From 81c59a207a2678f55454b7551d5d15b764b4a8dc Mon Sep 17 00:00:00 2001 From: github-actions Date: Sat, 14 Dec 2024 04:41:46 +0000 Subject: [PATCH] chore: update confs --- papers/cikm/cikm2023.md | 578 +++++++++++++++---------------- papers/cikm/cikm2024.md | 180 +++++----- papers/ecir/ecir2024.md | 4 +- papers/kdd/kdd2022.md | 46 +-- papers/kdd/kdd2023.md | 304 ++++++++-------- papers/kdd/kdd2024.md | 638 +++++++++++++++++----------------- papers/recsys/recsys2023.md | 30 +- papers/sigir/sigir2022.md | 354 +++++++++---------- papers/sigir/sigir2023.md | 304 ++++++++-------- papers/sigir/sigir2024.md | 310 ++++++++--------- papers/wsdm/wsdm2023.md | 174 +++++----- papers/wsdm/wsdm2024.md | 56 +-- papers/www/www2023.md | 672 ++++++++++++++++++------------------ papers/www/www2024.md | 8 +- results.json | 327 ++++++++++++++++-- 15 files changed, 2136 insertions(+), 1849 deletions(-) diff --git a/papers/cikm/cikm2023.md b/papers/cikm/cikm2023.md index 645006d9..9cd3646b 100644 --- a/papers/cikm/cikm2023.md +++ b/papers/cikm/cikm2023.md @@ -5,16 +5,16 @@ |[BI-GCN: Bilateral Interactive Graph Convolutional Network for Recommendation](https://doi.org/10.1145/3583780.3615232)|Yinan Zhang, Pei Wang, Congcong Liu, Xiwei Zhao, Hao Qi, Jie He, Junsheng Jin, Changping Peng, Zhangang Lin, Jingping Shao|JD.com, Beijing, China|Recently, Graph Convolutional Network (GCN) based methods have become novel state-of-the-arts for Collaborative Filtering (CF) based Recommender Systems. To obtain users' preferences over different items, it is a common practice to learn representations of users and items by performing embedding propagation on a user-item bipartite graph, and then calculate the preference scores based on the representations. However, in most existing algorithms, user/item representations are generated independently of target items/users. To address this problem, we propose a novel graph attention model named Bilateral Interactive GCN (BI-GCN), which introduces bilateral interactive guidance into each user-item pair and thus leads to target-aware representations for preference prediction. Specifically, to learn the user/item representation from its neighborhood, we assign higher attention weights to those neighbors similar to the target item/user. By this manner, we can obtain target-aware representations, i.e., the information of the target item/user is explicitly encoded in the corresponding user/item representation, for more precise matching. Extensive experiments on three benchmark datasets demonstrate the effectiveness and robustness of BI-GCN.|最近，基于图卷积网络(GCN)的方法已经成为基于协同过滤(CF)的推荐系统的最新技术。为了获得用户对不同项目的偏好，通常的做法是通过在用户-项目二部图上嵌入传播来学习用户和项目的表示，然后根据表示计算偏好得分。但是，在大多数现有算法中，用户/项表示是独立于目标项/用户生成的。为了解决这一问题，我们提出了一种新的图形注意模型——双边交互式 GCN (BI-GCN) ，该模型将双边交互式引导引入到每个用户项目对中，从而产生用于偏好预测的目标感知表示。具体来说，为了从邻域中学习用户/项目表示，我们给与目标项目/用户相似的邻域分配更高的注意力权重。通过这种方式，我们可以获得目标感知的表示，也就是说，目标项/用户的信息显式地编码在相应的用户/项表示中，以便进行更精确的匹配。在三个基准数据集上的大量实验证明了 BI-GCN 的有效性和鲁棒性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=BI-GCN:+Bilateral+Interactive+Graph+Convolutional+Network+for+Recommendation)|1| |[Monotonic Neural Ordinary Differential Equation: Time-series Forecasting for Cumulative Data](https://doi.org/10.1145/3583780.3615487)|Zhichao Chen, Leilei Ding, Zhixuan Chu, Yucheng Qi, Jianmin Huang, Hao Wang|Ant Group, Hangzhou, China|Time-Series Forecasting based on Cumulative Data (TSFCD) is a crucial problem in decision-making across various industrial scenarios. However, existing time-series forecasting methods often overlook two important characteristics of cumulative data, namely monotonicity and irregularity, which limit their practical applicability. To address this limitation, we propose a principled approach called Monotonic neural Ordinary Differential Equation (MODE) within the framework of neural ordinary differential equations. By leveraging MODE, we are able to effectively capture and represent the monotonicity and irregularity in practical cumulative data. Through extensive experiments conducted in a bonus allocation scenario, we demonstrate that MODE outperforms state-of-the-art methods, showcasing its ability to handle both monotonicity and irregularity in cumulative data and delivering superior forecasting performance.|基于累积数据的时间序列预测是各种工业情景下决策的关键问题。然而，现有的时间序列预测方法往往忽视了累积数据的两个重要特征，即单调性和不规则性，这限制了它们的实际应用。为了解决这一局限性，我们提出了一种在神经常微分方程框架内的原理性方法，称为单调神经常微分方程(MODE)。通过利用 MODE，我们能够有效地捕获和表示实际累积数据中的单调性和不规则性。通过在奖金分配场景中进行的大量实验，我们证明了 MODE 优于最先进的方法，展示了其处理累积数据中的单调性和不规则性的能力，并提供了卓越的预测性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Monotonic+Neural+Ordinary+Differential+Equation:+Time-series+Forecasting+for+Cumulative+Data)|1| |[Continual Learning in Predictive Autoscaling](https://doi.org/10.1145/3583780.3615463)|Hongyan Hao, Zhixuan Chu, Shiyi Zhu, Gangwei Jiang, Yan Wang, Caigao Jiang, James Y. Zhang, Wei Jiang, Siqiao Xue, Jun Zhou|Ant Group, Hangzhou, China; Ant Group, New York, NY, USA|Predictive Autoscaling is used to forecast the workloads of servers and prepare the resources in advance to ensure service level objectives (SLOs) in dynamic cloud environments.However, in practice, its prediction task often suffers from performance degradation under abnormal traffics caused by external events (such as sales promotional activities and applications' re-configurations), for which a common solution is to re-train the model with data of a long historical period, but at the expense of high computational and storage costs.To better address this problem, we propose a replay-based continual learning method, i.e., Density-based Memory Selection and Hint-based Network Learning Model (DMSHM), using only a small part of the historical log to achieve accurate predictions.First, we discover the phenomenon of sample overlap when applying replay-based continual learning in prediction tasks. In order to surmount this challenge and effectively integrate new sample distribution, we propose a density-based sample selection strategy that utilizes kernel density estimation to calculate sample density as a reference to compute sample weight, and employs weight sampling to construct a new memory set.Then we implement hint-based network learning based on hint representation to optimize the parameters.Finally, we conduct experiments on public and industrial datasets to demonstrate that our proposed method outperforms state-of-the-art continual learning methods in terms of memory capacity and prediction accuracy. Furthermore, we demonstrate remarkable practicability of DMSHM in real industrial applications.|预测性自动伸缩用于预测服务器的工作负载并提前准备资源，以确保动态云环境中的服务水平目标(SLOs)。然而，在实践中，其预测任务往往受到异常流量下的性能下降的外部事件(如销售促销活动和应用程序的重新配置) ，其中一个常见的解决方案是重新训练模型与长期的历史时期的数据，但以高计算和存储成本为代价。为了更好地解决这个问题，我们提出了一个基于重放的连续学习方法，即基于密度的记忆选择和基于提示的网络学习模型(DMSHM) ，只使用历史日志的一小部分来实现准确的预测。首先，我们发现了在预测任务中应用基于重播的连续学习时样本重叠的现象。为了克服这个挑战，并有效地整合新的样本分布，我们提出一个基于密度的样本选择策略，利用核密度估计计算样本密度作为计算样本权重的参考，并利用权重采样来构建一个新的记忆集。然后基于提示表示实现了基于提示的网络学习，对参数进行了优化。最后，我们在公共数据集和工业数据集上进行了实验，结果表明我们提出的方法在记忆容量和预测准确性方面优于最先进的连续学习方法。此外，我们证明了 DMSHM 在实际工业应用中的显著实用性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Continual+Learning+in+Predictive+Autoscaling)|1| -|[TEI2GO: A Multilingual Approach for Fast Temporal Expression Identification](https://doi.org/10.1145/3583780.3615130)|Hugo Sousa, Ricardo Campos, Alípio Jorge|University of Beira Interior & INESC TEC, Covilhã, Portugal; University of Porto & INESC TEC, Porto, Portugal|Temporal expression identification is crucial for understanding texts written in natural language. Although highly effective systems such as HeidelTime exist, their limited runtime performance hampers adoption in large-scale applications and production environments. In this paper, we introduce the TEI2GO models, matching HeidelTime's effectiveness but with significantly improved runtime, supporting six languages, and achieving state-of-the-art results in four of them. To train the TEI2GO models, we used a combination of manually annotated reference corpus and developed "Professor HeidelTime'', a comprehensive weakly labeled corpus of news texts annotated with HeidelTime. This corpus comprises a total of 138,069 documents (over six languages) with 1,050,921 temporal expressions, the largest open-source annotated dataset for temporal expression identification to date. By describing how the models were produced, we aim to encourage the research community to further explore, refine, and extend the set of models to additional languages and domains. Code, annotations, and models are openly available for community exploration and use. The models are conveniently on HuggingFace for seamless integration and application.|时间表达式识别是理解自然语言文本的关键。尽管存在像 HeidelTime 这样的高效系统，但它们有限的运行时性能阻碍了在大规模应用程序和生产环境中的采用。在本文中，我们介绍了 TEI2GO 模型，匹配 HeidelTime 的有效性，但有显著改进的运行时，支持六种语言，并在其中四种语言中取得了最先进的结果。为了训练 TEI2GO 模型，我们使用了人工注释的参考语料库的组合，并开发了“海德尔时间教授”，一个全面的弱标记的新闻文本的海德尔时间注释语料库。这个语料库包含138,069个文档(超过6种语言)和1,050,921个时态表达式，是迄今为止用于时态表达式识别的最大的开源注释数据集。通过描述模型是如何产生的，我们的目标是鼓励研究团体进一步探索、完善和扩展模型集到其他语言和领域。代码、注释和模型可供社区探索和使用。该模型可以方便地在 HuggingFace 上进行无缝集成和应用。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=TEI2GO:+A+Multilingual+Approach+for+Fast+Temporal+Expression+Identification)|1| -|[APGL4SR: A Generic Framework with Adaptive and Personalized Global Collaborative Information in Sequential Recommendation](https://doi.org/10.1145/3583780.3614781)|Mingjia Yin, Hao Wang, Xiang Xu, Likang Wu, Sirui Zhao, Wei Guo, Yong Liu, Ruiming Tang, Defu Lian, Enhong Chen|Huawei Singapore Research Center, Singapore, Singapore; Huawei Noah's Ark Lab, Shenzhen, China; University of Science and Technology of China & State Key Laboratory of Cognitive Intelligence, Hefei, China|The sequential recommendation system has been widely studied for its promising effectiveness in capturing dynamic preferences buried in users' sequential behaviors. Despite the considerable achievements, existing methods usually focus on intra-sequence modeling while overlooking exploiting global collaborative information by inter-sequence modeling, resulting in inferior recommendation performance. Therefore, previous works attempt to tackle this problem with a global collaborative item graph constructed by pre-defined rules. However, these methods neglect two crucial properties when capturing global collaborative information, i.e., adaptiveness and personalization, yielding sub-optimal user representations. To this end, we propose a graph-driven framework, named Adaptive and Personalized Graph Learning for Sequential Recommendation (APGL4SR), that incorporates adaptive and personalized global collaborative information into sequential recommendation systems. Specifically, we first learn an adaptive global graph among all items and capture global collaborative information with it in a self-supervised fashion, whose computational burden can be further alleviated by the proposed SVD-based accelerator. Furthermore, based on the graph, we propose to extract and utilize personalized item correlations in the form of relative positional encoding, which is a highly compatible manner of personalizing the utilization of global collaborative information. Finally, the entire framework is optimized in a multi-task learning paradigm, thus each part of APGL4SR can be mutually reinforced. As a generic framework, APGL4SR can not only outperform other baselines with significant margins, but also exhibit promising versatility, the ability to learn a meaningful global collaborative graph, and the ability to alleviate the dimensional collapse issue of item embeddings.|顺序推荐系统在捕获隐藏在用户顺序行为中的动态偏好方面有着广泛的应用前景。尽管已经取得了相当大的成就，但现有的方法往往只注重序列内建模，而忽视了通过序列间建模来开发全局协同信息，导致推荐性能较差。因此，以往的研究尝试利用预先定义的规则构造一个全局协同项目图来解决这个问题。然而，这些方法在获取全局协作信息时忽略了两个关键性质，即适应性和个性化，产生了次优的用户表示。为此，我们提出了一个图驱动的序列推荐自适应和个性化图学习框架(APGL4SR) ，该框架将自适应和个性化的全局协作信息融入到序列推荐系统中。具体来说，我们首先学习一个所有项目之间的自适应全局图，并以自监督的方式捕获全局协作信息，通过提出的基于奇异值分解的加速器可以进一步减轻全局协作信息的计算负担。在此基础上，提出了以相对位置编码的形式提取和利用个性化项目相关性，这是一种高度兼容的利用全球协同信息的个性化方式。最后，在多任务学习范式下对整个框架进行优化，使 APGL4SR 的各个部分得到相互增强。作为一个通用框架，APGL4SR 不仅具有显著的优势，而且具有良好的通用性、学习有意义的全局协作图的能力以及缓解项目嵌入的尺寸崩溃问题的能力。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=APGL4SR:+A+Generic+Framework+with+Adaptive+and+Personalized+Global+Collaborative+Information+in+Sequential+Recommendation)|0| +|[TEI2GO: A Multilingual Approach for Fast Temporal Expression Identification](https://doi.org/10.1145/3583780.3615130)|Hugo Sousa, Ricardo Campos, Alípio Jorge|University of Porto & INESC TEC, Porto, Portugal; University of Beira Interior & INESC TEC, Covilhã, Portugal|Temporal expression identification is crucial for understanding texts written in natural language. Although highly effective systems such as HeidelTime exist, their limited runtime performance hampers adoption in large-scale applications and production environments. In this paper, we introduce the TEI2GO models, matching HeidelTime's effectiveness but with significantly improved runtime, supporting six languages, and achieving state-of-the-art results in four of them. To train the TEI2GO models, we used a combination of manually annotated reference corpus and developed "Professor HeidelTime'', a comprehensive weakly labeled corpus of news texts annotated with HeidelTime. This corpus comprises a total of 138,069 documents (over six languages) with 1,050,921 temporal expressions, the largest open-source annotated dataset for temporal expression identification to date. By describing how the models were produced, we aim to encourage the research community to further explore, refine, and extend the set of models to additional languages and domains. Code, annotations, and models are openly available for community exploration and use. The models are conveniently on HuggingFace for seamless integration and application.|时间表达式识别是理解自然语言文本的关键。尽管存在像 HeidelTime 这样的高效系统，但它们有限的运行时性能阻碍了在大规模应用程序和生产环境中的采用。在本文中，我们介绍了 TEI2GO 模型，匹配 HeidelTime 的有效性，但有显著改进的运行时，支持六种语言，并在其中四种语言中取得了最先进的结果。为了训练 TEI2GO 模型，我们使用了人工注释的参考语料库的组合，并开发了“海德尔时间教授”，一个全面的弱标记的新闻文本的海德尔时间注释语料库。这个语料库包含138,069个文档(超过6种语言)和1,050,921个时态表达式，是迄今为止用于时态表达式识别的最大的开源注释数据集。通过描述模型是如何产生的，我们的目标是鼓励研究团体进一步探索、完善和扩展模型集到其他语言和领域。代码、注释和模型可供社区探索和使用。该模型可以方便地在 HuggingFace 上进行无缝集成和应用。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=TEI2GO:+A+Multilingual+Approach+for+Fast+Temporal+Expression+Identification)|1| +|[APGL4SR: A Generic Framework with Adaptive and Personalized Global Collaborative Information in Sequential Recommendation](https://doi.org/10.1145/3583780.3614781)|Mingjia Yin, Hao Wang, Xiang Xu, Likang Wu, Sirui Zhao, Wei Guo, Yong Liu, Ruiming Tang, Defu Lian, Enhong Chen|Huawei Noah's Ark Lab, Shenzhen, China; University of Science and Technology of China & State Key Laboratory of Cognitive Intelligence, Hefei, China; Huawei Singapore Research Center, Singapore, Singapore|The sequential recommendation system has been widely studied for its promising effectiveness in capturing dynamic preferences buried in users' sequential behaviors. Despite the considerable achievements, existing methods usually focus on intra-sequence modeling while overlooking exploiting global collaborative information by inter-sequence modeling, resulting in inferior recommendation performance. Therefore, previous works attempt to tackle this problem with a global collaborative item graph constructed by pre-defined rules. However, these methods neglect two crucial properties when capturing global collaborative information, i.e., adaptiveness and personalization, yielding sub-optimal user representations. To this end, we propose a graph-driven framework, named Adaptive and Personalized Graph Learning for Sequential Recommendation (APGL4SR), that incorporates adaptive and personalized global collaborative information into sequential recommendation systems. Specifically, we first learn an adaptive global graph among all items and capture global collaborative information with it in a self-supervised fashion, whose computational burden can be further alleviated by the proposed SVD-based accelerator. Furthermore, based on the graph, we propose to extract and utilize personalized item correlations in the form of relative positional encoding, which is a highly compatible manner of personalizing the utilization of global collaborative information. Finally, the entire framework is optimized in a multi-task learning paradigm, thus each part of APGL4SR can be mutually reinforced. As a generic framework, APGL4SR can not only outperform other baselines with significant margins, but also exhibit promising versatility, the ability to learn a meaningful global collaborative graph, and the ability to alleviate the dimensional collapse issue of item embeddings.|顺序推荐系统在捕获隐藏在用户顺序行为中的动态偏好方面有着广泛的应用前景。尽管已经取得了相当大的成就，但现有的方法往往只注重序列内建模，而忽视了通过序列间建模来开发全局协同信息，导致推荐性能较差。因此，以往的研究尝试利用预先定义的规则构造一个全局协同项目图来解决这个问题。然而，这些方法在获取全局协作信息时忽略了两个关键性质，即适应性和个性化，产生了次优的用户表示。为此，我们提出了一个图驱动的序列推荐自适应和个性化图学习框架(APGL4SR) ，该框架将自适应和个性化的全局协作信息融入到序列推荐系统中。具体来说，我们首先学习一个所有项目之间的自适应全局图，并以自监督的方式捕获全局协作信息，通过提出的基于奇异值分解的加速器可以进一步减轻全局协作信息的计算负担。在此基础上，提出了以相对位置编码的形式提取和利用个性化项目相关性，这是一种高度兼容的利用全球协同信息的个性化方式。最后，在多任务学习范式下对整个框架进行优化，使 APGL4SR 的各个部分得到相互增强。作为一个通用框架，APGL4SR 不仅具有显著的优势，而且具有良好的通用性、学习有意义的全局协作图的能力以及缓解项目嵌入的尺寸崩溃问题的能力。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=APGL4SR:+A+Generic+Framework+with+Adaptive+and+Personalized+Global+Collaborative+Information+in+Sequential+Recommendation)|0| |[Query-dominant User Interest Network for Large-Scale Search Ranking](https://doi.org/10.1145/3583780.3615022)|Tong Guo, Xuanping Li, Haitao Yang, Xiao Liang, Yong Yuan, Jingyou Hou, Bingqing Ke, Chao Zhang, Junlin He, Shunyu Zhang, Enyun Yu, Wenwu Ou|Kuaishou Technology Co., Ltd, Beijing, China; unaffiliated, Beijing, China|Historical behaviors have shown great effect and potential in various prediction tasks, including recommendation and information retrieval. The overall historical behaviors are various but noisy while search behaviors are always sparse. Most existing approaches in personalized search ranking adopt the sparse search behaviors to learn representation with bottleneck, which do not sufficiently exploit the crucial long-term interest. In fact, there is no doubt that user long-term interest is various but noisy for instant search, and how to exploit it well still remains an open problem. To tackle this problem, in this work, we propose a novel model named Query-dominant user Interest Network (QIN), including two cascade units to filter the raw user behaviors and reweigh the behavior subsequences. Specifically, we propose a relevance search unit (RSU), which aims to search a subsequence relevant to the query first and then search the sub-subsequences relevant to the target item. These items are then fed into an attention unit called Fused Attention Unit (FAU). It should be able to calculate attention scores from the ID field and attribute field separately, and then adaptively fuse the item embedding and content embedding based on the user engagement of past period. Extensive experiments and ablation studies on real-world datasets demonstrate the superiority of our model over state-of-the-art methods. The QIN now has been successfully deployed on Kuaishou search, an online video search platform, and obtained 7.6% improvement on CTR.|历史行为在各种预测任务中显示出巨大的效果和潜力，包括推荐和信息检索。整体的历史行为是多样的，但是有噪声，而搜索行为总是稀疏的。大多数现有的个性化检索排名方法采用稀疏搜索行为来学习带有瓶颈的表示，这种方法没有充分利用关键的长期兴趣。事实上，毫无疑问，用户对即时搜索的长期兴趣是多种多样的，但是噪音很大，如何很好地利用它仍然是一个悬而未决的问题。为了解决这一问题，本文提出了一种新的查询主导用户兴趣网络(QIN)模型，该模型包括两个级联单元，用于过滤原始用户行为和重新权衡行为子序列。具体来说，我们提出了一种相关搜索单元(RSU) ，它的目的是先搜索与查询相关的子序列，然后再搜索与目标项相关的子子序列。然后，这些物品被输入一个称为“融合注意力单元”(FAU)的注意力单元。它应该能够分别从 ID 字段和属性字段计算注意力得分，然后根据用户过去一段时间的参与度自适应地融合项目嵌入和内容嵌入。对真实世界数据集的大量实验和消融研究表明，我们的模型优于最先进的方法。QIN 现已成功部署在快手搜索(一个在线视频搜索平台)上，点击率提高了7.6% 。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Query-dominant+User+Interest+Network+for+Large-Scale+Search+Ranking)|0| |[Modeling Sequential Collaborative User Behaviors For Seller-Aware Next Basket Recommendation](https://doi.org/10.1145/3583780.3614973)|Ziyi Kou, Saurav Manchanda, ShihTing Lin, Min Xie, Haixun Wang, Xiangliang Zhang|University of Notre Dame, Notre Dame, IN, USA; Instacart, San Francisco, CA, USA|Next Basket Recommendation (NBR) aims to recommend a set of products as a basket to users based on their historical shopping behavior. In this paper, we investigate the problem of NBR in online marketplaces (e.g., Instacart, Uber Eats) that connect users with multiple sellers. In such scenarios, effective NBR can significantly enhance the shopping experience of users by recommending diversified and completed products based on specific sellers, especially when a user purchases from a seller they have not visited before. However, conventional NBR approaches assume that all considered products are from the same sellers, which overlooks the complex relationships between users, sellers, and products. To address such limitations, we develop SecGT, a sequential collaborative graph transformer framework that recommends users with baskets from specific sellers based on seller-aware user preference representations that are generated by collaboratively modeling the joint user-seller-product interactions and sequentially exploring the user-agnostic basket transitions in an interactive way. We evaluate the performance of SecGT on users from a leading online marketplace at multiple cities with various involved sellers. The results show that SecGT outperforms existing NBR and also traditional product recommendation approaches on recommending baskets from cold sellers for different types of users across all cities.|下一个购物篮推荐系统(NBR)的目标是根据用户的历史购物行为向他们推荐一组产品作为购物篮。在本文中，我们研究了在线市场(如 Instacart，Uber Eats)中连接用户和多个卖家的 NBR 问题。在这种情况下，有效的 NBR 可以通过推荐基于特定卖家的多样化和完整的产品来显著提高用户的购物体验，特别是当用户从他们以前没有访问过的卖家那里购买产品时。然而，传统的 NBR 方法假设所有被考虑的产品都来自同一个销售商，这忽略了用户、销售商和产品之间的复杂关系。为了解决这些局限性，我们开发了 SecGT，这是一个连续的协作图形转换框架，根据卖方感知的用户偏好表示，通过协作建模联合用户-卖方-产品交互并以交互方式顺序探索用户不可知的篮子转换来推荐用户。我们评估的性能，从一个领先的在线市场在多个城市的用户与各种参与的卖家。结果表明，在向各城市不同类型的用户推荐冷卖篮子方面，SecGT 的表现优于现有的 NBR 和传统的产品推荐方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Modeling+Sequential+Collaborative+User+Behaviors+For+Seller-Aware+Next+Basket+Recommendation)|0| |[Rec4Ad: A Free Lunch to Mitigate Sample Selection Bias for Ads CTR Prediction in Taobao](https://doi.org/10.1145/3583780.3615496)|Jingyue Gao, Shuguang Han, Han Zhu, Siran Yang, Yuning Jiang, Jian Xu, Bo Zheng|Alibaba Group, Beijing, China|Click-Through Rate (CTR) prediction serves as a fundamental component in online advertising. A common practice is to train a CTR model on advertisement (ad) impressions with user feedback. Since ad impressions are purposely selected by the model itself, their distribution differs from the inference distribution and thus exhibits sample selection bias (SSB) that affects model performance. Existing studies on SSB mainly employ sample re-weighting techniques which suffer from high variance and poor model calibration. Another line of work relies on costly uniform data that is inadequate to train industrial models. Thus mitigating SSB in industrial models with a uniform-data-free framework is worth exploring. Fortunately, many platforms display mixed results of organic items (i.e., recommendations) and sponsored items (i.e., ads) to users, where impressions of ads and recommendations are selected by different systems but share the same user decision rationales. Based on the above characteristics, we propose to leverage recommendations samples as a free lunch to mitigate SSB for ads CTR model (Rec4Ad). After elaborating data augmentation, Rec4Ad learns disentangled representations with alignment and decorrelation modules for enhancement. When deployed in Taobao display advertising system, Rec4Ad achieves substantial gains in key business metrics, with a lift of up to +6.6\% CTR and +2.9\% RPM.|点进率预测是在线广告的一个基本组成部分。一个常见的做法是训练广告(广告)印象与用户反馈的点击率模型。由于广告印象是由模型本身有目的地选择的，它们的分布不同于推断分布，因此表现出影响模型性能的样本选择偏差(SSB)。现有的 SSB 研究主要采用样本重权重技术，存在方差大、模型校正差等问题。另一项工作依赖于昂贵的统一数据，这些数据不足以培训工业模型。因此，在无统一数据框架的工业模型中减少 SSB 是值得探索的。幸运的是，许多平台向用户显示有机项目(即推荐)和赞助项目(即广告)的混合结果，其中广告和推荐的印象由不同的系统选择，但共享相同的用户决策理由。基于上述特点，我们建议利用推荐样本作为免费午餐，以减轻 SSB 广告点击率模型(Rec4Ad)。在详细阐述了数据增强之后，Rec4Ad 学习了利用对齐和去相关模块进行增强的解纠缠表示。在淘宝展示广告系统中部署 Rec4Ad 后，Rec4Ad 在关键业务指标上取得了实质性进展，点击率和转速分别提高了6.6% 和2.9% 。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Rec4Ad:+A+Free+Lunch+to+Mitigate+Sample+Selection+Bias+for+Ads+CTR+Prediction+in+Taobao)|0| |[Fragment and Integrate Network (FIN): A Novel Spatial-Temporal Modeling Based on Long Sequential Behavior for Online Food Ordering Click-Through Rate Prediction](https://doi.org/10.1145/3583780.3615478)|Jun Li, Ge Zhang||Spatial-temporal information has been proven to be of great significance for click-through rate prediction tasks in online Location-Based Services (LBS), especially in mainstream food ordering platforms such as DoorDash, Uber Eats, Meituan, and Ele.me. Modeling user spatial-temporal preferences with sequential behavior data has become a hot topic in recommendation systems and online advertising. However, most of existing methods either lack the representation of rich spatial-temporal information or only handle user behaviors with limited length, e.g. 100. In this paper, we tackle these problems by designing a new spatial-temporal modeling paradigm named Fragment and Integrate Network (FIN). FIN consists of two networks: (i) Fragment Network (FN) extracts Multiple Sub-Sequences (MSS) from lifelong sequential behavior data, and captures the specific spatial-temporal representation by modeling each MSS respectively. Here both a simplified attention and a complicated attention are adopted to balance the performance gain and resource consumption. (ii) Integrate Network (IN) builds a new integrated sequence by utilizing spatial-temporal interaction on MSS and captures the comprehensive spatial-temporal representation by modeling the integrated sequence with a complicated attention. Both public datasets and production datasets have demonstrated the accuracy and scalability of FIN. Since 2022, FIN has been fully deployed in the recommendation advertising system of Ele.me, one of the most popular online food ordering platforms in China, obtaining 5.7% improvement on Click-Through Rate (CTR) and 7.3% increase on Revenue Per Mille (RPM).|时空信息已被证明对于在线基于位置服务(LBS)的点进率预测任务具有重要意义，特别是在主流食品订购平台如 DoorDash、 Uber Eats、美团和 Ele.me 中。利用序列行为数据建立用户时空偏好模型已成为推荐系统和在线广告研究的热点。然而，大多数现有的方法要么缺乏丰富的时空信息的表示，要么只能处理有限长度的用户行为，例如100。针对这些问题，本文设计了一种新的时空建模范式——分段集成网络(FIN)。FIN 由两个网络组成: (1)片段网络(FN)从终身序列行为数据中提取多个子序列(MSS) ，并分别对每个子序列进行建模，获取特定的时空表示。这里采用了简化注意和复杂注意来平衡性能增益和资源消耗。(2)综合网络(IN)利用 MSS 上的时空相互作用建立一个新的综合序列，通过对综合序列进行复杂注意力建模，获取综合的时空表示。公共数据集和生产数据集都证明了 FIN 的准确性和可扩展性。自2022年以来，中国最受欢迎的在线食品订购平台之一饿了么的推荐广告系统中已经全面部署了财务识别系统，点进率点击率提高了5.7% ，每公里收入提高了7.3% 。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Fragment+and+Integrate+Network+(FIN):+A+Novel+Spatial-Temporal+Modeling+Based+on+Long+Sequential+Behavior+for+Online+Food+Ordering+Click-Through+Rate+Prediction)|0| -|[Batch-Mix Negative Sampling for Learning Recommendation Retrievers](https://doi.org/10.1145/3583780.3614789)|Yongfu Fan, Jin Chen, Yongquan Jiang, Defu Lian, Fangda Guo, Kai Zheng|University of Science and Technology of China, Hefei, China; Southwest Jiaotong University, Chengdu, China; University of Electronic Science and Technology of China, Chengdu, China; Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China|Recommendation retrievers commonly retrieve user potentially preferred items from numerous items, where the query and item representation are learned according to the dual encoders with the log-softmax loss. Under real scenarios, the number of items becomes considerably large, making it exceedingly difficult to calculate the partition function with the whole item corpus. Negative sampling, which samples a subset from the item corpus, is widely used to accelerate the model training. Among different samplers, the in-batch sampling is commonly adopted for online recommendation retrievers, which regards the other items within the mini-batch as the negative samples for the given query, owing to its time and memory efficiency. However, the sample selection bias occurs due to the skewed feedback, harming the retrieval quality. In this paper, we propose a negative sampling approach named Batch-Mix Negative Sampling (BMNS), which adopts batch mixing operation to generate additional negatives for model training. Concretely, BMNS first generates new negative items with the sampled mix coefficient from the Beta distribution, after which a tailored correct strategy guided by frequency is designed to match the sampled softmax loss. In this way, the effort of re-encoding items out of the mini-batch is reduced while also improving the representation space of the negative set. The empirical experiments on four real-world datasets demonstrate BMNS is superior to the competitive negative inbatch sampling method.|推荐检索器通常从许多项目中检索用户潜在首选项，其中根据带有 log-softmax 损失的双编码器学习查询和项表示。在实际情况下，条目的数量会变得相当大，这使得用整个条目语料库计算配分函数变得非常困难。负抽样是从项目语料库中抽取子集，广泛用于加速模型训练。在不同的抽样方法中，在线推荐检索通常采用批内抽样，由于时间和内存效率的原因，将小批内的其他项目作为给定查询的否定样本。然而，样本选择偏差的产生是由于反馈的偏差，影响了检索质量。本文提出了一种负抽样方法——批量混合负抽样(BMNS) ，该方法采用批量混合操作产生附加负数，用于模型训练。具体来说，BMNS 首先根据采样的混合系数生成新的负项，然后根据频率设计一个量身定制的正确策略，以匹配采样的软最大损失(softmax loss) Β分布。这样，既减少了从小批处理中重新编码项的工作量，又提高了负集的表示空间。在四个实际数据集上的实验表明，BMNS 方法优于竞争性负内批抽样方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Batch-Mix+Negative+Sampling+for+Learning+Recommendation+Retrievers)|0| -|[Dynamic Embedding Size Search with Minimum Regret for Streaming Recommender System](https://doi.org/10.1145/3583780.3615135)|Bowei He, Xu He, Renrui Zhang, Yingxue Zhang, Ruiming Tang, Chen Ma|City University of Hong Kong, Hong Kong, Hong Kong; The Chinese University of Hong Kong, Hong Kong, Hong Kong; Huawei Noah's Ark Lab Montreal, Montreal, PQ, Canada; Huawei Noah's Ark Lab, Shenzhen, China|With the continuous increase of users and items, conventional recommender systems trained on static datasets can hardly adapt to changing environments. The high-throughput data requires the model to be updated in a timely manner for capturing the user interest dynamics, which leads to the emergence of streaming recommender systems. Due to the prevalence of deep learning-based recommender systems, the embedding layer is widely adopted to represent the characteristics of users, items, and other features in low-dimensional vectors. However, it has been proved that setting an identical and static embedding size is sub-optimal in terms of recommendation performance and memory cost, especially for streaming recommendations. To tackle this problem, we first rethink the streaming model update process and model the dynamic embedding size search as a bandit problem. Then, we analyze and quantify the factors that influence the optimal embedding sizes from the statistics perspective. Based on this, we propose the \textbf{D}ynamic \textbf{E}mbedding \textbf{S}ize \textbf{S}earch (\textbf{DESS}) method to minimize the embedding size selection regret on both user and item sides in a non-stationary manner. Theoretically, we obtain a sublinear regret upper bound superior to previous methods. Empirical results across two recommendation tasks on four public datasets also demonstrate that our approach can achieve better streaming recommendation performance with lower memory cost and higher time efficiency.|随着用户和项目的不断增加，传统的基于静态数据集的推荐系统难以适应不断变化的环境。高吞吐量数据要求模型及时更新，以捕捉用户兴趣动态，从而导致流式推荐系统的出现。由于基于深度学习的推荐系统的普及，嵌入层被广泛采用来在低维向量中表示用户、项目等特征。然而，已经证明，设置一个相同的静态嵌入大小在推荐性能和内存成本方面是次优的，特别是对于流式推荐。为了解决这个问题，我们首先重新考虑了流模型更新过程，并将动态嵌入大小搜索模型建模为盗贼问题。然后，从统计学的角度对影响最优嵌入规模的因素进行了分析和量化。在此基础上，提出了一种动态 textbf { D }嵌入 textbf { E }嵌入 textbf { S } ize textbf { S } earch (textbf { DESS })方法，以非平稳方式最小化用户端和项目端的嵌入大小选择遗憾。从理论上，我们得到了一个次线性后悔上界优于以往的方法。对四个公共数据集的两个推荐任务的实验结果也表明，该方法能够以较低的内存开销和较高的时间效率获得较好的流推荐性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Dynamic+Embedding+Size+Search+with+Minimum+Regret+for+Streaming+Recommender+System)|0| -|[Text Matching Improves Sequential Recommendation by Reducing Popularity Biases](https://doi.org/10.1145/3583780.3615077)|Zhenghao Liu, Sen Mei, Chenyan Xiong, Xiaohua Li, Shi Yu, Zhiyuan Liu, Yu Gu, Ge Yu|Tsinghua University, Beijing, China; Carnegie Mellon University, Pittsburgh, PA, USA; Northeastern University, Shenyang, China|This paper proposes Text mAtching based SequenTial rEcommendation model (TASTE), which maps items and users in an embedding space and recommends items by matching their text representations. TASTE verbalizes items and user-item interactions using identifiers and attributes of items. To better characterize user behaviors, TASTE additionally proposes an attention sparsity method, which enables TASTE to model longer user-item interactions by reducing the self-attention computations during encoding. Our experiments show that TASTE outperforms the state-of-the-art methods on widely used sequential recommendation datasets. TASTE alleviates the cold start problem by representing long-tail items using full-text modeling and bringing the benefits of pretrained language models to recommendation systems. Our further analyses illustrate that TASTE significantly improves the recommendation accuracy by reducing the popularity bias of previous item id based recommendation models and returning more appropriate and text-relevant items to satisfy users. All codes are available at https://github.com/OpenMatch/TASTE.|提出了一种基于文本匹配的序列推荐模型(TASTE) ，该模型将项目和用户映射到嵌入空间中，通过匹配项目的文本表示来推荐项目。TASTE 使用项目的标识符和属性语言化项目和用户-项目交互。为了更好地描述用户行为，TASTE 还提出了一种注意稀疏方法，该方法通过减少编码过程中的自我注意计算，使 TASTE 能够模拟更长时间的用户-项目交互。我们的实验表明，在广泛使用的顺序推荐数据集上，TASTE 优于最先进的方法。TASTE 通过使用全文建模来表示长尾项目，并将预训练语言模型的优点带到推荐系统中，从而缓解了冷启动问题。我们进一步的分析表明，TASTE 通过减少以前基于项目 ID 的推荐模型的流行偏差和返回更合适的和文本相关的项目来满足用户，从而显著提高了推荐的准确性。所有密码都在 https://github.com/openmatch/taste。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Text+Matching+Improves+Sequential+Recommendation+by+Reducing+Popularity+Biases)|0| -|[Cracking the Code of Negative Transfer: A Cooperative Game Theoretic Approach for Cross-Domain Sequential Recommendation](https://doi.org/10.1145/3583780.3614828)|Chung Park, Taesan Kim, Taekyoon Choi, Junui Hong, Yelim Yu, Mincheol Cho, Kyunam Lee, Sungil Ryu, Hyungjun Yoon, Minsung Choi, Jaegul Choo|SK Telelcom, Seoul, Republic of Korea; SK Telelcom & Korea Advanced Institute of Science and Technology, Seoul & Daejeon, Republic of Korea; NAVER, Seongnam, Republic of Korea; Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea; SK Telecom & Korea Advanced Institute of Science and Technology, Seoul & Daejeon, Republic of Korea|This paper investigates Cross-Domain Sequential Recommendation (CDSR), a promising method that uses information from multiple domains (more than three) to generate accurate and diverse recommendations, and takes into account the sequential nature of user interactions. The effectiveness of these systems often depends on the complex interplay among the multiple domains. In this dynamic landscape, the problem of negative transfer arises, where heterogeneous knowledge between dissimilar domains leads to performance degradation due to differences in user preferences across these domains. As a remedy, we propose a new CDSR framework that addresses the problem of negative transfer by assessing the extent of negative transfer from one domain to another and adaptively assigning low weight values to the corresponding prediction losses. To this end, the amount of negative transfer is estimated by measuring the marginal contribution of each domain to model performance based on a cooperative game theory. In addition, a hierarchical contrastive learning approach that incorporates information from the sequence of coarse-level categories into that of fine-level categories (e.g., item level) when implementing contrastive learning was developed to mitigate negative transfer. Despite the potentially low relevance between domains at the fine-level, there may be higher relevance at the category level due to its generalised and broader preferences. We show that our model is superior to prior works in terms of model performance on two real-world datasets across ten different domains.|本文研究了跨域序列推荐(CDSR) ，这是一种利用多个域(超过三个)的信息来生成准确和多样化推荐的有前途的方法，并考虑了用户交互的序列特性。这些系统的有效性往往取决于多个领域之间复杂的相互作用。在这种动态环境中，出现了负迁移问题，即不同领域之间的异质知识由于这些领域的用户偏好不同而导致性能下降。作为补救措施，我们提出了一个新的 CDSR 框架，通过评估从一个领域到另一个领域的负转移程度，并自适应地为相应的预测损失赋予低权值来解决负转移问题。为此，负向转移的数量是通过衡量每个领域对基于合作博弈理论的绩效模型的边际贡献来估计的。此外，在实施对比学习的过程中，还开发了一种分层对比学习方法，将粗级别类别的信息整合到细级别类别的信息中(例如，项目级别) ，以减轻负迁移。尽管在精细层面上各领域之间的相关性可能较低，但在类别层面上可能具有更高的相关性，因为它具有普遍性和更广泛的偏好。我们表明，我们的模型是优于以往的工作方面的两个真实世界的数据集在十个不同的领域的模型性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Cracking+the+Code+of+Negative+Transfer:+A+Cooperative+Game+Theoretic+Approach+for+Cross-Domain+Sequential+Recommendation)|0| +|[Batch-Mix Negative Sampling for Learning Recommendation Retrievers](https://doi.org/10.1145/3583780.3614789)|Yongfu Fan, Jin Chen, Yongquan Jiang, Defu Lian, Fangda Guo, Kai Zheng|University of Electronic Science and Technology of China, Chengdu, China; University of Science and Technology of China, Hefei, China; Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China; Southwest Jiaotong University, Chengdu, China|Recommendation retrievers commonly retrieve user potentially preferred items from numerous items, where the query and item representation are learned according to the dual encoders with the log-softmax loss. Under real scenarios, the number of items becomes considerably large, making it exceedingly difficult to calculate the partition function with the whole item corpus. Negative sampling, which samples a subset from the item corpus, is widely used to accelerate the model training. Among different samplers, the in-batch sampling is commonly adopted for online recommendation retrievers, which regards the other items within the mini-batch as the negative samples for the given query, owing to its time and memory efficiency. However, the sample selection bias occurs due to the skewed feedback, harming the retrieval quality. In this paper, we propose a negative sampling approach named Batch-Mix Negative Sampling (BMNS), which adopts batch mixing operation to generate additional negatives for model training. Concretely, BMNS first generates new negative items with the sampled mix coefficient from the Beta distribution, after which a tailored correct strategy guided by frequency is designed to match the sampled softmax loss. In this way, the effort of re-encoding items out of the mini-batch is reduced while also improving the representation space of the negative set. The empirical experiments on four real-world datasets demonstrate BMNS is superior to the competitive negative inbatch sampling method.|推荐检索器通常从许多项目中检索用户潜在首选项，其中根据带有 log-softmax 损失的双编码器学习查询和项表示。在实际情况下，条目的数量会变得相当大，这使得用整个条目语料库计算配分函数变得非常困难。负抽样是从项目语料库中抽取子集，广泛用于加速模型训练。在不同的抽样方法中，在线推荐检索通常采用批内抽样，由于时间和内存效率的原因，将小批内的其他项目作为给定查询的否定样本。然而，样本选择偏差的产生是由于反馈的偏差，影响了检索质量。本文提出了一种负抽样方法——批量混合负抽样(BMNS) ，该方法采用批量混合操作产生附加负数，用于模型训练。具体来说，BMNS 首先根据采样的混合系数生成新的负项，然后根据频率设计一个量身定制的正确策略，以匹配采样的软最大损失(softmax loss) Β分布。这样，既减少了从小批处理中重新编码项的工作量，又提高了负集的表示空间。在四个实际数据集上的实验表明，BMNS 方法优于竞争性负内批抽样方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Batch-Mix+Negative+Sampling+for+Learning+Recommendation+Retrievers)|0| +|[Dynamic Embedding Size Search with Minimum Regret for Streaming Recommender System](https://doi.org/10.1145/3583780.3615135)|Bowei He, Xu He, Renrui Zhang, Yingxue Zhang, Ruiming Tang, Chen Ma|Huawei Noah's Ark Lab, Shenzhen, China; The Chinese University of Hong Kong, Hong Kong, Hong Kong; Huawei Noah's Ark Lab Montreal, Montreal, PQ, Canada; City University of Hong Kong, Hong Kong, Hong Kong|With the continuous increase of users and items, conventional recommender systems trained on static datasets can hardly adapt to changing environments. The high-throughput data requires the model to be updated in a timely manner for capturing the user interest dynamics, which leads to the emergence of streaming recommender systems. Due to the prevalence of deep learning-based recommender systems, the embedding layer is widely adopted to represent the characteristics of users, items, and other features in low-dimensional vectors. However, it has been proved that setting an identical and static embedding size is sub-optimal in terms of recommendation performance and memory cost, especially for streaming recommendations. To tackle this problem, we first rethink the streaming model update process and model the dynamic embedding size search as a bandit problem. Then, we analyze and quantify the factors that influence the optimal embedding sizes from the statistics perspective. Based on this, we propose the \textbf{D}ynamic \textbf{E}mbedding \textbf{S}ize \textbf{S}earch (\textbf{DESS}) method to minimize the embedding size selection regret on both user and item sides in a non-stationary manner. Theoretically, we obtain a sublinear regret upper bound superior to previous methods. Empirical results across two recommendation tasks on four public datasets also demonstrate that our approach can achieve better streaming recommendation performance with lower memory cost and higher time efficiency.|随着用户和项目的不断增加，传统的基于静态数据集的推荐系统难以适应不断变化的环境。高吞吐量数据要求模型及时更新，以捕捉用户兴趣动态，从而导致流式推荐系统的出现。由于基于深度学习的推荐系统的普及，嵌入层被广泛采用来在低维向量中表示用户、项目等特征。然而，已经证明，设置一个相同的静态嵌入大小在推荐性能和内存成本方面是次优的，特别是对于流式推荐。为了解决这个问题，我们首先重新考虑了流模型更新过程，并将动态嵌入大小搜索模型建模为盗贼问题。然后，从统计学的角度对影响最优嵌入规模的因素进行了分析和量化。在此基础上，提出了一种动态 textbf { D }嵌入 textbf { E }嵌入 textbf { S } ize textbf { S } earch (textbf { DESS })方法，以非平稳方式最小化用户端和项目端的嵌入大小选择遗憾。从理论上，我们得到了一个次线性后悔上界优于以往的方法。对四个公共数据集的两个推荐任务的实验结果也表明，该方法能够以较低的内存开销和较高的时间效率获得较好的流推荐性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Dynamic+Embedding+Size+Search+with+Minimum+Regret+for+Streaming+Recommender+System)|0| +|[Text Matching Improves Sequential Recommendation by Reducing Popularity Biases](https://doi.org/10.1145/3583780.3615077)|Zhenghao Liu, Sen Mei, Chenyan Xiong, Xiaohua Li, Shi Yu, Zhiyuan Liu, Yu Gu, Ge Yu|Carnegie Mellon University, Pittsburgh, PA, USA; Tsinghua University, Beijing, China; Northeastern University, Shenyang, China|This paper proposes Text mAtching based SequenTial rEcommendation model (TASTE), which maps items and users in an embedding space and recommends items by matching their text representations. TASTE verbalizes items and user-item interactions using identifiers and attributes of items. To better characterize user behaviors, TASTE additionally proposes an attention sparsity method, which enables TASTE to model longer user-item interactions by reducing the self-attention computations during encoding. Our experiments show that TASTE outperforms the state-of-the-art methods on widely used sequential recommendation datasets. TASTE alleviates the cold start problem by representing long-tail items using full-text modeling and bringing the benefits of pretrained language models to recommendation systems. Our further analyses illustrate that TASTE significantly improves the recommendation accuracy by reducing the popularity bias of previous item id based recommendation models and returning more appropriate and text-relevant items to satisfy users. All codes are available at https://github.com/OpenMatch/TASTE.|提出了一种基于文本匹配的序列推荐模型(TASTE) ，该模型将项目和用户映射到嵌入空间中，通过匹配项目的文本表示来推荐项目。TASTE 使用项目的标识符和属性语言化项目和用户-项目交互。为了更好地描述用户行为，TASTE 还提出了一种注意稀疏方法，该方法通过减少编码过程中的自我注意计算，使 TASTE 能够模拟更长时间的用户-项目交互。我们的实验表明，在广泛使用的顺序推荐数据集上，TASTE 优于最先进的方法。TASTE 通过使用全文建模来表示长尾项目，并将预训练语言模型的优点带到推荐系统中，从而缓解了冷启动问题。我们进一步的分析表明，TASTE 通过减少以前基于项目 ID 的推荐模型的流行偏差和返回更合适的和文本相关的项目来满足用户，从而显著提高了推荐的准确性。所有密码都在 https://github.com/openmatch/taste。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Text+Matching+Improves+Sequential+Recommendation+by+Reducing+Popularity+Biases)|0| +|[Cracking the Code of Negative Transfer: A Cooperative Game Theoretic Approach for Cross-Domain Sequential Recommendation](https://doi.org/10.1145/3583780.3614828)|Chung Park, Taesan Kim, Taekyoon Choi, Junui Hong, Yelim Yu, Mincheol Cho, Kyunam Lee, Sungil Ryu, Hyungjun Yoon, Minsung Choi, Jaegul Choo|SK Telecom & Korea Advanced Institute of Science and Technology, Seoul & Daejeon, Republic of Korea; SK Telelcom, Seoul, Republic of Korea; SK Telelcom & Korea Advanced Institute of Science and Technology, Seoul & Daejeon, Republic of Korea; NAVER, Seongnam, Republic of Korea; Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea|This paper investigates Cross-Domain Sequential Recommendation (CDSR), a promising method that uses information from multiple domains (more than three) to generate accurate and diverse recommendations, and takes into account the sequential nature of user interactions. The effectiveness of these systems often depends on the complex interplay among the multiple domains. In this dynamic landscape, the problem of negative transfer arises, where heterogeneous knowledge between dissimilar domains leads to performance degradation due to differences in user preferences across these domains. As a remedy, we propose a new CDSR framework that addresses the problem of negative transfer by assessing the extent of negative transfer from one domain to another and adaptively assigning low weight values to the corresponding prediction losses. To this end, the amount of negative transfer is estimated by measuring the marginal contribution of each domain to model performance based on a cooperative game theory. In addition, a hierarchical contrastive learning approach that incorporates information from the sequence of coarse-level categories into that of fine-level categories (e.g., item level) when implementing contrastive learning was developed to mitigate negative transfer. Despite the potentially low relevance between domains at the fine-level, there may be higher relevance at the category level due to its generalised and broader preferences. We show that our model is superior to prior works in terms of model performance on two real-world datasets across ten different domains.|本文研究了跨域序列推荐(CDSR) ，这是一种利用多个域(超过三个)的信息来生成准确和多样化推荐的有前途的方法，并考虑了用户交互的序列特性。这些系统的有效性往往取决于多个领域之间复杂的相互作用。在这种动态环境中，出现了负迁移问题，即不同领域之间的异质知识由于这些领域的用户偏好不同而导致性能下降。作为补救措施，我们提出了一个新的 CDSR 框架，通过评估从一个领域到另一个领域的负转移程度，并自适应地为相应的预测损失赋予低权值来解决负转移问题。为此，负向转移的数量是通过衡量每个领域对基于合作博弈理论的绩效模型的边际贡献来估计的。此外，在实施对比学习的过程中，还开发了一种分层对比学习方法，将粗级别类别的信息整合到细级别类别的信息中(例如，项目级别) ，以减轻负迁移。尽管在精细层面上各领域之间的相关性可能较低，但在类别层面上可能具有更高的相关性，因为它具有普遍性和更广泛的偏好。我们表明，我们的模型是优于以往的工作方面的两个真实世界的数据集在十个不同的领域的模型性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Cracking+the+Code+of+Negative+Transfer:+A+Cooperative+Game+Theoretic+Approach+for+Cross-Domain+Sequential+Recommendation)|0| |[Personalized Interest Sustainability Modeling for Sequential POI Recommendation](https://doi.org/10.1145/3583780.3615278)|Zewen Long, Liang Wang, Qiang Liu, Shu Wu|CRIPAC, MAIS, Institute of Automation, Chinese Academy of Sciences & University of Chinese Academy of Science, Beijing, China|Sequential point-of-interest (POI) recommendation endeavors to capture users' dynamic interests based on their historical check-ins, subsequently predicting the next POIs that they are most likely to visit.Existing methods conventionally capture users' personalized dynamic interests from their chronological sequences of visited POIs. However, these methods fail to explicitly consider personalized interest sustainability, which means whether each user's interest in specific POIs will sustain beyond the training time. In this work, we propose a personalized INterest Sustainability modeling framework for sequential POI REcommendation, INSPIRE for brevity. Different from existing methods that directly recommend next POIs through users' historical trajectories, our proposed INSPIRE focuses on users' personalized interest sustainability. Specifically, we first develop a new task to predict whether each user will visit the POIs in the recent period of the training time. Afterwards, to remedy the sparsity issue of users' check-in history, we propose to augment users' check-in history in three ways: geographical, intrinsic, and extrinsic schemes. Extensive experiments are conducted on two real-world datasets and results show that INSPIRE outperforms existing next POI solutions.|连续感兴趣点(POI)推荐努力根据用户的历史签入来捕获用户的动态兴趣，随后预测他们最有可能访问的下一个 POI。现有的方法通常从用户访问的 POI 的时间序列中获取用户的个性化动态兴趣。然而，这些方法没有明确考虑个性化兴趣的可持续性，这意味着每个用户对特定 POI 的兴趣是否会持续到培训时间之后。在这项工作中，我们提出了一个个性化的兴趣可持续性建模框架的顺序 POI 推荐，简短的 INSPIRE。与现有的通过用户历史轨迹直接推荐下一个 POI 的方法不同，我们提出的 INSPIRE 侧重于用户个性化兴趣的可持续性。具体来说，我们首先开发一个新的任务来预测每个用户是否会在最近的培训时间内访问 POI。然后，针对用户签入历史的稀疏性问题，提出了从地理方案、内部方案和外部方案三个方面增加用户签入历史的方法。在两个实际数据集上进行了广泛的实验，结果表明 INSPIRE 的性能优于现有的下一个 POI 解决方案。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Personalized+Interest+Sustainability+Modeling+for+Sequential+POI+Recommendation)|0| |[Differentiable Retrieval Augmentation via Generative Language Modeling for E-commerce Query Intent Classification](https://doi.org/10.1145/3583780.3615210)|Chenyu Zhao, Yunjiang Jiang, Yiming Qiu, Han Zhang, WenYun Yang|JD.com, Beijing, China|Retrieval augmentation, which enhances downstream models by a knowledge retriever and an external corpus instead of by merely increasing the number of model parameters, has been successfully applied to many natural language processing (NLP) tasks such as text classification, question answering and so on. However, existing methods that separately or asynchronously train the retriever and downstream model mainly due to the non-differentiability between the two parts, usually lead to degraded performance compared to end-to-end joint training.|检索增强技术通过知识检索器和外部语料库对下游模型进行增强，而不仅仅是增加模型参数，已经成功地应用于文本分类、问答等自然语言处理任务中。然而，现有的分别或异步训练检索器和下游模型的方法，主要是由于两部分之间的不可微性，通常导致性能下降相比，端到端联合训练。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Differentiable+Retrieval+Augmentation+via+Generative+Language+Modeling+for+E-commerce+Query+Intent+Classification)|0| |[Enhancing E-commerce Product Search through Reinforcement Learning-Powered Query Reformulation](https://doi.org/10.1145/3583780.3615474)|Sanjay Agrawal, Srujana Merugu, Vivek Sembium|Amazon.com Inc., Bengaluru, India|Query reformulation (QR) is a widely used technique in web and product search. In QR, we map a poorly formed or low coverage user query to a few semantically similar queries that are rich in product coverage, thereby enabling effective targeted searches with less cognitive load on the user. Recent QR approaches based on generative language models are superior to informational retrieval-based methods but exhibit key limitations: (i) generated reformulations often have low lexical diversity and fail to retrieve a large set of relevant products of a wider variety, (ii) the training objective of generative models does not incorporate a our goal of improving product coverage. In this paper, we propose RLQR (Reinforcement Learning for Query Reformulations), for generating high quality diverse reformulations which aim to maximize the product coverage (number of distinct relevant products returned). We evaluate our approach against supervised generative models and strong RL-based methods. Our experiments demonstrate a 28.6% increase in product coverage compared to a standard generative model, outperforming SOTA benchmarks by a significant margin. We also conduct our experiments on an external Amazon shopping dataset and demonstrate increased product coverage over SOTA algorithms.|查询重构(QR)是一种广泛应用于网络和产品搜索的技术。在 QR 中，我们将一个形式不正确或覆盖率低的用户查询映射到几个语义相似的查询，这些查询具有丰富的产品覆盖率，从而使得有效的目标搜索能够减轻用户的认知负荷。最近基于生成语言模型的 QR 方法优于基于信息检索的方法，但表现出关键的局限性: (i)生成的重新编排通常具有较低的词汇多样性，并且不能检索更多种类的大量相关产品，(ii)生成模型的培训目标不包含我们提高产品覆盖率的目标。在本文中，我们提出了 RLQR (查询重构的强化学习) ，用于产生高质量的多样化重构，目的是最大化产品覆盖率(返回的不同相关产品的数量)。我们评估我们的方法对监督生成模型和强 RL 为基础的方法。我们的实验表明，与标准生成模型相比，产品覆盖率提高了28.6% ，远远超过 SOTA 基准。我们还在一个外部 Amazon 购物数据集上进行了实验，并证明了 SOTA 算法对产品覆盖率的提高。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Enhancing+E-commerce+Product+Search+through+Reinforcement+Learning-Powered+Query+Reformulation)|0| @@ -24,527 +24,527 @@ |[CPMR: Context-Aware Incremental Sequential Recommendation with Pseudo-Multi-Task Learning](https://doi.org/10.1145/3583780.3615512)|Qingtian Bian, Jiaxing Xu, Hui Fang, Yiping Ke|Shanghai University of Finance and Economics, Shanghai, China; Nanyang Technological University, Singapore, Singapore|The motivations of users to make interactions can be divided into static preference and dynamic interest. To accurately model user representations over time, recent studies in sequential recommendation utilize information propagation and evolution to mine from batches of arriving interactions. However, they ignore the fact that people are easily influenced by the recent actions of other users in the contextual scenario, and applying evolution across all historical interactions dilutes the importance of recent ones, thus failing to model the evolution of dynamic interest accurately. To address this issue, we propose a Context-Aware Pseudo-Multi-Task Recommender System (CPMR) to model the evolution in both historical and contextual scenarios by creating three representations for each user and item under different dynamics: static embedding, historical temporal states, and contextual temporal states. To dually improve the performance of temporal states evolution and incremental recommendation, we design a Pseudo-Multi-Task Learning (PMTL) paradigm by stacking the incremental single-target recommendations into one multi-target task for joint optimization. Within the PMTL paradigm, CPMR employs a shared-bottom network to conduct the evolution of temporal states across historical and contextual scenarios, as well as the fusion of them at the user-item level. In addition, CPMR incorporates one real tower for incremental predictions, and two pseudo towers dedicated to updating the respective temporal states based on new batches of interactions. Experimental results on four benchmark recommendation datasets show that CPMR consistently outperforms state-of-the-art baselines and achieves significant gains on three of them. The code is available at: https://github.com/DiMarzioBian/CPMR.|用户进行交互的动机可以分为静态偏好和动态兴趣。为了随时间精确地建立用户表示模型，最近在顺序推荐方面的研究利用信息传播和进化从到达的交互批次中挖掘信息。然而，他们忽略了这样一个事实，即人们很容易受到其他用户最近在上下文场景中的行为的影响，并且在所有的历史交互中应用进化会削弱最近交互的重要性，从而无法精确地模拟动态兴趣的进化。为了解决这个问题，我们提出了一个上下文感知的伪多任务推荐系统(Context-Aware Pseudo-multi-Task) ，通过在不同的动态下为每个用户和项目创建三种表示(静态嵌入、历史时间状态和上下文时间状态)来模拟历史和上下文场景中的演化。为了同时提高时间状态演化和增量推荐的性能，我们设计了一个伪多任务学习(PMTL)范式，将增量的单目标推荐叠加到一个多目标任务中进行联合优化。在 PMTL 范式中，CPMR 使用一个共享底层网络来跨越历史和上下文场景进行时间状态的演化，以及在用户项目级别进行时间状态的融合。此外，CPMR 还包括一个用于增量预测的实际塔，以及两个用于根据新批量的相互作用更新各自的时间状态的伪塔。对四个基准推荐数据集的实验结果表明，CPMR 的性能始终优于最先进的基准，并在其中三个基准上取得了显著的增益。密码可于以下 https://github.com/dimarziobian/cpmr 索取:。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=CPMR:+Context-Aware+Incremental+Sequential+Recommendation+with+Pseudo-Multi-Task+Learning)|0| |[Leveraging Post-Click User Behaviors for Calibrated Conversion Rate Prediction Under Delayed Feedback in Online Advertising](https://doi.org/10.1145/3583780.3615161)|Yuyao Guo, Xiang Ao, Qiming Liu, Qing He|Institute of Computing Technology, Chinese Academy of Sciences & University of Chinese Academy of Sciences, Beijing, China|Obtaining accurately calibrated conversion rate predictions is essential for the bidding and ranking process in online advertising systems. Nevertheless, the inherent latency between clicks and conversions leads to delayed feedback, which may introduce bias into the prediction models. Compared to indefinitely long conversion delays, post-click user behaviors manifest within a relatively brief time and have been empirically validated to exert a favorable influence on the precision of conversion rate estimates. In light of this, we propose a novel approach that leverages post-click user behaviors to calibrate conversion rate predictions. Specifically, we treat user behaviors as predictable targets to improve accuracy and enhance timeliness. An adaptive loss function based on task uncertainty is employed for multi-task learning. To further reduce calibration error, we integrate the modified prediction model with a parameterized scaling technique. Experiments conducted on two real-world datasets demonstrate that our proposed method outperforms existing models in providing more calibrated predictions.|获得准确校准的转换率预测是必不可少的投标和排名过程中的在线广告系统。然而，点击和转换之间的内在延迟会导致延迟反馈，这可能会给预测模型带来偏差。与无限长的转换延迟相比，点击后的用户行为在相对较短的时间内表现出来，并且已经被经验验证对转换率估计的精度产生了有利的影响。鉴于此，我们提出了一种新的方法，利用点击后的用户行为来校准转换率预测。具体来说，我们将用户行为视为可预测的目标，以提高准确性和及时性。采用基于任务不确定性的自适应损失函数进行多任务学习。为了进一步减小校准误差，我们将改进的预测模型与参数化标度技术相结合。在两个真实世界数据集上进行的实验表明，我们提出的方法在提供更精确的预测方面优于现有的模型。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Leveraging+Post-Click+User+Behaviors+for+Calibrated+Conversion+Rate+Prediction+Under+Delayed+Feedback+in+Online+Advertising)|0| |[DAE: Distribution-Aware Embedding for Numerical Features in Click-Through Rate Prediction](https://doi.org/10.1145/3583780.3615212)|Bin Shen, Jingran Xu, Xu Min, Zeyu Ke, Yong He, Liang Zhang, Xin Dong, Linjian Mo|Ant Group, Hangzhou, China; Antgroup, Hangzhou, China|Numerical features are an important type of input for CTR prediction models. Recently, several discretization and numerical transformation methods have been proposed to deal with numerical features. However, existing approaches do not fully consider compatibility with different distributions. Here, we propose a novel numerical feature embedding framework, called Distribution-Aware Embedding (DAE), which is applicable to various numerical feature distributions. First, DAE efficiently approximates the cumulative distribution function by estimating the expectation of the order statistics. Then, the distribution information is applied to the embedding layer by nonlinear interpolation. Finally, to capture both local and global information, we aggregate the embeddings at multiple scales to obtain the final representation. Empirical results validate the effectiveness of DAE compared to the baselines, while demonstrating the adaptability to different CTR models and distributions.|数值特征是 CTR 预测模型的重要输入类型。近年来，人们提出了几种离散化和数值变换方法来处理数值特征。但是，现有的方法并不完全考虑与不同发行版的兼容性。在这里，我们提出了一种新的数值特征嵌入框架，称为分布感知嵌入(DAE) ，它适用于各种数值特征分布。首先，DAE 通过估计订单统计数据的期望值来有效地逼近累积分布函数。然后，通过非线性插值将分布信息应用到嵌入层。最后，为了同时捕获局部和全局信息，我们在多个尺度上聚合嵌入信息以获得最终的表示。实证结果验证了 DAE 与基线相比的有效性，同时证明了 DAE 对不同 CTR 模型和分布的适应性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=DAE:+Distribution-Aware+Embedding+for+Numerical+Features+in+Click-Through+Rate+Prediction)|0| -|[Unified Generative & Dense Retrieval for Query Rewriting in Sponsored Search](https://doi.org/10.1145/3583780.3615459)|Akash Kumar Mohankumar, Bhargav Dodla, Gururaj K, Amit Singh|New York University; Fudan University|Named Entity Recognition (NER) is the task of identifying spans that represent entities in sentences. Whether the entity spans are nested or discontinuous, the NER task can be categorized into the flat NER, nested NER, and discontinuous NER subtasks. These subtasks have been mainly solved by the token-level sequence labelling or span-level classification. However, these solutions can hardly tackle the three kinds of NER subtasks concurrently. To that end, we propose to formulate the NER subtasks as an entity span sequence generation task, which can be solved by a unified sequence-to-sequence (Seq2Seq) framework. Based on our unified framework, we can leverage the pre-trained Seq2Seq model to solve all three kinds of NER subtasks without the special design of the tagging schema or ways to enumerate spans. We exploit three types of entity representations to linearize entities into a sequence. Our proposed framework is easy-to-implement and achieves state-of-the-art (SoTA) or near SoTA performance on eight English NER datasets, including two flat NER datasets, three nested NER datasets, and three discontinuous NER datasets.|命名实体识别(NER)是识别句子中表示实体的跨度的任务。无论实体跨度是嵌套的还是不连续的，NER 任务都可以分为平面的 NER、嵌套的 NER 和不连续的 NER 子任务。这些子任务主要通过令牌级序列标记或跨级分类来解决。然而，这些解决方案很难同时处理三种 NER 子任务。为此，我们提出将 NER 子任务表示为一个实体跨度序列生成任务，该任务可以通过一个统一的序列到序列(Seq2Seq)框架来解决。基于我们的统一框架，我们可以利用预先训练的 Seq2Seq 模型来解决所有三种 NER 子任务，而不需要特别设计标记模式或枚举范围的方法。我们利用三种类型的实体表示将实体线性化为一个序列。我们提出的框架易于实现，并在八个英文 NER 数据集上实现了最先进的(SoTA)或接近 SoTA 的性能，包括两个平面 NER 数据集，三个嵌套的 NER 数据集和三个不连续的 NER 数据集。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Unified+Generative+&+Dense+Retrieval+for+Query+Rewriting+in+Sponsored+Search)|0| +|[Unified Generative & Dense Retrieval for Query Rewriting in Sponsored Search](https://doi.org/10.1145/3583780.3615459)|Akash Kumar Mohankumar, Bhargav Dodla, Gururaj K, Amit Singh|Fudan University; New York University|Named Entity Recognition (NER) is the task of identifying spans that represent entities in sentences. Whether the entity spans are nested or discontinuous, the NER task can be categorized into the flat NER, nested NER, and discontinuous NER subtasks. These subtasks have been mainly solved by the token-level sequence labelling or span-level classification. However, these solutions can hardly tackle the three kinds of NER subtasks concurrently. To that end, we propose to formulate the NER subtasks as an entity span sequence generation task, which can be solved by a unified sequence-to-sequence (Seq2Seq) framework. Based on our unified framework, we can leverage the pre-trained Seq2Seq model to solve all three kinds of NER subtasks without the special design of the tagging schema or ways to enumerate spans. We exploit three types of entity representations to linearize entities into a sequence. Our proposed framework is easy-to-implement and achieves state-of-the-art (SoTA) or near SoTA performance on eight English NER datasets, including two flat NER datasets, three nested NER datasets, and three discontinuous NER datasets.|命名实体识别(NER)是识别句子中表示实体的跨度的任务。无论实体跨度是嵌套的还是不连续的，NER 任务都可以分为平面的 NER、嵌套的 NER 和不连续的 NER 子任务。这些子任务主要通过令牌级序列标记或跨级分类来解决。然而，这些解决方案很难同时处理三种 NER 子任务。为此，我们提出将 NER 子任务表示为一个实体跨度序列生成任务，该任务可以通过一个统一的序列到序列(Seq2Seq)框架来解决。基于我们的统一框架，我们可以利用预先训练的 Seq2Seq 模型来解决所有三种 NER 子任务，而不需要特别设计标记模式或枚举范围的方法。我们利用三种类型的实体表示将实体线性化为一个序列。我们提出的框架易于实现，并在八个英文 NER 数据集上实现了最先进的(SoTA)或接近 SoTA 的性能，包括两个平面 NER 数据集，三个嵌套的 NER 数据集和三个不连续的 NER 数据集。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Unified+Generative+&+Dense+Retrieval+for+Query+Rewriting+in+Sponsored+Search)|0| |[SPM: Structured Pretraining and Matching Architectures for Relevance Modeling in Meituan Search](https://doi.org/10.1145/3583780.3615500)|Wen Zan, Yaopeng Han, Xiaotian Jiang, Yao Xiao, Yang Yang, Dayao Chen, Sheng Chen|Meituan Inc., Beijing, China|In e-commerce search, relevance between query and documents is an essential requirement for satisfying user experience. Different from traditional e-commerce platforms that offer products, users search on life service platforms such as Meituan mainly for product providers, which usually have abundant structured information, e.g. name, address, category, thousands of products. Modeling search relevance with these rich structured contents is challenging due to the following issues: (1) there is language distribution discrepancy among different fields of structured document, making it difficult to directly adopt off-the-shelf pretrained language model based methods like BERT. (2) different fields usually have different importance and their length vary greatly, making it difficult to extract document information helpful for relevance matching. To tackle these issues, in this paper we propose a novel two-stage pretraining and matching architecture for relevance matching with rich structured documents. At pretraining stage, we propose an effective pretraining method that employs both query and multiple fields of document as inputs, including an effective information compression method for lengthy fields. At relevance matching stage, a novel matching method is proposed by leveraging domain knowledge in search query to generate more effective document representations for relevance scoring. Extensive offline experiments and online A/B tests on millions of users verify that the proposed architectures effectively improve the performance of relevance modeling. The model has already been deployed online, serving the search traffic of Meituan for over a year.|在电子商务搜索中，查询与文档之间的相关性是满足用户体验的基本要求。与提供产品的传统电子商务平台不同，用户在生活服务平台(例如主要为产品供应商提供的美团)上进行搜索，这些平台通常有大量的结构化信息，例如名称、地址、类别、数以千计的产品。用这些丰富的结构化内容来建立搜索相关性具有挑战性，这是由于以下问题: (1)不同领域的结构性文件之间存在语言分布差异，使得很难直接采用现成的基于语言模型的方法，比如 BERT。(2)不同领域的重要性不同，领域长度差异较大，难以提取有助于相关匹配的文档信息。为了解决这些问题，本文提出了一种新的两阶段预训练和匹配体系结构，用于富结构化文档的相关性匹配。在预训练阶段，我们提出了一种有效的预训练方法，该方法同时使用文档的查询和多个字段作为输入，包括一种有效的长字段信息压缩方法。在相关匹配阶段，利用搜索查询中的领域知识，提出了一种新的匹配方法，为相关评分生成更有效的文档表示。大量的离线实验和对数百万用户的在线 A/B 测试验证了所提出的体系结构有效地提高了相关建模的性能。该模型已经在网上部署，为美团的搜索流量服务了一年多。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=SPM:+Structured+Pretraining+and+Matching+Architectures+for+Relevance+Modeling+in+Meituan+Search)|0| |[Towards Filling the Gap in Conversational Search: From Passage Retrieval to Conversational Response Generation](https://doi.org/10.1145/3583780.3615132)|Weronika Lajewska, Krisztian Balog|University of Stavanger, Stavanger, Norway|Research on conversational search has so far mostly focused on query rewriting and multi-stage passage retrieval. However, synthesizing the top retrieved passages into a complete, relevant, and concise response is still an open challenge. Having snippet-level annotations of relevant passages would enable both (1) the training of response generation models that are able to ground answers in actual statements and (2) the automatic evaluation of the generated responses in terms of completeness. In this paper, we address the problem of collecting high-quality snippet-level answer annotations for two of the TREC Conversational Assistance track datasets. To ensure quality, we first perform a preliminary annotation study, employing different task designs, crowdsourcing platforms, and workers with different qualifications. Based on the outcomes of this study, we refine our annotation protocol before proceeding with the full-scale data collection. Overall, we gather annotations for 1.8k question-paragraph pairs, each annotated by three independent crowd workers. The process of collecting data at this magnitude also led to multiple insights about the problem that can inform the design of future response-generation methods. This is an extended version of the article published with the same title in the Proceedings of CIKM'23.|会话搜索的研究目前主要集中在查询重写和多阶段文章检索两个方面。然而，将检索到的顶级段落综合成一个完整的、相关的、简洁的回应仍然是一个公开的挑战。对相关段落进行片段级注释，可以(1)训练能够在实际语句中找到答案的响应生成模型，(2)根据完整性自动评估生成的响应。在本文中，我们解决了为两个 TREC 会话辅助跟踪数据集收集高质量片段级答案注释的问题。为了保证质量，我们首先进行了初步的注释研究，采用了不同的任务设计，众包平台，以及不同资历的工人。基于本研究的结果，我们在进行全面的数据收集之前优化了我们的注释协议。总的来说，我们收集了1.8 k 问题-段落对的注释，每个注释由三个独立的人群工作者。在这种规模上收集数据的过程也导致了对这个问题的多种认识，这些认识可以为设计未来的响应生成方法提供信息。这是一个扩展版本的文章与同一标题发表在 CIKM’23会议记录。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Towards+Filling+the+Gap+in+Conversational+Search:+From+Passage+Retrieval+to+Conversational+Response+Generation)|0| |[IUI: Intent-Enhanced User Interest Modeling for Click-Through Rate Prediction](https://doi.org/10.1145/3583780.3614939)|Mao Pan, Tao Yu, Kun Zhou, Zheng Li, Dongyue Wang, Zhuoye Ding, Xiwei Zhao, Sulong Xu|JD.com, Inc., Beijing, China|Click-Through Rate (CTR) prediction is becoming increasingly vital in many industrial applications, such as recommendations and online advertising. How to precisely capture users' dynamic and evolving interests from previous interactions (e.g., clicks, purchases, etc.) is a challenging task in CTR prediction. Mainstream approaches focus on disentangling user interests in a heuristic way or modeling user interests into a static representation. However, these approaches overlook the importance of users' current intent and the complex interactions between their current intent and global interests. To address these concerns, in this paper, we propose a novel intent-enhanced user interest modeling for click-through rate prediction in large-scale e-commerce recommendations, abbreviated as IUI. Methodologically, different from existing works, we consider users' recent interactions to be inspired by their implicit intent and then leverage an intent-aware network to model their current local interests in a more precise and fine-grained manner. In addition, to obtain a more stable co-dependent global and local interest representation, we employ a co-attention network capable of activating the corresponding interest in global-level interactions and capturing the dynamic interactions between global- and local-level interaction behaviors. Finally, we incorporate self-supervised learning into the model training by maximizing the mutual information between the global and local representations obtained via the above two networks to enhance the CTR prediction performance. Compared with existing methods, IUI benefits from the different granularity of user interest to generate a more accurate and comprehensive preference representation. Experimental results demonstrate that the proposed model outperforms previous state-of-the-art methods in various metrics on three real-world datasets. In addition, an online A/B test deployed on the JD recommendation platforms shows a promising improvement across multiple evaluation metrics.|在诸如推荐和在线广告等许多工业应用中，点进率预测(ctrl)正变得越来越重要。如何从以前的交互(例如点击、购买等)中准确捕捉用户的动态和不断变化的兴趣是 CTR 预测中的一个具有挑战性的任务。主流方法侧重于以启发式方式分离用户兴趣，或者将用户兴趣建模为静态表示。然而，这些方法忽略了用户当前意图的重要性，以及用户当前意图与全局兴趣之间的复杂交互。为了解决这些问题，在本文中，我们提出了一种新的意图增强的用户兴趣模型，用于大规模电子商务推荐中的点进率预测，简称为 IUI。在方法论上，与现有的作品不同，我们认为用户最近的互动是受到他们的隐含意图的启发，然后利用意图感知网络，以更精确和细粒度的方式建模他们当前的本地兴趣。此外，为了获得更稳定的相互依赖的全局和局部兴趣表示，我们采用了能够激活全局层面相互作用中的相应兴趣并捕获全局和局部层面相互作用行为之间的动态相互作用的共注意网络。最后，将自监督学习算法引入模型训练中，通过最大化两个网络所获得的全局和局部表示之间的互信息来提高 CTR 预测性能。与现有的方法相比，IUI 受益于不同粒度的用户兴趣，以生成更准确和全面的偏好表示。实验结果表明，该模型在三个实际数据集的各种度量指标上优于先前的最新方法。此外，部署在 JD 推荐平台上的在线 A/B 测试显示了跨多个评估指标的有希望的改进。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=IUI:+Intent-Enhanced+User+Interest+Modeling+for+Click-Through+Rate+Prediction)|0| |[TPUF: Enhancing Cross-domain Sequential Recommendation via Transferring Pre-trained User Features](https://doi.org/10.1145/3583780.3615094)|Yujia Ding, Huan Li, Ke Chen, Lidan Shou|Zhejiang University, Hangzhou, China|Sequential recommendation has long been challenged by data sparsity issues. Most recently, cross-domain sequential recommendation (CDSR) techniques have been proposed to leverage sequential interaction data from other domains. However, accessing raw data from source domains is often restricted due to privacy concerns. To tackle this issue, we introduce TPUF, a novel CDSR model that transfers pre-trained latent user features from the source domain (UFS) instead of the original interaction data. By doing so, TPUF improves recommendation effectiveness while maintaining practicality. TPUF has three functional characteristics: (1) It is a feature mapping-and-aggregation framework that does not impose specific constraints on the nature of pre-trained UFS. (2) It incorporates a temporal feature mapping unit to effectively extract domain-shared information from UFS with temporal information recovered. (3) It additionally employs an adversarial feature alignment unit to align features across domains to combat feature transfer bias. Experimental results on real-world datasets demonstrate that TPUF outperforms other state-of-the-art cross-domain recommendation models and is compatible with multiple UFS types.|长期以来，连续推荐一直受到数据稀疏问题的挑战。最近，跨域顺序推荐(CDSR)技术被提出来利用来自其他域的顺序交互数据。然而，从源域访问原始数据通常由于隐私问题而受到限制。为了解决这个问题，我们引入了 TPUF，一种新的 CDSR 模型，它从源域(UFS)传输预先训练好的潜在用户特征，而不是传输原始的交互数据。通过这样做，TPUF 在保持实用性的同时提高了推荐的有效性。TPUF 具有三个功能特征: (1)它是一个特征映射和聚合框架，不对预先训练的 UFS 的性质施加特定的限制。(2)结合时态特征映射单元，有效地提取 UFS 中的域共享信息，并恢复时态信息。(3)采用对抗性特征对齐单元对特征进行跨域对齐，以对抗特征迁移偏差。在实际数据集上的实验结果表明，TPUF 的性能优于其他最先进的跨域推荐模型，并且可以兼容多种 UFS 类型。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=TPUF:+Enhancing+Cross-domain+Sequential+Recommendation+via+Transferring+Pre-trained+User+Features)|0| -|[Zero-shot Item-based Recommendation via Multi-task Product Knowledge Graph Pre-Training](https://doi.org/10.1145/3583780.3615110)|Ziwei Fan, Zhiwei Liu, Shelby Heinecke, Jianguo Zhang, Huan Wang, Caiming Xiong, Philip S. Yu|University of Illinois Chicago, Chicago, IL, USA; Salesforce AI Research, Palo Alto, CA, USA|Existing recommender systems face difficulties with zero-shot items, i.e. items that have no historical interactions with users during the training stage. Though recent works extract universal item representation via pre-trained language models (PLMs), they ignore the crucial item relationships. This paper presents a novel paradigm for the Zero-Shot Item-based Recommendation (ZSIR) task, which pre-trains a model on product knowledge graph (PKG) to refine the item features from PLMs. We identify three challenges for pre-training PKG, which are multi-type relations in PKG, semantic divergence between item generic information and relations and domain discrepancy from PKG to downstream ZSIR task. We address the challenges by proposing four pre-training tasks and novel task-oriented adaptation (ToA) layers. Moreover, this paper discusses how to fine-tune the model on new recommendation task such that the ToA layers are adapted to ZSIR task. Comprehensive experiments on 18 markets dataset are conducted to verify the effectiveness of the proposed model in both knowledge prediction and ZSIR task.|现有的推荐系统面临着“零拍摄”项目的困难，也就是说，这些项目在培训阶段与用户没有历史性的交互。近年来的研究虽然通过预训练语言模型(PLM)提取通用项表示，但忽视了关键项之间的关系。提出了一种新的基于零拍项目推荐(ZSIR)任务的实现方法，该方法通过对产品知识图(PKG)模型进行预训练来提取 PLM 中的项目特征。我们确定了 PKG 训练前的三个挑战，即 PKG 中的多类型关系、项目类属信息和关系之间的语义分歧以及从 PKG 到下游 ZSIR 任务之间的领域差异。我们通过提出四个训练前任务和新的面向任务的适应(ToA)层来应对这些挑战。此外，本文还讨论了如何对新推荐任务模型进行微调，使 ToA 层适应 ZSIR 任务。通过对18个市场数据集的综合实验，验证了该模型在知识预测和 ZSIR 任务中的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Zero-shot+Item-based+Recommendation+via+Multi-task+Product+Knowledge+Graph+Pre-Training)|0| +|[Zero-shot Item-based Recommendation via Multi-task Product Knowledge Graph Pre-Training](https://doi.org/10.1145/3583780.3615110)|Ziwei Fan, Zhiwei Liu, Shelby Heinecke, Jianguo Zhang, Huan Wang, Caiming Xiong, Philip S. Yu|Salesforce AI Research, Palo Alto, CA, USA; University of Illinois Chicago, Chicago, IL, USA|Existing recommender systems face difficulties with zero-shot items, i.e. items that have no historical interactions with users during the training stage. Though recent works extract universal item representation via pre-trained language models (PLMs), they ignore the crucial item relationships. This paper presents a novel paradigm for the Zero-Shot Item-based Recommendation (ZSIR) task, which pre-trains a model on product knowledge graph (PKG) to refine the item features from PLMs. We identify three challenges for pre-training PKG, which are multi-type relations in PKG, semantic divergence between item generic information and relations and domain discrepancy from PKG to downstream ZSIR task. We address the challenges by proposing four pre-training tasks and novel task-oriented adaptation (ToA) layers. Moreover, this paper discusses how to fine-tune the model on new recommendation task such that the ToA layers are adapted to ZSIR task. Comprehensive experiments on 18 markets dataset are conducted to verify the effectiveness of the proposed model in both knowledge prediction and ZSIR task.|现有的推荐系统面临着“零拍摄”项目的困难，也就是说，这些项目在培训阶段与用户没有历史性的交互。近年来的研究虽然通过预训练语言模型(PLM)提取通用项表示，但忽视了关键项之间的关系。提出了一种新的基于零拍项目推荐(ZSIR)任务的实现方法，该方法通过对产品知识图(PKG)模型进行预训练来提取 PLM 中的项目特征。我们确定了 PKG 训练前的三个挑战，即 PKG 中的多类型关系、项目类属信息和关系之间的语义分歧以及从 PKG 到下游 ZSIR 任务之间的领域差异。我们通过提出四个训练前任务和新的面向任务的适应(ToA)层来应对这些挑战。此外，本文还讨论了如何对新推荐任务模型进行微调，使 ToA 层适应 ZSIR 任务。通过对18个市场数据集的综合实验，验证了该模型在知识预测和 ZSIR 任务中的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Zero-shot+Item-based+Recommendation+via+Multi-task+Product+Knowledge+Graph+Pre-Training)|0| |[A Generalized Propensity Learning Framework for Unbiased Post-Click Conversion Rate Estimation](https://doi.org/10.1145/3583780.3614760)|Yuqing Zhou, Tianshu Feng, Mingrui Liu, Ziwei Zhu|George Mason University, Fairfax, VA, USA|This paper addresses the critical gap in the unbiased estimation of post-click conversion rate (CVR) in recommender systems. Existing CVR prediction methods, such as Inverse Propensity Score (IPS) and various Doubly Robust (DR) based estimators, overlook the impact of propensity estimation on the model bias and variance, thus leading to a debiasing performance gap. We propose a Generalized Propensity Learning (GPL) framework to directly minimize the bias and variance in CVR prediction models. The proposed method works as a complement to existing methods like IPS, DR, MRDR, and DRMSE to improve prediction performance by reducing their bias and variance. Extensive experiments on real-world datasets and semi-synthetic datasets demonstrate the significant performance promotion brought by our proposed method. Data and code can be found at: https://github.com/yuqing-zhou/GPL.|本文研究了推荐系统中点击后转换率(CVR)无偏估计的关键缺陷。现有的 CVR 预测方法，如逆倾向评分(IPS)和各种基于双稳健(DR)的估计方法，忽视了倾向估计对模型偏差和方差的影响，从而导致性能差距的消除。我们提出了一个广义倾向学习(GPL)框架，直接最小化 CVR 预测模型的偏差和方差。该方法对现有的 IPS、 DR、 MRDR 和 DRMSE 等预测方法进行了补充，减少了它们的偏差和方差，提高了预测性能。在实际数据集和半合成数据集上进行的大量实验表明，该方法能够显著提高系统的性能。数据和代码可在以下 https://github.com/yuqing-zhou/gpl 找到:。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Generalized+Propensity+Learning+Framework+for+Unbiased+Post-Click+Conversion+Rate+Estimation)|0| |[Satisfaction-Aware User Interest Network for Click-Through Rate Prediction](https://doi.org/10.1145/3583780.3615288)|Mao Pan, Wen Shi, Kun Zhou, Zheng Li, Dongyue Wang, Zhuoye Ding, Xiwei Zhao, Sulong Xu|JD.com, Inc., Beijing, China|Click-Through Rate (CTR) prediction plays a pivotal role in numerous industrial applications, including online advertising and recommender systems. Existing approaches primarily focus on modeling the correlation between user interests and candidate items. However, we argue that personalized user preferences for candidate items depend not only on correlation but also on the satisfaction of associated interests. To address this limitation, we propose SUIN, a novel CTR model that integrates satisfaction factors into user interest modeling for enhanced click-through rate prediction. Specifically, we employ a user interest satisfaction-aware network to capture the degree of satisfaction for each interest, thereby enabling adaptation of the user's personalized preference based on satisfaction levels. Additionally, we leverage the exposure-unclicked signal (recommended to the user but not clicked) as supervision during training, facilitating the interest satisfaction module to better model the satisfaction degree of user interests. Besides, this module serves as a foundational building block suitable for integration into mainstream sequential-based CTR models. Extensive experiments conducted on two real-world datasets demonstrate the superiority of our proposed model, outperforming state-of-the-art methods across various evaluation metrics. Furthermore, an online A/B test deployed on large-scale recommender systems shows significant improvements achieved by our model in diverse evaluation metrics.|在许多工业应用中，包括在线广告和推荐系统中，点进率(ctrl)预测起着关键的作用。现有的方法主要集中在建模用户兴趣和候选项之间的关系。然而，我们认为个性化用户对候选项的偏好不仅取决于相关性，而且还取决于相关兴趣的满意度。为了解决这一局限性，我们提出了 SUIN 模型，这是一种新型的点击率模型，它将满意度因素集成到用户兴趣模型中，以增强点进率预测。具体来说，我们使用一个感知用户兴趣满意度的网络来获取每个兴趣的满意度，从而能够根据满意度来适应用户的个性化偏好。此外，我们利用曝光-未点击信号(推荐给用户但未点击)作为培训期间的监督，促进兴趣满意度模块，以更好地模拟用户兴趣的满意度。此外，该模块作为一个基本的积木块，适合集成到主流的顺序为基础的 CTR 模型。在两个真实世界的数据集上进行的大量实验证明了我们提出的模型的优越性，在各种评估指标上优于最先进的方法。此外，部署在大型推荐系统上的在线 A/B 测试显示，我们的模型在不同的评估指标上取得了显著的改进。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Satisfaction-Aware+User+Interest+Network+for+Click-Through+Rate+Prediction)|0| |[Unsupervised Multi-Modal Representation Learning for High Quality Retrieval of Similar Products at E-commerce Scale](https://doi.org/10.1145/3583780.3615504)|Kushal Kumar, Tarik Arici, Tal Neiman, Jinyu Yang, Shioulin Sam, Yi Xu, Hakan Ferhatosmanoglu, Ismail B. Tutar|Amazon, Seattle, WA, USA; Amazon, New York, NY, USA|Identifying similar products in e-commerce is useful in discovering relationships between products, making recommendations, and increasing diversity in search results. Product representation learning is the first step to define a generalized product similarity metric for search. The second step is to extend similarity search to a large scale (e.g., e-commerce catalog scale) without sacrificing quality. In this work, we present a solution that interweaves both steps, i.e., learn representations suited to high quality retrieval using contrastive learning (CL) and retrieve similar items from a large search space using approximate nearest neighbor search (ANNS) to trade-off quality for speed. We propose a CL training strategy for learning uni-modal encoders suited to multi-modal similarity search for e-commerce. We study ANNS retrieval by generating Pareto Frontiers (PFs) without requiring labels. Our CL training strategy doubles retrieval@1 metric across categories (e.g., from 36% to 88% in category C). We also demonstrate that ANNS engine optimization using PFs help select configurations appropriately (e.g., we achieve 6.8× search speed with just 2% drop from the maximum retrieval accuracy in medium size datasets).|在电子商务中识别类似的产品有助于发现产品之间的关系，提出建议，并增加搜索结果的多样性。产品表示学习是定义用于搜索的广义产品相似度量的第一步。第二步是在不牺牲质量的情况下将最近邻搜索扩展到大规模(例如，电子商务目录规模)。在这项工作中，我们提出了一个解决方案，交织两个步骤，即，学习表示适合高质量的检索使用对比学习(CL)和检索相似的项目从一个大的搜索空间使用近似最近邻搜索(ANNS) ，以权衡质量的速度。我们建议采用 CL 培训策略，学习适用于电子商务多模态最近邻搜索的单模态编码器。我们通过生成不需要标签的帕累托前沿(PF)来研究 ANNS 检索。我们的 CL 训练策略将跨类别的检索@1指标加倍(例如，在 C 类中从36% 增加到88%)。我们还证明了使用 PF 的 ANNS 引擎优化有助于选择适当的配置(例如，我们实现了6.8倍的搜索速度，仅比中等大小数据集的最大检索精度下降2%)。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Unsupervised+Multi-Modal+Representation+Learning+for+High+Quality+Retrieval+of+Similar+Products+at+E-commerce+Scale)|0| -|[Graph Exploration Matters: Improving both Individual-Level and System-Level Diversity in WeChat Feed Recommendation](https://doi.org/10.1145/3583780.3614688)|Shuai Yang, Lixin Zhang, Feng Xia, Leyu Lin|Tencent Inc., Shenzhen, China; Tencent Inc., Beijing, China|There are roughly three stages in real industrial recommendation systems, candidates generation (retrieval), ranking and reranking. Individual-level diversity and system-level diversity are both important for industrial recommender systems. The former focus on each single user's experience, while the latter focus on the difference among users. Graph-based retrieval strategies are inevitably hijacked by heavy users and popular items, leading to the convergence of candidates for users and the lack of system-level diversity. Meanwhile, in the reranking phase, Determinantal Point Process (DPP) is deployed to increase individual-level diverisity. Heavily relying on the semantic information of items, DPP suffers from clickbait and inaccurate attributes. Besides, most studies only focus on one of the two levels of diversity, and ignore the mutual influence among different stages in real recommender systems. We argue that individual-level diversity and system-level diversity should be viewed as an integrated problem, and we provide an efficient and deployable solution for web-scale recommenders. Generally, we propose to employ the retrieval graph information in diversity-based reranking, by which to weaken the hidden similarity of items exposed to users, and consequently gain more graph explorations to improve the system-level diveristy. Besides, we argue that users' propensity for diversity changes over time in content feed recommendation. Therefore, with the explored graph, we also propose to capture the user's real-time personalized propensity to the diversity. We implement and deploy the combined system in WeChat App's Top Stories used by hundreds of millions of users. Offline simulations and online A/B tests show our solution can effectively improve both user engagement and system revenue.|在实际的行业推荐系统中，大致有三个阶段: 候选人的生成(检索)、排名和重新排名。个体层次的多样性和系统层次的多样性对于工业推荐系统都很重要。前者关注每个用户的体验，而后者关注用户之间的差异。基于图的检索策略不可避免地会受到大量用户和热门项目的劫持，导致用户候选项的收敛和系统级多样性的缺乏。与此同时，在重新排名阶段，行列式点过程(DPP)被用来增加个人层面的多样性。民进党严重依赖项目语义信息，饱受点击率和不准确属性的困扰。此外，大多数研究只关注两个层次中的一个层次的多样性，而忽略了实际推荐系统中不同层次之间的相互影响。我们认为，个人层面的多样性和系统层面的多样性应该被视为一个综合的问题，我们提供了一个有效的和可部署的解决方案，网络规模的推荐。一般来说，我们建议在基于多样性的重新排序中使用检索图信息来削弱暴露给用户的项目的隐藏相似性，从而获得更多的图探索以提高系统级的多样性。此外，我们认为用户的多样性倾向随着时间的推移而变化。因此，通过对图形的研究，我们还提出了捕捉用户对多样性的实时个性化倾向。我们在微信应用程序的热门故事中实现并部署了这个组合系统，被数亿用户使用。离线模拟和在线 A/B 测试表明，我们的解决方案可以有效地提高用户参与度和系统收入。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Graph+Exploration+Matters:+Improving+both+Individual-Level+and+System-Level+Diversity+in+WeChat+Feed+Recommendation)|0| -|[Build Faster with Less: A Journey to Accelerate Sparse Model Building for Semantic Matching in Product Search](https://doi.org/10.1145/3583780.3614661)|Jiong Zhang, YauShian Wang, WeiCheng Chang, Wei Li, JyunYu Jiang, ChoJui Hsieh, HsiangFu Yu|Amazon, Palo Alto, CA, USA; UCLA, Los Angeles, CA, USA|The semantic matching problem in product search seeks to retrieve all semantically relevant products given a user query. Recent studies have shown that extreme multi-label classification~(XMC) model enjoys both low inference latency and high recall in real-world scenarios. These XMC semantic matching models adopt TF-IDF vectorizers to extract query text features and use mainly sparse matrices for the model weights. However, limited availability of libraries for efficient parallel sparse modules may lead to tediously long model building time when the problem scales to hundreds of millions of labels. This incurs significant hardware cost and renders the semantic model stale even before it is deployed. In this paper, we investigate and accelerate the model building procedures in a tree-based XMC model. On a real-world semantic matching task with 100M labels, our enhancements achieve over 10 times acceleration (from 3.1 days to 6.7 hours) while reducing hardware cost by 25%.|产品搜索中的语义匹配问题寻求检索给定用户查询的所有语义相关的产品。最近的研究表明，极端多标签分类 ~ (XMC)模型在现实场景中具有较低的推理延迟和较高的召回率。这些 XMC 语义匹配模型采用 TF-IDF 向量提取查询文本特征，模型权重以稀疏矩阵为主。然而，对于高效的并行稀疏模块来说，库的有限可用性可能导致当问题扩展到数亿个标签时，冗长的模型构建时间。这会带来巨大的硬件成本，甚至在部署语义模型之前，它就已经过时了。在本文中，我们研究并加速了一个基于树的 XMC 模型中的模型建立过程。在一个现实世界的语义匹配任务与100M 标签，我们的增强实现了10倍以上的加速(从3.1天到6.7小时) ，同时减少了25% 的硬件成本。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Build+Faster+with+Less:+A+Journey+to+Accelerate+Sparse+Model+Building+for+Semantic+Matching+in+Product+Search)|0| +|[Graph Exploration Matters: Improving both Individual-Level and System-Level Diversity in WeChat Feed Recommendation](https://doi.org/10.1145/3583780.3614688)|Shuai Yang, Lixin Zhang, Feng Xia, Leyu Lin|Tencent Inc., Beijing, China; Tencent Inc., Shenzhen, China|There are roughly three stages in real industrial recommendation systems, candidates generation (retrieval), ranking and reranking. Individual-level diversity and system-level diversity are both important for industrial recommender systems. The former focus on each single user's experience, while the latter focus on the difference among users. Graph-based retrieval strategies are inevitably hijacked by heavy users and popular items, leading to the convergence of candidates for users and the lack of system-level diversity. Meanwhile, in the reranking phase, Determinantal Point Process (DPP) is deployed to increase individual-level diverisity. Heavily relying on the semantic information of items, DPP suffers from clickbait and inaccurate attributes. Besides, most studies only focus on one of the two levels of diversity, and ignore the mutual influence among different stages in real recommender systems. We argue that individual-level diversity and system-level diversity should be viewed as an integrated problem, and we provide an efficient and deployable solution for web-scale recommenders. Generally, we propose to employ the retrieval graph information in diversity-based reranking, by which to weaken the hidden similarity of items exposed to users, and consequently gain more graph explorations to improve the system-level diveristy. Besides, we argue that users' propensity for diversity changes over time in content feed recommendation. Therefore, with the explored graph, we also propose to capture the user's real-time personalized propensity to the diversity. We implement and deploy the combined system in WeChat App's Top Stories used by hundreds of millions of users. Offline simulations and online A/B tests show our solution can effectively improve both user engagement and system revenue.|在实际的行业推荐系统中，大致有三个阶段: 候选人的生成(检索)、排名和重新排名。个体层次的多样性和系统层次的多样性对于工业推荐系统都很重要。前者关注每个用户的体验，而后者关注用户之间的差异。基于图的检索策略不可避免地会受到大量用户和热门项目的劫持，导致用户候选项的收敛和系统级多样性的缺乏。与此同时，在重新排名阶段，行列式点过程(DPP)被用来增加个人层面的多样性。民进党严重依赖项目语义信息，饱受点击率和不准确属性的困扰。此外，大多数研究只关注两个层次中的一个层次的多样性，而忽略了实际推荐系统中不同层次之间的相互影响。我们认为，个人层面的多样性和系统层面的多样性应该被视为一个综合的问题，我们提供了一个有效的和可部署的解决方案，网络规模的推荐。一般来说，我们建议在基于多样性的重新排序中使用检索图信息来削弱暴露给用户的项目的隐藏相似性，从而获得更多的图探索以提高系统级的多样性。此外，我们认为用户的多样性倾向随着时间的推移而变化。因此，通过对图形的研究，我们还提出了捕捉用户对多样性的实时个性化倾向。我们在微信应用程序的热门故事中实现并部署了这个组合系统，被数亿用户使用。离线模拟和在线 A/B 测试表明，我们的解决方案可以有效地提高用户参与度和系统收入。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Graph+Exploration+Matters:+Improving+both+Individual-Level+and+System-Level+Diversity+in+WeChat+Feed+Recommendation)|0| +|[Build Faster with Less: A Journey to Accelerate Sparse Model Building for Semantic Matching in Product Search](https://doi.org/10.1145/3583780.3614661)|Jiong Zhang, YauShian Wang, WeiCheng Chang, Wei Li, JyunYu Jiang, ChoJui Hsieh, HsiangFu Yu|UCLA, Los Angeles, CA, USA; Amazon, Palo Alto, CA, USA|The semantic matching problem in product search seeks to retrieve all semantically relevant products given a user query. Recent studies have shown that extreme multi-label classification~(XMC) model enjoys both low inference latency and high recall in real-world scenarios. These XMC semantic matching models adopt TF-IDF vectorizers to extract query text features and use mainly sparse matrices for the model weights. However, limited availability of libraries for efficient parallel sparse modules may lead to tediously long model building time when the problem scales to hundreds of millions of labels. This incurs significant hardware cost and renders the semantic model stale even before it is deployed. In this paper, we investigate and accelerate the model building procedures in a tree-based XMC model. On a real-world semantic matching task with 100M labels, our enhancements achieve over 10 times acceleration (from 3.1 days to 6.7 hours) while reducing hardware cost by 25%.|产品搜索中的语义匹配问题寻求检索给定用户查询的所有语义相关的产品。最近的研究表明，极端多标签分类 ~ (XMC)模型在现实场景中具有较低的推理延迟和较高的召回率。这些 XMC 语义匹配模型采用 TF-IDF 向量提取查询文本特征，模型权重以稀疏矩阵为主。然而，对于高效的并行稀疏模块来说，库的有限可用性可能导致当问题扩展到数亿个标签时，冗长的模型构建时间。这会带来巨大的硬件成本，甚至在部署语义模型之前，它就已经过时了。在本文中，我们研究并加速了一个基于树的 XMC 模型中的模型建立过程。在一个现实世界的语义匹配任务与100M 标签，我们的增强实现了10倍以上的加速(从3.1天到6.7小时) ，同时减少了25% 的硬件成本。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Build+Faster+with+Less:+A+Journey+to+Accelerate+Sparse+Model+Building+for+Semantic+Matching+in+Product+Search)|0| |[Dual Interests-Aligned Graph Auto-Encoders for Cross-domain Recommendation in WeChat](https://doi.org/10.1145/3583780.3614676)|Jiawei Zheng, Hao Gu, Chonggang Song, Dandan Lin, Lingling Yi, Chuan Chen|WeChat, Tencent, Shenzhen, China|Recently, cross-domain recommendation (CDR) has been widely studied in both research and industry since it can alleviate a long-standing challenge of traditional recommendation methods, i.e., data sparsity issue, by transferring the information from a relatively richer domain (termed source domain) to a sparser domain (termed target domain). To our best knowledge, most (if not all) existing CDR methods focus on transferring either the similar content information or the user preferences embedding from the source domain to the target domain. However, they fail to improve the recommendation performance in real-world recommendation scenarios where the items in the source domain are totally different from those in the target domain in terms of attributes. To solve the above issues, we analyzed the historical interactions of users from different domains in the WeChat platform, and found that if two users have similar interests (interactions) in one domain, they are very likely to have similar interests in another domain even though the items of these two domains are totally different in terms of attributes. Based on this observation, in this paper, we propose a novel model named Dual Interests-Aligned Graph Auto-Encoders (DIAGAE) by utilizing the inter-domain interest alignment of users. Besides, our proposed model DIAGAE also leverages graph decoding objectives to align intra-domain user interests, which makes the representation of two users who have similar interests in a single domain closer. Comprehensive experimental results demonstrate that our model DIAGAE outperforms state-of-the-art methods on both public benchmark datasets and online A/B tests in WeChat live-stream recommendation scenario. Our model DIAGAE now serves the major online traffic in WeChat live-streaming recommendation scenario.|近年来，跨域推荐技术(CDR)在研究和工业界得到了广泛的研究，因为它可以通过将信息从相对丰富的域(称为源域)转移到较稀疏的域(称为目标域)来缓解传统推荐方法长期以来面临的挑战，即数据稀疏问题。据我们所知，大多数(如果不是全部的话)现有的 CDR 方法集中于将相似的内容信息或用户首选项从源域嵌入到目标域。但是，在源域中的条目在属性方面与目标域中的条目完全不同的现实推荐场景中，它们无法提高推荐性能。为了解决上述问题，我们分析了微信平台中不同领域用户的历史互动，发现如果两个用户在一个领域有相似的兴趣(互动) ，他们很可能在另一个领域有相似的兴趣，即使这两个领域的项目在属性方面完全不同。在此基础上，本文提出了一种利用用户域间兴趣对齐的双兴趣对齐图自动编码器(DIAGAE)模型。此外，我们提出的模型 DIAGAE 还利用图解码目标来调整域内用户的兴趣，使两个用户在一个单一的领域有相似的兴趣更接近的表示。综合实验结果表明，在微信实时流推荐场景中，我们的模型 DIAGAE 在公共基准数据集和在线 A/B 测试方面都优于最先进的方法。我们的模型 DIAGAE 现在服务于微信直播推荐场景中的主要在线流量。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Dual+Interests-Aligned+Graph+Auto-Encoders+for+Cross-domain+Recommendation+in+WeChat)|0| |[Self-supervised Contrastive Enhancement with Symmetric Few-shot Learning Towers for Cold-start News Recommendation](https://doi.org/10.1145/3583780.3615053)|Hao Jiang, Chuanzhen Li, Juanjuan Cai, Runyu Tian, Jingling Wang|Communication University of China, Beijing, China|Nowadays, news spreads faster than it is consumed. This, alongside the rapid news cycle and delayed updates, has led to a challenging news cold-start issue. Likewise, the user cold-start problem, due to limited user engagement, has long hindered recommendations. To tackle both of them, we introduce the Symmetric Few-shot Learning framework for Cold-start News Recommendation (SFCNR), built upon self-supervised contrastive enhancement. Our approach employs symmetric few-shot learning towers (SFTs) to transform warm user/news attributes into their behavior/content features during training. We design two innovative feature alignment strategies to enhance towers training. Subsequently, this tower generates virtual features for cold users/news during inference, leveraging tower-stored prior knowledge through a personalized gating network. We assess the SFCNR on four quality news recommendation models, conducting comprehensive experiments on two kinds of News dataset. Results showcase significant performance boosts for both warm and cold-start scenarios compared to baseline models.|如今，新闻的传播速度快于消费速度。这与快速的新闻周期和延迟更新一起，导致了一个具有挑战性的新闻冷启动问题。同样，由于用户参与有限，用户冷启动问题长期以来阻碍了推荐。为了解决这两个问题，我们引入了基于自监督对比增强的冷启动新闻推荐对称少镜头学习框架(SFCNR)。我们的方法使用对称的少镜头学习塔(SFT)来转换温暖的用户/新闻属性到他们的行为/内容特征在训练期间。我们设计了两个创新的特征对齐策略来加强塔训练。随后，该塔在推理过程中为冷用户/新闻生成虚拟特征，通过个性化门控网络利用塔存储的先验知识。我们在四种优质新闻推荐模型上对 SFCNR 进行了评估，并对两种新闻数据集进行了综合实验。结果显示，与基线模型相比，暖启动和冷启动方案的性能都有显著提高。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Self-supervised+Contrastive+Enhancement+with+Symmetric+Few-shot+Learning+Towers+for+Cold-start+News+Recommendation)|0| |[Capturing Popularity Trends: A Simplistic Non-Personalized Approach for Enhanced Item Recommendation](https://doi.org/10.1145/3583780.3614801)|Jiazheng Jing, Yinan Zhang, Xin Zhou, Zhiqi Shen|Nanyang Technological University, Singapore, Singapore|Recommender systems have been gaining increasing research attention over the years. Most existing recommendation methods focus on capturing users' personalized preferences through historical user-item interactions, which may potentially violate user privacy. Additionally, these approaches often overlook the significance of the temporal fluctuation in item popularity that can sway users' decision-making. To bridge this gap, we propose Popularity-Aware Recommender (PARE), which makes non-personalized recommendations by predicting the items that will attain the highest popularity. PARE consists of four modules, each focusing on a different aspect: popularity history, temporal impact, periodic impact, and side information. Finally, an attention layer is leveraged to fuse the outputs of four modules. To our knowledge, this is the first work to explicitly model item popularity in recommendation systems. Extensive experiments show that PARE performs on par or even better than sophisticated state-of-the-art recommendation methods. Since PARE prioritizes item popularity over personalized user preferences, it can enhance existing recommendation methods as a complementary component. Our experiments demonstrate that integrating PARE with existing recommendation methods significantly surpasses the performance of standalone models, highlighting PARE's potential as a complement to existing recommendation methods. Furthermore, the simplicity of PARE makes it immensely practical for industrial applications and a valuable baseline for future research.|推荐系统近年来得到了越来越多的研究关注。现有的大多数推荐方法都是通过历史的用户-项目交互来获取用户的个性化偏好，这可能会侵犯用户的隐私。此外，这些方法往往忽略了项目流行度的时间波动的重要性，可以影响用户的决策。为了弥补这一差距，我们提出了流行感知推荐(PARE) ，它通过预测将获得最高流行度的项目来提供非个性化的推荐。PARE 由四个模块组成，每个模块关注一个不同的方面: 流行历史、时间影响、周期性影响和侧面信息。最后，利用注意层融合四个模块的输出。据我们所知，这是第一个在推荐系统中对项目流行性进行明确建模的工作。大量的实验表明，价格调整汇率的表现与最先进的复杂推荐方法相当，甚至更好。由于 PARE 优先考虑项目流行度而不是个性化的用户偏好，它可以增强现有的推荐方法作为一个补充组件。我们的实验表明，将价格调整汇率与现有的推荐方法相结合，显著地超过了独立模型的性能，突出了价格调整汇率作为现有推荐方法的补充的潜力。此外，价格调整汇率的简单性使其对工业应用非常实用，并为未来的研究提供了宝贵的基准。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Capturing+Popularity+Trends:+A+Simplistic+Non-Personalized+Approach+for+Enhanced+Item+Recommendation)|0| -|[Multi-domain Recommendation with Embedding Disentangling and Domain Alignment](https://doi.org/10.1145/3583780.3614977)|Wentao Ning, Xiao Yan, Weiwen Liu, Reynold Cheng, Rui Zhang, Bo Tang|ruizhang.info, Beijing, China; The University of Hong Kong, Hong Kong, Hong Kong; Huawei Noah's Ark Lab, Shenzhen, China; Southern University of Science and Technology, Shenzhen, China|Multi-domain recommendation (MDR) aims to provide recommendations for different domains (e.g., types of products) with overlapping users/items and is common for platforms such as Amazon, Facebook, and LinkedIn that host multiple services. Existing MDR models face two challenges: First, it is difficult to disentangle knowledge that generalizes across domains (e.g., a user likes cheap items) and knowledge specific to a single domain (e.g., a user likes blue clothing but not blue cars). Second, they have limited ability to transfer knowledge across domains with small overlaps. We propose a new MDR method named EDDA with two key components, i.e., embedding disentangling recommender and domain alignment, to tackle the two challenges respectively. In particular, the embedding disentangling recommender separates both the model and embedding for the inter-domain part and the intra-domain part, while most existing MDR methods only focus on model-level disentangling. The domain alignment leverages random walks from graph processing to identify similar user/item pairs from different domains and encourages similar user/item pairs to have similar embeddings, enhancing knowledge transfer. We compare EDDA with 12 state-of-the-art baselines on 3 real datasets. The results show that EDDA consistently outperforms the baselines on all datasets and domains. All datasets and codes are available at https://github.com/Stevenn9981/EDDA.|多域名推荐(MDR)的目的是为不同领域(例如，产品类型)的重叠用户/项目提供推荐，这种推荐在亚马逊、 Facebook 和 LinkedIn 等提供多种服务的平台上很常见。现有的 MDR 模型面临两个挑战: 首先，很难区分跨领域概括的知识(例如，用户喜欢廉价商品)和特定于单个领域的知识(例如，用户喜欢蓝色衣服但不喜欢蓝色汽车)。其次，他们跨领域传递知识的能力有限，重叠的领域很小。我们提出了一种新的 MDR 方法 EDDA，该方法由两个关键部分组成，即嵌入分离推荐和域对齐，分别解决了这两个难题。特别是嵌入式解缠推荐器将域间部分和域内部分的模型和嵌入分离开来，而现有的 MDR 方法大多只关注模型级的解缠。领域对齐利用图形处理中的随机游走来识别来自不同领域的相似用户/项目对，并鼓励相似的用户/项目对具有相似的嵌入，增强知识转移。我们比较了3个实际数据集上的 EDDA 和12个最先进的基线。结果表明，EDDA 在所有数据集和域上的性能均优于基线。所有数据集和代码都可以在 https://github.com/stevenn9981/edda 获得。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Multi-domain+Recommendation+with+Embedding+Disentangling+and+Domain+Alignment)|0| -|[Dimension Independent Mixup for Hard Negative Sample in Collaborative Filtering](https://doi.org/10.1145/3583780.3614845)|Xi Wu, Liangwei Yang, Jibing Gong, Chao Zhou, Tianyu Lin, Xiaolong Liu, Philip S. Yu|University of Illinois Chicago, Chicago, IL, USA; YanShan University, Qinhuangdao, China|Collaborative filtering (CF) is a widely employed technique that predicts user preferences based on past interactions. Negative sampling plays a vital role in training CF-based models with implicit feedback. In this paper, we propose a novel perspective based on the sampling area to revisit existing sampling methods. We point out that current sampling methods mainly focus on Point-wise or Line-wise sampling, lacking flexibility and leaving a significant portion of the hard sampling area un-explored. To address this limitation, we propose Dimension Independent Mixup for Hard Negative Sampling (DINS), which is the first Area-wise sampling method for training CF-based models. DINS comprises three modules: Hard Boundary Definition, Dimension Independent Mixup, and Multi-hop Pooling. Experiments with real-world datasets on both matrix factorization and graph-based models demonstrate that DINS outperforms other negative sampling methods, establishing its effectiveness and superiority. Our work contributes a new perspective, introduces Area-wise sampling, and presents DINS as a novel approach that achieves state-of-the-art performance for negative sampling. Our implementations are available in PyTorch.|协同过滤(CF)是一种广泛应用的技术，它基于过去的交互预测用户偏好。负抽样在基于 CF 的隐式反馈模型的训练中起着至关重要的作用。在本文中，我们提出了一个新的视角基于抽样面积重新审视现有的抽样方法。我们指出，目前的抽样方法主要集中在点或线抽样，缺乏灵活性，并留下了很大一部分硬抽样区域未被探索。针对这一局限性，我们提出了硬负采样的尺寸无关混合(DINS)方法，这是第一种用于训练基于 CF 模型的区域采样方法。DINS 由三个模块组成: 硬边界定义模块、尺寸无关混合模块和多跳池模块。在矩阵分解模型和基于图形的模型上对真实世界数据集进行的实验表明，DINS 优于其他负采样方法，确立了它的有效性和优越性。我们的工作提供了一个新的视角，介绍了区域采样，并提出了 DINS 作为一种新颖的方法，实现了最先进的性能负采样。我们的实现在 PyTorch 中可用。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Dimension+Independent+Mixup+for+Hard+Negative+Sample+in+Collaborative+Filtering)|0| -|[Sequential Recommendation via an Adaptive Cross-domain Knowledge Decomposition](https://doi.org/10.1145/3583780.3615058)|Chuang Zhao, Xinyu Li, Ming He, Hongke Zhao, Jianping Fan|College of Management and Economics, Tianjin University, Tianjin, China; AI Lab at Lenovo Research, Beijing, China|Cross-domain recommendation, as an intelligent machine to alleviate data sparsity and cold start problems, has attracted extensive attention from scholars. Existing cross-domain recommendation frameworks usually leverage overlapping entities for knowledge transfer, the most popular of which are information aggregation and consistency maintenance. Despite decent improvements, the neglect of dynamic perspectives, the presence of confounding factors, and the disparities in domain properties inevitably constrain model performance. In view of this, this paper proposes a sequential recommendation framework via adaptive cross-domain knowledge decomposition, namely ARISEN, which focuses on employing adaptive causal learning to improve recommendation performance. Specifically, in order to facilitate sequence transfer, we align the user's behaviour sequences in the source domain and target domain according to the timestamps, expecting to use the abundant semantics of the former to augment the information of the latter. Regarding confounding factor removal, we introduce the causal learning technique and promote it as an adaptive representation decomposition framework on the basis of instrumental variables. For the sake of alleviating the impact of domain disparities, this paper endeavors to employ two mutually orthogonal transformation matrices for information fusion. Extensive experiments and detailed analyzes on large industrial and public data sets demonstrate that our framework can achieve substantial improvements over state-of-the-art algorithms.|跨域推荐作为一种缓解数据稀疏和冷启动问题的智能机器，已经引起了学者们的广泛关注。现有的跨领域推荐框架通常利用重叠实体进行知识转移，其中最流行的是信息聚合和一致性维护。尽管有了不错的改进，但是忽视动态视角、混杂因素的存在以及领域属性的差异不可避免地限制了模型的性能。鉴于此，本文提出了一种基于自适应跨领域知识分解的顺序推荐框架 ARISEN，重点研究了利用自适应因果学习来提高推荐性能的方法。具体来说，为了方便序列传输，我们根据时间戳对源域和目标域中的用户行为序列进行对齐，期望利用前者丰富的语义来增强后者的信息。关于去除混杂因素，我们引入了因果学习技术，并将其推广为一个基于工具变量的自适应表征分解框架。为了减轻领域差异的影响，本文尝试采用两个互正交的变换矩阵进行信息融合。大量的实验和对大型工业和公共数据集的详细分析表明，我们的框架可以比最先进的算法实现实质性的改进。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Sequential+Recommendation+via+an+Adaptive+Cross-domain+Knowledge+Decomposition)|0| +|[Multi-domain Recommendation with Embedding Disentangling and Domain Alignment](https://doi.org/10.1145/3583780.3614977)|Wentao Ning, Xiao Yan, Weiwen Liu, Reynold Cheng, Rui Zhang, Bo Tang|Huawei Noah's Ark Lab, Shenzhen, China; ruizhang.info, Beijing, China; The University of Hong Kong, Hong Kong, Hong Kong; Southern University of Science and Technology, Shenzhen, China|Multi-domain recommendation (MDR) aims to provide recommendations for different domains (e.g., types of products) with overlapping users/items and is common for platforms such as Amazon, Facebook, and LinkedIn that host multiple services. Existing MDR models face two challenges: First, it is difficult to disentangle knowledge that generalizes across domains (e.g., a user likes cheap items) and knowledge specific to a single domain (e.g., a user likes blue clothing but not blue cars). Second, they have limited ability to transfer knowledge across domains with small overlaps. We propose a new MDR method named EDDA with two key components, i.e., embedding disentangling recommender and domain alignment, to tackle the two challenges respectively. In particular, the embedding disentangling recommender separates both the model and embedding for the inter-domain part and the intra-domain part, while most existing MDR methods only focus on model-level disentangling. The domain alignment leverages random walks from graph processing to identify similar user/item pairs from different domains and encourages similar user/item pairs to have similar embeddings, enhancing knowledge transfer. We compare EDDA with 12 state-of-the-art baselines on 3 real datasets. The results show that EDDA consistently outperforms the baselines on all datasets and domains. All datasets and codes are available at https://github.com/Stevenn9981/EDDA.|多域名推荐(MDR)的目的是为不同领域(例如，产品类型)的重叠用户/项目提供推荐，这种推荐在亚马逊、 Facebook 和 LinkedIn 等提供多种服务的平台上很常见。现有的 MDR 模型面临两个挑战: 首先，很难区分跨领域概括的知识(例如，用户喜欢廉价商品)和特定于单个领域的知识(例如，用户喜欢蓝色衣服但不喜欢蓝色汽车)。其次，他们跨领域传递知识的能力有限，重叠的领域很小。我们提出了一种新的 MDR 方法 EDDA，该方法由两个关键部分组成，即嵌入分离推荐和域对齐，分别解决了这两个难题。特别是嵌入式解缠推荐器将域间部分和域内部分的模型和嵌入分离开来，而现有的 MDR 方法大多只关注模型级的解缠。领域对齐利用图形处理中的随机游走来识别来自不同领域的相似用户/项目对，并鼓励相似的用户/项目对具有相似的嵌入，增强知识转移。我们比较了3个实际数据集上的 EDDA 和12个最先进的基线。结果表明，EDDA 在所有数据集和域上的性能均优于基线。所有数据集和代码都可以在 https://github.com/stevenn9981/edda 获得。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Multi-domain+Recommendation+with+Embedding+Disentangling+and+Domain+Alignment)|0| +|[Dimension Independent Mixup for Hard Negative Sample in Collaborative Filtering](https://doi.org/10.1145/3583780.3614845)|Xi Wu, Liangwei Yang, Jibing Gong, Chao Zhou, Tianyu Lin, Xiaolong Liu, Philip S. Yu|YanShan University, Qinhuangdao, China; University of Illinois Chicago, Chicago, IL, USA|Collaborative filtering (CF) is a widely employed technique that predicts user preferences based on past interactions. Negative sampling plays a vital role in training CF-based models with implicit feedback. In this paper, we propose a novel perspective based on the sampling area to revisit existing sampling methods. We point out that current sampling methods mainly focus on Point-wise or Line-wise sampling, lacking flexibility and leaving a significant portion of the hard sampling area un-explored. To address this limitation, we propose Dimension Independent Mixup for Hard Negative Sampling (DINS), which is the first Area-wise sampling method for training CF-based models. DINS comprises three modules: Hard Boundary Definition, Dimension Independent Mixup, and Multi-hop Pooling. Experiments with real-world datasets on both matrix factorization and graph-based models demonstrate that DINS outperforms other negative sampling methods, establishing its effectiveness and superiority. Our work contributes a new perspective, introduces Area-wise sampling, and presents DINS as a novel approach that achieves state-of-the-art performance for negative sampling. Our implementations are available in PyTorch.|协同过滤(CF)是一种广泛应用的技术，它基于过去的交互预测用户偏好。负抽样在基于 CF 的隐式反馈模型的训练中起着至关重要的作用。在本文中，我们提出了一个新的视角基于抽样面积重新审视现有的抽样方法。我们指出，目前的抽样方法主要集中在点或线抽样，缺乏灵活性，并留下了很大一部分硬抽样区域未被探索。针对这一局限性，我们提出了硬负采样的尺寸无关混合(DINS)方法，这是第一种用于训练基于 CF 模型的区域采样方法。DINS 由三个模块组成: 硬边界定义模块、尺寸无关混合模块和多跳池模块。在矩阵分解模型和基于图形的模型上对真实世界数据集进行的实验表明，DINS 优于其他负采样方法，确立了它的有效性和优越性。我们的工作提供了一个新的视角，介绍了区域采样，并提出了 DINS 作为一种新颖的方法，实现了最先进的性能负采样。我们的实现在 PyTorch 中可用。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Dimension+Independent+Mixup+for+Hard+Negative+Sample+in+Collaborative+Filtering)|0| +|[Sequential Recommendation via an Adaptive Cross-domain Knowledge Decomposition](https://doi.org/10.1145/3583780.3615058)|Chuang Zhao, Xinyu Li, Ming He, Hongke Zhao, Jianping Fan|AI Lab at Lenovo Research, Beijing, China; College of Management and Economics, Tianjin University, Tianjin, China|Cross-domain recommendation, as an intelligent machine to alleviate data sparsity and cold start problems, has attracted extensive attention from scholars. Existing cross-domain recommendation frameworks usually leverage overlapping entities for knowledge transfer, the most popular of which are information aggregation and consistency maintenance. Despite decent improvements, the neglect of dynamic perspectives, the presence of confounding factors, and the disparities in domain properties inevitably constrain model performance. In view of this, this paper proposes a sequential recommendation framework via adaptive cross-domain knowledge decomposition, namely ARISEN, which focuses on employing adaptive causal learning to improve recommendation performance. Specifically, in order to facilitate sequence transfer, we align the user's behaviour sequences in the source domain and target domain according to the timestamps, expecting to use the abundant semantics of the former to augment the information of the latter. Regarding confounding factor removal, we introduce the causal learning technique and promote it as an adaptive representation decomposition framework on the basis of instrumental variables. For the sake of alleviating the impact of domain disparities, this paper endeavors to employ two mutually orthogonal transformation matrices for information fusion. Extensive experiments and detailed analyzes on large industrial and public data sets demonstrate that our framework can achieve substantial improvements over state-of-the-art algorithms.|跨域推荐作为一种缓解数据稀疏和冷启动问题的智能机器，已经引起了学者们的广泛关注。现有的跨领域推荐框架通常利用重叠实体进行知识转移，其中最流行的是信息聚合和一致性维护。尽管有了不错的改进，但是忽视动态视角、混杂因素的存在以及领域属性的差异不可避免地限制了模型的性能。鉴于此，本文提出了一种基于自适应跨领域知识分解的顺序推荐框架 ARISEN，重点研究了利用自适应因果学习来提高推荐性能的方法。具体来说，为了方便序列传输，我们根据时间戳对源域和目标域中的用户行为序列进行对齐，期望利用前者丰富的语义来增强后者的信息。关于去除混杂因素，我们引入了因果学习技术，并将其推广为一个基于工具变量的自适应表征分解框架。为了减轻领域差异的影响，本文尝试采用两个互正交的变换矩阵进行信息融合。大量的实验和对大型工业和公共数据集的详细分析表明，我们的框架可以比最先进的算法实现实质性的改进。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Sequential+Recommendation+via+an+Adaptive+Cross-domain+Knowledge+Decomposition)|0| |[Noisy Perturbations for Estimating Query Difficulty in Dense Retrievers](https://doi.org/10.1145/3583780.3615270)|Negar Arabzadeh, Radin Hamidi Rad, Maryam Khodabakhsh, Ebrahim Bagheri|University of Waterloo, Waterloo, ON, Canada; Toronto Metropolitan University, Toronto, ON, Canada; Shahrood University of Technology, Shahrood, Iran|Estimating query difficulty, also known as Query Performance Prediction (QPP), is concerned with assessing the retrieval quality of a ranking method for an input query. Most traditional unsupervised frequency-based models and many recent supervised neural methods have been designed specifically for predicting the performance of sparse retrievers such as BM25. In this paper we propose an unsupervised QPP method for dense neural retrievers which operates by redefining the well-known concept of query robustness i.e., a more robust query to perturbations is an easier query to handle. We propose to generate query perturbations for measuring query robustness by systematically injecting noise into the contextualized neural representation of each query. We then compare the retrieved list for the original query with that of the perturbed query as a way to measure query robustness. Our experiments on four different query sets including MS MARCO, TREC Deep Learning track 2019 and 2020 and TREC DL-Hard show consistently improved performance on linear and ranking correlation metrics over the state of the art.|评估查询难度，也称为查询性能预测(QueryPerformance預，QPP) ，与评估输入查询的排序方法的检索质量有关。大多数传统的基于频率的无监督模型和许多最近的监督神经网络方法已被专门设计用于预测稀疏检索器的性能，如 BM25。本文提出了一种密集型神经元检索器的无监督 QPP 方法，该方法通过重新定义查询鲁棒性的概念来实现。我们提出通过系统地在每个查询的上下文化神经表示中注入噪声来产生查询扰动来衡量查询的鲁棒性。然后，我们将检索到的原始查询列表与受干扰的查询列表进行比较，作为衡量查询健壮性的一种方法。我们在包括 MS MARCO，TREC Deep Learning track 2019和2020以及 TREC DL-Hard 在内的四个不同的查询集上进行的实验显示，在线性和排名相关性指标方面，相对于最先进的水平，性能持续改善。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Noisy+Perturbations+for+Estimating+Query+Difficulty+in+Dense+Retrievers)|0| -|[Deep Context Interest Network for Click-Through Rate Prediction](https://doi.org/10.1145/3583780.3615233)|Xuyang Hou, Zhe Wang, Qi Liu, Tan Qu, Jia Cheng, Jun Lei|University of Science and Technology of China, Hefei, China; Meituan, Beijing, China|Click-Through Rate (CTR) prediction, estimating the probability of a user clicking on an item, is essential in industrial applications, such as online advertising. Many works focus on user behavior modeling to improve CTR prediction performance. However, most of those methods only model users' positive interests from users' click items while ignoring the context information, which is the display items around the clicks, resulting in inferior performance. In this paper, we highlight the importance of context information on user behavior modeling and propose a novel model named Deep Context Interest Network (DCIN), which integrally models the click and its display context to learn users' context-aware interests. DCIN consists of three key modules: 1) Position-aware Context Aggregation Module (PCAM), which performs aggregation of display items with an attention mechanism; 2) Feedback-Context Fusion Module (FCFM), which fuses the representation of clicks and display contexts through non-linear feature interaction; 3) Interest Matching Module (IMM), which activates interests related with the target item. Moreover, we provide our hands-on solution to implement our DCIN model on large-scale industrial systems. The significant improvements in both offline and online evaluations demonstrate the superiority of our proposed DCIN method. Notably, DCIN has been deployed on our online advertising system serving the main traffic, which brings 1.5% CTR and 1.5% RPM lift.|点进率(ctrl)预测，估计用户点击一个项目的概率，在工业应用中是必不可少的，比如在线广告。许多工作集中在用户行为建模，以提高点击率预测性能。然而，这些方法大多只是从用户的点击项目中建立用户的积极兴趣模型，而忽略了上下文信息，即点击周围的显示项目，导致性能较差。本文强调了上下文信息在用户行为建模中的重要性，提出了一种新的模型——深度上下文兴趣网络(Deep Context Interest Network，DCIN)。DCIN 由三个关键模块组成: 1)位置感知上下文聚合模块(PCAM) ，利用注意机制对显示项目进行聚合; 2)反馈上下文融合模块(FCFM) ，通过非线性特征交互融合点击表示和显示上下文; 3)兴趣匹配模块(IMM) ，激活与目标项目相关的兴趣。此外，我们提供了我们的动手解决方案，以实现我们的 DCIN 模型的大规模工业系统。离线和在线评估的显著改进证明了我们提出的 DCIN 方法的优越性。值得注意的是，DCIN 已经部署在我们的在线广告系统服务的主要流量，这带来了1.5% 的点击率和1.5% 的转速提升。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Deep+Context+Interest+Network+for+Click-Through+Rate+Prediction)|0| +|[Deep Context Interest Network for Click-Through Rate Prediction](https://doi.org/10.1145/3583780.3615233)|Xuyang Hou, Zhe Wang, Qi Liu, Tan Qu, Jia Cheng, Jun Lei|Meituan, Beijing, China; University of Science and Technology of China, Hefei, China|Click-Through Rate (CTR) prediction, estimating the probability of a user clicking on an item, is essential in industrial applications, such as online advertising. Many works focus on user behavior modeling to improve CTR prediction performance. However, most of those methods only model users' positive interests from users' click items while ignoring the context information, which is the display items around the clicks, resulting in inferior performance. In this paper, we highlight the importance of context information on user behavior modeling and propose a novel model named Deep Context Interest Network (DCIN), which integrally models the click and its display context to learn users' context-aware interests. DCIN consists of three key modules: 1) Position-aware Context Aggregation Module (PCAM), which performs aggregation of display items with an attention mechanism; 2) Feedback-Context Fusion Module (FCFM), which fuses the representation of clicks and display contexts through non-linear feature interaction; 3) Interest Matching Module (IMM), which activates interests related with the target item. Moreover, we provide our hands-on solution to implement our DCIN model on large-scale industrial systems. The significant improvements in both offline and online evaluations demonstrate the superiority of our proposed DCIN method. Notably, DCIN has been deployed on our online advertising system serving the main traffic, which brings 1.5% CTR and 1.5% RPM lift.|点进率(ctrl)预测，估计用户点击一个项目的概率，在工业应用中是必不可少的，比如在线广告。许多工作集中在用户行为建模，以提高点击率预测性能。然而，这些方法大多只是从用户的点击项目中建立用户的积极兴趣模型，而忽略了上下文信息，即点击周围的显示项目，导致性能较差。本文强调了上下文信息在用户行为建模中的重要性，提出了一种新的模型——深度上下文兴趣网络(Deep Context Interest Network，DCIN)。DCIN 由三个关键模块组成: 1)位置感知上下文聚合模块(PCAM) ，利用注意机制对显示项目进行聚合; 2)反馈上下文融合模块(FCFM) ，通过非线性特征交互融合点击表示和显示上下文; 3)兴趣匹配模块(IMM) ，激活与目标项目相关的兴趣。此外，我们提供了我们的动手解决方案，以实现我们的 DCIN 模型的大规模工业系统。离线和在线评估的显著改进证明了我们提出的 DCIN 方法的优越性。值得注意的是，DCIN 已经部署在我们的在线广告系统服务的主要流量，这带来了1.5% 的点击率和1.5% 的转速提升。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Deep+Context+Interest+Network+for+Click-Through+Rate+Prediction)|0| |[MSRA: A Multi-Aspect Semantic Relevance Approach for E-Commerce via Multimodal Pre-Training](https://doi.org/10.1145/3583780.3615224)|Hanqi Jin, Jiwei Tan, Lixin Liu, Lisong Qiu, Shaowei Yao, Xi Chen, Xiaoyi Zeng|Alibaba Group, Hangzhou, China|To enhance the effectiveness of matching user requests with millions of online products, practitioners invest significant efforts in developing semantic relevance models on large-scale e-commerce platforms. Generally, such semantic relevance models are formulated as text-matching approaches, which measure the relevance between users' search queries and the titles of candidate items (i.e., products). However, we argue that conventional relevance methods may lead to sub-optimal performance due to the limited information provided by the titles of candidate items. To alleviate this issue, we suggest incorporating additional information about candidate items from multiple aspects, including their attributes and images. This could supplement the information that may not be fully provided by titles alone. To this end, we propose a multi-aspect semantic relevance model that takes into account the match between search queries and the title, attribute and image information of items simultaneously. The model is further enhanced through pre-training using several well-designed self-supervised and weakly-supervised tasks. Furthermore, the proposed model is fine-tuned using annotated data and distilled into a representation-based architecture for efficient online deployment. Experimental results show the proposed approach significantly improves relevance and leads to considerable enhancements in business metrics.|为了提高将用户请求与数百万在线产品匹配的有效性，从业人员投入大量精力在大规模电子商务平台上开发语义相关性模型。通常，这种语义相关模型都是以文本匹配的方式构建的，用于测量用户的搜索查询与候选项(即产品)标题之间的相关性。然而，由于候选项目标题提供的信息有限，传统的关联方法可能导致性能不理想。为了缓解这个问题，我们建议从多个方面整合关于候选项目的额外信息，包括它们的属性和图像。这可以补充标题本身可能无法完全提供的信息。为此，我们提出了一个多方面的语义相关模型，该模型同时考虑了搜索查询与项目的标题、属性和图像信息之间的匹配。该模型通过使用几个设计良好的自监督和弱监督任务进行预训练得到进一步增强。此外，该模型使用带注释的数据进行了微调，并提炼为一个基于表示的体系结构，以实现有效的在线部署。实验结果表明，提出的方法显著提高了相关性，并导致业务度量的显著增强。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=MSRA:+A+Multi-Aspect+Semantic+Relevance+Approach+for+E-Commerce+via+Multimodal+Pre-Training)|0| |[LEAD-ID: Language-Enhanced Denoising and Intent Distinguishing Graph Neural Network for Sponsored Search Broad Retrievals](https://doi.org/10.1145/3583780.3615175)|Xiao Zhou, Ran Wang, Haorui Li, Qiang Liu, Xingxing Wang, Dong Wang|Meituan.com, Beijing, China|As a local-based service (LBS), search ad retrieval in online meal delivery platforms should be broader to bridge the gap between vague consumption intentions of users and shortage of ad candidates limited by users' queries and positions. Recently, graph neural networks (GNNs) have been successfully applied to search ad retrieval task. However, directly applying GNNs suffer from noisy interactions and intents indistinguishability, which seriously degrades systems' effectiveness in the broad retrieval. In this paper, we propose a Language-EnhAnced Denoising and Intent Distinguishing graph neural network, LEAD-ID, which is developed and deployed at Meituan for sponsored search broad retrieval. To denoise interaction data, LEAD-ID designs hard- and soft- denoising strategies for GNNs based on a pretrained language model. A variational EM method is also employed to reduce high computational complexity of combining LMs and GNNs jointly. To distinguish various intents, LEAD-ID generates intent-aware node representations based on meticulously crafted LMs (language model) and GNNs; and then, it is guided by a contrastive learning object in an explicit and effective manner. According to offline experiments and online A/B tests, our framework significantly outperforms baselines in terms of recall and revenue.|作为一种基于本地的服务(LBS) ，在线送餐平台的搜索广告检索应该更加广泛，以弥补用户模糊的消费意图和受用户查询和位置限制的广告候选人短缺之间的差距。近年来，图神经网络(GNN)已成功地应用于广告检索任务中。然而，直接应用 GNN 存在着噪声交互和意图不可区分的问题，严重影响了系统在广义检索中的有效性。在本文中，我们提出了一个语言增强去噪和意图识别图神经网络，LEAD-ID，这是开发和部署在美团的赞助搜索广泛检索。为了对交互数据进行去噪，LEAD-ID 基于预先训练好的语言模型设计了 GNN 的软硬去噪策略。采用变分 EM 方法，降低了 LM 和 GNN 联合运算的高计算复杂度。为了区分不同的意图，LEAD-ID 基于精心制作的 LM (语言模型)和 GNN 生成意图感知的节点表示; 然后，在一个对比学习对象的指导下，以一种明确而有效的方式。根据离线实验和在线 A/B 测试，我们的框架在召回和收入方面明显优于基准。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=LEAD-ID:+Language-Enhanced+Denoising+and+Intent+Distinguishing+Graph+Neural+Network+for+Sponsored+Search+Broad+Retrievals)|0| -|[Regression Compatible Listwise Objectives for Calibrated Ranking with Binary Relevance](https://doi.org/10.1145/3583780.3614712)|Aijun Bai, Rolf Jagerman, Zhen Qin, Le Yan, Pratyush Kar, BingRong Lin, Xuanhui Wang, Michael Bendersky, Marc Najork|Google LLC, New York, NY, USA; Google LLC, Mountain View, CA, USA; Google LLC, Paris, France; Google LLC, Amsterdam, Netherlands|As Learning-to-Rank (LTR) approaches primarily seek to improve ranking quality, their output scores are not scale-calibrated by design. This fundamentally limits LTR usage in score-sensitive applications. Though a simple multi-objective approach that combines a regression and a ranking objective can effectively learn scale-calibrated scores, we argue that the two objectives are not necessarily compatible, which makes the trade-off less ideal for either of them. In this paper, we propose a practical regression compatible ranking (RCR) approach that achieves a better trade-off, where the two ranking and regression components are proved to be mutually aligned. Although the same idea applies to ranking with both binary and graded relevance, we mainly focus on binary labels in this paper. We evaluate the proposed approach on several public LTR benchmarks and show that it consistently achieves either best or competitive result in terms of both regression and ranking metrics, and significantly improves the Pareto frontiers in the context of multi-objective optimization. Furthermore, we evaluated the proposed approach on YouTube Search and found that it not only improved the ranking quality of the production pCTR model, but also brought gains to the click prediction accuracy. The proposed approach has been successfully deployed in the YouTube production system.|由于学习到排名(LTR)方法主要寻求提高排名质量，因此它们的输出分数不是按照设计进行标度校准的。这从根本上限制了对分数敏感的应用程序中 LTR 的使用。虽然一个简单的多目标方法，结合回归和排名目标可以有效地学习量表校准的分数，我们认为，这两个目标不一定相容，这使得权衡不太理想的任何一个。在本文中，我们提出了一个实用的回归相容排序(RCR)方法，以实现更好的权衡，其中两个排序和回归组件被证明是相互一致的。虽然同样的思想也适用于二进制和分级相关性的排序，但本文主要关注二进制标签。我们在几个公共 LTR 基准上对所提出的方法进行了评估，结果表明，该方法在回归和排序指标方面始终达到最佳或有竞争力的结果，并且在多目标优化的情况下显著改善了帕累托前沿。此外，我们在 YouTube 搜索中对该方法进行了评估，发现该方法不仅提高了产品 pCTR 模型的排序质量，而且提高了点击预测的准确性。提议的方法已经成功地部署在 YouTube 制作系统中。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Regression+Compatible+Listwise+Objectives+for+Calibrated+Ranking+with+Binary+Relevance)|0| +|[Regression Compatible Listwise Objectives for Calibrated Ranking with Binary Relevance](https://doi.org/10.1145/3583780.3614712)|Aijun Bai, Rolf Jagerman, Zhen Qin, Le Yan, Pratyush Kar, BingRong Lin, Xuanhui Wang, Michael Bendersky, Marc Najork|Google LLC, Amsterdam, Netherlands; Google LLC, Mountain View, CA, USA; Google LLC, New York, NY, USA; Google LLC, Paris, France|As Learning-to-Rank (LTR) approaches primarily seek to improve ranking quality, their output scores are not scale-calibrated by design. This fundamentally limits LTR usage in score-sensitive applications. Though a simple multi-objective approach that combines a regression and a ranking objective can effectively learn scale-calibrated scores, we argue that the two objectives are not necessarily compatible, which makes the trade-off less ideal for either of them. In this paper, we propose a practical regression compatible ranking (RCR) approach that achieves a better trade-off, where the two ranking and regression components are proved to be mutually aligned. Although the same idea applies to ranking with both binary and graded relevance, we mainly focus on binary labels in this paper. We evaluate the proposed approach on several public LTR benchmarks and show that it consistently achieves either best or competitive result in terms of both regression and ranking metrics, and significantly improves the Pareto frontiers in the context of multi-objective optimization. Furthermore, we evaluated the proposed approach on YouTube Search and found that it not only improved the ranking quality of the production pCTR model, but also brought gains to the click prediction accuracy. The proposed approach has been successfully deployed in the YouTube production system.|由于学习到排名(LTR)方法主要寻求提高排名质量，因此它们的输出分数不是按照设计进行标度校准的。这从根本上限制了对分数敏感的应用程序中 LTR 的使用。虽然一个简单的多目标方法，结合回归和排名目标可以有效地学习量表校准的分数，我们认为，这两个目标不一定相容，这使得权衡不太理想的任何一个。在本文中，我们提出了一个实用的回归相容排序(RCR)方法，以实现更好的权衡，其中两个排序和回归组件被证明是相互一致的。虽然同样的思想也适用于二进制和分级相关性的排序，但本文主要关注二进制标签。我们在几个公共 LTR 基准上对所提出的方法进行了评估，结果表明，该方法在回归和排序指标方面始终达到最佳或有竞争力的结果，并且在多目标优化的情况下显著改善了帕累托前沿。此外，我们在 YouTube 搜索中对该方法进行了评估，发现该方法不仅提高了产品 pCTR 模型的排序质量，而且提高了点击预测的准确性。提议的方法已经成功地部署在 YouTube 制作系统中。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Regression+Compatible+Listwise+Objectives+for+Calibrated+Ranking+with+Binary+Relevance)|0| |[An Unified Search and Recommendation Foundation Model for Cold-Start Scenario](https://doi.org/10.1145/3583780.3614657)|Yuqi Gong, Xichen Ding, Yehui Su, Kaiming Shen, Zhongyi Liu, Guannan Zhang|Ant Group, Hangzhou, China; Ant Group, Beijing, China|In modern commercial search engines and recommendation systems, data from multiple domains is available to jointly train the multi-domain model. Traditional methods train multi-domain models in the multi-task setting, with shared parameters to learn the similarity of multiple tasks, and task-specific parameters to learn the divergence of features, labels, and sample distributions of individual tasks. With the development of large language models, LLM can extract global domain-invariant text features that serve both search and recommendation tasks. We propose a novel framework called S\&R Multi-Domain Foundation, which uses LLM to extract domain invariant features, and Aspect Gating Fusion to merge the ID feature, domain invariant text features and task-specific heterogeneous sparse features to obtain the representations of query and item. Additionally, samples from multiple search and recommendation scenarios are trained jointly with Domain Adaptive Multi-Task module to obtain the multi-domain foundation model. We apply the S\&R Multi-Domain foundation model to cold start scenarios in the pretrain-finetune manner, which achieves better performance than other SOTA transfer learning methods. The S\&R Multi-Domain Foundation model has been successfully deployed in Alipay Mobile Application's online services, such as content query recommendation and service card recommendation, etc.|在现代商业搜索引擎和推荐系统中，来自多个域的数据可用于联合训练多域模型。传统方法在多任务环境下训练多领域模型，通过共享参数来学习多任务的相似性，通过任务特定参数来学习单个任务的特征、标签和样本分布的差异性。随着大型语言模型的发展，LLM 可以提取全局域不变的文本特征，这些特征可以同时服务于搜索和推荐任务。提出了一种新的基于领域不变特征提取的框架 S & R Multi-Domain Foundation，该框架利用 LLM 提取领域不变特征，利用方面门控融合技术融合 ID 特征、领域不变文本特征和任务特定的异构稀疏特征，得到查询和项目的表示。此外，将多个搜索和推荐场景的样本与领域自适应多任务模块联合训练，得到多领域基础模型。我们将 S & R 多领域基础模型应用于预训练-微调方式的冷启动场景，比其他 SOTA 迁移学习方法获得了更好的性能。S & R 多域基金会模式已成功应用于支付宝移动应用的在线服务，如内容查询推荐和服务卡推荐等。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=An+Unified+Search+and+Recommendation+Foundation+Model+for+Cold-Start+Scenario)|0| |[BOLT: An Automated Deep Learning Framework for Training and Deploying Large-Scale Search and Recommendation Models on Commodity CPU Hardware](https://doi.org/10.1145/3583780.3615458)|Nicholas Meisburger, Vihan Lakshman, Benito Geordie, Joshua Engels, David Torres Ramos, Pratik Pranav, Benjamin Coleman, Benjamin Meisburger, Shubh Gupta, Yashwanth Adunukota, Siddharth Jain, Tharun Medini, Anshumali Shrivastava|ThirdAI, Houston, TX, USA|Efficient large-scale neural network training and inference on commodity CPU hardware is of immense practical significance in democratizing deep learning (DL) capabilities. Presently, the process of training massive models consisting of hundreds of millions to billions of parameters requires the extensive use of specialized hardware accelerators, such as GPUs, which are only accessible to a limited number of institutions with considerable financial resources. Moreover, there is often an alarming carbon footprint associated with training and deploying these models. In this paper, we take a step towards addressing these challenges by introducing BOLT, a sparse deep learning library for training large-scale search and recommendation models on standard CPU hardware. BOLT provides a flexible, high-level API for constructing models that will be familiar to users of existing popular DL frameworks. By automatically tuning specialized hyperparameters, BOLT also abstracts away the algorithmic details of sparse network training. We evaluate BOLT on a number of information retrieval tasks including product recommendations, text classification, graph neural networks, and personalization. We find that our proposed system achieves competitive performance with state-of-the-art techniques at a fraction of the cost and energy consumption and an order-of-magnitude faster inference time. BOLT has also been successfully deployed by multiple businesses to address critical problems, and we highlight one customer case study in the field of e-commerce.|高效的大规模神经网络训练和推理对于普及深度学习(DL)能力具有重要的现实意义。目前，培训由数亿至数十亿个参数组成的大型模型的过程需要广泛使用专门的硬件加速器，如图形处理器，只有少数拥有大量财政资源的机构才能使用这些加速器。此外，在培训和部署这些模型时，往往存在令人担忧的碳足印。在本文中，我们通过引入 BOLT 向解决这些挑战迈出了一步，BOLT 是一个稀疏的深度学习库，用于在标准 CPU 硬件上培训大规模搜索和推荐模型。BOLT 提供了一个灵活的高级 API，用于构造现有流行的 DL 框架的用户所熟悉的模型。通过自动调整专门的超参数，BOLT 还抽象出稀疏网络训练的算法细节。我们评估 BOLT 的一些信息检索任务，包括产品推荐、文本分类、图形神经网络和个性化。我们发现，我们提出的系统实现了具有竞争力的性能与国家的最先进的技术在成本和能源消耗的一小部分和一个数量级更快的推理时间。BOLT 还被多个企业成功部署以解决关键问题，我们强调了电子商务领域的一个客户案例研究。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=BOLT:+An+Automated+Deep+Learning+Framework+for+Training+and+Deploying+Large-Scale+Search+and+Recommendation+Models+on+Commodity+CPU+Hardware)|0| -|[Graph Learning for Exploratory Query Suggestions in an Instant Search System](https://doi.org/10.1145/3583780.3615481)|Enrico Palumbo, Andreas Damianou, Alice Wang, Alva Liu, Ghazal Fazelnia, Francesco Fabbri, Rui Ferreira, Fabrizio Silvestri, Hugues Bouchard, Claudia Hauff, Mounia Lalmas, Ben Carterette, Praveen Chandar, David Nyhan|Spotify, Turin, Italy; Spotify, Cambridge, United Kingdom; Spotify, Barcelona, Spain; Spotify, Wilmington, USA; Spotify, Farsta, Sweden; Spotify, London, United Kingdom; Spotify, Alexandria, USA; Spotify, Delft, Netherlands; Spotify, New York, USA; Spotify, Rome, Italy|Search systems in online content platforms are typically biased toward a minority of highly consumed items, reflecting the most common user behavior of navigating toward content that is already familiar and popular. Query suggestions are a powerful tool to support query formulation and to encourage exploratory search and content discovery. However, classic approaches for query suggestions typically rely either on semantic similarity, which lacks diversity and does not reflect user searching behavior, or on a collaborative similarity measure mined from search logs, which suffers from data sparsity and is biased by highly popular queries. In this work, we argue that the task of query suggestion can be modelled as a link prediction task on a heterogeneous graph including queries and documents, enabling Graph Learning methods to effectively generate query suggestions encompassing both semantic and collaborative information. We perform an offline evaluation on an internal Spotify dataset of search logs and on two public datasets, showing that node2vec leads to an accurate and diversified set of results, especially on the large scale real-world data. We then describe the implementation in an instant search scenario and discuss a set of additional challenges tied to the specific production environment. Finally, we report the results of a large scale A/B test involving millions of users and prove that node2vec query suggestions lead to an increase in online metrics such as coverage (+1.42% shown search results pages with suggestions) and engagement (+1.21% clicks), with a specifically notable boost in the number of clicks on exploratory search queries (+9.37%).|在线内容平台中的搜索系统通常偏向于少数高消费项目，这反映了最常见的用户行为，即导航到已经熟悉和流行的内容。查询建议是一个强大的工具，可以支持查询表达，并鼓励探索性搜索和内容发现。然而，经典的查询建议方法通常依赖于语义相似性，这种相似性缺乏多样性，不能反映用户的搜索行为; 或者依赖于从搜索日志中挖掘出来的协作相似性度量，这种度量受到数据稀疏性的影响，并且受到非常流行的查询的影响。本文认为，查询建议任务可以建模为包含查询和文档的异构图上的链接预测任务，使图学习方法能够有效地生成包含语义和协作信息的查询建议。我们对搜索日志的内部 Spotify 数据集和两个公共数据集进行离线评估，表明 node2vec 导致准确和多样化的结果集，特别是在大规模的现实世界数据上。然后，我们在一个即时搜索场景中描述实现，并讨论一组与特定生产环境相关的附加挑战。最后，我们报告了一个涉及数百万用户的大规模 A/B 测试的结果，并证明 node2vec 查询建议导致在线指标的增加，如覆盖率(+ 1.42% 显示带有建议的搜索结果页面)和参与度(+ 1.21% 点击率) ，特别是探索性搜索查询的点击数显著增加(+ 9.37%)。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Graph+Learning+for+Exploratory+Query+Suggestions+in+an+Instant+Search+System)|0| -|[Learning and Optimization of Implicit Negative Feedback for Industrial Short-video Recommender System](https://doi.org/10.1145/3583780.3615482)|Yunzhu Pan, Nian Li, Chen Gao, Jianxin Chang, Yanan Niu, Yang Song, Depeng Jin, Yong Li|Tsinghua University, Beijing, China; University of Electronic Science and Technology of China, Chengdu, China; Beijing Kuaishou Technology Co., Ltd., Beijing, China; Department of Electronic Engineering, Tsinghua University, Beijing, China|Short-video recommendation is one of the most important recommendation applications in today's industrial information systems. Compared with other recommendation tasks, the enormous amount of feedback is the most typical characteristic. Specifically, in short-video recommendation, the easiest-to-collect user feedback is from the skipping behaviors, which leads to two critical challenges for the recommendation model. First, the skipping behavior reflects implicit user preferences, and thus it is challenging for interest extraction. Second, the kind of special feedback involves multiple objectives, such as total watching time, which is also very challenging. In this paper, we present our industrial solution in Kuaishou, which serves billion-level users every day. Specifically, we deploy a feedback-aware encoding module which well extracts user preference taking the impact of context into consideration. We further design a multi-objective prediction module which well distinguishes the relation and differences among different model objectives in the short-video recommendation. We conduct extensive online A/B testing, along with detailed and careful analysis, which verifies the effectiveness of our solution.|短视频推荐是当今工业信息系统中最重要的推荐应用之一。与其他推荐任务相比，大量的反馈是最典型的特征。具体来说，在短视频推荐中，最容易收集的用户反馈来自跳跃行为，这给推荐模型带来了两个关键的挑战。首先，跳跃行为反映了隐式用户偏好，因此对兴趣提取具有挑战性。其次，这种特殊的反馈涉及多个目标，如总观看时间，这也是非常具有挑战性的。在本文中，我们介绍了我们在 Kuaishou 的工业解决方案，这个方案每天为数十亿用户提供服务。具体来说，我们部署了一个反馈感知的编码模块，该模块在考虑上下文影响的情况下很好地提取了用户偏好。进一步设计了一个多目标预测模块，可以很好地区分短视频推荐中不同模型目标之间的关系和差异。我们进行了广泛的在线 A/B 测试，并进行了详细和仔细的分析，从而验证了我们的解决方案的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Learning+and+Optimization+of+Implicit+Negative+Feedback+for+Industrial+Short-video+Recommender+System)|0| +|[Graph Learning for Exploratory Query Suggestions in an Instant Search System](https://doi.org/10.1145/3583780.3615481)|Enrico Palumbo, Andreas Damianou, Alice Wang, Alva Liu, Ghazal Fazelnia, Francesco Fabbri, Rui Ferreira, Fabrizio Silvestri, Hugues Bouchard, Claudia Hauff, Mounia Lalmas, Ben Carterette, Praveen Chandar, David Nyhan|Spotify, London, United Kingdom; Spotify, Farsta, Sweden; Spotify, Rome, Italy; Spotify, Wilmington, USA; Spotify, Cambridge, United Kingdom; Spotify, Delft, Netherlands; Spotify, Alexandria, USA; Spotify, Barcelona, Spain; Spotify, Turin, Italy; Spotify, New York, USA|Search systems in online content platforms are typically biased toward a minority of highly consumed items, reflecting the most common user behavior of navigating toward content that is already familiar and popular. Query suggestions are a powerful tool to support query formulation and to encourage exploratory search and content discovery. However, classic approaches for query suggestions typically rely either on semantic similarity, which lacks diversity and does not reflect user searching behavior, or on a collaborative similarity measure mined from search logs, which suffers from data sparsity and is biased by highly popular queries. In this work, we argue that the task of query suggestion can be modelled as a link prediction task on a heterogeneous graph including queries and documents, enabling Graph Learning methods to effectively generate query suggestions encompassing both semantic and collaborative information. We perform an offline evaluation on an internal Spotify dataset of search logs and on two public datasets, showing that node2vec leads to an accurate and diversified set of results, especially on the large scale real-world data. We then describe the implementation in an instant search scenario and discuss a set of additional challenges tied to the specific production environment. Finally, we report the results of a large scale A/B test involving millions of users and prove that node2vec query suggestions lead to an increase in online metrics such as coverage (+1.42% shown search results pages with suggestions) and engagement (+1.21% clicks), with a specifically notable boost in the number of clicks on exploratory search queries (+9.37%).|在线内容平台中的搜索系统通常偏向于少数高消费项目，这反映了最常见的用户行为，即导航到已经熟悉和流行的内容。查询建议是一个强大的工具，可以支持查询表达，并鼓励探索性搜索和内容发现。然而，经典的查询建议方法通常依赖于语义相似性，这种相似性缺乏多样性，不能反映用户的搜索行为; 或者依赖于从搜索日志中挖掘出来的协作相似性度量，这种度量受到数据稀疏性的影响，并且受到非常流行的查询的影响。本文认为，查询建议任务可以建模为包含查询和文档的异构图上的链接预测任务，使图学习方法能够有效地生成包含语义和协作信息的查询建议。我们对搜索日志的内部 Spotify 数据集和两个公共数据集进行离线评估，表明 node2vec 导致准确和多样化的结果集，特别是在大规模的现实世界数据上。然后，我们在一个即时搜索场景中描述实现，并讨论一组与特定生产环境相关的附加挑战。最后，我们报告了一个涉及数百万用户的大规模 A/B 测试的结果，并证明 node2vec 查询建议导致在线指标的增加，如覆盖率(+ 1.42% 显示带有建议的搜索结果页面)和参与度(+ 1.21% 点击率) ，特别是探索性搜索查询的点击数显著增加(+ 9.37%)。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Graph+Learning+for+Exploratory+Query+Suggestions+in+an+Instant+Search+System)|0| +|[Learning and Optimization of Implicit Negative Feedback for Industrial Short-video Recommender System](https://doi.org/10.1145/3583780.3615482)|Yunzhu Pan, Nian Li, Chen Gao, Jianxin Chang, Yanan Niu, Yang Song, Depeng Jin, Yong Li|Department of Electronic Engineering, Tsinghua University, Beijing, China; Tsinghua University, Beijing, China; University of Electronic Science and Technology of China, Chengdu, China; Beijing Kuaishou Technology Co., Ltd., Beijing, China|Short-video recommendation is one of the most important recommendation applications in today's industrial information systems. Compared with other recommendation tasks, the enormous amount of feedback is the most typical characteristic. Specifically, in short-video recommendation, the easiest-to-collect user feedback is from the skipping behaviors, which leads to two critical challenges for the recommendation model. First, the skipping behavior reflects implicit user preferences, and thus it is challenging for interest extraction. Second, the kind of special feedback involves multiple objectives, such as total watching time, which is also very challenging. In this paper, we present our industrial solution in Kuaishou, which serves billion-level users every day. Specifically, we deploy a feedback-aware encoding module which well extracts user preference taking the impact of context into consideration. We further design a multi-objective prediction module which well distinguishes the relation and differences among different model objectives in the short-video recommendation. We conduct extensive online A/B testing, along with detailed and careful analysis, which verifies the effectiveness of our solution.|短视频推荐是当今工业信息系统中最重要的推荐应用之一。与其他推荐任务相比，大量的反馈是最典型的特征。具体来说，在短视频推荐中，最容易收集的用户反馈来自跳跃行为，这给推荐模型带来了两个关键的挑战。首先，跳跃行为反映了隐式用户偏好，因此对兴趣提取具有挑战性。其次，这种特殊的反馈涉及多个目标，如总观看时间，这也是非常具有挑战性的。在本文中，我们介绍了我们在 Kuaishou 的工业解决方案，这个方案每天为数十亿用户提供服务。具体来说，我们部署了一个反馈感知的编码模块，该模块在考虑上下文影响的情况下很好地提取了用户偏好。进一步设计了一个多目标预测模块，可以很好地区分短视频推荐中不同模型目标之间的关系和差异。我们进行了广泛的在线 A/B 测试，并进行了详细和仔细的分析，从而验证了我们的解决方案的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Learning+and+Optimization+of+Implicit+Negative+Feedback+for+Industrial+Short-video+Recommender+System)|0| |[3MN: Three Meta Networks for Multi-Scenario and Multi-Task Learning in Online Advertising Recommender Systems](https://doi.org/10.1145/3583780.3614651)|Yifei Zhang, Hua Hua, Hui Guo, Shuangyang Wang, Chongyu Zhong, Shijie Zhang|Interactive Entertainment Group, Tencent, Shenzhen, China|Recommender systems are widely applied on web. For example, online advertising systems rely on recommender systems to accurately estimate the value of display opportunities, which is critical to maximize the profits of advertisers. To reduce computational resource consumption, the core tactic of Multi-Scenario Multi-Task Learning (MSMTL) is to devise a single recommder system that is adapted to all contexts instead of implementing multiple scenario-oriented or task-oriented recommender systems. However, MSMTL is challenging because there are complicated task-task, scenario-scenario, and task-scenario interrelations; the characteristic of different tasks in different scenarios also largely varies; and samples of each context are often unevenly distributed. Previous MSMTL solutions focus on applying scenario knowledge to improve the performance of multi-task learning, while neglecting the complicated interrelations among tasks and scenarios. Moreover, samples derived from different scenarios are transferred into the latent embedding with the same dimension. This static embedding strategy impedes the practicality of model expressiveness, since the scenarios with sufficient samples are underrepresented and those with insufficient samples are over-represented. In this paper, we propose a novel three meta networks-based solution (3MN) to MSMTL that addresses all the limitations discussed above. Specifically, we innovatively bind the meta network with scenario-related input in bottom embedding layer, so that the embedding layer is capable of learning the scenario-related knowledge explicitly. To counteract the imbalanced scenario-related data distributions, our flexible embedding layer adaptively learns the representation of samples. This innovative embedding layer is also able to boost other solutions as a plug-in. Moreover, to fully capture the interrelations among scenarios and tasks, we enforce the task and scenario information into the other two meta networks, and transfer the resulted meta-knowledge into the top components (i.e., backbone network and classifier) of the recommender system, respectively. These three meta networks contribute to the superiority of our 3MN solution over state-of-the-art MSMTL solutions, which is demonstrated by extensive offline experiments. 3MN has been successfully deployed in our industrial online advertising system.|推荐系统在网络上得到了广泛的应用。例如，在线广告系统依赖于推荐系统来准确估计展示机会的价值，这对于广告商的利润最大化至关重要。为了减少计算资源消耗，多场景多任务学习(MSMTL)的核心策略是设计一个单一的推荐系统，以适应所有环境，而不是实施多场景或任务导向的推荐系统。然而，MSMTL 具有挑战性，因为存在复杂的任务-任务、场景-场景和任务-场景之间的相互关系; 不同场景中不同任务的特征也大不相同; 每个上下文的样本通常分布不均匀。以往的 MSMTL 解决方案侧重于应用场景知识来提高多任务学习的性能，而忽视了任务和场景之间复杂的相互关系。同时，将不同场景的样本转化为同维数的潜在嵌入。这种静态嵌入策略阻碍了模型表达的实用性，因为有足够样本的场景表示不足，而有不足样本的场景表示过多。在本文中，我们提出了一个新的三元网络为基础的解决方案(3MN)的 MSMTL，解决所有的限制上述讨论。具体来说，我们在底层嵌入层创新性地将元网络与场景相关的输入绑定在一起，使得嵌入层能够显式地学习场景相关的知识。为了抵消不平衡的场景相关数据分布，我们的灵活嵌入层自适应地学习样本的表示。这个创新的嵌入层还可以作为插件提升其他解决方案。此外，为了充分捕捉场景和任务之间的相互关系，我们将任务和场景信息强制加入另外两个元网络，并将得到的元知识分别转移到推荐系统的顶层组件(即骨干网络和分类器)中。这三个元网络有助于我们的3MN 解决方案优于最先进的 MSMTL 解决方案，这在大量的离线实验中得到了证明。3MN 已成功部署在我们的工业在线广告系统。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=3MN:+Three+Meta+Networks+for+Multi-Scenario+and+Multi-Task+Learning+in+Online+Advertising+Recommender+Systems)|0| -|[GripRank: Bridging the Gap between Retrieval and Generation via the Generative Knowledge Improved Passage Ranking](https://doi.org/10.1145/3583780.3614901)|Jiaqi Bai, Hongcheng Guo, Jiaheng Liu, Jian Yang, Xinnian Liang, Zhao Yan, Zhoujun Li|State Key Lab of Software Development Environment, Beihang University, Beijing, China; DAMO Academy, Alibaba Group, Beijing, China; Tencent Cloud AI, Beijing, China; Beihang University, Beijing, China|Retrieval-enhanced text generation, which aims to leverage passages retrieved from a large passage corpus for delivering a proper answer given the input query, has shown remarkable progress on knowledge-intensive language tasks such as open-domain question answering and knowledge-enhanced dialogue generation. However, the retrieved passages are not ideal for guiding answer generation because of the discrepancy between retrieval and generation, i.e., the candidate passages are all treated equally during the retrieval procedure without considering their potential to generate the proper answers. This discrepancy makes a passage retriever deliver a sub-optimal collection of candidate passages to generate answers. In this paper, we propose the GeneRative Knowledge Improved Passage Ranking (GripRank) approach, addressing the above challenge by distilling knowledge from a generative passage estimator (GPE) to a passage ranker, where the GPE is a generative language model used to measure how likely the candidate passages can generate the proper answer. We realize the distillation procedure by teaching the passage ranker learning to rank the passages ordered by the GPE. Furthermore, we improve the distillation quality by devising a curriculum knowledge distillation mechanism, which allows the knowledge provided by the GPE can be progressively distilled to the ranker through an easy-to-hard curriculum, enabling the passage ranker to correctly recognize the provenance of the answer from many plausible candidates. We conduct extensive experiments on four datasets across three knowledge-intensive language tasks. Experimental results show advantages over the state-of-the-art methods for both passage ranking and answer generation on the KILT benchmark.|检索增强型文本生成的目的是利用从大型文章语料库中检索到的段落来提供输入查询的正确答案，在开放领域问题回答和知识增强型对话生成等知识密集型语言任务方面取得了显著进展。然而，由于检索和生成之间的差异，被检索的段落并不是指导生成答案的理想选择，也就是说，候选段落在检索过程中都被平等对待，而没有考虑它们生成正确答案的潜力。这种差异使得文章检索器提供一个次优的候选文章集合来生成答案。在本文中，我们提出了生成知识改进通道排名(GripRank)方法，通过从生成通道估计(GPE)中提取知识到一个通道排名，其中 GPE 是一个生成语言模型，用于衡量候选通道产生正确答案的可能性，从而解决上述挑战。通过教导通道排序器学习对 GPE 排序的通道进行排序，实现了蒸馏过程。此外，我们通过设计一个课程知识蒸馏机制来提高蒸馏质量，该机制允许 GPE 提供的知识可以通过一个容易到难的课程逐步蒸馏到排名，使得通过排名能够正确地识别来自许多合理候选人的答案的来源。我们在三个知识密集型语言任务的四个数据集上进行了广泛的实验。实验结果表明，在 KILT 基准的文章排序和答案生成方面，该方法比目前最先进的方法具有更大的优势。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=GripRank:+Bridging+the+Gap+between+Retrieval+and+Generation+via+the+Generative+Knowledge+Improved+Passage+Ranking)|0| -|[CLosER: Conversational Legal Longformer with Expertise-Aware Passage Response Ranker for Long Contexts](https://doi.org/10.1145/3583780.3614812)|Arian Askari, Mohammad Aliannejadi, Amin Abolghasemi, Evangelos Kanoulas, Suzan Verberne|LIACS, Leiden University, Leiden, Netherlands; Leiden University, Amsterdam, Netherlands; University of Amsterdam, Amsterdam, Netherlands; Leiden Institute of Advanced Computer Science, Leiden University, Leiden, Netherlands|In this paper, we investigate the task of response ranking in conversational legal search. We propose a novel method for conversational passage response retrieval (ConvPR) for long conversations in domains with mixed levels of expertise. Conversational legal search is challenging because the domain includes long, multi-participant dialogues with domain-specific language. Furthermore, as opposed to other domains, there typically is a large knowledge gap between the questioner (a layperson) and the responders (lawyers), participating in the same conversation. We collect and release a large-scale real-world dataset called LegalConv with nearly one million legal conversations from a legal community question answering (CQA) platform. We address the particular challenges of processing legal conversations, with our novel Conversational Legal Longformer with Expertise-Aware Response Ranker, called CLosER. The proposed method has two main innovations compared to state-of-the-art methods for ConvPR: (i) Expertise-Aware Post-Training; a learning objective that takes into account the knowledge gap difference between participants to the conversation; and (ii) a simple but effective strategy for re-ordering the context utterances in long conversations to overcome the limitations of the sparse attention mechanism of the Longformer architecture. Evaluation on LegalConv shows that our proposed method substantially and significantly outperforms existing state-of-the-art models on the response selection task. Our analysis indicates that our Expertise-Aware PostTraining, i.e., continued pre-training or domain/task adaptation, plays an important role in the achieved effectiveness. Our proposed method is generalizable to other tasks with domain-specific challenges and can facilitate future research on conversational search in other domains.|本文研究了会话法律搜索中的回答排序问题。本文提出了一种基于混合专业知识水平的会话通道反应检索方法。对话式法律搜索具有挑战性，因为该领域包括与领域特定语言的长时间、多参与者对话。此外，与其他领域不同，提问者(外行)和回答者(律师)参与同一对话时，通常存在很大的知识差距。我们收集并发布了一个名为 LegalConv 的大规模现实世界数据集，其中包括来自法律社区问答(cQA)平台的近一百万个法律对话。我们解决处理法律对话的特殊挑战，我们的新颖的对话法律长期与专家意识的响应排名，所谓的关闭。与目前最先进的 ConvPR 方法相比，提出的方法有两个主要创新: (i)专业知识意识的后期培训; 一个考虑到会话参与者之间的知识差异的学习目标; 和(ii)一个简单但有效的策略来重新排序长时间会话中的上下文语句，以克服 Longform 架构的稀疏注意机制的局限性。对 LegalConv 的评估表明，我们提出的方法在响应选择任务上大大优于现有的最先进的模型。我们的分析表明，我们的专业意识后期培训，即持续的培训前或领域/任务适应，在实现有效性方面发挥了重要作用。我们提出的方法可推广到其他具有领域特定挑战的任务，并可促进未来其他领域的会话搜索研究。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=CLosER:+Conversational+Legal+Longformer+with+Expertise-Aware+Passage+Response+Ranker+for+Long+Contexts)|0| +|[GripRank: Bridging the Gap between Retrieval and Generation via the Generative Knowledge Improved Passage Ranking](https://doi.org/10.1145/3583780.3614901)|Jiaqi Bai, Hongcheng Guo, Jiaheng Liu, Jian Yang, Xinnian Liang, Zhao Yan, Zhoujun Li|Tencent Cloud AI, Beijing, China; State Key Lab of Software Development Environment, Beihang University, Beijing, China; DAMO Academy, Alibaba Group, Beijing, China; Beihang University, Beijing, China|Retrieval-enhanced text generation, which aims to leverage passages retrieved from a large passage corpus for delivering a proper answer given the input query, has shown remarkable progress on knowledge-intensive language tasks such as open-domain question answering and knowledge-enhanced dialogue generation. However, the retrieved passages are not ideal for guiding answer generation because of the discrepancy between retrieval and generation, i.e., the candidate passages are all treated equally during the retrieval procedure without considering their potential to generate the proper answers. This discrepancy makes a passage retriever deliver a sub-optimal collection of candidate passages to generate answers. In this paper, we propose the GeneRative Knowledge Improved Passage Ranking (GripRank) approach, addressing the above challenge by distilling knowledge from a generative passage estimator (GPE) to a passage ranker, where the GPE is a generative language model used to measure how likely the candidate passages can generate the proper answer. We realize the distillation procedure by teaching the passage ranker learning to rank the passages ordered by the GPE. Furthermore, we improve the distillation quality by devising a curriculum knowledge distillation mechanism, which allows the knowledge provided by the GPE can be progressively distilled to the ranker through an easy-to-hard curriculum, enabling the passage ranker to correctly recognize the provenance of the answer from many plausible candidates. We conduct extensive experiments on four datasets across three knowledge-intensive language tasks. Experimental results show advantages over the state-of-the-art methods for both passage ranking and answer generation on the KILT benchmark.|检索增强型文本生成的目的是利用从大型文章语料库中检索到的段落来提供输入查询的正确答案，在开放领域问题回答和知识增强型对话生成等知识密集型语言任务方面取得了显著进展。然而，由于检索和生成之间的差异，被检索的段落并不是指导生成答案的理想选择，也就是说，候选段落在检索过程中都被平等对待，而没有考虑它们生成正确答案的潜力。这种差异使得文章检索器提供一个次优的候选文章集合来生成答案。在本文中，我们提出了生成知识改进通道排名(GripRank)方法，通过从生成通道估计(GPE)中提取知识到一个通道排名，其中 GPE 是一个生成语言模型，用于衡量候选通道产生正确答案的可能性，从而解决上述挑战。通过教导通道排序器学习对 GPE 排序的通道进行排序，实现了蒸馏过程。此外，我们通过设计一个课程知识蒸馏机制来提高蒸馏质量，该机制允许 GPE 提供的知识可以通过一个容易到难的课程逐步蒸馏到排名，使得通过排名能够正确地识别来自许多合理候选人的答案的来源。我们在三个知识密集型语言任务的四个数据集上进行了广泛的实验。实验结果表明，在 KILT 基准的文章排序和答案生成方面，该方法比目前最先进的方法具有更大的优势。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=GripRank:+Bridging+the+Gap+between+Retrieval+and+Generation+via+the+Generative+Knowledge+Improved+Passage+Ranking)|0| +|[CLosER: Conversational Legal Longformer with Expertise-Aware Passage Response Ranker for Long Contexts](https://doi.org/10.1145/3583780.3614812)|Arian Askari, Mohammad Aliannejadi, Amin Abolghasemi, Evangelos Kanoulas, Suzan Verberne|Leiden University, Amsterdam, Netherlands; LIACS, Leiden University, Leiden, Netherlands; Leiden Institute of Advanced Computer Science, Leiden University, Leiden, Netherlands; University of Amsterdam, Amsterdam, Netherlands|In this paper, we investigate the task of response ranking in conversational legal search. We propose a novel method for conversational passage response retrieval (ConvPR) for long conversations in domains with mixed levels of expertise. Conversational legal search is challenging because the domain includes long, multi-participant dialogues with domain-specific language. Furthermore, as opposed to other domains, there typically is a large knowledge gap between the questioner (a layperson) and the responders (lawyers), participating in the same conversation. We collect and release a large-scale real-world dataset called LegalConv with nearly one million legal conversations from a legal community question answering (CQA) platform. We address the particular challenges of processing legal conversations, with our novel Conversational Legal Longformer with Expertise-Aware Response Ranker, called CLosER. The proposed method has two main innovations compared to state-of-the-art methods for ConvPR: (i) Expertise-Aware Post-Training; a learning objective that takes into account the knowledge gap difference between participants to the conversation; and (ii) a simple but effective strategy for re-ordering the context utterances in long conversations to overcome the limitations of the sparse attention mechanism of the Longformer architecture. Evaluation on LegalConv shows that our proposed method substantially and significantly outperforms existing state-of-the-art models on the response selection task. Our analysis indicates that our Expertise-Aware PostTraining, i.e., continued pre-training or domain/task adaptation, plays an important role in the achieved effectiveness. Our proposed method is generalizable to other tasks with domain-specific challenges and can facilitate future research on conversational search in other domains.|本文研究了会话法律搜索中的回答排序问题。本文提出了一种基于混合专业知识水平的会话通道反应检索方法。对话式法律搜索具有挑战性，因为该领域包括与领域特定语言的长时间、多参与者对话。此外，与其他领域不同，提问者(外行)和回答者(律师)参与同一对话时，通常存在很大的知识差距。我们收集并发布了一个名为 LegalConv 的大规模现实世界数据集，其中包括来自法律社区问答(cQA)平台的近一百万个法律对话。我们解决处理法律对话的特殊挑战，我们的新颖的对话法律长期与专家意识的响应排名，所谓的关闭。与目前最先进的 ConvPR 方法相比，提出的方法有两个主要创新: (i)专业知识意识的后期培训; 一个考虑到会话参与者之间的知识差异的学习目标; 和(ii)一个简单但有效的策略来重新排序长时间会话中的上下文语句，以克服 Longform 架构的稀疏注意机制的局限性。对 LegalConv 的评估表明，我们提出的方法在响应选择任务上大大优于现有的最先进的模型。我们的分析表明，我们的专业意识后期培训，即持续的培训前或领域/任务适应，在实现有效性方面发挥了重要作用。我们提出的方法可推广到其他具有领域特定挑战的任务，并可促进未来其他领域的会话搜索研究。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=CLosER:+Conversational+Legal+Longformer+with+Expertise-Aware+Passage+Response+Ranker+for+Long+Contexts)|0| |[Multi-modal Mixture of Experts Represetation Learning for Sequential Recommendation](https://doi.org/10.1145/3583780.3614978)|Shuqing Bian, Xingyu Pan, Wayne Xin Zhao, Jinpeng Wang, Chuyuan Wang, JiRong Wen|Renmin University of China, Beijing, China; Meituan, Beijing, China|Within online platforms, it is critical to capture the dynamic user preference from the sequential interaction behaviors for making accurate recommendation over time. Recently, significant progress has been made in sequential recommendation with deep learning. However, existing neural sequential recommender often suffer from the data sparsity issue in real-world applications. To tackle this problem, we propose a Multi-Modal Mixture of experts model for Sequential Recommendation, named M3SRec, which leverage rich multi-modal interaction data for improving sequential recommendation. Different from existing multi-modal recommendation models, our approach jointly considers reducing the semantic gap across modalities and adapts multi-modal semantics to fit recommender systems. For this purpose, we make two important technical contributions in architecture and training. Firstly, we design a novel multi-modal mixture-of-experts (MoE) fusion network, which can deeply fuse the across-modal semantics and largely enhance the modeling capacity of complex user intents. For training, we design specific pre-training tasks that can mimic the goal of the recommendation, which help model learn the semantic relatedness between the multi-modal sequential context and the target item. Extensive experiments conducted on both public and industry datasets demonstrate the superiority of our proposed method over existing state-of-the-art methods, especially when only limited training data is available.|在在线平台中，从连续的交互行为中捕获动态用户偏好对于随时间做出准确的推荐是至关重要的。近年来，随着深度学习在序贯推荐方面取得了显著的进展。然而，现有的神经顺序推荐系统在实际应用中经常遇到数据稀疏的问题。为了解决这个问题，我们提出了一个序贯推荐的多模态混合专家模型 M3SRec，该模型利用丰富的多模态交互数据来改进序贯推荐。与现有的多模态推荐模型不同，我们的方法共同考虑缩小模态间的语义差距，并采用多模态语义来适应推荐系统。为此，我们在建筑和培训方面做出了两项重要的技术贡献。首先，我们设计了一种新的多模态专家混合(MoE)融合网络，该网络能够深入融合跨模态语义，大大提高复杂用户意图的建模能力。对于训练，我们设计了能够模拟推荐目标的特定的预训练任务，帮助模型学习多模态顺序上下文和目标项之间的语义关系。在公共数据集和行业数据集上进行的大量实验表明，我们提出的方法优于现有的最先进的方法，特别是当只有有限的训练数据可用时。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Multi-modal+Mixture+of+Experts+Represetation+Learning+for+Sequential+Recommendation)|0| -|[BOMGraph: Boosting Multi-scenario E-commerce Search with a Unified Graph Neural Network](https://doi.org/10.1145/3583780.3614794)|Shuai Fan, Jinping Gou, Yang Li, Jiaxing Bai, Chen Lin, Wanxian Guan, Xubin Li, Hongbo Deng, Jian Xu, Bo Zheng|Alibaba Group, Hangzhou, China; Xiamen University, Xiamen, China|Mobile Taobao Application delivers search services on multiple scenarios that take textual, visual, or product queries. This paper aims to propose a unified graph neural network for these search scenarios to leverage data from multiple scenarios and jointly optimize search performances with less training and maintenance costs. Towards this end, this paper proposes BOMGraph, BOosting Multi-scenario E-commerce Search with a unified Graph neural network. BOMGraph is embodied with several components to address challenges in multi-scenario search. It captures heterogeneous information flow across scenarios by inter-scenario and intra-scenario metapaths. It learns robust item representations by disentangling specific characteristics for different scenarios and encoding common knowledge across scenarios. It alleviates label scarcity and long-tail problems in scenarios with low traffic by contrastive learning with cross-scenario augmentation. BOMGraph has been deployed in production by Alibaba's E-commerce search advertising platform. Both offline evaluations and online A/B tests demonstrate the effectiveness of BOMGraph.|移动淘宝应用程序提供多种场景的搜索服务，包括文本查询、视觉查询或产品查询。针对这些搜索场景，本文提出了一种统一的图形神经网络，以较少的训练和维护成本，充分利用多个场景的数据，共同优化搜索性能。为此，本文提出了基于统一图神经网络的 BOMGraph，以推动多场景电子商务搜索。BOMGraph 由几个组件组成，用于解决多场景搜索中的挑战。它通过场景间和场景内的元路径捕获跨场景的异构信息流。它通过分离不同场景的特定特征并跨场景编码公共知识来学习健壮的项表示。通过对比学习和跨场景扩展，缓解了低流量场景下的标签稀缺性和长尾问题。BOMGraph 已被阿里巴巴电子商务搜索广告平台投入生产。离线评估和在线 A/B 测试都证明了 BOMGraph 的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=BOMGraph:+Boosting+Multi-scenario+E-commerce+Search+with+a+Unified+Graph+Neural+Network)|0| -|[Large Language Models as Zero-Shot Conversational Recommenders](https://doi.org/10.1145/3583780.3614949)|Zhankui He, Zhouhang Xie, Rahul Jha, Harald Steck, Dawen Liang, Yesu Feng, Bodhisattwa Prasad Majumder, Nathan Kallus, Julian J. McAuley|Netflix Inc. & Cornell University, Los Gatos, CA, USA; University of California, San Diego, La Jolla, CA, USA; Netflix Inc., Los Gatos, CA, USA|In this paper, we present empirical studies on conversational recommendation tasks using representative large language models in a zero-shot setting with three primary contributions. (1) Data: To gain insights into model behavior in "in-the-wild" conversational recommendation scenarios, we construct a new dataset of recommendation-related conversations by scraping a popular discussion website. This is the largest public real-world conversational recommendation dataset to date. (2) Evaluation: On the new dataset and two existing conversational recommendation datasets, we observe that even without fine-tuning, large language models can outperform existing fine-tuned conversational recommendation models. (3) Analysis: We propose various probing tasks to investigate the mechanisms behind the remarkable performance of large language models in conversational recommendation. We analyze both the large language models' behaviors and the characteristics of the datasets, providing a holistic understanding of the models' effectiveness, limitations and suggesting directions for the design of future conversational recommenders|本文采用三个主要贡献的零击点模型对会话推荐任务进行了实证研究。(1)数据: 为了深入了解“野外”会话推荐场景中的模型行为，我们通过刮取一个流行的讨论网站，构建了一个新的推荐相关会话数据集。这是迄今为止最大的公共现实世界对话推荐数据集。(2)评估: 在新的数据集和两个现有的会话推荐数据集上，我们观察到即使没有微调，大型语言模型也能胜过现有的微调会话推荐模型。(3)分析: 我们提出了各种探究任务来研究大语言模型在会话推荐中显著表现的机制。我们分析了大型语言模型的行为和数据集的特点，为模型的有效性、局限性提供了全面的理解，并为未来会话推荐系统的设计提供了建议|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Large+Language+Models+as+Zero-Shot+Conversational+Recommenders)|0| +|[BOMGraph: Boosting Multi-scenario E-commerce Search with a Unified Graph Neural Network](https://doi.org/10.1145/3583780.3614794)|Shuai Fan, Jinping Gou, Yang Li, Jiaxing Bai, Chen Lin, Wanxian Guan, Xubin Li, Hongbo Deng, Jian Xu, Bo Zheng|Xiamen University, Xiamen, China; Alibaba Group, Hangzhou, China|Mobile Taobao Application delivers search services on multiple scenarios that take textual, visual, or product queries. This paper aims to propose a unified graph neural network for these search scenarios to leverage data from multiple scenarios and jointly optimize search performances with less training and maintenance costs. Towards this end, this paper proposes BOMGraph, BOosting Multi-scenario E-commerce Search with a unified Graph neural network. BOMGraph is embodied with several components to address challenges in multi-scenario search. It captures heterogeneous information flow across scenarios by inter-scenario and intra-scenario metapaths. It learns robust item representations by disentangling specific characteristics for different scenarios and encoding common knowledge across scenarios. It alleviates label scarcity and long-tail problems in scenarios with low traffic by contrastive learning with cross-scenario augmentation. BOMGraph has been deployed in production by Alibaba's E-commerce search advertising platform. Both offline evaluations and online A/B tests demonstrate the effectiveness of BOMGraph.|移动淘宝应用程序提供多种场景的搜索服务，包括文本查询、视觉查询或产品查询。针对这些搜索场景，本文提出了一种统一的图形神经网络，以较少的训练和维护成本，充分利用多个场景的数据，共同优化搜索性能。为此，本文提出了基于统一图神经网络的 BOMGraph，以推动多场景电子商务搜索。BOMGraph 由几个组件组成，用于解决多场景搜索中的挑战。它通过场景间和场景内的元路径捕获跨场景的异构信息流。它通过分离不同场景的特定特征并跨场景编码公共知识来学习健壮的项表示。通过对比学习和跨场景扩展，缓解了低流量场景下的标签稀缺性和长尾问题。BOMGraph 已被阿里巴巴电子商务搜索广告平台投入生产。离线评估和在线 A/B 测试都证明了 BOMGraph 的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=BOMGraph:+Boosting+Multi-scenario+E-commerce+Search+with+a+Unified+Graph+Neural+Network)|0| +|[Large Language Models as Zero-Shot Conversational Recommenders](https://doi.org/10.1145/3583780.3614949)|Zhankui He, Zhouhang Xie, Rahul Jha, Harald Steck, Dawen Liang, Yesu Feng, Bodhisattwa Prasad Majumder, Nathan Kallus, Julian J. McAuley|Netflix Inc., Los Gatos, CA, USA; University of California, San Diego, La Jolla, CA, USA; Netflix Inc. & Cornell University, Los Gatos, CA, USA|In this paper, we present empirical studies on conversational recommendation tasks using representative large language models in a zero-shot setting with three primary contributions. (1) Data: To gain insights into model behavior in "in-the-wild" conversational recommendation scenarios, we construct a new dataset of recommendation-related conversations by scraping a popular discussion website. This is the largest public real-world conversational recommendation dataset to date. (2) Evaluation: On the new dataset and two existing conversational recommendation datasets, we observe that even without fine-tuning, large language models can outperform existing fine-tuned conversational recommendation models. (3) Analysis: We propose various probing tasks to investigate the mechanisms behind the remarkable performance of large language models in conversational recommendation. We analyze both the large language models' behaviors and the characteristics of the datasets, providing a holistic understanding of the models' effectiveness, limitations and suggesting directions for the design of future conversational recommenders|本文采用三个主要贡献的零击点模型对会话推荐任务进行了实证研究。(1)数据: 为了深入了解“野外”会话推荐场景中的模型行为，我们通过刮取一个流行的讨论网站，构建了一个新的推荐相关会话数据集。这是迄今为止最大的公共现实世界对话推荐数据集。(2)评估: 在新的数据集和两个现有的会话推荐数据集上，我们观察到即使没有微调，大型语言模型也能胜过现有的微调会话推荐模型。(3)分析: 我们提出了各种探究任务来研究大语言模型在会话推荐中显著表现的机制。我们分析了大型语言模型的行为和数据集的特点，为模型的有效性、局限性提供了全面的理解，并为未来会话推荐系统的设计提供了建议|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Large+Language+Models+as+Zero-Shot+Conversational+Recommenders)|0| |[Adaptive Multi-Modalities Fusion in Sequential Recommendation Systems](https://doi.org/10.1145/3583780.3614775)|Hengchang Hu, Wei Guo, Yong Liu, MinYen Kan|Huawei Noah's Ark Lab, Singapore, Singapore; National University of Singapore, Singapore, Singapore|In sequential recommendation, multi-modal information (e.g., text or image) can provide a more comprehensive view of an item's profile. The optimal stage (early or late) to fuse modality features into item representations is still debated. We propose a graph-based approach (named MMSR) to fuse modality features in an adaptive order, enabling each modality to prioritize either its inherent sequential nature or its interplay with other modalities. MMSR represents each user's history as a graph, where the modality features of each item in a user's history sequence are denoted by cross-linked nodes. The edges between homogeneous nodes represent intra-modality sequential relationships, and the ones between heterogeneous nodes represent inter-modality interdependence relationships. During graph propagation, MMSR incorporates dual attention, differentiating homogeneous and heterogeneous neighbors. To adaptively assign nodes with distinct fusion orders, MMSR allows each node's representation to be asynchronously updated through an update gate. In scenarios where modalities exhibit stronger sequential relationships, the update gate prioritizes updates among homogeneous nodes. Conversely, when the interdependent relationships between modalities are more pronounced, the update gate prioritizes updates among heterogeneous nodes. Consequently, MMSR establishes a fusion order that spans a spectrum from early to late modality fusion. In experiments across six datasets, MMSR consistently outperforms state-of-the-art models, and our graph propagation methods surpass other graph neural networks. Additionally, MMSR naturally manages missing modalities.|在顺序推荐中，多模态信息(例如文本或图像)可以提供一个更全面的项目配置文件视图。最佳阶段(早期或晚期)融合情态特征的项目表示仍然存在争议。我们提出了一种基于图的方法(命名为 MMSR) ，以自适应的顺序融合模态特征，使每个模态优先考虑其固有的顺序性质或其与其他模态的相互作用。MMSR 将每个用户的历史表示为一个图，其中用户历史序列中每个项目的模态特征由交叉链接的节点表示。同质节点之间的边表示模态内部的顺序关系，异质节点之间的边表示模态间的相互依赖关系。在图的传播过程中，MMSR 融合了双重注意，区分了同质和异质邻居。为了自适应地分配具有不同融合顺序的节点，MMSR 允许通过更新门异步更新每个节点的表示。在模式表现出更强的顺序关系的场景中，更新门优先更新同质节点。相反，当模式之间的相互依赖关系更加明显时，更新门优先考虑异构节点之间的更新。因此，MMSR 建立了一个从早期到晚期的融合序列。在跨六个数据集的实验中，MMSR 始终优于最先进的模型，我们的图传播方法优于其他图神经网络。此外，MMSR 自然会管理缺失的模式。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Adaptive+Multi-Modalities+Fusion+in+Sequential+Recommendation+Systems)|0| |[AdaMCT: Adaptive Mixture of CNN-Transformer for Sequential Recommendation](https://doi.org/10.1145/3583780.3614773)|Juyong Jiang, Peiyan Zhang, Yingtao Luo, Chaozhuo Li, Jae Boum Kim, Kai Zhang, Senzhang Wang, Xing Xie, Sunghun Kim||Sequential recommendation (SR) aims to model users' dynamic preferences from their historical interactions. Recently, Transformer and convolution neural network (CNNs) have shown great success in learning representations for SR. Nevertheless, Transformer mainly focus on capturing content-based global interactions, while CNNs effectively exploit local features in practical recommendation scenarios. Thus, how to effectively aggregate CNNs and Transformer to model both local and global dependencies of historical item sequence still remains an open challenge and is rarely studied in SR. To this regard, we inject locality inductive bias into Transformer by combining its global attention mechanism with a local convolutional filter, and adaptively determine the mixing importance on a personalized basis through a module- and layer-aware adaptive mixture units, named AdaMCT. Moreover, considering that softmax-based attention may encourage unimodal activation, we introduce the Squeeze-Excitation Attention (with sigmoid activation) into sequential recommendation to capture multiple relevant items (keys) simultaneously. Extensive experiments on three widely used benchmark datasets demonstrate that AdaMCT significantly outperforms the previous Transformer and CNNs based models by an average of 18.46% and 60.85% respectively in terms of NDCG@5 and achieves state-of-the-art performance.|序贯推荐(SR)的目的是根据用户的历史交互对其动态偏好进行建模。近年来，变压器和卷积神经网络(CNN)在 SR 的学习表征方面取得了很大的成功。尽管如此，Transformer 主要关注于捕获基于内容的全局交互，而 CNN 在实际推荐场景中有效地利用了局部特征。因此，如何有效地聚合 CNN 和 Transformer 来模拟历史项目序列的局部和全局依赖关系仍然是一个开放的挑战，在 SR 中很少进行研究。为此，我们将变压器的全局注意机制与局部卷积滤波器相结合，将局部感应偏差注入变压器，并通过模块和层感知自适应混合单元 AdaMCT 自适应地确定混合重要性。此外，考虑到基于 softmax 的注意力可能会鼓励单峰激活，我们将挤压-兴奋注意力(具有乙状结肠激活)引入顺序推荐，以同时捕获多个相关项目(键)。在三个广泛使用的基准数据集上进行的大量实验表明，AdaMCT 在 NDCG@5方面的性能显著优于以前基于 former 和 CNN 的模型，平均分别为18.46% 和60.85% ，并且达到了最先进的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=AdaMCT:+Adaptive+Mixture+of+CNN-Transformer+for+Sequential+Recommendation)|0| |[AutoMRM: A Model Retrieval Method Based on Multimodal Query and Meta-learning](https://doi.org/10.1145/3583780.3614787)|Zhaotian Li, Binhang Qi, Hailong Sun, Xiang Gao|Beihang University, Beijing, China|With more and more Deep Neural Network (DNN) models are publicly available on model sharing platforms (e.g., HuggingFace), model reuse has become a promising way in practice to improve the efficiency of DNN model construction by avoiding the costs of model training. To that end, a pivotal step for model reuse is model retrieval, which facilitates discovering suitable models from a model hub that match the requirements of users. However, the existing model retrieval methods have inadequate performance and efficiency, since they focus on matching user requirements with the model names, and thus cannot work well for high-dimensional data such as images. In this paper, we propose a user-task-centric multimodal model retrieval method named AutoMRM. AutoMRM can retrieve DNN models suitable for the user's task according to both the dataset and description of the task. Moreover, AutoMRM utilizes meta-learning to retrieve models for previously unseen task queries. Specifically, given a task, AutoMRM extracts the latent meta-features from the dataset and description for training meta-learners offline and obtaining the representation of user task queries online. Experimental results demonstrate that AutoMRM outperforms existing model retrieval methods including the state-of-the-art method in both effectiveness and efficiency.|随着深度神经网络(DNN)模型在模型共享平台(如 HuggingFace)上的广泛应用，模型重用已成为提高 DNN 模型构建效率、避免模型训练成本的有效途径。为此，模型重用的关键步骤是模型检索，它有助于从模型中心发现符合用户需求的合适模型。然而，现有的模型检索方法由于侧重于匹配用户需求和模型名称，因此性能和效率不高，不能很好地适用于图像等高维数据。本文提出了一种以用户任务为中心的多模态模型检索方法 AutoMRM。AutoMRM 可以根据数据集和任务描述检索适合用户任务的 DNN 模型。此外，AutoMRM 利用元学习来检索以前未见到的任务查询的模型。具体来说，AutoMRM 从数据集中提取潜在的元特征，用于离线培训元学习者，并在线获取用户任务查询的表示。实验结果表明，AutoMRM 在有效性和效率方面都优于现有的模型检索方法，包括最先进的方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=AutoMRM:+A+Model+Retrieval+Method+Based+on+Multimodal+Query+and+Meta-learning)|0| -|[Retrieving GNN Architecture for Collaborative Filtering](https://doi.org/10.1145/3583780.3615035)|Fengqi Liang, Huan Zhao, Zhenyi Wang, Wei Fang, Chuan Shi|4Paradigm, Beijing, China; Beijing University of Posts and Telecommunications, Beijing, China; Beijing University of Potsts and Telecommunications, Beijing, China|Graph Neural Networks (GNNs) have been widely used in Collaborative Filtering (CF). However, when given a new recommendation scenario, the current options are either selecting from existing GNN architectures or employing Neural Architecture Search (NAS) to obtain a well-performing GNN model, both of which are expensive in terms of human expertise or computational resources.To address the problem, in this work,we propose a novel neural retrieval approach, dubbed RGCF, to search a well-performing architecture for GNN-based CF rapidly when handling new scenarios. Specifically, we design the neural retrieval approach based on meta-learning by developing two-level meta-features, ranking loss, and task-level data augmentation, and in a retrieval paradigm, RGCF can directly return a well-performing architecture given a new dataset (query), thus being efficient inherently. Experimental results on two mainstream tasks, i.e., rating prediction and item ranking, show that RGCF outperforms all models either by human-designed or NAS on two new datasets in terms of effectiveness and efficiency. Particularly, the efficiency improvement is significant, taking as an example that RGCF is 61.7-206.3x faster than a typical reinforcement learning based NAS approach on the two new datasets. Code and data are available at https://github.com/BUPT-GAMMA/RGCF.|图形神经网络(GNN)已广泛应用于协同过滤(CF)。然而，当给出一个新的推荐场景时，目前的选择要么是从现有的 GNN 架构中选择，要么是使用神经结构搜索(NAS)来获得一个性能良好的 GNN 模型，这两者在人类专业知识或计算资源方面都是昂贵的。为了解决这个问题，在这项工作中，我们提出了一种新的神经检索方法，称为 RGCF，在处理新的场景时快速搜索一个性能良好的基于 GNN 的 CF 架构。具体而言，我们通过开发两级元特征，排序丢失和任务级数据增强来设计基于元学习的神经检索方法，并且在检索范例中，RGCF 可以直接返回给定新数据集(查询)的性能良好的架构，从而本质上是有效的。在评分预测和项目排序这两个主流任务上的实验结果表明，RGCF 在两个新数据集上的有效性和效率均优于人工设计的或 NAS 的所有模型。特别是，效率的提高是显著的，例如在两个新的数据集上，RgCF 比典型的基于强化学习的 NAS 方法快61.7-206.3倍。代码和数据可在 https://github.com/bupt-gamma/rgcf 查阅。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Retrieving+GNN+Architecture+for+Collaborative+Filtering)|0| +|[Retrieving GNN Architecture for Collaborative Filtering](https://doi.org/10.1145/3583780.3615035)|Fengqi Liang, Huan Zhao, Zhenyi Wang, Wei Fang, Chuan Shi|Beijing University of Posts and Telecommunications, Beijing, China; 4Paradigm, Beijing, China; Beijing University of Potsts and Telecommunications, Beijing, China|Graph Neural Networks (GNNs) have been widely used in Collaborative Filtering (CF). However, when given a new recommendation scenario, the current options are either selecting from existing GNN architectures or employing Neural Architecture Search (NAS) to obtain a well-performing GNN model, both of which are expensive in terms of human expertise or computational resources.To address the problem, in this work,we propose a novel neural retrieval approach, dubbed RGCF, to search a well-performing architecture for GNN-based CF rapidly when handling new scenarios. Specifically, we design the neural retrieval approach based on meta-learning by developing two-level meta-features, ranking loss, and task-level data augmentation, and in a retrieval paradigm, RGCF can directly return a well-performing architecture given a new dataset (query), thus being efficient inherently. Experimental results on two mainstream tasks, i.e., rating prediction and item ranking, show that RGCF outperforms all models either by human-designed or NAS on two new datasets in terms of effectiveness and efficiency. Particularly, the efficiency improvement is significant, taking as an example that RGCF is 61.7-206.3x faster than a typical reinforcement learning based NAS approach on the two new datasets. Code and data are available at https://github.com/BUPT-GAMMA/RGCF.|图形神经网络(GNN)已广泛应用于协同过滤(CF)。然而，当给出一个新的推荐场景时，目前的选择要么是从现有的 GNN 架构中选择，要么是使用神经结构搜索(NAS)来获得一个性能良好的 GNN 模型，这两者在人类专业知识或计算资源方面都是昂贵的。为了解决这个问题，在这项工作中，我们提出了一种新的神经检索方法，称为 RGCF，在处理新的场景时快速搜索一个性能良好的基于 GNN 的 CF 架构。具体而言，我们通过开发两级元特征，排序丢失和任务级数据增强来设计基于元学习的神经检索方法，并且在检索范例中，RGCF 可以直接返回给定新数据集(查询)的性能良好的架构，从而本质上是有效的。在评分预测和项目排序这两个主流任务上的实验结果表明，RGCF 在两个新数据集上的有效性和效率均优于人工设计的或 NAS 的所有模型。特别是，效率的提高是显著的，例如在两个新的数据集上，RgCF 比典型的基于强化学习的 NAS 方法快61.7-206.3倍。代码和数据可在 https://github.com/bupt-gamma/rgcf 查阅。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Retrieving+GNN+Architecture+for+Collaborative+Filtering)|0| |[AutoSeqRec: Autoencoder for Efficient Sequential Recommendation](https://doi.org/10.1145/3583780.3614788)|Sijia Liu, Jiahao Liu, Hansu Gu, Dongsheng Li, Tun Lu, Peng Zhang, Ning Gu|Fudan University, Shanghai, China; Microsoft Research Asia, Shanghai, China; Independent, Seattle, WA, USA|Sequential recommendation demonstrates the capability to recommend items by modeling the sequential behavior of users. Traditional methods typically treat users as sequences of items, overlooking the collaborative relationships among them. Graph-based methods incorporate collaborative information by utilizing the user-item interaction graph. However, these methods sometimes face challenges in terms of time complexity and computational efficiency. To address these limitations, this paper presents AutoSeqRec, an incremental recommendation model specifically designed for sequential recommendation tasks. AutoSeqRec is based on autoencoders and consists of an encoder and three decoders within the autoencoder architecture. These components consider both the user-item interaction matrix and the rows and columns of the item transition matrix. The reconstruction of the user-item interaction matrix captures user long-term preferences through collaborative filtering. In addition, the rows and columns of the item transition matrix represent the item out-degree and in-degree hopping behavior, which allows for modeling the user's short-term interests. When making incremental recommendations, only the input matrices need to be updated, without the need to update parameters, which makes AutoSeqRec very efficient. Comprehensive evaluations demonstrate that AutoSeqRec outperforms existing methods in terms of accuracy, while showcasing its robustness and efficiency.|顺序推荐通过建模用户的顺序行为来展示推荐项目的能力。传统方法通常将用户视为项目序列，忽略了它们之间的协作关系。基于图的方法利用用户-项目交互图来整合协作信息。然而，这些方法有时面临着时间复杂性和计算效率方面的挑战。为了解决这些局限性，本文提出了 AutoSeqRec，一个专门为顺序推荐任务设计的增量推荐模型。AutoSeqRec 基于自动编码器，在自动编码器体系结构中由一个编码器和三个解码器组成。这些组件同时考虑用户-项目交互矩阵和项目转移矩阵的行和列。用户-项目交互矩阵的重建通过协同过滤捕捉用户的长期偏好。此外，项目转移矩阵中的行和列表示项目的出度和内度跳跃行为，这允许对用户的短期兴趣进行建模。当进行增量建议时，只需要更新输入矩阵，而不需要更新参数，这使得 AutoSeqRec 非常高效。综合评估表明，AutoSeqRec 在准确性方面优于现有方法，同时展示了其健壮性和效率。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=AutoSeqRec:+Autoencoder+for+Efficient+Sequential+Recommendation)|0| |[MATA*: Combining Learnable Node Matching with A* Algorithm for Approximate Graph Edit Distance Computation](https://doi.org/10.1145/3583780.3614959)|Junfeng Liu, Min Zhou, Shuai Ma, Lujia Pan|Huawei Noah's Ark Lab, Shenzhen, China; Beihang University, Beijing, China|Graph Edit Distance (GED) is a general and domain-agnostic metric to measure graph similarity, widely used in graph search or retrieving tasks. However, the exact GED computation is known to be NP-complete. For instance, the widely used A* algorithms explore the entire search space to find the optimal solution which inevitably suffers scalability issues. Learning-based methods apply graph representation techniques to learn the GED by formulating a regression task, which can not recover the edit path and lead to inaccurate GED approximation (i.e., the predicted GED is smaller than the exact). To this end, in this work, we present a data-driven hybrid approach MATA* for approximate GED computation based on Graph Neural Networks (GNNs) and A* algorithms, which models from the perspective of learning to match nodes instead of directly regressing GED. Specifically, aware of the structure-dominant operations (i.e., node and edge insertion/deletion) property in GED computation, a structure-enhanced GNN is firstly designed to jointly learn local and high-order structural information for node embeddings for node matchings. Second, top-k candidate nodes are produced via a differentiable top-k operation to enable the training for node matchings, which is adhering to another property of GED, i.e., multiple optimal node matchings. Third, benefiting from the candidate nodes, MATA* only performs on the promising search directions, reaching the solution efficiently. Finally, extensive experiments show the superiority of MATA* as it significantly outperforms the combinatorial search-based, learning-based and hybrid methods and scales well to large-size graphs.|图形编辑距离(GED)是度量图形相似度的一种通用的领域不可知度量方法，广泛应用于图形搜索或检索任务中。然而，精确的 GED 计算是已知的 NP 完全的。例如，广泛使用的 A * 算法探索整个搜索空间，寻找不可避免地存在可伸缩性问题的最优解。基于学习的方法应用图表示技术，通过制定回归任务来学习 GED，回归任务不能恢复编辑路径并导致不精确的 GED 近似(即，预测的 GED 小于精确的 GED)。为此，本文提出了一种基于图神经网络(GNNs)和 A * 算法的数据驱动混合 MATA * 算法用于 GED 近似计算，该算法从学习匹配节点而不是直接回归 GED 的角度进行建模。针对 GED 计算中的结构主导操作(即节点和边插入/删除操作)特性，设计了一种结构增强的 GNN，用于联合学习节点嵌入的局部和高阶结构信息，实现节点匹配。其次，通过可微 top-k 操作生成 top-k 候选节点，从而实现节点匹配的训练，这符合 GED 的另一个特性，即多个最优节点匹配。第三，利用候选节点，MATA * 只执行有希望的搜索方向，有效地达到解决方案。最后，广泛的实验表明，MATA * 的优越性，因为它明显优于基于组合搜索，基于学习和混合方法和规模以及大型图。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=MATA*:+Combining+Learnable+Node+Matching+with+A*+Algorithm+for+Approximate+Graph+Edit+Distance+Computation)|0| |[Leveraging Event Schema to Ask Clarifying Questions for Conversational Legal Case Retrieval](https://doi.org/10.1145/3583780.3614953)|Bulou Liu, Yiran Hu, Qingyao Ai, Yiqun Liu, Yueyue Wu, Chenliang Li, Weixing Shen|Tsinghua University, Beijing, China; Tsinghua University, Quan Cheng Laboratory, & Tsinghua University, Beijing, China; Wuhan University, Wuhan, China|Legal case retrieval is a special IR task aiming to retrieve supporting cases for a given query case. Existing works have shown that conversational search paradigm can improve users' search experience in legal case retrieval. One of the keys to a practical conversational search system is how to ask high-quality clarifying questions to initiate conversations with users and understand their search intents. Recently, Large Language Models, such as ChatGPT and GPT-4, have shown superior ability in both open-domain QA and conversations with human. Thus it is natural to believe that they could be applied to legal conversational search as well. However, our preliminary study has shown that generating clarifying questions in legal conversational search with SOTA LLMs (e.g., GPT-4) often suffers from several problems such as duplication and low-utility contents. To address these problems, we propose LeClari, which leverages legal event schema as external knowledge to instruct LLMs to generate effective clarifying questions for legal conversational search. LeClari is constructed with a prompt module and a novel legal event selection module. The former defines a prompt with legal events for clarifying question generation and the latter selects potential event types by modeling the relationships of legal event types, conversational context, and candidate cases. We also propose ranking-oriented rewards and employ the reward augmented maximum likelihood (RAML) method to optimize LeClari directly based on the final retrieval performance of the conversational legal search system. Empirical results over two widely adopted legal case retrieval datasets demonstrate the effectiveness of our approach as compared with the state-of-the-art baselines.|法律案例检索是一项特殊的信息检索任务，旨在为给定的查询案例检索支持案例。已有的研究表明，会话搜索范式可以提高用户在法律案件检索中的搜索体验。实际会话搜索系统的关键问题之一是如何提出高质量的澄清性问题来启动与用户的对话并理解他们的搜索意图。最近，大型语言模型，如 ChatGPT 和 GPT-4，在开放领域的 QA 和与人类的对话方面表现出了卓越的能力。因此，很自然地认为，它们也可以应用于合法的会话搜索。然而，我们的初步研究表明，在使用 SOTA LLM (例如，GPT-4)进行法律会话搜索时，产生澄清问题常常会遇到重复和低效用内容等问题。为了解决这些问题，我们提出了 LeClari，它利用法律事件模式作为外部知识，指导 LLM 为法律会话搜索生成有效的澄清问题。LeClari 由一个提示模块和一个新的法律事件选择模块构成。前者定义了一个带有法律事件的提示符，用于说明问题的产生，后者通过建模法律事件类型、会话语境和候选案例之间的关系来选择潜在的事件类型。我们还提出了面向排序的奖励方法，并利用奖励增加的最大似然(RAML)方法直接根据会话法律搜索系统的最终检索性能对 LeClari 进行优化。通过两个广泛采用的法律案例检索数据集的实证结果表明，与最先进的基线相比，我们的方法是有效的。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Leveraging+Event+Schema+to+Ask+Clarifying+Questions+for+Conversational+Legal+Case+Retrieval)|0| -|[Diffusion Augmentation for Sequential Recommendation](https://doi.org/10.1145/3583780.3615134)|Qidong Liu, Fan Yan, Xiangyu Zhao, Zhaocheng Du, Huifeng Guo, Ruiming Tang, Feng Tian|City University of Hong Kong, Hong Kong, Hong Kong; Xi'an Jiaotong University, Xi'an, China; Xi'an Jiaotong University & City University of Hong Kong, Xi'an, China; Huawei Noah's Ark Lab, Shenzhen, China|Sequential recommendation (SRS) has become the technical foundation in many applications recently, which aims to recommend the next item based on the user's historical interactions. However, sequential recommendation often faces the problem of data sparsity, which widely exists in recommender systems. Besides, most users only interact with a few items, but existing SRS models often underperform these users. Such a problem, named the long-tail user problem, is still to be resolved. Data augmentation is a distinct way to alleviate these two problems, but they often need fabricated training strategies or are hindered by poor-quality generated interactions. To address these problems, we propose a Diffusion Augmentation for Sequential Recommendation (DiffuASR) for a higher quality generation. The augmented dataset by DiffuASR can be used to train the sequential recommendation models directly, free from complex training procedures. To make the best of the generation ability of the diffusion model, we first propose a diffusion-based pseudo sequence generation framework to fill the gap between image and sequence generation. Then, a sequential U-Net is designed to adapt the diffusion noise prediction model U-Net to the discrete sequence generation task. At last, we develop two guide strategies to assimilate the preference between generated and origin sequences. To validate the proposed DiffuASR, we conduct extensive experiments on three real-world datasets with three sequential recommendation models. The experimental results illustrate the effectiveness of DiffuASR. As far as we know, DiffuASR is one pioneer that introduce the diffusion model to the recommendation.|序贯推荐(SRS)已经成为许多应用程序的技术基础，其目的是根据用户的历史交互情况推荐下一个项目。然而，在推荐系统中，顺序推荐常常面临数据稀疏的问题。此外，大多数用户只与少数几个项目交互，但现有的 SRS 模型往往表现不佳。这样一个被称为长尾用户问题的问题仍有待解决。数据增强是缓解这两个问题的一种独特方式，但它们往往需要编造的培训策略，或者受到低质量生成的交互作用的阻碍。为了解决这些问题，我们提出了一种扩散增强的序列推荐(区分 ASR)为更高的质量生成。区分扩展数据集可以直接用于训练序列推荐模型，避免了复杂的训练过程。为了充分利用扩散模型的生成能力，我们首先提出了一种基于扩散的伪序列生成框架，以填补图像和序列生成之间的空白。然后，设计了一个序列 U-Net，使扩散噪声预测模型 U-Net 适应离散序列生成任务。最后，我们提出了两种引导策略来同化生成序列和起源序列之间的偏好。为了验证所提出的 DISUASR，我们使用三个连续的推荐模型在三个真实世界的数据集上进行了广泛的实验。实验结果表明了该方法的有效性。据我们所知，DISUASR 是将扩散模型引入推荐系统的先驱者之一。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Diffusion+Augmentation+for+Sequential+Recommendation)|0| +|[Diffusion Augmentation for Sequential Recommendation](https://doi.org/10.1145/3583780.3615134)|Qidong Liu, Fan Yan, Xiangyu Zhao, Zhaocheng Du, Huifeng Guo, Ruiming Tang, Feng Tian|Huawei Noah's Ark Lab, Shenzhen, China; Xi'an Jiaotong University & City University of Hong Kong, Xi'an, China; Xi'an Jiaotong University, Xi'an, China; City University of Hong Kong, Hong Kong, Hong Kong|Sequential recommendation (SRS) has become the technical foundation in many applications recently, which aims to recommend the next item based on the user's historical interactions. However, sequential recommendation often faces the problem of data sparsity, which widely exists in recommender systems. Besides, most users only interact with a few items, but existing SRS models often underperform these users. Such a problem, named the long-tail user problem, is still to be resolved. Data augmentation is a distinct way to alleviate these two problems, but they often need fabricated training strategies or are hindered by poor-quality generated interactions. To address these problems, we propose a Diffusion Augmentation for Sequential Recommendation (DiffuASR) for a higher quality generation. The augmented dataset by DiffuASR can be used to train the sequential recommendation models directly, free from complex training procedures. To make the best of the generation ability of the diffusion model, we first propose a diffusion-based pseudo sequence generation framework to fill the gap between image and sequence generation. Then, a sequential U-Net is designed to adapt the diffusion noise prediction model U-Net to the discrete sequence generation task. At last, we develop two guide strategies to assimilate the preference between generated and origin sequences. To validate the proposed DiffuASR, we conduct extensive experiments on three real-world datasets with three sequential recommendation models. The experimental results illustrate the effectiveness of DiffuASR. As far as we know, DiffuASR is one pioneer that introduce the diffusion model to the recommendation.|序贯推荐(SRS)已经成为许多应用程序的技术基础，其目的是根据用户的历史交互情况推荐下一个项目。然而，在推荐系统中，顺序推荐常常面临数据稀疏的问题。此外，大多数用户只与少数几个项目交互，但现有的 SRS 模型往往表现不佳。这样一个被称为长尾用户问题的问题仍有待解决。数据增强是缓解这两个问题的一种独特方式，但它们往往需要编造的培训策略，或者受到低质量生成的交互作用的阻碍。为了解决这些问题，我们提出了一种扩散增强的序列推荐(区分 ASR)为更高的质量生成。区分扩展数据集可以直接用于训练序列推荐模型，避免了复杂的训练过程。为了充分利用扩散模型的生成能力，我们首先提出了一种基于扩散的伪序列生成框架，以填补图像和序列生成之间的空白。然后，设计了一个序列 U-Net，使扩散噪声预测模型 U-Net 适应离散序列生成任务。最后，我们提出了两种引导策略来同化生成序列和起源序列之间的偏好。为了验证所提出的 DISUASR，我们使用三个连续的推荐模型在三个真实世界的数据集上进行了广泛的实验。实验结果表明了该方法的有效性。据我们所知，DISUASR 是将扩散模型引入推荐系统的先驱者之一。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Diffusion+Augmentation+for+Sequential+Recommendation)|0| |[Deep Task-specific Bottom Representation Network for Multi-Task Recommendation](https://doi.org/10.1145/3583780.3614837)|Qi Liu, Zhilong Zhou, Gangwei Jiang, Tiezheng Ge, Defu Lian|Alibaba Group, Beijing, China; University of Science and Technology of China, Hefei, China|Neural-based multi-task learning (MTL) has gained significant improvement, and it has been successfully applied to recommendation system (RS). Recent deep MTL methods for RS (e.g. MMoE, PLE) focus on designing soft gating-based parameter-sharing networks that implicitly learn a generalized representation for each task. However, MTL methods may suffer from performance degeneration when dealing with conflicting tasks, as negative transfer effects can occur on the task-shared bottom representation. This can result in a reduced capacity for MTL methods to capture task-specific characteristics, ultimately impeding their effectiveness and hindering the ability to generalize well on all tasks. In this paper, we focus on the bottom representation learning of MTL in RS and propose the Deep Task-specific Bottom Representation Network (DTRN) to alleviate the negative transfer problem. DTRN obtains task-specific bottom representation explicitly by making each task has its own representation learning network in the bottom representation modeling stage. Specifically, it extracts the user's interests from multiple types of behavior sequences for each task through the parameter-efficient hypernetwork. To further obtain the dedicated representation for each task, DTRN refines the representation of each feature by employing a SENet-like network for each task. The two proposed modules can achieve the purpose of getting task-specific bottom representation to relieve tasks' mutual interference. Moreover, the proposed DTRN is flexible to combine with existing MTL methods. Experiments on one public dataset and one industrial dataset demonstrate the effectiveness of the proposed DTRN. Furthermore, we deploy DTRN in an industrial recommender system and gain remarkable improvements in multiple tasks.|基于神经网络的多任务学习(MTL)已经取得了显著的进步，并已成功地应用于推荐系统(RS)。最近的 RS 深层 MTL 方法(例如 MMoE，PLE)主要集中在设计基于软门控的参数共享网络，这种网络隐式地学习每个任务的通用表示。然而，MTL 方法在处理相互冲突的任务时可能会出现性能退化，因为任务共享的底层表示可能会受到负迁移效应的影响。这可能导致 MTL 方法捕获特定任务特征的能力下降，最终妨碍其有效性，并阻碍在所有任务中良好推广的能力。本文针对 RS 中 MTL 的底层表示学习问题，提出了基于深层任务的底层表示网络(DTRN)来解决负迁移问题。DTRN 通过在底层表示建模阶段使每个任务都有自己的表示学习网络，明确地获得任务特定的底层表示。具体来说，它通过参数有效的超网络从每个任务的多种类型的行为序列中提取用户的兴趣。为了进一步获得每个任务的专用表示，DTRN 通过为每个任务使用类似 SENet 的网络来改进每个特性的表示。这两个模块可以实现任务特定的底部表示，减少任务间的相互干扰。此外，提出的 DTRN 是灵活的结合现有的 MTL 方法。在一个公共数据集和一个工业数据集上的实验表明了所提出的 DTRN 方法的有效性。此外，我们在工业推荐系统部署 DTRN，并在多项任务中取得显著改进。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Deep+Task-specific+Bottom+Representation+Network+for+Multi-Task+Recommendation)|0| |[Post-hoc Selection of Pareto-Optimal Solutions in Search and Recommendation](https://doi.org/10.1145/3583780.3615010)|Vincenzo Paparella, Vito Walter Anelli, Franco Maria Nardini, Raffaele Perego, Tommaso Di Noia|Politecnico di Bari, Bari, Italy; ISTI-CNR, Pisa, Italy|Information Retrieval (IR) and Recommender Systems (RS) tasks are moving from computing a ranking of final results based on a single metric to multi-objective problems. Solving these problems leads to a set of Pareto-optimal solutions, known as Pareto frontier, in which no objective can be further improved without hurting the others. In principle, all the points on the Pareto frontier are potential candidates to represent the best model selected with respect to the combination of two, or more, metrics. To our knowledge, there are no well-recognized strategies to decide which point should be selected on the frontier. In this paper, we propose a novel, post-hoc, theoretically-justified technique, named "Population Distance from Utopia" (PDU), to identify and select the one-best Pareto-optimal solution from the frontier. In detail, PDU analyzes the distribution of the points by investigating how far each point is from its utopia point (the ideal performance for the objectives). The possibility of considering fine-grained utopia points allows PDU to select solutions tailored to individual user preferences, a novel feature we call "calibration". We compare PDU against existing state-of-the-art strategies through extensive experiments on tasks from both IR and RS. Experimental results show that PDU and combined with calibration notably impact the solution selection. Furthermore, the results show that the proposed framework selects a solution in a principled way, irrespective of its position on the frontier, thus overcoming the limits of other strategies.|信息检索(IR)和推荐系统(RS)任务正在从计算基于单一指标的最终结果排序过渡到多目标问题。解决这些问题导致一组帕累托最优解，称为帕累托边界，其中没有一个目标可以进一步改进而不损害其他目标。原则上，Pareto 前沿上的所有点都是潜在的候选者，可以代表就两个或更多指标的组合而选择的最佳模型。据我们所知，没有公认的战略来决定哪一点应该选择在前沿。本文提出了一种新的、事后的、理论证明的技术，称为“距离乌托邦的人口距离”(PDU) ，从前沿中识别和选择一个最佳的帕累托最优解。具体来说，PDU 通过调查每个点离它的乌托邦点(目标的理想性能)有多远来分析这些点的分布。考虑细粒度乌托邦点的可能性允许 PDU 选择适合个人用户偏好的解决方案，这个新特性我们称之为“校准”。通过对 IR 和 RS 任务的大量实验，我们比较了 PDU 和现有的最新策略。实验结果表明，PDU 和标定相结合对解决方案的选择有显著影响。此外，结果表明，所提出的框架以原则性的方式选择解决方案，而不考虑其在前沿的位置，从而克服了其他战略的局限性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Post-hoc+Selection+of+Pareto-Optimal+Solutions+in+Search+and+Recommendation)|0| -|[MERIT: A Merchant Incentive Ranking Model for Hotel Search & Ranking](https://doi.org/10.1145/3583780.3614964)|Shigang Quan, Hailong Tan, Shui Liu, Zhenzhe Zheng, Ruihao Zhu, Liangyue Li, Quan Lu, Fan Wu|Cornell University, Ithaca, NY, USA; Alibaba Group, Hangzhou, China; Shanghai Jiao Tong University, Shanghai, China|Online Travel Platforms (OTPs) have been working on improving their hotel Search & Ranking (S&R) systems that facilitate efficient matching between consumers and hotels. Existing OTPs focus on improving platform revenue. In this work, we take a first step in incorporating hotel merchants' objectives into the design of hotel S&R systems to achieve an incentive loop: the OTP tilts impressions and better-ranked positions to merchants with high service quality, and in return, the merchants provide better service to consumers. Three critical design challenges need to be resolved to achieve this incentive loop: Matthew Effect in the consumer feedback-loop, unclear relation between hotel service quality and performance, and conflicts between platform revenue and consumer experience. To address these challenges, we propose MERIT, a MERchant InceTive ranking model, which can simultaneously take the interests of merchants and consumers into account. We introduce information about the hotel service quality at the input-output level. At the input level, we incorporate factors of hotel service quality as features (as the underlying reasons for service quality), while at the output level, we introduce the metric Hotel Rating Score (HRS) as a label (as the evaluated outcome of service quality). Also, we design a monotonic structure for Merchant Tower to provide a clear relation between hotel quality and performance. Finally, we propose a Multi-objective Stratified Pairwise Loss, which can mitigate the conflicts between OTP's revenue and consumer experience. To demonstrate the effectiveness of MERIT, we compare our method with several state-of-the-art benchmarks. The offline experiment results indicate that MERIT outperforms these methods in optimizing the demands of consumers and merchants. Furthermore, we conduct an online A/B test and obtain an improvement of 3.02% for the HRS score. Based on these results, we have deployed MERIT online on Fliggy, one of the most popular OTPs in China, to serve tens of millions of consumers and hundreds of thousands of hotel merchants. To address these challenges, we propose MERIT, a MER chant I nceT ive ranking model, which can simultaneously take the interests of merchants and consumers into account. We introduce information about the hotel service quality at the input-output level. At the input level, we incorporate factors of hotel service quality as features (as the underlying reasons for service quality), while at the output level, we introduce the metric Hotel Rating Score (HRS) as a label (as the evaluated outcome of service quality). Also, we design a monotonic structure for Merchant Tower to provide a clear relation between hotel quality and performance. Finally, we propose a Multi-objective Stratified Pairwise Loss, which can mitigate the conflicts between OTP's revenue and consumer experience. To demonstrate the effectiveness of MERIT, we compare our method with several state-of-the-art benchmarks. The offline experiment results indicate that MERIT outperforms these methods in optimizing the demands of consumers and merchants. Furthermore, we conduct an online A/B test and obtain an improvement of 3.02% for the HRS score. Based on these results, we have deployed MERIT online on Fliggy, one of the most popular OTPs in China, to serve tens of millions of consumers and hundreds of thousands of hotel merchants.|在线旅游平台(OTP)一直致力于改进其酒店搜索和排名(S & R)系统，以促进消费者和酒店之间的有效匹配。现有的 OTP 侧重于提高平台收入。在这项工作中，我们首先将酒店商家的目标融入到酒店 S & R 系统的设计中，以实现激励循环: OTP 向服务质量较高的商家倾斜印象和排名较高的位置，作为回报，商家为消费者提供更好的服务。要实现这种激励回路，需要解决三个关键的设计难题: 消费者反馈回路中的马太效应、酒店服务质量与绩效之间的模糊关系以及平台收入与消费者体验之间的冲突。为了应对这些挑战，我们提出 MERIT，一个 MERchant InceTive 排名模型，它可以同时考虑商家和消费者的利益。我们从投入产出的角度介绍酒店服务质量的信息。在输入层面，我们将酒店服务质量的因素作为特征(作为服务质量的根本原因) ，而在输出层面，我们引入度量酒店评分(HRS)作为标签(作为评估服务质量的结果)。此外，我们设计了一个单调的结构商务大厦，以提供一个明确的关系，酒店质量和性能。最后，我们提出了一个多目标成对分层损失模型，它可以缓解 OTP 的收入和消费者体验之间的冲突。为了证明 MERIT 的有效性，我们将我们的方法与几个最先进的基准进行比较。离线实验结果表明，MERIT 在优化消费者和商家需求方面优于这些方法。此外，我们进行了在线 A/B 测试，HRS 得分提高了3.02% 。基于这些结果，我们在中国最受欢迎的 OTP 之一 Fliggy 上部署了 MERIT 在线服务，为数千万消费者和数十万酒店商家提供服务。为了应对这些挑战，我们提出了 MERIT 模型，这是一个可以同时考虑商家和消费者利益的 MER 商号排名模型。我们从投入产出的角度介绍酒店服务质量的信息。在输入层面，我们将酒店服务质量的因素作为特征(作为服务质量的根本原因) ，而在输出层面，我们引入度量酒店评分(HRS)作为标签(作为评估服务质量的结果)。此外，我们设计了一个单调的结构商务大厦，以提供一个明确的关系，酒店质量和性能。最后，我们提出了一个多目标成对分层损失模型，它可以缓解 OTP 的收入和消费者体验之间的冲突。为了证明 MERIT 的有效性，我们将我们的方法与几个最先进的基准进行比较。离线实验结果表明，MERIT 在优化消费者和商家需求方面优于这些方法。此外，我们进行了在线 A/B 测试，HRS 得分提高了3.02% 。基于这些结果，我们在中国最受欢迎的 OTP 之一 Fliggy 上部署了 MERIT 在线服务，为数千万消费者和数十万酒店商家提供服务。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=MERIT:+A+Merchant+Incentive+Ranking+Model+for+Hotel+Search+&+Ranking)|0| +|[MERIT: A Merchant Incentive Ranking Model for Hotel Search & Ranking](https://doi.org/10.1145/3583780.3614964)|Shigang Quan, Hailong Tan, Shui Liu, Zhenzhe Zheng, Ruihao Zhu, Liangyue Li, Quan Lu, Fan Wu|Alibaba Group, Hangzhou, China; Shanghai Jiao Tong University, Shanghai, China; Cornell University, Ithaca, NY, USA|Online Travel Platforms (OTPs) have been working on improving their hotel Search & Ranking (S&R) systems that facilitate efficient matching between consumers and hotels. Existing OTPs focus on improving platform revenue. In this work, we take a first step in incorporating hotel merchants' objectives into the design of hotel S&R systems to achieve an incentive loop: the OTP tilts impressions and better-ranked positions to merchants with high service quality, and in return, the merchants provide better service to consumers. Three critical design challenges need to be resolved to achieve this incentive loop: Matthew Effect in the consumer feedback-loop, unclear relation between hotel service quality and performance, and conflicts between platform revenue and consumer experience. To address these challenges, we propose MERIT, a MERchant InceTive ranking model, which can simultaneously take the interests of merchants and consumers into account. We introduce information about the hotel service quality at the input-output level. At the input level, we incorporate factors of hotel service quality as features (as the underlying reasons for service quality), while at the output level, we introduce the metric Hotel Rating Score (HRS) as a label (as the evaluated outcome of service quality). Also, we design a monotonic structure for Merchant Tower to provide a clear relation between hotel quality and performance. Finally, we propose a Multi-objective Stratified Pairwise Loss, which can mitigate the conflicts between OTP's revenue and consumer experience. To demonstrate the effectiveness of MERIT, we compare our method with several state-of-the-art benchmarks. The offline experiment results indicate that MERIT outperforms these methods in optimizing the demands of consumers and merchants. Furthermore, we conduct an online A/B test and obtain an improvement of 3.02% for the HRS score. Based on these results, we have deployed MERIT online on Fliggy, one of the most popular OTPs in China, to serve tens of millions of consumers and hundreds of thousands of hotel merchants. To address these challenges, we propose MERIT, a MER chant I nceT ive ranking model, which can simultaneously take the interests of merchants and consumers into account. We introduce information about the hotel service quality at the input-output level. At the input level, we incorporate factors of hotel service quality as features (as the underlying reasons for service quality), while at the output level, we introduce the metric Hotel Rating Score (HRS) as a label (as the evaluated outcome of service quality). Also, we design a monotonic structure for Merchant Tower to provide a clear relation between hotel quality and performance. Finally, we propose a Multi-objective Stratified Pairwise Loss, which can mitigate the conflicts between OTP's revenue and consumer experience. To demonstrate the effectiveness of MERIT, we compare our method with several state-of-the-art benchmarks. The offline experiment results indicate that MERIT outperforms these methods in optimizing the demands of consumers and merchants. Furthermore, we conduct an online A/B test and obtain an improvement of 3.02% for the HRS score. Based on these results, we have deployed MERIT online on Fliggy, one of the most popular OTPs in China, to serve tens of millions of consumers and hundreds of thousands of hotel merchants.|在线旅游平台(OTP)一直致力于改进其酒店搜索和排名(S & R)系统，以促进消费者和酒店之间的有效匹配。现有的 OTP 侧重于提高平台收入。在这项工作中，我们首先将酒店商家的目标融入到酒店 S & R 系统的设计中，以实现激励循环: OTP 向服务质量较高的商家倾斜印象和排名较高的位置，作为回报，商家为消费者提供更好的服务。要实现这种激励回路，需要解决三个关键的设计难题: 消费者反馈回路中的马太效应、酒店服务质量与绩效之间的模糊关系以及平台收入与消费者体验之间的冲突。为了应对这些挑战，我们提出 MERIT，一个 MERchant InceTive 排名模型，它可以同时考虑商家和消费者的利益。我们从投入产出的角度介绍酒店服务质量的信息。在输入层面，我们将酒店服务质量的因素作为特征(作为服务质量的根本原因) ，而在输出层面，我们引入度量酒店评分(HRS)作为标签(作为评估服务质量的结果)。此外，我们设计了一个单调的结构商务大厦，以提供一个明确的关系，酒店质量和性能。最后，我们提出了一个多目标成对分层损失模型，它可以缓解 OTP 的收入和消费者体验之间的冲突。为了证明 MERIT 的有效性，我们将我们的方法与几个最先进的基准进行比较。离线实验结果表明，MERIT 在优化消费者和商家需求方面优于这些方法。此外，我们进行了在线 A/B 测试，HRS 得分提高了3.02% 。基于这些结果，我们在中国最受欢迎的 OTP 之一 Fliggy 上部署了 MERIT 在线服务，为数千万消费者和数十万酒店商家提供服务。为了应对这些挑战，我们提出了 MERIT 模型，这是一个可以同时考虑商家和消费者利益的 MER 商号排名模型。我们从投入产出的角度介绍酒店服务质量的信息。在输入层面，我们将酒店服务质量的因素作为特征(作为服务质量的根本原因) ，而在输出层面，我们引入度量酒店评分(HRS)作为标签(作为评估服务质量的结果)。此外，我们设计了一个单调的结构商务大厦，以提供一个明确的关系，酒店质量和性能。最后，我们提出了一个多目标成对分层损失模型，它可以缓解 OTP 的收入和消费者体验之间的冲突。为了证明 MERIT 的有效性，我们将我们的方法与几个最先进的基准进行比较。离线实验结果表明，MERIT 在优化消费者和商家需求方面优于这些方法。此外，我们进行了在线 A/B 测试，HRS 得分提高了3.02% 。基于这些结果，我们在中国最受欢迎的 OTP 之一 Fliggy 上部署了 MERIT 在线服务，为数千万消费者和数十万酒店商家提供服务。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=MERIT:+A+Merchant+Incentive+Ranking+Model+for+Hotel+Search+&+Ranking)|0| |[Enhancing Repeat-Aware Recommendation from a Temporal-Sequential Perspective](https://doi.org/10.1145/3583780.3614866)|Shigang Quan, Shui Liu, Zhenzhe Zheng, Fan Wu|Alibaba Group, Hangzhou, China; Shanghai Jiao Tong University, Shanghai, China|Repeat consumption, such as re-purchasing items and re-listening songs, is a common scenario in daily life. To model repeat consumption, the repeat-aware recommendation has been proposed to predict which item will be re-interacted based on the user-item interactions. In this paper, we investigate various inherent characteristics to enhance the performance of repeat-aware recommendation. Specifically, we explore these characteristics from two aspects: one is from the temporal aspect where we consider the time interval relationship in user behavior sequence; the other is from the sequential aspect where we consider the sequential-level relationship. Our intuition is that both thetemporal pattern andsequential pattern reflect users' intentions of repeat consumption. By utilizing these two patterns, a novel model called Temporal and Sequential repeat-aware Recommendation(TSRec for short) is proposed to enhance repeat-aware recommendation. TSRec has three main components: 1) User-specific Temporal Representation Module (UTRM), which encodes and extracts user historical repeat temporal information. 2) Item-specific Temporal Representation Module (ITRM), which incorporates item time interval information as side information to alleviate the data sparsity problem of user repeat behavior sequence. 3) Sequential Repeat-Aware Module (SRAM), which represents the similarity between user's current and the last repeat sequences. Extensive experimental results on three public benchmarks demonstrate the superiority of TSRec over state-of-the-art methods. The code is released online.|重复消费，例如重新购买物品和重新听歌，是日常生活中常见的情况。为了对重复消费进行建模，提出了基于重复感知的推荐方法来预测基于用户-项目交互的重复消费项目。为了提高重复感知推荐的性能，本文研究了重复感知推荐的各种内在特征。具体来说，我们从两个方面来探讨这些特征: 一个是从时间方面考虑用户行为序列中的时间间隔关系; 另一个是从序列方面考虑序列级关系。我们的直觉是，时间模式和序列模式都反映了用户的重复消费意图。利用这两种模式，提出了一种新的时序重复感知推荐(TSRec)模型来增强重复感知推荐。TSRec 主要由三部分组成: 1)用户特定时态表示模块(UTRM) ，对用户历史重复时态信息进行编码和提取。2)项目特定时间表示模块(ITRM) ，该模块将项目时间间隔信息作为侧信息，解决了用户重复行为序列的数据稀疏性问题。3)顺序重复感知模块(SRAM) ，表示用户当前重复序列与最后重复序列的相似性。在三个公共基准上的大量实验结果证明了 TSRec 相对于最先进的方法的优越性。代码已经在网上公布了。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Enhancing+Repeat-Aware+Recommendation+from+a+Temporal-Sequential+Perspective)|0| |[ELTRA: An Embedding Method based on Learning-to-Rank to Preserve Asymmetric Information in Directed Graphs](https://doi.org/10.1145/3583780.3614862)|Masoud Reyhani Hamedani, JinSu Ryu, SangWook Kim|Hanyang University, Seoul, Republic of Korea|Double-vector embedding methods capture the asymmetric information in directed graphs first, and then preserve them in the embedding space by providingtwo latent vectors, i.e., source and target, per node. Although these methods are known to besuperior to the single-vector ones (i.e., providing asingle latent vector per node), wepoint out their three drawbacks as inability to preserve asymmetry on NU-paths, inability to preserve global nodes similarity, and impairing in/out-degree distributions. To address these, we first proposeCRW, anovel similarity measure for graphs that considers contributions ofboth in-links and out-links in similarity computation,without ignoring their directions. Then, we proposeELTRA, aneffective double-vector embedding method to preserve asymmetric information in directed graphs. ELTRA computesasymmetry preserving proximity scores (AP-scores) by employing CRW in which the contribution of out-links and in-links in similarity computation isupgraded anddowngraded, respectively. Then, for every node u, ELTRA selects its top-tclosest nodes based on AP-scores andconforms theranks of their corresponding target vectors w.r.t u's source vector in the embedding space to theiroriginal ranks. Our extensive experimental results withseven real-world datasets andsixteen embedding methods show that (1) CRWsignificantly outperforms Katz and RWR in computing nodes similarity in graphs, (2) ELTRAoutperforms the existing state-of-the-art methods in graph reconstruction, link prediction, and node classification tasks.|双向量嵌入方法首先捕获有向图中的不对称信息，然后通过每个节点提供源和目标两个潜在向量，将不对称信息保存在嵌入空间中。尽管这些方法已知优于单向量方法(即每个节点提供单个潜在向量) ，但我们指出了它们的三个缺点，即无法在 NU 路径上保持不对称性，无法保持全局节点相似性，以及损害/外度分布。为了解决这些问题，我们首先提出了 CRW，一种新的图的相似性度量，它考虑了相似性计算中的内链接和外链接的贡献，而没有忽略它们的方向。然后，我们提出了 ELTRA，一种有效的双向量嵌入方法来保持有向图中的不对称信息。ELTRA 通过使用 CRW 计算保持不对称性的邻近分数(AP 分数) ，其中外链和内链在相似性计算中的贡献分别被升级和降级。然后，对于每个节点 u，ELTRA 根据 AP 得分选择其最接近的节点，并将嵌入空间中相应的目标向量的排序与原始排序相一致。我们对7个实际数据集和16种嵌入方法的广泛实验结果表明: (1) CRW 在计算图中节点相似度方面明显优于 Katz 和 RWR; (2) ELTRAB 在图重构、链接预测和节点分类任务方面优于现有的最先进的方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=ELTRA:+An+Embedding+Method+based+on+Learning-to-Rank+to+Preserve+Asymmetric+Information+in+Directed+Graphs)|0| -|[Periodicity May Be Emanative: Hierarchical Contrastive Learning for Sequential Recommendation](https://doi.org/10.1145/3583780.3615007)|Changxin Tian, Binbin Hu, Wayne Xin Zhao, Zhiqiang Zhang, Jun Zhou|Renmin University of China, Beijing, China; Ant Group, Hangzhou, China|Nowadays, contrastive self-supervised learning has been widely incorporated into sequential recommender systems. However, most existing contrastive sequential recommender systems simply emphasize the overall information of interaction sequences, thereby neglecting the special periodic patterns of user behavior. In this study, we propose that users exhibit emanative periodicity towards a group of correlated items, i.e., user behavior follow a certain periodic pattern while their interests may shift from one item to other related items over time. In light of this observation, we present a hierarchical contrastive learning framework to model EmAnative periodicity for SEquential Recommendation (referred to as EASE). Specifically, we design dual-channel contrastive strategy from the perspective of correlation and periodicity to capture emanative periodic patterns. Furthermore, we extend the traditional binary contrastive loss with hierarchical constraint to handle hierarchical contrastive samples, thus preserving the inherent hierarchical information of correlation and periodicity. Comprehensive experiments conducted on five datasets substantiate the effectiveness of our proposed EASE in improving sequential recommendation.|目前，对比自监督学习已广泛应用于顺序推荐系统中。然而，现有的对比序列推荐系统大多只强调交互序列的整体信息，而忽视了用户行为的特殊周期模式。在本研究中，我们提出使用者对一组相关项目表现出发射周期性，即使用者的兴趣可能随时间由一个项目转移至其他相关项目，使用者的行为仍遵循一定的周期性模式。根据这一观察，我们提出了一个层次对比学习框架来模拟顺序推荐的 EmAnative 周期性(简称 EASE)。具体来说，我们从相关性和周期性的角度设计了双通道对比策略来捕捉发射周期图案。在此基础上，将传统的具有层次约束的二进制对比度损失算法扩展到层次对比度样本的处理，从而保留了相关性和周期性的固有层次信息。在五个数据集上进行的综合实验证实了我们提出的 EASE 在改进顺序推荐方面的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Periodicity+May+Be+Emanative:+Hierarchical+Contrastive+Learning+for+Sequential+Recommendation)|0| +|[Periodicity May Be Emanative: Hierarchical Contrastive Learning for Sequential Recommendation](https://doi.org/10.1145/3583780.3615007)|Changxin Tian, Binbin Hu, Wayne Xin Zhao, Zhiqiang Zhang, Jun Zhou|Ant Group, Hangzhou, China; Renmin University of China, Beijing, China|Nowadays, contrastive self-supervised learning has been widely incorporated into sequential recommender systems. However, most existing contrastive sequential recommender systems simply emphasize the overall information of interaction sequences, thereby neglecting the special periodic patterns of user behavior. In this study, we propose that users exhibit emanative periodicity towards a group of correlated items, i.e., user behavior follow a certain periodic pattern while their interests may shift from one item to other related items over time. In light of this observation, we present a hierarchical contrastive learning framework to model EmAnative periodicity for SEquential Recommendation (referred to as EASE). Specifically, we design dual-channel contrastive strategy from the perspective of correlation and periodicity to capture emanative periodic patterns. Furthermore, we extend the traditional binary contrastive loss with hierarchical constraint to handle hierarchical contrastive samples, thus preserving the inherent hierarchical information of correlation and periodicity. Comprehensive experiments conducted on five datasets substantiate the effectiveness of our proposed EASE in improving sequential recommendation.|目前，对比自监督学习已广泛应用于顺序推荐系统中。然而，现有的对比序列推荐系统大多只强调交互序列的整体信息，而忽视了用户行为的特殊周期模式。在本研究中，我们提出使用者对一组相关项目表现出发射周期性，即使用者的兴趣可能随时间由一个项目转移至其他相关项目，使用者的行为仍遵循一定的周期性模式。根据这一观察，我们提出了一个层次对比学习框架来模拟顺序推荐的 EmAnative 周期性(简称 EASE)。具体来说，我们从相关性和周期性的角度设计了双通道对比策略来捕捉发射周期图案。在此基础上，将传统的具有层次约束的二进制对比度损失算法扩展到层次对比度样本的处理，从而保留了相关性和周期性的固有层次信息。在五个数据集上进行的综合实验证实了我们提出的 EASE 在改进顺序推荐方面的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Periodicity+May+Be+Emanative:+Hierarchical+Contrastive+Learning+for+Sequential+Recommendation)|0| |[Diversity-aware Deep Ranking Network for Recommendation](https://doi.org/10.1145/3583780.3614848)|Zihong Wang, Yingxia Shao, Jiyuan He, Jinbao Liu, Shitao Xiao, Tao Feng, Ming Liu|Meituan Inc., Beijing, China; Beijing University of Posts and Telecommunications, Beijing, China|Diversity is a vital factor in recommendation systems.Improving the diversity in recommendations helps broaden users' horizons, bring good user experience and promote the enterprises' sales. In the past years, many efforts have been devoted to optimizing the diversity in the matching stage and the re-ranking stage of the recommendation system, but few in the ranking stage. The ranking stage is the intermediate stage of the recommendation system. Improving the diversity of the ranking stage can preserve the diversity of the matching stage, and provide a more diversified list for the re-ranking stage. Besides, the ranking models are able to achieve a better balance between accuracy and diversity. In this paper, we aim to improve the diversity in the ranking stage. To address the diversity challenges posed by the pointwise ranking model and biased user interaction history, we propose a Diversity-aware Deep Ranking Network by carefully designing two diversity-aware components that are diversity-aware listwise information fusion and balanced weighting loss. We conduct both offline and online experiments, and the results demonstrate that our proposed model effectively improves the recommendation diversity in the ranking stage while maintaining the accuracy. Moreover, the new model achieves 1.27%, 2.30% and 1.98% improvements in VBR, GMV and Coverage in Meituan, one of the world's largest E-commerce platforms.|多样性是推荐系统中的一个重要因素。提高推荐的多样性有助于拓宽用户的视野，带来良好的用户体验，促进企业的销售。近年来，在推荐系统的匹配阶段和重新排序阶段，人们致力于优化推荐系统的多样性，但在排序阶段却很少。排名阶段是推荐系统的中间阶段。提高排序阶段的多样性可以保持匹配阶段的多样性，为重排阶段提供更加多样化的列表。此外，排名模型能够在准确性和多样性之间取得更好的平衡。在本文中，我们的目标是提高排名阶段的多样性。针对逐点排序模型和有偏差的用户交互历史带来的多样性挑战，提出了一种多样性感知的深度排序网络，该网络通过精心设计两个多样性感知组件: 多样性感知列表信息融合和均衡加权损失。我们进行了离线和在线实验，结果表明，我们提出的模型有效地提高了排名阶段的推荐多样性，同时保持了准确性。此外，作为全球最大的电子商贸平台之一，新模式在视频比率、通用市场价格和美团覆盖率方面分别取得1.27% 、2.30% 和1.98% 的改善。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Diversity-aware+Deep+Ranking+Network+for+Recommendation)|0| |[Sentiment-aware Review Summarization with Personalized Multi-task Fine-tuning](https://doi.org/10.1145/3583780.3615056)|Hongyan Xu, Hongtao Liu, Zhepeng Lv, Qing Yang, Wenjun Wang|Tianjin University, Tianjin, China; Du Xiaoman Financial, Beijing, China|Personalized review summarization is a challenging task in recommender systems, which aims to generate condensed and readable summaries for product reviews. Recently, some methods propose to adopt the sentiment signals of reviews to enhance the review summarization. However, most previous works only share the semantic features of reviews via preliminary multi-task learning, while ignoring the rich personalized information of users and products, which is crucial to both sentiment identification and comprehensive review summarization. In this paper, we propose a sentiment-aware review summarization method with an elaborately designed multi-task fine-tuning framework to make full use of personalized information of users and products effectively based on Pretrained Language Models (PLMs). We first denote two types of personalized information including IDs and historical summaries to indicate their identification and semantics information respectively. Subsequently, we propose to incorporate the IDs of the user/product into the PLMs-based encoder to learn the personalized representations of input reviews and their historical summaries in a fine-tuning way. Based on this, an auxiliary context-aware review sentiment classification task and a further sentiment-guided personalized review summarization task are jointly learned. Specifically, the sentiment representation of input review is used to identify relevant historical summaries, which are then treated as additional semantic context features to enhance the summary generation process. Extensive experimental results show our approach could generate sentiment-consistent summaries and outperforms many competitive baselines on both review summarization and sentiment classification tasks.|在推荐系统中，个性化的评论摘要是一项具有挑战性的任务，它的目标是为产品评论生成简明易读的摘要。近年来，一些方法提出采用评论的情感信号来加强评论总结。然而，以往的研究大多只是通过初步的多任务学习来共享评论的语义特征，而忽略了用户和产品丰富的个性化信息，这对于情感识别和综合评论总结都是至关重要的。本文提出了一种基于预训练语言模型的情感感知评论摘要方法，该方法通过精心设计的多任务微调框架，有效地利用了用户和产品的个性化信息。我们首先表示两种类型的个性化信息，包括 ID 和历史汇总，分别表示它们的识别和语义信息。随后，我们建议将用户/产品的 ID 合并到基于 PLM 的编码器中，以微调的方式学习输入评论及其历史摘要的个性化表示。在此基础上，共同学习了一个辅助上下文感知的评论情感分类任务和一个进一步的情感引导的个性化评论摘要任务。具体来说，输入评论的情感表示被用来识别相关的历史摘要，然后将其作为额外的语义上下文特征来加强摘要的生成过程。广泛的实验结果表明，我们的方法可以产生情绪一致的摘要，并优于许多竞争基线审查摘要和情绪分类任务。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Sentiment-aware+Review+Summarization+with+Personalized+Multi-task+Fine-tuning)|0| |[A Two-tier Shared Embedding Method for Review-based Recommender Systems](https://doi.org/10.1145/3583780.3614770)|Zhen Yang, Junrui Liu, Tong Li, Di Wu, Shiqiu Yang, Huan Liu|Arizona State University, Tempe, AZ, USA; Beijing University of Technology, Beijing, China|Reviews are valuable resources that have been widely researched and used to improve the quality of recommendation services. Recent methods use multiple full embedding layers to model various levels of individual preferences, increasing the risk of the data sparsity issue. Although it is a potential way to deal with this issue that models homophily among users who have similar behaviors, the existing approaches are implemented in a coarse-grained way. They calculate user similarities by considering the homophily in their global behaviors but ignore their local behaviors under a specific context. In this paper, we propose a two-tier shared embedding model (TSE), which fuses coarse- and fine-grained ways of modeling homophily. It considers global behaviors to model homophily in a coarse-grained way, and the high-level feature in the process of each user-item interaction to model homophily in a fine-grained way. TSE designs a whole-to-part principle-based process to fuse these ways in the review-based recommendation. Experiments on five real-world datasets demonstrate that TSE significantly outperforms state-of-the-art models. It outperforms the best baseline by 20.50% on the root-mean-square error (RMSE) and 23.96% on the mean absolute error (MAE), respectively. The source code is available at https://github.com/dianziliu/TSE.git.|评论是有价值的资源，已被广泛研究和用于提高推荐服务的质量。最近的方法使用多个完整的嵌入层来模拟不同层次的个人偏好，增加了数据稀疏问题的风险。尽管在具有相似行为的用户之间建模同质性是解决这个问题的一种潜在方法，但是现有的方法都是以粗粒度的方式实现的。它们通过考虑全局行为中的同质性来计算用户相似性，但是忽略了特定环境下的局部行为。本文提出了一种两层共享嵌入模型(TSE) ，它融合了粗粒度和细粒度同构建模方法。它考虑了全局行为的粗粒度同质性建模，考虑了每个用户项交互过程中的高层次特征的细粒度同质性建模。TSE 设计了一个基于整体到部分原则的过程，将这些方法融合到基于评论的推荐中。在五个真实世界数据集上的实验表明，TSE 显著优于最先进的模型。它在均方根差(RMSE)和平均绝对误差(MAE)方面分别比最佳基线高出20.50% 和23.96% 。源代码可在 https://github.com/dianziliu/tse.git 下载。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Two-tier+Shared+Embedding+Method+for+Review-based+Recommender+Systems)|0| |[Improving Query Correction Using Pre-train Language Model In Search Engines](https://doi.org/10.1145/3583780.3614930)|Dezhi Ye, Bowen Tian, Jiabin Fan, Jie Liu, Tianhua Zhou, Xiang Chen, Mingming Li, Jin Ma|Tencent, Beijing, China|Query correction is a task that automatically detects and corrects errors in what users type into a search engine. Misspelled queries can lead to user dissatisfaction and churn. However, correcting a user query accurately is not an easy task. One major challenge is that a correction model must be capable of high-level language comprehension. Recently, pre-trained language models (PLMs) have been successfully applied to text correction tasks, but few works have been done on query correction. However, it is nontrivial to directly apply these PLMs to query correction in large-scale search systems due to the following challenging issues: 1) Expensive deployment. Deploying such a model requires expensive computations. 2) Lacking domain knowledge. A neural correction model needs massive training data to activate its power. To this end, we introduce KSTEM, a Knowledge-based Sequence To Edit Model for Chinese query correction. KSTEM transforms the sequence generation task into sequence tagging by mapping errors into five categories: KEEP, REPLACE, SWAP, DELETE, and INSERT, reducing computational complexity. Additionally, KSTEM adopts 2D position encoding, which is composed of the internal and external order of the words. Meanwhile, to compensate for the lack of domain knowledge, we propose a task-specific training paradigm for query correction, including edit strategy-based pre-training, user click-based post pre-train, and human label-based fine-tuning. Finally, we apply KSTEM to the industrial search system. Extensive offline and online experiments show that KSTEM significantly improves query correction performance. We hope that our experience will benefit frontier researchers.|查询纠正是一项自动检测和纠正用户在搜索引擎中输入的错误的任务。拼写错误的查询可能导致用户不满意和混乱。然而，准确地纠正用户查询并非易事。一个主要的挑战是，纠错模式必须能够高水平的语言理解。近年来，预训练语言模型(PLM)已经成功地应用于文本校正任务中，但在查询校正方面的研究还很少。然而，由于以下挑战性问题，直接应用这些 PLM 在大规模搜索系统中进行查询更正并非易事: 1)昂贵的部署。部署这样的模型需要昂贵的计算。2)缺乏领域知识。神经校正模型需要大量的训练数据来激活它的能量。为此，本文介绍了基于知识的汉语查询纠错序列编辑模型 KSTEM。KSTEM 通过将错误映射到 KEEP、 REPLACE、 SWAP、 DELETE 和 INSERT 五个类别，将序列生成任务转换为序列标记，降低了计算复杂度。此外，KSTEM 还采用了二维位置编码，由词的内部和外部顺序组成。同时，为了弥补领域知识的不足，本文提出了一种针对具体任务的查询修正训练范式，包括基于编辑策略的预训练、基于用户点击的后期预训练和基于人工标签的微调。最后，我们将 KSTEM 应用于产业搜索系统。大量的离线和在线实验表明，KSTEM 显著提高了查询纠错性能。我们希望我们的经验将有益于前沿研究人员。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Improving+Query+Correction+Using+Pre-train+Language+Model+In+Search+Engines)|0| |[iHAS: Instance-wise Hierarchical Architecture Search for Deep Learning Recommendation Models](https://doi.org/10.1145/3583780.3614925)|Yakun Yu, Shiang Qi, Jiuding Yang, Liyao Jiang, Di Niu|University of Alberta, Edmonton, AB, Canada|Current recommender systems employ large-sized embedding tables with uniform dimensions for all features, leading to overfitting, high computational cost, and suboptimal generalizing performance. Many techniques aim to solve this issue by feature selection or embedding dimension search. However, these techniques typically select a fixed subset of features or embedding dimensions for all instances and feed all instances into one recommender model without considering heterogeneity between items or users. This paper proposes a novel instance-wise Hierarchical Architecture Search framework, iHAS, which automates neural architecture search at the instance level. Specifically, iHAS incorporates three stages: searching, clustering, and retraining. The searching stage identifies optimal instance-wise embedding dimensions across different field features via carefully designed Bernoulli gates with stochastic selection and regularizers. After obtaining these dimensions, the clustering stage divides samples into distinct groups via a deterministic selection approach of Bernoulli gates. The retraining stage then constructs different recommender models, each one designed with optimal dimensions for the corresponding group. We conduct extensive experiments to evaluate the proposed iHAS on two public benchmark datasets from a real-world recommender system. The experimental results demonstrate the effectiveness of iHAS and its outstanding transferability to widely-used deep recommendation models.|目前的推荐系统采用大型嵌入表，所有特征尺寸统一，导致拟合过度，计算成本高，泛化性能不理想。许多技术都是通过特征选择或嵌入维搜索来解决这一问题。然而，这些技术通常为所有实例选择固定的特性子集或嵌入维度，并将所有实例提供给一个推荐模型，而不考虑项目或用户之间的异构性。提出了一种新的实例级层次体系结构搜索框架 iHAS，该框架在实例级自动进行神经体系结构搜索。具体来说，iHAS 包括三个阶段: 搜索、集群和再培训。搜索阶段通过精心设计的带有随机选择和正则化子的伯努利门来确定不同场特征之间的最佳实例嵌入维数。在获得这些维度之后，聚类阶段通过贝努利门的确定性选择方法将样本划分为不同的组。然后再培训阶段构建不同的推荐模型，每个模型都为相应的群体设计了最优维度。我们进行了广泛的实验，以评估来自真实世界的两个公共基准数据集的 iHAS 推荐系统。实验结果表明了 iHAS 的有效性及其对广泛使用的深度推荐模型的突出可转移性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=iHAS:+Instance-wise+Hierarchical+Architecture+Search+for+Deep+Learning+Recommendation+Models)|0| |[Search Result Diversification Using Query Aspects as Bottlenecks](https://doi.org/10.1145/3583780.3615050)|Puxuan Yu, Razieh Rahimi, Zhiqi Huang, James Allan|University of Massachusetts Amherst, Amherst, USA|We address some of the limitations of coverage-based search result diversification models, which often consist of separate components and rely on external systems for query aspects. To overcome these challenges, we introduce an end-to-end learning framework called DUB. Our approach preserves the intrinsic interpretability of coverage-based methods while enhancing diversification performance. Drawing inspiration from the information bottleneck method, we propose an aspect extractor that generates query aspect embeddings optimized as information bottlenecks for the task of diversified document re-ranking. Experimental results demonstrate that DUB outperforms state-of-the-art diversification models.|我们解决了基于覆盖率的搜索结果多样化模型的一些局限性，这些模型通常由单独的组件组成，并依赖于外部系统进行查询。为了克服这些挑战，我们引入了一个名为 DUB 的端到端学习框架。我们的方法保留了基于覆盖的方法的内在可解释性，同时增强了多样化性能。从信息瓶颈方法的启发出发，提出了一种方面提取器，该方面提取器生成的查询方面嵌入作为信息瓶颈进行优化，用于多样化的文档重排任务。实验结果表明，DUB 的性能优于国家的最先进的多元化模式。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Search+Result+Diversification+Using+Query+Aspects+as+Bottlenecks)|0| -|[Attention Calibration for Transformer-based Sequential Recommendation](https://doi.org/10.1145/3583780.3614785)|Peilin Zhou, Qichen Ye, Yueqi Xie, Jingqi Gao, Shoujin Wang, Jae Boum Kim, Chenyu You, Sunghun Kim|Upstage, Hong Kong, Hong Kong; The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China; Yale University, New Haven, CT, USA; Peking University, Beijing, China; The Hong Kong University of Science and Technology, Hong Kong, Hong Kong; University of Technology Sydney, Sydney, Australia|Transformer-based sequential recommendation (SR) has been booming in recent years, with the self-attention mechanism as its key component. Self-attention has been widely believed to be able to effectively select those informative and relevant items from a sequence of interacted items for next-item prediction via learning larger attention weights for these items. However, this may not always be true in reality. Our empirical analysis of some representative Transformer-based SR models reveals that it is not uncommon for large attention weights to be assigned to less relevant items, which can result in inaccurate recommendations. Through further in-depth analysis, we find two factors that may contribute to such inaccurate assignment of attention weights: sub-optimal position encoding and noisy input. To this end, in this paper, we aim to address this significant yet challenging gap in existing works. To be specific, we propose a simple yet effective framework called Attention Calibration for Transformer-based Sequential Recommendation (AC-TSR). In AC-TSR, a novel spatial calibrator and adversarial calibrator are designed respectively to directly calibrates those incorrectly assigned attention weights. The former is devised to explicitly capture the spatial relationships (i.e., order and distance) among items for more precise calculation of attention weights. The latter aims to redistribute the attention weights based on each item's contribution to the next-item prediction. AC-TSR is readily adaptable and can be seamlessly integrated into various existing transformer-based SR models. Extensive experimental results on four benchmark real-world datasets demonstrate the superiority of our proposed ACTSR via significant recommendation performance enhancements. The source code is available at https://github.com/AIM-SE/AC-TSR.|基于变压器的顺序推荐(SR)近年来兴起，自注意机制是其关键组成部分。人们普遍认为，自我注意能够有效地从一系列相互作用的项目中选择信息丰富的相关项目，通过学习这些项目的较大注意权重来进行下一个项目的预测。然而，这在现实中可能并不总是正确的。我们对一些有代表性的基于变压器的 SR 模型的实证分析表明，将大的注意力权重分配给相关性较低的项目并不罕见，这可能导致不准确的推荐。通过进一步的深入分析，我们发现了两个可能导致注意力权重分配不准确的因素: 次优位置编码和噪声输入。为此，在本文中，我们的目标是解决这一重大但具有挑战性的差距，在现有的工作。具体来说，我们提出了一个简单而有效的框架，称为基于变压器的顺序推荐注意力校正(AC-TSR)。在 AC-TSR 中，分别设计了一种新的空间校正器和对抗校正器，用于直接校正那些不正确分配的注意权重。前者旨在明确地捕捉项目之间的空间关系(即顺序和距离) ，以便更精确地计算注意力权重。后者的目的是重新分配注意权重的基础上，每个项目的贡献，下一个项目的预测。AC-TSR 很容易适应，可以无缝地集成到各种现有的基于变压器的 SR 模型中。在四个基准的真实世界数据集上的大量实验结果显示了我们提出的 ACTSR 通过显著的推荐性能增强的优越性。源代码可在 https://github.com/aim-se/ac-tsr 下载。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Attention+Calibration+for+Transformer-based+Sequential+Recommendation)|0| -|[HCL4QC: Incorporating Hierarchical Category Structures Into Contrastive Learning for E-commerce Query Classification](https://doi.org/10.1145/3583780.3614907)|Lvxing Zhu, Kexin Zhang, Hao Chen, Chao Wei, Weiru Zhang, Haihong Tang, Xiu Li|Tsinghua University, Shenzhen, China; Alibaba Group, Hangzhou, China|Query classification plays a crucial role in e-commerce, where the goal is to assign user queries to appropriate categories within a hierarchical product category taxonomy. However, existing methods rely on a limited number of words from the category description and often neglect the hierarchical structure of the category tree, resulting in suboptimal category representations. To overcome these limitations, we propose a novel approach named hierarchical contrastive learning framework for query classification (HCL4QC), which leverages the hierarchical category tree structure to improve the performance of query classification. Specifically, HCL4QC is designed as a plugin module that consists of two innovative losses, namely local hierarchical contrastive loss (LHCL) and global hierarchical contrastive loss (GHCL). LHCL adjusts representations of categories according to their positional relationship in the hierarchical tree, while GHCL ensures the semantic consistency between the parent category and its child categories. Our proposed method can be adapted to any query classification tasks that involve a hierarchical category structure. We conduct experiments on two real-world datasets to demonstrate the superiority of our hierarchical contrastive learning. The results demonstrate significant improvements in the query classification task, particularly for long-tail categories with sparse supervised information.|查询分类在电子商务中起着至关重要的作用，其目标是在分层的产品类别分类中将用户查询分配给适当的类别。然而，现有的方法仅仅依赖于类别描述中有限的词汇，往往忽略了类别树的层次结构，导致了类别表示的次优化。为了克服这些局限性，提出了一种新的查询分类层次对比学习框架(HCL4QC) ，该框架利用层次分类树结构来提高查询分类的性能。具体来说，HCL4QC 被设计成一个插件模块，由两个创新性的损失组成，即局部层次对比损失(LHCL)和全局层次对比损失(GHCL)。LHCL 根据类别在层次树中的位置关系来调整类别的表示，而 GHCL 保证了父类别与其子类别之间的语义一致性。该方法适用于任何涉及层次分类结构的查询分类任务。我们在两个真实世界的数据集上进行了实验，以验证我们的分层对比学习的优越性。实验结果表明，该算法在查询分类任务方面有明显的改进，特别是对于监督信息稀疏的长尾类别。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=HCL4QC:+Incorporating+Hierarchical+Category+Structures+Into+Contrastive+Learning+for+E-commerce+Query+Classification)|0| -|[Non-Recursive Cluster-Scale Graph Interacted Model for Click-Through Rate Prediction](https://doi.org/10.1145/3583780.3615180)|Yuanchen Bei, Hao Chen, Shengyuan Chen, Xiao Huang, Sheng Zhou, Feiran Huang|The Hong Kong Polytechnic University, Hung Hom, Hong Kong; Zhejiang University, Hangzhou, China; Jinan University, Guangzhou, China|Extracting users' interests from their behavior, particularly their 1-hop neighbors, has been shown to enhance Click-Through Rate (CTR) prediction performance. However, online recommender systems impose strict constraints on the inference time of CTR models, which necessitates pruning or filtering users' 1-hop neighbors to reduce computational complexity. Furthermore, while the graph information of users and items has been proven effective in collaborative filtering models, recursive graph convolution can be computationally costly and expensive to implement. To address these challenges, we propose the Non-Recursive Cluster-scale Graph Interacted (NRCGI) model, which reorganizes graph convolutional networks in a non-recursive and cluster-scale view to enable CTR models to consider deep graph information with low computational cost. NRCGI employs non-recursive cluster-scale graph aggregation, which allows the online recommendation computational complexity to shrink from tens of thousands of items to tens to hundreds of clusters. Additionally, since NRCGI aggregates neighbors in a non-recursive view, each hop of neighbors has a clear physical meaning. NRCGI explicitly constructs meaningful interactions between the hops of neighbors of users and items to fully model users' intent towards the given item. Experimental results demonstrate that NRCGI outperforms state-of-the-art baselines in three public datasets and one industrial dataset while maintaining efficient inference.|从用户的行为中提取他们的兴趣，特别是他们的一跳邻居，已经被证明可以提高点进率(CTR)预测性能。然而，在线推荐系统对 CTR 模型的推理时间有严格的限制，为了降低计算复杂度，需要对用户的1跳邻居进行剪枝或过滤。此外，虽然用户和项目的图形信息已被证明在协同过滤模型中是有效的，递归图形卷积可能在计算上是昂贵的，实现起来也是昂贵的。为了解决这些挑战，我们提出了非递归聚类尺度图交互(NRCGI)模型，该模型以非递归和聚类尺度视图重新组织图卷积网络，以使 CTR 模型能够以低计算成本考虑深层图信息。NRCGI 采用非递归聚类规模的图聚合技术，使在线推荐计算复杂度从几万个项目减少到几十到几百个聚类。此外，由于 NRCGI 以非递归视图的形式聚合邻居，因此邻居的每个跃点都有明确的物理意义。NRCGI 显式地在用户和项目的邻居之间构造有意义的交互，以充分模拟用户对给定项目的意图。实验结果表明，在保持有效推理的同时，NRCGI 在三个公共数据集和一个工业数据集中的表现优于最先进的基线。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Non-Recursive+Cluster-Scale+Graph+Interacted+Model+for+Click-Through+Rate+Prediction)|0| -|[SAFE: Sequential Attentive Face Embedding with Contrastive Learning for Deepfake Video Detection](https://doi.org/10.1145/3583780.3615279)|Juho Jung, Chaewon Kang, Jeewoo Yoon, Simon S. Woo, Jinyoung Han|Sungkyunkwan University, Seoul, Republic of Korea; Raondata, Seoul, Republic of Korea; Sungkyunkwan University, Suwon, Republic of Korea|The emergence of hyper-realistic deepfake videos has raised significant concerns regarding their potential misuse. However, prior research on deepfake detection has primarily focused on image-based approaches, with little emphasis on video. With the advancement of generation techniques enabling intricate and dynamic manipulation of entire faces as well as specific facial components in a video sequence, capturing dynamic changes in both global and local facial features becomes crucial in detecting deepfake videos. This paper proposes a novel sequential attentive face embedding, SAFE, that can capture facial dynamics in a deepfake video. The proposed SAFE can effectively integrate global and local dynamics of facial features revealed in a video sequence using contrastive learning. Through a comprehensive comparison with the state-of-the-art methods on the DFDC (Deepfake Detection Challenge) dataset and the FaceForensic++ benchmark, we show that our model achieves the highest accuracy in detecting deepfake videos on both datasets.|超真实的深度假视频的出现引起了人们对其潜在滥用的严重关切。然而，先前对深度伪造检测的研究主要集中在基于图像的方法上，对视频的研究很少。随着生成技术的进步，可以对视频序列中的整个面部以及特定的面部组件进行复杂和动态的操作，捕捉全局和局部面部特征的动态变化成为检测深度伪造视频的关键。提出了一种新的序列注意人脸嵌入方法 SAFE，该方法可以在深度伪造视频中捕获人脸动态特征。该方法利用对比学习，有效地整合了视频序列中人脸特征的全局和局部动态特征。通过全面比较 DFDC 数据集和 FaceForensic + + 基准上的最新方法，我们表明我们的模型在两个数据集上检测深度伪造视频时达到了最高的准确性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=SAFE:+Sequential+Attentive+Face+Embedding+with+Contrastive+Learning+for+Deepfake+Video+Detection)|0| -|[Can Embeddings Analysis Explain Large Language Model Ranking?](https://doi.org/10.1145/3583780.3615225)|Claudio Lucchese, Giorgia Minello, Franco Maria Nardini, Salvatore Orlando, Raffaele Perego, Alberto Veneri|Ca' Foscari University of Venice, Venice, Italy; Università Ca' Foscari Venezia, Venice, Italy; Ca' Foscari University of Venice & Institute of Information Science and Technologies, Venice, Italy; Institute of Information Science and Technologies "Alessandro Faedo" - National Research Council of Italy, Pisa, Italy|Understanding the behavior of deep neural networks for Information Retrieval (IR) is crucial to improve trust in these effective models. Current popular approaches to diagnose the predictions made by deep neural networks are mainly based on: i) the adherence of the retrieval model to some axiomatic property of the IR system, ii) the generation of free-text explanations, or iii) feature importance attributions. In this work, we propose a novel approach that analyzes the changes of document and query embeddings in the latent space and that might explain the inner workings of IR large pre-trained language models. In particular, we focus on predicting query/document relevance, and we characterize the predictions by analyzing the topological arrangement of the embeddings in their latent space and their evolution while passing through the layers of the network. We show that there exists a link between the embedding adjustment and the predicted score, based on how tokens cluster in the embedding space. This novel approach, grounded in the query and document tokens interplay over the latent space, provides a new perspective on neural ranker explanation and a promising strategy for improving the efficiency of the models and Query Performance Prediction (QPP).|理解信息检索深层神经网络的行为对于提高人们对这些有效模型的信任至关重要。目前流行的诊断深度神经网络预测的方法主要基于: i)检索模型对 IR 系统的某些公理化特性的依从性，ii)自由文本解释的生成，或 iii)特征重要性归属。在这项工作中，我们提出了一种新的方法，分析文档和查询嵌入的变化，在潜在的空间，这可能解释了内部工作的信息检索大型预训练语言模型。特别地，我们侧重于预测查询/文档的相关性，并且通过分析嵌入在其潜在空间中的拓扑排列以及它们在通过网络层时的演化来刻画预测的特征。基于标记在嵌入空间中的聚类，我们证明了嵌入调整和预测得分之间存在联系。该方法基于查询和文档标记在潜在空间上的相互作用，为神经排序解释提供了一个新的视角，为提高模型效率和查询性能预测(QPP)提供了一种有前途的策略。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Can+Embeddings+Analysis+Explain+Large+Language+Model+Ranking?)|0| +|[Attention Calibration for Transformer-based Sequential Recommendation](https://doi.org/10.1145/3583780.3614785)|Peilin Zhou, Qichen Ye, Yueqi Xie, Jingqi Gao, Shoujin Wang, Jae Boum Kim, Chenyu You, Sunghun Kim|Peking University, Beijing, China; University of Technology Sydney, Sydney, Australia; Yale University, New Haven, CT, USA; Upstage, Hong Kong, Hong Kong; The Hong Kong University of Science and Technology, Hong Kong, Hong Kong; The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China|Transformer-based sequential recommendation (SR) has been booming in recent years, with the self-attention mechanism as its key component. Self-attention has been widely believed to be able to effectively select those informative and relevant items from a sequence of interacted items for next-item prediction via learning larger attention weights for these items. However, this may not always be true in reality. Our empirical analysis of some representative Transformer-based SR models reveals that it is not uncommon for large attention weights to be assigned to less relevant items, which can result in inaccurate recommendations. Through further in-depth analysis, we find two factors that may contribute to such inaccurate assignment of attention weights: sub-optimal position encoding and noisy input. To this end, in this paper, we aim to address this significant yet challenging gap in existing works. To be specific, we propose a simple yet effective framework called Attention Calibration for Transformer-based Sequential Recommendation (AC-TSR). In AC-TSR, a novel spatial calibrator and adversarial calibrator are designed respectively to directly calibrates those incorrectly assigned attention weights. The former is devised to explicitly capture the spatial relationships (i.e., order and distance) among items for more precise calculation of attention weights. The latter aims to redistribute the attention weights based on each item's contribution to the next-item prediction. AC-TSR is readily adaptable and can be seamlessly integrated into various existing transformer-based SR models. Extensive experimental results on four benchmark real-world datasets demonstrate the superiority of our proposed ACTSR via significant recommendation performance enhancements. The source code is available at https://github.com/AIM-SE/AC-TSR.|基于变压器的顺序推荐(SR)近年来兴起，自注意机制是其关键组成部分。人们普遍认为，自我注意能够有效地从一系列相互作用的项目中选择信息丰富的相关项目，通过学习这些项目的较大注意权重来进行下一个项目的预测。然而，这在现实中可能并不总是正确的。我们对一些有代表性的基于变压器的 SR 模型的实证分析表明，将大的注意力权重分配给相关性较低的项目并不罕见，这可能导致不准确的推荐。通过进一步的深入分析，我们发现了两个可能导致注意力权重分配不准确的因素: 次优位置编码和噪声输入。为此，在本文中，我们的目标是解决这一重大但具有挑战性的差距，在现有的工作。具体来说，我们提出了一个简单而有效的框架，称为基于变压器的顺序推荐注意力校正(AC-TSR)。在 AC-TSR 中，分别设计了一种新的空间校正器和对抗校正器，用于直接校正那些不正确分配的注意权重。前者旨在明确地捕捉项目之间的空间关系(即顺序和距离) ，以便更精确地计算注意力权重。后者的目的是重新分配注意权重的基础上，每个项目的贡献，下一个项目的预测。AC-TSR 很容易适应，可以无缝地集成到各种现有的基于变压器的 SR 模型中。在四个基准的真实世界数据集上的大量实验结果显示了我们提出的 ACTSR 通过显著的推荐性能增强的优越性。源代码可在 https://github.com/aim-se/ac-tsr 下载。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Attention+Calibration+for+Transformer-based+Sequential+Recommendation)|0| +|[HCL4QC: Incorporating Hierarchical Category Structures Into Contrastive Learning for E-commerce Query Classification](https://doi.org/10.1145/3583780.3614907)|Lvxing Zhu, Kexin Zhang, Hao Chen, Chao Wei, Weiru Zhang, Haihong Tang, Xiu Li|Alibaba Group, Hangzhou, China; Tsinghua University, Shenzhen, China|Query classification plays a crucial role in e-commerce, where the goal is to assign user queries to appropriate categories within a hierarchical product category taxonomy. However, existing methods rely on a limited number of words from the category description and often neglect the hierarchical structure of the category tree, resulting in suboptimal category representations. To overcome these limitations, we propose a novel approach named hierarchical contrastive learning framework for query classification (HCL4QC), which leverages the hierarchical category tree structure to improve the performance of query classification. Specifically, HCL4QC is designed as a plugin module that consists of two innovative losses, namely local hierarchical contrastive loss (LHCL) and global hierarchical contrastive loss (GHCL). LHCL adjusts representations of categories according to their positional relationship in the hierarchical tree, while GHCL ensures the semantic consistency between the parent category and its child categories. Our proposed method can be adapted to any query classification tasks that involve a hierarchical category structure. We conduct experiments on two real-world datasets to demonstrate the superiority of our hierarchical contrastive learning. The results demonstrate significant improvements in the query classification task, particularly for long-tail categories with sparse supervised information.|查询分类在电子商务中起着至关重要的作用，其目标是在分层的产品类别分类中将用户查询分配给适当的类别。然而，现有的方法仅仅依赖于类别描述中有限的词汇，往往忽略了类别树的层次结构，导致了类别表示的次优化。为了克服这些局限性，提出了一种新的查询分类层次对比学习框架(HCL4QC) ，该框架利用层次分类树结构来提高查询分类的性能。具体来说，HCL4QC 被设计成一个插件模块，由两个创新性的损失组成，即局部层次对比损失(LHCL)和全局层次对比损失(GHCL)。LHCL 根据类别在层次树中的位置关系来调整类别的表示，而 GHCL 保证了父类别与其子类别之间的语义一致性。该方法适用于任何涉及层次分类结构的查询分类任务。我们在两个真实世界的数据集上进行了实验，以验证我们的分层对比学习的优越性。实验结果表明，该算法在查询分类任务方面有明显的改进，特别是对于监督信息稀疏的长尾类别。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=HCL4QC:+Incorporating+Hierarchical+Category+Structures+Into+Contrastive+Learning+for+E-commerce+Query+Classification)|0| +|[Non-Recursive Cluster-Scale Graph Interacted Model for Click-Through Rate Prediction](https://doi.org/10.1145/3583780.3615180)|Yuanchen Bei, Hao Chen, Shengyuan Chen, Xiao Huang, Sheng Zhou, Feiran Huang|Jinan University, Guangzhou, China; Zhejiang University, Hangzhou, China; The Hong Kong Polytechnic University, Hung Hom, Hong Kong|Extracting users' interests from their behavior, particularly their 1-hop neighbors, has been shown to enhance Click-Through Rate (CTR) prediction performance. However, online recommender systems impose strict constraints on the inference time of CTR models, which necessitates pruning or filtering users' 1-hop neighbors to reduce computational complexity. Furthermore, while the graph information of users and items has been proven effective in collaborative filtering models, recursive graph convolution can be computationally costly and expensive to implement. To address these challenges, we propose the Non-Recursive Cluster-scale Graph Interacted (NRCGI) model, which reorganizes graph convolutional networks in a non-recursive and cluster-scale view to enable CTR models to consider deep graph information with low computational cost. NRCGI employs non-recursive cluster-scale graph aggregation, which allows the online recommendation computational complexity to shrink from tens of thousands of items to tens to hundreds of clusters. Additionally, since NRCGI aggregates neighbors in a non-recursive view, each hop of neighbors has a clear physical meaning. NRCGI explicitly constructs meaningful interactions between the hops of neighbors of users and items to fully model users' intent towards the given item. Experimental results demonstrate that NRCGI outperforms state-of-the-art baselines in three public datasets and one industrial dataset while maintaining efficient inference.|从用户的行为中提取他们的兴趣，特别是他们的一跳邻居，已经被证明可以提高点进率(CTR)预测性能。然而，在线推荐系统对 CTR 模型的推理时间有严格的限制，为了降低计算复杂度，需要对用户的1跳邻居进行剪枝或过滤。此外，虽然用户和项目的图形信息已被证明在协同过滤模型中是有效的，递归图形卷积可能在计算上是昂贵的，实现起来也是昂贵的。为了解决这些挑战，我们提出了非递归聚类尺度图交互(NRCGI)模型，该模型以非递归和聚类尺度视图重新组织图卷积网络，以使 CTR 模型能够以低计算成本考虑深层图信息。NRCGI 采用非递归聚类规模的图聚合技术，使在线推荐计算复杂度从几万个项目减少到几十到几百个聚类。此外，由于 NRCGI 以非递归视图的形式聚合邻居，因此邻居的每个跃点都有明确的物理意义。NRCGI 显式地在用户和项目的邻居之间构造有意义的交互，以充分模拟用户对给定项目的意图。实验结果表明，在保持有效推理的同时，NRCGI 在三个公共数据集和一个工业数据集中的表现优于最先进的基线。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Non-Recursive+Cluster-Scale+Graph+Interacted+Model+for+Click-Through+Rate+Prediction)|0| +|[SAFE: Sequential Attentive Face Embedding with Contrastive Learning for Deepfake Video Detection](https://doi.org/10.1145/3583780.3615279)|Juho Jung, Chaewon Kang, Jeewoo Yoon, Simon S. Woo, Jinyoung Han|Sungkyunkwan University, Suwon, Republic of Korea; Raondata, Seoul, Republic of Korea; Sungkyunkwan University, Seoul, Republic of Korea|The emergence of hyper-realistic deepfake videos has raised significant concerns regarding their potential misuse. However, prior research on deepfake detection has primarily focused on image-based approaches, with little emphasis on video. With the advancement of generation techniques enabling intricate and dynamic manipulation of entire faces as well as specific facial components in a video sequence, capturing dynamic changes in both global and local facial features becomes crucial in detecting deepfake videos. This paper proposes a novel sequential attentive face embedding, SAFE, that can capture facial dynamics in a deepfake video. The proposed SAFE can effectively integrate global and local dynamics of facial features revealed in a video sequence using contrastive learning. Through a comprehensive comparison with the state-of-the-art methods on the DFDC (Deepfake Detection Challenge) dataset and the FaceForensic++ benchmark, we show that our model achieves the highest accuracy in detecting deepfake videos on both datasets.|超真实的深度假视频的出现引起了人们对其潜在滥用的严重关切。然而，先前对深度伪造检测的研究主要集中在基于图像的方法上，对视频的研究很少。随着生成技术的进步，可以对视频序列中的整个面部以及特定的面部组件进行复杂和动态的操作，捕捉全局和局部面部特征的动态变化成为检测深度伪造视频的关键。提出了一种新的序列注意人脸嵌入方法 SAFE，该方法可以在深度伪造视频中捕获人脸动态特征。该方法利用对比学习，有效地整合了视频序列中人脸特征的全局和局部动态特征。通过全面比较 DFDC 数据集和 FaceForensic + + 基准上的最新方法，我们表明我们的模型在两个数据集上检测深度伪造视频时达到了最高的准确性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=SAFE:+Sequential+Attentive+Face+Embedding+with+Contrastive+Learning+for+Deepfake+Video+Detection)|0| +|[Can Embeddings Analysis Explain Large Language Model Ranking?](https://doi.org/10.1145/3583780.3615225)|Claudio Lucchese, Giorgia Minello, Franco Maria Nardini, Salvatore Orlando, Raffaele Perego, Alberto Veneri|Università Ca' Foscari Venezia, Venice, Italy; Ca' Foscari University of Venice, Venice, Italy; Institute of Information Science and Technologies "Alessandro Faedo" - National Research Council of Italy, Pisa, Italy; Ca' Foscari University of Venice & Institute of Information Science and Technologies, Venice, Italy|Understanding the behavior of deep neural networks for Information Retrieval (IR) is crucial to improve trust in these effective models. Current popular approaches to diagnose the predictions made by deep neural networks are mainly based on: i) the adherence of the retrieval model to some axiomatic property of the IR system, ii) the generation of free-text explanations, or iii) feature importance attributions. In this work, we propose a novel approach that analyzes the changes of document and query embeddings in the latent space and that might explain the inner workings of IR large pre-trained language models. In particular, we focus on predicting query/document relevance, and we characterize the predictions by analyzing the topological arrangement of the embeddings in their latent space and their evolution while passing through the layers of the network. We show that there exists a link between the embedding adjustment and the predicted score, based on how tokens cluster in the embedding space. This novel approach, grounded in the query and document tokens interplay over the latent space, provides a new perspective on neural ranker explanation and a promising strategy for improving the efficiency of the models and Query Performance Prediction (QPP).|理解信息检索深层神经网络的行为对于提高人们对这些有效模型的信任至关重要。目前流行的诊断深度神经网络预测的方法主要基于: i)检索模型对 IR 系统的某些公理化特性的依从性，ii)自由文本解释的生成，或 iii)特征重要性归属。在这项工作中，我们提出了一种新的方法，分析文档和查询嵌入的变化，在潜在的空间，这可能解释了内部工作的信息检索大型预训练语言模型。特别地，我们侧重于预测查询/文档的相关性，并且通过分析嵌入在其潜在空间中的拓扑排列以及它们在通过网络层时的演化来刻画预测的特征。基于标记在嵌入空间中的聚类，我们证明了嵌入调整和预测得分之间存在联系。该方法基于查询和文档标记在潜在空间上的相互作用，为神经排序解释提供了一个新的视角，为提高模型效率和查询性能预测(QPP)提供了一种有前途的策略。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Can+Embeddings+Analysis+Explain+Large+Language+Model+Ranking?)|0| |[A Self-Learning Resource-Efficient Re-Ranking Method for Clinical Trials Search](https://doi.org/10.1145/3583780.3615174)|Maciej Rybinski, Vincent Nguyen, Sarvnaz Karimi|CSIRO, Sydney, Australia|Complex search scenarios, such as those in biomedical settings, can be challenging. One such scenario is matching a patient's profile to relevant clinical trials. There are multiple criteria that should match for a document (clinical trial) to be considered relevant to a query (patient's profile represented with an admission note). While different neural ranking methods have been proposed for searching clinical trials, resource-efficient approaches to ranker training are less studied. A resource-efficient method uses training data in moderation. We propose a self-learning reranking method that achieves results comparable to those of more complicated, fully supervised, systems. Our experiments demonstrate our method's robustness and competitiveness compared to the state-of-the-art approaches in clinical trial search.|复杂的搜索场景，例如在生物医学环境中，可能是具有挑战性的。其中一种情况是将患者的特征与相关的临床试验相匹配。有多个标准，应该匹配的文件(临床试验)被认为是相关的查询(病人的配置文件代表入院说明)。虽然不同的神经排序方法已被提出搜索临床试验，资源效率的方法排序训练的研究较少。一种节约资源的方法适度使用训练数据。我们提出了一种自学习重新排序的方法，实现的结果相当于那些更复杂的，完全监督的系统。我们的实验证明了我们的方法的稳健性和竞争力相比，在临床试验搜索的最先进的方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Self-Learning+Resource-Efficient+Re-Ranking+Method+for+Clinical+Trials+Search)|0| -|[Efficient Multi-Task Learning via Generalist Recommender](https://doi.org/10.1145/3583780.3615229)|Luyang Wang, Cangcheng Tang, Chongyang Zhang, Jun Ruan, Kai Huang, Jason Jinquan Dai|Verizon, Basking Ridge, NJ, USA; Verizon, Boston, MA, USA; Verizon, Alpharetta, GA, USA; Intel Corporation, Santa Clara, CA, USA|Multi-task learning (MTL) is a common machine learning technique that allows the model to share information across different tasks and improve the accuracy of recommendations for all of them. Many existing MTL implementations suffer from scalability issues as the training and inference performance can degrade with the increasing number of tasks, which can limit production use case scenarios for MTL-based recommender systems. Inspired by the recent advances of large language models, we developed an end-to-end efficient and scalable Generalist Recommender (GRec). GRec takes comprehensive data signals by utilizing NLP heads, parallel Transformers, as well as a wide and deep structure to process multi-modal inputs. These inputs are then combined and fed through a newly proposed task-sentence level routing mechanism to scale the model capabilities on multiple tasks without compromising performance. Offline evaluations and online experiments show that GRec significantly outperforms our previous recommender solutions. GRec has been successfully deployed on one of the largest telecom websites and apps, effectively managing high volumes of online traffic every day.|多任务学习(Multi-Task Learning，MTL)是一种常见的机器学习技术，它允许模型在不同的任务之间共享信息，并提高对所有任务的推荐的准确性。许多现有的 MTL 实现都存在可伸缩性问题，因为培训和推理性能可能随着任务数量的增加而下降，这可能会限制基于 MTL 的推荐系统的生产用例场景。受到大型语言模型最新进展的启发，我们开发了一个端到端高效且可扩展的通用推荐程序(Generalist Revimender，GRec)。GRec 利用自然语言处理(NLP)磁头、并联变压器以及广泛而深入的结构来处理多模态输入，从而获得全面的数据信号。然后，这些输入通过新提出的任务句子级路由机制进行组合和馈送，以在不影响性能的情况下扩展多任务的模型能力。脱机评估和在线实验表明，GRec 明显优于我们以前的推荐解决方案。GREc 已成功部署在世界上最大的电信网站和应用程序之一上，有效地管理了每天大量的在线流量。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Efficient+Multi-Task+Learning+via+Generalist+Recommender)|0| -|[MTKDN: Multi-Task Knowledge Disentanglement Network for Recommendation](https://doi.org/10.1145/3583780.3615271)|Haotian Wu, Bowen Xing, Ivor W. Tsang|CFAR, Agency for Science, Technology and Research & IHPC, Agency for Science, Technology and Research, Singapore, Singapore; University of Technology Sydney, CFAR, Agency for Science, Technology and Research, & IHPC, Agency for Science, Technology and Research, Sydney, NSW, Australia; Beijing Jiaotong University, CFAR, Agency for Science, Technology and Research, & IHPC, Agency for Science, Technology and Research, Beijing, China|Multi-task learning (MTL) is a widely adopted machine learning paradigm in recommender systems. However, existing MTL models often suffer from performance degeneration with negative transfer and seesaw phenomena. Some works attempt to alleviate the negative transfer and seesaw issues by separating task-specific and shared experts to mitigate the harmful interference between task-specific and shared knowledge. Despite the success of these efforts, task-specific and shared knowledge have still not been thoroughly decoupled. There may still exist unnecessary mixture between the shared and task-specific knowledge, which may harm MLT models' performances. To tackle this problem, in this paper, we propose multi-task knowledge disentanglement network (MTKDN) to further reduce harmful interference between the shared and task-specific knowledge. Specifically, we propose a novel contrastive disentanglement mechanism to explicitly decouple the shared and task-specific knowledge in corresponding hidden spaces. In this way, the unnecessary mixture between shared and task-specific knowledge can be reduced. As for optimization objectives, we propose individual optimization objectives for shared and task-specific experts, by which we can encourage these two kinds of experts to focus more on extracting the shared and task-specific knowledge, respectively. Additionally, we propose a margin regularization to ensure that the fusion of shared and task-specific knowledge can outperform exploiting either of them alone. We conduct extensive experiments on open-source large-scale recommendation datasets. The experimental results demonstrate that MTKDN significantly outperforms state-of-the-art MTL models. In addition, the ablation experiments further verify the necessity of our proposed contrastive disentanglement mechanism and the novel loss settings.|多任务学习(MTL)是推荐系统中广泛采用的机器学习范式。然而，现有的 MTL 模型往往遭受性能退化的负转移和跷跷板现象。有些作品试图通过将具体任务专家和共享专家分开来缓解负面转移和跷跷板问题，以减轻具体任务专家和共享知识之间的有害干扰。尽管这些努力取得了成功，但特定任务和共享知识仍然没有完全解耦。共享知识和特定任务知识之间可能仍然存在不必要的混合，这可能会损害 MLT 模型的性能。为了解决这一问题，本文提出了多任务知识解缠网络(MTKDN) ，以进一步减少共享知识和特定任务知识之间的有害干扰。具体来说，我们提出了一种新的对比解缠机制，以显式解耦共享和任务特定的知识在相应的隐藏空间。通过这种方式，可以减少共享知识和特定任务知识之间不必要的混合。对于优化目标，我们提出了共享专家和任务特定专家的个体优化目标，通过这些目标可以鼓励这两类专家更多地关注共享知识和任务特定知识的提取。此外，我们提出了一个边际正则化，以确保融合共享和任务特定的知识可以胜过利用任何一个单独。我们在开源的大规模推荐数据集上进行了广泛的实验。实验结果表明，MTKDN 的性能明显优于最先进的 MTL 模型。此外，烧蚀实验进一步验证了我们提出的对比解缠机制和新型损耗设置的必要性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=MTKDN:+Multi-Task+Knowledge+Disentanglement+Network+for+Recommendation)|0| -|[FiBiNet++: Reducing Model Size by Low Rank Feature Interaction Layer for CTR Prediction](https://doi.org/10.1145/3583780.3615242)|Pengtao Zhang, Zheng Zheng, Junlin Zhang|Brandeis University, Waltham, MA, USA; Sina Weibo, Beijing, China|Click-Through Rate (CTR) estimation has become one of the most fundamental tasks in many real-world applications and various deep models have been proposed. Some research has proved that FiBiNet is one of the best performance models and outperforms all other models on Avazu dataset. However, the large model size of FiBiNet hinders its wider application. In this paper, we propose a novel FiBiNet++ model to redesign FiBiNet's model structure, which greatly reduces model size while further improves its performance. One of the primary techniques involves our proposed "Low Rank Layer" focused on feature interaction, which serves as a crucial driver of achieving a superior compression ratio for models. Extensive experiments on three public datasets show that FiBiNet++ effectively reduces non-embedding model parameters of FiBiNet by 12x to 16x on three datasets. On the other hand, FiBiNet++ leads to significant performance improvements compared to state-of-the-art CTR methods, including FiBiNet. The source code is in https://github.com/recommendation-algorithm/FiBiNet.|点进率估计已经成为现实应用中最基本的任务之一，各种深度模型已经被提出。一些研究已经证明，FiBiNet 是最好的性能模型之一，在 Avazu 数据集上优于所有其他模型。然而，FiBiNet 的大型模型阻碍了它的广泛应用。本文提出了一种新的 FiBiNet + + 模型，重新设计了 FiBiNet 的模型结构，大大减小了模型尺寸，进一步提高了性能。其中一个主要的技术涉及我们提出的“低等级层”，重点是功能交互，这是一个关键的驱动器，以实现一个优越的压缩比模型。在三个公共数据集上进行的大量实验表明，FiBiNet + + 在三个数据集上有效地将 FiBiNet 的非嵌入模型参数减少了12 ~ 16倍。另一方面，与包括 FiBiNet 在内的最先进的 CTR 方法相比，FiBiNet + + 带来了显著的性能改进。源代码在 https://github.com/recommendation-algorithm/fibinet 里。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=FiBiNet++:+Reducing+Model+Size+by+Low+Rank+Feature+Interaction+Layer+for+CTR+Prediction)|0| -|[Learning To Rank Diversely At Airbnb](https://doi.org/10.1145/3583780.3614692)|Malay Haldar, Mustafa Abdool, Liwei He, Dillon Davis, Huiji Gao, Sanjeev Katariya|Airbnb, Inc., San Jose, CA, USA; Airbnb, Inc., Seattle, WA, USA; Airbnb, Inc., San Francisco, CA, USA; Airbnb, Inc., Cupertino, CA, USA; Airbnb, Inc., Sunnyvale, CA, USA|Airbnb is a two-sided marketplace, bringing together hosts who own listings for rent, with prospective guests from around the globe. Applying neural network-based learning to rank techniques has led to significant improvements in matching guests with hosts. These improvements in ranking were driven by a core strategy: order the listings by their estimated booking probabilities, then iterate on techniques to make these booking probability estimates more and more accurate. Embedded implicitly in this strategy was an assumption that the booking probability of a listing could be determined independently of other listings in search results. In this paper we discuss how this assumption, pervasive throughout the commonly-used learning to rank frameworks, is false. We provide a theoretical foundation correcting this assumption, followed by efficient neural network architectures based on the theory. Explicitly accounting for possible similarities between listings, and reducing them to diversify the search results generated strong positive impact. We discuss these metric wins as part of the online A/B tests of the theory. Our method provides a practical way to diversify search results for large-scale production ranking systems.|Airbnb 是一个双面市场，汇集了拥有出租房源的房东，以及来自世界各地的潜在客户。将基于神经网络的学习应用于排序技术已经导致了客户与主机匹配方面的重大改进。排名方面的这些改进是由一个核心策略驱动的: 根据预订概率的估计对列表进行排序，然后迭代技术，使这些预订概率估计越来越准确。该策略隐含的假设是，一个列表的预订概率可以独立于搜索结果中的其他列表来确定。在本文中，我们将讨论这个贯穿于常用的对框架进行排序的学习过程中的假设是如何错误的。我们提供了一个理论基础，纠正这一假设，其次是有效的神经网络架构的基础上的理论。明确说明列表之间可能存在的相似之处，并减少列表数量以使搜索结果多样化，产生了强有力的积极影响。我们讨论这些度量胜利作为在线 A/B 理论测试的一部分。该方法为大规模生产排序系统提供了一种实用的搜索结果多样化方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Learning+To+Rank+Diversely+At+Airbnb)|0| +|[Efficient Multi-Task Learning via Generalist Recommender](https://doi.org/10.1145/3583780.3615229)|Luyang Wang, Cangcheng Tang, Chongyang Zhang, Jun Ruan, Kai Huang, Jason Jinquan Dai|Verizon, Boston, MA, USA; Verizon, Alpharetta, GA, USA; Intel Corporation, Santa Clara, CA, USA; Verizon, Basking Ridge, NJ, USA|Multi-task learning (MTL) is a common machine learning technique that allows the model to share information across different tasks and improve the accuracy of recommendations for all of them. Many existing MTL implementations suffer from scalability issues as the training and inference performance can degrade with the increasing number of tasks, which can limit production use case scenarios for MTL-based recommender systems. Inspired by the recent advances of large language models, we developed an end-to-end efficient and scalable Generalist Recommender (GRec). GRec takes comprehensive data signals by utilizing NLP heads, parallel Transformers, as well as a wide and deep structure to process multi-modal inputs. These inputs are then combined and fed through a newly proposed task-sentence level routing mechanism to scale the model capabilities on multiple tasks without compromising performance. Offline evaluations and online experiments show that GRec significantly outperforms our previous recommender solutions. GRec has been successfully deployed on one of the largest telecom websites and apps, effectively managing high volumes of online traffic every day.|多任务学习(Multi-Task Learning，MTL)是一种常见的机器学习技术，它允许模型在不同的任务之间共享信息，并提高对所有任务的推荐的准确性。许多现有的 MTL 实现都存在可伸缩性问题，因为培训和推理性能可能随着任务数量的增加而下降，这可能会限制基于 MTL 的推荐系统的生产用例场景。受到大型语言模型最新进展的启发，我们开发了一个端到端高效且可扩展的通用推荐程序(Generalist Revimender，GRec)。GRec 利用自然语言处理(NLP)磁头、并联变压器以及广泛而深入的结构来处理多模态输入，从而获得全面的数据信号。然后，这些输入通过新提出的任务句子级路由机制进行组合和馈送，以在不影响性能的情况下扩展多任务的模型能力。脱机评估和在线实验表明，GRec 明显优于我们以前的推荐解决方案。GREc 已成功部署在世界上最大的电信网站和应用程序之一上，有效地管理了每天大量的在线流量。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Efficient+Multi-Task+Learning+via+Generalist+Recommender)|0| +|[MTKDN: Multi-Task Knowledge Disentanglement Network for Recommendation](https://doi.org/10.1145/3583780.3615271)|Haotian Wu, Bowen Xing, Ivor W. Tsang|CFAR, Agency for Science, Technology and Research & IHPC, Agency for Science, Technology and Research, Singapore, Singapore; Beijing Jiaotong University, CFAR, Agency for Science, Technology and Research, & IHPC, Agency for Science, Technology and Research, Beijing, China; University of Technology Sydney, CFAR, Agency for Science, Technology and Research, & IHPC, Agency for Science, Technology and Research, Sydney, NSW, Australia|Multi-task learning (MTL) is a widely adopted machine learning paradigm in recommender systems. However, existing MTL models often suffer from performance degeneration with negative transfer and seesaw phenomena. Some works attempt to alleviate the negative transfer and seesaw issues by separating task-specific and shared experts to mitigate the harmful interference between task-specific and shared knowledge. Despite the success of these efforts, task-specific and shared knowledge have still not been thoroughly decoupled. There may still exist unnecessary mixture between the shared and task-specific knowledge, which may harm MLT models' performances. To tackle this problem, in this paper, we propose multi-task knowledge disentanglement network (MTKDN) to further reduce harmful interference between the shared and task-specific knowledge. Specifically, we propose a novel contrastive disentanglement mechanism to explicitly decouple the shared and task-specific knowledge in corresponding hidden spaces. In this way, the unnecessary mixture between shared and task-specific knowledge can be reduced. As for optimization objectives, we propose individual optimization objectives for shared and task-specific experts, by which we can encourage these two kinds of experts to focus more on extracting the shared and task-specific knowledge, respectively. Additionally, we propose a margin regularization to ensure that the fusion of shared and task-specific knowledge can outperform exploiting either of them alone. We conduct extensive experiments on open-source large-scale recommendation datasets. The experimental results demonstrate that MTKDN significantly outperforms state-of-the-art MTL models. In addition, the ablation experiments further verify the necessity of our proposed contrastive disentanglement mechanism and the novel loss settings.|多任务学习(MTL)是推荐系统中广泛采用的机器学习范式。然而，现有的 MTL 模型往往遭受性能退化的负转移和跷跷板现象。有些作品试图通过将具体任务专家和共享专家分开来缓解负面转移和跷跷板问题，以减轻具体任务专家和共享知识之间的有害干扰。尽管这些努力取得了成功，但特定任务和共享知识仍然没有完全解耦。共享知识和特定任务知识之间可能仍然存在不必要的混合，这可能会损害 MLT 模型的性能。为了解决这一问题，本文提出了多任务知识解缠网络(MTKDN) ，以进一步减少共享知识和特定任务知识之间的有害干扰。具体来说，我们提出了一种新的对比解缠机制，以显式解耦共享和任务特定的知识在相应的隐藏空间。通过这种方式，可以减少共享知识和特定任务知识之间不必要的混合。对于优化目标，我们提出了共享专家和任务特定专家的个体优化目标，通过这些目标可以鼓励这两类专家更多地关注共享知识和任务特定知识的提取。此外，我们提出了一个边际正则化，以确保融合共享和任务特定的知识可以胜过利用任何一个单独。我们在开源的大规模推荐数据集上进行了广泛的实验。实验结果表明，MTKDN 的性能明显优于最先进的 MTL 模型。此外，烧蚀实验进一步验证了我们提出的对比解缠机制和新型损耗设置的必要性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=MTKDN:+Multi-Task+Knowledge+Disentanglement+Network+for+Recommendation)|0| +|[FiBiNet++: Reducing Model Size by Low Rank Feature Interaction Layer for CTR Prediction](https://doi.org/10.1145/3583780.3615242)|Pengtao Zhang, Zheng Zheng, Junlin Zhang|Sina Weibo, Beijing, China; Brandeis University, Waltham, MA, USA|Click-Through Rate (CTR) estimation has become one of the most fundamental tasks in many real-world applications and various deep models have been proposed. Some research has proved that FiBiNet is one of the best performance models and outperforms all other models on Avazu dataset. However, the large model size of FiBiNet hinders its wider application. In this paper, we propose a novel FiBiNet++ model to redesign FiBiNet's model structure, which greatly reduces model size while further improves its performance. One of the primary techniques involves our proposed "Low Rank Layer" focused on feature interaction, which serves as a crucial driver of achieving a superior compression ratio for models. Extensive experiments on three public datasets show that FiBiNet++ effectively reduces non-embedding model parameters of FiBiNet by 12x to 16x on three datasets. On the other hand, FiBiNet++ leads to significant performance improvements compared to state-of-the-art CTR methods, including FiBiNet. The source code is in https://github.com/recommendation-algorithm/FiBiNet.|点进率估计已经成为现实应用中最基本的任务之一，各种深度模型已经被提出。一些研究已经证明，FiBiNet 是最好的性能模型之一，在 Avazu 数据集上优于所有其他模型。然而，FiBiNet 的大型模型阻碍了它的广泛应用。本文提出了一种新的 FiBiNet + + 模型，重新设计了 FiBiNet 的模型结构，大大减小了模型尺寸，进一步提高了性能。其中一个主要的技术涉及我们提出的“低等级层”，重点是功能交互，这是一个关键的驱动器，以实现一个优越的压缩比模型。在三个公共数据集上进行的大量实验表明，FiBiNet + + 在三个数据集上有效地将 FiBiNet 的非嵌入模型参数减少了12 ~ 16倍。另一方面，与包括 FiBiNet 在内的最先进的 CTR 方法相比，FiBiNet + + 带来了显著的性能改进。源代码在 https://github.com/recommendation-algorithm/fibinet 里。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=FiBiNet++:+Reducing+Model+Size+by+Low+Rank+Feature+Interaction+Layer+for+CTR+Prediction)|0| +|[Learning To Rank Diversely At Airbnb](https://doi.org/10.1145/3583780.3614692)|Malay Haldar, Mustafa Abdool, Liwei He, Dillon Davis, Huiji Gao, Sanjeev Katariya|Airbnb, Inc., Cupertino, CA, USA; Airbnb, Inc., San Francisco, CA, USA; Airbnb, Inc., Sunnyvale, CA, USA; Airbnb, Inc., Seattle, WA, USA; Airbnb, Inc., San Jose, CA, USA|Airbnb is a two-sided marketplace, bringing together hosts who own listings for rent, with prospective guests from around the globe. Applying neural network-based learning to rank techniques has led to significant improvements in matching guests with hosts. These improvements in ranking were driven by a core strategy: order the listings by their estimated booking probabilities, then iterate on techniques to make these booking probability estimates more and more accurate. Embedded implicitly in this strategy was an assumption that the booking probability of a listing could be determined independently of other listings in search results. In this paper we discuss how this assumption, pervasive throughout the commonly-used learning to rank frameworks, is false. We provide a theoretical foundation correcting this assumption, followed by efficient neural network architectures based on the theory. Explicitly accounting for possible similarities between listings, and reducing them to diversify the search results generated strong positive impact. We discuss these metric wins as part of the online A/B tests of the theory. Our method provides a practical way to diversify search results for large-scale production ranking systems.|Airbnb 是一个双面市场，汇集了拥有出租房源的房东，以及来自世界各地的潜在客户。将基于神经网络的学习应用于排序技术已经导致了客户与主机匹配方面的重大改进。排名方面的这些改进是由一个核心策略驱动的: 根据预订概率的估计对列表进行排序，然后迭代技术，使这些预订概率估计越来越准确。该策略隐含的假设是，一个列表的预订概率可以独立于搜索结果中的其他列表来确定。在本文中，我们将讨论这个贯穿于常用的对框架进行排序的学习过程中的假设是如何错误的。我们提供了一个理论基础，纠正这一假设，其次是有效的神经网络架构的基础上的理论。明确说明列表之间可能存在的相似之处，并减少列表数量以使搜索结果多样化，产生了强有力的积极影响。我们讨论这些度量胜利作为在线 A/B 理论测试的一部分。该方法为大规模生产排序系统提供了一种实用的搜索结果多样化方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Learning+To+Rank+Diversely+At+Airbnb)|0| |[Dynamic Group Parameter Modeling for Click-Through-Rate Prediction](https://doi.org/10.1145/3583780.3615471)|Xuan Ma, Jian Wang, Zhiyuan Chen, Zehua Zhang, Jie He, Changping Peng, Zhangang Lin, Jingping Shao|JD.com, Beijing, China|It is noted that Click-Through-Rate(CTR) prediction plays an important part in recommendation systems and online advertising. Over the past few years, numerous studies have been conducted to improve the accuracy of CTR prediction by exploring data inherent patterns. These studies indicate that training CTR models with group-specific parameters on divided data groups can lead to significant improvements. However, most works generally divide groups manually with some prior knowledge, and such a fixed group division method may hinder the expression of user common interests. To address this limitation, we propose a novel group parameter modeling method, where the user group division and group parameter learning processes are completed in an automatic and dynamic way. Our method employs a three-stage approach, consisting of group information selection, group representation learning, and group parameter generation, which allows the efficient expression of user common interests. We conduct experiments on both public datasets and industrial datasets, and the experimental results demonstrate the effectiveness of our method. We have also deployed the model in an online advertising system and observed significant improvements in both CTR and Revenue Per Mille (RPM).|点击率(CTR)预测在推荐系统和在线广告中起着重要作用。在过去的几年中，通过探索数据固有的模式，已经进行了大量的研究来提高 CTR 预测的准确性。这些研究表明，在分组的数据组上训练具有组特定参数的 CTR 模型可以导致显著的改进。然而，大多数工作一般都是利用一定的先验知识进行人工分组，这种固定的分组方法可能会阻碍用户共同兴趣的表达。针对这一局限性，提出了一种新的组参数建模方法，用户组划分和组参数学习过程可以自动动态地完成。我们的方法采用了三个阶段的方法，包括群体信息选择、群表示论学习和群体参数生成，这样可以有效地表达用户的共同兴趣。我们在公共数据集和工业数据集上进行了实验，实验结果表明了该方法的有效性。我们还在一个在线广告系统中部署了该模型，并观察到在点击率和每公里收入(RPM)方面的显著改进。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Dynamic+Group+Parameter+Modeling+for+Click-Through-Rate+Prediction)|0| |[Practice on Effectively Extracting NLP Features for Click-Through Rate Prediction](https://doi.org/10.1145/3583780.3614707)|Hao Yang, Ziliang Wang, Weijie Bian, Yifan Zeng|Shopee Discovery Ads, Beijing, China|Click-through rate (CTR) prediction is critical for industrial applications such as recommendation system and online advertising. Practically, there are a series of research proved that Natural Language Processing (NLP) features are helpful to improve CTR task performance. As these works show, there are different ways to extract NLP features. For example, keywords of item title are extracted as open-box feature by term frequency?inverse document frequency (tf-idf) method while item semantic embedding is extracted as black-box feature by shallow models (\emphe.g., word2vec) or deep learning models (e.g., BERT). However, these NLP models are pre-trained on NLP task, which is very different from the CTR task. Then it leads to the limited improvement of Area Under the ROC Curve (AUC) in CTR task. On the other hand, traditional NLP models for CTR task only consider open-box feature or black-box feature separately, which also leads to the discounted effect. Lastly, many NLP models are mainly used to extract semantic features only on item side. These methods take little account of user side information, or only IDs related features (\emphe.g., item's IDs) in user behavior sequence are introduced. In our work, we proposed a new network named BERT Attention method based on both Item and User information (BAIU). The target of BAIU is consistent with the CTR task, which is helpful to extract more effective NLP features. Also, both open-box and black-box features are simultaneously extracted by this network, which makes the model to learn more useful NLP features for CTR task and also makes the model more interpretable. Extensive experiments on both public data and our commercial data validate the effectiveness of our approach. Finally, the online experiment brings 2.2% gain of CTR on recommendation.|对于推荐系统和在线广告等工业应用而言，点进率(ctrl)预测至关重要。实践证明，自然语言处理(NLP)特性有助于提高 CTR 任务性能。正如这些工作所显示的，有不同的方法提取自然语言处理特征。例如，项目标题的关键词通过词频-逆文档频率(tf-idf)方法提取为开箱特征，而项目语义嵌入通过浅层模型(word2vec)或深层学习模型(BERT)提取为黑箱特征。然而，这些自然语言处理模型是在自然语言处理任务上进行预训练的，这与 CTR 任务有很大的不同。然后，它导致 ROC 曲线下面积(aUC)在 CTR 任务中的有限改进。另一方面，传统的点击率任务自然语言处理模型只考虑开箱特征和黑箱特征，这也导致了模型的折现效应。最后，许多自然语言处理模型主要用于仅在项目一侧提取语义特征。这些方法很少考虑用户端信息，或者只引入用户行为序列中与 ID 相关的特征(例如，项的 ID)。在我们的工作中，我们提出了一种新的基于项目和用户信息(BAIU)的网络方法—— BERT 注意方法。BAIU 的目标与 CTR 任务一致，有助于提取更有效的自然语言处理特征。该网络同时提取了开箱特征和黑箱特征，使模型学习到更多有用的自然语言处理特征，使模型更具可解释性。对公共数据和我们的商业数据的大量实验验证了我们的方法的有效性。最后，在线实验给推荐带来了2.2% 的点击率增益。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Practice+on+Effectively+Extracting+NLP+Features+for+Click-Through+Rate+Prediction)|0| -|[DiscoverPath: A Knowledge Refinement and Retrieval System for Interdisciplinarity on Biomedical Research](https://doi.org/10.1145/3583780.3614739)|YuNeng Chuang, Guanchu Wang, ChiaYuan Chang, KweiHerng Lai, Daochen Zha, Ruixiang Tang, Fan Yang, Alfredo CostillaReyes, Kaixiong Zhou, Xiaoqian Jiang, Xia Hu|Rice University, Houston, TX, USA; UTHealth at Houston, Houston, TX, USA; Texas A&M University, College Station, TX, USA|The exponential growth in scholarly publications necessitates advanced tools for efficient article retrieval, especially in interdisciplinary fields where diverse terminologies are used to describe similar research. Traditional keyword-based search engines often fall short in assisting users who may not be familiar with specific terminologies. To address this, we present a knowledge graph-based paper search engine for biomedical research to enhance the user experience in discovering relevant queries and articles. The system, dubbed DiscoverPath, employs Named Entity Recognition (NER) and part-of-speech (POS) tagging to extract terminologies and relationships from article abstracts to create a KG. To reduce information overload, DiscoverPath presents users with a focused subgraph containing the queried entity and its neighboring nodes and incorporates a query recommendation system, enabling users to iteratively refine their queries. The system is equipped with an accessible Graphical User Interface that provides an intuitive visualization of the KG, query recommendations, and detailed article information, enabling efficient article retrieval, thus fostering interdisciplinary knowledge exploration. DiscoverPath is open-sourced at https://github.com/ynchuang/DiscoverPath.|学术出版物的指数增长需要先进的工具来进行有效的文章检索，特别是在跨学科领域，在这些领域中，不同的术语被用来描述类似的研究。传统的基于关键字的搜索引擎往往不能帮助那些可能不熟悉特定术语的用户。为了解决这个问题，我们提供了一个基于知识图表的生物医学研究纸质搜索引擎，以增强用户发现相关查询和文章的体验。这个名为 Discover Path 的系统使用命名实体识别(NER)和词性标签(POS)从文章摘要中提取术语和关系来创建 KG。为了减少信息超载，Discover Path 为用户提供了一个包含被查询实体及其邻近节点的聚焦子图，并结合了一个查询推荐系统，使用户能够迭代地完善他们的查询。该系统配备了一个易于使用的图形用户界面，可以直观地显示幼稚园、查询建议和详细的文章信息，从而提供有效的文章检索，从而促进跨学科的知识探索。发现路径在 https://github.com/ynchuang/DiscoverPath 是开源的。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=DiscoverPath:+A+Knowledge+Refinement+and+Retrieval+System+for+Interdisciplinarity+on+Biomedical+Research)|0| -|[KuaiSAR: A Unified Search And Recommendation Dataset](https://doi.org/10.1145/3583780.3615123)|Zhongxiang Sun, Zihua Si, Xiaoxue Zang, Dewei Leng, Yanan Niu, Yang Song, Xiao Zhang, Jun Xu|Renmin Unversity of China, Beijing, China; Kuaishou Technology Co., Ltd, Beijing, China|The confluence of Search and Recommendation services is a vital aspect of online content platforms like Kuaishou and TikTok. The integration of S&R modeling is a highly intuitive approach adopted by industry practitioners. However, there is a noticeable lack of research conducted in this area within the academia, primarily due to the absence of publicly available datasets. Consequently, a substantial gap has emerged between academia and industry regarding research endeavors in this field. To bridge this gap, we introduce the first large-scale, real-world dataset KuaiSAR of integrated Search And Recommendation behaviors collected from Kuaishou, a leading short-video app in China with over 300 million daily active users. Previous research in this field has predominantly employed publicly available datasets that are semi-synthetic and simulated, with artificially fabricated search behaviors. Distinct from previous datasets, KuaiSAR records genuine user behaviors, the occurrence of each interaction within either search or recommendation service, and the users' transitions between the two services. This work aids in joint modeling of S&R, and the utilization of search data for recommenders (and recommendation data for search engines). Additionally, due to the diverse feedback labels of user-video interactions, KuaiSAR also supports a wide range of other tasks, including intent recommendation, multi-task learning, and long sequential multi-behavior modeling etc. We believe this dataset will facilitate innovative research and enrich our understanding of S&R services integration in real-world applications.|搜索和推荐服务的融合是 Kuaishou 和 TikTok 等在线内容平台的一个重要方面。S & R 建模的集成是业界从业人员采用的一种高度直观的方法。然而，学术界在这一领域明显缺乏研究，主要是由于缺乏公开可用的数据集。因此，在这个领域的研究工作方面，学术界和工业界之间出现了巨大的差距。为了弥补这一差距，我们介绍了第一个大规模的，真实世界的数据集 KuaiSAR 的集成搜索和推荐行为收集自 Kuaishou，一个领先的短视频应用程序在中国有超过3亿日活跃用户。以前在这个领域的研究主要使用公开可用的数据集，这些数据集是半合成的和模拟的，具有人为制造的搜索行为。与以前的数据集不同，KuaiSAR 记录了真实的用户行为、搜索或推荐服务中每个交互的发生情况以及用户在两个服务之间的转换。这项工作有助于 S & R 的联合建模，以及对推荐者的搜索数据(和搜索引擎的推荐数据)的利用。此外，由于用户与视频交互的反馈标签多种多样，KuaiSAR 还支持广泛的其他任务，包括意图推荐、多任务学习和长顺序多行为建模等。我们相信这个数据集将促进创新研究，丰富我们对现实世界应用中的 S & R 服务集成的理解。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=KuaiSAR:+A+Unified+Search+And+Recommendation+Dataset)|0| -|[HyperBandit: Contextual Bandit with Hypernewtork for Time-Varying User Preferences in Streaming Recommendation](https://doi.org/10.1145/3583780.3614921)|Chenglei Shen, Xiao Zhang, Wei Wei, Jun Xu|Renmin University of China, Beijing, China; Huazhong University of Science and Technology, Wuhan, China|In real-world streaming recommender systems, user preferences often dynamically change over time (e.g., a user may have different preferences during weekdays and weekends). Existing bandit-based streaming recommendation models only consider time as a timestamp, without explicitly modeling the relationship between time variables and time-varying user preferences. This leads to recommendation models that cannot quickly adapt to dynamic scenarios. To address this issue, we propose a contextual bandit approach using hypernetwork, called HyperBandit, which takes time features as input and dynamically adjusts the recommendation model for time-varying user preferences. Specifically, HyperBandit maintains a neural network capable of generating the parameters for estimating time-varying rewards, taking into account the correlation between time features and user preferences. Using the estimated time-varying rewards, a bandit policy is employed to make online recommendations by learning the latent item contexts. To meet the real-time requirements in streaming recommendation scenarios, we have verified the existence of a low-rank structure in the parameter matrix and utilize low-rank factorization for efficient training. Theoretically, we demonstrate a sublinear regret upper bound against the best policy. Extensive experiments on real-world datasets show that the proposed HyperBandit consistently outperforms the state-of-the-art baselines in terms of accumulated rewards.|在现实世界的流媒体推荐系统中，用户的偏好通常会随着时间而动态变化(例如，用户在工作日和周末可能会有不同的偏好)。现有的基于盗贼的流媒体推荐模型只考虑时间作为时间戳，而没有明确建模时间变量和时变用户偏好之间的关系。这导致推荐模型不能快速适应动态场景。为了解决这个问题，我们提出了一种使用超网络的上下文绑架方法，称为 HyperBandit，它以时间特征作为输入，并动态调整推荐模型以适应时变的用户偏好。具体来说，HyperBandit 维护了一个神经网络，该网络能够生成用于估计时变奖励的参数，同时考虑到时间特征和用户偏好之间的相关性。利用估计的时变奖励，采用强盗策略，通过学习潜在项目上下文提出在线推荐。为了满足流媒体推荐场景的实时性要求，我们验证了参数矩阵中低秩结构的存在性，并利用低秩分解进行有效的训练。从理论上证明了最优策略的次线性后悔上界。在真实世界数据集上的大量实验表明，提出的 HyperBandit 在累积奖励方面始终优于最先进的基线。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=HyperBandit:+Contextual+Bandit+with+Hypernewtork+for+Time-Varying+User+Preferences+in+Streaming+Recommendation)|0| +|[DiscoverPath: A Knowledge Refinement and Retrieval System for Interdisciplinarity on Biomedical Research](https://doi.org/10.1145/3583780.3614739)|YuNeng Chuang, Guanchu Wang, ChiaYuan Chang, KweiHerng Lai, Daochen Zha, Ruixiang Tang, Fan Yang, Alfredo CostillaReyes, Kaixiong Zhou, Xiaoqian Jiang, Xia Hu|Texas A&M University, College Station, TX, USA; UTHealth at Houston, Houston, TX, USA; Rice University, Houston, TX, USA|The exponential growth in scholarly publications necessitates advanced tools for efficient article retrieval, especially in interdisciplinary fields where diverse terminologies are used to describe similar research. Traditional keyword-based search engines often fall short in assisting users who may not be familiar with specific terminologies. To address this, we present a knowledge graph-based paper search engine for biomedical research to enhance the user experience in discovering relevant queries and articles. The system, dubbed DiscoverPath, employs Named Entity Recognition (NER) and part-of-speech (POS) tagging to extract terminologies and relationships from article abstracts to create a KG. To reduce information overload, DiscoverPath presents users with a focused subgraph containing the queried entity and its neighboring nodes and incorporates a query recommendation system, enabling users to iteratively refine their queries. The system is equipped with an accessible Graphical User Interface that provides an intuitive visualization of the KG, query recommendations, and detailed article information, enabling efficient article retrieval, thus fostering interdisciplinary knowledge exploration. DiscoverPath is open-sourced at https://github.com/ynchuang/DiscoverPath.|学术出版物的指数增长需要先进的工具来进行有效的文章检索，特别是在跨学科领域，在这些领域中，不同的术语被用来描述类似的研究。传统的基于关键字的搜索引擎往往不能帮助那些可能不熟悉特定术语的用户。为了解决这个问题，我们提供了一个基于知识图表的生物医学研究纸质搜索引擎，以增强用户发现相关查询和文章的体验。这个名为 Discover Path 的系统使用命名实体识别(NER)和词性标签(POS)从文章摘要中提取术语和关系来创建 KG。为了减少信息超载，Discover Path 为用户提供了一个包含被查询实体及其邻近节点的聚焦子图，并结合了一个查询推荐系统，使用户能够迭代地完善他们的查询。该系统配备了一个易于使用的图形用户界面，可以直观地显示幼稚园、查询建议和详细的文章信息，从而提供有效的文章检索，从而促进跨学科的知识探索。发现路径在 https://github.com/ynchuang/DiscoverPath 是开源的。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=DiscoverPath:+A+Knowledge+Refinement+and+Retrieval+System+for+Interdisciplinarity+on+Biomedical+Research)|0| +|[KuaiSAR: A Unified Search And Recommendation Dataset](https://doi.org/10.1145/3583780.3615123)|Zhongxiang Sun, Zihua Si, Xiaoxue Zang, Dewei Leng, Yanan Niu, Yang Song, Xiao Zhang, Jun Xu|Kuaishou Technology Co., Ltd, Beijing, China; Renmin Unversity of China, Beijing, China|The confluence of Search and Recommendation services is a vital aspect of online content platforms like Kuaishou and TikTok. The integration of S&R modeling is a highly intuitive approach adopted by industry practitioners. However, there is a noticeable lack of research conducted in this area within the academia, primarily due to the absence of publicly available datasets. Consequently, a substantial gap has emerged between academia and industry regarding research endeavors in this field. To bridge this gap, we introduce the first large-scale, real-world dataset KuaiSAR of integrated Search And Recommendation behaviors collected from Kuaishou, a leading short-video app in China with over 300 million daily active users. Previous research in this field has predominantly employed publicly available datasets that are semi-synthetic and simulated, with artificially fabricated search behaviors. Distinct from previous datasets, KuaiSAR records genuine user behaviors, the occurrence of each interaction within either search or recommendation service, and the users' transitions between the two services. This work aids in joint modeling of S&R, and the utilization of search data for recommenders (and recommendation data for search engines). Additionally, due to the diverse feedback labels of user-video interactions, KuaiSAR also supports a wide range of other tasks, including intent recommendation, multi-task learning, and long sequential multi-behavior modeling etc. We believe this dataset will facilitate innovative research and enrich our understanding of S&R services integration in real-world applications.|搜索和推荐服务的融合是 Kuaishou 和 TikTok 等在线内容平台的一个重要方面。S & R 建模的集成是业界从业人员采用的一种高度直观的方法。然而，学术界在这一领域明显缺乏研究，主要是由于缺乏公开可用的数据集。因此，在这个领域的研究工作方面，学术界和工业界之间出现了巨大的差距。为了弥补这一差距，我们介绍了第一个大规模的，真实世界的数据集 KuaiSAR 的集成搜索和推荐行为收集自 Kuaishou，一个领先的短视频应用程序在中国有超过3亿日活跃用户。以前在这个领域的研究主要使用公开可用的数据集，这些数据集是半合成的和模拟的，具有人为制造的搜索行为。与以前的数据集不同，KuaiSAR 记录了真实的用户行为、搜索或推荐服务中每个交互的发生情况以及用户在两个服务之间的转换。这项工作有助于 S & R 的联合建模，以及对推荐者的搜索数据(和搜索引擎的推荐数据)的利用。此外，由于用户与视频交互的反馈标签多种多样，KuaiSAR 还支持广泛的其他任务，包括意图推荐、多任务学习和长顺序多行为建模等。我们相信这个数据集将促进创新研究，丰富我们对现实世界应用中的 S & R 服务集成的理解。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=KuaiSAR:+A+Unified+Search+And+Recommendation+Dataset)|0| +|[HyperBandit: Contextual Bandit with Hypernewtork for Time-Varying User Preferences in Streaming Recommendation](https://doi.org/10.1145/3583780.3614921)|Chenglei Shen, Xiao Zhang, Wei Wei, Jun Xu|Huazhong University of Science and Technology, Wuhan, China; Renmin University of China, Beijing, China|In real-world streaming recommender systems, user preferences often dynamically change over time (e.g., a user may have different preferences during weekdays and weekends). Existing bandit-based streaming recommendation models only consider time as a timestamp, without explicitly modeling the relationship between time variables and time-varying user preferences. This leads to recommendation models that cannot quickly adapt to dynamic scenarios. To address this issue, we propose a contextual bandit approach using hypernetwork, called HyperBandit, which takes time features as input and dynamically adjusts the recommendation model for time-varying user preferences. Specifically, HyperBandit maintains a neural network capable of generating the parameters for estimating time-varying rewards, taking into account the correlation between time features and user preferences. Using the estimated time-varying rewards, a bandit policy is employed to make online recommendations by learning the latent item contexts. To meet the real-time requirements in streaming recommendation scenarios, we have verified the existence of a low-rank structure in the parameter matrix and utilize low-rank factorization for efficient training. Theoretically, we demonstrate a sublinear regret upper bound against the best policy. Extensive experiments on real-world datasets show that the proposed HyperBandit consistently outperforms the state-of-the-art baselines in terms of accumulated rewards.|在现实世界的流媒体推荐系统中，用户的偏好通常会随着时间而动态变化(例如，用户在工作日和周末可能会有不同的偏好)。现有的基于盗贼的流媒体推荐模型只考虑时间作为时间戳，而没有明确建模时间变量和时变用户偏好之间的关系。这导致推荐模型不能快速适应动态场景。为了解决这个问题，我们提出了一种使用超网络的上下文绑架方法，称为 HyperBandit，它以时间特征作为输入，并动态调整推荐模型以适应时变的用户偏好。具体来说，HyperBandit 维护了一个神经网络，该网络能够生成用于估计时变奖励的参数，同时考虑到时间特征和用户偏好之间的相关性。利用估计的时变奖励，采用强盗策略，通过学习潜在项目上下文提出在线推荐。为了满足流媒体推荐场景的实时性要求，我们验证了参数矩阵中低秩结构的存在性，并利用低秩分解进行有效的训练。从理论上证明了最优策略的次线性后悔上界。在真实世界数据集上的大量实验表明，提出的 HyperBandit 在累积奖励方面始终优于最先进的基线。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=HyperBandit:+Contextual+Bandit+with+Hypernewtork+for+Time-Varying+User+Preferences+in+Streaming+Recommendation)|0| |[Dually Enhanced Delayed Feedback Modeling for Streaming Conversion Rate Prediction](https://doi.org/10.1145/3583780.3614856)|Sunhao Dai, Yuqi Zhou, Jun Xu, JiRong Wen|Renmin University of China, Beijing, China|In online industrial advertising systems, conversion actions (e.g., purchases or downloads) often occur significantly delayed, even up to several days or weeks after the user clicks. This phenomenon leads to the crucial challenge calleddelayed feedback problem in streaming CVR prediction, that is, the online systems cannot receive the true label of conversions immediately for continuous training. To mitigate the delayed feedback problem, recent state-of-the-art methods often apply sample duplicate mechanisms to introduce early certain conversion information. Nevertheless, these works have overlooked a crucial issue of rapid shifts in data distribution and considered both the newly observed data and duplicated early data together, resulting in biases in both distributions. In this work, we propose a Dually enhanced Delayed Feedback Model (DDFM), which tackles the above issues by treating the newly observed data and duplicated early data separately. DDFM consists of dual unbiased CVR estimators that share the same form but utilize different latent variables as weights: one for the newly observed data and the other for the duplicated early data. To avoid high variance, we adopt an addition-only formula for these latent variables, eliminating multiplication or division operations. Furthermore, we design a shared-bottom network that efficiently and jointly estimates the latent variables in DDFM. Theoretical analysis demonstrates the unbiasedness and convergence properties of DDFM. Extensive experiments on both public and industrial large-scale real-world datasets exhibit that our proposed DDFM consistently outperforms existing state-of-the-art methods.|在在线工业广告系统中，转换操作(例如购买或下载)经常出现明显延迟，甚至在用户点击后几天或几周。这种现象导致了流式 CVR 预测中的关键问题——延迟反馈问题，即在线系统不能立即得到连续训练所需的转换的真实标签。为了缓解延迟反馈问题，最新的技术方法通常应用样本重复机制来引入早期确定的转换信息。然而，这些工作忽略了数据分布快速变化的关键问题，同时考虑了新观测数据和重复的早期数据，导致两种分布的偏差。在这项工作中，我们提出了一个双增强延迟反馈模型(DDFM) ，它通过分别处理新观测数据和重复早期数据来解决上述问题。DDFM 由双无偏 CVR 估计器组成，它们具有相同的形式，但利用不同的潜变量作为权值: 一个用于新观测数据，另一个用于重复的早期数据。为了避免高方差，我们对这些潜在变量采用一个只加的公式，消除了乘法或除法运算。此外，我们还设计了一个共享底层网络，可以有效地联合估计 DDFM 中的潜变量。理论分析证明了 DDFM 的无偏性和收敛性。在公共和工业大规模真实世界数据集上的大量实验表明，我们提出的 DDFM 始终优于现有的最先进的方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Dually+Enhanced+Delayed+Feedback+Modeling+for+Streaming+Conversion+Rate+Prediction)|0| |[Knowledge-Aware Cross-Semantic Alignment for Domain-Level Zero-Shot Recommendation](https://doi.org/10.1145/3583780.3614945)|Junji Jiang, Hongke Zhao, Ming He, Likang Wu, Kai Zhang, Jianping Fan|Tianjin University, Tianjin, China; University of Science and Technology of China & State Key Laboratory of Cognitive Intelligence, Hefei, China; AI Lab at Lenovo Research, Beijing, China|Recommendation systems have attracted attention from academia and industry due to their wide range of application scenarios. However, cold start remains a challenging problem limited by sparse user interactions. Some scholars propose to transfer the dense information from the source domain to the target domain through cross-domain recommendation, but most of the work assumes that there is a small amount of historical interaction in the target domain. However, this approach essentially presupposes the existence of at least some historical interaction within the target domain. In this paper, we focus on the domain-level zero-shot recommendation (DZSR) problem. To address the above challenges, we propose a knowledge-aware cross-semantic alignment (K-CSA) framework to learn transferable source domain semantic information. The motivation is to establish stable alignments of interests in different domains through class semantic descriptions (CSDs). Specifically, due to the lack of effective information in the target domain, we learn semantic representations of source and target domain items based on knowledge graphs. Moreover, we conduct multi-view K-means to extract item CSDs from the learned semantic representations. Further, K-CSA learns universal user CSDs through the designed multi-head self-attention. To facilitate the transference of user interest from the source domain to the target domain, we devise a cross-semantic contrastive learning strategy, grounded in the prototype distribution matrix. We conduct extensive experiments on several real-world cross-domain datasets, and the experimental results clearly demonstrate the superiority of our proposed K-CSA compared with other baselines.|推荐系统由于其广泛的应用场景吸引了学术界和工业界的注意。然而，受稀疏用户交互的限制，冷启动仍然是一个具有挑战性的问题。一些学者提出通过跨域推荐将密集信息从源域转移到目标域，但大多数工作假设目标域中存在少量的历史交互。然而，这种方法基本上是以目标域内至少存在一些历史交互为前提的。本文主要研究领域级零拍推荐(DZSR)问题。为了应对上述挑战，我们提出了一个知识感知跨语义比对(k-CSA)框架来学习可转移源域语义信息。其动机是通过类语义描述(CSD)在不同领域建立稳定的兴趣对齐。具体来说，由于目标领域缺乏有效的信息，我们基于知识图学习源和目标领域项目的语义表示。此外，我们还进行了多视图 K- 均值从学习的语义表征中提取项目 CSD。此外，K-CSA 通过设计的多头自我注意来学习通用用户 CSD。为了促进用户兴趣从源域向目标域的转移，我们设计了一种基于原型分布矩阵的跨语义对比学习策略。我们在几个实际的跨域数据集上进行了广泛的实验，实验结果清楚地表明了我们提出的 K-CSA 相对于其他基线的优越性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Knowledge-Aware+Cross-Semantic+Alignment+for+Domain-Level+Zero-Shot+Recommendation)|0| -|[Continuous Personalized Knowledge Tracing: Modeling Long-Term Learning in Online Environments](https://doi.org/10.1145/3583780.3614822)|Chunpai Wang, Shaghayegh Sahebi|University at Albany - SUNY, Albany, NY, USA; JPMorgan Chase & Co., New York, NY, USA|With the advance of online education systems, accessibility to learning materials has increased. In these systems, students can practice independently and learn from different learning materials over long periods of time. As a result, it is essential to trace students' knowledge states over long learning sequences while maintaining a personalized model of each individual student's progress. However, the existing deep learning-based knowledge tracing models are either not personalized or not tailored for handling long sequences. Handling long sequences are especially essential in the online education environments, in where models are preferred to be updated with the newly collected user data in a timely manner as students could acquire knowledge on each learning activity. In this paper, we propose a knowledge tracing model, Continuous Personalized Knowledge Tracing (CPKT), that can mimic the real-world long-term continuous learning scenario by incorporating a novel online model training paradigm that is suitable for the knowledge tracing problem. To achieve personalized knowledge tracing, we propose two model components: 1) personalized memory slots to maintain learner's knowledge in a lifelong manner, and 2) personalized user embeddings that help to accurately predict the individual responses, correctly detect the personalized knowledge acquisition and forgetting patterns, and better interpret and analyze the learner's progress. Additionally, we propose transition-aware stochastic shared embedding according to the learning transition matrix to regularize the online model training. Extensive experiments on four real-world datasets showcase the effectiveness and superiority of CPKT, especially for students with longer sequences.|随着在线教育系统的发展，获取学习材料的机会越来越多。在这些系统中，学生可以长时间独立练习和从不同的学习材料中学习。因此，在长时间的学习序列中追踪学生的知识状态，同时保持每个学生进步的个性化模型是至关重要的。然而，现有的基于深度学习的知识跟踪模型要么不个性化，要么不适合处理长序列。处理长序列在在线教育环境中尤为重要，在这种环境中，由于学生可以获得关于每项学习活动的知识，因此倾向于使用新收集的用户数据及时更新模型。在本文中，我们提出了一个知识跟踪模型，连续个性化知识跟踪(CPKT) ，它可以模拟真实世界的长期连续学习情景，通过引入一个新的在线模型训练范式，适合于知识跟踪问题。为了实现个性化的知识追踪，我们提出了两个模型组件: 1)个性化的记忆槽，以终身的方式维护学习者的知识; 2)个性化的用户嵌入，有助于准确预测个体的反应，正确检测个性化的知识获取和遗忘模式，并更好地解释和分析学习者的进步。此外，我们根据学习转移矩阵提出了具有过渡意识的随机共享嵌入，以规范在线模型训练。在四个实际数据集上的大量实验表明了 CPKT 的有效性和优越性，特别是对于序列较长的学生。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Continuous+Personalized+Knowledge+Tracing:+Modeling+Long-Term+Learning+in+Online+Environments)|0| +|[Continuous Personalized Knowledge Tracing: Modeling Long-Term Learning in Online Environments](https://doi.org/10.1145/3583780.3614822)|Chunpai Wang, Shaghayegh Sahebi|JPMorgan Chase & Co., New York, NY, USA; University at Albany - SUNY, Albany, NY, USA|With the advance of online education systems, accessibility to learning materials has increased. In these systems, students can practice independently and learn from different learning materials over long periods of time. As a result, it is essential to trace students' knowledge states over long learning sequences while maintaining a personalized model of each individual student's progress. However, the existing deep learning-based knowledge tracing models are either not personalized or not tailored for handling long sequences. Handling long sequences are especially essential in the online education environments, in where models are preferred to be updated with the newly collected user data in a timely manner as students could acquire knowledge on each learning activity. In this paper, we propose a knowledge tracing model, Continuous Personalized Knowledge Tracing (CPKT), that can mimic the real-world long-term continuous learning scenario by incorporating a novel online model training paradigm that is suitable for the knowledge tracing problem. To achieve personalized knowledge tracing, we propose two model components: 1) personalized memory slots to maintain learner's knowledge in a lifelong manner, and 2) personalized user embeddings that help to accurately predict the individual responses, correctly detect the personalized knowledge acquisition and forgetting patterns, and better interpret and analyze the learner's progress. Additionally, we propose transition-aware stochastic shared embedding according to the learning transition matrix to regularize the online model training. Extensive experiments on four real-world datasets showcase the effectiveness and superiority of CPKT, especially for students with longer sequences.|随着在线教育系统的发展，获取学习材料的机会越来越多。在这些系统中，学生可以长时间独立练习和从不同的学习材料中学习。因此，在长时间的学习序列中追踪学生的知识状态，同时保持每个学生进步的个性化模型是至关重要的。然而，现有的基于深度学习的知识跟踪模型要么不个性化，要么不适合处理长序列。处理长序列在在线教育环境中尤为重要，在这种环境中，由于学生可以获得关于每项学习活动的知识，因此倾向于使用新收集的用户数据及时更新模型。在本文中，我们提出了一个知识跟踪模型，连续个性化知识跟踪(CPKT) ，它可以模拟真实世界的长期连续学习情景，通过引入一个新的在线模型训练范式，适合于知识跟踪问题。为了实现个性化的知识追踪，我们提出了两个模型组件: 1)个性化的记忆槽，以终身的方式维护学习者的知识; 2)个性化的用户嵌入，有助于准确预测个体的反应，正确检测个性化的知识获取和遗忘模式，并更好地解释和分析学习者的进步。此外，我们根据学习转移矩阵提出了具有过渡意识的随机共享嵌入，以规范在线模型训练。在四个实际数据集上的大量实验表明了 CPKT 的有效性和优越性，特别是对于序列较长的学生。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Continuous+Personalized+Knowledge+Tracing:+Modeling+Long-Term+Learning+in+Online+Environments)|0| |[Modeling Preference as Weighted Distribution over Functions for User Cold-start Recommendation](https://doi.org/10.1145/3583780.3614972)|Jingxuan Wen, Huafeng Liu, Liping Jing|Beijing Jiaotong University, Beijing, China|=User cold-start recommendation is a well-known challenge in current recommender systems. The cause is that the number of user interactions is too few to accurately estimate user preferences. Furthermore, the uncertainty of user interactions intensifies along with the number of user interactions decreasing. Although existing meta-learning based models with globally sharing knowledge show good performance in most cold-start scenarios, the ability of handling challenges on intention importance and prediction uncertainty is missing: (1) Intra-user uncertainty. When estimating user preferences (reflected in the user's latent representation), each of user interactions is independently considered in the form of user-item pair, which cannot capture the correlation between user interactions, as well as considering the global intent under user interactions. (2) Inter-user importance. During the model training, all users are treated as equally important, which cannot distinguish the contribution of users in the model training process. Assigning the same weight to all users may lead to users with high uncertainty incorrectly guiding the model learning in the early stage of training. To tackle the above challenges, in this paper, we focus on modeling user preference as a weighted distribution over functions (WDoF) for user cold-start recommendation, which not only models the intra-user uncertainty through neural processes with Multinomial likelihood but also considers the importance of different users with curriculum learning during the model training process. Furthermore, we provide a theoretical explanation that why the proposed model performs better than regular neural processes based recommendation methods. Experiments on four real-world datasets demonstrate the effectiveness of the proposed model over several state-of-the-art cold-start recommendation methods.|= 用户冷启动推荐在当前的推荐系统中是一个众所周知的挑战。原因是用户交互的数量太少，无法准确估计用户偏好。此外，用户交互的不确定性随着用户交互次数的减少而增加。虽然现有的基于元学习的全局知识共享模型在大多数冷启动情景下表现出良好的性能，但缺乏处理意图重要性和预测不确定性挑战的能力: (1)用户内部的不确定性。在估计用户偏好时(反映在用户的潜在表示中) ，每个用户交互都以用户项对的形式独立考虑，不能捕获用户交互之间的相关性，也不能考虑用户交互下的全局意图。(2)用户之间的重要性。在模型训练过程中，所有用户都被视为同等重要的用户，不能区分用户在模型训练过程中的贡献。给所有用户赋予相同的权重可能会导致高不确定性用户在训练的早期阶段错误地指导模型学习。针对上述挑战，本文将用户偏好建模为用户冷启动推荐的加权函数分布(WDoF) ，不仅通过多项式似然的神经过程模拟用户内部的不确定性，而且在模型训练过程中考虑了不同用户课程学习的重要性。此外，我们提供了一个理论上的解释，为什么所提出的模型性能优于常规的神经过程为基础的推荐方法。在四个实际数据集上的实验结果表明了该模型对于几种最新的冷启动推荐方法的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Modeling+Preference+as+Weighted+Distribution+over+Functions+for+User+Cold-start+Recommendation)|0| -|[Multimodal Optimal Transport Knowledge Distillation for Cross-domain Recommendation](https://doi.org/10.1145/3583780.3614983)|Wei Yang, Jie Yang, Yuan Liu|Institute of Automation, Chinese Academy of Sciences, Beijing, China; Tencent Technology, Beijing, China|Recommendation systems have been widely used in e-commerce, news media, and short video platforms. With the abundance of images, text, and audio information, users often engage in personalized interactions based on their multimodal preferences. With the continuous expansion of application scenarios, cross domain recommendation issues have become important, such as recommendations in both the public and private domains of e-commerce. The current cross domain recommendation methods have achieved certain results through methods such as shared encoders and contrastive learning. However, few studies have focused on the effective extraction and utilization of multimodal information in cross domain recommendations. Furthermore, due to the existence of distribution drift issues, directly constructing feature alignment between source domain and target domain representations is not an effective way. Therefore, we propose a Multimodal Optimal Transport Knowledge Distillation (MOTKD) method for cross domain recommendation. Specifically, we propose a multimodal graph attention network to model the multimodal preference representation of users. Then, we introduce a proxy distribution space as a bridge between the source and target domains. Based on the common proxy distribution, we utilize the optimal transport method to achieve cross domain knowledge transfer. Further, in order to improve the auxiliary training effect of source domain supervised signals on target domain, we design a multi-level cross domain knowledge distillation module. We conducted extensive experiments on two pairs of cross domain datasets composed of four datasets. The experimental results indicate that our proposed MOTKD method outperforms other state-of-the-art models.|推荐系统已广泛应用于电子商务、新闻媒体和短视频平台。随着丰富的图像，文字和音频信息，用户往往从事个性化的交互基于他们的多模态偏好。随着应用场景的不断扩展，跨域推荐问题变得越来越重要，例如电子商务的公共和私人领域的推荐。现有的跨域推荐方法通过共享编码器和对比学习等方法取得了一定的效果。然而，很少有研究关注跨领域推荐中多模态信息的有效提取和利用。此外，由于分布漂移问题的存在，直接构造源域和目标域表示之间的特征对齐并不是一种有效的方法。因此，我们提出了一种跨域推荐的多模式最优运输知识提取(MOTKD)方法。具体来说，我们提出了一个多模态图注意网络来模拟用户的多模态偏好表示。然后，引入一个代理分布空间作为源域和目标域之间的桥梁。在公共代理分布的基础上，利用最优传输方法实现跨领域的知识转移。进一步，为了提高源域监督信号对目标域的辅助训练效果，我们设计了一个多级跨域知识提取模块。我们对由四个数据集组成的两对跨域数据集进行了广泛的实验。实验结果表明，我们提出的 MOTKD 方法优于其他最先进的模型。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Multimodal+Optimal+Transport+Knowledge+Distillation+for+Cross-domain+Recommendation)|0| +|[Multimodal Optimal Transport Knowledge Distillation for Cross-domain Recommendation](https://doi.org/10.1145/3583780.3614983)|Wei Yang, Jie Yang, Yuan Liu|Tencent Technology, Beijing, China; Institute of Automation, Chinese Academy of Sciences, Beijing, China|Recommendation systems have been widely used in e-commerce, news media, and short video platforms. With the abundance of images, text, and audio information, users often engage in personalized interactions based on their multimodal preferences. With the continuous expansion of application scenarios, cross domain recommendation issues have become important, such as recommendations in both the public and private domains of e-commerce. The current cross domain recommendation methods have achieved certain results through methods such as shared encoders and contrastive learning. However, few studies have focused on the effective extraction and utilization of multimodal information in cross domain recommendations. Furthermore, due to the existence of distribution drift issues, directly constructing feature alignment between source domain and target domain representations is not an effective way. Therefore, we propose a Multimodal Optimal Transport Knowledge Distillation (MOTKD) method for cross domain recommendation. Specifically, we propose a multimodal graph attention network to model the multimodal preference representation of users. Then, we introduce a proxy distribution space as a bridge between the source and target domains. Based on the common proxy distribution, we utilize the optimal transport method to achieve cross domain knowledge transfer. Further, in order to improve the auxiliary training effect of source domain supervised signals on target domain, we design a multi-level cross domain knowledge distillation module. We conducted extensive experiments on two pairs of cross domain datasets composed of four datasets. The experimental results indicate that our proposed MOTKD method outperforms other state-of-the-art models.|推荐系统已广泛应用于电子商务、新闻媒体和短视频平台。随着丰富的图像，文字和音频信息，用户往往从事个性化的交互基于他们的多模态偏好。随着应用场景的不断扩展，跨域推荐问题变得越来越重要，例如电子商务的公共和私人领域的推荐。现有的跨域推荐方法通过共享编码器和对比学习等方法取得了一定的效果。然而，很少有研究关注跨领域推荐中多模态信息的有效提取和利用。此外，由于分布漂移问题的存在，直接构造源域和目标域表示之间的特征对齐并不是一种有效的方法。因此，我们提出了一种跨域推荐的多模式最优运输知识提取(MOTKD)方法。具体来说，我们提出了一个多模态图注意网络来模拟用户的多模态偏好表示。然后，引入一个代理分布空间作为源域和目标域之间的桥梁。在公共代理分布的基础上，利用最优传输方法实现跨领域的知识转移。进一步，为了提高源域监督信号对目标域的辅助训练效果，我们设计了一个多级跨域知识提取模块。我们对由四个数据集组成的两对跨域数据集进行了广泛的实验。实验结果表明，我们提出的 MOTKD 方法优于其他最先进的模型。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Multimodal+Optimal+Transport+Knowledge+Distillation+for+Cross-domain+Recommendation)|0| |[Task-Difficulty-Aware Meta-Learning with Adaptive Update Strategies for User Cold-Start Recommendation](https://doi.org/10.1145/3583780.3615074)|Xuhao Zhao, Yanmin Zhu, Chunyang Wang, Mengyuan Jing, Jiadi Yu, Feilong Tang|Shanghai Jiao Tong University, Shanghai, China|User cold-start recommendation is one of the most challenging problems that limit the effectiveness of recommender systems. Meta-learning-based methods are introduced to address this problem by learning initialization parameters for cold-start tasks. Recent studies attempt to enhance the initialization methods. They first represent each task by the cold-start user and interacted items. Then they distinguish tasks based on the task relevance to learn adaptive initialization. However, this manner is based on the assumption that user preferences can be reflected by the interacted items saliently, which is not always true in reality. In addition, we argue that previous approaches suffer from their adaptive framework (e.g., adaptive initialization), which reduces the adaptability in the process of transferring meta-knowledge to personalized RSs. In response to the issues, we propose a task-difficulty-aware meta-learning with adaptive update strategies (TDAS) for user cold-start recommendation. First, we design a task difficulty encoder, which can represent user preference salience, task relevance, and other task characteristics by modeling task difficulty information. Second, we adopt a novel framework with task-adaptive local update strategies by optimizing the initialization parameters with task-adaptive per-step and per-layer hyperparameters. Extensive experiments based on three real-world datasets demonstrate that our TDAS outperforms the state-of-the-art methods. The source code is available at https://github.com/XuHao-bit/TDAS.|用户冷启动推荐是限制推荐系统有效性的最具挑战性的问题之一。为了解决这个问题，引入了基于元学习的方法，学习冷启动任务的初始化参数。最近的研究试图改进初始化方法。它们首先由冷启动用户和交互项表示每个任务。然后根据任务相关性区分任务，学习自适应初始化。然而，这种方式是基于这样的假设，即用户的偏好可以通过交互的项显著地反映出来，这在现实中并不总是正确的。此外，我们认为以前的方法受到其自适应框架(例如，自适应初始化)的影响，这降低了元知识向个性化 RSS 传输过程中的适应性。针对这些问题，我们提出了一种基于任务难度感知的元学习算法，该算法采用自适应更新策略(TDAS)对用户进行冷启动推荐。首先，我们设计了一个任务难度编码器，通过对任务难度信息进行建模来表示用户偏好显著性、任务相关性等任务特征。其次，采用任务自适应局部更新策略，通过每步任务自适应和每层超参数对初始化参数进行优化，提出了一种新的任务自适应局部更新策略框架。基于三个真实世界数据集的大量实验表明，我们的 TDAS 优于最先进的方法。源代码可在 https://github.com/xuhao-bit/tdas 下载。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Task-Difficulty-Aware+Meta-Learning+with+Adaptive+Update+Strategies+for+User+Cold-Start+Recommendation)|0| -|[Retrievability Bias Estimation Using Synthetically Generated Queries](https://doi.org/10.1145/3583780.3615221)|Amin Abolghasemi, Suzan Verberne, Arian Askari, Leif Azzopardi|University of Strathclyde, Glasgow, United Kingdom; Leiden University, Leiden, Netherlands|Ranking with pre-trained language models (PLMs) has shown to be highly effective for various Information Retrieval tasks. Previous studies investigated the performance of these models in terms of effectiveness and efficiency. However, there is no prior work on evaluating PLM-based rankers in terms of their retrievability bias. In this paper, we evaluate the retrievability bias of PLM-based rankers with the use of synthetically generated queries. We compare the retrievability bias in two of the most common PLM-based rankers, a Bi-Encoder BERT ranker and a Cross-Encoder BERT re-ranker against BM25, which was found to be one of the least biased models in prior work. We conduct a series of experiments with which we explore the plausibility of using synthetic queries generated with a generative model, docT5query, in the evaluation of retrievability bias. Our experiments show promising results on the use of synthetically generated queries for the purpose of retrievability bias estimation. Moreover, we find that the estimated bias values resulting from synthetically generated queries are lower than the ones estimated with user-generated queries on the MS MARCO evaluation benchmark. This indicates that synthetically generated queries might cause less bias than user-generated queries and therefore, by using such queries in training PLM-based rankers, we might be able to reduce the retrievability bias in these models.|使用预先训练好的语言模型(PLM)进行排名已被证明对于各种信息检索任务非常有效。以往的研究从效能和效率的角度研究了这些模型的性能。然而，目前还没有关于评估基于 PLM 的排名在他们的检索偏差方面的工作。本文利用综合生成的查询来评估基于 PLM 的排序器的可检索性偏差。我们比较了两种最常见的基于 PLM 的排序器，双编码器 BERT 排序器和交叉编码器 BERT 重排序器对 BM25的可检索性偏差，这被发现是先前工作中偏差最小的模型之一。我们进行了一系列的实验，通过这些实验，我们探索了使用一个名为 doct5 query 的生成模型生成的合成查询来评估可检索性偏差的可行性。实验结果表明，综合生成的查询用于可检索性偏差估计具有良好的效果。此外，我们发现在 MS MARCO 评估基准上，由合成生成查询所得到的估计偏差值低于由用户生成查询所得到的估计偏差值。这表明综合生成的查询可能比用户生成的查询引起的偏差更小，因此，通过在训练基于 PLM 的排名中使用这样的查询，我们可能能够减少这些模型中的可检索性偏差。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Retrievability+Bias+Estimation+Using+Synthetically+Generated+Queries)|0| +|[Retrievability Bias Estimation Using Synthetically Generated Queries](https://doi.org/10.1145/3583780.3615221)|Amin Abolghasemi, Suzan Verberne, Arian Askari, Leif Azzopardi|Leiden University, Leiden, Netherlands; University of Strathclyde, Glasgow, United Kingdom|Ranking with pre-trained language models (PLMs) has shown to be highly effective for various Information Retrieval tasks. Previous studies investigated the performance of these models in terms of effectiveness and efficiency. However, there is no prior work on evaluating PLM-based rankers in terms of their retrievability bias. In this paper, we evaluate the retrievability bias of PLM-based rankers with the use of synthetically generated queries. We compare the retrievability bias in two of the most common PLM-based rankers, a Bi-Encoder BERT ranker and a Cross-Encoder BERT re-ranker against BM25, which was found to be one of the least biased models in prior work. We conduct a series of experiments with which we explore the plausibility of using synthetic queries generated with a generative model, docT5query, in the evaluation of retrievability bias. Our experiments show promising results on the use of synthetically generated queries for the purpose of retrievability bias estimation. Moreover, we find that the estimated bias values resulting from synthetically generated queries are lower than the ones estimated with user-generated queries on the MS MARCO evaluation benchmark. This indicates that synthetically generated queries might cause less bias than user-generated queries and therefore, by using such queries in training PLM-based rankers, we might be able to reduce the retrievability bias in these models.|使用预先训练好的语言模型(PLM)进行排名已被证明对于各种信息检索任务非常有效。以往的研究从效能和效率的角度研究了这些模型的性能。然而，目前还没有关于评估基于 PLM 的排名在他们的检索偏差方面的工作。本文利用综合生成的查询来评估基于 PLM 的排序器的可检索性偏差。我们比较了两种最常见的基于 PLM 的排序器，双编码器 BERT 排序器和交叉编码器 BERT 重排序器对 BM25的可检索性偏差，这被发现是先前工作中偏差最小的模型之一。我们进行了一系列的实验，通过这些实验，我们探索了使用一个名为 doct5 query 的生成模型生成的合成查询来评估可检索性偏差的可行性。实验结果表明，综合生成的查询用于可检索性偏差估计具有良好的效果。此外，我们发现在 MS MARCO 评估基准上，由合成生成查询所得到的估计偏差值低于由用户生成查询所得到的估计偏差值。这表明综合生成的查询可能比用户生成的查询引起的偏差更小，因此，通过在训练基于 PLM 的排名中使用这样的查询，我们可能能够减少这些模型中的可检索性偏差。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Retrievability+Bias+Estimation+Using+Synthetically+Generated+Queries)|0| |[On the Reliability of User Feedback for Evaluating the Quality of Conversational Agents](https://doi.org/10.1145/3583780.3615286)|Jordan Massiah, Emine Yilmaz, Yunlong Jiao, Gabriella Kazai|Amazon & University College London, London, United Kingdom; Amazon, London, United Kingdom|We analyse the reliability of users' explicit feedback for evaluating the quality of conversational agents. Using data from a commercial conversational system, we analyse how user feedback compares with human annotations; how well it aligns with implicit user satisfaction signals, such as retention; and how much user feedback is needed to reliably evaluate the quality of a conversational system.|我们分析了用户显性反馈对于评价会话代理质量的可靠性。使用来自商业会话系统的数据，我们分析了用户反馈与人工注释的比较; 它与隐含的用户满意信号(如保留)的匹配程度; 以及需要多少用户反馈才能可靠地评估会话系统的质量。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=On+the+Reliability+of+User+Feedback+for+Evaluating+the+Quality+of+Conversational+Agents)|0| -|[SeqGen: A Sequence Generator via User Side Information for Behavior Sparsity in Recommendation](https://doi.org/10.1145/3583780.3615244)|Xu Min, Xiaolu Zhang, Bin Shen, Shuhan Wang, Yong He, Changsheng Li, Jun Zhou|Ant Group, Hangzhou, China; Ant Group, Beijing, China; Beijing Institute of Technology, Beijing, China|In real-world industrial advertising systems, user behavior sparsity is a key issue that affects online recommendation performance. We observe that users with rich behaviors can obtain better recommendation results than those with sparse behaviors in a conversion-rate (CVR) prediction model. Inspired by this phenomenon, we propose a new method SeqGen, in an effort to exploit user side information to bridge the gap between rich and sparse behaviors. SeqGen is a learnable and pluggable module, which can be easily integrated into any CVR model and no longer requires two-stage training as in previous works. In particular, SeqGen learns a mapping relationship between the user side information and behavior sequences, only on the basis of the users with long behavior sequences. After that, SeqGen can generate rich sequence features for users with sparse behaviors based on their side information, so as to alleviate the issue of user behavior sparsity. The generated sequence features will then be fed into the classifier tower of an arbitrary CVR model together with the original sequence features. To the best of our knowledge, our approach constitutes the first attempt to exploit user side information for addressing the user behavior sparsity issue. We validate the effectiveness of SeqGen on the publicly available dataset MovieLens-1M, and our method receives an improvement of up to 0.5% in terms of the AUC score. More importantly, we successfully deploy SeqGen in the commercial advertising system Xlight of Alipay, which improves the grouped AUC of the CVR model by 0.6% and brings a boost of 0.49% in terms of the conversion rate on A/B testing.|在现实工业广告系统中，用户行为稀疏性是影响在线推荐性能的关键问题。在 CVR 预测模型中，我们观察到行为丰富的用户比行为稀疏的用户获得更好的推荐结果。受到这一现象的启发，我们提出了一种新的方法 SeqGen，尝试利用用户端信息来弥补丰富和稀疏行为之间的差距。SeqGen 是一个可学习和可插拔的模块，它可以很容易地集成到任何 CVR 模型中，不再像以前的作品那样需要两阶段的培训。特别是，SeqGen 只在具有长行为序列的用户的基础上学习用户端信息和行为序列之间的映射关系。然后，SeqGen 可以根据用户的侧信息为稀疏行为的用户生成丰富的序列特征，从而缓解用户行为稀疏的问题。然后将生成的序列特征与原始序列特征一起反馈到任意 CVR 模型的分类器塔中。据我们所知，我们的方法是第一次尝试利用用户端信息来解决用户行为稀疏性问题。我们验证了 SeqGen 在公开数据集 MovieLens-1M 上的有效性，并且我们的方法在 AUC 评分方面获得了高达0.5% 的改善。更重要的是，我们成功地在支付宝的商业广告系统 Xlight 中部署了 SeqGen，它将 CVR 模型的分组 AUC 提高了0.6% ，在 A/B 测试中的转换率提高了0.49% 。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=SeqGen:+A+Sequence+Generator+via+User+Side+Information+for+Behavior+Sparsity+in+Recommendation)|0| +|[SeqGen: A Sequence Generator via User Side Information for Behavior Sparsity in Recommendation](https://doi.org/10.1145/3583780.3615244)|Xu Min, Xiaolu Zhang, Bin Shen, Shuhan Wang, Yong He, Changsheng Li, Jun Zhou|Beijing Institute of Technology, Beijing, China; Ant Group, Hangzhou, China; Ant Group, Beijing, China|In real-world industrial advertising systems, user behavior sparsity is a key issue that affects online recommendation performance. We observe that users with rich behaviors can obtain better recommendation results than those with sparse behaviors in a conversion-rate (CVR) prediction model. Inspired by this phenomenon, we propose a new method SeqGen, in an effort to exploit user side information to bridge the gap between rich and sparse behaviors. SeqGen is a learnable and pluggable module, which can be easily integrated into any CVR model and no longer requires two-stage training as in previous works. In particular, SeqGen learns a mapping relationship between the user side information and behavior sequences, only on the basis of the users with long behavior sequences. After that, SeqGen can generate rich sequence features for users with sparse behaviors based on their side information, so as to alleviate the issue of user behavior sparsity. The generated sequence features will then be fed into the classifier tower of an arbitrary CVR model together with the original sequence features. To the best of our knowledge, our approach constitutes the first attempt to exploit user side information for addressing the user behavior sparsity issue. We validate the effectiveness of SeqGen on the publicly available dataset MovieLens-1M, and our method receives an improvement of up to 0.5% in terms of the AUC score. More importantly, we successfully deploy SeqGen in the commercial advertising system Xlight of Alipay, which improves the grouped AUC of the CVR model by 0.6% and brings a boost of 0.49% in terms of the conversion rate on A/B testing.|在现实工业广告系统中，用户行为稀疏性是影响在线推荐性能的关键问题。在 CVR 预测模型中，我们观察到行为丰富的用户比行为稀疏的用户获得更好的推荐结果。受到这一现象的启发，我们提出了一种新的方法 SeqGen，尝试利用用户端信息来弥补丰富和稀疏行为之间的差距。SeqGen 是一个可学习和可插拔的模块，它可以很容易地集成到任何 CVR 模型中，不再像以前的作品那样需要两阶段的培训。特别是，SeqGen 只在具有长行为序列的用户的基础上学习用户端信息和行为序列之间的映射关系。然后，SeqGen 可以根据用户的侧信息为稀疏行为的用户生成丰富的序列特征，从而缓解用户行为稀疏的问题。然后将生成的序列特征与原始序列特征一起反馈到任意 CVR 模型的分类器塔中。据我们所知，我们的方法是第一次尝试利用用户端信息来解决用户行为稀疏性问题。我们验证了 SeqGen 在公开数据集 MovieLens-1M 上的有效性，并且我们的方法在 AUC 评分方面获得了高达0.5% 的改善。更重要的是，我们成功地在支付宝的商业广告系统 Xlight 中部署了 SeqGen，它将 CVR 模型的分组 AUC 提高了0.6% ，在 A/B 测试中的转换率提高了0.49% 。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=SeqGen:+A+Sequence+Generator+via+User+Side+Information+for+Behavior+Sparsity+in+Recommendation)|0| |[Beyond Semantics: Learning a Behavior Augmented Relevance Model with Self-supervised Learning](https://doi.org/10.1145/3583780.3615457)|Zeyuan Chen, Wei Chen, Jia Xu, Zhongyi Liu, Wei Zhang|Ant Group, Hangzhou, China; East China Normal University, Shanghai, China|Relevance modeling aims to locate desirable items for corresponding queries, which is crucial for search engines to ensure user experience. Although most conventional approaches address this problem by assessing the semantic similarity between the query and item, pure semantic matching is not everything. In reality, auxiliary query-item interactions extracted from user historical behavior data of the search log could provide hints to reveal users' search intents further. Drawing inspiration from this, we devise a novel Behavior Augmented Relevance Learning model for Alipay Search (BARL-ASe) that leverages neighbor queries of target item and neighbor items of target query to complement target query-item semantic matching. Specifically, our model builds multi-level co-attention for distilling coarse-grained and fine-grained semantic representations from both neighbor and target views. The model subsequently employs neighbor-target self-supervised learning to improve the accuracy and robustness of BARL-ASe by strengthening representation and logit learning. Furthermore, we discuss how to deal with the long-tail query-item matching of the mini apps search scenario of Alipay practically. Experiments on real-world industry data and online A/B testing demonstrate our proposal achieves promising performance with low latency.|相关性建模的目的是为相应的查询定位合适的条目，这对于搜索引擎保证用户体验是至关重要的。尽管大多数传统的方法都是通过评估查询和条目之间的语义相似性来解决这个问题，但是纯语义匹配并不是一切。实际上，从搜索日志的用户历史行为数据中提取的辅助查询项交互可以为进一步揭示用户的搜索意图提供提示。基于此，我们设计了一种新的支付宝搜索行为增强关联学习模型(BARL-ASE) ，该模型利用目标项的邻居查询和目标查询的邻居查询来补充目标查询项语义匹配。具体来说，我们的模型建立了多级共注意，从邻居视图和目标视图中提取粗粒度和细粒度的语义表示。该模型随后采用邻居-目标自监督学习，通过加强表示和逻辑学习来提高 BARL-ASE 的准确性和鲁棒性。此外，本文还讨论了如何实际处理支付宝迷你应用搜索场景中的长尾查询项匹配问题。在现实工业数据和在线 A/B 测试中的实验表明，该方案具有良好的性能和较低的延迟。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Beyond+Semantics:+Learning+a+Behavior+Augmented+Relevance+Model+with+Self-supervised+Learning)|0| -|[Entire Space Cascade Delayed Feedback Modeling for Effective Conversion Rate Prediction](https://doi.org/10.1145/3583780.3615475)|Yunfeng Zhao, Xu Yan, Xiaoqiang Gui, Shuguang Han, XiangRong Sheng, Guoxian Yu, Jufeng Chen, Zhao Xu, Bo Zheng|School of Software, Shandong University, Jinan, China; Alibaba Group, Hangzhou, China|Conversion rate (CVR) prediction is an essential task for large-scale e-commerce platforms. However, refund behaviors frequently occur after conversion in online shopping systems, which drives us to pay attention to effective conversion for building healthier shopping services. This paper defines the probability of item purchasing without any subsequent refund as an effective conversion rate (ECVR). A simple paradigm for ECVR prediction is to decompose it into two sub-tasks: CVR prediction and post-conversion refund rate (RFR) prediction. However, RFR prediction suffers from data sparsity (DS) and sample selection bias (SSB) issues, as the refund behaviors are only available after user purchase. Furthermore, there is delayed feedback in both conversion and refund events and they are sequentially dependent, named cascade delayed feedback (CDF), which significantly harms data freshness for model training. Previous studies mainly focus on tackling DS and SSB or delayed feedback for a single event. To jointly tackle these issues in ECVR prediction, we propose an Entire space CAscade Delayed feedback modeling (ECAD) method. Specifically, ECAD deals with DS and SSB by constructing two tasks including CVR prediction and conversion \& refund rate (CVRFR) prediction using the entire space modeling framework. In addition, it carefully schedules auxiliary tasks to leverage both conversion and refund time within data to alleviate CDF. Experimental results on the offline industrial dataset and online A/B testing demonstrate the effectiveness of ECAD. In addition, ECAD has been deployed in one of the recommender systems in Alibaba, contributing to a significant improvement of ECVR.|转化率(CVR)预测是大型电子商务平台的一项基本任务。然而，在网上购物系统中，退款行为经常发生在转换之后，这促使我们关注有效的转换以建立更健康的购物服务。本文将不随后退款的物品购买概率定义为有效转换率(ECVR)。ECVR 预测的一个简单范例是将其分解为两个子任务: CVR 预测和转换后退款率(RFR)预测。然而，RFR 预测存在数据稀疏(DS)和样本选择偏差(SSB)问题，因为退款行为只能在用户购买之后才能得到。此外，在转换事件和退款事件中都存在延迟反馈，并且它们是相互依赖的，称为级联延迟反馈(CDF) ，这严重损害了模型训练的数据新鲜度。以往的研究主要集中在处理 DS 和 SSB 或单个事件的延迟反馈。为了共同解决 ECVR 预测中的这些问题，我们提出了一种全空间级联延迟反馈建模(ECAD)方法。具体来说，ECAD 利用整个空间建模框架构造了 CVR 预测和转换退款率(CVRFR)预测两个任务来处理 DS 和 SSB。此外，它仔细地安排辅助任务，以利用数据中的转换和退款时间来缓解 CDF。离线工业数据集和在线 A/B 测试的实验结果证明了 ECAD 的有效性。此外，阿里巴巴其中一个推荐系统已采用 ECAD，有助显著改善 ECVR。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Entire+Space+Cascade+Delayed+Feedback+Modeling+for+Effective+Conversion+Rate+Prediction)|0| +|[Entire Space Cascade Delayed Feedback Modeling for Effective Conversion Rate Prediction](https://doi.org/10.1145/3583780.3615475)|Yunfeng Zhao, Xu Yan, Xiaoqiang Gui, Shuguang Han, XiangRong Sheng, Guoxian Yu, Jufeng Chen, Zhao Xu, Bo Zheng|Alibaba Group, Hangzhou, China; School of Software, Shandong University, Jinan, China|Conversion rate (CVR) prediction is an essential task for large-scale e-commerce platforms. However, refund behaviors frequently occur after conversion in online shopping systems, which drives us to pay attention to effective conversion for building healthier shopping services. This paper defines the probability of item purchasing without any subsequent refund as an effective conversion rate (ECVR). A simple paradigm for ECVR prediction is to decompose it into two sub-tasks: CVR prediction and post-conversion refund rate (RFR) prediction. However, RFR prediction suffers from data sparsity (DS) and sample selection bias (SSB) issues, as the refund behaviors are only available after user purchase. Furthermore, there is delayed feedback in both conversion and refund events and they are sequentially dependent, named cascade delayed feedback (CDF), which significantly harms data freshness for model training. Previous studies mainly focus on tackling DS and SSB or delayed feedback for a single event. To jointly tackle these issues in ECVR prediction, we propose an Entire space CAscade Delayed feedback modeling (ECAD) method. Specifically, ECAD deals with DS and SSB by constructing two tasks including CVR prediction and conversion \& refund rate (CVRFR) prediction using the entire space modeling framework. In addition, it carefully schedules auxiliary tasks to leverage both conversion and refund time within data to alleviate CDF. Experimental results on the offline industrial dataset and online A/B testing demonstrate the effectiveness of ECAD. In addition, ECAD has been deployed in one of the recommender systems in Alibaba, contributing to a significant improvement of ECVR.|转化率(CVR)预测是大型电子商务平台的一项基本任务。然而，在网上购物系统中，退款行为经常发生在转换之后，这促使我们关注有效的转换以建立更健康的购物服务。本文将不随后退款的物品购买概率定义为有效转换率(ECVR)。ECVR 预测的一个简单范例是将其分解为两个子任务: CVR 预测和转换后退款率(RFR)预测。然而，RFR 预测存在数据稀疏(DS)和样本选择偏差(SSB)问题，因为退款行为只能在用户购买之后才能得到。此外，在转换事件和退款事件中都存在延迟反馈，并且它们是相互依赖的，称为级联延迟反馈(CDF) ，这严重损害了模型训练的数据新鲜度。以往的研究主要集中在处理 DS 和 SSB 或单个事件的延迟反馈。为了共同解决 ECVR 预测中的这些问题，我们提出了一种全空间级联延迟反馈建模(ECAD)方法。具体来说，ECAD 利用整个空间建模框架构造了 CVR 预测和转换退款率(CVRFR)预测两个任务来处理 DS 和 SSB。此外，它仔细地安排辅助任务，以利用数据中的转换和退款时间来缓解 CDF。离线工业数据集和在线 A/B 测试的实验结果证明了 ECAD 的有效性。此外，阿里巴巴其中一个推荐系统已采用 ECAD，有助显著改善 ECVR。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Entire+Space+Cascade+Delayed+Feedback+Modeling+for+Effective+Conversion+Rate+Prediction)|0| |[MUSER: A Multi-View Similar Case Retrieval Dataset](https://doi.org/10.1145/3583780.3615125)|Qingquan Li, Yiran Hu, Feng Yao, Chaojun Xiao, Zhiyuan Liu, Maosong Sun, Weixing Shen|Tsinghua University, Beijing, China|Similar case retrieval (SCR) is a representative legal AI application that plays a pivotal role in promoting judicial fairness. However, existing SCR datasets only focus on the fact description section when judging the similarity between cases, ignoring other valuable sections (e.g., the court's opinion) that can provide insightful reasoning process behind. Furthermore, the case similarities are typically measured solely by the textual semantics of the fact descriptions, which may fail to capture the full complexity of legal cases from the perspective of legal knowledge. In this work, we present MUSER, a similar case retrieval dataset based on multi-view similarity measurement and comprehensive legal element with sentence-level legal element annotations. Specifically, we select three perspectives (legal fact, dispute focus, and law statutory) and build a comprehensive and structured label schema of legal elements for each of them, to enable accurate and knowledgeable evaluation of case similarities. The constructed dataset originates from Chinese civil cases and contains 100 query cases and 4,024 candidate cases. We implement several text classification algorithms for legal element prediction and various retrieval methods for retrieving similar cases on MUSER. The experimental results indicate that incorporating legal elements can benefit the performance of SCR models, but further efforts are still required to address the remaining challenges posed by MUSER. The source code and dataset are released at https://github.com/THUlawtech/MUSER.|类似案件检索(SCR)是一种具有代表性的法律人工智能应用，对促进司法公正起着举足轻重的作用。然而，现有的 SCR 数据集只集中在事实描述部分，当判断案件之间的相似性时，忽略了其他有价值的部分(例如，法院的意见) ，可以提供深刻的推理过程背后。此外，案件的相似性通常仅通过事实描述的文本语义来衡量，这可能无法从法律知识的角度捕捉到法律案件的全部复杂性。本文提出了一个基于多视图相似度量和综合法律要素的相似案例检索数据集 MUSER。具体来说，我们选取法律事实、争议焦点和法律成文法三个视角，为每一个视角建立一个全面而结构化的法律要素标签模式，以便能够准确而知识化地评价案件的相似性。所构建的数据集来源于中国民事案件，包含100个查询案件和4024个候选案件。在 MUSER 上实现了法律元素预测的文本分类算法和相似案例检索的各种检索方法。实验结果表明，纳入法律因素有利于可持续性研究模型的性能，但仍需作出进一步努力，以解决 MUSER 提出的其余挑战。源代码和数据集在 https://github.com/thulawtech/muser 发布。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=MUSER:+A+Multi-View+Similar+Case+Retrieval+Dataset)|0| |[A Multi-Task Semantic Decomposition Framework with Task-specific Pre-training for Few-Shot NER](https://doi.org/10.1145/3583780.3614766)|Guanting Dong, Zechen Wang, Jinxu Zhao, Gang Zhao, Daichi Guo, Dayuan Fu, Tingfeng Hui, Chen Zeng, Keqing He, Xuefeng Li, Liwen Wang, Xinyue Cui, Weiran Xu|Beijing University of Posts and Telecommunications, Beijing, China; Meituan Group, Beijing, China|The objective of few-shot named entity recognition is to identify named entities with limited labeled instances. Previous works have primarily focused on optimizing the traditional token-wise classification framework, while neglecting the exploration of information based on NER data characteristics. To address this issue, we propose a Multi-Task Semantic Decomposition Framework via Joint Task-specific Pre-training (MSDP) for few-shot NER. Drawing inspiration from demonstration-based and contrastive learning, we introduce two novel pre-training tasks: Demonstration-based Masked Language Modeling (MLM) and Class Contrastive Discrimination. These tasks effectively incorporate entity boundary information and enhance entity representation in Pre-trained Language Models (PLMs). In the downstream main task, we introduce a multi-task joint optimization framework with the semantic decomposing method, which facilitates the model to integrate two different semantic information for entity classification. Experimental results of two few-shot NER benchmarks demonstrate that MSDP consistently outperforms strong baselines by a large margin. Extensive analyses validate the effectiveness and generalization of MSDP.|少镜头命名实体识别的目标是识别具有有限标记实例的命名实体。以往的工作主要集中在优化传统的标记分类框架，而忽视了基于 NER 数据特征的信息探索。针对这一问题，提出了一种基于联合任务特定预训练(MSDP)的多任务语义分解框架。借鉴基于演示和对比学习的方法，我们介绍了两种新颖的预训练任务: 基于演示的掩蔽语言建模(MLM)和类别对比鉴别。这些任务有效地整合了实体边界信息，增强了预训练语言模型(PLM)中的实体表示。在下游的主要任务中，我们引入了一个基于语义分解的多任务联合优化框架，该框架有利于模型集成两个不同的语义信息进行实体分类。两个短镜头 NER 基准测试的实验结果表明，MSDP 的性能始终大大优于强基准测试。广泛的分析验证了 MSDP 的有效性和推广性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Multi-Task+Semantic+Decomposition+Framework+with+Task-specific+Pre-training+for+Few-Shot+NER)|0| |[CLSPRec: Contrastive Learning of Long and Short-term Preferences for Next POI Recommendation](https://doi.org/10.1145/3583780.3614813)|Chenghua Duan, Wei Fan, Wei Zhou, Hu Liu, Junhao Wen|Chongqing University, Chongqing, China|Next point-of-interest (POI) recommendation optimizes user travel experiences and enhances platform revenues by providing users with potentially appealing next location choices. In recent research, scholars have successfully mined users' general tastes and varying interests by modeling long-term and short-term check-in sequences. However, conventional methods for long and short-term modeling predominantly employ distinct encoders to process long and short-term interaction data independently, with disparities in encoders and data limiting the ultimate performance of these models. Instead, we propose a shared trajectory encoder and a novel Contrastive learning of Long and Short-term Preferences for next POI Recommendation (CLSPRec) model to better utilize the preference similarity among the same users and distinguish different users' travel preferences for more accurate next POI prediction. CLSPRec adopts a masking strategy in long-term sequences to enhance model robustness and further strengthens user representation through short-term sequences. Extensive experiments on three real-world datasets validate the superiority of our model. Our code is publicly available at https://github.com/Wonderdch/CLSPRec.|下一个兴趣点(Next Point-of-interest，POI)推荐通过为用户提供具有潜在吸引力的下一个地点选择，优化了用户的旅游体验，增强了平台收入。在最近的研究中，学者们通过建立长期和短期的签入序列模型，成功地挖掘了用户的一般品味和不同的兴趣。然而，用于长期和短期建模的传统方法主要使用不同的编码器来独立处理长期和短期的交互数据，编码器和数据的差异限制了这些模型的最终性能。相反，我们提出了一个共享的轨迹编码器和一个新的下一个 POI 推荐的长期和短期偏好对比学习(CLSPRec)模型，以更好地利用相同用户之间的偏好相似性，并区分不同用户的出行偏好，以便更准确地预测下一个 POI。CLSPRec 在长序列中采用掩蔽策略来增强模型的鲁棒性，并通过短序列进一步增强用户表示。在三个实际数据集上的大量实验验证了该模型的优越性。我们的代码可以在 https://github.com/wonderdch/clsprec 上公开获取。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=CLSPRec:+Contrastive+Learning+of+Long+and+Short-term+Preferences+for+Next+POI+Recommendation)|0| -|[Predictive Uncertainty-based Bias Mitigation in Ranking](https://doi.org/10.1145/3583780.3615011)|Maria Heuss, Daniel Cohen, Masoud Mansoury, Maarten de Rijke, Carsten Eickhoff|University of Tübingen, Tübingen, Germany; University of Amsterdam, Amsterdam, Netherlands; Dataminr, NYC, NY, USA|Societal biases that are contained in retrieved documents have received increased interest. Such biases, which are often prevalent in the training data and learned by the model, can cause societal harms, by misrepresenting certain groups, and by enforcing stereotypes. Mitigating such biases demands algorithms that balance the trade-off between maximized utility for the user with fairness objectives, which incentivize unbiased rankings. Prior work on bias mitigation often assumes that ranking scores, which correspond to the utility that a document holds for a user, can be accurately determined. In reality, there is always a degree of uncertainty in the estimate of expected document utility. This uncertainty can be approximated by viewing ranking models through a Bayesian perspective, where the standard deterministic score becomes a distribution. In this work, we investigate whether uncertainty estimates can be used to decrease the amount of bias in the ranked results, while minimizing loss in measured utility. We introduce a simple method that uses the uncertainty of the ranking scores for an uncertainty-aware, post hoc approach to bias mitigation. We compare our proposed method with existing baselines for bias mitigation with respect to the utility-fairness trade-off, the controllability of methods, and computational costs. We show that an uncertainty-based approach can provide an intuitive and flexible trade-off that outperforms all baselines without additional training requirements, allowing for the post hoc use of this approach on top of arbitrary retrieval models.|检索到的文件中包含的社会偏见越来越受到关注。这样的偏见，通常在培训数据中普遍存在，并被模型学习到，通过歪曲特定群体和强制执行刻板印象，可能造成社会危害。减轻这种偏见需要算法，平衡之间的权衡最大效用的用户和公平的目标，激励无偏排名。先前关于减少偏差的工作通常假设排名分数，这对应于一个文档为用户持有的效用，可以被准确地确定。实际上，在预期文档效用的估计中总是存在一定程度的不确定性。这种不确定性可以通过贝叶斯透视图查看排名模型来近似化，其中标准的确定性得分成为一个分布。在这项工作中，我们调查是否不确定性估计可以用来减少排名结果中的偏差量，同时最小化测量效用的损失。我们介绍了一个简单的方法，使用不确定性的排名得分的不确定性的不确定性，事后的方法来减少偏见。在效用-公平权衡、方法的可控性和计算成本方面，我们将提出的方法与现有的减少偏差的基线进行了比较。我们展示了一个基于不确定性的方法可以提供一个直观和灵活的权衡，在没有额外的训练要求的情况下优于所有的基线，允许在任意检索模型之上事后使用这种方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Predictive+Uncertainty-based+Bias+Mitigation+in+Ranking)|0| +|[Predictive Uncertainty-based Bias Mitigation in Ranking](https://doi.org/10.1145/3583780.3615011)|Maria Heuss, Daniel Cohen, Masoud Mansoury, Maarten de Rijke, Carsten Eickhoff|University of Tübingen, Tübingen, Germany; Dataminr, NYC, NY, USA; University of Amsterdam, Amsterdam, Netherlands|Societal biases that are contained in retrieved documents have received increased interest. Such biases, which are often prevalent in the training data and learned by the model, can cause societal harms, by misrepresenting certain groups, and by enforcing stereotypes. Mitigating such biases demands algorithms that balance the trade-off between maximized utility for the user with fairness objectives, which incentivize unbiased rankings. Prior work on bias mitigation often assumes that ranking scores, which correspond to the utility that a document holds for a user, can be accurately determined. In reality, there is always a degree of uncertainty in the estimate of expected document utility. This uncertainty can be approximated by viewing ranking models through a Bayesian perspective, where the standard deterministic score becomes a distribution. In this work, we investigate whether uncertainty estimates can be used to decrease the amount of bias in the ranked results, while minimizing loss in measured utility. We introduce a simple method that uses the uncertainty of the ranking scores for an uncertainty-aware, post hoc approach to bias mitigation. We compare our proposed method with existing baselines for bias mitigation with respect to the utility-fairness trade-off, the controllability of methods, and computational costs. We show that an uncertainty-based approach can provide an intuitive and flexible trade-off that outperforms all baselines without additional training requirements, allowing for the post hoc use of this approach on top of arbitrary retrieval models.|检索到的文件中包含的社会偏见越来越受到关注。这样的偏见，通常在培训数据中普遍存在，并被模型学习到，通过歪曲特定群体和强制执行刻板印象，可能造成社会危害。减轻这种偏见需要算法，平衡之间的权衡最大效用的用户和公平的目标，激励无偏排名。先前关于减少偏差的工作通常假设排名分数，这对应于一个文档为用户持有的效用，可以被准确地确定。实际上，在预期文档效用的估计中总是存在一定程度的不确定性。这种不确定性可以通过贝叶斯透视图查看排名模型来近似化，其中标准的确定性得分成为一个分布。在这项工作中，我们调查是否不确定性估计可以用来减少排名结果中的偏差量，同时最小化测量效用的损失。我们介绍了一个简单的方法，使用不确定性的排名得分的不确定性的不确定性，事后的方法来减少偏见。在效用-公平权衡、方法的可控性和计算成本方面，我们将提出的方法与现有的减少偏差的基线进行了比较。我们展示了一个基于不确定性的方法可以提供一个直观和灵活的权衡，在没有额外的训练要求的情况下优于所有的基线，允许在任意检索模型之上事后使用这种方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Predictive+Uncertainty-based+Bias+Mitigation+in+Ranking)|0| |[Single-User Injection for Invisible Shilling Attack against Recommender Systems](https://doi.org/10.1145/3583780.3615062)|Chengzhi Huang, Hui Li|Xiamen University, Xiamen, China|Recommendation systems (RS) are crucial for alleviating the information overload problem. Due to its pivotal role in guiding users to make decisions, unscrupulous parties are lured to launch attacks against RS to affect the decisions of normal users and gain illegal profits. Among various types of attacks, shilling attack is one of the most subsistent and profitable attacks. In shilling attack, an adversarial party injects a number of well-designed fake user profiles into the system to mislead RS so that the attack goal can be achieved. Although existing shilling attack methods have achieved promising results, they all adopt the attack paradigm of multi-user injection, where some fake user profiles are required. This paper provides the first study of shilling attack in an extremely limited scenario: only one fake user profile is injected into the victim RS to launch shilling attacks (i.e., single-user injection). We propose a novel single-user injection method SUI-Attack for invisible shilling attack. SUI-Attack is a graph based attack method that models shilling attack as a node generation task over the user-item bipartite graph of the victim RS, and it constructs the fake user profile by generating user features and edges that link the fake user to items. Extensive experiments demonstrate that SUI-Attack can achieve promising attack results in single-user injection. In addition to its attack power, SUI-Attack increases the stealthiness of shilling attack and reduces the risk of being detected. We provide our implementation at: https://github.com/KDEGroup/SUI-Attack.|推荐系统(RS)对于缓解信息超载问题至关重要。由于 RS 在引导用户做出决策方面发挥着关键作用，因此引诱不法分子对 RS 发起攻击，以影响正常用户的决策并获取非法利润。在各种类型的攻击中，先令攻击是最具生命力和最有利可图的攻击之一。在先令攻击中，敌对方向系统中注入大量精心设计的虚假用户资料，以误导 RS，从而达到攻击目的。现有的先令攻击方法虽然取得了良好的效果，但都采用了多用户注入的攻击范式，需要一些伪造的用户配置文件。本文提供了在极其有限的情况下的先令攻击的第一个研究: 只有一个假的用户配置文件被注入到受害者的 RS 发动先令攻击(即，单用户注入)。针对隐形先令攻击，提出了一种新的单用户注入方法 SUI- 攻击。SUI 攻击是一种基于图的攻击方法，在受害者 RS 的用户-项目二分图上将先令攻击建模为一个节点生成任务，通过生成用户特征和边将假用户与项目连接起来，构造假用户轮廓。大量的实验表明，SUI 攻击可以在单用户注入中获得良好的攻击效果。除了它的攻击威力，SUI 攻击增加了先令攻击的隐蔽性，并降低了被发现的风险。我们在以下 https://github.com/kdegroup/sui-attack 提供实施方案:。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Single-User+Injection+for+Invisible+Shilling+Attack+against+Recommender+Systems)|0| |[Meta-Learning with Adaptive Weighted Loss for Imbalanced Cold-Start Recommendation](https://doi.org/10.1145/3583780.3614965)|Minchang Kim, Yongjin Yang, Jung Hyun Ryu, Taesup Kim|Seoul National University, Seoul, Republic of Korea|Sequential recommenders have made great strides in capturing a user's preferences. Nevertheless, the cold-start recommendation remains a fundamental challenge in which only a few user-item interactions are available for personalization. Gradient-based meta-learning approaches have recently emerged in the sequential recommendation field due to their fast adaptation and easy-to-integrate abilities. The meta-learning algorithms formulate the cold-start recommendation as a few-shot learning problem, where each user is represented as a task to be adapted. However, while meta-learning algorithms generally assume that task-wise samples are evenly distributed over classes or values, user-item interactions are not that way in real-world applications (e.g., watching favorite videos multiple times, leaving only good ratings and no bad ones). As a result, in the real-world, imbalanced user feedback that accounts for most task training data may dominate the user adaptation and prevent meta-learning algorithms from learning meaningful meta-knowledge for personalized recommendations. To alleviate this limitation, we propose a novel sequential recommendation framework based on gradient-based meta-learning that captures the imbalance of each user's rating distribution and accordingly computes adaptive loss for user-specific learning. It is the first work to tackle the impact of imbalanced ratings in cold-start sequential recommendation scenarios. We design adaptive weighted loss and improve the existing meta-learning algorithms for state-of-the-art sequential recommendation methods. Extensive experiments conducted on real-world datasets demonstrate the effectiveness of our framework.|顺序推荐系统在捕获用户的首选项方面取得了长足的进步。然而，冷启动建议仍然是一个根本性的挑战，因为只有少数用户项交互可用于个性化。基于梯度的元学习方法由于其快速的适应性和易于集成的能力，近年来出现在顺序推荐领域。元学习算法将冷启动推荐表示为一个短镜头学习问题，将每个用户表示为一个需要调整的任务。然而，虽然元学习算法通常假设任务智能样本均匀分布在类或值上，但在现实世界的应用程序中，用户项交互并非如此(例如，多次观看最喜欢的视频，只留下好的评分，没有坏的)。因此，在现实世界中，占用大多数任务训练数据的不平衡的用户反馈可能会主导用户适应性，并阻碍元学习算法学习有意义的元知识以获得个性化推荐。为了解决这一问题，本文提出了一种基于梯度元学习的顺序推荐框架，该框架能够捕捉每个用户评分分布的不平衡性，从而计算用户特定学习的自适应损失。这是第一个处理冷启动顺序推荐情景中评分不平衡的影响的工作。设计了自适应加权损失算法，并对现有的元学习算法进行了改进。在真实世界数据集上进行的大量实验证明了我们框架的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Meta-Learning+with+Adaptive+Weighted+Loss+for+Imbalanced+Cold-Start+Recommendation)|0| |[Learning the Co-evolution Process on Live Stream Platforms with Dual Self-attention for Next-topic Recommendations](https://doi.org/10.1145/3583780.3614952)|HsuChao Lai, Philip S. Yu, JiunLong Huang|University of Illinois at Chicago, Chicago, IL, USA; National Yang Ming Chiao Tung University, Hsinchu, Taiwan Roc|Live stream platforms have gained popularity in light of emerging social media platforms. Unlike traditional on-demand video platforms, viewers and streamers on the live stream platforms are able to interact in real-time, and this makes viewer interests and live stream topics mutually affect each other on the fly, which is the unique co-evolution phenomenon on live stream platforms. In this paper, we make the first attempt to introduce a novel next-topic recommendation problem for the streamers, LSNR, which incorporates the co-evolution phenomenon. A novel framework CENTR introducing the Co-evolutionary Sequence Embedding Structure that captures the temporal relations of viewer interests and live stream topic sequences with two stacks of self-attention layers is proposed. Instead of learning the sequences individually, a novel dual self-attention mechanism is designed to model interactions between the sequences. The dual self-attention includes two modules, LCA and LVA, to leverage viewer loyalty to improve efficiency and flexibility. Finally, to facilitate cold-start recommendations for new streamers, a collaborative diffusion mechanism is implemented to improve a meta learner. Through the experiments in real datasets, CENTR outperforms state-of-the-art recommender systems in both regular and cold-start scenarios.|随着社交媒体平台的兴起，直播平台越来越受欢迎。与传统的视频点播平台不同，直播平台上的观众和流媒体可以实时互动，这使得观众的兴趣和直播话题在运动中相互影响，这是直播平台上独特的协同进化现象。在本文中，我们首次尝试引入一个新的流媒体下一主题推荐问题，LSNR，它结合了协同进化现象。提出了一种引入协同进化序列嵌入结构的新框架 CENTR，该结构能够捕获观看者兴趣与实时流主题序列之间的时间关系。提出了一种新的双重自我注意机制来模拟序列之间的相互作用，而不是单独学习序列。双重自我关注包括两个模块，LCA 和 LVA，以利用观众忠诚度，提高效率和灵活性。最后，为了促进新流媒体的冷启动建议，实现了一个协作扩散机制，以改善元学习者。通过在实际数据集中的实验，CENTR 在常规和冷启动情况下都优于最先进的推荐系统。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Learning+the+Co-evolution+Process+on+Live+Stream+Platforms+with+Dual+Self-attention+for+Next-topic+Recommendations)|0| -|[HAMUR: Hyper Adapter for Multi-Domain Recommendation](https://doi.org/10.1145/3583780.3615137)|Xiaopeng Li, Fan Yan, Xiangyu Zhao, Yichao Wang, Bo Chen, Huifeng Guo, Ruiming Tang|City University of Hong Kong, Hong Kong, Hong Kong; Huawei Noah's Ark Lab, Shenzhen, China|Multi-Domain Recommendation (MDR) has gained significant attention in recent years, which leverages data from multiple domains to enhance their performance concurrently.However, current MDR models are confronted with two limitations. Firstly, the majority of these models adopt an approach that explicitly shares parameters between domains, leading to mutual interference among them. Secondly, due to the distribution differences among domains, the utilization of static parameters in existing methods limits their flexibility to adapt to diverse domains. To address these challenges, we propose a novel model Hyper Adapter for Multi-Domain Recommendation (HAMUR). Specifically, HAMUR consists of two components: (1). Domain-specific adapter, designed as a pluggable module that can be seamlessly integrated into various existing multi-domain backbone models, and (2). Domain-shared hyper-network, which implicitly captures shared information among domains and dynamically generates the parameters for the adapter. We conduct extensive experiments on two public datasets using various backbone networks. The experimental results validate the effectiveness and scalability of the proposed model.|多域推荐(MDR)近年来受到了广泛的关注，它利用来自多个域的数据来同时提高它们的性能。然而，当前的 MDR 模型面临两个限制。首先，这些模型中的大多数都采用了域之间显式共享参数的方法，导致了域之间的相互干扰。其次，由于领域之间的分布差异，现有方法中静态参数的使用限制了它们适应不同领域的灵活性。为了应对这些挑战，我们提出了一种新型的多域推荐超级适配器(HAMUR)模型。具体来说，HAMUR 由两部分组成: (1)。特定于领域的适配器，设计为可插入模块，可以无缝集成到各种现有的多领域主干模型，以及(2)。域共享超网络，它隐式地捕获域之间的共享信息并动态地生成适配器的参数。我们使用不同的骨干网络对两个公共数据集进行了广泛的实验。实验结果验证了该模型的有效性和可扩展性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=HAMUR:+Hyper+Adapter+for+Multi-Domain+Recommendation)|0| +|[HAMUR: Hyper Adapter for Multi-Domain Recommendation](https://doi.org/10.1145/3583780.3615137)|Xiaopeng Li, Fan Yan, Xiangyu Zhao, Yichao Wang, Bo Chen, Huifeng Guo, Ruiming Tang|Huawei Noah's Ark Lab, Shenzhen, China; City University of Hong Kong, Hong Kong, Hong Kong|Multi-Domain Recommendation (MDR) has gained significant attention in recent years, which leverages data from multiple domains to enhance their performance concurrently.However, current MDR models are confronted with two limitations. Firstly, the majority of these models adopt an approach that explicitly shares parameters between domains, leading to mutual interference among them. Secondly, due to the distribution differences among domains, the utilization of static parameters in existing methods limits their flexibility to adapt to diverse domains. To address these challenges, we propose a novel model Hyper Adapter for Multi-Domain Recommendation (HAMUR). Specifically, HAMUR consists of two components: (1). Domain-specific adapter, designed as a pluggable module that can be seamlessly integrated into various existing multi-domain backbone models, and (2). Domain-shared hyper-network, which implicitly captures shared information among domains and dynamically generates the parameters for the adapter. We conduct extensive experiments on two public datasets using various backbone networks. The experimental results validate the effectiveness and scalability of the proposed model.|多域推荐(MDR)近年来受到了广泛的关注，它利用来自多个域的数据来同时提高它们的性能。然而，当前的 MDR 模型面临两个限制。首先，这些模型中的大多数都采用了域之间显式共享参数的方法，导致了域之间的相互干扰。其次，由于领域之间的分布差异，现有方法中静态参数的使用限制了它们适应不同领域的灵活性。为了应对这些挑战，我们提出了一种新型的多域推荐超级适配器(HAMUR)模型。具体来说，HAMUR 由两部分组成: (1)。特定于领域的适配器，设计为可插入模块，可以无缝集成到各种现有的多领域主干模型，以及(2)。域共享超网络，它隐式地捕获域之间的共享信息并动态地生成适配器的参数。我们使用不同的骨干网络对两个公共数据集进行了广泛的实验。实验结果验证了该模型的有效性和可扩展性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=HAMUR:+Hyper+Adapter+for+Multi-Domain+Recommendation)|0| |[Prompt Distillation for Efficient LLM-based Recommendation](https://doi.org/10.1145/3583780.3615017)|Lei Li, Yongfeng Zhang, Li Chen|Hong Kong Baptist University, Hong Kong, Hong Kong; Rutgers University, New Brunswick, NJ, USA|Large language models (LLM) have manifested unparalleled modeling capability on various tasks, e.g., multi-step reasoning, but the input to these models is mostly limited to plain text, which could be very long and contain noisy information. Long text could take long time to process, and thus may not be efficient enough for recommender systems that require immediate response. In LLM-based recommendation models, user and item IDs are usually filled in a template (i.e., discrete prompt) to allow the models to understand a given task, but the models usually need extensive fine-tuning to bridge the user/item IDs and the template words and to unleash the power of LLM for recommendation. To address the problems, we propose to distill the discrete prompt for a specific task to a set of continuous prompt vectors so as to bridge IDs and words and to reduce the inference time. We also design a training strategy with an attempt to improve the efficiency of training these models. Experimental results on three real-world datasets demonstrate the effectiveness of our PrOmpt Distillation (POD) approach on both sequential recommendation and top-N recommendation tasks. Although the training efficiency can be significantly improved, the improvement of inference efficiency is limited. This finding may inspire researchers in the community to further improve the inference efficiency of LLM-based recommendation models.|大型语言模型(LLM)在多种任务(如多步推理)上表现出了无与伦比的建模能力，但是这些模型的输入大多局限于纯文本，这些文本可能非常长并且包含有噪声的信息。处理冗长的文本可能需要很长的时间，因此对于需要立即响应的推荐系统来说，效率可能不够高。在基于 LLM 的推荐模型中，用户和项目 ID 通常填写在一个模板中(即，离散提示符) ，以使模型能够理解给定的任务，但是模型通常需要大量的微调来连接用户/项目 ID 和模板词，并释放 LLM 的推荐功能。为了解决这个问题，我们提出将特定任务的离散提示提取到一组连续的提示向量中，从而桥接 ID 和单词，减少推理时间。我们还设计了一个训练策略，试图提高这些模型的训练效率。在三个实际数据集上的实验结果表明了本文提示精馏(POD)方法在顺序推荐和前 N 推荐任务上的有效性。虽然训练效率可以显著提高，但推理效率的提高是有限的。这一发现可能会激励社区研究人员进一步提高基于 LLM 的推荐模型的推理效率。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Prompt+Distillation+for+Efficient+LLM-based+Recommendation)|0| |[Bias Invariant Approaches for Improving Word Embedding Fairness](https://doi.org/10.1145/3583780.3614792)|Siyu Liao, Ringting Zhang, Barbara Poblete, Vanessa Murdock|University of Chile & Amazon.com, Santiago, Chile; Amazon.com, Seattle, WA, USA|Many public pre-trained word embeddings have been shown to encode different types of biases. Embeddings are often obtained from training on large pre-existing corpora, and therefore resulting biases can be a reflection of unfair representations in the original data. Bias, in this scenario, is a challenging problem since current mitigation techniques require knowing and understanding existing biases in the embedding, which is not always possible. In this work, we propose to improve word embedding fairness by borrowing methods from the field of data privacy. The idea behind this approach is to treat bias as if it were a special type of training data leakage. This has the unique advantage of not requiring prior knowledge of potential biases in word embeddings. We investigated two types of privacy algorithms, and measured their effect on bias using four different metrics. To investigate techniques from differential privacy, we applied Gaussian perturbation to public pre-trained word embeddings. To investigate noiseless privacy, we applied vector quantization during training. Experiments show that both approaches improve fairness for commonly used embeddings, and additionally, noiseless privacy techniques reduce the size of the resulting embedding representation.|许多公开的预先训练的词语嵌入已经被证明可以编码不同类型的偏见。嵌入常常是通过对大型预先存在的语料库进行训练而获得的，因此产生的偏差可能是原始数据中不公平表示的反映。在这种情况下，偏差是一个具有挑战性的问题，因为当前的缓解技术需要了解和理解嵌入中存在的偏差，而这并不总是可能的。本文从数据隐私的角度出发，提出了一种改进嵌入公平性的方法。这种方法背后的思想是把偏差当作一种特殊类型的训练数据泄漏来处理。这样做的独特优点是不需要事先知道嵌入词中的潜在偏差。我们研究了两种类型的隐私算法，并使用四种不同的指标来测量它们对偏差的影响。为了研究基于差分隐私的嵌入技术，我们将高斯扰动应用于公共预先训练的单词嵌入。为了研究无声的私隐，我们在训练期间使用了向量量化。实验结果表明，这两种方法都提高了常用嵌入算法的公平性，并且无噪隐私技术减小了嵌入表示的大小。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Bias+Invariant+Approaches+for+Improving+Word+Embedding+Fairness)|0| |[PopDCL: Popularity-aware Debiased Contrastive Loss for Collaborative Filtering](https://doi.org/10.1145/3583780.3615009)|Zhuang Liu, Haoxuan Li, Guanming Chen, Yuanxin Ouyang, Wenge Rong, Zhang Xiong|Beihang University, Beijing, China|Collaborative filtering (CF) is the basic method for recommendation with implicit feedback. Recently, various state-of-the-art CF integrates graph neural networks. However, they often suffer from popularity bias, causing recommendations to deviate from users' genuine preferences. Additionally, several contrastive learning methods based on the in-batch sample strategy have been proposed to train the CF model effectively, but they are prone to suffering from sample bias. To address this problem, debiased contrastive loss has been employed in the recommendation, but instead of personalized debiasing, it treats each user equally. In this paper, we propose a popularity-aware debiased contrastive loss for CF, which can adaptively correct the positive and negative scores based on the popularity of users and items. Our approach aims to reduce the negative impact of popularity and sample bias simultaneously. We theoretically analyze the effectiveness of the proposed method and reveal the relationship between popularity and gradient, which justifies the correction strategy. We extensively evaluate our method on three public benchmarks over balanced and imbalanced settings. The results demonstrate its superiority over the existing debiased strategies, not only on the entire datasets but also when segmenting the datasets based on item popularity.|协同过滤(CF)是内隐反馈推荐的基本方法。最近，各种最先进的 CF 集成了图神经网络。然而，他们经常受到流行偏见的影响，导致推荐偏离用户的真实偏好。此外，为了有效地训练 CF 模型，人们提出了几种基于批内样本策略的对比学习方法，但这些方法容易产生样本偏差。为了解决这个问题，去偏对比度损失被用于推荐，但它不是个性化的去偏，它对每个用户一视同仁。本文提出了一种基于流行度的消偏对比度损失算法，该算法可以根据用户和项目的流行度自适应地修正正负分值。我们的方法旨在同时减少受欢迎程度和样本偏差的负面影响。从理论上分析了该方法的有效性，揭示了流行度与梯度的关系，从而验证了该方法的正确性。我们广泛评估我们的方法在三个公共基准平衡和不平衡的设置。实验结果表明，无论是在整个数据集上，还是在基于项目知名度的数据集分割上，该方法都优于现有的去偏策略。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=PopDCL:+Popularity-aware+Debiased+Contrastive+Loss+for+Collaborative+Filtering)|0| -|[ForeSeer: Product Aspect Forecasting Using Temporal Graph Embedding](https://doi.org/10.1145/3583780.3614887)|Zixuan Liu, Gaurush Hiranandani, Kun Qian, Edward W. Huang, Yi Xu, Belinda Zeng, Karthik Subbian, Sheng Wang|; University of Washington, Seattle, WA, USA; Amazon, Seattle, WA, USA; Amazon, Palo Alto, CA, USA|Developing text mining approaches to mine aspects from customer reviews has been well-studied due to its importance in understanding customer needs and product attributes. In contrast, it remains unclear how to predict the future emerging aspects of a new product that currently has little review information. This task, which we named product aspect forecasting, is critical for recommending new products, but also challenging because of the missing reviews. Here, we propose ForeSeer, a novel textual mining and product embedding approach progressively trained on temporal product graphs for this novel product aspect forecasting task. ForeSeer transfers reviews from similar products on a large product graph and exploits these reviews to predict aspects that might emerge in future reviews. A key novelty of our method is to jointly provide review, product, and aspect embeddings that are both time-sensitive and less affected by extremely imbalanced aspect frequencies. We evaluated ForeSeer on a real-world product review system containing 11,536,382 reviews and 11,000 products over 3 years. We observe that ForeSeer substantially outperformed existing approaches with at least 49.1\% AUPRC improvement under the real setting where aspect associations are not given. ForeSeer further improves future link prediction on the product graph and the review aspect association prediction. Collectively, Foreseer offers a novel framework for review forecasting by effectively integrating review text, product network, and temporal information, opening up new avenues for online shopping recommendation and e-commerce applications.|由于文本挖掘在理解客户需求和产品属性方面的重要性，开发从客户评论中挖掘方面的文本挖掘方法已经得到了很好的研究。相比之下，目前尚不清楚如何预测新产品的未来新出现的方面，目前几乎没有审查信息。这项任务，我们命名为产品方面的预测，是至关重要的推荐新产品，但也具有挑战性，因为缺少审查。在这里，我们提出了 ForeSeer，一种新的文本挖掘和产品嵌入方法，逐步训练的时间产品图为这种新的产品方面的预测任务。ForeSeer 将类似产品的评论转移到一个较大的产品图上，并利用这些评论来预测未来评论中可能出现的方面。我们的方法的一个关键的新颖之处是联合提供审查、产品和方面嵌入，它们都是时间敏感的，并且受极不平衡的方面频率的影响较小。我们在一个包含11,536,382个评论和11,000个产品的真实世界产品评论系统上对 ForeSeer 进行了评估。我们观察到 ForeSeer 在没有给出方面关联的实际情况下，至少有49.1% 的 AUPRC 改进，大大优于现有的方法。ForeSeer 进一步改进了产品图上的未来链接预测和评论方面的关联预测。通过有效地整合评论文本、产品网络和时间信息，为在线购物推荐和电子商务应用开辟了新的途径，Foreseer 提供了一个新颖的评论预测框架。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=ForeSeer:+Product+Aspect+Forecasting+Using+Temporal+Graph+Embedding)|0| +|[ForeSeer: Product Aspect Forecasting Using Temporal Graph Embedding](https://doi.org/10.1145/3583780.3614887)|Zixuan Liu, Gaurush Hiranandani, Kun Qian, Edward W. Huang, Yi Xu, Belinda Zeng, Karthik Subbian, Sheng Wang|; University of Washington, Seattle, WA, USA; Amazon, Palo Alto, CA, USA; Amazon, Seattle, WA, USA|Developing text mining approaches to mine aspects from customer reviews has been well-studied due to its importance in understanding customer needs and product attributes. In contrast, it remains unclear how to predict the future emerging aspects of a new product that currently has little review information. This task, which we named product aspect forecasting, is critical for recommending new products, but also challenging because of the missing reviews. Here, we propose ForeSeer, a novel textual mining and product embedding approach progressively trained on temporal product graphs for this novel product aspect forecasting task. ForeSeer transfers reviews from similar products on a large product graph and exploits these reviews to predict aspects that might emerge in future reviews. A key novelty of our method is to jointly provide review, product, and aspect embeddings that are both time-sensitive and less affected by extremely imbalanced aspect frequencies. We evaluated ForeSeer on a real-world product review system containing 11,536,382 reviews and 11,000 products over 3 years. We observe that ForeSeer substantially outperformed existing approaches with at least 49.1\% AUPRC improvement under the real setting where aspect associations are not given. ForeSeer further improves future link prediction on the product graph and the review aspect association prediction. Collectively, Foreseer offers a novel framework for review forecasting by effectively integrating review text, product network, and temporal information, opening up new avenues for online shopping recommendation and e-commerce applications.|由于文本挖掘在理解客户需求和产品属性方面的重要性，开发从客户评论中挖掘方面的文本挖掘方法已经得到了很好的研究。相比之下，目前尚不清楚如何预测新产品的未来新出现的方面，目前几乎没有审查信息。这项任务，我们命名为产品方面的预测，是至关重要的推荐新产品，但也具有挑战性，因为缺少审查。在这里，我们提出了 ForeSeer，一种新的文本挖掘和产品嵌入方法，逐步训练的时间产品图为这种新的产品方面的预测任务。ForeSeer 将类似产品的评论转移到一个较大的产品图上，并利用这些评论来预测未来评论中可能出现的方面。我们的方法的一个关键的新颖之处是联合提供审查、产品和方面嵌入，它们都是时间敏感的，并且受极不平衡的方面频率的影响较小。我们在一个包含11,536,382个评论和11,000个产品的真实世界产品评论系统上对 ForeSeer 进行了评估。我们观察到 ForeSeer 在没有给出方面关联的实际情况下，至少有49.1% 的 AUPRC 改进，大大优于现有的方法。ForeSeer 进一步改进了产品图上的未来链接预测和评论方面的关联预测。通过有效地整合评论文本、产品网络和时间信息，为在线购物推荐和电子商务应用开辟了新的途径，Foreseer 提供了一个新颖的评论预测框架。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=ForeSeer:+Product+Aspect+Forecasting+Using+Temporal+Graph+Embedding)|0| |[Neural Personalized Topic Modeling for Mining User Preferences on Social Media](https://doi.org/10.1145/3583780.3614987)|Luyang Liu, Qunyang Lin, Haonan Tong, Hongyin Zhu, Ke Liu, Min Wang, Chuang Zhang|Inspur Electronic Information Industry Co., Ltd., Beijing, China|With the rapid development of web services, social media has been a prevalent and readily way for people to express themselves and share their daily lives. Consequently, numerous user-generated content is accumulated on social media platforms. These data usually contain rich information and knowledge for users, which is a viable source for user data mining. As one of the prevalent techniques in user data mining, mining personalized topics and discovering user preferences from social media data attract much interest in academic and industrial communities. The emerging Neural Topic Models(NTMs) have recently shown leading performance and scalability by employing neural networks. However, most existing NTMs usually model topics simply from observed document token information and do not explicitly take user preferences into the generative process, which inevitably fails to model personalized topics. To address this issue, we introduce Neural Personalized Topic Model(NPTM), a novel NTM that can discover personalized topics and user preferences. NPTM introduces a novel hybrid generative process for combining user preferences and contextualized document codes in modeling personalized topics. A transformer-based document encoder to obtain contextualized document codes. For user preference modeling, NPTM regards user-related information as trainable user embeddings, further determining user preferences over the topics. Following the proposed hybrid generative process, we present a module-wise asynchronous optimization strategy to get coherent topics and user preferences. Then, we apply our model to two challenging real-world social media post collections and compare them against several baseline methods to verify our contributions. The experimental results demonstrate the effectiveness of the proposed method.|随着网络服务的快速发展，社交媒体已经成为人们表达自己和分享日常生活的一种流行和便捷的方式。因此，社交媒体平台上积累了大量的用户生成内容。这些数据通常包含丰富的用户信息和知识，这是用户数据挖掘的可行来源。作为用户数据挖掘的一种流行技术，从社会媒体数据中挖掘个性化主题和发现用户偏好引起了学术界和工业界的广泛关注。新兴的神经主题模型(NTMs)通过使用神经网络表现出领先的性能和可扩展性。然而，大多数现有的 NTM 通常只是根据观察到的文档令牌信息对主题进行建模，并没有明确地将用户偏好引入到生成过程中，这就不可避免地无法对个性化主题进行建模。为了解决这个问题，我们引入了神经个性化主题模型(NPTM) ，这是一种新的可以发现个性化主题和用户偏好的神经个性化主题模型。NPTM 引入了一种新的混合生成过程，将用户偏好和上下文文档代码相结合，对个性化主题进行建模。一种基于转换器的获取上下文文档编码的文档编码器。对于用户偏好建模，NPTM 将用户相关信息视为可训练的用户嵌入，进一步确定用户对主题的偏好。根据所提出的混合生成过程，我们提出了一个模块化的异步优化策略，以获得一致的主题和用户偏好。然后，我们将我们的模型应用于两个具有挑战性的现实社会媒体帖子集合，并将它们与几个基线方法进行比较，以验证我们的贡献。实验结果表明了该方法的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Neural+Personalized+Topic+Modeling+for+Mining+User+Preferences+on+Social+Media)|0| -|[Improving Long-Tail Item Recommendation with Graph Augmentation](https://doi.org/10.1145/3583780.3614929)|Sichun Luo, Chen Ma, Yuanzhang Xiao, Linqi Song|City University of Hong Kong & City University of Hong Kong Shenzhen Research Institute, Hong Kong, Hong Kong; City University of Hong Kong, Hong Kong, Hong Kong; University of Hawaii at Manoa, Honolulu, HI, USA|The ubiquitous long-tail distribution of inherent user behaviors results in worse recommendation performance for the items with fewer user records (i.e., tail items) than those with richer ones (i.e., head items). Graph-based recommendation methods (e.g., using graph neural networks) have recently emerged as a powerful tool for recommender systems, often outperforming traditional methods. However, existing techniques for alleviating the long-tail problem mainly focus on traditional methods. There is a lack of graph-based methods that can efficiently deal with the long-tail problem. In this paper, we propose a novel approach, Graph Augmentation for Long-tail Recommendation (GALORE), which can be plugged into any graph-based recommendation models to improve the performance for tail items. GALORE incorporates an edge addition module that enriches the graph's connectivity for tail items by injecting additional item-to-item edges. To further balance the graph structure, GALORE utilizes a degree-aware edge dropping strategy, preserving the more valuable edges from the tail items while selectively discarding less informative edges from the head items. Beyond structural augmentation, we synthesize new data samples, thereby addressing the data scarcity issue for tail items. We further introduce a two-stage training strategy to facilitate the learning for both head and tail items. Comprehensive empirical studies conducted on four datasets show that GALORE outperforms existing methods in terms of the performance for tail items as well as the overall performance.|无处不在的固有用户行为的长尾分布导致用户记录较少的条目(即尾条目)的推荐性能低于用户记录较丰富的条目(即头条目)的推荐性能。基于图形的推荐方法(例如，使用图形神经网络)最近已经成为推荐系统的一个强大工具，其性能通常优于传统方法。然而，现有的解决长尾问题的技术主要集中在传统方法上。缺乏基于图论的方法来有效地处理长尾问题。在本文中，我们提出了一种新的方法，图增强的长尾推荐(GALORE) ，可以插入到任何基于图的推荐模型，以提高性能的尾项。GALORE 合并了一个边缘添加模块，通过注入额外的项目到项目的边缘，丰富了图形对尾部项目的连通性。为了进一步平衡图形结构，GALORE 使用了一种度感知的边缘丢弃策略，保留了尾部项目中更有价值的边缘，同时选择性地丢弃了头部项目中信息量较小的边缘。除了结构增强，我们还合成了新的数据样本，从而解决了尾部项目的数据稀缺问题。我们进一步引入了一个两阶段的训练策略，以促进头部和尾部项目的学习。对四个数据集进行的综合实证研究表明，GALORE 在尾部项目的性能以及整体性能方面优于现有的方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Improving+Long-Tail+Item+Recommendation+with+Graph+Augmentation)|0| +|[Improving Long-Tail Item Recommendation with Graph Augmentation](https://doi.org/10.1145/3583780.3614929)|Sichun Luo, Chen Ma, Yuanzhang Xiao, Linqi Song|University of Hawaii at Manoa, Honolulu, HI, USA; City University of Hong Kong & City University of Hong Kong Shenzhen Research Institute, Hong Kong, Hong Kong; City University of Hong Kong, Hong Kong, Hong Kong|The ubiquitous long-tail distribution of inherent user behaviors results in worse recommendation performance for the items with fewer user records (i.e., tail items) than those with richer ones (i.e., head items). Graph-based recommendation methods (e.g., using graph neural networks) have recently emerged as a powerful tool for recommender systems, often outperforming traditional methods. However, existing techniques for alleviating the long-tail problem mainly focus on traditional methods. There is a lack of graph-based methods that can efficiently deal with the long-tail problem. In this paper, we propose a novel approach, Graph Augmentation for Long-tail Recommendation (GALORE), which can be plugged into any graph-based recommendation models to improve the performance for tail items. GALORE incorporates an edge addition module that enriches the graph's connectivity for tail items by injecting additional item-to-item edges. To further balance the graph structure, GALORE utilizes a degree-aware edge dropping strategy, preserving the more valuable edges from the tail items while selectively discarding less informative edges from the head items. Beyond structural augmentation, we synthesize new data samples, thereby addressing the data scarcity issue for tail items. We further introduce a two-stage training strategy to facilitate the learning for both head and tail items. Comprehensive empirical studies conducted on four datasets show that GALORE outperforms existing methods in terms of the performance for tail items as well as the overall performance.|无处不在的固有用户行为的长尾分布导致用户记录较少的条目(即尾条目)的推荐性能低于用户记录较丰富的条目(即头条目)的推荐性能。基于图形的推荐方法(例如，使用图形神经网络)最近已经成为推荐系统的一个强大工具，其性能通常优于传统方法。然而，现有的解决长尾问题的技术主要集中在传统方法上。缺乏基于图论的方法来有效地处理长尾问题。在本文中，我们提出了一种新的方法，图增强的长尾推荐(GALORE) ，可以插入到任何基于图的推荐模型，以提高性能的尾项。GALORE 合并了一个边缘添加模块，通过注入额外的项目到项目的边缘，丰富了图形对尾部项目的连通性。为了进一步平衡图形结构，GALORE 使用了一种度感知的边缘丢弃策略，保留了尾部项目中更有价值的边缘，同时选择性地丢弃了头部项目中信息量较小的边缘。除了结构增强，我们还合成了新的数据样本，从而解决了尾部项目的数据稀缺问题。我们进一步引入了一个两阶段的训练策略，以促进头部和尾部项目的学习。对四个数据集进行的综合实证研究表明，GALORE 在尾部项目的性能以及整体性能方面优于现有的方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Improving+Long-Tail+Item+Recommendation+with+Graph+Augmentation)|0| |[Parallel Knowledge Enhancement based Framework for Multi-behavior Recommendation](https://doi.org/10.1145/3583780.3615004)|Chang Meng, Chenhao Zhai, Yu Yang, Hengyu Zhang, Xiu Li|Tsinghua University, Shenzhen, China|Multi-behavior recommendation algorithms aim to leverage the multiplex interactions between users and items to learn users' latent preferences. Recent multi-behavior recommendation frameworks contain two steps: fusion and prediction. In the fusion step, advanced neural networks are used to model the hierarchical correlations between user behaviors. In the prediction step, multiple signals are utilized to jointly optimize the model with a multi-task learning (MTL) paradigm. However, recent approaches have not addressed the issue caused by imbalanced data distribution in the fusion step, resulting in the learned relationships being dominated by high-frequency behaviors. In the prediction step, the existing methods use a gate mechanism to directly aggregate expert information generated by coupling input, leading to negative information transfer. To tackle these issues, we propose a Parallel Knowledge Enhancement Framework (PKEF) for multi-behavior recommendation. Specifically, we enhance the hierarchical information propagation in the fusion step using parallel knowledge (PKF). Meanwhile, in the prediction step, we decouple the representations to generate expert information and introduce a projection mechanism during aggregation to eliminate gradient conflicts and alleviate negative transfer (PME). We conduct comprehensive experiments on three real-world datasets to validate the effectiveness of our model. The results further demonstrate the rationality and effectiveness of the designed PKF and PME modules. The source code and datasets are available at https://github.com/MC-CV/PKEF.|多行为推荐算法旨在利用用户和项目之间的多重交互来了解用户的潜在偏好。最近的多行为推荐框架包含两个步骤: 融合和预测。在融合步骤中，采用先进的神经网络对用户行为之间的层次关系进行建模。在预测步骤中，利用多个信号与多任务学习(MTL)范式联合优化模型。然而，最近的方法还没有解决由于融合步骤中数据分布不平衡所引起的问题，导致学习关系被高频行为所主导。在预测步骤中，现有的方法采用门机制直接聚合由耦合输入产生的专家信息，导致负信息传递。为了解决这些问题，我们提出了一个用于多行为推荐的并行知识增强框架(PKEF)。具体地说，我们在融合步骤中使用并行知识(PKF)来增强层次信息的传播。同时，在预测步骤中，对表示进行解耦，生成专家信息，并在聚合过程中引入投影机制，消除梯度冲突，减轻负迁移(PME)。为了验证模型的有效性，我们在三个实际数据集上进行了综合实验。仿真结果进一步验证了所设计的 PKF 模块和 PME 模块的合理性和有效性。源代码和数据集可在 https://github.com/mc-cv/pkef 下载。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Parallel+Knowledge+Enhancement+based+Framework+for+Multi-behavior+Recommendation)|0| -|[DebCSE: Rethinking Unsupervised Contrastive Sentence Embedding Learning in the Debiasing Perspective](https://doi.org/10.1145/3583780.3614833)|Pu Miao, Zeyao Du, Junlin Zhang|Sina Weibo, BeiJing, China; China Literature Limited, Shang Hai, China; Sina Weibo, Beijing, China|Several prior studies have suggested that word frequency biases can cause the Bert model to learn indistinguishable sentence embeddings. Contrastive learning schemes such as SimCSE and ConSERT have already been adopted successfully in unsupervised sentence embedding to improve the quality of embeddings by reducing this bias. However, these methods still introduce new biases such as sentence length bias and false negative sample bias, that hinders model's ability to learn more fine-grained semantics. In this paper, we reexamine the challenges of contrastive sentence embedding learning from a debiasing perspective and argue that effectively eliminating the influence of various biases is crucial for learning high-quality sentence embeddings. We think all those biases are introduced by simple rules for constructing training data in contrastive learning and the key for contrastive learning sentence embedding is to mimic the distribution of training data in supervised machine learning in unsupervised way. We propose a novel contrastive framework for sentence embedding, termed DebCSE, which can eliminate the impact of these biases by an inverse propensity weighted sampling method to select high-quality positive and negative pairs according to both the surface and semantic similarity between sentences. Extensive experiments on semantic textual similarity (STS) benchmarks reveal that DebCSE significantly outperforms the latest state-of-the-art models with an average Spearman's correlation coefficient of 80.33% on BERTbase.|已有的研究表明，词频偏差可以导致 Bert 模型学习不可区分的句子嵌入。对比学习方法如 SimCSE 和 ConSERT 已经成功地应用于无监督句子嵌入中，通过减少这种偏差来提高嵌入质量。然而，这些方法仍然引入了新的偏差，如句子长度偏差和错误的否定样本偏差，阻碍了模型学习更细粒度语义的能力。本文从消除偏差的角度重新审视对比句嵌入学习的挑战，认为有效地消除各种偏差的影响对于学习高质量的句子嵌入是至关重要的。我们认为所有这些偏差都是由构建对比学习中的训练数据的简单规则引入的，而对比学习句子嵌入的关键是以无监督的方式模拟训练数据在监督式学习中的分布。我们提出了一个新的句子嵌入对比框架，称为 DebCSE，它可以消除这些偏见的影响，通过反倾向加权抽样方法，根据句子之间的表面和语义相似性选择高质量的正负对。对语义文本相似度(STS)测试的大量实验表明，DebCSE 的性能明显优于最新的最先进的模型，在 BERTbase 上 Spearman 的平均相关系数为80.33% 。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=DebCSE:+Rethinking+Unsupervised+Contrastive+Sentence+Embedding+Learning+in+the+Debiasing+Perspective)|0| +|[DebCSE: Rethinking Unsupervised Contrastive Sentence Embedding Learning in the Debiasing Perspective](https://doi.org/10.1145/3583780.3614833)|Pu Miao, Zeyao Du, Junlin Zhang|Sina Weibo, Beijing, China; Sina Weibo, BeiJing, China; China Literature Limited, Shang Hai, China|Several prior studies have suggested that word frequency biases can cause the Bert model to learn indistinguishable sentence embeddings. Contrastive learning schemes such as SimCSE and ConSERT have already been adopted successfully in unsupervised sentence embedding to improve the quality of embeddings by reducing this bias. However, these methods still introduce new biases such as sentence length bias and false negative sample bias, that hinders model's ability to learn more fine-grained semantics. In this paper, we reexamine the challenges of contrastive sentence embedding learning from a debiasing perspective and argue that effectively eliminating the influence of various biases is crucial for learning high-quality sentence embeddings. We think all those biases are introduced by simple rules for constructing training data in contrastive learning and the key for contrastive learning sentence embedding is to mimic the distribution of training data in supervised machine learning in unsupervised way. We propose a novel contrastive framework for sentence embedding, termed DebCSE, which can eliminate the impact of these biases by an inverse propensity weighted sampling method to select high-quality positive and negative pairs according to both the surface and semantic similarity between sentences. Extensive experiments on semantic textual similarity (STS) benchmarks reveal that DebCSE significantly outperforms the latest state-of-the-art models with an average Spearman's correlation coefficient of 80.33% on BERTbase.|已有的研究表明，词频偏差可以导致 Bert 模型学习不可区分的句子嵌入。对比学习方法如 SimCSE 和 ConSERT 已经成功地应用于无监督句子嵌入中，通过减少这种偏差来提高嵌入质量。然而，这些方法仍然引入了新的偏差，如句子长度偏差和错误的否定样本偏差，阻碍了模型学习更细粒度语义的能力。本文从消除偏差的角度重新审视对比句嵌入学习的挑战，认为有效地消除各种偏差的影响对于学习高质量的句子嵌入是至关重要的。我们认为所有这些偏差都是由构建对比学习中的训练数据的简单规则引入的，而对比学习句子嵌入的关键是以无监督的方式模拟训练数据在监督式学习中的分布。我们提出了一个新的句子嵌入对比框架，称为 DebCSE，它可以消除这些偏见的影响，通过反倾向加权抽样方法，根据句子之间的表面和语义相似性选择高质量的正负对。对语义文本相似度(STS)测试的大量实验表明，DebCSE 的性能明显优于最新的最先进的模型，在 BERTbase 上 Spearman 的平均相关系数为80.33% 。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=DebCSE:+Rethinking+Unsupervised+Contrastive+Sentence+Embedding+Learning+in+the+Debiasing+Perspective)|0| |[Bi-channel Multiple Sparse Graph Attention Networks for Session-based Recommendation](https://doi.org/10.1145/3583780.3614791)|Shutong Qiao, Wei Zhou, Junhao Wen, Hongyu Zhang, Min Gao|Chongqing University, Chongqing, China|Session-based Recommendation (SBR) has recently received significant attention due to its ability to provide personalized recommendations based on the interaction sequences of anonymous session users. The challenges facing SBR consist mainly of how to utilize information other than the current session and how to reduce the negative impact of irrelevant information in the session data on the prediction. To address these challenges, we propose a novel graph attention network-based model called Multiple Sparse Graph Attention Networks (MSGAT). MSGAT leverages two parallel channels to model intra-session and inter-session information. In the intra-session channel, we utilize a gated graph neural network to perform initial encoding, followed by a self-attention mechanism to generate the target representation. The global representation is then noise-reduced based on the target representation. Additionally, the target representation is used as a medium to connect the two channels. In the inter-session channel, the noise-reduced relation representation is generated using the global attention mechanism of target perception. Moreover, MSGAT fully considers session similarity from the intent perspective by integrating valid information from both channels. Finally, the intent neighbor collaboration module effectively combines relevant information to enhance the current session representation. Extensive experiments on five datasets demonstrate that simultaneous modeling of intra-session and inter-session data can effectively enhance the performance of the SBR model.|基于会话的推荐技术(SBS)由于能够根据匿名会话用户的交互序列提供个性化的推荐，近年来受到了广泛的关注。SBR 面临的挑战主要包括如何利用本届会议以外的信息，以及如何减少会议数据中不相关信息对预测的负面影响。为了应对这些挑战，我们提出了一种新的基于图注意网络的模型，称为多稀疏图注意网络(MSGAT)。MSGAT 利用两个并行通道对会话内和会话间信息建模。在会话内信道中，利用门控图神经网络进行初始编码，然后利用自注意机制生成目标表示。然后在目标表示的基础上对全局表示进行噪声抑制。此外，目标表示形式被用作连接两个通道的媒介。在会话间信道中，利用目标感知的全局注意机制生成降噪关系表示。此外，MSGAT 通过整合来自两个通道的有效信息，从意图的角度充分考虑了会话相似性。最后，意向邻居协作模块有效地结合了相关信息，增强了当前的会话表示。对五个数据集的大量实验表明，同时建模会话内和会话间数据可以有效地提高 SBR 模型的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Bi-channel+Multiple+Sparse+Graph+Attention+Networks+for+Session-based+Recommendation)|0| -|[CDR: Conservative Doubly Robust Learning for Debiased Recommendation](https://doi.org/10.1145/3583780.3614805)|Zijie Song, Jiawei Chen, Sheng Zhou, Qihao Shi, Yan Feng, Chun Chen, Can Wang|Hangzhou City University, Hangzhou, China; Zhejiang University, Hangzhou, China|In recommendation systems (RS), user behavior data is observational rather than experimental, resulting in widespread bias in the data. Consequently, tackling bias has emerged as a major challenge in the field of recommendation systems. Recently, Doubly Robust Learning (DR) has gained significant attention due to its remarkable performance and robust properties. However, our experimental findings indicate that existing DR methods are severely impacted by the presence of so-called Poisonous Imputation, where the imputation significantly deviates from the truth and becomes counterproductive. To address this issue, this work proposes Conservative Doubly Robust strategy (CDR) which filters imputations by scrutinizing their mean and variance. Theoretical analyses show that CDR offers reduced variance and improved tail bounds.In addition, our experimental investigations illustrate that CDR significantly enhances performance and can indeed reduce the frequency of poisonous imputation.|在推荐系统(RS)中，用户行为数据是观察性的而不是实验性的，这导致了数据中广泛的偏差。因此，处理偏差已经成为推荐系统领域的一个主要挑战。近年来，双鲁棒学习(DR)以其显著的性能和鲁棒性得到了广泛的关注。然而，我们的实验结果表明，现有的 DR 方法受到所谓的有毒归责的严重影响，其中归责明显偏离真相，并成为适得其反。为了解决这个问题，本文提出了保守的双稳健策略(CDR) ，它通过检查估计的均值和方差来过滤估计。理论分析表明，CDR 方差减小，尾界改善，实验结果表明，CDR 方差显著提高了性能，并且确实能够减少有毒插补的频率。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=CDR:+Conservative+Doubly+Robust+Learning+for+Debiased+Recommendation)|0| -|[Disentangled Interest importance aware Knowledge Graph Neural Network for Fund Recommendation](https://doi.org/10.1145/3583780.3614846)|Ke Tu, Wei Qu, Zhengwei Wu, Zhiqiang Zhang, Zhongyi Liu, Yiming Zhao, Le Wu, Jun Zhou, Guannan Zhang|Ant Financial Services Group, Hangzhou, China; Hefei University of Technology, Hefei, China; Ant Group, Hangzhou, China; Alipay, Beijing, China; Ant Financial, Hangzhou, China; Ant Group, Beijing, China|At present, people are gradually becoming aware of financial management and thus fund recommendation attracts more and more attention to help them find suitable funds quickly. As a user usually takes many factors (e.g., fund theme, fund manager) into account when investing a fund and the fund usually consists of a substantial collection of investments, effectively modeling multi-interest representations is more crucial for personalized fund recommendation than the traditional goods recommendation. However, existing multi-interest methods are largely sub-optimal for fund recommendation, since they ignore financial domain knowledge and diverse fund investment intentions. In this work, we propose a Disentangled Interest importance aware Knowledge Graph Neural Network (DIKGNN) for personalized fund recommendation on FinTech platforms. In particular, we restrict the multiple intent spaces by introducing the attribute nodes from the fund knowledge graph as the minimum intent modeling unit to utilize financial domain knowledge and provide interpretability. In the intent space, we define disentangled intent representations, equipped with intent importance distributions to describe the diverse fund investment intentions. Then we design a new neighbor aggregation mechanism with the learned intent importance distribution upon the interaction graph and knowledge graph to collect multi-intent information. Furthermore, we leverage micro independence and macro balance constraints on the representations and distributions respectively to encourage intent independence and diversity. The extensive experiments on public recommendation benchmarks demonstrate that DIKGNN can achieve substantial improvement over state-of-the-art methods. Our proposed model is also evaluated over one real-world industrial fund dataset from a FinTech platform and has been deployed online.|目前，人们逐渐意识到财务管理的重要性，因此基金推荐越来越受到人们的重视，以帮助他们尽快找到合适的基金。由于投资者在投资基金时通常会考虑多种因素(如基金主题、基金经理等) ，而基金通常由大量的投资组合构成，因此有效地建立多利益表示模型对于个性化基金推荐比传统的商品推荐更为重要。然而，现有的多利率基金推荐方法由于忽视了金融领域的知识和基金投资意向的多样性，在很大程度上不能满足基金推荐的要求。在这项工作中，我们提出了一个分离利益重要性感知知识图神经网络(DIKGNN)个性化基金推荐在金融科技平台。特别地，我们通过引入基金知识图中的属性节点作为最小意图建模单元来限制多意图空间，以利用金融领域的知识并提供可解释性。在意向空间中，我们定义了非纠缠意向表示，并配备了意向重要性分布来描述不同的基金投资意向。然后设计了一种新的邻居聚集机制，在交互图和知识图上分布学习意图的重要性，以收集多意图信息。此外，我们利用微观独立性和宏观平衡约束分别表示和分布，以鼓励意向独立性和多样性。对公众推荐基准的广泛实验表明，DIKGNN 可以取得实质性的改善国家的最新方法。我们提出的模型也评估了一个来自金融科技平台的真实世界的工业基金数据集，并已在线部署。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Disentangled+Interest+importance+aware+Knowledge+Graph+Neural+Network+for+Fund+Recommendation)|0| -|[Node-dependent Semantic Search over Heterogeneous Graph Neural Networks](https://doi.org/10.1145/3583780.3614989)|Zhenyi Wang, Huan Zhao, Fengqi Liang, Chuan Shi|4Paradigm Inc., Beijing, China; Beijing University of Posts and Telecommunications, Beijing, China|In recent years, Heterogeneous Graph Neural Networks (HGNNs) have been the state-of-the-art approaches for various tasks on Heterogeneous Graphs (HGs), e.g., recommendation and social network analysis. Despite the success of existing HGNNs, the utilization of the intricate semantic information in HGs is still insufficient. In this work, we study the problem of how to design powerful HGNNs under the guidance of node-dependent semantics. Specifically, to perform semantic search over HGNNs, we propose to develop semantic structures in terms of relation selection and connection selection, which could guide a task-relevant message flow. Furthermore, to better capture the diversified property of different node samples in HGs, we design predictors to adaptively decide the semantic structures per node. Extensive experiments on seven benchmarking datasets across different downstream tasks, i.e., node classification and recommendation, show that our method can consistently outperform various state-of-the-art baselines with shorter inference latency, which justifies its effectiveness and efficiency. The code and data are available at https://github.com/BUPT-GAMMA/NDS.|近年来，异构图神经网络(HGNN)已经成为异构图(HGs)上各种任务(如推荐和社会网络分析)的最新研究方法。尽管现有的 HGNN 取得了成功，但在 HGs 中使用的复杂语义信息仍然不足。本文主要研究如何在节点依赖语义的指导下设计强大的 HGNN。具体来说，为了在 HGNN 上进行语义搜索，我们提出了在关系选择和连接选择方面开发语义结构，以指导任务相关的消息流。此外，为了更好地捕捉 HG 中不同节点样本的多样性，我们设计了预测器来自适应地确定每个节点的语义结构。对不同下游任务(即节点分类和推荐)的7个基准测试数据集进行的大量实验表明，我们的方法能够以更短的推理延迟持续优于各种最先进的基线，这证明了其有效性和效率。代码和数据可在 https://github.com/bupt-gamma/nds 查阅。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Node-dependent+Semantic+Search+over+Heterogeneous+Graph+Neural+Networks)|0| +|[CDR: Conservative Doubly Robust Learning for Debiased Recommendation](https://doi.org/10.1145/3583780.3614805)|Zijie Song, Jiawei Chen, Sheng Zhou, Qihao Shi, Yan Feng, Chun Chen, Can Wang|Zhejiang University, Hangzhou, China; Hangzhou City University, Hangzhou, China|In recommendation systems (RS), user behavior data is observational rather than experimental, resulting in widespread bias in the data. Consequently, tackling bias has emerged as a major challenge in the field of recommendation systems. Recently, Doubly Robust Learning (DR) has gained significant attention due to its remarkable performance and robust properties. However, our experimental findings indicate that existing DR methods are severely impacted by the presence of so-called Poisonous Imputation, where the imputation significantly deviates from the truth and becomes counterproductive. To address this issue, this work proposes Conservative Doubly Robust strategy (CDR) which filters imputations by scrutinizing their mean and variance. Theoretical analyses show that CDR offers reduced variance and improved tail bounds.In addition, our experimental investigations illustrate that CDR significantly enhances performance and can indeed reduce the frequency of poisonous imputation.|在推荐系统(RS)中，用户行为数据是观察性的而不是实验性的，这导致了数据中广泛的偏差。因此，处理偏差已经成为推荐系统领域的一个主要挑战。近年来，双鲁棒学习(DR)以其显著的性能和鲁棒性得到了广泛的关注。然而，我们的实验结果表明，现有的 DR 方法受到所谓的有毒归责的严重影响，其中归责明显偏离真相，并成为适得其反。为了解决这个问题，本文提出了保守的双稳健策略(CDR) ，它通过检查估计的均值和方差来过滤估计。理论分析表明，CDR 方差减小，尾界改善，实验结果表明，CDR 方差显著提高了性能，并且确实能够减少有毒插补的频率。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=CDR:+Conservative+Doubly+Robust+Learning+for+Debiased+Recommendation)|0| +|[Disentangled Interest importance aware Knowledge Graph Neural Network for Fund Recommendation](https://doi.org/10.1145/3583780.3614846)|Ke Tu, Wei Qu, Zhengwei Wu, Zhiqiang Zhang, Zhongyi Liu, Yiming Zhao, Le Wu, Jun Zhou, Guannan Zhang|Ant Financial, Hangzhou, China; Hefei University of Technology, Hefei, China; Ant Financial Services Group, Hangzhou, China; Ant Group, Hangzhou, China; Alipay, Beijing, China; Ant Group, Beijing, China|At present, people are gradually becoming aware of financial management and thus fund recommendation attracts more and more attention to help them find suitable funds quickly. As a user usually takes many factors (e.g., fund theme, fund manager) into account when investing a fund and the fund usually consists of a substantial collection of investments, effectively modeling multi-interest representations is more crucial for personalized fund recommendation than the traditional goods recommendation. However, existing multi-interest methods are largely sub-optimal for fund recommendation, since they ignore financial domain knowledge and diverse fund investment intentions. In this work, we propose a Disentangled Interest importance aware Knowledge Graph Neural Network (DIKGNN) for personalized fund recommendation on FinTech platforms. In particular, we restrict the multiple intent spaces by introducing the attribute nodes from the fund knowledge graph as the minimum intent modeling unit to utilize financial domain knowledge and provide interpretability. In the intent space, we define disentangled intent representations, equipped with intent importance distributions to describe the diverse fund investment intentions. Then we design a new neighbor aggregation mechanism with the learned intent importance distribution upon the interaction graph and knowledge graph to collect multi-intent information. Furthermore, we leverage micro independence and macro balance constraints on the representations and distributions respectively to encourage intent independence and diversity. The extensive experiments on public recommendation benchmarks demonstrate that DIKGNN can achieve substantial improvement over state-of-the-art methods. Our proposed model is also evaluated over one real-world industrial fund dataset from a FinTech platform and has been deployed online.|目前，人们逐渐意识到财务管理的重要性，因此基金推荐越来越受到人们的重视，以帮助他们尽快找到合适的基金。由于投资者在投资基金时通常会考虑多种因素(如基金主题、基金经理等) ，而基金通常由大量的投资组合构成，因此有效地建立多利益表示模型对于个性化基金推荐比传统的商品推荐更为重要。然而，现有的多利率基金推荐方法由于忽视了金融领域的知识和基金投资意向的多样性，在很大程度上不能满足基金推荐的要求。在这项工作中，我们提出了一个分离利益重要性感知知识图神经网络(DIKGNN)个性化基金推荐在金融科技平台。特别地，我们通过引入基金知识图中的属性节点作为最小意图建模单元来限制多意图空间，以利用金融领域的知识并提供可解释性。在意向空间中，我们定义了非纠缠意向表示，并配备了意向重要性分布来描述不同的基金投资意向。然后设计了一种新的邻居聚集机制，在交互图和知识图上分布学习意图的重要性，以收集多意图信息。此外，我们利用微观独立性和宏观平衡约束分别表示和分布，以鼓励意向独立性和多样性。对公众推荐基准的广泛实验表明，DIKGNN 可以取得实质性的改善国家的最新方法。我们提出的模型也评估了一个来自金融科技平台的真实世界的工业基金数据集，并已在线部署。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Disentangled+Interest+importance+aware+Knowledge+Graph+Neural+Network+for+Fund+Recommendation)|0| +|[Node-dependent Semantic Search over Heterogeneous Graph Neural Networks](https://doi.org/10.1145/3583780.3614989)|Zhenyi Wang, Huan Zhao, Fengqi Liang, Chuan Shi|Beijing University of Posts and Telecommunications, Beijing, China; 4Paradigm Inc., Beijing, China|In recent years, Heterogeneous Graph Neural Networks (HGNNs) have been the state-of-the-art approaches for various tasks on Heterogeneous Graphs (HGs), e.g., recommendation and social network analysis. Despite the success of existing HGNNs, the utilization of the intricate semantic information in HGs is still insufficient. In this work, we study the problem of how to design powerful HGNNs under the guidance of node-dependent semantics. Specifically, to perform semantic search over HGNNs, we propose to develop semantic structures in terms of relation selection and connection selection, which could guide a task-relevant message flow. Furthermore, to better capture the diversified property of different node samples in HGs, we design predictors to adaptively decide the semantic structures per node. Extensive experiments on seven benchmarking datasets across different downstream tasks, i.e., node classification and recommendation, show that our method can consistently outperform various state-of-the-art baselines with shorter inference latency, which justifies its effectiveness and efficiency. The code and data are available at https://github.com/BUPT-GAMMA/NDS.|近年来，异构图神经网络(HGNN)已经成为异构图(HGs)上各种任务(如推荐和社会网络分析)的最新研究方法。尽管现有的 HGNN 取得了成功，但在 HGs 中使用的复杂语义信息仍然不足。本文主要研究如何在节点依赖语义的指导下设计强大的 HGNN。具体来说，为了在 HGNN 上进行语义搜索，我们提出了在关系选择和连接选择方面开发语义结构，以指导任务相关的消息流。此外，为了更好地捕捉 HG 中不同节点样本的多样性，我们设计了预测器来自适应地确定每个节点的语义结构。对不同下游任务(即节点分类和推荐)的7个基准测试数据集进行的大量实验表明，我们的方法能够以更短的推理延迟持续优于各种最先进的基线，这证明了其有效性和效率。代码和数据可在 https://github.com/bupt-gamma/nds 查阅。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Node-dependent+Semantic+Search+over+Heterogeneous+Graph+Neural+Networks)|0| |[Towards Communication-Efficient Model Updating for On-Device Session-Based Recommendation](https://doi.org/10.1145/3583780.3615088)|Xin Xia, Junliang Yu, Guandong Xu, Hongzhi Yin|The University of Queensland, Brisbane, QLD, Australia; University of Technology Sydney, Sydney, NSW, Australia|On-device recommender systems recently have garnered increasing attention due to their advantages of providing prompt response and securing privacy. To stay current with evolving user interests, cloud-based recommender systems are periodically updated with new interaction data. However, on-device models struggle to retrain themselves because of limited onboard computing resources. As a solution, we consider the scenario where the model retraining occurs on the server side and then the updated parameters are transferred to edge devices via network communication. While this eliminates the need for local retraining, it incurs a regular transfer of parameters that significantly taxes network bandwidth. To mitigate this issue, we develop an efficient approach based on compositional codes to compress the model update. This approach ensures the on-device model is updated flexibly with minimal additional parameters whilst utilizing previous knowledge. The extensive experiments conducted on multiple session-based recommendation models with distinctive architectures demonstrate that the on-device model can achieve comparable accuracy to the retrained server-side counterpart through transferring an update 60x smaller in size. The codes are available at \url{https://github.com/xiaxin1998/ODUpdate}.|设备上推荐系统最近受到越来越多的关注，因为它们具有提供快速响应和保护隐私的优点。为了与不断变化的用户兴趣保持同步，基于云的推荐系统定期更新新的交互数据。然而，由于机载计算资源有限，在设备上的模型很难进行再培训。作为解决方案，我们考虑在服务器端进行模型再训练，然后通过网络通信将更新后的参数传输到边缘设备。虽然这消除了对本地再培训的需要，但它引起定期参数传输，大大增加了网络带宽的负担。为了解决这一问题，我们提出了一种基于复合代码的模型更新压缩方法。这种方法确保在设备上的模型更新灵活，最小的额外参数，同时利用以前的知识。在具有独特体系结构的多会话推荐模型上进行的大量实验表明，设备上模型可以通过传输小60倍的更新来达到与再训练的服务器端模型相当的精度。这些代码可以在 url { https://github.com/xiaxin1998/odupdate }获得。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Towards+Communication-Efficient+Model+Updating+for+On-Device+Session-Based+Recommendation)|0| -|[CoSaR: Combating Label Noise Using Collaborative Sample Selection and Adversarial Regularization](https://doi.org/10.1145/3583780.3614826)|Xiaobo Zhang, Yutao Liu, Hao Wang, Wei Wang, Panpan Ni, Ji Zhang|The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China; Southwest Jiaotong University, Chendu, China; Southwest Jiaotong University, Chengdu, China; University of Southern Queensland, Toowoomba, Australia|Learning with noisy labels is nontrivial for deep learning models. Sample selection is a widely investigated research topic for handling noisy labels. However, most existing methods face challenges such as imprecise selection, a lack of global selection capabilities, and the need for tedious hyperparameter tuning. In this paper, we propose CoSaR (Collaborative Selection and adversarial Regularization ), a twin-networks based model that performs globally adaptive sample selection to tackle label noise. Specifically, the collaborative selection estimates the average distribution distances between predictions and generation labels through the collaboration of two networks to address the bias of the average distribution distances and the manual tuning of hyperparameters. Adversarial regularization is integrated into CoSaR to restrict the network's tendency to fit and memorize noisy labels, thereby enhancing its collaborative selection capability. In addition, we employ a label smoothing regularization and two types of data augmentation to enhance the robustness of the model further. Extensive experiments on both synthetic and real-world noisy datasets demonstrate that the proposed model outperforms baseline methods remarkably, with an accuracy improvement ranging between +0.56% and +15.14%.|对于深度学习模型来说，使用噪声标签进行学习是非常重要的。样本选择是一个广泛研究的课题，处理噪声标签。然而，大多数现有的方法都面临挑战，如选择不精确、缺乏全局选择能力以及需要冗长的超参数调优。本文提出了一种基于双网络的协同选择与对抗正则化(CoSaR)模型，该模型对标签噪声进行全局自适应样本选择。具体而言，协作选择通过两个网络的协作来估计预测和产生标签之间的平均分布距离，以解决平均分布距离的偏差和手动调整超参数。将对抗性规则化集成到 CoSaR 中，以限制网络适配和记忆噪声标签的倾向，从而增强其协同选择能力。此外，我们采用了标签平滑正则化和两种数据增强方法来进一步增强模型的鲁棒性。在合成和真实噪声数据集上的大量实验表明，该模型的性能明显优于基线方法，精度提高幅度在 + 0.56% 和 + 15.14% 之间。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=CoSaR:+Combating+Label+Noise+Using+Collaborative+Sample+Selection+and+Adversarial+Regularization)|0| -|[HST-GT: Heterogeneous Spatial-Temporal Graph Transformer for Delivery Time Estimation in Warehouse-Distribution Integration E-Commerce](https://doi.org/10.1145/3583780.3614918)|Xiaohui Zhao, Shuai Wang, Hai Wang, Tian He, Desheng Zhang, Guang Wang|Florida State University, Tallahassee, FL, USA; Southeast University, Nanjing, China; JD Logistics, Beijing, China; Rutgers University, New Brunswick, NJ, USA|Warehouse-distribution integration has been adopted by many e-commerce retailers (e.g., Amazon, TAOBAO, and JD) as an efficient business mode. In warehouse-distribution integration e-commerce, one of the most important problems is to estimate the full-link delivery time for better decision-making. Existing solutions for traditional warehouse-distribution separation mode are challenging to address this problem due to two unique features in the integration mode including (i) contextual influence caused by neighbor units in heterogeneous delivery networks, (ii) uncertain delivery time caused by the dynamic temporal data (e.g., online sales volume) and heterogeneity of delivery units. To incorporate these new factors, we propose Heterogeneous Spatial-Temporal Graph Transformer (HST-GT), a novel full-link delivery time estimation method under the warehouse-distribution integration mode, where we (i) develop heterogeneous graph transformers to capture hierarchical heterogeneous information; and (ii) design a set of spatial-temporal transformers based on heterogeneous features to fully exploit the correlation of spatial and temporal information. We extensively evaluate our method based on one-month real-world data consisting of hundreds of warehouses and sorting centers, and millions of historical orders collected from one of the largest e-commerce retailers in the world. Experimental results demonstrate that our method outperforms state-of-the-art baselines in various metrics.|仓储-分销集成已被许多电子商务零售商(如亚马逊、淘宝和 JD)采用为一种高效的商业模式。在仓储-配送一体化电子商务中，为了更好地进行决策，需要解决的一个重要问题就是如何估计全程配送时间。由于集成模式中的两个独特特征，传统的仓储-分销分离模式的现有解决方案难以解决这一问题，这两个特征包括: (i)异构配送网络中邻居单元引起的上下文影响，(ii)动态时态数据(如在线销售量)引起的不确定配送时间和配送单元的异质性。为了吸收这些新的因素，我们提出了异构时空图形转换器(HST-GT) ，这是一种在仓库-分布式集成模式下的新的全链路传输时间估计方法。我们(i)开发异构图形转换器来捕获层次化的异构信息; (ii)设计一组基于异构特征的时空转换器来充分利用空间和时间信息的相关性。我们广泛评估我们的方法基于一个月的真实世界数据，包括数百个仓库和分拣中心，以及从世界上最大的电子商务零售商之一收集的数百万历史订单。实验结果表明，我们的方法优于国家的最先进的基线在各种指标。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=HST-GT:+Heterogeneous+Spatial-Temporal+Graph+Transformer+for+Delivery+Time+Estimation+in+Warehouse-Distribution+Integration+E-Commerce)|0| +|[CoSaR: Combating Label Noise Using Collaborative Sample Selection and Adversarial Regularization](https://doi.org/10.1145/3583780.3614826)|Xiaobo Zhang, Yutao Liu, Hao Wang, Wei Wang, Panpan Ni, Ji Zhang|University of Southern Queensland, Toowoomba, Australia; Southwest Jiaotong University, Chendu, China; The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China; Southwest Jiaotong University, Chengdu, China|Learning with noisy labels is nontrivial for deep learning models. Sample selection is a widely investigated research topic for handling noisy labels. However, most existing methods face challenges such as imprecise selection, a lack of global selection capabilities, and the need for tedious hyperparameter tuning. In this paper, we propose CoSaR (Collaborative Selection and adversarial Regularization ), a twin-networks based model that performs globally adaptive sample selection to tackle label noise. Specifically, the collaborative selection estimates the average distribution distances between predictions and generation labels through the collaboration of two networks to address the bias of the average distribution distances and the manual tuning of hyperparameters. Adversarial regularization is integrated into CoSaR to restrict the network's tendency to fit and memorize noisy labels, thereby enhancing its collaborative selection capability. In addition, we employ a label smoothing regularization and two types of data augmentation to enhance the robustness of the model further. Extensive experiments on both synthetic and real-world noisy datasets demonstrate that the proposed model outperforms baseline methods remarkably, with an accuracy improvement ranging between +0.56% and +15.14%.|对于深度学习模型来说，使用噪声标签进行学习是非常重要的。样本选择是一个广泛研究的课题，处理噪声标签。然而，大多数现有的方法都面临挑战，如选择不精确、缺乏全局选择能力以及需要冗长的超参数调优。本文提出了一种基于双网络的协同选择与对抗正则化(CoSaR)模型，该模型对标签噪声进行全局自适应样本选择。具体而言，协作选择通过两个网络的协作来估计预测和产生标签之间的平均分布距离，以解决平均分布距离的偏差和手动调整超参数。将对抗性规则化集成到 CoSaR 中，以限制网络适配和记忆噪声标签的倾向，从而增强其协同选择能力。此外，我们采用了标签平滑正则化和两种数据增强方法来进一步增强模型的鲁棒性。在合成和真实噪声数据集上的大量实验表明，该模型的性能明显优于基线方法，精度提高幅度在 + 0.56% 和 + 15.14% 之间。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=CoSaR:+Combating+Label+Noise+Using+Collaborative+Sample+Selection+and+Adversarial+Regularization)|0| +|[HST-GT: Heterogeneous Spatial-Temporal Graph Transformer for Delivery Time Estimation in Warehouse-Distribution Integration E-Commerce](https://doi.org/10.1145/3583780.3614918)|Xiaohui Zhao, Shuai Wang, Hai Wang, Tian He, Desheng Zhang, Guang Wang|Southeast University, Nanjing, China; JD Logistics, Beijing, China; Florida State University, Tallahassee, FL, USA; Rutgers University, New Brunswick, NJ, USA|Warehouse-distribution integration has been adopted by many e-commerce retailers (e.g., Amazon, TAOBAO, and JD) as an efficient business mode. In warehouse-distribution integration e-commerce, one of the most important problems is to estimate the full-link delivery time for better decision-making. Existing solutions for traditional warehouse-distribution separation mode are challenging to address this problem due to two unique features in the integration mode including (i) contextual influence caused by neighbor units in heterogeneous delivery networks, (ii) uncertain delivery time caused by the dynamic temporal data (e.g., online sales volume) and heterogeneity of delivery units. To incorporate these new factors, we propose Heterogeneous Spatial-Temporal Graph Transformer (HST-GT), a novel full-link delivery time estimation method under the warehouse-distribution integration mode, where we (i) develop heterogeneous graph transformers to capture hierarchical heterogeneous information; and (ii) design a set of spatial-temporal transformers based on heterogeneous features to fully exploit the correlation of spatial and temporal information. We extensively evaluate our method based on one-month real-world data consisting of hundreds of warehouses and sorting centers, and millions of historical orders collected from one of the largest e-commerce retailers in the world. Experimental results demonstrate that our method outperforms state-of-the-art baselines in various metrics.|仓储-分销集成已被许多电子商务零售商(如亚马逊、淘宝和 JD)采用为一种高效的商业模式。在仓储-配送一体化电子商务中，为了更好地进行决策，需要解决的一个重要问题就是如何估计全程配送时间。由于集成模式中的两个独特特征，传统的仓储-分销分离模式的现有解决方案难以解决这一问题，这两个特征包括: (i)异构配送网络中邻居单元引起的上下文影响，(ii)动态时态数据(如在线销售量)引起的不确定配送时间和配送单元的异质性。为了吸收这些新的因素，我们提出了异构时空图形转换器(HST-GT) ，这是一种在仓库-分布式集成模式下的新的全链路传输时间估计方法。我们(i)开发异构图形转换器来捕获层次化的异构信息; (ii)设计一组基于异构特征的时空转换器来充分利用空间和时间信息的相关性。我们广泛评估我们的方法基于一个月的真实世界数据，包括数百个仓库和分拣中心，以及从世界上最大的电子商务零售商之一收集的数百万历史订单。实验结果表明，我们的方法优于国家的最先进的基线在各种指标。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=HST-GT:+Heterogeneous+Spatial-Temporal+Graph+Transformer+for+Delivery+Time+Estimation+in+Warehouse-Distribution+Integration+E-Commerce)|0| |[Scalable Neural Contextual Bandit for Recommender Systems](https://doi.org/10.1145/3583780.3615048)|Zheqing Zhu, Benjamin Van Roy|Stanford University, Stanford, USA; Meta AI, Stanford University, Menlo Park, USA|High-quality recommender systems ought to deliver both innovative and relevant content through effective and exploratory interactions with users. Yet, supervised learning-based neural networks, which form the backbone of many existing recommender systems, only leverage recognized user interests, falling short when it comes to efficiently uncovering unknown user preferences. While there has been some progress with neural contextual bandit algorithms towards enabling online exploration through neural networks, their onerous computational demands hinder widespread adoption in real-world recommender systems. In this work, we propose a scalable sample-efficient neural contextual bandit algorithm for recommender systems. To do this, we design an epistemic neural network architecture, Epistemic Neural Recommendation (ENR), that enables Thompson sampling at a large scale. In two distinct large-scale experiments with real-world tasks, ENR significantly boosts click-through rates and user ratings by at least 9% and 6% respectively compared to state-of-the-art neural contextual bandit algorithms. Furthermore, it achieves equivalent performance with at least 29% fewer user interactions compared to the best-performing baseline algorithm. Remarkably, while accomplishing these improvements, ENR demands orders of magnitude fewer computational resources than neural contextual bandit baseline algorithms.|高质量的推荐系统应该通过与用户有效和探索性的互动交付创新和相关的内容。然而，作为许多现有推荐系统骨干的基于监督学习的神经网络，只能利用已识别的用户兴趣，在有效发现未知用户偏好方面存在不足。虽然神经上下文盗贼算法在通过神经网络实现在线探索方面取得了一些进展，但是它们繁重的计算需求阻碍了在现实世界中推荐系统的广泛采用。在这项工作中，我们提出了一个可扩展的样本效率神经上下文盗贼算法的推荐系统。为了做到这一点，我们设计了一个认知神经网络结构，认知神经推荐(ENR) ，使汤普森采样在大规模。在两个不同的大规模实验与现实世界的任务，ENR 显着提高点击率和用户评分至少9% 和6% 分别相比，国家的最先进的神经上下文土匪算法。此外，与性能最好的基线算法相比，它至少减少了29% 的用户交互，从而实现了相同的性能。值得注意的是，在完成这些改进的同时，ENR 所需的计算资源数量级比神经上下文强盗基线算法要少。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Scalable+Neural+Contextual+Bandit+for+Recommender+Systems)|0| |[PCENet: Psychological Clues Exploration Network for Multimodal Personality Assessment](https://doi.org/10.1145/3583780.3615005)|Yangfu Zhu, Yuting Wei, Meiling Li, Tingting Zhang, Siqi Wei, Bin Wu|Beijing University of Posts and Telecommunications, Beijing, China; Beijing University of Posts and Telecommunications,, Beijing, China|Multimodal personality assessment aims to identify and express human personality traits in videos. Existing methods primarily focus on multimodal fusion while ignoring the inherent psychological clues essential for this interdisciplinary task. Modality clues: personality traits are stable over time due to their genetic and environmental origins, resulting in stable personality traits in the multimodal data. Trait clues: multiple traits often co-occur with non-negligible correlations, which can collectively aid trait identification. To simultaneously capture the above psychological clues, we propose a novel Psychological Clues Exploration Network (PCENet) for multimodal personality assessment, which is a human-like judgment paradigm with more generalization capability. Specifically, we first devise a multimodal hierarchical disentanglement, which clearly aligns stable representations among different modalities and separates the mutability of each modality. Subsequently, a Transformer-backbone decoder equipped with modality-to-trait attention is exploited to adaptively generate a tailored representation for each trait with the guidance of trait semantics. The trait semantics are obtained by exploiting trait correlations through self-attention. Extensive experiments on the First Impression V2 dataset demonstrate that our PCENet outperforms the state-of-the-art methods for multimodal personality assessment.|多模态人格评估旨在识别和表达视频中的人格特征。现有的方法主要侧重于多模态融合，而忽视了这一跨学科任务所必需的内在心理线索。情态线索: 由于遗传和环境起源，人格特征随着时间的推移是稳定的，在多模态数据中导致稳定的人格特征。性状线索: 多个性状常常与不可忽视的相关性共同出现，这可以共同帮助性状识别。为了同时捕捉上述心理线索，我们提出了一个新的心理线索探索网络(PCENet)的多模态人格评估，这是一个类人的判断范式，具有更多的泛化能力。具体来说，我们首先设计了一个多模态层次分离，它清楚地调整了不同模态之间的稳定表示，并分离了每种模态的可变性。然后，在特征语义的指导下，利用具有模态-特征注意的主干变压器解码器自适应地为每个特征生成一个量身定制的表示。特质语义是通过自我注意利用特质相关获得的。对第一印象 V2数据集的大量实验表明，我们的 PCENet 优于最先进的多模态人格评估方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=PCENet:+Psychological+Clues+Exploration+Network+for+Multimodal+Personality+Assessment)|0| -|[G-STO: Sequential Main Shopping Intention Detection via Graph-Regularized Stochastic Transformer](https://doi.org/10.1145/3583780.3614890)|Yuchen Zhuang, Xin Shen, Yan Zhao, Chaosheng Dong, Ming Wang, Jin Li, Chao Zhang|Amazon, New York, NY, USA; Amazon, Seattle, WA, USA; Georgia Institute of Technology, Atlanta, GA, USA|Sequential recommendation requires understanding the dynamic patterns of users' behaviors, contexts, and preferences from their historical interactions. Most existing works focus on modeling user-item interactions only from the item level, ignoring that they are driven by latent shopping intentions (e.g., ballpoint pens, miniatures, etc). The detection of the underlying shopping intentions of users based on their historical interactions is a crucial aspect for e-commerce platforms, such as Amazon, to enhance the convenience and efficiency of their customers' shopping experiences. Despite its significance, the area of main shopping intention detection remains under-investigated in the academic literature. To fill this gap, we propose a graph-regularized stochastic Transformer method, G-STO. By considering intentions as sets of products and user preferences as compositions of intentions, we model both of them as stochastic Gaussian embeddings in the latent representation space. Instead of training the stochastic representations from scratch, we develop a global intention relational graph as prior knowledge for regularization, allowing relevant shopping intentions to be distributionally close. Finally, we feed the newly regularized stochastic embeddings into Transformer-based models to encode sequential information from the intention transitions. We evaluate our main shopping intention identification model on three different real-world datasets, where G-STO achieves significantly superior performances to the baselines by 18.08% in Hit@1, 7.01% in Hit@10, and 6.11% in NDCG@10 on average.|顺序推荐需要从用户的历史交互中理解用户行为、上下文和偏好的动态模式。大多数现有的作品只关注于从商品层次建模用户-商品交互，忽略了它们是由潜在的购物意图驱动的(例如，圆珠笔，微缩模型等)。基于用户历史交互的潜在购物意图的检测是亚马逊等电子商务平台提高用户购物体验的便利性和效率的一个关键方面。尽管其意义重大，主要的购物意图检测领域仍然没有得到充分的研究在学术文献。为了填补这个空白，我们提出了一个图正则化的随机变压器方法，G-STO。通过将意图看作是产品集合，将用户偏好看作是意图的组合，我们将两者建模为潜在表征空间中的随机高斯嵌入。我们不需要从头开始训练随机表示，而是开发一个全局意图关系图作为正则化的先验知识，允许相关的购物意图分布接近。最后，我们将新的正则化随机嵌入输入到基于变压器的模型中，从意图转换中编码序列信息。我们在三个不同的真实世界数据集上评估了我们的主要购物意向识别模型，其中 G-STO 在 Hit@1中的性能明显优于基线18.08% ，在 Hit@10中的性能为7.01% ，在 NDCG@10中的性能平均为6.11% 。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=G-STO:+Sequential+Main+Shopping+Intention+Detection+via+Graph-Regularized+Stochastic+Transformer)|0| -|[TCCM: Time and Content-Aware Causal Model for Unbiased News Recommendation](https://doi.org/10.1145/3583780.3615272)|Yewang Chen, Weiyao Ye, Guipeng Xv, Chen Lin, Xiaomin Zhu|Huaqiao University, Xiamen, China; Xiamen University, Xiamen, China; Academy of Military Sciences, Beijing, China|Popularity bias significantly impacts news recommendation systems, as popular news articles receive more exposure and are often delivered to irrelevant users, resulting in unsatisfactory performance. Existing methods have not adequately addressed the issue of popularity bias in news recommendations, largely due to the neglect of the time factor and the impact of news content on popularity. In this paper, we propose a novel approach called Time and Content-aware Causal Model, namely TCCM. It models the effects of three factors on user interaction behavior, i.e., the time factor, the news popularity, and the matching between news content and user interest. TCCM also estimates news popularity more accurately by incorporating the news content, i.e., the popularity of entity and words. Causal intervention techniques are applied to obtain debiased recommendations. Extensive experiments on well-known benchmark datasets demonstrate that the proposed approach outperforms a range of state-of-the-art techniques.|受欢迎度偏差对新闻推荐系统有显著影响，因为受欢迎的新闻文章曝光率更高，而且常常被传递给不相关的用户，从而导致不令人满意的性能。现有的研究方法没有充分解决新闻推荐中的受欢迎程度偏差问题，这主要是由于忽视了时间因素和新闻内容对受欢迎程度的影响。在本文中，我们提出了一种新的方法称为时间和内容感知的因果模型，即 TCCM。它模拟了三个因素对用户交互行为的影响，即时间因素、新闻受欢迎程度以及新闻内容与用户兴趣的匹配程度。TCCM 还通过结合新闻内容，即实体和词汇的受欢迎程度，更准确地估计新闻的受欢迎程度。应用因果干预技术来获得消除偏见的建议。在著名基准数据集上的大量实验表明，所提出的方法优于一系列最先进的技术。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=TCCM:+Time+and+Content-Aware+Causal+Model+for+Unbiased+News+Recommendation)|0| +|[G-STO: Sequential Main Shopping Intention Detection via Graph-Regularized Stochastic Transformer](https://doi.org/10.1145/3583780.3614890)|Yuchen Zhuang, Xin Shen, Yan Zhao, Chaosheng Dong, Ming Wang, Jin Li, Chao Zhang|Amazon, Seattle, WA, USA; Amazon, New York, NY, USA; Georgia Institute of Technology, Atlanta, GA, USA|Sequential recommendation requires understanding the dynamic patterns of users' behaviors, contexts, and preferences from their historical interactions. Most existing works focus on modeling user-item interactions only from the item level, ignoring that they are driven by latent shopping intentions (e.g., ballpoint pens, miniatures, etc). The detection of the underlying shopping intentions of users based on their historical interactions is a crucial aspect for e-commerce platforms, such as Amazon, to enhance the convenience and efficiency of their customers' shopping experiences. Despite its significance, the area of main shopping intention detection remains under-investigated in the academic literature. To fill this gap, we propose a graph-regularized stochastic Transformer method, G-STO. By considering intentions as sets of products and user preferences as compositions of intentions, we model both of them as stochastic Gaussian embeddings in the latent representation space. Instead of training the stochastic representations from scratch, we develop a global intention relational graph as prior knowledge for regularization, allowing relevant shopping intentions to be distributionally close. Finally, we feed the newly regularized stochastic embeddings into Transformer-based models to encode sequential information from the intention transitions. We evaluate our main shopping intention identification model on three different real-world datasets, where G-STO achieves significantly superior performances to the baselines by 18.08% in Hit@1, 7.01% in Hit@10, and 6.11% in NDCG@10 on average.|顺序推荐需要从用户的历史交互中理解用户行为、上下文和偏好的动态模式。大多数现有的作品只关注于从商品层次建模用户-商品交互，忽略了它们是由潜在的购物意图驱动的(例如，圆珠笔，微缩模型等)。基于用户历史交互的潜在购物意图的检测是亚马逊等电子商务平台提高用户购物体验的便利性和效率的一个关键方面。尽管其意义重大，主要的购物意图检测领域仍然没有得到充分的研究在学术文献。为了填补这个空白，我们提出了一个图正则化的随机变压器方法，G-STO。通过将意图看作是产品集合，将用户偏好看作是意图的组合，我们将两者建模为潜在表征空间中的随机高斯嵌入。我们不需要从头开始训练随机表示，而是开发一个全局意图关系图作为正则化的先验知识，允许相关的购物意图分布接近。最后，我们将新的正则化随机嵌入输入到基于变压器的模型中，从意图转换中编码序列信息。我们在三个不同的真实世界数据集上评估了我们的主要购物意向识别模型，其中 G-STO 在 Hit@1中的性能明显优于基线18.08% ，在 Hit@10中的性能为7.01% ，在 NDCG@10中的性能平均为6.11% 。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=G-STO:+Sequential+Main+Shopping+Intention+Detection+via+Graph-Regularized+Stochastic+Transformer)|0| +|[TCCM: Time and Content-Aware Causal Model for Unbiased News Recommendation](https://doi.org/10.1145/3583780.3615272)|Yewang Chen, Weiyao Ye, Guipeng Xv, Chen Lin, Xiaomin Zhu|Huaqiao University, Xiamen, China; Academy of Military Sciences, Beijing, China; Xiamen University, Xiamen, China|Popularity bias significantly impacts news recommendation systems, as popular news articles receive more exposure and are often delivered to irrelevant users, resulting in unsatisfactory performance. Existing methods have not adequately addressed the issue of popularity bias in news recommendations, largely due to the neglect of the time factor and the impact of news content on popularity. In this paper, we propose a novel approach called Time and Content-aware Causal Model, namely TCCM. It models the effects of three factors on user interaction behavior, i.e., the time factor, the news popularity, and the matching between news content and user interest. TCCM also estimates news popularity more accurately by incorporating the news content, i.e., the popularity of entity and words. Causal intervention techniques are applied to obtain debiased recommendations. Extensive experiments on well-known benchmark datasets demonstrate that the proposed approach outperforms a range of state-of-the-art techniques.|受欢迎度偏差对新闻推荐系统有显著影响，因为受欢迎的新闻文章曝光率更高，而且常常被传递给不相关的用户，从而导致不令人满意的性能。现有的研究方法没有充分解决新闻推荐中的受欢迎程度偏差问题，这主要是由于忽视了时间因素和新闻内容对受欢迎程度的影响。在本文中，我们提出了一种新的方法称为时间和内容感知的因果模型，即 TCCM。它模拟了三个因素对用户交互行为的影响，即时间因素、新闻受欢迎程度以及新闻内容与用户兴趣的匹配程度。TCCM 还通过结合新闻内容，即实体和词汇的受欢迎程度，更准确地估计新闻的受欢迎程度。应用因果干预技术来获得消除偏见的建议。在著名基准数据集上的大量实验表明，所提出的方法优于一系列最先进的技术。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=TCCM:+Time+and+Content-Aware+Causal+Model+for+Unbiased+News+Recommendation)|0| |[Attribute-enhanced Dual Channel Representation Learning for Session-based Recommendation](https://doi.org/10.1145/3583780.3615245)|Qian Chen, Jianjun Li, Zhiqiang Guo, Guohui Li, Zhiying Deng|Huazhong University of Science and Technology, Wuhan, China|Session-based recommendation (SBR) aims to predict the anonymous user's next-click items by modeling the short-term sequence pattern. As most existing SBR models generally generate item representations based only on information propagation over the short sequence while ignoring additional valuable knowledge, their expressive abilities are somewhat limited by data sparsity caused by short sequence. Though there have been some attempts on utilizing items' attributes, they basically embed attributes into items directly, ignoring the fact that 1) there is no contextual relationship among attributes; and 2) users have varying levels of attention to different attributes, which still leads to unsatisfactory performance. To tackle the issues, we propose a novel Attribute-enhanced Dual Channel Representation Learning (ADRL) model for SBR, in which we independently model session representations in attribute-related pattern and sequence-related pattern. Specifically, we learn session representations with sequence patterns from the session graph, and we further design an frequency-driven attribute aggregator to generate the attribute-related session representations within a session. The proposed attribute aggregator is plug-and-play, as it can be coupled with most existing SBR models. Extensive experiments on three real-world public datasets demonstrate the superiority of the proposed ADRL over several state-of-the-art baselines, as well as the effectiveness and efficiency of our attribute aggregator module.|基于会话的推荐(SBS)通过建立短期序列模式来预测匿名用户的下一次点击项目。由于现有的 SBR 模型大多只是基于短序列的信息传播来产生条目表示，而忽略了附加的有价值的知识，因此它们的表示能力受到短序列造成的数据稀疏的限制。尽管已经有一些尝试利用项目的属性，他们基本上直接将属性嵌入到项目中，忽略了这样一个事实: 1)属性之间没有上下文关系; 2)用户对不同属性的关注程度不同，这仍然会导致不令人满意的性能。为了解决这一问题，我们提出了一种新的基于属性增强的双通道表示学习(ADRL)模型，该模型独立地对会话表示进行属性相关模式和序列相关模式的建模。具体来说，我们从会话图中学习具有序列模式的会话表示，并且进一步设计一个频率驱动的属性聚合器来在会话中生成与属性相关的会话表示。提出的属性聚合器是即插即用的，因为它可以与大多数现有的 SBR 模型耦合。在三个真实世界的公共数据集上的大量实验表明了所提出的 ADRL 相对于几个最先进的基线的优越性，以及我们的属性聚合器模块的有效性和效率。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Attribute-enhanced+Dual+Channel+Representation+Learning+for+Session-based+Recommendation)|0| |[Simulating Users in Interactive Web Table Retrieval](https://doi.org/10.1145/3583780.3615187)|Björn Engelmann, Timo Breuer, Philipp Schaer|TH Köln (University of Applied Sciences), Köln, Germany|Considering the multimodal signals of search items is beneficial for retrieval effectiveness. Especially in web table retrieval (WTR) experiments, accounting for multimodal properties of tables boosts effectiveness. However, it still remains an open question how the single modalities affect user experience in particular. Previous work analyzed WTR performance in ad-hoc retrieval benchmarks, which neglects interactive search behavior and limits the conclusion about the implications for real-world user environments. To this end, this work presents an in-depth evaluation of simulated interactive WTR search sessions as a more cost-efficient and reproducible alternative to real user studies. As a first of its kind, we introduce interactive query reformulation strategies based on Doc2Query, incorporating cognitive states of simulated user knowledge. Our evaluations include two perspectives on user effectiveness by considering different cost paradigms, namely query-wise and time-oriented measures of effort. Our multi-perspective evaluation scheme reveals new insights about query strategies, the impact of modalities, and different user types in simulated WTR search sessions.|考虑检索项的多模态信号有利于提高检索效率。特别是在 Web 表检索(WTR)实验中，考虑表的多模态特性可以提高检索效率。然而，单一模式如何特别影响用户体验仍然是一个悬而未决的问题。以往的工作分析了自组织检索基准的 WTR 性能，忽略了交互式搜索行为，限制了对实际用户环境影响的结论。为此，这项工作提出了一个深入的评价模拟交互式 WTR 搜索会话作为一个更具成本效益和可重复的替代真正的用户研究。首次提出了基于 Doc2Query 的交互式查询重构策略，该策略融合了模拟用户知识的认知状态。通过考虑不同的成本范式，我们的评估包括对用户有效性的两个视角，即查询式和面向时间的工作量度。我们的多视角评估方案揭示了在模拟 WTR 搜索会话中关于查询策略、模式影响和不同用户类型的新见解。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Simulating+Users+in+Interactive+Web+Table+Retrieval)|0| |[Test-Time Embedding Normalization for Popularity Bias Mitigation](https://doi.org/10.1145/3583780.3615281)|Dain Kim, Jinhyeok Park, Dongwoo Kim|POSTECH, Pohang, Republic of Korea|Popularity bias is a widespread problem in the field of recommender systems, where popular items tend to dominate recommendation results. In this work, we propose 'Test Time Embedding Normalization' as a simple yet effective strategy for mitigating popularity bias, which surpasses the performance of the previous mitigation approaches by a significant margin. Our approach utilizes the normalized item embedding during the inference stage to control the influence of embedding magnitude, which is highly correlated with item popularity. Through extensive experiments, we show that our method combined with the sampled softmax loss effectively reduces popularity bias compare to previous approaches for bias mitigation. We further investigate the relationship between user and item embeddings and find that the angular similarity between embeddings distinguishes preferable and non-preferable items regardless of their popularity. The analysis explains the mechanism behind the success of our approach in eliminating the impact of popularity bias. Our code is available at https://github.com/ml-postech/TTEN.|在推荐系统领域，流行度偏差是一个普遍存在的问题，在这个领域中，流行项目往往占据推荐结果的主导地位。在这项工作中，我们提出了“测试时间嵌入规范化”作为一个简单而有效的策略，以减轻流行偏差，这超过了以前的缓解方法的性能显着差距。该方法在推理阶段利用归一化项目嵌入来控制项目嵌入量的影响，项目嵌入量与项目知名度高度相关。通过大量的实验，我们发现与以往的偏差抑制方法相比，我们的方法结合采样软最大损失有效地降低了流行偏差。我们进一步研究了用户与项目嵌入之间的关系，发现无论项目受欢迎程度如何，用户与项目嵌入之间的角度相似度都能区分出优选项目和不优选项目。该分析解释了我们的方法在消除流行偏见影响方面取得成功背后的机制。我们的代码可以在 https://github.com/ml-postech/tten 找到。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Test-Time+Embedding+Normalization+for+Popularity+Bias+Mitigation)|0| -|[Boosting Meta-Learning Cold-Start Recommendation with Graph Neural Network](https://doi.org/10.1145/3583780.3615283)|Han Liu, Hongxiang Lin, Xiaotong Zhang, Fenglong Ma, Hongyang Chen, Lei Wang, Hong Yu, Xianchao Zhang|Zhejiang Lab, Hangzhou, China; Peking University, Beijing, China; The Pennsylvania State University, University Park, USA; Dalian University of Technology, Dalian, China; Meituan, Beijing, China|Meta-learning methods have shown to be effective in dealing with cold-start recommendation. However, most previous methods rely on an ideal assumption that there exists a similar data distribution between source and target tasks, which are unsuitable for the scenario that only extremely limited number of new user or item interactions are available. In this paper, we propose to boost meta-learning cold-start recommendation with graph neural network (MeGNN). First, it utilizes the global neighborhood translation learning to obtain consistent potential interactions for all new user and item nodes, which can refine their representations. Second, it employs the local neighborhood translation learning to predict specific potential interactions for each node, thus guaranteeing the personalized requirement. In experiments, we combine MeGNN with two representative meta-learning models MeLU and TaNP. Extensive results on two widely-used datasets show the superiority of MeGNN in four different scenarios.|元学习方法已被证明在处理冷启动推荐时是有效的。但是，大多数以前的方法都依赖于一个理想的假设，即在源任务和目标任务之间存在类似的数据分布，这种假设不适合于只有极其有限的新用户或项交互可用的场景。本文提出了一种基于图神经网络(MeGNN)的元学习冷启动推荐算法。首先，利用全局邻域翻译学习来获得所有新的用户和项目节点的一致的潜在交互，从而改进它们的表示。其次，利用局部邻域翻译学习来预测每个节点的特定潜在交互，从而保证个性化需求。在实验中，我们将 MeGNN 与两个有代表性的元学习模型 MeLU 和 TaNP 相结合。在两个广泛使用的数据集上的广泛结果显示了 MeGNN 在四种不同场景下的优越性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Boosting+Meta-Learning+Cold-Start+Recommendation+with+Graph+Neural+Network)|0| +|[Boosting Meta-Learning Cold-Start Recommendation with Graph Neural Network](https://doi.org/10.1145/3583780.3615283)|Han Liu, Hongxiang Lin, Xiaotong Zhang, Fenglong Ma, Hongyang Chen, Lei Wang, Hong Yu, Xianchao Zhang|Peking University, Beijing, China; Zhejiang Lab, Hangzhou, China; Dalian University of Technology, Dalian, China; Meituan, Beijing, China; The Pennsylvania State University, University Park, USA|Meta-learning methods have shown to be effective in dealing with cold-start recommendation. However, most previous methods rely on an ideal assumption that there exists a similar data distribution between source and target tasks, which are unsuitable for the scenario that only extremely limited number of new user or item interactions are available. In this paper, we propose to boost meta-learning cold-start recommendation with graph neural network (MeGNN). First, it utilizes the global neighborhood translation learning to obtain consistent potential interactions for all new user and item nodes, which can refine their representations. Second, it employs the local neighborhood translation learning to predict specific potential interactions for each node, thus guaranteeing the personalized requirement. In experiments, we combine MeGNN with two representative meta-learning models MeLU and TaNP. Extensive results on two widely-used datasets show the superiority of MeGNN in four different scenarios.|元学习方法已被证明在处理冷启动推荐时是有效的。但是，大多数以前的方法都依赖于一个理想的假设，即在源任务和目标任务之间存在类似的数据分布，这种假设不适合于只有极其有限的新用户或项交互可用的场景。本文提出了一种基于图神经网络(MeGNN)的元学习冷启动推荐算法。首先，利用全局邻域翻译学习来获得所有新的用户和项目节点的一致的潜在交互，从而改进它们的表示。其次，利用局部邻域翻译学习来预测每个节点的特定潜在交互，从而保证个性化需求。在实验中，我们将 MeGNN 与两个有代表性的元学习模型 MeLU 和 TaNP 相结合。在两个广泛使用的数据集上的广泛结果显示了 MeGNN 在四种不同场景下的优越性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Boosting+Meta-Learning+Cold-Start+Recommendation+with+Graph+Neural+Network)|0| |[STGIN: Spatial-Temporal Graph Interaction Network for Large-scale POI Recommendation](https://doi.org/10.1145/3583780.3615200)|Shaohua Liu, Yu Qi, Gen Li, Mingjian Chen, Teng Zhang, Jia Cheng, Jun Lei|Meituan, Shanghai, China|In Location-Based Services, Point-Of-Interest(POI) recommendation plays a crucial role in both user experience and business opportunities. Graph neural networks have been proven effective in providing personalized POI recommendation services. However, there are still two critical challenges. First, existing graph models attempt to capture users' diversified interests through a unified graph, which limits their ability to express interests in various spatial-temporal contexts. Second, the efficiency limitations of graph construction and graph sampling in large-scale systems make it difficult to adapt quickly to new real-time interests. To tackle the above challenges, we propose a novel Spatial-Temporal Graph Interaction Network. Specifically, we construct subgraphs of spatial, temporal, spatial-temporal, and global views respectively to precisely characterize the user's interests in various contexts. In addition, we design an industry-friendly framework to track the user's latest interests. Extensive experiments on the real-world dataset show that our method outperforms state-of-the-art models. This work has been successfully deployed in a large e-commerce platform, delivering a 1.1% CTR and 6.3% RPM improvement.|在基于位置的服务中，兴趣点(POI)推荐在用户体验和商业机会中都扮演着至关重要的角色。图形神经网络在提供个性化 POI 推荐服务方面已被证明是有效的。然而，仍然存在两个关键的挑战。首先，现有的图模型试图通过一个统一的图来捕捉用户的多样化兴趣，这限制了用户在不同的时空背景下表达兴趣的能力。其次，在大规模系统中，由于图的构造和采样效率的限制，很难快速适应新的实时需求。为了解决上述问题，我们提出了一种新的时空图交互网络。具体来说，我们分别构造了空间、时间、空间-时间和全局视图的子图，以精确表征用户在不同情境下的兴趣。此外，我们还设计了一个行业友好的框架来跟踪用户的最新兴趣。在真实世界数据集上的大量实验表明，我们的方法优于最先进的模型。这项工作已经成功地部署在一个大型电子商务平台，提供了1.1% 的点击率和6.3% 的 RPM 改进。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=STGIN:+Spatial-Temporal+Graph+Interaction+Network+for+Large-scale+POI+Recommendation)|0| |[FairGraph: Automated Graph Debiasing with Gradient Matching](https://doi.org/10.1145/3583780.3615176)|Yezi Liu|University of California, Irvine, Irvine, CA, USA|As a prevalence data structure in the real world, graphs have found extensive applications ranging from modeling social networks to molecules. However, the existence of diverse biases within graphs gives rise to unfair representations learned by graph neural networks (GNNs). Addressing this issue has typically been approached from a modeling perspective, which not only compromises the integrity of the model structure but also entails additional effort and cost for retraining model parameters when the architecture changes. In this study, we adopt a data-centric standpoint to tackle the problem of fairness, focusing on graph debiasing for Graph Neural Networks. Our specific objective is to eliminate various biases from the input graph by generating a fair synthetic graph. By training GNNs on this fair graph, we aim to achieve an optimal accuracy-fairness trade-off. To this end, we propose FairGraph, which approaches the graph debiasing problem by mimicking the GNN training trajectory of the input graph through an optimization process involving a gradient-matching loss and fairness constraints. Through extensive experiments conducted on three benchmark datasets, we demonstrate the effectiveness of FairGraph and its ability to automatedly generate fair graphs that are transferable across different GNN architectures.|作为现实世界中流行的数据结构，图表已经发现了广泛的应用，从建模社会网络到分子。然而，图中存在不同的偏差会导致图神经网络学习到的不公平表示。解决这个问题通常是从建模的角度出发的，这不仅损害了模型结构的完整性，而且在体系结构发生变化时需要额外的努力和成本来重新训练模型参数。在本研究中，我们采用以数据为中心的观点来解决公平性问题，重点是图神经网络的图形消偏。我们的具体目标是通过生成一个公平的综合图来消除输入图中的各种偏差。通过在这个公平图上训练 GNN，我们的目标是实现最佳的精度-公平权衡。为此，我们提出了 FairGraph，它通过一个包含梯度匹配损失和公平约束的优化过程来模拟输入图的 GNN 训练轨迹，从而解决图的去偏问题。通过在三个基准数据集上进行的大量实验，我们证明了 FairGraph 的有效性及其自动生成可跨不同 GNN 架构转移的 Fair 图的能力。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=FairGraph:+Automated+Graph+Debiasing+with+Gradient+Matching)|0| |[Product Entity Matching via Tabular Data](https://doi.org/10.1145/3583780.3615172)|Ali Naeim abadi, Mir Tafseer Nayeem, Davood Rafiei|University of Alberta, Edmonton, AB, Canada|Product Entity Matching (PEM)--a subfield of record linkage that focuses on linking records that refer to the same product--is a challenging task for many entity matching models. For example, recent transformer models report a near-perfect performance score on many datasets while their performance is the lowest on PEM datasets. In this paper, we study PEM under the common setting where the information is spread over text and tables. We show that adding tables can enrich the existing PEM datasets and those tables can act as a bridge between the entities being matched. We also propose TATEM, an effective solution that leverages Pre-trained Language Models (PLMs) with a novel serialization technique to encode tabular product data and an attribute ranking module to make our model more data-efficient. Our experiments on both current benchmark datasets and our proposed datasets show significant improvements compared to state-of-the-art methods, including Large Language Models (LLMs) in zero-shot and few-shot settings.|产品实体匹配(Product Entity Matching，PEM)——记录链接的一个子领域，其重点是链接引用同一产品的记录——对于许多实体匹配模型来说是一项具有挑战性的任务。例如，最近的变压器模型在许多数据集上报告了接近完美的性能得分，而在 PEM 数据集上它们的性能最低。在本文中，我们研究质子交换膜下的公共设置，其中的信息是分布在文本和表格。我们表明，添加表可以丰富现有的 PEM 数据集，并且这些表可以作为匹配实体之间的桥梁。我们还提出了 TATEM，一个有效的解决方案，利用预训练语言模型(PLM)与一种新的序列化技术来编码表格产品数据和属性排序模块，使我们的模型更有效的数据。我们对当前基准数据集和我们提出的数据集的实验表明，与最先进的方法相比，包括大语言模型(LLM)在零拍摄和少拍摄设置方面有显著的改进。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Product+Entity+Matching+via+Tabular+Data)|0| -|[Neural Disentanglement of Query Difficulty and Semantics](https://doi.org/10.1145/3583780.3615189)|Sara Salamat, Negar Arabzadeh, Shirin Seyedsalehi, Amin Bigdeli, Morteza Zihayat, Ebrahim Bagheri|University of Waterloo, Waterloo, ON, Canada; Toronto Metropolitan University, Toronto, ON, Canada|Researchers have shown that the retrieval effectiveness of queries may depend on other factors in addition to the semantics of the query. In other words, several queries expressed with the same intent, and even using overlapping keywords, may exhibit completely different degrees of retrieval effectiveness. As such, the objective of our work in this paper is to propose a neural disentanglement method that is able to disentangle query semantics from query difficulty. The disentangled query semantics representation provides the means to determine semantic association between queries whereas the disentangled query difficulty representation would allow for the estimation of query effectiveness. We show through our experiments on the query performance prediction; and, query similarity calculation tasks that our proposed disentanglement method is able to show better performance compared to the state of the art.|研究表明，查询的检索效果除了取决于查询的语义外，还取决于其他因素。换句话说，几个具有相同意图的查询，甚至使用重叠关键字，可能表现出完全不同程度的检索效率。因此，本文的工作目标是提出一种能够将查询语义从查询难度中分离出来的神经网络分离方法。分离查询语义表示提供了确定查询之间语义关联的方法，而分离查询难度表示则可以评估查询的有效性。通过对查询性能预测和查询相似度计算任务的实验表明，本文提出的分离方法能够比现有方法表现出更好的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Neural+Disentanglement+of+Query+Difficulty+and+Semantics)|0| +|[Neural Disentanglement of Query Difficulty and Semantics](https://doi.org/10.1145/3583780.3615189)|Sara Salamat, Negar Arabzadeh, Shirin Seyedsalehi, Amin Bigdeli, Morteza Zihayat, Ebrahim Bagheri|Toronto Metropolitan University, Toronto, ON, Canada; University of Waterloo, Waterloo, ON, Canada|Researchers have shown that the retrieval effectiveness of queries may depend on other factors in addition to the semantics of the query. In other words, several queries expressed with the same intent, and even using overlapping keywords, may exhibit completely different degrees of retrieval effectiveness. As such, the objective of our work in this paper is to propose a neural disentanglement method that is able to disentangle query semantics from query difficulty. The disentangled query semantics representation provides the means to determine semantic association between queries whereas the disentangled query difficulty representation would allow for the estimation of query effectiveness. We show through our experiments on the query performance prediction; and, query similarity calculation tasks that our proposed disentanglement method is able to show better performance compared to the state of the art.|研究表明，查询的检索效果除了取决于查询的语义外，还取决于其他因素。换句话说，几个具有相同意图的查询，甚至使用重叠关键字，可能表现出完全不同程度的检索效率。因此，本文的工作目标是提出一种能够将查询语义从查询难度中分离出来的神经网络分离方法。分离查询语义表示提供了确定查询之间语义关联的方法，而分离查询难度表示则可以评估查询的有效性。通过对查询性能预测和查询相似度计算任务的实验表明，本文提出的分离方法能够比现有方法表现出更好的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Neural+Disentanglement+of+Query+Difficulty+and+Semantics)|0| |[EdgeNet : Encoder-decoder generative Network for Auction Design in E-commerce Online Advertising](https://doi.org/10.1145/3583780.3615192)|Guangyuan Shen, Shengjie Sun, Dehong Gao, Duanxiao Song, Libin Yang, Zhen Wang, Yongping Shi, Wei Ning||We present a new encoder-decoder generative network dubbed EdgeNet, which introduces a novel encoder-decoder framework for data-driven auction design in online e-commerce advertising. We break the neural auction paradigm of Generalized-Second-Price(GSP), and improve the utilization efficiency of data while ensuring the economic characteristics of the auction mechanism. Specifically, EdgeNet introduces a transformer-based encoder to better capture the mutual influence among different candidate advertisements. In contrast to GSP based neural auction model, we design an autoregressive decoder to better utilize the rich context information in online advertising auctions. EdgeNet is conceptually simple and easy to extend to the existing end-to-end neural auction framework. We validate the efficiency of EdgeNet on a wide range of e-commercial advertising auction, demonstrating its potential in improving user experience and platform revenue.|提出了一种新的编解码生成网络 EdgeNet，该网络为在线电子商务广告中的数据驱动拍卖设计提供了一种新的编解码框架。我们打破了广义二级价格(GSP)的神经拍卖范式，在保证拍卖机制的经济性的同时，提高了数据的利用效率。具体来说，EdgeNet 引入了一种基于变压器的编码器，以更好地捕捉不同候选广告之间的相互影响。与基于 GSP 的神经拍卖模型相比，我们设计了一个自回归解码器，以更好地利用在线广告拍卖中丰富的上下文信息。EdgeNet 概念简单，易于扩展到现有的端到端神经拍卖框架。我们验证了 EdgeNet 在广泛的电子商务广告拍卖中的效率，证明了它在改善用户体验和平台收入方面的潜力。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=EdgeNet+:+Encoder-decoder+generative+Network+for+Auction+Design+in+E-commerce+Online+Advertising)|0| |[G-Meta: Distributed Meta Learning in GPU Clusters for Large-Scale Recommender Systems](https://doi.org/10.1145/3583780.3615208)|Youshao Xiao, Shangchun Zhao, Zhenglei Zhou, Zhaoxin Huan, Lin Ju, Xiaolu Zhang, Lin Wang, Jun Zhou|Ant Group, Hangzhou, China|Recently, a new paradigm, meta learning, has been widely applied to Deep Learning Recommendation Models (DLRM) and significantly improves statistical performance, especially in cold-start scenarios. However, the existing systems are not tailored for meta learning based DLRM models and have critical problems regarding efficiency in distributed training in the GPU cluster. It is because the conventional deep learning pipeline is not optimized for two task-specific datasets and two update loops in meta learning. This paper provides a high-performance framework for large-scale training for Optimization-based Meta DLRM models over the G PU cluster, namely G -Meta. Firstly, G-Meta utilizes both data parallelism and model parallelism with careful orchestration regarding computation and communication efficiency, to enable high-speed distributed training. Secondly, it proposes a Meta-IO pipeline for efficient data ingestion to alleviate the I/O bottleneck. Various experimental results show that G-Meta achieves notable training speed without loss of statistical performance. Since early 2022, G-Meta has been deployed in Alipay's core advertising and recommender system, shrinking the continuous delivery of models by four times. It also obtains 6.48% improvement in Conversion Rate (CVR) and 1.06% increase in CPM (Cost Per Mille) in Alipay's homepage display advertising, with the benefit of larger training samples and tasks.|最近，一种新的学习模式元学习被广泛应用于深度学习推荐模型(DLRM) ，并显著提高了统计性能，特别是在冷启动情景下。然而，现有的系统并不适合基于元学习的 DLRM 模型，并且在 GPU 集群的分布式培训效率方面存在关键问题。这是因为传统的深度学习流水线没有针对元学习中的两个任务特定的数据集和两个更新循环进行优化。本文提供了一个在 G PU 集群上进行基于优化的元 DLRM 模型大规模培训的高性能框架，即 G-Meta。首先，G-Meta 利用了资料平行和模型的并行性，在计算和通信效率方面进行了精心的编排，从而实现了高速的分布式训练。其次，提出了一种有效的数据摄取元 IO 管道，以缓解 I/O 瓶颈。各种实验结果表明，G-Meta 在不损失统计性能的前提下，达到了显著的训练速度。自2022年初以来，g-Meta 一直部署在支付宝的核心广告和推荐系统中，将模型的持续交付缩减了4倍。在支付宝主页显示广告中，转化率(CVR)提高了6.48% ，每公里成本(CPM)提高了1.06% ，这些都得益于更大的培训样本和任务。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=G-Meta:+Distributed+Meta+Learning+in+GPU+Clusters+for+Large-Scale+Recommender+Systems)|0| |[MEBS: Multi-task End-to-end Bid Shading for Multi-slot Display Advertising](https://doi.org/10.1145/3583780.3615486)|Zhen Gong, Lvyin Niu, Yang Zhao, Miao Xu, Haoqi Zhang, Zhenzhe Zheng, Zhilin Zhang, Rongquan Bai, Chuan Yu, Jian Xu, Bo Zheng, Fan Wu|Alibaba Group, Beijing, China; Shanghai Jiao Tong University, Shanghai, China|Online bidding and auction are crucial aspects of the online advertising industry. Conventionally, there is only one slot for ad display and most current studies focus on it. Nowadays, multi-slot display advertising is gradually becoming popular where many ads could be displayed in a list and shown as a whole to users. However, multi-slot display advertising leads to different cost-effectiveness. Advertisers have the incentive to adjust bid prices so as to win the most economical ad positions. In this study, we introduce bid shading into multi-slot display advertising for bid price adjustment with a Multi-task End-to-end Bid Shading~(MEBS) method. We prove the optimality of our method theoretically and examine its performance experimentally. Through extensive offline and online experiments, we demonstrate the effectiveness and efficiency of our method, and we obtain a 7.01% lift in Gross Merchandise Volume, a 7.42% lift in Return on Investment, and a 3.26% lift in ad buy count.|在线招标和拍卖是在线广告业的重要方面。传统上，只有一个广告展示时段，目前大多数研究集中在它。如今，多插槽显示广告正逐渐流行，许多广告可以显示在一个列表中，并作为一个整体显示给用户。然而，多插槽显示广告导致不同的成本效益。广告商有动机调整投标价格，以赢得最经济的广告位置。在本研究中，我们利用多任务端到端的投标底纹 ~ (MEBS)方法，将投标底纹引入多时隙显示广告中，以调整投标价格。从理论上证明了该方法的最优性，并对其性能进行了实验验证。通过大量的线下和线上实验，我们证明了我们的方法的有效性和效率，我们获得了7.01% 的商品总量增长，7.42% 的投资回报增长，3.26% 的广告购买数量增长。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=MEBS:+Multi-task+End-to-end+Bid+Shading+for+Multi-slot+Display+Advertising)|0| -|[DFFM: Domain Facilitated Feature Modeling for CTR Prediction](https://doi.org/10.1145/3583780.3615469)|Wei Guo, Chenxu Zhu, Fan Yan, Bo Chen, Weiwen Liu, Huifeng Guo, Hongkun Zheng, Yong Liu, Ruiming Tang|Huawei Noah's Ark Lab, Huawei, Shanghai, China; Huawei Technologies Co Ltd, Shenzhen, China; Huawei Noah's Ark Lab, Shanghai, China|CTR prediction is critical to industrial recommender systems. Recently, with the growth of business domains in enterprises, much attention has been focused on the multi-domain CTR recommendation. Numerous models have been proposed that attempt to use a unified model to serve multiple domains. Although much progress has been made, we argue that they ignore the importance of feature interactions and user behaviors when modeling cross-domain relations, which is a coarse-grained utilizing of domain information. To solve this problem, we propose Domain Facilitated Feature Modeling (DFFM) for CTR prediction. It incorporates domain-related information into the parameters of the feature interaction and user behavior modules, allowing for domain-specific learning of these two aspects. Extensive experiments are conducted on two public datasets and one industrial dataset to demonstrate the effectiveness of DFFM. We deploy the DFFM model in Huawei advertising platform and gain a 4.13% improvement of revenue on a two week online A/B test. Currently DFFM model has been used as the main traffic model, serving for hundreds of millions of people.|CTR 预测是工业推荐系统的关键。近年来，随着企业业务领域的不断扩大，多领域 CTR 推荐引起了人们的广泛关注。许多模型试图使用一个统一的模型来服务于多个领域。虽然已经取得了很大的进展，但是我们认为他们忽视了特征交互和用户行为在跨领域关系建模中的重要性，这是对领域信息的粗粒度利用。为了解决这个问题，我们提出了领域简化特征建模(DFFM)的 CTR 预测。它将领域相关信息整合到特征交互和用户行为模块的参数中，允许特定领域学习这两个方面。为了验证 DFFM 算法的有效性，在两个公共数据集和一个工业数据集上进行了大量的实验。我们在华为广告平台采用了 dFFM 模式，通过两周的在线 A/B 测试，收入提高了4.13% 。目前 DFFM 模型已经成为主要的流量模型，服务于数亿人。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=DFFM:+Domain+Facilitated+Feature+Modeling+for+CTR+Prediction)|0| +|[DFFM: Domain Facilitated Feature Modeling for CTR Prediction](https://doi.org/10.1145/3583780.3615469)|Wei Guo, Chenxu Zhu, Fan Yan, Bo Chen, Weiwen Liu, Huifeng Guo, Hongkun Zheng, Yong Liu, Ruiming Tang|Huawei Noah's Ark Lab, Shanghai, China; Huawei Noah's Ark Lab, Huawei, Shanghai, China; Huawei Technologies Co Ltd, Shenzhen, China|CTR prediction is critical to industrial recommender systems. Recently, with the growth of business domains in enterprises, much attention has been focused on the multi-domain CTR recommendation. Numerous models have been proposed that attempt to use a unified model to serve multiple domains. Although much progress has been made, we argue that they ignore the importance of feature interactions and user behaviors when modeling cross-domain relations, which is a coarse-grained utilizing of domain information. To solve this problem, we propose Domain Facilitated Feature Modeling (DFFM) for CTR prediction. It incorporates domain-related information into the parameters of the feature interaction and user behavior modules, allowing for domain-specific learning of these two aspects. Extensive experiments are conducted on two public datasets and one industrial dataset to demonstrate the effectiveness of DFFM. We deploy the DFFM model in Huawei advertising platform and gain a 4.13% improvement of revenue on a two week online A/B test. Currently DFFM model has been used as the main traffic model, serving for hundreds of millions of people.|CTR 预测是工业推荐系统的关键。近年来，随着企业业务领域的不断扩大，多领域 CTR 推荐引起了人们的广泛关注。许多模型试图使用一个统一的模型来服务于多个领域。虽然已经取得了很大的进展，但是我们认为他们忽视了特征交互和用户行为在跨领域关系建模中的重要性，这是对领域信息的粗粒度利用。为了解决这个问题，我们提出了领域简化特征建模(DFFM)的 CTR 预测。它将领域相关信息整合到特征交互和用户行为模块的参数中，允许特定领域学习这两个方面。为了验证 DFFM 算法的有效性，在两个公共数据集和一个工业数据集上进行了大量的实验。我们在华为广告平台采用了 dFFM 模式，通过两周的在线 A/B 测试，收入提高了4.13% 。目前 DFFM 模型已经成为主要的流量模型，服务于数亿人。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=DFFM:+Domain+Facilitated+Feature+Modeling+for+CTR+Prediction)|0| |[Masked Multi-Domain Network: Multi-Type and Multi-Scenario Conversion Rate Prediction with a Single Model](https://doi.org/10.1145/3583780.3614697)|Wentao Ouyang, Xiuwu Zhang, Chaofeng Guo, Shukui Ren, Yupei Sui, Kun Zhang, Jinmei Luo, Yunfeng Chen, Dongbo Xu, Xiangzheng Liu, Yanlong Du|Alibaba Group, Beijing, China|In real-world advertising systems, conversions have different types in nature and ads can be shown in different display scenarios, both of which highly impact the actual conversion rate (CVR). This results in the multi-type and multi-scenario CVR prediction problem. A desired model for this problem should satisfy the following requirements: 1) Accuracy: the model should achieve fine-grained accuracy with respect to any conversion type in any display scenario. 2) Scalability: the model parameter size should be affordable. 3) Convenience: the model should not require a large amount of effort in data partitioning, subset processing and separate storage. Existing approaches cannot simultaneously satisfy these requirements. For example, building a separate model for each (conversion type, display scenario) pair is neither scalable nor convenient. Building a unified model trained on all the data with conversion type and display scenario included as two features is not accurate enough. In this paper, we propose the Masked Multi-domain Network (MMN) to solve this problem. To achieve the accuracy requirement, we model domain-specific parameters and propose a dynamically weighted loss to account for the loss scale imbalance issue within each mini-batch. To achieve the scalability requirement, we propose a parameter sharing and composition strategy to reduce model parameters from a product space to a sum space. To achieve the convenience requirement, we propose an auto-masking strategy which can take mixed data from all the domains as input. It avoids the overhead caused by data partitioning, individual processing and separate storage. Both offline and online experimental results validate the superiority of MMN for multi-type and multi-scenario CVR prediction. MMN is now the serving model for real-time CVR prediction in UC Toutiao.|在现实世界的广告系统中，转化率具有不同的性质，广告可以在不同的显示场景中显示，这两者都对实际转化率(CVR)有很大的影响。这导致了多类型和多情景 CVR 预测问题。这个问题的期望模型应该满足以下要求: 1)精度: 模型应该达到细粒度的精度相对于任何转换类型在任何显示场景。2)可伸缩性: 模型参数的大小应该是可以承受的。3)方便性: 模型不需要在数据分区、子集处理和独立存储方面做大量的工作。现有的方法不能同时满足这些需求。例如，为每个(转换类型，显示场景)对构建单独的模型既不可伸缩也不方便。将转换类型和显示场景作为两个特征包含在内，对所有数据建立统一的训练模型是不够准确的。本文提出了掩蔽多域网络(MMN)来解决这一问题。为了达到精度要求，我们对特定领域的参数进行建模，并提出一个动态加权损失，以解决每个小批量内损失规模不平衡的问题。为了满足可扩展性的要求，我们提出了一种参数共享和合成策略，将模型参数从乘积空间减少到和空间。为了达到方便的要求，我们提出了一种自动掩蔽的策略，可以从所有领域的混合数据作为输入。它避免了由于数据分区、单独处理和单独存储而造成的开销。离线和在线实验结果验证了 MMN 在多类型、多情景 CVR 预测中的优越性。MMN 现在是 UC 今日头条实时 CVR 预测的服务模型。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Masked+Multi-Domain+Network:+Multi-Type+and+Multi-Scenario+Conversion+Rate+Prediction+with+a+Single+Model)|0| |[TrendSpotter: Forecasting E-commerce Product Trends](https://doi.org/10.1145/3583780.3615503)|Gayatri Ryali, Shreyas S, Sivaramakrishnan Kaveri, Prakash Mandayam Comar|Amazon.com Inc., Bengaluru, India|Internet users actively search for trending products on various social media services like Instagram and YouTube which serve as popular hubs for discovering and exploring fashionable and popular items. It is imperative for e-commerce giants to have the capability to accurately identify, predict and subsequently showcase these trending products to the customers. E-commerce stores can effectively cater to the evolving demands of the customer base and enhance the overall shopping experience by offering recent and most sought-after products in a timely manner. In this work we propose a framework for predicting and surfacing trending products in e-commerce stores, the first of its kind to the best of our knowledge. We begin by defining what constitutes a trending product using sound statistical tests. We then introduce a machine learning-based early trend prediction system called TrendSpotter to help users identify upcoming product trends. TrendSpotter is a unique adaptation of the state-of-the-art InceptionTime model\citeInceptionTime that predicts the future popularity of a product based on its current customer engagement, such as clicks, purchases, and other relevant product attributes. The effectiveness of our approach is demonstrated through A/B tests, where we first showcase the effectiveness of our statistical test based labeling strategy, resulting in an incremental sales lift of 59 bps\footnotebps or basis points are a measure of percentages. 1 bps = 0.01% across two experiments on home page and search page. Subsequently, we conduct a comparison between our machine learning model and the statistical labeling baseline and observe an additional sales gain of 14 bps, reflecting the importance of early identification of trending products.|互联网用户在 Instagram 和 YouTube 等社交媒体上积极搜索流行产品，这些社交媒体是发现和探索时尚和流行物品的热门中心。电子商务巨头必须具备准确识别、预测和随后向客户展示这些趋势产品的能力。电子商务商店能够有效地满足顾客群不断变化的需求，并通过及时提供最新和最受欢迎的产品，提高整体购物体验。在这项工作中，我们提出了一个框架，预测和表面趋势的产品在电子商务商店，这是第一个类似的最好的我们的知识。我们首先使用可靠的统计检验来定义什么是趋势产品。然后，我们引入一个基于机器学习的早期趋势预测系统，称为趋势观察器，以帮助用户识别即将出现的产品趋势。TrendSpotter 是对最先进的 InceptionTime 模型 citeInceptionTime 的独特改编，该模型根据当前的客户参与度，如点击、购买和其他相关产品属性，预测产品未来的受欢迎程度。我们的方法的有效性通过 A/B 测试得到了证明，我们首先展示了我们基于统计测试的标签策略的有效性，从而使销售额增加了59个基点。1bps = 0.01% ，通过主页和搜索页面的两个实验。随后，我们对我们的机器学习模型和统计标签基线进行了比较，观察到额外的14个基点的销售收益，反映了早期识别趋势产品的重要性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=TrendSpotter:+Forecasting+E-commerce+Product+Trends)|0| |[An Incremental Update Framework for Online Recommenders with Data-Driven Prior](https://doi.org/10.1145/3583780.3615456)|Chen Yang, Jin Chen, Qian Yu, Xiangdong Wu, Kui Ma, Zihao Zhao, Zhiwei Fang, Wenlong Chen, Chaosheng Fan, Jie He, Changping Peng, Zhangang Lin, Jingping Shao|JD.com, Beijing, China; UESTC, Chengdu, China|Online recommenders have attained growing interest and created great revenue for businesses. Given numerous users and items, incremental update becomes a mainstream paradigm for learning large-scale models in industrial scenarios, where only newly arrived data within a sliding window is fed into the model, meeting the strict requirements of quick response. However, this strategy would be prone to overfitting to newly arrived data. When there exists a significant drift of data distribution, the long-term information would be discarded, which harms the recommendation performance. Conventional methods address this issue through native model-based continual learning methods, without analyzing the data characteristics for online recommenders. To address the aforementioned issue, we propose an incremental update framework for online recommenders with Data-Driven Prior (DDP), which is composed of Feature Prior (FP) and Model Prior (MP). The FP performs the click estimation for each specific value to enhance the stability of the training process. The MP incorporates previous model output into the current update while strictly following the Bayes rules, resulting in a theoretically provable prior for the robust update. In this way, both the FP and MP are well integrated into the unified framework, which is model-agnostic and can accommodate various advanced interaction models. Extensive experiments on two publicly available datasets as well as an industrial dataset demonstrate the superior performance of the proposed framework.|在线推荐已经获得了越来越多的兴趣，并为企业创造了巨大的收入。鉴于用户和项目众多，增量更新成为工业场景中学习大规模模型的主流范式，在工业场景中，只有滑动窗口中新到达的数据才被输入模型，以满足快速响应的严格要求。但是，这种策略容易过度适应新到达的数据。当数据分布存在显著漂移时，长期信息会被丢弃，从而影响推荐性能。传统的方法通过基于本地模型的连续学习方法来解决这个问题，而没有分析在线推荐的数据特征。为了解决上述问题，我们提出了一种基于数据驱动优先级(DDP)的在线推荐增量更新框架，该框架由特征优先级(FP)和模型优先级(MP)组成。FP 对每个特定值执行点击估计，以增强培训过程的稳定性。MP 在严格遵循贝叶斯规则的同时，将以前的模型输出合并到当前更新中，从而为鲁棒更新提供了一个理论上可证明的先验。通过这种方式，FP 和 MP 很好地集成到统一框架中，这是模型不可知的，可以适应各种先进的交互模型。在两个公开可用的数据集和一个工业数据集上的大量实验证明了该框架的优越性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=An+Incremental+Update+Framework+for+Online+Recommenders+with+Data-Driven+Prior)|0| |[SHARK: A Lightweight Model Compression Approach for Large-scale Recommender Systems](https://doi.org/10.1145/3583780.3615499)|Beichuan Zhang, Chenggen Sun, Jianchao Tan, Xinjun Cai, Jun Zhao, Mengqi Miao, Kang Yin, Chengru Song, Na Mou, Yang Song|Kuaishou, Beijing, China|Increasing the size of embedding layers has shown to be effective in improving the performance of recommendation models, yet gradually causing their sizes to exceed terabytes in industrial recommender systems, and hence the increase of computing and storage costs. To save resources while maintaining model performances, we propose SHARK, the model compression practice we have summarized in the recommender system of industrial scenarios. SHARK consists of two main components. First, we use the novel first-order component of Taylor expansion as importance scores to prune the number of embedding tables (feature fields). Second, we introduce a new row-wise quantization method to apply different quantization strategies to each embedding. We conduct extensive experiments on both public and industrial datasets, demonstrating that each component of our proposed SHARK framework outperforms previous approaches. We conduct A/B tests in multiple models on Kuaishou, such as short video, e-commerce, and advertising recommendation models. The results of the online A/B test showed SHARK can effectively reduce the memory footprint of the embedded layer. For the short-video scenarios, the compressed model without any performance drop significantly saves 70% storage and thousands of machines, improves 30\% queries per second (QPS), and has been deployed to serve hundreds of millions of users and process tens of billions of requests every day.|在工业推荐系统中，增加嵌入层的大小可以有效地提高推荐模型的性能，但是会逐渐导致其大小超过 TB，从而增加计算和存储成本。为了在保持模型性能的同时节省资源，我们提出了 SHARK，这是我们在工业场景推荐系统中总结的模型压缩实践。鲨鱼由两个主要部分组成。首先，我们利用泰勒展开的一阶分量作为重要性分数来裁剪嵌入表(特征域)的数目。其次，我们介绍了一种新的行量化方法，对每个嵌入应用不同的量化策略。我们在公共和工业数据集上进行了广泛的实验，证明了我们提出的 SHARK 框架的每个组件都优于以前的方法。我们在 Kuaishou 进行多种模式的 A/B 测试，例如短片、电子商务和广告推荐模式。在线 A/B 测试结果表明，SHARK 可以有效地减少嵌入层的内存占用。对于短视频场景，没有任何性能下降的压缩模型显著地节省了70% 的存储和数千台机器，提高了每秒30% 的查询(QPS) ，并且已经被部署用于服务数亿用户和每天处理数百亿个请求。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=SHARK:+A+Lightweight+Model+Compression+Approach+for+Large-scale+Recommender+Systems)|0| -|[Leveraging Watch-time Feedback for Short-Video Recommendations: A Causal Labeling Framework](https://doi.org/10.1145/3583780.3615483)|Yang Zhang, Yimeng Bai, Jianxin Chang, Xiaoxue Zang, Song Lu, Jing Lu, Fuli Feng, Yanan Niu, Yang Song|University of Science and Technology of China, Hefei, China; University of Science and Technology of China & USTC Beijing Research Institute, Hefei, China; Kuaishou Technology, Beijing, China|With the proliferation of short video applications, the significance of short video recommendations has vastly increased. Unlike other recommendation scenarios, short video recommendation systems heavily rely on feedback from watch time. Existing approaches simply treat watch time as a direct label, failing to effectively harness its extensive semantics and introduce bias, thereby limiting the potential for modeling user interests based on watch time. To overcome this challenge, we propose a framework named Debiasied Multiple-semantics-extracting Labeling (DML). DML constructs labels that encompass various semantics by utilizing quantiles derived from the distribution of watch time, prioritizing relative order rather than absolute label values. This approach facilitates easier model learning while aligning with the ranking objective of recommendations. Furthermore, we introduce a method inspired by causal adjustment to refine label definitions, thereby reducing the impact of bias on the label and directly mitigating bias at the label level. We substantiate the effectiveness of our DML framework through both online and offline experiments. Extensive results demonstrate that our DML could effectively leverage watch time to discover users' real interests, enhancing their engagement in our application.|随着短视频应用程序的激增，短视频推荐的重要性大大增加。与其他推荐场景不同，短视频推荐系统严重依赖于观看时间的反馈。现有的方法只是简单地将手表时间作为一个直接标签，未能有效地利用其广泛的语义并引入偏见，从而限制了基于手表时间建模用户兴趣的潜力。为了克服这一挑战，我们提出了一个名为 Debiasied 多语义抽取标记(DML)的框架。DML 通过利用从手表时间分布派生的分位数构造包含各种语义的标签，优先考虑相对顺序而不是绝对标签值。这种方法促进了更容易的模型学习，同时与建议的排名目标保持一致。此外，我们引入了一种方法，受因果调整的启发，以完善标签的定义，从而减少偏见的影响，标签和直接减轻偏见的水平。我们通过两个在线和离线的实验证实了我们的 DML 框架的有效性。大量的结果表明，我们的 DML 可以有效地利用观看时间来发现用户的真正兴趣，提高他们在我们的应用程序中的参与度。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Leveraging+Watch-time+Feedback+for+Short-Video+Recommendations:+A+Causal+Labeling+Framework)|0| +|[Leveraging Watch-time Feedback for Short-Video Recommendations: A Causal Labeling Framework](https://doi.org/10.1145/3583780.3615483)|Yang Zhang, Yimeng Bai, Jianxin Chang, Xiaoxue Zang, Song Lu, Jing Lu, Fuli Feng, Yanan Niu, Yang Song|University of Science and Technology of China & USTC Beijing Research Institute, Hefei, China; University of Science and Technology of China, Hefei, China; Kuaishou Technology, Beijing, China|With the proliferation of short video applications, the significance of short video recommendations has vastly increased. Unlike other recommendation scenarios, short video recommendation systems heavily rely on feedback from watch time. Existing approaches simply treat watch time as a direct label, failing to effectively harness its extensive semantics and introduce bias, thereby limiting the potential for modeling user interests based on watch time. To overcome this challenge, we propose a framework named Debiasied Multiple-semantics-extracting Labeling (DML). DML constructs labels that encompass various semantics by utilizing quantiles derived from the distribution of watch time, prioritizing relative order rather than absolute label values. This approach facilitates easier model learning while aligning with the ranking objective of recommendations. Furthermore, we introduce a method inspired by causal adjustment to refine label definitions, thereby reducing the impact of bias on the label and directly mitigating bias at the label level. We substantiate the effectiveness of our DML framework through both online and offline experiments. Extensive results demonstrate that our DML could effectively leverage watch time to discover users' real interests, enhancing their engagement in our application.|随着短视频应用程序的激增，短视频推荐的重要性大大增加。与其他推荐场景不同，短视频推荐系统严重依赖于观看时间的反馈。现有的方法只是简单地将手表时间作为一个直接标签，未能有效地利用其广泛的语义并引入偏见，从而限制了基于手表时间建模用户兴趣的潜力。为了克服这一挑战，我们提出了一个名为 Debiasied 多语义抽取标记(DML)的框架。DML 通过利用从手表时间分布派生的分位数构造包含各种语义的标签，优先考虑相对顺序而不是绝对标签值。这种方法促进了更容易的模型学习，同时与建议的排名目标保持一致。此外，我们引入了一种方法，受因果调整的启发，以完善标签的定义，从而减少偏见的影响，标签和直接减轻偏见的水平。我们通过两个在线和离线的实验证实了我们的 DML 框架的有效性。大量的结果表明，我们的 DML 可以有效地利用观看时间来发现用户的真正兴趣，提高他们在我们的应用程序中的参与度。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Leveraging+Watch-time+Feedback+for+Short-Video+Recommendations:+A+Causal+Labeling+Framework)|0| |[COPR: Consistency-Oriented Pre-Ranking for Online Advertising](https://doi.org/10.1145/3583780.3615465)|Zhishan Zhao, Jingyue Gao, Yu Zhang, Shuguang Han, Siyuan Lou, XiangRong Sheng, Zhe Wang, Han Zhu, Yuning Jiang, Jian Xu, Bo Zheng|Alibaba Group, Beijing, China|Cascading architecture has been widely adopted in large-scale advertising systems to balance efficiency and effectiveness. In this architecture, the pre-ranking model is expected to be a lightweight approximation of the ranking model, which handles more candidates with strict latency requirements. Due to the gap in model capacity, the pre-ranking and ranking models usually generate inconsistent ranked results, thus hurting the overall system effectiveness. The paradigm of score alignment is proposed to regularize their raw scores to be consistent. However, it suffers from inevitable alignment errors and error amplification by bids when applied in online advertising. To this end, we introduce a consistency-oriented pre-ranking framework for online advertising, which employs a chunk-based sampling module and a plug-and-play rank alignment module to explicitly optimize consistency of ECPM-ranked results. A $\Delta NDCG$-based weighting mechanism is adopted to better distinguish the importance of inter-chunk samples in optimization. Both online and offline experiments have validated the superiority of our framework. When deployed in Taobao display advertising system, it achieves an improvement of up to +12.3\% CTR and +5.6\% RPM.|为了平衡效率和效果，级联体系结构在大规模广告系统中得到了广泛的应用。在这种体系结构中，预排序模型被期望是排序模型的轻量级近似，它处理具有严格延迟要求的更多候选者。由于模型容量的差距，预排序模型和排序模型通常会产生不一致的排序结果，从而影响系统的整体有效性。提出了分数对齐的范式，以规范他们的原始分数是一致的。然而，在网络广告中应用时，不可避免地会出现一致性错误和出价放大错误。为此，我们引入了一个面向一致性的在线广告预排序框架，该框架采用基于块的抽样模块和即插即用的排序对齐模块来显式优化 ECPM 排序结果的一致性。为了更好地区分块间样本在优化中的重要性，采用了基于 $Delta NDCG 的加权机制。这两个在线和离线实验都验证了我们框架的优越性。在淘宝展示广告系统中，点击率和转速分别提高了12.3% 和5.6% 。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=COPR:+Consistency-Oriented+Pre-Ranking+for+Online+Advertising)|0| |[Learning What to Ask: Mining Product Attributes for E-commerce Sales from Massive Dialogue Corpora](https://doi.org/10.1145/3583780.3614745)|Yan Fan, Chengyu Wang, Fan Feng, Hengbin Cui, Yuchuan Wu, Yongbin Li|Alibaba Group, Hangzhou, China|Conversational Recommender Systems (CRSs) are extensively applied in e-commercial platforms that recommend items to users. To ensure accurate recommendation, agents usually ask for users' preferences towards specific product attributes which are pre-defined by humans. In e-commercial platforms, however, the number of products easily reaches to billions, making it prohibitive to pre-define decisive attributes for efficient recommendation due to the lack of substantial human resources and the scarce domain expertise. In this work, we present AliMeMOSAIC, a novel knowledge mining and conversational assistance framework that extracts core product attributes from massive dialogue corpora for better conversational recommendation experience. It first extracts user-agent interaction utterances from massive corpora that contain product attributes. A Joint Attribute and Value Extraction (JAVE) network is designed to extract product attributes from user-agent interaction utterances. Finally, AliMeMOSAIC generates attribute sets that frequently appear in dialogues as the target attributes for agents to request, and serve as an assistant to guide the dialogue flow. To prove the effectiveness of AliMeMOSAIC, we show that it consistently improves the overall recommendation performance of our CRS system. An industrial demonstration scenario is further presented to show how it benefits online shopping experiences.|会话推荐系统(CRS)广泛应用于向用户推荐项目的电子商务平台。为了确保准确的推荐，代理通常会询问用户对特定产品属性的偏好，这些属性是由人工预先定义的。然而，在电子商务平台上，产品的数量很容易达到数十亿，由于缺乏大量人力资源和稀缺的领域专门知识，因此无法预先确定有效推荐的决定性属性。在这项工作中，我们提出了 AliMeMOSAIC，一个新的知识挖掘和会话辅助框架，提取核心产品属性从大量的对话语料库，以更好的会话推荐体验。它首先从包含产品属性的海量语料库中提取用户-代理交互语句。设计了一个联合属性和价值提取(JAVE)网络，从用户-代理交互语句中提取产品属性。最后，AliMeMOSAIC 生成对话中经常出现的属性集，作为代理请求的目标属性，并作为指导对话流的助手。为了证明 AliMeMOSAIC 的有效性，我们展示了它持续改善了我们的 CRS 系统的整体推荐性能。进一步介绍了一个工业示范场景，以说明它如何有利于在线购物体验。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Learning+What+to+Ask:+Mining+Product+Attributes+for+E-commerce+Sales+from+Massive+Dialogue+Corpora)|0| -|[Uplift Modeling: From Causal Inference to Personalization](https://doi.org/10.1145/3583780.3615298)|Felipe Moraes, Hugo Manuel Proença, Anastasiia Kornilova, Javier Albert, Dmitri Goldenberg|Booking.com, Tel-Aviv, Israel; Booking.com, Amsterdam, Netherlands|Uplift modeling is a collection of machine learning techniques for estimating causal effects of a treatment at the individual or subgroup levels. Over the last years, causality and uplift modeling have become key trends in personalization at online e-commerce platforms, enabling the selection of the best treatment for each user in order to maximize the target business metric. Uplift modeling can be particularly useful for personalized promotional campaigns, where the potential benefit caused by a promotion needs to be weighed against the potential costs. In this tutorial we will cover basic concepts of causality and introduce the audience to state-of-the-art techniques in uplift modeling. We will discuss the advantages and the limitations of different approaches and dive into the unique setup of constrained uplift modeling. Finally, we will present real-life applications and discuss challenges in implementing these models in production.|提升建模是机器学习技术的集合，用于估计个体或亚组水平治疗的因果效应。在过去几年中，因果关系和提升建模已成为在线电子商务平台个性化的主要趋势，使得能够为每个用户选择最佳待遇，以最大限度地实现目标业务指标。提升模型对于个性化的促销活动特别有用，因为需要权衡促销活动带来的潜在收益和潜在成本。在本教程中，我们将涵盖因果关系的基本概念，并向观众介绍最先进的提升建模技术。我们将讨论不同方法的优点和局限性，并深入探讨约束抬升模型的独特设置。最后，我们将展示实际应用程序，并讨论在生产中实现这些模型的挑战。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Uplift+Modeling:+From+Causal+Inference+to+Personalization)|0| +|[Uplift Modeling: From Causal Inference to Personalization](https://doi.org/10.1145/3583780.3615298)|Felipe Moraes, Hugo Manuel Proença, Anastasiia Kornilova, Javier Albert, Dmitri Goldenberg|Booking.com, Amsterdam, Netherlands; Booking.com, Tel-Aviv, Israel|Uplift modeling is a collection of machine learning techniques for estimating causal effects of a treatment at the individual or subgroup levels. Over the last years, causality and uplift modeling have become key trends in personalization at online e-commerce platforms, enabling the selection of the best treatment for each user in order to maximize the target business metric. Uplift modeling can be particularly useful for personalized promotional campaigns, where the potential benefit caused by a promotion needs to be weighed against the potential costs. In this tutorial we will cover basic concepts of causality and introduce the audience to state-of-the-art techniques in uplift modeling. We will discuss the advantages and the limitations of different approaches and dive into the unique setup of constrained uplift modeling. Finally, we will present real-life applications and discuss challenges in implementing these models in production.|提升建模是机器学习技术的集合，用于估计个体或亚组水平治疗的因果效应。在过去几年中，因果关系和提升建模已成为在线电子商务平台个性化的主要趋势，使得能够为每个用户选择最佳待遇，以最大限度地实现目标业务指标。提升模型对于个性化的促销活动特别有用，因为需要权衡促销活动带来的潜在收益和潜在成本。在本教程中，我们将涵盖因果关系的基本概念，并向观众介绍最先进的提升建模技术。我们将讨论不同方法的优点和局限性，并深入探讨约束抬升模型的独特设置。最后，我们将展示实际应用程序，并讨论在生产中实现这些模型的挑战。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Uplift+Modeling:+From+Causal+Inference+to+Personalization)|0| |[Vigil: Effective End-to-end Monitoring for Large-scale Recommender Systems at Glance](https://doi.org/10.1145/3583780.3615997)|Priyansh Saxena, Manisha R|Glance, Bangalore, India|The success of large-scale recommender systems hinges upon their ability to deliver accurate and timely recommendations to a diverse user base. At Glance, we deliver snackable personalized content to the lock screens of 200M smartphones. In this context, continuous monitoring is paramount as it safeguards data integrity, detects drifts, addresses evolving user preferences, optimizes system downtime, and ultimately augments the system's effectiveness and user satisfaction. In this talk, we delve into Vigil, a set of monitoring practices developed to provide comprehensive end-to-end monitoring of recommender systems at Glance. These practices revolve around three key pillars: mitigating developer fatigue, ensuring precise predictions, and establishing a centralized monitoring framework. By adopting these practices, we have observed a 30% reduction in compute cost, a 26% drop in downtime, and a surge in developer productivity demonstrated by a 45% decrease in turnaround time.|大型推荐系统的成功取决于它们向不同用户群提供准确和及时的推荐的能力。在 Glance，我们为2亿部智能手机的锁定屏幕提供可点心的个性化内容。在这种情况下，持续监控是至关重要的，因为它可以保证数据的完整性，检测漂移，解决不断变化的用户偏好，优化系统停机时间，并最终提高系统的有效性和用户满意度。在这个演讲中，我们深入研究了 Vigil，这是一组监控实践，开发它们是为了在 Glance 中提供对推荐系统的全面的端到端监控。这些实践围绕着三个关键支柱: 减轻开发人员疲劳、确保精确的预测以及建立一个集中的监控框架。通过采用这些实践，我们观察到计算成本降低了30% ，停机时间减少了26% ，开发人员生产力大幅提高，周转时间减少了45% 。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Vigil:+Effective+End-to-end+Monitoring+for+Large-scale+Recommender+Systems+at+Glance)|0| |[Prod2Vec-Var: A Session Based Recommendation System with Enhanced Diversity](https://doi.org/10.1145/3583780.3615995)|Hacer Turgut, Tan Doruk Yetki, Ömür Bali, Tayfun Arda Yücel|iLab Ventures, Istanbul, Turkey|Understanding user behavior and leveraging this information in recommendation systems pose challenges for websites lacking a login system or with limited logged-in users. This study introduces the Prod2Vec-Var recommendation system, a modified version of a session-based recommendation algorithm aimed at enhancing the performance of product recommendation systems with a cold-start extension. The proposed model builds upon the original prod2vec algorithm, incorporating an additional step to improve the diversity of product recommendations. The project entails a well-designed data pipeline, effectively processing user actions to align them with the model, and implementing unique functions that expand the range of products capturing users' attention. Moreover, a straightforward yet effective cold-start model is developed to address newly added products that have not been viewed by users. The outcome of our project, namely product suggestions, is presented to users of cimri.com, one of iLab's affiliated companies, which attracts millions of daily visits, thereby enabling seamless access to desired products. Experimental results demonstrated the superior performance of our model compared to the other two different strategies in running recommendation on popular products, as evidenced by favorable R@1, R@5, R@10, and R@15 metrics. Concerning less popular products, we observed an improvement in our model's performance as the value of K increased, ultimately achieving optimal results in terms of R@15. Additionally, our cold-start model for new products substantiated the efficacy of our methodology, yielding the highest scores across R@5, R@10, and R@15 metrics.|理解用户行为并在推荐系统中利用这些信息对缺乏登录系统或登录用户有限的网站构成挑战。本研究介绍了 Prod2Vec-Var 推荐系统，它是基于会话的推荐算法的一个修改版本，旨在通过冷启动扩展来提高产品推荐系统的性能。提出的模型建立在原始的 prod2vec 算法的基础上，包含了一个额外的步骤来改善产品推荐的多样性。该项目需要一个设计良好的数据管道，有效地处理用户行为，使其与模型保持一致，并实现独特的功能，以扩大产品的范围，吸引用户的注意力。此外，还开发了一个简单而有效的冷启动模型，以处理用户尚未查看的新增产品。我们的项目成果，即产品建议，将呈现给 cimri.com 的用户，cimri.com 是 iLab 的附属公司之一，每天吸引数百万的访问量，从而使人们能够无缝地访问想要的产品。实验结果表明，与其他两种不同的策略相比，我们的模型在推荐流行产品方面表现出更好的性能，这可以通过有利的 R@1、 R@5、 R@10和 R@15指标来证明。关于不太受欢迎的产品，我们观察到随着 K 值的增加，我们模型的性能有所改善，最终达到 R@15的最佳结果。此外，我们新产品的冷启动模型证实了我们方法的有效性，在 R@5、 R@10和 R@15指标上得分最高。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Prod2Vec-Var:+A+Session+Based+Recommendation+System+with+Enhanced+Diversity)|0| |[RePair: An Extensible Toolkit to Generate Large-Scale Datasets for Query Refinement via Transformers](https://doi.org/10.1145/3583780.3615129)|Yogeswar Lakshmi Narayanan, Hossein Fani|University of Windsor, Windsor, ON, Canada|Query refinement is the process of transforming users' queries into newrefined versions without semantic drift to enhance the relevance of search results. Prior query refiners were benchmarked on web query logs followingweak assumptions that users' input queries within a search session are about a single topic and improve gradually, which is not necessarily accurate in practice. In this paper, we contribute RePair, an open-source configurable toolkit to generatelarge-scale gold-standard benchmark datasets whose pairs of (original query, refined versions) arealmost surely guaranteed to be in the same semantic context. RePair takes a dataset of queries and their relevance judgements (e.g., msmarco or aol), a sparse or dense retrieval method (e.g., bm25 or colbert ), and an evaluation metric (e.g., map or mrr), and outputs refined versions of queries, each of which with the relevance improvement guarantees under the retrieval method in terms of the evaluation metric. RePair benefits from text-to-text-transfer-transformer (t5) to generate gold-standard datasets for any input query sets and is designed with extensibility in mind. Out of the box, RePair includes gold-standard datasets for aol and msmarco.passage as well as benchmark results of state-of-the-art supervised query suggestion methods on the generated datasets at https://github.com/fani-lab/RePair.|查询精化是将用户的查询转换为不带语义漂移的新精化版本以增强搜索结果相关性的过程。之前的查询精炼器在网络查询日志上进行了基准测试，这是基于一个薄弱的假设，即用户在搜索会话中的输入查询是关于单个主题的，并且是逐渐改进的，这在实践中并不一定准确。在本文中，我们提供了 RePair，一个开源的可配置工具包，用于生成大规模的黄金标准基准数据集，这些数据集的对(原始查询，精化版本)几乎肯定是在相同的语义上下文中。RePair 采用查询及其相关性判断(例如，mmarco 或 aol)、稀疏或密集检索方法(例如，bm25或 colbert)和评估度量(例如，map 或 mrr)的数据集，并输出精化版本的查询，其中每个查询在检索方法下的相关性改进都在评估度量方面得到保证。RePair 受益于文本到文本传输转换器(t5) ，可以为任何输入查询集生成黄金标准的数据集，并且在设计时考虑到了可扩展性。开箱即用，RePair 包括美国在线和 mmarco.pass 的黄金标准数据集，以及最先进的监督查询建议方法在 https://github.com/fani-lab/RePair 生成的数据集上的基准结果。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=RePair:+An+Extensible+Toolkit+to+Generate+Large-Scale+Datasets+for+Query+Refinement+via+Transformers)|0| -|[Combining Inductive and Deductive Reasoning for Query Answering over Incomplete Knowledge Graphs](https://doi.org/10.1145/3583780.3614816)|Medina Andresel, TrungKien Tran, Csaba Domokos, Pasquale Minervini, Daria Stepanova|Bosch Center for Artificial Intelligence, Renningen, Germany; University of Edinburgh, Edinburgh, United Kingdom; AIT Austrian Institute of Technology, Vienna, Austria|Current methods for embedding-based query answering over incomplete Knowledge Graphs (KGs) only focus on inductive reasoning, i.e., predicting answers by learning patterns from the data, and lack the complementary ability to do deductive reasoning, which requires the application of domain knowledge to infer further information. To address this shortcoming, we investigate the problem of incorporating ontologies into embedding-based query answering models by defining the task of embedding-based ontology-mediated query answering. We propose various integration strategies into prominent representatives of embedding models that involve (1) different ontology-driven data augmentation techniques and (2) adaptation of the loss function to enforce the ontology axioms. We design novel benchmarks for the considered task based on the LUBM and the NELL KGs and evaluate our methods on them. The achieved improvements in the setting that requires both inductive and deductive reasoning are from 20% to 55% in HITS@3.|目前基于嵌入式查询回答的不完整知识图(kGs)方法只关注归纳推理，即通过学习数据模式来预测答案，缺乏做演绎推理的互补能力，这需要应用领域知识来推断进一步的信息。针对这一缺陷，本文通过定义基于嵌入本体的查询回答任务，研究了将本体融入基于嵌入的查询回答模型中的问题。我们提出了各种集成策略的嵌入模型的突出代表，涉及(1)不同的本体驱动的数据增强技术和(2)适应的损失函数，以执行本体公理。我们基于 LUBM 和 NELL 幼儿园设计了新的任务基准，并对我们的方法进行了评估。在 HITS@3中，要求同时具有归纳性和演绎推理的环境改善率由20% 提高到55% 。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Combining+Inductive+and+Deductive+Reasoning+for+Query+Answering+over+Incomplete+Knowledge+Graphs)|0| +|[Combining Inductive and Deductive Reasoning for Query Answering over Incomplete Knowledge Graphs](https://doi.org/10.1145/3583780.3614816)|Medina Andresel, TrungKien Tran, Csaba Domokos, Pasquale Minervini, Daria Stepanova|AIT Austrian Institute of Technology, Vienna, Austria; Bosch Center for Artificial Intelligence, Renningen, Germany; University of Edinburgh, Edinburgh, United Kingdom|Current methods for embedding-based query answering over incomplete Knowledge Graphs (KGs) only focus on inductive reasoning, i.e., predicting answers by learning patterns from the data, and lack the complementary ability to do deductive reasoning, which requires the application of domain knowledge to infer further information. To address this shortcoming, we investigate the problem of incorporating ontologies into embedding-based query answering models by defining the task of embedding-based ontology-mediated query answering. We propose various integration strategies into prominent representatives of embedding models that involve (1) different ontology-driven data augmentation techniques and (2) adaptation of the loss function to enforce the ontology axioms. We design novel benchmarks for the considered task based on the LUBM and the NELL KGs and evaluate our methods on them. The achieved improvements in the setting that requires both inductive and deductive reasoning are from 20% to 55% in HITS@3.|目前基于嵌入式查询回答的不完整知识图(kGs)方法只关注归纳推理，即通过学习数据模式来预测答案，缺乏做演绎推理的互补能力，这需要应用领域知识来推断进一步的信息。针对这一缺陷，本文通过定义基于嵌入本体的查询回答任务，研究了将本体融入基于嵌入的查询回答模型中的问题。我们提出了各种集成策略的嵌入模型的突出代表，涉及(1)不同的本体驱动的数据增强技术和(2)适应的损失函数，以执行本体公理。我们基于 LUBM 和 NELL 幼儿园设计了新的任务基准，并对我们的方法进行了评估。在 HITS@3中，要求同时具有归纳性和演绎推理的环境改善率由20% 提高到55% 。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Combining+Inductive+and+Deductive+Reasoning+for+Query+Answering+over+Incomplete+Knowledge+Graphs)|0| |[GraphERT- Transformers-based Temporal Dynamic Graph Embedding](https://doi.org/10.1145/3583780.3614899)|Moran Beladev, Gilad Katz, Lior Rokach, Uriel Singer, Kira Radinsky|Technion, Haifa, Israel; Ben Gurion University of the Negev, Beer Sheva, Israel|Dynamic temporal graphs evolve over time, adding and removing nodes and edges between time snapshots. The tasks performed on such graphs are diverse and include detecting temporal trends, finding graph-to-graph similarities, and graph visualization and clustering. For all these tasks, it is necessary to embed the entire graph in a low-dimensional space by using graph-level representations instead of the more common node-level representations. This embedding requires handling the appearance of new nodes over time as well as capturing temporal patterns of the entire graph. Most existing methods perform temporal node embeddings and focus on different methods of aggregating them for a graph-based representation. In this work, we propose an end-to-end architecture that captures both the node embeddings and their influence in a structural context during a specific time period of the graph. We present GraphERT (Graph Embedding Representation using Transformers), a novel approach to temporal graph-level embeddings. Our method pioneers the use of Transformers to seamlessly integrate graph structure learning with temporal analysis. By employing a masked language model on sequences of graph random walks, together with a novel temporal classification task, our model not only comprehends the intricate graph dynamics but also unravels the temporal significance of each node and path. This novel training paradigm empowers GraphERT to capture the essence of both the structural and temporal aspects of graphs, surpassing state-of-the-art approaches across multiple tasks on real-world datasets.|动态时间图随着时间的推移而发展，在时间快照之间添加和删除节点和边。在这些图上执行的任务是多种多样的，包括检测时间趋势，发现图到图的相似性，以及图的可视化和聚类。对于所有这些任务，有必要通过使用图级表示代替更常见的节点级表示将整个图嵌入到低维空间中。这种嵌入需要随着时间的推移处理新节点的出现，并捕获整个图的时间模式。大多数现有的方法执行时间节点嵌入，并集中在不同的方法聚合它们为一个基于图的表示。在这项工作中，我们提出了一个端到端的架构，捕获两个节点嵌入和它们的影响在一个结构上下文在一个特定的时间段的图。本文提出了一种新的时态图级嵌入方法——图形嵌入表示(GraphERT)。我们的方法率先使用变压器，以无缝集成图结构学习与时间分析。该模型通过对图的随机游动序列采用掩蔽语言模型，结合一种新的时间分类任务，不仅理解了复杂的图动态，而且揭示了每个节点和路径的时间意义。这种新颖的训练范式使 GraphERT 能够捕捉图形的结构和时间方面的本质，超越现实世界数据集上多个任务的最新方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=GraphERT-+Transformers-based+Temporal+Dynamic+Graph+Embedding)|0| |[Faster Approximation Algorithms for Parameterized Graph Clustering and Edge Labeling](https://doi.org/10.1145/3583780.3614878)|Vedangi Bengali, Nate Veldt|Texas A&M University, College Station, TX, USA|Graph clustering is a fundamental task in network analysis where the goal is to detect sets of nodes that are well-connected to each other but sparsely connected to the rest of the graph. We present faster approximation algorithms for an NP-hard parameterized clustering framework called LambdaCC, which is governed by a tunable resolution parameter and generalizes many other clustering objectives such as modularity, sparsest cut, and cluster deletion. Previous LambdaCC algorithms are either heuristics with no approximation guarantees, or computationally expensive approximation algorithms. We provide fast new approximation algorithms that can be made purely combinatorial. These rely on a new parameterized edge labeling problem we introduce that generalizes previous edge labeling problems that are based on the principle of strong triadic closure and are of independent interest in social network analysis. Our methods are orders of magnitude more scalable than previous approximation algorithms and our lower bounds allow us to obtain a posteriori approximation guarantees for previous heuristics that have no approximation guarantees of their own.|图聚类是网络分析中的一个基本任务，其目标是检测彼此连接良好但与图的其余部分连接稀疏的节点集。我们提出了一个名为 LambdaCC 的 NP- 硬参数化聚类框架的更快的近似算法，该框架由一个可调的分辨率参数控制，并推广了许多其他聚类目标，如模块化，最稀疏切割和集群删除。以前的 LambdaCC 算法要么是没有近似保证的启发式算法，要么是计算昂贵的近似算法。我们提供了新的快速近似算法，可以使纯组合。这些都依赖于一个新的参数化边标注问题，我们引入了推广以前的边标注问题是基于强三元闭包原理，并在社会网络分析中的独立兴趣。我们的方法比以前的近似算法具有更大的数量级，我们的下限允许我们为以前的启发式算法获得一个没有近似保证的后验近似保证。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Faster+Approximation+Algorithms+for+Parameterized+Graph+Clustering+and+Edge+Labeling)|0| -|[Relevance-based Infilling for Natural Language Counterfactuals](https://doi.org/10.1145/3583780.3615029)|Lorenzo Betti, Carlo Abrate, Francesco Bonchi, Andreas Kaltenbrunner|ISI Foundation & Central European University, Turin, Italy; CENTAI & Sapienza University, Turin, Italy; CENTAI & Eurecat, Turin, Italy; ISI Foundation & Universitat Oberta de Catalunya, Turin, Italy|Counterfactual explanations are a natural way for humans to gain understanding and trust in the outcomes of complex machine learning algorithms. In the context of natural language processing, generating counterfactuals is particularly challenging as it requires the generated text to be fluent, grammatically correct, and meaningful. In this study, we improve the current state of the art for the generation of such counterfactual explanations for text classifiers. Our approach, named RELITC (Relevance-based Infilling for Textual Counterfactuals), builds on the idea of masking a fraction of text tokens based on their importance in a given prediction task and employs a novel strategy, based on the entropy of their associated probability distributions, to determine the infilling order of these tokens. Our method uses less time than competing methods to generate counterfactuals that require less changes, are closer to the original text and preserve its content better, while being competitive in terms of fluency. We demonstrate the effectiveness of the method on four different datasets and show the quality of its outcomes in a comparison with human generated counterfactuals.|反事实解释是人类理解和信任复杂机器学习算法结果的一种自然方式。在自然语言处理的环境中，生成反事实特别具有挑战性，因为它要求生成的文本流畅、语法正确和有意义。在这项研究中，我们改善了目前的技术状况，以生成这样的反事实解释的文本量词。我们的方法被命名为 RELITC (基于相关性的文本反事实填充) ，它基于根据文本标记在给定预测任务中的重要性来掩盖一小部分文本标记的想法，并采用一种新的策略，基于相关概率分布的熵来确定这些标记的填充顺序。我们的方法比竞争的方法用更少的时间来产生反事实，需要更少的变化，更接近原始文本，更好地保存其内容，同时在流畅性方面具有竞争力。我们证明了该方法在四个不同的数据集上的有效性，并通过与人类产生的反事实的比较显示了其结果的质量。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Relevance-based+Infilling+for+Natural+Language+Counterfactuals)|0| +|[Relevance-based Infilling for Natural Language Counterfactuals](https://doi.org/10.1145/3583780.3615029)|Lorenzo Betti, Carlo Abrate, Francesco Bonchi, Andreas Kaltenbrunner|ISI Foundation & Central European University, Turin, Italy; CENTAI & Sapienza University, Turin, Italy; ISI Foundation & Universitat Oberta de Catalunya, Turin, Italy; CENTAI & Eurecat, Turin, Italy|Counterfactual explanations are a natural way for humans to gain understanding and trust in the outcomes of complex machine learning algorithms. In the context of natural language processing, generating counterfactuals is particularly challenging as it requires the generated text to be fluent, grammatically correct, and meaningful. In this study, we improve the current state of the art for the generation of such counterfactual explanations for text classifiers. Our approach, named RELITC (Relevance-based Infilling for Textual Counterfactuals), builds on the idea of masking a fraction of text tokens based on their importance in a given prediction task and employs a novel strategy, based on the entropy of their associated probability distributions, to determine the infilling order of these tokens. Our method uses less time than competing methods to generate counterfactuals that require less changes, are closer to the original text and preserve its content better, while being competitive in terms of fluency. We demonstrate the effectiveness of the method on four different datasets and show the quality of its outcomes in a comparison with human generated counterfactuals.|反事实解释是人类理解和信任复杂机器学习算法结果的一种自然方式。在自然语言处理的环境中，生成反事实特别具有挑战性，因为它要求生成的文本流畅、语法正确和有意义。在这项研究中，我们改善了目前的技术状况，以生成这样的反事实解释的文本量词。我们的方法被命名为 RELITC (基于相关性的文本反事实填充) ，它基于根据文本标记在给定预测任务中的重要性来掩盖一小部分文本标记的想法，并采用一种新的策略，基于相关概率分布的熵来确定这些标记的填充顺序。我们的方法比竞争的方法用更少的时间来产生反事实，需要更少的变化，更接近原始文本，更好地保存其内容，同时在流畅性方面具有竞争力。我们证明了该方法在四个不同的数据集上的有效性，并通过与人类产生的反事实的比较显示了其结果的质量。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Relevance-based+Infilling+for+Natural+Language+Counterfactuals)|0| |[How Expressive are Graph Neural Networks in Recommendation?](https://doi.org/10.1145/3583780.3614917)|Xuheng Cai, Lianghao Xia, Xubin Ren, Chao Huang|The University of Hong Kong, Hong Kong, China|Graph Neural Networks (GNNs) have demonstrated superior performance on various graph learning tasks, including recommendation, where they leverage user-item collaborative filtering signals in graphs. However, theoretical formulations of their capability are scarce, despite their empirical effectiveness in state-of-the-art recommender models. Recently, research has explored the expressiveness of GNNs in general, demonstrating that message passing GNNs are at most as powerful as the Weisfeiler-Lehman test, and that GNNs combined with random node initialization are universal. Nevertheless, the concept of "expressiveness" for GNNs remains vaguely defined. Most existing works adopt the graph isomorphism test as the metric of expressiveness, but this graph-level task may not effectively assess a model's ability in recommendation, where the objective is to distinguish nodes of different closeness. In this paper, we provide a comprehensive theoretical analysis of the expressiveness of GNNs in recommendation, considering three levels of expressiveness metrics: graph isomorphism (graph-level), node automorphism (node-level), and topological closeness (link-level). We propose the topological closeness metric to evaluate GNNs' ability to capture the structural distance between nodes, which aligns closely with the objective of recommendation. To validate the effectiveness of this new metric in evaluating recommendation performance, we introduce a learning-less GNN algorithm that is optimal on the new metric and can be optimal on the node-level metric with suitable modification. We conduct extensive experiments comparing the proposed algorithm against various types of state-of-the-art GNN models to explore the explainability of the new metric in the recommendation task. For reproducibility, implementation codes are available at https://github.com/HKUDS/GTE.|图形神经网络(GNN)在各种图形学习任务中表现出了卓越的性能，包括推荐，它们利用图形中的用户项目协同过滤信号。然而，他们的能力的理论公式是稀缺的，尽管他们的经验有效性在国家的最先进的推荐模型。最近，研究人员对 GNN 的表达能力进行了一般性的探索，证明了信息传递 GNN 的最大强度与 Weisfeiler-Lehman 检验相当，并且 GNN 与随机节点初始化相结合是通用的。尽管如此，GNN 的“表现性”概念仍然含糊不清。现有的大多数工作都采用图同构测试作为表达能力的度量标准，但是这种图级任务可能不能有效地评估模型的推荐能力，其目标是区分不同亲密度的节点。本文从图同构(图级)、节点自同构(节点级)和拓扑亲密度(链路级)三个层次对推荐中 GNN 的表达能力进行了全面的理论分析。我们提出拓扑贴近度量来评估 GNN 捕获节点间结构距离的能力，这与推荐的目标非常接近。为了验证这一新指标在评估推荐性能方面的有效性，我们引入了一种无学习 GNN 算法，该算法在新指标上是最优的，并且在适当修改后可以在节点级指标上达到最优。我们进行了广泛的实验比较提出的算法与各种类型的国家最先进的 GNN 模型，以探索新的指标在推荐任务的可解释性。为确保可重复性，实施守则可于 https://github.com/hkuds/gte 索取。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=How+Expressive+are+Graph+Neural+Networks+in+Recommendation?)|0| |[L2R: Lifelong Learning for First-stage Retrieval with Backward-Compatible Representations](https://doi.org/10.1145/3583780.3614947)|Yinqiong Cai, Keping Bi, Yixing Fan, Jiafeng Guo, Wei Chen, Xueqi Cheng|CAS Key Lab of Network Data Science and Technology, ICT, CAS & University of Chinese Academy of Sciences, Beijing, China|First-stage retrieval is a critical task that aims to retrieve relevant document candidates from a large-scale collection. While existing retrieval models have achieved impressive performance, they are mostly studied on static data sets, ignoring that in the real-world, the data on the Web is continuously growing with potential distribution drift. Consequently, retrievers trained on static old data may not suit new-coming data well and inevitably produce sub-optimal results. In this work, we study lifelong learning for first-stage retrieval, especially focusing on the setting where the emerging documents are unlabeled since relevance annotation is expensive and may not keep up with data emergence. Under this setting, we aim to develop model updating with two goals: (1) to effectively adapt to the evolving distribution with the unlabeled new-coming data, and (2) to avoid re-inferring all embeddings of old documents to efficiently update the index each time the model is updated. We first formalize the task and then propose a novel Lifelong Learning method for the first-stage Retrieval, namely L2R. L2R adopts the typical memory mechanism for lifelong learning, and incorporates two crucial components: (1) selecting diverse support negatives for model training and memory updating for effective model adaptation, and (2) a ranking alignment objective to ensure the backward-compatibility of representations to save the cost of index rebuilding without hurting the model performance. For evaluation, we construct two new benchmarks from LoTTE and Multi-CPR datasets to simulate the document distribution drift in realistic retrieval scenarios. Extensive experiments show that L^2R significantly outperforms competitive lifelong learning baselines.|第一阶段检索是一项关键任务，其目的是从大规模的文档集合中检索相关的候选文档。虽然现有的检索模型已经取得了令人印象深刻的性能，但它们大多是在静态数据集上进行研究，忽略了在现实世界中，Web 上的数据随着潜在的分布漂移而不断增长。因此，对静态旧数据进行训练的检索器可能不能很好地适应新数据，并不可避免地产生次优结果。在这项工作中，我们研究了第一阶段检索的终身学习，特别关注于新出现的文档没有标记的情况，因为相关注释的成本很高，而且可能跟不上数据出现的速度。在这种情况下，我们的目标是开发模型更新有两个目标: (1)有效地适应演化的分布与未标记的新来的数据，(2)避免重新推断所有嵌入的旧文档，以有效地更新索引每次模型更新。我们首先将任务形式化，然后为第一阶段的检索提出一种新的终身学习方法，即 L2R。L2R 采用了典型的终身学习记忆机制，包括两个关键部分: (1)选择不同的支持否定来进行模型训练和记忆更新，以便有效地进行模型适应; (2)排序对齐目标，以确保表征的向后兼容性，从而在不损害模型性能的情况下节省索引重建的成本。为了进行评估，我们从 LoTTE 和 Multi-CPR 数据集中构建了两个新的基准来模拟真实检索场景中的文档分布漂移。大量实验表明，L ^ 2R 的表现明显优于竞争性的终身学习基线。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=L2R:+Lifelong+Learning+for+First-stage+Retrieval+with+Backward-Compatible+Representations)|0| |[Incorporating Constituent Syntax into Grammatical Error Correction with Multi-Task Learning](https://doi.org/10.1145/3583780.3614931)|Chen Chen, Bo He, Jing Yuan, Chunyan Hou, Xiaojie Yuan|Nankai University, Tianjin, China; Tianjin University of Technology, Tianjin, China|Grammatical Error Correction (GEC) is usually considered as a translation task where an erroneous sentence is treated as the source language and the corrected sentence as the target language. The state-of-the-art GEC models often adopt transformer-based sequence-to-sequence architecture of machine translation. However, most of these approaches ignore the syntactic information because the syntax of an erroneous sentence is also full of errors and not beneficial to GEC. In this paper, we propose a novel Error-Correction Constituent Parsing (ECCP) task which uses the constituent parsing of corrected sentences to avoid the harmful effect of the erroneous sentence. We also propose an architecture that includes one encoder and two decoders. There are millions of parameters in transformer-based GEC models, and the labeled training data is substantially less than synthetic pre-training data. Therefore, adapter layers are added to the proposed architecture, and adapter tuning is used for fine-tuning our model to alleviate the low-resource issue. We conduct experiments on CoNLL-2014, BEA-2019, and JFLEG test datasets in unsupervised and supervised settings. Experimental results show that our method outperforms the-state-of-art baselines and achieves superior performance on all datasets.|语法错误纠正(GEC)通常被认为是一个翻译任务，其中错误的句子被视为源语言，被纠正的句子被视为目标语言。目前最先进的 GEC 模型通常采用基于变压器的序列到序列的机器翻译体系结构。然而，这些方法大多忽略了句法信息，因为错误句子的句法也充满了错误，不利于 GEC。本文提出了一种新的纠错成分分析(ECCP)任务，利用纠错句的成分分析来避免错误句的有害影响。我们还提出了一个包括一个编码器和两个解码器的体系结构。在基于变压器的 GEC 模型中有数百万个参数，标记的训练数据大大少于合成的训练前数据。因此，将适配器层添加到提出的体系结构中，并使用适配器调优来微调我们的模型，以缓解资源不足的问题。我们在 CoNLL-2014、 BEA-2019和 JFLEG 测试数据集上进行了无监督和监督环境下的实验。实验结果表明，该方法的性能优于现有的基线方法，在所有数据集上都取得了较好的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Incorporating+Constituent+Syntax+into+Grammatical+Error+Correction+with+Multi-Task+Learning)|0| -|[HEProto: A Hierarchical Enhancing ProtoNet based on Multi-Task Learning for Few-shot Named Entity Recognition](https://doi.org/10.1145/3583780.3614908)|Wei Chen, Lili Zhao, Pengfei Luo, Tong Xu, Yi Zheng, Enhong Chen|Huawei Cloud Computing Technologies Co., Ltd, Hangzhou, China; University of Science and Technology of China & State Key Laboratory of Cognitive Intelligence, Hefei, China|Few-shot Named Entity Recognition (NER) task, which aims to identify and classify entities from different domains with limited training samples, has long been treated as a basic step for knowledge graph (KG) construction. Great efforts have been made on this task with competitive performance, however, they usually treat the two subtasks, namely span detection and type classification, as mutually independent, and the integrity and correlation between subtasks have been largely ignored. Moreover, prior arts may fail to absorb the coarse-grained features of entities, resulting in a semantic-insufficient representation of entity types. To that end, in this paper, we propose a Hierarchical Enhancing ProtoNet (HEProto) based on multi-task learning, which is utilized to jointly learn these two subtasks and model their correlation. Specifically, we adopt contrastive learning to enhance the span boundary information and the type semantic representations in these two subtasks. Then, the hierarchical prototypical network is designed to leverage the coarse-grained information of entities in the type classification stage, which could help the model to better learn the fine-grained semantic representations. Along this line, we construct a similarity margin loss to reduce the similarity between fine-grained entities and other irrelevant coarse-grained prototypes. Finally, extensive experiments on the Few-NERD dataset prove that our solution outperforms competitive baseline methods. The source code of HEProto is available at \hrefhttps://github.com/fanshu6hao/HEProto https://github.com/fanshu6hao/HEProto.|少镜头命名实体识别(NER)任务是利用有限的训练样本对来自不同领域的实体进行识别和分类，长期以来一直被视为构建知识图(KG)的基本步骤。但是，人们往往将跨度检测和类型分类这两个子任务看作是相互独立的，而忽视了子任务之间的完整性和相关性。此外，现有技术可能无法吸收实体的粗粒度特征，导致实体类型的语义表示不足。为此，本文提出了一种基于多任务学习的分层增强协议网(HEProto) ，利用它来联合学习这两个子任务并建立它们之间的关联模型。具体来说，我们采用对比学习来增强这两个子任务中的跨度边界信息和类型语义表示。然后，设计层次化原型网络，在类型分类阶段利用实体的粗粒度信息，帮助模型更好地学习细粒度的语义表示。沿着这条线，我们构造一个相似性边界损失来减少细粒度实体和其他不相关的粗粒度原型之间的相似性。最后，在极少数 NERD 数据集上的大量实验证明了我们的解决方案优于竞争基线方法。HEproto 的源代码可以在 hrefhttps:// github.com/fanshu6hao/HEProto https://github.com/fanshu6hao/HEProto 找到。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=HEProto:+A+Hierarchical+Enhancing+ProtoNet+based+on+Multi-Task+Learning+for+Few-shot+Named+Entity+Recognition)|0| +|[HEProto: A Hierarchical Enhancing ProtoNet based on Multi-Task Learning for Few-shot Named Entity Recognition](https://doi.org/10.1145/3583780.3614908)|Wei Chen, Lili Zhao, Pengfei Luo, Tong Xu, Yi Zheng, Enhong Chen|University of Science and Technology of China & State Key Laboratory of Cognitive Intelligence, Hefei, China; Huawei Cloud Computing Technologies Co., Ltd, Hangzhou, China|Few-shot Named Entity Recognition (NER) task, which aims to identify and classify entities from different domains with limited training samples, has long been treated as a basic step for knowledge graph (KG) construction. Great efforts have been made on this task with competitive performance, however, they usually treat the two subtasks, namely span detection and type classification, as mutually independent, and the integrity and correlation between subtasks have been largely ignored. Moreover, prior arts may fail to absorb the coarse-grained features of entities, resulting in a semantic-insufficient representation of entity types. To that end, in this paper, we propose a Hierarchical Enhancing ProtoNet (HEProto) based on multi-task learning, which is utilized to jointly learn these two subtasks and model their correlation. Specifically, we adopt contrastive learning to enhance the span boundary information and the type semantic representations in these two subtasks. Then, the hierarchical prototypical network is designed to leverage the coarse-grained information of entities in the type classification stage, which could help the model to better learn the fine-grained semantic representations. Along this line, we construct a similarity margin loss to reduce the similarity between fine-grained entities and other irrelevant coarse-grained prototypes. Finally, extensive experiments on the Few-NERD dataset prove that our solution outperforms competitive baseline methods. The source code of HEProto is available at \hrefhttps://github.com/fanshu6hao/HEProto https://github.com/fanshu6hao/HEProto.|少镜头命名实体识别(NER)任务是利用有限的训练样本对来自不同领域的实体进行识别和分类，长期以来一直被视为构建知识图(KG)的基本步骤。但是，人们往往将跨度检测和类型分类这两个子任务看作是相互独立的，而忽视了子任务之间的完整性和相关性。此外，现有技术可能无法吸收实体的粗粒度特征，导致实体类型的语义表示不足。为此，本文提出了一种基于多任务学习的分层增强协议网(HEProto) ，利用它来联合学习这两个子任务并建立它们之间的关联模型。具体来说，我们采用对比学习来增强这两个子任务中的跨度边界信息和类型语义表示。然后，设计层次化原型网络，在类型分类阶段利用实体的粗粒度信息，帮助模型更好地学习细粒度的语义表示。沿着这条线，我们构造一个相似性边界损失来减少细粒度实体和其他不相关的粗粒度原型之间的相似性。最后，在极少数 NERD 数据集上的大量实验证明了我们的解决方案优于竞争基线方法。HEproto 的源代码可以在 hrefhttps:// github.com/fanshu6hao/HEProto https://github.com/fanshu6hao/HEProto 找到。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=HEProto:+A+Hierarchical+Enhancing+ProtoNet+based+on+Multi-Task+Learning+for+Few-shot+Named+Entity+Recognition)|0| |[Continual Learning for Generative Retrieval over Dynamic Corpora](https://doi.org/10.1145/3583780.3614821)|Jiangui Chen, Ruqing Zhang, Jiafeng Guo, Maarten de Rijke, Wei Chen, Yixing Fan, Xueqi Cheng|ICT, CAS & University of Chinese Academy of Sciences, Beijing, China; University of Amsterdam, Amsterdam, Netherlands|Generative retrieval (GR) directly predicts the identifiers of relevant documents (i.e., docids) based on a parametric model. It has achieved solid performance on many ad-hoc retrieval tasks. So far, these tasks have assumed a static document collection. In many practical scenarios, however, document collections are dynamic, where new documents are continuously added to the corpus. The ability to incrementally index new documents while preserving the ability to answer queries with both previously and newly indexed relevant documents is vital to applying GR models. In this paper, we address this practical continual learning problem for GR. We put forward a novel Continual-LEarner for generatiVE Retrieval (CLEVER) model and make two major contributions to continual learning for GR: (i) To encode new documents into docids with low computational cost, we present Incremental Product Quantization, which updates a partial quantization codebook according to two adaptive thresholds; and (ii) To memorize new documents for querying without forgetting previous knowledge, we propose a memory-augmented learning mechanism, to form meaningful connections between old and new documents. Empirical results demonstrate the effectiveness and efficiency of the proposed model.|生成检索(GR)基于参数模型直接预测相关文档(即文档)的标识符。它在许多自组织检索任务中取得了可靠的性能。到目前为止，这些任务都假定为静态文档集合。然而，在许多实际场景中，文档集合是动态的，其中新文档不断地添加到语料库中。增量索引新文档的能力，同时保留用先前和新索引的相关文档回答查询的能力，对于应用 GR 模型至关重要。在本文中，我们解决了这个实际的 GR 连续学习问题。我们提出了一种新的生成式虚拟检索(CLEVER)的 Continual-LEarner 模型，并为 GR 的持续学习做出了两个主要贡献: (i)为了以较低的计算成本将新文档编码成文档，我们提出了增量产品量化，它根据两个自适应阈值更新部分量化码书; (ii)为了在不忘记先前知识的情况下记忆查询新文档，我们提出了一种记忆增强学习机制，在新旧文档之间形成有意义的联系。实证结果证明了该模型的有效性和有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Continual+Learning+for+Generative+Retrieval+over+Dynamic+Corpora)|0| |[I3 Retriever: Incorporating Implicit Interaction in Pre-trained Language Models for Passage Retrieval](https://doi.org/10.1145/3583780.3614923)|Qian Dong, Yiding Liu, Qingyao Ai, Haitao Li, Shuaiqiang Wang, Yiqun Liu, Dawei Yin, Shaoping Ma|Tsinghua University, Beijing, China; Baidu Inc., Beijing, China|Passage retrieval is a fundamental task in many information systems, such as web search and question answering, where both efficiency and effectiveness are critical concerns. In recent years, neural retrievers based on pre-trained language models (PLM), such as dual-encoders, have achieved huge success. Yet, studies have found that the performance of dual-encoders are often limited due to the neglecting of the interaction information between queries and candidate passages. Therefore, various interaction paradigms have been proposed to improve the performance of vanilla dual-encoders. Particularly, recent state-of-the-art methods often introduce late-interaction during the model inference process. However, such late-interaction based methods usually bring extensive computation and storage cost on large corpus. Despite their effectiveness, the concern of efficiency and space footprint is still an important factor that limits the application of interaction-based neural retrieval models. To tackle this issue, we Incorporate Implicit Interaction into dual-encoders, and propose I3 retriever. In particular, our implicit interaction paradigm leverages generated pseudo-queries to simulate query-passage interaction, which jointly optimizes with query and passage encoders in an end-to-end manner. It can be fully pre-computed and cached, and its inference process only involves simple dot product operation of the query vector and passage vector, which makes it as efficient as the vanilla dual encoders. We conduct comprehensive experiments on MSMARCO and TREC2019 Deep Learning Datasets, demonstrating the I3 retriever's superiority in terms of both effectiveness and efficiency. Moreover, the proposed implicit interaction is compatible with special pre-training and knowledge distillation for passage retrieval, which brings a new state-of-the-art performance. The codes are available at https://github.com/Deriq-Qian-Dong/III-Retriever.|短文检索是许多信息系统(如网络搜索和问答系统)的基本任务，其效率和有效性是关键问题。近年来，基于预训练语言模型(PLM)的神经检索器(如双编码器)取得了巨大的成功。然而，研究发现，由于忽略了查询和候选段之间的交互信息，双编码器的性能往往受到限制。因此，人们提出了各种交互模式来提高普通双编码器的性能。特别是，最近最先进的方法经常在模型推理过程中引入后期交互。然而，这种基于后期交互的方法通常会在大型语料库上带来大量的计算和存储开销。尽管有效，但对效率和空间足迹的关注仍然是限制基于交互的神经检索模型应用的一个重要因素。为了解决这个问题，我们将隐式交互集成到双编码器中，并提出了 I3检索器。特别是，我们的隐式交互范例利用生成的伪查询来模拟查询-通道交互，它与查询和通道编码器以端到端的方式进行联合优化。它可以完全预先计算和缓存，其推理过程只涉及查询向量和通道向量的简单点乘操作，这使得它像普通的双重编码器一样高效。我们在 MSMARCO 和 TREC2019深度学习数据集上进行了全面的实验，证明了 I3检索器在有效性和效率方面的优势。此外，提出的隐式交互与文章检索的专门预训练和知识提取兼容，带来了新的技术水平的性能。密码可以在 https://github.com/deriq-qian-dong/iii-retriever 找到。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=I3+Retriever:+Incorporating+Implicit+Interaction+in+Pre-trained+Language+Models+for+Passage+Retrieval)|0| |[Optimal Linear Subspace Search: Learning to Construct Fast and High-Quality Schedulers for Diffusion Models](https://doi.org/10.1145/3583780.3614999)|Zhongjie Duan, Chengyu Wang, Cen Chen, Jun Huang, Weining Qian|Alibaba Group, Hangzhou, China; East China Normal University, Shanghai, China|In recent years, diffusion models have become the most popular and powerful methods in the field of image synthesis, even rivaling human artists in artistic creativity. However, the key issue currently limiting the application of diffusion models is its extremely slow generation process. Although several methods were proposed to speed up the generation process, there still exists a trade-off between efficiency and quality. In this paper, we first provide a detailed theoretical and empirical analysis of the generation process of the diffusion models based on schedulers. We transform the designing problem of schedulers into the determination of several parameters, and further transform the accelerated generation process into an expansion process of the linear subspace. Based on these analyses, we consequently propose a novel method called Optimal Linear Subspace Search (OLSS), which accelerates the generation process by searching for the optimal approximation process of the complete generation process in the linear subspaces spanned by latent variables. OLSS is able to generate high-quality images with a very small number of steps. To demonstrate the effectiveness of our method, we conduct extensive comparative experiments on open-source diffusion models. Experimental results show that with a given number of steps, OLSS can significantly improve the quality of generated images. Using an NVIDIA A100 GPU, we make it possible to generate a high-quality image by Stable Diffusion within only one second without other optimization techniques.|近年来，扩散模型已经成为图像合成领域中最流行、最有力的方法，甚至在艺术创造力方面可以与人类艺术家相媲美。然而，目前限制扩散模型应用的关键问题是其生成过程极其缓慢。虽然提出了几种方法来加快生成过程，但仍然存在效率和质量之间的平衡。本文首先对基于调度器的扩散模型的生成过程进行了详细的理论和实证分析。将调度器的设计问题转化为多个参数的确定问题，并将加速生成过程进一步转化为线性子空间的展开过程。在此基础上，提出了一种新的最优线性子空间搜索(OLSS)方法，该方法通过在潜变量跨度的线性子空间中搜索完全生成过程的最优逼近过程来加速生成过程。OLSS 只需很少的步骤就能生成高质量的图像。为了证明我们方法的有效性，我们对开源扩散模型进行了广泛的比较实验。实验结果表明，在给定步长的情况下，OLSS 可以显著提高图像的质量。使用 NVIDIA A100图形处理器，我们可以在不使用其他优化技术的情况下，通过稳定扩散在仅仅一秒钟内生成高质量的图像。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Optimal+Linear+Subspace+Search:+Learning+to+Construct+Fast+and+High-Quality+Schedulers+for+Diffusion+Models)|0| |[KG4Ex: An Explainable Knowledge Graph-Based Approach for Exercise Recommendation](https://doi.org/10.1145/3583780.3614943)|Quanlong Guan, Fang Xiao, Xinghe Cheng, Liangda Fang, Ziliang Chen, Guanliang Chen, Weiqi Luo|Monash University, Melbourne, Australia; Jinan University, Guangzhou, China|Effective exercise recommendation is crucial for guiding students' learning trajectories and fostering their interest in the subject matter. However, the vast exercise resource and the varying learning abilities of individual students pose a significant challenge in selecting appropriate exercise questions. Collaborative filtering-based methods often struggle with recommending suitable exercises, while deep learning-based methods lack explanation, limiting their practical adoption. To address these limitations, this paper proposes KG4Ex, a knowledge graph-based exercise recommendation method. KG4Ex facilitates the matching of diverse students with suitable exercises while providing recommendation reasons. Specifically, we introduce a feature extraction module to represent students' learning states and construct a knowledge graph for exercise recommendation. This knowledge graph comprises three key entities (knowledge concepts, students, and exercises) and their interrelationships, and can be used to recommend suitable exercises. Extensive experiments on three real-world datasets and expert interviews demonstrate the superiority of KG4Ex over existing baseline methods and highlight its strong explainability.|有效的练习推荐对于引导学生的学习轨迹和培养他们对科目的兴趣是至关重要的。然而，大量的练习资源和个别学生不同的学习能力对选择合适的练习题提出了重大挑战。基于协作过滤的方法往往难以推荐合适的练习，而基于深度学习的方法缺乏解释，限制了它们的实际应用。针对这些局限性，本文提出了一种基于知识图的练习推荐方法 KG4Ex。KG4Ex 在提供推荐理由的同时，为不同类型的学生提供合适的练习。具体地说，我们引入了一个特征提取模块来表示学生的学习状态，并构造了一个用于练习推荐的知识图。这个知识图包括三个关键实体(知识概念、学生和练习)及其相互关系，可以用来推荐合适的练习。通过对三个现实世界数据集的大量实验和专家访谈，证明了 KG4Ex 相对于现有基线方法的优越性，并突出了其强大的可解释性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=KG4Ex:+An+Explainable+Knowledge+Graph-Based+Approach+for+Exercise+Recommendation)|0| -|[Targeted Shilling Attacks on GNN-based Recommender Systems](https://doi.org/10.1145/3583780.3615073)|Sihan Guo, Ting Bai, Weihong Deng|Beijing University of Posts and Telecommunications, Beijing, China; Beijing University of Posts and Telecommunications, Beijing , China|GNN-based recommender systems have shown their vulnerability to shilling attacks in recent studies. By conducting shilling attacks on recommender systems, the attackers aim to have homogeneous impacts on all users. However, such indiscriminate attacks suffer from a waste of resources because even if the target item is promoted to users who are not interested, they are unlikely to click on them. In this paper, we conduct targeted shilling attacks in GNN-based recommender systems. By automatically constructing the features and edges of the fake users, our proposed framework AutoAttack achieves accurate attacks on a specific group of users while minimizing the impact on non-target users. Specifically, the features of fake users are generated based on a similarity function, which is optimized according to the features of target users. The structure of fake users is learned by conducting spectral clustering on the target users based on their graph Laplacian matrix, which contains the degree and adjacency information that provides guidance to the edge generation of fake users. We conduct extensive experiments on four real-world datasets in different GNN-based RS and evaluate the performance of our method on the shilling attack and recommendation tasks comprehensively, showing the effectiveness and flexibility of our framework.|在最近的研究中，基于 GNN 的推荐系统已经显示了它们对先令攻击的脆弱性。通过对推荐系统进行先令式攻击，攻击者的目标是对所有用户产生同样的影响。然而，这种不分青红皂白的攻击造成资源浪费，因为即使目标项目被推广给不感兴趣的用户，他们也不太可能点击这些项目。本文在基于 GNN 的推荐系统中进行有针对性的先令攻击。通过自动构造虚假用户的特征和边缘，我们提出的框架自动攻击实现了对特定用户群的准确攻击，同时最小化了对非目标用户的影响。具体来说，基于相似度函数生成虚假用户的特征，并根据目标用户的特征进行优化。虚假用户的结构是通过基于目标用户的图形 SVD 来了解的，图形 Laplacian Matrix 包含程度和邻接信息，为虚假用户的边缘生成提供指导。我们在四个不同的基于 GNN 的 RS 的真实世界数据集上进行了广泛的实验，全面评估了我们的方法在先令攻击和推荐任务上的性能，显示了我们的框架的有效性和灵活性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Targeted+Shilling+Attacks+on+GNN-based+Recommender+Systems)|0| +|[Targeted Shilling Attacks on GNN-based Recommender Systems](https://doi.org/10.1145/3583780.3615073)|Sihan Guo, Ting Bai, Weihong Deng|Beijing University of Posts and Telecommunications, Beijing , China; Beijing University of Posts and Telecommunications, Beijing, China|GNN-based recommender systems have shown their vulnerability to shilling attacks in recent studies. By conducting shilling attacks on recommender systems, the attackers aim to have homogeneous impacts on all users. However, such indiscriminate attacks suffer from a waste of resources because even if the target item is promoted to users who are not interested, they are unlikely to click on them. In this paper, we conduct targeted shilling attacks in GNN-based recommender systems. By automatically constructing the features and edges of the fake users, our proposed framework AutoAttack achieves accurate attacks on a specific group of users while minimizing the impact on non-target users. Specifically, the features of fake users are generated based on a similarity function, which is optimized according to the features of target users. The structure of fake users is learned by conducting spectral clustering on the target users based on their graph Laplacian matrix, which contains the degree and adjacency information that provides guidance to the edge generation of fake users. We conduct extensive experiments on four real-world datasets in different GNN-based RS and evaluate the performance of our method on the shilling attack and recommendation tasks comprehensively, showing the effectiveness and flexibility of our framework.|在最近的研究中，基于 GNN 的推荐系统已经显示了它们对先令攻击的脆弱性。通过对推荐系统进行先令式攻击，攻击者的目标是对所有用户产生同样的影响。然而，这种不分青红皂白的攻击造成资源浪费，因为即使目标项目被推广给不感兴趣的用户，他们也不太可能点击这些项目。本文在基于 GNN 的推荐系统中进行有针对性的先令攻击。通过自动构造虚假用户的特征和边缘，我们提出的框架自动攻击实现了对特定用户群的准确攻击，同时最小化了对非目标用户的影响。具体来说，基于相似度函数生成虚假用户的特征，并根据目标用户的特征进行优化。虚假用户的结构是通过基于目标用户的图形 SVD 来了解的，图形 Laplacian Matrix 包含程度和邻接信息，为虚假用户的边缘生成提供指导。我们在四个不同的基于 GNN 的 RS 的真实世界数据集上进行了广泛的实验，全面评估了我们的方法在先令攻击和推荐任务上的性能，显示了我们的框架的有效性和灵活性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Targeted+Shilling+Attacks+on+GNN-based+Recommender+Systems)|0| |[Robust Basket Recommendation via Noise-tolerated Graph Contrastive Learning](https://doi.org/10.1145/3583780.3615039)|Xinrui He, Tianxin Wei, Jingrui He|University of Illinois at Urbana-Champaign, Champaign, USA|The growth of e-commerce has seen a surge in popularity of platforms like Amazon, eBay, and Taobao. This has given rise to a unique shopping behavior involving baskets - sets of items purchased together. As a less studied interaction mode in the community, the question of how should shopping basket complement personalized recommendation systems remains under-explored. While previous attempts focused on jointly modeling user purchases and baskets, the distinct semantic nature of these elements can introduce noise when directly integrated. This noise negatively impacts the model's performance, further exacerbated by significant noise (e.g., a user is misled to click an item or recognizes it as uninteresting after consuming it) within both user and basket behaviors. In order to cope with the above difficulties, we propose a novel Basket recommendation framework via Noise-tolerated Contrastive Learning, named BNCL, to handle the noise existing in the cross-behavior integration and within-behavior modeling. First, we represent the basket-item interactions as the hypergraph to model the complex basket behavior, where all items appearing in the same basket are treated as a single hyperedge. Second, cross-behavior contrastive learning is designed to suppress the noise during the fusion of diverse behaviors. Next, to further inhibit the within-behavior noise of the user and basket interactions, we propose to exploit invariant properties of the recommenders w.r.t augmentations through within-behavior contrastive learning. A novel consistency-aware augmentation approach is further designed to better identify the noisy interactions with the consideration of the above two types of interactions. Our framework BNCL offers a generic training paradigm that is applicable to different backbones. Extensive experiments on three shopping transaction datasets verify the effectiveness of our proposed method.|随着电子商务的发展，像亚马逊、 eBay 和淘宝这样的平台越来越受欢迎。这就产生了一种独特的购物行为，包括一篮子一套的商品一起购买。作为一种研究较少的社区互动模式，购物篮应该如何补充个性化推荐系统的问题仍然没有得到充分的探讨。虽然以前的尝试侧重于联合建模用户购买和购物篮，但是当直接集成时，这些元素独特的语义特性可能会引入噪音。这种噪音会对模型的性能产生负面影响，而在用户和购物篮的行为中，显著的噪音会进一步加剧这种影响(例如，用户被误导去点击一个项目，或者在消费之后认为它没有意思)。为了克服上述困难，我们提出了一种新的基于噪声容忍对比学习的 Basket 推荐框架 BNCL，用于处理跨行为集成和行为内建模中存在的噪声。首先，我们将篮子项目的交互作用表示为超图来模拟复杂的篮子行为，其中出现在同一个篮子中的所有项目都被视为一个单一的超边。其次，设计交叉行为对比学习来抑制不同行为融合过程中的噪声。接下来，为了进一步抑制用户和篮子交互的行为内噪声，我们建议通过行为内对比学习来利用推荐者 w.r.t 增强的不变性。进一步设计了一种新的一致性增强方法，以便在考虑上述两类相互作用的情况下更好地识别噪声相互作用。我们的框架 BNCL 提供了一个通用的培训范例，适用于不同的骨干。在三个购物交易数据集上的大量实验验证了该方法的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Robust+Basket+Recommendation+via+Noise-tolerated+Graph+Contrastive+Learning)|0| -|[Search-Efficient Computerized Adaptive Testing](https://doi.org/10.1145/3583780.3615049)|Yuting Hong, Shiwei Tong, Wei Huang, Yan Zhuang, Qi Liu, Enhong Chen, Xin Li, Yuanjing He|Open University of China, Beijing, China; University of Science and Technology of China & State Key Laboratory of Cognitive Intelligence, Hefei, China; University of Science and Technology of China & iFLYTEK Co., Ltd, Hefei, China|Computerized Adaptive Testing (CAT) arises as a promising personalized test mode in online education, targeting at revealing students' latent knowledge state by selecting test items adaptively. The item selection strategy is the core component of CAT, which searches for the best suitable test item based on students' current estimated ability at each test step. However, existing selection strategies behave in a brute-force manner, which results in the time complexity being linear to the number of items (N) in the item pool, i.e., O(N). Thus, in reality, the search latency becomes the bottleneck for CAT with a large-scale item pool. To this end, we propose a Search-Efficient Computerized Adaptive Testing framework (SECAT), which aims at enhancing CAT with an efficient selection strategy. Specifically, SECAT contains two main phases: item pool indexing and item search. In the item pool indexing phase, we apply a student-aware spatial partition method on the item pool to divide the test items into many sub-spaces, considering the adaptability of test items. In the item search phase, we optimize the traditional single-round search strategy with the asymptotic theory and propose a multi-round search strategy that can further improve the time efficiency. Compared with existing strategies, the time complexity of SECAT decreases from O(N) to O(logN). Across two real-world datasets, SECAT achieves over 200x speed up with negligible accuracy degradation.|计算机自适应测试(CAT)作为一种新兴的网络教育个性化测试模式，旨在通过自适应选择测试项目来揭示学生的潜在知识状态。试题选择策略是计算机辅助测试(CAT)的核心组成部分，它根据学生在每个测试步骤中的当前估计能力来寻找最适合的试题。然而，现有的选择策略表现出一种蛮力的方式，导致时间复杂度与项目池中的项目数(N)成线性关系，即 O (N)。因此，在现实中，搜索延迟成为大规模项目池 CAT 的瓶颈。为此，我们提出了一个搜索效率高的计算机自适应测试框架(SECAT) ，旨在通过一种有效的选择策略来增强 CAT。具体来说，SECAT 包含两个主要阶段: 项目池索引和项目搜索。在试题库索引阶段，考虑试题的适应性，在试题库上采用学生感知的空间划分方法，将试题划分为多个子空间。在项目搜索阶段，我们利用渐近理论优化了传统的单轮搜索策略，并提出了多轮搜索策略，进一步提高了时间效率。与已有策略相比，SECAT 的时间复杂度从 O (N)降低到 O (logN)。通过两个真实世界的数据集，SECAT 在可以忽略不计的精度降低的情况下实现了超过200倍的速度。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Search-Efficient+Computerized+Adaptive+Testing)|0| +|[Search-Efficient Computerized Adaptive Testing](https://doi.org/10.1145/3583780.3615049)|Yuting Hong, Shiwei Tong, Wei Huang, Yan Zhuang, Qi Liu, Enhong Chen, Xin Li, Yuanjing He|University of Science and Technology of China & iFLYTEK Co., Ltd, Hefei, China; University of Science and Technology of China & State Key Laboratory of Cognitive Intelligence, Hefei, China; Open University of China, Beijing, China|Computerized Adaptive Testing (CAT) arises as a promising personalized test mode in online education, targeting at revealing students' latent knowledge state by selecting test items adaptively. The item selection strategy is the core component of CAT, which searches for the best suitable test item based on students' current estimated ability at each test step. However, existing selection strategies behave in a brute-force manner, which results in the time complexity being linear to the number of items (N) in the item pool, i.e., O(N). Thus, in reality, the search latency becomes the bottleneck for CAT with a large-scale item pool. To this end, we propose a Search-Efficient Computerized Adaptive Testing framework (SECAT), which aims at enhancing CAT with an efficient selection strategy. Specifically, SECAT contains two main phases: item pool indexing and item search. In the item pool indexing phase, we apply a student-aware spatial partition method on the item pool to divide the test items into many sub-spaces, considering the adaptability of test items. In the item search phase, we optimize the traditional single-round search strategy with the asymptotic theory and propose a multi-round search strategy that can further improve the time efficiency. Compared with existing strategies, the time complexity of SECAT decreases from O(N) to O(logN). Across two real-world datasets, SECAT achieves over 200x speed up with negligible accuracy degradation.|计算机自适应测试(CAT)作为一种新兴的网络教育个性化测试模式，旨在通过自适应选择测试项目来揭示学生的潜在知识状态。试题选择策略是计算机辅助测试(CAT)的核心组成部分，它根据学生在每个测试步骤中的当前估计能力来寻找最适合的试题。然而，现有的选择策略表现出一种蛮力的方式，导致时间复杂度与项目池中的项目数(N)成线性关系，即 O (N)。因此，在现实中，搜索延迟成为大规模项目池 CAT 的瓶颈。为此，我们提出了一个搜索效率高的计算机自适应测试框架(SECAT) ，旨在通过一种有效的选择策略来增强 CAT。具体来说，SECAT 包含两个主要阶段: 项目池索引和项目搜索。在试题库索引阶段，考虑试题的适应性，在试题库上采用学生感知的空间划分方法，将试题划分为多个子空间。在项目搜索阶段，我们利用渐近理论优化了传统的单轮搜索策略，并提出了多轮搜索策略，进一步提高了时间效率。与已有策略相比，SECAT 的时间复杂度从 O (N)降低到 O (logN)。通过两个真实世界的数据集，SECAT 在可以忽略不计的精度降低的情况下实现了超过200倍的速度。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Search-Efficient+Computerized+Adaptive+Testing)|0| |[Celebrity-aware Graph Contrastive Learning Framework for Social Recommendation](https://doi.org/10.1145/3583780.3614806)|Zheng Hu, Satoshi Nakagawa, Liang Luo, Yu Gu, Fuji Ren|The University of Tokyo, Tokyo, Japan; University of Electronic Science and Technology of China, Chengdu, China|Social networks exhibit a distinct "celebrity effect" whereby influential individuals have a more significant impact on others compared to ordinary individuals, unlike other network structures such as citation networks and knowledge graphs. Despite its common occurrence in social networks, the celebrity effect is frequently overlooked by existing social recommendation methods when modeling social relationships, thereby hindering the full exploitation of social networks to mine similarities between users. In this paper, we fill this gap and propose a Celebrity-aware Graph Contrastive Learning Framework for Social Recommendation (CGCL), which explicitly models the celebrity effect in the social domain. Technically, we measure the different influences of celebrity and ordinary nodes by mining social network structure features, such as closeness centrality. To model the celebrity effect in social networks, we design a novel user-user impact-aware aggregation method, which incorporates the celebrity-aware influence information into the message propagation process. Additionally, we design a graph neural network-based framework which incorporates social semantics into the user-item interaction modeling with contrastive learning-enhanced data augmentation. The experimental results on three real-world datasets show the effectiveness of the proposed framework. We conduct ablation experiments to prove that the key components of our model benefit the recommendation performance improvement.|与引用网络和知识图表等其他网络结构不同，社交网络表现出明显的“名人效应”，与普通个体相比，有影响力的个体对他人的影响更为显著。尽管名人效应在社交网络中普遍存在，但现有的社交推荐方法在对社交关系建模时往往忽视了名人效应，从而阻碍了对社交网络的充分利用以挖掘用户之间的相似性。在本文中，我们填补了这一空白，并提出了一个名人感知的图形对比学习框架(CGCL)的社会推荐，显式模型的名人效应的社会领域。在技术上，我们通过挖掘社会网络结构特征，如亲密度中心性，来衡量名人和普通节点的不同影响。为了模拟社交网络中的名人效应，我们设计了一种新的用户-用户影响感知聚合方法，该方法将名人感知的影响信息融入到信息传播过程中。此外，我们还设计了一个基于图神经网络的框架，该框架将社会语义引入到用户项目交互建模中，通过对比学习增强数据增强。在三个实际数据集上的实验结果表明了该框架的有效性。通过烧蚀实验证明了该模型的关键部分有利于推荐性能的提高。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Celebrity-aware+Graph+Contrastive+Learning+Framework+for+Social+Recommendation)|0| |[Independent Distribution Regularization for Private Graph Embedding](https://doi.org/10.1145/3583780.3614933)|Qi Hu, Yangqiu Song|HKUST, Hong Kong, Hong Kong|Learning graph embeddings is a crucial task in graph mining tasks. An effective graph embedding model can learn low-dimensional representations from graph-structured data for data publishing benefiting various downstream applications such as node classification, link prediction, etc. However, recent studies have revealed that graph embeddings are susceptible to attribute inference attacks, which allow attackers to infer private node attributes from the learned graph embeddings. To address these concerns, privacy-preserving graph embedding methods have emerged, aiming to simultaneously consider primary learning and privacy protection through adversarial learning. However, most existing methods assume that representation models have access to all sensitive attributes in advance during the training stage, which is not always the case due to diverse privacy preferences. Furthermore, the commonly used adversarial learning technique in privacy-preserving representation learning suffers from unstable training issues. In this paper, we propose a novel approach called Private Variational Graph AutoEncoders (PVGAE) with the aid of independent distribution penalty as a regularization term. Specifically, we split the original variational graph autoencoder (VGAE) to learn sensitive and non-sensitive latent representations using two sets of encoders. Additionally, we introduce a novel regularization to enforce the independence of the encoders. We prove the theoretical effectiveness of regularization from the perspective of mutual information. Experimental results on three real-world datasets demonstrate that PVGAE outperforms other baselines in private embedding learning regarding utility performance and privacy protection.|图嵌入学习是图挖掘任务中的一个关键问题。一个有效的图嵌入模型可以从图结构化数据中学习低维表示，用于数据发布，有利于各种下游应用，如节点分类、链路预测等。然而，最近的研究表明，图嵌入容易受到属性推理攻击，使攻击者能够从学习的图嵌入中推断出私有节点的属性。为了解决这些问题，出现了保护隐私的图嵌入方法，旨在同时考虑初级学习和通过对抗学习保护隐私。然而，现有的大多数方法都假设表示模型在训练阶段可以提前访问所有敏感属性，但由于隐私偏好的不同，这种假设并不总是成立。此外，隐私保护表征学习中常用的对抗学习技术存在不稳定的训练问题。在本文中，我们提出了一种新的方法称为私有变分图自动编码器(PVGAE)的援助下，独立分布罚金作为一个正则化项。具体来说，我们将原始的变分图自动编码器(VGAE)分离，使用两组编码器来学习敏感和非敏感的潜在表示。此外，我们还引入了一种新的正则化方法来增强编码器的独立性。从互信息的角度证明了正则化的理论有效性。在三个实际数据集上的实验结果表明，PVGAE 在效用性能和隐私保护方面优于其他基线的私有嵌入学习。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Independent+Distribution+Regularization+for+Private+Graph+Embedding)|0| -|[Enhancing the Robustness via Adversarial Learning and Joint Spatial-Temporal Embeddings in Traffic Forecasting](https://doi.org/10.1145/3583780.3614868)|Juyong Jiang, Binqing Wu, Ling Chen, Kai Zhang, Sunghun Kim|The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China; Zhejiang University, Hangzhou, China; East China Normal University, Shanghai, China|Traffic forecasting is an essential problem in urban planning and computing. The complex dynamic spatial-temporal dependencies among traffic objects (e.g., sensors and road segments) have been calling for highly flexible models; unfortunately, sophisticated models may suffer from poor robustness especially in capturing the trend of the time series (1st-order derivatives with time), leading to unrealistic forecasts. To address the challenge of balancing dynamics and robustness, we propose TrendGCN, a new scheme that extends the flexibility of GCNs and the distribution-preserving capacity of generative and adversarial loss for handling sequential data with inherent statistical correlations. On the one hand, our model simultaneously incorporates spatial (node-wise) embeddings and temporal (time-wise) embeddings to account for heterogeneous space-and-time convolutions; on the other hand, it uses GAN structure to systematically evaluate statistical consistencies between the real and the predicted time series in terms of both the temporal trending and the complex spatial-temporal dependencies. Compared with traditional approaches that handle step-wise predictive errors independently, our approach can produce more realistic and robust forecasts. Experiments on six benchmark traffic forecasting datasets and theoretical analysis both demonstrate the superiority and the state-of-the-art performance of TrendGCN. Source code is available at https://github.com/juyongjiang/TrendGCN.|交通量预测是城市规划和计算中的一个基本问题。交通对象(如传感器和路段)之间复杂的动态时空依赖性一直要求高度灵活的模型; 不幸的是，复杂的模型可能会受到鲁棒性差的影响，特别是在捕捉时间序列的趋势(随时间的一阶导数)时，导致不切实际的预测。为了解决平衡动态和稳健性的挑战，我们提出了 TrendGCN，这是一种新的方案，扩展了 GCNs 的灵活性以及生成和对抗性损失的分布保持能力，用于处理具有固有统计相关性的顺序数据。一方面，我们的模型同时结合了空间(节点)嵌入和时间(时间)嵌入来解释异质的时空卷积; 另一方面，它使用 GAN 结构来系统地评估实际时间序列和预测时间序列之间的统计一致性，包括时间趋势和复杂的时空相关性。与独立处理逐步预测误差的传统方法相比，我们的方法可以产生更加真实和稳健的预测。通过对6个基准流量预测数据集的实验和理论分析，验证了趋势 GCN 的优越性和最新性能。源代码可在 https://github.com/juyongjiang/trendgcn 下载。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Enhancing+the+Robustness+via+Adversarial+Learning+and+Joint+Spatial-Temporal+Embeddings+in+Traffic+Forecasting)|0| -|[Replace Scoring with Arrangement: A Contextual Set-to-Arrangement Framework for Learning-to-Rank](https://doi.org/10.1145/3583780.3615031)|Jiarui Jin, Xianyu Chen, Weinan Zhang, Mengyue Yang, Yang Wang, Yali Du, Yong Yu, Jun Wang|King's College London, London, United Kingdom; University College London, London, United Kingdom; Shanghai Jiao Tong University, Shanghai, China; East China Normal University, Shanghai, China|Learning-to-rank is a core technique in the top-N recommendation task, where an ideal ranker would be a mapping from an item set to an arrangement (a.k.a. permutation). Most existing solutions fall in the paradigm of probabilistic ranking principle (PRP), i.e., first score each item in the candidate set and then perform a sort operation to generate the top ranking list. However, these approaches neglect the contextual dependence among candidate items during individual scoring, and the sort operation is non-differentiable. To bypass the above issues, we propose Set-To-Arrangement Ranking (STARank), a new framework directly generates the permutations of the candidate items without the need for individually scoring and sort operations; and is end-to-end differentiable. As a result, STARank can operate when only the ground-truth permutations are accessible without requiring access to the ground-truth relevance scores for items. For this purpose, STARank first reads the candidate items in the context of the user browsing history, whose representations are fed into a Plackett-Luce module to arrange the given items into a list. To effectively utilize the given ground-truth permutations for supervising STARank, we leverage the internal consistency property of Plackett-Luce models to derive a computationally efficient list-wise loss. Experimental comparisons against 9 the state-of-the-art methods on 2 learning-to-rank benchmark datasets and 3 top-N real-world recommendation datasets demonstrate the superiority of STARank in terms of conventional ranking metrics. Notice that these ranking metrics do not consider the effects of the contextual dependence among the items in the list, we design a new family of simulation-based ranking metrics, where existing metrics can be regarded as special cases. STARank can consistently achieve better performance in terms of PBM and UBM simulation-based metrics.|学习排名是排名前 N 的推荐任务中的一项核心技术，理想的排名应该是从一个项目集到一个排列(又称排列)的映射。大多数现有的解决方案属于概率排序原则(PRP)的范式，即首先给候选集中的每个项目打分，然后执行排序操作来生成最高排名列表。然而，这些方法忽略了个体评分过程中候选项之间的上下文依赖性，且排序操作是不可微的。为了绕过上述问题，我们提出了一个新的框架集到排列排序(STARank) ，它直接生成候选项的排列，而不需要单独的评分和排序操作，并且是端到端可微的。因此，STARank 可以在只有地面真相排列可以访问时运行，而不需要访问项目的地面真相相关分数。为此，STARank 首先在用户浏览历史记录的上下文中读取候选项，其表示被提供给 Plackett-Luce 模块，以便将给定的项排列成列表。为了有效地利用给定的地面真理排列来监督 STARank，我们利用 Plackett-Luce 模型的内部一致性特性来导出一个计算有效的列表损失。通过对2个学习排名基准数据集和3个排名前 N 的实际推荐数据集的9种最新方法的实验比较，证明了 STARank 在常规排名指标方面的优越性。请注意，这些排名指标没有考虑列表中项目之间的上下文相关性的影响，我们设计了一个新的基于模拟的排名指标家族，其中现有的指标可以被视为特殊情况。STARank 在基于 PBM 和基于 UBM 仿真的度量方面能够持续地获得更好的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Replace+Scoring+with+Arrangement:+A+Contextual+Set-to-Arrangement+Framework+for+Learning-to-Rank)|0| +|[Enhancing the Robustness via Adversarial Learning and Joint Spatial-Temporal Embeddings in Traffic Forecasting](https://doi.org/10.1145/3583780.3614868)|Juyong Jiang, Binqing Wu, Ling Chen, Kai Zhang, Sunghun Kim|East China Normal University, Shanghai, China; The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China; Zhejiang University, Hangzhou, China|Traffic forecasting is an essential problem in urban planning and computing. The complex dynamic spatial-temporal dependencies among traffic objects (e.g., sensors and road segments) have been calling for highly flexible models; unfortunately, sophisticated models may suffer from poor robustness especially in capturing the trend of the time series (1st-order derivatives with time), leading to unrealistic forecasts. To address the challenge of balancing dynamics and robustness, we propose TrendGCN, a new scheme that extends the flexibility of GCNs and the distribution-preserving capacity of generative and adversarial loss for handling sequential data with inherent statistical correlations. On the one hand, our model simultaneously incorporates spatial (node-wise) embeddings and temporal (time-wise) embeddings to account for heterogeneous space-and-time convolutions; on the other hand, it uses GAN structure to systematically evaluate statistical consistencies between the real and the predicted time series in terms of both the temporal trending and the complex spatial-temporal dependencies. Compared with traditional approaches that handle step-wise predictive errors independently, our approach can produce more realistic and robust forecasts. Experiments on six benchmark traffic forecasting datasets and theoretical analysis both demonstrate the superiority and the state-of-the-art performance of TrendGCN. Source code is available at https://github.com/juyongjiang/TrendGCN.|交通量预测是城市规划和计算中的一个基本问题。交通对象(如传感器和路段)之间复杂的动态时空依赖性一直要求高度灵活的模型; 不幸的是，复杂的模型可能会受到鲁棒性差的影响，特别是在捕捉时间序列的趋势(随时间的一阶导数)时，导致不切实际的预测。为了解决平衡动态和稳健性的挑战，我们提出了 TrendGCN，这是一种新的方案，扩展了 GCNs 的灵活性以及生成和对抗性损失的分布保持能力，用于处理具有固有统计相关性的顺序数据。一方面，我们的模型同时结合了空间(节点)嵌入和时间(时间)嵌入来解释异质的时空卷积; 另一方面，它使用 GAN 结构来系统地评估实际时间序列和预测时间序列之间的统计一致性，包括时间趋势和复杂的时空相关性。与独立处理逐步预测误差的传统方法相比，我们的方法可以产生更加真实和稳健的预测。通过对6个基准流量预测数据集的实验和理论分析，验证了趋势 GCN 的优越性和最新性能。源代码可在 https://github.com/juyongjiang/trendgcn 下载。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Enhancing+the+Robustness+via+Adversarial+Learning+and+Joint+Spatial-Temporal+Embeddings+in+Traffic+Forecasting)|0| +|[Replace Scoring with Arrangement: A Contextual Set-to-Arrangement Framework for Learning-to-Rank](https://doi.org/10.1145/3583780.3615031)|Jiarui Jin, Xianyu Chen, Weinan Zhang, Mengyue Yang, Yang Wang, Yali Du, Yong Yu, Jun Wang|University College London, London, United Kingdom; Shanghai Jiao Tong University, Shanghai, China; East China Normal University, Shanghai, China; King's College London, London, United Kingdom|Learning-to-rank is a core technique in the top-N recommendation task, where an ideal ranker would be a mapping from an item set to an arrangement (a.k.a. permutation). Most existing solutions fall in the paradigm of probabilistic ranking principle (PRP), i.e., first score each item in the candidate set and then perform a sort operation to generate the top ranking list. However, these approaches neglect the contextual dependence among candidate items during individual scoring, and the sort operation is non-differentiable. To bypass the above issues, we propose Set-To-Arrangement Ranking (STARank), a new framework directly generates the permutations of the candidate items without the need for individually scoring and sort operations; and is end-to-end differentiable. As a result, STARank can operate when only the ground-truth permutations are accessible without requiring access to the ground-truth relevance scores for items. For this purpose, STARank first reads the candidate items in the context of the user browsing history, whose representations are fed into a Plackett-Luce module to arrange the given items into a list. To effectively utilize the given ground-truth permutations for supervising STARank, we leverage the internal consistency property of Plackett-Luce models to derive a computationally efficient list-wise loss. Experimental comparisons against 9 the state-of-the-art methods on 2 learning-to-rank benchmark datasets and 3 top-N real-world recommendation datasets demonstrate the superiority of STARank in terms of conventional ranking metrics. Notice that these ranking metrics do not consider the effects of the contextual dependence among the items in the list, we design a new family of simulation-based ranking metrics, where existing metrics can be regarded as special cases. STARank can consistently achieve better performance in terms of PBM and UBM simulation-based metrics.|学习排名是排名前 N 的推荐任务中的一项核心技术，理想的排名应该是从一个项目集到一个排列(又称排列)的映射。大多数现有的解决方案属于概率排序原则(PRP)的范式，即首先给候选集中的每个项目打分，然后执行排序操作来生成最高排名列表。然而，这些方法忽略了个体评分过程中候选项之间的上下文依赖性，且排序操作是不可微的。为了绕过上述问题，我们提出了一个新的框架集到排列排序(STARank) ，它直接生成候选项的排列，而不需要单独的评分和排序操作，并且是端到端可微的。因此，STARank 可以在只有地面真相排列可以访问时运行，而不需要访问项目的地面真相相关分数。为此，STARank 首先在用户浏览历史记录的上下文中读取候选项，其表示被提供给 Plackett-Luce 模块，以便将给定的项排列成列表。为了有效地利用给定的地面真理排列来监督 STARank，我们利用 Plackett-Luce 模型的内部一致性特性来导出一个计算有效的列表损失。通过对2个学习排名基准数据集和3个排名前 N 的实际推荐数据集的9种最新方法的实验比较，证明了 STARank 在常规排名指标方面的优越性。请注意，这些排名指标没有考虑列表中项目之间的上下文相关性的影响，我们设计了一个新的基于模拟的排名指标家族，其中现有的指标可以被视为特殊情况。STARank 在基于 PBM 和基于 UBM 仿真的度量方面能够持续地获得更好的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Replace+Scoring+with+Arrangement:+A+Contextual+Set-to-Arrangement+Framework+for+Learning-to-Rank)|0| |[Real-time Emotion Pre-Recognition in Conversations with Contrastive Multi-modal Dialogue Pre-training](https://doi.org/10.1145/3583780.3615024)|Xincheng Ju, Dong Zhang, Suyang Zhu, Junhui Li, Shoushan Li, Guodong Zhou|Soochow University, Suzhou, China|This paper presents our pioneering effort in addressing a new and realistic scenario in multi-modal dialogue systems called Multi-modal Real-time Emotion Pre-recognition in Conversations (MREPC). The objective is to predict the emotion of a forthcoming target utterance that is highly likely to occur. We believe that this task can enhance the dialogue system's understanding of the interlocutor's state of mind, enabling it to prepare an appropriate response in advance. However, addressing MREPC poses the following challenges:1) Previous studies on emotion elicitation typically focus on textual modality and perform sentiment forecasting within a fixed contextual scenario. 2) Previous studies on multi-modal emotion recognition aim to predict the emotion of existing utterances, making it difficult to extend these approaches to MREPC due to the absence of the target utterance. To tackle these challenges, we construct two benchmark multi-modal datasets for MREPC and propose a task-specific multi-modal contrastive pre-training approach. This approach leverages large-scale unlabeled multi-modal dialogues to facilitate emotion pre-recognition for potential utterances of specific target speakers. Through detailed experiments and extensive analysis, we demonstrate that our proposed multi-modal contrastive pre-training architecture effectively enhances the performance of multi-modal real-time emotion pre-recognition in conversations.|本文介绍了我们在处理多模态对话系统中的一个新的和现实的场景，称为多模态实时情绪会话预识别(MREPC)的开拓性工作。目的是预测一个即将到来的目标话语的情绪，这是非常可能发生的。我们相信，这一任务可以提高对话系统对对话者心理状态的理解，使其能够提前准备好适当的应对措施。然而，解决 MREPC 问题带来了以下挑战: 1)以往的情绪诱发研究主要集中在语篇情态上，在固定的语境情景下进行情绪预测。2)以往对多模态情绪识别的研究主要是针对已有话语的情绪预测，但由于目标话语的缺失，这些方法难以推广到 MREPC。为了应对这些挑战，我们构建了两个基准的 MREPC 多模态数据集，并提出了一种针对特定任务的多模态对比预训练方法。该方法利用大规模未标记的多模态对话，促进对特定目标说话人潜在话语的情感预识别。通过详细的实验和广泛的分析表明，本文提出的多模态对比预训练结构有效地提高了会话中多模态实时情绪预识别的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Real-time+Emotion+Pre-Recognition+in+Conversations+with+Contrastive+Multi-modal+Dialogue+Pre-training)|0| -|[Nudging Neural Click Prediction Models to Pay Attention to Position](https://doi.org/10.1145/3583780.3614994)|Efi Karra Taniskidou, Wenjie Zhao, Iain Murray, Roberto Pellegrini|Amazon, Edinburgh, United Kingdom; Amazon & The University of Edinburgh, Edinburgh, United Kingdom|Predicting the click-through rate (CTR) of an item is a fundamental task in online advertising and recommender systems. CTR prediction models are typically trained on user click data from traffic logs. However, users are more likely to interact with items that were shown prominently on a website. CTR models often over-estimate the value of such items and show them more often, at the expense of items of higher quality that were previously shown at less prominent positions. This self-reinforcing position bias effect reduces both the immediate and long-term quality of recommendations for users. In this paper, we revisit position bias in a family of state-of-the-art neural models for CTR prediction, and use synthetic data to demonstrate the difficulty of controlling for position. We propose an approach that encourages neural networks to use position (or other confounding variables) as much as possible to explain the training data, and a metric that can directly measure bias. Experiments on two real-world datasets demonstrate the effectiveness of our approach in correcting for position-like features in 2 state-of-the-art CTR prediction models.|在在线广告和推荐系统中，预测商品的点进率是一项基本任务。CTR 预测模型通常根据流量日志中的用户点击数据进行训练。然而，用户更可能与网站上显著显示的项目进行交互。CTR 模型往往高估这些项目的价值，并更经常地显示它们，以牺牲以前显示在不太突出位置的高质量项目为代价。这种自我强化的位置偏差效应降低了对用户推荐的即时和长期质量。在本文中，我们回顾了位置偏差在一个国家的最先进的 CTR 预测神经模型的家庭，并使用合成数据来说明位置控制的困难。我们提出了一种方法，鼓励神经网络使用位置(或其他混杂变量)尽可能多地解释训练数据，并指标，可以直接测量偏倚。在两个真实世界的数据集上的实验证明了我们的方法在两个最先进的 CTR 预测模型中校正位置类特征的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Nudging+Neural+Click+Prediction+Models+to+Pay+Attention+to+Position)|0| +|[Nudging Neural Click Prediction Models to Pay Attention to Position](https://doi.org/10.1145/3583780.3614994)|Efi Karra Taniskidou, Wenjie Zhao, Iain Murray, Roberto Pellegrini|Amazon & The University of Edinburgh, Edinburgh, United Kingdom; Amazon, Edinburgh, United Kingdom|Predicting the click-through rate (CTR) of an item is a fundamental task in online advertising and recommender systems. CTR prediction models are typically trained on user click data from traffic logs. However, users are more likely to interact with items that were shown prominently on a website. CTR models often over-estimate the value of such items and show them more often, at the expense of items of higher quality that were previously shown at less prominent positions. This self-reinforcing position bias effect reduces both the immediate and long-term quality of recommendations for users. In this paper, we revisit position bias in a family of state-of-the-art neural models for CTR prediction, and use synthetic data to demonstrate the difficulty of controlling for position. We propose an approach that encourages neural networks to use position (or other confounding variables) as much as possible to explain the training data, and a metric that can directly measure bias. Experiments on two real-world datasets demonstrate the effectiveness of our approach in correcting for position-like features in 2 state-of-the-art CTR prediction models.|在在线广告和推荐系统中，预测商品的点进率是一项基本任务。CTR 预测模型通常根据流量日志中的用户点击数据进行训练。然而，用户更可能与网站上显著显示的项目进行交互。CTR 模型往往高估这些项目的价值，并更经常地显示它们，以牺牲以前显示在不太突出位置的高质量项目为代价。这种自我强化的位置偏差效应降低了对用户推荐的即时和长期质量。在本文中，我们回顾了位置偏差在一个国家的最先进的 CTR 预测神经模型的家庭，并使用合成数据来说明位置控制的困难。我们提出了一种方法，鼓励神经网络使用位置(或其他混杂变量)尽可能多地解释训练数据，并指标，可以直接测量偏倚。在两个真实世界的数据集上的实验证明了我们的方法在两个最先进的 CTR 预测模型中校正位置类特征的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Nudging+Neural+Click+Prediction+Models+to+Pay+Attention+to+Position)|0| |[A Model-Agnostic Method to Interpret Link Prediction Evaluation of Knowledge Graph Embeddings](https://doi.org/10.1145/3583780.3614763)|Narayanan Asuri Krishnan, Carlos R. Rivero|Rochester Institute of Technology, Rochester, NY, USA|In link prediction evaluation, an embedding model assigns plausibility scores to unseen triples in a knowledge graph using an input partial triple. Performance metrics like mean rank are useful to compare models side by side, but do not shed light on their behavior. Interpreting link prediction evaluation and comparing models based on such interpretation are appealing. Current interpretation methods have mainly focused on single predictions or other tasks different from link prediction. Since knowledge graph embedding methods are diverse, interpretation methods that are applicable only to certain machine learning approaches cannot be used. In this paper, we propose a model-agnostic method for interpreting link prediction evaluation as a whole. The interpretation consists of Horn rules mined from the knowledge graph containing the triples a model deems plausible. We combine precision and recall measurements of mined rules using Fβ score to quantify interpretation accuracy. To maximize interpretation accuracy when comparing models, we study two approximations to the hard problem of merging rules. Our quantitative study shows that interpretation accuracy serves to compare diverse models side by side, and that these comparisons are different from those using ranks. Our qualitative study shows that several models globally capture expected semantics, and that models make a common set of predictions despite of redundancy reduction.|在链接预测评价中，嵌入模型使用输入部分三元组将合理性分值赋给知识图中的未知三元组。像平均排名这样的性能指标对于并排比较模型很有用，但是不能说明它们的行为。解释链接预测评价和基于这种解释的比较模型是很有吸引力的。目前的解释方法主要集中在单个预测或其他不同于链接预测的任务。由于知识图嵌入方法多种多样，不能采用仅适用于某些机器学习方法的解释方法。本文提出了一种从整体上解释链路预测评价的模型不可知方法。解释包括从知识图中挖掘的 Horn 规则，其中包含模型认为合理的三元组。我们结合了准确率召回率挖掘规则的测量结果，使用 Fβ 评分来量化解释的准确性。为了在比较模型时最大限度地提高解释的准确性，我们研究了合并规则这一难题的两个近似值。我们的定量研究表明，解释的准确性服务于比较不同的模型并排，这些比较是不同的使用等级。我们的定性研究表明，几个模型全局捕获预期的语义，模型作出了一个共同的预测集，尽管冗余减少。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Model-Agnostic+Method+to+Interpret+Link+Prediction+Evaluation+of+Knowledge+Graph+Embeddings)|0| |[Towards Automatic ICD Coding via Knowledge Enhanced Multi-Task Learning](https://doi.org/10.1145/3583780.3615087)|Xinhang Li, Xiangyu Zhao, Yong Zhang, Chunxiao Xing|Tsinghua University, Beijing, China; City University of Hong Kong, Hong Kong, Hong Kong|The aim of ICD coding is to assign International Classification of Diseases (ICD) codes to unstructured clinical notes or discharge summaries. Numerous methods have been proposed for automatic ICD coding in an effort to reduce human labor and errors. However, existing works disregard the data imbalance problem of clinical notes. In addition, the noisy clinical note issue has not been thoroughly investigated. To address such issues, we propose a knowledge enhanced Graph Attention Network (GAT) under multi-task learning setting. Specifically, multi-level information transitions and interactions have been implemented. On the one hand, a large heterogeneous text graph is constructed to capture both intra- and inter-note correlations between various semantic concepts, thereby alleviating the data imbalance issue. On the other hand, two auxiliary healthcare tasks have been proposed to facilitate the sharing of information across tasks. Moreover, to tackle the issue of noisy clinical notes, we propose to utilize the rich structured knowledge facts and information provided by medical domain knowledge, thereby encouraging the model to focus on the clinical notes' noteworthy portion and valuable information. The experimental results on the widely-used medical dataset, MIMIC-III, demonstrate the advantages of our proposed framework.|ICD 编码的目的是为非结构化的临床记录或出院摘要分配国际疾病与相关健康问题统计分类(ICD)编码。为了减少人工劳动和错误，人们提出了许多 ICD 自动编码的方法。然而，现有的研究忽视了临床注释的数据不平衡问题。此外，噪音临床注意事项并没有被彻底调查。为了解决这些问题，我们提出了一种多任务学习环境下的知识增强型图注意网络(GAT)。具体来说，已经实现了多级信息转换和交互。一方面，构造了一个大型的异构文本图来捕捉各种语义概念之间的注释内和注释间的相关性，从而缓解数据不平衡问题。另一方面，我们提出了两项辅助保健工作，以促进各项工作之间的信息共享。此外，为了解决临床记录噪声的问题，我们建议利用医学领域知识所提供的丰富的结构化知识事实和信息，从而鼓励模型关注临床记录的值得注意的部分和有价值的信息。在广泛使用的医学数据集 MIMIC-III 上的实验结果证明了该框架的优越性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Towards+Automatic+ICD+Coding+via+Knowledge+Enhanced+Multi-Task+Learning)|0| -|[Graph Enhanced Hierarchical Reinforcement Learning for Goal-oriented Learning Path Recommendation](https://doi.org/10.1145/3583780.3614897)|Qingyao Li, Wei Xia, Li'ang Yin, Jian Shen, Renting Rui, Weinan Zhang, Xianyu Chen, Ruiming Tang, Yong Yu|Huawei Noah's Art Lab, Shenzhen, China; Huawei Noah's Ark Lab, Shenzhen, China; Shanghai Jiao Tong University, Shanghai, China; Shanghai Jiaotong University, Shanghai, China|Goal-oriented Learning path recommendation aims to recommend learning items (concepts or exercises) step-by-step to a learner to promote the mastery level of her specific learning goals. By formulating this task as a Markov decision process, reinforcement learning (RL) methods have demonstrated great power. Although extensive research efforts have been made, previous methods still fail to recommend effective goal-oriented paths due to the under-utilizing of goals. Specifically, it is mainly reflected in two aspects: (1)The lack of goal planning. When learners have multiple goals with different difficulties, the previous methods can't fully utilize the difficulties and dependencies between goal learning items to plan the sequence of achieving these goals, making the path chaotic and inefficient; (2)The lack of efficiency in goal achieving. When pursuing a single goal, the path may contain learning items unrelated to the goal, which makes realizing a certain goal inefficient. To address these challenges, we present a novel Graph Enhanced Hierarchical Reinforcement Learning (GEHRL) framework for goal-oriented learning path recommendation. The framework divides learning path recommendation into two parts: sub-goal selection(planning) and sub-goal achieving(learning item recommendation). Specifically, we employ a high-level agent as a sub-goal selector to select sub-goals for the low-level agent to achieve. The low-level agent in the framework is to recommend learning items to the learner. To make the path only contain goal-related learning items to improve the efficiency of achieving the goal, we develop a graph-based candidate selector to constrain the action space of the low-level agent based on the sub-goal and knowledge graph. We also develop test-based internal reward for low-level training so that the sparsity problem of external reward can be alleviated. Extensive experiments on three different simulators demonstrate our framework achieves state-of-the-art performance.|目标导向学习路径推荐旨在向学习者逐步推荐学习项目(概念或练习) ，以提高其对特定学习目标的掌握程度。通过把这个任务作为一个马可夫决策过程，强化学习(RL)方法已经证明了它的巨大威力。尽管已经做了大量的研究工作，以往的方法仍然不能推荐有效的目标导向的路径，由于目标利用不足。具体来说，主要体现在两个方面: (1)目标规划的缺失。当学习者有不同困难的多个目标时，以往的方法不能充分利用目标学习项目之间的困难和依赖关系来规划实现这些目标的顺序，使得路径混乱和效率低下; (2)目标实现效率低下。在追求单一目标时，路径中可能包含与目标无关的学习项，使得实现某一目标效率低下。为了应对这些挑战，我们提出了一个新的图形增强层次强化学习(GEHRL)框架，用于面向目标的学习路径推荐。该框架将学习路径推荐分为子目标选择(规划)和子目标实现(学习项目推荐)两部分。具体来说，我们使用高级代理作为子目标选择器，为低级代理选择要实现的子目标。框架中的底层代理是向学习者推荐学习项目。为了使路径只包含与目标相关的学习项，提高实现目标的效率，提出了一种基于子目标和知识图的候选选择器来约束底层智能体的行为空间。我们还针对低水平培训开发了基于测试的内部奖励，以缓解外部奖励稀缺的问题。在三个不同的模拟器上进行的大量实验表明，我们的框架实现了最先进的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Graph+Enhanced+Hierarchical+Reinforcement+Learning+for+Goal-oriented+Learning+Path+Recommendation)|0| -|[THGNN: An Embedding-based Model for Anomaly Detection in Dynamic Heterogeneous Social Networks](https://doi.org/10.1145/3583780.3615079)|Yilin Li, Jiaqi Zhu, Congcong Zhang, Yi Yang, Jiawen Zhang, Ying Qiao, Hongan Wang|Institute of Software, Chinese Academy of Sciences, Beijing, China; The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China; Institute of Software, Chinese Academy of Sciences & University of Chinese Academy of Sciences, Beijing, China|Anomaly detection, particularly the detection of anomalous behaviors in dynamic and heterogeneous social networks, is becoming more and more crucial in real life. Traditional rule-based and feature-based methods cannot well capture the structural and temporal patterns of ever-changing user behaviors. Moreover, most of the existing works based on network embedding either rely on discretized snapshots, which have ignored accurate temporal relations among user behaviors and weakened the impact of new edges, or fail to utilize dynamic and heterogeneous information simultaneously to distinguish varying effects of new edges on existing nodes. In this paper, we propose an end-to-end continuous-time model, named Temporal Heterogeneous Graph Neural Network (THGNN), to detect anomalous behaviors (edges) in dynamic heterogeneous social networks. Specifically, the model constantly updates node embeddings by propagating the information of a new edge to its source and target nodes as well as their neighbors. In this process, heterogeneous encoders are employed to handle different types of nodes and edges. What is more, a novel dual-level distributive attention mechanism is designed to allocate the influence degree of a currently interacting node to its multiple neighbors, considering the combined effect of edge type and time interval information. That can be regarded as an extension of the classical aggregative attention mechanism in the opposite direction. Extensive experiments on four real-world datasets demonstrate that THGNN outperforms all the baselines on the task of anomalous edge detection, achieving an average AUC gain of 6% across all datasets.|异常检测，特别是在动态和异构的社交网络中发现异常行为，在现实生活中变得越来越重要。传统的基于规则和基于特征的方法不能很好地捕获不断变化的用户行为的结构和时间模式。此外，现有的基于网络嵌入的工作大多依赖于离散快照，忽略了用户行为之间精确的时间关系，削弱了新边的影响，或者未能同时利用动态和异构信息来区分新边对现有节点的不同影响。本文提出了一种端到端连续时间模型——时态异构图神经网络(THGNN) ，用于检测动态异构社会网络中的异常行为(边)。具体来说，该模型通过将新边的信息传播到源节点和目标节点以及它们的邻居，不断地更新节点嵌入。在这个过程中，异构编码器被用来处理不同类型的节点和边缘。同时，考虑到边缘类型和时间间隔信息的组合效应，设计了一种新的双层分布式注意机制来分配当前交互节点对其多个邻居的影响程度。这可以看作是对经典的聚集性注意机制在相反方向上的延伸。在四个实际数据集上的大量实验表明，THGNN 在异常边缘检测任务上优于所有基线，在所有数据集上平均获得6% 的 AUC 增益。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=THGNN:+An+Embedding-based+Model+for+Anomaly+Detection+in+Dynamic+Heterogeneous+Social+Networks)|0| -|[MadSGM: Multivariate Anomaly Detection with Score-based Generative Models](https://doi.org/10.1145/3583780.3614956)|Haksoo Lim, Sewon Park, Minjung Kim, Jaehoon Lee, Seonkyu Lim, Noseong Park|Yonsei University, Seoul, Republic of Korea; Samsung SDS, Seoul, Republic of Korea; LG AI Research, Seoul, Republic of Korea|The time-series anomaly detection is one of the most fundamental tasks for time-series. Unlike the time-series forecasting and classification, the time-series anomaly detection typically requires unsupervised (or self-supervised) training since collecting and labeling anomalous observations are difficult. In addition, most existing methods resort to limited forms of anomaly measurements and therefore, it is not clear whether they are optimal in all circumstances. To this end, we present a multivariate time-series anomaly detector based on score-based generative models, called MadSGM, which considers the broadest ever set of anomaly measurement factors: i) reconstruction-based, ii) density-based, and iii) gradient-based anomaly measurements. We also design a conditional score network and its denoising score matching loss for the time-series anomaly detection. Experiments on five real-world benchmark datasets illustrate that MadSGM achieves the most robust and accurate predictions.|时间序列异常检测是时间序列最基本的任务之一。与时间序列预测和分类不同，时间序列异常检测通常需要无监督(或自我监督)的训练，因为收集和标记异常观测是困难的。此外，大多数现有方法采用有限形式的异常测量，因此，不清楚它们是否在所有情况下都是最佳的。为此，我们提出了一个基于基于评分的生成模型的多变量时间序列异常检测器，称为 MadSGM，其考虑了有史以来最广泛的一组异常测量因素: i)基于重建的，ii)基于密度的，和 iii)基于梯度的异常测量。我们还设计了一个条件得分网络及其去噪得分匹配丢失的时间序列异常检测。对五个真实世界基准数据集的实验表明，MadSGM 能够实现最稳健和准确的预测。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=MadSGM:+Multivariate+Anomaly+Detection+with+Score-based+Generative+Models)|0| -|[Hierarchical Prompt Tuning for Few-Shot Multi-Task Learning](https://doi.org/10.1145/3583780.3614913)|Jingping Liu, Tao Chen, Zujie Liang, Haiyun Jiang, Yanghua Xiao, Feng Wei, Yuxi Qian, Zhenghong Hao, Bing Han|East China University of Science and Technology, Shanghai, China; Ant Group, Shanghai, China; Fudan University, Shanghai, China|Prompt tuning has enhanced the performance of Pre-trained Language Models for multi-task learning in few-shot scenarios. However, existing studies fail to consider that the prompts among different layers in Transformer are different due to the diverse information learned at each layer. In general, the bottom layers in the model tend to capture low-level semantic or structural information, while the upper layers primarily acquire task-specific knowledge. Hence, we propose a novel hierarchical prompt tuning model for few-shot multi-task learning to capture this regularity. The designed model mainly consists of three types of prompts: shared prompts, auto-adaptive prompts, and task-specific prompts. Shared prompts facilitate the sharing of general information across all tasks. Auto-adaptive prompts dynamically select and integrate relevant prompt information from all tasks into the current task. Task-specific prompts concentrate on learning task-specific knowledge. To enhance the model's adaptability to diverse inputs, we introduce deep instance-aware language prompts as the foundation for constructing the above prompts. To evaluate the effectiveness of our proposed method, we conduct extensive experiments on multiple widely-used datasets. The experimental results demonstrate that the proposed method achieves state-of-the-art performance for multi-task learning in few-shot settings and outperforms ChatGPT in the full-data setting.|及时调优提高了预训练语言模型在少镜头情景下多任务学习的性能。然而，现有的研究没有考虑到变压器不同层之间的提示是不同的，因为在每一层学习的不同信息。一般来说，模型的底层倾向于捕获低层次的语义或结构信息，而上层主要获取特定于任务的知识。因此，我们提出了一个新的分层提示调整模型来捕捉这种规律性的少拍多任务学习。所设计的模型主要包括三种类型的提示: 共享提示、自适应提示和任务特定提示。共享提示有助于在所有任务之间共享一般信息。自适应提示动态选择并将所有任务中的相关提示信息集成到当前任务中。特定于任务的提示集中于学习特定于任务的知识。为了增强模型对不同输入的适应性，我们引入了深度实例感知语言提示作为构造上述提示的基础。为了评估我们提出的方法的有效性，我们在多个广泛使用的数据集上进行了广泛的实验。实验结果表明，该方法在少镜头情况下获得了最佳的多任务学习性能，在全数据情况下优于 ChatGPT。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Hierarchical+Prompt+Tuning+for+Few-Shot+Multi-Task+Learning)|0| -|[SMEF: Social-aware Multi-dimensional Edge Features-based Graph Representation Learning for Recommendation](https://doi.org/10.1145/3583780.3615063)|Xiao Liu, Shunmei Meng, Qianmu Li, Lianyong Qi, Xiaolong Xu, Wanchun Dou, Xuyun Zhang|Digital Economy Research Institute, Nanjing University of Science & Technology, Nanjing, China; Nanjing University of Information Science & Technology, Nanjing, China; Department of Computer Science and Engineering, Nanjing University of Science and Technology & State Key Lab. for Novel Software Technology, Nanjing University, Nanjing, China; Department of Cyber Science and Engineering, Nanjing University of Science and Technology, Nanjing, China; College of Computer Science and Technology, China University of Petroleum (East China), Qingdao, China; Macquarie University, Sydney, Australia; Nanjing University, Nanjing, China|Exploring user-item interaction cues is crucial for the performance of recommender systems. Explicit investigation of interaction cues is made possible by using graph-based models, where each user-item relationship is described by an edge, and the introduction of user-user social network. While existing graph-based recommendation methods use only a single-value edge to define the relationship between a pair of user and item, which limits the ability to represent complex user-item interactions. Furthermore, some social recommendation methods overlook the heterogeneous user behavior patterns in social and interaction relationships, resulting in the suboptimal performance of existing systems. In this paper, we propose a novel Social-aware Multi-dimensional Edge Feature-based Graph Representation Learning method, called SMEF. It represents all users and items as a graph and deep learns a multi-dimensional edge feature to explicitly describe the task-specific relationships of each user-item pair. Specifically, the proposed SMEF focuses on two distinct user behavior patterns toward social friends and interactive items, which explore the underlying heterogeneous relationship cues within them. This way, the learned multi-dimensional edge features encode user information from both social and interaction aspects. The proposed SMEF is a plug-and-play module that can be combined with different recommendation frameworks and Graph Neural Networks (GNNs) backbones to generate high quality user representations. The experimental results achieved on three publicly accessible datasets show that our SMEF-based method outperforms strong baselines.|探索用户项交互线索对于推荐系统的性能至关重要。通过使用基于图的模型(其中每个用户-项目关系由一个边描述)和引入用户-用户社交网络，可以对交互线索进行明确的调查。而现有的基于图的推荐方法只使用单值边界来定义一对用户和项目之间的关系，这限制了表示复杂用户-项目交互的能力。此外，一些社交推荐方法忽视了社交和交互关系中的异构用户行为模式，导致现有系统的性能不理想。本文提出了一种新的基于社会感知的多维边缘特征的图形表示学习方法，称为 SMF。它将所有用户和项目表示为一个图形，并深入学习一个多维边缘特性，以显式地描述每个用户-项目对的特定于任务的关系。具体而言，本研究针对两种不同的使用者行为模式，分别针对社交朋友与互动项目，探讨其中潜在的异质性关系线索。这样，学习的多维边缘特征从社会和交互两个方面对用户信息进行编码。该模块可以与不同的推荐框架和图形神经网络(GNN)骨干结合，生成高质量的用户表示。在三个公开可访问数据集上的实验结果表明，我们提出的基于 MESF 的方法性能优于强基线。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=SMEF:+Social-aware+Multi-dimensional+Edge+Features-based+Graph+Representation+Learning+for+Recommendation)|0| -|[Self-Supervised Dynamic Hypergraph Recommendation based on Hyper-Relational Knowledge Graph](https://doi.org/10.1145/3583780.3615054)|Yi Liu, Hongrui Xuan, Bohan Li, Meng Wang, Tong Chen, Hongzhi Yin|The University of Queensland, Brisbane, Australia; Nanjing University of Aeronautics and Astronautics, Nanjing, China; Tongji University, Shanghai, China|Knowledge graphs (KGs) are commonly used as side information to enhance collaborative signals and improve recommendation quality. In the context of knowledge-aware recommendation (KGR), graph neural networks (GNNs) have emerged as promising solutions for modeling factual and semantic information in KGs. However, the long-tail distribution of entities leads to sparsity in supervision signals, which weakens the quality of item representation when utilizing KG enhancement. Additionally, the binary relation representation of KGs simplifies hyper-relational facts, making it challenging to model complex real-world information. Furthermore, the over-smoothing phenomenon results in indistinguishable representations and information loss. To address these challenges, we propose the SDK (Self-Supervised Dynamic Hypergraph Recommendation based on Hyper-Relational Knowledge Graph) framework. This framework establishes a cross-view hypergraph self-supervised learning mechanism for KG enhancement. Specifically, we model hyper-relational facts in KGs to capture interdependencies between entities under complete semantic conditions. With the refined representation, a hypergraph is dynamically constructed to preserve features in the deep vector space, thereby alleviating the over-smoothing problem. Furthermore, we mine external supervision signals from both the global perspective of the hypergraph and the local perspective of collaborative filtering (CF) to guide the model prediction process. Extensive experiments conducted on different datasets demonstrate the superiority of the SDK framework over state-of-the-art models. The results showcase its ability to alleviate the effects of over-smoothing and supervision signal sparsity.|知识图作为辅助信息被广泛应用于增强协作信号和提高推荐质量。在知识感知推荐(KGR)的背景下，图形神经网络(GNN)已经成为幼儿园建立事实和语义信息模型的有希望的解决方案。然而，实体的长尾分布导致监督信号的稀疏性，使得 KG 增强的项目表示质量下降。此外，KGs 的二进制关系表示简化了超关系事实，使得对复杂的现实世界信息进行建模具有挑战性。此外，过度平滑现象导致不可区分的表示和信息损失。针对这些挑战，本文提出了基于超关系知识图的自监督动态超图推荐(SDK)框架。该框架建立了一种用于 KG 增强的跨视图超图自监督学习机制。具体来说，我们在 KG 中对超关系事实建模，以在完全语义条件下捕获实体之间的相互依赖性。该方法通过动态构造超图来保留深向量空间中的特征，从而解决了超图的过平滑问题。此外，我们从超图的全局视角和协同过滤的局部视角来挖掘外部监督信号，以指导模型的预测过程。在不同数据集上进行的大量实验表明，SDK 框架优于最先进的模型。实验结果表明，该算法能够有效地缓解过平滑和监控信号稀疏的影响。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Self-Supervised+Dynamic+Hypergraph+Recommendation+based+on+Hyper-Relational+Knowledge+Graph)|0| +|[Graph Enhanced Hierarchical Reinforcement Learning for Goal-oriented Learning Path Recommendation](https://doi.org/10.1145/3583780.3614897)|Qingyao Li, Wei Xia, Li'ang Yin, Jian Shen, Renting Rui, Weinan Zhang, Xianyu Chen, Ruiming Tang, Yong Yu|Huawei Noah's Ark Lab, Shenzhen, China; Shanghai Jiao Tong University, Shanghai, China; Huawei Noah's Art Lab, Shenzhen, China; Shanghai Jiaotong University, Shanghai, China|Goal-oriented Learning path recommendation aims to recommend learning items (concepts or exercises) step-by-step to a learner to promote the mastery level of her specific learning goals. By formulating this task as a Markov decision process, reinforcement learning (RL) methods have demonstrated great power. Although extensive research efforts have been made, previous methods still fail to recommend effective goal-oriented paths due to the under-utilizing of goals. Specifically, it is mainly reflected in two aspects: (1)The lack of goal planning. When learners have multiple goals with different difficulties, the previous methods can't fully utilize the difficulties and dependencies between goal learning items to plan the sequence of achieving these goals, making the path chaotic and inefficient; (2)The lack of efficiency in goal achieving. When pursuing a single goal, the path may contain learning items unrelated to the goal, which makes realizing a certain goal inefficient. To address these challenges, we present a novel Graph Enhanced Hierarchical Reinforcement Learning (GEHRL) framework for goal-oriented learning path recommendation. The framework divides learning path recommendation into two parts: sub-goal selection(planning) and sub-goal achieving(learning item recommendation). Specifically, we employ a high-level agent as a sub-goal selector to select sub-goals for the low-level agent to achieve. The low-level agent in the framework is to recommend learning items to the learner. To make the path only contain goal-related learning items to improve the efficiency of achieving the goal, we develop a graph-based candidate selector to constrain the action space of the low-level agent based on the sub-goal and knowledge graph. We also develop test-based internal reward for low-level training so that the sparsity problem of external reward can be alleviated. Extensive experiments on three different simulators demonstrate our framework achieves state-of-the-art performance.|目标导向学习路径推荐旨在向学习者逐步推荐学习项目(概念或练习) ，以提高其对特定学习目标的掌握程度。通过把这个任务作为一个马可夫决策过程，强化学习(RL)方法已经证明了它的巨大威力。尽管已经做了大量的研究工作，以往的方法仍然不能推荐有效的目标导向的路径，由于目标利用不足。具体来说，主要体现在两个方面: (1)目标规划的缺失。当学习者有不同困难的多个目标时，以往的方法不能充分利用目标学习项目之间的困难和依赖关系来规划实现这些目标的顺序，使得路径混乱和效率低下; (2)目标实现效率低下。在追求单一目标时，路径中可能包含与目标无关的学习项，使得实现某一目标效率低下。为了应对这些挑战，我们提出了一个新的图形增强层次强化学习(GEHRL)框架，用于面向目标的学习路径推荐。该框架将学习路径推荐分为子目标选择(规划)和子目标实现(学习项目推荐)两部分。具体来说，我们使用高级代理作为子目标选择器，为低级代理选择要实现的子目标。框架中的底层代理是向学习者推荐学习项目。为了使路径只包含与目标相关的学习项，提高实现目标的效率，提出了一种基于子目标和知识图的候选选择器来约束底层智能体的行为空间。我们还针对低水平培训开发了基于测试的内部奖励，以缓解外部奖励稀缺的问题。在三个不同的模拟器上进行的大量实验表明，我们的框架实现了最先进的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Graph+Enhanced+Hierarchical+Reinforcement+Learning+for+Goal-oriented+Learning+Path+Recommendation)|0| +|[THGNN: An Embedding-based Model for Anomaly Detection in Dynamic Heterogeneous Social Networks](https://doi.org/10.1145/3583780.3615079)|Yilin Li, Jiaqi Zhu, Congcong Zhang, Yi Yang, Jiawen Zhang, Ying Qiao, Hongan Wang|The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China; Institute of Software, Chinese Academy of Sciences & University of Chinese Academy of Sciences, Beijing, China; Institute of Software, Chinese Academy of Sciences, Beijing, China|Anomaly detection, particularly the detection of anomalous behaviors in dynamic and heterogeneous social networks, is becoming more and more crucial in real life. Traditional rule-based and feature-based methods cannot well capture the structural and temporal patterns of ever-changing user behaviors. Moreover, most of the existing works based on network embedding either rely on discretized snapshots, which have ignored accurate temporal relations among user behaviors and weakened the impact of new edges, or fail to utilize dynamic and heterogeneous information simultaneously to distinguish varying effects of new edges on existing nodes. In this paper, we propose an end-to-end continuous-time model, named Temporal Heterogeneous Graph Neural Network (THGNN), to detect anomalous behaviors (edges) in dynamic heterogeneous social networks. Specifically, the model constantly updates node embeddings by propagating the information of a new edge to its source and target nodes as well as their neighbors. In this process, heterogeneous encoders are employed to handle different types of nodes and edges. What is more, a novel dual-level distributive attention mechanism is designed to allocate the influence degree of a currently interacting node to its multiple neighbors, considering the combined effect of edge type and time interval information. That can be regarded as an extension of the classical aggregative attention mechanism in the opposite direction. Extensive experiments on four real-world datasets demonstrate that THGNN outperforms all the baselines on the task of anomalous edge detection, achieving an average AUC gain of 6% across all datasets.|异常检测，特别是在动态和异构的社交网络中发现异常行为，在现实生活中变得越来越重要。传统的基于规则和基于特征的方法不能很好地捕获不断变化的用户行为的结构和时间模式。此外，现有的基于网络嵌入的工作大多依赖于离散快照，忽略了用户行为之间精确的时间关系，削弱了新边的影响，或者未能同时利用动态和异构信息来区分新边对现有节点的不同影响。本文提出了一种端到端连续时间模型——时态异构图神经网络(THGNN) ，用于检测动态异构社会网络中的异常行为(边)。具体来说，该模型通过将新边的信息传播到源节点和目标节点以及它们的邻居，不断地更新节点嵌入。在这个过程中，异构编码器被用来处理不同类型的节点和边缘。同时，考虑到边缘类型和时间间隔信息的组合效应，设计了一种新的双层分布式注意机制来分配当前交互节点对其多个邻居的影响程度。这可以看作是对经典的聚集性注意机制在相反方向上的延伸。在四个实际数据集上的大量实验表明，THGNN 在异常边缘检测任务上优于所有基线，在所有数据集上平均获得6% 的 AUC 增益。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=THGNN:+An+Embedding-based+Model+for+Anomaly+Detection+in+Dynamic+Heterogeneous+Social+Networks)|0| +|[MadSGM: Multivariate Anomaly Detection with Score-based Generative Models](https://doi.org/10.1145/3583780.3614956)|Haksoo Lim, Sewon Park, Minjung Kim, Jaehoon Lee, Seonkyu Lim, Noseong Park|Samsung SDS, Seoul, Republic of Korea; LG AI Research, Seoul, Republic of Korea; Yonsei University, Seoul, Republic of Korea|The time-series anomaly detection is one of the most fundamental tasks for time-series. Unlike the time-series forecasting and classification, the time-series anomaly detection typically requires unsupervised (or self-supervised) training since collecting and labeling anomalous observations are difficult. In addition, most existing methods resort to limited forms of anomaly measurements and therefore, it is not clear whether they are optimal in all circumstances. To this end, we present a multivariate time-series anomaly detector based on score-based generative models, called MadSGM, which considers the broadest ever set of anomaly measurement factors: i) reconstruction-based, ii) density-based, and iii) gradient-based anomaly measurements. We also design a conditional score network and its denoising score matching loss for the time-series anomaly detection. Experiments on five real-world benchmark datasets illustrate that MadSGM achieves the most robust and accurate predictions.|时间序列异常检测是时间序列最基本的任务之一。与时间序列预测和分类不同，时间序列异常检测通常需要无监督(或自我监督)的训练，因为收集和标记异常观测是困难的。此外，大多数现有方法采用有限形式的异常测量，因此，不清楚它们是否在所有情况下都是最佳的。为此，我们提出了一个基于基于评分的生成模型的多变量时间序列异常检测器，称为 MadSGM，其考虑了有史以来最广泛的一组异常测量因素: i)基于重建的，ii)基于密度的，和 iii)基于梯度的异常测量。我们还设计了一个条件得分网络及其去噪得分匹配丢失的时间序列异常检测。对五个真实世界基准数据集的实验表明，MadSGM 能够实现最稳健和准确的预测。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=MadSGM:+Multivariate+Anomaly+Detection+with+Score-based+Generative+Models)|0| +|[Hierarchical Prompt Tuning for Few-Shot Multi-Task Learning](https://doi.org/10.1145/3583780.3614913)|Jingping Liu, Tao Chen, Zujie Liang, Haiyun Jiang, Yanghua Xiao, Feng Wei, Yuxi Qian, Zhenghong Hao, Bing Han|Fudan University, Shanghai, China; East China University of Science and Technology, Shanghai, China; Ant Group, Shanghai, China|Prompt tuning has enhanced the performance of Pre-trained Language Models for multi-task learning in few-shot scenarios. However, existing studies fail to consider that the prompts among different layers in Transformer are different due to the diverse information learned at each layer. In general, the bottom layers in the model tend to capture low-level semantic or structural information, while the upper layers primarily acquire task-specific knowledge. Hence, we propose a novel hierarchical prompt tuning model for few-shot multi-task learning to capture this regularity. The designed model mainly consists of three types of prompts: shared prompts, auto-adaptive prompts, and task-specific prompts. Shared prompts facilitate the sharing of general information across all tasks. Auto-adaptive prompts dynamically select and integrate relevant prompt information from all tasks into the current task. Task-specific prompts concentrate on learning task-specific knowledge. To enhance the model's adaptability to diverse inputs, we introduce deep instance-aware language prompts as the foundation for constructing the above prompts. To evaluate the effectiveness of our proposed method, we conduct extensive experiments on multiple widely-used datasets. The experimental results demonstrate that the proposed method achieves state-of-the-art performance for multi-task learning in few-shot settings and outperforms ChatGPT in the full-data setting.|及时调优提高了预训练语言模型在少镜头情景下多任务学习的性能。然而，现有的研究没有考虑到变压器不同层之间的提示是不同的，因为在每一层学习的不同信息。一般来说，模型的底层倾向于捕获低层次的语义或结构信息，而上层主要获取特定于任务的知识。因此，我们提出了一个新的分层提示调整模型来捕捉这种规律性的少拍多任务学习。所设计的模型主要包括三种类型的提示: 共享提示、自适应提示和任务特定提示。共享提示有助于在所有任务之间共享一般信息。自适应提示动态选择并将所有任务中的相关提示信息集成到当前任务中。特定于任务的提示集中于学习特定于任务的知识。为了增强模型对不同输入的适应性，我们引入了深度实例感知语言提示作为构造上述提示的基础。为了评估我们提出的方法的有效性，我们在多个广泛使用的数据集上进行了广泛的实验。实验结果表明，该方法在少镜头情况下获得了最佳的多任务学习性能，在全数据情况下优于 ChatGPT。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Hierarchical+Prompt+Tuning+for+Few-Shot+Multi-Task+Learning)|0| +|[SMEF: Social-aware Multi-dimensional Edge Features-based Graph Representation Learning for Recommendation](https://doi.org/10.1145/3583780.3615063)|Xiao Liu, Shunmei Meng, Qianmu Li, Lianyong Qi, Xiaolong Xu, Wanchun Dou, Xuyun Zhang|Macquarie University, Sydney, Australia; Department of Computer Science and Engineering, Nanjing University of Science and Technology & State Key Lab. for Novel Software Technology, Nanjing University, Nanjing, China; Nanjing University, Nanjing, China; Department of Cyber Science and Engineering, Nanjing University of Science and Technology, Nanjing, China; College of Computer Science and Technology, China University of Petroleum (East China), Qingdao, China; Nanjing University of Information Science & Technology, Nanjing, China; Digital Economy Research Institute, Nanjing University of Science & Technology, Nanjing, China|Exploring user-item interaction cues is crucial for the performance of recommender systems. Explicit investigation of interaction cues is made possible by using graph-based models, where each user-item relationship is described by an edge, and the introduction of user-user social network. While existing graph-based recommendation methods use only a single-value edge to define the relationship between a pair of user and item, which limits the ability to represent complex user-item interactions. Furthermore, some social recommendation methods overlook the heterogeneous user behavior patterns in social and interaction relationships, resulting in the suboptimal performance of existing systems. In this paper, we propose a novel Social-aware Multi-dimensional Edge Feature-based Graph Representation Learning method, called SMEF. It represents all users and items as a graph and deep learns a multi-dimensional edge feature to explicitly describe the task-specific relationships of each user-item pair. Specifically, the proposed SMEF focuses on two distinct user behavior patterns toward social friends and interactive items, which explore the underlying heterogeneous relationship cues within them. This way, the learned multi-dimensional edge features encode user information from both social and interaction aspects. The proposed SMEF is a plug-and-play module that can be combined with different recommendation frameworks and Graph Neural Networks (GNNs) backbones to generate high quality user representations. The experimental results achieved on three publicly accessible datasets show that our SMEF-based method outperforms strong baselines.|探索用户项交互线索对于推荐系统的性能至关重要。通过使用基于图的模型(其中每个用户-项目关系由一个边描述)和引入用户-用户社交网络，可以对交互线索进行明确的调查。而现有的基于图的推荐方法只使用单值边界来定义一对用户和项目之间的关系，这限制了表示复杂用户-项目交互的能力。此外，一些社交推荐方法忽视了社交和交互关系中的异构用户行为模式，导致现有系统的性能不理想。本文提出了一种新的基于社会感知的多维边缘特征的图形表示学习方法，称为 SMF。它将所有用户和项目表示为一个图形，并深入学习一个多维边缘特性，以显式地描述每个用户-项目对的特定于任务的关系。具体而言，本研究针对两种不同的使用者行为模式，分别针对社交朋友与互动项目，探讨其中潜在的异质性关系线索。这样，学习的多维边缘特征从社会和交互两个方面对用户信息进行编码。该模块可以与不同的推荐框架和图形神经网络(GNN)骨干结合，生成高质量的用户表示。在三个公开可访问数据集上的实验结果表明，我们提出的基于 MESF 的方法性能优于强基线。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=SMEF:+Social-aware+Multi-dimensional+Edge+Features-based+Graph+Representation+Learning+for+Recommendation)|0| +|[Self-Supervised Dynamic Hypergraph Recommendation based on Hyper-Relational Knowledge Graph](https://doi.org/10.1145/3583780.3615054)|Yi Liu, Hongrui Xuan, Bohan Li, Meng Wang, Tong Chen, Hongzhi Yin|The University of Queensland, Brisbane, Australia; Tongji University, Shanghai, China; Nanjing University of Aeronautics and Astronautics, Nanjing, China|Knowledge graphs (KGs) are commonly used as side information to enhance collaborative signals and improve recommendation quality. In the context of knowledge-aware recommendation (KGR), graph neural networks (GNNs) have emerged as promising solutions for modeling factual and semantic information in KGs. However, the long-tail distribution of entities leads to sparsity in supervision signals, which weakens the quality of item representation when utilizing KG enhancement. Additionally, the binary relation representation of KGs simplifies hyper-relational facts, making it challenging to model complex real-world information. Furthermore, the over-smoothing phenomenon results in indistinguishable representations and information loss. To address these challenges, we propose the SDK (Self-Supervised Dynamic Hypergraph Recommendation based on Hyper-Relational Knowledge Graph) framework. This framework establishes a cross-view hypergraph self-supervised learning mechanism for KG enhancement. Specifically, we model hyper-relational facts in KGs to capture interdependencies between entities under complete semantic conditions. With the refined representation, a hypergraph is dynamically constructed to preserve features in the deep vector space, thereby alleviating the over-smoothing problem. Furthermore, we mine external supervision signals from both the global perspective of the hypergraph and the local perspective of collaborative filtering (CF) to guide the model prediction process. Extensive experiments conducted on different datasets demonstrate the superiority of the SDK framework over state-of-the-art models. The results showcase its ability to alleviate the effects of over-smoothing and supervision signal sparsity.|知识图作为辅助信息被广泛应用于增强协作信号和提高推荐质量。在知识感知推荐(KGR)的背景下，图形神经网络(GNN)已经成为幼儿园建立事实和语义信息模型的有希望的解决方案。然而，实体的长尾分布导致监督信号的稀疏性，使得 KG 增强的项目表示质量下降。此外，KGs 的二进制关系表示简化了超关系事实，使得对复杂的现实世界信息进行建模具有挑战性。此外，过度平滑现象导致不可区分的表示和信息损失。针对这些挑战，本文提出了基于超关系知识图的自监督动态超图推荐(SDK)框架。该框架建立了一种用于 KG 增强的跨视图超图自监督学习机制。具体来说，我们在 KG 中对超关系事实建模，以在完全语义条件下捕获实体之间的相互依赖性。该方法通过动态构造超图来保留深向量空间中的特征，从而解决了超图的过平滑问题。此外，我们从超图的全局视角和协同过滤的局部视角来挖掘外部监督信号，以指导模型的预测过程。在不同数据集上进行的大量实验表明，SDK 框架优于最先进的模型。实验结果表明，该算法能够有效地缓解过平滑和监控信号稀疏的影响。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Self-Supervised+Dynamic+Hypergraph+Recommendation+based+on+Hyper-Relational+Knowledge+Graph)|0| |[Black-box Adversarial Attacks against Dense Retrieval Models: A Multi-view Contrastive Learning Method](https://doi.org/10.1145/3583780.3614793)|YuAn Liu, Ruqing Zhang, Jiafeng Guo, Maarten de Rijke, Wei Chen, Yixing Fan, Xueqi Cheng|ICT, CAS & University of Chinese Academy of Sciences, Beijing, China; University of Amsterdam, Amsterdam, Netherlands|Neural ranking models (NRMs) and dense retrieval (DR) models have given rise to substantial improvements in overall retrieval performance. In addition to their effectiveness, and motivated by the proven lack of robustness of deep learning-based approaches in other areas, there is growing interest in the robustness of deep learning-based approaches to the core retrieval problem. Adversarial attack methods that have so far been developed mainly focus on attacking NRMs, with very little attention being paid to the robustness of DR models. In this paper, we introduce the adversarial retrieval attack (AREA) task. The AREA task is meant to trick DR models into retrieving a target document that is outside the initial set of candidate documents retrieved by the DR model in response to a query. We consider the decision-based black-box adversarial setting, which is realistic in real-world search engines. To address the AREA task, we first employ existing adversarial attack methods designed for NRMs. We find that the promising results that have previously been reported on attacking NRMs, do not generalize to DR models: these methods underperform a simple term spamming method. We attribute the observed lack of generalizability to the interaction-focused architecture of NRMs, which emphasizes fine-grained relevance matching. DR models follow a different representation-focused architecture that prioritizes coarse-grained representations. We propose to formalize attacks on DR models as a contrastive learning problem in a multi-view representation space. The core idea is to encourage the consistency between each view representation of the target document and its corresponding viewer via view-wise supervision signals. Experimental results demonstrate that the proposed method can significantly outperform existing attack strategies in misleading the DR model with small indiscernible text perturbations.|神经排序模型(NRM)和密集检索(DR)模型在总体检索性能方面有了很大的提高。除了这些方法的有效性外，由于在其他领域基于深度学习的方法缺乏稳健性，人们对基于深度学习的方法解决核心检索问题的稳健性越来越感兴趣。目前发展起来的对抗性攻击方法主要集中在攻击 NRM，很少关注 DR 模型的鲁棒性。本文介绍了对抗性检索攻击(AREA)任务。AREA 任务是为了欺骗 DR 模型来检索目标文档，该目标文档位于 DR 模型在响应查询时检索的候选文档的初始集之外。我们考虑基于决策的黑盒对抗设置，这在现实世界的搜索引擎中是现实的。为了解决 AREA 任务，我们首先使用为 NRM 设计的现有对手攻击方法。我们发现，以前报道的攻击 NRM 的有希望的结果并没有推广到 DR 模型: 这些方法不如一个简单的术语发送方法。我们将观察到的缺乏普遍性归因于以交互为中心的 NRM 体系结构，它强调细粒度的相关性匹配。DR 模型遵循不同的以表示为中心的体系结构，该体系结构对粗粒度表示进行优先级排序。我们提出将对 DR 模型的攻击形式化为一个多视图表示空间中的对比学习问题。其核心思想是通过视图监控信号鼓励目标文档的每个视图表示与其相应的查看器之间的一致性。实验结果表明，该方法能够明显优于现有的攻击策略，具有较小的不可分辨文本扰动误导 DR 模型。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Black-box+Adversarial+Attacks+against+Dense+Retrieval+Models:+A+Multi-view+Contrastive+Learning+Method)|0| |[Selecting Walk Schemes for Database Embedding](https://doi.org/10.1145/3583780.3615052)|Yuval Lev Lubarsky, Jan Tönshoff, Martin Grohe, Benny Kimelfeld|Technion, Haifa, Israel; RWTH Aachen University, Aachen, Germany|Machinery for data analysis often requires a numeric representation of the input. Towards that, a common practice is to embed components of structured data into a high-dimensional vector space. We study the embedding of the tuples of a relational database, where existing techniques are often based on optimization tasks over a collection of random walks from the database. The focus of this paper is on the recent FoRWaRD algorithm that is designed for dynamic databases, where walks are sampled by following foreign keys between tuples. Importantly, different walks have different schemas, or ?walk schemes," that are derived by listing the relations and attributes along the walk. Also importantly, different walk schemes describe relationships of different natures in the database. We show that by focusing on a few informative walk schemes, we can obtain tuple embedding significantly faster, while retaining the quality. We define the problem of scheme selection for tuple embedding, devise several approaches and strategies for scheme selection, and conduct a thorough empirical study of the performance over a collection of downstream tasks. Our results confirm that with effective strategies for scheme selection, we can obtain high-quality embeddings considerably (e.g., three times) faster, preserve the extensibility to newly inserted tuples, and even achieve an increase in the precision of some tasks.|用于数据分析的机器通常需要输入的数字表示。为此，一个常见的做法是将结构化数据的组件嵌入到高维向量空间中。我们研究了关系数据库元组的嵌入，其中现有的技术通常是基于优化任务，从数据库中随机游走的集合。本文的重点是针对动态数据库设计的最新的 ForRWaRD 算法，该算法通过在元组之间跟随外键来采样行走。重要的是，不同的散步有不同的模式，还是？这是通过列出步行过程中的关系和属性得出的。同样重要的是，不同的遍历方案描述数据库中不同性质的关系。实验结果表明，在保证质量的前提下，通过对几种信息量较大的步进方案进行分析，可以显著提高元组嵌入的速度。我们定义了元组嵌入的方案选择问题，设计了几种方案选择的方法和策略，并对下游任务集的性能进行了深入的实证研究。实验结果表明，采用有效的方案选择策略，可以更快地获得高质量的嵌入，保持新插入元组的可扩展性，甚至可以提高某些任务的精度。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Selecting+Walk+Schemes+for+Database+Embedding)|0| -|[Timestamps as Prompts for Geography-Aware Location Recommendation](https://doi.org/10.1145/3583780.3615083)|Yan Luo, Haoyi Duan, Ye Liu, FuLai Chung|Zhejiang University, Hangzhou, China; The Hong Kong Polytechnic University, Hong Kong, Hong Kong|Location recommendation plays a vital role in improving users' travel experience. The timestamp of the POI to be predicted is of great significance, since a user will go to different places at different times. However, most existing methods either do not use this kind of temporal information, or just implicitly fuse it with other contextual information. In this paper, we revisit the problem of location recommendation and point out that explicitly modeling temporal information is a great help when the model needs to predict not only the next location but also further locations. In addition, state-of-the-art methods do not make effective use of geographic information and suffer from the hard boundary problem when encoding geographic information by gridding. To this end, a Temporal Prompt-based and Geography-aware (TPG) framework is proposed. The temporal prompt is firstly designed to incorporate temporal information of any further check-in. A shifted window mechanism is then devised to augment geographic data for addressing the hard boundary problem. Via extensive comparisons with existing methods and ablation studies on five real-world datasets, we demonstrate the effectiveness and superiority of the proposed method under various settings. Most importantly, our proposed model has the superior ability of interval prediction. In particular, the model can predict the location that a user wants to go to at a certain time while the most recent check-in behavioral data is masked, or it can predict specific future check-in (not just the next one) at a given timestamp.|位置推荐在改善用户的旅游体验方面起着至关重要的作用。预测的 POI 的时间戳非常重要，因为用户将在不同的时间到达不同的地点。然而，大多数现有的方法要么不使用这种时间信息，要么只是隐式地将其与其他上下文信息融合在一起。在本文中，我们再次回顾了位置推荐的问题，并指出当模型不仅需要预测下一个位置，而且需要预测更远的位置时，显式地建立时间信息是一个很大的帮助。另外，现有的方法在对地理信息进行网格化编码时，不能有效地利用地理信息，存在硬边界问题。为此，提出了一个基于时态提示和地理感知(TPG)的框架。时间提示符首先设计为合并任何进一步签入的时间信息。然后设计了一种移动窗口机制来增加地理数据，以解决硬边界问题。通过与现有方法的广泛比较以及对五个实际数据集的烧蚀研究，我们证明了该方法在不同环境下的有效性和优越性。最重要的是，我们提出的模型具有优越的区间预测能力。特别是，当最近的签入行为数据被掩盖时，该模型可以预测用户在特定时间想要去的位置，或者它可以预测给定时间戳的特定未来签入(不仅仅是下一次)。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Timestamps+as+Prompts+for+Geography-Aware+Location+Recommendation)|0| +|[Timestamps as Prompts for Geography-Aware Location Recommendation](https://doi.org/10.1145/3583780.3615083)|Yan Luo, Haoyi Duan, Ye Liu, FuLai Chung|The Hong Kong Polytechnic University, Hong Kong, Hong Kong; Zhejiang University, Hangzhou, China|Location recommendation plays a vital role in improving users' travel experience. The timestamp of the POI to be predicted is of great significance, since a user will go to different places at different times. However, most existing methods either do not use this kind of temporal information, or just implicitly fuse it with other contextual information. In this paper, we revisit the problem of location recommendation and point out that explicitly modeling temporal information is a great help when the model needs to predict not only the next location but also further locations. In addition, state-of-the-art methods do not make effective use of geographic information and suffer from the hard boundary problem when encoding geographic information by gridding. To this end, a Temporal Prompt-based and Geography-aware (TPG) framework is proposed. The temporal prompt is firstly designed to incorporate temporal information of any further check-in. A shifted window mechanism is then devised to augment geographic data for addressing the hard boundary problem. Via extensive comparisons with existing methods and ablation studies on five real-world datasets, we demonstrate the effectiveness and superiority of the proposed method under various settings. Most importantly, our proposed model has the superior ability of interval prediction. In particular, the model can predict the location that a user wants to go to at a certain time while the most recent check-in behavioral data is masked, or it can predict specific future check-in (not just the next one) at a given timestamp.|位置推荐在改善用户的旅游体验方面起着至关重要的作用。预测的 POI 的时间戳非常重要，因为用户将在不同的时间到达不同的地点。然而，大多数现有的方法要么不使用这种时间信息，要么只是隐式地将其与其他上下文信息融合在一起。在本文中，我们再次回顾了位置推荐的问题，并指出当模型不仅需要预测下一个位置，而且需要预测更远的位置时，显式地建立时间信息是一个很大的帮助。另外，现有的方法在对地理信息进行网格化编码时，不能有效地利用地理信息，存在硬边界问题。为此，提出了一个基于时态提示和地理感知(TPG)的框架。时间提示符首先设计为合并任何进一步签入的时间信息。然后设计了一种移动窗口机制来增加地理数据，以解决硬边界问题。通过与现有方法的广泛比较以及对五个实际数据集的烧蚀研究，我们证明了该方法在不同环境下的有效性和优越性。最重要的是，我们提出的模型具有优越的区间预测能力。特别是，当最近的签入行为数据被掩盖时，该模型可以预测用户在特定时间想要去的位置，或者它可以预测给定时间戳的特定未来签入(不仅仅是下一次)。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Timestamps+as+Prompts+for+Geography-Aware+Location+Recommendation)|0| |[LambdaRank Gradients are Incoherent](https://doi.org/10.1145/3583780.3614948)|Federico Marcuzzi, Claudio Lucchese, Salvatore Orlando|Università Ca' Foscari Venezia, Venice, Italy|In Information Retrieval (IR), the Learning-to-Rank (LTR) task requires building a ranking model that optimises a specific IR metric. One of the most effective approaches to do so is the well-known LambdaRank algorithm. LambdaRank uses gradient descent optimisation, and at its core, it defines approximate gradients, the so-called lambdas, for a non-differentiable IR metric. Intuitively, each lambda describes how much a document's score should be "pushed" up/down to reduce the ranking error. In this work, we show that lambdas may be incoherent w.r.t. the metric being optimised: e.g., a document with high relevance in the ground truth may receive a smaller gradient push than a document with lower relevance. This behaviour goes far beyond the expected degree of approximation. We analyse such behaviour of LambdaRank gradients and we introduce some strategies to reduce their incoherencies. We demonstrate through extensive experiments, conducted using publicly available datasets, that the proposed approach reduces the frequency of the incoherencies in LambdaRank and derivatives, and leads to models that achieve statistically significant improvements in the NDCG metric, without compromising the training efficiency.|在信息检索中，学习排名(Learning-to-Rank，LTR)任务需要建立一个排名模型来优化一个特定的 IR 指标。最有效的方法之一是著名的 LambdaRank 算法。LambdaRank 使用梯度下降法优化，在其核心，它定义了近似梯度，即所谓的 lambdas，用于不可微 IR 度量。直观地说，每个 lambda 描述了一个文档的分数应该“上推”多少，以减少排名错误。在这项工作中，我们表明，lambdas 可能是不连贯的 W.R.T。的度量被优化: 例如，一个文件与地面真相高相关性可能会收到一个较小的梯度推动比一个文件与低相关性。这种行为远远超出了预期的近似程度。我们分析了这种行为的 LambdaRank 梯度和我们介绍了一些策略，以减少他们的不一致性。我们通过使用公开可用的数据集进行的广泛实验证明，所提出的方法降低了 LambdaRank 和衍生物中不相干的频率，并导致模型在 NDCG 指标中实现统计学显着的改善，而不损害训练效率。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=LambdaRank+Gradients+are+Incoherent)|0| |[System Initiative Prediction for Multi-turn Conversational Information Seeking](https://doi.org/10.1145/3583780.3615070)|Chuan Meng, Mohammad Aliannejadi, Maarten de Rijke|University of Amsterdam, Amsterdam, Netherlands|Identifying the right moment for a system to take the initiative is essential to conversational information seeking (CIS). Existing studies have extensively studied the clarification need prediction task, i.e., predicting when to ask a clarifying question, however, it only covers one specific system-initiative action. We define the system initiative prediction (SIP) task as predicting whether a CIS system should take the initiative at the next turn. Our analysis reveals that for effective modeling of SIP, it is crucial to capture dependencies between adjacent user?system initiative-taking decisions. We propose to model SIP by CRFs. Due to their graphical nature, CRFs are effective in capturing such dependencies and have greater transparency than more complex methods, e.g., LLMs. Applying CRFs to SIP comes with two challenges: (i) CRFs need to be given the unobservable system utterance at the next turn, and (ii) they do not explicitly model multi-turn features. We model SIP as an input-incomplete sequence labeling problem and propose a multi-turn system initiative predictor (MuSIc) that has (i) prior-posterior inter-utterance encoders to eliminate the need to be given the unobservable system utterance, and (ii) a multi-turn feature-aware CRF layer to incorporate multi-turn features into the dependencies between adjacent initiative-taking decisions. Experiments show that MuSIc outperforms LLM-based baselines including LLaMA, achieving state-of-the-art results on SIP. We also show the benefits of SIP on clarification need prediction and action prediction.|确定系统采取主动的合适时机对于会话信息搜索(CIS)至关重要。现有的研究已经广泛地研究了澄清需求预测任务，即预测何时提出澄清问题，但它只涉及一个具体的系统-主动行为。我们将系统主动预测(SIP)任务定义为预测一个 CIS 系统是否应该在下一轮采取主动。我们的分析表明，对于 SIP 的有效建模，捕获相邻用户系统主动决策之间的依赖性是至关重要的。我们提出用 CRF 来建立 SIP 模型。由于它们的图形化特性，通用报告格式能够有效地捕获这种依赖关系，并且比更复杂的方法(例如 LLM)具有更大的透明度。将 CRF 应用于 SIP 有两个挑战: (i) CRF 需要在下一个回合中被赋予不可观察的系统语句，以及(ii)它们没有明确地建模多回合特征。我们将 SIP 建模为一个输入不完全序列标记问题，并提出了一个多回合系统主动预测器(MuSic) ，它具有(i)先验-后验语音编码器以消除给定不可观测系统语音的需要，以及(ii)一个多回合特征感知 CRF 层以将多回合特征纳入相邻主动决策之间的依赖关系。实验结果表明，MuSIC 的性能优于基于 LLM 的基线(包括 LLaMA) ，在 SIP 上取得了一流的效果。我们还展示了 SIP 在澄清需求预测和作用预测方面的优势。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=System+Initiative+Prediction+for+Multi-turn+Conversational+Information+Seeking)|0| |[Hybrid Contrastive Constraints for Multi-Scenario Ad Ranking](https://doi.org/10.1145/3583780.3614920)|Shanlei Mu, Penghui Wei, Wayne Xin Zhao, Shaoguo Liu, Liang Wang, Bo Zheng|Alibaba Group, Beijing, China; Gaoling School of Artificial Intelligence, Renmin University of China & Beijing Key Laboratory of Big Data Management and Analysis Methods, Beijing, China|Multi-scenario ad ranking aims at leveraging the data from multiple domains or channels for training a unified ranking model to improve the performance at each individual scenario. Although the research on this task has made important progress, it still lacks the consideration of cross-scenario relations, thus leading to limitation in learning capability and difficulty in interrelation modeling. In this paper, we propose a Hybrid Contrastive Constrained approach (HC^2) for multi-scenario ad ranking. To enhance the modeling of data interrelation, we elaborately design a hybrid contrastive learning approach to capture commonalities and differences among multiple scenarios. The core of our approach consists of two elaborated contrastive losses, namely generalized and individual contrastive loss, which aim at capturing common knowledge and scenario-specific knowledge, respectively. To adapt contrastive learning to the complex multi-scenario setting, we propose a series of important improvements. For generalized contrastive loss, we enhance contrastive learning by extending the contrastive samples (label-aware and diffusion noise enhanced contrastive samples) and reweighting the contrastive samples (reciprocal similarity weighting). For individual contrastive loss, we use the strategies of dropout-based augmentation and {cross-scenario encoding} for generating meaningful positive and negative contrastive samples, respectively. Extensive experiments on both offline evaluation and online test have demonstrated the effectiveness of the proposed HC$^2$ by comparing it with a number of competitive baselines.|多场景广告排名的目的是利用来自多个领域或渠道的数据来训练一个统一的排名模型，以提高每个场景的性能。本课题的研究虽然取得了重要进展，但仍然缺乏对跨场景关系的考虑，从而导致学习能力的局限性和相互关系建模的困难。本文提出了一种基于混合对比约束的多场景广告排名方法(HC ^ 2)。为了加强数据相关性的建模，我们精心设计了一种混合对比学习方法来捕捉多个场景之间的共性和差异。我们的方法的核心包括两个详细的对比损失，即广义对比损失和个体对比损失，分别旨在获取共同知识和情景特定的知识。为了使对比学习适应复杂的多场景环境，我们提出了一系列重要的改进措施。对于广义对比损失，我们通过扩展对比样本(标签感知和扩散噪声增强的对比样本)和重新加权对比样本(互惠相似性加权)来增强对比学习。对于个体对比损失，我们分别使用基于辍学的增强策略和{交叉情景编码}来产生有意义的正向和负向对比样本。离线评估和在线测试的广泛实验已经证明了所提出的 HC $^ 2 $的有效性，通过比较它与一些竞争性的基线。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Hybrid+Contrastive+Constraints+for+Multi-Scenario+Ad+Ranking)|0| -|[Contrastive Learning of Temporal Distinctiveness for Survival Analysis in Electronic Health Records](https://doi.org/10.1145/3583780.3614824)|Mohsen Nayebi Kerdabadi, Arya Hadizadeh Moghaddam, Bin Liu, Mei Liu, Zijun Yao|University of Kansas, Lawrence, KS, USA; West Virginia University, Morgantown, WV, USA; University of Florida, Gainesville, FL, USA|Survival analysis plays a crucial role in many healthcare decisions, where the risk prediction for the events of interest can support an informative outlook for a patient's medical journey. Given the existence of data censoring, an effective way of survival analysis is to enforce the pairwise temporal concordance between censored and observed data, aiming to utilize the time interval before censoring as partially observed time-to-event labels for supervised learning. Although existing studies mostly employed ranking methods to pursue an ordering objective, contrastive methods which learn a discriminative embedding by having data contrast against each other, have not been explored thoroughly for survival analysis. Therefore, in this paper, we propose a novel Ontology-aware Temporality-based Contrastive Survival (OTCSurv) analysis framework that utilizes survival durations from both censored and observed data to define temporal distinctiveness and construct negative sample pairs with adjustable hardness for contrastive learning. Specifically, we first use an ontological encoder and a sequential self-attention encoder to represent the longitudinal EHR data with rich contexts. Second, we design a temporal contrastive loss to capture varying survival durations in a supervised setting through a hardness-aware negative sampling mechanism. Last, we incorporate the contrastive task into the time-to-event predictive task with multiple loss components. We conduct extensive experiments using a large EHR dataset to forecast the risk of hospitalized patients who are in danger of developing acute kidney injury (AKI), a critical and urgent medical condition. The effectiveness and explainability of the proposed model are validated through comprehensive quantitative and qualitative studies.|生存分析在许多医疗决策中起着至关重要的作用，其中对感兴趣的事件的风险预测可以支持对患者医疗旅程的信息性展望。鉴于数据审查的存在，一个有效的生存分析方法是加强审查数据和观察数据之间的成对时间一致性，目的是利用审查前的时间间隔作为部分观察到的监督式学习时间-事件标签。虽然现有的研究大多采用排序方法来追求排序目标，但对比分析方法通过数据对比来学习判别嵌入，尚未深入探讨用于生存分析的方法。因此，本文提出了一种新的基于本体感知时间性的对比生存(OTCSurv)分析框架，该框架利用截尾数据和观测数据的生存时间来定义时间差异性，并构造具有可调硬度的负样本对用于对比学习。具体来说，我们首先使用一个本体编码器和一个顺序自我注意编码器来表示具有丰富上下文的纵向 EHR 数据。其次，我们设计了一个时间对比损失，通过硬度感知的负采样机制，在监督环境下捕获不同的生存期。最后，我们将对比任务融入到具有多个损失分量的时间-事件预测任务中。我们使用一个大型的 EHR 数据集进行了广泛的实验，以预测有发展为急性肾损伤(AKI)危险的住院患者的风险，急性肾损伤是一种危险和紧急的医疗状况。通过全面的定量和定性研究，验证了该模型的有效性和可解释性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Contrastive+Learning+of+Temporal+Distinctiveness+for+Survival+Analysis+in+Electronic+Health+Records)|0| +|[Contrastive Learning of Temporal Distinctiveness for Survival Analysis in Electronic Health Records](https://doi.org/10.1145/3583780.3614824)|Mohsen Nayebi Kerdabadi, Arya Hadizadeh Moghaddam, Bin Liu, Mei Liu, Zijun Yao|University of Florida, Gainesville, FL, USA; West Virginia University, Morgantown, WV, USA; University of Kansas, Lawrence, KS, USA|Survival analysis plays a crucial role in many healthcare decisions, where the risk prediction for the events of interest can support an informative outlook for a patient's medical journey. Given the existence of data censoring, an effective way of survival analysis is to enforce the pairwise temporal concordance between censored and observed data, aiming to utilize the time interval before censoring as partially observed time-to-event labels for supervised learning. Although existing studies mostly employed ranking methods to pursue an ordering objective, contrastive methods which learn a discriminative embedding by having data contrast against each other, have not been explored thoroughly for survival analysis. Therefore, in this paper, we propose a novel Ontology-aware Temporality-based Contrastive Survival (OTCSurv) analysis framework that utilizes survival durations from both censored and observed data to define temporal distinctiveness and construct negative sample pairs with adjustable hardness for contrastive learning. Specifically, we first use an ontological encoder and a sequential self-attention encoder to represent the longitudinal EHR data with rich contexts. Second, we design a temporal contrastive loss to capture varying survival durations in a supervised setting through a hardness-aware negative sampling mechanism. Last, we incorporate the contrastive task into the time-to-event predictive task with multiple loss components. We conduct extensive experiments using a large EHR dataset to forecast the risk of hospitalized patients who are in danger of developing acute kidney injury (AKI), a critical and urgent medical condition. The effectiveness and explainability of the proposed model are validated through comprehensive quantitative and qualitative studies.|生存分析在许多医疗决策中起着至关重要的作用，其中对感兴趣的事件的风险预测可以支持对患者医疗旅程的信息性展望。鉴于数据审查的存在，一个有效的生存分析方法是加强审查数据和观察数据之间的成对时间一致性，目的是利用审查前的时间间隔作为部分观察到的监督式学习时间-事件标签。虽然现有的研究大多采用排序方法来追求排序目标，但对比分析方法通过数据对比来学习判别嵌入，尚未深入探讨用于生存分析的方法。因此，本文提出了一种新的基于本体感知时间性的对比生存(OTCSurv)分析框架，该框架利用截尾数据和观测数据的生存时间来定义时间差异性，并构造具有可调硬度的负样本对用于对比学习。具体来说，我们首先使用一个本体编码器和一个顺序自我注意编码器来表示具有丰富上下文的纵向 EHR 数据。其次，我们设计了一个时间对比损失，通过硬度感知的负采样机制，在监督环境下捕获不同的生存期。最后，我们将对比任务融入到具有多个损失分量的时间-事件预测任务中。我们使用一个大型的 EHR 数据集进行了广泛的实验，以预测有发展为急性肾损伤(AKI)危险的住院患者的风险，急性肾损伤是一种危险和紧急的医疗状况。通过全面的定量和定性研究，验证了该模型的有效性和可解释性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Contrastive+Learning+of+Temporal+Distinctiveness+for+Survival+Analysis+in+Electronic+Health+Records)|0| |[MUSE: Music Recommender System with Shuffle Play Recommendation Enhancement](https://doi.org/10.1145/3583780.3614976)|Yunhak Oh, Sukwon Yun, Dongmin Hyun, Sein Kim, Chanyoung Park|KAIST, Daejeon, Republic of Korea; POSTECH, Pohang, Republic of Korea|Recommender systems have become indispensable in music streaming services, enhancing user experiences by personalizing playlists and facilitating the serendipitous discovery of new music. However, the existing recommender systems overlook the unique challenges inherent in the music domain, specifically shuffle play, which provides subsequent tracks in a random sequence. Based on our observation that the shuffle play sessions hinder the overall training process of music recommender systems mainly due to the high unique transition rates of shuffle play sessions, we propose a Music Recommender System with Shuffle Play Recommendation Enhancement (MUSE). MUSE employs the self-supervised learning framework that maximizes the agreement between the original session and the augmented session, which is augmented by our novel session augmentation method, called transition-based augmentation. To further facilitate the alignment of the representations between the two views, we devise two fine-grained matching strategies, i.e., item- and similarity-based matching strategies. Through rigorous experiments conducted across diverse environments, we demonstrate MUSE's efficacy over 12 baseline models on a large-scale Music Streaming Sessions Dataset (MSSD) from Spotify. The source code of MUSE is available at \url{https://github.com/yunhak0/MUSE}.|在音乐流媒体服务中，推荐系统已经变得不可或缺，它通过个性化播放列表和促进偶然发现新音乐来增强用户体验。然而，现有的推荐系统忽略了音乐领域固有的独特挑战，特别是随机播放，它提供随机序列的后续曲目。根据我们的观察，主要由于洗牌游戏游戏游戏游戏游戏游戏游戏游戏游戏游戏游戏游戏游戏游戏游戏游戏游戏游戏游戏游戏游戏游戏游戏游戏游戏游戏游戏游戏游戏游戏游戏游戏游戏游戏游戏游戏游戏游戏游戏游戏游戏游戏游戏游戏游戏游戏游戏游戏游戏游戏游戏游戏游戏游戏游。MUSE 采用自监督学习框架，最大限度地提高了原始会话和扩展会话之间的一致性，并通过我们新颖的会话扩展方法(称为基于转换的扩展)进行了扩展。为了进一步促进两个视图之间的匹配，我们设计了两种细粒度匹配策略，即基于项目和相似性的匹配策略。通过在不同环境中进行的严格实验，我们在来自 Spotify 的大规模音乐流媒体会话数据集(MSSD)上证明了 MUSE 超过12个基线模型的功效。MUSE 的源代码可以在 url { https://github.com/yunhak0/MUSE }找到。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=MUSE:+Music+Recommender+System+with+Shuffle+Play+Recommendation+Enhancement)|0| |[Dual-Oriented Contrast for Recommendation with A Stop-Gradient Operation](https://doi.org/10.1145/3583780.3614852)|Byungkook Oh, Yul Kim, Bumky Min|Samsung Research, Seoul, Republic of Korea|Recently, contrastive loss is adopted as a main objective of recommender systems. InfoNCE-like losses penalize hard negative items more and control the strength of penalties with a temperature, called hardness-aware sensitivity. However, since they leverageuser->item patterns in a non-symmetric way, negative items are pushed away from anchor users and attract semantically-similar items to each other, focusing on the distribution of item embeddings. We point out that user embeddings also have inherent semantic structures that can be captured fromitem->user patterns. This paper presents Dual-oriented Contrast(DuCo), a novel symmetric learning objective for recommendation to learn more comprehensive representations fromusereftrightarrowitem patterns. DuCo controls user-/item-centric hardness-aware sensitivities and simultaneously optimizes the score distributions over sampled items (user-oriented contrast) and users (item-oriented contrast). This aims to explore ideal user and item distributions that are locally clustered and globally uniform. However, since user-/item-side temperatures are interdependent, naive control over temperatures may break the underlying semantic structures of the other side. To this end, we employ a stop-gradient operation to preserve the individual characteristics of user/item embedding distributions. Furthermore, we balance user-/item-oriented contrasts during learning to maintain consistent high-rank performance (e.g., recall@1). Empirical results show that DuCo contributes to the top-k user and item prediction simultaneously, and outperforms state-of-the-art learning objectives across different backbones from ID-based to neighbor-based encoders.|近年来，对比损失被作为推荐系统的主要目标。类似于 InfoNCE 的损失更多地惩罚硬负面项目，并用温度控制惩罚的强度，称为硬度感知灵敏度。然而，由于它们以一种非对称的方式利用 user-> item 模式，负面条目被推离锚用户，并且相互吸引语义相似的条目，关注条目嵌入的分布。我们指出，用户嵌入还具有可以从 mitem-> 用户模式捕获的固有语义结构。本文提出了一种新的对称推荐学习目标——双向对比度(DuCo)。DuCo 控制以用户/项目为中心的硬度感知敏感性，并同时优化分数分布的抽样项目(面向用户的对比)和用户(面向项目的对比)。这旨在探索理想的本地集群和全局统一的用户和项目分布。但是，由于用户/项目端的温度是相互依赖的，因此对温度的天真控制可能会破坏另一端的底层语义结构。为此，我们使用了一个停止梯度操作来保留用户/项嵌入分布的各自特征。此外，我们在学习过程中平衡用户/项目导向的对比，以保持一致的高等级性能(例如，召回@1)。实证结果表明，DuCo 能够同时提供最佳用户和项目预测，并且在从基于 ID 的编码器到基于邻居的编码器的不同骨干网络中，其学习效果优于最先进的学习目标。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Dual-Oriented+Contrast+for+Recommendation+with+A+Stop-Gradient+Operation)|0| |[Quad-Tier Entity Fusion Contrastive Representation Learning for Knowledge Aware Recommendation System](https://doi.org/10.1145/3583780.3615020)|Rongqing Kenneth Ong, Wei Qiu, Andy W. H. Khong|Nanyang Technological University, Singapore, Singapore|Knowledge graph (KG) has recently emerged as a powerful source of auxiliary information in the realm of knowledge-aware recommendation (KGR) systems. However, due to the lack of supervision signals caused by the sparse nature of user-item interactions, existing supervised graph neural network (GNN) models suffer from performance degradation. Moreover, the over-smoothing issue further limits the number of GNN layers or hops required to propagate messages - these models ignore the non-local information concealed deep within the knowledge graph. We propose the Quad-Tier Entity Fusion Contrastive Representation Learning (QTEF-CRL) knowledge-aware framework to achieve learning of deep user preferences from four perspectives: the collaborative, semantic, preference, and structural view. Unlike existing methods, the proposed tri-local and single-global quad-tier architecture exploits the knowledge graph holistically to achieve effective self-supervised representation learning. The newly-introduced preference view constructed from the collaborative knowledge graph (CKG) comprises a preference graph and preference-guided GNN that are specifically designed to capture non-local information explicitly. Experiments conducted on three datasets highlight the efficacy of our proposed model.|在知识感知推荐(KGR)系统领域，知识图(KG)已经成为一个强大的辅助信息源。然而，由于用户-项目交互的稀疏性造成监督信号的缺乏，现有的监督图神经网络(GNN)模型存在性能下降的问题。此外，过于平滑的问题进一步限制了传播消息所需的 GNN 层数或跳数——这些模型忽略了隐藏在知识图深处的非本地信息。我们提出了四层实体融合对比表示学习(QTEF-CRL)知识感知框架，从协作、语义、偏好和结构四个角度实现深度用户偏好的学习。与现有的方法不同，本文提出的三局部和单全局四层结构全面地利用知识图来实现有效的自监督表示学习。由协同知识图(CKG)构造的新的偏好视图包括偏好图和偏好引导的 GNN，它们是专门为显式捕获非局部信息而设计的。在三个数据集上进行的实验突出了我们提出的模型的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Quad-Tier+Entity+Fusion+Contrastive+Representation+Learning+for+Knowledge+Aware+Recommendation+System)|0| -|[A Retrieve-and-Read Framework for Knowledge Graph Link Prediction](https://doi.org/10.1145/3583780.3614769)|Vardaan Pahuja, Boshi Wang, Hugo Latapie, Jayanth Srinivasa, Yu Su|Cisco Research, San Jose, CA, USA; The Ohio State University, Columbus, OH, USA|Knowledge graph (KG) link prediction aims to infer new facts based on existing facts in the KG. Recent studies have shown that using the graph neighborhood of a node via graph neural networks (GNNs) provides more useful information compared to just using the query information. Conventional GNNs for KG link prediction follow the standard message-passing paradigm on the entire KG, which leads to over-smoothing of representations and also limits their scalability. On a large scale, it becomes computationally expensive to aggregate useful information from the entire KG for inference. To address the limitations of existing KG link prediction frameworks, we propose a novel retrieve-and-read framework, which first retrieves a relevant subgraph context for the query and then jointly reasons over the context and the query with a high-capacity reader. As part of our exemplar instantiation for the new framework, we propose a novel Transformer-based GNN as the reader, which incorporates graph-based attention structure and cross-attention between query and context for deep fusion. This design enables the model to focus on salient context information relevant to the query. Empirical results on two standard KG link prediction datasets demonstrate the competitive performance of the proposed method.|知识图(KG)链接预测的目的是根据 KG 中已有的事实推断出新的事实。最近的研究表明，通过图神经网络(GNN)使用节点的图邻域比仅仅使用查询信息提供更多有用的信息。用于 KG 链路预测的传统 GNN 遵循整个 KG 上的标准消息传递范式，这导致表示过于平滑，也限制了它们的可伸缩性。在大规模情况下，从整个 KG 中聚合有用的信息进行推理的计算开销变得很大。针对现有 KG 链路预测框架的局限性，提出了一种新的检索-读取框架，该框架首先检索查询的相关子图上下文，然后与大容量阅读器对上下文和查询进行联合推理。作为新框架示例实例的一部分，我们提出了一种新的基于 Transform- 的 GNN 作为读者，它结合了基于图的注意结构和查询与上下文之间的交叉注意来进行深度融合。这种设计使模型能够关注与查询相关的显著上下文信息。在两个标准 KG 链路预测数据集上的实验结果证明了该方法的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Retrieve-and-Read+Framework+for+Knowledge+Graph+Link+Prediction)|0| +|[A Retrieve-and-Read Framework for Knowledge Graph Link Prediction](https://doi.org/10.1145/3583780.3614769)|Vardaan Pahuja, Boshi Wang, Hugo Latapie, Jayanth Srinivasa, Yu Su|The Ohio State University, Columbus, OH, USA; Cisco Research, San Jose, CA, USA|Knowledge graph (KG) link prediction aims to infer new facts based on existing facts in the KG. Recent studies have shown that using the graph neighborhood of a node via graph neural networks (GNNs) provides more useful information compared to just using the query information. Conventional GNNs for KG link prediction follow the standard message-passing paradigm on the entire KG, which leads to over-smoothing of representations and also limits their scalability. On a large scale, it becomes computationally expensive to aggregate useful information from the entire KG for inference. To address the limitations of existing KG link prediction frameworks, we propose a novel retrieve-and-read framework, which first retrieves a relevant subgraph context for the query and then jointly reasons over the context and the query with a high-capacity reader. As part of our exemplar instantiation for the new framework, we propose a novel Transformer-based GNN as the reader, which incorporates graph-based attention structure and cross-attention between query and context for deep fusion. This design enables the model to focus on salient context information relevant to the query. Empirical results on two standard KG link prediction datasets demonstrate the competitive performance of the proposed method.|知识图(KG)链接预测的目的是根据 KG 中已有的事实推断出新的事实。最近的研究表明，通过图神经网络(GNN)使用节点的图邻域比仅仅使用查询信息提供更多有用的信息。用于 KG 链路预测的传统 GNN 遵循整个 KG 上的标准消息传递范式，这导致表示过于平滑，也限制了它们的可伸缩性。在大规模情况下，从整个 KG 中聚合有用的信息进行推理的计算开销变得很大。针对现有 KG 链路预测框架的局限性，提出了一种新的检索-读取框架，该框架首先检索查询的相关子图上下文，然后与大容量阅读器对上下文和查询进行联合推理。作为新框架示例实例的一部分，我们提出了一种新的基于 Transform- 的 GNN 作为读者，它结合了基于图的注意结构和查询与上下文之间的交叉注意来进行深度融合。这种设计使模型能够关注与查询相关的显著上下文信息。在两个标准 KG 链路预测数据集上的实验结果证明了该方法的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Retrieve-and-Read+Framework+for+Knowledge+Graph+Link+Prediction)|0| |[Toward a Better Understanding of Loss Functions for Collaborative Filtering](https://doi.org/10.1145/3583780.3615086)|Seongmin Park, Mincheol Yoon, Jaewoong Lee, Hogun Park, Jongwuk Lee|Sungkyunkwan University, Suwon, Republic of Korea|Collaborative filtering (CF) is a pivotal technique in modern recommender systems. The learning process of CF models typically consists of three components: interaction encoder, loss function, and negative sampling. Although many existing studies have proposed various CF models to design sophisticated interaction encoders, recent work shows that simply reformulating the loss functions can achieve significant performance gains. This paper delves into analyzing the relationship among existing loss functions. Our mathematical analysis reveals that the previous loss functions can be interpreted as alignment and uniformity functions: (i) the alignment matches user and item representations, and (ii) the uniformity disperses user and item distributions. Inspired by this analysis, we propose a novel loss function that improves the design of alignment and uniformity considering the unique patterns of datasets called Margin-aware Alignment and Weighted Uniformity (MAWU). The key novelty of MAWU is two-fold: (i) margin-aware alignment (MA) mitigates user/item-specific popularity biases, and (ii) weighted uniformity (WU) adjusts the significance between user and item uniformities to reflect the inherent characteristics of datasets. Extensive experimental results show that MF and LightGCN equipped with MAWU are comparable or superior to state-of-the-art CF models with various loss functions on three public datasets.|协同过滤(CF)是现代推荐系统中的一项关键技术。CF 模型的学习过程通常由三部分组成: 交互编码器、损耗函数和负采样。虽然许多现有的研究已经提出了各种 CF 模型来设计复杂的交互编码器，最近的工作表明，简单地重新制定损失函数可以取得显著的性能增益。本文深入分析了现有损失函数之间的关系。我们的数学分析表明，先前的损失函数可以解释为对齐和一致性函数: (i)对齐匹配用户和项目表示，和(ii)一致性分散用户和项目分布。受此分析的启发，我们提出了一种新的损失函数，改进了排列和均匀性的设计，考虑到独特的模式数据集称为边缘感知排列和加权均匀性(MAWU)。MAWU 的关键新颖性有两个方面: (i)边际感知对齐(MA)减轻用户/项目特定的流行偏见，和(ii)加权一致性(WU)调整用户和项目一致性之间的显着性以反映数据集的固有特征。大量的实验结果表明，配备 MAWU 的 MF 和 LightGCN 在三个公共数据集上具有各种损失函数，与最先进的 CF 模型相比具有可比性或优越性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Toward+a+Better+Understanding+of+Loss+Functions+for+Collaborative+Filtering)|0| |[Evaluating and Optimizing the Effectiveness of Neural Machine Translation in Supporting Code Retrieval Models: A Study on the CAT Benchmark](https://doi.org/10.1145/3583780.3614869)|Hung Phan, Ali Jannesari|Iowa State University, Ames, IA, USA|Neural Machine Translation (NMT) is widely applied in software engineering tasks. The effectiveness of NMT for code retrieval relies on the ability to learn from the sequence of tokens in the source language to the sequence of tokens in the target language. While NMT performs well in pseudocode-to-code translation, it might have challenges in learning to translate from natural language query to source code in newly curated real-world code documentation/ implementation datasets. In this work, we analyze the performance of NMT in natural language-to-code translation in the newly curated CAT benchmark that includes the optimized versions of three Java datasets TLCodeSum, CodeSearchNet, Funcom, and a Python dataset PCSD. Our evaluation shows that NMT has low accuracy, measured by CrystalBLEU and Meteor metrics in this task. To alleviate the duty of NMT in learning complex representation of source code, we propose ASTTrans Representation, a tailored representation of an Abstract Syntax Tree (AST) using a subset of non-terminal nodes. We show that the classical approach NMT performs significantly better in learning ASTTrans Representation over code tokens with up to 36% improvement on Meteor score. Moreover, we leverage ASTTrans Representation to conduct combined code search processes from the state-of-the-art code search processes using GraphCodeBERT and UniXcoder. Our NMT models of learning ASTTrans Representation can boost the Mean Reciprocal Rank of these state-of-the-art code search processes by up to 3.08% and improve 23.08% of queries' results over the CAT benchmark.|神经机器翻译(NMT)在软件工程任务中有着广泛的应用。NMT 对于代码检索的有效性依赖于从源语言中的令牌序列学习到目标语言中的令牌序列的能力。虽然 NMT 在伪代码到代码的转换中表现良好，但是在学习如何在新策划的真实世界代码文档/实现数据集中从自然语言查询转换为源代码时，可能会遇到挑战。在这项工作中，我们分析了 NMT 在自然语言到代码的翻译中的性能，新策划的 CAT 基准包括三个 Java 数据集 tlcodeSum，codeSearchNet，Funcom 和一个 Python 数据集 PCSD 的优化版本。我们的评估表明，NMT 的准确度较低，测量晶体 BLEU 和流星指标在这个任务。为了减轻 NMT 在学习源代码复杂表示方面的责任，我们提出了一种基于非终端节点子集的抽象语法树表示(AST)。我们表明，经典的方法 NMT 在学习 ASTTrans 表示明显优于代码令牌，提高了36% 的流星分数。此外，我们利用 ASTTrans 表示来使用 GraphCodeBERT 和 UniXcoder 从最先进的代码搜索过程中进行组合代码搜索过程。我们的学习 ASTTrans 表示的 NMT 模型可以将这些最先进的代码搜索过程的平均倒数排名提高3.08% ，比 CAT 基准提高23.08% 的查询结果。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Evaluating+and+Optimizing+the+Effectiveness+of+Neural+Machine+Translation+in+Supporting+Code+Retrieval+Models:+A+Study+on+the+CAT+Benchmark)|0| -|[Dual-Process Graph Neural Network for Diversified Recommendation](https://doi.org/10.1145/3583780.3614853)|Yuanyi Ren, Hang Ni, Yingxue Zhang, Xi Wang, Guojie Song, Dong Li, Jianye Hao|Huawei Noah's Ark Lab, Beijing, China; Northwestern Polytechnical University, Xi'an, China; Peking University, Beijing, China; Huawei Noah's Ark Lab, Markham, Canada|The recommender system is one of the most fundamental information services. A significant effort has been devoted to improving prediction accuracy, inevitably leading to the potential degradation of recommendation diversity. Moreover, individuals have different needs for diversity. To address these problems, diversity-enhanced approaches are proposed to modify the recommender models. However, these methods fail to break free from the relevance-oriented paradigm and are mostly haunted by sharply-declined accuracy and high computational costs. To tackle these challenges, we propose the Dual-Process Graph Neural Network (DPGNN), an efficient diversity-enhanced recommender system, resonating with the dual-process model of human cognition and the arousal theory of human interest. The first stage reduces the risk of suboptimal output during the training procedure, which helps to find a solution outside the relevance-oriented paradigm. Moreover, the second stage utilizes user-specific rating adjustments, boosting the recommendation diversity and accommodating users' distinctive needs with minimum computational costs. Extensive experiments on real-world datasets verify the effectiveness of our method in improving diversity, while maintaining accuracy with low computational costs.|推荐系统是最基本的信息服务之一。为了提高预测的准确性，人们付出了巨大的努力，这不可避免地导致了推荐多样性的潜在退化。此外，个体对多样性有不同的需求。为了解决这些问题，提出了基于多样性增强的方法来修改推荐模型。然而，这些方法未能摆脱以关联为导向的范式，并且大多受到精度急剧下降和计算成本高的困扰。为了应对这些挑战，我们提出了双进程图形神经网络(DPGNN) ，一种有效的多样性增强推荐系统，与人类认知的双进程模型和人类兴趣唤醒理论产生共鸣。第一阶段降低了培训过程中产出不理想的风险，这有助于找到一个以关联为导向的范式之外的解决方案。此外，第二阶段利用用户特定的评分调整，提高推荐的多样性，以最小的计算成本满足用户的特殊需求。在真实世界数据集上的大量实验证明了该方法在提高多样性的同时以较低的计算成本保持准确性方面的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Dual-Process+Graph+Neural+Network+for+Diversified+Recommendation)|0| -|[Dual-view Contrastive Learning for Auction Recommendation](https://doi.org/10.1145/3583780.3614854)|Dan Ni Ren, Leong Hou U, Wei Liu|Sun Yat-sen University, Guangdong, China; University of Macau, Macau SAR, Macao|Recommendation systems in auction platforms like eBay function differently in comparison to those found in traditional trading platforms. The bidding process involves multiple users competing for a product, with the highest bidder winning the item. As a result, each transaction is independent and characterized by varying transaction prices. The individual nature of auction items means that users cannot purchase identical items, adding to the uniqueness of the purchasing history. Bidders in auction systems rely on their judgment to determine the value of a product, as bidding prices reflect preferences rather than cost-free actions like clicking or collecting. Conventional methodologies that heavily rely on user-item purchase history are ill-suited to handle these unique and extreme product features. Unfortunately, prior recommendation approaches have failed to give due attention to the contextual intricacies of auction items, thereby missing out on the full potential of the invaluable bidding record at hand. This paper introduces a novel contrastive learning approach for auction recommendation, addressing the challenges of data sparsity and uniqueness in auction recommendation. Our method focuses on capturing multiple behavior relations and item context through contrastive pairs construction, contrastive embedding, and contrastive optimization techniques from both user and item perspectives. By overcoming the limitations of previous approaches, our method delivers promising results on two auction datasets, highlighting the practicality and effectiveness of our model.|EBay 等拍卖平台中的推荐系统与传统交易平台中的推荐系统功能不同。投标过程涉及多个用户竞争一个产品，出价最高者中标。因此，每笔交易都是独立的，拥有属性不同的交易价格。拍卖物品的个别性质意味着用户不能购买相同的物品，增加了购买历史的唯一性。拍卖系统中的投标人依靠自己的判断来确定产品的价值，因为投标价格反映的是偏好，而不是点击或收集等无成本的行为。严重依赖于用户商品购买历史的传统方法不适合处理这些独特和极端的产品特性。遗憾的是，先前的推荐办法未能适当注意到拍卖物品的复杂背景，从而错过了手头宝贵的投标记录的全部潜力。针对拍卖推荐中存在的数据稀疏性和唯一性等问题，提出了一种新的拍卖推荐对比学习方法。该方法从用户和项目的角度出发，通过对比对构造、对比嵌入和对比优化技术来捕获多个行为关系和项目上下文。通过克服以往方法的局限性，我们的方法在两个拍卖数据集上提供了有希望的结果，突出了我们的模型的实用性和有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Dual-view+Contrastive+Learning+for+Auction+Recommendation)|0| +|[Dual-Process Graph Neural Network for Diversified Recommendation](https://doi.org/10.1145/3583780.3614853)|Yuanyi Ren, Hang Ni, Yingxue Zhang, Xi Wang, Guojie Song, Dong Li, Jianye Hao|Peking University, Beijing, China; Northwestern Polytechnical University, Xi'an, China; Huawei Noah's Ark Lab, Beijing, China; Huawei Noah's Ark Lab, Markham, Canada|The recommender system is one of the most fundamental information services. A significant effort has been devoted to improving prediction accuracy, inevitably leading to the potential degradation of recommendation diversity. Moreover, individuals have different needs for diversity. To address these problems, diversity-enhanced approaches are proposed to modify the recommender models. However, these methods fail to break free from the relevance-oriented paradigm and are mostly haunted by sharply-declined accuracy and high computational costs. To tackle these challenges, we propose the Dual-Process Graph Neural Network (DPGNN), an efficient diversity-enhanced recommender system, resonating with the dual-process model of human cognition and the arousal theory of human interest. The first stage reduces the risk of suboptimal output during the training procedure, which helps to find a solution outside the relevance-oriented paradigm. Moreover, the second stage utilizes user-specific rating adjustments, boosting the recommendation diversity and accommodating users' distinctive needs with minimum computational costs. Extensive experiments on real-world datasets verify the effectiveness of our method in improving diversity, while maintaining accuracy with low computational costs.|推荐系统是最基本的信息服务之一。为了提高预测的准确性，人们付出了巨大的努力，这不可避免地导致了推荐多样性的潜在退化。此外，个体对多样性有不同的需求。为了解决这些问题，提出了基于多样性增强的方法来修改推荐模型。然而，这些方法未能摆脱以关联为导向的范式，并且大多受到精度急剧下降和计算成本高的困扰。为了应对这些挑战，我们提出了双进程图形神经网络(DPGNN) ，一种有效的多样性增强推荐系统，与人类认知的双进程模型和人类兴趣唤醒理论产生共鸣。第一阶段降低了培训过程中产出不理想的风险，这有助于找到一个以关联为导向的范式之外的解决方案。此外，第二阶段利用用户特定的评分调整，提高推荐的多样性，以最小的计算成本满足用户的特殊需求。在真实世界数据集上的大量实验证明了该方法在提高多样性的同时以较低的计算成本保持准确性方面的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Dual-Process+Graph+Neural+Network+for+Diversified+Recommendation)|0| +|[Dual-view Contrastive Learning for Auction Recommendation](https://doi.org/10.1145/3583780.3614854)|Dan Ni Ren, Leong Hou U, Wei Liu|University of Macau, Macau SAR, Macao; Sun Yat-sen University, Guangdong, China|Recommendation systems in auction platforms like eBay function differently in comparison to those found in traditional trading platforms. The bidding process involves multiple users competing for a product, with the highest bidder winning the item. As a result, each transaction is independent and characterized by varying transaction prices. The individual nature of auction items means that users cannot purchase identical items, adding to the uniqueness of the purchasing history. Bidders in auction systems rely on their judgment to determine the value of a product, as bidding prices reflect preferences rather than cost-free actions like clicking or collecting. Conventional methodologies that heavily rely on user-item purchase history are ill-suited to handle these unique and extreme product features. Unfortunately, prior recommendation approaches have failed to give due attention to the contextual intricacies of auction items, thereby missing out on the full potential of the invaluable bidding record at hand. This paper introduces a novel contrastive learning approach for auction recommendation, addressing the challenges of data sparsity and uniqueness in auction recommendation. Our method focuses on capturing multiple behavior relations and item context through contrastive pairs construction, contrastive embedding, and contrastive optimization techniques from both user and item perspectives. By overcoming the limitations of previous approaches, our method delivers promising results on two auction datasets, highlighting the practicality and effectiveness of our model.|EBay 等拍卖平台中的推荐系统与传统交易平台中的推荐系统功能不同。投标过程涉及多个用户竞争一个产品，出价最高者中标。因此，每笔交易都是独立的，拥有属性不同的交易价格。拍卖物品的个别性质意味着用户不能购买相同的物品，增加了购买历史的唯一性。拍卖系统中的投标人依靠自己的判断来确定产品的价值，因为投标价格反映的是偏好，而不是点击或收集等无成本的行为。严重依赖于用户商品购买历史的传统方法不适合处理这些独特和极端的产品特性。遗憾的是，先前的推荐办法未能适当注意到拍卖物品的复杂背景，从而错过了手头宝贵的投标记录的全部潜力。针对拍卖推荐中存在的数据稀疏性和唯一性等问题，提出了一种新的拍卖推荐对比学习方法。该方法从用户和项目的角度出发，通过对比对构造、对比嵌入和对比优化技术来捕获多个行为关系和项目上下文。通过克服以往方法的局限性，我们的方法在两个拍卖数据集上提供了有希望的结果，突出了我们的模型的实用性和有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Dual-view+Contrastive+Learning+for+Auction+Recommendation)|0| |[GiGaMAE: Generalizable Graph Masked Autoencoder via Collaborative Latent Space Reconstruction](https://doi.org/10.1145/3583780.3614894)|Yucheng Shi, Yushun Dong, Qiaoyu Tan, Jundong Li, Ninghao Liu|University of Virginia, Charlottesville, VA, USA; University of Georgia, Athens, GA, USA; Texas A&M University, College Station, TX, USA|Self-supervised learning with masked autoencoders has recently gained popularity for its ability to produce effective image or textual representations, which can be applied to various downstream tasks without retraining. However, we observe that the current masked autoencoder models lack good generalization ability on graph data. To tackle this issue, we propose a novel graph masked autoencoder framework called GiGaMAE. Different from existing masked autoencoders that learn node presentations by explicitly reconstructing the original graph components (e.g., features or edges), in this paper, we propose to collaboratively reconstruct informative and integrated latent embeddings. By considering embeddings encompassing graph topology and attribute information as reconstruction targets, our model could capture more generalized and comprehensive knowledge. Furthermore, we introduce a mutual information based reconstruction loss that enables the effective reconstruction of multiple targets. This learning objective allows us to differentiate between the exclusive knowledge learned from a single target and common knowledge shared by multiple targets. We evaluate our method on three downstream tasks with seven datasets as benchmarks. Extensive experiments demonstrate the superiority of GiGaMAE against state-of-the-art baselines. We hope our results will shed light on the design of foundation models on graph-structured data. Our code is available at: https://github.com/sycny/GiGaMAE.|掩蔽自动编码器的自监督学习因其能够产生有效的图像或文本表示而得到广泛应用，可以在不需要再培训的情况下应用于各种下游任务。然而，我们观察到目前的掩码自动编码器模型缺乏良好的图形数据泛化能力。为了解决这个问题，我们提出了一种新的图形掩码自动编码框架，称为 GiGaMAE。与现有的隐式自动编码器不同，隐式自动编码器通过显式重构原始的图形组件(如特征或边)来学习节点表示，本文提出了协同重构信息化和集成化的潜在嵌入。该模型以包含图形拓扑和属性信息的嵌入为重构目标，可以获得更广泛、更全面的知识。此外，我们还引入了一种基于互信息的重建损失算法，可以有效地重建多个目标。这种学习目标使我们能够区分从单个目标学习的专有知识和由多个目标共享的共同知识。我们用七个数据集作为基准，在三个下游任务上评估我们的方法。大量的实验证明了 GiGaMAE 相对于最先进的基线的优越性。我们希望我们的研究结果能够对基于图结构数据的基础模型的设计有所帮助。我们的代码可以在以下 https://github.com/sycny/gigamae 找到。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=GiGaMAE:+Generalizable+Graph+Masked+Autoencoder+via+Collaborative+Latent+Space+Reconstruction)|0| |[Joint Rebalancing and Charging for Shared Electric Micromobility Vehicles with Energy-informed Demand](https://doi.org/10.1145/3583780.3614942)|Heng Tan, Yukun Yuan, Shuxin Zhong, Yu Yang|Lehigh University, Bethlehem, PA, USA; University of Tennessee at Chattanooga, Chattanooga, TN, USA; Rutgers University, New Brunswick, NJ, USA|Shared electric micromobility (e.g., shared electric bikes and electric scooters), as an emerging way of urban transportation, has been increasingly popular in recent years. However, managing thousands of micromobility vehicles in a city, such as rebalancing and charging vehicles to meet spatial-temporally varied demand, is challenging. Existing management frameworks generally consider demand as the number of requests without the energy consumption of these requests, which can lead to less effective management. To address this limitation, we design RECOMMEND, a rebalancing and charging framework for shared electric micromobility vehicles with energy-informed demand to improve the system revenue. Specifically, we first re-define the demand from the perspective of energy consumption and predict the future energy-informed demand based on the state-of-the-art spatial-temporal prediction method. Then we fuse the predicted energy-informed demand into different components of a rebalancing and charging framework based on reinforcement learning. We evaluate the RECOMMEND system with 2-month real-world electric micromobility system operation data. Experimental results show that our method can be easily integrated into a general RL framework and outperform state-of-the-art baselines by at least 26.89% in terms of net revenue.|共享电动微型交通(如共享电动自行车和电动滑板车) ，作为一种新兴的城市交通方式，近年来越来越受欢迎。然而，在一个城市管理数以千计的微型移动车辆，如重新平衡和充电车辆，以满足时空不同的需求，是具有挑战性的。现有管理框架一般将需求视为没有这些需求的能源消耗的需求数量，这可能导致管理效率较低。为了解决这一局限性，我们设计了一个再平衡和充电框架 RECOMMEND，用于具有能源信息需求的共享电动微型移动车辆，以提高系统收入。具体来说，我们首先从能源消费的角度重新定义需求，并基于最新的时空预测方法对未来的能源知情需求进行预测。然后，我们将预测的能源需求融入基于强化学习的再平衡和收费框架的不同组成部分。我们使用2个月的实际电子微移动系统运行数据对 RECOMMEND 系统进行了评估。实验结果表明，该方法可以很容易地集成到一个通用的 RL 框架中，并且在净收入方面比最先进的基线至少高出26.89% 。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Joint+Rebalancing+and+Charging+for+Shared+Electric+Micromobility+Vehicles+with+Energy-informed+Demand)|0| -|[Experience and Evidence are the eyes of an excellent summarizer! Towards Knowledge Infused Multi-modal Clinical Conversation Summarization](https://doi.org/10.1145/3583780.3614870)|Abhisek Tiwari, Anisha Saha, Sriparna Saha, Pushpak Bhattacharyya, Minakshi Dhar|Indian Institute of Technology, Bombay, Bombay, India; Indian Institute of Technology, Patna, Patna, India; All India Institute of Medical Sciences, Rishikesh, Rishikesh, India|With the advancement of telemedicine, both researchers and medical practitioners are working hand-in-hand to develop various techniques to automate various medical operations, such as diagnosis report generation. In this paper, we first present a multi-modal clinical conversation summary generation task that takes a clinician-patient interaction (both textual and visual information) and generates a succinct synopsis of the conversation. We propose a knowledge-infused, multi-modal, multi-tasking medical domain identification and clinical conversation summary generation (MM-CliConSummation) framework. It leverages an adapter to infuse knowledge and visual features and unify the fused feature vector using a gated mechanism. Furthermore, we developed a multi-modal, multi-intent clinical conversation summarization corpus annotated with intent, symptom, and summary. The extensive set of experiments, both quantitatively and qualitatively, led to the following findings: (a) critical significance of visuals, (b) more precise and medical entity preserving summary with additional knowledge infusion, and (c) a correlation between medical department identification and clinical synopsis generation. Furthermore, the dataset and source code are available at https://github.com/NLP-RL/MM-CliConSummation.|随着远程医疗的发展，研究人员和医务人员正携手合作，开发各种技术，以自动化各种医疗操作，如诊断报告生成。在本文中，我们首先提出了一个多模态的临床会话摘要生成任务，采用临床医生-患者的互动(文本和视觉信息) ，并生成一个简洁的会话概要。我们提出了一个知识注入、多模态、多任务的医学领域识别和临床会话摘要生成(MM-CliConsum)框架。它利用适配器来注入知识和可视化特征，并使用门控机制统一融合特征向量。此外，我们开发了一个多模式，多意图临床会话摘要语料库注释意图，症状和总结。广泛的一系列定量和定性实验导致了以下发现: (a)视觉的关键意义，(b)更精确的医疗实体保存总结与额外的知识输入，以及(c)医疗部门识别和临床概要生成之间的相关性。此外，数据集和源代码也可以在 https://github.com/nlp-rl/mm-cliconsummation 中找到。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Experience+and+Evidence+are+the+eyes+of+an+excellent+summarizer!+Towards+Knowledge+Infused+Multi-modal+Clinical+Conversation+Summarization)|0| +|[Experience and Evidence are the eyes of an excellent summarizer! Towards Knowledge Infused Multi-modal Clinical Conversation Summarization](https://doi.org/10.1145/3583780.3614870)|Abhisek Tiwari, Anisha Saha, Sriparna Saha, Pushpak Bhattacharyya, Minakshi Dhar|Indian Institute of Technology, Patna, Patna, India; All India Institute of Medical Sciences, Rishikesh, Rishikesh, India; Indian Institute of Technology, Bombay, Bombay, India|With the advancement of telemedicine, both researchers and medical practitioners are working hand-in-hand to develop various techniques to automate various medical operations, such as diagnosis report generation. In this paper, we first present a multi-modal clinical conversation summary generation task that takes a clinician-patient interaction (both textual and visual information) and generates a succinct synopsis of the conversation. We propose a knowledge-infused, multi-modal, multi-tasking medical domain identification and clinical conversation summary generation (MM-CliConSummation) framework. It leverages an adapter to infuse knowledge and visual features and unify the fused feature vector using a gated mechanism. Furthermore, we developed a multi-modal, multi-intent clinical conversation summarization corpus annotated with intent, symptom, and summary. The extensive set of experiments, both quantitatively and qualitatively, led to the following findings: (a) critical significance of visuals, (b) more precise and medical entity preserving summary with additional knowledge infusion, and (c) a correlation between medical department identification and clinical synopsis generation. Furthermore, the dataset and source code are available at https://github.com/NLP-RL/MM-CliConSummation.|随着远程医疗的发展，研究人员和医务人员正携手合作，开发各种技术，以自动化各种医疗操作，如诊断报告生成。在本文中，我们首先提出了一个多模态的临床会话摘要生成任务，采用临床医生-患者的互动(文本和视觉信息) ，并生成一个简洁的会话概要。我们提出了一个知识注入、多模态、多任务的医学领域识别和临床会话摘要生成(MM-CliConsum)框架。它利用适配器来注入知识和可视化特征，并使用门控机制统一融合特征向量。此外，我们开发了一个多模式，多意图临床会话摘要语料库注释意图，症状和总结。广泛的一系列定量和定性实验导致了以下发现: (a)视觉的关键意义，(b)更精确的医疗实体保存总结与额外的知识输入，以及(c)医疗部门识别和临床概要生成之间的相关性。此外，数据集和源代码也可以在 https://github.com/nlp-rl/mm-cliconsummation 中找到。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Experience+and+Evidence+are+the+eyes+of+an+excellent+summarizer!+Towards+Knowledge+Infused+Multi-modal+Clinical+Conversation+Summarization)|0| |[Towards Deeper, Lighter and Interpretable Cross Network for CTR Prediction](https://doi.org/10.1145/3583780.3615089)|Fangye Wang, Hansu Gu, Dongsheng Li, Tun Lu, Peng Zhang, Ning Gu||Click Through Rate (CTR) prediction plays an essential role in recommender systems and online advertising. It is crucial to effectively model feature interactions to improve the prediction performance of CTR models. However, existing methods face three significant challenges. First, while most methods can automatically capture high-order feature interactions, their performance tends to diminish as the order of feature interactions increases. Second, existing methods lack the ability to provide convincing interpretations of the prediction results, especially for high-order feature interactions, which limits the trustworthiness of their predictions. Third, many methods suffer from the presence of redundant parameters, particularly in the embedding layer. This paper proposes a novel method called Gated Deep Cross Network (GDCN) and a Field-level Dimension Optimization (FDO) approach to address these challenges. As the core structure of GDCN, Gated Cross Network (GCN) captures explicit high-order feature interactions and dynamically filters important interactions with an information gate in each order. Additionally, we use the FDO approach to learn condensed dimensions for each field based on their importance. Comprehensive experiments on five datasets demonstrate the effectiveness, superiority and interpretability of GDCN. Moreover, we verify the effectiveness of FDO in learning various dimensions and reducing model parameters. The code is available on https://github.com/anonctr/GDCN.|点击通过率(CTR)预测在推荐系统和在线广告中起着至关重要的作用。有效的特征交互建模对于提高 CTR 模型的预测性能至关重要。然而，现有的方法面临三个重大挑战。首先，虽然大多数方法可以自动捕获高阶特征交互，但是它们的性能往往会随着特征交互次序的增加而下降。其次，现有的方法缺乏对预测结果提供令人信服的解释的能力，特别是对于高阶特征相互作用，这限制了它们的预测的可信度。第三，许多方法都存在冗余参数，特别是在嵌入层。本文提出了一种新的方法，称为门控深交叉网络(GDCN)和场级尺寸优化(FDO)的方法来解决这些挑战。门限交叉网络(GCN)作为 GDCN 的核心结构，捕获显式的高阶特征交互，并动态过滤每个阶段与信息门的重要交互。此外，我们使用 FDO 方法根据每个领域的重要性来学习压缩维度。通过对五个数据集的综合实验，验证了 GDCN 算法的有效性、优越性和可解释性。此外，我们还验证了 FDO 在学习各种维数和降低模型参数方面的有效性。密码可以在 https://github.com/anonctr/gdcn 上找到。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Towards+Deeper,+Lighter+and+Interpretable+Cross+Network+for+CTR+Prediction)|0| |[AFRF: Angle Feature Retrieval Based Popularity Forecasting](https://doi.org/10.1145/3583780.3614776)|Haoyu Wang, Zongxia Xie, Meiyao Liu, Canhua Guan|Tianjin University, Tianjin, China|Social media popularity forecasting has become a hot research topic in recent years. It is of great significance in assisting public opinion monitoring and advertising placement. Time series prediction is one of the simple and commonly used methods for popularity forecasting, which takes the popularity of the first few time steps in the observed data as inputs. However, the complete popularity trend of each social media is known in the training dataset, while the historical time series information except for the first few time steps is neglected in the existing models. In order to utilize the complete historical information from the observed data, a retrieval method is introduced in this paper. Therefore, how to retrieve similar social media based on the first few steps time series and how to integrate the similar historical information have become two challenges. A two-stage prediction method named Angle Feature Retrieval based Forecasting (AFRF) is proposed in this paper to solve the upper two problems. In the first stage, based on the angle features of series, we retrieve K similar series from the historical posts and concatenate them with the target series as the model's input. In the second stage, an attention mechanism is used to learn the temporal relationships among the series and generate future popularity forecasts. We evaluated the multi-step and single-point forecasting performance of AFRF on three real-world datasets and compared it with state-of-the-art popularity forecasting methods, such as temporal feature-based and cascade-based methods, verifying the effectiveness of AFRF.|社交媒体受欢迎程度预测已成为近年来的研究热点。它对辅助舆论监督和广告投放具有重要意义。时间序列预测是一种简单、常用的流行度预测方法，它以观测数据中前几个时间步长的流行度作为输入。然而，在训练数据集中已经知道每个社会媒体的完全流行趋势，而在现有的模型中，除了前几个时间步骤之外的历史时间序列信息被忽略了。为了充分利用观测数据中的完整历史信息，本文提出了一种检索方法。因此，如何基于前几步时间序列检索相似的社会媒体，如何整合相似的历史信息，已成为两大挑战。针对上述两个问题，提出了一种基于角度特征反演的两阶段预测方法(AFRF)。在第一阶段，根据序列的角度特征，从历史文章中提取出 K 个相似序列，并将它们与目标序列连接起来作为模型的输入。在第二阶段，使用注意机制来学习序列之间的时间关系，并生成未来的流行预测。对 AFRF 在三个实际数据集上的多步预测性能和单点预测性能进行了评估，并与基于时间特征和基于级联的流行性预测方法进行了比较，验证了 AFRF 的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=AFRF:+Angle+Feature+Retrieval+Based+Popularity+Forecasting)|0| |[SplitGNN: Spectral Graph Neural Network for Fraud Detection against Heterophily](https://doi.org/10.1145/3583780.3615067)|Bin Wu, Xinyu Yao, Boyan Zhang, KuoMing Chao, Yinsheng Li|Fudan University, Shanghai, China; University of Roehampton, London, United Kingdom|Fraudsters in the real world frequently add more legitimate links while concealing their direct ones with other fraudsters, leading to heterophily in fraud graphs, which is a problem that most GNN-based techniques are not built to solve. Several works have been proposed to tackle the issue from the spatial domain. However, researches on addressing the heterophily problem in the spectral domain are still limited due to a lack of understanding of spectral energy distribution in graphs with heterophily. In this paper, we analyze the spectral distribution with different heterophily degrees and observe that the heterophily of fraud nodes leads to the spectral energy moving from low-frequency to high-frequency. Further, we verify that splitting graphs using heterophilic and homophilic edges can obtain more significant expressions of signals in different frequency bands. The observation drives us to propose the spectral graph neural network, SplitGNN, to capture signals for fraud detection against heterophily. SplitGNN uses an edge classifier to split the original graph and adopts flexible band-pass graph filters to learn representations. Extensive experiments on real-world datasets demonstrate the effectiveness of our proposed method. The code and data are available at https://github.com/Split-GNN/SplitGNN.|现实世界中的欺诈者经常添加更多的合法链接，同时隐藏他们与其他欺诈者的直接链接，从而导致欺诈图中的异质性，这是大多数基于 GNN 的技术无法解决的问题。已经提出了几个工作，以解决从空间领域的问题。然而，由于缺乏对具有异质性的图的光谱能量分布的理解，有关谱域异质性问题的研究仍然十分有限。本文分析了欺诈节点不同异质性程度的频谱分布，发现欺诈节点的异质性导致频谱能量由低频向高频移动。进一步，我们验证了使用异质和同质边的分裂图可以在不同的频带获得更有意义的信号表达式。这一观测结果促使我们提出了谱图神经网络 SplitGNN 来捕获信号用于对异质性的欺诈检测。SplitGNN 使用边分类器对原始图进行分割，并采用灵活的带通图滤波器来学习表示。在实际数据集上的大量实验证明了该方法的有效性。代码和数据可在 https://github.com/split-gnn/splitgnn 查阅。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=SplitGNN:+Spectral+Graph+Neural+Network+for+Fraud+Detection+against+Heterophily)|0| -|[DPGN: Denoising Periodic Graph Network for Life Service Recommendation](https://doi.org/10.1145/3583780.3614850)|Hao Xu, Huixuan Chi, Danyang Liu, Sheng Zhou, Mengdi Zhang|Zhejiang University, Hangzhou, China; Meituan, Beijing, China; Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China|Different from traditional e-commerce platforms, life service recommender systems provide hundreds of millions of users with daily necessities services such as nearby food ordering. In this scenario, users have instant intentions and living habits, which exhibit a periodic tendency to click or buy products with similar intentions. This can be summarized as the intentional periodicity problem, which was not well-studied in previous works. Existing periodic-related recommenders exploit time-sensitive functions to capture the evolution of user preferences. However, these methods are easily affected by the real noisy signal in life service platforms, wherein the recent noisy signals can mislead the instant intention and living habits modeling. We summarize it as the noise issue. Although there are some denoising recommenders, these methods cannot effectively solve the noise issue for intentional periodicity modeling. To alleviate the issues, we propose a novel Denoising Periodic Graph Network (DPGN) for life service recommendation. First, to alleviate the noisy signals and model the instant intention accurately, we propose (i) temporal pooling (TP) to encode the most representative information shared by recent behaviors; (ii) temporal encoding (TE) to encode the relative time intervals. Second, to capture the user's living habits accurately, we propose the memory mechanism to maintain a series of instant intentions in different time periods. Third, to further capture the intentional periodicity, we propose the temporal graph transformer (TGT) layer to aggregate temporal information. Last, the denoising task is further proposed to alleviate the noisy signals. Extensive experiments on both real-world public and industrial datasets validate the state-of-the-art performance of DPGN. Code is available in https://github.com/ytchx1999/DPGN|与传统的电子商务平台不同，生活服务推荐系统为数亿用户提供日常必需品服务，如附近的食品订购。在这种情况下，用户有即时的意图和生活习惯，这表现出一个周期性的趋势，点击或购买具有相似意图的产品。这可以概括为有意识的周期性问题，在以前的工作中没有得到很好的研究。现有的与周期相关的推荐程序利用时间敏感的功能来捕获用户偏好的演变。然而，这些方法很容易受到生活服务平台中真实噪声信号的影响，其中最近的噪声信号会误导人们的即时意图和生活习惯建模。我们把它归结为噪音问题。虽然有一些去噪建议，但这些方法不能有效地解决有意识的周期性建模的噪声问题。为了解决这些问题，我们提出了一种新的消噪周期图网络(DPGN)用于终身服务推荐。首先，为了减轻噪声信号的影响，准确地模拟瞬时意图，我们提出: (1)时间池(TP)对最近行为共享的最有代表性的信息进行编码; (2)时间编码(TE)对相对时间间隔进行编码。其次，为了准确地捕捉用户的生活习惯，我们提出了在不同时间段保持一系列即时意图的记忆机制。第三，为了进一步捕获有意识的周期性，我们提出了时间图转换(TGT)层来聚集时间信息。最后，进一步提出降噪任务，以减轻噪声信号。在真实世界的公共数据集和工业数据集上的大量实验验证了 DPGN 的最新性能。代码可在 https://github.com/ytchx1999/dpgn 下载|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=DPGN:+Denoising+Periodic+Graph+Network+for+Life+Service+Recommendation)|0| +|[DPGN: Denoising Periodic Graph Network for Life Service Recommendation](https://doi.org/10.1145/3583780.3614850)|Hao Xu, Huixuan Chi, Danyang Liu, Sheng Zhou, Mengdi Zhang|Meituan, Beijing, China; Zhejiang University, Hangzhou, China; Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China|Different from traditional e-commerce platforms, life service recommender systems provide hundreds of millions of users with daily necessities services such as nearby food ordering. In this scenario, users have instant intentions and living habits, which exhibit a periodic tendency to click or buy products with similar intentions. This can be summarized as the intentional periodicity problem, which was not well-studied in previous works. Existing periodic-related recommenders exploit time-sensitive functions to capture the evolution of user preferences. However, these methods are easily affected by the real noisy signal in life service platforms, wherein the recent noisy signals can mislead the instant intention and living habits modeling. We summarize it as the noise issue. Although there are some denoising recommenders, these methods cannot effectively solve the noise issue for intentional periodicity modeling. To alleviate the issues, we propose a novel Denoising Periodic Graph Network (DPGN) for life service recommendation. First, to alleviate the noisy signals and model the instant intention accurately, we propose (i) temporal pooling (TP) to encode the most representative information shared by recent behaviors; (ii) temporal encoding (TE) to encode the relative time intervals. Second, to capture the user's living habits accurately, we propose the memory mechanism to maintain a series of instant intentions in different time periods. Third, to further capture the intentional periodicity, we propose the temporal graph transformer (TGT) layer to aggregate temporal information. Last, the denoising task is further proposed to alleviate the noisy signals. Extensive experiments on both real-world public and industrial datasets validate the state-of-the-art performance of DPGN. Code is available in https://github.com/ytchx1999/DPGN|与传统的电子商务平台不同，生活服务推荐系统为数亿用户提供日常必需品服务，如附近的食品订购。在这种情况下，用户有即时的意图和生活习惯，这表现出一个周期性的趋势，点击或购买具有相似意图的产品。这可以概括为有意识的周期性问题，在以前的工作中没有得到很好的研究。现有的与周期相关的推荐程序利用时间敏感的功能来捕获用户偏好的演变。然而，这些方法很容易受到生活服务平台中真实噪声信号的影响，其中最近的噪声信号会误导人们的即时意图和生活习惯建模。我们把它归结为噪音问题。虽然有一些去噪建议，但这些方法不能有效地解决有意识的周期性建模的噪声问题。为了解决这些问题，我们提出了一种新的消噪周期图网络(DPGN)用于终身服务推荐。首先，为了减轻噪声信号的影响，准确地模拟瞬时意图，我们提出: (1)时间池(TP)对最近行为共享的最有代表性的信息进行编码; (2)时间编码(TE)对相对时间间隔进行编码。其次，为了准确地捕捉用户的生活习惯，我们提出了在不同时间段保持一系列即时意图的记忆机制。第三，为了进一步捕获有意识的周期性，我们提出了时间图转换(TGT)层来聚集时间信息。最后，进一步提出降噪任务，以减轻噪声信号。在真实世界的公共数据集和工业数据集上的大量实验验证了 DPGN 的最新性能。代码可在 https://github.com/ytchx1999/dpgn 下载|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=DPGN:+Denoising+Periodic+Graph+Network+for+Life+Service+Recommendation)|0| |[Identifying Regional Driving Risks via Transductive Cross-City Transfer Learning Under Negative Transfer](https://doi.org/10.1145/3583780.3614924)|Hua Yan, Hao Wang, Desheng Zhang, Yu Yang|Lehigh University, Bethlehem, PA, USA; Rutgers University, Piscataway, NJ, USA|Identifying regional driving risks is important for real-world applications such as driving safety warning applications, public safety management, and insurance company premium pricing. Previous approaches are either based on traffic accident reports or vehicular sensor data. They either fail to identify potential risks, such as near-miss collisions, which would need other important measurements (e.g., hard break, acceleration, etc.), or fail to generalize to cities without vehicular sensor data, severely limiting their practicality. In this work, we address these two challenges and successfully identify regional driving risks in a target city without vehicular sensor data via cross-city transfer learning. Specifically, we design a novel framework RiskTrans by optimizing both the predictor and the relationship between cities to achieve transfer learning. We advance the existing works from two aspects: (i) we achieve it in a transductive manner without accessing labeled data in the target cities; (ii) we identify and address the problem of negative transfer in cross-city transfer learning, a prominent issue that is often (surprisingly) neglected in previous works. Finally, we conduct extensive experiments based on data collected from 175 thousand vehicles in six cities. The results show RiskTrans outperforms baselines by at least 50.2% and reduces negative transfer by 49.4%.|识别区域驾驶风险对于驾驶安全警告应用程序、公共安全管理和保险公司保险费定价等实际应用程序非常重要。以前的方法要么基于交通事故报告，要么基于车辆传感器数据。他们要么不能识别潜在的风险，例如差点撞上，这将需要其他重要的测量(例如，硬碰撞，加速度等) ，或者不能推广到没有车辆传感器数据的城市，严重限制了他们的实用性。在这项工作中，我们解决这两个挑战，并成功地识别区域驾驶风险在目标城市没有车辆传感器数据通过跨城市转移学习。具体来说，我们设计了一个新的框架 RiskTrans，通过优化预测器和城市之间的关系来实现迁移学习。我们从两个方面推进现有的工作: (1)我们实现了转换的方式，而没有访问目标城市的标记数据; (2)我们识别和解决跨城市迁移学习中的负迁移问题，这是一个突出的问题，往往(令人惊讶)被忽视在以前的工作。最后，我们在六个城市17.5万辆汽车的数据基础上进行了广泛的实验。结果显示 RiskTrans 的业绩至少比基线水平高出50.2% ，负转移减少了49.4% 。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Identifying+Regional+Driving+Risks+via+Transductive+Cross-City+Transfer+Learning+Under+Negative+Transfer)|0| -|[FARA: Future-aware Ranking Algorithm for Fairness Optimization](https://doi.org/10.1145/3583780.3614877)|Tao Yang, Zhichao Xu, Zhenduo Wang, Qingyao Ai|University of Utah, Salt Lake City, UT, USA; DCST, Tsinghua University, Quan Cheng Laboratory, Zhongguancun Laboratory, Beijing, China|Ranking systems are the key components of modern Information Retrieval (IR) applications, such as search engines and recommender systems. Besides the ranking relevance to users, the exposure fairness to item providers has also been considered an important factor in ranking optimization. Many fair ranking algorithms have been proposed to jointly optimize both ranking relevance and fairness. However, we find that most existing fair ranking methods adopt greedy algorithms that only optimize rankings for the next immediate session or request. As shown in this paper, such a myopic paradigm could limit the upper bound of ranking optimization and lead to suboptimal performance in the long term. To this end, we propose FARA, a novel Future-Aware Ranking Algorithm for ranking relevance and fairness optimization. Instead of greedily optimizing rankings for the next immediate session, FARA plans ahead by jointly optimizing multiple ranklists together and saving them for future sessions. Particularly, FARA first uses the Taylor expansion to investigate how future ranklists will influence the overall fairness of the system. Then, based on the analysis of the Taylor expansion, FARA adopts a two-phase optimization algorithm where we first solve an optimal future exposure planning problem and then construct the optimal ranklists according to the optimal future exposure planning. Theoretically, we show that FARA is optimal for ranking relevance and fairness joint optimization. Empirically, our extensive experiments on three semi-synthesized datasets show that FARA is efficient, effective, and can deliver significantly better ranking performance compared to state-of-the-art fair ranking methods.|排名系统是现代信息检索应用(如搜索引擎和推荐系统)的关键组成部分。除了与用户的排名相关性之外，项目提供者的曝光公平性也被认为是排名优化的一个重要因素。许多公平排序算法被提出来共同优化排序的相关性和公平性。然而，我们发现大多数现有的公平排名方法采用贪婪算法，只优化下一个即时会话或请求的排名。如本文所述，这种短视的范式可能会限制排序优化的上界，并导致长期的次优性能。为此，我们提出了一种新的未来感知排序算法 FARA，用于排序相关性和公平性优化。FARA 没有贪婪地优化下一阶段的排名，而是提前计划，联合优化多个排名，并将它们保存到未来的阶段。特别是，FARA 首先利用泰勒扩展来调查未来的排行榜将如何影响系统的整体公平性。然后，在分析泰勒展开的基础上，采用两阶段优化算法，首先解决未来曝光规划问题，然后根据未来曝光规划的优化结果构造最优排名表。从理论上证明了 FARA 对于排序相关性和公平性联合优化是最优的。经验上，我们在三个半合成数据集上的大量实验表明，FARA 是有效的，有效的，并且能够提供比最先进的公平排序方法更好的排序性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=FARA:+Future-aware+Ranking+Algorithm+for+Fairness+Optimization)|0| -|[Co-guided Random Walk for Polarized Communities Search](https://doi.org/10.1145/3583780.3614814)|Fanyi Yang, Huifang Ma, Cairui Yan, Zhixin Li, Liang Chang|Guilin University of Electronic Technology, Guilin, China; Guangxi Normal University, Guilin, China; Northwest Normal University, Lanzhou, China; NorthWest Normal University, Lanzhou, China|Polarized Communities Search (PCS) aims to identify query-dependent communities where positive links predominantly connect nodes within each community, while negative links primarily connect nodes across different communities. Existing solutions primarily focus on modeling network topology, disregarding the crucial factor of node attributes. However, it is non-trivial to incorporate node attributes into PCS. In this paper, we propose a novel method called CO-guided RAndom walk in attributed signed networks (CORA) for PCS. Our approach involves constructing an attribute-based signed network to represent the auxiliary relations between nodes. We introduce a weight assignment mechanism to assess the reliability of edges in the signed network. Then, we design a co-guided random walk scheme that operates on two signed networks to model the connections between network topology and node attributes, thereby enhancing the search outcomes. Finally, we identify polarized communities using the Rayleigh quotient in the signed network. Extensive experiments conducted on three public datasets demonstrate the superior performance of CORA compared to state-of-the-art baselines for polarized communities search.|极化社区搜索(PCS)旨在识别查询依赖的社区，其中正向链接主要连接每个社区内的节点，而负向链接主要连接不同社区的节点。现有的解决方案主要侧重于建模网络拓扑，忽略了节点属性的关键因素。然而，将节点属性合并到 PCS 中是非常重要的。在本文中，我们提出了一种新的方法称为 CO 引导的随机游走在属性签名网络(CORA)的 PCS。我们的方法包括构造一个基于属性的有符号网络来表示节点之间的辅助关系。我们引入了一种权重分配机制来评估有符号网络中边的可靠性。然后，我们设计一个共同引导的随机游走方案，在两个签名网络上操作，建立网络拓扑和节点属性之间的联系模型，从而提高搜索结果。最后，我们利用有符号网络中的瑞利商来识别极化群落。在三个公共数据集上进行的大量实验表明，与最先进的极化社区搜索基线相比，CORA 具有更好的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Co-guided+Random+Walk+for+Polarized+Communities+Search)|0| +|[FARA: Future-aware Ranking Algorithm for Fairness Optimization](https://doi.org/10.1145/3583780.3614877)|Tao Yang, Zhichao Xu, Zhenduo Wang, Qingyao Ai|DCST, Tsinghua University, Quan Cheng Laboratory, Zhongguancun Laboratory, Beijing, China; University of Utah, Salt Lake City, UT, USA|Ranking systems are the key components of modern Information Retrieval (IR) applications, such as search engines and recommender systems. Besides the ranking relevance to users, the exposure fairness to item providers has also been considered an important factor in ranking optimization. Many fair ranking algorithms have been proposed to jointly optimize both ranking relevance and fairness. However, we find that most existing fair ranking methods adopt greedy algorithms that only optimize rankings for the next immediate session or request. As shown in this paper, such a myopic paradigm could limit the upper bound of ranking optimization and lead to suboptimal performance in the long term. To this end, we propose FARA, a novel Future-Aware Ranking Algorithm for ranking relevance and fairness optimization. Instead of greedily optimizing rankings for the next immediate session, FARA plans ahead by jointly optimizing multiple ranklists together and saving them for future sessions. Particularly, FARA first uses the Taylor expansion to investigate how future ranklists will influence the overall fairness of the system. Then, based on the analysis of the Taylor expansion, FARA adopts a two-phase optimization algorithm where we first solve an optimal future exposure planning problem and then construct the optimal ranklists according to the optimal future exposure planning. Theoretically, we show that FARA is optimal for ranking relevance and fairness joint optimization. Empirically, our extensive experiments on three semi-synthesized datasets show that FARA is efficient, effective, and can deliver significantly better ranking performance compared to state-of-the-art fair ranking methods.|排名系统是现代信息检索应用(如搜索引擎和推荐系统)的关键组成部分。除了与用户的排名相关性之外，项目提供者的曝光公平性也被认为是排名优化的一个重要因素。许多公平排序算法被提出来共同优化排序的相关性和公平性。然而，我们发现大多数现有的公平排名方法采用贪婪算法，只优化下一个即时会话或请求的排名。如本文所述，这种短视的范式可能会限制排序优化的上界，并导致长期的次优性能。为此，我们提出了一种新的未来感知排序算法 FARA，用于排序相关性和公平性优化。FARA 没有贪婪地优化下一阶段的排名，而是提前计划，联合优化多个排名，并将它们保存到未来的阶段。特别是，FARA 首先利用泰勒扩展来调查未来的排行榜将如何影响系统的整体公平性。然后，在分析泰勒展开的基础上，采用两阶段优化算法，首先解决未来曝光规划问题，然后根据未来曝光规划的优化结果构造最优排名表。从理论上证明了 FARA 对于排序相关性和公平性联合优化是最优的。经验上，我们在三个半合成数据集上的大量实验表明，FARA 是有效的，有效的，并且能够提供比最先进的公平排序方法更好的排序性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=FARA:+Future-aware+Ranking+Algorithm+for+Fairness+Optimization)|0| +|[Co-guided Random Walk for Polarized Communities Search](https://doi.org/10.1145/3583780.3614814)|Fanyi Yang, Huifang Ma, Cairui Yan, Zhixin Li, Liang Chang|Guilin University of Electronic Technology, Guilin, China; NorthWest Normal University, Lanzhou, China; Guangxi Normal University, Guilin, China; Northwest Normal University, Lanzhou, China|Polarized Communities Search (PCS) aims to identify query-dependent communities where positive links predominantly connect nodes within each community, while negative links primarily connect nodes across different communities. Existing solutions primarily focus on modeling network topology, disregarding the crucial factor of node attributes. However, it is non-trivial to incorporate node attributes into PCS. In this paper, we propose a novel method called CO-guided RAndom walk in attributed signed networks (CORA) for PCS. Our approach involves constructing an attribute-based signed network to represent the auxiliary relations between nodes. We introduce a weight assignment mechanism to assess the reliability of edges in the signed network. Then, we design a co-guided random walk scheme that operates on two signed networks to model the connections between network topology and node attributes, thereby enhancing the search outcomes. Finally, we identify polarized communities using the Rayleigh quotient in the signed network. Extensive experiments conducted on three public datasets demonstrate the superior performance of CORA compared to state-of-the-art baselines for polarized communities search.|极化社区搜索(PCS)旨在识别查询依赖的社区，其中正向链接主要连接每个社区内的节点，而负向链接主要连接不同社区的节点。现有的解决方案主要侧重于建模网络拓扑，忽略了节点属性的关键因素。然而，将节点属性合并到 PCS 中是非常重要的。在本文中，我们提出了一种新的方法称为 CO 引导的随机游走在属性签名网络(CORA)的 PCS。我们的方法包括构造一个基于属性的有符号网络来表示节点之间的辅助关系。我们引入了一种权重分配机制来评估有符号网络中边的可靠性。然后，我们设计一个共同引导的随机游走方案，在两个签名网络上操作，建立网络拓扑和节点属性之间的联系模型，从而提高搜索结果。最后，我们利用有符号网络中的瑞利商来识别极化群落。在三个公共数据集上进行的大量实验表明，与最先进的极化社区搜索基线相比，CORA 具有更好的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Co-guided+Random+Walk+for+Polarized+Communities+Search)|0| |[Federated News Recommendation with Fine-grained Interpolation and Dynamic Clustering](https://doi.org/10.1145/3583780.3614881)|Sanshi Lei Yu, Qi Liu, Fei Wang, Yang Yu, Enhong Chen|Anhui Province Key Laboratory of Big Data Analysis and Application, University of Science and Technology of China & State Key Laboratory of Cognitive Intelligence, Hefei, China|Researchers have successfully adapted the privacy-preserving Federated Learning (FL) to news recommendation tasks to better protect users' privacy, although typically at the cost of performance degradation due to the data heterogeneity issue. To address this issue, Personalized Federated Learning (PFL) has emerged, among which model interpolation is a promising approach that interpolates the local personalized models with the global model. However, the existing model interpolation method may not work well for news recommendation tasks for some reasons. First, it neglects the fine-grained personalization needs at both the temporal and spatial levels in news recommendation tasks. Second, due to the cold-user problem in real-world news recommendation tasks, the local personalized models may perform poorly, thus limiting the performance gain from model interpolation. To this end, we propose FINDING (Federated News Recommendation with Fine-grained Interpolation and Dynamic Clustering ), a novel personalized federated learning framework based on model interpolation. Specifically, we first propose the fine-grained model interpolation strategy which interpolates the local personalized models with the global model in a time-aware and layer-aware way. Then, to address the cold-user problem in news recommendation tasks, we adopt the group-level personalization approach where users are dynamically clustered into groups and the group-level personalized models are used for interpolation. Extensive experiments on two real-world datasets show that our method can effectively handle the above limitations of the current model interpolation method and alleviate the heterogeneity issue faced by traditional FL.|研究人员已经成功地将保护隐私的联邦学习(FL)应用到新闻推荐任务中，以更好地保护用户的隐私，尽管由于数据异构性问题，这通常会以性能下降为代价。为了解决这一问题，个性化联邦学习(PFL)应运而生，其中模型插值是一种很有前途的方法，它利用全局模型对局部个性化模型进行插值。然而，由于某些原因，现有的模型插值方法在新闻推荐任务中可能不能很好地工作。首先，它忽略了新闻推荐任务在时间和空间层面上的细粒度个性化需求。其次，由于现实新闻推荐任务中的冷用户问题，本地个性化模型可能表现不佳，从而限制了模型插值的性能增益。为此，本文提出了一种新的基于模型插值的个性化联邦学习框架 FINING (具有细粒度插值和动态聚类的联邦新闻推荐)。具体地说，我们首先提出了细粒度模型插值策略，该策略以时间感知和层感知的方式将局部个性化模型插值到全局模型中。然后，针对新闻推荐任务中的冷用户问题，采用组级个性化方法，将用户动态聚类成组，并使用组级个性化模型进行插值。在两个实际数据集上的大量实验表明，该方法能有效地处理现有模型插值方法的上述局限性，缓解传统 FL 所面临的异构性问题。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Federated+News+Recommendation+with+Fine-grained+Interpolation+and+Dynamic+Clustering)|0| -|[VILE: Block-Aware Visual Enhanced Document Retrieval](https://doi.org/10.1145/3583780.3615107)|Huaying Yuan, Zhicheng Dou, Yujia Zhou, Yu Guo, JiRong Wen|Renmin University of China, Beijing, China; Engineering Research Center of Next-Generation Intelligent Search and Recommendation, Ministry of Education & Renmin University of China, Beijing, China|Document retrieval has always been a crucial problem in Web search. Recent works leverage pre-trained language models to represent documents in dense vectors. However, these works focus on the textual content but ignore the appearance of web pages (e.g., the visual style, the layout, and the images), which are actually essential for information delivery. To alleviate this problem, we propose a new dense retrieval model, namely VILE, to incorporate visual features into document representations. However, because a web page is usually very large and contains diverse information, simply concatenating its textual and visual features may result in a cluttered multi-modal representation that lacks focus on the important parts of the page. We observe that web pages often have a structured content organization, comprising multiple blocks that convey different information. Motivated by the observation, we propose building a multi-modal document representation by aggregating the fine-grained multi-modal block representations, to enable a more comprehensive understanding of the page. Specifically, we first segment a web page into multiple blocks, then create multi-modal features for each block. %allowing for more effective capture of its content and visual information. The representations of all blocks are then integrated into the final multi-modal page representation. VILE can better model the importance of different content regions, leading to a high-quality multi-modal representation. We collect screenshots and the corresponding layout information of some web pages in the MS MARCO Document Ranking dataset, resulting in a new multi-modal document retrieval dataset. Experimental results conducted on this dataset demonstrate that our model exhibits significant improvements over existing document retrieval models. Our code is available at https://github.com/yhy-2000/VILE.|文献检索一直是网络搜索中的一个关键问题。最近的工作利用预训练语言模型来表示密集向量文档。然而，这些作品关注的是文本内容，而忽略了网页的外观(例如，视觉风格、布局和图像) ，这些实际上是信息传递所必需的。为了解决这一问题，我们提出了一种新的密集检索模型，即 VILE，将视觉特征融入到文档表示中。然而，由于一个网页通常非常大，并包含不同的信息，简单地连接其文本和视觉特征可能会导致一个混乱的多模态表示，缺乏重点页面的重要部分。我们观察到，网页通常有一个结构化内容的组织，由多个块组成，传达不同的信息。基于这种观察，我们提出通过聚合细粒度的多模态块表示来构建多模态文档表示，以便更全面地理解页面。具体来说，我们首先将网页分割成多个块，然后为每个块创建多模态特性。% ，以便更有效地捕捉其内容和视觉信息。然后将所有块的表示集成到最终的多模态页表示中。VILE 可以更好地为不同内容区域的重要性建模，从而实现高质量的多模态表示。我们收集了微软 MARCO 文件排名数据集中一些网页的截图和相应的布局信息，产生了一个新的多模式文献检索数据集。在这个数据集上进行的实验结果表明，我们的模型比现有的文献检索模型有显著的改进。我们的代码可以在 https://github.com/yhy-2000/vile 找到。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=VILE:+Block-Aware+Visual+Enhanced+Document+Retrieval)|0| +|[VILE: Block-Aware Visual Enhanced Document Retrieval](https://doi.org/10.1145/3583780.3615107)|Huaying Yuan, Zhicheng Dou, Yujia Zhou, Yu Guo, JiRong Wen|Engineering Research Center of Next-Generation Intelligent Search and Recommendation, Ministry of Education & Renmin University of China, Beijing, China; Renmin University of China, Beijing, China|Document retrieval has always been a crucial problem in Web search. Recent works leverage pre-trained language models to represent documents in dense vectors. However, these works focus on the textual content but ignore the appearance of web pages (e.g., the visual style, the layout, and the images), which are actually essential for information delivery. To alleviate this problem, we propose a new dense retrieval model, namely VILE, to incorporate visual features into document representations. However, because a web page is usually very large and contains diverse information, simply concatenating its textual and visual features may result in a cluttered multi-modal representation that lacks focus on the important parts of the page. We observe that web pages often have a structured content organization, comprising multiple blocks that convey different information. Motivated by the observation, we propose building a multi-modal document representation by aggregating the fine-grained multi-modal block representations, to enable a more comprehensive understanding of the page. Specifically, we first segment a web page into multiple blocks, then create multi-modal features for each block. %allowing for more effective capture of its content and visual information. The representations of all blocks are then integrated into the final multi-modal page representation. VILE can better model the importance of different content regions, leading to a high-quality multi-modal representation. We collect screenshots and the corresponding layout information of some web pages in the MS MARCO Document Ranking dataset, resulting in a new multi-modal document retrieval dataset. Experimental results conducted on this dataset demonstrate that our model exhibits significant improvements over existing document retrieval models. Our code is available at https://github.com/yhy-2000/VILE.|文献检索一直是网络搜索中的一个关键问题。最近的工作利用预训练语言模型来表示密集向量文档。然而，这些作品关注的是文本内容，而忽略了网页的外观(例如，视觉风格、布局和图像) ，这些实际上是信息传递所必需的。为了解决这一问题，我们提出了一种新的密集检索模型，即 VILE，将视觉特征融入到文档表示中。然而，由于一个网页通常非常大，并包含不同的信息，简单地连接其文本和视觉特征可能会导致一个混乱的多模态表示，缺乏重点页面的重要部分。我们观察到，网页通常有一个结构化内容的组织，由多个块组成，传达不同的信息。基于这种观察，我们提出通过聚合细粒度的多模态块表示来构建多模态文档表示，以便更全面地理解页面。具体来说，我们首先将网页分割成多个块，然后为每个块创建多模态特性。% ，以便更有效地捕捉其内容和视觉信息。然后将所有块的表示集成到最终的多模态页表示中。VILE 可以更好地为不同内容区域的重要性建模，从而实现高质量的多模态表示。我们收集了微软 MARCO 文件排名数据集中一些网页的截图和相应的布局信息，产生了一个新的多模式文献检索数据集。在这个数据集上进行的实验结果表明，我们的模型比现有的文献检索模型有显著的改进。我们的代码可以在 https://github.com/yhy-2000/vile 找到。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=VILE:+Block-Aware+Visual+Enhanced+Document+Retrieval)|0| |[MemoNet: Memorizing All Cross Features' Representations Efficiently via Multi-Hash Codebook Network for CTR Prediction](https://doi.org/10.1145/3583780.3614963)|Pengtao Zhang, Junlin Zhang|Sina Weibo, Beijing, China|New findings in natural language processing (NLP) demonstrate that the strong memorization capability contributes a lot to the success of Large Language Models (LLM). This inspires us to explicitly bring an independent memory mechanism into CTR ranking model to learn and memorize cross features' representations. In this paper, we propose multi-Hash Codebook NETwork (HCNet) as the memory mechanism for efficiently learning and memorizing representations of cross features in CTR tasks. HCNet uses a multi-hash codebook as the main memory place and the whole memory procedure consists of three phases: multi-hash addressing, memory restoring, and feature shrinking. We also propose a new CTR model named MemoNet which combines HCNet with a DNN backbone. Extensive experimental results on three public datasets and online test show that MemoNet reaches superior performance over state-of-the-art approaches. Besides, MemoNet shows scaling law of large language model in NLP, which means we can enlarge the size of the codebook in HCNet to sustainably obtain performance gains. Our work demonstrates the importance and feasibility of learning and memorizing representations of cross features, which sheds light on a new promising research direction. The source code is in https://github.com/ptzhangAlg/RecAlg.|自然语言处理(NLP)的新发现表明，强记忆能力对大语言模型(LLM)的成功有很大的贡献。这促使我们将独立的记忆机制明确地引入到 CTR 排名模型中，以便学习和记忆交叉特征的表示。本文提出了多哈希码书网络(HCNet)作为 CTR 任务中有效学习和记忆交叉特征表示的记忆机制。HCNet 使用多哈希码本作为主存储位置，整个存储过程由三个阶段组成: 多哈希寻址、存储恢复和特征收缩。我们还提出了一种新的 CTR 模型—— MemoNet，它将 HCNet 和 DNN 骨干网结合在一起。在三个公共数据集上的大量实验结果和在线测试表明，MemoNet 的性能优于最先进的方法。此外，MemoNet 在自然语言处理中显示了大语言模型的尺度规律，这意味着我们可以在 HCNet 中扩大码书的尺寸，以获得可持续的性能增益。我们的工作证明了学习和记忆交叉特征表征的重要性和可行性，这为一个新的有前途的研究方向指明了方向。源代码在 https://github.com/ptzhangalg/recalg 里。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=MemoNet:+Memorizing+All+Cross+Features'+Representations+Efficiently+via+Multi-Hash+Codebook+Network+for+CTR+Prediction)|0| |[FATA-Trans: Field And Time-Aware Transformer for Sequential Tabular Data](https://doi.org/10.1145/3583780.3614879)|Dongyu Zhang, Liang Wang, Xin Dai, Shubham Jain, Junpeng Wang, Yujie Fan, ChinChia Michael Yeh, Yan Zheng, Zhongfang Zhuang, Wei Zhang|Visa Research, Palo Alto, CA, USA; Worcester Polytechnic Institute, Worcester, MA, USA|Sequential tabular data is one of the most commonly used data types in real-world applications. Different from conventional tabular data, where rows in a table are independent, sequential tabular data contains rich contextual and sequential information, where some fields aredynamically changing over time and others arestatic. Existing transformer-based approaches analyzing sequential tabular data overlook the differences between dynamic and static fields by replicating and filling static fields into each record, and ignore temporal information between rows, which leads to three major disadvantages: (1) computational overhead, (2) artificially simplified data for masked language modeling pre-training task that may yield less meaningful representations, and (3) disregarding the temporal behavioral patterns implied by time intervals. In this work, we propose FATA-Trans, a model with two field transformers for modeling sequential tabular data, where each processes static and dynamic field information separately. FATA-Trans isfield - andtime -aware for sequential tabular data. Thefield -type embedding in the method enables FATA-Trans to capture differences between static and dynamic fields. Thetime -aware position embedding exploits both order and time interval information between rows, which helps the model detect underlying temporal behavior in a sequence. Our experiments on three benchmark datasets demonstrate that the learned representations from FATA-Trans consistently outperform state-of-the-art solutions in the downstream tasks. We also present visualization studies to highlight the insights captured by the learned representations, enhancing our understanding of the underlying data. Our codes are available at https://github.com/zdy93/FATA-Trans.|序列表数据是实际应用中最常用的数据类型之一。与传统的表格数据(表中的行是独立的)不同，顺序表格数据包含丰富的上下文和顺序信息，其中一些字段随时间动态变化，其他字段是静态的。现有的基于转换器的序列表格数据分析方法通过在每个记录中复制和填充静态字段忽略了动态字段和静态字段之间的差异，忽略了行之间的时间信息，这导致了三个主要的缺点: (1)计算开销，(2)人为地简化了掩蔽语言建模预训练任务的数据，这可能产生意义不大的表示，(3)忽略了时间间隔隐含的时间行为模式。在这项工作中，我们提出了 FATA-Trans 模型，一个有两个场变换器的模型用于建模顺序表格数据，其中每个场分别处理静态和动态的场信息。FATA-用于顺序表格数据的 Trans isfield-and time-aware。该方法中的字段类型嵌入使 FATA-Trans 能够捕获静态字段和动态字段之间的差异。时间感知位置嵌入利用行之间的顺序和时间间隔信息，帮助模型检测序列中潜在的时间行为。我们在三个基准数据集上的实验表明，FATA-Trans 的学习表示在下游任务中始终优于最先进的解决方案。我们还提出可视化研究，以突出所获得的见解表示，提高了我们对基础数据的理解。我们的代码可以在 https://github.com/zdy93/fata-trans 找到。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=FATA-Trans:+Field+And+Time-Aware+Transformer+for+Sequential+Tabular+Data)|0| |[Efficient Exact Minimum k-Core Search in Real-World Graphs](https://doi.org/10.1145/3583780.3614861)|Qifan Zhang, Shengxin Liu|Harbin Institute of Technology, Shenzhen, Shenzhen, China|The k-core, which refers to the induced subgraph with a minimum degree of at least k, is widely used in cohesive subgraph discovery and has various applications. However, the k-core in real-world graphs tends to be extremely large, which hinders its effectiveness in practical applications. This challenge has motivated researchers to explore a variant of the k-core problem known as the minimum k-core search problem. This problem has been proven to be NP-Hard, and most of the existing studies naturally either deal with approximate solutions or suffer from inefficiency in practice. In this paper, we focus on designing efficient exact algorithms for the minimum k-core search problem. In particular, we develop an iterative-based framework that decomposes an instance of the minimum k-core search problem into a list of problem instances on another well-structured graph pattern. Based on this framework, we propose an iterative-based branch-and-bound algorithm, namely IBB, with additional pruning and reduction techniques. We show that, with a n-vertex graph, IBB runs in cn nO(1) time for some c < 2, achieving better theoretical performance than the trivial bound of 2n nO(1). Finally, our experiments on real-world graphs demonstrate that IBB is up to three orders of magnitude faster than the state-of-the-art algorithms on real-world datasets.|K 核是指至少 k 个最小度的诱导子图，广泛应用于内聚子图的发现，具有多种应用。然而，现实世界图中的 k 核往往非常大，这阻碍了它在实际应用中的有效性。这一挑战促使研究人员探索一种称为最小 k 核搜索问题的 k 核问题的变体。这个问题已经被证明是 NP 难的，现有的大多数研究要么自然地处理近似解，要么在实际应用中效率低下。本文主要研究最小 k 核搜索问题的高效精确算法设计。特别地，我们开发了一个基于迭代的框架，该框架将最小 k 核搜索问题的一个实例分解为另一个结构良好的图模式上的问题实例列表。在此框架的基础上，提出了一种基于迭代的分枝定界算法 IBB，该算法采用了附加的剪枝和约简技术。我们证明了，使用 n 个顶点图，IBB 在 cnnO (1)时间内运行一些 c < 2，比2nnO (1)的平凡界取得了更好的理论性能。最后，我们在真实世界图表上的实验表明，IBB 比现实世界数据集上的最先进算法快了三个数量级。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Efficient+Exact+Minimum+k-Core+Search+in+Real-World+Graphs)|0| |[Decentralized Graph Neural Network for Privacy-Preserving Recommendation](https://doi.org/10.1145/3583780.3614834)|Xiaolin Zheng, Zhongyu Wang, Chaochao Chen, Jiashu Qian, Yao Yang|Zhejiang Lab, Hangzhou, China; Zhejiang University, Hangzhou, China|Building a graph neural network (GNN)-based recommender system without violating user privacy proves challenging. Existing methods can be divided into federated GNNs and decentralized GNNs. But both methods have undesirable effects, i.e., low communication efficiency and privacy leakage. This paper proposes DGREC, a novel decentralized GNN for privacy-preserving recommendations, where users can choose to publicize their interactions. It includes three stages, i.e., graph construction, local gradient calculation, and global gradient passing. The first stage builds a local inner-item hypergraph for each user and a global inter-user graph. The second stage models user preference and calculates gradients on each local device. The third stage designs a local differential privacy mechanism named secure gradient-sharing, which proves strong privacy-preserving of users' private data. We conduct extensive experiments on three public datasets to validate the consistent superiority of our framework.|在不侵犯用户隐私的情况下构建一个基于图形神经网络(GNN)的推荐系统是具有挑战性的。现有的方法可以分为联邦 GNN 和分散 GNN。但这两种方法都存在通信效率低、隐私泄露等不良后果。本文提出了 DGREC，一种新型的用于隐私保护建议的分散式 GNN，用户可以选择在其中公开他们的交互。它包括图的构造、局部梯度计算和全局梯度传递三个阶段。第一阶段为每个用户构建一个本地内部项超图和一个全局用户间图。第二阶段建立用户偏好模型，并计算每个本地设备上的渐变。第三阶段设计了一种本地差分隐私机制——安全梯度共享机制，该机制对用户的私人数据具有很强的隐私保护能力。我们在三个公共数据集上进行了广泛的实验，以验证我们的框架的一致优越性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Decentralized+Graph+Neural+Network+for+Privacy-Preserving+Recommendation)|0| -|[FedPSE: Personalized Sparsification with Element-wise Aggregation for Federated Learning](https://doi.org/10.1145/3583780.3614882)|Longfei Zheng, Yingting Liu, Xiaolong Xu, Chaochao Chen, Yuzhou Tang, Lei Wang, Xiaolong Hu|; University of Science and Technology of China; University of Chinese Academy of Sciences; Shanghai Jiaotong University; Zhejiang University; University of Hong Kong|Federated learning (FL) is a popular distributed machine learning framework in which clients aggregate models' parameters instead of sharing their individual data. In FL, clients communicate with the server under limited network bandwidth frequently, which arises the communication challenge. To resolve this challenge, multiple compression methods have been proposed to reduce the transmitted parameters. However, these techniques show that the federated performance degrades significantly with Non-IID (non-identically independently distributed) datasets. To address this issue, we propose an effective method, called FedPSE, which solves the efficiency challenge of FL with heterogeneous data. FedPSE compresses the local updates on clients using Top-K sparsification and aggregates these updates on the server by element-wise average. Then clients download the personalized sparse updates from the server to update their individual local models. We then theoretically analyze the convergence of FedPSE under the non-convex setting. Moreover, extensive experiments on four benchmark tasks demonstrate that our FedPSE outperforms the state-of-the-art methods on Non-IID datasets in terms of both efficiency and accuracy.|联邦学习(FL)是一种流行的分布式机器学习框架，客户端聚合模型参数而不是共享个体数据。在 FL 中，客户端频繁地在有限的网络带宽下与服务器进行通信，这给通信带来了挑战。为了解决这一问题，人们提出了多种压缩方法来减少传输参数。但是，这些技术表明，使用 Non-IID (非相同独立分布的)数据集时，联邦性能会显著下降。为了解决这个问题，我们提出了一种有效的方法，称为 FedPSE，它解决了异构数据的 FL 的效率挑战。FedPSE 使用 Top-K 稀疏化压缩客户机上的本地更新，并按照元素的平均值聚合服务器上的这些更新。然后客户端从服务器下载个性化稀疏更新，以更新各自的本地模型。然后从理论上分析了 FedPSE 在非凸集下的收敛性。此外，在四个基准任务上的大量实验表明，我们的 FedPSE 在效率和准确性方面都优于非 IID 数据集上的最新方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=FedPSE:+Personalized+Sparsification+with+Element-wise+Aggregation+for+Federated+Learning)|0| -|[Contrastive Counterfactual Learning for Causality-aware Interpretable Recommender Systems](https://doi.org/10.1145/3583780.3614823)|Guanglin Zhou, Chengkai Huang, Xiaocong Chen, Xiwei Xu, Chen Wang, Liming Zhu, Lina Yao|Data61, CSIRO, Eveleigh, Australia; Data61, CSIRO & The University of New South Wales, Eveleigh & Sydney, Australia; The University of New South Wales, Sydney, NSW, Australia|The field of generating recommendations within the framework of causal inference has seen a recent surge.This approach enhances insights into the influence of recommendations on user behavior and helps in identifying the underlying factors. Existing research has often leveraged propensity scores to mitigate bias, albeit at the risk of introducing additional variance. Others have explored the use of unbiased data from randomized controlled trials, although this comes with assumptions that may prove challenging in practice. In this paper, we first present the causality-aware interpretation of recommendations and reveal how the underlying exposure mechanism can bias the maximum likelihood estimation (MLE) of observational feedback. Recognizing that confounders may be elusive, we propose a contrastive self-supervised learning to minimize exposure bias, employing inverse propensity scores and expanding the positive sample set. Building on this foundation, we present a novel contrastive counterfactual learning method (CCL) that incorporates three unique positive sampling strategies grounded in estimated exposure probability or random counterfactual samples. Through extensive experiments on two real-world datasets, we demonstrate that our CCL outperforms the state-of-the-art methods.|在因果推理框架内提出建议的领域最近出现了激增。这种方法增强了对推荐对用户行为的影响的洞察力，并有助于识别潜在的因素。现有的研究经常利用倾向分数来减轻偏差，尽管有引入额外差异的风险。其他人已经探索了随机对照试验中无偏倚数据的使用，尽管这些假设在实践中可能被证明是具有挑战性的。在本文中，我们首先提出了因果关系意识的解释建议，并揭示了如何潜在的暴露机制可以偏差的最大似然估计(MLE)的观察反馈。认识到混杂因素可能是难以捉摸的，我们提出一个对比的自我监督学习，以尽量减少暴露偏差，使用逆倾向得分和扩大正样本集。在此基础上，我们提出了一种新的对比反事实学习方法(CCL) ，该方法结合了基于估计暴露概率或随机反事实样本的三种独特的正抽样策略。通过在两个实际数据集上的大量实验，我们证明了我们的 CCL 优于最先进的方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Contrastive+Counterfactual+Learning+for+Causality-aware+Interpretable+Recommender+Systems)|0| +|[FedPSE: Personalized Sparsification with Element-wise Aggregation for Federated Learning](https://doi.org/10.1145/3583780.3614882)|Longfei Zheng, Yingting Liu, Xiaolong Xu, Chaochao Chen, Yuzhou Tang, Lei Wang, Xiaolong Hu|; Zhejiang University; University of Hong Kong; University of Science and Technology of China; Shanghai Jiaotong University; University of Chinese Academy of Sciences|Federated learning (FL) is a popular distributed machine learning framework in which clients aggregate models' parameters instead of sharing their individual data. In FL, clients communicate with the server under limited network bandwidth frequently, which arises the communication challenge. To resolve this challenge, multiple compression methods have been proposed to reduce the transmitted parameters. However, these techniques show that the federated performance degrades significantly with Non-IID (non-identically independently distributed) datasets. To address this issue, we propose an effective method, called FedPSE, which solves the efficiency challenge of FL with heterogeneous data. FedPSE compresses the local updates on clients using Top-K sparsification and aggregates these updates on the server by element-wise average. Then clients download the personalized sparse updates from the server to update their individual local models. We then theoretically analyze the convergence of FedPSE under the non-convex setting. Moreover, extensive experiments on four benchmark tasks demonstrate that our FedPSE outperforms the state-of-the-art methods on Non-IID datasets in terms of both efficiency and accuracy.|联邦学习(FL)是一种流行的分布式机器学习框架，客户端聚合模型参数而不是共享个体数据。在 FL 中，客户端频繁地在有限的网络带宽下与服务器进行通信，这给通信带来了挑战。为了解决这一问题，人们提出了多种压缩方法来减少传输参数。但是，这些技术表明，使用 Non-IID (非相同独立分布的)数据集时，联邦性能会显著下降。为了解决这个问题，我们提出了一种有效的方法，称为 FedPSE，它解决了异构数据的 FL 的效率挑战。FedPSE 使用 Top-K 稀疏化压缩客户机上的本地更新，并按照元素的平均值聚合服务器上的这些更新。然后客户端从服务器下载个性化稀疏更新，以更新各自的本地模型。然后从理论上分析了 FedPSE 在非凸集下的收敛性。此外，在四个基准任务上的大量实验表明，我们的 FedPSE 在效率和准确性方面都优于非 IID 数据集上的最新方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=FedPSE:+Personalized+Sparsification+with+Element-wise+Aggregation+for+Federated+Learning)|0| +|[Contrastive Counterfactual Learning for Causality-aware Interpretable Recommender Systems](https://doi.org/10.1145/3583780.3614823)|Guanglin Zhou, Chengkai Huang, Xiaocong Chen, Xiwei Xu, Chen Wang, Liming Zhu, Lina Yao|The University of New South Wales, Sydney, NSW, Australia; Data61, CSIRO & The University of New South Wales, Eveleigh & Sydney, Australia; Data61, CSIRO, Eveleigh, Australia|The field of generating recommendations within the framework of causal inference has seen a recent surge.This approach enhances insights into the influence of recommendations on user behavior and helps in identifying the underlying factors. Existing research has often leveraged propensity scores to mitigate bias, albeit at the risk of introducing additional variance. Others have explored the use of unbiased data from randomized controlled trials, although this comes with assumptions that may prove challenging in practice. In this paper, we first present the causality-aware interpretation of recommendations and reveal how the underlying exposure mechanism can bias the maximum likelihood estimation (MLE) of observational feedback. Recognizing that confounders may be elusive, we propose a contrastive self-supervised learning to minimize exposure bias, employing inverse propensity scores and expanding the positive sample set. Building on this foundation, we present a novel contrastive counterfactual learning method (CCL) that incorporates three unique positive sampling strategies grounded in estimated exposure probability or random counterfactual samples. Through extensive experiments on two real-world datasets, we demonstrate that our CCL outperforms the state-of-the-art methods.|在因果推理框架内提出建议的领域最近出现了激增。这种方法增强了对推荐对用户行为的影响的洞察力，并有助于识别潜在的因素。现有的研究经常利用倾向分数来减轻偏差，尽管有引入额外差异的风险。其他人已经探索了随机对照试验中无偏倚数据的使用，尽管这些假设在实践中可能被证明是具有挑战性的。在本文中，我们首先提出了因果关系意识的解释建议，并揭示了如何潜在的暴露机制可以偏差的最大似然估计(MLE)的观察反馈。认识到混杂因素可能是难以捉摸的，我们提出一个对比的自我监督学习，以尽量减少暴露偏差，使用逆倾向得分和扩大正样本集。在此基础上，我们提出了一种新的对比反事实学习方法(CCL) ，该方法结合了基于估计暴露概率或随机反事实样本的三种独特的正抽样策略。通过在两个实际数据集上的大量实验，我们证明了我们的 CCL 优于最先进的方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Contrastive+Counterfactual+Learning+for+Causality-aware+Interpretable+Recommender+Systems)|0| |[Personalized Location-Preference Learning for Federated Task Assignment in Spatial Crowdsourcing](https://doi.org/10.1145/3583780.3615008)|Xiaolong Zhong, Hao Miao, Dazhuo Qiu, Yan Zhao, Kai Zheng|Aalborg University, Aalborg, Denmark; University of Electronic Science and Technology of China, Chengdu, China|With the proliferation of wireless and mobile devices, Spatial Crowdsourcing (SC) attracts increasing attention, where task assignment plays a critically important role. However, recent task assignment solutions in SC often assume that data is stored in a central station while ignoring the issue of privacy leakage. To enable decentralized training and privacy protection, we propose a federated task assignment framework with personalized location-preference learning, which performs efficient task assignment while keeping the data decentralized and private in each platform center (e.g., a delivery center of an SC company). The framework consists of two phases: personalized federated location-preference learning and task assignment. Specifically, in the first phase, we design a personalized location-preference learning model for each platform center by simultaneously considering the location information and data heterogeneity across platform centers. Based on workers' location preference, the task assignment phase aims to achieve effective and efficient task assignment by means of the Kuhn-Munkres (KM) algorithm and the newly proposed conditional degree-reduction algorithm. Extensive experiments on real-world data show the effectiveness of the proposed framework.|随着无线和移动设备的普及，空间众包越来越受到人们的关注，其中任务分配起着至关重要的作用。然而，最近 SC 中的任务分配解决方案往往假设数据存储在中央站，而忽略了隐私泄露问题。为了实现分散式训练和隐私保护，提出了一种基于个性化位置偏好学习的联邦任务分配框架，该框架在保持平台中心(如 SC 公司的交付中心)数据分散和私有的同时，实现了高效的任务分配。该框架包括两个阶段: 个性化联邦位置偏好学习和任务分配。具体来说，在第一阶段，我们同时考虑了平台中心之间的位置信息和数据异构性，为每个平台中心设计了一个个性化的位置偏好学习模型。任务分配阶段基于工人的位置偏好，采用 Kuhn-Munkres (KM)算法和新提出的条件降度算法实现高效的任务分配。对实际数据的大量实验表明了该框架的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Personalized+Location-Preference+Learning+for+Federated+Task+Assignment+in+Spatial+Crowdsourcing)|0| -|[Improving Adversarial Transferability via Frequency-based Stationary Point Search](https://doi.org/10.1145/3583780.3614927)|Zhiyu Zhu, Huaming Chen, Jiayu Zhang, Xinyi Wang, Zhibo Jin, Qinghua Lu, Jun Shen, KimKwang Raymond Choo|Suzhou Yierqi, Suzhou, China; SCIT, University of Wollongong, Australia, Wollongong, Australia; Jiangsu University, Zhenjiang, China; The University of Sydney, Sydney, NSW, Australia; Data61, CSIRO, Sydney, NSW, Australia; University of Texas at San Antonio, San Antonio, TX, USA|Deep neural networks (DNNs) have been shown vulnerable to interference from adversarial samples, leading to erroneous predictions. Investigating adversarial attacks can effectively improve the reliability as well as the performance of deep neural models in real-world applications. Since it is generally challenging to infer the parameters in black-box models, high transferability becomes an important factor for the success rate of an attack method. Recently, the Spectrum Simulation Attack method exhibits promising results based on the frequency domain. In light of SSA, we propose a novel attack approach in this paper, which achieves the best results among diverse state-of-the-art transferable adversarial attack methods. Our method aims to find a stationary point, which extends the ability to find multiple local optima with the optimal local attack effect. After finding the stationary point, a frequency-based search is employed to explore the best adversarial samples in the neighbouring space, utilmately determining the final adversarial direction. We compare our method against a variety of cutting-edge transferable adversarial methods. Extensive experiments validate that our method improves the attack success rate by 4.7% for conventionally trained models and 53.1% for adversarially trained models. Our code is available at https://github.com/LMBTough/FSPS|深层神经网络(DNN)已被证明易受敌对样本的干扰，从而导致错误的预测。研究对手攻击可以有效地提高深度神经网络模型在现实应用中的可靠性和性能。由于黑盒模型中的参数推断通常具有挑战性，因此高可转移性成为影响攻击方法成功率的一个重要因素。近年来，基于频域的频谱仿真攻击方法取得了很好的效果。针对 SSA 的特点，本文提出了一种新的攻击方法，在各种最新的可转移对手攻击方法中取得了最好的效果。我们的方法的目标是找到一个驻点，扩展了寻找具有最佳局部攻击效果的多个局部最优解的能力。在找到驻点后，使用基于频率的搜索来探索邻近空间中最好的对手样本，最终确定最终的对手方向。我们将我们的方法与各种尖端的可转移的对抗性方法进行比较。大量的实验验证了该方法对传统训练模型的攻击成功率提高了4.7% ，对对抗训练模型的攻击成功率提高了53.1% 。我们的代码可以在 https://github.com/lmbtough/fsps 找到|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Improving+Adversarial+Transferability+via+Frequency-based+Stationary+Point+Search)|0| +|[Improving Adversarial Transferability via Frequency-based Stationary Point Search](https://doi.org/10.1145/3583780.3614927)|Zhiyu Zhu, Huaming Chen, Jiayu Zhang, Xinyi Wang, Zhibo Jin, Qinghua Lu, Jun Shen, KimKwang Raymond Choo|Data61, CSIRO, Sydney, NSW, Australia; University of Texas at San Antonio, San Antonio, TX, USA; SCIT, University of Wollongong, Australia, Wollongong, Australia; Suzhou Yierqi, Suzhou, China; The University of Sydney, Sydney, NSW, Australia; Jiangsu University, Zhenjiang, China|Deep neural networks (DNNs) have been shown vulnerable to interference from adversarial samples, leading to erroneous predictions. Investigating adversarial attacks can effectively improve the reliability as well as the performance of deep neural models in real-world applications. Since it is generally challenging to infer the parameters in black-box models, high transferability becomes an important factor for the success rate of an attack method. Recently, the Spectrum Simulation Attack method exhibits promising results based on the frequency domain. In light of SSA, we propose a novel attack approach in this paper, which achieves the best results among diverse state-of-the-art transferable adversarial attack methods. Our method aims to find a stationary point, which extends the ability to find multiple local optima with the optimal local attack effect. After finding the stationary point, a frequency-based search is employed to explore the best adversarial samples in the neighbouring space, utilmately determining the final adversarial direction. We compare our method against a variety of cutting-edge transferable adversarial methods. Extensive experiments validate that our method improves the attack success rate by 4.7% for conventionally trained models and 53.1% for adversarially trained models. Our code is available at https://github.com/LMBTough/FSPS|深层神经网络(DNN)已被证明易受敌对样本的干扰，从而导致错误的预测。研究对手攻击可以有效地提高深度神经网络模型在现实应用中的可靠性和性能。由于黑盒模型中的参数推断通常具有挑战性，因此高可转移性成为影响攻击方法成功率的一个重要因素。近年来，基于频域的频谱仿真攻击方法取得了很好的效果。针对 SSA 的特点，本文提出了一种新的攻击方法，在各种最新的可转移对手攻击方法中取得了最好的效果。我们的方法的目标是找到一个驻点，扩展了寻找具有最佳局部攻击效果的多个局部最优解的能力。在找到驻点后，使用基于频率的搜索来探索邻近空间中最好的对手样本，最终确定最终的对手方向。我们将我们的方法与各种尖端的可转移的对抗性方法进行比较。大量的实验验证了该方法对传统训练模型的攻击成功率提高了4.7% ，对对抗训练模型的攻击成功率提高了53.1% 。我们的代码可以在 https://github.com/lmbtough/fsps 找到|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Improving+Adversarial+Transferability+via+Frequency-based+Stationary+Point+Search)|0| |[Counterfactual Graph Augmentation for Consumer Unfairness Mitigation in Recommender Systems](https://doi.org/10.1145/3583780.3615165)|Ludovico Boratto, Francesco Fabbri, Gianni Fenu, Mirko Marras, Giacomo Medda|University of Cagliari, Cagliari, Italy; Spotify, Barcelona, Spain|In recommendation literature, explainability and fairness are becoming two prominent perspectives to consider. However, prior works have mostly addressed them separately, for instance by explaining to consumers why a certain item was recommended or mitigating disparate impacts in recommendation utility. None of them has leveraged explainability techniques to inform unfairness mitigation. In this paper, we propose an approach that relies on counterfactual explanations to augment the set of user-item interactions, such that using them while inferring recommendations leads to fairer outcomes. Modeling user-item interactions as a bipartite graph, our approach augments the latter by identifying new user-item edges that not only can explain the original unfairness by design, but can also mitigate it. Experiments on two public data sets show that our approach effectively leads to a better trade-off between fairness and recommendation utility compared with state-of-the-art mitigation procedures. We further analyze the characteristics of added edges to highlight key unfairness patterns. Source code available at https://github.com/jackmedda/RS-BGExplainer/tree/cikm2023.|在推荐文献中，可解释性和公平性正成为两个需要考虑的重要方面。然而，以前的工作大多是单独处理这些问题，例如向消费者解释为什么要推荐某个项目，或者减轻推荐实用程序中的不同影响。它们都没有利用可解释性技术来缓解不公平性。在本文中，我们提出了一种方法，依赖于反事实的解释，以增加用户项目的交互集，使用他们，而推断建议导致更公平的结果。将用户-项目交互建模为二分图，我们的方法通过识别新的用户-项目边缘来扩展后者，这些边缘不仅可以通过设计来解释原始的不公平，而且还可以减轻它。在两个公共数据集上的实验表明，与最先进的缓解过程相比，我们的方法有效地在公平性和推荐效用之间取得了更好的平衡。我们进一步分析了附加边缘的特征，以突出关键的不公平模式。源代码可在 https://github.com/jackmedda/rs-bgexplainer/tree/cikm2023下载。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Counterfactual+Graph+Augmentation+for+Consumer+Unfairness+Mitigation+in+Recommender+Systems)|0| |[Region-Wise Attentive Multi-View Representation Learning For Urban Region Embedding](https://doi.org/10.1145/3583780.3615194)|Weiliang Chan, Qianqian Ren||Urban region embedding is an important and yet highly challenging issue due to the complexity and constantly changing nature of urban data. To address the challenges, we propose a Region-Wise Multi-View Representation Learning (ROMER) to capture multi-view dependencies and learn expressive representations of urban regions without the constraints of rigid neighbourhood region conditions. Our model focus on learn urban region representation from multi-source urban data. First, we capture the multi-view correlations from mobility flow patterns, POI semantics and check-in dynamics. Then, we adopt global graph attention networks to learn similarity of any two vertices in graphs. To comprehensively consider and share features of multiple views, a two-stage fusion module is further proposed to learn weights with external attention to fuse multi-view embeddings. Extensive experiments for two downstream tasks on real-world datasets demonstrate that our model outperforms state-of-the-art methods by up to 17\% improvement.|由于城市数据的复杂性和不断变化的性质，城市区域嵌入是一个重要而又极具挑战性的问题。为了应对这些挑战，我们提出了一种区域智能多视图表示学习(ROMER)方法来捕获多视图依赖关系，并在不受严格的邻近区域条件约束的情况下学习城市地区的表示。我们的模型侧重于从多源城市数据中学习城市区域表示。首先，我们从移动流模式、 POI 语义和签入动态中获取多视图相关性。然后，我们采用全局图注意网络来学习图中任意两个顶点的相似性。为了综合考虑和共享多视图的特征，进一步提出了一个两阶段融合模块，该模块通过学习权值和外部注意力来融合多视图嵌入。对真实世界数据集上的两个下游任务进行的大量实验表明，我们的模型比最先进的方法提高了17% 。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Region-Wise+Attentive+Multi-View+Representation+Learning+For+Urban+Region+Embedding)|0| -|['Choose your Data Wisely': Active Learning based Selection with Multi-Objective Optimisation for Mitigating Stereotypes](https://doi.org/10.1145/3583780.3615261)|Manish Chandra, Debasis Ganguly, Tulika Saha, Iadh Ounis|University of Glasgow, Glasgow, United Kingdom; University of Liverpool, Liverpool, United Kingdom|Data-driven (deep) learning methods has led to parameterised abstractions of the data, often leading to stereotype societal biases in their predictions, e.g., predicting more frequently that women are weaker than men, or that African Americans are more likely to commit crimes than Caucasians. Standard approaches of mitigating such stereotypical biases from deep neural models include modifying the training dataset (pre-processing), or adjusting the model parameters with a bias-specific objective (in-processing). In our work, we approach this bias mitigation from a different perspective - that of an active learning-based selection of a subset of data instances towards training a model optimised for both effectiveness and fairness. Specifically speaking, the imbalances in the attribute value priors can be alleviated by constructing a balanced subset of the data instances with two selection objectives - first, of improving the model confidence of the primary task itself (a standard practice in active learning), and the second, of taking into account the parity of the model predictions with respect to the sensitive attributes, such as gender and race etc. We demonstrate that our proposed selection function achieves better results in terms of both the primary task effectiveness and fairness. The results are further shown to improve when this active learning-based data selection is combined with an in-process method of multi-objective training.|数据驱动(深度)学习方法已经导致数据的参数化抽象，往往导致他们的预测中的刻板的社会偏见，例如，更频繁地预测女性比男性弱，或非裔美国人比白种人更有可能犯罪。从深度神经模型中减轻这种定型偏差的标准方法包括修改训练数据集(预处理) ，或者用偏差特定的目标(在处理中)调整模型参数。在我们的工作中，我们从一个不同的角度来处理这种偏差缓解-这是一个积极的基于学习的选择一个子集的数据实例，以训练一个模型优化的有效性和公平性。具体来说，可以通过构建一个具有两个选择目标的数据实例的平衡子集来缓解属性值先验的不平衡——第一，提高主要任务本身的模型置信度(主动学习的标准实践) ，第二，考虑模型预测相对于敏感属性的平等性，如性别和种族等。实验结果表明，本文提出的选择函数在主要任务有效性和公平性方面都取得了较好的效果。结果进一步表明，当这种基于主动学习的数据选择与过程中的多目标训练方法相结合时，结果得到了改善。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q='Choose+your+Data+Wisely':+Active+Learning+based+Selection+with+Multi-Objective+Optimisation+for+Mitigating+Stereotypes)|0| -|[MI-DPG: Decomposable Parameter Generation Network Based on Mutual Information for Multi-Scenario Recommendation](https://doi.org/10.1145/3583780.3615223)|Wenzhuo Cheng, Ke Ding, Xin Dong, Yong He, Liang Zhang, Linjian Mo|Ant Group, Shanghai, China; Ant Group, Hangzhou, China|Conversion rate (CVR) prediction models play a vital role in recommendation systems. Recent research shows that learning a unified model to serve multiple scenarios is effective for improving overall performance. However, it remains challenging to improve model prediction performance across scenarios at low model parameter cost, and current solutions are hard to robustly model multi-scenario diversity. In this paper, we propose MI-DPG for the multi-scenario CVR prediction, which learns scenario-conditioned dynamic model parameters for each scenario in a more efficient and effective manner. Specifically, we introduce an auxiliary network to generate scenario-conditioned dynamic weighting matrices, which are obtained by combining decomposed scenario-specific and scenario-shared low-rank matrices with parameter efficiency. For each scenario, weighting the backbone model parameters by the weighting matrix helps to specialize the model parameters for different scenarios. It can not only modulate the complete parameter space of the backbone model but also improve the model effectiveness. Furthermore, we design a mutual information regularization to enhance the diversity of model parameters across scenarios by maximizing the mutual information between the scenario-aware input and the scenario-conditioned dynamic weighting matrix. Experiments from three real-world datasets show that MI-DPG outperforms previous multi-scenario recommendation models.|转化率(CVR)预测模型在推荐系统中起着至关重要的作用。最近的研究表明，学习一个统一的模型来服务于多种场景对于提高整体性能是有效的。然而，在模型参数成本较低的情景下提高模型预测性能仍然是一个挑战，目前的解决方案难以对多情景多样性进行稳健的模型建模。本文提出了多情景 CVR 预测的 MI-DPG 方法，该方法能够更有效地学习每个情景的情景条件动态模型参数。具体地说，我们引入了一个辅助网络来生成情景条件下的动态权重矩阵，这些权重矩阵是通过将分解的情景特定的和情景共享的低秩矩阵与参数有效性相结合而得到的。对于每个场景，通过加权矩阵对骨干模型参数进行加权，有助于为不同场景专门化模型参数。它不仅可以调节骨干模型的完整参数空间，而且可以提高模型的有效性。此外，我们设计了一个互信息正则化方案，通过最大化情景感知输入和情景条件下的动态加权矩阵之间的互信息来增强不同情景下模型参数的多样性。通过对三个实际数据集的实验表明，MI-DPG 的性能优于以往的多场景推荐模型。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=MI-DPG:+Decomposable+Parameter+Generation+Network+Based+on+Mutual+Information+for+Multi-Scenario+Recommendation)|0| +|['Choose your Data Wisely': Active Learning based Selection with Multi-Objective Optimisation for Mitigating Stereotypes](https://doi.org/10.1145/3583780.3615261)|Manish Chandra, Debasis Ganguly, Tulika Saha, Iadh Ounis|University of Liverpool, Liverpool, United Kingdom; University of Glasgow, Glasgow, United Kingdom|Data-driven (deep) learning methods has led to parameterised abstractions of the data, often leading to stereotype societal biases in their predictions, e.g., predicting more frequently that women are weaker than men, or that African Americans are more likely to commit crimes than Caucasians. Standard approaches of mitigating such stereotypical biases from deep neural models include modifying the training dataset (pre-processing), or adjusting the model parameters with a bias-specific objective (in-processing). In our work, we approach this bias mitigation from a different perspective - that of an active learning-based selection of a subset of data instances towards training a model optimised for both effectiveness and fairness. Specifically speaking, the imbalances in the attribute value priors can be alleviated by constructing a balanced subset of the data instances with two selection objectives - first, of improving the model confidence of the primary task itself (a standard practice in active learning), and the second, of taking into account the parity of the model predictions with respect to the sensitive attributes, such as gender and race etc. We demonstrate that our proposed selection function achieves better results in terms of both the primary task effectiveness and fairness. The results are further shown to improve when this active learning-based data selection is combined with an in-process method of multi-objective training.|数据驱动(深度)学习方法已经导致数据的参数化抽象，往往导致他们的预测中的刻板的社会偏见，例如，更频繁地预测女性比男性弱，或非裔美国人比白种人更有可能犯罪。从深度神经模型中减轻这种定型偏差的标准方法包括修改训练数据集(预处理) ，或者用偏差特定的目标(在处理中)调整模型参数。在我们的工作中，我们从一个不同的角度来处理这种偏差缓解-这是一个积极的基于学习的选择一个子集的数据实例，以训练一个模型优化的有效性和公平性。具体来说，可以通过构建一个具有两个选择目标的数据实例的平衡子集来缓解属性值先验的不平衡——第一，提高主要任务本身的模型置信度(主动学习的标准实践) ，第二，考虑模型预测相对于敏感属性的平等性，如性别和种族等。实验结果表明，本文提出的选择函数在主要任务有效性和公平性方面都取得了较好的效果。结果进一步表明，当这种基于主动学习的数据选择与过程中的多目标训练方法相结合时，结果得到了改善。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q='Choose+your+Data+Wisely':+Active+Learning+based+Selection+with+Multi-Objective+Optimisation+for+Mitigating+Stereotypes)|0| +|[MI-DPG: Decomposable Parameter Generation Network Based on Mutual Information for Multi-Scenario Recommendation](https://doi.org/10.1145/3583780.3615223)|Wenzhuo Cheng, Ke Ding, Xin Dong, Yong He, Liang Zhang, Linjian Mo|Ant Group, Hangzhou, China; Ant Group, Shanghai, China|Conversion rate (CVR) prediction models play a vital role in recommendation systems. Recent research shows that learning a unified model to serve multiple scenarios is effective for improving overall performance. However, it remains challenging to improve model prediction performance across scenarios at low model parameter cost, and current solutions are hard to robustly model multi-scenario diversity. In this paper, we propose MI-DPG for the multi-scenario CVR prediction, which learns scenario-conditioned dynamic model parameters for each scenario in a more efficient and effective manner. Specifically, we introduce an auxiliary network to generate scenario-conditioned dynamic weighting matrices, which are obtained by combining decomposed scenario-specific and scenario-shared low-rank matrices with parameter efficiency. For each scenario, weighting the backbone model parameters by the weighting matrix helps to specialize the model parameters for different scenarios. It can not only modulate the complete parameter space of the backbone model but also improve the model effectiveness. Furthermore, we design a mutual information regularization to enhance the diversity of model parameters across scenarios by maximizing the mutual information between the scenario-aware input and the scenario-conditioned dynamic weighting matrix. Experiments from three real-world datasets show that MI-DPG outperforms previous multi-scenario recommendation models.|转化率(CVR)预测模型在推荐系统中起着至关重要的作用。最近的研究表明，学习一个统一的模型来服务于多种场景对于提高整体性能是有效的。然而，在模型参数成本较低的情景下提高模型预测性能仍然是一个挑战，目前的解决方案难以对多情景多样性进行稳健的模型建模。本文提出了多情景 CVR 预测的 MI-DPG 方法，该方法能够更有效地学习每个情景的情景条件动态模型参数。具体地说，我们引入了一个辅助网络来生成情景条件下的动态权重矩阵，这些权重矩阵是通过将分解的情景特定的和情景共享的低秩矩阵与参数有效性相结合而得到的。对于每个场景，通过加权矩阵对骨干模型参数进行加权，有助于为不同场景专门化模型参数。它不仅可以调节骨干模型的完整参数空间，而且可以提高模型的有效性。此外，我们设计了一个互信息正则化方案，通过最大化情景感知输入和情景条件下的动态加权矩阵之间的互信息来增强不同情景下模型参数的多样性。通过对三个实际数据集的实验表明，MI-DPG 的性能优于以往的多场景推荐模型。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=MI-DPG:+Decomposable+Parameter+Generation+Network+Based+on+Mutual+Information+for+Multi-Scenario+Recommendation)|0| |[Incorporating Co-purchase Correlation for Next-basket Recommendation](https://doi.org/10.1145/3583780.3615257)|Yu Hao Chou, PuJen Cheng|National Taiwan University, Taipei, Taiwan Roc|Next-basket recommendation (NBR) aims to recommend a set of items that users would most likely purchase together. Existing approaches use deep learning to capture basket-level preference and traditional statistical methods to model user behavior sequences. However, these methods neglect the correlation of co-purchase items among users. We, therefore, propose a novel model that incorporates Co-purchase Correlation with Bidirectional Transformer (CCBT) to enhance item representation by exploiting the correlation among users' baskets. The results of experiments conducted on four real-world datasets demonstrate the proposed model outperforms state-of-the-art NBR methods. The relative improvement for Recall@20 ranges from 11% to 27%.|下一篮子推荐(NBR)旨在推荐一组用户最有可能一起购买的商品。现有的方法使用深度学习来捕获篮子级别的偏好，使用传统的统计方法来建模用户行为序列。然而，这些方法忽略了用户之间共同购买项目的相关性。因此，我们提出了一个新的模型，结合双向变压器(CCBT)的共同购买相关性，以提高项目表示的利用用户篮之间的相关性。在四个实际数据集上进行的实验结果表明，该模型的性能优于目前最先进的 NBR 方法。召回@20的相对改善幅度从11% 到27% 不等。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Incorporating+Co-purchase+Correlation+for+Next-basket+Recommendation)|0| |[DPAN: Dynamic Preference-based and Attribute-aware Network for Relevant Recommendations](https://doi.org/10.1145/3583780.3615218)|Wei Dai, Yingmin Su, Xiaofeng Pan, Yufeng Wang, Zhenyu Zhu, Nan Xu, Chengjun Mao, Bo Cao||In e-commerce platforms, the relevant recommendation is a unique scenario providing related items for a trigger item that users are interested in. However, users' preferences for the similarity and diversity of recommendation results are dynamic and vary under different conditions. Moreover, individual item-level diversity is too coarse-grained since all recommended items are related to the trigger item. Thus, the two main challenges are to learn fine-grained representations of similarity and diversity and capture users' dynamic preferences for them under different conditions. To address these challenges, we propose a novel method called the Dynamic Preference-based and Attribute-aware Network (DPAN) for predicting Click-Through Rate (CTR) in relevant recommendations. Specifically, based on Attribute-aware Activation Values Generation (AAVG), Bi-dimensional Compression-based Re-expression (BCR) is designed to obtain similarity and diversity representations of user interests and item information. Then Shallow and Deep Union-based Fusion (SDUF) is proposed to capture users' dynamic preferences for the diverse degree of recommendation results according to various conditions. DPAN has demonstrated its effectiveness through extensive offline experiments and online A/B testing, resulting in a significant 7.62% improvement in CTR. Currently, DPAN has been successfully deployed on our e-commerce platform serving the primary traffic for relevant recommendations. The code of DPAN has been made publicly available.|在电子商务平台中，相关建议是为用户感兴趣的触发项目提供相关项目的独特场景。然而，用户对推荐结果的相似性和多样性的偏好是动态的，并且在不同的条件下会有所不同。此外，由于所有推荐的项目都与触发项目相关，因此单个项目级别的多样性过于粗粒度。因此，两个主要的挑战是学习相似性和多样性的细粒度表示，并捕获用户在不同条件下的动态偏好。为了应对这些挑战，我们提出了一种新的方法，称为基于动态偏好和属性感知网络(dpAN) ，用于预测相关建议中的点进率。在基于属性感知的激活值生成(AAVG)方法的基础上，设计了基于二维压缩的重新表达(BCR)方法来获得用户兴趣和项目信息的相似性和多样性表示。然后提出了基于浅联盟和深联盟的融合方法(SDUF) ，根据不同的条件获取用户对不同程度推荐结果的动态偏好。DPAN 通过大量的离线实验和在线 A/B 测试证明了其有效性，使 CTR 显著提高了7.62% 。目前，DPAN 已成功应用于我们的电子商务平台，为主要流量提供相关建议。DPAN 的代码已经向公众开放。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=DPAN:+Dynamic+Preference-based+and+Attribute-aware+Network+for+Relevant+Recommendations)|0| |[Learning Sparse Lexical Representations Over Specified Vocabularies for Retrieval](https://doi.org/10.1145/3583780.3615207)|Jeffrey M. Dudek, Weize Kong, Cheng Li, Mingyang Zhang, Michael Bendersky|Google Research, Mountain View, CA, USA|A recent line of work in first-stage Neural Information Retrieval has focused on learning sparse lexical representations instead of dense embeddings. One such work is SPLADE, which has been shown to lead to state-of-the-art results in both the in-domain and zero-shot settings, can leverage inverted indices for efficient retrieval, and offers enhanced interpretability. However, existing SPLADE models are fundamentally limited to learning a sparse representation based on the native BERT WordPiece vocabulary. In this work, we extend SPLADE to support learning sparse representations over arbitrary sets of tokens to improve flexibility and aid integration with existing retrieval systems. As an illustrative example, we focus on learning a sparse representation over a large (300k) set of unigrams. We add an unsupervised pretraining task on C4 to learn internal representations for new tokens. Our experiments show that our Expanded-SPLADE model maintains the performance of WordPiece-SPLADE on both in-domain and zero-shot retrieval while allowing for custom output vocabularies.|最近，第一阶段神经信息检索的工作重点是学习稀疏的词汇表征，而不是密集的嵌入。SPLADE 就是这样一个工作，它已经被证明可以在领域内和零拍摄设置中获得最先进的结果，可以利用反向索引进行有效的检索，并且提供了增强的可解释性。然而，现有的 SPLADE 模型基本上仅限于学习基于本地 BERT WordPiece 词汇表的稀疏表示。在这项工作中，我们扩展了 SPLADE 来支持对任意标记集的稀疏表示学习，以提高灵活性并帮助与现有检索系统的集成。作为一个说明性的例子，我们关注于在一个大的(300k) Unigram 集合上学习稀疏表示。我们在 C4上添加一个无监督的预训练任务来学习新标记的内部表示。我们的实验表明，我们的扩展 SPLADE 模型保持了 WordPiece-SPLADE 在域内和零镜头检索方面的性能，同时允许自定义输出词汇表。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Learning+Sparse+Lexical+Representations+Over+Specified+Vocabularies+for+Retrieval)|0| |[KGPR: Knowledge Graph Enhanced Passage Ranking](https://doi.org/10.1145/3583780.3615252)|Jinyuan Fang, Zaiqiao Meng, Craig Macdonald|University of Glasgow, Glasgow, United Kingdom|Passage ranking aims to rank a set of passages based on their relevance to a query. Current state-of-the-art models for this task typically employ a cross-encoder structure. However, these models lack access to background knowledge, i.e., information related to the query that can be helpful in retrieving relevant passages. Knowledge Graphs (KGs) provide a structured way of storing information about entities and their relationships, offering valuable background knowledge about entities. While KGs have been used to augment pretrained language models (LMs) to perform several reasoning tasks such as question answering, it remains an open question of how to utilise the information from KGs to enhance the performance of cross-encoders on the passage ranking task. Therefore, we propose KGPR, a KG-enhanced cross-encoder for the Passage Retrieval task. KGPR is built upon LUKE, an entity-aware pretrained LM, with an additional module that fuses information from KGs into LUKE. By leveraging the background knowledge from KGs, KGPR enhances the model's comprehension of queries and passages, resulting in improved ranking performance. Experimental results demonstrate that using KGs can enhance the performance of LUKE in the passage retrieval task, and KGPR can outperform state-of-the-art monoT5 cross-encoder by 3.32% and 10.77% on the MS MARCO development set and TREC DL-HARD query set respectively, using a model with a similar number of parameters.|短文排名的目的是根据一组短文与查询的相关性对它们进行排名。当前用于此任务的最先进模型通常采用交叉编码器结构。然而，这些模型缺乏对背景知识的访问，即与查询相关的信息，这些信息有助于检索相关段落。知识图(KGs)提供了一种结构化的方式来存储有关实体及其关系的信息，并提供有关实体的有价值的背景知识。虽然幼稚园已被用来增加预先训练的语言模型，以执行多项推理任务，例如问题回答，但如何利用幼稚园提供的资料，以提高交叉编码器在短文排名任务中的表现，仍然是一个悬而未决的问题。因此，我们提出 KGPR，一个 KG 增强的交叉编码器的通道检索任务。KGPR 建立在 LUKE 之上，这是一个实体感知的预训练 LM，还有一个附加的模块，将来自 KGs 的信息融合到 LUKE 中。通过利用来自 KG 的背景知识，KGPR 增强了模型对查询和段落的理解，从而提高了排名性能。实验结果表明，在文章检索任务中使用 KGs 可以提高 LUKE 的性能，在参数相似的模型下，KGPR 在 MS MARCO 开发集和 TREC DL-HARD 查询集上的性能分别比单 T5交叉编码器的性能提高3.32% 和10.77% 。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=KGPR:+Knowledge+Graph+Enhanced+Passage+Ranking)|0| -|[Multi-step Prompting for Few-shot Emotion-Grounded Conversations](https://doi.org/10.1145/3583780.3615265)|Mauzama Firdaus, Gopendra Vikram Singh, Asif Ekbal, Pushpak Bhattacharyya|IIT Patna, Patna, India; University of Alberta, Edmonton, AB, Canada; IIT Bombay, Maharashtra, India|Conversational systems have shown immense growth in their ability to communicate like humans. With the emergence of large pre-trained language models (PLMs) the ability to provide informative responses have improved significantly. Despite the success of PLMs, the ability to identify and generate engaging and empathetic responses is largely dependent on labelled-data. In this work, we design a prompting approach that identifies the emotion of a given utterance and uses the emotion information for generating the appropriate responses for conversational systems. We propose a two-step prompting method that first recognises the emotion in the dialogue utterance and in the second-step uses the predicted emotion to prompt the PLM to generate the corresponding em- pathetic response in a few-shot setting. Experimental results on three publicly available datasets show that our proposed approach outperforms the state-of-the-art approaches for both automatic and manual evaluation.|会话系统已经显示出它们像人类一样交流能力的巨大增长。随着大型预训练语言模型(PLM)的出现，提供信息反馈的能力得到了显著提高。尽管 PLM 取得了成功，但是识别和产生引人入胜和富有同情心的反应的能力在很大程度上依赖于标记数据。在这项工作中，我们设计了一个提示方法，识别一个给定的话语的情绪，并利用情绪信息产生适当的反应的会话系统。我们提出了一种两步提示方法，首先在对话中识别情绪，然后在第二步利用预测的情绪提示 PLM 产生相应的情感反应。在三个公开数据集上的实验结果表明，我们提出的方法在自动和手动评估方面都优于最先进的方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Multi-step+Prompting+for+Few-shot+Emotion-Grounded+Conversations)|0| -|[Extracting Methodology Components from AI Research Papers: A Data-driven Factored Sequence Labeling Approach](https://doi.org/10.1145/3583780.3615258)|Madhusudan Ghosh, Debasis Ganguly, Partha Basuchowdhuri, Sudip Kumar Naskar|Indian Association for the Cultivation of Science, Kolkata, India; University of Glasgow, Glasgow, United Kingdom; Dept. of Computer Science & Engineering, Jadavpur University, India, Kolkata, India|Extraction of methodology component names from scientific articles is a challenging task due to the diversified contexts around the occurrences of these entities, and the different levels of granularity and containment relationships exhibited by these entities. We hypothesize that standard sequence labeling approaches may not adequately model the dependence of methodology name mentions with their contexts, due to the problems of their large, fast evolving, and domain-specific vocabulary. As a solution, we propose a factored approach, where the mention-context dependencies are represented in a more fine-grained manner, thus allowing the model parameters to better adjust to the different characteristic patterns inherent within the data. In particular, we experiment with two variants of this factored approach - one that uses the per-entity category information derived from an ontology, and the other that makes use of the topology of the sentence embedding space to infer a category for each entity constituting that sentence. We demonstrate that both these factored variants of SciBERT outperform their non-factored counterpart, a state-of-the-art model for scientific concept extraction.|从科技论文中提取方法论组件名称是一项具有挑战性的任务，因为这些实体出现的背景多种多样，而且这些实体表现出不同层次的粒度和包含关系。我们假设，标准的序列标签方法可能不能充分模拟方法名称提及与其上下文的依赖性，由于它们的大型，快速发展和领域特定的词汇表的问题。作为一种解决方案，我们提出了一种分因式方法，其中提及上下文依赖关系以更细粒度的方式表示，从而允许模型参数更好地调整以适应数据中固有的不同特征模式。具体来说，我们用这种因素分析方法的两种变体进行了实验——一种使用从本体派生出来的每个实体的类别信息，另一种使用句子嵌入空间的拓扑结构来推断构成该句子的每个实体的类别。我们证明 SciBERT 的这两个因子变体都优于它们的非因子变体，后者是科学概念提取的最先进模型。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Extracting+Methodology+Components+from+AI+Research+Papers:+A+Data-driven+Factored+Sequence+Labeling+Approach)|0| +|[Multi-step Prompting for Few-shot Emotion-Grounded Conversations](https://doi.org/10.1145/3583780.3615265)|Mauzama Firdaus, Gopendra Vikram Singh, Asif Ekbal, Pushpak Bhattacharyya|IIT Bombay, Maharashtra, India; University of Alberta, Edmonton, AB, Canada; IIT Patna, Patna, India|Conversational systems have shown immense growth in their ability to communicate like humans. With the emergence of large pre-trained language models (PLMs) the ability to provide informative responses have improved significantly. Despite the success of PLMs, the ability to identify and generate engaging and empathetic responses is largely dependent on labelled-data. In this work, we design a prompting approach that identifies the emotion of a given utterance and uses the emotion information for generating the appropriate responses for conversational systems. We propose a two-step prompting method that first recognises the emotion in the dialogue utterance and in the second-step uses the predicted emotion to prompt the PLM to generate the corresponding em- pathetic response in a few-shot setting. Experimental results on three publicly available datasets show that our proposed approach outperforms the state-of-the-art approaches for both automatic and manual evaluation.|会话系统已经显示出它们像人类一样交流能力的巨大增长。随着大型预训练语言模型(PLM)的出现，提供信息反馈的能力得到了显著提高。尽管 PLM 取得了成功，但是识别和产生引人入胜和富有同情心的反应的能力在很大程度上依赖于标记数据。在这项工作中，我们设计了一个提示方法，识别一个给定的话语的情绪，并利用情绪信息产生适当的反应的会话系统。我们提出了一种两步提示方法，首先在对话中识别情绪，然后在第二步利用预测的情绪提示 PLM 产生相应的情感反应。在三个公开数据集上的实验结果表明，我们提出的方法在自动和手动评估方面都优于最先进的方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Multi-step+Prompting+for+Few-shot+Emotion-Grounded+Conversations)|0| +|[Extracting Methodology Components from AI Research Papers: A Data-driven Factored Sequence Labeling Approach](https://doi.org/10.1145/3583780.3615258)|Madhusudan Ghosh, Debasis Ganguly, Partha Basuchowdhuri, Sudip Kumar Naskar|Dept. of Computer Science & Engineering, Jadavpur University, India, Kolkata, India; Indian Association for the Cultivation of Science, Kolkata, India; University of Glasgow, Glasgow, United Kingdom|Extraction of methodology component names from scientific articles is a challenging task due to the diversified contexts around the occurrences of these entities, and the different levels of granularity and containment relationships exhibited by these entities. We hypothesize that standard sequence labeling approaches may not adequately model the dependence of methodology name mentions with their contexts, due to the problems of their large, fast evolving, and domain-specific vocabulary. As a solution, we propose a factored approach, where the mention-context dependencies are represented in a more fine-grained manner, thus allowing the model parameters to better adjust to the different characteristic patterns inherent within the data. In particular, we experiment with two variants of this factored approach - one that uses the per-entity category information derived from an ontology, and the other that makes use of the topology of the sentence embedding space to infer a category for each entity constituting that sentence. We demonstrate that both these factored variants of SciBERT outperform their non-factored counterpart, a state-of-the-art model for scientific concept extraction.|从科技论文中提取方法论组件名称是一项具有挑战性的任务，因为这些实体出现的背景多种多样，而且这些实体表现出不同层次的粒度和包含关系。我们假设，标准的序列标签方法可能不能充分模拟方法名称提及与其上下文的依赖性，由于它们的大型，快速发展和领域特定的词汇表的问题。作为一种解决方案，我们提出了一种分因式方法，其中提及上下文依赖关系以更细粒度的方式表示，从而允许模型参数更好地调整以适应数据中固有的不同特征模式。具体来说，我们用这种因素分析方法的两种变体进行了实验——一种使用从本体派生出来的每个实体的类别信息，另一种使用句子嵌入空间的拓扑结构来推断构成该句子的每个实体的类别。我们证明 SciBERT 的这两个因子变体都优于它们的非因子变体，后者是科学概念提取的最先进模型。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Extracting+Methodology+Components+from+AI+Research+Papers:+A+Data-driven+Factored+Sequence+Labeling+Approach)|0| |[Lightweight Adaptation of Neural Language Models via Subspace Embedding](https://doi.org/10.1145/3583780.3615269)|Amit Kumar Jaiswal, Haiming Liu|University of Southampton, Southampton, United Kingdom; University of Surrey, Guildford, United Kingdom|Traditional neural word embeddings are usually dependent on a richer diversity of vocabulary. However, the language models recline to cover major vocabularies via the word embedding parameters, in particular, for multilingual language models that generally cover a significant part of their overall learning parameters. In this work, we present a new compact embedding structure to reduce the memory footprint of the pre-trained language models with a sacrifice of up to 4% absolute accuracy. The embeddings vectors reconstruction follows a set of subspace embeddings and an assignment procedure via the contextual relationship among tokens from pre-trained language models. The subspace embedding structure calibrates to masked language models, to evaluate our compact embedding structure on similarity and textual entailment tasks, sentence and paraphrase tasks. Our experimental evaluation shows that the subspace embeddings achieve compression rates beyond 99.8% in comparison with the original embeddings for the language models on XNLI and GLUE benchmark suites.|传统的神经词汇嵌入通常依赖于更丰富的词汇多样性。然而，语言模型通过单词嵌入参数覆盖了主要词汇，特别是多语言语言模型通常覆盖了其总体学习参数的很大一部分。在这项工作中，我们提出了一个新的紧凑的嵌入结构，以减少预训练的语言模型的内存占用，高达4% 的绝对准确率的牺牲。嵌入向量重构遵循一组子空间嵌入和一个赋值过程，通过预训练语言模型中标记之间的上下文关系实现。子空间嵌入结构校准到掩盖语言模型，评估我们紧凑的嵌入结构的相似性和文字蕴涵任务，句子和释义任务。实验结果表明，与基于 XNLI 和 GLUE 基准测试套件的语言模型原始嵌入相比，子空间嵌入的压缩率达到了99.8% 以上。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Lightweight+Adaptation+of+Neural+Language+Models+via+Subspace+Embedding)|0| -|[Multi-Granularity Attention Model for Group Recommendation](https://doi.org/10.1145/3583780.3615140)|Jianye Ji, Jiayan Pei, Shaochuan Lin, Taotao Zhou, Hengxu He, Jia Jia, Ning Hu|Alibaba Group, Hangzhou, China; Alibaba Group, Shanghai, China|Group recommendation provides personalized recommendations to a group of users based on their shared interests, preferences, and characteristics. Current studies have explored different methods for integrating individual preferences and making collective decisions that benefit the group as a whole. However, most of them heavily rely on users with rich behavior and ignore latent preferences of users with relatively sparse behavior, leading to insufficient learning of individual interests. To address this challenge, we present the Multi-Granularity Attention Model (MGAM), a novel approach that utilizes multiple levels of granularity (i.e., subsets, groups, and supersets) to uncover group members' latent preferences and mitigate recommendation noise. Specially, we propose a Subset Preference Extraction module that enhances the representation of users' latent subset-level preferences by incorporating their previous interactions with items and utilizing a hierarchical mechanism. Additionally, our method introduces a Group Preference Extraction module and a Superset Preference Extraction module, which explore users' latent preferences on two levels: the group-level, which maintains users' original preferences, and the superset-level, which includes group-group exterior information. By incorporating the subset-level embedding, group-level embedding, and superset-level embedding, our proposed method effectively reduces group recommendation noise across multiple granularities and comprehensively learns individual interests. Extensive offline and online experiments have demonstrated the superiority of our method in terms of performance.|群组推荐根据用户的共同兴趣、偏好和特征，向用户群体提供个性化的推荐。目前的研究探索了整合个人偏好和做出有利于整个群体的集体决策的不同方法。然而，它们大多严重依赖于行为丰富的用户，忽视了行为相对稀疏的用户的潜在偏好，导致个体兴趣学习不足。为了解决这一挑战，我们提出了多粒度注意力模型(MGAM) ，这是一种利用多个粒度级别(即子集，组和超集)来揭示组成员的潜在偏好并减轻推荐噪音的新方法。特别地，我们提出了一个子集偏好提取模块，该模块通过合并用户以前与项目的交互并利用层次化机制来增强用户潜在子集级偏好的表示。此外，该方法还引入了群组偏好提取模块和超集偏好提取模块，分别从两个层次探索用户的潜在偏好: 保持用户原始偏好的群组层次和包含群组外部信息的超集层次。该方法融合了子集级嵌入、组级嵌入和超集级嵌入，有效地降低了跨多个粒度的群推荐噪声，全面地学习了个人兴趣。大量的离线和在线实验已经证明了我们的方法在性能方面的优越性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Multi-Granularity+Attention+Model+for+Group+Recommendation)|0| +|[Multi-Granularity Attention Model for Group Recommendation](https://doi.org/10.1145/3583780.3615140)|Jianye Ji, Jiayan Pei, Shaochuan Lin, Taotao Zhou, Hengxu He, Jia Jia, Ning Hu|Alibaba Group, Shanghai, China; Alibaba Group, Hangzhou, China|Group recommendation provides personalized recommendations to a group of users based on their shared interests, preferences, and characteristics. Current studies have explored different methods for integrating individual preferences and making collective decisions that benefit the group as a whole. However, most of them heavily rely on users with rich behavior and ignore latent preferences of users with relatively sparse behavior, leading to insufficient learning of individual interests. To address this challenge, we present the Multi-Granularity Attention Model (MGAM), a novel approach that utilizes multiple levels of granularity (i.e., subsets, groups, and supersets) to uncover group members' latent preferences and mitigate recommendation noise. Specially, we propose a Subset Preference Extraction module that enhances the representation of users' latent subset-level preferences by incorporating their previous interactions with items and utilizing a hierarchical mechanism. Additionally, our method introduces a Group Preference Extraction module and a Superset Preference Extraction module, which explore users' latent preferences on two levels: the group-level, which maintains users' original preferences, and the superset-level, which includes group-group exterior information. By incorporating the subset-level embedding, group-level embedding, and superset-level embedding, our proposed method effectively reduces group recommendation noise across multiple granularities and comprehensively learns individual interests. Extensive offline and online experiments have demonstrated the superiority of our method in terms of performance.|群组推荐根据用户的共同兴趣、偏好和特征，向用户群体提供个性化的推荐。目前的研究探索了整合个人偏好和做出有利于整个群体的集体决策的不同方法。然而，它们大多严重依赖于行为丰富的用户，忽视了行为相对稀疏的用户的潜在偏好，导致个体兴趣学习不足。为了解决这一挑战，我们提出了多粒度注意力模型(MGAM) ，这是一种利用多个粒度级别(即子集，组和超集)来揭示组成员的潜在偏好并减轻推荐噪音的新方法。特别地，我们提出了一个子集偏好提取模块，该模块通过合并用户以前与项目的交互并利用层次化机制来增强用户潜在子集级偏好的表示。此外，该方法还引入了群组偏好提取模块和超集偏好提取模块，分别从两个层次探索用户的潜在偏好: 保持用户原始偏好的群组层次和包含群组外部信息的超集层次。该方法融合了子集级嵌入、组级嵌入和超集级嵌入，有效地降低了跨多个粒度的群推荐噪声，全面地学习了个人兴趣。大量的离线和在线实验已经证明了我们的方法在性能方面的优越性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Multi-Granularity+Attention+Model+for+Group+Recommendation)|0| |[CSPM: A Contrastive Spatiotemporal Preference Model for CTR Prediction in On-Demand Food Delivery Services](https://doi.org/10.1145/3583780.3615239)|Guyu Jiang, Xiaoyun Li, Rongrong Jing, Ruoqi Zhao, Xingliang Ni, Guodong Cao, Ning Hu||Click-through rate (CTR) prediction is a crucial task in the context of an online on-demand food delivery (OFD) platform for precisely estimating the probability of a user clicking on food items. Unlike universal e-commerce platforms such as Taobao and Amazon, user behaviors and interests on the OFD platform are more location and time-sensitive due to limited delivery ranges and regional commodity supplies. However, existing CTR prediction algorithms in OFD scenarios concentrate on capturing interest from historical behavior sequences, which fails to effectively model the complex spatiotemporal information within features, leading to poor performance. To address this challenge, this paper introduces the Contrastive Sres under different search states using three modules: contrastive spatiotemporal representation learning (CSRL), spatiotemporal preference extractor (StPE), and spatiotemporal information filter (StIF). CSRL utilizes a contrastive learning framework to generate a spatiotemporal activation representation (SAR) for the search action. StPE employs SAR to activate users' diverse preferences related to location and time from the historical behavior sequence field, using a multi-head attention mechanism. StIF incorporates SAR into a gating network to automatically capture important features with latent spatiotemporal effects. Extensive experiments conducted on two large-scale industrial datasets demonstrate the state-of-the-art performance of CSPM. Notably, CSPM has been successfully deployed in Alibaba's online OFD platform Ele.me, resulting in a significant 0.88% lift in CTR, which has substantial business implications.|点进率预测是网上按需供应食物平台的一项重要工作，目的是准确估计使用者点击食物项目的机会。与淘宝和亚马逊等通用电子商务平台不同，OFD 平台上的用户行为和兴趣更具地点和时间敏感性，因为交付范围和区域性商品供应有限。然而，现有的 OFD 场景下的 CTR 预测算法主要集中于从历史行为序列中获取兴趣，未能有效地模拟特征中复杂的时空信息，导致性能较差。为了解决这一问题，本文使用了三个模块: 对比时空表示学习(CSRL)、时空偏好提取器(StPE)和时空信息过滤器(StIF) ，介绍了不同搜索状态下的对比 Sres。CSRL 利用对比学习框架生成搜索动作的时空激活表示(SAR)。STPE 利用多头注意机制，从历史行为序列场中激活用户与位置和时间相关的多样化偏好。STIF 将合成孔径雷达(SAR)与门控网络相结合，自动捕获具有潜在时空效应的重要特征。在两个大规模工业数据集上进行的大量实验证明了 CSPM 的最新性能。值得注意的是，cSPM 已成功部署在阿里巴巴的在线 OFD 平台 Ele.me 上，使点击率大幅提高了0.88% ，这对商业具有重大意义。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=CSPM:+A+Contrastive+Spatiotemporal+Preference+Model+for+CTR+Prediction+in+On-Demand+Food+Delivery+Services)|0| -|[MvFS: Multi-view Feature Selection for Recommender System](https://doi.org/10.1145/3583780.3615243)|Youngjune Lee, Yeongjong Jeong, Keunchan Park, SeongKu Kang|University of Illinois at Urbana-Champaign, Urbana, IL, USA; NAVER Corporation, Seongnam, Republic of Korea|Feature selection, which is a technique to select key features in recommender systems, has received increasing research attention. Recently, Adaptive Feature Selection (AdaFS) has shown remarkable performance by adaptively selecting features for each data instance, considering that the importance of a given feature field can vary significantly across data. However, this method still has limitations in that its selection process could be easily biased to major features that frequently occur. To address these problems, we propose Multi-view Feature Selection (MvFS), which selects informative features for each instance more effectively. Most importantly, MvFS employs a multi-view network consisting of multiple sub-networks, each of which learns to measure the feature importance of a part of data with different feature patterns. By doing so, MvFS promotes a more balanced feature selection process mitigating the bias problem towards dominant patterns. Moreover, MvFS adopts an effective importance score modeling strategy which is applied independently to each field without incurring dependency among features. Experimental results on real-world datasets demonstrate the effectiveness of MvFS compared to state-of-the-art baselines.|特征选择作为推荐系统中的一种关键特征选择技术，越来越受到研究者的重视。最近，自适应特征选择(AdaFS)通过为每个数据实例自适应地选择特征来表现出显著的性能，考虑到给定特征字段的重要性在不同数据之间可能有显著的差异。然而，这种方法仍然有局限性，因为它的选择过程很容易偏向于频繁出现的主要特征。为了解决这些问题，我们提出了多视图特征选择(MvFS) ，它能够更有效地为每个实例选择信息特征。最重要的是，MvFS 使用一个由多个子网络组成的多视图网络，每个子网络学习测量具有不同特征模式的部分数据的特征重要性。通过这样做，MvFS 促进了一个更加平衡的特征选择过程，减轻了对主导模式的偏见问题。此外，MvFS 采用了一种有效的重要性评分建模策略，该策略可以独立地应用于各个领域，不会引起特征之间的依赖性。在真实世界数据集上的实验结果表明，与最先进的基线相比，MvFS 是有效的。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=MvFS:+Multi-view+Feature+Selection+for+Recommender+System)|0| -|[HEPT Attack: Heuristic Perpendicular Trial for Hard-label Attacks under Limited Query Budgets](https://doi.org/10.1145/3583780.3615198)|Qi Li, Xingyu Li, Xiaodong Cui, Keke Tang, Peican Zhu|Guangzhou University, Guangzhou, China; University of Alberta, Edmonton, AB, Canada; Northwestern Polytechnical University, Xi'an, China; National University of Singapore, Singapore, Singapore|Exploring adversarial attacks on deep neural networks (DNNs) is crucial for assessing and enhancing their adversarial robustness. Among various attack types, hard-label attacks that rely only on predicted labels offer a practical approach. This paper focuses on the challenging task of hard-label attacks within an extremely limited query budget, which is a significant achievement rarely accomplished by existing methods. To tackle this, we propose an attack framework that leverages geometric information from previous perturbation directions to form triangles and employs a heuristic perpendicular trial to effectively utilize the intermediate directions. Extensive experiments validate the effectiveness of our approach under strict query constraints and demonstrate its superiority to the state-of-the-art methods.|研究深层神经网络(DNN)的对抗性攻击对于评估和增强其对抗性鲁棒性至关重要。在各种攻击类型中，只依赖于预测标签的硬标签攻击提供了一种实用的方法。本文重点研究了在极其有限的查询预算内进行硬标签攻击的具有挑战性的任务，这是现有方法很少能够完成的一项重大成就。为了解决这个问题，我们提出了一个攻击框架，它利用先前摄动方向的几何信息来形成三角形，并采用启发式垂直试验来有效地利用中间方向。大量的实验验证了该方法在严格查询约束下的有效性，并证明了该方法相对于现有方法的优越性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=HEPT+Attack:+Heuristic+Perpendicular+Trial+for+Hard-label+Attacks+under+Limited+Query+Budgets)|0| -|[Retrieval-Based Unsupervised Noisy Label Detection on Text Data](https://doi.org/10.1145/3583780.3615146)|Peiyang Liu, Jinyu Yang, Lin Wang, Sen Wang, Yunlai Hao, Huihui Bai|PX Securities, Shen Zhen, China; Shanxi Institute of Energy, JIn Zhong, China|The success of deep neural networks hinges on both high-quality annotations and copious amounts of data; however, in practice, a compromise between dataset size and quality frequently arises. Data collection and cleansing are often resource-intensive and time-consuming, leading to real-world datasets containing label noise that can introduce incorrect correlation patterns, adversely affecting model generalization capabilities. The efficient identification of corrupted patterns is indispensable, with prevalent methods predominantly concentrating on devising robust training techniques to preclude models from internalizing these patterns. Nevertheless, these supervised approaches often necessitate tailored training procedures, potentially resulting in overfitting corrupted patterns and a decline in detection performance. This paper presents a retrieval-based unsupervised solution for the detection of noisy labels, surpassing the performance of three current competitive methods in this domain.|深度神经网络的成功取决于高质量的注释和大量的数据; 然而，在实践中，数据集大小和质量之间的妥协经常出现。数据收集和清理通常是资源密集型和耗时的，导致包含标签噪声的真实世界数据集可能引入不正确的相关模式，从而对模型泛化能力产生不利影响。腐败模式的有效识别是必不可少的，普遍的方法主要集中在设计稳健的训练技术，以排除模型内化这些模式。然而，这些监督的方法往往需要量身定制的训练程序，潜在地导致过度拟合损坏的模式和检测性能的下降。本文提出了一种基于检索的无监督的噪声标签检测方法，其性能超过了目前该领域三种竞争方法的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Retrieval-Based+Unsupervised+Noisy+Label+Detection+on+Text+Data)|0| -|[Counterfactual Adversarial Learning for Recommendation](https://doi.org/10.1145/3583780.3615152)|Jialin Liu, Zijian Zhang, Xiangyu Zhao, Jun Li|Computer Network Information Center, Chinese Academy of Sciences, Beijing, China; Computer Network Information Center, Chinese Academy of Sciences, University of Chinese Academy Sciences, Beijing, China; Jilin University, City University of Hong Kong, Changchun, China; City University of Hong Kong, Hong Kong, China|Long-term user responses, i.e., clicks or purchases on e-commerce platforms, are crucial for sequential recommender systems. Recent off-policy evaluation methods involve these responses by simultaneously maximizing expected cumulative rewards. However, two aspects of these methods require further consideration. Firstly, from the system's point of view, candidates with various values are interchangeable, which may result in contradictory future recommendations despite having the same interaction history. Secondly, rewards are manually designed, which necessitates a trial-and-error approach to strike a balance between training stabilization and reward distinction. To address these issues, we propose a new sequential recommender system called NCM4Rec. Specifically, for the distinction problem, NCM4Rec achieves counterfactual consistency via a neural causal model, which is learnable yet equally expressive as classic structural causal models. Such consistency is maintained by a Gumbel-Max design. For the representing problem, NCM4Rec encodes different types of responses as one-hot vectors and captures the long-term preference via adversarial learning. As a consequence, NCM4Rec is both adaptive and identifiable. Both theoretical analyses of the consistency and empirical studies over two real-world datasets demonstrate the effectiveness of our method.|长期的用户响应，即电子商务平台上的点击或购买，对于顺序推荐系统至关重要。最近的非政策性评估方法通过同时最大化预期累积回报来涉及这些反应。然而，这些方法的两个方面需要进一步考虑。首先，从系统的角度来看，具有不同价值观的候选人是可以互换的，这可能会导致相互矛盾的未来推荐，尽管有相同的互动历史。其次，奖励是手工设计的，这就需要一个试错法来平衡训练的稳定性和奖励的区别。为了解决这些问题，我们提出了一个新的连续推荐系统，称为 NCM4rec。具体来说，对于区分问题，NCM4Rec 通过一个神经因果模型来实现反事实的一致性，这个模型是可以学习的，但是与经典的结构因果模型同样具有表现力。这种一致性是由 Gumbel-Max 设计保持的。对于表示问题，NCM4Rec 将不同类型的响应编码为一个热向量，并通过对抗学习获取长期偏好。因此，NCM4Rec 既是自适应的，也是可识别的。对两个实际数据集的一致性理论分析和实证研究都证明了该方法的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Counterfactual+Adversarial+Learning+for+Recommendation)|0| +|[MvFS: Multi-view Feature Selection for Recommender System](https://doi.org/10.1145/3583780.3615243)|Youngjune Lee, Yeongjong Jeong, Keunchan Park, SeongKu Kang|NAVER Corporation, Seongnam, Republic of Korea; University of Illinois at Urbana-Champaign, Urbana, IL, USA|Feature selection, which is a technique to select key features in recommender systems, has received increasing research attention. Recently, Adaptive Feature Selection (AdaFS) has shown remarkable performance by adaptively selecting features for each data instance, considering that the importance of a given feature field can vary significantly across data. However, this method still has limitations in that its selection process could be easily biased to major features that frequently occur. To address these problems, we propose Multi-view Feature Selection (MvFS), which selects informative features for each instance more effectively. Most importantly, MvFS employs a multi-view network consisting of multiple sub-networks, each of which learns to measure the feature importance of a part of data with different feature patterns. By doing so, MvFS promotes a more balanced feature selection process mitigating the bias problem towards dominant patterns. Moreover, MvFS adopts an effective importance score modeling strategy which is applied independently to each field without incurring dependency among features. Experimental results on real-world datasets demonstrate the effectiveness of MvFS compared to state-of-the-art baselines.|特征选择作为推荐系统中的一种关键特征选择技术，越来越受到研究者的重视。最近，自适应特征选择(AdaFS)通过为每个数据实例自适应地选择特征来表现出显著的性能，考虑到给定特征字段的重要性在不同数据之间可能有显著的差异。然而，这种方法仍然有局限性，因为它的选择过程很容易偏向于频繁出现的主要特征。为了解决这些问题，我们提出了多视图特征选择(MvFS) ，它能够更有效地为每个实例选择信息特征。最重要的是，MvFS 使用一个由多个子网络组成的多视图网络，每个子网络学习测量具有不同特征模式的部分数据的特征重要性。通过这样做，MvFS 促进了一个更加平衡的特征选择过程，减轻了对主导模式的偏见问题。此外，MvFS 采用了一种有效的重要性评分建模策略，该策略可以独立地应用于各个领域，不会引起特征之间的依赖性。在真实世界数据集上的实验结果表明，与最先进的基线相比，MvFS 是有效的。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=MvFS:+Multi-view+Feature+Selection+for+Recommender+System)|0| +|[HEPT Attack: Heuristic Perpendicular Trial for Hard-label Attacks under Limited Query Budgets](https://doi.org/10.1145/3583780.3615198)|Qi Li, Xingyu Li, Xiaodong Cui, Keke Tang, Peican Zhu|Guangzhou University, Guangzhou, China; National University of Singapore, Singapore, Singapore; Northwestern Polytechnical University, Xi'an, China; University of Alberta, Edmonton, AB, Canada|Exploring adversarial attacks on deep neural networks (DNNs) is crucial for assessing and enhancing their adversarial robustness. Among various attack types, hard-label attacks that rely only on predicted labels offer a practical approach. This paper focuses on the challenging task of hard-label attacks within an extremely limited query budget, which is a significant achievement rarely accomplished by existing methods. To tackle this, we propose an attack framework that leverages geometric information from previous perturbation directions to form triangles and employs a heuristic perpendicular trial to effectively utilize the intermediate directions. Extensive experiments validate the effectiveness of our approach under strict query constraints and demonstrate its superiority to the state-of-the-art methods.|研究深层神经网络(DNN)的对抗性攻击对于评估和增强其对抗性鲁棒性至关重要。在各种攻击类型中，只依赖于预测标签的硬标签攻击提供了一种实用的方法。本文重点研究了在极其有限的查询预算内进行硬标签攻击的具有挑战性的任务，这是现有方法很少能够完成的一项重大成就。为了解决这个问题，我们提出了一个攻击框架，它利用先前摄动方向的几何信息来形成三角形，并采用启发式垂直试验来有效地利用中间方向。大量的实验验证了该方法在严格查询约束下的有效性，并证明了该方法相对于现有方法的优越性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=HEPT+Attack:+Heuristic+Perpendicular+Trial+for+Hard-label+Attacks+under+Limited+Query+Budgets)|0| +|[Retrieval-Based Unsupervised Noisy Label Detection on Text Data](https://doi.org/10.1145/3583780.3615146)|Peiyang Liu, Jinyu Yang, Lin Wang, Sen Wang, Yunlai Hao, Huihui Bai|Shanxi Institute of Energy, JIn Zhong, China; PX Securities, Shen Zhen, China|The success of deep neural networks hinges on both high-quality annotations and copious amounts of data; however, in practice, a compromise between dataset size and quality frequently arises. Data collection and cleansing are often resource-intensive and time-consuming, leading to real-world datasets containing label noise that can introduce incorrect correlation patterns, adversely affecting model generalization capabilities. The efficient identification of corrupted patterns is indispensable, with prevalent methods predominantly concentrating on devising robust training techniques to preclude models from internalizing these patterns. Nevertheless, these supervised approaches often necessitate tailored training procedures, potentially resulting in overfitting corrupted patterns and a decline in detection performance. This paper presents a retrieval-based unsupervised solution for the detection of noisy labels, surpassing the performance of three current competitive methods in this domain.|深度神经网络的成功取决于高质量的注释和大量的数据; 然而，在实践中，数据集大小和质量之间的妥协经常出现。数据收集和清理通常是资源密集型和耗时的，导致包含标签噪声的真实世界数据集可能引入不正确的相关模式，从而对模型泛化能力产生不利影响。腐败模式的有效识别是必不可少的，普遍的方法主要集中在设计稳健的训练技术，以排除模型内化这些模式。然而，这些监督的方法往往需要量身定制的训练程序，潜在地导致过度拟合损坏的模式和检测性能的下降。本文提出了一种基于检索的无监督的噪声标签检测方法，其性能超过了目前该领域三种竞争方法的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Retrieval-Based+Unsupervised+Noisy+Label+Detection+on+Text+Data)|0| +|[Counterfactual Adversarial Learning for Recommendation](https://doi.org/10.1145/3583780.3615152)|Jialin Liu, Zijian Zhang, Xiangyu Zhao, Jun Li|Computer Network Information Center, Chinese Academy of Sciences, Beijing, China; Jilin University, City University of Hong Kong, Changchun, China; City University of Hong Kong, Hong Kong, China; Computer Network Information Center, Chinese Academy of Sciences, University of Chinese Academy Sciences, Beijing, China|Long-term user responses, i.e., clicks or purchases on e-commerce platforms, are crucial for sequential recommender systems. Recent off-policy evaluation methods involve these responses by simultaneously maximizing expected cumulative rewards. However, two aspects of these methods require further consideration. Firstly, from the system's point of view, candidates with various values are interchangeable, which may result in contradictory future recommendations despite having the same interaction history. Secondly, rewards are manually designed, which necessitates a trial-and-error approach to strike a balance between training stabilization and reward distinction. To address these issues, we propose a new sequential recommender system called NCM4Rec. Specifically, for the distinction problem, NCM4Rec achieves counterfactual consistency via a neural causal model, which is learnable yet equally expressive as classic structural causal models. Such consistency is maintained by a Gumbel-Max design. For the representing problem, NCM4Rec encodes different types of responses as one-hot vectors and captures the long-term preference via adversarial learning. As a consequence, NCM4Rec is both adaptive and identifiable. Both theoretical analyses of the consistency and empirical studies over two real-world datasets demonstrate the effectiveness of our method.|长期的用户响应，即电子商务平台上的点击或购买，对于顺序推荐系统至关重要。最近的非政策性评估方法通过同时最大化预期累积回报来涉及这些反应。然而，这些方法的两个方面需要进一步考虑。首先，从系统的角度来看，具有不同价值观的候选人是可以互换的，这可能会导致相互矛盾的未来推荐，尽管有相同的互动历史。其次，奖励是手工设计的，这就需要一个试错法来平衡训练的稳定性和奖励的区别。为了解决这些问题，我们提出了一个新的连续推荐系统，称为 NCM4rec。具体来说，对于区分问题，NCM4Rec 通过一个神经因果模型来实现反事实的一致性，这个模型是可以学习的，但是与经典的结构因果模型同样具有表现力。这种一致性是由 Gumbel-Max 设计保持的。对于表示问题，NCM4Rec 将不同类型的响应编码为一个热向量，并通过对抗学习获取长期偏好。因此，NCM4Rec 既是自适应的，也是可识别的。对两个实际数据集的一致性理论分析和实证研究都证明了该方法的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Counterfactual+Adversarial+Learning+for+Recommendation)|0| |[Understanding the Multi-vector Dense Retrieval Models](https://doi.org/10.1145/3583780.3615282)|Qi Liu, Jiaxin Mao|Renmin University of China, Beijing, China|While dense retrieval has become a promising alternative to the traditional text retrieval models, such as BM25, some recent studies show that multi-vector dense retrieval models are more effective than the single-vector method in retrieval tasks. However, due to a lack of interpretability, why the multi-vector method outperforms its single-vector counterpart has not been fully studied. To fill this research gap, in this work, we investigate and compare the behaviors of single-vector and multi-vector models in retrieval. Specifically, we analyze the vocabulary distribution of dense representations by mapping them back to the sparse, vocabulary space. Our empirical findings show that the multi-vector representation has more lexical overlaps between queries and passages. Additionally, we show that this feature of multi-vector representation can enhance its ranking performance when a given passage can fulfill different information needs and thus can be retrieved by different queries. These results shed light on the internal mechanisms of multi-vector representation and may provide new perspectives for future research.|虽然密集检索已经成为传统文本检索模型(如 BM25)的一个有前途的替代方案，但最近的一些研究表明，多向量密集检索模型在检索任务中比单向量方法更有效。然而，由于缺乏可解释性，为什么多向量方法的性能优于单向量方法还没有得到充分的研究。为了填补这一研究空白，本文对单向量模型和多向量模型在检索中的行为进行了研究和比较。具体来说，我们通过将密集表示映射回稀疏的词汇空间来分析它们的词汇分布。我们的实证结果表明，多向量表示在查询和段落之间有更多的词汇重叠。此外，我们还发现，当一个给定的段落可以满足不同的信息需求时，这种多向量表示特征可以提高其排序性能，从而可以通过不同的查询进行检索。这些结果揭示了多向量表示的内在机制，为今后的研究提供了新的视角。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Understanding+the+Multi-vector+Dense+Retrieval+Models)|0| |[Spatio-Temporal Adaptive Embedding Makes Vanilla Transformer SOTA for Traffic Forecasting](https://doi.org/10.1145/3583780.3615160)|Hangchen Liu, Zheng Dong, Renhe Jiang, Jiewen Deng, Jinliang Deng, Quanjun Chen, Xuan Song|University of Technology Sydney, Sydney, Australia; The University of Tokyo, Kashiwa, Japan; Southern University of Science and Technology, Shenzhen, China|With the rapid development of the Intelligent Transportation System (ITS), accurate traffic forecasting has emerged as a critical challenge. The key bottleneck lies in capturing the intricate spatio-temporal traffic patterns. In recent years, numerous neural networks with complicated architectures have been proposed to address this issue. However, the advancements in network architectures have encountered diminishing performance gains. In this study, we present a novel component called spatio-temporal adaptive embedding that can yield outstanding results with vanilla transformers. Our proposed Spatio-Temporal Adaptive Embedding transformer (STAEformer) achieves state-of-the-art performance on five real-world traffic forecasting datasets. Further experiments demonstrate that spatio-temporal adaptive embedding plays a crucial role in traffic forecasting by effectively capturing intrinsic spatio-temporal relations and chronological information in traffic time series.|随着智能交通系统(ITS)的快速发展，准确的交通量预测已经成为智能交通系统面临的一个重要挑战。关键的瓶颈在于捕获复杂的时空交通模式。近年来，许多具有复杂结构的神经网络被提出来解决这个问题。然而，网络体系结构的改进遇到了性能收益递减的问题。在这项研究中，我们提出了一个新的组成部分称为时空自适应嵌入，可以产生突出的结果与香草变压器。我们提出的时空自适应嵌入变压器(STAEformer)在五个真实世界的交通预测数据集上实现了最先进的性能。进一步的实验表明，时空自适应嵌入通过有效地捕获交通时间序列中固有的时空关系和时间信息，在交通预测中发挥了重要作用。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Spatio-Temporal+Adaptive+Embedding+Makes+Vanilla+Transformer+SOTA+for+Traffic+Forecasting)|0| |[Personalized Differentially Private Federated Learning without Exposing Privacy Budgets](https://doi.org/10.1145/3583780.3615247)|Junxu Liu, Jian Lou, Li Xiong, Xiaofeng Meng|Renmin University of China, Beijing, China; Emory University, Atlanta, GA, USA; Zhejiang University, Hangzhou, China|The meteoric rise of cross-silo Federated Learning (FL) is due to its ability to mitigate data breaches during collaborative training. To further provide rigorous privacy protection with consideration of the varying privacy requirements across different clients, a privacy-enhanced line of work on personalized differentially private federated learning (PDP-FL) has been proposed. However, the existing solution for PDP-FL [20] assumes the raw privacy budgets of all clients should be collected by the server. These values are then directly utilized to improve the model utility via facilitating the privacy preferences partitioning (i.e., partitioning all clients into multiple privacy groups). It is however non-realistic because the raw privacy budgets can be quite informative and sensitive. In this work, our goal is to achieve PDP-FL without exposing clients' raw privacy budgets by indirectly partitioning the privacy preferences solely based on clients' noisy model updates. The crux lies in the fact that the noisy updates could be influenced by two entangled factors of DP noises and non-IID clients' data, leaving it unknown whether it is possible to uncover privacy preferences by disentangling the two affecting factors. To overcome the hurdle, we systematically investigate the unexplored question of under what conditions can the model updates of clients be primarily influenced by noise levels rather than data distribution. Then, we propose a simple yet effective strategy based on clustering the L2 norm of the noisy updates, which can be integrated into the vanilla PDP-FL to maintain the same performance. Experimental results demonstrate the effectiveness and feasibility of our privacy-budget-agnostic PDP-FL method.|跨竖井联邦学习(FL)的迅速崛起是由于它在协作培训期间减轻数据泄露的能力。为了进一步提供严格的隐私保护，考虑到不同客户端的不同隐私要求，提出了一种个性化差异私有联邦学习(PDP-FL)的隐私增强工作路线。然而，现有的 PDP-FL [20]解决方案假设所有客户端的原始隐私预算都应该由服务器收集。然后，这些值被直接用于通过促进隐私首选项分区(即，将所有客户端分成多个隐私组)来改进模型实用程序。然而，这是不现实的，因为原始的隐私预算可以相当信息量和敏感性。在这项工作中，我们的目标是实现 PDP-FL，而不暴露客户的原始隐私预算，通过间接分割的隐私偏好完全基于客户的噪声模型更新。问题的关键在于噪声更新可能受到 DP 噪声和非 IID 用户数据两个纠缠因素的影响，这使得是否可以通过分离这两个影响因素来揭示隐私偏好成为一个未知数。为了克服这一障碍，我们系统地研究了在什么条件下客户端的模型更新主要受噪声水平而不是数据分布的影响这一尚未探索的问题。然后，我们提出了一个简单而有效的策略，基于聚类的 L2范数的噪声更新，可以集成到普通的 PDP-FL，以保持相同的性能。实验结果证明了我们提出的隐私预算不可知的 PDP-FL 方法的有效性和可行性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Personalized+Differentially+Private+Federated+Learning+without+Exposing+Privacy+Budgets)|0| -|[A Flash Attention Transformer for Multi-Behaviour Recommendation](https://doi.org/10.1145/3583780.3615206)|Tendai Mukande, Esraa Ali, Annalina Caputo, Ruihai Dong, Noel E. O'Connor|University College Dublin, Dublin, Ireland; Dublin City University, Dublin, Ireland|\beginabstract Recently, modelling heterogeneous interactions in recommender systems has attracted research interest. Real-world scenarios involve sequential multi-type user-item interactions such as ''shape view'', ''shape add-to-favourites'', ''shape add-to-cart'' and ''shape purchase''. Graph Neural Network (GNN) methods have been widely adopted in Representation Learning of similar sequential user-item interactions. Promising results have been achieved by the integration of GNNs and transformers for self-attention. However, GNN based methods suffer from limited capability in handling global user-item interaction dependencies, particularly for long sequences. Moreover, these models require high computational cost of transformers, due to the quadratic memory and time complexity with respect to sequence length. This results in memory bottlenecks and slow training especially in computational resource-constrained environments. To address these challenges, we propose the FATH model which employs Flash Attention mechanism to reduce the high-bandwidth memory usage over higher-order user-item interaction sequences. Experimental results show that our model improves the training speed and reduces the memory usage with better recommendation performance in comparison with the state-of the art baselines.|近年来，在推荐系统中建立异构交互模型引起了人们的研究兴趣。真实世界的场景涉及连续的多类型用户项交互，如“形状视图”、“形状添加到收藏夹”、“形状添加到购物车”和“形状购买”。图神经网络(GNN)方法已被广泛应用于相似序列用户-项目交互的表示学习。全球导航卫星系统和变压器的结合为自我关注取得了令人鼓舞的成果。然而，基于 GNN 的方法在处理全局用户项交互依赖性方面能力有限，特别是对于长序列。此外，这些模型需要高计算成本的变压器，由于二次记忆和时间复杂度相对序列长度。这导致了内存瓶颈和训练速度缓慢，特别是在计算资源受限的环境中。为了解决这些问题，我们提出了基于 Flash 注意机制的 FATH 模型，以降低高阶用户项交互序列的高带宽内存使用。实验结果表明，该模型提高了训练速度，减少了内存使用，与目前最先进的基线相比，具有更好的推荐性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Flash+Attention+Transformer+for+Multi-Behaviour+Recommendation)|0| +|[A Flash Attention Transformer for Multi-Behaviour Recommendation](https://doi.org/10.1145/3583780.3615206)|Tendai Mukande, Esraa Ali, Annalina Caputo, Ruihai Dong, Noel E. O'Connor|Dublin City University, Dublin, Ireland; University College Dublin, Dublin, Ireland|\beginabstract Recently, modelling heterogeneous interactions in recommender systems has attracted research interest. Real-world scenarios involve sequential multi-type user-item interactions such as ''shape view'', ''shape add-to-favourites'', ''shape add-to-cart'' and ''shape purchase''. Graph Neural Network (GNN) methods have been widely adopted in Representation Learning of similar sequential user-item interactions. Promising results have been achieved by the integration of GNNs and transformers for self-attention. However, GNN based methods suffer from limited capability in handling global user-item interaction dependencies, particularly for long sequences. Moreover, these models require high computational cost of transformers, due to the quadratic memory and time complexity with respect to sequence length. This results in memory bottlenecks and slow training especially in computational resource-constrained environments. To address these challenges, we propose the FATH model which employs Flash Attention mechanism to reduce the high-bandwidth memory usage over higher-order user-item interaction sequences. Experimental results show that our model improves the training speed and reduces the memory usage with better recommendation performance in comparison with the state-of the art baselines.|近年来，在推荐系统中建立异构交互模型引起了人们的研究兴趣。真实世界的场景涉及连续的多类型用户项交互，如“形状视图”、“形状添加到收藏夹”、“形状添加到购物车”和“形状购买”。图神经网络(GNN)方法已被广泛应用于相似序列用户-项目交互的表示学习。全球导航卫星系统和变压器的结合为自我关注取得了令人鼓舞的成果。然而，基于 GNN 的方法在处理全局用户项交互依赖性方面能力有限，特别是对于长序列。此外，这些模型需要高计算成本的变压器，由于二次记忆和时间复杂度相对序列长度。这导致了内存瓶颈和训练速度缓慢，特别是在计算资源受限的环境中。为了解决这些问题，我们提出了基于 Flash 注意机制的 FATH 模型，以降低高阶用户项交互序列的高带宽内存使用。实验结果表明，该模型提高了训练速度，减少了内存使用，与目前最先进的基线相比，具有更好的推荐性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Flash+Attention+Transformer+for+Multi-Behaviour+Recommendation)|0| |[Differential Privacy in HyperNetworks for Personalized Federated Learning](https://doi.org/10.1145/3583780.3615203)|Vaisnavi Nemala, Phung Lai, NhatHai Phan|New Jersey Institute of Technology, Newark, NJ, USA; University at Albany - State University of New York, Albany, NY, USA|Federated learning (FL) is a framework for collaborative learning among users through a coordinating server. A recent HyperNetwork-based personalized FL framework, called HyperNetFL, is used to generate local models using personalized descriptors optimized for each user independently. However, HyperNetFL introduces unknown privacy risks. This paper introduces a novel approach to preserve user-level differential privacy, dubbed User-level DP, by providing formal privacy protection for data owners in training a HyperNetFL model. To achieve that, our proposed algorithm, called UDP-Alg, optimizes the trade-off between privacy loss and model utility by tightening sensitivity bounds. An intensive evaluation using benchmark datasets shows that our proposed UDP-Alg significantly improves privacy protection at a modest cost in utility.|联合学习(FL)是一个通过协调服务器在用户之间建立合作学习的框架。最近一个基于 HyperNetwork 的个性化 FL 框架，称为 HyperNetFL，用于使用为每个用户独立优化的个性化描述符生成本地模型。然而，HyperNetFL 引入了未知的隐私风险。本文介绍了一种新的方法来保护用户级别的差分隐私，称为用户级 DP，通过提供正式的隐私保护数据所有者在培训 HypernetFL 模型。为了实现这一目标，我们提出的算法，称为 UDP-Alg，通过收紧灵敏度界限，优化了隐私损失和模型实用性之间的权衡。使用基准数据集进行的深入评估表明，我们提出的 UDP-Alg 显著改善了隐私保护，而且成本不高。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Differential+Privacy+in+HyperNetworks+for+Personalized+Federated+Learning)|0| -|[Pre-training with Aspect-Content Text Mutual Prediction for Multi-Aspect Dense Retrieval](https://doi.org/10.1145/3583780.3615157)|Xiaojie Sun, Keping Bi, Jiafeng Guo, Xinyu Ma, Yixing Fan, Hongyu Shan, Qishen Zhang, Zhongyi Liu|; ICT, CAS & University of Chinese Academy of Sciences, Beijing, China; Ant Group, Beijing, China|Grounded on pre-trained language models (PLMs), dense retrieval has been studied extensively on plain text. In contrast, there has been little research on retrieving data with multiple aspects using dense models. In the scenarios such as product search, the aspect information plays an essential role in relevance matching, e.g., category: Electronics, Computers, and Pet Supplies. A common way of leveraging aspect information for multi-aspect retrieval is to introduce an auxiliary classification objective, i.e., using item contents to predict the annotated value IDs of item aspects. However, by learning the value embeddings from scratch, this approach may not capture the various semantic similarities between the values sufficiently. To address this limitation, we leverage the aspect information as text strings rather than class IDs during pre-training so that their semantic similarities can be naturally captured in the PLMs. To facilitate effective retrieval with the aspect strings, we propose mutual prediction objectives between the text of the item aspect and content. In this way, our model makes more sufficient use of aspect information than conducting undifferentiated masked language modeling (MLM) on the concatenated text of aspects and content. Extensive experiments on two real-world datasets (product and mini-program search) show that our approach can outperform competitive baselines both treating aspect values as classes and conducting the same MLM for aspect and content strings. Code and related dataset will be available at the URL \footnote{https://github.com/sunxiaojie99/ATTEMPT}.|基于预训练语言模型(PLM)的密集检索已经在纯文本上得到了广泛的研究。相比之下，使用密集模型检索多方面数据的研究很少。在诸如产品搜索这样的场景中，方面信息在相关性匹配中扮演着重要的角色，例如，类别: 电子产品、计算机和宠物用品。利用方面信息进行多方面检索的一种常见方法是引入辅助分类目标，即使用项目内容来预测项目方面的注释值 ID。然而，通过从头学习值嵌入，这种方法可能无法充分捕获值之间的各种语义相似性。为了解决这个限制，我们在预训练期间利用方面信息作为文本字符串而不是类 ID，这样它们的语义相似性可以自然地在 PLM 中捕获。为了方便有效的检索与方面字符串，我们提出了项目方面的文本和内容之间的相互预测目标。通过这种方式，我们的模型比对方面和内容的连接文本进行无区别掩蔽语言建模(MLM)更充分地利用了方面信息。对两个真实世界数据集(产品和小型程序搜索)的大量实验表明，我们的方法可以胜过将方面值作为类处理以及对方面和内容字符串执行相同的传销的竞争性基线。代码和相关数据集可在网址脚注{ https://github.com/sunxiaojie99/attempt }查阅。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Pre-training+with+Aspect-Content+Text+Mutual+Prediction+for+Multi-Aspect+Dense+Retrieval)|0| +|[Pre-training with Aspect-Content Text Mutual Prediction for Multi-Aspect Dense Retrieval](https://doi.org/10.1145/3583780.3615157)|Xiaojie Sun, Keping Bi, Jiafeng Guo, Xinyu Ma, Yixing Fan, Hongyu Shan, Qishen Zhang, Zhongyi Liu|; Ant Group, Beijing, China; ICT, CAS & University of Chinese Academy of Sciences, Beijing, China|Grounded on pre-trained language models (PLMs), dense retrieval has been studied extensively on plain text. In contrast, there has been little research on retrieving data with multiple aspects using dense models. In the scenarios such as product search, the aspect information plays an essential role in relevance matching, e.g., category: Electronics, Computers, and Pet Supplies. A common way of leveraging aspect information for multi-aspect retrieval is to introduce an auxiliary classification objective, i.e., using item contents to predict the annotated value IDs of item aspects. However, by learning the value embeddings from scratch, this approach may not capture the various semantic similarities between the values sufficiently. To address this limitation, we leverage the aspect information as text strings rather than class IDs during pre-training so that their semantic similarities can be naturally captured in the PLMs. To facilitate effective retrieval with the aspect strings, we propose mutual prediction objectives between the text of the item aspect and content. In this way, our model makes more sufficient use of aspect information than conducting undifferentiated masked language modeling (MLM) on the concatenated text of aspects and content. Extensive experiments on two real-world datasets (product and mini-program search) show that our approach can outperform competitive baselines both treating aspect values as classes and conducting the same MLM for aspect and content strings. Code and related dataset will be available at the URL \footnote{https://github.com/sunxiaojie99/ATTEMPT}.|基于预训练语言模型(PLM)的密集检索已经在纯文本上得到了广泛的研究。相比之下，使用密集模型检索多方面数据的研究很少。在诸如产品搜索这样的场景中，方面信息在相关性匹配中扮演着重要的角色，例如，类别: 电子产品、计算机和宠物用品。利用方面信息进行多方面检索的一种常见方法是引入辅助分类目标，即使用项目内容来预测项目方面的注释值 ID。然而，通过从头学习值嵌入，这种方法可能无法充分捕获值之间的各种语义相似性。为了解决这个限制，我们在预训练期间利用方面信息作为文本字符串而不是类 ID，这样它们的语义相似性可以自然地在 PLM 中捕获。为了方便有效的检索与方面字符串，我们提出了项目方面的文本和内容之间的相互预测目标。通过这种方式，我们的模型比对方面和内容的连接文本进行无区别掩蔽语言建模(MLM)更充分地利用了方面信息。对两个真实世界数据集(产品和小型程序搜索)的大量实验表明，我们的方法可以胜过将方面值作为类处理以及对方面和内容字符串执行相同的传销的竞争性基线。代码和相关数据集可在网址脚注{ https://github.com/sunxiaojie99/attempt }查阅。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Pre-training+with+Aspect-Content+Text+Mutual+Prediction+for+Multi-Aspect+Dense+Retrieval)|0| |[Sequential Text-based Knowledge Update with Self-Supervised Learning for Generative Language Models](https://doi.org/10.1145/3583780.3615188)|HaoRu Sung, YingJhe Tang, YuChung Cheng, PaiLin Chen, TsaiYen Li, HenHsen Huang|National Chengchi University, Taipei City, Taiwan Roc; Academia Sinica, Taipei City, Taiwan Roc|This work proposes a new natural language processing (NLP) task to tackle the issue of multi-round, sequential text-based knowledge update. The study introduces a hybrid learning architecture and a novel self-supervised training strategy to enable generative language models to consolidate knowledge in the same way as humans. A dataset was also created for evaluation and results showed the effectiveness of our methodology. Experimental results confirm the superiority of the proposed approach over existing models and large language models (LLMs). The proposed task and model framework have the potential to significantly improve the automation of knowledge organization, making text-based knowledge an increasingly crucial resource for powerful LLMs to perform various tasks for humans.|提出了一种新的自然语言处理(NLP)任务来解决基于文本序列的多轮知识更新问题。该研究引入了一种混合学习架构和一种新的自我监督训练策略，使生成语言模型能够像人类一样巩固知识。还创建了一个用于评估的数据集，结果显示了我们的方法的有效性。实验结果证实了该方法相对于现有模型和大语言模型(LLM)的优越性。提出的任务和模型框架有可能显著提高知识组织的自动化程度，使基于文本的知识成为强大的 LLM 执行人类各种任务的日益重要的资源。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Sequential+Text-based+Knowledge+Update+with+Self-Supervised+Learning+for+Generative+Language+Models)|0| -|[RecRec: Algorithmic Recourse for Recommender Systems](https://doi.org/10.1145/3583780.3615181)|Sahil Verma, Ashudeep Singh, Varich Boonsanong, John P. Dickerson, Chirag Shah|University of Maryland, College Park, MD, USA; University of Washington, Seattle, WA, USA; Pinterest, Inc., San Francisco, CA, USA|Recommender systems play an essential role in the choices people make in domains such as entertainment, shopping, food, news, employment, and education. The machine learning models underlying these recommender systems are often enormously large and black-box in nature for users, content providers, and system developers alike. It is often crucial for all stakeholders to understand the model's rationale behind making certain predictions and recommendations. This is especially true for the content providers whose livelihoods depend on the recommender system. Drawing motivation from the practitioners' need, in this work, we propose a recourse framework for recommender systems, targeted towards the content providers. Algorithmic recourse in the recommendation setting is a set of actions that, if executed, would modify the recommendations (or ranking) of an item in the desired manner. A recourse suggests actions of the form: "if a feature changes X to Y, then the ranking of that item for a set of users will change to Z." Furthermore, we demonstrate that RecRec is highly effective in generating valid, sparse, and actionable recourses through an empirical evaluation of recommender systems trained on three real-world datasets. To the best of our knowledge, this work is the first to conceptualize and empirically test a generalized framework for generating recourses for recommender systems.|推荐系统在人们在娱乐、购物、食品、新闻、就业和教育等领域做出的选择中起着至关重要的作用。对于用户、内容提供商和系统开发人员来说，这些推荐系统背后的机器学习模型通常是非常巨大的黑盒子。对于所有的利益相关者来说，理解模型做出某些预测和建议背后的基本原理通常是至关重要的。对于内容提供商来说尤其如此，因为他们的生计取决于推荐系统。在这项工作中，我们从实践者的需要中汲取动机，提出了一个面向内容提供者的推荐系统的追索框架。推荐设置中的算法追索权是一组操作，如果执行，将以期望的方式修改项目的推荐(或排名)。资源建议的行动的形式: “如果一个功能改变 X 到 Y，那么该项目的排名为一组用户将改变为 Z。”此外，我们证明，RecRec 是非常有效的生成有效的，稀疏，可操作的资源，通过实证评估的推荐系统训练的三个现实世界的数据集。据我们所知，这项工作是第一个概念化和经验测试一个通用的框架生成推荐系统的资源。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=RecRec:+Algorithmic+Recourse+for+Recommender+Systems)|0| +|[RecRec: Algorithmic Recourse for Recommender Systems](https://doi.org/10.1145/3583780.3615181)|Sahil Verma, Ashudeep Singh, Varich Boonsanong, John P. Dickerson, Chirag Shah|University of Washington, Seattle, WA, USA; Pinterest, Inc., San Francisco, CA, USA; University of Maryland, College Park, MD, USA|Recommender systems play an essential role in the choices people make in domains such as entertainment, shopping, food, news, employment, and education. The machine learning models underlying these recommender systems are often enormously large and black-box in nature for users, content providers, and system developers alike. It is often crucial for all stakeholders to understand the model's rationale behind making certain predictions and recommendations. This is especially true for the content providers whose livelihoods depend on the recommender system. Drawing motivation from the practitioners' need, in this work, we propose a recourse framework for recommender systems, targeted towards the content providers. Algorithmic recourse in the recommendation setting is a set of actions that, if executed, would modify the recommendations (or ranking) of an item in the desired manner. A recourse suggests actions of the form: "if a feature changes X to Y, then the ranking of that item for a set of users will change to Z." Furthermore, we demonstrate that RecRec is highly effective in generating valid, sparse, and actionable recourses through an empirical evaluation of recommender systems trained on three real-world datasets. To the best of our knowledge, this work is the first to conceptualize and empirically test a generalized framework for generating recourses for recommender systems.|推荐系统在人们在娱乐、购物、食品、新闻、就业和教育等领域做出的选择中起着至关重要的作用。对于用户、内容提供商和系统开发人员来说，这些推荐系统背后的机器学习模型通常是非常巨大的黑盒子。对于所有的利益相关者来说，理解模型做出某些预测和建议背后的基本原理通常是至关重要的。对于内容提供商来说尤其如此，因为他们的生计取决于推荐系统。在这项工作中，我们从实践者的需要中汲取动机，提出了一个面向内容提供者的推荐系统的追索框架。推荐设置中的算法追索权是一组操作，如果执行，将以期望的方式修改项目的推荐(或排名)。资源建议的行动的形式: “如果一个功能改变 X 到 Y，那么该项目的排名为一组用户将改变为 Z。”此外，我们证明，RecRec 是非常有效的生成有效的，稀疏，可操作的资源，通过实证评估的推荐系统训练的三个现实世界的数据集。据我们所知，这项工作是第一个概念化和经验测试一个通用的框架生成推荐系统的资源。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=RecRec:+Algorithmic+Recourse+for+Recommender+Systems)|0| |[Network Embedding with Adaptive Multi-hop Contrast](https://doi.org/10.1145/3583780.3615179)|Chenhao Wang, Yong Liu, Yan Yang|Heilongjiang University, Harbin, China|\beginabstract Graph neural networks (GNNs) have shown strong performance in graph-based analysis tasks. Despite their remarkable success, the inherent homophilic message-passing mechanism (MP) makes GNNs challenging to generalize to heterophilic graphs. In addition, the MP explicitly exploits the connection relationships between local neighbor nodes making GNNs unable to maintain stable performance in the face of adversarial perturbation attacks. In this paper, we propose a new method to explore graph structure by removing explicit message-passing mechanisms and present a network embedding framework AMCNE with Adaptive Multi-hop Contrast loss (AMCLoss) to address these challenges. AMCNE only relies on a simple autoencoder to obtain node representations for classification and uses elaborate contrastive loss to drive nodes capturing complex structural information on heterophilic graphs. The comprehensive experiments show that AMCNE outperforms state-of-the-art baseline models on homophilic and heterophilic graphs and is more robust in the node classification task. \endabstract|初级抽象图神经网络(GNN)在基于图的分析任务中表现出很强的性能。尽管它们取得了显著的成功，但是固有的同质信息传递机制(MP)使得 GNN 难以推广到异质图。此外，MP 算法明确地利用了本地邻居节点之间的连接关系，使得 GNN 在面对对抗性扰动攻击时无法保持稳定的性能。本文提出了一种通过去除显式消息传递机制来探索图结构的新方法，并提出了一种带有自适应多跳对比度损失(AMCLoss)的网络嵌入框架 AMCNE 来解决这些问题。AMCNE 只依靠一个简单的自动编码器来获得分类的节点表示，并使用精细的对比损失来驱动节点捕获异质图上的复杂结构信息。综合实验表明，AMCNE 在同亲和异亲图上优于最先进的基线模型，在节点分类任务中具有更强的鲁棒性。结束摘要|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Network+Embedding+with+Adaptive+Multi-hop+Contrast)|0| |[A Joint Training-Calibration Framework for Test-Time Personalization with Label Shift in Federated Learning](https://doi.org/10.1145/3583780.3615173)|Jian Xu, ShaoLun Huang|Tsinghua University, Shenzhen, China|The data heterogeneity has been a challenging issue in federated learning in both training and inference stages, which motivates a variety of approaches to learn either personalized models for participating clients or test-time adaptations for unseen clients. One such approach is employing a shared feature representation and a customized classifier head for each client. However, previous works either neglect the global head with rich knowledge or assume the new clients have enough labeled data, which significantly limit their broader practicality. In this work, we propose a lightweight framework to tackle with the label shift issue during the model deployment by test priors estimation and model prediction calibration. We also demonstrate the importance of training a balanced global model in FL so as to guarantee the general effectiveness of prior estimation approaches. Evaluation results on benchmark datasets demonstrate the superiority of our framework for model adaptation in unseen clients with unknown label shifts.|数据异构性在联邦学习的训练和推理阶段都是一个具有挑战性的问题，它激发了各种方法来学习参与客户的个性化模型或者为看不见的客户进行测试时间适应。其中一种方法是为每个客户机使用一个共享特征表示和一个定制的分类器头。然而，以往的研究忽略了具有丰富知识的全局负责人，或者假设新客户拥有足够的标记数据，从而大大限制了其更广泛的实用性。在这项工作中，我们提出了一个轻量级的框架，通过测试先验估计和模型预测校准来解决模型部署过程中的标签移位问题。我们还证明了在 FL 中训练一个平衡的全局模型以保证先验估计方法的一般有效性的重要性。对基准数据集的评估结果表明了我们的模型适应框架在未知客户端的未知标签位移方面的优越性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Joint+Training-Calibration+Framework+for+Test-Time+Personalization+with+Label+Shift+in+Federated+Learning)|0| |[Geometry Interaction Augmented Graph Collaborative Filtering](https://doi.org/10.1145/3583780.3615204)|Jie Xu, Chaozhuo Li|Beijing Foreign Studies University, Beijing, China; Microsoft Research Asia, Beijing, China|Graph collaborative filtering, which could capture the abundant collaborative signal from the high-order connectivity of the tree-likeness user-item interaction graph, has received considerable research attention recently. Most graph collaborative filtering methods embed graphs in the Euclidean spaces, but that could have high distortion when embedding graphs with tree-likeness structure. Recently, some researchers address this problem by learning the feature representations in the hyperbolic spaces. However, because the user-item interaction graphs also have cyclic structure, the high-order collaborative signal cannot be well captured by hyperbolic spaces. From this point of view, neither Euclidean spaces nor hyperbolic spaces can capture the full information from the complexity of user-item interactions. Therefore, how to construct a suitable embedding space for graph collaboration filtering is an important problem. In this paper, we analyze the properties of hyperbolic geometry in graph collaborative filtering tasks and proposed a novel geometry interaction augmented graph collaborative filtering (GeoGCF) method, which leverages both Euclidean and hyperbolic geometry to model the user-item interactions. Experimental results show the effectiveness of the proposed method.|图形协同过滤能够从树形用户-项目交互图的高阶连通性中捕捉到丰富的协作信号，近年来得到了广泛的研究关注。大多数图形协同过滤方法都是在欧几里德空间中嵌入图形，但是当嵌入具有树形结构的图形时，这种方法会产生很大的失真。最近，一些研究者通过学习双曲空间中的特征表示来解决这个问题。然而，由于用户项交互图也具有循环结构，双曲空间不能很好地捕获高阶协作信号。从这个角度来看，无论是欧几里德空间还是双曲空间都不能从用户-项目交互的复杂性中获取完整的信息。因此，如何构造合适的嵌入空间进行图协同过滤是一个重要问题。在这篇文章中，我们分析了图形双曲几何任务的协同过滤特性，提出了一种新的几何交互增强图形协同过滤(GeogCF)方法，该方法利用欧几里得和双曲几何对用户-项目的交互进行建模。实验结果表明了该方法的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Geometry+Interaction+Augmented+Graph+Collaborative+Filtering)|0| -|[Graph-based Alignment and Uniformity for Recommendation](https://doi.org/10.1145/3583780.3615185)|Liangwei Yang, Zhiwei Liu, Chen Wang, Mingdai Yang, Xiaolong Liu, Jing Ma, Philip S. Yu|University of Electronic Science and Technology of China, Chengdu, China; University of Illinois Chicago, Chicago, IL, USA; Salesforce AI Research, Palo Alto, CA, USA|Collaborative filtering-based recommender systems (RecSys) rely on learning representations for users and items to predict preferences accurately. Representation learning on the hypersphere is a promising approach due to its desirable properties, such as alignment and uniformity. However, the sparsity issue arises when it encounters RecSys. To address this issue, we propose a novel approach, graph-based alignment and uniformity (GraphAU), that explicitly considers high-order connectivities in the user-item bipartite graph. GraphAU aligns the user/item embedding to the dense vector representations of high-order neighbors using a neighborhood aggregator, eliminating the need to compute the burdensome alignment to high-order neighborhoods individually. To address the discrepancy in alignment losses, GraphAU includes a layer-wise alignment pooling module to integrate alignment losses layer-wise. Experiments on four datasets show that GraphAU significantly alleviates the sparsity issue and achieves state-of-the-art performance. We open-source GraphAU at https://github.com/YangLiangwei/GraphAU.|基于协同过滤的推荐系统(RecSys)依赖于用户和项目的学习表示来准确预测偏好。超球面上的表示学习是一种很有前途的学习方法，因为它具有良好的对齐性和一致性等特性。然而，当它遇到 RecSys 时，稀疏性问题就出现了。为了解决这个问题，我们提出了一种新的方法——基于图的对齐和一致性(GraphAU) ，它明确地考虑了用户项二部图中的高阶连通性。GraphAU 使用邻域聚合器将嵌入的用户/项目对齐到高阶邻居的密集向量表示，从而消除了单独计算高阶邻居的繁琐对齐的需要。为了解决对齐损失中的差异，GraphAU 包括一个分层对齐池模块，以分层集成对齐损失。在四个数据集上的实验表明，GraphAU 显著地缓解了稀疏性问题，并达到了最先进的性能。我们 https://github.com/yangliangwei/GraphAU 开源 GraphAU。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Graph-based+Alignment+and+Uniformity+for+Recommendation)|0| -|[Predicting Interaction Quality of Conversational Assistants With Spoken Language Understanding Model Confidences](https://doi.org/10.1145/3583780.3615493)|Yue Gao, Enrico Piovano, Tamer Soliman, Monir Moniruzzaman, Anoop Kumar, Melanie Bradford, Subhrangshu Nandi|Amazon Alexa AI, Sunnyvale, USA; University of Wisconsin-Madison, Madison, USA; Amazon Alexa AI, Seattle, USA; Amazon Alexa AI, Berlin, Germany|In conversational AI assistants, SLU models are part of a complex pipeline composed of several modules working in harmony. Hence, an update to the SLU model needs to ensure improvements not only in the model specific metrics but also in the overall conversational assistant performance. Specifically, the impact on user interaction quality metrics must be factored in, while integrating interactions with distal modules upstream and downstream of the SLU component. We develop a ML model that makes it possible to gauge the interaction quality metrics due to SLU model changes before a production launch. The proposed model is a multi-modal transformer with a gated mechanism that conditions on text embeddings, output of a BERT model pre-trained on conversational data, and the hypotheses of the SLU classifiers with the corresponding confidence scores. We show that the proposed model predicts defect with more than 76% correlation with live interaction quality defects, compared to 46% baseline.|在会话型人工智能助手领域，SLU 模型是由几个协调工作的模块组成的复杂管道的一部分。因此，对 SLU 模型的更新不仅需要确保模型特定指标的改进，还需要确保整体会话助理性能的改进。具体来说，在与 SLU 组件的远程模块集成交互时，必须考虑到对用户交互质量指标的影响上游或下游。我们开发了一个机器学习模型，可以在产品投入生产之前测量由于 SLU 模型变化而产生的交互质量指标。该模型是一个具有门控机制的多模态转换器，其条件为文本嵌入、基于会话数据的 BERT 模型的输出以及 SLU 分类器的假设和相应的置信度。我们表明，与46% 的基线相比，提出的模型预测缺陷与实时交互质量缺陷的相关性超过76% 。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Predicting+Interaction+Quality+of+Conversational+Assistants+With+Spoken+Language+Understanding+Model+Confidences)|0| +|[Graph-based Alignment and Uniformity for Recommendation](https://doi.org/10.1145/3583780.3615185)|Liangwei Yang, Zhiwei Liu, Chen Wang, Mingdai Yang, Xiaolong Liu, Jing Ma, Philip S. Yu|University of Electronic Science and Technology of China, Chengdu, China; Salesforce AI Research, Palo Alto, CA, USA; University of Illinois Chicago, Chicago, IL, USA|Collaborative filtering-based recommender systems (RecSys) rely on learning representations for users and items to predict preferences accurately. Representation learning on the hypersphere is a promising approach due to its desirable properties, such as alignment and uniformity. However, the sparsity issue arises when it encounters RecSys. To address this issue, we propose a novel approach, graph-based alignment and uniformity (GraphAU), that explicitly considers high-order connectivities in the user-item bipartite graph. GraphAU aligns the user/item embedding to the dense vector representations of high-order neighbors using a neighborhood aggregator, eliminating the need to compute the burdensome alignment to high-order neighborhoods individually. To address the discrepancy in alignment losses, GraphAU includes a layer-wise alignment pooling module to integrate alignment losses layer-wise. Experiments on four datasets show that GraphAU significantly alleviates the sparsity issue and achieves state-of-the-art performance. We open-source GraphAU at https://github.com/YangLiangwei/GraphAU.|基于协同过滤的推荐系统(RecSys)依赖于用户和项目的学习表示来准确预测偏好。超球面上的表示学习是一种很有前途的学习方法，因为它具有良好的对齐性和一致性等特性。然而，当它遇到 RecSys 时，稀疏性问题就出现了。为了解决这个问题，我们提出了一种新的方法——基于图的对齐和一致性(GraphAU) ，它明确地考虑了用户项二部图中的高阶连通性。GraphAU 使用邻域聚合器将嵌入的用户/项目对齐到高阶邻居的密集向量表示，从而消除了单独计算高阶邻居的繁琐对齐的需要。为了解决对齐损失中的差异，GraphAU 包括一个分层对齐池模块，以分层集成对齐损失。在四个数据集上的实验表明，GraphAU 显著地缓解了稀疏性问题，并达到了最先进的性能。我们 https://github.com/yangliangwei/GraphAU 开源 GraphAU。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Graph-based+Alignment+and+Uniformity+for+Recommendation)|0| +|[Predicting Interaction Quality of Conversational Assistants With Spoken Language Understanding Model Confidences](https://doi.org/10.1145/3583780.3615493)|Yue Gao, Enrico Piovano, Tamer Soliman, Monir Moniruzzaman, Anoop Kumar, Melanie Bradford, Subhrangshu Nandi|University of Wisconsin-Madison, Madison, USA; Amazon Alexa AI, Seattle, USA; Amazon Alexa AI, Sunnyvale, USA; Amazon Alexa AI, Berlin, Germany|In conversational AI assistants, SLU models are part of a complex pipeline composed of several modules working in harmony. Hence, an update to the SLU model needs to ensure improvements not only in the model specific metrics but also in the overall conversational assistant performance. Specifically, the impact on user interaction quality metrics must be factored in, while integrating interactions with distal modules upstream and downstream of the SLU component. We develop a ML model that makes it possible to gauge the interaction quality metrics due to SLU model changes before a production launch. The proposed model is a multi-modal transformer with a gated mechanism that conditions on text embeddings, output of a BERT model pre-trained on conversational data, and the hypotheses of the SLU classifiers with the corresponding confidence scores. We show that the proposed model predicts defect with more than 76% correlation with live interaction quality defects, compared to 46% baseline.|在会话型人工智能助手领域，SLU 模型是由几个协调工作的模块组成的复杂管道的一部分。因此，对 SLU 模型的更新不仅需要确保模型特定指标的改进，还需要确保整体会话助理性能的改进。具体来说，在与 SLU 组件的远程模块集成交互时，必须考虑到对用户交互质量指标的影响上游或下游。我们开发了一个机器学习模型，可以在产品投入生产之前测量由于 SLU 模型变化而产生的交互质量指标。该模型是一个具有门控机制的多模态转换器，其条件为文本嵌入、基于会话数据的 BERT 模型的输出以及 SLU 分类器的假设和相应的置信度。我们表明，与46% 的基线相比，提出的模型预测缺陷与实时交互质量缺陷的相关性超过76% 。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Predicting+Interaction+Quality+of+Conversational+Assistants+With+Spoken+Language+Understanding+Model+Confidences)|0| |[pADR: Towards Personalized Adverse Drug Reaction Prediction by Modeling Multi-sourced Data](https://doi.org/10.1145/3583780.3615490)|Junyu Luo, Cheng Qian, Xiaochen Wang, Lucas Glass, Fenglong Ma|The Pennsylvania State University, University Park, PA, USA; IQVIA, Chicago, IL, USA|Predicting adverse drug reactions (ADRs) of drugs is one of the most critical steps in drug development. By pre-estimating the adverse reactions, researchers and drug development companies can greatly prevent the potential ADR risks and tragedies. However, the current ADR prediction methods suffer from several limitations. First, the prediction results are based on pure drug-related information, which makes them impossible to be directly applied for the personalized ADR prediction task. The lack of personalization of models also makes rare adverse events hard to be predicted. Therefore, it is of great interest to develop a new personalized ADR prediction method by introducing additional sources, e.g., patient health records. However, few methods have tried to use additional sources. In the meantime, the variety of different source formats and structures makes this task more challenging. To address the above challenges, we propose a novel personalized multi-sourced-based drug adverse reaction prediction model named pADR. pADR first works on every single source to transform them into proper representations. Next, a hierarchical multi-sourced Transformer is designed to automatically model the interactions between different sources and fuse them together for the final adverse event prediction. Experimental results on a new multi-sourced ADR prediction dataset show that pADR1 outperforms state-of-the-art drug-based baselines. Moreover, the case and ablation studies also illustrate the effectiveness of our proposed fusion strategies and the reasonableness of each module design.|预测药物不良反应(ADR)是药物开发过程中最关键的步骤之一。通过预测不良反应，研究人员和药物开发公司可以大大防止潜在的不良反应风险和悲剧。然而，目前的药品不良反应预测方法存在一些局限性。首先，预测结果基于纯药物相关信息，不可能直接应用于个性化的药品不良反应预测任务。缺乏个性化的模型也使罕见的不良事件难以预测。因此，开发一种新的个性化的 ADR 预测方法，通过引入额外的资源，如病人健康记录，是非常有意义的。然而，很少有方法尝试使用其他来源。与此同时，各种不同的源格式和结构使这项任务更具挑战性。为了应对上述挑战，我们提出了一种新的个性化的基于多来源的药物不良反应预测模型 pADR。PADR 首先对每一个源进行处理，将它们转换成适当的表示。接下来，设计了一个分层的多源变压器，自动建模不同源之间的交互，并将它们融合在一起进行最终的不良事件预测。在一个新的多源 ADR 预测数据集上的实验结果表明，pADR1的表现优于最先进的基于药物的基线。此外，实例和烧蚀研究也说明了我们提出的融合策略的有效性和每个模块设计的合理性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=pADR:+Towards+Personalized+Adverse+Drug+Reaction+Prediction+by+Modeling+Multi-sourced+Data)|0| -|[Exploiting Sequential Music Preferences via Optimisation-Based Sequencing](https://doi.org/10.1145/3583780.3615476)|Dmitrii Moor, Yi Yuan, Rishabh Mehrotra, Zhenwen Dai, Mounia Lalmas|Spotify, New York, NY, USA; Spotify, London, United Kingdom; ShareChat, London, United Kingdom|Users in music streaming platforms typically consume tracks sequentially in sessions by interacting with personalised playlists. To satisfy users, music platforms usually rely on recommender systems that learn users' preferences over individual tracks and rank the tracks within each playlist according to the learned preferences. However, such rankings often do not fully exploit the sequential nature of the users' consumption, which may result in a lower within-a-session consumption. In this paper, we model the sequential within-a-session preferences of users and propose an optimisation-based sequencing approach that allows for optimally incorporating such preferences into the rankings. To this end, we rely on interaction data of a major music streaming service to identify two most common aspects of the users' sequential preferences: (1) Position-Aware preferences, and (2) Local-Sequential preferences. We propose a sequencing model that can leverage each of these aspects optimally to maximise the expected total consumption from the session. We further perform an extensive offline and off-policy evaluation of our model, and carry out a large scale online randomised control trial with 7M users across 80 countries. Our findings confirm that we can effectively incorporate sequential preferences of users into our sequencer to make users complete more and skip less tracks within their listening sessions.|音乐流媒体平台的用户通常通过与个性化播放列表交互，在会话中连续消费歌曲。为了满足用户的需求，音乐平台通常依赖于推荐系统来了解用户对单曲的偏好，并根据学到的偏好对每个播放列表中的曲目进行排名。然而，这样的排名往往没有充分利用用户消费的顺序性质，这可能导致较低的会话内消费。在这篇论文中，我们模拟了用户在一个会话内的顺序偏好，并提出了一个基于优化的排序方法，允许将这些偏好最佳地纳入到排名中。为此，我们依靠一个主流音乐流媒体服务的交互数据来识别用户顺序偏好的两个最常见的方面: (1)位置感知偏好和(2)本地顺序偏好。我们提出了一个排序模型，可以最佳地利用这些方面中的每一个来最大化会话的预期总消耗。我们进一步对我们的模型进行了广泛的离线和非政策评估，并在80个国家的700万用户中进行了大规模的在线随机对照试验。我们的研究结果证实，我们可以有效地将用户的顺序偏好纳入我们的音序器，使用户完成更多，跳过更少的轨道在他们的听力会议。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Exploiting+Sequential+Music+Preferences+via+Optimisation-Based+Sequencing)|0| +|[Exploiting Sequential Music Preferences via Optimisation-Based Sequencing](https://doi.org/10.1145/3583780.3615476)|Dmitrii Moor, Yi Yuan, Rishabh Mehrotra, Zhenwen Dai, Mounia Lalmas|ShareChat, London, United Kingdom; Spotify, London, United Kingdom; Spotify, New York, NY, USA|Users in music streaming platforms typically consume tracks sequentially in sessions by interacting with personalised playlists. To satisfy users, music platforms usually rely on recommender systems that learn users' preferences over individual tracks and rank the tracks within each playlist according to the learned preferences. However, such rankings often do not fully exploit the sequential nature of the users' consumption, which may result in a lower within-a-session consumption. In this paper, we model the sequential within-a-session preferences of users and propose an optimisation-based sequencing approach that allows for optimally incorporating such preferences into the rankings. To this end, we rely on interaction data of a major music streaming service to identify two most common aspects of the users' sequential preferences: (1) Position-Aware preferences, and (2) Local-Sequential preferences. We propose a sequencing model that can leverage each of these aspects optimally to maximise the expected total consumption from the session. We further perform an extensive offline and off-policy evaluation of our model, and carry out a large scale online randomised control trial with 7M users across 80 countries. Our findings confirm that we can effectively incorporate sequential preferences of users into our sequencer to make users complete more and skip less tracks within their listening sessions.|音乐流媒体平台的用户通常通过与个性化播放列表交互，在会话中连续消费歌曲。为了满足用户的需求，音乐平台通常依赖于推荐系统来了解用户对单曲的偏好，并根据学到的偏好对每个播放列表中的曲目进行排名。然而，这样的排名往往没有充分利用用户消费的顺序性质，这可能导致较低的会话内消费。在这篇论文中，我们模拟了用户在一个会话内的顺序偏好，并提出了一个基于优化的排序方法，允许将这些偏好最佳地纳入到排名中。为此，我们依靠一个主流音乐流媒体服务的交互数据来识别用户顺序偏好的两个最常见的方面: (1)位置感知偏好和(2)本地顺序偏好。我们提出了一个排序模型，可以最佳地利用这些方面中的每一个来最大化会话的预期总消耗。我们进一步对我们的模型进行了广泛的离线和非政策评估，并在80个国家的700万用户中进行了大规模的在线随机对照试验。我们的研究结果证实，我们可以有效地将用户的顺序偏好纳入我们的音序器，使用户完成更多，跳过更少的轨道在他们的听力会议。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Exploiting+Sequential+Music+Preferences+via+Optimisation-Based+Sequencing)|0| |[Deep Query Rewriting For Geocoding](https://doi.org/10.1145/3583780.3615466)|Pravakar Roy, Chirag Sharma, Chao Gao, Kumarswamy Valegerepura|Microsoft, Mountain View, CA, USA; Microsoft, Bellevue, WA, USA|Query rewriting aims to bridge the gap between user queries and indexed data. In the context of geocoding, it augments the user query to match all relevant entities in indexed data. This work presents an end-to-end trainable attention-based query rewriting model for geocoding. Most recent works attempt to solve query rewriting with sequence-to-sequence models, which often fail to satisfy the tight latency constraints of production environments. Toward developing a low latency model, we formulate it as a combination of multiple token classification tasks. We introduce numerous novel techniques, such as constructing a separate output space for alterations containing whole words to tackle many-to-one/one-to-many associations, using regular expression rules as prediction classes to handle the limitations of having a non-word piece prediction space, and curbing the output space for each wordpiece in our vocabulary to reduce inference time by 20x in average. We also present a data generation pipeline that generates train/test data without human judgment from the query logs. Through offline and online experiments, we demonstrate that the proposed model is orders of magnitude faster than their seq2seq and SMT-based counterparts in inference time, achieves better/on-par performance, and improves the relevance of overall geocoding.|查询重写旨在弥合用户查询和索引数据之间的差距。在地理编码的上下文中，它扩展用户查询以匹配索引数据中的所有相关实体。提出了一种基于端到端可训练注意的地理编码查询重写模型。最近的工作尝试用序列到序列模型来解决查询重写问题，这种模型往往不能满足生产环境的严格延迟约束。为了开发一个低延迟模型，我们将其描述为多个令牌分类任务的组合。我们引入了许多新颖的技术，例如构建一个单独的输出空间，用于包含整个单词的修改以处理多对一/一对多的关联，使用正则表达式规则作为预测类来处理具有非单词片段预测空间的局限性，以及限制词汇表中每个单词片段的输出空间以平均减少20倍的推理时间。我们还提供了一个数据生成流水线，该流水线生成列车/测试数据，而不需要从查询日志中进行人工判断。通过离线和在线实验，我们证明了所提出的模型在推理时间上比基于 seq2seq 和基于 SMT 的对应数量级更快，获得了更好/同等的性能，并提高了整体地理编码的相关性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Deep+Query+Rewriting+For+Geocoding)|0| -|[An Efficient Content-based Time Series Retrieval System](https://doi.org/10.1145/3583780.3614655)|ChinChia Michael Yeh, Huiyuan Chen, Xin Dai, Yan Zheng, Junpeng Wang, Vivian Lai, Yujie Fan, Audrey Der, Zhongfang Zhuang, Liang Wang, Wei Zhang, Jeff M. Phillips|University of California, Riverside, Riverside, CA, USA; University of Utah, Salt Lake City, UT, USA; Visa Research, Palo Alto, CA, USA; Visa Resarch, Palo Alto, CA, USA|A Content-based Time Series Retrieval (CTSR) system is an information retrieval system for users to interact with time series emerged from multiple domains, such as finance, healthcare, and manufacturing. For example, users seeking to learn more about the source of a time series can submit the time series as a query to the CTSR system and retrieve a list of relevant time series with associated metadata. By analyzing the retrieved metadata, users can gather more information about the source of the time series. Because the CTSR system is required to work with time series data from diverse domains, it needs a high-capacity model to effectively measure the similarity between different time series. On top of that, the model within the CTSR system has to compute the similarity scores in an efficient manner as the users interact with the system in real-time. In this paper, we propose an effective and efficient CTSR model that outperforms alternative models, while still providing reasonable inference runtimes. To demonstrate the capability of the proposed method in solving business problems, we compare it against alternative models using our in-house transaction data. Our findings reveal that the proposed model is the most suitable solution compared to others for our transaction data problem.|基于内容的时间序列检索系统是一个信息检索系统，用户可以通过该系统与来自多个领域的时间序列进行交互，如金融、医疗和制造业。例如，希望进一步了解时间序列来源的用户可以将时间序列作为一个查询提交给 CTSR 系统，并检索带有相关元数据的相关时间序列清单。通过分析检索到的元数据，用户可以收集关于时间序列源的更多信息。由于 CTSR 系统需要处理来自不同领域的时间序列数据，因此需要一个高容量的模型来有效地度量不同时间序列之间的相似性。此外，CTSR 系统中的模型必须在用户与系统进行实时交互时有效地计算相似度得分。在本文中，我们提出了一个有效和高效的 CTSR 模型，其性能优于其他模型，同时仍然提供合理的推理运行时。为了证明该方法解决业务问题的能力，我们将其与使用内部事务数据的替代模型进行比较。我们的研究结果表明，所提出的模型是最适合的解决方案比其他人为我们的交易数据问题。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=An+Efficient+Content-based+Time+Series+Retrieval+System)|0| +|[An Efficient Content-based Time Series Retrieval System](https://doi.org/10.1145/3583780.3614655)|ChinChia Michael Yeh, Huiyuan Chen, Xin Dai, Yan Zheng, Junpeng Wang, Vivian Lai, Yujie Fan, Audrey Der, Zhongfang Zhuang, Liang Wang, Wei Zhang, Jeff M. Phillips|University of California, Riverside, Riverside, CA, USA; Visa Research, Palo Alto, CA, USA; Visa Resarch, Palo Alto, CA, USA; University of Utah, Salt Lake City, UT, USA|A Content-based Time Series Retrieval (CTSR) system is an information retrieval system for users to interact with time series emerged from multiple domains, such as finance, healthcare, and manufacturing. For example, users seeking to learn more about the source of a time series can submit the time series as a query to the CTSR system and retrieve a list of relevant time series with associated metadata. By analyzing the retrieved metadata, users can gather more information about the source of the time series. Because the CTSR system is required to work with time series data from diverse domains, it needs a high-capacity model to effectively measure the similarity between different time series. On top of that, the model within the CTSR system has to compute the similarity scores in an efficient manner as the users interact with the system in real-time. In this paper, we propose an effective and efficient CTSR model that outperforms alternative models, while still providing reasonable inference runtimes. To demonstrate the capability of the proposed method in solving business problems, we compare it against alternative models using our in-house transaction data. Our findings reveal that the proposed model is the most suitable solution compared to others for our transaction data problem.|基于内容的时间序列检索系统是一个信息检索系统，用户可以通过该系统与来自多个领域的时间序列进行交互，如金融、医疗和制造业。例如，希望进一步了解时间序列来源的用户可以将时间序列作为一个查询提交给 CTSR 系统，并检索带有相关元数据的相关时间序列清单。通过分析检索到的元数据，用户可以收集关于时间序列源的更多信息。由于 CTSR 系统需要处理来自不同领域的时间序列数据，因此需要一个高容量的模型来有效地度量不同时间序列之间的相似性。此外，CTSR 系统中的模型必须在用户与系统进行实时交互时有效地计算相似度得分。在本文中，我们提出了一个有效和高效的 CTSR 模型，其性能优于其他模型，同时仍然提供合理的推理运行时。为了证明该方法解决业务问题的能力，我们将其与使用内部事务数据的替代模型进行比较。我们的研究结果表明，所提出的模型是最适合的解决方案比其他人为我们的交易数据问题。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=An+Efficient+Content-based+Time+Series+Retrieval+System)|0| |[Multi-gate Mixture-of-Contrastive-Experts with Graph-based Gating Mechanism for TV Recommendation](https://doi.org/10.1145/3583780.3615488)|Cong Zhang, Dongyang Liu, Lin Zuo, Junlan Feng, Chao Deng, Jian Sun, Haitao Zeng, Yaohong Zhao|China Mobile Research Institute, Xi'an, China; China Mobile Research Institute, Beijing, China|With the rapid development of smart TV, TV recommendation is attracting more and more users. TV users usually distribute in multiple regions with different cultures and hence have diverse TV program preferences. From the perspective of engineering practice and performance improvement, it's very essential to model users from multiple regions with one single model. In previous work, Multi-gate Mixture-of-Expert (MMoE) has been widely adopted in multi-task and multi-domain recommendation scenarios. In practice, however, we first observe the embeddings generated by experts tend to be homogeneous which may result in high semantic similarities among embeddings that reduce the capability of Multi-gate Mixture-of-Expert (MMoE) model. Secondly, we also find there are lots of commonalities and differences between multiple regions regarding user preferences. Therefore, it's meaningful to model the complicated relationships between regions. In this paper, we first introduce contrastive learning to overcome the expert representation degeneration problem. The embeddings of two augmented samples generated by the same experts are pushed closer to enhance the alignment, and the embeddings of the same samples generated by different experts are pushed away in vector space to improve uniformity. Then we propose a Graph-based Gating Mechanism to empower typical Multi-gate Mixture-of-Experts. Graph-based MMoE is able to recognize the commonalities and differences among multiple regions by introducing a Graph Neural Network (GNN) with region similarity prior. We name our model Multi-gate Mixture-of-Contrastive-Experts model with Graph-based Gating Mechanism (MMoCEG). Extensive offline experiments and online A/B tests on a commercial TV service provider over 100 million users and 2.3 million items demonstrate the efficacy of MMoCEG compared to the existing models.|随着智能电视的迅速发展，电视推荐正在吸引越来越多的用户。电视用户通常分布在具有不同文化的多个地区，因此有不同的电视节目偏好。从工程实践和性能改进的角度来看，用一个单一的模型对来自多个地区的用户进行建模是非常必要的。在以往的工作中，多门专家混合(MMoE)已经被广泛应用于多任务和多领域的推荐场景中。然而，在实际应用中，我们首先观察到由专家生成的嵌入趋于同质，这可能导致嵌入之间的语义相似度很高，从而降低了多门专家混合(MMoE)模型的性能。其次，我们还发现，在用户偏好方面，不同地区之间存在很多共性和差异。因此，建立区域间的复杂关系模型具有重要意义。在本文中，我们首先引入对比学习来克服专家表示退化问题。同一专家产生的两个增广样本的嵌入被推近以提高对齐度，同时不同专家产生的相同样本的嵌入被推远以提高向量空间的均匀性。然后提出了一种基于图的门控机制来增强典型的多门混合专家系统的能力。基于图的 MMoE 能够通过引入具有区域相似性的图神经网络(GNN)来识别多个区域之间的共性和差异。我们将该模型命名为基于图形的门控机制(MMoCEG)的多门控混合对比专家模型。与现有模型相比，MMoCEG 在一个拥有1亿用户和230万条目的商业电视服务提供商上进行了大量的离线实验和在线 A/B 测试，证明了 MMoCEG 的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Multi-gate+Mixture-of-Contrastive-Experts+with+Graph-based+Gating+Mechanism+for+TV+Recommendation)|0| -|[Popularity-aware Distributionally Robust Optimization for Recommendation System](https://doi.org/10.1145/3583780.3615492)|Jujia Zhao, Wenjie Wang, Xinyu Lin, Leigang Qu, Jizhi Zhang, TatSeng Chua|University of Science and Technology of China, Hefei, China, China; National University of Singapore, Singapore, Singapore|Collaborative Filtering (CF) has been widely applied for personalized recommendations in various industrial applications. However, due to the training strategy of Empirical Risk Minimization, CF models tend to favor popular items, resulting in inferior performance on sparse users and items. To enhance the CF representation learning of sparse users and items without sacrificing the performance of popular items, we propose a novel Popularity- aware Distributionally Robust Optimization (PDRO) framework. In particular, PDRO emphasizes the optimization of sparse users/items, while incorporating item popularity to preserve the performance of popular items through two modules. First, an implicit module develops a new popularity-aware DRO objective, paying more attention to items that will potentially become popular over time. Second, an explicit module that directly predicts the popularity of items to help the estimation of user-item matching scores. We apply PDRO to a micro-video recommendation scenario and implement it on two representative backend models. Extensive experiments on a real-world industrial dataset, as well as two public benchmark datasets, validate the efficacy of our proposed PDRO. Additionally, we perform an offline A/B test on the industrial dataset, further demonstrating the superiority of PDRO in real-world application scenarios.|协同过滤(CF)已广泛应用于各种工业应用中的个性化推荐。然而，由于经验风险最小化的训练策略，CF 模型倾向于流行项目，导致稀疏用户和项目的绩效较差。为了增强稀疏用户和项目的 CF 表示学习，同时不牺牲流行项目的性能，提出了一种新的流行感知分布鲁棒优化(PDRO)框架。特别是，PDRO 强调稀疏用户/项目的优化，同时通过两个模块结合项目流行性来保持流行项目的性能。首先，一个隐式模块开发一个新的流行感知 DRO 目标，更多地关注随着时间的推移可能变得流行的项目。第二，一个直接预测项目流行程度的显式模块，以帮助估计用户项目匹配得分。我们将 PDRO 应用于一个微视频推荐场景，并在两个有代表性的后端模型上实现它。对一个真实世界的工业数据集以及两个公共基准数据集的大量实验验证了我们提出的 PDRO 的有效性。此外，我们还对工业数据集进行了离线 A/B 测试，进一步证明了 PDRO 在实际应用场景中的优越性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Popularity-aware+Distributionally+Robust+Optimization+for+Recommendation+System)|0| +|[Popularity-aware Distributionally Robust Optimization for Recommendation System](https://doi.org/10.1145/3583780.3615492)|Jujia Zhao, Wenjie Wang, Xinyu Lin, Leigang Qu, Jizhi Zhang, TatSeng Chua|National University of Singapore, Singapore, Singapore; University of Science and Technology of China, Hefei, China, China|Collaborative Filtering (CF) has been widely applied for personalized recommendations in various industrial applications. However, due to the training strategy of Empirical Risk Minimization, CF models tend to favor popular items, resulting in inferior performance on sparse users and items. To enhance the CF representation learning of sparse users and items without sacrificing the performance of popular items, we propose a novel Popularity- aware Distributionally Robust Optimization (PDRO) framework. In particular, PDRO emphasizes the optimization of sparse users/items, while incorporating item popularity to preserve the performance of popular items through two modules. First, an implicit module develops a new popularity-aware DRO objective, paying more attention to items that will potentially become popular over time. Second, an explicit module that directly predicts the popularity of items to help the estimation of user-item matching scores. We apply PDRO to a micro-video recommendation scenario and implement it on two representative backend models. Extensive experiments on a real-world industrial dataset, as well as two public benchmark datasets, validate the efficacy of our proposed PDRO. Additionally, we perform an offline A/B test on the industrial dataset, further demonstrating the superiority of PDRO in real-world application scenarios.|协同过滤(CF)已广泛应用于各种工业应用中的个性化推荐。然而，由于经验风险最小化的训练策略，CF 模型倾向于流行项目，导致稀疏用户和项目的绩效较差。为了增强稀疏用户和项目的 CF 表示学习，同时不牺牲流行项目的性能，提出了一种新的流行感知分布鲁棒优化(PDRO)框架。特别是，PDRO 强调稀疏用户/项目的优化，同时通过两个模块结合项目流行性来保持流行项目的性能。首先，一个隐式模块开发一个新的流行感知 DRO 目标，更多地关注随着时间的推移可能变得流行的项目。第二，一个直接预测项目流行程度的显式模块，以帮助估计用户项目匹配得分。我们将 PDRO 应用于一个微视频推荐场景，并在两个有代表性的后端模型上实现它。对一个真实世界的工业数据集以及两个公共基准数据集的大量实验验证了我们提出的 PDRO 的有效性。此外，我们还对工业数据集进行了离线 A/B 测试，进一步证明了 PDRO 在实际应用场景中的优越性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Popularity-aware+Distributionally+Robust+Optimization+for+Recommendation+System)|0| |[DeepTagger: Knowledge Enhanced Named Entity Recognition for Web-Based Ads Queries](https://doi.org/10.1145/3583780.3615467)|Simiao Zuo, Pengfei Tang, Xinyu Hu, Qiang Lou, Jian Jiao, Denis Charles|Microsoft, Redmond, USA|Named entity recognition (NER) is a crucial task for online advertisement. State-of-the-art solutions leverage pre-trained language models for this task. However, three major challenges remain unresolved: web queries differ from natural language, on which pre-trained models are trained; web queries are short and lack contextual information; and labeled data for NER is scarce. We propose DeepTagger, a knowledge-enhanced NER model for web-based ads queries. The proposed knowledge enhancement framework leverages both model-free and model-based approaches. For model-free enhancement, we collect unlabeled web queries to augment domain knowledge; and we collect web search results to enrich the information of ads queries. We further leverage effective prompting methods to automatically generate labels using large language models such as ChatGPT. Additionally, we adopt a model-based knowledge enhancement method based on adversarial data augmentation. We employ a three-stage training framework to train DeepTagger models. Empirical results in various NER tasks demonstrate the effectiveness of the proposed framework.|命名实体识别(NER)是网络广告中的一项重要任务。最先进的解决方案利用预先训练好的语言模型完成这项任务。然而，三个主要的挑战仍然没有得到解决: 网络查询与自然语言不同，预先训练的模型是在自然语言的基础上进行训练的; 网络查询很短，缺乏上下文信息; NER 的标记数据很少。我们提出 DeepTagger，一个基于知识增强的网络广告查询 NER 模型。建议的知识增强框架利用了无模型和基于模型的方法。对于无模型增强，我们收集未标记的网页查询来增强领域知识; 收集网页搜索结果来增强广告查询的信息。我们进一步利用有效的提示方法，使用 ChatGPT 等大型语言模型自动生成标签。此外，我们还采用了一种基于模型的知识增强方法。我们采用了一个三阶段的训练框架来训练 DeepTagger 模型。在各种 NER 任务中的实证结果证明了该框架的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=DeepTagger:+Knowledge+Enhanced+Named+Entity+Recognition+for+Web-Based+Ads+Queries)|0| -|[A Data-Driven Index Recommendation System for Slow Queries](https://doi.org/10.1145/3583780.3614731)|Gan Peng, Peng Cai, Kaikai Ye, Kai Li, Jinlong Cai, Yufeng Shen, Han Su|Meituan, Shanghai, China; East China Normal University, Shanghai, China|The Database Autonomy Service (DAS) is a platform designed to assist database administrators in managing a large number of database instances within major internet companies. One of the key tasks in DAS is to find missing indexes to improve the slow query execution. In Meituan, a vast array of business lines deploy tens of thousands of MySQL database instances. Consequently, a great number of human-generated index cases are accumulated in the DAS platform. This motivates us to build a data-driven index recommendation system, referred to as idxLearner, which can learn index creation knowledge from human-generated index cases. In this demonstration, users can interact with idxLearner by choosing source databases to construct the training data, training the recommendation model, inputting slow queries for various target databases, and observing the recommended indexes and their evaluation results.|数据库自主服务(DAS)是一个平台，旨在帮助数据库管理员管理大型互联网公司的大量数据库实例。DAS 的关键任务之一是查找丢失的索引，以提高查询执行速度。在美团中，大量的业务部门部署了成千上万的 MySQL 数据库实例。因此，在 DAS 平台上积累了大量的人工索引案例。这促使我们建立一个数据驱动的索引推荐系统，称为 idxLearner，它可以从人工生成的索引案例中学习索引创建知识。在这个演示中，用户可以通过选择源数据库来构建训练数据，训练推荐模型，输入各种目标数据库的慢速查询，观察推荐索引及其评估结果，从而与 idxLearner 进行交互。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Data-Driven+Index+Recommendation+System+for+Slow+Queries)|0| -|[IMinimize: A System for Negative Influence Minimization via Vertex Blocking](https://doi.org/10.1145/3583780.3614743)|Siyi Teng, Jiadong Xie, Mingkai Zhang, Kai Wang, Fan Zhang|Guangzhou University, Guangzhou, China; Shanghai Jiao Tong University, Shanghai, China; University of New South Wales, Sydney, NSW, Australia; East China Normal University, Shanghai, China|The rapid rise and prevalence of social platforms have created great demands on effective schemes to limit the influence of negative information, e.g., blocking key vertices for influence minimization. However, there is currently no system providing practical schemes to solve the negative influence minimization problem with a blocking budget effectively and efficiently in the literature. In this demo, we present IMinimize, the first interactive system that provides audiences with vertex-blocking schemes over different budgets and demonstrates via visualization for comparison vividly and directly, aiming to help minimize the negative influence spreading in networks. Our IMinimize system applies an advanced greedy algorithm to select blocked vertices with both high efficiency and effectiveness. Furthermore, we extend IMinimize to the application of epidemic controlling and prevention and show the usability of IMinimize through two case studies of real-life applications.|社交平台的迅速兴起和普及对限制负面信息影响的有效方案提出了巨大要求，例如，为尽量减少影响而屏蔽关键顶点。然而，目前还没有系统提供切实可行的方案，以解决负面影响最小化问题的阻塞预算有效和高效的文献。在这个演示中，我们介绍了 IMiniize，这是第一个交互式系统，它在不同的预算下为受众提供顶点阻塞方案，并通过可视化演示进行生动直观的比较，旨在帮助最小化网络中的负面影响传播。我们的最小化系统采用了一种先进的贪婪算法来选择阻塞顶点，具有很高的效率和有效性。进一步，我们将 IMinimize 推广到流行病控制和预防的应用中，并通过两个实际应用的案例分析，说明了 IMinimize 的可用性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=IMinimize:+A+System+for+Negative+Influence+Minimization+via+Vertex+Blocking)|0| +|[A Data-Driven Index Recommendation System for Slow Queries](https://doi.org/10.1145/3583780.3614731)|Gan Peng, Peng Cai, Kaikai Ye, Kai Li, Jinlong Cai, Yufeng Shen, Han Su|East China Normal University, Shanghai, China; Meituan, Shanghai, China|The Database Autonomy Service (DAS) is a platform designed to assist database administrators in managing a large number of database instances within major internet companies. One of the key tasks in DAS is to find missing indexes to improve the slow query execution. In Meituan, a vast array of business lines deploy tens of thousands of MySQL database instances. Consequently, a great number of human-generated index cases are accumulated in the DAS platform. This motivates us to build a data-driven index recommendation system, referred to as idxLearner, which can learn index creation knowledge from human-generated index cases. In this demonstration, users can interact with idxLearner by choosing source databases to construct the training data, training the recommendation model, inputting slow queries for various target databases, and observing the recommended indexes and their evaluation results.|数据库自主服务(DAS)是一个平台，旨在帮助数据库管理员管理大型互联网公司的大量数据库实例。DAS 的关键任务之一是查找丢失的索引，以提高查询执行速度。在美团中，大量的业务部门部署了成千上万的 MySQL 数据库实例。因此，在 DAS 平台上积累了大量的人工索引案例。这促使我们建立一个数据驱动的索引推荐系统，称为 idxLearner，它可以从人工生成的索引案例中学习索引创建知识。在这个演示中，用户可以通过选择源数据库来构建训练数据，训练推荐模型，输入各种目标数据库的慢速查询，观察推荐索引及其评估结果，从而与 idxLearner 进行交互。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Data-Driven+Index+Recommendation+System+for+Slow+Queries)|0| +|[IMinimize: A System for Negative Influence Minimization via Vertex Blocking](https://doi.org/10.1145/3583780.3614743)|Siyi Teng, Jiadong Xie, Mingkai Zhang, Kai Wang, Fan Zhang|Guangzhou University, Guangzhou, China; East China Normal University, Shanghai, China; University of New South Wales, Sydney, NSW, Australia; Shanghai Jiao Tong University, Shanghai, China|The rapid rise and prevalence of social platforms have created great demands on effective schemes to limit the influence of negative information, e.g., blocking key vertices for influence minimization. However, there is currently no system providing practical schemes to solve the negative influence minimization problem with a blocking budget effectively and efficiently in the literature. In this demo, we present IMinimize, the first interactive system that provides audiences with vertex-blocking schemes over different budgets and demonstrates via visualization for comparison vividly and directly, aiming to help minimize the negative influence spreading in networks. Our IMinimize system applies an advanced greedy algorithm to select blocked vertices with both high efficiency and effectiveness. Furthermore, we extend IMinimize to the application of epidemic controlling and prevention and show the usability of IMinimize through two case studies of real-life applications.|社交平台的迅速兴起和普及对限制负面信息影响的有效方案提出了巨大要求，例如，为尽量减少影响而屏蔽关键顶点。然而，目前还没有系统提供切实可行的方案，以解决负面影响最小化问题的阻塞预算有效和高效的文献。在这个演示中，我们介绍了 IMiniize，这是第一个交互式系统，它在不同的预算下为受众提供顶点阻塞方案，并通过可视化演示进行生动直观的比较，旨在帮助最小化网络中的负面影响传播。我们的最小化系统采用了一种先进的贪婪算法来选择阻塞顶点，具有很高的效率和有效性。进一步，我们将 IMinimize 推广到流行病控制和预防的应用中，并通过两个实际应用的案例分析，说明了 IMinimize 的可用性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=IMinimize:+A+System+for+Negative+Influence+Minimization+via+Vertex+Blocking)|0| |[Towards Improving Accuracy and Computation Cost Optimization of Recommendation Systems](https://doi.org/10.1145/3583780.3616006)|Abdelghani Azri|Hassan First University of Settat, Settat, Morocco|It is hard to avoid recommender systems (RS) these days which play a vital role in various domains, such as e-commerce, online streaming platforms, and personalized content delivery. These systems assist users in discovering relevant items based on their preferences and past interactions. A variety of methods are developed by RS communities, to address the accuracy issue. But, most of these methods are sequential and omit the item features and user side information which contains relevant and rich information that can increase the accuracy of these systems. However, enhancing the accuracy of recommendations often comes at the expense of increased computational costs. This PhD thesis aims to address the challenge of improving the accuracy of RS following a hybrid approach that allows leveraging user-item features based on some relevant state-of-the-art models and using deep learning techniques such as the contractive autoencoder (CAE) while optimizing the cost of computation using parallel/distributed paradigms.|推荐系统(RS)在电子商务、在线流媒体平台和个性化内容发布等领域发挥着重要作用。这些系统帮助用户根据他们的偏好和过去的交互发现相关项目。RS 社区开发了各种各样的方法来解决准确性问题。但是，这些方法大多是顺序的，省略了项目特征和用户侧信息，这些信息包含了相关的、丰富的信息，可以提高这些系统的准确性。然而，提高建议的准确性往往是以增加计算成本为代价的。这篇博士论文旨在解决提高 RS 准确性的挑战，采用一种混合方法，允许利用基于一些相关的最先进模型的用户项特征，并使用深度学习技术，如压缩式自动编码器(CAE) ，同时使用并行/分布式范例优化计算成本。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Towards+Improving+Accuracy+and+Computation+Cost+Optimization+of+Recommendation+Systems)|0| |[Explaining Learning to Rank Methods to Improve Them](https://doi.org/10.1145/3583780.3616002)|Alberto Veneri|Ca' Foscari University of Venice & ISTI-CNR, Venice, Italy|State-of-the-art methods for Learning to Rank (LtR), either designed for tabular or textual data, are incredibly complex. Increasing the complexity of the models has many drawbacks, including difficulties in understanding the logic behind each prediction and a lack of trust in the system during its deployment. In this paper, which describes the author's goals during his Ph.D., there is an analysis and discussion of how we can use the ideas and tools coming from the eXplainable Artificial Intelligence (XAI) field to make the most effective methods for LtR understandable to the practitioners with the final goal of making them more efficient and/or understand better when they can be improved. The strategies adopted to achieve the aforementioned goals are different and based on the type of models analyzed, which go from more traditional LtR models based on ensembles of decision trees and using handcrafted features to fairly new neural LtR models using text data.|最先进的学习排名的方法，无论是为表格还是文本数据设计的，都是难以置信的复杂。增加模型的复杂性有许多缺点，包括难以理解每个预测背后的逻辑，以及在部署期间对系统缺乏信任。本文描述了作者在博士期间的目标，分析和讨论了如何利用可解释人工智能(XAI)领域的思想和工具，使从业者能够理解最有效的 LT R 方法，最终目标是使它们更有效和/或更好地理解当它们可以被改进时。为实现上述目标所采取的策略是不同的，它们基于所分析的模型类型，从更传统的基于决策树集合和使用手工特征的 LTR 模型转变为使用文本数据的相当新的神经 LTR 模型。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Explaining+Learning+to+Rank+Methods+to+Improve+Them)|0| |[Data Augmentation for Conversational AI](https://doi.org/10.1145/3583780.3615291)|Heydar Soudani, Evangelos Kanoulas, Faegheh Hasibi|Radboud University, Nijmegen, Netherlands; University of Amsterdam, Amsterdam, Netherlands|Advancements in conversational systems have revolutionized information access, surpassing the limitations of single queries. However, developing dialogue systems requires a large amount of training data, which is a challenge in low-resource domains and languages. Traditional data collection methods like crowd-sourcing are labor-intensive and time-consuming, making them ineffective in this context. Data augmentation (DA) is an affective approach to alleviate the data scarcity problem in conversational systems. This tutorial provides a comprehensive and up-to-date overview of DA approaches in the context of conversational systems. It highlights recent advances in conversation augmentation, open domain and task-oriented conversation generation, and different paradigms of evaluating these models. We also discuss current challenges and future directions in order to help researchers and practitioners to further advance the field in this area.|会话系统的进步彻底改变了信息访问，超越了单个查询的局限性。然而，开发对话系统需要大量的训练数据，这在资源不足的领域和语言中是一个挑战。传统的数据收集方法，如众包，是劳动密集型和耗时的，使他们在这种情况下无效。数据增强是解决会话系统中数据稀缺问题的有效途径。本教程提供了在会话系统上下文中 DA 方法的全面和最新概述。它强调了会话增强，开放领域和面向任务的会话生成的最新进展，以及评估这些模型的不同范式。我们还讨论了当前的挑战和未来的方向，以帮助研究人员和从业人员进一步推进该领域。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Data+Augmentation+for+Conversational+AI)|0| -|[Tutorial: Data Denoising Metrics in Recommender Systems](https://doi.org/10.1145/3583780.3615297)|Pengfei Wang, Chenliang Li, Lixin Zou, Zhichao Feng, Kaiyuan Li, Xiaochen Li, Xialong Liu, Shangguang Wang|School of Cyber Science and Engineering, Wuhan University, Wuhan, China; Beijing University of Posts and Telecommunications, Beijing, China; Chinese Academy of Sciences, Beijing, China|Recommender systems play a pivotal role in navigating users through vast reservoirs of information. However, data sparseness can compromise recommendation accuracy, making it challenging to improve recommendation performance. To address this issue, researchers have explored incorporating multiple data types. Yet, this approach can introduce noise that impairs the recommendations' accuracy. Therefore, it is crucial to denoise the data to enhance recommendation quality. This tutorial highlights the importance of data denoising metrics for improving the accuracy and quality of recommendations. Four groups of data denoising metrics are introduced: feature, item, pattern, and modality level. For each group, various denoising methods are presented. The tutorial emphasizes the significance of selecting the right data denoising methods to enhance recommendation quality. It provides valuable guidance for practitioners and researchers implementing reliable data denoising metrics in recommender systems. Finally, the tutorial proposes open research questions for future studies, making it a valuable resource for the research community.|推荐系统在通过大量信息库为用户导航方面发挥着关键作用。然而，数据稀疏会影响推荐的准确性，从而给提高推荐性能带来挑战。为了解决这个问题，研究人员已经探索了合并多种数据类型。然而，这种方法会引入噪音，影响建议的准确性。因此，对数据进行去噪处理以提高推荐质量至关重要。本教程强调了数据去噪指标对于提高建议的准确性和质量的重要性。介绍了四组数据去噪指标: 特征、项目、模式和模态水平。对于每一组，提出了各种去噪方法。本教程强调了选择正确的数据去噪方法以提高推荐质量的重要性。它为在推荐系统中实现可靠的数据去噪指标的从业人员和研究人员提供了有价值的指导。最后，本教程提出了未来研究的开放性研究问题，使其成为研究社区的宝贵资源。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Tutorial:+Data+Denoising+Metrics+in+Recommender+Systems)|0| -|[Reasoning beyond Triples: Recent Advances in Knowledge Graph Embeddings](https://doi.org/10.1145/3583780.3615294)|Bo Xiong, Mojtaba Nayyeri, Daniel Daza, Michael Cochez|Vrije Universiteit Amsterdam, Amsterdam, Netherlands; University of Stuttgart, Stuttgart, Germany; Vrije Universiteit Amsterdam & University of Amsterdam, Amsterdam, Netherlands|Knowledge Graphs (KGs) are a collection of facts describing entities connected by relationships. KG embeddings map entities and relations into a vector space while preserving their relational semantics. This enables effective inference of missing knowledge from the embedding space. Most KG embedding approaches focused on triple-shaped KGs. A great amount of real-world knowledge, however, cannot simply be represented by triples. In this tutorial, we give a systematic introduction to KG embeddings that go beyond the triple representation. In particular, the tutorial will focus on temporal facts where the triples are enriched with temporal information, hyper-relational facts where the triples are enriched with qualifiers, n-ary facts describing relationships between multiple entities, and also facts that are augmented with literal and text descriptions. During the tutorial, we will introduce both fundamental knowledge and advanced topics for understanding recent embedding approaches for beyond-triple representations.|知识图(KGs)是描述通过关系连接的实体的事实的集合。KG 嵌入将实体和关系映射到向量空间，同时保留它们的关系语义。这使得能够从嵌入空间有效地推断缺失的知识。大多数幼稚园的嵌入方法集中在三重形状的幼稚园。然而，现实世界中的大量知识不能简单地用三元组来表示。在本教程中，我们将系统地介绍超越三重表示的 KG 嵌入。特别是，本教程将重点关注三元组充满时间信息的时间事实，三元组充满修饰符的超关系事实，描述多个实体之间关系的 n 元事实，以及用文字和文本描述增强的事实。在本教程中，我们将介绍基础知识和高级主题，以便理解超越三重表示的最新嵌入方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Reasoning+beyond+Triples:+Recent+Advances+in+Knowledge+Graph+Embeddings)|0| +|[Tutorial: Data Denoising Metrics in Recommender Systems](https://doi.org/10.1145/3583780.3615297)|Pengfei Wang, Chenliang Li, Lixin Zou, Zhichao Feng, Kaiyuan Li, Xiaochen Li, Xialong Liu, Shangguang Wang|Beijing University of Posts and Telecommunications, Beijing, China; Chinese Academy of Sciences, Beijing, China; School of Cyber Science and Engineering, Wuhan University, Wuhan, China|Recommender systems play a pivotal role in navigating users through vast reservoirs of information. However, data sparseness can compromise recommendation accuracy, making it challenging to improve recommendation performance. To address this issue, researchers have explored incorporating multiple data types. Yet, this approach can introduce noise that impairs the recommendations' accuracy. Therefore, it is crucial to denoise the data to enhance recommendation quality. This tutorial highlights the importance of data denoising metrics for improving the accuracy and quality of recommendations. Four groups of data denoising metrics are introduced: feature, item, pattern, and modality level. For each group, various denoising methods are presented. The tutorial emphasizes the significance of selecting the right data denoising methods to enhance recommendation quality. It provides valuable guidance for practitioners and researchers implementing reliable data denoising metrics in recommender systems. Finally, the tutorial proposes open research questions for future studies, making it a valuable resource for the research community.|推荐系统在通过大量信息库为用户导航方面发挥着关键作用。然而，数据稀疏会影响推荐的准确性，从而给提高推荐性能带来挑战。为了解决这个问题，研究人员已经探索了合并多种数据类型。然而，这种方法会引入噪音，影响建议的准确性。因此，对数据进行去噪处理以提高推荐质量至关重要。本教程强调了数据去噪指标对于提高建议的准确性和质量的重要性。介绍了四组数据去噪指标: 特征、项目、模式和模态水平。对于每一组，提出了各种去噪方法。本教程强调了选择正确的数据去噪方法以提高推荐质量的重要性。它为在推荐系统中实现可靠的数据去噪指标的从业人员和研究人员提供了有价值的指导。最后，本教程提出了未来研究的开放性研究问题，使其成为研究社区的宝贵资源。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Tutorial:+Data+Denoising+Metrics+in+Recommender+Systems)|0| +|[Reasoning beyond Triples: Recent Advances in Knowledge Graph Embeddings](https://doi.org/10.1145/3583780.3615294)|Bo Xiong, Mojtaba Nayyeri, Daniel Daza, Michael Cochez|University of Stuttgart, Stuttgart, Germany; Vrije Universiteit Amsterdam, Amsterdam, Netherlands; Vrije Universiteit Amsterdam & University of Amsterdam, Amsterdam, Netherlands|Knowledge Graphs (KGs) are a collection of facts describing entities connected by relationships. KG embeddings map entities and relations into a vector space while preserving their relational semantics. This enables effective inference of missing knowledge from the embedding space. Most KG embedding approaches focused on triple-shaped KGs. A great amount of real-world knowledge, however, cannot simply be represented by triples. In this tutorial, we give a systematic introduction to KG embeddings that go beyond the triple representation. In particular, the tutorial will focus on temporal facts where the triples are enriched with temporal information, hyper-relational facts where the triples are enriched with qualifiers, n-ary facts describing relationships between multiple entities, and also facts that are augmented with literal and text descriptions. During the tutorial, we will introduce both fundamental knowledge and advanced topics for understanding recent embedding approaches for beyond-triple representations.|知识图(KGs)是描述通过关系连接的实体的事实的集合。KG 嵌入将实体和关系映射到向量空间，同时保留它们的关系语义。这使得能够从嵌入空间有效地推断缺失的知识。大多数幼稚园的嵌入方法集中在三重形状的幼稚园。然而，现实世界中的大量知识不能简单地用三元组来表示。在本教程中，我们将系统地介绍超越三重表示的 KG 嵌入。特别是，本教程将重点关注三元组充满时间信息的时间事实，三元组充满修饰符的超关系事实，描述多个实体之间关系的 n 元事实，以及用文字和文本描述增强的事实。在本教程中，我们将介绍基础知识和高级主题，以便理解超越三重表示的最新嵌入方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Reasoning+beyond+Triples:+Recent+Advances+in+Knowledge+Graph+Embeddings)|0| |[Comparative Analysis of Open Source and Commercial Embedding Models for Question Answering](https://doi.org/10.1145/3583780.3615994)|Georgios Balikas|Salesforce Inc., Grenoble, France|In this industry track presentation, we will provide a comprehensive tour of the best performing embedding models for question answering, as determined by the Massive Text Embedding Benchmark1. We showcase these models while also considering solutions offered by OpenAI and Cohere, renowned for their state-of-the-art performance. Through rigorous evaluations on internal Salesforce datasets tailored for Question Answering on Knowledge articles, we compare the performance of these models using standardized metrics. Our analysis sheds light on the current state-of-the-art in question answering using embedding models across three diverse domains. We hope that this talk's outcomes will empower practitioners and researchers to make informed decisions when selecting the most suitable solution for their specific requirements.|在这个行业的轨道演示中，我们将提供一个全面的旅游，最佳表现嵌入模型的问题回答，由海量文本嵌入基准1。我们展示了这些模型，同时也考虑了 OpenAI 和 Cohere 提供的解决方案，它们以最先进的性能而闻名。通过对内部 Salesforce 数据集的严格评估，我们使用标准化指标比较了这些模型的性能。我们的分析揭示了目前使用三个不同领域的嵌入模型进行问题回答的最新技术。我们希望这次演讲的结果能够使从业者和研究人员在为他们的具体需求选择最合适的解决方案时做出明智的决定。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Comparative+Analysis+of+Open+Source+and+Commercial+Embedding+Models+for+Question+Answering)|0| |[Practical Lessons Learned From Detecting, Preventing and Mitigating Harmful Experiences on Facebook](https://doi.org/10.1145/3583780.3615511)|Prathyusha Senthil Kumar|Meta Platforms Inc., Menlo Park, CA, USA|Social media's explosive growth brings with it a variety of societal risks ranging from severely harmful issues such as dangerous organizations and child sexual exploitation to moderately harmful content like displays of aggression, borderline nudity to benign or distasteful contents like gross videos and baity content. In recent times, the multitude and magnitude of these harms is being further exacerbated with the advent of generative AI [5]. Meta is committed to ensuring that Facebook is a place where people feel empowered to communicate and we take our role seriously in keeping abuse off the platform [7]. In this talk, I will describe practical challenges and lessons learned from tackling bad experiences for users on Facebook, particularly in the subjective, borderline and low quality spectrum of harms using state of the art, scalable machine learning approaches to content understanding, user behavior understanding and personalized ranking.|社交媒体的爆炸式增长带来了各种各样的社会风险，从严重有害的问题，如危险的组织和儿童性剥削，到中等有害的内容，如暴力行为的展示，边缘性的裸体，以良性或令人厌恶的内容，如恶心的视频和色情内容。近年来，随着生成性人工智能的出现，这些危害的多样性和严重性正在进一步加剧[5]。Meta 致力于确保 Facebook 是一个让人们感到有能力进行交流的地方，我们认真对待自己在防止滥用平台方面的作用[7]。在这个演讲中，我将描述实际的挑战和经验教训，从解决 Facebook 用户的不良体验，特别是在主观，边缘和低质量的危害范围使用最先进的，可扩展的机器学习方法的内容理解，用户行为理解和个性化排名。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Practical+Lessons+Learned+From+Detecting,+Preventing+and+Mitigating+Harmful+Experiences+on+Facebook)|0| -|[A Test Collection of Synthetic Documents for Training Rankers: ChatGPT vs. Human Experts](https://doi.org/10.1145/3583780.3615111)|Arian Askari, Mohammad Aliannejadi, Evangelos Kanoulas, Suzan Verberne|LIACS, Leiden University, Leiden, Netherlands; University of Amsterdam, Amsterdam, Netherlands; Leiden Institute of Advanced Computer Science, Leiden University, Leiden, Netherlands|In this resource paper, we investigate the usefulness of generative Large Language Models (LLMs) in generating training data for cross-encoder re-rankers in a novel direction: generating synthetic documents instead of synthetic queries. We introduce a new dataset, ChatGPT-RetrievalQA, and compare the effectiveness of strong models fine-tuned on both LLM-generated and human-generated data. We build ChatGPT-RetrievalQA based on an existing dataset, human ChatGPT Comparison Corpus (HC3), consisting of public question collections with human responses and answers from ChatGPT. We fine-tune a range of cross-encoder re-rankers on either human-generated or ChatGPT-generated data. Our evaluation on MS MARCO DEV, TREC DL'19, and TREC DL'20 demonstrates that cross-encoder re-ranking models trained on LLM-generated responses are significantly more effective for out-of-domain re-ranking than those trained on human responses. For in-domain re-ranking, the human-trained re-rankers outperform the LLM-trained re-rankers. Our novel findings suggest that generative LLMs have high potential in generating training data for neural retrieval models and can be used to augment training data, especially in domains with smaller amounts of labeled data. We believe that our dataset, ChatGPT-RetrievalQA, presents various opportunities for analyzing and improving rankers with human and synthetic data. We release our data, code, and model checkpoints for future work.|在这篇资源论文中，我们研究了生成大语言模型(LLM)在生成交叉编码器重新排序的训练数据方面的有用性: 生成合成文档而不是合成查询。我们引入了一个新的数据集 ChatGPT-RetrievalQA，并比较了对 LLM 生成的数据和人类生成的数据进行微调的强模型的有效性。我们基于现有的数据集，人类 ChatGPT 比较语料库(HC3)构建 ChatGPT 检索 QA，该语料库由公共问题集合和来自 ChatGPT 的答案组成。我们微调一系列的交叉编码器重新排序的人类生成或 ChatGPT 生成的数据。我们对 MS MARCO DEV，TREC DL’19和 TREC DL’20的评估表明，在 LLM 生成的响应上训练的交叉编码器重新排序模型对于域外重新排序显着比那些训练人类响应更有效。对于域内重新排序，人类训练的重新排序优于 LLM 训练的重新排序。我们的新发现表明，生成性 LLM 在为神经检索模型生成训练数据方面具有很大的潜力，可用于增强训练数据，特别是在标记数据量较小的领域。我们相信我们的数据集，ChatGPT-RetrievalQA，提供了各种机会来分析和提高排名与人类和合成数据。我们为将来的工作发布数据、代码和模型检查点。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Test+Collection+of+Synthetic+Documents+for+Training+Rankers:+ChatGPT+vs.+Human+Experts)|0| +|[A Test Collection of Synthetic Documents for Training Rankers: ChatGPT vs. Human Experts](https://doi.org/10.1145/3583780.3615111)|Arian Askari, Mohammad Aliannejadi, Evangelos Kanoulas, Suzan Verberne|LIACS, Leiden University, Leiden, Netherlands; Leiden Institute of Advanced Computer Science, Leiden University, Leiden, Netherlands; University of Amsterdam, Amsterdam, Netherlands|In this resource paper, we investigate the usefulness of generative Large Language Models (LLMs) in generating training data for cross-encoder re-rankers in a novel direction: generating synthetic documents instead of synthetic queries. We introduce a new dataset, ChatGPT-RetrievalQA, and compare the effectiveness of strong models fine-tuned on both LLM-generated and human-generated data. We build ChatGPT-RetrievalQA based on an existing dataset, human ChatGPT Comparison Corpus (HC3), consisting of public question collections with human responses and answers from ChatGPT. We fine-tune a range of cross-encoder re-rankers on either human-generated or ChatGPT-generated data. Our evaluation on MS MARCO DEV, TREC DL'19, and TREC DL'20 demonstrates that cross-encoder re-ranking models trained on LLM-generated responses are significantly more effective for out-of-domain re-ranking than those trained on human responses. For in-domain re-ranking, the human-trained re-rankers outperform the LLM-trained re-rankers. Our novel findings suggest that generative LLMs have high potential in generating training data for neural retrieval models and can be used to augment training data, especially in domains with smaller amounts of labeled data. We believe that our dataset, ChatGPT-RetrievalQA, presents various opportunities for analyzing and improving rankers with human and synthetic data. We release our data, code, and model checkpoints for future work.|在这篇资源论文中，我们研究了生成大语言模型(LLM)在生成交叉编码器重新排序的训练数据方面的有用性: 生成合成文档而不是合成查询。我们引入了一个新的数据集 ChatGPT-RetrievalQA，并比较了对 LLM 生成的数据和人类生成的数据进行微调的强模型的有效性。我们基于现有的数据集，人类 ChatGPT 比较语料库(HC3)构建 ChatGPT 检索 QA，该语料库由公共问题集合和来自 ChatGPT 的答案组成。我们微调一系列的交叉编码器重新排序的人类生成或 ChatGPT 生成的数据。我们对 MS MARCO DEV，TREC DL’19和 TREC DL’20的评估表明，在 LLM 生成的响应上训练的交叉编码器重新排序模型对于域外重新排序显着比那些训练人类响应更有效。对于域内重新排序，人类训练的重新排序优于 LLM 训练的重新排序。我们的新发现表明，生成性 LLM 在为神经检索模型生成训练数据方面具有很大的潜力，可用于增强训练数据，特别是在标记数据量较小的领域。我们相信我们的数据集，ChatGPT-RetrievalQA，提供了各种机会来分析和提高排名与人类和合成数据。我们为将来的工作发布数据、代码和模型检查点。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Test+Collection+of+Synthetic+Documents+for+Training+Rankers:+ChatGPT+vs.+Human+Experts)|0| |[Anserini Gets Dense Retrieval: Integration of Lucene's HNSW Indexes](https://doi.org/10.1145/3583780.3615112)|Xueguang Ma, Tommaso Teofili, Jimmy Lin|University of Waterloo, Waterloo, ON, Canada; Roma Tre University, Rome, Italy|Anserini is a Lucene-based toolkit for reproducible information retrieval research in Java that has been gaining traction in the community. It provides retrieval capabilities for both "traditional" bag-of-words retrieval models such as BM25 as well as retrieval using learned sparse representations such as SPLADE. With Pyserini, which provides a Python interface to Anserini, users gain access to both sparse and dense retrieval models, as Pyserini implements bindings to the Faiss vector search library alongside Lucene inverted indexes in a uniform, consistent interface. Nevertheless, hybrid fusion techniques that integrate sparse and dense retrieval models need to stitch together results from two completely different "software stacks", which creates unnecessary complexities and inefficiencies. However, the introduction of HNSW indexes for dense vector search in Lucene promises the integration of both dense and sparse retrieval within a single software framework. We explore exactly this integration in the context of Anserini. Experiments on the MS MARCO passage and BEIR datasets show that our Anserini HNSW integration supports (reasonably) effective and (reasonably) efficient approximate nearest neighbor search for dense retrieval models, using only Lucene.|Anserini 是一个基于 Lucene 的工具包，用于在 Java 中进行可重复的信息检索研究，在社区中获得了越来越多的关注。它为“传统的”单词检索模型(如 BM25)以及使用学习稀疏表示(如 SPLADE)的检索提供了检索能力。Pyserini 为 Anserini 提供了一个 Python 界面，用户可以访问稀疏和密集的检索模型，因为 Pyserini 实现了对 Faiss 矢量搜索库的绑定，以及在统一、一致的界面中的 Lucene 反向索引。然而，集成稀疏和密集检索模型的混合融合技术需要将来自两个完全不同的“软件栈”的结果拼接在一起，这造成了不必要的复杂性和效率低下。然而，在 Lucene 引入用于密集向量搜索的 HNSW 索引有望在单一软件框架内实现密集和稀疏检索的集成。我们正是在 Anserini 的背景下探讨这种一体化。对 MS MARCO 通道和 BEIR 数据集的实验表明，我们的 Anserini HNSW 集成只使用 Lucene 支持(合理地)有效和(合理地)近似最近邻搜索密集检索模型。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Anserini+Gets+Dense+Retrieval:+Integration+of+Lucene's+HNSW+Indexes)|0| |[ITA-ELECTION-2022: A Multi-Platform Dataset of Social Media Conversations Around the 2022 Italian General Election](https://doi.org/10.1145/3583780.3615121)|Francesco Pierri, Geng Liu, Stefano Ceri|Politecnico di Milano, Milano, Italy|Online social media play a major role in shaping public discourse and opinion, especially during political events. We present the first public multi-platform dataset of Italian-language political conversations, focused on the 2022 Italian general election taking place on September 25th. Leveraging public APIs and a keyword-based search, we collected millions of posts published by users, pages and groups on Facebook, Instagram and Twitter, along with metadata of TikTok and YouTube videos shared on these platforms, over a period of four months. We augmented the dataset with a collection of political ads sponsored on Meta platforms, and a list of social media handles associated with political representatives. Our data resource will allow researchers and academics to further our understanding of the role of social media in the democratic process.|在线社交媒体在塑造公共话语和舆论方面发挥着重要作用，特别是在政治事件期间。我们提出了第一个公开的多平台意大利语政治对话数据集，重点是2022年意大利大选在9月25日举行。我们利用公共 API 和基于关键词的搜索，在四个月的时间里收集了 Facebook、 Instagram 和 Twitter 上用户、页面和群组发布的数百万条帖子，以及 TikTok 和 YouTube 视频在这些平台上分享的元数据。我们通过 Meta 平台上赞助的一系列政治广告以及与政治代表相关的社交媒体处理列表来扩充数据集。我们的数据资源将使研究人员和学者进一步了解社会媒体在民主进程中的作用。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=ITA-ELECTION-2022:+A+Multi-Platform+Dataset+of+Social+Media+Conversations+Around+the+2022+Italian+General+Election)|0| -|[Explore Epistemic Uncertainty in Domain Adaptive Semantic Segmentation](https://doi.org/10.1145/3583780.3614872)|Kai Yao, Zixian Su, Xi Yang, Jie Sun, Kaizhu Huang|Duke Kunshan University, Kunshan, China; University of Liverpool & Xi'an Jiaotong-Liverpool University, Liverpool, United Kingdom; Xi'an Jiaotong-Liverpool University, Suzhou, China|In domain adaptive segmentation, domain shift may cause erroneous high-confidence predictions on the target domain, resulting in poor self-training. To alleviate the potential error, most previous works mainly consider aleatoric uncertainty arising from the inherit data noise. This may however lead to overconfidence in incorrect predictions and thus limit the performance. In this paper, we take advantage of Deterministic Uncertainty Methods (DUM) to explore the epistemic uncertainty, which reflects accurately the domain gap depending on the model choice and parameter fitting trained on source domain. The epistemic uncertainty on target domain is evaluated on-the-fly to facilitate online reweighting and correction in the self-training process. Meanwhile, to tackle the class-wise quantity and learning difficulty imbalance problem, we introduce a novel data resampling strategy to promote simultaneous convergence across different categories. This strategy prevents the class-level over-fitting in source domain and further boosts the adaptation performance by better quantifying the uncertainty in target domain. We illustrate the superiority of our method compared with the state-of-the-art methods.|在领域自适应分割中，领域移位可能导致目标领域的高置信度预测错误，从而导致自学习效果差。为了减小潜在的误差，以往的工作主要考虑由继承数据噪声引起的任意不确定性。然而，这可能导致对错误预测的过度自信，从而限制了性能。本文利用确定性不确定性方法(DUM)对认知不确定性进行了研究，根据源域上训练的模型选择和参数拟合，准确地反映了领域间的差异。在自我训练过程中，对目标域上的认知不确定性进行动态评估，以便于在线加权和校正。同时，为了解决类别数量和学习困难不平衡的问题，我们引入了一种新的数据重采样策略，以促进不同类别之间的同时收敛。该策略防止了源域中的类级过拟合，并通过更好地量化目标域中的不确定性进一步提高了自适应性能。我们说明了我们的方法相对于最先进的方法的优越性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Explore+Epistemic+Uncertainty+in+Domain+Adaptive+Semantic+Segmentation)|0| +|[Explore Epistemic Uncertainty in Domain Adaptive Semantic Segmentation](https://doi.org/10.1145/3583780.3614872)|Kai Yao, Zixian Su, Xi Yang, Jie Sun, Kaizhu Huang|Xi'an Jiaotong-Liverpool University, Suzhou, China; Duke Kunshan University, Kunshan, China; University of Liverpool & Xi'an Jiaotong-Liverpool University, Liverpool, United Kingdom|In domain adaptive segmentation, domain shift may cause erroneous high-confidence predictions on the target domain, resulting in poor self-training. To alleviate the potential error, most previous works mainly consider aleatoric uncertainty arising from the inherit data noise. This may however lead to overconfidence in incorrect predictions and thus limit the performance. In this paper, we take advantage of Deterministic Uncertainty Methods (DUM) to explore the epistemic uncertainty, which reflects accurately the domain gap depending on the model choice and parameter fitting trained on source domain. The epistemic uncertainty on target domain is evaluated on-the-fly to facilitate online reweighting and correction in the self-training process. Meanwhile, to tackle the class-wise quantity and learning difficulty imbalance problem, we introduce a novel data resampling strategy to promote simultaneous convergence across different categories. This strategy prevents the class-level over-fitting in source domain and further boosts the adaptation performance by better quantifying the uncertainty in target domain. We illustrate the superiority of our method compared with the state-of-the-art methods.|在领域自适应分割中，领域移位可能导致目标领域的高置信度预测错误，从而导致自学习效果差。为了减小潜在的误差，以往的工作主要考虑由继承数据噪声引起的任意不确定性。然而，这可能导致对错误预测的过度自信，从而限制了性能。本文利用确定性不确定性方法(DUM)对认知不确定性进行了研究，根据源域上训练的模型选择和参数拟合，准确地反映了领域间的差异。在自我训练过程中，对目标域上的认知不确定性进行动态评估，以便于在线加权和校正。同时，为了解决类别数量和学习困难不平衡的问题，我们引入了一种新的数据重采样策略，以促进不同类别之间的同时收敛。该策略防止了源域中的类级过拟合，并通过更好地量化目标域中的不确定性进一步提高了自适应性能。我们说明了我们的方法相对于最先进的方法的优越性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Explore+Epistemic+Uncertainty+in+Domain+Adaptive+Semantic+Segmentation)|0| |[Temporal Convolutional Explorer Helps Understand 1D-CNN's Learning Behavior in Time Series Classification from Frequency Domain](https://doi.org/10.1145/3583780.3615076)|Junru Zhang, Lang Feng, Yang He, Yuhan Wu, Yabo Dong|Zhejiang University, Hangzhou, China|While one-dimensional convolutional neural networks (1D-CNNs) have been empirically proven effective in time series classification tasks, we find that there remain undesirable outcomes that could arise in their application, motivating us to further investigate and understand their underlying mechanisms. In this work, we propose a Temporal Convolutional Explorer (TCE) to empirically explore the learning behavior of 1D-CNNs from the perspective of the frequency domain. Our TCE analysis highlights that deeper 1D-CNNs tend to distract the focus from the low-frequency components leading to the accuracy degradation phenomenon, and the disturbing convolution is the driving factor. Then, we leverage our findings to the practical application and propose a regulatory framework, which can easily be integrated into existing 1D-CNNs. It aims to rectify the suboptimal learning behavior by enabling the network to selectively bypass the specified disturbing convolutions. Finally, through comprehensive experiments on widely-used UCR, UEA, and UCI benchmarks, we demonstrate that 1) TCE's insight into 1D-CNN's learning behavior; 2) our regulatory framework enables state-of-the-art 1D-CNNs to get improved performances with less consumption of memory and computational overhead.|虽然一维卷积神经网络(1D-CNN)已经被经验证明在时间序列分类任务中是有效的，但是我们发现在其应用中仍然存在可能出现的不良结果，促使我们进一步调查和理解其潜在的机制。在本研究中，我们提出了一个时间卷积探索器(TCE) ，从频率域的角度对1D-CNN 的学习行为进行实证研究。我们的 TCE 分析强调，更深的1D-CNN 往往会分散对低频分量的关注，导致精度退化现象，干扰卷积是驱动因素。然后，我们利用我们的研究结果的实际应用，并提出了一个监管框架，这可以很容易地集成到现有的1D-CNN。它的目的是纠正次优学习行为，使网络能够有选择地绕过指定的干扰卷积。最后，通过对广泛使用的 UCR，UEA 和 UCI 基准的全面实验，我们证明了1) TCE 对1D-CNN 学习行为的洞察力; 2)我们的监管框架使最先进的1D-CNN 能够以更少的内存消耗和计算开销获得改善的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Temporal+Convolutional+Explorer+Helps+Understand+1D-CNN's+Learning+Behavior+in+Time+Series+Classification+from+Frequency+Domain)|0| -|[PS-SA: An Efficient Self-Attention via Progressive Sampling for User Behavior Sequence Modeling](https://doi.org/10.1145/3583780.3615495)|Jiacen Hu, Zhangming Chan, Yu Zhang, Shuguang Han, Siyuan Lou, Baolin Liu, Han Zhu, Yuning Jiang, Jian Xu, Bo Zheng|Alibaba Group, Beijing, China; USTB, Beijing, China|As the self-attention mechanism offers powerful capabilities for capturing sequential relationships, it has become increasingly popular to use it for modeling user behavior sequences in recommender systems. However, the self-attention mechanism has a quadratic computational complexity of O(n^2), as it conducts interactions among all item pairs in the sequence. This can lead to expensive model training and slow inference speeds, which may hinder practical deployment. To this end, we pursue to develop alternative approaches to improve the efficiency of the self-attention mechanism. We observe that the attention scores calculated from each item interacting with other items (including itself) are sparse, indicating that there are limited valuable item pairs (with non-zero attention weight) that contribute to the final output. This motivates us to develop effective strategies for discerning valuable items and computing attention scores solely for these items, thereby minimizing the consumption of unnecessary computations. Herein, we present a novel Progressive Sampling-based Self-Attention (PS-SA) mechanism, which utilizes a learnable progressive sampling strategy to identify the most valuable items. Subsequently, we solely utilize these selected items to produce the final output. Experiments on academic and production datasets demonstrate PS-SA could still achieve promising results while reducing computational costs. It is notable that we have successfully deployed it on Alibaba display advertising system, resulting in a 2.6% CTR and 1.3% RPM increase.|由于自我注意机制提供了捕获序列关系的强大功能，因此在推荐系统中使用自我注意机制对用户行为序列进行建模越来越受到欢迎。然而，自我注意机制具有 O (n ^ 2)的二次计算复杂度，因为它在序列中的所有项目对之间进行交互作用。这可能导致昂贵的模型训练和缓慢的推理速度，这可能会阻碍实际部署。为此，我们寻求发展替代方法，以提高自我注意机制的效率。我们观察到，从每个项目与其他项目(包括它自己)的相互作用中计算出的注意力得分是稀疏的，表明有限的有价值的项目对(非零注意力重量)有助于最终的输出。这促使我们制定有效的策略来识别有价值的项目，并仅仅为这些项目计算注意力分数，从而最大限度地减少不必要的计算消耗。本文提出了一种新的基于递进抽样的自我注意机制(PS-SA) ，该机制利用一种可学习的递进抽样策略来识别最有价值的项目。随后，我们只利用这些选定的项目来产生最终的输出。在学术和生产数据集上的实验表明，PS-SA 在降低计算成本的同时，仍然可以取得有希望的结果。值得注意的是，我们已经成功地在阿里巴巴展示广告系统中使用了它，点击率和每分钟转速分别提高了2.6% 和1.3% 。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=PS-SA:+An+Efficient+Self-Attention+via+Progressive+Sampling+for+User+Behavior+Sequence+Modeling)|0| +|[PS-SA: An Efficient Self-Attention via Progressive Sampling for User Behavior Sequence Modeling](https://doi.org/10.1145/3583780.3615495)|Jiacen Hu, Zhangming Chan, Yu Zhang, Shuguang Han, Siyuan Lou, Baolin Liu, Han Zhu, Yuning Jiang, Jian Xu, Bo Zheng|USTB, Beijing, China; Alibaba Group, Beijing, China|As the self-attention mechanism offers powerful capabilities for capturing sequential relationships, it has become increasingly popular to use it for modeling user behavior sequences in recommender systems. However, the self-attention mechanism has a quadratic computational complexity of O(n^2), as it conducts interactions among all item pairs in the sequence. This can lead to expensive model training and slow inference speeds, which may hinder practical deployment. To this end, we pursue to develop alternative approaches to improve the efficiency of the self-attention mechanism. We observe that the attention scores calculated from each item interacting with other items (including itself) are sparse, indicating that there are limited valuable item pairs (with non-zero attention weight) that contribute to the final output. This motivates us to develop effective strategies for discerning valuable items and computing attention scores solely for these items, thereby minimizing the consumption of unnecessary computations. Herein, we present a novel Progressive Sampling-based Self-Attention (PS-SA) mechanism, which utilizes a learnable progressive sampling strategy to identify the most valuable items. Subsequently, we solely utilize these selected items to produce the final output. Experiments on academic and production datasets demonstrate PS-SA could still achieve promising results while reducing computational costs. It is notable that we have successfully deployed it on Alibaba display advertising system, resulting in a 2.6% CTR and 1.3% RPM increase.|由于自我注意机制提供了捕获序列关系的强大功能，因此在推荐系统中使用自我注意机制对用户行为序列进行建模越来越受到欢迎。然而，自我注意机制具有 O (n ^ 2)的二次计算复杂度，因为它在序列中的所有项目对之间进行交互作用。这可能导致昂贵的模型训练和缓慢的推理速度，这可能会阻碍实际部署。为此，我们寻求发展替代方法，以提高自我注意机制的效率。我们观察到，从每个项目与其他项目(包括它自己)的相互作用中计算出的注意力得分是稀疏的，表明有限的有价值的项目对(非零注意力重量)有助于最终的输出。这促使我们制定有效的策略来识别有价值的项目，并仅仅为这些项目计算注意力分数，从而最大限度地减少不必要的计算消耗。本文提出了一种新的基于递进抽样的自我注意机制(PS-SA) ，该机制利用一种可学习的递进抽样策略来识别最有价值的项目。随后，我们只利用这些选定的项目来产生最终的输出。在学术和生产数据集上的实验表明，PS-SA 在降低计算成本的同时，仍然可以取得有希望的结果。值得注意的是，我们已经成功地在阿里巴巴展示广告系统中使用了它，点击率和每分钟转速分别提高了2.6% 和1.3% 。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=PS-SA:+An+Efficient+Self-Attention+via+Progressive+Sampling+for+User+Behavior+Sequence+Modeling)|0| |[Delivery Optimized Discovery in Behavioral User Segmentation under Budget Constraint](https://doi.org/10.1145/3583780.3614839)|Harshita Chopra, Atanu R. Sinha, Sunav Choudhary, Ryan A. Rossi, Paavan Kumar Indela, Veda Pranav Parwatala, Srinjayee Paul, Aurghya Maiti|Adobe Research, Bangalore, India; Adobe Research, San Jose, CA, USA|Users' behavioral footprints online enable firms to discover behavior-based user segments (or, segments) and deliver segment specific messages to users. Following the discovery of segments, delivery of messages to users through preferred media channels like Facebook and Google can be challenging, as only a portion of users in a behavior segment find match in a medium, and only a fraction of those matched actually see the message (exposure). Even high quality discovery becomes futile when delivery fails. Many sophisticated algorithms exist for discovering behavioral segments; however, these ignore the delivery component. The problem is compounded because (i) the discovery is performed on the behavior data space in firms' data (e.g., user clicks), while the delivery is predicated on the static data space (e.g., geo, age) as defined by media; and (ii) firms work under budget constraint. We introduce a stochastic optimization based algorithm for delivery optimized discovery of behavioral user segments and offer new metrics to address the joint optimization. We leverage optimization under a budget constraint for delivery combined with a learning-based component for discovery. Extensive experiments on a public dataset from Google and a proprietary dataset show the effectiveness of our approach by simultaneously improving delivery metrics, reducing budget spend and achieving strong predictive performance in discovery.|用户的在线行为足迹使企业能够发现基于行为的用户段(或段) ，并向用户传递段特定的信息。随着信息片段的发现，通过 Facebook 和 Google 这样的首选媒体渠道向用户传递信息可能是一个挑战，因为在行为片段中只有一部分用户在媒介中找到匹配，而且只有一小部分匹配的用户实际上看到了信息(曝光)。当交付失败时，即使是高质量的发现也是徒劳的。存在许多发现行为片段的复杂算法; 然而，这些算法忽略了传递组件。这个问题是复杂的，因为(i)发现是在企业数据的行为数据空间(例如，用户点击)上执行的，而交付是在媒体定义的静态数据空间(例如，地理位置，年龄)上进行的; (ii)企业在预算线下工作。提出了一种基于随机优化的行为用户段发现算法，并提出了一种新的度量方法来解决联合优化问题。我们利用优化的预算线，结合基于学习的组件进行发现。对来自 Google 的公共数据集和专有数据集进行的大量实验表明，我们的方法通过同时改进交付指标、减少预算开支和在发现方面实现强大的预测性能来显示有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Delivery+Optimized+Discovery+in+Behavioral+User+Segmentation+under+Budget+Constraint)|0| |[Understanding User Immersion in Online Short Video Interaction](https://doi.org/10.1145/3583780.3615099)|Zhiyu He, Shaorun Zhang, Peijie Sun, Jiayu Li, Xiaohui Xie, Min Zhang, Yiqun Liu|Tsinghua University, Beijing, China|Short video~(SV) online streaming has been one of the most popular Internet applications in recent years. When browsing SVs, users gradually immerse themselves and derive relaxation or knowledge. Whereas prolonged browsing will lead to a decline in positive feelings, users continue due to inertia, resulting in decreased satisfaction. Immersion is shown to be an essential factor for users' positive experience and highly related to users' interactions in film, games, and virtual reality. However, immersion in SV interaction is still unexplored, which differs from the previously studied scenarios essentially because SV delivery is fragmented, discrete, and with limited time for each video. In this paper, we aim to make an extensive understanding of user immersion in online short video interaction, include related factors, detecting possibility, and satisfaction representation. We conduct a three-step user study on real SV browsing, including an online survey, a field study, and a lab study with EEG signals. The user study reveals that immersion is a common feeling in SV interaction, and it is related to video features, personalization of recommendations, user mood, and interaction behaviors. Specifically, prolonged browsing leads to a significant decrease in immersion. Furthermore, analyses of EEG signals demonstrate that the prefrontal lobe and parietal lobe of the gamma band are associated with immersion. Besides, immersion prediction experiments achieve encouraging results, showing that user immersion status is predictable and EEG signals do help improve prediction performance. Moreover, correlation analysis indicates that the predicted immersion is more representative of user satisfaction than user behaviors, revealing the potential of immersion as an indicator of satisfaction in the recommender system. To the best of our knowledge, it is the first study on user immersion in real online SV interaction scenarios, and our findings are enlightening for SV users and recommender system designers.|短视频 ~ (SV)在线流媒体是近年来最流行的互联网应用之一。浏览 SVs 时，用户逐渐沉浸其中，从中获得放松或知识。而长时间的浏览会导致积极情绪的下降，用户继续由于惯性，导致满意度下降。沉浸被证明是一个重要因素，用户的积极体验和高度相关的用户在电影，游戏和虚拟现实的互动。然而，沉浸在 SV 交互中仍然是未知的，这与以前研究的场景不同，本质上是因为 SV 交付是零碎的，离散的，并且每个视频的时间有限。本文旨在深入了解在线短视频交互中的用户沉浸现象，包括相关因素、检测可能性、满意度表征等。我们对真实的 SV 浏览进行了三个步骤的用户研究，包括在线调查、实地研究和脑电信号的实验室研究。用户研究表明，沉浸感是 SV 交互中的一种常见感觉，它与视频特性、推荐的个性化、用户情绪和交互行为有关。具体来说，长时间的浏览会导致沉浸感的显著降低。此外，脑电信号分析表明，伽玛波段的前额叶和顶叶与浸泡有关。此外，沉浸预测实验取得了令人鼓舞的结果，表明用户沉浸状态是可预测的，脑电信号有助于提高预测性能。此外，相关分析表明，预测沉浸比用户行为更能代表用户满意度，揭示了沉浸作为推荐系统满意度指标的潜力。据我们所知，这是第一个关于用户沉浸在真实在线 SV 交互场景中的研究，我们的研究结果对于 SV 用户和推荐系统设计者都是有启发的。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Understanding+User+Immersion+in+Online+Short+Video+Interaction)|0| |[Tight-Sketch: A High-Performance Sketch for Heavy Item-Oriented Data Stream Mining with Limited Memory Size](https://doi.org/10.1145/3583780.3615080)|Weihe Li, Paul Patras|The University of Edinburgh, Edinburgh, United Kingdom|Accurate and fast data stream mining is critical and fundamental to many tasks, including time series database handling, big data management and machine learning. Different heavy-based detection tasks, such as heavy hitter, heavy changer, persistent item and significant item detection, have drawn much attention from both the industry and academia. Unfortunately, due to the growing data stream speeds and limited memory (L1 cache) available for real-time processing, existing schemes face challenges in simultaneously achieving high detection accuracy, high memory efficiency, and fast update throughput, as we reveal. To tackle this conundrum, we propose a versatile and elegant sketch framework named Tight-Sketch, which supports a spectrum of heavy-based detection tasks. Considering that most items are cold (non-heavy/persistent/significant) in practice, we employ different eviction treatments for different types of items to discard these potentially cold ones as soon as possible, and offer more protection to those that are hot (heavy/persistent/significant). In addition, we propose an eviction method that follows a stochastic decay strategy, enabling Tight-Sketch to only bear small one-sided errors (no overestimation). We present a theoretical analysis of the error bounds and conduct extensive experiments on diverse detection tasks to demonstrate that Tight-Sketch significantly outperforms existing methods in terms of accuracy and update speed. Lastly, we accelerate Tight-Sketch's update throughput by up to 36% with Single Instruction Multiple Data (SIMD) instructions.|准确、快速的数据流挖掘是时间序列数据库处理、大数据管理和机器学习等许多工作的关键和基础。不同的重基检测任务，如重点检测、重变换、持久性项目和重要项目检测等，已经引起了业界和学术界的广泛关注。不幸的是，由于不断增长的数据流速度和有限的内存(L1缓存)可用于实时处理，现有的方案在同时实现高检测精度、高内存效率和快速更新吞吐量方面面临挑战，正如我们所揭示的。为了解决这个难题，我们提出了一个通用而优雅的草图框架 Tight-Sketch，它支持一系列基于重量的检测任务。考虑到大多数物品在实践中是冷的(非重的/持久的/重要的) ，我们对不同类型的物品采用不同的驱逐处理方法，以尽快丢弃这些可能冷的物品，并为那些热的(重的/持久的/重要的)提供更多的保护。此外，我们提出了一个驱逐方法，遵循随机衰减策略，使紧凑草图只承受小的单方面误差(没有过高估计)。我们提出了一个误差界限的理论分析，并进行了广泛的实验在不同的检测任务，以证明紧致草图显着优于现有的方法在准确性和更新速度方面。最后，我们使用单指令流多数据流指令(SIMD)将 Tight-Sketch 的更新吞吐量提高了36% 。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Tight-Sketch:+A+High-Performance+Sketch+for+Heavy+Item-Oriented+Data+Stream+Mining+with+Limited+Memory+Size)|0| |[printf: Preference Modeling Based on User Reviews with Item Images and Textual Information via Graph Learning](https://doi.org/10.1145/3583780.3615012)|HaoLun Lin, JyunYu Jiang, MingHao Juan, PuJen Cheng|National Taiwan University, Taipei, Taiwan Roc; Amazon Search, Palo Alto, CA, USA|Nowadays, modern recommender systems usually leverage textual and visual contents as auxiliary information to predict user preference. For textual information, review texts are one of the most popular contents to model user behaviors. Nevertheless, reviews usually lose their shine when it comes to top-N recommender systems because those that solely utilize textual reviews as features struggle to adequately capture the interaction relationships between users and items. For visual one, it is usually modeled with naive convolutional networks and also hard to capture high-order relationships between users and items. Moreover, previous works did not collaboratively use both texts and images in a proper way. In this paper, we propose printf, preference modeling based on user reviews with item images and textual information via graph learning, to address the above challenges. Specifically, the dimension-based attention mechanism directs relations between user reviews and interacted items, allowing each dimension to contribute different importance weights to derive user representations. Extensive experiments are conducted on three publicly available datasets. The experimental results demonstrate that our proposed printf consistently outperforms baseline methods with the relative improvements for NDCG@5 of 26.80%, 48.65%, and 25.74% on Amazon-Grocery, Amazon-Tools, and Amazon-Electronics datasets, respectively. The in-depth analysis also indicates the dimensions of review representations definitely have different topics and aspects, assisting the validity of our model design.|现代推荐系统通常利用文本和可视化内容作为辅助信息来预测用户偏好。对于文本信息来说，评论文本是建立用户行为模型最常用的内容之一。尽管如此，当涉及到排名前 N 的推荐系统时，评论通常会失去它们的光芒，因为那些仅仅利用文本评论作为特性的系统很难充分捕捉到用户和项目之间的交互关系。对于可视化模型，它通常使用天真的卷积网络进行建模，并且很难捕捉用户和项目之间的高阶关系。此外，以前的作品没有合作使用文本和图像在适当的方式。针对上述挑战，本文提出了基于用户评论的 printf 偏好建模方法，该方法通过图形学习，利用项目图像和文本信息建立用户评论的 printf 偏好模型。具体来说，基于维度的注意机制指导用户评论和交互项目之间的关系，允许每个维度贡献不同的重要性权重来推导用户表示。在三个公开的数据集上进行了广泛的实验。实验结果表明，我们提出的 printf 始终优于基线方法，分别在 Amazon-Grocery，Amazon-Tools 和 Amazon-Electronics 数据集上对 NDCG@5的相对改进分别为26.80% ，48.65% 和25.74% 。深入的分析还表明，评论表征的维度肯定有不同的主题和方面，有助于我们的模型设计的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=printf:+Preference+Modeling+Based+on+User+Reviews+with+Item+Images+and+Textual+Information+via+Graph+Learning)|0| -|[The Role of Unattributed Behavior Logs in Predictive User Segmentation](https://doi.org/10.1145/3583780.3615078)|Atanu R. Sinha, Harshita Chopra, Aurghya Maiti, Atishay Ganesh, Sarthak Kapoor, Saili Myana, Saurabh Mahapatra|Adobe Inc., San Jose, USA; Adobe Research, Bangalore, India|Online browsing on firms' sites generates user behavior logs (or, logs). These logs are mainstays that drive several user modeling tasks. The logs that inform user modeling are the ones that are attributed to each user, termed Attributed Behaviors (AB). But, a lot more logs are anonymous, upwards of 85%. For example, many users do not sign in while browsing. These logs are not attributed to users, termed Unattributed Behaviors (UB), and are not recognized in user modeling. We examine whether and how UB can benefit user modeling. We focus on a common task, that of user segmentation, for which the prior art uses only AB. We demonstrate that information from UBs, although unattributed to any individual, when used along with ABs, enriches performance of machine learning model for user segmentation. We perform predictive segmentation, whereby predicted outcomes for each segment are evaluated against actual outcomes. Multiple evaluations on two datasets, one of which is public, relative to state of the art baseline, show strong performance of our model in predicting outcomes and in reducing user segmentation error.|在线浏览公司网站会产生用户行为日志(或者，日志)。这些日志是驱动多个用户建模任务的支柱。通知用户建模的日志是属于每个用户的日志，称为归属行为(AttributedBehavior，AB)。但是，更多的日志是匿名的，高达85% 。例如，许多用户在浏览时不登录。这些日志不属于用户，称为非属性化行为(UB) ，并且在用户建模中不被识别。我们研究 UB 是否以及如何有利于用户建模。我们的重点是一个共同的任务，即用户分割，其中现有的技术只使用 AB。我们证明了来自 UB 的信息虽然不属于任何个体，但是当与 ABs 一起使用时，丰富了用户分割的机器学习模型的性能。我们进行预测性分割，即根据实际结果评估每个部分的预测结果。对两个数据集(其中一个是公开的)的多重评估相对于最先进的基线，显示了我们的模型在预测结果和减少用户分割误差方面的强大性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=The+Role+of+Unattributed+Behavior+Logs+in+Predictive+User+Segmentation)|0| +|[The Role of Unattributed Behavior Logs in Predictive User Segmentation](https://doi.org/10.1145/3583780.3615078)|Atanu R. Sinha, Harshita Chopra, Aurghya Maiti, Atishay Ganesh, Sarthak Kapoor, Saili Myana, Saurabh Mahapatra|Adobe Research, Bangalore, India; Adobe Inc., San Jose, USA|Online browsing on firms' sites generates user behavior logs (or, logs). These logs are mainstays that drive several user modeling tasks. The logs that inform user modeling are the ones that are attributed to each user, termed Attributed Behaviors (AB). But, a lot more logs are anonymous, upwards of 85%. For example, many users do not sign in while browsing. These logs are not attributed to users, termed Unattributed Behaviors (UB), and are not recognized in user modeling. We examine whether and how UB can benefit user modeling. We focus on a common task, that of user segmentation, for which the prior art uses only AB. We demonstrate that information from UBs, although unattributed to any individual, when used along with ABs, enriches performance of machine learning model for user segmentation. We perform predictive segmentation, whereby predicted outcomes for each segment are evaluated against actual outcomes. Multiple evaluations on two datasets, one of which is public, relative to state of the art baseline, show strong performance of our model in predicting outcomes and in reducing user segmentation error.|在线浏览公司网站会产生用户行为日志(或者，日志)。这些日志是驱动多个用户建模任务的支柱。通知用户建模的日志是属于每个用户的日志，称为归属行为(AttributedBehavior，AB)。但是，更多的日志是匿名的，高达85% 。例如，许多用户在浏览时不登录。这些日志不属于用户，称为非属性化行为(UB) ，并且在用户建模中不被识别。我们研究 UB 是否以及如何有利于用户建模。我们的重点是一个共同的任务，即用户分割，其中现有的技术只使用 AB。我们证明了来自 UB 的信息虽然不属于任何个体，但是当与 ABs 一起使用时，丰富了用户分割的机器学习模型的性能。我们进行预测性分割，即根据实际结果评估每个部分的预测结果。对两个数据集(其中一个是公开的)的多重评估相对于最先进的基线，显示了我们的模型在预测结果和减少用户分割误差方面的强大性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=The+Role+of+Unattributed+Behavior+Logs+in+Predictive+User+Segmentation)|0| |[Treatment Effect Estimation across Domains](https://doi.org/10.1145/3583780.3615096)|YiXuan Sun, YaLin Zhang, Wei Wang, Longfei Li, Jun Zhou|Ant Group, Hangzhou, China; Nanjing University, Nanjing, China|Treatment effect estimation is essential in the causal inference literature, which has attracted increasing attention in recent years. Most previous methods assume that the training and test data are drawn from the same distribution, which may not hold in practice since the effect estimators may need to be deployed across domains. Meanwhile, in real-world applications, little or no targeted treatments may be conducted in the new domain. Therefore, we focus on a more realistic scenario in this paper, where treatments and outcomes can be observed in the source domain, but the target domain only contains some unlabeled data, i.e., only features are available. In this scenario, thedistribution shift exists not only in the source data due to the selection bias between the control and treated groups, but also between the source and target data. We propose a novel direct learning framework along with the distribution adaptation and reliable scoring modules. In the distribution adaptation module, we design three specialized density ratio estimators to aid the issue of complex distribution shifts. Even so, we may face the challenge of unreliable pseudo-effects in this framework. To address that, we also design the uncertainty-based reliable scoring module as a vital support, which makes the method more reliable. The experiments are conducted on synthetic data and benchmark datasets, which demonstrate the superiority of our method.|治疗效果评估是因果推理文献中必不可少的内容，近年来受到越来越多的关注。大多数以前的方法假定训练和测试数据来自同一个分布，这在实践中可能不成立，因为效应估计器可能需要跨领域部署。与此同时，在现实世界的应用，很少或没有针对性的治疗可能进行了新的领域。因此，我们在本文中关注一个更加现实的场景，在这个场景中，治疗和结果可以在源域中观察到，但是目标域只包含一些未标记的数据，也就是说，只有特征可用。在这种情况下，由于控制组和治疗组之间的选择偏差，不仅源数据中存在分布偏移，而且源数据和目标数据之间也存在分布偏移。我们提出了一个新的直接学习框架，以及分布适应和可靠的评分模块。在分布自适应模块中，我们设计了三个专门的密度比估计器，以解决复杂分布移位的问题。即便如此，我们也可能面临这一框架中不可靠的伪效应的挑战。为了解决这一问题，我们还设计了基于不确定性的可靠性评分模块作为重要的支持，使得该方法更加可靠。在综合数据和基准数据集上进行了实验，验证了该方法的优越性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Treatment+Effect+Estimation+across+Domains)|0| |[CLOCK: Online Temporal Hierarchical Framework for Multi-scale Multi-granularity Forecasting of User Impression](https://doi.org/10.1145/3583780.3614810)|Xiaoyu Wang, Yonghui Guo, Xiaoyang Ma, Dongbo Huang, Lan Xu, Haisheng Tan, Hao Zhou, XiangYang Li|Tencent Advertising, Shanghai, China; University of Science and Technology of China, Hefei, China|User impression forecasting underpins various commercial activities, from long-term strategic decisions to short-term automated operations. As a representative that involves both kinds, the highly profitable Guaranteed Delivery (GD) advertising focuses mainly on promoting brand effect by allowing advertisers to order target impressions weeksin advance and get allocatedonline at the scheduled time. Such a business mode naturally incurs three issues making existing solutions inferior: 1) Timescale-granularity dilemma of coherently supporting the sales of day-level impressions of the distant future and the corresponding fine-grained allocation in real-time. 2) High dimensionality due to the Cartesian product of user attribute combinations. 3) Stability-plasticity dilemma of instant adaptation to emerging patterns of temporal dependency withoutcatastrophic forgetting of repeated ones facing the non-stationary traffic. To overcome the obstacles, we propose an online temporal hierarchical framework that functions analogously to a CLOCK and hence its name. Long-timescale, coarse-grained temporal data (e.g., the daily impression of one quarter) and short-timescale but fine-grained ones are handled separately by dedicated models, just like the hour/minute/second hands. Each tier in the hierarchy is triggered for forecasting and updating by need at different frequencies, thus saving the maintenance overhead. Furthermore, we devise a reconciliation mechanism to coordinate tiers by aggregating the separately learned local variance and global trends tier by tier. CLOCK solves the dimensionality dilemma by subsuming the autoencoder design to achieve an end-to-end, nonlinear factorization of streaming data into a low-dimension latent space, where a neural predictor produces predictions for the decoder to project them back to the high dimension. Lastly, we regulate the CLOCK's continual refinement by combining the complementary Experience Replay (ER) and Knowledge Distillation (KD) techniques to consolidate and recall previously learned temporal patterns. We conduct extensive evaluations on three public datasets and the real-life user impression log from the Tencent advertising system, and the results demonstrate CLOCK's efficacy.|用户印象预测是各种商业活动的基础，从长期战略决策到短期自动化操作。作为这两类广告的代表，利润丰厚的保证送达广告主要侧重于提升品牌效应，允许广告主提前订购目标印象周，并在预定时间在线分配。这种商业模式自然会产生三个问题，使得现有的解决方案相形见绌: 1)连贯支持销售遥远未来的日级印象和相应的实时细粒度分配的时间尺度-粒度困境。2)由于用户属性组合的笛卡儿积，导致维度较高。3)即时适应新兴时间依赖模式的稳定性-可塑性困境，不会灾难性遗忘面对非平稳流量的重复模式。为了克服这些障碍，我们提出了一个类似于时钟的在线时间层次结构框架。长时间尺度的粗粒度时间数据(例如，一个季度的每日印象)和短时间尺度但细粒度的数据由专用模型分别处理，就像小时/分钟/秒针一样。层次结构中的每一层都被触发，以便在不同的频率根据需要进行预测和更新，从而节省维护开销。此外，我们设计了一个协调机制，通过聚合分别学习的局部方差和全球趋势层层协调层。CLOCK 通过包含自动编码器设计来解决维度困境，实现流数据的端到端非线性因子分解到一个低维潜在空间，其中神经预测器为解码器产生预测，将它们投射回高维空间。最后，我们通过结合互补的体验重放(ER)和知识提取(KD)技术来调节时钟的持续细化，以巩固和回忆先前学习的时间模式。我们对来自腾讯广告系统的三个公共数据集和现实生活中的用户印象日志进行了广泛的评估，结果证明了时钟的功效。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=CLOCK:+Online+Temporal+Hierarchical+Framework+for+Multi-scale+Multi-granularity+Forecasting+of+User+Impression)|0| |[Optimizing Upstream Representations for Out-of-Domain Detection with Supervised Contrastive Learning](https://doi.org/10.1145/3583780.3615001)|Bo Wang, Tsunenori Mine|Kyushu University, Fukuoka, Japan|Out-of-Domain (OOD) text detection has attracted significant research interest. However, conventional approaches primarily employ Cross-Entropy loss during upstream encoder training and seldom focus on optimizing discriminative In-Domain (IND) and OOD representations. To fill this gap, we introduce a novel method that applies supervised contrastive learning (SCL) to IND data for upstream representation optimization. This effectively brings the embeddings of semantically similar texts together while pushing dissimilar ones further apart, leading to more compact and distinct IND representations. This optimization subsequently improves the differentiation between IND and OOD representations, thereby enhancing the detection effect in downstream tasks. To further strengthen the ability of SCL to consolidate IND embedding clusters, and to improve the generalizability of the encoder, we propose a method that generates two different variations of the same text as "views". This is achieved by applying a twice "dropped-out" on the embeddings before performing SCL. Extensive experiments indicate that our method not only outperforms state-of-the-art approaches, but also reduces the requirement for training a large 354M-parameter model down to a more efficient 110M-parameter model, highlighting its superiority in both effectiveness and computational economy.|域外(OOD)文本检测已经引起了人们的极大兴趣。然而，传统的方法主要使用交叉熵损失在上游编码器训练，很少重点优化区分在域(IND)和面向对象的表示。为了填补这一空白，我们提出了一种新的方法，将监督对比学习(SCL)应用于 IND 数据的上游表示优化。这有效地将语义相似的文本嵌入在一起，同时将不同的文本进一步分离，从而产生更紧凑、更独特的 IND 表示。这种优化随后改进了 IND 和 OOD 表示之间的区别，从而增强了下游任务的检测效果。为了进一步增强 SCL 对 IND 嵌入簇的整合能力，提高编码器的通用性，提出了一种生成同一文本中“视图”的两种不同变体的方法。这是通过在执行 SCL 之前对嵌入应用两次“退出”来实现的。大量实验表明，该方法不仅优于最新的方法，而且降低了对大型354M 参数模型的训练要求，使其降低到更高效的110M 参数模型，突出了其在有效性和计算经济性方面的优越性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Optimizing+Upstream+Representations+for+Out-of-Domain+Detection+with+Supervised+Contrastive+Learning)|0| -|[Dual Intents Graph Modeling for User-centric Group Discovery](https://doi.org/10.1145/3583780.3614855)|Xixi Wu, Yun Xiong, Yao Zhang, Yizhu Jiao, Jiawei Zhang|Fudan University, Shanghai, China; University of Illinois at Urbana-Champaign, Champaign, IL, USA; University of California, Davis, Davis, CA, USA|Online groups have become increasingly prevalent, providing users with space to share experiences and explore interests. Therefore, user-centric group discovery task, i.e., recommending groups to users can help both users' online experiences and platforms' long-term developments. Existing recommender methods can not deal with this task as modeling user-group participation into a bipartite graph overlooks their item-side interests. Although there exist a few works attempting to address this task, they still fall short in fully preserving the social context and ensuring effective interest representation learning. In this paper, we focus on exploring the intents that motivate users to participate in groups, which can be categorized into different types, like the social-intent and the personal interest-intent. The former refers to users joining a group affected by their social links, while the latter relates to users joining groups with like-minded people for self-enjoyment. To comprehend different intents, we propose a novel model, DiRec, that first models each intent separately and then fuses them together for predictions. Specifically, for social-intent, we introduce the hypergraph structure to model the relationship between groups and members, leading to a richer understanding of the social context. As for interest-intent, we employ novel structural refinement on the interactive graph to uncover more intricate user behaviors and group interests, realizing better representation learning of interests. Furthermore, we also observe the intent overlapping in real-world scenarios and devise a novel self-supervised learning loss that encourages such alignment for final recommendations. Extensive experiments on three public datasets show the significant improvement of DiRec over the state-of-the-art methods.|在线小组已经变得越来越普遍，为用户提供了分享经验和探索兴趣的空间。因此，以用户为中心的群组发现任务，即向用户推荐群组，可以帮助用户的在线体验和平台的长期发展。现有的推荐方法无法处理这个任务，因为将用户组参与建模成二分图忽略了它们的项目侧兴趣。虽然有一些工作试图解决这一任务，但他们仍然不能充分保存的社会背景和确保有效的兴趣表征学习。本文着重探讨了激发用户参与群体的意图，这些意图可以分为社会意图和个人兴趣意图。前者是指使用者加入一个受其社交联系影响的群组，而后者是指使用者加入与志同道合者的群组以自娱自乐。为了理解不同的意图，我们提出了一个新的模型，DiRec，它首先分别模拟每个意图，然后将它们融合在一起进行预测。具体地说，对于社会意图，我们引入超图结构来建模群体和成员之间的关系，从而更加丰富地理解社会背景。对于兴趣意图，我们在交互图上采用了新颖的结构细化方法，以揭示更复杂的用户行为和群体兴趣，实现更好的兴趣表示学习。此外，我们还观察到在现实世界的情况下意图重叠，并设计了一种新的自我监督学习丢失，鼓励这种调整为最终的建议。在三个公共数据集上的大量实验表明，DiRec 相对于最先进的方法有显著的改进。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Dual+Intents+Graph+Modeling+for+User-centric+Group+Discovery)|0| +|[Dual Intents Graph Modeling for User-centric Group Discovery](https://doi.org/10.1145/3583780.3614855)|Xixi Wu, Yun Xiong, Yao Zhang, Yizhu Jiao, Jiawei Zhang|University of Illinois at Urbana-Champaign, Champaign, IL, USA; Fudan University, Shanghai, China; University of California, Davis, Davis, CA, USA|Online groups have become increasingly prevalent, providing users with space to share experiences and explore interests. Therefore, user-centric group discovery task, i.e., recommending groups to users can help both users' online experiences and platforms' long-term developments. Existing recommender methods can not deal with this task as modeling user-group participation into a bipartite graph overlooks their item-side interests. Although there exist a few works attempting to address this task, they still fall short in fully preserving the social context and ensuring effective interest representation learning. In this paper, we focus on exploring the intents that motivate users to participate in groups, which can be categorized into different types, like the social-intent and the personal interest-intent. The former refers to users joining a group affected by their social links, while the latter relates to users joining groups with like-minded people for self-enjoyment. To comprehend different intents, we propose a novel model, DiRec, that first models each intent separately and then fuses them together for predictions. Specifically, for social-intent, we introduce the hypergraph structure to model the relationship between groups and members, leading to a richer understanding of the social context. As for interest-intent, we employ novel structural refinement on the interactive graph to uncover more intricate user behaviors and group interests, realizing better representation learning of interests. Furthermore, we also observe the intent overlapping in real-world scenarios and devise a novel self-supervised learning loss that encourages such alignment for final recommendations. Extensive experiments on three public datasets show the significant improvement of DiRec over the state-of-the-art methods.|在线小组已经变得越来越普遍，为用户提供了分享经验和探索兴趣的空间。因此，以用户为中心的群组发现任务，即向用户推荐群组，可以帮助用户的在线体验和平台的长期发展。现有的推荐方法无法处理这个任务，因为将用户组参与建模成二分图忽略了它们的项目侧兴趣。虽然有一些工作试图解决这一任务，但他们仍然不能充分保存的社会背景和确保有效的兴趣表征学习。本文着重探讨了激发用户参与群体的意图，这些意图可以分为社会意图和个人兴趣意图。前者是指使用者加入一个受其社交联系影响的群组，而后者是指使用者加入与志同道合者的群组以自娱自乐。为了理解不同的意图，我们提出了一个新的模型，DiRec，它首先分别模拟每个意图，然后将它们融合在一起进行预测。具体地说，对于社会意图，我们引入超图结构来建模群体和成员之间的关系，从而更加丰富地理解社会背景。对于兴趣意图，我们在交互图上采用了新颖的结构细化方法，以揭示更复杂的用户行为和群体兴趣，实现更好的兴趣表示学习。此外，我们还观察到在现实世界的情况下意图重叠，并设计了一种新的自我监督学习丢失，鼓励这种调整为最终的建议。在三个公共数据集上的大量实验表明，DiRec 相对于最先进的方法有显著的改进。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Dual+Intents+Graph+Modeling+for+User-centric+Group+Discovery)|0| |[DSformer: A Double Sampling Transformer for Multivariate Time Series Long-term Prediction](https://doi.org/10.1145/3583780.3614851)|Chengqing Yu, Fei Wang, Zezhi Shao, Tao Sun, Lin Wu, Yongjun Xu|Institute of Computing Technology, Chinese Academy of Sciences & University of Chinese Academy of Sciences, Beijing, China; Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China|Multivariate time series long-term prediction, which aims to predict the change of data in a long time, can provide references for decision-making. Although transformer-based models have made progress in this field, they usually do not make full use of three features of multivariate time series: global information, local information, and variables correlation. To effectively mine the above three features and establish a high-precision prediction model, we propose a double sampling transformer (DSformer), which consists of the double sampling (DS) block and the temporal variable attention (TVA) block. Firstly, the DS block employs down sampling and piecewise sampling to transform the original series into feature vectors that focus on global information and local information respectively. Then, TVA block uses temporal attention and variable attention to mine these feature vectors from different dimensions and extract key information. Finally, based on a parallel structure, DSformer uses multiple TVA blocks to mine and integrate different features obtained from DS blocks respectively. The integrated feature information is passed to the generative decoder based on a multi-layer perceptron to realize multivariate time series long-term prediction. Experimental results on nine real-world datasets show that DSformer can outperform eight existing baselines.|多变量时间序列长期预测旨在预测数据在较长时间内的变化，可以为决策提供参考。虽然基于变压器的模型在这方面取得了一定的进展，但是它们往往没有充分利用多变量时间序列的三个特征: 全局信息、局部信息和变量相关性。为了有效地挖掘上述三个特征，建立高精度的预测模型，提出了一种双采样变压器(DSformer) ，它由双采样(DS)块和时变注意(TVA)块组成。首先，DS 块采用下采样和分段采样的方法，将原始序列分别转换成全局信息和局部信息的特征向量。然后，TVA 块利用时间注意和变量注意从不同维度挖掘特征向量，提取关键信息。最后，在并行结构的基础上，采用多个 TVA 块分别挖掘和集成 DS 块获得的不同特征。将集成的特征信息传递给基于多层感知器的生成解码器，实现多变量时间序列的长期预测。在九个实际数据集上的实验结果表明，DSformer 的性能优于现有的八个基线。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=DSformer:+A+Double+Sampling+Transformer+for+Multivariate+Time+Series+Long-term+Prediction)|0| |[Manipulating Out-Domain Uncertainty Estimation in Deep Neural Networks via Targeted Clean-Label Poisoning](https://doi.org/10.1145/3583780.3614957)|Huimin Zeng, Zhenrui Yue, Yang Zhang, Lanyu Shang, Dong Wang|University of Illinois Urbana-Champaign, Urbana-Champaign, IL, USA|Robust out-domain uncertainty estimation has gained growing attention for its capacity of providing adversary-resistant uncertainty estimates on out-domain samples. However, existing work on robust uncertainty estimation mainly focuses on evasion attacks that happen during test time. The threat of poisoning attacks against uncertainty models is largely unexplored. Compared to evasion attacks, poisoning attacks do not necessarily modify test data, and therefore, would be more practical in real-world applications. In this work, we systematically investigate the robustness of state-of-the-art uncertainty estimation algorithms against data poisoning attacks, with the ultimate objective of developing robust uncertainty training methods. In particular, we focus on attacking the out-domain uncertainty estimation. Under the proposed attack, the training process of models is affected. A fake high-confidence region is established around the targeted out-domain sample, which originally would have been rejected by the model due to low confidence. More fatally, our attack is clean-label and targeted: it leaves the poisoned data with clean labels and attacks a specific targeted test sample without degrading the overall model performance. We evaluate the proposed attack on several image benchmark datasets and a real-world application of COVID-19 misinformation detection. The extensive experimental results on different tasks suggest that the state-of-the-art uncertainty estimation methods could be extremely vulnerable and easily corrupted by our proposed attack.|鲁棒域外不确定性估计由于具有提供域外样本抗对手攻击不确定性估计的能力而受到越来越多的关注。然而，现有的鲁棒不确定性估计工作主要集中在测试期间发生的规避攻击。针对不确定性模型的中毒攻击的威胁在很大程度上尚未得到探索。与规避攻击相比，中毒攻击不一定会修改测试数据，因此在实际应用中会更加实用。在这项工作中，我们系统地研究了最先进的不确定性估计算法对数据中毒攻击的鲁棒性，最终目标是开发鲁棒的不确定性训练方法。特别地，我们重点攻击域外不确定性估计。在所提出的攻击下，模型的训练过程会受到影响。在目标域外样本周围建立一个假的高置信区域，这个区域原本会因为低置信度而被模型拒绝。更致命的是，我们的攻击是干净标签和有针对性的: 它使被污染的数据保持干净标签，并攻击特定的目标测试样本，而不会降低整体模型性能。我们评估了针对几个图像基准数据集的攻击，以及2019冠状病毒疾病错误信息检测的实际应用。在不同任务上的广泛实验结果表明，最先进的不确定性估计方法可能是极其脆弱的，很容易受到我们提出的攻击。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Manipulating+Out-Domain+Uncertainty+Estimation+in+Deep+Neural+Networks+via+Targeted+Clean-Label+Poisoning)|0| -|[All about Sample-Size Calculations for A/B Testing: Novel Extensions & Practical Guide](https://doi.org/10.1145/3583780.3614779)|Jing Zhou, Jiannan Lu, Anas Shallah|Apple, Cupertino, CA, USA; Apple, Seattle, WA, USA|While there exists a large amount of literature on the general challenges and best practices for trustworthy online A/B testing, there are limited studies on sample size estimation, which plays a crucial role in trustworthy and efficient A/B testing that ensures the resulting inference has a sufficient power and type I error control. For example, when sample size is under-estimated, the statistical inference, even with the correct analysis methods, will not be able to detect the true significant improvement leading to misinformed and costly decisions. This paper addresses this fundamental gap by developing new sample size calculation methods for correlated data, as well as absolute vs. relative treatment effects, both ubiquitous in online experiments. Additionally, we address a practical question of the minimal observed difference that will be statistically significant and how it relates to average treatment effect and sample size calculation. All proposed methods are accompanied by mathematical proofs, illustrative examples, and simulations. We end by sharing some best practices on various practical topics on sample size calculation and experimental design.|尽管存在大量关于可信的在线 A/B 测试的一般挑战和最佳实践的文献，但是关于样本量估计的研究有限，这在可信和高效的 A/B 测试中起着关键作用，确保所得到的推断具有足够的功效和 I 型错误控制。例如，当样本数量被低估时，即使使用正确的分析方法，推论统计学也无法发现导致错误信息和昂贵决策的真正显著改善。本文通过开发新的相关数据样本量计算方法，以及在线实验中普遍存在的绝对与相对处理效应，解决了这一根本性差距。此外，我们解决了一个实际问题，即观察到的最小差异将具有统计学意义，以及它如何与平均治疗效果和样本量计算有关。所有提出的方法都伴随着数学证明，说明性的例子，和模拟。最后，我们分享了一些关于样本量计算和实验设计的实际问题的最佳实践。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=All+about+Sample-Size+Calculations+for+A/B+Testing:+Novel+Extensions+&+Practical+Guide)|0| -|[Enhancing Information Diffusion Prediction with Self-Supervised Disentangled User and Cascade Representations](https://doi.org/10.1145/3583780.3615230)|Zhangtao Cheng, Wenxue Ye, Leyuan Liu, Wenxin Tai, Fan Zhou|University of Electronic Science and Technology of China & Kash Instiute of Electronics and Information Industry, Chengdu & Kashgar, China; University of Electronic Science and Technology of China, Chengdu, China; University of Electronic Science and Technology of ChinaUniversity of Electronic Science and Technology of China & Kash Instiute of Electronics and Information Industry, Chengdu & Kashgar, China|Accurately predicting information diffusion is critical for a vast range of applications. Existing methods generally consider user re-sharing behaviors to be driven by a single intent, and/or assume cascade temporal influence to be unchanged, which might not be consistent with real-world scenarios. To address these issues, we propose a self-supervised disentanglement framework (DisenIDP) for information diffusion prediction. First, we construct intent-aware hypergraphs to capture users' potential intents from different perspectives, and then perform the light hypergraph convolution to adaptively activate disentangled intents. Second, we extract long-term and short-term cascade influence via independent attention-based encoders. Finally, we set a self-supervised disentanglement task to alleviate the information loss and learn better-disentanglement representations. Extensive experiments conducted on two real-world social datasets demonstrate that DisenIDP outperforms state-of-the-art models across several settings.|准确预测信息扩散对于广泛的应用至关重要。现有的方法通常认为用户重新共享行为是由单一意图驱动的，并且/或者假定级联时间影响不变，这可能与现实世界的情况不一致。为了解决这些问题，我们提出了一个用于信息扩散预测的自监督解缠框架(DisenIDP)。首先构造意图感知超图，从不同角度捕捉用户的潜在意图，然后进行光超图卷积，自适应地激活解纠缠意图。其次，通过独立的基于注意的编码器提取长期和短期的级联影响。最后，我们设置了一个自我监督的解缠任务，以减轻信息丢失和学习更好的解缠表示。在两个真实世界的社会数据集上进行的大量实验表明，DisenIDP 在几个设置中的表现优于最先进的模型。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Enhancing+Information+Diffusion+Prediction+with+Self-Supervised+Disentangled+User+and+Cascade+Representations)|0| -|[Unlocking the Potential of User Feedback: Leveraging Large Language Model as User Simulators to Enhance Dialogue System](https://doi.org/10.1145/3583780.3615220)|Zhiyuan Hu, Yue Feng, Anh Tuan Luu, Bryan Hooi, Aldo Lipani|University College London, London, United Kingdom; Nanyang Technological University,, Singapore, Singapore; National University of Singapore, Singapore, Singapore|Dialogue systems and large language models (LLMs) have gained considerable attention. However, the direct utilization of LLMs as task-oriented dialogue (TOD) models has been found to underperform compared to smaller task-specific models. Nonetheless, it is crucial to acknowledge the significant potential of LLMs and explore improved approaches for leveraging their impressive abilities. Motivated by the goal of leveraging LLMs, we propose an alternative approach called User-Guided Response Optimization (UGRO) to combine it with a smaller TOD model. This approach uses LLM as annotation-free user simulator to assess dialogue responses, combining them with smaller fine-tuned end-to-end TOD models. By utilizing the satisfaction feedback generated by LLMs, UGRO further optimizes the supervised fine-tuned TOD model. Specifically, the TOD model takes the dialogue history as input and, with the assistance of the user simulator's feedback, generates high-satisfaction responses that meet the user's requirements. Through empirical experiments on two TOD benchmarks, we validate the effectiveness of our method. The results demonstrate that our approach outperforms previous state-of-the-art (SOTA) results.|对话系统和大语言模型(LLM)已经引起了人们的广泛关注。然而，直接利用 LLM 作为任务导向对话(TOD)模型被发现表现不如较小的任务特定模型。尽管如此，承认 LLM 的巨大潜力并探索利用其令人印象深刻的能力的改进方法是至关重要的。受到利用 LLM 目标的激励，我们提出了一种称为用户引导响应优化(UGRO)的替代方法，将其与较小的 TOD 模型相结合。这种方法使用 LLM 作为无注释的用户模拟器来评估对话响应，并将它们与较小的经过微调的端到端 TOD 模型相结合。通过利用 LLM 产生的满意度反馈，UGRO 进一步优化了监督微调 TOD 模型。具体来说，TOD 模型以对话历史为输入，在用户模拟器反馈的帮助下，产生满足用户需求的高满意度响应。通过对两个 TOD 基准的实验，验证了该方法的有效性。结果表明，我们的方法优于以前的最先进的(SOTA)结果。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Unlocking+the+Potential+of+User+Feedback:+Leveraging+Large+Language+Model+as+User+Simulators+to+Enhance+Dialogue+System)|0| -|[Training Heterogeneous Graph Neural Networks using Bandit Sampling](https://doi.org/10.1145/3583780.3615276)|TaYang Wang, Rajgopal Kannan, Viktor K. Prasanna|DEVCOM Army Research Lab, Los Angeles, CA, USA; University of Southern California, Los Angeles, CA, USA|Graph neural networks (GNNs) have gained significant attention across diverse areas due to their superior performance in learning graph representations. While GNNs exhibit superior performance compared to other methods, they are primarily designed for homogeneous graphs, where all nodes and edges are of the same type. Training a GNN model for large-scale graphs incurs high computation and storage costs, especially when considering the heterogeneous structural information of each node. To address the demand for efficient GNN training, various sampling methods have been proposed. In this paper, we propose a sampling method based on bandit sampling, an online learning algorithm with provable convergence under weak assumptions on the learning objective. To the best of our knowledge, this is the first bandit-based sampling method applied to heterogeneous GNNs with a theoretical guarantee. The main idea is to prioritize node types with more informative connections with respect to the learning objective. Compared with existing techniques for GNN training on heterogeneous graphs, extensive experiments using the Open Academic Graph (OAG) dataset demonstrate that our proposed method outperforms the state-of-the-art in terms of the runtime across various tasks with a speed-up of 1.5-2x, while achieving similar accuracy.|图神经网络(GNN)由于其在学习图表示方面的优越性能，在各个领域得到了广泛的关注。虽然 GNN 表现出比其他方法更好的性能，但它们主要是为同质图设计的，其中所有节点和边都是相同类型的。针对大规模图的 GNN 模型的训练需要很高的计算和存储成本，尤其是在考虑每个节点的异构结构信息时。为了满足有效的 GNN 训练需求，提出了多种抽样方法。本文提出了一种基于强盗抽样的抽样方法，这是一种在学习目标弱假设下具有可证明收敛性的在线学习算法。据我们所知，这是第一个基于强盗的抽样方法应用于异构 GNN 的理论保证。其主要思想是优先考虑与学习目标有更多信息联系的节点类型。与现有的在异构图上进行 GNN 训练的技术相比，使用开放学术图(OAG)数据集的大量实验表明，我们提出的方法在不同任务的运行时间方面优于最先进的技术，速度提高了1.5 -2倍，同时达到了类似的精度。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Training+Heterogeneous+Graph+Neural+Networks+using+Bandit+Sampling)|0| -|[Robust User Behavioral Sequence Representation via Multi-scale Stochastic Distribution Prediction](https://doi.org/10.1145/3583780.3614714)|Chilin Fu, Weichang Wu, Xiaolu Zhang, Jun Hu, Jing Wang, Jun Zhou|AntGroup, Hangzhou, China; Ant Group, Hangzhou, China|User behavior representation learned by self-supervised pre-training tasks is widely used in various domains and applications. Conventional methods usually follow the methodology in Natural Language Processing (NLP) to set the pre-training tasks. They either randomly mask some of the behaviors in the sequence and predict the masked ones or predict the next k behaviors. These methods fit for text sequence, in which the tokens are sequentially arranged subject to linguistic criterion. However, the user behavior sequences can be stochastic with noise and randomness. The same paradigm is intractable for learning a robust user behavioral representation. Though the next user behavior can be stochastic, the behavior distribution over a period of time is much more stable and less noisy. Based on this, we propose a Multi-scale Stochastic Distribution Prediction (MSDP) algorithm for learning robust user behavioral sequence representation. Instead of using predictions on concrete behavior as pre-training tasks, we take the prediction on user's behaviors distribution over a period of time as the self-supervision signal. Moreover, inspired by the recent success of the multi-task prompt training method on Large Language Models (LLM), we propose using the window size of the predicted time period as a prompt, enabling the model to learn user behavior representations that can be applied to prediction tasks across various future time periods. We generate different window size prompts through stochastic sampling. It effectively improves the generalization capability of the learned sequence representation. Extensive experiments demonstrate that our approach can learn robust user behavior representation successfully, which significantly outperforms state-of-the-art (SOTA) baselines.|自监督预训练任务学习的用户行为表示在各个领域和应用中得到了广泛的应用。传统的方法通常遵循自然语言处理(NLP)的方法来设置预训练任务。他们要么随机掩盖序列中的一些行为并预测掩盖的行为，要么预测下一个 k 行为。这些方法适用于符号按照语言规范顺序排列的文本序列。然而，用户行为序列具有随机性和噪声性。同样的范例对于学习一个健壮的用户行为表示是难以实现的。虽然下一个用户行为可能是随机的，但是在一段时间内的行为分布更加稳定，噪声更小。在此基础上，提出了一种学习鲁棒用户行为序列表示的多尺度随机分布预测(MSDP)算法。我们不再把对具体行为的预测作为训练前的任务，而是把对用户一段时间内行为分布的预测作为自我监督的信号。此外，受到最近大型语言模型(LLM)多任务提示训练方法的成功启发，我们建议使用预测时间段的窗口大小作为提示，使模型能够学习用户行为表示，可以应用于未来不同时间段的预测任务。我们通过随机抽样产生不同的窗口大小提示。有效地提高了学习序列表示的泛化能力。大量的实验表明，我们的方法可以学习鲁棒的用户行为表示成功，这显着优于国家的最先进的(SOTA)基线。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Robust+User+Behavioral+Sequence+Representation+via+Multi-scale+Stochastic+Distribution+Prediction)|0| -|[Addressing Selection Bias in Computerized Adaptive Testing: A User-Wise Aggregate Influence Function Approach](https://doi.org/10.1145/3583780.3615455)|Soonwoo Kwon, Sojung Kim, Seunghyun Lee, JinYoung Kim, Suyeong An, Kyuseok Kim|Riiid AI Research, Seoul, Republic of Korea; Riiid AI Research, Seoul, South Korea|Computerized Adaptive Testing (CAT) is a widely used, efficient test mode that adapts to the examinee's proficiency level in the test domain. CAT requires pre-trained item profiles, for CAT iteratively assesses the student real-time based on the registered items' profiles, and selects the next item to administer using candidate items' profiles. However, obtaining such item profiles is a costly process that involves gathering a large, dense item-response data, then training a diagnostic model on the collected data. In this paper, we explore the possibility of leveraging response data collected in the CAT service. We first show that this poses a unique challenge due to the inherent selection bias introduced by CAT, i.e., more proficient students will receive harder questions. Indeed, when naively training the diagnostic model using CAT response data, we observe that item profiles deviate significantly from the ground-truth. To tackle the selection bias issue, we propose the user-wise aggregate influence function method. Our intuition is to filter out users whose response data is heavily biased in an aggregate manner, as judged by how much perturbation the added data will introduce during parameter estimation. This way, we may enhance the performance of CAT while introducing minimal bias to the item profiles. We provide extensive experiments to demonstrate the superiority of our proposed method based on the three public datasets and one dataset that contains real-world CAT response data.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Addressing+Selection+Bias+in+Computerized+Adaptive+Testing:+A+User-Wise+Aggregate+Influence+Function+Approach)|0| +|[All about Sample-Size Calculations for A/B Testing: Novel Extensions & Practical Guide](https://doi.org/10.1145/3583780.3614779)|Jing Zhou, Jiannan Lu, Anas Shallah|Apple, Seattle, WA, USA; Apple, Cupertino, CA, USA|While there exists a large amount of literature on the general challenges and best practices for trustworthy online A/B testing, there are limited studies on sample size estimation, which plays a crucial role in trustworthy and efficient A/B testing that ensures the resulting inference has a sufficient power and type I error control. For example, when sample size is under-estimated, the statistical inference, even with the correct analysis methods, will not be able to detect the true significant improvement leading to misinformed and costly decisions. This paper addresses this fundamental gap by developing new sample size calculation methods for correlated data, as well as absolute vs. relative treatment effects, both ubiquitous in online experiments. Additionally, we address a practical question of the minimal observed difference that will be statistically significant and how it relates to average treatment effect and sample size calculation. All proposed methods are accompanied by mathematical proofs, illustrative examples, and simulations. We end by sharing some best practices on various practical topics on sample size calculation and experimental design.|尽管存在大量关于可信的在线 A/B 测试的一般挑战和最佳实践的文献，但是关于样本量估计的研究有限，这在可信和高效的 A/B 测试中起着关键作用，确保所得到的推断具有足够的功效和 I 型错误控制。例如，当样本数量被低估时，即使使用正确的分析方法，推论统计学也无法发现导致错误信息和昂贵决策的真正显著改善。本文通过开发新的相关数据样本量计算方法，以及在线实验中普遍存在的绝对与相对处理效应，解决了这一根本性差距。此外，我们解决了一个实际问题，即观察到的最小差异将具有统计学意义，以及它如何与平均治疗效果和样本量计算有关。所有提出的方法都伴随着数学证明，说明性的例子，和模拟。最后，我们分享了一些关于样本量计算和实验设计的实际问题的最佳实践。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=All+about+Sample-Size+Calculations+for+A/B+Testing:+Novel+Extensions+&+Practical+Guide)|0| +|[Enhancing Information Diffusion Prediction with Self-Supervised Disentangled User and Cascade Representations](https://doi.org/10.1145/3583780.3615230)|Zhangtao Cheng, Wenxue Ye, Leyuan Liu, Wenxin Tai, Fan Zhou|University of Electronic Science and Technology of ChinaUniversity of Electronic Science and Technology of China & Kash Instiute of Electronics and Information Industry, Chengdu & Kashgar, China; University of Electronic Science and Technology of China, Chengdu, China; University of Electronic Science and Technology of China & Kash Instiute of Electronics and Information Industry, Chengdu & Kashgar, China|Accurately predicting information diffusion is critical for a vast range of applications. Existing methods generally consider user re-sharing behaviors to be driven by a single intent, and/or assume cascade temporal influence to be unchanged, which might not be consistent with real-world scenarios. To address these issues, we propose a self-supervised disentanglement framework (DisenIDP) for information diffusion prediction. First, we construct intent-aware hypergraphs to capture users' potential intents from different perspectives, and then perform the light hypergraph convolution to adaptively activate disentangled intents. Second, we extract long-term and short-term cascade influence via independent attention-based encoders. Finally, we set a self-supervised disentanglement task to alleviate the information loss and learn better-disentanglement representations. Extensive experiments conducted on two real-world social datasets demonstrate that DisenIDP outperforms state-of-the-art models across several settings.|准确预测信息扩散对于广泛的应用至关重要。现有的方法通常认为用户重新共享行为是由单一意图驱动的，并且/或者假定级联时间影响不变，这可能与现实世界的情况不一致。为了解决这些问题，我们提出了一个用于信息扩散预测的自监督解缠框架(DisenIDP)。首先构造意图感知超图，从不同角度捕捉用户的潜在意图，然后进行光超图卷积，自适应地激活解纠缠意图。其次，通过独立的基于注意的编码器提取长期和短期的级联影响。最后，我们设置了一个自我监督的解缠任务，以减轻信息丢失和学习更好的解缠表示。在两个真实世界的社会数据集上进行的大量实验表明，DisenIDP 在几个设置中的表现优于最先进的模型。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Enhancing+Information+Diffusion+Prediction+with+Self-Supervised+Disentangled+User+and+Cascade+Representations)|0| +|[Unlocking the Potential of User Feedback: Leveraging Large Language Model as User Simulators to Enhance Dialogue System](https://doi.org/10.1145/3583780.3615220)|Zhiyuan Hu, Yue Feng, Anh Tuan Luu, Bryan Hooi, Aldo Lipani|University College London, London, United Kingdom; National University of Singapore, Singapore, Singapore; Nanyang Technological University,, Singapore, Singapore|Dialogue systems and large language models (LLMs) have gained considerable attention. However, the direct utilization of LLMs as task-oriented dialogue (TOD) models has been found to underperform compared to smaller task-specific models. Nonetheless, it is crucial to acknowledge the significant potential of LLMs and explore improved approaches for leveraging their impressive abilities. Motivated by the goal of leveraging LLMs, we propose an alternative approach called User-Guided Response Optimization (UGRO) to combine it with a smaller TOD model. This approach uses LLM as annotation-free user simulator to assess dialogue responses, combining them with smaller fine-tuned end-to-end TOD models. By utilizing the satisfaction feedback generated by LLMs, UGRO further optimizes the supervised fine-tuned TOD model. Specifically, the TOD model takes the dialogue history as input and, with the assistance of the user simulator's feedback, generates high-satisfaction responses that meet the user's requirements. Through empirical experiments on two TOD benchmarks, we validate the effectiveness of our method. The results demonstrate that our approach outperforms previous state-of-the-art (SOTA) results.|对话系统和大语言模型(LLM)已经引起了人们的广泛关注。然而，直接利用 LLM 作为任务导向对话(TOD)模型被发现表现不如较小的任务特定模型。尽管如此，承认 LLM 的巨大潜力并探索利用其令人印象深刻的能力的改进方法是至关重要的。受到利用 LLM 目标的激励，我们提出了一种称为用户引导响应优化(UGRO)的替代方法，将其与较小的 TOD 模型相结合。这种方法使用 LLM 作为无注释的用户模拟器来评估对话响应，并将它们与较小的经过微调的端到端 TOD 模型相结合。通过利用 LLM 产生的满意度反馈，UGRO 进一步优化了监督微调 TOD 模型。具体来说，TOD 模型以对话历史为输入，在用户模拟器反馈的帮助下，产生满足用户需求的高满意度响应。通过对两个 TOD 基准的实验，验证了该方法的有效性。结果表明，我们的方法优于以前的最先进的(SOTA)结果。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Unlocking+the+Potential+of+User+Feedback:+Leveraging+Large+Language+Model+as+User+Simulators+to+Enhance+Dialogue+System)|0| +|[Training Heterogeneous Graph Neural Networks using Bandit Sampling](https://doi.org/10.1145/3583780.3615276)|TaYang Wang, Rajgopal Kannan, Viktor K. Prasanna|University of Southern California, Los Angeles, CA, USA; DEVCOM Army Research Lab, Los Angeles, CA, USA|Graph neural networks (GNNs) have gained significant attention across diverse areas due to their superior performance in learning graph representations. While GNNs exhibit superior performance compared to other methods, they are primarily designed for homogeneous graphs, where all nodes and edges are of the same type. Training a GNN model for large-scale graphs incurs high computation and storage costs, especially when considering the heterogeneous structural information of each node. To address the demand for efficient GNN training, various sampling methods have been proposed. In this paper, we propose a sampling method based on bandit sampling, an online learning algorithm with provable convergence under weak assumptions on the learning objective. To the best of our knowledge, this is the first bandit-based sampling method applied to heterogeneous GNNs with a theoretical guarantee. The main idea is to prioritize node types with more informative connections with respect to the learning objective. Compared with existing techniques for GNN training on heterogeneous graphs, extensive experiments using the Open Academic Graph (OAG) dataset demonstrate that our proposed method outperforms the state-of-the-art in terms of the runtime across various tasks with a speed-up of 1.5-2x, while achieving similar accuracy.|图神经网络(GNN)由于其在学习图表示方面的优越性能，在各个领域得到了广泛的关注。虽然 GNN 表现出比其他方法更好的性能，但它们主要是为同质图设计的，其中所有节点和边都是相同类型的。针对大规模图的 GNN 模型的训练需要很高的计算和存储成本，尤其是在考虑每个节点的异构结构信息时。为了满足有效的 GNN 训练需求，提出了多种抽样方法。本文提出了一种基于强盗抽样的抽样方法，这是一种在学习目标弱假设下具有可证明收敛性的在线学习算法。据我们所知，这是第一个基于强盗的抽样方法应用于异构 GNN 的理论保证。其主要思想是优先考虑与学习目标有更多信息联系的节点类型。与现有的在异构图上进行 GNN 训练的技术相比，使用开放学术图(OAG)数据集的大量实验表明，我们提出的方法在不同任务的运行时间方面优于最先进的技术，速度提高了1.5 -2倍，同时达到了类似的精度。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Training+Heterogeneous+Graph+Neural+Networks+using+Bandit+Sampling)|0| +|[Robust User Behavioral Sequence Representation via Multi-scale Stochastic Distribution Prediction](https://doi.org/10.1145/3583780.3614714)|Chilin Fu, Weichang Wu, Xiaolu Zhang, Jun Hu, Jing Wang, Jun Zhou|Ant Group, Hangzhou, China; AntGroup, Hangzhou, China|User behavior representation learned by self-supervised pre-training tasks is widely used in various domains and applications. Conventional methods usually follow the methodology in Natural Language Processing (NLP) to set the pre-training tasks. They either randomly mask some of the behaviors in the sequence and predict the masked ones or predict the next k behaviors. These methods fit for text sequence, in which the tokens are sequentially arranged subject to linguistic criterion. However, the user behavior sequences can be stochastic with noise and randomness. The same paradigm is intractable for learning a robust user behavioral representation. Though the next user behavior can be stochastic, the behavior distribution over a period of time is much more stable and less noisy. Based on this, we propose a Multi-scale Stochastic Distribution Prediction (MSDP) algorithm for learning robust user behavioral sequence representation. Instead of using predictions on concrete behavior as pre-training tasks, we take the prediction on user's behaviors distribution over a period of time as the self-supervision signal. Moreover, inspired by the recent success of the multi-task prompt training method on Large Language Models (LLM), we propose using the window size of the predicted time period as a prompt, enabling the model to learn user behavior representations that can be applied to prediction tasks across various future time periods. We generate different window size prompts through stochastic sampling. It effectively improves the generalization capability of the learned sequence representation. Extensive experiments demonstrate that our approach can learn robust user behavior representation successfully, which significantly outperforms state-of-the-art (SOTA) baselines.|自监督预训练任务学习的用户行为表示在各个领域和应用中得到了广泛的应用。传统的方法通常遵循自然语言处理(NLP)的方法来设置预训练任务。他们要么随机掩盖序列中的一些行为并预测掩盖的行为，要么预测下一个 k 行为。这些方法适用于符号按照语言规范顺序排列的文本序列。然而，用户行为序列具有随机性和噪声性。同样的范例对于学习一个健壮的用户行为表示是难以实现的。虽然下一个用户行为可能是随机的，但是在一段时间内的行为分布更加稳定，噪声更小。在此基础上，提出了一种学习鲁棒用户行为序列表示的多尺度随机分布预测(MSDP)算法。我们不再把对具体行为的预测作为训练前的任务，而是把对用户一段时间内行为分布的预测作为自我监督的信号。此外，受到最近大型语言模型(LLM)多任务提示训练方法的成功启发，我们建议使用预测时间段的窗口大小作为提示，使模型能够学习用户行为表示，可以应用于未来不同时间段的预测任务。我们通过随机抽样产生不同的窗口大小提示。有效地提高了学习序列表示的泛化能力。大量的实验表明，我们的方法可以学习鲁棒的用户行为表示成功，这显着优于国家的最先进的(SOTA)基线。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Robust+User+Behavioral+Sequence+Representation+via+Multi-scale+Stochastic+Distribution+Prediction)|0| +|[Addressing Selection Bias in Computerized Adaptive Testing: A User-Wise Aggregate Influence Function Approach](https://doi.org/10.1145/3583780.3615455)|Soonwoo Kwon, Sojung Kim, Seunghyun Lee, JinYoung Kim, Suyeong An, Kyuseok Kim|Riiid AI Research, Seoul, South Korea; Riiid AI Research, Seoul, Republic of Korea|Computerized Adaptive Testing (CAT) is a widely used, efficient test mode that adapts to the examinee's proficiency level in the test domain. CAT requires pre-trained item profiles, for CAT iteratively assesses the student real-time based on the registered items' profiles, and selects the next item to administer using candidate items' profiles. However, obtaining such item profiles is a costly process that involves gathering a large, dense item-response data, then training a diagnostic model on the collected data. In this paper, we explore the possibility of leveraging response data collected in the CAT service. We first show that this poses a unique challenge due to the inherent selection bias introduced by CAT, i.e., more proficient students will receive harder questions. Indeed, when naively training the diagnostic model using CAT response data, we observe that item profiles deviate significantly from the ground-truth. To tackle the selection bias issue, we propose the user-wise aggregate influence function method. Our intuition is to filter out users whose response data is heavily biased in an aggregate manner, as judged by how much perturbation the added data will introduce during parameter estimation. This way, we may enhance the performance of CAT while introducing minimal bias to the item profiles. We provide extensive experiments to demonstrate the superiority of our proposed method based on the three public datasets and one dataset that contains real-world CAT response data.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Addressing+Selection+Bias+in+Computerized+Adaptive+Testing:+A+User-Wise+Aggregate+Influence+Function+Approach)|0| |[The Price is Right: Removing A/B Test Bias in a Marketplace of Expirable Goods](https://doi.org/10.1145/3583780.3615502)|Thu Le, Alex Deng|Airbnb, Seattle, WA, USA; Airbnb, San Francisco, CA, USA|Pricing Guidance tools at Airbnb aim to help hosts maximize the earning for each night of stay. For a given listing, the earning-maximization price point of a night can vary greatly with lead-day - the number of days from now until the night of stay. This introduces systematic bias in running marketplace A/B tests to compare the performances of two pricing strategies. Lead-day bias can cause the short-term experiment result to move in the opposite direction to the long-term impact, possibly leading to the suboptimal business decision and customer dissatisfaction. We propose an efficient experimentation approach that corrects for the bias, minimizes the possible negative impact of experimenting, and greatly accelerates the R&D cycle. This paper is the first of its kind to lays out the theoretical framework along with the real-world example that demonstrates the magnitude of the bias. It serves as a conversation starter for such insidious type of experimentation bias that is likely present in other marketplaces of expirable goods such as vacation nights, car rentals, and airline tickets, concert passes, or ride-hailings.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=The+Price+is+Right:+Removing+A/B+Test+Bias+in+a+Marketplace+of+Expirable+Goods)|0| -|[Optimal Real-Time Bidding Strategy for Position Auctions in Online Advertising](https://doi.org/10.1145/3583780.3614727)|Weitong Ou, Bo Chen, Weiwen Liu, Xinyi Dai, Weinan Zhang, Wei Xia, Xuan Li, Ruiming Tang, Yong Yu|Huawei Noah's Ark Lab, Nanjing, China; Huawei Noah's Ark Lab, Shenzhen, China; Shanghai Jiao Tong University, Shanghai, China|Position auctions are widely studied in the context of sponsored search advertising, where multiple ad slots are sold in a single auction. In traditional sponsored search, bids are submitted at the keyword level, while recent works have explored transitioning to impression-level bidding using Real-Time Bidding (RTB) techniques to achieve finer bidding. However, position auctions introduce varying user appeal across different positions and more dynamic auction landscape, which RTB, originally devised for single-slot display advertising, fails to address adequately. In this work, we are the first to study the optimal bidding strategy for position auctions in RTB. The position auctions we study belong to a broader class, including both sponsored search and display advertising with multiple ad slots. In particular, we aim at maximizing the advertising value within the budget constraint for an advertiser. We mathematically formulate the problem with explicit modeling of position effects. By leveraging the Lagrange multiplier, we derive the optimal bid price and prove its uniqueness under mild assumptions. Efficient numeric methods are applied to obtain the solution practically. Extensive experiments are conducted on a public semi-synthetic dataset and a private industry dataset to validate the effectiveness and feasibility in practice.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Optimal+Real-Time+Bidding+Strategy+for+Position+Auctions+in+Online+Advertising)|0| +|[Optimal Real-Time Bidding Strategy for Position Auctions in Online Advertising](https://doi.org/10.1145/3583780.3614727)|Weitong Ou, Bo Chen, Weiwen Liu, Xinyi Dai, Weinan Zhang, Wei Xia, Xuan Li, Ruiming Tang, Yong Yu|Huawei Noah's Ark Lab, Shenzhen, China; Shanghai Jiao Tong University, Shanghai, China; Huawei Noah's Ark Lab, Nanjing, China|Position auctions are widely studied in the context of sponsored search advertising, where multiple ad slots are sold in a single auction. In traditional sponsored search, bids are submitted at the keyword level, while recent works have explored transitioning to impression-level bidding using Real-Time Bidding (RTB) techniques to achieve finer bidding. However, position auctions introduce varying user appeal across different positions and more dynamic auction landscape, which RTB, originally devised for single-slot display advertising, fails to address adequately. In this work, we are the first to study the optimal bidding strategy for position auctions in RTB. The position auctions we study belong to a broader class, including both sponsored search and display advertising with multiple ad slots. In particular, we aim at maximizing the advertising value within the budget constraint for an advertiser. We mathematically formulate the problem with explicit modeling of position effects. By leveraging the Lagrange multiplier, we derive the optimal bid price and prove its uniqueness under mild assumptions. Efficient numeric methods are applied to obtain the solution practically. Extensive experiments are conducted on a public semi-synthetic dataset and a private industry dataset to validate the effectiveness and feasibility in practice.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Optimal+Real-Time+Bidding+Strategy+for+Position+Auctions+in+Online+Advertising)|0| |[STREAMER 3.0: Towards Online Monitoring and Distributed Learning](https://doi.org/10.1145/3583780.3614755)|Baudouin Naline, Sandra GarciaRodriguez, Karine Zeitouni|DAVID Lab, University of Versailles, University of Paris-Saclay, Versailles, France; Université Paris-Saclay, CEA, List, Saclay, France|Applications that generate continuous data have proliferated in recent years, and thus the challenge of processing those data streams has emerged. This requires Data Stream Processing frameworks with monitoring capabilities able to detect and react to any non-desired situation. Many streaming use cases deal with distributed sources of data which, for privacy and communication saving purposes, need to be tackled in a distributed manner. Based on the mentioned challenges, this paper presents STREAMER 3.0, an improvement on the former data stream framework with two new modules: (i) a monitoring manager with detection algorithms, alert raising and automatic model updater; and (ii) a distributed learning module relying on federated learning. We showcase these new functionalities with an example of remaining useful life estimation of turbofan engines using an LSTM.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=STREAMER+3.0:+Towards+Online+Monitoring+and+Distributed+Learning)|0| -|[Proactive Streaming Analytics at Scale: A Journey from the State-of-the-art to a Production Platform](https://doi.org/10.1145/3583780.3615293)|Nikos Giatrakos, Elias Alevizos, Antonios Deligiannakis, Ralf Klinkenberg, Alexander Artikis|National Centre for Scientific Research Demokritos, Agia Paraskevi, Greece; Altair Engineering GmbH, Dortmund, Germany; Technical University of Crete, Chania, Greece|Proactive streaming analytics continuously extract real-time business value from massive data that stream in data centers or clouds. This requires (a) to process the data while they are still in motion; (b) to scale the processing to multiple machines, often over various, dispersed computer clusters, with diverse Big Data technologies; and (c) to forecast complex business events for proactive decision-making. Combining the necessary facilities for proactive streaming analytics at scale entails: (I) deep knowledge of the relevant state-of-the-art, (II) cherry-picking cutting edge research outcomes based on desired features and with the prospect of building interoperable components, and (III) building components and deploying them into a holistic architecture within a real-world platform. In this tutorial, we drive the audience through the whole journey from (I) to (III), delivering cutting edge research into a commercial analytics platform, for which we provide a hands-on experience.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Proactive+Streaming+Analytics+at+Scale:+A+Journey+from+the+State-of-the-art+to+a+Production+Platform)|0| -|[Generalization Bound for Estimating Causal Effects from Observational Network Data](https://doi.org/10.1145/3583780.3614892)|Ruichu Cai, Zeqin Yang, Weilin Chen, Yuguang Yan, Zhifeng Hao|Guangdong University of Technology, Guangzhou, China; Shantou University, Shantou, China|Estimating causal effects from observational network data is a significant but challenging problem. Existing works in causal inference for observational network data lack an analysis of the generalization bound, which can theoretically provide support for alleviating the complex confounding bias and practically guide the design of learning objectives in a principled manner. To fill this gap, we derive a generalization bound for causal effect estimation in network scenarios by exploiting 1) the reweighting schema based on joint propensity score and 2) the representation learning schema based on Integral Probability Metric (IPM). We provide two perspectives on the generalization bound in terms of reweighting and representation learning, respectively. Motivated by the analysis of the bound, we propose a weighting regression method based on the joint propensity score augmented with representation learning. Extensive experimental studies on two real-world networks with semi-synthetic data demonstrate the effectiveness of our algorithm.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Generalization+Bound+for+Estimating+Causal+Effects+from+Observational+Network+Data)|0| -|[Beyond Trading Data: The Hidden Influence of Public Awareness and Interest on Cryptocurrency Volatility](https://doi.org/10.1145/3583780.3614790)|Zeyd Boukhers, Azeddine Bouabdallah, Cong Yang, Jan Jürjens|University of Koblenz, Koblenz, Germany; Soochow University, Suzhou, China; Fraunhofer Institute for Applied Information Technology, Sankt Augustin, Germany|Since Bitcoin first appeared on the scene in 2009, cryptocurrencies have become a worldwide phenomenon as important decentralized financial assets. Their decentralized nature, however, leads to notable volatility against traditional fiat currencies, making the task of accurately forecasting the crypto-fiat exchange rate complex. In this study, we examine the various independent factors that affect the Bitcoin-Dollar exchange rate's volatility. To this end, we propose CoMForE, a multimodal AdaBoost-LSTM ensemble model, which not only utilizes historical trading data but also incorporates public sentiments from related tweets, public interest demonstrated by search volumes, and blockchain hash-rate data. Our developed model goes a step further by predicting fluctuations in the overall cryptocurrency value distribution, thus increasing its value for investment decision-making. We have subjected this method to extensive testing via comprehensive experiments, thereby validating the importance of multimodal combination over exclusive reliance on trading data. Further experiments show that our method significantly surpasses existing forecasting tools and methodologies, demonstrating a 19.29% improvement. This result underscores the influence of external independent factors on cryptocurrency volatility.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Beyond+Trading+Data:+The+Hidden+Influence+of+Public+Awareness+and+Interest+on+Cryptocurrency+Volatility)|0| -|[Causality and Independence Enhancement for Biased Node Classification](https://doi.org/10.1145/3583780.3614804)|Guoxin Chen, Yongqing Wang, Fangda Guo, Qinglang Guo, Jiangli Shao, Huawei Shen, Xueqi Cheng|University of Science and Technology of China & China Academic of Electronics and Information Technology, HeFei, China; Chinese Academy of Sciences, Beijing, China; Chinese Academy of Sciences & University of Chinese Academy of Sciences, Beijing, China|Most existing methods that address out-of-distribution (OOD) generalization for node classification on graphs primarily focus on a specific type of data biases, such as label selection bias or structural bias. However, anticipating the type of bias in advance is extremely challenging, and designing models solely for one specific type may not necessarily improve overall generalization performance. Moreover, limited research has focused on the impact of mixed biases, which are more prevalent and demanding in real-world scenarios. To address these limitations, we propose a novel Causality and Independence Enhancement (CIE) framework, applicable to various graph neural networks (GNNs). Our approach estimates causal and spurious features at the node representation level and mitigates the influence of spurious correlations through the backdoor adjustment. Meanwhile, independence constraint is introduced to improve the discriminability and stability of causal and spurious features in complex biased environments. Essentially, CIE eliminates different types of data biases from a unified perspective, without the need to design separate methods for each bias as before. To evaluate the performance under specific types of data biases, mixed biases, and low-resource scenarios, we conducted comprehensive experiments on five publicly available datasets. Experimental results demonstrate that our approach CIE not only significantly enhances the performance of GNNs but outperforms state-of-the-art debiased node classification methods.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Causality+and+Independence+Enhancement+for+Biased+Node+Classification)|0| -|[Knowledge-inspired Subdomain Adaptation for Cross-Domain Knowledge Transfer](https://doi.org/10.1145/3583780.3614946)|Liyue Chen, Linian Wang, Jinyu Xu, Shuai Chen, Weiqiang Wang, Wenbiao Zhao, Qiyu Li, Leye Wang|Peking University, Beijing, China; Key Lab of High Confidence Software Technologies (Peking University), Ministry of Education, Beijing, China; Alipay (Hangzhou) Information & Technology Co., Ltd, Hangzhou, China|Most state-of-the-art deep domain adaptation techniques align source and target samples in a global fashion. That is, after alignment, each source sample is expected to become similar to any target sample. However, global alignment may not always be optimal or necessary in practice. For example, consider cross-domain fraud detection, where there are two types of transactions: credit and non-credit. Aligning credit and non-credit transactions separately may yield better performance than global alignment, as credit transactions are unlikely to exhibit patterns similar to non-credit transactions. To enable such fine-grained domain adaption, we propose a novel Knowledge-Inspired Subdomain Adaptation (KISA) framework. In particular, (1) We provide the theoretical insight that KISA minimizes the shared expected loss which is the premise for the success of domain adaptation methods. (2) We propose the knowledge-inspired subdomain division problem that plays a crucial role in fine-grained domain adaption. (3) We design a knowledge fusion network to exploit diverse domain knowledge. Extensive experiments demonstrate that KISA achieves remarkable results on fraud detection and traffic demand prediction tasks.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Knowledge-inspired+Subdomain+Adaptation+for+Cross-Domain+Knowledge+Transfer)|0| -|[Rebalancing Social Feed to Minimize Polarization and Disagreement](https://doi.org/10.1145/3583780.3615025)|Federico Cinus, Aristides Gionis, Francesco Bonchi|CENTAI & Eurecat, Turin, Italy; KTH Royal Institute of Technology, Stockholm, Sweden; Sapienza University & CENTAI, Rome, Italy|Social media have great potential for enabling public discourse on important societal issues. However, adverse effects, such as polarization and echo chambers, greatly impact the benefits of social media and call for algorithms that mitigate these effects. In this paper, we propose a novel problem formulation aimed at slightly nudging users' social feeds in order to strike a balance between relevance and diversity, thus mitigating the emergence of polarization, without lowering the quality of the feed. Our approach is based on re-weighting the relative importance of the accounts that a user follows, so as to calibrate the frequency with which the content produced by various accounts is shown to the user. We analyze the convexity properties of the problem, demonstrating the non-matrix convexity of the objective function and the convexity of the feasible set. To efficiently address the problem, we develop a scalable algorithm based on projected gradient descent. We also prove that our problem statement is a proper generalization of the undirected-case problem so that our method can also be adopted for undirected social networks. As a baseline for comparison in the undirected case, we develop a semidefinite programming approach, which provides the optimal solution. Through extensive experiments on synthetic and real-world datasets, we validate the effectiveness of our approach, which outperforms non-trivial baselines, underscoring its ability to foster healthier and more cohesive online communities.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Rebalancing+Social+Feed+to+Minimize+Polarization+and+Disagreement)|0| -|[AI in the Gray: Exploring Moderation Policies in Dialogic Large Language Models vs. Human Answers in Controversial Topics](https://doi.org/10.1145/3583780.3614777)|Vahid Ghafouri, Vibhor Agarwal, Yong Zhang, Nishanth Sastry, Jose M. Such, Guillermo SuarezTangil|IMDEA Networks Institute, Leganes, Spain; University of Surrey, Guildford, United Kingdom; IMDEA Networks Institute & Universidad Carlos III de Madrid, Leganes, Spain; King's College London & Universitat Politecnica de Valencia, London, United Kingdom|The introduction of ChatGPT and the subsequent improvement of Large Language Models (LLMs) have prompted more and more individuals to turn to the use of ChatBots, both for information and assistance with decision-making. However, the information the user is after is often not formulated by these ChatBots objectively enough to be provided with a definite, globally accepted answer. Controversial topics, such as "religion", "gender identity", "freedom of speech", and "equality", among others, can be a source of conflict as partisan or biased answers can reinforce preconceived notions or promote disinformation. By exposing ChatGPT to such debatable questions, we aim to understand its level of awareness and if existing models are subject to socio-political and/or economic biases. We also aim to explore how AI-generated answers compare to human ones. For exploring this, we use a dataset of a social media platform created for the purpose of debating human-generated claims on polemic subjects among users, dubbed Kialo. Our results show that while previous versions of ChatGPT have had important issues with controversial topics, more recent versions of ChatGPT (gpt-3.5-turbo) are no longer manifesting significant explicit biases in several knowledge areas. In particular, it is well-moderated regarding economic aspects. However, it still maintains degrees of implicit libertarian leaning toward right-winged ideals which suggest the need for increased moderation from the socio-political point of view. In terms of domain knowledge on controversial topics, with the exception of the "Philosophical" category, ChatGPT is performing well in keeping up with the collective human level of knowledge. Finally, we see that sources of Bing AI have slightly more tendency to the center when compared to human answers. All the analyses we make are generalizable to other types of biases and domains.|ChatGPT 的引入和随后对大型语言模型(LLM)的改进促使越来越多的人转向使用 ChatBots，以获取信息和帮助决策。然而，用户所需要的信息通常不是由这些聊天机器人客观地制定的，不足以提供一个明确的、全球公认的答案。有争议的话题，如“宗教”、“性别认同”、“言论自由”和“平等”等等，可能成为冲突的根源，因为党派或有偏见的答案可能会强化先入为主的观念或助长虚假信息。通过让 ChatGPT 接触这些有争议的问题，我们旨在了解其认识水平，以及现有模式是否受到社会政治和/或经济偏见的影响。我们还打算探索人工智能生成的答案与人类的答案之间的比较。为了探索这个问题，我们使用了一个社交媒体平台的数据集，该平台被命名为 Kialo，目的是对用户之间的争论话题进行辩论。我们的研究结果表明，虽然以前版本的 ChatGPT 在有争议的话题上存在重要问题，但是最近版本的 ChatGPT (gpt-3.5-turbo)在几个知识领域不再显示出明显的偏见。特别是，在经济方面，它是很有节制的。然而，它仍然保持着一定程度的隐性自由意志主义倾向于右翼理想，这表明从社会政治的角度来看，需要增加温和。就有争议话题的领域知识而言，除了“哲学”范畴外，ChatGPT 在与人类集体知识水平保持一致方面表现良好。最后，我们看到，与人类的答案相比，Bing AI 的来源稍微更倾向于中心。我们所做的所有分析都可以推广到其他类型的偏差和领域。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=AI+in+the+Gray:+Exploring+Moderation+Policies+in+Dialogic+Large+Language+Models+vs.+Human+Answers+in+Controversial+Topics)|0| -|[Safe-NORA: Safe Reinforcement Learning-based Mobile Network Resource Allocation for Diverse User Demands](https://doi.org/10.1145/3583780.3615043)|Wenzhen Huang, Tong Li, Yuting Cao, Zhe Lyu, Yanping Liang, Li Yu, Depeng Jin, Junge Zhang, Yong Li|Tsinghua University, Beijing, China; Institute of Automation, Chinese Academy of Sciences, Beijing, China; China Mobile Research Institute, Beijing, China|As mobile communication technologies advance, mobile networks become increasingly complex, and user requirements become increasingly diverse. To satisfy the diverse demands of users while improving the overall performance of the network system, the limited wireless network resources should be efficiently and dynamically allocated to them based on the magnitude of their demands and their relative location to the base stations. We separated the problem into four constrained subproblems, which we then solved using a safe reinforcement learning method. In addition, we design a reward mechanism to encourage agent cooperation in distributed training environments. We test our methodology in a simulated scenario with thousands of users and hundreds of base stations. According to experimental findings, our method guarantees that over 95% of user demands are satisfied while also maximizing the overall system throughput.|随着移动通信技术的发展，移动网络变得越来越复杂，用户需求也变得越来越多样化。为了满足用户的多样化需求，同时提高网络系统的整体性能，需要根据用户的需求量及其与基站的相对位置，对有限的无线网络资源进行有效的动态分配。我们把问题分成四个约束子问题，然后用安全强化学习法解决。此外，我们还设计了一个奖励机制来鼓励分布式培训环境中的代理合作。我们在一个有数千用户和数百个基站的模拟场景中测试我们的方法。根据实验结果，我们的方法保证超过95% 的用户需求得到满足，同时也使系统总吞吐量最大化。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Safe-NORA:+Safe+Reinforcement+Learning-based+Mobile+Network+Resource+Allocation+for+Diverse+User+Demands)|0| +|[Proactive Streaming Analytics at Scale: A Journey from the State-of-the-art to a Production Platform](https://doi.org/10.1145/3583780.3615293)|Nikos Giatrakos, Elias Alevizos, Antonios Deligiannakis, Ralf Klinkenberg, Alexander Artikis|Altair Engineering GmbH, Dortmund, Germany; Technical University of Crete, Chania, Greece; National Centre for Scientific Research Demokritos, Agia Paraskevi, Greece|Proactive streaming analytics continuously extract real-time business value from massive data that stream in data centers or clouds. This requires (a) to process the data while they are still in motion; (b) to scale the processing to multiple machines, often over various, dispersed computer clusters, with diverse Big Data technologies; and (c) to forecast complex business events for proactive decision-making. Combining the necessary facilities for proactive streaming analytics at scale entails: (I) deep knowledge of the relevant state-of-the-art, (II) cherry-picking cutting edge research outcomes based on desired features and with the prospect of building interoperable components, and (III) building components and deploying them into a holistic architecture within a real-world platform. In this tutorial, we drive the audience through the whole journey from (I) to (III), delivering cutting edge research into a commercial analytics platform, for which we provide a hands-on experience.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Proactive+Streaming+Analytics+at+Scale:+A+Journey+from+the+State-of-the-art+to+a+Production+Platform)|0| +|[Generalization Bound for Estimating Causal Effects from Observational Network Data](https://doi.org/10.1145/3583780.3614892)|Ruichu Cai, Zeqin Yang, Weilin Chen, Yuguang Yan, Zhifeng Hao|Shantou University, Shantou, China; Guangdong University of Technology, Guangzhou, China|Estimating causal effects from observational network data is a significant but challenging problem. Existing works in causal inference for observational network data lack an analysis of the generalization bound, which can theoretically provide support for alleviating the complex confounding bias and practically guide the design of learning objectives in a principled manner. To fill this gap, we derive a generalization bound for causal effect estimation in network scenarios by exploiting 1) the reweighting schema based on joint propensity score and 2) the representation learning schema based on Integral Probability Metric (IPM). We provide two perspectives on the generalization bound in terms of reweighting and representation learning, respectively. Motivated by the analysis of the bound, we propose a weighting regression method based on the joint propensity score augmented with representation learning. Extensive experimental studies on two real-world networks with semi-synthetic data demonstrate the effectiveness of our algorithm.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Generalization+Bound+for+Estimating+Causal+Effects+from+Observational+Network+Data)|0| +|[Beyond Trading Data: The Hidden Influence of Public Awareness and Interest on Cryptocurrency Volatility](https://doi.org/10.1145/3583780.3614790)|Zeyd Boukhers, Azeddine Bouabdallah, Cong Yang, Jan Jürjens|Soochow University, Suzhou, China; Fraunhofer Institute for Applied Information Technology, Sankt Augustin, Germany; University of Koblenz, Koblenz, Germany|Since Bitcoin first appeared on the scene in 2009, cryptocurrencies have become a worldwide phenomenon as important decentralized financial assets. Their decentralized nature, however, leads to notable volatility against traditional fiat currencies, making the task of accurately forecasting the crypto-fiat exchange rate complex. In this study, we examine the various independent factors that affect the Bitcoin-Dollar exchange rate's volatility. To this end, we propose CoMForE, a multimodal AdaBoost-LSTM ensemble model, which not only utilizes historical trading data but also incorporates public sentiments from related tweets, public interest demonstrated by search volumes, and blockchain hash-rate data. Our developed model goes a step further by predicting fluctuations in the overall cryptocurrency value distribution, thus increasing its value for investment decision-making. We have subjected this method to extensive testing via comprehensive experiments, thereby validating the importance of multimodal combination over exclusive reliance on trading data. Further experiments show that our method significantly surpasses existing forecasting tools and methodologies, demonstrating a 19.29% improvement. This result underscores the influence of external independent factors on cryptocurrency volatility.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Beyond+Trading+Data:+The+Hidden+Influence+of+Public+Awareness+and+Interest+on+Cryptocurrency+Volatility)|0| +|[Causality and Independence Enhancement for Biased Node Classification](https://doi.org/10.1145/3583780.3614804)|Guoxin Chen, Yongqing Wang, Fangda Guo, Qinglang Guo, Jiangli Shao, Huawei Shen, Xueqi Cheng|Chinese Academy of Sciences & University of Chinese Academy of Sciences, Beijing, China; University of Science and Technology of China & China Academic of Electronics and Information Technology, HeFei, China; Chinese Academy of Sciences, Beijing, China|Most existing methods that address out-of-distribution (OOD) generalization for node classification on graphs primarily focus on a specific type of data biases, such as label selection bias or structural bias. However, anticipating the type of bias in advance is extremely challenging, and designing models solely for one specific type may not necessarily improve overall generalization performance. Moreover, limited research has focused on the impact of mixed biases, which are more prevalent and demanding in real-world scenarios. To address these limitations, we propose a novel Causality and Independence Enhancement (CIE) framework, applicable to various graph neural networks (GNNs). Our approach estimates causal and spurious features at the node representation level and mitigates the influence of spurious correlations through the backdoor adjustment. Meanwhile, independence constraint is introduced to improve the discriminability and stability of causal and spurious features in complex biased environments. Essentially, CIE eliminates different types of data biases from a unified perspective, without the need to design separate methods for each bias as before. To evaluate the performance under specific types of data biases, mixed biases, and low-resource scenarios, we conducted comprehensive experiments on five publicly available datasets. Experimental results demonstrate that our approach CIE not only significantly enhances the performance of GNNs but outperforms state-of-the-art debiased node classification methods.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Causality+and+Independence+Enhancement+for+Biased+Node+Classification)|0| +|[Knowledge-inspired Subdomain Adaptation for Cross-Domain Knowledge Transfer](https://doi.org/10.1145/3583780.3614946)|Liyue Chen, Linian Wang, Jinyu Xu, Shuai Chen, Weiqiang Wang, Wenbiao Zhao, Qiyu Li, Leye Wang|Alipay (Hangzhou) Information & Technology Co., Ltd, Hangzhou, China; Key Lab of High Confidence Software Technologies (Peking University), Ministry of Education, Beijing, China; Peking University, Beijing, China|Most state-of-the-art deep domain adaptation techniques align source and target samples in a global fashion. That is, after alignment, each source sample is expected to become similar to any target sample. However, global alignment may not always be optimal or necessary in practice. For example, consider cross-domain fraud detection, where there are two types of transactions: credit and non-credit. Aligning credit and non-credit transactions separately may yield better performance than global alignment, as credit transactions are unlikely to exhibit patterns similar to non-credit transactions. To enable such fine-grained domain adaption, we propose a novel Knowledge-Inspired Subdomain Adaptation (KISA) framework. In particular, (1) We provide the theoretical insight that KISA minimizes the shared expected loss which is the premise for the success of domain adaptation methods. (2) We propose the knowledge-inspired subdomain division problem that plays a crucial role in fine-grained domain adaption. (3) We design a knowledge fusion network to exploit diverse domain knowledge. Extensive experiments demonstrate that KISA achieves remarkable results on fraud detection and traffic demand prediction tasks.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Knowledge-inspired+Subdomain+Adaptation+for+Cross-Domain+Knowledge+Transfer)|0| +|[Rebalancing Social Feed to Minimize Polarization and Disagreement](https://doi.org/10.1145/3583780.3615025)|Federico Cinus, Aristides Gionis, Francesco Bonchi|Sapienza University & CENTAI, Rome, Italy; KTH Royal Institute of Technology, Stockholm, Sweden; CENTAI & Eurecat, Turin, Italy|Social media have great potential for enabling public discourse on important societal issues. However, adverse effects, such as polarization and echo chambers, greatly impact the benefits of social media and call for algorithms that mitigate these effects. In this paper, we propose a novel problem formulation aimed at slightly nudging users' social feeds in order to strike a balance between relevance and diversity, thus mitigating the emergence of polarization, without lowering the quality of the feed. Our approach is based on re-weighting the relative importance of the accounts that a user follows, so as to calibrate the frequency with which the content produced by various accounts is shown to the user. We analyze the convexity properties of the problem, demonstrating the non-matrix convexity of the objective function and the convexity of the feasible set. To efficiently address the problem, we develop a scalable algorithm based on projected gradient descent. We also prove that our problem statement is a proper generalization of the undirected-case problem so that our method can also be adopted for undirected social networks. As a baseline for comparison in the undirected case, we develop a semidefinite programming approach, which provides the optimal solution. Through extensive experiments on synthetic and real-world datasets, we validate the effectiveness of our approach, which outperforms non-trivial baselines, underscoring its ability to foster healthier and more cohesive online communities.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Rebalancing+Social+Feed+to+Minimize+Polarization+and+Disagreement)|0| +|[AI in the Gray: Exploring Moderation Policies in Dialogic Large Language Models vs. Human Answers in Controversial Topics](https://doi.org/10.1145/3583780.3614777)|Vahid Ghafouri, Vibhor Agarwal, Yong Zhang, Nishanth Sastry, Jose M. Such, Guillermo SuarezTangil|IMDEA Networks Institute & Universidad Carlos III de Madrid, Leganes, Spain; King's College London & Universitat Politecnica de Valencia, London, United Kingdom; University of Surrey, Guildford, United Kingdom; IMDEA Networks Institute, Leganes, Spain|The introduction of ChatGPT and the subsequent improvement of Large Language Models (LLMs) have prompted more and more individuals to turn to the use of ChatBots, both for information and assistance with decision-making. However, the information the user is after is often not formulated by these ChatBots objectively enough to be provided with a definite, globally accepted answer. Controversial topics, such as "religion", "gender identity", "freedom of speech", and "equality", among others, can be a source of conflict as partisan or biased answers can reinforce preconceived notions or promote disinformation. By exposing ChatGPT to such debatable questions, we aim to understand its level of awareness and if existing models are subject to socio-political and/or economic biases. We also aim to explore how AI-generated answers compare to human ones. For exploring this, we use a dataset of a social media platform created for the purpose of debating human-generated claims on polemic subjects among users, dubbed Kialo. Our results show that while previous versions of ChatGPT have had important issues with controversial topics, more recent versions of ChatGPT (gpt-3.5-turbo) are no longer manifesting significant explicit biases in several knowledge areas. In particular, it is well-moderated regarding economic aspects. However, it still maintains degrees of implicit libertarian leaning toward right-winged ideals which suggest the need for increased moderation from the socio-political point of view. In terms of domain knowledge on controversial topics, with the exception of the "Philosophical" category, ChatGPT is performing well in keeping up with the collective human level of knowledge. Finally, we see that sources of Bing AI have slightly more tendency to the center when compared to human answers. All the analyses we make are generalizable to other types of biases and domains.|ChatGPT 的引入和随后对大型语言模型(LLM)的改进促使越来越多的人转向使用 ChatBots，以获取信息和帮助决策。然而，用户所需要的信息通常不是由这些聊天机器人客观地制定的，不足以提供一个明确的、全球公认的答案。有争议的话题，如“宗教”、“性别认同”、“言论自由”和“平等”等等，可能成为冲突的根源，因为党派或有偏见的答案可能会强化先入为主的观念或助长虚假信息。通过让 ChatGPT 接触这些有争议的问题，我们旨在了解其认识水平，以及现有模式是否受到社会政治和/或经济偏见的影响。我们还打算探索人工智能生成的答案与人类的答案之间的比较。为了探索这个问题，我们使用了一个社交媒体平台的数据集，该平台被命名为 Kialo，目的是对用户之间的争论话题进行辩论。我们的研究结果表明，虽然以前版本的 ChatGPT 在有争议的话题上存在重要问题，但是最近版本的 ChatGPT (gpt-3.5-turbo)在几个知识领域不再显示出明显的偏见。特别是，在经济方面，它是很有节制的。然而，它仍然保持着一定程度的隐性自由意志主义倾向于右翼理想，这表明从社会政治的角度来看，需要增加温和。就有争议话题的领域知识而言，除了“哲学”范畴外，ChatGPT 在与人类集体知识水平保持一致方面表现良好。最后，我们看到，与人类的答案相比，Bing AI 的来源稍微更倾向于中心。我们所做的所有分析都可以推广到其他类型的偏差和领域。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=AI+in+the+Gray:+Exploring+Moderation+Policies+in+Dialogic+Large+Language+Models+vs.+Human+Answers+in+Controversial+Topics)|0| +|[Safe-NORA: Safe Reinforcement Learning-based Mobile Network Resource Allocation for Diverse User Demands](https://doi.org/10.1145/3583780.3615043)|Wenzhen Huang, Tong Li, Yuting Cao, Zhe Lyu, Yanping Liang, Li Yu, Depeng Jin, Junge Zhang, Yong Li|China Mobile Research Institute, Beijing, China; Tsinghua University, Beijing, China; Institute of Automation, Chinese Academy of Sciences, Beijing, China|As mobile communication technologies advance, mobile networks become increasingly complex, and user requirements become increasingly diverse. To satisfy the diverse demands of users while improving the overall performance of the network system, the limited wireless network resources should be efficiently and dynamically allocated to them based on the magnitude of their demands and their relative location to the base stations. We separated the problem into four constrained subproblems, which we then solved using a safe reinforcement learning method. In addition, we design a reward mechanism to encourage agent cooperation in distributed training environments. We test our methodology in a simulated scenario with thousands of users and hundreds of base stations. According to experimental findings, our method guarantees that over 95% of user demands are satisfied while also maximizing the overall system throughput.|随着移动通信技术的发展，移动网络变得越来越复杂，用户需求也变得越来越多样化。为了满足用户的多样化需求，同时提高网络系统的整体性能，需要根据用户的需求量及其与基站的相对位置，对有限的无线网络资源进行有效的动态分配。我们把问题分成四个约束子问题，然后用安全强化学习法解决。此外，我们还设计了一个奖励机制来鼓励分布式培训环境中的代理合作。我们在一个有数千用户和数百个基站的模拟场景中测试我们的方法。根据实验结果，我们的方法保证超过95% 的用户需求得到满足，同时也使系统总吞吐量最大化。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Safe-NORA:+Safe+Reinforcement+Learning-based+Mobile+Network+Resource+Allocation+for+Diverse+User+Demands)|0| |[Deep Variational Bayesian Modeling of Haze Degradation Process](https://doi.org/10.1145/3583780.3614838)|Eun Woo Im, Junsung Shin, Sungyong Baik, Tae Hyun Kim|Hanyang University, Seoul, Republic of Korea|Relying on the representation power of neural networks, most recent works have often neglected several factors involved in haze degradation, such as transmission (the amount of light reaching an observer from a scene over distance) and atmospheric light. These factors are generally unknown, making dehazing problems ill-posed and creating inherent uncertainties. To account for such uncertainties and factors involved in haze degradation, we introduce a variational Bayesian framework for single image dehazing. We propose to take not only a clean image and but also transmission map as latent variables, the posterior distributions of which are parameterized by corresponding neural networks: dehazing and transmission networks, respectively. Based on a physical model for haze degradation, our variational Bayesian framework leads to a new objective function that encourages the cooperation between them, facilitating the joint training of and thereby boosting the performance of each other. In our framework, a dehazing network can estimate a clean image independently of a transmission map estimation during inference, introducing no overhead. Furthermore, our model-agnostic framework can be seamlessly incorporated with other existing dehazing networks, greatly enhancing the performance consistently across datasets and models.|依赖于神经网络的表现能力，大多数最近的工作往往忽略了几个因素涉及到阴霾退化，如透射(从远处的场景到达观察者的光量)和大气光。这些因素通常是未知的，使得令人厌烦的问题不适定，并造成固有的不确定性。为了解决这些不确定性和影响霾退化的因素，我们引入了一个变分贝叶斯框架来处理单幅图像的除霾问题。我们提出不仅以清晰图像作为潜变量，而且以传输图作为潜变量，其后验分布分别由相应的神经网络参数化: 去雾网络和传输网络。基于雾霾退化的物理模型，我们的变化贝叶斯框架导致一个新的目标函数，鼓励他们之间的合作，促进联合训练，从而提高彼此的表现。在我们的框架中，去雾网络可以在推理过程中独立于传输图估计来估计干净的图像，不会引入开销。此外，我们的模型无关框架可以与其他现有的去雾网络无缝结合，极大地提高了跨数据集和模型的一致性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Deep+Variational+Bayesian+Modeling+of+Haze+Degradation+Process)|0| |[Diffusion Variational Autoencoder for Tackling Stochasticity in Multi-Step Regression Stock Price Prediction](https://doi.org/10.1145/3583780.3614844)|Kelvin J. L. Koa, Yunshan Ma, Ritchie Ng, TatSeng Chua|Eastspring Investments, Singapore, Singapore; National University of Singapore, Singapore, Singapore|Multi-step stock price prediction over a long-term horizon is crucial for forecasting its volatility, allowing financial institutions to price and hedge derivatives, and banks to quantify the risk in their trading books. Additionally, most financial regulators also require a liquidity horizon of several days for institutional investors to exit their risky assets, in order to not materially affect market prices. However, the task of multi-step stock price prediction is challenging, given the highly stochastic nature of stock data. Current solutions to tackle this problem are mostly designed for single-step, classification-based predictions, and are limited to low representation expressiveness. The problem also gets progressively harder with the introduction of the target price sequence, which also contains stochastic noise and reduces generalizability at test-time. To tackle these issues, we combine a deep hierarchical variational-autoencoder (VAE) and diffusion probabilistic techniques to do seq2seq stock prediction through a stochastic generative process. The hierarchical VAE allows us to learn the complex and low-level latent variables for stock prediction, while the diffusion probabilistic model trains the predictor to handle stock price stochasticity by progressively adding random noise to the stock data. Our Diffusion-VAE (D-Va) model is shown to outperform state-of-the-art solutions in terms of its prediction accuracy and variance. More importantly, the multi-step outputs can also allow us to form a stock portfolio over the prediction length. We demonstrate the effectiveness of our model outputs in the portfolio investment task through the Sharpe ratio metric and highlight the importance of dealing with different types of prediction uncertainties.|长期多步股价预测对于预测其波动性至关重要，它使金融机构能够对衍生品进行定价和对冲，使银行能够量化其交易账户中的风险。此外，大多数金融监管机构还要求机构投资者在几天内退出风险资产，以免对市场价格产生重大影响。然而，由于股票数据的高度随机性，多步股价预测的任务是具有挑战性的。目前解决这个问题的解决方案大多是为单步、基于分类的预测而设计的，并且仅限于低表示表达性。随着目标价格序列的引入，这个问题也变得越来越难，因为目标价格序列还包含随机噪声，降低了测试时的通用性。为了解决这些问题，我们将深层变分自动编码器(VAE)和扩散概率技术结合起来，通过一个随机生成过程进行 seq2seq 股票预测。分层 VAE 使我们能够学习复杂和低层潜变量的股票预测，而扩散概率模型训练预测器处理股票价格的随机性，逐步增加随机噪声的股票数据。我们的扩散 VAE (D-Va)模型在预测精度和方差方面表现优于最先进的解。更重要的是，多步输出还可以让我们形成一个股票投资组合的预测长度。我们证明了我们的模型输出在证券投资任务中的有效性，通过 Sharpe 比率度量，并强调了处理不同类型的预测不确定性的重要性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Diffusion+Variational+Autoencoder+for+Tackling+Stochasticity+in+Multi-Step+Regression+Stock+Price+Prediction)|0| -|[Non-Compliant Bandits](https://doi.org/10.1145/3583780.3614990)|Branislav Kveton, Yi Liu, Johan Matteo Kruijssen, Yisu Nie|Amazon, Santa Clara, USA; Amazon, New York, USA; Amazon, Seattle, USA|Bandit algorithms arose as a standard approach to learning better models online. As they become more popular, they are increasingly deployed in complex machine learning pipelines, where their actions can be overwritten. For example, in ranking problems, a list of recommended items can be modified by a downstream algorithm to increase diversity. This may break the classic bandit algorithms and lead to linear regret. Specifically, if the proposed action is not taken, uncertainty in its estimated mean reward may not get reduced. In this work, we study this setting and call it non-compliant bandits; as the agent tries to learn rewarding actions that comply with a downstream task. We propose two algorithms, compliant contextual UCB (CompUCB) and Thompson sampling (CompTS), which learn separate reward and compliance models. The compliance model allows the agent to avoid non-compliant actions. We derive a sublinear regret bound for CompUCB. We also conduct experiments that compare our algorithms to classic bandit baselines. The experiments show failures of the baselines and that we mitigate them by learning compliance models.|盗贼算法作为在线学习更好模型的标准方法而兴起。随着它们变得越来越流行，它们越来越多地部署在复杂的机器学习管道中，在那里它们的行为可以被覆盖。例如，在排序问题中，可以通过下游算法修改推荐项目的列表，以增加多样性。这可能会打破经典的强盗算法，并导致线性遗憾。具体来说，如果没有采取建议的行动，其估计的平均报酬的不确定性可能不会得到减少。在这项工作中，我们研究这种情况，并称之为不顺从的土匪; 因为代理人试图学习符合下游任务的奖励行动。我们提出了两个算法，遵从上下文 UCB (CompUCB)和汤普森抽样(CompTS) ，它们学习单独的奖励和遵从模型。遵从性模型允许代理避免不遵从的操作。我们得到了 CompUCB 的一个次线性后悔界。我们还进行实验，将我们的算法与传统的强盗基线进行比较。实验显示了基线的失败，并且我们通过学习遵从性模型来减轻它们。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Non-Compliant+Bandits)|0| -|[ST-MoE: Spatio-Temporal Mixture-of-Experts for Debiasing in Traffic Prediction](https://doi.org/10.1145/3583780.3615068)|Shuhao Li, Yue Cui, Yan Zhao, Weidong Yang, Ruiyuan Zhang, Xiaofang Zhou|Fudan University, Shanghai, China; Aalborg University, Aalborg, Denmark; The Hong Kong University of Science and Technology, Hong Kong SAR, China|The pervasiveness of GPS-enabled devices and wireless communication technologies results in a proliferation of traffic data in intelligent transportation systems, where traffic prediction is often essential to enable reliability and safety. Many recent studies target traffic prediction using deep learning techniques. They model spatio-temporal dependencies among traffic states by deep learning and achieve good overall performance. However, existing studies ignore the bias on traffic prediction models, which refers to non-uniformed performance distribution across road segments, especially the significantly poor prediction results on certain road segments. To solve this issue, we propose a framework named spatio-temporal mixture-of-experts (ST-MoE) that aims to eliminate the bias on traffic prediction. In general, we refer to any traffic prediction model as the based model, and adopt the proposed ST-MoE framework as a plug-in to debias. ST-MoE uses stacked convolution-based networks to learn spatio-temporal representations of individual patterns of road segments and then adaptively assigns appropriate expert layers (sub-networks) to different patterns through a spatio-temporal gating network. To this end, the patterns can be distinguished, and biased performance among road segments can be eliminated by experts tailored for specific patterns, which also further improves the overall prediction accuracy of the base model. Extensive experimental results on various base models and real-world datasets prove the effectiveness of ST-MoE.|具有全球定位系统功能的设备和无线通信技术的普及导致智能交通系统中交通数据的激增，在这些系统中，交通预测往往对可靠性和安全性至关重要。最近的许多研究目标交通预测使用深度学习技术。它们通过深度学习模拟交通状态之间的时空依赖关系，取得了良好的综合性能。然而，现有的研究忽略了交通预测模型的偏差，即路段间的性能分布不均匀，特别是某些路段的预测效果明显较差。为了解决这一问题，我们提出了一种时空混合专家模型(ST-MoE) ，旨在消除交通预测中的偏差。一般来说，我们把任何一种交通预测模型作为基础模型，并采用所提出的 ST-MoE 框架作为插件进行偏置。ST-MoE 使用基于叠加卷积的网络来学习路段单个模式的时空表示，然后通过时空门控网络自适应地为不同的模式分配适当的专家层(子网)。为此，专家针对特定的模式，可以区分模式，消除路段之间的偏差性能，从而进一步提高基本模型的整体预测精度。在各种基本模型和实际数据集上的大量实验结果证明了 ST-MoE 方法的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=ST-MoE:+Spatio-Temporal+Mixture-of-Experts+for+Debiasing+in+Traffic+Prediction)|0| -|[UniTE: A Unified Treatment Effect Estimation Method for One-sided and Two-sided Marketing](https://doi.org/10.1145/3583780.3615100)|Runshi Liu, Zhipeng Hou|Ant Group, Shanghai, China; Ant Group, Hangzhou, China|Many internet platforms are two-sided markets that involve two types of participants. Examples include e-commerce platforms like Taobao (retailers and consumers) and ride-hailing platforms like Uber (drivers and passengers). Participants of different types in the two-sided market have relationships (i.e., supply and demand) that provide externalities and network benefits. On two-sided platforms, marketing campaigns are designed by subsidizing supply or demand. Uplift models built in this scenario usually consider the treatment assignment for only one of the two sides. However, ignoring the interaction of treatments between two sides or treating them as noises may result in incomplete models and inaccurate predictions. As far as we know, there is not much work related to modeling the combinational treatment effects in the two-sided market. In this paper, we first introduce the two-sided treatment effects estimation problem and then propose a Unified Treatment effect Estimation (UniTE) method for one-sided and two-sided marketing. We extend the Robinson Decomposition to two-sided, in which the relationship of the three involved tasks, namely the outcome, the propensity, and the treatment effect, is theoretically derived. Based on the decomposition result, a multi-task-based neural network model is proposed to integrate the three tasks and learn the inter-task-related common information, which prompts the model to estimate the treatment effects better. We also propose a unified synthetic data generation method that adapts to one/two-sided situations to verify the treatment effects estimation performance. Extensive and comprehensive experimental results show that our method outperforms the other methods.|许多互联网平台是双边市场，涉及两种类型的参与者。例如电子商务平台如淘宝(零售商和消费者)和叫车平台如优步(司机和乘客)。双边市场中不同类型的参与者之间的关系(即供求关系)提供了外部性和网络效益。在双边平台上，营销活动是通过补贴供给或需求来设计的。在这种情况下建立的提升模型通常只考虑两侧中的一侧的处理分配。然而，忽略两侧处理之间的相互作用或将它们视为噪声可能导致模型不完整和预测不准确。据我们所知，目前还没有多少工作涉及到在双边市场中建立联合治疗效果的模型。在本文中，我们首先介绍了双边治疗效果估计问题，然后提出了单边和双边营销的统一治疗效果估计(UniTE)方法。我们将鲁滨逊分解推广到双侧，从理论上导出了三个相关任务，即结果、倾向和治疗效果之间的关系。在分解结果的基础上，提出了一种基于多任务的神经网络模型来整合三个任务，学习任务间相关的共同信息，从而更好地估计治疗效果。我们还提出了一个统一的综合数据生成方法，适用于单/双边情况，以验证治疗效果估计性能。广泛而全面的实验结果表明，我们的方法优于其他方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=UniTE:+A+Unified+Treatment+Effect+Estimation+Method+for+One-sided+and+Two-sided+Marketing)|0| -|[Online Efficient Secure Logistic Regression based on Function Secret Sharing](https://doi.org/10.1145/3583780.3614998)|Jing Liu, Jamie Cui, Cen Chen|Ant Group & East China Normal University, Hangzhou, China; East China Normal University & Ant Group, Shanghai, China; East China Normal University, Shanghai, China|Logistic regression is an algorithm widely used for binary classification in various real-world applications such as fraud detection, medical diagnosis, and recommendation systems. However, training a logistic regression model with data from different parties raises privacy concerns. Secure Multi-Party Computation (MPC) is a cryptographic tool that allows multiple parties to train a logistic regression model jointly without compromising privacy. The efficiency of the online training phase becomes crucial when dealing with large-scale data in practice. In this paper, we propose an online efficient protocol for privacy-preserving logistic regression based on Function Secret Sharing (FSS). Our protocols are designed in the two non-colluding servers setting and assume the existence of a third-party dealer who only poses correlated randomness to the computing parties. During the online phase, two servers jointly train a logistic regression model on their private data by utilizing pre-generated correlated randomness. Furthermore, we propose accurate and MPC-friendly alternatives to the sigmoid function and encapsulate the logistic regression training process into a function secret sharing gate. The online communication overhead significantly decreases compared with the traditional secure logistic regression training based on secret sharing. We provide both theoretical and experimental analyses to demonstrate the efficiency and effectiveness of our method.|Logit模型是一种广泛用于二进制分类的算法，在现实世界的各种应用中，如欺诈检测、医疗诊断和推荐系统。然而，利用来自不同方面的数据训练一个 Logit模型模型会引起隐私方面的担忧。安全多方计算是一种加密工具，允许多方在不损害隐私的情况下联合训练一个 Logit模型模型。在实际处理大规模数据时，在线训练阶段的效率变得至关重要。在这篇文章中，我们提出了一个基于功能秘密共享(fSS)的在线高效的保护隐私的 Logit模型协议。我们的协议设计在两个非合谋的服务器设置，并假设存在一个第三方经销商谁只提出了相关的随机性的计算各方。在联机阶段，两台服务器利用预先生成的相关随机性，共同训练一个私有数据的 Logit模型模型。此外，我们还提出了精确的 MPC 友好的替代 S形函数，并将 Logit模型培训过程封装成一个功能秘密共享门。与传统的基于秘密共享的安全 Logit模型训练相比，在线通信开销显著降低。我们提供了理论和实验分析，以证明我们的方法的效率和有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Online+Efficient+Secure+Logistic+Regression+based+on+Function+Secret+Sharing)|0| -|[Khronos: A Real-Time Indexing Framework for Time Series Databases on Large-Scale Performance Monitoring Systems](https://doi.org/10.1145/3583780.3614944)|Xinyu Liu, Zijing Wei, Wenqing Yu, Shaozhi Liu, Gang Wang, Xiaoguang Liu, Yusen Li|Nankai University, Tianjin, China; Alibaba Group Holding Limited, Beijing, China|Time series databases play a critical role in large-scale performance monitoring systems. Metrics are required to be observable immediately after being generated to support real-time analysis. However, the commonly used Log-Structured Merge-Tree structure suffers from periodically visible delay spikes when a new segment is created due to the instantaneous index construction pressure. In this paper, we present Khronos, an asynchronous indexing framework tailored for high-cardinal monitoring data, aiming at reducing the visible delay. Firstly, we analyze the temporal locality nature of time series and propose a complementary index construction algorithm by only indexing series not reported before to relieve indexing workload. Secondly, we design index structures based on Minimum Excluded value function to effectively reuse indexes of previous segments. Thirdly, we take advantage of the non-repetitive feature of complementary indexes and further develop an intermediate query results reusing approach for deduplicating index traversal among segments. Moreover, we propose an index dependency management strategy that cuts off the previous reusing dependency before persistence to avoid extended dependency overhead. Experimental results show that our framework significantly reduces the visible delay from minutes to milliseconds. Khronos outperforms the state-of-the-art databases InfluxDB and TimeScaleDB with at least 4 times higher write throughput, hundreds of times lower visible delay, and 6 times lower query latency. Khronos has been deployed in production since 2020 and has become the largest performance monitoring database in Alibaba.|时间序列数据库在大规模性能监测系统中起着关键的作用。为了支持实时分析，需要在生成度量值后立即可观察到。但是，常用的日志结构化合并树结构在创建新段时，由于瞬时索引构造压力，会出现周期性可见的延迟峰值。本文提出了一种异步索引框架 Khronos，该框架针对高基数监测数据，旨在减少可见的延迟。首先分析了时间序列的时间局部性，提出了一种只索引以前未报道的时间序列的互补索引构造算法，以减轻索引工作量。其次，设计了基于最小排除值函数的索引结构，有效地重用了前面分段的索引。第三，利用互补索引的非重复性特点，进一步提出了一种中间查询结果重用方法，用于去除段间索引遍历的重复性。此外，我们提出了一种索引依赖关系管理策略，在持久化之前切断先前的重用依赖关系，以避免扩展依赖开销。实验结果表明，我们的框架显著减少了可见的延迟从分钟到毫秒。Khronos 的写入吞吐量至少比最先进的数据库 FluxDB 和 TimeScaleDB 高4倍，可见延迟低数百倍，查询延迟低6倍。Khronos 自2020年开始投入生产，已成为阿里巴巴最大的性能监控数据库。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Khronos:+A+Real-Time+Indexing+Framework+for+Time+Series+Databases+on+Large-Scale+Performance+Monitoring+Systems)|0| +|[Non-Compliant Bandits](https://doi.org/10.1145/3583780.3614990)|Branislav Kveton, Yi Liu, Johan Matteo Kruijssen, Yisu Nie|Amazon, New York, USA; Amazon, Seattle, USA; Amazon, Santa Clara, USA|Bandit algorithms arose as a standard approach to learning better models online. As they become more popular, they are increasingly deployed in complex machine learning pipelines, where their actions can be overwritten. For example, in ranking problems, a list of recommended items can be modified by a downstream algorithm to increase diversity. This may break the classic bandit algorithms and lead to linear regret. Specifically, if the proposed action is not taken, uncertainty in its estimated mean reward may not get reduced. In this work, we study this setting and call it non-compliant bandits; as the agent tries to learn rewarding actions that comply with a downstream task. We propose two algorithms, compliant contextual UCB (CompUCB) and Thompson sampling (CompTS), which learn separate reward and compliance models. The compliance model allows the agent to avoid non-compliant actions. We derive a sublinear regret bound for CompUCB. We also conduct experiments that compare our algorithms to classic bandit baselines. The experiments show failures of the baselines and that we mitigate them by learning compliance models.|盗贼算法作为在线学习更好模型的标准方法而兴起。随着它们变得越来越流行，它们越来越多地部署在复杂的机器学习管道中，在那里它们的行为可以被覆盖。例如，在排序问题中，可以通过下游算法修改推荐项目的列表，以增加多样性。这可能会打破经典的强盗算法，并导致线性遗憾。具体来说，如果没有采取建议的行动，其估计的平均报酬的不确定性可能不会得到减少。在这项工作中，我们研究这种情况，并称之为不顺从的土匪; 因为代理人试图学习符合下游任务的奖励行动。我们提出了两个算法，遵从上下文 UCB (CompUCB)和汤普森抽样(CompTS) ，它们学习单独的奖励和遵从模型。遵从性模型允许代理避免不遵从的操作。我们得到了 CompUCB 的一个次线性后悔界。我们还进行实验，将我们的算法与传统的强盗基线进行比较。实验显示了基线的失败，并且我们通过学习遵从性模型来减轻它们。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Non-Compliant+Bandits)|0| +|[ST-MoE: Spatio-Temporal Mixture-of-Experts for Debiasing in Traffic Prediction](https://doi.org/10.1145/3583780.3615068)|Shuhao Li, Yue Cui, Yan Zhao, Weidong Yang, Ruiyuan Zhang, Xiaofang Zhou|The Hong Kong University of Science and Technology, Hong Kong SAR, China; Fudan University, Shanghai, China; Aalborg University, Aalborg, Denmark|The pervasiveness of GPS-enabled devices and wireless communication technologies results in a proliferation of traffic data in intelligent transportation systems, where traffic prediction is often essential to enable reliability and safety. Many recent studies target traffic prediction using deep learning techniques. They model spatio-temporal dependencies among traffic states by deep learning and achieve good overall performance. However, existing studies ignore the bias on traffic prediction models, which refers to non-uniformed performance distribution across road segments, especially the significantly poor prediction results on certain road segments. To solve this issue, we propose a framework named spatio-temporal mixture-of-experts (ST-MoE) that aims to eliminate the bias on traffic prediction. In general, we refer to any traffic prediction model as the based model, and adopt the proposed ST-MoE framework as a plug-in to debias. ST-MoE uses stacked convolution-based networks to learn spatio-temporal representations of individual patterns of road segments and then adaptively assigns appropriate expert layers (sub-networks) to different patterns through a spatio-temporal gating network. To this end, the patterns can be distinguished, and biased performance among road segments can be eliminated by experts tailored for specific patterns, which also further improves the overall prediction accuracy of the base model. Extensive experimental results on various base models and real-world datasets prove the effectiveness of ST-MoE.|具有全球定位系统功能的设备和无线通信技术的普及导致智能交通系统中交通数据的激增，在这些系统中，交通预测往往对可靠性和安全性至关重要。最近的许多研究目标交通预测使用深度学习技术。它们通过深度学习模拟交通状态之间的时空依赖关系，取得了良好的综合性能。然而，现有的研究忽略了交通预测模型的偏差，即路段间的性能分布不均匀，特别是某些路段的预测效果明显较差。为了解决这一问题，我们提出了一种时空混合专家模型(ST-MoE) ，旨在消除交通预测中的偏差。一般来说，我们把任何一种交通预测模型作为基础模型，并采用所提出的 ST-MoE 框架作为插件进行偏置。ST-MoE 使用基于叠加卷积的网络来学习路段单个模式的时空表示，然后通过时空门控网络自适应地为不同的模式分配适当的专家层(子网)。为此，专家针对特定的模式，可以区分模式，消除路段之间的偏差性能，从而进一步提高基本模型的整体预测精度。在各种基本模型和实际数据集上的大量实验结果证明了 ST-MoE 方法的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=ST-MoE:+Spatio-Temporal+Mixture-of-Experts+for+Debiasing+in+Traffic+Prediction)|0| +|[UniTE: A Unified Treatment Effect Estimation Method for One-sided and Two-sided Marketing](https://doi.org/10.1145/3583780.3615100)|Runshi Liu, Zhipeng Hou|Ant Group, Hangzhou, China; Ant Group, Shanghai, China|Many internet platforms are two-sided markets that involve two types of participants. Examples include e-commerce platforms like Taobao (retailers and consumers) and ride-hailing platforms like Uber (drivers and passengers). Participants of different types in the two-sided market have relationships (i.e., supply and demand) that provide externalities and network benefits. On two-sided platforms, marketing campaigns are designed by subsidizing supply or demand. Uplift models built in this scenario usually consider the treatment assignment for only one of the two sides. However, ignoring the interaction of treatments between two sides or treating them as noises may result in incomplete models and inaccurate predictions. As far as we know, there is not much work related to modeling the combinational treatment effects in the two-sided market. In this paper, we first introduce the two-sided treatment effects estimation problem and then propose a Unified Treatment effect Estimation (UniTE) method for one-sided and two-sided marketing. We extend the Robinson Decomposition to two-sided, in which the relationship of the three involved tasks, namely the outcome, the propensity, and the treatment effect, is theoretically derived. Based on the decomposition result, a multi-task-based neural network model is proposed to integrate the three tasks and learn the inter-task-related common information, which prompts the model to estimate the treatment effects better. We also propose a unified synthetic data generation method that adapts to one/two-sided situations to verify the treatment effects estimation performance. Extensive and comprehensive experimental results show that our method outperforms the other methods.|许多互联网平台是双边市场，涉及两种类型的参与者。例如电子商务平台如淘宝(零售商和消费者)和叫车平台如优步(司机和乘客)。双边市场中不同类型的参与者之间的关系(即供求关系)提供了外部性和网络效益。在双边平台上，营销活动是通过补贴供给或需求来设计的。在这种情况下建立的提升模型通常只考虑两侧中的一侧的处理分配。然而，忽略两侧处理之间的相互作用或将它们视为噪声可能导致模型不完整和预测不准确。据我们所知，目前还没有多少工作涉及到在双边市场中建立联合治疗效果的模型。在本文中，我们首先介绍了双边治疗效果估计问题，然后提出了单边和双边营销的统一治疗效果估计(UniTE)方法。我们将鲁滨逊分解推广到双侧，从理论上导出了三个相关任务，即结果、倾向和治疗效果之间的关系。在分解结果的基础上，提出了一种基于多任务的神经网络模型来整合三个任务，学习任务间相关的共同信息，从而更好地估计治疗效果。我们还提出了一个统一的综合数据生成方法，适用于单/双边情况，以验证治疗效果估计性能。广泛而全面的实验结果表明，我们的方法优于其他方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=UniTE:+A+Unified+Treatment+Effect+Estimation+Method+for+One-sided+and+Two-sided+Marketing)|0| +|[Online Efficient Secure Logistic Regression based on Function Secret Sharing](https://doi.org/10.1145/3583780.3614998)|Jing Liu, Jamie Cui, Cen Chen|East China Normal University, Shanghai, China; Ant Group & East China Normal University, Hangzhou, China; East China Normal University & Ant Group, Shanghai, China|Logistic regression is an algorithm widely used for binary classification in various real-world applications such as fraud detection, medical diagnosis, and recommendation systems. However, training a logistic regression model with data from different parties raises privacy concerns. Secure Multi-Party Computation (MPC) is a cryptographic tool that allows multiple parties to train a logistic regression model jointly without compromising privacy. The efficiency of the online training phase becomes crucial when dealing with large-scale data in practice. In this paper, we propose an online efficient protocol for privacy-preserving logistic regression based on Function Secret Sharing (FSS). Our protocols are designed in the two non-colluding servers setting and assume the existence of a third-party dealer who only poses correlated randomness to the computing parties. During the online phase, two servers jointly train a logistic regression model on their private data by utilizing pre-generated correlated randomness. Furthermore, we propose accurate and MPC-friendly alternatives to the sigmoid function and encapsulate the logistic regression training process into a function secret sharing gate. The online communication overhead significantly decreases compared with the traditional secure logistic regression training based on secret sharing. We provide both theoretical and experimental analyses to demonstrate the efficiency and effectiveness of our method.|Logit模型是一种广泛用于二进制分类的算法，在现实世界的各种应用中，如欺诈检测、医疗诊断和推荐系统。然而，利用来自不同方面的数据训练一个 Logit模型模型会引起隐私方面的担忧。安全多方计算是一种加密工具，允许多方在不损害隐私的情况下联合训练一个 Logit模型模型。在实际处理大规模数据时，在线训练阶段的效率变得至关重要。在这篇文章中，我们提出了一个基于功能秘密共享(fSS)的在线高效的保护隐私的 Logit模型协议。我们的协议设计在两个非合谋的服务器设置，并假设存在一个第三方经销商谁只提出了相关的随机性的计算各方。在联机阶段，两台服务器利用预先生成的相关随机性，共同训练一个私有数据的 Logit模型模型。此外，我们还提出了精确的 MPC 友好的替代 S形函数，并将 Logit模型培训过程封装成一个功能秘密共享门。与传统的基于秘密共享的安全 Logit模型训练相比，在线通信开销显著降低。我们提供了理论和实验分析，以证明我们的方法的效率和有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Online+Efficient+Secure+Logistic+Regression+based+on+Function+Secret+Sharing)|0| +|[Khronos: A Real-Time Indexing Framework for Time Series Databases on Large-Scale Performance Monitoring Systems](https://doi.org/10.1145/3583780.3614944)|Xinyu Liu, Zijing Wei, Wenqing Yu, Shaozhi Liu, Gang Wang, Xiaoguang Liu, Yusen Li|Alibaba Group Holding Limited, Beijing, China; Nankai University, Tianjin, China|Time series databases play a critical role in large-scale performance monitoring systems. Metrics are required to be observable immediately after being generated to support real-time analysis. However, the commonly used Log-Structured Merge-Tree structure suffers from periodically visible delay spikes when a new segment is created due to the instantaneous index construction pressure. In this paper, we present Khronos, an asynchronous indexing framework tailored for high-cardinal monitoring data, aiming at reducing the visible delay. Firstly, we analyze the temporal locality nature of time series and propose a complementary index construction algorithm by only indexing series not reported before to relieve indexing workload. Secondly, we design index structures based on Minimum Excluded value function to effectively reuse indexes of previous segments. Thirdly, we take advantage of the non-repetitive feature of complementary indexes and further develop an intermediate query results reusing approach for deduplicating index traversal among segments. Moreover, we propose an index dependency management strategy that cuts off the previous reusing dependency before persistence to avoid extended dependency overhead. Experimental results show that our framework significantly reduces the visible delay from minutes to milliseconds. Khronos outperforms the state-of-the-art databases InfluxDB and TimeScaleDB with at least 4 times higher write throughput, hundreds of times lower visible delay, and 6 times lower query latency. Khronos has been deployed in production since 2020 and has become the largest performance monitoring database in Alibaba.|时间序列数据库在大规模性能监测系统中起着关键的作用。为了支持实时分析，需要在生成度量值后立即可观察到。但是，常用的日志结构化合并树结构在创建新段时，由于瞬时索引构造压力，会出现周期性可见的延迟峰值。本文提出了一种异步索引框架 Khronos，该框架针对高基数监测数据，旨在减少可见的延迟。首先分析了时间序列的时间局部性，提出了一种只索引以前未报道的时间序列的互补索引构造算法，以减轻索引工作量。其次，设计了基于最小排除值函数的索引结构，有效地重用了前面分段的索引。第三，利用互补索引的非重复性特点，进一步提出了一种中间查询结果重用方法，用于去除段间索引遍历的重复性。此外，我们提出了一种索引依赖关系管理策略，在持久化之前切断先前的重用依赖关系，以避免扩展依赖开销。实验结果表明，我们的框架显著减少了可见的延迟从分钟到毫秒。Khronos 的写入吞吐量至少比最先进的数据库 FluxDB 和 TimeScaleDB 高4倍，可见延迟低数百倍，查询延迟低6倍。Khronos 自2020年开始投入生产，已成为阿里巴巴最大的性能监控数据库。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Khronos:+A+Real-Time+Indexing+Framework+for+Time+Series+Databases+on+Large-Scale+Performance+Monitoring+Systems)|0| |[Exploring Low-Dimensional Manifolds of Deep Neural Network Parameters for Improved Model Optimization](https://doi.org/10.1145/3583780.3614873)|Ke Lu, Xiaotong He, Ze Qin, Xinyao Li, Zhekai Du|University of Electronic Science and Technology of China, Chengdu, China|Manifold learning techniques have significantly enhanced the comprehension of massive data by exploring the geometric properties of the data manifold in low-dimensional subspaces. However, existing research on manifold learning primarily focuses on understanding the intricate data, overlooking the explosive growth of the scale and complexity of deep neural networks (DNNs), which presents a significant challenge for model optimization. In this work, we propose to explore the intrinsic low-dimensional manifold of network parameters for efficient model optimization. Specifically, we analyze parameter distributions in a deep model and perform sampling to map them onto a low-dimensional parameter manifold using the local tangent space alignment (LTSA). Since our focus is on studying parameter manifolds to guide model optimization, we therefore select dynamic optimal training trajectories for sampling and approximate tangent spaces to obtain low-dimensional representations of DNNs. By applying manifold learning techniques and employing a two-step alternate optimization method, we achieve a fixed subspace that reduces training time and resource costs for commonly used deep networks. The trained low-dimensional network can be mapped back to the original parameter space for further use. We demonstrate the benefits of learning low-dimensional parameterization of DNNs on both noisy label learning and federated learning tasks. Extensive experimental results on various benchmarks show the effectiveness of our method concerning both superior accuracy and reduced resource consumption.|流形学习技术通过研究低维子空间中数据流形的几何性质，极大地提高了对海量数据的理解能力。然而，现有的流形学习研究主要集中在对复杂数据的理解上，忽视了深层神经网络(DNN)规模和复杂性的爆炸性增长，这对模型优化提出了严峻的挑战。在这项工作中，我们提出探索内在的低维流形的网络参数为有效的模型优化。具体来说，我们分析深度模型中的参数分布，并使用局部切线空间对齐(LTSA)进行抽样，将它们映射到低维参数流形上。由于我们的重点是研究参数流形，以指导模型优化，因此我们选择动态最优的采样训练轨迹和近似切线空间，以获得 DNN 的低维表示。通过应用流形学习技术和采用两步交替优化方法，我们得到了一个固定的子空间，降低了常用深层网络的训练时间和资源成本。训练后的低维网络可以映射回原始参数空间以供进一步使用。我们展示了学习低维参量化的 DNN 在噪声标签学习和联合学习任务中的好处。在各种基准上的大量实验结果表明，我们的方法在提高精度和降低资源消耗方面是有效的。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Exploring+Low-Dimensional+Manifolds+of+Deep+Neural+Network+Parameters+for+Improved+Model+Optimization)|0| -|[Integrating Priors into Domain Adaptation Based on Evidence Theory](https://doi.org/10.1145/3583780.3614935)|Ying Lv, Jianpeng Ma, Yiqiu Zhang, Gang Xu|Shanghai Artificial Intelligence Laboratory & Fudan University, Shanghai, China; Shanghai Artificial Intelligence Laboratory, Shanghai, China|Domain adaptation aims to build up a learning model for target domain by leveraging transferable knowledge from different but related source domains. Existing domain adaptation methods generally transfer the knowledge from source domain to target domain through measuring the consistency between the different domains. Under this strategy, if the data of source domain is not sufficient to guarantee the consistency, the transferable knowledge will be very limited. On the other hand, we often have priors about target domain which facilitate knowledge transfer but are neglected in the extant domain adaptation methods. To tackle the problems, we integrate the priors of target domain into transfer process and propose a domain adaptation method based on evidence evidence theory. We represent the priors with evidential belief function and reformulate the domain adaptation objective based on likelihood principle, in which the priors are used to adjust transferred knowledge to suit for target domain. Based on this, we propose an improved coordinate ascent algorithm to optimize likelihood objective of domain adaption. Experimental results on both text and image datasets validate that the proposed method is effective to improve the knowledge transferability in domain adaptation, especially when the source domain is limited.|领域适应旨在通过利用来自不同但相关源领域的可转移知识，建立目标领域的学习模型。现有的领域自适应方法一般通过测量不同领域之间的一致性将知识从源领域转移到目标领域。在这种策略下，如果源域的数据不足以保证一致性，可转移的知识就会非常有限。另一方面，我们对目标领域的研究往往存在一些先例，这些先例有利于知识转移，但在现有的领域适应方法中却被忽视了。为了解决这些问题，我们将目标域的先验信息融入到转移过程中，提出了一种基于证据理论的域自适应方法。利用证据信度函数对先验知识进行表示，并基于似然原理重新制定领域适应性目标，利用先验知识对传递知识进行调整以适应目标领域。在此基础上，提出了一种改进的坐标提升算法来优化域自适应的似然目标。在文本和图像数据集上的实验结果验证了该方法在提高领域自适应知识转移能力方面的有效性，特别是在源领域有限的情况下。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Integrating+Priors+into+Domain+Adaptation+Based+on+Evidence+Theory)|0| -|[A Principled Decomposition of Pointwise Mutual Information for Intention Template Discovery](https://doi.org/10.1145/3583780.3614767)|Denghao Ma, Kevin ChenChuan Chang, Yueguo Chen, Xueqiang Lv, Liang Shen|Renmin University of China, Beijing, China; University of Illinois at Urbana-Champaign, URBANA, IL, USA; Meituan, Beijing, China; Beijing Information Science and Technology University, Beijing, China|With the rise of Artificial Intelligence (AI), question answering systems have become common for users to interact with computers, e.g., ChatGPT and Siri. These systems require a substantial amount of labeled data to train their models. However, the labeled data is scarce and challenging to be constructed. The construction process typically involves two stages: discovering potential sample candidates and manually labeling these candidates. To discover high-quality candidate samples, we study the intention paraphrase template discovery task: Given some seed questions or templates of an intention, discover new paraphrase templates that describe the intention and are diverse to the seeds enough in text. As the first exploration of the task, we identify the new quality requirements, i.e., relevance, divergence and popularity, and identify the new challenges, i.e., the paradox of divergent yet relevant paraphrases, and the conflict of popular yet relevant paraphrases. To untangle the paradox of divergent yet relevant paraphrases, in which the traditional bag of words falls short, we develop usage-centric modeling, which represents a question/template/answer as a bag of usages that users engaged (e.g., up-votes), and uses a usage-flow graph to interrelate templates, questions and answers. To balance the conflict of popular yet relevant paraphrases, we propose a new and principled decomposition for the well-known Pointwise Mutual Information from the usage perspective (usage-PMI), and then develop a Bayesian inference framework over the usage-flow graph to estimate the usage-PMI. Extensive experiments over three large CQA corpora show strong performance advantage over the baselines adopted from paraphrase identification task. We release 885,000 paraphrase templates of high quality discovered by our proposed PMI decomposition model, and the data is available in site https://github.com/Para-Questions/Intention\_template\_discovery.|随着人工智能(AI)的兴起，问答系统已经成为用户与计算机交互的常见方式，例如 ChatGPT 和 Siri。这些系统需要大量的标记数据来训练它们的模型。然而，标记数据是稀缺和具有挑战性的构建。构建过程通常包括两个阶段: 发现潜在的候选样本和手动标记这些候选样本。为了发现高质量的候选样本，我们研究了意向释义模板发现任务: 给定一些意向的种子问题或模板，发现新的释义模板描述意向，并在文本中对种子足够多样化。作为任务的第一次探索，我们确定了新的质量要求，即相关性，分歧和流行，并确定了新的挑战，即矛盾的分歧但相关的释义，以及流行但相关的释义的冲突。为了解决这个矛盾，我们开发了以用法为中心的模型，它将问题/模板/答案表示为用户参与的一组用法(例如赞成票) ，并使用用法流图来相互关联模板、问题和答案。为了平衡流行但相关的转述之间的冲突，我们从使用角度(使用-采购经理指数)提出了一个新的和原则性的分解点间互信息，然后在使用-流程图上开发了一个贝叶斯推断框架来估计使用-采购经理指数。通过对三个大型 CQA 语料库的大量实验表明，该方法比释义识别任务所采用的基线方法具有更强的性能优势。我们发布了885,000个由我们提出的 PMI 分解模型发现的高质量的转述模板，数据可以在现场 https://github.com/para-questions/intention_template_discovery 中获得。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Principled+Decomposition+of+Pointwise+Mutual+Information+for+Intention+Template+Discovery)|0| +|[Integrating Priors into Domain Adaptation Based on Evidence Theory](https://doi.org/10.1145/3583780.3614935)|Ying Lv, Jianpeng Ma, Yiqiu Zhang, Gang Xu|Shanghai Artificial Intelligence Laboratory, Shanghai, China; Shanghai Artificial Intelligence Laboratory & Fudan University, Shanghai, China|Domain adaptation aims to build up a learning model for target domain by leveraging transferable knowledge from different but related source domains. Existing domain adaptation methods generally transfer the knowledge from source domain to target domain through measuring the consistency between the different domains. Under this strategy, if the data of source domain is not sufficient to guarantee the consistency, the transferable knowledge will be very limited. On the other hand, we often have priors about target domain which facilitate knowledge transfer but are neglected in the extant domain adaptation methods. To tackle the problems, we integrate the priors of target domain into transfer process and propose a domain adaptation method based on evidence evidence theory. We represent the priors with evidential belief function and reformulate the domain adaptation objective based on likelihood principle, in which the priors are used to adjust transferred knowledge to suit for target domain. Based on this, we propose an improved coordinate ascent algorithm to optimize likelihood objective of domain adaption. Experimental results on both text and image datasets validate that the proposed method is effective to improve the knowledge transferability in domain adaptation, especially when the source domain is limited.|领域适应旨在通过利用来自不同但相关源领域的可转移知识，建立目标领域的学习模型。现有的领域自适应方法一般通过测量不同领域之间的一致性将知识从源领域转移到目标领域。在这种策略下，如果源域的数据不足以保证一致性，可转移的知识就会非常有限。另一方面，我们对目标领域的研究往往存在一些先例，这些先例有利于知识转移，但在现有的领域适应方法中却被忽视了。为了解决这些问题，我们将目标域的先验信息融入到转移过程中，提出了一种基于证据理论的域自适应方法。利用证据信度函数对先验知识进行表示，并基于似然原理重新制定领域适应性目标，利用先验知识对传递知识进行调整以适应目标领域。在此基础上，提出了一种改进的坐标提升算法来优化域自适应的似然目标。在文本和图像数据集上的实验结果验证了该方法在提高领域自适应知识转移能力方面的有效性，特别是在源领域有限的情况下。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Integrating+Priors+into+Domain+Adaptation+Based+on+Evidence+Theory)|0| +|[A Principled Decomposition of Pointwise Mutual Information for Intention Template Discovery](https://doi.org/10.1145/3583780.3614767)|Denghao Ma, Kevin ChenChuan Chang, Yueguo Chen, Xueqiang Lv, Liang Shen|Renmin University of China, Beijing, China; Beijing Information Science and Technology University, Beijing, China; Meituan, Beijing, China; University of Illinois at Urbana-Champaign, URBANA, IL, USA|With the rise of Artificial Intelligence (AI), question answering systems have become common for users to interact with computers, e.g., ChatGPT and Siri. These systems require a substantial amount of labeled data to train their models. However, the labeled data is scarce and challenging to be constructed. The construction process typically involves two stages: discovering potential sample candidates and manually labeling these candidates. To discover high-quality candidate samples, we study the intention paraphrase template discovery task: Given some seed questions or templates of an intention, discover new paraphrase templates that describe the intention and are diverse to the seeds enough in text. As the first exploration of the task, we identify the new quality requirements, i.e., relevance, divergence and popularity, and identify the new challenges, i.e., the paradox of divergent yet relevant paraphrases, and the conflict of popular yet relevant paraphrases. To untangle the paradox of divergent yet relevant paraphrases, in which the traditional bag of words falls short, we develop usage-centric modeling, which represents a question/template/answer as a bag of usages that users engaged (e.g., up-votes), and uses a usage-flow graph to interrelate templates, questions and answers. To balance the conflict of popular yet relevant paraphrases, we propose a new and principled decomposition for the well-known Pointwise Mutual Information from the usage perspective (usage-PMI), and then develop a Bayesian inference framework over the usage-flow graph to estimate the usage-PMI. Extensive experiments over three large CQA corpora show strong performance advantage over the baselines adopted from paraphrase identification task. We release 885,000 paraphrase templates of high quality discovered by our proposed PMI decomposition model, and the data is available in site https://github.com/Para-Questions/Intention\_template\_discovery.|随着人工智能(AI)的兴起，问答系统已经成为用户与计算机交互的常见方式，例如 ChatGPT 和 Siri。这些系统需要大量的标记数据来训练它们的模型。然而，标记数据是稀缺和具有挑战性的构建。构建过程通常包括两个阶段: 发现潜在的候选样本和手动标记这些候选样本。为了发现高质量的候选样本，我们研究了意向释义模板发现任务: 给定一些意向的种子问题或模板，发现新的释义模板描述意向，并在文本中对种子足够多样化。作为任务的第一次探索，我们确定了新的质量要求，即相关性，分歧和流行，并确定了新的挑战，即矛盾的分歧但相关的释义，以及流行但相关的释义的冲突。为了解决这个矛盾，我们开发了以用法为中心的模型，它将问题/模板/答案表示为用户参与的一组用法(例如赞成票) ，并使用用法流图来相互关联模板、问题和答案。为了平衡流行但相关的转述之间的冲突，我们从使用角度(使用-采购经理指数)提出了一个新的和原则性的分解点间互信息，然后在使用-流程图上开发了一个贝叶斯推断框架来估计使用-采购经理指数。通过对三个大型 CQA 语料库的大量实验表明，该方法比释义识别任务所采用的基线方法具有更强的性能优势。我们发布了885,000个由我们提出的 PMI 分解模型发现的高质量的转述模板，数据可以在现场 https://github.com/para-questions/intention_template_discovery 中获得。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Principled+Decomposition+of+Pointwise+Mutual+Information+for+Intention+Template+Discovery)|0| |[Good Intentions: Adaptive Parameter Management via Intent Signaling](https://doi.org/10.1145/3583780.3614895)|Alexander RenzWieland, Andreas Kieslinger, Robert Gericke, Rainer Gemulla, Zoi Kaoudi, Volker Markl|IT University of Copenhagen, Copenhagen, Denmark; Technische Universität Berlin, Berlin, Germany; Universität Mannheim, Mannheim, Germany|Parameter management is essential for distributed training of large machine learning (ML) tasks. Some ML tasks are hard to distribute because common approaches to parameter management can be highly inefficient. Advanced parameter management approaches -- such as selective replication or dynamic parameter allocation -- can improve efficiency, but to do so, they typically need to be integrated manually into each task's implementation and they require expensive upfront experimentation to tune correctly. In this work, we explore whether these two problems can be avoided. We first propose a novel intent signaling mechanism that integrates naturally into existing ML stacks and provides the parameter manager with crucial information about parameter accesses. We then describe AdaPM, a fully adaptive, zero-tuning parameter manager based on this mechanism. In contrast to prior systems, this approach separates providing information (simple, done by the task) from exploiting it effectively (hard, done automatically by AdaPM). In our experimental evaluation, AdaPM matched or outperformed state-of-the-art parameter managers out of the box, suggesting that automatic parameter management is possible.|参数管理是大型机器学习任务分布式训练的基础。一些机器学习任务很难分发，因为常见的参数管理方法效率很低。高级的参数管理方法——比如选择性复制或动态参数分配——可以提高效率，但是要做到这一点，它们通常需要手动集成到每个任务的实现中，并且需要昂贵的前期试验来正确调优。在这项工作中，我们探讨这两个问题是否可以避免。我们首先提出了一种新的意图信号机制，该机制自然地集成到现有的 ML 栈中，并为参数管理器提供关于参数访问的关键信息。然后，我们描述 AdaPM，一个基于此机制的完全自适应、零调整的参数管理器。与以前的系统不同，这种方法将提供信息(简单的，由任务完成)和有效的利用信息(难的，由 AdaPM 自动完成)分离开来。在我们的实验评估中，AdaPM 匹配或超过了现有的最先进的参数管理器，这表明自动参数管理是可能的。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Good+Intentions:+Adaptive+Parameter+Management+via+Intent+Signaling)|0| -|[Automatic and Precise Data Validation for Machine Learning](https://doi.org/10.1145/3583780.3614786)|Shreya Shankar, Labib Fawaz, Karl Gyllstrom, Aditya G. Parameswaran|Meta, Menlo Park, CA, USA; University of California, Berkeley, Berkeley, CA, USA|Machine learning (ML) models in production pipelines are frequently retrained on the latest partitions of large, continually- growing datasets. Due to engineering bugs, partitions in such datasets almost always have some corrupted features; thus, it's critical to find data issues and block retraining before downstream ML accuracy decreases. However, current ML data validation methods are difficult to operationalize: they yield too many false positive alerts, require manual tuning, or are infeasible at scale. In this pa- per, we present an automatic, precise, and scalable data validation system for ML pipelines, employing a simple idea that we call a Partition Summarization (PS) approach to data validation: each timestamp-based partition of data is summarized with data quality metrics, and summaries are compared to detect corrupted partitions. We demonstrate how to adapt PS for any data validation method in a robust manner and evaluate several adaptations-which by themselves provide limited precision. Finally, we present gate, our data validation method that leverages these adaptations, giving a 2.1× average improvement in precision over the baseline from prior work on a case study within our large tech company.|生产管道中的机器学习(ML)模型经常在大型、不断增长的数据集的最新分区上重新训练。由于工程缺陷，这类数据集中的分区几乎总是有一些损坏的特征; 因此，在下游机器学习精度下降之前，发现数据问题和阻塞再训练是至关重要的。然而，目前的机器学习数据验证方法很难操作: 它们产生了太多的误报警，需要人工调整，或者在规模上不可行。在本文中，我们提出了一个自动的，精确的，可扩展的机器学习管道数据验证系统，采用一个简单的想法，我们称之为分区总结(PS)方法的数据验证: 每个时间戳为基础的数据分区总结与数据质量指标，并总结进行比较，以检测损坏的分区。我们演示了如何以一种健壮的方式将 PS 适应于任何数据验证方法，并评估了几种适应性——这些适应性本身提供了有限的精度。最后，我们介绍了我们的数据验证方法 gate，它利用了这些适应性，从我们大型科技公司以前的案例研究中得到了2.1倍的平均精度提高。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Automatic+and+Precise+Data+Validation+for+Machine+Learning)|0| -|[TOAK: A Topology-oriented Attack Strategy for Degrading User Identity Linkage in Cross-network Learning](https://doi.org/10.1145/3583780.3615084)|Jiangli Shao, Yongqing Wang, Fangda Guo, Boshen Shi, Huawei Shen, Xueqi Cheng|Institute of Computing Technology, CAS, Beijing, China; Institute of Computing Technology, CAS & University of Chinese Academy of Sciences, Beijing, China|Privacy concerns on social networks have received extensive attention in recent years. The task of user identity linkage (UIL), which aims to identify corresponding users across different social networks, poses a threat to privacy if applied unethically. Sensitive user information would be inferred with cross-network identity linkages. A feasible solution to this issue is to design an adversarial strategy that degrades the matching performance of UIL models. Nevertheless, most of the current adversarial attacks on graphs are tailored towards models working within a single network, failing to account for the challenges presented by cross-network learning tasks such as UIL. Also, in real-world scenarios, the adversarial strategy against UIL has more constraints as service providers can only add perturbations to their own networks. To tackle these challenges, this paper proposes a novel poisoning strategy to prevent nodes in a target network from being linked to other networks by UIL algorithms. Specifically, the UIL problem is formalized in the kernelized topology consistency perspective, and the objective is formulated as maximizing the structural variations in the target network before and after modifications. To achieve this, a novel graph kernel is defined based on earth mover's distance (EMD) in the edge-embedding space. In terms of efficiency, a fast attack strategy is proposed using greedy searching and a lower bound approximation of EMD. Results on three real-world datasets demonstrate that the proposed method outperforms six baselines and reaches a balance between effectiveness and imperceptibility while being efficient.|近年来，社交网络上的隐私问题受到广泛关注。用户身份链接(UIL)任务旨在识别不同社交网络上的相应用户，如果应用不道德，将对隐私构成威胁。敏感用户信息将通过跨网络身份链接推断出来。一个可行的解决方案是设计一个对抗策略，降低 UIL 模型的匹配性能。然而，目前大多数对图形的敌对攻击都是针对单一网络内工作的模型，没有考虑到跨网络学习任务(如 UIL)所带来的挑战。此外，在现实世界的情况下，对 UIL 的对抗策略有更多的限制，因为服务提供商只能添加扰动到他们自己的网络。为了解决这些问题，本文提出了一种新的中毒策略，以防止目标网络中的节点被 UIL 算法链接到其他网络。具体地说，UIL 问题被形式化为核拓扑一致性视角，目标被表述为在目标网络修改前后使结构变化最大化。为了实现这一点，在边嵌入空间中定义了一种新的基于地球动子距离(EMD)的图核。在效率方面，提出了一种基于贪婪搜索和 EMD 下界近似的快速攻击策略。在三个实际数据集上的实验结果表明，该方法的性能优于六个基线，在有效性和不可感知性之间达到了平衡。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=TOAK:+A+Topology-oriented+Attack+Strategy+for+Degrading+User+Identity+Linkage+in+Cross-network+Learning)|0| -|[Improving Graph Domain Adaptation with Network Hierarchy](https://doi.org/10.1145/3583780.3614928)|Boshen Shi, Yongqing Wang, Fangda Guo, Jiangli Shao, Huawei Shen, Xueqi Cheng|Institute of Computing Technology, CAS, Beijing, China; Institute of Computing Technology, CAS & University of Chinese Academy of Sciences, Beijing, China|Graph domain adaptation models have become instrumental in addressing cross-network learning problems due to their ability to transfer abundant label and structural knowledge from source graphs to target graphs. A crucial step in transfer involves measuring domain discrepancy, which refers to distribution shifts between graphs from source and target domains. While conventional models simply provide a node-level measurement, exploiting information from different levels of network hierarchy is intuitive. As each hierarchical level characterizes distinct and meaningful properties or functionalities of the original graph, integrating domain discrepancy based on such hierarchies should contribute to a more precise domain discrepancy measurement. Moreover, class conditional distribution shift is often overlooked in node classification tasks, which could potentially lead to sub-optimal performance. To address the above limitations, we propose a new graph domain adaptation model and apply it to cross-network node classification tasks. Specifically, a hierarchical pooling model to extract meaningful and adaptive hierarchical structures is designed, where both marginal and class conditional distribution shifts on each hierarchical level are jointly minimized. The effectiveness is demonstrated through theoretical analysis and experimental studies across various datasets.|图域自适应模型由于能够将丰富的标签和结构知识从源图转移到目标图，因而在解决跨网络学习问题方面发挥了重要作用。转移中的一个关键步骤涉及到测量域差异，这是指来自源域和目标域的图之间的分布移位。虽然传统的模型只提供节点级的度量，但是利用来自不同层次的网络层次结构的信息是直观的。由于每个层次结构都表征了原始图的独特和有意义的属性或功能，基于这种层次结构的领域差异整合应该有助于更精确的领域差异度量。此外，在节点分类任务中，类条件分布移位往往被忽视，这可能导致性能不理想。针对上述局限性，提出了一种新的图域自适应模型，并将其应用于跨网络节点分类任务。具体地说，设计了一个分层池模型来提取有意义的和自适应的分层结构，其中边际和类别条件分布移动在每个分层级别共同最小化。通过对不同数据集的理论分析和实验研究，验证了该方法的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Improving+Graph+Domain+Adaptation+with+Network+Hierarchy)|0| +|[Automatic and Precise Data Validation for Machine Learning](https://doi.org/10.1145/3583780.3614786)|Shreya Shankar, Labib Fawaz, Karl Gyllstrom, Aditya G. Parameswaran|University of California, Berkeley, Berkeley, CA, USA; Meta, Menlo Park, CA, USA|Machine learning (ML) models in production pipelines are frequently retrained on the latest partitions of large, continually- growing datasets. Due to engineering bugs, partitions in such datasets almost always have some corrupted features; thus, it's critical to find data issues and block retraining before downstream ML accuracy decreases. However, current ML data validation methods are difficult to operationalize: they yield too many false positive alerts, require manual tuning, or are infeasible at scale. In this pa- per, we present an automatic, precise, and scalable data validation system for ML pipelines, employing a simple idea that we call a Partition Summarization (PS) approach to data validation: each timestamp-based partition of data is summarized with data quality metrics, and summaries are compared to detect corrupted partitions. We demonstrate how to adapt PS for any data validation method in a robust manner and evaluate several adaptations-which by themselves provide limited precision. Finally, we present gate, our data validation method that leverages these adaptations, giving a 2.1× average improvement in precision over the baseline from prior work on a case study within our large tech company.|生产管道中的机器学习(ML)模型经常在大型、不断增长的数据集的最新分区上重新训练。由于工程缺陷，这类数据集中的分区几乎总是有一些损坏的特征; 因此，在下游机器学习精度下降之前，发现数据问题和阻塞再训练是至关重要的。然而，目前的机器学习数据验证方法很难操作: 它们产生了太多的误报警，需要人工调整，或者在规模上不可行。在本文中，我们提出了一个自动的，精确的，可扩展的机器学习管道数据验证系统，采用一个简单的想法，我们称之为分区总结(PS)方法的数据验证: 每个时间戳为基础的数据分区总结与数据质量指标，并总结进行比较，以检测损坏的分区。我们演示了如何以一种健壮的方式将 PS 适应于任何数据验证方法，并评估了几种适应性——这些适应性本身提供了有限的精度。最后，我们介绍了我们的数据验证方法 gate，它利用了这些适应性，从我们大型科技公司以前的案例研究中得到了2.1倍的平均精度提高。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Automatic+and+Precise+Data+Validation+for+Machine+Learning)|0| +|[TOAK: A Topology-oriented Attack Strategy for Degrading User Identity Linkage in Cross-network Learning](https://doi.org/10.1145/3583780.3615084)|Jiangli Shao, Yongqing Wang, Fangda Guo, Boshen Shi, Huawei Shen, Xueqi Cheng|Institute of Computing Technology, CAS & University of Chinese Academy of Sciences, Beijing, China; Institute of Computing Technology, CAS, Beijing, China|Privacy concerns on social networks have received extensive attention in recent years. The task of user identity linkage (UIL), which aims to identify corresponding users across different social networks, poses a threat to privacy if applied unethically. Sensitive user information would be inferred with cross-network identity linkages. A feasible solution to this issue is to design an adversarial strategy that degrades the matching performance of UIL models. Nevertheless, most of the current adversarial attacks on graphs are tailored towards models working within a single network, failing to account for the challenges presented by cross-network learning tasks such as UIL. Also, in real-world scenarios, the adversarial strategy against UIL has more constraints as service providers can only add perturbations to their own networks. To tackle these challenges, this paper proposes a novel poisoning strategy to prevent nodes in a target network from being linked to other networks by UIL algorithms. Specifically, the UIL problem is formalized in the kernelized topology consistency perspective, and the objective is formulated as maximizing the structural variations in the target network before and after modifications. To achieve this, a novel graph kernel is defined based on earth mover's distance (EMD) in the edge-embedding space. In terms of efficiency, a fast attack strategy is proposed using greedy searching and a lower bound approximation of EMD. Results on three real-world datasets demonstrate that the proposed method outperforms six baselines and reaches a balance between effectiveness and imperceptibility while being efficient.|近年来，社交网络上的隐私问题受到广泛关注。用户身份链接(UIL)任务旨在识别不同社交网络上的相应用户，如果应用不道德，将对隐私构成威胁。敏感用户信息将通过跨网络身份链接推断出来。一个可行的解决方案是设计一个对抗策略，降低 UIL 模型的匹配性能。然而，目前大多数对图形的敌对攻击都是针对单一网络内工作的模型，没有考虑到跨网络学习任务(如 UIL)所带来的挑战。此外，在现实世界的情况下，对 UIL 的对抗策略有更多的限制，因为服务提供商只能添加扰动到他们自己的网络。为了解决这些问题，本文提出了一种新的中毒策略，以防止目标网络中的节点被 UIL 算法链接到其他网络。具体地说，UIL 问题被形式化为核拓扑一致性视角，目标被表述为在目标网络修改前后使结构变化最大化。为了实现这一点，在边嵌入空间中定义了一种新的基于地球动子距离(EMD)的图核。在效率方面，提出了一种基于贪婪搜索和 EMD 下界近似的快速攻击策略。在三个实际数据集上的实验结果表明，该方法的性能优于六个基线，在有效性和不可感知性之间达到了平衡。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=TOAK:+A+Topology-oriented+Attack+Strategy+for+Degrading+User+Identity+Linkage+in+Cross-network+Learning)|0| +|[Improving Graph Domain Adaptation with Network Hierarchy](https://doi.org/10.1145/3583780.3614928)|Boshen Shi, Yongqing Wang, Fangda Guo, Jiangli Shao, Huawei Shen, Xueqi Cheng|Institute of Computing Technology, CAS & University of Chinese Academy of Sciences, Beijing, China; Institute of Computing Technology, CAS, Beijing, China|Graph domain adaptation models have become instrumental in addressing cross-network learning problems due to their ability to transfer abundant label and structural knowledge from source graphs to target graphs. A crucial step in transfer involves measuring domain discrepancy, which refers to distribution shifts between graphs from source and target domains. While conventional models simply provide a node-level measurement, exploiting information from different levels of network hierarchy is intuitive. As each hierarchical level characterizes distinct and meaningful properties or functionalities of the original graph, integrating domain discrepancy based on such hierarchies should contribute to a more precise domain discrepancy measurement. Moreover, class conditional distribution shift is often overlooked in node classification tasks, which could potentially lead to sub-optimal performance. To address the above limitations, we propose a new graph domain adaptation model and apply it to cross-network node classification tasks. Specifically, a hierarchical pooling model to extract meaningful and adaptive hierarchical structures is designed, where both marginal and class conditional distribution shifts on each hierarchical level are jointly minimized. The effectiveness is demonstrated through theoretical analysis and experimental studies across various datasets.|图域自适应模型由于能够将丰富的标签和结构知识从源图转移到目标图，因而在解决跨网络学习问题方面发挥了重要作用。转移中的一个关键步骤涉及到测量域差异，这是指来自源域和目标域的图之间的分布移位。虽然传统的模型只提供节点级的度量，但是利用来自不同层次的网络层次结构的信息是直观的。由于每个层次结构都表征了原始图的独特和有意义的属性或功能，基于这种层次结构的领域差异整合应该有助于更精确的领域差异度量。此外，在节点分类任务中，类条件分布移位往往被忽视，这可能导致性能不理想。针对上述局限性，提出了一种新的图域自适应模型，并将其应用于跨网络节点分类任务。具体地说，设计了一个分层池模型来提取有意义的和自适应的分层结构，其中边际和类别条件分布移动在每个分层级别共同最小化。通过对不同数据集的理论分析和实验研究，验证了该方法的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Improving+Graph+Domain+Adaptation+with+Network+Hierarchy)|0| |[EmFore: Online Learning of Email Folder Classification Rules](https://doi.org/10.1145/3583780.3614863)|Mukul Singh, José Cambronero, Sumit Gulwani, Vu Le, Gust Verbruggen|Microsoft, Redmond, WA, USA; Microsoft, Delhi, India; Microsoft, Keerbergen, Belgium|Modern email clients support predicate-based folder assignment rules that can automatically organize emails. Unfortunately, users still need to write these rules manually. Prior machine learning approaches have framed automatically assigning email to folders as a classification task and do not produce symbolic rules. Prior inductive logic programming (ILP) approaches, which generate symbolic rules, fail to learn efficiently in the online environment needed for email management. To close this gap, we present EmFORE, an online system that learns symbolic rules for email classification from observations. Our key insights to do this successfully are: (1) learning rules over a folder abstraction that supports quickly determining candidate predicates to add or replace terms in a rule, (2) ensuring that rules remain consistent with historical assignments, (3) ranking rule updates based on existing predicate and folder name similarity, and (4) building a rule suppression model to avoid surfacing low-confidence folder predictions while keeping the rule for future use. We evaluate on two popular public email corpora and compare to 13 baselines, including state-of-the-art folder assignment systems, incremental machine learning, ILP and transformer-based approaches. We find that EmFORE performs significantly better, updates four orders of magnitude faster, and is more robust than existing methods and baselines.|现代电子邮件客户端支持基于谓词的文件夹分配规则，可以自动组织电子邮件。不幸的是，用户仍然需要手动编写这些规则。先前的机器学习方法将自动分配电子邮件到文件夹作为一个分类任务，而不产生符号规则。先前的归纳逻辑编程(ILP)方法产生符号规则，无法在电子邮件管理所需的在线环境中有效地学习。为了缩小这个差距，我们提出了 EmFORE，一个在线系统，它从观察中学习电子邮件分类的符号规则。我们成功做到这一点的关键见解是: (1)通过文件夹抽象学习规则，支持快速确定候选谓词在规则中添加或替换术语; (2)确保规则与历史分配保持一致; (3)根据现有谓词和文件夹名相似性对规则更新进行排序; (4)建立规则抑制模型，以避免出现低信任度的文件夹预测，同时保留规则供将来使用。我们评估了两个流行的公共电子邮件语料库，并比较了13个基线，包括最先进的文件夹分配系统，增量机器学习，ILP 和转换器为基础的方法。我们发现 EmFORE 的性能明显更好，更新数量级更快，比现有的方法和基线更加健壮。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=EmFore:+Online+Learning+of+Email+Folder+Classification+Rules)|0| |[SAND: Semantic Annotation of Numeric Data in Web Tables](https://doi.org/10.1145/3583780.3615046)|Yuchen Su, Davood Rafiei, Behrad Khorram Nazari|University of Alberta, Edmonton, Canada|A large portion of quantitative information about entities is expressed as Web tables, and these tables often lack proper schema and annotation, which introduces challenges for the purpose of querying and analysis. In this paper, we introduce SAND, a novel approach for annotating numeric columns of Web tables by linking them to properties in a knowledge graph. Our approach relies only on the semantic information readily available in knowledge graphs and not on contextual information that can be missing or labelled data which may be difficult to obtain. We show that our approach can reliably detect both semantic types (e.g., height) and unit labels (e.g., Centimeter) when the semantic type is present in the knowledge graph. Our evaluation on real-world web tables shows that our method outperforms by a large margin, in terms of accuracy, some of the state-of-the-art approaches on semantic labeling and unit detection.|有关实体的大部分定量信息表示为 Web 表，而这些表往往缺乏适当的模式和注释，这为查询和分析带来了挑战。在本文中，我们介绍了 SAND，一种新颖的方法来标注网络表格中的数字列，通过链接它们到一个知识图中的属性。我们的方法只依赖于知识图表中容易获得的语义信息，而不依赖于可能丢失或难以获得的标记数据的上下文信息。我们证明了我们的方法可以可靠地检测语义类型(例如，高度)和单位标签(例如，厘米)当语义类型存在于知识图中。我们对现实世界中的网络表格的评估表明，我们的方法在准确性方面大大优于一些最先进的语义标记和单元检测方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=SAND:+Semantic+Annotation+of+Numeric+Data+in+Web+Tables)|0| |[Graph Inference via the Energy-efficient Dynamic Precision Matrix Estimation with One-bit Data](https://doi.org/10.1145/3583780.3614898)|Xiao Tan, Yangyang Shen, Meng Wang, Beilun Wang|Southeast University, Nanjing, China|Graph knowledge discovery from graph-structured data is a fascinating data mining topic in various domains, especially in the Internet of Things, where inferring the graph structure from such informative data can benefit many downstream tasks. Deep neural networks are typically used to perform such predictions, but they produce unreliable results without sufficient high-quality data. Therefore, researchers introduce lightweight statistical precision matrix learning to infer the graph structure in many IoT scenarios with limited communication and resolution of sensors. However, these methods still suffer from low-resolution data or the omission of hidden information in time-series data. To address the challenges, we propose a novel approach for Energy-efficient Dynamic Sparse Graph Structure Estimation with one-bit data, EDGE. Our method proposes a novel estimator to estimate the covariance matrix from one-bit data, and then utilize the covariance matrices to capture the dynamic structure. We theoretically demonstrate the effectiveness of the estimators by deriving two non-asymptotic estimation error bounds for the estimated covariance matrix and precision matrix, respectively. The theoretical results show that our method can achieve a consistent result of the precision matrix at the rate O(log p/n). On multiple synthetic and real-world datasets, the experimental results demonstrate that our proposed estimator is able to obtain a relatively high detection rate using one-bit data, which exceeds the baseline by 35%, and identify potentially perturbed nodes in real-time dynamic network inference.|基于图结构数据的图形知识发现是各个领域，尤其是物联网领域中一个引人注目的数据挖掘话题，从这些信息性数据中推断出图形结构可以使许多下游任务受益。深度神经网络通常用于执行这样的预测，但是它们在没有足够高质量数据的情况下产生不可靠的结果。因此，研究人员引入了轻量级统计精度矩阵学习来推断在传感器通信和分辨率有限的物联网场景中的图结构。然而，这些方法仍然存在低分辨率数据或时间序列数据中隐藏信息的遗漏问题。为了解决这一问题，我们提出了一种新的基于一位数据的能量有效的动态稀疏图结构估计方法 EDGE。我们的方法提出了一个新的估计器来估计单位数据的协方差矩阵，然后利用协方差矩阵来捕获动态结构。我们从理论上证明了估计的有效性，通过导出估计的协方差矩阵和精度矩阵的两个非渐近估计误差界。理论分析结果表明，该方法在速率为 O (log p/n)时，可以得到精度矩阵一致的结果。在多个合成和真实数据集上，实验结果表明，我们提出的估计器能够使用单位数据获得相对较高的检测率，超过基线35% ，并在实时动态网络推理中识别潜在的干扰节点。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Graph+Inference+via+the+Energy-efficient+Dynamic+Precision+Matrix+Estimation+with+One-bit+Data)|0| |[Multi-Representation Variational Autoencoder via Iterative Latent Attention and Implicit Differentiation](https://doi.org/10.1145/3583780.3614980)|NhuThuat Tran, Hady W. Lauw|Singapore Management University, Singapore, Singapore|Variational Autoencoder (VAE) offers a non-linear probabilistic modeling of user's preferences. While it has achieved remarkable performance at collaborative filtering, it typically samples a single vector for representing user's preferences, which may be insufficient to capture the user's diverse interests. Existing solutions extend VAE to model multiple interests of users by resorting a variant of self-attentive method, i.e., employing prototypes to group items into clusters, each capturing one topic of user's interests. Despite showing improvements, the current design could be more effective since prototypes are randomly initialized and shared across users, resulting in uninformative and non-personalized clusters. To fill the gap, firstly, we introduce iterative latent attention for personalized item grouping into VAE framework to infer multiple interests of users. Secondly, we propose to incorporate implicit differentiation to improve training of our iterative refinement model. Thirdly, we study the self-attention to refine cluster prototypes for item grouping, which is largely ignored by existing works. Extensive experiments on three real-world datasets demonstrate stronger performance of our method over those of baselines.|变量自动编码器(VAE)提供用户偏好的非线性概率建模。虽然它在协同过滤上取得了显著的性能，但它通常采用一个单一的向量来表示用户的偏好，这可能不足以捕捉用户的不同兴趣。现有的解决方案将 VAE 扩展到多用户兴趣模型，采用了一种变体的自我关注方法，即利用原型将项目分组到集群中，每个项目捕获用户兴趣的一个主题。尽管有所改进，但当前的设计可能更有效，因为原型是随机初始化的，并在用户之间共享，导致无信息和非个性化的集群。为了填补这一空白，我们首先在 VAE 框架中引入个性化项目分组的迭代潜在注意，以推断用户的多重兴趣。其次，我们提出加入隐式微分来改善我们的迭代求精模型的训练。再次，研究了自注意对项目分组的聚类原型进行细化的问题，但现有的研究大多忽略了这一问题。在三个真实世界数据集上的大量实验表明，我们的方法比基线方法的性能更强。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Multi-Representation+Variational+Autoencoder+via+Iterative+Latent+Attention+and+Implicit+Differentiation)|0| -|[Citation Intent Classification and Its Supporting Evidence Extraction for Citation Graph Construction](https://doi.org/10.1145/3583780.3614808)|HongJin Tsai, AnZi Yen, HenHsen Huang, HsinHsi Chen|National Taiwan University, Taipei, Taiwan Roc; National Yang Ming Chiao Tung University, Hsinchu, Taiwan Roc; Institute of Information Science, Academia Sinica, Taipei, Taiwan Roc|As the significant growth of scientific publications in recent years, an efficient way to extract scholarly knowledge and organize the relationship among literature is necessitated. Previous works constructed scientific knowledge graph with authors, papers, citations, and scientific entities. To assist researchers to grasp the research context comprehensively, this paper constructs a fine-grained citation graph in which citation intents and their supporting evidence are labeled between citing and cited papers instead. We propose a model with a Transformer encoder to encode the long-lengthy paper. To capture the coreference relations of words and sentences in a paper, a coreference graph is created by utilizing Gated Graph Convolution Network (GGCN). We further propose a graph modification mechanism to dynamically update the coreference links. Experimental results show that our model achieves promising results on identifying multiple citation intents in sentences.|近年来，随着科技出版物的大量增加，有必要采用一种有效的方法来提取学术知识，组织文献之间的关系。以往的著作构建了包括作者、论文、引文和科学实体的科学知识图表。为了帮助研究者全面把握研究背景，本文构建了一个细粒度的引文图，在引文和被引文之间标注引文意图及其支持证据。我们提出了一个模型与变压器编码器编码的长篇论文。利用门限图卷积网络(GGCN)构造共指图，以获取文章中单词和句子之间的共指关系。我们进一步提出了一个图修改机制来动态更新共引用链接。实验结果表明，该模型在识别句子中的多引文意图方面取得了较好的效果。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Citation+Intent+Classification+and+Its+Supporting+Evidence+Extraction+for+Citation+Graph+Construction)|0| +|[Citation Intent Classification and Its Supporting Evidence Extraction for Citation Graph Construction](https://doi.org/10.1145/3583780.3614808)|HongJin Tsai, AnZi Yen, HenHsen Huang, HsinHsi Chen|Institute of Information Science, Academia Sinica, Taipei, Taiwan Roc; National Yang Ming Chiao Tung University, Hsinchu, Taiwan Roc; National Taiwan University, Taipei, Taiwan Roc|As the significant growth of scientific publications in recent years, an efficient way to extract scholarly knowledge and organize the relationship among literature is necessitated. Previous works constructed scientific knowledge graph with authors, papers, citations, and scientific entities. To assist researchers to grasp the research context comprehensively, this paper constructs a fine-grained citation graph in which citation intents and their supporting evidence are labeled between citing and cited papers instead. We propose a model with a Transformer encoder to encode the long-lengthy paper. To capture the coreference relations of words and sentences in a paper, a coreference graph is created by utilizing Gated Graph Convolution Network (GGCN). We further propose a graph modification mechanism to dynamically update the coreference links. Experimental results show that our model achieves promising results on identifying multiple citation intents in sentences.|近年来，随着科技出版物的大量增加，有必要采用一种有效的方法来提取学术知识，组织文献之间的关系。以往的著作构建了包括作者、论文、引文和科学实体的科学知识图表。为了帮助研究者全面把握研究背景，本文构建了一个细粒度的引文图，在引文和被引文之间标注引文意图及其支持证据。我们提出了一个模型与变压器编码器编码的长篇论文。利用门限图卷积网络(GGCN)构造共指图，以获取文章中单词和句子之间的共指关系。我们进一步提出了一个图修改机制来动态更新共引用链接。实验结果表明，该模型在识别句子中的多引文意图方面取得了较好的效果。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Citation+Intent+Classification+and+Its+Supporting+Evidence+Extraction+for+Citation+Graph+Construction)|0| |[FAMC-Net: Frequency Domain Parity Correction Attention and Multi-Scale Dilated Convolution for Time Series Forecasting](https://doi.org/10.1145/3583780.3614876)|Min Wang, Hua Wang, Fan Zhang|Shandong Technology and Business University, Yantai, China; Ludong University, Yantai, China|In recent years, time series forecasting models based on the Transformer framework have shown great potential, but they suffer from the inherent drawback of high computational complexity and only focus on global modeling. Inspired by trend-seasonality decomposition, we propose a method that combines global modeling with local feature extraction within the seasonal cycle. It aims at capturing the global view while fully exploring the potential features within each seasonal cycle and better expressing the long-term and periodic characteristics of time series. We introduce a frequency domain parity correction block to compute global attention and utilize multi-scale dilated convolution to extract local correlations within each cycle. Additionally, we adopt a dual-branch structure to separately model the seasonality and trend based on their intrinsic features, improving prediction performance and enhancing model interpretability. This model is implemented on a completely single-layer decoder architecture, breaking through the traditional encoder-decoder architecture paradigm and reducing computational complexity to a certain extent. We conducted sufficient experimental validation on eight benchmark datasets, and the results demonstrate its superior performance compared to existing methods in both univariate and multivariate forecasting.|近年来，基于变压器框架的时间序列预测模型已经显示出巨大的潜力，但它们存在着计算复杂度高的固有缺陷，只关注于全局建模。受趋势-季节性分解的启发，本文提出了一种在季节周期内将全局建模和局部特征提取相结合的方法。它的目的是捕捉全球视野，同时充分挖掘每个季节周期的潜在特征，更好地表达时间序列的长期和周期特征。我们引入一个频域奇偶校正块来计算全局注意力，并利用多尺度扩张卷积来提取每个周期内的局部相关性。此外，我们采用双分支结构，根据季节性和趋势的内在特征分别建立模型，提高了预测性能和模型的可解释性。该模型采用完全单层的解码器体系结构，突破了传统的编解码器体系结构范式，在一定程度上降低了计算复杂度。我们对八个基准数据集进行了充分的实验验证，结果表明其性能优于现有的单变量和多变量预测方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=FAMC-Net:+Frequency+Domain+Parity+Correction+Attention+and+Multi-Scale+Dilated+Convolution+for+Time+Series+Forecasting)|0| -|[MultiPLe: Multilingual Prompt Learning for Relieving Semantic Confusions in Few-shot Event Detection](https://doi.org/10.1145/3583780.3614984)|Siyuan Wang, Jianming Zheng, Wanyu Chen, Fei Cai, Xueshan Luo|National University of Defense Technology, Changsha, China; National University of Defense Technology, Hefei, China|Event detection (ED) is a challenging task in the field of information extraction. Due to the monolingual text and rampant confusing triggers, traditional ED models suffer from semantic confusions in terms of polysemy and synonym, leading to severe detection mistakes. Such semantic confusions can be further exacerbated in a practical situation where scarce labeled data cannot provide sufficient semantic clues. To mitigate such bottleneck, we propose a multilingual prompt learning (MultiPLe) framework for few-shot event detection (FSED), including three components, i.e., a multilingual prompt, a hierarchical prototype and a quadruplet contrastive learning module. In detail, to ease the polysemy confusion, the multilingual prompt module develops the in-context semantics of triggers via the multilingual disambiguation and prior knowledge in pretrained language models. Then, the hierarchical prototype module is adopted to diminish the synonym confusion by connecting the captured inmost semantics of fuzzy triggers with labels at a fine granularity. Finally, we employ the quadruplet contrastive learning module to tackle the insufficient label representation and potential noise. Experiments on two public datasets show that MultiPLe outperforms the state-of-the-art baselines in weighted F1-score, presenting a maximum improvement of 13.63% for FSED.|事件检测(ED)是信息抽取领域的一项具有挑战性的任务。由于单语文本和猖獗的混淆触发，传统的 ED 模型在多义词和同义词方面存在语义混淆，导致严重的检测错误。在实际情况下，这种语义混淆可能会进一步加剧，因为稀缺的标记数据不能提供足够的语义线索。为了缓解这种瓶颈，我们提出了一个多语言提示学习(MultiPLe)框架用于少镜头事件检测(FSED) ，该框架包括三个组件，即多语言提示、层次原型和四元组对比学习模块。具体来说，为了缓解多义现象，多语言提示模块通过多语言消歧和预训练语言模型中的先验知识，开发了触发器的上下文语义。然后，采用层次化原型模块，将模糊触发器捕获的最内层语义与标签细粒度地连接起来，以消除同义词混淆。最后，我们利用四元组对比学习模组来处理不充分的标签表示和潜在的噪声。在两个公共数据集上的实验表明，MultiPLe 在加权 F1得分方面优于最先进的基线，FSED 的最大改善率为13.63% 。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=MultiPLe:+Multilingual+Prompt+Learning+for+Relieving+Semantic+Confusions+in+Few-shot+Event+Detection)|0| +|[MultiPLe: Multilingual Prompt Learning for Relieving Semantic Confusions in Few-shot Event Detection](https://doi.org/10.1145/3583780.3614984)|Siyuan Wang, Jianming Zheng, Wanyu Chen, Fei Cai, Xueshan Luo|National University of Defense Technology, Hefei, China; National University of Defense Technology, Changsha, China|Event detection (ED) is a challenging task in the field of information extraction. Due to the monolingual text and rampant confusing triggers, traditional ED models suffer from semantic confusions in terms of polysemy and synonym, leading to severe detection mistakes. Such semantic confusions can be further exacerbated in a practical situation where scarce labeled data cannot provide sufficient semantic clues. To mitigate such bottleneck, we propose a multilingual prompt learning (MultiPLe) framework for few-shot event detection (FSED), including three components, i.e., a multilingual prompt, a hierarchical prototype and a quadruplet contrastive learning module. In detail, to ease the polysemy confusion, the multilingual prompt module develops the in-context semantics of triggers via the multilingual disambiguation and prior knowledge in pretrained language models. Then, the hierarchical prototype module is adopted to diminish the synonym confusion by connecting the captured inmost semantics of fuzzy triggers with labels at a fine granularity. Finally, we employ the quadruplet contrastive learning module to tackle the insufficient label representation and potential noise. Experiments on two public datasets show that MultiPLe outperforms the state-of-the-art baselines in weighted F1-score, presenting a maximum improvement of 13.63% for FSED.|事件检测(ED)是信息抽取领域的一项具有挑战性的任务。由于单语文本和猖獗的混淆触发，传统的 ED 模型在多义词和同义词方面存在语义混淆，导致严重的检测错误。在实际情况下，这种语义混淆可能会进一步加剧，因为稀缺的标记数据不能提供足够的语义线索。为了缓解这种瓶颈，我们提出了一个多语言提示学习(MultiPLe)框架用于少镜头事件检测(FSED) ，该框架包括三个组件，即多语言提示、层次原型和四元组对比学习模块。具体来说，为了缓解多义现象，多语言提示模块通过多语言消歧和预训练语言模型中的先验知识，开发了触发器的上下文语义。然后，采用层次化原型模块，将模糊触发器捕获的最内层语义与标签细粒度地连接起来，以消除同义词混淆。最后，我们利用四元组对比学习模组来处理不充分的标签表示和潜在的噪声。在两个公共数据集上的实验表明，MultiPLe 在加权 F1得分方面优于最先进的基线，FSED 的最大改善率为13.63% 。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=MultiPLe:+Multilingual+Prompt+Learning+for+Relieving+Semantic+Confusions+in+Few-shot+Event+Detection)|0| |[Understanding and Modeling Collision Avoidance Behavior for Realistic Crowd Simulation](https://doi.org/10.1145/3583780.3615098)|Zihan Yu, Guozhen Zhang, Yong Li, Depeng Jin|Tsinghua University, Beijing, China|For walking pedestrians, when they are blocked by obstacles or other pedestrians, they adjust their speeds and directions to avoid colliding with them, which is called collision avoidance behavior. This behavior is the most complex part of pedestrians' walking processes and its modeling and simulation are the keys to realistic crowd simulation, which serves as the foundation for various applications. However, most existing methods either lack the representation power to accurately model the complex collision behavior or do not model it explicitly, which leads to a poor level of realism of the simulation. To realize realistic crowd simulation, we propose to analyze, understand, and model the collision avoidance behavior in a data-driven way. First, to automatically detect collision avoidance behavior for further analysis, we propose a domain transformation algorithm that detects it by transforming the trajectories in the spatial domain into a new domain where the behavior is much more apparent and is thus easier to detect. The new domain also provides a new perspective for understanding collision avoidance behavior. Second, since there are no mature metrics to evaluate the level of realism, we propose a new evaluation metric based on the least-effort theory, which evaluates the realism of collision avoidance behavior by its physical and mental consumption. This evaluation metric also provides the foundation of modeling. Third, for realistic crowd simulation, we design a reinforcement learning model. It trains agents with our proposed reward function that models pedestrians' intrinsic needs of "reducing effort consumption'' and thus can guide agents to behave realistically when avoiding collisions. Extensive experiments show our model is 55.9% and 52.5% more realistic in collision avoidance behavior than the best baselines on two real-world datasets. We release our codes at https://github.com/tsinghua-fib-lab/TECRL.|对于行走的行人，当他们被障碍物或其他行人阻挡时，他们会调整自己的速度和方向以避免与他们发生碰撞，这种行为被称为避碰行为。这种行为是行人行走过程中最复杂的部分，其建模与模拟是真实人群模拟的关键，也是各种应用的基础。然而，现有的大多数方法要么缺乏对复杂碰撞行为进行精确建模的表示能力，要么没有对其进行明确的建模，从而导致仿真的真实性较差。为了实现真实的人群仿真，我们提出用数据驱动的方法来分析、理解和建模避碰行为。首先，为了自动检测避碰行为以便进一步分析，我们提出了一种域变换算法，该算法通过将空间域中的轨迹转换为一个新的域来检测避碰行为，该域的行为更加明显，因此更容易检测。这一新领域也为理解避碰行为提供了一个新的视角。其次，由于目前还没有成熟的指标来评价避碰行为的实际水平，本文提出了一种新的基于最小努力理论的避碰行为实际水平评价指标，即通过避碰行为的身心消耗来评价避碰行为的实际水平。这个评估指标也提供了建模的基础。第三，为了实现真实的人群模拟，我们设计了一个强化学习模型。它用我们提出的奖励函数训练代理，该函数模拟行人“减少努力消耗”的内在需求，从而可以指导代理在避免碰撞时实际行动。大量实验表明，该模型在避免碰撞行为方面比两个真实数据集上的最佳基线分别提高了55.9% 和52.5% 。我们在 https://github.com/tsinghua-fib-lab/tecrl 公布密码。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Understanding+and+Modeling+Collision+Avoidance+Behavior+for+Realistic+Crowd+Simulation)|0| |[iLoRE: Dynamic Graph Representation with Instant Long-term Modeling and Re-occurrence Preservation](https://doi.org/10.1145/3583780.3614926)|Siwei Zhang, Yun Xiong, Yao Zhang, Xixi Wu, Yiheng Sun, Jiawei Zhang|Fudan University, Shanghai, China; University of California, Davis, Davis, CA, USA; Tencent Weixin Group, Shenzhen, China|Continuous-time dynamic graph modeling is a crucial task for many real-world applications, such as financial risk management and fraud detection. Though existing dynamic graph modeling methods have achieved satisfactory results, they still suffer from three key limitations, hindering their scalability and further applicability. i) Indiscriminate updating. For incoming edges, existing methods would indiscriminately deal with them, which may lead to more time consumption and unexpected noisy information. ii) Ineffective node-wise long-term modeling. They heavily rely on recurrent neural networks (RNNs) as a backbone, which has been demonstrated to be incapable of fully capturing node-wise long-term dependencies in event sequences. iii) Neglect of re-occurrence patterns. Dynamic graphs involve the repeated occurrence of neighbors that indicates their importance, which is disappointedly neglected by existing methods. In this paper, we present iLoRE, a novel dynamic graph modeling method with instant node-wise Long-term modeling and Re-occurrence preservation. To overcome the indiscriminate updating issue, we introduce the Adaptive Short-term Updater module that will automatically discard the useless or noisy edges, ensuring iLoRE's effectiveness and instant ability. We further propose the Long-term Updater to realize more effective node-wise long-term modeling, where we innovatively propose the Identity Attention mechanism to empower a Transformer-based updater, bypassing the limited effectiveness of typical RNN-dominated designs. Finally, the crucial re-occurrence patterns are also encoded into a graph module for informative representation learning, which will further improve the expressiveness of our method. Our experimental results on real-world datasets demonstrate the effectiveness of our iLoRE for dynamic graph modeling.|连续时间动态图建模是金融风险管理和欺诈检测等实际应用中的关键问题。现有的动态图建模方法虽然取得了令人满意的效果，但仍然存在三个关键的局限性，阻碍了其可扩展性和进一步的适用性。I)不加选择地更新。对于传入的边缘，现有的方法会不加区分地处理它们，这可能会导致更多的时间消耗和意外的噪声信息。Ii)无效的节点长期建模。它们严重依赖于回归神经网络(RNN)作为骨干，已被证明不能完全捕获事件序列中的节点长期依赖性。Iii)忽略重复出现的模式。动态图涉及到邻居的重复出现，表明了它们的重要性，这是令人失望的忽略了现有的方法。本文提出了一种新的动态图形建模方法 iLoRE，该方法具有即时节点长期建模和再现保持的特点。为了克服不加区分的更新问题，引入了自适应短期更新模块，该模块能够自动丢弃无用或有噪声的边缘，保证了 iLoRE 的有效性和即时性。我们进一步提出长期更新器来实现更有效的节点式长期建模，其中我们创新性地提出了身份注意机制来赋予基于变压器的更新器权力，绕过典型的 RNN 主导设计的有限效率。最后，将关键的重现模式编码成图模块，用于信息表示学习，进一步提高了该方法的表示能力。我们在真实世界数据集上的实验结果证明了我们的 iLoRE 对动态图形建模的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=iLoRE:+Dynamic+Graph+Representation+with+Instant+Long-term+Modeling+and+Re-occurrence+Preservation)|0| -|[Task Relation Distillation and Prototypical Pseudo Label for Incremental Named Entity Recognition](https://doi.org/10.1145/3583780.3615075)|Duzhen Zhang, Hongliu Li, Wei Cong, Rongtao Xu, Jiahua Dong, Xiuyi Chen|Shenyang Institute of Automation, Chinese Academy of Science, Shenyang, China; Baidu Inc., Beijing, China; Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China; Beijing Academy of Artificial Intelligence, Beijing, China; The Hong Kong Polytechnic University, Hong Kong, China|Incremental Named Entity Recognition (INER) involves the sequential learning of new entity types without accessing the training data of previously learned types. However, INER faces the challenge of catastrophic forgetting specific for incremental learning, further aggravated by background shift (i.e., old and future entity types are labeled as the non-entity type in the current task). To address these challenges, we propose a method called task Relation Distillation and Prototypical pseudo label (RDP) for INER. Specifically, to tackle catastrophic forgetting, we introduce a task relation distillation scheme that serves two purposes: 1) ensuring inter-task semantic consistency across different incremental learning tasks by minimizing inter-task relation distillation loss, and 2) enhancing the model's prediction confidence by minimizing intra-task self-entropy loss. Simultaneously, to mitigate background shift, we develop a prototypical pseudo label strategy that distinguishes old entity types from the current non-entity type using the old model. This strategy generates high-quality pseudo labels by measuring the distances between token embeddings and type-wise prototypes. We conducted extensive experiments on ten INER settings of three benchmark datasets (i.e., CoNLL2003, I2B2, and OntoNotes5). The results demonstrate that our method achieves significant improvements over the previous state-of-the-art methods, with an average increase of 6.08% in Micro F1 score and 7.71% in Macro F1 score.|增量命名实体识别(INER)涉及到新实体类型的连续学习，而不需要访问先前学习的类型的训练数据。然而，INER 面临着特定于在线机机器学习的灾难性遗忘的挑战，背景变化进一步加剧了这一挑战(即，旧的和未来的实体类型在当前任务中被标记为非实体类型)。为了解决这些问题，我们提出了一种基于 INER 的任务关系提取和原型伪标签(RDP)方法。具体来说，为了解决灾难性遗忘问题，我们引入了一个任务关系精馏方案，该方案有两个目的: 1)通过最小化任务间关系精馏损失来确保不同在线机机器学习任务间的语义一致性; 2)通过最小化任务内自熵损失来提高模型的预测置信度。同时，为了减少背景移位，我们开发了一个原型的伪标签策略，使用旧的模型区分旧的实体类型和当前的非实体类型。该策略通过测量令牌嵌入和类型原型之间的距离来生成高质量的伪标签。我们对三个基准数据集(即 CoNLL2003、 I2B2和 OntoNotes5)的10个 INER 设置进行了广泛的实验。实验结果表明，该方法在微 F1评分和宏 F1评分上的平均增长率分别为6.08% 和7.71% ，明显优于以往的最新评分方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Task+Relation+Distillation+and+Prototypical+Pseudo+Label+for+Incremental+Named+Entity+Recognition)|0| -|[Communication-Efficient Decentralized Online Continuous DR-Submodular Maximization](https://doi.org/10.1145/3583780.3614817)|Qixin Zhang, Zengde Deng, Xiangru Jian, Zaiyi Chen, Haoyuan Hu, Yu Yang|University of Waterloo, ON, Canada; Cainiao Network, Hangzhou, China; City University of Hong Kong, Hong Kong, China|Maximizing a monotone submodular function is a fundamental task in machine learning, economics, and statistics. In this paper, we present two communication-efficient decentralized online algorithms for the monotone continuous DR-submodular maximization problem, both of which reduce the number of per-function gradient evaluations and per-round communication complexity from $T^{3/2}$ to $1$. The first one, One-shot Decentralized Meta-Frank-Wolfe (Mono-DMFW), achieves a $(1-1/e)$-regret bound of $O(T^{4/5})$. As far as we know, this is the first one-shot and projection-free decentralized online algorithm for monotone continuous DR-submodular maximization. Next, inspired by the non-oblivious boosting function \citep{zhang2022boosting}, we propose the Decentralized Online Boosting Gradient Ascent (DOBGA) algorithm, which attains a $(1-1/e)$-regret of $O(\sqrt{T})$. To the best of our knowledge, this is the first result to obtain the optimal $O(\sqrt{T})$ against a $(1-1/e)$-approximation with only one gradient inquiry for each local objective function per step. Finally, various experimental results confirm the effectiveness of the proposed methods.|最大化单调子模函数是机器学习、经济学和统计学中的一个基本任务。本文针对单调连续 DR 子模极大化问题，提出了两种通信效率高的分散在线算法，它们都将每个函数的梯度估计次数和每轮通信复杂度从 $T ^ {3/2} $减少到 $1 $。第一种是一次性分散元 Frank-Wolfe (Mono-DMFW) ，它实现了 $(1-1/e) $- 后悔界 $O (T ^ {4/5}) $。据我们所知，这是第一个单调连续 DR 子模极大化的一次无投影分散在线算法。接下来，受到非遗忘增强函数 citep { zhang2022booting }的启发，我们提出了分散在线增强梯度上升(DOBGA)算法，该算法获得了 $(1-1/e) $- 后悔的 $O (sqrt { T }) $。据我们所知，这是第一个获得最优 $O (sqrt { T }) $与 $(1-1/e) $- 近似的结果，每个步骤对每个局部目标函数只有一个梯度查询。最后，各种实验结果证实了该方法的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Communication-Efficient+Decentralized+Online+Continuous+DR-Submodular+Maximization)|0| -|[HTMapper: Bidirectional Head-Tail Mapping for Nested Named Entity Recognition](https://doi.org/10.1145/3583780.3614919)|Jin Zhao, Zhixu Li, Yanghua Xiao, Jiaqing Liang, Jingping Liu|Fudan University, Shanghai, China; East China University of Science and Technology, Shanghai, China; Fudan University & Fudan-Aishu Cognitive Intelligence Joint Research Center, Shanghai, China|Nested named entity recognition (Nested NER) aims to identify entities with nested structures from the given text, which is a fundamental task in Natural Language Processing. The region-based approach is the current mainstream approach, which first generates candidate spans and then classifies them into predefined categories. However, this method suffers from several drawbacks, including over-reliance on span representation, vulnerability to unbalanced category distribution, and inaccurate span boundary detection. To address these problems, we propose to model the nested NER problem into a head-tail mapping problem, namely, HTMapper, which detects head boundaries first and then models a conditional mapping from head to tail under a given category. Based on this mapping, we can find corresponding tails under different categories for each detected head by enumerating all entity categories. Our approach directly models the head boundary and tail boundary of entities, avoiding over-reliance on the span representation. Additionally, Our approach utilizes category information as an indicator signal to address the imbalance of category distribution during category prediction. Furthermore, our approach enhances the detection of span boundaries by capturing the correlation between head and tail boundaries. Extensive experiments on three nested NER datasets and two flat NER datasets demonstrate that our HTMapper achieves excellent performance with F1 scores of 89.09%, 88.30%, 81.57% on ACE2004,ACE2005, GENIA, and 94.26%, 91.40% on CoNLL03, OntoNotes, respectively.|嵌套命名实体识别(NER)是自然语言处理中的一项基础性工作，其目的是从给定的文本中识别具有嵌套结构的实体。基于区域的方法是当前的主流方法，它首先生成候选范围，然后将它们分类为预定义的类别。然而，该方法存在着对跨度表示的过度依赖，容易受到类别分布不平衡的影响，以及跨度边界检测不准确等缺点。为了解决这些问题，我们提出将嵌套的 NER 问题建模为一个头尾映射问题，即 HTMapper，它首先检测头部边界，然后在给定类别下建模从头部到尾部的条件映射。基于这种映射，我们可以通过枚举所有的实体类别，为每个检测到的头找到不同类别下的相应尾。我们的方法直接建模实体的头部边界和尾部边界，避免了对跨度表示的过度依赖。此外，我们的方法利用类别信息作为指标信号，以解决类别预测过程中类别分布不均衡的问题。此外，我们的方法通过捕捉头尾边界之间的相关性来提高跨度边界的检测。在三个嵌套的 NER 数据集和两个平面的 NER 数据集上的大量实验表明，我们的 HTMapper 在 ACE2004，ACe2005，GENIA 和94.26% ，91.40% 的 CoNLL03，OntoNotes 上分别取得了89.09% ，88.30% ，81.57% 的 F1得分，取得了很好的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=HTMapper:+Bidirectional+Head-Tail+Mapping+for+Nested+Named+Entity+Recognition)|0| -|[GCformer: An Efficient Solution for Accurate and Scalable Long-Term Multivariate Time Series Forecasting](https://doi.org/10.1145/3583780.3615136)|Yanjun Zhao, Ziqing Ma, Tian Zhou, Mengni Ye, Liang Sun, Yi Qian|Alibaba Group, Bellevue, WA, USA; Alibaba Group, Hangzhou, China; Xi'an Jiaotong University, Xi'an, China; Xi'an Jiaotong University & Alibaba Group, hangzhou, China|Transformer-based models have emerged as promising tools for time series forecasting. However, these models cannot make accurate prediction for long input time series. On the one hand, they failed to capture long-range dependency within time series data. On the other hand, the long input sequence usually leads to large model size and high time complexity. To address these limitations, we present GCformer, which combines a structured global convolutional branch for processing long input sequences with a local Transformer-based branch for capturing short, recent signals. A cohesive framework for a global convolution kernel has been introduced, utilizing three distinct parameterization methods. The selected structured convolutional kernel in the global branch has been specifically crafted with sublinear complexity, thereby allowing for the efficient and effective processing of lengthy and noisy input signals. Empirical studies on six benchmark datasets demonstrate that GCformer outperforms state-of-the-art methods, reducing MSE error in multivariate time series benchmarks by 4.38% and model parameters by 61.92%. In particular, the global convolutional branch can serve as a plug-in block to enhance the performance of other models, with an average improvement of 31.93%, including various recently published Transformer-based models. Our code is publicly available at https://github.com/Yanjun-Zhao/GCformer.|基于变压器的模型已经成为时间序列预测的有前途的工具。然而，这些模型不能对长输入时间序列做出准确的预测。一方面，它们未能捕捉到时间序列数据的长期依赖性。另一方面，长的输入序列通常会导致大的模型规模和高的时间复杂度。为了解决这些局限性，我们提出了 GCformer，它结合了用于处理长输入序列的结构化全局卷积分支和用于捕获短的、最近的信号的基于局部变压器的分支。引入了一个全局卷积内核的内聚框架，使用了三种不同的参量化方法。全局分支中所选择的结构化卷积核具有特定的次线性复杂度，从而可以有效地处理冗长而嘈杂的输入信号。对6个基准数据集的实证研究表明，GCform 方法的性能优于最先进的方法，使多变量时间序列基准的 MSE 误差降低了4.38% ，模型参数的 MSE 误差降低了61.92% 。特别是，全局卷积分支可以作为一个插件块来增强其他模型的性能，平均改进率为31.93% ，其中包括最近发布的各种基于 Transformer 的模型。我们的代码可以在 https://github.com/yanjun-zhao/gcformer 上公开获取。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=GCformer:+An+Efficient+Solution+for+Accurate+and+Scalable+Long-Term+Multivariate+Time+Series+Forecasting)|0| +|[Task Relation Distillation and Prototypical Pseudo Label for Incremental Named Entity Recognition](https://doi.org/10.1145/3583780.3615075)|Duzhen Zhang, Hongliu Li, Wei Cong, Rongtao Xu, Jiahua Dong, Xiuyi Chen|The Hong Kong Polytechnic University, Hong Kong, China; Shenyang Institute of Automation, Chinese Academy of Science, Shenyang, China; Beijing Academy of Artificial Intelligence, Beijing, China; Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China; Baidu Inc., Beijing, China|Incremental Named Entity Recognition (INER) involves the sequential learning of new entity types without accessing the training data of previously learned types. However, INER faces the challenge of catastrophic forgetting specific for incremental learning, further aggravated by background shift (i.e., old and future entity types are labeled as the non-entity type in the current task). To address these challenges, we propose a method called task Relation Distillation and Prototypical pseudo label (RDP) for INER. Specifically, to tackle catastrophic forgetting, we introduce a task relation distillation scheme that serves two purposes: 1) ensuring inter-task semantic consistency across different incremental learning tasks by minimizing inter-task relation distillation loss, and 2) enhancing the model's prediction confidence by minimizing intra-task self-entropy loss. Simultaneously, to mitigate background shift, we develop a prototypical pseudo label strategy that distinguishes old entity types from the current non-entity type using the old model. This strategy generates high-quality pseudo labels by measuring the distances between token embeddings and type-wise prototypes. We conducted extensive experiments on ten INER settings of three benchmark datasets (i.e., CoNLL2003, I2B2, and OntoNotes5). The results demonstrate that our method achieves significant improvements over the previous state-of-the-art methods, with an average increase of 6.08% in Micro F1 score and 7.71% in Macro F1 score.|增量命名实体识别(INER)涉及到新实体类型的连续学习，而不需要访问先前学习的类型的训练数据。然而，INER 面临着特定于在线机机器学习的灾难性遗忘的挑战，背景变化进一步加剧了这一挑战(即，旧的和未来的实体类型在当前任务中被标记为非实体类型)。为了解决这些问题，我们提出了一种基于 INER 的任务关系提取和原型伪标签(RDP)方法。具体来说，为了解决灾难性遗忘问题，我们引入了一个任务关系精馏方案，该方案有两个目的: 1)通过最小化任务间关系精馏损失来确保不同在线机机器学习任务间的语义一致性; 2)通过最小化任务内自熵损失来提高模型的预测置信度。同时，为了减少背景移位，我们开发了一个原型的伪标签策略，使用旧的模型区分旧的实体类型和当前的非实体类型。该策略通过测量令牌嵌入和类型原型之间的距离来生成高质量的伪标签。我们对三个基准数据集(即 CoNLL2003、 I2B2和 OntoNotes5)的10个 INER 设置进行了广泛的实验。实验结果表明，该方法在微 F1评分和宏 F1评分上的平均增长率分别为6.08% 和7.71% ，明显优于以往的最新评分方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Task+Relation+Distillation+and+Prototypical+Pseudo+Label+for+Incremental+Named+Entity+Recognition)|0| +|[Communication-Efficient Decentralized Online Continuous DR-Submodular Maximization](https://doi.org/10.1145/3583780.3614817)|Qixin Zhang, Zengde Deng, Xiangru Jian, Zaiyi Chen, Haoyuan Hu, Yu Yang|Cainiao Network, Hangzhou, China; University of Waterloo, ON, Canada; City University of Hong Kong, Hong Kong, China|Maximizing a monotone submodular function is a fundamental task in machine learning, economics, and statistics. In this paper, we present two communication-efficient decentralized online algorithms for the monotone continuous DR-submodular maximization problem, both of which reduce the number of per-function gradient evaluations and per-round communication complexity from $T^{3/2}$ to $1$. The first one, One-shot Decentralized Meta-Frank-Wolfe (Mono-DMFW), achieves a $(1-1/e)$-regret bound of $O(T^{4/5})$. As far as we know, this is the first one-shot and projection-free decentralized online algorithm for monotone continuous DR-submodular maximization. Next, inspired by the non-oblivious boosting function \citep{zhang2022boosting}, we propose the Decentralized Online Boosting Gradient Ascent (DOBGA) algorithm, which attains a $(1-1/e)$-regret of $O(\sqrt{T})$. To the best of our knowledge, this is the first result to obtain the optimal $O(\sqrt{T})$ against a $(1-1/e)$-approximation with only one gradient inquiry for each local objective function per step. Finally, various experimental results confirm the effectiveness of the proposed methods.|最大化单调子模函数是机器学习、经济学和统计学中的一个基本任务。本文针对单调连续 DR 子模极大化问题，提出了两种通信效率高的分散在线算法，它们都将每个函数的梯度估计次数和每轮通信复杂度从 $T ^ {3/2} $减少到 $1 $。第一种是一次性分散元 Frank-Wolfe (Mono-DMFW) ，它实现了 $(1-1/e) $- 后悔界 $O (T ^ {4/5}) $。据我们所知，这是第一个单调连续 DR 子模极大化的一次无投影分散在线算法。接下来，受到非遗忘增强函数 citep { zhang2022booting }的启发，我们提出了分散在线增强梯度上升(DOBGA)算法，该算法获得了 $(1-1/e) $- 后悔的 $O (sqrt { T }) $。据我们所知，这是第一个获得最优 $O (sqrt { T }) $与 $(1-1/e) $- 近似的结果，每个步骤对每个局部目标函数只有一个梯度查询。最后，各种实验结果证实了该方法的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Communication-Efficient+Decentralized+Online+Continuous+DR-Submodular+Maximization)|0| +|[HTMapper: Bidirectional Head-Tail Mapping for Nested Named Entity Recognition](https://doi.org/10.1145/3583780.3614919)|Jin Zhao, Zhixu Li, Yanghua Xiao, Jiaqing Liang, Jingping Liu|Fudan University, Shanghai, China; Fudan University & Fudan-Aishu Cognitive Intelligence Joint Research Center, Shanghai, China; East China University of Science and Technology, Shanghai, China|Nested named entity recognition (Nested NER) aims to identify entities with nested structures from the given text, which is a fundamental task in Natural Language Processing. The region-based approach is the current mainstream approach, which first generates candidate spans and then classifies them into predefined categories. However, this method suffers from several drawbacks, including over-reliance on span representation, vulnerability to unbalanced category distribution, and inaccurate span boundary detection. To address these problems, we propose to model the nested NER problem into a head-tail mapping problem, namely, HTMapper, which detects head boundaries first and then models a conditional mapping from head to tail under a given category. Based on this mapping, we can find corresponding tails under different categories for each detected head by enumerating all entity categories. Our approach directly models the head boundary and tail boundary of entities, avoiding over-reliance on the span representation. Additionally, Our approach utilizes category information as an indicator signal to address the imbalance of category distribution during category prediction. Furthermore, our approach enhances the detection of span boundaries by capturing the correlation between head and tail boundaries. Extensive experiments on three nested NER datasets and two flat NER datasets demonstrate that our HTMapper achieves excellent performance with F1 scores of 89.09%, 88.30%, 81.57% on ACE2004,ACE2005, GENIA, and 94.26%, 91.40% on CoNLL03, OntoNotes, respectively.|嵌套命名实体识别(NER)是自然语言处理中的一项基础性工作，其目的是从给定的文本中识别具有嵌套结构的实体。基于区域的方法是当前的主流方法，它首先生成候选范围，然后将它们分类为预定义的类别。然而，该方法存在着对跨度表示的过度依赖，容易受到类别分布不平衡的影响，以及跨度边界检测不准确等缺点。为了解决这些问题，我们提出将嵌套的 NER 问题建模为一个头尾映射问题，即 HTMapper，它首先检测头部边界，然后在给定类别下建模从头部到尾部的条件映射。基于这种映射，我们可以通过枚举所有的实体类别，为每个检测到的头找到不同类别下的相应尾。我们的方法直接建模实体的头部边界和尾部边界，避免了对跨度表示的过度依赖。此外，我们的方法利用类别信息作为指标信号，以解决类别预测过程中类别分布不均衡的问题。此外，我们的方法通过捕捉头尾边界之间的相关性来提高跨度边界的检测。在三个嵌套的 NER 数据集和两个平面的 NER 数据集上的大量实验表明，我们的 HTMapper 在 ACE2004，ACe2005，GENIA 和94.26% ，91.40% 的 CoNLL03，OntoNotes 上分别取得了89.09% ，88.30% ，81.57% 的 F1得分，取得了很好的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=HTMapper:+Bidirectional+Head-Tail+Mapping+for+Nested+Named+Entity+Recognition)|0| +|[GCformer: An Efficient Solution for Accurate and Scalable Long-Term Multivariate Time Series Forecasting](https://doi.org/10.1145/3583780.3615136)|Yanjun Zhao, Ziqing Ma, Tian Zhou, Mengni Ye, Liang Sun, Yi Qian|Alibaba Group, Hangzhou, China; Alibaba Group, Bellevue, WA, USA; Xi'an Jiaotong University, Xi'an, China; Xi'an Jiaotong University & Alibaba Group, hangzhou, China|Transformer-based models have emerged as promising tools for time series forecasting. However, these models cannot make accurate prediction for long input time series. On the one hand, they failed to capture long-range dependency within time series data. On the other hand, the long input sequence usually leads to large model size and high time complexity. To address these limitations, we present GCformer, which combines a structured global convolutional branch for processing long input sequences with a local Transformer-based branch for capturing short, recent signals. A cohesive framework for a global convolution kernel has been introduced, utilizing three distinct parameterization methods. The selected structured convolutional kernel in the global branch has been specifically crafted with sublinear complexity, thereby allowing for the efficient and effective processing of lengthy and noisy input signals. Empirical studies on six benchmark datasets demonstrate that GCformer outperforms state-of-the-art methods, reducing MSE error in multivariate time series benchmarks by 4.38% and model parameters by 61.92%. In particular, the global convolutional branch can serve as a plug-in block to enhance the performance of other models, with an average improvement of 31.93%, including various recently published Transformer-based models. Our code is publicly available at https://github.com/Yanjun-Zhao/GCformer.|基于变压器的模型已经成为时间序列预测的有前途的工具。然而，这些模型不能对长输入时间序列做出准确的预测。一方面，它们未能捕捉到时间序列数据的长期依赖性。另一方面，长的输入序列通常会导致大的模型规模和高的时间复杂度。为了解决这些局限性，我们提出了 GCformer，它结合了用于处理长输入序列的结构化全局卷积分支和用于捕获短的、最近的信号的基于局部变压器的分支。引入了一个全局卷积内核的内聚框架，使用了三种不同的参量化方法。全局分支中所选择的结构化卷积核具有特定的次线性复杂度，从而可以有效地处理冗长而嘈杂的输入信号。对6个基准数据集的实证研究表明，GCform 方法的性能优于最先进的方法，使多变量时间序列基准的 MSE 误差降低了4.38% ，模型参数的 MSE 误差降低了61.92% 。特别是，全局卷积分支可以作为一个插件块来增强其他模型的性能，平均改进率为31.93% ，其中包括最近发布的各种基于 Transformer 的模型。我们的代码可以在 https://github.com/yanjun-zhao/gcformer 上公开获取。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=GCformer:+An+Efficient+Solution+for+Accurate+and+Scalable+Long-Term+Multivariate+Time+Series+Forecasting)|0| |[DynED: Dynamic Ensemble Diversification in Data Stream Classification](https://doi.org/10.1145/3583780.3615266)|Soheil Abadifard, Sepehr Bakhshi, Sanaz Gheibuni, Fazli Can|Bilkent University, Ankara, Turkey|Ensemble methods are commonly used in classification due to their remarkable performance. Achieving high accuracy in a data stream environment is a challenging task considering disruptive changes in the data distribution, also known as concept drift. A greater diversity of ensemble components is known to enhance prediction accuracy in such settings. Despite the diversity of components within an ensemble, not all contribute as expected to its overall performance. This necessitates a method for selecting components that exhibit high performance and diversity. We present a novel ensemble construction and maintenance approach based on MMR (Maximal Marginal Relevance) that dynamically combines the diversity and prediction accuracy of components during the process of structuring an ensemble. The experimental results on both four real and 11 synthetic datasets demonstrate that the proposed approach (DynED) provides a higher average mean accuracy compared to the five state-of-the-art baselines.|集成方法由于其显著的性能，在分类中得到了广泛的应用。在数据流环境中实现高精度是一项具有挑战性的任务，考虑到数据分布的破坏性变化，也称为概念漂移。众所周知，集合分量的更大多样性可以提高这种情况下的预测精度。尽管一个集合中的组件多种多样，但并非所有组件都如预期的那样对其整体性能有贡献。这就需要一种方法来选择具有高性能和多样性的组件。提出了一种基于最大边际相关(MMR)的集成构建和维护方法，该方法在集成构建过程中动态地综合了组件的多样性和预测精度。在4个实际数据集和11个合成数据集上的实验结果表明，与5个最先进的基线相比，该方法提供了更高的平均精度。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=DynED:+Dynamic+Ensemble+Diversification+in+Data+Stream+Classification)|0| |[HOVER: Homophilic Oversampling via Edge Removal for Class-Imbalanced Bot Detection on Graphs](https://doi.org/10.1145/3583780.3615264)|Bradley Ashmore, Lingwei Chen|Wright State University, Dayton, OH, USA|As malicious bots reside in a network to disrupt network stability, graph neural networks (GNNs) have emerged as one of the most popular bot detection methods. However, in most cases these graphs are significantly class-imbalanced. To address this issue, graph oversampling has recently been proposed to synthesize nodes and edges, which still suffers from graph heterophily, leading to suboptimal performance. In this paper, we propose HOVER, which implements Homophilic Oversampling Via Edge Removal for bot detection on graphs. Instead of oversampling nodes and edges within initial graph structure, HOVER designs a simple edge removal method with heuristic criteria to mitigate heterophily and learn distinguishable node embeddings, which are then used to oversample minority bots to generate a balanced class distribution without edge synthesis. Experiments on TON IoT networks demonstrate the state-of-the-art performance of HOVER on bot detection with high graph heterophily and extreme class imbalance.|由于恶意机器人驻留在网络中以破坏网络的稳定性，图神经网络(GNN)已经成为最流行的机器人检测方法之一。然而，在大多数情况下，这些图是显着的类不平衡。为了解决这个问题，最近提出了图过采样来综合节点和边，这仍然受到图异构性的影响，导致次优性能。本文提出了 HOVER 方法，通过边缘去除实现图上机器人的同态过采样检测。HOVER 设计了一种简单的基于启发式标准的边去除方法，用以消除异构性，学习可区分的节点嵌入，而不需要对初始图结构中的节点和边进行过采样，然后利用过采样的少数机器人生成一个不需要边合成的均衡类分布。在 TON 物联网上的实验表明，HOVER 在具有高度图异构性和极端类不平衡的机器人检测方面具有很好的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=HOVER:+Homophilic+Oversampling+via+Edge+Removal+for+Class-Imbalanced+Bot+Detection+on+Graphs)|0| |[Accelerating Concept Learning via Sampling](https://doi.org/10.1145/3583780.3615158)|Alkid Baci, Stefan Heindorf|Paderborn University, Paderborn, Germany|Node classification is an important task in many fields, e.g., predicting entity types in knowledge graphs, classifying papers in citation graphs, or classifying nodes in social networks. In many cases, it is crucial to explain why certain predictions are made. Towards this end, concept learning has been proposed as a means of interpretable node classification: given positive and negative examples in a knowledge base, concepts in description logics are learned that serve as classification models. However, state-of-the-art concept learners, including EvoLearner and CELOE exhibit long runtimes. In this paper, we propose to accelerate concept learning with graph sampling techniques. We experiment with seven techniques and tailor them to the setting of concept learning. In our experiments, we achieve a reduction in training size by over 90% while maintaining a high predictive performance.|节点分类是知识图中实体类型预测、引文图中论文分类、社会网络中节点分类等领域的重要研究课题。在许多情况下，解释为什么做出某些预测是至关重要的。为此，概念学习被提出作为可解释节点分类的一种手段: 在知识库中给出正反两方面的例子，学习描述逻辑中作为分类模型的概念。然而，最先进的概念学习者，包括 EvoLearner 和 CELOE 展示了长时间的运行。本文提出利用图抽样技术来加速概念学习。我们试验了七种技巧，并根据概念学习的环境对它们进行调整。在我们的实验中，我们在保持高预测性能的同时，将训练规模减少了90% 以上。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Accelerating+Concept+Learning+via+Sampling)|0| -|[Unsupervised Anomaly Detection & Diagnosis: A Stein Variational Gradient Descent Approach](https://doi.org/10.1145/3583780.3615167)|Zhichao Chen, Leilei Ding, Jianmin Huang, Zhixuan Chu, Qingyang Dai, Hao Wang|EURECOM & Orange, Sophia Antipolis, France; Orange Labs, Sophia Antipolis, France; EURECOM, Biot, France; Orange, Sophia Antipolis, France|The automatic supervision of IT systems is a current challenge at Orange. Given the size and complexity reached by its IT operations, the number of sensors needed to obtain measurements over time, used to infer normal and abnormal behaviors, has increased dramatically making traditional expert-based supervision methods slow or prone to errors. In this paper, we propose a fast and stable method called UnSupervised Anomaly Detection for multivariate time series (USAD) based on adversely trained autoencoders. Its autoencoder architecture makes it capable of learning in an unsupervised way. The use of adversarial training and its architecture allows it to isolate anomalies while providing fast training. We study the properties of our methods through experiments on five public datasets, thus demonstrating its robustness, training speed and high anomaly detection performance. Through a feasibility study using Orange's proprietary data we have been able to validate Orange's requirements on scalability, stability, robustness, training speed and high performance.|IT 系统的自动监控是奥兰治目前面临的一个挑战。鉴于其 IT 操作的规模和复杂性，随着时间的推移，用于推断正常和异常行为的获取测量数据所需的传感器数量急剧增加，使得传统的基于专家的监督方法变得缓慢或容易出错。在这篇文章中，我们提出了一个快速而稳定的方法，称为多变量时间序列的无监督异常检测(USAD) ，它基于受过不良训练的自动编码器。它的自动编码器结构使它能够以无监督的方式学习。对抗性训练的使用及其架构允许它在提供快速训练的同时隔离异常。我们通过在五个公共数据集上的实验来研究我们的方法的特性，从而证明了它的鲁棒性、训练速度和高异常检测性能。通过使用 Orange 专有数据的可行性研究，我们已经能够验证 Orange 在可扩展性、稳定性、健壮性、培训速度和高性能方面的要求。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Unsupervised+Anomaly+Detection+&+Diagnosis:+A+Stein+Variational+Gradient+Descent+Approach)|0| -|[Segment Augmentation and Prediction Consistency Neural Network for Multi-label Unknown Intent Detection](https://doi.org/10.1145/3583780.3615163)|Miaoxin Chen, Cao Liu, Boqi Dai, HaiTao Zheng, Ting Song, Jiansong Chen, Guanglu Wan, Rui Xie|Meituan, Shanghai, China; Tsinghua Shenzhen International Graduate School & Pengcheng Laboratory, Shenzhen, China; Meituan, Beijing, China; Tsinghua Shenzhen International Graduate School, Shenzhen, China|Multi-label unknown intent detection is a challenging task where each utterance may contain not only multiple known but also unknown intents. To tackle this challenge, pioneers proposed to predict the intent number of the utterance first, then compare it with the results of known intent matching to decide whether the utterance contains unknown intent(s). Though they have made remarkable progress on this task, their method still suffers from two important issues: 1) It is inadequate to extract multiple intents using only utterance encoding; 2) Optimizing two sub-tasks (intent number prediction and known intent matching) independently leads to inconsistent predictions. In this paper, we propose to incorporate segment augmentation rather than only use utterance encoding to better detect multiple intents. We also design a prediction consistency module to bridge the gap between the two sub-tasks. Empirical results on MultiWOZ2.3 show that our method achieves state-of-the-art performance and improves the best baseline significantly.|多标签未知意图检测是一项具有挑战性的任务，每个话语不仅包含多个已知意图，而且还包含未知意图。为了应对这一挑战，先驱者提出先预测话语的意图数量，然后将其与已知意图匹配的结果进行比较，以确定话语是否包含未知意图。尽管他们在这个任务上取得了显著的进步，但是他们的方法仍然面临两个重要的问题: 1)仅仅使用话语编码提取多个意图是不够的; 2)优化两个子任务(意图数预测和已知意图匹配)独立导致不一致的预测。在本文中，我们提出采用分段增强，而不仅仅使用话语编码，以更好地检测多个意图。同时设计了一个预测一致性模块来弥补这两个子任务之间的差距。在 MultiWOZ2.3上的实验结果表明，该方法达到了最先进的性能，并显著提高了最佳基线的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Segment+Augmentation+and+Prediction+Consistency+Neural+Network+for+Multi-label+Unknown+Intent+Detection)|0| -|[Assessing Student Performance with Multi-granularity Attention from Online Classroom Dialogue](https://doi.org/10.1145/3583780.3615143)|Jiahao Chen, Zitao Liu, Shuyan Huang, Yaying Huang, Xiangyu Zhao, Boyu Gao, Weiqi Luo|City University of Hong Kong, Hong Kong, Hong Kong; The Primary School attached to Jinan University, Guangzhou, China; TAL Education Group, Beijing, China; Guangdong Institute of Smart Education, Jinan University, Guangzhou, China|Accurately judging students' ongoing performance is very crucial for real-world educational scenarios. In this work, we focus on the task of automatically predicting students' levels of mastery of math questions from teacher-student classroom dialogue data in the online learning environment. We propose a novel neural network armed with a multi-granularity attention mechanism to capture the personalized pedagogical instructions from the very noisy teacher-student dialogue transcriptions. We conduct experiments on a real-world educational dataset and the results demonstrate the superiority and availability of our model in terms of various evaluation metrics.|准确地判断学生正在进行的表现对于真实世界的教育情景是非常关键的。在本研究中，我们主要探讨在网路学习环境下，利用师生课堂对话资料，自动预测学生数学问题的掌握程度。我们提出了一种新的神经网络，该网络具有多粒度的注意机制，可以从非常嘈杂的师生对话转录中获取个性化的教学指令。我们在一个真实的教育数据集上进行了实验，结果表明了我们的模型在各种评价指标方面的优越性和可用性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Assessing+Student+Performance+with+Multi-granularity+Attention+from+Online+Classroom+Dialogue)|0| -|[Learning Invariant Representations for New Product Sales Forecasting via Multi-Granularity Adversarial Learning](https://doi.org/10.1145/3583780.3615219)|Zhenzhen Chu, Chengyu Wang, Cen Chen, Dawei Cheng, Yuqi Liang, Weining Qian|Tongji University, Shanghai, China; Alibaba Group, Hangzhou, China; Seek Data Group, Emoney Inc., Shanghai, China; East China Normal University, Shanghai, China|Sales forecasting during the launch of new products has always been a challenging task, due to the lack of historical sales data. The dynamic market environment and consumer preferences also increase the uncertainty of predictions. Large chains face even greater difficulties due to their extensive presence across various regions. Traditional time-series forecasting methods usually rely on statistical models and empirical judgments, which are difficult to handle large, variable data and often fail to achieve satisfactory performance for new products. In this paper, we propose a Multi-granularity AdversaRial Learning framework (MARL) to leverage knowledge from old products and improve the quality of invariant representations for more accurate sales predictions. To evaluate our proposed method, we conducted extensive experiments on both a real-world dataset from a prominent international Café chain and a public dataset. The results demonstrated that our method is more effective than the existing state-of-the-art baselines for new product sales forecasting.|由于缺乏历史销售数据，新产品推出期间的销售预测一直是一项具有挑战性的任务。动态的市场环境和消费者偏好也增加了预测的不确定性。由于大型连锁企业遍布各个区域，它们面临的困难更大。传统的时间序列预测方法往往依赖于统计模型和经验判断，这些方法难以处理大量的可变数据，往往无法使新产品取得令人满意的性能。在本文中，我们提出了一个多粒度逆向学习框架(MARL) ，以利用知识从旧产品和提高质量的不变表示更准确的销售预测。为了评估我们提出的方法，我们进行了广泛的实验，从一个著名的国际咖啡连锁店和公共数据集的真实世界的数据集。结果表明，该方法比现有的新产品销售预测基准更有效。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Learning+Invariant+Representations+for+New+Product+Sales+Forecasting+via+Multi-Granularity+Adversarial+Learning)|0| +|[Unsupervised Anomaly Detection & Diagnosis: A Stein Variational Gradient Descent Approach](https://doi.org/10.1145/3583780.3615167)|Zhichao Chen, Leilei Ding, Jianmin Huang, Zhixuan Chu, Qingyang Dai, Hao Wang|EURECOM, Biot, France; Orange, Sophia Antipolis, France; Orange Labs, Sophia Antipolis, France; EURECOM & Orange, Sophia Antipolis, France|The automatic supervision of IT systems is a current challenge at Orange. Given the size and complexity reached by its IT operations, the number of sensors needed to obtain measurements over time, used to infer normal and abnormal behaviors, has increased dramatically making traditional expert-based supervision methods slow or prone to errors. In this paper, we propose a fast and stable method called UnSupervised Anomaly Detection for multivariate time series (USAD) based on adversely trained autoencoders. Its autoencoder architecture makes it capable of learning in an unsupervised way. The use of adversarial training and its architecture allows it to isolate anomalies while providing fast training. We study the properties of our methods through experiments on five public datasets, thus demonstrating its robustness, training speed and high anomaly detection performance. Through a feasibility study using Orange's proprietary data we have been able to validate Orange's requirements on scalability, stability, robustness, training speed and high performance.|IT 系统的自动监控是奥兰治目前面临的一个挑战。鉴于其 IT 操作的规模和复杂性，随着时间的推移，用于推断正常和异常行为的获取测量数据所需的传感器数量急剧增加，使得传统的基于专家的监督方法变得缓慢或容易出错。在这篇文章中，我们提出了一个快速而稳定的方法，称为多变量时间序列的无监督异常检测(USAD) ，它基于受过不良训练的自动编码器。它的自动编码器结构使它能够以无监督的方式学习。对抗性训练的使用及其架构允许它在提供快速训练的同时隔离异常。我们通过在五个公共数据集上的实验来研究我们的方法的特性，从而证明了它的鲁棒性、训练速度和高异常检测性能。通过使用 Orange 专有数据的可行性研究，我们已经能够验证 Orange 在可扩展性、稳定性、健壮性、培训速度和高性能方面的要求。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Unsupervised+Anomaly+Detection+&+Diagnosis:+A+Stein+Variational+Gradient+Descent+Approach)|0| +|[Segment Augmentation and Prediction Consistency Neural Network for Multi-label Unknown Intent Detection](https://doi.org/10.1145/3583780.3615163)|Miaoxin Chen, Cao Liu, Boqi Dai, HaiTao Zheng, Ting Song, Jiansong Chen, Guanglu Wan, Rui Xie|Tsinghua Shenzhen International Graduate School, Shenzhen, China; Meituan, Beijing, China; Tsinghua Shenzhen International Graduate School & Pengcheng Laboratory, Shenzhen, China; Meituan, Shanghai, China|Multi-label unknown intent detection is a challenging task where each utterance may contain not only multiple known but also unknown intents. To tackle this challenge, pioneers proposed to predict the intent number of the utterance first, then compare it with the results of known intent matching to decide whether the utterance contains unknown intent(s). Though they have made remarkable progress on this task, their method still suffers from two important issues: 1) It is inadequate to extract multiple intents using only utterance encoding; 2) Optimizing two sub-tasks (intent number prediction and known intent matching) independently leads to inconsistent predictions. In this paper, we propose to incorporate segment augmentation rather than only use utterance encoding to better detect multiple intents. We also design a prediction consistency module to bridge the gap between the two sub-tasks. Empirical results on MultiWOZ2.3 show that our method achieves state-of-the-art performance and improves the best baseline significantly.|多标签未知意图检测是一项具有挑战性的任务，每个话语不仅包含多个已知意图，而且还包含未知意图。为了应对这一挑战，先驱者提出先预测话语的意图数量，然后将其与已知意图匹配的结果进行比较，以确定话语是否包含未知意图。尽管他们在这个任务上取得了显著的进步，但是他们的方法仍然面临两个重要的问题: 1)仅仅使用话语编码提取多个意图是不够的; 2)优化两个子任务(意图数预测和已知意图匹配)独立导致不一致的预测。在本文中，我们提出采用分段增强，而不仅仅使用话语编码，以更好地检测多个意图。同时设计了一个预测一致性模块来弥补这两个子任务之间的差距。在 MultiWOZ2.3上的实验结果表明，该方法达到了最先进的性能，并显著提高了最佳基线的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Segment+Augmentation+and+Prediction+Consistency+Neural+Network+for+Multi-label+Unknown+Intent+Detection)|0| +|[Assessing Student Performance with Multi-granularity Attention from Online Classroom Dialogue](https://doi.org/10.1145/3583780.3615143)|Jiahao Chen, Zitao Liu, Shuyan Huang, Yaying Huang, Xiangyu Zhao, Boyu Gao, Weiqi Luo|Guangdong Institute of Smart Education, Jinan University, Guangzhou, China; The Primary School attached to Jinan University, Guangzhou, China; TAL Education Group, Beijing, China; City University of Hong Kong, Hong Kong, Hong Kong|Accurately judging students' ongoing performance is very crucial for real-world educational scenarios. In this work, we focus on the task of automatically predicting students' levels of mastery of math questions from teacher-student classroom dialogue data in the online learning environment. We propose a novel neural network armed with a multi-granularity attention mechanism to capture the personalized pedagogical instructions from the very noisy teacher-student dialogue transcriptions. We conduct experiments on a real-world educational dataset and the results demonstrate the superiority and availability of our model in terms of various evaluation metrics.|准确地判断学生正在进行的表现对于真实世界的教育情景是非常关键的。在本研究中，我们主要探讨在网路学习环境下，利用师生课堂对话资料，自动预测学生数学问题的掌握程度。我们提出了一种新的神经网络，该网络具有多粒度的注意机制，可以从非常嘈杂的师生对话转录中获取个性化的教学指令。我们在一个真实的教育数据集上进行了实验，结果表明了我们的模型在各种评价指标方面的优越性和可用性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Assessing+Student+Performance+with+Multi-granularity+Attention+from+Online+Classroom+Dialogue)|0| +|[Learning Invariant Representations for New Product Sales Forecasting via Multi-Granularity Adversarial Learning](https://doi.org/10.1145/3583780.3615219)|Zhenzhen Chu, Chengyu Wang, Cen Chen, Dawei Cheng, Yuqi Liang, Weining Qian|Seek Data Group, Emoney Inc., Shanghai, China; Alibaba Group, Hangzhou, China; East China Normal University, Shanghai, China; Tongji University, Shanghai, China|Sales forecasting during the launch of new products has always been a challenging task, due to the lack of historical sales data. The dynamic market environment and consumer preferences also increase the uncertainty of predictions. Large chains face even greater difficulties due to their extensive presence across various regions. Traditional time-series forecasting methods usually rely on statistical models and empirical judgments, which are difficult to handle large, variable data and often fail to achieve satisfactory performance for new products. In this paper, we propose a Multi-granularity AdversaRial Learning framework (MARL) to leverage knowledge from old products and improve the quality of invariant representations for more accurate sales predictions. To evaluate our proposed method, we conducted extensive experiments on both a real-world dataset from a prominent international Café chain and a public dataset. The results demonstrated that our method is more effective than the existing state-of-the-art baselines for new product sales forecasting.|由于缺乏历史销售数据，新产品推出期间的销售预测一直是一项具有挑战性的任务。动态的市场环境和消费者偏好也增加了预测的不确定性。由于大型连锁企业遍布各个区域，它们面临的困难更大。传统的时间序列预测方法往往依赖于统计模型和经验判断，这些方法难以处理大量的可变数据，往往无法使新产品取得令人满意的性能。在本文中，我们提出了一个多粒度逆向学习框架(MARL) ，以利用知识从旧产品和提高质量的不变表示更准确的销售预测。为了评估我们提出的方法，我们进行了广泛的实验，从一个著名的国际咖啡连锁店和公共数据集的真实世界的数据集。结果表明，该方法比现有的新产品销售预测基准更有效。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Learning+Invariant+Representations+for+New+Product+Sales+Forecasting+via+Multi-Granularity+Adversarial+Learning)|0| |[OnlineAutoClust: A Framework for Online Automated Clustering](https://doi.org/10.1145/3583780.3615148)|Radwa El Shawi, Dmitri Rozgonjuk|Tartu University, Tartu, Estonia|Automated Machine Learning (AutoML) has been successful when the learning task is assumed to be static. However, it remains unclear whether AutoML methods can efficiently create online pipelines in dynamic environments. The current online AutoML frameworks primarily focus on supervised learning. However, unsupervised learning, particularly clustering, also requires AutoML solutions, especially with the ambiguity associated with evaluating clustering results. In this paper, we introduce OnlineAutoClust, a framework for online automated clustering for algorithm selection and hyperparameter tuning. OnlineAutoClust combines the inherent adaptation capabilities of online learners with automated pipeline optimization using Bayesian optimization. OnlineAutoClust develops a collaborative mechanism based on clustering ensemble to combine optimized pipelines based on different internal cluster validity indices. The proposed framework is based on River library and utilizes five clustering algorithms. Empirical evaluation on several real and synthetic data streams with varying types of concept drift demonstrates the effectiveness of the proposed approach compared to existing methods|当学习任务是静态的时候，自动机器学习(AutoML)就取得了成功。然而，目前尚不清楚 AutoML 方法是否能够在动态环境中有效地创建联机管道。当前的在线 AutoML 框架主要关注监督式学习。然而，非监督式学习，尤其是集群，也需要 AutoML 解决方案，尤其是在评估集群结果时，存在模糊性。本文介绍了 OnlineAutoClust，一个用于算法选择和超参数调整的在线自动聚类框架。OnlineAutoClust 将在线学习者固有的自适应能力与使用贝叶斯优化的自动流水线优化相结合。OnlineAutoClust 开发了一种基于集群集成的协作机制，将基于不同内部集群有效性指标的优化流水线结合起来。该框架基于 River 库，采用了五种聚类算法。对具有不同类型概念漂移的实际和合成数据流进行了实证评估，结果表明该方法与现有方法相比具有较好的有效性|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=OnlineAutoClust:+A+Framework+for+Online+Automated+Clustering)|0| -|[Synergistic Disease Similarity Measurement via Unifying Hierarchical Relation Perception and Association Capturing](https://doi.org/10.1145/3583780.3615274)|Zihao Gao, Huifang Ma, Yike Wang, Zhixin Li, Liang Chang|Guilin University of Electronic Technology, Guilin, China; Guangxi Normal University, Guilin, China; NorthWest Normal University & Guilin University of Electronic Technology, Lanzhou, China; NorthWest Normal University, Lanzhou, China|Quantifying similarities among human diseases is crucial to enhance our understanding of disease biology. Deep learning efforts have been devoted to quantifying disease similarity by integrating multi-view data sources from disparate biological data. However, disease data are often sparse, leading to suboptimal representation of disease given biological entity relationships and labeled disease data are not adequately modeled. In this paper, we propose an effective Synergistic disease Similarity measurement model called SynerSim. SynerSim possesses two key components: a hierarchical biological entity relation perception module to capture disease features from various biological entities, and a disease association capturing module based on signed random walk to model precious disease data. Additionally, SynerSim leverages dual granularity contrastive learning to enhance the representation of diverse biological entities, owing to the ability to enable the synergistic supervision of diseases represented by both homogeneous and heterogeneous information. Experimental results demonstrate that SynerSim achieves outstanding performance in the disease similarity measurement.|量化人类疾病之间的相似性对于提高我们对疾病生物学的理解是至关重要的。深度学习致力于通过整合来自不同生物学数据的多视图数据源来量化疾病相似性。然而，疾病数据往往是稀疏的，导致疾病的次优表示给予生物实体关系和标记的疾病数据没有充分建模。在本文中，我们提出了一个有效的协同疾病相似性度量模型 SynerSim。SynerSim 具有两个关键部分: 一个层次化的生物实体关系感知模块，用于从各种生物实体中获取疾病特征; 一个基于签名随机游走的疾病关联获取模块，用于建模珍贵的疾病数据。此外，SynerSim 利用双粒度对比学习来增强不同生物实体的表示，这是由于能够对同质和异质信息所代表的疾病进行协同监督。实验结果表明，SynerSim 在疾病相似性度量中取得了优异的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Synergistic+Disease+Similarity+Measurement+via+Unifying+Hierarchical+Relation+Perception+and+Association+Capturing)|0| +|[Synergistic Disease Similarity Measurement via Unifying Hierarchical Relation Perception and Association Capturing](https://doi.org/10.1145/3583780.3615274)|Zihao Gao, Huifang Ma, Yike Wang, Zhixin Li, Liang Chang|Guilin University of Electronic Technology, Guilin, China; NorthWest Normal University, Lanzhou, China; Guangxi Normal University, Guilin, China; NorthWest Normal University & Guilin University of Electronic Technology, Lanzhou, China|Quantifying similarities among human diseases is crucial to enhance our understanding of disease biology. Deep learning efforts have been devoted to quantifying disease similarity by integrating multi-view data sources from disparate biological data. However, disease data are often sparse, leading to suboptimal representation of disease given biological entity relationships and labeled disease data are not adequately modeled. In this paper, we propose an effective Synergistic disease Similarity measurement model called SynerSim. SynerSim possesses two key components: a hierarchical biological entity relation perception module to capture disease features from various biological entities, and a disease association capturing module based on signed random walk to model precious disease data. Additionally, SynerSim leverages dual granularity contrastive learning to enhance the representation of diverse biological entities, owing to the ability to enable the synergistic supervision of diseases represented by both homogeneous and heterogeneous information. Experimental results demonstrate that SynerSim achieves outstanding performance in the disease similarity measurement.|量化人类疾病之间的相似性对于提高我们对疾病生物学的理解是至关重要的。深度学习致力于通过整合来自不同生物学数据的多视图数据源来量化疾病相似性。然而，疾病数据往往是稀疏的，导致疾病的次优表示给予生物实体关系和标记的疾病数据没有充分建模。在本文中，我们提出了一个有效的协同疾病相似性度量模型 SynerSim。SynerSim 具有两个关键部分: 一个层次化的生物实体关系感知模块，用于从各种生物实体中获取疾病特征; 一个基于签名随机游走的疾病关联获取模块，用于建模珍贵的疾病数据。此外，SynerSim 利用双粒度对比学习来增强不同生物实体的表示，这是由于能够对同质和异质信息所代表的疾病进行协同监督。实验结果表明，SynerSim 在疾病相似性度量中取得了优异的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Synergistic+Disease+Similarity+Measurement+via+Unifying+Hierarchical+Relation+Perception+and+Association+Capturing)|0| |[Knowledge-Enhanced Multi-Label Few-Shot Product Attribute-Value Extraction](https://doi.org/10.1145/3583780.3615142)|Jiaying Gong, WeiTe Chen, Hoda Eldardiry|Virginia Polytechnic Institute and State University, Blacksburg, VA, USA; Rakuten Institute of Technology, Boston, MA, USA|Existing attribute-value extraction (AVE) models require large quantities of labeled data for training. However, new products with new attribute-value pairs enter the market every day in real-world e-Commerce. Thus, we formulate AVE in multi-label few-shot learning (FSL), aiming to extract unseen attribute value pairs based on a small number of training examples. We propose a Knowledge-Enhanced Attentive Framework (KEAF) based on prototypical networks, leveraging the generated label description and category information to learn more discriminative prototypes. Besides, KEAF integrates with hybrid attention to reduce noise and capture more informative semantics for each class by calculating the label-relevant and query-related weights. To achieve multi-label inference, KEAF further learns a dynamic threshold by integrating the semantic information from both the support set and the query set. Extensive experiments with ablation studies conducted on two datasets demonstrate that KEAF outperforms other SOTA models for information extraction in FSL. The code can be found at: https://github.com/gjiaying/KEAF|现有的属性值提取(AVE)模型需要大量的标记数据进行训练。然而，在现实的电子商务中，每天都有新的属性-价值对产品进入市场。因此，我们在多标签少镜头学习(FSL)中构造 AVE，目的是在少量训练样本的基础上提取不可见的属性值对。我们提出了一个基于原型网络的知识增强注意框架(KEAF) ，利用生成的标签描述和类别信息来学习更多的区分原型。此外，KEAF 与混合注意相结合，通过计算与标签相关和查询相关的权重来降低噪声并为每个类捕获更多的信息语义。为了实现多标签推理，KEAF 进一步通过集成来自支持集和查询集的语义信息来学习动态阈值。在两个数据集上进行的消融研究的广泛实验表明，在 FSL 中，KEAF 的性能优于其他 SOTA 模型的信息抽取。密码可在以下 https://github.com/gjiaying/keaf 找到:|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Knowledge-Enhanced+Multi-Label+Few-Shot+Product+Attribute-Value+Extraction)|0| -|[Perturbation-Based Two-Stage Multi-Domain Active Learning](https://doi.org/10.1145/3583780.3615222)|Rui He, Zeyu Dai, Shan He, Ke Tang|Southern University of Science and Technology & University of Birmingham, Shenzhen / Birmingham, China; University of Birmingham, Birmingham, United Kingdom; The Hong Kong Polytechnic University & Southern University of Science and Technology, Hong Kong, Hong Kong; Southern University of Science and Technology, Shenzhen, China|In multi-domain learning (MDL) scenarios, high labeling effort is required due to the complexity of collecting data from various domains. Active Learning (AL) presents an encouraging solution to this issue by annotating a smaller number of highly informative instances, thereby reducing the labeling effort. Previous research has relied on conventional AL strategies for MDL scenarios, which underutilize the domain-shared information of each instance during the selection procedure. To mitigate this issue, we propose a novel perturbation-based two-stage multi-domain active learning (P2S-MDAL) method incorporated into the well-regarded ASP-MTL model. Specifically, P2S-MDAL involves allocating budgets for domains and establishing regions for diversity selection, which are further used to select the most cross-domain influential samples in each region. A perturbation metric has been introduced to evaluate the robustness of the shared feature extractor of the model, facilitating the identification of potentially cross-domain influential samples. Experiments are conducted on three real-world datasets, encompassing both texts and images. The superior performance over conventional AL strategies shows the effectiveness of the proposed strategy. Additionally, an ablation study has been carried out to demonstrate the validity of each component. Finally, we outline several intriguing potential directions for future MDAL research, thus catalyzing the field's advancement.|在多领域学习(MDL)场景中，由于从不同领域收集数据的复杂性，需要进行大量的标记工作。主动学习(AL)提出了一个令人鼓舞的解决方案，通过注释数量较少的高信息量的实例，从而减少了标记工作。以往的研究都依赖于传统的 AL 策略来处理 MDL 场景，这种策略在选择过程中没有充分利用每个实例的域共享信息。为了解决这一问题，我们提出了一种新的基于扰动的两阶段多域主动学习(P2S-MDAL)方法，并将其引入到广受好评的 ASP-MTL 模型中。具体来说，P2S-MDAL 涉及到为域分配预算和建立区域进行多样性选择，进一步用于选择每个区域中最具跨域影响力的样本。引入摄动度量来评估模型的共享特征提取器的鲁棒性，便于识别潜在的跨域影响样本。实验在三个真实世界的数据集上进行，包括文本和图像。相对于传统 AL 策略的优越性能表明了该策略的有效性。此外，还进行了消融研究，以证明各组分的有效性。最后，我们概述了未来 MDAL 研究的几个有趣的潜在方向，从而促进该领域的进步。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Perturbation-Based+Two-Stage+Multi-Domain+Active+Learning)|0| +|[Perturbation-Based Two-Stage Multi-Domain Active Learning](https://doi.org/10.1145/3583780.3615222)|Rui He, Zeyu Dai, Shan He, Ke Tang|Southern University of Science and Technology, Shenzhen, China; The Hong Kong Polytechnic University & Southern University of Science and Technology, Hong Kong, Hong Kong; University of Birmingham, Birmingham, United Kingdom; Southern University of Science and Technology & University of Birmingham, Shenzhen / Birmingham, China|In multi-domain learning (MDL) scenarios, high labeling effort is required due to the complexity of collecting data from various domains. Active Learning (AL) presents an encouraging solution to this issue by annotating a smaller number of highly informative instances, thereby reducing the labeling effort. Previous research has relied on conventional AL strategies for MDL scenarios, which underutilize the domain-shared information of each instance during the selection procedure. To mitigate this issue, we propose a novel perturbation-based two-stage multi-domain active learning (P2S-MDAL) method incorporated into the well-regarded ASP-MTL model. Specifically, P2S-MDAL involves allocating budgets for domains and establishing regions for diversity selection, which are further used to select the most cross-domain influential samples in each region. A perturbation metric has been introduced to evaluate the robustness of the shared feature extractor of the model, facilitating the identification of potentially cross-domain influential samples. Experiments are conducted on three real-world datasets, encompassing both texts and images. The superior performance over conventional AL strategies shows the effectiveness of the proposed strategy. Additionally, an ablation study has been carried out to demonstrate the validity of each component. Finally, we outline several intriguing potential directions for future MDAL research, thus catalyzing the field's advancement.|在多领域学习(MDL)场景中，由于从不同领域收集数据的复杂性，需要进行大量的标记工作。主动学习(AL)提出了一个令人鼓舞的解决方案，通过注释数量较少的高信息量的实例，从而减少了标记工作。以往的研究都依赖于传统的 AL 策略来处理 MDL 场景，这种策略在选择过程中没有充分利用每个实例的域共享信息。为了解决这一问题，我们提出了一种新的基于扰动的两阶段多域主动学习(P2S-MDAL)方法，并将其引入到广受好评的 ASP-MTL 模型中。具体来说，P2S-MDAL 涉及到为域分配预算和建立区域进行多样性选择，进一步用于选择每个区域中最具跨域影响力的样本。引入摄动度量来评估模型的共享特征提取器的鲁棒性，便于识别潜在的跨域影响样本。实验在三个真实世界的数据集上进行，包括文本和图像。相对于传统 AL 策略的优越性能表明了该策略的有效性。此外，还进行了消融研究，以证明各组分的有效性。最后，我们概述了未来 MDAL 研究的几个有趣的潜在方向，从而促进该领域的进步。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Perturbation-Based+Two-Stage+Multi-Domain+Active+Learning)|0| |[KGrEaT: A Framework to Evaluate Knowledge Graphs via Downstream Tasks](https://doi.org/10.1145/3583780.3615241)|Nicolas Heist, Sven Hertling, Heiko Paulheim|University of Mannheim, Mannheim, Germany|In recent years, countless research papers have addressed the topics of knowledge graph creation, extension, or completion in order to create knowledge graphs that are larger, more correct, or more diverse. This research is typically motivated by the argumentation that using such enhanced knowledge graphs to solve downstream tasks will improve performance. Nonetheless, this is hardly ever evaluated. Instead, the predominant evaluation metrics - aiming at correctness and completeness - are undoubtedly valuable but fail to capture the complete picture, i.e., how useful the created or enhanced knowledge graph actually is. Further, the accessibility of such a knowledge graph is rarely considered (e.g., whether it contains expressive labels, descriptions, and sufficient context information to link textual mentions to the entities of the knowledge graph). To better judge how well knowledge graphs perform on actual tasks, we present KGrEaT - a framework to estimate the quality of knowledge graphs via actual downstream tasks like classification, clustering, or recommendation. Instead of comparing different methods of processing knowledge graphs with respect to a single task, the purpose of KGrEaT is to compare various knowledge graphs as such by evaluating them on a fixed task setup. The framework takes a knowledge graph as input, automatically maps it to the datasets to be evaluated on, and computes performance metrics for the defined tasks. It is built in a modular way to be easily extendable with additional tasks and datasets.|近年来，为了创建更大、更正确或更多样化的知识图，无数的研究论文都涉及到知识图的创建、扩展或完成等主题。这项研究是典型的动机论证，使用这种增强的知识图解决下游任务将提高性能。尽管如此，这几乎从未被评估过。相反，主要的评估指标——旨在正确性和完整性——无疑是有价值的，但是没有捕捉到完整的图景，也就是说，创建或增强的知识图实际上是多么有用。此外，这样一个知识图表的可访问性很少被考虑(例如，它是否包含表达性标签、描述和足够的上下文信息来链接文本提及到知识图表的实体)。为了更好地判断知识图表在实际任务中的表现，我们提出了 KGraT-一个通过分类、聚类或推荐等实际下游任务来评估知识图表质量的框架。KGreaT 的目的不是针对单个任务比较不同的知识图处理方法，而是通过在一个固定的任务设置上评估不同的知识图来比较不同的知识图。该框架将知识图作为输入，自动将其映射到要评估的数据集，并计算已定义任务的性能指标。它是以一种模块化的方式构建的，可以很容易地通过附加的任务和数据集进行扩展。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=KGrEaT:+A+Framework+to+Evaluate+Knowledge+Graphs+via+Downstream+Tasks)|0| |[Forgetting-aware Linear Bias for Attentive Knowledge Tracing](https://doi.org/10.1145/3583780.3615191)|Yoonjin Im, Eunseong Choi, Heejin Kook, Jongwuk Lee|Sungkyunkwan University, Suwon, Republic of Korea|Knowledge Tracing (KT) aims to track proficiency based on a question-solving history, allowing us to offer a streamlined curriculum. Recent studies actively utilize attention-based mechanisms to capture the correlation between questions and combine it with the learner's characteristics for responses. However, our empirical study shows that existing attention-based KT models neglect the learner's forgetting behavior, especially as the interaction history becomes longer. This problem arises from the bias that overprioritizes the correlation of questions while inadvertently ignoring the impact of forgetting behavior. This paper proposes a simple-yet-effective solution, namely Forgetting-aware Linear Bias (FoLiBi), to reflect forgetting behavior as a linear bias. Despite its simplicity, FoLiBi is readily equipped with existing attentive KT models by effectively decomposing question correlations with forgetting behavior. FoLiBi plugged with several KT models yields a consistent improvement of up to 2.58% in AUC over state-of-the-art KT models on four benchmark datasets.|知识追踪(KT)旨在追踪基于问题解决历史的熟练程度，使我们能够提供一个简化的课程。最近的研究积极利用基于注意的机制来捕捉问题之间的相关性，并将其与学习者的反应特征结合起来。然而，我们的实证研究表明，现有的基于注意的 KT 模型忽视了学习者的遗忘行为，特别是随着交互历史的增长。这个问题产生于一种偏见，即过度优先考虑问题的相关性，而无意中忽略了遗忘行为的影响。本文提出了一种简单而有效的解决方案，即遗忘感知线性偏差(FoLiBi) ，以反映遗忘行为作为一种线性偏差。尽管 FoLiBi 很简单，但它可以通过有效地分解与遗忘行为的问题相关性，很容易地配备现有的注意 KT 模型。在四个基准数据集上，与最先进的 KT 模型相比，用几个 KT 模型封装的 FoLiBi 在 AUC 上获得了高达2.58% 的一致性改进。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Forgetting-aware+Linear+Bias+for+Attentive+Knowledge+Tracing)|0| -|[Exploring Cohesive Subgraphs in Hypergraphs: The (k, g)-core Approach](https://doi.org/10.1145/3583780.3615275)|Dahee Kim, Junghoon Kim, Sungsu Lim, Hyun Ji Jeong|Ulsan National Institute of Science & Technology, Ulsan, Republic of Korea; Chungnam National University, Daejeon, Republic of Korea; Kongju National University, Cheonan, Republic of Korea|Identifying cohesive subgraphs in hypergraphs is a fundamental problem that has received recent attention in data mining and engineering fields. Existing approaches mainly focus on a strongly induced subhypergraph or edge cardinality, overlooking the importance of the frequency of co-occurrence. In this paper, we propose a new cohesive subgraph named (k,g)-core, which considers both neighbour and co-occurrence simultaneously. The $(k,g)$-core has various applications including recommendation system, network analysis, and fraud detection. To the best of our knowledge, this is the first work to combine these factors. We extend an existing efficient algorithm to find solutions for $(k,g)$-core. Finally, we conduct extensive experimental studies that demonstrate the efficiency and effectiveness of our proposed algorithm.|在超图中识别内聚子图是近年来数据挖掘和工程领域关注的一个基本问题。现有的方法主要集中在强诱导子超图或边基数上，忽略了共现频率的重要性。本文提出了一个新的内聚子图(k，g)-core，它同时考虑了邻域和共现。$(k，g) $- 核心具有各种应用程序，包括推荐系统、网络分析和欺诈检测。据我们所知，这是第一项将这些因素结合起来的工作。我们扩展了一个已有的有效算法来寻找 $(k，g) $- 核的解。最后，我们进行了广泛的实验研究，证明了我们提出的算法的效率和有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Exploring+Cohesive+Subgraphs+in+Hypergraphs:+The+(k,+g)-core+Approach)|0| -|[AmpliBias: Mitigating Dataset Bias through Bias Amplification in Few-shot Learning for Generative Models](https://doi.org/10.1145/3583780.3615184)|Donggeun Ko, Dongjun Lee, Namjun Park, Kyoungrae Noh, Hyeonjin Park, Jaekwang Kim|Sungkyunkwan University, Seoul, Republic of Korea; Sungkyunkwan University, Suwon, Republic of Korea|Deep learning models exhibit a dependency on peripheral attributes of input data, such as shapes and colors, leading the models to become biased towards these certain attributes that result in subsequent degradation of performance. In this paper, we alleviate this problem by presenting~\sysname, a novel framework that tackles dataset bias by leveraging generative models to amplify bias and facilitate the learning of debiased representations of the classifier. Our method involves three major steps. We initially train a biased classifier, denoted as f_b, on a biased dataset and extract the top-K biased-conflict samples. Next, we train a generator solely on a bias-conflict dataset comprised of these top-K samples, aiming to learn the distribution of bias-conflict samples. Finally, we re-train the classifier on the newly constructed debiased dataset, which combines the original and amplified data. This allows the biased classifier to competently learn debiased representation. Extensive experiments validate that our proposed method effectively debiases the biased classifier.|深度学习模型表现出对输入数据的外围属性(如形状和颜色)的依赖性，导致模型偏向于这些特定属性，从而导致随后的性能下降。在本文中，我们通过提出 ~ sysname 来缓解这个问题，这是一个新的框架，它通过利用生成模型来放大偏差和促进分类器去偏表示的学习来处理数据集偏差。我们的方法包括三个主要步骤。我们首先在一个有偏的数据集上训练一个有偏的分类器，表示为 f _ b，然后提取上 K 个有偏冲突的样本。接下来，我们训练一个生成器单独的偏差冲突数据集包含这些顶 K 样本，旨在学习偏差冲突样本的分布。最后，在新构造的去偏数据集上重新训练分类器，将原始数据和放大数据结合起来。这允许有偏分类器胜任地学习去偏表示。大量的实验验证了我们提出的方法有效地降低了有偏分类器。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=AmpliBias:+Mitigating+Dataset+Bias+through+Bias+Amplification+in+Few-shot+Learning+for+Generative+Models)|0| -|[Causal Discovery in Temporal Domain from Interventional Data](https://doi.org/10.1145/3583780.3615177)|Peiwen Li, Yuan Meng, Xin Wang, Fang Shen, Yue Li, Jialong Wang, Wenwu Zhu|Tsinghua University, Beijing, China; Alibaba Group, Hangzhou, China; Tsinghua University & Alibaba Group, Shenzhen, China|Causal learning from observational data has garnered attention as controlled experiments can be costly. To enhance identifiability, incorporating intervention data has become a mainstream approach. However, these methods have yet to be explored in the context of time series data, despite their success in static data. To address this research gap, this paper presents a novel contribution. Firstly, a temporal interventional dataset with causal labels is introduced, derived from a data center IT room of a cloud service company. Secondly, this paper introduces TECDI, a novel approach for temporal causal discovery. TECDI leverages the smooth, algebraic characterization of acyclicity in causal graphs to efficiently uncover causal relationships. Experimental results on simulated and proposed real-world datasets validate the effectiveness of TECDI in accurately uncovering temporal causal relationships. The introduction of the temporal interventional dataset and the superior performance of TECDI contribute to advancing research in temporal causal discovery. Our datasets and codes have released at~\hrefhttps://github.com/lpwpower/TECDI https://github.com/lpwpower/TECDI.|从观测数据中进行因果学习已经引起了人们的注意，因为对照实验的成本可能很高。为了提高可识别性，合并干预数据已成为一种主流方法。然而，尽管这些方法在静态数据方面取得了成功，但在时间序列数据方面还有待进一步研究。为了解决这一研究差距，本文提出了一个新的贡献。首先，介绍了一个基于因果标签的时间干预数据集，该数据集来源于一家云服务公司的数据中心 IT 机房。其次，介绍了时间因果发现的新方法 TECDI。TECDI 利用因果图中平滑的代数角色塑造来有效地揭示因果关系。实验结果验证了 TECDI 在准确揭示时间因果关系方面的有效性。时间干预数据集的引入和 TECDI 的优越性能有助于推进时间因果发现的研究。我们的数据集和代码已经在 ~ hrefhttps:// github.com/lpwpower/tecdi https://github.com/lpwpower/tecdi 发布。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Causal+Discovery+in+Temporal+Domain+from+Interventional+Data)|0| -|[T-SaS: Toward Shift-aware Dynamic Adaptation for Streaming Data](https://doi.org/10.1145/3583780.3615267)|Weijieying Ren, Tianxiang Zhao, Wei Qin, Kunpeng Liu|Hefei University of Technology, Hefei, China; Portland State University, Portland , OR, USA; The Pennsylvania State University, State College, PA, USA|In many real-world scenarios, distribution shifts exist in the streaming data across time steps. Many complex sequential data can be effectively divided into distinct regimes that exhibit persistent dynamics. Discovering the shifted behaviors and the evolving patterns underlying the streaming data are important to understand the dynamic system. Existing methods typically train one robust model to work for the evolving data of distinct distributions or sequentially adapt the model utilizing explicitly given regime boundaries. However, there are two challenges: (1) shifts in data streams could happen drastically and abruptly without precursors. Boundaries of distribution shifts are usually unavailable, and (2) training a shared model for all domains could fail to capture varying patterns. This paper aims to solve the problem of sequential data modeling in the presence of sudden distribution shifts that occur without any precursors. Specifically, we design a Bayesian framework, dubbed as T-SaS, with a discrete distribution-modeling variable to capture abrupt shifts of data. Then, we design a model that enable adaptation with dynamic network selection conditioned on that discrete variable. The proposed method learns specific model parameters for each distribution by learning which neurons should be activated in the full network. A dynamic masking strategy is adopted here to support inter-distribution transfer through the overlapping of a set of sparse networks. Extensive experiments show that our proposed method is superior in both accurately detecting shift boundaries to get segments of varying distributions and effectively adapting to downstream forecast or classification tasks.|在许多实际场景中，流数据中存在跨时间步长的分布变化。许多复杂的顺序数据可以有效地划分为具有持续动态的不同区域。发现流数据背后的移动行为和演化模式对于理解动态系统非常重要。现有的方法通常训练一个稳健的模型来处理不同分布的演化数据，或者利用明确给定的制度边界顺序调整模型。然而，有两个挑战: (1)数据流的转移可能发生剧烈和突然没有前兆。分布转移的边界通常是不可用的，(2)为所有领域训练一个共享模型可能无法捕获不同的模式。本文旨在解决在没有任何前兆的情况下发生突变分布的时序数据建模问题。具体来说，我们设计了一个被称为 T-SaS 的贝叶斯框架，其中包含一个离散的分布建模变量来捕获数据的突变。然后，我们设计了一个以该离散变量为条件的动态网络选择自适应模型。该方法通过学习在整个网络中哪些神经元应该被激活来学习每个分布的特定模型参数。该算法采用动态掩蔽策略，通过一组稀疏网络的重叠来支持分布间转移。大量实验表明，该方法在准确检测移位边界以获得变化分布的片段和有效适应下游预测或分类任务方面具有优势。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=T-SaS:+Toward+Shift-aware+Dynamic+Adaptation+for+Streaming+Data)|0| +|[Exploring Cohesive Subgraphs in Hypergraphs: The (k, g)-core Approach](https://doi.org/10.1145/3583780.3615275)|Dahee Kim, Junghoon Kim, Sungsu Lim, Hyun Ji Jeong|Kongju National University, Cheonan, Republic of Korea; Ulsan National Institute of Science & Technology, Ulsan, Republic of Korea; Chungnam National University, Daejeon, Republic of Korea|Identifying cohesive subgraphs in hypergraphs is a fundamental problem that has received recent attention in data mining and engineering fields. Existing approaches mainly focus on a strongly induced subhypergraph or edge cardinality, overlooking the importance of the frequency of co-occurrence. In this paper, we propose a new cohesive subgraph named (k,g)-core, which considers both neighbour and co-occurrence simultaneously. The $(k,g)$-core has various applications including recommendation system, network analysis, and fraud detection. To the best of our knowledge, this is the first work to combine these factors. We extend an existing efficient algorithm to find solutions for $(k,g)$-core. Finally, we conduct extensive experimental studies that demonstrate the efficiency and effectiveness of our proposed algorithm.|在超图中识别内聚子图是近年来数据挖掘和工程领域关注的一个基本问题。现有的方法主要集中在强诱导子超图或边基数上，忽略了共现频率的重要性。本文提出了一个新的内聚子图(k，g)-core，它同时考虑了邻域和共现。$(k，g) $- 核心具有各种应用程序，包括推荐系统、网络分析和欺诈检测。据我们所知，这是第一项将这些因素结合起来的工作。我们扩展了一个已有的有效算法来寻找 $(k，g) $- 核的解。最后，我们进行了广泛的实验研究，证明了我们提出的算法的效率和有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Exploring+Cohesive+Subgraphs+in+Hypergraphs:+The+(k,+g)-core+Approach)|0| +|[AmpliBias: Mitigating Dataset Bias through Bias Amplification in Few-shot Learning for Generative Models](https://doi.org/10.1145/3583780.3615184)|Donggeun Ko, Dongjun Lee, Namjun Park, Kyoungrae Noh, Hyeonjin Park, Jaekwang Kim|Sungkyunkwan University, Suwon, Republic of Korea; Sungkyunkwan University, Seoul, Republic of Korea|Deep learning models exhibit a dependency on peripheral attributes of input data, such as shapes and colors, leading the models to become biased towards these certain attributes that result in subsequent degradation of performance. In this paper, we alleviate this problem by presenting~\sysname, a novel framework that tackles dataset bias by leveraging generative models to amplify bias and facilitate the learning of debiased representations of the classifier. Our method involves three major steps. We initially train a biased classifier, denoted as f_b, on a biased dataset and extract the top-K biased-conflict samples. Next, we train a generator solely on a bias-conflict dataset comprised of these top-K samples, aiming to learn the distribution of bias-conflict samples. Finally, we re-train the classifier on the newly constructed debiased dataset, which combines the original and amplified data. This allows the biased classifier to competently learn debiased representation. Extensive experiments validate that our proposed method effectively debiases the biased classifier.|深度学习模型表现出对输入数据的外围属性(如形状和颜色)的依赖性，导致模型偏向于这些特定属性，从而导致随后的性能下降。在本文中，我们通过提出 ~ sysname 来缓解这个问题，这是一个新的框架，它通过利用生成模型来放大偏差和促进分类器去偏表示的学习来处理数据集偏差。我们的方法包括三个主要步骤。我们首先在一个有偏的数据集上训练一个有偏的分类器，表示为 f _ b，然后提取上 K 个有偏冲突的样本。接下来，我们训练一个生成器单独的偏差冲突数据集包含这些顶 K 样本，旨在学习偏差冲突样本的分布。最后，在新构造的去偏数据集上重新训练分类器，将原始数据和放大数据结合起来。这允许有偏分类器胜任地学习去偏表示。大量的实验验证了我们提出的方法有效地降低了有偏分类器。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=AmpliBias:+Mitigating+Dataset+Bias+through+Bias+Amplification+in+Few-shot+Learning+for+Generative+Models)|0| +|[Causal Discovery in Temporal Domain from Interventional Data](https://doi.org/10.1145/3583780.3615177)|Peiwen Li, Yuan Meng, Xin Wang, Fang Shen, Yue Li, Jialong Wang, Wenwu Zhu|Alibaba Group, Hangzhou, China; Tsinghua University, Beijing, China; Tsinghua University & Alibaba Group, Shenzhen, China|Causal learning from observational data has garnered attention as controlled experiments can be costly. To enhance identifiability, incorporating intervention data has become a mainstream approach. However, these methods have yet to be explored in the context of time series data, despite their success in static data. To address this research gap, this paper presents a novel contribution. Firstly, a temporal interventional dataset with causal labels is introduced, derived from a data center IT room of a cloud service company. Secondly, this paper introduces TECDI, a novel approach for temporal causal discovery. TECDI leverages the smooth, algebraic characterization of acyclicity in causal graphs to efficiently uncover causal relationships. Experimental results on simulated and proposed real-world datasets validate the effectiveness of TECDI in accurately uncovering temporal causal relationships. The introduction of the temporal interventional dataset and the superior performance of TECDI contribute to advancing research in temporal causal discovery. Our datasets and codes have released at~\hrefhttps://github.com/lpwpower/TECDI https://github.com/lpwpower/TECDI.|从观测数据中进行因果学习已经引起了人们的注意，因为对照实验的成本可能很高。为了提高可识别性，合并干预数据已成为一种主流方法。然而，尽管这些方法在静态数据方面取得了成功，但在时间序列数据方面还有待进一步研究。为了解决这一研究差距，本文提出了一个新的贡献。首先，介绍了一个基于因果标签的时间干预数据集，该数据集来源于一家云服务公司的数据中心 IT 机房。其次，介绍了时间因果发现的新方法 TECDI。TECDI 利用因果图中平滑的代数角色塑造来有效地揭示因果关系。实验结果验证了 TECDI 在准确揭示时间因果关系方面的有效性。时间干预数据集的引入和 TECDI 的优越性能有助于推进时间因果发现的研究。我们的数据集和代码已经在 ~ hrefhttps:// github.com/lpwpower/tecdi https://github.com/lpwpower/tecdi 发布。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Causal+Discovery+in+Temporal+Domain+from+Interventional+Data)|0| +|[T-SaS: Toward Shift-aware Dynamic Adaptation for Streaming Data](https://doi.org/10.1145/3583780.3615267)|Weijieying Ren, Tianxiang Zhao, Wei Qin, Kunpeng Liu|Hefei University of Technology, Hefei, China; The Pennsylvania State University, State College, PA, USA; Portland State University, Portland , OR, USA|In many real-world scenarios, distribution shifts exist in the streaming data across time steps. Many complex sequential data can be effectively divided into distinct regimes that exhibit persistent dynamics. Discovering the shifted behaviors and the evolving patterns underlying the streaming data are important to understand the dynamic system. Existing methods typically train one robust model to work for the evolving data of distinct distributions or sequentially adapt the model utilizing explicitly given regime boundaries. However, there are two challenges: (1) shifts in data streams could happen drastically and abruptly without precursors. Boundaries of distribution shifts are usually unavailable, and (2) training a shared model for all domains could fail to capture varying patterns. This paper aims to solve the problem of sequential data modeling in the presence of sudden distribution shifts that occur without any precursors. Specifically, we design a Bayesian framework, dubbed as T-SaS, with a discrete distribution-modeling variable to capture abrupt shifts of data. Then, we design a model that enable adaptation with dynamic network selection conditioned on that discrete variable. The proposed method learns specific model parameters for each distribution by learning which neurons should be activated in the full network. A dynamic masking strategy is adopted here to support inter-distribution transfer through the overlapping of a set of sparse networks. Extensive experiments show that our proposed method is superior in both accurately detecting shift boundaries to get segments of varying distributions and effectively adapting to downstream forecast or classification tasks.|在许多实际场景中，流数据中存在跨时间步长的分布变化。许多复杂的顺序数据可以有效地划分为具有持续动态的不同区域。发现流数据背后的移动行为和演化模式对于理解动态系统非常重要。现有的方法通常训练一个稳健的模型来处理不同分布的演化数据，或者利用明确给定的制度边界顺序调整模型。然而，有两个挑战: (1)数据流的转移可能发生剧烈和突然没有前兆。分布转移的边界通常是不可用的，(2)为所有领域训练一个共享模型可能无法捕获不同的模式。本文旨在解决在没有任何前兆的情况下发生突变分布的时序数据建模问题。具体来说，我们设计了一个被称为 T-SaS 的贝叶斯框架，其中包含一个离散的分布建模变量来捕获数据的突变。然后，我们设计了一个以该离散变量为条件的动态网络选择自适应模型。该方法通过学习在整个网络中哪些神经元应该被激活来学习每个分布的特定模型参数。该算法采用动态掩蔽策略，通过一组稀疏网络的重叠来支持分布间转移。大量实验表明，该方法在准确检测移位边界以获得变化分布的片段和有效适应下游预测或分类任务方面具有优势。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=T-SaS:+Toward+Shift-aware+Dynamic+Adaptation+for+Streaming+Data)|0| |[Adversarial Density Ratio Estimation for Change Point Detection](https://doi.org/10.1145/3583780.3615248)|Shreyas S, Prakash Mandayam Comar, Sivaramakrishnan Kaveri|Amazon, Bengaluru, India|Change Point Detection (CPD) models are used to identify abrupt changes in the distribution of a data stream and have a widespread practical use. CPD methods generally compare the distribution of data sequences before and after a given time step to infer if there is a shift in distribution at the said time step. Numerous divergence measures, which measure distance between data distributions of sequence pairs, have been proposed for CPD \citeMStatisticNIPS, BergCPD and often the choice of divergence measure depends on the data used. Density Ratio Estimation (DRE) \citeRelDivCPD,BergCPD can be used to estimate a broad family of f-divergences, which includes widely used CPD divergences like Kullback-Leibler (KL) and Pearson, and thus DRE is a popular approach for CPD. In this work, we improve upon the existing DRE techniques for CPD, by proposing a novel objective that combines DRE seamlessly with adversarial sample generation. The adversarial samples allows for a robust CPD with DRE to track subtle changes in distribution, leading to a reduction in false negatives. We experiment on a wide variety of real-world, public benchmark datasets to show that our approach improves upon existing state-of-the-art (SoTA) methods, including DRE based CPD methods, by demonstrating an \sim 5% increase in F-score.|变点检测(CPD)模型用于识别数据流分布的突变，有着广泛的实际应用。CPD 方法通常比较给定时间步长前后数据序列的分布情况，以推断在给定时间步长内数据序列的分布是否有偏移。对于 CPD 引用 MStatistics-NIPS，BergCPD，已经提出了许多测量序列对数据分布之间距离的发散度量，而且发散度量的选择往往取决于所使用的数据。密度比估计(DRE) citeRelDivCPD，BergCPD 可以用来估计广泛的 f 散度族，其中包括广泛使用的 CPD 散度，如 Kullback-Leibler (KL)和 Pearson，因此 DRE 是一种流行的 CPD 方法。在这项工作中，我们改进了现有的 DRE 技术的 CPD，提出了一个新的目标，结合 DRE 无缝的对手样本生成。对手样本允许一个强大的 CPD 与 DRE 跟踪分布的微妙变化，导致减少假阴性。我们在大量真实世界的公共基准数据集上进行了实验，结果表明我们的方法比现有的最先进的(SoTA)方法(包括基于 DRE 的 CPD 方法)提高了5% 的 F 值。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Adversarial+Density+Ratio+Estimation+for+Change+Point+Detection)|0| |[Improving Diversity in Unsupervised Keyphrase Extraction with Determinantal Point Process](https://doi.org/10.1145/3583780.3615141)|Mingyang Song, Huafeng Liu, Liping Jing|Beijing Jiaotong University, Beijing, China|Keyphrase extraction aims to provide readers with high-level information about the central ideas or important topics described in a given source text. Recent advances in embedding-based models have made remarkable progress on unsupervised keyphrase extraction, demonstrated through improved quality metrics such as F1-score. However, the diversity in the keyphrase extraction task needs to be addressed. In this paper, we focus on diverse keyphrase extraction, which entails extracting keyphrases that cover different central information or essential topics in the document. To achieve this goal, we propose a re-ranking-based approach that employs determinantal point processes utilizing BERT as kernels, which we call DiversityRank. Specifically, DiversityRank jointly considers phrase-document relevance and cross-phrase similarities to select candidate keyphrases that are document-relevant and diverse. Results demonstrate that our re-ranking strategy outperforms the state-of-the-art unsupervised keyphrase extraction baselines on three benchmark datasets.|关键词提取旨在为读者提供关于给定源文本中描述的中心思想或重要主题的高级信息。基于嵌入的模型在无监督关键词提取方面取得了显著的进展，通过改进质量指标(如 F1得分)可以证明这一点。然而，关键词提取任务的多样性需要得到解决。在本文中，我们重点研究了不同的关键词提取，这涉及到提取涵盖文档中不同中心信息或基本主题的关键词。为了实现这一目标，我们提出了一种基于重新排序的方法，该方法采用行列式点过程，利用 BERT 作为内核，我们称之为 DiversityRank。具体来说，DiversityRank 联合考虑短语文档相关性和跨短语相似性，以选择与文档相关且多样化的候选关键短语。结果表明，我们的重新排序策略优于国家的最先进的无监督关键字提取基线的三个基准数据集。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Improving+Diversity+in+Unsupervised+Keyphrase+Extraction+with+Determinantal+Point+Process)|0| -|[Exposing Model Theft: A Robust and Transferable Watermark for Thwarting Model Extraction Attacks](https://doi.org/10.1145/3583780.3615211)|Ruixiang Tang, Hongye Jin, Mengnan Du, Curtis Wigington, Rajiv Jain, Xia Hu|Adobe, San Francisco, USA; Texas A&M Univeristy, College Station, TX, USA; Rice Univeristy, Houston, USA; Rice University, Houston, USA; New Jersey Institute of Technology, Newark, USA|The increasing prevalence of Deep Neural Networks (DNNs) in cloud-based services has led to their widespread use through various APIs. However, recent studies reveal the susceptibility of these public APIs to model extraction attacks, where adversaries attempt to create a local duplicate of the private model using data and API-generated predictions. Existing defense methods often involve perturbing prediction distributions to hinder an attacker's training goals, inadvertently affecting API utility. In this study, we extend the concept of digital watermarking to protect DNNs' APIs. We suggest embedding a watermark into the safeguarded APIs; thus, any model attempting to copy will inherently carry the watermark, allowing the defender to verify any suspicious models. We propose a simple yet effective framework to increase watermark transferability. By requiring the model to memorize the preset watermarks in the final decision layers, we significantly enhance the transferability of watermarks. Comprehensive experiments show that our proposed framework not only successfully watermarks APIs but also maintains their utility.|深度神经网络(DNN)在基于云的服务中日益流行，导致它们通过各种 API 被广泛使用。然而，最近的研究揭示了这些公共 API 对模型提取攻击的敏感性，在这种攻击中，对手试图使用数据和 API 生成的预测来创建私有模型的局部副本。现有的防御方法通常会扰乱预测分布，从而阻碍攻击者的训练目标，无意中影响 API 的实用性。在这项研究中，我们扩展了数字水印的概念，以保护 DNN 的 API。我们建议在受保护的 API 中嵌入一个水印; 因此，任何试图复制的模型都会带有水印，允许防御者验证任何可疑的模型。我们提出了一个简单而有效的框架来提高水印的可转移性。通过要求模型在最终决策层中存储预设置的水印，我们显著提高了水印的可转移性。综合实验表明，我们提出的框架不仅成功地实现了水印 API，而且保持了它们的实用性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Exposing+Model+Theft:+A+Robust+and+Transferable+Watermark+for+Thwarting+Model+Extraction+Attacks)|0| -|[Mitigating Semantic Confusion from Hostile Neighborhood for Graph Active Learning](https://doi.org/10.1145/3583780.3615199)|Tianmeng Yang, Min Zhou, Yujing Wang, Zhengjie Lin, Lujia Pan, Bin Cui, Yunhai Tong|Huawei Cloud, Shenzhen, China; Peking University, Beijing, China; Huawei Noah's Ark Lab, Shenzhen, China; School of Intelligence Science and Technology, Peking University, Beijing, China|Graph Active Learning (GAL), which aims to find the most informative nodes in graphs for annotation to maximize the Graph Neural Networks (GNNs) performance, has attracted many research efforts but remains non-trivial challenges. One major challenge is that existing GAL strategies may introduce semantic confusion to the selected training set, particularly when graphs are noisy. Specifically, most existing methods assume all aggregating features to be helpful, ignoring the semantically negative effect between inter-class edges under the message-passing mechanism. In this work, we present Semantic-aware Active learning framework for Graphs (SAG) to mitigate the semantic confusion problem. Pairwise similarities and dissimilarities of nodes with semantic features are introduced to jointly evaluate the node influence. A new prototype-based criterion and query policy are also designed to maintain diversity and class balance of the selected nodes, respectively. Extensive experiments on the public benchmark graphs and a real-world financial dataset demonstrate that SAG significantly improves node classification performances and consistently outperforms previous methods. Moreover, comprehensive analysis and ablation study also verify the effectiveness of the proposed framework.|图形主动学习(Graph Active Learning，GAL)旨在寻找图形中信息量最大的节点进行注释，以最大限度地提高图形神经网络(Graph NeuroNetworks，GNN)的性能。一个主要的挑战是现有的 GAL 策略可能会给选定的训练集引入语义混淆，特别是当图是有噪声的时候。具体来说，大多数现有方法都假定所有聚合特性都是有用的，忽略了消息传递机制下类间边缘之间的语义负面影响。本文提出了一种基于语义感知的图主动学习框架(SAG)来缓解语义混淆问题。引入具有语义特征的节点的成对相似性和不相似性来联合评价节点的影响。设计了一种新的基于原型的准则和查询策略，分别保持所选节点的多样性和类平衡。在公共基准图和真实世界金融数据集上的大量实验表明，SAG 显著提高了节点分类性能，并始终优于以前的方法。此外，综合分析和烧蚀研究也验证了该框架的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Mitigating+Semantic+Confusion+from+Hostile+Neighborhood+for+Graph+Active+Learning)|0| -|[Target-oriented Few-shot Transferring via Measuring Task Similarity](https://doi.org/10.1145/3583780.3615149)|Zhipeng Zhou, Wei Gong, Haoquan Zhou|The First Affiliated Hospital of USTC, Hefei, China; University of Science and Technology of China, Hefei, China|Despite significant progress in recent years, few-shot learning (FSL) still faces two critical challenges. Firstly, most FSL solutions in the training phase rely on exploiting auxiliary tasks, while target tasks are underutilized. Secondly, current benchmarks sample numerous target tasks, each with only an N-way C-shot shot query set in the evaluation phase, which is not representative of real-world scenarios. To address these issues, we propose Guidepost, a target-oriented FSL method that can implicitly learn task similarities using a task-level learn-to-learn mechanism and then re-weight auxiliary tasks. Additionally, we introduce a new FSL benchmark that satisfies realistic needs and aligns with our target-oriented approach. Mainstream FSL methods struggle under this new experimental setting. Extensive experiments demonstrate that Guidepost outperforms two classical few-shot learners, i.e., MAML and ProtoNet, and one state-of-the-art few-shot learner, i.e., RENet, on several FSL image datasets. Furthermore, we implement Guidepost as a domain adaptor to achieve high accuracy wireless sensing on our collected WiFi-based human activity recognition dataset.|尽管近年来取得了显著的进展，但是少镜头学习(FSL)仍然面临着两个严峻的挑战。首先，大多数 FSL 解决方案在训练阶段依赖于开发辅助任务，而目标任务未得到充分利用。其次，目前的基准测试对大量的目标任务进行抽样，每个任务在评估阶段只有一个 N 路 C 镜头查询集，不能代表真实场景。为了解决这些问题，我们提出了 Guidepost，一种面向目标的 FSL 方法，它可以使用任务级的学习学习机制隐式地学习任务的相似性，然后重新加权辅助任务。此外，我们还引入了一个新的 FSL 基准测试，它满足了现实的需求，并与我们的面向目标的方法保持一致。主流的 FSL 方法在这个新的实验环境下挣扎。大量的实验表明，Guidepost 在几个 FSL 图像数据集上优于两个经典的少镜头学习者，即 MAML 和 ProtoNet，以及一个最先进的少镜头学习者，即 RENet。此外，我们将 Guidepost 作为一个域适配器来实现在我们收集的基于 WiFi 的人类活动识别数据集上的高精度无线传感。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Target-oriented+Few-shot+Transferring+via+Measuring+Task+Similarity)|0| -|[Enhancing Catalog Relationship Problems with Heterogeneous Graphs and Graph Neural Networks Distillation](https://doi.org/10.1145/3583780.3615472)|Boxin Du, Robert A. Barton, Grant Galloway, Junzhou Huang, Shioulin Sam, Ismail B. Tutar, Changhe Yuan|Amazon, Edinburgh, United Kingdom; Amazon, New York, NY, USA; Amazon, Seattle, WA, USA|Traditionally, catalog relationship problems in e-commerce stores have been handled as pairwise classification tasks, which limit the ability of machine learning models to learn from the diverse relationships among different entities in the catalog. In this paper, we leverage heterogeneous graphs and Graph Neural Networks (GNNs) for improving catalog relationship inference. We start from investigating how to create multi-entity, multi-relationship graphs from diverse relationship data sources, and then explore how to utilizing GNNs to leverage the knowledge of the constructed graph in a self-supervised fashion. We finally propose a distillation approach to transfer the knowledge learned by GNNs into a pairwise neural network for seamless deployment in the catalog pipeline that relies on pairwise input for inductive relationship inference. Our experiments exhibit that in two of the representative catalog relationship problems, Title Authority/Contributor Authority and Broken Variation, the proposed framework is able to improve the recall at 95% precision of a pairwise baseline by up to 33.6% and 14.0%, respectively. Our findings highlight the effectiveness of this approach in advancing catalog quality maintenance and accurate relationship modeling, with potential for broader industry adoption.|传统上，电子商务商店中的目录关系问题被当作成对分类任务来处理，这限制了机器学习模型从目录中不同实体之间的不同关系中学习的能力。在本文中，我们利用异构图和图神经网络(GNN)来改进目录关系推理。本文从研究如何从不同的关系数据源创建多实体、多关系图入手，然后探讨如何利用 GNN 以自我监督的方式利用已构建图的知识。最后，我们提出了一种精馏方法，将 GNN 所学到的知识转化成成对的神经网络，以便在目录流水线中进行无缝部署，该网络依赖于成对的输入来进行归纳关系推理。我们的实验表明，在两个具有代表性的目录关系问题，标题权威/贡献者权威和断裂变异，提出的框架能够提高召回的95% 精度的成对基线高达33.6% 和14.0% ，分别。我们的研究结果强调了这种方法在提高目录质量维护和准确的关系建模方面的有效性，具有更广泛的行业采用的潜力。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Enhancing+Catalog+Relationship+Problems+with+Heterogeneous+Graphs+and+Graph+Neural+Networks+Distillation)|0| +|[Exposing Model Theft: A Robust and Transferable Watermark for Thwarting Model Extraction Attacks](https://doi.org/10.1145/3583780.3615211)|Ruixiang Tang, Hongye Jin, Mengnan Du, Curtis Wigington, Rajiv Jain, Xia Hu|Adobe, San Francisco, USA; Rice University, Houston, USA; Rice Univeristy, Houston, USA; Texas A&M Univeristy, College Station, TX, USA; New Jersey Institute of Technology, Newark, USA|The increasing prevalence of Deep Neural Networks (DNNs) in cloud-based services has led to their widespread use through various APIs. However, recent studies reveal the susceptibility of these public APIs to model extraction attacks, where adversaries attempt to create a local duplicate of the private model using data and API-generated predictions. Existing defense methods often involve perturbing prediction distributions to hinder an attacker's training goals, inadvertently affecting API utility. In this study, we extend the concept of digital watermarking to protect DNNs' APIs. We suggest embedding a watermark into the safeguarded APIs; thus, any model attempting to copy will inherently carry the watermark, allowing the defender to verify any suspicious models. We propose a simple yet effective framework to increase watermark transferability. By requiring the model to memorize the preset watermarks in the final decision layers, we significantly enhance the transferability of watermarks. Comprehensive experiments show that our proposed framework not only successfully watermarks APIs but also maintains their utility.|深度神经网络(DNN)在基于云的服务中日益流行，导致它们通过各种 API 被广泛使用。然而，最近的研究揭示了这些公共 API 对模型提取攻击的敏感性，在这种攻击中，对手试图使用数据和 API 生成的预测来创建私有模型的局部副本。现有的防御方法通常会扰乱预测分布，从而阻碍攻击者的训练目标，无意中影响 API 的实用性。在这项研究中，我们扩展了数字水印的概念，以保护 DNN 的 API。我们建议在受保护的 API 中嵌入一个水印; 因此，任何试图复制的模型都会带有水印，允许防御者验证任何可疑的模型。我们提出了一个简单而有效的框架来提高水印的可转移性。通过要求模型在最终决策层中存储预设置的水印，我们显著提高了水印的可转移性。综合实验表明，我们提出的框架不仅成功地实现了水印 API，而且保持了它们的实用性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Exposing+Model+Theft:+A+Robust+and+Transferable+Watermark+for+Thwarting+Model+Extraction+Attacks)|0| +|[Mitigating Semantic Confusion from Hostile Neighborhood for Graph Active Learning](https://doi.org/10.1145/3583780.3615199)|Tianmeng Yang, Min Zhou, Yujing Wang, Zhengjie Lin, Lujia Pan, Bin Cui, Yunhai Tong|Huawei Noah's Ark Lab, Shenzhen, China; Huawei Cloud, Shenzhen, China; Peking University, Beijing, China; School of Intelligence Science and Technology, Peking University, Beijing, China|Graph Active Learning (GAL), which aims to find the most informative nodes in graphs for annotation to maximize the Graph Neural Networks (GNNs) performance, has attracted many research efforts but remains non-trivial challenges. One major challenge is that existing GAL strategies may introduce semantic confusion to the selected training set, particularly when graphs are noisy. Specifically, most existing methods assume all aggregating features to be helpful, ignoring the semantically negative effect between inter-class edges under the message-passing mechanism. In this work, we present Semantic-aware Active learning framework for Graphs (SAG) to mitigate the semantic confusion problem. Pairwise similarities and dissimilarities of nodes with semantic features are introduced to jointly evaluate the node influence. A new prototype-based criterion and query policy are also designed to maintain diversity and class balance of the selected nodes, respectively. Extensive experiments on the public benchmark graphs and a real-world financial dataset demonstrate that SAG significantly improves node classification performances and consistently outperforms previous methods. Moreover, comprehensive analysis and ablation study also verify the effectiveness of the proposed framework.|图形主动学习(Graph Active Learning，GAL)旨在寻找图形中信息量最大的节点进行注释，以最大限度地提高图形神经网络(Graph NeuroNetworks，GNN)的性能。一个主要的挑战是现有的 GAL 策略可能会给选定的训练集引入语义混淆，特别是当图是有噪声的时候。具体来说，大多数现有方法都假定所有聚合特性都是有用的，忽略了消息传递机制下类间边缘之间的语义负面影响。本文提出了一种基于语义感知的图主动学习框架(SAG)来缓解语义混淆问题。引入具有语义特征的节点的成对相似性和不相似性来联合评价节点的影响。设计了一种新的基于原型的准则和查询策略，分别保持所选节点的多样性和类平衡。在公共基准图和真实世界金融数据集上的大量实验表明，SAG 显著提高了节点分类性能，并始终优于以前的方法。此外，综合分析和烧蚀研究也验证了该框架的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Mitigating+Semantic+Confusion+from+Hostile+Neighborhood+for+Graph+Active+Learning)|0| +|[Target-oriented Few-shot Transferring via Measuring Task Similarity](https://doi.org/10.1145/3583780.3615149)|Zhipeng Zhou, Wei Gong, Haoquan Zhou|University of Science and Technology of China, Hefei, China; The First Affiliated Hospital of USTC, Hefei, China|Despite significant progress in recent years, few-shot learning (FSL) still faces two critical challenges. Firstly, most FSL solutions in the training phase rely on exploiting auxiliary tasks, while target tasks are underutilized. Secondly, current benchmarks sample numerous target tasks, each with only an N-way C-shot shot query set in the evaluation phase, which is not representative of real-world scenarios. To address these issues, we propose Guidepost, a target-oriented FSL method that can implicitly learn task similarities using a task-level learn-to-learn mechanism and then re-weight auxiliary tasks. Additionally, we introduce a new FSL benchmark that satisfies realistic needs and aligns with our target-oriented approach. Mainstream FSL methods struggle under this new experimental setting. Extensive experiments demonstrate that Guidepost outperforms two classical few-shot learners, i.e., MAML and ProtoNet, and one state-of-the-art few-shot learner, i.e., RENet, on several FSL image datasets. Furthermore, we implement Guidepost as a domain adaptor to achieve high accuracy wireless sensing on our collected WiFi-based human activity recognition dataset.|尽管近年来取得了显著的进展，但是少镜头学习(FSL)仍然面临着两个严峻的挑战。首先，大多数 FSL 解决方案在训练阶段依赖于开发辅助任务，而目标任务未得到充分利用。其次，目前的基准测试对大量的目标任务进行抽样，每个任务在评估阶段只有一个 N 路 C 镜头查询集，不能代表真实场景。为了解决这些问题，我们提出了 Guidepost，一种面向目标的 FSL 方法，它可以使用任务级的学习学习机制隐式地学习任务的相似性，然后重新加权辅助任务。此外，我们还引入了一个新的 FSL 基准测试，它满足了现实的需求，并与我们的面向目标的方法保持一致。主流的 FSL 方法在这个新的实验环境下挣扎。大量的实验表明，Guidepost 在几个 FSL 图像数据集上优于两个经典的少镜头学习者，即 MAML 和 ProtoNet，以及一个最先进的少镜头学习者，即 RENet。此外，我们将 Guidepost 作为一个域适配器来实现在我们收集的基于 WiFi 的人类活动识别数据集上的高精度无线传感。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Target-oriented+Few-shot+Transferring+via+Measuring+Task+Similarity)|0| +|[Enhancing Catalog Relationship Problems with Heterogeneous Graphs and Graph Neural Networks Distillation](https://doi.org/10.1145/3583780.3615472)|Boxin Du, Robert A. Barton, Grant Galloway, Junzhou Huang, Shioulin Sam, Ismail B. Tutar, Changhe Yuan|Amazon, Edinburgh, United Kingdom; Amazon, Seattle, WA, USA; Amazon, New York, NY, USA|Traditionally, catalog relationship problems in e-commerce stores have been handled as pairwise classification tasks, which limit the ability of machine learning models to learn from the diverse relationships among different entities in the catalog. In this paper, we leverage heterogeneous graphs and Graph Neural Networks (GNNs) for improving catalog relationship inference. We start from investigating how to create multi-entity, multi-relationship graphs from diverse relationship data sources, and then explore how to utilizing GNNs to leverage the knowledge of the constructed graph in a self-supervised fashion. We finally propose a distillation approach to transfer the knowledge learned by GNNs into a pairwise neural network for seamless deployment in the catalog pipeline that relies on pairwise input for inductive relationship inference. Our experiments exhibit that in two of the representative catalog relationship problems, Title Authority/Contributor Authority and Broken Variation, the proposed framework is able to improve the recall at 95% precision of a pairwise baseline by up to 33.6% and 14.0%, respectively. Our findings highlight the effectiveness of this approach in advancing catalog quality maintenance and accurate relationship modeling, with potential for broader industry adoption.|传统上，电子商务商店中的目录关系问题被当作成对分类任务来处理，这限制了机器学习模型从目录中不同实体之间的不同关系中学习的能力。在本文中，我们利用异构图和图神经网络(GNN)来改进目录关系推理。本文从研究如何从不同的关系数据源创建多实体、多关系图入手，然后探讨如何利用 GNN 以自我监督的方式利用已构建图的知识。最后，我们提出了一种精馏方法，将 GNN 所学到的知识转化成成对的神经网络，以便在目录流水线中进行无缝部署，该网络依赖于成对的输入来进行归纳关系推理。我们的实验表明，在两个具有代表性的目录关系问题，标题权威/贡献者权威和断裂变异，提出的框架能够提高召回的95% 精度的成对基线高达33.6% 和14.0% ，分别。我们的研究结果强调了这种方法在提高目录质量维护和准确的关系建模方面的有效性，具有更广泛的行业采用的潜力。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Enhancing+Catalog+Relationship+Problems+with+Heterogeneous+Graphs+and+Graph+Neural+Networks+Distillation)|0| |[DoRA: Domain-Based Self-Supervised Learning Framework for Low-Resource Real Estate Appraisal](https://doi.org/10.1145/3583780.3615470)|WeiWei Du, WeiYao Wang, WenChih Peng|National Yang Ming Chiao Tung University, Hsinchu, Taiwan Roc|The marketplace system connecting demands and supplies has been explored to develop unbiased decision-making in valuing properties. Real estate appraisal serves as one of the high-cost property valuation tasks for financial institutions since it requires domain experts to appraise the estimation based on the corresponding knowledge and the judgment of the market. Existing automated valuation models reducing the subjectivity of domain experts require a large number of transactions for effective evaluation, which is predominantly limited to not only the labeling efforts of transactions but also the generalizability of new developing and rural areas. To learn representations from unlabeled real estate sets, existing self-supervised learning (SSL) for tabular data neglects various important features, and fails to incorporate domain knowledge. In this paper, we propose DoRA, a Domain-based self-supervised learning framework for low-resource Real estate Appraisal. DoRA is pre-trained with an intra-sample geographic prediction as the pretext task based on the metadata of the real estate for equipping the real estate representations with prior domain knowledge. Furthermore, inter-sample contrastive learning is employed to generalize the representations to be robust for limited transactions of downstream tasks. Our benchmark results on three property types of real-world transactions show that DoRA significantly outperforms the SSL baselines for tabular data, the graph-based methods, and the supervised approaches in the few-shot scenarios by at least 7.6% for MAPE, 11.59% for MAE, and 3.34% for HR10%. We expect DoRA to be useful to other financial practitioners with similar marketplace applications who need general models for properties that are newly built and have limited records. The source code is available at https://github.com/wwweiwei/DoRA.|探讨了连接需求和供应的市场体系，以便在估价财产时做出公正的决策。不动产估价师是金融机构的高成本物业估值工作之一，因为它需要专业人士根据相应的知识和市场判断来评估估值。现有的自动化评估模型降低了领域专家的主观性，需要大量的交易才能进行有效的评估，这主要限于交易的标识工作以及新发展中国家和农村地区的普遍性。为了从未标记的不动产集合中学习表示，现有的表格数据自监督学习(SSL)忽视了表格数据的各种重要特征，并且未能整合领域知识。在这篇文章中，我们提出了一个基于领域的自我监督学习框架 DoRA，用于低资源不动产估价师。DoRA 预先训练了一个样本内地理预测作为借口任务的基础上的元数据的房地产装备与先前的领域知识的房地产表示。此外，采用样本间对比学习方法，对下游任务的有限事务进行鲁棒表示。我们对现实世界事务的三种属性类型的基准测试结果显示，DoRA 显着优于表格数据的 SSL 基线，基于图表的方法和监督方法，MAPE 至少为7.6% ，MAE 为11.59% ，HR10% 为3.34% 。我们希望 DORA 对其他有类似市场应用程序的金融从业者有用，这些从业者需要针对新建房产和有限记录的通用模型。源代码可在 https://github.com/wwweiwei/dora 下载。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=DoRA:+Domain-Based+Self-Supervised+Learning+Framework+for+Low-Resource+Real+Estate+Appraisal)|0| |[A Stochastic Online Forecast-and-Optimize Framework for Real-Time Energy Dispatch in Virtual Power Plants under Uncertainty](https://doi.org/10.1145/3583780.3614653)|Wei Jiang, Zhongkai Yi, Li Wang, Hanwei Zhang, Jihai Zhang, Fangquan Lin, Cheng Yang|Alibaba Group, Hangzhou, China|Aggregating distributed energy resources in power systems significantly increases uncertainties, in particular caused by the fluctuation of renewable energy generation. This issue has driven the necessity of widely exploiting advanced predictive control techniques under uncertainty to ensure long-term economics and decarbonization. In this paper, we propose a real-time uncertainty-aware energy dispatch framework, which is composed of two key elements: (i) A hybrid forecast-and-optimize sequential task, integrating deep learning-based forecasting and stochastic optimization, where these two stages are connected by the uncertainty estimation at multiple temporal resolutions; (ii) An efficient online data augmentation scheme, jointly involving model pre-training and online fine-tuning stages. In this way, the proposed framework is capable to rapidly adapt to the real-time data distribution, as well as to target on uncertainties caused by data drift, model discrepancy and environment perturbations in the control process, and finally to realize an optimal and robust dispatch solution. The proposed framework won the championship in CityLearn Challenge 2022, which provided an influential opportunity to investigate the potential of AI application in the energy domain. In addition, comprehensive experiments are conducted to interpret its effectiveness in the real-life scenario of smart building energy management.|电力系统中分布式能源资源的聚集显著增加了系统的不确定性，尤其是可再生能源发电量的波动。这一问题促使人们有必要在不确定条件下广泛利用先进的预测控制技术，以确保长期的经济效益和脱碳。本文提出了一种基于不确定性的实时能量调度框架，该框架由两个关键部分组成: (1)基于深度学习的预测与随机优化相结合的混合预测与优化序列任务，这两个阶段通过多时间分辨率的不确定性估计相连接; (2)一种高效的在线数据增强方案，共同包括模型预训练阶段和在线微调阶段。这样，该框架能够快速适应实时数据分布，并能够针对控制过程中数据漂移、模型偏差和环境扰动等引起的不确定性，最终实现最优的鲁棒调度解决方案。提出的框架赢得了2022年城市学习挑战赛的冠军，这为研究人工智能在能源领域的应用潜力提供了一个有影响力的机会。此外，还进行了综合实验，以解释其在智能建筑能源管理的实际情景中的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Stochastic+Online+Forecast-and-Optimize+Framework+for+Real-Time+Energy+Dispatch+in+Virtual+Power+Plants+under+Uncertainty)|0| -|[Generating Product Insights from Community Q&A](https://doi.org/10.1145/3583780.3615480)|Lital Kuchy, Ran Levy, Avihai Mejer, Noam Segev, Shunit Agmon, Miriam Farber|Amazon, Tel Aviv, Israel; Amazon, Haifa, Israel; Technion - Israel Institute of Technology, Haifa, Israel; Unaffiliated, Haifa, Israel|In e-commerce sites, customer questions on the product details-page express the customers' information needs about the product. The answers to these questions often provide the necessary information. In this work, we present and address the novel task of generating product insights from community questions and answers (Q&A). These insights can be presented to customers to assist them in their shopping journey. Our method first generates concise, self-contained sentences based on the information in the Q&A. Then insights are selected based on the prominence of their associated questions. Empirical evaluation attests to the effectiveness of our approach in generating well-formed, objective, and helpful insights that are often not available in the product description or in summaries of customer reviews.|在电子商务网站中，客户在产品详细信息页面上提出的问题表达了客户对产品的信息需求。这些问题的答案往往提供了必要的信息。在这项工作中，我们提出并解决从社区问答(Q & A)中产生产品见解的新任务。这些见解可以呈现给客户，以帮助他们在他们的购物之旅。我们的方法首先根据问答环节中的信息生成简洁、自成一体的句子。然后根据相关问题的突出性来选择洞察力。经验性评估证明了我们的方法在产生形式良好、客观和有用的见解方面的有效性，而这些见解往往在产品描述或客户评论摘要中无法获得。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Generating+Product+Insights+from+Community+Q&A)|0| -|[GBTTE: Graph Attention Network Based Bus Travel Time Estimation](https://doi.org/10.1145/3583780.3614730)|Yuecheng Rong, Juntao Yao, Jun Liu, Yifan Fang, Wei Luo, Hao Liu, Jie Ma, Zepeng Dan, Jinzhu Lin, Zhi Wu, Yan Zhang, Chuanming Zhang|Baidu Inc., Beijing, China; The Hong Kong University of Science and Technology (Guangzhou) & The Hong Kong University of Science and Technology, Guangzhou, HongKong, China; Xi'an Jiaotong University, Baidu Inc., Xi'an, Beijing, China; Xi'an Jiaotong University, Xi'an, China|Real-time bus travel time is crucial for the smart public transportation system and is beneficial for improving user satisfaction for online map services. However, it faces great challenges due to fine-grained spatial dependencies and dynamic temporal dependencies. To address the above problem, we propose GBTTE, a novel end-to-end graph attention network framework to estimate bus travel time. Specifically, we construct a novel graph structure of bus routes and use a graph attention network to capture the fine-grained spatial features of bus routes. Then, we fully exploit the joint spatial-temporal relations of bus stops through a spatial-temporal graph attention network and also capture the dynamic correlation between the route and the bus transportation network with a cross graph attention network. Finally, we integrate the route representation, the spatial-temporal representation and contextual information to estimate bus travel time. Extensive experiments carried out on two large-scale real-world datasets demonstrate the effectiveness of GBTTE. In addition, GBTTE has been deployed in production at Baidu Maps, handling tens of millions of requests every day.|实时公交出行时间是智能公交系统的关键，有利于提高用户对在线地图服务的满意度。然而，由于细粒度的空间依赖性和动态的时间依赖性，它面临着巨大的挑战。为了解决上述问题，我们提出了一种新的端到端图注意网络框架 GBTTE 来估计公共汽车行驶时间。具体来说，我们构造了一种新的公交线路图结构，并利用图注意网络来捕捉公交线路的细粒度空间特征。然后，通过时空图形注意网络充分利用公交站点之间的联合时空关系，并利用交叉图形注意网络捕捉公交线路与公交网络之间的动态相关性。最后，结合路径表示、时空表示和上下文信息对公交行程时间进行估计。在两个大规模实际数据集上进行的大量实验证明了 GBTTE 算法的有效性。此外，GBTTE 已在百度地图投入生产，每天处理数千万个请求。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=GBTTE:+Graph+Attention+Network+Based+Bus+Travel+Time+Estimation)|0| +|[Generating Product Insights from Community Q&A](https://doi.org/10.1145/3583780.3615480)|Lital Kuchy, Ran Levy, Avihai Mejer, Noam Segev, Shunit Agmon, Miriam Farber|Technion - Israel Institute of Technology, Haifa, Israel; Unaffiliated, Haifa, Israel; Amazon, Haifa, Israel; Amazon, Tel Aviv, Israel|In e-commerce sites, customer questions on the product details-page express the customers' information needs about the product. The answers to these questions often provide the necessary information. In this work, we present and address the novel task of generating product insights from community questions and answers (Q&A). These insights can be presented to customers to assist them in their shopping journey. Our method first generates concise, self-contained sentences based on the information in the Q&A. Then insights are selected based on the prominence of their associated questions. Empirical evaluation attests to the effectiveness of our approach in generating well-formed, objective, and helpful insights that are often not available in the product description or in summaries of customer reviews.|在电子商务网站中，客户在产品详细信息页面上提出的问题表达了客户对产品的信息需求。这些问题的答案往往提供了必要的信息。在这项工作中，我们提出并解决从社区问答(Q & A)中产生产品见解的新任务。这些见解可以呈现给客户，以帮助他们在他们的购物之旅。我们的方法首先根据问答环节中的信息生成简洁、自成一体的句子。然后根据相关问题的突出性来选择洞察力。经验性评估证明了我们的方法在产生形式良好、客观和有用的见解方面的有效性，而这些见解往往在产品描述或客户评论摘要中无法获得。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Generating+Product+Insights+from+Community+Q&A)|0| +|[GBTTE: Graph Attention Network Based Bus Travel Time Estimation](https://doi.org/10.1145/3583780.3614730)|Yuecheng Rong, Juntao Yao, Jun Liu, Yifan Fang, Wei Luo, Hao Liu, Jie Ma, Zepeng Dan, Jinzhu Lin, Zhi Wu, Yan Zhang, Chuanming Zhang|The Hong Kong University of Science and Technology (Guangzhou) & The Hong Kong University of Science and Technology, Guangzhou, HongKong, China; Xi'an Jiaotong University, Baidu Inc., Xi'an, Beijing, China; Xi'an Jiaotong University, Xi'an, China; Baidu Inc., Beijing, China|Real-time bus travel time is crucial for the smart public transportation system and is beneficial for improving user satisfaction for online map services. However, it faces great challenges due to fine-grained spatial dependencies and dynamic temporal dependencies. To address the above problem, we propose GBTTE, a novel end-to-end graph attention network framework to estimate bus travel time. Specifically, we construct a novel graph structure of bus routes and use a graph attention network to capture the fine-grained spatial features of bus routes. Then, we fully exploit the joint spatial-temporal relations of bus stops through a spatial-temporal graph attention network and also capture the dynamic correlation between the route and the bus transportation network with a cross graph attention network. Finally, we integrate the route representation, the spatial-temporal representation and contextual information to estimate bus travel time. Extensive experiments carried out on two large-scale real-world datasets demonstrate the effectiveness of GBTTE. In addition, GBTTE has been deployed in production at Baidu Maps, handling tens of millions of requests every day.|实时公交出行时间是智能公交系统的关键，有利于提高用户对在线地图服务的满意度。然而，由于细粒度的空间依赖性和动态的时间依赖性，它面临着巨大的挑战。为了解决上述问题，我们提出了一种新的端到端图注意网络框架 GBTTE 来估计公共汽车行驶时间。具体来说，我们构造了一种新的公交线路图结构，并利用图注意网络来捕捉公交线路的细粒度空间特征。然后，通过时空图形注意网络充分利用公交站点之间的联合时空关系，并利用交叉图形注意网络捕捉公交线路与公交网络之间的动态相关性。最后，结合路径表示、时空表示和上下文信息对公交行程时间进行估计。在两个大规模实际数据集上进行的大量实验证明了 GBTTE 算法的有效性。此外，GBTTE 已在百度地图投入生产，每天处理数千万个请求。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=GBTTE:+Graph+Attention+Network+Based+Bus+Travel+Time+Estimation)|0| |[STREAMS: Towards Spatio-Temporal Causal Discovery with Reinforcement Learning for Streamflow Rate Prediction](https://doi.org/10.1145/3583780.3614719)|Paras Sheth, Ahmadreza Mosallanezhad, Kaize Ding, Reepal Shah, John Sabo, Huan Liu, K. Selçuk Candan|Arizona State University, Tempe, AZ, USA; Tulane University, New Orleans, LA, USA|The capacity to anticipate streamflow is critical to the efficient functioning of reservoir systems as it gives vital information to reservoir operators about water release quantities as well as help quantify the impact of environmental factors on downstream water quality. Yet, streamflow modelling is difficult owing to the intricate interactions between different watershed outlets. In this paper, we argue that one possible solution to this problem is to identify the causal structure of these outlets, which would allow for the identification of crucial watershed outlets while capturing the spatiotemporally informed complex relationships leading to improved hydrological resource management. However, due to the inherent complexity of spatiotemporal causal learning problems, extending existing causal discovery methods to a whole basin is a major hurdle. To address these issues, we offer STREAMS, a new framework that uses Reinforcement Learning (RL) to optimize the search space for causal discovery and an LSTM-GCN based autoencoder to infer spatiotemporal causal features for streamflow rate prediction. We conduct extensive experiments on the Brazos river basin carried out within the scope of a US Army Corps of Engineers, Engineering With Nature Initiative project, including empirical studies of generalization performance to verify the nature of the inferred relationships.|预测流量的能力对于水库系统的有效运作至关重要，因为它向水库管理人员提供关于水释放量的重要信息，并有助于量化环境因素对下游水质的影响。然而，由于不同流域出口之间错综复杂的相互作用，流量建模是困难的。在本文中，我们认为一个可能的解决方案是确定这些出口的因果结构，这将有助于确定关键的流域出口，同时捕捉时空信息的复杂关系，从而改善水文资源管理。然而，由于时空因果学习问题的固有复杂性，将现有的因果发现方法推广到整个流域是一个主要障碍。为了解决这些问题，我们提供了一个新的框架—— STREAMS，它使用强化学习(RL)来优化因果发现的搜索空间，以及一个基于 LSTM-gcn 的自动编码器来推断用于流量预测的时空因果特征。我们在美国陆军工兵（印度）“自然工程倡议”项目范围内对布拉索斯河流域进行了广泛的实验，包括对推断关系的性质进行泛化性能的实证研究。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=STREAMS:+Towards+Spatio-Temporal+Causal+Discovery+with+Reinforcement+Learning+for+Streamflow+Rate+Prediction)|0| |[Combating Ad Fatigue via Frequency-Recency Features in Online Advertising Systems](https://doi.org/10.1145/3583780.3615461)|Natalia Silberstein, Or Shoham, Assaf Klein|Outbrain, Netanya, Israel|Online advertising is a driving force of Internet services today. One of the main challenges in advertising systems is finding the right balance between user experience and overall revenue. In this paper we address one of the problems that negatively impacts the user experience, specifically, the repeated display of identical ads to the same user. This problem leads to the phenomenon called "ad fatigue", characterized by diminished interest in the ad, resulting in a decrease in the click-through rate (CTR) as users encounter the same ad repeatedly. Naive solutions such as placing a hard limit on the number of times a specific ad is displayed to a specific user, usually come at the cost of reduced revenue. To address the ad fatigue problem, we introduce a new family of features, called FoRI (Frequency over Recent Intervals). FoRI features integrate information about frequency and recency of previous user-ad interactions within the CTR prediction model. This approach involves allocating these interactions to unevenly distributed time intervals, enabling a higher emphasis on more recent interactions. Furthermore, we introduce new metrics to assess ad fatigue in terms of the repetitiveness and novelty of the displayed ads. We conducted a comprehensive large-scale online evaluation which shows that integrating FoRI features into our CTR prediction model offers two-fold benefits. Firstly, it improves user experience by reducing the occurrence of repeated ads by 15%, and increasing the exposure to unseen ads by 5% (ads not previously displayed to the user), leading to a substantial boost in CTR. Secondly, it significantly increases revenue.|在线广告是当今互联网服务的驱动力。广告系统的主要挑战之一是在用户体验和总收入之间找到正确的平衡。在本文中，我们解决了一个对用户体验产生负面影响的问题，具体来说，就是对同一用户重复显示相同的广告。这个问题导致了所谓的“广告疲劳”现象，即拥有属性对广告的兴趣降低，导致用户反复看到同一个广告时点进率(ctrr)下降。一些简单的解决方案，比如对某个特定的广告向某个特定的用户显示的次数设置严格的限制，通常是以减少收入为代价的。为了解决广告疲劳问题，我们引入了一系列新的特性，称为 FoRI (最近间隔的频率)。ForRI 在 CTR 预测模型中整合了以前用户-广告交互的频率和近期信息。这种方法涉及到将这些交互分配给不均匀分布的时间间隔，从而能够更加强调最近的交互。此外，我们引入了新的指标来评估广告疲劳的重复性和新颖性的显示广告。我们进行了一次全面的大规模在线评估，结果表明将 FoRI 特征集成到我们的 CTR 预测模型中可以带来双重好处。首先，它通过减少15% 的重复广告和增加5% 的无形广告(之前没有显示给用户的广告)提高了用户体验，从而大大提高了点击率。其次，它显著增加了收入。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Combating+Ad+Fatigue+via+Frequency-Recency+Features+in+Online+Advertising+Systems)|0| |[PRODIGY: Product Design Guidance at Scale](https://doi.org/10.1145/3583780.3615494)|Sambeet Tiady, Anirban Majumder, Deepak Gupta|Amazon, Bangalore, India|Growth of e-commerce has enabled the creation of thousands of small-scale brands. However, these brands lack information on a) what new products to develop and b) how to refine existing products to improve on business metrics. We present a comprehensive Product Design Insights and Guidance service (named PRODIGY) that mines product attributes data available on e-commerce platforms and surface insights on a) new product development and b) product refinement. Our core contribution is a novel demand forecasting model for product designs based on a notable extension of the recently proposed FTTransformer architecture combined with a self-supervised pre-training task, akin to Masked Language Modeling (MLM) objective. For the product refinement use-case, we present a novel algorithm by embedding the design search in a data-density approximator, namely Conditional Variational Autoencoder. We run a thorough and comprehensive set of experiments and establish that PRODIGY achieves significant improvement in demand prediction as compared to state-of-the-art alternatives. Finally, we present our findings from an online experiment where PRODIGY helps to launch new products with +20% lift in sales and +1.3% lift in product ratings.|电子商务的发展创造了数以千计的小规模品牌。然而，这些品牌缺乏以下信息: a)开发什么新产品; b)如何改进现有产品以提高业务指标。我们提出了一个全面的产品设计洞察和指导服务(命名为 PRODIGY) ，挖掘产品属性数据可用的电子商务平台和表面见解 a)新产品开发和 b)产品改进。我们的核心贡献是一个新颖的产品设计需求预测模型，该模型基于最近提出的 FTTransformer 架构的显著扩展，结合自我监督的预训练任务，类似于蒙版语言建模(MLM)目标。对于产品细化用例，我们提出了一种新的算法，将设计搜索嵌入到数据密度近似器中，即条件变分自动编码器。我们进行了一系列全面彻底的实验，结果表明，与最先进的方案相比，PRODIGY 在需求预测方面取得了显著的改善。最后，我们展示了一个在线实验的结果，PRODIGY 帮助推出的新产品销售额提高了20% ，产品评级提高了1.3% 。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=PRODIGY:+Product+Design+Guidance+at+Scale)|0| |[LittleMu: Deploying an Online Virtual Teaching Assistant via Heterogeneous Sources Integration and Chain of Teach Prompts](https://doi.org/10.1145/3583780.3615484)|Shangqing Tu, Zheyuan Zhang, Jifan Yu, Chunyang Li, Siyu Zhang, Zijun Yao, Lei Hou, Juanzi Li|Tsinghua University, Beijing, China|Teaching assistants have played essential roles in the long history of education. However, few MOOC platforms are providing human or virtual teaching assistants to support learning for massive online students due to the complexity of real-world online education scenarios and the lack of training data. In this paper, we present a virtual MOOC teaching assistant, LittleMu with minimum labeled training data, to provide question answering and chit-chat services. Consisting of two interactive modules of heterogeneous retrieval and language model prompting, LittleMu first integrates structural, semi- and unstructured knowledge sources to support accurate answers for a wide range of questions. Then, we design delicate demonstrations named "Chain of Teach" prompts to exploit the large-scale pre-trained model to handle complex uncollected questions. Except for question answering, we develop other educational services such as knowledge-grounded chit-chat. We test the system's performance via both offline evaluation and online deployment. Since May 2020, our LittleMu system has served over 80,000 users with over 300,000 queries from over 500 courses on XuetangX MOOC platform, which continuously contributes to a more convenient and fair education. Our code, services, and dataset will be available at https://github.com/THU-KEG/VTA.|教学助理在教育的悠久历史中发挥了重要作用。然而，由于现实世界在线教育场景的复杂性和缺乏培训数据，很少有 MOOC 平台提供人工或虚拟教学助理来支持大量在线学生的学习。本文介绍了一个虚拟的 MOOC 教学助手 LittleMu，它使用最少的标记训练数据来提供问答和聊天服务。LittleMu 首先集成了结构化、半结构化和非结构化的知识源，以支持对广泛问题的准确回答，它由异构检索和语言模型提示两个交互模块组成。然后，我们设计了一个名为“教学链”的精致演示，利用大规模的预训练模型来处理复杂的未收集问题。除了回答问题，我们还开发其他教育服务，例如基于知识的闲聊。我们通过离线评估和在线部署来测试系统的性能。自2020年5月以来，我们的 LittleMu 系统在学堂 X MOOC 平台上为超过80,000名用户提供了超过300,000条来自500多门课程的查询，不断为更加便利和公平的教育做出贡献。我们的代码、服务和数据集将在 https://github.com/thu-keg/vta 提供。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=LittleMu:+Deploying+an+Online+Virtual+Teaching+Assistant+via+Heterogeneous+Sources+Integration+and+Chain+of+Teach+Prompts)|0| -|[MArBLE: Hierarchical Multi-Armed Bandits for Human-in-the-Loop Set Expansion](https://doi.org/10.1145/3583780.3615485)|Muntasir Wahed, Daniel Gruhl, Ismini Lourentzou|IBM Almaden Research Center, San Jose, CA, USA; Virginia Tech, Blacksburg, VA, USA|The modern-day research community has an embarrassment of riches regarding pre-trained AI models. Even for a simple task such as lexicon set expansion, where an AI model suggests new entities to add to a predefined seed set of entities, thousands of models are available. However, deciding which model to use for a given set expansion task is non-trivial. In hindsight, some models can be 'off topic' for specific set expansion tasks, while others might work well initially but quickly exhaust what they have to offer. Additionally, certain models may require more careful priming in the form of samples or feedback before being finetuned to the task at hand. In this work, we frame this model selection as a sequential non-stationary problem, where there exist a large number of diverse pre-trained models that may or may not fit a task at hand, and an expert is shown one suggestion at a time to include in the set or not, i.e., accept or reject the suggestion. The goal is to expand the list with the most entities as quickly as possible. We introduce MArBLE, a hierarchical multi-armed bandit method for this task, and two strategies designed to address cold-start problems. Experimental results on three set expansion tasks demonstrate MArBLE's effectiveness compared to baselines.|现代研究界对于预先训练好的人工智能模型有着丰富的资料，这令人尴尬。即使是像词典集扩展这样的简单任务(AI 模型建议新的实体添加到预定义的实体种子集中) ，也有数千个模型可用。然而，决定对给定的集合扩展任务使用哪个模型是非常重要的。事后看来，对于特定的集合扩展任务，一些模型可能会“偏离主题”，而其他模型可能一开始工作得很好，但很快就会用尽它们所能提供的东西。此外，某些模型可能需要以样本或反馈的形式进行更仔细的启动，然后才能适应手头的任务。在这项工作中，我们将这个模型选择框架为一个连续的非平稳问题，其中存在大量不同的预先训练的模型，这些模型可能适合也可能不适合手头的任务，并且每次向专家展示一个建议，以便将其包含在集合中，也就是说，接受或拒绝这个建议。目标是尽可能快地扩展包含最多实体的列表。我们将介绍一种用于这项任务的分层多臂老虎机方法—— MARBLE，以及用于解决冷启动问题的两种策略。在三个集合扩展任务上的实验结果证明了该算法的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=MArBLE:+Hierarchical+Multi-Armed+Bandits+for+Human-in-the-Loop+Set+Expansion)|0| -|[SEDAR: A Semantic Data Reservoir for Heterogeneous Datasets](https://doi.org/10.1145/3583780.3614753)|Sayed Hoseini, Ahmed Ali, Haron Shaker, Christoph Quix|Fraunhofer FIT & Hochschule Niederrhein, St. Augustin & Krefeld, Germany; Hochschule Niederrhein, Krefeld, Germany; RWTH Aachen University, Aachen, Germany|Data lakes have emerged as a solution for managing vast and diverse datasets for modern data analytics. To prevent them from becoming ungoverned, semantic data management techniques are crucial, which involve connecting metadata with knowledge graphs, following the principles of Linked Data. This semantic layer enables more expressive data management, integration from various sources and enhances data access utilizing the concepts and relations to semantically enrich the data. Some frameworks have been proposed, but requirements like data versioning, linking of datasets, managing machine learning projects, automated semantic modeling and ontology-based data access are not supported in one uniform system. We demonstrate SEDAR, a comprehensive semantic data lake that includes support for data ingestion, storage, processing, and governance with a special focus on semantic data management. The demo will showcase how the system allows for various ingestion scenarios, metadata enrichment, data source linking, profiling, semantic modeling, data integration and processing inside a machine learning life cycle.|数据湖已经成为现代数据分析中管理大量多样化数据集的解决方案。为了防止它们变得无法治理，语义数据管理技术是至关重要的，其中包括按照关联数据的原则将元数据与知识图连接起来。这个语义层支持更具表现力的数据管理、来自不同来源的集成，并利用概念和关系增强数据访问，从而在语义上丰富数据。虽然已经提出了一些框架，但是数据版本化、数据集链接、机器学习项目管理、自动语义建模和基于本体的数据访问等需求并不能在一个统一的系统中得到支持。我们演示了 SEDAR，这是一个全面的语义数据湖，包括对数据摄入、存储、处理和治理的支持，特别关注语义数据管理。演示将展示该系统如何在机器学习生命周期内实现各种摄取场景、元数据充实、数据源链接、剖析、语义建模、数据集成和处理。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=SEDAR:+A+Semantic+Data+Reservoir+for+Heterogeneous+Datasets)|0| +|[MArBLE: Hierarchical Multi-Armed Bandits for Human-in-the-Loop Set Expansion](https://doi.org/10.1145/3583780.3615485)|Muntasir Wahed, Daniel Gruhl, Ismini Lourentzou|Virginia Tech, Blacksburg, VA, USA; IBM Almaden Research Center, San Jose, CA, USA|The modern-day research community has an embarrassment of riches regarding pre-trained AI models. Even for a simple task such as lexicon set expansion, where an AI model suggests new entities to add to a predefined seed set of entities, thousands of models are available. However, deciding which model to use for a given set expansion task is non-trivial. In hindsight, some models can be 'off topic' for specific set expansion tasks, while others might work well initially but quickly exhaust what they have to offer. Additionally, certain models may require more careful priming in the form of samples or feedback before being finetuned to the task at hand. In this work, we frame this model selection as a sequential non-stationary problem, where there exist a large number of diverse pre-trained models that may or may not fit a task at hand, and an expert is shown one suggestion at a time to include in the set or not, i.e., accept or reject the suggestion. The goal is to expand the list with the most entities as quickly as possible. We introduce MArBLE, a hierarchical multi-armed bandit method for this task, and two strategies designed to address cold-start problems. Experimental results on three set expansion tasks demonstrate MArBLE's effectiveness compared to baselines.|现代研究界对于预先训练好的人工智能模型有着丰富的资料，这令人尴尬。即使是像词典集扩展这样的简单任务(AI 模型建议新的实体添加到预定义的实体种子集中) ，也有数千个模型可用。然而，决定对给定的集合扩展任务使用哪个模型是非常重要的。事后看来，对于特定的集合扩展任务，一些模型可能会“偏离主题”，而其他模型可能一开始工作得很好，但很快就会用尽它们所能提供的东西。此外，某些模型可能需要以样本或反馈的形式进行更仔细的启动，然后才能适应手头的任务。在这项工作中，我们将这个模型选择框架为一个连续的非平稳问题，其中存在大量不同的预先训练的模型，这些模型可能适合也可能不适合手头的任务，并且每次向专家展示一个建议，以便将其包含在集合中，也就是说，接受或拒绝这个建议。目标是尽可能快地扩展包含最多实体的列表。我们将介绍一种用于这项任务的分层多臂老虎机方法—— MARBLE，以及用于解决冷启动问题的两种策略。在三个集合扩展任务上的实验结果证明了该算法的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=MArBLE:+Hierarchical+Multi-Armed+Bandits+for+Human-in-the-Loop+Set+Expansion)|0| +|[SEDAR: A Semantic Data Reservoir for Heterogeneous Datasets](https://doi.org/10.1145/3583780.3614753)|Sayed Hoseini, Ahmed Ali, Haron Shaker, Christoph Quix|Hochschule Niederrhein, Krefeld, Germany; RWTH Aachen University, Aachen, Germany; Fraunhofer FIT & Hochschule Niederrhein, St. Augustin & Krefeld, Germany|Data lakes have emerged as a solution for managing vast and diverse datasets for modern data analytics. To prevent them from becoming ungoverned, semantic data management techniques are crucial, which involve connecting metadata with knowledge graphs, following the principles of Linked Data. This semantic layer enables more expressive data management, integration from various sources and enhances data access utilizing the concepts and relations to semantically enrich the data. Some frameworks have been proposed, but requirements like data versioning, linking of datasets, managing machine learning projects, automated semantic modeling and ontology-based data access are not supported in one uniform system. We demonstrate SEDAR, a comprehensive semantic data lake that includes support for data ingestion, storage, processing, and governance with a special focus on semantic data management. The demo will showcase how the system allows for various ingestion scenarios, metadata enrichment, data source linking, profiling, semantic modeling, data integration and processing inside a machine learning life cycle.|数据湖已经成为现代数据分析中管理大量多样化数据集的解决方案。为了防止它们变得无法治理，语义数据管理技术是至关重要的，其中包括按照关联数据的原则将元数据与知识图连接起来。这个语义层支持更具表现力的数据管理、来自不同来源的集成，并利用概念和关系增强数据访问，从而在语义上丰富数据。虽然已经提出了一些框架，但是数据版本化、数据集链接、机器学习项目管理、自动语义建模和基于本体的数据访问等需求并不能在一个统一的系统中得到支持。我们演示了 SEDAR，这是一个全面的语义数据湖，包括对数据摄入、存储、处理和治理的支持，特别关注语义数据管理。演示将展示该系统如何在机器学习生命周期内实现各种摄取场景、元数据充实、数据源链接、剖析、语义建模、数据集成和处理。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=SEDAR:+A+Semantic+Data+Reservoir+for+Heterogeneous+Datasets)|0| |[EFFECTS: Explorable and Explainable Feature Extraction Framework for Multivariate Time-Series Classification](https://doi.org/10.1145/3583780.3614740)|Ido Ikar, Amit Somech|Bar-Ilan University, Ramat Gan, Israel|We demonstrate EFFECTS, an automated system for explorable and explainable feature extraction for multivariate time series classification. EFFECTS has a twofold contribution: (1) It significantly facilitates the exploration of MTSC data, and (2) it generates informative yet intuitive and explainable features to be used by the classification model. EFFECTS first mines the MTS data and extracts a set of interpretable features using an optimized transform-slice-aggregate process. To evaluate the quality of EFFECTS features, we gauge how well each feature distinguishes between every two classes, and how well they characterize each single class. Users can then explore the MTS data via the EFFECTS Explorer, which facilitates the visual inspection of important features, dimensions, and time slices. Last, the user can use the top features for each class when building a classification pipeline. We demonstrate EFFECTS on several real-world MTSC datasets, inviting the audience to investigate the data via EFFECTS Explorer and obtain initial insights on the time series data. Then, we will show how EFFECTS features are used in an ML model, and obtain accuracy that is on par with state-of-the-art MTSC models that do not optimize on explainability.|我们展示了 EFFECTS，一个用于多变量时间序列分类的可探索性和可解释性特征提取的自动化系统。EFFECTS 有两方面的贡献: (1)它极大地促进了 MTSC 数据的探索; (2)它产生了信息丰富但直观和可解释的特征，供分类模型使用。EFFECTS 首先挖掘 MTS 数据，并使用优化的转换切片聚合过程提取一组可解释的特征。为了评估 EFFECTS 特征的质量，我们衡量每个特征在每两个类之间的区别，以及它们如何很好地表征每个单一类。然后，用户可以通过 EFFECTS Explorer 浏览 MTS 数据，该浏览器便于对重要特征、尺寸和时间片进行目视检查。最后，用户可以在构建分类管道时使用每个类的顶部特性。我们在几个真实的 MTSC 数据集上演示 EFFECTS，邀请观众通过 EFFECTS 资源管理器调查数据，并获得对时间序列数据的初步认识。然后，我们将展示如何在机器学习模型中使用 EFFECTS 特征，并获得与不优化可解释性的最新 MTSC 模型相当的准确性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=EFFECTS:+Explorable+and+Explainable+Feature+Extraction+Framework+for+Multivariate+Time-Series+Classification)|0| -|[Misinformation Concierge: A Proof-of-Concept with Curated Twitter Dataset on COVID-19 Vaccination](https://doi.org/10.1145/3583780.3614746)|Shakshi Sharma, Anwitaman Datta, Vigneshwaran Shankaran, Rajesh Sharma|University of Tartu, Tartu, Estonia; University of Surrey, Guildford, United Kingdom; Nanyang Technological University, Singapore, Singapore|We demonstrate the Misinformation Concierge, a proof-of-concept that provides actionable intelligence on misinformation prevalent in social media. Specifically, it uses language processing and machine learning tools to identify subtopics of discourse and discern non/misleading posts; presents statistical reports for policy-makers to understand the big picture of prevalent misinformation in a timely manner; and recommends rebuttal messages for specific pieces of misinformation, identified from within the corpus of data - providing means to intervene and counter misinformation promptly. The Misinformation Concierge proof-of-concept using a curated dataset is accessible at: https://demo-frontend-uy34.onrender.com/|我们展示了错误信息门房，一个概念的证明，提供可行的情报在社会媒体流行的错误信息。具体而言，它使用语言处理和机器学习工具来确定话语的次主题和辨别非误导性的帖子; 为决策者提供统计报告，以便及时了解流行的错误信息的大局; 并就从数据提供手段中查明的具体错误信息建议反驳信息，以便迅速干预和反击错误信息。错误信息礼宾使用策划数据集的概念验证可以在以下 https://demo-frontend-uy34.onrender.com/找到|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Misinformation+Concierge:+A+Proof-of-Concept+with+Curated+Twitter+Dataset+on+COVID-19+Vaccination)|0| +|[Misinformation Concierge: A Proof-of-Concept with Curated Twitter Dataset on COVID-19 Vaccination](https://doi.org/10.1145/3583780.3614746)|Shakshi Sharma, Anwitaman Datta, Vigneshwaran Shankaran, Rajesh Sharma|University of Surrey, Guildford, United Kingdom; University of Tartu, Tartu, Estonia; Nanyang Technological University, Singapore, Singapore|We demonstrate the Misinformation Concierge, a proof-of-concept that provides actionable intelligence on misinformation prevalent in social media. Specifically, it uses language processing and machine learning tools to identify subtopics of discourse and discern non/misleading posts; presents statistical reports for policy-makers to understand the big picture of prevalent misinformation in a timely manner; and recommends rebuttal messages for specific pieces of misinformation, identified from within the corpus of data - providing means to intervene and counter misinformation promptly. The Misinformation Concierge proof-of-concept using a curated dataset is accessible at: https://demo-frontend-uy34.onrender.com/|我们展示了错误信息门房，一个概念的证明，提供可行的情报在社会媒体流行的错误信息。具体而言，它使用语言处理和机器学习工具来确定话语的次主题和辨别非误导性的帖子; 为决策者提供统计报告，以便及时了解流行的错误信息的大局; 并就从数据提供手段中查明的具体错误信息建议反驳信息，以便迅速干预和反击错误信息。错误信息礼宾使用策划数据集的概念验证可以在以下 https://demo-frontend-uy34.onrender.com/找到|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Misinformation+Concierge:+A+Proof-of-Concept+with+Curated+Twitter+Dataset+on+COVID-19+Vaccination)|0| |[NumJoin: Discovering Numeric Joinable Tables with Semantically Related Columns](https://doi.org/10.1145/3583780.3614750)|Pranav Subramaniam, Udayan Khurana, Kavitha Srinivas, Horst Samulowitz|IBM Research, Yorktown Heights, NY, USA; The University of Chicago, Chicago, IL, USA|Join discovery is a crucial part of exploration on data lakes. It often involves finding joinable tables that are semantically relevant. However, data lakes often contain numeric tables with unreliable column headers, and ID columns whose text names have been lost. Finding semantically relevant joins over numeric tables is a challenge. State-of-the-art describes join discovery using semantic similarity, but do not consider purely numeric tables. In this paper, we describe a system, NumJoin that includes two novel approaches for discovering joinable tables in a data lake: one that maps tables to knowledge graphs, and another that leverages numeric types and distributions. We demonstrate the effectiveness of NumJoin on a large data lake, including transportation data and finance data.|联合发现是数据湖勘探的重要组成部分。它通常包括寻找语义相关的可接合表。但是，数据湖通常包含具有不可靠列标题的数字表和文本名已丢失的 ID 列。在数字表上查找语义相关的联接是一个挑战。最先进的技术使用语义相似性描述联接发现，但是不考虑纯数值表。在本文中，我们描述了一个系统 NumJoin，它包括两种在数据湖中发现可接合表的新方法: 一种是将表映射到知识图，另一种是利用数值类型和分布。我们展示了 NumJoin 在大型数据湖(包括运输数据和财务数据)上的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=NumJoin:+Discovering+Numeric+Joinable+Tables+with+Semantically+Related+Columns)|0| |[Cluster-Explorer: An interactive Framework for Explaining Black-Box Clustering Results](https://doi.org/10.1145/3583780.3614734)|Sariel Tutay, Amit Somech|Bar-Ilan University, Ramat Gan, Israel|Interpreting clustering results is a challenging, manual task, that often requires the user to perform additional analytical queries and visualizations. To this end, we demonstrate Cluster-Explorer, an interactive, easy-to-use framework that provides explanations for black-box clustering results. Cluster-Explorer takes as input the raw dataset alongside cluster labels, and automatically generates multiple coherent explanations that characterize each cluster. We first propose a threefold quality measure that considers the conciseness, cluster coverage, and separation error of an explanation. We tackle the challenge of efficiently computing high-quality explanations using a modified version of a generalized frequent-itemsets mining (gFIM) algorithm. The gFIM algorithm is employed over multiple filter predicates which are extracted by applying various binning methods of different granularities. We implemented Cluster-Explorer as a Python library that can be easily used by data scientists in their ongoing workflows. After employing the clustering pipeline of their choice, Cluster-Explorer opens an integrated, interactive interface for the user to explore the various different explanations for each cluster. In our demonstration, the audience is invited to use Cluster-Explorer on numerous real-life datasets and different clustering pipelines and examine the usefulness of the cluster explanations provided by the system, as well as its efficiency of computation.|解释聚类结果是一项具有挑战性的手工任务，通常需要用户执行额外的分析查询和可视化。为此，我们演示了 Cluster-Explorer，这是一个交互式的、易于使用的框架，它为黑盒集群结果提供了解释。Cluster-Explorer 将原始数据集与簇标签一起作为输入，并自动生成描述每个簇特征的多个一致的解释。我们首先提出了一个三重质量度量，它考虑了解释的简洁性、聚类覆盖率和分离误差。我们使用广义频繁项集挖掘(gFIM)算法的修改版本来解决有效计算高质量解释的挑战。GFIM 算法适用于多个过滤谓词，这些谓词是通过应用不同粒度的不同分组方法提取出来的。我们将 Cluster-Explorer 实现为一个 Python 库，数据科学家可以很容易地在他们正在进行的工作流程中使用它。在使用了他们选择的集群管道之后，Cluster-Explorer 为用户打开了一个集成的交互式界面，以探索每个集群的各种不同解释。在我们的演示中，我们邀请观众在大量的实际数据集和不同的聚类管道上使用 Cluster-Explorer，并检验系统提供的聚类解释的有用性和计算效率。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Cluster-Explorer:+An+interactive+Framework+for+Explaining+Black-Box+Clustering+Results)|0| |[Investigating Natural and Artificial Dynamics in Graph Data Mining and Machine Learning](https://doi.org/10.1145/3583780.3616007)|Dongqi Fu|University of Illinois at Urbana-Champaign, Urbana, IL, USA|The complexity of relationships between entities is increasing in the era of big data, leading to a growing interest in graph (network) data, owing to its ability to encode intricate relational information. Graph data mining and machine learning methods extract informative node and graph representations to support broad applications, which have been proven effective for various high-impact tasks in the fields such as computer vision, natural language processing, and recommendation systems. Despite their effectiveness, graph data mining and machine learning methods face practical challenges in real-world scenarios. First, the input graphs may change over time, making it necessary to integrate time-evolving information to enhance representation capabilities. Second, the input graphs may contain unreliable, noisy, or sub-optimal information, requiring researchers and practitioners to intentionally modify the graph topology and node features to improve downstream performance. Facing these two phenomena, our research works focus on natural and artificial dynamics for benefiting graph data mining and machine learning. In this paper, we will briefly introduce our recent works in investigative natural and artificial dynamics and point out some under-explored research problems in the interaction of these dynamics.|随着大数据时代的到来，实体间关系的复杂性日益增加，由于图形(网络)数据能够对复杂的关系信息进行编码，因此人们对图形(网络)数据的兴趣日益浓厚。图形数据挖掘和机器学习方法提取信息的节点和图形表示，以支持广泛的应用，这已被证明是有效的各种高影响力的任务，如计算机视觉，自然语言处理和推荐系统。尽管图形数据挖掘和机器学习方法很有效，但它们在现实世界中面临着实际的挑战。首先，输入图可能随着时间的推移而改变，因此有必要整合随时间演变的信息以增强表示能力。其次，输入图可能包含不可靠的、有噪声的或次优的信息，需要研究人员和从业人员有意地修改图的拓扑结构和节点特征，以改善下游性能。面对这两种现象，我们的研究工作集中在自然和人工动力学有利于图形数据挖掘和机器学习。本文简要介绍了我们近年来在自然动力学和人工动力学研究方面的工作，并指出了在这两种动力学相互作用方面尚未得到充分研究的一些问题。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Investigating+Natural+and+Artificial+Dynamics+in+Graph+Data+Mining+and+Machine+Learning)|0| |[Intersectional Bias Mitigation in Pre-trained Language Models: A Quantum-Inspired Approach](https://doi.org/10.1145/3583780.3616003)|Omid Shokrollahi|Toronto Metropolitan University, Toronto, ON, Canada|The growing criticality of contextualized language models has raised concerns about the perpetuation of biases. Current fairness research often concentrates on single aspects of social groups. However, our study brings a multifaceted approach to the table, reflecting the intricate realities of intersectionality. We propose a bias mitigation algorithm inspired by quantum theory to fine-tune pre-trained language models. Our method applies Jensen-Shannon divergence alongside an entanglement entropy measure to quantify the complexity of entwined identities. Moreover, we utilize an innovative entanglement embedding neural network to address emergent features from different intersectional groups. An Actor-Critic setup facilitates effective fine-tuning. Our approach broadens the scope of intersectional fairness beyond just statistical parity, providing a strategic tool to tackle complex, interrelated biases. We anticipate that this fresh approach to bias mitigation will substantially enhance fairness in a wide range of language model applications.|语境化语言模式的日益严重性引起了人们对偏见长期存在的关注。当前的公平研究往往集中在社会群体的单一方面。然而，我们的研究带来了一个多方面的方法表，反映了错综复杂的现实的交叉性。我们提出了一个受量子理论启发的偏差抑制算法来微调预训练语言模型。我们的方法应用 Jensen-Shannon 散度和纠缠熵度量来量化纠缠恒等式的复杂性。此外，我们利用一个创新的纠缠嵌入式神经网络来处理不同交叉群的突现特征。演员-评论家设置有助于进行有效的微调。我们的方法扩大了交叉公平的范围，不仅仅是统计平价，提供了一个战略工具来解决复杂的，相互关联的偏见。我们期望这种新的减少偏差的方法将大大提高在广泛的语言模型应用中的公平性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Intersectional+Bias+Mitigation+in+Pre-trained+Language+Models:+A+Quantum-Inspired+Approach)|0| |[Tutorial on User Simulation for Evaluating Information Access Systems](https://doi.org/10.1145/3583780.3615296)|Krisztian Balog, ChengXiang Zhai|University of Stavanger, Stavanger, Norway; University of Illinois at Urbana-Champaign, Urbana, IL, USA|With the emergence of various information access systems exhibiting increasing complexity, there is a critical need for sound and scalable means of automatic evaluation. To address this challenge, user simulation emerges as a promising solution. This half-day tutorial focuses on providing a thorough understanding of user simulation techniques designed specifically for evaluation purposes. We systematically review major research progress, covering both general frameworks for designing user simulators, and specific models and algorithms for simulating user interactions with search engines, recommender systems, and conversational assistants. We also highlight some important future research directions.|随着越来越复杂的各种信息获取系统的出现，迫切需要健全和可扩展的自动评价手段。为了应对这一挑战，用户模拟成为一种有前途的解决方案。本教程为期半天，重点介绍为评估目的专门设计的用户模拟技术。我们系统地回顾了主要的研究进展，包括用于设计用户模拟器的一般框架，以及用于模拟用户与搜索引擎、推荐系统和会话助手的交互的具体模型和算法。我们还强调了一些重要的未来研究方向。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Tutorial+on+User+Simulation+for+Evaluating+Information+Access+Systems)|0| -|[Leveraging Graph Neural Networks for User Profiling: Recent Advances and Open Challenges](https://doi.org/10.1145/3583780.3615292)|Erasmo Purificato, Ludovico Boratto, Ernesto William De Luca|University of Cagliari, Cagliari, Italy; Otto von Guericke University Magdeburg & Georg Eckert Institute, Magdeburg, Germany; Otto von Guericke University Magdeburg & Georg Eckert Institute, Magdeburg & Brunswick, Germany|The proposed tutorial aims to familiarise the CIKM community with modern user profiling techniques that utilise Graph Neural Networks (GNNs). Initially, we will delve into the foundational principles of user profiling and GNNs, accompanied by an overview of relevant literature. We will subsequently systematically examine cutting-edge GNN architectures specifically developed for user profiling, highlighting the typical data utilised in this context. Furthermore, ethical considerations and beyond-accuracy perspectives, e.g. fairness and explainability, will be discussed regarding the potential applications of GNNs in user profiling. During the hands-on session, participants will gain practical insights into constructing and training recent GNN models for user profiling using open-source tools and publicly available datasets. The audience will actively explore the impact of these models through case studies focused on bias analysis and explanations of user profiles. To conclude the tutorial, we will analyse existing and emerging challenges in the field and discuss future research directions.|建议的教程旨在使 CIKM 社区熟悉使用图形神经网络(GNN)的现代用户分析技术。首先，我们将深入研究用户分析和 GNN 的基本原则，并对相关文献进行概述。随后，我们将系统地研究专门为用户剖析开发的前沿 GNN 体系结构，突出在这种情况下使用的典型数据。此外，关于 GNN 在用户特征分析中的潜在应用，还将讨论道德考虑和超越准确性的观点，例如公平性和可解释性。在实践会议上，与会者将获得使用开放源码工具和公开数据集构建和培训最新 GNN 模型用于用户特征分析的实用见解。受众将通过侧重于偏见分析和用户简介解释的案例研究，积极探索这些模式的影响。在结束本教程时，我们将分析该领域现有的和新出现的挑战，并讨论未来的研究方向。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Leveraging+Graph+Neural+Networks+for+User+Profiling:+Recent+Advances+and+Open+Challenges)|0| -|[From User Activity Traces to Navigation Graph for Software Enhancement: An Application of Graph Neural Network (GNN) on a Real-World Non-Attributed Graph](https://doi.org/10.1145/3583780.3615998)|Ikram Boukharouba, Florence Sèdes, Christophe Bortolaso, Florent Mouysset|IRIT Universite Toulouse 3 Paul Sabatier, Toulouse, France; Berger-Levrault, Labège, France; IRIT Universite Toulouse 3 Paul Sabatier; Berger-Levrault, Toulouse, France|Understanding software's user behavior is key to personalizing and enriching the user experience and improving the quality of the software. In this paper, we consider the use of user navigation graphs issued from user activity traces. The aim of our study is to do node classification over the user graph navigation in order to understand better the composition of the software and to offer a better experience to the users. Traditional baseline methods has shown good performance in the node classification task, but can't be applied for tasks as link prediction. Graph Neural Network on the contrary can satisfy both node classification and link prediction. However, GNN produce significant results when the features on the nodes are numerous enough. This is not always the case in real-world problems, because too many features implies too much data, storage issues, affect the performances of apps, etc. Indeed, due to the origin of the data and their uncontrolled generation, the resulting graphs contain few or no features (AKA non-attributed graphs). In addition, in industrial fields, some external requirements particularly legal may limit the collection and the use of data. In this article, we show that graphs issued from real-world data also have such limitations, and we propose the generation of artificial features on the nodes as a solution to this problem. The obtained results showed that the usage of artificial node features is a promising solution to overcome the greediness of GNN in terms of node features and applying GNN on the non-attributed graphs.|理解软件的用户行为是个性化和丰富用户体验以及提高软件质量的关键。在本文中，我们考虑使用用户导航图发出的用户活动跟踪。我们的研究目的是在用户图导航上进行节点分类，以便更好地了解软件的组成，并为用户提供更好的体验。传统的基线方法在节点分类任务中表现出了良好的性能，但不适用于链路预测任务。相反，图神经网络可以同时满足节点分类和链路预测。然而，当节点上的特征足够多时，GNN 会产生显著的结果。在现实世界的问题中并不总是这样，因为太多的特性意味着太多的数据、存储问题、影响应用程序的性能等等。事实上，由于数据的来源及其不受控制的生成，所得到的图包含很少或没有特征(AKA 非属性图)。此外，在工业领域，一些特别合法的外部要求可能会限制数据的收集和使用。在这篇文章中，我们表明，从现实世界的数据发布的图也有这样的局限性，我们提出了在节点上生成人工特征作为这个问题的解决方案。结果表明，利用人工节点特征克服 GNN 在节点特征方面的贪婪性，并将 GNN 应用于非属性图是一种有前途的解决方案。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=From+User+Activity+Traces+to+Navigation+Graph+for+Software+Enhancement:+An+Application+of+Graph+Neural+Network+(GNN)+on+a+Real-World+Non-Attributed+Graph)|0| -|[Proactive and Automatic Detection of Product Misclassifications at Massive Scale](https://doi.org/10.1145/3583780.3615510)|Ling Jiang, Xiaoyu Chu, Saaransh Gulati, Pulkit Garg, Andrew Borthwick, Gang Luo|Amazon, Sunnyvale, CA, USA; Amazon, Seattle, WA, USA; Amazon & University of Washington, Seattle, WA, USA|In e-commerce, product classification is widely used for various purposes. Misclassifying products can cause compliance issues and hurt the company's reputation. To address this problem, we propose an automated system to proactively detect product misclassifications by overcoming several challenges. A large e-commerce retailer can sell billions of distinct products, on which many thousands of classification tasks are performed. At this massive scale, we need to quickly detect misclassifications under a limited budget. In this talk, we point out these challenges and show how we design our system to handle them. When evaluated on a set of Amazon's product classification data, at an overhead of <10% of the classification cost, our system automatically identified and corrected many misclassifications, which would take a human many thousand years to manually find and 14.6 years to manually review and correct if our system were not used.|在电子商务中，产品分类被广泛用于各种目的。错误分类产品可能会导致法规遵循问题，并损害公司的声誉。为了解决这个问题，我们提出了一个自动化系统，通过克服几个挑战来主动检测产品错误分类。大型电子商务零售商可以销售数十亿种不同的产品，在这些产品上执行成千上万的分类任务。在如此巨大的规模下，我们需要在有限的预算下快速发现错误分类。在这个演讲中，我们指出了这些挑战，并展示了我们如何设计系统来应对它们。当在亚马逊的一组产品分类数据上进行评估时，我们的系统自动识别和纠正了许多错误分类，如果我们的系统没有被使用，这将需要人类数千年的时间来手动发现和14.6年的时间来手动审查和纠正。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Proactive+and+Automatic+Detection+of+Product+Misclassifications+at+Massive+Scale)|0| -|[KID34K: A Dataset for Online Identity Card Fraud Detection](https://doi.org/10.1145/3583780.3615122)|EunJu Park, SeungYeon Back, Jeongho Kim, Simon S. Woo|Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea; Sungkyunkwan University, Suwon, Republic of Korea|Though digital financial systems have provided users with convenient and accessible services, such as supporting banking or payment services anywhere, it is necessary to have robust security to protect against identity misuse. Thus, online digital identity (ID) verification plays a crucial role in securing financial services on mobile platforms. One of the most widely employed techniques for digital ID verification is that mobile applications request users to take and upload a picture of their own ID cards. However, this approach has vulnerabilities where someone takes pictures of the ID cards belonging to another person displayed on a screen, or printed on paper to be verified as the ID card owner. To mitigate the risks associated with fraudulent ID card verification, we present a novel dataset for classifying cases where the ID card images that users upload to the verification system are genuine or digitally represented. Our dataset is replicas designed to resemble real ID cards, making it available while avoiding privacy issues. Through extensive experiments, we demonstrate that our dataset is effective for detecting digitally represented ID card images, not only in our replica dataset but also in the dataset consisting of real ID cards. Our dataset is available at https://github.com/DASH-Lab/idcard_fraud_detection.|虽然数字金融系统为用户提供了方便易用的服务，例如在任何地方支持银行或支付服务，但必须有强大的安全保障，以防止身份被滥用。因此，在线数字身份(ID)验证在保障移动平台上的金融服务安全方面起着至关重要的作用。数字身份验证最广泛使用的技术之一是，移动应用程序要求用户拍摄并上传自己身份证的照片。然而，这种方法存在一些漏洞，即有人将属于另一个人的身份证照片显示在屏幕上，或打印在纸上，以便核实身份证持有人。为了减低验证伪造身份证的风险，我们提供了一个新的数据集，用于分类用户上载到验证系统的身份证图像是真实或数码表示的情况。我们的数据集是复制品，设计得像真正的身份证，使其可用，同时避免隐私问题。通过大量的实验，我们证明了我们的数据集对于检测数字表示的身份证图像是有效的，不仅在我们的副本数据集中，而且在由真实身份证组成的数据集中。我们的数据集 https://github.com/dash-lab/idcard_fraud_detection 可用。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=KID34K:+A+Dataset+for+Online+Identity+Card+Fraud+Detection)|0| -|[OpenGDA: Graph Domain Adaptation Benchmark for Cross-network Learning](https://doi.org/10.1145/3583780.3615127)|Boshen Shi, Yongqing Wang, Fangda Guo, Jiangli Shao, Huawei Shen, Xueqi Cheng|Institute of Computing Technology, CAS, Beijing, China; Institute of Computing Technology, CAS & University of Chinese Academy of Sciences, Beijing, China|Graph domain adaptation models are widely adopted in cross-network learning tasks, with the aim of transferring labeling or structural knowledge. Currently, there mainly exist two limitations in evaluating graph domain adaptation models. On one side, they are primarily tested for the specific cross-network node classification task, leaving tasks at edge-level and graph-level largely under-explored. Moreover, they are primarily tested in limited scenarios, such as social networks or citation networks, lacking validation of model's capability in richer scenarios. As comprehensively assessing models could enhance model practicality in real-world applications, we propose a benchmark, known as OpenGDA. It provides abundant pre-processed and unified datasets for different types of tasks (node, edge, graph). They originate from diverse scenarios, covering web information systems, urban systems and natural systems. Furthermore, it integrates state-of-the-art models with standardized and end-to-end pipelines. Overall, OpenGDA provides a user-friendly, scalable and reproducible benchmark for evaluating graph domain adaptation models. The benchmark experiments highlight the challenges of applying GDA models to real-world applications with consistent good performance, and potentially provide insights to future research. As an emerging project, OpenGDA will be regularly updated with new datasets and models. It could be accessed from https://github.com/Skyorca/OpenGDA.|图域自适应模型广泛应用于跨网络学习任务中，其目的是传递标记或结构化知识。目前，评估图域自适应模型主要存在两个局限性。一方面，它们主要针对特定的跨网络节点分类任务进行测试，使得边缘层和图层的任务在很大程度上没有得到充分的探索。此外，它们主要在有限的场景中进行测试，如社交网络或引文网络，缺乏对模型在更丰富场景中的能力的验证。由于综合评估模型可以增强模型在实际应用中的实用性，我们提出了一个基准，称为 OpenGDA。它为不同类型的任务(节点、边、图)提供了丰富的预处理和统一的数据集。它们来源于不同的场景，包括网络信息系统、城市系统和自然系统。此外，它还将最先进的模型与标准化的端到端管道集成在一起。总的来说，OpenGDA 为评估图形域适应模型提供了一个用户友好、可伸缩和可重复的基准。基准实验突出了将 GDA 模型应用于具有一致良好性能的现实世界应用程序的挑战，并可能为未来的研究提供深刻见解。作为一个新兴的项目，OpenGDA 将定期更新新的数据集和模型。可以从 https://github.com/skyorca/opengda 进入。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=OpenGDA:+Graph+Domain+Adaptation+Benchmark+for+Cross-network+Learning)|0| +|[Leveraging Graph Neural Networks for User Profiling: Recent Advances and Open Challenges](https://doi.org/10.1145/3583780.3615292)|Erasmo Purificato, Ludovico Boratto, Ernesto William De Luca|Otto von Guericke University Magdeburg & Georg Eckert Institute, Magdeburg, Germany; Otto von Guericke University Magdeburg & Georg Eckert Institute, Magdeburg & Brunswick, Germany; University of Cagliari, Cagliari, Italy|The proposed tutorial aims to familiarise the CIKM community with modern user profiling techniques that utilise Graph Neural Networks (GNNs). Initially, we will delve into the foundational principles of user profiling and GNNs, accompanied by an overview of relevant literature. We will subsequently systematically examine cutting-edge GNN architectures specifically developed for user profiling, highlighting the typical data utilised in this context. Furthermore, ethical considerations and beyond-accuracy perspectives, e.g. fairness and explainability, will be discussed regarding the potential applications of GNNs in user profiling. During the hands-on session, participants will gain practical insights into constructing and training recent GNN models for user profiling using open-source tools and publicly available datasets. The audience will actively explore the impact of these models through case studies focused on bias analysis and explanations of user profiles. To conclude the tutorial, we will analyse existing and emerging challenges in the field and discuss future research directions.|建议的教程旨在使 CIKM 社区熟悉使用图形神经网络(GNN)的现代用户分析技术。首先，我们将深入研究用户分析和 GNN 的基本原则，并对相关文献进行概述。随后，我们将系统地研究专门为用户剖析开发的前沿 GNN 体系结构，突出在这种情况下使用的典型数据。此外，关于 GNN 在用户特征分析中的潜在应用，还将讨论道德考虑和超越准确性的观点，例如公平性和可解释性。在实践会议上，与会者将获得使用开放源码工具和公开数据集构建和培训最新 GNN 模型用于用户特征分析的实用见解。受众将通过侧重于偏见分析和用户简介解释的案例研究，积极探索这些模式的影响。在结束本教程时，我们将分析该领域现有的和新出现的挑战，并讨论未来的研究方向。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Leveraging+Graph+Neural+Networks+for+User+Profiling:+Recent+Advances+and+Open+Challenges)|0| +|[From User Activity Traces to Navigation Graph for Software Enhancement: An Application of Graph Neural Network (GNN) on a Real-World Non-Attributed Graph](https://doi.org/10.1145/3583780.3615998)|Ikram Boukharouba, Florence Sèdes, Christophe Bortolaso, Florent Mouysset|Berger-Levrault, Labège, France; IRIT Universite Toulouse 3 Paul Sabatier; Berger-Levrault, Toulouse, France; IRIT Universite Toulouse 3 Paul Sabatier, Toulouse, France|Understanding software's user behavior is key to personalizing and enriching the user experience and improving the quality of the software. In this paper, we consider the use of user navigation graphs issued from user activity traces. The aim of our study is to do node classification over the user graph navigation in order to understand better the composition of the software and to offer a better experience to the users. Traditional baseline methods has shown good performance in the node classification task, but can't be applied for tasks as link prediction. Graph Neural Network on the contrary can satisfy both node classification and link prediction. However, GNN produce significant results when the features on the nodes are numerous enough. This is not always the case in real-world problems, because too many features implies too much data, storage issues, affect the performances of apps, etc. Indeed, due to the origin of the data and their uncontrolled generation, the resulting graphs contain few or no features (AKA non-attributed graphs). In addition, in industrial fields, some external requirements particularly legal may limit the collection and the use of data. In this article, we show that graphs issued from real-world data also have such limitations, and we propose the generation of artificial features on the nodes as a solution to this problem. The obtained results showed that the usage of artificial node features is a promising solution to overcome the greediness of GNN in terms of node features and applying GNN on the non-attributed graphs.|理解软件的用户行为是个性化和丰富用户体验以及提高软件质量的关键。在本文中，我们考虑使用用户导航图发出的用户活动跟踪。我们的研究目的是在用户图导航上进行节点分类，以便更好地了解软件的组成，并为用户提供更好的体验。传统的基线方法在节点分类任务中表现出了良好的性能，但不适用于链路预测任务。相反，图神经网络可以同时满足节点分类和链路预测。然而，当节点上的特征足够多时，GNN 会产生显著的结果。在现实世界的问题中并不总是这样，因为太多的特性意味着太多的数据、存储问题、影响应用程序的性能等等。事实上，由于数据的来源及其不受控制的生成，所得到的图包含很少或没有特征(AKA 非属性图)。此外，在工业领域，一些特别合法的外部要求可能会限制数据的收集和使用。在这篇文章中，我们表明，从现实世界的数据发布的图也有这样的局限性，我们提出了在节点上生成人工特征作为这个问题的解决方案。结果表明，利用人工节点特征克服 GNN 在节点特征方面的贪婪性，并将 GNN 应用于非属性图是一种有前途的解决方案。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=From+User+Activity+Traces+to+Navigation+Graph+for+Software+Enhancement:+An+Application+of+Graph+Neural+Network+(GNN)+on+a+Real-World+Non-Attributed+Graph)|0| +|[Proactive and Automatic Detection of Product Misclassifications at Massive Scale](https://doi.org/10.1145/3583780.3615510)|Ling Jiang, Xiaoyu Chu, Saaransh Gulati, Pulkit Garg, Andrew Borthwick, Gang Luo|Amazon & University of Washington, Seattle, WA, USA; Amazon, Sunnyvale, CA, USA; Amazon, Seattle, WA, USA|In e-commerce, product classification is widely used for various purposes. Misclassifying products can cause compliance issues and hurt the company's reputation. To address this problem, we propose an automated system to proactively detect product misclassifications by overcoming several challenges. A large e-commerce retailer can sell billions of distinct products, on which many thousands of classification tasks are performed. At this massive scale, we need to quickly detect misclassifications under a limited budget. In this talk, we point out these challenges and show how we design our system to handle them. When evaluated on a set of Amazon's product classification data, at an overhead of <10% of the classification cost, our system automatically identified and corrected many misclassifications, which would take a human many thousand years to manually find and 14.6 years to manually review and correct if our system were not used.|在电子商务中，产品分类被广泛用于各种目的。错误分类产品可能会导致法规遵循问题，并损害公司的声誉。为了解决这个问题，我们提出了一个自动化系统，通过克服几个挑战来主动检测产品错误分类。大型电子商务零售商可以销售数十亿种不同的产品，在这些产品上执行成千上万的分类任务。在如此巨大的规模下，我们需要在有限的预算下快速发现错误分类。在这个演讲中，我们指出了这些挑战，并展示了我们如何设计系统来应对它们。当在亚马逊的一组产品分类数据上进行评估时，我们的系统自动识别和纠正了许多错误分类，如果我们的系统没有被使用，这将需要人类数千年的时间来手动发现和14.6年的时间来手动审查和纠正。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Proactive+and+Automatic+Detection+of+Product+Misclassifications+at+Massive+Scale)|0| +|[KID34K: A Dataset for Online Identity Card Fraud Detection](https://doi.org/10.1145/3583780.3615122)|EunJu Park, SeungYeon Back, Jeongho Kim, Simon S. Woo|Sungkyunkwan University, Suwon, Republic of Korea; Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea|Though digital financial systems have provided users with convenient and accessible services, such as supporting banking or payment services anywhere, it is necessary to have robust security to protect against identity misuse. Thus, online digital identity (ID) verification plays a crucial role in securing financial services on mobile platforms. One of the most widely employed techniques for digital ID verification is that mobile applications request users to take and upload a picture of their own ID cards. However, this approach has vulnerabilities where someone takes pictures of the ID cards belonging to another person displayed on a screen, or printed on paper to be verified as the ID card owner. To mitigate the risks associated with fraudulent ID card verification, we present a novel dataset for classifying cases where the ID card images that users upload to the verification system are genuine or digitally represented. Our dataset is replicas designed to resemble real ID cards, making it available while avoiding privacy issues. Through extensive experiments, we demonstrate that our dataset is effective for detecting digitally represented ID card images, not only in our replica dataset but also in the dataset consisting of real ID cards. Our dataset is available at https://github.com/DASH-Lab/idcard_fraud_detection.|虽然数字金融系统为用户提供了方便易用的服务，例如在任何地方支持银行或支付服务，但必须有强大的安全保障，以防止身份被滥用。因此，在线数字身份(ID)验证在保障移动平台上的金融服务安全方面起着至关重要的作用。数字身份验证最广泛使用的技术之一是，移动应用程序要求用户拍摄并上传自己身份证的照片。然而，这种方法存在一些漏洞，即有人将属于另一个人的身份证照片显示在屏幕上，或打印在纸上，以便核实身份证持有人。为了减低验证伪造身份证的风险，我们提供了一个新的数据集，用于分类用户上载到验证系统的身份证图像是真实或数码表示的情况。我们的数据集是复制品，设计得像真正的身份证，使其可用，同时避免隐私问题。通过大量的实验，我们证明了我们的数据集对于检测数字表示的身份证图像是有效的，不仅在我们的副本数据集中，而且在由真实身份证组成的数据集中。我们的数据集 https://github.com/dash-lab/idcard_fraud_detection 可用。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=KID34K:+A+Dataset+for+Online+Identity+Card+Fraud+Detection)|0| +|[OpenGDA: Graph Domain Adaptation Benchmark for Cross-network Learning](https://doi.org/10.1145/3583780.3615127)|Boshen Shi, Yongqing Wang, Fangda Guo, Jiangli Shao, Huawei Shen, Xueqi Cheng|Institute of Computing Technology, CAS & University of Chinese Academy of Sciences, Beijing, China; Institute of Computing Technology, CAS, Beijing, China|Graph domain adaptation models are widely adopted in cross-network learning tasks, with the aim of transferring labeling or structural knowledge. Currently, there mainly exist two limitations in evaluating graph domain adaptation models. On one side, they are primarily tested for the specific cross-network node classification task, leaving tasks at edge-level and graph-level largely under-explored. Moreover, they are primarily tested in limited scenarios, such as social networks or citation networks, lacking validation of model's capability in richer scenarios. As comprehensively assessing models could enhance model practicality in real-world applications, we propose a benchmark, known as OpenGDA. It provides abundant pre-processed and unified datasets for different types of tasks (node, edge, graph). They originate from diverse scenarios, covering web information systems, urban systems and natural systems. Furthermore, it integrates state-of-the-art models with standardized and end-to-end pipelines. Overall, OpenGDA provides a user-friendly, scalable and reproducible benchmark for evaluating graph domain adaptation models. The benchmark experiments highlight the challenges of applying GDA models to real-world applications with consistent good performance, and potentially provide insights to future research. As an emerging project, OpenGDA will be regularly updated with new datasets and models. It could be accessed from https://github.com/Skyorca/OpenGDA.|图域自适应模型广泛应用于跨网络学习任务中，其目的是传递标记或结构化知识。目前，评估图域自适应模型主要存在两个局限性。一方面，它们主要针对特定的跨网络节点分类任务进行测试，使得边缘层和图层的任务在很大程度上没有得到充分的探索。此外，它们主要在有限的场景中进行测试，如社交网络或引文网络，缺乏对模型在更丰富场景中的能力的验证。由于综合评估模型可以增强模型在实际应用中的实用性，我们提出了一个基准，称为 OpenGDA。它为不同类型的任务(节点、边、图)提供了丰富的预处理和统一的数据集。它们来源于不同的场景，包括网络信息系统、城市系统和自然系统。此外，它还将最先进的模型与标准化的端到端管道集成在一起。总的来说，OpenGDA 为评估图形域适应模型提供了一个用户友好、可伸缩和可重复的基准。基准实验突出了将 GDA 模型应用于具有一致良好性能的现实世界应用程序的挑战，并可能为未来的研究提供深刻见解。作为一个新兴的项目，OpenGDA 将定期更新新的数据集和模型。可以从 https://github.com/skyorca/opengda 进入。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=OpenGDA:+Graph+Domain+Adaptation+Benchmark+for+Cross-network+Learning)|0| |[Generative AI and the Future of Information Access](https://doi.org/10.1145/3583780.3615317)|Chirag Shah|University of Washington, Seattle, WA, USA|The prominent model of retrieving, evaluating, and using relevant information from databases, collections, and the web is going through a significant transformation. This is largely due to wide-scale availability of various generative AI systems that can take in natural language inputs and generate highly customized natural language text, images, audio, and videos. This transformation in how people seek and access information will have profound impacts on users, developers, and policymakers. It is already changing many sectors including education, health, and commerce. But the hopes and hypes of generative AI are often not clear as we get swept up by either the current capabilities and limitations of this technology in the short term or fear from speculative future in the long term. Instead, I believe we need to approach this area pragmatically and with scientific curiosity, scholarly rigor, and societal responsibility. In this talk, I will highlight some of the opportunities and challenges for information access stemming from recent advancements in generative AI. For instance, there are new possibilities now for addressing accessibility, low-resource domains, and bias in training data using generative AI tools. On the other hand, there are new challenges concerning hallucination, toxicity, and information provenance. It is clear that we want to benefit from what AI systems are capable of, but how do we do that while curbing some of these problems? I will argue that the solution is multifaceted and complex -- some will require technical advancements and others will call for policy changes. We will need to not only build information systems with fairness, transparency, and accountability in mind, but also train a new generation of developers, policymakers, and of course the users. The goal here is to cut through both hype and fear and think pragmatically about the future of information access.|检索、评估和使用来自数据库、集合和网络的相关信息的突出模式正在经历一个重大转变。这在很大程度上归功于各种生成 AI 系统的广泛应用，这些系统可以接收自然语言输入并生成高度定制的自然语言文本、图像、音频和视频。人们如何寻找和获取信息的这种转变将对用户、开发人员和决策者产生深远的影响。它已经改变了包括教育、卫生和商业在内的许多领域。但是，生成性人工智能的希望和炒作往往并不明朗，因为我们要么在短期内被这种技术的当前能力和局限性所席卷，要么在长期内对投机性未来感到恐惧。相反，我认为我们需要以务实的态度，带着科学的好奇心、学术的严谨性和社会责任感来处理这个问题。在这次演讲中，我将强调一些信息获取的机遇和挑战，这些机遇和挑战来自生成性人工智能的最新进展。例如，现在有新的可能性解决可访问性，低资源领域，和偏见的训练数据使用生成人工智能工具。另一方面，有关幻觉，毒性和信息来源的新挑战。很明显，我们希望从人工智能系统的能力中获益，但我们如何做到这一点，同时抑制其中一些问题？我认为解决方案是多方面和复杂的——有些需要技术进步，有些需要政策变更。我们不仅需要建立公平、透明和负责任的信息系统，还需要培训新一代的开发人员、决策者，当然还有用户。这里的目标是消除大肆宣传和恐惧，实事求是地思考信息访问的未来。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Generative+AI+and+the+Future+of+Information+Access)|0| |[Knowledge Graphs for Knowing More and Knowing for Sure](https://doi.org/10.1145/3583780.3615316)|Steffen Staab|University of Stuttgart, Sttutgart, Germany|Knowledge graphs have been conceived to collect heterogeneous data and knowledge about large domains, e.g. medical or engineering domains, and to allow versatile access to such collections by means of querying and logical reasoning. A surge of methods has responded to additional requirements in recent years. (i) Knowledge graph embeddings use similarity and analogy of structures to speculatively add to the collected data and knowledge. (ii) Queries with shapes and schema information can be typed to provide certainty about results. We survey both developments and find that the development of techniques happens in disjoint communities that mostly do not understand each other, thus limiting the proper and most versatile use of knowledge graphs.|知识图表被设计用来收集关于大型领域(例如医疗或工程领域)的异质数据和知识，并允许通过查询和逻辑推理的方式对这些收集进行多种访问。近年来，随着需求的增加，各种方法应运而生。(i)知识图嵌入使用结构的相似性和类比性来推测性地增加收集的数据和知识。(ii)可以键入带有形状和模式信息的查询，以提供关于结果的确定性。我们调查了这两方面的发展，发现技术的发展发生在不相连的社区，这些社区大多彼此不了解，从而限制了知识图表的正确和最多才多艺的使用。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Knowledge+Graphs+for+Knowing+More+and+Knowing+for+Sure)|0| -|[Optimizing for Member Value in an Edge Building Marketplace](https://doi.org/10.1145/3583780.3615000)|Ayan Acharya, Siyuan Gao, Ankan Saha, Borja Ocejo, Kinjal Basu, Sathiya Keerthi Selvaraj, Rahul Mazumder, Aman Gupta, Parag Agrawal, Ayan Acharya|Aliveo AI, Austin, TX, USA; LinkedIn Inc. (MIT), Sunnyvale (Cambridge), USA; LinkedIn Inc., Sunnyvale, CA, USA|Social networks are prosperous marketplaces where creators and consumers congregate to share and consume various content. In general, products that rank content for distribution (such as newsfeeds, stories, and notifications) and are related to edge recommendations (such as connect to members, follow celebrities or groups or hashtags) optimize the experience of active users. Typically, such users generate ample interaction data amenable to accurate model training and prediction. In contrast, we prioritize enhancing the experience of inactive members (IMs) who do not have a rich connection network. We formulate strategies for recommending superior edges to help members grow their connection network. Adapting the recommendations provides enormous value to the IMs and can significantly influence their future behaviour and engagement with the ecosystem. To that end, we propose a general and scalable multi-objective optimization (MOO) framework to provide more value to IMs as invitation recipients on LinkedIn, a professional network with over 900M members. To deal with the enormous scale, we formulate the problem as a massive constrained linear optimization involving billions of variables and millions of constraints and efficiently solve it using accelerated gradient descent,making this the largest deployment of LP-based recommender systems worldwide. Furthermore, the proposed MOO paradigm can solve the general problem of matching different types of entities in an m-sided marketplace. Finally, we discuss the challenges and benefits of implementing and ramping our method in production at scale at LinkedIn and report our findings about the core business metrics related to users' engagement and network health.|社交网络是繁荣的市场，创作者和消费者聚集在一起分享和消费各种内容。一般来说，对发布内容进行排名的产品(如新闻源、故事和通知)和与边缘推荐相关的产品(如与成员联系、关注名人或群组或标签)优化了活跃用户的体验。通常，这样的用户生成大量的交互数据，可以进行精确的模型训练和预测。相比之下，我们优先考虑增强没有丰富连接网络的非活动成员(IM)的体验。我们制定策略，推荐优势，以帮助会员发展他们的联系网络。采纳这些建议为管理系统提供了巨大的价值，可以对它们今后的行为和与生态系统的接触产生重大影响。为此，我们提出了一个通用的和可扩展的多目标优化(MOO)框架，以提供更多的价值即时通讯作为邀请收件人在 LinkedIn，一个专业网络超过900万成员。为了解决这个庞大的问题，我们将这个问题表述为一个涉及数十亿变量和数百万约束的大规模约束线性优化问题，并使用加速梯度下降法有效地解决这个问题，从而使这个问题成为全球范围内基于 LP 的推荐系统中规模最大的部署。此外，提出的 MOO 范式可以解决在多边市场中匹配不同类型实体的一般问题。最后，我们在 LinkedIn 上讨论了在大规模生产中实施和扩展我们的方法的挑战和好处，并报告了我们关于与用户参与和网络健康相关的核心业务指标的发现。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Optimizing+for+Member+Value+in+an+Edge+Building+Marketplace)|0| -|[Interpretable Natural Language Understanding](https://doi.org/10.1145/3583780.3615315)|Yulan He|Microsoft Research AI; Sun Yat-sen University; South China University of Technology; Mila; Tsinghua University; Beihang University|Recently generating natural language explanations has shown very promising results in not only offering interpretable explanations but also providing additional information and supervision for prediction. However, existing approaches usually require a large set of human annotated explanations for training while collecting a large set of explanations is not only time consuming but also expensive. In this paper, we develop a general framework for interpretable natural language understanding that requires only a small set of human annotated explanations for training. Our framework treats natural language explanations as latent variables that model the underlying reasoning process of a neural model. We develop a variational EM framework for optimization where an explanation generation module and an explanation-augmented prediction module are alternatively optimized and mutually enhance each other. Moreover, we further propose an explanation-based self-training method under this framework for semi-supervised learning. It alternates between assigning pseudo-labels to unlabeled data and generating new explanations to iteratively improve each other. Experiments on two natural language understanding tasks demonstrate that our framework can not only make effective predictions in both supervised and semi-supervised settings, but also generate good natural language explanation.|最近生成的自然语言解释已经显示出非常有希望的结果，不仅提供可解释的解释，而且提供额外的信息和监督预测。然而，现有的方法通常需要大量的人工注释解释来进行培训，而收集大量的解释不仅耗费时间，而且成本高昂。在本文中，我们开发了一个可解释的自然语言理解的一般框架，只需要一小组人工注释的训练解释。我们的框架将自然语言解释作为潜在变量，模拟神经模型的潜在推理过程。本文提出了一个变分 EM 优化框架，其中解释生成模块和解释增强预测模块交替优化并相互增强。此外，我们进一步建议在这个架构下，采用以解释为本的自我训练方法，训练半监督学习。它可以将伪标签分配给未标记的数据，也可以生成新的解释以反复改进彼此。在两个自然语言理解任务上的实验表明，该框架不仅能够在监督和半监督环境下进行有效的预测，而且能够产生良好的自然语言解释。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Interpretable+Natural+Language+Understanding)|0| -|[Deep Integrated Explanations](https://doi.org/10.1145/3583780.3614836)|Oren Barkan, Yehonatan Elisha, Jonathan Weill, Yuval Asher, Amit Eshel, Noam Koenigstein|The Open University, Raanana, Israel; Tel Aviv University, Tel Aviv, Israel|This paper presents Deep Integrated Explanations (DIX) - a universal method for explaining vision models. DIX generates explanation maps by integrating information from the intermediate representations of the model, coupled with their corresponding gradients. Through an extensive array of both objective and subjective evaluations spanning diverse tasks, datasets, and model configurations, we showcase the efficacy of DIX in generating faithful and accurate explanation maps, while surpassing current state-of-the-art methods. Our code is available at: https://github.com/dix-cikm23/dix|本文介绍了深度集成解释(DIX)——一种解释视觉模型的通用方法。DIX 通过集成来自模型中间表示的信息以及它们相应的梯度来生成解释映射。通过跨越不同任务，数据集和模型配置的客观和主观评估的广泛阵列，我们展示了 DIX 在生成忠实和准确的解释地图方面的功效，同时超越了当前的最先进的方法。我们的代码可以在以下 https://github.com/dix-cikm23/dix 找到|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Deep+Integrated+Explanations)|0| +|[Optimizing for Member Value in an Edge Building Marketplace](https://doi.org/10.1145/3583780.3615000)|Ayan Acharya, Siyuan Gao, Ankan Saha, Borja Ocejo, Kinjal Basu, Sathiya Keerthi Selvaraj, Rahul Mazumder, Aman Gupta, Parag Agrawal, Ayan Acharya|LinkedIn Inc., Sunnyvale, CA, USA; LinkedIn Inc. (MIT), Sunnyvale (Cambridge), USA; Aliveo AI, Austin, TX, USA|Social networks are prosperous marketplaces where creators and consumers congregate to share and consume various content. In general, products that rank content for distribution (such as newsfeeds, stories, and notifications) and are related to edge recommendations (such as connect to members, follow celebrities or groups or hashtags) optimize the experience of active users. Typically, such users generate ample interaction data amenable to accurate model training and prediction. In contrast, we prioritize enhancing the experience of inactive members (IMs) who do not have a rich connection network. We formulate strategies for recommending superior edges to help members grow their connection network. Adapting the recommendations provides enormous value to the IMs and can significantly influence their future behaviour and engagement with the ecosystem. To that end, we propose a general and scalable multi-objective optimization (MOO) framework to provide more value to IMs as invitation recipients on LinkedIn, a professional network with over 900M members. To deal with the enormous scale, we formulate the problem as a massive constrained linear optimization involving billions of variables and millions of constraints and efficiently solve it using accelerated gradient descent,making this the largest deployment of LP-based recommender systems worldwide. Furthermore, the proposed MOO paradigm can solve the general problem of matching different types of entities in an m-sided marketplace. Finally, we discuss the challenges and benefits of implementing and ramping our method in production at scale at LinkedIn and report our findings about the core business metrics related to users' engagement and network health.|社交网络是繁荣的市场，创作者和消费者聚集在一起分享和消费各种内容。一般来说，对发布内容进行排名的产品(如新闻源、故事和通知)和与边缘推荐相关的产品(如与成员联系、关注名人或群组或标签)优化了活跃用户的体验。通常，这样的用户生成大量的交互数据，可以进行精确的模型训练和预测。相比之下，我们优先考虑增强没有丰富连接网络的非活动成员(IM)的体验。我们制定策略，推荐优势，以帮助会员发展他们的联系网络。采纳这些建议为管理系统提供了巨大的价值，可以对它们今后的行为和与生态系统的接触产生重大影响。为此，我们提出了一个通用的和可扩展的多目标优化(MOO)框架，以提供更多的价值即时通讯作为邀请收件人在 LinkedIn，一个专业网络超过900万成员。为了解决这个庞大的问题，我们将这个问题表述为一个涉及数十亿变量和数百万约束的大规模约束线性优化问题，并使用加速梯度下降法有效地解决这个问题，从而使这个问题成为全球范围内基于 LP 的推荐系统中规模最大的部署。此外，提出的 MOO 范式可以解决在多边市场中匹配不同类型实体的一般问题。最后，我们在 LinkedIn 上讨论了在大规模生产中实施和扩展我们的方法的挑战和好处，并报告了我们关于与用户参与和网络健康相关的核心业务指标的发现。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Optimizing+for+Member+Value+in+an+Edge+Building+Marketplace)|0| +|[Interpretable Natural Language Understanding](https://doi.org/10.1145/3583780.3615315)|Yulan He|Beihang University; Tsinghua University; South China University of Technology; Sun Yat-sen University; Mila; Microsoft Research AI|Recently generating natural language explanations has shown very promising results in not only offering interpretable explanations but also providing additional information and supervision for prediction. However, existing approaches usually require a large set of human annotated explanations for training while collecting a large set of explanations is not only time consuming but also expensive. In this paper, we develop a general framework for interpretable natural language understanding that requires only a small set of human annotated explanations for training. Our framework treats natural language explanations as latent variables that model the underlying reasoning process of a neural model. We develop a variational EM framework for optimization where an explanation generation module and an explanation-augmented prediction module are alternatively optimized and mutually enhance each other. Moreover, we further propose an explanation-based self-training method under this framework for semi-supervised learning. It alternates between assigning pseudo-labels to unlabeled data and generating new explanations to iteratively improve each other. Experiments on two natural language understanding tasks demonstrate that our framework can not only make effective predictions in both supervised and semi-supervised settings, but also generate good natural language explanation.|最近生成的自然语言解释已经显示出非常有希望的结果，不仅提供可解释的解释，而且提供额外的信息和监督预测。然而，现有的方法通常需要大量的人工注释解释来进行培训，而收集大量的解释不仅耗费时间，而且成本高昂。在本文中，我们开发了一个可解释的自然语言理解的一般框架，只需要一小组人工注释的训练解释。我们的框架将自然语言解释作为潜在变量，模拟神经模型的潜在推理过程。本文提出了一个变分 EM 优化框架，其中解释生成模块和解释增强预测模块交替优化并相互增强。此外，我们进一步建议在这个架构下，采用以解释为本的自我训练方法，训练半监督学习。它可以将伪标签分配给未标记的数据，也可以生成新的解释以反复改进彼此。在两个自然语言理解任务上的实验表明，该框架不仅能够在监督和半监督环境下进行有效的预测，而且能够产生良好的自然语言解释。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Interpretable+Natural+Language+Understanding)|0| +|[Deep Integrated Explanations](https://doi.org/10.1145/3583780.3614836)|Oren Barkan, Yehonatan Elisha, Jonathan Weill, Yuval Asher, Amit Eshel, Noam Koenigstein|Tel Aviv University, Tel Aviv, Israel; The Open University, Raanana, Israel|This paper presents Deep Integrated Explanations (DIX) - a universal method for explaining vision models. DIX generates explanation maps by integrating information from the intermediate representations of the model, coupled with their corresponding gradients. Through an extensive array of both objective and subjective evaluations spanning diverse tasks, datasets, and model configurations, we showcase the efficacy of DIX in generating faithful and accurate explanation maps, while surpassing current state-of-the-art methods. Our code is available at: https://github.com/dix-cikm23/dix|本文介绍了深度集成解释(DIX)——一种解释视觉模型的通用方法。DIX 通过集成来自模型中间表示的信息以及它们相应的梯度来生成解释映射。通过跨越不同任务，数据集和模型配置的客观和主观评估的广泛阵列，我们展示了 DIX 在生成忠实和准确的解释地图方面的功效，同时超越了当前的最先进的方法。我们的代码可以在以下 https://github.com/dix-cikm23/dix 找到|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Deep+Integrated+Explanations)|0| |[MPMRC-MNER: A Unified MRC framework for Multimodal Named Entity Recognition based Multimodal Prompt](https://doi.org/10.1145/3583780.3614975)|Xigang Bao, Mengyuan Tian, Zhiyuan Zha, Biao Qin|Renmin University of China, Beijing, China|Multimodal named entity recognition (MNER) is a vision-language task, which aims to detect entity spans and classify them to corresponding entity types given a sentence-image pair. Existing methods often regard an image as a set of visual objects, trying to explicitly capture the relations between visual objects and entities. However, since visual objects are often not identical to entities in quantity and type, they may suffer the bias introduced by visual objects rather than aid. Inspired by the success of textual prompt-based fine-tuning (PF) approaches in many methods, in this paper, we propose a Multimodal Prompt-based Machine Reading Comprehension based framework to implicit alignment between text and image for improving MNER, namely MPMRC-MNER. Specifically, we transform text-only query in MRC into multimodal prompt containing image tokens and text tokens. To better integrate image tokens and text tokens, we design a prompt-aware attention mechanism for better cross-modal fusion. At last, contrastive learning with two types of contrastive losses is designed to learn more consistent representation of two modalities and reduce noise. Extensive experiments and analyses on two public MNER datasets, Twitter2015 and Twitter2017, demonstrate the better performance of our model against the state-of-the-art methods.|多模态命名实体识别(MNER)是一个视觉语言任务，其目的是检测实体跨度，并将其分类为相应的实体类型给出一个句子-图像对。现有的方法通常将图像视为一组可视对象，试图显式地捕获可视对象和实体之间的关系。然而，由于视觉对象在数量和类型上往往与实体不完全相同，它们可能受到视觉对象引入的偏见的影响，而不是受到辅助的影响。受基于文本提示的微调(PF)方法在许多方法中取得成功的启发，本文提出了一种基于多模式提示的机器阅读理解的框架，用于文本和图像之间的隐式对齐以改善 MNER，即 MPMRC-MNER。具体来说，我们将 MRC 中的纯文本查询转换为包含图像标记和文本标记的多模式提示。为了更好地集成图像标记和文本标记，我们设计了一种更好的跨模态融合的快速感知注意机制。最后，设计了两种对比损失下的对比学习算法，使两种模式的表示更加一致，从而降低了噪声。对两个公开的 MNER 数据集 Twitter2015和 Twitter2017进行了大量的实验和分析，证明了我们的模型比最先进的方法具有更好的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=MPMRC-MNER:+A+Unified+MRC+framework+for+Multimodal+Named+Entity+Recognition+based+Multimodal+Prompt)|0| |[Bridged-GNN: Knowledge Bridge Learning for Effective Knowledge Transfer](https://doi.org/10.1145/3583780.3614796)|Wendong Bi, Xueqi Cheng, Bingbing Xu, Xiaoqian Sun, Easton Li Xu, Huawei Shen|Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China|The data-hungry problem, characterized by insufficiency and low-quality of data, poses obstacles for deep learning models. Transfer learning has been a feasible way to transfer knowledge from high-quality external data of source domains to limited data of target domains, which follows a domain-level knowledge transfer to learn a shared posterior distribution. However, they are usually built on strong assumptions, e.g., the domain invariant posterior distribution, which is usually unsatisfied and may introduce noises, resulting in poor generalization ability on target domains. Inspired by Graph Neural Networks (GNNs) that aggregate information from neighboring nodes, we redefine the paradigm as learning a knowledge-enhanced posterior distribution for target domains, namely Knowledge Bridge Learning (KBL). KBL first learns the scope of knowledge transfer by constructing a Bridged-Graph that connects knowledgeable samples to each target sample and then performs sample-wise knowledge transfer via GNNs.KBL is free from strong assumptions and is robust to noises in the source data. Guided by KBL, we propose the Bridged-GNN} including an Adaptive Knowledge Retrieval module to build Bridged-Graph and a Graph Knowledge Transfer module. Comprehensive experiments on both un-relational and relational data-hungry scenarios demonstrate the significant improvements of Bridged-GNN compared with SOTA methods|数据匮乏、拥有属性不足和数据质量低下的问题，对深度学习模型构成了障碍。迁移学习是一种可行的方法，可以将来源领域的高质量外部数据转移到目标领域的有限数据中，这种方法遵循领域级别的知识转移，以学习共享的后验概率。然而，它们通常建立在强烈的假设之上，例如，领域不变后验概率，这通常是不满足的，并可能引入噪声，导致对目标领域的概括能力差。受到聚集邻近节点信息的图形神经网络(GNN)的启发，我们将该范式重新定义为学习目标领域的知识增强后验概率，即知识桥梁学习(Knowledge Bridge Learning，KBL)。KBL 首先通过构造一个连接知识样本和目标样本的桥图来学习知识转移的范围，然后通过 GNNs 进行样本智能知识转移。 KBL 不受强假设的约束，对源数据中的噪声具有鲁棒性。在 KBL 的指导下，本文提出了桥接 GNN 模型，包括自适应知识检索模块和图形知识传递模块。在非关系和关系数据饥饿场景下的综合实验表明，与 SOTA 方法相比，桥接 GNN 方法有显著的改进|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Bridged-GNN:+Knowledge+Bridge+Learning+for+Effective+Knowledge+Transfer)|0| |[Enabling Health Data Sharing with Fine-Grained Privacy](https://doi.org/10.1145/3583780.3614864)|Luca Bonomi, Sepand Gousheh, Liyue Fan|University of North Carolina at Charlotte, Charlotte, NC, USA; Vanderbilt University Medical Center, Nashville, TN, USA|Sharing health data is vital in advancing medical research and transforming knowledge into clinical practice. Meanwhile, protecting the privacy of data contributors is of paramount importance. To that end, several privacy approaches have been proposed to protect individual data contributors in data sharing, including data anonymization and data synthesis techniques. These approaches have shown promising results in providing privacy protection at the dataset level. In this work, we study the privacy challenges in enabling fine-grained privacy in health data sharing. Our work is motivated by recent research findings, in which patients and healthcare providers may have different privacy preferences and policies that need to be addressed. Specifically, we propose a novel and effective privacy solution that enables data curators (e.g., healthcare providers) to protect sensitive data elements while preserving data usefulness. Our solution builds on randomized techniques to provide rigorous privacy protection for sensitive elements and leverages graphical models to mitigate privacy leakage due to dependent elements. To enhance the usefulness of the shared data, our randomized mechanism incorporates domain knowledge to preserve semantic similarity and adopts a block-structured design to minimize utility loss. Evaluations with real-world health data demonstrate the effectiveness of our approach and the usefulness of the shared data for health applications.|共享健康数据对于推进医学研究和将知识转化为临床实践至关重要。同时，保护数据提供者的隐私至关重要。为此，提出了几种保护数据共享中个人数据贡献者的隐私方法，包括数据匿名化和数据合成技术。这些方法在提供数据集级别的隐私保护方面显示了有希望的结果。在这项工作中，我们研究了在健康数据共享中实现细粒度隐私的隐私挑战。我们的工作是由最近的研究发现，其中病人和医疗保健提供者可能有不同的隐私偏好和政策，需要加以解决。具体来说，我们提出了一个新颖而有效的隐私解决方案，使数据管理者(例如，医疗保健提供者)能够在保护数据有用性的同时保护敏感的数据元素。我们的解决方案建立在随机技术的基础上，为敏感元素提供严格的隐私保护，并利用图形化模型来缓解因依赖元素而导致的隐私泄漏。为了提高共享数据的有用性，我们的随机机制结合了领域知识来保持语义相似性，并采用了块结构设计来最小化效用损失。使用真实世界卫生数据进行的评估表明了我们的方法的有效性以及共享数据对卫生应用的有用性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Enabling+Health+Data+Sharing+with+Fine-Grained+Privacy)|0| |[Fair&Share: Fast and Fair Multi-Criteria Selections](https://doi.org/10.1145/3583780.3614874)|Kathleen Cachel, Elke A. Rundensteiner|USC-ISI|AbstractWith the widespread use of artificial intelligence (AI) systems and applications in our everyday lives, accounting for fairness has gained significant importance in designing and engineering of such systems. AI systems can be used in many sensitive environments to make important and life-changing decisions; thus, it is crucial to ensure that these decisions do not reflect discriminatory behavior toward certain groups or populations. More recently some work has been developed in traditional machine learning and deep learning that address such challenges in different subdomains. With the commercialization of these systems, researchers are becoming more aware of the biases that these applications can contain and are attempting to address them. In this survey, we investigated different real-world applications that have shown biases in various ways, and we listed different sources of biases that can affect AI applications. We then created a taxonomy for fairness definitions that machine learning researchers have defined to avoid the existing bias in AI systems. In addition to that, we examined different domains and subdomains in AI showing what researchers have observed with regard to unfair outcomes in the state-of-the-art methods and ways they have tried to address them. There are still many future directions and solutions that can be taken to mitigate the problem of bias in AI systems. We are hoping that this survey will motivate researchers to tackle these issues in the near future by observing existing work in their respective fields.|随着人工智能(AI)系统和应用在日常生活中的广泛应用，公平会计在人工智能系统的设计和工程中显得越来越重要。人工智能系统可以在许多敏感的环境中作出重要和改变生活的决定; 因此，至关重要的是确保这些决定不反映对某些群体或人口的歧视行为。最近，在传统的机器学习和深度学习领域开展了一些工作，以应对不同子领域的此类挑战。随着这些系统的商业化，研究人员越来越意识到这些应用程序可能存在的偏差，并试图解决这些偏差。在这项调查中，我们调查了不同的现实世界中的应用程序，它们以不同的方式表现出偏差，我们列出了可能影响人工智能应用程序的不同偏差来源。然后，我们创建了一个公平定义的分类，机器学习研究人员已经定义，以避免在人工智能系统中存在的偏见。除此之外，我们研究了 AI 中的不同领域和子领域，显示了研究人员在最先进的方法和他们试图解决这些问题的方法中所观察到的不公平结果。为了减轻人工智能系统中的偏差问题，仍然有许多未来的方向和解决方案可以采取。我们希望这项调查能够激励研究人员在不久的将来通过观察他们各自领域的现有工作来解决这些问题。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Fair&Share:+Fast+and+Fair+Multi-Criteria+Selections)|0| |[MemDA: Forecasting Urban Time Series with Memory-based Drift Adaptation](https://doi.org/10.1145/3583780.3614962)|Zekun Cai, Renhe Jiang, Xinyu Yang, Zhaonan Wang, Diansheng Guo, Hill Hiroki Kobayashi, Xuan Song, Ryosuke Shibasaki|The University of Tokyo, Tokyo, Japan; Tencent Corporation, Beijing, China|Urban time series data forecasting featuring significant contributions to sustainable development is widely studied as an essential task of the smart city. However, with the dramatic and rapid changes in the world environment, the assumption that data obey Independent Identically Distribution is undermined by the subsequent changes in data distribution, known as concept drift, leading to weak replicability and transferability of the model over unseen data. To address the issue, previous approaches typically retrain the model, forcing it to fit the most recent observed data. However, retraining is problematic in that it leads to model lag, consumption of resources, and model re-invalidation, causing the drift problem to be not well solved in realistic scenarios. In this study, we propose a new urban time series prediction model for the concept drift problem, which encodes the drift by considering the periodicity in the data and makes on-the-fly adjustments to the model based on the drift using a meta-dynamic network. Experiments on real-world datasets show that our design significantly outperforms state-of-the-art methods and can be well generalized to existing prediction backbones by reducing their sensitivity to distribution changes.|城市时间序列数据预测作为智能城市的一项重要任务，对城市可持续发展作出了重要贡献。然而，随着世界环境的急剧和迅速变化，数据服从独立同一分布的假设被随后的数据分布变化(称为概念漂移)所破坏，导致模型对无形数据的可复制性和可转移性较弱。为了解决这个问题，以前的方法通常会重新训练模型，迫使它适应最近观察到的数据。然而，再训练会导致模型滞后、资源消耗和模型再失效，使得漂移问题在现实情况下得不到很好的解决。针对概念漂移问题，提出了一种新的城市时间序列预测模型。在真实世界数据集上的实验表明，我们的设计明显优于最先进的方法，可以很好地推广到现有的预测骨干，降低其对分布变化的敏感性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=MemDA:+Forecasting+Urban+Time+Series+with+Memory-based+Drift+Adaptation)|0| |[Inducing Causal Structure for Abstractive Text Summarization](https://doi.org/10.1145/3583780.3614934)|Lu Chen, Ruqing Zhang, Wei Huang, Wei Chen, Jiafeng Guo, Xueqi Cheng|ICT, CAS & University of Chinese Academy of Sciences, Beijing, China|The mainstream of data-driven abstractive summarization models tends to explore the correlations rather than the causal relationships. Among such correlations, there can be spurious ones which suffer from the language prior learned from the training corpus and therefore undermine the overall effectiveness of the learned model. To tackle this issue, we introduce a Structural Causal Model (SCM) to induce the underlying causal structure of the summarization data. We assume several latent causal factors and non-causal factors, representing the content and style of the document and summary. Theoretically, we prove that the latent factors in our SCM can be identified by fitting the observed training data under certain conditions. On the basis of this, we propose a Causality Inspired Sequence-to-Sequence model (CI-Seq2Seq) to learn the causal representations that can mimic the causal factors, guiding us to pursue causal information for summary generation. The key idea is to reformulate the Variational Auto-encoder (VAE) to fit the joint distribution of the document and summary variables from the training corpus. Experimental results on two widely used text summarization datasets demonstrate the advantages of our approach.|数据驱动的抽象摘要模型的主流趋向于探索相关性，而不是因果关系。在这些相关性中，可能存在一些虚假的相关性，这些相关性受到事先从培训语料库中学到的语言的影响，因此会破坏所学模型的整体有效性。为了解决这个问题，我们引入了一个结构性因果模型(SCM)来归纳总结数据的潜在因果结构。我们假设几个潜在的因果因素和非因果因素，代表的内容和风格的文件和总结。从理论上证明了在一定条件下，通过拟合观测训练数据可以识别供应链管理中的潜在因素。在此基础上，我们提出了一个受因果关系启发的序列到序列模型(CI-Seq2Seq)来学习可以模拟因果因素的因果表征，指导我们追求因果信息以进行摘要生成。其核心思想是对变分自动编码器(VAE)进行重构，以适应训练语料中文档和汇总变量的联合分布。在两个广泛使用的文本摘要数据集上的实验结果证明了该方法的优越性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Inducing+Causal+Structure+for+Abstractive+Text+Summarization)|0| -|[Region Profile Enhanced Urban Spatio-Temporal Prediction via Adaptive Meta-Learning](https://doi.org/10.1145/3583780.3615027)|Jie Chen, Tong Liu, Ruiyuan Li|Chongqing University, Chongqing, China; Shanghai University, Shanghai, China|Urban spatio-temporal (ST) prediction plays a crucial role in smart city construction. Due to the high cost of ST data collection, improving ST prediction in a lack of data is significant. For this purpose, existing meta-learning methods have been demonstrated powerful by learning an initial network from training tasks and adjusting to target tasks with limited data. However, such shared knowledge from a set of tasks may contain irrelevant noise due to the gap of region-varying ST dynamics, resulting in the negative transfer issue. As a revelation of regional functional patterns, region profiles give rise to the diversity of ST dynamics. Thus, we design a novel adaptive meta-optimized model MetaRSTP, which conducts the initial prediction model in a finer-granularity of region level with region profiles as semantic evidence. To enhance the expressiveness of profiles, we firstly build a semantic alignment space to explore the inter-view co-semantics. Fusing it with view-specific uniqueness, the multi-view region profiles can be better applied in urban tasks. Then, a regional bias generator derives non-shared parameters in terms of profiles, which alleviates the divergence among regions. We set a new meta-learning strategy as initialize the network with fixed generalizable parameters and region-adaptive bias, thus enhancing the personalized prediction performance even in few-shot scenarios. Extensive experiments on real-world datasets illustrate the effectiveness of our MetaRSTP and our learned region profiles.|城市时空预测在智能城市建设中起着至关重要的作用。由于 ST 段数据采集成本较高，因此在缺乏数据的情况下改进 ST 段预测具有重要意义。为此，现有的元学习方法通过从训练任务中学习初始网络并根据有限的数据调整到目标任务已被证明是有效的。然而，由于区域变化的 ST 动力学之间的差异，来自一组任务的共享知识可能含有不相关的噪声，从而导致负迁移问题。区域剖面揭示了区域功能格局，引起了 ST 动力学的多样性。因此，我们设计了一个新的自适应元优化模型 MetaRSTP，该模型以区域轮廓作为语义证据，在较细粒度的区域水平上进行初始预测模型。为了提高配置文件的表达能力，我们首先构建了一个语义对齐空间来探索访问共语义。结合视图特定的唯一性，多视图区域轮廓可以更好地应用于城市任务。然后，区域偏差发生器根据轮廓推导出非共享参数，减小了区域间的差异。我们设置了一种新的元学习策略，即在固定的可推广参数和区域自适应偏差的情况下对网络进行初始化，从而提高了在少镜头情况下的个性化预测性能。对真实世界数据集的大量实验说明了我们的 MetaRSTP 和我们学习的区域概况的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Region+Profile+Enhanced+Urban+Spatio-Temporal+Prediction+via+Adaptive+Meta-Learning)|0| +|[Region Profile Enhanced Urban Spatio-Temporal Prediction via Adaptive Meta-Learning](https://doi.org/10.1145/3583780.3615027)|Jie Chen, Tong Liu, Ruiyuan Li|Shanghai University, Shanghai, China; Chongqing University, Chongqing, China|Urban spatio-temporal (ST) prediction plays a crucial role in smart city construction. Due to the high cost of ST data collection, improving ST prediction in a lack of data is significant. For this purpose, existing meta-learning methods have been demonstrated powerful by learning an initial network from training tasks and adjusting to target tasks with limited data. However, such shared knowledge from a set of tasks may contain irrelevant noise due to the gap of region-varying ST dynamics, resulting in the negative transfer issue. As a revelation of regional functional patterns, region profiles give rise to the diversity of ST dynamics. Thus, we design a novel adaptive meta-optimized model MetaRSTP, which conducts the initial prediction model in a finer-granularity of region level with region profiles as semantic evidence. To enhance the expressiveness of profiles, we firstly build a semantic alignment space to explore the inter-view co-semantics. Fusing it with view-specific uniqueness, the multi-view region profiles can be better applied in urban tasks. Then, a regional bias generator derives non-shared parameters in terms of profiles, which alleviates the divergence among regions. We set a new meta-learning strategy as initialize the network with fixed generalizable parameters and region-adaptive bias, thus enhancing the personalized prediction performance even in few-shot scenarios. Extensive experiments on real-world datasets illustrate the effectiveness of our MetaRSTP and our learned region profiles.|城市时空预测在智能城市建设中起着至关重要的作用。由于 ST 段数据采集成本较高，因此在缺乏数据的情况下改进 ST 段预测具有重要意义。为此，现有的元学习方法通过从训练任务中学习初始网络并根据有限的数据调整到目标任务已被证明是有效的。然而，由于区域变化的 ST 动力学之间的差异，来自一组任务的共享知识可能含有不相关的噪声，从而导致负迁移问题。区域剖面揭示了区域功能格局，引起了 ST 动力学的多样性。因此，我们设计了一个新的自适应元优化模型 MetaRSTP，该模型以区域轮廓作为语义证据，在较细粒度的区域水平上进行初始预测模型。为了提高配置文件的表达能力，我们首先构建了一个语义对齐空间来探索访问共语义。结合视图特定的唯一性，多视图区域轮廓可以更好地应用于城市任务。然后，区域偏差发生器根据轮廓推导出非共享参数，减小了区域间的差异。我们设置了一种新的元学习策略，即在固定的可推广参数和区域自适应偏差的情况下对网络进行初始化，从而提高了在少镜头情况下的个性化预测性能。对真实世界数据集的大量实验说明了我们的 MetaRSTP 和我们学习的区域概况的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Region+Profile+Enhanced+Urban+Spatio-Temporal+Prediction+via+Adaptive+Meta-Learning)|0| |[Learning Pair-Centric Representation for Link Sign Prediction with Subgraph](https://doi.org/10.1145/3583780.3614951)|Jushuo Chen, Feifei Dai, Xiaoyan Gu, Haihui Fan, Jiang Zhou, Bo Li, Weiping Wang|Institute of Information Engineering, Chinese Academy of Sciences & School of Cyber Security, University of Chinese Academy of Sciences, Beijing, China; Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China|Signed graphs are prevalent data structures containing both positive and negative links. Recently, the fundamental network analysis task on signed graphs, namely link sign prediction, has received careful attention. Existing methods learn two target node representations independently, and the sign between these two nodes is predicted based on similarity. However, such a paradigm is node-centric that cannot distinguish node pairs with distinct contexts, thus lowering the prediction performance. Learning pair-centric representation is therefore a rewarding way to be aware of differences between pairs. There is no study yet on how to build such an appropriate representation that can effectively infer the sign between the target node pair. In this paper, we provide a new perspective to conduct link sign prediction within the paradigm of subgraph classification and propose a novel Subgraph-based link Sign Prediction (SSP) model. Technically, SSP uses importance-based sampling to extract an informative subgraph around each target node pair. For each subgraph, an innovative node labeling scheme is designed to encode its structural and signed information for representation learning. To further utilize the subgraph representation for imbalanced sign classification, SSP employs self-pruning contrastive learning to gain balanced representations. Extensive experiments on real-world datasets demonstrate that SSP consistently and significantly outperforms all the state-of-the-art baselines.|签名图是一种普遍的数据结构，包含正向和负向链接。近年来，有符号图的基本网络分析任务，即链路符号预测，受到了广泛的关注。现有的方法分别学习两个目标节点的表示，并根据相似性对这两个节点之间的符号进行预测。然而，这种模式是以节点为中心的，不能区分具有不同上下文的节点对，从而降低了预测性能。因此，学习以配对为中心的表征是了解配对之间差异的一种有益的方法。目前还没有研究如何建立这样一个适当的表示，可以有效地推断符号之间的目标节点对。本文从子图分类的角度提出了一种新的链路符号预测方法，并提出了一种新的基于子图的链路符号预测(SSP)模型。从技术上讲，SSP 使用基于重要性的抽样来提取每个目标节点对周围的信息子图。针对每个子图，设计了一种新的节点标记方案，对其结构信息和符号信息进行编码，用于表示学习。为了进一步利用子图表示进行不平衡符号分类，SSP 采用自剪枝对比学习来获得平衡表示。对真实世界数据集的大量实验表明，SSP 始终如一地显著优于所有最先进的基线。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Learning+Pair-Centric+Representation+for+Link+Sign+Prediction+with+Subgraph)|0| -|[Meta-Transfer-Learning for Time Series Data with Extreme Events: An Application to Water Temperature Prediction](https://doi.org/10.1145/3583780.3614966)|Shengyu Chen, Nasrin Kalanat, Simon Topp, Jeffrey M. Sadler, Yiqun Xie, Zhe Jiang, Xiaowei Jia|University of Maryland, College Park, MD, USA; University of Florida, Gainesville, FL, USA; Oklahoma State University, Stillwater, OK, USA; Upstream Tech, Fridley, MN, USA; University of Pittsburgh, Pittsburgh, PA, USA|This paper proposes a meta-transfer-learning method for predicting daily maximum water temperature in stream networks with explicit modeling of extreme events. Accurate prediction of these extreme events is challenging because of their sparsity in the training data and their distinct responses to external drivers when compared to non-extreme observations. To overcome these challenges, we propose a sample reweighting strategy to escalate the importance of extreme events in the training process while preserving the predictive performance in normal time periods. The sample weight for each training data point is estimated as the similarity with the target test data point using contextual information and physical simulation. The obtained sample weight values are then used to fine-tune the initial model to transfer it to the test data. This method is further enhanced by an extreme value theory-based loss function to enforce the distribution of extreme data points and accelerated by a clustering algorithm based on the estimated similarities. Additionally, we introduce an online learning strategy to further refine the predictive model using newly collected observed data. The experimental results using real stream data from the Delaware River Basin over the past 36 years demonstrate that our meta-transfer-learning method produces more accurate predictions in both normal and extreme time periods when compared to baselines without the sample re-weighting scheme. The similarity learning method can reveal meaningful relationships amongst data points. We also show that the clustering algorithm can be used to accelerate the prediction while not compromising the predictive performance. The online learning strategy is shown to further improve predictive performance using recently observed data.|提出了一种基于显式极端事件模型的元传递学习方法来预测河网日最高水温。这些极端事件的准确预测是具有挑战性的，因为它们的训练数据稀少，与非极端观测相比，它们对外部驱动因素的反应截然不同。为了克服这些挑战，我们提出了一个样本重新加权策略，以提高极端事件在训练过程中的重要性，同时保持在正常时间段的预测性能。利用上下文信息和物理仿真，将每个训练数据点的样本权重估计为与目标测试数据点的相似度。然后使用获得的样本权重值对初始模型进行微调，将其传输到测试数据。该方法进一步通过基于极值理论的损失函数增强极值数据点的分布，并通过基于估计相似度的聚类算法加速。此外，我们引入了一个在线学习策略，使用新收集的观测数据进一步完善预测模型。使用特拉华河流域过去36年的实际水流数据的实验结果表明，与没有样本重新加权方案的基线相比，我们的元转移学习方法在正常和极端时间段产生更准确的预测。相似性学习方法可以揭示数据点之间有意义的关系。我们还表明，聚类算法可以在不影响预测性能的前提下加快预测速度。在线学习策略利用最近的观测数据进一步提高了预测性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Meta-Transfer-Learning+for+Time+Series+Data+with+Extreme+Events:+An+Application+to+Water+Temperature+Prediction)|0| -|[Hadamard Adapter: An Extreme Parameter-Efficient Adapter Tuning Method for Pre-trained Language Models](https://doi.org/10.1145/3583780.3614904)|Yuyan Chen, Qiang Fu, Ge Fan, Lun Du, JianGuang Lou, Shi Han, Dongmei Zhang, Zhixu Li, Yanghua Xiao|Shanghai Key Laboratory of Data Science, School of Computer Science, Fudan University & Fudan-Aishu Cognitive Intelligence Joint Research Center, Shanghai, China; Shanghai Key Laboratory of Data Science, School of Computer Science, Fudan University, Shanghai, China; Microsoft, Beijing, China; Tencent, Shenzhen, China|Recent years, Pre-trained Language models (PLMs) have swept into various fields of artificial intelligence and achieved great success. However, most PLMs, such as T5 and GPT3, have a huge amount of parameters, fine-tuning them is often expensive and time consuming, and storing them takes up a lot of space. Therefore, it is necessary to adopt a parameter-efficient approach to reduce parameters of PLMs in fine-tuning without compromising their performance in downstream tasks. In this paper, we design a novel adapter which only acts on self-attention outputs in PLMs. This adapter adopts element-wise linear transformation using Hadamard product, hence named as Hadamard adapter, requires the fewest parameters compared to previous parameter-efficient adapters. In addition, we also summarize some tuning patterns for Hadamard adapter shared by various downstream tasks, expecting to provide some guidance for further parameter reduction with shared adapters in future studies. The experiments conducted on the widely-used GLUE benchmark with several SOTA PLMs prove that the Hadamard adapter achieves competitive performance with only 0.033% parameters compared with full fine-tuning, and it has the fewest parameters compared with other adapters. Moreover, we further find that there is also some redundant layers in the Hadamard adapter which can be removed to achieve more parameter efficiency with only 0.022% parameters.|近年来，预训练语言模型(PLM)已经深入人工智能的各个领域，并取得了巨大的成功。然而，大多数 PLM，如 T5和 GPT3，都有大量的参数，对它们进行微调通常代价高昂且耗时，并且存储它们占用大量空间。因此，有必要采取一种参数有效的方法，以减少参数的 PLM 在微调，而不损害其在下游任务的性能。在本文中，我们设计了一种新颖的适配器，它只作用于 PLM 中的自注意输出。这个适配器采用了使用 Hadamard 产品的元素线性映射，因此被命名为 Hadamard 适配器，与以前的参数高效适配器相比，需要的参数最少。此外，我们还总结了各种下游任务共享的 Hadamard 适配器的一些调优模式，希望在未来的研究中为共享适配器的进一步参数减少提供一些指导。利用多个 SOTA PLM 在广泛使用的 GLUE 基准上进行的实验表明，与完全微调相比，Hadamard 适配器的参数只有0.033% ，而且参数最少，具有一定的竞争力。此外，我们进一步发现，在 Hadamard 适配器中也存在一些冗余层，可以删除这些冗余层，以便在只有0.022% 参数的情况下实现更高的参数效率。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Hadamard+Adapter:+An+Extreme+Parameter-Efficient+Adapter+Tuning+Method+for+Pre-trained+Language+Models)|0| -|[Hallucination Detection: Robustly Discerning Reliable Answers in Large Language Models](https://doi.org/10.1145/3583780.3614905)|Yuyan Chen, Qiang Fu, Yichen Yuan, Zhihao Wen, Ge Fan, Dayiheng Liu, Dongmei Zhang, Zhixu Li, Yanghua Xiao|Shanghai Key Laboratory of Data Science & School of Computer Science, Fudan University, Shanghai, China; Shanghai Key Laboratory of Data Science, Beijing, China; Microsoft, Beijing, China; Tencent, Shenzhen, China; DAMO Academy, Hangzhou, China; Singapore Management University, Singapore, Singapore; Shanghai Key Laboratory of Data Science & School of Computer Science, Fudan University & Fudan-Aishu Cognitive Intelligence Joint Research Center, Shanghai, China; Shanghai Key Laboratory of Data Science & School of Computer Science, Fudan University & Fudan-Aishu Cognitive Intelligence Joint Research Center, Beijing, China; Microsoft, Shanghai, China|Large language models (LLMs) have gained widespread adoption in various natural language processing tasks, including question answering and dialogue systems. However, a major drawback of LLMs is the issue of hallucination, where they generate unfaithful or inconsistent content that deviates from the input source, leading to severe consequences. In this paper, we propose a robust discriminator named RelD to effectively detect hallucination in LLMs' generated answers. RelD is trained on the constructed RelQA, a bilingual question-answering dialogue dataset along with answers generated by LLMs and a comprehensive set of metrics. Our experimental results demonstrate that the proposed RelD successfully detects hallucination in the answers generated by diverse LLMs. Moreover, it performs well in distinguishing hallucination in LLMs' generated answers from both in-distribution and out-of-distribution datasets. Additionally, we also conduct a thorough analysis of the types of hallucinations that occur and present valuable insights. This research significantly contributes to the detection of reliable answers generated by LLMs and holds noteworthy implications for mitigating hallucination in the future work.|大语言模型(LLM)已经广泛应用于各种自然语言处理任务，包括问答和对话系统。然而，LLM 的一个主要缺点是产生幻觉的问题，它们产生不忠实或不一致的内容，偏离输入源，导致严重的后果。在本文中，我们提出了一个鲁棒的鉴别器 RelD 来有效地检测 LLM 生成的答案中的幻觉。RelD 是基于构建的 RelQA 进行训练的，RelQA 是一个双语问答对话数据集，以及由 LLM 生成的答案和一套全面的度量标准。我们的实验结果表明，提出的 RelD 成功地检测幻觉的答案所产生的不同的 LLM。此外，它还能很好地区分 LLM 生成的分布内和分布外数据集中的幻觉。此外，我们还对出现的幻觉类型进行了全面分析，并提出了有价值的见解。这项研究显著有助于发现可靠的答案所产生的 LLM 和举行值得注意的影响，以减轻幻觉在未来的工作。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Hallucination+Detection:+Robustly+Discerning+Reliable+Answers+in+Large+Language+Models)|0| -|[Deep Generative Imputation Model for Missing Not At Random Data](https://doi.org/10.1145/3583780.3614835)|Jialei Chen, Yuanbo Xu, Pengyang Wang, Yongjian Yang|MIC Lab, Department of Computer Science and Technology, Jilin University, Changchun, China; SKL-IOTSC, Department of Computer and Information Science, University of Macau, Macao, China|Data analysis usually suffers from the Missing Not At Random (MNAR) problem, where the cause of the value missing is not fully observed. Compared to the naive Missing Completely At Random (MCAR) problem, it is more in line with the realistic scenario whereas more complex and challenging. Existing statistical methods model the MNAR mechanism by different decomposition of the joint distribution of the complete data and the missing mask. But we empirically find that directly incorporating these statistical methods into deep generative models is sub-optimal. Specifically, it would neglect the confidence of the reconstructed mask during the MNAR imputation process, which leads to insufficient information extraction and less-guaranteed imputation quality. In this paper, we revisit the MNAR problem from a novel perspective that the complete data and missing mask are two modalities of incomplete data on an equal footing. Along with this line, we put forward a generative-model-specific joint probability decomposition method, conjunction model, to represent the distributions of two modalities in parallel and extract sufficient information from both complete data and missing mask. Taking a step further, we exploit a deep generative imputation model, namely GNR, to process the real-world missing mechanism in the latent space and concurrently impute the incomplete data and reconstruct the missing mask. The experimental results show that our GNR surpasses state-of-the-art MNAR baselines with significant margins (averagely improved from 9.9% to 18.8% in RMSE) and always gives a better mask reconstruction accuracy which makes the imputation more principle.|数据分析通常会遇到丢失不随机(MNAR)问题，其中值丢失的原因没有得到充分的观察。与天真的完全随机缺失(MCAR)问题相比，它更符合现实情况，而更复杂和具有挑战性。现有的统计方法通过对完整数据和缺失模板的联合分布进行不同的分解来模拟 MNAR 机制。但我们经验发现，直接将这些统计方法纳入深层生成模型是次优的。具体来说，它会忽略重建掩码在 MNAR 估算过程中的置信度，导致信息抽取不足和估算质量得不到保证。本文从一个新的角度重新讨论了 MNAR 问题，即完全数据和缺失掩模是不完全数据在同等条件下的两种形式。在此基础上，提出了一种特定于生成模型的联合概率分解方法——联合模型，用于并行表示两种模式的分布，并从完整数据和缺失模板中提取足够的信息。进一步，我们利用一个深层生成插补模型，即 GNR，来处理潜在空间中真实世界的缺失机制，同时对不完全数据进行插补并重构缺失掩模。实验结果表明，我们的 GNR 超越了最先进的 MNAR 基线，具有显著的边界(RMSE 平均值从9.9% 提高到18.8%) ，并且总是具有较好的掩模重建精度，使得插补更加原则。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Deep+Generative+Imputation+Model+for+Missing+Not+At+Random+Data)|0| +|[Meta-Transfer-Learning for Time Series Data with Extreme Events: An Application to Water Temperature Prediction](https://doi.org/10.1145/3583780.3614966)|Shengyu Chen, Nasrin Kalanat, Simon Topp, Jeffrey M. Sadler, Yiqun Xie, Zhe Jiang, Xiaowei Jia|Oklahoma State University, Stillwater, OK, USA; University of Pittsburgh, Pittsburgh, PA, USA; University of Florida, Gainesville, FL, USA; Upstream Tech, Fridley, MN, USA; University of Maryland, College Park, MD, USA|This paper proposes a meta-transfer-learning method for predicting daily maximum water temperature in stream networks with explicit modeling of extreme events. Accurate prediction of these extreme events is challenging because of their sparsity in the training data and their distinct responses to external drivers when compared to non-extreme observations. To overcome these challenges, we propose a sample reweighting strategy to escalate the importance of extreme events in the training process while preserving the predictive performance in normal time periods. The sample weight for each training data point is estimated as the similarity with the target test data point using contextual information and physical simulation. The obtained sample weight values are then used to fine-tune the initial model to transfer it to the test data. This method is further enhanced by an extreme value theory-based loss function to enforce the distribution of extreme data points and accelerated by a clustering algorithm based on the estimated similarities. Additionally, we introduce an online learning strategy to further refine the predictive model using newly collected observed data. The experimental results using real stream data from the Delaware River Basin over the past 36 years demonstrate that our meta-transfer-learning method produces more accurate predictions in both normal and extreme time periods when compared to baselines without the sample re-weighting scheme. The similarity learning method can reveal meaningful relationships amongst data points. We also show that the clustering algorithm can be used to accelerate the prediction while not compromising the predictive performance. The online learning strategy is shown to further improve predictive performance using recently observed data.|提出了一种基于显式极端事件模型的元传递学习方法来预测河网日最高水温。这些极端事件的准确预测是具有挑战性的，因为它们的训练数据稀少，与非极端观测相比，它们对外部驱动因素的反应截然不同。为了克服这些挑战，我们提出了一个样本重新加权策略，以提高极端事件在训练过程中的重要性，同时保持在正常时间段的预测性能。利用上下文信息和物理仿真，将每个训练数据点的样本权重估计为与目标测试数据点的相似度。然后使用获得的样本权重值对初始模型进行微调，将其传输到测试数据。该方法进一步通过基于极值理论的损失函数增强极值数据点的分布，并通过基于估计相似度的聚类算法加速。此外，我们引入了一个在线学习策略，使用新收集的观测数据进一步完善预测模型。使用特拉华河流域过去36年的实际水流数据的实验结果表明，与没有样本重新加权方案的基线相比，我们的元转移学习方法在正常和极端时间段产生更准确的预测。相似性学习方法可以揭示数据点之间有意义的关系。我们还表明，聚类算法可以在不影响预测性能的前提下加快预测速度。在线学习策略利用最近的观测数据进一步提高了预测性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Meta-Transfer-Learning+for+Time+Series+Data+with+Extreme+Events:+An+Application+to+Water+Temperature+Prediction)|0| +|[Hadamard Adapter: An Extreme Parameter-Efficient Adapter Tuning Method for Pre-trained Language Models](https://doi.org/10.1145/3583780.3614904)|Yuyan Chen, Qiang Fu, Ge Fan, Lun Du, JianGuang Lou, Shi Han, Dongmei Zhang, Zhixu Li, Yanghua Xiao|Tencent, Shenzhen, China; Microsoft, Beijing, China; Shanghai Key Laboratory of Data Science, School of Computer Science, Fudan University, Shanghai, China; Shanghai Key Laboratory of Data Science, School of Computer Science, Fudan University & Fudan-Aishu Cognitive Intelligence Joint Research Center, Shanghai, China|Recent years, Pre-trained Language models (PLMs) have swept into various fields of artificial intelligence and achieved great success. However, most PLMs, such as T5 and GPT3, have a huge amount of parameters, fine-tuning them is often expensive and time consuming, and storing them takes up a lot of space. Therefore, it is necessary to adopt a parameter-efficient approach to reduce parameters of PLMs in fine-tuning without compromising their performance in downstream tasks. In this paper, we design a novel adapter which only acts on self-attention outputs in PLMs. This adapter adopts element-wise linear transformation using Hadamard product, hence named as Hadamard adapter, requires the fewest parameters compared to previous parameter-efficient adapters. In addition, we also summarize some tuning patterns for Hadamard adapter shared by various downstream tasks, expecting to provide some guidance for further parameter reduction with shared adapters in future studies. The experiments conducted on the widely-used GLUE benchmark with several SOTA PLMs prove that the Hadamard adapter achieves competitive performance with only 0.033% parameters compared with full fine-tuning, and it has the fewest parameters compared with other adapters. Moreover, we further find that there is also some redundant layers in the Hadamard adapter which can be removed to achieve more parameter efficiency with only 0.022% parameters.|近年来，预训练语言模型(PLM)已经深入人工智能的各个领域，并取得了巨大的成功。然而，大多数 PLM，如 T5和 GPT3，都有大量的参数，对它们进行微调通常代价高昂且耗时，并且存储它们占用大量空间。因此，有必要采取一种参数有效的方法，以减少参数的 PLM 在微调，而不损害其在下游任务的性能。在本文中，我们设计了一种新颖的适配器，它只作用于 PLM 中的自注意输出。这个适配器采用了使用 Hadamard 产品的元素线性映射，因此被命名为 Hadamard 适配器，与以前的参数高效适配器相比，需要的参数最少。此外，我们还总结了各种下游任务共享的 Hadamard 适配器的一些调优模式，希望在未来的研究中为共享适配器的进一步参数减少提供一些指导。利用多个 SOTA PLM 在广泛使用的 GLUE 基准上进行的实验表明，与完全微调相比，Hadamard 适配器的参数只有0.033% ，而且参数最少，具有一定的竞争力。此外，我们进一步发现，在 Hadamard 适配器中也存在一些冗余层，可以删除这些冗余层，以便在只有0.022% 参数的情况下实现更高的参数效率。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Hadamard+Adapter:+An+Extreme+Parameter-Efficient+Adapter+Tuning+Method+for+Pre-trained+Language+Models)|0| +|[Hallucination Detection: Robustly Discerning Reliable Answers in Large Language Models](https://doi.org/10.1145/3583780.3614905)|Yuyan Chen, Qiang Fu, Yichen Yuan, Zhihao Wen, Ge Fan, Dayiheng Liu, Dongmei Zhang, Zhixu Li, Yanghua Xiao|Microsoft, Shanghai, China; Shanghai Key Laboratory of Data Science & School of Computer Science, Fudan University & Fudan-Aishu Cognitive Intelligence Joint Research Center, Beijing, China; Singapore Management University, Singapore, Singapore; Microsoft, Beijing, China; Shanghai Key Laboratory of Data Science & School of Computer Science, Fudan University & Fudan-Aishu Cognitive Intelligence Joint Research Center, Shanghai, China; Tencent, Shenzhen, China; Shanghai Key Laboratory of Data Science & School of Computer Science, Fudan University, Shanghai, China; DAMO Academy, Hangzhou, China; Shanghai Key Laboratory of Data Science, Beijing, China|Large language models (LLMs) have gained widespread adoption in various natural language processing tasks, including question answering and dialogue systems. However, a major drawback of LLMs is the issue of hallucination, where they generate unfaithful or inconsistent content that deviates from the input source, leading to severe consequences. In this paper, we propose a robust discriminator named RelD to effectively detect hallucination in LLMs' generated answers. RelD is trained on the constructed RelQA, a bilingual question-answering dialogue dataset along with answers generated by LLMs and a comprehensive set of metrics. Our experimental results demonstrate that the proposed RelD successfully detects hallucination in the answers generated by diverse LLMs. Moreover, it performs well in distinguishing hallucination in LLMs' generated answers from both in-distribution and out-of-distribution datasets. Additionally, we also conduct a thorough analysis of the types of hallucinations that occur and present valuable insights. This research significantly contributes to the detection of reliable answers generated by LLMs and holds noteworthy implications for mitigating hallucination in the future work.|大语言模型(LLM)已经广泛应用于各种自然语言处理任务，包括问答和对话系统。然而，LLM 的一个主要缺点是产生幻觉的问题，它们产生不忠实或不一致的内容，偏离输入源，导致严重的后果。在本文中，我们提出了一个鲁棒的鉴别器 RelD 来有效地检测 LLM 生成的答案中的幻觉。RelD 是基于构建的 RelQA 进行训练的，RelQA 是一个双语问答对话数据集，以及由 LLM 生成的答案和一套全面的度量标准。我们的实验结果表明，提出的 RelD 成功地检测幻觉的答案所产生的不同的 LLM。此外，它还能很好地区分 LLM 生成的分布内和分布外数据集中的幻觉。此外，我们还对出现的幻觉类型进行了全面分析，并提出了有价值的见解。这项研究显著有助于发现可靠的答案所产生的 LLM 和举行值得注意的影响，以减轻幻觉在未来的工作。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Hallucination+Detection:+Robustly+Discerning+Reliable+Answers+in+Large+Language+Models)|0| +|[Deep Generative Imputation Model for Missing Not At Random Data](https://doi.org/10.1145/3583780.3614835)|Jialei Chen, Yuanbo Xu, Pengyang Wang, Yongjian Yang|SKL-IOTSC, Department of Computer and Information Science, University of Macau, Macao, China; MIC Lab, Department of Computer Science and Technology, Jilin University, Changchun, China|Data analysis usually suffers from the Missing Not At Random (MNAR) problem, where the cause of the value missing is not fully observed. Compared to the naive Missing Completely At Random (MCAR) problem, it is more in line with the realistic scenario whereas more complex and challenging. Existing statistical methods model the MNAR mechanism by different decomposition of the joint distribution of the complete data and the missing mask. But we empirically find that directly incorporating these statistical methods into deep generative models is sub-optimal. Specifically, it would neglect the confidence of the reconstructed mask during the MNAR imputation process, which leads to insufficient information extraction and less-guaranteed imputation quality. In this paper, we revisit the MNAR problem from a novel perspective that the complete data and missing mask are two modalities of incomplete data on an equal footing. Along with this line, we put forward a generative-model-specific joint probability decomposition method, conjunction model, to represent the distributions of two modalities in parallel and extract sufficient information from both complete data and missing mask. Taking a step further, we exploit a deep generative imputation model, namely GNR, to process the real-world missing mechanism in the latent space and concurrently impute the incomplete data and reconstruct the missing mask. The experimental results show that our GNR surpasses state-of-the-art MNAR baselines with significant margins (averagely improved from 9.9% to 18.8% in RMSE) and always gives a better mask reconstruction accuracy which makes the imputation more principle.|数据分析通常会遇到丢失不随机(MNAR)问题，其中值丢失的原因没有得到充分的观察。与天真的完全随机缺失(MCAR)问题相比，它更符合现实情况，而更复杂和具有挑战性。现有的统计方法通过对完整数据和缺失模板的联合分布进行不同的分解来模拟 MNAR 机制。但我们经验发现，直接将这些统计方法纳入深层生成模型是次优的。具体来说，它会忽略重建掩码在 MNAR 估算过程中的置信度，导致信息抽取不足和估算质量得不到保证。本文从一个新的角度重新讨论了 MNAR 问题，即完全数据和缺失掩模是不完全数据在同等条件下的两种形式。在此基础上，提出了一种特定于生成模型的联合概率分解方法——联合模型，用于并行表示两种模式的分布，并从完整数据和缺失模板中提取足够的信息。进一步，我们利用一个深层生成插补模型，即 GNR，来处理潜在空间中真实世界的缺失机制，同时对不完全数据进行插补并重构缺失掩模。实验结果表明，我们的 GNR 超越了最先进的 MNAR 基线，具有显著的边界(RMSE 平均值从9.9% 提高到18.8%) ，并且总是具有较好的掩模重建精度，使得插补更加原则。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Deep+Generative+Imputation+Model+for+Missing+Not+At+Random+Data)|0| |[Towards Spoken Language Understanding via Multi-level Multi-grained Contrastive Learning](https://doi.org/10.1145/3583780.3615093)|Xuxin Cheng, Wanshi Xu, Zhihong Zhu, Hongxiang Li, Yuexian Zou|Peking University, Shenzhen, China|Spoken language understanding (SLU) is a core task in task-oriented dialogue systems, which aims at understanding user's current goal through constructing semantic frames. SLU usually consists of two subtasks, including intent detection and slot filling. Although there are some SLU frameworks joint modeling the two subtasks and achieve the high performance, most of them still overlook the inherent relationships between intents and slots, and fail to achieve mutual guidance between the two subtasks. To solve the problem, we propose a multi-level multi-grained SLU framework MMCL to apply contrastive learning at three levels, including utterance level, slot level, and word level to enable intent and slot to mutually guide each other. For the utterance level, our framework implements coarse granularity contrastive learning and fine granularity contrastive learning simultaneously. Besides, we also apply the self-distillation method to improve the robustness of the model. Experimental results and further analysis demonstrate that our proposed model achieves new state-of-the-art results on two public multi-intent SLU datasets, obtaining a 2.6 overall accuracy improvement on MixATIS dataset compared to previous best models.|口语理解是面向任务的对话系统的核心任务，其目的是通过构建语义框架来理解用户当前的目标。SLU 通常由两个子任务组成，包括意图检测和插槽填充。虽然有一些 SLU 框架对这两个子任务进行了联合建模并实现了高性能，但大多数框架仍然忽视了意图和插槽之间的内在关系，无法实现两个子任务之间的相互引导。为了解决这个问题，我们提出了一个多层次多粒度 SLU 框架 MMMCL，在话语层次、时隙层次和词语层次三个层次上应用对比学习，使意图和时隙能够相互引导。在话语层面，该框架同时实现了粗粒度对比学习和细粒度对比学习。此外，我们还应用自蒸馏方法来提高模型的鲁棒性。实验结果和进一步的分析表明，我们提出的模型在两个公共的多意图 SLU 数据集上获得了新的最新的结果，与以前的最佳模型相比，在 MixATIS 数据集上获得了2.6的整体精度提高。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Towards+Spoken+Language+Understanding+via+Multi-level+Multi-grained+Contrastive+Learning)|0| |[DAS-CL: Towards Multimodal Machine Translation via Dual-Level Asymmetric Contrastive Learning](https://doi.org/10.1145/3583780.3614832)|Xuxin Cheng, Zhihong Zhu, Yaowei Li, Hongxiang Li, Yuexian Zou|Peking University, Shenzhen, China|Multimodal machine translation (MMT) aims to exploit visual information to improve neural machine translation (NMT). It has been demonstrated that image captioning and object detection can further improve MMT. In this paper, to leverage image captioning and object detection more effectively, we propose a Dual-level ASymmetric Contrastive Learning (DAS-CL) framework. Specifically, we leverage image captioning and object detection to generate more pairs of visual inputs and textual inputs. At the utterance level, we introduce an image captioning model to generate more coarse-grained pairs. At the word level, we introduce an object detection model to generate more fine-grained pairs. To mitigate the negative impact of noise in generated pairs, we apply asymmetric contrastive learning at these two levels. Experiments on the Multi30K dataset of three translation directions demonstrate that DAS-CL significantly outperforms existing MMT frameworks and achieves new state-of-the-art performance. More encouragingly, further analysis displays that DAS-CL is more robust to irrelevant visual information.|多模态机器翻译(MMT)旨在利用视觉信息改善神经机器翻译(NMT)。研究结果显示，影像字幕及目标检测可进一步改善 MMT。在本文中，为了更有效地利用图像字幕和目标检测，我们提出了一个双层非对称对比学习(das-CL)框架。具体来说，我们利用图像字幕和目标检测来生成更多的视觉输入和文本输入。在话语层面，我们引入了一个图像字幕模型来生成更多的粗粒度对。在单词层面，我们引入一个目标检测模型来生成更多的细粒度对。为了减轻噪声对生成对的负面影响，我们在这两个水平上应用了非对称对比学习。在三个平移方向的 Multi30K 数据集上进行的实验表明，DAS-CL 的性能明显优于现有的 MMT 框架，达到了新的性能水平。更令人鼓舞的是，进一步的分析表明，DAS-CL 对不相关的视觉信息更加健壮。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=DAS-CL:+Towards+Multimodal+Machine+Translation+via+Dual-Level+Asymmetric+Contrastive+Learning)|0| |[PCT-CycleGAN: Paired Complementary Temporal Cycle-Consistent Adversarial Networks for Radar-Based Precipitation Nowcasting](https://doi.org/10.1145/3583780.3615006)|Jaeho Choi, Yura Kim, KwangHo Kim, SungHwa Jung, Ikhyun Cho|Korea Meteorological Administration, Seoul, Republic of Korea|The precipitation nowcasting methods have been elaborated over the centuries because rain has a crucial impact on human life. Not only quantitative precipitation forecast (QPF) models and convolutional long short-term memory (ConvLSTM), but also various sophisticated methods such as the latest MetNet-2 are emerging. In this paper, we propose a paired complementary temporal cycle-consistent adversarial networks (PCT-CycleGAN) for radar-based precipitation nowcasting, inspired by cycle-consistent adversarial networks (CycleGAN), which shows strong performance in image-to-image translation. PCT-CycleGAN generates temporal causality using two generator networks with forward and backward temporal dynamics in paired complementary cycles. Each generator network learns a huge number of one-to-one mappings about time-dependent radar-based precipitation data to approximate a mapping function representing the temporal dynamics in each direction. To create robust temporal causality between paired complementary cycles, novel connection loss is proposed. The generator network learning forward temporal dynamics in PCT-CycleGAN generates radar-based precipitation data 10 minutes from the current time. Also, it provides a reliable prediction of up to 2 hours with iterative forecasting. The superiority of PCT-CycleGAN is demonstrated through qualitative and quantitative comparisons with several previous methods.|由于降雨对人类生活有着至关重要的影响，降雨临近预报方法已经研究了几个世纪。不仅仅是定量降水预报模型和卷积长期记忆模型，还有各种复杂的方法，比如最新的 MetNet-2也正在出现。本文在循环一致对抗网络(CycleGAN)的启发下，提出了一种用于雷达降水临近预报的成对互补时间循环一致对抗网络(PCT-CycleGAN)。PCT-CycleGAN 使用两个具有正向和反向时间动态的生成器网络在成对的互补循环中产生时间因果关系。每个发生器网络学习大量与时间相关的基于雷达的降水数据的一对一映射，以近似表示每个方向的时间动态的映射函数。为了在成对互补周期之间建立鲁棒的时间因果关系，提出了一种新的连接损失方法。在 PCT-CycleGAN 中学习前向时间动态的发生器网络从当前时间起10分钟生成基于雷达的降水数据。此外，它提供了一个可靠的预测长达2小时的迭代预测。通过与以往几种方法的定性和定量比较，证明了 PCT-CycleGAN 方法的优越性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=PCT-CycleGAN:+Paired+Complementary+Temporal+Cycle-Consistent+Adversarial+Networks+for+Radar-Based+Precipitation+Nowcasting)|0| |[Can Knowledge Graphs Simplify Text?](https://doi.org/10.1145/3583780.3615514)|Anthony Colas, Haodi Ma, Xuanli He, Yang Bai, Daisy Zhe Wang|University College London, London, United Kingdom; University of Florida, Gainesville, FL, USA|Knowledge Graph (KG)-to-Text Generation has seen recent improvements in generating fluent and informative sentences which describe a given KG. As KGs are widespread across multiple domains and contain important entity-relation information, and as text simplification aims to reduce the complexity of a text while preserving the meaning of the original text, we propose KGSimple, a novel approach to unsupervised text simplification which infuses KG-established techniques in order to construct a simplified KG path and generate a concise text which preserves the original input's meaning. Through an iterative and sampling KG-first approach, our model is capable of simplifying text when starting from a KG by learning to keep important information while harnessing KG-to-text generation to output fluent and descriptive sentences. We evaluate various settings of the KGSimple model on currently-available KG-to-text datasets, demonstrating its effectiveness compared to unsupervised text simplification models which start with a given complex text. Our code is available on GitHub.|知识图(KG)到文本的生成在生成描述给定 KG 的流畅且信息丰富的句子方面已经看到了最近的改进。由于 KG 广泛存在于多个领域，并且包含重要的实体关系信息，而且文本简化的目的是在保留原文意义的同时降低文本的复杂度，因此我们提出了一种新的无监督文本简化方法 KGSimple，该方法融入了 KG 建立的技术，以构造一个简化的 KG 路径并生成一个保留原文意义的简洁文本。通过一个迭代和抽样的 KG 优先的方法，我们的模型能够简化文本，当从一个 KG 开始，学习保持重要的信息，同时利用 KG 到文本的生成输出流畅和描述性的句子。我们评估了 KGSimple 模型在当前可用的 KG-to-text 数据集上的各种设置，证明了它与从给定复杂文本开始的无监督文本简化模型相比的有效性。我们的代码可以在 GitHub 上找到。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Can+Knowledge+Graphs+Simplify+Text?)|0| |[Cross-heterogeneity Graph Few-shot Learning](https://doi.org/10.1145/3583780.3614830)|Pengfei Ding, Yan Wang, Guanfeng Liu|Macquarie University, Sydney, NSW, Australia|In recent years, heterogeneous graph few-shot learning has been proposed to address the label sparsity issue in heterogeneous graphs (HGs), which contain various types of nodes and edges. The existing methods have achieved good performance by transferring generalized knowledge extracted from rich-labeled classes in source HG(s) to few-labeled classes in a target HG. However, these methods only consider the single-heterogeneity scenario where the source and target HGs share a fixed set of node/edge types, ignoring the more general scenario of cross-heterogeneity, where each HG can have a different and non-fixed set of node/edge types. To this end, we focus on the unexplored cross-heterogeneity scenario and propose a novel model for Cross-heterogeneity Graph Few-shot Learning, namely CGFL. In CGFL, we first extract meta-patterns to capture heterogeneous information and propose a multi-view heterogeneous graph neural network (MHGN) to learn meta-patterns across HGs. Then, we propose a score module to measure the informativeness of labeled samples and determine the transferability of each source HG. Finally, by integrating MHGN and the score module into a meta-learning mechanism, CGFL can effectively transfer generalized knowledge to predict new classes with few-labeled data. Extensive experiments on four real-world datasets have demonstrated the superior performance of CGFL over the state-of-the-art methods.|近年来，异构图少镜头学习被提出来解决包含各种节点和边的异构图中的标签稀疏问题。现有的方法通过将源 HG 中富标记类提取的广义知识转移到目标 HG 中的少标记类，获得了良好的性能。然而，这些方法只考虑单一异质性场景，其中源和目标 HG 共享一组固定的节点/边缘类型，忽略了更一般的交叉异质性场景，其中每个 HG 可以具有不同的和非固定的节点/边缘类型集。为此，我们着眼于未探索的跨异构情景，提出了一种新的跨异构图少镜头学习模型，即 CGFL。在 CGFL 中，我们首先提取元模式来捕获异构信息，并提出了一种多视图异构图神经网络(MHGN)来学习跨 HG 的元模式。然后，我们提出了一个评分模块来衡量被标记样本的信息性，并确定每个源 HG 的可转移性。最后，通过将 MHGN 和评分模块集成到一个元学习机制中，CGFL 可以有效地传递广义知识，利用少量标记数据预测新的类别。在四个真实世界数据集上的大量实验已经证明了 CGFL 比最先进的方法具有更好的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Cross-heterogeneity+Graph+Few-shot+Learning)|0| -|[NeoMaPy: A Parametric Framework for Reasoning with MAP Inference on Temporal Markov Logic Networks](https://doi.org/10.1145/3583780.3614757)|Victor David, Raphaël FournierS'niehotta, Nicolas Travers|Léonard de Vinci Pôle Universitaire, Research Center, Paris La Défense, France; Conservatoire National des Arts et Métiers, Paris, France; University of Perugia, Perugia, Italy|Reasoning on inconsistent and uncertain data is challenging, especially for Knowledge-Graphs (KG) to abide temporal consistency. Our goal is to enhance inference with more general time interval semantics that specify their validity, as regularly found in historical sciences. We propose a new Temporal Markov Logic Networks (TMLN) model which extends the Markov Logic Networks (MLN) model with uncertain temporal facts and rules. Total and partial temporal (in)consistency relations between sets of temporal formulae are examined. We then propose a new Temporal Parametric Semantics (TPS) which allows combining several sub-functions leading to different assessment strategies. Finally, we present the new NeoMaPy tool, to compute the MAP inference on MLNs and TMLNs with several TPS. We compare our performances with state-of-the-art inference tools and exhibit faster and higher quality results.|对不一致和不确定的数据进行推理具有挑战性，尤其是知识图(KG)要遵守时间一致性。我们的目标是使用更一般的时间间隔语义来加强推理，这些语义指定了它们的有效性，这在历史科学中是经常发现的。提出了一种新的时态马尔可夫逻辑网络(TMLN)模型，该模型扩展了具有不确定时态事实和规则的马尔可夫逻辑网络(MLN)模型。研究了时态公式集之间的全时态和部分时态一致性关系。然后，我们提出了一个新的时态参数语义(TPS) ，它允许组合多个子功能导致不同的评估策略。最后，我们提出了新的 NeoMaPy 工具，用于计算多个 TPS 对 MLN 和 TMLN 的 MAP 推断。我们将我们的性能与最先进的推理工具进行比较，并显示出更快和更高质量的结果。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=NeoMaPy:+A+Parametric+Framework+for+Reasoning+with+MAP+Inference+on+Temporal+Markov+Logic+Networks)|0| +|[NeoMaPy: A Parametric Framework for Reasoning with MAP Inference on Temporal Markov Logic Networks](https://doi.org/10.1145/3583780.3614757)|Victor David, Raphaël FournierS'niehotta, Nicolas Travers|Conservatoire National des Arts et Métiers, Paris, France; University of Perugia, Perugia, Italy; Léonard de Vinci Pôle Universitaire, Research Center, Paris La Défense, France|Reasoning on inconsistent and uncertain data is challenging, especially for Knowledge-Graphs (KG) to abide temporal consistency. Our goal is to enhance inference with more general time interval semantics that specify their validity, as regularly found in historical sciences. We propose a new Temporal Markov Logic Networks (TMLN) model which extends the Markov Logic Networks (MLN) model with uncertain temporal facts and rules. Total and partial temporal (in)consistency relations between sets of temporal formulae are examined. We then propose a new Temporal Parametric Semantics (TPS) which allows combining several sub-functions leading to different assessment strategies. Finally, we present the new NeoMaPy tool, to compute the MAP inference on MLNs and TMLNs with several TPS. We compare our performances with state-of-the-art inference tools and exhibit faster and higher quality results.|对不一致和不确定的数据进行推理具有挑战性，尤其是知识图(KG)要遵守时间一致性。我们的目标是使用更一般的时间间隔语义来加强推理，这些语义指定了它们的有效性，这在历史科学中是经常发现的。提出了一种新的时态马尔可夫逻辑网络(TMLN)模型，该模型扩展了具有不确定时态事实和规则的马尔可夫逻辑网络(MLN)模型。研究了时态公式集之间的全时态和部分时态一致性关系。然后，我们提出了一个新的时态参数语义(TPS) ，它允许组合多个子功能导致不同的评估策略。最后，我们提出了新的 NeoMaPy 工具，用于计算多个 TPS 对 MLN 和 TMLN 的 MAP 推断。我们将我们的性能与最先进的推理工具进行比较，并显示出更快和更高质量的结果。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=NeoMaPy:+A+Parametric+Framework+for+Reasoning+with+MAP+Inference+on+Temporal+Markov+Logic+Networks)|0| |[Reveal the Unknown: Out-of-Knowledge-Base Mention Discovery with Entity Linking](https://doi.org/10.1145/3583780.3615036)|Hang Dong, Jiaoyan Chen, Yuan He, Yinan Liu, Ian Horrocks||Discovering entity mentions that are out of a Knowledge Base (KB) from texts plays a critical role in KB maintenance, but has not yet been fully explored. The current methods are mostly limited to the simple threshold-based approach and feature-based classification, and the datasets for evaluation are relatively rare. We propose BLINKout, a new BERT-based Entity Linking (EL) method which can identify mentions that do not have corresponding KB entities by matching them to a special NIL entity. To better utilize BERT, we propose new techniques including NIL entity representation and classification, with synonym enhancement. We also propose KB Pruning and Versioning strategies to automatically construct out-of-KB datasets from common in-KB EL datasets. Results on five datasets of clinical notes, biomedical publications, and Wikipedia articles in various domains show the advantages of BLINKout over existing methods to identify out-of-KB mentions for the medical ontologies, UMLS, SNOMED CT, and the general KB, WikiData.|从文本中发现知识库(KB)之外的实体提及在知识库维护中起着至关重要的作用，但是还没有得到充分的研究。目前的方法大多局限于简单的基于阈值的方法和基于特征的分类，用于评价的数据集相对较少。我们提出了 BLINKout，一种新的基于 BERT 的实体链接(EL)方法，它可以通过将没有相应知识库实体的提及与一个特殊的 NIL 实体匹配来识别它们。为了更好地利用 BERT，我们提出了新的技术，包括 NIL 实体表示和分类，同义词增强。我们还提出了 KB 修剪和版本控制策略，从常见的 in-KB EL 数据集中自动构造超出 KB 的数据集。临床注释，生物医学出版物和维基百科文章在不同领域的五个数据集的结果显示，BLINKout 优于现有的方法来识别医学本体论，UMLS，SNOMED CT 和一般知识库 WikiData 的外部知识库提及。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Reveal+the+Unknown:+Out-of-Knowledge-Base+Mention+Discovery+with+Entity+Linking)|0| |[Spatial-Temporal Graph Boosting Networks: Enhancing Spatial-Temporal Graph Neural Networks via Gradient Boosting](https://doi.org/10.1145/3583780.3615066)|Yujie Fan, ChinChia Michael Yeh, Huiyuan Chen, Yan Zheng, Liang Wang, Junpeng Wang, Xin Dai, Zhongfang Zhuang, Wei Zhang|Visa Research, Palo Alto, CA, USA|Spatial-temporal graph neural networks (STGNNs) are promising in solving real-world spatial-temporal forecasting problems. Recognizing the inherent sequential relationship of spatial-temporal data, it is natural to explore the integration of boosting training mechanism to further enhance the performance of STGNNs. However, few studies have touched this research area. To bridge this gap, in this work, we propose spatial-temporal graph boosting networks, namely STGBN, which to the best of our knowledge is the first attempt to leverage gradient boosting for enhancing STGNNs. STGBN follows the general training procedure of conventional gradient boosting, but incorporates two distinctive designs to improve its efficiency in training on spatial-temporal graphs. Specifically, we design an incremental learning strategy that progressively includes spatial-temporal data into training. Additionally, we enforce an identical architecture for the base learner in all boosting iterations with each base learner inheriting from the one in the previous iteration. These designs facilitate rapid convergence of the base learner and expedite the overall training process. The base learner in STGBN is designed as a Transformer sandwich, which consists of two temporal Transformers on the top and bottom and a spatial Transformer in the middle. Structuring them in such a way helps the model capture long-range temporal dynamics, global spatial dependencies, and deep spatial-temporal interactions. We perform extensive spatial-temporal forecasting experiments on four spatial-temporal graph benchmarks. Promising results demonstrate the outstanding performance of STGBN against a wide range of state-of-the-art baseline models.|时空图形神经网络(STGNN)在解决实际时空预测问题方面具有广阔的应用前景。认识到时空数据的内在时序关系，探索增强训练机制的整合以进一步提高 STGNN 的性能是很自然的。然而，很少有研究涉及到这个研究领域。为了弥合这一差距，在这项工作中，我们提出了时空图增强网络，即 STGBN，据我们所知，这是第一次尝试利用梯度提升增强 STGNN。STGBN 遵循传统梯度提升的一般训练程序，但结合了两种独特的设计，以提高其在时空图形上的训练效率。具体来说，我们设计了一个在线机机器学习策略，逐步将时空数据纳入训练。此外，我们在所有提升迭代中为基础学习者实施一个相同的体系结构，每个基础学习者从前一个迭代继承。这些设计有利于基础学习者的快速融合，加快整个培训过程。STGBN 的基础学习器设计为一个变压器夹层，由顶部和底部的两个时间变压器和中间的一个空间变压器组成。以这种方式构造它们有助于模型捕获长期的时间动态、全局的空间依赖性和深入的时空交互。我们在四个时空图基准上进行了广泛的时空预测实验。有希望的结果表明，STGBN 的出色性能对广泛的国家最先进的基线模型。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Spatial-Temporal+Graph+Boosting+Networks:+Enhancing+Spatial-Temporal+Graph+Neural+Networks+via+Gradient+Boosting)|0| |[Cognitive-inspired Graph Redundancy Networks for Multi-source Information Fusion](https://doi.org/10.1145/3583780.3614815)|Yao Fu, Junhong Wan, Junlan Yu, Weihao Jiang, Shiliang Pu|Hikvision Research Institute, Hangzhou, China|The recent developments in technologies bring not only increasing amount of information but also multiple information sources for Graph Representation Learning. With the success of Graph Neural Networks (GNN), there have been increasing attempts to learn representation of multi-source information leveraging its graph structures. However, existing graph methods basically combine multi-source information with different contribution scores and over-simplify the graph structures based on prior knowledge, which fail to unify complex and conflicting multi-source information. Multisensory Processing theory in cognitive neuroscience reveals human mechanism of learning multi-source information by identifying the redundancy and complementarity. Inspired by that, we propose Graph Redundancy Network (GRN) that: 1). learns a suitable representation space that maximizes multi-source interactions; 2). encodes the redundant and complementary information according to Graph Intersection and Difference of their graph structures; 3). further reinforces and explores the redundant and complementary information through low-pass and high-pass graph filters. The empirical study shows that GRN outperforms existing methods on various tasks.|近年来技术的发展不仅为图形表示学习带来了越来越多的信息，而且为图形表示学习带来了多种信息源。随着图形神经网络(GNN)的成功，人们越来越多地尝试利用其图形结构来学习多源信息的表示。然而，现有的图论方法基本上是将不同贡献分数的多源信息结合起来，过分简化了基于先验知识的图论结构，无法统一复杂、冲突的多源信息。认知神经科学的多感官加工理论揭示了人类通过识别冗余和互补来学习多源信息的机制。受此启发，我们提出了图冗余网络(GRN) : 1)。学习最大化多源交互的合适的表示空间;。根据图结构的交差对冗余信息和互补信息进行编码;。通过低通和高通图形滤波器进一步加强和探索冗余和互补信息。实证研究表明，GRN 在各种任务中的表现优于现有的方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Cognitive-inspired+Graph+Redundancy+Networks+for+Multi-source+Information+Fusion)|0| |[Cross-Scenario Maneuver Decision with Adaptive Perception for Autonomous Driving](https://doi.org/10.1145/3583780.3614831)|Yuan Fu, Shuncheng Liu, Yuyang Xia, Fangda Guo, Kai Zheng|University of Electronic Science and Technology of China, Chengdu, China; Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China|Autonomous driving is a rapidly advancing field that promises to revolutionize the transportation industry through an intelligent perception-and-decision paradigm. Despite decades of research, existing methods are limited in adapting to complex scenarios or expanding to unseen situations, which pose significant challenges to the development of autonomous driving. Inspired by the process of human learning to drive, autonomous vehicles can prioritize developing driving capabilities in basic scenarios and then extending the atomic abilities to more complex scenarios. To this end, we proposed a perception-and-decision framework, called ATEND, which consists of an adaptive perception module and a maneuver decision module. Specifically, the perception module based on Variational Autoencoder is proposed to map perceptual data of complex scenarios into basic scenarios. Then the reinforcement learning-based decision module can make high-level decisions in transformed scenarios. Once ATEND learns to drive in basic scenarios, it can achieve safe and efficient driving in real scenarios without additional training. Extensive experiments in different traffic scenarios evidence that the proposed framework advances the state of the art in terms of both macroscopic and microscopic effectiveness.|自动驾驶是一个迅速发展的领域，有望通过智能感知和决策范式彻底改革交通行业。尽管经过了几十年的研究，现有的方法在适应复杂的情景或扩展到未知的情况方面仍有局限性，这对自动驾驶的发展构成了重大挑战。受到人类学习驾驶过程的启发，自动驾驶汽车可以优先发展基本场景中的驾驶能力，然后将原子能力扩展到更复杂的场景中。为此，我们提出了一个感知与决策框架 ATEND，它由一个自适应感知模块和一个机动决策模块组成。提出了基于变分自动编码器的感知模块，将复杂场景的感知数据映射到基本场景。然后基于强化学习的决策模块可以在变换后的情景中进行高层决策。一旦 ATEND 学会驾驶在基本的情况下，它可以实现安全和有效的驾驶在真正的情况下没有额外的培训。在不同的交通情景下进行的大量实验表明，所提出的框架在宏观和微观效果方面都提高了技术水平。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Cross-Scenario+Maneuver+Decision+with+Adaptive+Perception+for+Autonomous+Driving)|0| -|[On the Trade-off between Over-smoothing and Over-squashing in Deep Graph Neural Networks](https://doi.org/10.1145/3583780.3614997)|Jhony H. Giraldo, Konstantinos Skianis, Thierry Bouwmans, Fragkiskos D. Malliaros|LTCI, Télécom Paris - Institut Polytechnique de Paris, Palaiseau, France; BLUAI, Athens, Greece; Université Paris-Saclay, CentraleSupélec, Inria, Gif-sur-Yvette, France; Laboratoire MIA, La Rochelle Université, La Rochelle, France|Graph Neural Networks (GNNs) have succeeded in various computer science applications, yet deep GNNs underperform their shallow counterparts despite deep learning's success in other domains. Over-smoothing and over-squashing are key challenges when stacking graph convolutional layers, hindering deep representation learning and information propagation from distant nodes. Our work reveals that over-smoothing and over-squashing are intrinsically related to the spectral gap of the graph Laplacian, resulting in an inevitable trade-off between these two issues, as they cannot be alleviated simultaneously. To achieve a suitable compromise, we propose adding and removing edges as a viable approach. We introduce the Stochastic Jost and Liu Curvature Rewiring (SJLR) algorithm, which is computationally efficient and preserves fundamental properties compared to previous curvature-based methods. Unlike existing approaches, SJLR performs edge addition and removal during GNN training while maintaining the graph unchanged during testing. Comprehensive comparisons demonstrate SJLR's competitive performance in addressing over-smoothing and over-squashing.|图形神经网络(GNN)已经在各种计算机科学应用中取得了成功，然而深层神经网络在其他领域的成功表现不如浅层神经网络。过度平滑和过度压缩是叠加图卷积层时面临的主要挑战，它们阻碍了深度表示学习和远距离节点的信息传播。我们的工作揭示了过度平滑和过度压缩与图的拉普拉斯谱间隙有着内在的联系，由于这两个问题不能同时得到解决，因此在这两个问题之间必然会出现权衡。为了达到一个合适的折衷，我们建议添加和去除边缘作为一个可行的方法。我们介绍了随机 Jost 和 Liu 曲率重布线(SJLR)算法，与以前的基于曲率的方法相比，SJLR 算法具有计算效率高和保持基本性质的优点。与现有的方法不同，SJLR 在 GNN 训练期间执行边添加和去除，同时在测试期间保持图形不变。全面的比较证明了 SJLR 在解决过度平滑和过度压缩方面的竞争表现。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=On+the+Trade-off+between+Over-smoothing+and+Over-squashing+in+Deep+Graph+Neural+Networks)|0| -|[Homophily-enhanced Structure Learning for Graph Clustering](https://doi.org/10.1145/3583780.3614915)|Ming Gu, Gaoming Yang, Sheng Zhou, Ning Ma, Jiawei Chen, Qiaoyu Tan, Meihan Liu, Jiajun Bu|Zhejiang University, Hangzhou, China; New York University Shanghai, Shanghai, China|Graph clustering is a fundamental task in graph analysis, and recent advances in utilizing graph neural networks (GNNs) have shown impressive results. Despite the success of existing GNN-based graph clustering methods, they often overlook the quality of graph structure, which is inherent in real-world graphs due to their sparse and multifarious nature, leading to subpar performance. Graph structure learning allows refining the input graph by adding missing links and removing spurious connections. However, previous endeavors in graph structure learning have predominantly centered around supervised settings, and cannot be directly applied to our specific clustering tasks due to the absence of ground-truth labels. To bridge the gap, we propose a novel method called \textbf{ho}mophily-enhanced structure \textbf{le}arning for graph clustering (HoLe). Our motivation stems from the observation that subtly enhancing the degree of homophily within the graph structure can significantly improve GNNs and clustering outcomes. To realize this objective, we develop two clustering-oriented structure learning modules, i.e., hierarchical correlation estimation and cluster-aware sparsification. The former module enables a more accurate estimation of pairwise node relationships by leveraging guidance from latent and clustering spaces, while the latter one generates a sparsified structure based on the similarity matrix and clustering assignments. Additionally, we devise a joint optimization approach alternating between training the homophily-enhanced structure learning and GNN-based clustering, thereby enforcing their reciprocal effects. Extensive experiments on seven benchmark datasets of various types and scales, across a range of clustering metrics, demonstrate the superiority of HoLe against state-of-the-art baselines.|图聚类是图分析的基本任务，近年来利用图神经网络(GNN)的研究取得了令人瞩目的成果。尽管现有的基于 GNN 的图聚类方法已经取得了一定的成功，但是它们往往忽视了图结构的质量。图结构学习允许通过添加缺失链接和消除伪连接来精炼输入图。然而，以前在图结构学习的努力主要集中在监督设置，不能直接应用到我们的具体聚类任务，由于没有地面真理标签。为了弥补这一缺陷，我们提出了一种新的图聚类方法 textbf { ho } mophily 增强结构 textbf { le } arning。我们的动机来自于这样一个观察: 微妙地提高图结构中的同质性程度可以显著改善 GNN 和聚类结果。为了实现这一目标，我们开发了两个面向聚类的结构学习模块，即层次相关估计和聚类感知的稀疏化。前一个模块利用潜在空间和聚类空间的引导，更准确地估计成对节点关系，而后一个模块基于相似矩阵和聚类分配生成稀疏结构。此外，我们设计了一个联合优化方法，交替训练同质增强结构学习和基于 GNN 的聚类，从而增强它们的互惠效应。在七个不同类型和尺度的基准数据集上进行了大量的实验，通过一系列聚类度量，证明了 HoLe 相对于最先进的基准线的优越性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Homophily-enhanced+Structure+Learning+for+Graph+Clustering)|0| +|[On the Trade-off between Over-smoothing and Over-squashing in Deep Graph Neural Networks](https://doi.org/10.1145/3583780.3614997)|Jhony H. Giraldo, Konstantinos Skianis, Thierry Bouwmans, Fragkiskos D. Malliaros|LTCI, Télécom Paris - Institut Polytechnique de Paris, Palaiseau, France; Laboratoire MIA, La Rochelle Université, La Rochelle, France; Université Paris-Saclay, CentraleSupélec, Inria, Gif-sur-Yvette, France; BLUAI, Athens, Greece|Graph Neural Networks (GNNs) have succeeded in various computer science applications, yet deep GNNs underperform their shallow counterparts despite deep learning's success in other domains. Over-smoothing and over-squashing are key challenges when stacking graph convolutional layers, hindering deep representation learning and information propagation from distant nodes. Our work reveals that over-smoothing and over-squashing are intrinsically related to the spectral gap of the graph Laplacian, resulting in an inevitable trade-off between these two issues, as they cannot be alleviated simultaneously. To achieve a suitable compromise, we propose adding and removing edges as a viable approach. We introduce the Stochastic Jost and Liu Curvature Rewiring (SJLR) algorithm, which is computationally efficient and preserves fundamental properties compared to previous curvature-based methods. Unlike existing approaches, SJLR performs edge addition and removal during GNN training while maintaining the graph unchanged during testing. Comprehensive comparisons demonstrate SJLR's competitive performance in addressing over-smoothing and over-squashing.|图形神经网络(GNN)已经在各种计算机科学应用中取得了成功，然而深层神经网络在其他领域的成功表现不如浅层神经网络。过度平滑和过度压缩是叠加图卷积层时面临的主要挑战，它们阻碍了深度表示学习和远距离节点的信息传播。我们的工作揭示了过度平滑和过度压缩与图的拉普拉斯谱间隙有着内在的联系，由于这两个问题不能同时得到解决，因此在这两个问题之间必然会出现权衡。为了达到一个合适的折衷，我们建议添加和去除边缘作为一个可行的方法。我们介绍了随机 Jost 和 Liu 曲率重布线(SJLR)算法，与以前的基于曲率的方法相比，SJLR 算法具有计算效率高和保持基本性质的优点。与现有的方法不同，SJLR 在 GNN 训练期间执行边添加和去除，同时在测试期间保持图形不变。全面的比较证明了 SJLR 在解决过度平滑和过度压缩方面的竞争表现。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=On+the+Trade-off+between+Over-smoothing+and+Over-squashing+in+Deep+Graph+Neural+Networks)|0| +|[Homophily-enhanced Structure Learning for Graph Clustering](https://doi.org/10.1145/3583780.3614915)|Ming Gu, Gaoming Yang, Sheng Zhou, Ning Ma, Jiawei Chen, Qiaoyu Tan, Meihan Liu, Jiajun Bu|New York University Shanghai, Shanghai, China; Zhejiang University, Hangzhou, China|Graph clustering is a fundamental task in graph analysis, and recent advances in utilizing graph neural networks (GNNs) have shown impressive results. Despite the success of existing GNN-based graph clustering methods, they often overlook the quality of graph structure, which is inherent in real-world graphs due to their sparse and multifarious nature, leading to subpar performance. Graph structure learning allows refining the input graph by adding missing links and removing spurious connections. However, previous endeavors in graph structure learning have predominantly centered around supervised settings, and cannot be directly applied to our specific clustering tasks due to the absence of ground-truth labels. To bridge the gap, we propose a novel method called \textbf{ho}mophily-enhanced structure \textbf{le}arning for graph clustering (HoLe). Our motivation stems from the observation that subtly enhancing the degree of homophily within the graph structure can significantly improve GNNs and clustering outcomes. To realize this objective, we develop two clustering-oriented structure learning modules, i.e., hierarchical correlation estimation and cluster-aware sparsification. The former module enables a more accurate estimation of pairwise node relationships by leveraging guidance from latent and clustering spaces, while the latter one generates a sparsified structure based on the similarity matrix and clustering assignments. Additionally, we devise a joint optimization approach alternating between training the homophily-enhanced structure learning and GNN-based clustering, thereby enforcing their reciprocal effects. Extensive experiments on seven benchmark datasets of various types and scales, across a range of clustering metrics, demonstrate the superiority of HoLe against state-of-the-art baselines.|图聚类是图分析的基本任务，近年来利用图神经网络(GNN)的研究取得了令人瞩目的成果。尽管现有的基于 GNN 的图聚类方法已经取得了一定的成功，但是它们往往忽视了图结构的质量。图结构学习允许通过添加缺失链接和消除伪连接来精炼输入图。然而，以前在图结构学习的努力主要集中在监督设置，不能直接应用到我们的具体聚类任务，由于没有地面真理标签。为了弥补这一缺陷，我们提出了一种新的图聚类方法 textbf { ho } mophily 增强结构 textbf { le } arning。我们的动机来自于这样一个观察: 微妙地提高图结构中的同质性程度可以显著改善 GNN 和聚类结果。为了实现这一目标，我们开发了两个面向聚类的结构学习模块，即层次相关估计和聚类感知的稀疏化。前一个模块利用潜在空间和聚类空间的引导，更准确地估计成对节点关系，而后一个模块基于相似矩阵和聚类分配生成稀疏结构。此外，我们设计了一个联合优化方法，交替训练同质增强结构学习和基于 GNN 的聚类，从而增强它们的互惠效应。在七个不同类型和尺度的基准数据集上进行了大量的实验，通过一系列聚类度量，证明了 HoLe 相对于最先进的基准线的优越性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Homophily-enhanced+Structure+Learning+for+Graph+Clustering)|0| |[Hierarchical Meta-Learning with Hyper-Tasks for Few-Shot Learning](https://doi.org/10.1145/3583780.3614911)|Yunchuan Guan, Yu Liu, Ke Zhou, Junyuan Huang|Huazhong University of Science and Technology, Wuhan, China|Meta-learning excels in few-shot learning by extracting shared knowledge from the observed tasks. However, it needs the tasks to adhere to the i.i.d. constraint, which is challenging to achieve due to complex task relationships between data content. Current methods that create tasks in a one-dimensional structure and use meta-learning to learn all tasks flatly struggle with extracting shared knowledge from tasks with overlapping concepts. To address this issue, we propose further constructing tasks from the same environment into hyper-tasks. Since the distributions of hyper-tasks and tasks in a hyper-task can both be approximated as i.i.d. due to further summarization, the meta-learning algorithm can capture shared knowledge more efficiently. Based on the hyper-task, we propose a hierarchical meta-learning paradigm to meta-learn the meta-learning algorithm. The paradigm builds a customized meta-learner for each hyper-task, which makes meta-learners more flexible and expressive. We apply the paradigm to three classic meta-learning algorithms and conduct extensive experiments on public datasets, which confirm the superiority of hierarchical meta-learning in the few-shot learning setting. The code is released at https://github.com/tuantuange/H-meta-learning.|元学习通过从观察到的任务中提取共享知识，在少镜头学习中表现出色。然而，它需要任务遵守 i.d. 约束，由于数据内容之间复杂的任务关系，这一点很难实现。当前的方法是在一维结构中创建任务，并使用元学习来学习所有任务，这些方法在从概念重叠的任务中提取共享知识时显然遇到了困难。为了解决这个问题，我们提出进一步构造任务从相同的环境到超任务。由于超任务和超任务中任务的分布可以通过进一步的归纳近似为标识符，因此元学习算法可以更有效地获取共享的知识。基于超任务，本文提出了一种分层元学习范式来元学习元学习算法。该范式为每个超任务建立一个定制的元学习者，使元学习者更加灵活和富有表现力。我们将该范式应用于三种经典的元学习算法，并在公共数据集上进行了广泛的实验，证实了分层元学习在少镜头学习环境下的优越性。密码在 https://github.com/tuantuange/h-meta-learning 发布。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Hierarchical+Meta-Learning+with+Hyper-Tasks+for+Few-Shot+Learning)|0| |[RoCourseNet: Robust Training of a Prediction Aware Recourse Model](https://doi.org/10.1145/3583780.3615040)|Hangzhi Guo, Feiran Jia, Jinghui Chen, Anna Cinzia Squicciarini, Amulya Yadav|The Pennsylvania State University, State College, PA, USA|Counterfactual (CF) explanations for machine learning (ML) models are preferred by end-users, as they explain the predictions of ML models by providing a recourse (or contrastive) case to individuals who are adversely impacted by predicted outcomes. Existing CF explanation methods generate recourses under the assumption that the underlying target ML model remains stationary over time. However, due to commonly occurring distributional shifts in training data, ML models constantly get updated in practice, which might render previously generated recourses invalid and diminish end-users trust in our algorithmic framework. To address this problem, we propose RoCourseNet, a training framework that jointly optimizes predictions and recourses that are robust to future data shifts. This work contains four key contributions: (1) We formulate the robust recourse generation problem as a tri-level optimization problem which consists of two sub-problems: (i) a bi-level problem that finds the worst-case adversarial shift in the training data, and (ii) an outer minimization problem to generate robust recourses against this worst-case shift. (2) We leverage adversarial training to solve this tri-level optimization problem by: (i) proposing a novel virtual data shift (VDS) algorithm to find worst-case shifted ML models via explicitly considering the worst-case data shift in the training dataset, and (ii) a block-wise coordinate descent procedure to optimize for prediction and corresponding robust recourses. (3) We evaluate RoCourseNet's performance on three real-world datasets, and show that RoCourseNet consistently achieves more than 96% robust validity and outperforms state-of-the-art baselines by at least 10% in generating robust CF explanations. (4) Finally, we generalize the RoCourseNet framework to accommodate any parametric post-hoc methods for improving robust validity.|机器学习(ML)模型的反事实(CF)解释是最终用户的首选，因为他们解释机器学习模型的预测，通过提供一个追索权(或对比)的情况下，受到预测结果的不利影响的个人。现有的 CF 解释方法在假设潜在目标 ML 模型随时间保持稳定的前提下生成资源。然而，由于训练数据中普遍存在的分布变化，机器学习模型在实际应用中不断更新，这可能导致以前生成的资源无效，并削弱最终用户对我们算法框架的信任。为了解决这个问题，我们提出了 RoCourseNet，一个联合优化预测和资源的培训框架，这些预测和资源对未来的数据转移是健壮的。这项工作包含四个关键的贡献: (1)我们把鲁棒追索生成问题描述成一个三层次的最佳化问题，其中包括两个子问题: (1)一个在训练数据中发现最坏情况下的对手变化的双层次问题，和(2)一个针对最坏情况变化生成鲁棒追索的外部最小化问题。(2)我们利用对抗性训练来解决这个三级最佳化问题: (i)提出一种新的虚拟数据移位(VDS)算法，通过明确考虑训练数据集中的最坏情况数据移位来找到最坏情况移位的机器学习模型，以及(ii)一个分块的坐标下降法程序来优化预测和相应的稳健资源。(3)我们对 RoCourseNet 在三个实际数据集上的表现进行了评估，结果显示 RoCourseNet 在产生稳健的 CF 解释方面始终达到超过96% 的稳健有效性，并且比最先进的基线至少高出10% 。(4)最后，我们推广了 RoCourseNet 框架，以适应任何参数化的事后方法来提高鲁棒有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=RoCourseNet:+Robust+Training+of+a+Prediction+Aware+Recourse+Model)|0| -|[Attacking Neural Networks with Neural Networks: Towards Deep Synchronization for Backdoor Attacks](https://doi.org/10.1145/3583780.3614784)|Zihan Guan, Lichao Sun, Mengnan Du, Ninghao Liu|New Jersey Institute of Technology, Newark, NJ, USA; Lehigh University, Bethlehem, PA, USA; University of Georgia, Athens, GA, USA|Backdoor attacks inject poisoned samples into training data, where backdoor triggers are embedded into the model trained on the mixture of poisoned and clean samples. An interesting phenomenon can be observed in the training process: the loss of poisoned samples tends to drop significantly faster than that of clean samples, which we call the early-fitting phenomenon. Early-fitting provides a simple but effective evidence to defend against backdoor attacks, where the poisoned samples can be detected by selecting the samples with the lowest loss values in the early training epochs. Then, two questions naturally arise: (1) What characteristics of poisoned samples cause early-fitting? (2) Does a stronger attack exist which could circumvent the defense methods? To answer the first question, we find that early-fitting could be attributed to a unique property among poisoned samples called synchronization, which depicts the similarity between two samples at different layers of a model. Meanwhile, the degree of synchronization could be controlled based on whether it is captured by shallow or deep layers of the model. Then, we give an affirmative answer to the second question by proposing a new backdoor attack method, Deep Backdoor Attack (DBA), which utilizes deep synchronization to reverse engineer trigger patterns by activating neurons in the deep layer of a base neural network. Experimental results validate our propositions and the effectiveness of DBA. Our code is available at https://github.com/GuanZihan/Deep-Backdoor-Attack.|后门攻击将有毒样本注入到训练数据中，其中后门触发器被嵌入到对有毒样本和无毒样本混合进行训练的模型中。在训练过程中可以观察到一个有趣的现象: 中毒样本的损失下降速度明显快于未中毒样本，我们称之为早期拟合现象。早期拟合提供了一个简单而有效的证据来抵御后门攻击，其中中毒样本可以通过选择在早期训练阶段损失值最低的样本来检测。然后，自然而然地出现了两个问题: (1)中毒样本的什么特征会导致早期拟合？(2)是否存在可以规避防御手段的更强攻击？为了回答第一个问题，我们发现早期拟合可以归因于中毒样本之间的一个独特属性，即同步，它描述了模型不同层次上两个样本之间的相似性。同时，可以根据模型的浅层或深层对同步进行控制。然后，我们提出了一种新的后门攻击方法——深度后门攻击(Deep Backdoor Strategy，DBA) ，该方法利用深度同步技术通过激活基础神经网络深层的神经元来逆向工程触发模式，从而对第二个问题给出了肯定的回答。实验结果验证了我们的提议和 DBA 的有效性。我们的代码可以在 https://github.com/guanzihan/deep-backdoor-attack 找到。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Attacking+Neural+Networks+with+Neural+Networks:+Towards+Deep+Synchronization+for+Backdoor+Attacks)|0| +|[Attacking Neural Networks with Neural Networks: Towards Deep Synchronization for Backdoor Attacks](https://doi.org/10.1145/3583780.3614784)|Zihan Guan, Lichao Sun, Mengnan Du, Ninghao Liu|Lehigh University, Bethlehem, PA, USA; New Jersey Institute of Technology, Newark, NJ, USA; University of Georgia, Athens, GA, USA|Backdoor attacks inject poisoned samples into training data, where backdoor triggers are embedded into the model trained on the mixture of poisoned and clean samples. An interesting phenomenon can be observed in the training process: the loss of poisoned samples tends to drop significantly faster than that of clean samples, which we call the early-fitting phenomenon. Early-fitting provides a simple but effective evidence to defend against backdoor attacks, where the poisoned samples can be detected by selecting the samples with the lowest loss values in the early training epochs. Then, two questions naturally arise: (1) What characteristics of poisoned samples cause early-fitting? (2) Does a stronger attack exist which could circumvent the defense methods? To answer the first question, we find that early-fitting could be attributed to a unique property among poisoned samples called synchronization, which depicts the similarity between two samples at different layers of a model. Meanwhile, the degree of synchronization could be controlled based on whether it is captured by shallow or deep layers of the model. Then, we give an affirmative answer to the second question by proposing a new backdoor attack method, Deep Backdoor Attack (DBA), which utilizes deep synchronization to reverse engineer trigger patterns by activating neurons in the deep layer of a base neural network. Experimental results validate our propositions and the effectiveness of DBA. Our code is available at https://github.com/GuanZihan/Deep-Backdoor-Attack.|后门攻击将有毒样本注入到训练数据中，其中后门触发器被嵌入到对有毒样本和无毒样本混合进行训练的模型中。在训练过程中可以观察到一个有趣的现象: 中毒样本的损失下降速度明显快于未中毒样本，我们称之为早期拟合现象。早期拟合提供了一个简单而有效的证据来抵御后门攻击，其中中毒样本可以通过选择在早期训练阶段损失值最低的样本来检测。然后，自然而然地出现了两个问题: (1)中毒样本的什么特征会导致早期拟合？(2)是否存在可以规避防御手段的更强攻击？为了回答第一个问题，我们发现早期拟合可以归因于中毒样本之间的一个独特属性，即同步，它描述了模型不同层次上两个样本之间的相似性。同时，可以根据模型的浅层或深层对同步进行控制。然后，我们提出了一种新的后门攻击方法——深度后门攻击(Deep Backdoor Strategy，DBA) ，该方法利用深度同步技术通过激活基础神经网络深层的神经元来逆向工程触发模式，从而对第二个问题给出了肯定的回答。实验结果验证了我们的提议和 DBA 的有效性。我们的代码可以在 https://github.com/guanzihan/deep-backdoor-attack 找到。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Attacking+Neural+Networks+with+Neural+Networks:+Towards+Deep+Synchronization+for+Backdoor+Attacks)|0| |[MGICL: Multi-Grained Interaction Contrastive Learning for Multimodal Named Entity Recognition](https://doi.org/10.1145/3583780.3614967)|Aibo Guo, Xiang Zhao, Zhen Tan, Weidong Xiao|National University of Defense Technology, Changsha, China|Multimodal Named Entity Recognition (MNER) aims to combine data from different modalities (e.g. text, images, videos, etc.) for recognition and classification of named entities, which is crucial for constructing Multimodal Knowledge Graphs (MMKGs). However, existing researches suffer from two prominant issues: over-reliance on textual features while neglecting visual features, and the lack of effective reduction of the feature space discrepancy of multimodal data. To overcome these challenges, this paper proposes a Multi-Grained Interaction Contrastive Learning framework for MNER task, namely MGICL. MGICL slices data into different granularities, i.e., sentence level/word token level for text, and image level/object level for image. By utilizing multimodal features with different granularities, the framework enables cross-contrast and narrows down the feature space discrepancy between modalities. Moreover, it facilitates the acquisition of valuable visual features by the text. Additionally, a visual gate control mechanism is introduced to dynamically select relevant visual information, thereby reducing the impact of visual noise. Experimental results demonstrate that the proposed MGICL framework satisfactorily tackles the challenges of MNER through enhancing information interaction of multimodal data and reducing the effect of noise, and hence, effectively improves the performance of MNER.|多模态命名实体识别(MNER)的目的是将来自不同模式(如文本、图像、视频等)的数据结合起来，对命名实体进行识别和分类，这对于构建多模态知识图(MMKG)至关重要。然而，现有的研究存在两个突出问题: 过度依赖文本特征而忽视视觉特征，以及缺乏有效的减少多模态数据的特征空间差异。为了克服这些挑战，本文提出了一个用于 MNER 任务的多粒度交互对比学习框架，即 MGICL。MGICL 将数据切割成不同的粒度，即，文本的句子级/词标记级，图像的图像级/对象级。该框架利用不同粒度的多模态特征，实现了模态间的交叉对比，缩小了模态间的特征空间差异。此外，它还有助于文本获得有价值的视觉特征。此外，还引入了视觉门控制机制，动态选择相关的视觉信息，从而减少视觉噪声的影响。实验结果表明，所提出的 MGICL 框架通过增强多模态数据的信息交互和降低噪声的影响，较好地解决了 MNER 的挑战，从而有效地提高了 MNER 的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=MGICL:+Multi-Grained+Interaction+Contrastive+Learning+for+Multimodal+Named+Entity+Recognition)|0| |[Interpretable Fake News Detection with Graph Evidence](https://doi.org/10.1145/3583780.3614936)|Hao Guo, Weixin Zeng, Jiuyang Tang, Xiang Zhao|National University of Defense Technology, Changsha, China|Automatic detection of fake news has received widespread attentions over recent years. A pile of efforts has been put forward to address the problem with high accuracy, while most of them lack convincing explanations, making it difficult to curb the continued spread of false news in real-life cases. Although some models leverage external resources to provide preliminary interpretability, such external signals are not always available. To fill in this gap, in this work, we put forward an interpretable fake news detection model IKA by making use of the historical evidence in the form of graphs. Specifically, we establish both positive and negative evidence graphs by collecting the signals from the historical news, i.e., training data. Then, given a piece of news to be detected, in addition to the common features used for detecting false news, we compare the news and evidence graphs to generate both the matching vector and the related graph evidence for explaining the prediction. We conduct extensive experiments on both Chinese and English datasets. The experiment results show that the detection accuracy of IKA exceeds the state-of-the-art approaches and IKA can provide useful explanations for the prediction results. Besides, IKA is general and can be applied on other models to improve their interpretability.|虚假新闻的自动检测近年来受到了广泛的关注。为了解决这一问题，人们付出了大量努力，但大多数努力都缺乏令人信服的解释，因此难以遏制在现实案件中继续传播虚假新闻。虽然有些模型利用外部资源提供初步的可解释性，但这种外部信号并不总是可用的。为了填补这一空白，本文利用图表形式的历史证据，提出了一种可解释的假新闻检测模型 IKA。具体来说，我们通过收集历史新闻的信号，即训练数据，建立了正证据图和负证据图。然后，给定一条待检测的新闻，除了用于检测虚假新闻的常见特征之外，对新闻图和证据图进行比较，生成匹配向量和相关图证据来解释预测。我们对中文和英文数据集进行了广泛的实验。实验结果表明，IKA 算法的检测精度超过了现有的方法，为预测结果提供了有用的解释。此外，IKA 具有通用性，可以应用于其他模型以提高模型的可解释性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Interpretable+Fake+News+Detection+with+Graph+Evidence)|0| |[Towards Fair Graph Neural Networks via Graph Counterfactual](https://doi.org/10.1145/3583780.3615092)|Zhimeng Guo, Jialiang Li, Teng Xiao, Yao Ma, Suhang Wang|Rensselaer Polytechnic Institute, Troy, USA; New Jersey Institute of Technology, Newark, NJ, USA; The Pennsylvania State University, State College, PA, USA|Graph neural networks have shown great ability in representation (GNNs) learning on graphs, facilitating various tasks. Despite their great performance in modeling graphs, recent works show that GNNs tend to inherit and amplify the bias from training data, causing concerns of the adoption of GNNs in high-stake scenarios. Hence, many efforts have been taken for fairness-aware GNNs. However, most existing fair GNNs learn fair node representations by adopting statistical fairness notions, which may fail to alleviate bias in the presence of statistical anomalies. Motivated by causal theory, there are several attempts utilizing graph counterfactual fairness to mitigate root causes of unfairness. However, these methods suffer from non-realistic counterfactuals obtained by perturbation or generation. In this paper, we take a causal view on fair graph learning problem. Guided by the casual analysis, we propose a novel framework CAF, which can select counterfactuals from training data to avoid non-realistic counterfactuals and adopt selected counterfactuals to learn fair node representations for node classification task. Extensive experiments on synthetic and real-world datasets show the effectiveness of CAF. Our code is available at https://github.com/TimeLovercc/CAF-GNN.|图形神经网络具有良好的图形表示学习能力，能够方便地完成各种任务。尽管 GNN 在建模图中表现出色，但近年来的研究表明，GNN 倾向于继承和放大训练数据中的偏差，这引起了人们对在高风险场景中采用 GNN 的担忧。因此，对于公平感知的 GNN，人们付出了很多努力。然而，现有的公平 GNN 大多通过采用统计公平概念来学习公平节点表示，这可能不能减轻存在统计异常时的偏差。受因果关系理论的启发，人们多次尝试利用图的反事实公平性来减轻不公平的根本原因。然而，这些方法遭受非现实的反事实由摄动或生成获得。本文对公平图学习问题提出了因果观点。在随机分析的指导下，提出了一种新的 CAF 框架，该框架可以从训练数据中选择反事实，避免非现实反事实，并采用选择的反事实来学习节点分类任务的公平节点表示。在合成数据集和真实数据集上的大量实验表明了 CAF 的有效性。我们的代码可以在 https://github.com/timelovercc/caf-gnn 找到。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Towards+Fair+Graph+Neural+Networks+via+Graph+Counterfactual)|0| |[James ate 5 oranges = Steve bought 5 pencils: Structure-Aware Denoising for Paraphrasing Word Problems](https://doi.org/10.1145/3583780.3614940)|Rishabh Gupta, Venktesh V, Mukesh K. Mohania, Vikram Goyal|IIIT Delhi, Delhi, India|We propose SCANING, an unsupervised framework for paraphrasing via controlled noise injection. We focus on the novel task of paraphrasing algebraic word problems having practical applications in online pedagogy as a means to reduce plagiarism as well as evoke reasoning capabilities on the part of the student instead of rote memorization. This task is more complex than paraphrasing general-domain corpora due to the difficulty in preserving critical information for solution consistency of the paraphrased word problem, managing the increased length of the text and ensuring diversity in the generated paraphrase. Existing approaches fail to demonstrate adequate performance on at least one, if not all, of these facets, necessitating the need for a more comprehensive solution. To this end, we model the noising search space as a composition of contextual and syntactic aspects to sample noising functions. This allows for learning a denoising function, that operates over both aspects and produces semantically equivalent and syntactically diverse outputs through grounded noise injection. The denoising function serves as a foundation for training a paraphrasing function, which operates solely in the input-paraphrase space without carrying any direct dependency on noise. We demonstrate that SCANING improves performance in terms of producing semantically equivalent and syntactically diverse paraphrases by 35% through extensive automated and human evaluation across 4 datasets.|我们提出扫描，一个无监督的框架释义通过控制噪声注入。我们的重点是解释代数词问题的新颖任务有实际应用在在线教学作为一种手段，以减少剽窃，并唤起部分学生的推理能力，而不是死记硬背。这项任务比解释一般领域语料库更为复杂，因为在解决解释词问题的一致性方面保留关键信息、管理增加的文本长度和确保生成的解释的多样性方面存在困难。现有方法未能在至少一个(如果不是全部的话)这些方面展示出足够的性能，因此需要一个更全面的解决方案。为此，我们将噪声搜索空间建模为由上下文和句法两个方面组成的样本噪声函数。这允许学习一个去噪函数，通过接地噪声注入，该函数在两个方面都起作用，并产生语义等效和语法不同的输出。去噪函数是训练复述函数的基础，复述函数只在输入-复述空间中运行，不直接依赖于噪声。我们证明，SCANING 通过在4个数据集中广泛的自动化和人工评估，提高了35% 的语义等效和句法多样性转述的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=James+ate+5+oranges+=+Steve+bought+5+pencils:+Structure-Aware+Denoising+for+Paraphrasing+Word+Problems)|0| -|[Enhancing Spatio-temporal Traffic Prediction through Urban Human Activity Analysis](https://doi.org/10.1145/3583780.3614867)|Sumin Han, Youngjun Park, Minji Lee, Jisun An, Dongman Lee|Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea; Indiana University Bloomington (IUB), Bloomington, IN, USA|Traffic prediction is one of the key elements to ensure the safety and convenience of citizens. Existing traffic prediction models primarily focus on deep learning architectures to capture spatial and temporal correlation. They often overlook the underlying nature of traffic. Specifically, the sensor networks in most traffic datasets do not accurately represent the actual road network exploited by vehicles, failing to provide insights into the traffic patterns in urban activities. To overcome these limitations, we propose an improved traffic prediction method based on graph convolution deep learning algorithms. We leverage human activity frequency data from National Household Travel Survey to enhance the inference capability of a causal relationship between activity and traffic patterns. Despite making minimal modifications to the conventional graph convolutional recurrent networks and graph convolutional transformer architectures, our approach achieves state-of-the-art performance without introducing excessive computational overhead.|交通预测是保障市民安全和便利的关键因素之一。现有的流量预测模型主要集中在深度学习体系结构上，以捕捉空间和时间的相关性。他们常常忽视交通的潜在本质。具体来说，大多数交通数据集中的传感器网络不能准确地表示车辆实际使用的道路网络，无法提供对城市活动中交通模式的深入了解。为了克服这些局限性，我们提出了一种改进的基于图卷积深度学习算法的流量预测方法。我们利用全国家庭旅行调查中的人类活动频率数据来加强对活动和交通模式之间因果关系的推断能力。尽管对传统的图卷积递归网络和图卷积转换器结构进行了最小的修改，但是我们的方法在不引入过多计算开销的情况下实现了最先进的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Enhancing+Spatio-temporal+Traffic+Prediction+through+Urban+Human+Activity+Analysis)|0| -|[On Root Cause Localization and Anomaly Mitigation through Causal Inference](https://doi.org/10.1145/3583780.3614995)|Xiao Han, Lu Zhang, Yongkai Wu, Shuhan Yuan|Utah State University, Logan, UT, USA; Clemson University, Clemson, SC, USA; University of Arkansas, Fayetteville, AR, USA|Due to a wide spectrum of applications in the real world, such as security, financial surveillance, and health risk, various deep anomaly detection models have been proposed and achieved state-of-the-art performance. However, besides being effective, in practice, the practitioners would further like to know what causes the abnormal outcome and how to further fix it. In this work, we propose RootCLAM, which aims to achieve Root Cause Localization and Anomaly Mitigation from a causal perspective. Especially, we formulate anomalies caused by external interventions on the normal causal mechanism and aim to locate the abnormal features with external interventions as root causes. After that, we further propose an anomaly mitigation approach that aims to recommend mitigation actions on abnormal features to revert the abnormal outcomes such that the counterfactuals guided by the causal mechanism are normal. Experiments on three datasets show that our approach can locate the root causes and further flip the abnormal labels.|由于在现实世界中的应用范围广泛，例如安全、金融监管和健康风险，各种深度异常检测模型已被提出，并取得了最先进的性能。然而，在实践中，除了有效之外，实践者还想进一步了解导致异常结果的原因以及如何进一步修复它。在这项工作中，我们提出了 RootCLAM，旨在从因果关系的角度实现根源定位和异常缓解。特别是在正常因果机制上提出了外部干预引起的异常，并以外部干预为根本原因定位异常特征。然后，我们进一步提出了一种异常缓解方法，目的是建议对异常特征采取缓解行动，以恢复异常结果，使由因果机制引导的反事实是正常的。在三个数据集上的实验表明，该方法能够定位异常标签的根本原因，并进一步翻转异常标签。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=On+Root+Cause+Localization+and+Anomaly+Mitigation+through+Causal+Inference)|0| -|[SANN: Programming Code Representation Using Attention Neural Network with Optimized Subtree Extraction](https://doi.org/10.1145/3583780.3615047)|Muntasir Hoq, Sushanth Reddy Chilla, Melika Ahmadi Ranjbar, Peter Brusilovsky, Bita Akram|North Carolina State University, Raleigh, NC, USA; University of Pittsburgh, Pittsburgh, PA, USA|Automated analysis of programming data using code representation methods offers valuable services for programmers, from code completion to clone detection to bug detection. Recent studies show the effectiveness of Abstract Syntax Trees (AST), pre-trained Transformer-based models, and graph-based embeddings in programming code representation. However, pre-trained large language models lack interpretability, while other embedding-based approaches struggle with extracting important information from large ASTs. This study proposes a novel Subtree-based Attention Neural Network (SANN) to address these gaps by integrating different components: an optimized sequential subtree extraction process using Genetic algorithm optimization, a two-way embedding approach, and an attention network. We investigate the effectiveness of SANN by applying it to two different tasks: program correctness prediction and algorithm detection on two educational datasets containing both small and large-scale code snippets written in Java and C, respectively. The experimental results show SANN's competitive performance against baseline models from the literature, including code2vec, ASTNN, TBCNN, CodeBERT, GPT-2, and MVG, regarding accurate predictive power. Finally, a case study is presented to show the interpretability of our model prediction and its application for an important human-centered computing application, student modeling. Our results indicate the effectiveness of the SANN model in capturing important syntactic and semantic information from students' code, allowing the construction of accurate student models, which serve as the foundation for generating adaptive instructional support such as individualized hints and feedback.|使用代码表示方法自动分析编程数据为程序员提供了有价值的服务，从代码完成到克隆检测到 bug 检测。最近的研究表明抽象语法树(AST)、预训练的基于变压器的模型和基于图的嵌入在编程代码表示中是有效的。然而，预先训练的大型语言模型缺乏可解释性，而其他基于嵌入的方法难以从大型 AST 中提取重要信息。本研究提出一种新的基于子树的注意力神经网络(SANN) ，通过集成不同的组成部分来解决这些差距: 使用遗传算法优化的优化顺序子树提取过程，双向嵌入方法，和一个注意力网络。通过将 SANN 应用于两个不同的任务: 程序正确性预测和算法检测，我们研究了 SANN 在两个教育数据集上的有效性，这两个数据集分别包含用 Java 和 C 编写的小代码片段和大代码片段。实验结果显示，在准确预测能力方面，SANN 与文献中的基线模型(包括 code2vec、 ASTNN、 TBCNN、 CodeBERT、 GPT-2和 MVG)的竞争表现。最后，通过一个实例说明了模型预测的可解释性及其在以人为中心的重要计算应用——学生建模中的应用。我们的研究结果表明了 SANN 模型在从学生代码中捕捉重要的句法和语义信息方面的有效性，允许建立精确的学生模型，这些模型作为产生适应性教学支持(如个性化提示和反馈)的基础。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=SANN:+Programming+Code+Representation+Using+Attention+Neural+Network+with+Optimized+Subtree+Extraction)|0| -|[Designing and Evaluating Presentation Strategies for Fact-Checked Content](https://doi.org/10.1145/3583780.3614841)|Danula Hettiachchi, Kaixin Ji, Jenny Kennedy, Anthony McCosker, Flora D. Salim, Mark Sanderson, Falk Scholer, Damiano Spina|UNSW, Sydney, Australia; Swinburne University of Technology, Melbourne, Australia; RMIT University, Melbourne, Australia|With the rapid growth of online misinformation, it is crucial to have reliable fact-checking methods. Recent research on finding check-worthy claims and automated fact-checking have made significant advancements. However, limited guidance exists regarding the presentation of fact-checked content to effectively convey verified information to users. We address this research gap by exploring the critical design elements in fact-checking reports and investigating whether credibility and presentation-based design improvements can enhance users' ability to interpret the report accurately. We co-developed potential content presentation strategies through a workshop involving fact-checking professionals, communication experts, and researchers. The workshop examined the significance and utility of elements such as veracity indicators and explored the feasibility of incorporating interactive components for enhanced information disclosure. Building on the workshop outcomes, we conducted an online experiment involving 76 crowd workers to assess the efficacy of different design strategies. The results indicate that proposed strategies significantly improve users' ability to accurately interpret the verdict of fact-checking articles. Our findings underscore the critical role of effective presentation of fact reports in addressing the spread of misinformation. By adopting appropriate design enhancements, the effectiveness of fact-checking reports can be maximized, enabling users to make informed judgments.|随着网络虚假信息的快速增长，建立可靠的事实核查方法至关重要。最近关于寻找值得核实的索赔和自动事实核查的研究取得了重大进展。然而，在提供经事实核查的内容以有效地向用户传达经核实的信息方面，存在着有限的指导。我们通过探索事实核查报告中的关键设计元素，以及调查可信度和基于表现的设计改进是否能够提高用户准确解释报告的能力来弥补这一研究差距。我们通过一个由事实核查专家、沟通专家和研究人员参加的研讨会，共同开发了潜在的内容展示策略。讲习班审查了诸如真实性指标等要素的重要性和效用，并探讨了纳入互动性要素以加强信息披露的可行性。在研讨会成果的基础上，我们进行了一项在线实验，涉及76名群体工作者，以评估不同设计策略的有效性。结果表明，提出的策略显著提高了用户对事实核查文章结论的准确理解能力。我们的研究结果强调了有效介绍事实报告在解决错误信息传播方面的关键作用。通过采用适当的设计改进，可以最大限度地提高事实核查报告的有效性，使用户能够做出明智的判断。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Designing+and+Evaluating+Presentation+Strategies+for+Fact-Checked+Content)|0| -|[HyperFormer: Enhancing Entity and Relation Interaction for Hyper-Relational Knowledge Graph Completion](https://doi.org/10.1145/3583780.3614922)|Zhiwei Hu, Víctor GutiérrezBasulto, Zhiliang Xiang, Ru Li, Jeff Z. Pan|Shanxi University, Taiyuan , China; Shanxi University, Taiyuan, China; Cardiff University, Cardiff, United Kingdom; University of Edinburgh, Edinburgh, United Kingdom|Hyper-relational knowledge graphs (HKGs) extend standard knowledge graphs by associating attribute-value qualifiers to triples, which effectively represent additional fine-grained information about its associated triple. Hyper-relational knowledge graph completion (HKGC) aims at inferring unknown triples while considering its qualifiers. Most existing approaches to HKGC exploit a global-level graph structure to encode hyper-relational knowledge into the graph convolution message passing process. However, the addition of multi-hop information might bring noise into the triple prediction process. To address this problem, we propose HyperFormer, a model that considers local-level sequential information, which encodes the content of the entities, relations and qualifiers of a triple. More precisely, HyperFormer is composed of three different modules: an entity neighbor aggregator module allowing to integrate the information of the neighbors of an entity to capture different perspectives of it; a relation qualifier aggregator module to integrate hyper-relational knowledge into the corresponding relation to refine the representation of relational content; a convolution-based bidirectional interaction module based on a convolutional operation, capturing pairwise bidirectional interactions of entity-relation, entity-qualifier, and relation-qualifier. realize the depth perception of the content related to the current statement. Furthermore, we introduce a Mixture-of-Experts strategy into the feed-forward layers of HyperFormer to strengthen its representation capabilities while reducing the amount of model parameters and computation. Extensive experiments on three well-known datasets with four different conditions demonstrate HyperFormer's effectiveness. Datasets and code are available at https://github.com/zhiweihu1103/HKGC-HyperFormer.|超关系知识图(HKG)通过将属性值限定符关联到三元组来扩展标准知识图，三元组有效地表示关联三元组的附加细粒度信息。超关系知识图完备化(HKGC)的目的是在考虑其限定词的同时推断出未知的三元组。大部分现有的 HKGC 方法都采用全球层次的图结构，将超关系知识编码到图卷积信息传递过程中。然而，多跳信息的加入可能会给三重预测过程带来噪声。为了解决这个问题，我们提出了一种考虑局部层次序列信息的 HyperForm 模型，它对三元组的实体、关系和限定符的内容进行编码。更准确地说，HyperForter 由三个不同的模块组成: 一个实体邻居聚合器模块，允许集成实体邻居的信息来捕获不同的视角; 一个关系限定器聚合器模块，将超关系知识集成到相应的关系中，以精化关系内容的表示; 一个基于卷积运算的双向交互模块，捕获实体关系、实体限定器和关系限定器的成对双向交互。了解与当前声明有关的内容的深度知觉。在此基础上，我们引入了一种专家混合策略，在减少模型参数和计算量的同时，增强了 HyperForm 的前馈层的表示能力。在三个著名的数据集上进行的四种不同条件的大量实验证明了 HyperForm 的有效性。数据集和代码可在 https://github.com/zhiweihu1103/hkgc-hyperformer 获得。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=HyperFormer:+Enhancing+Entity+and+Relation+Interaction+for+Hyper-Relational+Knowledge+Graph+Completion)|0| -|[Liberate Pseudo Labels from Over-Dependence: Label Information Migration on Sparsely Labeled Graphs](https://doi.org/10.1145/3583780.3614954)|Zhihui Hu, Yao Fu, Hong Zhao, Xiaoyu Cai, Weihao Jiang, Shiliang Pu|Hikvision Research Institute & Key Laboratory of Peace-building Big Data of Zhejiang Province, Hangzhou, China; Hikvision Research Institute, Hangzhou, China|Graph Convolutional Networks (GCNs) have made outstanding achievements in many tasks on graphs in recent years, but their success relies on sufficient training data. In practice, sparsely labeled graphs widely exist in the real world so self-training methods have become popular approaches by adding pseudo labeled nodes to enhance the performance of GCNs. However, we observe that most selected high-confidence pseudo labeled nodes by the existing methods would surround the true labeled nodes. It is what we called pseudo label over-dependence, which could lead to the non-uniform pseudo label distribution. Furthermore, a thorough experiment shows that the classification accuracy changes significantly under different label densities and the label-sparse regions show great potential improvement in the model performance. Based on the above findings, we theoretically analyze the constraint factors in the label-sparse regions and further propose reducing the feature distribution difference between the label-dense regions and label-sparse regions can effectively decrease the classification error. Thus, in this paper, we propose a novel Graph Label Information Migration framework (GLIM) to liberate pseudo labels from over-dependence. Specifically, we first propose a training dynamics module (TDM) that uses abundant training process information to find more reliable node labels and improve the model robustness against label noise. Then we propose a label migration module (LMM) that migrates label information from label-dense regions to label-sparse regions by a spectral based graph matching algorithm. These migrated labels are like the glimmers in the darkness, providing the supervision signals for the unlabeled nodes in label-sparse regions. Finally, we conduct extensive experiments to demonstrate the effectiveness of the proposed GLIM.|图卷积网络(GCNs)近年来在许多图任务中取得了突出的成就，但它的成功依赖于充分的训练数据。在实际应用中，稀疏标记图广泛存在于现实世界中，因此通过增加伪标记节点来提高 GCNs 性能的自训练方法已经成为一种流行的方法。然而，我们观察到，现有方法选择的大多数高置信度伪标记节点将包围真正的标记节点。这就是我们所说的伪标签过度依赖，它可能导致非均匀的伪标签分布。实验表明，在不同的标签密度下，分类精度有显著的变化，标签稀疏区域对模型性能有很大的改善潜力。在此基础上，从理论上分析了标签稀疏区域的约束因素，进一步提出减小标签密集区域与标签稀疏区域之间的特征分布差异可以有效地减小分类误差。因此，本文提出了一种新的图形标签信息迁移框架(GLIM) ，将伪标签从过度依赖中解放出来。具体来说，我们首先提出了一个训练动力学模块(TDM) ，利用大量的训练过程信息来寻找更可靠的节点标签，提高模型对标签噪声的鲁棒性。然后提出了一种基于谱图匹配算法的标签迁移模块(LMM) ，将标签信息从标签密集区域迁移到标签稀疏区域。这些迁移的标签就像黑暗中的微光，为标签稀疏区域的未标记节点提供监控信号。最后，我们进行了广泛的实验来验证所提出的 GLIM 的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Liberate+Pseudo+Labels+from+Over-Dependence:+Label+Information+Migration+on+Sparsely+Labeled+Graphs)|0| +|[Enhancing Spatio-temporal Traffic Prediction through Urban Human Activity Analysis](https://doi.org/10.1145/3583780.3614867)|Sumin Han, Youngjun Park, Minji Lee, Jisun An, Dongman Lee|Indiana University Bloomington (IUB), Bloomington, IN, USA; Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea|Traffic prediction is one of the key elements to ensure the safety and convenience of citizens. Existing traffic prediction models primarily focus on deep learning architectures to capture spatial and temporal correlation. They often overlook the underlying nature of traffic. Specifically, the sensor networks in most traffic datasets do not accurately represent the actual road network exploited by vehicles, failing to provide insights into the traffic patterns in urban activities. To overcome these limitations, we propose an improved traffic prediction method based on graph convolution deep learning algorithms. We leverage human activity frequency data from National Household Travel Survey to enhance the inference capability of a causal relationship between activity and traffic patterns. Despite making minimal modifications to the conventional graph convolutional recurrent networks and graph convolutional transformer architectures, our approach achieves state-of-the-art performance without introducing excessive computational overhead.|交通预测是保障市民安全和便利的关键因素之一。现有的流量预测模型主要集中在深度学习体系结构上，以捕捉空间和时间的相关性。他们常常忽视交通的潜在本质。具体来说，大多数交通数据集中的传感器网络不能准确地表示车辆实际使用的道路网络，无法提供对城市活动中交通模式的深入了解。为了克服这些局限性，我们提出了一种改进的基于图卷积深度学习算法的流量预测方法。我们利用全国家庭旅行调查中的人类活动频率数据来加强对活动和交通模式之间因果关系的推断能力。尽管对传统的图卷积递归网络和图卷积转换器结构进行了最小的修改，但是我们的方法在不引入过多计算开销的情况下实现了最先进的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Enhancing+Spatio-temporal+Traffic+Prediction+through+Urban+Human+Activity+Analysis)|0| +|[On Root Cause Localization and Anomaly Mitigation through Causal Inference](https://doi.org/10.1145/3583780.3614995)|Xiao Han, Lu Zhang, Yongkai Wu, Shuhan Yuan|Utah State University, Logan, UT, USA; University of Arkansas, Fayetteville, AR, USA; Clemson University, Clemson, SC, USA|Due to a wide spectrum of applications in the real world, such as security, financial surveillance, and health risk, various deep anomaly detection models have been proposed and achieved state-of-the-art performance. However, besides being effective, in practice, the practitioners would further like to know what causes the abnormal outcome and how to further fix it. In this work, we propose RootCLAM, which aims to achieve Root Cause Localization and Anomaly Mitigation from a causal perspective. Especially, we formulate anomalies caused by external interventions on the normal causal mechanism and aim to locate the abnormal features with external interventions as root causes. After that, we further propose an anomaly mitigation approach that aims to recommend mitigation actions on abnormal features to revert the abnormal outcomes such that the counterfactuals guided by the causal mechanism are normal. Experiments on three datasets show that our approach can locate the root causes and further flip the abnormal labels.|由于在现实世界中的应用范围广泛，例如安全、金融监管和健康风险，各种深度异常检测模型已被提出，并取得了最先进的性能。然而，在实践中，除了有效之外，实践者还想进一步了解导致异常结果的原因以及如何进一步修复它。在这项工作中，我们提出了 RootCLAM，旨在从因果关系的角度实现根源定位和异常缓解。特别是在正常因果机制上提出了外部干预引起的异常，并以外部干预为根本原因定位异常特征。然后，我们进一步提出了一种异常缓解方法，目的是建议对异常特征采取缓解行动，以恢复异常结果，使由因果机制引导的反事实是正常的。在三个数据集上的实验表明，该方法能够定位异常标签的根本原因，并进一步翻转异常标签。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=On+Root+Cause+Localization+and+Anomaly+Mitigation+through+Causal+Inference)|0| +|[SANN: Programming Code Representation Using Attention Neural Network with Optimized Subtree Extraction](https://doi.org/10.1145/3583780.3615047)|Muntasir Hoq, Sushanth Reddy Chilla, Melika Ahmadi Ranjbar, Peter Brusilovsky, Bita Akram|University of Pittsburgh, Pittsburgh, PA, USA; North Carolina State University, Raleigh, NC, USA|Automated analysis of programming data using code representation methods offers valuable services for programmers, from code completion to clone detection to bug detection. Recent studies show the effectiveness of Abstract Syntax Trees (AST), pre-trained Transformer-based models, and graph-based embeddings in programming code representation. However, pre-trained large language models lack interpretability, while other embedding-based approaches struggle with extracting important information from large ASTs. This study proposes a novel Subtree-based Attention Neural Network (SANN) to address these gaps by integrating different components: an optimized sequential subtree extraction process using Genetic algorithm optimization, a two-way embedding approach, and an attention network. We investigate the effectiveness of SANN by applying it to two different tasks: program correctness prediction and algorithm detection on two educational datasets containing both small and large-scale code snippets written in Java and C, respectively. The experimental results show SANN's competitive performance against baseline models from the literature, including code2vec, ASTNN, TBCNN, CodeBERT, GPT-2, and MVG, regarding accurate predictive power. Finally, a case study is presented to show the interpretability of our model prediction and its application for an important human-centered computing application, student modeling. Our results indicate the effectiveness of the SANN model in capturing important syntactic and semantic information from students' code, allowing the construction of accurate student models, which serve as the foundation for generating adaptive instructional support such as individualized hints and feedback.|使用代码表示方法自动分析编程数据为程序员提供了有价值的服务，从代码完成到克隆检测到 bug 检测。最近的研究表明抽象语法树(AST)、预训练的基于变压器的模型和基于图的嵌入在编程代码表示中是有效的。然而，预先训练的大型语言模型缺乏可解释性，而其他基于嵌入的方法难以从大型 AST 中提取重要信息。本研究提出一种新的基于子树的注意力神经网络(SANN) ，通过集成不同的组成部分来解决这些差距: 使用遗传算法优化的优化顺序子树提取过程，双向嵌入方法，和一个注意力网络。通过将 SANN 应用于两个不同的任务: 程序正确性预测和算法检测，我们研究了 SANN 在两个教育数据集上的有效性，这两个数据集分别包含用 Java 和 C 编写的小代码片段和大代码片段。实验结果显示，在准确预测能力方面，SANN 与文献中的基线模型(包括 code2vec、 ASTNN、 TBCNN、 CodeBERT、 GPT-2和 MVG)的竞争表现。最后，通过一个实例说明了模型预测的可解释性及其在以人为中心的重要计算应用——学生建模中的应用。我们的研究结果表明了 SANN 模型在从学生代码中捕捉重要的句法和语义信息方面的有效性，允许建立精确的学生模型，这些模型作为产生适应性教学支持(如个性化提示和反馈)的基础。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=SANN:+Programming+Code+Representation+Using+Attention+Neural+Network+with+Optimized+Subtree+Extraction)|0| +|[Designing and Evaluating Presentation Strategies for Fact-Checked Content](https://doi.org/10.1145/3583780.3614841)|Danula Hettiachchi, Kaixin Ji, Jenny Kennedy, Anthony McCosker, Flora D. Salim, Mark Sanderson, Falk Scholer, Damiano Spina|Swinburne University of Technology, Melbourne, Australia; RMIT University, Melbourne, Australia; UNSW, Sydney, Australia|With the rapid growth of online misinformation, it is crucial to have reliable fact-checking methods. Recent research on finding check-worthy claims and automated fact-checking have made significant advancements. However, limited guidance exists regarding the presentation of fact-checked content to effectively convey verified information to users. We address this research gap by exploring the critical design elements in fact-checking reports and investigating whether credibility and presentation-based design improvements can enhance users' ability to interpret the report accurately. We co-developed potential content presentation strategies through a workshop involving fact-checking professionals, communication experts, and researchers. The workshop examined the significance and utility of elements such as veracity indicators and explored the feasibility of incorporating interactive components for enhanced information disclosure. Building on the workshop outcomes, we conducted an online experiment involving 76 crowd workers to assess the efficacy of different design strategies. The results indicate that proposed strategies significantly improve users' ability to accurately interpret the verdict of fact-checking articles. Our findings underscore the critical role of effective presentation of fact reports in addressing the spread of misinformation. By adopting appropriate design enhancements, the effectiveness of fact-checking reports can be maximized, enabling users to make informed judgments.|随着网络虚假信息的快速增长，建立可靠的事实核查方法至关重要。最近关于寻找值得核实的索赔和自动事实核查的研究取得了重大进展。然而，在提供经事实核查的内容以有效地向用户传达经核实的信息方面，存在着有限的指导。我们通过探索事实核查报告中的关键设计元素，以及调查可信度和基于表现的设计改进是否能够提高用户准确解释报告的能力来弥补这一研究差距。我们通过一个由事实核查专家、沟通专家和研究人员参加的研讨会，共同开发了潜在的内容展示策略。讲习班审查了诸如真实性指标等要素的重要性和效用，并探讨了纳入互动性要素以加强信息披露的可行性。在研讨会成果的基础上，我们进行了一项在线实验，涉及76名群体工作者，以评估不同设计策略的有效性。结果表明，提出的策略显著提高了用户对事实核查文章结论的准确理解能力。我们的研究结果强调了有效介绍事实报告在解决错误信息传播方面的关键作用。通过采用适当的设计改进，可以最大限度地提高事实核查报告的有效性，使用户能够做出明智的判断。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Designing+and+Evaluating+Presentation+Strategies+for+Fact-Checked+Content)|0| +|[HyperFormer: Enhancing Entity and Relation Interaction for Hyper-Relational Knowledge Graph Completion](https://doi.org/10.1145/3583780.3614922)|Zhiwei Hu, Víctor GutiérrezBasulto, Zhiliang Xiang, Ru Li, Jeff Z. Pan|Shanxi University, Taiyuan, China; Cardiff University, Cardiff, United Kingdom; Shanxi University, Taiyuan , China; University of Edinburgh, Edinburgh, United Kingdom|Hyper-relational knowledge graphs (HKGs) extend standard knowledge graphs by associating attribute-value qualifiers to triples, which effectively represent additional fine-grained information about its associated triple. Hyper-relational knowledge graph completion (HKGC) aims at inferring unknown triples while considering its qualifiers. Most existing approaches to HKGC exploit a global-level graph structure to encode hyper-relational knowledge into the graph convolution message passing process. However, the addition of multi-hop information might bring noise into the triple prediction process. To address this problem, we propose HyperFormer, a model that considers local-level sequential information, which encodes the content of the entities, relations and qualifiers of a triple. More precisely, HyperFormer is composed of three different modules: an entity neighbor aggregator module allowing to integrate the information of the neighbors of an entity to capture different perspectives of it; a relation qualifier aggregator module to integrate hyper-relational knowledge into the corresponding relation to refine the representation of relational content; a convolution-based bidirectional interaction module based on a convolutional operation, capturing pairwise bidirectional interactions of entity-relation, entity-qualifier, and relation-qualifier. realize the depth perception of the content related to the current statement. Furthermore, we introduce a Mixture-of-Experts strategy into the feed-forward layers of HyperFormer to strengthen its representation capabilities while reducing the amount of model parameters and computation. Extensive experiments on three well-known datasets with four different conditions demonstrate HyperFormer's effectiveness. Datasets and code are available at https://github.com/zhiweihu1103/HKGC-HyperFormer.|超关系知识图(HKG)通过将属性值限定符关联到三元组来扩展标准知识图，三元组有效地表示关联三元组的附加细粒度信息。超关系知识图完备化(HKGC)的目的是在考虑其限定词的同时推断出未知的三元组。大部分现有的 HKGC 方法都采用全球层次的图结构，将超关系知识编码到图卷积信息传递过程中。然而，多跳信息的加入可能会给三重预测过程带来噪声。为了解决这个问题，我们提出了一种考虑局部层次序列信息的 HyperForm 模型，它对三元组的实体、关系和限定符的内容进行编码。更准确地说，HyperForter 由三个不同的模块组成: 一个实体邻居聚合器模块，允许集成实体邻居的信息来捕获不同的视角; 一个关系限定器聚合器模块，将超关系知识集成到相应的关系中，以精化关系内容的表示; 一个基于卷积运算的双向交互模块，捕获实体关系、实体限定器和关系限定器的成对双向交互。了解与当前声明有关的内容的深度知觉。在此基础上，我们引入了一种专家混合策略，在减少模型参数和计算量的同时，增强了 HyperForm 的前馈层的表示能力。在三个著名的数据集上进行的四种不同条件的大量实验证明了 HyperForm 的有效性。数据集和代码可在 https://github.com/zhiweihu1103/hkgc-hyperformer 获得。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=HyperFormer:+Enhancing+Entity+and+Relation+Interaction+for+Hyper-Relational+Knowledge+Graph+Completion)|0| +|[Liberate Pseudo Labels from Over-Dependence: Label Information Migration on Sparsely Labeled Graphs](https://doi.org/10.1145/3583780.3614954)|Zhihui Hu, Yao Fu, Hong Zhao, Xiaoyu Cai, Weihao Jiang, Shiliang Pu|Hikvision Research Institute, Hangzhou, China; Hikvision Research Institute & Key Laboratory of Peace-building Big Data of Zhejiang Province, Hangzhou, China|Graph Convolutional Networks (GCNs) have made outstanding achievements in many tasks on graphs in recent years, but their success relies on sufficient training data. In practice, sparsely labeled graphs widely exist in the real world so self-training methods have become popular approaches by adding pseudo labeled nodes to enhance the performance of GCNs. However, we observe that most selected high-confidence pseudo labeled nodes by the existing methods would surround the true labeled nodes. It is what we called pseudo label over-dependence, which could lead to the non-uniform pseudo label distribution. Furthermore, a thorough experiment shows that the classification accuracy changes significantly under different label densities and the label-sparse regions show great potential improvement in the model performance. Based on the above findings, we theoretically analyze the constraint factors in the label-sparse regions and further propose reducing the feature distribution difference between the label-dense regions and label-sparse regions can effectively decrease the classification error. Thus, in this paper, we propose a novel Graph Label Information Migration framework (GLIM) to liberate pseudo labels from over-dependence. Specifically, we first propose a training dynamics module (TDM) that uses abundant training process information to find more reliable node labels and improve the model robustness against label noise. Then we propose a label migration module (LMM) that migrates label information from label-dense regions to label-sparse regions by a spectral based graph matching algorithm. These migrated labels are like the glimmers in the darkness, providing the supervision signals for the unlabeled nodes in label-sparse regions. Finally, we conduct extensive experiments to demonstrate the effectiveness of the proposed GLIM.|图卷积网络(GCNs)近年来在许多图任务中取得了突出的成就，但它的成功依赖于充分的训练数据。在实际应用中，稀疏标记图广泛存在于现实世界中，因此通过增加伪标记节点来提高 GCNs 性能的自训练方法已经成为一种流行的方法。然而，我们观察到，现有方法选择的大多数高置信度伪标记节点将包围真正的标记节点。这就是我们所说的伪标签过度依赖，它可能导致非均匀的伪标签分布。实验表明，在不同的标签密度下，分类精度有显著的变化，标签稀疏区域对模型性能有很大的改善潜力。在此基础上，从理论上分析了标签稀疏区域的约束因素，进一步提出减小标签密集区域与标签稀疏区域之间的特征分布差异可以有效地减小分类误差。因此，本文提出了一种新的图形标签信息迁移框架(GLIM) ，将伪标签从过度依赖中解放出来。具体来说，我们首先提出了一个训练动力学模块(TDM) ，利用大量的训练过程信息来寻找更可靠的节点标签，提高模型对标签噪声的鲁棒性。然后提出了一种基于谱图匹配算法的标签迁移模块(LMM) ，将标签信息从标签密集区域迁移到标签稀疏区域。这些迁移的标签就像黑暗中的微光，为标签稀疏区域的未标记节点提供监控信号。最后，我们进行了广泛的实验来验证所提出的 GLIM 的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Liberate+Pseudo+Labels+from+Over-Dependence:+Label+Information+Migration+on+Sparsely+Labeled+Graphs)|0| |[Enhanced Template-Free Reaction Prediction with Molecular Graphs and Sequence-based Data Augmentation](https://doi.org/10.1145/3583780.3614865)|Haozhe Hu, Yongquan Jiang, Yan Yang, Jim X. Chen|George Mason University, Fairfax, USA; Southwest Jiaotong University, Chengdu, China|Retrosynthesis and forward synthesis prediction are fundamental challenges in organic synthesis, computer-aided synthesis planning (CASP), and computer-aided drug design (CADD). The objective is to predict plausible reactants for a given target product and its corresponding inverse task. With the rapid development of deep learning, numerous approaches have been proposed to solve this problem from various perspectives. The methods based on molecular graphs benefit from their rich features embedded inside but face difficulties in applying existing sequence-based data augmentations due to the permutation invariance of graph structures. In this work, we propose SeqAGraph, a template-free approach that annotates input graphs with its root atom index to ensure compatibility with sequence-based data augmentation. The matrix product for global attention in graph encoders is implemented by indexing, elementwise product, and aggregation to fuse global attention with local message passing without graph padding. Experiments demonstrate that SeqAGraph fully benefits from molecular graphs and sequence-based data augmentation and achieves state-of-the-art accuracy in template-free approaches.|回溯合成和正向合成预测是有机合成、计算机辅助合成计划(cASP)和计算机辅助药物设计(cADD)的基本挑战。目的是预测给定目标产物的合理反应物及其相应的逆任务。随着深度学习的迅速发展，人们从不同的角度提出了许多解决这一问题的方法。基于分子图的方法得益于其内嵌的丰富特征，但由于图结构的排列不变性，使得现有的基于序列的数据增广方法难以应用。在这项工作中，我们提出了 SeqAGGraph，一种无模板的方法，用它的根原子索引对输入图进行注释，以确保与基于序列的数据增强的兼容性。在图形编码器中，通过索引、元素乘积和聚合实现全局注意力矩阵积，使全局注意力与无图形填充的局部消息传递相融合。实验表明，SeqAGGraph 完全受益于分子图和基于序列的数据增强，并在无模板的方法中实现了最先进的准确性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Enhanced+Template-Free+Reaction+Prediction+with+Molecular+Graphs+and+Sequence-based+Data+Augmentation)|0| |[Spans, Not Tokens: A Span-Centric Model for Multi-Span Reading Comprehension](https://doi.org/10.1145/3583780.3615064)|Zixian Huang, Jiaying Zhou, Chenxu Niu, Gong Cheng|Nanjing University, Nanjing, China|Many questions should be answered by not a single answer but a set of multiple answers. This emerging Multi-Span Reading Comprehension (MSRC) task requires extracting multiple non-contiguous spans from a given context to answer a question. Existing methods extend conventional single-span models to predict the positions of the start and end tokens of answer spans, or predict the beginning-inside-outside tag of each token. Such token-centric paradigms can hardly capture dependencies among span-level answers which are critical to MSRC. In this paper, we propose SpanQualifier, a span-centric scheme where spans, as opposed to tokens, are directly represented and scored to qualify as answers. Explicit span representations enable their interaction which exploits their dependencies to enhance representations. Experiments on three MSRC datasets demonstrate the effectiveness of our span-centric scheme and show that SpanQualifier achieves state-of-the-art results.|许多问题的答案不应该是一个单一的答案，而应该是一系列的多重答案。这个新兴的多阅读理解任务需要从给定的上下文中提取多个非连续的跨度来回答问题。现有的方法扩展了传统的单跨度模型来预测答案跨度的开始和结束标记的位置，或者预测每个标记的开始-内部-外部标记。这种以令牌为中心的范例很难捕获对 MSRC 至关重要的跨级应答之间的依赖性。在本文中，我们提出了 SpanQualifier，这是一个以跨度为中心的方案，其中跨度(而不是标记)直接表示并打分以符合答案的要求。显式跨度表示使它们的交互能够利用它们的依赖性来增强表示。在三个 MSRC 数据集上的实验表明了我们的跨度中心方案的有效性，并且表明 SpanQualifier 达到了最先进的结果。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Spans,+Not+Tokens:+A+Span-Centric+Model+for+Multi-Span+Reading+Comprehension)|0| |[STAMINA (Spatial-Temporal Aligned Meteorological INformation Attention) and FPL (Focal Precip Loss): Advancements in Precipitation Nowcasting for Heavy Rainfall Events](https://doi.org/10.1145/3583780.3615069)|PingChia Huang, YuehLi Chen, YiSyuan Liou, BingChen Tsai, ChunChieh Wu, Winston H. Hsu|National Taiwan University, Taipei, Taiwan Roc|Precipitation nowcasting is crucial for weather-dependent decision-making in various sectors, providing accurate and high-resolution predictions of precipitation within a typical two-hour timeframe. Deep learning techniques have shown promise in improving nowcasting accuracy by leveraging large radar datasets. However, accurately predicting heavy rainfall events remains challenging due to several persistent problems in previous work. These include spatial-temporal misalignment between meteorological information and precipitation data, as well as the performance gap between different rainfall levels. To address these challenges, we propose two innovative modules: Spatial-Temporal Aligned Meteorological INformation Attention (STAMINA) and Focal Precip Loss (FPL). STAMINA integrates meteorological information using spatial-temporal embedding and pixelwise linear attention mechanisms to overcome spatial-temporal misalignment. FPL addresses event imbalance through event weighting and a penalty mechanism. Through extensive experiments, we demonstrate significant performance improvements achieved by STAMINA and FPL, with an 8% improvement in predicting light rainfall and, more significantly, a 30% improvement in heavy rainfall compared to the state-of-the-art DGMR model. These modules offer practical and effective solutions for enhancing nowcasting accuracy, with a specific focus on improving predictions for heavy rainfall events. By tackling the persistent problems in previous work, our proposed approach represents a significant advancement in the field of precipitation nowcasting.|降水临近预报对于各部门依赖天气的决策至关重要，能够在典型的两小时时间范围内提供准确和高分辨率的降水预报。深度学习技术在利用大型雷达数据集提高临近预报精度方面已显示出前景。然而，由于以往工作中的一些持续性问题，准确预测强降水事件仍然具有挑战性。其中包括气象信息与降水数据之间的时空错位，以及不同降水水平之间的性能差距。为了应对这些挑战，我们提出了两个创新模块: 时空对齐气象信息注意(STAMINA)和焦点降水损失(FPL)。STAMINA 利用时空嵌入和像素线性注意机制对气象信息进行集成，克服了时空失调问题。FPL 通过事件加权和惩罚机制解决事件不平衡问题。通过广泛的实验，我们证明了 STAMINA 和 FPL 实现的显著性能改进，与最先进的 DGMR 模型相比，在预测小降雨方面提高了8% ，更显著的是，在强降雨方面提高了30% 。这些模块为提高临近预报的准确性提供了切实有效的解决方案，特别侧重于改进对强降雨事件的预报。通过解决以往工作中存在的问题，我们提出的方法代表了降水临近预报领域的一个重大进展。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=STAMINA+(Spatial-Temporal+Aligned+Meteorological+INformation+Attention)+and+FPL+(Focal+Precip+Loss):+Advancements+in+Precipitation+Nowcasting+for+Heavy+Rainfall+Events)|0| -|[SAGE: A Storage-Based Approach for Scalable and Efficient Sparse Generalized Matrix-Matrix Multiplication](https://doi.org/10.1145/3583780.3615044)|MyungHwan Jang, YunYong Ko, HyuckMoo Gwon, Ikhyeon Jo, Yongjun Park, SangWook Kim|Hanyang University, Seoul, Republic of Korea; Yonsei University, Seoul, Republic of Korea|Sparse generalized matrix-matrix multiplication (SpGEMM) is a fundamental operation for real-world network analysis. With the increasing size of real-world networks, the single-machine-based SpGEMM approach cannot perform SpGEMM on large-scale networks, exceeding the size of main memory (i.e., not scalable). Although the distributed-system-based approach could handle large-scale SpGEMM based on multiple machines, it suffers from severe inter-machine communication overhead to aggregate results of multiple machines (i.e., not efficient). To address this dilemma, in this paper, we propose a novel storage-based SpGEMM approach (SAGE) that stores given networks in storage (e.g., SSD) and loads only the necessary parts of the networks into main memory when they are required for processing via a 3-layer architecture. Furthermore, we point out three challenges that could degrade the overall performance of SAGE and propose three effective strategies to address them: (1) block-based workload allocation for balancing workloads across threads, (2) in-memory partial aggregation for reducing the amount of unnecessarily generated storage-memory I/Os, and (3) distribution-aware memory allocation for preventing unexpected buffer overflows in main memory. Via extensive evaluation, we verify the superiority of SAGE over existing SpGEMM methods in terms of scalability and efficiency.|稀疏广义矩阵乘法(SpGEMM)是实际网络分析的基本运算。随着现实网络规模的不断扩大，基于单机的 SpGEMM 方法不能在大规模网络上运行，超出了主存的规模(即不可伸缩性)。尽管基于分布式系统的方法可以处理基于多台机器的大规模 SpGEMM，但是由于需要对多台机器的结果进行聚合(即效率不高) ，使得该方法存在严重的机器间通信开销。为了解决这一难题，本文提出了一种新的基于存储的 SpGEMM 方法(SAGE) ，该方法将给定的网络存储在存储器中(如 SSD) ，当需要通过3层架构进行处理时，只将网络的必要部分加载到主存中。此外，我们指出了可能降低 SAGE 整体性能的三个挑战，并提出了三种有效的策略来解决这些问题: (1)基于块的工作负载分配，用于平衡线程间的工作负载; (2)内存部分聚合，用于减少不必要的生成存储内存 I/O 的数量; (3)分布式感知内存分配，用于防止主存中意外的缓冲区溢出。通过广泛的评估，我们验证了 SAGE 方法在可扩展性和效率方面优于现有的 SpGEMM 方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=SAGE:+A+Storage-Based+Approach+for+Scalable+and+Efficient+Sparse+Generalized+Matrix-Matrix+Multiplication)|0| -|[Relevant Entity Selection: Knowledge Graph Bootstrapping via Zero-Shot Analogical Pruning](https://doi.org/10.1145/3583780.3615030)|Lucas Jarnac, Miguel Couceiro, Pierre Monnin|Orange, Belfort, France; Orange & Université de Lorraine, CNRS, LORIA, Belfort & Nancy, France; Université de Lorraine, CNRS, LORIA, Nancy, France|Knowledge Graph Construction (KGC) can be seen as an iterative process starting from a high quality nucleus that is refined by knowledge extraction approaches in a virtuous loop. Such a nucleus can be obtained from knowledge existing in an open KG like Wikidata. However, due to the size of such generic KGs, integrating them as a whole may entail irrelevant content and scalability issues. We propose an analogy-based approach that starts from seed entities of interest in a generic KG, and keeps or prunes their neighboring entities. We evaluate our approach on Wikidata through two manually labeled datasets that contain either domain-homogeneous or -heterogeneous seed entities. We empirically show that our analogy-based approach outperforms LSTM, Random Forest, SVM, and MLP, with a drastically lower number of parameters. We also evaluate its generalization potential in a transfer learning setting. These results advocate for the further integration of analogy-based inference in tasks related to the KG lifecycle.|知识图构建(KGC)可以看作是一个迭代过程，它从一个高质量的核心开始，通过知识抽取方法在一个良性循环中精炼。这样的细胞核可以从像 Wikidata 这样的开放幼儿园中存在的知识中获得。然而，由于这些通用幼稚园的规模，将它们作为一个整体进行集成可能会带来不相关的内容和可伸缩性问题。我们提出了一种基于类比的方法，它从一般 KG 中感兴趣的种子实体开始，并保留或删除它们的相邻实体。我们评估我们的方法对 Wikidata 通过两个手动标记的数据集，包含领域同质或异质种子实体。我们的经验表明，我们的类比为基础的方法优于 LSTM，随机森林，支持向量机和 MLP，大大减少了参数数量。我们还评估了它在迁移学习环境中的推广潜力。这些结果提倡在与 KG 生命周期相关的任务中进一步集成基于类比的推理。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Relevant+Entity+Selection:+Knowledge+Graph+Bootstrapping+via+Zero-Shot+Analogical+Pruning)|0| +|[SAGE: A Storage-Based Approach for Scalable and Efficient Sparse Generalized Matrix-Matrix Multiplication](https://doi.org/10.1145/3583780.3615044)|MyungHwan Jang, YunYong Ko, HyuckMoo Gwon, Ikhyeon Jo, Yongjun Park, SangWook Kim|Yonsei University, Seoul, Republic of Korea; Hanyang University, Seoul, Republic of Korea|Sparse generalized matrix-matrix multiplication (SpGEMM) is a fundamental operation for real-world network analysis. With the increasing size of real-world networks, the single-machine-based SpGEMM approach cannot perform SpGEMM on large-scale networks, exceeding the size of main memory (i.e., not scalable). Although the distributed-system-based approach could handle large-scale SpGEMM based on multiple machines, it suffers from severe inter-machine communication overhead to aggregate results of multiple machines (i.e., not efficient). To address this dilemma, in this paper, we propose a novel storage-based SpGEMM approach (SAGE) that stores given networks in storage (e.g., SSD) and loads only the necessary parts of the networks into main memory when they are required for processing via a 3-layer architecture. Furthermore, we point out three challenges that could degrade the overall performance of SAGE and propose three effective strategies to address them: (1) block-based workload allocation for balancing workloads across threads, (2) in-memory partial aggregation for reducing the amount of unnecessarily generated storage-memory I/Os, and (3) distribution-aware memory allocation for preventing unexpected buffer overflows in main memory. Via extensive evaluation, we verify the superiority of SAGE over existing SpGEMM methods in terms of scalability and efficiency.|稀疏广义矩阵乘法(SpGEMM)是实际网络分析的基本运算。随着现实网络规模的不断扩大，基于单机的 SpGEMM 方法不能在大规模网络上运行，超出了主存的规模(即不可伸缩性)。尽管基于分布式系统的方法可以处理基于多台机器的大规模 SpGEMM，但是由于需要对多台机器的结果进行聚合(即效率不高) ，使得该方法存在严重的机器间通信开销。为了解决这一难题，本文提出了一种新的基于存储的 SpGEMM 方法(SAGE) ，该方法将给定的网络存储在存储器中(如 SSD) ，当需要通过3层架构进行处理时，只将网络的必要部分加载到主存中。此外，我们指出了可能降低 SAGE 整体性能的三个挑战，并提出了三种有效的策略来解决这些问题: (1)基于块的工作负载分配，用于平衡线程间的工作负载; (2)内存部分聚合，用于减少不必要的生成存储内存 I/O 的数量; (3)分布式感知内存分配，用于防止主存中意外的缓冲区溢出。通过广泛的评估，我们验证了 SAGE 方法在可扩展性和效率方面优于现有的 SpGEMM 方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=SAGE:+A+Storage-Based+Approach+for+Scalable+and+Efficient+Sparse+Generalized+Matrix-Matrix+Multiplication)|0| +|[Relevant Entity Selection: Knowledge Graph Bootstrapping via Zero-Shot Analogical Pruning](https://doi.org/10.1145/3583780.3615030)|Lucas Jarnac, Miguel Couceiro, Pierre Monnin|Orange & Université de Lorraine, CNRS, LORIA, Belfort & Nancy, France; Orange, Belfort, France; Université de Lorraine, CNRS, LORIA, Nancy, France|Knowledge Graph Construction (KGC) can be seen as an iterative process starting from a high quality nucleus that is refined by knowledge extraction approaches in a virtuous loop. Such a nucleus can be obtained from knowledge existing in an open KG like Wikidata. However, due to the size of such generic KGs, integrating them as a whole may entail irrelevant content and scalability issues. We propose an analogy-based approach that starts from seed entities of interest in a generic KG, and keeps or prunes their neighboring entities. We evaluate our approach on Wikidata through two manually labeled datasets that contain either domain-homogeneous or -heterogeneous seed entities. We empirically show that our analogy-based approach outperforms LSTM, Random Forest, SVM, and MLP, with a drastically lower number of parameters. We also evaluate its generalization potential in a transfer learning setting. These results advocate for the further integration of analogy-based inference in tasks related to the KG lifecycle.|知识图构建(KGC)可以看作是一个迭代过程，它从一个高质量的核心开始，通过知识抽取方法在一个良性循环中精炼。这样的细胞核可以从像 Wikidata 这样的开放幼儿园中存在的知识中获得。然而，由于这些通用幼稚园的规模，将它们作为一个整体进行集成可能会带来不相关的内容和可伸缩性问题。我们提出了一种基于类比的方法，它从一般 KG 中感兴趣的种子实体开始，并保留或删除它们的相邻实体。我们评估我们的方法对 Wikidata 通过两个手动标记的数据集，包含领域同质或异质种子实体。我们的经验表明，我们的类比为基础的方法优于 LSTM，随机森林，支持向量机和 MLP，大大减少了参数数量。我们还评估了它在迁移学习环境中的推广潜力。这些结果提倡在与 KG 生命周期相关的任务中进一步集成基于类比的推理。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Relevant+Entity+Selection:+Knowledge+Graph+Bootstrapping+via+Zero-Shot+Analogical+Pruning)|0| |[PriSHAP: Prior-guided Shapley Value Explanations for Correlated Features](https://doi.org/10.1145/3583780.3615013)|Guanyu Jiang, Fuzhen Zhuang, Bowen Song, Tianyi Zhang, Deqing Wang|Ant Group, Hangzhou, China; Beihang University & Zhongguancun Laboratory, Beijing, China; Beihang University, Beijing, China|Among numerous explainable AI (XAI) methods proposed in recent years, model explanations based on Shapley values are widely accepted for their solid theoretical support from game theory. However, most existing methods approximate Shapley values based on a feature independence assumption considering the complexity of calculating exact Shapley values. This assumption could bring some counterfactual problems when interpreted features are highly correlated and result in explanations contrary to human intuition. In this paper, we propose PriSHAP to explicitly model the dependency relationship between correlated features and provide reasonable explanations for tabular data. Feature dependencies are analyzed and taken as prior information to guide the process of estimating Shapley values. Additionally, PriSHAP is free to be applied in popular Shapley value-based explainers to address counterfactual problems while providing more faithful explanations. A pipeline is given to apply PriSHAP in existing explainers with simple adjustments. Extensive experiments on both public datasets and artificial datasets are provided to demonstrate the effectiveness of our method.|在近年来提出的众多可解释 AI (XAI)方法中，基于 Shapley 值的模型解释因其博弈论的坚实理论支持而被广泛接受。然而，考虑到计算准确的 Shapley 值的复杂性，现有的方法大多基于特征无关的假设来逼近 Shapley 值。当解释特征高度相关时，这种假设会带来一些反事实问题，导致解释与人的直觉相悖。在本文中，我们提出了 PriSHAP 显式模型相关特征之间的依赖关系，并提供合理的解释表数据。将特征依赖关系作为先验信息进行分析，指导 Shapley 值的估计过程。此外，PriSHAP 可以自由地应用于流行的 Shapley 价值为基础的解释，以解决反事实的问题，同时提供更忠实的解释。通过简单的调整，给出了在现有解释器中应用 PriSHAP 的流水线。在公共数据集和人工数据集上进行了大量的实验，验证了该方法的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=PriSHAP:+Prior-guided+Shapley+Value+Explanations+for+Correlated+Features)|0| -|[A Momentum Loss Reweighting Method for Improving Recall](https://doi.org/10.1145/3583780.3614764)|Chenzhi Jiang, Yin Jin, Ningtao Wang, Ruofan Wu, Xing Fu, Weiqiang Wang|Tiansuan Lab, Ant Group, Hangzhou, China; Tiansuan Lab, Ant Group, Shanghai, China|In many practical binary classification applications, such as financial fraud detection or medical diagnosis, it is crucial to optimize a model's performance on high-confidence samples whose scores are higher than a specific threshold, which is calculated by a given false positive rate according to practical requirements. However, the proportion of high-confidence samples is typically extremely small, especially in long-tailed datasets, which can lead to poor recall results and an alignment bias between realistic goals and loss. To address this challenge, we propose a novel loss reweighting framework called Momentum Threshold-Oriented Loss (MTOL) for binary classification tasks and propose two instantiated losses of it. Given a limited FPR range, MTOL aims to improve the recall of binary classification models at that FPR range by incorporating a batch memory queue and momentum estimation mechanisms. The MTOL adaptively estimates thresholds of FPR during the model training iterations and up-weights the loss of samples in the threshold range, with little consumption of storage and computation. Our experimental results on various datasets, including CIFAR-10, CIFAR-100, Tiny-ImageNet, demonstrate the significant effect of MTOL in improving the recall at low FPR especially in class imbalance settings. These results suggest that MTOL is a promising approach in scenarios where the model's performance in the low FPR range is of utmost importance.|在许多实际的二进制分类应用中，如金融欺诈检测或医疗诊断，优化模型对高置信度样本的性能是至关重要的，这些样本的得分高于特定的阈值，根据实际需求，该阈值是通过给定的假阳性率计算的。然而，高置信度样本的比例通常非常小，尤其是在长尾数据集中，这可能导致较差的召回结果和现实目标与损失之间的一致性偏差。为了解决这个问题，我们提出了一个新的损失重新加权框架，称为动量阈值导向损失(MTOL)的二进制分类任务，并提出了两个实例化的损失。在有限的 FPR 范围内，MTOL 旨在通过引入批记忆队列和动量估计机制来提高该 FPR 范围内二进制分类模型的召回率。在模型训练迭代过程中，MTOL 自适应估计 FPR 的阈值，并在阈值范围内增加样本损失的权重，同时减少存储和计算的消耗。我们在各种数据集上的实验结果，包括 CIFAR-10，CIFAR-100，Tiny-ImageNet，证明了 MTOL 在改善低 FPR 的召回方面的显着效果，特别是在类不平衡设置中。这些结果表明，MTOL 是一种有前途的方法在情况下，模型的性能在低 FPR 范围是至关重要的。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Momentum+Loss+Reweighting+Method+for+Improving+Recall)|0| +|[A Momentum Loss Reweighting Method for Improving Recall](https://doi.org/10.1145/3583780.3614764)|Chenzhi Jiang, Yin Jin, Ningtao Wang, Ruofan Wu, Xing Fu, Weiqiang Wang|Tiansuan Lab, Ant Group, Shanghai, China; Tiansuan Lab, Ant Group, Hangzhou, China|In many practical binary classification applications, such as financial fraud detection or medical diagnosis, it is crucial to optimize a model's performance on high-confidence samples whose scores are higher than a specific threshold, which is calculated by a given false positive rate according to practical requirements. However, the proportion of high-confidence samples is typically extremely small, especially in long-tailed datasets, which can lead to poor recall results and an alignment bias between realistic goals and loss. To address this challenge, we propose a novel loss reweighting framework called Momentum Threshold-Oriented Loss (MTOL) for binary classification tasks and propose two instantiated losses of it. Given a limited FPR range, MTOL aims to improve the recall of binary classification models at that FPR range by incorporating a batch memory queue and momentum estimation mechanisms. The MTOL adaptively estimates thresholds of FPR during the model training iterations and up-weights the loss of samples in the threshold range, with little consumption of storage and computation. Our experimental results on various datasets, including CIFAR-10, CIFAR-100, Tiny-ImageNet, demonstrate the significant effect of MTOL in improving the recall at low FPR especially in class imbalance settings. These results suggest that MTOL is a promising approach in scenarios where the model's performance in the low FPR range is of utmost importance.|在许多实际的二进制分类应用中，如金融欺诈检测或医疗诊断，优化模型对高置信度样本的性能是至关重要的，这些样本的得分高于特定的阈值，根据实际需求，该阈值是通过给定的假阳性率计算的。然而，高置信度样本的比例通常非常小，尤其是在长尾数据集中，这可能导致较差的召回结果和现实目标与损失之间的一致性偏差。为了解决这个问题，我们提出了一个新的损失重新加权框架，称为动量阈值导向损失(MTOL)的二进制分类任务，并提出了两个实例化的损失。在有限的 FPR 范围内，MTOL 旨在通过引入批记忆队列和动量估计机制来提高该 FPR 范围内二进制分类模型的召回率。在模型训练迭代过程中，MTOL 自适应估计 FPR 的阈值，并在阈值范围内增加样本损失的权重，同时减少存储和计算的消耗。我们在各种数据集上的实验结果，包括 CIFAR-10，CIFAR-100，Tiny-ImageNet，证明了 MTOL 在改善低 FPR 的召回方面的显着效果，特别是在类不平衡设置中。这些结果表明，MTOL 是一种有前途的方法在情况下，模型的性能在低 FPR 范围是至关重要的。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Momentum+Loss+Reweighting+Method+for+Improving+Recall)|0| |[Hierarchical Multi-Label Classification with Partial Labels and Unknown Hierarchy](https://doi.org/10.1145/3583780.3614912)|Suhyeon Jo, DongHyeok Shin, Byeonghu Na, JoonHo Jang, IlChul Moon|Korea Advanced Institute of Science & Technology, Daejeon, Republic of Korea|Hierarchical multi-label classification aims at learning a multi-label classifier from a dataset whose labels are organized into a hierarchical structure. To the best of our knowledge, we propose for the first time the problem of finding a multi-label classifier given a partially labeled hierarchical multi-label dataset. We also assume the situation where the classifier cannot access hierarchical information during training. This work proposes an iterative framework for learning both multi-labels and a hierarchical structure of classes. When training a multi-label classifier from partial labels, our model extracts a class hierarchy from the classifier output using our hierarchy extraction algorithm. Then, our proposed loss exploits the extracted hierarchy to train the classifier. Theoretically, we show that our hierarchy extraction algorithm correctly finds the unknown hierarchy under a mild condition, and we prove that our loss function of multi-label classification with such hierarchy becomes an unbiased estimator of true multi-label classification risk. Our experiments show that our model obtains a class hierarchy close to the ground-truth dataset hierarchy, and simultaneously, our method outperforms previous methods for hierarchical multi-label classification and multi-label classification from partial labels.|分层多标签分类旨在从标签组织成分层结构的数据集中学习多标签分类器。根据我们的知识，我们首次提出了在给定部分标记的层次化多标记数据集的情况下寻找多标记分类器的问题。我们还假设分类器在训练期间不能访问层次信息的情况。这项工作提出了一个迭代的框架学习两个多标签和分层结构的类。当从部分标签中训练多标签分类器时，我们的模型使用我们的层次提取算法从分类器输出中提取类层次。然后，我们提出的损失利用提取的层次结构来训练分类器。从理论上证明了我们的层次提取算法在温和的条件下正确地找到了未知的层次，并且证明了我们的具有这种层次的多标签分类的损失函数成为真正的多标签分类风险的无偏估计。实验结果表明，该模型获得了一个接近于地面真实数据集层次结构的类层次结构，同时该方法在多标签分类和部分标签多标签分类方面优于以往的方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Hierarchical+Multi-Label+Classification+with+Partial+Labels+and+Unknown+Hierarchy)|0| |[Robust Graph Clustering via Meta Weighting for Noisy Graphs](https://doi.org/10.1145/3583780.3615038)|Hyeonsoo Jo, Fanchen Bu, Kijung Shin||How can we find meaningful clusters in a graph robustly against noise edges? Graph clustering (i.e., dividing nodes into groups of similar ones) is a fundamental problem in graph analysis with applications in various fields. Recent studies have demonstrated that graph neural network (GNN) based approaches yield promising results for graph clustering. However, we observe that their performance degenerates significantly on graphs with noise edges, which are prevalent in practice. In this work, we propose MetaGC for robust GNN-based graph clustering. MetaGC employs a decomposable clustering loss function, which can be rephrased as a sum of losses over node pairs. We add a learnable weight to each node pair, and MetaGC adaptively adjusts the weights of node pairs using meta-weighting so that the weights of meaningful node pairs increase and the weights of less-meaningful ones (e.g., noise edges) decrease. We show empirically that MetaGC learns weights as intended and consequently outperforms the state-of-the-art GNN-based competitors, even when they are equipped with separate denoising schemes, on five real-world graphs under varying levels of noise. Our code and datasets are available at https://github.com/HyeonsooJo/MetaGC.|我们怎样才能在一个图中找到有意义的集群对噪声边强健？图聚类(即将节点划分为相似的节点组)是图分析中的一个基本问题，在各个领域都有应用。最近的研究表明，基于图神经网络(GNN)的方法在图聚类方面取得了很好的效果。然而，我们观察到它们的性能在具有噪声边的图上明显退化，这在实际中是普遍存在的。在这项工作中，我们提出了 MetaGC 的健壮 GNN 为基础的图聚类。MetaGC 使用了一个可分解的聚类损失函数，它可以被重新表述为节点对损失的总和。我们给每个节点对添加一个可学习的权重，MetaGC 使用元加权自适应地调整节点对的权重，以便有意义的节点对的权重增加，而无意义的节点对(例如噪声边缘)的权重减少。我们的经验表明，MetaGC 按照预期学习权重，因此优于最先进的基于 GNN 的竞争对手，即使它们配备了独立的去噪方案，在五个不同噪声水平的真实世界图上。我们的代码和数据集 https://github.com/hyeonsoojo/metagc 可用。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Robust+Graph+Clustering+via+Meta+Weighting+for+Noisy+Graphs)|0| |[CFOM: Lead Optimization For Drug Discovery With Limited Data](https://doi.org/10.1145/3583780.3614807)|Natan Kaminsky, Uriel Singer, Kira Radinsky|Technion - Israel Institute of Technology, Haifa, Israel; Meta AI Research, Tel-Aviv, Israel|Drug development is a long and costly process consisting of several stages that can take many years to complete. One of the early stage's goals is to optimize a novel chemical compound to be active against a target protein associated with the disease. Often machine learning techniques are used to improve the procedure of discovering and optimizing potential drug candidates. The goal of molecule optimization is, given an input molecule, to produce a new molecule that is chemically similar to the input molecule but with an improved property. We present a novel algorithm that during optimization divides a molecule into two disjoint substructures that we call: the molecule chains and the molecule core. Our approach is inspired by expert design of chemical compounds that employ a fundamental molecular template and add to it chemical functional groups to generate compounds with desired properties. We train a model to generate the molecule chains with the desired properties for optimization, which are then attached to the molecule core to construct a novel molecule with high similarity to the input molecule. This is achieved by selective masking of pairs of input molecules' chains and cores during training. Additionally, we demonstrate the extension of this approach to data-scarce tasks, like targeting a drug to a novel protein. We first evaluate our method on standard molecule optimization tasks such as inhibition against glycogen synthase kinase-3 beta (GSK3β). We then empirically compared the model performance with the state-of-the-art algorithms over 21 novel proteins and show superior performance.|药物开发是一个漫长而昂贵的过程，包括几个阶段，可能需要许多年才能完成。早期阶段的目标之一是优化一种新的化合物，使其对与疾病相关的靶蛋白具有活性。机器学习技术经常被用来改进发现和优化潜在候选药物的过程。分子优化的目标是，给定一个输入分子，以产生一个新的分子，这是化学类似的输入分子，但具有改进的性能。我们提出了一个新的算法，在优化过程中分裂成两个不相交的子结构，我们称之为: 分子链和分子核心。我们的方法的灵感来自专家设计的化合物，采用一个基本的分子模板，并添加到它的化学功能基团，以产生具有期望的性质的化合物。我们训练一个模型来产生具有优化所需特性的分子链，然后将这些分子链连接到分子核心以构建一个与输入分子具有高度相似性的新分子。这是通过在训练过程中对输入分子的链和核进行选择性掩蔽来实现的。此外，我们还展示了这种方法在数据稀缺任务中的应用，比如将药物定位于一种新的蛋白质。我们首先评估我们的方法在标准分子优化任务，如抑制糖原合成酶激酶 -3β (GSK3β)。然后，我们经验性地比较了模型性能与国家的最先进的算法超过21个新的蛋白质和显示优越的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=CFOM:+Lead+Optimization+For+Drug+Discovery+With+Limited+Data)|0| |[Diving into a Sea of Opinions: Multi-modal Abstractive Summarization with Comment Sensitivity](https://doi.org/10.1145/3583780.3614849)|Raghvendra Kumar, Ratul Chakraborty, Abhishek Tiwari, Sriparna Saha, Naveen Saini|Ramakrishna Mission Vivekananda Educational & Research Institute Kolkata, Kolkata, India; Indian Institute of Information Technology Lucknow, Lucknow, India; Indian Institute of Technology Patna, Patna, India|In the modern era, the rapid expansion of social media and the proliferation of the internet community has led to a multi-fold increase in the richness and range of views and outlooks expressed by readers and viewers. To obtain valuable insights from this vast sea of opinions, we present an inventive and holistic procedure for multi-modal abstractive summarization with comment sensitivity. Our proposed model utilizes both textual and visual modalities and examines the remarks provided by the readers to produce summaries that apprehend the significant points and opinions made by them. Our model features a transformer-based encoder that seamlessly processes both news articles and comments, merging them before transmitting the amalgamated information to the decoder. Additionally, the core segment of our architecture consists of an attention-based merging technique which is trained adversarially by means of a generator and discriminator to bridge the semantic gap between comments and articles. We have used a Bi-LSTM-based branch for image pointer generation. We assess our model on the reader-aware multi-document summarization (RA-MDS) dataset which contains news articles, their summaries, and related comments. We have extended the dataset by adding images pertaining to news articles in the corpus to increase the richness and diversity of the dataset. Our comprehensive experiments reveal that our model outperforms similar pre-trained models and baselines across two of the four evaluated metrics, showcasing its superior performance.|在现代社会，社会媒体的迅速扩张和互联网社区的扩散，使得读者和观众表达的观点和观点的丰富性和范围增加了数倍。为了从这一浩瀚的意见海洋中获得有价值的见解，我们提出了一个创造性和整体性的多模态抽象摘要过程，具有评论敏感性。我们提出的模型利用文本和视觉模式，检查读者提供的评论，以产生理解他们所提出的重要观点和意见的摘要。我们的模型具有一个基于转换器的编码器，它可以无缝地处理新闻文章和评论，在将合并后的信息传输给解码器之前将它们合并。此外，我们的体系结构的核心部分包括一个基于注意力的合并技术，该技术通过生成器和鉴别器进行对立训练，以消除评论和文章之间的语义差距。我们使用了一个基于双 LSTM 的分支来生成图像指针。我们在读者感知的多文档摘要(RA-MDS)数据集上评估我们的模型，该数据集包含新闻文章、它们的摘要和相关的评论。我们通过在语料库中添加与新闻文章相关的图像来扩展数据集，以增加数据集的丰富性和多样性。我们的综合实验表明，我们的模型优于类似的预训练模型和基线，在四个评估指标中的两个，显示了其优越的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Diving+into+a+Sea+of+Opinions:+Multi-modal+Abstractive+Summarization+with+Comment+Sensitivity)|0| |[Prompting Strategies for Citation Classification](https://doi.org/10.1145/3583780.3615018)|Suchetha N. Kunnath, David Pride, Petr Knoth|The Open University, Milton Keynes, United Kingdom|Citation classification aims to identify the purpose of the cited article in the citing article. Previous citation classification methods rely largely on supervised approaches. The models are trained on datasets with citing sentences or citation contexts annotated for a citation's purpose or function or intent. Recent advancements in Large Language Models (LLMs) have dramatically improved the ability of NLP systems to achieve state-of-the-art performances under zero or few-shot settings. This makes LLMs particularly suitable for tasks where sufficiently large labelled datasets are not yet available, which remains to be the case for citation classification. This paper systematically investigates the effectiveness of different prompting strategies for citation classification and compares them to promptless strategies as a baseline. Specifically, we evaluate the following four strategies, two of which we introduce for the first time, which involve updating Language Model (LM) parameters while training the model: (1) Promptless fine-tuning, (2) Fixed-prompt LM tuning, (3) Dynamic Context-prompt LM tuning (proposed), (4) Prompt + LM fine-tuning (proposed). Additionally, we test the zero-shot performance of LLMs, GPT3.5, a (5) Tuning-free prompting strategy that involves no parameter updating. Our results show that prompting methods based on LM parameter updating significantly improve citation classification performances on both domain-specific and multi-disciplinary citation classifications. Moreover, our Dynamic Context-prompting method achieves top scores both for the ACL-ARC and ACT2 citation classification datasets, surpassing the highest-performing system in the 3C shared task benchmark. Interestingly, we observe zero-shot GPT3.5 to perform well on ACT2 but poorly on the ACL-ARC dataset.|引文分类旨在识别引文中被引文章的目的。以往的引文分类方法主要依赖于监督方法。这些模型在数据集上进行训练，引用的句子或引用上下文被注释为引用的目的、功能或意图。大语言模型(LLM)的最新进展极大地提高了自然语言处理(NLP)系统的能力，使其能够在零或少镜头设置下实现最先进的性能。这使得 LLM 特别适合于那些没有足够大标记的数据集的任务，这仍然是引文分类的情况。本文系统地研究了引文分类中不同激励策略的有效性，并将其与非激励策略进行了比较。具体来说，我们评估了以下四种策略，其中两种是我们首次引入的，它们涉及在训练语言模型时更新语言模型(LM)参数: (1)无提示微调，(2)固定提示 LM 微调，(3)动态上下文提示 LM 微调(建议) ，(4)提示 + LM 微调(建议)。此外，我们还测试了 LLM，GPT3.5，这是一种不需要参数更新的无调优提示策略。研究结果表明，基于 LM 参数更新的提示方法显著提高了特定领域和多学科引文分类的分类性能。此外，我们的动态上下文提示方法在 ACL-ARC 和 ACT2引文分类数据集中都获得了最高分，超过了3C 共享任务基准中表现最好的系统。有趣的是，我们观察到零射 GPT3.5在 ACT2上表现良好，但在 ACL-ARC 数据集上表现不佳。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Prompting+Strategies+for+Citation+Classification)|0| |[VFedAD: A Defense Method Based on the Information Mechanism Behind the Vertical Federated Data Poisoning Attack](https://doi.org/10.1145/3583780.3615106)|Jinrong Lai, Tong Wang, Chuan Chen, Yihao Li, Zibin Zheng|Sun Yat-sen University, Guangzhou, China|In recent years, federated learning has achieved remarkable results in the medical and financial fields, but various attacks have always plagued federated learning. Data poisoning attack and defense research in horizontal federated learning are sufficient, yet vertical federated data poisoning attack and defense remains an open area due to two challenges: (1) Complex data distributions lead to immense attack possibilities, and (2) defense methods are insufficient for complex data distributions. We have discovered that from the perspective of information theory, the above challenges can be addressed elegantly and succinctly with a solution. We first reveal the information-theoretic mechanisms underlying vertical federated data poisoning attacks and then propose an unsupervised vertical federated data poisoning defense method (VFedAD) based on information theory. VFedAD learns semantic-rich client data representations through contrastive learning task and cross-client prediction task to identify anomalies. Experiments show VFedAD effectively detects vertical federated anomalies, protecting subsequent algorithms from vertical federated data poisoning attacks.|近年来，联邦学习在医学和金融领域取得了显著的成绩，但各种攻击一直困扰着联邦学习。水平联邦学习中的数据中毒攻击和防御研究已经足够，但垂直联邦数据中毒攻击和防御仍然是一个开放的领域，这是由于两个挑战: (1)复杂的数据分布导致巨大的攻击可能性，(2)防御方法不足以应对复杂的数据分布。我们发现，从信息论的角度来看，上述挑战可以通过一个解决方案得到优雅而简洁的解决。首先揭示了垂直联邦数据中毒攻击的信息论机制，然后提出了一种基于信息论的无监督垂直联邦数据中毒防御方法(VFedAD)。VFedAD 通过对比学习任务和跨客户端预测任务学习语义丰富的客户端数据表示来识别异常。实验表明，VFedAD 可以有效地检测垂直联邦异常，保护后续算法免受垂直联邦数据中毒攻击。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=VFedAD:+A+Defense+Method+Based+on+the+Information+Mechanism+Behind+the+Vertical+Federated+Data+Poisoning+Attack)|0| -|[A Re-evaluation of Deep Learning Methods for Attributed Graph Clustering](https://doi.org/10.1145/3583780.3614768)|Xinying Lai, Dingming Wu, Christian S. Jensen, Kezhong Lu|Aalborg University, Aalborg, Denmark; Shenzhen University, Shenzhen, China|Attributed graph clustering aims to partition the nodes in a graph into groups such that the nodes in the same group are close in terms of graph proximity and also have similar attribute values. Recently, deep learning methods have achieved state-of-the-art clustering performance. However, the effectiveness of existing methods remains unclear due to two reasons. First, the datasets used for evaluation do not support fully the goal of attributed graph clustering. The category labels of nodes are only relevant to node attributes, and nodes with the same category label are often distant in the graph. Second, existing methods for the attributed graph clustering are complex and consist of several components. There is lack of comparisons of methods composed of different components from existing methods. This study proposes six benchmark datasets that support better the goal of attributed graph clustering and reports the performance of existing representative methods. Given that existing methods leave room for improvement on the proposed benchmark datasets, we systematically analyze five aspects of existing methods: encoded information, training networks, fusion mechanisms, loss functions, and clustering result generation. Based on these aspects, we decompose existing methods into modules and evaluate the performance of reconfigured methods based on these modules. According to the experimental results on the proposed benchmark datasets, we identify two promising configurations: (i) taking the attribute matrix as input to a graph convolutional network and (ii) layer-wise linear fusing deep neural network and graph attention network. And we also find that complex loss function fails to improve the clustering performance.|属性图聚类的目的是将一个图中的节点划分为若干组，使得同一组中的节点在图的邻近性方面相近，并且具有相似的属性值。近年来，深度学习方法取得了一流的聚类性能。然而，由于两个原因，现有方法的有效性仍不清楚。首先，用于评估的数据集不完全支持属性图聚类的目标。节点的类别标签只与节点属性相关，具有相同类别标签的节点在图中往往相距较远。其次，现有的属性图聚类方法是复杂的，由多个部分组成。由现有方法的不同组件组成的方法缺乏比较。本研究提出了六个基准数据集，更好地支持属性图聚类的目标，并报告了现有的代表性方法的性能。鉴于现有的方法在基准数据集上还有改进的空间，我们系统地分析了现有方法的五个方面: 编码信息、训练网络、融合机制、损失函数和聚类结果生成。基于这些方面，我们将现有的方法分解为模块，并对基于这些模块的重新配置方法的性能进行评估。根据基准数据集的实验结果，我们确定了两种有前景的构型: (i)将属性矩阵作为图卷积网络的输入; (ii)分层线性融合深度神经网络和图注意网络。同时发现复杂损失函数不能提高聚类性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Re-evaluation+of+Deep+Learning+Methods+for+Attributed+Graph+Clustering)|0| -|[Tackling Diverse Minorities in Imbalanced Classification](https://doi.org/10.1145/3583780.3615071)|KweiHerng Lai, Daochen Zha, Huiyuan Chen, Mangesh Bendre, Yuzhong Chen, Mahashweta Das, Hao Yang, Xia Hu|Rice University, Houston, TX, USA; Visa Research, Palo Alto, CA, USA; Visa Research, PAlo Alto, CA, USA|Imbalanced datasets are commonly observed in various real-world applications, presenting significant challenges in training classifiers. When working with large datasets, the imbalanced issue can be further exacerbated, making it exceptionally difficult to train classifiers effectively. To address the problem, over-sampling techniques have been developed to linearly interpolating data instances between minorities and their neighbors. However, in many real-world scenarios such as anomaly detection, minority instances are often dispersed diversely in the feature space rather than clustered together. Inspired by domain-agnostic data mix-up, we propose generating synthetic samples iteratively by mixing data samples from both minority and majority classes. It is non-trivial to develop such a framework, the challenges include source sample selection, mix-up strategy selection, and the coordination between the underlying model and mix-up strategies. To tackle these challenges, we formulate the problem of iterative data mix-up as a Markov decision process (MDP) that maps data attributes onto an augmentation strategy. To solve the MDP, we employ an actor-critic framework to adapt the discrete-continuous decision space. This framework is utilized to train a data augmentation policy and design a reward signal that explores classifier uncertainty and encourages performance improvement, irrespective of the classifier's convergence. We demonstrate the effectiveness of our proposed framework through extensive experiments conducted on seven publicly available benchmark datasets using three different types of classifiers. The results of these experiments showcase the potential and promise of our framework in addressing imbalanced datasets with diverse minorities.|不平衡数据集在各种实际应用中普遍存在，对分类器的训练提出了严峻的挑战。在处理大型数据集时，不平衡问题会进一步加剧，使得有效地训练分类器变得异常困难。为了解决这个问题，已经开发了过采样技术来线性插值少数民族和他们的邻居之间的数据实例。然而，在许多实际场景中，例如异常检测，少数实例通常分散在不同的特性空间中，而不是聚集在一起。受领域不可知数据混合的启发，我们提出通过混合来自少数和多数类的数据样本迭代生成合成样本。开发这样一个框架并非易事，其挑战包括源样本选择、混合策略选择以及底层模型与混合策略之间的协调。为了应对这些挑战，我们将迭代数据混淆的问题制定为一个马可夫决策过程(mDP) ，将数据属性映射到一个增强策略上。为了解决 MDP 问题，我们采用了一个行为者-批评框架来适应离散-连续决策空间。该框架用于训练数据增强策略和设计奖励信号，以探索分类器的不确定性，并鼓励性能的改善，无论分类器的收敛。我们通过使用三种不同类型的分类器对七个公开可用的基准数据集进行广泛的实验，证明了我们提出的框架的有效性。这些实验的结果展示了我们的框架在处理不同少数群体的不平衡数据集方面的潜力和希望。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Tackling+Diverse+Minorities+in+Imbalanced+Classification)|0| +|[A Re-evaluation of Deep Learning Methods for Attributed Graph Clustering](https://doi.org/10.1145/3583780.3614768)|Xinying Lai, Dingming Wu, Christian S. Jensen, Kezhong Lu|Shenzhen University, Shenzhen, China; Aalborg University, Aalborg, Denmark|Attributed graph clustering aims to partition the nodes in a graph into groups such that the nodes in the same group are close in terms of graph proximity and also have similar attribute values. Recently, deep learning methods have achieved state-of-the-art clustering performance. However, the effectiveness of existing methods remains unclear due to two reasons. First, the datasets used for evaluation do not support fully the goal of attributed graph clustering. The category labels of nodes are only relevant to node attributes, and nodes with the same category label are often distant in the graph. Second, existing methods for the attributed graph clustering are complex and consist of several components. There is lack of comparisons of methods composed of different components from existing methods. This study proposes six benchmark datasets that support better the goal of attributed graph clustering and reports the performance of existing representative methods. Given that existing methods leave room for improvement on the proposed benchmark datasets, we systematically analyze five aspects of existing methods: encoded information, training networks, fusion mechanisms, loss functions, and clustering result generation. Based on these aspects, we decompose existing methods into modules and evaluate the performance of reconfigured methods based on these modules. According to the experimental results on the proposed benchmark datasets, we identify two promising configurations: (i) taking the attribute matrix as input to a graph convolutional network and (ii) layer-wise linear fusing deep neural network and graph attention network. And we also find that complex loss function fails to improve the clustering performance.|属性图聚类的目的是将一个图中的节点划分为若干组，使得同一组中的节点在图的邻近性方面相近，并且具有相似的属性值。近年来，深度学习方法取得了一流的聚类性能。然而，由于两个原因，现有方法的有效性仍不清楚。首先，用于评估的数据集不完全支持属性图聚类的目标。节点的类别标签只与节点属性相关，具有相同类别标签的节点在图中往往相距较远。其次，现有的属性图聚类方法是复杂的，由多个部分组成。由现有方法的不同组件组成的方法缺乏比较。本研究提出了六个基准数据集，更好地支持属性图聚类的目标，并报告了现有的代表性方法的性能。鉴于现有的方法在基准数据集上还有改进的空间，我们系统地分析了现有方法的五个方面: 编码信息、训练网络、融合机制、损失函数和聚类结果生成。基于这些方面，我们将现有的方法分解为模块，并对基于这些模块的重新配置方法的性能进行评估。根据基准数据集的实验结果，我们确定了两种有前景的构型: (i)将属性矩阵作为图卷积网络的输入; (ii)分层线性融合深度神经网络和图注意网络。同时发现复杂损失函数不能提高聚类性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Re-evaluation+of+Deep+Learning+Methods+for+Attributed+Graph+Clustering)|0| +|[Tackling Diverse Minorities in Imbalanced Classification](https://doi.org/10.1145/3583780.3615071)|KweiHerng Lai, Daochen Zha, Huiyuan Chen, Mangesh Bendre, Yuzhong Chen, Mahashweta Das, Hao Yang, Xia Hu|Visa Research, PAlo Alto, CA, USA; Rice University, Houston, TX, USA; Visa Research, Palo Alto, CA, USA|Imbalanced datasets are commonly observed in various real-world applications, presenting significant challenges in training classifiers. When working with large datasets, the imbalanced issue can be further exacerbated, making it exceptionally difficult to train classifiers effectively. To address the problem, over-sampling techniques have been developed to linearly interpolating data instances between minorities and their neighbors. However, in many real-world scenarios such as anomaly detection, minority instances are often dispersed diversely in the feature space rather than clustered together. Inspired by domain-agnostic data mix-up, we propose generating synthetic samples iteratively by mixing data samples from both minority and majority classes. It is non-trivial to develop such a framework, the challenges include source sample selection, mix-up strategy selection, and the coordination between the underlying model and mix-up strategies. To tackle these challenges, we formulate the problem of iterative data mix-up as a Markov decision process (MDP) that maps data attributes onto an augmentation strategy. To solve the MDP, we employ an actor-critic framework to adapt the discrete-continuous decision space. This framework is utilized to train a data augmentation policy and design a reward signal that explores classifier uncertainty and encourages performance improvement, irrespective of the classifier's convergence. We demonstrate the effectiveness of our proposed framework through extensive experiments conducted on seven publicly available benchmark datasets using three different types of classifiers. The results of these experiments showcase the potential and promise of our framework in addressing imbalanced datasets with diverse minorities.|不平衡数据集在各种实际应用中普遍存在，对分类器的训练提出了严峻的挑战。在处理大型数据集时，不平衡问题会进一步加剧，使得有效地训练分类器变得异常困难。为了解决这个问题，已经开发了过采样技术来线性插值少数民族和他们的邻居之间的数据实例。然而，在许多实际场景中，例如异常检测，少数实例通常分散在不同的特性空间中，而不是聚集在一起。受领域不可知数据混合的启发，我们提出通过混合来自少数和多数类的数据样本迭代生成合成样本。开发这样一个框架并非易事，其挑战包括源样本选择、混合策略选择以及底层模型与混合策略之间的协调。为了应对这些挑战，我们将迭代数据混淆的问题制定为一个马可夫决策过程(mDP) ，将数据属性映射到一个增强策略上。为了解决 MDP 问题，我们采用了一个行为者-批评框架来适应离散-连续决策空间。该框架用于训练数据增强策略和设计奖励信号，以探索分类器的不确定性，并鼓励性能的改善，无论分类器的收敛。我们通过使用三种不同类型的分类器对七个公开可用的基准数据集进行广泛的实验，证明了我们提出的框架的有效性。这些实验的结果展示了我们的框架在处理不同少数群体的不平衡数据集方面的潜力和希望。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Tackling+Diverse+Minorities+in+Imbalanced+Classification)|0| |[DuoGAT: Dual Time-oriented Graph Attention Networks for Accurate, Efficient and Explainable Anomaly Detection on Time-series](https://doi.org/10.1145/3583780.3614857)|Jongsoo Lee, Byeongtae Park, DongKyu Chae|Hanyang University, Seoul, Republic of Korea|Recently, Graph Neural Networks (GNNs) have achieved state-of-the-art performance on the multivariate time-series anomaly detection task by learning relationships between variables (sensors). However, they show limitations in capturing temporal dependencies due to lack of sufficient consideration on the characteristics of time to their graph structure. Several studies constructed a time-oriented graph, where each node represents a timestamp within a certain sliding window, to model temporal dependencies, but they failed to learn the trend of changes in time-series. This paper proposes Dual time-oriented Graph ATtention networks (DuoGAT) that resolves the aforementioned problems. Unlike previous work that uses the simple complete undirected structure for time-oriented graphs, our work models directed graphs with weighted edges that only connect from prior events to posterior events, and the edges that connect nearby events are given higher weights. In addition, another time-oriented graph is used to model time series stationary via differencing, which especially focuses on capturing the series of changes. Empirically, our method outperformed the existing state-of-the-art work with the highest F1-score for the four real-world dataset while maintaining low training cost. We also proposed a novel explanation method for anomaly detection using DuoGAT, which provides time-oriented reasoning via hierarchically tracking time points critical in a specific anomaly detection. Our code is available at: https://github.com/ByeongtaePark/DuoGAT|最近，图形神经网络(GNN)通过学习变量(传感器)之间的关系，在多变量时间序列异常检测任务中取得了最先进的性能。然而，由于缺乏对图结构时间特性的充分考虑，它们在捕获时间依赖性方面存在局限性。一些研究构建了一个面向时间的图表，其中每个节点代表一个特定滑动窗口中的时间戳，以模拟时间依赖，但他们未能了解时间序列的变化趋势。针对上述问题，本文提出了面向双时间的图注意网络(DuoGAT)。不同于以往的工作，使用简单的完全无向结构的时间导向图，我们的工作模型指导图的加权边，只连接从先前的事件到后续事件，并连接附近的事件的边被赋予更高的权重。此外，还利用另一种时间导向图通过差分方法对时间序列进行建模，重点捕捉时间序列的变化。经验上，我们的方法在保持低训练成本的同时，以四个真实世界数据集的最高 F1分数超过了现有的最先进的工作。我们还提出了一种使用 DuogAT 的新颖的异常检测解释方法，该方法通过分层跟踪特定异常检测中关键的时间点，提供面向时间的推理。我们的代码可以在以下 https://github.com/byeongtaepark/duogat 找到|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=DuoGAT:+Dual+Time-oriented+Graph+Attention+Networks+for+Accurate,+Efficient+and+Explainable+Anomaly+Detection+on+Time-series)|0| |[GUARD: Graph Universal Adversarial Defense](https://doi.org/10.1145/3583780.3614903)|Jintang Li, Jie Liao, Ruofan Wu, Liang Chen, Zibin Zheng, Jiawang Dan, Changhua Meng, Weiqiang Wang||Recently, graph convolutional networks (GCNs) have shown to be vulnerable to small adversarial perturbations, which becomes a severe threat and largely limits their applications in security-critical scenarios. To mitigate such a threat, considerable research efforts have been devoted to increasing the robustness of GCNs against adversarial attacks. However, current approaches for defense are typically designed for the whole graph and consider the global performance, posing challenges in protecting important local nodes from stronger adversarial targeted attacks. In this work, we present a simple yet effective method, named \textbf{\underline{G}}raph \textbf{\underline{U}}niversal \textbf{\underline{A}}dve\textbf{\underline{R}}sarial \textbf{\underline{D}}efense (GUARD). Unlike previous works, GUARD protects each individual node from attacks with a universal defensive patch, which is generated once and can be applied to any node (node-agnostic) in a graph. Extensive experiments on four benchmark datasets demonstrate that our method significantly improves robustness for several established GCNs against multiple adversarial attacks and outperforms existing adversarial defense methods by large margins. Our code is publicly available at https://github.com/EdisonLeeeee/GUARD.|最近，图卷积网络(GCNs)已被证明易受小的对抗性扰动的影响，这成为一个严重的威胁，并在很大程度上限制了它在安全关键场景中的应用。为了减轻这种威胁，已经进行了大量的研究工作，以提高全球网络对抗敌对攻击的能力。然而，目前的防御方法通常是为整个图设计的，并考虑到全局性能，在保护重要的本地节点免受更强的敌对目标攻击方面提出了挑战。在这项工作中，我们提出了一个简单而有效的方法，命名为 textbf {下划线{ G }} raph textbf {下划线{ U }}通用 textbf {下划线{ A }} dve textbf {下划线{ R }} sarial textbf {下划线{ D }防御(GUARD)。与以前的工作不同，GUARD 使用一个通用的防御补丁保护每个节点免受攻击，该补丁只生成一次，可以应用于图中的任何节点(节点不可知)。在四个基准数据集上的大量实验表明，我们的方法显著提高了几个已建立的 GCNs 对多个对手攻击的鲁棒性，并大大优于现有的对手防御方法。我们的代码可以在 https://github.com/edisonleeeee/guard 上公开获取。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=GUARD:+Graph+Universal+Adversarial+Defense)|0| -|[Class-Specific Word Sense Aware Topic Modeling via Soft Orthogonalized Topics](https://doi.org/10.1145/3583780.3614809)|Wenbo Li, Yao Yang, Einoshin Suzuki|Zhejiang Laboratory, Hangzhou, China; Kyushu University, Fukuoka, Japan|We propose a word sense aware topic model for document classification based on soft orthogonalized topics. An essential problem for this task is to capture word senses related to classes, i.e., class-specific word senses. Traditional models mainly introduce semantic information of knowledge libraries for word sense discovery. However, this information may not align with the classification targets, because these targets are often subjective and task-related. We aim to model the class-specific word senses in topic space. The challenge is to optimize the class separability of the senses, i.e., obtaining sense vectors with (a) high intra-class and (b) low inter-class similarities. Most existing models predefine specific topics for each class to specify the class-specific sense vectors. We call them hard orthogonalization based methods. These methods can hardly achieve both (a) and (b) since they assume the conditional independence of topics to classes and inevitably lose topic information. To this problem, we propose soft orthogonalization for topics. Specifically, we reserve all the topics and introduce a group of class-specific weights for each word to handle the importance of topic dimensions to class separability. Besides, we detect and use highly class-specific words in each document to guide sense estimation. Our experiments on two standard datasets show that our proposal outperforms other state-of-the-art models in terms of accuracy of sense estimation, document classification, and topic modeling. In addition, our joint learning experiments with the pre-trained language model BERT showcased the best complementarity of our model in most cases compared to other topic models.|我们提出了一个基于软正交主题的词义感知主题文档分类模型。这个任务的一个基本问题是捕获与类相关的词义，也就是特定于类的词义。传统的模式主要是引入语义信息知识库来发现词义。但是，这些信息可能与分类目标不一致，因为这些目标通常是主观的和与任务相关的。我们的目标是在主题空间中建立类别特定的词义模型。挑战在于优化感官的类可分性，即获得具有(a)高的类内和(b)低的类间相似性的感官向量。大多数现有的模型为每个类预先定义特定的主题，以指定特定于类的感觉向量。我们称之为基于正交化的方法。这些方法很难同时实现(a)和(b)两个目标，因为它们承担了课程的主题条件独立，并且不可避免地丢失了主题信息。针对这个问题，我们建议对话题采用软正交化。具体来说，我们保留所有的主题，并为每个单词引入一组特定于类别的权重，以处理主题维度对类别可分性的重要性。此外，我们在每个文档中检测并使用高度类别特异性的词语来指导意义估计。我们在两个标准数据集上的实验表明，我们的方案在感觉估计、文档分类和主题建模的准确性方面优于其他最先进的模型。此外，我们的联合学习实验与预先训练的语言模型 BERT 显示了我们的模型在大多数情况下最好的互补性相比，其他主题模型。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Class-Specific+Word+Sense+Aware+Topic+Modeling+via+Soft+Orthogonalized+Topics)|0| +|[Class-Specific Word Sense Aware Topic Modeling via Soft Orthogonalized Topics](https://doi.org/10.1145/3583780.3614809)|Wenbo Li, Yao Yang, Einoshin Suzuki|Kyushu University, Fukuoka, Japan; Zhejiang Laboratory, Hangzhou, China|We propose a word sense aware topic model for document classification based on soft orthogonalized topics. An essential problem for this task is to capture word senses related to classes, i.e., class-specific word senses. Traditional models mainly introduce semantic information of knowledge libraries for word sense discovery. However, this information may not align with the classification targets, because these targets are often subjective and task-related. We aim to model the class-specific word senses in topic space. The challenge is to optimize the class separability of the senses, i.e., obtaining sense vectors with (a) high intra-class and (b) low inter-class similarities. Most existing models predefine specific topics for each class to specify the class-specific sense vectors. We call them hard orthogonalization based methods. These methods can hardly achieve both (a) and (b) since they assume the conditional independence of topics to classes and inevitably lose topic information. To this problem, we propose soft orthogonalization for topics. Specifically, we reserve all the topics and introduce a group of class-specific weights for each word to handle the importance of topic dimensions to class separability. Besides, we detect and use highly class-specific words in each document to guide sense estimation. Our experiments on two standard datasets show that our proposal outperforms other state-of-the-art models in terms of accuracy of sense estimation, document classification, and topic modeling. In addition, our joint learning experiments with the pre-trained language model BERT showcased the best complementarity of our model in most cases compared to other topic models.|我们提出了一个基于软正交主题的词义感知主题文档分类模型。这个任务的一个基本问题是捕获与类相关的词义，也就是特定于类的词义。传统的模式主要是引入语义信息知识库来发现词义。但是，这些信息可能与分类目标不一致，因为这些目标通常是主观的和与任务相关的。我们的目标是在主题空间中建立类别特定的词义模型。挑战在于优化感官的类可分性，即获得具有(a)高的类内和(b)低的类间相似性的感官向量。大多数现有的模型为每个类预先定义特定的主题，以指定特定于类的感觉向量。我们称之为基于正交化的方法。这些方法很难同时实现(a)和(b)两个目标，因为它们承担了课程的主题条件独立，并且不可避免地丢失了主题信息。针对这个问题，我们建议对话题采用软正交化。具体来说，我们保留所有的主题，并为每个单词引入一组特定于类别的权重，以处理主题维度对类别可分性的重要性。此外，我们在每个文档中检测并使用高度类别特异性的词语来指导意义估计。我们在两个标准数据集上的实验表明，我们的方案在感觉估计、文档分类和主题建模的准确性方面优于其他最先进的模型。此外，我们的联合学习实验与预先训练的语言模型 BERT 显示了我们的模型在大多数情况下最好的互补性相比，其他主题模型。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Class-Specific+Word+Sense+Aware+Topic+Modeling+via+Soft+Orthogonalized+Topics)|0| |[Relation-Aware Diffusion Model for Controllable Poster Layout Generation](https://doi.org/10.1145/3583780.3615028)|Fengheng Li, An Liu, Wei Feng, Honghe Zhu, Yaoyu Li, Zheng Zhang, Jingjing Lv, Xin Zhu, Junjie Shen, Zhangang Lin, Jingping Shao|JD, Beijing, China; Nankai University, Tianjin, China|Poster layout is a crucial aspect of poster design. Prior methods primarily focus on the correlation between visual content and graphic elements. However, a pleasant layout should also consider the relationship between visual and textual contents and the relationship between elements. In this study, we introduce a relation-aware diffusion model for poster layout generation that incorporates these two relationships in the generation process. Firstly, we devise a visual-textual relation-aware module that aligns the visual and textual representations across modalities, thereby enhancing the layout's efficacy in conveying textual information. Subsequently, we propose a geometry relation-aware module that learns the geometry relationship between elements by comprehensively considering contextual information. Additionally, the proposed method can generate diverse layouts based on user constraints. To advance research in this field, we have constructed a poster layout dataset named CGL-Dataset V2. Our proposed method outperforms state-of-the-art methods on CGL-Dataset V2. The data and code will be available at https://github.com/liuan0803/RADM.|海报布局是海报设计的一个重要方面。先前的方法主要集中在视觉内容和图形元素之间的相关性。然而，一个愉快的布局还应该考虑视觉和文本内容之间的关系以及元素之间的关系。在这项研究中，我们引入了一个关系感知的海报布局生成扩散模型，在生成过程中结合了这两个关系。首先，我们设计了一个视觉-文本关系感知模块，将视觉和文本表征跨模式对齐，从而提高布局在传递文本信息方面的效率。随后，我们提出了一个几何关系感知模块，通过综合考虑上下文信息来学习元素之间的几何关系。此外，该方法还可以根据用户约束生成不同的布局。为了推进这一领域的研究，我们构建了一个海报布局数据集 CGL-DatasetV2。我们提出的方法在 CGL-Dataset V2上优于最先进的方法。数据和代码将在 https://github.com/liuan0803/radm 公布。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Relation-Aware+Diffusion+Model+for+Controllable+Poster+Layout+Generation)|0| |[ACGAN-GNNExplainer: Auxiliary Conditional Generative Explainer for Graph Neural Networks](https://doi.org/10.1145/3583780.3614772)|Yiqiao Li, Jianlong Zhou, Yifei Dong, Niusha Shafiabady, Fang Chen|University of Technology Sydney, Sydney, Australia; Charles Darwin University, Sydney, Australia|Graph neural networks (GNNs) have proven their efficacy in a variety of real-world applications, but their underlying mechanisms remain a mystery. To address this challenge and enable reliable decision-making, many GNN explainers have been proposed in recent years. However, these methods often encounter limitations, including their dependence on specific instances, lack of generalizability to unseen graphs, producing potentially invalid explanations, and yielding inadequate fidelity. To overcome these limitations, we, in this paper, introduce the Auxiliary Classifier Generative Adversarial Network (ACGAN) into the field of GNN explanation and propose a new GNN explainer dubbed~\emph{ACGAN-GNNExplainer}. Our approach leverages a generator to produce explanations for the original input graphs while incorporating a discriminator to oversee the generation process, ensuring explanation fidelity and improving accuracy. Experimental evaluations conducted on both synthetic and real-world graph datasets demonstrate the superiority of our proposed method compared to other existing GNN explainers.|图形神经网络(GNN)已经证明了它们在各种现实应用中的有效性，但是它们的基本机制仍然是一个谜。为了应对这一挑战，并使可靠的决策，许多 GNN 解释者已提出近年来。然而，这些方法经常遇到限制，包括它们依赖于特定的实例，缺乏对看不见的图的普遍性，产生可能无效的解释，以及产生不足的保真度。为了克服这些局限性，本文将辅助分类器生成对抗网络(ACGAN)引入 GNN 解释领域，提出了一种新的 GNN 解释器，命名为 ~ emph { ACGAN-GNNExplainer }。我们的方法利用一个生成器为原始输入图生成解释，同时加入一个鉴别器来监督生成过程，确保解释的保真度和提高准确性。实验结果表明，与现有的 GNN 解释器相比，本文提出的方法具有明显的优越性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=ACGAN-GNNExplainer:+Auxiliary+Conditional+Generative+Explainer+for+Graph+Neural+Networks)|0| -|[REST: Drug-Drug Interaction Prediction via Reinforced Student-Teacher Curriculum Learning](https://doi.org/10.1145/3583780.3615033)|Xinhang Li, Zhaopeng Qiu, Xiangyu Zhao, Yong Zhang, Chunxiao Xing, Xian Wu|Tsinghua University, Beijing, China; City University of Hong Kong, Hong Kong, Hong Kong; BOSS Zhipin, Beijing, China; Tencent Jarvis Lab, Beijing, China|Accurate prediction of drug-drug interaction (DDI) is crucial to achieving effective decision-making in medical treatment for both doctors and patients. Recently, many deep learning based methods have been proposed to learn from drug-related features and conduct DDI prediction. These works have achieved promising results. However, the extreme imbalance of medical data poses a serious problem to DDI prediction, where a small fraction of DDI types occupy the majority training data. A straightforward way is to develop an appropriate policy to sample the data. Due to the high complexity and speciality of medical science, a dynamic learnable policy is required instead of a heuristic, uniform or static one. Therefore, we propose a REinforced Student-Teacher curriculum learning model (REST) for effective sampling to tackle this imbalance problem. Specifically, REST consists of two interactive parts, which are a heterogeneous graph neural network as the student and a reinforced sampler as the teacher. In each interaction, the teacher model takes action to sample an appropriate batch to train the student model according to the student model state while the cumulated improvement in performance of the student model is treated as the reward for policy gradient of the teacher model. The experimental results on two benchmarking datasets have demonstrated the significant effectiveness of our proposed model in DDI prediction, especially for the DDI types with low frequency.|准确预测药物相互作用(DDI)是实现医患双方有效决策的关键。近年来，人们提出了许多基于深度学习的方法来学习药物相关特征并进行 DDI 预测。这些工作取得了可喜的成果。然而，医学数据的极端不平衡性给 DDI 预测带来了严重的问题，其中一小部分 DDI 类型占据了大多数训练数据。一个直接的方法是开发一个适当的策略来抽样数据。由于医学科学的高度复杂性和特殊性，需要一个动态的可学习策略，而不是一个启发式的、统一的或静态的策略。因此，我们提出了一个强化的学生-教师课程学习模式(REST) ，以有效的抽样来解决这一不平衡问题。具体来说，REST 由两个交互部分组成，即作为学生的异构图形神经网络和作为教师的强化采样器。在每个交互过程中，教师模型根据学生模型的状态采取行动抽样适当的批次来训练学生模型，而学生模型的绩效的累积提高被视为对教师模型的政策梯度的奖励。在两个基准测试数据集上的实验结果表明，该模型在 DDI 预测中具有显著的效果，特别是对于低频率的 DDI 类型。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=REST:+Drug-Drug+Interaction+Prediction+via+Reinforced+Student-Teacher+Curriculum+Learning)|0| +|[REST: Drug-Drug Interaction Prediction via Reinforced Student-Teacher Curriculum Learning](https://doi.org/10.1145/3583780.3615033)|Xinhang Li, Zhaopeng Qiu, Xiangyu Zhao, Yong Zhang, Chunxiao Xing, Xian Wu|Tsinghua University, Beijing, China; Tencent Jarvis Lab, Beijing, China; BOSS Zhipin, Beijing, China; City University of Hong Kong, Hong Kong, Hong Kong|Accurate prediction of drug-drug interaction (DDI) is crucial to achieving effective decision-making in medical treatment for both doctors and patients. Recently, many deep learning based methods have been proposed to learn from drug-related features and conduct DDI prediction. These works have achieved promising results. However, the extreme imbalance of medical data poses a serious problem to DDI prediction, where a small fraction of DDI types occupy the majority training data. A straightforward way is to develop an appropriate policy to sample the data. Due to the high complexity and speciality of medical science, a dynamic learnable policy is required instead of a heuristic, uniform or static one. Therefore, we propose a REinforced Student-Teacher curriculum learning model (REST) for effective sampling to tackle this imbalance problem. Specifically, REST consists of two interactive parts, which are a heterogeneous graph neural network as the student and a reinforced sampler as the teacher. In each interaction, the teacher model takes action to sample an appropriate batch to train the student model according to the student model state while the cumulated improvement in performance of the student model is treated as the reward for policy gradient of the teacher model. The experimental results on two benchmarking datasets have demonstrated the significant effectiveness of our proposed model in DDI prediction, especially for the DDI types with low frequency.|准确预测药物相互作用(DDI)是实现医患双方有效决策的关键。近年来，人们提出了许多基于深度学习的方法来学习药物相关特征并进行 DDI 预测。这些工作取得了可喜的成果。然而，医学数据的极端不平衡性给 DDI 预测带来了严重的问题，其中一小部分 DDI 类型占据了大多数训练数据。一个直接的方法是开发一个适当的策略来抽样数据。由于医学科学的高度复杂性和特殊性，需要一个动态的可学习策略，而不是一个启发式的、统一的或静态的策略。因此，我们提出了一个强化的学生-教师课程学习模式(REST) ，以有效的抽样来解决这一不平衡问题。具体来说，REST 由两个交互部分组成，即作为学生的异构图形神经网络和作为教师的强化采样器。在每个交互过程中，教师模型根据学生模型的状态采取行动抽样适当的批次来训练学生模型，而学生模型的绩效的累积提高被视为对教师模型的政策梯度的奖励。在两个基准测试数据集上的实验结果表明，该模型在 DDI 预测中具有显著的效果，特别是对于低频率的 DDI 类型。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=REST:+Drug-Drug+Interaction+Prediction+via+Reinforced+Student-Teacher+Curriculum+Learning)|0| |[Simplifying Temporal Heterogeneous Network for Continuous-Time Link prediction](https://doi.org/10.1145/3583780.3615059)|Ce Li, Rongpei Hong, Xovee Xu, Goce Trajcevski, Fan Zhou|Iowa State University, Ames, IA, USA; University of Electronic Science and Technology of China, Chengdu, China; University of Electronic Science and Technology of China & Kash Institute of Electronics and Information Industry, Chengdu, China|Temporal heterogeneous networks (THNs) investigate the structural interactions and their evolution over time in graphs with multiple types of nodes or edges. Existing THNs describe evolving networks as a sequence of graph snapshots and adopt mechanisms from static heterogeneous networks to capture the spatial-temporal correlation. However, these works are confined to the discrete-time setting and the implementation of stacked mechanisms often introduces a high level of complexity, both conceptually and computationally. Here, we conduct comprehensive examinations and propose STHN, a simplifying THN for continuous-time link prediction. Concretely, to integrate continuous dynamics, we maintain a historical interaction memory for each node. A link encoder that incorporates two components - type encoding and relative time encoding - is introduced to encapsulate implicit heterogeneous characteristics of interaction and extract the most informative temporal information. We further propose to use a patching technique that assists with Transformer feature extractor to support the interaction sequence with long histories. Extensive experiments on three real-world datasets empirically demonstrate that STHN outperforms state-of-the-art methods with competitive task accuracy and predictive efficiency on both transductive and inductive settings.|时间异质网络(THN)研究具有多种节点或边的图的结构相互作用及其随时间的演化。现有的 THN 将演化网络描述为一系列图形快照，并采用静态异构网络的机制来捕获时空相关性。然而，这些工作仅限于离散时间设置和实现堆叠机制往往引入了高水平的复杂性，无论是在概念上和计算上。在这里，我们进行了全面的检验，并提出了一种简化的连续时间链路预测的 THN。具体地说，为了整合连续动态，我们为每个节点维护一个历史交互记忆。提出了一种结合类型编码和相对时间编码的链路编码器，用于封装隐式异构交互特性，提取信息量最大的时间信息。我们进一步建议使用补丁技术，协助变压器特征提取，以支持交互序列的长历史。在三个真实世界数据集上的大量实验表明，STHN 在传导性和归纳性设置上都优于最先进的方法，具有竞争性的任务准确性和预测效率。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Simplifying+Temporal+Heterogeneous+Network+for+Continuous-Time+Link+prediction)|0| |[Heterogeneous Temporal Graph Neural Network Explainer](https://doi.org/10.1145/3583780.3614909)|Jiazheng Li, Chunhui Zhang, Chuxu Zhang|Brandeis University, Waltham, MA, USA|Graph Neural Networks (GNNs) have been a prominent research area and have been widely deployed in various high-stakes applications in recent years, leading to a growing demand for explanations. While existing explainer methods focus on explaining homogeneous and static GNNs, none of them have attempted to explain heterogeneous temporal GNNs. However, in practice, many real-world databases should be represented as heterogeneous temporal graphs (HTGs), which serve as the fundamental data structure for GNN backbone models in applications. To address this gap, in this paper, we propose HTGExplainer, a novel method for explaining heterogeneous temporal GNNs by considering temporal dependencies and preserving heterogeneity when generating subgraphs as explanations. HTGExplainer employs a deep neural network to re-parameterize the generation process of explanations and incorporates effective heterogeneous and temporal edge embeddings to capture informative semantics used for generating explanatory subgraphs. Extensive experiments are conducted on multiple HTG datasets constructed from real-world scenarios, and the results demonstrate the superior performance of HTGExplainer compared to state-of-the-art baselines.|图形神经网络(GNN)近年来已成为一个突出的研究领域，并被广泛应用于各种高风险的应用领域，导致对其解释的需求日益增长。虽然现有的解释方法集中于解释同质和静态 GNN，但没有一种方法尝试解释异质时态 GNN。然而，在实际应用中，许多现实世界的数据库应该表示为异构时态图(HTGs) ，它们作为 GNN 骨干网模型在应用中的基本数据结构。为了解决这一问题，本文提出了一种新的解释异构时态 GNN 的方法—— HTGExplainer，该方法在生成子图时考虑了时态依赖性并保留了异构性。HTGExplainer 使用深层神经网络重新参数化解释的生成过程，并结合有效的异构和时间边缘嵌入来捕获用于生成解释子图的信息语义。在实际场景构建的多个 HTG 数据集上进行了广泛的实验，结果表明 HTGExplainer 的性能优于最先进的基线。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Heterogeneous+Temporal+Graph+Neural+Network+Explainer)|0| |[Harnessing the Power of Pre-trained Vision-Language Models for Efficient Medical Report Generation](https://doi.org/10.1145/3583780.3614961)|Qi Li|Tsinghua University, Shenzhen, China|Medical images are commonly used in clinical practice. But the need for diagnosis and reporting from image-based examinations far excels the current medical capacity. Automatic Medical Report Generation (MRG) can help to ease the burden of radiologists. Vision-Language Pre-training (VLP) has received tremendous success on various tasks, therefore it is naturally expected that MRG can harvest from this rapid advancement. However, directly applying existing VLP models in the medical domain is impracticable due to their data-hungry nature, the need for aligning different modalities, prohibitive training time, exorbitant hardware barrier, and the challenge of open-ended text generation. To address these problems, we propose MedEPT, a parameter-efficient approach for MRG that can utilize ever-ignored image-only datasets. It employs parameter-efficient tuning (PET) for VLP adaption to mitigate inefficiency in fine-tuning time and hardware. MedEPT also employs MRGPID to augment and expand adaption datasets by synthesizing meaningful text for image-only datasets. We perform a systematic evaluation of our method. Empirical results show that we obtain a better performance than the state-of-the-art method while using less than 10% trainable parameters and not more than 30% training time than ever before.|医学影像在临床实践中有着广泛的应用。但是，通过图像检查进行诊断和报告的需要远远超过了目前的医疗能力。自动医疗报告生成(MRG)有助于减轻放射科医师的负担。视觉语言预训练(VLP)已经在各种任务中取得了巨大的成功，因此 MRG 能够从这种快速发展中获得收获是很自然的事情。然而，直接应用现有的 VLP 模型在医学领域是不切实际的，因为它们的数据饥饿的性质，需要调整不同的模式，禁止训练时间，过高的硬件障碍，以及开放式文本生成的挑战。为了解决这些问题，我们提出了 MedEPT，一种 MRG 的参数效率方法，它可以利用被忽略的仅有图像的数据集。它采用参数有效调整(PET)的 VLP 自适应，以减少微调时间和硬件效率低下。MedEPT 还使用 MRGPID 通过为纯图像数据集合成有意义的文本来增强和扩展自适应数据集。我们对我们的方法进行系统的评估。实验结果表明，在使用可训练参数少于10% 、训练时间不超过30% 的情况下，该方法具有比现有方法更好的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Harnessing+the+Power+of+Pre-trained+Vision-Language+Models+for+Efficient+Medical+Report+Generation)|0| |[Contrastive Representation Learning Based on Multiple Node-centered Subgraphs](https://doi.org/10.1145/3583780.3614825)|Dong Li, Wenjun Wang, Minglai Shao, Chen Zhao|Tianjin University, Tianjin, China; Baylor University, Waco, TX, USA|As the basic element of graph-structured data, node has been recognized as the main object of study in graph representation learning. A single node intuitively has multiple node-centered subgraphs from the whole graph (e.g., one person in a social network has multiple social circles based on his different relationships). We study this intuition under the framework of graph contrastive learning, and propose a multiple node-centered subgraphs contrastive representation learning method to learn node representation on graphs in a self-supervised way. Specifically, we carefully design a series of node-centered regional subgraphs of the central node. Then, the mutual information between different subgraphs of the same node is maximized by contrastive loss. Experiments on various real-world datasets and different downstream tasks demonstrate that our model has achieved state-of-the-art results.|节点作为图结构化数据的基本元素，已经成为图表示学习的主要研究对象。一个节点直观地拥有来自整个图的多个以节点为中心的子图(例如，一个社交网络中的一个人根据其不同的关系拥有多个社交圈)。本文在图的对比学习框架下研究了这种直觉，提出了一种以多个节点为中心的子图对比表示学习方法，用于自监督的方法学习图上的节点表示。具体地说，我们精心设计了一系列以节点为中心的中心节点区域子图。然后，利用对比损失最大化同一节点的不同子图之间的互信息。在各种真实世界数据集和不同的下游任务上的实验表明，我们的模型已经取得了最先进的结果。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Contrastive+Representation+Learning+Based+on+Multiple+Node-centered+Subgraphs)|0| |[Multi-Order Relations Hyperbolic Fusion for Heterogeneous Graphs](https://doi.org/10.1145/3583780.3614979)|Junlin Li, Yueheng Sun, Minglai Shao|School of New Media and Communication, Tianjin University, Tianjin, China; College of Intelligence and Computing, Tianjin University, Tianjin, China|Heterogeneous graphs with multiple node and edge types are prevalent in real-world scenarios. However, most methods use meta-paths on the original graph structure to learn information in heterogeneous graphs, and these methods only consider pairwise relations and rely on meta-paths. In this paper, we use simplicial complexes to extract higher-order relations containing multiple nodes from heterogeneous graphs. We also discover power-law structures in both the heterogeneous graph and the extracted simplicial complex. Thus, we propose the Simplicial Hyperbolic Attention Network (SHAN), a graph neural network for heterogeneous graphs. SHAN extracts simplicial complexes and the original graph structure from the heterogeneous graph to represent multi-order relations between nodes. Next, SHAN uses hyperbolic multi-perspective attention to learn the importance of different neighbors and relations in hyperbolic space. Finally, SHAN integrates multi-order relations to obtain a more comprehensive node representation. We conducted extensive experiments to verify the effectiveness of SHAN and the results of node classification experiments on three publicly available heterogeneous graph datasets demonstrate that SHAN outperforms representative baseline models.|具有多个节点和边类型的异构图在现实世界中普遍存在。然而，大多数方法在原始图结构上使用元路径来学习异构图中的信息，这些方法只考虑成对关系，依赖于元路径。本文利用单纯复形从异质图中提取包含多个节点的高阶关系。我们还发现幂律结构的异质图和提取的单纯复形。因此，我们提出了单纯双曲注意网络(SHAN) ，一个适用于异构图的图神经网络。SHAN 从异构图中提取单纯复形和原始图结构来表示节点之间的多阶关系。接下来，SHAN 使用双曲线多视角注意力来学习不同邻居和关系在双曲空间中的重要性。最后，SHAN 整合了多阶关系，得到了更全面的节点表示。为了验证 SHAN 算法的有效性，我们进行了大量的实验，并在三个公开的异构图形数据集上进行了节点分类实验，实验结果表明，SHAN 算法的性能优于典型的基线模型。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Multi-Order+Relations+Hyperbolic+Fusion+for+Heterogeneous+Graphs)|0| -|[SAILOR: Structural Augmentation Based Tail Node Representation Learning](https://doi.org/10.1145/3583780.3615045)|Jie Liao, Jintang Li, Liang Chen, Bingzhe Wu, Yatao Bian, Zibin Zheng|Tecent AI Lab, Shenzhen, China; Sun Yat-sen University, Guangzhou, China; Tencent AI Lab, Shenzhen, China|Graph Neural Networks (GNNs) have achieved state-of-the-art performance in representation learning for graphs recently. However, the effectiveness of GNNs, which capitalize on the key operation of message propagation, highly depends on the quality of the topology structure. Most of the graphs in real-world scenarios follow a long-tailed distribution on their node degrees, that is, a vast majority of the nodes in the graph are tail nodes with only a few connected edges. GNNs produce inferior node representations for tail nodes since they lack structural information. In the pursuit of promoting the expressiveness of GNNs for tail nodes, we explore how the deficiency of structural information deteriorates the performance of tail nodes and propose a general Structural Augmentation based taIL nOde Representation learning framework, dubbed as SAILOR, which can jointly learn to augment the graph structure and extract more informative representations for tail nodes. Extensive experiments on public benchmark datasets demonstrate that SAILOR can significantly improve the tail node representations and outperform the state-of-the-art baselines.|近年来，图神经网络在图表示学习方面取得了较好的效果。然而，利用消息传播关键操作的 GNN 的有效性在很大程度上取决于拓扑结构的质量。现实场景中的大多数图在节点度上遵循长尾分布，即图中的绝大多数节点都是尾节点，只有少数连接边。GNN 由于缺乏结构信息，对尾节点产生较差的节点表示。为了提高 GNN 对尾节点的表达能力，研究了结构信息不足对尾节点表达能力的影响，提出了一种通用的基于结构增强的尾节点表示学习框架 SAILOR。在公共基准数据集上的大量实验表明，SAILOR 能够显著改善尾节点的表示，并优于最先进的基准线。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=SAILOR:+Structural+Augmentation+Based+Tail+Node+Representation+Learning)|0| +|[SAILOR: Structural Augmentation Based Tail Node Representation Learning](https://doi.org/10.1145/3583780.3615045)|Jie Liao, Jintang Li, Liang Chen, Bingzhe Wu, Yatao Bian, Zibin Zheng|Sun Yat-sen University, Guangzhou, China; Tecent AI Lab, Shenzhen, China; Tencent AI Lab, Shenzhen, China|Graph Neural Networks (GNNs) have achieved state-of-the-art performance in representation learning for graphs recently. However, the effectiveness of GNNs, which capitalize on the key operation of message propagation, highly depends on the quality of the topology structure. Most of the graphs in real-world scenarios follow a long-tailed distribution on their node degrees, that is, a vast majority of the nodes in the graph are tail nodes with only a few connected edges. GNNs produce inferior node representations for tail nodes since they lack structural information. In the pursuit of promoting the expressiveness of GNNs for tail nodes, we explore how the deficiency of structural information deteriorates the performance of tail nodes and propose a general Structural Augmentation based taIL nOde Representation learning framework, dubbed as SAILOR, which can jointly learn to augment the graph structure and extract more informative representations for tail nodes. Extensive experiments on public benchmark datasets demonstrate that SAILOR can significantly improve the tail node representations and outperform the state-of-the-art baselines.|近年来，图神经网络在图表示学习方面取得了较好的效果。然而，利用消息传播关键操作的 GNN 的有效性在很大程度上取决于拓扑结构的质量。现实场景中的大多数图在节点度上遵循长尾分布，即图中的绝大多数节点都是尾节点，只有少数连接边。GNN 由于缺乏结构信息，对尾节点产生较差的节点表示。为了提高 GNN 对尾节点的表达能力，研究了结构信息不足对尾节点表达能力的影响，提出了一种通用的基于结构增强的尾节点表示学习框架 SAILOR。在公共基准数据集上的大量实验表明，SAILOR 能够显著改善尾节点的表示，并优于最先进的基准线。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=SAILOR:+Structural+Augmentation+Based+Tail+Node+Representation+Learning)|0| |[Adaptation Speed Analysis for Fairness-aware Causal Models](https://doi.org/10.1145/3583780.3614774)|Yujie Lin, Chen Zhao, Minglai Shao, Xujiang Zhao, Haifeng Chen||For example, in machine translation tasks, to achieve bidirectional translation between two languages, the source corpus is often used as the target corpus, which involves the training of two models with opposite directions. The question of which one can adapt most quickly to a domain shift is of significant importance in many fields. Specifically, consider an original distribution p that changes due to an unknown intervention, resulting in a modified distribution p*. In aligning p with p*, several factors can affect the adaptation rate, including the causal dependencies between variables in p. In real-life scenarios, however, we have to consider the fairness of the training process, and it is particularly crucial to involve a sensitive variable (bias) present between a cause and an effect variable. To explore this scenario, we examine a simple structural causal model (SCM) with a cause-bias-effect structure, where variable A acts as a sensitive variable between cause (X) and effect (Y). The two models, respectively, exhibit consistent and contrary cause-effect directions in the cause-bias-effect SCM. After conducting unknown interventions on variables within the SCM, we can simulate some kinds of domain shifts for analysis. We then compare the adaptation speeds of two models across four shift scenarios. Additionally, we prove the connection between the adaptation speeds of the two models across all interventions.|例如，在机器翻译任务中，为了实现两种语言之间的双向翻译，常常使用源语料库作为目标语料库，这涉及到训练两种方向相反的模型。在许多领域中，哪一个人能够最快地适应领域转移的问题是非常重要的。具体来说，考虑一个原始分布 p，该分布 p 由于未知的干预而发生变化，从而导致修改分布 p * 。在 p 与 p * 对齐时，有几个因素可以影响适应率，包括 p 中变量之间的因果依赖关系。然而，在现实生活中，我们必须考虑训练过程的公平性，尤其是涉及到一个敏感的变量(偏见)之间的原因和结果变量。为了探索这种情况，我们研究了一个简单的结构性因果模型(SCM) ，其中变量 A 作为原因(X)和结果(Y)之间的敏感变量。这两个模型在因果-偏倚-效应供应链管理中分别表现出一致和相反的因果方向。在对供应链管理中的变量进行未知的干预之后，我们可以模拟某些领域的移位进行分析。然后我们比较了两个模型在四个移位情景下的适应速度。此外，我们证明了两个模型在所有干预措施中的适应速度之间的联系。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Adaptation+Speed+Analysis+for+Fairness-aware+Causal+Models)|0| -|[On the Thresholding Strategy for Infrequent Labels in Multi-label Classification](https://doi.org/10.1145/3583780.3614996)|YuJen Lin, ChihJen Lin|National Taiwan University, Taipei, Taiwan Roc; National Taiwan University & Mohamed bin Zayed University of Artificial Intelligence, Taipei, Taiwan Roc|In multi-label classification, the imbalance between labels is often a concern. For a label that seldom occurs, the default threshold used to generate binarized predictions of that label is usually sub-optimal. However, directly tuning the threshold to optimize F-measure has been observed to overfit easily. In this work, we explain why this overfitting occurs. Then, we analyze the FBR heuristic, a previous technique proposed to address the overfitting issue. We explain its success but also point out some problems unobserved before. Then, we first propose a variant of the FBR heuristic that not only fixes the problems but is also more justifiable. Second, we propose a new technique based on smoothing the F-measure when tuning the threshold. We theoretically prove that, with proper parameters, smoothing results in desirable properties of the tuned threshold. Based on the idea of smoothing, we then propose jointly optimizing micro-F and macro-F as a lightweight alternative free from extra hyperparameters. Our methods are empirically evaluated on text and node classification datasets. The results show that our methods consistently outperform the FBR heuristic.|在多标签分类中，标签之间的不平衡往往是一个值得关注的问题。对于很少出现的标签，用于生成该标签的二进制预测的默认阈值通常是次优的。然而，直接调整阈值以优化 F 测度已被观察到很容易过拟合。在这项工作中，我们解释了为什么会发生这种过拟合。然后，我们分析了 FBR 启发式算法，这是一种先前提出的解决过拟合问题的技术。我们解释了它的成功，但也指出了一些以前没有注意到的问题。然后，我们首先提出了 FBR 启发式的一个变体，它不仅解决了问题，而且更加合理。其次，提出了一种基于平滑 F- 测度的阈值调整新方法。我们从理论上证明，在适当的参数下，光滑化可以得到理想的调谐阈值特性。基于平滑的思想，我们提出联合优化微 F 和宏 F 作为一个轻量级的替代方案，不需要额外的超参数。在文本和节点分类数据集上对我们的方法进行了实证评估。结果表明，我们的方法始终优于 FBR 启发式。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=On+the+Thresholding+Strategy+for+Infrequent+Labels+in+Multi-label+Classification)|0| -|[Cross-city Few-Shot Traffic Forecasting via Traffic Pattern Bank](https://doi.org/10.1145/3583780.3614829)|Zhanyu Liu, Guanjie Zheng, Yanwei Yu|Ocean University of China, Qingdao, China; Shanghai Jiao Tong University, Shanghai, China|Traffic forecasting is a critical service in Intelligent Transportation Systems (ITS). Utilizing deep models to tackle this task relies heavily on data from traffic sensors or vehicle devices, while some cities might lack device support and thus have few available data. So, it is necessary to learn from data-rich cities and transfer the knowledge to data-scarce cities in order to improve the performance of traffic forecasting. To address this problem, we propose a cross-city few-shot traffic forecasting framework via Traffic Pattern Bank (TPB) due to that the traffic patterns are similar across cities. TPB utilizes a pre-trained traffic patch encoder to project raw traffic data from data-rich cities into high-dimensional space, from which a traffic pattern bank is generated through clustering. Then, the traffic data of the data-scarce city could query the traffic pattern bank and explicit relations between them are constructed. The metaknowledge is aggregated based on these relations and an adjacency matrix is constructed to guide a downstream spatial-temporal model in forecasting future traffic. The frequently used meta-training framework Reptile is adapted to find a better initial parameter for the learnable modules. Experiments on real-world traffic datasets show that TPB outperforms existing methods and demonstrates the effectiveness of our approach in cross-city few-shot traffic forecasting.|交通预测是智能交通系统(ITS)中的一项重要业务。利用深度模型来解决这个问题主要依赖于来自交通传感器或车辆设备的数据，而一些城市可能缺乏设备支持，因此可用的数据很少。因此，有必要向数据丰富的城市学习，向数据稀缺的城市转移知识，以提高交通预测的性能。针对这一问题，针对城市间交通模式相似的特点，提出了一种基于交通模式库(TPB)的跨城市少镜头交通预测框架。TPB 利用预先训练好的交通补丁编码器将数据丰富的城市的原始交通数据投影到高维空间中，通过聚类生成交通模式库。然后，利用数据稀缺城市的交通数据对交通模式库进行查询，建立二者之间的显式关系。基于这些关系，元知识被聚合起来，并构建一个邻接矩阵来指导下游的时空模型来预测未来的交通。为了给可学习模块提供一个更好的初始参数，对经常使用的元训练框架爬行动物进行了改进。在实际交通数据集上的实验结果表明，TPB 方法的性能优于现有的方法，并证明了该方法在城市间少镜头交通预测中的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Cross-city+Few-Shot+Traffic+Forecasting+via+Traffic+Pattern+Bank)|0| -|[GranCATs: Cross-Lingual Enhancement through Granularity-Specific Contrastive Adapters](https://doi.org/10.1145/3583780.3614896)|Meizhen Liu, Jiakai He, Xu Guo, Jianye Chen, Siu Cheung Hui, Fengyu Zhou|Shandong University, Jinan, China; Nanyang Technological University, Singapore, Singapore; Shandong University, Jinan city, China; Nanyang Technological University, Singapore, China|Multilingual language models (MLLMs) have demonstrated remarkable success in various cross-lingual downstream tasks, facilitating the transfer of knowledge across numerous languages, whereas this transfer is not universally effective. Our study reveals that while existing MLLMs like mBERT can capturephrase-level alignments across the language families, they struggle to effectively capturesentence-level andparagraph-level alignments. To address this limitation, we propose GranCATs, Granularity-specific Contrastive AdapTers. We collect a new dataset that observes each sample at three distinct levels of granularity and employ contrastive learning as a pre-training task to train GranCATs on this dataset. Our objective is to enhance MLLMs' adaptation to a broader range of cross-lingual tasks by equipping them with improved capabilities to capture global information at different levels of granularity. Extensive experiments show that MLLMs with GranCATs yield significant performance advancements across various language tasks with different text granularities, including entity alignment, relation extraction, sentence classification and retrieval, and question-answering. These results validate the effectiveness of our proposed GranCATs in enhancing cross-lingual alignments across various text granularities and effectively transferring this knowledge to downstream tasks.|多语言语言模型(MLLM)在各种跨语言的下游任务中取得了显著的成功，促进了跨多种语言的知识转移，然而这种转移并非普遍有效。我们的研究表明，现有的 MLLM，如 mBERT，可以捕捉跨语系的短语水平对齐，他们努力有效捕捉句子水平和段落水平对齐。为了解决这个限制，我们提出了 GranCATs，特定于粒度的对比适配器。我们收集了一个新的数据集，在三个不同的粒度级别上观察每个样本，并将对比学习作为一个预训练任务来训练这个数据集上的 GranCATs。我们的目标是提高 MLLM 的适应性，使其能够更好地捕获不同粒度级别的全球信息，从而适应更广泛的跨语言任务。大量实验表明，使用 GranCATs 的 MLLM 在不同文本粒度的不同语言任务中，包括实体对齐、关系提取、句子分类和检索以及问答，都取得了显著的性能提升。这些结果验证了我们提出的 GranCATs 在增强不同文本粒度之间的跨语言对齐以及有效地将这些知识传递给下游任务方面的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=GranCATs:+Cross-Lingual+Enhancement+through+Granularity-Specific+Contrastive+Adapters)|0| -|[Quantifying the Effectiveness of Advertising: A Bootstrap Proportion Test for Brand Lift Testing](https://doi.org/10.1145/3583780.3615021)|Wanjun Liu, Xiufan Yu, Jialiang Mao, Xiaoxu Wu, Justin Dyer|LinkedIn Corporation, Sunnyvale, CA, USA; University of Notre Dame, Notre Dame, IN, USA|Brand Lift test is a widely deployed statistical tool for measuring the effectiveness of online advertisements on brand perception such as ad recall, brand familiarity and favorability. By formulating the problem of interest into a two-sample test on the binomial proportions from the control group (p_0) and the treatment group (p_1), Brand Lift test evaluates ads impact based on the statistical significance of test results. Traditional approaches construct the test statistics based on the absolute difference between the two observed proportions, a.k.a, absolute lift. In this work, we propose a new bootstrap test based on the percentage difference between the two observed proportions, i.e., relative lift. We provide rigorous theoretical guarantees on the asymptotic validity of the proposed relative-lift-based test. Our numerical studies suggest that the relative-lift-based test requires less stringent conditions than the absolute-lift-based test for controlling the type-I error rate. Interestingly, we also prove that the relative-lift-based test is more powerful than the absolute-lift-based test when the alternative is positive (i.e., p1 - p0 > 0), but less powerful when the alternative is negative (i.e., p1 - p0 < 0). The empirical performance of the proposed test is demonstrated by extensive simulation studies, an application to a publicly available A/B testing dataset from advertising, and real datasets collected from the Brand Lift Testing platform at LinkedIn.|品牌提升测试(Brand Lift test)是一种广泛应用的统计工具，用于测量在线广告对品牌认知的有效性，如广告回忆、品牌熟悉度和好感度。通过对对照组(p _ 0)和治疗组(p _ 1)的二项式比例的双样本检验，Brand Lift 检验基于检验结果的统计显著性来评估广告的影响。传统的方法建立测试统计基于两个观察比例之间的绝对差异，即绝对提升。在这项工作中，我们提出了一个新的自举测试基于两个观察比例之间的百分比差异，即相对提升。我们提供了严格的理论保证，渐近有效的相对提升为基础的检验。我们的数值研究表明，为了控制 I 型错误率，基于相对升力的测试比基于绝对升力的测试要求的条件要宽松。有趣的是，我们还证明，当替代方案为阳性(即 p1-p0 > 0)时，基于相对提升的测试比基于绝对提升的测试更强大，但是当替代方案为阴性(即 p1-p0 < 0)时则不那么强大。广泛的模拟研究，广告中公开的 A/B 测试数据集的应用，以及从 LinkedIn 的 Brand Lift 测试平台收集的真实数据集，都证明了该测试的经验性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Quantifying+the+Effectiveness+of+Advertising:+A+Bootstrap+Proportion+Test+for+Brand+Lift+Testing)|0| +|[On the Thresholding Strategy for Infrequent Labels in Multi-label Classification](https://doi.org/10.1145/3583780.3614996)|YuJen Lin, ChihJen Lin|National Taiwan University & Mohamed bin Zayed University of Artificial Intelligence, Taipei, Taiwan Roc; National Taiwan University, Taipei, Taiwan Roc|In multi-label classification, the imbalance between labels is often a concern. For a label that seldom occurs, the default threshold used to generate binarized predictions of that label is usually sub-optimal. However, directly tuning the threshold to optimize F-measure has been observed to overfit easily. In this work, we explain why this overfitting occurs. Then, we analyze the FBR heuristic, a previous technique proposed to address the overfitting issue. We explain its success but also point out some problems unobserved before. Then, we first propose a variant of the FBR heuristic that not only fixes the problems but is also more justifiable. Second, we propose a new technique based on smoothing the F-measure when tuning the threshold. We theoretically prove that, with proper parameters, smoothing results in desirable properties of the tuned threshold. Based on the idea of smoothing, we then propose jointly optimizing micro-F and macro-F as a lightweight alternative free from extra hyperparameters. Our methods are empirically evaluated on text and node classification datasets. The results show that our methods consistently outperform the FBR heuristic.|在多标签分类中，标签之间的不平衡往往是一个值得关注的问题。对于很少出现的标签，用于生成该标签的二进制预测的默认阈值通常是次优的。然而，直接调整阈值以优化 F 测度已被观察到很容易过拟合。在这项工作中，我们解释了为什么会发生这种过拟合。然后，我们分析了 FBR 启发式算法，这是一种先前提出的解决过拟合问题的技术。我们解释了它的成功，但也指出了一些以前没有注意到的问题。然后，我们首先提出了 FBR 启发式的一个变体，它不仅解决了问题，而且更加合理。其次，提出了一种基于平滑 F- 测度的阈值调整新方法。我们从理论上证明，在适当的参数下，光滑化可以得到理想的调谐阈值特性。基于平滑的思想，我们提出联合优化微 F 和宏 F 作为一个轻量级的替代方案，不需要额外的超参数。在文本和节点分类数据集上对我们的方法进行了实证评估。结果表明，我们的方法始终优于 FBR 启发式。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=On+the+Thresholding+Strategy+for+Infrequent+Labels+in+Multi-label+Classification)|0| +|[Cross-city Few-Shot Traffic Forecasting via Traffic Pattern Bank](https://doi.org/10.1145/3583780.3614829)|Zhanyu Liu, Guanjie Zheng, Yanwei Yu|Shanghai Jiao Tong University, Shanghai, China; Ocean University of China, Qingdao, China|Traffic forecasting is a critical service in Intelligent Transportation Systems (ITS). Utilizing deep models to tackle this task relies heavily on data from traffic sensors or vehicle devices, while some cities might lack device support and thus have few available data. So, it is necessary to learn from data-rich cities and transfer the knowledge to data-scarce cities in order to improve the performance of traffic forecasting. To address this problem, we propose a cross-city few-shot traffic forecasting framework via Traffic Pattern Bank (TPB) due to that the traffic patterns are similar across cities. TPB utilizes a pre-trained traffic patch encoder to project raw traffic data from data-rich cities into high-dimensional space, from which a traffic pattern bank is generated through clustering. Then, the traffic data of the data-scarce city could query the traffic pattern bank and explicit relations between them are constructed. The metaknowledge is aggregated based on these relations and an adjacency matrix is constructed to guide a downstream spatial-temporal model in forecasting future traffic. The frequently used meta-training framework Reptile is adapted to find a better initial parameter for the learnable modules. Experiments on real-world traffic datasets show that TPB outperforms existing methods and demonstrates the effectiveness of our approach in cross-city few-shot traffic forecasting.|交通预测是智能交通系统(ITS)中的一项重要业务。利用深度模型来解决这个问题主要依赖于来自交通传感器或车辆设备的数据，而一些城市可能缺乏设备支持，因此可用的数据很少。因此，有必要向数据丰富的城市学习，向数据稀缺的城市转移知识，以提高交通预测的性能。针对这一问题，针对城市间交通模式相似的特点，提出了一种基于交通模式库(TPB)的跨城市少镜头交通预测框架。TPB 利用预先训练好的交通补丁编码器将数据丰富的城市的原始交通数据投影到高维空间中，通过聚类生成交通模式库。然后，利用数据稀缺城市的交通数据对交通模式库进行查询，建立二者之间的显式关系。基于这些关系，元知识被聚合起来，并构建一个邻接矩阵来指导下游的时空模型来预测未来的交通。为了给可学习模块提供一个更好的初始参数，对经常使用的元训练框架爬行动物进行了改进。在实际交通数据集上的实验结果表明，TPB 方法的性能优于现有的方法，并证明了该方法在城市间少镜头交通预测中的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Cross-city+Few-Shot+Traffic+Forecasting+via+Traffic+Pattern+Bank)|0| +|[GranCATs: Cross-Lingual Enhancement through Granularity-Specific Contrastive Adapters](https://doi.org/10.1145/3583780.3614896)|Meizhen Liu, Jiakai He, Xu Guo, Jianye Chen, Siu Cheung Hui, Fengyu Zhou|Nanyang Technological University, Singapore, China; Shandong University, Jinan city, China; Shandong University, Jinan, China; Nanyang Technological University, Singapore, Singapore|Multilingual language models (MLLMs) have demonstrated remarkable success in various cross-lingual downstream tasks, facilitating the transfer of knowledge across numerous languages, whereas this transfer is not universally effective. Our study reveals that while existing MLLMs like mBERT can capturephrase-level alignments across the language families, they struggle to effectively capturesentence-level andparagraph-level alignments. To address this limitation, we propose GranCATs, Granularity-specific Contrastive AdapTers. We collect a new dataset that observes each sample at three distinct levels of granularity and employ contrastive learning as a pre-training task to train GranCATs on this dataset. Our objective is to enhance MLLMs' adaptation to a broader range of cross-lingual tasks by equipping them with improved capabilities to capture global information at different levels of granularity. Extensive experiments show that MLLMs with GranCATs yield significant performance advancements across various language tasks with different text granularities, including entity alignment, relation extraction, sentence classification and retrieval, and question-answering. These results validate the effectiveness of our proposed GranCATs in enhancing cross-lingual alignments across various text granularities and effectively transferring this knowledge to downstream tasks.|多语言语言模型(MLLM)在各种跨语言的下游任务中取得了显著的成功，促进了跨多种语言的知识转移，然而这种转移并非普遍有效。我们的研究表明，现有的 MLLM，如 mBERT，可以捕捉跨语系的短语水平对齐，他们努力有效捕捉句子水平和段落水平对齐。为了解决这个限制，我们提出了 GranCATs，特定于粒度的对比适配器。我们收集了一个新的数据集，在三个不同的粒度级别上观察每个样本，并将对比学习作为一个预训练任务来训练这个数据集上的 GranCATs。我们的目标是提高 MLLM 的适应性，使其能够更好地捕获不同粒度级别的全球信息，从而适应更广泛的跨语言任务。大量实验表明，使用 GranCATs 的 MLLM 在不同文本粒度的不同语言任务中，包括实体对齐、关系提取、句子分类和检索以及问答，都取得了显著的性能提升。这些结果验证了我们提出的 GranCATs 在增强不同文本粒度之间的跨语言对齐以及有效地将这些知识传递给下游任务方面的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=GranCATs:+Cross-Lingual+Enhancement+through+Granularity-Specific+Contrastive+Adapters)|0| +|[Quantifying the Effectiveness of Advertising: A Bootstrap Proportion Test for Brand Lift Testing](https://doi.org/10.1145/3583780.3615021)|Wanjun Liu, Xiufan Yu, Jialiang Mao, Xiaoxu Wu, Justin Dyer|University of Notre Dame, Notre Dame, IN, USA; LinkedIn Corporation, Sunnyvale, CA, USA|Brand Lift test is a widely deployed statistical tool for measuring the effectiveness of online advertisements on brand perception such as ad recall, brand familiarity and favorability. By formulating the problem of interest into a two-sample test on the binomial proportions from the control group (p_0) and the treatment group (p_1), Brand Lift test evaluates ads impact based on the statistical significance of test results. Traditional approaches construct the test statistics based on the absolute difference between the two observed proportions, a.k.a, absolute lift. In this work, we propose a new bootstrap test based on the percentage difference between the two observed proportions, i.e., relative lift. We provide rigorous theoretical guarantees on the asymptotic validity of the proposed relative-lift-based test. Our numerical studies suggest that the relative-lift-based test requires less stringent conditions than the absolute-lift-based test for controlling the type-I error rate. Interestingly, we also prove that the relative-lift-based test is more powerful than the absolute-lift-based test when the alternative is positive (i.e., p1 - p0 > 0), but less powerful when the alternative is negative (i.e., p1 - p0 < 0). The empirical performance of the proposed test is demonstrated by extensive simulation studies, an application to a publicly available A/B testing dataset from advertising, and real datasets collected from the Brand Lift Testing platform at LinkedIn.|品牌提升测试(Brand Lift test)是一种广泛应用的统计工具，用于测量在线广告对品牌认知的有效性，如广告回忆、品牌熟悉度和好感度。通过对对照组(p _ 0)和治疗组(p _ 1)的二项式比例的双样本检验，Brand Lift 检验基于检验结果的统计显著性来评估广告的影响。传统的方法建立测试统计基于两个观察比例之间的绝对差异，即绝对提升。在这项工作中，我们提出了一个新的自举测试基于两个观察比例之间的百分比差异，即相对提升。我们提供了严格的理论保证，渐近有效的相对提升为基础的检验。我们的数值研究表明，为了控制 I 型错误率，基于相对升力的测试比基于绝对升力的测试要求的条件要宽松。有趣的是，我们还证明，当替代方案为阳性(即 p1-p0 > 0)时，基于相对提升的测试比基于绝对提升的测试更强大，但是当替代方案为阴性(即 p1-p0 < 0)时则不那么强大。广泛的模拟研究，广告中公开的 A/B 测试数据集的应用，以及从 LinkedIn 的 Brand Lift 测试平台收集的真实数据集，都证明了该测试的经验性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Quantifying+the+Effectiveness+of+Advertising:+A+Bootstrap+Proportion+Test+for+Brand+Lift+Testing)|0| |[BRep-BERT: Pre-training Boundary Representation BERT with Sub-graph Node Contrastive Learning](https://doi.org/10.1145/3583780.3614795)|Yunzhong Lou, Xueyang Li, Haotian Chen, Xiangdong Zhou|Fudan University, Shanghai, China|Obtaining effective entity feature representations is crucial in the field of Boundary Representation (B-Rep), a key parametric representation method in Computer-Aided Design (CAD). However, the lack of labeled large-scale database and the scarcity of task-specific label sets pose significant challenges. To address these problems, we propose an innovative unsupervised neural network approach called BRep-BERT, which extends the concept of BERT to the B-Rep domain. Specifically, we utilize Graph Neural Network (GNN) Tokenizer to generate discrete entity labels with geometric and structural semantic information. We construct new entity representation sequences based on the structural relationships and pre-train the model through the Masked Entity Modeling (MEM) task. To address the attention sparsity issue in large-scale geometric models, we incorporate graph structure information and learnable relative position encoding into the attention module to optimize feature updates. Additionally, we employ geometric sub-graphs and multi-level contrastive learning techniques to enhance the model's ability to learn regional features. Comparisons with previous methods demonstrate that BRep-BERT achieves the state-of-the-art performance on both full-data training and few-shot learning tasks across multiple B-Rep datasets. Particularly, BRep-BERT outperforms previous methods significantly in the few-shot learning scenarios. Comprehensive experiments demonstrate the substantial advantages and potential of BRep-BERT in handling B-Rep data representation. Code will be released at https://github.com/louyz1026/Brep_Bert.|获得有效的实体特征表示在边界表示领域(B-Rep)是至关重要的，这是计算机辅助设计领域(CAD)中一种关键的参数表示方法。然而，缺乏大规模的标签数据库和缺乏特定任务的标签集构成了重大的挑战。为了解决这些问题，我们提出了一种新的无监督神经网络方法 BRep-BERT，它将 BERT 的概念扩展到了 B-Rep 域。具体来说，我们利用图形神经网络(GNN)标记器生成具有几何和结构语义信息的离散实体标签。我们基于结构关系构造新的实体表示序列，并通过掩模实体建模(MEM)任务对模型进行预训练。为了解决大规模几何模型中的注意稀疏问题，将图结构信息和可学习的相对位置编码结合到注意模块中，优化特征更新。此外，采用几何子图和多层次对比学习技术，提高了模型学习区域特征的能力。与以往的方法比较表明，BRep-BERT 在跨多个 B-Rep 数据集的全数据训练和少镜头学习任务中都取得了最好的性能。特别是，BRep-BERT 在少镜头学习场景中的性能明显优于以前的方法。综合实验表明，BRep-BERT 在处理 B-Rep 数据表示方面具有很大的优势和潜力。密码将在 https://github.com/louyz1026/brep_bert 公布。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=BRep-BERT:+Pre-training+Boundary+Representation+BERT+with+Sub-graph+Node+Contrastive+Learning)|0| |[Forward Creation, Reverse Selection: Achieving Highly Pertinent Multimodal Responses in Dialogue Contexts](https://doi.org/10.1145/3583780.3614888)|Ge Luo, Manman Zhang, Yuchen Ma, Sheng Li, Zhenxing Qian, Xinpeng Zhang|Fudan University, Shanghai, China|Multimodal Dialogue agents are often required to respond to conversation history using both textual and visual content. Even though current dialogue studies predominantly strive to generate natural texts or images, they fall short in considering the relevance of multimodal responses within a dialogue context, consequently confining agents from making prudent choices based on multiple alternatives and their associated relevance scores for decision-making. In this paper, we present a bidirectional multimodal dialogue framework that skillfully combines the forward generation of multiple text and image response candidates with reverse selection guided by relevance scores evaluated on dialogue context, facilitating agents in selecting the most suitable multimodal responses. Specifically, the forward generation aspect of our framework leverages a stage-wise approach, first producing textual replies and composite visual descriptions from the dialogue context, followed by the generation of visual responses aligned with the descriptions. In the reverse selection process, visual responses are translated into tangible descriptive texts that, in conjunction with textual responses, are inversely tied back to the dialogue context for relevance assessment, assigning a reference score to each multimodal response candidate to assist the intelligent agent in making informed decisions. Experimental outcomes demonstrate that our proposed bidirectional dialogue response framework markedly elevates performance in both automatic and human evaluations, yielding a range of contextually fitting multimodal responses for selection.|多模式对话代理经常需要使用文本和视觉内容来响应会话历史。尽管目前的对话研究主要致力于产生自然的文本或图像，但它们在对话背景下考虑多式联运反应的相关性方面存在不足，从而限制了行为体根据多种备选方案及其相关的决策相关性得分作出谨慎的选择。本文提出了一个双向多模式对话框架，该框架巧妙地将多个文本和图像响应候选者的前向生成与在对话背景下评估的相关性分数指导下的反向选择相结合，促进代理人选择最合适的多模式响应。具体来说，我们框架的前向生成方面采用了分阶段的方法，首先从对话上下文生成文本回复和组合视觉描述，然后生成与描述相一致的视觉回复。在反向选择过程中，视觉反应被转化为有形的描述性文本，与文字反应一起，与相关性评估的对话背景成反比，为每个多式联运反应候选人指定一个参考评分，以协助智能代理人作出知情决定。实验结果表明，我们提出的双向对话响应框架显着提高了自动和人工评估的绩效，产生了一系列符合上下文的多模式选择响应。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Forward+Creation,+Reverse+Selection:+Achieving+Highly+Pertinent+Multimodal+Responses+in+Dialogue+Contexts)|0| |[Context-Aware Prompt for Generation-based Event Argument Extraction with Diffusion Models](https://doi.org/10.1145/3583780.3614820)|Lei Luo, Yajing Xu|Beijing University of Posts and Telecommunications, Beijing, China|Event argument extraction (EAE) has attracted increasing attention via generation-based methods. However, most existing works tend to independently extract arguments for each role, ignoring the correlation between different arguments, especially in long contexts. Motivated by these observations and the high-quality generation results of recent diffusion models, we propose an effective model called PGAD (Context-Aware Prompt for Generation-based EAE with Diffusion models) for both sentence-level and document-level EAE. In PGAD, a text diffusion model is designed to generate diverse context-aware prompt representations in conjunction with a series of random Gaussian noise. Firstly, cross-attention is employed between the designed prompt and input context within the text diffusion model in order to generate the context-aware prompt. Through this interaction, the context-aware prompt is able to capture multiple role-specific argument span queriers. Secondly, the context-aware prompt is aligned with the context to generate event arguments by joint optimization. Extensive experiments on three publicly available EAE datasets demonstrate the superiority of our proposed PGAD model over existing approaches.|事件参数提取(EAE)通过基于生成的方法得到了越来越多的关注。然而，大多数现有的作品倾向于为每个角色独立提取论点，忽略了不同论点之间的相关性，特别是在较长的上下文中。基于这些观察结果和最近扩散模型的高质量生成结果，我们提出了一个有效的模型 PGAD (Context-Aware Prompt for Generation-based EAE with Disversation model)用于句子级和文档级 EAE。在 PGAD 中，文本扩散模型被设计用来产生不同的上下文感知的提示表示和一系列随机高斯噪声。首先，在文本扩散模型中，在设计的提示语和输入语之间引入交叉注意，生成感知上下文的提示语。通过这种交互，上下文感知提示符能够捕获多个特定于角色的参数 span 查询器。其次，将上下文感知提示符与上下文对齐，通过联合优化生成事件参数。在三个公开的 EAE 数据集上的大量实验证明了我们提出的 PGAD 模型优于现有的方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Context-Aware+Prompt+for+Generation-based+Event+Argument+Extraction+with+Diffusion+Models)|0| |[Multi-scale Graph Pooling Approach with Adaptive Key Subgraph for Graph Representations](https://doi.org/10.1145/3583780.3614981)|Yiqin Lv, Zhiliang Tian, Zheng Xie, Yiping Song|National University of Defense Technology, China, China|The recent progress in graph representation learning boosts the development of many graph classification tasks, such as protein classification and social network classification. One of the mainstream approaches for graph representation learning is the hierarchical pooling method. It learns the graph representation by gradually reducing the scale of the graph, so it can be easily adapted to large-scale graphs. However, existing graph pooling methods discard the original graph structure during downsizing the graph, resulting in a lack of graph topological structure. In this paper, we propose a multi-scale graph neural network (MSGNN) model that not only retains the topological information of the graph but also maintains the key-subgraph for better interpretability. MSGNN gradually discards the unimportant nodes and retains the important subgraph structure during the iteration. The key subgraphs are first chosen by experience and then adaptively evolved to tailor specific graph structures for downstream tasks. The extensive experiments on seven datasets show that MSGNN improves the SOTA performance on graph classification and better retains key subgraphs.|图表示学习的最新进展促进了许多图分类任务的发展，如蛋白质分类和社会网络分类。图表示学习的主流方法之一是层次化池方法。它通过逐渐缩小图的尺度来学习图的表示，因此可以很容易地适应大尺度的图。然而，现有的图池方法在缩小图的尺寸时会丢弃原有的图结构，从而导致图拓扑结构的缺失。本文提出了一种多尺度图神经网络(MSGNN)模型，该模型不仅保留了图的拓扑信息，而且保留了关键子图，从而提高了图的可解释性。在迭代过程中，MSGNN 逐渐丢弃不重要的节点，保留重要的子图结构。首先根据经验选择关键子图，然后自适应地进化，为下游任务裁剪特定的图结构。通过对7个数据集的大量实验表明，MSGNN 提高了 SOTA 在图分类方面的性能，并且能够更好地保留关键子图。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Multi-scale+Graph+Pooling+Approach+with+Adaptive+Key+Subgraph+for+Graph+Representations)|0| -|[Rethinking Sensors Modeling: Hierarchical Information Enhanced Traffic Forecasting](https://doi.org/10.1145/3583780.3614910)|Qian Ma, Zijian Zhang, Xiangyu Zhao, Haoliang Li, Hongwei Zhao, Yiqi Wang, Zitao Liu, Wanyu Wang|Michigan State University, Michigan , MI, USA; City University of Hong Kong, Hong Kong, Hong Kong; Jilin University, Changchun, China; Jinan University, Guangzhou, China|With the acceleration of urbanization, traffic forecasting has become an essential role in smart city construction. In the context of spatio-temporal prediction, the key lies in how to model the dependencies of sensors. However, existing works basically only consider the micro relationships between sensors, where the sensors are treated equally, and their macroscopic dependencies are neglected. In this paper, we argue to rethink the sensor's dependency modeling from two hierarchies: regional and global perspectives. Particularly, we merge original sensors with high intra-region correlation as a region node to preserve the inter-region dependency. Then, we generate representative and common spatio-temporal patterns as global nodes to reflect a global dependency between sensors and provide auxiliary information for spatio-temporal dependency learning. In pursuit of the generality and reality of node representations, we incorporate a Meta GCN to calibrate the regional and global nodes in the physical data space. Furthermore, we devise the cross-hierarchy graph convolution to propagate information from different hierarchies. In a nutshell, we propose a Hierarchical Information Enhanced Spatio-Temporal prediction method, HIEST, to create and utilize the regional dependency and common spatio-temporal patterns. Extensive experiments have verified the leading performance of our HIEST against state-of-the-art baselines. We publicize the code to ease reproducibility.|随着城市化进程的加快，交通预测已经成为智能城市建设的重要内容。在时空预测方面，关键在于如何对传感器的依赖关系进行建模。然而，现有的工作基本上只考虑传感器之间的微观关系，其中传感器被平等对待，它们的宏观依赖性被忽略。本文从区域和全局两个层次对传感器的依赖性建模进行了反思。特别地，我们将具有高区域内相关性的原始传感器合并为区域节点，以保持区域间的相关性。然后，我们生成具有代表性和共性的时空模式作为全局节点，以反映传感器之间的全局依赖关系，并为时空依赖学习提供辅助信息。为了追求节点表示的通用性和真实性，我们引入了一个元 GCN 来标定物理数据空间中的区域和全局节点。此外，我们还设计了跨层次图卷积来传播来自不同层次的信息。简而言之，我们提出了一种分层信息增强的时空预测方法，HIEST，来创建和利用区域依赖性和常见的时空模式。广泛的实验已经证实了我们的领先性能的 HIEST 对国家的最先进的基线。我们公布代码以减轻重复性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Rethinking+Sensors+Modeling:+Hierarchical+Information+Enhanced+Traffic+Forecasting)|0| +|[Rethinking Sensors Modeling: Hierarchical Information Enhanced Traffic Forecasting](https://doi.org/10.1145/3583780.3614910)|Qian Ma, Zijian Zhang, Xiangyu Zhao, Haoliang Li, Hongwei Zhao, Yiqi Wang, Zitao Liu, Wanyu Wang|Jinan University, Guangzhou, China; Jilin University, Changchun, China; Michigan State University, Michigan , MI, USA; City University of Hong Kong, Hong Kong, Hong Kong|With the acceleration of urbanization, traffic forecasting has become an essential role in smart city construction. In the context of spatio-temporal prediction, the key lies in how to model the dependencies of sensors. However, existing works basically only consider the micro relationships between sensors, where the sensors are treated equally, and their macroscopic dependencies are neglected. In this paper, we argue to rethink the sensor's dependency modeling from two hierarchies: regional and global perspectives. Particularly, we merge original sensors with high intra-region correlation as a region node to preserve the inter-region dependency. Then, we generate representative and common spatio-temporal patterns as global nodes to reflect a global dependency between sensors and provide auxiliary information for spatio-temporal dependency learning. In pursuit of the generality and reality of node representations, we incorporate a Meta GCN to calibrate the regional and global nodes in the physical data space. Furthermore, we devise the cross-hierarchy graph convolution to propagate information from different hierarchies. In a nutshell, we propose a Hierarchical Information Enhanced Spatio-Temporal prediction method, HIEST, to create and utilize the regional dependency and common spatio-temporal patterns. Extensive experiments have verified the leading performance of our HIEST against state-of-the-art baselines. We publicize the code to ease reproducibility.|随着城市化进程的加快，交通预测已经成为智能城市建设的重要内容。在时空预测方面，关键在于如何对传感器的依赖关系进行建模。然而，现有的工作基本上只考虑传感器之间的微观关系，其中传感器被平等对待，它们的宏观依赖性被忽略。本文从区域和全局两个层次对传感器的依赖性建模进行了反思。特别地，我们将具有高区域内相关性的原始传感器合并为区域节点，以保持区域间的相关性。然后，我们生成具有代表性和共性的时空模式作为全局节点，以反映传感器之间的全局依赖关系，并为时空依赖学习提供辅助信息。为了追求节点表示的通用性和真实性，我们引入了一个元 GCN 来标定物理数据空间中的区域和全局节点。此外，我们还设计了跨层次图卷积来传播来自不同层次的信息。简而言之，我们提出了一种分层信息增强的时空预测方法，HIEST，来创建和利用区域依赖性和常见的时空模式。广泛的实验已经证实了我们的领先性能的 HIEST 对国家的最先进的基线。我们公布代码以减轻重复性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Rethinking+Sensors+Modeling:+Hierarchical+Information+Enhanced+Traffic+Forecasting)|0| |[MultiCAD: Contrastive Representation Learning for Multi-modal 3D Computer-Aided Design Models](https://doi.org/10.1145/3583780.3614982)|Weijian Ma, Minyang Xu, Xueyang Li, Xiangdong Zhou|Fudan University, Shanghai, China|CAD models are multimodal data where information and knowledge contained in construction sequences and shapes are complementary to each other and representation learning methods should consider both of them. Such traits have been neglected in previous methods learning unimodal representations. To leverage the information from both modalities, we develop a multimodal contrastive learning strategy where features from different modalities interact via contrastive learning paradigm, driven by a novel multimodal contrastive loss. Two pretext tasks on both geometry and sequence domains are designed along with a two-stage training strategy to make the representation focus on encoding geometric details and decoding representations into construction sequences, thus being more applicable to downstream tasks such as multimodal retrieval and CAD sequence reconstruction. Experimental results show that the performance of our multimodal representation learning scheme has surpassed the baselines and unimodal methods significantly.|CAD 模型是多模态的数据，其中包含的信息和知识建设序列和形状是互补的，表示学习方法应考虑这两者。这些特征在以前的单峰表征学习方法中被忽略了。为了利用这两种模式的信息，我们开发了一个多模式对比学习策略，其中来自不同模式的特征通过对比学习范式相互作用，由一个新的多模式对比损失驱动。设计了几何域和序列域上的两个托词任务，并采用两阶段训练策略，使表示集中于编码几何细节，并将表示解码为结构序列，从而更适用于多模态检索和 CAD 序列重构等下游任务。实验结果表明，我们的多模态表示学习方案的性能明显优于基线和单模态方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=MultiCAD:+Contrastive+Representation+Learning+for+Multi-modal+3D+Computer-Aided+Design+Models)|0| -|[A Graph Neural Network Model for Concept Prerequisite Relation Extraction](https://doi.org/10.1145/3583780.3614761)|Debjani Mazumder, Jiaul H. Paik, Anupam Basu|Indian Institute of Technology, Kharagpur, Kharagpur, India; National Institute of Technology Durgapur, Kolkata, India|In recent years, with the emergence of online learning platforms and e-learning resources, many documents are available for a particular topic. For a better learning experience, the learner often needs to know and learn first the prerequisite concepts for a given concept. Traditionally, the identification of such prerequisite concepts is done manually by subject experts, which in turn, often limits self-paced learning. Recently, machine learning models have found encouraging success for the task, obviating manual effort. In this paper, we propose a graph neural network based approach that leverages node attention over a heterogeneous graph to extract the prerequisite concepts for a given concept. Experiments on a set of benchmark data show that the proposed model outperforms the existing models by large margins almost always, making the model a new state-of-the-art for the task.|近年来，随着在线学习平台和电子学习资源的出现，针对特定主题的文献越来越多。为了获得更好的学习体验，学习者往往需要首先了解和学习给定概念的前提概念。传统上，这种先决条件概念的识别是由学科专家手工完成的，这反过来又常常限制了自主学习。最近，机器学习模型发现这项任务取得了令人鼓舞的成功，避免了人工劳动。本文提出了一种基于图神经网络的方法，该方法利用异构图上的节点注意力来提取给定概念的前提概念。在一组基准数据上的实验表明，该模型的性能几乎总是大大优于现有的模型，使得该模型成为该任务的一种新的技术状态。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Graph+Neural+Network+Model+for+Concept+Prerequisite+Relation+Extraction)|0| +|[A Graph Neural Network Model for Concept Prerequisite Relation Extraction](https://doi.org/10.1145/3583780.3614761)|Debjani Mazumder, Jiaul H. Paik, Anupam Basu|National Institute of Technology Durgapur, Kolkata, India; Indian Institute of Technology, Kharagpur, Kharagpur, India|In recent years, with the emergence of online learning platforms and e-learning resources, many documents are available for a particular topic. For a better learning experience, the learner often needs to know and learn first the prerequisite concepts for a given concept. Traditionally, the identification of such prerequisite concepts is done manually by subject experts, which in turn, often limits self-paced learning. Recently, machine learning models have found encouraging success for the task, obviating manual effort. In this paper, we propose a graph neural network based approach that leverages node attention over a heterogeneous graph to extract the prerequisite concepts for a given concept. Experiments on a set of benchmark data show that the proposed model outperforms the existing models by large margins almost always, making the model a new state-of-the-art for the task.|近年来，随着在线学习平台和电子学习资源的出现，针对特定主题的文献越来越多。为了获得更好的学习体验，学习者往往需要首先了解和学习给定概念的前提概念。传统上，这种先决条件概念的识别是由学科专家手工完成的，这反过来又常常限制了自主学习。最近，机器学习模型发现这项任务取得了令人鼓舞的成功，避免了人工劳动。本文提出了一种基于图神经网络的方法，该方法利用异构图上的节点注意力来提取给定概念的前提概念。在一组基准数据上的实验表明，该模型的性能几乎总是大大优于现有的模型，使得该模型成为该任务的一种新的技术状态。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Graph+Neural+Network+Model+for+Concept+Prerequisite+Relation+Extraction)|0| |[Disparity, Inequality, and Accuracy Tradeoffs in Graph Neural Networks for Node Classification](https://doi.org/10.1145/3583780.3614847)|Arpit Merchant, Carlos Castillo|University of Helsinki, Helsinki, Finland; ICREA & Universitat Pompeu Fabra, Barcelona, Spain|Graph neural networks (GNNs) are increasingly used in critical human applications for predicting node labels in attributed graphs. Their ability to aggregate features from nodes' neighbors for accurate classification also has the capacity to exacerbate existing biases in data or to introduce new ones towards members from protected demographic groups. Thus, it is imperative to quantify how GNNs may be biased and to what extent their harmful effects may be mitigated. To this end, we propose two new GNN-agnostic interventions namely, (i) PFR-AX which decreases the separability between nodes in protected and non-protected groups, and (ii) PostProcess which updates model predictions based on a blackbox policy to minimize differences between error rates across demographic groups. Through a large set of experiments on four datasets, we frame the efficacies of our approaches (and three variants) in terms of their algorithmic fairness-accuracy tradeoff and benchmark our results against three strong baseline interventions on three state-of-the-art GNN models. Our results show that no single intervention offers a universally optimal tradeoff, but PFR-AX and PostProcess provide granular control and improve model confidence when correctly predicting positive outcomes for nodes in protected groups.|图神经网络(GNN)越来越多地应用于关键的人类应用中，用于预测属性图中的节点标记。他们聚合节点邻居特征进行精确分类的能力也有能力加剧现有的数据偏差，或者向来自受保护人口群体的成员引入新的偏差。因此，必须量化 GNN 如何可能带有偏见，以及在多大程度上可以减轻其有害影响。为此，我们提出了两种新的 GNN 不可知干预措施，即(i) PFR-AX，其降低了受保护组和非受保护组中节点之间的可分性，以及(ii) PostProcess，其根据黑盒政策更新模型预测，以尽量减少人口组之间的差异。通过对四个数据集的大量实验，我们构建了我们的方法(和三个变体)在算法公平性-准确性权衡方面的功效，并将我们的结果与三个最先进的 GNN 模型上的三个强基线干预进行比较。我们的研究结果表明，没有单一的干预提供了普遍的最佳折衷，但 PFR-AX 和 PostProcess 提供了粒度控制，并提高了模型的置信度，当正确预测保护组中节点的积极结果。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Disparity,+Inequality,+and+Accuracy+Tradeoffs+in+Graph+Neural+Networks+for+Node+Classification)|0| |[Unlocking the Potential of Non-PSD Kernel Matrices: A Polar Decomposition-based Transformation for Improved Prediction Models](https://doi.org/10.1145/3583780.3615102)|Maximilian Münch, Manuel Röder, FrankMichael Schleif|Technical University of Applied Sciences Würzburg-Schweinfurt, Center for Artificial Intelligence and Robotics, Würzburg, Germany; University of Groningen, Groningen, Netherlands|Kernel functions are a key element in many machine learning methods to capture the similarity between data points. However, a considerable number of these functions do not meet all mathematical requirements to be a valid positive semi-definite kernel, a crucial precondition for kernel-based classifiers such as Support Vector Machines or Kernel Fisher Discriminant classifiers. In this paper, we propose a novel strategy employing a polar decomposition to effectively transform invalid kernel matrices to positive semi-definite matrices, while preserving the topological structure inherent to the data points. Utilizing polar decomposition allows the effective transformation of indefinite kernel matrices from Krein space to positive semi-definite matrices in Hilbert space, thereby providing an efficient out-of-sample extension for new unseen data and enhancing kernel method applicability across diverse classification tasks. We evaluate our approach on a variety of benchmark datasets and demonstrate its superiority over competitive methods.|核函数是许多机器学习方法中捕捉数据点之间相似性的关键元素。然而，这些函数中有相当一部分不能满足作为有效正半定核的所有数学要求，这是支持向量机或核 Fisher 鉴别分类器等基于核的分类器的关键先决条件。在本文中，我们提出了一个新的策略，使用极分解有效地转换无效的核矩阵到正的半定矩阵，同时保留数据点固有的拓扑结构。利用极分解可以有效地将不定核矩阵从 Krein 空间转化为 Hilbert 空间中的正半定矩阵，从而为新的不可见数据提供了有效的样本外扩展，并增强了核方法在不同分类任务中的适用性。我们评估了我们的方法在各种基准数据集，并证明了其优势竞争的方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Unlocking+the+Potential+of+Non-PSD+Kernel+Matrices:+A+Polar+Decomposition-based+Transformation+for+Improved+Prediction+Models)|0| |[Joint Link Prediction Via Inference from a Model](https://doi.org/10.1145/3583780.3614941)|Parmis Naddaf, Erfaneh Mahmoudzaheh Ahmadi Nejad, Kiarash Zahirnia, Manfred Jaeger, Oliver Schulte|Aalborg University, Aalborg, Denmark; Simon Fraser University, Burnaby, BC, Canada|A Joint Link Prediction Query (JLPQ) specifies a set of links to be predicted, given another set of links as well as node attributes as evidence. While single link prediction has been well studied in literature on deep graph learning, predicting multiple links together has gained little attention. This paper presents a novel framework for computing JLPQs using a probabilistic deep Graph Generative Model. Specifically, we develop inference procedures for an inductively trained Variational Graph Auto-Encoder (VGAE) that estimates the joint link probability for any input JLPQ, without retraining. For evaluation, we apply inference to a range of joint link prediction queries on six benchmark datasets. We find that for most datasets and query types, joint link prediction via inference from a model achieves good predictive performance, better than the independent link prediction baselines (by 0.02-0.4 AUC points depending on the dataset).|联合链接预测查询(JLPQ)指定一组要预测的链接，给定另一组链接以及节点属性作为证据。在深度图学习的文献中，单链接预测已经得到了很好的研究，但是多链接一起预测却很少受到关注。本文提出了一个使用概率深度图生成模型计算 JLPQs 的新框架。具体来说，我们开发了一个归纳训练的变分图自动编码器(VGAE)的推理程序，估计任何输入 JLPQ 的联合链接概率，没有再训练。为了进行评估，我们对六个基准数据集上的一系列联合链接预测查询进行了推断。我们发现，对于大多数数据集和查询类型，通过模型推断的联合链接预测实现了良好的预测性能，优于独立链接预测基线(根据数据集的不同，增加0.02 -0.4 AUC 点)。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Joint+Link+Prediction+Via+Inference+from+a+Model)|0| |[Non-Uniform Adversarial Perturbations for Discrete Tabular Datasets](https://doi.org/10.1145/3583780.3614992)|Jay Nandy, Jatin Chauhan, Rishi Saket, Aravindan Raghuveer|Google Research, Bangalore, India|We study the problem of adversarial attack and robustness on tabular datasets with discrete features. The discrete features of a tabular dataset represent high-level meaningful concepts, with different sets of vocabularies, leading to requiring non-uniform robustness. Further, the notion of distance between tabular input instances is not well defined, making the problem of producing adversarial examples with minor perturbations qualitatively more challenging compared to existing methods. Towards this, our paper defines the notion of distance through the lens of feature embeddings, learnt to represent the discrete features. We then formulate the task of generating adversarial examples as abinary set selection problem under non-uniform feature importance. Next, we propose an efficient approximate gradient-descent based algorithm, calledDiscrete Non-uniform Approximation (DNA) attack, by reformulating the problem into a continuous domain to solve the original optimization problem for generating adversarial examples. We demonstrate the effectiveness of our proposed DNA attack using two large real-world discrete tabular datasets from e-commerce domains for binary classification, where the datasets are heavily biased for one-class. We also analyze challenges for existing adversarial training frameworks for such datasets under our DNA attack.|研究了具有离散特征的表格数据集的对抗性攻击和鲁棒性问题。表格数据集的离散特征代表了高层次的有意义的概念，具有不同的词汇集，导致需要非统一的鲁棒性。此外，表格输入实例之间的距离的概念没有得到很好的定义，使得产生具有微小扰动的对抗性实例的问题在质量上比现有方法更具挑战性。为此，本文通过特征嵌入的透镜定义了距离的概念，学会了表示离散的特征。然后将生成对手例子的任务表述为非一致特征重要度下的二进制集合选择问题。接下来，我们提出了一种有效的基于梯度下降的近似算法，称为离散非均匀近似(DNA)攻击，通过将问题重新构造成一个连续的域来解决生成对抗性例子的原始最佳化问题。我们证明了我们提出的 DNA 攻击的有效性，使用两个大型现实世界的离散表格数据集从电子商务领域的二进制分类，其中数据集是严重偏见的一类。我们还分析了在我们的 DNA 攻击下，针对这些数据集的现有对抗性训练框架所面临的挑战。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Non-Uniform+Adversarial+Perturbations+for+Discrete+Tabular+Datasets)|0| |[Measuring the Effect of Causal Disentanglement on the Adversarial Robustness of Neural Network Models](https://doi.org/10.1145/3583780.3614960)|Preben M. Ness, Dusica Marijan, Sunanda Bose|Simula Research Laboratory, Oslo, Norway|Causal Neural Network models have shown high levels of robustness to adversarial attacks as well as an increased capacity for generalisation tasks such as few-shot learning and rare-context classification compared to traditional Neural Networks. This robustness is argued to stem from the disentanglement of causal and confounder input signals. However, no quantitative study has yet measured the level of disentanglement achieved by these types of causal models or assessed how this relates to their adversarial robustness. Existing causal disentanglement metrics are not applicable to deterministic models trained on real-world datasets. We, therefore, utilise metrics of content/style disentanglement from the field of Computer Vision to measure different aspects of the causal disentanglement for four state-of-the-art causal Neural Network models. By re-implementing these models with a common ResNet18 architecture we are able to fairly measure their adversarial robustness on three standard image classification benchmarking datasets under seven common white-box attacks. We find a strong association (r=0.820, p=0.001) between the degree to which models decorrelate causal and confounder signals and their adversarial robustness. Additionally, we find a moderate negative association between the pixel-level information content of the confounder signal and adversarial robustness (r=-0.597, p=0.040).|与传统神经网络相比，因果神经网络模型显示了对敌对攻击的高水平鲁棒性，以及对概括性任务(如少镜头学习和罕见背景分类)的增强能力。这种稳健性被认为源于因果和混杂输入信号的分离。然而，还没有定量研究测量这些类型的因果模型所达到的解纠缠水平，或评估这与它们的对抗稳健性的关系。现有的因果解缠度量不适用于在真实世界数据集上训练的确定性模型。因此，我们利用计算机视觉领域的内容/风格脱离度量来衡量四个最先进的因果神经网络模型的因果脱离的不同方面。通过使用一个通用的 ResNet18架构重新实现这些模型，我们能够在七种常见的白盒攻击下，在三种标准图像分类基准数据集上公平地测量它们的对抗鲁棒性。我们发现在模型去除因果信号和混杂信号的程度与它们的对抗稳健性之间有很强的相关性(r = 0.820，p = 0.001)。此外，我们发现混杂信号的像素级信息含量与对抗鲁棒性之间存在中度负相关(r = -0.597，p = 0.040)。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Measuring+the+Effect+of+Causal+Disentanglement+on+the+Adversarial+Robustness+of+Neural+Network+Models)|0| -|[How Discriminative Are Your Qrels? How To Study the Statistical Significance of Document Adjudication Methods](https://doi.org/10.1145/3583780.3614916)|David Otero, Javier Parapar, Nicola Ferro|Universidade da Coruña, A Coruña, Spain; Univesidade da Coruña, A Coruña, Spain; University of Padua, Padova, Italy|Creating test collections for offline retrieval evaluation requires human effort to judge documents' relevance. This expensive activity motivated much work in developing methods for constructing benchmarks with fewer assessment costs. In this respect, adjudication methods actively decide both which documents and the order in which experts review them, in order to better exploit the assessment budget or to lower it. Researchers evaluate the quality of those methods by measuring the correlation between the known gold ranking of systems under the full collection and the observed ranking of systems under the lower-cost one. This traditional analysis ignores whether and how the low-cost judgements impact on the statistically significant differences among systems with respect to the full collection. We fill this void by proposing a novel methodology to evaluate how the low-cost adjudication methods preserve the pairwise significant differences between systems as the full collection. In other terms, while traditional approaches look for stability in answering the question "is system A better than system B?", our proposed approach looks for stability in answering the question "is system A significantly better than system B?", which is the ultimate questions researchers need to answer to guarantee the generalisability of their results. Among other results, we found that the best methods in terms of ranking of systems correlation do not always match those preserving statistical significance.|为离线检索评估创建测试集需要人力来判断文档的相关性。这项昂贵的活动促使人们开展大量工作，制定方法以构建评估成本较低的基准。在这方面，裁定方法积极决定哪些文件和专家审查这些文件的顺序，以便更好地利用或降低评估预算。研究人员评估这些方法的质量，通过测量之间的相关性已知黄金排名的系统下的全部收集和观察排名的系统下的低成本。这种传统的分析忽略了低成本判断是否以及如何影响各系统在完整收集方面的统计显著差异。为了填补这一空白，我们提出了一种新的方法来评估低成本的判决方法如何保持系统之间的成对显著差异作为完整的集合。换句话说，虽然传统方法在回答“系统 A 比系统 B 更好吗?”在回答“系统 A 是否明显优于系统 B?”这个问题时，我们提出的方法寻求稳定性这是研究人员需要回答的最终问题，以保证他们的结果的普遍性。除了其他结果之外，我们发现在系统相关性排序方面的最佳方法并不总是与那些保持统计显著性的方法相匹配。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=How+Discriminative+Are+Your+Qrels?+How+To+Study+the+Statistical+Significance+of+Document+Adjudication+Methods)|0| +|[How Discriminative Are Your Qrels? How To Study the Statistical Significance of Document Adjudication Methods](https://doi.org/10.1145/3583780.3614916)|David Otero, Javier Parapar, Nicola Ferro|University of Padua, Padova, Italy; Univesidade da Coruña, A Coruña, Spain; Universidade da Coruña, A Coruña, Spain|Creating test collections for offline retrieval evaluation requires human effort to judge documents' relevance. This expensive activity motivated much work in developing methods for constructing benchmarks with fewer assessment costs. In this respect, adjudication methods actively decide both which documents and the order in which experts review them, in order to better exploit the assessment budget or to lower it. Researchers evaluate the quality of those methods by measuring the correlation between the known gold ranking of systems under the full collection and the observed ranking of systems under the lower-cost one. This traditional analysis ignores whether and how the low-cost judgements impact on the statistically significant differences among systems with respect to the full collection. We fill this void by proposing a novel methodology to evaluate how the low-cost adjudication methods preserve the pairwise significant differences between systems as the full collection. In other terms, while traditional approaches look for stability in answering the question "is system A better than system B?", our proposed approach looks for stability in answering the question "is system A significantly better than system B?", which is the ultimate questions researchers need to answer to guarantee the generalisability of their results. Among other results, we found that the best methods in terms of ranking of systems correlation do not always match those preserving statistical significance.|为离线检索评估创建测试集需要人力来判断文档的相关性。这项昂贵的活动促使人们开展大量工作，制定方法以构建评估成本较低的基准。在这方面，裁定方法积极决定哪些文件和专家审查这些文件的顺序，以便更好地利用或降低评估预算。研究人员评估这些方法的质量，通过测量之间的相关性已知黄金排名的系统下的全部收集和观察排名的系统下的低成本。这种传统的分析忽略了低成本判断是否以及如何影响各系统在完整收集方面的统计显著差异。为了填补这一空白，我们提出了一种新的方法来评估低成本的判决方法如何保持系统之间的成对显著差异作为完整的集合。换句话说，虽然传统方法在回答“系统 A 比系统 B 更好吗?”在回答“系统 A 是否明显优于系统 B?”这个问题时，我们提出的方法寻求稳定性这是研究人员需要回答的最终问题，以保证他们的结果的普遍性。除了其他结果之外，我们发现在系统相关性排序方面的最佳方法并不总是与那些保持统计显著性的方法相匹配。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=How+Discriminative+Are+Your+Qrels?+How+To+Study+the+Statistical+Significance+of+Document+Adjudication+Methods)|0| |[Rule-based Knowledge Graph Completion with Canonical Models](https://doi.org/10.1145/3583780.3615042)|Simon Ott, Patrick Betz, Daria Stepanova, Mohamed H. GadElrab, Christian Meilicke, Heiner Stuckenschmidt|Bosch Center for Artificial Intelligence, Renningen, Germany; Bosch Center for Artificial Intelligence & AIT Austrian Institute of Technology GmbH, Renningen, Germany; University Mannheim, Mannheim, Germany|Rule-based approaches have proven to be an efficient and explainable method for knowledge base completion. Their predictive quality is on par with classic knowledge graph embedding models such as TransE or ComplEx, however, they cannot achieve the results of neural models proposed recently. The performance of a rule-based approach depends crucially on the solution of the rule aggregation problem, which is concerned with the computation of a score for a prediction that is generated by several rules. Within this paper, we propose a supervised approach to learn a reweighted confidence value for each rule to get an optimal explanation for the training set given a specific aggregation function. In particular, we apply our approach to two aggregation functions: We learn weights for a noisy-or multiplication and apply logistic regression, which computes the score of a prediction as a sum of these weights. Due to the simplicity of both models the final score is fully explainable. Our experimental results show that we can significantly improve the predictive quality of a rule-based approach. We compare our method with current state-of-the-art latent models that lack explainability, and achieve promising results.|基于规则的方法已被证明是完成知识库的一种有效和可解释的方法。它们的预测性能与传统的知识图嵌入模型如 TransE 或 CompleEx 相当，但是还不能达到最近提出的神经模型的预测效果。基于规则的方法的性能关键取决于规则聚合问题的解决方案，该问题涉及由多个规则生成的预测的得分计算。在本文中，我们提出了一种监督方法来学习每个规则的重新加权置信值，以获得一个给定特定聚集函数的训练集的最优解释。特别是，我们将我们的方法应用于两个聚合函数: 我们学习噪声或乘法的权重，并应用 Logit模型，它将预测的分数计算为这些权重的总和。由于两个模型的简单性，最终得分是完全可以解释的。实验结果表明，我们可以显著提高基于规则的方法的预测质量。我们比较了我们的方法与目前最先进的潜在模型，缺乏解释性，并取得了有希望的结果。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Rule-based+Knowledge+Graph+Completion+with+Canonical+Models)|0| |[Concept Evolution in Deep Learning Training: A Unified Interpretation Framework and Discoveries](https://doi.org/10.1145/3583780.3614819)|Haekyu Park, Seongmin Lee, Benjamin Hoover, Austin P. Wright, Omar Shaikh, Rahul Duggal, Nilaksh Das, Kevin Li, Judy Hoffman, Duen Horng Chau|Georgia Tech, Atlanta, GA, USA|We present ConceptEvo, a unified interpretation framework for deep neural networks (DNNs) that reveals the inception and evolution of learned concepts during training. Our work addresses a critical gap in DNN interpretation research, as existing methods primarily focus on post-training interpretation. ConceptEvo introduces two novel technical contributions: (1) an algorithm that generates a unified semantic space, enabling side-by-side comparison of different models during training, and (2) an algorithm that discovers and quantifies important concept evolutions for class predictions. Through a large-scale human evaluation and quantitative experiments, we demonstrate that ConceptEvo successfully identifies concept evolutions across different models, which are not only comprehensible to humans but also crucial for class predictions. ConceptEvo is applicable to both modern DNN architectures, such as ConvNeXt, and classic DNNs, such as VGGs and InceptionV3.|我们提出概念 Evo，一个深度神经网络(DNN)的统一解释框架，揭示了在训练过程中学习概念的开始和发展。我们的工作解决了 DNN 口译研究中的一个关键差距，因为现有的方法主要集中在训练后口译。概念 Evo 介绍了两个新的技术贡献: (1)一个算法，生成一个统一的语义空间，使不同的模型在训练期间并排比较，和(2)一个算法，发现和量化重要的概念进化类预测。通过一个大规模的人类评估和定量实验，我们证明 ConeptEvo 成功地识别了不同模型之间的概念进化，这不仅对人类是可以理解的，而且对类别预测也是至关重要的。概念 Evo 既适用于现代 DNN 体系结构，如 ConvNeXt，也适用于经典 DNN，如 VGGs 和 InceptionV3。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Concept+Evolution+in+Deep+Learning+Training:+A+Unified+Interpretation+Framework+and+Discoveries)|0| -|[RotDiff: A Hyperbolic Rotation Representation Model for Information Diffusion Prediction](https://doi.org/10.1145/3583780.3615041)|Hongliang Qiao, Shanshan Feng, Xutao Li, Huiwei Lin, Han Hu, Wei Wei, Yunming Ye|Huazhong University of Science and Technology, Wuhan, China; Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, China; Wecar Technology Co., Ltd., Shenzhen, China; Harbin Institute of Technology, Shenzhen, Shenzhen, China; Beijing Institute of Technology, Beijing, China|The massive amounts of online user behavior data on social networks allow for the investigation of information diffusion prediction, which is essential to comprehend how information propagates among users. The main difficulty in diffusion prediction problem is to effectively model the complex social factors in social networks and diffusion cascades. However, existing methods are mainly based on Euclidean space, which cannot well preserve the underlying hierarchical structures that could better reflect the strength of user influence. Meanwhile, existing methods cannot accurately model the obvious asymmetric features of the diffusion process. To alleviate these limitations, we utilize rotation transformation in the hyperbolic to model complex diffusion patterns. The modulus of representations in the hyperbolic space could effectively describe the strength of the user's influence. Rotation transformations could represent a variety of complex asymmetric features. Further, rotation transformation could model various social factors without changing the strength of influence. In this paper, we propose a novel hyperbolic rotation representation model RotDiff for the diffusion prediction problem. Specifically, we first map each social user to a Lorentzian vector and use two groups of transformations to encode global social factors in the social graph and the diffusion graph. Then, we combine attention mechanism in the hyperbolic space with extra rotation transformations to capture local diffusion dependencies within a given cascade. Experimental results on five real-world datasets demonstrate that the proposed model RotDiff outperforms various state-of-the-art diffusion prediction models.|社交网络中海量的在线用户行为数据为信息扩散预测提供了研究平台，对于理解信息在用户之间的传播方式具有重要意义。扩散预测问题的主要困难在于如何有效地模拟社会网络和扩散级联中的复杂社会因素。然而，现有的方法主要是基于欧几里德空间，不能很好地保留潜在的层次结构，可以更好地反映用户的影响力。同时，现有的方法不能准确地模拟扩散过程明显的非对称特征。为了减轻这些限制，我们利用双曲线中的旋转变换来模拟复杂的扩散模式。双曲空间中的表示模数可以有效地描述用户影响力的强度。旋转变换可以代表各种复杂的不对称特征。此外，轮换转换可以在不改变影响力的情况下模拟各种社会因素。本文针对扩散预测问题，提出了一种新的双曲旋转表示模型 RotDiff。具体来说，我们首先将每个社会用户映射到一个洛伦兹向量，并使用两组转换在社会图和扩散图中编码全球社会因素。然后，我们将双曲空间中的注意机制与额外的旋转变换结合起来，捕捉给定级联中的局部扩散依赖。在五个实际数据集上的实验结果表明，该模型的性能优于各种最先进的扩散预测模型。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=RotDiff:+A+Hyperbolic+Rotation+Representation+Model+for+Information+Diffusion+Prediction)|0| +|[RotDiff: A Hyperbolic Rotation Representation Model for Information Diffusion Prediction](https://doi.org/10.1145/3583780.3615041)|Hongliang Qiao, Shanshan Feng, Xutao Li, Huiwei Lin, Han Hu, Wei Wei, Yunming Ye|Huazhong University of Science and Technology, Wuhan, China; Harbin Institute of Technology, Shenzhen, Shenzhen, China; Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, China; Beijing Institute of Technology, Beijing, China; Wecar Technology Co., Ltd., Shenzhen, China|The massive amounts of online user behavior data on social networks allow for the investigation of information diffusion prediction, which is essential to comprehend how information propagates among users. The main difficulty in diffusion prediction problem is to effectively model the complex social factors in social networks and diffusion cascades. However, existing methods are mainly based on Euclidean space, which cannot well preserve the underlying hierarchical structures that could better reflect the strength of user influence. Meanwhile, existing methods cannot accurately model the obvious asymmetric features of the diffusion process. To alleviate these limitations, we utilize rotation transformation in the hyperbolic to model complex diffusion patterns. The modulus of representations in the hyperbolic space could effectively describe the strength of the user's influence. Rotation transformations could represent a variety of complex asymmetric features. Further, rotation transformation could model various social factors without changing the strength of influence. In this paper, we propose a novel hyperbolic rotation representation model RotDiff for the diffusion prediction problem. Specifically, we first map each social user to a Lorentzian vector and use two groups of transformations to encode global social factors in the social graph and the diffusion graph. Then, we combine attention mechanism in the hyperbolic space with extra rotation transformations to capture local diffusion dependencies within a given cascade. Experimental results on five real-world datasets demonstrate that the proposed model RotDiff outperforms various state-of-the-art diffusion prediction models.|社交网络中海量的在线用户行为数据为信息扩散预测提供了研究平台，对于理解信息在用户之间的传播方式具有重要意义。扩散预测问题的主要困难在于如何有效地模拟社会网络和扩散级联中的复杂社会因素。然而，现有的方法主要是基于欧几里德空间，不能很好地保留潜在的层次结构，可以更好地反映用户的影响力。同时，现有的方法不能准确地模拟扩散过程明显的非对称特征。为了减轻这些限制，我们利用双曲线中的旋转变换来模拟复杂的扩散模式。双曲空间中的表示模数可以有效地描述用户影响力的强度。旋转变换可以代表各种复杂的不对称特征。此外，轮换转换可以在不改变影响力的情况下模拟各种社会因素。本文针对扩散预测问题，提出了一种新的双曲旋转表示模型 RotDiff。具体来说，我们首先将每个社会用户映射到一个洛伦兹向量，并使用两组转换在社会图和扩散图中编码全球社会因素。然后，我们将双曲空间中的注意机制与额外的旋转变换结合起来，捕捉给定级联中的局部扩散依赖。在五个实际数据集上的实验结果表明，该模型的性能优于各种最先进的扩散预测模型。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=RotDiff:+A+Hyperbolic+Rotation+Representation+Model+for+Information+Diffusion+Prediction)|0| |[Federated Competing Risk Analysis](https://doi.org/10.1145/3583780.3614880)|Md Mahmudur Rahman, Sanjay Purushotham|University of Maryland, Baltimore County, Baltimore, MD, USA|Conducting survival analysis on distributed healthcare data is an important research problem, as privacy laws and emerging data-sharing regulations prohibit the sharing of sensitive patient data across multiple institutions. The distributed healthcare survival data often exhibit heterogeneity, non-uniform censoring and involve patients with multiple health conditions (competing risks), which can result in biased and unreliable risk predictions. To address these challenges, we propose employing federated learning (FL) for survival analysis with competing risks. In this work, we present two main contributions. Firstly, we propose a simple algorithm for estimating consistent federated pseudo values (FPV) for survival analysis with competing risks and censoring. Secondly, we introduce a novel and flexible FPV-based deep learning framework named Fedora, which jointly trains our proposed transformer-based model, TransPseudo, specific to the participating institutions (clients) within the Fedora framework without accessing clients' data, thus, preserving data privacy. We conducted extensive experiments on both real-world distributed healthcare datasets characterized by non-IID and non-uniform censoring properties, as well as synthetic data with various censoring settings. Our results demonstrate that our Fedora framework with the TransPseudo model performs better than the federated learning frameworks employing state-of-the-art survival models for competing risk analysis.|对分布式医疗数据进行生存分析是一个重要的研究问题，因为隐私法和新出现的数据共享规定禁止在多个机构之间共享敏感的患者数据。分布式医疗生存数据往往表现出异质性，审查不统一，并涉及多种健康状况(竞争风险)的患者，这可能导致有偏见和不可靠的风险预测。为了应对这些挑战，我们建议使用联邦学习(FL)进行具有竞争风险的生存分析。在这项工作中，我们提出了两个主要贡献。首先，我们提出了一个简单的算法估计一致性联邦伪值(FPV)的生存分析竞争风险和审查。其次，我们引入了一个新颖而灵活的基于 FPV 的深度学习框架 Fedora，该框架在不访问客户数据的情况下，联合训练我们提出的基于转换器的模型 TransPseuto，该模型特定于 Fedora 框架中的参与机构(客户) ，从而保护了数据隐私。我们在真实世界的分布式医疗数据集上进行了广泛的实验，这些数据集包括非拥有属性 ID 和非统一审查属性，以及具有各种审查设置的合成数据。我们的研究结果表明，我们的 Fedora 框架与 TransPseuto 模型执行优于联邦学习框架使用最先进的生存模型竞争风险分析。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Federated+Competing+Risk+Analysis)|0| -|[Incremental Graph Classification by Class Prototype Construction and Augmentation](https://doi.org/10.1145/3583780.3614932)|Yixin Ren, Li Ke, Dong Li, Hui Xue, Zhao Li, Shuigeng Zhou|Fudan University, Shanghai, China; Hangzhou Yugu Technology, Hangzhou, China; Alibaba Group, Hangzhou, China; Alibaba, Hangzhou, China|Graph neural networks (GNNs) are prone to catastrophic forgetting of past experience in continuous learning scenarios. In this work, we propose a novel method for class-incremental graph learning (CGL) by class prototype construction and augmentation, which can effectively overcome catastrophic forgetting and requires no storage of exemplars (i.e., data-free). Concretely, on the one hand, we construct class prototypes in the embedding space that contain rich topological information of nodes or graphs to represent past data, which are then used for future learning. On the other hand, to boost the adaptability of the model to new classes, we employ class prototype augmentation (PA) to create virtual classes by combining current prototypes. Theoretically, we show that PA can promote the model's adaptation to new data and reduce the inconsistency of old prototypes in the embedding space, therefore further mitigate catastrophic forgetting. Extensive experiments on both node and graph classification datasets show that our method significantly outperforms the existing methods in reducing catastrophic forgetting, and beats the existing methods in most cases in terms of classification accuracy.|在连续学习情境中，图神经网络容易发生灾难性遗忘。本文提出了一种基于类原型构造和扩充的类增量图学习方法，该方法可以有效地克服灾难性遗忘，不需要存储样本(即无数据)。具体来说，我们一方面在嵌入空间中构造类原型，包含丰富的节点或图的拓扑信息来表示过去的数据，然后用于未来的学习。另一方面，为了提高模型对新类的适应性，我们采用类原型增强(PA)的方法，通过组合当前的原型来创建虚拟类。从理论上说，PA 可以提高模型对新数据的适应性，减少嵌入空间中原型的不一致性，从而进一步减轻灾难性遗忘。在节点和图分类数据集上的大量实验表明，该方法在减少灾难性遗忘方面明显优于现有方法，并且在大多数情况下在分类精度方面优于现有方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Incremental+Graph+Classification+by+Class+Prototype+Construction+and+Augmentation)|0| -|[Seq-HyGAN: Sequence Classification via Hypergraph Attention Network](https://doi.org/10.1145/3583780.3615057)|Khaled Mohammed Saifuddin, Corey May, Farhan Tanvir, Muhammad Ifte Khairul Islam, Esra Akbas|Oklahoma State University, Stillwater, OK, USA; Georgia State University, Atlanta, GA, USA; Arkansas Tech University, Russellville, AR, USA|Sequence classification has a wide range of real-world applications in different domains, such as genome classification in health and anomaly detection in business. However, the lack of explicit features in sequence data makes it difficult for machine learning models. While Neural Network (NN) models address this with learning features automatically, they are limited to capturing adjacent structural connections and ignore global, higher-order information between the sequences. To address these challenges in the sequence classification problems, we propose a novel Hypergraph Attention Network model, namely Seq-HyGAN. To capture the complex structural similarity between sequence data, we first create a hypergraph where the sequences are depicted as hyperedges and subsequences extracted from sequences are depicted as nodes. Additionally, we introduce an attention-based Hypergraph Neural Network model that utilizes a two-level attention mechanism. This model generates a sequence representation as a hyperedge while simultaneously learning the crucial subsequences for each sequence. We conduct extensive experiments on four data sets to assess and compare our model with several state-of-the-art methods. Experimental results demonstrate that our proposed Seq-HyGAN model can effectively classify sequence data and significantly outperform the baselines. We also conduct case studies to investigate the contribution of each module in Seq-HyGAN.|序列分类在不同的领域有着广泛的实际应用，例如健康领域的基因组分类和商业领域的异常检测分类。然而，由于序列数据缺乏明确的特征，使得机器学习模型难以建立。虽然神经网络(NN)模型解决了这一问题，自动学习功能，他们仅限于捕获相邻的结构连接和忽略全局，高阶信息之间的序列。为了解决序列分类问题中的这些挑战，我们提出了一种新的 Hypergraph 注意网络模型，即 Seq-HyGAN。为了捕获序列数据之间的复杂结构相似性，我们首先创建一个超图，其中序列被描述为超边，从序列中提取的子序列被描述为节点。此外，我们还介绍了一个基于注意的超图神经网络模型，该模型采用了两级注意机制。该模型以超边界的形式生成序列表示，同时学习每个序列的关键子序列。我们对四个数据集进行了广泛的实验，以评估和比较我们的模型与几种最先进的方法。实验结果表明，我们提出的 Seq-HyGAN 模型能够有效地对序列数据进行分类，并明显优于基线。我们还进行了案例研究，以调查每个模块在 Seq-HyGAN 的贡献。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Seq-HyGAN:+Sequence+Classification+via+Hypergraph+Attention+Network)|0| -|[PaperLM: A Pre-trained Model for Hierarchical Examination Paper Representation Learning](https://doi.org/10.1145/3583780.3615003)|Minghui Shan, Yixiao Ma, Shulan Ruan, Zhi Cao, Shiwei Tong, Qi Liu, Yu Su, Shijin Wang|iFLYTEK AI Research (Central China) & State Key Laboratory of Cognitive Intelligence, Hefei, China; University of Science and Technology of China & State Key Laboratory of Cognitive Intelligence, Hefei, China; Hefei Normal University,Institute of Artificial Intelligence & Hefei Comprehensive National Science Center, Hefei, China|Representation learning of examination papers is significantly crucial for online education systems, as it benefits various applications such as estimating paper difficulty and examination paper retrieval. Previous works mainly explore the representation learning of individual questions in an examination paper, with limited attention given to the examination paper as a whole. In fact, the structure of examination papers is strongly correlated with paper properties such as paper difficulty, which existing paper representation methods fail to capture adequately. To this end, we propose a pre-trained model namely PaperLM to learn the representation of examination papers. Our model integrates both the text content and hierarchical structure of examination papers within a single framework by converting the path of the Examination Organization Tree (EOT) into embedding. Furthermore, we specially design three pre-training objectives for PaperLM, namely EOT Node Relationship Prediction (ENRP), Question Type Prediction (QTP) and Paper Contrastive Learning (PCL), aiming to capture features from text and structure effectively. We pre-train our model on a real-world examination paper dataset, and then evaluate the model with three down-stream tasks: paper difficulty estimation, examination paper retrieval, and paper clustering. The experimental results demonstrate the effectiveness of our method.|试卷表征学习对于在线教育系统具有重要意义，因为它有利于评估试卷难度和检索试卷等多种应用。以往的研究主要探讨个别试题在试卷中的表征学习，对试卷整体的关注有限。事实上，试卷的结构与试卷的难度等特性密切相关，而现有的试卷表示方法未能充分捕捉到这些特性。为此，我们提出了一种预训练模型，即 PaperLM 来学习试卷的表示。该模型通过将考试组织树(EOT)的路径转换为嵌入式，将试卷的文本内容和层次结构集成在一个框架内。此外，我们特别设计了三个 PaperLM 的预训目标，即 EOT 节点关系预测(ENRP)、问题类型预测(QTP)和纸张对比学习(PCL) ，旨在有效地从文本和结构中捕捉特征。我们在一个真实的试卷数据集上预训练我们的模型，然后通过三个下游任务来评估模型: 试卷难度估计，试卷检索和试卷聚类。实验结果表明了该方法的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=PaperLM:+A+Pre-trained+Model+for+Hierarchical+Examination+Paper+Representation+Learning)|0| +|[Incremental Graph Classification by Class Prototype Construction and Augmentation](https://doi.org/10.1145/3583780.3614932)|Yixin Ren, Li Ke, Dong Li, Hui Xue, Zhao Li, Shuigeng Zhou|Alibaba Group, Hangzhou, China; Fudan University, Shanghai, China; Hangzhou Yugu Technology, Hangzhou, China; Alibaba, Hangzhou, China|Graph neural networks (GNNs) are prone to catastrophic forgetting of past experience in continuous learning scenarios. In this work, we propose a novel method for class-incremental graph learning (CGL) by class prototype construction and augmentation, which can effectively overcome catastrophic forgetting and requires no storage of exemplars (i.e., data-free). Concretely, on the one hand, we construct class prototypes in the embedding space that contain rich topological information of nodes or graphs to represent past data, which are then used for future learning. On the other hand, to boost the adaptability of the model to new classes, we employ class prototype augmentation (PA) to create virtual classes by combining current prototypes. Theoretically, we show that PA can promote the model's adaptation to new data and reduce the inconsistency of old prototypes in the embedding space, therefore further mitigate catastrophic forgetting. Extensive experiments on both node and graph classification datasets show that our method significantly outperforms the existing methods in reducing catastrophic forgetting, and beats the existing methods in most cases in terms of classification accuracy.|在连续学习情境中，图神经网络容易发生灾难性遗忘。本文提出了一种基于类原型构造和扩充的类增量图学习方法，该方法可以有效地克服灾难性遗忘，不需要存储样本(即无数据)。具体来说，我们一方面在嵌入空间中构造类原型，包含丰富的节点或图的拓扑信息来表示过去的数据，然后用于未来的学习。另一方面，为了提高模型对新类的适应性，我们采用类原型增强(PA)的方法，通过组合当前的原型来创建虚拟类。从理论上说，PA 可以提高模型对新数据的适应性，减少嵌入空间中原型的不一致性，从而进一步减轻灾难性遗忘。在节点和图分类数据集上的大量实验表明，该方法在减少灾难性遗忘方面明显优于现有方法，并且在大多数情况下在分类精度方面优于现有方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Incremental+Graph+Classification+by+Class+Prototype+Construction+and+Augmentation)|0| +|[Seq-HyGAN: Sequence Classification via Hypergraph Attention Network](https://doi.org/10.1145/3583780.3615057)|Khaled Mohammed Saifuddin, Corey May, Farhan Tanvir, Muhammad Ifte Khairul Islam, Esra Akbas|Georgia State University, Atlanta, GA, USA; Arkansas Tech University, Russellville, AR, USA; Oklahoma State University, Stillwater, OK, USA|Sequence classification has a wide range of real-world applications in different domains, such as genome classification in health and anomaly detection in business. However, the lack of explicit features in sequence data makes it difficult for machine learning models. While Neural Network (NN) models address this with learning features automatically, they are limited to capturing adjacent structural connections and ignore global, higher-order information between the sequences. To address these challenges in the sequence classification problems, we propose a novel Hypergraph Attention Network model, namely Seq-HyGAN. To capture the complex structural similarity between sequence data, we first create a hypergraph where the sequences are depicted as hyperedges and subsequences extracted from sequences are depicted as nodes. Additionally, we introduce an attention-based Hypergraph Neural Network model that utilizes a two-level attention mechanism. This model generates a sequence representation as a hyperedge while simultaneously learning the crucial subsequences for each sequence. We conduct extensive experiments on four data sets to assess and compare our model with several state-of-the-art methods. Experimental results demonstrate that our proposed Seq-HyGAN model can effectively classify sequence data and significantly outperform the baselines. We also conduct case studies to investigate the contribution of each module in Seq-HyGAN.|序列分类在不同的领域有着广泛的实际应用，例如健康领域的基因组分类和商业领域的异常检测分类。然而，由于序列数据缺乏明确的特征，使得机器学习模型难以建立。虽然神经网络(NN)模型解决了这一问题，自动学习功能，他们仅限于捕获相邻的结构连接和忽略全局，高阶信息之间的序列。为了解决序列分类问题中的这些挑战，我们提出了一种新的 Hypergraph 注意网络模型，即 Seq-HyGAN。为了捕获序列数据之间的复杂结构相似性，我们首先创建一个超图，其中序列被描述为超边，从序列中提取的子序列被描述为节点。此外，我们还介绍了一个基于注意的超图神经网络模型，该模型采用了两级注意机制。该模型以超边界的形式生成序列表示，同时学习每个序列的关键子序列。我们对四个数据集进行了广泛的实验，以评估和比较我们的模型与几种最先进的方法。实验结果表明，我们提出的 Seq-HyGAN 模型能够有效地对序列数据进行分类，并明显优于基线。我们还进行了案例研究，以调查每个模块在 Seq-HyGAN 的贡献。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Seq-HyGAN:+Sequence+Classification+via+Hypergraph+Attention+Network)|0| +|[PaperLM: A Pre-trained Model for Hierarchical Examination Paper Representation Learning](https://doi.org/10.1145/3583780.3615003)|Minghui Shan, Yixiao Ma, Shulan Ruan, Zhi Cao, Shiwei Tong, Qi Liu, Yu Su, Shijin Wang|University of Science and Technology of China & State Key Laboratory of Cognitive Intelligence, Hefei, China; Hefei Normal University,Institute of Artificial Intelligence & Hefei Comprehensive National Science Center, Hefei, China; iFLYTEK AI Research (Central China) & State Key Laboratory of Cognitive Intelligence, Hefei, China|Representation learning of examination papers is significantly crucial for online education systems, as it benefits various applications such as estimating paper difficulty and examination paper retrieval. Previous works mainly explore the representation learning of individual questions in an examination paper, with limited attention given to the examination paper as a whole. In fact, the structure of examination papers is strongly correlated with paper properties such as paper difficulty, which existing paper representation methods fail to capture adequately. To this end, we propose a pre-trained model namely PaperLM to learn the representation of examination papers. Our model integrates both the text content and hierarchical structure of examination papers within a single framework by converting the path of the Examination Organization Tree (EOT) into embedding. Furthermore, we specially design three pre-training objectives for PaperLM, namely EOT Node Relationship Prediction (ENRP), Question Type Prediction (QTP) and Paper Contrastive Learning (PCL), aiming to capture features from text and structure effectively. We pre-train our model on a real-world examination paper dataset, and then evaluate the model with three down-stream tasks: paper difficulty estimation, examination paper retrieval, and paper clustering. The experimental results demonstrate the effectiveness of our method.|试卷表征学习对于在线教育系统具有重要意义，因为它有利于评估试卷难度和检索试卷等多种应用。以往的研究主要探讨个别试题在试卷中的表征学习，对试卷整体的关注有限。事实上，试卷的结构与试卷的难度等特性密切相关，而现有的试卷表示方法未能充分捕捉到这些特性。为此，我们提出了一种预训练模型，即 PaperLM 来学习试卷的表示。该模型通过将考试组织树(EOT)的路径转换为嵌入式，将试卷的文本内容和层次结构集成在一个框架内。此外，我们特别设计了三个 PaperLM 的预训目标，即 EOT 节点关系预测(ENRP)、问题类型预测(QTP)和纸张对比学习(PCL) ，旨在有效地从文本和结构中捕捉特征。我们在一个真实的试卷数据集上预训练我们的模型，然后通过三个下游任务来评估模型: 试卷难度估计，试卷检索和试卷聚类。实验结果表明了该方法的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=PaperLM:+A+Pre-trained+Model+for+Hierarchical+Examination+Paper+Representation+Learning)|0| |[Transferable Structure-based Adversarial Attack of Heterogeneous Graph Neural Network](https://doi.org/10.1145/3583780.3615095)|Yu Shang, Yudong Zhang, Jiansheng Chen, Depeng Jin, Yong Li|Tsinghua University, Beijing, China; University of Science and Technology Beijing, Beijing, China|Heterogeneous graph neural networks (HGNNs) have achieved remarkable development recently and exhibited superior performance in various tasks. However, recently HGNNs have been shown to have robustness weakness towards adversarial perturbations, which brings critical pitfalls for real applications, e.g. node classification and recommender systems. In particular, the transfer-based black-box attack is the most practical method to attack unknown models and poses a great threat to the reliability of HGNNs. In this work, we take the first step to explore the transferability of adversarial examples of HGNNs. Due to the overfitting of the source model, the adversarial perturbations generated by traditional methods usually exhibit unpromising transferability. To address this problem and boost adversarial transferability, we expect to seek common vulnerable directions of different models to attack. Inspired by the observation of the notable commonality of edge attention distribution between different HGNNs, we propose to guide the perturbation generation toward disrupting edge attention distribution. This edge attention-guided attack prioritizes the perturbation on edges that are more likely to be given common attention by different models, which benefits the transferability of adversarial perturbations. Finally, we develop two edge attention-guided attack methods towards heterogeneous relations tailored for HGNNs, called EA-FGSM and EA-PGD. Extensive experiments on six representative models and two datasets verify the effectiveness of our methods and form an unprecedented transfer robustness benchmark for HGNNs.|异构图神经网络(HGNN)近年来取得了显著的发展，在各种任务中表现出了优异的性能。然而，最近 HGNN 已被证明对抗性扰动具有鲁棒性弱点，这给实际应用带来了关键的陷阱，如节点分类和推荐系统。特别是基于传输的黑盒攻击是最实用的攻击未知模型的方法，对 HGNN 的可靠性构成了很大的威胁。在这项工作中，我们采取的第一步，探讨可转移的对抗性例子的 HGNN。由于源模型的过度拟合，传统方法产生的对抗扰动通常表现出不良的可转移性。为了解决这一问题，提高对抗性的可转移性，我们期望寻找不同模型共同的易受攻击的方向。受不同 HGNN 之间边缘注意分布的显著共性的启发，我们提出将扰动产生引向破坏边缘注意分布的方向。这种边缘注意引导攻击优先考虑边缘上的扰动，这些扰动更有可能被不同的模型给予共同的关注，这有利于对抗性扰动的可转移性。最后，我们针对 HGNN 的异构关系提出了两种边缘注意引导攻击方法: EA-FGSM 和 EA-PGD。在六个典型模型和两个数据集上的大量实验验证了该方法的有效性，为 HGNN 提供了一个前所未有的传输鲁棒性基准。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Transferable+Structure-based+Adversarial+Attack+of+Heterogeneous+Graph+Neural+Network)|0| -|[CANA: Causal-enhanced Social Network Alignment](https://doi.org/10.1145/3583780.3614799)|Jiangli Shao, Yongqing Wang, Fangda Guo, Boshen Shi, Huawei Shen, Xueqi Cheng|Institute of Computing Technology, CAS, Beijing, China; Institute of Computing Technology, CAS & University of Chinese Academy of Sciences, Beijing, China|Social network alignment is widely applied in web applications for identifying corresponding nodes across different networks, such as linking users across two social networks. Existing methods for social network alignment primarily rely on alignment consistency, assuming that nodes with similar attributes and neighbors are more likely to be aligned. However, distributional discrepancies in node attributes and neighbors across different networks would bring biases in alignment consistency, leading to inferior alignment performance. To address this issue, we conduct a causal analysis of alignment consistency. Based on this analysis, we propose a novel model called CANA that uses causal inference approaches to mitigate biases and enhance social network alignment. Firstly, we disentangle observed node attributes into endogenous features and exogenous features with multi-task learning. Only endogenous features are retained to overcome node attribute discrepancies. To eliminate biases caused by neighbors discrepancies, we propose causal-aware attention mechanisms and integrate them in graph neural network to reweight contributions of different neighbors in alignment consistency comparison. Additionally, backdoor adjustment is applied to reduce confounding effects and estimate unbiased alignment probability. Through experimental evaluation on four real-world datasets, the proposed method demonstrates superior performance in terms of alignment accuracy and top-k hits precision.|社交网络对齐被广泛应用于 Web 应用程序中，用于识别跨不同网络的相应节点，例如在两个社交网络之间链接用户。现有的社会网络对齐方法主要依赖于对齐的一致性，假设具有相似属性和邻居的节点更有可能进行对齐。然而，不同网络中节点属性和邻居属性的分布差异会导致对齐一致性的偏差，从而导致对齐性能的下降。为了解决这个问题，我们对比对一致性进行了因果分析。在此基础上，我们提出了一个新的模型，称为 CANA，使用因果推理的方法，以减轻偏见和增强社会网络的一致性。首先，利用多任务学习将观测节点属性分解为内生特征和外生特征。只保留内生特征以克服节点属性差异。为了消除邻居间差异造成的偏差，本文提出了因果感知注意机制，并将其集成到图神经网络中，以重新权重不同邻居对对齐一致性比较的贡献。此外，后门调整的应用，以减少混杂效应和估计无偏的对齐概率。通过对四个实际数据集的实验评估，表明该方法在对准精度和 top-k 命中精度方面具有较好的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=CANA:+Causal-enhanced+Social+Network+Alignment)|0| +|[CANA: Causal-enhanced Social Network Alignment](https://doi.org/10.1145/3583780.3614799)|Jiangli Shao, Yongqing Wang, Fangda Guo, Boshen Shi, Huawei Shen, Xueqi Cheng|Institute of Computing Technology, CAS & University of Chinese Academy of Sciences, Beijing, China; Institute of Computing Technology, CAS, Beijing, China|Social network alignment is widely applied in web applications for identifying corresponding nodes across different networks, such as linking users across two social networks. Existing methods for social network alignment primarily rely on alignment consistency, assuming that nodes with similar attributes and neighbors are more likely to be aligned. However, distributional discrepancies in node attributes and neighbors across different networks would bring biases in alignment consistency, leading to inferior alignment performance. To address this issue, we conduct a causal analysis of alignment consistency. Based on this analysis, we propose a novel model called CANA that uses causal inference approaches to mitigate biases and enhance social network alignment. Firstly, we disentangle observed node attributes into endogenous features and exogenous features with multi-task learning. Only endogenous features are retained to overcome node attribute discrepancies. To eliminate biases caused by neighbors discrepancies, we propose causal-aware attention mechanisms and integrate them in graph neural network to reweight contributions of different neighbors in alignment consistency comparison. Additionally, backdoor adjustment is applied to reduce confounding effects and estimate unbiased alignment probability. Through experimental evaluation on four real-world datasets, the proposed method demonstrates superior performance in terms of alignment accuracy and top-k hits precision.|社交网络对齐被广泛应用于 Web 应用程序中，用于识别跨不同网络的相应节点，例如在两个社交网络之间链接用户。现有的社会网络对齐方法主要依赖于对齐的一致性，假设具有相似属性和邻居的节点更有可能进行对齐。然而，不同网络中节点属性和邻居属性的分布差异会导致对齐一致性的偏差，从而导致对齐性能的下降。为了解决这个问题，我们对比对一致性进行了因果分析。在此基础上，我们提出了一个新的模型，称为 CANA，使用因果推理的方法，以减轻偏见和增强社会网络的一致性。首先，利用多任务学习将观测节点属性分解为内生特征和外生特征。只保留内生特征以克服节点属性差异。为了消除邻居间差异造成的偏差，本文提出了因果感知注意机制，并将其集成到图神经网络中，以重新权重不同邻居对对齐一致性比较的贡献。此外，后门调整的应用，以减少混杂效应和估计无偏的对齐概率。通过对四个实际数据集的实验评估，表明该方法在对准精度和 top-k 命中精度方面具有较好的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=CANA:+Causal-enhanced+Social+Network+Alignment)|0| |[Representation Learning in Continuous-Time Dynamic Signed Networks](https://doi.org/10.1145/3583780.3615032)|Kartik Sharma, Mohit Raghavendra, YeonChang Lee, Anand Kumar M, Srijan Kumar|National Institute of Technology Karnataka, Surathkal, India; Georgia Institute of Technology, Atlanta, GA, USA|Signed networks allow us to model conflicting relationships and interactions, such as friend/enemy and support/oppose. These signed interactions happen in real-time. Modeling such dynamics of signed networks is crucial to understanding the evolution of polarization in the network and enabling effective prediction of the signed structure (i.e., link signs and signed weights) in the future. However, existing works have modeled either (static) signed networks or dynamic (unsigned) networks but not dynamic signed networks. Since both sign and dynamics inform the graph structure in different ways, it is non-trivial to model how to combine the two features. In this work, we propose a new Graph Neural Network (GNN)-based approach to model dynamic signed networks, named SEMBA: Signed link's Evolution using Memory modules and Balanced Aggregation. Here, the idea is to incorporate the signs of temporal interactions using separate modules guided by balance theory and to evolve the embeddings from a higher-order neighborhood. Experiments on 4 real-world datasets and 4 different tasks demonstrate that SEMBA consistently and significantly outperforms the baselines by up to $80\%$ on the tasks of predicting signs of future links while matching the state-of-the-art performance on predicting the existence of these links in the future. We find that this improvement is due specifically to the superior performance of SEMBA on the minority negative class.|签名网络允许我们建立冲突的关系和互动，例如朋友/敌人和支持/反对。这些签名的交互实时发生。建立符号网络的动力学模型对于理解网络中极化的演化过程以及有效预测未来的符号结构(即链路符号和符号权重)是至关重要的。然而，现有的作品已经模拟了(静态)有符号网络或动态(无符号)网络，但没有动态有符号网络。由于符号和动力学都以不同的方式告知图形结构，因此建立如何结合这两个特征的模型是非常重要的。在这项工作中，我们提出了一个新的图神经网络(GNN)为基础的模型动态签名网络，称为 SEMBA: 签名链路的演化使用记忆模块和平衡聚合。在这里，我们的想法是使用平衡理论指导下的独立模块合并时间相互作用的标志，并从一个高阶邻域演化嵌入。在4个真实世界的数据集和4个不同的任务上的实验表明，SEMBA 在预测未来链接迹象的任务上持续而显著地优于基线80% ，同时在预测未来链接存在的任务上匹配最先进的表现。我们发现，这种改善主要是由于 SEMBA 在少数消极阶层中的卓越表现。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Representation+Learning+in+Continuous-Time+Dynamic+Signed+Networks)|0| |[Investigating the Impact of Multimodality and External Knowledge in Aspect-level Complaint and Sentiment Analysis](https://doi.org/10.1145/3583780.3614937)|Apoorva Singh, Apoorv Verma, Raghav Jain, Sriparna Saha|IIT Patna, Patna, India|Automated complaint analysis is vital for generating critical insights, which in turn enhance customer satisfaction, product quality, and overall business performance. Nevertheless, conventional methods frequently fail to capture the nuances of aspect-level complaints and inadequately utilize external knowledge, thus creating a gap in effective complaint detection and analysis. In response to this issue, we proactively explore the role of external knowledge and multimodality in this domain. This leads to the development of MGasD (Multimodal Generative framework for aspect-based complaint and sentiment Detection), a multimodal knowledge-infused unified framework. MGasD diverges from traditional methods by reframing the complaint detection problem as a multimodal text-to-text generation task. Significantly, our research includes the development of a novel aspect-level dataset. Annotated for both complaint and sentiment categories across diverse domains such as books, electronics, edibles, fashion, and miscellaneous, this dataset provides a comprehensive platform for the concurrent study of complaints and sentiment. This resource facilitates a more robust understanding of consumer feedback. Our proposed methodology establishes a benchmark performance in the novel aspect-based complaint and sentiment detection tasks based on extensive evaluation. We also demonstrate that our model consistently outperforms all other baselines and state-of-the-art models in both full and few-shot settings (The dataset and code are available at:https://github.com/appy1608/CIKM2023).|自动化的投诉分析对于产生关键见解至关重要，这反过来又能提高客户满意度、产品质量和整体业务表现。然而，传统方法往往无法捕捉方面一级投诉的细微差别，也不能充分利用外部知识，从而在有效的投诉侦测和分析方面造成空白。针对这一问题，我们积极探索外部知识和多模态在这一领域的作用。这导致了 MGasD (用于基于方面的投诉和情绪检测的多模态生成框架)的开发，这是一个基于多模态知识的统一框架。MGasD 将抱怨检测问题重新定义为一个多模态的文本到文本生成任务，从而背离了传统的方法。值得注意的是，我们的研究包括开发一个新的方面级别的数据集。该数据集涵盖了书籍、电子产品、食品、时尚和杂项等不同领域的抱怨和情绪类别，为同时研究抱怨和情绪提供了一个全面的平台。这种资源有助于更好地理解消费者的反馈。我们提出的方法建立了一个新的基于方面的投诉和情绪检测任务的基准性能的广泛评估的基础上。我们还展示了我们的模型在完整和少量的设置中始终优于所有其他基线和最先进的模型(数据集和代码可在以下 https://github.com/appy1608/cikm2023获得)。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Investigating+the+Impact+of+Multimodality+and+External+Knowledge+in+Aspect-level+Complaint+and+Sentiment+Analysis)|0| -|[Follow the Will of the Market: A Context-Informed Drift-Aware Method for Stock Prediction](https://doi.org/10.1145/3583780.3614886)|ChenHui Song, Xi Xiao, Bin Zhang, ShuTao Xia|Tsinghua University & Peng Cheng Laboratory, Shenzhen, China; Tsinghua University, Shenzhen, China; Peng Cheng Laboratory, Shenzhen, China|The dynamic nature of stock market styles, referred to as concept drift, poses a formidable challenge when applying deep learning to stock prediction. Models trained on historical data often struggle to adapt to the latest market styles, as the patterns they have learned may no longer hold true over time. To alleviate this issue, the recently popularized concept of In-Context learning has provided us with valuable insights. In this approach, large language models (LLMs) are exposed to multiple examples of input-label pairs, also known as demonstrations, as part of the prompt before performing a task on an unseen example. By thoroughly analyzing these demonstrations, LLMs can uncover potential patterns and effectively adapt to new tasks. Building upon this concept, we propose a Context-Informed drift-aware method for Stock Prediction (CISP), which continually adjusts to the latest market styles and offers more accurate predictions. Our proposed method consists of two key parts. Firstly, we introduce a straightforward and efficient technique for designing demonstrations that aggregate current market information, thereby indicating the prevailing stock market style. Secondly, we incorporate a prediction module with dynamic parameters, allowing it to appropriately adjust its model parameters based on the market patterns embedded in the aforementioned demonstrations. Through extensive experiments conducted on real-world stock market datasets, our approach consistently outperforms the most advanced existing methods for stock prediction.|股票市场风格的动态特性，即概念漂移，对深度学习在股票预测中的应用提出了严峻的挑战。受过历史数据训练的模型往往难以适应最新的市场风格，因为他们所学到的模式可能不再适用于未来。为了解决这个问题，最近推广的 In-Context 学习概念为我们提供了宝贵的见解。在这种方法中，大型语言模型(LLM)在对一个看不见的示例执行任务之前，作为提示的一部分向多个输入标签对示例(也称为演示)公开。通过深入分析这些演示，LLM 可以发现潜在的模式，并有效地适应新的任务。基于这一概念，我们提出了一种基于上下文信息的漂移感知股票预测方法(CISP) ，该方法可以不断地根据最新的市场风格进行调整，并提供更准确的预测。我们提出的方法包括两个关键部分。首先，我们介绍了一种简单有效的方法来设计演示，聚合当前的市场信息，从而表明流行的股票市场风格。其次，我们引入了一个具有动态参数的预测模块，使其能够根据上述示范中所包含的市场模式适当地调整其模型参数。通过在真实股市数据集上进行的大量实验，我们的方法始终优于现有的最先进的股票预测方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Follow+the+Will+of+the+Market:+A+Context-Informed+Drift-Aware+Method+for+Stock+Prediction)|0| +|[Follow the Will of the Market: A Context-Informed Drift-Aware Method for Stock Prediction](https://doi.org/10.1145/3583780.3614886)|ChenHui Song, Xi Xiao, Bin Zhang, ShuTao Xia|Tsinghua University & Peng Cheng Laboratory, Shenzhen, China; Peng Cheng Laboratory, Shenzhen, China; Tsinghua University, Shenzhen, China|The dynamic nature of stock market styles, referred to as concept drift, poses a formidable challenge when applying deep learning to stock prediction. Models trained on historical data often struggle to adapt to the latest market styles, as the patterns they have learned may no longer hold true over time. To alleviate this issue, the recently popularized concept of In-Context learning has provided us with valuable insights. In this approach, large language models (LLMs) are exposed to multiple examples of input-label pairs, also known as demonstrations, as part of the prompt before performing a task on an unseen example. By thoroughly analyzing these demonstrations, LLMs can uncover potential patterns and effectively adapt to new tasks. Building upon this concept, we propose a Context-Informed drift-aware method for Stock Prediction (CISP), which continually adjusts to the latest market styles and offers more accurate predictions. Our proposed method consists of two key parts. Firstly, we introduce a straightforward and efficient technique for designing demonstrations that aggregate current market information, thereby indicating the prevailing stock market style. Secondly, we incorporate a prediction module with dynamic parameters, allowing it to appropriately adjust its model parameters based on the market patterns embedded in the aforementioned demonstrations. Through extensive experiments conducted on real-world stock market datasets, our approach consistently outperforms the most advanced existing methods for stock prediction.|股票市场风格的动态特性，即概念漂移，对深度学习在股票预测中的应用提出了严峻的挑战。受过历史数据训练的模型往往难以适应最新的市场风格，因为他们所学到的模式可能不再适用于未来。为了解决这个问题，最近推广的 In-Context 学习概念为我们提供了宝贵的见解。在这种方法中，大型语言模型(LLM)在对一个看不见的示例执行任务之前，作为提示的一部分向多个输入标签对示例(也称为演示)公开。通过深入分析这些演示，LLM 可以发现潜在的模式，并有效地适应新的任务。基于这一概念，我们提出了一种基于上下文信息的漂移感知股票预测方法(CISP) ，该方法可以不断地根据最新的市场风格进行调整，并提供更准确的预测。我们提出的方法包括两个关键部分。首先，我们介绍了一种简单有效的方法来设计演示，聚合当前的市场信息，从而表明流行的股票市场风格。其次，我们引入了一个具有动态参数的预测模块，使其能够根据上述示范中所包含的市场模式适当地调整其模型参数。通过在真实股市数据集上进行的大量实验，我们的方法始终优于现有的最先进的股票预测方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Follow+the+Will+of+the+Market:+A+Context-Informed+Drift-Aware+Method+for+Stock+Prediction)|0| |[Towards Fair Financial Services for All: A Temporal GNN Approach for Individual Fairness on Transaction Networks](https://doi.org/10.1145/3583780.3615091)|Zixing Song, Yuji Zhang, Irwin King|The Hong Kong Polytechnic University, Kowloon, Hong Kong; The Chinese University of Hong Kong, New Territories, Hong Kong|Discrimination against minority groups within the banking sector has long resulted in unequal treatment in financial services. Recent works in the general machine learning domain can promote group fairness for predictions on static tabular data, but their direct application in finance often proves ineffective. Financial losses of banks may arise from inaccurate predictions due to the overlooked dynamic nature of data, and illegal discrimination against some individual clients could still occur since fairness is promoted on the subgroup level. Therefore, we model the data as a dynamic or temporal transaction network for better utility and investigate individual fairness on this dynamic graph for the loan approval task. We define two novel individual fairness properties on temporal graphs with a theoretical analysis of their respective regret. Using these notions, we design a temporally fair graph neural network (TF-GNN) approach under a new real-time evaluation scheme for dynamic transaction networks. Experiments on real-world datasets demonstrate the superiority of the proposed method for both utility improvement in accuracy and fairness promotion in NDCG@k.|长期以来，银行部门对少数群体的歧视导致了金融服务方面的不平等待遇。最近在一般机器学习领域的研究工作可以促进对静态表格数据的预测的群公平性，但它们在金融中的直接应用往往被证明是无效的。由于数据的动态性质被忽视，银行的财务损失可能来自不准确的预测，而且由于在小组一级促进公平，仍然可能发生对某些个别客户的非法歧视。因此，我们将数据建模为一个动态或时态的交易网络，以获得更好的效用，并在这个动态图上研究贷款批准任务的个体公平性。在时间图上定义了两个新的个体公平性质，并对它们各自的遗憾进行了理论分析。利用这些概念，我们设计了一种新的动态事务网络实时评估方案下的时间公平图神经网络(TF-GNN)方法。实际数据集的实验结果表明，该方法在提高 NDCG@k 的准确性和公平性方面具有优越性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Towards+Fair+Financial+Services+for+All:+A+Temporal+GNN+Approach+for+Individual+Fairness+on+Transaction+Networks)|0| |[Topic-Aware Contrastive Learning and K-Nearest Neighbor Mechanism for Stance Detection](https://doi.org/10.1145/3583780.3615085)|Yepeng Sun, Jicang Lu, Ling Wang, Shunhang Li, Ningbo Huang|State Key Laboratory of Mathematical Engineering and Advanced Computing, Zhengzhou, Henan, China|The goal of stance detection is to automatically recognize the author's expressed attitude in text towards a given target. However, social media users often express themselves briefly and implicitly, which leads to a significant number of comments lacking explicit reference information to the target, posing a challenge for stance detection. To address the missing relationship between text and target, existing studies primarily focus on incorporating external knowledge, which inevitably introduces noise information. In contrast to their work, we are dedicated to mining implicit relational information within data. Typically, users tend to emphasize their attitudes towards a relevant topic or aspect of the target while concealing others when expressing opinions. Motivated by this phenomenon, we suggest that the potential correlation between text and target can be learned from instances with similar topics. Therefore, we design a pretext task to mine the topic associations between samples and model this topic association as a dynamic weight introduced into contrastive learning. In this way, we can selectively cluster samples that have similar topics and consistent stances, while enlarging the gap between samples with different stances in the feature space. Additionally, we propose a nearest-neighbor prediction mechanism for stance classification to better utilize the features we constructed. Our experiments on two datasets demonstrate the advanced and generalization ability of our method, yielding the state-of-the-art results.|姿势检测的目的是自动识别作者在文本中对给定目标所表达的态度。然而，社会媒体用户往往表达自己的简短和隐含，这导致大量的评论缺乏明确的参考信息的目标，造成了立场检测的挑战。为了解决文本与目标语之间缺失的关系，现有的研究主要集中在外部知识的融合上，这就不可避免地引入了噪声信息。与他们的工作相反，我们致力于在数据中挖掘隐式关系信息。通常情况下，用户倾向于强调他们对目标相关话题或方面的态度，而在表达意见时隐瞒其他人。基于这一现象，我们认为文本与目标语之间的潜在相关性可以通过类似话题的实例来学习。因此，我们设计了一个借口任务来挖掘样本之间的主题关联，并将这种主题关联作为一个动态权重引入到对比学习中。这样，我们可以选择性地对具有相似主题和一致立场的样本进行聚类，同时在特征空间中扩大具有不同立场的样本之间的差距。此外，为了更好地利用所构造的特征，我们提出了一种姿态分类的最近邻预测机制。我们在两个数据集上的实验证明了我们方法的先进性和泛化能力，得到了最先进的结果。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Topic-Aware+Contrastive+Learning+and+K-Nearest+Neighbor+Mechanism+for+Stance+Detection)|0| |[Fairness through Aleatoric Uncertainty](https://doi.org/10.1145/3583780.3614875)|Anique Tahir, Lu Cheng, Huan Liu|Arizona State University, Tempe, AZ, USA; University of Illinois Chicago, Chicago, IL, USA|We propose a unique solution to tackle the often-competing goals of fairness and utility in machine learning classification tasks. While fairness ensures that the model's predictions are unbiased and do not discriminate against any particular group, utility focuses on maximizing the accuracy of the model's predictions. Our aim is to investigate the relationship between uncertainty and fairness. Our approach leverages this concept by employing Bayesian learning to estimate the uncertainty in sample predictions where the estimation is independent of confounding effects related to the protected attribute. Through empirical evidence, we show that samples with low classification uncertainty are modeled more accurately and fairly than those with high uncertainty, which may have biased representations and higher prediction errors. To address the challenge of balancing fairness and utility, we propose a novel fairness-utility objective that is defined based on uncertainty quantification. The weights in this objective are determined by the level of uncertainty, allowing us to optimize both fairness and utility simultaneously. Experiments on real-world datasets demonstrate the effectiveness of our approach. Our results show that our method outperforms state-of-the-art methods in terms of the fairness-utility tradeoff and this applies to both group and individual fairness metrics. This work presents a fresh perspective on the trade-off between accuracy and fairness in machine learning and highlights the potential of using uncertainty as a means to achieve optimal fairness and utility.|针对机器学习分类任务中公平性和效用性这两个经常相互竞争的目标，提出了一种独特的解决方案。虽然公平性确保模型的预测是无偏见的，不歧视任何特定的群体，效用的重点是最大限度地提高模型的预测的准确性。我们的目的是研究不确定性和公平性之间的关系。我们的方法利用这个概念，通过使用贝叶斯学习来估计样本预测中的不确定性，其中估计独立于与受保护属性相关的混杂效应。通过经验证明分析，我们发现分类不确定性低的样本比不确定性高的样本建模更加准确和公平，因为不确定性高的样本可能有偏差表示和更高的预测误差。为了解决平衡公平与效用的难题，我们提出了一种新的基于不确定性量化的公平效用目标。该目标中的权重由不确定性水平决定，使我们能够同时优化公平性和效用。在实际数据集上的实验证明了该方法的有效性。我们的结果表明，我们的方法在公平-效用权衡方面优于最先进的方法，这适用于群体和个人的公平度量。这项工作提出了一个新的视角之间的权衡准确性和公平的机器学习，并强调了潜在的使用不确定性作为一种手段，以实现最佳的公平性和效用。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Fairness+through+Aleatoric+Uncertainty)|0| |[EAGLE: Enhance Target-Oriented Dialogs by Global Planning and Topic Flow Integration](https://doi.org/10.1145/3583780.3614860)|Zee Hen Tang, MiYen Yeh|National Taiwan University and Academia Sinica, Taipei, Taiwan Roc|In this study, we propose a novel model EAGLE for target-oriented dialogue generation. Without relying on any knowledge graphs, our method integrates the global planning strategy in both topic path generation and response generation given the initial and target topics. EAGLE comprises three components: a topic path sampling strategy, a topic flow generator, and a global planner. Our approach confers a number of advantages: EAGLE is robust to the target that has never appeared in the training data set and able to plan the topic flow globally. The topic path sampling strategy samples topic paths based on two predefined rules and use the sampled paths to train the topic path generator. The topic flow generator then applies a non-autoregressive method to generate intermediate topics that link the initial and target topics smoothly. In addition, the global planner is a response generator that generates a response based on the future topic sequence and conversation history, enabling it to plan how to transition to future topics smoothly. Our experimental results demonstrate that EAGLE produces more coherent responses and smoother transitions than state-of-the-art baselines, with an overall success rate improvement of approximately 25% and an average smoothness score improvement of 10% in both offline and human evaluations.|在这项研究中，我们提出了一个新的模型 EAGLE 面向目标的对话生成。该方法在不依赖任何知识图的情况下，将全局规划策略集成到给定初始和目标主题的主题路径生成和响应生成中。EAGLE 由三部分组成: 主题路径抽样策略、主题流生成器和全局规划器。我们的方法提供了许多优点: EAGLE 对从未出现在训练数据集中的目标是健壮的，并且能够在全局范围内规划主题流。主题路径采样策略根据两个预定义的规则对主题路径进行采样，并利用采样后的路径对主题路径生成器进行训练。然后，主题流生成器应用非自回归方法来生成中间主题，这些中间主题将初始主题和目标主题平滑地连接起来。此外，全局规划器是一个响应生成器，它根据未来的主题顺序和会话历史生成响应，使其能够规划如何平稳地过渡到未来的主题。我们的实验结果表明，与最先进的基线相比，EAGLE 产生更连贯的响应和更平滑的转换，总体成功率提高约25% ，在离线和人类评估中平均平滑评分提高10% 。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=EAGLE:+Enhance+Target-Oriented+Dialogs+by+Global+Planning+and+Topic+Flow+Integration)|0| -|[Spatio-Temporal Meta Contrastive Learning](https://doi.org/10.1145/3583780.3615065)|Jiabin Tang, Lianghao Xia, Jie Hu, Chao Huang|University of Hong Kong, Hong Kong, China; Southwest Jiaotong University, Chengdu, China|Spatio-temporal prediction is crucial in numerous real-world applications, including traffic forecasting and crime prediction, which aim to improve public transportation and safety management. Many state-of-the-art models demonstrate the strong capability of spatio-temporal graph neural networks (STGNN) to capture complex spatio-temporal correlations. However, despite their effectiveness, existing approaches do not adequately address several key challenges. Data quality issues, such as data scarcity and sparsity, lead to data noise and a lack of supervised signals, which significantly limit the performance of STGNN. Although recent STGNN models with contrastive learning aim to address these challenges, most of them use pre-defined augmentation strategies that heavily depend on manual design and cannot be customized for different Spatio-Temporal Graph (STG) scenarios. To tackle these challenges, we propose a new spatio-temporal contrastive learning (CL4ST) framework to encode robust and generalizable STG representations via the STG augmentation paradigm. Specifically, we design the meta view generator to automatically construct node and edge augmentation views for each disentangled spatial and temporal graph in a data-driven manner. The meta view generator employs meta networks with parameterized generative model to customize the augmentations for each input. This personalizes the augmentation strategies for every STG and endows the learning framework with spatio-temporal-aware information. Additionally, we integrate a unified spatio-temporal graph attention network with the proposed meta view generator and two-branch graph contrastive learning paradigms. Extensive experiments demonstrate that our CL4ST significantly improves performance over various state-of-the-art baselines in traffic and crime prediction. Our model implementation is available at the link: https://github.com/HKUDS/CL4ST.|时空预测在许多实际应用中至关重要，包括交通预测和犯罪预测，其目的是改善公共交通和安全管理。许多国家的最新模型表明，强大的能力时空图神经网络(STGNN)捕捉复杂的时空相关性。然而，尽管有效，现有的方法并没有充分解决几个关键的挑战。数据质量问题，如数据稀缺性和稀疏性，导致数据噪声和监督信号的缺乏，严重限制了 STGNN 的性能。虽然最近的 STGNN 模型与对比学习旨在解决这些挑战，他们中的大多数使用预定义的增强策略，严重依赖于手工设计，不能定制为不同的时空图(STG)场景。为了应对这些挑战，我们提出了一个新的时空对比学习(CL4ST)框架，通过 STG 增强范式编码健壮的和可推广的 STG 表示。具体来说，我们设计了元视图生成器，以数据驱动的方式自动构造每个分离的空间和时间图的节点和边增强视图。元视图生成器使用带有参数化生成模型的元网络来定制每个输入的扩展。这使每个 STG 的增强策略个性化，并赋予学习框架具有时空感知的信息。此外，我们将统一的时空图形注意网络与提出的元视图生成器和两分支图形对比学习范式相结合。大量的实验表明，我们的 CL4ST 显著提高了各种最先进的交通和犯罪预测基线的性能。我们的模型实现可在以下链接获得: https://github.com/hkuds/cl4st。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Spatio-Temporal+Meta+Contrastive+Learning)|0| +|[Spatio-Temporal Meta Contrastive Learning](https://doi.org/10.1145/3583780.3615065)|Jiabin Tang, Lianghao Xia, Jie Hu, Chao Huang|Southwest Jiaotong University, Chengdu, China; University of Hong Kong, Hong Kong, China|Spatio-temporal prediction is crucial in numerous real-world applications, including traffic forecasting and crime prediction, which aim to improve public transportation and safety management. Many state-of-the-art models demonstrate the strong capability of spatio-temporal graph neural networks (STGNN) to capture complex spatio-temporal correlations. However, despite their effectiveness, existing approaches do not adequately address several key challenges. Data quality issues, such as data scarcity and sparsity, lead to data noise and a lack of supervised signals, which significantly limit the performance of STGNN. Although recent STGNN models with contrastive learning aim to address these challenges, most of them use pre-defined augmentation strategies that heavily depend on manual design and cannot be customized for different Spatio-Temporal Graph (STG) scenarios. To tackle these challenges, we propose a new spatio-temporal contrastive learning (CL4ST) framework to encode robust and generalizable STG representations via the STG augmentation paradigm. Specifically, we design the meta view generator to automatically construct node and edge augmentation views for each disentangled spatial and temporal graph in a data-driven manner. The meta view generator employs meta networks with parameterized generative model to customize the augmentations for each input. This personalizes the augmentation strategies for every STG and endows the learning framework with spatio-temporal-aware information. Additionally, we integrate a unified spatio-temporal graph attention network with the proposed meta view generator and two-branch graph contrastive learning paradigms. Extensive experiments demonstrate that our CL4ST significantly improves performance over various state-of-the-art baselines in traffic and crime prediction. Our model implementation is available at the link: https://github.com/HKUDS/CL4ST.|时空预测在许多实际应用中至关重要，包括交通预测和犯罪预测，其目的是改善公共交通和安全管理。许多国家的最新模型表明，强大的能力时空图神经网络(STGNN)捕捉复杂的时空相关性。然而，尽管有效，现有的方法并没有充分解决几个关键的挑战。数据质量问题，如数据稀缺性和稀疏性，导致数据噪声和监督信号的缺乏，严重限制了 STGNN 的性能。虽然最近的 STGNN 模型与对比学习旨在解决这些挑战，他们中的大多数使用预定义的增强策略，严重依赖于手工设计，不能定制为不同的时空图(STG)场景。为了应对这些挑战，我们提出了一个新的时空对比学习(CL4ST)框架，通过 STG 增强范式编码健壮的和可推广的 STG 表示。具体来说，我们设计了元视图生成器，以数据驱动的方式自动构造每个分离的空间和时间图的节点和边增强视图。元视图生成器使用带有参数化生成模型的元网络来定制每个输入的扩展。这使每个 STG 的增强策略个性化，并赋予学习框架具有时空感知的信息。此外，我们将统一的时空图形注意网络与提出的元视图生成器和两分支图形对比学习范式相结合。大量的实验表明，我们的 CL4ST 显著提高了各种最先进的交通和犯罪预测基线的性能。我们的模型实现可在以下链接获得: https://github.com/hkuds/cl4st。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Spatio-Temporal+Meta+Contrastive+Learning)|0| |[Single-Cell Multimodal Prediction via Transformers](https://doi.org/10.1145/3583780.3615061)|Wenzhuo Tang, Hongzhi Wen, Renming Liu, Jiayuan Ding, Wei Jin, Yuying Xie, Hui Liu, Jiliang Tang|Emory University, Atlanta, GA, USA; Michigan State University, East Lansing, MI, USA|The recent development of multimodal single-cell technology has made the possibility of acquiring multiple omics data from individual cells, thereby enabling a deeper understanding of cellular states and dynamics. Nevertheless, the proliferation of multimodal single-cell data also introduces tremendous challenges in modeling the complex interactions among different modalities. The recently advanced methods focus on constructing static interaction graphs and applying graph neural networks (GNNs) to learn from multimodal data. However, such static graphs can be suboptimal as they do not take advantage of the downstream task information; meanwhile GNNs also have some inherent limitations when deeply stacking GNN layers. To tackle these issues, in this work, we investigate how to leverage transformers for multimodal single-cell data in an end-to-end manner while exploiting downstream task information. In particular, we propose a scMoFormer framework which can readily incorporate external domain knowledge and model the interactions within each modality and cross modalities. Extensive experiments demonstrate that scMoFormer achieves superior performance on various benchmark datasets. Note that scMoFormer won a Kaggle silver medal with the rank of $24\ /\ 1221$ (Top 2%) without ensemble in a NeurIPS 2022 competition. Our implementation is publicly available at Github.|多模式单细胞技术的最新发展使得从单个细胞获取多组学数据成为可能，从而能够更深入地理解细胞状态和动力学。然而，多模态单细胞数据的增殖也给建立不同模态之间复杂的相互作用模型带来了巨大的挑战。最近提出的方法主要集中在构造静态交互图和应用图神经网络(GNN)学习多模态数据。然而，这样的静态图可能是次优的，因为它们没有利用下游任务信息; 同时 GNN 在深度堆叠 GNN 层时也有一些固有的局限性。为了解决这些问题，在这项工作中，我们研究如何利用变压器的多模式单细胞数据的端到端方式，同时利用下游任务信息。特别是，我们提出了一个 scMoForm 框架，它可以很容易地结合外部领域的知识，并在每个模式和交叉模式的交互模型。大量的实验表明，scMoForm 在各种基准数据集上取得了优越的性能。值得注意的是 scMoForm 在 NeurIPS 2022比赛中赢得了 Kaggle 银牌，没有合唱的成绩为 $24/1221 $(最高2%)。我们的实现可以在 Github 上公开获得。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Single-Cell+Multimodal+Prediction+via+Transformers)|0| |[Explainable Spatio-Temporal Graph Neural Networks](https://doi.org/10.1145/3583780.3614871)|Jiabin Tang, Lianghao Xia, Chao Huang|University of Hong Kong, Hong Kong SAR, China|Spatio-temporal graph neural networks (STGNNs) have gained popularity as a powerful tool for effectively modeling spatio-temporal dependencies in diverse real-world urban applications, including intelligent transportation and public safety. However, the black-box nature of STGNNs limits their interpretability, hindering their application in scenarios related to urban resource allocation and policy formulation. To bridge this gap, we propose an Explainable Spatio-Temporal Graph Neural Networks (STExplainer) framework that enhances STGNNs with inherent explainability, enabling them to provide accurate predictions and faithful explanations simultaneously. Our framework integrates a unified spatio-temporal graph attention network with a positional information fusion layer as the STG encoder and decoder, respectively. Furthermore, we propose a structure distillation approach based on the Graph Information Bottleneck (GIB) principle with an explainable objective, which is instantiated by the STG encoder and decoder. Through extensive experiments, we demonstrate that our STExplainer outperforms state-of-the-art baselines in terms of predictive accuracy and explainability metrics (i.e., sparsity and fidelity) on traffic and crime prediction tasks. Furthermore, our model exhibits superior representation ability in alleviating data missing and sparsity issues. The implementation code is available at: https://github.com/HKUDS/STExplainer.|时空图形神经网络(STGNNs)作为一种有效的时空依赖建模工具，在智能交通和公共安全等多种现实世界的城市应用中得到了广泛的应用。然而，STGNN 的黑匣子性质限制了它们的可解释性，阻碍了它们在与城市资源分配和政策制定有关的情景中的应用。为了弥合这一差距，我们提出了一个可解释的时空图形神经网络(STExplainer)框架，增强了固有的可解释性，使他们能够同时提供准确的预测和忠实的解释。该框架将统一的时空图形注意网络与位置信息融合层分别作为 STG 编码器和解码器集成在一起。在此基础上，本文提出了一种基于图形信息瓶颈(GIB)原理的结构提取方法，该方法以 STG 编解码器为实例，具有可解释的目标。通过大量的实验，我们证明了我们的 STExplainer 在交通和犯罪预测任务的预测准确性和可解释性指标(即稀疏性和保真度)方面优于最先进的基线。此外，我们的模型表现出优越的表示能力，以减轻数据丢失和稀疏问题。实施守则可于以下 https://github.com/hkuds/stexplainer 索取:。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Explainable+Spatio-Temporal+Graph+Neural+Networks)|0| -|[PSLF: Defending Against Label Leakage in Split Learning](https://doi.org/10.1145/3583780.3615019)|Xinwei Wan, Jiankai Sun, Shengjie Wang, Lei Chen, Zhenzhe Zheng, Fan Wu, Guihai Chen|Bytedance Inc., San Jose, USA; Bytedance Inc., Seattle, USA; Shanghai Jiao Tong University, Shanghai, China|With increasing concern over data privacy, split learning has become a widely used distributed machine learning paradigm in practice, where two participants (namely the non-label party and the label party) own raw features and raw labels respectively, and jointly train a model. Although no raw data is communicated between the two parties during model training, several works have demonstrated that data privacy, especially label privacy, is still vulnerable in split learning, and have proposed several defense algorithms against label attacks. However, the theoretical guarantee on the privacy preservation of these algorithms is limited. In this work, we propose a novel Private Split Learning Framework (PSLF). In PSLF, the label party shares only the gradients computed by flipped labels with the non-label party, which improves privacy preservation on raw labels, and meanwhile, we further design an extra sub-model from true labels to improve prediction accuracy. We also design a Flipped Multi-Label Generation mechanism (FMLG) based on randomized response for the label party to generate flipped labels. FMLG is proven differentially private and the label party could make a trade-off between privacy and utility by setting the DP budget. In addition, we design an upsampling method to further protect the labels against some existing attacks. We have evaluated PSLF over real-world datasets to demonstrate its effectiveness in protecting label privacy and achieving promising prediction accuracy.|随着人们对数据隐私问题的日益关注，分离学习已经成为实践中广泛使用的分布式机器学习范式，其中两个参与者(即非标签方和标签方)分别拥有原始特征和原始标签，并共同训练一个模型。虽然在模型训练过程中双方之间没有原始数据的交流，但是已有的研究表明，数据隐私，特别是标签隐私，在分割学习中仍然是脆弱的，并且提出了几种防御标签攻击的算法。然而，这些算法在保护隐私方面的理论保障是有限的。在这项工作中，我们提出了一个新颖的私人拆分学习框架(PSLF)。在 PSLF 中，标签方只与非标签方共享由翻转标签计算的梯度，从而提高了对原始标签的隐私保护，同时，我们进一步从真实标签中设计了一个额外的子模型，以提高预测的准确性。我们还设计了一个基于随机化回答的翻转多标签生成机制(FMLG) ，供标签方生成翻转标签。FMLG 被证明是不同的私人和标签方可以作出权衡之间的隐私和公用事业设置 DP 预算。此外，我们设计了一个向上采样的方法来进一步保护标签免受一些现有的攻击。我们已经在现实世界的数据集上评估了 PSLF，以证明它在保护标签隐私和实现有希望的预测准确性方面的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=PSLF:+Defending+Against+Label+Leakage+in+Split+Learning)|0| +|[PSLF: Defending Against Label Leakage in Split Learning](https://doi.org/10.1145/3583780.3615019)|Xinwei Wan, Jiankai Sun, Shengjie Wang, Lei Chen, Zhenzhe Zheng, Fan Wu, Guihai Chen|Bytedance Inc., Seattle, USA; Shanghai Jiao Tong University, Shanghai, China; Bytedance Inc., San Jose, USA|With increasing concern over data privacy, split learning has become a widely used distributed machine learning paradigm in practice, where two participants (namely the non-label party and the label party) own raw features and raw labels respectively, and jointly train a model. Although no raw data is communicated between the two parties during model training, several works have demonstrated that data privacy, especially label privacy, is still vulnerable in split learning, and have proposed several defense algorithms against label attacks. However, the theoretical guarantee on the privacy preservation of these algorithms is limited. In this work, we propose a novel Private Split Learning Framework (PSLF). In PSLF, the label party shares only the gradients computed by flipped labels with the non-label party, which improves privacy preservation on raw labels, and meanwhile, we further design an extra sub-model from true labels to improve prediction accuracy. We also design a Flipped Multi-Label Generation mechanism (FMLG) based on randomized response for the label party to generate flipped labels. FMLG is proven differentially private and the label party could make a trade-off between privacy and utility by setting the DP budget. In addition, we design an upsampling method to further protect the labels against some existing attacks. We have evaluated PSLF over real-world datasets to demonstrate its effectiveness in protecting label privacy and achieving promising prediction accuracy.|随着人们对数据隐私问题的日益关注，分离学习已经成为实践中广泛使用的分布式机器学习范式，其中两个参与者(即非标签方和标签方)分别拥有原始特征和原始标签，并共同训练一个模型。虽然在模型训练过程中双方之间没有原始数据的交流，但是已有的研究表明，数据隐私，特别是标签隐私，在分割学习中仍然是脆弱的，并且提出了几种防御标签攻击的算法。然而，这些算法在保护隐私方面的理论保障是有限的。在这项工作中，我们提出了一个新颖的私人拆分学习框架(PSLF)。在 PSLF 中，标签方只与非标签方共享由翻转标签计算的梯度，从而提高了对原始标签的隐私保护，同时，我们进一步从真实标签中设计了一个额外的子模型，以提高预测的准确性。我们还设计了一个基于随机化回答的翻转多标签生成机制(FMLG) ，供标签方生成翻转标签。FMLG 被证明是不同的私人和标签方可以作出权衡之间的隐私和公用事业设置 DP 预算。此外，我们设计了一个向上采样的方法来进一步保护标签免受一些现有的攻击。我们已经在现实世界的数据集上评估了 PSLF，以证明它在保护标签隐私和实现有希望的预测准确性方面的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=PSLF:+Defending+Against+Label+Leakage+in+Split+Learning)|0| |[GraphFADE: Field-aware Decorrelation Neural Network for Graphs with Tabular Features](https://doi.org/10.1145/3583780.3614900)|Junhong Wan, Yao Fu, Junlan Yu, Weihao Jiang, Shiliang Pu, Ruiheng Yang|Hikvision Research Institute, Hangzhou, China|Graph Neural Networks (GNNs) have achieved great success in recent years for their remarkable ability to extract effective representations from both node features and graph structures. Most of GNNs only focus on graphs with homogeneous features that correspond to one single feature field. For tabular features that are heterogeneous with multiple feature fields, GNNs often perform less favorably compared to machine learning methods such as boosted trees. In this work, we propose a new perspective to uncover the problem of GNNs on graphs with tabular features through both empirical study and theoretical analysis. The assumption of GNNs that connected nodes exhibit similar patterns can barely hold true for tabular features since multiple feature fields already exhibit different patterns. And propagation on such mismatched graph causes propagated features overcorrelated on graphs, which leads to the reduction of feature diversity and the increase of information redundancy. Therefore, we propose Field-aware Decorrelation Neural Network for graphs with tabular features (GraphFADE), a novel framework that directly optimizes the overcorrelation problem for graphs with tabular features. We first hierarchically partition the dataset into subsets with minimal correlation and then according to the decorrelation clustering results assemble the optimal matched graphs for each feature dimension to propagate on. The empirical study shows that our method achieves superior performance on multiple graphs with tabular features, demonstrating the effectiveness of our model.|近年来，图神经网络(GNN)以其从节点特征和图结构中提取有效表示的能力取得了巨大的成功。大多数 GNN 只关注对应于单一特征域的具有同质特征的图。对于具有多个特征域的异构表格特征，GNN 的性能往往不如增强树等机器学习方法。本文通过实证研究和理论分析，提出了一个新的视角来揭示具有表格特征的图的 GNN 问题。由于多个特征字段已经表现出不同的模式，所以连接节点表现出相似模式的 GNN 假设对于表格特征几乎不成立。而在这种不匹配图上的传播会导致图上的传播特征过度相关，从而导致特征多样性的降低和信息冗余的增加。因此，我们提出了基于场感知的具有表格特征图的去相关神经网络(GraphFADE) ，这是一种直接优化具有表格特征图的过相关问题的新框架。首先对数据集进行最小相关性分层划分，然后根据去相关聚类结果组合出每个特征维数传播的最优匹配图。实证研究表明，该方法对具有表格特征的多个图形具有较好的性能，证明了该模型的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=GraphFADE:+Field-aware+Decorrelation+Neural+Network+for+Graphs+with+Tabular+Features)|0| -|[MPerformer: An SE(3) Transformer-based Molecular Perceptron](https://doi.org/10.1145/3583780.3614974)|Fanmeng Wang, Hongteng Xu, Xi Chen, Shuqi Lu, Yuqing Deng, Wenbing Huang|Renmin University of China, Beijing, China; DP Technology, Beijing, China; Renmin University of China & DP Technology, Beijing, China|Molecular perception aims to construct 3D molecules from 3D atom clouds (i.e., atom types and corresponding 3D coordinates), determining bond connections, bond orders, and other molecular attributes within molecules. It is essential for realizing many applications in cheminformatics and bioinformatics, such as modeling quantum chemistry-derived molecular structures in protein-ligand complexes. Additionally, many molecular generation methods can only generate molecular 3D atom clouds, requiring molecular perception as a necessary post-processing. However, existing molecular perception methods mainly rely on predefined chemical rules and fail to leverage 3D geometric information, whose performance is sub-optimal fully. In this study, we propose MPerformer, an SE(3) Transformer-based molecular perceptron exhibiting SE(3)-invariance, to construct 3D molecules from 3D atom clouds efficiently. Besides, we propose a multi-task pretraining-and-finetuning paradigm to learn this model. In the pretraining phase, we jointly minimize an attribute prediction loss and an atom cloud reconstruction loss, mitigating the data imbalance issue of molecular attributes and enhancing the robustness and generalizability of the model. Experiments show that MPerformer significantly outperforms state-of-the-art molecular perception methods in precision and robustness, benefiting various molecular generation scenarios.|分子感知旨在从三维原子云(即原子类型和相应的三维坐标)构建三维分子，确定分子内的键连接、键顺序和其他分子属性。这对于实现化学信息学和生物信息学中的许多应用，例如在蛋白质-配体复合物中建立量子化学衍生的分子结构是必不可少的。此外，许多分子生成方法只能生成分子三维原子云，需要分子感知作为必要的后处理。然而，现有的分子感知方法主要依赖于预定义的化学规则，不能充分利用三维几何信息，其性能完全是次优的。在这项研究中，我们提出了一个基于 SE (3)变压器的分子感知器，它具有 SE (3)不变性，可以有效地从三维原子云中构建三维分子。此外，我们提出了一个多任务预训练和微调范式来学习这个模型。在预训练阶段，我们将属性预测损失和原子云重构损失最小化，减轻了分子属性的数据不平衡问题，提高了模型的鲁棒性和通用性。实验结果表明，该算法在精度和鲁棒性方面明显优于目前最先进的分子感知方法，有利于各种分子生成场景。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=MPerformer:+An+SE(3)+Transformer-based+Molecular+Perceptron)|0| -|[Iteratively Learning Representations for Unseen Entities with Inter-Rule Correlations](https://doi.org/10.1145/3583780.3614938)|Zihan Wang, Kai Zhao, Yongquan He, Zhumin Chen, Pengjie Ren, Maarten de Rijke, Zhaochun Ren|Georgia State University, Atlanta, GA, USA; Shandong University & University of Amsterdam, Qingdao, China; Shandong University, Qingdao, China; Leiden University, Leiden, Netherlands; Meituan, Beijing, China; University of Amsterdam, Amsterdam, Netherlands|Recent work on knowledge graph completion (KGC) focused on learning embeddings of entities and relations in knowledge graphs. These embedding methods require that all test entities are observed at training time, resulting in a time-consuming retraining process for out-of-knowledge-graph (OOKG) entities. To address this issue, current inductive knowledge embedding methods employ graph neural networks (GNNs) to represent unseen entities by aggregating information of known neighbors. They face three important challenges: (i) data sparsity, (ii) the presence of complex patterns in knowledge graphs (e.g., inter-rule correlations), and (iii) the presence of interactions among rule mining, rule inference, and embedding. In this paper, we propose a virtual neighbor network with inter-rule correlations (VNC) that consists of three stages: (i) rule mining, (ii) rule inference, and (iii) embedding. In the rule mining process, to identify complex patterns in knowledge graphs, both logic rules and inter-rule correlations are extracted from knowledge graphs based on operations over relation embeddings. To reduce data sparsity, virtual neighbors for OOKG entities are predicted and assigned soft labels by optimizing a rule-constrained problem. We also devise an iterative framework to capture the underlying relations between rule learning and embedding learning. In our experiments, results on both link prediction and triple classification tasks show that the proposed VNC framework achieves state-of-the-art performance on four widely-used knowledge graphs. Further analysis reveals that VNC is robust to the proportion of unseen entities and effectively mitigates data sparsity.|最近关于知识图完备化(KGC)的研究主要集中在知识图中实体的学习嵌入和关系的学习。这些嵌入方法要求所有的测试实体在训练时都被观察到，从而导致对知识外图(OOKG)实体进行耗时的再训练过程。为了解决这一问题，现有的归纳知识嵌入方法采用图神经网络(GNN)通过聚合已知邻居的信息来表示未知实体。他们面临三个重要的挑战: (i)数据稀疏，(ii)知识图中存在复杂模式(例如，规则间相关性) ，以及(iii)规则挖掘，规则推理和嵌入之间存在交互作用。本文提出了一种具有规则间相关性的虚拟邻居网络(VNC) ，该网络由三个阶段组成: (i)规则挖掘、(ii)规则推理和(iii)嵌入。在规则挖掘过程中，为了识别知识图中的复杂模式，基于关系嵌入操作从知识图中提取逻辑规则和规则间相关性。为了减少数据稀疏性，通过优化规则约束问题，对 OOKG 实体的虚拟邻居进行预测并分配软标签。我们还设计了一个迭代框架来捕捉规则学习和嵌入式学习之间的潜在关系。在我们的实验中，链路预测和三重分类任务的结果表明，所提出的 VNC 框架在四个广泛使用的知识图上实现了最先进的性能。进一步的分析表明，VNC 对不可见实体的比例具有鲁棒性，并有效地减少了数据稀疏性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Iteratively+Learning+Representations+for+Unseen+Entities+with+Inter-Rule+Correlations)|0| -|[UrbanFloodKG: An Urban Flood Knowledge Graph System for Risk Assessment](https://doi.org/10.1145/3583780.3615105)|Yu Wang, Feng Ye, Binquan Li, Gaoyang Jin, Dong Xu, Fengsheng Li|Hydrologic Bureau (Information Center) of Huaihe River Commission, Bengbu, China; Hohai University, Nanjing, China; China Water Resources Pearl River Planning, Surveying & Designing Co., Ltd, Guangzhou, China|Increasing numbers of people live in flood-prone areas worldwide. With continued development, urban flood will become more frequent, which has caused casualties and property damage. Researchers have been dedicating to urban flood risk assessments in recent years. However, current research is still facing the challenges of multi-modal data fusion and knowledge representation of urban flood events. Therefore, in this paper, we propose an Urban Flood Knowledge Graph (UrbanFloodKG) system that enables KG to support urban flood risk assessment. The system consists of data layer, graph layer, algorithm layer, and application layer, which implements knowledge extraction and storage functions, integrates knowledge representation learning models and graph neural network models to support link prediction and node classification tasks. We conduct model comparison experiments on link prediction and node classification tasks based on urban flood event data from Guangzhou, and demonstrate the effectiveness of the models used. Our experiments prove that the accuracy of risk assessment can reach 91% when using GEN, which provides a a promising research direction for urban flood risk assessment.|全世界越来越多的人生活在洪水易发地区。随着城市的不断发展，城市洪水将越来越频繁，造成人员伤亡和财产损失。近年来，研究人员一直致力于城市洪水风险评估。然而，目前的研究仍然面临着城市洪水事件多模态数据融合和知识表示的挑战。因此，本文提出了一个城市洪水知识图(UrbanFloodKG)系统，以支持城市洪水风险评估。该系统由数据层、图层、算法层和应用层组成，实现知识提取和存储功能，集成知识表示学习模型和图神经网络模型，支持链路预测和节点分类任务。基于广州市的城市洪水事件数据，对链路预测和节点分类任务进行了模型对比实验，验证了模型的有效性。实验证明，利用遗传网络进行城市洪水风险评价的准确率可达91% ，为城市洪水风险评价提供了一个有前途的研究方向。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=UrbanFloodKG:+An+Urban+Flood+Knowledge+Graph+System+for+Risk+Assessment)|0| -|[Flexible and Robust Counterfactual Explanations with Minimal Satisfiable Perturbations](https://doi.org/10.1145/3583780.3614885)|Yongjie Wang, Hangwei Qian, Yongjie Liu, Wei Guo, Chunyan Miao|Shandong University, Jinan, China; A*STAR, Singapore, Singapore; Nanyang Technological University, Singapore, Singapore|Counterfactual explanations (CFEs) exemplify how to minimally modify a feature vector to achieve a different prediction for an instance. CFEs can enhance informational fairness and trustworthiness, and provide suggestions for users who receive adverse predictions. However, recent research has shown that multiple CFEs can be offered for the same instance or instances with slight differences. Multiple CFEs provide flexible choices and cover diverse desiderata for user selection. However, individual fairness and model reliability will be damaged if unstable CFEs with different costs are returned. Existing methods fail to exploit flexibility and address the concerns of non-robustness simultaneously. To address these issues, we propose a conceptually simple yet effective solution named Counterfactual Explanations with Minimal Satisfiable Perturbations (CEMSP). Specifically, CEMSP constrains changing values of abnormal features with the help of their semantically meaningful normal ranges. For efficiency, we model the problem as a Boolean satisfiability problem to modify as few features as possible. Additionally, CEMSP is a general framework and can easily accommodate more practical requirements, e.g., casualty and actionability. Compared to existing methods, we conduct comprehensive experiments on both synthetic and real-world datasets to demonstrate that our method provides more robust explanations while preserving flexibility.|反事实解释(CFE)举例说明了如何最小限度地修改特征向量以实现对实例的不同预测。CFE 可以提高信息的公平性和可信度，并为收到负面预测的用户提供建议。然而，最近的研究表明，对于相同的实例或者稍有不同的实例，可以提供多个 CFE。多个 CFE 提供灵活的选择，并涵盖用户选择所需的各种数据。然而，如果返回不稳定且成本不同的 CFE，则会损害个体的公平性和模型的可靠性。现有的方法不能同时利用灵活性和解决非健壮性问题。为了解决这些问题，我们提出了一个概念上简单但有效的解决方案，称为最小可满足扰动反事实解释(CEMSP)。具体来说，CEMSP 利用异常特征的语义有意义的正常范围来约束异常特征的变化值。为了提高效率，我们将问题建模为一个布尔可满足性问题，以尽可能少地修改功能。此外，CEMSP 是一个通用框架，可以很容易地适应更多的实际需求，例如，伤亡和可操作性。与已有的方法相比，我们对合成和真实数据集进行了全面的实验，结果表明该方法在保持灵活性的同时提供了更加稳健的解释。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Flexible+and+Robust+Counterfactual+Explanations+with+Minimal+Satisfiable+Perturbations)|0| -|[Low-bit Quantization for Deep Graph Neural Networks with Smoothness-aware Message Propagation](https://doi.org/10.1145/3583780.3614955)|Shuang Wang, Bahaeddin Eravci, Rustam Guliyev, Hakan Ferhatosmanoglu|University of Warwick, Coventry, United Kingdom; TOBB University of Economics and Technology, Ankara, Turkey|Graph Neural Network (GNN) training and inference involve significant challenges of scalability with respect to both model sizes and number of layers, resulting in degradation of efficiency and accuracy for large and deep GNNs. We present an end-to-end solution that aims to address these challenges for efficient GNNs in resource constrained environments while avoiding the oversmoothing problem in deep GNNs. We introduce a quantization based approach for all stages of GNNs, from message passing in training to node classification, compressing the model and enabling efficient processing. The proposed GNN quantizer learns quantization ranges and reduces the model size with comparable accuracy even under low-bit quantization. To scale with the number of layers, we devise a message propagation mechanism in training that controls layer-wise changes of similarities between neighboring nodes. This objective is incorporated into a Lagrangian function with constraints and a differential multiplier method is utilized to iteratively find optimal embeddings. This mitigates oversmoothing and suppresses the quantization error to a bound. Significant improvements are demonstrated over state-of-the-art quantization methods and deep GNN approaches in both full-precision and quantized models. The proposed quantizer demonstrates superior performance in INT2 configurations across all stages of GNN, achieving a notable level of accuracy. In contrast, existing quantization approaches fail to generate satisfactory accuracy levels. Finally, the inference with INT2 and INT4 representations exhibits a speedup of 5.11 $\times$ and 4.70 $\times$ compared to full precision counterparts, respectively.|图形神经网络(GNN)的训练和推理涉及到模型大小和层数的可扩展性方面的重大挑战，导致大型和深度 GNN 的效率和精度下降。我们提出了一个端到端的解决方案，旨在解决这些挑战，有效的 GNN 在资源受限的环境中，同时避免深 GNN 过于平滑的问题。我们介绍了一种基于量化的方法，适用于 GNN 的各个阶段，从训练中的消息传递到节点分类，压缩模型并实现有效的处理。所提出的 GNN 量化器即使在低位量化情况下也能学习量化范围并以相当的精度缩小模型尺寸。为了根据层数进行调整，我们设计了一种训练中的消息传播机制，控制相邻节点之间相似性的层次变化。将该目标引入具有约束条件的拉格朗日函数中，并利用微分乘子法迭代寻找最优嵌入。这减轻了过度平滑，并将量化噪声压缩到一定程度。在全精度和量化模型方面，对最先进的量化方法和深度 GNN 方法都作了重大改进。所提议的量化器在 GNN 的所有阶段都表现出优越的 INT2配置性能，达到了显著的精确度水平。相比之下，现有的量化方法不能产生令人满意的精度水平。最后，使用 INT2和 INT4表示的推断与完全精确的对应物相比，分别显示出5.11 $乘以 $和4.70 $乘以 $的加速效果。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Low-bit+Quantization+for+Deep+Graph+Neural+Networks+with+Smoothness-aware+Message+Propagation)|0| +|[MPerformer: An SE(3) Transformer-based Molecular Perceptron](https://doi.org/10.1145/3583780.3614974)|Fanmeng Wang, Hongteng Xu, Xi Chen, Shuqi Lu, Yuqing Deng, Wenbing Huang|Renmin University of China, Beijing, China; Renmin University of China & DP Technology, Beijing, China; DP Technology, Beijing, China|Molecular perception aims to construct 3D molecules from 3D atom clouds (i.e., atom types and corresponding 3D coordinates), determining bond connections, bond orders, and other molecular attributes within molecules. It is essential for realizing many applications in cheminformatics and bioinformatics, such as modeling quantum chemistry-derived molecular structures in protein-ligand complexes. Additionally, many molecular generation methods can only generate molecular 3D atom clouds, requiring molecular perception as a necessary post-processing. However, existing molecular perception methods mainly rely on predefined chemical rules and fail to leverage 3D geometric information, whose performance is sub-optimal fully. In this study, we propose MPerformer, an SE(3) Transformer-based molecular perceptron exhibiting SE(3)-invariance, to construct 3D molecules from 3D atom clouds efficiently. Besides, we propose a multi-task pretraining-and-finetuning paradigm to learn this model. In the pretraining phase, we jointly minimize an attribute prediction loss and an atom cloud reconstruction loss, mitigating the data imbalance issue of molecular attributes and enhancing the robustness and generalizability of the model. Experiments show that MPerformer significantly outperforms state-of-the-art molecular perception methods in precision and robustness, benefiting various molecular generation scenarios.|分子感知旨在从三维原子云(即原子类型和相应的三维坐标)构建三维分子，确定分子内的键连接、键顺序和其他分子属性。这对于实现化学信息学和生物信息学中的许多应用，例如在蛋白质-配体复合物中建立量子化学衍生的分子结构是必不可少的。此外，许多分子生成方法只能生成分子三维原子云，需要分子感知作为必要的后处理。然而，现有的分子感知方法主要依赖于预定义的化学规则，不能充分利用三维几何信息，其性能完全是次优的。在这项研究中，我们提出了一个基于 SE (3)变压器的分子感知器，它具有 SE (3)不变性，可以有效地从三维原子云中构建三维分子。此外，我们提出了一个多任务预训练和微调范式来学习这个模型。在预训练阶段，我们将属性预测损失和原子云重构损失最小化，减轻了分子属性的数据不平衡问题，提高了模型的鲁棒性和通用性。实验结果表明，该算法在精度和鲁棒性方面明显优于目前最先进的分子感知方法，有利于各种分子生成场景。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=MPerformer:+An+SE(3)+Transformer-based+Molecular+Perceptron)|0| +|[Iteratively Learning Representations for Unseen Entities with Inter-Rule Correlations](https://doi.org/10.1145/3583780.3614938)|Zihan Wang, Kai Zhao, Yongquan He, Zhumin Chen, Pengjie Ren, Maarten de Rijke, Zhaochun Ren|Shandong University, Qingdao, China; Shandong University & University of Amsterdam, Qingdao, China; Leiden University, Leiden, Netherlands; Georgia State University, Atlanta, GA, USA; Meituan, Beijing, China; University of Amsterdam, Amsterdam, Netherlands|Recent work on knowledge graph completion (KGC) focused on learning embeddings of entities and relations in knowledge graphs. These embedding methods require that all test entities are observed at training time, resulting in a time-consuming retraining process for out-of-knowledge-graph (OOKG) entities. To address this issue, current inductive knowledge embedding methods employ graph neural networks (GNNs) to represent unseen entities by aggregating information of known neighbors. They face three important challenges: (i) data sparsity, (ii) the presence of complex patterns in knowledge graphs (e.g., inter-rule correlations), and (iii) the presence of interactions among rule mining, rule inference, and embedding. In this paper, we propose a virtual neighbor network with inter-rule correlations (VNC) that consists of three stages: (i) rule mining, (ii) rule inference, and (iii) embedding. In the rule mining process, to identify complex patterns in knowledge graphs, both logic rules and inter-rule correlations are extracted from knowledge graphs based on operations over relation embeddings. To reduce data sparsity, virtual neighbors for OOKG entities are predicted and assigned soft labels by optimizing a rule-constrained problem. We also devise an iterative framework to capture the underlying relations between rule learning and embedding learning. In our experiments, results on both link prediction and triple classification tasks show that the proposed VNC framework achieves state-of-the-art performance on four widely-used knowledge graphs. Further analysis reveals that VNC is robust to the proportion of unseen entities and effectively mitigates data sparsity.|最近关于知识图完备化(KGC)的研究主要集中在知识图中实体的学习嵌入和关系的学习。这些嵌入方法要求所有的测试实体在训练时都被观察到，从而导致对知识外图(OOKG)实体进行耗时的再训练过程。为了解决这一问题，现有的归纳知识嵌入方法采用图神经网络(GNN)通过聚合已知邻居的信息来表示未知实体。他们面临三个重要的挑战: (i)数据稀疏，(ii)知识图中存在复杂模式(例如，规则间相关性) ，以及(iii)规则挖掘，规则推理和嵌入之间存在交互作用。本文提出了一种具有规则间相关性的虚拟邻居网络(VNC) ，该网络由三个阶段组成: (i)规则挖掘、(ii)规则推理和(iii)嵌入。在规则挖掘过程中，为了识别知识图中的复杂模式，基于关系嵌入操作从知识图中提取逻辑规则和规则间相关性。为了减少数据稀疏性，通过优化规则约束问题，对 OOKG 实体的虚拟邻居进行预测并分配软标签。我们还设计了一个迭代框架来捕捉规则学习和嵌入式学习之间的潜在关系。在我们的实验中，链路预测和三重分类任务的结果表明，所提出的 VNC 框架在四个广泛使用的知识图上实现了最先进的性能。进一步的分析表明，VNC 对不可见实体的比例具有鲁棒性，并有效地减少了数据稀疏性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Iteratively+Learning+Representations+for+Unseen+Entities+with+Inter-Rule+Correlations)|0| +|[UrbanFloodKG: An Urban Flood Knowledge Graph System for Risk Assessment](https://doi.org/10.1145/3583780.3615105)|Yu Wang, Feng Ye, Binquan Li, Gaoyang Jin, Dong Xu, Fengsheng Li|Hydrologic Bureau (Information Center) of Huaihe River Commission, Bengbu, China; China Water Resources Pearl River Planning, Surveying & Designing Co., Ltd, Guangzhou, China; Hohai University, Nanjing, China|Increasing numbers of people live in flood-prone areas worldwide. With continued development, urban flood will become more frequent, which has caused casualties and property damage. Researchers have been dedicating to urban flood risk assessments in recent years. However, current research is still facing the challenges of multi-modal data fusion and knowledge representation of urban flood events. Therefore, in this paper, we propose an Urban Flood Knowledge Graph (UrbanFloodKG) system that enables KG to support urban flood risk assessment. The system consists of data layer, graph layer, algorithm layer, and application layer, which implements knowledge extraction and storage functions, integrates knowledge representation learning models and graph neural network models to support link prediction and node classification tasks. We conduct model comparison experiments on link prediction and node classification tasks based on urban flood event data from Guangzhou, and demonstrate the effectiveness of the models used. Our experiments prove that the accuracy of risk assessment can reach 91% when using GEN, which provides a a promising research direction for urban flood risk assessment.|全世界越来越多的人生活在洪水易发地区。随着城市的不断发展，城市洪水将越来越频繁，造成人员伤亡和财产损失。近年来，研究人员一直致力于城市洪水风险评估。然而，目前的研究仍然面临着城市洪水事件多模态数据融合和知识表示的挑战。因此，本文提出了一个城市洪水知识图(UrbanFloodKG)系统，以支持城市洪水风险评估。该系统由数据层、图层、算法层和应用层组成，实现知识提取和存储功能，集成知识表示学习模型和图神经网络模型，支持链路预测和节点分类任务。基于广州市的城市洪水事件数据，对链路预测和节点分类任务进行了模型对比实验，验证了模型的有效性。实验证明，利用遗传网络进行城市洪水风险评价的准确率可达91% ，为城市洪水风险评价提供了一个有前途的研究方向。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=UrbanFloodKG:+An+Urban+Flood+Knowledge+Graph+System+for+Risk+Assessment)|0| +|[Flexible and Robust Counterfactual Explanations with Minimal Satisfiable Perturbations](https://doi.org/10.1145/3583780.3614885)|Yongjie Wang, Hangwei Qian, Yongjie Liu, Wei Guo, Chunyan Miao|A*STAR, Singapore, Singapore; Shandong University, Jinan, China; Nanyang Technological University, Singapore, Singapore|Counterfactual explanations (CFEs) exemplify how to minimally modify a feature vector to achieve a different prediction for an instance. CFEs can enhance informational fairness and trustworthiness, and provide suggestions for users who receive adverse predictions. However, recent research has shown that multiple CFEs can be offered for the same instance or instances with slight differences. Multiple CFEs provide flexible choices and cover diverse desiderata for user selection. However, individual fairness and model reliability will be damaged if unstable CFEs with different costs are returned. Existing methods fail to exploit flexibility and address the concerns of non-robustness simultaneously. To address these issues, we propose a conceptually simple yet effective solution named Counterfactual Explanations with Minimal Satisfiable Perturbations (CEMSP). Specifically, CEMSP constrains changing values of abnormal features with the help of their semantically meaningful normal ranges. For efficiency, we model the problem as a Boolean satisfiability problem to modify as few features as possible. Additionally, CEMSP is a general framework and can easily accommodate more practical requirements, e.g., casualty and actionability. Compared to existing methods, we conduct comprehensive experiments on both synthetic and real-world datasets to demonstrate that our method provides more robust explanations while preserving flexibility.|反事实解释(CFE)举例说明了如何最小限度地修改特征向量以实现对实例的不同预测。CFE 可以提高信息的公平性和可信度，并为收到负面预测的用户提供建议。然而，最近的研究表明，对于相同的实例或者稍有不同的实例，可以提供多个 CFE。多个 CFE 提供灵活的选择，并涵盖用户选择所需的各种数据。然而，如果返回不稳定且成本不同的 CFE，则会损害个体的公平性和模型的可靠性。现有的方法不能同时利用灵活性和解决非健壮性问题。为了解决这些问题，我们提出了一个概念上简单但有效的解决方案，称为最小可满足扰动反事实解释(CEMSP)。具体来说，CEMSP 利用异常特征的语义有意义的正常范围来约束异常特征的变化值。为了提高效率，我们将问题建模为一个布尔可满足性问题，以尽可能少地修改功能。此外，CEMSP 是一个通用框架，可以很容易地适应更多的实际需求，例如，伤亡和可操作性。与已有的方法相比，我们对合成和真实数据集进行了全面的实验，结果表明该方法在保持灵活性的同时提供了更加稳健的解释。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Flexible+and+Robust+Counterfactual+Explanations+with+Minimal+Satisfiable+Perturbations)|0| +|[Low-bit Quantization for Deep Graph Neural Networks with Smoothness-aware Message Propagation](https://doi.org/10.1145/3583780.3614955)|Shuang Wang, Bahaeddin Eravci, Rustam Guliyev, Hakan Ferhatosmanoglu|TOBB University of Economics and Technology, Ankara, Turkey; University of Warwick, Coventry, United Kingdom|Graph Neural Network (GNN) training and inference involve significant challenges of scalability with respect to both model sizes and number of layers, resulting in degradation of efficiency and accuracy for large and deep GNNs. We present an end-to-end solution that aims to address these challenges for efficient GNNs in resource constrained environments while avoiding the oversmoothing problem in deep GNNs. We introduce a quantization based approach for all stages of GNNs, from message passing in training to node classification, compressing the model and enabling efficient processing. The proposed GNN quantizer learns quantization ranges and reduces the model size with comparable accuracy even under low-bit quantization. To scale with the number of layers, we devise a message propagation mechanism in training that controls layer-wise changes of similarities between neighboring nodes. This objective is incorporated into a Lagrangian function with constraints and a differential multiplier method is utilized to iteratively find optimal embeddings. This mitigates oversmoothing and suppresses the quantization error to a bound. Significant improvements are demonstrated over state-of-the-art quantization methods and deep GNN approaches in both full-precision and quantized models. The proposed quantizer demonstrates superior performance in INT2 configurations across all stages of GNN, achieving a notable level of accuracy. In contrast, existing quantization approaches fail to generate satisfactory accuracy levels. Finally, the inference with INT2 and INT4 representations exhibits a speedup of 5.11 $\times$ and 4.70 $\times$ compared to full precision counterparts, respectively.|图形神经网络(GNN)的训练和推理涉及到模型大小和层数的可扩展性方面的重大挑战，导致大型和深度 GNN 的效率和精度下降。我们提出了一个端到端的解决方案，旨在解决这些挑战，有效的 GNN 在资源受限的环境中，同时避免深 GNN 过于平滑的问题。我们介绍了一种基于量化的方法，适用于 GNN 的各个阶段，从训练中的消息传递到节点分类，压缩模型并实现有效的处理。所提出的 GNN 量化器即使在低位量化情况下也能学习量化范围并以相当的精度缩小模型尺寸。为了根据层数进行调整，我们设计了一种训练中的消息传播机制，控制相邻节点之间相似性的层次变化。将该目标引入具有约束条件的拉格朗日函数中，并利用微分乘子法迭代寻找最优嵌入。这减轻了过度平滑，并将量化噪声压缩到一定程度。在全精度和量化模型方面，对最先进的量化方法和深度 GNN 方法都作了重大改进。所提议的量化器在 GNN 的所有阶段都表现出优越的 INT2配置性能，达到了显著的精确度水平。相比之下，现有的量化方法不能产生令人满意的精度水平。最后，使用 INT2和 INT4表示的推断与完全精确的对应物相比，分别显示出5.11 $乘以 $和4.70 $乘以 $的加速效果。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Low-bit+Quantization+for+Deep+Graph+Neural+Networks+with+Smoothness-aware+Message+Propagation)|0| |[A Mix-up Strategy to Enhance Adversarial Training with Imbalanced Data](https://doi.org/10.1145/3583780.3614762)|Wentao Wang, Harry Shomer, Yuxuan Wan, Yaxin Li, Jiangtao Huang, Hui Liu||||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Mix-up+Strategy+to+Enhance+Adversarial+Training+with+Imbalanced+Data)|0| |[NOVO: Learnable and Interpretable Document Identifiers for Model-Based IR](https://doi.org/10.1145/3583780.3614993)|Zihan Wang, Yujia Zhou, Yiteng Tu, Zhicheng Dou|Renmin University of China, Beijing, China|Model-based Information Retrieval (Model-based IR) has gained attention due to advancements in generative language models. Unlike traditional dense retrieval methods relying on dense vector representations of documents, model-based IR leverages language models to retrieve documents by generating their unique discrete identifiers (docids). This approach effectively reduces the requirements to store separate document representations in an index. Most existing model-based IR approaches utilize pre-defined static docids, i.e., these docids are fixed and are not learnable by training on the retrieval tasks. However, these docids are not specifically optimized for retrieval tasks, which makes it difficult to learn semantics and relationships between documents and achieve satisfactory retrieval performance. To address the above limitations, we propose Neural Optimized VOcabularial (NOVO) docids. NOVO docids are unique n-gram sets identifying each document. They can be generated in any order to retrieve the corresponding document and can be optimized through training to better learn semantics and relationships between documents. We propose to optimize NOVO docids through query denoising modeling and retrieval tasks, allowing for optimizing both semantic and token representations for such docids. Experiments on two datasets under the normal and zero-shot settings show that NOVO exhibits strong performance in more effective and interpretable model-based IR.|基于模型的信息检索(Model-based IR)因为生成语言模型的进步而受到关注。与传统的依赖于文档密集向量表示的密集检索方法不同，基于模型的 IR 利用语言模型通过生成独特的离散标识符(docids)来检索文档。这种方法有效地减少了在索引中存储单独文档表示形式的需求。大多数现有的基于模型的检索方法利用预定义的静态文档，即这些文档是固定的，不能通过检索任务的训练来学习。然而，这些文档并没有针对检索任务进行专门的优化，这使得学习文档之间的语义和关系以及获得令人满意的检索性能变得十分困难。针对上述局限性，我们提出了神经优化词汇表(NOVO)文档。NOVO docids 是唯一的 n-gram 集合，用于标识每个文档。它们可以以任何顺序生成，以检索相应的文档，并且可以通过培训进行优化，以更好地学习文档之间的语义和关系。我们建议通过查询去噪建模和检索任务来优化 NOVO 文档，允许优化此类文档的语义和令牌表示。在两个数据集上的实验表明，NOVO 在更有效、更易于解释的基于模型的 IR 中表现出很强的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=NOVO:+Learnable+and+Interpretable+Document+Identifiers+for+Model-Based+IR)|0| -|[WOT-Class: Weakly Supervised Open-world Text Classification](https://doi.org/10.1145/3583780.3615109)|Tianle Wang, Zihan Wang, Weitang Liu, Jingbo Shang|University of California, San Diego, La Jolla, CA, USA; Shanghai Jiao Tong University, Shanghai, China|State-of-the-art weakly supervised text classification methods, while significantly reduced the required human supervision, still requires the supervision to cover all the classes of interest. This is never easy to meet in practice when human explore new, large corpora without complete pictures. In this paper, we work on a novel yet important problem of weakly supervised open-world text classification, where supervision is only needed for a few examples from a few known classes and the machine should handle both known and unknown classes in test time. General open-world classification has been studied mostly using image classification; however, existing methods typically assume the availability of sufficient known-class supervision and strong unknown-class prior knowledge (e.g., the number and/or data distribution). We propose a novel framework WOT-Class that lifts those strong assumptions. Specifically, it follows an iterative process of (a) clustering text to new classes, (b) mining and ranking indicative words for each class, and (c) merging redundant classes by using the overlapped indicative words as a bridge. Extensive experiments on 7 popular text classification datasets demonstrate that WOT-Class outperforms strong baselines consistently with a large margin, attaining 23.33% greater average absolute macro-F1 over existing approaches across all datasets. Such competent accuracy illuminates the practical potential of further reducing human effort for text classification.|最先进的弱监督文本分类方法，虽然大大减少了所需的人工监督，但仍然要求监督覆盖所有感兴趣的类别。当人们在没有完整图片的情况下探索新的、大型的语料库时，这在实践中是不容易实现的。本文研究了弱监督开放世界文本分类的一个新的重要问题。一般的开放世界分类主要是利用图像分类进行研究，然而，现有的分类方法通常假定有足够的已知类监督和强大的未知类先验知识(例如，数字和/或数据分布)。我们提出了一个新的框架 WOT-Class，消除了这些强烈的假设。具体来说，它遵循以下迭代过程: (a)将文本聚类到新类; (b)为每个类挖掘和排序指示性词; (c)通过使用重叠的指示性词作为桥梁来合并冗余类。在7个流行的文本分类数据集上进行的大量实验表明，WOT-Class 一直以大幅度优于强基线，在所有数据集上比现有方法获得23.33% 更高的平均绝对宏 F1。这种称职的准确性说明了进一步减少文本分类工作的实际潜力。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=WOT-Class:+Weakly+Supervised+Open-world+Text+Classification)|0| +|[WOT-Class: Weakly Supervised Open-world Text Classification](https://doi.org/10.1145/3583780.3615109)|Tianle Wang, Zihan Wang, Weitang Liu, Jingbo Shang|Shanghai Jiao Tong University, Shanghai, China; University of California, San Diego, La Jolla, CA, USA|State-of-the-art weakly supervised text classification methods, while significantly reduced the required human supervision, still requires the supervision to cover all the classes of interest. This is never easy to meet in practice when human explore new, large corpora without complete pictures. In this paper, we work on a novel yet important problem of weakly supervised open-world text classification, where supervision is only needed for a few examples from a few known classes and the machine should handle both known and unknown classes in test time. General open-world classification has been studied mostly using image classification; however, existing methods typically assume the availability of sufficient known-class supervision and strong unknown-class prior knowledge (e.g., the number and/or data distribution). We propose a novel framework WOT-Class that lifts those strong assumptions. Specifically, it follows an iterative process of (a) clustering text to new classes, (b) mining and ranking indicative words for each class, and (c) merging redundant classes by using the overlapped indicative words as a bridge. Extensive experiments on 7 popular text classification datasets demonstrate that WOT-Class outperforms strong baselines consistently with a large margin, attaining 23.33% greater average absolute macro-F1 over existing approaches across all datasets. Such competent accuracy illuminates the practical potential of further reducing human effort for text classification.|最先进的弱监督文本分类方法，虽然大大减少了所需的人工监督，但仍然要求监督覆盖所有感兴趣的类别。当人们在没有完整图片的情况下探索新的、大型的语料库时，这在实践中是不容易实现的。本文研究了弱监督开放世界文本分类的一个新的重要问题。一般的开放世界分类主要是利用图像分类进行研究，然而，现有的分类方法通常假定有足够的已知类监督和强大的未知类先验知识(例如，数字和/或数据分布)。我们提出了一个新的框架 WOT-Class，消除了这些强烈的假设。具体来说，它遵循以下迭代过程: (a)将文本聚类到新类; (b)为每个类挖掘和排序指示性词; (c)通过使用重叠的指示性词作为桥梁来合并冗余类。在7个流行的文本分类数据集上进行的大量实验表明，WOT-Class 一直以大幅度优于强基线，在所有数据集上比现有方法获得23.33% 更高的平均绝对宏 F1。这种称职的准确性说明了进一步减少文本分类工作的实际潜力。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=WOT-Class:+Weakly+Supervised+Open-world+Text+Classification)|0| |[Selecting Top-k Data Science Models by Example Dataset](https://doi.org/10.1145/3583780.3615051)|Mengying Wang, Sheng Guan, Hanchao Ma, Yiyang Bian, Haolai Che, Abhishek Daundkar, Alp Sehirlioglu, Yinghui Wu|Case Western Reserve University, Cleveland, OH, USA|Data analytical pipelines routinely involve various domain-specific data science models. Such models require expensive manual or training effort and often incur expensive validation costs (e.g., via scientific simulation analysis). Meanwhile, high-value models remain to be ad-hocly created, isolated, and underutilized for a broad community. Searching and accessing proper models for data analysis pipelines is desirable yet challenging for users without domain knowledge. This paper introduces ModsNet, a novel MODel SelectioN framework that only requires an Example daTaset. (1) We investigate the following problem: Given a library of pre-trained models, a limited amount of historical observations of their performance, and an "example" dataset as a query, return k models that are expected to perform the best over the query dataset. (2) We formulate a regression problem and introduce a knowledge-enhanced framework using a model-data interaction graph. Unlike traditional methods, (1) ModsNet uses a dynamic, cost-bounded "probe-and-select" strategy to incrementally identify promising pre-trained models in a strict cold-start scenario (when a new dataset without any interaction with existing models is given). (2) To reduce the learning cost, we develop a clustering-based sparsification strategy to prune unpromising models and their interactions. (3) We showcase of ModsNet built on top of a crowdsourced materials knowledge base platform. Our experiments verified its effectiveness, efficiency, and applications over real-world analytical pipelines.|数据分析管道通常涉及各种领域特定的数据科学模型。这样的模型需要昂贵的人工或培训工作，并且经常产生昂贵的验证成本(例如，通过科学模拟分析)。与此同时，高价值的模式仍然是临时创造的，孤立的，未充分利用的一个广泛的社区。对于没有领域知识的用户来说，搜索和访问数据分析管道的适当模型是可取的，但也是具有挑战性的。本文介绍了一种新的 MODel 选择框架 ModsNet，它只需要一个示例数据集。(1)我们研究以下问题: 给定一个预先训练的模型库，有限数量的历史观察它们的性能，以及一个“示例”数据集作为查询，返回预期在查询数据集上表现最好的 k 模型。(2)利用模型-数据交互图构造回归问题并引入知识增强框架。与传统方法不同，(1) ModsNet 使用一种动态的、成本有限的“探测-选择”策略，在一个严格的冷启动场景(当给定一个与现有模型没有任何交互的新数据集时)中增量识别有前景的预先训练的模型。(2)为了降低学习成本，本文提出了一种基于聚类的稀疏化策略，用于修剪无希望模型及其相互作用。(3)我们展示了建立在众包材料知识库平台之上的 ModsNet。我们的实验验证了它的有效性、效率和在实际分析管道上的应用。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Selecting+Top-k+Data+Science+Models+by+Example+Dataset)|0| -|[A Multi-Modality Framework for Drug-Drug Interaction Prediction by Harnessing Multi-source Data](https://doi.org/10.1145/3583780.3614765)|Qianlong Wen, Jiazheng Li, Chuxu Zhang, Yanfang Ye|Brandeis University, Waltham, MA, USA; University of Notre Dame, Notre Dame, IN, USA|Drug-drug interaction (DDI), as a possible result of drug combination treatment, could lead to adverse physiological reactions and increasing mortality rates of patients. Therefore, predicting potential DDI has always been an important and challenging issue in medical health applications. Owing to the extensive pharmacological research, we can get access to various drug-related features for DDI predictions; however, most of the existing works on DDI prediction do not incorporate comprehensive features to analyze the DDI patterns. Despite the high performance that the existing works have achieved, the incomplete and noisy information generated from limited sources usually leads to sub-optimal performance and poor generalization ability on the unknown DDI pairs. In this work, we propose a holistic framework, namely Multi-modality Feature Optimal Fusion for Drug-Drug Interaction Prediction (MOF-DDI), that incorporates the features from multiple data sources to resolve the DDI predictions. Specifically, the proposed model jointly considers DDIs literature descriptions, biomedical knowledge graphs, and drug molecular structures to make the prediction. To overcome the issue induced by directly aggregating features in different modalities, we bring a new insight by mapping the representations learned from different sources to a unified hidden space before the combination. The empirical results show that MOF-DDI achieves a large performance gain on different DDI datasets compared with multiple state-of-the-art baselines, especially under the inductive setting.|药物相互作用(DDI)是药物联合治疗的可能结果，可能导致不良生理反应，增加患者的死亡率。因此，预测潜在的 DDI 一直是医疗卫生应用中的一个重要和具有挑战性的问题。由于广泛的药理学研究，我们可以获得各种 DDI 预测的药物相关特征，然而，现有的 DDI 预测工作大多没有包含全面的特征来分析 DDI 模式。尽管现有的工作已经取得了很高的性能，但是由有限信源产生的不完全信息和噪声信息通常会导致未知 DDI 对的性能次优和泛化能力较差。在这项工作中，我们提出了一个整体框架，即多模态特征最优融合药物-药物相互作用预测(MOF-DDI) ，它结合了来自多个数据源的特征来解决 DDI 预测。具体地说，该模型综合考虑了 DDI 文献描述、生物医学知识图和药物分子结构等因素进行预测。为了克服在不同模式下直接聚合特征所引起的问题，我们将从不同来源学到的表征映射到合并前的统一隐藏空间，提出了一种新的观点。实验结果表明，MOF-DDI 在不同的 DDI 数据集上，特别是在归纳设置下，比多个最新的基线数据集获得了更大的性能增益。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Multi-Modality+Framework+for+Drug-Drug+Interaction+Prediction+by+Harnessing+Multi-source+Data)|0| +|[A Multi-Modality Framework for Drug-Drug Interaction Prediction by Harnessing Multi-source Data](https://doi.org/10.1145/3583780.3614765)|Qianlong Wen, Jiazheng Li, Chuxu Zhang, Yanfang Ye|University of Notre Dame, Notre Dame, IN, USA; Brandeis University, Waltham, MA, USA|Drug-drug interaction (DDI), as a possible result of drug combination treatment, could lead to adverse physiological reactions and increasing mortality rates of patients. Therefore, predicting potential DDI has always been an important and challenging issue in medical health applications. Owing to the extensive pharmacological research, we can get access to various drug-related features for DDI predictions; however, most of the existing works on DDI prediction do not incorporate comprehensive features to analyze the DDI patterns. Despite the high performance that the existing works have achieved, the incomplete and noisy information generated from limited sources usually leads to sub-optimal performance and poor generalization ability on the unknown DDI pairs. In this work, we propose a holistic framework, namely Multi-modality Feature Optimal Fusion for Drug-Drug Interaction Prediction (MOF-DDI), that incorporates the features from multiple data sources to resolve the DDI predictions. Specifically, the proposed model jointly considers DDIs literature descriptions, biomedical knowledge graphs, and drug molecular structures to make the prediction. To overcome the issue induced by directly aggregating features in different modalities, we bring a new insight by mapping the representations learned from different sources to a unified hidden space before the combination. The empirical results show that MOF-DDI achieves a large performance gain on different DDI datasets compared with multiple state-of-the-art baselines, especially under the inductive setting.|药物相互作用(DDI)是药物联合治疗的可能结果，可能导致不良生理反应，增加患者的死亡率。因此，预测潜在的 DDI 一直是医疗卫生应用中的一个重要和具有挑战性的问题。由于广泛的药理学研究，我们可以获得各种 DDI 预测的药物相关特征，然而，现有的 DDI 预测工作大多没有包含全面的特征来分析 DDI 模式。尽管现有的工作已经取得了很高的性能，但是由有限信源产生的不完全信息和噪声信息通常会导致未知 DDI 对的性能次优和泛化能力较差。在这项工作中，我们提出了一个整体框架，即多模态特征最优融合药物-药物相互作用预测(MOF-DDI) ，它结合了来自多个数据源的特征来解决 DDI 预测。具体地说，该模型综合考虑了 DDI 文献描述、生物医学知识图和药物分子结构等因素进行预测。为了克服在不同模式下直接聚合特征所引起的问题，我们将从不同来源学到的表征映射到合并前的统一隐藏空间，提出了一种新的观点。实验结果表明，MOF-DDI 在不同的 DDI 数据集上，特别是在归纳设置下，比多个最新的基线数据集获得了更大的性能增益。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Multi-Modality+Framework+for+Drug-Drug+Interaction+Prediction+by+Harnessing+Multi-source+Data)|0| |[Prompt-and-Align: Prompt-Based Social Alignment for Few-Shot Fake News Detection](https://doi.org/10.1145/3583780.3615015)|Jiaying Wu, Shen Li, Ailin Deng, Miao Xiong, Bryan Hooi|National University of Singapore, Singapore, Singapore|Despite considerable advances in automated fake news detection, due to the timely nature of news, it remains a critical open question how to effectively predict the veracity of news articles based on limited fact-checks. Existing approaches typically follow a "Train-from-Scratch" paradigm, which is fundamentally bounded by the availability of large-scale annotated data. While expressive pre-trained language models (PLMs) have been adapted in a "Pre-Train-and-Fine-Tune" manner, the inconsistency between pre-training and downstream objectives also requires costly task-specific supervision. In this paper, we propose "Prompt-and-Align" (P&A), a novel prompt-based paradigm for few-shot fake news detection that jointly leverages the pre-trained knowledge in PLMs and the social context topology. Our approach mitigates label scarcity by wrapping the news article in a task-related textual prompt, which is then processed by the PLM to directly elicit task-specific knowledge. To supplement the PLM with social context without inducing additional training overheads, motivated by empirical observation on user veracity consistency (i.e., social users tend to consume news of the same veracity type), we further construct a news proximity graph among news articles to capture the veracity-consistent signals in shared readerships, and align the prompting predictions along the graph edges in a confidence-informed manner. Extensive experiments on three real-world benchmarks demonstrate that P&A sets new states-of-the-art for few-shot fake news detection performance by significant margins.|尽管假新闻自动检测技术取得了长足的进步，但由于新闻的及时性，如何在有限的事实核查基础上有效地预测新闻报道的真实性仍然是一个悬而未决的问题。现有的方法通常遵循“从头开始训练”的范式，这种范式从根本上受到大规模注释数据可用性的限制。虽然表达式预训练语言模型(PLM)已经以“预训练和微调”的方式进行了调整，但预训练和下游目标之间的不一致性也需要昂贵的特定任务监督。在本文中，我们提出了“提示-对齐”(P & A) ，一个新颖的基于提示的范例，用于少拍摄假新闻检测，共同利用 PLM 中的预训练知识和社会上下文拓扑。我们的方法通过将新闻文章包装在一个与任务相关的文本提示符中来缓解标签稀缺性，然后由 PLM 处理该提示符直接获取特定任务的知识。为了在不引入额外培训开销的情况下，通过对用户准确性一致性(即社交用户倾向于消费相同准确性类型的新闻)的实证观察来补充 PLM，我们进一步构建了新闻文章之间的新闻接近度图，以捕获共享读者群中的准确性一致信号，并以信心知情的方式沿图边缘对齐提示预测。在三个现实世界的基准上进行的大量实验表明，P & A 为几乎没有镜头的假新闻检测性能提供了新的最高水平。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Prompt-and-Align:+Prompt-Based+Social+Alignment+for+Few-Shot+Fake+News+Detection)|0| -|[Community-Based Hierarchical Positive-Unlabeled (PU) Model Fusion for Chronic Disease Prediction](https://doi.org/10.1145/3583780.3614818)|Yang Wu, Xurui Li, Xuhong Zhang, Yangyang Kang, Changlong Sun, Xiaozhong Liu|Alibaba Group, Hangzhou, China; Worcester Polytechnic Institute, Worcester, MA, USA; Indiana University Bloomington, Bloomington, IN, USA|Positive-Unlabeled (PU) Learning is a challenge presented by binary classification problems where there is an abundance of unlabeled data along with a small number of positive data instances, which can be used to address chronic disease screening problem. State-of-the-art PU learning methods have resulted in the development of various risk estimators, yet they neglect the differences among distinct populations. To address this issue, we present a novel Positive-Unlabeled Learning Tree (PUtree) algorithm. PUtree is designed to take into account communities such as different age or income brackets, in tasks of chronic disease prediction. We propose a novel approach for binary decision-making, which hierarchically builds community-based PU models and then aggregates their deliverables. Our method can explicate each PU model on the tree for the optimized non-leaf PU node splitting. Furthermore, a mask-recovery data augmentation strategy enables sufficient training of the model in individual communities. Additionally, the proposed approach includes an adversarial PU risk estimator to capture hierarchical PU-relationships, and a model fusion network that integrates data from each tree path, resulting in robust binary classification results. We demonstrate the superior performance of PUtree as well as its variants on two benchmarks and a new diabetes-prediction dataset.|阳性未标记(PU)学习是由二元分类问题提出的一个挑战，其中存在大量的未标记数据和少量的阳性数据实例，可用于解决慢性疾病筛查问题。最先进的 PU 学习方法导致了各种风险评估的发展，但它们忽略了不同人群之间的差异。为了解决这个问题，我们提出了一种新的正无标记学习树(PUtree)算法。PUtree 的设计是为了在慢性病预测任务中考虑到不同年龄段或收入等级的社区。我们提出了一种新的二元决策方法，它分层建立基于社区的 PU 模型，然后聚合它们的可交付成果。我们的方法可以解释树上的每个 PU 模型来优化无叶 PU 节点的分裂。此外，掩码恢复数据增强策略可以在个别社区中对模型进行充分的培训。此外，提出的方法还包括一个对抗性 PU 风险估计器来捕获分层的 PU 关系，以及一个模型融合网络，该网络整合来自每个树路径的数据，从而产生鲁棒的二进制分类结果。我们在两个基准和一个新的糖尿病预测数据集上展示了 PUtree 及其变体的优越性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Community-Based+Hierarchical+Positive-Unlabeled+(PU)+Model+Fusion+for+Chronic+Disease+Prediction)|0| -|[Rethinking Sentiment Analysis under Uncertainty](https://doi.org/10.1145/3583780.3615034)|Yuefei Wu, Bin Shi, Jiarun Chen, Yuhang Liu, Bo Dong, Qinghua Zheng, Hua Wei|Xi'an Jiaotong University, Xi'an, China; Arizona State University, Phoenix, AZ, USA|Sentiment Analysis (SA) is a fundamental task in natural language processing, which is widely used in public decision-making. Recently, deep learning have demonstrated great potential to deal with this task. However, prior works have mostly treated SA as a deterministic classification problem, and meanwhile, without quantifying the predictive uncertainty. This presents a serious problem in the SA, different annotator, due to the differences in beliefs, values, and experiences, may have different perspectives on how to label the text sentiment. Such situation will lead to inevitable data uncertainty and make the deterministic classification models feel puzzle to make decision. To address this issue, we propose a new SA paradigm with the consideration of uncertainty and conduct an expensive empirical study. Specifically, we treat SA as the regression task and introduce uncertainty quantification to obtain confidence intervals for predictions, which enables the risk assessment ability of the model and can improve the credibility of SA-aids decision-making. Experiments on five datasets show that our proposed new paradigm effectively quantifies uncertainty in SA while remaining competitive performance to point estimation, in addition to being capable of Out-Of-Distribution~(OOD) detection.|情感分析是自然语言处理中的一项基础性工作，在公共决策中得到了广泛的应用。近年来，深度学习在解决这一问题上显示出巨大的潜力。然而，以往的工作大多将模拟退火视为一个确定性的分类问题，同时没有量化预测的不确定性。由于信仰、价值观和经验的差异，不同的注释者对于如何标注文本情感可能有不同的看法。这种情况将导致不可避免的数据不确定性，使确定性分类模型感到困惑而作出决策。为了解决这个问题，我们提出了一个考虑不确定性的 SA 范式，并进行了一个昂贵的实证研究。具体来说，我们将模拟退火作为回归任务，引入不确定性量化来获得预测的置信区间，从而使模型具有风险评估能力，提高了模拟退火辅助决策的可信度。在5个数据集上的实验表明，我们提出的新范式有效地量化了 SA 中的不确定性，同时保持了对点估计的竞争性能，此外还能够检测到外部分布的 ~ (OOD)。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Rethinking+Sentiment+Analysis+under+Uncertainty)|0| +|[Community-Based Hierarchical Positive-Unlabeled (PU) Model Fusion for Chronic Disease Prediction](https://doi.org/10.1145/3583780.3614818)|Yang Wu, Xurui Li, Xuhong Zhang, Yangyang Kang, Changlong Sun, Xiaozhong Liu|Indiana University Bloomington, Bloomington, IN, USA; Alibaba Group, Hangzhou, China; Worcester Polytechnic Institute, Worcester, MA, USA|Positive-Unlabeled (PU) Learning is a challenge presented by binary classification problems where there is an abundance of unlabeled data along with a small number of positive data instances, which can be used to address chronic disease screening problem. State-of-the-art PU learning methods have resulted in the development of various risk estimators, yet they neglect the differences among distinct populations. To address this issue, we present a novel Positive-Unlabeled Learning Tree (PUtree) algorithm. PUtree is designed to take into account communities such as different age or income brackets, in tasks of chronic disease prediction. We propose a novel approach for binary decision-making, which hierarchically builds community-based PU models and then aggregates their deliverables. Our method can explicate each PU model on the tree for the optimized non-leaf PU node splitting. Furthermore, a mask-recovery data augmentation strategy enables sufficient training of the model in individual communities. Additionally, the proposed approach includes an adversarial PU risk estimator to capture hierarchical PU-relationships, and a model fusion network that integrates data from each tree path, resulting in robust binary classification results. We demonstrate the superior performance of PUtree as well as its variants on two benchmarks and a new diabetes-prediction dataset.|阳性未标记(PU)学习是由二元分类问题提出的一个挑战，其中存在大量的未标记数据和少量的阳性数据实例，可用于解决慢性疾病筛查问题。最先进的 PU 学习方法导致了各种风险评估的发展，但它们忽略了不同人群之间的差异。为了解决这个问题，我们提出了一种新的正无标记学习树(PUtree)算法。PUtree 的设计是为了在慢性病预测任务中考虑到不同年龄段或收入等级的社区。我们提出了一种新的二元决策方法，它分层建立基于社区的 PU 模型，然后聚合它们的可交付成果。我们的方法可以解释树上的每个 PU 模型来优化无叶 PU 节点的分裂。此外，掩码恢复数据增强策略可以在个别社区中对模型进行充分的培训。此外，提出的方法还包括一个对抗性 PU 风险估计器来捕获分层的 PU 关系，以及一个模型融合网络，该网络整合来自每个树路径的数据，从而产生鲁棒的二进制分类结果。我们在两个基准和一个新的糖尿病预测数据集上展示了 PUtree 及其变体的优越性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Community-Based+Hierarchical+Positive-Unlabeled+(PU)+Model+Fusion+for+Chronic+Disease+Prediction)|0| +|[Rethinking Sentiment Analysis under Uncertainty](https://doi.org/10.1145/3583780.3615034)|Yuefei Wu, Bin Shi, Jiarun Chen, Yuhang Liu, Bo Dong, Qinghua Zheng, Hua Wei|Arizona State University, Phoenix, AZ, USA; Xi'an Jiaotong University, Xi'an, China|Sentiment Analysis (SA) is a fundamental task in natural language processing, which is widely used in public decision-making. Recently, deep learning have demonstrated great potential to deal with this task. However, prior works have mostly treated SA as a deterministic classification problem, and meanwhile, without quantifying the predictive uncertainty. This presents a serious problem in the SA, different annotator, due to the differences in beliefs, values, and experiences, may have different perspectives on how to label the text sentiment. Such situation will lead to inevitable data uncertainty and make the deterministic classification models feel puzzle to make decision. To address this issue, we propose a new SA paradigm with the consideration of uncertainty and conduct an expensive empirical study. Specifically, we treat SA as the regression task and introduce uncertainty quantification to obtain confidence intervals for predictions, which enables the risk assessment ability of the model and can improve the credibility of SA-aids decision-making. Experiments on five datasets show that our proposed new paradigm effectively quantifies uncertainty in SA while remaining competitive performance to point estimation, in addition to being capable of Out-Of-Distribution~(OOD) detection.|情感分析是自然语言处理中的一项基础性工作，在公共决策中得到了广泛的应用。近年来，深度学习在解决这一问题上显示出巨大的潜力。然而，以往的工作大多将模拟退火视为一个确定性的分类问题，同时没有量化预测的不确定性。由于信仰、价值观和经验的差异，不同的注释者对于如何标注文本情感可能有不同的看法。这种情况将导致不可避免的数据不确定性，使确定性分类模型感到困惑而作出决策。为了解决这个问题，我们提出了一个考虑不确定性的 SA 范式，并进行了一个昂贵的实证研究。具体来说，我们将模拟退火作为回归任务，引入不确定性量化来获得预测的置信区间，从而使模型具有风险评估能力，提高了模拟退火辅助决策的可信度。在5个数据集上的实验表明，我们提出的新范式有效地量化了 SA 中的不确定性，同时保持了对点估计的竞争性能，此外还能够检测到外部分布的 ~ (OOD)。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Rethinking+Sentiment+Analysis+under+Uncertainty)|0| |[HiPo: Detecting Fake News via Historical and Multi-Modal Analyses of Social Media Posts](https://doi.org/10.1145/3583780.3614914)|Tianshu Xiao, Sichang Guo, Jingcheng Huang, Riccardo Spolaor, Xiuzhen Cheng|Shandong University, Qingdao, China|In recent years, fake news has been a primary concern as it plays a significant role in influencing the political, economic, and social spheres. The scientific community has proposed several solutions to detect such fraudulent information. However, such solutions are unsuitable for social media posts since they cannot extract sufficient information from one-line textual and graphical content or are highly dependent on prior knowledge, which may be unavailable in the case of unprecedented events (e.g., breaking news). This paper tackles this issue by proposing HiPo, a novel multi-modal historical post-based fake news detection method. By combining the features extracted from the graphical and textual content, HiPo assesses the truthfulness of a social media post by building its historical context from prior off-label posts with high similarity, therefore, achieving online detection without maintaining a context or knowledge database. We evaluate the performance of HiPo via an exhaustive set of experiments involving four real-world datasets. Our method achieves a detection accuracy higher than 84%, outperforming the state-of-the-art methods in most experimental instances.|近年来，假新闻在政治、经济、社会等领域发挥着重要的影响力，成为人们关注的焦点。科学界已经提出了几种方案来检测这种欺诈性信息。然而，这样的解决方案并不适用于社交媒体帖子，因为它们不能从单行文本和图形内容中提取足够的信息，或者高度依赖于先前的知识，而这些知识在发生前所未有的事件(例如突发新闻)时可能无法获得。为了解决这个问题，本文提出了一种新的多模态历史邮政假新闻检测方法—— HiPo。通过结合从图形和文本内容中提取的特征，HiPo 评估了一个社交媒体帖子的真实性，通过建立其历史背景，从以前的标签外的帖子具有高度相似性，因此，实现在线检测，而不需要维护背景或知识数据库。我们通过一系列包含四个现实世界数据集的详尽实验来评估 HiPo 的表现。我们的方法实现了高于84% 的检测准确率，在大多数实验实例中优于最先进的方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=HiPo:+Detecting+Fake+News+via+Historical+and+Multi-Modal+Analyses+of+Social+Media+Posts)|0| |[An Efficient Selective Ensemble Learning with Rejection Approach for Classification](https://doi.org/10.1145/3583780.3614780)|Hao Xu, Chiranjeet Chetia|Visa Research, Austin, TX, USA|Recent studies found that selective ensemble learning (e.g., dynamic ensemble selection) shows better predictive performance for classification tasks, compared to traditional static ensemble. However, there are some limitations of the available methods, such as high computational cost and multiple restrictions in base model ranking and aggregation (especially for class-imbalanced data modeling). Besides, the current methods make predictions for all data without measuring the credibility regarding different data patterns. This paper proposes a selective ensemble learning with rejection approach that aggregates base models from a different perspective. The approach introduces rejection measures to quantify base model credibility, and learns how to use the models according to their credibility on different sample patterns. It avoids the complexity in base model ranking and therefore is computationally more efficient than current methods. Any common evaluation metrics can be adopted in the selective ensemble strategy, which allows the developed model to handle class-imbalanced data properly. Also, a global rejection region is developed which indicates whether the ensemble model can provide reliable predictions for the targets. We implement the approach in the modeling of 12 datasets, including both class-imbalanced and class-balanced cases. Results show that the approach significantly reduces the inference time while showing promising performance, compared to 8 dynamic ensemble selection methods in the literature. Feature contributions and impacts of different rejection ratios on performance are also investigated to better demonstrate the approach.|最近的研究发现，与传统的静态集合相比，选择性集成学习(例如，动态集合选择)对分类任务有更好的预测性能。然而，现有的方法存在一些局限性，如计算成本高和基本模型排序和聚合的多重限制(特别是对于类不平衡的数据建模)。此外，目前的方法对所有的数据进行预测，而没有测量关于不同数据模式的可信度。这篇文章提出了一个选择性的集成学习和拒绝方法，从不同的角度聚合了基础模型。该方法引入拒绝测度来量化基本模型的可信度，并学习如何根据模型在不同样本模式上的可信度来使用模型。它避免了基本模型排序的复杂性，因此计算效率比目前的方法。在选择性集成策略中可以采用任何通用的评价指标，使得开发的模型能够正确处理类不平衡的数据。此外，还建立了一个全局抑制区域，用以表明系综模型能否为目标提供可靠的预测。我们在12个数据集的建模中实现了该方法，包括类不平衡和类平衡两种情况。结果表明，与文献中的8种动态集合选择方法相比，该方法在显示出良好性能的同时显著缩短了推理时间。为了更好地验证该方法，还研究了不同拒绝率对性能的影响和特征贡献。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=An+Efficient+Selective+Ensemble+Learning+with+Rejection+Approach+for+Classification)|0| -|[Density-Aware Temporal Attentive Step-wise Diffusion Model For Medical Time Series Imputation](https://doi.org/10.1145/3583780.3614840)|Jingwen Xu, Fei Lyu, Pong C. Yuen|Hong Kong Baptist University, Hong Kong, Hong Kong; Hong Kong Baptist University, Hong Kong, China|Medical time series have been widely employed for disease prediction. Missing data hinders accurate prediction. While existing imputation methods partially solve the problem, there are two challenges for medical time series: (1) High dimensionality: Existing imputation methods existing methods suffer from the trade-off between accuracy and computational efficiency. (2) Irregularity: Medical time series exhibit the dynamic temporal relationship that changes over varying sampling densities. However, existing methods mainly take the stationary mechanism, which struggles with capturing the dynamic temporal relationships. To overcome the above deficiencies, we propose a Density-Aware Temporal Attentive Step-wise Diffusion Model (DA-TASWDM), which imputes each time step based on a non-iterative diffusion model and captures inter-step dependency with the density-aware time similarity. Specifically, DA-TASWDM exploits two novel modules: (1) Density-Aware Temporal Attention (DA-TA): It correlates inter-step values from the time embedding similarity adjusted with varying sampling densities. (2) Non-Iterative Step-wise Diffusion Imputer (NI-SWDI): It directly recovers the missing values at each time step from noise without diffusion iteration. Compared with the existing methods, DA-TASWDM can achieve promising accuracy without sacrificing computational efficiency. Extensive experimental results on three real-world datasets demonstrate that our method can significantly outperform state-of-the-art methods in both imputation and post-imputation performance.|医学时间序列已被广泛应用于疾病预测。缺少数据妨碍准确预测。现有的插补方法虽然部分地解决了这个问题，但是对于医学时间序列来说，仍然存在两个挑战: (1)高维度: 现有的插补方法在精度和计算效率之间存在折衷。(2)不规则性: 医学时间序列表现出随采样密度变化而变化的动态时间关系。然而，现有的方法主要采用平稳机制，难以捕捉动态的时间关系。为了克服上述缺陷，我们提出了一种基于非迭代扩散模型的密度感知时态注意逐步扩散模型(DA-TASWDM) ，该模型基于非迭代扩散模型计算每个时间步长，并捕获具有密度感知时间相似性的步间相关性。具体而言，DA-TASWDM 采用了两个新颖的模块: (1)密度感知时间注意(DA-TA) : 它通过改变采样密度来调整时间嵌入相似度的步间值。(2)非迭代逐步扩散计算器(NI-SWDI) : 它不需要扩散迭代，直接从噪声中恢复每个时间步的缺失值。与现有方法相比，DA-TASWDM 在不牺牲计算效率的前提下，能够获得较高的精度。在三个实际数据集上的大量实验结果表明，该方法在插补性能和插补后性能方面都明显优于最新的插补方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Density-Aware+Temporal+Attentive+Step-wise+Diffusion+Model+For+Medical+Time+Series+Imputation)|0| +|[Density-Aware Temporal Attentive Step-wise Diffusion Model For Medical Time Series Imputation](https://doi.org/10.1145/3583780.3614840)|Jingwen Xu, Fei Lyu, Pong C. Yuen|Hong Kong Baptist University, Hong Kong, China; Hong Kong Baptist University, Hong Kong, Hong Kong|Medical time series have been widely employed for disease prediction. Missing data hinders accurate prediction. While existing imputation methods partially solve the problem, there are two challenges for medical time series: (1) High dimensionality: Existing imputation methods existing methods suffer from the trade-off between accuracy and computational efficiency. (2) Irregularity: Medical time series exhibit the dynamic temporal relationship that changes over varying sampling densities. However, existing methods mainly take the stationary mechanism, which struggles with capturing the dynamic temporal relationships. To overcome the above deficiencies, we propose a Density-Aware Temporal Attentive Step-wise Diffusion Model (DA-TASWDM), which imputes each time step based on a non-iterative diffusion model and captures inter-step dependency with the density-aware time similarity. Specifically, DA-TASWDM exploits two novel modules: (1) Density-Aware Temporal Attention (DA-TA): It correlates inter-step values from the time embedding similarity adjusted with varying sampling densities. (2) Non-Iterative Step-wise Diffusion Imputer (NI-SWDI): It directly recovers the missing values at each time step from noise without diffusion iteration. Compared with the existing methods, DA-TASWDM can achieve promising accuracy without sacrificing computational efficiency. Extensive experimental results on three real-world datasets demonstrate that our method can significantly outperform state-of-the-art methods in both imputation and post-imputation performance.|医学时间序列已被广泛应用于疾病预测。缺少数据妨碍准确预测。现有的插补方法虽然部分地解决了这个问题，但是对于医学时间序列来说，仍然存在两个挑战: (1)高维度: 现有的插补方法在精度和计算效率之间存在折衷。(2)不规则性: 医学时间序列表现出随采样密度变化而变化的动态时间关系。然而，现有的方法主要采用平稳机制，难以捕捉动态的时间关系。为了克服上述缺陷，我们提出了一种基于非迭代扩散模型的密度感知时态注意逐步扩散模型(DA-TASWDM) ，该模型基于非迭代扩散模型计算每个时间步长，并捕获具有密度感知时间相似性的步间相关性。具体而言，DA-TASWDM 采用了两个新颖的模块: (1)密度感知时间注意(DA-TA) : 它通过改变采样密度来调整时间嵌入相似度的步间值。(2)非迭代逐步扩散计算器(NI-SWDI) : 它不需要扩散迭代，直接从噪声中恢复每个时间步的缺失值。与现有方法相比，DA-TASWDM 在不牺牲计算效率的前提下，能够获得较高的精度。在三个实际数据集上的大量实验结果表明，该方法在插补性能和插补后性能方面都明显优于最新的插补方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Density-Aware+Temporal+Attentive+Step-wise+Diffusion+Model+For+Medical+Time+Series+Imputation)|0| |[Minimizing Polarization in Noisy Leader-Follower Opinion Dynamics](https://doi.org/10.1145/3583780.3614968)|Wanyue Xu, Zhongzhi Zhang|Fudan University, Shanghai, China|The operation of creating edges has been widely applied to optimize relevant quantities of opinion dynamics. In this paper, we consider a problem of polarization optimization for the leader-follower opinion dynamics in a noisy social network with $n$ nodes and $m$ edges, where a group $Q$ of $q$ nodes are leaders, and the remaining $n-q$ nodes are followers. We adopt the popular leader-follower DeGroot model, where the opinion of every leader is identical and remains unchanged, while the opinion of every follower is subject to white noise. The polarization is defined as the steady-state variance of the deviation of each node's opinion from leaders' opinion, which equals one half of the effective resistance $\mathcal{R}_Q$ between the node group $Q$ and all other nodes. Concretely, we propose and study the problem of minimizing $\mathcal{R}_Q$ by adding $k$ new edges with each incident to a node in $Q$. We show that the objective function is monotone and supermodular. We then propose a simple greedy algorithm with an approximation factor $1-1/e$ that approximately solves the problem in $O((n-q)^3)$ time. To speed up the computation, we also provide a fast algorithm to compute $(1-1/e-\eps)$-approximate effective resistance $\mathcal{R}_Q$, the running time of which is $\Otil (mk\eps^{-2})$ for any $\eps>0$, where the $\Otil (\cdot)$ notation suppresses the ${\rm poly} (\log n)$ factors. Extensive experiment results show that our second algorithm is both effective and efficient.|创建边界运算已被广泛应用于优化相关数量的舆论动力学。本文研究了一类具有 $n $节点和 $m $棱边的噪声社交网络中的领导者-追随者意见动力学的极化优化问题，其中 $q $节点的群体 $Q $为领导者，剩余的 $n-q $节点为追随者。我们采用流行的领导者-追随者 DeGroot 模型，每个领导者的观点都是一致的，并且保持不变，而每个追随者的观点都受到白噪音的影响。极化定义为每个节点的意见偏离领导意见的稳态方差，等于节点组 $Q $与所有其他节点之间的有效阻力 $R } _ Q $的一半。具体地，我们提出并研究了通过在 $Q $中的一个节点上添加每个事件的 $k $新边来使 $mathcal { R } _ Q $最小化的问题。证明了目标函数是单调的、超模的。然后，我们提出了一个简单的贪婪算法，其近似因子为 $1-1/e $，大致可以在 $O ((n-q) ^ 3) $时间内解决问题。为了加快计算速度，我们还提供了一个快速算法来计算 $(1-1/e-ep) $- 近似有效电阻 $数学{ R } _ Q $，其运行时间是 $Otil (mk ep ^ {-2}) $对于任何 $ep > 0 $，其中 $Otil (cdot) $符号抑制 ${ rm poly}(log n) $因子。广泛的实验结果表明，第二种算法是有效和高效的。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Minimizing+Polarization+in+Noisy+Leader-Follower+Opinion+Dynamics)|0| |[Time-series Shapelets with Learnable Lengths](https://doi.org/10.1145/3583780.3615082)|Akihiro Yamaguchi, Ken Ueno, Hisashi Kashima|Kyoto University, Kyoto, Japan; Toshiba Corporation, Kawasaki, Japan|Shapelets are subsequences that are effective for classifying time-series instances. Learning shapelets by a continuous optimization has recently been studied to improve computational efficiency and classification performance. However, existing methods have employed predefined and fixed shapelet lengths during the continuous optimization, despite the fact that shapelets and their lengths are inherently interdependent and thus should be jointly optimized. To efficiently explore shapelets of high quality in terms of interpretability and inter-class separability, this study makes the shapelet lengths continuous and learnable. The proposed formulation jointly optimizes not only a binary classifier and shapelets but also shapelet lengths. The derived SGD optimization can be theoretically interpreted as improving the quality of shapelets in terms of shapelet closeness to the time series for target / off-target classes. We demonstrate improvements in area under the curve, total training time, and shapelet interpretability on UCR binary datasets.|形状是对时间序列实例进行有效分类的子序列。为了提高计算效率和分类性能，最近研究了连续优化学习形状的方法。然而，现有的方法在连续优化过程中采用了预定义和固定的小波长度，尽管事实上小波长度和它们的长度本质上是相互依赖的，因此应该联合优化。为了在可解释性和类间可分性方面有效地探索高质量的形状，本研究使得形状长度是连续的和可学习的。该公式不仅对二元分类器和形状进行了联合优化，而且对形状长度进行了联合优化。推导出的 SGD 优化算法可以从理论上解释为提高目标/非目标类的小波接近时间序列的形状质量。我们在 UCR 二值数据集上展示了曲线下面积、总训练时间和小波可解释性的改进。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Time-series+Shapelets+with+Learnable+Lengths)|0| |[Few-Shot Learning via Task-Aware Discriminant Local Descriptors Network](https://doi.org/10.1145/3583780.3614883)|Leilei Yan, Fanzhang Li, Xiaohan Zheng, Li Zhang|Soochow University, Suzhou, China|Few-shot learning for image classification task aims to classify images from several novel classes with limited number of samples. Recent studies have shown that the deep local descriptors have better representation ability than image-level features, and achieve great success. However, most of these methods often use all local descriptors or over-screening local descriptors for classification. The former contains some task-irrelevant descriptors, which may misguide the final classification result. The latter is likely to lose some key descriptors. In this paper, we propose a novel Task-Aware Discriminant local descriptors Network (TADNet) to address these issues, which can adaptively select the discriminative query descriptors and eliminate the task-irrelevant query descriptors among the entire task. Specifically, TADNet assigns a value to each query descriptor by comparing its similarity to all support classes to represent its discriminant power for classification. Then the discriminative query descriptors can be preserved via a task-aware attention map. Extensive experiments on both fine-grained and generalized datasets demonstrate that the proposed TADNet outperforms the existing state-of-the-art methods.|图像分类任务中的少镜头学习是针对有限样本数的几类新图像进行分类。近年来的研究表明，深层局部描述符比图像级特征具有更好的表示能力，并取得了很大的成功。然而，大多数这些方法往往使用所有的局部描述符或过度筛选局部描述符进行分类。前者包含一些与任务无关的描述符，可能会误导最终的分类结果。后者可能会丢失一些关键的描述符。针对这些问题，本文提出了一种新的任务感知鉴别局部描述符网络(TADNet) ，该网络可以自适应地选择鉴别查询描述符，消除整个任务中与任务无关的查询描述符。具体来说，TADNet 通过比较每个查询描述符与所有支持类的相似性，为每个查询描述符分配一个值，以表示其分类的判别能力。然后通过任务感知的注意图保留区分查询描述符。在细粒度和广义数据集上的大量实验表明，所提出的 TADNet 优于现有的最先进的方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Few-Shot+Learning+via+Task-Aware+Discriminant+Local+Descriptors+Network)|0| |[A Bipartite Graph is All We Need for Enhancing Emotional Reasoning with Commonsense Knowledge](https://doi.org/10.1145/3583780.3614758)|Kailai Yang, Tianlin Zhang, Shaoxiong Ji, Sophia Ananiadou|University of Helsinki, Helsinki, Finland; The University of Manchester, Manchester, United Kingdom|The context-aware emotional reasoning ability of AI systems, especially in conversations, is of vital importance in applications such as online opinion mining from social media and empathetic dialogue systems. Due to the implicit nature of conveying emotions in many scenarios, commonsense knowledge is widely utilized to enrich utterance semantics and enhance conversation modeling. However, most previous knowledge infusion methods perform empirical knowledge filtering and design highly customized architectures for knowledge interaction with the utterances, which can discard useful knowledge aspects and limit their generalizability to different knowledge sources. Based on these observations, we propose a Bipartite Heterogeneous Graph (BHG) method for enhancing emotional reasoning with commonsense knowledge. In BHG, the extracted context-aware utterance representations and knowledge representations are modeled as heterogeneous nodes. Two more knowledge aggregation node types are proposed to perform automatic knowledge filtering and interaction. BHG-based knowledge infusion can be directly generalized to multi-type and multi-grained knowledge sources. In addition, we propose a Multi-dimensional Heterogeneous Graph Transformer (MHGT) to perform graph reasoning, which can retain unchanged feature spaces and unequal dimensions for heterogeneous node types during inference to prevent unnecessary loss of information. Experiments show that BHG-based methods significantly outperform state-of-the-art knowledge infusion methods and show generalized knowledge infusion ability with higher efficiency. Further analysis proves that previous empirical knowledge filtering methods do not guarantee to provide the most useful knowledge information. Our code is available at: https://github.com/SteveKGYang/BHG.|人工智能系统的上下文感知情感推理能力，尤其是在对话中，在社交媒体和移情对话系统的在线意见挖掘等应用中至关重要。由于在许多场景中表达情感的内隐性，常识性知识被广泛地用于丰富话语语义和加强会话建模。然而，以往的知识灌输方法大多采用经验知识过滤的方法，设计高度自定义的知识交互体系结构，放弃了有用的知识方面，限制了它们对不同知识源的推广能力。在此基础上，我们提出了一种基于常识知识增强情绪推理的二部异质图(BHG)方法。在 BHG 中，提取的上下文感知话语表示和知识表示被建模为异构节点。为了实现知识的自动过滤和交互，提出了两种新的知识聚合节点类型。基于 BHG 的知识灌输可以直接推广到多类型、多粒度的知识源。此外，本文还提出了一种多维异构图形变换器(MHGT)来进行图形推理，在推理过程中对异构节点类型保持不变的特征空间和不等的维数，以防止不必要的信息丢失。实验结果表明，基于 BHG 的方法明显优于现有的知识灌输方法，具有更高的广义知识灌输效率。进一步分析表明，以往的经验知识过滤方法不能保证提供最有用的知识信息。我们的代码可以在以下 https://github.com/stevekgyang/bhg 找到。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Bipartite+Graph+is+All+We+Need+for+Enhancing+Emotional+Reasoning+with+Commonsense+Knowledge)|0| |[CARPG: Cross-City Knowledge Transfer for Traffic Accident Prediction via Attentive Region-Level Parameter Generation](https://doi.org/10.1145/3583780.3614802)|Guang Yang, Yuequn Zhang, Jinquan Hang, Xinyue Feng, Zejun Xie, Desheng Zhang, Yu Yang|Lehigh University, Bethlehem, PA, USA; Rutgers University, Piscataway, NJ, USA|Traffic accident prediction is a crucial problem for public safety, emergency treatment, and urban management. Existing works leverage extensive data collected from city infrastructures to achieve encouraging performance based on various machine learning techniques but cannot achieve a good performance in situations with limited data (i.e., data scarcity). Recent developments in transfer learning bring a new opportunity to solve the data scarcity problem. In this paper, we design a novel cross-city transfer learning framework named CARPG for predicting traffic accidents in data-scarce cities. We address the unique challenge of predicting traffic accidents caused by its two fundamental characteristics, i.e., spatial heterogeneity and inherent rareness, which result in the biased performance of the state-of-the-art transfer learning methods. Specifically, we build cross-city region connections by jointly learning the spatial region representations for both source and target cities with an inter-city global graph knowledge transfer process. Further, we design an efficient attention-based parameter-generating mechanism to learn region-specific traffic accident patterns, while controlling the total number of parameters. Built upon that, we ensure that only relevant patterns are transferred to each target region during the knowledge transfer process and further to be fine-tuned. We conduct extensive experiments on three real-world datasets, and the evaluation results demonstrate the superiority of our framework compared with state-of-the-art baseline models.|交通事故预测是公共安全、应急处置和城市管理的关键问题。现有的工程利用从城市基础设施中收集的大量数据，基于各种机器学习技术实现令人鼓舞的性能，但在数据有限(即数据稀缺)的情况下无法实现良好的性能。近年来迁移学习的发展为解决数据稀缺问题带来了新的机遇。在本文中，我们设计了一个新颖的跨城市交通事故转移学习框架 CARPG 来预测数据稀缺城市的交通事故。由于交通事故的两个基本特征，即空间异质性和固有的稀有性，导致了最新的迁移学习方法在预测交通事故时存在偏差。具体来说，我们通过城市间全局图形知识转移过程来联合学习源城市和目标城市的空间区域表示，从而建立城市间的区域联系。进一步，我们设计了一个有效的基于注意的参数生成机制来学习区域特定的交通事故模式，同时控制参数的总数。在此基础上，我们确保在知识转移过程中只将相关模式转移到每个目标区域，并进一步进行微调。我们在三个真实世界的数据集上进行了广泛的实验，评估结果显示了我们的框架相对于最先进的基线模型的优越性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=CARPG:+Cross-City+Knowledge+Transfer+for+Traffic+Accident+Prediction+via+Attentive+Region-Level+Parameter+Generation)|0| -|[Group Identification via Transitional Hypergraph Convolution with Cross-view Self-supervised Learning](https://doi.org/10.1145/3583780.3614902)|Mingdai Yang, Zhiwei Liu, Liangwei Yang, Xiaolong Liu, Chen Wang, Hao Peng, Philip S. Yu|Beihang University & Kunming University of Science and Technology, Beijing, China; University of Illinois at Chicago, Chicago, IL, USA; Salesforce AI Research, Palo Alto, CA, USA|With the proliferation of social media, a growing number of users search for and join group activities in their daily life. This develops a need for the study on the group identification (GI) task, i.e., recommending groups to users. The major challenge in this task is how to predict users' preferences for groups based on not only previous group participation of users but also users' interests in items. Although recent developments in Graph Neural Networks (GNNs) accomplish embedding multiple types of objects in graph-based recommender systems, they, however, fail to address this GI problem comprehensively. In this paper, we propose a novel framework named Group Identification via Transitional Hypergraph Convolution with Graph Self-supervised Learning (GTGS). We devise a novel transitional hypergraph convolution layer to leverage users' preferences for items as prior knowledge when seeking their group preferences. To construct comprehensive user/group representations for GI task, we design the cross-view self-supervised learning to encourage the intrinsic consistency between item and group preferences for each user, and the group-based regularization to enhance the distinction among group embeddings. Experimental results on three benchmark datasets verify the superiority of GTGS. Additional detailed investigations are conducted to demonstrate the effectiveness of the proposed framework.|随着社交媒体的普及，越来越多的用户在日常生活中搜索和参加小组活动。这就需要对群体识别(GI)任务进行研究，即向用户推荐群体。这项任务的主要挑战是如何预测用户对群组的偏好，不仅基于用户以前的群组参与，而且基于用户对项目的兴趣。尽管近年来图形神经网络(GNN)已经实现了在基于图形的推荐系统中嵌入多种类型的对象，但是它们并没有全面地解决这个 GI 问题。本文提出了一种基于过渡超图卷积的图自监督学习(GTGS)的群体识别方法。我们设计了一个新的过渡超图卷积层，以利用用户的项目偏好作为先验知识时，寻找他们的群体偏好。为了构建 GI 任务的全面的用户/组表示，我们设计了跨视图自监督学习来鼓励每个用户项目和组偏好之间的内在一致性，以及基于组的正则化来增强组嵌入之间的区别。在三个基准数据集上的实验结果验证了 GTGS 算法的优越性。还进行了更多的详细调查，以证明拟议框架的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Group+Identification+via+Transitional+Hypergraph+Convolution+with+Cross-view+Self-supervised+Learning)|0| -|[Mulco: Recognizing Chinese Nested Named Entities through Multiple Scopes](https://doi.org/10.1145/3583780.3615026)|Jiuding Yang, Jinwen Luo, Weidong Guo, Jerry Chen, Di Niu, Yu Xu|University of Alberta, Edmonton, AB, Canada; Platform and Content Group, Tencent, Beijing, China|Nested Named Entity Recognition (NNER) has been a long-term challenge to researchers as an important sub-area of Named Entity Recognition. NNER is where one entity may be part of a longer entity, and this may happen on multiple levels, as the term nested suggests. These nested structures make traditional sequence labeling methods unable to properly recognize all entities. While recent researches focus on designing better recognition methods for NNER in a variety of languages, the Chinese NNER (CNNER) still lacks attention, where a free-for-access, CNNER-specialized benchmark is absent. In this paper, we aim to solve CNNER problems by providing a Chinese dataset and a learning-based model to tackle the issue. To facilitate the research on this task, we release ChiNesE, a CNNER dataset with 20,000 sentences sampled from online passages of multiple domains, containing 117,284 entities failing in 10 categories, where 43.8 percent of those entities are nested. Based on ChiNesE, we propose Mulco, a novel method that can recognize named entities in nested structures through multiple scopes. Each scope use a designed scope-based sequence labeling method, which predicts an anchor and the length of a named entity to recognize it. Experiment results show that Mulco has outperformed several baseline methods with the different recognizing schemes on ChiNesE. We also conduct extensive experiments on ACE2005 Chinese corpus, where Mulco has achieved the best performance compared with the baseline methods.|嵌套命名实体识别(NNER)作为命名实体识别的一个重要分支，长期以来一直是研究者面临的挑战。NNER 是指一个实体可能是一个较长实体的一部分，这可能发生在多个层次上，正如术语嵌套所暗示的那样。这些嵌套结构使得传统的序列标记方法无法正确识别所有实体。目前的研究主要集中在设计多种语言的 NNER 识别方法，但对于中文 NNER (CNNER)识别仍缺乏足够的重视，缺乏自由访问的 CNNER 专用基准。本文旨在通过提供一个中文数据集和一个基于学习的模型来解决 CNNER 问题。为了促进这项任务的研究，我们发布了 ChiNesE，一个 CNNER 数据集，从多个域的在线段落中抽取了20,000个句子，包含117,284个在10个类别中失败的实体，其中43.8% 是嵌套的。提出了一种基于嵌套结构的多作用域命名实体识别方法—— Mulco。每个作用域使用一个设计的基于作用域的序列标记方法，该方法预测一个锚和一个命名实体的长度来识别它。实验结果表明，采用不同的识别方案，Mulco 算法的识别性能优于几种基线方法。我们还在 ACE2005中文语料库上进行了广泛的实验，其中 Mulco 的性能优于基线方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Mulco:+Recognizing+Chinese+Nested+Named+Entities+through+Multiple+Scopes)|0| +|[Group Identification via Transitional Hypergraph Convolution with Cross-view Self-supervised Learning](https://doi.org/10.1145/3583780.3614902)|Mingdai Yang, Zhiwei Liu, Liangwei Yang, Xiaolong Liu, Chen Wang, Hao Peng, Philip S. Yu|University of Illinois at Chicago, Chicago, IL, USA; Salesforce AI Research, Palo Alto, CA, USA; Beihang University & Kunming University of Science and Technology, Beijing, China|With the proliferation of social media, a growing number of users search for and join group activities in their daily life. This develops a need for the study on the group identification (GI) task, i.e., recommending groups to users. The major challenge in this task is how to predict users' preferences for groups based on not only previous group participation of users but also users' interests in items. Although recent developments in Graph Neural Networks (GNNs) accomplish embedding multiple types of objects in graph-based recommender systems, they, however, fail to address this GI problem comprehensively. In this paper, we propose a novel framework named Group Identification via Transitional Hypergraph Convolution with Graph Self-supervised Learning (GTGS). We devise a novel transitional hypergraph convolution layer to leverage users' preferences for items as prior knowledge when seeking their group preferences. To construct comprehensive user/group representations for GI task, we design the cross-view self-supervised learning to encourage the intrinsic consistency between item and group preferences for each user, and the group-based regularization to enhance the distinction among group embeddings. Experimental results on three benchmark datasets verify the superiority of GTGS. Additional detailed investigations are conducted to demonstrate the effectiveness of the proposed framework.|随着社交媒体的普及，越来越多的用户在日常生活中搜索和参加小组活动。这就需要对群体识别(GI)任务进行研究，即向用户推荐群体。这项任务的主要挑战是如何预测用户对群组的偏好，不仅基于用户以前的群组参与，而且基于用户对项目的兴趣。尽管近年来图形神经网络(GNN)已经实现了在基于图形的推荐系统中嵌入多种类型的对象，但是它们并没有全面地解决这个 GI 问题。本文提出了一种基于过渡超图卷积的图自监督学习(GTGS)的群体识别方法。我们设计了一个新的过渡超图卷积层，以利用用户的项目偏好作为先验知识时，寻找他们的群体偏好。为了构建 GI 任务的全面的用户/组表示，我们设计了跨视图自监督学习来鼓励每个用户项目和组偏好之间的内在一致性，以及基于组的正则化来增强组嵌入之间的区别。在三个基准数据集上的实验结果验证了 GTGS 算法的优越性。还进行了更多的详细调查，以证明拟议框架的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Group+Identification+via+Transitional+Hypergraph+Convolution+with+Cross-view+Self-supervised+Learning)|0| +|[Mulco: Recognizing Chinese Nested Named Entities through Multiple Scopes](https://doi.org/10.1145/3583780.3615026)|Jiuding Yang, Jinwen Luo, Weidong Guo, Jerry Chen, Di Niu, Yu Xu|Platform and Content Group, Tencent, Beijing, China; University of Alberta, Edmonton, AB, Canada|Nested Named Entity Recognition (NNER) has been a long-term challenge to researchers as an important sub-area of Named Entity Recognition. NNER is where one entity may be part of a longer entity, and this may happen on multiple levels, as the term nested suggests. These nested structures make traditional sequence labeling methods unable to properly recognize all entities. While recent researches focus on designing better recognition methods for NNER in a variety of languages, the Chinese NNER (CNNER) still lacks attention, where a free-for-access, CNNER-specialized benchmark is absent. In this paper, we aim to solve CNNER problems by providing a Chinese dataset and a learning-based model to tackle the issue. To facilitate the research on this task, we release ChiNesE, a CNNER dataset with 20,000 sentences sampled from online passages of multiple domains, containing 117,284 entities failing in 10 categories, where 43.8 percent of those entities are nested. Based on ChiNesE, we propose Mulco, a novel method that can recognize named entities in nested structures through multiple scopes. Each scope use a designed scope-based sequence labeling method, which predicts an anchor and the length of a named entity to recognize it. Experiment results show that Mulco has outperformed several baseline methods with the different recognizing schemes on ChiNesE. We also conduct extensive experiments on ACE2005 Chinese corpus, where Mulco has achieved the best performance compared with the baseline methods.|嵌套命名实体识别(NNER)作为命名实体识别的一个重要分支，长期以来一直是研究者面临的挑战。NNER 是指一个实体可能是一个较长实体的一部分，这可能发生在多个层次上，正如术语嵌套所暗示的那样。这些嵌套结构使得传统的序列标记方法无法正确识别所有实体。目前的研究主要集中在设计多种语言的 NNER 识别方法，但对于中文 NNER (CNNER)识别仍缺乏足够的重视，缺乏自由访问的 CNNER 专用基准。本文旨在通过提供一个中文数据集和一个基于学习的模型来解决 CNNER 问题。为了促进这项任务的研究，我们发布了 ChiNesE，一个 CNNER 数据集，从多个域的在线段落中抽取了20,000个句子，包含117,284个在10个类别中失败的实体，其中43.8% 是嵌套的。提出了一种基于嵌套结构的多作用域命名实体识别方法—— Mulco。每个作用域使用一个设计的基于作用域的序列标记方法，该方法预测一个锚和一个命名实体的长度来识别它。实验结果表明，采用不同的识别方案，Mulco 算法的识别性能优于几种基线方法。我们还在 ACE2005中文语料库上进行了广泛的实验，其中 Mulco 的性能优于基线方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Mulco:+Recognizing+Chinese+Nested+Named+Entities+through+Multiple+Scopes)|0| |[FINRule: Feature Interactive Neural Rule Learning](https://doi.org/10.1145/3583780.3614884)|Lu Yu, Meng Li, YaLin Zhang, Longfei Li, Jun Zhou|Ant Group, Hangzhou, China|Though neural networks have achieved impressive prediction performance, it's still hard for people to understand what neural networks have learned from the data. The black-box property of neural networks already becomes one of the main obstacles preventing from being applied to many high-stakes applications, such as finance and medicine that have critical requirement on the model transparency and interpretability. In order to enhance the explainability of neural networks, we propose a neural rule learning method-Feature Interactive Neural Rule Learning (FINRule) to incorporate the expressivity of neural networks and the interpretability of rule-based systems. Specifically, we conduct rule learning as differential discrete combination encoded by a feedforward neural network, in which each layer acts as a logical operator of explainable decision conditions. The first hidden layer can act as sharable atomic conditions which are connected to next hidden layer for formulating decision rules. Moreover, we propose to represent both atomic condition and rules with contextual embeddings, with aim to enrich the expressivity power by capturing high-order feature interactions. We conduct comprehensive experiments on real-world datasets to validate both effectiveness and explainability of the proposed method.|尽管神经网络已经取得了令人印象深刻的预测性能，但人们仍然很难理解神经网络从数据中学到了什么。神经网络的黑盒特性已经成为阻碍其应用于金融、医药等对模型透明度和可解释性有关键要求的高风险应用领域的主要障碍之一。为了提高神经网络的可解释性，提出了一种神经规则学习方法——特征交互式神经规则学习(FINRule) ，将神经网络的表达能力和基于规则的系统的可解释性结合起来。具体来说，我们将规则学习作为由前馈神经网络编码的微分离散组合，其中每一层作为可解释决策条件的逻辑运算符。第一个隐藏层可以作为可共享的原子条件，这些原子条件连接到下一个隐藏层，用于制定决策规则。此外，我们提出用语境嵌入来表示原子条件和规则，目的是通过捕捉高阶特征交互来丰富表达能力。为了验证该方法的有效性和可解释性，我们对真实世界的数据集进行了全面的实验。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=FINRule:+Feature+Interactive+Neural+Rule+Learning)|0| |[MUSE: Multi-view Contrastive Learning for Heterophilic Graphs via Information Reconstruction](https://doi.org/10.1145/3583780.3614985)|Mengyi Yuan, Minjie Chen, Xiang Li|East China Normal University, Shanghai, China|In recent years, self-supervised learning has emerged as a promising approach in addressing the issues of label dependency and poor generalization performance in traditional GNNs. However, existing self-supervised methods have limited effectiveness on heterophilic graphs, due to the homophily assumption that results in similar node representations for connected nodes. In this work, we propose a multi-view contrastive learning model for heterophilic graphs, namely, MUSE. Specifically, we construct two views to capture the information of the ego node and its neighborhood by GNNs enhanced with contrastive learning, respectively. Then we integrate the information from these two views to fuse the node representations. Fusion contrast is utilized to enhance the effectiveness of fused node representations. Further, considering that the influence of neighboring contextual information on information fusion may vary across different ego nodes, we employ an information fusion controller to model the diversity of node-neighborhood similarity at both the local and global levels. Finally, an alternating training scheme is adopted to ensure that unsupervised node representation learning and information fusion controller can mutually reinforce each other. We conduct extensive experiments to evaluate the performance of MUSE on 9 benchmark datasets. Our results show the effectiveness of MUSE on both node classification and clustering tasks. We provide our data and codes at https://github.com/dcxr969/MUSE.|近年来，自监督学习已经成为解决传统 GNN 中标签依赖和泛化性能差等问题的一种有前途的方法。然而，现有的自监督方法对异质图的有效性是有限的，因为同质假设会导致连通节点的相似节点表示。在这项工作中，我们提出了一个异质图的多视图对比学习模型，即 MUSE。具体来说，我们构造了两个视图，分别通过对比学习增强的 GNN 来捕获自我节点及其邻域的信息。然后我们整合这两个视图的信息来融合节点表示。融合对比度用于提高融合节点表示的有效性。此外，考虑到相邻上下文信息对信息融合的影响在不同的自我节点之间可能不同，我们采用信息融合控制器在局部和全局两个层次上对节点-邻域相似性的多样性进行建模。最后，采用交替训练方案，保证无监督节点表示学习与信息融合控制相互补充。我们进行了广泛的实验来评估 MUSE 在9个基准数据集上的性能。我们的结果显示了 MUSE 在节点分类和聚类任务上的有效性。我们在 https://github.com/dcxr969/muse 提供数据和代码。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=MUSE:+Multi-view+Contrastive+Learning+for+Heterophilic+Graphs+via+Information+Reconstruction)|0| |[Target-Oriented Maneuver Decision for Autonomous Vehicle: A Rule-Aided Reinforcement Learning Framework](https://doi.org/10.1145/3583780.3615072)|Ximu Zeng, Quanlin Yu, Shuncheng Liu, Yuyang Xia, Han Su, Kai Zheng|University of Electronic Science and Technology of China, Chengdu, China; Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, China|Autonomous driving systems (ADSs) have the potential to revolutionize transportation by improving traffic safety and efficiency. As the core component of ADSs, maneuver decision aims to make tactical decisions to accomplish road following, obstacle avoidance, and efficient driving. In this work, we consider a typical but rarely studied task, called Target-Lane-Entering (TLE), where an autonomous vehicle should enter a target lane before reaching an intersection to ensure a smooth transition to another road. For navigation-assisted autonomous driving, a maneuver decision module chooses the optimal timing to enter the target lane in each road section, thus avoiding rerouting and reducing travel time. To achieve the TLE task, we propose a ruLe-aided reINforcement lEarning framework, called LINE, which combines the advantages of RL-based policy and rule-based strategy, allowing the autonomous vehicle to make target-oriented maneuver decisions. Specifically, an RL-based policy with a hybrid reward function is able to make safe, efficient, and comfortable decisions while considering the factors of target lanes. Then a strategy of rule revision aims to help the policy learn from intervention and block the risk of missing target lanes. Extensive experiments based on the SUMO simulator confirm the effectiveness of our framework. The results show that LINE achieves state-of-the-art driving performance with over 95% task success rate.|自主驾驶系统(ADS)具有通过提高交通安全性和效率来彻底改变交通的潜力。机动决策作为自动驾驶系统的核心组成部分，其目的是为了实现道路跟踪、避障和高效驾驶而进行战术决策。在这项工作中，我们考虑了一个典型但很少被研究的任务，称为目标车道进入(TLE) ，其中无人机应该在到达一个十字路口之前进入一个目标车道，以确保平稳过渡到另一条道路。对于导航辅助自主驾驶，机动决策模块选择最佳时机进入每个路段的目标车道，从而避免了改道，减少了行驶时间。为了完成 TLE 任务，我们提出了一个基于规则的辅助强化学习框架，称为 LINE，它结合了基于规则的策略和基于规则的策略的优势，允许无人机做出面向目标的机动决策。具体来说，基于 RL 的混合报酬策略能够在考虑目标车道因素的情况下做出安全、有效和舒适的决策。然后采用规则修正策略，帮助政策从干预中吸取教训，防止错过目标车道的风险。基于 SUMO 模拟器的大量实验证实了该框架的有效性。结果表明，LINE 具有最先进的驱动性能，任务成功率达到95% 以上。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Target-Oriented+Maneuver+Decision+for+Autonomous+Vehicle:+A+Rule-Aided+Reinforcement+Learning+Framework)|0| |[AKE-GNN: Effective Graph Learning with Adaptive Knowledge Exchange](https://doi.org/10.1145/3583780.3614778)|Liang Zeng, Jin Xu, Zijun Yao, Yanqiao Zhu, Jian Li|Tsinghua University, Beijing, China; University of California, Los Angeles, Los Angeles, CA, USA|Graph Neural Networks (GNNs) have already been widely used in various graph mining tasks. However, recent works reveal that the learned weights (channels) in well-trained GNNs are highly redundant, which inevitably limits the performance of GNNs. Instead of removing these redundant channels for efficiency consideration, we aim to reactivate them to enlarge the representation capacity of GNNs for effective graph learning. In this paper, we propose to substitute these redundant channels with other informative channels to achieve this goal. We introduce a novel GNN learning framework named AKE-GNN, which performs the Adaptive Knowledge Exchange strategy among multiple graph views generated by graph augmentations. AKE-GNN first trains multiple GNNs each corresponding to one graph view to obtain informative channels. Then, AKE-GNN iteratively exchanges redundant channels in the weight parameter matrix of one GNN with informative channels of another GNN in a layer-wise manner. Additionally, existing GNNs can be seamlessly incorporated into our framework. AKE-GNN achieves superior performance compared with various baselines across a suite of experiments on node classification, link prediction, and graph classification. In particular, we conduct a series of experiments on 15 public benchmark datasets, 8 popular GNN models, and 3 graph tasks and show that AKE-GNN consistently outperforms existing popular GNN models and even their ensembles. Extensive ablation studies and analyses on knowledge exchange methods validate the effectiveness of AKE-GNN.|图神经网络(GNN)已经广泛应用于各种图挖掘任务中。然而，最近的研究表明，训练有素的 GNN 中的学习权重(信道)是高度冗余的，这不可避免地限制了 GNN 的性能。我们的目标是重新激活这些冗余通道，以扩大 GNN 的表示能力，从而达到有效的图学习，而不是去除这些冗余通道以考虑效率。在本文中，我们建议用其他信息通道替代这些冗余通道来实现这一目标。本文介绍了一种新的 GNN 学习框架 AKE-GNN，它在通过图增强生成的多个图视图之间执行自适应知识交换策略。AKE-GNN 首先训练多个 GNN，每个 GNN 对应于一个图视图，以获得信息通道。然后，AKE-GNN 将一个 GNN 的权参数矩阵中的冗余信道与另一个 GNN 的信息信道分层迭代地交换。此外，现有的 GNN 可以无缝地合并到我们的框架中。通过一系列关于节点分类、链路预测和图分类的实验，AKE-GNN 获得了比各种基线更好的性能。特别是，我们在15个公共基准数据集、8个流行的 GNN 模型和3个图任务上进行了一系列的实验，结果表明 AKE-GNN 一直优于现有流行的 GNN 模型，甚至优于它们的集合。广泛的消融研究和知识交换方法的分析验证了 AKE-GNN 的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=AKE-GNN:+Effective+Graph+Learning+with+Adaptive+Knowledge+Exchange)|0| -|[DYANE: DYnamic Attributed Node rolEs Generative Model](https://doi.org/10.1145/3583780.3614858)|Giselle Zeno, Jennifer Neville|Microsoft Research & Purdue University, West Lafayette, IN, USA; Purdue University, West Lafayette, IN, USA|Recent work has shown that modeling higher-order structures, such as motifs or graphlets, can capture the complex network structure and dynamics in a variety of graph domains (e.g., social sciences, biology, chemistry). However, many dynamic networks are not only rich in structure, but also in content information. For example, an academic citation network has content such as the title and abstracts of the papers. Currently, there is a lack of generative models for dynamic networks that also generate content. To address this gap, in this work we propose DYnamic Attributed Node rolEs (DYANE)-a generative model that (i) captures network structure dynamics through temporal motifs, and (ii) extends the structural roles of nodes in motifs (e.g., a node acting as a hub in a wedge) to roles that generate content embeddings. We evaluate DYANE on real-world networks against other dynamic graph generative model baselines. DYANE outperforms the baselines in graph structure and node behavior, improving the KS score for graph metrics by 21-31% and node metrics by 17-27% on average, and produces content embeddings similar to the observed network. We also derive a methodology to evaluate the content embeddings generated by nodes, taking into account keywords extracted from the content (as topic representations), and using distance metrics.|最近的工作已经表明，建模高阶结构，如图案或图形，可以捕捉复杂的网络结构和动态在各种图形领域(如，社会科学，生物学，化学)。然而，许多动态网络不仅具有丰富的结构，而且具有丰富的内容信息。例如，一个学术引文网络包含论文的标题和摘要等内容。目前，动态网络还缺乏生成内容的生成模型。为了解决这一差距，在这项工作中，我们提出了动态属性节点角色(dYANE)-一个生成模型，(i)捕获网络结构动态通过时间图案，和(ii)扩展结构角色的节点在图案(例如，一个节点作为一个中心在楔形)的角色，生成内容嵌入。我们根据其他动态图形生成模型基线来评估现实世界网络上的 DYANE。DYANE 在图结构和节点行为方面优于基线，平均提高了图度量的 KS 分数21-31% ，节点度量的 KS 分数提高了17-27% ，并且产生了类似于观察网络的内容嵌入。我们还推导了一种评估节点生成的内容嵌入的方法，考虑了从内容中提取的关键字(作为主题表示) ，并使用距离度量。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=DYANE:+DYnamic+Attributed+Node+rolEs+Generative+Model)|0| +|[DYANE: DYnamic Attributed Node rolEs Generative Model](https://doi.org/10.1145/3583780.3614858)|Giselle Zeno, Jennifer Neville|Purdue University, West Lafayette, IN, USA; Microsoft Research & Purdue University, West Lafayette, IN, USA|Recent work has shown that modeling higher-order structures, such as motifs or graphlets, can capture the complex network structure and dynamics in a variety of graph domains (e.g., social sciences, biology, chemistry). However, many dynamic networks are not only rich in structure, but also in content information. For example, an academic citation network has content such as the title and abstracts of the papers. Currently, there is a lack of generative models for dynamic networks that also generate content. To address this gap, in this work we propose DYnamic Attributed Node rolEs (DYANE)-a generative model that (i) captures network structure dynamics through temporal motifs, and (ii) extends the structural roles of nodes in motifs (e.g., a node acting as a hub in a wedge) to roles that generate content embeddings. We evaluate DYANE on real-world networks against other dynamic graph generative model baselines. DYANE outperforms the baselines in graph structure and node behavior, improving the KS score for graph metrics by 21-31% and node metrics by 17-27% on average, and produces content embeddings similar to the observed network. We also derive a methodology to evaluate the content embeddings generated by nodes, taking into account keywords extracted from the content (as topic representations), and using distance metrics.|最近的工作已经表明，建模高阶结构，如图案或图形，可以捕捉复杂的网络结构和动态在各种图形领域(如，社会科学，生物学，化学)。然而，许多动态网络不仅具有丰富的结构，而且具有丰富的内容信息。例如，一个学术引文网络包含论文的标题和摘要等内容。目前，动态网络还缺乏生成内容的生成模型。为了解决这一差距，在这项工作中，我们提出了动态属性节点角色(dYANE)-一个生成模型，(i)捕获网络结构动态通过时间图案，和(ii)扩展结构角色的节点在图案(例如，一个节点作为一个中心在楔形)的角色，生成内容嵌入。我们根据其他动态图形生成模型基线来评估现实世界网络上的 DYANE。DYANE 在图结构和节点行为方面优于基线，平均提高了图度量的 KS 分数21-31% ，节点度量的 KS 分数提高了17-27% ，并且产生了类似于观察网络的内容嵌入。我们还推导了一种评估节点生成的内容嵌入的方法，考虑了从内容中提取的关键字(作为主题表示) ，并使用距离度量。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=DYANE:+DYnamic+Attributed+Node+rolEs+Generative+Model)|0| |[TriD-MAE: A Generic Pre-trained Model for Multivariate Time Series with Missing Values](https://doi.org/10.1145/3583780.3615097)|Kai Zhang, Chao Li, Qinmin Yang|Zhejiang University, Hangzhou, China|Multivariate time series(MTS) is a universal data type related to various real-world applications. Data imputation methods are widely used in MTS applications to deal with the frequent data missing problem. However, these methods inevitably introduce biased imputation and training-redundancy problems in downstream training. To address these challenges, we propose TriD-MAE, a generic pre-trained model for MTS data with missing values. Firstly, we introduce TriD-TCN, an end-to-end module based on TCN that effectively extracts temporal features by integrating dynamic kernel mechanisms and a time-flipping trick. Building upon that, we designed an MAE-based pre-trained model as the precursor of specialized downstream models. Our model cooperates with a dynamic positional embedding mechanism to represent the missing information and generate transferable representation through our proposed encoder units. The overall mixed data feed-in strategy and weighted loss function are established to ensure adequate training of the whole model. Comparative experiment results in time series prediction and classification manifest that our TriD-MAE model outperforms the other state-of-the-art methods within six real-world datasets. Moreover, ablation and interpretability experiments are delivered to verify the validity of TriD-MAE's|多变量时间序列(MTS)是一种与各种实际应用相关的通用数据类型。数据填补方法在 MTS 应用中被广泛应用于处理频繁的数据丢失问题。然而，这些方法不可避免地在下游训练中引入了有偏的归责和训练冗余问题。为了应对这些挑战，我们提出了 TriD-MAE，一个通用的 MTS 数据缺失值预训练模型。首先介绍了基于 TCN 的端到端模块 TriD-TCN，该模块集成了动态内核机制和时间翻转技术，有效地提取了时间特征。在此基础上，我们设计了一个基于 MAE 的预训练模型，作为专门的下游模型的前身。该模型采用动态位置嵌入机制来表示缺失的信息，并通过编码单元生成可转移的表示。建立了整体混合数据输入策略和加权损失函数，保证了整个模型的充分训练。时间序列预测和分类的对比实验结果表明，我们的 TriD-MAE 模型在六个实际数据集中优于其他最先进的方法。此外，还进行了烧蚀实验和可解释性实验，验证了 TriD-MAE 方法的有效性|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=TriD-MAE:+A+Generic+Pre-trained+Model+for+Multivariate+Time+Series+with+Missing+Values)|0| -|[RDGSL: Dynamic Graph Representation Learning with Structure Learning](https://doi.org/10.1145/3583780.3615023)|Siwei Zhang, Yun Xiong, Yao Zhang, Yiheng Sun, Xi Chen, Yizhu Jiao, Yangyong Zhu|Fudan University, Shanghai, China; University of Illinois Urbana-Champaign, Champaign, IL, USA; Tencent Weixin Group, Shenzhen, China|Temporal Graph Networks (TGNs) have shown remarkable performance in learning representation for continuous-time dynamic graphs. However, real-world dynamic graphs typically contain diverse and intricate noise. Noise can significantly degrade the quality of representation generation, impeding the effectiveness of TGNs in downstream tasks. Though structure learning is widely applied to mitigate noise in static graphs, its adaptation to dynamic graph settings poses two significant challenges. i) Noise dynamics. Existing structure learning methods are ill-equipped to address the temporal aspect of noise, hampering their effectiveness in such dynamic and ever-changing noise patterns. ii) More severe noise. Noise may be introduced along with multiple interactions between two nodes, leading to the re-pollution of these nodes and consequently causing more severe noise compared to static graphs. In this paper, we present RDGSL, a representation learning method in continuous-time dynamic graphs. Meanwhile, we propose dynamic graph structure learning, a novel supervisory signal that empowers RDGSL with the ability to effectively combat noise in dynamic graphs. To address the noise dynamics issue, we introduce the Dynamic Graph Filter, where we innovatively propose a dynamic noise function that dynamically captures both current and historical noise, enabling us to assess the temporal aspect of noise and generate a denoised graph. We further propose the Temporal Embedding Learner to tackle the challenge of more severe noise, which utilizes an attention mechanism to selectively turn a blind eye to noisy edges and hence focus on normal edges, enhancing the expressiveness for representation generation that remains resilient to noise. Our method demonstrates robustness towards downstream tasks, resulting in up to 5.1% absolute AUC improvement in evolving classification versus the second-best baseline.|时态图网络(TGNs)在连续时间动态图的学习表示方面表现出显著的性能。然而，真实世界的动态图通常包含不同和复杂的噪音。噪声可以显著降低表示生成的质量，阻碍 TGN 在下游任务中的有效性。尽管结构学习被广泛应用于降低静态图中的噪声，但其对动态图设置的适应性提出了两个重要的挑战。I)噪音动态。现有的结构学习方法在处理噪声的时间方面装备不足，妨碍了它们在这种动态和不断变化的噪声模式中的有效性。Ii)更严重的噪音。噪声可能随着两个节点之间的多重相互作用而引入，导致这些节点的重新污染，从而导致比静态图更严重的噪声。本文提出了连续时间动态图表示学习方法 RDGSL。同时，我们提出了动态图结构学习，这是一种新颖的监控信号，使 RDGSL 能够有效地对抗动态图中的噪声。为了解决噪声动态问题，我们引入了动态图形滤波器，在这里我们创新地提出了一个动态噪声函数，动态捕获当前和历史噪声，使我们能够评估噪声的时间方面，并生成一个去噪图。我们进一步建议时态嵌入学习器来解决更严重的噪声的挑战，其利用注意机制选择性地对噪声边缘视而不见，从而集中在正常边缘上，增强表示生成的表达能力，仍然对噪声具有弹性。我们的方法证明了对下游任务的鲁棒性，导致高达5.1% 的绝对 AUC 改善进化分类相对第二最佳基线。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=RDGSL:+Dynamic+Graph+Representation+Learning+with+Structure+Learning)|0| -|[PromptST: Prompt-Enhanced Spatio-Temporal Multi-Attribute Prediction](https://doi.org/10.1145/3583780.3615016)|Zijian Zhang, Xiangyu Zhao, Qidong Liu, Chunxu Zhang, Qian Ma, Wanyu Wang, Hongwei Zhao, Yiqi Wang, Zitao Liu|City University of Hong Kong, Hong Kong, Hong Kong; Michigan State University, East Lansing, MI, USA; Jinan University, Guangzhou, China; Xi'an Jiaotong University & City University of Hong Kong, Xi'an, China; Jilin University, Changchun, China; Jilin University & City University of Hong Kong, Changchun, China|In the era of information explosion, spatio-temporal data mining serves as a critical part of urban management. Considering the various fields demanding attention, e.g., traffic state, human activity, and social event, predicting multiple spatio-temporal attributes simultaneously can alleviate regulatory pressure and foster smart city construction. However, current research can not handle the spatio-temporal multi-attribute prediction well due to the complex relationships between diverse attributes. The key challenge lies in how to address the common spatio-temporal patterns while tackling their distinctions. In this paper, we propose an effective solution for spatio-temporal multi-attribute prediction, PromptST. We devise a spatio-temporal transformer and a parameter-sharing training scheme to address the common knowledge among different spatio-temporal attributes. Then, we elaborate a spatio-temporal prompt tuning strategy to fit the specific attributes in a lightweight manner. Through the pretrain and prompt tuning phases, our PromptST is able to enhance the specific spatio-temoral characteristic capture by prompting the backbone model to fit the specific target attribute while maintaining the learned common knowledge. Extensive experiments on real-world datasets verify that our PromptST attains state-of-the-art performance. Furthermore, we also prove PromptST owns good transferability on unseen spatio-temporal attributes, which brings promising application potential in urban computing. The implementation code is available to ease reproducibility.|在信息爆炸时代，时空数据挖掘是城市管理的重要组成部分。考虑到交通状态、人类活动、社会事件等需要关注的各个领域，同时预测多个时空属性可以缓解调控压力，促进智能城市建设。然而，由于不同属性之间关系复杂，目前的研究不能很好地处理时空多属性预测问题。关键的挑战在于如何处理共同的时空模式，同时处理它们的区别。本文提出了一种有效的时空多属性预测方法 PromptST。为了解决不同时空属性之间的共同知识，我们设计了一个时空变换器和一个参数共享训练方案。然后，我们阐述了一个时空提示调优策略，以轻量级的方式适应特定的属性。通过预训练和快速调整阶段，我们的 PromptST 能够通过提示骨干模型来适应特定的目标属性，同时保持所学到的公共知识，从而增强特定的时空特征捕获。在真实世界数据集上的大量实验证明，我们的 PromptST 获得了最先进的性能。此外，我们还证明了 PromptST 在不可见的时空属性上具有良好的可转移性，在城市计算中具有广阔的应用前景。实现代码可用于简化重现性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=PromptST:+Prompt-Enhanced+Spatio-Temporal+Multi-Attribute+Prediction)|0| +|[RDGSL: Dynamic Graph Representation Learning with Structure Learning](https://doi.org/10.1145/3583780.3615023)|Siwei Zhang, Yun Xiong, Yao Zhang, Yiheng Sun, Xi Chen, Yizhu Jiao, Yangyong Zhu|University of Illinois Urbana-Champaign, Champaign, IL, USA; Fudan University, Shanghai, China; Tencent Weixin Group, Shenzhen, China|Temporal Graph Networks (TGNs) have shown remarkable performance in learning representation for continuous-time dynamic graphs. However, real-world dynamic graphs typically contain diverse and intricate noise. Noise can significantly degrade the quality of representation generation, impeding the effectiveness of TGNs in downstream tasks. Though structure learning is widely applied to mitigate noise in static graphs, its adaptation to dynamic graph settings poses two significant challenges. i) Noise dynamics. Existing structure learning methods are ill-equipped to address the temporal aspect of noise, hampering their effectiveness in such dynamic and ever-changing noise patterns. ii) More severe noise. Noise may be introduced along with multiple interactions between two nodes, leading to the re-pollution of these nodes and consequently causing more severe noise compared to static graphs. In this paper, we present RDGSL, a representation learning method in continuous-time dynamic graphs. Meanwhile, we propose dynamic graph structure learning, a novel supervisory signal that empowers RDGSL with the ability to effectively combat noise in dynamic graphs. To address the noise dynamics issue, we introduce the Dynamic Graph Filter, where we innovatively propose a dynamic noise function that dynamically captures both current and historical noise, enabling us to assess the temporal aspect of noise and generate a denoised graph. We further propose the Temporal Embedding Learner to tackle the challenge of more severe noise, which utilizes an attention mechanism to selectively turn a blind eye to noisy edges and hence focus on normal edges, enhancing the expressiveness for representation generation that remains resilient to noise. Our method demonstrates robustness towards downstream tasks, resulting in up to 5.1% absolute AUC improvement in evolving classification versus the second-best baseline.|时态图网络(TGNs)在连续时间动态图的学习表示方面表现出显著的性能。然而，真实世界的动态图通常包含不同和复杂的噪音。噪声可以显著降低表示生成的质量，阻碍 TGN 在下游任务中的有效性。尽管结构学习被广泛应用于降低静态图中的噪声，但其对动态图设置的适应性提出了两个重要的挑战。I)噪音动态。现有的结构学习方法在处理噪声的时间方面装备不足，妨碍了它们在这种动态和不断变化的噪声模式中的有效性。Ii)更严重的噪音。噪声可能随着两个节点之间的多重相互作用而引入，导致这些节点的重新污染，从而导致比静态图更严重的噪声。本文提出了连续时间动态图表示学习方法 RDGSL。同时，我们提出了动态图结构学习，这是一种新颖的监控信号，使 RDGSL 能够有效地对抗动态图中的噪声。为了解决噪声动态问题，我们引入了动态图形滤波器，在这里我们创新地提出了一个动态噪声函数，动态捕获当前和历史噪声，使我们能够评估噪声的时间方面，并生成一个去噪图。我们进一步建议时态嵌入学习器来解决更严重的噪声的挑战，其利用注意机制选择性地对噪声边缘视而不见，从而集中在正常边缘上，增强表示生成的表达能力，仍然对噪声具有弹性。我们的方法证明了对下游任务的鲁棒性，导致高达5.1% 的绝对 AUC 改善进化分类相对第二最佳基线。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=RDGSL:+Dynamic+Graph+Representation+Learning+with+Structure+Learning)|0| +|[PromptST: Prompt-Enhanced Spatio-Temporal Multi-Attribute Prediction](https://doi.org/10.1145/3583780.3615016)|Zijian Zhang, Xiangyu Zhao, Qidong Liu, Chunxu Zhang, Qian Ma, Wanyu Wang, Hongwei Zhao, Yiqi Wang, Zitao Liu|Xi'an Jiaotong University & City University of Hong Kong, Xi'an, China; Jilin University, Changchun, China; Jilin University & City University of Hong Kong, Changchun, China; City University of Hong Kong, Hong Kong, Hong Kong; Jinan University, Guangzhou, China; Michigan State University, East Lansing, MI, USA|In the era of information explosion, spatio-temporal data mining serves as a critical part of urban management. Considering the various fields demanding attention, e.g., traffic state, human activity, and social event, predicting multiple spatio-temporal attributes simultaneously can alleviate regulatory pressure and foster smart city construction. However, current research can not handle the spatio-temporal multi-attribute prediction well due to the complex relationships between diverse attributes. The key challenge lies in how to address the common spatio-temporal patterns while tackling their distinctions. In this paper, we propose an effective solution for spatio-temporal multi-attribute prediction, PromptST. We devise a spatio-temporal transformer and a parameter-sharing training scheme to address the common knowledge among different spatio-temporal attributes. Then, we elaborate a spatio-temporal prompt tuning strategy to fit the specific attributes in a lightweight manner. Through the pretrain and prompt tuning phases, our PromptST is able to enhance the specific spatio-temoral characteristic capture by prompting the backbone model to fit the specific target attribute while maintaining the learned common knowledge. Extensive experiments on real-world datasets verify that our PromptST attains state-of-the-art performance. Furthermore, we also prove PromptST owns good transferability on unseen spatio-temporal attributes, which brings promising application potential in urban computing. The implementation code is available to ease reproducibility.|在信息爆炸时代，时空数据挖掘是城市管理的重要组成部分。考虑到交通状态、人类活动、社会事件等需要关注的各个领域，同时预测多个时空属性可以缓解调控压力，促进智能城市建设。然而，由于不同属性之间关系复杂，目前的研究不能很好地处理时空多属性预测问题。关键的挑战在于如何处理共同的时空模式，同时处理它们的区别。本文提出了一种有效的时空多属性预测方法 PromptST。为了解决不同时空属性之间的共同知识，我们设计了一个时空变换器和一个参数共享训练方案。然后，我们阐述了一个时空提示调优策略，以轻量级的方式适应特定的属性。通过预训练和快速调整阶段，我们的 PromptST 能够通过提示骨干模型来适应特定的目标属性，同时保持所学到的公共知识，从而增强特定的时空特征捕获。在真实世界数据集上的大量实验证明，我们的 PromptST 获得了最先进的性能。此外，我们还证明了 PromptST 在不可见的时空属性上具有良好的可转移性，在城市计算中具有广阔的应用前景。实现代码可用于简化重现性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=PromptST:+Prompt-Enhanced+Spatio-Temporal+Multi-Attribute+Prediction)|0| |[Out of the Box Thinking: Improving Customer Lifetime Value Modelling via Expert Routing and Game Whale Detection](https://doi.org/10.1145/3583780.3615002)|Shijie Zhang, Xin Yan, Xuejiao Yang, Binfeng Jia, Shuangyang Wang|Tencent, Shenzhen, China|Customer lifetime value (LTV) prediction is essential for mobile game publishers trying to optimize the advertising investment for each user acquisition based on the estimated worth. In mobile games, deploying microtransactions is a simple yet effective monetization strategy, which attracts a tiny group of game whales who splurge on in-game purchases. The presence of such game whales may impede the practicality of existing LTV prediction models, since game whales' purchase behaviours always exhibit varied distribution from general users. Consequently, identifying game whales can open up new opportunities to improve the accuracy of LTV prediction models. However, little attention has been paid to applying game whale detection in LTV prediction, and existing works are mainly specialized for the long-term LTV prediction with the assumption that the high-quality user features are available, which is not applicable in the UA stage. In this paper, we propose ExpLTV, a novel multi-task framework to perform LTV prediction and game whale detection in a unified way. In ExpLTV, we first innovatively design a deep neural network-based game whale detector that can not only infer the intrinsic order in accordance with monetary value, but also precisely identify high spenders (i.e., game whales) and low spenders. Then, by treating the game whale detector as a gating network to decide the different mixture patterns of LTV experts assembling, we can thoroughly leverage the shared information and scenario-specific information (i.e., game whales modelling and low spenders modelling). Finally, instead of separately designing a purchase rate estimator for two tasks, we design a shared estimator that can preserve the inner task relationships. The superiority of ExpLTV is further validated via extensive experiments on three industrial datasets.|客户生命周期价值(LTV)预测是手机游戏发行商基于估计价值优化每次用户获取广告投资的关键。在手机游戏中，部署微交易是一种简单而有效的货币化策略，它吸引了一小群游戏巨头在游戏中大肆消费。这种游戏鲸的存在可能会阻碍现有 LTV 预测模型的实用性，因为游戏鲸的购买行为总是表现出不同于一般用户的分布。因此，识别猎鲸可以为提高 LTV 预测模型的准确性开辟新的机会。然而，现有的工作主要是专门用于长期的按揭成数预测，假设有高质量的用户功能，这在 UA 阶段是不适用的。在本文中，我们提出了一个新的多任务框架 ExpLTV，以统一的方式执行 LTV 预测和游戏鲸鱼检测。在 ExpLTV 中，我们首先创新性地设计了一个基于深度神经网络的游戏鲸探测器，它不仅可以根据货币价值推断内在顺序，而且还可以精确地识别高消费者(即，游戏鲸)和低消费者。然后，通过把游戏鲸鱼探测器当作一个门控网络来决定不同的 LTV 专家组合的混合模式，我们可以充分利用共享的信息和特定场景的信息(例如，游戏鲸鱼模型和低消费者模型)。最后，我们设计了一个能够保持内部任务关系的共享估计器，而不是分别设计两个任务的购买率估计器。通过在三个工业数据集上的大量实验，进一步验证了 ExpLTV 的优越性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Out+of+the+Box+Thinking:+Improving+Customer+Lifetime+Value+Modelling+via+Expert+Routing+and+Game+Whale+Detection)|0| |[No Length Left Behind: Enhancing Knowledge Tracing for Modeling Sequences of Excessive or Insufficient Lengths](https://doi.org/10.1145/3583780.3614988)|Moyu Zhang, Xinning Zhu, Chunhong Zhang, Feng Pan, Wenchen Qian, Hui Zhao|Beijing University of Posts and Telecommunications, Beijing, China|Knowledge tracing (KT) aims to predict students' responses to practices based on their historical question-answering behaviors. However, most current KT methods focus on improving overall AUC, leaving ample room for optimization in modeling sequences of excessive or insufficient lengths. As sequences get longer, computational costs will increase exponentially. Therefore, KT methods usually truncate sequences to an acceptable length, which makes it difficult for models on online service systems to capture complete historical practice behaviors of students with too long sequences. Conversely, modeling students with short practice sequences using most KT methods may result in overfitting due to limited observation samples. To address the above limitations, we propose a model called Sequence-Flexible Knowledge Tracing (SFKT).|知识追踪(KT)旨在根据学生历史问答行为预测学生对实践的反应。然而，大多数当前的 KT 方法侧重于改善整体 AUC，为过长或不足长度的建模序列留下了充足的优化空间。随着序列变长，计算成本将呈指数增长。因此，KT 方法通常将序列截断到一个可接受的长度，这使得在线服务系统的模型很难捕捉到序列过长的学生的完整的历史实践行为。相反，使用大多数 KT 方法对练习序列较短的学生进行建模，由于观察样本有限，可能导致过度拟合。针对上述局限性，本文提出了一种序列柔性知识追踪模型(SFKT)。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=No+Length+Left+Behind:+Enhancing+Knowledge+Tracing+for+Modeling+Sequences+of+Excessive+or+Insufficient+Lengths)|0| |[Counterfactual Monotonic Knowledge Tracing for Assessing Students' Dynamic Mastery of Knowledge Concepts](https://doi.org/10.1145/3583780.3614827)|Moyu Zhang, Xinning Zhu, Chunhong Zhang, Wenchen Qian, Feng Pan, Hui Zhao|Beijing University of Posts and Telecommunications, Beijing, China|As the core of the Knowledge Tracking (KT) task, assessing students' dynamic mastery of knowledge concepts is crucial for both offline teaching and online educational applications. Since students' mastery of knowledge concepts is often unlabeled, existing KT methods rely on the implicit paradigm of historical practice to mastery of knowledge concepts to students' responses to practices to address the challenge of unlabeled concept mastery. However, purely predicting student responses without imposing specific constraints on hidden concept mastery values does not guarantee the accuracy of these intermediate values as concept mastery values. To address this issue, we propose a principled approach called Counterfactual Monotonic Knowledge Tracing (CMKT), which builds on the implicit paradigm described above by using a counterfactual assumption to constrain the evolution of students' mastery of knowledge concepts.|作为知识跟踪(KT)任务的核心，评估学生对知识概念的动态掌握对离线教学和网络教育应用都至关重要。由于学生对知识概念的掌握往往是未标记的，现有的 KT 方法依赖于历史实践的内隐范式来掌握知识概念，以学生对实践的反应来应对未标记概念掌握的挑战。然而，纯粹预测学生的反应而不对隐藏的概念掌握价值施加特定的限制，并不能保证这些中间价值作为概念掌握价值的准确性。为了解决这一问题，我们提出了一种原则性的方法，称为反事实单调知识追踪(CMKT) ，它建立在上述内隐范式的基础上，通过使用反事实假设来约束学生对知识概念掌握的进化。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Counterfactual+Monotonic+Knowledge+Tracing+for+Assessing+Students'+Dynamic+Mastery+of+Knowledge+Concepts)|0| |[Non-IID always Bad? Semi-Supervised Heterogeneous Federated Learning with Local Knowledge Enhancement](https://doi.org/10.1145/3583780.3614991)|Chao Zhang, Fangzhao Wu, Jingwei Yi, Derong Xu, Yang Yu, Jindong Wang, Yidong Wang, Tong Xu, Xing Xie, Enhong Chen|University of Science and Technology of China, Hefei, China; Microsoft Research Asia, Beijing, China|Federated learning (FL) is important for privacy-preserving services by training models without collecting raw user data. Most FL algorithms assume all data is annotated, which is impractical due to the high cost of labeling data in real applications. To alleviate the reliance on labeled data, semi-supervised federated learning (SSFL) has been proposed to utilize unlabeled data on clients to improve model performance. However, most existing methods either have privacy issues which share models trained on other clients, or generate pseudo-labels for unlabeled local datasets with the global model, which is usually biased towards the global data distribution. The latter may lead to sub-optimal accuracy of pseudo-labels, due to the gap between the local data distribution and the global model, especially in non-IID settings. In this paper, we propose a semi-supervised heterogeneous federated learning method with local knowledge enhancement, called FedLoKe, which aims to train an accurate global model from both labeled and unlabeled local data with non-IID distributions. Specifically, in FedLoKe, the server maintains a global model to capture global data distribution, and each client learns a local model to capture local data distribution. Since the distribution captured by the local model is aligned with the local data distribution, we utilize it to generate high-accuracy pseudo-labels of the unlabeled dataset for global model training. To prevent the local model from severely overfitting the small number of local labeled data, we further use the exponential moving average and apply the global model to generate pseudo-labels for local modeling training. Experiments on four datasets show the effectiveness of FedLoKe. Our code is available at: https://github.com/zcfinal/FedLoKe.|联邦学习(FL)通过不收集原始用户数据的训练模型对保护隐私的服务非常重要。大多数 FL 算法都假设所有数据都是注释的，这是不切实际的，因为在实际应用中标记数据的成本很高。为了减少对标记数据的依赖，半监督联邦学习(SSFL)已被提出，利用客户端的未标记数据来提高模型的性能。然而，大多数现有的方法或者存在隐私问题，这些隐私问题共享在其他客户机上训练的模型，或者在全局模型下为未标记的本地数据集生成伪标签，这通常偏向于全局数据分布。由于本地数据分布与全局模型之间的差距，特别是在非 IID 设置中，后者可能导致伪标签的准确性不理想。本文提出了一种基于局部知识增强的半监督异构联邦学习方法 FedLoke，该方法旨在从带有非 IID 分布的标记和未标记的局部数据中训练出一个精确的全局模型。具体来说，在 FedLoke 中，服务器维护一个全局模型来捕获全局数据分布，每个客户机学习一个本地模型来捕获本地数据分布。由于局部模型所捕获的分布与局部数据分布是一致的，因此我们利用局部模型生成未标记数据集的高精度伪标签，用于全局模型训练。为了防止局部模型严重过度拟合少量的局部标记数据，我们进一步使用指数移动平均，并应用全局模型生成伪标记进行局部建模训练。在四个数据集上的实验表明了 FedLoke 算法的有效性。我们的代码可以在以下 https://github.com/zcfinal/fedloke 找到。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Non-IID+always+Bad?+Semi-Supervised+Heterogeneous+Federated+Learning+with+Local+Knowledge+Enhancement)|0| |[Mutual Information-Driven Multi-View Clustering](https://doi.org/10.1145/3583780.3614986)|Lei Zhang, Lele Fu, Tong Wang, Chuan Chen, Chuanfu Zhang|Sun Yat-Sen University, Guangzhou, China|In deep multi-view clustering, three intractable problems are posed ahead of researchers, namely, the complementarity exploration problem, the information preservation problem, and the cluster structure discovery problem. In this paper, we consider the deep multi-view clustering from the perspective of mutual information (MI), and attempt to address the three important concerns with a Mutual Information-Driven Multi-View Clustering (MIMC) method, which extracts the common and view-specific information hidden in multi-view data and constructs a clustering-oriented comprehensive representation. Specifically, three constraints based on MI are devised in response to three issues. Correspondingly, we minimize the MI between the common representation and view-specific representations to exploit the inter-view complementary information. Further, we maximize the MI between the refined data representations and original data representations to preserve the principal information. Moreover, to learn a clustering-friendly comprehensive representation, the MI between the comprehensive embedding space and cluster structure is maximized. Finally, we conduct extensive experiments on six benchmark datasets, and the experimental results indicate that the proposed MIMC outperforms other clustering methods.|在深度多视图聚类中，研究人员面临三个棘手的问题，即互补性探索问题、信息保存问题和聚类结构发现问题。本文从互信息的角度考虑深度多视图聚类问题，尝试用互信息驱动的多视图聚类(MIMC)方法解决深度多视图聚类的三个重要问题，提取隐藏在多视图数据中的公共信息和特定视图信息，构建面向聚类的综合表示。具体而言，针对三个问题设计了基于 MI 的三个约束条件。相应地，我们将公共表示和视图特定表示之间的 MI 最小化，以利用视图间的互补信息。进一步，我们最大化精化数据表示和原始数据表示之间的 MI，以保留主信息。此外，为了学习一种聚类友好的综合表示方法，最大化了综合嵌入空间与聚类结构之间的 MI。最后，我们在六个基准数据集上进行了广泛的实验，实验结果表明所提出的 MIMC 方法优于其他聚类方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Mutual+Information-Driven+Multi-View+Clustering)|0| -|[Closed-form Machine Unlearning for Matrix Factorization](https://doi.org/10.1145/3583780.3614811)|Shuijing Zhang, Jian Lou, Li Xiong, Xiaoyu Zhang, Jing Liu|Xidian University, Guangzhou, China; Emory University, Atlanta, GA, USA; Zhejiang University, Guangzhou, China; Xidian University, Xi'an, China|Matrix factorization (MF) is a fundamental model in data mining and machine learning, which finds wide applications in diverse application areas, including recommendation systems with user-item rating matrices, phenotype extraction from electronic health records, and spatial-temporal data analysis for check-in records. The "right to be forgotten" has become an indispensable privacy consideration due to the widely enforced data protection regulations, which allow personal users having contributed their data for model training to revoke their data through a data deletion request. Consequently, it gives rise to the emerging task of machine unlearning for the MF model, which removes the influence of the matrix rows/columns from the trained MF factors upon receiving the deletion requests from the data owners of these rows/columns. The central goal is to effectively remove the influence of the rows/columns to be forgotten, while avoiding the computationally prohibitive baseline approach of retraining from scratch. Existing machine unlearning methods are either designed for single-variable models and not compatible with MF that has two factors as coupled model variables, or require alternative updates that are not efficient enough. In this paper, we propose a closed-form machine unlearning method. In particular, we explicitly capture the implicit dependency between the two factors, which yields the total Hessian-based Newton step as the closed-form unlearning update. In addition, we further introduce a series of efficiency-enhancement strategies by exploiting the structural properties of the total Hessian. Extensive experiments on five real-world datasets from three application areas as well as synthetic datasets validate the efficiency, effectiveness, and utility of the proposed method.|矩阵分解是数据挖掘和机器学习的基本模型，在不同的应用领域有广泛的应用，包括使用用户项目评分矩阵的推荐系统、从电子健康记录中提取表型，以及为签到记录进行时空数据分析。“被遗忘的权利”已成为一个不可或缺的隐私考虑，由于广泛执行的数据保护法规，允许个人用户贡献了他们的数据模型培训，撤销他们的数据通过一个数据删除请求。因此，它产生了 MF 模型的机器学习新任务，消除了训练后的 MF 因子的矩阵行/列在接收到这些行/列的数据所有者的删除请求时的影响。中心目标是有效地消除要忘记的行/列的影响，同时避免从头开始再训练的计算上禁止的基线方法。现有的机器去学习方法要么是为单变量模型设计的，与有两个因素作为耦合模型变量的 MF 不兼容，要么需要不够有效的替代更新。本文提出了一种闭式机器去学习方法。特别地，我们显式地捕捉了这两个因素之间的隐式依赖关系，从而产生了基于 Hessian 的牛顿步作为闭合形式的无学习更新。此外，我们进一步介绍了一系列的效率提高策略，利用结构性质的总黑森。通过对来自三个应用领域的五个实际数据集以及合成数据集的大量实验，验证了该方法的有效性和实用性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Closed-form+Machine+Unlearning+for+Matrix+Factorization)|0| +|[Closed-form Machine Unlearning for Matrix Factorization](https://doi.org/10.1145/3583780.3614811)|Shuijing Zhang, Jian Lou, Li Xiong, Xiaoyu Zhang, Jing Liu|Zhejiang University, Guangzhou, China; Xidian University, Guangzhou, China; Emory University, Atlanta, GA, USA; Xidian University, Xi'an, China|Matrix factorization (MF) is a fundamental model in data mining and machine learning, which finds wide applications in diverse application areas, including recommendation systems with user-item rating matrices, phenotype extraction from electronic health records, and spatial-temporal data analysis for check-in records. The "right to be forgotten" has become an indispensable privacy consideration due to the widely enforced data protection regulations, which allow personal users having contributed their data for model training to revoke their data through a data deletion request. Consequently, it gives rise to the emerging task of machine unlearning for the MF model, which removes the influence of the matrix rows/columns from the trained MF factors upon receiving the deletion requests from the data owners of these rows/columns. The central goal is to effectively remove the influence of the rows/columns to be forgotten, while avoiding the computationally prohibitive baseline approach of retraining from scratch. Existing machine unlearning methods are either designed for single-variable models and not compatible with MF that has two factors as coupled model variables, or require alternative updates that are not efficient enough. In this paper, we propose a closed-form machine unlearning method. In particular, we explicitly capture the implicit dependency between the two factors, which yields the total Hessian-based Newton step as the closed-form unlearning update. In addition, we further introduce a series of efficiency-enhancement strategies by exploiting the structural properties of the total Hessian. Extensive experiments on five real-world datasets from three application areas as well as synthetic datasets validate the efficiency, effectiveness, and utility of the proposed method.|矩阵分解是数据挖掘和机器学习的基本模型，在不同的应用领域有广泛的应用，包括使用用户项目评分矩阵的推荐系统、从电子健康记录中提取表型，以及为签到记录进行时空数据分析。“被遗忘的权利”已成为一个不可或缺的隐私考虑，由于广泛执行的数据保护法规，允许个人用户贡献了他们的数据模型培训，撤销他们的数据通过一个数据删除请求。因此，它产生了 MF 模型的机器学习新任务，消除了训练后的 MF 因子的矩阵行/列在接收到这些行/列的数据所有者的删除请求时的影响。中心目标是有效地消除要忘记的行/列的影响，同时避免从头开始再训练的计算上禁止的基线方法。现有的机器去学习方法要么是为单变量模型设计的，与有两个因素作为耦合模型变量的 MF 不兼容，要么需要不够有效的替代更新。本文提出了一种闭式机器去学习方法。特别地，我们显式地捕捉了这两个因素之间的隐式依赖关系，从而产生了基于 Hessian 的牛顿步作为闭合形式的无学习更新。此外，我们进一步介绍了一系列的效率提高策略，利用结构性质的总黑森。通过对来自三个应用领域的五个实际数据集以及合成数据集的大量实验，验证了该方法的有效性和实用性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Closed-form+Machine+Unlearning+for+Matrix+Factorization)|0| |[Time-aware Graph Structure Learning via Sequence Prediction on Temporal Graphs](https://doi.org/10.1145/3583780.3615081)|Haozhen Zhang, Xueting Han, Xi Xiao, Jing Bai|Microsoft Research Asia, Beijing, China; Shenzhen International Graduate School, Tsinghua University, Shenzhen, China|Temporal Graph Learning, which aims to model the time-evolving nature of graphs, has gained increasing attention and achieved remarkable performance recently. However, in reality, graph structures are often incomplete and noisy, which hinders temporal graph networks (TGNs) from learning informative representations. Graph contrastive learning uses data augmentation to generate plausible variations of existing data and learn robust representations. However, rule-based augmentation approaches may be suboptimal as they lack learnability and fail to leverage rich information from downstream tasks. To address these issues, we propose a Time-aware Graph Structure Learning (TGSL) approach via sequence prediction on temporal graphs, which learns better graph structures for downstream tasks through adding potential temporal edges. In particular, it predicts time-aware context embedding based on previously observed interactions and uses the Gumble-Top-K to select the closest candidate edges to this context embedding. Additionally, several candidate sampling strategies are proposed to ensure both efficiency and diversity. Furthermore, we jointly learn the graph structure and TGNs in an end-to-end manner and perform inference on the refined graph. Extensive experiments on temporal link prediction benchmarks demonstrate that TGSL yields significant gains for the popular TGNs such as TGAT and GraphMixer, and it outperforms other contrastive learning methods on temporal graphs. We will release the code in the future.|时态图学习作为一种对图的时间演化特性进行建模的学习方法，近年来得到了越来越多的关注，并取得了显著的效果。然而，在现实中，图结构往往是不完整和噪声，这阻碍了时间图网络(TGNs)学习信息表示。图形对比学习使用数据增强来生成现有数据的合理变化，并学习健壮的表示。然而，基于规则的增强方法可能是次优的，因为它们缺乏可学性，并且无法从下游任务中利用丰富的信息。为了解决这些问题，我们提出了一种基于时间图序列预测的时间感知图结构学习(TGSL)方法，该方法通过增加潜在的时间边来学习下游任务的图结构。特别是，它预测基于先前观察到的交互的时间感知上下文嵌入，并使用 Gumble-Top-K 选择最接近这种上下文嵌入的候选边缘。此外，本文还提出了几种候选抽样策略，以保证抽样的有效性和多样性。此外，我们还以端到端的方式共同学习图的结构和 TGN，并对精化后的图进行推理。通过对时间链路预测基准的大量实验表明，TGSL 在 TGAT 和 GraphMixer 等流行的时间链路预测基准上取得了显著的效果，并且在时间图上优于其他对比学习方法。我们将来会发布代码。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Time-aware+Graph+Structure+Learning+via+Sequence+Prediction+on+Temporal+Graphs)|0| -|[Mask- and Contrast-Enhanced Spatio-Temporal Learning for Urban Flow Prediction](https://doi.org/10.1145/3583780.3614958)|Xu Zhang, Yongshun Gong, Xinxin Zhang, Xiaoming Wu, Chengqi Zhang, Xiangjun Dong|Key Laboratory of Computing Power Network and Information Security, Ministry of Education, Shandong Computer Science Center, Qilu University of Technology (Shandong Academy of Sciences) & Shandong Provincial Key Laboratory of Computer Networks, Shandong Fundamental Research Center for Computer Science, Jinan, China; School of Software, Shandong University, Jinan, China; Centre for Artificial inteligence, Facuty of Engineering and informalion Technology, Universily of Technology Sydney, Sydney, NSW, Australia|As a critical mission of intelligent transportation systems, urban flow prediction (UFP) benefits in many city services including trip planning, congestion control, and public safety. Despite the achievements of previous studies, limited efforts have been observed on simultaneous investigation of the heterogeneity in both space and time aspects. That is, regional correlations would be variable at different timestamps. In this paper, we propose a spatio-temporal learning framework with mask and contrast enhancements to capture spatio-temporal variabilities among city regions. We devise a mask-enhanced pre-training task to learn latent correlations across the spatial and temporal dimensions, and then a graph-based method is developed to extract the significance of regions by using the inter-regional attention weights. To further acquire contrastive correlations of regions, we elaborate a pre-trained contrastive learning task with the global-local cross-attention mechanism. Thereafter, two well-trained encoders have strong capability to capture latent spatio-temporal representations for the flow forecasting with time-varying. Extensive experiments conducted on real-world urban flow datasets demonstrate that our method compares favorably with other state-of-the-art models.|作为智能交通系统的一项关键任务，城市流量预测(uFP)在许多城市服务领域都有着重要的应用价值，包括出行规划、拥塞控制和公共安全。尽管以前的研究已经取得了一些成果，但是在同时考察空间和时间方面的异质性方面所做的努力还是有限的。也就是说，在不同的时间戳下，地区间的相关性是可变的。在本文中，我们提出了一个具有掩模和对比度增强的时空学习框架来捕捉城市地区之间的时空变量。提出了一种基于面具增强的训练前任务来学习跨时间和空间维度的潜在相关性，然后利用区域间注意权重提取区域的显著性。为了进一步获得区域的对比相关性，我们设计了一个预训练的具有全局-局部交叉注意机制的对比学习任务。此后，两个训练有素的编码器具有强大的能力，捕获潜在的时空表示的流量预测与时变。在真实世界的城市流量数据集上进行的大量实验表明，我们的方法与其他最先进的模型相比具有良好的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Mask-+and+Contrast-Enhanced+Spatio-Temporal+Learning+for+Urban+Flow+Prediction)|0| -|[A Co-training Approach for Noisy Time Series Learning](https://doi.org/10.1145/3583780.3614759)|Weiqi Zhang, Jianfeng Zhang, Jia Li, Fugee Tsung|The Hong Kong University of Science and Technology (Guangzhou) & Hong Kong University of Science and Technology, Guangzhou & Hong Kong SAR, China; The Hong Kong University of Science and Technology, Hong Kong SAR, Hong Kong; Huawei Noah's Ark Lab, Shenzhen, China|In this work, we focus on robust time series representation learning. Our assumption is that real-world time series is noisy and complementary information from different views of the same time series plays an important role while analyzing noisy input. Based on this, we create two views for the input time series through two different encoders. We conduct co-training based contrastive learning iteratively to learn the encoders. Our experiments demonstrate that this co-training approach leads to a significant improvement in performance. Especially, by leveraging the complementary information from different views, our proposed TS-CoT method can mitigate the impact of data noise and corruption. Empirical evaluations on four time series benchmarks in unsupervised and semi-supervised settings reveal that TS-CoT outperforms existing methods. Furthermore, the representations learned by TS-CoT can transfer well to downstream tasks through fine-tuning.|在这项工作中，我们着重于鲁棒的时间序列表示学习。我们假设现实世界的时间序列是有噪声的，同一时间序列的不同观点的互补信息在分析有噪声的输入时起着重要作用。在此基础上，我们通过两个不同的编码器为输入时间序列创建两个视图。我们反复进行基于协同训练的对比学习来学习编码器。我们的实验表明，这种协同训练方法可以显著提高性能。特别是，通过利用来自不同视图的互补信息，我们提出的 TS-CoT 方法可以减轻数据噪声和腐败的影响。对四个时间序列基准在无监督和半监督情况下的实证评估表明，TS-CoT 优于现有的方法。此外，通过微调，TS-CoT 学到的表示能够很好地传递到下游任务。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Co-training+Approach+for+Noisy+Time+Series+Learning)|0| -|[Unleashing the Power of Shared Label Structures for Human Activity Recognition](https://doi.org/10.1145/3583780.3615101)|Xiyuan Zhang, Ranak Roy Chowdhury, Jiayun Zhang, Dezhi Hong, Rajesh K. Gupta, Jingbo Shang|Amazon, Seattle, WA, USA; University of California, San Diego, La Jolla, CA, USA|Current human activity recognition (HAR) techniques regard activity labels as integer class IDs without explicitly modeling the semantics of class labels. We observe that different activity names often have shared structures. For example, "open door" and "open fridge" both have "open" as the action; "kicking soccer ball" and "playing tennis ball" both have "ball" as the object. Such shared structures in label names can be translated to the similarity in sensory data and modeling common structures would help uncover knowledge across different activities, especially for activities with limited samples. In this paper, we propose SHARE, a HAR framework that takes into account shared structures of label names for different activities. To exploit the shared structures, SHARE comprises an encoder for extracting features from input sensory time series and a decoder for generating label names as a token sequence. We also propose three label augmentation techniques to help the model more effectively capture semantic structures across activities, including a basic token-level augmentation, and two enhanced embedding-level and sequence-level augmentations utilizing the capabilities of pre-trained models. SHARE outperforms state-of-the-art HAR models in extensive experiments on seven HAR benchmark datasets. We also evaluate in few-shot learning and label imbalance settings and observe even more significant performance gap.|当前的人类活动识别(HAR)技术将活动标签视为整数类 ID，而没有明确建模类标签的语义。我们注意到，不同的活动名称通常具有共享的结构。例如，“开门”和“开冰箱”都以“开”为动作; “踢足球”和“打网球”都以“球”为对象。标签名称中的这种共享结构可以转化为感官数据中的相似性，建立共同结构模型将有助于揭示不同活动之间的知识，特别是对于样本有限的活动。在本文中，我们提出了 SHARE，一个考虑到不同活动的标签名共享结构的 HAR 框架。为了利用共享结构，SHARE 包括用于从输入感官时间序列中提取特征的编码器和用于生成标签名作为标记序列的解码器。我们还提出了三种标签增强技术，以帮助模型更有效地捕获跨活动的语义结构，包括一个基本的令牌级增强，以及两个增强的嵌入级和序列级增强利用预训练模型的能力。在七个 HAR 基准数据集的广泛实验中，SHARE 优于最先进的 HAR 模型。我们也评估在少镜头学习和标签不平衡的设置，并观察到更显着的性能差距。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Unleashing+the+Power+of+Shared+Label+Structures+for+Human+Activity+Recognition)|0| +|[Mask- and Contrast-Enhanced Spatio-Temporal Learning for Urban Flow Prediction](https://doi.org/10.1145/3583780.3614958)|Xu Zhang, Yongshun Gong, Xinxin Zhang, Xiaoming Wu, Chengqi Zhang, Xiangjun Dong|Centre for Artificial inteligence, Facuty of Engineering and informalion Technology, Universily of Technology Sydney, Sydney, NSW, Australia; School of Software, Shandong University, Jinan, China; Key Laboratory of Computing Power Network and Information Security, Ministry of Education, Shandong Computer Science Center, Qilu University of Technology (Shandong Academy of Sciences) & Shandong Provincial Key Laboratory of Computer Networks, Shandong Fundamental Research Center for Computer Science, Jinan, China|As a critical mission of intelligent transportation systems, urban flow prediction (UFP) benefits in many city services including trip planning, congestion control, and public safety. Despite the achievements of previous studies, limited efforts have been observed on simultaneous investigation of the heterogeneity in both space and time aspects. That is, regional correlations would be variable at different timestamps. In this paper, we propose a spatio-temporal learning framework with mask and contrast enhancements to capture spatio-temporal variabilities among city regions. We devise a mask-enhanced pre-training task to learn latent correlations across the spatial and temporal dimensions, and then a graph-based method is developed to extract the significance of regions by using the inter-regional attention weights. To further acquire contrastive correlations of regions, we elaborate a pre-trained contrastive learning task with the global-local cross-attention mechanism. Thereafter, two well-trained encoders have strong capability to capture latent spatio-temporal representations for the flow forecasting with time-varying. Extensive experiments conducted on real-world urban flow datasets demonstrate that our method compares favorably with other state-of-the-art models.|作为智能交通系统的一项关键任务，城市流量预测(uFP)在许多城市服务领域都有着重要的应用价值，包括出行规划、拥塞控制和公共安全。尽管以前的研究已经取得了一些成果，但是在同时考察空间和时间方面的异质性方面所做的努力还是有限的。也就是说，在不同的时间戳下，地区间的相关性是可变的。在本文中，我们提出了一个具有掩模和对比度增强的时空学习框架来捕捉城市地区之间的时空变量。提出了一种基于面具增强的训练前任务来学习跨时间和空间维度的潜在相关性，然后利用区域间注意权重提取区域的显著性。为了进一步获得区域的对比相关性，我们设计了一个预训练的具有全局-局部交叉注意机制的对比学习任务。此后，两个训练有素的编码器具有强大的能力，捕获潜在的时空表示的流量预测与时变。在真实世界的城市流量数据集上进行的大量实验表明，我们的方法与其他最先进的模型相比具有良好的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Mask-+and+Contrast-Enhanced+Spatio-Temporal+Learning+for+Urban+Flow+Prediction)|0| +|[A Co-training Approach for Noisy Time Series Learning](https://doi.org/10.1145/3583780.3614759)|Weiqi Zhang, Jianfeng Zhang, Jia Li, Fugee Tsung|Huawei Noah's Ark Lab, Shenzhen, China; The Hong Kong University of Science and Technology (Guangzhou) & Hong Kong University of Science and Technology, Guangzhou & Hong Kong SAR, China; The Hong Kong University of Science and Technology, Hong Kong SAR, Hong Kong|In this work, we focus on robust time series representation learning. Our assumption is that real-world time series is noisy and complementary information from different views of the same time series plays an important role while analyzing noisy input. Based on this, we create two views for the input time series through two different encoders. We conduct co-training based contrastive learning iteratively to learn the encoders. Our experiments demonstrate that this co-training approach leads to a significant improvement in performance. Especially, by leveraging the complementary information from different views, our proposed TS-CoT method can mitigate the impact of data noise and corruption. Empirical evaluations on four time series benchmarks in unsupervised and semi-supervised settings reveal that TS-CoT outperforms existing methods. Furthermore, the representations learned by TS-CoT can transfer well to downstream tasks through fine-tuning.|在这项工作中，我们着重于鲁棒的时间序列表示学习。我们假设现实世界的时间序列是有噪声的，同一时间序列的不同观点的互补信息在分析有噪声的输入时起着重要作用。在此基础上，我们通过两个不同的编码器为输入时间序列创建两个视图。我们反复进行基于协同训练的对比学习来学习编码器。我们的实验表明，这种协同训练方法可以显著提高性能。特别是，通过利用来自不同视图的互补信息，我们提出的 TS-CoT 方法可以减轻数据噪声和腐败的影响。对四个时间序列基准在无监督和半监督情况下的实证评估表明，TS-CoT 优于现有的方法。此外，通过微调，TS-CoT 学到的表示能够很好地传递到下游任务。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Co-training+Approach+for+Noisy+Time+Series+Learning)|0| +|[Unleashing the Power of Shared Label Structures for Human Activity Recognition](https://doi.org/10.1145/3583780.3615101)|Xiyuan Zhang, Ranak Roy Chowdhury, Jiayun Zhang, Dezhi Hong, Rajesh K. Gupta, Jingbo Shang|University of California, San Diego, La Jolla, CA, USA; Amazon, Seattle, WA, USA|Current human activity recognition (HAR) techniques regard activity labels as integer class IDs without explicitly modeling the semantics of class labels. We observe that different activity names often have shared structures. For example, "open door" and "open fridge" both have "open" as the action; "kicking soccer ball" and "playing tennis ball" both have "ball" as the object. Such shared structures in label names can be translated to the similarity in sensory data and modeling common structures would help uncover knowledge across different activities, especially for activities with limited samples. In this paper, we propose SHARE, a HAR framework that takes into account shared structures of label names for different activities. To exploit the shared structures, SHARE comprises an encoder for extracting features from input sensory time series and a decoder for generating label names as a token sequence. We also propose three label augmentation techniques to help the model more effectively capture semantic structures across activities, including a basic token-level augmentation, and two enhanced embedding-level and sequence-level augmentations utilizing the capabilities of pre-trained models. SHARE outperforms state-of-the-art HAR models in extensive experiments on seven HAR benchmark datasets. We also evaluate in few-shot learning and label imbalance settings and observe even more significant performance gap.|当前的人类活动识别(HAR)技术将活动标签视为整数类 ID，而没有明确建模类标签的语义。我们注意到，不同的活动名称通常具有共享的结构。例如，“开门”和“开冰箱”都以“开”为动作; “踢足球”和“打网球”都以“球”为对象。标签名称中的这种共享结构可以转化为感官数据中的相似性，建立共同结构模型将有助于揭示不同活动之间的知识，特别是对于样本有限的活动。在本文中，我们提出了 SHARE，一个考虑到不同活动的标签名共享结构的 HAR 框架。为了利用共享结构，SHARE 包括用于从输入感官时间序列中提取特征的编码器和用于生成标签名作为标记序列的解码器。我们还提出了三种标签增强技术，以帮助模型更有效地捕获跨活动的语义结构，包括一个基本的令牌级增强，以及两个增强的嵌入级和序列级增强利用预训练模型的能力。在七个 HAR 基准数据集的广泛实验中，SHARE 优于最先进的 HAR 模型。我们也评估在少镜头学习和标签不平衡的设置，并观察到更显着的性能差距。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Unleashing+the+Power+of+Shared+Label+Structures+for+Human+Activity+Recognition)|0| |[AspectMMKG: A Multi-modal Knowledge Graph with Aspect-aware Entities](https://doi.org/10.1145/3583780.3614782)|Jingdan Zhang, Jiaan Wang, Xiaodan Wang, Zhixu Li, Yanghua Xiao|Fudan University, Shanghai, China; Soochow University, Suzhou, China|Multi-modal knowledge graphs (MMKGs) combine different modal data (e.g., text and image) for a comprehensive understanding of entities. Despite the recent progress of large-scale MMKGs, existing MMKGs neglect the multi-aspect nature of entities, limiting the ability to comprehend entities from various perspectives. In this paper, we construct AspectMMKG, the first MMKG with aspect-related images by matching images to different entity aspects. Specifically, we collect aspect-related images from a knowledge base, and further extract aspect-related sentences from the knowledge base as queries to retrieve a large number of aspect-related images via an online image search engine. Finally, AspectMMKG contains 2,380 entities, 18,139 entity aspects, and 645,383 aspect-related images. We demonstrate the usability of AspectMMKG in entity aspect linking (EAL) downstream task and show that previous EAL models achieve a new state-of-the-art performance with the help of AspectMMKG. To facilitate the research on aspect-related MMKG, we further propose an aspect-related image retrieval (AIR) model, that aims to correct and expand aspect-related images in AspectMMKG. We train an AIR model to learn the relationship between entity image and entity aspect-related images by incorporating entity image, aspect, and aspect image information. Experimental results indicate that the AIR model could retrieve suitable images for a given entity w.r.t different aspects.|多模态知识图(MMKGs)将不同的模态数据(例如文本和图像)组合在一起，以便对实体进行全面的理解。尽管大规模 MMKG 近年来取得了一些进展，但是现有的 MMKG 忽视了实体的多面性，限制了从不同角度理解实体的能力。本文通过对不同实体方面的图像进行匹配，构造了第一个具有方面相关图像的 MMKG-AspectMMKG。具体来说，我们从知识库中收集与方面相关的图像，并进一步从知识库中提取与方面相关的句子作为查询，通过在线图像搜索引擎检索大量与方面相关的图像。最后，AspectMMKG 包含2,380个实体、18,139个实体方面和645,383个与方面相关的图像。我们证明了 AspectMMKG 在实体方面连接(EAL)下游任务中的可用性，并表明以前的 EAL 模型在 AspectMMKG 的帮助下取得了新的最佳性能。为了方便对 AspectMMKG 的研究，我们进一步提出了一种 AIR 模型，用于校正和扩展 AspectMMKG 中与 AspectMMKG 相关的图像。我们训练一个 AIR 模型来学习实体图像和实体体相关图像之间的关系，通过合并实体图像、体相和体相图像信息。实验结果表明，AIR 模型能够在给定实体的不同方面提取出合适的图像。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=AspectMMKG:+A+Multi-modal+Knowledge+Graph+with+Aspect-aware+Entities)|0| -|[Towards Dynamic and Reliable Private Key Management for Hierarchical Access Structure in Decentralized Storage](https://doi.org/10.1145/3583780.3615090)|Yifang Zhang, Mingyue Wang, Yu Guo, Fangda Guo|City University of Hong Kong, Hong Kong, Hong Kong; Beijing Normal University, Beijing, China; Chinese Academy of Sciences, Beijing, China|With the widespread development of decentralized storage, it is increasingly popular for users to store their data to the decentralized database systems for the well-understood benefits of outsourced storage. To ensure the data privacy, systems commonly require users to securely keep their private keys. Thus, the secure storage of private keys is an important issue in these systems. However, existing key-management schemes commonly rely on a Trusted Third Party (TTP), which raises critical security concerns such as the single point of failure and Distributed Denial of Service (DDoS) attacks. In this paper, we propose HasDPSS, a secure and efficient blockchain-based key-management scheme for decentralized storage systems. It uses secret sharing, a lightweight cryptographic technique, to build the decentralized key-management scheme. Considering that the reliability of managing participants has inherent heterogeneity, we introduce the hierarchical access structure to achieve fine-grained key management. Meanwhile, to adapt the node churn of decentralized key management, HasDPSS enables a dynamic management committee to provide reliable services with a proactive refresh mechanism while protecting the integrity and security of private keys. In our design, we use the dimension switch method of polynomials in the evolving process to achieve the committee change of the hierarchical access structure. The reliability of participants is guaranteed by the customized commitment protocol and the immutable property of the blockchain. We thoroughly analyze security strengths and conduct extensive experiments to demonstrate the practicality of our design.|随着分散存储的广泛发展，用户将数据存储到分散数据库系统中以获得外包存储的好处越来越受欢迎。为了确保数据的私密性，系统通常要求用户安全地保存他们的私钥。因此，私钥的安全存储是这些系统中的一个重要问题。然而，现有的密钥管理方案通常依赖可信第三方(tTP) ，这引起了严重的安全问题，例如单点故障和分布式分布式拒绝服务攻击(dDoS)攻击。本文提出了一种基于区块链的分散存储系统密钥管理方案 HasDPSS。它使用秘密共享这种轻量级密码技术来构建分散式密钥管理方案。考虑到管理参与者的可靠性具有内在的异构性，引入分层访问结构实现细粒度密钥管理。同时，为了适应分散密钥管理的节点流失，HasDPSS 使得动态管理委员会能够在保护私钥完整性和安全性的同时，通过主动刷新机制提供可靠的服务。在我们的设计中，我们使用多项式在演化过程中的维数切换方法来实现层级访问结构的委员会变化。定制的承诺协议和区块链的不变性保证了参与者的可靠性。我们深入分析了安全优势，并进行了广泛的实验，以证明我们的设计的实用性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Towards+Dynamic+and+Reliable+Private+Key+Management+for+Hierarchical+Access+Structure+in+Decentralized+Storage)|0| -|[MLPST: MLP is All You Need for Spatio-Temporal Prediction](https://doi.org/10.1145/3583780.3614969)|Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S. Joe Qin, Hongwei Zhao|City University of Hong Kong, Hong Kong, Hong Kong; Jinan University, Guangzhou, China; Lingnan University, Hong Kong, Hong Kong; JD Intelligent Cities Research & JD iCity, JD Technology, Beijing, China; Jilin University, Changchun, China; City University of Hong Kong, Hong Kong, China|Traffic prediction is a typical spatio-temporal data mining task and has great significance to the public transportation system. Considering the demand for its grand application, we recognize key factors for an ideal spatio-temporal prediction method: efficient, lightweight, and effective. However, the current deep model-based spatio-temporal prediction solutions generally own intricate architectures with cumbersome optimization, which can hardly meet these expectations. To accomplish the above goals, we propose an intuitive and novel framework, MLPST, a pure multi-layer perceptron architecture for traffic prediction. Specifically, we first capture spatial relationships from both local and global receptive fields. Then, temporal dependencies in different intervals are comprehensively considered. Through compact and swift MLP processing, MLPST can well capture the spatial and temporal dependencies while requiring only linear computational complexity, as well as model parameters that are more than an order of magnitude lower than baselines. Extensive experiments validated the superior effectiveness and efficiency of MLPST against advanced baselines, and among models with optimal accuracy, MLPST achieves the best time and space efficiency.|交通预测是一个典型的时空数据挖掘任务，对公共交通系统具有重要意义。考虑到其广泛应用的需求，我们认识到一个理想的时空预测方法的关键因素: 高效、轻量和有效。然而，目前基于深度模型的时空预测解决方案通常具有复杂的体系结构和繁琐的优化，难以满足这些期望。为了实现上述目标，我们提出了一个直观和新颖的框架，MLPST，一个纯粹的多层感知器体系结构的交通预测。具体来说，我们首先从局部和全局接收字段捕获空间关系。然后，综合考虑不同区间的时间依赖关系。通过紧凑和快速的 MLP 处理，MLPST 可以很好地捕获空间和时间的依赖性，同时只需要线性计算复杂度，以及比基线低一个数量级以上的模型参数。大量的实验验证了 MLPST 算法对高精度基线的有效性和效率，在精度最高的模型中，MLPST 算法获得了最佳的时间和空间效率。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=MLPST:+MLP+is+All+You+Need+for+Spatio-Temporal+Prediction)|0| -|[Geometric Graph Learning for Protein Mutation Effect Prediction](https://doi.org/10.1145/3583780.3614893)|Kangfei Zhao, Yu Rong, Biaobin Jiang, Jianheng Tang, Hengtong Zhang, Jeffrey Xu Yu, Peilin Zhao|The Chinese University of Hong Kong, Hong Kong, China; Beijing Institute of Technology, Tencent AI Lab, None, China; Tencent AI Lab, Shenzhen, China; Hong Kong University of Science and Technology, Hong Kong, China|Proteins govern a wide range of biological systems. Evaluating the changes in protein properties upon protein mutation is a fundamental application of protein design, where modeling the 3D protein structure is a principal task for AI-driven computational approaches. Existing deep learning (DL) approaches represent the protein structure as a 3D geometric graph and simplify the graph modeling to different degrees, thereby failing to capture the low-level atom patterns and high-level amino acid patterns simultaneously. In addition, limited training samples with ground truth labels and protein structures further restrict the effectiveness of DL approaches. In this paper, we propose a new graph learning framework, Hierarchical Graph Invariant Network (HGIN), a fine-grained and data-efficient graph neural encoder for encoding protein structures and predicting the mutation effect on protein properties. For fine-grained modeling, HGIN hierarchically models the low-level interactions of atoms and the high-level interactions of amino acid residues by Graph Neural Networks. For data efficiency, HGIN preserves the invariant encoding for atom permutation and coordinate transformation, which is an intrinsic inductive bias of property prediction that bypasses data augmentations. We integrate HGIN into a Siamese network to predict the quantitative effect on protein properties upon mutations. Our approach outperforms 9 state-of-the-art approaches on 3 protein datasets. More inspiringly, when predicting the neutralizing ability of human antibodies against COVID-19 mutant viruses, HGIN achieves an absolute improvement of 0.23 regarding the Spearman coefficient.|蛋白质控制着广泛的生物系统。评估蛋白质突变后蛋白质特性的变化是蛋白质设计的一个基本应用，其中建立三维蛋白质结构是人工智能驱动的计算方法的主要任务。现有的深度学习(DL)方法将蛋白质结构表示为一个三维几何图形，并对图形建模进行了不同程度的简化，从而无法同时捕获低级原子模式和高级氨基酸模式。此外，有限的训练样本与地面真相标签和蛋白质结构进一步限制了 DL 方法的有效性。本文提出了一种新的图形学习框架——层次图不变网络(HGIN) ，它是一种细粒度、数据高效的图形神经编码器，用于编码蛋白质结构和预测突变对蛋白质性质的影响。对于细粒度建模，HGIN 通过图形神经网络对原子间的低能级相互作用和氨基酸残基间的高能级相互作用进行分层建模。为了提高数据效率，HGIN 保留了原子置换和坐标变换的不变编码，这是一种绕过数据增广的属性预测的内在归纳偏差。我们将 HGIN 整合到一个暹罗网络中，以预测突变对蛋白质特性的定量影响。我们的方法在3个蛋白质数据集上优于9个最先进的方法。更鼓舞人心的是，当预测人类抗体对2019冠状病毒疾病突变病毒的中和能力时，HGIN 在斯皮尔曼系数方面实现了0.23的绝对改善。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Geometric+Graph+Learning+for+Protein+Mutation+Effect+Prediction)|0| +|[Towards Dynamic and Reliable Private Key Management for Hierarchical Access Structure in Decentralized Storage](https://doi.org/10.1145/3583780.3615090)|Yifang Zhang, Mingyue Wang, Yu Guo, Fangda Guo|Beijing Normal University, Beijing, China; Chinese Academy of Sciences, Beijing, China; City University of Hong Kong, Hong Kong, Hong Kong|With the widespread development of decentralized storage, it is increasingly popular for users to store their data to the decentralized database systems for the well-understood benefits of outsourced storage. To ensure the data privacy, systems commonly require users to securely keep their private keys. Thus, the secure storage of private keys is an important issue in these systems. However, existing key-management schemes commonly rely on a Trusted Third Party (TTP), which raises critical security concerns such as the single point of failure and Distributed Denial of Service (DDoS) attacks. In this paper, we propose HasDPSS, a secure and efficient blockchain-based key-management scheme for decentralized storage systems. It uses secret sharing, a lightweight cryptographic technique, to build the decentralized key-management scheme. Considering that the reliability of managing participants has inherent heterogeneity, we introduce the hierarchical access structure to achieve fine-grained key management. Meanwhile, to adapt the node churn of decentralized key management, HasDPSS enables a dynamic management committee to provide reliable services with a proactive refresh mechanism while protecting the integrity and security of private keys. In our design, we use the dimension switch method of polynomials in the evolving process to achieve the committee change of the hierarchical access structure. The reliability of participants is guaranteed by the customized commitment protocol and the immutable property of the blockchain. We thoroughly analyze security strengths and conduct extensive experiments to demonstrate the practicality of our design.|随着分散存储的广泛发展，用户将数据存储到分散数据库系统中以获得外包存储的好处越来越受欢迎。为了确保数据的私密性，系统通常要求用户安全地保存他们的私钥。因此，私钥的安全存储是这些系统中的一个重要问题。然而，现有的密钥管理方案通常依赖可信第三方(tTP) ，这引起了严重的安全问题，例如单点故障和分布式分布式拒绝服务攻击(dDoS)攻击。本文提出了一种基于区块链的分散存储系统密钥管理方案 HasDPSS。它使用秘密共享这种轻量级密码技术来构建分散式密钥管理方案。考虑到管理参与者的可靠性具有内在的异构性，引入分层访问结构实现细粒度密钥管理。同时，为了适应分散密钥管理的节点流失，HasDPSS 使得动态管理委员会能够在保护私钥完整性和安全性的同时，通过主动刷新机制提供可靠的服务。在我们的设计中，我们使用多项式在演化过程中的维数切换方法来实现层级访问结构的委员会变化。定制的承诺协议和区块链的不变性保证了参与者的可靠性。我们深入分析了安全优势，并进行了广泛的实验，以证明我们的设计的实用性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Towards+Dynamic+and+Reliable+Private+Key+Management+for+Hierarchical+Access+Structure+in+Decentralized+Storage)|0| +|[MLPST: MLP is All You Need for Spatio-Temporal Prediction](https://doi.org/10.1145/3583780.3614969)|Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S. Joe Qin, Hongwei Zhao|Jilin University, Changchun, China; City University of Hong Kong, Hong Kong, Hong Kong; City University of Hong Kong, Hong Kong, China; JD Intelligent Cities Research & JD iCity, JD Technology, Beijing, China; Jinan University, Guangzhou, China; Lingnan University, Hong Kong, Hong Kong|Traffic prediction is a typical spatio-temporal data mining task and has great significance to the public transportation system. Considering the demand for its grand application, we recognize key factors for an ideal spatio-temporal prediction method: efficient, lightweight, and effective. However, the current deep model-based spatio-temporal prediction solutions generally own intricate architectures with cumbersome optimization, which can hardly meet these expectations. To accomplish the above goals, we propose an intuitive and novel framework, MLPST, a pure multi-layer perceptron architecture for traffic prediction. Specifically, we first capture spatial relationships from both local and global receptive fields. Then, temporal dependencies in different intervals are comprehensively considered. Through compact and swift MLP processing, MLPST can well capture the spatial and temporal dependencies while requiring only linear computational complexity, as well as model parameters that are more than an order of magnitude lower than baselines. Extensive experiments validated the superior effectiveness and efficiency of MLPST against advanced baselines, and among models with optimal accuracy, MLPST achieves the best time and space efficiency.|交通预测是一个典型的时空数据挖掘任务，对公共交通系统具有重要意义。考虑到其广泛应用的需求，我们认识到一个理想的时空预测方法的关键因素: 高效、轻量和有效。然而，目前基于深度模型的时空预测解决方案通常具有复杂的体系结构和繁琐的优化，难以满足这些期望。为了实现上述目标，我们提出了一个直观和新颖的框架，MLPST，一个纯粹的多层感知器体系结构的交通预测。具体来说，我们首先从局部和全局接收字段捕获空间关系。然后，综合考虑不同区间的时间依赖关系。通过紧凑和快速的 MLP 处理，MLPST 可以很好地捕获空间和时间的依赖性，同时只需要线性计算复杂度，以及比基线低一个数量级以上的模型参数。大量的实验验证了 MLPST 算法对高精度基线的有效性和效率，在精度最高的模型中，MLPST 算法获得了最佳的时间和空间效率。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=MLPST:+MLP+is+All+You+Need+for+Spatio-Temporal+Prediction)|0| +|[Geometric Graph Learning for Protein Mutation Effect Prediction](https://doi.org/10.1145/3583780.3614893)|Kangfei Zhao, Yu Rong, Biaobin Jiang, Jianheng Tang, Hengtong Zhang, Jeffrey Xu Yu, Peilin Zhao|The Chinese University of Hong Kong, Hong Kong, China; Hong Kong University of Science and Technology, Hong Kong, China; Tencent AI Lab, Shenzhen, China; Beijing Institute of Technology, Tencent AI Lab, None, China|Proteins govern a wide range of biological systems. Evaluating the changes in protein properties upon protein mutation is a fundamental application of protein design, where modeling the 3D protein structure is a principal task for AI-driven computational approaches. Existing deep learning (DL) approaches represent the protein structure as a 3D geometric graph and simplify the graph modeling to different degrees, thereby failing to capture the low-level atom patterns and high-level amino acid patterns simultaneously. In addition, limited training samples with ground truth labels and protein structures further restrict the effectiveness of DL approaches. In this paper, we propose a new graph learning framework, Hierarchical Graph Invariant Network (HGIN), a fine-grained and data-efficient graph neural encoder for encoding protein structures and predicting the mutation effect on protein properties. For fine-grained modeling, HGIN hierarchically models the low-level interactions of atoms and the high-level interactions of amino acid residues by Graph Neural Networks. For data efficiency, HGIN preserves the invariant encoding for atom permutation and coordinate transformation, which is an intrinsic inductive bias of property prediction that bypasses data augmentations. We integrate HGIN into a Siamese network to predict the quantitative effect on protein properties upon mutations. Our approach outperforms 9 state-of-the-art approaches on 3 protein datasets. More inspiringly, when predicting the neutralizing ability of human antibodies against COVID-19 mutant viruses, HGIN achieves an absolute improvement of 0.23 regarding the Spearman coefficient.|蛋白质控制着广泛的生物系统。评估蛋白质突变后蛋白质特性的变化是蛋白质设计的一个基本应用，其中建立三维蛋白质结构是人工智能驱动的计算方法的主要任务。现有的深度学习(DL)方法将蛋白质结构表示为一个三维几何图形，并对图形建模进行了不同程度的简化，从而无法同时捕获低级原子模式和高级氨基酸模式。此外，有限的训练样本与地面真相标签和蛋白质结构进一步限制了 DL 方法的有效性。本文提出了一种新的图形学习框架——层次图不变网络(HGIN) ，它是一种细粒度、数据高效的图形神经编码器，用于编码蛋白质结构和预测突变对蛋白质性质的影响。对于细粒度建模，HGIN 通过图形神经网络对原子间的低能级相互作用和氨基酸残基间的高能级相互作用进行分层建模。为了提高数据效率，HGIN 保留了原子置换和坐标变换的不变编码，这是一种绕过数据增广的属性预测的内在归纳偏差。我们将 HGIN 整合到一个暹罗网络中，以预测突变对蛋白质特性的定量影响。我们的方法在3个蛋白质数据集上优于9个最先进的方法。更鼓舞人心的是，当预测人类抗体对2019冠状病毒疾病突变病毒的中和能力时，HGIN 在斯皮尔曼系数方面实现了0.23的绝对改善。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Geometric+Graph+Learning+for+Protein+Mutation+Effect+Prediction)|0| |[Simulating Student Interactions with Two-stage Imitation Learning for Intelligent Educational Systems](https://doi.org/10.1145/3583780.3615060)|Guanhao Zhao, Zhenya Huang, Yan Zhuang, Jiayu Liu, Qi Liu, Zhiding Liu, Jinze Wu, Enhong Chen|iFLYTEK AI Research (Central China), iFLYTEK Co., Ltd, Hefei, China; School of Computer Science and Technology, University of Science and Technology of China & State Key Laboratory of Cognitive Intelligence, Hefei, China; School of Data Science, University of Science and Technology of China & State Key Laboratory of Cognitive Intelligence, Hefei, China; Anhui Province Key Laboratory of Big Data Analysis and Application, University of Science and Technology of China & State Key Laboratory of Cognitive Intelligence, Hefei, China|The fundamental task of intelligent educational systems is to offer adaptive learning services to students, such as exercise recommendations and computerized adaptive testing. However, optimizing required models in these systems would always encounter the collection difficulty of high-quality interaction data in practice. Therefore, establishing a student simulator is of great value since it can generate valid interactions to help optimize models. Existing advances have achieved success but generally suffer from exposure bias and overlook long-term intentions. To tackle these problems, we propose a novel Direct-Adversarial Imitation Student Simulator (DAISim) by formulating it as a Markov Decision Process (MDP), which unifies the workflow of the simulator in training and generating to alleviate the exposure bias and single-step optimization problems. To construct the intentions underlying the complex student interactions, we first propose a direct imitation strategy to mimic the interactions with a simple reward function. Then, we propose an adversarial imitation strategy to learn a rational distribution with the reward given by a parameterized discriminator. Furthermore, we optimize the discriminator in adversarial imitation in a pairwise manner, and the theoretical analysis shows that the pairwise discriminator would improve the generation quality. We conduct extensive experiments on real-world datasets, where the results demonstrate that our DAISim can simulate high-quality student interactions whose distribution is close to real distribution and can promote several downstream services.|智能教育系统的基本任务是为学生提供在线机机器学习服务，如练习推荐和计算机自适应测试。然而，在这些系统中优化所需的模型在实践中总是会遇到收集高质量交互数据的困难。因此，建立一个学生模拟器是非常有价值的，因为它可以产生有效的交互，以帮助优化模型。现有的进展取得了成功，但普遍存在暴露偏见，忽视了长期意图。为了解决这些问题，我们提出了一种新颖的直接对抗模拟学生模拟器(DAISim) ，将其设计为一个马可夫决策过程(mDP) ，统一了模拟器在训练和生成过程中的工作流程，以减少暴露偏差和单步优化问题。为了构建复杂学生互动的意图，我们首先提出了一种直接模仿策略，通过一个简单的奖励函数来模拟学生互动。然后，我们提出了一个对抗模仿策略来学习一个由参数化鉴别器给出报酬的有理分布。进一步，我们以成对的方式优化了对抗模仿中的鉴别器，理论分析表明成对鉴别器可以提高生成质量。我们在真实世界的数据集上进行了广泛的实验，结果表明我们的 DAISim 可以模拟高质量的学生交互，其分布接近真实分布，并可以促进几个下游服务。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Simulating+Student+Interactions+with+Two-stage+Imitation+Learning+for+Intelligent+Educational+Systems)|0| |[Highly-Optimized Forgetting for Creating Signature-Based Views of Ontologies](https://doi.org/10.1145/3583780.3614771)|Yizheng Zhao|Nanjing University, Nanjing, China|Uniform interpolation (UI) is a non-standard reasoning service that seeks to project an ontology down to its sub-signature --- given an ontology taking a certain signature, and a subset Σ of "relevant names'' of that signature, compute a new ontology, called a uniform interpolant, that uses only the relevant names while preserving the semantics of the relevant names in the uniform interpolant. UI is of great potential importance since it may be used in a variety of applications where suitable views of ontologies need to be computed. However, this potential can only be fully realized if a highly optimized method for computing such views exists. Previous research has shown that computing uniform interpolants of ELH-ontologies is a computationally extremely hard problem --- a finite uniform interpolant does not always exist for ELH, and if it exists, then there exists one of at most triple exponential size in terms of the original ontology, and that, in the worst case, no shorter interpolant exists. Despite the inherent difficulty of the problem, in this paper, we present a highly optimized forgetting method for computing uniform interpolants of ELH-ontologies, and show however that, with good reduction and inference strategies, such uniform interpolants can be efficiently computed. The method is an improvement of the one presented in our previous work. What sets it apart is its flexibility to treat concept names of different types differently, effectively cutting down on the inferences involved. This treatment is primarily driven by the polarities of the concept names within an ontology. A comprehensive evaluation with a prototypical implementation of the method shows >95% average success rates over two popular benchmark datasets and demonstrates a clear computational advantage over state-of-the-art systems.|统一插值(UI)是一种非标准的推理服务，它试图将一个本体投射到它的子签名——给定一个带有特定签名的本体，以及该签名的“相关名称”的子集 Σ，计算一个新的本体，称为统一插值，它只使用相关名称，同时在统一插值中保留相关名称的语义。UI 具有很大的潜在重要性，因为它可以用于需要计算合适的本体视图的各种应用程序中。然而，只有存在一种高度优化的计算这种视图的方法，这种潜力才能完全实现。先前的研究已经表明，计算 ELH 本体的一致插值是一个计算上非常困难的问题——-一个有限的一致插值并不总是存在于 ELH 中，如果它存在，那么就存在一个最多三倍指数大小的插值，在最坏的情况下，没有更短的插值存在。本文提出了一种计算 ELH- 本体一致插值的高度优化遗忘方法，并证明了采用良好的约简和推理策略，可以有效地计算 ELH- 本体的一致插值。该方法是对我们以前工作中提出的方法的改进。它之所以与众不同，是因为它可以灵活地区别对待不同类型的概念名称，有效地减少了所涉及的推论。这种处理主要是由本体中概念名称的极性驱动的。通过该方法的原型实现进行的综合评估显示，与两个流行的基准数据集相比，该方法的平均成功率 > 95% ，并且显示出与最先进的系统相比，该方法具有明显的计算优势。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Highly-Optimized+Forgetting+for+Creating+Signature-Based+Views+of+Ontologies)|0| -|[Unveiling the Role of Message Passing in Dual-Privacy Preservation on GNNs](https://doi.org/10.1145/3583780.3615104)|Tianyi Zhao, Hui Hu, Lu Cheng|University of Illinois Chicago, Chicago, IL, USA; Wyoming University, Larami, WY, USA; University of Southern California, Los Angeles, CA, USA|Graph Neural Networks (GNNs) are powerful tools for learning representations on graphs, such as social networks. However, their vulnerability to privacy inference attacks restricts their practicality, especially in high-stake domains. To address this issue, privacy-preserving GNNs have been proposed, focusing on preserving node and/or link privacy. This work takes a step back and investigates how GNNs contribute to privacy leakage. Through theoretical analysis and simulations, we identify message passing under structural bias as the core component that allows GNNs to \textit{propagate} and \textit{amplify} privacy leakage. Building upon these findings, we propose a principled privacy-preserving GNN framework that effectively safeguards both node and link privacy, referred to as dual-privacy preservation. The framework comprises three major modules: a Sensitive Information Obfuscation Module that removes sensitive information from node embeddings, a Dynamic Structure Debiasing Module that dynamically corrects the structural bias, and an Adversarial Learning Module that optimizes the privacy-utility trade-off. Experimental results on four benchmark datasets validate the effectiveness of the proposed model in protecting both node and link privacy while preserving high utility for downstream tasks, such as node classification.|图形神经网络(GNN)是学习图形表示的强大工具，例如社交网络。然而，它们易受隐私推断攻击的弱点限制了它们的实用性，尤其是在高风险领域。为了解决这个问题，提出了保护隐私的 GNN，重点是保护节点和/或链路的隐私。这项工作退一步来研究 GNN 是如何导致隐私泄露的。通过理论分析和仿真，我们确定了结构偏差下的消息传递是 GNN 发送传播信息和发送放大隐私泄露信息的核心组件。在这些发现的基础上，我们提出了一个原则性的隐私保护 GNN 框架，有效地保护节点和链路的隐私，称为双重隐私保护。该框架由三个主要模块组成: 一个敏感信息模糊模块，从节点嵌入中删除敏感信息; 一个动态结构消偏模块，动态纠正结构偏差; 以及一个对抗性学习模块，优化隐私-效用的权衡。在四个基准数据集上的实验结果验证了该模型在保护节点和链路隐私的同时，对下游任务(如节点分类)保持较高效用的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Unveiling+the+Role+of+Message+Passing+in+Dual-Privacy+Preservation+on+GNNs)|0| -|[DiffUFlow: Robust Fine-grained Urban Flow Inference with Denoising Diffusion Model](https://doi.org/10.1145/3583780.3614842)|Yuhao Zheng, Lian Zhong, Senzhang Wang, Yu Yang, Weixi Gu, Junbo Zhang, Jianxin Wang|China Academy of Industrial Internet, Beijing, China; JD Intelligent Cities Research, Beijing, China; The Hong Kong Polytechnic University, Hong Kong, Hong Kong; Central South University, Changsha, China|Inferring the fine-grained urban flows based on the coarse-grained flow observations is practically important to many smart city-related applications. However, the collected human/vehicle trajectory flows are usually rather unreliable, may contain various noise and sometimes are incomplete, thus posing great challenges to existing approaches. In this paper, we present a pioneering study on robust fine-grained urban flow inference with noisy and incomplete urban flow observations, and propose a denoising diffusion model named DiffUFlow to effectively address it. Specifically, we propose an improved reverse diffusion strategy. A spatial-temporal feature extraction network called STFormer and a semantic features extraction network called ELFetcher are also proposed. Then, we overlay the spatial-temporal feature map extracted by STFormer onto the coarse-grained flow map, serving as a conditional guidance for the reverse diffusion process. We further integrate the semantic features extracted by ELFetcher to cross-attention layers, enabling the comprehensive consideration of semantic information encompassing the entirety of urban data in fine-grained inference. Extensive experiments on two large real-world datasets validate the effectiveness of our method compared with the state-of-the-art baselines.|基于粗粒度流观测的细粒度城市流推断对于智能城市相关应用具有重要的实际意义。然而，收集的人/车轨迹流通常是相当不可靠的，可能包含各种噪声，有时是不完整的，从而对现有的方法提出了巨大的挑战。本文首次提出了一种基于噪声和不完全城市流量观测的鲁棒细粒度城市流量推断方法，并提出了一种去噪扩散模型，称之为 DISUFlow，以有效地解决这一问题。具体来说，我们提出了一种改进的反向扩散策略。提出了一种时空特征提取网络 STForm 和一种语义特征提取网络 ELFetcher。在此基础上，将时空特征图叠加到粗粒度流图上，作为反向扩散过程的条件指导。我们进一步将 ELfetcher 提取的语义特征整合到交叉注意层，使得我们能够在细粒度推理中全面考虑包含整个城市数据的语义信息。在两个大型真实世界数据集上的大量实验验证了我们的方法与最先进的基线相比的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=DiffUFlow:+Robust+Fine-grained+Urban+Flow+Inference+with+Denoising+Diffusion+Model)|0| +|[Unveiling the Role of Message Passing in Dual-Privacy Preservation on GNNs](https://doi.org/10.1145/3583780.3615104)|Tianyi Zhao, Hui Hu, Lu Cheng|University of Southern California, Los Angeles, CA, USA; University of Illinois Chicago, Chicago, IL, USA; Wyoming University, Larami, WY, USA|Graph Neural Networks (GNNs) are powerful tools for learning representations on graphs, such as social networks. However, their vulnerability to privacy inference attacks restricts their practicality, especially in high-stake domains. To address this issue, privacy-preserving GNNs have been proposed, focusing on preserving node and/or link privacy. This work takes a step back and investigates how GNNs contribute to privacy leakage. Through theoretical analysis and simulations, we identify message passing under structural bias as the core component that allows GNNs to \textit{propagate} and \textit{amplify} privacy leakage. Building upon these findings, we propose a principled privacy-preserving GNN framework that effectively safeguards both node and link privacy, referred to as dual-privacy preservation. The framework comprises three major modules: a Sensitive Information Obfuscation Module that removes sensitive information from node embeddings, a Dynamic Structure Debiasing Module that dynamically corrects the structural bias, and an Adversarial Learning Module that optimizes the privacy-utility trade-off. Experimental results on four benchmark datasets validate the effectiveness of the proposed model in protecting both node and link privacy while preserving high utility for downstream tasks, such as node classification.|图形神经网络(GNN)是学习图形表示的强大工具，例如社交网络。然而，它们易受隐私推断攻击的弱点限制了它们的实用性，尤其是在高风险领域。为了解决这个问题，提出了保护隐私的 GNN，重点是保护节点和/或链路的隐私。这项工作退一步来研究 GNN 是如何导致隐私泄露的。通过理论分析和仿真，我们确定了结构偏差下的消息传递是 GNN 发送传播信息和发送放大隐私泄露信息的核心组件。在这些发现的基础上，我们提出了一个原则性的隐私保护 GNN 框架，有效地保护节点和链路的隐私，称为双重隐私保护。该框架由三个主要模块组成: 一个敏感信息模糊模块，从节点嵌入中删除敏感信息; 一个动态结构消偏模块，动态纠正结构偏差; 以及一个对抗性学习模块，优化隐私-效用的权衡。在四个基准数据集上的实验结果验证了该模型在保护节点和链路隐私的同时，对下游任务(如节点分类)保持较高效用的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Unveiling+the+Role+of+Message+Passing+in+Dual-Privacy+Preservation+on+GNNs)|0| +|[DiffUFlow: Robust Fine-grained Urban Flow Inference with Denoising Diffusion Model](https://doi.org/10.1145/3583780.3614842)|Yuhao Zheng, Lian Zhong, Senzhang Wang, Yu Yang, Weixi Gu, Junbo Zhang, Jianxin Wang|China Academy of Industrial Internet, Beijing, China; Central South University, Changsha, China; The Hong Kong Polytechnic University, Hong Kong, Hong Kong; JD Intelligent Cities Research, Beijing, China|Inferring the fine-grained urban flows based on the coarse-grained flow observations is practically important to many smart city-related applications. However, the collected human/vehicle trajectory flows are usually rather unreliable, may contain various noise and sometimes are incomplete, thus posing great challenges to existing approaches. In this paper, we present a pioneering study on robust fine-grained urban flow inference with noisy and incomplete urban flow observations, and propose a denoising diffusion model named DiffUFlow to effectively address it. Specifically, we propose an improved reverse diffusion strategy. A spatial-temporal feature extraction network called STFormer and a semantic features extraction network called ELFetcher are also proposed. Then, we overlay the spatial-temporal feature map extracted by STFormer onto the coarse-grained flow map, serving as a conditional guidance for the reverse diffusion process. We further integrate the semantic features extracted by ELFetcher to cross-attention layers, enabling the comprehensive consideration of semantic information encompassing the entirety of urban data in fine-grained inference. Extensive experiments on two large real-world datasets validate the effectiveness of our method compared with the state-of-the-art baselines.|基于粗粒度流观测的细粒度城市流推断对于智能城市相关应用具有重要的实际意义。然而，收集的人/车轨迹流通常是相当不可靠的，可能包含各种噪声，有时是不完整的，从而对现有的方法提出了巨大的挑战。本文首次提出了一种基于噪声和不完全城市流量观测的鲁棒细粒度城市流量推断方法，并提出了一种去噪扩散模型，称之为 DISUFlow，以有效地解决这一问题。具体来说，我们提出了一种改进的反向扩散策略。提出了一种时空特征提取网络 STForm 和一种语义特征提取网络 ELFetcher。在此基础上，将时空特征图叠加到粗粒度流图上，作为反向扩散过程的条件指导。我们进一步将 ELfetcher 提取的语义特征整合到交叉注意层，使得我们能够在细粒度推理中全面考虑包含整个城市数据的语义信息。在两个大型真实世界数据集上的大量实验验证了我们的方法与最先进的基线相比的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=DiffUFlow:+Robust+Fine-grained+Urban+Flow+Inference+with+Denoising+Diffusion+Model)|0| |[Assessing the Continuous Causal Responses of Typhoon-related Weather on Human Mobility: An Empirical Study in Japan](https://doi.org/10.1145/3583780.3615513)|Zhiwen Zhang, Hongjun Wang, Zipei Fan, Ryosuke Shibasaki, Xuan Song|The University of Tokyo, Tokyo, Japan; SUSTech, Shenzhen, China|To understand human mobility following the typhoon, analyzing the causal impact of extreme typhoon weather on human mobility is important for disaster emergency management. However, the unobserved confounders (e.g., the characteristic of each region) correlate with the strength of typhoon weather and also affect human mobility during typhoon, which may generate biased influences on the causal analysis process. Besides, these confounders may be time-varying following the dynamic movements of typhoon. In this work, we develop a neural network-based continuous causal effect estimation framework to mitigate the interference from (unobserved) confounders and assess the continuous causal responses of typhoon-related weather (treatment) on several types of human mobility (outcome) across different counties at any given period. To this end, we integrate the big data from two huge typhoons in Japan (i.e., Typhoon Faxai and Hagibis) and leverage multiple sources of covariates (i.e., residents' vigilance and basic mobility patterns) from different counties to learn the representations of time-varying confounders. The experimental results indicate the effectiveness of our proposed framework in capturing the confounders for quantifying the causal impact of extreme weather during the typhoon process, compared with several existing causal studies.|为了了解台风过后的人员流动情况，分析极端台风天气对人员流动的影响对灾害应急管理具有重要意义。但未观测到的混杂因素(如各区域的特征)与台风天气强度相关，并影响台风期间的人口流动，可能对因果分析过程产生偏差影响。此外，这些混杂因子可能随台风的动态变化而变化。在这项工作中，我们开发了一个基于神经网络的连续因果效应估计框架，以减轻(未观察到的)混杂因素的干扰，并评估台风相关天气(处理)对几种类型的人口流动(结果)的连续因果响应，在不同的县在任何给定的时期。为此，我们整合了来自日本两个巨大台风(即台风法赛和哈吉比斯)的大数据，并利用来自不同县的多个协变量来源(即居民的警觉性和基本流动模式)来学习时变混杂因素的表征。实验结果表明，与已有的因果关系研究相比，我们提出的框架在捕获混杂因子以量化台风过程中极端天气的因果影响方面是有效的。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Assessing+the+Continuous+Causal+Responses+of+Typhoon-related+Weather+on+Human+Mobility:+An+Empirical+Study+in+Japan)|0| -|[Learning Node Abnormality with Weak Supervision](https://doi.org/10.1145/3583780.3614950)|Qinghai Zhou, Kaize Ding, Huan Liu, Hanghang Tong|Arizona State University, Tempe, AZ, USA; Northwestern University, Evanston, IL, USA; University of Illinois at Urbana-Champaign, Urbana, IL, USA|Graph anomaly detection aims to identify the atypical substructures and has attracted an increasing amount of research attention due to its profound impacts in a variety of application domains, including social network analysis, security, finance, and many more. The lack of prior knowledge of the ground-truth anomaly has been a major obstacle in acquiring fine-grained annotations (e.g., anomalous nodes), therefore, a plethora of existing methods have been developed either with a limited number of node-level supervision or in an unsupervised manner. Nonetheless, annotations for coarse-grained graph elements (e.g., a suspicious group of nodes), which often require marginal human effort in terms of time and expertise, are comparatively easier to obtain. Therefore, it is appealing to investigate anomaly detection in a weakly-supervised setting and to establish the intrinsic relationship between annotations at different levels of granularity. In this paper, we tackle the challenging problem of weakly-supervised graph anomaly detection with coarse-grained supervision by (1) proposing a novel architecture of graph neural network with attention mechanism named WEDGE that can identify the critical node-level anomaly given a few labels of anomalous subgraphs, and (2) designing a novel objective with contrastive loss that facilitates node representation learning by enforcing distinctive representations between normal and abnormal graph elements. Through extensive evaluations on real-world datasets, we corroborate the efficacy of our proposed method, improving AUC-ROC by up to 16.48% compared to the best competitor.|图形异常检测旨在识别非典型的子结构，由于其在各种应用领域(包括社交网络分析、安全、金融等)的深远影响，已经吸引了越来越多的研究注意力。缺乏对地面真实异常的事先知识一直是获取细粒度注释(例如异常节点)的主要障碍，因此，已经开发了大量现有的方法，要么是有限数量的节点级监督，要么是以无监督的方式。尽管如此，粗粒度图元素(例如，一组可疑的节点)的注释相对容易获得，这些注释通常需要人们在时间和专业知识方面的边际努力。因此，在弱监督环境下研究异常检测，并在不同粒度级别上建立注释之间的内在关系，是很有吸引力的。在本文中，我们解决了粗粒度监督的弱监督图异常检测的挑战性问题，通过(1)提出了一种新的带注意机制的图神经网络结构，称为 WEDGE，它可以识别临界节点级异常，给出几个异常子图的标签，和(2)设计一个新的对比损失的目标，通过强制正常和异常图元之间的独特表示，促进节点表示学习。通过对真实世界数据集的广泛评估，我们证实了我们提出的方法的有效性，与最好的竞争对手相比，AUC-ROC 提高了16.48% 。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Learning+Node+Abnormality+with+Weak+Supervision)|0| -|[Privacy-Preserving Federated Learning via Disentanglement](https://doi.org/10.1145/3583780.3615014)|Wenjie Zhou, Piji Li, Zhaoyang Han, Xiaozhen Lu, Juan Li, Zhaochun Ren, Zhe Liu|Shandong University, Jinan, China; Nanjing University of Aeronautics and Astronautics, Nanjing, China|The trade-off between privacy and accuracy presents a challenge for current federated learning (FL) frameworks, hindering their progress from theory to application. The main issues with existing FL frameworks stem from a lack of interpretability and targeted privacy protections. To cope with these, we proposed Disentangled Federated Learning for Privacy (DFLP) which employes disentanglement, one of interpretability techniques, in private FL frameworks. Since sensitive properties are client-specific in nature, our main idea is to turn this feature into a tool that strikes the balance between data privacy and FL model performance, enabling the sensitive attributes to be private. DFLP disentangles the client-specific and class-invariant attributes to mask the sensitive attributes precisely. To our knowledge, this is the first work that successfully integrates disentanglement and the nature of sensitive attributes to achieve privacy protection while ensuring high FL model performance. Extensive experiments validate that disentanglement is an effective method for accuracy-aware privacy protection in FL frameworks.|隐私和准确性之间的权衡给当前的联邦学习(FL)框架带来了挑战，阻碍了它们从理论到应用的进步。现有 FL 框架的主要问题源于缺乏可解释性和有针对性的隐私保护。为了解决这些问题，我们提出了在私人 FL 框架中采用解释技术之一的“解缠”技术的联邦隐私分离学习(DFLP)。由于敏感属性本质上是特定于客户端的，因此我们的主要想法是将这个特性转换为一个工具，在数据隐私和 FL 模型性能之间取得平衡，使敏感属性成为私有的。DFLP 解开了客户特定属性和类不变属性之间的关系，精确地屏蔽了敏感属性。据我们所知，这是第一个工作，成功地集成解纠缠和敏感属性的性质，以实现隐私保护，同时确保高 FL 模型性能。大量的实验证明，在 FL 框架中，解缠是一种有效的精确隐私保护方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Privacy-Preserving+Federated+Learning+via+Disentanglement)|0| +|[Learning Node Abnormality with Weak Supervision](https://doi.org/10.1145/3583780.3614950)|Qinghai Zhou, Kaize Ding, Huan Liu, Hanghang Tong|Arizona State University, Tempe, AZ, USA; University of Illinois at Urbana-Champaign, Urbana, IL, USA; Northwestern University, Evanston, IL, USA|Graph anomaly detection aims to identify the atypical substructures and has attracted an increasing amount of research attention due to its profound impacts in a variety of application domains, including social network analysis, security, finance, and many more. The lack of prior knowledge of the ground-truth anomaly has been a major obstacle in acquiring fine-grained annotations (e.g., anomalous nodes), therefore, a plethora of existing methods have been developed either with a limited number of node-level supervision or in an unsupervised manner. Nonetheless, annotations for coarse-grained graph elements (e.g., a suspicious group of nodes), which often require marginal human effort in terms of time and expertise, are comparatively easier to obtain. Therefore, it is appealing to investigate anomaly detection in a weakly-supervised setting and to establish the intrinsic relationship between annotations at different levels of granularity. In this paper, we tackle the challenging problem of weakly-supervised graph anomaly detection with coarse-grained supervision by (1) proposing a novel architecture of graph neural network with attention mechanism named WEDGE that can identify the critical node-level anomaly given a few labels of anomalous subgraphs, and (2) designing a novel objective with contrastive loss that facilitates node representation learning by enforcing distinctive representations between normal and abnormal graph elements. Through extensive evaluations on real-world datasets, we corroborate the efficacy of our proposed method, improving AUC-ROC by up to 16.48% compared to the best competitor.|图形异常检测旨在识别非典型的子结构，由于其在各种应用领域(包括社交网络分析、安全、金融等)的深远影响，已经吸引了越来越多的研究注意力。缺乏对地面真实异常的事先知识一直是获取细粒度注释(例如异常节点)的主要障碍，因此，已经开发了大量现有的方法，要么是有限数量的节点级监督，要么是以无监督的方式。尽管如此，粗粒度图元素(例如，一组可疑的节点)的注释相对容易获得，这些注释通常需要人们在时间和专业知识方面的边际努力。因此，在弱监督环境下研究异常检测，并在不同粒度级别上建立注释之间的内在关系，是很有吸引力的。在本文中，我们解决了粗粒度监督的弱监督图异常检测的挑战性问题，通过(1)提出了一种新的带注意机制的图神经网络结构，称为 WEDGE，它可以识别临界节点级异常，给出几个异常子图的标签，和(2)设计一个新的对比损失的目标，通过强制正常和异常图元之间的独特表示，促进节点表示学习。通过对真实世界数据集的广泛评估，我们证实了我们提出的方法的有效性，与最好的竞争对手相比，AUC-ROC 提高了16.48% 。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Learning+Node+Abnormality+with+Weak+Supervision)|0| +|[Privacy-Preserving Federated Learning via Disentanglement](https://doi.org/10.1145/3583780.3615014)|Wenjie Zhou, Piji Li, Zhaoyang Han, Xiaozhen Lu, Juan Li, Zhaochun Ren, Zhe Liu|Nanjing University of Aeronautics and Astronautics, Nanjing, China; Shandong University, Jinan, China|The trade-off between privacy and accuracy presents a challenge for current federated learning (FL) frameworks, hindering their progress from theory to application. The main issues with existing FL frameworks stem from a lack of interpretability and targeted privacy protections. To cope with these, we proposed Disentangled Federated Learning for Privacy (DFLP) which employes disentanglement, one of interpretability techniques, in private FL frameworks. Since sensitive properties are client-specific in nature, our main idea is to turn this feature into a tool that strikes the balance between data privacy and FL model performance, enabling the sensitive attributes to be private. DFLP disentangles the client-specific and class-invariant attributes to mask the sensitive attributes precisely. To our knowledge, this is the first work that successfully integrates disentanglement and the nature of sensitive attributes to achieve privacy protection while ensuring high FL model performance. Extensive experiments validate that disentanglement is an effective method for accuracy-aware privacy protection in FL frameworks.|隐私和准确性之间的权衡给当前的联邦学习(FL)框架带来了挑战，阻碍了它们从理论到应用的进步。现有 FL 框架的主要问题源于缺乏可解释性和有针对性的隐私保护。为了解决这些问题，我们提出了在私人 FL 框架中采用解释技术之一的“解缠”技术的联邦隐私分离学习(DFLP)。由于敏感属性本质上是特定于客户端的，因此我们的主要想法是将这个特性转换为一个工具，在数据隐私和 FL 模型性能之间取得平衡，使敏感属性成为私有的。DFLP 解开了客户特定属性和类不变属性之间的关系，精确地屏蔽了敏感属性。据我们所知，这是第一个工作，成功地集成解纠缠和敏感属性的性质，以实现隐私保护，同时确保高 FL 模型性能。大量的实验证明，在 FL 框架中，解缠是一种有效的精确隐私保护方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Privacy-Preserving+Federated+Learning+via+Disentanglement)|0| |[CANDY: A Causality-Driven Model for Hotel Dynamic Pricing](https://doi.org/10.1145/3583780.3614800)|Ruitao Zhu, Wendong Xiao, Yao Yu, Yizhi Yu, Zhenzhe Zheng, Ke Bu, Dong Li, Fan Wu|Alibaba Group, Hangzhou, China; Shanghai Jiao Tong University, Shanghai, China|Broad adoption of online travel platforms (OTPs) has led to increasing focus on hotel dynamic pricing algorithms, which directly affect the revenue of platform and hotels. Existing approaches, which directly model the correlation between price and occupancy, have limitations in improving occupancy prediction accuracy while ensuring interpretability for dynamic pricing. Moreover, these methods struggle to address the significant data sparsity issue in hotel pricing scenarios. To overcome these limitations, we propose a novel Causality-driven Hotel Dynamic Pricing Model (CANDY) that captures the essential causal relationship between price and occupancy, enhancing occupancy prediction accuracy and interpretability for dynamic pricing. Specifically, we decompose confounders into three orthogonal groups of factors: characteristic factors, competitive factors, and temporal factors, and design submodules to capture the features of each dimension. To address the treatment bias and sample imbalance issues faced by existing causal inference methods in hotel pricing scenarios, we propose a novel data augmentation method based on the monotonic relationship between price and occupancy, and further design a multi-task learning framework tailored to multi-valued treatment scenarios, simultaneously alleviating the data sparsity issue. Both offline and online experiments demonstrate the effectiveness of CANDY in occupancy prediction and dynamic pricing. CANDY has been successfully deployed to provide price suggestion service at Fliggy, a leading OTP in China, serving thousands of hotel operators.|在线旅游平台(OTP)的广泛应用导致人们越来越关注酒店动态定价算法，这直接影响到平台和酒店的收入。现有的方法直接模拟价格与入住率之间的相关性，在提高入住率预测精度的同时保证动态定价的可解释性方面存在局限性。此外，这些方法很难解决酒店定价场景中的重大数据稀疏问题。为了克服这些限制，我们提出了一个新的因果关系驱动的酒店动态定价模型(CANDY) ，捕捉价格和入住率之间的基本因果关系，提高入住率预测的准确性和动态定价的可解释性。具体来说，我们将混杂因素分解为三组正交的因素: 特征因素、竞争因素和时间因素，并设计子模块来捕获每个维度的特征。针对现有因果推理方法在酒店定价场景中存在的治疗偏差和样本不平衡问题，提出了一种基于价格与入住率单调关系的数据增强方法，并进一步设计了适合多值治疗场景的多任务学习框架，同时缓解了数据稀疏问题。离线和在线实验都证明了 CANDY 在入住率预测和动态定价方面的有效性。CANDY 已成功部署于 Fliggy，为数以千计的酒店运营商提供价格建议服务。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=CANDY:+A+Causality-Driven+Model+for+Hotel+Dynamic+Pricing)|0| -|[FVW: Finding Valuable Weight on Deep Neural Network for Model Pruning](https://doi.org/10.1145/3583780.3614889)|Zhiyu Zhu, Huaming Chen, Zhibo Jin, Xinyi Wang, Jiayu Zhang, Minhui Xue, Qinghua Lu, Jun Shen, KimKwang Raymond Choo|Suzhou Yierqi, Suzhou, China; Jiangsu University, Zhenjiang, China; The University of Sydney, Sydney, NSW, Australia; Data61, CSIRO, Sydney, NSW, Australia; University of Texas at San Antonio, San Antonio, TX, USA; SCIT, University of Wollongong, Australia, Wollongong , Australia|The rapid development of deep learning has demonstrated its potential for deployment in many intelligent service systems. However, some issues such as optimisation (e.g., how to reduce the deployment resources costs and further improve the detection speed), especially in scenarios where limited resources are available, remain challenging to address. In this paper, we aim to delve into the principles of deep neural networks, focusing on the importance of network neurons. The goal is to identify the neurons that exert minimal impact on model performances, thereby aiding in the process of model pruning. In this work, we have thoroughly considered the deep learning model pruning process with and without fine-tuning step, ensuring the model performance consistency. To achieve our objectives, we propose a methodology that employs adversarial attack methods to explore deep neural network parameters. This approach is combined with an innovative attribution algorithm to analyse the level of network neurons involvement. In our experiments, our approach can effectively quantify the importance of network neuron. We extend the evaluation through comprehensive experiments conducted on a range of datasets, including CIFAR-10, CIFAR-100 and Caltech101. The results demonstrate that, our method have consistently achieved the state-of-the-art performance over many existing methods. We anticipate that this work will help to reduce the heavy training and inference cost of deep neural network models where a lightweight deep learning enhanced service and system is possible. The source code is open source at https://github.com/LMBTough/FVW.|深度学习的迅速发展已经证明了它在许多智能服务系统中的部署潜力。然而，一些问题，如优化(例如，如何降低部署资源成本和进一步提高检测速度) ，特别是在有限的资源可用的情况下，仍然具有挑战性的解决。本文旨在深入研究深层神经网络的原理，重点研究网络神经元的重要性。目标是识别对模型性能影响最小的神经元，从而有助于模型修剪过程。在这项工作中，我们充分考虑了有和没有微调步骤的深度学习模型修剪过程，保证了模型性能的一致性。为了实现我们的目标，我们提出了一种使用对抗性攻击方法来探索深层神经网络参数的方法。该方法结合了一种创新的归因算法来分析网络神经元参与的水平。在我们的实验中，我们的方法可以有效地量化网络神经元的重要性。我们通过在 CIFAR-10、 CIFAR-100和 Caltech101等一系列数据集上进行的综合实验来扩展评估。结果表明，我们的方法在许多现有的方法中始终达到了最先进的性能。我们期望这项工作将有助于减少沉重的训练和推理成本的深度神经网络模型，其中一个轻量级的深度学习增强服务和系统是可能的。源代码在 https://github.com/lmbtough/fvw 上是开源的。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=FVW:+Finding+Valuable+Weight+on+Deep+Neural+Network+for+Model+Pruning)|0| -|[Fine-Grained Socioeconomic Prediction from Satellite Images with Distributional Adjustment](https://doi.org/10.1145/3583780.3615226)|Donghyun Ahn, Minhyuk Song, SeungEon Lee, Yubin Choi, Jihee Kim, Sangyoon Park, Hyunjoo Yang, Meeyoung Cha|IBS, KAIST, Daejeon, Republic of Korea; Sogang University, Seoul, Republic of Korea; KAIST, IBS, Daejeon, Republic of Korea; HKUST, Hong Kong, China|While measuring socioeconomic indicators is critical for local governments to make informed policy decisions, such measurements are often unavailable at fine-grained levels like municipality. This study employs deep learning-based predictions from satellite images to close the gap. We propose a method that assigns a socioeconomic score to each satellite image by capturing the distributional behavior observed in larger areas based on the ground truth. We train an ordinal regression scoring model and adjust the scores to follow the common power law within and across regions. Evaluation based on official statistics in South Korea shows that our method outperforms previous models in predicting population and employment size at both the municipality and grid levels. Our method also demonstrates robust performance in districts with uneven development, suggesting its potential use in developing countries where reliable, fine-grained data is scarce.|虽然衡量社会经济指标对于地方政府作出知情的政策决定至关重要，但在细粒度级别(如市政当局)往往无法获得这种衡量。这项研究利用卫星图像中基于深度学习的预测来缩小差距。我们提出了一种方法，通过捕获在更大的区域观察到的基于地面真实的分布行为为每个卫星图像分配一个社会经济评分。我们训练一个有序回归得分模型，并调整得分以遵循区域内和区域间的普遍幂律。基于韩国官方统计数据的评估表明，我们的方法在预测市级和电网级的人口和就业规模方面优于以前的模型。我们的方法还在发展不平衡的地区表现出强劲的性能，表明它在缺乏可靠、细粒度数据的发展中国家有潜力使用。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Fine-Grained+Socioeconomic+Prediction+from+Satellite+Images+with+Distributional+Adjustment)|0| +|[FVW: Finding Valuable Weight on Deep Neural Network for Model Pruning](https://doi.org/10.1145/3583780.3614889)|Zhiyu Zhu, Huaming Chen, Zhibo Jin, Xinyi Wang, Jiayu Zhang, Minhui Xue, Qinghua Lu, Jun Shen, KimKwang Raymond Choo|Data61, CSIRO, Sydney, NSW, Australia; University of Texas at San Antonio, San Antonio, TX, USA; Suzhou Yierqi, Suzhou, China; SCIT, University of Wollongong, Australia, Wollongong , Australia; The University of Sydney, Sydney, NSW, Australia; Jiangsu University, Zhenjiang, China|The rapid development of deep learning has demonstrated its potential for deployment in many intelligent service systems. However, some issues such as optimisation (e.g., how to reduce the deployment resources costs and further improve the detection speed), especially in scenarios where limited resources are available, remain challenging to address. In this paper, we aim to delve into the principles of deep neural networks, focusing on the importance of network neurons. The goal is to identify the neurons that exert minimal impact on model performances, thereby aiding in the process of model pruning. In this work, we have thoroughly considered the deep learning model pruning process with and without fine-tuning step, ensuring the model performance consistency. To achieve our objectives, we propose a methodology that employs adversarial attack methods to explore deep neural network parameters. This approach is combined with an innovative attribution algorithm to analyse the level of network neurons involvement. In our experiments, our approach can effectively quantify the importance of network neuron. We extend the evaluation through comprehensive experiments conducted on a range of datasets, including CIFAR-10, CIFAR-100 and Caltech101. The results demonstrate that, our method have consistently achieved the state-of-the-art performance over many existing methods. We anticipate that this work will help to reduce the heavy training and inference cost of deep neural network models where a lightweight deep learning enhanced service and system is possible. The source code is open source at https://github.com/LMBTough/FVW.|深度学习的迅速发展已经证明了它在许多智能服务系统中的部署潜力。然而，一些问题，如优化(例如，如何降低部署资源成本和进一步提高检测速度) ，特别是在有限的资源可用的情况下，仍然具有挑战性的解决。本文旨在深入研究深层神经网络的原理，重点研究网络神经元的重要性。目标是识别对模型性能影响最小的神经元，从而有助于模型修剪过程。在这项工作中，我们充分考虑了有和没有微调步骤的深度学习模型修剪过程，保证了模型性能的一致性。为了实现我们的目标，我们提出了一种使用对抗性攻击方法来探索深层神经网络参数的方法。该方法结合了一种创新的归因算法来分析网络神经元参与的水平。在我们的实验中，我们的方法可以有效地量化网络神经元的重要性。我们通过在 CIFAR-10、 CIFAR-100和 Caltech101等一系列数据集上进行的综合实验来扩展评估。结果表明，我们的方法在许多现有的方法中始终达到了最先进的性能。我们期望这项工作将有助于减少沉重的训练和推理成本的深度神经网络模型，其中一个轻量级的深度学习增强服务和系统是可能的。源代码在 https://github.com/lmbtough/fvw 上是开源的。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=FVW:+Finding+Valuable+Weight+on+Deep+Neural+Network+for+Model+Pruning)|0| +|[Fine-Grained Socioeconomic Prediction from Satellite Images with Distributional Adjustment](https://doi.org/10.1145/3583780.3615226)|Donghyun Ahn, Minhyuk Song, SeungEon Lee, Yubin Choi, Jihee Kim, Sangyoon Park, Hyunjoo Yang, Meeyoung Cha|Sogang University, Seoul, Republic of Korea; IBS, KAIST, Daejeon, Republic of Korea; KAIST, IBS, Daejeon, Republic of Korea; HKUST, Hong Kong, China|While measuring socioeconomic indicators is critical for local governments to make informed policy decisions, such measurements are often unavailable at fine-grained levels like municipality. This study employs deep learning-based predictions from satellite images to close the gap. We propose a method that assigns a socioeconomic score to each satellite image by capturing the distributional behavior observed in larger areas based on the ground truth. We train an ordinal regression scoring model and adjust the scores to follow the common power law within and across regions. Evaluation based on official statistics in South Korea shows that our method outperforms previous models in predicting population and employment size at both the municipality and grid levels. Our method also demonstrates robust performance in districts with uneven development, suggesting its potential use in developing countries where reliable, fine-grained data is scarce.|虽然衡量社会经济指标对于地方政府作出知情的政策决定至关重要，但在细粒度级别(如市政当局)往往无法获得这种衡量。这项研究利用卫星图像中基于深度学习的预测来缩小差距。我们提出了一种方法，通过捕获在更大的区域观察到的基于地面真实的分布行为为每个卫星图像分配一个社会经济评分。我们训练一个有序回归得分模型，并调整得分以遵循区域内和区域间的普遍幂律。基于韩国官方统计数据的评估表明，我们的方法在预测市级和电网级的人口和就业规模方面优于以前的模型。我们的方法还在发展不平衡的地区表现出强劲的性能，表明它在缺乏可靠、细粒度数据的发展中国家有潜力使用。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Fine-Grained+Socioeconomic+Prediction+from+Satellite+Images+with+Distributional+Adjustment)|0| |[Logarithmic Dimension Reduction for Quantum Neural Networks](https://doi.org/10.1145/3583780.3615240)|Hankyul Baek, Soohyun Park, Joongheon Kim|Korea University, Seoul, Republic of Korea|In recent years, quantum neural network (QNN) based on quantum computing has attracted attention due to its potential for computation-acceleration and parallelism. However, the intrinsic limitations of QNN, where the output (i.e., observables) can only be obtained through a measurement process, pose scalability challenges. Motivated by this, this paper aims to address the scalability challenges by incorporating Pauli-Z measurement and Basis measurement. In conventional frameworks, QNN typically relies on classical fully connected networks (FCNs) or increases the number of qubits to achieve large output dimensions. However, by leveraging our proposed framework, this paper successfully expands the output dimensions to an exponential scale, surpassing the limitations imposed by the limited number of qubits without relying on FCNs. Through extensive experiments, this paper demonstrates that the proposed framework outperforms existing QNN frameworks in multi-class classification tasks that require numerous output dimensions.|近年来，基于量子计算的量子神经网络(QNN)由于其在计算加速和并行性方面的潜力而引起了人们的关注。然而，QNN 的内在局限性，其中的输出(即，可观测的)只能通过测量过程获得，提出了可伸缩性的挑战。基于此，本文旨在通过结合 Pauli-Z 度量和基础度量来解决可伸缩性方面的挑战。在传统的框架中，QNN 通常依赖于经典的全连通网络(FCNs)或增加量子位的数量来实现较大的输出维数。然而，通过利用我们提出的框架，本文成功地将输出维度扩展到指数级，超越了不依赖 FCNs 的有限量子比特数量所带来的限制。通过大量的实验证明，该框架在多类分类任务中优于现有的 QNN 框架，因为多类分类任务需要大量的输出维数。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Logarithmic+Dimension+Reduction+for+Quantum+Neural+Networks)|0| -|[A Comparative Study of Reference Reliability in Multiple Language Editions of Wikipedia](https://doi.org/10.1145/3583780.3615254)|Aitolkyn Baigutanova, Diego SáezTrumper, Miriam Redi, Meeyoung Cha, Pablo Aragón|IBS, KAIST, Daejeon, Republic of Korea; Wikimedia Foundation, London, United Kingdom; KAIST, IBS, Daejeon, Republic of Korea; Wikimedia Foundation, Barcelona, Spain|Information presented in Wikipedia articles must be attributable to reliable published sources in the form of references. This study examines over 5 million Wikipedia articles to assess the reliability of references in multiple language editions. We quantify the cross-lingual patterns of the perennial sources list, a collection of reliability labels for web domains identified and collaboratively agreed upon by Wikipedia editors. We discover that some sources (or web domains) deemed untrustworthy in one language (i.e., English) continue to appear in articles in other languages. This trend is especially evident with sources tailored for smaller communities. Furthermore, non-authoritative sources found in the English version of a page tend to persist in other language versions of that page. We finally present a case study on the Chinese, Russian, and Swedish Wikipedias to demonstrate a discrepancy in reference reliability across cultures. Our finding highlights future challenges in coordinating global knowledge on source reliability.|维基百科条目中提供的信息必须归因于以参考文献形式出版的可靠来源。这项研究检查了超过500万维基百科文章，以评估在多种语言版本的参考文献的可靠性。我们量化了多年来源列表的跨语言模式，这是一个由维基百科编辑共同确定和认可的网络域名的可靠性标签集合。我们发现一些在一种语言(如英语)中被认为不可信的来源(或网络域名)继续出现在其他语言的文章中。这种趋势在为小型社区量身定制的信息来源中尤为明显。此外，在英文版本的网页中发现的非权威来源往往会保留在该网页的其他语言版本中。最后，我们提出了一个案例研究的中国，俄罗斯和瑞典的维基百科，以证明在不同文化的参考可靠性的差异。我们的发现突出了未来在协调源可靠性全球知识方面的挑战。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Comparative+Study+of+Reference+Reliability+in+Multiple+Language+Editions+of+Wikipedia)|0| +|[A Comparative Study of Reference Reliability in Multiple Language Editions of Wikipedia](https://doi.org/10.1145/3583780.3615254)|Aitolkyn Baigutanova, Diego SáezTrumper, Miriam Redi, Meeyoung Cha, Pablo Aragón|Wikimedia Foundation, London, United Kingdom; KAIST, IBS, Daejeon, Republic of Korea; IBS, KAIST, Daejeon, Republic of Korea; Wikimedia Foundation, Barcelona, Spain|Information presented in Wikipedia articles must be attributable to reliable published sources in the form of references. This study examines over 5 million Wikipedia articles to assess the reliability of references in multiple language editions. We quantify the cross-lingual patterns of the perennial sources list, a collection of reliability labels for web domains identified and collaboratively agreed upon by Wikipedia editors. We discover that some sources (or web domains) deemed untrustworthy in one language (i.e., English) continue to appear in articles in other languages. This trend is especially evident with sources tailored for smaller communities. Furthermore, non-authoritative sources found in the English version of a page tend to persist in other language versions of that page. We finally present a case study on the Chinese, Russian, and Swedish Wikipedias to demonstrate a discrepancy in reference reliability across cultures. Our finding highlights future challenges in coordinating global knowledge on source reliability.|维基百科条目中提供的信息必须归因于以参考文献形式出版的可靠来源。这项研究检查了超过500万维基百科文章，以评估在多种语言版本的参考文献的可靠性。我们量化了多年来源列表的跨语言模式，这是一个由维基百科编辑共同确定和认可的网络域名的可靠性标签集合。我们发现一些在一种语言(如英语)中被认为不可信的来源(或网络域名)继续出现在其他语言的文章中。这种趋势在为小型社区量身定制的信息来源中尤为明显。此外，在英文版本的网页中发现的非权威来源往往会保留在该网页的其他语言版本中。最后，我们提出了一个案例研究的中国，俄罗斯和瑞典的维基百科，以证明在不同文化的参考可靠性的差异。我们的发现突出了未来在协调源可靠性全球知识方面的挑战。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Comparative+Study+of+Reference+Reliability+in+Multiple+Language+Editions+of+Wikipedia)|0| |[Linkage Attack on Skeleton-based Motion Visualization](https://doi.org/10.1145/3583780.3615263)|Thomas Carr, Aidong Lu, Depeng Xu|University of North Carolina at Charlotte, Charlotte, NC, USA|Skeleton-based motion capture and visualization is an important computer vision task, especially in the virtual reality (VR) environment. It has grown increasingly popular due to the ease of gathering skeleton data and the high demand of virtual socialization. The captured skeleton data seems anonymous but can still be used to extract personal identifiable information (PII). This can lead to an unintended privacy leakage inside a VR meta-verse. We propose a novel linkage attack on skeleton-based motion visualization. It detects if a target and a reference skeleton are the same individual. The proposed model, called Linkage Attack Neural Network (LAN), is based on the principles of a Siamese Network. It incorporates deep neural networks to embed the relevant PII then uses a classifier to match the reference and target skeletons. We also employ classical and deep motion retargeting (MR) to cast the target skeleton onto a dummy skeleton such that the motion sequence is anonymized for privacy protection. Our evaluation shows that the effectiveness of LAN in the linkage attack and the effectiveness of MR in anonymization.|基于骨架的运动捕获与可视化是计算机视觉的一项重要任务，尤其是在虚拟现实(VR)环境中。由于收集骨架数据的便捷性和虚拟社交的高要求，它已经变得越来越流行。捕获的骨骼数据似乎是匿名的，但仍然可以用来提取个人识别信息(PII)。这可能会导致虚拟现实元宇宙中意外的隐私泄露。针对基于骨架的运动可视化提出了一种新的联动攻击方法。它检测目标和引用框架是否是同一个个体。提出的连锁攻击神经网络(LAN)模型是基于暹罗网络的原理。它结合了深度神经网络来嵌入相关的 PII，然后使用分类器来匹配参考和目标骨架。我们还采用经典的和深度运动重定向(MR)将目标骨架投射到一个虚拟骨架上，使运动序列匿名以保护隐私。我们的评估表明局域网在链路攻击中的有效性和 MR 在匿名化中的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Linkage+Attack+on+Skeleton-based+Motion+Visualization)|0| |[Identify Risky Rules to Reduce Side Effects in Association Rule Hiding](https://doi.org/10.1145/3583780.3615259)|Peng Cheng|Southwest Univeristy, Chongqing, China|Data sharing is necessary for many practical applications. People do, however, frequently worry about the problem of privacy leaking.This study focuses on preventing the disclosure of sensitive information using association rules and frequent itemsets, which are frequently utilized in numerous applications. How to minimize side effects while hiding, particularly side effects on non-sensitive knowledge, is the difficult part of the problem. The majority of association rule hiding techniques currently in use solely consider reducing side effects on frequent itemsets (patterns), rather than rules, in order to conceal sensitive rules by reducing the statistical disclosure of the itemsets that generate such rules. In this study, we provide a concealment technique utilizing potentially risky rules to lessen adverse impacts on non-sensitive rules, not only itemsets. In addition, this method can be tailored to conceal sensitive itemsets, instead of rules. Extensive experiments show that in most cases the proposed solution can bring fewer side effects on rules, frequent patterns or data quality than existing methods.|数据共享对于许多实际应用是必要的。然而，人们确实经常担心隐私泄露的问题。本研究的重点是利用关联规则和频繁项目集来防止敏感信息的泄露，这些关联规则和频繁项目集在许多应用中经常被使用。如何在隐藏的同时尽量减少副作用，尤其是对非敏感知识的副作用，是问题的难点。目前使用的大多数关联规则隐藏技术只考虑减少对频繁项集(模式)的副作用，而不考虑规则，以通过减少生成此类规则的项集的统计信息披露来隐藏敏感规则。在这项研究中，我们提供了一种隐藏技术，利用潜在的风险规则，以减少对非敏感性规则的不利影响，而不仅仅是项集。此外，可以对此方法进行修改以隐藏敏感项集，而不是隐藏规则。大量实验表明，在大多数情况下，与现有方法相比，提出的解决方案对规则、频繁模式或数据质量带来的副作用更小。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Identify+Risky+Rules+to+Reduce+Side+Effects+in+Association+Rule+Hiding)|0| -|[Patient Clustering via Integrated Profiling of Clinical and Digital Data](https://doi.org/10.1145/3583780.3615262)|Dongjin Choi, Andy Xiang, Ozgur Ozturk, Deep Shrestha, Barry L. Drake, Hamid Haidarian, Faizan Javed, Haesun Park|Kaiser Permanente, Atlanta, GA, USA; Georgia Institute of Technology, Atlanta, GA, USA; Kaiser Permanente, Los Angeles, CA, USA|We introduce a novel profile-based patient clustering model designed for clinical data in healthcare. By utilizing a method grounded on constrained low-rank approximation, our model takes advantage of patients' clinical data and digital interaction data, including browsing and search, to construct patient profiles. As a result of the method, nonnegative embedding vectors are generated, serving as a low-dimensional representation of the patients. Our model was assessed using real-world patient data from a healthcare web portal, with a comprehensive evaluation approach which considered clustering and recommendation capabilities. In comparison to other baselines, our approach demonstrated superior performance in terms of clustering coherence and recommendation accuracy.|我们介绍了一种新的基于特征的患者聚类模型，该模型是为医疗保健领域的临床数据而设计的。该模型采用基于约束低秩近似的方法，利用患者的临床数据和数字交互数据，包括浏览和搜索，构建患者的个人资料。作为该方法的结果，非负嵌入向量生成，作为低维表示的患者。我们的模型使用来自医疗保健门户网站的真实世界患者数据进行评估，并采用综合评估方法，其中考虑了聚类和推荐功能。与其他基线相比，我们的方法在聚类一致性和推荐准确性方面表现出更好的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Patient+Clustering+via+Integrated+Profiling+of+Clinical+and+Digital+Data)|0| +|[Patient Clustering via Integrated Profiling of Clinical and Digital Data](https://doi.org/10.1145/3583780.3615262)|Dongjin Choi, Andy Xiang, Ozgur Ozturk, Deep Shrestha, Barry L. Drake, Hamid Haidarian, Faizan Javed, Haesun Park|Kaiser Permanente, Los Angeles, CA, USA; Kaiser Permanente, Atlanta, GA, USA; Georgia Institute of Technology, Atlanta, GA, USA|We introduce a novel profile-based patient clustering model designed for clinical data in healthcare. By utilizing a method grounded on constrained low-rank approximation, our model takes advantage of patients' clinical data and digital interaction data, including browsing and search, to construct patient profiles. As a result of the method, nonnegative embedding vectors are generated, serving as a low-dimensional representation of the patients. Our model was assessed using real-world patient data from a healthcare web portal, with a comprehensive evaluation approach which considered clustering and recommendation capabilities. In comparison to other baselines, our approach demonstrated superior performance in terms of clustering coherence and recommendation accuracy.|我们介绍了一种新的基于特征的患者聚类模型，该模型是为医疗保健领域的临床数据而设计的。该模型采用基于约束低秩近似的方法，利用患者的临床数据和数字交互数据，包括浏览和搜索，构建患者的个人资料。作为该方法的结果，非负嵌入向量生成，作为低维表示的患者。我们的模型使用来自医疗保健门户网站的真实世界患者数据进行评估，并采用综合评估方法，其中考虑了聚类和推荐功能。与其他基线相比，我们的方法在聚类一致性和推荐准确性方面表现出更好的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Patient+Clustering+via+Integrated+Profiling+of+Clinical+and+Digital+Data)|0| |[Reconciling Training and Evaluation Objectives in Location Agnostic Surrogate Explainers](https://doi.org/10.1145/3583780.3615284)|Matthew Clifford, Jonathan Erskine, Alexander Hepburn, Peter A. Flach, Raúl SantosRodríguez|University of Bristol, Bristol, United Kingdom|Transparency in AI models is crucial to designing, auditing, and deploying AI systems. However, 'black box' models are still used in practice for their predictive power despite their lack of transparency. This has led to a demand for post-hoc, model-agnostic surrogate explainers which provide explanations for decisions of any model by approximating its behaviour close to a query point with a surrogate model. However, it is often overlooked how the location of the query point in the decision surface of the black box model affects the faithfulness of the surrogate explainer. Here, we show that when using standard techniques, there is a decrease in agreement between the black box and the surrogate model for query points towards the edge of the test dataset and when moving away from the decision boundary. This originates from a mismatch between the data distributions used to train and evaluate surrogate explainers. We address this by leveraging knowledge about the test data distribution captured in the class labels of the black box model. By addressing this and encouraging users to take care in understanding the alignment of training and evaluation objectives, we empower them to construct more faithful surrogate explainers.|人工智能模型的透明度对于人工智能系统的设计、审核和部署至关重要。然而，尽管缺乏透明度，“黑匣子”模型在实践中仍然被用来预测未来。这导致了对事后的、模型无关的代理解释器的需求，这些代理解释器通过使用代理模型在接近查询点的地方近似模型的行为，为任何模型的决策提供解释。然而，人们往往忽视了查询点在黑盒模型决策表面中的位置对代理解释器的忠实性的影响。在这里，我们展示了当使用标准技术时，黑匣子和查询点的替代模型之间的一致性在测试数据集的边缘以及远离决策边界时有所下降。这源于用于训练和评估代理解释器的数据分布之间的不匹配。我们通过利用在黑盒模型的类标签中捕获的关于测试数据分布的知识来解决这个问题。通过解决这个问题，并鼓励用户注意理解培训和评估目标的一致性，我们授权他们构建更忠实的代理解释器。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Reconciling+Training+and+Evaluation+Objectives+in+Location+Agnostic+Surrogate+Explainers)|0| |[Efficient Variant Calling on Human Genome Sequences Using a GPU-Enabled Commodity Cluster](https://doi.org/10.1145/3583780.3615268)|Manas Jyoti Das, Khawar Shehzad, Praveen Rao|University of Missouri, Columbia, MO, USA|Human genome sequences are very large in size and require significant compute and storage resources for processing and analysis. Variant calling is a key task performed on an individual's genome to identify different types of variants. Knowing these variants can lead to new advances in disease diagnosis and treatment. In this work, we propose a new approach for accelerating variant calling pipelines on a large workload of human genomes using a commodity cluster with graphics processing units (GPUs). Our approach has two salient features: First, it enables a pipeline stage to use GPUs and/or CPUs based on the availability of resources in the cluster. Second, it employs a mutual exclusion strategy for executing a pipeline stage on the GPUs of a cluster node so that the stages (for other sequences) can be executed using CPUs if needed. We evaluated our approach on a 8-node cluster with bare metal servers and virtual machines (VMs) containing different types of GPUs. On publicly available genome sequences, our approach was 3.6X-5X faster compared to an approach that used only the cluster CPUs.|人类基因组序列非常大，需要大量的计算和存储资源进行处理和分析。变异调用是对个体基因组进行识别不同类型变异的关键任务。了解这些变异可以导致疾病诊断和治疗的新进展。在这项工作中，我们提出了一种新的方法，加速变体调用管道上的大量工作负载的人类基因组使用商品集群的图形处理单元(GPU)。我们的方法有两个显著特性: 首先，它允许管道阶段根据集群中资源的可用性使用 GPU 和/或 CPU。其次，它采用了一种互斥锁策略，在集群节点的 GPU 上执行流水线阶段，以便在需要时可以使用 CPU 执行这些阶段(对于其他序列)。我们在一个8节点集群上评估了我们的方法，该集群包含裸金属服务器和包含不同类型 GPU 的虚拟机(VM)。对于公开可用的基因组序列，我们的方法比仅使用集群 CPU 的方法快3.6X-5X 倍。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Efficient+Variant+Calling+on+Human+Genome+Sequences+Using+a+GPU-Enabled+Commodity+Cluster)|0| |[Self-supervised Learning and Graph Classification under Heterophily](https://doi.org/10.1145/3583780.3615166)|Yilin Ding, Zhen Liu, Hao Hao|School of Computer and Information Technology, Beijing Jiaotong University, Beijing, China|Self-supervised learning has shown its promising capability in graph representation learning in recent work. Most existing pre-training strategies usually choose the popular Graph neural networks (GNNs), which can be seen as a special form of low-pass filter, fail to effectively capture heterophily. In this paper, we first present an experimental investigation exploring the performance of low-pass and high-pass filters in heterophily graph classification, where the results clearly show that high-frequency signal is important for learning heterophily graph representation. On the other hand, it is still unclear how to effectively capture the structural pattern of graphs and how to measure the capability of the self-supervised pre-training strategy in capturing graph structure. To address the problem, we first design a quantitative metric to Measure Graph Structure (MGS), which analyzes correlation between structural similarity and embedding similarity of graph pairs. Then, to enhance the graph structural information captured by self-supervised learning, we propose a novel self-supervised strategy for Pre-training GNNs based on the Metric (PGM). Extensive experiments validate our pre-training strategy achieves state-of-the-art performance for molecular property prediction and protein function prediction. In addition, we find choosing the suitable filter sometimes may be better than designing good pre-training strategies for heterophily graph classification.|近年来，自监督学习在图表示学习方面表现出了很好的应用前景。现有的预训练策略大多采用流行的图形神经网络(GNN) ，它可以看作是一种特殊形式的低通滤波器，不能有效地捕获异质性。本文首先对异构图分类中的低通和高通滤波器的性能进行了实验研究，结果表明高频信号对于学习异构图表示是非常重要的。另一方面，如何有效地捕获图的结构模式以及如何衡量自监督预训练策略在捕获图结构方面的能力，目前尚不清楚。为了解决这个问题，我们首先设计了一个量化度量来度量图结构(MGS) ，它分析了图对的结构相似性和嵌入相似度之间的相关性。然后，为了增强自监督学习所获得的图结构信息，提出了一种新的基于度量(PGM)的自监督预训练 GNN 策略。大量的实验验证了我们的预训练策略在分子特性预测和蛋白质功能预测方面达到了最先进的性能。此外，我们发现选择合适的滤波器有时可能比设计好的异构图分类预训练策略更好。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Self-supervised+Learning+and+Graph+Classification+under+Heterophily)|0| -|[Bridging the KB-Text Gap: Leveraging Structured Knowledge-aware Pre-training for KBQA](https://doi.org/10.1145/3583780.3615150)|Guanting Dong, Rumei Li, Sirui Wang, Yupeng Zhang, Yunsen Xian, Weiran Xu|Beijing University of Aeronautics and Astronautics, Beijing, China; Beijing University of Posts and Telecommunications, Beijing, China; Meituan Group, Beijing, China|Knowledge Base Question Answering (KBQA) aims to answer natural language questions with factual information such as entities and relations in KBs. However, traditional Pre-trained Language Models (PLMs) are directly pre-trained on large-scale natural language corpus, which poses challenges for them in understanding and representing complex subgraphs in structured KBs. To bridge the gap between texts and structured KBs, we propose a Structured Knowledge-aware Pre-training method (SKP). In the pre-training stage, we introduce two novel structured knowledge-aware tasks, guiding the model to effectively learn the implicit relationship and better representations of complex subgraphs. In downstream KBQA task, we further design an efficient linearization strategy and an interval attention mechanism, which assist the model to better encode complex subgraphs and shield the interference of irrelevant subgraphs during reasoning respectively. Detailed experiments and analyses on WebQSP verify the effectiveness of SKP, especially the significant improvement in subgraph retrieval (+4.08% H@10).|知识库问题解答(KBQA)的目的是利用知识库中的实体和关系等事实信息来回答自然语言问题。然而，传统的预训练语言模型直接在大规模的自然语言语料库上进行预训练，这对它们在结构化知识库中理解和表示复杂子图提出了挑战。为了弥合文本和结构化知识库之间的差距，我们提出了一种结构化知识感知的预训练方法(SKP)。在预训练阶段，我们引入了两个新的结构化知识感知任务，引导模型有效地学习复杂子图的隐含关系和更好的表示。在后续的 KBQA 任务中，我们进一步设计了有效的线性化策略和区间注意机制，分别帮助模型更好地编码复杂子图和屏蔽推理过程中不相关子图的干扰。通过对 WebQSP 的详细实验和分析，验证了 SKP 的有效性，尤其是子图检索的显著改善(+ 4.08% H@10)。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Bridging+the+KB-Text+Gap:+Leveraging+Structured+Knowledge-aware+Pre-training+for+KBQA)|0| -|[Geometric Matrix Completion via Sylvester Multi-Graph Neural Network](https://doi.org/10.1145/3583780.3615170)|Boxin Du, Changhe Yuan, Fei Wang, Hanghang Tong|Amazon, New York, USA; University of Illinois at Urbana-Champaign, Urbana, USA; Amazon, San Francisco, USA|Despite the success of the Sylvester equation empowered methods on various graph mining applications, such as semi-supervised label learning and network alignment, there also exists several limitations. The Sylvester equation's inability of modeling non-linear relations and the inflexibility of tuning towards different tasks restrict its performance. In this paper, we propose an end-to-end neural framework, SYMGNN, which consists of a multi-network neural aggregation module and a prior multi-network association incorporation learning module. The proposed framework inherits the key ideas of the Sylvester equation, and meanwhile generalizes it to overcome aforementioned limitations. Empirical evaluations on real-world datasets show that the instantiations of SYMGNN overall outperform the baselines in geometric matrix completion task, and its low-rank instantiation could further reduce the memory consumption by 16.98\% on average.|尽管 Sylvester 方程在半监督标记学习和网络对齐等各种图挖掘应用中取得了成功，但也存在一些局限性。Sylvester 方程不能建立非线性关系，对不同任务的调整也不灵活，这些都限制了它的性能。在本文中，我们提出了一个端到端的神经元架构，SYMGNN，它由一个多网络神经元聚合模块和一个先前的多网络关联结合学习模块组成。该框架继承了 Sylvester 方程的核心思想，同时对其进行了推广，克服了上述局限性。对实际数据集的实证分析表明，SYMGNN 的实例化效果总体优于几何矩阵完成任务的基线，其低秩实例化平均可以进一步降低内存消耗16.98% 。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Geometric+Matrix+Completion+via+Sylvester+Multi-Graph+Neural+Network)|0| +|[Bridging the KB-Text Gap: Leveraging Structured Knowledge-aware Pre-training for KBQA](https://doi.org/10.1145/3583780.3615150)|Guanting Dong, Rumei Li, Sirui Wang, Yupeng Zhang, Yunsen Xian, Weiran Xu|Beijing University of Posts and Telecommunications, Beijing, China; Meituan Group, Beijing, China; Beijing University of Aeronautics and Astronautics, Beijing, China|Knowledge Base Question Answering (KBQA) aims to answer natural language questions with factual information such as entities and relations in KBs. However, traditional Pre-trained Language Models (PLMs) are directly pre-trained on large-scale natural language corpus, which poses challenges for them in understanding and representing complex subgraphs in structured KBs. To bridge the gap between texts and structured KBs, we propose a Structured Knowledge-aware Pre-training method (SKP). In the pre-training stage, we introduce two novel structured knowledge-aware tasks, guiding the model to effectively learn the implicit relationship and better representations of complex subgraphs. In downstream KBQA task, we further design an efficient linearization strategy and an interval attention mechanism, which assist the model to better encode complex subgraphs and shield the interference of irrelevant subgraphs during reasoning respectively. Detailed experiments and analyses on WebQSP verify the effectiveness of SKP, especially the significant improvement in subgraph retrieval (+4.08% H@10).|知识库问题解答(KBQA)的目的是利用知识库中的实体和关系等事实信息来回答自然语言问题。然而，传统的预训练语言模型直接在大规模的自然语言语料库上进行预训练，这对它们在结构化知识库中理解和表示复杂子图提出了挑战。为了弥合文本和结构化知识库之间的差距，我们提出了一种结构化知识感知的预训练方法(SKP)。在预训练阶段，我们引入了两个新的结构化知识感知任务，引导模型有效地学习复杂子图的隐含关系和更好的表示。在后续的 KBQA 任务中，我们进一步设计了有效的线性化策略和区间注意机制，分别帮助模型更好地编码复杂子图和屏蔽推理过程中不相关子图的干扰。通过对 WebQSP 的详细实验和分析，验证了 SKP 的有效性，尤其是子图检索的显著改善(+ 4.08% H@10)。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Bridging+the+KB-Text+Gap:+Leveraging+Structured+Knowledge-aware+Pre-training+for+KBQA)|0| +|[Geometric Matrix Completion via Sylvester Multi-Graph Neural Network](https://doi.org/10.1145/3583780.3615170)|Boxin Du, Changhe Yuan, Fei Wang, Hanghang Tong|Amazon, New York, USA; Amazon, San Francisco, USA; University of Illinois at Urbana-Champaign, Urbana, USA|Despite the success of the Sylvester equation empowered methods on various graph mining applications, such as semi-supervised label learning and network alignment, there also exists several limitations. The Sylvester equation's inability of modeling non-linear relations and the inflexibility of tuning towards different tasks restrict its performance. In this paper, we propose an end-to-end neural framework, SYMGNN, which consists of a multi-network neural aggregation module and a prior multi-network association incorporation learning module. The proposed framework inherits the key ideas of the Sylvester equation, and meanwhile generalizes it to overcome aforementioned limitations. Empirical evaluations on real-world datasets show that the instantiations of SYMGNN overall outperform the baselines in geometric matrix completion task, and its low-rank instantiation could further reduce the memory consumption by 16.98\% on average.|尽管 Sylvester 方程在半监督标记学习和网络对齐等各种图挖掘应用中取得了成功，但也存在一些局限性。Sylvester 方程不能建立非线性关系，对不同任务的调整也不灵活，这些都限制了它的性能。在本文中，我们提出了一个端到端的神经元架构，SYMGNN，它由一个多网络神经元聚合模块和一个先前的多网络关联结合学习模块组成。该框架继承了 Sylvester 方程的核心思想，同时对其进行了推广，克服了上述局限性。对实际数据集的实证分析表明，SYMGNN 的实例化效果总体优于几何矩阵完成任务的基线，其低秩实例化平均可以进一步降低内存消耗16.98% 。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Geometric+Matrix+Completion+via+Sylvester+Multi-Graph+Neural+Network)|0| |[Neighborhood Homophily-based Graph Convolutional Network](https://doi.org/10.1145/3583780.3615195)|Shengbo Gong, Jiajun Zhou, Chenxuan Xie, Qi Xuan|Zhejiang University of Technology & Binjiang Cyberspace Security Institute of ZJUT, Hangzhou, China|Graph neural networks (GNNs) have been proved powerful in graph-oriented tasks. However, many real-world graphs are heterophilous, challenging the homophily assumption of classical GNNs. To solve the universality problem, many studies deepen networks or concatenate intermediate representations, which does not inherently change neighbor aggregation and introduces noise. Recent studies propose new metrics to characterize the homophily, but rarely consider the correlation of the proposed metrics and models. In this paper, we first design a new metric, Neighborhood Homophily (NH), to measure the label complexity or purity in node neighborhoods. Furthermore, we incorporate the metric into the classical graph convolutional network (GCN) architecture and propose Neighborhood Homophily-based Graph Convolutional Network (NHGCN). In this framework, neighbors are grouped by estimated NH values and aggregated from different channels, and the resulting node predictions are then used in turn to estimate and update NH values. The two processes of metric estimation and model inference are alternately optimized to achieve better node classification. NHGCN achieves top overall performance on both homophilous and heterophilous benchmarks, with an improvement of up to 7.4% compared to the current SOTA methods.|图神经网络(GNN)已被证明在面向图的任务中是强大的。然而，许多现实世界的图是异质的，挑战了经典 GNN 的同质假设。为了解决通用性问题，许多研究深化了网络或级联了中间表示，这种方法本身并不改变邻居聚集，而是引入了噪声。最近的研究提出了新的度量来表征同质性，但很少考虑提出的度量和模型的相关性。在本文中，我们首先设计了一个新的度量，邻域同伦(NH) ，来度量标签的复杂度或节点邻域的纯度。此外，我们将度量结合到经典的图卷积网络(GCN)体系结构中，并提出了基于邻域同构的图卷积网络(NHGCN)。在这个框架中，根据估计的 NH 值对邻居进行分组，并从不同的通道汇总，然后使用所得到的节点预测来估计和更新 NH 值。交替优化度量估计和模型推理两个过程，以实现更好的节点分类。NHGCN 在同质性和异质性基准测试上都取得了最好的总体性能，与目前的 SOTA 方法相比提高了7.4% 。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Neighborhood+Homophily-based+Graph+Convolutional+Network)|0| |[Explainable and Accurate Natural Language Understanding for Voice Assistants and Beyond](https://doi.org/10.1145/3583780.3615277)|Kalpa Gunaratna, Vijay Srinivasan, Hongxia Jin|Samsung Research America, Mountain View, CA, USA|Joint intent detection and slot filling, which is also termed as joint NLU (Natural Language Understanding) is invaluable for smart voice assistants. Recent advancements in this area have been heavily focusing on improving accuracy using various techniques. Explainability is undoubtedly an important aspect for deep learning-based models including joint NLU models. Without explainability, their decisions are opaque to the outside world and hence, have tendency to lack user trust. Therefore to bridge this gap, we transform the full joint NLU model to be `inherently' explainable at granular levels without compromising on accuracy. Further, as we enable the full joint NLU model explainable, we show that our extension can be successfully used in other general classification tasks. We demonstrate this using sentiment analysis and named entity recognition.|联合意图检测和插槽填充，也被称为联合 NLU (自然语言理解)是非常宝贵的智能语音助手。该领域的最新进展主要集中在利用各种技术提高精度。可解释性无疑是包括联合 NLU 模型在内的基于深度学习模型的一个重要方面。如果没有可解释性，他们的决策对于外部世界是不透明的，因此有缺乏用户信任的倾向。因此，为了弥合这一差距，我们将完整的联合 NLU 模型转换为“固有的”可解释的粒度级别，而不影响精度。进一步，当我们能够解释完整的联合 NLU 模型，我们表明我们的扩展可以成功地用于其他一般的分类任务。我们使用情感分析和命名实体识别来证明这一点。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Explainable+and+Accurate+Natural+Language+Understanding+for+Voice+Assistants+and+Beyond)|0| -|[Hateful Comment Detection and Hate Target Type Prediction for Video Comments](https://doi.org/10.1145/3583780.3615260)|Shrey Gupta, Pratyush Priyadarshi, Manish Gupta|IIIT-Hyderabad, Hyderabad, India; IIIT-Hyderabad & Microsoft, Hyderabad, India|With the widespread increase in hateful content on the web, hate detection has become more crucial than ever. Although vast literature exists on hate detection from text, images and videos, interestingly, there has been no previous work on hateful comment detection (HCD) from video pages. HCD is critical for comment moderation and for flagging controversial videos. Comments are often short, contextual and convoluted making the problem challenging. Toward solving this problem, we contribute a dataset, HateComments, consisting of 2071 comments for 401 videos obtained from two popular video sharing platforms. We investigate two related tasks: binary HCD and 4-class multi-label hate target-type prediction (HTP). We systematically explore the importance of various forms of context for effective HCD. Our initial experiments show that our best method which leverages rich video context (like description, transcript and visual input) leads to an HCD accuracy of ~78.6% and an ROC AUC score of ~0.61 for HTP. Code and data is at https://drive.google.com/file/d/1EUbWDUokv1CYkWKlwByUC6yIuBGUw2MN/.|随着互联网上仇恨内容的广泛增加，仇恨检测变得比以往任何时候都更加重要。虽然大量的文献存在从文本，图像和视频仇恨检测，有趣的是，还没有以前的工作在仇恨评论检测(HCD)从视频网页。HCD 对于评论节制和标记有争议的视频至关重要。评论通常很短，上下文相关，而且令人费解，使问题具有挑战性。为了解决这个问题，我们贡献了一个数据集，仇恨评论，包括从两个流行的视频分享平台获得的401个视频的2071条评论。我们研究了两个相关的任务: 二进制 HCD 和4类多标签仇恨目标类型预测(HTTP)。我们系统地探讨了各种形式的背景对有效 HCD 的重要性。我们最初的实验表明，我们利用丰富的视频上下文(如描述，转录本和视觉输入)的最佳方法导致 HCD 准确率约为78.6% ，对于 HTTP，ROC AUC 评分约为0.61。代码和数据处于 https://drive.google.com/file/d/1eubwduokv1cykwklwbyuc6yiubguw2mn/。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Hateful+Comment+Detection+and+Hate+Target+Type+Prediction+for+Video+Comments)|0| -|[Latent Aspect Detection via Backtranslation Augmentation](https://doi.org/10.1145/3583780.3615205)|Farinam Hemmatizadeh, Christine Wong, Alice Yu, Hossein Fani|Vincent Massey Secondary School, Windsor, ON, Canada; University of Windsor, Windsor, ON, Canada|Within the context of review analytics, aspects are the features of products and services at which customers target their opinions and sentiments. Aspect detection helps product owners and service providers identify shortcomings and prioritize customers' needs. Existing methods focus on detecting the surface form of an aspect falling short when aspects are latent in reviews, especially in an informal context like in social posts. In this paper, we propose data augmentation via natural language backtranslation to extract latent occurrences of aspects. We presume that backtranslation (1) can reveal latent aspects because they may not be commonly known in the target language and can be generated through backtranslation; (2) augments context-aware synonymous aspects from a target language to the original language, hence addressing the out-of-vocabulary issue; and (3) helps with the semantic disambiguation of polysemous words and collocations. Through our experiments on well-known aspect detection methods across semeval datasets of restaurant and laptop reviews, we demonstrate that review augmentation via backtranslation yields a steady performance boost in baselines. We further contribute LADy at https://github.com/fani-lab/LADy, a benchmark library to support the reproducibility of our research.|在评论分析的背景下，方面是产品和服务的特征，客户针对这些特征提出他们的意见和看法。方面检测可以帮助产品所有者和服务提供者识别缺点并优先考虑客户的需求。现有的研究方法主要集中在检测评论中，尤其是在非正式场合，如社会职位中，某一方面的表面形式不足。本文提出了一种基于自然语言反翻译的数据增强方法来提取潜在的方面。我们假设，反向翻译(1)可以揭示潜在的方面，因为它们可能在目的语中并不常见，可以通过反向翻译产生; (2)从目的语到原语增加了上下文感知的同义方面，从而解决了词汇表外的问题; (3)有助于多义词和搭配的语义消歧。通过我们在餐馆和笔记本电脑评论的中期数据集上的著名方面检测方法的实验，我们证明了通过反向翻译的评论增强在基线上产生了稳定的性能提升。我们进一步贡献了 https://github.com/fani-lab/LADy ，一个基准图书馆，以支持我们的研究的可重复性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Latent+Aspect+Detection+via+Backtranslation+Augmentation)|0| +|[Hateful Comment Detection and Hate Target Type Prediction for Video Comments](https://doi.org/10.1145/3583780.3615260)|Shrey Gupta, Pratyush Priyadarshi, Manish Gupta|IIIT-Hyderabad & Microsoft, Hyderabad, India; IIIT-Hyderabad, Hyderabad, India|With the widespread increase in hateful content on the web, hate detection has become more crucial than ever. Although vast literature exists on hate detection from text, images and videos, interestingly, there has been no previous work on hateful comment detection (HCD) from video pages. HCD is critical for comment moderation and for flagging controversial videos. Comments are often short, contextual and convoluted making the problem challenging. Toward solving this problem, we contribute a dataset, HateComments, consisting of 2071 comments for 401 videos obtained from two popular video sharing platforms. We investigate two related tasks: binary HCD and 4-class multi-label hate target-type prediction (HTP). We systematically explore the importance of various forms of context for effective HCD. Our initial experiments show that our best method which leverages rich video context (like description, transcript and visual input) leads to an HCD accuracy of ~78.6% and an ROC AUC score of ~0.61 for HTP. Code and data is at https://drive.google.com/file/d/1EUbWDUokv1CYkWKlwByUC6yIuBGUw2MN/.|随着互联网上仇恨内容的广泛增加，仇恨检测变得比以往任何时候都更加重要。虽然大量的文献存在从文本，图像和视频仇恨检测，有趣的是，还没有以前的工作在仇恨评论检测(HCD)从视频网页。HCD 对于评论节制和标记有争议的视频至关重要。评论通常很短，上下文相关，而且令人费解，使问题具有挑战性。为了解决这个问题，我们贡献了一个数据集，仇恨评论，包括从两个流行的视频分享平台获得的401个视频的2071条评论。我们研究了两个相关的任务: 二进制 HCD 和4类多标签仇恨目标类型预测(HTTP)。我们系统地探讨了各种形式的背景对有效 HCD 的重要性。我们最初的实验表明，我们利用丰富的视频上下文(如描述，转录本和视觉输入)的最佳方法导致 HCD 准确率约为78.6% ，对于 HTTP，ROC AUC 评分约为0.61。代码和数据处于 https://drive.google.com/file/d/1eubwduokv1cykwklwbyuc6yiubguw2mn/。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Hateful+Comment+Detection+and+Hate+Target+Type+Prediction+for+Video+Comments)|0| +|[Latent Aspect Detection via Backtranslation Augmentation](https://doi.org/10.1145/3583780.3615205)|Farinam Hemmatizadeh, Christine Wong, Alice Yu, Hossein Fani|University of Windsor, Windsor, ON, Canada; Vincent Massey Secondary School, Windsor, ON, Canada|Within the context of review analytics, aspects are the features of products and services at which customers target their opinions and sentiments. Aspect detection helps product owners and service providers identify shortcomings and prioritize customers' needs. Existing methods focus on detecting the surface form of an aspect falling short when aspects are latent in reviews, especially in an informal context like in social posts. In this paper, we propose data augmentation via natural language backtranslation to extract latent occurrences of aspects. We presume that backtranslation (1) can reveal latent aspects because they may not be commonly known in the target language and can be generated through backtranslation; (2) augments context-aware synonymous aspects from a target language to the original language, hence addressing the out-of-vocabulary issue; and (3) helps with the semantic disambiguation of polysemous words and collocations. Through our experiments on well-known aspect detection methods across semeval datasets of restaurant and laptop reviews, we demonstrate that review augmentation via backtranslation yields a steady performance boost in baselines. We further contribute LADy at https://github.com/fani-lab/LADy, a benchmark library to support the reproducibility of our research.|在评论分析的背景下，方面是产品和服务的特征，客户针对这些特征提出他们的意见和看法。方面检测可以帮助产品所有者和服务提供者识别缺点并优先考虑客户的需求。现有的研究方法主要集中在检测评论中，尤其是在非正式场合，如社会职位中，某一方面的表面形式不足。本文提出了一种基于自然语言反翻译的数据增强方法来提取潜在的方面。我们假设，反向翻译(1)可以揭示潜在的方面，因为它们可能在目的语中并不常见，可以通过反向翻译产生; (2)从目的语到原语增加了上下文感知的同义方面，从而解决了词汇表外的问题; (3)有助于多义词和搭配的语义消歧。通过我们在餐馆和笔记本电脑评论的中期数据集上的著名方面检测方法的实验，我们证明了通过反向翻译的评论增强在基线上产生了稳定的性能提升。我们进一步贡献了 https://github.com/fani-lab/LADy ，一个基准图书馆，以支持我们的研究的可重复性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Latent+Aspect+Detection+via+Backtranslation+Augmentation)|0| |[Stochastic Subgraph Neighborhood Pooling for Subgraph Classification](https://doi.org/10.1145/3583780.3615227)|Shweta Ann Jacob, Paul Louis, Amirali SalehiAbari|University of Ontario Institute of Technology, Oshawa, ON, Canada|Subgraph classification is an emerging field in graph representation learning where the task is to classify a group of nodes (i.e., a subgraph) within a graph. Subgraph classification has applications such as predicting the cellular function of a group of proteins or identifying rare diseases given a collection of phenotypes. Graph neural networks (GNNs) are the de facto solution for node, link, and graph-level tasks but fail to perform well on subgraph classification tasks. Even GNNs tailored for graph classification are not directly transferable to subgraph classification as they ignore the external topology of the subgraph, thus failing to capture how the subgraph is located within the larger graph. The current state-of-the-art models for subgraph classification address this shortcoming through either labeling tricks or multiple message-passing channels, both of which impose a computation burden and are not scalable to large graphs. To address the scalability issue while maintaining generalization, we propose Stochastic Subgraph Neighborhood Pooling (SSNP), which jointly aggregates the subgraph and its neighborhood (i.e., external topology) information without any computationally expensive operations such as labeling tricks. To improve scalability and generalization further, we also propose a simple data augmentation pre-processing step for SSNP that creates multiple sparse views of the subgraph neighborhood. We show that our model is more expressive than GNNs without labeling tricks. Our extensive experiments demonstrate that our models outperform current state-of-the-art methods (with a margin of up to 2%) while being up to 3X faster in training.|子图分类是图表示学习中的一个新兴领域，其任务是对图中的一组节点(即子图)进行分类。子图分类的应用，如预测一组蛋白质的细胞功能或鉴定罕见疾病的表型集合。图神经网络(GNN)是节点、链路和图级任务的实际解决方案，但在子图分类任务中表现不佳。即使是专门用于图分类的 GNN 也不能直接转移到子图分类，因为它们忽略了子图的外部拓扑结构，因此无法捕捉子图在较大图中的位置。目前最先进的子图分类模型通过标记技巧或多个消息传递通道来解决这一缺陷，这两种方法都增加了计算负担，而且不能扩展到大图。为了在保持泛化的同时解决可扩展性问题，我们提出了随机子图邻域池(SSNP) ，它不需要任何计算代价高昂的操作，如标记技巧，就可以联合聚集子图及其邻域(即外部拓扑)信息。为了进一步提高可扩展性和泛化能力，我们还提出了一个简单的数据增强预处理步骤，用于产生子图邻域的多个稀疏视图。我们展示了我们的模型比没有标记技巧的 GNN 更有表现力。我们广泛的实验表明，我们的模型表现优于目前的最先进的方法(利润率高达2%) ，同时在训练快达3倍。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Stochastic+Subgraph+Neighborhood+Pooling+for+Subgraph+Classification)|0| |[Uncertainty Quantification via Spatial-Temporal Tweedie Model for Zero-inflated and Long-tail Travel Demand Prediction](https://doi.org/10.1145/3583780.3615215)|Xinke Jiang, Dingyi Zhuang, Xianghui Zhang, Hao Chen, Jiayuan Luo, Xiaowei Gao||Understanding Origin-Destination (O-D) travel demand is crucial for transportation management. However, traditional spatial-temporal deep learning models grapple with addressing the sparse and long-tail characteristics in high-resolution O-D matrices and quantifying prediction uncertainty. This dilemma arises from the numerous zeros and over-dispersed demand patterns within these matrices, which challenge the Gaussian assumption inherent to deterministic deep learning models. To address these challenges, we propose a novel approach: the Spatial-Temporal Tweedie Graph Neural Network (STTD). The STTD introduces the Tweedie distribution as a compelling alternative to the traditional 'zero-inflated' model and leverages spatial and temporal embeddings to parameterize travel demand distributions. Our evaluations using real-world datasets highlight STTD's superiority in providing accurate predictions and precise confidence intervals, particularly in high-resolution scenarios.|理解起点-目的地(O-D)旅游需求对于运输管理至关重要。然而，传统的时空深度学习模型致力于解决高分辨率 O-D 矩阵中的稀疏和长尾特征以及预测不确定性的量化问题。这种困境源于这些矩阵中大量的零和过分分散的需求模式，这挑战了确定性深度学习模型固有的高斯假设。为了应对这些挑战，我们提出了一种新的方法: 时空 Tweedie 图神经网络(STTD)。STTD 引入了 Tweedie 发行版，作为传统“零膨胀”模型的一个引人注目的替代品，并利用空间和时间嵌入来参数化旅游需求分布。我们使用真实世界数据集的评估突出了 STTD 在提供准确预测和精确置信区间方面的优势，特别是在高分辨率情况下。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Uncertainty+Quantification+via+Spatial-Temporal+Tweedie+Model+for+Zero-inflated+and+Long-tail+Travel+Demand+Prediction)|0| |[Effective Slogan Generation with Noise Perturbation](https://doi.org/10.1145/3583780.3615193)|Jongeun Kim, MinChung Kim, Taehwan Kim|UNIST, Ulsan, Republic of Korea|Slogans play a crucial role in building the brand's identity of the firm. A slogan is expected to reflect firm's vision and the brand's value propositions in memorable and likeable ways. Automating the generation of slogans with such characteristics is challenging. Previous studies developed and tested slogan generation with syntactic control and summarization models which are not capable of generating distinctive slogans. We introduce a novel approach that leverages pre-trained transformer T5 model with noise perturbation on newly proposed 1:N matching pair dataset. This approach serves as a contributing factor in generating distinctive and coherent slogans. Furthermore, the proposed approach incorporates descriptions about the firm and brand into the generation of slogans. We evaluate generated slogans based on ROUGE-1, ROUGE-L and Cosine Similarity metrics and also assess them with human subjects in terms of slogan's distinctiveness, coherence, and fluency. The results demonstrate that our approach yields better performance than baseline models and other transformer-based models.|口号在树立公司品牌形象方面起着至关重要的作用。一个口号被期望以令人难忘和可爱的方式反映公司的愿景和品牌的价值主张。自动生成具有这些特征的口号具有挑战性。以往的研究开发和测试口号生成的句法控制和总结模型，不能产生独特的口号。我们提出了一种新的方法，利用预训练的变压器 T5模型与噪声扰动新提出的1: N 匹配对数据集。这种方法在产生与众不同和连贯的口号方面起到了影响因素的作用。此外，建议的方法将有关企业和品牌的描述纳入口号的生成。我们基于 ROUge-1、 ROUge-L 和余弦距离度量对生成的口号进行评估，同时也根据口号的独特性、连贯性和流畅性对人类受试者进行评估。结果表明，我们的方法比基线模型和其他基于变压器的模型产生更好的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Effective+Slogan+Generation+with+Noise+Perturbation)|0| @@ -552,38 +552,38 @@ |[Class Label-aware Graph Anomaly Detection](https://doi.org/10.1145/3583780.3615249)|Junghoon Kim, Yeonjun In, Kanghoon Yoon, Junmo Lee, Chanyoung Park|KAIST, Daejeon, Republic of Korea|Unsupervised GAD methods assume the lack of anomaly labels, i.e., whether a node is anomalous or not. One common observation we made from previous unsupervised methods is that they not only assume the absence of such anomaly labels, but also the absence of class labels (the class a node belongs to used in a general node classification task). In this work, we study the utility of class labels for unsupervised GAD; in particular, how they enhance the detection of structural anomalies. To this end, we propose a Class Label-aware Graph Anomaly Detection framework (CLAD) that utilizes a limited amount of labeled nodes to enhance the performance of unsupervised GAD. Extensive experiments on ten datasets demonstrate the superior performance of CLAD in comparison to existing unsupervised GAD methods, even in the absence of ground-truth class label information. The source code for CLAD is available at \url{https://github.com/jhkim611/CLAD}.|无监督的 GAD 方法假定缺乏异常标签，即节点是否异常。我们从以前的无监督方法中得到的一个常见的观察结果是，它们不仅假设没有这种异常标签，而且还假设没有类标签(节点所属的类在一般节点分类任务中使用)。在这项工作中，我们研究了类标签对于无监督广义系统的效用，特别是它们如何增强结构异常的检测。为此，我们提出了一个类标签感知图形异常检测框架(CLAD) ，该框架利用有限数量的标记节点来提高无监督广义相关设计的性能。在10个数据集上的大量实验表明，与现有的无监督 GAD 方法相比，即使在没有地面真值类标签信息的情况下，CLAD 的性能也更好。CLAD 的源代码可以在 url { https://github.com/jhkim611/CLAD }找到。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Class+Label-aware+Graph+Anomaly+Detection)|0| |[Can a Chatbot be Useful in Childhood Cancer Survivorship? Development of a Chatbot for Survivors of Childhood Cancer](https://doi.org/10.1145/3583780.3615234)|Mirae Kim, Kyubum Hwang, Hayoung Oh, Heejin Kim, MinAh Kim|Sungkyunkwan University, Seoul, Republic of Korea|This study introduces an informational and empathetic chatbot for childhood cancer survivors. As the survival rates for childhood cancer around the world have increased, survivors often face various psychosocial challenges during and after cancer treatment. However, they rarely seek support from psychosocial professionals due to the low availability of resources and stigma toward cancer survivors in countries like South Korea. This study aimed to develop a chatbot tailed to the unique characteristics of childhood cancer survivors in need of informational and emotional support. Given the limited availability of empirical data on childhood cancer survivors, quotes from survivors were gathered from academic articles and social media, then large language models were employed to generate appropriate responses. Furthermore, we incorporated domain learning techniques to ensure a more tailored and suitable model for addressing the needs of survivors.|这项研究为儿童癌症幸存者介绍了一个信息和移情聊天机器人。随着世界各地儿童癌症存活率的提高，幸存者在癌症治疗期间和治疗后往往面临各种心理社会挑战。然而，他们很少寻求社会心理学专业人士的支持，因为在韩国等国家，资源的可用性很低，癌症幸存者也被视为耻辱。这项研究旨在开发一个聊天机器人尾随儿童癌症幸存者的独特特征，需要信息和情感支持。鉴于儿童癌症幸存者的经验数据有限，从学术文章和社交媒体中收集幸存者的引文，然后使用大型语言模型来产生适当的反应。此外，我们纳入了领域学习技术，以确保更加量身定制和适当的模式，以满足幸存者的需求。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Can+a+Chatbot+be+Useful+in+Childhood+Cancer+Survivorship?+Development+of+a+Chatbot+for+Survivors+of+Childhood+Cancer)|0| |[You're Not Alone in Battle: Combat Threat Analysis Using Attention Networks and a New Open Benchmark](https://doi.org/10.1145/3583780.3615196)|Soo Yong Lee, Juwon Kim, Kiwoong Park, Dong Kuk Ryu, Sang Heun Shim, Kijung Shin|KAIST, Seoul, Republic of Korea; Agency for Defense Development, Seoul, Republic of Korea|For military commands, combat threat analysis is crucial in predicting future outcomes and informing consequent decisions. Its primary objectives include determining the intention and attack likelihood of the hostiles. The complex, dynamic, and noisy nature of combat, however, presents significant challenges in its analysis. The prior research has been limited in accounting for such characteristics, assuming independence of each entity, no unobserved tactics, and clean combat data. As such, we present spatio-temporal attention for threat analysis (SAFETY) to encode complex interactions that arise within combat. We test the model performance for unobserved tactics and with various perturbations. To do so, we also present the first open-source benchmark for combat threat analysis with two downstream tasks of predicting entity intention and attack probability. Our experiments show that SAFETY achieves a significant improvement in model performance, with enhancements of up to 13% in intention prediction and 7% in attack prediction compared to the strongest competitor, even when confronted with noisy or missing data. This result highlights the importance of encoding dynamic interactions among entities for combat threat analysis. Our codes and dataset are available at https://github.com/syleeheal/SAFETY.|对于军事指挥来说，战斗威胁分析在预测未来结果和通知后续决策方面是至关重要的。它的主要目标包括确定敌人的意图和攻击可能性。然而，战斗的复杂性、动态性和嘈杂性在其分析中提出了重大的挑战。先前的研究在考虑这些特征方面受到限制，假设每个实体独立，没有未观察到的战术，以及清晰的作战数据。因此，我们提出时空注意力的威胁分析(安全) ，以编码复杂的相互作用中出现的战斗。我们测试模型的性能未观测的战术和各种摄动。为此，我们还提出了第一个用于作战威胁分析的开源基准，其下游任务是预测实体意图和攻击概率。我们的实验表明，SAFETY 在模型性能方面取得了显著的改进，与最强大的竞争对手相比，即使面对有噪音或丢失的数据，在意图预测和攻击预测方面的改进分别高达13% 和7% 。该结果突出了编码实体间动态交互对于作战威胁分析的重要性。我们的代码和数据集 https://github.com/syleeheal/safety 可查。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=You're+Not+Alone+in+Battle:+Combat+Threat+Analysis+Using+Attention+Networks+and+a+New+Open+Benchmark)|0| -|[Look At Me, No Replay! SurpriseNet: Anomaly Detection Inspired Class Incremental Learning](https://doi.org/10.1145/3583780.3615236)|Anton Lee, Yaqian Zhang, Heitor Murilo Gomes, Albert Bifet, Bernhard Pfahringer|Victoria University of Wellington, Wellington, New Zealand; Victoria University of Wellington & University of Waikato, Wellington, New Zealand; University of Waikato, Hamilton, New Zealand; University of Waikato & Institute Polytechnique de Paris, Hamilton, New Zealand|Continual learning aims to create artificial neural networks capable of accumulating knowledge and skills through incremental training on a sequence of tasks. The main challenge of continual learning is catastrophic interference, wherein new knowledge overrides or interferes with past knowledge, leading to forgetting. An associated issue is the problem of learning "cross-task knowledge," where models fail to acquire and retain knowledge that helps differentiate classes across task boundaries. A common solution to both problems is "replay," where a limited buffer of past instances is utilized to learn cross-task knowledge and mitigate catastrophic interference. However, a notable drawback of these methods is their tendency to overfit the limited replay buffer. In contrast, our proposed solution, SurpriseNet, addresses catastrophic interference by employing a parameter isolation method and learning cross-task knowledge using an auto-encoder inspired by anomaly detection. SurpriseNet is applicable to both structured and unstructured data, as it does not rely on image-specific inductive biases. We have conducted empirical experiments demonstrating the strengths of SurpriseNet on various traditional vision continual-learning benchmarks, as well as on structured data datasets. Source code made available at https://doi.org/10.5281/zenodo.8247906 and https://github.com/tachyonicClock/SurpriseNet-CIKM-23|持续学习的目的是创建人工神经网络，能够通过一系列任务的增量训练来积累知识和技能。持续学习的主要挑战是灾难性干扰，即新知识凌驾于过去的知识之上或干扰过去的知识，导致遗忘。一个相关的问题是学习“跨任务知识”的问题，其中模型无法获取和保留有助于跨任务边界区分类别的知识。这两个问题的一个共同解决方案是“重播”，即利用过去实例的有限缓冲区来学习跨任务知识和减轻灾难性干扰。然而，这些方法的一个显著缺点是它们倾向于过载有限的重播缓冲区。相比之下，我们提出的解决方案“惊喜网络”通过使用参数隔离方法和使用受灾难性干扰异常检测启发的自动编码器学习跨任务知识来解决问题。因为它不依赖于特定于图像的归纳偏差，所以它同时适用于结构化和非结构化数据化。我们已经进行了实证实验，证明了在各种传统的视觉连续学习基准，以及结构化数据集的优势惊喜网络。源代码可在 https://doi.org/10.5281/zenodo.8247906和 https://github.com/tachyonicclock/surprisenet-cikm-23获得|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Look+At+Me,+No+Replay!+SurpriseNet:+Anomaly+Detection+Inspired+Class+Incremental+Learning)|0| +|[Look At Me, No Replay! SurpriseNet: Anomaly Detection Inspired Class Incremental Learning](https://doi.org/10.1145/3583780.3615236)|Anton Lee, Yaqian Zhang, Heitor Murilo Gomes, Albert Bifet, Bernhard Pfahringer|Victoria University of Wellington, Wellington, New Zealand; University of Waikato, Hamilton, New Zealand; University of Waikato & Institute Polytechnique de Paris, Hamilton, New Zealand; Victoria University of Wellington & University of Waikato, Wellington, New Zealand|Continual learning aims to create artificial neural networks capable of accumulating knowledge and skills through incremental training on a sequence of tasks. The main challenge of continual learning is catastrophic interference, wherein new knowledge overrides or interferes with past knowledge, leading to forgetting. An associated issue is the problem of learning "cross-task knowledge," where models fail to acquire and retain knowledge that helps differentiate classes across task boundaries. A common solution to both problems is "replay," where a limited buffer of past instances is utilized to learn cross-task knowledge and mitigate catastrophic interference. However, a notable drawback of these methods is their tendency to overfit the limited replay buffer. In contrast, our proposed solution, SurpriseNet, addresses catastrophic interference by employing a parameter isolation method and learning cross-task knowledge using an auto-encoder inspired by anomaly detection. SurpriseNet is applicable to both structured and unstructured data, as it does not rely on image-specific inductive biases. We have conducted empirical experiments demonstrating the strengths of SurpriseNet on various traditional vision continual-learning benchmarks, as well as on structured data datasets. Source code made available at https://doi.org/10.5281/zenodo.8247906 and https://github.com/tachyonicClock/SurpriseNet-CIKM-23|持续学习的目的是创建人工神经网络，能够通过一系列任务的增量训练来积累知识和技能。持续学习的主要挑战是灾难性干扰，即新知识凌驾于过去的知识之上或干扰过去的知识，导致遗忘。一个相关的问题是学习“跨任务知识”的问题，其中模型无法获取和保留有助于跨任务边界区分类别的知识。这两个问题的一个共同解决方案是“重播”，即利用过去实例的有限缓冲区来学习跨任务知识和减轻灾难性干扰。然而，这些方法的一个显著缺点是它们倾向于过载有限的重播缓冲区。相比之下，我们提出的解决方案“惊喜网络”通过使用参数隔离方法和使用受灾难性干扰异常检测启发的自动编码器学习跨任务知识来解决问题。因为它不依赖于特定于图像的归纳偏差，所以它同时适用于结构化和非结构化数据化。我们已经进行了实证实验，证明了在各种传统的视觉连续学习基准，以及结构化数据集的优势惊喜网络。源代码可在 https://doi.org/10.5281/zenodo.8247906和 https://github.com/tachyonicclock/surprisenet-cikm-23获得|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Look+At+Me,+No+Replay!+SurpriseNet:+Anomaly+Detection+Inspired+Class+Incremental+Learning)|0| |[UNDO: Effective and Accurate Unlearning Method for Deep Neural Networks](https://doi.org/10.1145/3583780.3615235)|Sangyong Lee, Simon S. Woo|Sungkyunkwan University, Suwon, Republic of Korea|Machine learning has evolved through extensive data usage, including personal and private information. Regulations like GDPR highlight the "Right to be forgotten" for user and data privacy. Research in machine unlearning aims to remove specific data from pre-trained models. We introduce a novel two-step unlearning method, UNDO. First, we selectively disrupt the decision boundary of forgetting data at the coarse-grained level. However, this can also inadvertently affect the decision boundary of other remaining data, lowering the overall performance of classification task. Hence, we subsequently repair and refining the decision boundary for each class at the fine-grained level by introducing a loss for maintain the overall performance, while completely removing the class. Our approach is validated through experiments on two datasets, outperforming other methods in effectiveness and efficiency.|机器学习已经通过广泛的数据使用，包括个人和私人信息的演变。GDPR 等法规强调了用户和数据隐私的“被遗忘的权利”。机器学习研究的目的是从预先训练的模型中去除特定的数据。我们介绍了一种新的两步去学习方法，UNDO。首先，我们在粗粒度层面有选择性地中断遗忘数据的决策边界。然而，这也会无意中影响其他数据的决策边界，降低分类任务的整体性能。因此，我们随后在细粒度水平上修复和改进每个类的决策边界，引入损失以保持整体性能，同时完全删除该类。我们的方法是通过两个数据集的实验验证，在有效性和效率方面优于其他方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=UNDO:+Effective+and+Accurate+Unlearning+Method+for+Deep+Neural+Networks)|0| -|[ST-RAP: A Spatio-Temporal Framework for Real Estate Appraisal](https://doi.org/10.1145/3583780.3615168)|Hojoon Lee, Hawon Jeong, Byungkun Lee, Kyungyup Daniel Lee, Jaegul Choo|KAIST AI, Seongnam-si, Republic of Korea; Spacewalk Inc., Seoul-si, Republic of Korea|In this paper, we introduce ST-RAP, a novel Spatio-Temporal framework for Real estate APpraisal. ST-RAP employs a hierarchical architecture with a heterogeneous graph neural network to encapsulate temporal dynamics and spatial relationships simultaneously. Through comprehensive experiments on a large-scale real estate dataset, ST-RAP outperforms previous methods, demonstrating the significant benefits of integrating spatial and temporal aspects in real estate appraisal. Our code and dataset are available at https://github.com/dojeon-ai/STRAP.|在这篇文章中，我们介绍了 ST-RAP，一个新的不动产估价师时空框架。RAP 采用层次结构和异构图形神经网络来同时封装时间动态和空间关系。通过在大规模房地产数据集上的全面实验，ST-RAP 优于以往的方法，展示了在不动产估价师中整合空间和时间方面的显著好处。我们的代码和数据集可在 https://github.com/dojeon-ai/strap 下载。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=ST-RAP:+A+Spatio-Temporal+Framework+for+Real+Estate+Appraisal)|0| +|[ST-RAP: A Spatio-Temporal Framework for Real Estate Appraisal](https://doi.org/10.1145/3583780.3615168)|Hojoon Lee, Hawon Jeong, Byungkun Lee, Kyungyup Daniel Lee, Jaegul Choo|Spacewalk Inc., Seoul-si, Republic of Korea; KAIST AI, Seongnam-si, Republic of Korea|In this paper, we introduce ST-RAP, a novel Spatio-Temporal framework for Real estate APpraisal. ST-RAP employs a hierarchical architecture with a heterogeneous graph neural network to encapsulate temporal dynamics and spatial relationships simultaneously. Through comprehensive experiments on a large-scale real estate dataset, ST-RAP outperforms previous methods, demonstrating the significant benefits of integrating spatial and temporal aspects in real estate appraisal. Our code and dataset are available at https://github.com/dojeon-ai/STRAP.|在这篇文章中，我们介绍了 ST-RAP，一个新的不动产估价师时空框架。RAP 采用层次结构和异构图形神经网络来同时封装时间动态和空间关系。通过在大规模房地产数据集上的全面实验，ST-RAP 优于以往的方法，展示了在不动产估价师中整合空间和时间方面的显著好处。我们的代码和数据集可在 https://github.com/dojeon-ai/strap 下载。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=ST-RAP:+A+Spatio-Temporal+Framework+for+Real+Estate+Appraisal)|0| |[Temporal and Topological Augmentation-based Cross-view Contrastive Learning Model for Temporal Link Prediction](https://doi.org/10.1145/3583780.3615231)|Dongyuan Li, Shiyin Tan, Yusong Wang, Kotaro Funakoshi, Manabu Okumura|Tokyo Institute of Technology, Tokyo, Japan; Kuaishou Technology, Beijing, China|With the booming development of social media, temporal link prediction (TLP), as a core technology, has been receiving increasing attention. However, current methods are based on graph neural networks, which suffer from the over-smoothing issue and easily yield indistinguishable node representations, degrading the prediction accuracy. Besides, they lack the ability to eliminate noisy temporal information and ignore the importance of high-order neighbor information for measuring the link probability between nodes. To solve these issues, we design a cross-view graph contrastive learning (GCL) framework for TLP, called Tacl. We first design two augmented views for GCL by enhancing the temporal and topological information to obtain distinguishable node representations. Then, we learn the evolution rule of temporal networks to help constrain consistency of node representations and eliminate noise. Finally, we incorporate the high-order neighbor information to measure the link probability between nodes. Extensive experiments demonstrate the effectiveness and robustness of Tacl.|随着社交媒体的蓬勃发展，时间链接预测(TLP)作为一种核心技术，受到越来越多的关注。然而，目前的方法都是基于图神经网络的，这种方法存在过于平滑的问题，容易产生难以区分的节点表示，降低了预测的准确性。此外，它们缺乏消除噪声时间信息的能力，忽略了高阶邻居信息对于测量节点间链路概率的重要性。为了解决这些问题，我们设计了一个跨视图图形对比学习(GCL)的 TLP 框架，称为 Tacl。首先通过增强 GCL 的时间和拓扑信息设计两个增强视图，以获得可区分的节点表示。然后通过学习时间网络的演化规律来约束节点表示的一致性，消除噪声。最后，结合高阶邻居信息来度量节点之间的链路概率。大量的实验证明了 Tacl 的有效性和鲁棒性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Temporal+and+Topological+Augmentation-based+Cross-view+Contrastive+Learning+Model+for+Temporal+Link+Prediction)|0| -|[CORD: A Three-Stage Coarse-to-Fine Framework for Relation Detection in Knowledge Base Question Answering](https://doi.org/10.1145/3583780.3615178)|Yanzeng Li, Sen Hu, Wenjuan Han, Lei Zou|Beijing Jiaotong University & DUOMO, Beijing, China; Peking University, Beijing, China; Peking University & National Key Laboratory of General Artificial Intelligence, BIGAI, Beijing, China; Ant Group, Beijing, China|As a fundamental subtask of Knowledge Base Question Answering (KBQA), Relation Detection (KBQA-RD) plays a crucial role to detect the KB relations between entities or variables in natural language questions. It remains, however, a challenging task, particularly for significant large-scale relations and in the presence of easily confused relations. Recent state-of-the-art methods not only struggle with such scenarios, but often take into account only one facet and fail to incorporate the subtle discrepancy among the relations. In this paper, we propose a simple and efficient three-stage framework to exploit the coarse-to-fine paradigm. Specifically, we employ a natural clustering over all KB relations and perform a coarse-to-fine relation recognition process based on the relation clustering. In this way, our framework (i.e., CORD) refines the detection of relations, so as to scale well with large-scale relations. Experiments on both single-relation (i.e., SimpleQuestions (SQ)) and multi-relation (i.e., WebQSP (WQ)) benchmarks show that CORD not only achieves the outstanding relation detection performance in KBQA-RD subtask; but more importantly, further improves the accuracy of KBQA systems.|关系检测作为知识库问题回答(KBQA)的一个基本子任务，对于检测自然语言问题中实体或变量之间的知识库关系起着至关重要的作用。然而，这仍然是一项具有挑战性的任务，特别是对于重大的大规模关系和容易混淆的关系。最近的最先进的方法不仅与这种情况作斗争，而且往往只考虑一个方面，而且未能纳入关系之间的微妙差异。在本文中，我们提出了一个简单而有效的三阶段框架来开发从粗到精的范式。具体来说，我们在所有知识库关系上采用自然聚类，并在关系聚类的基础上进行从粗到精的关系识别过程。通过这种方式，我们的框架(即 CORD)改进了关系的检测，从而可以很好地扩展大规模关系。在单关系(SimpleQuestions，SQ)和多关系(WebQSP，WQ)基准上的实验表明，CORD 不仅在 KBQA-RD 子任务中取得了优异的关系检测性能，更重要的是进一步提高了 KBQA 系统的准确性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=CORD:+A+Three-Stage+Coarse-to-Fine+Framework+for+Relation+Detection+in+Knowledge+Base+Question+Answering)|0| -|[Homogeneous Cohort-Aware Group Cognitive Diagnosis: A Multi-grained Modeling Perspective](https://doi.org/10.1145/3583780.3615287)|Shuhuan Liu, Xiaoshan Yu, Haiping Ma, Ziwen Wang, Chuan Qin, Xingyi Zhang|Institutes of Physical Science and Information Technology, Anhui University, Hefei, China; BOSS Zhipin, Beijing, China; School of Artificial Intelligence, Anhui University, Hefei, China|Cognitive Diagnosis has been widely investigated as a fundamental task in the field of education, aiming at effectively assessing the students' knowledge proficiency level by mining their exercise records. Recently, group-level cognitive diagnosis is also attracting attention, which measures the group-level knowledge proficiency on specific concepts by modeling the response behaviors of all students within the classes. However, existing work tends to explore group characteristics with a coarse-grained perspective while ignoring the inter-individual variability within groups, which is prone to unstable diagnosis results. To this end, in this paper, we propose a novel Homogeneous cohort-aware Group Cognitive Diagnosis model, namely HomoGCD, to effectively model the group's knowledge proficiency level from a multi-grained modeling perspective. Specifically, we first design a homogeneous cohort mining module to explore subgroups of students with similar ability status within a class by modeling their routine exercising performance. Then, we construct the mined cohorts into fine-grained organizations for exploring stable and uniformly distributed features of groups. Subsequently, we develop a multi-grained modeling module to comprehensively learn the cohort and group ability status, which jointly trains both interactions with the exercises. In particular, an extensible diagnosis module is introduced to support the incorporation of different diagnosis functions. Finally, extensive experiments on two real-world datasets clearly demonstrate the generality and effectiveness of our HomoGCD in group as well as cohort~assessments.|认知诊断作为教育领域的一项基础性课题已经得到了广泛的研究，其目的是通过挖掘学生的运动记录来有效地评估学生的知识水平。近年来，群体认知诊断也引起了人们的关注，它通过建立班级内所有学生的反应行为模型来衡量群体对特定概念的认知水平。然而，现有的研究倾向于从粗粒度的角度探索群体特征，而忽视了群体内个体间的差异性，这容易导致诊断结果不稳定。为此，本文提出了一种新的同质队列感知群体认知诊断模型 HomoGCD，从多粒度建模的角度对群体的知识水平进行有效建模。具体来说，我们首先设计了一个同质队列挖掘模块，通过模拟学生的日常锻炼表现来探索班级内具有相似能力地位的学生群体。然后，我们将挖掘的队列构造成细粒度组织，以探索群体的稳定和均匀分布特征。随后，我们开发了一个多粒度建模模块来全面学习队列和群体能力状态，并将这两种能力状态与练习相互作用进行联合训练。特别地，引入了一个可扩展的诊断模块来支持不同诊断功能的集成。最后，在两个实际数据集上的广泛实验清楚地证明了我们的 HomoGCD 在群体和队列 ~ 评估中的普遍性和有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Homogeneous+Cohort-Aware+Group+Cognitive+Diagnosis:+A+Multi-grained+Modeling+Perspective)|0| +|[CORD: A Three-Stage Coarse-to-Fine Framework for Relation Detection in Knowledge Base Question Answering](https://doi.org/10.1145/3583780.3615178)|Yanzeng Li, Sen Hu, Wenjuan Han, Lei Zou|Peking University & National Key Laboratory of General Artificial Intelligence, BIGAI, Beijing, China; Ant Group, Beijing, China; Peking University, Beijing, China; Beijing Jiaotong University & DUOMO, Beijing, China|As a fundamental subtask of Knowledge Base Question Answering (KBQA), Relation Detection (KBQA-RD) plays a crucial role to detect the KB relations between entities or variables in natural language questions. It remains, however, a challenging task, particularly for significant large-scale relations and in the presence of easily confused relations. Recent state-of-the-art methods not only struggle with such scenarios, but often take into account only one facet and fail to incorporate the subtle discrepancy among the relations. In this paper, we propose a simple and efficient three-stage framework to exploit the coarse-to-fine paradigm. Specifically, we employ a natural clustering over all KB relations and perform a coarse-to-fine relation recognition process based on the relation clustering. In this way, our framework (i.e., CORD) refines the detection of relations, so as to scale well with large-scale relations. Experiments on both single-relation (i.e., SimpleQuestions (SQ)) and multi-relation (i.e., WebQSP (WQ)) benchmarks show that CORD not only achieves the outstanding relation detection performance in KBQA-RD subtask; but more importantly, further improves the accuracy of KBQA systems.|关系检测作为知识库问题回答(KBQA)的一个基本子任务，对于检测自然语言问题中实体或变量之间的知识库关系起着至关重要的作用。然而，这仍然是一项具有挑战性的任务，特别是对于重大的大规模关系和容易混淆的关系。最近的最先进的方法不仅与这种情况作斗争，而且往往只考虑一个方面，而且未能纳入关系之间的微妙差异。在本文中，我们提出了一个简单而有效的三阶段框架来开发从粗到精的范式。具体来说，我们在所有知识库关系上采用自然聚类，并在关系聚类的基础上进行从粗到精的关系识别过程。通过这种方式，我们的框架(即 CORD)改进了关系的检测，从而可以很好地扩展大规模关系。在单关系(SimpleQuestions，SQ)和多关系(WebQSP，WQ)基准上的实验表明，CORD 不仅在 KBQA-RD 子任务中取得了优异的关系检测性能，更重要的是进一步提高了 KBQA 系统的准确性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=CORD:+A+Three-Stage+Coarse-to-Fine+Framework+for+Relation+Detection+in+Knowledge+Base+Question+Answering)|0| +|[Homogeneous Cohort-Aware Group Cognitive Diagnosis: A Multi-grained Modeling Perspective](https://doi.org/10.1145/3583780.3615287)|Shuhuan Liu, Xiaoshan Yu, Haiping Ma, Ziwen Wang, Chuan Qin, Xingyi Zhang|School of Artificial Intelligence, Anhui University, Hefei, China; BOSS Zhipin, Beijing, China; Institutes of Physical Science and Information Technology, Anhui University, Hefei, China|Cognitive Diagnosis has been widely investigated as a fundamental task in the field of education, aiming at effectively assessing the students' knowledge proficiency level by mining their exercise records. Recently, group-level cognitive diagnosis is also attracting attention, which measures the group-level knowledge proficiency on specific concepts by modeling the response behaviors of all students within the classes. However, existing work tends to explore group characteristics with a coarse-grained perspective while ignoring the inter-individual variability within groups, which is prone to unstable diagnosis results. To this end, in this paper, we propose a novel Homogeneous cohort-aware Group Cognitive Diagnosis model, namely HomoGCD, to effectively model the group's knowledge proficiency level from a multi-grained modeling perspective. Specifically, we first design a homogeneous cohort mining module to explore subgroups of students with similar ability status within a class by modeling their routine exercising performance. Then, we construct the mined cohorts into fine-grained organizations for exploring stable and uniformly distributed features of groups. Subsequently, we develop a multi-grained modeling module to comprehensively learn the cohort and group ability status, which jointly trains both interactions with the exercises. In particular, an extensible diagnosis module is introduced to support the incorporation of different diagnosis functions. Finally, extensive experiments on two real-world datasets clearly demonstrate the generality and effectiveness of our HomoGCD in group as well as cohort~assessments.|认知诊断作为教育领域的一项基础性课题已经得到了广泛的研究，其目的是通过挖掘学生的运动记录来有效地评估学生的知识水平。近年来，群体认知诊断也引起了人们的关注，它通过建立班级内所有学生的反应行为模型来衡量群体对特定概念的认知水平。然而，现有的研究倾向于从粗粒度的角度探索群体特征，而忽视了群体内个体间的差异性，这容易导致诊断结果不稳定。为此，本文提出了一种新的同质队列感知群体认知诊断模型 HomoGCD，从多粒度建模的角度对群体的知识水平进行有效建模。具体来说，我们首先设计了一个同质队列挖掘模块，通过模拟学生的日常锻炼表现来探索班级内具有相似能力地位的学生群体。然后，我们将挖掘的队列构造成细粒度组织，以探索群体的稳定和均匀分布特征。随后，我们开发了一个多粒度建模模块来全面学习队列和群体能力状态，并将这两种能力状态与练习相互作用进行联合训练。特别地，引入了一个可扩展的诊断模块来支持不同诊断功能的集成。最后，在两个实际数据集上的广泛实验清楚地证明了我们的 HomoGCD 在群体和队列 ~ 评估中的普遍性和有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Homogeneous+Cohort-Aware+Group+Cognitive+Diagnosis:+A+Multi-grained+Modeling+Perspective)|0| |[Epidemiology-aware Deep Learning for Infectious Disease Dynamics Prediction](https://doi.org/10.1145/3583780.3615139)|Mutong Liu, Yang Liu, Jiming Liu|Hong Kong Baptist University, Kowloon, Hong Kong|Infectious disease risk prediction plays a vital role in disease control and prevention. Recent studies in machine learning have attempted to incorporate epidemiological knowledge into the learning process to enhance the accuracy and informativeness of prediction results for decision-making. However, these methods commonly involve single-patch mechanistic models, overlooking the disease spread across multiple locations caused by human mobility. Additionally, these methods often require extra information beyond the infection data, which is typically unavailable in reality. To address these issues, this paper proposes a novel epidemiology-aware deep learning framework that integrates a fundamental epidemic component, the next-generation matrix (NGM), into the deep architecture and objective function. This integration enables the inclusion of both mechanistic models and human mobility in the learning process to characterize within- and cross-location disease transmission. From this framework, two novel methods, Epi-CNNRNN-Res and Epi-Cola-GNN, are further developed to predict epidemics, with experimental results validating their effectiveness.|传染病风险预测在疾病控制和预防方面起着至关重要的作用。最近的机器学习研究试图将流行病学知识纳入学习过程，以提高决策预测结果的准确性和信息量。然而，这些方法通常涉及单一斑块的机制模型，忽略了疾病传播的多个地点造成的人类流动性。此外，这些方法通常需要感染数据之外的额外信息，而这些数据在现实中通常是不可用的。为了解决这些问题，本文提出了一种新的流行病学意识深度学习框架，它将流行病学的基本组成部分——下一代矩阵(NGM)集成到深度体系结构和目标函数中。这种整合使机械模型和人类流动性都能纳入学习过程，以描述疾病传播的内部和跨部位特征。在此框架下，进一步发展了两种新的流行病预测方法 Epi-CNNRNN-Res 和 Epi-Cola-GNN，实验结果验证了它们的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Epidemiology-aware+Deep+Learning+for+Infectious+Disease+Dynamics+Prediction)|0| |[Towards Trustworthy Rumor Detection with Interpretable Graph Structural Learning](https://doi.org/10.1145/3583780.3615228)|Leyuan Liu, Junyi Chen, Zhangtao Cheng, Wenxin Tai, Fan Zhou|University of Electronic Science and Technology of China, Chengdu, China; University of Electronic Science and Technology of China & Kash Institute of Electronics and Information Industry, Chengdu, China|The exponential growth of digital information has amplified the necessity for effective rumor detection on social media. However, existing approaches often neglect the inherent noise and uncertainty in rumor propagation, leading to obscure learning mechanisms. Moreover, current deep-learning methodologies, despite their top-tier performance, are heavily dependent on supervised learning, which is labor-intensive and inefficient. Their prediction credibility is also questionable. To tackle these issues, we present a new framework, TrustRD, for reliable rumor detection. Our framework incorporates a self-supervised learning module, designed to derive interpretable and informative representations with less reliance on large labeled data sets. A downstream model based on Bayesian networks, which is further refined with adversarial training, enhances performance while providing a quantifiable trustworthiness assessment of results. Our methods' effectiveness is confirmed through experiments on two benchmark datasets.|数字信息的指数增长增强了有效侦测社交媒体谣言的必要性。然而，现有的方法往往忽视了谣言传播中固有的噪声和不确定性，导致模糊的学习机制。此外，目前的深度学习方法，尽管表现一流，但严重依赖于监督式学习，这是劳动密集型和效率低下的。他们的预测可信度也值得怀疑。为了解决这些问题，我们提出了一个新的框架 TrustRD，用于可靠的谣言检测。我们的框架结合了一个自我监督的学习模块，旨在获得可解释和信息表示，较少依赖于大型标记数据集。一个基于贝叶斯网络的下游模型，通过对抗性训练进一步完善，提高了性能，同时提供了一个可量化的结果可信度评估。通过对两个基准数据集的实验验证了该方法的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Towards+Trustworthy+Rumor+Detection+with+Interpretable+Graph+Structural+Learning)|0| |[TemDep: Temporal Dependency Priority for Multivariate Time Series Prediction](https://doi.org/10.1145/3583780.3615164)|Shu Liu, Jiaheng Wang, Jiamin Chen, Jianliang Gao, Yuhui Zhong|Central South University, Changsha, China|The multivariate fusion transformation is ubiquitous in multivariate time series prediction (MTSP) problems. The previous multivariate fusion transformation fuses the feature of different variates at a time step, then projects them to a new feature space for effective feature representation. However, temporal dependency is the most fundamental property of time series. The previous manner fails to capture the temporal dependency of the feature, which is destroyed in the transformed feature matrix. Multivariate feature extraction based on the feature matrix with missing temporal dependency leads to the loss of predictive performance of MTSP. To address this problem, we propose the Temporal Dependency Priority for Multivariate Time Series Prediction (TemDep) method. Specifically, TemDep extracts feature temporal dependency of multivariate time series first and then considers multivariate feature fusion. Moreover, the low-dimensional and high-dimensional feature fusion manners are designed with the temporal dependency priority to fit different dimensional multivariate time series. The extensive experimental results of different datasets show that our proposed method can outperform all state-of-the-art baseline methods. It proves the significance of temporal dependency priority for MTSP.|多元融合变换在多元时间序列预测(MTSP)问题中普遍存在。以往的多变量融合变换是在一个时间步上对不同变量的特征进行融合，然后将它们投影到一个新的特征空间中进行有效的特征表示。然而，时间依赖性是时间序列最基本的性质。前一种方法不能捕获特征的时间依赖关系，这种依赖关系在转换后的特征矩阵中被破坏。基于时间相关性缺失的特征矩阵的多变量特征提取导致 MTSP 预测性能的损失。为了解决这个问题，我们提出了多变量时间序列预测(TemDep)的时间依赖优先级方法。具体来说，TemDep 首先提取多元时间序列的特征时间相关性，然后考虑多元特征融合。同时设计了具有时间依赖优先级的低维和高维特征融合方法，以适应不同维数的多元时间序列。不同数据集的大量实验结果表明，我们提出的方法可以优于所有最先进的基线方法。证明了时间依赖优先级对 MTSP 的重要性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=TemDep:+Temporal+Dependency+Priority+for+Multivariate+Time+Series+Prediction)|0| |[DCGNN: Dual-Channel Graph Neural Network for Social Bot Detection](https://doi.org/10.1145/3583780.3615237)|Nuoyan Lyu, Bingbing Xu, Fangda Guo, Huawei Shen|Beijing University of Posts and Telecommunications, Beijing, China; Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China|The importance of social bot detection has been increasingly recognized due to its profound impact on information dissemination. Existing methodologies can be categorized into feature engineering and deep learning-based methods, which mainly focus on static features, e.g., post characteristics and user profiles.However, existing methods often overlook the burst phenomena when distinguishing social bots and genuine users, i.e, the sudden and intense activity or behavior of bots after prolonged inter. Through comprehensive analysis, we find that both burst behavior and static features play pivotal roles in social bot detection. To capture such properties, the dual-channel GNN (DCGNN) is proposed which consists of a burst-aware channel with an adaptive-pass filter and a static-aware channel with a low-pass filter to model user characteristics effectively. Experimental results demonstrate the superiority of this method over competitive baselines.|由于社会机器人检测对信息传播的深刻影响，其重要性得到了越来越多的重视。现有的方法可以分为特征工程方法和基于深度学习的方法，主要集中在静态特征方面，如文章特征和用户概况。然而，现有的方法在区分社交机器人和真正的用户时往往忽略了突发现象，即机器人在长时间间隔后突然的、强烈的活动或行为。通过综合分析，我们发现突发行为和静态特征在社会机器人检测中起着关键作用。为了捕获这些特性，提出了一种双信道 GNN (DCGNN) ，它由一个带有自适应通滤波器的突发感知信道和一个带有低通滤波器的静态感知信道组成，可以有效地模拟用户特性。实验结果表明，该方法优于竞争基线。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=DCGNN:+Dual-Channel+Graph+Neural+Network+for+Social+Bot+Detection)|0| -|[Contrastive Learning for Rumor Detection via Fitting Beta Mixture Model](https://doi.org/10.1145/3583780.3615138)|Jiachen Ma, Jing Dai, Yong Liu, Meng Han, Chunyu Ai|University of South Carolina Upstate, Spartanburg, SC, USA; Heilongjiang University, Harbin, China; Zhejiang University, Hangzhou, China|The rise of social media has posed a challenging problem of effectively identifying rumors. With the great success of contrastive learning in many fields, many contrastive learning models for rumor detection have been proposed. However, existing models usually use the propagation structure of other events as negative samples and regard more similar samples to anchor events as hard ones across all the training processes, resulting in undesirably pushing away the samples of the same class. Thus, we propose a novel contrastive learning model (CRFB) to solve the above problem. Specifically, we employ contrastive learning between two augmented propagation structure and fit a two-component (true-false) beta mixture model (BMM) to measure the probability of negative samples being true. In addition, we propose a CNN-based model to capture the consistent and complementary information between two augmented propagation structure. The experimental results on public datasets demonstrate that our CRFB outperforms the existing state-of-the-art models for rumor detection.|社交媒体的兴起提出了一个有效识别谣言的挑战性问题。随着对比学习在许多领域取得的巨大成功，人们提出了许多用于谣言检测的对比学习模型。然而，现有的模型通常将其他事件的传播结构作为负样本，并将更多相似样本作为硬样本锚定在所有训练过程中，导致不必要地推开同类样本。因此，我们提出了一种新的对比学习模型(CRFB)来解决上述问题。具体来说，我们使用两个增广传播结构之间的对比学习，并拟合一个双组分(真假)混合模型(BMM)来测量负样本为真的概率。此外，我们提出了一个基于 CNN 的模型来捕获两个增强传播结构之间的一致性和互补信息。在公共数据集上的实验结果表明，我们的 CRFB 优于现有的最先进的谣言检测模型。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Contrastive+Learning+for+Rumor+Detection+via+Fitting+Beta+Mixture+Model)|0| +|[Contrastive Learning for Rumor Detection via Fitting Beta Mixture Model](https://doi.org/10.1145/3583780.3615138)|Jiachen Ma, Jing Dai, Yong Liu, Meng Han, Chunyu Ai|University of South Carolina Upstate, Spartanburg, SC, USA; Zhejiang University, Hangzhou, China; Heilongjiang University, Harbin, China|The rise of social media has posed a challenging problem of effectively identifying rumors. With the great success of contrastive learning in many fields, many contrastive learning models for rumor detection have been proposed. However, existing models usually use the propagation structure of other events as negative samples and regard more similar samples to anchor events as hard ones across all the training processes, resulting in undesirably pushing away the samples of the same class. Thus, we propose a novel contrastive learning model (CRFB) to solve the above problem. Specifically, we employ contrastive learning between two augmented propagation structure and fit a two-component (true-false) beta mixture model (BMM) to measure the probability of negative samples being true. In addition, we propose a CNN-based model to capture the consistent and complementary information between two augmented propagation structure. The experimental results on public datasets demonstrate that our CRFB outperforms the existing state-of-the-art models for rumor detection.|社交媒体的兴起提出了一个有效识别谣言的挑战性问题。随着对比学习在许多领域取得的巨大成功，人们提出了许多用于谣言检测的对比学习模型。然而，现有的模型通常将其他事件的传播结构作为负样本，并将更多相似样本作为硬样本锚定在所有训练过程中，导致不必要地推开同类样本。因此，我们提出了一种新的对比学习模型(CRFB)来解决上述问题。具体来说，我们使用两个增广传播结构之间的对比学习，并拟合一个双组分(真假)混合模型(BMM)来测量负样本为真的概率。此外，我们提出了一个基于 CNN 的模型来捕获两个增强传播结构之间的一致性和互补信息。在公共数据集上的实验结果表明，我们的 CRFB 优于现有的最先进的谣言检测模型。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Contrastive+Learning+for+Rumor+Detection+via+Fitting+Beta+Mixture+Model)|0| |[Generating News-Centric Crossword Puzzles As A Constraint Satisfaction and Optimization Problem](https://doi.org/10.1145/3583780.3615151)|Kaito Majima, Shotaro Ishihara|Nikkei Inc., Otemachi, Tokyo, Japan|Crossword puzzles have traditionally served not only as entertainment but also as an educational tool that can be used to acquire vocabulary and language proficiency. One strategy to enhance the educational purpose is personalization, such as including more words on a particular topic. This paper focuses on the case of encouraging people's interest in news and proposes a framework for automatically generating news-centric crossword puzzles. We designed possible scenarios and built a prototype as a constraint satisfaction and optimization problem, that is, containing as many news-derived words as possible. Our experiments reported the generation probabilities and time required under several conditions. The results showed that news-centric crossword puzzles can be generated even with few news-derived words. We summarize the current issues and future research directions through a qualitative evaluation of the prototype. This is the first proposal that a formulation of a constraint satisfaction and optimization problem can be beneficial as an educational application.|纵横字谜传统上不仅作为一种娱乐，而且作为一种教育工具，可以用来获得词汇和语言熟练程度。增强教育目的的一个策略是个性化，例如包含更多关于特定主题的单词。本文以激发人们对新闻的兴趣为例，提出了一个自动生成以新闻为中心的填字游戏的框架。我们设计了可能的场景，并建立了一个原型作为约束补偿和最佳化问题，也就是说，包含尽可能多的新闻衍生词。我们的实验报告了在几种条件下所需的生成概率和时间。结果表明，即使只有很少的新闻词汇，也可以生成以新闻为中心的填字游戏。通过对样机的定性评价，总结了目前存在的问题和未来的研究方向。这是第一个建议，一个约束补偿和最佳化问题的制定可以作为一个有益的教育应用。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Generating+News-Centric+Crossword+Puzzles+As+A+Constraint+Satisfaction+and+Optimization+Problem)|0| |[Age-Aware Guidance via Masking-Based Attention in Face Aging](https://doi.org/10.1145/3583780.3615183)|Junyeong Maeng, Kwanseok Oh, HeungIl Suk|Korea University, Seoul, Republic of Korea|Face age transformation aims to convert reference images into synthesized images so that they portray the specified target ages. The crux of this task is to change only age-related areas of the given image while maintaining the age-irrelevant areas unchanged. Nevertheless, a common limitation among most existing models is the struggle to generate high-quality aging images that effectively consider both crucial properties. To address this problem, we propose a novel GAN-based face-aging framework that utilizes age-aware Guidance via Masking-Based Attention (GMBA). Specifically, we devise an age-aware guidance module to adjust age-relevant and age-irrelevant attributes within the image seamlessly. By virtue of its capability, it enables the model to produce realistic age-transformed images that certainly preserve the input's identities while delicately imposing age-related properties. Experimental results show that our proposed GMBA outperformed other state-of-the-art methods in terms of identity preservation and accurate age conversion, as well as providing superior visual quality for age-transformed images.|人脸年龄变换的目的是将参考图像转换为合成图像，从而刻画出特定的目标年龄。这项任务的关键是只改变给定图像中与年龄相关的区域，同时保持与年龄无关的区域不变。尽管如此，大多数现有模型的一个共同限制是难以产生高质量的老化图像，有效地考虑到这两个关键属性。为了解决这一问题，我们提出了一种新的基于 GAN 的面孔老化框架，该框架利用基于掩蔽注意的年龄感知指导(GMBA)。具体来说，我们设计了一个年龄感知的指导模块来无缝地调整图像中与年龄相关和与年龄无关的属性。凭借其能力，它使模型能够产生真实的年龄转换的图像，当然保留输入的身份，同时微妙地施加与年龄有关的属性。实验结果表明，我们提出的 gMBA 在食品生产履历和准确的年龄转换方面优于其他最先进的方法，并且为年龄转换图像提供了优越的视觉质量。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Age-Aware+Guidance+via+Masking-Based+Attention+in+Face+Aging)|0| |[Metapath-Guided Data-Augmentation For Knowledge Graphs](https://doi.org/10.1145/3583780.3615186)|Saurav Manchanda|Instacart, San Francisco, CA, USA|Knowledge graph (KG) embedding techniques use relationships between entities to learn low-dimensional representations of entities and relations. The traditional KG embedding techniques (such as TransE and DistMult) estimate these embeddings using the observed KG triplets and differ in their triplet scoring loss functions. As these models only use the observed triplets to estimate the embeddings, they are prone to suffer through data sparsity that usually occurs in the real-world knowledge graphs, i.e., the lack of enough triplets per entity. In this paper, we propose an efficient method to augment the triplets to address the problem of data sparsity. We use random walks to create additional triplets, such that the relations carried by these introduced triplets correspond to the metapath (sequence of underlying relations) induced by the random walks. We also provide approaches to accurately and efficiently choose the informative metapaths from the possible set of metapaths. The proposed augmentation approaches can be used with any KG embedding approach out of the box. Experimental results on benchmarks show the advantages of the proposed approach.|知识图(KG)嵌入技术利用实体之间的关系来学习实体和关系的低维表示。传统的 KG 嵌入技术(如 TransE 和 distMult)使用观察到的 KG 三联体估计这些嵌入，并且它们的三联体得分损失函数不同。由于这些模型只使用观察到的三联体来估计嵌入，因此它们容易遭受通常出现在现实世界知识图中的数据稀疏问题，即每个实体缺乏足够的三联体。在本文中，我们提出了一种有效的方法来增加三联体，以解决数据稀疏的问题。我们使用随机游走来创建额外的三联体，使得这些引入的三联体所携带的关系对应于由随机游走诱导的元路径(基础关系序列)。我们还提供了从可能的元路径集合中准确有效地选择信息元路径的方法。所提出的增广方法可以用于任何 KG 嵌入方法开箱即用。基准测试的实验结果表明了该方法的优越性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Metapath-Guided+Data-Augmentation+For+Knowledge+Graphs)|0| |[Learning Visibility Attention Graph Representation for Time Series Forecasting](https://doi.org/10.1145/3583780.3615289)|Shengzhong Mao, XiaoJun Zeng|The University of Manchester, Manchester, United Kingdom|Visibility algorithm acts as a mapping that bridges graph representation learning with time series analysis, which has been broadly investigated for forecasting tasks. However, the intrinsic nature of visibility encoding yields graphs structured exclusively by binary adjacency matrix, leading to inevitable information loss of temporal sequence during the mapping. To this end, we introduce Angular Visibility Graph Networks (AVGNets), designed with two core features: (i) The framework reconstructs weighted graphs to encode time series by leveraging topological insights derived from visual angles of visibility networks, which capture sequential and structural information within weighted angular matrix. (ii) ProbAttention module is proposed for evaluating probabilistic attention of weighted networks, with remarkable capabilities to extract intrinsic and extrinsic temporal dependencies across multi-layer graphs. Extensive experiments and ablation studies on real-world datasets covering diverse ranges demonstrate that AVGNets achieve state-of-the-art performance, offering an innovative perspective on graph representation for sequence modeling.|可见性算法作为一种将图表示学习与时间序列分析相结合的映射方法，在预测任务中得到了广泛的研究。然而，可见性编码的固有特性产生了完全由二进制邻接矩阵构成的图形，导致在映射过程中不可避免地会丢失时间序列的信息。为此，我们介绍了角度可见性图形网络(AVGNets) ，其设计具有两个核心特征: (i)该框架通过利用从可见性网络的视角获得的拓扑洞察力来重构加权图，以编码时间序列，这些拓扑洞察力捕获加权角矩阵中的序列和结构信息。(ii)利用概率注意模型对加权网络的概率注意进行评估，该模型具有显著的跨多层图提取内在和外在时间依赖的能力。对现实世界中覆盖不同范围的数据集进行的大量实验和消融研究表明，AVGNets 实现了最先进的性能，为序列建模的图表示提供了一个创新的视角。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Learning+Visibility+Attention+Graph+Representation+for+Time+Series+Forecasting)|0| |[A Robust Backward Compatibility Metric for Model Retraining](https://doi.org/10.1145/3583780.3615213)|Ryuta Matsuno, Keita Sakuma|NEC Corporation, Minato-ku, Japan|Model retraining and updating are essential processes in AI applications. However, during updates, there is a potential for performance degradation, in which the overall performance improves, but local performance deteriorates. This study proposes a backward compatibility metric that focuses on the compatibility of local predictive performance. The score of the proposed metric increases if the accuracy over the conditional distribution for each input is higher than before. Furthermore, we propose a model retraining method based on the proposed metric. Due to the use of the conditional distribution, our metric and retraining method are robust against label noises, while existing sample-based backward compatibility metrics are often affected by noise. We perform a theoretical analysis of our method and derive an upper bound for the generalization error. Numerical experiments demonstrate that our retraining method enhances compatibility while achieving equal or better trade-offs in overall performance compared to existing methods.|模型再训练和更新是人工智能应用中必不可少的环节。但是，在更新期间，存在性能下降的可能性，即总体性能提高，但局部性能下降。这项研究提出了一个向下兼容的衡量标准，着重于局部预测性能的兼容性。如果每个输入的条件分布的精度高于以前，则提出的度量的得分会增加。在此基础上，提出了一种基于该度量的模型再训练方法。由于使用了条件分布，我们的度量和再训练方法对标签噪声具有鲁棒性，而现有的基于样本的向下兼容度量常常受到噪声的影响。我们对这个方法进行了理论分析，得到了泛化误差的上界。数值实验表明，我们的再训练方法增强了兼容性，同时在整体性能方面取得了与现有方法相同或更好的平衡。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Robust+Backward+Compatibility+Metric+for+Model+Retraining)|0| |[Graph Contrastive Learning with Graph Info-Min](https://doi.org/10.1145/3583780.3615162)|En Meng, Yong Liu|Heilongjiang University, Haerbin, China|The complexity of the graph structure poses a challenge for graph representation learning. Contrastive learning offers a straightforward and efficient unsupervised framework for graph representation learning. It achieves unsupervised learning by augmenting the original views and comparing them with the augmented views. Several methods based on this framework have achieved significant progress in the field of graph representation learning. Despite its success, the factors contributing to good augmented views in graph contrast learning have received less attention. In order to address this issue, we introduce the graph info-min principle. We investigate the relationship between mutual information (MI) and good augmented views through experimental and theoretical analysis. Additionally, we present a new contrastive learning method called Info-min Contrastive Learning (IMCL). Specifically, The method comprises an adaptive graph augmentation generator and a pseudo-label generator. The graph augmentation generator ensures sufficient differentiation between the augmented and original views. The pseudo-label generator generates pseudo-labels as supervision signals, ensuring consistency between the classification results of augmented views and original views. Our method demonstrates excellent performance through extensive experimental results on various datasets.|图结构的复杂性对图表示学习提出了挑战。对比学习为图形表示学习提供了一个简单有效的无监督学习框架。它通过增强原始视图并将其与增强视图进行比较来达到非监督式学习。基于该框架的几种方法在图表示学习领域取得了显著的进展。尽管在图形对比度学习中取得了一定的成功，但有助于提高图形对比度学习效果的因素却没有得到足够的重视。为了解决这个问题，我们引入了图的信息最小原则。通过实验和理论分析，研究了互信息(MI)与良好增广视图之间的关系。此外，我们提出了一种新的对比学习方法称为信息最小对比学习(IMCL)。具体地说，该方法包括一个自适应图增强生成器和一个伪标记生成器。图增强生成器确保了增强视图和原始视图之间的充分区分。伪标签生成器生成伪标签作为监控信号，保证了增强视图分类结果与原始视图分类结果的一致性。我们的方法通过在各种数据集上的广泛的实验结果证明了优异的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Graph+Contrastive+Learning+with+Graph+Info-Min)|0| -|[Generative Graph Augmentation for Minority Class in Fraud Detection](https://doi.org/10.1145/3583780.3615255)|Lin Meng, Hesham Mostafa, Marcel Nassar, Xiaonan Zhang, Jiawei Zhang|Florida State University, Tallahassee, FL, USA; Intel Labs, San Diego, CA, USA; University of California, Davis, Davis, CA, USA|Class imbalance is a well-recognized challenge in GNN-based fraud detection. Traditional methods like re-sampling and re-weighting address this issue by balancing class distribution. However, node class balancing with simple re-sampling or re-weighting may greatly distort the data distributions and eventually lead to the ineffective performance of GNNs. In this paper, we propose a novel approach named Graph Generative Node Augmentation (GGA), which improves GNN-based fraud detection models by augmenting synthetic nodes of the minority class. GGA utilizes the GAN framework to synthesize node features and related edges of fake fraudulent nodes. To introduce greater variety in the generated nodes, we employ an MLP for feature generation. We also introduce an attention module to encode feature-level information before graph convolutional layers for edge generation. Our empirical results on two real-world fraud datasets demonstrate that GGA improves the performance of GNN-based fraud detection models by a large margin with much fewer nodes than traditional class balance methods, and outperforms recent graph augmentation methods with the same number of synthetic nodes.|类不平衡是基于 GNN 的欺诈检测中公认的挑战。传统的方法如重新抽样和重新加权通过平衡类的分布来解决这个问题。然而，通过简单的重采样或重加权来实现节点类平衡，可能会严重扭曲数据分布，最终导致 GNN 的无效性能。本文提出了一种新的图生成节点增强(GGA)方法，通过增强少数类的合成节点来改进基于 GNN 的欺诈检测模型。GGA 利用 GAN 框架综合假冒节点的节点特征和相关边。为了在生成的节点中引入更大的多样性，我们使用 MLP 进行特征生成。在图卷积层之前引入注意模块对特征层信息进行编码，实现边缘生成。我们对两个实际欺诈数据集的实验结果表明，与传统的类平衡方法相比，GGA 在节点较少的情况下大大提高了基于 GNN 的欺诈检测模型的性能，并且在合成节点数相同的情况下优于现有的图增强方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Generative+Graph+Augmentation+for+Minority+Class+in+Fraud+Detection)|0| +|[Generative Graph Augmentation for Minority Class in Fraud Detection](https://doi.org/10.1145/3583780.3615255)|Lin Meng, Hesham Mostafa, Marcel Nassar, Xiaonan Zhang, Jiawei Zhang|University of California, Davis, Davis, CA, USA; Intel Labs, San Diego, CA, USA; Florida State University, Tallahassee, FL, USA|Class imbalance is a well-recognized challenge in GNN-based fraud detection. Traditional methods like re-sampling and re-weighting address this issue by balancing class distribution. However, node class balancing with simple re-sampling or re-weighting may greatly distort the data distributions and eventually lead to the ineffective performance of GNNs. In this paper, we propose a novel approach named Graph Generative Node Augmentation (GGA), which improves GNN-based fraud detection models by augmenting synthetic nodes of the minority class. GGA utilizes the GAN framework to synthesize node features and related edges of fake fraudulent nodes. To introduce greater variety in the generated nodes, we employ an MLP for feature generation. We also introduce an attention module to encode feature-level information before graph convolutional layers for edge generation. Our empirical results on two real-world fraud datasets demonstrate that GGA improves the performance of GNN-based fraud detection models by a large margin with much fewer nodes than traditional class balance methods, and outperforms recent graph augmentation methods with the same number of synthetic nodes.|类不平衡是基于 GNN 的欺诈检测中公认的挑战。传统的方法如重新抽样和重新加权通过平衡类的分布来解决这个问题。然而，通过简单的重采样或重加权来实现节点类平衡，可能会严重扭曲数据分布，最终导致 GNN 的无效性能。本文提出了一种新的图生成节点增强(GGA)方法，通过增强少数类的合成节点来改进基于 GNN 的欺诈检测模型。GGA 利用 GAN 框架综合假冒节点的节点特征和相关边。为了在生成的节点中引入更大的多样性，我们使用 MLP 进行特征生成。在图卷积层之前引入注意模块对特征层信息进行编码，实现边缘生成。我们对两个实际欺诈数据集的实验结果表明，与传统的类平衡方法相比，GGA 在节点较少的情况下大大提高了基于 GNN 的欺诈检测模型的性能，并且在合成节点数相同的情况下优于现有的图增强方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Generative+Graph+Augmentation+for+Minority+Class+in+Fraud+Detection)|0| |[Efficient Differencing of System-level Provenance Graphs](https://doi.org/10.1145/3583780.3615171)|Yuta Nakamura, Iyad Kanj, Tanu Malik|DePaul University, Chicago, IL, USA|Data provenance, when audited at the operating system level, generates a large volume of low-level events. Current provenance systems infer causal flow from these event traces, but do not infer application structure, such as loops and branches. The absence of these inferred structures decreases accuracy when comparing two event traces, leading to low-quality answers from a provenance system. In this paper, we infer nested natural and unnatural loop structures over a collection of provenance event traces. We describe an 'unrolling method' that uses the inferred nested loop structure to systematically mark loop iterations. Our loop-based unrolling improves the accuracy of trace comparison by 20-70% over trace comparisons that do not rely on inferred structures.|当在操作系统级审计数据来源时，会生成大量低级事件。当前的起源系统从这些事件跟踪推断因果流，但不推断应用程序结构，如循环和分支。缺少这些推断结构会降低比较两个事件轨迹的准确性，从而导致来源系统的低质量答案。在本文中，我们推断嵌套的自然和非自然循环结构上的起源事件痕迹的集合。我们描述了一种“展开方法”，它使用推断的嵌套循环结构来系统地标记循环迭代。我们基于循环的展开比不依赖于推断结构的跟踪比较提高了20-70% 的跟踪比较准确性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Efficient+Differencing+of+System-level+Provenance+Graphs)|0| -|[Camaraderie: Content-based Knowledge Transfer for Medical Image Labelling using Supervised Autoencoders in a Decentralized Setting](https://doi.org/10.1145/3583780.3615216)|Advait Padhye, Shreeja Bhakat, Humaira Firdowse, Atharv Savarkar, Ganesh Ramakrishnan, Kshitij S. Jadhav|Indian Institute of Technology, Bombay, Mumbai, India; Indian Institute Of Technology, Bombay, Mumbai, India; Indian Statistical Institute, Kolkata, Kolkata, India|Deep neural networks for medical imaging require large high-quality labelled data, a huge bottleneck for resource poor settings. Given the privacy requirements of medical data, institutes are un-willing to share data, causing an hindrance in resource poor settings. In the present paper, (Camaraderie: Content-based Knowledge Transfer for Medical Image Labelling using Supervised Autoencoders in a Decentralized Setting) we propose to use Discrete Classifier Supervised Autoencoder (DC-SAE) to generate low-dimensional representations of a few annotated images at the Donor client and transfer both the DC-SAE's encoder part and the latent space representations to the Recipient client without sharing raw data. We then pass the unlabelled images of the Recipient Client through this encoder to obtain their latent space representation. In a supervised setting, using latent space representation of Donor client's labelled images, we accurately annotate images of Recipient client. Camaraderie demonstrates that DC-SAE outperforms Recipient end label accuracy beyond classical VAE based classification and anomaly detection based VAE. Thus, given a limited amount of labelled data in a decentralized privacy preserving scenario, one can transfer latent space representation across clients to annotate large number of unlabelled images with high accuracy.|医学成像的深层神经网络需要大量高质量的标记数据，这是资源匮乏设置的一个巨大瓶颈。鉴于医疗数据的隐私要求，研究机构不愿意共享数据，在资源匮乏的环境中造成了障碍。在本文中(Camaraderie: 基于内容的医学图像标签知识转移使用监督自动编码器在分散的设置) ，我们建议使用离散分类器监督自动编码器(DC-SAE)在捐助客户端生成几个注释图像的低维表示，并转移 DC-SAE 的编码器部分和潜在空间表示到接收客户端，而不共享原始数据。然后，我们通过这个编码器传递收件人客户端的未标记图像，以获得它们的潜在空间表示。在监督设置下，利用捐献者客户标记图像的潜在空间表示，准确地对接受者客户的图像进行注释。Camaraderie 表明，DC-SAE 的收件人终端标签准确性优于经典的基于 VAE 的分类和基于异常检测的 VAE。因此，在分散保护隐私的场景中，给定有限数量的标记数据，可以跨客户机传输潜在空间表示，以高精度注释大量未标记的图像。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Camaraderie:+Content-based+Knowledge+Transfer+for+Medical+Image+Labelling+using+Supervised+Autoencoders+in+a+Decentralized+Setting)|0| +|[Camaraderie: Content-based Knowledge Transfer for Medical Image Labelling using Supervised Autoencoders in a Decentralized Setting](https://doi.org/10.1145/3583780.3615216)|Advait Padhye, Shreeja Bhakat, Humaira Firdowse, Atharv Savarkar, Ganesh Ramakrishnan, Kshitij S. Jadhav|Indian Institute of Technology, Bombay, Mumbai, India; Indian Statistical Institute, Kolkata, Kolkata, India; Indian Institute Of Technology, Bombay, Mumbai, India|Deep neural networks for medical imaging require large high-quality labelled data, a huge bottleneck for resource poor settings. Given the privacy requirements of medical data, institutes are un-willing to share data, causing an hindrance in resource poor settings. In the present paper, (Camaraderie: Content-based Knowledge Transfer for Medical Image Labelling using Supervised Autoencoders in a Decentralized Setting) we propose to use Discrete Classifier Supervised Autoencoder (DC-SAE) to generate low-dimensional representations of a few annotated images at the Donor client and transfer both the DC-SAE's encoder part and the latent space representations to the Recipient client without sharing raw data. We then pass the unlabelled images of the Recipient Client through this encoder to obtain their latent space representation. In a supervised setting, using latent space representation of Donor client's labelled images, we accurately annotate images of Recipient client. Camaraderie demonstrates that DC-SAE outperforms Recipient end label accuracy beyond classical VAE based classification and anomaly detection based VAE. Thus, given a limited amount of labelled data in a decentralized privacy preserving scenario, one can transfer latent space representation across clients to annotate large number of unlabelled images with high accuracy.|医学成像的深层神经网络需要大量高质量的标记数据，这是资源匮乏设置的一个巨大瓶颈。鉴于医疗数据的隐私要求，研究机构不愿意共享数据，在资源匮乏的环境中造成了障碍。在本文中(Camaraderie: 基于内容的医学图像标签知识转移使用监督自动编码器在分散的设置) ，我们建议使用离散分类器监督自动编码器(DC-SAE)在捐助客户端生成几个注释图像的低维表示，并转移 DC-SAE 的编码器部分和潜在空间表示到接收客户端，而不共享原始数据。然后，我们通过这个编码器传递收件人客户端的未标记图像，以获得它们的潜在空间表示。在监督设置下，利用捐献者客户标记图像的潜在空间表示，准确地对接受者客户的图像进行注释。Camaraderie 表明，DC-SAE 的收件人终端标签准确性优于经典的基于 VAE 的分类和基于异常检测的 VAE。因此，在分散保护隐私的场景中，给定有限数量的标记数据，可以跨客户机传输潜在空间表示，以高精度注释大量未标记的图像。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Camaraderie:+Content-based+Knowledge+Transfer+for+Medical+Image+Labelling+using+Supervised+Autoencoders+in+a+Decentralized+Setting)|0| |[Quantum Split Learning for Privacy-Preserving Information Management](https://doi.org/10.1145/3583780.3615144)|Soohyun Park, Hankyul Baek, Joongheon Kim|Korea University, Seoul, Republic of Korea|Recently, research on quantum neural network (QNN) architectures has been attracted in various fields. Among them, the distributed computation of QNN has been actively discussed for privacy-preserving information management due to data and model distribution over multiple computing devices. Based on this concept, this paper proposes quantum split learning (QSL) which splits a single QNN architecture across multiple distributed computing devices to avoid entire QNN architecture exposure. In order to realize QSL design, this paper also proposes cross-channel pooling, which utilizes quantum state tomography. Our evaluation results verifies that QSL preserves privacy in classification tasks and also improves accuracy at most by 6.83% compared to existing methods.|近年来，量子神经网络(QNN)体系结构的研究引起了各个领域的关注。其中，由于数据和模型在多个计算设备上的分布，QNN 的分布式计算在保护隐私的信息管理中得到了积极的讨论。基于这个概念，本文提出量子分裂学习(QSL) ，它将一个单一的量子神经网络体系结构分割到多个分布式计算设备上，以避免整个量子神经网络体系结构的暴露。为了实现 QSL 的设计，本文还提出了利用量子态层析成像技术的跨信道池技术。我们的评估结果验证了 QSL 在分类任务中保护了隐私，并且与现有的方法相比，最多提高了6.83% 的准确率。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Quantum+Split+Learning+for+Privacy-Preserving+Information+Management)|0| |[Quantitative Decomposition of Prediction Errors Revealing Multi-Cause Impacts: An Insightful Framework for MLOps](https://doi.org/10.1145/3583780.3615238)|Keita Sakuma, Ryuta Matsuno, Yoshio Kameda|NEC Corporation, Minato-ku, Japan|As machine learning applications expand in various industries, MLOps, which enables continuous model operation and improvement, becomes increasingly significant. Identifying causes of prediction errors, such as low model performance or anomalous samples, and implementing appropriate countermeasures are essential for effective MLOps. Furthermore, quantitatively evaluating each cause's impact is necessary to determine the effectiveness of countermeasures. In this study, we propose a method to quantitatively decompose a single sample's prediction error into contributions from multiple causes. Our method involves four steps: calculating the prediction error, computing metrics related to error causes, using a regression model to learn the relationship between the error and metrics, and applying SHAP to interpret the model's predictions and calculate the contribution of each cause to the prediction error. Numerical experiments with open data show that our method offers valuable insights for model improvement, confirming the effectiveness of our approach.|随着机器学习应用在各个行业的不断扩展，使模型持续运行和改进成为可能的 MLOPs 变得越来越重要。识别预测错误的原因，如低模型性能或异常样本，并实施适当的对策是有效的 MLOP 所必需的。此外，定量评估每个原因的影响是必要的，以确定对策的有效性。在这项研究中，我们提出了一种方法，定量分解单个样本的预测误差，以多种原因的贡献。我们的方法包括四个步骤: 计算预测误差，计算与误差原因相关的指标，使用回归模型来了解误差与指标之间的关系，以及应用 SHAP 来解释模型的预测，并计算每个原因对预测误差的贡献。开放数据的数值实验表明，我们的方法为模型改进提供了有价值的见解，证实了我们的方法的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Quantitative+Decomposition+of+Prediction+Errors+Revealing+Multi-Cause+Impacts:+An+Insightful+Framework+for+MLOps)|0| |[VN-Solver: Vision-based Neural Solver for Combinatorial Optimization over Graphs](https://doi.org/10.1145/3583780.3615156)|Mina Samizadeh, Guangmo Tong|University of Delaware, Newark, DE, USA|Data-driven approaches have been proven effective in solving combinatorial optimization problems over graphs such as the traveling salesman problems and the vehicle routing problem. The rationale behind such methods is that the input instances may follow distributions with salient patterns that can be leveraged to overcome the worst-case computational hardness. For optimization problems over graphs, the common practice of neural combinatorial solvers consumes the inputs in the form of adjacency matrices. In this paper, we explore a vision-based method that is conceptually novel: can neural models solve graph optimization problems by \textit{taking a look at the graph pattern}? Our results suggest that the performance of such vision-based methods is not only non-trivial but also comparable to the state-of-the-art matrix-based methods, which opens a new avenue for developing data-driven optimization solvers.|数据驱动的方法已经被证明可以有效地解决图表中的组合优化问题，比如旅行推销员问题和车辆路径问题问题。这些方法背后的基本原理是，输入实例可能遵循具有显著模式的分布，这些模式可以用来克服最坏情况下的计算难度。对于图上的最优化问题，神经组合求解器通常以邻接矩阵的形式消耗输入。在本文中，我们探索了一种概念上新颖的基于视觉的方法: 神经模型能否通过文本{查看图形模式}来解决图形优化问题？我们的研究结果表明，这种基于视觉的方法的性能不仅是不平凡的，而且可以与最先进的基于矩阵的方法相媲美，这为开发数据驱动的优化求解器开辟了一条新的途径。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=VN-Solver:+Vision-based+Neural+Solver+for+Combinatorial+Optimization+over+Graphs)|0| |[Findability: A Novel Measure of Information Accessibility](https://doi.org/10.1145/3583780.3615256)|Aman Sinha, Priyanshu Raj Mall, Dwaipayan Roy|Indian Institute of Science Education and Research, Kolkata, Kolkata, India|The overwhelming volume of data generated and indexed by search engines poses a significant challenge in retrieving documents from the index efficiently and effectively. Even with a well-crafted query, several relevant documents often get buried among a multitude of competing documents, resulting in reduced accessibility or `findability' of the desired document. Consequently, it is crucial to develop a robust methodology for assessing this dimension of Information Retrieval (IR) system performance. While previous studies have focused on measuring document accessibility disregarding user queries and document relevance, there exists no metric to quantify the findability of a document within a given IR system without resorting to manual labor. This paper aims to address this gap by defining and deriving a metric to evaluate the findability of documents as perceived by end-users. Through experiments, we demonstrate the varying impact of different retrieval models and collections on the findability of documents. Furthermore, we establish the findability measure as an independent metric distinct from retrievability, an accessibility measure introduced in prior literature.|搜索引擎生成和索引的大量数据对从索引中有效和高效地检索文件提出了重大挑战。即使是一个精心设计的查询，几个相关的文档通常也会被淹没在众多相互竞争的文档中，从而降低了所需文档的可访问性或“可找到性”。因此，开发一套可靠的方法来评估信息检索系统的性能是至关重要的。虽然以前的研究侧重于无视用户查询和文档相关性来衡量文档的可访问性，但是在给定的 IR 系统中，没有一个度量标准可以量化文档的可查找性，而不需要人工劳动。本文旨在通过定义和推导一个度量标准来评估最终用户所感知的文档的可查找性，从而弥补这一差距。通过实验，我们证明了不同的检索模型和集合对文档可查找性的不同影响。此外，我们建立了可查找性度量作为一个独立的度量区别于可检索性，一个可访问性度量介绍了以往的文献。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Findability:+A+Novel+Measure+of+Information+Accessibility)|0| |[Learning to Simulate Complex Physical Systems: A Case Study](https://doi.org/10.1145/3583780.3615169)|Jiasheng Shi, Fu Lin, Weixiong Rao|Tongji University, Shanghai, China|Complex physical system simulation is important in many real world applications. We study the general simulation scenario to generate the response result when a physical object is applied by external factors. Traditional solvers on Partial Differential Equations (PDEs) suffer from significantly high computational cost. Many recent learning-based approaches focus on multivariate time series alike simulation prediction problem and do not work for our case. In this paper, we propose a novel two-level graph neural networks (GNNs) to learn the simulation result of a physical object applied by external factors. The key is a two-level graph structure where one fine mesh graph is mapped to multiple coarse one. Our preliminary evaluation on both synthetic and real datasets demonstrates that our work outperforms three state-of-the-arts by much lower errors.|复杂物理系统仿真在许多实际应用中具有重要意义。我们研究了一般的模拟场景，以产生响应结果时，一个物理对象的外部因素应用。偏微分方程(PDE)的传统求解方法计算量大。近年来，许多基于学习的方法都集中在多变量时间序列的仿真预测问题上，这些方法对我们的案例都不起作用。本文提出了一种新的两级图形神经网络(GNN) ，用于学习外部因素对物理对象的模拟结果。其关键是一个两层图结构，其中一个细网格图映射到多个粗网格图。我们对合成数据集和真实数据集的初步评估表明，我们的工作比三种最先进的数据集有更低的误差。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Learning+to+Simulate+Complex+Physical+Systems:+A+Case+Study)|0| -|[Higher-Order Peak Decomposition](https://doi.org/10.1145/3583780.3615209)|Xingyu Tan, Jingya Qian, Chen Chen, Sima Qing, Yanping Wu, Xiaoyang Wang, Wenjie Zhang|University of Wollongong, Wollongong, Australia; Zhejiang Gongshang University, Hangzhou, China; University of Technology Sydney, Sydney, NSW, Australia; University of New South Wales, Sydney, NSW, Australia|k-peak is a well-regarded cohesive subgraph model in graph analysis. However, the k-peak model only considers the direct neighbors of a vertex, consequently limiting its capacity to uncover higher-order structural information of the graph. To address this limitation, we propose a new model in this paper, named (k,h)-peak, which incorporates higher-order (h-hops) neighborhood information of vertices. Employing the (k,h)-peak model, we explore the higher-order peak decomposition problem that calculates the vertex peakness for all conceivable k values given a particular h. To tackle this problem efficiently, we propose an advanced local computation based algorithm, which is parallelizable, and additionally, devise novel pruning strategies to mitigate unnecessary computation. Experiments as well as case studies are conducted on real-world datasets to evaluate the efficiency and effectiveness of our proposed solutions.|K- 峰是图分析中一个被广泛认可的内聚子图模型。然而，k- 峰模型只考虑顶点的直接邻居，从而限制了它揭示图的高阶结构信息的能力。针对这一局限性，本文提出了一种新的模型——(k，h)-峰模型，该模型结合了顶点的高阶邻域信息。利用(k，h)-峰值模型，我们研究了高阶峰值分解问题，该问题计算给定一个特定的 h 的所有可能 k 值的顶点峰值。为了有效地解决这个问题，我们提出了一种改进的基于局部计算的并行算法，此外，设计了新的剪枝策略来减少不必要的计算。在实际数据集上进行了实验和案例研究，以评估我们提出的解决方案的效率和有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Higher-Order+Peak+Decomposition)|0| +|[Higher-Order Peak Decomposition](https://doi.org/10.1145/3583780.3615209)|Xingyu Tan, Jingya Qian, Chen Chen, Sima Qing, Yanping Wu, Xiaoyang Wang, Wenjie Zhang|Zhejiang Gongshang University, Hangzhou, China; University of Technology Sydney, Sydney, NSW, Australia; University of New South Wales, Sydney, NSW, Australia; University of Wollongong, Wollongong, Australia|k-peak is a well-regarded cohesive subgraph model in graph analysis. However, the k-peak model only considers the direct neighbors of a vertex, consequently limiting its capacity to uncover higher-order structural information of the graph. To address this limitation, we propose a new model in this paper, named (k,h)-peak, which incorporates higher-order (h-hops) neighborhood information of vertices. Employing the (k,h)-peak model, we explore the higher-order peak decomposition problem that calculates the vertex peakness for all conceivable k values given a particular h. To tackle this problem efficiently, we propose an advanced local computation based algorithm, which is parallelizable, and additionally, devise novel pruning strategies to mitigate unnecessary computation. Experiments as well as case studies are conducted on real-world datasets to evaluate the efficiency and effectiveness of our proposed solutions.|K- 峰是图分析中一个被广泛认可的内聚子图模型。然而，k- 峰模型只考虑顶点的直接邻居，从而限制了它揭示图的高阶结构信息的能力。针对这一局限性，本文提出了一种新的模型——(k，h)-峰模型，该模型结合了顶点的高阶邻域信息。利用(k，h)-峰值模型，我们研究了高阶峰值分解问题，该问题计算给定一个特定的 h 的所有可能 k 值的顶点峰值。为了有效地解决这个问题，我们提出了一种改进的基于局部计算的并行算法，此外，设计了新的剪枝策略来减少不必要的计算。在实际数据集上进行了实验和案例研究，以评估我们提出的解决方案的效率和有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Higher-Order+Peak+Decomposition)|0| |[Leveraging Knowledge and Reinforcement Learning for Enhanced Reliability of Language Models](https://doi.org/10.1145/3583780.3615273)|Nancy Tyagi, Surjodeep Sarkar, Manas Gaur|University of Maryland, Baltimore County, MD, USA|The Natural Language Processing(NLP) community has been using crowd sourcing techniques to create benchmark datasets such as General Language Understanding and Evaluation(GLUE) for training modern Language Models such as BERT. GLUE tasks measure the reliability scores using inter annotator metrics i.e. Cohens Kappa. However, the reliability aspect of LMs has often been overlooked. To counter this problem, we explore a knowledge-guided LM ensembling approach that leverages reinforcement learning to integrate knowledge from ConceptNet and Wikipedia as knowledge graph embeddings. This approach mimics human annotators resorting to external knowledge to compensate for information deficits in the datasets. Across nine GLUE datasets, our research shows that ensembling strengthens reliability and accuracy scores, outperforming state of the art.|自然语言处理(NLP)社区一直在使用众包技术来创建基准数据集，如通用语言理解和评估(GLUE) ，用于培训现代语言模型，如 BERT。GLUE 任务使用注释器间指标(即 Cohen Kappa)来度量可靠性得分。然而，LM 的可靠性方面往往被忽视。为了解决这个问题，我们探索了一种知识引导的 LM 集成方法，这种方法利用强化学习来整合概念网和维基百科的知识，将其作为知识图表嵌入。这种方法模仿人类注释者求助于外部知识来弥补数据集中的信息缺陷。在九个 GLUE 数据集中，我们的研究表明，集合增强了可靠性和准确性得分，表现优于最先进的水平。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Leveraging+Knowledge+and+Reinforcement+Learning+for+Enhanced+Reliability+of+Language+Models)|0| |[Clustering-property Matters: A Cluster-aware Network for Large Scale Multivariate Time Series Forecasting](https://doi.org/10.1145/3583780.3615253)|Yuan Wang, Zezhi Shao, Tao Sun, Chengqing Yu, Yongjun Xu, Fei Wang|Institute of Computing Technology, Chinese Academy of Sciences & University of Chinese Academy of Sciences, Beijing, China|Large-scale Multivariate Time Series(MTS) widely exist in various real-world systems, imposing significant demands on model efficiency. A recent work, STID, addressed the high complexity issue of popular Spatial-Temporal Graph Neural Networks(STGNNs). Despite its success, when applied to large-scale MTS data, the number of parameters of STID for modeling spatial dependencies increases substantially, leading to over-parameterization issues and suboptimal performance. These observations motivate us to explore new approaches for modeling spatial dependencies in a parameter-friendly manner. In this paper, we argue that the spatial properties of variables are essentially the superposition of multiple cluster centers. Accordingly, we propose a Cluster-Aware Network(CANet), which effectively captures spatial dependencies by mining the implicit cluster centers of variables. CANet solely optimizes the cluster centers instead of the spatial information of all nodes, thereby significantly reducing the parameter amount. Extensive experiments on two large-scale datasets validate our motivation and demonstrate the superiority of CANet.|大规模多变量时间序列(MTS)广泛存在于各种实际系统中，对模型的效率提出了很高的要求。最近的一项工作，STID，解决了流行的时空图形神经网络(STGNN)的高复杂性问题。尽管 STID 在大规模 MTS 数据建模方面取得了成功，但 STID 用于空间依赖建模的参数数量却大幅增加，导致过度参数化问题和性能欠佳。这些观察促使我们探索新的方法，以参数友好的方式建模空间依赖性。本文认为变量的空间性质实质上是多个聚类中心的叠加。因此，我们提出了一个集群感知网络(CANet) ，它通过挖掘变量的隐式集群中心来有效地捕获空间依赖。CANet 只优化集群中心，而不优化所有节点的空间信息，从而大大减少了参数量。在两个大规模数据集上的大量实验验证了我们的动机，并证明了 CANet 的优越性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Clustering-property+Matters:+A+Cluster-aware+Network+for+Large+Scale+Multivariate+Time+Series+Forecasting)|0| -|[Adaptive Graph Neural Diffusion for Traffic Demand Forecasting](https://doi.org/10.1145/3583780.3615153)|Yiling Wu, Xinfeng Zhang, Yaowei Wang|University of Chinese Academy of Sciences, Beijing, China; Peng Cheng Laboratory, Shenzhen, China|This paper studies the problem of spatial-temporal modeling for traffic demand forecasting. In practice, the temporal-spatial dependencies are complex. Conventional methods using graph convolutional networks and gated recurrent units cannot fully explore the patterns of demand evolution. Therefore, we propose Adaptive Graph Neural Diffusion (AGND) for spatial-temporal graph modeling. Specifically, complex spatial relations are modeled with a diffusion process by the graph neural diffusion. The spatial attention mechanism and a data-driven semantic adjacency matrix are used to describe the diffusivity function in the graph neural diffusion, which provides both local and global spatial information. Long-term temporal dependencies are modeled by the temporal attention mechanism. The proposed method is applied to two real-world datasets, and the results show that the proposed method outperforms state-of-the-art methods.|研究了交通需求预测的时空建模问题。在实践中，时空依赖性是复杂的。传统的使用图卷积网络和门限递归单元的方法不能完全探索需求演化的模式。因此，我们提出自适应图神经扩散(AGND)的时空图建模。具体地说，复杂的空间关系是通过图神经扩散过程建模的。空间注意机制和数据驱动的语义邻接矩阵被用来描述图形神经扩散中的扩散函数，它提供局部和全局的空间信息。长期的时间依赖由时间注意机制建模。将该方法应用于两个实际数据集，结果表明，该方法的性能优于现有方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Adaptive+Graph+Neural+Diffusion+for+Traffic+Demand+Forecasting)|0| +|[Adaptive Graph Neural Diffusion for Traffic Demand Forecasting](https://doi.org/10.1145/3583780.3615153)|Yiling Wu, Xinfeng Zhang, Yaowei Wang|Peng Cheng Laboratory, Shenzhen, China; University of Chinese Academy of Sciences, Beijing, China|This paper studies the problem of spatial-temporal modeling for traffic demand forecasting. In practice, the temporal-spatial dependencies are complex. Conventional methods using graph convolutional networks and gated recurrent units cannot fully explore the patterns of demand evolution. Therefore, we propose Adaptive Graph Neural Diffusion (AGND) for spatial-temporal graph modeling. Specifically, complex spatial relations are modeled with a diffusion process by the graph neural diffusion. The spatial attention mechanism and a data-driven semantic adjacency matrix are used to describe the diffusivity function in the graph neural diffusion, which provides both local and global spatial information. Long-term temporal dependencies are modeled by the temporal attention mechanism. The proposed method is applied to two real-world datasets, and the results show that the proposed method outperforms state-of-the-art methods.|研究了交通需求预测的时空建模问题。在实践中，时空依赖性是复杂的。传统的使用图卷积网络和门限递归单元的方法不能完全探索需求演化的模式。因此，我们提出自适应图神经扩散(AGND)的时空图建模。具体地说，复杂的空间关系是通过图神经扩散过程建模的。空间注意机制和数据驱动的语义邻接矩阵被用来描述图形神经扩散中的扩散函数，它提供局部和全局的空间信息。长期的时间依赖由时间注意机制建模。将该方法应用于两个实际数据集，结果表明，该方法的性能优于现有方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Adaptive+Graph+Neural+Diffusion+for+Traffic+Demand+Forecasting)|0| |[Promoting Diversity in Mixed Complex Cooperative and Competitive Multi-Agent Environment](https://doi.org/10.1145/3583780.3615217)|Jia Wu, Zixiao Huang|University of Electronic Science and Technology of China, ChengDu, China|This paper introduces a new approach for promoting diversity of behavior in complex multi-agent environments that pose three challenges: 1) competition or collaboration among agents of diverse types, 2) the need for complex multi-agent coordination, which makes it challenging to achieve risky cooperation strategies, and 3) a large number of agents in the environment, leading to increased complexity when considering agent-to-agent relationships. To address the first two challenges, we leverage Reward Randomization in combination with Bayesian Optimization to train agents to exhibit diverse strategic behaviors, thereby mitigating the issue of risky cooperation. To address the challenge of learning in a large number of agents, we utilize MAPPO with parameter sharing to enhance learning efficiency. Experimental results demonstrate that within this multi-agent environment, agents can effectively learn multiple visually distinct behaviors, and the incorporation of these two techniques significantly improves agents' performance.|本文介绍了一种在复杂的多主体环境中促进行为多样性的新方法，它提出了三个挑战: 1)不同类型的主体之间的竞争或协作，2)需要复杂的多主体协作，这使得实现有风险的合作策略具有挑战性，3)环境中存在大量的主体，导致在考虑主体-主体关系时复杂性增加。为了解决前两个挑战，我们利用奖励随机化和贝叶斯优化相结合的方法来训练代理人展示多样化的战略行为，从而减轻风险合作的问题。为了解决大量智能体中学习的难题，我们利用参数共享的 MAPPO 来提高学习效率。实验结果表明，在这种多智能体环境下，智能体可以有效地学习多种视觉上不同的行为，这两种技术的结合显著提高了智能体的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Promoting+Diversity+in+Mixed+Complex+Cooperative+and+Competitive+Multi-Agent+Environment)|0| -|[MC-DRE: Multi-Aspect Cross Integration for Drug Event/Entity Extraction](https://doi.org/10.1145/3583780.3615190)|Jie Yang, Soyeon Caren Han, Siqu Long, Josiah Poon, Goran Nenadic|The University of Sydney, Sydney, NSW, Australia; The University of Manchester, Manchester, United Kingdom; The University of Western Australia, Perth, WA, Australia|Extracting meaningful drug-related information chunks, such as adverse drug events (ADE), is crucial for preventing morbidity and saving many lives. Most ADE are reported via an unstructured conversation with the medical context. Hence, applying a general entity recognition approach is not sufficient enough. The key is how to integrate and align multiple crucial aspects to detect drug event information, including drug event semantics, syntactic structures, and medical domain terminology. In this paper, we propose a new multi-aspect cross-integration framework for drug entity/event detection by capturing and aligning different context/language/knowledge properties from drug-related documents. We first construct multi-aspect encoders to describe semantic, syntactic, and medical document contextual information by conducting those slot tagging tasks, main drug entity/event detection, part-of-speech tagging, and general medical named entity recognition. Then, each encoder conducts cross integration and alignment with other contextual information in three ways, including the key-value cross, attention cross, and feedforward cross, so the multi-encoders are integrated in depth. Then, we perform extensive experiments on two widely used drug-related entity recognition downstream tasks, flat entity detection and discontinuous event extraction. Our model significantly outperforms all recent twelve state-of-the-art models. The implementation code will be released at~\url{https://github.com/adlnlp/mc-dre}.|提取有意义的药物相关信息块，如药物不良事件(ADE) ，对于预防发病率和挽救许多生命至关重要。大多数 ADE 是通过与医学背景的非结构化对话报告的。因此，仅仅采用一般的实体识别方法是不够的。关键是如何整合和调整多个关键方面来检测药物事件信息，包括药物事件语义、句法结构和医学领域术语。本文提出了一种新的多方面交叉集成框架，通过捕获和对齐药物相关文献中不同的上下文、语言和知识属性，实现了药物实体/事件检测的多方面交叉集成。我们首先构造多方面编码器来描述语义、句法和医学文献上下文信息，包括时隙标记任务、主要药物实体/事件检测、词性标记和一般医学命名实体识别。然后，每个编码器通过键值交叉、注意交叉和前馈交叉三种方式对其他上下文信息进行交叉整合和对齐，从而实现多个编码器的深度整合。然后，我们对两个广泛使用的药物相关实体识别下游任务，平面实体检测和不连续事件提取进行了广泛的实验。我们的模型明显优于最近的十二个最先进的模型。实现代码将在 ~ url { https://github.com/adlnlp/mc-dre }发布。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=MC-DRE:+Multi-Aspect+Cross+Integration+for+Drug+Event/Entity+Extraction)|0| -|[Positive-Unlabeled Node Classification with Structure-aware Graph Learning](https://doi.org/10.1145/3583780.3615250)|Hansi Yang, Yongqi Zhang, Quanming Yao, James T. Kwok|4Paradigm, Beijing, China; Hong Kong University of Science and Technology, Hong Kong, Hong Kong; Tsinghua University, Beijing, China|Node classification on graphs is an important research problem with many applications. Real-world graph data sets may not be balanced and accurate as assumed by most existing works. A challenging setting is positive-unlabeled (PU) node classification, where labeled nodes are restricted to positive nodes. It has diverse applications, e.g., pandemic prediction or network anomaly detection. Existing works on PU node classification overlook information in the graph structure, which can be critical. In this paper, we propose to better utilize graph structure for PU node classification. We first propose a distance-aware PU loss that uses homophily in graphs to introduce more accurate supervision. We also propose a regularizer to align the model with graph structure. Theoretical analysis shows that minimizing the proposed loss also leads to minimizing the expected loss with both positive and negative labels. Extensive empirical evaluation on diverse graph data sets demonstrates its superior performance over existing state-of-the-art methods.|图的节点分类是一个具有广泛应用前景的重要研究课题。现实世界中的图形数据集可能并不像大多数现有工作所假定的那样平衡和准确。一个具有挑战性的设置是阳性未标记(PU)节点分类，其中标记节点仅限于阳性节点。它有多种应用，例如流行病预测或网络异常检测。现有的 PU 节点分类工作忽略了图结构中的信息，这可能是至关重要的。在本文中，我们提出了更好地利用图结构对 PU 节点进行分类。我们首先提出了一种距离感知的 PU 损失，它使用图中的同质性来引入更准确的监督。我们还提出了一个正则化器来使模型与图结构相一致。理论分析表明，最小化建议的损失也导致最小化的预期损失与正面和负面的标签。对各种图形数据集的广泛实证评估表明，其性能优于现有的最先进的方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Positive-Unlabeled+Node+Classification+with+Structure-aware+Graph+Learning)|0| +|[MC-DRE: Multi-Aspect Cross Integration for Drug Event/Entity Extraction](https://doi.org/10.1145/3583780.3615190)|Jie Yang, Soyeon Caren Han, Siqu Long, Josiah Poon, Goran Nenadic|The University of Western Australia, Perth, WA, Australia; The University of Manchester, Manchester, United Kingdom; The University of Sydney, Sydney, NSW, Australia|Extracting meaningful drug-related information chunks, such as adverse drug events (ADE), is crucial for preventing morbidity and saving many lives. Most ADE are reported via an unstructured conversation with the medical context. Hence, applying a general entity recognition approach is not sufficient enough. The key is how to integrate and align multiple crucial aspects to detect drug event information, including drug event semantics, syntactic structures, and medical domain terminology. In this paper, we propose a new multi-aspect cross-integration framework for drug entity/event detection by capturing and aligning different context/language/knowledge properties from drug-related documents. We first construct multi-aspect encoders to describe semantic, syntactic, and medical document contextual information by conducting those slot tagging tasks, main drug entity/event detection, part-of-speech tagging, and general medical named entity recognition. Then, each encoder conducts cross integration and alignment with other contextual information in three ways, including the key-value cross, attention cross, and feedforward cross, so the multi-encoders are integrated in depth. Then, we perform extensive experiments on two widely used drug-related entity recognition downstream tasks, flat entity detection and discontinuous event extraction. Our model significantly outperforms all recent twelve state-of-the-art models. The implementation code will be released at~\url{https://github.com/adlnlp/mc-dre}.|提取有意义的药物相关信息块，如药物不良事件(ADE) ，对于预防发病率和挽救许多生命至关重要。大多数 ADE 是通过与医学背景的非结构化对话报告的。因此，仅仅采用一般的实体识别方法是不够的。关键是如何整合和调整多个关键方面来检测药物事件信息，包括药物事件语义、句法结构和医学领域术语。本文提出了一种新的多方面交叉集成框架，通过捕获和对齐药物相关文献中不同的上下文、语言和知识属性，实现了药物实体/事件检测的多方面交叉集成。我们首先构造多方面编码器来描述语义、句法和医学文献上下文信息，包括时隙标记任务、主要药物实体/事件检测、词性标记和一般医学命名实体识别。然后，每个编码器通过键值交叉、注意交叉和前馈交叉三种方式对其他上下文信息进行交叉整合和对齐，从而实现多个编码器的深度整合。然后，我们对两个广泛使用的药物相关实体识别下游任务，平面实体检测和不连续事件提取进行了广泛的实验。我们的模型明显优于最近的十二个最先进的模型。实现代码将在 ~ url { https://github.com/adlnlp/mc-dre }发布。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=MC-DRE:+Multi-Aspect+Cross+Integration+for+Drug+Event/Entity+Extraction)|0| +|[Positive-Unlabeled Node Classification with Structure-aware Graph Learning](https://doi.org/10.1145/3583780.3615250)|Hansi Yang, Yongqi Zhang, Quanming Yao, James T. Kwok|Hong Kong University of Science and Technology, Hong Kong, Hong Kong; Tsinghua University, Beijing, China; 4Paradigm, Beijing, China|Node classification on graphs is an important research problem with many applications. Real-world graph data sets may not be balanced and accurate as assumed by most existing works. A challenging setting is positive-unlabeled (PU) node classification, where labeled nodes are restricted to positive nodes. It has diverse applications, e.g., pandemic prediction or network anomaly detection. Existing works on PU node classification overlook information in the graph structure, which can be critical. In this paper, we propose to better utilize graph structure for PU node classification. We first propose a distance-aware PU loss that uses homophily in graphs to introduce more accurate supervision. We also propose a regularizer to align the model with graph structure. Theoretical analysis shows that minimizing the proposed loss also leads to minimizing the expected loss with both positive and negative labels. Extensive empirical evaluation on diverse graph data sets demonstrates its superior performance over existing state-of-the-art methods.|图的节点分类是一个具有广泛应用前景的重要研究课题。现实世界中的图形数据集可能并不像大多数现有工作所假定的那样平衡和准确。一个具有挑战性的设置是阳性未标记(PU)节点分类，其中标记节点仅限于阳性节点。它有多种应用，例如流行病预测或网络异常检测。现有的 PU 节点分类工作忽略了图结构中的信息，这可能是至关重要的。在本文中，我们提出了更好地利用图结构对 PU 节点进行分类。我们首先提出了一种距离感知的 PU 损失，它使用图中的同质性来引入更准确的监督。我们还提出了一个正则化器来使模型与图结构相一致。理论分析表明，最小化建议的损失也导致最小化的预期损失与正面和负面的标签。对各种图形数据集的广泛实证评估表明，其性能优于现有的最先进的方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Positive-Unlabeled+Node+Classification+with+Structure-aware+Graph+Learning)|0| |[Toward a Foundation Model for Time Series Data](https://doi.org/10.1145/3583780.3615155)|ChinChia Michael Yeh, Xin Dai, Huiyuan Chen, Yan Zheng, Yujie Fan, Audrey Der, Vivian Lai, Zhongfang Zhuang, Junpeng Wang, Liang Wang, Wei Zhang|University of California, Riverside, Riverside, CA, USA; Visa Research, Palo Alto, CA, USA; Visa Resarch, Palo Alto, CA, USA|A foundation model is a machine learning model trained on a large and diverse set of data, typically using self-supervised learning-based pre-training techniques, that can be adapted to various downstream tasks. However, current research on time series pre-training has mostly focused on models pre-trained solely on data from a single domain, resulting in a lack of knowledge about other types of time series. However, current research on time series pre-training has predominantly focused on models trained exclusively on data from a single domain. As a result, these models possess domain-specific knowledge that may not be easily transferable to time series from other domains. In this paper, we aim to develop an effective time series foundation model by leveraging unlabeled samples from multiple domains. To achieve this, we repurposed the publicly available UCR Archive and evaluated four existing self-supervised learning-based pre-training methods, along with a novel method, on the datasets. We tested these methods using four popular neural network architectures for time series to understand how the pre-training methods interact with different network designs. Our experimental results show that pre-training improves downstream classification tasks by enhancing the convergence of the fine-tuning process. Furthermore, we found that the proposed pre-training method, when combined with the Transformer model, outperforms the alternatives.|基础模型是对大量不同数据集进行训练的机器学习模型，通常使用基于自监督学习的预训练技术，可以适应各种下游任务。然而，目前关于时间序列预训练的研究主要集中在仅仅根据单一领域的数据进行预训练的模型上，导致对其他类型的时间序列缺乏了解。然而，目前关于时间序列预训练的研究主要集中在专门针对来自单一领域的数据进行训练的模型上。因此，这些模型具有领域特定的知识，可能不容易从其他领域转移到时间序列。本文旨在利用来自多个领域的未标记样本，建立一个有效的时间序列基础模型。为了实现这一目标，我们重新利用公开可用的 UCR 档案，并评估了四种现有的基于自我监督学习的预训练方法，以及一种新的方法，对数据集。我们使用四种流行的时间序列神经网络结构来测试这些方法，以了解预训练方法如何与不同的网络设计相互作用。实验结果表明，预训练通过提高微调过程的收敛性来改善下游分类任务。此外，我们发现，提出的预训练方法，当结合变压器模型，优于替代方案。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Toward+a+Foundation+Model+for+Time+Series+Data)|0| |[Simplex2vec Backward: From Vectors Back to Simplicial Complex](https://doi.org/10.1145/3583780.3615147)|Huixin Zhan, Kun Zhang, Zhong Chen, Victor S. Sheng|Xavier University of Louisiana, New Orleans, LA, USA; Texas Tech University, Lubbock, TX, USA|Simplicial neural networks (SNNs) were proposed to generate higher-order simplicial complex representations as vectors that encode not only pairwise relationships but also higher-order interactions between nodes. Although these vectors allowing us to consider richer data representations compared to typical graph convolution, most real-world graphs associated with molecule or human-related activities are often sensitive and might contain confidential information, e.g., molecular geometry or friend lists. However, little works investigate the potential threats for these simplicial complexes (higher-order interactions between nodes). We name this threat by Simplicial Complexes Reconstruction Attack (SCRA) and conduct this attack by studying whether the vectors can be inverted to (approximately) recover the simplicial complexes who used to generate them. Specifically, we first generate the vectors via a k-simplex2vec approach that extends the node2vec algorithm to simplices of higher dimensions to associate Euclidean vectors to simplicial complexes. We then present a Simplex2vec Backward algorithm to perform the SCRA on k-simplex2vec vectors by pointwise mutual information (PMI) matrix reconstruction.|简单神经网络(SNN)被提出来生成高阶单纯复形表示，作为矢量，不仅编码成对的关系，而且编码节点之间的高阶相互作用。虽然这些向量允许我们考虑比典型的图卷积更丰富的数据表示，但大多数与分子或人类相关活动相关的现实世界图往往是敏感的，可能包含机密信息，例如分子结构或朋友列表。然而，很少有工作研究这些单纯复合体(节点之间的高阶相互作用)的潜在威胁。我们将这种威胁命名为简单复合体重构攻击(SCRA) ，并通过研究向量是否可以反转来(近似地)恢复用来产生它们的简单复合体来实施这种攻击。具体来说，我们首先通过 k-simplex2vec 方法生成向量，该方法将 node2vec 算法扩展到更高维的单纯形，以将欧氏向量关联到单纯形复合体。然后，我们提出了一个单纯形向量2/vec 向后算法，通过点间互信息矩阵重构(PMI)对 k- 单纯形向量2/vec 向量进行 SCRA。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Simplex2vec+Backward:+From+Vectors+Back+to+Simplicial+Complex)|0| |[Unlocking the Potential of Deep Learning in Peak-Hour Series Forecasting](https://doi.org/10.1145/3583780.3615159)|Zhenwei Zhang, Xin Wang, Jingyuan Xie, Heling Zhang, Yuantao Gu|Tsinghua University, Beijing, China|Unlocking the potential of deep learning in Peak-Hour Series Forecasting (PHSF) remains a critical yet underexplored task in various domains. While state-of-the-art deep learning models excel in regular Time Series Forecasting (TSF), they struggle to achieve comparable results in PHSF. This can be attributed to the challenges posed by the high degree of non-stationarity in peak-hour series, which makes direct forecasting more difficult than standard TSF. Additionally, manually extracting the maximum value from regular forecasting results leads to suboptimal performance due to models minimizing the mean deficit. To address these issues, this paper presents Seq2Peak, a novel framework designed specifically for PHSF tasks, bridging the performance gap observed in TSF models. Seq2Peak offers two key components: the CyclicNorm pipeline to mitigate the non-stationarity issue and a simple yet effective trainable-parameter-free peak-hour decoder with a hybrid loss function that utilizes both the original series and peak-hour series as supervised signals. Extensive experimentation on publicly available time series datasets demonstrates the effectiveness of the proposed framework, yielding a remarkable average relative improvement of 37.7% across four real-world datasets for both transformer- and non-transformer-based TSF models.|在高峰时间序列预测(PHSF)中，挖掘深度学习的潜力仍然是各个领域中一个至关重要但尚未得到充分开发的课题。虽然最先进的深度学习模型在常规时间序列预测(TSF)中表现出色，但在 PHSF 中却难以取得可比较的结果。这可归因于高峰时段序列的高度非平稳性所带来的挑战，这使得直接预测比标准 TSF 更加困难。此外，由于模型的平均亏损最小，人工从常规预测结果中提取最大值会导致性能不理想。为了解决这些问题，本文提出了专门为 PHSF 任务设计的新框架 Seq2Peak，它弥补了 TSF 模型中观察到的性能差距。Seq2Peak 提供了两个关键组件: CyclicNorm 流水线以减轻非平稳性问题，以及一个简单而有效的无可训练参数的峰时解码器，该解码器具有利用原始序列和峰时序列作为监督信号的混合损耗功能。对公开可用的时间序列数据集的广泛实验证明了所提出的框架的有效性，在基于变压器和非变压器的 TSF 模型的四个现实世界数据集中产生了37.7% 的显着平均相对改善。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Unlocking+the+Potential+of+Deep+Learning+in+Peak-Hour+Series+Forecasting)|0| @@ -594,76 +594,76 @@ |[FCT-GAN: Enhancing Global Correlation of Table Synthesis via Fourier Transform](https://doi.org/10.1145/3583780.3615202)|Zilong Zhao, Robert Birke, Lydia Y. Chen|Delft University of Technology, Delft, Netherlands; University of Turin, Turin, Italy|An alternative method for sharing knowledge while complying with strict data access regulations, such as the European General Data Protection Regulation (GDPR), is the emergence of synthetic tabular data. Mainstream table synthesizers utilize methodologies derived from Generative Adversarial Networks (GAN). Although several state-of-the-art (SOTA) tabular GAN algorithms inherit Convolutional Neural Network (CNN)-based architectures, which have proven effective for images, they tend to overlook two critical properties of tabular data: (i) the global correlation across columns, and (ii) the semantic invariance to the column order. Permuting columns in a table does not alter the semantic meaning of the data, but features extracted by CNNs can change significantly due to their limited convolution filter kernel size. To address the above problems, we propose FCT-GAN the first conditional tabular GAN to adopt Fourier networks into table synthesis. FCT-GAN enhances permutation invariant GAN training by strengthening the learning of global correlations via Fourier layers. Extensive evaluation on benchmarks and real-world datasets show that FCT-GAN can synthesize tabular data with better (up to 27.8%) machine learning utility (i.e. a proxy of global correlations) and higher (up to 26.5%) statistical similarity to real data. FCT-GAN also has the least variation on synthetic data quality among 7 SOTA baselines on 3 different training-data column orders.|在遵守严格的数据访问规定(如欧洲通用数据保护条例(GDPR))的同时，共享知识的另一种方法是出现合成表格数据。主流表合成器利用源自生成对抗网络(GAN)的方法。尽管一些最先进的(SOTA)表格式 GAN 算法继承了基于卷积神经网络(CNN)的架构，这些架构已被证明对图像有效，但它们往往忽略了表格数据的两个关键属性: (i)跨列的全局相关性，以及(ii)对列顺序的语义不变性。对表中的列进行排列并不会改变数据的语义含义，但由于 CNN 的卷积过滤器内核大小有限，所以提取的特征可能会发生显著变化。针对上述问题，我们提出了 FCT-GAN 第一个条件表 GAN，采用傅里叶网络进行表综合。FCT-GAN 通过傅里叶层强化全局相关性的学习来增强置换不变 GAN 训练。对基准和真实世界数据集的广泛评估表明，FCT-GAN 可以综合表格数据，具有更好(高达27.8%)的机器学习效用(即全球相关性的代理)和更高(高达26.5%)与真实数据的统计相似性。FCT-GAN 在3种不同训练数据列顺序的7个 SOTA 基线中，综合数据质量变化最小。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=FCT-GAN:+Enhancing+Global+Correlation+of+Table+Synthesis+via+Fourier+Transform)|0| |[A Semi-Supervised Anomaly Network Traffic Detection Framework via Multimodal Traffic Information Fusion](https://doi.org/10.1145/3583780.3615214)|Yu Zheng, Xinglin Lian, Zhangxuan Dang, Chunlei Peng, Chao Yang, Jianfeng Ma|Xidian University, Xi'An, China|Anomaly traffic detection is a crucial issue in the cyber-security field. Previously, many researchers regarded anomaly traffic detection as a supervised classification problem. However, in real scenarios, anomaly network traffic is unpredictable, dynamically changing and difficult to collect. To address these limitations, we employ anomaly detection setting to propose a novel semi-supervised anomaly network traffic detection framework. It only learns features of normal samples during the training phase. Our framework utilizes low-pass filtering to extract multi-scale low-frequency information from 2-D traffic image. Furthermore, we design a two-stage fusion scheme to incorporate information from original and multi-scale low-frequency traffic image modalities. We conduct experiments on two public datasets: ISCX Tor-nonTor and USTC-TFC2016. The experimental results show that our method outperforms current state-of-the-art anomaly detection methods.|异常流量检测是网络安全领域的一个关键问题。以前，许多研究者将异常流量检测视为一个有监督的分类问题。然而，在实际场景中，异常网络流量是不可预测的、动态变化的，并且难以收集。为了解决这些局限性，我们使用异常检测设置来提出一种新的半监督异常网络流量检测框架。它只在训练阶段学习正常样本的特征。该框架利用低通滤波从二维交通图像中提取多尺度低频信息。此外，我们设计了一个两阶段的融合方案，融合了来自原始和多尺度低频交通图像模式的信息。我们在两个公共数据集上进行了实验: ISCX Tor-nonTor 和 USTC-TFC2016。实验结果表明，我们的方法优于目前最先进的异常检测方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Semi-Supervised+Anomaly+Network+Traffic+Detection+Framework+via+Multimodal+Traffic+Information+Fusion)|0| |[Nowcast-to-Forecast: Token-Based Multiple Remote Sensing Data Fusion for Precipitation Forecast](https://doi.org/10.1145/3583780.3614702)|Sojung An|Korea Institute of Atmospheric Prediction Systems, Seoul, Republic of Korea|Accurate short-term precipitation forecast is of social and economic significance for preventing severe weather damage. Deep learning has been rapidly adopted in nowcasting based on weather radar, which plays a key role in preventing dangerous weather conditions such as torrential rainfall. However, the limited observation range of the radar imposes constraints on shorter forecast lead times. Securing a sufficient lead time for timely flood warnings and emergency responses is crucial. Here, we propose a novel GAN-based framework that combines radar and satellite data to extend forecast lead time. First, we tokenize the satellite image to align with radar dimensions and combine the satellite and radar data. We then apply positional encoding to add positional information. Second, we design the self-conditioned generator to estimate distributions of various rainfall intensities. Finally, we employ Gaussian Fourier features to map the input noise into a continuous representation. The proposed framework realistically and accurately produces time series images of various precipitation types. Furthermore, our multisource data-driven system outperforms numerical weather prediction at forecasts of up to 6 hours in South Korea.|准确的短期降水预报对于防止严重天气灾害的发生具有重要的社会和经济意义。深度学习在基于天气雷达的临近预报中得到了迅速的应用，对于防止暴雨等危险天气状况的发生起到了关键作用。然而，雷达有限的观测范围限制了较短的预报提前时间。确保有足够的准备时间以及时发布洪水警报和作出应急反应至关重要。在这里，我们提出了一个新的基于广域网的框架，结合雷达和卫星数据，以扩大预测提前期。首先，我们对卫星图像进行标记，以便与雷达尺寸对齐，并结合卫星和雷达数据。然后，我们应用位置编码来添加位置信息。其次，我们设计了自调节发生器来估计不同降雨强度的分布。最后，利用高斯傅里叶特征将输入噪声映射成一个连续的表示。建议的框架实际而准确地生成不同锋面雨的时间序列图像。此外，我们的多源数据驱动系统在韩国的预测时间长达6小时，表现优于数值天气预报。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Nowcast-to-Forecast:+Token-Based+Multiple+Remote+Sensing+Data+Fusion+for+Precipitation+Forecast)|0| -|[CallMine: Fraud Detection and Visualization of Million-Scale Call Graphs](https://doi.org/10.1145/3583780.3614662)|Mirela Teixeira Cazzolato, Saranya Vijayakumar, MengChieh Lee, Catalina Vajiac, Namyong Park, Pedro Fidalgo, Agma J. M. Traina, Christos Faloutsos|Carnegie Mellon University, Pittsburgh, PA, USA; Mobileum and ISCTE-IUL, Lisbon, Portugal; Carnegie Mellon University & University of Sao Paulo, Sao Carlos, Brazil; University of Sao Paulo, Sao Carlos, Brazil|Given a million-scale dataset of who-calls-whom data containing imperfect labels, how can we detect existing and new fraud patterns? We propose CallMine, with carefully designed features and visualizations. Our CallMine method has the following properties: (a) Scalable, being linear on the input size, handling about 35 million records in around one hour on a stock laptop; (b) Effective, allowing natural interaction with human analysts; (c) Flexible, being applicable in both supervised and unsupervised settings; (d) Automatic, requiring no user-defined parameters. In the real world, in a multi-million-scale dataset, CallMine was able to detect fraudsters 7,000x faster, namely in a matter of hours, while expert humans took over 10 months to detect them. CIKM-ARP Categories: Application; Analytics and machine learning; Data presentation.|给定一个百万级别的数据集谁打电话-谁的数据包含不完美的标签，我们如何检测现有的和新的欺诈模式？我们提出 CallMine，它具有精心设计的特性和可视化。我们的 CallMine 方法具有以下特性: (a)可扩展，输入大小呈线性，在一台普通笔记本电脑上大约一小时内处理3500万条记录; (b)有效，允许与人类分析师自然交互; (c)灵活，适用于监督和非监督设置; (d)自动，不需要用户定义的参数。在现实世界中，在一个数百万规模的数据集中，CallMine 能够以7000倍的速度发现欺诈者，也就是在几个小时内，而专家人类花了10个月才发现他们。CIKM-ARP 分类: 应用; 分析和机器学习; 数据表示。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=CallMine:+Fraud+Detection+and+Visualization+of+Million-Scale+Call+Graphs)|0| +|[CallMine: Fraud Detection and Visualization of Million-Scale Call Graphs](https://doi.org/10.1145/3583780.3614662)|Mirela Teixeira Cazzolato, Saranya Vijayakumar, MengChieh Lee, Catalina Vajiac, Namyong Park, Pedro Fidalgo, Agma J. M. Traina, Christos Faloutsos|University of Sao Paulo, Sao Carlos, Brazil; Carnegie Mellon University, Pittsburgh, PA, USA; Mobileum and ISCTE-IUL, Lisbon, Portugal; Carnegie Mellon University & University of Sao Paulo, Sao Carlos, Brazil|Given a million-scale dataset of who-calls-whom data containing imperfect labels, how can we detect existing and new fraud patterns? We propose CallMine, with carefully designed features and visualizations. Our CallMine method has the following properties: (a) Scalable, being linear on the input size, handling about 35 million records in around one hour on a stock laptop; (b) Effective, allowing natural interaction with human analysts; (c) Flexible, being applicable in both supervised and unsupervised settings; (d) Automatic, requiring no user-defined parameters. In the real world, in a multi-million-scale dataset, CallMine was able to detect fraudsters 7,000x faster, namely in a matter of hours, while expert humans took over 10 months to detect them. CIKM-ARP Categories: Application; Analytics and machine learning; Data presentation.|给定一个百万级别的数据集谁打电话-谁的数据包含不完美的标签，我们如何检测现有的和新的欺诈模式？我们提出 CallMine，它具有精心设计的特性和可视化。我们的 CallMine 方法具有以下特性: (a)可扩展，输入大小呈线性，在一台普通笔记本电脑上大约一小时内处理3500万条记录; (b)有效，允许与人类分析师自然交互; (c)灵活，适用于监督和非监督设置; (d)自动，不需要用户定义的参数。在现实世界中，在一个数百万规模的数据集中，CallMine 能够以7000倍的速度发现欺诈者，也就是在几个小时内，而专家人类花了10个月才发现他们。CIKM-ARP 分类: 应用; 分析和机器学习; 数据表示。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=CallMine:+Fraud+Detection+and+Visualization+of+Million-Scale+Call+Graphs)|0| |[Continually-Adaptive Representation Learning Framework for Time-Sensitive Healthcare Applications](https://doi.org/10.1145/3583780.3615464)|Akash Choudhuri, Hankyu Jang, Alberto M. Segre, Philip M. Polgreen, Kishlay Jha, Bijaya Adhikari|The University of Iowa, Iowa City, IA, USA|Continual learning has emerged as a powerful approach to address the challenges of non-stationary environments, allowing machine learning models to adapt to new data while retaining the previously acquired knowledge. In time-sensitive healthcare applications, where entities such as physicians, hospital rooms, and medications exhibit continuous changes over time, continual learning holds great promise, yet its application remains relatively unexplored. This paper aims to bridge this gap by proposing a novel framework, i.e., Continually-Adaptive Representation Learning, designed to adapt representations in response to changing data distributions in evolving healthcare applications. Specifically, the proposed approach develops a continual learning strategy wherein the context information (e.g., interactions) of healthcare entities is exploited to continually identify and retrain the representations of those entities whose context evolved over time. Moreover, different from existing approaches, the proposed approach leverages the valuable patient information present in clinical notes to generate accurate and robust healthcare embeddings. Notably, the proposed continually-adaptive representations have practical benefits in low-resource clinical settings where it is difficult to training machine learning models from scratch to accommodate the newly available data streams. Experimental evaluations on real-world healthcare datasets demonstrate the effectiveness of our approach in time-sensitive healthcare applications such as Clostridioides difficile (C.diff) Infection (CDI) incidence prediction task and medical intensive care unit transfer prediction task.|连续学习已成为解决非平稳环境挑战的有力方法，使机器学习模型能够适应新的数据，同时保留以前获得的知识。在时间敏感的医疗保健应用中，医生、医院病房和药物等实体随着时间的推移显示出持续的变化，持续学习具有很大的前景，但其应用仍然相对未被探索。本文旨在通过提出一个新的框架，即连续自适应表示学习来弥补这一差距，该框架旨在适应不断变化的医疗保健应用数据分布的变化。具体而言，所提出的方法开发了一种持续学习策略，其中利用医疗实体的上下文信息(例如，交互)来不断识别和重新训练那些其上下文随时间演变的实体的表示。此外，不同于现有的方法，建议的方法利用临床记录中存在的有价值的患者信息来生成准确和健壮的医疗保健嵌入。值得注意的是，提出的持续自适应表示在低资源临床环境中具有实际好处，因为在这些环境中难以从头开始训练机器学习模型以适应新的可用数据流。对现实世界医疗数据集的实验评估证明了我们的方法在时间敏感的医疗应用中的有效性，例如艰难梭状芽孢杆菌感染(cdiff)发病率预测任务和医疗重症监护室转移预测任务。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Continually-Adaptive+Representation+Learning+Framework+for+Time-Sensitive+Healthcare+Applications)|0| -|[Content-Based Email Classification at Scale](https://doi.org/10.1145/3583780.3615462)|Kirstin Early, Neil O'Hare, Christopher C. LuVogt|Yahoo Research, San Francisco, CA, USA; Yahoo Research, Mountain View, CA, USA|Understanding the content of email messages can enable new features that highlight what matters to users, making email a more useful tool for people to manage their lives. We present work from a consumer email platform to build multilabel models to classify messages according to a mail-specific, content-based taxonomy that represents the topic, type, and objective of an email. While state-of-the-art Transformer-based language models can achieve impressive results for text classification, these models are too costly to deploy at the scale of email. Using a knowledge distillation framework, we first build a complex, accurate teacher model from limited human-labeled training data and then use a large amount of teacher-labeled data to train lightweight student models that are suitable for deployment. The student models retain up to 91% of the predictive performance of the teacher model while reducing inference cost by three orders of magnitude. Deployed to production in Yahoo Mail, these models classify billions of emails every day and power features that help people tackle their inboxes.|了解电子邮件的内容可以使新的功能，突出什么对用户重要，使电子邮件成为一个更有用的工具，人们管理他们的生活。我们从一个消费者电子邮件平台开始构建多标签模型，以根据邮件特定的、基于内容的分类法对邮件进行分类，该分类法代表电子邮件的主题、类型和目标。虽然最先进的基于 Transformer 的语言模型可以在文本分类方面取得令人印象深刻的结果，但是这些模型的开销太大，无法在电子邮件规模上进行部署。使用知识提取框架，我们首先从有限的人类标记的训练数据中建立一个复杂的、精确的教师模型，然后使用大量的教师标记的数据来训练适合部署的轻量级学生模型。学生模型保留了教师模型91% 的预测性能，同时减少了三个数量级的推理成本。这些模型被部署到雅虎邮箱，每天为数十亿封电子邮件分类，并提供强大的功能帮助人们处理他们的收件箱。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Content-Based+Email+Classification+at+Scale)|0| -|[AutoBuild: Automatic Community Building Labeling for Last-mile Delivery](https://doi.org/10.1145/3583780.3614658)|Zhiqing Hong, Dongjiang Cao, Haotian Wang, Guang Wang, Tian He, Desheng Zhang|Florida State University, Tallahassee, FL, USA; Rutgers University, New Brunswick, NJ, USA; JD Logistics, Beijing, China; JD Logistics & Rutgers University, New Brunswick, NJ, USA|Fine-grained community-building information, such as building names and accurate geographical coordinates, is critical for a range of practical applications like navigation and door-to-door services (e.g., on-demand delivery and last-mile delivery). A common practice of traditional methods to gather community-building information usually relies on manual collection, which is typically labor-intensive and time-consuming. To address these issues, we utilize the massive data generated from e-commerce delivery services and design a framework, AutoBuild, for fine-grained large-scale community-building labeling. AutoBuild consists of two main components: (i) a Location Candidate Detection Module that identifies potential building names and coordinates from multi-source delivery data, and (ii) a Progressive Building Matching Model that employs trajectory modeling, human behavior analysis, and heterogeneous graph alignment to match building names and coordinates. To evaluate the performance of AutoBuild, we applied it to two real-world multi-modal datasets from Beijing City and Chengdu City. The results reveal that AutoBuild significantly outperforms multiple baseline models by 50-meter accuracy of 81.8% and 100-meter accuracy of 95.9% in Beijing City. More importantly, we conduct a real-world case study to demonstrate the practical impact of AutoBuild in last-mile delivery.|细粒度的社区建设信息，如建筑物名称和准确的经纬度，对于一系列实际应用，如导航和门到门服务(如按需配送和最后一英里配送)至关重要。收集社区建设信息的传统方法通常依赖于人工收集，这通常是劳动密集型和耗时的。为了解决这些问题，我们利用电子商务交付服务产生的大量数据，并设计了一个框架 AutoBuild，用于细粒度的大规模社区建设标签。AutoBuild 由两个主要组成部分组成: (i)位置候选检测模块，从多源交付数据中识别潜在的建筑名称和坐标; (ii)渐进式建筑匹配模型，采用轨迹建模，人类行为分析和异质图形对齐来匹配建筑名称和坐标。为了评价 AutoBuild 的性能，我们将其应用于来自北京和成都两个真实世界的多模态数据集。结果表明，在北京市，AutoBuild 的50米准确率为81.8% ，100米准确率为95.9% ，明显优于多基线模型。更重要的是，我们进行了一个真实世界的案例研究，以演示 AutoBuild 在最后一英里交付中的实际影响。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=AutoBuild:+Automatic+Community+Building+Labeling+for+Last-mile+Delivery)|0| -|[Urban-scale POI Updating with Crowd Intelligence](https://doi.org/10.1145/3583780.3614724)|Zhiqing Hong, Haotian Wang, Wenjun Lyu, Hai Wang, Yunhuai Liu, Guang Wang, Tian He, Desheng Zhang|Southeast University, Nanjing, China; Florida State University, Tallahassee, FL, USA; Peking University, Beijing, China; JD Logistics, Beijing, China; Rutgers University, New Brunswick, NJ, USA|Points of Interest (POIs), such as entertainment, dining, and living, are crucial for urban planning and location-based services. However, the high dynamics and expensive updating costs of POIs pose a key roadblock for their urban applications. This is especially true for developing countries, where active economic activities lead to frequent POI updates (e.g., merchants closing down and new ones opening). Therefore, POI updating, i.e., detecting new POIs and different names of the same POIs (alias) to update the POI database, has become an urgent but challenging problem to address. In this paper, we attempt to answer the research question of how to detect and update large-scale POIs via a low-cost approach. To do so, we propose a novel framework called UrbanPOI, which formulates the POI updating problem as a tagging and detection problem based on multi-modal logistics delivery data. UrbanPOI consists of two key modules: (i) a hierarchical POI candidate generation module based on the POINet model that detects POIs from shipping addresses; and (ii) a new POI detection module based on the Siamese Attention Network that models multi-modal data and crowd intelligence. We evaluate our framework on real-world logistics delivery datasets from two Chinese cities. Extensive results show that our model outperforms state-of-the-art models in Beijing City by 26.2% in precision and 10.7% in F1-score, respectively.|兴趣点(POI) ，如娱乐、餐饮和生活，对于城市规划和基于位置的服务至关重要。然而，POI 的高动态性和昂贵的更新成本对其城市应用构成了一个关键的障碍。发展中国家尤其如此，在这些国家，活跃的经济活动导致频繁的 POI 更新(例如，商家关闭和新的开业)。因此，检测新的 POI 和同一 POI (别名)的不同名称以更新 POI 数据库，已成为一个迫切但具有挑战性的问题。本文试图回答如何利用低成本方法检测和更新大规模 POI 的研究问题。为此，我们提出了一个名为 UrbanPOI 的新框架，该框架将 POI 更新问题表述为一个基于多模式物流配送数据的标记和检测问题。UrbanPOI 由两个关键模块组成: (i)基于 POINet 模型的分层 POI 候选生成模块，该模块从运输地址中检测 POI; (ii)基于暹罗注意网络的新 POI 检测模块，该模块对多模态数据和人群智能进行建模。我们评估了我们的框架在现实世界的物流配送数据集从两个中国城市。大量的实验结果表明，该模型在精度上优于北京市最先进的模型26.2% ，在 F1评分上优于北京市最先进的模型10.7% 。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Urban-scale+POI+Updating+with+Crowd+Intelligence)|0| -|[Climate Intervention Analysis using AI Model Guided by Statistical Physics Principles](https://doi.org/10.1145/3583780.3615460)|Soo Kyung Kim, Kalai Ramea, Salva Rühling Cachay, Haruki Hirasawa, Subhashis Hazarika, Dipti Hingmire, Peetak Mitra, Philip J. Rasch, Hansi A. Singh|Palo Alto Research Center, Palo Alto , CA, USA; University of Victoria, Victoria, BC, Canada; Excarta, San Francisco, CA, USA; University of California, San Diego, La Jolla, CA, USA; Pacific Northwest National Laboratory, Richland, WA, USA; Palo Alto Research Center, Palo Alto, CA, USA|The availability of training data remains a significant obstacle for the implementation of machine learning in scientific applications. In particular, estimating how a system might respond to external forcings or perturbations requires specialized labeled data or targeted simulations, which may be computationally intensive to generate at scale. In this study, we propose a novel solution to this challenge by utilizing a principle from statistical physics known as the Fluctuation-Dissipation Theorem (FDT) to discover knowledge using an AI model that can rapidly produce scenarios for different external forcings. By leveraging FDT, we are able to extract information encoded in a large dataset produced by Earth System Models, which includes 8250 years of internal climate fluctuations, to estimate the climate system's response to forcings. Our model, AiBEDO, is capable of capturing the complex, multi-timescale effects of radiation perturbations on global and regional surface climate, allowing for a substantial acceleration of the exploration of the impacts of spatially-heterogenous climate forcers. To demonstrate the utility of AiBEDO, we use the example of a climate intervention technique called Marine Cloud Brightening, with the ultimate goal of optimizing the spatial pattern of cloud brightening to achieve regional climate targets and prevent known climate tipping points. While we showcase the effectiveness of our approach in the context of climate science, it is generally applicable to other scientific disciplines that are limited by the extensive computational demands of domain simulation models. Source code of AiBEDO framework is made available at https://github.com/kramea/kdd_aibedo. A sample dataset is made available at https://doi.org/10.5281/zenodo.7597027. Additional data available upon request.|训练数据的可用性仍然是在科学应用中实施机器学习的一个重大障碍。特别是，估计一个系统可能如何响应外部强迫或扰动需要专门的标记数据或有针对性的模拟，这可能是计算密集型产生的规模。在这项研究中，我们提出了一个新颖的解决方案，通过利用统计物理学中的一个原理——涨落耗散定理(FDT) ，使用人工智能模型来发现知识，该模型可以快速地为不同的外部压力生成场景。通过利用 FDT，我们能够提取由地球系统模型产生的大型数据集中编码的信息，其中包括8250年的内部气候波动，以估计气候系统对外力的反应。我们的模型 AiBEDO 能够捕捉辐射扰动对全球和区域表面气候的复杂的、多时间尺度的影响，允许大幅度加速探索空间异质性气候力量的影响。为了证明 AiBEDO 的实用性，我们使用了一个名为海洋云亮化的气候干预技术的例子，其最终目标是优化云亮化的空间模式，以实现区域气候目标，并防止已知的气候临界点。虽然我们在气候科学的背景下展示了我们的方法的有效性，但它通常适用于其他受到领域模拟模型广泛的计算需求限制的科学分支。AiBEDO 框架的源代码可在 https://github.com/kramea/kdd_aibedo 查阅。Https://doi.org/10.5281/zenodo.7597027提供样本数据集。可根据要求提供其他数据。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Climate+Intervention+Analysis+using+AI+Model+Guided+by+Statistical+Physics+Principles)|0| -|[A Hierarchical Imitation Learning-based Decision Framework for Autonomous Driving](https://doi.org/10.1145/3583780.3615454)|Hebin Liang, Zibin Dong, Yi Ma, Xiaotian Hao, Yan Zheng, Jianye Hao|Tianjin University, Tianjin, China; Harbin Institution of Technology (Shenzhen), Shenzhen, China|In this paper, we focus on the decision-making challenge in autonomous driving, a central and intricate problem influencing the safety and practicality of autonomous vehicles. We propose an innovative hierarchical imitation learning framework that effectively alleviates the complexity of learning in autonomous driving decision-making problems by decoupling decision-making tasks into sub-problems. Specifically, the decision-making process is divided into two levels of sub-problems: the upper level directs the vehicle's lane selection and qualitative speed management, while the lower level implements precise control of the driving speed and direction. We harness Transformer-based models for solving each sub-problem, enabling overall hierarchical framework to comprehend and navigate diverse and various road conditions, ultimately resulting in improved decision-making. Through an evaluation in several typical driving scenarios within the SMARTS autonomous driving simulation environment, our proposed hierarchical decision-making framework significantly outperforms end-to-end reinforcement learning algorithms and behavior cloning algorithm, achieving an average pass rate of over 90%. Our framework's effectiveness is substantiated by its commendable achievements at the NeurIPS 2022 Driving SMARTS competition, where it secures dual track championships.|本文主要研究影响自主驾驶汽车安全性和实用性的核心问题——自主驾驶决策挑战。提出了一种创新的层次模拟学习框架，通过将决策任务解耦为子问题，有效地降低了自主驾驶决策问题学习的复杂性。具体来说，决策过程分为两个层次的子问题: 上层指导车辆的车道选择和定性速度管理，下层实现对行驶速度和方向的精确控制。我们利用变压器为基础的模型来解决每个子问题，使整体层次框架理解和导航不同的和各种各样的道路条件，最终导致改进的决策。通过对 SMARTS 自主驾驶模拟环境中几个典型驾驶场景的评估，我们提出的分层决策框架显著优于端到端强化学习算法和行为克隆算法，平均合格率超过90% 。我们的框架的有效性是由其值得赞扬的成就在 NeurIPS 2022驾驶 SMARTS 竞赛，其中它确保双轨锦标赛。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Hierarchical+Imitation+Learning-based+Decision+Framework+for+Autonomous+Driving)|0| +|[Content-Based Email Classification at Scale](https://doi.org/10.1145/3583780.3615462)|Kirstin Early, Neil O'Hare, Christopher C. LuVogt|Yahoo Research, Mountain View, CA, USA; Yahoo Research, San Francisco, CA, USA|Understanding the content of email messages can enable new features that highlight what matters to users, making email a more useful tool for people to manage their lives. We present work from a consumer email platform to build multilabel models to classify messages according to a mail-specific, content-based taxonomy that represents the topic, type, and objective of an email. While state-of-the-art Transformer-based language models can achieve impressive results for text classification, these models are too costly to deploy at the scale of email. Using a knowledge distillation framework, we first build a complex, accurate teacher model from limited human-labeled training data and then use a large amount of teacher-labeled data to train lightweight student models that are suitable for deployment. The student models retain up to 91% of the predictive performance of the teacher model while reducing inference cost by three orders of magnitude. Deployed to production in Yahoo Mail, these models classify billions of emails every day and power features that help people tackle their inboxes.|了解电子邮件的内容可以使新的功能，突出什么对用户重要，使电子邮件成为一个更有用的工具，人们管理他们的生活。我们从一个消费者电子邮件平台开始构建多标签模型，以根据邮件特定的、基于内容的分类法对邮件进行分类，该分类法代表电子邮件的主题、类型和目标。虽然最先进的基于 Transformer 的语言模型可以在文本分类方面取得令人印象深刻的结果，但是这些模型的开销太大，无法在电子邮件规模上进行部署。使用知识提取框架，我们首先从有限的人类标记的训练数据中建立一个复杂的、精确的教师模型，然后使用大量的教师标记的数据来训练适合部署的轻量级学生模型。学生模型保留了教师模型91% 的预测性能，同时减少了三个数量级的推理成本。这些模型被部署到雅虎邮箱，每天为数十亿封电子邮件分类，并提供强大的功能帮助人们处理他们的收件箱。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Content-Based+Email+Classification+at+Scale)|0| +|[AutoBuild: Automatic Community Building Labeling for Last-mile Delivery](https://doi.org/10.1145/3583780.3614658)|Zhiqing Hong, Dongjiang Cao, Haotian Wang, Guang Wang, Tian He, Desheng Zhang|JD Logistics, Beijing, China; Rutgers University, New Brunswick, NJ, USA; JD Logistics & Rutgers University, New Brunswick, NJ, USA; Florida State University, Tallahassee, FL, USA|Fine-grained community-building information, such as building names and accurate geographical coordinates, is critical for a range of practical applications like navigation and door-to-door services (e.g., on-demand delivery and last-mile delivery). A common practice of traditional methods to gather community-building information usually relies on manual collection, which is typically labor-intensive and time-consuming. To address these issues, we utilize the massive data generated from e-commerce delivery services and design a framework, AutoBuild, for fine-grained large-scale community-building labeling. AutoBuild consists of two main components: (i) a Location Candidate Detection Module that identifies potential building names and coordinates from multi-source delivery data, and (ii) a Progressive Building Matching Model that employs trajectory modeling, human behavior analysis, and heterogeneous graph alignment to match building names and coordinates. To evaluate the performance of AutoBuild, we applied it to two real-world multi-modal datasets from Beijing City and Chengdu City. The results reveal that AutoBuild significantly outperforms multiple baseline models by 50-meter accuracy of 81.8% and 100-meter accuracy of 95.9% in Beijing City. More importantly, we conduct a real-world case study to demonstrate the practical impact of AutoBuild in last-mile delivery.|细粒度的社区建设信息，如建筑物名称和准确的经纬度，对于一系列实际应用，如导航和门到门服务(如按需配送和最后一英里配送)至关重要。收集社区建设信息的传统方法通常依赖于人工收集，这通常是劳动密集型和耗时的。为了解决这些问题，我们利用电子商务交付服务产生的大量数据，并设计了一个框架 AutoBuild，用于细粒度的大规模社区建设标签。AutoBuild 由两个主要组成部分组成: (i)位置候选检测模块，从多源交付数据中识别潜在的建筑名称和坐标; (ii)渐进式建筑匹配模型，采用轨迹建模，人类行为分析和异质图形对齐来匹配建筑名称和坐标。为了评价 AutoBuild 的性能，我们将其应用于来自北京和成都两个真实世界的多模态数据集。结果表明，在北京市，AutoBuild 的50米准确率为81.8% ，100米准确率为95.9% ，明显优于多基线模型。更重要的是，我们进行了一个真实世界的案例研究，以演示 AutoBuild 在最后一英里交付中的实际影响。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=AutoBuild:+Automatic+Community+Building+Labeling+for+Last-mile+Delivery)|0| +|[Urban-scale POI Updating with Crowd Intelligence](https://doi.org/10.1145/3583780.3614724)|Zhiqing Hong, Haotian Wang, Wenjun Lyu, Hai Wang, Yunhuai Liu, Guang Wang, Tian He, Desheng Zhang|Southeast University, Nanjing, China; Peking University, Beijing, China; Florida State University, Tallahassee, FL, USA; Rutgers University, New Brunswick, NJ, USA; JD Logistics, Beijing, China|Points of Interest (POIs), such as entertainment, dining, and living, are crucial for urban planning and location-based services. However, the high dynamics and expensive updating costs of POIs pose a key roadblock for their urban applications. This is especially true for developing countries, where active economic activities lead to frequent POI updates (e.g., merchants closing down and new ones opening). Therefore, POI updating, i.e., detecting new POIs and different names of the same POIs (alias) to update the POI database, has become an urgent but challenging problem to address. In this paper, we attempt to answer the research question of how to detect and update large-scale POIs via a low-cost approach. To do so, we propose a novel framework called UrbanPOI, which formulates the POI updating problem as a tagging and detection problem based on multi-modal logistics delivery data. UrbanPOI consists of two key modules: (i) a hierarchical POI candidate generation module based on the POINet model that detects POIs from shipping addresses; and (ii) a new POI detection module based on the Siamese Attention Network that models multi-modal data and crowd intelligence. We evaluate our framework on real-world logistics delivery datasets from two Chinese cities. Extensive results show that our model outperforms state-of-the-art models in Beijing City by 26.2% in precision and 10.7% in F1-score, respectively.|兴趣点(POI) ，如娱乐、餐饮和生活，对于城市规划和基于位置的服务至关重要。然而，POI 的高动态性和昂贵的更新成本对其城市应用构成了一个关键的障碍。发展中国家尤其如此，在这些国家，活跃的经济活动导致频繁的 POI 更新(例如，商家关闭和新的开业)。因此，检测新的 POI 和同一 POI (别名)的不同名称以更新 POI 数据库，已成为一个迫切但具有挑战性的问题。本文试图回答如何利用低成本方法检测和更新大规模 POI 的研究问题。为此，我们提出了一个名为 UrbanPOI 的新框架，该框架将 POI 更新问题表述为一个基于多模式物流配送数据的标记和检测问题。UrbanPOI 由两个关键模块组成: (i)基于 POINet 模型的分层 POI 候选生成模块，该模块从运输地址中检测 POI; (ii)基于暹罗注意网络的新 POI 检测模块，该模块对多模态数据和人群智能进行建模。我们评估了我们的框架在现实世界的物流配送数据集从两个中国城市。大量的实验结果表明，该模型在精度上优于北京市最先进的模型26.2% ，在 F1评分上优于北京市最先进的模型10.7% 。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Urban-scale+POI+Updating+with+Crowd+Intelligence)|0| +|[Climate Intervention Analysis using AI Model Guided by Statistical Physics Principles](https://doi.org/10.1145/3583780.3615460)|Soo Kyung Kim, Kalai Ramea, Salva Rühling Cachay, Haruki Hirasawa, Subhashis Hazarika, Dipti Hingmire, Peetak Mitra, Philip J. Rasch, Hansi A. Singh|University of California, San Diego, La Jolla, CA, USA; Excarta, San Francisco, CA, USA; Pacific Northwest National Laboratory, Richland, WA, USA; Palo Alto Research Center, Palo Alto , CA, USA; Palo Alto Research Center, Palo Alto, CA, USA; University of Victoria, Victoria, BC, Canada|The availability of training data remains a significant obstacle for the implementation of machine learning in scientific applications. In particular, estimating how a system might respond to external forcings or perturbations requires specialized labeled data or targeted simulations, which may be computationally intensive to generate at scale. In this study, we propose a novel solution to this challenge by utilizing a principle from statistical physics known as the Fluctuation-Dissipation Theorem (FDT) to discover knowledge using an AI model that can rapidly produce scenarios for different external forcings. By leveraging FDT, we are able to extract information encoded in a large dataset produced by Earth System Models, which includes 8250 years of internal climate fluctuations, to estimate the climate system's response to forcings. Our model, AiBEDO, is capable of capturing the complex, multi-timescale effects of radiation perturbations on global and regional surface climate, allowing for a substantial acceleration of the exploration of the impacts of spatially-heterogenous climate forcers. To demonstrate the utility of AiBEDO, we use the example of a climate intervention technique called Marine Cloud Brightening, with the ultimate goal of optimizing the spatial pattern of cloud brightening to achieve regional climate targets and prevent known climate tipping points. While we showcase the effectiveness of our approach in the context of climate science, it is generally applicable to other scientific disciplines that are limited by the extensive computational demands of domain simulation models. Source code of AiBEDO framework is made available at https://github.com/kramea/kdd_aibedo. A sample dataset is made available at https://doi.org/10.5281/zenodo.7597027. Additional data available upon request.|训练数据的可用性仍然是在科学应用中实施机器学习的一个重大障碍。特别是，估计一个系统可能如何响应外部强迫或扰动需要专门的标记数据或有针对性的模拟，这可能是计算密集型产生的规模。在这项研究中，我们提出了一个新颖的解决方案，通过利用统计物理学中的一个原理——涨落耗散定理(FDT) ，使用人工智能模型来发现知识，该模型可以快速地为不同的外部压力生成场景。通过利用 FDT，我们能够提取由地球系统模型产生的大型数据集中编码的信息，其中包括8250年的内部气候波动，以估计气候系统对外力的反应。我们的模型 AiBEDO 能够捕捉辐射扰动对全球和区域表面气候的复杂的、多时间尺度的影响，允许大幅度加速探索空间异质性气候力量的影响。为了证明 AiBEDO 的实用性，我们使用了一个名为海洋云亮化的气候干预技术的例子，其最终目标是优化云亮化的空间模式，以实现区域气候目标，并防止已知的气候临界点。虽然我们在气候科学的背景下展示了我们的方法的有效性，但它通常适用于其他受到领域模拟模型广泛的计算需求限制的科学分支。AiBEDO 框架的源代码可在 https://github.com/kramea/kdd_aibedo 查阅。Https://doi.org/10.5281/zenodo.7597027提供样本数据集。可根据要求提供其他数据。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Climate+Intervention+Analysis+using+AI+Model+Guided+by+Statistical+Physics+Principles)|0| +|[A Hierarchical Imitation Learning-based Decision Framework for Autonomous Driving](https://doi.org/10.1145/3583780.3615454)|Hebin Liang, Zibin Dong, Yi Ma, Xiaotian Hao, Yan Zheng, Jianye Hao|Harbin Institution of Technology (Shenzhen), Shenzhen, China; Tianjin University, Tianjin, China|In this paper, we focus on the decision-making challenge in autonomous driving, a central and intricate problem influencing the safety and practicality of autonomous vehicles. We propose an innovative hierarchical imitation learning framework that effectively alleviates the complexity of learning in autonomous driving decision-making problems by decoupling decision-making tasks into sub-problems. Specifically, the decision-making process is divided into two levels of sub-problems: the upper level directs the vehicle's lane selection and qualitative speed management, while the lower level implements precise control of the driving speed and direction. We harness Transformer-based models for solving each sub-problem, enabling overall hierarchical framework to comprehend and navigate diverse and various road conditions, ultimately resulting in improved decision-making. Through an evaluation in several typical driving scenarios within the SMARTS autonomous driving simulation environment, our proposed hierarchical decision-making framework significantly outperforms end-to-end reinforcement learning algorithms and behavior cloning algorithm, achieving an average pass rate of over 90%. Our framework's effectiveness is substantiated by its commendable achievements at the NeurIPS 2022 Driving SMARTS competition, where it secures dual track championships.|本文主要研究影响自主驾驶汽车安全性和实用性的核心问题——自主驾驶决策挑战。提出了一种创新的层次模拟学习框架，通过将决策任务解耦为子问题，有效地降低了自主驾驶决策问题学习的复杂性。具体来说，决策过程分为两个层次的子问题: 上层指导车辆的车道选择和定性速度管理，下层实现对行驶速度和方向的精确控制。我们利用变压器为基础的模型来解决每个子问题，使整体层次框架理解和导航不同的和各种各样的道路条件，最终导致改进的决策。通过对 SMARTS 自主驾驶模拟环境中几个典型驾驶场景的评估，我们提出的分层决策框架显著优于端到端强化学习算法和行为克隆算法，平均合格率超过90% 。我们的框架的有效性是由其值得赞扬的成就在 NeurIPS 2022驾驶 SMARTS 竞赛，其中它确保双轨锦标赛。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Hierarchical+Imitation+Learning-based+Decision+Framework+for+Autonomous+Driving)|0| |[Enhancing Dynamic On-demand Food Order Dispatching via Future-informed and Spatial-temporal Extended Decisions](https://doi.org/10.1145/3583780.3615473)|Yile Liang, Donghui Li, Jiuxia Zhao, Xuetao Ding, Huanjia Lian, Jinghua Hao, Renqing He|Meituan Inc., Beijing, China; Meituan, Beijing, China|On-demand food delivery (OFD) service has gained fast-growing popularity all around the world. Order dispatching is instrumental to large-scale OFD platforms, such as Meituan, which continuously match food order requests to couriers at a scale of tens of millions each day to satisfy the needs of consumers, couriers, and merchants. However, due to high dynamism and inevitable uncertainties in the real-world environment, it is not an easy task to achieve long-term global objective optimization through continuous isolated optimization decisions at each dispatch moment. Our work proposes the concept of "courier occupancy" (CO) to precisely quantify the impact of order assignment on the courier's delivery efficiency, realizing a decomposition of long-term and macro goals into various dispatch moments and micro decision-making dimensions. Then in the prediction phase, an improved and universally applicable distribution estimation method is designed to quantify CO which is a stochastic variable and contains future information, combining Monte Carlo dropout and knowledge distillation. In the optimization phase, we use CO to model the objective function at each dispatch moment to introduce future information and extend dispatch decisions from merely who to assign the order to both when and who to assign it, significantly enhancing the long-term optimization capability of dispatching decisions and avoiding local greed. We conduct extensive offline simulations based on real dispatching data as well as online AB tests through Meituan's platform. Results show that our method consistently improves the couriers' delivery efficiency and consumers' satisfaction.|按需送餐(OFD)服务在全世界迅速普及。订单调度对于大型的 OfD 平台来说非常重要，例如美团，它每天以数千万的规模不断地将食品订单请求与快递匹配，以满足消费者、快递员和商家的需求。然而，由于现实环境中的高动态性和不可避免的不确定性，通过每个调度时刻的连续孤立优化决策来实现长期的全局目标优化是一项不容易的任务。我们的工作提出了“快递员占用”(CO)的概念，以精确量化订单分配对快递员配送效率的影响，实现了长期和宏观目标分解为各种分配时刻和微观决策维度。然后在预测阶段，结合蒙特卡罗退出和知识提取，设计了一种改进的、普遍适用的分布估计方法，对含有未来信息的随机变量 CO 进行量化。在优化阶段，利用 CO 对每个调度时刻的目标函数进行建模，引入未来信息，并将调度决策从单纯的由谁分配指令扩展到何时分配和由谁分配，显著提高了调度决策的长期优化能力，避免了局部贪婪。我们基于真实的调度数据进行大量的离线模拟，并通过美团平台进行在线 AB 测试。结果表明，该方法一致地提高了快递员的配送效率和消费者的满意度。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Enhancing+Dynamic+On-demand+Food+Order+Dispatching+via+Future-informed+and+Spatial-temporal+Extended+Decisions)|0| |[FAF: A Risk Detection Framework on Industry-Scale Graphs](https://doi.org/10.1145/3583780.3615477)|Yice Luo, Guannan Wang, Yongchao Liu, Jiaxin Yue, Weihong Cheng, Binjie Fei|Ant Group, Hangzhou, China|It is neither effective nor profitable for individuals to attempt to bypass Ant Group's comprehensive risk control system. However, there are still criminals (such as underground loan sharks and professional hackers,), who may teach borrowers how to use hacking techniques to circumvent Ant Group and its partners' risk control system. Despite the fact that our risk control system has greatly reduced fraud losses for merchants in practice, a significant number of intermediary-related frauds still occur. During our investigation into fraud events at Zhima Credit Renting (ZCR), we discovered that more than 30 percent of fraud cases were directly linked to malicious intermediaries. To address this issue, we propose an anti-abettor fraud detection framework, called FAF (Fraud-Abettor-Fraud), specifically designed to combat intermediary-related frauds on an industry-wide scale. We have developed a series of algorithms under the FAF framework, which outperform commonly used risk detection methods and meet real-world business requirements. In this paper, we use ZCR's risk management application as a real-world example-which has deployed FAF for over 1 year-to demonstrate the superiority of the FAF framework compared to existing methods.|对个人而言，试图绕开蚂蚁集团的全面风险控制体系既无效果，也无利可图。然而，仍然有犯罪分子(如地下高利贷者和专业黑客) ，他们可能会教导借款人如何使用黑客技术来规避蚂蚁金服及其合作伙伴的风险控制系统。尽管我国的风险控制体系在实践中大大减少了商家的欺诈损失，但仍然存在大量与中介相关的欺诈行为。在我们对芝马信用租赁(ZCR)欺诈事件的调查中，我们发现超过30% 的欺诈案件直接与恶意中介有关。为了解决这一问题，我们提出了一个名为 FAF (Fraud-Abettor-Fraud)的反教唆欺诈侦查框架，专门用于在整个行业范围内打击与中介相关的欺诈行为。我们在 FAF 框架下开发了一系列算法，它们的性能优于常用的风险检测方法，并且满足了现实世界的业务需求。本文以 ZCR 的风险管理应用程序为实例，论证了 FAF 框架相对于现有方法的优越性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=FAF:+A+Risk+Detection+Framework+on+Industry-Scale+Graphs)|0| -|[Retention is All You Need](https://doi.org/10.1145/3583780.3615497)|Karishma Mohiuddin, Mirza Ariful Alam, Mirza Mohtashim Alam, Pascal Welke, Michael Martin, Jens Lehmann, Sahar Vahdati|FIZ Karlsruhe, Karlsruhe, Germany; InfAI, Dresden, Germany; TU Dresden, InfAI, & Amazon, Dresden, Germany; University of Bonn, Bonn, Germany; University of Rajshahi, Rajshahi, Bangladesh; TU Wien, Vienna, Austria; InfAI, Leipzig, Germany|Skilled employees are usually seen as the most important pillar of an organization. Despite this, most organizations face high attrition and turnover rates. While several machine learning models have been developed for analyzing attrition and its causal factors, the interpretations of those models remain opaque. In this paper, we propose the HR-DSS approach, which stands for Human Resource Decision Support System, and uses explainable AI for employee attrition problems. The system is designed to assist human resource departments in interpreting the predictions provided by machine learning models. In our experiments, eight machine learning models are employed to provide predictions, and the results achieved by the best-performing model are further processed by the SHAP explainability process. We optimize both the correctness and explanation of the results. Furthermore, using "What-if-analysis", we aim to observe plausible causes for attrition of an individual employee. The results show that by adjusting the specific dominant features of each individual, employee attrition can turn into employee retention through informative business decisions. Reducing attrition is not only a problem for any specific organization but also, in some countries, becomes a significant societal problem that impacts the well-being of both employers and employees.|有技能的员工通常被视为一个组织最重要的支柱。尽管如此，大多数组织都面临着高流失率和更替率。虽然一些机器学习模型已经开发了分析磨损及其原因的因素，这些模型的解释仍然是不透明的。本文提出了人力资源决策支持系统的人力资源决策支持系统(HR-DSS)方法，并将可解释人工智能应用于员工流失问题。该系统旨在帮助人力资源部门解释机器学习模型提供的预测。在我们的实验中，我们使用了八个机器学习模型来提供预测，并且使用 SHAP 可解释性过程来进一步处理表现最佳的模型所得到的结果。我们优化了结果的正确性和解释性。此外，使用“如果-如果-分析”，我们的目的是观察合理的原因，个别员工的磨损。研究结果表明，通过调整个体的显性特征，员工流失可以通过信息化的商业决策转化为员工留住。减少自然减员不仅是任何特定组织的问题，而且在一些国家成为影响雇主和雇员福祉的重大社会问题。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Retention+is+All+You+Need)|0| -|[GraphFC: Customs Fraud Detection with Label Scarcity](https://doi.org/10.1145/3583780.3614690)|Karandeep Singh, YuChe Tsai, ChengTe Li, Meeyoung Cha, ShouDe Lin|National Taiwan University, Taipei, Taiwan Roc; Institute for Basic Science & KAIST, Daejeon, Republic of Korea; National Cheng Kung University, Tainan, Taiwan Roc; Institute for Basic Science, Daejeon, Republic of Korea|Custom officials across the world encounter huge volumes of transactions. With increased connectivity and globalization, the customs transactions continue to grow every year. Associated with customs transactions is the customs fraud - the intentional manipulation of goods declarations to avoid the taxes and duties. With limited manpower, the custom offices can only undertake manual inspection of a limited number of declarations. This necessitates the need for automating the customs fraud detection by machine learning (ML) techniques. Due the limited manual inspection for labeling the new-incoming declarations, the ML approach should have robust performance subject to the scarcity of labeled data. However, current approaches for customs fraud detection are not well suited and designed for this real-world setting. In this work, we propose $\textbf{GraphFC}$ ($\textbf{Graph}$ neural networks for $\textbf{C}$ustoms $\textbf{F}$raud), a model-agnostic, domain-specific, semi-supervised graph neural network based customs fraud detection algorithm that has strong semi-supervised and inductive capabilities. With upto 252% relative increase in recall over the present state-of-the-art, extensive experimentation on real customs data from customs administrations of three different countries demonstrate that GraphFC consistently outperforms various baselines and the present state-of-art by a large margin.|世界各地的海关官员都会遇到大量的交易。随着互联互通和全球化程度的提高，海关交易每年都在增长。与海关交易有关的是海关欺诈——蓄意操纵货物申报以逃避税收和关税。在人力有限的情况下，海关只能对有限数量的报关单进行人工检查。这就需要通过机器学习(ML)技术实现海关欺诈检测的自动化。由于对新传入声明进行标记的人工检查有限，因此在标记数据稀缺的情况下，机器学习方法应该具有健壮的性能。然而，目前的海关欺诈检测方法并不十分适合这种现实环境。在这项工作中，我们提出了 $textbf { GraphFC } $($textbf { Graph } $神经网络为 $textbf { C } $ustoms $textbf { F } $raud) ，一个模型无关的，领域特定的，基于半监督图神经网络的海关欺诈检测算法，具有强大的半监督和归纳能力。与目前的最新技术水平相比，召回率相对增加了252% ，三个不同国家海关当局对真实海关数据的广泛实验表明，GraphFC 的表现始终大大优于各种基准和目前的最新技术水平。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=GraphFC:+Customs+Fraud+Detection+with+Label+Scarcity)|0| +|[Retention is All You Need](https://doi.org/10.1145/3583780.3615497)|Karishma Mohiuddin, Mirza Ariful Alam, Mirza Mohtashim Alam, Pascal Welke, Michael Martin, Jens Lehmann, Sahar Vahdati|InfAI, Leipzig, Germany; TU Wien, Vienna, Austria; University of Bonn, Bonn, Germany; FIZ Karlsruhe, Karlsruhe, Germany; TU Dresden, InfAI, & Amazon, Dresden, Germany; University of Rajshahi, Rajshahi, Bangladesh; InfAI, Dresden, Germany|Skilled employees are usually seen as the most important pillar of an organization. Despite this, most organizations face high attrition and turnover rates. While several machine learning models have been developed for analyzing attrition and its causal factors, the interpretations of those models remain opaque. In this paper, we propose the HR-DSS approach, which stands for Human Resource Decision Support System, and uses explainable AI for employee attrition problems. The system is designed to assist human resource departments in interpreting the predictions provided by machine learning models. In our experiments, eight machine learning models are employed to provide predictions, and the results achieved by the best-performing model are further processed by the SHAP explainability process. We optimize both the correctness and explanation of the results. Furthermore, using "What-if-analysis", we aim to observe plausible causes for attrition of an individual employee. The results show that by adjusting the specific dominant features of each individual, employee attrition can turn into employee retention through informative business decisions. Reducing attrition is not only a problem for any specific organization but also, in some countries, becomes a significant societal problem that impacts the well-being of both employers and employees.|有技能的员工通常被视为一个组织最重要的支柱。尽管如此，大多数组织都面临着高流失率和更替率。虽然一些机器学习模型已经开发了分析磨损及其原因的因素，这些模型的解释仍然是不透明的。本文提出了人力资源决策支持系统的人力资源决策支持系统(HR-DSS)方法，并将可解释人工智能应用于员工流失问题。该系统旨在帮助人力资源部门解释机器学习模型提供的预测。在我们的实验中，我们使用了八个机器学习模型来提供预测，并且使用 SHAP 可解释性过程来进一步处理表现最佳的模型所得到的结果。我们优化了结果的正确性和解释性。此外，使用“如果-如果-分析”，我们的目的是观察合理的原因，个别员工的磨损。研究结果表明，通过调整个体的显性特征，员工流失可以通过信息化的商业决策转化为员工留住。减少自然减员不仅是任何特定组织的问题，而且在一些国家成为影响雇主和雇员福祉的重大社会问题。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Retention+is+All+You+Need)|0| +|[GraphFC: Customs Fraud Detection with Label Scarcity](https://doi.org/10.1145/3583780.3614690)|Karandeep Singh, YuChe Tsai, ChengTe Li, Meeyoung Cha, ShouDe Lin|Institute for Basic Science, Daejeon, Republic of Korea; National Cheng Kung University, Tainan, Taiwan Roc; Institute for Basic Science & KAIST, Daejeon, Republic of Korea; National Taiwan University, Taipei, Taiwan Roc|Custom officials across the world encounter huge volumes of transactions. With increased connectivity and globalization, the customs transactions continue to grow every year. Associated with customs transactions is the customs fraud - the intentional manipulation of goods declarations to avoid the taxes and duties. With limited manpower, the custom offices can only undertake manual inspection of a limited number of declarations. This necessitates the need for automating the customs fraud detection by machine learning (ML) techniques. Due the limited manual inspection for labeling the new-incoming declarations, the ML approach should have robust performance subject to the scarcity of labeled data. However, current approaches for customs fraud detection are not well suited and designed for this real-world setting. In this work, we propose $\textbf{GraphFC}$ ($\textbf{Graph}$ neural networks for $\textbf{C}$ustoms $\textbf{F}$raud), a model-agnostic, domain-specific, semi-supervised graph neural network based customs fraud detection algorithm that has strong semi-supervised and inductive capabilities. With upto 252% relative increase in recall over the present state-of-the-art, extensive experimentation on real customs data from customs administrations of three different countries demonstrate that GraphFC consistently outperforms various baselines and the present state-of-art by a large margin.|世界各地的海关官员都会遇到大量的交易。随着互联互通和全球化程度的提高，海关交易每年都在增长。与海关交易有关的是海关欺诈——蓄意操纵货物申报以逃避税收和关税。在人力有限的情况下，海关只能对有限数量的报关单进行人工检查。这就需要通过机器学习(ML)技术实现海关欺诈检测的自动化。由于对新传入声明进行标记的人工检查有限，因此在标记数据稀缺的情况下，机器学习方法应该具有健壮的性能。然而，目前的海关欺诈检测方法并不十分适合这种现实环境。在这项工作中，我们提出了 $textbf { GraphFC } $($textbf { Graph } $神经网络为 $textbf { C } $ustoms $textbf { F } $raud) ，一个模型无关的，领域特定的，基于半监督图神经网络的海关欺诈检测算法，具有强大的半监督和归纳能力。与目前的最新技术水平相比，召回率相对增加了252% ，三个不同国家海关当局对真实海关数据的广泛实验表明，GraphFC 的表现始终大大优于各种基准和目前的最新技术水平。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=GraphFC:+Customs+Fraud+Detection+with+Label+Scarcity)|0| |[Generating Optimized Molecules without Patent Infringement](https://doi.org/10.1145/3583780.3615479)|Sally Turutov, Kira Radinsky|Technion - Israel Institute of Technology, Haifa, Israel|Molecular optimization seeks to improve a given molecule's therapeutic profile. It is a key challenge in drug development, but it is difficult due to the constraints of molecular similarity to the original molecule and the size of the chemical space to explore. Numerous works tackled this problem with initial success. Unlike previous works that focus on generating molecules that optimize chemical properties, we focus on the optimization while attempting to "move away" from patented molecules. We present a novel loss function and its utilization in numerous types of molecular optimization algorithms. The loss allows to improve molecular properties while decreasing patent-infringement. We perform empirical evaluation showing superior performance of state-of-the-art models when using the novel loss function. The deployment of the system is underway at the Targeted Drug Delivery and Personalized Medicine labs. It will be utilized to generate targeted carriers of mRNA, providing a new method of drug delivery. The system is also producing non-patented candidates for industrial use, making it a valuable tool in the field of personalized medicine.|分子优化试图改善给定分子的治疗特性。这是药物开发中的一个关键挑战，但由于与原始分子的分子相似性和化学空间大小的限制而难以探索。许多著作初步成功地解决了这个问题。不像以前的工作，重点是生成分子，优化化学性质，我们关注的优化，同时试图“远离”专利分子。我们提出了一种新的损失函数及其在许多类型的分子优化算法中的应用。这种损失可以在减少专利侵权的同时提高分子性质。我们进行的实证评估表明，当使用新的损失函数时，最先进的模型的优越性能。该系统正在靶向药物输送和个体化医学实验室进行部署。它将被用于产生靶向的 mRNA 载体，为药物传递提供一种新的方法。该系统还为工业用途生产非专利候选产品，使其成为个体化医学领域的一个有价值的工具。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Generating+Optimized+Molecules+without+Patent+Infringement)|0| -|[Voucher Abuse Detection with Prompt-based Fine-tuning on Graph Neural Networks](https://doi.org/10.1145/3583780.3615505)|Zhihao Wen, Yuan Fang, Yihan Liu, Yang Guo, Shuji Hao|Lazada Inc., Singapore, Singapore; Singapore Management University, Singapore, Singapore|Voucher abuse detection is an important anomaly detection problem in E-commerce. While many GNN-based solutions have emerged, the supervised paradigm depends on a large quantity of labeled data. A popular alternative is to adopt self-supervised pre-training using label-free data, and further fine-tune on a downstream task with limited labels. Nevertheless, the "pre-train, fine-tune" paradigm is often plagued by the objective gap between pre-training and downstream tasks. Hence, we propose VPGNN, a prompt-based fine-tuning framework on GNNs for voucher abuse detection. We design a novel graph prompting function to reformulate the downstream task into a similar template as the pretext task in pre-training, thereby narrowing the objective gap. Extensive experiments on both proprietary and public datasets demonstrate the strength of VPGNN in both few-shot and semi-supervised scenarios. Moreover, an online deployment of VPGNN in a production environment shows a 23.4% improvement over two existing deployed models.|凭单滥用侦测是电子商务中一个重要的异常检测问题。虽然许多基于 GNN 的解决方案已经出现，监督范式依赖于大量的标记数据。一个流行的替代方案是采用自我监督的预先训练，使用无标签数据，并进一步微调下游任务与有限的标签。尽管如此，“预训练，微调”范式经常被预训练和下游任务之间的客观差距所困扰。因此，我们提出了 VPGNN，一个基于提示的微调架构 GNN，用于票据滥用检测。我们设计了一个新颖的图形提示函数，将下游任务重新表述为一个类似的模板，作为预训练中的借口任务，从而缩小了目标差距。在专有和公开数据集上的大量实验证明了 VPGNN 在少镜头和半监督场景中的强度。此外，在生产环境中在线部署 VPGNN 比现有的两个部署模型提高了23.4% 。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Voucher+Abuse+Detection+with+Prompt-based+Fine-tuning+on+Graph+Neural+Networks)|0| +|[Voucher Abuse Detection with Prompt-based Fine-tuning on Graph Neural Networks](https://doi.org/10.1145/3583780.3615505)|Zhihao Wen, Yuan Fang, Yihan Liu, Yang Guo, Shuji Hao|Singapore Management University, Singapore, Singapore; Lazada Inc., Singapore, Singapore|Voucher abuse detection is an important anomaly detection problem in E-commerce. While many GNN-based solutions have emerged, the supervised paradigm depends on a large quantity of labeled data. A popular alternative is to adopt self-supervised pre-training using label-free data, and further fine-tune on a downstream task with limited labels. Nevertheless, the "pre-train, fine-tune" paradigm is often plagued by the objective gap between pre-training and downstream tasks. Hence, we propose VPGNN, a prompt-based fine-tuning framework on GNNs for voucher abuse detection. We design a novel graph prompting function to reformulate the downstream task into a similar template as the pretext task in pre-training, thereby narrowing the objective gap. Extensive experiments on both proprietary and public datasets demonstrate the strength of VPGNN in both few-shot and semi-supervised scenarios. Moreover, an online deployment of VPGNN in a production environment shows a 23.4% improvement over two existing deployed models.|凭单滥用侦测是电子商务中一个重要的异常检测问题。虽然许多基于 GNN 的解决方案已经出现，监督范式依赖于大量的标记数据。一个流行的替代方案是采用自我监督的预先训练，使用无标签数据，并进一步微调下游任务与有限的标签。尽管如此，“预训练，微调”范式经常被预训练和下游任务之间的客观差距所困扰。因此，我们提出了 VPGNN，一个基于提示的微调架构 GNN，用于票据滥用检测。我们设计了一个新颖的图形提示函数，将下游任务重新表述为一个类似的模板，作为预训练中的借口任务，从而缩小了目标差距。在专有和公开数据集上的大量实验证明了 VPGNN 在少镜头和半监督场景中的强度。此外，在生产环境中在线部署 VPGNN 比现有的两个部署模型提高了23.4% 。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Voucher+Abuse+Detection+with+Prompt-based+Fine-tuning+on+Graph+Neural+Networks)|0| |[Logistics Audience Expansion via Temporal Knowledge Graph](https://doi.org/10.1145/3583780.3614695)|Hua Yan, Yingqiang Ge, Haotian Wang, Desheng Zhang, Yu Yang|Lehigh University, Bethlehem, PA, USA; JD Logistics, Beijing, China; Rutgers University, Piscataway, NJ, USA|Logistics audience expansion, the process for logistics companies to find potential long-term customers, is one of the most important tasks for business growth. However, existing methods for conventional audience expansion fall short due to two significant challenges, the intricate interplay of multiple complex factors in the logistics scenario and the emphasis on long-term logistics service usage instead of one-time promotions. To address the above limitations, we design LOGAE-TKG, a logistics audience expansion method based on a temporal knowledge graph, which consists of three components: (i) a temporal logistics knowledge graph pre-trained model to model the effect of multiple complex factors and build a solid logistics knowledge base for contracting and usage prediction; (ii) an intention learning model with data augmentation-based comparison to capture the contracting intention; (iii) a future pattern discovery model to uncover post-contract patterns. We evaluate and deploy our method on the JingDong e-commerce platform. Extensive offline experiment results and real-world deployment results demonstrate the effectiveness of our method.|物流受众拓展是物流企业寻找潜在长期客户的过程，是企业发展的重要任务之一。然而，由于物流情景中多种复杂因素错综复杂的相互作用以及强调物流服务的长期使用而不是一次性促销这两个重大挑战，现有的传统扩大受众的方法存在不足。针对上述局限性，我们设计了一种基于时态知识图的物流受众扩展方法 LOGAE-TKG，该方法由三个部分组成: (1)一个时态物流知识图预训练模型，用于模拟多个复杂因素的影响，建立一个可靠的物流知识库，用于订约和使用预测; (2)一个基于数据增强的比较意向学习模型，用于捕获订约意向; (3)一个未来模式发现模型，用于揭示后订约模式。我们在京东电子商务平台上对我们的方法进行了评估和部署。大量的离线实验结果和实际部署结果证明了该方法的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Logistics+Audience+Expansion+via+Temporal+Knowledge+Graph)|0| -|[DeepSTA: A Spatial-Temporal Attention Network for Logistics Delivery Timely Rate Prediction in Anomaly Conditions](https://doi.org/10.1145/3583780.3614671)|Jinhui Yi, Huan Yan, Haotian Wang, Jian Yuan, Yong Li|Tsinghua University, Beijing, China; JD Logistics, Beijing, China; Tsinghua University & JD Logistics, Beijing, China|Prediction of couriers' delivery timely rates in advance is essential to the logistics industry, enabling companies to take preemptive measures to ensure the normal operation of delivery services. This becomes even more critical during anomaly conditions like the epidemic outbreak, during which couriers' delivery timely rate will decline markedly and fluctuates significantly. Existing studies pay less attention to the logistics scenario. Moreover, many works focusing on prediction tasks in anomaly scenarios fail to explicitly model abnormal events, e.g., treating external factors equally with other features, resulting in great information loss. Further, since some anomalous events occur infrequently, traditional data-driven methods perform poorly in these scenarios. To deal with them, we propose a deep spatial-temporal attention model, named DeepSTA. To be specific, to avoid information loss, we design an anomaly spatio-temporal learning module that employs a recurrent neural network to model incident information. Additionally, we utilize Node2vec to model correlations between road districts, and adopt graph neural networks and long short-term memory to capture the spatial-temporal dependencies of couriers. To tackle the issue of insufficient training data in abnormal circumstances, we propose an anomaly pattern attention module that adopts a memory network for couriers' anomaly feature patterns storage via attention mechanisms. The experiments on real-world logistics datasets during the COVID-19 outbreak in 2022 show the model outperforms the best baselines by 12.11% in MAE and 13.71% in MSE, demonstrating its superior performance over multiple competitive baselines.|提前预测快递员的送货及时率对物流业至关重要，使公司能够采取先发制人的措施，以确保送货服务的正常运作。在疫情爆发等异常情况下，这种情况变得更加严重，在这种情况下，送货员的及时送货率将显著下降并出现大幅波动。现有的研究对物流情景的关注较少。此外，许多侧重于异常场景中的预测任务的工作未能对异常事件进行明确的建模，例如，将外部因素与其他特征等同对待，造成了巨大的信息损失。此外，由于一些异常事件很少发生，传统的数据驱动方法在这些场景中表现不佳。为了解决这些问题，我们提出了一个深度时空注意模型 DeepSTA。具体来说，为了避免信息丢失，我们设计了一个异常时空学习模块，该模块使用一个递归神经网络来模拟事件信息。此外，我们利用 Node2vec 建立道路区域之间的相关性模型，并采用图形神经网络和长期短期记忆来捕捉信使的时空依赖性。针对异常情况下训练数据不足的问题，提出了一种异常模式注意模块，该模块采用记忆网络通过注意机制对信使的异常特征模式进行存储。在2022年2019冠状病毒疾病暴发期间对现实世界物流数据集的实验表明，该模型在 MAE 和 MSE 中的表现优于最佳基线12.11% 和13.71% ，表明其优于多个竞争基线。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=DeepSTA:+A+Spatial-Temporal+Attention+Network+for+Logistics+Delivery+Timely+Rate+Prediction+in+Anomaly+Conditions)|0| +|[DeepSTA: A Spatial-Temporal Attention Network for Logistics Delivery Timely Rate Prediction in Anomaly Conditions](https://doi.org/10.1145/3583780.3614671)|Jinhui Yi, Huan Yan, Haotian Wang, Jian Yuan, Yong Li|JD Logistics, Beijing, China; Tsinghua University, Beijing, China; Tsinghua University & JD Logistics, Beijing, China|Prediction of couriers' delivery timely rates in advance is essential to the logistics industry, enabling companies to take preemptive measures to ensure the normal operation of delivery services. This becomes even more critical during anomaly conditions like the epidemic outbreak, during which couriers' delivery timely rate will decline markedly and fluctuates significantly. Existing studies pay less attention to the logistics scenario. Moreover, many works focusing on prediction tasks in anomaly scenarios fail to explicitly model abnormal events, e.g., treating external factors equally with other features, resulting in great information loss. Further, since some anomalous events occur infrequently, traditional data-driven methods perform poorly in these scenarios. To deal with them, we propose a deep spatial-temporal attention model, named DeepSTA. To be specific, to avoid information loss, we design an anomaly spatio-temporal learning module that employs a recurrent neural network to model incident information. Additionally, we utilize Node2vec to model correlations between road districts, and adopt graph neural networks and long short-term memory to capture the spatial-temporal dependencies of couriers. To tackle the issue of insufficient training data in abnormal circumstances, we propose an anomaly pattern attention module that adopts a memory network for couriers' anomaly feature patterns storage via attention mechanisms. The experiments on real-world logistics datasets during the COVID-19 outbreak in 2022 show the model outperforms the best baselines by 12.11% in MAE and 13.71% in MSE, demonstrating its superior performance over multiple competitive baselines.|提前预测快递员的送货及时率对物流业至关重要，使公司能够采取先发制人的措施，以确保送货服务的正常运作。在疫情爆发等异常情况下，这种情况变得更加严重，在这种情况下，送货员的及时送货率将显著下降并出现大幅波动。现有的研究对物流情景的关注较少。此外，许多侧重于异常场景中的预测任务的工作未能对异常事件进行明确的建模，例如，将外部因素与其他特征等同对待，造成了巨大的信息损失。此外，由于一些异常事件很少发生，传统的数据驱动方法在这些场景中表现不佳。为了解决这些问题，我们提出了一个深度时空注意模型 DeepSTA。具体来说，为了避免信息丢失，我们设计了一个异常时空学习模块，该模块使用一个递归神经网络来模拟事件信息。此外，我们利用 Node2vec 建立道路区域之间的相关性模型，并采用图形神经网络和长期短期记忆来捕捉信使的时空依赖性。针对异常情况下训练数据不足的问题，提出了一种异常模式注意模块，该模块采用记忆网络通过注意机制对信使的异常特征模式进行存储。在2022年2019冠状病毒疾病暴发期间对现实世界物流数据集的实验表明，该模型在 MAE 和 MSE 中的表现优于最佳基线12.11% 和13.71% ，表明其优于多个竞争基线。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=DeepSTA:+A+Spatial-Temporal+Attention+Network+for+Logistics+Delivery+Timely+Rate+Prediction+in+Anomaly+Conditions)|0| |[Detecting Social Bot on the Fly using Contrastive Learning](https://doi.org/10.1145/3583780.3615468)|Ming Zhou, Dan Zhang, Yuandong Wang, YangliAo Geng, Jie Tang|Tsinghua University, Beijing, China|Social bot detection is becoming a task of wide concern in social security. All along, the development of social bot detection technology is hindered by the lack of high-quality annotated data. Besides, the rapid development of AI Generated Content (AIGC) technology is dramatically improving the creative ability of social bots. For example, the recently released ChatGPT [2] can fool the state-of-the-art AI-text-detection method with a probability of 74%, bringing a large challenge to content-based bot detection methods. To address the above drawbacks, we propose a Contrastive Learning-driven Social Bot Detection framework (CBD). The core of CBD is characterized by a two-stage model learning strategy: a contrastive pre-training stage to mine generalization patterns from massive unlabeled social graphs, followed by a semi-supervised fine-tuning stage to model task-specific knowledge latent in social graphs with a few annotations. The above strategy endows our model with promising detection performance under an extreme scarcity of labeled data. In terms of system architecture, we propose a smart feedback mechanism to further improve detection performance. Comprehensive experiments on a real bot detection dataset show that CBD consistently outperforms 10 state-of-the-art baselines by a large margin for few-shot bot detection using very little (5-shot) labeled data. CBD has been deployed online.|社会机器人检测正成为社会保障领域广泛关注的课题。一直以来，由于缺乏高质量的注释数据，阻碍了社交机器人检测技术的发展。此外，人工智能生成内容(AIGC)技术的快速发展也极大地提高了社交机器人的创造能力。例如，最近发布的 ChatGPT [2]可以以74% 的概率欺骗最先进的 AI 文本检测方法，给基于内容的机器人检测方法带来了巨大的挑战。针对上述缺点，我们提出了一个对比学习驱动的社会机器人检测框架(CBD)。CBD 的核心拥有属性是一个两阶段的模型学习策略: 一个对比性的预训练阶段，从大量未标记的社交图中挖掘概括模式，然后是一个半监督的微调阶段，用一些注释来模拟社交图中潜在的特定任务知识。上述策略使我们的模型在标记数据极度稀缺的情况下具有良好的检测性能。在系统结构方面，我们提出了一种智能反馈机制来进一步提高检测性能。在一个真实的机器人检测数据集上的综合实验表明，CBD 在使用非常少(5次)标记数据进行少镜头机器人检测方面始终优于10个最先进的基线。CBD 已经在线部署。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Detecting+Social+Bot+on+the+Fly+using+Contrastive+Learning)|0| -|[SNAKE Challenge: Sanitization Algorithms under Attack](https://doi.org/10.1145/3583780.3614754)|Tristan Allard, Louis Béziaud, Sébastien Gambs|Université du Québec à Montréal, Montréal, PQ, Canada; Univ Rennes, CNRS, IRISA, Rennes, France; Univ Rennes, CNRS, IRISA & Université du Québec à Montréal, Rennes, France|While there were already some privacy challenges organized in the domain of data sanitization, they have mainly focused on the defense side of the problem. To favor the organization of successful challenges focusing on attacks, we introduce the SNAKE framework that is designed to facilitate the organization of challenges dedicated to attacking existing data sanitization mechanisms. In particular, it enables to easily automate the redundant tasks that are inherent to any such challenge and exhibits the following salient features: genericity with respect to attacks, ease of use and extensibility. We propose to demonstrate the main features of the SNAKE framework through a specific instantiation focusing on membership inference attacks over differentially-private synthetic data generation schemes. This instance of the SNAKE framework is currently being used for supporting a challenge co-located with APVP 2023 (the French workshop on the protection of privacy).|虽然在数据清理领域已经存在一些隐私挑战，但它们主要集中在问题的防御方面。为了有利于组织针对攻击的成功挑战，我们引入了 SNAKE 框架，该框架旨在促进组织针对攻击现有数据消毒机制的挑战。特别是，它能够轻松地自动化任何此类挑战所固有的冗余任务，并显示出以下突出特性: 与攻击相关的通用性、易用性和可扩展性。我们建议通过一个特定的实例来说明 SNAKE 框架的主要特征，该实例着重于针对差分私有合成数据生成方案的成员推理攻击。SNAKE 框架的这个实例目前正在用于支持与 APVP 2023(法国隐私保护研讨会)共处的一个挑战。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=SNAKE+Challenge:+Sanitization+Algorithms+under+Attack)|0| +|[SNAKE Challenge: Sanitization Algorithms under Attack](https://doi.org/10.1145/3583780.3614754)|Tristan Allard, Louis Béziaud, Sébastien Gambs|Univ Rennes, CNRS, IRISA & Université du Québec à Montréal, Rennes, France; Université du Québec à Montréal, Montréal, PQ, Canada; Univ Rennes, CNRS, IRISA, Rennes, France|While there were already some privacy challenges organized in the domain of data sanitization, they have mainly focused on the defense side of the problem. To favor the organization of successful challenges focusing on attacks, we introduce the SNAKE framework that is designed to facilitate the organization of challenges dedicated to attacking existing data sanitization mechanisms. In particular, it enables to easily automate the redundant tasks that are inherent to any such challenge and exhibits the following salient features: genericity with respect to attacks, ease of use and extensibility. We propose to demonstrate the main features of the SNAKE framework through a specific instantiation focusing on membership inference attacks over differentially-private synthetic data generation schemes. This instance of the SNAKE framework is currently being used for supporting a challenge co-located with APVP 2023 (the French workshop on the protection of privacy).|虽然在数据清理领域已经存在一些隐私挑战，但它们主要集中在问题的防御方面。为了有利于组织针对攻击的成功挑战，我们引入了 SNAKE 框架，该框架旨在促进组织针对攻击现有数据消毒机制的挑战。特别是，它能够轻松地自动化任何此类挑战所固有的冗余任务，并显示出以下突出特性: 与攻击相关的通用性、易用性和可扩展性。我们建议通过一个特定的实例来说明 SNAKE 框架的主要特征，该实例着重于针对差分私有合成数据生成方案的成员推理攻击。SNAKE 框架的这个实例目前正在用于支持与 APVP 2023(法国隐私保护研讨会)共处的一个挑战。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=SNAKE+Challenge:+Sanitization+Algorithms+under+Attack)|0| |[AQUAPLANE: The Argument Quality Explainer App](https://doi.org/10.1145/3583780.3614733)|Sebastian Britner, Lorik Dumani, Ralf Schenkel|Trier University, Trier, Germany|In computational argumentation, so-called quality dimensions such as coherence or rhetoric are often used for ranking arguments. However, the literature often only predicts which argument is more persuasive, but not why this is the case. In this paper, we introduce AQUAPLANE, a transparent and easy-to-extend application that not only decides for a pair of arguments which one is more convincing with respect to a statement, but also provides an explanation.|在计算论证中，所谓的质量维度，如连贯性或修辞学，经常被用来对论证进行排序。然而，文献往往只能预测哪种论点更有说服力，而不能预测为什么会出现这种情况。本文介绍了 AQUAPLANE，这是一个透明的、易于扩展的应用程序，它不仅决定了一对论证中哪一个对于一个陈述更有说服力，而且还提供了一个解释。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=AQUAPLANE:+The+Argument+Quality+Explainer+App)|0| -|[Contrastive Keyword Extraction from Versioned Documents](https://doi.org/10.1145/3583780.3614735)|Lukas Eder, Ricardo Campos, Adam Jatowt|University of Innsbruck, Innsbruck, Austria; University of Beira Interior & INESC TEC, Covilha, Portugal|Versioned documents are common in many situations and play a vital part in numerous applications enabling an overview of the revisions made to a document or document collection. However, as documents increase in size, it gets difficult to summarize and comprehend all the changes made to versioned documents. In this paper, we propose a novel research problem of contrastive keyword extraction from versioned documents, and introduce an unsupervised approach that extracts keywords to reflect the key changes made to an earlier document version. In order to provide an easy-to-use comparison and summarization tool, an open-source demonstration is made available which can be found at https://contrastive-keyword-extraction.streamlit.app/|版本化的文档在许多情况下都很常见，并且在许多应用程序中发挥着至关重要的作用，从而可以对文档或文档集合的修订进行概述。然而，随着文档规模的增加，很难总结和理解对版本化文档所做的所有更改。本文提出了一个新的对比关键字提取的研究课题，并介绍了一种无监督的方法，提取关键字以反映早期文档版本的关键字变化。为了提供一个易于使用的比较和总结工具，我们提供了一个开源的演示，可以在 https://contrastive-keyword-extraction.streamlit.app/找到|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Contrastive+Keyword+Extraction+from+Versioned+Documents)|0| +|[Contrastive Keyword Extraction from Versioned Documents](https://doi.org/10.1145/3583780.3614735)|Lukas Eder, Ricardo Campos, Adam Jatowt|University of Beira Interior & INESC TEC, Covilha, Portugal; University of Innsbruck, Innsbruck, Austria|Versioned documents are common in many situations and play a vital part in numerous applications enabling an overview of the revisions made to a document or document collection. However, as documents increase in size, it gets difficult to summarize and comprehend all the changes made to versioned documents. In this paper, we propose a novel research problem of contrastive keyword extraction from versioned documents, and introduce an unsupervised approach that extracts keywords to reflect the key changes made to an earlier document version. In order to provide an easy-to-use comparison and summarization tool, an open-source demonstration is made available which can be found at https://contrastive-keyword-extraction.streamlit.app/|版本化的文档在许多情况下都很常见，并且在许多应用程序中发挥着至关重要的作用，从而可以对文档或文档集合的修订进行概述。然而，随着文档规模的增加，很难总结和理解对版本化文档所做的所有更改。本文提出了一个新的对比关键字提取的研究课题，并介绍了一种无监督的方法，提取关键字以反映早期文档版本的关键字变化。为了提供一个易于使用的比较和总结工具，我们提供了一个开源的演示，可以在 https://contrastive-keyword-extraction.streamlit.app/找到|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Contrastive+Keyword+Extraction+from+Versioned+Documents)|0| |[ParkFlow: Intelligent Dispersal for Mitigating Parking Shortages Using Multi-Granular Spatial-Temporal Analysis](https://doi.org/10.1145/3583780.3614751)|Yang Fan Chiang, ChunWei Shen, JheWei Tsai, PeiXuan Li, TzuChang Lee, HsunPing Hsieh|National Cheng Kung University, Tainan, Taiwan Roc|Parking behaviors near popular destinations often exhibit a preference for proximity, resulting in poor habits, limited parking availability, and a range of consequential issues such as traffic chaos, economic challenges due to congestion, and imbalanced parking utilization. Taiwan has also faced escalating challenges in this regard. To effectively address these issues, the Government of Taiwan has initiated the Smart City program, encompassing various initiatives to enhance urban functionality. One notable solution implemented under this program is the Smart Parking Meter System (SPMS), designed to enhance the overall parking experience. The SPMS incorporates intelligent billing and secure parking data transmission, ensuring a safer and improved parking environment. In this paper, we propose ParkFlow, a comprehensive software-based solution that seamlessly integrates with smart parking hardware, presenting a holistic approach to tackling these challenges. ParkFlow intelligently disperses parking shortages in highly frequented areas and addresses the problem from multiple perspectives, including user, engineering, and government scenarios. By exploring and addressing these scenarios, we aim to provide valuable insights and inspiration to regions worldwide grappling with similar parking-related difficulties. Based on historical data analysis, the implementation of ParkFlow in resolving the parking imbalance problem is anticipated to lead to a significant increase of up to 10% to 20% in available parking hours in popular areas of Tainan, Taiwan. ParkFlow is in the process of being integrated into the Tainan City Government's Parking application, indicating its potential to address real-world parking challenges.|受欢迎的目的地附近的停车行为往往表现出对邻近性的偏好，导致不良的停车习惯，有限的停车可用性，以及一系列相应的问题，如交通混乱，由于拥堵造成的经济挑战，以及停车利用不平衡。台湾在这方面也面临着不断升级的挑战。为了有效地解决这些问题，台湾政府启动了智能城市计划，包括各种提高城市功能的举措。一个值得注意的解决方案是智能停车计时系统(SPMS) ，旨在提高整体停车体验。该系统集成了智能计费和安全的停车数据传输，确保了更安全和更好的停车环境。在本文中，我们提出了 ParkFlow，一个基于软件的综合解决方案，它与智能停车硬件无缝集成，提出了一个解决这些挑战的整体方法。ParkFlow 智能地将停车位短缺分散在高度频繁的地区，并从多个角度解决问题，包括用户、工程和政府场景。通过探索和处理这些情景，我们的目标是为世界各地努力解决类似停车相关困难的地区提供宝贵的见解和启发。根据历史数据分析，在台南市热门地区实施“泊车流量”以解决泊车不平衡问题，预计可令可供泊车的时间大幅增加10% 至20% 。ParkFlow 正在整合到台南市政府的泊车应用程序中，这表明它有潜力应对现实世界的泊车挑战。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=ParkFlow:+Intelligent+Dispersal+for+Mitigating+Parking+Shortages+Using+Multi-Granular+Spatial-Temporal+Analysis)|0| |[The µ-RA System for Recursive Path Queries over Graphs](https://doi.org/10.1145/3583780.3614756)|Amela Fejza, Pierre Genevès, Nabil Layaïda, Sarah Chlyah|Univ. Grenoble Alpes, CNRS, Inria, Grenoble, France|We demonstrate a system for recursive query answering over graphs. The system is based on a complete implementation of the recursive relational algebra µ-RA, extended with parsers and compilers adapted for queries over knowledge and property graphs. Each component of the system comes with novelty for processing recursion. As a result, one can formulate, optimize and efficiently answer expressive queries that navigate recursively along paths in different types of graphs. We demonstrate the system on real datasets and show how it performs considering other state-of-the-art systems.|我们展示了一个图上递归查询回答系统。该系统基于完全实施递归关系代数 μRA，并扩展了解析器和编译器，以适应对知识和属性图表的查询。系统的每个组件都带有处理递归的新颖性。因此，可以制定、优化和有效地回答表达式查询，这些查询在不同类型的图中沿着路径递归导航。我们在实际数据集上演示了该系统，并展示了在考虑其他最先进系统的情况下该系统的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=The+µ-RA+System+for+Recursive+Path+Queries+over+Graphs)|0| -|[DataDoc Analyzer: A Tool for Analyzing the Documentation of Scientific Datasets](https://doi.org/10.1145/3583780.3614737)|Joan GinerMiguelez, Abel Gómez, Jordi Cabot|Universitat Oberta de Catalunya, Barcelona, Spain; Luxembourg Institute of Science and Technology (LIST), Esch-sur-Alzette, Luxembourg|Recent public regulatory initiatives and relevant voices in the ML community have identified the need to document datasets according to several dimensions to ensure the fairness and trustworthiness of machine learning systems. In this sense, the data-sharing practices in the scientific field have been quickly evolving in the last years, with more and more research works publishing technical documentation together with the data for replicability purposes. However, this documentation is written in natural language, and its structure, content focus, and composition vary, making them challenging to analyze. We present DataDoc Analyzer, a tool for analyzing the documentation of scientific datasets by extracting the details of the main dimensions required to analyze the fairness and potential biases. We believe that our tool could help improve the quality of scientific datasets, aid dataset curators during its documentation process, and be a helpful tool for empirical studies on the overall quality of the datasets used in the ML field. The tool implements an ML pipeline that uses Large Language Models at its core for information retrieval. DataDoc is open-source, and a public demo is published online.|最近的公共监管倡议和机器学习社区中的相关声音已经确定需要根据几个维度来记录数据集，以确保机器学习系统的公平性和可信性。从这个意义上说，科学领域的数据共享做法在过去几年中迅速发展，越来越多的研究工作出版了技术文件和数据，以便复制。然而，这些文档是用自然语言编写的，其结构、内容重点和组成各不相同，使得分析它们具有挑战性。我们介绍了 DataDoc Analyzer，它是一个通过提取分析公平性和潜在偏差所需的主要维度的细节来分析科学数据集文档的工具。我们相信，我们的工具可以帮助提高科学数据集的质量，在其文档编制过程中帮助数据集管理人员，并成为一个有用的工具，对机器学习领域中使用的数据集的整体质量进行实证研究。该工具实现了一个机器学习管道，其核心使用大型语言模型进行信息检索。DataDoc 是开源的，公共演示在线发布。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=DataDoc+Analyzer:+A+Tool+for+Analyzing+the+Documentation+of+Scientific+Datasets)|0| +|[DataDoc Analyzer: A Tool for Analyzing the Documentation of Scientific Datasets](https://doi.org/10.1145/3583780.3614737)|Joan GinerMiguelez, Abel Gómez, Jordi Cabot|Luxembourg Institute of Science and Technology (LIST), Esch-sur-Alzette, Luxembourg; Universitat Oberta de Catalunya, Barcelona, Spain|Recent public regulatory initiatives and relevant voices in the ML community have identified the need to document datasets according to several dimensions to ensure the fairness and trustworthiness of machine learning systems. In this sense, the data-sharing practices in the scientific field have been quickly evolving in the last years, with more and more research works publishing technical documentation together with the data for replicability purposes. However, this documentation is written in natural language, and its structure, content focus, and composition vary, making them challenging to analyze. We present DataDoc Analyzer, a tool for analyzing the documentation of scientific datasets by extracting the details of the main dimensions required to analyze the fairness and potential biases. We believe that our tool could help improve the quality of scientific datasets, aid dataset curators during its documentation process, and be a helpful tool for empirical studies on the overall quality of the datasets used in the ML field. The tool implements an ML pipeline that uses Large Language Models at its core for information retrieval. DataDoc is open-source, and a public demo is published online.|最近的公共监管倡议和机器学习社区中的相关声音已经确定需要根据几个维度来记录数据集，以确保机器学习系统的公平性和可信性。从这个意义上说，科学领域的数据共享做法在过去几年中迅速发展，越来越多的研究工作出版了技术文件和数据，以便复制。然而，这些文档是用自然语言编写的，其结构、内容重点和组成各不相同，使得分析它们具有挑战性。我们介绍了 DataDoc Analyzer，它是一个通过提取分析公平性和潜在偏差所需的主要维度的细节来分析科学数据集文档的工具。我们相信，我们的工具可以帮助提高科学数据集的质量，在其文档编制过程中帮助数据集管理人员，并成为一个有用的工具，对机器学习领域中使用的数据集的整体质量进行实证研究。该工具实现了一个机器学习管道，其核心使用大型语言模型进行信息检索。DataDoc 是开源的，公共演示在线发布。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=DataDoc+Analyzer:+A+Tool+for+Analyzing+the+Documentation+of+Scientific+Datasets)|0| |[MORPHER: Structural Transformation of Ill-formed Rows](https://doi.org/10.1145/3583780.3614747)|Mazhar Hameed, Gerardo Vitagliano, Felix Naumann|Hasso Plattner Institute, University of Potsdam, Potsdam, Germany|Open data portals contain a plethora of data files, with comma-separated value (CSV) files being particularly popular with users and businesses due to their flexible standard. However, this flexibility comes with much responsibility for data consumers, as many files contain various structural problems, e.g., a different number of cells across data rows, multiple value formats within the same column, different variants of quoted fields due to user specifications, etc. We refer to rows that contain such structural inconsistencies as ill-formed. Consequently, ingesting them into a host system, such as a database or an analytics platform, often requires prior data preparation steps. We propose to demonstrate MORPHER, a desktop-based system that incorporates our state-of-the-art error detection system, SURAGH and extends it to also clean the files at hand. MORPHER facilitates ingesting CSV files by automatically identifying and cleaning ill-formed rows while preserving all data. It comprises three key components: 1) The pattern modeler, which generates syntax-based patterns for each row of the input file. The system uses these patterns to classify rows into ill-formed and well-formed. 2) The pattern classifier obtains row patterns for ill-formed rows and uses them to distinguish ill-formed but wanted rows from ill-formed unwanted rows. 3) The pattern wrangler transforms the identified wanted rows into well-formed rows, effectively repairing a wide range of formatting problems.|开放数据门户包含大量的数据文件，逗号分隔值(CSV)文件由于其灵活的标准而特别受到用户和企业的欢迎。然而，这种灵活性伴随着对数据消费者的责任，因为许多文件包含各种结构性问题，例如，跨数据行的单元格数量不同，同一列中的多个值格式，由于用户规范而引用字段的不同变体，等等。我们将包含结构不一致的行称为格式不正确的行。因此，将它们摄取到主机系统(如数据库或分析平台)中通常需要事先的数据准备步骤。我们建议演示 MORPHER，这是一个基于桌面的系统，它结合了我们最先进的错误检测系统 SURAGH，并将其扩展到清理手边的文件。MORPHER 通过自动识别和清理格式不正确的行，同时保留所有数据，促进了 CSV 文件的摄取。它包括三个关键组件: 1)模式建模器，它为输入文件的每一行生成基于语法的模式。系统使用这些模式将行分为格式不良和格式良好的两类。2)模式分类器获取格式不正确的行的行模式，并使用它们来区分格式不正确但需要的行和格式不正确的不需要的行。3)模式管理器将识别出的所需行转换为格式良好的行，有效地修复了大量的格式问题。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=MORPHER:+Structural+Transformation+of+Ill-formed+Rows)|0| |[LARCH: Large Language Model-based Automatic Readme Creation with Heuristics](https://doi.org/10.1145/3583780.3614744)|Yuta Koreeda, Terufumi Morishita, Osamu Imaichi, Yasuhiro Sogawa|Hitachi, Ltd., Kokubunji, Japan|Writing a readme is a crucial aspect of software development as it plays a vital role in managing and reusing program code. Though it is a pain point for many developers, automatically creating one remains a challenge even with the recent advancements in large language models (LLMs), because it requires generating abstract description from thousands of lines of code. In this demo paper, we show that LLMs are capable of generating a coherent and factually correct readmes if we can identify a code fragment that is representative of the repository. Building upon this finding, we developed LARCH (LLM-based Automatic Readme Creation with Heuristics) which leverages representative code identification with heuristics and weak supervision. Through human and automated evaluations, we illustrate that LARCH can generate coherent and factually correct readmes in the majority of cases, outperforming a baseline that does not rely on representative code identification. We have made LARCH open-source and provided a cross-platform Visual Studio Code interface and command-line interface, accessible at https://github.com/hitachi-nlp/larch . A demo video showcasing LARCH's capabilities is available at https://youtu.be/ZUKkh5ED-O4 .|编写自述文件是软件开发的一个关键方面，因为它在管理和重用程序代码方面起着至关重要的作用。尽管对于许多开发人员来说这是一个痛点，但是自动创建一个仍然是一个挑战，即使是在大型语言模型(LLM)最近的进步中，因为它需要从成千上万行代码中生成抽象描述。在这篇演示文章中，我们展示了 LLM 能够生成一个连贯的、事实上正确的自述文件，如果我们能够识别代表存储库的代码片段。在这个发现的基础上，我们开发了 LARCH (基于 LLM 的启发式自动自述创建) ，它利用启发式和弱监督的代表性代码标识。通过人工和自动评估，我们说明 LARCH 可以在大多数情况下产生连贯和事实上正确的自述，优于不依赖于代表性代码识别的基线。我们使 LARCH 开源，并提供了一个跨平台的 Visual Studio 代码界面和命令行界面，可以在 https://github.com/hitachi-nlp/LARCH 访问。展示 LARCH 能力的演示视频可在 https://youtu.be/zukkh5ed-o4下载。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=LARCH:+Large+Language+Model-based+Automatic+Readme+Creation+with+Heuristics)|0| -|[CRUISE-Screening: Living Literature Reviews Toolbox](https://doi.org/10.1145/3583780.3614736)|Wojciech Kusa, Petr Knoth, Allan Hanbury|TU Wien, Vienna, Austria; The Open University, Milton Keynes, United Kingdom|Keeping up with research and finding related work is still a time-consuming task for academics. Researchers sift through thousands of studies to identify a few relevant ones. Automation techniques can help by increasing the efficiency and effectiveness of this task. To this end, we developed CRUISE-Screening, a web-based application for conducting living literature reviews - a type of literature review that is continuously updated to reflect the latest research in a particular field. CRUISE-Screening is connected to several search engines via an API, which allows for updating the search results periodically. Moreover, it can facilitate the process of screening for relevant publications by using text classification and question answering models. CRUISE-Screening can be used both by researchers conducting literature reviews and by those working on automating the citation screening process to validate their algorithms. The application is open-source: https://github.com/ProjectDoSSIER/cruise-screening, and a demo is available under this URL: https://citation-screening.ec.tuwien.ac.at. We discuss the limitations of our tool in Appendix A.|对于学术界来说，跟上研究进度和寻找相关工作仍然是一项耗时的任务。研究人员筛选了数以千计的研究，以确定一些相关的。自动化技术可以通过提高此任务的效率和有效性来提供帮助。为此，我们开发了“ CRUISE 筛选”，一种用于进行活体文献评论的网络应用程序——一种不断更新以反映特定领域最新研究的文献评论类型。CRUISE-筛选通过一个 API 连接到几个搜索引擎，它允许定期更新搜索结果。此外，利用文本分类和问答模型，可以方便相关出版物的筛选过程。CRUISE-筛选既可以用于进行文献综述的研究人员，也可以用于那些致力于引文筛选过程自动化以验证其算法的研究人员。这个应用程序是开源的: https://github.com/projectdossier/cruise-screening 的，并且可以在这个网址下面找到一个演示 https://citation-screening.ec.tuwien.ac.at。我们在附录 A 中讨论了我们的工具的局限性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=CRUISE-Screening:+Living+Literature+Reviews+Toolbox)|0| -|[HugNLP: A Unified and Comprehensive Library for Natural Language Processing](https://doi.org/10.1145/3583780.3614742)|Jianing Wang, Nuo Chen, Qiushi Sun, Wenkang Huang, Chengyu Wang, Ming Gao|National University of Singapore, Singapore, Singapore; Alibaba Group, Hangzhou, China; Ant Group, Hangzhou, China; East China Normal University, Shanghai, China|In this paper, we introduce HugNLP, a unified and comprehensive library for natural language processing (NLP) with the prevalent backend of HuggingFace Transformers, which is designed for NLP researchers to easily utilize off-the-shelf algorithms and develop novel methods with user-defined models and tasks in real-world scenarios. HugNLP consists of a hierarchical structure including models, processors and applications that unifies the learning process of pre-trained language models (PLMs) on different NLP tasks. Additionally, we present some featured NLP applications to show the effectiveness of HugNLP, such as knowledge-enhanced PLMs, universal information extraction, low-resource mining, and code understanding and generation, etc. The source code will be released on GitHub (https://github.com/wjn1996/HugNLP).|本文介绍了 HugNLP，这是一个自然语言处理(NLP)的统一和综合库，它的后端是 HuggingFace Transformers，它是为自然语言处理的研究人员设计的，可以方便地利用现成的算法，并开发新的方法与用户定义的模型和任务在真实世界的场景。HugNLP 由模型、处理器和应用程序组成的层次结构，统一了针对不同 NLP 任务的预训练语言模型(PLM)的学习过程。此外，我们还介绍了一些特色的自然语言处理应用程序，以展示 HugNLP 的有效性，例如知识增强 PLM、通用信息抽取、低资源挖掘、代码理解和生成等。源代码将在 GitHub ( https://GitHub.com/wjn1996/hugnlp )上发布。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=HugNLP:+A+Unified+and+Comprehensive+Library+for+Natural+Language+Processing)|0| +|[CRUISE-Screening: Living Literature Reviews Toolbox](https://doi.org/10.1145/3583780.3614736)|Wojciech Kusa, Petr Knoth, Allan Hanbury|The Open University, Milton Keynes, United Kingdom; TU Wien, Vienna, Austria|Keeping up with research and finding related work is still a time-consuming task for academics. Researchers sift through thousands of studies to identify a few relevant ones. Automation techniques can help by increasing the efficiency and effectiveness of this task. To this end, we developed CRUISE-Screening, a web-based application for conducting living literature reviews - a type of literature review that is continuously updated to reflect the latest research in a particular field. CRUISE-Screening is connected to several search engines via an API, which allows for updating the search results periodically. Moreover, it can facilitate the process of screening for relevant publications by using text classification and question answering models. CRUISE-Screening can be used both by researchers conducting literature reviews and by those working on automating the citation screening process to validate their algorithms. The application is open-source: https://github.com/ProjectDoSSIER/cruise-screening, and a demo is available under this URL: https://citation-screening.ec.tuwien.ac.at. We discuss the limitations of our tool in Appendix A.|对于学术界来说，跟上研究进度和寻找相关工作仍然是一项耗时的任务。研究人员筛选了数以千计的研究，以确定一些相关的。自动化技术可以通过提高此任务的效率和有效性来提供帮助。为此，我们开发了“ CRUISE 筛选”，一种用于进行活体文献评论的网络应用程序——一种不断更新以反映特定领域最新研究的文献评论类型。CRUISE-筛选通过一个 API 连接到几个搜索引擎，它允许定期更新搜索结果。此外，利用文本分类和问答模型，可以方便相关出版物的筛选过程。CRUISE-筛选既可以用于进行文献综述的研究人员，也可以用于那些致力于引文筛选过程自动化以验证其算法的研究人员。这个应用程序是开源的: https://github.com/projectdossier/cruise-screening 的，并且可以在这个网址下面找到一个演示 https://citation-screening.ec.tuwien.ac.at。我们在附录 A 中讨论了我们的工具的局限性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=CRUISE-Screening:+Living+Literature+Reviews+Toolbox)|0| +|[HugNLP: A Unified and Comprehensive Library for Natural Language Processing](https://doi.org/10.1145/3583780.3614742)|Jianing Wang, Nuo Chen, Qiushi Sun, Wenkang Huang, Chengyu Wang, Ming Gao|Alibaba Group, Hangzhou, China; Ant Group, Hangzhou, China; East China Normal University, Shanghai, China; National University of Singapore, Singapore, Singapore|In this paper, we introduce HugNLP, a unified and comprehensive library for natural language processing (NLP) with the prevalent backend of HuggingFace Transformers, which is designed for NLP researchers to easily utilize off-the-shelf algorithms and develop novel methods with user-defined models and tasks in real-world scenarios. HugNLP consists of a hierarchical structure including models, processors and applications that unifies the learning process of pre-trained language models (PLMs) on different NLP tasks. Additionally, we present some featured NLP applications to show the effectiveness of HugNLP, such as knowledge-enhanced PLMs, universal information extraction, low-resource mining, and code understanding and generation, etc. The source code will be released on GitHub (https://github.com/wjn1996/HugNLP).|本文介绍了 HugNLP，这是一个自然语言处理(NLP)的统一和综合库，它的后端是 HuggingFace Transformers，它是为自然语言处理的研究人员设计的，可以方便地利用现成的算法，并开发新的方法与用户定义的模型和任务在真实世界的场景。HugNLP 由模型、处理器和应用程序组成的层次结构，统一了针对不同 NLP 任务的预训练语言模型(PLM)的学习过程。此外，我们还介绍了一些特色的自然语言处理应用程序，以展示 HugNLP 的有效性，例如知识增强 PLM、通用信息抽取、低资源挖掘、代码理解和生成等。源代码将在 GitHub ( https://GitHub.com/wjn1996/hugnlp )上发布。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=HugNLP:+A+Unified+and+Comprehensive+Library+for+Natural+Language+Processing)|0| |[PyABSA: A Modularized Framework for Reproducible Aspect-based Sentiment Analysis](https://doi.org/10.1145/3583780.3614752)|Heng Yang, Chen Zhang, Ke Li||The advancement of aspect-based sentiment analysis (ABSA) has urged the lack of a user-friendly framework that can largely lower the difficulty of reproducing state-of-the-art ABSA performance, especially for beginners. To meet the demand, we present \our, a modularized framework built on PyTorch for reproducible ABSA. To facilitate ABSA research, PyABSA supports several ABSA subtasks, including aspect term extraction, aspect sentiment classification, and end-to-end aspect-based sentiment analysis. Concretely, PyABSA integrates 29 models and 26 datasets. With just a few lines of code, the result of a model on a specific dataset can be reproduced. With a modularized design, PyABSA can also be flexiblely extended to considered models, datasets, and other related tasks. Besides, PyABSA highlights its data augmentation and annotation features, which significantly address data scarity. All are welcome to have a try at \url{https://github.com/yangheng95/PyABSA}.|基于方面的情绪分析(ABSA)的进步促使缺乏一个用户友好的框架，可以在很大程度上降低复制最先进的 ABSA 性能的难度，特别是对于初学者。为了满足这一需求，我们提出了一个基于 PyTorch 的模块化框架，用于可重现的 ABSA。为了便于 ABSA 研究，PyABSA 支持几个 ABSA 子任务，包括方面词提取、方面情绪分类和端到端基于方面的情绪分析。具体来说，PyABSA 集成了29个模型和26个数据集。只需几行代码，就可以复制特定数据集上模型的结果。通过模块化设计，PyABSA 还可以灵活地扩展到所考虑的模型、数据集和其他相关任务。此外，PyABSA 强调了其数据增强和注释特性，这些特性显著地解决了数据稀缺问题。欢迎大家到 url { https://github.com/yangheng95/pyabsa }试一试。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=PyABSA:+A+Modularized+Framework+for+Reproducible+Aspect-based+Sentiment+Analysis)|0| -|[NP-SSL: A Modular and Extensible Self-supervised Learning Library with Neural Processes](https://doi.org/10.1145/3583780.3614749)|Zesheng Ye, Jing Du, Yao Liu, Yihong Zhang, Lina Yao|Osaka University, Osaka, Japan; The University of New South Wales, Sydney, NSW, Australia; CSIRO's Data61 & UNSW, Sydney, NSW, Australia|Neural Processes (NPs) are a family of supervised density estimators devoted to probabilistic function approximation with meta-learning. Despite extensive research on the subject, the absence of a unified framework for NPs leads to varied architectural solutions across diverse studies. This non-consensus poses challenges to reproducing and benchmarking different NPs. Moreover, existing codebases mainly prioritize generative density estimation, yet rarely consider expanding the capability of NPs to self-supervised representation learning, which however has gained growing importance in data mining applications. To this end, we present NP-SSL, a modular and configurable framework with built-in support, requiring minimal effort to 1) implement classical NPs architectures; 2) customize specific components; 3) integrate hybrid training scheme (e.g., contrastive); and 4) extend NPs to act as a self-supervised learning toolkit, producing latent representations of data, and facilitating diverse downstream predictive tasks. To illustrate, we discuss a case study that applies NP-SSL to model time-series data. We interpret that NP-SSL can handle different predictive tasks such as imputation and forecasting, by a simple switch in data samplings, without significant change to the underlying structure. We hope this study can reduce the workload of future research on leveraging NPs to tackle more a broader range of real-world data mining applications. Code and documentation are at https://github.com/zyecs/NP-SSL.|神经过程(NPs)是一个有监督的密度估计家族，致力于元学习的概率函数逼近。尽管对这一课题进行了广泛的研究，但由于缺乏一个统一的 NPs 框架，导致了不同研究领域的建筑解决方案各不相同。这种不一致性对复制和基准测试不同的 NPs 提出了挑战。此外，现有的代码库主要优先考虑生成密度估计，但很少考虑扩展能力的 NP 的自我监督表示学习，但在数据挖掘应用越来越重要。为此，我们提出了 NP-SSL，一个内置支持的模块化和可配置框架，只需要最小的努力: 1)实现经典的 NP 架构; 2)定制特定的组件; 3)集成混合训练方案(例如，对比) ; 4)扩展 NP 作为自我监督的学习工具包，产生数据的潜在表示，并促进多样化的下游预测任务。为了说明，我们讨论了一个案例研究，应用 NP-SSL 模型的时间序列数据。我们解释说，NP-SSL 可以处理不同的预测任务，如插补和预测，通过一个简单的切换数据采样，没有显着的变化，基础结构。我们希望这项研究能够减少未来利用 NPs 处理更广泛的现实世界数据挖掘应用的研究工作量。代码和文档都在 https://github.com/zyecs/np-ssl。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=NP-SSL:+A+Modular+and+Extensible+Self-supervised+Learning+Library+with+Neural+Processes)|0| +|[NP-SSL: A Modular and Extensible Self-supervised Learning Library with Neural Processes](https://doi.org/10.1145/3583780.3614749)|Zesheng Ye, Jing Du, Yao Liu, Yihong Zhang, Lina Yao|The University of New South Wales, Sydney, NSW, Australia; CSIRO's Data61 & UNSW, Sydney, NSW, Australia; Osaka University, Osaka, Japan|Neural Processes (NPs) are a family of supervised density estimators devoted to probabilistic function approximation with meta-learning. Despite extensive research on the subject, the absence of a unified framework for NPs leads to varied architectural solutions across diverse studies. This non-consensus poses challenges to reproducing and benchmarking different NPs. Moreover, existing codebases mainly prioritize generative density estimation, yet rarely consider expanding the capability of NPs to self-supervised representation learning, which however has gained growing importance in data mining applications. To this end, we present NP-SSL, a modular and configurable framework with built-in support, requiring minimal effort to 1) implement classical NPs architectures; 2) customize specific components; 3) integrate hybrid training scheme (e.g., contrastive); and 4) extend NPs to act as a self-supervised learning toolkit, producing latent representations of data, and facilitating diverse downstream predictive tasks. To illustrate, we discuss a case study that applies NP-SSL to model time-series data. We interpret that NP-SSL can handle different predictive tasks such as imputation and forecasting, by a simple switch in data samplings, without significant change to the underlying structure. We hope this study can reduce the workload of future research on leveraging NPs to tackle more a broader range of real-world data mining applications. Code and documentation are at https://github.com/zyecs/NP-SSL.|神经过程(NPs)是一个有监督的密度估计家族，致力于元学习的概率函数逼近。尽管对这一课题进行了广泛的研究，但由于缺乏一个统一的 NPs 框架，导致了不同研究领域的建筑解决方案各不相同。这种不一致性对复制和基准测试不同的 NPs 提出了挑战。此外，现有的代码库主要优先考虑生成密度估计，但很少考虑扩展能力的 NP 的自我监督表示学习，但在数据挖掘应用越来越重要。为此，我们提出了 NP-SSL，一个内置支持的模块化和可配置框架，只需要最小的努力: 1)实现经典的 NP 架构; 2)定制特定的组件; 3)集成混合训练方案(例如，对比) ; 4)扩展 NP 作为自我监督的学习工具包，产生数据的潜在表示，并促进多样化的下游预测任务。为了说明，我们讨论了一个案例研究，应用 NP-SSL 模型的时间序列数据。我们解释说，NP-SSL 可以处理不同的预测任务，如插补和预测，通过一个简单的切换数据采样，没有显着的变化，基础结构。我们希望这项研究能够减少未来利用 NPs 处理更广泛的现实世界数据挖掘应用的研究工作量。代码和文档都在 https://github.com/zyecs/np-ssl。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=NP-SSL:+A+Modular+and+Extensible+Self-supervised+Learning+Library+with+Neural+Processes)|0| |[MOSS: AI Platform for Discovery of Corrosion-Resistant Materials](https://doi.org/10.1145/3583780.3614748)|Biao Yin, Nicholas Josselyn, Ziming Zhang, Elke A. Rundensteiner, Thomas A. Considine, John V. Kelley, Berend C. Rinderspacher, Robert E. Jensen, James F. Snyder|DEVCOM Army Research Laboratory, Aberdeen Proving Ground, MD, USA; Worcester Polytechnic Institute, Worcester, MA, USA|Amid corrosion degradation of metallic structures causing expenses nearing 3 trillion or 4% of the GDP annually along with major safety risks, the adoption of AI technologies for accelerating the materials science life-cycle for developing materials with better corrosive properties is paramount. While initial machine learning models for corrosion assessment are being proposed in the literature, their incorporation into end-to-end tools for field experimentation by corrosion scientists remains largely unexplored. To fill this void, our university data science team in collaboration with the materials science unit at the Army Research Lab have jointly developed MOSS, an innovative AI-based digital platform to support material science corrosion research. MOSS features user-friendly iPadOS app for in-field corrosion progression data collection, deep-learning corrosion assessor, robust data repository system for long-term experimental data modeling, and visual analytics web portal for material science research. In this demonstration, we showcase the key innovations of the MOSS platform via use cases supporting the corrosion exploration processes, with the promise of accelerating the discovery of new materials. We open a MOSS video demo at: https://www.youtube.com/watch?v=CzcxMMRsxkE|由于金属结构的腐蚀退化导致每年的支出接近国内生产总值的3万亿或4% ，并伴随着重大的安全风险，因此采用人工智能技术加快材料科学的生命周期以开发具有更好腐蚀性能的材料是至关重要的。虽然文献中提出了腐蚀评估的初始机器学习模型，但是将其纳入腐蚀科学家现场试验的端到端工具中仍然大部分未被探索。为了填补这一空白，我们的大学数据科学团队与美国陆军研究实验室的材料科学部门合作，共同开发了 MOSS，一个创新的基于人工智能的数字平台，以支持材料科学腐蚀研究。MOSS 具有用于现场腐蚀进程数据收集的用户友好的 iPadOS 应用程序，深度学习腐蚀评估器，用于长期实验数据建模的健壮的数据存储系统，以及用于材料科学研究的可视化分析门户网站。在这个演示中，我们通过支持腐蚀探测过程的用例展示了 MOSS 平台的关键创新，并承诺加速新材料的发现。我们在 https://www.youtube.com/watch?v=czcxmmrsxke 打开一个 MOSS 视频演示|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=MOSS:+AI+Platform+for+Discovery+of+Corrosion-Resistant+Materials)|0| -|[Demonstration of ViTA: Visualizing, Testing and Analyzing Index Advisors](https://doi.org/10.1145/3583780.3614738)|Wei Zhou, Chen Lin, Xuanhe Zhou, Guoliang Li, Tianqing Wang|Tsinghua University, Beijing, China; Xiamen University, Xiamen, China; Huawei, Beijing, China|Index advisors have become an essential tool to optimize index selection and accelerate query processing. Various index advisors have been developed in recent years, and comprehensively assessing their performance from multiple aspects is necessary. In this demonstration, we introduce VITA, a user-friendly and informative tool for interactively Visualizing, Testing, and Analyzing index advisors. For a user-given workload, VITA can visualize the main steps of the index selection procedure in ten existing index advisors to facilitate the management of index advisors. Moreover, VITA can assess the index advisor's robustness w.r.t. workload drift by generating testing workloads, i.e., potentially future workloads that may damage the index advisor's performance. Finally, VITA provides a comparative analysis across index advisors on four aspects, including the index advisor's utility (i.e., the ratio of the reduced workload cost), robustness (i.e., the performance under dynamic workload), overhead (i.e., the time to acquire the final configuration), and scalability (i.e., the volume of the enumerated index candidates). Therefore, VITA can thoroughly compare existing index advisors to help users determine the most suitable index advisor that meets their requirements. VITA is now being integrated into the openGauss platform as a plug-in.|索引顾问已经成为优化索引选择和加速查询处理的重要工具。指数顾问是近年来发展起来的各种指数顾问，需要从多方面对其业绩进行综合评价。在本演示中，我们将介绍 VITA，这是一个用户友好的、信息丰富的工具，用于交互式地对索引顾问进行可视化、测试和分析。对于用户给定的工作量，VITA 可以在现有的10个索引顾问中直观地显示索引选择程序的主要步骤，以方便索引顾问的管理。此外，VITA 还可以通过生成测试工作负载来评估索引顾问的健壮性。最后，VITA 从四个方面对索引顾问进行了比较分析，包括索引顾问的效用(即降低工作负载成本的比率)、健壮性(即动态工作负载下的性能)、开销(即获得最终配置的时间)和可伸缩性(即枚举索引候选者的数量)。因此，VITA 可以彻底比较现有的索引顾问，以帮助用户确定最合适的索引顾问，以满足他们的要求。VITA 现在正作为一个插件集成到 openGauss 平台中。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Demonstration+of+ViTA:+Visualizing,+Testing+and+Analyzing+Index+Advisors)|0| +|[Demonstration of ViTA: Visualizing, Testing and Analyzing Index Advisors](https://doi.org/10.1145/3583780.3614738)|Wei Zhou, Chen Lin, Xuanhe Zhou, Guoliang Li, Tianqing Wang|Xiamen University, Xiamen, China; Tsinghua University, Beijing, China; Huawei, Beijing, China|Index advisors have become an essential tool to optimize index selection and accelerate query processing. Various index advisors have been developed in recent years, and comprehensively assessing their performance from multiple aspects is necessary. In this demonstration, we introduce VITA, a user-friendly and informative tool for interactively Visualizing, Testing, and Analyzing index advisors. For a user-given workload, VITA can visualize the main steps of the index selection procedure in ten existing index advisors to facilitate the management of index advisors. Moreover, VITA can assess the index advisor's robustness w.r.t. workload drift by generating testing workloads, i.e., potentially future workloads that may damage the index advisor's performance. Finally, VITA provides a comparative analysis across index advisors on four aspects, including the index advisor's utility (i.e., the ratio of the reduced workload cost), robustness (i.e., the performance under dynamic workload), overhead (i.e., the time to acquire the final configuration), and scalability (i.e., the volume of the enumerated index candidates). Therefore, VITA can thoroughly compare existing index advisors to help users determine the most suitable index advisor that meets their requirements. VITA is now being integrated into the openGauss platform as a plug-in.|索引顾问已经成为优化索引选择和加速查询处理的重要工具。指数顾问是近年来发展起来的各种指数顾问，需要从多方面对其业绩进行综合评价。在本演示中，我们将介绍 VITA，这是一个用户友好的、信息丰富的工具，用于交互式地对索引顾问进行可视化、测试和分析。对于用户给定的工作量，VITA 可以在现有的10个索引顾问中直观地显示索引选择程序的主要步骤，以方便索引顾问的管理。此外，VITA 还可以通过生成测试工作负载来评估索引顾问的健壮性。最后，VITA 从四个方面对索引顾问进行了比较分析，包括索引顾问的效用(即降低工作负载成本的比率)、健壮性(即动态工作负载下的性能)、开销(即获得最终配置的时间)和可伸缩性(即枚举索引候选者的数量)。因此，VITA 可以彻底比较现有的索引顾问，以帮助用户确定最合适的索引顾问，以满足他们的要求。VITA 现在正作为一个插件集成到 openGauss 平台中。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Demonstration+of+ViTA:+Visualizing,+Testing+and+Analyzing+Index+Advisors)|0| |[An AI-based Simulation and Optimization Framework for Logistic Systems](https://doi.org/10.1145/3583780.3614732)|Zefang Zong, Huan Yan, Hongjie Sui, Haoxiang Li, Peiqi Jiang, Yong Li|Tsinghua University, Beijing, China|Improving logistics efficiency is a challenging task in logistic systems, since planning the vehicle routes highly relies on the changing traffic conditions and diverse demand scenarios. However, most existing approaches either neglect the dynamic traffic environment or adopt manually designed rules, which fails to efficiently find a high-quality routing strategy. In this paper, we present a novel artificial intelligence (AI) based framework for logistic systems. This framework can simulate the spatio-temporal traffic conditions to form a dynamic environment in a data-driven manner. Under such a simulated environment, it adopts deep reinforcement learning techniques to intelligently generate the optimized routing strategy. Meanwhile, we also design an interactive frontend to visualize the simulated environment and routing strategies, which help operators evaluate the task performance. We will showcase the results of AI-based simulation and optimization in our demonstration.|在物流系统中，提高物流效率是一项具有挑战性的任务，因为车辆路线的规划高度依赖于不断变化的交通条件和不同的需求情景。然而，现有的路由策略大多忽视了动态流量环境，或者采用了人工设计的规则，无法有效地找到高质量的路由策略。本文提出了一种新的基于人工智能的物流系统框架。该框架能够模拟时空交通状况，以数据驱动的方式形成动态环境。在这样的模拟环境下，它采用深度强化学习技术智能地生成优化的路由策略。同时，我们还设计了一个交互式前端，可视化模拟环境和路由策略，帮助操作员评估任务性能。我们将在演示中展示基于人工智能的仿真和优化的结果。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=An+AI-based+Simulation+and+Optimization+Framework+for+Logistic+Systems)|0| |[Data and Decision Fusion with Uncertainty Quantification for ML-based Healthcare Decision Systems](https://doi.org/10.1145/3583780.3616004)|Grigor Bezirganyan|Aix-Marseille University, CNRS, LIS, Marseille, France|This paper outlines the PhD research plan to develop a comprehensive, uncertainty-aware, multimodal deep learning approach to be used in the healthcare domain. The goal is to design a multimodal deep learning framework that can leverage the complex interconnections between various modalities in order to generate highly precise predictions for intricate healthcare datasets. In addition, the framework should also incorporate methods for quantifying and dealing with uncertainty, which is an important consideration in many real-world healthcare applications. The approach will be tested on real-world multimodal datasets from Marseilles hospitals in France. We further represent some preliminary results of our early stage experiments with uncertainty quantification on multimodal datasets.|本文概述了博士研究计划，开发一个全面的，不确定性意识，多模式深入学习方法，在医疗领域使用。目标是设计一个多模式深度学习框架，可以利用各种模式之间复杂的相互联系，以便为复杂的医疗保健数据集生成高度精确的预测。此外，该框架还应该包含量化和处理不确定性的方法，这是许多现实世界医疗保健应用程序中的一个重要考虑因素。该方法将在法国马赛医院的现实世界多式联运数据集上进行测试。我们进一步展示了我们的早期实验的一些初步结果与不确定性量化的多模态数据集。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Data+and+Decision+Fusion+with+Uncertainty+Quantification+for+ML-based+Healthcare+Decision+Systems)|0| |[A Neuro-symbolic Approach to Enhance Interpretability of Graph Neural Network through the Integration of External Knowledge](https://doi.org/10.1145/3583780.3616008)|Kislay Raj|Dublin City University, Dublin, Ireland|Graph Neural Networks (GNNs) have shown remarkable performance in tackling complex tasks. However, interpreting the decision-making process of GNNs remains a challenge. To address the challenge, we explore representing the behaviour of a GNN in a representation space that is more transparent such as a knowledge graph, in a way that captures the behaviour of a GNN as a graph. Our initial experiments on the node classification task can represent the trained graph convolutional neural network (GCN) behaviour with some semantics uncovered by state-of-the-art approaches. This research offers a promising direction for enhancing GNN interpretability and understanding by providing structured, human-understandable representations and incorporating external knowledge for more accurate predictions.|图形神经网络(GNN)在处理复杂任务方面表现出显著的性能。然而，解释 GNN 的决策过程仍然是一个挑战。为了应对这一挑战，我们探索在一个更加透明的表示空间(如知识图)中表示 GNN 的行为，以一种将 GNN 的行为捕获为图的方式。我们在节点分类任务上的初步实验可以用最先进的方法发现的一些语义来表示经过训练的图形卷积神经网络(GCN)行为。本研究通过提供结构化的、人类可理解的表示，并结合外部知识进行更精确的预测，为提高 GNN 的可解释性和理解性提供了一个有希望的方向。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Neuro-symbolic+Approach+to+Enhance+Interpretability+of+Graph+Neural+Network+through+the+Integration+of+External+Knowledge)|0| |[Enhancing Badminton Player Performance via a Closed-Loop AI Approach: Imitation, Simulation, Optimization, and Execution](https://doi.org/10.1145/3583780.3616001)|KuangDa Wang|National Yang Ming Chiao Tung University, Hsinchu, Taiwan Roc|In recent years, the sports industry has witnessed a significant rise in interest in leveraging artificial intelligence to enhance players' performance. However, the application of deep learning to improve badminton athletes' performance faces challenges related to identifying weaknesses, generating winning suggestions, and validating strategy effectiveness. These challenges arise due to the limited availability of realistic environments and agents. This paper aims to address these research gaps and make contributions to the badminton community. To achieve this goal, we propose a closed-loop approach consisting of six key components: Badminton Data Acquisition, Imitating Players' Styles, Simulating Matches, Optimizing Strategies, Training Execution, and Real-World Competitions. Specifically, we developed a novel model called RallyNet, which excels at imitating players' styles, allowing agents to accurately replicate real players' behavior. Secondly, we created a sophisticated badminton simulation environment that incorporates real-world physics, faithfully recreating game situations. Thirdly, we employed reinforcement learning techniques to improve players' strategies, enhancing their chances of winning while preserving their unique playing styles. By comparing strategy differences before and after improvement, we provide winning suggestions to players, which can be validated against diverse opponents within our carefully designed environment. Lastly, through collaborations with badminton venues and players, we apply the generated suggestions to the players' training and competitions, ensuring the effectiveness of our approach. Moreover, we continuously gather data from training and competitions, incorporating it into the closed-loop cycle to refine strategies and suggestions. This research presents an innovative approach for continuously improving players' performance, contributing to the field of AI-driven sports performance enhancement. This dissertation is supervised by Wen-Chih Peng (wcp [email protected] ) and Ping-Chun Hsieh ( [email protected] ).|近年来，体育产业对利用人工智能提高运动员表现的兴趣显著上升。然而，应用深度学习来提高羽毛球运动员的成绩面临着识别弱点、产生获胜建议和验证策略有效性等方面的挑战。由于现实环境和代理的可用性有限，出现了这些挑战。本文旨在填补这些研究空白，为羽毛球界做出贡献。为了实现这一目标，我们提出了一个由六个关键部分组成的闭环方法: 羽毛球数据采集、模仿运动员的风格、模拟比赛、优化策略、训练执行和真实世界的比赛。具体来说，我们开发了一个名为 RallyNet 的新模型，它擅长模仿玩家的风格，允许代理精确地复制真实玩家的行为。其次，我们创建了一个复杂的羽毛球模拟环境，结合了真实世界的物理学，忠实地再现了比赛场景。第三，我们采用强化学习技术来改善球员的战术，提高他们赢球的机会，同时保留他们独特的踢球风格。通过比较改进前后的策略差异，我们为玩家提供获胜的建议，这些建议可以在我们精心设计的环境中针对不同的对手进行验证。最后，通过与羽毛球场地和运动员的合作，我们将生成的建议应用到运动员的训练和比赛中，确保我们的方法的有效性。此外，我们不断从训练和比赛中收集数据，将其纳入闭环周期，以完善战略和建议。本研究提出了一种不断提升运动员成绩的创新方法，有助于人工智能驱动的运动成绩提升领域。本论文由彭(wcp [ email protected ])和谢平春(Ping-Chun Hsieh ([ email protected ])指导。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Enhancing+Badminton+Player+Performance+via+a+Closed-Loop+AI+Approach:+Imitation,+Simulation,+Optimization,+and+Execution)|0| |[Exploiting Homeostatic Synaptic Modulation in Spiking Neural Networks for Semi-Supervised Graph Learning](https://doi.org/10.1145/3583780.3616000)|Mingkun Xu|Guangdong Institute of Intelligence Science and Technology & Tsinghua University, Zhuhai & Beijing, China|Semi-supervised graph learning (SSL) is an important task in machine learning that aims to make predictions based on a limited amount of labeled data and a larger set of unlabeled structured data, which can be effectively processed by biological neural networks. In this paper, we investigate the effects of the underlying homeostatic synaptic modulation (HSM) in spiking neural networks (SNNs) on such scenario. We propose a novel framework that integrates HSM into the spiking graph convolutional network to maintain stability by regulating the strength of synapses based on the activity of neurons, allowing for stable graph learning in a semi-supervised setting. Experimental results on citation benchmark datasets demonstrate that the proposed HSM mechanism can enable SNNs with superior capabilities of convergence and generalization, meanwhile possessing expected characteristics of sparsity and call-back phenomenon. The proposed framework provides a promising approach for exploiting HSM in neural network architectures for efficient graph learning.|半监督图学习(SSL)是机器学习中的一项重要任务，其目标是基于有限的标记数据和大量的未标记结构化数据进行预测，这些数据能够被生物神经网络有效地处理。本文研究了刺激神经网络中潜在的稳态突触调制(HSM)对这种情况的影响。我们提出了一个新的框架，将 HSM 集成到峰值图卷积网络，通过调节突触的强度来维持稳定性，基于神经元的活动，允许在半监督环境下稳定的图学习。对引文基准数据集的实验结果表明，所提出的 HSM 机制能够使 SNN 具有较强的收敛和泛化能力，同时具有稀疏和回调现象的预期特征。该框架为利用神经网络结构中的 HSM 进行有效的图形学习提供了一种有前途的方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Exploiting+Homeostatic+Synaptic+Modulation+in+Spiking+Neural+Networks+for+Semi-Supervised+Graph+Learning)|0| -|[Some Useful Things to Know When Combining IR and NLP: the Easy, the Hard and the Ugly](https://doi.org/10.1145/3583780.3615295)|Omar Alonso, Kenneth Church|Amazon, Santa Clara, CA, USA; Northeastern University, San Jose, CA, USA|Deep nets such as GPT are at the core of the current advances in many systems and applications. Things are moving very fast, and it appears that techniques are out of date within weeks. How can we take advantage of new discoveries and incorporate them into our existing work? Are these radical new developments, repetitions of older concepts, or both? In this tutorial, we aim to bring interested researchers and practitioners up to speed on the recent and ongoing techniques around ML and Deep learning in the context of IR and NLP. Additionally, our goal is to clarify terminology, emphasize fundamentals, and outline new research opportunities.|像 GPT 这样的深网是当前许多系统和应用进展的核心。事情发展得非常快，而且技术似乎在几周内就过时了。我们如何利用新的发现，并将其纳入我们现有的工作？这些是激进的新发展，旧观念的重复，还是两者兼而有之？在本教程中，我们的目的是让感兴趣的研究人员和从业人员加快最近和正在进行的技术在机器学习和深度学习的背景下的 IR 和 NLP。此外，我们的目标是澄清术语，强调基本原理，并概述新的研究机会。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Some+Useful+Things+to+Know+When+Combining+IR+and+NLP:+the+Easy,+the+Hard+and+the+Ugly)|0| +|[Some Useful Things to Know When Combining IR and NLP: the Easy, the Hard and the Ugly](https://doi.org/10.1145/3583780.3615295)|Omar Alonso, Kenneth Church|Northeastern University, San Jose, CA, USA; Amazon, Santa Clara, CA, USA|Deep nets such as GPT are at the core of the current advances in many systems and applications. Things are moving very fast, and it appears that techniques are out of date within weeks. How can we take advantage of new discoveries and incorporate them into our existing work? Are these radical new developments, repetitions of older concepts, or both? In this tutorial, we aim to bring interested researchers and practitioners up to speed on the recent and ongoing techniques around ML and Deep learning in the context of IR and NLP. Additionally, our goal is to clarify terminology, emphasize fundamentals, and outline new research opportunities.|像 GPT 这样的深网是当前许多系统和应用进展的核心。事情发展得非常快，而且技术似乎在几周内就过时了。我们如何利用新的发现，并将其纳入我们现有的工作？这些是激进的新发展，旧观念的重复，还是两者兼而有之？在本教程中，我们的目的是让感兴趣的研究人员和从业人员加快最近和正在进行的技术在机器学习和深度学习的背景下的 IR 和 NLP。此外，我们的目标是澄清术语，强调基本原理，并概述新的研究机会。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Some+Useful+Things+to+Know+When+Combining+IR+and+NLP:+the+Easy,+the+Hard+and+the+Ugly)|0| |[Application of Deep Clustering Algorithms](https://doi.org/10.1145/3583780.3615290)|Collin Leiber, Lukas Miklautz, Claudia Plant, Christian Böhm|University of Vienna, Vienna, Austria; LMU Munich, Munich, Germany|Deep clustering algorithms have gained popularity for clustering complex, large-scale data sets, but getting started is difficult because of numerous decisions regarding architecture, optimizer, and other hyperparameters. Theoretical foundations must be known to obtain meaningful results. At the same time, ease of use is necessary to get used by a broader audience. Therefore, we require a unified framework that allows for easy execution in diverse settings. While this applies to established clustering methods like k-Means and DBSCAN, deep clustering algorithms lack a standard structure, resulting in significant programming overhead. This complicates empirical evaluations, which are essential in both scientific and practical applications. We present a solution to this problem by providing a theoretical background on deep clustering as well as practical implementation techniques and a unified structure with predefined neural networks. For the latter, we use the Python package ClustPy. The aim is to share best practices and facilitate community participation in deep clustering research.|深度聚类算法已经在对复杂的、大规模的数据集进行聚类方面获得了普及，但是由于在体系结构、优化器和其他超参数方面的许多决策，开始这项工作变得很困难。要想得到有意义的结果，必须了解理论基础。同时，易于使用对于更广泛的受众来说是必要的。因此，我们需要一个统一的框架，以便在不同的设置中轻松执行。虽然这适用于已建立的聚类方法，如 k-Means 和 DBSCAN，但是深度聚类算法缺乏标准结构，导致大量的编程开销。这使得经验性评估复杂化，而这些评估在科学和实际应用中都是必不可少的。我们提出了一个解决这个问题的方案，提供了深度聚类的理论背景和实际的实现技术，并提出了一个统一的结构与预定义的神经网络。对于后者，我们使用 Python 包 ClustPy。其目的是分享最佳做法，促进社区参与深度集群研究。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Application+of+Deep+Clustering+Algorithms)|0| |[RT2S: A Framework for Learning with Noisy Labels](https://doi.org/10.1145/3583780.3615996)|Indranil Bhattacharya, Ze Ye, Kaushik Pavani, Sunny Dasgupta|Amazon.com, Inc., Seattle, WA, USA|We introduce Robust Training with Trust Scores (RT2S), a framework to train machine learning classifiers with potentially noisy labels. RT2S calculates a trust score for each training sample, which indicates the quality of its corresponding label. These trust scores are employed as sample weights during training and optionally during threshold optimization. The trust scores are generated from two sources: (i) the model's confidence in the observed label, leveraging out-of-fold prediction scores to detect anomalous labels in the training data, and (ii) the probability of the correct label, ascertained by a Large Language Model with the ability to identify biased label noise. We evaluate RT2S by training machine learning models on 6 product classification datasets that utilize low-quality labels generated by a rule-based classification engine acting as a surrogate labeler. Our experimental findings indicate that RT2S outperforms all baselines, and achieves an average accuracy improvement of 4.38% (max 7.18%) over rule-based classifiers in particular.|我们引入了带信任分数的鲁棒训练(RT2S) ，这是一个用潜在噪声标签训练机器学习分类器的框架。RT2S 为每个训练样本计算一个信任评分，表明其相应标签的质量。这些信任得分被用作训练期间和阈值优化期间的样本权重。信任分数来自两个来源: (i)模型对观察标签的信心，利用外部预测分数来检测训练数据中的异常标签，以及(ii)由具有识别偏倚标签噪声能力的大语言模型确定的正确标签的概率。通过对6个产品分类数据集的机器学习模型进行训练，利用基于规则的分类引擎作为替代标签生成的低质量标签，对 RT2S 进行评估。实验结果表明，RT2S 分类器的性能优于所有基线分类器，特别是与基于规则的分类器相比，RT2S 分类器的平均准确率提高了4.38% (最大7.18%)。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=RT2S:+A+Framework+for+Learning+with+Noisy+Labels)|0| |[Type Theory as a Unifying Paradigm for Modern Databases](https://doi.org/10.1145/3583780.3615999)|Christoph Dorn, Haikal Pribadi|Vaticle, London, United Kingdom|Over the past decades, data modeling has become a highly diversified discipline with many competing paradigms emerging across various application domains. We argue that higher levels of abstraction including, in particular, the integration of high-level programming and reasoning techniques, will pave the way forward for future knowledge management systems. As a programmatic foundation for this endeavor, we will discuss a novel type theoretical modeling and reasoning paradigm, which aims to strike a powerful balance between what can be naturally semantically modeled and what can be practically implemented. TypeQL is a multi-purpose database language rooted in these foundations: it is designed around the expressivity of natural language and backed by type theoretical principles. This rigorous high-level approach to database language reduces development and maintenance loads, preventing hard to spot data integrity and logic errors through its underlying type system, while also providing a unifying toolset for a large class of domain-specific applications ranging from querying connected data in knowledge graphs for drug discovery to reasoning and adaptive decision making in cognitive robotics.|在过去的几十年中，数据建模已经成为一个高度多样化的学科，在不同的应用领域中出现了许多相互竞争的范例。我们认为，更高层次的抽象，尤其是高层次编程和推理技术的集成，将为未来的知识管理系统铺平道路。作为这一努力的纲领性基础，我们将讨论一种新型的理论建模和推理范式，其目的是在可以自然语义建模和可以实际实现之间达成有力的平衡。TypeQL 是一种基于这些基础的多用途数据库语言: 它是围绕自然语言的表达性设计的，并以类型理论原则为支撑。这种严格的高级数据库语言方法减少了开发和维护的负荷，通过其底层类型系统防止难以发现数据完整性和逻辑错误，同时也为一大类特定领域的应用程序提供了统一的工具集，从查询知识图中的连接数据用于药物发现，到认知机器人中的推理和自适应决策。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Type+Theory+as+a+Unifying+Paradigm+for+Modern+Databases)|0| -|[Comparing Fine-Tuned Transformers and Large Language Models for Sales Call Classification: A Case Study](https://doi.org/10.1145/3583780.3615509)|Roy Eisenstadt, Abedelkader Asi, Royi Ronen|Microsoft Dynamics 365 Sales, Sammamish, WA, USA; Microsoft Dynamics 365 Sales, Tel Aviv, Israel|We present a research project carried out to enable a Call Categorization Service (CCS) for Dynamics 365 Sales Conversation Intelligence. CCS identifies prevalent types of sales calls based on their transcription, for the purpose of automating manual sales processes and making informed business decisions. We sift through R&D process, and provide clear evidence that purpose-focused fine-tuned transformers out-perform GPT-3 in this text classification task. Additionally we share: an efficient, non-trivial data annotation approach suited to the problem of finding data related to rare categories in a highly unbalanced data source; Considerations regarding zero-shot and in-context learning (i.e. few-shot learning) when using LLMs for classification and cost and performance analysis that opt in favor of fine-tuned transformers as well.|我们提出了一个研究项目，以启用呼叫分类服务(CCS)的动态365销售会话智能。CCS 根据销售电话的转录来识别流行的销售电话类型，目的是实现手工销售流程的自动化，并做出明智的商业决策。我们通过研发过程中筛选，并提供明确的证据表明，目的为重点的微调变压器性能优于 GPT-3在这个文本分类任务。此外，我们共享: 一个有效的，非平凡的数据注释方法，适合于在一个高度不平衡的数据源中找到与罕见类别相关的数据的问题; 关于零拍摄和上下文学习(即少拍摄学习)的考虑，当使用 LLM 进行分类，成本和性能分析，选择有利于微调变压器以及。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Comparing+Fine-Tuned+Transformers+and+Large+Language+Models+for+Sales+Call+Classification:+A+Case+Study)|0| +|[Comparing Fine-Tuned Transformers and Large Language Models for Sales Call Classification: A Case Study](https://doi.org/10.1145/3583780.3615509)|Roy Eisenstadt, Abedelkader Asi, Royi Ronen|Microsoft Dynamics 365 Sales, Tel Aviv, Israel; Microsoft Dynamics 365 Sales, Sammamish, WA, USA|We present a research project carried out to enable a Call Categorization Service (CCS) for Dynamics 365 Sales Conversation Intelligence. CCS identifies prevalent types of sales calls based on their transcription, for the purpose of automating manual sales processes and making informed business decisions. We sift through R&D process, and provide clear evidence that purpose-focused fine-tuned transformers out-perform GPT-3 in this text classification task. Additionally we share: an efficient, non-trivial data annotation approach suited to the problem of finding data related to rare categories in a highly unbalanced data source; Considerations regarding zero-shot and in-context learning (i.e. few-shot learning) when using LLMs for classification and cost and performance analysis that opt in favor of fine-tuned transformers as well.|我们提出了一个研究项目，以启用呼叫分类服务(CCS)的动态365销售会话智能。CCS 根据销售电话的转录来识别流行的销售电话类型，目的是实现手工销售流程的自动化，并做出明智的商业决策。我们通过研发过程中筛选，并提供明确的证据表明，目的为重点的微调变压器性能优于 GPT-3在这个文本分类任务。此外，我们共享: 一个有效的，非平凡的数据注释方法，适合于在一个高度不平衡的数据源中找到与罕见类别相关的数据的问题; 关于零拍摄和上下文学习(即少拍摄学习)的考虑，当使用 LLM 进行分类，成本和性能分析，选择有利于微调变压器以及。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Comparing+Fine-Tuned+Transformers+and+Large+Language+Models+for+Sales+Call+Classification:+A+Case+Study)|0| |[Application and Evaluation of Large Language Models for the Generation of Survey Questions](https://doi.org/10.1145/3583780.3615506)|Antonio Maiorino, Zoe Padgett, Chun Wang, Misha Yakubovskiy, Peng Jiang|SurveyMonkey, San Mateo, USA|Generative Language Models have shown promising results in various domains, and some of the most successful applications are related to "concept expansion", which is the task of generating extensive text based on concise instructions provided through a "seed" prompt. In this presentation we will discuss the recent work conducted by the Data Science team at SurveyMonkey, where we have recently introduced a new feature that harnesses Generative AI models to streamline the survey design process. With this feature users can effortlessly initiate this process by specifying their desired objectives through a prompt, allowing them to automate the creation of surveys that include the critical aspects they wish to investigate. We will share our findings regarding some of the challenges encountered during the development of this feature. These include techniques for conditioning the model outputs, integrating generated text with industry-standard questions, fine-tuning Language Models using semi-synthetic Data Generation techniques, and more. Moreover, we will showcase the Evaluation Methodology that we have developed to measure the quality of the generated surveys across several dimensions. This evaluation process is crucial in ensuring that the generated surveys align well with user expectations and serve their intended purpose effectively. Our goal is to demonstrate the promising potential of Generative Language Models in the context of Survey Research, and we believe that sharing our learnings on these challenges and how we addressed them will be useful for practitioners working with Language Models on similar problems.|生成语言模型已经在各个领域显示了有希望的结果，并且一些最成功的应用与“概念扩展”有关，这是一个基于通过“种子”提示提供的简洁指令生成大量文本的任务。在本次演讲中，我们将讨论 SurveyMonkey 数据科学团队最近的工作，我们最近引入了一个新特性，利用生成人工智能模型来简化调查设计过程。有了这个功能，用户可以通过迅速指定他们想要的目标，轻松地启动这个过程，允许他们自动创建包括他们想要调查的关键方面的调查。我们将分享我们关于在开发这一功能过程中遇到的一些挑战的调查结果。这些技术包括调节模型输出、将生成的文本与行业标准问题集成、使用半合成数据生成技术微调语言模型等。此外，我们将展示我们开发的评价方法，以衡量所生成的调查在几个方面的质量。这一评价过程对于确保生成的调查符合用户的期望并有效地达到预期目的至关重要。我们的目标是在调查研究的背景下展示生成语言模型的潜力，我们相信分享我们在这些挑战上的经验以及我们如何解决这些挑战将对从事语言模型工作的人员在类似问题上有所帮助。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Application+and+Evaluation+of+Large+Language+Models+for+the+Generation+of+Survey+Questions)|0| |[Harnessing GPT for Topic-Based Call Segmentation in Microsoft Dynamics 365 Sales](https://doi.org/10.1145/3583780.3615508)|Itzik Malkiel, Uri Alon, Yakir Yehuda, Shahar Keren, Oren Barkan, Royi Ronen, Noam Koenigstein|Microsoft, Tel Aviv Yaffo, Israel|Transcriptions of phone calls hold significant value in sales, customer service, healthcare, law enforcement, and more. However, analyzing recorded conversations can be a time-consuming process, especially for complex dialogues. In Microsoft Dynamics 365 Sales, a novel system, named GPT-Calls, is applied for efficient and accurate topic-based call segmentation. GPT-Calls comprises offline and online phases. In the offline phase, the system leverages a GPT model to generate synthetic sentences and extract anchor vectors for predefined topics. This phase, performed once on a given topic list, significantly reduces the computational burden. The online phase scores the similarity between the transcribed conversation and the topic anchors from the offline phase, followed by time domain analysis to group utterances into segments and tag them with topics. The GPT-Calls scheme offers an accurate and efficient approach to call segmentation and topic extraction, eliminating the need for labeled data. It is a versatile solution applicable to various industry domains. GPT-Calls operates in production under Dynamics 365 Sales Conversation Intelligence, applied to real sales conversations from diverse Dynamics 365 Sales tenants, streamlining call analysis, and saving time and resources while ensuring accuracy and effectiveness.|电话记录在销售、客户服务、医疗保健、执法等方面具有重要价值。然而，分析记录的对话可能是一个耗时的过程，特别是对于复杂的对话。在 Microsoft Dynamics 365 Sales 中，一个名为 GPT-Call 的新系统被应用于高效、准确的基于主题的呼叫分类。GPT-通话包括离线和在线两个阶段。在离线阶段，系统利用 GPT 模型生成合成语句，并为预定义的主题提取锚向量。这个阶段在给定的主题列表上执行一次，就可以大大减少计算负担。在线阶段记录转录的会话与离线阶段的主题锚之间的相似性，然后进行时域分析，将话语分成片段，并给它们加上主题标签。GPT-Call 方案提供了一种准确有效的呼叫分割和主题提取方法，从而消除了对标记数据的需求。它是一个适用于各种行业领域的通用解决方案。GPT-Call 在 Dynamics 365 Sales Conversation Intelligence 下运作，应用于来自不同 Dynamics 365销售租户的真实销售对话，简化呼叫分析，节省时间和资源，同时确保准确性和有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Harnessing+GPT+for+Topic-Based+Call+Segmentation+in+Microsoft+Dynamics+365+Sales)|0| |[Astrolabe: Visual Graph Database Queries with Tabular Output](https://doi.org/10.1145/3583780.3615992)|Michael Miller|Northrop Grumman Space Systems, Roy, UT, USA|Graph databases are an established solution for large, highly connected datasets. One challenge associated with deploying graph databases in industrial settings is usability. Typically, developers interact with graph databases through queries in languages such as Cypher or GraphQL. Many end-users, analysts, and administrators are not familiar with these specialized languages. Additionally, these queries return hierarchical data in formats such as JSON (JavaScript Object Notation) or XML (Extensible Markup Language). Additional scripts and interfaces are needed to convert hierarchical data into more easily digested tables. To overcome these challenges, each graph database use-case typically involves significant custom software to explore, view, and export data. We introduce Astrolabe, a generalized interface that addresses the challenges of querying graph databases. In Astrolabe, queries are constructed visually, so users do not need to learn new graph query languages. Results are returned as tables, which can be easily digested by end users or down-stream applications. Astrolabe was designed to function with arbitrary graph databases, so schema definition is not required. Astrolabe revolutionizes graph exploration and querying by allowing graph databases to be viewed as tables, without the need for custom software adapters.|图形数据库是大型、高度连接数据集的一种已建立的解决方案。在工业环境中部署图形数据库的一个挑战是可用性。通常，开发人员通过 Cypher 或 GraphQL 等语言中的查询与图形数据库交互。许多最终用户、分析人员和管理员不熟悉这些专用语言。此外，这些查询以 JSON (JSON)或 XML (XML)等格式返回分层数据。需要额外的脚本和接口将分层数据转换为更容易消化的表。为了克服这些挑战，每个图形数据库用例通常需要使用重要的定制软件来探索、查看和导出数据。我们介绍 Astrolabe，它是一个通用的接口，用于解决查询图形数据库的挑战。在 Astrolabe 中，查询是可视化构建的，因此用户不需要学习新的图形查询语言。结果以表的形式返回，终端用户或下游应用程序可以很容易地消化这些结果。星盘被设计用于任意图形数据库，因此不需要模式定义。Astrolabe 通过允许将图形数据库视为表格，而不需要自定义软件适配器，彻底改革了图形探索和查询。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Astrolabe:+Visual+Graph+Database+Queries+with+Tabular+Output)|0| -|[LAMM: Language Aware Active Learning for Multilingual Models](https://doi.org/10.1145/3583780.3615507)|Ze Ye, Dantong Liu, Kaushik Pavani, Sunny Dasgupta|Amazon.com, Inc., Seattle, WA, USA; Amazon.com, Inc., Sunnyvale, CA, USA|In industrial settings, it is often necessary to achieve language-level accuracy targets. For example, Amazon business teams need to build multilingual product classifiers that operate accurately in all European languages. It is unacceptable for the accuracy of product classification to meet the target in one language (e.g, English), while falling below the target in other languages (e.g, Portuguese). To fix such issues, we propose Language Aware Active Learning for Multilingual Models (LAMM), an active learning strategy that enables a classifier to learn from a small amount of labeled data in a targeted manner to improve the accuracy of Low-resource languages (LRLs) with limited amounts of data for model training. Our empirical results on two open-source datasets and two proprietary product classification datasets demonstrate that LAMM is able to improve the LRL performance by 4%--11% when compared to strong baselines.|在工业设置中，通常需要达到语言级别的精度目标。例如，亚马逊的业务团队需要构建多语言的产品分类器，这些分类器可以准确地在所有欧洲语言中运行。以一种语言(例如英文)达到目标，而以其他语言(例如葡萄牙文)达不到目标，产品分类的准确性是不可接受的。为了解决这些问题，我们提出了语言感知的多语言模型主动学习(LAMM) ，这是一种主动学习策略，使分类器能够以有针对性的方式从少量的标记数据中学习，以提高低资源语言(LRL)的准确性，用有限的数据进行模型训练。我们对两个开源数据集和两个专有产品分类数据集的实证结果表明，与强基线相比，LAMM 能够提高 LRL 性能4% -11% 。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=LAMM:+Language+Aware+Active+Learning+for+Multilingual+Models)|0| -|[Unleashing the Power of Large Language Models for Legal Applications](https://doi.org/10.1145/3583780.3615993)|Dell Zhang, Alina Petrova, Dietrich Trautmann, Frank Schilder|Thomson Reuters Labs, London, United Kingdom; Thomson Reuters Labs, Eagan, MN, USA; Thomson Reuters Labs, Zug, Switzerland|The use of Large Language Models (LLMs) is revolutionizing the legal industry. In this technical talk, we would like to explore the various use cases of LLMs in legal tasks, discuss the best practices, investigate the available resources, examine the ethical concerns, and suggest promising research directions.|大型语言模型(LLM)的使用正在给法律行业带来革命性的变化。在这个技术演讲中，我们将探讨法律任务中 LLM 的各种用例，讨论最佳实践，调查可用资源，审查伦理问题，并提出有前途的研究方向。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Unleashing+the+Power+of+Large+Language+Models+for+Legal+Applications)|0| -|[Info-Wild: Knowledge Extraction and Management for Wildlife Conservation](https://doi.org/10.1145/3583780.3615313)|Prasenjit Mitra, Shreya Ghosh, Bistra Dilkina, Thomas Müller|Goethe University and Senckenberg Biodiversity & Climate Research Centre, Senckenberg, Germany; Pennsylvania State University, State College, PA, USA; University of Southern California, Los Angeles, CA, USA|Our primary objective is to explore and enhance AI's role for wildlife conservation, in brief, Nature Through the Lens of AI. It seeks to address crucial challenges related to data heterogeneity, scale integration, data privacy, mitigating biases, and decision-making under uncertainty. This workshop is centred around leveraging AI's prowess in deciphering complex spatio-temporal data patterns for wildlife conservation, thereby contributing significantly to the broader canvas of AI for social good. The workshop intends to create an interdisciplinary platform bringing together computer scientists, data scientists, geospatial experts, ecologists, and conservation practitioners, fostering collaboration and driving real-world impact. The program will include keynote speeches, panel discussions, and interactive sessions focusing on efficient knowledge extraction and management, remote sensing technologies, predictive modeling, species distribution modeling, habitat quality assessment, and human-wildlife conflict mitigation. With an em- phasis on CIKM's primary interests, our aim is not only to enrich understanding of AI's symbiotic potential with ecology but also to utilize it to address pressing societal and environmental challenges.|我们的主要目标是探索和加强人工智能在野生动物保护方面的作用，简而言之，就是通过人工智能的镜头来观察自然。它寻求解决与数据异构性、规模集成、数据隐私、减少偏差和不确定性下的决策相关的关键挑战。这个研讨会的中心是利用人工智能破译复杂的野生动物保护时空数据模式的能力，从而为更广泛的人工智能社会公益事业做出重大贡献。研讨会打算创建一个跨学科平台，将计算机科学家、数据科学家、地理空间专家、生态学家和环境保护从业人员聚集在一起，促进合作，推动现实世界的影响。该项目将包括主题演讲、小组讨论和互动会议，重点是有效的知识提取和管理、遥感技术、预测建模、物种分布建模、栖息地质量评估和人类与野生动植物冲突的缓解。重点关注 CIKM 的主要兴趣，我们的目标不仅是丰富对人工智能与生态共生潜力的理解，而且利用它来应对紧迫的社会和环境挑战。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Info-Wild:+Knowledge+Extraction+and+Management+for+Wildlife+Conservation)|0| +|[LAMM: Language Aware Active Learning for Multilingual Models](https://doi.org/10.1145/3583780.3615507)|Ze Ye, Dantong Liu, Kaushik Pavani, Sunny Dasgupta|Amazon.com, Inc., Sunnyvale, CA, USA; Amazon.com, Inc., Seattle, WA, USA|In industrial settings, it is often necessary to achieve language-level accuracy targets. For example, Amazon business teams need to build multilingual product classifiers that operate accurately in all European languages. It is unacceptable for the accuracy of product classification to meet the target in one language (e.g, English), while falling below the target in other languages (e.g, Portuguese). To fix such issues, we propose Language Aware Active Learning for Multilingual Models (LAMM), an active learning strategy that enables a classifier to learn from a small amount of labeled data in a targeted manner to improve the accuracy of Low-resource languages (LRLs) with limited amounts of data for model training. Our empirical results on two open-source datasets and two proprietary product classification datasets demonstrate that LAMM is able to improve the LRL performance by 4%--11% when compared to strong baselines.|在工业设置中，通常需要达到语言级别的精度目标。例如，亚马逊的业务团队需要构建多语言的产品分类器，这些分类器可以准确地在所有欧洲语言中运行。以一种语言(例如英文)达到目标，而以其他语言(例如葡萄牙文)达不到目标，产品分类的准确性是不可接受的。为了解决这些问题，我们提出了语言感知的多语言模型主动学习(LAMM) ，这是一种主动学习策略，使分类器能够以有针对性的方式从少量的标记数据中学习，以提高低资源语言(LRL)的准确性，用有限的数据进行模型训练。我们对两个开源数据集和两个专有产品分类数据集的实证结果表明，与强基线相比，LAMM 能够提高 LRL 性能4% -11% 。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=LAMM:+Language+Aware+Active+Learning+for+Multilingual+Models)|0| +|[Unleashing the Power of Large Language Models for Legal Applications](https://doi.org/10.1145/3583780.3615993)|Dell Zhang, Alina Petrova, Dietrich Trautmann, Frank Schilder|Thomson Reuters Labs, Zug, Switzerland; Thomson Reuters Labs, Eagan, MN, USA; Thomson Reuters Labs, London, United Kingdom|The use of Large Language Models (LLMs) is revolutionizing the legal industry. In this technical talk, we would like to explore the various use cases of LLMs in legal tasks, discuss the best practices, investigate the available resources, examine the ethical concerns, and suggest promising research directions.|大型语言模型(LLM)的使用正在给法律行业带来革命性的变化。在这个技术演讲中，我们将探讨法律任务中 LLM 的各种用例，讨论最佳实践，调查可用资源，审查伦理问题，并提出有前途的研究方向。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Unleashing+the+Power+of+Large+Language+Models+for+Legal+Applications)|0| +|[Info-Wild: Knowledge Extraction and Management for Wildlife Conservation](https://doi.org/10.1145/3583780.3615313)|Prasenjit Mitra, Shreya Ghosh, Bistra Dilkina, Thomas Müller|University of Southern California, Los Angeles, CA, USA; Pennsylvania State University, State College, PA, USA; Goethe University and Senckenberg Biodiversity & Climate Research Centre, Senckenberg, Germany|Our primary objective is to explore and enhance AI's role for wildlife conservation, in brief, Nature Through the Lens of AI. It seeks to address crucial challenges related to data heterogeneity, scale integration, data privacy, mitigating biases, and decision-making under uncertainty. This workshop is centred around leveraging AI's prowess in deciphering complex spatio-temporal data patterns for wildlife conservation, thereby contributing significantly to the broader canvas of AI for social good. The workshop intends to create an interdisciplinary platform bringing together computer scientists, data scientists, geospatial experts, ecologists, and conservation practitioners, fostering collaboration and driving real-world impact. The program will include keynote speeches, panel discussions, and interactive sessions focusing on efficient knowledge extraction and management, remote sensing technologies, predictive modeling, species distribution modeling, habitat quality assessment, and human-wildlife conflict mitigation. With an em- phasis on CIKM's primary interests, our aim is not only to enrich understanding of AI's symbiotic potential with ecology but also to utilize it to address pressing societal and environmental challenges.|我们的主要目标是探索和加强人工智能在野生动物保护方面的作用，简而言之，就是通过人工智能的镜头来观察自然。它寻求解决与数据异构性、规模集成、数据隐私、减少偏差和不确定性下的决策相关的关键挑战。这个研讨会的中心是利用人工智能破译复杂的野生动物保护时空数据模式的能力，从而为更广泛的人工智能社会公益事业做出重大贡献。研讨会打算创建一个跨学科平台，将计算机科学家、数据科学家、地理空间专家、生态学家和环境保护从业人员聚集在一起，促进合作，推动现实世界的影响。该项目将包括主题演讲、小组讨论和互动会议，重点是有效的知识提取和管理、遥感技术、预测建模、物种分布建模、栖息地质量评估和人类与野生动植物冲突的缓解。重点关注 CIKM 的主要兴趣，我们的目标不仅是丰富对人工智能与生态共生潜力的理解，而且利用它来应对紧迫的社会和环境挑战。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Info-Wild:+Knowledge+Extraction+and+Management+for+Wildlife+Conservation)|0| |[Anomaly and Novelty detection for Satellite and Drone systems (ANSD '23)](https://doi.org/10.1145/3583780.3615306)|Shahroz Tariq, Daewon Chung, Simon Woo, Youjin Shin|The Catholic University of Korea, Bucheon, South Korea; Sungkyunkwan University, Suwon, South Korea; Korea Aerospace Research Institute (KARI), Daejeon, South Korea; CSIRO's Data61, Sydney, Australia|In recent times, there has been a notable surge in the amount of vision and sensing/time-series data obtained from drones and satellites. This data can be utilized in various fields, such as precision agriculture, disaster management, environmental monitoring, and others. However, the analysis of such data poses significant challenges due to its complexity, heterogeneity, and scale. Furthermore, it is critical to identify anomalies and maintain/monitor the health of drones and satellite systems to enable the aforementioned applications and sciences. This workshop presents an excellent opportunity to explore solutions that specifically target the detection of anomalies and novel occurrences in drones and satellite systems and their data. For more information, visit our website at https://sites.google.com/view/ansd23|近年来，从无人机和卫星获得的视觉和传感/时间序列数据量显著增加。这些数据可用于各种领域，如精细农业、灾害管理、环境监测等。然而，由于其复杂性、异构性和规模，对这些数据的分析提出了重大挑战。此外，必须查明异常情况并维持/监测无人机和卫星系统的健康状况，以便能够开展上述应用和科学研究。这次研讨会提供了一个极好的机会，可以探讨具体针对探测无人机和卫星系统及其数据中的异常和新现象的解决办法。如欲查询更多资料，请浏览本署网页( https://sites.google.com/view/ansd23)|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Anomaly+and+Novelty+detection+for+Satellite+and+Drone+systems+(ANSD+'23))|0| -|[Knowledge-driven Analytics and Systems Impacting Human Quality of Life- Neurosymbolic AI, Explainable AI and Beyond](https://doi.org/10.1145/3583780.3615300)|Arijit Ukil, Joao Gama, Antonio J. Jara, Leandro Marín|University of Porto, Porto, Portugal; Libelium, Murcia, Spain; TCS Research, Kolkata, India; University of Murcia, Murcia, Spain|The management of knowledge-driven artificial intelligence technologies is essential in order to evaluate their impact on human life and society. Social networks and tech use can have a negative impact on us physically, emotionally, socially and mentally. On the other hand, intelligent systems can have a positive effect on people's lives. Currently, we are witnessing the power of large language models (LLMs) like chatGPT and its influence towards the society. The objective of the workshop is to contribute to the advancement of intelligent technologies designed to address the human condition. This could include precise and personalized medicine, better care for elderly people, reducing private data leaks, using AI to manage resources better, using AI to predict risks, augmenting human capabilities, and more. The workshop's objective is to present research findings and perspectives that demonstrate how knowledge-enabled technologies and applications improve human well-being. This workshop indeed focuses on the impacts at different granularity levels made by Artificial Intelligence (AI) research on the micro granular level, where the daily or regular functioning of human life is affected, and also the macro granulate level, where the long-term or far-future effects of artificial intelligence on people's lives and the human society could be pretty high. In conclusion, this workshop explores how AI research can potentially address the most pressing challenges facing modern societies, and how knowledge management can potentially contribute to these solutions.|知识驱动的人工智能技术的管理对于评估其对人类生活和社会的影响至关重要。社交网络和科技的使用会对我们的身体、情感、社交和精神产生负面影响。另一方面，智能系统可以对人们的生活产生积极的影响。目前，我们正在见证像 chatGPT 这样的大型语言模型(LLM)的力量及其对社会的影响。讲习班的目的是促进智能技术的进步，以解决人类的条件。这可能包括精确和个体化医学，更好地照顾老年人，减少私人数据泄露，使用人工智能更好地管理资源，使用人工智能预测风险，增强人类能力等等。研讨会的目的是展示研究结果和观点，说明知识技术和应用如何改善人类福祉。这个研讨会确实关注人工智能(AI)研究在不同粒度水平上对微观粒度水平(人类生活的日常或正常功能受到影响)和宏观粒度水平(人工智能对人类生活和人类社会的长期或远期影响可能相当高)的影响。最后，本次研讨会探讨了人工智能研究如何能够潜在地应对现代社会面临的最紧迫挑战，以及知识管理如何能够潜在地为这些解决方案做出贡献。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Knowledge-driven+Analytics+and+Systems+Impacting+Human+Quality+of+Life-+Neurosymbolic+AI,+Explainable+AI+and+Beyond)|0| +|[Knowledge-driven Analytics and Systems Impacting Human Quality of Life- Neurosymbolic AI, Explainable AI and Beyond](https://doi.org/10.1145/3583780.3615300)|Arijit Ukil, Joao Gama, Antonio J. Jara, Leandro Marín|Libelium, Murcia, Spain; University of Porto, Porto, Portugal; University of Murcia, Murcia, Spain; TCS Research, Kolkata, India|The management of knowledge-driven artificial intelligence technologies is essential in order to evaluate their impact on human life and society. Social networks and tech use can have a negative impact on us physically, emotionally, socially and mentally. On the other hand, intelligent systems can have a positive effect on people's lives. Currently, we are witnessing the power of large language models (LLMs) like chatGPT and its influence towards the society. The objective of the workshop is to contribute to the advancement of intelligent technologies designed to address the human condition. This could include precise and personalized medicine, better care for elderly people, reducing private data leaks, using AI to manage resources better, using AI to predict risks, augmenting human capabilities, and more. The workshop's objective is to present research findings and perspectives that demonstrate how knowledge-enabled technologies and applications improve human well-being. This workshop indeed focuses on the impacts at different granularity levels made by Artificial Intelligence (AI) research on the micro granular level, where the daily or regular functioning of human life is affected, and also the macro granulate level, where the long-term or far-future effects of artificial intelligence on people's lives and the human society could be pretty high. In conclusion, this workshop explores how AI research can potentially address the most pressing challenges facing modern societies, and how knowledge management can potentially contribute to these solutions.|知识驱动的人工智能技术的管理对于评估其对人类生活和社会的影响至关重要。社交网络和科技的使用会对我们的身体、情感、社交和精神产生负面影响。另一方面，智能系统可以对人们的生活产生积极的影响。目前，我们正在见证像 chatGPT 这样的大型语言模型(LLM)的力量及其对社会的影响。讲习班的目的是促进智能技术的进步，以解决人类的条件。这可能包括精确和个体化医学，更好地照顾老年人，减少私人数据泄露，使用人工智能更好地管理资源，使用人工智能预测风险，增强人类能力等等。研讨会的目的是展示研究结果和观点，说明知识技术和应用如何改善人类福祉。这个研讨会确实关注人工智能(AI)研究在不同粒度水平上对微观粒度水平(人类生活的日常或正常功能受到影响)和宏观粒度水平(人工智能对人类生活和人类社会的长期或远期影响可能相当高)的影响。最后，本次研讨会探讨了人工智能研究如何能够潜在地应对现代社会面临的最紧迫挑战，以及知识管理如何能够潜在地为这些解决方案做出贡献。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Knowledge-driven+Analytics+and+Systems+Impacting+Human+Quality+of+Life-+Neurosymbolic+AI,+Explainable+AI+and+Beyond)|0| |[Knowledge-enhanced Artificial Intelligence in Drug Discovery (KAIDD)](https://doi.org/10.1145/3583780.3615309)|Qingpeng Zhang, Jiannan Yang|The University of Hong Kong, Hong Kong SAR, Hong Kong|Artificial Intelligence (AI) in drug discovery is a rapidly evolving field that combines computational methods with biological knowledge and applications. Traditionally, the process of developing a new drug has been time-consuming and expensive, spanning several years and costing billions of dollars. The emergence of AI technologies offers the potential to significantly reduce both the timeline and cost involved in this critical endeavour. However, it is crucial to acknowledge that AI applications in pharmacy and drug discovery require a high degree of interpretability and transparency. The integration of domain knowledge into AI models becomes paramount to ensure the reliability and trustworthiness of the generated results. In light of these considerations, we propose a workshop on "Knowledge-enhanced Artificial Intelligence in Drug Discovery (KAIDD)." This workshop aims to explore the profound impact of incorporating various knowledge databases into the development of explainable AI models for drug discovery. Participants will have the opportunity to delve into cutting-edge research, methodologies, and practical applications that leverage the fusion of AI techniques with domain-specific knowledge. Authors of accepted papers will have the opportunity to submit extended versions of their work for a full-paper review process and potential publication in Philosophical Transactions of the Royal Society B.|药物发现中的人工智能(AI)是一个快速发展的领域，它将计算方法与生物学知识和应用相结合。传统上，开发一种新药的过程既耗时又昂贵，需要花费数年时间和数十亿美元。人工智能技术的出现有可能大大缩短这一关键工作的时间和成本。然而，认识到 AI 在药剂学和药物发现中的应用需要高度的可解释性和透明度是至关重要的。将领域知识集成到人工智能模型中对于确保所生成结果的可靠性和可信度至关重要。基于这些考虑，我们提议举办“药物发现中的知识增强型人工智能(KAIDD)”研讨会这次研讨会的目的是探讨将各种知识数据库纳入药物发现可解释的人工智能模型开发的深远影响。与会者将有机会深入研究前沿的研究，方法学和实际应用，利用人工智能技术与特定领域的知识融合。被接受的论文的作者将有机会提交他们的工作的扩展版本的全文审查过程和潜在的出版物在英国皇家学会哲学会报 B。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Knowledge-enhanced+Artificial+Intelligence+in+Drug+Discovery+(KAIDD))|0| |[Ontology Enrichment from Texts: A Biomedical Dataset for Concept Discovery and Placement](https://doi.org/10.1145/3583780.3615126)|Hang Dong, Jiaoyan Chen, Yuan He, Ian Horrocks|University of Oxford, Oxford, United Kingdom; The University of Manchester & University of Oxford, Manchester, United Kingdom|Mentions of new concepts appear regularly in texts and require automated approaches to harvest and place them into Knowledge Bases (KB), e.g., ontologies and taxonomies. Existing datasets suffer from three issues, (i) mostly assuming that a new concept is pre-discovered and cannot support out-of-KB mention discovery; (ii) only using the concept label as the input along with the KB and thus lacking the contexts of a concept label; and (iii) mostly focusing on concept placement w.r.t a taxonomy of atomic concepts, instead of complex concepts, i.e., with logical operators. To address these issues, we propose a new benchmark, adapting MedMentions dataset (PubMed abstracts) with SNOMED CT versions in 2014 and 2017 under the Diseases sub-category and the broader categories of Clinical finding, Procedure, and Pharmaceutical / biologic product. We provide usage on the evaluation with the dataset for out-of-KB mention discovery and concept placement, adapting recent Large Language Model based methods.|新概念的提及经常出现在文本中，需要自动化的方法来收集并放入知识库(KB)中，例如，本体论和分类法。现有的数据集存在三个问题，(i)主要假设一个新的概念是预先发现的，不能支持知识库之外的发现; (ii)只使用概念标签作为输入以及知识库，因此缺乏概念标签的上下文; 以及(iii)主要关注概念放置.r.t 原子概念的分类，而不是复杂的概念，即逻辑运算符。为了解决这些问题，我们提出了一个新的基准，在2014年和2017年在疾病子类别和更广泛的临床发现，程序和药物/生物产品类别下使用 SNOMED CT 版本的 MedMations 数据集(PubMed 摘要)。我们提供了使用数据集进行评估的方法，用于知识库以外的提及发现和概念放置，并采用了最新的基于大语言模型的方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Ontology+Enrichment+from+Texts:+A+Biomedical+Dataset+for+Concept+Discovery+and+Placement)|0| |[FlaCGEC: A Chinese Grammatical Error Correction Dataset with Fine-grained Linguistic Annotation](https://doi.org/10.1145/3583780.3615119)|Hanyue Du, Yike Zhao, Qingyuan Tian, Jiani Wang, Lei Wang, Yunshi Lan, Xuesong Lu||Chinese Grammatical Error Correction (CGEC) has been attracting growing attention from researchers recently. In spite of the fact that multiple CGEC datasets have been developed to support the research, these datasets lack the ability to provide a deep linguistic topology of grammar errors, which is critical for interpreting and diagnosing CGEC approaches. To address this limitation, we introduce FlaCGEC, which is a new CGEC dataset featured with fine-grained linguistic annotation. Specifically, we collect raw corpus from the linguistic schema defined by Chinese language experts, conduct edits on sentences via rules, and refine generated samples manually, which results in 10k sentences with 78 instantiated grammar points and 3 types of edits. We evaluate various cutting-edge CGEC methods on the proposed FlaCGEC dataset and their unremarkable results indicate that this dataset is challenging in covering a large range of grammatical errors. In addition, we also treat FlaCGEC as a diagnostic dataset for testing generalization skills and conduct a thorough evaluation of existing CGEC models.|近年来，汉语语法错误纠正(CGEC)越来越受到研究者的关注。尽管已经开发了多个 CGEC 数据集来支持这项研究，但是这些数据集缺乏提供深层语法错误语言拓扑的能力，这对于解释和诊断 CGEC 方法至关重要。为了解决这个问题，我们引入了 FlaCGEC，它是一个新的具有细粒度语言标注的 CGEC 数据集。具体来说，我们从汉语专家定义的语言图式中收集原始语料，通过规则对句子进行编辑，并对生成的样本进行人工精炼，得到10000个句子，其中78个实例化语法点，3种类型的编辑。我们评估了各种尖端的 CGEC 方法对提出的 FlaCGEC 数据集和他们的不起眼的结果表明，这个数据集是具有挑战性的，以涵盖大范围的语法错误。此外，我们还将 FlaCGEC 作为一个诊断数据集，用于测试泛化技能，并对现有的 CGEC 模型进行全面评估。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=FlaCGEC:+A+Chinese+Grammatical+Error+Correction+Dataset+with+Fine-grained+Linguistic+Annotation)|0| |[PGB: A PubMed Graph Benchmark for Heterogeneous Network Representation Learning](https://doi.org/10.1145/3583780.3615128)|Eric Wonhee Lee, Joyce C. Ho|Emory University, Atlanta, GA, USA|There has been a rapid growth in biomedical literature, yet capturing the heterogeneity of the bibliographic information of these articles remains relatively understudied. Although graph mining research via heterogeneous graph neural networks has taken center stage, it remains unclear whether these approaches capture the heterogeneity of the PubMed database, a vast digital repository containing over 33 million articles. We introduce PubMed Graph Benchmark (PGB), a new benchmark dataset for evaluating heterogeneous graph embeddings for biomedical literature. PGB is one of the largest heterogeneous networks to date and consists of 30 million English articles. The benchmark contains rich metadata including abstract, authors, citations, MeSH terms, MeSH hierarchy, and some other information. The benchmark contains an evaluation task of 21 systematic reviews topics from 3 different datasets. In PGB, we aggregate the metadata associated with the biomedical articles from PubMed into a unified source and make the benchmark publicly available for any future works.|生物医学文献发展迅速，然而对这些文献书目信息的异质性的捕捉仍然相对缺乏研究。尽管通过异构图形神经网络进行的图形挖掘研究已经占据了中心地位，但是这些方法是否能够捕捉到 PubMed 数据库的异构性仍然是个未知数。 PubMed 数据库是一个包含超过3300万篇文章的庞大数字图书馆。我们介绍了 PubMed 图基准(PGB) ，一个新的基准数据集评估异构图嵌入生物医学文献。PGB 是迄今为止最大的异构网络之一，拥有3000万篇英文文章。基准测试包含丰富的元数据，包括摘要、作者、引用、 MeSH 术语、 MeSH 层次结构和其他一些信息。该基准包含来自3个不同数据集的21个系统评价主题的评价任务。在 PGB，我们将来自 PubMed 的生物医学文章的元数据聚合成一个统一的来源，并将基准公开发布给未来的任何工作。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=PGB:+A+PubMed+Graph+Benchmark+for+Heterogeneous+Network+Representation+Learning)|0| -|[CTCam: Enhancing Transportation Evaluation through Fusion of Cellular Traffic and Camera-Based Vehicle Flows](https://doi.org/10.1145/3583780.3615116)|ChungYi Lin, ShenLung Tung, HungTing Su, Winston H. Hsu|National Taiwan University, Taipei City, Taiwan Roc; Chunghwa Telecom Laboratories, Taipei City, Taiwan Roc; National Taiwan University & Mobile Drive Technology, Taipei City, Taiwan Roc; National Taiwan University & Chunghwa Telecom Laboratories, Taipei City, Taiwan Roc|Traffic prediction utility often faces infrastructural limitations, which restrict its coverage. To overcome this challenge, we present Geographical Cellular Traffic (GCT) flow that leverages cellular network data as a new source for transportation evaluation. The broad coverage of cellular networks allows GCT flow to capture various mobile user activities across regions, aiding city authorities in resource management through precise predictions. Acknowledging the complexity arising from the diversity of mobile users in GCT flow, we supplement it with camera-based vehicle flow data from limited deployments and verify their spatio-temporal attributes and correlations through extensive data analysis. Our two-stage fusion approach integrates these multi-source data, addressing their coverage and magnitude discrepancies, thereby enhancing the prediction of GCT flow for accurate transportation evaluation. Overall, we propose novel uses of telecom data in transportation and verify its effectiveness in multi-source fusion with vision-based data.|交通预测效用往往面临基础设施的限制，从而限制了其覆盖范围。为了克服这一挑战，我们提出地理蜂窝流量(GCT)流，利用蜂窝网络数据作为一个新的来源，运输评估。蜂窝网络的广泛覆盖使得 GCT 流能够捕获跨区域的各种移动用户活动，通过精确的预测帮助城市当局进行资源管理。认识到 GCT 流中移动用户的多样性所带来的复杂性，我们补充了有限部署的基于摄像头的车流数据，并通过广泛的数据分析验证了它们的时空属性和相关性。我们的两阶段融合方法集成了这些多源数据，解决了它们的覆盖范围和数量级差异，从而提高了 GCT 流量的预测，以便准确地进行交通评估。总之，我们提出了电信数据在交通运输中的新用途，并验证了其在基于视觉数据的多源融合中的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=CTCam:+Enhancing+Transportation+Evaluation+through+Fusion+of+Cellular+Traffic+and+Camera-Based+Vehicle+Flows)|0| -|[Datasets and Interfaces for Benchmarking Heterogeneous Graph Neural Networks](https://doi.org/10.1145/3583780.3615117)|Yijian Liu, Hongyi Zhang, Cheng Yang, Ao Li, Yugang Ji, Luhao Zhang, Tao Li, Jinyu Yang, Tianyu Zhao, Juan Yang, Hai Huang, Chuan Shi|Orange Shield Technology, Hangzhou, China; Beijing University of Posts and Telecommunications, Beijing, China; Meituan, Beijing, China|In recent years, Heterogeneous Graph Neural Networks (HGNNs) have gained increasing attention due to their excellent performance in applications. However, the lack of high-quality benchmarks in new fields has become a critical limitation for developing and applying HGNNs. To accommodate the urgent need for emerging fields and the advancement of HGNNs, we present two large-scale, real-world, and challenging heterogeneous graph datasets from real scenarios: risk commodity detection and takeout recommendation. Meanwhile, we establish standard benchmark interfaces that provide over 40 heterogeneous graph datasets. We provide initial data split, unified evaluation metrics, and baseline results for future work, making it fair and handy to explore state-of-the-art HGNNs. Our interfaces also offer a comprehensive toolkit to research the characteristics of graph datasets. The above new datasets are publicly available on https://zenodo.org/communities/hgd, and the interface codes are available at https://github.com/BUPT-GAMMA/hgbi.|近年来，异构图神经网络(HGNN)以其优异的性能在应用中受到越来越多的关注。然而，在新领域缺乏高质量的基准已经成为开发和应用 HGNN 的一个关键限制因素。为了适应新兴领域的迫切需要和 HGNN 的发展，我们提出了两个来自真实场景的大规模、真实世界和具有挑战性的异构图形数据集: 风险商品检测和外卖推荐。同时，我们建立了标准的基准接口，提供了40多个异构图形数据集。我们为未来的工作提供了初始数据分割、统一的评估指标和基线结果，使得探索最先进的 HGNN 变得公平和方便。我们的接口还提供了一个全面的工具包来研究图形数据集的特征。上述新的数据集可在 https://zenodo.org/communities/hgd 上公开查阅，而界面代码则可在 https://github.com/bupt-gamma/hgbi 上查阅。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Datasets+and+Interfaces+for+Benchmarking+Heterogeneous+Graph+Neural+Networks)|0| -|[ContributionSum: Generating Disentangled Contributions for Scientific Papers](https://doi.org/10.1145/3583780.3615115)|MengHuan Liu, AnZi Yen, HenHsen Huang, HsinHsi Chen|National Taiwan University, Taipei, Taiwan Roc; National Yang Ming Chiao Tung University, Hsinchu, Taiwan Roc; Institute of Information Science, Academia Sinica, Taipei, Taiwan Roc|Contributions are essentially the core of every scientific research, highlighting their key values to the academic community. Systems that are capable of identifying the contributions from scientific papers precisely and organizing them into well-structured summaries can facilitate both text processing and human comprehension. In this paper, we present ContributionSum, a dataset consisting of 24K computer science papers with contributions explicitly listed by the authors, which are further classified into different contribution types based on a newly-proposed annotation scheme. In addition, we study the task of generating disentangled contributions that summarize the values of scientific papers into key points. We propose a fine-grained post-training strategy tailored to our task and leverage salient information of different contribution types in the papers. To assess the coherency and coverage of each contribution aspect, we perform summary-level and contribution-level evaluations for our task. Experimental results show that our method improves upon mainstream baselines.|贡献本质上是每项科学研究的核心，突出其对学术界的关键价值。能够准确识别科学论文的贡献并将其组织成结构良好的摘要的系统能够促进文本处理和人类理解。本文提出了一个由24K 计算机科学论文组成的数据集，其中包括作者明确列出的贡献，并根据新提出的注释方案将这些贡献进一步分为不同的贡献类型。此外，我们研究的任务，产生分离的贡献，总结成关键点的科学论文的价值。我们提出了一个细粒度的后培训策略，针对我们的任务和利用不同贡献类型的突出信息的文件。为了评估每个贡献方面的一致性和覆盖面，我们对我们的任务进行了总结一级和贡献一级的评价。实验结果表明，该方法在主流基线的基础上得到了改进。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=ContributionSum:+Generating+Disentangled+Contributions+for+Scientific+Papers)|0| -|[ClinicalRisk: A New Therapy-related Clinical Trial Dataset for Predicting Trial Status and Failure Reasons](https://doi.org/10.1145/3583780.3615113)|Junyu Luo, Zhi Qiao, Lucas Glass, Cao Xiao, Fenglong Ma|United Imaging Healthcare, Beijing, China; GE HealthCare, Chicago, IL, USA; The Pennsylvania State University, University Park, PA, USA; IQVIA, Chicago, IL, USA|Clinical trials aim to study new tests and evaluate their effects on human health outcomes, which has a huge market size. However, carrying out clinical trials is expensive and time-consuming and often ends in no results. It will revolutionize clinical practice if we can develop an effective model to automatically estimate the status of a clinical trial and find out possible failure reasons. However, it is challenging to develop such a model because of the lack of a benchmark dataset. To address these challenges, in this paper, we first build a new dataset by extracting the publicly available clinical trial reports from ClinicalTrials.gov. The associated status of each report is treated as the status label. To analyze the failure reasons, domain experts help us manually annotate each failed report based on the description associated with it. More importantly, we examine several state-of-the-art text classification baselines on this task and find out that the unique format of the clinical trial protocols plays an essential role in affecting prediction accuracy, demonstrating the need for specially designed clinical trial classification models.|临床试验的目的是研究新的测试方法，并评估其对人类健康结果的影响，这方面的市场规模巨大。然而，进行临床试验是昂贵和费时的，往往没有结果。如果我们能够开发一个有效的模型来自动估计临床试验的状态，并找出可能的失败原因，这将彻底改变临床实践。然而，由于缺乏基准数据集，开发这样一个模型是具有挑战性的。为了应对这些挑战，在本文中，我们首先从 clinicaltrials.gov 中提取公开可用的临床试验报告，建立一个新的数据集。每个报表的关联状态被视为状态标签。为了分析失败的原因，领域专家帮助我们根据相关的描述手动注释每个失败的报告。更重要的是，我们在这项任务上检查了几个最先进的文本分类基线，发现临床试验方案的独特格式在影响预测准确性方面起着至关重要的作用，表明需要特别设计的临床试验分类模型。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=ClinicalRisk:+A+New+Therapy-related+Clinical+Trial+Dataset+for+Predicting+Trial+Status+and+Failure+Reasons)|0| -|[ThyExp: An explainable AI-assisted Decision Making Toolkit for Thyroid Nodule Diagnosis based on Ultra-sound Images](https://doi.org/10.1145/3583780.3615131)|Jamie Morris, Zehao Liu, Huizhi Liang, Sidhartha Nagala, Xia Hong|The University of Reading, Reading, United Kingdom; Royal Berkshire Hospital, Reading, United Kingdom; Newcastle University, Newcastle Upon Tyne, United Kingdom|Radiologists have an important task of diagnosing thyroid nodules present in ultra sound images. Although reporting systems exist to aid in the diagnosis process, these systems do not provide explanations about the diagnosis results. We present ThyExp -- a web based toolkit for it use by medical professionals, allowing for accurate diagnosis with explanations of thyroid nodules present in ultrasound images utilising artificial intelligence models. The proposed web-based toolkit can be easily incorporated into current medical workflows, and allows medical professionals to have the confidence of a highly accurate machine learning model with explanations to provide supplementary diagnosis data. The solution provides classification results with their probability accuracy, as well as the explanations in the form of presenting the key features or characteristics that contribute to the classification results. The experiments conducted on a real-world UK NHS hospital patient dataset demonstrate the effectiveness of the proposed approach. This toolkit can improve the trust of medical professional to understand the confidence of the model in its predictions. This toolkit can improve the trust of medical professionals in understanding the models reasoning behind its predictions.|放射科医生的一项重要任务是诊断甲状腺结节存在于超声图像。虽然报告系统的存在有助于诊断过程，这些系统不提供对诊断结果的解释。我们介绍 ThyExp ——一个基于网络的医疗专业人员使用的工具包，允许使用人工智能模型对超声图像中出现的甲状腺结节进行精确诊断。建议的基于网络的工具包可以很容易地纳入当前的医疗工作流程，并允许医疗专业人员有信心的一个高度准确的机器学习模型与解释，以提供补充诊断数据。该解决方案提供了分类结果及其概率准确性，以及以显示有助于分类结果的关键特征或特征的形式提供的解释。在一个真实的英国 NHS 医院病人数据集上进行的实验证明了该方法的有效性。该工具包可以提高医疗专业人员的信任，以了解模型的信心，在其预测。这个工具包可以提高医疗专业人员在理解其预测背后的模型推理的信任。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=ThyExp:+An+explainable+AI-assisted+Decision+Making+Toolkit+for+Thyroid+Nodule+Diagnosis+based+on+Ultra-sound+Images)|0| +|[CTCam: Enhancing Transportation Evaluation through Fusion of Cellular Traffic and Camera-Based Vehicle Flows](https://doi.org/10.1145/3583780.3615116)|ChungYi Lin, ShenLung Tung, HungTing Su, Winston H. Hsu|National Taiwan University & Chunghwa Telecom Laboratories, Taipei City, Taiwan Roc; Chunghwa Telecom Laboratories, Taipei City, Taiwan Roc; National Taiwan University & Mobile Drive Technology, Taipei City, Taiwan Roc; National Taiwan University, Taipei City, Taiwan Roc|Traffic prediction utility often faces infrastructural limitations, which restrict its coverage. To overcome this challenge, we present Geographical Cellular Traffic (GCT) flow that leverages cellular network data as a new source for transportation evaluation. The broad coverage of cellular networks allows GCT flow to capture various mobile user activities across regions, aiding city authorities in resource management through precise predictions. Acknowledging the complexity arising from the diversity of mobile users in GCT flow, we supplement it with camera-based vehicle flow data from limited deployments and verify their spatio-temporal attributes and correlations through extensive data analysis. Our two-stage fusion approach integrates these multi-source data, addressing their coverage and magnitude discrepancies, thereby enhancing the prediction of GCT flow for accurate transportation evaluation. Overall, we propose novel uses of telecom data in transportation and verify its effectiveness in multi-source fusion with vision-based data.|交通预测效用往往面临基础设施的限制，从而限制了其覆盖范围。为了克服这一挑战，我们提出地理蜂窝流量(GCT)流，利用蜂窝网络数据作为一个新的来源，运输评估。蜂窝网络的广泛覆盖使得 GCT 流能够捕获跨区域的各种移动用户活动，通过精确的预测帮助城市当局进行资源管理。认识到 GCT 流中移动用户的多样性所带来的复杂性，我们补充了有限部署的基于摄像头的车流数据，并通过广泛的数据分析验证了它们的时空属性和相关性。我们的两阶段融合方法集成了这些多源数据，解决了它们的覆盖范围和数量级差异，从而提高了 GCT 流量的预测，以便准确地进行交通评估。总之，我们提出了电信数据在交通运输中的新用途，并验证了其在基于视觉数据的多源融合中的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=CTCam:+Enhancing+Transportation+Evaluation+through+Fusion+of+Cellular+Traffic+and+Camera-Based+Vehicle+Flows)|0| +|[Datasets and Interfaces for Benchmarking Heterogeneous Graph Neural Networks](https://doi.org/10.1145/3583780.3615117)|Yijian Liu, Hongyi Zhang, Cheng Yang, Ao Li, Yugang Ji, Luhao Zhang, Tao Li, Jinyu Yang, Tianyu Zhao, Juan Yang, Hai Huang, Chuan Shi|Beijing University of Posts and Telecommunications, Beijing, China; Meituan, Beijing, China; Orange Shield Technology, Hangzhou, China|In recent years, Heterogeneous Graph Neural Networks (HGNNs) have gained increasing attention due to their excellent performance in applications. However, the lack of high-quality benchmarks in new fields has become a critical limitation for developing and applying HGNNs. To accommodate the urgent need for emerging fields and the advancement of HGNNs, we present two large-scale, real-world, and challenging heterogeneous graph datasets from real scenarios: risk commodity detection and takeout recommendation. Meanwhile, we establish standard benchmark interfaces that provide over 40 heterogeneous graph datasets. We provide initial data split, unified evaluation metrics, and baseline results for future work, making it fair and handy to explore state-of-the-art HGNNs. Our interfaces also offer a comprehensive toolkit to research the characteristics of graph datasets. The above new datasets are publicly available on https://zenodo.org/communities/hgd, and the interface codes are available at https://github.com/BUPT-GAMMA/hgbi.|近年来，异构图神经网络(HGNN)以其优异的性能在应用中受到越来越多的关注。然而，在新领域缺乏高质量的基准已经成为开发和应用 HGNN 的一个关键限制因素。为了适应新兴领域的迫切需要和 HGNN 的发展，我们提出了两个来自真实场景的大规模、真实世界和具有挑战性的异构图形数据集: 风险商品检测和外卖推荐。同时，我们建立了标准的基准接口，提供了40多个异构图形数据集。我们为未来的工作提供了初始数据分割、统一的评估指标和基线结果，使得探索最先进的 HGNN 变得公平和方便。我们的接口还提供了一个全面的工具包来研究图形数据集的特征。上述新的数据集可在 https://zenodo.org/communities/hgd 上公开查阅，而界面代码则可在 https://github.com/bupt-gamma/hgbi 上查阅。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Datasets+and+Interfaces+for+Benchmarking+Heterogeneous+Graph+Neural+Networks)|0| +|[ContributionSum: Generating Disentangled Contributions for Scientific Papers](https://doi.org/10.1145/3583780.3615115)|MengHuan Liu, AnZi Yen, HenHsen Huang, HsinHsi Chen|Institute of Information Science, Academia Sinica, Taipei, Taiwan Roc; National Yang Ming Chiao Tung University, Hsinchu, Taiwan Roc; National Taiwan University, Taipei, Taiwan Roc|Contributions are essentially the core of every scientific research, highlighting their key values to the academic community. Systems that are capable of identifying the contributions from scientific papers precisely and organizing them into well-structured summaries can facilitate both text processing and human comprehension. In this paper, we present ContributionSum, a dataset consisting of 24K computer science papers with contributions explicitly listed by the authors, which are further classified into different contribution types based on a newly-proposed annotation scheme. In addition, we study the task of generating disentangled contributions that summarize the values of scientific papers into key points. We propose a fine-grained post-training strategy tailored to our task and leverage salient information of different contribution types in the papers. To assess the coherency and coverage of each contribution aspect, we perform summary-level and contribution-level evaluations for our task. Experimental results show that our method improves upon mainstream baselines.|贡献本质上是每项科学研究的核心，突出其对学术界的关键价值。能够准确识别科学论文的贡献并将其组织成结构良好的摘要的系统能够促进文本处理和人类理解。本文提出了一个由24K 计算机科学论文组成的数据集，其中包括作者明确列出的贡献，并根据新提出的注释方案将这些贡献进一步分为不同的贡献类型。此外，我们研究的任务，产生分离的贡献，总结成关键点的科学论文的价值。我们提出了一个细粒度的后培训策略，针对我们的任务和利用不同贡献类型的突出信息的文件。为了评估每个贡献方面的一致性和覆盖面，我们对我们的任务进行了总结一级和贡献一级的评价。实验结果表明，该方法在主流基线的基础上得到了改进。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=ContributionSum:+Generating+Disentangled+Contributions+for+Scientific+Papers)|0| +|[ClinicalRisk: A New Therapy-related Clinical Trial Dataset for Predicting Trial Status and Failure Reasons](https://doi.org/10.1145/3583780.3615113)|Junyu Luo, Zhi Qiao, Lucas Glass, Cao Xiao, Fenglong Ma|United Imaging Healthcare, Beijing, China; The Pennsylvania State University, University Park, PA, USA; IQVIA, Chicago, IL, USA; GE HealthCare, Chicago, IL, USA|Clinical trials aim to study new tests and evaluate their effects on human health outcomes, which has a huge market size. However, carrying out clinical trials is expensive and time-consuming and often ends in no results. It will revolutionize clinical practice if we can develop an effective model to automatically estimate the status of a clinical trial and find out possible failure reasons. However, it is challenging to develop such a model because of the lack of a benchmark dataset. To address these challenges, in this paper, we first build a new dataset by extracting the publicly available clinical trial reports from ClinicalTrials.gov. The associated status of each report is treated as the status label. To analyze the failure reasons, domain experts help us manually annotate each failed report based on the description associated with it. More importantly, we examine several state-of-the-art text classification baselines on this task and find out that the unique format of the clinical trial protocols plays an essential role in affecting prediction accuracy, demonstrating the need for specially designed clinical trial classification models.|临床试验的目的是研究新的测试方法，并评估其对人类健康结果的影响，这方面的市场规模巨大。然而，进行临床试验是昂贵和费时的，往往没有结果。如果我们能够开发一个有效的模型来自动估计临床试验的状态，并找出可能的失败原因，这将彻底改变临床实践。然而，由于缺乏基准数据集，开发这样一个模型是具有挑战性的。为了应对这些挑战，在本文中，我们首先从 clinicaltrials.gov 中提取公开可用的临床试验报告，建立一个新的数据集。每个报表的关联状态被视为状态标签。为了分析失败的原因，领域专家帮助我们根据相关的描述手动注释每个失败的报告。更重要的是，我们在这项任务上检查了几个最先进的文本分类基线，发现临床试验方案的独特格式在影响预测准确性方面起着至关重要的作用，表明需要特别设计的临床试验分类模型。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=ClinicalRisk:+A+New+Therapy-related+Clinical+Trial+Dataset+for+Predicting+Trial+Status+and+Failure+Reasons)|0| +|[ThyExp: An explainable AI-assisted Decision Making Toolkit for Thyroid Nodule Diagnosis based on Ultra-sound Images](https://doi.org/10.1145/3583780.3615131)|Jamie Morris, Zehao Liu, Huizhi Liang, Sidhartha Nagala, Xia Hong|The University of Reading, Reading, United Kingdom; Newcastle University, Newcastle Upon Tyne, United Kingdom; Royal Berkshire Hospital, Reading, United Kingdom|Radiologists have an important task of diagnosing thyroid nodules present in ultra sound images. Although reporting systems exist to aid in the diagnosis process, these systems do not provide explanations about the diagnosis results. We present ThyExp -- a web based toolkit for it use by medical professionals, allowing for accurate diagnosis with explanations of thyroid nodules present in ultrasound images utilising artificial intelligence models. The proposed web-based toolkit can be easily incorporated into current medical workflows, and allows medical professionals to have the confidence of a highly accurate machine learning model with explanations to provide supplementary diagnosis data. The solution provides classification results with their probability accuracy, as well as the explanations in the form of presenting the key features or characteristics that contribute to the classification results. The experiments conducted on a real-world UK NHS hospital patient dataset demonstrate the effectiveness of the proposed approach. This toolkit can improve the trust of medical professional to understand the confidence of the model in its predictions. This toolkit can improve the trust of medical professionals in understanding the models reasoning behind its predictions.|放射科医生的一项重要任务是诊断甲状腺结节存在于超声图像。虽然报告系统的存在有助于诊断过程，这些系统不提供对诊断结果的解释。我们介绍 ThyExp ——一个基于网络的医疗专业人员使用的工具包，允许使用人工智能模型对超声图像中出现的甲状腺结节进行精确诊断。建议的基于网络的工具包可以很容易地纳入当前的医疗工作流程，并允许医疗专业人员有信心的一个高度准确的机器学习模型与解释，以提供补充诊断数据。该解决方案提供了分类结果及其概率准确性，以及以显示有助于分类结果的关键特征或特征的形式提供的解释。在一个真实的英国 NHS 医院病人数据集上进行的实验证明了该方法的有效性。该工具包可以提高医疗专业人员的信任，以了解模型的信心，在其预测。这个工具包可以提高医疗专业人员在理解其预测背后的模型推理的信任。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=ThyExp:+An+explainable+AI-assisted+Decision+Making+Toolkit+for+Thyroid+Nodule+Diagnosis+based+on+Ultra-sound+Images)|0| |[GOAL: A Challenging Knowledge-grounded Video Captioning Benchmark for Real-time Soccer Commentary Generation](https://doi.org/10.1145/3583780.3615120)|Ji Qi, Jifan Yu, Teng Tu, Kunyu Gao, Yifan Xu, Xinyu Guan, Xiaozhi Wang, Bin Xu, Lei Hou, Juanzi Li, Jie Tang||Despite the recent emergence of video captioning models, how to generate vivid, fine-grained video descriptions based on the background knowledge (i.e., long and informative commentary about the domain-specific scenes with appropriate reasoning) is still far from being solved, which however has great applications such as automatic sports narrative. In this paper, we present GOAL, a benchmark of over 8.9k soccer video clips, 22k sentences, and 42k knowledge triples for proposing a challenging new task setting as Knowledge-grounded Video Captioning (KGVC). Moreover, we conduct experimental adaption of existing methods to show the difficulty and potential directions for solving this valuable and applicable task.|尽管最近出现了视频字幕模型，但是如何基于背景知识生成生动的、细粒度的视频描述(例如，对特定领域场景进行长时间的、信息丰富的评论，并进行适当的推理)仍然是一个有待解决的问题。在本文中，我们提出了目标，一个基准的超过8.9 k 足球视频剪辑，22k 句子，和42k 知识三元组提出了一个具有挑战性的新任务设置作为知识为基础的视频字幕(KGVC)。此外，我们对现有的方法进行了实验改编，以显示解决这一有价值和适用的任务的困难和潜在的方向。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=GOAL:+A+Challenging+Knowledge-grounded+Video+Captioning+Benchmark+for+Real-time+Soccer+Commentary+Generation)|0| -|[DynamicESG: A Dataset for Dynamically Unearthing ESG Ratings from News Articles](https://doi.org/10.1145/3583780.3615118)|YuMin Tseng, ChungChi Chen, HenHsen Huang, HsinHsi Chen|National Taiwan University, Taipei, Taiwan Roc; AIST, Tokyo, Japan; Institute of Information Science, Academia Sinica, Taipei, Taiwan Roc|This paper introduces the DynamicESG dataset, a unique resource for dynamically extracting ESG ratings from news articles. The ESG rating, a novel metric employed annually to gauge a company's sustainability, relies heavily on corporate disclosure and other external information, especially news narratives. Our dataset, comprising a wide spectrum of news over a twelve-year span, annotates articles in accordance with MSCI ESG ratings methodology and SASB standards, with relevance to ESG issues. DynamicESG provides a comprehensive means of investigating the relationship between public discourse, ESG-related events, and subsequent ESG rating adjustments. We detail our data collection, curation, annotation procedure, and inter-rater agreement, ensuring high data quality and usability. Importantly, our dataset includes a temporal dimension, enabling the analysis of longitudinal trends in ESG ratings and their correlation with news coverage. Moreover, the dataset incorporates an opportunity/risk tendency, thus permitting analysis from diverse perspectives to discern if the news is beneficial or detrimental to the company. We believe this dataset will serve as a valuable resource for researchers in fields such as corporate social responsibility, sustainable investing, machine learning, and natural language processing. Initial analysis using the dataset underscores its potential to facilitate new insights into the dynamics of ESG ratings and the influence of news media on these ratings.|本文介绍了 DynamicESG 数据集，这是一个从新闻文章中动态提取 ESG 评分的独特资源。ESG 评级是每年用来衡量一家公司可持续性的新指标，严重依赖于公司信息披露和其他外部信息，尤其是新闻报道。我们的数据集，包括一个12年跨度的广泛的新闻频谱，根据摩根士丹利资本国际 ESG 评级方法和 SASB 标准注释文章，与 ESG 问题相关。DynamicESG 提供了一种全面的方法来调查公共话语、 ESG 相关事件和随后的 ESG 评级调整之间的关系。我们详细说明了我们的数据收集、管理、注释程序和评价者之间的协议，确保了高数据质量和可用性。重要的是，我们的数据集包括一个时间维度，使 ESG 评级的纵向趋势及其与新闻报道的相关性分析成为可能。此外，数据集包含了机会/风险倾向，从而允许从不同角度进行分析，以辨别新闻对公司是有利还是有害。我们相信，这个数据集将成为企业社会责任、可持续投资、机器学习和自然语言处理等领域的研究人员的宝贵资源。使用数据集进行的初步分析强调了其促进对 ESG 评级动态和新闻媒体对这些评级的影响的新见解的潜力。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=DynamicESG:+A+Dataset+for+Dynamically+Unearthing+ESG+Ratings+from+News+Articles)|0| +|[DynamicESG: A Dataset for Dynamically Unearthing ESG Ratings from News Articles](https://doi.org/10.1145/3583780.3615118)|YuMin Tseng, ChungChi Chen, HenHsen Huang, HsinHsi Chen|Institute of Information Science, Academia Sinica, Taipei, Taiwan Roc; National Taiwan University, Taipei, Taiwan Roc; AIST, Tokyo, Japan|This paper introduces the DynamicESG dataset, a unique resource for dynamically extracting ESG ratings from news articles. The ESG rating, a novel metric employed annually to gauge a company's sustainability, relies heavily on corporate disclosure and other external information, especially news narratives. Our dataset, comprising a wide spectrum of news over a twelve-year span, annotates articles in accordance with MSCI ESG ratings methodology and SASB standards, with relevance to ESG issues. DynamicESG provides a comprehensive means of investigating the relationship between public discourse, ESG-related events, and subsequent ESG rating adjustments. We detail our data collection, curation, annotation procedure, and inter-rater agreement, ensuring high data quality and usability. Importantly, our dataset includes a temporal dimension, enabling the analysis of longitudinal trends in ESG ratings and their correlation with news coverage. Moreover, the dataset incorporates an opportunity/risk tendency, thus permitting analysis from diverse perspectives to discern if the news is beneficial or detrimental to the company. We believe this dataset will serve as a valuable resource for researchers in fields such as corporate social responsibility, sustainable investing, machine learning, and natural language processing. Initial analysis using the dataset underscores its potential to facilitate new insights into the dynamics of ESG ratings and the influence of news media on these ratings.|本文介绍了 DynamicESG 数据集，这是一个从新闻文章中动态提取 ESG 评分的独特资源。ESG 评级是每年用来衡量一家公司可持续性的新指标，严重依赖于公司信息披露和其他外部信息，尤其是新闻报道。我们的数据集，包括一个12年跨度的广泛的新闻频谱，根据摩根士丹利资本国际 ESG 评级方法和 SASB 标准注释文章，与 ESG 问题相关。DynamicESG 提供了一种全面的方法来调查公共话语、 ESG 相关事件和随后的 ESG 评级调整之间的关系。我们详细说明了我们的数据收集、管理、注释程序和评价者之间的协议，确保了高数据质量和可用性。重要的是，我们的数据集包括一个时间维度，使 ESG 评级的纵向趋势及其与新闻报道的相关性分析成为可能。此外，数据集包含了机会/风险倾向，从而允许从不同角度进行分析，以辨别新闻对公司是有利还是有害。我们相信，这个数据集将成为企业社会责任、可持续投资、机器学习和自然语言处理等领域的研究人员的宝贵资源。使用数据集进行的初步分析强调了其促进对 ESG 评级动态和新闻媒体对这些评级的影响的新见解的潜力。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=DynamicESG:+A+Dataset+for+Dynamically+Unearthing+ESG+Ratings+from+News+Articles)|0| |[MDCC: A Multimodal Dynamic Dataset for Donation-based Crowdfunding Campaigns](https://doi.org/10.1145/3583780.3615124)|Xovee Xu, Jiayang Li, Fan Zhou|University of Electronic Science and Technology of China, Chengdu, China; University of Electronic Science and Technology of China & Kash Institute of Electronics and Information Industry, Chengdu, China|Crowdfunding platforms have become pivotal financial support avenues for diverse causes, yet the success rates are surprisingly low. Previous research has largely focused on reward-based crowdfunding, leaving donation-based platforms under-studied. In addition, the roles of multimodal data (e.g., textual descriptions and visual photos) and dynamic elements (e.g., sequences of donations, project updates, and comments) in influencing campaign success have been largely overlooked. This paper introduces MDCC, a Multimodal Dynamic dataset for donation-based Crowdfunding Campaigns, collected from 14,961 projects on GoFundMe, incorporates multimodal project information and captures project dynamics, thus providing a comprehensive tool for analyzing donation-based crowdfunding. The dataset is expected to inspire innovative methodologies and facilitate understanding of project success determinants. Our preliminary experiments demonstrate the significance of multimodal and dynamic crowdfunding data on predicting the success of donation-based projects.|众筹平台已经成为各种不同事业的关键金融支持渠道，但成功率却低得惊人。以前的研究主要集中在基于奖励的众筹，而基于捐赠的平台研究不足。此外，多模态数据(例如文本描述和视觉照片)和动态元素(例如捐赠序列、项目更新和评论)在影响活动成功方面的作用在很大程度上被忽视了。本文介绍了从 GoFundMe 上的14,961个项目收集的基于捐赠的众筹活动的多模式动态数据集 MDCC，它整合了多模式项目信息并捕捉项目动态，从而为分析基于捐赠的众筹活动提供了一个全面的工具。预计该数据集将启发创新方法并促进对项目成功决定因素的理解。我们的初步实验证明了多模式和动态众筹数据在预测基于捐赠的项目成功方面的重要性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=MDCC:+A+Multimodal+Dynamic+Dataset+for+Donation-based+Crowdfunding+Campaigns)|0| -|[Causality-guided Graph Learning for Session-based Recommendation](https://doi.org/10.1145/3583780.3614803)|Dianer Yu, Qian Li, Hongzhi Yin, Guandong Xu|University of Technology, Sydney, Sydney, NSW, Australia; University of Queensland, Brisbane, QLD, Australia; Curtin University, Perth, WA, Australia|Session-based recommendation systems (SBRs) aim to capture user preferences over time by taking into account the sequential order of interactions within sessions. One promising approach within this domain is session graph-based recommendation, which leverages graph-based models to represent and analyze user sessions. However, current graph-based methods for SBRs mainly rely on attention or pooling mechanisms that are prone to exploiting shortcut paths and thus lead to suboptimal recommendations. To address this issue, we propose Causality-guided Graph Learning for Session-based Recommendation (CGSR) that is capable of blocking shortcut paths on the session graph and exploring robust causal connections capturing users' true preferences. Specifically, by employing back-door adjustment of causality, we can generate a distilled causal session graph capturing causal relations among items. CGSR then performs high-order aggregation on the distilled graph, incorporating information from various edge types, to estimate the session preference of the user. This enables us to provide more accurate recommendations grounded in causality while offering fine-grained interaction explanations by highlighting influential items in the graph. Extensive experiments on three datasets show the superior performance of CGSR compared to state-of-the-art SBRs.|基于会话的推荐系统(SBRs)旨在通过考虑会话内交互的顺序顺序来获取用户的偏好。这个领域中一个很有前途的方法是基于会话图的推荐，它利用基于图的模型来表示和分析用户会话。然而，目前基于图表的 SBR 方法主要依赖于注意力或汇集机制，这些机制易于利用捷径，从而导致次优建议。为了解决这个问题，我们提出了基于因果关系的会话推荐图学习(CGSR) ，它能够阻塞会话图上的快捷路径，并探索捕捉用户真实偏好的健壮的因果关系。具体来说，通过因果关系的后门调整，我们可以生成一个提取的因果会话图，捕捉项目之间的因果关系。然后 CGSR 对提取的图进行高阶聚合，结合来自各种边缘类型的信息，以估计用户的会话偏好。这使我们能够提供更准确的建议，基于因果关系，同时提供细粒度的交互解释，突出显示图中有影响力的项目。在三个数据集上的大量实验表明，CGSR 的性能优于最先进的 SBR。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Causality-guided+Graph+Learning+for+Session-based+Recommendation)|-1| +|[Causality-guided Graph Learning for Session-based Recommendation](https://doi.org/10.1145/3583780.3614803)|Dianer Yu, Qian Li, Hongzhi Yin, Guandong Xu|University of Queensland, Brisbane, QLD, Australia; University of Technology, Sydney, Sydney, NSW, Australia; Curtin University, Perth, WA, Australia|Session-based recommendation systems (SBRs) aim to capture user preferences over time by taking into account the sequential order of interactions within sessions. One promising approach within this domain is session graph-based recommendation, which leverages graph-based models to represent and analyze user sessions. However, current graph-based methods for SBRs mainly rely on attention or pooling mechanisms that are prone to exploiting shortcut paths and thus lead to suboptimal recommendations. To address this issue, we propose Causality-guided Graph Learning for Session-based Recommendation (CGSR) that is capable of blocking shortcut paths on the session graph and exploring robust causal connections capturing users' true preferences. Specifically, by employing back-door adjustment of causality, we can generate a distilled causal session graph capturing causal relations among items. CGSR then performs high-order aggregation on the distilled graph, incorporating information from various edge types, to estimate the session preference of the user. This enables us to provide more accurate recommendations grounded in causality while offering fine-grained interaction explanations by highlighting influential items in the graph. Extensive experiments on three datasets show the superior performance of CGSR compared to state-of-the-art SBRs.|基于会话的推荐系统(SBRs)旨在通过考虑会话内交互的顺序顺序来获取用户的偏好。这个领域中一个很有前途的方法是基于会话图的推荐，它利用基于图的模型来表示和分析用户会话。然而，目前基于图表的 SBR 方法主要依赖于注意力或汇集机制，这些机制易于利用捷径，从而导致次优建议。为了解决这个问题，我们提出了基于因果关系的会话推荐图学习(CGSR) ，它能够阻塞会话图上的快捷路径，并探索捕捉用户真实偏好的健壮的因果关系。具体来说，通过因果关系的后门调整，我们可以生成一个提取的因果会话图，捕捉项目之间的因果关系。然后 CGSR 对提取的图进行高阶聚合，结合来自各种边缘类型的信息，以估计用户的会话偏好。这使我们能够提供更准确的建议，基于因果关系，同时提供细粒度的交互解释，突出显示图中有影响力的项目。在三个数据集上的大量实验表明，CGSR 的性能优于最先进的 SBR。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Causality-guided+Graph+Learning+for+Session-based+Recommendation)|-1| |[gFOV: A Full-Stack SPARQL Query Optimizer & Plan Visualizer](https://doi.org/10.1145/3583780.3614741)|Yue Pang, Linglin Yang, Lei Zou, M. Tamer Özsu|Peking University, Beijing, China; University of Waterloo, Waterloo, Canada|SPARQL is the standard query language for RDF data. A SPARQL query consists of basic graph patterns (BGPs), which are matched onto the data graph, and graph pattern operators, which specify how to merge the matched results. Despite the prevalence of graph pattern operators in real-world SPARQL workloads, the optimization of SPARQL queries with graph pattern operators has scarcely been studied. We hence propose gFOV, a full-stack SPARQL query optimizer and plan visualizer targeting both BGPs and graph pattern operators. As its basis, we propose a novel BGP-based evaluation tree (BE-tree) plan representation for SPARQL queries that integrates the physical plan for BGPs, which directly accesses the RDF store, and the logical plan for graph pattern operators, which operates on existent results in memory. On top of it, we devise a full-stack cost-based optimization scheme, combining logical and physical plan optimization, that outperforms the state-of-the-art. In the demonstration, we present an interactive interface that explains our optimization scheme and shows its efficiency by visualizing changes in the query plan and allowing the audience to inspect and execute alternative plans.|SPARQL 是 RDF 数据的标准查询语言。SPARQL 查询包括与数据图匹配的基本图形模式(BGP)和指定如何合并匹配结果的图形模式运算符。尽管在实际的 SPARQL 工作负载中普遍使用图形模式算子，但是对于使用图形模式算子优化 SPARQL 查询的研究却很少。因此，我们提出了 gFOV，一个针对 BGP 和图形模式运算符的全栈 SPARQL 查询优化器和计划可视化器。作为其基础，我们提出了一种新的基于 BGP 的 SPARQL 查询计划表示(BE-tree) ，它集成了 BGP 的物理计划(直接访问 RDF 存储)和图模式运算符的逻辑计划(对存储器中已有的结果进行操作)。在此基础上，我们设计了一个基于全堆栈成本的优化方案，将逻辑和物理计划优化相结合，优于最先进的水平。在演示中，我们展示了一个交互式界面，它解释了我们的优化方案，并通过可视化查询计划中的更改来显示其效率，并允许受众检查和执行替代计划。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=gFOV:+A+Full-Stack+SPARQL+Query+Optimizer+&+Plan+Visualizer)|-1| |[Semantic-aware Node Synthesis for Imbalanced Heterogeneous Information Networks](https://doi.org/10.1145/3583780.3615055)|Xinyi Gao, Wentao Zhang, Tong Chen, Junliang Yu, Nguyen Quoc Viet Hung, Hongzhi Yin|The University of Queensland, Brisbane, QLD, Australia; Peking University, Beijing, China; Griffith University, Gold Coast, SEQ, Australia|Heterogeneous graph neural networks (HGNNs) have exhibited exceptional efficacy in modeling the complex heterogeneity in heterogeneous information networks (HINs). The critical advantage of HGNNs is their ability to handle diverse node and edge types in HINs by extracting and utilizing the abundant semantic information for effective representation learning. However, as a widespread phenomenon in many real-world scenarios, the class-imbalance distribution in HINs creates a performance bottleneck for existing HGNNs. Apart from the quantity imbalance of nodes, another more crucial and distinctive challenge in HINs is semantic imbalance. Minority classes in HINs often lack diverse and sufficient neighbor nodes, resulting in biased and incomplete semantic information. This semantic imbalance further compounds the difficulty of accurately classifying minority nodes, leading to the performance degradation of HGNNs. To tackle the imbalance of minority classes and supplement their inadequate semantics, we present the first method for the semantic imbalance problem in imbalanced HINs named Semantic-aware Node Synthesis (SNS). By assessing the influence on minority classes, SNS adaptively selects the heterogeneous neighbor nodes and augments the network with synthetic nodes while preserving the minority semantics. In addition, we introduce two regularization approaches for HGNNs that constrain the representation of synthetic nodes from both semantic and class perspectives to effectively suppress the potential noises from synthetic nodes, facilitating more expressive embeddings for classification. The comprehensive experimental study demonstrates that SNS consistently outperforms existing methods by a large margin in different benchmark datasets.|异构图神经网络(HGNNs)在异构信息网络(HINs)的复杂异构性建模中表现出了非凡的功效。HGNN 的关键优势在于它们能够通过提取和利用丰富的语义信息来处理 HINs 中不同的节点和边缘类型，从而有效地进行表示学习。然而，作为现实世界中普遍存在的一种现象，HIN 中的类不平衡分布为现有的 HGNN 造成了性能瓶颈。除了节点的数量不平衡外，HIN 中另一个更为关键和独特的挑战是语义不平衡。HIN 中的少数族群往往缺乏多样性和足够的邻居节点，导致偏倚和不完全的语义信息。这种语义不平衡进一步加大了对少数节点进行准确分类的难度，导致 HGNN 的性能下降。为了解决不平衡 HIN 中少数类的语义不平衡问题，并补充其不足的语义，我们提出了第一种解决不平衡 HIN 中语义不平衡问题的方法——语义感知节点合成(SNS)。通过评估对少数族群的影响，SNS 自适应地选择异构邻居节点，在保留少数族群语义的前提下使用合成节点对网络进行扩展。此外，我们还介绍了两种 HGNN 的正则化方法，它们从语义和类的角度约束合成节点的表示，以有效地抑制合成节点的潜在噪声，促进更具表达性的分类嵌入。综合实验研究表明，在不同的基准数据集中，SNS 的性能一直大大优于现有的方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Semantic-aware+Node+Synthesis+for+Imbalanced+Heterogeneous+Information+Networks)|-1| |[Unsupervised Aspect Term Extraction by Integrating Sentence-level Curriculum Learning with Token-level Self-paced Learning](https://doi.org/10.1145/3583780.3615103)|Jihong Ouyang, Zhiyao Yang, Chang Xuan, Bing Wang, Yiyuan Wang, Ximing Li|Jilin University, Changchun, China; Northeast Normal University, Changchun, China|Aspect Term Extraction (ATE), a key sub-task of aspect-based sentiment analysis, aims to extract aspect terms from review sentences on which users express opinions. Existing studies mainly treat ATE as a sequence labeling problem, and the aspect terms of training data are annotated at the token level, such as "BIO'' tagging. However, such fine-grained annotations are often too costly to collect in many real applications, giving rise to the urgent demand for the challenging Unsupervised ATE (UATE). This paper suggests a novel UATE method by integrating sentence-level curriculum learning with token-level self-paced learning, namely UATE-SCTS. We design a set of hand-crafted rules to generate pseudo-labels but with noise. To combat this issue, our key idea is to train the ATE model from easier samples to harder samples to achieve a more robust model with more precise predictions at the early training epochs. This enables better refining of the noisy pseudo-labels. At the sentence level, we propose a frequency-induced pseudo-label cardinality to measure the learning difficulty of the review sentence and train the model in a curriculum-learning manner. At the token level, we formulate a self-paced learning objective that can adaptively select easier samples for training. We compare UATE-SCTS with baseline methods on benchmark collections of reviews from different domains. Empirical results demonstrate that UATE-SCTS can outperform existing UATE baselines.|体词提取(ATE)是基于体词的情感分析的一个关键子任务，旨在从用户表达意见的复习句中提取体词。现有的研究主要将 ATE 作为一个序列标注问题来处理，训练数据的方面术语在标记层次上进行标注，如“ BIO”标注。然而，这样的细粒度注释往往是太昂贵的收集在许多实际应用中，引起了对具有挑战性的无监督 ATE (UATE)的迫切需求。本文提出了一种将句子层次课程学习与表征层次自主学习相结合的 UATE 方法，即 UATE-SCS。我们设计了一组手工制作的规则来生成伪标签，但是带有噪音。为了解决这个问题，我们的关键思想是训练 ATE 模型从更容易的样本到更难的样本，以实现一个更稳健的模型与更精确的预测在早期训练阶段。这样可以更好地精炼嘈杂的伪标签。在句子层面，我们提出了一个频率诱导的伪标记基数来度量复习句的学习难度，并以课程学习的方式训练该模型。在表征层面，我们制定了一个自定步调的学习目标，可以自适应地选择更容易的样本进行训练。我们比较了 UATE-SCT 和基准方法对不同领域的评论的基准收集。实证结果表明，UATE-SCS 的性能优于现有的 UATE 基线。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Unsupervised+Aspect+Term+Extraction+by+Integrating+Sentence-level+Curriculum+Learning+with+Token-level+Self-paced+Learning)|-1| -|[Weak Regression Enhanced Lifelong Learning for Improved Performance and Reduced Training Data](https://doi.org/10.1145/3583780.3615108)|Tong Liu, Xulong Wang, He Huang, Po Yang|Department of Computer Science, University of Sheffield, Sheffield, United Kingdom; Institute of Intelligent Machines, Chinese Academy Of Sciences, Hefei, China|As an emerging learning paradigm, lifelong learning intends to solve multiple consecutive tasks over long-time scales upon previously accumulated knowledge. When facing with a new task, existing lifelong learning approaches need first gather sufficient training data to identify task relationships before knowledge transfer can succeed. However, annotating large number of training data persistently for every coming task is time-consuming, which can be prohibitive for real-world lifelong regression problems. To reduce this burden, we propose to incorporate weak regression into lifelong learning so as to enhance training data and improve predictive performance. Specifically, the weak prediction is first produced by single-task predictor, which is encoded as feature vectors that contain essential prior output information. This weak regression is further linked with task model via coupled dictionary learning. The integration of weak regression and task model can facilitate both cross-task and inter-task knowledge transfer, thus improving the overall performance. More critically, the weak regression can backup the task model especially when there is insufficient training data to construct an accurate model. Three real-world datasets are used to evaluate the effectiveness of our proposed method. Results show that our method outperforms existing lifelong models and single-task models even if training data is minimal.|作为一种新兴的学习范式，终身学习试图在以前积累的知识的基础上，在长时间尺度上解决多个连续的任务。当面对一项新任务时，现有的终身学习方法需要首先收集足够的训练数据，以确定任务关系，然后才能成功地进行知识转移。然而，为每个即将到来的任务持久地注释大量的训练数据是非常耗时的，这对于现实世界中的终身回归问题来说是非常困难的。为了减轻这种负担，我们建议在终身学习中加入弱回归，以增强训练数据，提高预测性能。具体来说，弱预测首先由单任务预测器产生，该预测器被编码为包含必要先验输出信息的特征向量。这种弱回归通过耦合词典学习进一步与任务模型联系起来。弱回归与任务模型的结合可以促进跨任务和跨任务的知识转移，从而提高整体绩效。更关键的是，弱回归可以备份任务模型，特别是当没有足够的训练数据来建立一个准确的模型。利用三个实际数据集对该方法的有效性进行了评估。结果表明，即使训练数据最少，该方法仍优于现有的终身模型和单任务模型。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Weak+Regression+Enhanced+Lifelong+Learning+for+Improved+Performance+and+Reduced+Training+Data)|-1| +|[Weak Regression Enhanced Lifelong Learning for Improved Performance and Reduced Training Data](https://doi.org/10.1145/3583780.3615108)|Tong Liu, Xulong Wang, He Huang, Po Yang|Institute of Intelligent Machines, Chinese Academy Of Sciences, Hefei, China; Department of Computer Science, University of Sheffield, Sheffield, United Kingdom|As an emerging learning paradigm, lifelong learning intends to solve multiple consecutive tasks over long-time scales upon previously accumulated knowledge. When facing with a new task, existing lifelong learning approaches need first gather sufficient training data to identify task relationships before knowledge transfer can succeed. However, annotating large number of training data persistently for every coming task is time-consuming, which can be prohibitive for real-world lifelong regression problems. To reduce this burden, we propose to incorporate weak regression into lifelong learning so as to enhance training data and improve predictive performance. Specifically, the weak prediction is first produced by single-task predictor, which is encoded as feature vectors that contain essential prior output information. This weak regression is further linked with task model via coupled dictionary learning. The integration of weak regression and task model can facilitate both cross-task and inter-task knowledge transfer, thus improving the overall performance. More critically, the weak regression can backup the task model especially when there is insufficient training data to construct an accurate model. Three real-world datasets are used to evaluate the effectiveness of our proposed method. Results show that our method outperforms existing lifelong models and single-task models even if training data is minimal.|作为一种新兴的学习范式，终身学习试图在以前积累的知识的基础上，在长时间尺度上解决多个连续的任务。当面对一项新任务时，现有的终身学习方法需要首先收集足够的训练数据，以确定任务关系，然后才能成功地进行知识转移。然而，为每个即将到来的任务持久地注释大量的训练数据是非常耗时的，这对于现实世界中的终身回归问题来说是非常困难的。为了减轻这种负担，我们建议在终身学习中加入弱回归，以增强训练数据，提高预测性能。具体来说，弱预测首先由单任务预测器产生，该预测器被编码为包含必要先验输出信息的特征向量。这种弱回归通过耦合词典学习进一步与任务模型联系起来。弱回归与任务模型的结合可以促进跨任务和跨任务的知识转移，从而提高整体绩效。更关键的是，弱回归可以备份任务模型，特别是当没有足够的训练数据来建立一个准确的模型。利用三个实际数据集对该方法的有效性进行了评估。结果表明，即使训练数据最少，该方法仍优于现有的终身模型和单任务模型。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Weak+Regression+Enhanced+Lifelong+Learning+for+Improved+Performance+and+Reduced+Training+Data)|-1| |[Calibrate Graph Neural Networks under Out-of-Distribution Nodes via Deep Q-learning](https://doi.org/10.1145/3583780.3614797)|Weili Shi, Xueying Yang, Xujiang Zhao, Haifeng Chen, Zhiqiang Tao, Sheng Li|University of Virginia, Charlottesville, VA, USA; Rochester Institute of Technology, Rochester, NY, USA; NEC-Labs, Princeton, NJ, USA; Santa Clara University, Santa Clara, CA, USA|Graph neural networks (GNNs) have achieved great success in dealing with graph-structured data that are prevalent in the real world. The core of graph neural networks is the message passing mechanism that aims to generate the embeddings of nodes by aggregating the neighboring node information. However, recent work suggests that GNNs also suffer the trustworthiness issues. Our empirical study shows that the calibration error of the in-distribution (ID) nodes would be exacerbated if a graph is mixed with out-of-distribution (OOD) nodes, and we assume that the noisy information from OOD nodes is the root for the worsened calibration error. Both previous study and our empirical study suggest that adjusting the weights of edges could be a promising way to reduce the adverse impact from the OOD nodes. However, how to precisely select the desired edges and modify the corresponding weights is not trivial, since the distribution of OOD nodes is unknown to us. To tackle this problem, we propose a Graph Edge Re-weighting via Deep Q-learning (GERDQ) framework to calibrate the graph neural networks. Our framework aims to explore the potential influence of the change of the edge weights on target ID nodes by sampling and traversing the edges in the graph, and we formulate this process as a Markov Decision Process (MDP). Many existing GNNs could be seamlessly incorporated into our framework. Experimental results show that when wrapped with our method, the existing GNN models can yield lower calibration error under OOD nodes as well as comparable accuracy compared to the original ones and other strong baselines. The source code is available at:|图形神经网络(GNN)在处理现实世界中普遍存在的图形结构数据方面取得了巨大的成功。图神经网络的核心是消息传递机制，其目的是通过聚合相邻节点的信息来产生节点的嵌入。然而，最近的工作表明，GNN 也遭受信任问题。我们的实证研究表明，当一个图与分布外(OOD)节点混合时，分布内(ID)节点的校准误差将会加剧，并且我们假设来自 OOD 节点的噪声信息是校准误差加剧的根源。以往的研究和我们的实证研究都表明，调整边缘的权重可能是一个有希望的方法，以减少不利的影响从面向对象的节点。然而，如何精确地选择所需的边缘和修改相应的权重是不容易的，因为我们不知道面向对象的节点的分布。为了解决这一问题，我们提出了一种基于深度 Q 学习(GERDQ)的图边重新加权方法来标定图神经网络。我们的框架旨在通过采样和遍历图中的边来探索边权重变化对目标 ID 节点的潜在影响，并将这一过程表述为一个马可夫决策过程(mDP)。许多现有的 GNN 可以无缝地并入到我们的框架中。实验结果表明，与原始模型和其他强基线相比，现有的 GNN 模型在 OOD 节点下可以获得较低的标定误差和可比较的精度。源代码可在以下网址查阅:|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Calibrate+Graph+Neural+Networks+under+Out-of-Distribution+Nodes+via+Deep+Q-learning)|-1| |[MoCLIM: Towards Accurate Cancer Subtyping via Multi-Omics Contrastive Learning with Omics-Inference Modeling](https://doi.org/10.1145/3583780.3614970)|Ziwei Yang, Zheng Chen, Yasuko Matsubara, Yasushi Sakurai|Bioinformatics Center, Institute for Chemical Research, Kyoto University, Kyoto, Japan; SANKEN, Osaka University, Osaka, Japan|Precision medicine fundamentally aims to establish causality between dysregulated biochemical mechanisms and cancer subtypes. Omics-based cancer subtyping has emerged as a revolutionary approach, as different level of omics records the biochemical products of multistep processes in cancers. This paper focuses on fully exploiting the potential of multi-omics data to improve cancer subtyping outcomes, and hence developed MoCLIM, a representation learning framework. MoCLIM independently extracts the informative features from distinct omics modalities. Using a unified representation informed by contrastive learning of different omics modalities, we can well-cluster the subtypes, given cancer, into a lower latent space. This contrast can be interpreted as a projection of inter-omics inference observed in biological networks. Experimental results on six cancer datasets demonstrate that our approach significantly improves data fit and subtyping performance in fewer high-dimensional cancer instances. Moreover, our framework incorporates various medical evaluations as the final component, providing high interpretability in medical analysis.|精准医学的基本目标是建立失调的生化机制和癌症亚型之间的因果关系。基于组学的癌症分型已经成为一种革命性的方法，因为不同水平的组学记录癌症中多步骤过程的生化产物。本文着重于充分利用多组学数据的潜力，以改善癌症亚型的结果，因此开发了 MoCLIM，一个代表性学习框架。MoCLIM 独立地从不同的组学模式中提取信息特征。通过对不同组学模式的对比学习，使用统一的表示方法，我们可以很好地将给定癌症的亚型聚类到一个较低的潜伏空间。这种对比可以解释为在生物网络中观察到的组间推断的投影。在六个癌症数据集上的实验结果表明，我们的方法在较少的高维癌症实例中显著提高了数据拟合和分型性能。此外，我们的框架整合了各种医疗评价作为最终组成部分，提供了高解释性的医疗分析。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=MoCLIM:+Towards+Accurate+Cancer+Subtyping+via+Multi-Omics+Contrastive+Learning+with+Omics-Inference+Modeling)|-1| -|[RLIFE: Remaining Lifespan Prediction for E-scooters](https://doi.org/10.1145/3583780.3615037)|Shuxin Zhong, William Yubeaton, Wenjun Lyu, Guang Wang, Desheng Zhang, Yu Yang|Florida State University, Tallahassee, FL, USA; Lehigh University, Bethlehem, PA, USA; New York University, New York, NY, USA; Rutgers University, New Brunswick, NJ, USA|Shared electric scooters (e-scooters) have been increasingly popular because of their characteristics of convenience and eco-friendliness. Due to their shared nature and widespread usage, e-scooters usually have a short lifespan (e.g., two to five months[2]), which makes it important to predict the remaining lifespan accurately, ensuring timely replacements. While several studies have focused on the lifespan prediction of various systems, such as batteries and bridges, they present a two-fold drawback. Firstly, they require significant manual labor or additional sensor resources to ascertain the explicit status of the object, rendering them cost-ineffective. Secondly, these studies assume that future usage is similar as the historical usage. To solve these limitations, we aim at accurately predicting the remaining lifespan of e-scooters without extra cost, and its essence is to accurately represent its current status and anticipate its future usage. However, it is challenging because: i) lack of explicit rules for the e-scooters' status representation; and ii) e-scooters' future usage may significantly differ from their historical usage. In this paper, we design a framework called RLIFE, whose key insight is modeling user behaviors from trip transactions is of great importance in predicting the Remaining LIFespan of shared E-scooters. Specifically, we introduce an unsupervised contrastive learning component to learn the e-scooters' status representation over time considering degradation, where user preferences are served as a status reflector; We further design an LSTM-based recursive component to dynamically predict uncertain future usage, upon which we fuse the current status and predicted usage of the e-scooter for its remaining lifespan prediction. Extensive experiments are conducted on large-scale, real-world datasets collected from an e-scooter company. It shows that RLIFE improves the baselines by 35.67% and benefits from the learned user preferences and predicted future usage.|共享电动滑板车(e-scooter)因其方便、环保的特点而越来越受到人们的青睐。由于它们的共同性质和广泛使用，电动滑板车通常寿命较短(例如，2至5个月[2]) ，这使得准确预测剩余寿命，确保及时更换非常重要。虽然一些研究集中在各种系统的寿命预测，如电池和桥梁，他们提出了一个双重的缺点。首先，它们需要大量的人工或额外的传感器资源来确定目标的明确状态，从而降低成本。其次，这些研究假设未来的用法与历史上的用法相似。为了解决这些局限性，我们的目标是准确地预测电动滑板车的剩余寿命，而不需要额外的费用，其本质是准确地表示其目前的状态和预测其未来的使用。然而，这是具有挑战性的，因为: i)缺乏明确的规则，电动滑板车的地位表示; 和 ii)电动滑板车的未来使用可能与其历史使用明显不同。在本文中，我们设计了一个名为 RLIFE 的框架，其核心思想是从出行事务中建立用户行为模型，这对于预测共享电动滑板车的剩余寿命非常重要。具体来说，我们引入了一个无监督的对比学习组件来学习电动滑板车的状态表示随着时间的推移考虑退化，其中用户偏好被用作状态反映器; 我们进一步设计了一个基于 LSTM 的递归组件来动态预测不确定的未来使用，在此基础上，我们融合了电动滑板车的当前状态和预测使用寿命预测。广泛的实验进行了大规模，真实世界的数据集收集从电动滑板车公司。结果表明，RLIFE 提高了35.67% 的基线，并且受益于学习到的用户偏好和预测未来的使用。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=RLIFE:+Remaining+Lifespan+Prediction+for+E-scooters)|-1| +|[RLIFE: Remaining Lifespan Prediction for E-scooters](https://doi.org/10.1145/3583780.3615037)|Shuxin Zhong, William Yubeaton, Wenjun Lyu, Guang Wang, Desheng Zhang, Yu Yang|New York University, New York, NY, USA; Florida State University, Tallahassee, FL, USA; Lehigh University, Bethlehem, PA, USA; Rutgers University, New Brunswick, NJ, USA|Shared electric scooters (e-scooters) have been increasingly popular because of their characteristics of convenience and eco-friendliness. Due to their shared nature and widespread usage, e-scooters usually have a short lifespan (e.g., two to five months[2]), which makes it important to predict the remaining lifespan accurately, ensuring timely replacements. While several studies have focused on the lifespan prediction of various systems, such as batteries and bridges, they present a two-fold drawback. Firstly, they require significant manual labor or additional sensor resources to ascertain the explicit status of the object, rendering them cost-ineffective. Secondly, these studies assume that future usage is similar as the historical usage. To solve these limitations, we aim at accurately predicting the remaining lifespan of e-scooters without extra cost, and its essence is to accurately represent its current status and anticipate its future usage. However, it is challenging because: i) lack of explicit rules for the e-scooters' status representation; and ii) e-scooters' future usage may significantly differ from their historical usage. In this paper, we design a framework called RLIFE, whose key insight is modeling user behaviors from trip transactions is of great importance in predicting the Remaining LIFespan of shared E-scooters. Specifically, we introduce an unsupervised contrastive learning component to learn the e-scooters' status representation over time considering degradation, where user preferences are served as a status reflector; We further design an LSTM-based recursive component to dynamically predict uncertain future usage, upon which we fuse the current status and predicted usage of the e-scooter for its remaining lifespan prediction. Extensive experiments are conducted on large-scale, real-world datasets collected from an e-scooter company. It shows that RLIFE improves the baselines by 35.67% and benefits from the learned user preferences and predicted future usage.|共享电动滑板车(e-scooter)因其方便、环保的特点而越来越受到人们的青睐。由于它们的共同性质和广泛使用，电动滑板车通常寿命较短(例如，2至5个月[2]) ，这使得准确预测剩余寿命，确保及时更换非常重要。虽然一些研究集中在各种系统的寿命预测，如电池和桥梁，他们提出了一个双重的缺点。首先，它们需要大量的人工或额外的传感器资源来确定目标的明确状态，从而降低成本。其次，这些研究假设未来的用法与历史上的用法相似。为了解决这些局限性，我们的目标是准确地预测电动滑板车的剩余寿命，而不需要额外的费用，其本质是准确地表示其目前的状态和预测其未来的使用。然而，这是具有挑战性的，因为: i)缺乏明确的规则，电动滑板车的地位表示; 和 ii)电动滑板车的未来使用可能与其历史使用明显不同。在本文中，我们设计了一个名为 RLIFE 的框架，其核心思想是从出行事务中建立用户行为模型，这对于预测共享电动滑板车的剩余寿命非常重要。具体来说，我们引入了一个无监督的对比学习组件来学习电动滑板车的状态表示随着时间的推移考虑退化，其中用户偏好被用作状态反映器; 我们进一步设计了一个基于 LSTM 的递归组件来动态预测不确定的未来使用，在此基础上，我们融合了电动滑板车的当前状态和预测使用寿命预测。广泛的实验进行了大规模，真实世界的数据集收集从电动滑板车公司。结果表明，RLIFE 提高了35.67% 的基线，并且受益于学习到的用户偏好和预测未来的使用。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=RLIFE:+Remaining+Lifespan+Prediction+for+E-scooters)|-1| |[Semi-supervised Curriculum Ensemble Learning for Financial Precision Marketing](https://doi.org/10.1145/3583780.3615251)|HsinYu Chen, ChengTe Li, TingYu Chen|Bank SinoPac, Taipei, Taiwan Roc; National Cheng Kung University, Tainan, Taiwan Roc|This paper tackles precision marketing in financial technology, focusing on the accurate prediction of potential customers' interest in specific financial products amidst extreme class imbalance and a significant volume of unlabeled data. We propose the innovative Semi-supervised Curriculum Ensemble (SSCE) framework, which integrates curriculum pseudo-labeling and balanced bagging with tree-based models. This novel approach enables the effective utilization of high-confidence predicted instances from unlabeled data and mitigates the impact of extreme class imbalance. Experiments conducted on a large-scale real-world banking dataset, featuring five financial products, demonstrate that the SSCE consistently outperforms existing methods, thereby promising significant advances in the domain of financial precision marketing.|本文针对金融技术中的精确营销问题，重点在于在极端的等级不平衡和大量未标记数据的情况下，准确预测潜在客户对特定金融产品的兴趣。提出了一种创新的半监督课程集成(SSCE)框架，该框架将课程伪标注和平衡包装与基于树的模型集成在一起。这种新颖的方法能够有效地利用来自未标记数据的高置信度预测实例，并减轻极端类不平衡的影响。在一个以五种金融产品为特征的大规模现实世界银行数据集上进行的实验表明，SSCE 始终优于现有方法，从而有望在金融精确营销领域取得重大进展。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Semi-supervised+Curriculum+Ensemble+Learning+for+Financial+Precision+Marketing)|-1| -|[A Deep Conditional Generative Approach for Constrained Community Detection](https://doi.org/10.1145/3583780.3615145)|Chaobo He, Junwei Cheng, Quanlong Guan, Xiang Fei, Hanchao Li, Yong Tang|Department of Computing, Coventry University, Coventry, United Kingdom; Institute for Digital Technologies, Loughborough University, Leicester, United Kingdom; School of Computer Science, South China Normal University, Pazhou Lab, Guangzhou, China; School of Computer Science, South China Normal University, Guangzhou, China; College of Information Science and Technology, Jinan University, Guangzhou, China|Constrained community detection is one of the popular topics in graph data mining, and it aims to improve the performance by exploiting prior pairwise constraints, such as must-link and cannot-link constraints. However, most of existing methods for constrained community detection are shallow approaches, and are also not robust to handle constraints information with noises. In view of this, we propose a deep conditional generative approach CGMVGAE. It firstly treats pairwise constraints as the priors with different degrees of certainty, and then integrates them into the conditional Gaussian mixture model. By further combing variational graph auto-encoders and the Wasserstein regularization, CGMVGAE can learn the latent node representations preserving community structures in a deep generative manner. Experimental results show that CGMVGAE outperforms state-of-the-art approaches, and is also more robust.|约束社区检测是图形数据挖掘中的热点问题之一，其目的是利用先验的成对约束，如必须链接约束和不能链接约束，提高社区检测的性能。然而，现有的约束群体检测方法大多是浅层检测方法，对于带有噪声的约束信息也不具有鲁棒性。鉴于此，我们提出了一种深度条件生成方法 CGMVGAE。它首先将成对约束作为不同确定度的先验条件，然后将其集成到条件高斯混合模型中。通过进一步结合变分图自动编码器和 Wasserstein 正则化，CGMVGAE 能够以深层生成的方式学习保持群体结构的潜在节点表示。实验结果表明，CGMVGAE 算法的性能优于目前最先进的算法，并且具有更强的鲁棒性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Deep+Conditional+Generative+Approach+for+Constrained+Community+Detection)|-1| -|[Pseudo Triplet Networks for Classification Tasks with Cross-Source Feature Incompleteness](https://doi.org/10.1145/3583780.3615154)|Cayon Liow, ChengTe Li, ChunPai Yang, ShouDe Lin|National Taiwan University, Taipei, Taiwan Roc; National Cheng Kung University, Tainan, Taiwan Roc|Cross-source feature incompleteness -- a scenario where certain features are only available in one data source but missing in another -- is a common and significant challenge in machine learning. It typically arises in situations where the training data and testing data are collected from different sources with distinct feature sets. Addressing this challenge has the potential to greatly improve the utility of valuable datasets that might otherwise be considered incomplete and enhance model performance. This paper introduces the novel Pseudo Triplet Network (PTN) to address cross-source feature incompleteness. PTN fuses two Siamese network architectures -- Triplet Networks and Pseudo Networks. By segregating data into instance, positive, and negative subsets, PTN facilitates effectively contrastive learning through a hybrid loss function design. The model was rigorously evaluated on six benchmark datasets from the UCI Repository, in comparison with five other methods for managing missing data, under a range of feature overlap and missing data scenarios. The PTN consistently exhibited superior performance, displaying resilience in high missing ratio situations and maintaining robust stability across various data scenarios.|跨源特性不完整性——某些特性只在一个数据源中可用，而在另一个数据源中缺失——是机器学习中常见的重大挑战。它通常出现在训练数据和测试数据是从具有不同特征集的不同来源收集的情况下。应对这一挑战有可能大大提高可能被认为不完整的宝贵数据集的效用，并提高模型性能。针对跨源特征不完全性问题，提出了一种新的伪三重网(PTN)算法。PTN 融合了两种连体网络结构——三联网络和伪网络。PTN 通过将数据分为实例子集、正子集和负子集，通过混合损失函数设计有效地促进了对比学习。在一系列特征重叠和缺失数据情景下，对 UCI 知识库的六个基准数据集进行了严格的评估，并与其他五种管理缺失数据的方法进行了比较。PTN 始终表现出优越的性能，在高缺失率情况下显示出弹性，并在各种数据场景中保持稳健的稳定性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Pseudo+Triplet+Networks+for+Classification+Tasks+with+Cross-Source+Feature+Incompleteness)|-1| -|[Towards Understanding of Deepfake Videos in the Wild](https://doi.org/10.1145/3583780.3614729)|Beomsang Cho, Binh M. Le, Jiwon Kim, Simon S. Woo, Shahroz Tariq, Alsharif Abuadbba, Kristen Moore|CSIRO's Data61, Melbourne, Australia; Sungkyunkwan University, Suwon, Republic of Korea; CSIRO's Data61, Sydney, Australia|Deepfakes have become a growing concern in recent years, prompting researchers to develop benchmark datasets and detection algorithms to tackle the issue. However, existing datasets suffer from significant drawbacks that hamper their effectiveness. Notably, these datasets fail to encompass the latest deepfake videos produced by state-of-the-art methods that are being shared across various platforms. This limitation impedes the ability to keep pace with the rapid evolution of generative AI techniques employed in real-world deepfake production. Our contributions in this IRB-approved study are to bridge this knowledge gap from current real-world deepfakes by providing in-depth analysis. We first present the largest and most diverse and recent deepfake dataset (RWDF-23) collected from the wild to date, consisting of 2,000 deepfake videos collected from 4 platforms targeting 4 different languages span created from 21 countries: Reddit, YouTube, TikTok, and Bilibili. By expanding the dataset's scope beyond the previous research, we capture a broader range of real-world deepfake content, reflecting the ever-evolving landscape of online platforms. Also, we conduct a comprehensive analysis encompassing various aspects of deepfakes, including creators, manipulation strategies, purposes, and real-world content production methods. This allows us to gain valuable insights into the nuances and characteristics of deepfakes in different contexts. Lastly, in addition to the video content, we also collect viewer comments and interactions, enabling us to explore the engagements of internet users with deepfake content. By considering this rich contextual information, we aim to provide a holistic understanding of the {evolving} deepfake phenomenon and its impact on online platforms.|近年来，Deepfakes 已经成为一个日益令人担忧的问题，促使研究人员开发基准数据集和检测算法来解决这个问题。但是，现有的数据集存在严重的缺陷，妨碍了它们的有效性。值得注意的是，这些数据集未能涵盖最新的深度伪造视频产生的最先进的方法，正在分享各种平台。这种局限性阻碍了生成性人工智能技术在现实世界深度伪造生产中的快速发展。我们在这个 IRB 批准的研究中的贡献是通过提供深入的分析来弥补与现实世界中的深度造假者之间的知识差距。我们首先展示迄今为止从野外收集的最大、最多样化和最新的深度伪造数据集(rWDF-23) ，包括从4个平台收集的2000个深度伪造视频，这些平台针对来自21个国家的4种不同语言: Reddit、 YouTube、 tikTok 和 Bilibili。通过扩大数据集的范围超越以前的研究，我们捕获了更广泛的现实世界深度假内容，反映了不断发展的在线平台景观。此外，我们进行了全面的分析，包括各个方面的深假，包括创作者，操作策略，目的，和现实世界的内容生产方法。这使我们能够获得有价值的洞察深度假的细微差别和特点在不同的背景下。最后，除了视频内容，我们还收集观众的评论和互动，使我们能够探索互联网用户与深度假内容的参与。通过考虑这丰富的上下文信息，我们旨在提供一个整体的理解{演变}深度假现象及其对在线平台的影响。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Towards+Understanding+of+Deepfake+Videos+in+the+Wild)|-1| -|[Commonsense Temporal Action Knowledge (CoTAK) Dataset](https://doi.org/10.1145/3583780.3615114)|Steven J. Lynden, Hailemariam Mehari Yohannes, KyoungSook Kim, Adam Jatowt, Akiyoshi Matono, HaiTao Yu, Xin Liu, Yijun Duan|University of Innsbruck, Innsbruck, Austria; National Institute of Advanced Industrial Science and Technology, Tokyo, Japan; University of Tsukuba, Tsukuba, Japan|This paper presents a publicly available, large-scale dataset resource, CoTAK (COmmonsense Temporal Action Knowledge) consisting of short descriptions of action-describing sentences manually annotated with temporal commonsense knowledge. The dataset consists of over 300K instructional sentences extracted from WikiHow, which are annotated with commonsense knowledge-based temporal labels indicating implicitly understood information about the actions described by the sentences, including approximately how long an action takes to perform and approximately how long its effects last for. For short duration actions labeled as taking seconds or minutes, which would be of relevance to automated task planning, e.g. in robotics applications, the dataset also provides scalar values to accurately label the temporal durations of how long actions take to perform. Experimental results are presented demonstrating that state-of-the-art machine learning techniques such as fine-tuning of large language models are effective in making predictions of commonsense temporal knowledge using the dataset, with up to 80% accuracy, showing the high utility and promising impact of the constructed resource and its applicability towards generating commonsense temporal knowledge relevant to various|本文提出了一个公开的、大规模的数据集资源——常识时态行为知识 CoTAK (COmmonsense 颞 al Action Knowledge) ，它包含用时态常识知识手工注释的行为描述句子的简短描述。该数据集包括从 WikiHow 中提取的超过300K 的指导性句子，这些句子被注释为基于常识的时间标签，表明隐含地理解了句子所描述的行为的信息，包括大约多长时间的行为需要执行，以及大约多长时间的影响持续。对于那些被标记为“花费几秒钟或几分钟”的短时间动作，这些动作与自动化任务规划有关，例如在机器人应用中，数据集还提供标量值来准确标记动作执行的时间长度。实验结果表明，最先进的机器学习技术，如大型语言模型的微调，是有效的预测常识时间知识使用数据集，高达80% 的准确率，显示了高效用和有希望的影响构造的资源和它的适用性，生成常识时间知识相关的各种|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Commonsense+Temporal+Action+Knowledge+(CoTAK)+Dataset)|-1| +|[A Deep Conditional Generative Approach for Constrained Community Detection](https://doi.org/10.1145/3583780.3615145)|Chaobo He, Junwei Cheng, Quanlong Guan, Xiang Fei, Hanchao Li, Yong Tang|College of Information Science and Technology, Jinan University, Guangzhou, China; School of Computer Science, South China Normal University, Guangzhou, China; Department of Computing, Coventry University, Coventry, United Kingdom; Institute for Digital Technologies, Loughborough University, Leicester, United Kingdom; School of Computer Science, South China Normal University, Pazhou Lab, Guangzhou, China|Constrained community detection is one of the popular topics in graph data mining, and it aims to improve the performance by exploiting prior pairwise constraints, such as must-link and cannot-link constraints. However, most of existing methods for constrained community detection are shallow approaches, and are also not robust to handle constraints information with noises. In view of this, we propose a deep conditional generative approach CGMVGAE. It firstly treats pairwise constraints as the priors with different degrees of certainty, and then integrates them into the conditional Gaussian mixture model. By further combing variational graph auto-encoders and the Wasserstein regularization, CGMVGAE can learn the latent node representations preserving community structures in a deep generative manner. Experimental results show that CGMVGAE outperforms state-of-the-art approaches, and is also more robust.|约束社区检测是图形数据挖掘中的热点问题之一，其目的是利用先验的成对约束，如必须链接约束和不能链接约束，提高社区检测的性能。然而，现有的约束群体检测方法大多是浅层检测方法，对于带有噪声的约束信息也不具有鲁棒性。鉴于此，我们提出了一种深度条件生成方法 CGMVGAE。它首先将成对约束作为不同确定度的先验条件，然后将其集成到条件高斯混合模型中。通过进一步结合变分图自动编码器和 Wasserstein 正则化，CGMVGAE 能够以深层生成的方式学习保持群体结构的潜在节点表示。实验结果表明，CGMVGAE 算法的性能优于目前最先进的算法，并且具有更强的鲁棒性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Deep+Conditional+Generative+Approach+for+Constrained+Community+Detection)|-1| +|[Pseudo Triplet Networks for Classification Tasks with Cross-Source Feature Incompleteness](https://doi.org/10.1145/3583780.3615154)|Cayon Liow, ChengTe Li, ChunPai Yang, ShouDe Lin|National Cheng Kung University, Tainan, Taiwan Roc; National Taiwan University, Taipei, Taiwan Roc|Cross-source feature incompleteness -- a scenario where certain features are only available in one data source but missing in another -- is a common and significant challenge in machine learning. It typically arises in situations where the training data and testing data are collected from different sources with distinct feature sets. Addressing this challenge has the potential to greatly improve the utility of valuable datasets that might otherwise be considered incomplete and enhance model performance. This paper introduces the novel Pseudo Triplet Network (PTN) to address cross-source feature incompleteness. PTN fuses two Siamese network architectures -- Triplet Networks and Pseudo Networks. By segregating data into instance, positive, and negative subsets, PTN facilitates effectively contrastive learning through a hybrid loss function design. The model was rigorously evaluated on six benchmark datasets from the UCI Repository, in comparison with five other methods for managing missing data, under a range of feature overlap and missing data scenarios. The PTN consistently exhibited superior performance, displaying resilience in high missing ratio situations and maintaining robust stability across various data scenarios.|跨源特性不完整性——某些特性只在一个数据源中可用，而在另一个数据源中缺失——是机器学习中常见的重大挑战。它通常出现在训练数据和测试数据是从具有不同特征集的不同来源收集的情况下。应对这一挑战有可能大大提高可能被认为不完整的宝贵数据集的效用，并提高模型性能。针对跨源特征不完全性问题，提出了一种新的伪三重网(PTN)算法。PTN 融合了两种连体网络结构——三联网络和伪网络。PTN 通过将数据分为实例子集、正子集和负子集，通过混合损失函数设计有效地促进了对比学习。在一系列特征重叠和缺失数据情景下，对 UCI 知识库的六个基准数据集进行了严格的评估，并与其他五种管理缺失数据的方法进行了比较。PTN 始终表现出优越的性能，在高缺失率情况下显示出弹性，并在各种数据场景中保持稳健的稳定性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Pseudo+Triplet+Networks+for+Classification+Tasks+with+Cross-Source+Feature+Incompleteness)|-1| +|[Towards Understanding of Deepfake Videos in the Wild](https://doi.org/10.1145/3583780.3614729)|Beomsang Cho, Binh M. Le, Jiwon Kim, Simon S. Woo, Shahroz Tariq, Alsharif Abuadbba, Kristen Moore|Sungkyunkwan University, Suwon, Republic of Korea; CSIRO's Data61, Melbourne, Australia; CSIRO's Data61, Sydney, Australia|Deepfakes have become a growing concern in recent years, prompting researchers to develop benchmark datasets and detection algorithms to tackle the issue. However, existing datasets suffer from significant drawbacks that hamper their effectiveness. Notably, these datasets fail to encompass the latest deepfake videos produced by state-of-the-art methods that are being shared across various platforms. This limitation impedes the ability to keep pace with the rapid evolution of generative AI techniques employed in real-world deepfake production. Our contributions in this IRB-approved study are to bridge this knowledge gap from current real-world deepfakes by providing in-depth analysis. We first present the largest and most diverse and recent deepfake dataset (RWDF-23) collected from the wild to date, consisting of 2,000 deepfake videos collected from 4 platforms targeting 4 different languages span created from 21 countries: Reddit, YouTube, TikTok, and Bilibili. By expanding the dataset's scope beyond the previous research, we capture a broader range of real-world deepfake content, reflecting the ever-evolving landscape of online platforms. Also, we conduct a comprehensive analysis encompassing various aspects of deepfakes, including creators, manipulation strategies, purposes, and real-world content production methods. This allows us to gain valuable insights into the nuances and characteristics of deepfakes in different contexts. Lastly, in addition to the video content, we also collect viewer comments and interactions, enabling us to explore the engagements of internet users with deepfake content. By considering this rich contextual information, we aim to provide a holistic understanding of the {evolving} deepfake phenomenon and its impact on online platforms.|近年来，Deepfakes 已经成为一个日益令人担忧的问题，促使研究人员开发基准数据集和检测算法来解决这个问题。但是，现有的数据集存在严重的缺陷，妨碍了它们的有效性。值得注意的是，这些数据集未能涵盖最新的深度伪造视频产生的最先进的方法，正在分享各种平台。这种局限性阻碍了生成性人工智能技术在现实世界深度伪造生产中的快速发展。我们在这个 IRB 批准的研究中的贡献是通过提供深入的分析来弥补与现实世界中的深度造假者之间的知识差距。我们首先展示迄今为止从野外收集的最大、最多样化和最新的深度伪造数据集(rWDF-23) ，包括从4个平台收集的2000个深度伪造视频，这些平台针对来自21个国家的4种不同语言: Reddit、 YouTube、 tikTok 和 Bilibili。通过扩大数据集的范围超越以前的研究，我们捕获了更广泛的现实世界深度假内容，反映了不断发展的在线平台景观。此外，我们进行了全面的分析，包括各个方面的深假，包括创作者，操作策略，目的，和现实世界的内容生产方法。这使我们能够获得有价值的洞察深度假的细微差别和特点在不同的背景下。最后，除了视频内容，我们还收集观众的评论和互动，使我们能够探索互联网用户与深度假内容的参与。通过考虑这丰富的上下文信息，我们旨在提供一个整体的理解{演变}深度假现象及其对在线平台的影响。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Towards+Understanding+of+Deepfake+Videos+in+the+Wild)|-1| +|[Commonsense Temporal Action Knowledge (CoTAK) Dataset](https://doi.org/10.1145/3583780.3615114)|Steven J. Lynden, Hailemariam Mehari Yohannes, KyoungSook Kim, Adam Jatowt, Akiyoshi Matono, HaiTao Yu, Xin Liu, Yijun Duan|National Institute of Advanced Industrial Science and Technology, Tokyo, Japan; University of Tsukuba, Tsukuba, Japan; University of Innsbruck, Innsbruck, Austria|This paper presents a publicly available, large-scale dataset resource, CoTAK (COmmonsense Temporal Action Knowledge) consisting of short descriptions of action-describing sentences manually annotated with temporal commonsense knowledge. The dataset consists of over 300K instructional sentences extracted from WikiHow, which are annotated with commonsense knowledge-based temporal labels indicating implicitly understood information about the actions described by the sentences, including approximately how long an action takes to perform and approximately how long its effects last for. For short duration actions labeled as taking seconds or minutes, which would be of relevance to automated task planning, e.g. in robotics applications, the dataset also provides scalar values to accurately label the temporal durations of how long actions take to perform. Experimental results are presented demonstrating that state-of-the-art machine learning techniques such as fine-tuning of large language models are effective in making predictions of commonsense temporal knowledge using the dataset, with up to 80% accuracy, showing the high utility and promising impact of the constructed resource and its applicability towards generating commonsense temporal knowledge relevant to various|本文提出了一个公开的、大规模的数据集资源——常识时态行为知识 CoTAK (COmmonsense 颞 al Action Knowledge) ，它包含用时态常识知识手工注释的行为描述句子的简短描述。该数据集包括从 WikiHow 中提取的超过300K 的指导性句子，这些句子被注释为基于常识的时间标签，表明隐含地理解了句子所描述的行为的信息，包括大约多长时间的行为需要执行，以及大约多长时间的影响持续。对于那些被标记为“花费几秒钟或几分钟”的短时间动作，这些动作与自动化任务规划有关，例如在机器人应用中，数据集还提供标量值来准确标记动作执行的时间长度。实验结果表明，最先进的机器学习技术，如大型语言模型的微调，是有效的预测常识时间知识使用数据集，高达80% 的准确率，显示了高效用和有希望的影响构造的资源和它的适用性，生成常识时间知识相关的各种|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Commonsense+Temporal+Action+Knowledge+(CoTAK)+Dataset)|-1| diff --git a/papers/cikm/cikm2024.md b/papers/cikm/cikm2024.md index 237f05c7..25a5fc77 100644 --- a/papers/cikm/cikm2024.md +++ b/papers/cikm/cikm2024.md @@ -2,13 +2,13 @@ |论文|作者|组织|摘要|翻译|代码|引用数| |---|---|---|---|---|---|---| -|[Enhancing Click-through Rate Prediction in Recommendation Domain with Search Query Representation](https://doi.org/10.1145/3627673.3679849)|Yuening Wang, Man Chen, Yaochen Hu, Wei Guo, Yingxue Zhang, Huifeng Guo, Yong Liu, Mark Coates|Huawei Noah's Ark Lab, Markham, Canada; Huawei Noah's Ark Lab, Shenzhen, China; McGill University, Montreal, Canada; Huawei Noah's Ark Lab, Montreal, Canada; Huawei Noah's Ark Lab, Singapore, Singapore|Many platforms, such as e-commerce websites, offer both search and recommendation services simultaneously to better meet users' diverse needs. Recommendation services suggest items based on user preferences, while search services allow users to search for items before providing recommendations. Since users and items are often shared between the search and recommendation domains, there is a valuable opportunity to enhance the recommendation domain by leveraging user preferences extracted from the search domain. Existing approaches either overlook the shift in user intention between these domains or fail to capture the significant impact of learning from users' search queries on understanding their interests. In this paper, we propose a framework that learns from user search query embeddings within the context of user preferences in the recommendation domain. Specifically, user search query sequences from the search domain are used to predict the items users will click at the next time point in the recommendation domain. Additionally, the relationship between queries and items is explored through contrastive learning. To address issues of data sparsity, the diffusion model is incorporated to infer positive items the user will select after searching with certain queries in a denoising manner, which is particularly effective in preventing false positives. Effectively extracting this information, the queries are integrated into click-through rate prediction in the recommendation domain. Experimental analysis demonstrates that our model outperforms state-of-the-art models in the recommendation domain.|许多平台，如电子商务网站，同时提供搜索和推荐服务，以更好地满足用户多样化的需求。推荐服务根据用户的偏好推荐商品，而搜索服务则允许用户在提供推荐之前搜索商品。由于用户和商品通常在搜索和推荐领域之间共享，因此有机会通过利用从搜索领域提取的用户偏好来增强推荐领域。现有方法要么忽略了这两个领域之间用户意图的转变，要么未能捕捉到从用户搜索查询中学习对理解用户兴趣的重大影响。本文提出了一种框架，该框架在推荐领域的用户偏好背景下学习用户搜索查询嵌入。具体来说，使用搜索领域的用户搜索查询序列来预测用户在推荐领域中下一次点击的商品。此外，通过对比学习探索查询与商品之间的关系。为了解决数据稀疏性问题，采用了扩散模型以去噪方式推断用户在使用某些查询进行搜索后将选择的正向商品，这在防止误报方面特别有效。有效地提取这些信息后，将查询整合到推荐领域的点击率预测中。实验分析表明，我们的模型在推荐领域的表现优于最先进的模型。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Enhancing+Click-through+Rate+Prediction+in+Recommendation+Domain+with+Search+Query+Representation)|0| +|[Enhancing Click-through Rate Prediction in Recommendation Domain with Search Query Representation](https://doi.org/10.1145/3627673.3679849)|Yuening Wang, Man Chen, Yaochen Hu, Wei Guo, Yingxue Zhang, Huifeng Guo, Yong Liu, Mark Coates|Huawei Noah's Ark Lab, Shenzhen, China; Huawei Noah's Ark Lab, Singapore, Singapore; McGill University, Montreal, Canada; Huawei Noah's Ark Lab, Markham, Canada; Huawei Noah's Ark Lab, Montreal, Canada|Many platforms, such as e-commerce websites, offer both search and recommendation services simultaneously to better meet users' diverse needs. Recommendation services suggest items based on user preferences, while search services allow users to search for items before providing recommendations. Since users and items are often shared between the search and recommendation domains, there is a valuable opportunity to enhance the recommendation domain by leveraging user preferences extracted from the search domain. Existing approaches either overlook the shift in user intention between these domains or fail to capture the significant impact of learning from users' search queries on understanding their interests. In this paper, we propose a framework that learns from user search query embeddings within the context of user preferences in the recommendation domain. Specifically, user search query sequences from the search domain are used to predict the items users will click at the next time point in the recommendation domain. Additionally, the relationship between queries and items is explored through contrastive learning. To address issues of data sparsity, the diffusion model is incorporated to infer positive items the user will select after searching with certain queries in a denoising manner, which is particularly effective in preventing false positives. Effectively extracting this information, the queries are integrated into click-through rate prediction in the recommendation domain. Experimental analysis demonstrates that our model outperforms state-of-the-art models in the recommendation domain.|许多平台，如电子商务网站，同时提供搜索和推荐服务，以更好地满足用户多样化的需求。推荐服务根据用户的偏好推荐商品，而搜索服务则允许用户在提供推荐之前搜索商品。由于用户和商品通常在搜索和推荐领域之间共享，因此有机会通过利用从搜索领域提取的用户偏好来增强推荐领域。现有方法要么忽略了这两个领域之间用户意图的转变，要么未能捕捉到从用户搜索查询中学习对理解用户兴趣的重大影响。本文提出了一种框架，该框架在推荐领域的用户偏好背景下学习用户搜索查询嵌入。具体来说，使用搜索领域的用户搜索查询序列来预测用户在推荐领域中下一次点击的商品。此外，通过对比学习探索查询与商品之间的关系。为了解决数据稀疏性问题，采用了扩散模型以去噪方式推断用户在使用某些查询进行搜索后将选择的正向商品，这在防止误报方面特别有效。有效地提取这些信息后，将查询整合到推荐领域的点击率预测中。实验分析表明，我们的模型在推荐领域的表现优于最先进的模型。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Enhancing+Click-through+Rate+Prediction+in+Recommendation+Domain+with+Search+Query+Representation)|0| |[Calibration-Disentangled Learning and Relevance-Prioritized Reranking for Calibrated Sequential Recommendation](https://doi.org/10.1145/3627673.3679728)|Hyunsik Jeon, Seeun Yoon, Julian J. McAuley||Calibrated recommendation, which aims to maintain personalized proportions of categories within recommendations, is crucial in practical scenarios since it enhances user satisfaction by reflecting diverse interests. However, achieving calibration in a sequential setting (i.e., calibrated sequential recommendation) is challenging due to the need to adapt to users' evolving preferences. Previous methods typically leverage reranking algorithms to calibrate recommendations after training a model without considering the effect of calibration and do not effectively tackle the conflict between relevance and calibration during the reranking process. In this work, we propose LeapRec (Calibration-Disentangled Learning and Relevance-Prioritized Reranking), a novel approach for the calibrated sequential recommendation that addresses these challenges. LeapRec consists of two phases, model training phase and reranking phase. In the training phase, a backbone model is trained using our proposed calibration-disentangled learning-to-rank loss, which optimizes personalized rankings while integrating calibration considerations. In the reranking phase, relevant items are prioritized at the top of the list, with items needed for calibration following later to address potential conflicts between relevance and calibration. Through extensive experiments on four real-world datasets, we show that LeapRec consistently outperforms previous methods in the calibrated sequential recommendation. Our code is available at https://github.com/jeon185/LeapRec.|校准推荐旨在保持推荐中类别的个性化比例，这在实际场景中至关重要，因为它通过反映多样化的兴趣来增强用户满意度。然而，在序列环境中实现校准（即校准序列推荐）具有挑战性，因为需要适应用户不断变化的偏好。先前的方法通常利用重新排序算法在训练模型后进行推荐校准，而没有考虑校准效果，并且在重新排序过程中未能有效解决相关性与校准之间的冲突。在这项工作中，我们提出了LeapRec（校准解耦学习和相关性优先重新排序），这是一种新颖的校准序列推荐方法，旨在解决这些挑战。LeapRec包括两个阶段，模型训练阶段和重新排序阶段。在训练阶段，使用我们提出的校准解耦学习排序损失训练骨干模型，该损失在优化个性化排序的同时整合了校准考虑。在重新排序阶段，相关项目优先置于列表顶部，而需要校准的项目随后放置，以解决相关性与校准之间可能的冲突。通过在四个真实世界数据集上的广泛实验，我们展示了LeapRec在校准序列推荐方面始终优于先前的方法。我们的代码可在https://github.com/jeon185/LeapRec获取。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Calibration-Disentangled+Learning+and+Relevance-Prioritized+Reranking+for+Calibrated+Sequential+Recommendation)|0| |[Aligning Query Representation with Rewritten Query and Relevance Judgments in Conversational Search](https://doi.org/10.1145/3627673.3679534)|Fengran Mo, Chen Qu, Kelong Mao, Yihong Wu, Zhan Su, Kaiyu Huang, JianYun Nie||Conversational search supports multi-turn user-system interactions to solve complex information needs. Different from the traditional single-turn ad-hoc search, conversational search encounters a more challenging problem of context-dependent query understanding with the lengthy and long-tail conversational history context. While conversational query rewriting methods leverage explicit rewritten queries to train a rewriting model to transform the context-dependent query into a stand-stone search query, this is usually done without considering the quality of search results. Conversational dense retrieval methods use fine-tuning to improve a pre-trained ad-hoc query encoder, but they are limited by the conversational search data available for training. In this paper, we leverage both rewritten queries and relevance judgments in the conversational search data to train a better query representation model. The key idea is to align the query representation with those of rewritten queries and relevant documents. The proposed model – Query Representation Alignment Conversational Dense Retriever, QRACDR, is tested on eight datasets, including various settings in conversational search and ad-hoc search. The results demonstrate the strong performance of QRACDR compared with state-of-the-art methods, and confirm the effectiveness of representation alignment.|对话搜索支持多轮用户-系统交互，以解决复杂的信息需求。与传统的单轮即席搜索不同，对话搜索面临着一个更具挑战性的问题，即在长篇且长尾的对话历史背景下进行依赖上下文的查询理解。虽然对话查询重写方法利用显式的重写查询来训练重写模型，将依赖上下文的查询转换为独立的搜索查询，但这通常不考虑搜索结果的质量。对话密集检索方法通过微调预训练的即席查询编码器来改进，但受限于可用于训练的对话搜索数据。本文中，我们利用对话搜索数据中的重写查询和相关性判断来训练一个更好的查询表示模型。关键思想是将查询表示与重写查询和相关文档的表示对齐。提出的模型——查询表示对齐对话密集检索器（QRACDR），在八个数据集上进行了测试，包括对话搜索和即席搜索的各种设置。结果显示，QRACDR相比最先进的方法表现出强劲的性能，并证实了表示对齐的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Aligning+Query+Representation+with+Rewritten+Query+and+Relevance+Judgments+in+Conversational+Search)|0| |[Improved Estimation of Ranks for Learning Item Recommenders with Negative Sampling](https://doi.org/10.1145/3627673.3679943)|Anushya Subbiah, Steffen Rendle, Vikram Aggarwal||In recommendation systems, there has been a growth in the number of recommendable items (# of movies, music, products). When the set of recommendable items is large, training and evaluation of item recommendation models becomes computationally expensive. To lower this cost, it has become common to sample negative items. However, the recommendation quality can suffer from biases introduced by traditional negative sampling mechanisms. In this work, we demonstrate the benefits from correcting the bias introduced by sampling of negatives. We first provide sampled batch version of the well-studied WARP and LambdaRank methods. Then, we present how these methods can benefit from improved ranking estimates. Finally, we evaluate the recommendation quality as a result of correcting rank estimates and demonstrate that WARP and LambdaRank can be learned efficiently with negative sampling and our proposed correction technique.|在推荐系统中，可推荐项目的数量（如电影、音乐、产品）有所增加。当可推荐项目的集合规模较大时，训练和评估项目推荐模型的计算成本会变得非常高。为了降低这一成本，通常会采用负样本采样的方法。然而，传统的负样本采样机制可能会引入偏差，从而影响推荐质量。在这项工作中，我们展示了通过纠正负样本采样引入的偏差所带来的好处。我们首先提供了经过深入研究的WARP和LambdaRank方法的采样批次版本。然后，我们展示了这些方法如何从改进的排序估计中受益。最后，我们评估了纠正排序估计后的推荐质量，并证明WARP和LambdaRank可以通过负样本采样和我们的修正技术高效地进行学习。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Improved+Estimation+of+Ranks+for+Learning+Item+Recommenders+with+Negative+Sampling)|0| |[Scalable Dynamic Embedding Size Search for Streaming Recommendation](https://doi.org/10.1145/3627673.3679638)|Yunke Qu, Liang Qu, Tong Chen, Xiangyu Zhao, Quoc Viet Hung Nguyen, Hongzhi Yin||Recommender systems typically represent users and items by learning their embeddings, which are usually set to uniform dimensions and dominate the model parameters. However, real-world recommender systems often operate in streaming recommendation scenarios, where the number of users and items continues to grow, leading to substantial storage resource consumption for these embeddings. Although a few methods attempt to mitigate this by employing embedding size search strategies to assign different embedding dimensions in streaming recommendations, they assume that the embedding size grows with the frequency of users/items, which eventually still exceeds the predefined memory budget over time. To address this issue, this paper proposes to learn Scalable Lightweight Embeddings for streaming recommendation, called SCALL, which can adaptively adjust the embedding sizes of users/items within a given memory budget over time. Specifically, we propose to sample embedding sizes from a probabilistic distribution, with the guarantee to meet any predefined memory budget. By fixing the memory budget, the proposed embedding size sampling strategy can increase and decrease the embedding sizes in accordance to the frequency of the corresponding users or items. Furthermore, we develop a reinforcement learning-based search paradigm that models each state with mean pooling to keep the length of the state vectors fixed, invariant to the changing number of users and items. As a result, the proposed method can provide embedding sizes to unseen users and items. Comprehensive empirical evaluations on two public datasets affirm the advantageous effectiveness of our proposed method.|推荐系统通常通过学习嵌入来表示用户和物品，这些嵌入通常设置为统一的维度，并且主导模型参数。然而，现实世界的推荐系统经常在流式推荐场景中运行，其中用户和物品的数量持续增长，导致这些嵌入的存储资源消耗巨大。尽管一些方法试图通过采用嵌入大小搜索策略在流式推荐中分配不同的嵌入维度来缓解这一问题，但它们假设嵌入大小随着用户/物品的频率增长，最终仍然会超过预定义的内存预算。为了解决这个问题，本文提出了一种名为SCALL的流式推荐可扩展轻量级嵌入学习方法，它能够在给定的内存预算内随时间自适应地调整用户/物品的嵌入大小。具体来说，我们提出从概率分布中采样嵌入大小，以确保满足任何预定义的内存预算。通过固定内存预算，所提出的嵌入大小采样策略可以根据相应用户或物品的频率增加或减少嵌入大小。此外，我们开发了一种基于强化学习的搜索范式，该范式通过均值池化来建模每个状态，以保持状态向量的长度固定，不受用户和物品数量变化的影响。因此，所提出的方法可以为未见过的用户和物品提供嵌入大小。在两个公共数据集上的综合实证评估证实了我们提出的方法的优势有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Scalable+Dynamic+Embedding+Size+Search+for+Streaming+Recommendation)|0| -|[Ask or Recommend: An Empirical Study on Conversational Product Search](https://doi.org/10.1145/3627673.3679875)|Heli Ma, Jie Zou, Mohammad Aliannejadi, Evangelos Kanoulas, Yi Bin, Yang Yang|University of Electronic Science and Technology of China, Chengdu, China; University of Science and Technology of China, Chengdu, China; University of Amsterdam, Amstedam, Netherlands; Tongji University, Shanghai, China; University of Amsterdam, Amsterdam, Netherlands|Conversational Product Search (CPS) provides an engaging way for users to find products through effective natural language conversations. However, understanding the effect of conversational characteristics on user search performance and when to ask clarifying questions or recommend products remains unexplored. To fill the gap, we conduct an empirical study in this paper. Specifically, we developed a conversational system that allows participants to join as customers or shopping assistants, to simulate the conversational product search activity. Data collected from conversations and participant feedback indicate that: (a) CPS systems tend to ask clarifying questions early in the conversation when users express the intent of issuing a new query and chitchat, while they tend to recommend products at a later stage of conversations; asking clarifying questions early and recommending products lately can significantly improve search performance and user's satisfaction; (b) asking clarifying questions and more fine-grained search keywords positively influence search performance in terms of finding relevant products; (c) although the conversation time has a positive impact on the number of recommended products, the performance gain diminishes with longer conversation time; (d) more clarifying questions, more conversation turns, and longer system response time lead to decreased user satisfaction.|对话式产品搜索（Conversational Product Search, CPS）为用户提供了一种通过高效自然语言对话寻找产品的互动方式。然而，对话特性对用户搜索表现的影响以及何时提问澄清问题或推荐产品的问题尚未得到深入研究。为了填补这一空白，本文进行了一项实证研究。具体而言，我们开发了一个对话系统，允许参与者扮演顾客或购物助手的角色，模拟对话式产品搜索活动。从对话中收集的数据及参与者反馈表明：(a) CPS系统在用户表达新查询意图和闲聊时，倾向于在对话初期提问澄清问题，而在对话后期则更倾向于推荐产品；尽早提问澄清问题和延迟推荐产品可以显著提升搜索表现和用户满意度；(b)提问澄清问题和更细粒度的搜索关键词对查找相关产品有正面影响，从而提高搜索表现；(c)尽管对话时间对推荐产品数量有正面影响，但随对话时间延长，性能提升逐渐减少；(d)更多的澄清问题、更多的对话轮次和更长的系统响应时间会导致用户满意度下降。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Ask+or+Recommend:+An+Empirical+Study+on+Conversational+Product+Search)|0| -|[Towards Better Seach Query Classification with Distribution-Diverse Multi-Expert Knowledge Distillation in JD Ads Search](https://doi.org/10.1145/3627673.3680049)|KunPeng Ning, Ming Pang, Zheng Fang, Xue Jiang, XiWei Zhao, Changping Peng, Zhangang Lin, Jinghe Hu, Jingping Shao, Li Yuan|Peking University, ShenZhen, China; Business Growth BU, JD.COM, Beijing, China; Peking University, Shenzhen, China|In the dynamic landscape of online advertising, decoding user intent remains a pivotal challenge, particularly in the context of query classification. Swift classification models, exemplified by FastText, cater to the demand for real-time responses but encounter limitations in handling intricate queries. Conversely, accuracy-centric models like BERT introduce challenges associated with increased latency. This paper undertakes a nuanced exploration, navigating the delicate balance between efficiency and accuracy. It unveils FastText's latent potential as an 'online dictionary' for historical queries while harnessing the semantic robustness of BERT for novel and complex scenarios. The proposed Distribution-Diverse Multi-Expert (DDME) framework employs multiple teacher models trained from diverse data distributions. Through meticulous data categorization and enrichment, it elevates the classification performance across the query spectrum. Empirical results within the JD ads search system validate the superiority of our proposed approaches.|在在线广告的动态环境中，解读用户意图仍然是一个关键挑战，尤其是在查询分类的背景下。以FastText为代表的快速分类模型满足了实时响应的需求，但在处理复杂查询时存在局限性。相反，以准确性为中心的模型如BERT，虽然引入了延迟增加的挑战，但在处理复杂查询时表现出色。本文深入探讨了在效率和准确性之间寻求微妙平衡的问题。研究发现，FastText作为历史查询的“在线词典”具有潜在价值，同时利用BERT的语义丰富性来应对新颖和复杂的场景。提出的分布多样多专家（DDME）框架采用了从不同数据分布中训练的多个教师模型。通过细致的数据分类和丰富化处理，该框架提升了查询分类的整体性能。在京东广告搜索系统中的实证结果验证了我们提出的方法的优越性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Towards+Better+Seach+Query+Classification+with+Distribution-Diverse+Multi-Expert+Knowledge+Distillation+in+JD+Ads+Search)|0| +|[Ask or Recommend: An Empirical Study on Conversational Product Search](https://doi.org/10.1145/3627673.3679875)|Heli Ma, Jie Zou, Mohammad Aliannejadi, Evangelos Kanoulas, Yi Bin, Yang Yang|University of Electronic Science and Technology of China, Chengdu, China; University of Amsterdam, Amstedam, Netherlands; University of Science and Technology of China, Chengdu, China; Tongji University, Shanghai, China; University of Amsterdam, Amsterdam, Netherlands|Conversational Product Search (CPS) provides an engaging way for users to find products through effective natural language conversations. However, understanding the effect of conversational characteristics on user search performance and when to ask clarifying questions or recommend products remains unexplored. To fill the gap, we conduct an empirical study in this paper. Specifically, we developed a conversational system that allows participants to join as customers or shopping assistants, to simulate the conversational product search activity. Data collected from conversations and participant feedback indicate that: (a) CPS systems tend to ask clarifying questions early in the conversation when users express the intent of issuing a new query and chitchat, while they tend to recommend products at a later stage of conversations; asking clarifying questions early and recommending products lately can significantly improve search performance and user's satisfaction; (b) asking clarifying questions and more fine-grained search keywords positively influence search performance in terms of finding relevant products; (c) although the conversation time has a positive impact on the number of recommended products, the performance gain diminishes with longer conversation time; (d) more clarifying questions, more conversation turns, and longer system response time lead to decreased user satisfaction.|对话式产品搜索（Conversational Product Search, CPS）为用户提供了一种通过高效自然语言对话寻找产品的互动方式。然而，对话特性对用户搜索表现的影响以及何时提问澄清问题或推荐产品的问题尚未得到深入研究。为了填补这一空白，本文进行了一项实证研究。具体而言，我们开发了一个对话系统，允许参与者扮演顾客或购物助手的角色，模拟对话式产品搜索活动。从对话中收集的数据及参与者反馈表明：(a) CPS系统在用户表达新查询意图和闲聊时，倾向于在对话初期提问澄清问题，而在对话后期则更倾向于推荐产品；尽早提问澄清问题和延迟推荐产品可以显著提升搜索表现和用户满意度；(b)提问澄清问题和更细粒度的搜索关键词对查找相关产品有正面影响，从而提高搜索表现；(c)尽管对话时间对推荐产品数量有正面影响，但随对话时间延长，性能提升逐渐减少；(d)更多的澄清问题、更多的对话轮次和更长的系统响应时间会导致用户满意度下降。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Ask+or+Recommend:+An+Empirical+Study+on+Conversational+Product+Search)|0| +|[Towards Better Seach Query Classification with Distribution-Diverse Multi-Expert Knowledge Distillation in JD Ads Search](https://doi.org/10.1145/3627673.3680049)|KunPeng Ning, Ming Pang, Zheng Fang, Xue Jiang, XiWei Zhao, Changping Peng, Zhangang Lin, Jinghe Hu, Jingping Shao, Li Yuan|Peking University, Shenzhen, China; Business Growth BU, JD.COM, Beijing, China; Peking University, ShenZhen, China|In the dynamic landscape of online advertising, decoding user intent remains a pivotal challenge, particularly in the context of query classification. Swift classification models, exemplified by FastText, cater to the demand for real-time responses but encounter limitations in handling intricate queries. Conversely, accuracy-centric models like BERT introduce challenges associated with increased latency. This paper undertakes a nuanced exploration, navigating the delicate balance between efficiency and accuracy. It unveils FastText's latent potential as an 'online dictionary' for historical queries while harnessing the semantic robustness of BERT for novel and complex scenarios. The proposed Distribution-Diverse Multi-Expert (DDME) framework employs multiple teacher models trained from diverse data distributions. Through meticulous data categorization and enrichment, it elevates the classification performance across the query spectrum. Empirical results within the JD ads search system validate the superiority of our proposed approaches.|在在线广告的动态环境中，解读用户意图仍然是一个关键挑战，尤其是在查询分类的背景下。以FastText为代表的快速分类模型满足了实时响应的需求，但在处理复杂查询时存在局限性。相反，以准确性为中心的模型如BERT，虽然引入了延迟增加的挑战，但在处理复杂查询时表现出色。本文深入探讨了在效率和准确性之间寻求微妙平衡的问题。研究发现，FastText作为历史查询的“在线词典”具有潜在价值，同时利用BERT的语义丰富性来应对新颖和复杂的场景。提出的分布多样多专家（DDME）框架采用了从不同数据分布中训练的多个教师模型。通过细致的数据分类和丰富化处理，该框架提升了查询分类的整体性能。在京东广告搜索系统中的实证结果验证了我们提出的方法的优越性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Towards+Better+Seach+Query+Classification+with+Distribution-Diverse+Multi-Expert+Knowledge+Distillation+in+JD+Ads+Search)|0| |[Spectral and Geometric Spaces Representation Regularization for Multi-Modal Sequential Recommendation](https://doi.org/10.1145/3627673.3679647)|Zihao Li, Xuekong Xu, Zuoli Tang, Lixin Zou, Qian Wang, Chenliang Li||Recent works demonstrate the effectiveness of multi-modal information for sequential recommendation. However, the computational cost and representation degeneration fail to be focused specifically and addressed adequately in multi-modality recommendation. To this end, we first identify and formalize three properties i.e., diversity, compactness, and consistency from the geometric space and spectrum perspective. Building upon this foundation, we devise tailored loss functions to regularize the above three properties for representation optimization. Theoretical underpinnings and experimental results demonstrate the efficacy of an enhanced item representation in ameliorating degeneration. Furthermore, we propose an efficient and expandable image-centered method, named E2 ImgRec, to mitigate the immense cost of computation. Concretely, we substitute the linear projection operations in the self-attention module and feed-forward network layer with two learnable rescaling vectors or efficient recommendation, then leverage cross-attention for multi-modality information fusion. Extensive experiments on three public datasets illustrate our method outperforms representative ID-based solutions and multi-modal based state-of-the-arts with only up to 39.9% in memory usage and 4.3× acceleration in training time. The code for replication is available at https://github.com/WHUIR/E2ImgRec.|近期的研究展示了多模态信息在序列推荐中的有效性。然而，多模态推荐中的计算成本和表示退化问题尚未得到充分关注和解决。为此，我们首先从几何空间和频谱的角度识别并形式化了三个特性，即多样性、紧凑性和一致性。在此基础上，我们设计了定制的损失函数来规范上述三个特性，以优化表示。理论基础和实验结果表明，增强的物品表示能够有效改善退化问题。此外，我们提出了一种高效且可扩展的以图像为中心的方法，名为E2 ImgRec，以缓解巨大的计算成本。具体而言，我们用两个可学习的重缩放向量替代了自注意力模块和前馈网络层中的线性投影操作，并利用交叉注意力进行多模态信息融合。在三个公开数据集上的广泛实验表明，我们的方法在内存使用率最高仅为39.9%和训练时间加速4.3倍的情况下，优于基于ID的代表性解决方案和多模态的最新技术。可复现代码已发布在https://github.com/WHUIR/E2ImgRec。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Spectral+and+Geometric+Spaces+Representation+Regularization+for+Multi-Modal+Sequential+Recommendation)|0| |[Retrieval-Oriented Knowledge for Click-Through Rate Prediction](https://doi.org/10.1145/3627673.3679842)|Huanshuo Liu, Bo Chen, Menghui Zhu, Jianghao Lin, Jiarui Qin, Hao Zhang, Yang Yang, Ruiming Tang||Click-through rate (CTR) prediction plays an important role in personalizedrecommendations. Recently, sample-level retrieval-based models (e.g., RIM) haveachieved remarkable performance by retrieving and aggregating relevant samples.However, their inefficiency at the inference stage makes them impractical forindustrial applications. To overcome this issue, this paper proposes auniversal plug-and-play Retrieval-Oriented Knowledge (ROK) framework.Specifically, a knowledge base, consisting of a retrieval-oriented embeddinglayer and a knowledge encoder, is designed to preserve and imitate theretrieved aggregated representations in a decomposition-reconstructionparadigm. Knowledge distillation and contrastive learning methods are utilizedto optimize the knowledge base, and the learned retrieval-enhancedrepresentations can be integrated with arbitrary CTR models in bothinstance-wise and feature-wise manners. Extensive experiments on threelarge-scale datasets show that ROK achieves competitive performance with theretrieval-based CTR models while reserving superior inference efficiency andmodel compatibility.|点击率（CTR）预测在个性化推荐中扮演着重要角色。近期，基于样本级检索的模型（如RIM）通过检索并聚合相关样本来取得了显著的性能。然而，这些模型在推理阶段的效率低下使其难以应用于工业场景。为解决这一问题，本文提出了一种通用的即插即用型检索导向知识（ROK）框架。具体而言，设计了一个由检索导向嵌入层和知识编码器组成的知识库，该知识库在分解-重构范式中保留并模仿检索到的聚合表示。利用知识蒸馏和对比学习方法来优化知识库，所学到的检索增强表示可以与任意CTR模型在实例级和特征级方式上进行集成。在三个大规模数据集上的广泛实验表明，ROK在保留优越的推理效率和模型兼容性的同时，实现了与基于检索的CTR模型相当的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Retrieval-Oriented+Knowledge+for+Click-Through+Rate+Prediction)|0| |[Mitigating Exposure Bias in Online Learning to Rank Recommendation: A Novel Reward Model for Cascading Bandits](https://doi.org/10.1145/3627673.3679763)|Masoud Mansoury, Bamshad Mobasher, Herke van Hoof||Exposure bias is a well-known issue in recommender systems where items and suppliers are not equally represented in the recommendation results. This bias becomes particularly problematic over time as a few items are repeatedly over-represented in recommendation lists, leading to a feedback loop that further amplifies this bias. Although extensive research has addressed this issue in model-based or neighborhood-based recommendation algorithms, less attention has been paid to online recommendation models, such as those based on top-K contextual bandits, where recommendation models are dynamically updated with ongoing user feedback. In this paper, we study exposure bias in a class of well-known contextual bandit algorithms known as Linear Cascading Bandits. We analyze these algorithms in their ability to handle exposure bias and provide a fair representation of items in the recommendation results. Our analysis reveals that these algorithms fail to mitigate exposure bias in the long run during the course of ongoing user interactions. We propose an Exposure-Aware reward model that updates the model parameters based on two factors: 1) implicit user feedback and 2) the position of the item in the recommendation list. The proposed model mitigates exposure bias by controlling the utility assigned to the items based on their exposure in the recommendation list. Our experiments with two real-world datasets show that our proposed reward model improves the exposure fairness of the linear cascading bandits over time while maintaining the recommendation accuracy. It also outperforms the current baselines. Finally, we prove a high probability upper regret bound for our proposed model, providing theoretical guarantees for its performance.|曝光偏差是推荐系统中一个众所周知的问题，其中物品和供应商在推荐结果中的表现并不均衡。随着时间的推移，这种偏差变得尤为严重，因为少数物品在推荐列表中被过度重复展示，形成了一个反馈循环，进一步加剧了这种偏差。尽管大量研究已经解决了基于模型或基于邻域的推荐算法中的这一问题，但对于在线推荐模型（如基于top-K上下文强盗的模型）的关注较少，这些模型会根据用户的持续反馈动态更新推荐模型。在本文中，我们研究了一类著名的上下文强盗算法——线性级联强盗算法中的曝光偏差问题。我们分析了这些算法在处理曝光偏差和在推荐结果中公平展示物品方面的能力。我们的分析表明，这些算法在长期用户交互过程中无法有效缓解曝光偏差。我们提出了一种曝光感知奖励模型，该模型根据两个因素更新模型参数：1）隐式用户反馈和2）物品在推荐列表中的位置。所提出的模型通过根据物品在推荐列表中的曝光程度调整分配给它们的效用，来缓解曝光偏差。我们在两个真实世界数据集上的实验表明，所提出的奖励模型随着时间的推移提高了线性级联强盗算法的曝光公平性，同时保持了推荐准确性。此外，它还优于当前的基线模型。最后，我们证明了所提出模型的高概率上界遗憾界限，为其性能提供了理论保证。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Mitigating+Exposure+Bias+in+Online+Learning+to+Rank+Recommendation:+A+Novel+Reward+Model+for+Cascading+Bandits)|0| @@ -22,84 +22,84 @@ |[Relevance Filtering for Embedding-based Retrieval](https://doi.org/10.1145/3627673.3680095)|Nicholas Rossi, Juexin Lin, Feng Liu, Zhen Yang, Tony Lee, Alessandro Magnani, Ciya Liao||In embedding-based retrieval, Approximate Nearest Neighbor (ANN) search enables efficient retrieval of similar items from large-scale datasets. While maximizing recall of relevant items is usually the goal of retrieval systems, a low precision may lead to a poor search experience. Unlike lexical retrieval, which inherently limits the size of the retrieved set through keyword matching, dense retrieval via ANN search has no natural cutoff. Moreover, the cosine similarity scores of embedding vectors are often optimized via contrastive or ranking losses, which make them difficult to interpret. Consequently, relying on top-K or cosine-similarity cutoff is often insufficient to filter out irrelevant results effectively. This issue is prominent in product search, where the number of relevant products is often small. This paper introduces a novel relevance filtering component (called "Cosine Adapter") for embedding-based retrieval to address this challenge. Our approach maps raw cosine similarity scores to interpretable scores using a query-dependent mapping function. We then apply a global threshold on the mapped scores to filter out irrelevant results. We are able to significantly increase the precision of the retrieved set, at the expense of a small loss of recall. The effectiveness of our approach is demonstrated through experiments on both public MS MARCO dataset and internal Walmart product search data. Furthermore, online A/B testing on the Walmart site validates the practical value of our approach in real-world e-commerce settings.|在基于嵌入的检索中，近似最近邻（ANN）搜索能够从大规模数据集中高效地检索相似项目。尽管最大化相关项目的召回率通常是检索系统的目标，但低精度可能会导致糟糕的搜索体验。与通过关键词匹配自然限制检索集大小的词法检索不同，通过ANN搜索的密集检索没有自然的截止点。此外，嵌入向量的余弦相似度分数通常通过对比或排序损失进行优化，这使得它们难以解释。因此，仅依赖于前K个结果或余弦相似度截止点往往不足以有效过滤掉不相关的结果。在产品搜索中，这一问题尤为突出，因为相关产品的数量通常较少。本文为基于嵌入的检索引入了一种新颖的相关性过滤组件（称为“余弦适配器”），以应对这一挑战。我们的方法使用查询依赖的映射函数将原始余弦相似度分数映射为可解释的分数，然后对映射后的分数应用全局阈值以过滤掉不相关的结果。我们能够在召回率小幅损失的情况下显著提高检索集的精度。通过在公共MS MARCO数据集和内部沃尔玛产品搜索数据上的实验，证明了我们方法的有效性。此外，在沃尔玛网站上的在线A/B测试验证了我们的方法在实际电子商务环境中的实用价值。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Relevance+Filtering+for+Embedding-based+Retrieval)|0| |[Advancing Re-Ranking with Multimodal Fusion and Target-Oriented Auxiliary Tasks in E-Commerce Search](https://doi.org/10.1145/3627673.3680063)|Enqiang Xu, Xinhui Li, Zhigong Zhou, Jiahao Ji, Jinyuan Zhao, Dadong Miao, Songlin Wang, Lin Liu, Sulong Xu||In the rapidly evolving field of e-commerce, the effectiveness of search re-ranking models is crucial for enhancing user experience and driving conversion rates. Despite significant advancements in feature representation and model architecture, the integration of multimodal information remains underexplored. This study addresses this gap by investigating the computation and fusion of textual and visual information in the context of re-ranking. We propose Advancing Re-Ranking with Multimodal Fusion and Target-Oriented Auxiliary Tasks (ARMMT), which integrates an attention-based multimodal fusion technique and an auxiliary ranking-aligned task to enhance item representation and improve targeting capabilities. This method not only enriches the understanding of product attributes but also enables more precise and personalized recommendations. Experimental evaluations on JD.com's search platform demonstrate that ARMMT achieves state-of-the-art performance in multimodal information integration, evidenced by a 0.22% increase in the Conversion Rate (CVR), significantly contributing to Gross Merchandise Volume (GMV). This pioneering approach has the potential to revolutionize e-commerce re-ranking, leading to elevated user satisfaction and business growth.|在电子商务快速发展的领域中，搜索重排序模型的有效性对于提升用户体验和推动转化率至关重要。尽管在特征表示和模型架构方面取得了显著进展，但多模态信息的整合仍未得到充分探索。本研究通过探讨重排序情境下文本和视觉信息的计算与融合，填补了这一空白。我们提出了基于多模态融合与面向目标的辅助任务的进阶重排序模型（ARMMT），该模型整合了基于注意力的多模态融合技术与辅助排序对齐任务，以增强商品表示并提升目标定位能力。这种方法不仅丰富了对产品属性的理解，还实现了更精确和个性化的推荐。在京东搜索平台上的实验评估表明，ARMMT在多模态信息整合方面达到了最先进的性能，体现在转化率（CVR）提高了0.22%，显著促进了商品交易总额（GMV）的增长。这一开创性方法有望革新电子商务重排序，带来用户满意度和业务增长的双重提升。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Advancing+Re-Ranking+with+Multimodal+Fusion+and+Target-Oriented+Auxiliary+Tasks+in+E-Commerce+Search)|0| |[Missing Interest Modeling with Lifelong User Behavior Data for Retrieval Recommendation](https://doi.org/10.1145/3627673.3680019)|Gaode Chen, Yuezihan Jiang, Rui Huang, Kuo Cai, Yunze Luo, Ruina Sun, Qi Zhang, Han Li, Kun Gai|Kuaishou Technology, Beijing, China|Rich user behavior data has been proven to be of great value for recommendation systems. Modeling lifelong user behavior data in the retrieval stage to explore user long-term preference and obtain comprehensive retrieval results is crucial. Existing lifelong modeling methods cannot applied to the retrieval stage because they extract target-relevant items through the coupling between the user and the target item. Moreover, the current retrieval methods fail to precisely capture user interests when the length of the user behavior sequence increases further. That leads to a gap in the ability of retrieval models to model lifelong user behavior data. In this paper, we propose the concept of missing interest, leveraging the idea of complementarity, which serves as a supplement to short-term interest based on lifelong behavior data in the retrieval stage. Specifically, we design a missing interest operator and deploy it in Kafka data stream, without incurring latency or storage costs. This operator derives categories and authors of items that the user was previously interested in but has recently missed, and uses these as triggers to output missing features to the downstream retrieval model. Our retrieval model is a complete dual-tower structure that combines short-term and missing interests on the user side to provide a comprehensive depiction of lifelong behaviors. Since 2023, the presented solution has been deployed in Kuaishou, one of the most popular short-video streaming platforms in China with hundreds of millions of active users.|丰富的用户行为数据已被证明对推荐系统具有巨大价值。在检索阶段对终身用户行为数据进行建模，以探索用户的长期偏好并获得全面的检索结果至关重要。现有的终身建模方法无法应用于检索阶段，因为它们通过用户与目标项目之间的耦合来提取目标相关项目。此外，当前的检索方法在用户行为序列长度进一步增加时无法精确捕捉用户兴趣。这导致了检索模型在终身用户行为数据建模能力上的差距。本文提出了缺失兴趣的概念，利用互补的思想，作为基于终身行为数据在检索阶段对短期兴趣的补充。具体来说，我们设计了一个缺失兴趣操作符，并将其部署在Kafka数据流中，不会产生延迟或存储成本。该操作符推导出用户之前感兴趣但最近错过的项目的类别和作者，并使用这些作为触发器向下游检索模型输出缺失特征。我们的检索模型是一个完整的双塔结构，结合了用户端的短期兴趣和缺失兴趣，全面描绘了终身行为。自2023年以来，所提出的解决方案已部署在中国最受欢迎的短视频流媒体平台之一——快手，该平台拥有数亿活跃用户。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Missing+Interest+Modeling+with+Lifelong+User+Behavior+Data+for+Retrieval+Recommendation)|0| -|[Relative Contrastive Learning for Sequential Recommendation with Similarity-based Positive Sample Selection](https://doi.org/10.1145/3627673.3679681)|Zhikai Wang, Yanyan Shen, Zexi Zhang, Li He, Yichun Li, Hao Gu, Yinghua Zhang|Meituan, Shanghai, China; Shanghai Jiao Tong University, Shanghai, China|Contrastive Learning (CL) enhances the training of sequential recommendation (SR) models through informative self-supervision signals. Existing methods often rely on data augmentation strategies to create positive samples and promote representation invariance. Some strategies such as item reordering and item substitution may inadvertently alter user intent. Supervised Contrastive Learning (SCL) based methods find an alternative to augmentation-based CL methods by selecting same-target sequences (interaction sequences with the same target item) to form positive samples. However, SCL-based methods suffer from the scarcity of same-target sequences and consequently lack enough signals for contrastive learning. In this work, we propose to use similar sequences (with different target items) as additional positive samples and introduce a Relative Contrastive Learning (RCL) framework for sequential recommendation. RCL comprises a dual-tiered positive sample selection module and a relative contrastive learning module. The former module selects same-target sequences as strong positive samples and selects similar sequences as weak positive samples. The latter module employs a weighted relative contrastive loss, ensuring that each sequence is represented closer to its strong positive samples than its weak positive samples. We apply RCL on two mainstream deep learning-based SR models, and our empirical results reveal that RCL can achieve 4.88% improvement averagely than the state-of-the-art SR methods on five public datasets and one private dataset. The code can be found at https://github.com/Cloudcatcher888/RCL.|对比学习（CL）通过提供信息丰富的自监督信号，增强了序列推荐（SR）模型的训练。现有方法通常依赖于数据增强策略来创建正样本并促进表示的不变性。一些策略如物品重新排序和物品替换可能会无意中改变用户意图。基于监督对比学习（SCL）的方法通过选择相同目标序列（与相同目标物品的交互序列）来形成正样本，从而为基于增强的CL方法提供了替代方案。然而，SCL方法面临相同目标序列稀缺的问题，因此缺乏足够的对比学习信号。在这项工作中，我们提出使用相似序列（具有不同目标物品）作为额外的正样本，并引入了一个相对对比学习（RCL）框架用于序列推荐。RCL包括一个双层正样本选择模块和一个相对对比学习模块。前者模块选择相同目标序列作为强正样本，并选择相似序列作为弱正样本。后者模块采用加权相对对比损失，确保每个序列的表示更接近其强正样本而非弱正样本。我们将RCL应用于两个主流的基于深度学习的SR模型，我们的实验结果显示，RCL在五个公共数据集和一个私有数据集上平均比最先进的SR方法提高了4.88%。代码可在https://github.com/Cloudcatcher888/RCL找到。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Relative+Contrastive+Learning+for+Sequential+Recommendation+with+Similarity-based+Positive+Sample+Selection)|0| +|[Relative Contrastive Learning for Sequential Recommendation with Similarity-based Positive Sample Selection](https://doi.org/10.1145/3627673.3679681)|Zhikai Wang, Yanyan Shen, Zexi Zhang, Li He, Yichun Li, Hao Gu, Yinghua Zhang|Shanghai Jiao Tong University, Shanghai, China; Meituan, Shanghai, China|Contrastive Learning (CL) enhances the training of sequential recommendation (SR) models through informative self-supervision signals. Existing methods often rely on data augmentation strategies to create positive samples and promote representation invariance. Some strategies such as item reordering and item substitution may inadvertently alter user intent. Supervised Contrastive Learning (SCL) based methods find an alternative to augmentation-based CL methods by selecting same-target sequences (interaction sequences with the same target item) to form positive samples. However, SCL-based methods suffer from the scarcity of same-target sequences and consequently lack enough signals for contrastive learning. In this work, we propose to use similar sequences (with different target items) as additional positive samples and introduce a Relative Contrastive Learning (RCL) framework for sequential recommendation. RCL comprises a dual-tiered positive sample selection module and a relative contrastive learning module. The former module selects same-target sequences as strong positive samples and selects similar sequences as weak positive samples. The latter module employs a weighted relative contrastive loss, ensuring that each sequence is represented closer to its strong positive samples than its weak positive samples. We apply RCL on two mainstream deep learning-based SR models, and our empirical results reveal that RCL can achieve 4.88% improvement averagely than the state-of-the-art SR methods on five public datasets and one private dataset. The code can be found at https://github.com/Cloudcatcher888/RCL.|对比学习（CL）通过提供信息丰富的自监督信号，增强了序列推荐（SR）模型的训练。现有方法通常依赖于数据增强策略来创建正样本并促进表示的不变性。一些策略如物品重新排序和物品替换可能会无意中改变用户意图。基于监督对比学习（SCL）的方法通过选择相同目标序列（与相同目标物品的交互序列）来形成正样本，从而为基于增强的CL方法提供了替代方案。然而，SCL方法面临相同目标序列稀缺的问题，因此缺乏足够的对比学习信号。在这项工作中，我们提出使用相似序列（具有不同目标物品）作为额外的正样本，并引入了一个相对对比学习（RCL）框架用于序列推荐。RCL包括一个双层正样本选择模块和一个相对对比学习模块。前者模块选择相同目标序列作为强正样本，并选择相似序列作为弱正样本。后者模块采用加权相对对比损失，确保每个序列的表示更接近其强正样本而非弱正样本。我们将RCL应用于两个主流的基于深度学习的SR模型，我们的实验结果显示，RCL在五个公共数据集和一个私有数据集上平均比最先进的SR方法提高了4.88%。代码可在https://github.com/Cloudcatcher888/RCL找到。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Relative+Contrastive+Learning+for+Sequential+Recommendation+with+Similarity-based+Positive+Sample+Selection)|0| |[Momentum Contrastive Bidirectional Encoding with Self-Distillation for Sequential Recommendation](https://doi.org/10.1145/3627673.3679965)|Dingyi Zhang, Haoyu Wenren, Yue Wang, Yingming Li|Alipay (Hangzhou) Information Technology Co., Ltd, Hangzhou, China; Zhejiang University, Hangzhou, China|In this paper, we propose a new Momentum Contrastive Bidirectional Encoding network with S elf-D istillation (MoCoBE-SD) to alleviate the data sparsity and noise issues in sequential recommendation by providing rich informative supervisions from both sequence-level and item-level perspectives. In particular, a Momentum Contrastive Bidirectional Encoding (MoCoBE) network is first proposed by constructing momentum updated encoder based on an online bidirectional self-attention encoder, where a momentum contrastive learning task and a masked item prediction task are simultaneously optimized. Building upon MoCoBE, a well-elaborated Self-Distillation (SD) scheme is incorporated to further suppress the noise influence. Specifically, a well-trained sequence encoder by MoCoBE is adopted as the teacher encoder to provide refined supervision for the masked item prediction, which constitutes our MoCoBE-SD framework. Extensive experiments on three public datasets show that MoCoBE-SD outperforms the existing state-of-the-art methods consistently.|本文提出了一种新的动量对比双向编码网络，结合自蒸馏技术（MoCoBE-SD），以缓解序列推荐中数据稀疏和噪声问题。通过从序列级和项目级两个角度提供丰富的信息监督来实现这一目标。具体而言，首先提出了一种动量对比双向编码（MoCoBE）网络，该网络基于在线双向自注意力编码器构建了动量更新的编码器，同时优化了动量对比学习任务和掩码项目预测任务。在MoCoBE的基础上，引入了一种精心设计的自蒸馏（SD）方案，以进一步抑制噪声的影响。具体来说，通过MoCoBE训练好的序列编码器被用作教师编码器，为掩码项目预测提供精细化的监督，从而构成了我们的MoCoBE-SD框架。在三个公共数据集上的广泛实验表明，MoCoBE-SD在性能上持续优于现有的最先进方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Momentum+Contrastive+Bidirectional+Encoding+with+Self-Distillation+for+Sequential+Recommendation)|0| |[A Real-Time Adaptive Multi-Stream GPU System For Online Approximate Nearest Neighborhood Search](https://doi.org/10.1145/3627673.3680054)|Yiping Sun, Yang Shi, Jiaolong Du||In recent years, Approximate Nearest Neighbor Search (ANNS) has played a pivotal role in modern search and recommendation systems, especially in emerging LLM applications like Retrieval-Augmented Generation. There is a growing exploration into harnessing the parallel computing capabilities of GPUs to meet the substantial demands of ANNS. However, existing systems primarily focus on offline scenarios, overlooking the distinct requirements of online applications that necessitate real-time insertion of new vectors. This limitation renders such systems inefficient for real-world scenarios. Moreover, previous architectures struggled to effectively support real-time insertion due to their reliance on serial execution streams. In this paper, we introduce a novel Real-Time Adaptive Multi-Stream GPU ANNS System (RTAMS-GANNS). Our architecture achieves its objectives through three key advancements: 1) We initially examined the real-time insertion mechanisms in existing GPU ANNS systems and discovered their reliance on repetitive copying and memory allocation, which significantly hinders real-time effectiveness on GPUs. As a solution, we introduce a dynamic vector insertion algorithm based on memory blocks, which includes in-place rearrangement. 2) To enable real-time vector insertion in parallel, we introduce a multi-stream parallel execution mode, which differs from existing systems that operate serially within a single stream. Our system utilizes a dynamic resource pool, allowing multiple streams to execute concurrently without additional execution blocking. 3) Through extensive experiments and comparisons, our approach effectively handles varying QPS levels across different datasets, reducing latency by up to 40 proposed system has also been deployed in real-world industrial search and recommendation systems, serving hundreds of millions of users daily, and has achieved good results.|近年来，近似最近邻搜索（ANNS）在现代搜索和推荐系统中发挥了关键作用，特别是在诸如增强检索生成（Retrieval-Augmented Generation）等新兴的大型语言模型（LLM）应用中。越来越多的研究致力于利用GPU的并行计算能力来满足ANNS的巨大需求。然而，现有的系统主要关注离线场景，忽视了在线应用的独特需求，这些需求需要实时插入新向量。这种局限性使得这些系统在现实场景中效率低下。此外，先前的架构由于依赖串行执行流，难以有效支持实时插入。在本文中，我们介绍了一种新型实时自适应多流GPU ANNS系统（RTAMS-GANNS）。我们的架构通过三个关键进展实现了其目标：1）我们首先研究了现有GPU ANNS系统中的实时插入机制，发现它们依赖于重复的复制和内存分配，这严重阻碍了GPU上的实时效率。作为解决方案，我们引入了一种基于内存块的动态向量插入算法，包括就地重排。2）为了实现并行实时向量插入，我们引入了一种多流并行执行模式，这与现有系统在单一流中串行操作不同。我们的系统利用动态资源池，允许多个流并发执行而无需额外的执行阻塞。3）通过广泛的实验和比较，我们的方法有效地处理了不同数据集上的不同QPS水平，延迟降低了高达40%。所提出的系统也已部署在实际的工业搜索和推荐系统中，每天服务于数亿用户，并取得了良好的效果。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Real-Time+Adaptive+Multi-Stream+GPU+System+For+Online+Approximate+Nearest+Neighborhood+Search)|0| |[MERLIN: Multimodal & Multilingual Embedding for Recommendations at Large-scale via Item Associations](https://doi.org/10.1145/3627673.3680106)|Sambeet Tiady, Arihant Jain, Dween Rabius Sanny, Khushi Gupta, Srinivas Virinchi, Swapnil Gupta, Anoop Saladi, Deepak Gupta|Amazon.com, Bangalore, India|Product recommendations incentivize customers to make multi-unit purchases by surfacing relevant products, leading to lower cost per unit for e-commerce stores and lower prices for their customers. However, the humongous scale of products, implicit co-purchase asymmetry and variation in co-purchase behavior across different categories, are orthogonal problems to solve. To address these problems, we propose MERLIN (Multimodal & Multilingual Embedding for Recommendations at Large-scale via Item associations), a Graph Neural Network that generates product recommendations from a heterogeneous and directed product graph. We mine category associations to remove noisy product co-purchase associations, leading to higher quality recommendations. Leveraging product co-view relationships, we finetune SentenceBERT model for textual representation, and train a self-supervised knowledge distillation model to learn visual representation, which allows us to learn product representations which are multi-lingual and multi-modal in nature. We selectively align node embeddings leveraging co-viewed products. MERLIN model can handle node asymmetry by learning dual embeddings for each product, and can generate recommendations for cold-start products by employing catalog metadata such as title, category and image. Extensive offline experiments on internal and external datasets show that MERLIN model outperforms state-of-the-art baselines for node recommendation and link prediction task. We conduct ablations to quantify the impact of our model components and choices. Further, MERLIN model delivers significant improvement in sales measured through an A/B experiment.|产品推荐通过展示相关产品激励顾客进行多单位购买，从而降低电商商店的单位成本和顾客的购买价格。然而，产品规模的巨大、隐含的共同购买不对称性以及不同类别间共同购买行为的变化，是相互独立的问题。为了解决这些问题，我们提出了MERLIN（通过项目关联进行大规模推荐的多模态与多语言嵌入），这是一个从异构且有向的产品图中生成产品推荐的图神经网络。我们挖掘类别关联来消除噪声产品共同购买关联，从而提高推荐质量。利用产品共同浏览关系，我们微调了SentenceBERT模型以获取文本表示，并训练了一个自监督的知识蒸馏模型来学习视觉表示，这使我们能够学习到本质上是多语言和多模态的产品表示。我们通过共同浏览的产品有选择地对齐节点嵌入。MERLIN模型通过为每个产品学习双重嵌入来处理节点不对称性，并通过使用标题、类别和图像等目录元数据为冷启动产品生成推荐。在内、外部数据集上的广泛离线实验表明，MERLIN模型在节点推荐和链接预测任务上优于最先进的基线模型。我们进行了消融实验，以量化我们模型组件和选择的影响。此外，通过A/B实验测量的销售数据显示，MERLIN模型带来了显著的改进。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=MERLIN:+Multimodal+&+Multilingual+Embedding+for+Recommendations+at+Large-scale+via+Item+Associations)|0| |[Towards Advancing Text-Based User and Item Representation in Personalized Recommendation](https://doi.org/10.1145/3627673.3680270)|Hanjia Lyu|University of Rochester, Rochester, NY, USA|In the realm of personalized recommendation systems, accurately capturing user preferences and item characteristics is important for delivering relevant and satisfying recommendations. This study introduces innovative approaches using Large Language Models (LLMs) to generate detailed textual descriptions that enhance both user and item representations. We propose a dual strategy: for user representation, we employ supervised fine-tuning coupled with Retrieval-Augmented Generation (RAG) to keep the model current with dynamic user preferences; for item representation, we leverage the extensive knowledge base of LLMs to enrich item descriptions and infer traits from user interactions. These methods promise a deeper, more nuanced understanding of both users and items, potentially leading to superior recommendation accuracy. We adopt a rigorous evaluation methodology, ensuring the reliability of our results and the effectiveness of our proposed system. This paper discusses these methodologies, presents our preliminary findings, and highlights the potential of text-augmented profiles in advancing recommendation systems.|在个性化推荐系统领域，准确捕捉用户偏好和物品特征对于提供相关且令人满意的推荐至关重要。本研究引入了利用大型语言模型（LLMs）生成详细文本描述的创新方法，以增强用户和物品的表示。我们提出了一种双重策略：对于用户表示，我们采用监督微调结合检索增强生成（RAG），以使模型与动态用户偏好保持同步；对于物品表示，我们利用LLMs的广泛知识库来丰富物品描述，并从用户交互中推断特征。这些方法有望对用户和物品实现更深入、更细致的理解，从而可能提高推荐准确性。我们采用严格的评估方法，确保结果的可靠性和所提出系统的有效性。本文讨论了这些方法，展示了初步研究成果，并强调了文本增强型用户和物品描述在推进推荐系统方面的潜力。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Towards+Advancing+Text-Based+User+and+Item+Representation+in+Personalized+Recommendation)|0| |[Contrastive Learning on Medical Intents for Sequential Prescription Recommendation](https://doi.org/10.1145/3627673.3679836)|Arya Hadizadeh Moghaddam, Mohsen Nayebi Kerdabadi, Mei Liu, Zijun Yao||Recent advancements in sequential modeling applied to Electronic Health Records (EHR) have greatly influenced prescription recommender systems. While the recent literature on drug recommendation has shown promising performance, the study of discovering a diversity of coexisting temporal relationships at the level of medical codes over consecutive visits remains less explored. The goal of this study can be motivated from two perspectives. First, there is a need to develop a sophisticated sequential model capable of disentangling the complex relationships across sequential visits. Second, it is crucial to establish multiple and diverse health profiles for the same patient to ensure a comprehensive consideration of different medical intents in drug recommendation. To achieve this goal, we introduce Attentive Recommendation with Contrasted Intents (ARCI), a multi-level transformer-based method designed to capture the different but coexisting temporal paths across a shared sequence of visits. Specifically, we propose a novel intent-aware method with contrastive learning, that links specialized medical intents of the patients to the transformer heads for extracting distinct temporal paths associated with different health profiles. We conducted experiments on two real-world datasets for the prescription recommendation task using both ranking and classification metrics. Our results demonstrate that ARCI has outperformed the state-of-the-art prescription recommendation methods and is capable of providing interpretable insights for healthcare practitioners.|近年来，应用于电子健康记录（EHR）的序列建模的进展极大地影响了处方推荐系统。尽管最近关于药物推荐的文献展示了令人鼓舞的性能，但在连续就诊中，在医疗代码层面发现多种共存的时间关系的研究仍较少探索。本研究的目标可以从两个角度来推动。首先，需要开发一种复杂的序列模型，能够解开跨连续就诊的复杂关系。其次，为同一患者建立多个多样化的健康档案至关重要，以确保在药物推荐中全面考虑不同的医疗意图。为了实现这一目标，我们引入了带有对比意图的注意力推荐（ARCI），这是一种基于多层变换器的方法，旨在捕捉共享序列就诊中不同的但共存的时间路径。具体来说，我们提出了一种新颖的意图感知方法，结合对比学习，将患者的专门医疗意图链接到变换器头部，以提取与不同健康档案相关的独特时间路径。我们在两个真实世界的数据集上进行了处方推荐任务的实验，使用了排名和分类指标。结果表明，ARCI在性能上优于最先进的处方推荐方法，并能够为医疗从业者提供可解释的见解。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Contrastive+Learning+on+Medical+Intents+for+Sequential+Prescription+Recommendation)|0| |[Behavior-Dependent Linear Recurrent Units for Efficient Sequential Recommendation](https://doi.org/10.1145/3627673.3679717)|Chengkai Liu, Jianghao Lin, Hanzhou Liu, Jianling Wang, James Caverlee||Sequential recommender systems aims to predict the users' next interaction through user behavior modeling with various operators like RNNs and attentions. However, existing models generally fail to achieve the three golden principles for sequential recommendation simultaneously, i.e., training efficiency, low-cost inference, and strong performance. To this end, we propose RecBLR, an Efficient Sequential Recommendation Model based on Behavior-Dependent Linear Recurrent Units to accomplish the impossible triangle of the three principles. By incorporating gating mechanisms and behavior-dependent designs into linear recurrent units, our model significantly enhances user behavior modeling and recommendation performance. Furthermore, we unlock the parallelizable training as well as inference efficiency for our model by designing a hardware-aware scanning acceleration algorithm with a customized CUDA kernel. Extensive experiments on real-world datasets with varying lengths of user behavior sequences demonstrate RecBLR's remarkable effectiveness in simultaneously achieving all three golden principles - strong recommendation performance, training efficiency, and low-cost inference, while exhibiting excellent scalability to datasets with long user interaction histories.|顺序推荐系统旨在通过使用RNN和注意力等操作符对用户行为进行建模来预测用户的下一次交互。然而，现有的模型通常无法同时实现顺序推荐的三个黄金原则，即训练效率、低成本推理和强大的性能。为此，我们提出了RecBLR，一种基于行为依赖线性循环单元的高效顺序推荐模型，以实现这三个原则的不可能三角。通过将门控机制和行为依赖设计融入线性循环单元，我们的模型显著增强了用户行为建模和推荐性能。此外，我们通过设计一个硬件感知的扫描加速算法和定制的CUDA内核，为我们的模型解锁了可并行化的训练以及推理效率。在具有不同长度用户行为序列的实际数据集上的广泛实验表明，RecBLR在同时实现所有三个黄金原则方面表现出色——强大的推荐性能、训练效率和低成本推理，同时在处理具有长用户交互历史的数据集时展现出卓越的可扩展性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Behavior-Dependent+Linear+Recurrent+Units+for+Efficient+Sequential+Recommendation)|0| -|[MultiLoRA: Multi-Directional Low Rank Adaptation for Multi-Domain Recommendation](https://doi.org/10.1145/3627673.3679549)|Zijian Song, Wenhan Zhang, Lifang Deng, Jiandong Zhang, Kaigui Bian, Bin Cui|School of CS, Peking University & AI Innovation Center, Peking University, Beijing, China; Lazada Group, Beijing, China|To address the business needs of industrial recommendation systems, an increasing number of Multi-Domain Recommendation (MDR) methods are designed to improve recommendation performance on multiple domains simultaneously. Most MDR methods follow a multi-task learning paradigm, suffering from poor deployability and negative transfer. Due to the great success of large pre-trained models, the pre-train & fine-tune paradigm is attracting increasing attention. The latest methods introduce parameter-efficient fine-tuning techniques like prompt-tuning, showcasing high efficiency and effectiveness. However, these methods neglect the fundamental differences between recommendation and NLP tasks. The inadequate capacity of recommendation models restricts the effectiveness of prompts and adapters. Worse still, traditional natural domain division may group non-identically distributed samples into the same domain, violating the assumption of independent and identically distributed (i.i.d.) data. In this paper, we propose MultiLoRA, a Multi-directional Low Rank Adaptation paradigm for multi-domain recommendation. First we pre-train a universal model using all data samples. Then we conduct multiple domain divisions on the sample space. Under each division, we fine-tune the pre-trained model to obtain a set of domain-specific LoRAs. Finally, we learn a LoRA fusion module to integrate domain-specific preference patterns across multiple divisions. Experimental results on real-world datasets demonstrate notable advantages of MultiLoRA: (1) achieving SOTA performance, (2) showcasing remarkable compatibility, and (3) proving highly efficient, featuring only 2% trainable parameters compared to the backbone.|为了满足工业推荐系统的业务需求，越来越多的多领域推荐（MDR）方法被设计出来，以同时提升多个领域的推荐性能。大多数MDR方法遵循多任务学习的范式，存在部署性差和负迁移的问题。由于大型预训练模型取得了巨大成功，预训练与微调的范式正受到越来越多的关注。最新的方法引入了如提示调优等参数高效的微调技术，展示了高效性和有效性。然而，这些方法忽略了推荐任务与自然语言处理任务之间的根本差异。推荐模型的不足能力限制了提示词和适配器的有效性。更糟糕的是，传统的自然领域划分可能将非同分布的样本归入同一领域，违反了独立同分布（i.i.d.）数据的假设。在本文中，我们提出了MultiLoRA，一种面向多领域推荐的多向低秩适应范式。首先，我们使用所有数据样本预训练一个通用模型。然后，我们在样本空间上进行多次领域划分。在每次划分下，我们对预训练模型进行微调，以获得一组领域特定的LoRAs。最后，我们学习一个LoRA融合模块，以整合多个划分中的领域特定偏好模式。在真实世界数据集上的实验结果显示了MultiLoRA的显著优势：（1）实现了SOTA性能，（2）展示了出色的兼容性，（3）证明了高效率，仅具有与骨干模型相比2%的可训练参数。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=MultiLoRA:+Multi-Directional+Low+Rank+Adaptation+for+Multi-Domain+Recommendation)|0| +|[MultiLoRA: Multi-Directional Low Rank Adaptation for Multi-Domain Recommendation](https://doi.org/10.1145/3627673.3679549)|Zijian Song, Wenhan Zhang, Lifang Deng, Jiandong Zhang, Kaigui Bian, Bin Cui|Lazada Group, Beijing, China; School of CS, Peking University & AI Innovation Center, Peking University, Beijing, China|To address the business needs of industrial recommendation systems, an increasing number of Multi-Domain Recommendation (MDR) methods are designed to improve recommendation performance on multiple domains simultaneously. Most MDR methods follow a multi-task learning paradigm, suffering from poor deployability and negative transfer. Due to the great success of large pre-trained models, the pre-train & fine-tune paradigm is attracting increasing attention. The latest methods introduce parameter-efficient fine-tuning techniques like prompt-tuning, showcasing high efficiency and effectiveness. However, these methods neglect the fundamental differences between recommendation and NLP tasks. The inadequate capacity of recommendation models restricts the effectiveness of prompts and adapters. Worse still, traditional natural domain division may group non-identically distributed samples into the same domain, violating the assumption of independent and identically distributed (i.i.d.) data. In this paper, we propose MultiLoRA, a Multi-directional Low Rank Adaptation paradigm for multi-domain recommendation. First we pre-train a universal model using all data samples. Then we conduct multiple domain divisions on the sample space. Under each division, we fine-tune the pre-trained model to obtain a set of domain-specific LoRAs. Finally, we learn a LoRA fusion module to integrate domain-specific preference patterns across multiple divisions. Experimental results on real-world datasets demonstrate notable advantages of MultiLoRA: (1) achieving SOTA performance, (2) showcasing remarkable compatibility, and (3) proving highly efficient, featuring only 2% trainable parameters compared to the backbone.|为了满足工业推荐系统的业务需求，越来越多的多领域推荐（MDR）方法被设计出来，以同时提升多个领域的推荐性能。大多数MDR方法遵循多任务学习的范式，存在部署性差和负迁移的问题。由于大型预训练模型取得了巨大成功，预训练与微调的范式正受到越来越多的关注。最新的方法引入了如提示调优等参数高效的微调技术，展示了高效性和有效性。然而，这些方法忽略了推荐任务与自然语言处理任务之间的根本差异。推荐模型的不足能力限制了提示词和适配器的有效性。更糟糕的是，传统的自然领域划分可能将非同分布的样本归入同一领域，违反了独立同分布（i.i.d.）数据的假设。在本文中，我们提出了MultiLoRA，一种面向多领域推荐的多向低秩适应范式。首先，我们使用所有数据样本预训练一个通用模型。然后，我们在样本空间上进行多次领域划分。在每次划分下，我们对预训练模型进行微调，以获得一组领域特定的LoRAs。最后，我们学习一个LoRA融合模块，以整合多个划分中的领域特定偏好模式。在真实世界数据集上的实验结果显示了MultiLoRA的显著优势：（1）实现了SOTA性能，（2）展示了出色的兼容性，（3）证明了高效率，仅具有与骨干模型相比2%的可训练参数。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=MultiLoRA:+Multi-Directional+Low+Rank+Adaptation+for+Multi-Domain+Recommendation)|0| |[Bridging User Dynamics: Transforming Sequential Recommendations with Schrödinger Bridge and Diffusion Models](https://doi.org/10.1145/3627673.3679756)|Wenjia Xie, Rui Zhou, Hao Wang, Tingjia Shen, Enhong Chen||Sequential recommendation has attracted increasing attention due to its ability to accurately capture the dynamic changes in user interests. We have noticed that generative models, especially diffusion models, which have achieved significant results in fields like image and audio, hold considerable promise in the field of sequential recommendation. However, existing sequential recommendation methods based on diffusion models are constrained by a prior distribution limited to Gaussian distribution, hindering the possibility of introducing user-specific information for each recommendation and leading to information loss. To address these issues, we introduce the Schrödinger Bridge into diffusion-based sequential recommendation models, creating the SdifRec model. This allows us to replace the Gaussian prior of the diffusion model with the user's current state, directly modeling the process from a user's current state to the target recommendation. Additionally, to better utilize collaborative information in recommendations, we propose an extended version of SdifRec called con-SdifRec, which utilizes user clustering information as a guiding condition to further enhance the posterior distribution. Finally, extensive experiments on multiple public benchmark datasets have demonstrated the effectiveness of SdifRec and con-SdifRec through comparison with several state-of-the-art methods. Further in-depth analysis has validated their efficiency and robustness.|顺序推荐因其能够准确捕捉用户兴趣的动态变化而受到越来越多的关注。我们注意到，生成模型，特别是扩散模型，在图像和音频等领域取得了显著成果，在顺序推荐领域也展现出巨大的潜力。然而，现有的基于扩散模型的顺序推荐方法受限于仅限于高斯分布的先验分布，这阻碍了在每次推荐中引入特定用户信息的可能性，导致信息损失。为了解决这些问题，我们将薛定谔桥引入基于扩散的顺序推荐模型，创建了SdifRec模型。这使得我们能够将扩散模型的高斯先验替换为用户当前状态，直接模拟从用户当前状态到目标推荐的过程。此外，为了更好地利用推荐中的协同信息，我们提出了SdifRec的扩展版本，称为con-SdifRec，它利用用户聚类信息作为指导条件，进一步增强后验分布。最后，在多个公共基准数据集上的广泛实验通过与几种最先进方法的比较，证明了SdifRec和con-SdifRec的有效性。进一步的深入分析验证了它们的效率和鲁棒性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Bridging+User+Dynamics:+Transforming+Sequential+Recommendations+with+Schrödinger+Bridge+and+Diffusion+Models)|0| |[Generating Intent-aware Clarifying Questions in Conversational Information Retrieval Systems](https://doi.org/10.1145/3627673.3679851)|Ziliang Zhao, Zhicheng Dou, Yujia Zhou|Gaoling School of Artificial Intelligence, Renmin University of China, Beijing, China|Generating clarifying questions can effectively clarify users' complicated search intent in conversational search systems. However, existing methods based on pre-defined templates are inadequate in understanding explicit user intents, making generated questions monotonous or inaccurate in some cases. In this paper, we define the ''intent'' of a query as a verb representing the potential behavior, action, or task the user may take. We study generating clarifying questions from a new perspective by incorporating the intents explicitly to form ''intent-aware'' questions with high informativeness and accuracy. Since obtaining gold intent-aware questions is expensive, we propose a rule-based method and a continual learning model to generate intent-aware questions as weak supervision signals. The former leverages search results to mine contextual intent-aware words or phrases, and the latter relies on parallel corpora to paraphrase template-based questions by incorporating the intents. The generated weak supervision data are then applied to fine-tune a BART-based model for end-to-end intent-aware question generation. We also explore to prompt a large language model to generate intent-aware questions. Experimental results on a public clarification dataset demonstrate that our proposed methods improve users' search experience compared to existing methods.|生成澄清问题可以有效澄清对话搜索系统中用户复杂的搜索意图。然而，现有基于预定义模板的方法在理解明确用户意图方面存在不足，导致生成的问题在某些情况下单调或不准确。本文中，我们将查询的“意图”定义为用户可能采取的潜在行为、动作或任务的动词表示。我们通过明确结合意图，从新的角度研究生成信息丰富且准确的“意图感知”澄清问题。由于获取黄金标准的意图感知问题成本高昂，我们提出了一种基于规则的方法和一种持续学习模型，以生成意图感知的弱监督信号。前者利用搜索结果挖掘上下文中的意图感知词或短语，后者则依赖平行语料库通过结合意图对基于模板的问题进行释义。生成的弱监督数据随后用于微调基于BART的模型，以实现端到端的意图感知问题生成。我们还探索了引导大型语言模型生成意图感知问题的方法。在公开的澄清数据集上的实验结果表明，与现有方法相比，我们提出的方法提升了用户的搜索体验。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Generating+Intent-aware+Clarifying+Questions+in+Conversational+Information+Retrieval+Systems)|0| -|[Enhancing E-Commerce Query Rewriting: A Large Language Model Approach with Domain-Specific Pre-Training and Reinforcement Learning](https://doi.org/10.1145/3627673.3680109)|Aijun Dai, Zhenyu Zhu, Haiqing Hu, Guoyu Tang, Lin Liu, Sulong Xu|JD.com, Bejing, China; JD.com, Beijing, China|In the domain of e-commerce, query rewriting is a potent strategy for bridging the lexical gap between search queries and product descriptions, thereby enhancing the recall rate of search engines. This research introduces a query rewriting framework predicated on large language models (LLM), encompassing three phases of training: domain-specific pre-training, supervised fine-tuning (SFT) and reinforcement learning (RL) for objective alignment. To detail, the process initiates with domain-specific pre-training using consumer behavior data and product descriptions from JD.com. Subsequently, we filter and utilize high-quality query-rewrite pairs for SFT. The final stage employs RL to refine the model's objective alignment, utilizing an offline search system as the simulation environment. The RL's training reward is derived from the recall rate, aiming to optimize the number of relevant products the rewrites retrieve. Through offline evaluations, our method has demonstrated its capacity to substantially enhance the efficacy of LLMs for e-commerce query rewriting. Moreover, online A/B testing has corroborated that our approach significantly boosts the number of purchases made per user (UCVR). Since December 2023, our approach has been successfully implemented on JD.com, one of China's most frequented online shopping platforms.|在电子商务领域，查询重写是弥合搜索查询与产品描述之间词汇鸿沟的有效策略，从而提高搜索引擎的召回率。本研究引入了一种基于大型语言模型（LLM）的查询重写框架，该框架包括三个训练阶段：领域特定的预训练、有监督的微调（SFT）和用于目标对齐的强化学习（RL）。具体而言，该过程首先使用京东的消费行为数据和产品描述进行领域特定的预训练。随后，我们筛选并利用高质量的查询-重写对进行SFT。最后阶段采用RL来优化模型的目标对齐，利用离线搜索系统作为模拟环境。RL的训练奖励基于召回率，旨在优化重写查询所检索到的相关产品数量。通过离线评估，我们的方法展示了其显著提升LLM在电子商务查询重写中效能的能力。此外，在线A/B测试证实了我们的方法显著提高了每位用户的购买转化率（UCVR）。自2023年12月以来，我们的方法已成功应用于京东，这是中国访问量最大的在线购物平台之一。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Enhancing+E-Commerce+Query+Rewriting:+A+Large+Language+Model+Approach+with+Domain-Specific+Pre-Training+and+Reinforcement+Learning)|0| +|[Enhancing E-Commerce Query Rewriting: A Large Language Model Approach with Domain-Specific Pre-Training and Reinforcement Learning](https://doi.org/10.1145/3627673.3680109)|Aijun Dai, Zhenyu Zhu, Haiqing Hu, Guoyu Tang, Lin Liu, Sulong Xu|JD.com, Beijing, China; JD.com, Bejing, China|In the domain of e-commerce, query rewriting is a potent strategy for bridging the lexical gap between search queries and product descriptions, thereby enhancing the recall rate of search engines. This research introduces a query rewriting framework predicated on large language models (LLM), encompassing three phases of training: domain-specific pre-training, supervised fine-tuning (SFT) and reinforcement learning (RL) for objective alignment. To detail, the process initiates with domain-specific pre-training using consumer behavior data and product descriptions from JD.com. Subsequently, we filter and utilize high-quality query-rewrite pairs for SFT. The final stage employs RL to refine the model's objective alignment, utilizing an offline search system as the simulation environment. The RL's training reward is derived from the recall rate, aiming to optimize the number of relevant products the rewrites retrieve. Through offline evaluations, our method has demonstrated its capacity to substantially enhance the efficacy of LLMs for e-commerce query rewriting. Moreover, online A/B testing has corroborated that our approach significantly boosts the number of purchases made per user (UCVR). Since December 2023, our approach has been successfully implemented on JD.com, one of China's most frequented online shopping platforms.|在电子商务领域，查询重写是弥合搜索查询与产品描述之间词汇鸿沟的有效策略，从而提高搜索引擎的召回率。本研究引入了一种基于大型语言模型（LLM）的查询重写框架，该框架包括三个训练阶段：领域特定的预训练、有监督的微调（SFT）和用于目标对齐的强化学习（RL）。具体而言，该过程首先使用京东的消费行为数据和产品描述进行领域特定的预训练。随后，我们筛选并利用高质量的查询-重写对进行SFT。最后阶段采用RL来优化模型的目标对齐，利用离线搜索系统作为模拟环境。RL的训练奖励基于召回率，旨在优化重写查询所检索到的相关产品数量。通过离线评估，我们的方法展示了其显著提升LLM在电子商务查询重写中效能的能力。此外，在线A/B测试证实了我们的方法显著提高了每位用户的购买转化率（UCVR）。自2023年12月以来，我们的方法已成功应用于京东，这是中国访问量最大的在线购物平台之一。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Enhancing+E-Commerce+Query+Rewriting:+A+Large+Language+Model+Approach+with+Domain-Specific+Pre-Training+and+Reinforcement+Learning)|0| |[Deep Uncertainty-Based Explore for Index Construction and Retrieval in Recommendation System](https://doi.org/10.1145/3627673.3680077)|Xin Jiang, Kaiqiang Wang, Yinlong Wang, Fengchang Lv, Taiyang Peng, Shuai Yang, Xianteng Wu, Pengye Zhang, Shuo Yuan, Yifan Zeng||In recommendation systems, the relevance and novelty of the final results are selected through a cascade system of Matching -> Ranking -> Strategy. The matching model serves as the starting point of the pipeline and determines the upper bound of the subsequent stages. Balancing the relevance and novelty of matching results is a crucial step in the design and optimization of recommendation systems, contributing significantly to improving recommendation quality. However, the typical matching algorithms have not simultaneously addressed the relevance and novelty perfectly. One main reason is that deep matching algorithms exhibit significant uncertainty when estimating items in the long tail (e.g., due to insufficient training samples) items.The uncertainty not only affects the training of the models but also influences the confidence in the index construction and beam search retrieval process of these models. This paper proposes the UICR (Uncertainty-based explore for Index Construction and Retrieval) algorithm, which introduces the concept of uncertainty modeling in the matching stage and achieves multi-task modeling of model uncertainty and index uncertainty. The final matching results are obtained by combining the relevance score and uncertainty score infered by the model. Experimental results demonstrate that the UICR improves novelty without sacrificing relevance on realworld industrial productive environments and multiple open-source datasets. Remarkably, online A/B test results of display advertising in Shopee demonstrates the effectiveness of the proposed algorithm.|在推荐系统中，最终结果的相关性和新颖性是通过匹配（Matching）->排序（Ranking）->策略（Strategy）的级联系统来选择的。匹配模型作为管道的起点，决定了后续阶段的上限。平衡匹配结果的相关性和新颖性是推荐系统设计和优化的关键步骤，对提高推荐质量有显著贡献。然而，典型的匹配算法并没有同时完美地解决相关性和新颖性问题。主要原因之一是深度匹配算法在估计长尾（例如，由于训练样本不足）项目时表现出显著的不确定性。这种不确定性不仅影响模型的训练，还影响这些模型在索引构建和束搜索检索过程中的置信度。本文提出了基于不确定性的索引构建和检索（UICR）算法，该算法在匹配阶段引入了不确定性建模的概念，实现了模型不确定性和索引不确定性的多任务建模。最终的匹配结果是通过结合模型推断的相关性分数和不确定性分数获得的。实验结果表明，UICR在不牺牲相关性的情况下提高了新颖性，在现实世界的工业生产环境和多个开源数据集上都得到了验证。值得注意的是，Shopee展示广告的在线A/B测试结果证明了所提出算法的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Deep+Uncertainty-Based+Explore+for+Index+Construction+and+Retrieval+in+Recommendation+System)|0| |[Towards Seamless User Query to REST API Conversion](https://doi.org/10.1145/3627673.3680275)|Han Xu|University of Illinois Urbana-Champaign, Urbana, IL, USA|Integrating Large Language Models (LLMs) with external tools and APIs is essential for fields such as information retrieval and knowledge management. While LLMs have made significant strides, their effective integration with external APIs-essential for real-world applications-remains challenging. This paper introduces RESTful-Llama, a novel method designed to empower open-source LLMs to accurately convert natural language instructions into well-formed RESTful API calls. Moreover, RESTful-Llama utilizes DOC-Prompt, a newly proposed technique for generating fine-tuning datasets from publicly available API documentation. Initial experiments demonstrate that RESTful-Llama significantly enhances the accuracy of generated REST API requests.|将大型语言模型（LLMs）与外部工具和API集成对于信息检索和知识管理等领域至关重要。尽管LLMs取得了显著进展，但它们与外部API的有效集成——这对于实际应用至关重要——仍然具有挑战性。本文介绍了RESTful-Llama，这是一种新颖的方法，旨在使开源LLMs能够准确地将自然语言指令转换为格式良好的RESTful API调用。此外，RESTful-Llama利用了DOC-Prompt，这是一种新提出的技术，用于从公开可用的API文档生成微调数据集。初步实验表明，RESTful-Llama显著提高了生成的REST API请求的准确性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Towards+Seamless+User+Query+to+REST+API+Conversion)|0| -|[Product Retrieval and Ranking for Alphanumeric Queries](https://doi.org/10.1145/3627673.3679080)|Hadeel Saadany, Swapnil Bhosale, Samarth Agrawal, Zhe Wu, Constantin Orasan, Diptesh Kanojia|People-Centred AI, University of Surrey, Guildford, United Kingdom; Centre for Translation Studies, University of Surrey, Guildford, United Kingdom; eBay Inc., Seattle, USA; eBay Inc., San Jose, USA|This talk addresses the challenge of improving user experience on e-commerce platforms by enhancing product ranking relevant to user's search queries. Queries such as S2716DG consist of alphanumeric characters where a letter or number can signify important detail for the product/model. Speaker describes recent research where we curate samples from existing datasets at eBay, manually annotated with buyer-centric relevance scores, and centrality scores which reflect how well the product title matches the user's intent. We introduce a User-intent Centrality Optimization (UCO) approach for existing models, which optimizes for the user intent in semantic product search. To that end, we propose a dual-loss based optimization to handle hard negatives, i.e., product titles that are semantically relevant but do not reflect the user's intent. Our contributions include curating a challenging evaluation set and implementing UCO, resulting in significant improvements in product ranking efficiency, observed for different evaluation metrics. Our work aims to ensure that the most buyer-centric titles for a query are ranked higher, thereby, enhancing the user experience on e-commerce platforms.|本次演讲探讨了通过提升与用户搜索查询相关的产品排序来改善电子商务平台用户体验的挑战。诸如S2716DG之类的查询包含字母数字字符，其中字母或数字可能代表产品/型号的重要细节。演讲者描述了最近的研究，我们在eBay的现有数据集中精选样本，这些样本经过手动标注，具有以买家为中心的相关性评分和中心性评分，后者反映了产品标题与用户意图的匹配程度。我们引入了一种用户意图中心性优化（User-intent Centrality Optimization, UCO）方法，用于现有模型，该方法优化了语义产品搜索中的用户意图。为此，我们提出了一种基于双重损失的优化方法来处理硬负样本，即那些在语义上相关但未反映用户意图的产品标题。我们的贡献包括策划一个具有挑战性的评估集和实现UCO，从而在不同的评估指标下显著提高了产品排序效率。我们的工作旨在确保对于一个查询，最符合买家意图的标题排名更高，从而增强电子商务平台的用户体验。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Product+Retrieval+and+Ranking+for+Alphanumeric+Queries)|0| +|[Product Retrieval and Ranking for Alphanumeric Queries](https://doi.org/10.1145/3627673.3679080)|Hadeel Saadany, Swapnil Bhosale, Samarth Agrawal, Zhe Wu, Constantin Orasan, Diptesh Kanojia|Centre for Translation Studies, University of Surrey, Guildford, United Kingdom; eBay Inc., Seattle, USA; People-Centred AI, University of Surrey, Guildford, United Kingdom; eBay Inc., San Jose, USA|This talk addresses the challenge of improving user experience on e-commerce platforms by enhancing product ranking relevant to user's search queries. Queries such as S2716DG consist of alphanumeric characters where a letter or number can signify important detail for the product/model. Speaker describes recent research where we curate samples from existing datasets at eBay, manually annotated with buyer-centric relevance scores, and centrality scores which reflect how well the product title matches the user's intent. We introduce a User-intent Centrality Optimization (UCO) approach for existing models, which optimizes for the user intent in semantic product search. To that end, we propose a dual-loss based optimization to handle hard negatives, i.e., product titles that are semantically relevant but do not reflect the user's intent. Our contributions include curating a challenging evaluation set and implementing UCO, resulting in significant improvements in product ranking efficiency, observed for different evaluation metrics. Our work aims to ensure that the most buyer-centric titles for a query are ranked higher, thereby, enhancing the user experience on e-commerce platforms.|本次演讲探讨了通过提升与用户搜索查询相关的产品排序来改善电子商务平台用户体验的挑战。诸如S2716DG之类的查询包含字母数字字符，其中字母或数字可能代表产品/型号的重要细节。演讲者描述了最近的研究，我们在eBay的现有数据集中精选样本，这些样本经过手动标注，具有以买家为中心的相关性评分和中心性评分，后者反映了产品标题与用户意图的匹配程度。我们引入了一种用户意图中心性优化（User-intent Centrality Optimization, UCO）方法，用于现有模型，该方法优化了语义产品搜索中的用户意图。为此，我们提出了一种基于双重损失的优化方法来处理硬负样本，即那些在语义上相关但未反映用户意图的产品标题。我们的贡献包括策划一个具有挑战性的评估集和实现UCO，从而在不同的评估指标下显著提高了产品排序效率。我们的工作旨在确保对于一个查询，最符合买家意图的标题排名更高，从而增强电子商务平台的用户体验。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Product+Retrieval+and+Ranking+for+Alphanumeric+Queries)|0| |[PTSR: Prefix-Target Graph-based Sequential Recommendation](https://doi.org/10.1145/3627673.3679718)|Jiayu Chen, Xiaoyu Du, Yonghua Pan, Jinhui Tang|Nanjing University of Science and Technology, Nanjing, China|Sequential recommendation approaches predict the next items (targets) by analyzing prefix subsequences. These methods primarily model the correlations between prefixes and targets but often neglect the inherent correlations among prefixes and items. In this paper, we propose a Prefix-Target Graph-based Sequential Recommendation Approach (PTSR), which constructs a prefix-target graph (PTG) to collect observed correlations among prefixes and targets. It utilizes a graph neural network to model these inherent correlations, thus improving the item representations used in the predictive model. Specifically, prefixes linked to the same target reflect similar intents, while targets linked to the same prefix indicate available choices. This allows the graph neural network to effectively capture high-level correlations among prefixes and items, enhancing recommendation accuracy. We conduct extensive experiments on four real-world datasets to demonstrate the superiority of PTSR compared to state-of-the-art (SOTA) sequential recommendation methods. The source code of the PTSR is available at https://github.com/TosakRin/PTSR.|顺序推荐方法通过分析前缀子序列来预测下一个项目（目标）。这些方法主要建模前缀与目标之间的关联，但往往忽略了前缀和项目之间固有的关联。本文提出了一种基于前缀-目标图的顺序推荐方法（PTSR），该方法构建了一个前缀-目标图（PTG）以收集前缀与目标之间观察到的关联。它利用图神经网络来建模这些固有关联，从而改进预测模型中使用的项目表示。具体而言，与同一目标相连的前缀反映了相似的意图，而与同一前缀相连的目标则表示可用的选择。这使得图神经网络能够有效地捕捉前缀和项目之间的高层次关联，从而提高推荐准确性。我们在四个真实世界数据集上进行了广泛的实验，以证明PTSR相较于最先进的（SOTA）顺序推荐方法的优越性。PTSR的源代码可在https://github.com/TosakRin/PTSR获取。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=PTSR:+Prefix-Target+Graph-based+Sequential+Recommendation)|0| -|[PACIFIC: Enhancing Sequential Recommendation via Preference-aware Causal Intervention and Counterfactual Data Augmentation](https://doi.org/10.1145/3627673.3679803)|Jinpeng Chen, Huachen Guan, Huan Li, Fan Zhang, Liwei Huang, Guangyao Pang, Xiongnan Jin|; The State Key Laboratory of Blockchain and Data Security, Zhejiang University, Hangzhou, China; Beijing Institute of Remote Sensing, Beijing, China|Sequential recommendation has been receiving increasing attention from researchers. Existing sequential recommendation models leverage deep learning models to capture sequential features. However, these methods ignore confounders in the recommendation process, which can lead the model to learn incorrect correlations and fail to accurately capture users' true preferences. Moreover, these methods rely on extensive interaction sequences, but sequential data often suffers from sparsity issues. To address these limitations, this paper proposes a P reference- a ware C ausal I ntervention and Counter f a c tual Data Augmentation ( Pacific ) framework to enhance sequential recommendation. Initially, we model the causal graph of sequential recommendation and categorize user preferences into global long-term preferences, local long-term preferences, and short-term preferences. Then, we introduce the front-door criterion to eliminate the interference of confounders and design different self-attention mechanisms to estimate the causal effects, aiming to capture users' true preferences. In addition, based on counterfactual thinking, we design a counterfactual data augmentation module to generate enriched sequences. Experimental results on four real-world datasets demonstrate the superiority of our proposed approach over state-of-the-art sequential recommendation methods.|序列推荐近年来引起了研究人员的广泛关注。现有的序列推荐模型利用深度学习模型来捕捉序列特征。然而，这些方法忽略了推荐过程中的混杂因素，这可能导致模型学习到错误的关联，无法准确捕捉用户的真实偏好。此外，这些方法依赖于大量的交互序列，但序列数据往往存在稀疏性问题。为了解决这些局限性，本文提出了一个P reference-a ware C ausal I ntervention and Counter f a c tual Data Augmentation（Pacific）框架，以增强序列推荐。首先，我们建模了序列推荐的因果图，并将用户偏好分为全局长期偏好、局部长期偏好和短期偏好。然后，我们引入了前门准则来消除混杂因素的干扰，并设计了不同的自注意力机制来估计因果效应，旨在捕捉用户的真实偏好。此外，基于反事实思维，我们设计了一个反事实数据增强模块，以生成丰富的序列。在四个真实世界数据集上的实验结果表明，我们提出的方法优于最先进的序列推荐方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=PACIFIC:+Enhancing+Sequential+Recommendation+via+Preference-aware+Causal+Intervention+and+Counterfactual+Data+Augmentation)|0| +|[PACIFIC: Enhancing Sequential Recommendation via Preference-aware Causal Intervention and Counterfactual Data Augmentation](https://doi.org/10.1145/3627673.3679803)|Jinpeng Chen, Huachen Guan, Huan Li, Fan Zhang, Liwei Huang, Guangyao Pang, Xiongnan Jin|; Beijing Institute of Remote Sensing, Beijing, China; The State Key Laboratory of Blockchain and Data Security, Zhejiang University, Hangzhou, China|Sequential recommendation has been receiving increasing attention from researchers. Existing sequential recommendation models leverage deep learning models to capture sequential features. However, these methods ignore confounders in the recommendation process, which can lead the model to learn incorrect correlations and fail to accurately capture users' true preferences. Moreover, these methods rely on extensive interaction sequences, but sequential data often suffers from sparsity issues. To address these limitations, this paper proposes a P reference- a ware C ausal I ntervention and Counter f a c tual Data Augmentation ( Pacific ) framework to enhance sequential recommendation. Initially, we model the causal graph of sequential recommendation and categorize user preferences into global long-term preferences, local long-term preferences, and short-term preferences. Then, we introduce the front-door criterion to eliminate the interference of confounders and design different self-attention mechanisms to estimate the causal effects, aiming to capture users' true preferences. In addition, based on counterfactual thinking, we design a counterfactual data augmentation module to generate enriched sequences. Experimental results on four real-world datasets demonstrate the superiority of our proposed approach over state-of-the-art sequential recommendation methods.|序列推荐近年来引起了研究人员的广泛关注。现有的序列推荐模型利用深度学习模型来捕捉序列特征。然而，这些方法忽略了推荐过程中的混杂因素，这可能导致模型学习到错误的关联，无法准确捕捉用户的真实偏好。此外，这些方法依赖于大量的交互序列，但序列数据往往存在稀疏性问题。为了解决这些局限性，本文提出了一个P reference-a ware C ausal I ntervention and Counter f a c tual Data Augmentation（Pacific）框架，以增强序列推荐。首先，我们建模了序列推荐的因果图，并将用户偏好分为全局长期偏好、局部长期偏好和短期偏好。然后，我们引入了前门准则来消除混杂因素的干扰，并设计了不同的自注意力机制来估计因果效应，旨在捕捉用户的真实偏好。此外，基于反事实思维，我们设计了一个反事实数据增强模块，以生成丰富的序列。在四个真实世界数据集上的实验结果表明，我们提出的方法优于最先进的序列推荐方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=PACIFIC:+Enhancing+Sequential+Recommendation+via+Preference-aware+Causal+Intervention+and+Counterfactual+Data+Augmentation)|0| |[Context Matters: Enhancing Sequential Recommendation with Context-aware Diffusion-based Contrastive Learning](https://doi.org/10.1145/3627673.3679655)|Ziqiang Cui, Haolun Wu, Bowei He, Ji Cheng, Chen Ma|City University of Hong Kong, Hong Kong SAR, Hong Kong; McGill University, Montréal, Canada|Contrastive learning has been effectively utilized to enhance the training of sequential recommendation models by leveraging informative self-supervised signals. Most existing approaches generate augmented views of the same user sequence through random augmentation and subsequently maximize their agreement in the representation space. However, these methods often neglect the rationality of the augmented samples. Due to significant uncertainty, random augmentation can disrupt the semantic information and interest evolution patterns inherent in the original user sequences. Moreover, pulling semantically inconsistent sequences closer in the representation space can render the user sequence embeddings insensitive to variations in user preferences, which contradicts the primary objective of sequential recommendation. To address these limitations, we propose the Context-aware Diffusion-based Contrastive Learning for Sequential Recommendation, named CaDiRec. The core idea is to leverage context information to generate more reasonable augmented views. Specifically, CaDiRec employs a context-aware diffusion model to generate alternative items for the given positions within a sequence. These generated items are aligned with their respective context information and can effectively replace the corresponding original items, thereby generating a positive view of the original sequence. By considering two different augmentations of the same user sequence, we can construct a pair of positive samples for contrastive learning. To ensure representation cohesion, we train the entire framework in an end-to-end manner, with shared item embeddings between the diffusion model and the recommendation model. Extensive experiments on five benchmark datasets demonstrate the advantages of our proposed method over existing baselines.|对比学习已被有效利用，通过利用信息丰富的自监督信号来增强序列推荐模型的训练。大多数现有方法通过随机增强生成同一用户序列的增强视图，并在表示空间中最大化它们的共识。然而，这些方法往往忽略了增强样本的合理性。由于存在显著的不确定性，随机增强可能会破坏原始用户序列中固有的语义信息和兴趣演变模式。此外，在表示空间中将语义不一致的序列拉近会导致用户序列嵌入对用户偏好变化的敏感性降低，这与序列推荐的主要目标相悖。为了解决这些局限性，我们提出了基于上下文感知的扩散对比学习用于序列推荐，命名为CaDiRec。其核心思想是利用上下文信息生成更合理的增强视图。具体来说，CaDiRec采用上下文感知的扩散模型为序列中给定位置生成替代项。这些生成的项与其上下文信息对齐，并能有效替换相应的原始项，从而生成原始序列的正视图。通过考虑同一用户序列的两种不同增强，我们可以构建一对用于对比学习的正样本。为确保表示的一致性，我们以端到端的方式训练整个框架，并在扩散模型和推荐模型之间共享项嵌入。在五个基准数据集上的广泛实验证明了我们提出的方法相对于现有基线的优势。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Context+Matters:+Enhancing+Sequential+Recommendation+with+Context-aware+Diffusion-based+Contrastive+Learning)|0| |[A General Strategy Graph Collaborative Filtering for Recommendation Unlearning](https://doi.org/10.1145/3627673.3679637)|Yongjing Hao, Fuzhen Zhuang, Deqing Wang, Guanfeng Liu, Victor S. Sheng, Pengpeng Zhao|Macquarie University, Sydney, Australia; Soochow University, Suzhou, Jiangsu, China; Texas Tech University, Lubbock, USA; Beihang University, Beijing, China|Recommender systems play a crucial role in delivering personalized services to users, but the increasing volume of user data raises significant concerns about privacy, security, and utility. However, existing machine unlearning methods cannot be directly applied to recommendation systems as they overlook the collaborative information shared across users and items. More recently, a method known as RecEraser was introduced, offering partitioning and aggregation-based approaches. Nevertheless, these approaches have limitations due to their inadequate handling of additional overhead costs. In this paper, we propose A General Strategy Graph Collaborative Filtering for Recommendation Unlearning (GSGCF-RU), which is a novel model-agnostic learnable delete operator that optimizes unlearning edge consistency and feature representation consistency. Specifically, the GSGCF-RU model utilizes unlearning edge consistency to eliminate the influence of deleted elements, followed by feature representation consistency to retain knowledge after deletion. Lastly, experimental results on three real-world public benchmarks demonstrate that GSGCF-RU not only achieves efficient recommendation unlearning but also surpasses state-of-the-art methods in terms of model utility. The source code can be found at https://github.com/YongjingHao/GSGCF-RU.|推荐系统在为用户提供个性化服务方面起着至关重要的作用，但随着用户数据量的增加，隐私、安全和效用问题日益突出。然而，现有的机器遗忘方法无法直接应用于推荐系统，因为它们忽略了用户和物品之间共享的协作信息。最近，一种名为RecEraser的方法被提出，采用了基于分区和聚合的策略。然而，这些方法由于未能充分处理额外的开销成本而存在局限性。本文提出了一种通用策略图协同过滤推荐遗忘（GSGCF-RU）方法，这是一种新颖的模型无关可学习删除操作符，优化了遗忘边缘一致性和特征表示一致性。具体而言，GSGCF-RU模型利用遗忘边缘一致性来消除已删除元素的影响，随后通过特征表示一致性来保留删除后的知识。最后，在三个真实世界的公共基准上的实验结果表明，GSGCF-RU不仅实现了高效的推荐遗忘，而且在模型效用方面超越了最先进的方法。源代码可在https://github.com/YongjingHao/GSGCF-RU找到。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+General+Strategy+Graph+Collaborative+Filtering+for+Recommendation+Unlearning)|0| |[Interpretable Triplet Importance for Personalized Ranking](https://doi.org/10.1145/3627673.3679536)|Bowei He, Chen Ma||Personalized item ranking has been a crucial component contributing to the performance of recommender systems. As a representative approach, pairwise ranking directly optimizes the ranking with user implicit feedback by constructing (user, positive item, negative item) triplets. Several recent works have noticed that treating all triplets equally may hardly achieve the best effects. They assign different importance scores to negative items, user-item pairs, or triplets, respectively. However, almost all the generated importance scores are groundless and hard to interpret, thus far from trustworthy and transparent. To tackle these, we propose the Triplet Shapley – a Shapely value-based method to measure the triplet importance in an interpretable manner. Due to the huge number of triplets, we transform the original Shapley value calculation to the Monte Carlo (MC) approximation, where the guarantee for the approximation unbiasedness is also provided. To stabilize the MC approximation, we adopt a control covariates-based method. Finally, we utilize the triplet Shapley value to guide the resampling of important triplets for benefiting the model learning. Extensive experiments are conducted on six public datasets involving classical matrix factorization- and graph neural network-based recommendation models. Empirical results and subsequent analysis show that our model consistently outperforms the state-of-the-art methods.|个性化项目排序已成为提升推荐系统性能的关键组成部分。作为代表性方法，成对排序通过构建（用户，正项，负项）三元组，直接优化用户隐式反馈的排序。近期研究注意到，同等对待所有三元组可能难以达到最佳效果。因此，它们分别对负项、用户-项目对或三元组分配不同的重要性分数。然而，几乎所有生成的重要性分数都缺乏依据且难以解释，因此远非可靠和透明。为解决这些问题，我们提出了三元组Shapley——一种基于Shapley值的方法，以可解释的方式衡量三元组的重要性。由于三元组数量庞大，我们将原始Shapley值计算转换为蒙特卡洛（MC）近似，并提供了近似无偏性的保证。为稳定MC近似，我们采用了基于控制协变量的方法。最后，我们利用三元组Shapley值来指导重要三元组的重新采样，以促进模型学习。在涉及经典矩阵分解和图神经网络推荐模型的六个公开数据集上进行了广泛的实验。实证结果和后续分析表明，我们的模型始终优于最先进的方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Interpretable+Triplet+Importance+for+Personalized+Ranking)|0| -|[CausalMed: Causality-Based Personalized Medication Recommendation Centered on Patient Health State](https://doi.org/10.1145/3627673.3679542)|Xiang Li, Shunpan Liang, Yu Lei, Chen Li, Yulei Hou, Dashun Zheng, Tengfei Ma|; Hunan University School of Computer Science and Engineering; Yanshan University School of Information Science and Engineering; Xinjiang University of Science & Technology School of Information Science and Engineering; Yanshan University School of Mechanical Engineering|Medication recommendation systems are developed to recommend suitable medications tailored to specific patient. Previous researches primarily focus on learning medication representations, which have yielded notable advances. However, these methods are limited to capturing personalized patient representations due to the following primary limitations: (i) unable to capture the differences in the impact of diseases/procedures on patients across various patient health states; (ii) fail to model the direct causal relationships between medications and specific health state of patients, resulting in an inability to determine which specific disease each medication is treating. To address these limitations, we propose CausalMed, a patient health state-centric model capable of enhancing the personalization of patient representations. Specifically, CausalMed first captures the causal relationship between diseases/procedures and medications through causal discovery and evaluates their causal effects. Building upon this, CausalMed focuses on analyzing the health state of patients, capturing the dynamic differences of diseases/procedures in different health states of patients, and transforming diseases/procedures into medications on direct causal relationships. Ultimately, CausalMed integrates information from longitudinal visits to recommend medication combinations. Extensive experiments on real-world datasets show that our method learns more personalized patient representation and outperforms state-of-the-art models in accuracy and safety.|药物推荐系统旨在为特定患者推荐合适的药物。以往的研究主要集中在学习药物表征上，取得了显著进展。然而，这些方法由于以下主要限制，无法捕捉个性化的患者表征：（i）无法捕捉疾病/程序对不同患者健康状态影响的差异；（ii）未能建模药物与患者特定健康状态之间的直接因果关系，导致无法确定每种药物具体治疗哪种疾病。为解决这些限制，我们提出了CausalMed，一种以患者健康状态为中心的模型，能够增强患者表征的个性化。具体来说，CausalMed首先通过因果发现捕捉疾病/程序与药物之间的因果关系，并评估其因果效应。在此基础上，CausalMed专注于分析患者的健康状态，捕捉疾病/程序在不同健康状态下的动态差异，并基于直接因果关系将疾病/程序转化为药物。最终，CausalMed整合了纵向就诊信息，推荐药物组合。在真实世界数据集上的广泛实验表明，我们的方法学习到了更个性化的患者表征，并在准确性和安全性方面优于最先进的模型。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=CausalMed:+Causality-Based+Personalized+Medication+Recommendation+Centered+on+Patient+Health+State)|0| +|[CausalMed: Causality-Based Personalized Medication Recommendation Centered on Patient Health State](https://doi.org/10.1145/3627673.3679542)|Xiang Li, Shunpan Liang, Yu Lei, Chen Li, Yulei Hou, Dashun Zheng, Tengfei Ma|; Hunan University School of Computer Science and Engineering; Yanshan University School of Information Science and Engineering; Yanshan University School of Mechanical Engineering; Xinjiang University of Science & Technology School of Information Science and Engineering|Medication recommendation systems are developed to recommend suitable medications tailored to specific patient. Previous researches primarily focus on learning medication representations, which have yielded notable advances. However, these methods are limited to capturing personalized patient representations due to the following primary limitations: (i) unable to capture the differences in the impact of diseases/procedures on patients across various patient health states; (ii) fail to model the direct causal relationships between medications and specific health state of patients, resulting in an inability to determine which specific disease each medication is treating. To address these limitations, we propose CausalMed, a patient health state-centric model capable of enhancing the personalization of patient representations. Specifically, CausalMed first captures the causal relationship between diseases/procedures and medications through causal discovery and evaluates their causal effects. Building upon this, CausalMed focuses on analyzing the health state of patients, capturing the dynamic differences of diseases/procedures in different health states of patients, and transforming diseases/procedures into medications on direct causal relationships. Ultimately, CausalMed integrates information from longitudinal visits to recommend medication combinations. Extensive experiments on real-world datasets show that our method learns more personalized patient representation and outperforms state-of-the-art models in accuracy and safety.|药物推荐系统旨在为特定患者推荐合适的药物。以往的研究主要集中在学习药物表征上，取得了显著进展。然而，这些方法由于以下主要限制，无法捕捉个性化的患者表征：（i）无法捕捉疾病/程序对不同患者健康状态影响的差异；（ii）未能建模药物与患者特定健康状态之间的直接因果关系，导致无法确定每种药物具体治疗哪种疾病。为解决这些限制，我们提出了CausalMed，一种以患者健康状态为中心的模型，能够增强患者表征的个性化。具体来说，CausalMed首先通过因果发现捕捉疾病/程序与药物之间的因果关系，并评估其因果效应。在此基础上，CausalMed专注于分析患者的健康状态，捕捉疾病/程序在不同健康状态下的动态差异，并基于直接因果关系将疾病/程序转化为药物。最终，CausalMed整合了纵向就诊信息，推荐药物组合。在真实世界数据集上的广泛实验表明，我们的方法学习到了更个性化的患者表征，并在准确性和安全性方面优于最先进的模型。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=CausalMed:+Causality-Based+Personalized+Medication+Recommendation+Centered+on+Patient+Health+State)|0| |[PSNE: Efficient Spectral Sparsification Algorithms for Scaling Network Embedding](https://doi.org/10.1145/3627673.3679540)|Longlong Lin, Yunfeng Yu, Zihao Wang, Zeli Wang, Yuying Zhao, Jin Zhao, Tao Jia||Network embedding has numerous practical applications and has received extensive attention in graph learning, which aims at mapping vertices into a low-dimensional and continuous dense vector space by preserving the underlying structural properties of the graph. Many network embedding methods have been proposed, among which factorization of the Personalized PageRank (PPR for short) matrix has been empirically and theoretically well supported recently. However, several fundamental issues cannot be addressed. (1) Existing methods invoke a seminal Local Push subroutine to approximate \textit{a single} row or column of the PPR matrix. Thus, they have to execute $n$ ($n$ is the number of nodes) Local Push subroutines to obtain a provable PPR matrix, resulting in prohibitively high computational costs for large $n$. (2) The PPR matrix has limited power in capturing the structural similarity between vertices, leading to performance degradation. To overcome these dilemmas, we propose PSNE, an efficient spectral s\textbf{P}arsification method for \textbf{S}caling \textbf{N}etwork \textbf{E}mbedding, which can fast obtain the embedding vectors that retain strong structural similarities. Specifically, PSNE first designs a matrix polynomial sparser to accelerate the calculation of the PPR matrix, which has a theoretical guarantee in terms of the Frobenius norm. Subsequently, PSNE proposes a simple but effective multiple-perspective strategy to enhance further the representation power of the obtained approximate PPR matrix. Finally, PSNE applies a randomized singular value decomposition algorithm on the sparse and multiple-perspective PPR matrix to get the target embedding vectors. Experimental evaluation of real-world and synthetic datasets shows that our solutions are indeed more efficient, effective, and scalable compared with ten competitors.|网络嵌入在图学习领域具有众多实际应用，并受到了广泛关注。其目标是将顶点映射到一个低维且连续的密集向量空间，同时保留图的底层结构特性。已有多种网络嵌入方法被提出，其中个性化PageRank（简称PPR）矩阵的分解方法近期在实践和理论上都得到了良好的支持。然而，仍存在几个基本问题无法解决。（1）现有方法调用一个开创性的Local Push子程序来近似PPR矩阵的单行或单列。因此，它们需要执行n（n为节点数量）次Local Push子程序以获得可证明的PPR矩阵，这对于大规模n来说计算成本极高。（2）PPR矩阵在捕捉顶点间结构相似性方面能力有限，导致性能下降。为解决这些问题，我们提出了PSNE，这是一种高效的谱稀疏化方法，用于扩展网络嵌入，能够快速获取保留强结构相似性的嵌入向量。具体而言，PSNE首先设计了一种矩阵多项式稀疏化方法，以加速PPR矩阵的计算，该方法在Frobenius范数方面具有理论保证。随后，PSNE提出了一种简单但有效的多视角策略，进一步增强所获得的近似PPR矩阵的表示能力。最后，PSNE对稀疏且多视角的PPR矩阵应用随机奇异值分解算法，以获取目标嵌入向量。对真实世界和合成数据集的实验评估表明，与十个竞争对手相比，我们的解决方案确实更加高效、有效且可扩展。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=PSNE:+Efficient+Spectral+Sparsification+Algorithms+for+Scaling+Network+Embedding)|0| |[UniRec: A Dual Enhancement of Uniformity and Frequency in Sequential Recommendations](https://doi.org/10.1145/3627673.3679689)|Yang Liu, Yitong Wang, Chenyue Feng||Representation learning in sequential recommendation is critical for accurately modeling user interaction patterns and improving recommendation precision. However, existing approaches predominantly emphasize item-to-item transitions, often neglecting the time intervals between interactions, which are closely related to behavior pattern changes. Additionally, broader interaction attributes, such as item frequency, are frequently overlooked. We found that both sequences with more uniform time intervals and items with higher frequency yield better prediction performance. Conversely, non-uniform sequences exacerbate user interest drift and less-frequent items are difficult to model due to sparse sampling, presenting unique challenges inadequately addressed by current methods. In this paper, we propose UniRec, a novel bidirectional enhancement sequential recommendation method. UniRec leverages sequence uniformity and item frequency to enhance performance, particularly improving the representation of non-uniform sequences and less-frequent items. These two branches mutually reinforce each other, driving comprehensive performance optimization in complex sequential recommendation scenarios. Additionally, we present a multidimensional time module to further enhance adaptability. To the best of our knowledge, UniRec is the first method to utilize the characteristics of uniformity and frequency for feature augmentation. Comparing with eleven advanced models across four datasets, we demonstrate that UniRec outperforms SOTA models significantly. The code is available at https://github.com/Linxi000/UniRec.|在序列推荐中的表示学习对于准确建模用户交互模式和提升推荐精度至关重要。然而，现有方法主要侧重于项目间的转换，往往忽视了交互之间的时间间隔，这些时间间隔与行为模式的变化密切相关。此外，更广泛的交互属性，如项目频率，也经常被忽略。我们发现，时间间隔更均匀的序列和频率更高的项目能带来更好的预测性能。相反，时间间隔不均匀的序列会加剧用户兴趣的漂移，而采样稀疏的低频项目则难以建模，这为当前方法带来了独特的挑战。在本文中，我们提出了UniRec，一种新颖的双向增强序列推荐方法。UniRec利用序列均匀性和项目频率来提升性能，特别是在改进非均匀序列和低频项目的表示方面。这两个分支相互强化，推动在复杂序列推荐场景中的全面性能优化。此外，我们还引入了一个多维度时间模块，以进一步增强适应性。据我们所知，UniRec是首个利用均匀性和频率特性进行特征增强的方法。通过与四个数据集上的十一种先进模型进行比较，我们展示了UniRec显著优于现有的最先进模型。代码已公开在https://github.com/Linxi000/UniRec。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=UniRec:+A+Dual+Enhancement+of+Uniformity+and+Frequency+in+Sequential+Recommendations)|0| |[Collaborative Cross-modal Fusion with Large Language Model for Recommendation](https://doi.org/10.1145/3627673.3679596)|Zhongzhou Liu, Hao Zhang, Kuicai Dong, Yuan Fang||Despite the success of conventional collaborative filtering (CF) approaches for recommendation systems, they exhibit limitations in leveraging semantic knowledge within the textual attributes of users and items. Recent focus on the application of large language models for recommendation (LLM4Rec) has highlighted their capability for effective semantic knowledge capture. However, these methods often overlook the collaborative signals in user behaviors. Some simply instruct-tune a language model, while others directly inject the embeddings of a CF-based model, lacking a synergistic fusion of different modalities. To address these issues, we propose a framework of Collaborative Cross-modal Fusion with Large Language Models, termed CCF-LLM, for recommendation. In this framework, we translate the user-item interactions into a hybrid prompt to encode both semantic knowledge and collaborative signals, and then employ an attentive cross-modal fusion strategy to effectively fuse latent embeddings of both modalities. Extensive experiments demonstrate that CCF-LLM outperforms existing methods by effectively utilizing semantic and collaborative signals in the LLM4Rec context.|尽管传统的协同过滤（CF）方法在推荐系统中取得了成功，但它们在利用用户和项目文本属性中的语义知识方面存在局限性。最近，大语言模型在推荐系统中的应用（LLM4Rec）强调了其在有效捕捉语义知识方面的能力。然而，这些方法往往忽略了用户行为中的协同信号。有些方法仅仅是通过指令调整语言模型，而另一些方法则直接注入基于协同过滤模型的嵌入，缺乏不同模态之间的协同融合。为了解决这些问题，我们提出了一种名为CCF-LLM的大语言模型协同跨模态融合框架，用于推荐系统。在该框架中，我们将用户-项目交互转换为混合提示，以编码语义知识和协同信号，然后采用一种注意力跨模态融合策略，有效地融合两种模态的潜在嵌入。广泛的实验表明，CCF-LLM在LLM4Rec背景下，通过有效利用语义和协同信号，优于现有的方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Collaborative+Cross-modal+Fusion+with+Large+Language+Model+for+Recommendation)|0| -|[Re-evaluating the Command-and-Control Paradigm in Conversational Search Interactions](https://doi.org/10.1145/3627673.3679588)|Johanne R. Trippas, Luke Gallagher, Joel Mackenzie|The University of Queensland, Brisbane, Australia; The University of Melbourne, Melbourne, Australia; RMIT University, Melbourne, Australia|Conversational assistants are becoming prevalent among the wider population due to their simplicity and increasing utility. However, the shortcomings of these tools are as renowned as their benefits. In this work, we present a "first look" at an extensive collection of conversational queries, aiming to identify limitations and improvement opportunities specifically related to information access (i.e., search interactions). We explore over 600,000 Google Assistant interactions from 173 unique users, examining usage trends and the resulting deficiencies and strengths of these assistants. We aim to provide a balanced assessment, highlighting the assistant's shortcomings in supporting users and delivering relevant information to user needs and areas where it demonstrates a reasonable response to user inputs. Our analysis shows that, although most users conduct information-seeking tasks, there is little evidence of complex information-seeking behaviour, with most interactions consisting of simple, imperative instructions. Finally, we find that conversational devices allow users to benefit from increased naturalistic interactions and the ability to apply acquired information in situ, a novel observation for conversational information seeking.|对话助手因其简便性和日益增加的实用性，在更广泛的人群中变得普及。然而，这些工具的缺点与其优点同样为人所知。在这项工作中，我们首次对大量对话查询进行了深入分析，旨在识别与信息访问（即搜索交互）相关的局限性和改进机会。我们研究了来自173名独特用户的超过600,000次Google Assistant交互，考察了使用趋势以及这些助手的缺陷和优势。我们的目标是提供一个平衡的评估，突出助手在支持用户和传递相关信息以满足用户需求方面的不足，以及在合理响应用户输入的领域。我们的分析显示，尽管大多数用户进行信息检索任务，但几乎没有证据表明存在复杂的信息检索行为，大多数交互由简单的、命令式的指令组成。最后，我们发现对话设备使用户能够受益于更加自然的交互，并能够在现场应用所获取的信息，这是对话信息检索的一个新颖观察。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Re-evaluating+the+Command-and-Control+Paradigm+in+Conversational+Search+Interactions)|0| -|[Collaborative Alignment for Recommendation](https://doi.org/10.1145/3627673.3679535)|Chen Wang, Liangwei Yang, Zhiwei Liu, Xiaolong Liu, Mingdai Yang, Yueqing Liang, Philip S. Yu|University of Illinois Chicago, Chicgao, IL, USA; University of Illinois Chicago, Chicago, IL, USA; Salesforce AI Research, Palo Alto, CA, USA; Illinois Institute of Technology, Chicago, IL, USA|Traditional recommender systems have primarily relied on identity representations (IDs) to model users and items. Recently, the integration of pre-trained language models (PLMs) has enhanced the capability to capture semantic descriptions of items. However, while PLMs excel in few-shot, zero-shot, and unified modeling scenarios, they often overlook the crucial signals from collaborative filtering (CF), resulting in suboptimal performance when sufficient training data is available. To effectively combine semantic representations with the CF signal and enhance recommender system performance in both warm and cold settings, two major challenges must be addressed: (1) bridging the gap between semantic and collaborative representation spaces, and (2) refining while preserving the integrity of semantic representations. In this paper, we introduce CARec, a novel model that adeptly integrates collaborative filtering signals with semantic representations, ensuring alignment within the semantic space while maintaining essential semantics. We present experimental results from four real-world datasets, which demonstrate significant improvements. By leveraging collaborative alignment, CARec also shows remarkable effectiveness in cold-start scenarios, achieving notable enhancements in recommendation performance. The code is available at https://github.com/ChenMetanoia/CARec **REMOVE 2nd URL**://github.com/ChenMetanoia/CARec.|传统的推荐系统主要依赖于身份表示（IDs）来建模用户和物品。近年来，预训练语言模型（PLMs）的引入增强了捕捉物品语义描述的能力。然而，尽管PLMs在少样本、零样本和统一建模场景中表现出色，但它们往往忽略了协同过滤（CF）中的关键信号，导致在有足够训练数据时性能不佳。为了有效结合语义表示与CF信号，并在冷启动和热启动场景中提升推荐系统性能，必须解决两个主要挑战：（1）弥合语义与协同表示空间之间的差距，（2）在保持语义表示完整性的同时进行优化。本文介绍了CARec，这是一种新型模型，能够巧妙地将协同过滤信号与语义表示相结合，确保在语义空间内的对齐同时保持基本语义。我们在四个真实世界数据集上进行了实验，结果显示了显著的改进。通过利用协同对齐，CARec在冷启动场景中也表现出显著的有效性，实现了推荐性能的显著提升。代码可在https://github.com/ChenMetanoia/CARec获取。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Collaborative+Alignment+for+Recommendation)|0| +|[Re-evaluating the Command-and-Control Paradigm in Conversational Search Interactions](https://doi.org/10.1145/3627673.3679588)|Johanne R. Trippas, Luke Gallagher, Joel Mackenzie|The University of Queensland, Brisbane, Australia; RMIT University, Melbourne, Australia; The University of Melbourne, Melbourne, Australia|Conversational assistants are becoming prevalent among the wider population due to their simplicity and increasing utility. However, the shortcomings of these tools are as renowned as their benefits. In this work, we present a "first look" at an extensive collection of conversational queries, aiming to identify limitations and improvement opportunities specifically related to information access (i.e., search interactions). We explore over 600,000 Google Assistant interactions from 173 unique users, examining usage trends and the resulting deficiencies and strengths of these assistants. We aim to provide a balanced assessment, highlighting the assistant's shortcomings in supporting users and delivering relevant information to user needs and areas where it demonstrates a reasonable response to user inputs. Our analysis shows that, although most users conduct information-seeking tasks, there is little evidence of complex information-seeking behaviour, with most interactions consisting of simple, imperative instructions. Finally, we find that conversational devices allow users to benefit from increased naturalistic interactions and the ability to apply acquired information in situ, a novel observation for conversational information seeking.|对话助手因其简便性和日益增加的实用性，在更广泛的人群中变得普及。然而，这些工具的缺点与其优点同样为人所知。在这项工作中，我们首次对大量对话查询进行了深入分析，旨在识别与信息访问（即搜索交互）相关的局限性和改进机会。我们研究了来自173名独特用户的超过600,000次Google Assistant交互，考察了使用趋势以及这些助手的缺陷和优势。我们的目标是提供一个平衡的评估，突出助手在支持用户和传递相关信息以满足用户需求方面的不足，以及在合理响应用户输入的领域。我们的分析显示，尽管大多数用户进行信息检索任务，但几乎没有证据表明存在复杂的信息检索行为，大多数交互由简单的、命令式的指令组成。最后，我们发现对话设备使用户能够受益于更加自然的交互，并能够在现场应用所获取的信息，这是对话信息检索的一个新颖观察。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Re-evaluating+the+Command-and-Control+Paradigm+in+Conversational+Search+Interactions)|0| +|[Collaborative Alignment for Recommendation](https://doi.org/10.1145/3627673.3679535)|Chen Wang, Liangwei Yang, Zhiwei Liu, Xiaolong Liu, Mingdai Yang, Yueqing Liang, Philip S. Yu|University of Illinois Chicago, Chicgao, IL, USA; Salesforce AI Research, Palo Alto, CA, USA; Illinois Institute of Technology, Chicago, IL, USA; University of Illinois Chicago, Chicago, IL, USA|Traditional recommender systems have primarily relied on identity representations (IDs) to model users and items. Recently, the integration of pre-trained language models (PLMs) has enhanced the capability to capture semantic descriptions of items. However, while PLMs excel in few-shot, zero-shot, and unified modeling scenarios, they often overlook the crucial signals from collaborative filtering (CF), resulting in suboptimal performance when sufficient training data is available. To effectively combine semantic representations with the CF signal and enhance recommender system performance in both warm and cold settings, two major challenges must be addressed: (1) bridging the gap between semantic and collaborative representation spaces, and (2) refining while preserving the integrity of semantic representations. In this paper, we introduce CARec, a novel model that adeptly integrates collaborative filtering signals with semantic representations, ensuring alignment within the semantic space while maintaining essential semantics. We present experimental results from four real-world datasets, which demonstrate significant improvements. By leveraging collaborative alignment, CARec also shows remarkable effectiveness in cold-start scenarios, achieving notable enhancements in recommendation performance. The code is available at https://github.com/ChenMetanoia/CARec **REMOVE 2nd URL**://github.com/ChenMetanoia/CARec.|传统的推荐系统主要依赖于身份表示（IDs）来建模用户和物品。近年来，预训练语言模型（PLMs）的引入增强了捕捉物品语义描述的能力。然而，尽管PLMs在少样本、零样本和统一建模场景中表现出色，但它们往往忽略了协同过滤（CF）中的关键信号，导致在有足够训练数据时性能不佳。为了有效结合语义表示与CF信号，并在冷启动和热启动场景中提升推荐系统性能，必须解决两个主要挑战：（1）弥合语义与协同表示空间之间的差距，（2）在保持语义表示完整性的同时进行优化。本文介绍了CARec，这是一种新型模型，能够巧妙地将协同过滤信号与语义表示相结合，确保在语义空间内的对齐同时保持基本语义。我们在四个真实世界数据集上进行了实验，结果显示了显著的改进。通过利用协同对齐，CARec在冷启动场景中也表现出显著的有效性，实现了推荐性能的显著提升。代码可在https://github.com/ChenMetanoia/CARec获取。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Collaborative+Alignment+for+Recommendation)|0| |[Sparks of Surprise: Multi-objective Recommendations with Hierarchical Decision Transformers for Diversity, Novelty, and Serendipity](https://doi.org/10.1145/3627673.3679533)|Jie Wang, Alexandros Karatzoglou, Ioannis Arapakis, Xin Xin, Xuri Ge, Joemon M. Jose|ShanDong University, Qingdao, China; University of Glasgow, Glasgow, United Kingdom; Amazon, BARCELONA, Spain; Telefonica Research, BARCELONA, Spain|Personalized Session-based Recommendation (PSR) extends the traditional sequential recommendation models-which typically recommends the next item based on a recent active session-to leverage historical sessions of a user for short-term recommendations in current session. However, existing PSR methods face two limitations: (1) treating offline sessions uniformly as static data and relying on user embeddings to represent personalized information overlook the dynamic evolution of interests over time, which can change significantly as sessions progress in practical application. (2) focusing on accuracy, i.e., recommending items relevant to recent interactions, ignores the balance of multi-faceted requirements for user satisfaction, i.e., diversity, novelty, and serendipity. Therefore, we introduce Multi-objective PSR (MOPSR) task and propose Hierarchical Decision Transformers (HDT) framework, which models strictly sequential preference transitions of users across and within sessions to balance recommendation accuracy with the mentioned objectives. To address the first problem, Inter-session DT dynamically tracks the user's long-term preference across sessions by maintaining a goal state. This goal state serves as personalized information to collaboratively make recommendations with short-term state via the Intra-session DT. To tackle the second limitation, we propose inter-session and intra-session unexpected returns to trade off relevant recommendations and user preferences on diversity, novelty, and serendipity. The hierarchical returns help the recommender accurately identify signals of the user's expectations and changes in multi-objective preferences. To verify the effectiveness of our method on the MOPSR, we apply HDT to four state-of-the-art sequential recommendation models and conduct experiments on two publicly available datasets. Experimental results demonstrate that (1) HDT can widely generalize sequential models to solve the MOPSR task in scenarios with incrementally generated sessions, and (2) our method can balance multi-objectives by maintaining and even enhancing accuracy while effectively improving the diversity, novelty, and serendipity objectives.|个性化会话推荐（PSR）扩展了传统的序列推荐模型——这些模型通常根据最近的活动会话推荐下一个项目——以利用用户的历史会话为当前会话提供短期推荐。然而，现有的PSR方法存在两个局限性：（1）将离线会话统一视为静态数据，并依赖用户嵌入来表示个性化信息，忽略了兴趣随时间的动态演变，这在实际应用中随着会话的进展可能会发生显著变化。（2）专注于准确性，即推荐与最近交互相关的项目，忽略了用户满意度的多方面需求平衡，即多样性、新颖性和意外性。因此，我们引入了多目标PSR（MOPSR）任务，并提出了层次决策变换器（HDT）框架，该框架对用户在会话内外严格顺序的偏好转移进行建模，以平衡推荐准确性与上述目标。为解决第一个问题，会话间DT通过维护一个目标状态来动态追踪用户在会话间的长期偏好。该目标状态作为个性化信息，通过会话内DT与短期状态协作进行推荐。为应对第二个局限性，我们提出了会话间和会话内的意外回报，以权衡相关推荐与用户对多样性、新颖性和意外性的偏好。层次回报有助于推荐系统准确识别用户期望的信号和多目标偏好的变化。为验证我们的方法在MOPSR上的有效性，我们将HDT应用于四种最先进的序列推荐模型，并在两个公开数据集上进行实验。实验结果表明：（1）HDT能够广泛推广序列模型，以解决在会话增量生成场景下的MOPSR任务；（2）我们的方法能够在保持甚至提高准确性的同时，有效提升多样性、新颖性和意外性目标，从而平衡多目标。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Sparks+of+Surprise:+Multi-objective+Recommendations+with+Hierarchical+Decision+Transformers+for+Diversity,+Novelty,+and+Serendipity)|0| -|[Content-Based Collaborative Generation for Recommender Systems](https://doi.org/10.1145/3627673.3679692)|Yidan Wang, Zhaochun Ren, Weiwei Sun, Jiyuan Yang, Zhixiang Liang, Xin Chen, Ruobing Xie, Su Yan, Xu Zhang, Pengjie Ren, Zhumin Chen, Xin Xin|Shandong University, Qingdao, China; Tencent, Beijing, China; WeChat, Tencent, Beijing, China; Zhejiang University, Hangzhou, China; Leiden University, Leiden, Netherlands|Generative models have emerged as a promising utility to enhance recommender systems. It is essential to model both item content and user-item collaborative interactions in a unified generative framework for better recommendation. Although some existing large language model (LLM)-based methods contribute to fusing content information and collaborative signals, they fundamentally rely on textual language generation, which is not fully aligned with the recommendation task. How to integrate content knowledge and collaborative interaction signals in a generative framework tailored for item recommendation is still an open research challenge. In this paper, we propose co ntent-based col la borative generation for rec ommender systems, namely ColaRec. ColaRec is a sequence-to-sequence framework which is tailored for directly generating the recommended item identifier. Precisely, the input sequence comprises data pertaining to the user's interacted items, and the output sequence represents the generative identifier (GID) for the suggested item. To model collaborative signals, the GIDs are constructed from a pretrained collaborative filtering model, and the user is represented as the content aggregation of interacted items. To this end, ColaRec captures both collaborative signals and content information in a unified framework. Then an item indexing task is proposed to conduct the alignment between the content-based semantic space and the interaction-based collaborative space. Besides, a contrastive loss is further introduced to ensure that items with similar collaborative GIDs have similar content representations. To verify the effectiveness of ColaRec, we conduct experiments on four benchmark datasets. Empirical results demonstrate the superior performance of ColaRec.|生成模型已成为增强推荐系统的有力工具。为了实现更好的推荐效果，在一个统一的生成框架中同时建模项目内容和用户-项目协同交互是至关重要的。尽管一些现有的基于大型语言模型（LLM）的方法有助于融合内容信息和协同信号，但它们本质上依赖于文本语言生成，这并未完全适应推荐任务的需求。如何在专为项目推荐设计的生成框架中整合内容知识和协同交互信号，仍然是一个开放的研究挑战。本文提出了一种基于内容的协同生成推荐系统方法，命名为ColaRec。ColaRec是一个序列到序列框架，专门用于直接生成推荐项目的标识符。具体而言，输入序列包含用户交互过的项目数据，输出序列表示推荐项目的生成标识符（GID）。为了建模协同信号，GID由预训练的协同过滤模型构建，用户则表示为交互项目的聚合内容。通过这种方式，ColaRec在一个统一的框架中捕捉了协同信号和内容信息。随后，提出了一项项目索引任务，以实现基于内容语义空间与基于交互的协同空间之间的对齐。此外，引入对比损失以确保具有相似协同GID的项目具有相似的内容表示。为了验证ColaRec的有效性，我们在四个基准数据集上进行了实验。实证结果表明，ColaRec具有优越的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Content-Based+Collaborative+Generation+for+Recommender+Systems)|0| +|[Content-Based Collaborative Generation for Recommender Systems](https://doi.org/10.1145/3627673.3679692)|Yidan Wang, Zhaochun Ren, Weiwei Sun, Jiyuan Yang, Zhixiang Liang, Xin Chen, Ruobing Xie, Su Yan, Xu Zhang, Pengjie Ren, Zhumin Chen, Xin Xin|WeChat, Tencent, Beijing, China; Shandong University, Qingdao, China; Tencent, Beijing, China; Zhejiang University, Hangzhou, China; Leiden University, Leiden, Netherlands|Generative models have emerged as a promising utility to enhance recommender systems. It is essential to model both item content and user-item collaborative interactions in a unified generative framework for better recommendation. Although some existing large language model (LLM)-based methods contribute to fusing content information and collaborative signals, they fundamentally rely on textual language generation, which is not fully aligned with the recommendation task. How to integrate content knowledge and collaborative interaction signals in a generative framework tailored for item recommendation is still an open research challenge. In this paper, we propose co ntent-based col la borative generation for rec ommender systems, namely ColaRec. ColaRec is a sequence-to-sequence framework which is tailored for directly generating the recommended item identifier. Precisely, the input sequence comprises data pertaining to the user's interacted items, and the output sequence represents the generative identifier (GID) for the suggested item. To model collaborative signals, the GIDs are constructed from a pretrained collaborative filtering model, and the user is represented as the content aggregation of interacted items. To this end, ColaRec captures both collaborative signals and content information in a unified framework. Then an item indexing task is proposed to conduct the alignment between the content-based semantic space and the interaction-based collaborative space. Besides, a contrastive loss is further introduced to ensure that items with similar collaborative GIDs have similar content representations. To verify the effectiveness of ColaRec, we conduct experiments on four benchmark datasets. Empirical results demonstrate the superior performance of ColaRec.|生成模型已成为增强推荐系统的有力工具。为了实现更好的推荐效果，在一个统一的生成框架中同时建模项目内容和用户-项目协同交互是至关重要的。尽管一些现有的基于大型语言模型（LLM）的方法有助于融合内容信息和协同信号，但它们本质上依赖于文本语言生成，这并未完全适应推荐任务的需求。如何在专为项目推荐设计的生成框架中整合内容知识和协同交互信号，仍然是一个开放的研究挑战。本文提出了一种基于内容的协同生成推荐系统方法，命名为ColaRec。ColaRec是一个序列到序列框架，专门用于直接生成推荐项目的标识符。具体而言，输入序列包含用户交互过的项目数据，输出序列表示推荐项目的生成标识符（GID）。为了建模协同信号，GID由预训练的协同过滤模型构建，用户则表示为交互项目的聚合内容。通过这种方式，ColaRec在一个统一的框架中捕捉了协同信号和内容信息。随后，提出了一项项目索引任务，以实现基于内容语义空间与基于交互的协同空间之间的对齐。此外，引入对比损失以确保具有相似协同GID的项目具有相似的内容表示。为了验证ColaRec的有效性，我们在四个基准数据集上进行了实验。实证结果表明，ColaRec具有优越的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Content-Based+Collaborative+Generation+for+Recommender+Systems)|0| |[Multi-Task Recommendation with Task Information Decoupling](https://doi.org/10.1145/3627673.3679621)|Ruiran Yan, Rui Fan, Defu Lian||Multi-task learning (MTL) has become increasingly prevalent in e-commerce recommender systems. However, existing MTL methods, particularly those utilizing the Multi-gate Mixture-of-Experts (MMoE) architecture, face challenges due to their implicit routing mechanisms. These mechanisms can inadvertently lead to negative knowledge transfer, failing to resolve conflicts among tasks and resulting in gradient contradictions on shared parameters. Such issues undermine the generalization capability of MTL models across various tasks. To address these limitations, we introduce the Task Information Decoupling Model (TIDM), designed to alleviate negative transfer by decoupling task knowledge. TIDM incorporates two innovative modules following the expert layer: the Maximize Information Aggregation Module (MIA) and the Automatic Information Selection Module (AIS). The MIA module employs an auxiliary loss to filter out irrelevant task information and aggregates task-specific knowledge using a dissimilar self-attention network. Subsequently, the AIS module automatically selects the most pertinent task-specific information to facilitate task tower learning. Our experiments demonstrate that TIDM outperforms five contemporary MTL models across two datasets, showcasing its effectiveness in extracting task-specific information. This advancement is crucial for enhancing the performance of recommender systems in e-commerce and other complex domains.|多任务学习（MTL）在电子商务推荐系统中变得越来越普遍。然而，现有的MTL方法，特别是那些采用多门混合专家（MMoE）架构的方法，面临着由于其隐式路由机制带来的挑战。这些机制可能会无意中导致负知识转移，无法解决任务间的冲突，并在共享参数上产生梯度矛盾。这些问题削弱了MTL模型在各种任务中的泛化能力。为了解决这些局限性，我们引入了任务信息解耦模型（TIDM），旨在通过解耦任务知识来减轻负转移。TIDM在专家层之后包含了两个创新模块：最大化信息聚合模块（MIA）和自动信息选择模块（AIS）。MIA模块采用辅助损失来过滤无关任务信息，并使用不相似的自注意力网络聚合任务特定知识。随后，AIS模块自动选择最相关的任务特定信息，以促进任务塔的学习。我们的实验表明，TIDM在两个数据集上优于五种当代MTL模型，展示了其在提取任务特定信息方面的有效性。这一进展对于提升电子商务及其他复杂领域中推荐系统的性能至关重要。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Multi-Task+Recommendation+with+Task+Information+Decoupling)|0| -|[MMLRec: A Unified Multi-Task and Multi-Scenario Learning Benchmark for Recommendation](https://doi.org/10.1145/3627673.3679691)|Guanghu Yuan, Jieyu Yang, Shujie Li, Mingjie Zhong, Ang Li, Ke Ding, Yong He, Min Yang, Liang Zhang, Xiaolu Zhang, Linjian Mo|Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China; Ant Group, Hangzhou, China|In recent years, there has been a trend in the field of recommender systems towards multi-task modeling and multi-scenario modeling. The aim is to enhance the performance of various tasks and scenarios by jointly training on multiple tasks or scenarios to learn common patterns and features. Joint modeling of tasks and scenarios has also received widespread attention recently. However, despite the rich proposals of methods for Multi-Task Learning (MTL), Multi-Scenario Learning (MSL), and Multi-Task-Multi-Scenario Learning (MTMSL) in recent years, there still lacks a comprehensive benchmark to evaluate these methods. Previous studies often employed different datasets, data processing techniques, data partitioning strategies, and hyperparameter settings, making replication of existing research and fair comparison of experimental results challenging. To address this challenge, we introduce MMLRec, the first unified comprehensive benchmark for evaluating MTL, MSL and MTMSL, featuring consistent dataset processing and identical parameter settings. This benchmark implements a range of MTL, MSL, and MTMSL algorithms, and evaluates them on multiple commonly used recommender systems datasets. Through fair comparative experiments, we find that some structurally simplistic recommendation algorithms are underestimated, as they can achieve comparable results to more complex algorithms while maintaining lower complexity. Furthermore, our experimental analysis indicates that more complex methods exhibit better robustness when there are significant differences between tasks or scenarios. By providing a unified framework (MMLRec), our goal is to promote rapid evaluation and inspire innovative research in this continuously evolving field. We hope that our open-source benchmark can facilitate swift, equitable evaluations, while also fostering further breakthrough research in the domains of MTL, MSL, and MTMSL.|近年来，推荐系统领域出现了一种趋势，即向多任务建模和多场景建模发展。其目的是通过联合训练多个任务或场景，以学习共同的模型和特征，从而提升各个任务和场景的性能。任务和场景的联合建模也近来受到了广泛关注。然而，尽管近年来针对多任务学习（MTL）、多场景学习（MSL）以及多任务多场景学习（MTMSL）的方法提出了丰富的建议，但仍缺乏一个全面的基准来评估这些方法。以往的研究往往采用不同的数据集、数据处理技术、数据划分策略和超参数设置，这使得现有研究的复现和实验结果的公平比较变得困难。为了应对这一挑战，我们引入了MMLRec，这是首个用于评估MTL、MSL和MTMSL的统一综合性基准，具有一致的数据集处理和相同的参数设置。该基准实现了一系列MTL、MSL和MTMSL算法，并在多个常用的推荐系统数据集上对其进行了评估。通过公平的比较实验，我们发现一些结构上较为简单的推荐算法被低估了，因为它们能够在保持较低复杂度的同时，取得与更复杂算法相当的结果。此外，我们的实验分析表明，当任务或场景之间存在显著差异时，更复杂的方法表现出更好的鲁棒性。通过提供一个统一的框架（MMLRec），我们的目标是促进该领域的快速评估，并激发创新研究。我们希望我们的开源基准能够促进快速、公平的评估，同时也推动MTL、MSL和MTMSL领域的进一步突破性研究。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=MMLRec:+A+Unified+Multi-Task+and+Multi-Scenario+Learning+Benchmark+for+Recommendation)|0| +|[MMLRec: A Unified Multi-Task and Multi-Scenario Learning Benchmark for Recommendation](https://doi.org/10.1145/3627673.3679691)|Guanghu Yuan, Jieyu Yang, Shujie Li, Mingjie Zhong, Ang Li, Ke Ding, Yong He, Min Yang, Liang Zhang, Xiaolu Zhang, Linjian Mo|Ant Group, Hangzhou, China; Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China|In recent years, there has been a trend in the field of recommender systems towards multi-task modeling and multi-scenario modeling. The aim is to enhance the performance of various tasks and scenarios by jointly training on multiple tasks or scenarios to learn common patterns and features. Joint modeling of tasks and scenarios has also received widespread attention recently. However, despite the rich proposals of methods for Multi-Task Learning (MTL), Multi-Scenario Learning (MSL), and Multi-Task-Multi-Scenario Learning (MTMSL) in recent years, there still lacks a comprehensive benchmark to evaluate these methods. Previous studies often employed different datasets, data processing techniques, data partitioning strategies, and hyperparameter settings, making replication of existing research and fair comparison of experimental results challenging. To address this challenge, we introduce MMLRec, the first unified comprehensive benchmark for evaluating MTL, MSL and MTMSL, featuring consistent dataset processing and identical parameter settings. This benchmark implements a range of MTL, MSL, and MTMSL algorithms, and evaluates them on multiple commonly used recommender systems datasets. Through fair comparative experiments, we find that some structurally simplistic recommendation algorithms are underestimated, as they can achieve comparable results to more complex algorithms while maintaining lower complexity. Furthermore, our experimental analysis indicates that more complex methods exhibit better robustness when there are significant differences between tasks or scenarios. By providing a unified framework (MMLRec), our goal is to promote rapid evaluation and inspire innovative research in this continuously evolving field. We hope that our open-source benchmark can facilitate swift, equitable evaluations, while also fostering further breakthrough research in the domains of MTL, MSL, and MTMSL.|近年来，推荐系统领域出现了一种趋势，即向多任务建模和多场景建模发展。其目的是通过联合训练多个任务或场景，以学习共同的模型和特征，从而提升各个任务和场景的性能。任务和场景的联合建模也近来受到了广泛关注。然而，尽管近年来针对多任务学习（MTL）、多场景学习（MSL）以及多任务多场景学习（MTMSL）的方法提出了丰富的建议，但仍缺乏一个全面的基准来评估这些方法。以往的研究往往采用不同的数据集、数据处理技术、数据划分策略和超参数设置，这使得现有研究的复现和实验结果的公平比较变得困难。为了应对这一挑战，我们引入了MMLRec，这是首个用于评估MTL、MSL和MTMSL的统一综合性基准，具有一致的数据集处理和相同的参数设置。该基准实现了一系列MTL、MSL和MTMSL算法，并在多个常用的推荐系统数据集上对其进行了评估。通过公平的比较实验，我们发现一些结构上较为简单的推荐算法被低估了，因为它们能够在保持较低复杂度的同时，取得与更复杂算法相当的结果。此外，我们的实验分析表明，当任务或场景之间存在显著差异时，更复杂的方法表现出更好的鲁棒性。通过提供一个统一的框架（MMLRec），我们的目标是促进该领域的快速评估，并激发创新研究。我们希望我们的开源基准能够促进快速、公平的评估，同时也推动MTL、MSL和MTMSL领域的进一步突破性研究。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=MMLRec:+A+Unified+Multi-Task+and+Multi-Scenario+Learning+Benchmark+for+Recommendation)|0| |[Reformulating Conversational Recommender Systems as Tri-Phase Offline Policy Learning](https://doi.org/10.1145/3627673.3679792)|Gangyi Zhang, Chongming Gao, Hang Pan, Runzhe Teng, Ruizhe Li||Existing Conversational Recommender Systems (CRS) predominantly utilize user simulators for training and evaluating recommendation policies. These simulators often oversimplify the complexity of user interactions by focusing solely on static item attributes, neglecting the rich, evolving preferences that characterize real-world user behavior. This limitation frequently leads to models that perform well in simulated environments but falter in actual deployment. Addressing these challenges, this paper introduces the Tri-Phase Offline Policy Learning-based Conversational Recommender System (TPCRS), which significantly reduces dependency on real-time interactions and mitigates overfitting issues prevalent in traditional approaches. TPCRS integrates a model-based offline learning strategy with a controllable user simulation that dynamically aligns with both personalized and evolving user preferences. Through comprehensive experiments, TPCRS demonstrates enhanced robustness, adaptability, and accuracy in recommendations, outperforming traditional CRS models in diverse user scenarios. This approach not only provides a more realistic evaluation environment but also facilitates a deeper understanding of user behavior dynamics, thereby refining the recommendation process.|现有的对话推荐系统（CRS）主要依赖用户模拟器进行推荐策略的训练和评估。这些模拟器通常过于简化用户交互的复杂性，仅关注静态的物品属性，而忽略了现实世界中用户行为所具有的丰富且不断演变的偏好。这一局限性往往导致模型在模拟环境中表现良好，但在实际应用中却表现不佳。为了应对这些挑战，本文提出了基于三阶段离线策略学习的对话推荐系统（TPCRS），该系统显著减少了对实时交互的依赖，并缓解了传统方法中常见的过拟合问题。TPCRS结合了基于模型的离线学习策略与可控的用户模拟器，后者能够动态地与个性化且不断演变的用户偏好相匹配。通过全面的实验，TPCRS在推荐系统的鲁棒性、适应性和准确性方面表现出色，在多种用户场景下均优于传统的CRS模型。这种方法不仅提供了一个更为真实的评估环境，还促进了对于用户行为动态的深入理解，从而优化了推荐过程。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Reformulating+Conversational+Recommender+Systems+as+Tri-Phase+Offline+Policy+Learning)|0| |[HGCH: A Hyperbolic Graph Convolution Network Model for Heterogeneous Collaborative Graph Recommendation](https://doi.org/10.1145/3627673.3679701)|Lu Zhang, Ning Wu|Huazhong University of Science and Technology, Wuhan, China; Beihang University, Beijing, China|User-item interaction data in collaborative filtering and graph modeling tasks often exhibit power-law characteristics, which suggest the suitability of hyperbolic space modeling. Hyperbolic Graph Convolution Neural Networks (HGCNs) are a novel technique that leverages the advantages of GCN and hyperbolic space, and then achieves remarkable results. However, existing HGCN methods have several drawbacks: they fail to fully leverage hyperbolic space properties due to arbitrary embedding initialization and imprecise tangent space aggregation; they overlook auxiliary information that could enrich the collaborative graph; and their training convergence is slow due to margin ranking loss and random negative sampling. To overcome these challenges, we propose Hyperbolic Graph Collaborative for Heterogeneous Recommendation (HGCH), an enhanced HGCN-based model for collaborative filtering that integrates diverse side information into a heterogeneous collaborative graph and improves training convergence speed. HGCH first preserves the long-tailed nature of the graph by initializing node embeddings with power law prior; then it aggregates neighbors in hyperbolic space using the gyromidpoint method for accurate computation; finally, it fuses multiple embeddings from different hyperbolic spaces by the gate fusion with prior. Moreover, HGCH employs a hyperbolic user-specific negative sampling to speed up convergence. We evaluate HGCH on four real datasets, and the results show that HGCH achieves competitive results and outperforms leading baselines, including HGCNs. Extensive ablation studies further confirm its effectiveness.|在协同过滤和图建模任务中的用户-物品交互数据往往呈现出幂律分布特征，这表明双曲空间建模的适用性。双曲图卷积神经网络（HGCNs）是一种利用GCN和双曲空间优势的新技术，并取得了显著成果。然而，现有的HGCN方法存在几个缺点：由于任意嵌入初始化和不精确的切空间聚合，未能充分利用双曲空间特性；忽略了可以丰富协同图的辅助信息；由于边缘排序损失和随机负采样，训练收敛速度慢。为了克服这些挑战，我们提出了异构推荐的双曲图协同（HGCH），这是一个基于HGCN的协同过滤增强模型，它将多样化的辅助信息整合到异构协同图中，并提高了训练收敛速度。HGCH首先通过使用幂律先验初始化节点嵌入来保留图的长尾特性；然后使用双曲空间中的中点方法聚合邻居以进行精确计算；最后，通过带先验的门融合方法融合来自不同双曲空间的多个嵌入。此外，HGCH采用双曲用户特定的负采样来加速收敛。我们在四个真实数据集上评估了HGCH，结果显示HGCH取得了竞争性的结果，并优于包括HGCNs在内的领先基线。广泛的消融研究进一步证实了其有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=HGCH:+A+Hyperbolic+Graph+Convolution+Network+Model+for+Heterogeneous+Collaborative+Graph+Recommendation)|0| -|[EMERGE: Enhancing Multimodal Electronic Health Records Predictive Modeling with Retrieval-Augmented Generation](https://doi.org/10.1145/3627673.3679582)|Yinghao Zhu, Changyu Ren, Zixiang Wang, Xiaochen Zheng, Shiyun Xie, Junlan Feng, Xi Zhu, Zhoujun Li, Liantao Ma, Chengwei Pan|Beihang University & Peking University, Beijing, China; Peking University, Beijing, China; Beihang University, Beijing, China; Beihang University & Zhongguancun Laboratory, Beijing, China; China Mobile Research Institute, Beijing, China; ETH Zürich, Zürich, Switzerland|The integration of multimodal Electronic Health Records (EHR) data has significantly advanced clinical predictive capabilities. Existing models, which utilize clinical notes and multivariate time-series EHR data, often fall short of incorporating the necessary medical context for accurate clinical tasks, while previous approaches with knowledge graphs (KGs) primarily focus on structured knowledge extraction. In response, we propose EMERGE, a Retrieval-Augmented Generation (RAG) driven framework to enhance multimodal EHR predictive modeling. We extract entities from both time-series data and clinical notes by prompting Large Language Models (LLMs) and align them with professional PrimeKG, ensuring consistency. In addition to triplet relationships, we incorporate entities' definitions and descriptions for richer semantics. The extracted knowledge is then used to generate task-relevant summaries of patients' health statuses. Finally, we fuse the summary with other modalities using an adaptive multimodal fusion network with cross-attention. Extensive experiments on the MIMIC-III and MIMIC-IV datasets' in-hospital mortality and 30-day readmission tasks demonstrate the superior performance of the EMERGE framework over baseline models. Comprehensive ablation studies and analysis highlight the efficacy of each designed module and robustness to data sparsity. EMERGE contributes to refining the utilization of multimodal EHR data in healthcare, bridging the gap with nuanced medical contexts essential for informed clinical predictions. We have publicly released the code at https://github.com/yhzhu99/EMERGE.|多模态电子健康记录（EHR）数据的整合显著提升了临床预测能力。现有的模型，尽管利用了临床笔记和多元时间序列EHR数据，但往往未能充分纳入必要的医学背景信息，以实现精准的临床任务；而先前基于知识图谱（KGs）的方法则主要集中在结构化知识的提取上。为此，我们提出了EMERGE，一个由检索增强生成（RAG）驱动框架，旨在提升多模态EHR预测建模。我们通过提示大型语言模型（LLMs）从时间序列数据和临床笔记中提取实体，并将其与专业的PrimeKG对齐，以确保一致性。除了三元组关系外，我们还纳入了实体的定义和描述，以丰富语义。提取的知识随后用于生成与任务相关的患者健康状况摘要。最后，我们利用具有交叉注意力的自适应多模态融合网络将该摘要与其他模态数据融合。在MIMIC-III和MIMIC-IV数据集上的住院死亡率和30天再入院任务的广泛实验表明，EMERGE框架优于基线模型。全面的消融研究和分析突显了每个设计模块的有效性及其对数据稀疏性的稳健性。EMERGE有助于优化多模态EHR数据在医疗领域的应用，弥合了与精细医学背景之间的差距，这对于精准的临床预测至关重要。我们已经公开发布了代码，地址为https://github.com/yhzhu99/EMERGE。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=EMERGE:+Enhancing+Multimodal+Electronic+Health+Records+Predictive+Modeling+with+Retrieval-Augmented+Generation)|0| +|[EMERGE: Enhancing Multimodal Electronic Health Records Predictive Modeling with Retrieval-Augmented Generation](https://doi.org/10.1145/3627673.3679582)|Yinghao Zhu, Changyu Ren, Zixiang Wang, Xiaochen Zheng, Shiyun Xie, Junlan Feng, Xi Zhu, Zhoujun Li, Liantao Ma, Chengwei Pan|China Mobile Research Institute, Beijing, China; Peking University, Beijing, China; Beihang University & Peking University, Beijing, China; Beihang University & Zhongguancun Laboratory, Beijing, China; ETH Zürich, Zürich, Switzerland; Beihang University, Beijing, China|The integration of multimodal Electronic Health Records (EHR) data has significantly advanced clinical predictive capabilities. Existing models, which utilize clinical notes and multivariate time-series EHR data, often fall short of incorporating the necessary medical context for accurate clinical tasks, while previous approaches with knowledge graphs (KGs) primarily focus on structured knowledge extraction. In response, we propose EMERGE, a Retrieval-Augmented Generation (RAG) driven framework to enhance multimodal EHR predictive modeling. We extract entities from both time-series data and clinical notes by prompting Large Language Models (LLMs) and align them with professional PrimeKG, ensuring consistency. In addition to triplet relationships, we incorporate entities' definitions and descriptions for richer semantics. The extracted knowledge is then used to generate task-relevant summaries of patients' health statuses. Finally, we fuse the summary with other modalities using an adaptive multimodal fusion network with cross-attention. Extensive experiments on the MIMIC-III and MIMIC-IV datasets' in-hospital mortality and 30-day readmission tasks demonstrate the superior performance of the EMERGE framework over baseline models. Comprehensive ablation studies and analysis highlight the efficacy of each designed module and robustness to data sparsity. EMERGE contributes to refining the utilization of multimodal EHR data in healthcare, bridging the gap with nuanced medical contexts essential for informed clinical predictions. We have publicly released the code at https://github.com/yhzhu99/EMERGE.|多模态电子健康记录（EHR）数据的整合显著提升了临床预测能力。现有的模型，尽管利用了临床笔记和多元时间序列EHR数据，但往往未能充分纳入必要的医学背景信息，以实现精准的临床任务；而先前基于知识图谱（KGs）的方法则主要集中在结构化知识的提取上。为此，我们提出了EMERGE，一个由检索增强生成（RAG）驱动框架，旨在提升多模态EHR预测建模。我们通过提示大型语言模型（LLMs）从时间序列数据和临床笔记中提取实体，并将其与专业的PrimeKG对齐，以确保一致性。除了三元组关系外，我们还纳入了实体的定义和描述，以丰富语义。提取的知识随后用于生成与任务相关的患者健康状况摘要。最后，我们利用具有交叉注意力的自适应多模态融合网络将该摘要与其他模态数据融合。在MIMIC-III和MIMIC-IV数据集上的住院死亡率和30天再入院任务的广泛实验表明，EMERGE框架优于基线模型。全面的消融研究和分析突显了每个设计模块的有效性及其对数据稀疏性的稳健性。EMERGE有助于优化多模态EHR数据在医疗领域的应用，弥合了与精细医学背景之间的差距，这对于精准的临床预测至关重要。我们已经公开发布了代码，地址为https://github.com/yhzhu99/EMERGE。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=EMERGE:+Enhancing+Multimodal+Electronic+Health+Records+Predictive+Modeling+with+Retrieval-Augmented+Generation)|0| |[Pairing Clustered Inverted Indexes with κ-NN Graphs for Fast Approximate Retrieval over Learned Sparse Representations](https://doi.org/10.1145/3627673.3679977)|Sebastian Bruch, Franco Maria Nardini, Cosimo Rulli, Rossano Venturini||Learned sparse representations form an effective and interpretable class of embeddings for text retrieval. While exact top-k retrieval over such embeddings faces efficiency challenges, a recent algorithm called Seismic has enabled remarkably fast, highly-accurate approximate retrieval. Seismic statically prunes inverted lists, organizes each list into geometrically-cohesive blocks, and augments each block with a summary vector. At query time, each inverted list associated with a query term is traversed one block at a time in an arbitrary order, with the inner product between the query and summaries determining if a block must be evaluated. When a block is deemed promising, its documents are fully evaluated with a forward index. Seismic is one to two orders of magnitude faster than state-of-the-art inverted index-based solutions and significantly outperforms the winning graph-based submissions to the BigANN 2023 Challenge. In this work, we speed up Seismic further by introducing two innovations to its query processing subroutine. First, we traverse blocks in order of importance, rather than arbitrarily. Second, we take the list of documents retrieved by Seismic and expand it to include the neighbors of each document using an offline k-regular nearest neighbor graph; the expanded list is then ranked to produce the final top-k set. Experiments on two public datasets show that our extension, named SeismicWave, can reach almost-exact accuracy levels and is up to 2.2x faster than Seismic.|学习到的稀疏表示为文本检索提供了一类有效且可解释的嵌入。尽管在这种嵌入上进行精确的top-k检索面临效率挑战，但最近的一种名为Seismic的算法实现了非常快速且高度精确的近似检索。Seismic静态地修剪倒排列表，将每个列表组织成几何上内聚的块，并为每个块增加一个摘要向量。在查询时，与查询词相关的每个倒排列表按任意顺序逐块遍历，通过查询与摘要之间的内积来确定是否需要评估该块。当一个块被认为有前景时，其文档会通过正向索引进行全面评估。Seismic比最先进的基于倒排索引的解决方案快一到两个数量级，并且在2023年BigANN挑战赛中显著优于基于图的获胜提交方案。在本研究中，我们通过在查询处理子程序中引入两项创新来进一步加速Seismic。首先，我们按重要性顺序遍历块，而不是任意顺序。其次，我们使用离线的k-正则最近邻图，将Seismic检索到的文档列表扩展为包含每个文档的邻居；然后对扩展后的列表进行排序以生成最终的top-k集合。在两个公开数据集上的实验表明，我们的扩展版本SeismicWave几乎可以达到完全精确的准确度，并且比Seismic快2.2倍。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Pairing+Clustered+Inverted+Indexes+with+κ-NN+Graphs+for+Fast+Approximate+Retrieval+over+Learned+Sparse+Representations)|0| |[PP4RNR: Popularity- and Position-Aware Contrastive Learning for Retrieval-Driven News Recommendation](https://doi.org/10.1145/3627673.3679979)|Wenwei Chen, Yewang Chen|College of Computer Science and Technology, Huaqiao University, Xiamen, China|Existing news recommendation systems often overlook the diversity of recommended content and exhibit popularity bias, resulting in suboptimal performance. To address this issue, this paper introduces a novel news recommendation approach, Popularity- and Position-Aware Contrastive Learning for Retrieval-Driven News Recommendation (PP4RNR). It consists of two modules: Entity-Level Retrieval Augmentation (ERA) and Popularity- and Position-Aware Contrastive Learning (PPCL). The ERA module utilizes both entities and titles to retrieve relevant news. Subsequently, retrieval-augmented news is fused with candidate news using our innovative cascaded attention network, leading to richer and more diverse news semantics. The PPCL module introduces perturbations in the news representation using a Gaussian perturbation vector based on the popularity and position information and then employs contrastive learning to regularize the representation space. Hence, this approach not only deepens the understanding of content diversity but also implicitly mitigates the popularity bias prevalent in current models. Rigorous testing on benchmark datasets demonstrates that our method significantly outperforms a range of state-of-the-art techniques.|现有的新闻推荐系统往往忽视推荐内容的多样性，并表现出流行度偏差，导致推荐效果不佳。为解决这一问题，本文提出了一种新颖的新闻推荐方法——基于检索的新闻推荐的流行度和位置感知对比学习（PP4RNR）。该方法包含两个模块：实体级检索增强（ERA）和流行度与位置感知的对比学习（PPCL）。ERA模块利用实体和标题来检索相关新闻。随后，通过我们创新的级联注意力网络将检索增强的新闻与候选新闻融合，从而丰富和多样化新闻语义。PPCL模块基于新闻的流行度和位置信息引入高斯扰动向量，对新闻表示进行扰动，然后利用对比学习来规范化表示空间。因此，这种方法不仅加深了对内容多样性的理解，还隐式地缓解了当前模型中普遍存在的流行度偏差。在基准数据集上的严格测试表明，我们的方法显著优于一系列最先进的技术。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=PP4RNR:+Popularity-+and+Position-Aware+Contrastive+Learning+for+Retrieval-Driven+News+Recommendation)|0| |[Exploiting Preferences in Loss Functions for Sequential Recommendation via Weak Transitivity](https://doi.org/10.1145/3627673.3679920)|Hyunsoo Chung, Jungtaek Kim, Hyungeun Jo, Hyungwon Choi||A choice of optimization objective is immensely pivotal in the design of a recommender system as it affects the general modeling process of a user's intent from previous interactions. Existing approaches mainly adhere to three categories of loss functions: pairwise, pointwise, and setwise loss functions. Despite their effectiveness, a critical and common drawback of such objectives is viewing the next observed item as a unique positive while considering all remaining items equally negative. Such a binary label assignment is generally limited to assuring a higher recommendation score of the positive item, neglecting potential structures induced by varying preferences between other unobserved items. To alleviate this issue, we propose a novel method that extends original objectives to explicitly leverage the different levels of preferences as relative orders between their scores. Finally, we demonstrate the superior performance of our method compared to baseline objectives.|优化目标的选择在推荐系统设计中极为关键，因为它影响从用户先前交互中对用户意图的一般建模过程。现有方法主要遵循三类损失函数：成对损失、逐点损失和集合损失函数。尽管这些方法有效，但它们的一个关键且普遍的缺点是将下一个观察到的项目视为唯一的正样本，而将所有剩余项目视为等同的负样本。这种二元标签分配通常仅限于确保正样本的推荐得分更高，而忽略了其他未观察项目之间因偏好差异所诱导的潜在结构。为解决这一问题，我们提出了一种新方法，将原始目标扩展为显式利用其得分之间的相对顺序来表示不同级别的偏好。最后，我们展示了我们的方法相对于基线目标的优越性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Exploiting+Preferences+in+Loss+Functions+for+Sequential+Recommendation+via+Weak+Transitivity)|0| |[RECE: Reduced Cross-Entropy Loss for Large-Catalogue Sequential Recommenders](https://doi.org/10.1145/3627673.3679986)|Danil Gusak, Gleb Mezentsev, Ivan V. Oseledets, Evgeny Frolov||Scalability is a major challenge in modern recommender systems. In sequential recommendations, full Cross-Entropy (CE) loss achieves state-of-the-art recommendation quality but consumes excessive GPU memory with large item catalogs, limiting its practicality. Using a GPU-efficient locality-sensitive hashing-like algorithm for approximating large tensor of logits, this paper introduces a novel RECE (REduced Cross-Entropy) loss. RECE significantly reduces memory consumption while allowing one to enjoy the state-of-the-art performance of full CE loss. Experimental results on various datasets show that RECE cuts training peak memory usage by up to 12 times compared to existing methods while retaining or exceeding performance metrics of CE loss. The approach also opens up new possibilities for large-scale applications in other domains.|可扩展性是现代推荐系统面临的主要挑战之一。在序列推荐中，全交叉熵（CE）损失实现了最先进的推荐质量，但在处理大型物品目录时会消耗过多的GPU内存，限制了其实用性。本文介绍了一种新的RECE（缩减交叉熵）损失，通过使用GPU高效的类似局部敏感哈希算法来近似大的logits张量。RECE显著减少了内存消耗，同时允许用户享受到全CE损失的最先进性能。在各种数据集上的实验结果表明，与现有方法相比，RECE将训练峰值内存使用量减少了高达12倍，同时保持或超过了CE损失的性能指标。该方法还为其他领域的大规模应用开辟了新的可能性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=RECE:+Reduced+Cross-Entropy+Loss+for+Large-Catalogue+Sequential+Recommenders)|0| -|[Enhanced Retrieval Effectiveness through Selective Query Generation](https://doi.org/10.1145/3627673.3679912)|Seyed Mohammad Hosseini, Negar Arabzadeh, Morteza Zihayat, Ebrahim Bagheri|Toronto Metropolitan University, Toronto, Ontario, Canada; University of Waterloo, Waterloo, Ontario, Canada|Prior research has demonstrated that reformulation of queries can significantly enhance retrieval effectiveness. Despite notable successes in neural-based query reformulation methods, identifying optimal reformulations that cover the same information need while enhancing retrieval effectiveness is still challenging. This paper introduces a two-step query reformulation framework for generating and selecting optimal target query variants which not only achieve higher retrieval performance but also preserve the original query's information need. Our comprehensive evaluations on the MS MARCO dataset and TREC Deep Learning tracks demonstrate substantial improvements over original query's performance.|先前研究表明，查询的重构可以显著提升检索效果。尽管基于神经网络的查询重构方法取得了显著成功，但识别出既能覆盖相同信息需求又能增强检索效果的最佳重构查询仍然具有挑战性。本文提出了一种两步走的查询重构框架，用于生成和选择最优的目标查询变体，这些变体不仅实现了更高的检索性能，还保留了原始查询的信息需求。我们在MS MARCO数据集和TREC深度学习赛道上的全面评估显示，相较于原始查询，性能有显著提升。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Enhanced+Retrieval+Effectiveness+through+Selective+Query+Generation)|0| +|[Enhanced Retrieval Effectiveness through Selective Query Generation](https://doi.org/10.1145/3627673.3679912)|Seyed Mohammad Hosseini, Negar Arabzadeh, Morteza Zihayat, Ebrahim Bagheri|University of Waterloo, Waterloo, Ontario, Canada; Toronto Metropolitan University, Toronto, Ontario, Canada|Prior research has demonstrated that reformulation of queries can significantly enhance retrieval effectiveness. Despite notable successes in neural-based query reformulation methods, identifying optimal reformulations that cover the same information need while enhancing retrieval effectiveness is still challenging. This paper introduces a two-step query reformulation framework for generating and selecting optimal target query variants which not only achieve higher retrieval performance but also preserve the original query's information need. Our comprehensive evaluations on the MS MARCO dataset and TREC Deep Learning tracks demonstrate substantial improvements over original query's performance.|先前研究表明，查询的重构可以显著提升检索效果。尽管基于神经网络的查询重构方法取得了显著成功，但识别出既能覆盖相同信息需求又能增强检索效果的最佳重构查询仍然具有挑战性。本文提出了一种两步走的查询重构框架，用于生成和选择最优的目标查询变体，这些变体不仅实现了更高的检索性能，还保留了原始查询的信息需求。我们在MS MARCO数据集和TREC深度学习赛道上的全面评估显示，相较于原始查询，性能有显著提升。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Enhanced+Retrieval+Effectiveness+through+Selective+Query+Generation)|0| |[Post-Training Embedding Enhancement for Long-Tail Recommendation](https://doi.org/10.1145/3627673.3679978)|Geon Lee, Kyungho Kim, Kijung Shin|KAIST, Seoul, Republic of Korea|Item popularity in real-world data follows a long-tail distribution, where a few items attract most of the attention, while the majority receive much less. This disparity results in high-quality embeddings for popular (head) items, but lower-quality embeddings for unpopular (tail) items, leading to less accurate recommendations for the latter. Our observations confirm that embeddings of tail items often exhibit (1) magnitudes (i.e., norms) that are less reflective of actual popularity and (2) directions that are less effective in capturing user preferences, compared to those of head items. To address this issue, we propose EDGE, a post-training embedding enhancement method for long-tail recommendations. EDGE employs two key strategies: (1) refining embedding magnitudes to better reflect item popularity and (2) adjusting embedding directions by leveraging knowledge from head items. Importantly, EDGE is model-agnostic and can be applied to embeddings learned from any trained recommender system. Experimental results show that EDGE significantly improves tail item recommendation performance and overall system performance, achieving up to an improvement of 211.23% in NDCG@20 over the state-of-the-art method. Our code and datasets are available at https://github.com/geon0325/EDGE.|现实世界数据中的物品流行度遵循长尾分布，其中少数物品吸引了大部分关注，而大多数物品则受到较少的关注。这种差异导致流行（头部）物品的嵌入质量较高，但不流行（尾部）物品的嵌入质量较低，从而使得后者的推荐准确性降低。我们的观察证实了尾部物品的嵌入通常表现出（1）范数（即模）不太能反映实际流行度，以及（2）方向不太能有效捕捉用户偏好，相比头部物品的嵌入。为了解决这一问题，我们提出了EDGE，一种用于长尾推荐的后训练嵌入增强方法。EDGE采用两种关键策略：（1）优化嵌入范数以更好地反映物品流行度，以及（2）通过利用头部物品的知识来调整嵌入方向。重要的是，EDGE与模型无关，可以应用于从任何训练好的推荐系统中学习到的嵌入。实验结果表明，EDGE显著提升了尾部物品的推荐性能和整体系统性能，在NDCG@20指标上相比最先进的方法提升了高达211.23%。我们的代码和数据集可在https://github.com/geon0325/EDGE获取。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Post-Training+Embedding+Enhancement+for+Long-Tail+Recommendation)|0| |[Scalable and Adaptive Spectral Embedding for Attributed Graph Clustering](https://doi.org/10.1145/3627673.3679992)|Yunhui Liu, Tieke He, Qing Wu, Tao Zheng, Jianhua Zhao||Attributed graph clustering, which aims to group the nodes of an attributed graph into disjoint clusters, has made promising advancements in recent years. However, most existing methods face challenges when applied to large graphs due to the expensive computational cost and high memory usage. In this paper, we introduce Scalable and Adaptive Spectral Embedding (SASE), a simple attributed graph clustering method devoid of parameter learning. SASE comprises three main components: node features smoothing via $k$-order simple graph convolution, scalable spectral clustering using random Fourier features, and adaptive order selection. With these designs, SASE not only effectively captures global cluster structures but also exhibits linear time and space complexity relative to the graph size. Empirical results demonstrate the superiority of SASE. For example, on the ArXiv dataset with 169K nodes and 1.17M edges, SASE achieves a 6.9\% improvement in ACC and a $5.87\times$ speedup compared to the runner-up, S3GC.|属性图聚类旨在将属性图的节点分组为不相交的集群，近年来取得了显著进展。然而，大多数现有方法在应用于大规模图时面临挑战，主要是因为计算成本高且内存使用量大。本文提出了一种名为可扩展自适应谱嵌入（SASE）的简单属性图聚类方法，该方法无需参数学习。SASE包含三个主要组件：通过$k$阶简单图卷积进行节点特征平滑、使用随机傅里叶特征的可扩展谱聚类以及自适应阶数选择。这些设计使得SASE不仅能够有效捕捉全局聚类结构，而且相对于图的大小表现出线性的时间和空间复杂度。实证结果表明，SASE具有优越性。例如，在拥有169K节点和1.17M边的ArXiv数据集上，SASE在ACC上比次优的S3GC提高了6.9%，并且速度提升了$5.87$倍。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Scalable+and+Adaptive+Spectral+Embedding+for+Attributed+Graph+Clustering)|0| |[P-Rank+: A Scalable Efficient P-Rank Search Algorithm](https://doi.org/10.1145/3627673.3679976)|Maoyin Zhang, Weiren Yu|Warwick University, Coventry, United Kingdom; Nanjing University of Sci. & Tech., Jiangsu, China|P-Rank (Penetrating-Rank) is a charming measure of structural similarity between objects based on graph topology. It recursively follows the principle that "two objects are considered similar if (a) they are referenced by similar objects and (b) they reference similar objects''. The best-known algorithm for computing P-Rank employs two repeated Singular Value Decompositions (SVDs) coupled with the Woodbury matrix identity. However, this method does not scale well on billion-sized graphs. Worse yet, this algorithm only provides a linear approximation of the P-Rank model and cannot deliver accurate P-Rank values. In this paper, we propose P-Rank+, a fast and efficient algorithm for computing P-Rank similarities, which scales well on large graphs with billions of edges. P-Rank+ leverages dimensionality reduction techniques by performing only one SVD of the graph integrated with Hadamard products in the reduced subspace. Moreover, we provide provable error guarantees for P-Rank+ computation. Experiments on various datasets validate that P-Rank+ is 1--3 orders of magnitude faster than the best-known competitor while achieving excellent scalability on massive graphs.|P-Rank（渗透排序）是一种基于图拓扑结构的对象间结构相似性的迷人度量方法。它递归地遵循以下原则：“如果两个对象（a）被相似的对象引用，并且（b）引用相似的对象，则认为它们是相似的”。计算P-Rank最著名的算法采用了两个重复的奇异值分解（SVD），并与Woodbury矩阵恒等式相结合。然而，这种方法在处理十亿级大小的图时扩展性不佳。更糟糕的是，该算法仅提供了P-Rank模型的线性近似，无法提供精确的P-Rank值。在本文中，我们提出了P-Rank+，一种快速且高效的计算P-Rank相似性的算法，该算法在拥有数十亿条边的大型图上具有良好的扩展性。P-Rank+通过在降维子空间中执行一次图的SVD结合Hadamard积来利用降维技术。此外，我们为P-Rank+的计算提供了可证明的误差保证。在各种数据集上的实验验证了P-Rank+比已知的最优竞争对手快1到3个数量级，同时在大型图上表现出卓越的可扩展性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=P-Rank+:+A+Scalable+Efficient+P-Rank+Search+Algorithm)|0| |[Learning the Dynamics in Sequential Recommendation by Exploiting Real-time Information](https://doi.org/10.1145/3627673.3679955)|Rujiao Zhang, Hao Zhang, Yucong Luo, Zhiding Liu, Mingyue Cheng, Qi Liu, Enhong Chen||Sequential recommender systems offer personalized suggestions by modeling users' interactions chronologically to capture dynamic user interest. Existing approaches typically fail to adequately describe the dynamics of the entire recommender system, including shifts in both user interest and item availability. To address this, we propose a simple yet effective framework with three key perspectives, tailored to the dynamics of recommender system by fully exploiting the time information. Firstly, we propose a dynamic candidate set construction approach to prevent the model from learning future interactions. Secondly, assuming that user behaviors remain consistent over short terms but may evolve over long terms, we employ a interval-weighted optimization target to model the correlation of users' historical interactions. Finally, we introduce a specialized time-aware attention module to enhance recommendations within specific temporal contexts. Extensive experiments demonstrate the effectiveness and generalizability of our framework. We make our codes publicly available.|顺序推荐系统通过按时间顺序建模用户的交互来捕捉动态用户兴趣，从而提供个性化建议。现有的方法通常未能充分描述整个推荐系统的动态变化，包括用户兴趣和物品可用性的变化。为了解决这一问题，我们提出了一个简单而有效的框架，该框架从三个关键角度出发，充分利用时间信息来适应推荐系统的动态变化。首先，我们提出了一种动态候选集构建方法，以防止模型学习未来的交互。其次，假设用户行为在短期内保持一致，但在长期内可能发生变化，我们采用了一种区间加权优化目标来建模用户历史交互的相关性。最后，我们引入了一个专门的时间感知注意力模块，以增强在特定时间上下文中的推荐效果。大量实验证明了我们框架的有效性和普适性。我们将代码公开发布。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Learning+the+Dynamics+in+Sequential+Recommendation+by+Exploiting+Real-time+Information)|0| |[VIER: Visual Imagination Enhanced Retrieval in Sponsored Search](https://doi.org/10.1145/3627673.3680005)|Yadong Zhang, Yuqing Song, Siyu Lu, Qiang Liu, Xingxing Wang|Meituan, Beijing, China|Embedding-based Retrieval (EBR) has been a fundamental component in sponsored-search systems, which retrieves high-quality products for the user's search query by encoding the information of the query, user and product into dense embeddings. However, due to the characteristic of location-based service, the user input queries suffer from two extremes: overly brief queries with vague intentions and lengthy queries with substantial noise, both of which make it challenging to discern the exact user search intent. In fact, the e-consumers typically have a mental imagery of the product they intend to search for, reflecting their specific purchasing intentions. In this paper, we propose a Visual Imagination Enhanced Retrieval model (VIER) to explore the implicit imagery of users. Specifically, we design a visual imagination network to reconstruct the imagery embeddings that capture both coarse-grained query commonalities and fine-grained user personalities. These pseudo-image representations are integrated with the query and user behavior to enhance the understanding of user search intentions for improved retrieval. According to online A/B tests on Meituan sponsored-search system, our method significantly outperforms baselines in terms of revenue, clicks and click-through rate.|基于嵌入的检索（EBR）已成为赞助搜索系统中的基础组件，通过将查询、用户和产品的信息编码为密集嵌入，检索出高质量的产品以满足用户的搜索需求。然而，由于基于位置服务的特性，用户输入的查询呈现出两种极端：过于简短且意图模糊的查询，以及冗长但包含大量噪音的查询，这两者都使得准确识别用户的搜索意图变得困难。实际上，电子消费者通常对其意图搜索的产品有一个心理意象，这反映了他们特定的购买意向。在本文中，我们提出了一种视觉想象力增强的检索模型（VIER），以探索用户的隐含意象。具体而言，我们设计了一个视觉想象力网络，用于重建捕捉粗粒度查询共性和细粒度用户个性的意象嵌入。这些伪图像表示与查询和用户行为相结合，以增强对用户搜索意图的理解，从而改进检索效果。根据在美团赞助搜索系统上的在线A/B测试结果，我们的方法在收入、点击量和点击率方面显著优于基线方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=VIER:+Visual+Imagination+Enhanced+Retrieval+in+Sponsored+Search)|0| |[Transforming Location Retrieval at Airbnb: A Journey from Heuristics to Reinforcement Learning](https://doi.org/10.1145/3627673.3680089)|Dillon Davis, Huiji Gao, Thomas Legrand, Malay Haldar, Alex Deng, Han Zhao, Liwei He, Sanjeev Katariya||The Airbnb search system grapples with many unique challenges as it continues to evolve. We oversee a marketplace that is nuanced by geography, diversity of homes, and guests with a variety of preferences. Crafting an efficient search system that can accommodate diverse guest needs, while showcasing relevant homes lies at the heart of Airbnb's success. Airbnb search has many challenges that parallel other recommendation and search systems but it has a unique information retrieval problem, upstream of ranking, called location retrieval. It requires defining a topological map area that is relevant to the searched query for homes listing retrieval. The purpose of this paper is to demonstrate the methodology, challenges, and impact of building a machine learning based location retrieval product from the ground up. Despite the lack of suitable, prevalent machine learning based approaches, we tackle cold start, generalization, differentiation and algorithmic bias. We detail the efficacy of heuristics, statistics, machine learning, and reinforcement learning approaches to solve these challenges, particularly for systems that are often unexplored by current literature.|随着不断发展，Airbnb搜索系统面临着许多独特的挑战。我们管理着一个由地理位置、房屋多样性和具有各种偏好的客人所构成的复杂市场。打造一个能够满足不同客人需求的高效搜索系统，同时展示相关房屋，是Airbnb成功的核心。Airbnb搜索系统面临许多与其他推荐和搜索系统相似的挑战，但它有一个独特的信息检索问题，即在排序之前的位置检索。这需要定义一个与搜索查询相关的拓扑地图区域，以便进行房屋列表检索。本文旨在展示从头构建一个基于机器学习的位置检索产品的过程、挑战及其影响。尽管缺乏合适且普遍的基于机器学习的方法，我们解决了冷启动、泛化、差异化和算法偏差等问题。我们详细介绍了启发式、统计学、机器学习和强化学习方法在这些挑战中的有效性，特别是针对当前文献中较少探索的系统。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Transforming+Location+Retrieval+at+Airbnb:+A+Journey+from+Heuristics+to+Reinforcement+Learning)|0| -|[Pareto-based Multi-Objective Recommender System with Forgetting Curve](https://doi.org/10.1145/3627673.3680080)|Jipeng Jin, Zhaoxiang Zhang, Zhiheng Li, Xiaofeng Gao, Xiongwen Yang, Lei Xiao, Jie Jiang|Shanghai Jiao Tong University; |Recommender systems with cascading architecture play an increasinglysignificant role in online recommendation platforms, where the approach todealing with negative feedback is a vital issue. For instance, in short videoplatforms, users tend to quickly slip away from candidates that they feelaversive, and recommender systems are expected to receive these explicitnegative feedbacks and make adjustments to avoid these recommendations.Considering recency effect in memories, we propose a forgetting model based onEbbinghaus Forgetting Curve to cope with negative feedback. In addition, weintroduce a Pareto optimization solver to guarantee a better trade-off betweenrecency and model performance. In conclusion, we propose Pareto-basedMulti-Objective Recommender System with forgetting curve (PMORS), which can beapplied to any multi-objective recommendation and show sufficiently superioritywhen facing explicit negative feedback. We have conducted evaluations of PMORSand achieved favorable outcomes in short-video scenarios on both public datasetand industrial dataset. After being deployed on an online short video platformnamed WeChat Channels in May, 2023, PMORS has not only demonstrated promisingresults for both consistency and recency but also achieved an improvement of upto +1.45|具有级联架构的推荐系统在在线推荐平台中扮演着越来越重要的角色，其中如何处理负面反馈是一个关键问题。例如，在短视频平台中，用户往往会对感到不喜欢的候选内容迅速失去兴趣，推荐系统需要接收这些明确的负面反馈并进行调整，以避免此类推荐。考虑到记忆中的时效性效应，我们提出了一种基于艾宾浩斯遗忘曲线的遗忘模型来处理负面反馈。此外，我们引入了一种帕累托优化求解器，以确保在时效性和模型性能之间取得更好的平衡。综上所述，我们提出了基于帕累托的多目标推荐系统（PMORS），该系统可以应用于任何多目标推荐，并且在面对明确的负面反馈时表现出足够的优越性。我们对PMORS进行了评估，并在公共数据集和工业数据集的短视频场景中取得了良好的结果。自2023年5月在名为微信视频号的在线短视频平台上部署以来，PMORS不仅在一致性和时效性方面展示了有前景的结果，还实现了高达+1.45的改进。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Pareto-based+Multi-Objective+Recommender+System+with+Forgetting+Curve)|0| -|[Ads Supply Personalization via Doubly Robust Learning](https://doi.org/10.1145/3627673.3680035)|Wei Shi, Chen Fu, Qi Xu, Sanjian Chen, Jizhe Zhang, Qinqin Zhu, Zhigang Hua, Shuang Yang|Meta Platforms, Inc., Menlo Park, CA, USA; Meta Platforms, Inc., Sunnyvale, CA, USA|Ads supply personalization aims to balance the revenue and user engagement, two long-term objectives in social media ads, by tailoring the ad quantity and density. In the industry-scale system, the challenge for ads supply lies in modeling the counterfactual effects of a conservative supply treatment (e.g., a small density change) over an extended duration. In this paper, we present a streamlined framework for personalized ad supply. This framework optimally utilizes information from data collection policies through the doubly robust learning. Consequently, it significantly improves the accuracy of long-term treatment effect estimates. Additionally, its low-complexity design not only results in computational cost savings compared to existing methods, but also makes it scalable for billion-scale applications. Through both offline experiments and online production tests, the framework consistently demonstrated significant improvements in top-line business metrics over months. The framework has been fully deployed to live traffic in one of the world's largest social media platforms.|广告供应个性化旨在通过调整广告数量和密度，平衡社交媒体广告中的两个长期目标：收入和用户参与度。在行业规模的系统中，广告供应的挑战在于对保守供应处理（例如，小幅密度变化）在长时间内的反事实效应进行建模。本文提出了一种简化的个性化广告供应框架。该框架通过双重稳健学习最优地利用数据收集策略中的信息，从而显著提高了长期处理效应估计的准确性。此外，其低复杂度的设计不仅在计算成本上优于现有方法，还使其适用于亿级规模的应用。通过离线实验和在线生产测试，该框架在数月内持续显示出对业务关键指标的显著改进。该框架已全面部署到全球最大社交媒体平台之一的实时流量中。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Ads+Supply+Personalization+via+Doubly+Robust+Learning)|0| -|[DivNet: Diversity-Aware Self-Correcting Sequential Recommendation Networks](https://doi.org/10.1145/3627673.3680059)|Shuai Xiao, Zaifan Jiang|Alibaba Group, Beijing, China; Alibaba Group, Shanghai, China|As the last stage of a typical recommendation system, collective recommendation aims to give the final touches to the recommended items and their layout so as to optimize overall objectives such as diversity and whole-page relevance. In practice, however, the interaction dynamics among the recommended items, their visual appearances and meta-data such as specifications are often too complex to be captured by experts' heuristics or simple models. To address this issue, we propose a div ersity-aware self-correcting sequential recommendation net works (DivNet) that is able to estimate utility by capturing the complex interactions among sequential items and diversify recommendations simultaneously. Experiments on both offline and online settings demonstrate that DivNet can achieve better results compared to baselines with or without collective recommendations.|在典型的推荐系统的最后一个阶段，集体推荐旨在对推荐项目及其布局进行最后的调整，以优化多样性和整个页面的相关性等总体目标。然而，在实践中，推荐项目之间的交互动态、它们的视觉外观和规格等元数据往往过于复杂，难以被专家的启发式方法或简单的模型捕捉。为了解决这个问题，我们提出了一种多样性感知的自校正序列推荐网络（DivNet），它能够通过捕捉序列项目之间的复杂交互来估计效用，并同时实现推荐项目的多样化。在离线和在线设置中的实验表明，与有无集体推荐的基线相比，DivNet能够取得更好的结果。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=DivNet:+Diversity-Aware+Self-Correcting+Sequential+Recommendation+Networks)|0| +|[Pareto-based Multi-Objective Recommender System with Forgetting Curve](https://doi.org/10.1145/3627673.3680080)|Jipeng Jin, Zhaoxiang Zhang, Zhiheng Li, Xiaofeng Gao, Xiongwen Yang, Lei Xiao, Jie Jiang|; Shanghai Jiao Tong University|Recommender systems with cascading architecture play an increasinglysignificant role in online recommendation platforms, where the approach todealing with negative feedback is a vital issue. For instance, in short videoplatforms, users tend to quickly slip away from candidates that they feelaversive, and recommender systems are expected to receive these explicitnegative feedbacks and make adjustments to avoid these recommendations.Considering recency effect in memories, we propose a forgetting model based onEbbinghaus Forgetting Curve to cope with negative feedback. In addition, weintroduce a Pareto optimization solver to guarantee a better trade-off betweenrecency and model performance. In conclusion, we propose Pareto-basedMulti-Objective Recommender System with forgetting curve (PMORS), which can beapplied to any multi-objective recommendation and show sufficiently superioritywhen facing explicit negative feedback. We have conducted evaluations of PMORSand achieved favorable outcomes in short-video scenarios on both public datasetand industrial dataset. After being deployed on an online short video platformnamed WeChat Channels in May, 2023, PMORS has not only demonstrated promisingresults for both consistency and recency but also achieved an improvement of upto +1.45|具有级联架构的推荐系统在在线推荐平台中扮演着越来越重要的角色，其中如何处理负面反馈是一个关键问题。例如，在短视频平台中，用户往往会对感到不喜欢的候选内容迅速失去兴趣，推荐系统需要接收这些明确的负面反馈并进行调整，以避免此类推荐。考虑到记忆中的时效性效应，我们提出了一种基于艾宾浩斯遗忘曲线的遗忘模型来处理负面反馈。此外，我们引入了一种帕累托优化求解器，以确保在时效性和模型性能之间取得更好的平衡。综上所述，我们提出了基于帕累托的多目标推荐系统（PMORS），该系统可以应用于任何多目标推荐，并且在面对明确的负面反馈时表现出足够的优越性。我们对PMORS进行了评估，并在公共数据集和工业数据集的短视频场景中取得了良好的结果。自2023年5月在名为微信视频号的在线短视频平台上部署以来，PMORS不仅在一致性和时效性方面展示了有前景的结果，还实现了高达+1.45的改进。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Pareto-based+Multi-Objective+Recommender+System+with+Forgetting+Curve)|0| +|[Ads Supply Personalization via Doubly Robust Learning](https://doi.org/10.1145/3627673.3680035)|Wei Shi, Chen Fu, Qi Xu, Sanjian Chen, Jizhe Zhang, Qinqin Zhu, Zhigang Hua, Shuang Yang|Meta Platforms, Inc., Sunnyvale, CA, USA; Meta Platforms, Inc., Menlo Park, CA, USA|Ads supply personalization aims to balance the revenue and user engagement, two long-term objectives in social media ads, by tailoring the ad quantity and density. In the industry-scale system, the challenge for ads supply lies in modeling the counterfactual effects of a conservative supply treatment (e.g., a small density change) over an extended duration. In this paper, we present a streamlined framework for personalized ad supply. This framework optimally utilizes information from data collection policies through the doubly robust learning. Consequently, it significantly improves the accuracy of long-term treatment effect estimates. Additionally, its low-complexity design not only results in computational cost savings compared to existing methods, but also makes it scalable for billion-scale applications. Through both offline experiments and online production tests, the framework consistently demonstrated significant improvements in top-line business metrics over months. The framework has been fully deployed to live traffic in one of the world's largest social media platforms.|广告供应个性化旨在通过调整广告数量和密度，平衡社交媒体广告中的两个长期目标：收入和用户参与度。在行业规模的系统中，广告供应的挑战在于对保守供应处理（例如，小幅密度变化）在长时间内的反事实效应进行建模。本文提出了一种简化的个性化广告供应框架。该框架通过双重稳健学习最优地利用数据收集策略中的信息，从而显著提高了长期处理效应估计的准确性。此外，其低复杂度的设计不仅在计算成本上优于现有方法，还使其适用于亿级规模的应用。通过离线实验和在线生产测试，该框架在数月内持续显示出对业务关键指标的显著改进。该框架已全面部署到全球最大社交媒体平台之一的实时流量中。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Ads+Supply+Personalization+via+Doubly+Robust+Learning)|0| +|[DivNet: Diversity-Aware Self-Correcting Sequential Recommendation Networks](https://doi.org/10.1145/3627673.3680059)|Shuai Xiao, Zaifan Jiang|Alibaba Group, Shanghai, China; Alibaba Group, Beijing, China|As the last stage of a typical recommendation system, collective recommendation aims to give the final touches to the recommended items and their layout so as to optimize overall objectives such as diversity and whole-page relevance. In practice, however, the interaction dynamics among the recommended items, their visual appearances and meta-data such as specifications are often too complex to be captured by experts' heuristics or simple models. To address this issue, we propose a div ersity-aware self-correcting sequential recommendation net works (DivNet) that is able to estimate utility by capturing the complex interactions among sequential items and diversify recommendations simultaneously. Experiments on both offline and online settings demonstrate that DivNet can achieve better results compared to baselines with or without collective recommendations.|在典型的推荐系统的最后一个阶段，集体推荐旨在对推荐项目及其布局进行最后的调整，以优化多样性和整个页面的相关性等总体目标。然而，在实践中，推荐项目之间的交互动态、它们的视觉外观和规格等元数据往往过于复杂，难以被专家的启发式方法或简单的模型捕捉。为了解决这个问题，我们提出了一种多样性感知的自校正序列推荐网络（DivNet），它能够通过捕捉序列项目之间的复杂交互来估计效用，并同时实现推荐项目的多样化。在离线和在线设置中的实验表明，与有无集体推荐的基线相比，DivNet能够取得更好的结果。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=DivNet:+Diversity-Aware+Self-Correcting+Sequential+Recommendation+Networks)|0| |[Enhancing Playback Performance in Video Recommender Systems with an On-Device Gating and Ranking Framework](https://doi.org/10.1145/3627673.3680076)|Yunfei Yang, Zhenghao Qi, Honghuan Wu, Qi Song, Tieyao Zhang, Hao Li, Yimin Tu, Kaiqiao Zhan, Ben Wang||Video recommender systems (RSs) have gained increasing attention in recent years. Existing mainstream RSs focus on optimizing the matching function between users and items. However, we noticed that users frequently encounter playback issues such as slow loading or stuttering while browsing the videos, especially in weak network conditions, which will lead to a subpar browsing experience, and may cause users to leave, even when the video content and recommendations are superior. It is quite a serious issue, yet easily overlooked. To tackle this issue, we propose an on-device Gating and Ranking Framework (GRF) that cooperates with server-side RS. Specifically, we utilize a gate model to identify videos that may have playback issues in real-time, and then we employ a ranking model to select the optimal result from a locally-cached pool to replace the stuttering videos. Our solution has been fully deployed on Kwai, a large-scale short video platform with hundreds of millions of users globally. Moreover, it significantly enhances video playback performance and improves overall user experience and retention rates.|视频推荐系统（RSs）近年来受到了越来越多的关注。现有的主流RSs专注于优化用户与项目之间的匹配函数。然而，我们注意到用户在浏览视频时经常遇到播放问题，如加载缓慢或卡顿，尤其是在网络条件较差的情况下，这将导致浏览体验不佳，甚至可能使用户流失，即使视频内容和推荐本身是优质的。这是一个相当严重但容易被忽视的问题。为了解决这个问题，我们提出了一个设备端门控与排序框架（GRF），该框架与服务器端RS协同工作。具体来说，我们利用门控模型实时识别可能存在播放问题的视频，然后使用排序模型从本地缓存池中选择最佳结果来替换卡顿的视频。我们的解决方案已在快手这一全球拥有数亿用户的大型短视频平台上全面部署。此外，它显著提升了视频播放性能，并改善了整体用户体验和留存率。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Enhancing+Playback+Performance+in+Video+Recommender+Systems+with+an+On-Device+Gating+and+Ranking+Framework)|0| |[An Enhanced Batch Query Architecture in Real-time Recommendation](https://doi.org/10.1145/3627673.3680034)|Qiang Zhang, Zhipeng Teng, Disheng Wu, Jiayin Wang||In industrial recommendation systems on websites and apps, it is essential to recall and predict top-n results relevant to user interests from a content pool of billions within milliseconds. To cope with continuous data growth and improve real-time recommendation performance, we have designed and implemented a high-performance batch query architecture for real-time recommendation systems. Our contributions include optimizing hash structures with a cacheline-aware probing method to enhance coalesced hashing, as well as the implementation of a hybrid storage key-value service built upon it. Our experiments indicate this approach significantly surpasses conventional hash tables in batch query throughput, achieving up to 90 of random memory access when incorporating parallel optimization. The support for NVMe, integrating two-tier storage for hot and cold data, notably reduces resource consumption. Additionally, the system facilitates dynamic updates, automated sharding of attributes and feature embedding tables, and introduces innovative protocols for consistency in batch queries, thereby enhancing the effectiveness of real-time incremental learning updates. This architecture has been deployed and in use in the bilibili recommendation system for over a year, a video content community with hundreds of millions of users, supporting 10x increase in model computation with minimal resource growth, improving outcomes while preserving the system's real-time performance.|在网站和应用的工业推荐系统中，从数十亿内容库中毫秒级召回并预测与用户兴趣相关的前N个结果至关重要。为应对持续的数据增长并提升实时推荐性能，我们设计并实现了一种高性能的实时推荐系统批量查询架构。我们的贡献包括通过缓存行感知的探测方法优化哈希结构以增强聚合哈希，以及基于此构建的混合存储键值服务。实验表明，该方法在批量查询吞吐量方面显著超越传统哈希表，结合并行优化时随机内存访问可达90%。对NVMe的支持，结合冷热数据的两级存储，显著降低了资源消耗。此外，系统支持动态更新、属性和特征嵌入表的自动分片，并引入了创新的批量查询一致性协议，从而提升了实时增量学习更新的效果。该架构已在拥有数亿用户的视频内容社区bilibili推荐系统中部署并使用超过一年，支持模型计算量10倍增长的同时资源增长最小化，既提升了推荐效果又保持了系统的实时性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=An+Enhanced+Batch+Query+Architecture+in+Real-time+Recommendation)|0| -|[Voting with Generative AI for German Compound Splitting in E-commerce Search](https://doi.org/10.1145/3627673.3679074)|Ümit Yilmaz, Kilian Merkelbach, Daniel Stein, Hasan Oezkan|eBay Inc., Dreilinden, Germany; eBay Inc., Aachen, Germany|Compound words are a grammatical structure that allows forming new words by composing existing words. For e-commerce search in German, it is essential to split these compounds into meaningful parts because item titles often use the joint form while search queries are often split. We propose a method for German compound splitting leveraging a large language model (LLM) with a voting mechanism and a hyperparameter search for automatically optimizing prompt and parameter combinations. Our evaluation of the proposed method on human-created gold standard data for e-commerce shows that it outperforms existing methods for compound splitting in this domain.|复合词是一种语法结构，通过组合现有词汇来形成新词。在德语的电子商务搜索中，将这些复合词拆分为有意义的组成部分至关重要，因为商品标题通常使用联合形式，而搜索查询则通常是拆分后的形式。我们提出了一种利用大型语言模型（LLM）进行德语复合词拆分的方法，该方法结合了投票机制和超参数搜索，以自动优化提示和参数组合。我们对所提出的方法在人工创建的电子商务金标准数据上的评估显示，它在复合词拆分方面优于现有的方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Voting+with+Generative+AI+for+German+Compound+Splitting+in+E-commerce+Search)|0| -|[AI Agent for Information Retrieval: Generating and Ranking](https://doi.org/10.1145/3627673.3680120)|Yongfeng Zhang, Zhiwei Liu, Qingsong Wen, Linsey Pang, Wei Liu, Philip S. Yu|University of Illinois at Chicago, Chicago, IL, USA; Salesforce AI Research, Palo Alto, CA, USA; Salesforce, San Francisco, CA, USA; Squirrel Ai Learning, Seattle, WA, USA; Rutgers University, New Brunswick, NJ, USA; University of Technology Sydney, Sydney, NSW, Australia|The field of information retrieval has significantly transformed with the integration of AI technologies. AI agents, especially those leveraging LLMs and vast computational power, have revolutionized information retrieval, processing, and presentation. LLM agents, with advanced memory, reasoning, and planning capabilities, can perform complex tasks, engage in coherent conversations, and provide personalized responses. Despite these advancements, challenges such as ensuring relevance and accuracy, mitigating biases, providing real-time responses, and maintaining data security remain. This workshop aims to explore these challenges, share innovative solutions, and discuss future directions. It will provide a platform to bring together researchers, practitioners to discuss the latest theoretical advancements and practical implementations of AI agents in information retrieval. Topics include AI in search, recommendation, and personalization systems. By gathering a diverse group of experts, the workshop seeks to deepen the understanding of AI agents in information retrieval, advance the field, and enhance its societal impact. Participants will gain insights into cutting-edge research, emerging trends, and foster knowledge exchange and collaboration within the community.|信息检索领域随着AI技术的融合发生了显著变革。AI代理，尤其是那些利用大型语言模型（LLMs）和强大计算能力的代理，已经彻底改变了信息检索、处理和呈现的方式。具备先进记忆、推理和规划能力的LLM代理能够执行复杂任务、进行连贯对话并提供个性化响应。尽管取得了这些进展，但仍面临确保相关性和准确性、减轻偏见、提供实时响应以及维护数据安全等挑战。本次研讨会旨在探讨这些挑战，分享创新解决方案，并讨论未来的发展方向。研讨会将提供一个平台，让研究人员和从业者能够讨论AI代理在信息检索中最新的理论进展和实际应用。主题包括AI在搜索、推荐和个人化系统中的应用。通过汇集多元化的专家群体，研讨会旨在深化对信息检索中AI代理的理解，推动该领域的发展，并增强其社会影响力。参与者将获得关于尖端研究、新兴趋势的见解，并促进社区内的知识交流与合作。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=AI+Agent+for+Information+Retrieval:+Generating+and+Ranking)|0| -|[UniEmbedding: Learning Universal Multi-Modal Multi-Domain Item Embeddings via User-View Contrastive Learning](https://doi.org/10.1145/3627673.3680098)|Boqi Dai, Zhaocheng Du, Jieming Zhu, Jintao Xu, Deqing Zou, Quanyu Dai, Zhenhua Dong, Rui Zhang, HaiTao Zheng|Shenzhen International Graduate School, Tsinghua University & Pengcheng Laboratory, Shenzhen, China; Huazhong University of Science and Technology, Shenzhen, China; Huawei Noah's Ark Lab, Shenzhen, China; Shenzhen International Graduate School, Tsinghua University, Shenzhen, China|Learning high-quality item embeddings is crucial for recommendation tasks such as matching and ranking. However, existing methods often rely on ID-based item embeddings learned end-to-end with downstream recommendation models, which may suffer from overfitting and limited generalizability. In this paper, we aim to learn universal item embeddings (dubbed UniEmbedding) that capture multi-modal semantics, generalize across multiple domains, and serve different downstream tasks. To achieve this goal, we introduce the UniEmbedding pretraining framework, which includes three modules: a domain-aware multi-modal adapter, a user-view projection module, and contrastive learning objectives across domains. Compared to naive ID embeddings, UniEmbedding provides rich semantic information that generalizes more effectively across domains. Unlike multi-modal embeddings directly extracted from off-the-shelf pretrained models, UniEmbedding achieves better alignment between content semantics and behaviors. We evaluated UniEmbedding on both public and industrial datasets, demonstrating its effectiveness in matching and ranking tasks. Furthermore, UniEmbedding has been deployed in multiple recommendation applications at Huawei, resulting in significant gains in user engagement metrics.|学习高质量的物品嵌入对于匹配和排序等推荐任务至关重要。然而，现有方法通常依赖于基于ID的物品嵌入，这些嵌入与下游推荐模型端到端学习，可能会遭受过拟合和泛化能力有限的问题。本文旨在学习一种通用的物品嵌入（称为UniEmbedding），这种嵌入能够捕捉多模态语义，跨多个领域泛化，并服务于不同的下游任务。为实现这一目标，我们引入了UniEmbedding预训练框架，该框架包括三个模块：领域感知的多模态适配器、用户视角投影模块以及跨领域的对比学习目标。与简单的ID嵌入相比，UniEmbedding提供了更丰富的语义信息，能更有效地跨领域泛化。与直接从现成的预训练模型中提取的多模态嵌入不同，UniEmbedding在内容语义和行为之间实现了更好的对齐。我们在公共和工业数据集上评估了UniEmbedding，证明了其在匹配和排序任务中的有效性。此外，UniEmbedding已在华为的多个推荐应用中部署，显著提升了用户参与度指标。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=UniEmbedding:+Learning+Universal+Multi-Modal+Multi-Domain+Item+Embeddings+via+User-View+Contrastive+Learning)|0| +|[Voting with Generative AI for German Compound Splitting in E-commerce Search](https://doi.org/10.1145/3627673.3679074)|Ümit Yilmaz, Kilian Merkelbach, Daniel Stein, Hasan Oezkan|eBay Inc., Aachen, Germany; eBay Inc., Dreilinden, Germany|Compound words are a grammatical structure that allows forming new words by composing existing words. For e-commerce search in German, it is essential to split these compounds into meaningful parts because item titles often use the joint form while search queries are often split. We propose a method for German compound splitting leveraging a large language model (LLM) with a voting mechanism and a hyperparameter search for automatically optimizing prompt and parameter combinations. Our evaluation of the proposed method on human-created gold standard data for e-commerce shows that it outperforms existing methods for compound splitting in this domain.|复合词是一种语法结构，通过组合现有词汇来形成新词。在德语的电子商务搜索中，将这些复合词拆分为有意义的组成部分至关重要，因为商品标题通常使用联合形式，而搜索查询则通常是拆分后的形式。我们提出了一种利用大型语言模型（LLM）进行德语复合词拆分的方法，该方法结合了投票机制和超参数搜索，以自动优化提示和参数组合。我们对所提出的方法在人工创建的电子商务金标准数据上的评估显示，它在复合词拆分方面优于现有的方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Voting+with+Generative+AI+for+German+Compound+Splitting+in+E-commerce+Search)|0| +|[AI Agent for Information Retrieval: Generating and Ranking](https://doi.org/10.1145/3627673.3680120)|Yongfeng Zhang, Zhiwei Liu, Qingsong Wen, Linsey Pang, Wei Liu, Philip S. Yu|University of Technology Sydney, Sydney, NSW, Australia; Squirrel Ai Learning, Seattle, WA, USA; Rutgers University, New Brunswick, NJ, USA; Salesforce, San Francisco, CA, USA; University of Illinois at Chicago, Chicago, IL, USA; Salesforce AI Research, Palo Alto, CA, USA|The field of information retrieval has significantly transformed with the integration of AI technologies. AI agents, especially those leveraging LLMs and vast computational power, have revolutionized information retrieval, processing, and presentation. LLM agents, with advanced memory, reasoning, and planning capabilities, can perform complex tasks, engage in coherent conversations, and provide personalized responses. Despite these advancements, challenges such as ensuring relevance and accuracy, mitigating biases, providing real-time responses, and maintaining data security remain. This workshop aims to explore these challenges, share innovative solutions, and discuss future directions. It will provide a platform to bring together researchers, practitioners to discuss the latest theoretical advancements and practical implementations of AI agents in information retrieval. Topics include AI in search, recommendation, and personalization systems. By gathering a diverse group of experts, the workshop seeks to deepen the understanding of AI agents in information retrieval, advance the field, and enhance its societal impact. Participants will gain insights into cutting-edge research, emerging trends, and foster knowledge exchange and collaboration within the community.|信息检索领域随着AI技术的融合发生了显著变革。AI代理，尤其是那些利用大型语言模型（LLMs）和强大计算能力的代理，已经彻底改变了信息检索、处理和呈现的方式。具备先进记忆、推理和规划能力的LLM代理能够执行复杂任务、进行连贯对话并提供个性化响应。尽管取得了这些进展，但仍面临确保相关性和准确性、减轻偏见、提供实时响应以及维护数据安全等挑战。本次研讨会旨在探讨这些挑战，分享创新解决方案，并讨论未来的发展方向。研讨会将提供一个平台，让研究人员和从业者能够讨论AI代理在信息检索中最新的理论进展和实际应用。主题包括AI在搜索、推荐和个人化系统中的应用。通过汇集多元化的专家群体，研讨会旨在深化对信息检索中AI代理的理解，推动该领域的发展，并增强其社会影响力。参与者将获得关于尖端研究、新兴趋势的见解，并促进社区内的知识交流与合作。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=AI+Agent+for+Information+Retrieval:+Generating+and+Ranking)|0| +|[UniEmbedding: Learning Universal Multi-Modal Multi-Domain Item Embeddings via User-View Contrastive Learning](https://doi.org/10.1145/3627673.3680098)|Boqi Dai, Zhaocheng Du, Jieming Zhu, Jintao Xu, Deqing Zou, Quanyu Dai, Zhenhua Dong, Rui Zhang, HaiTao Zheng|Huawei Noah's Ark Lab, Shenzhen, China; Huazhong University of Science and Technology, Shenzhen, China; Shenzhen International Graduate School, Tsinghua University, Shenzhen, China; Shenzhen International Graduate School, Tsinghua University & Pengcheng Laboratory, Shenzhen, China|Learning high-quality item embeddings is crucial for recommendation tasks such as matching and ranking. However, existing methods often rely on ID-based item embeddings learned end-to-end with downstream recommendation models, which may suffer from overfitting and limited generalizability. In this paper, we aim to learn universal item embeddings (dubbed UniEmbedding) that capture multi-modal semantics, generalize across multiple domains, and serve different downstream tasks. To achieve this goal, we introduce the UniEmbedding pretraining framework, which includes three modules: a domain-aware multi-modal adapter, a user-view projection module, and contrastive learning objectives across domains. Compared to naive ID embeddings, UniEmbedding provides rich semantic information that generalizes more effectively across domains. Unlike multi-modal embeddings directly extracted from off-the-shelf pretrained models, UniEmbedding achieves better alignment between content semantics and behaviors. We evaluated UniEmbedding on both public and industrial datasets, demonstrating its effectiveness in matching and ranking tasks. Furthermore, UniEmbedding has been deployed in multiple recommendation applications at Huawei, resulting in significant gains in user engagement metrics.|学习高质量的物品嵌入对于匹配和排序等推荐任务至关重要。然而，现有方法通常依赖于基于ID的物品嵌入，这些嵌入与下游推荐模型端到端学习，可能会遭受过拟合和泛化能力有限的问题。本文旨在学习一种通用的物品嵌入（称为UniEmbedding），这种嵌入能够捕捉多模态语义，跨多个领域泛化，并服务于不同的下游任务。为实现这一目标，我们引入了UniEmbedding预训练框架，该框架包括三个模块：领域感知的多模态适配器、用户视角投影模块以及跨领域的对比学习目标。与简单的ID嵌入相比，UniEmbedding提供了更丰富的语义信息，能更有效地跨领域泛化。与直接从现成的预训练模型中提取的多模态嵌入不同，UniEmbedding在内容语义和行为之间实现了更好的对齐。我们在公共和工业数据集上评估了UniEmbedding，证明了其在匹配和排序任务中的有效性。此外，UniEmbedding已在华为的多个推荐应用中部署，显著提升了用户参与度指标。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=UniEmbedding:+Learning+Universal+Multi-Modal+Multi-Domain+Item+Embeddings+via+User-View+Contrastive+Learning)|0| |[Modeling User Intent Beyond Trigger: Incorporating Uncertainty for Trigger-Induced Recommendation](https://doi.org/10.1145/3627673.3680065)|Jianxing Ma, Zhibo Xiao, Luwei Yang, Hansheng Xue, Xuanzhou Liu, Wen Jiang, Wei Ning, Guannan Zhang||To cater to users' desire for an immersive browsing experience, numerous e-commerce platforms provide various recommendation scenarios, with a focus on Trigger-Induced Recommendation (TIR) tasks. However, the majority of current TIR methods heavily rely on the trigger item to understand user intent, lacking a higher-level exploration and exploitation of user intent (e.g., popular items and complementary items), which may result in an overly convergent understanding of users' short-term intent and can be detrimental to users' long-term purchasing experiences. Moreover, users' short-term intent shows uncertainty and is affected by various factors such as browsing context and historical behaviors, which poses challenges to user intent modeling. To address these challenges, we propose a novel model called Deep Uncertainty Intent Network (DUIN), comprising three essential modules: i) Explicit Intent Exploit Module extracting explicit user intent using the contrastive learning paradigm; ii) Latent Intent Explore Module exploring latent user intent by leveraging the multi-view relationships between items; iii) Intent Uncertainty Measurement Module offering a distributional estimation and capturing the uncertainty associated with user intent. Experiments on three real-world datasets demonstrate the superior performance of DUIN compared to existing baselines. Notably, DUIN has been deployed across all TIR scenarios in our e-commerce platform, with online A/B testing results conclusively validating its superiority.|为了满足用户对沉浸式浏览体验的需求，众多电商平台提供了多种推荐场景，着重于触发式推荐（Trigger-Induced Recommendation, TIR）任务。然而，当前大多数TIR方法过于依赖触发项来理解用户意图，缺乏对用户意图的高层次探索和利用（例如，流行商品和互补商品），这可能导致对用户短期意图的理解过于集中，从而对用户的长期购买体验产生不利影响。此外，用户的短期意图表现出不确定性，并受到浏览上下文和历史行为等多种因素的影响，这对用户意图建模提出了挑战。为了应对这些挑战，我们提出了一种名为深度不确定性意图网络（Deep Uncertainty Intent Network, DUIN）的新模型，该模型包含三个核心模块：i) 显式意图利用模块，通过对比学习范式提取显式用户意图；ii) 潜在意图探索模块，利用商品之间的多视角关系来探索潜在用户意图；iii) 意图不确定性度量模块，提供分布估计并捕捉用户意图的不确定性。在三个真实世界数据集上的实验表明，DUIN相比现有基线方法表现出优越的性能。值得注意的是，DUIN已在我们电商平台的所有TIR场景中部署，在线A/B测试结果有力地验证了其优越性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Modeling+User+Intent+Beyond+Trigger:+Incorporating+Uncertainty+for+Trigger-Induced+Recommendation)|0| |[Confidence-aware Self-Semantic Distillation on Knowledge Graph Embedding](https://doi.org/10.1145/3627673.3679683)|Yichen Liu, Jiawei Chen, Defang Chen, Zhehui Zhou, Yan Feng, Can Wang||Knowledge Graph Embedding (KGE), which projects entities and relations intocontinuous vector spaces, have garnered significant attention. Althoughhigh-dimensional KGE methods offer better performance, they come at the expenseof significant computation and memory overheads. Decreasing embeddingdimensions significantly deteriorates model performance. While several recentefforts utilize knowledge distillation or non-Euclidean representation learningto augment the effectiveness of low-dimensional KGE, they either necessitate apre-trained high-dimensional teacher model or involve complex non-Euclideanoperations, thereby incurring considerable additional computational costs. Toaddress this, this work proposes Confidence-aware Self-Knowledge Distillation(CSD) that learns from model itself to enhance KGE in a low-dimensional space.Specifically, CSD extracts knowledge from embeddings in previous iterations,which would be utilized to supervise the learning of the model in the nextiterations. Moreover, a specific semantic module is developed to filterreliable knowledge by estimating the confidence of previously learnedembeddings. This straightforward strategy bypasses the need for time-consumingpre-training of teacher models and can be integrated into various KGE methodsto improve their performance. Our comprehensive experiments on six KGEbackbones and four datasets underscore the effectiveness of the proposed CSD.|知识图谱嵌入（KGE）将实体和关系投影到连续的向量空间中，引起了广泛关注。尽管高维KGE方法提供了更好的性能，但它们也带来了显著的计算和内存开销。降低嵌入维度会显著降低模型性能。虽然最近的一些研究利用知识蒸馏或非欧几里得表示学习来增强低维KGE的有效性，但它们要么需要预训练的高维教师模型，要么涉及复杂的非欧几里得操作，从而产生了大量的额外计算成本。为了解决这一问题，本文提出了置信度感知的自知识蒸馏（CSD），该方法通过从模型自身学习来增强低维空间的KGE。具体而言，CSD从先前迭代中的嵌入中提取知识，这些知识将被用于监督模型在后续迭代中的学习。此外，本文还开发了一个特定的语义模块，通过估计先前学习嵌入的置信度来过滤可靠的知识。这种直接的策略避免了耗时的教师模型预训练，并且可以集成到各种KGE方法中以提高其性能。我们在六个KGE基线和四个数据集上的综合实验验证了所提出CSD的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Confidence-aware+Self-Semantic+Distillation+on+Knowledge+Graph+Embedding)|0| -|[SAQRec: Aligning Recommender Systems to User Satisfaction via Questionnaire Feedback](https://doi.org/10.1145/3627673.3679643)|Kepu Zhang, Teng Shi, Sunhao Dai, Xiao Zhang, Yinfeng Li, Jing Lu, Xiaoxue Zang, Yang Song, Jun Xu|Kuaishou Technology Co., Ltd., Beijing, China; Gaoling School of Artificial Intelligence, Renmin University of China, Beijing, China|In real-world recommender systems, user engagement and subjective feedback play pivotal roles in shaping the content distribution mechanism of the platform. When platforms reach a certain scale, they often gather valuable questionnaire feedback data from users to evaluate their satisfaction with recommended items. Compared to traditional user feedback such as likes, questionnaires explicitly capture both satisfaction and dissatisfaction and are unaffected by other users' questionnaires, thus better expressing users' true preferences. In this paper, we aim to leverage the questionnaire feedback to align the recommendation model with users' true preferences. However, due to the platform distribution mechanism and divergent user attitudes toward questionnaires, the questionnaire feedback data frequently becomes sparse and exhibits selection biases, resulting in challenges in feature integration and training process. To address these issues, we introduce a novel user Satisfaction Alignment framework that effectively leverages Questionnaire feedback to enhance Recommendation, named SAQRec. SAQRec begins by training an unbiased satisfaction model to impute satisfaction, addressing selection bias and data sparsity. Then, SAQRec aligns features with users' true preferences by disentangling satisfaction and dissatisfaction from click history and categorizing clicked items into multiple satisfaction levels through the imputed satisfactions. Additionally, the imputed satisfactions from the pre-trained unbiased satisfaction model serve as pseudo-labels to align the model's outputs with users' true preferences. Extensive experiments on both public and commercial datasets demonstrate SAQRec's superior integration of questionnaire feedback in recommendation models. Online A/B testing on a short video platform confirms its effectiveness in boosting user watch time and positive-to-negative feedback ratio, enhancing overall performance and user satisfaction.|在实际的推荐系统中，用户参与度和主观反馈在塑造平台内容分发机制方面起着关键作用。当平台达到一定规模时，通常会收集用户对推荐项目的满意度问卷反馈数据，以评估用户的满意度。与传统的用户反馈（如点赞）相比，问卷能够明确捕捉用户的满意和不满意情况，并且不受其他用户问卷的影响，因此更能表达用户的真实偏好。本文旨在利用问卷反馈来使推荐模型与用户的真实偏好相一致。然而，由于平台分发机制和用户对问卷的不同态度，问卷反馈数据往往变得稀疏并存在选择偏差，导致特征整合和训练过程面临挑战。为解决这些问题，我们提出了一种新的用户满意度对齐框架，该框架有效利用问卷反馈来增强推荐，命名为SAQRec。SAQRec首先训练一个无偏的满意度模型来填补满意度，解决选择偏差和数据稀疏问题。然后，SAQRec通过对点击历史进行解耦，将满意和不满意分离，并通过填补的满意度将点击项目分类为多个满意度级别，从而使特征与用户的真实偏好对齐。此外，预训练的无偏满意度模型产生的填补满意度作为伪标签，用于使模型的输出与用户的真实偏好对齐。在公共和商业数据集上的广泛实验表明，SAQRec在推荐模型中对问卷反馈的整合具有优越性。在一个短视频平台上的在线A/B测试证实了其在提升用户观看时间和正面反馈与负面反馈比例方面的有效性，从而提高了整体性能和用户满意度。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=SAQRec:+Aligning+Recommender+Systems+to+User+Satisfaction+via+Questionnaire+Feedback)|0| +|[SAQRec: Aligning Recommender Systems to User Satisfaction via Questionnaire Feedback](https://doi.org/10.1145/3627673.3679643)|Kepu Zhang, Teng Shi, Sunhao Dai, Xiao Zhang, Yinfeng Li, Jing Lu, Xiaoxue Zang, Yang Song, Jun Xu|Gaoling School of Artificial Intelligence, Renmin University of China, Beijing, China; Kuaishou Technology Co., Ltd., Beijing, China|In real-world recommender systems, user engagement and subjective feedback play pivotal roles in shaping the content distribution mechanism of the platform. When platforms reach a certain scale, they often gather valuable questionnaire feedback data from users to evaluate their satisfaction with recommended items. Compared to traditional user feedback such as likes, questionnaires explicitly capture both satisfaction and dissatisfaction and are unaffected by other users' questionnaires, thus better expressing users' true preferences. In this paper, we aim to leverage the questionnaire feedback to align the recommendation model with users' true preferences. However, due to the platform distribution mechanism and divergent user attitudes toward questionnaires, the questionnaire feedback data frequently becomes sparse and exhibits selection biases, resulting in challenges in feature integration and training process. To address these issues, we introduce a novel user Satisfaction Alignment framework that effectively leverages Questionnaire feedback to enhance Recommendation, named SAQRec. SAQRec begins by training an unbiased satisfaction model to impute satisfaction, addressing selection bias and data sparsity. Then, SAQRec aligns features with users' true preferences by disentangling satisfaction and dissatisfaction from click history and categorizing clicked items into multiple satisfaction levels through the imputed satisfactions. Additionally, the imputed satisfactions from the pre-trained unbiased satisfaction model serve as pseudo-labels to align the model's outputs with users' true preferences. Extensive experiments on both public and commercial datasets demonstrate SAQRec's superior integration of questionnaire feedback in recommendation models. Online A/B testing on a short video platform confirms its effectiveness in boosting user watch time and positive-to-negative feedback ratio, enhancing overall performance and user satisfaction.|在实际的推荐系统中，用户参与度和主观反馈在塑造平台内容分发机制方面起着关键作用。当平台达到一定规模时，通常会收集用户对推荐项目的满意度问卷反馈数据，以评估用户的满意度。与传统的用户反馈（如点赞）相比，问卷能够明确捕捉用户的满意和不满意情况，并且不受其他用户问卷的影响，因此更能表达用户的真实偏好。本文旨在利用问卷反馈来使推荐模型与用户的真实偏好相一致。然而，由于平台分发机制和用户对问卷的不同态度，问卷反馈数据往往变得稀疏并存在选择偏差，导致特征整合和训练过程面临挑战。为解决这些问题，我们提出了一种新的用户满意度对齐框架，该框架有效利用问卷反馈来增强推荐，命名为SAQRec。SAQRec首先训练一个无偏的满意度模型来填补满意度，解决选择偏差和数据稀疏问题。然后，SAQRec通过对点击历史进行解耦，将满意和不满意分离，并通过填补的满意度将点击项目分类为多个满意度级别，从而使特征与用户的真实偏好对齐。此外，预训练的无偏满意度模型产生的填补满意度作为伪标签，用于使模型的输出与用户的真实偏好对齐。在公共和商业数据集上的广泛实验表明，SAQRec在推荐模型中对问卷反馈的整合具有优越性。在一个短视频平台上的在线A/B测试证实了其在提升用户观看时间和正面反馈与负面反馈比例方面的有效性，从而提高了整体性能和用户满意度。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=SAQRec:+Aligning+Recommender+Systems+to+User+Satisfaction+via+Questionnaire+Feedback)|0| |[CXSimulator: A User Behavior Simulation using LLM Embeddings for Web-Marketing Campaign Assessment](https://doi.org/10.1145/3627673.3679894)|Akira Kasuga, Ryo Yonetani||This paper presents the Customer Experience (CX) Simulator, a novel framework designed to assess the effects of untested web-marketing campaigns through user behavior simulations. The proposed framework leverages large language models (LLMs) to represent various events in a user's behavioral history, such as viewing an item, applying a coupon, or purchasing an item, as semantic embedding vectors. We train a model to predict transitions between events from their LLM embeddings, which can even generalize to unseen events by learning from diverse training data. In web-marketing applications, we leverage this transition prediction model to simulate how users might react differently when new campaigns or products are presented to them. This allows us to eliminate the need for costly online testing and enhance the marketers' abilities to reveal insights. Our numerical evaluation and user study, utilizing BigQuery Public Datasets from the Google Merchandise Store, demonstrate the effectiveness of our framework.|本文介绍了客户体验（CX）模拟器，这是一个新颖的框架，旨在通过用户行为模拟来评估未经测试的网络营销活动的效果。该框架利用大型语言模型（LLMs）将用户行为历史中的各种事件，如查看商品、使用优惠券或购买商品，表示为语义嵌入向量。我们训练了一个模型，从这些LLM嵌入中预测事件之间的转换，该模型甚至可以通过从多样化的训练数据中学习来泛化到未见过的事件。在网络营销应用中，我们利用这种转换预测模型来模拟用户在新活动或新产品呈现给他们时可能产生的不同反应。这使我们能够消除昂贵的在线测试需求，并增强营销人员揭示洞察的能力。我们的数值评估和用户研究，利用了Google商品商店的BigQuery公共数据集，证明了我们框架的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=CXSimulator:+A+User+Behavior+Simulation+using+LLM+Embeddings+for+Web-Marketing+Campaign+Assessment)|0| -|[Exploring High-Order User Preference with Knowledge Graph for Recommendation](https://doi.org/10.1145/3627673.3679921)|Caijun Xu, Fuwei Zhang, Zhao Zhang, Fuzhen Zhuang, Rui Liu|School of Computer Science, Beihang University, Beijing, China; Institute of Artificial Intelligence, Beihang University, Beijing, China; Institute of Artificial Intelligence, Beihang University & Zhongguancun Laboratory, Beijing, China; Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China|Knowledge Graph (KG) has proven its effectiveness in recommendation systems. Recent knowledge-aware recommendation methods, which utilize graph neural networks and contrastive learning, underestimate two issues: 1) The neglect of modeling the latent relationships between users and entities; 2) The insufficiency of traditional cross-view contrastive learning whose domain is incapable of covering all nodes in a graph. To address these issues, we propose a novel model named Knowledge-aware User Preference Network (KUPN). Specifically, KUPN first constructs the relational preference view containing a new graph named User Preference Graph (UPG) to model the potential relationships between users and entities. Then, we adopt a novel attentive information aggregation to learn the UPG. In addition, we obtain semantic information of users and entities from collaborative knowledge view which consists of KG and Interaction Graph (IG) as supplementary. Finally, we apply a cross-view contrastive learning for complete domains between dynamic relational preference view and collaborative knowledge view. Extensive experiments on three real-world datasets demonstrate the superiority of KUPN against the state-of-the-art methods.|知识图谱（KG）在推荐系统中已证明其有效性。近年来，利用图神经网络和对比学习的知识感知推荐方法低估了两个问题：1）忽视了用户与实体之间潜在关系的建模；2）传统跨视图对比学习的领域不足以覆盖图中的所有节点。为解决这些问题，我们提出了一种名为知识感知用户偏好网络（KUPN）的新模型。具体而言，KUPN首先构建了包含用户偏好图（UPG）的关系偏好视图，以建模用户与实体之间的潜在关系。接着，我们采用了一种新颖的注意力信息聚合方法来学习UPG。此外，我们从协同知识视图中获取用户和实体的语义信息，该视图由KG和交互图（IG）组成，作为补充。最后，我们在动态关系偏好视图和协同知识视图之间应用了跨视图对比学习，以实现完整领域的覆盖。在三个真实世界数据集上的广泛实验表明，KUPN相较于最先进的方法具有优越性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Exploring+High-Order+User+Preference+with+Knowledge+Graph+for+Recommendation)|0| +|[Exploring High-Order User Preference with Knowledge Graph for Recommendation](https://doi.org/10.1145/3627673.3679921)|Caijun Xu, Fuwei Zhang, Zhao Zhang, Fuzhen Zhuang, Rui Liu|Institute of Artificial Intelligence, Beihang University & Zhongguancun Laboratory, Beijing, China; School of Computer Science, Beihang University, Beijing, China; Institute of Artificial Intelligence, Beihang University, Beijing, China; Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China|Knowledge Graph (KG) has proven its effectiveness in recommendation systems. Recent knowledge-aware recommendation methods, which utilize graph neural networks and contrastive learning, underestimate two issues: 1) The neglect of modeling the latent relationships between users and entities; 2) The insufficiency of traditional cross-view contrastive learning whose domain is incapable of covering all nodes in a graph. To address these issues, we propose a novel model named Knowledge-aware User Preference Network (KUPN). Specifically, KUPN first constructs the relational preference view containing a new graph named User Preference Graph (UPG) to model the potential relationships between users and entities. Then, we adopt a novel attentive information aggregation to learn the UPG. In addition, we obtain semantic information of users and entities from collaborative knowledge view which consists of KG and Interaction Graph (IG) as supplementary. Finally, we apply a cross-view contrastive learning for complete domains between dynamic relational preference view and collaborative knowledge view. Extensive experiments on three real-world datasets demonstrate the superiority of KUPN against the state-of-the-art methods.|知识图谱（KG）在推荐系统中已证明其有效性。近年来，利用图神经网络和对比学习的知识感知推荐方法低估了两个问题：1）忽视了用户与实体之间潜在关系的建模；2）传统跨视图对比学习的领域不足以覆盖图中的所有节点。为解决这些问题，我们提出了一种名为知识感知用户偏好网络（KUPN）的新模型。具体而言，KUPN首先构建了包含用户偏好图（UPG）的关系偏好视图，以建模用户与实体之间的潜在关系。接着，我们采用了一种新颖的注意力信息聚合方法来学习UPG。此外，我们从协同知识视图中获取用户和实体的语义信息，该视图由KG和交互图（IG）组成，作为补充。最后，我们在动态关系偏好视图和协同知识视图之间应用了跨视图对比学习，以实现完整领域的覆盖。在三个真实世界数据集上的广泛实验表明，KUPN相较于最先进的方法具有优越性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Exploring+High-Order+User+Preference+with+Knowledge+Graph+for+Recommendation)|0| |[EXIT: An EXplicit Interest Transfer Framework for Cross-Domain Recommendation](https://doi.org/10.1145/3627673.3680055)|Lei Huang, Weitao Li, Chenrui Zhang, Jinpeng Wang, Xianchun Yi, Sheng Chen||Cross-domain recommendation has attracted substantial interest in industrial apps such as Meituan, which serves multiple business domains via knowledge transfer and meets the diverse interests of users. However, existing methods typically follow an implicit modeling paradigm that blends the knowledge from both the source and target domains, and design intricate network structures to share learned embeddings or patterns between domains to improve recommendation accuracy. Since the transfer of interest signals is unsupervised, these implicit paradigms often struggle with the negative transfer resulting from differences in service functions and presentation forms across different domains. In this paper, we propose a simple and effective EXplicit Interest Transfer framework named EXIT to address the stated challenge. Specifically, we propose a novel label combination approach that enables the model to directly learn beneficial source domain interests through supervised learning, while excluding inappropriate interest signals. Moreover, we introduce a scene selector network to model the interest transfer intensity under fine-grained scenes. Offline experiments conducted on the industrial production dataset and online A/B tests validate the superiority and effectiveness of our proposed framework. Without complex network structures or training processes, EXIT can be easily deployed in the industrial recommendation system. EXIT has been successfully deployed in the online homepage recommendation system of Meituan App, serving the main traffic.|跨领域推荐在美团等工业应用中引起了广泛关注，通过知识转移服务于多个业务领域，满足用户的多样化兴趣。然而，现有方法通常采用隐式建模范式，将源域和目标域的知识混合，设计复杂的网络结构以在域间共享学习到的嵌入或模式，从而提高推荐准确性。由于兴趣信号的转移是无监督的，这些隐式范式往往因不同域间服务功能和呈现形式的差异而遭遇负迁移问题。本文提出了一种简单而有效的显式兴趣转移框架，名为EXIT，以应对上述挑战。具体而言，我们提出了一种新颖的标签组合方法，使模型能够通过监督学习直接学习有益的源域兴趣，同时排除不适当的兴趣信号。此外，我们引入了一个场景选择器网络，以在细粒度场景下建模兴趣转移强度。在工业生产数据集上的离线实验和在线A/B测试验证了我们提出的框架的优越性和有效性。EXIT无需复杂的网络结构或训练过程，可以轻松部署在工业推荐系统中。EXIT已成功部署在美团App的在线首页推荐系统中，服务于主要流量。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=EXIT:+An+EXplicit+Interest+Transfer+Framework+for+Cross-Domain+Recommendation)|0| -|[To Explore or Exploit? A Gradient-informed Framework to Address the Feedback Loop for Graph based Recommendation](https://doi.org/10.1145/3627673.3680061)|Zhigang Huangfu, Binbin Hu, Zhengwei Wu, Fengyu Han, GongDuo Zhang, Lihong Gu, Zhiqiang Zhang|Ant Group, Hang Zhou, China; Ant Group, Hangzhou, China|Graph-based Recommendation Systems (GRSs) have gained prominence for their ability to enhance the accuracy and effectiveness of recommender systems by exploiting structural relationships in user-item interaction data. Despite their advanced capabilities, we find GRSs are susceptible to feedback-loop phenomena that disproportionately diminish the visibility of new and long-tail items, leading to a homogenization of recommendations and the potential emergence of echo chambers. To mitigate this feedback-loop issue, exploration and exploitation (E&E) strategies have been extensively researched. However, conventional E&E methods rest on the assumption that recommendations are independent and identically distributed-an assumption that is not valid for GRSs. To forge an effective E&E approach tailored to GRSs, we introduce a novel framework, the GRADient-informed Exploration and Exploitation (GRADE), designed to adaptively seek out underrepresented or new items with promising rewards. Our method evaluates the potential benefit of exploring an item by assessing the change in the system's empirical risk error pre- and post-exposure. For practical implementation, we approximate this measure using the gradients of potential edges and model parameters, alongside their associated uncertainties. We then orchestrate the balance between exploration and exploitation utilizing Thompson sampling and the Upper Confidence Bound (UCB) strategy. Empirical tests on datasets from two industrial environments demonstrate that GRADE consistently outperforms existing state-of-the-art methods. Additionally, our approach has been successfully integrated into actual industrial systems.|基于图的推荐系统（GRSs）因其能够通过利用用户-项目交互数据中的结构关系来提高推荐系统的准确性和有效性而备受关注。尽管其功能强大，我们发现GRSs易受反馈循环现象的影响，这种现象不均衡地降低了新项目和长尾项目的可见性，导致推荐内容的同质化，并可能催生回音壁效应。为缓解这一反馈循环问题，探索与利用（E&E）策略得到了广泛研究。然而，传统的E&E方法基于推荐是独立同分布的假设，这一假设对于GRSs并不成立。为了针对GRSs开发一种有效的E&E方法，我们引入了一个新的框架——基于梯度的探索与利用（GRADE），该框架旨在自适应地发掘具有潜在回报的未充分代表或新项目。我们的方法通过评估系统在项目曝光前后的经验风险误差变化，来评估探索某一项目的潜在收益。在实际应用中，我们利用潜在边的梯度和模型参数及其相关的不确定性来近似这一度量。随后，我们利用汤普森采样和上置信界（UCB）策略来协调探索与利用之间的平衡。在两个工业环境数据集上的实证测试表明，GRADE始终优于现有的最先进方法。此外，我们的方法已成功集成到实际的工业系统中。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=To+Explore+or+Exploit?+A+Gradient-informed+Framework+to+Address+the+Feedback+Loop+for+Graph+based+Recommendation)|0| -|[Sequential Optimum Test with Multi-armed Bandits for Online Experimentation](https://doi.org/10.1145/3627673.3680040)|Fang Kong, Penglei Zhao, Shichao Han, Yong Wang, Shuai Li|Tencent Inc., Shenzhen, China; Shanghai Jiao Tong University, Shanghai, China|In large-scale online experimentation platforms, experimenters aim to discover the best treatment (arm) among multiple candidates. Traditional A/B testing and multi-armed bandits (MAB) algorithms are two popular designs. The former usually achieves a higher power but may hurt the customers' satisfaction when always recommending a poor arm, while the latter aims at improving the customers' experience (collecting more rewards) but faces the loss of testing power. Recently, [26] combine the advantage of A/B testing and MAB algorithms to maximize the testing power while maintaining more rewards for experiments with two-arm and Bernoulli rewards. However, in practice, the number of arms is usually larger than two and the reward type also varies. In multi-arm experiments, the required sample size to find the optimal arm blows up to guarantee a false discovery rate with the increase of arm numbers, bringing high opportunity costs to experimenters. To save the cost during the long experimental process, we propose a more efficient sequential test framework named Soptima that can work with general reward types. Inspired by the design of traditional MAB algorithms in chasing rewards and A/B testing in maximizing power, we propose an Elimination-type strategy adapted to this framework to dynamically adjust the traffic split on arms. This strategy cooperating with Soptima simultaneously maintains the advantage of the A/B testing in maximizing the testing power, the sequential test methods in saving the sample size, and the MAB algorithms in collecting rewards. The theoretical analysis gives guarantees on the Type-I, Type-II, and optimality error rates of the proposed approach. A series of experiments from both simulation and industrial historical data sets are conducted to verify the superiority of our approach compared with available baselines.|在大规模在线实验平台中，实验者的目标是从多个候选方案（臂）中找出最佳方案。传统的A/B测试和多臂老虎机（MAB）算法是两种流行的设计方案。前者通常具有更高的测试效能，但当总是推荐效果不佳的方案时，可能会损害客户的满意度；而后者旨在提升客户体验（收集更多奖励），但面临测试效能的损失。最近，[26]结合了A/B测试和MAB算法的优势，以最大化测试效能，同时在两臂和伯努利奖励的实验中保持更多的奖励。然而，在实践中，臂的数量通常大于两个，且奖励类型也多种多样。在多臂实验中，随着臂数量的增加，为了保证错误发现率，找到最佳臂所需的样本量会急剧增加，这给实验者带来了高昂的机会成本。为了在漫长的实验过程中节省成本，我们提出了一种名为Soptima的高效序列测试框架，该框架适用于一般的奖励类型。受传统MAB算法在追逐奖励和A/B测试在最大化效能的设计启发，我们提出了一种适应此框架的淘汰型策略，以动态调整对各臂的流量分配。这种策略与Soptima合作，同时保持了A/B测试在最大化测试效能、序列测试方法在节省样本量以及MAB算法在收集奖励方面的优势。理论分析为所提出方法的I类错误率、II类错误率和最优性错误率提供了保障。通过一系列来自模拟和工业历史数据集的实验，验证了我们的方法相对于现有基线的优越性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Sequential+Optimum+Test+with+Multi-armed+Bandits+for+Online+Experimentation)|0| +|[To Explore or Exploit? A Gradient-informed Framework to Address the Feedback Loop for Graph based Recommendation](https://doi.org/10.1145/3627673.3680061)|Zhigang Huangfu, Binbin Hu, Zhengwei Wu, Fengyu Han, GongDuo Zhang, Lihong Gu, Zhiqiang Zhang|Ant Group, Hangzhou, China; Ant Group, Hang Zhou, China|Graph-based Recommendation Systems (GRSs) have gained prominence for their ability to enhance the accuracy and effectiveness of recommender systems by exploiting structural relationships in user-item interaction data. Despite their advanced capabilities, we find GRSs are susceptible to feedback-loop phenomena that disproportionately diminish the visibility of new and long-tail items, leading to a homogenization of recommendations and the potential emergence of echo chambers. To mitigate this feedback-loop issue, exploration and exploitation (E&E) strategies have been extensively researched. However, conventional E&E methods rest on the assumption that recommendations are independent and identically distributed-an assumption that is not valid for GRSs. To forge an effective E&E approach tailored to GRSs, we introduce a novel framework, the GRADient-informed Exploration and Exploitation (GRADE), designed to adaptively seek out underrepresented or new items with promising rewards. Our method evaluates the potential benefit of exploring an item by assessing the change in the system's empirical risk error pre- and post-exposure. For practical implementation, we approximate this measure using the gradients of potential edges and model parameters, alongside their associated uncertainties. We then orchestrate the balance between exploration and exploitation utilizing Thompson sampling and the Upper Confidence Bound (UCB) strategy. Empirical tests on datasets from two industrial environments demonstrate that GRADE consistently outperforms existing state-of-the-art methods. Additionally, our approach has been successfully integrated into actual industrial systems.|基于图的推荐系统（GRSs）因其能够通过利用用户-项目交互数据中的结构关系来提高推荐系统的准确性和有效性而备受关注。尽管其功能强大，我们发现GRSs易受反馈循环现象的影响，这种现象不均衡地降低了新项目和长尾项目的可见性，导致推荐内容的同质化，并可能催生回音壁效应。为缓解这一反馈循环问题，探索与利用（E&E）策略得到了广泛研究。然而，传统的E&E方法基于推荐是独立同分布的假设，这一假设对于GRSs并不成立。为了针对GRSs开发一种有效的E&E方法，我们引入了一个新的框架——基于梯度的探索与利用（GRADE），该框架旨在自适应地发掘具有潜在回报的未充分代表或新项目。我们的方法通过评估系统在项目曝光前后的经验风险误差变化，来评估探索某一项目的潜在收益。在实际应用中，我们利用潜在边的梯度和模型参数及其相关的不确定性来近似这一度量。随后，我们利用汤普森采样和上置信界（UCB）策略来协调探索与利用之间的平衡。在两个工业环境数据集上的实证测试表明，GRADE始终优于现有的最先进方法。此外，我们的方法已成功集成到实际的工业系统中。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=To+Explore+or+Exploit?+A+Gradient-informed+Framework+to+Address+the+Feedback+Loop+for+Graph+based+Recommendation)|0| +|[Sequential Optimum Test with Multi-armed Bandits for Online Experimentation](https://doi.org/10.1145/3627673.3680040)|Fang Kong, Penglei Zhao, Shichao Han, Yong Wang, Shuai Li|Shanghai Jiao Tong University, Shanghai, China; Tencent Inc., Shenzhen, China|In large-scale online experimentation platforms, experimenters aim to discover the best treatment (arm) among multiple candidates. Traditional A/B testing and multi-armed bandits (MAB) algorithms are two popular designs. The former usually achieves a higher power but may hurt the customers' satisfaction when always recommending a poor arm, while the latter aims at improving the customers' experience (collecting more rewards) but faces the loss of testing power. Recently, [26] combine the advantage of A/B testing and MAB algorithms to maximize the testing power while maintaining more rewards for experiments with two-arm and Bernoulli rewards. However, in practice, the number of arms is usually larger than two and the reward type also varies. In multi-arm experiments, the required sample size to find the optimal arm blows up to guarantee a false discovery rate with the increase of arm numbers, bringing high opportunity costs to experimenters. To save the cost during the long experimental process, we propose a more efficient sequential test framework named Soptima that can work with general reward types. Inspired by the design of traditional MAB algorithms in chasing rewards and A/B testing in maximizing power, we propose an Elimination-type strategy adapted to this framework to dynamically adjust the traffic split on arms. This strategy cooperating with Soptima simultaneously maintains the advantage of the A/B testing in maximizing the testing power, the sequential test methods in saving the sample size, and the MAB algorithms in collecting rewards. The theoretical analysis gives guarantees on the Type-I, Type-II, and optimality error rates of the proposed approach. A series of experiments from both simulation and industrial historical data sets are conducted to verify the superiority of our approach compared with available baselines.|在大规模在线实验平台中，实验者的目标是从多个候选方案（臂）中找出最佳方案。传统的A/B测试和多臂老虎机（MAB）算法是两种流行的设计方案。前者通常具有更高的测试效能，但当总是推荐效果不佳的方案时，可能会损害客户的满意度；而后者旨在提升客户体验（收集更多奖励），但面临测试效能的损失。最近，[26]结合了A/B测试和MAB算法的优势，以最大化测试效能，同时在两臂和伯努利奖励的实验中保持更多的奖励。然而，在实践中，臂的数量通常大于两个，且奖励类型也多种多样。在多臂实验中，随着臂数量的增加，为了保证错误发现率，找到最佳臂所需的样本量会急剧增加，这给实验者带来了高昂的机会成本。为了在漫长的实验过程中节省成本，我们提出了一种名为Soptima的高效序列测试框架，该框架适用于一般的奖励类型。受传统MAB算法在追逐奖励和A/B测试在最大化效能的设计启发，我们提出了一种适应此框架的淘汰型策略，以动态调整对各臂的流量分配。这种策略与Soptima合作，同时保持了A/B测试在最大化测试效能、序列测试方法在节省样本量以及MAB算法在收集奖励方面的优势。理论分析为所提出方法的I类错误率、II类错误率和最优性错误率提供了保障。通过一系列来自模拟和工业历史数据集的实验，验证了我们的方法相对于现有基线的优越性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Sequential+Optimum+Test+with+Multi-armed+Bandits+for+Online+Experimentation)|0| |[TWIN V2: Scaling Ultra-Long User Behavior Sequence Modeling for Enhanced CTR Prediction at Kuaishou](https://doi.org/10.1145/3627673.3680030)|Zihua Si, Lin Guan, Zhongxiang Sun, Xiaoxue Zang, Jing Lu, Yiqun Hui, Xingchao Cao, Zeyu Yang, Yichen Zheng, Dewei Leng, Kai Zheng, Chenbin Zhang, Yanan Niu, Yang Song, Kun Gai||The significance of modeling long-term user interests for CTR prediction tasks in large-scale recommendation systems is progressively gaining attention among researchers and practitioners. Existing work, such as SIM and TWIN, typically employs a two-stage approach to model long-term user behavior sequences for efficiency concerns. The first stage rapidly retrieves a subset of sequences related to the target item from a long sequence using a search-based mechanism namely the General Search Unit (GSU), while the second stage calculates the interest scores using the Exact Search Unit (ESU) on the retrieved results. Given the extensive length of user behavior sequences spanning the entire life cycle, potentially reaching up to 10^6 in scale, there is currently no effective solution for fully modeling such expansive user interests. To overcome this issue, we introduced TWIN-V2, an enhancement of TWIN, where a divide-and-conquer approach is applied to compress life-cycle behaviors and uncover more accurate and diverse user interests. Specifically, a hierarchical clustering method groups items with similar characteristics in life-cycle behaviors into a single cluster during the offline phase. By limiting the size of clusters, we can compress behavior sequences well beyond the magnitude of 10^5 to a length manageable for online inference in GSU retrieval. Cluster-aware target attention extracts comprehensive and multi-faceted long-term interests of users, thereby making the final recommendation results more accurate and diverse. Extensive offline experiments on a multi-billion-scale industrial dataset and online A/B tests have demonstrated the effectiveness of TWIN-V2. Under an efficient deployment framework, TWIN-V2 has been successfully deployed to the primary traffic that serves hundreds of millions of daily active users at Kuaishou.|在大规模推荐系统中，建模长期用户兴趣对点击率（CTR）预测任务的重要性正逐渐受到研究者和从业者的关注。现有的研究工作，如SIM和TWIN，通常采用两阶段方法来高效地建模长期用户行为序列。第一阶段通过基于搜索的机制，即通用搜索单元（GSU），从长序列中快速检索与目标项目相关的子集序列；第二阶段则使用精确搜索单元（ESU）对检索结果计算兴趣分数。鉴于用户行为序列的广泛长度可能跨越整个生命周期，规模可达10^6，目前尚无有效解决方案来全面建模如此广泛的用户兴趣。为解决这一问题，我们引入了TWIN-V2，即TWIN的增强版本，采用分而治之的方法来压缩生命周期行为并揭示更准确和多样的用户兴趣。具体而言，层次聚类方法在离线阶段将具有相似特征的生命周期行为项目分组为一个集群。通过限制集群大小，我们可以将行为序列压缩到远超10^5的规模，使其适合在线推理中的GSU检索。集群感知的目标注意力机制提取了用户全面且多方面的长期兴趣，从而使最终的推荐结果更加准确和多样化。在多十亿规模工业数据集上的广泛离线实验和在线A/B测试证明了TWIN-V2的有效性。在高效部署框架下，TWIN-V2已成功部署到快手的主要流量中，服务数亿日活用户。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=TWIN+V2:+Scaling+Ultra-Long+User+Behavior+Sequence+Modeling+for+Enhanced+CTR+Prediction+at+Kuaishou)|0| |[Understanding the User: An Intent-Based Ranking Dataset](https://doi.org/10.1145/3627673.3679166)|Abhijit Anand, Jurek Leonhardt, Venktesh V, Avishek Anand||As information retrieval systems continue to evolve, accurate evaluation and benchmarking of these systems become pivotal. Web search datasets, such as MS MARCO, primarily provide short keyword queries without accompanying intent or descriptions, posing a challenge in comprehending the underlying information need. This paper proposes an approach to augmenting such datasets to annotate informative query descriptions, with a focus on two prominent benchmark datasets: TREC-DL-21 and TREC-DL-22. Our methodology involves utilizing state-of-the-art LLMs to analyze and comprehend the implicit intent within individual queries from benchmark datasets. By extracting key semantic elements, we construct detailed and contextually rich descriptions for these queries. To validate the generated query descriptions, we employ crowdsourcing as a reliable means of obtaining diverse human perspectives on the accuracy and informativeness of the descriptions. This information can be used as an evaluation set for tasks such as ranking, query rewriting, or others.|随着信息检索系统不断演进，对其进行准确的评估和基准测试变得至关重要。诸如MS MARCO等网络搜索数据集主要提供简短的关键词查询，缺乏伴随的意图或描述，这给理解背后的信息需求带来了挑战。本文提出了一种增强此类数据集的方法，旨在为查询添加信息丰富的描述，重点关注两个著名的基准数据集：TREC-DL-21和TREC-DL-22。我们的方法涉及利用最先进的LLMs（大型语言模型）来分析和理解基准数据集中各个查询的隐含意图。通过提取关键的语义元素，我们为这些查询构建了详细且上下文丰富的描述。为了验证生成的查询描述，我们采用众包作为获取多样化人类视角的可靠手段，以评估描述的准确性和信息量。这些信息可以作为排序、查询重写等任务的评估集使用。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Understanding+the+User:+An+Intent-Based+Ranking+Dataset)|0| -|[Domain Alignment with Large Vision-language Models for Cross-domain Remote Sensing Image Retrieval](https://doi.org/10.1145/3627673.3679612)|Yan Chen, Guocan Cai, Fufang Li, Yangtao Wang, Xin Tan, Xiaocui Li|School of Computer Science and Cyber Engineering, Guangzhou University, Guangzhou, China; Hunan University of Technology and Business, Changsha, China; East China Normal University, Shanghai, China|Cross-domain remote sensing image retrieval has been a hotspot in the past few years. Most of the existing methods focus on combining semantic learning with domain adaptation on well-labeled source domain and unlabeled target domain. However, they face two serious challenges. (1) They cannot deal with practical scenarios where the source domain lacks sufficient label supervision. (2) They suffer from severe performance degradation when the data distribution between the source domain and target domain becomes highly inconsistent. To address these challenges, we propose D omain A lignment with L arge V ision-language models for cross-domain remote sensing image retrieval (termed as DALV). First, we design a dual-modality prototype guided pseudo-labeling mechanism, which leverages the pre-trained large vision-language model (i.e., CLIP) to assign pseudo-labels for all unlabeled source domain images and target domain images. Second, we compute the confidence scores for these pseudo-labels to distinguish their reliability. Next, we devise a loss reweighting strategy, which incorporates the confidence scores as weight values into the contrastive loss to mitigate the impact of noisy pseudo-labels. Finally, the low-rank adaptation fine-tuning means is adapted to update our model and achieve domain alignment to obtain class discriminative features. Extensive experiments on 12 cross-domain remote sensing image retrieval tasks show that our proposed DALV outperforms the state-of-the-art approaches. The source code is available at https://github.com/ptyy01/DALV.|跨领域遥感图像检索近年来成为研究热点。现有方法大多集中在结合语义学习和领域适应于标签丰富的源域和无标签的目标域。然而，这些方法面临两个严重挑战：（1）无法处理源域缺乏足够标签监督的实际场景；（2）当源域和目标域的数据分布高度不一致时，性能严重下降。为应对这些挑战，我们提出了基于大规模视觉语言模型的跨领域遥感图像检索的领域对齐方法（简称DALV）。首先，我们设计了一种双模态原型引导的伪标签机制，利用预训练的大规模视觉语言模型（如CLIP）为所有无标签的源域图像和目标域图像分配伪标签。其次，我们计算这些伪标签的置信度分数以区分其可靠性。接着，我们设计了一种损失重加权策略，将置信度分数作为权重值融入对比损失，以减轻噪声伪标签的影响。最后，采用低秩适应微调方法更新模型，实现领域对齐，获取具有类别区分性的特征。在12个跨领域遥感图像检索任务上的广泛实验表明，我们提出的DALV方法优于现有最先进的方法。源代码可在https://github.com/ptyy01/DALV获取。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Domain+Alignment+with+Large+Vision-language+Models+for+Cross-domain+Remote+Sensing+Image+Retrieval)|0| +|[Domain Alignment with Large Vision-language Models for Cross-domain Remote Sensing Image Retrieval](https://doi.org/10.1145/3627673.3679612)|Yan Chen, Guocan Cai, Fufang Li, Yangtao Wang, Xin Tan, Xiaocui Li|School of Computer Science and Cyber Engineering, Guangzhou University, Guangzhou, China; East China Normal University, Shanghai, China; Hunan University of Technology and Business, Changsha, China|Cross-domain remote sensing image retrieval has been a hotspot in the past few years. Most of the existing methods focus on combining semantic learning with domain adaptation on well-labeled source domain and unlabeled target domain. However, they face two serious challenges. (1) They cannot deal with practical scenarios where the source domain lacks sufficient label supervision. (2) They suffer from severe performance degradation when the data distribution between the source domain and target domain becomes highly inconsistent. To address these challenges, we propose D omain A lignment with L arge V ision-language models for cross-domain remote sensing image retrieval (termed as DALV). First, we design a dual-modality prototype guided pseudo-labeling mechanism, which leverages the pre-trained large vision-language model (i.e., CLIP) to assign pseudo-labels for all unlabeled source domain images and target domain images. Second, we compute the confidence scores for these pseudo-labels to distinguish their reliability. Next, we devise a loss reweighting strategy, which incorporates the confidence scores as weight values into the contrastive loss to mitigate the impact of noisy pseudo-labels. Finally, the low-rank adaptation fine-tuning means is adapted to update our model and achieve domain alignment to obtain class discriminative features. Extensive experiments on 12 cross-domain remote sensing image retrieval tasks show that our proposed DALV outperforms the state-of-the-art approaches. The source code is available at https://github.com/ptyy01/DALV.|跨领域遥感图像检索近年来成为研究热点。现有方法大多集中在结合语义学习和领域适应于标签丰富的源域和无标签的目标域。然而，这些方法面临两个严重挑战：（1）无法处理源域缺乏足够标签监督的实际场景；（2）当源域和目标域的数据分布高度不一致时，性能严重下降。为应对这些挑战，我们提出了基于大规模视觉语言模型的跨领域遥感图像检索的领域对齐方法（简称DALV）。首先，我们设计了一种双模态原型引导的伪标签机制，利用预训练的大规模视觉语言模型（如CLIP）为所有无标签的源域图像和目标域图像分配伪标签。其次，我们计算这些伪标签的置信度分数以区分其可靠性。接着，我们设计了一种损失重加权策略，将置信度分数作为权重值融入对比损失，以减轻噪声伪标签的影响。最后，采用低秩适应微调方法更新模型，实现领域对齐，获取具有类别区分性的特征。在12个跨领域遥感图像检索任务上的广泛实验表明，我们提出的DALV方法优于现有最先进的方法。源代码可在https://github.com/ptyy01/DALV获取。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Domain+Alignment+with+Large+Vision-language+Models+for+Cross-domain+Remote+Sensing+Image+Retrieval)|0| |[DIIT: A Domain-Invariant Information Transfer Method for Industrial Cross-Domain Recommendation](https://doi.org/10.1145/3627673.3679782)|Heyuan Huang, Xingyu Lou, Chaochao Chen, Pengxiang Cheng, Yue Xin, Chengwei He, Xiang Liu, Jun Wang||Cross-Domain Recommendation (CDR) have received widespread attention due to their ability to utilize rich information across domains. However, most existing CDR methods assume an ideal static condition that is not practical in industrial recommendation systems (RS). Therefore, simply applying existing CDR methods in the industrial RS environment may lead to low effectiveness and efficiency. To fill this gap, we propose DIIT, an end-to-end Domain-Invariant Information Transfer method for industrial cross-domain recommendation. Specifically, We first simulate the industrial RS environment that maintains respective models in multiple domains, each of them is trained in the incremental mode. Then, for improving the effectiveness, we design two extractors to fully extract domain-invariant information from the latest source domain models at the domain level and the representation level respectively. Finally, for improving the efficiency, we design a migrator to transfer the extracted information to the latest target domain model, which only need the target domain model for inference. Experiments conducted on one production dataset and two public datasets verify the effectiveness and efficiency of DIIT.|跨域推荐（CDR）因其能够利用跨领域的丰富信息而受到广泛关注。然而，大多数现有的CDR方法假设了一个理想的静态条件，这在工业推荐系统（RS）中并不实际。因此，简单地将现有的CDR方法应用于工业RS环境中可能导致效果和效率低下。为了填补这一空白，我们提出了DIIT，一种用于工业跨域推荐的端到端领域不变信息传递方法。具体而言，我们首先模拟了工业RS环境，该环境在多个领域中维护各自的模型，每个模型都以增量模式进行训练。然后，为了提高效果，我们设计了两个提取器，分别从领域级别和表示级别充分提取最新源域模型中的领域不变信息。最后，为了提高效率，我们设计了一个迁移器，将提取的信息传递到最新的目标域模型，该模型仅需要进行目标域模型的推理。在一个生产数据集和两个公共数据集上进行的实验验证了DIIT的有效性和效率。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=DIIT:+A+Domain-Invariant+Information+Transfer+Method+for+Industrial+Cross-Domain+Recommendation)|0| |[The Devil is in the Sources! Knowledge Enhanced Cross-Domain Recommendation in an Information Bottleneck Perspective](https://doi.org/10.1145/3627673.3679595)|Binbin Hu, Weifan Wang, Shuhan Wang, Ziqi Liu, Bin Shen, Yong He, Jiawei Chen||Cross-domain Recommendation (CDR) aims to alleviate the data sparsity and the cold-start problems in traditional recommender systems by leveraging knowledge from an informative source domain. However, previously proposed CDR models pursue an imprudent assumption that the entire information from the source domain is equally contributed to the target domain, neglecting the evil part that is completely irrelevant to users' intrinsic interest. To address this concern, in this paper, we propose a novel knowledge enhanced cross-domain recommendation framework named CoTrans, which remolds the core procedures of CDR models with: Compression on the knowledge from the source domain and Transfer of the purity to the target domain. Specifically, following the theory of Graph Information Bottleneck, CoTrans first compresses the source behaviors with the perception of information from the target domain. Then to preserve all the important information for the CDR task, the feedback signals from both domains are utilized to promote the effectiveness of the transfer procedure. Additionally, a knowledge-enhanced encoder is employed to narrow gaps caused by the non-overlapped items across separate domains. Comprehensive experiments on three widely used cross-domain datasets demonstrate that CoTrans significantly outperforms both single-domain and state-of-the-art cross-domain recommendation approaches.|跨域推荐（CDR）旨在通过利用信息丰富的源域知识，缓解传统推荐系统中的数据稀疏性和冷启动问题。然而，先前提出的CDR模型追求一个不谨慎的假设，即源域的全部信息对目标域的贡献是均等的，忽视了与用户内在兴趣完全无关的有害部分。为了解决这一问题，本文提出了一种名为CoTrans的新型知识增强跨域推荐框架，该框架通过以下核心步骤重构了CDR模型的流程：源域知识的压缩和目标域纯净信息的转移。具体而言，遵循图信息瓶颈理论，CoTrans首先根据目标域的信息感知压缩源域行为。然后，为了保留CDR任务的所有重要信息，利用来自两个域的反馈信号来提升转移过程的有效性。此外，采用知识增强编码器来缩小各域之间非重叠项目造成的差距。在三个广泛使用的跨域数据集上的综合实验表明，CoTrans显著优于单一域和最先进的跨域推荐方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=The+Devil+is+in+the+Sources!+Knowledge+Enhanced+Cross-Domain+Recommendation+in+an+Information+Bottleneck+Perspective)|0| -|[MuLe: Multi-Grained Graph Learning for Multi-Behavior Recommendation](https://doi.org/10.1145/3627673.3679709)|Seunghan Lee, Geonwoo Ko, HyunJe Song, Jinhong Jung|School of Software, Soongsil University, Seoul, Republic of Korea; Dept. of CSAI, Jeonbuk Nat'l Univ., Jeonju, Republic of Korea|Multi-behavior recommender systems, rapidly advancing across various domains, utilize plentiful auxiliary interactions on a variety of user behaviors to enhance recommendations for the target behavior, such as purchases. While previous methods have made strides in leveraging such interactions with advanced machine learning methods, they still face challenges in adequately using multi-faceted relationships among behaviors and handling uncertain auxiliary interactions that could potentially lead to purchases or not. In this paper, we propose MuLe (Multi-Grained Graph Learning), a novel graph-based model designed to address these limitations. We design a multi-grained graph learning strategy to capture diverse aspects of behaviors, ranging from unified to specific, and then to target-related behavior interactions. To handle uncertain interactions, we use graph attention, weighting the importance of those interactions related to the target behavior. Afterward, we use an attention mechanism to effectively aggregate diverse behavior embeddings obtained from the multi-grained graph encoders. Extensive experiments show that MuLe significantly outperforms the state-of-the-art methods, achieving improvements of up to 44.6% in HR@10 and 52.9% in NDCG@10, respectively. Our code and datasets are available at https://github.com/geonwooko/MULE.|多行为推荐系统在各个领域迅速发展，利用丰富的用户行为辅助交互来增强对目标行为（如购买）的推荐。尽管先前的方法通过先进的机器学习方法在这一领域取得了进展，但它们在充分使用行为之间的多方面关系以及处理可能导致或不导致购买的模糊辅助交互方面仍面临挑战。本文提出了MuLe（多粒度图学习），这是一种新颖的基于图的模型，旨在解决这些局限性。我们设计了一种多粒度图学习策略，以捕捉从统一到具体再到与目标相关的行为交互的多样性。为了处理不确定的交互，我们使用图注意力机制，对与目标行为相关的交互进行重要性加权。随后，我们采用注意力机制，有效地聚合从多粒度图编码器获得的各种行为嵌入。广泛的实验表明，MuLe显著优于最先进的方法，HR@10和NDCG@10分别提高了44.6%和52.9%。我们的代码和数据集可在https://github.com/geonwooko/MULE获取。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=MuLe:+Multi-Grained+Graph+Learning+for+Multi-Behavior+Recommendation)|0| +|[MuLe: Multi-Grained Graph Learning for Multi-Behavior Recommendation](https://doi.org/10.1145/3627673.3679709)|Seunghan Lee, Geonwoo Ko, HyunJe Song, Jinhong Jung|Dept. of CSAI, Jeonbuk Nat'l Univ., Jeonju, Republic of Korea; School of Software, Soongsil University, Seoul, Republic of Korea|Multi-behavior recommender systems, rapidly advancing across various domains, utilize plentiful auxiliary interactions on a variety of user behaviors to enhance recommendations for the target behavior, such as purchases. While previous methods have made strides in leveraging such interactions with advanced machine learning methods, they still face challenges in adequately using multi-faceted relationships among behaviors and handling uncertain auxiliary interactions that could potentially lead to purchases or not. In this paper, we propose MuLe (Multi-Grained Graph Learning), a novel graph-based model designed to address these limitations. We design a multi-grained graph learning strategy to capture diverse aspects of behaviors, ranging from unified to specific, and then to target-related behavior interactions. To handle uncertain interactions, we use graph attention, weighting the importance of those interactions related to the target behavior. Afterward, we use an attention mechanism to effectively aggregate diverse behavior embeddings obtained from the multi-grained graph encoders. Extensive experiments show that MuLe significantly outperforms the state-of-the-art methods, achieving improvements of up to 44.6% in HR@10 and 52.9% in NDCG@10, respectively. Our code and datasets are available at https://github.com/geonwooko/MULE.|多行为推荐系统在各个领域迅速发展，利用丰富的用户行为辅助交互来增强对目标行为（如购买）的推荐。尽管先前的方法通过先进的机器学习方法在这一领域取得了进展，但它们在充分使用行为之间的多方面关系以及处理可能导致或不导致购买的模糊辅助交互方面仍面临挑战。本文提出了MuLe（多粒度图学习），这是一种新颖的基于图的模型，旨在解决这些局限性。我们设计了一种多粒度图学习策略，以捕捉从统一到具体再到与目标相关的行为交互的多样性。为了处理不确定的交互，我们使用图注意力机制，对与目标行为相关的交互进行重要性加权。随后，我们采用注意力机制，有效地聚合从多粒度图编码器获得的各种行为嵌入。广泛的实验表明，MuLe显著优于最先进的方法，HR@10和NDCG@10分别提高了44.6%和52.9%。我们的代码和数据集可在https://github.com/geonwooko/MULE获取。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=MuLe:+Multi-Grained+Graph+Learning+for+Multi-Behavior+Recommendation)|0| |[Inferring Visualization Intent from Conversation](https://doi.org/10.1145/3627673.3679589)|Haotian Li, Nithin Chalapathi, Huamin Qu, Alvin Cheung, Aditya G. Parameswaran|UC Berkeley, Berkeley, CA, USA; HKUST, Hong Kong, China|During visual data analysis, users often explore visualizations one at a time, with each visualization leading to new directions of exploration. We consider a conversational approach to visualization, where users specify their needs at each step in natural language, with a visualization being returned in turn. Prior work has shown that visualization generation can be boiled down to the identification of visualization intent and visual encodings. Recognizing that the latter is a well-studied problem with standard solutions, we focus on the former, i.e., identifying visualization intent during conversation. We develop Luna, a framework that comprises a novel combination of language models adapted from BERT and rule-based inference, that together predict various aspects of visualization intent. We compare Luna with other conversational NL-to-visualization and NL-to-SQL approaches (adapted to visualization intent), including GPT-3.5 and GPT-4, and demonstrate that Luna has 14.3% higher accuracy than the state-of-the-art. We also apply Luna to a usage scenario on a dataset of police misconduct, showcasing its benefits relative to other approaches.|在视觉数据分析过程中，用户通常一次只探索一个可视化图表，每个图表都引导出新的探索方向。我们考虑了一种对话式的可视化方法，用户在每一步以自然语言指定其需求，并依次返回一个可视化图表。先前的研究表明，可视化生成可以简化为可视化意图和视觉编码的识别。鉴于后者是一个已有标准解决方案的成熟问题，我们将重点放在前者，即在对话过程中识别可视化意图。我们开发了Luna框架，该框架结合了从BERT改编的语言模型和基于规则的推理，共同预测可视化意图的各个方面。我们将Luna与其他对话式自然语言到可视化和自然语言到SQL的方法（适配于可视化意图）进行了比较，包括GPT-3.5和GPT-4，并展示了Luna比现有技术高出14.3%的准确性。我们还应用Luna到一个关于警察不当行为的实际数据集场景中，展示了其相对于其他方法的优势。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Inferring+Visualization+Intent+from+Conversation)|0| |[GUME: Graphs and User Modalities Enhancement for Long-Tail Multimodal Recommendation](https://doi.org/10.1145/3627673.3679620)|Guojiao Lin, Zhen Meng, Dongjie Wang, Qingqing Long, Yuanchun Zhou, Meng Xiao||Multimodal recommendation systems (MMRS) have received considerable attention from the research community due to their ability to jointly utilize information from user behavior and product images and text. Previous research has two main issues. First, many long-tail items in recommendation systems have limited interaction data, making it difficult to learn comprehensive and informative representations. However, past MMRS studies have overlooked this issue. Secondly, users' modality preferences are crucial to their behavior. However, previous research has primarily focused on learning item modality representations, while user modality representations have remained relatively simplistic.To address these challenges, we propose a novel Graphs and User Modalities Enhancement (GUME) for long-tail multimodal recommendation. Specifically, we first enhance the user-item graph using multimodal similarity between items. This improves the connectivity of long-tail items and helps them learn high-quality representations through graph propagation. Then, we construct two types of user modalities: explicit interaction features and extended interest features. By using the user modality enhancement strategy to maximize mutual information between these two features, we improve the generalization ability of user modality representations. Additionally, we design an alignment strategy for modality data to remove noise from both internal and external perspectives. Extensive experiments on four publicly available datasets demonstrate the effectiveness of our approach.|多模态推荐系统（MMRS）因其能够联合利用用户行为、产品图像和文本信息而受到研究界的广泛关注。以往的研究存在两个主要问题。首先，推荐系统中许多长尾项目（long-tail items）的交互数据有限，这使得学习全面且信息丰富的表示变得困难。然而，过去的MMRS研究忽视了这一问题。其次，用户的模态偏好对其行为至关重要。然而，以往的研究主要集中在学习项目模态表示上，而用户模态表示则相对简单。为了解决这些挑战，我们提出了一种新的针对长尾多模态推荐的图与用户模态增强（GUME）方法。具体来说，我们首先通过项目间的多模态相似性来增强用户-项目图，这提高了长尾项目的连通性，并帮助它们通过图传播学习高质量的表示。然后，我们构建了两种用户模态：显式交互特征和扩展兴趣特征。通过使用用户模态增强策略来最大化这两种特征之间的互信息，我们提高了用户模态表示的泛化能力。此外，我们还设计了一种模态数据对齐策略，以从内部和外部角度去除噪声。在四个公开可用的数据集上进行的广泛实验证明了我们方法的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=GUME:+Graphs+and+User+Modalities+Enhancement+for+Long-Tail+Multimodal+Recommendation)|0| |[Multi-Behavior Generative Recommendation](https://doi.org/10.1145/3627673.3679730)|Zihan Liu, Yupeng Hou, Julian J. McAuley|University of California, San Diego, San Diego, CA, USA|Multi-behavior sequential recommendation (MBSR) aims to incorporate behaviortypes of interactions for better recommendations. Existing approaches focus onthe next-item prediction objective, neglecting the value of integrating thetarget behavior type into the learning objective. In this paper, we proposeMBGen, a novel Multi-Behavior sequential Generative recommendation framework.We formulate the MBSR task into a consecutive two-step process: (1) given itemsequences, MBGen first predicts the next behavior type to frame the userintention, (2) given item sequences and a target behavior type, MBGen thenpredicts the next items. To model such a two-step process, we tokenize bothbehaviors and items into tokens and construct one single token sequence withboth behaviors and items placed interleaved. Furthermore, MBGen learns toautoregressively generate the next behavior and item tokens in a unifiedgenerative recommendation paradigm, naturally enabling a multi-task capability.Additionally, we exploit the heterogeneous nature of token sequences in thegenerative recommendation and propose a position-routed sparse architecture toefficiently and effectively scale up models. Extensive experiments on publicdatasets demonstrate that MBGen significantly outperforms existing MBSR modelsacross multiple tasks.|多行为序列推荐（MBSR）旨在整合交互行为类型以实现更佳的推荐效果。现有方法主要聚焦于下一项预测目标，忽略了将目标行为类型整合到学习目标中的价值。本文提出了一种名为MBGen的新型多行为序列生成推荐框架。我们将MBSR任务构建成一个连续的两步过程：（1）在给定项目序列的情况下，MBGen首先预测下一行为类型以构建用户意图；（2）在给定项目序列和目标行为类型的基础上，MBGen随后预测下一项目。为模拟这一两步过程，我们将行为和项目都标记化为令牌，并构建一个包含交错放置的行为和项目的单一令牌序列。此外，MBGen在统一的生成推荐范式中自回归地生成下一行为和项目令牌，自然地实现了多任务能力。我们还利用生成推荐中令牌序列的异构性，提出了一种位置路由稀疏架构，以高效且有效地扩展模型规模。在公共数据集上的广泛实验表明，MBGen在多个任务中显著优于现有的MBSR模型。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Multi-Behavior+Generative+Recommendation)|0| |[Veracity Estimation for Entity-Oriented Search with Knowledge Graphs](https://doi.org/10.1145/3627673.3679561)|Stefano Marchesin, Gianmaria Silvello, Omar Alonso|University of Padua, Padua, Italy; Amazon, Palo Alto, California, USA|In this paper, we discuss the potential costs that emerge from using a Knowledge Graph (KG) in entity-oriented search without considering its data veracity. We argue for the need for KG veracity analysis to gain insights and propose a scalable assessment framework. Previous assessments focused on relevance, assuming correct KGs, and overlooking the potential risks of misinformation. Our approach strategically allocates annotation resources, optimizing utility and revealing the significant impact of veracity on entity search and card generation. Contributions include a fresh perspective on entity-oriented search extending beyond the conventional focus on relevance, a scalable assessment framework, exploratory experiments highlighting the impact of veracity on ranking and user experience, as well as outlining associated challenges and opportunities.|本文探讨了在面向实体的搜索中使用知识图谱（KG）而不考虑其数据真实性所可能产生的潜在成本。我们主张进行知识图谱真实性分析以获取洞察，并提出一个可扩展的评估框架。以往的评估主要集中在相关性上，假设知识图谱是正确的，而忽视了错误信息可能带来的潜在风险。我们的方法策略性地分配标注资源，优化效用并揭示真实性对实体搜索和卡片生成的重要影响。主要贡献包括：对面向实体的搜索提出了超越传统相关性关注的新视角，一个可扩展的评估框架，探索性实验突显了真实性对排名和用户体验的影响，以及概述了相关的挑战和机遇。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Veracity+Estimation+for+Entity-Oriented+Search+with+Knowledge+Graphs)|0| |[Inductive Knowledge Graph Embedding via Exploring Interaction Patterns of Relations](https://doi.org/10.1145/3627673.3679667)|Chong Mu, Lizong Zhang, Jinchuan Zhang, Qian Huang, Zhiguo Wang||Recent research in inductive reasoning has focused on predicting missing links between entities that are not observed during training. However, most approaches usually require that the relations are known at the inference time. In the real world, new entities and new relations usually emerge concurrently, which greatly challenges the model's generalization ability. In this paper, we propose a novel inductive knowledge graph embedding model that effectively handles unknown entities and relations by capturing their local structural features. Specifically, a relation graph is constructed to learn relation representations. In the relation graph, we employ a four-dimensional vector to represent the interaction patterns between nodes (relations), where each dimension corresponds to a specific type of interaction. For entity representations, our model dynamically initializes entity features using relation features and attentively aggregates neighboring features of entities to update entity features. By modeling interaction patterns between relations and incorporating structural information of entities, our model learns how to aggregate neighboring embeddings using attention mechanisms, thus generating high-quality embeddings for new entities and relations. Extensive experiments on benchmark datasets demonstrate that our model outperforms state-of-the-art methods, particularly in scenarios involving completely new relations.|最近的研究集中在归纳推理，即预测训练过程中未观察到的实体之间的缺失链接。然而，大多数方法通常要求在推理时已知关系。在现实世界中，新实体和新关系通常同时出现，这对模型的泛化能力提出了巨大挑战。在本文中，我们提出了一种新颖的归纳知识图谱嵌入模型，该模型通过捕捉实体和关系的局部结构特征，有效处理未知实体和关系。具体来说，我们构建了一个关系图来学习关系表示。在关系图中，我们使用一个四维向量来表示节点（关系）之间的交互模式，其中每个维度对应一种特定的交互类型。对于实体表示，我们的模型使用关系特征动态初始化实体特征，并注意聚合实体的邻居特征以更新实体特征。通过建模关系之间的交互模式并结合实体的结构信息，我们的模型学习如何使用注意力机制聚合邻居嵌入，从而生成新实体和关系的高质量嵌入。在基准数据集上的广泛实验表明，我们的模型优于最先进的方法，特别是在涉及完全新关系的场景中。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Inductive+Knowledge+Graph+Embedding+via+Exploring+Interaction+Patterns+of+Relations)|0| |[When LLM Meets Hypergraph: A Sociological Analysis on Personality via Online Social Networks](https://doi.org/10.1145/3627673.3679646)|Zhiyao Shu, Xiangguo Sun, Hong Cheng||Individual personalities significantly influence our perceptions, decisions, and social interactions, which is particularly crucial for gaining insights into human behavior patterns in online social network analysis. Many psychological studies have observed that personalities are strongly reflected in their social behaviors and social environments. In light of these problems, this paper proposes a sociological analysis framework for one's personality in an environment-based view instead of individual-level data mining. Specifically, to comprehensively understand an individual's behavior from low-quality records, we leverage the powerful associative ability of LLMs by designing an effective prompt. In this way, LLMs can integrate various scattered information with their external knowledge to generate higher-quality profiles, which can significantly improve the personality analysis performance. To explore the interactive mechanism behind the users and their online environments, we design an effective hypergraph neural network where the hypergraph nodes are users and the hyperedges in the hypergraph are social environments. We offer a useful dataset with user profile data, personality traits, and several detected environments from the real-world social platform. To the best of our knowledge, this is the first network-based dataset containing both hypergraph structure and social information, which could push forward future research in this area further. By employing the framework on this dataset, we can effectively capture the nuances of individual personalities and their online behaviors, leading to a deeper understanding of human interactions in the digital world.|个体性格显著影响我们的感知、决策和社会互动，这对于深入理解在线社交网络分析中的人类行为模式尤为关键。许多心理学研究观察到，性格在其社会行为和社会环境中得到了强烈体现。鉴于这些问题，本文提出了一种基于环境视角而非个体层面数据挖掘的社会学分析框架，用于分析个体性格。具体而言，为了从低质量记录中全面理解个体行为，我们利用大型语言模型（LLMs）强大的关联能力，通过设计有效的提示词，使LLMs能够整合各种分散的信息与其外部知识，生成更高质量的个体画像，从而显著提升性格分析的性能。为了探索用户与其在线环境之间的交互机制，我们设计了一种有效的超图神经网络，其中超图节点为用户，超图的超边为社会环境。我们提供了一个包含用户画像数据、性格特征及从真实社交平台检测到的多种环境的实用数据集。据我们所知，这是首个同时包含超图结构和社会信息的网络数据集，有望推动该领域的未来研究。通过在该数据集上应用该框架，我们能够有效捕捉个体性格及其在线行为的细微差别，从而更深入地理解数字世界中的人类互动。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=When+LLM+Meets+Hypergraph:+A+Sociological+Analysis+on+Personality+via+Online+Social+Networks)|0| -|[FABLE: Approximate Butterfly Counting in Bipartite Graph Stream with Duplicate Edges](https://doi.org/10.1145/3627673.3679812)|Guozhang Sun, Yuhai Zhao, Yuan Li|Northeastern University, Shenyang, China; North China University of Technology, Beijing, China|Bipartite graph models the relationship between two different sets of entities. Such graph data become more dynamic and are organized as stream with duplicate edges in real-word applications such as customer-product in e-commerce. A butterfly, (2,2)-biclique, is the simplest cohesive substructure and of great importance in a bipartite graph. However, it is challenging to estimate the number of butterflies in large scale and high dynamic bipartite graph stream when given a limited memory. Besides, existing works for butterfly counting assume no duplicate edges in the bipartite graph stream, which cause less accuracy in bipartite graph stream with duplicate edges. In this paper, we propose FABLE, a Fixed-size memory Approximate Butterfly counting algorithm for dupLicate Edges in bipartite graph stream. In FABLE, we compute the number of distinct edges by maintaining an ordered list of edge priorities for replacement and sampling. We provide theoretical proof of unbiasedness and derive the variance of butterfly count. Our extensive experiments on 5 real-world datasets confirm that our approach has higher accuracy compared with the baseline method under the same memory usage.|二分图模型描述了两组不同实体之间的关系。在电子商务等实际应用中，如客户-产品关系，这种图数据变得更加动态，并以包含重复边的流形式组织。蝴蝶结构，即(2,2)-二分团，是二分图中最重要的简单凝聚子结构。然而，在有限的内存条件下，估计大规模和高动态二分图流中的蝴蝶数量是一个挑战。此外，现有的蝴蝶计数方法假设二分图流中没有重复边，这导致在包含重复边的二分图流中计数精度较低。本文提出了FABLE，一种用于二分图流中重复边的固定内存近似蝴蝶计数算法。在FABLE中，我们通过维护一个用于替换和采样的边优先级有序列表来计算不同边的数量。我们提供了无偏性的理论证明，并推导了蝴蝶计数的方差。在5个真实世界数据集上的广泛实验证实，在相同内存使用条件下，我们的方法相比基线方法具有更高的精度。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=FABLE:+Approximate+Butterfly+Counting+in+Bipartite+Graph+Stream+with+Duplicate+Edges)|0| +|[FABLE: Approximate Butterfly Counting in Bipartite Graph Stream with Duplicate Edges](https://doi.org/10.1145/3627673.3679812)|Guozhang Sun, Yuhai Zhao, Yuan Li|North China University of Technology, Beijing, China; Northeastern University, Shenyang, China|Bipartite graph models the relationship between two different sets of entities. Such graph data become more dynamic and are organized as stream with duplicate edges in real-word applications such as customer-product in e-commerce. A butterfly, (2,2)-biclique, is the simplest cohesive substructure and of great importance in a bipartite graph. However, it is challenging to estimate the number of butterflies in large scale and high dynamic bipartite graph stream when given a limited memory. Besides, existing works for butterfly counting assume no duplicate edges in the bipartite graph stream, which cause less accuracy in bipartite graph stream with duplicate edges. In this paper, we propose FABLE, a Fixed-size memory Approximate Butterfly counting algorithm for dupLicate Edges in bipartite graph stream. In FABLE, we compute the number of distinct edges by maintaining an ordered list of edge priorities for replacement and sampling. We provide theoretical proof of unbiasedness and derive the variance of butterfly count. Our extensive experiments on 5 real-world datasets confirm that our approach has higher accuracy compared with the baseline method under the same memory usage.|二分图模型描述了两组不同实体之间的关系。在电子商务等实际应用中，如客户-产品关系，这种图数据变得更加动态，并以包含重复边的流形式组织。蝴蝶结构，即(2,2)-二分团，是二分图中最重要的简单凝聚子结构。然而，在有限的内存条件下，估计大规模和高动态二分图流中的蝴蝶数量是一个挑战。此外，现有的蝴蝶计数方法假设二分图流中没有重复边，这导致在包含重复边的二分图流中计数精度较低。本文提出了FABLE，一种用于二分图流中重复边的固定内存近似蝴蝶计数算法。在FABLE中，我们通过维护一个用于替换和采样的边优先级有序列表来计算不同边的数量。我们提供了无偏性的理论证明，并推导了蝴蝶计数的方差。在5个真实世界数据集上的广泛实验证实，在相同内存使用条件下，我们的方法相比基线方法具有更高的精度。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=FABLE:+Approximate+Butterfly+Counting+in+Bipartite+Graph+Stream+with+Duplicate+Edges)|0| |[Learnable Item Tokenization for Generative Recommendation](https://doi.org/10.1145/3627673.3679569)|Wenjie Wang, Honghui Bao, Xinyu Lin, Jizhi Zhang, Yongqi Li, Fuli Feng, SeeKiong Ng, TatSeng Chua||Utilizing powerful Large Language Models (LLMs) for generative recommendation has attracted much attention. Nevertheless, a crucial challenge is transforming recommendation data into the language space of LLMs through effective item tokenization. Current approaches, such as ID, textual, and codebook-based identifiers, exhibit shortcomings in encoding semantic information, incorporating collaborative signals, or handling code assignment bias. To address these limitations, we propose LETTER (a LEarnable Tokenizer for generaTivE Recommendation), which integrates hierarchical semantics, collaborative signals, and code assignment diversity to satisfy the essential requirements of identifiers. LETTER incorporates Residual Quantized VAE for semantic regularization, a contrastive alignment loss for collaborative regularization, and a diversity loss to mitigate code assignment bias. We instantiate LETTER on two models and propose a ranking-guided generation loss to augment their ranking ability theoretically. Experiments on three datasets validate the superiority of LETTER, advancing the state-of-the-art in the field of LLM-based generative recommendation.|利用强大的大型语言模型（LLMs）进行生成式推荐引起了广泛关注。然而，一个关键挑战是如何通过有效的物品标记化将推荐数据转换为LLMs的语言空间。当前的方法，如基于ID、文本和码本的标识符，在编码语义信息、整合协作信号或处理码分配偏差方面存在不足。为了解决这些限制，我们提出了LETTER（一种用于生成式推荐的LEarnable Tokenizer），它整合了层次语义、协作信号和码分配多样性，以满足标识符的基本要求。LETTER结合了残差量化VAE进行语义正则化，对比对齐损失进行协作正则化，以及多样性损失以减轻码分配偏差。我们在两个模型上实例化了LETTER，并提出了一种排名引导的生成损失，以理论增强其排名能力。在三个数据集上的实验验证了LETTER的优越性，推动了基于LLM的生成式推荐领域的前沿进展。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Learnable+Item+Tokenization+for+Generative+Recommendation)|0| -|[Improving Adversarial Transferability via Frequency-Guided Sample Relevance Attack](https://doi.org/10.1145/3627673.3679858)|Xinyi Wang, Zhibo Jin, Zhiyu Zhu, Jiayu Zhang, Huaming Chen|Suzhou Yierqi, Suzhou, China; The University of Sydney, Sydney, Australia; University of Malaya, Kuala Lumpur, Malaysia|Deep neural networks (DNNs) are known to be vulnerable to adversarial examples. To facilitate model safety, transfer-based attacks employ surrogate models to craft adversarial examples. In this work, we firstly study the intricate mechanisms of such attacks. We observe a correlation between the sharpness of decision boundaries in model sensitive regions and overfitting during adversarial training, which hampers the adversarial examples' transferability. To address this issue, we propose a novel approach termed Frequency-Guided Sample Relevance Attack (FGSRA). Specifically, we leverage frequency information to explore similar sensitive regions across different models, thereby generating neighborhood samples. Additional similarity weights are subsequently introduced to assess the adversarial contribution of the neighborhood samples. A hybrid gradient is then obtained to thoroughly exploit neighborhood information within input samples. Extensive experiments demonstrate the prominent performance of our approach. Compared to other state-of-the-art benchmarks on surrogate model Inc-v3, our method has an average improvement of 27.21% for normally trained CNNs and 42.1% for adversarially trained CNNs. Moreover, we achieve an average improvement of 24.6% for ViTs. Our code is available at:https://github.com/LMBTough/FGSRA|深度神经网络（DNNs）在面对对抗样本时表现出脆弱性。为了提升模型的安全性，基于迁移的攻击方法利用代理模型来生成对抗样本。在本研究中，我们首先探讨了这类攻击的复杂机制。我们观察到，在模型的敏感区域中，决策边界的锐度与对抗训练中的过拟合现象之间存在关联，这影响了对抗样本的迁移性。为解决这一问题，我们提出了一种名为频率引导样本相关性攻击（Frequency-Guided Sample Relevance Attack, FGSRA）的新方法。具体而言，我们利用频率信息来探索不同模型间相似的敏感区域，从而生成邻域样本。随后，引入额外的相似性权重来评估这些邻域样本的对抗贡献。通过这种方式，我们获得了一种混合梯度，以充分挖掘输入样本中的邻域信息。广泛的实验结果表明，我们的方法表现出色。与基于代理模型Inc-v3的其他最先进基准相比，我们的方法在常规训练的CNNs上平均提升了27.21%，在对抗训练的CNNs上平均提升了42.1%。此外，我们在ViTs上也实现了平均24.6%的提升。我们的代码已公开，可访问：https://github.com/LMBTough/FGSRA。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Improving+Adversarial+Transferability+via+Frequency-Guided+Sample+Relevance+Attack)|0| -|[Image-text Retrieval with Main Semantics Consistency](https://doi.org/10.1145/3627673.3679619)|Yi Xie, Yangtao Wang, Yanzhao Xie, Xin Tan, Jingjing Li, Xiaocui Li, Weilong Peng, Maobin Tang, Meie Fang|School of Computer Science and Cyber Engineering, Guangzhou University, Guangzhou, China; University of Electronic Science and Technology of China, Chengdu, China; Hunan University of Technology and Business, Changsha, China; East China Normal University, Shanghai, China|Image-text retrieval (ITR) has been one of the primary tasks in cross-modal retrieval, serving as a crucial bridge between computer vision and natural language processing. Significant progress has been made to achieve global alignment and local alignment between images and texts by mapping images and texts into a common space to establish correspondences between these two modalities. However, the rich semantic content contained in each image may bring false matches, resulting in the matched text ignoring the main semantics but focusing on the secondary or other semantics of this image. To address this issue, this paper proposes a semantically optimized approach with a novel Main Semantics Consistency (MSC) loss function, which aims to rank the semantically most similar images (or texts) corresponding to the given query at the top position during the retrieval process. First, in each batch of image-text pairs, we separately compute (i) the image-image similarity, i.e., the similarity between every two images, (ii) the text-text similarity, i.e., the similarity between a group of texts (that belong to a certain image) and another group of texts (that belong to another image), and (iii) the image-text similarity, i.e., the similarity between each image and each text. Afterward, our proposed MSC effectively aligns the above image-image, image-text, and text-text similarity, since the main semantics of every two images will be highly close if their text descriptions remain highly semantically consistent. By this means, we can capture the main semantics of each image to be matched with its corresponding texts, prioritizing the semantically most related retrieval results. Extensive experiments on MSCOCO and FLICKR30K verify the superior performance of MSC compared with the SOTA image-text retrieval methods. The source code of this project is released at GitHub: https://github.com/xyi007/MSC.|图像-文本检索（ITR）一直是跨模态检索中的主要任务之一，作为连接计算机视觉和自然语言处理的关键桥梁。通过将图像和文本映射到共同空间以建立这两种模态之间的对应关系，已经取得了显著的进展，实现了图像与文本之间的全局对齐和局部对齐。然而，每张图像中丰富的语义内容可能会带来错误的匹配，导致匹配的文本忽略了主要语义，而聚焦于次要或其他语义。为了解决这一问题，本文提出了一种语义优化的方法，并引入了一种新颖的主语义一致性（MSC）损失函数，旨在检索过程中将语义上最相似的图像（或文本）排在给定查询结果的最前面。首先，在每一批图像-文本对中，我们分别计算（i）图像-图像相似度，即每两张图像之间的相似度；（ii）文本-文本相似度，即属于某张图像的一组文本与属于另一张图像的另一组文本之间的相似度；以及（iii）图像-文本相似度，即每张图像与每个文本之间的相似度。随后，我们提出的MSC有效地对齐了上述的图像-图像、图像-文本和文本-文本相似度，因为如果两张图像的文本描述在语义上保持高度一致，那么这两张图像的主要语义将会高度接近。通过这种方式，我们可以捕捉每张图像的主要语义，并将其与相应的文本匹配，优先考虑语义上最相关的检索结果。在MSCOCO和FLICKR30K上的大量实验验证了MSC相比最先进的图像-文本检索方法的优越性能。本项目的源代码已在GitHub上发布：https://github.com/xyi007/MSC。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Image-text+Retrieval+with+Main+Semantics+Consistency)|0| -|[Post-Quantum Searchable Encryption Supporting User-Authorization for Outsourced Data Management](https://doi.org/10.1145/3627673.3679522)|Shiyuan Xu, Yibo Cao, Xue Chen, Yu Guo, Yuer Yang, Fangda Guo, SiuMing Yiu|; School of Cyberspace Security, Beijing University of Posts and Telecommunications, Beijing, China; School of Artificial Intelligence, Beijing Normal University, Beijing, China; Department of Computer Science, The University of Hong Kong, Hong Kong, China|With the widespread development of database systems, data security has become crucial when it comes to sharing among users and servers. A straightforward approach involves using searchable encryption to ensure the confidentiality of shared data. However, in certain scenarios, varying user tiers are granted disparate data searching privileges, and administrators need to restrict the searchability of ciphertexts to select users exclusively. To address this issue, public key encryption with authorized keyword search (PEAKS) was proposed, wherein solely authorized users possess the ability to conduct targeted keyword searches. Nonetheless, it is vulnerable to resist quantum computing attacks. As a result, research focusing on authorizing users to search for keywords while achieving quantum security is far-reaching. In this paper, we propose a lattice-based variant of PEAKS (L-PEAKS) that enables keyword dataset authorization for outsourced data management. Unlike existing schemes, our design incorporates identity-based encryption (IBE) to overcome the bottleneck of public key management. Besides, we utilize several lattice sampling algorithms to defend against attacks from quantum adversaries. Specifically, each authorized user must obtain a search privilege from an authority. The authority distributes an authorized token to the user within a specific time period, and the user generates a trapdoor for any authorized keywords. Our scheme is proven to be secure against IND-sID-CKA and T-EUF security in a quantum setting. We also conduct comprehensive evaluations on a commodity machine to assess completeness and provide theoretical complexity comparisons with existing state-of-the-art schemes.|随着数据库系统的广泛发展，数据安全在用户和服务器之间的共享过程中变得至关重要。一个直接的方法是使用可搜索加密来确保共享数据的机密性。然而，在某些情况下，不同的用户层级被授予不同的数据搜索权限，管理员需要将密文的可搜索性限制为仅对选定的用户开放。为了解决这个问题，提出了基于授权关键词搜索的公钥加密（PEAKS），其中只有授权用户能够进行目标关键词搜索。然而，这种方案易受量子计算攻击的影响。因此，研究授权用户进行关键词搜索同时实现量子安全的方案具有深远意义。本文提出了一种基于格的PEAKS变体（L-PEAKS），该变体支持外包数据管理的关键词数据集授权。与现有方案不同，我们的设计结合了基于身份的加密（IBE）来解决公钥管理的瓶颈问题。此外，我们利用多种格采样算法来防御量子敌手的攻击。具体而言，每个授权用户必须从权威机构获取搜索权限。权威机构在特定时间段内向用户分发授权令牌，用户为任何授权关键词生成陷门。我们的方案在量子环境下被证明对IND-sID-CKA和T-EUF安全是安全的。我们还对商用机器进行了全面的评估，以评估其完整性，并提供了与现有最先进方案的理论复杂度比较。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Post-Quantum+Searchable+Encryption+Supporting+User-Authorization+for+Outsourced+Data+Management)|0| +|[Improving Adversarial Transferability via Frequency-Guided Sample Relevance Attack](https://doi.org/10.1145/3627673.3679858)|Xinyi Wang, Zhibo Jin, Zhiyu Zhu, Jiayu Zhang, Huaming Chen|Suzhou Yierqi, Suzhou, China; University of Malaya, Kuala Lumpur, Malaysia; The University of Sydney, Sydney, Australia|Deep neural networks (DNNs) are known to be vulnerable to adversarial examples. To facilitate model safety, transfer-based attacks employ surrogate models to craft adversarial examples. In this work, we firstly study the intricate mechanisms of such attacks. We observe a correlation between the sharpness of decision boundaries in model sensitive regions and overfitting during adversarial training, which hampers the adversarial examples' transferability. To address this issue, we propose a novel approach termed Frequency-Guided Sample Relevance Attack (FGSRA). Specifically, we leverage frequency information to explore similar sensitive regions across different models, thereby generating neighborhood samples. Additional similarity weights are subsequently introduced to assess the adversarial contribution of the neighborhood samples. A hybrid gradient is then obtained to thoroughly exploit neighborhood information within input samples. Extensive experiments demonstrate the prominent performance of our approach. Compared to other state-of-the-art benchmarks on surrogate model Inc-v3, our method has an average improvement of 27.21% for normally trained CNNs and 42.1% for adversarially trained CNNs. Moreover, we achieve an average improvement of 24.6% for ViTs. Our code is available at:https://github.com/LMBTough/FGSRA|深度神经网络（DNNs）在面对对抗样本时表现出脆弱性。为了提升模型的安全性，基于迁移的攻击方法利用代理模型来生成对抗样本。在本研究中，我们首先探讨了这类攻击的复杂机制。我们观察到，在模型的敏感区域中，决策边界的锐度与对抗训练中的过拟合现象之间存在关联，这影响了对抗样本的迁移性。为解决这一问题，我们提出了一种名为频率引导样本相关性攻击（Frequency-Guided Sample Relevance Attack, FGSRA）的新方法。具体而言，我们利用频率信息来探索不同模型间相似的敏感区域，从而生成邻域样本。随后，引入额外的相似性权重来评估这些邻域样本的对抗贡献。通过这种方式，我们获得了一种混合梯度，以充分挖掘输入样本中的邻域信息。广泛的实验结果表明，我们的方法表现出色。与基于代理模型Inc-v3的其他最先进基准相比，我们的方法在常规训练的CNNs上平均提升了27.21%，在对抗训练的CNNs上平均提升了42.1%。此外，我们在ViTs上也实现了平均24.6%的提升。我们的代码已公开，可访问：https://github.com/LMBTough/FGSRA。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Improving+Adversarial+Transferability+via+Frequency-Guided+Sample+Relevance+Attack)|0| +|[Image-text Retrieval with Main Semantics Consistency](https://doi.org/10.1145/3627673.3679619)|Yi Xie, Yangtao Wang, Yanzhao Xie, Xin Tan, Jingjing Li, Xiaocui Li, Weilong Peng, Maobin Tang, Meie Fang|School of Computer Science and Cyber Engineering, Guangzhou University, Guangzhou, China; East China Normal University, Shanghai, China; University of Electronic Science and Technology of China, Chengdu, China; Hunan University of Technology and Business, Changsha, China|Image-text retrieval (ITR) has been one of the primary tasks in cross-modal retrieval, serving as a crucial bridge between computer vision and natural language processing. Significant progress has been made to achieve global alignment and local alignment between images and texts by mapping images and texts into a common space to establish correspondences between these two modalities. However, the rich semantic content contained in each image may bring false matches, resulting in the matched text ignoring the main semantics but focusing on the secondary or other semantics of this image. To address this issue, this paper proposes a semantically optimized approach with a novel Main Semantics Consistency (MSC) loss function, which aims to rank the semantically most similar images (or texts) corresponding to the given query at the top position during the retrieval process. First, in each batch of image-text pairs, we separately compute (i) the image-image similarity, i.e., the similarity between every two images, (ii) the text-text similarity, i.e., the similarity between a group of texts (that belong to a certain image) and another group of texts (that belong to another image), and (iii) the image-text similarity, i.e., the similarity between each image and each text. Afterward, our proposed MSC effectively aligns the above image-image, image-text, and text-text similarity, since the main semantics of every two images will be highly close if their text descriptions remain highly semantically consistent. By this means, we can capture the main semantics of each image to be matched with its corresponding texts, prioritizing the semantically most related retrieval results. Extensive experiments on MSCOCO and FLICKR30K verify the superior performance of MSC compared with the SOTA image-text retrieval methods. The source code of this project is released at GitHub: https://github.com/xyi007/MSC.|图像-文本检索（ITR）一直是跨模态检索中的主要任务之一，作为连接计算机视觉和自然语言处理的关键桥梁。通过将图像和文本映射到共同空间以建立这两种模态之间的对应关系，已经取得了显著的进展，实现了图像与文本之间的全局对齐和局部对齐。然而，每张图像中丰富的语义内容可能会带来错误的匹配，导致匹配的文本忽略了主要语义，而聚焦于次要或其他语义。为了解决这一问题，本文提出了一种语义优化的方法，并引入了一种新颖的主语义一致性（MSC）损失函数，旨在检索过程中将语义上最相似的图像（或文本）排在给定查询结果的最前面。首先，在每一批图像-文本对中，我们分别计算（i）图像-图像相似度，即每两张图像之间的相似度；（ii）文本-文本相似度，即属于某张图像的一组文本与属于另一张图像的另一组文本之间的相似度；以及（iii）图像-文本相似度，即每张图像与每个文本之间的相似度。随后，我们提出的MSC有效地对齐了上述的图像-图像、图像-文本和文本-文本相似度，因为如果两张图像的文本描述在语义上保持高度一致，那么这两张图像的主要语义将会高度接近。通过这种方式，我们可以捕捉每张图像的主要语义，并将其与相应的文本匹配，优先考虑语义上最相关的检索结果。在MSCOCO和FLICKR30K上的大量实验验证了MSC相比最先进的图像-文本检索方法的优越性能。本项目的源代码已在GitHub上发布：https://github.com/xyi007/MSC。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Image-text+Retrieval+with+Main+Semantics+Consistency)|0| +|[Post-Quantum Searchable Encryption Supporting User-Authorization for Outsourced Data Management](https://doi.org/10.1145/3627673.3679522)|Shiyuan Xu, Yibo Cao, Xue Chen, Yu Guo, Yuer Yang, Fangda Guo, SiuMing Yiu|Department of Computer Science, The University of Hong Kong, Hong Kong, China; ; School of Cyberspace Security, Beijing University of Posts and Telecommunications, Beijing, China; School of Artificial Intelligence, Beijing Normal University, Beijing, China|With the widespread development of database systems, data security has become crucial when it comes to sharing among users and servers. A straightforward approach involves using searchable encryption to ensure the confidentiality of shared data. However, in certain scenarios, varying user tiers are granted disparate data searching privileges, and administrators need to restrict the searchability of ciphertexts to select users exclusively. To address this issue, public key encryption with authorized keyword search (PEAKS) was proposed, wherein solely authorized users possess the ability to conduct targeted keyword searches. Nonetheless, it is vulnerable to resist quantum computing attacks. As a result, research focusing on authorizing users to search for keywords while achieving quantum security is far-reaching. In this paper, we propose a lattice-based variant of PEAKS (L-PEAKS) that enables keyword dataset authorization for outsourced data management. Unlike existing schemes, our design incorporates identity-based encryption (IBE) to overcome the bottleneck of public key management. Besides, we utilize several lattice sampling algorithms to defend against attacks from quantum adversaries. Specifically, each authorized user must obtain a search privilege from an authority. The authority distributes an authorized token to the user within a specific time period, and the user generates a trapdoor for any authorized keywords. Our scheme is proven to be secure against IND-sID-CKA and T-EUF security in a quantum setting. We also conduct comprehensive evaluations on a commodity machine to assess completeness and provide theoretical complexity comparisons with existing state-of-the-art schemes.|随着数据库系统的广泛发展，数据安全在用户和服务器之间的共享过程中变得至关重要。一个直接的方法是使用可搜索加密来确保共享数据的机密性。然而，在某些情况下，不同的用户层级被授予不同的数据搜索权限，管理员需要将密文的可搜索性限制为仅对选定的用户开放。为了解决这个问题，提出了基于授权关键词搜索的公钥加密（PEAKS），其中只有授权用户能够进行目标关键词搜索。然而，这种方案易受量子计算攻击的影响。因此，研究授权用户进行关键词搜索同时实现量子安全的方案具有深远意义。本文提出了一种基于格的PEAKS变体（L-PEAKS），该变体支持外包数据管理的关键词数据集授权。与现有方案不同，我们的设计结合了基于身份的加密（IBE）来解决公钥管理的瓶颈问题。此外，我们利用多种格采样算法来防御量子敌手的攻击。具体而言，每个授权用户必须从权威机构获取搜索权限。权威机构在特定时间段内向用户分发授权令牌，用户为任何授权关键词生成陷门。我们的方案在量子环境下被证明对IND-sID-CKA和T-EUF安全是安全的。我们还对商用机器进行了全面的评估，以评估其完整性，并提供了与现有最先进方案的理论复杂度比较。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Post-Quantum+Searchable+Encryption+Supporting+User-Authorization+for+Outsourced+Data+Management)|0| |[Decoupled Behavior-based Contrastive Recommendation](https://doi.org/10.1145/3627673.3679636)|Mengduo Yang, Jie Zhou, Meng Xi, Xiaohua Pan, Yi Yuan, Ying Li, Yangyang Wu, Jinshan Zhang, Jianwei Yin|; School of Software Technology, Zhejiang University, Ningbo, Zhejiang, China|Recommender systems are crucial tools in online applications, assisting users in discovering relevant content efficiently. Recent studies demonstrate that contrastive learning (CL) based methods yield significant results in collaborative filtering (CF) recommendations, due to their ability to address the issue of data sparsity. However, two inherent limitations remain unexplored in these methods. a) Since the datasets commonly used are binary (0: no interaction; 1: interaction), current methods only provide rudimentary modeling of user behaviors in binary form, which fails to model complex user-item interactions and relationships in real-world recommendation scenarios. b) Existing CL-based methods mostly construct contrastive views through heuristic-based embedding or structure perturbation, which are prone to introduce noise or discard important information, leading to a decreased representation quality. To address these issues, we propose a Decoupled Behavior-based Contrastive Recommendation model (DBCR) that effectively decouples user behaviors from binary datasets for better user-item interaction modeling. The core idea is to decouple latent user behaviors from unlabelled user-item interactions (binary datasets) and utilize self-supervised contrastive learning to optimize CF-based recommendation jointly. Specifically, we introduce latent behavior variables and embed them into user-item interaction modeling within the generalized expectation-maximization (EM) framework. Moreover, we design a contrastive learning task by constructing a preference view instead of unreasonable perturbation to further improve the learned representation. Experimental results and analyses on three real-world datasets demonstrate the effectiveness of DBCR and its high efficiency, with an average improvement of 16.9% over state-of-the-art methods. Our code is available on https://github.com/Du-danger/DBCR.|推荐系统是在线应用中的关键工具，能够帮助用户高效地发现相关内容。最近的研究表明，基于对比学习（Contrastive Learning, CL）的方法在协同过滤（Collaborative Filtering, CF）推荐中取得了显著成果，这主要归功于它们解决数据稀疏性问题的能力。然而，这些方法存在两个内在限制尚未得到充分探讨。a) 由于常用的数据集通常是二值的（0：无交互；1：有交互），当前的方法仅以二值形式对用户行为进行初步建模，未能捕捉真实推荐场景中复杂的用户-物品交互和关系。b) 现有的基于CL的方法大多通过启发式嵌入或结构扰动来构建对比视图，这容易引入噪声或丢弃重要信息，导致表示质量下降。为了解决这些问题，我们提出了一种解耦行为对比推荐模型（Decoupled Behavior-based Contrastive Recommendation model, DBCR），该模型有效地将用户行为从二值数据集中解耦，以更好地建模用户-物品交互。其核心思想是将潜在用户行为从无标签的用户-物品交互（二值数据集）中解耦，并利用自监督对比学习来联合优化基于CF的推荐。具体来说，我们引入了潜在行为变量，并在广义期望最大化（EM）框架内将其嵌入到用户-物品交互建模中。此外，我们设计了一个对比学习任务，通过构建偏好视图而非不合理的扰动来进一步提高学习到的表示质量。在三个真实世界数据集上的实验结果和分析表明，DBCR的有效性和高效性，平均比最先进的方法提高了16.9%。我们的代码可在https://github.com/Du-danger/DBCR获取。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Decoupled+Behavior-based+Contrastive+Recommendation)|0| -|[Hyperbolic Contrastive Learning for Cross-Domain Recommendation](https://doi.org/10.1145/3627673.3679572)|Xin Yang, Heng Chang, Zhijian Lai, Jinze Yang, Xingrun Li, Yu Lu, Shuaiqiang Wang, Dawei Yin, Erxue Min|University of Tsukuba, Tsukuba, Japan; Kyoto University, Kyoto, Japan; Baidu Inc., Beijing, China; Peking University, Beijing, China; Tsinghua University, Beijing, China; University of Tokyo, Tokyo, Japan|Cross-Domain Recommendation (CDR) seeks to utilize knowledge from different domains to alleviate the problem of data sparsity in the target recommendation domain, and has been gaining more attention in recent years. Although there have been notable advances in this area, most current methods represent users and items in Euclidean space, which is not ideal for handling long-tail distributed data in recommendation systems. Additionally, adding data from other domains can worsen the long-tail characteristics of the entire dataset, making it harder to train CDR models effectively. Recent studies have shown that hyperbolic methods are particularly suitable for modeling long-tail distributions, which has led us to explore hyperbolic representations for users and items in CDR scenarios. However, due to the distinct characteristics of the different domains, applying hyperbolic representation learning to CDR tasks is quite challenging. In this paper, we introduce a new framework called Hyperbolic Contrastive Learning (HCTS), designed to capture the unique features of each domain while enabling efficient knowledge transfer between domains. We achieve this by embedding users and items from each domain separately and mapping them onto distinct hyperbolic manifolds with adjustable curvatures for prediction. To improve the representations of users and items in the target domain, we develop a hyperbolic contrastive learning module for knowledge transfer. Extensive experiments on real-world datasets demonstrate that hyperbolic manifolds are a promising alternative to Euclidean space for CDR tasks. The codes are available at https://github.com/EnkiXin/hcts.|跨域推荐（CDR）旨在利用不同领域的知识来缓解目标推荐领域中的数据稀疏问题，近年来受到越来越多的关注。尽管在这一领域取得了显著进展，但大多数现有方法将用户和物品表示在欧几里得空间中，这对于处理推荐系统中的长尾分布数据并不理想。此外，添加其他领域的数据可能会加剧整个数据集的长尾特性，使得有效训练CDR模型变得更加困难。最近的研究表明，双曲方法特别适合于建模长尾分布，这促使我们探索在CDR场景中使用双曲表示用户和物品。然而，由于不同领域的特性各异，将双曲表示学习应用于CDR任务相当具有挑战性。在本文中，我们引入了一种称为双曲对比学习（HCTS）的新框架，旨在捕捉每个领域的独特特征，同时实现领域间高效的知识转移。我们通过将每个领域的用户和物品分别嵌入，并将它们映射到具有可调曲率的独立双曲流形上进行预测来实现这一点。为了改进目标领域中用户和物品的表示，我们开发了一个双曲对比学习模块用于知识转移。在真实世界数据集上的广泛实验表明，双曲流形是CDR任务中欧几里得空间的一个有前景的替代方案。代码可在https://github.com/EnkiXin/hcts获取。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Hyperbolic+Contrastive+Learning+for+Cross-Domain+Recommendation)|0| +|[Hyperbolic Contrastive Learning for Cross-Domain Recommendation](https://doi.org/10.1145/3627673.3679572)|Xin Yang, Heng Chang, Zhijian Lai, Jinze Yang, Xingrun Li, Yu Lu, Shuaiqiang Wang, Dawei Yin, Erxue Min|Peking University, Beijing, China; Tsinghua University, Beijing, China; University of Tokyo, Tokyo, Japan; Kyoto University, Kyoto, Japan; University of Tsukuba, Tsukuba, Japan; Baidu Inc., Beijing, China|Cross-Domain Recommendation (CDR) seeks to utilize knowledge from different domains to alleviate the problem of data sparsity in the target recommendation domain, and has been gaining more attention in recent years. Although there have been notable advances in this area, most current methods represent users and items in Euclidean space, which is not ideal for handling long-tail distributed data in recommendation systems. Additionally, adding data from other domains can worsen the long-tail characteristics of the entire dataset, making it harder to train CDR models effectively. Recent studies have shown that hyperbolic methods are particularly suitable for modeling long-tail distributions, which has led us to explore hyperbolic representations for users and items in CDR scenarios. However, due to the distinct characteristics of the different domains, applying hyperbolic representation learning to CDR tasks is quite challenging. In this paper, we introduce a new framework called Hyperbolic Contrastive Learning (HCTS), designed to capture the unique features of each domain while enabling efficient knowledge transfer between domains. We achieve this by embedding users and items from each domain separately and mapping them onto distinct hyperbolic manifolds with adjustable curvatures for prediction. To improve the representations of users and items in the target domain, we develop a hyperbolic contrastive learning module for knowledge transfer. Extensive experiments on real-world datasets demonstrate that hyperbolic manifolds are a promising alternative to Euclidean space for CDR tasks. The codes are available at https://github.com/EnkiXin/hcts.|跨域推荐（CDR）旨在利用不同领域的知识来缓解目标推荐领域中的数据稀疏问题，近年来受到越来越多的关注。尽管在这一领域取得了显著进展，但大多数现有方法将用户和物品表示在欧几里得空间中，这对于处理推荐系统中的长尾分布数据并不理想。此外，添加其他领域的数据可能会加剧整个数据集的长尾特性，使得有效训练CDR模型变得更加困难。最近的研究表明，双曲方法特别适合于建模长尾分布，这促使我们探索在CDR场景中使用双曲表示用户和物品。然而，由于不同领域的特性各异，将双曲表示学习应用于CDR任务相当具有挑战性。在本文中，我们引入了一种称为双曲对比学习（HCTS）的新框架，旨在捕捉每个领域的独特特征，同时实现领域间高效的知识转移。我们通过将每个领域的用户和物品分别嵌入，并将它们映射到具有可调曲率的独立双曲流形上进行预测来实现这一点。为了改进目标领域中用户和物品的表示，我们开发了一个双曲对比学习模块用于知识转移。在真实世界数据集上的广泛实验表明，双曲流形是CDR任务中欧几里得空间的一个有前景的替代方案。代码可在https://github.com/EnkiXin/hcts获取。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Hyperbolic+Contrastive+Learning+for+Cross-Domain+Recommendation)|0| |[Guaranteeing Accuracy and Fairness under Fluctuating User Traffic: A Bankruptcy-Inspired Re-ranking Approach](https://doi.org/10.1145/3627673.3679590)|Xiaopeng Ye, Chen Xu, Jun Xu, Xuyang Xie, Gang Wang, Zhenhua Dong||Out of sustainable and economical considerations, two-sided recommendation platforms must satisfy the needs of both users and providers. Previous studies often show that the two sides' needs show different urgency: providers need a relatively long-term exposure demand while users want more short-term and accurate service. However, our empirical study reveals that previous methods for trading off fairness-accuracy often fail to guarantee long-term fairness and short-term accuracy simultaneously in real applications of fluctuating user traffic. Especially, when user traffic is low, the user experience often drops a lot. Our theoretical analysis also confirms that user traffic is a key factor in such a trade-off problem. How to guarantee accuracy and fairness under fluctuating user traffic remains a problem. Inspired by the bankruptcy problem in economics, we propose a novel fairness-aware re-ranking approach named BankFair. Intuitively, BankFair employs the Talmud rule to leverage periods of abundant user traffic to offset periods of user traffic scarcity, ensuring consistent user service at every period while upholding long-term fairness. Specifically, BankFair consists of two modules: (1) employing the Talmud rule to determine the required fairness degree under varying periods of user traffic; and (2) conducting an online re-ranking algorithm based on the fairness degree determined by the Talmud rule. Experiments on two real-world recommendation datasets show that BankFair outperforms all baselines regarding accuracy and provider fairness.|出于可持续性和经济性的考虑，双边推荐平台必须同时满足用户和提供者的需求。以往的研究通常表明，双方的需求显示出不同的紧迫性：提供者需要相对长期的曝光需求，而用户则希望获得更多短期且准确的服务。然而，我们的实证研究揭示，先前用于权衡公平性与准确性的方法在面对用户流量波动的实际应用中，往往无法同时保证长期的公平性和短期的准确性。特别是在用户流量较低时，用户体验往往会大幅下降。我们的理论分析也证实，用户流量是此类权衡问题中的关键因素。如何在用户流量波动的情况下保证准确性和公平性仍然是一个问题。受经济学中破产问题的启发，我们提出了一种名为BankFair的新型公平感知重排序方法。直观上，BankFair利用用户流量充足期来弥补用户流量稀缺期，通过使用塔木德规则确保每个时期用户服务的连续性，同时维护长期的公平性。具体而言，BankFair包含两个模块：（1）利用塔木德规则确定在不同用户流量时期所需的公平性程度；（2）基于塔木德规则确定的公平性程度进行在线重排序算法。在两个真实世界推荐数据集上的实验表明，BankFair在准确性和提供者公平性方面均优于所有基线方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Guaranteeing+Accuracy+and+Fairness+under+Fluctuating+User+Traffic:+A+Bankruptcy-Inspired+Re-ranking+Approach)|0| |[EFVAE: Efficient Federated Variational Autoencoder for Collaborative Filtering](https://doi.org/10.1145/3627673.3679818)|Lu Zhang, Qian Rong, Xuanang Ding, Guohui Li, Ling Yuan|Huazhong University of Science and Technology, Wuhan, China|Federated recommender systems are used to address privacy issues in recommendations. Among them, FedVAE extends the representative non-linear recommendation method MultVAE. However, the bottleneck of FedVAE lies in its communication load during training, as the parameter volume of its first and last layers is correlated with the number of items. This leads to significant communication cost during the model's transmission phases (distribution and upload), making FedVAE's implementation extremely challenging. To address these challenges, we propose an Efficient Federated Variational AutoEncoder for collaborative filtering, EFVAE, which core is the Federated Collaborative Importance Sampling (FCIS) method. FCIS reduces communication costs through a client-to-server collaborative sampling mechanism and provides satisfactory recommendation performance through dynamic multi-stage approximation of the decoding distribution. Extensive experiments and analyses on real-world datasets confirm that EFVAE significantly reduces communication costs by up to 94.51% while maintaining the recommendation performance. Moreover, its recommendation performance is better on sparse datasets, with improvements reaching up to 13.79%.|联邦推荐系统用于解决推荐中的隐私问题。其中，FedVAE扩展了代表性的非线性推荐方法MultVAE。然而，FedVAE的瓶颈在于其训练过程中的通信负载，因为其第一层和最后一层的参数体积与项目数量相关。这导致模型传输阶段（分发和上传）的通信成本显著增加，使得FedVAE的实现极为困难。为了应对这些挑战，我们提出了一种高效的联邦变分自编码器用于协同过滤，EFVAE，其核心是联邦协同重要性采样（FCIS）方法。FCIS通过客户端到服务器的协同采样机制减少通信成本，并通过解码分布的动态多阶段近似提供令人满意的推荐性能。在真实世界数据集上的广泛实验和分析证实，EFVAE显著减少了高达94.51%的通信成本，同时保持了推荐性能。此外，它在稀疏数据集上的推荐性能更好，提升幅度最高可达13.79%。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=EFVAE:+Efficient+Federated+Variational+Autoencoder+for+Collaborative+Filtering)|0| |[MSKR: Advancing Multi-modal Structured Knowledge Representation with Synergistic Hard Negative Samples](https://doi.org/10.1145/3627673.3679680)|Shuili Zhang, Hongzhang Mu, Tingwen Liu, Qianqian Tong, Jiawei Sheng|; Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China|Despite the notable progress achieved by large-scale vision-language pre-training models in a wide range of multi-modal tasks, their performance often falls short in image-text matching challenges that require an in-depth understanding of structured representations. For instance, when distinguishing between texts or images that are generally similar but have distinct structured knowledge (such as entities and relationships in text, or objects and object attributes in images), the model's capabilities are limited. In this paper, we propose a advancing Multi-modal Structured Knowledge Representation with synergistic hard negative samples (MSKR), thereby significantly improving the model's matching capability for such data. Specifically, our model comprises a structured knowledge-enhanced encoder designed to bolster the structured knowledge inherent in textual data, such as entities, their attributes, and the relationships among these entities as well as structured knowledge within images, focusing on elements like objects and their attributes. To further refine the model's learning process, we produce both image and text challenging negative samples. Extensive experimental evaluations on the Winoground, InpaintCOCO, and MSCOCO benchmark reveal that MSKR significantly outperforms the baseline model, showcasing marked improvements 2.66% on average in structured representation learning compared to the baseline. Moreover, general representation results illustrate that our model not only excels in structured representation learning but also maintains its proficiency in general representation learning.|尽管大规模视觉-语言预训练模型在多模态任务中取得了显著进展，但在需要深入理解结构化表示的图像-文本匹配挑战中，其表现往往不尽如人意。例如，在区分文本或图像时，当这些文本或图像总体相似但具有不同的结构化知识（如文本中的实体及其关系，或图像中的对象及其属性）时，模型的能力受到限制。本文提出了一种通过协同硬负样本推进多模态结构化知识表示（MSKR）的方法，从而显著提升了模型对这类数据的匹配能力。具体而言，我们的模型包括一个结构化知识增强的编码器，旨在强化文本数据中固有的结构化知识，如实体、实体属性及其关系，以及图像中关注对象及其属性的结构化知识。为了进一步优化模型的学习过程，我们生成了图像和文本的挑战性负样本。在Winoground、InpaintCOCO和MSCOCO基准上的广泛实验评估表明，MSKR显著优于基线模型，与基线相比，在结构化表示学习中平均提高了2.66%。此外，通用表示结果显示，我们的模型不仅在结构化表示学习中表现出色，而且在通用表示学习中也保持了其熟练度。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=MSKR:+Advancing+Multi-modal+Structured+Knowledge+Representation+with+Synergistic+Hard+Negative+Samples)|0| @@ -116,30 +116,30 @@ |[Osprey 🪶: A Reference Framework for Online Grooming Detection via Neural Models and Conversation Features](https://doi.org/10.1145/3627673.3679974)|Hamed Waezi, Reza Barzegar, Hossein Fani|School of Computer Science, University of Windsor, Windsor, ON, Canada|Online grooming is the process of an adult initiating a sexual relationship with a minor through online conversation platforms. While neural models are developed to detect such incidents, their practical implications in real-world settings remain moot for their closed, irreproducible, and poor evaluation methodologies under the sparse distribution of grooming conversations in the training datasets, like undermining recall over precision. Furthermore, proposed models overlook characteristic features of grooming in online conversations, including the number of participants, message exchange patterns, and temporal signals, such as the elapsed times between messages. In this paper, we foremost contribute Osprey, an open-source library to support a standard pipeline and experimental details, incorporating canonical neural models and a variety of vector representation learning for conversations while accommodating new models and training datasets. Further, we incorporate conversation features into the models to improve recall while maintaining precision. Our experiments across neural baselines and vector representations of conversations demonstrated that recurrent neural models, particularly gru, on the sequence of pretrained transformer-based embeddings of messages in a conversation along with conversation features obtain state-of-the-art performance, winning the best recall with competitive precision. Osprey is available at https://github.com/fani-lab/Osprey/tree/cikm24.|在线“狩猎”是指成年人通过在线聊天平台与未成年人建立性关系的过程。尽管已开发出神经模型来检测此类事件，但由于训练数据集中“狩猎”对话的分布稀疏，这些模型在实际应用中的效果仍不明确，其封闭、不可复现且评估方法不当的问题削弱了召回率而偏重精度。此外，现有模型忽视了在线对话中“狩猎”行为的特征，如参与者数量、消息交换模式及时间信号（如消息之间的时间间隔）。本文首先贡献了Osprey，一个开源库，支持标准流程和实验细节，整合了经典的神经模型和多种对话向量表示学习方法，并兼容新模型和训练数据集。进一步，我们将对话特征融入模型，以提高召回率的同时保持精度。我们的实验表明，在神经基线和对话向量表示中，基于预训练变换器嵌入的消息序列及对话特征的循环神经模型，特别是GRU，取得了最先进的性能，获得了最佳召回率并保持了竞争力的精度。Osprey可通过以下链接获取：https://github.com/fani-lab/Osprey/tree/cikm24。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Osprey+🪶:+A+Reference+Framework+for+Online+Grooming+Detection+via+Neural+Models+and+Conversation+Features)|0| |[The Effect of Icon Semantic Distance on Preschool Children's Information Search: Evidence from an Eye-Tracking Study](https://doi.org/10.1145/3627673.3680001)|Jiaqi Yang, Pianran Wang|Department of Information Management, Peking University, Beijing, China|Icons are frequently employed in children-oriented information systems due to children's limited literacy. However, the inherent semantic distances of icons, which may influence their affordance to children, are often overlooked in the development of such systems and related research. In this study, we apply semantic distance to measure the explicitness of icons in children-oriented book search, utilizing self-developed icons tailored for indexing picture books. We first gathered data from children through questionnaires to assess the perceived semantic distance of each icon. Subsequently, we conducted eye-tracking experiments with 50 preschool children, measuring their search accuracy, response time, and eye movement patterns while using icons to locate specific picture books. Our findings indicate that preschool children are easier to use icons with close semantic distance and single icons for searching. Additionally, the ability to use icons with distant semantic distances and combination icons significantly improves with age. These findings may contribute to the development of more effective and children-friendly information search systems.|由于儿童的识字能力有限，图标经常被用于面向儿童的信息系统中。然而，图标固有的语义距离，这可能会影响它们对儿童的可操作性，在开发此类系统和相关研究中往往被忽视。在本研究中，我们应用语义距离来衡量面向儿童的图书搜索中图标的显性程度，使用为索引图画书而定制的自开发图标。我们首先通过问卷从儿童那里收集数据，以评估每个图标的感知语义距离。随后，我们对50名学前儿童进行了眼动追踪实验，测量他们在使用图标定位特定图画书时的搜索准确性、反应时间和眼动模式。我们的研究结果表明，学前儿童更容易使用语义距离接近的单个图标进行搜索。此外，随着年龄的增长，使用语义距离较远的组合图标的能力显著提高。这些发现可能有助于开发更有效且更适合儿童的信息搜索系统。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=The+Effect+of+Icon+Semantic+Distance+on+Preschool+Children's+Information+Search:+Evidence+from+an+Eye-Tracking+Study)|0| |[Contrastive Disentangled Representation Learning for Debiasing Recommendation with Uniform Data](https://doi.org/10.1145/3627673.3679889)|Xinxin Yang, Zhen Liu, Xiaoman Lu, Yafan Yuan, Sibo Lu, Yibo Gao|Beijing Jiaotong University, Beijing, China|In recommender systems, learning high-quality user and item representations is crucial for predicting user preferences. However, there are various confounding factors in observational data, resulting in data bias, which hinders the learning of user and item representations. Recent work proposed to use uniform data to alleviate bias problem. However, these methods fail to learn pure representations for unbiased prediction, which are not affected by confounding factors. This paper introduces a novel disentangled framework, named CDLRec, for learning unbiased representations, leveraging uniform data as supervisory signal for disentangling. Furthermore, to address the scarcity problem of uniform data, the contrastive learning is utilized to implement disentanglement by providing augmented samples. Specifically, two contrastive strategies are designed based on different sampling ways for positives and negatives. Extensive experiments are conducted over two real-world datasets and the results demonstrate the superior performance of our proposed method.|在推荐系统中，学习高质量的用户和物品表示对于预测用户偏好至关重要。然而，观察数据中存在多种混淆因素，导致数据偏差，这阻碍了用户和物品表示的学习。最近的工作提出使用均匀数据来缓解偏差问题。然而，这些方法未能学习到不受混淆因素影响的纯表示，从而无法进行无偏预测。本文引入了一种名为CDLRec的新型解耦框架，利用均匀数据作为解耦的监督信号来学习无偏表示。此外，为了解决均匀数据稀缺的问题，本文利用对比学习通过提供增强样本来实现解耦。具体来说，基于正负样本的不同采样方式设计了两种对比策略。在两个真实世界数据集上进行了大量实验，结果表明我们提出的方法具有优越的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Contrastive+Disentangled+Representation+Learning+for+Debiasing+Recommendation+with+Uniform+Data)|0| -|[Dual-level Intents Modeling for Knowledge-aware Recommendation](https://doi.org/10.1145/3627673.3679902)|Jin Zeng, Nan Wang, Jinbao Li|Heilongjiang University, Harbin, China; Qilu University of Technology, Jinan, China|Previous user-item interaction graphs have typically focused on simple interaction between users and items, failing to identify the important effects of user's intents in the interaction. While recent studies have ventured into exploring intent relationships between users and items for modeling, they predominantly emphasize user preferences manifesting in the interaction, overlooking knowledge-driven insight, thereby limiting the interpretability of intent. In this paper, we utilize the rich interpretable knowledge information in the knowledge graph to design a novel dual-level intents modeling framework called DIM. DIM aims to mine user's true intents, which usually include user popularity preference and personalized preference. Therefore, we extract both the popular and personalized user preferences from attribute tuples within the knowledge graph at the global and local levels, respectively. Experimental results on three datasets demonstrate the superiority of DIM over various state-of-the-art approaches.|以往的用户-项目交互图主要关注用户与项目之间的简单交互，未能识别出用户意图在交互中的重要影响。虽然近期研究开始探索用户与项目之间的意图关系以进行建模，但它们主要强调交互中表现出的用户偏好，忽视了基于知识的洞察，从而限制了意图的可解释性。本文利用知识图谱中丰富的可解释知识信息，设计了一种新颖的双层次意图建模框架，称为DIM。DIM旨在挖掘用户的真实意图，这些意图通常包括用户的热门偏好和个性化偏好。因此，我们从知识图谱的属性元组中分别提取了全局和局部层面的热门和个性化用户偏好。在三个数据集上的实验结果表明，DIM优于各种最先进的方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Dual-level+Intents+Modeling+for+Knowledge-aware+Recommendation)|0| -|[Distilling Knowledge Based on Curriculum Learning for Temporal Knowledge Graph Embeddings](https://doi.org/10.1145/3627673.3679896)|Bin Zhang, Jiayin Li, Yuanfei Dai|Fujian Normal University, Fuzhou, China; Nanjing Tech University, Nanjing, China|Lower-dimensional temporal knowledge graph embedding (TKGE) models are crucial for practical applications and resource-limited scenarios, although existing models employ higher-dimensional embeddings in training. In this paper, we propose a new framework for distilling TKGE models via an easy to hard pedagogical principle. The framework utilizes a learnable curriculum temperature (CT) module to optimize and guide the knowledge distillation process dynamically, ensuring that the entire procedure adheres to the principle. It also employs a self-adaptive attention mechanism to endeavor to achieve efficient transfer of knowledge from higher-dimensional models to lower-dimensional ones. Evaluation on various TKGE models and datasets demonstrates the proposed approach significantly reduces the model's parameters without noticeably affecting its performance.|低维时间知识图谱嵌入（TKGE）模型对于实际应用和资源有限场景至关重要，尽管现有模型在训练中采用高维嵌入。本文提出了一种基于由易到难教学原则的TKGE模型蒸馏新框架。该框架利用可学习的课程温度（CT）模块动态优化并指导知识蒸馏过程，确保整个过程遵循该原则。同时，采用自适应注意力机制，努力实现从高维模型到低维模型的知识高效传递。在多种TKGE模型和数据集上的评估表明，所提出的方法显著减少了模型参数，且对性能影响甚微。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Distilling+Knowledge+Based+on+Curriculum+Learning+for+Temporal+Knowledge+Graph+Embeddings)|0| +|[Dual-level Intents Modeling for Knowledge-aware Recommendation](https://doi.org/10.1145/3627673.3679902)|Jin Zeng, Nan Wang, Jinbao Li|Qilu University of Technology, Jinan, China; Heilongjiang University, Harbin, China|Previous user-item interaction graphs have typically focused on simple interaction between users and items, failing to identify the important effects of user's intents in the interaction. While recent studies have ventured into exploring intent relationships between users and items for modeling, they predominantly emphasize user preferences manifesting in the interaction, overlooking knowledge-driven insight, thereby limiting the interpretability of intent. In this paper, we utilize the rich interpretable knowledge information in the knowledge graph to design a novel dual-level intents modeling framework called DIM. DIM aims to mine user's true intents, which usually include user popularity preference and personalized preference. Therefore, we extract both the popular and personalized user preferences from attribute tuples within the knowledge graph at the global and local levels, respectively. Experimental results on three datasets demonstrate the superiority of DIM over various state-of-the-art approaches.|以往的用户-项目交互图主要关注用户与项目之间的简单交互，未能识别出用户意图在交互中的重要影响。虽然近期研究开始探索用户与项目之间的意图关系以进行建模，但它们主要强调交互中表现出的用户偏好，忽视了基于知识的洞察，从而限制了意图的可解释性。本文利用知识图谱中丰富的可解释知识信息，设计了一种新颖的双层次意图建模框架，称为DIM。DIM旨在挖掘用户的真实意图，这些意图通常包括用户的热门偏好和个性化偏好。因此，我们从知识图谱的属性元组中分别提取了全局和局部层面的热门和个性化用户偏好。在三个数据集上的实验结果表明，DIM优于各种最先进的方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Dual-level+Intents+Modeling+for+Knowledge-aware+Recommendation)|0| +|[Distilling Knowledge Based on Curriculum Learning for Temporal Knowledge Graph Embeddings](https://doi.org/10.1145/3627673.3679896)|Bin Zhang, Jiayin Li, Yuanfei Dai|Nanjing Tech University, Nanjing, China; Fujian Normal University, Fuzhou, China|Lower-dimensional temporal knowledge graph embedding (TKGE) models are crucial for practical applications and resource-limited scenarios, although existing models employ higher-dimensional embeddings in training. In this paper, we propose a new framework for distilling TKGE models via an easy to hard pedagogical principle. The framework utilizes a learnable curriculum temperature (CT) module to optimize and guide the knowledge distillation process dynamically, ensuring that the entire procedure adheres to the principle. It also employs a self-adaptive attention mechanism to endeavor to achieve efficient transfer of knowledge from higher-dimensional models to lower-dimensional ones. Evaluation on various TKGE models and datasets demonstrates the proposed approach significantly reduces the model's parameters without noticeably affecting its performance.|低维时间知识图谱嵌入（TKGE）模型对于实际应用和资源有限场景至关重要，尽管现有模型在训练中采用高维嵌入。本文提出了一种基于由易到难教学原则的TKGE模型蒸馏新框架。该框架利用可学习的课程温度（CT）模块动态优化并指导知识蒸馏过程，确保整个过程遵循该原则。同时，采用自适应注意力机制，努力实现从高维模型到低维模型的知识高效传递。在多种TKGE模型和数据集上的评估表明，所提出的方法显著减少了模型参数，且对性能影响甚微。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Distilling+Knowledge+Based+on+Curriculum+Learning+for+Temporal+Knowledge+Graph+Embeddings)|0| |[Feedback Reciprocal Graph Collaborative Filtering](https://doi.org/10.1145/3627673.3680015)|Weijun Chen, Yuanchen Bei, Qijie Shen, Hao Chen, Xiao Huang, Feiran Huang||Collaborative filtering on user-item interaction graphs has achieved success in the industrial recommendation. However, recommending users' truly fascinated items poses a seesaw dilemma for collaborative filtering models learned from the interaction graph. On the one hand, not all items that users interact with are equally appealing. Some items are genuinely fascinating to users, while others are unfascinated. Training graph collaborative filtering models in the absence of distinction between them can lead to the recommendation of unfascinating items to users. On the other hand, disregarding the interacted but unfascinating items during graph collaborative filtering will result in an incomplete representation of users' interaction intent, leading to a decline in the model's recommendation capabilities. To address this seesaw problem, we propose Feedback Reciprocal Graph Collaborative Filtering (FRGCF), which emphasizes the recommendation of fascinating items while attenuating the recommendation of unfascinating items. Specifically, FRGCF first partitions the entire interaction graph into the Interacted Fascinated (I F) graph and the Interacted Unfascinated (I U) graph based on the user feedback. Then, FRGCF introduces separate collaborative filtering on the I F graph and the I U graph with feedback-reciprocal contrastive learning and macro-level feedback modeling. This enables the I F graph recommender to learn multi-grained interaction characteristics from the I U graph without being misdirected by it. Extensive experiments on four benchmark datasets and a billion-scale industrial dataset demonstrate that FRGCF improves the performance by recommending more fascinating items and fewer unfascinating items. Besides, online A/B tests on Taobao's recommender system verify the superiority of FRGCF.|在用户-物品交互图上的协同过滤在工业推荐中取得了成功。然而，从交互图中学习的协同过滤模型在推荐用户真正感兴趣的物品时面临着跷跷板难题。一方面，用户交互的所有物品并非同样吸引人。有些物品确实能引起用户的兴趣，而有些则不能。在没有区分这些物品的情况下训练图协同过滤模型可能会导致向用户推荐不吸引人的物品。另一方面，在图协同过滤过程中忽略那些虽被交互但不吸引人的物品会导致用户交互意图的表示不完整，从而降低模型的推荐能力。为了解决这一跷跷板问题，我们提出了反馈互惠图协同过滤（FRGCF），该方法强调推荐吸引人的物品，同时减弱对不吸引人物品的推荐。具体来说，FRGCF首先根据用户反馈将整个交互图划分为交互且吸引人（I F）图和交互但不吸引人（I U）图。然后，FRGCF在I F图和I U图上分别引入协同过滤，并结合反馈互惠对比学习和宏观层次的反馈建模。这使得I F图推荐器能够从I U图中学习多粒度的交互特征，而不会被其误导。在四个基准数据集和一个亿级工业数据集上的广泛实验表明，FRGCF通过推荐更多吸引人的物品和更少不吸引人的物品，提升了性能。此外，在淘宝推荐系统上的在线A/B测试验证了FRGCF的优越性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Feedback+Reciprocal+Graph+Collaborative+Filtering)|0| -|[DIFN: A Dual Intention-aware Network for Repurchase Recommendation with Hierarchical Spatio-temporal Fusion](https://doi.org/10.1145/3627673.3680071)|Li Lin, Xin Xu, Hai Wang, Tian He, Desheng Zhang, Shuai Wang|Southeast University, Nanjing, Jiangsu, China; JD Logistics, Beijing, Beijing, China; Rutgers University, Piscataway, New Jersey, USA|Recommendation systems play a crucial role in both industrial applications and research fields, which target to understand user preferences and intentions to provide personalized services. Compared to conventional recommendations, repurchase recommendations aim to suggest suitable products to users that they used to buy based on their intention evolution. Existing research on product recommendation can mainly be divided into behavior sequence-based methods and graph-based methods. Although these methods represent user interests and preference features effectively, they still fail to model repurchase behaviors because (i) the environment causing repurchase intention change is neglected and (ii) the lack of feedback after purchasing makes it difficult to learn the impacts of diverse behaviors. To comprehensively consider these limitations, we design a D ual I ntention-aware F usion N etwork framework (DIFN) to understand the effects of environment and after-purchasing feedback on users' intentions. Firstly, a hierarchical graph-based multi-level relational attention module is designed to effectively extract basic user features and spatial features from complex environmental information. Then, we introduce a behavior intention module and a usage intention module for different types of feedback data. Finally, we propose a dual intention fusion network that effectively fuses user basic features with spatial attributes and user intention features with temporal attributes for recommendation. Comprehensive evaluations on real-world datasets show that our method exceeds state-of-the-art baselines, which show an average of 8.2% improvements in different metrics.|推荐系统在工业应用和研究领域中扮演着至关重要的角色，其目标是通过理解用户偏好和意图来提供个性化服务。与传统推荐相比，复购推荐旨在根据用户意图的演变，向其推荐他们曾经购买过的合适产品。现有的产品推荐研究主要可分为基于行为序列的方法和基于图的方法。尽管这些方法能够有效表示用户的兴趣和偏好特征，但它们未能对复购行为进行建模，原因有二：(i) 忽视了导致复购意图变化的环境因素；(ii) 购买后缺乏反馈，使得难以学习多样行为的影响。为了全面考虑这些限制，我们设计了一个双意图感知融合网络框架（DIFN），以理解环境和购买后反馈对用户意图的影响。首先，我们设计了一个基于层次图的多级关系注意力模块，以有效从复杂的环境信息中提取基本用户特征和空间特征。接着，我们引入了行为意图模块和使用意图模块，分别处理不同类型的反馈数据。最后，我们提出了一种双意图融合网络，该网络能够有效融合用户基本特征与空间属性以及用户意图特征与时间属性，用于推荐。在真实世界数据集上的综合评估表明，我们的方法超越了最先进的基线方法，在不同指标上平均提升了8.2%。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=DIFN:+A+Dual+Intention-aware+Network+for+Repurchase+Recommendation+with+Hierarchical+Spatio-temporal+Fusion)|0| +|[DIFN: A Dual Intention-aware Network for Repurchase Recommendation with Hierarchical Spatio-temporal Fusion](https://doi.org/10.1145/3627673.3680071)|Li Lin, Xin Xu, Hai Wang, Tian He, Desheng Zhang, Shuai Wang|JD Logistics, Beijing, Beijing, China; Southeast University, Nanjing, Jiangsu, China; Rutgers University, Piscataway, New Jersey, USA|Recommendation systems play a crucial role in both industrial applications and research fields, which target to understand user preferences and intentions to provide personalized services. Compared to conventional recommendations, repurchase recommendations aim to suggest suitable products to users that they used to buy based on their intention evolution. Existing research on product recommendation can mainly be divided into behavior sequence-based methods and graph-based methods. Although these methods represent user interests and preference features effectively, they still fail to model repurchase behaviors because (i) the environment causing repurchase intention change is neglected and (ii) the lack of feedback after purchasing makes it difficult to learn the impacts of diverse behaviors. To comprehensively consider these limitations, we design a D ual I ntention-aware F usion N etwork framework (DIFN) to understand the effects of environment and after-purchasing feedback on users' intentions. Firstly, a hierarchical graph-based multi-level relational attention module is designed to effectively extract basic user features and spatial features from complex environmental information. Then, we introduce a behavior intention module and a usage intention module for different types of feedback data. Finally, we propose a dual intention fusion network that effectively fuses user basic features with spatial attributes and user intention features with temporal attributes for recommendation. Comprehensive evaluations on real-world datasets show that our method exceeds state-of-the-art baselines, which show an average of 8.2% improvements in different metrics.|推荐系统在工业应用和研究领域中扮演着至关重要的角色，其目标是通过理解用户偏好和意图来提供个性化服务。与传统推荐相比，复购推荐旨在根据用户意图的演变，向其推荐他们曾经购买过的合适产品。现有的产品推荐研究主要可分为基于行为序列的方法和基于图的方法。尽管这些方法能够有效表示用户的兴趣和偏好特征，但它们未能对复购行为进行建模，原因有二：(i) 忽视了导致复购意图变化的环境因素；(ii) 购买后缺乏反馈，使得难以学习多样行为的影响。为了全面考虑这些限制，我们设计了一个双意图感知融合网络框架（DIFN），以理解环境和购买后反馈对用户意图的影响。首先，我们设计了一个基于层次图的多级关系注意力模块，以有效从复杂的环境信息中提取基本用户特征和空间特征。接着，我们引入了行为意图模块和使用意图模块，分别处理不同类型的反馈数据。最后，我们提出了一种双意图融合网络，该网络能够有效融合用户基本特征与空间属性以及用户意图特征与时间属性，用于推荐。在真实世界数据集上的综合评估表明，我们的方法超越了最先进的基线方法，在不同指标上平均提升了8.2%。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=DIFN:+A+Dual+Intention-aware+Network+for+Repurchase+Recommendation+with+Hierarchical+Spatio-temporal+Fusion)|0| |[Building Natural Language Interface for Product Search](https://doi.org/10.1145/3627673.3680070)|Vijit Malik, Vinayak Puranik, Anirban Majumder, Vivek Sembium|Amazon, Bangalore, India|Automatic extraction of attribute preferences from search queries is a critical problem in providing accurate product recommendations to customer. The task becomes even more challenging in cold-start settings where we do not have any supervised/labelled data available to train ML models. In this work, we implement a novel dataset generation pipeline (LLM-API) that leverages Large Language Models (LLMs), search logs and proprietary product information data from an ecommerce website to create a high quality dataset. Our proposed pipeline of LLM-API is robust as it can generalize to any product category with minimal changes in the LLM prompts. For the problem of converting product search queries to API calls we propose a multi-task schema generator model which we train on our generated dataset. Experiments on an internal test set reveals that our proposed model achieves an improvement of ≈9.6% and ≈5% in Exact Match and Micro-F1 respectively, over competitive baselines. Benchmarking our approach on public test set of search queries further reveals a gain of ≈8.6% and ≈10.5% in Exact Match and Micro-F1. We further demonstrate that our approach outperforms a state-of-the-art LLM (Claude) applied on our task using few-shot prompting and CoT reasoning, while at the same time, achieves improvement in inference latency.|从搜索查询中自动提取属性偏好是向客户提供准确产品推荐的关键问题。在冷启动场景下，这一任务变得更加具有挑战性，因为我们没有任何监督/标注数据来训练机器学习模型。在这项工作中，我们实现了一种新颖的数据集生成管道（LLM-API），该管道利用大型语言模型（LLMs）、搜索日志以及来自电子商务网站的专有产品信息数据，来创建高质量的数据集。我们提出的LLM-API管道具有鲁棒性，因为它可以推广到任何产品类别，只需对LLM提示进行最小的更改。对于将产品搜索查询转换为API调用的问题，我们提出了一种多任务模式生成器模型，并在我们生成的数据集上进行训练。在内部测试集上的实验表明，我们提出的模型在精确匹配和微观F1得分上分别比竞争基线提高了约9.6%和5%。在公共测试集上的基准测试进一步显示，精确匹配和微观F1得分分别提高了约8.6%和10.5%。我们进一步证明，我们的方法在少样本提示和CoT推理下，优于应用于我们任务的最先进的LLM（Claude），同时在推理延迟方面也实现了改进。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Building+Natural+Language+Interface+for+Product+Search)|0| -|[EASE: Learning Lightweight Semantic Feature Adapters from Large Language Models for CTR Prediction](https://doi.org/10.1145/3627673.3680048)|Zexuan Qiu, Jieming Zhu, Yankai Chen, Guohao Cai, Weiwen Liu, Zhenhua Dong, Irwin King|Conrnell University, Ithaca, USA; The Chinese University of Hong Kong, Hong Kong, China; Huawei Noah's Ark Lab, Shenzhen, China|Recent studies highlight the potential of large language models (LLMs) to enhance content integration in recommender systems by leveraging their semantic understanding capabilities. However, directly incorporating LLMs into an online inference pipeline significantly increases computation costs for large-scale deployment, posing a practical challenge in balancing their benefits and costs. In this work, we propose the EASE framework, which enriches and aligns semantic feature embeddings using LLMs during the training phase while establishing a lightweight inference pipeline that does not directly involve LLMs. Specifically, we train a semantic adapter to align item features with LLMs and simultaneously enrich semantic embeddings through reconstruction tasks from LLMs. During inference, we retain only the item feature encoder and lightweight semantic adapter, thereby eliminating the computation overhead of resource-intensive LLMs. Our EASE framework is flexible, supporting not only text and visual features but also other pre-processed embedding features. Extensive experiments on both public and industrial datasets demonstrate that enriching semantic feature embeddings with our EASE framework yields consistent improvements in downstream click-through rate prediction tasks.|近期研究强调了大型语言模型（LLMs）通过利用其语义理解能力来增强推荐系统中内容整合的潜力。然而，直接将LLMs整合到在线推理管道中显著增加了大规模部署的计算成本，这使得在平衡其收益和成本方面面临实际挑战。在此工作中，我们提出了EASE框架，该框架在训练阶段利用LLMs丰富和校准语义特征嵌入，同时建立一个不直接涉及LLMs的轻量级推理管道。具体而言，我们训练一个语义适配器来校准项目特征与LLMs，并通过从LLMs进行重建任务来同时丰富语义嵌入。在推理阶段，我们仅保留项目特征编码器和轻量级语义适配器，从而消除了资源密集型LLMs的计算开销。我们的EASE框架具有灵活性，不仅支持文本和视觉特征，还支持其他预处理的嵌入特征。在公共和工业数据集上的广泛实验表明，通过我们的EASE框架丰富语义特征嵌入在下游点击率预测任务中持续提升了性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=EASE:+Learning+Lightweight+Semantic+Feature+Adapters+from+Large+Language+Models+for+CTR+Prediction)|0| -|[Mitigating Extreme Cold Start in Graph-based RecSys through Re-ranking](https://doi.org/10.1145/3627673.3680069)|Alessandro Sbandi, Federico Siciliano, Fabrizio Silvestri|Sapienza University of Rome, Rome, Italy; TIM S.p.A. & Sapienza University of Rome, Rome, Italy|Recommender systems based on Graph Neural Networks (GNN) have become the state-of-the-art approach in recommendation, but they struggle with in extreme cold-start settings, where most users or items lack interaction data. This paper proposes a novel framework to address this challenge in four steps: (i) a propensity model to predict item purchase behaviour, with associated explainability to identify the most relevant features, (ii) a link augmentation module to connect users based on previously obtained similarities, (iii) a GNN-based link prediction step on the obtained dense graph and (iv) a final re-ranking stage to increase diversity in predictions leveraging users embeddings. By exploiting the enriched graph structure, the framework generates embeddings for cold-start users and items, enabling diverse recommendations, containing long tail and unsold items, for both established and new users. We validate the framework's effectiveness on real-world industrial data from TIM S.p.A.|基于图神经网络（GNN）的推荐系统已成为推荐领域的最先进方法，但在极端冷启动情况下表现不佳，此时大多数用户或物品缺乏交互数据。本文提出了一种新颖的框架，通过四个步骤来解决这一挑战：（i）一个倾向模型用于预测物品购买行为，并提供相关解释性以识别最相关的特征；（ii）一个链接增强模块，基于先前获得的相似性连接用户；（iii）在获得的密集图上进行基于GNN的链接预测步骤；（iv）一个最终的重新排序阶段，利用用户嵌入来增加预测的多样性。通过利用丰富的图结构，该框架为冷启动用户和物品生成嵌入，从而能够为既有用户和新用户提供包含长尾和未售物品的多样化推荐。我们在TIM S.p.A.的真实工业数据上验证了该框架的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Mitigating+Extreme+Cold+Start+in+Graph-based+RecSys+through+Re-ranking)|0| +|[EASE: Learning Lightweight Semantic Feature Adapters from Large Language Models for CTR Prediction](https://doi.org/10.1145/3627673.3680048)|Zexuan Qiu, Jieming Zhu, Yankai Chen, Guohao Cai, Weiwen Liu, Zhenhua Dong, Irwin King|The Chinese University of Hong Kong, Hong Kong, China; Huawei Noah's Ark Lab, Shenzhen, China; Conrnell University, Ithaca, USA|Recent studies highlight the potential of large language models (LLMs) to enhance content integration in recommender systems by leveraging their semantic understanding capabilities. However, directly incorporating LLMs into an online inference pipeline significantly increases computation costs for large-scale deployment, posing a practical challenge in balancing their benefits and costs. In this work, we propose the EASE framework, which enriches and aligns semantic feature embeddings using LLMs during the training phase while establishing a lightweight inference pipeline that does not directly involve LLMs. Specifically, we train a semantic adapter to align item features with LLMs and simultaneously enrich semantic embeddings through reconstruction tasks from LLMs. During inference, we retain only the item feature encoder and lightweight semantic adapter, thereby eliminating the computation overhead of resource-intensive LLMs. Our EASE framework is flexible, supporting not only text and visual features but also other pre-processed embedding features. Extensive experiments on both public and industrial datasets demonstrate that enriching semantic feature embeddings with our EASE framework yields consistent improvements in downstream click-through rate prediction tasks.|近期研究强调了大型语言模型（LLMs）通过利用其语义理解能力来增强推荐系统中内容整合的潜力。然而，直接将LLMs整合到在线推理管道中显著增加了大规模部署的计算成本，这使得在平衡其收益和成本方面面临实际挑战。在此工作中，我们提出了EASE框架，该框架在训练阶段利用LLMs丰富和校准语义特征嵌入，同时建立一个不直接涉及LLMs的轻量级推理管道。具体而言，我们训练一个语义适配器来校准项目特征与LLMs，并通过从LLMs进行重建任务来同时丰富语义嵌入。在推理阶段，我们仅保留项目特征编码器和轻量级语义适配器，从而消除了资源密集型LLMs的计算开销。我们的EASE框架具有灵活性，不仅支持文本和视觉特征，还支持其他预处理的嵌入特征。在公共和工业数据集上的广泛实验表明，通过我们的EASE框架丰富语义特征嵌入在下游点击率预测任务中持续提升了性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=EASE:+Learning+Lightweight+Semantic+Feature+Adapters+from+Large+Language+Models+for+CTR+Prediction)|0| +|[Mitigating Extreme Cold Start in Graph-based RecSys through Re-ranking](https://doi.org/10.1145/3627673.3680069)|Alessandro Sbandi, Federico Siciliano, Fabrizio Silvestri|TIM S.p.A. & Sapienza University of Rome, Rome, Italy; Sapienza University of Rome, Rome, Italy|Recommender systems based on Graph Neural Networks (GNN) have become the state-of-the-art approach in recommendation, but they struggle with in extreme cold-start settings, where most users or items lack interaction data. This paper proposes a novel framework to address this challenge in four steps: (i) a propensity model to predict item purchase behaviour, with associated explainability to identify the most relevant features, (ii) a link augmentation module to connect users based on previously obtained similarities, (iii) a GNN-based link prediction step on the obtained dense graph and (iv) a final re-ranking stage to increase diversity in predictions leveraging users embeddings. By exploiting the enriched graph structure, the framework generates embeddings for cold-start users and items, enabling diverse recommendations, containing long tail and unsold items, for both established and new users. We validate the framework's effectiveness on real-world industrial data from TIM S.p.A.|基于图神经网络（GNN）的推荐系统已成为推荐领域的最先进方法，但在极端冷启动情况下表现不佳，此时大多数用户或物品缺乏交互数据。本文提出了一种新颖的框架，通过四个步骤来解决这一挑战：（i）一个倾向模型用于预测物品购买行为，并提供相关解释性以识别最相关的特征；（ii）一个链接增强模块，基于先前获得的相似性连接用户；（iii）在获得的密集图上进行基于GNN的链接预测步骤；（iv）一个最终的重新排序阶段，利用用户嵌入来增加预测的多样性。通过利用丰富的图结构，该框架为冷启动用户和物品生成嵌入，从而能够为既有用户和新用户提供包含长尾和未售物品的多样化推荐。我们在TIM S.p.A.的真实工业数据上验证了该框架的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Mitigating+Extreme+Cold+Start+in+Graph-based+RecSys+through+Re-ranking)|0| |[Sequence-level Semantic Representation Fusion for Recommender Systems](https://doi.org/10.1145/3627673.3680037)|Lanling Xu, Zhen Tian, Bingqian Li, Junjie Zhang, Daoyuan Wang, Hongyu Wang, Jinpeng Wang, Sheng Chen, Wayne Xin Zhao||With the rapid development of recommender systems, there is increasing side information that can be employed to improve the recommendation performance. Specially, we focus on the utilization of the associated textual data of items (eg product title) and study how text features can be effectively fused with ID features in sequential recommendation. However, there exists distinct data characteristics for the two kinds of item features, making a direct fusion method (eg adding text and ID embeddings as item representation) become less effective. To address this issue, we propose a novel Text-ID semantic fusion approach for sequential Recommendation, namely . The core idea of our approach is to conduct a sequence-level semantic fusion approach by better integrating global contexts. The key strategy lies in that we transform the text embeddings and ID embeddings by Fourier Transform from time domain to frequency domain. In the frequency domain, the global sequential characteristics of the original sequences are inherently aggregated into the transformed representations, so that we can employ simple multiplicative operations to effectively fuse the two kinds of item features. Our fusion approach can be proved to have the same effects of contextual convolution, so as to achieving sequence-level semantic fusion. In order to further improve the fusion performance, we propose to enhance the discriminability of the text embeddings from the text encoder, by adaptively injecting positional information via a mixture-of-experts (MoE) modulation method. Our implementation is available at this repository: .|随着推荐系统的快速发展，越来越多的辅助信息可用于提升推荐性能。特别是，我们专注于利用项目的关联文本数据（如产品标题），并研究如何在序列推荐中有效地将文本特征与ID特征融合。然而，这两类项目特征存在显著的数据特性，使得直接的融合方法（如将文本和ID嵌入作为项目表示相加）效果不佳。为了解决这一问题，我们提出了一种新的文本-ID语义融合方法，用于序列推荐，即。我们的方法的核心思想是通过更好地整合全局上下文，进行序列级别的语义融合。关键策略在于，我们通过傅里叶变换将文本嵌入和ID嵌入从时域转换到频域。在频域中，原始序列的全局序列特性自然地聚合到转换后的表示中，从而我们可以使用简单的乘法操作来有效融合这两类项目特征。我们的融合方法可以证明具有与上下文卷积相同的效果，从而实现序列级别的语义融合。为了进一步提高融合性能，我们提出通过混合专家（MoE）调制方法自适应地注入位置信息，以增强文本编码器中文本嵌入的区分性。我们的实现代码可在以下仓库中获取：。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Sequence-level+Semantic+Representation+Fusion+for+Recommender+Systems)|0| |[Effective Utilization of Large-scale Unobserved Data in Recommendation Systems](https://doi.org/10.1145/3627673.3680067)|Feng Zhang, Yulin Xu, Hongjie Chen, Xu Yuan, Qingwen Liu, Yuning Jiang|Taotian Group, Beijing, China; Taotian Group, Hangzhou, China|Ranking models play an important role in industrial recommendation systems. However, most ranking models are trained only with the observed items but used to retrieve all items in the entire space, which may suffer from the sample selection bias and the exposure bias. Inspired by the entire space learning framework, we carry out detailed data analyses on large-scale unobserved items and find that they contain quite a few "potentially-positive" samples. In this paper, we propose an "Extract and Transfer" (EAT) framework, utilizing quantities of unobserved items and other domains' data to construct more training data for ranking models. Specifically, we first extract "potentially-positive" samples and negative ones according to their ranking scores from the unobserved data, and then design an Entire Space Transfer Learning (ESTL) model to transfer knowledge between observed and unobserved samples, instead of directly mixing them together to avoid negative transfer. Experiments on production data collected from Taobao validate the proposed method's superiority. Besides, we have deployed EAT on the Taobao recommendation system, obtaining 6.22% IPV (Item Page View) and 3.77% CTR improvement. The code is available at https://github.com/Recommender1/EAT.git1.|排名模型在工业推荐系统中扮演着重要角色。然而，大多数排名模型仅使用观察到的项目进行训练，但在整个项目空间中检索所有项目时使用，这可能导致样本选择偏差和曝光偏差。受整体空间学习框架的启发，我们对大规模未观察到的项目进行了详细的数据分析，发现其中包含相当数量的“潜在正面”样本。本文提出了一种“提取与迁移”（EAT）框架，利用大量未观察到的项目和其他领域的数据来构建更多用于排名模型的训练数据。具体而言，我们首先根据排名分数从未观察到的数据中提取“潜在正面”样本和负样本，然后设计了一个整体空间迁移学习（ESTL）模型，用于在观察到的样本和未观察到的样本之间进行知识迁移，而不是直接将它们混合在一起，以避免负迁移。在从淘宝收集的生产数据上的实验验证了所提出方法的优越性。此外，我们已经在淘宝推荐系统中部署了EAT，获得了6.22%的IPV（商品页面浏览量）和3.77%的CTR提升。代码可在https://github.com/Recommender1/EAT.git1获取。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Effective+Utilization+of+Large-scale+Unobserved+Data+in+Recommendation+Systems)|0| |[ECRT: Flexible Sequence Enhancement Framework for Cross-Domain Information Reuse in Recommendation](https://doi.org/10.1145/3627673.3680038)|Weiqiang Zhao, ZiYuan Wu, Yatao Yang, Lifeng Hua, Hao Xiong|Alibaba Group, Hangzhou, China; Nanjing University, Nanjing, China|Chronological sequence of user-item interactions is a key feature in recommender systems, as it reveals the transition of users' interests as well as contextual relevance between nearby items. In modern e-commerce applications, various scenarios are usually integrated in one entry page, and the behavior sequence tend to be a combination of user-item interactions across multiple domains, such as on-sale goods, search queries, short videos, livestreams, etc. However, traditional domain-specified recommendations only deal with the interactions within the target domain, which neglects the overall profiles depicted by the behavior across the entire application, leading to overestimation of retargeted items as well as underestimation of unseen ones. So it is crucial to leverage cross-domain data from prominent domains to better supplement user behavior sequences for our targets. To tackle this problem, we propose the Enhanced Cross-domain Ralation Transfer (ECRT) framework to make flexible sequence augmentation with the assist of cross-domain information from other domains. We first employ similarity-based retrieval to obtain relevant sequence information from neighbor domains and build a heterogeneous graph to represent the complex behavior of users. Then we use innovative mining approaches to sample relevant information from the graph to supplement users' behavior sequences, and a hierarchical gated attention structure is used to aggregate these augmented information. We apply our proposed method in the livestream recommendation of Taobao channel pages, and the final experimental results indicate that our method demonstrates excellent performance in both online and offline environments, with an excess of up to 3.6% in main online indicators beyond past SOTA methods.|用户与物品交互的时间序列是推荐系统中的一个关键特征，因为它揭示了用户兴趣的转变以及相邻物品之间的上下文相关性。在现代电子商务应用中，各种场景通常集成在一个入口页面上，行为序列往往是跨多个领域（如在售商品、搜索查询、短视频、直播等）的用户-物品交互的组合。然而，传统的特定领域推荐系统仅处理目标领域内的交互，忽略了整个应用中行为所描绘的整体轮廓，导致对再定位物品的高估和对未见物品的低估。因此，利用来自显著领域的跨领域数据来更好地补充用户行为序列以实现我们的目标至关重要。为了解决这个问题，我们提出了增强的跨领域关系迁移（ECRT）框架，通过借助其他领域的跨领域信息来实现灵活的序列增强。我们首先采用基于相似性的检索方法从邻近领域获取相关的序列信息，并构建一个异构图来表示用户的复杂行为。然后，我们使用创新的挖掘方法从图中采样相关信息来补充用户的行为序列，并使用分层门控注意力结构来聚合这些增强的信息。我们将所提出的方法应用于淘宝频道页面的直播推荐，最终的实验结果表明，我们的方法在在线和离线环境中均表现出色，主要在线指标比过去的SOTA方法高出多达3.6%。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=ECRT:+Flexible+Sequence+Enhancement+Framework+for+Cross-Domain+Information+Reuse+in+Recommendation)|0| |[Collaborative Scope: Encountering the Substitution Effect within the Delivery Scope in Online Food Delivery Platform](https://doi.org/10.1145/3627673.3680029)|Yida Zhu, Liying Chen, Chen Zheng, Jia Shi, Daping Xiong, Zewen Huang, Shihao Ren, Shuiping Chen, Jinghua Hao, Renqing He|Meituan, Beijing, China|Online food delivery (OFD) services, known for offering varied meals at home, have gained global popularity. Meituan has recently ventured into the affordable market segment with its "Pinhaofan'' service, highlighting the imperative to delivery efficiency. To achieve this, delivery scope is regarded as one of the most effective operational tools. The delivery scope of a merchant refers to the geo-graphical area where they can serve customers. Current methods for generating delivery scopes primarily focus on optimizing a single merchant's efficiency or rely on manual delineated from the merchant's perspective, neglecting the merchant substitution effect and potentially resulting in order loss. In this paper, we propose a novel method, named Collaborative Scope, which views the delivery scope as an assortment optimization problem, considering the substitution effect between merchants from the user's perspective. We introduce the discrete choice model of econometrics and use the Enhanced Multinomial Logit Model to predict user conversion rates in the merchant list. Next, we formulate the delivery scope optimization problem of multiple merchants as a mixed integer programming problem. The city-wide solution of this problem, owing to the large-scale combinatorial optimization triggered by high-dimensional decision variables, incurs high computational complexity. To address this, we propose an approximate solution to the original problem through a first-order Taylor series approximation, which significantly reduces the computation complexity at the expense of a slight decrease in solution accuracy. Offline and online A/B test results indicate that, compared to existing methods, Collaborative Scope significantly improves delivery efficiency by reducing delivery difficulty without hurt of order volume. Notably, Collaborative Scope is currently deployed on "Pinhaofan'', serving tens of millions of online users.|在线食品配送（OFD）服务因其能够将多样化的餐食送至家中而风靡全球。美团最近通过其“拼好饭”服务进军了平价市场领域，突显了配送效率的重要性。为此，配送范围被视为最有效的运营工具之一。商家的配送范围指的是他们能够服务顾客的地理区域。目前生成配送范围的方法主要集中在优化单个商家的效率，或依赖商家视角的手动划定，忽视了商家之间的替代效应，可能导致订单损失。本文提出了一种名为“协同范围”的新方法，将配送范围视为一个组合优化问题，从用户的角度考虑商家之间的替代效应。我们引入了计量经济学的离散选择模型，并使用增强的多项式逻辑回归模型来预测用户在商家列表中的转化率。接着，我们将多个商家的配送范围优化问题形式化为一个混合整数规划问题。由于高维决策变量引发的大规模组合优化，该问题的城市级解决方案计算复杂度高。为此，我们通过一阶泰勒级数近似提出了原问题的近似解，显著降低了计算复杂度，但略微降低了解决方案的精度。离线和在线A/B测试结果表明，与现有方法相比，协同范围显著提高了配送效率，减少了配送难度，且不影响订单量。值得注意的是，协同范围目前已在“拼好饭”上部署，服务于数千万在线用户。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Collaborative+Scope:+Encountering+the+Substitution+Effect+within+the+Delivery+Scope+in+Online+Food+Delivery+Platform)|0| |[EDGE: A Conversational Interface driven by Large Language Models for Educational Knowledge Graphs Exploration](https://doi.org/10.1145/3627673.3679231)|Neda Afreen, Giacomo Balloccu, Ludovico Boratto, Gianni Fenu, Francesca Maridina Malloci, Mirko Marras, Andrea Giovanni Martis|University of Cagliari, Cagliari, Italy|As education adopts digital platforms, the vast amount of information from various sources, such as learning management systems and learning object repositories, presents challenges in navigation and elaboration. Traditional interfaces involve a steep learning curve, limited user accessibility, and lack flexibility. Language models alone cannot address these issues as they do not have access to structured information specific to the educational organization. In this paper, we propose EDGE (EDucational knowledge Graph Explorer), a natural language interface that uses knowledge graphs to organize educational information. EDGE translates natural language requests into queries and converts the results back into natural language responses. We show EDGE's versatility using knowledge graphs built from public datasets, providing example interactions of different stakeholders. Demo video: https://u.garr.it/eYq63.|随着教育转向数字化平台，来自学习管理系统、学习对象资源库等各种来源的海量信息在导航和详细阐述方面带来了挑战。传统界面存在学习曲线陡峭、用户可访问性有限以及缺乏灵活性的问题。仅依赖语言模型无法解决这些问题，因为它们无法访问教育机构特有的结构化信息。本文提出了EDGE（教育知识图谱探索器），这是一个利用知识图谱组织教育信息的自然语言界面。EDGE将自然语言请求转换为查询，并将结果转换回自然语言响应。我们展示了EDGE的多样性，使用了从公共数据集构建的知识图谱，并提供了不同利益相关者的示例交互。演示视频链接：https://u.garr.it/eYq63。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=EDGE:+A+Conversational+Interface+driven+by+Large+Language+Models+for+Educational+Knowledge+Graphs+Exploration)|0| -|[A Supervised BERT Model for Identifying Core-Intent Bearing Phrases in e-Commerce Queries](https://doi.org/10.1145/3627673.3679072)|Abhishek Sudhakar Deshmukh, Arnab Dutta|eBay GmbH, Aachen, Germany; eBay GmbH, Dreilinden, Germany|In the realm of e-Commerce, a fundamental problem is accurate interpretation of users' core intent. The intent is often subtly expressed implicitly or stated explicitly with the usage of verbose tokens or key phrases in a user query. In this work, we focus on the later class of problems where we identify a subset of query tokens which are the primary intent bearing phrases that convey explicit intents. We did not solve this as an intent detection problem but rather an immutable component detection problem because we believe that discovering the immutable phrases in a query entails that those are the intent bearing phrases. Furthermore, identifying a certain set of query tokens as immutable ensures better downstream processing in terms of unprecedented token handling, query category detection or query rewrites. We have developed a BERT based supervised learned model which can identify core-intent tokens, thereby improving F1 score over the baseline by over 35%. Furthermore, we integrated our proposed approach for a query recovery strategy which produces approximately 11.9% improvement in offline relevance scores compared to the production model.|在电子商务领域，一个基本问题是如何准确解读用户的中心意图。用户的意图通常通过隐晦的隐式表达或使用冗长的标记或关键词在用户查询中明确表达。在这项工作中，我们关注的是后一类问题，即识别查询标记中的一部分，这些标记是主要承载意图的短语，传达明确的意图。我们没有将这个问题视为意图检测问题，而是视为不可变组件检测问题，因为我们认为，发现查询中的不可变短语意味着这些短语是承载意图的短语。此外，将某些查询标记识别为不可变，确保了在处理前所未见的标记、查询类别检测或查询重写方面的下游处理效果更好。我们开发了一个基于BERT的有监督学习模型，该模型能够识别核心意图标记，从而将F1得分比基线提高了35%以上。此外，我们将提出的方法集成到一个查询恢复策略中，与生产模型相比，离线相关性评分提高了约11.9%。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Supervised+BERT+Model+for+Identifying+Core-Intent+Bearing+Phrases+in+e-Commerce+Queries)|0| +|[A Supervised BERT Model for Identifying Core-Intent Bearing Phrases in e-Commerce Queries](https://doi.org/10.1145/3627673.3679072)|Abhishek Sudhakar Deshmukh, Arnab Dutta|eBay GmbH, Dreilinden, Germany; eBay GmbH, Aachen, Germany|In the realm of e-Commerce, a fundamental problem is accurate interpretation of users' core intent. The intent is often subtly expressed implicitly or stated explicitly with the usage of verbose tokens or key phrases in a user query. In this work, we focus on the later class of problems where we identify a subset of query tokens which are the primary intent bearing phrases that convey explicit intents. We did not solve this as an intent detection problem but rather an immutable component detection problem because we believe that discovering the immutable phrases in a query entails that those are the intent bearing phrases. Furthermore, identifying a certain set of query tokens as immutable ensures better downstream processing in terms of unprecedented token handling, query category detection or query rewrites. We have developed a BERT based supervised learned model which can identify core-intent tokens, thereby improving F1 score over the baseline by over 35%. Furthermore, we integrated our proposed approach for a query recovery strategy which produces approximately 11.9% improvement in offline relevance scores compared to the production model.|在电子商务领域，一个基本问题是如何准确解读用户的中心意图。用户的意图通常通过隐晦的隐式表达或使用冗长的标记或关键词在用户查询中明确表达。在这项工作中，我们关注的是后一类问题，即识别查询标记中的一部分，这些标记是主要承载意图的短语，传达明确的意图。我们没有将这个问题视为意图检测问题，而是视为不可变组件检测问题，因为我们认为，发现查询中的不可变短语意味着这些短语是承载意图的短语。此外，将某些查询标记识别为不可变，确保了在处理前所未见的标记、查询类别检测或查询重写方面的下游处理效果更好。我们开发了一个基于BERT的有监督学习模型，该模型能够识别核心意图标记，从而将F1得分比基线提高了35%以上。此外，我们将提出的方法集成到一个查询恢复策略中，与生产模型相比，离线相关性评分提高了约11.9%。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Supervised+BERT+Model+for+Identifying+Core-Intent+Bearing+Phrases+in+e-Commerce+Queries)|0| |[Traversing the Journey of Data and AI: From Convergence to Translation](https://doi.org/10.1145/3627673.3679026)|Nitesh V. Chawla|University of Notre Dame, Notre Dame, IN, USA|In this talk, I will present our work on fundamental advances in AI, inspired by interdisciplinary problem statements and societal challenges. I will also highlight our innovation journey that encapsulates both the opportunities and challenges inherent in harnessing the full potential of AI for societal benefit, in particular highlighting the realization of societal impact through translational work and partnerships. Additionally, I will highlight our educational endeavors, emphasizing experiential learning and interdisciplinary approaches as fundamental elements of the student experience.|在本次演讲中，我将介绍我们在人工智能基础性进展方面的工作，这些工作受到跨学科问题陈述和社会挑战的启发。我还将重点介绍我们的创新历程，这一历程既体现了利用人工智能全部潜力造福社会的机遇，也揭示了其中的挑战，特别强调了通过转化工作和合作伙伴关系实现社会影响的重要性。此外，我将强调我们的教育努力，突出体验式学习和跨学科方法作为学生体验的基本要素。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Traversing+the+Journey+of+Data+and+AI:+From+Convergence+to+Translation)|0| |[Is the Search Engine of the Future a Chatbot?](https://doi.org/10.1145/3627673.3679059)|Suzan Verberne|Leiden Institute of Advanced Computer Science, Leiden University, Leiden, Netherlands|The rise of Large Language Models (LLMs) has had a huge impact on the interaction of users with information. Many people argue that the age of search engines as we know them has ended, while other people argue that retrieval technology is more relevant than ever before, because we need information to be grounded in sources. In my talk I will argue that both statements are true. I will discuss the multiple relations between LLMs and Information Retrieval: how can they strengthen each other, what are the challenges we face, and what directions should we go in our research?|大型语言模型（LLMs）的崛起对用户与信息的交互方式产生了巨大影响。许多人认为，我们所熟知的搜索引擎时代已经结束，而另一些人则认为，检索技术比以往任何时候都更加重要，因为我们需要的不仅仅是信息，而是基于来源的信息。在我的演讲中，我将主张这两种观点都是正确的。我将探讨LLMs与信息检索之间的多重关系：它们如何相互增强，我们面临的挑战是什么，以及我们的研究应该朝着哪些方向发展？|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Is+the+Search+Engine+of+the+Future+a+Chatbot?)|0| -|[Navigating the Landscape of Reproducible Research: A Predictive Modeling Approach](https://doi.org/10.1145/3627673.3679831)|Akhil Pandey Akella, Sagnik Ray Choudhury, David Koop, Hamed Alhoori|University of North Texas, Denton, TX, USA; Northern Illinois University, Dekalb, IL, USA; Northern Illinois University & Northwestern University, Dekalb, IL, USA|The reproducibility of scientific articles is central to the advancement of science. Despite this importance, evaluating reproducibility remains challenging due to the scarcity of ground truth data. Predictive models can address this limitation by streamlining the tedious evaluation process. Typically, a paper's reproducibility is inferred based on the availability of artifacts such as code, data, or supplemental information, often without extensive empirical investigation. To address these issues, we utilized artifacts of papers as fundamental units to develop a novel, dual-spectrum framework that focuses on author-centric and external-agent perspectives. We used the author-centric spectrum, followed by the external-agent spectrum, to guide a structured, model-based approach to quantify and assess reproducibility. We explored the interdependencies between different factors influencing reproducibility and found that linguistic features such as readability and lexical diversity are strongly correlated with papers achieving the highest statuses on both spectrums. Our work provides a model-driven pathway for evaluating the reproducibility of scientific research. The code, methods, and artifacts for our study are publicly available at: https://github.com/reproducibilityproject/NLRR/|科学文章的可重复性对于科学的进步至关重要。尽管其重要性不言而喻，但由于缺乏真实数据，评估可重复性仍然具有挑战性。预测模型可以通过简化繁琐的评估过程来解决这一限制。通常，一篇论文的可重复性是基于代码、数据或补充信息等资源的存在来推断的，而往往没有进行广泛的实证调查。为了解决这些问题，我们利用论文的资源作为基本单位，开发了一种新颖的双光谱框架，该框架侧重于以作者为中心和外部代理的视角。我们使用以作者为中心的光谱，随后是外部代理的光谱，来指导一种结构化的、基于模型的方法来量化和评估可重复性。我们探讨了影响可重复性的不同因素之间的相互依赖关系，并发现语言特征如可读性和词汇多样性与论文在两个光谱上达到最高状态之间存在强烈的相关性。我们的工作为评估科学研究的可重复性提供了一种模型驱动的途径。我们研究的代码、方法和资源可在以下公开获取：https://github.com/reproducibilityproject/NLRR/。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Navigating+the+Landscape+of+Reproducible+Research:+A+Predictive+Modeling+Approach)|0| -|[Aligning Large Language Model with Direct Multi-Preference Optimization for Recommendation](https://doi.org/10.1145/3627673.3679611)|Zhuoxi Bai, Ning Wu, Fengyu Cai, Xinyi Zhu, Yun Xiong|CashCat, Hangzhou, China; Shanghai University, Shanghai, China; Beihang University, Beijing, China; Fudan University, Shanghai, China; Technical University of Darmstadt, Darmstadt, Germany|Large Language Models (LLMs) have shown impressive performance in various domains, prompting researchers to explore their potential application in recommendation systems. However, directly applying LLMs to recommendation tasks has proven to be less effective due to the significant gap between the data used for pre-training LLMs and the specific requirements of recommendation tasks. In this study, we propose Direct Multi-Preference Optimization (DMPO), a streamlined framework to bridge this gap and enhance the alignment of LLMs for recommendation tasks. DMPO can be viewed as a pair-wise ranking loss to distinguish between positive and negative samples in recommendation tasks. Furthermore, DMPO improves the performance of LLM-based recommenders by maximizing the probability of positive samples and minimizing the probability of multiple negative samples at the same time. Experimental evaluations are conducted to compare DMPO with traditional recommendation methods and other LLM-based recommendation methods. The results reveal that DMPO significantly enhances the recommendation capabilities of LLMs across three real-world public datasets in few-shot scenarios. Furthermore, the experiments also demonstrate that DMPO exhibits superior generalization ability in cross-domain recommendation. A case study elucidates the reasons behind these consistent improvements and also underscores DMPO's potential as an explainable recommendation system. Our code and data are available at https://github.com/BZX667/DMPO.|大型语言模型（LLMs）在多个领域展示了卓越的性能，促使研究人员探索其在推荐系统中的潜在应用。然而，直接将LLMs应用于推荐任务已被证明效果不佳，因为用于预训练LLMs的数据与推荐任务的具体需求之间存在显著差距。在本研究中，我们提出了直接多偏好优化（Direct Multi-Preference Optimization, DMPO），这是一个简化的框架，旨在弥合这一差距并增强LLMs在推荐任务中的适应性。DMPO可以视为一种成对排序损失，用于区分推荐任务中的正样本和负样本。此外，DMPO通过最大化正样本的概率并同时最小化多个负样本的概率，提升了基于LLM的推荐系统的性能。我们进行了实验评估，将DMPO与传统的推荐方法以及其他基于LLM的推荐方法进行了比较。结果显示，在少样本场景下，DMPO在三个真实世界的公开数据集上显著增强了LLMs的推荐能力。此外，实验还表明，DMPO在跨领域推荐中展现出优越的泛化能力。一项案例研究阐明了这些持续改进的原因，并强调了DMPO作为可解释推荐系统的潜力。我们的代码和数据可在https://github.com/BZX667/DMPO 获取。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Aligning+Large+Language+Model+with+Direct+Multi-Preference+Optimization+for+Recommendation)|0| +|[Navigating the Landscape of Reproducible Research: A Predictive Modeling Approach](https://doi.org/10.1145/3627673.3679831)|Akhil Pandey Akella, Sagnik Ray Choudhury, David Koop, Hamed Alhoori|Northern Illinois University & Northwestern University, Dekalb, IL, USA; University of North Texas, Denton, TX, USA; Northern Illinois University, Dekalb, IL, USA|The reproducibility of scientific articles is central to the advancement of science. Despite this importance, evaluating reproducibility remains challenging due to the scarcity of ground truth data. Predictive models can address this limitation by streamlining the tedious evaluation process. Typically, a paper's reproducibility is inferred based on the availability of artifacts such as code, data, or supplemental information, often without extensive empirical investigation. To address these issues, we utilized artifacts of papers as fundamental units to develop a novel, dual-spectrum framework that focuses on author-centric and external-agent perspectives. We used the author-centric spectrum, followed by the external-agent spectrum, to guide a structured, model-based approach to quantify and assess reproducibility. We explored the interdependencies between different factors influencing reproducibility and found that linguistic features such as readability and lexical diversity are strongly correlated with papers achieving the highest statuses on both spectrums. Our work provides a model-driven pathway for evaluating the reproducibility of scientific research. The code, methods, and artifacts for our study are publicly available at: https://github.com/reproducibilityproject/NLRR/|科学文章的可重复性对于科学的进步至关重要。尽管其重要性不言而喻，但由于缺乏真实数据，评估可重复性仍然具有挑战性。预测模型可以通过简化繁琐的评估过程来解决这一限制。通常，一篇论文的可重复性是基于代码、数据或补充信息等资源的存在来推断的，而往往没有进行广泛的实证调查。为了解决这些问题，我们利用论文的资源作为基本单位，开发了一种新颖的双光谱框架，该框架侧重于以作者为中心和外部代理的视角。我们使用以作者为中心的光谱，随后是外部代理的光谱，来指导一种结构化的、基于模型的方法来量化和评估可重复性。我们探讨了影响可重复性的不同因素之间的相互依赖关系，并发现语言特征如可读性和词汇多样性与论文在两个光谱上达到最高状态之间存在强烈的相关性。我们的工作为评估科学研究的可重复性提供了一种模型驱动的途径。我们研究的代码、方法和资源可在以下公开获取：https://github.com/reproducibilityproject/NLRR/。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Navigating+the+Landscape+of+Reproducible+Research:+A+Predictive+Modeling+Approach)|0| +|[Aligning Large Language Model with Direct Multi-Preference Optimization for Recommendation](https://doi.org/10.1145/3627673.3679611)|Zhuoxi Bai, Ning Wu, Fengyu Cai, Xinyi Zhu, Yun Xiong|Shanghai University, Shanghai, China; CashCat, Hangzhou, China; Technical University of Darmstadt, Darmstadt, Germany; Beihang University, Beijing, China; Fudan University, Shanghai, China|Large Language Models (LLMs) have shown impressive performance in various domains, prompting researchers to explore their potential application in recommendation systems. However, directly applying LLMs to recommendation tasks has proven to be less effective due to the significant gap between the data used for pre-training LLMs and the specific requirements of recommendation tasks. In this study, we propose Direct Multi-Preference Optimization (DMPO), a streamlined framework to bridge this gap and enhance the alignment of LLMs for recommendation tasks. DMPO can be viewed as a pair-wise ranking loss to distinguish between positive and negative samples in recommendation tasks. Furthermore, DMPO improves the performance of LLM-based recommenders by maximizing the probability of positive samples and minimizing the probability of multiple negative samples at the same time. Experimental evaluations are conducted to compare DMPO with traditional recommendation methods and other LLM-based recommendation methods. The results reveal that DMPO significantly enhances the recommendation capabilities of LLMs across three real-world public datasets in few-shot scenarios. Furthermore, the experiments also demonstrate that DMPO exhibits superior generalization ability in cross-domain recommendation. A case study elucidates the reasons behind these consistent improvements and also underscores DMPO's potential as an explainable recommendation system. Our code and data are available at https://github.com/BZX667/DMPO.|大型语言模型（LLMs）在多个领域展示了卓越的性能，促使研究人员探索其在推荐系统中的潜在应用。然而，直接将LLMs应用于推荐任务已被证明效果不佳，因为用于预训练LLMs的数据与推荐任务的具体需求之间存在显著差距。在本研究中，我们提出了直接多偏好优化（Direct Multi-Preference Optimization, DMPO），这是一个简化的框架，旨在弥合这一差距并增强LLMs在推荐任务中的适应性。DMPO可以视为一种成对排序损失，用于区分推荐任务中的正样本和负样本。此外，DMPO通过最大化正样本的概率并同时最小化多个负样本的概率，提升了基于LLM的推荐系统的性能。我们进行了实验评估，将DMPO与传统的推荐方法以及其他基于LLM的推荐方法进行了比较。结果显示，在少样本场景下，DMPO在三个真实世界的公开数据集上显著增强了LLMs的推荐能力。此外，实验还表明，DMPO在跨领域推荐中展现出优越的泛化能力。一项案例研究阐明了这些持续改进的原因，并强调了DMPO作为可解释推荐系统的潜力。我们的代码和数据可在https://github.com/BZX667/DMPO 获取。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Aligning+Large+Language+Model+with+Direct+Multi-Preference+Optimization+for+Recommendation)|0| |[Wise Fusion: Group Fairness Enhanced Rank Fusion](https://doi.org/10.1145/3627673.3679649)|Kathleen Cachel, Elke A. Rundensteiner|Worcester Polytechnic Institute, Worcester, MA, USA|Rank fusion is a technique for combining multiple rankings into a single aggregated ranking, commonly used in high-stakes applications. For hiring decisions, a fused ranking might combine evaluations of different candidates from various job boards into one list. Ideally, such fused rankings are fair. Meaning they do not withhold opportunities or resources from marginalized groups of candidates, even if such biases may be present in the to-be-fused rankings. Prior work fairly aggregating rankings is limited to ensuring proportional (not addressing equality) fairness when combining ranked lists containing the same candidate items. Yet, real-world fusion tasks often combine rankings of varying candidate sets, may also contain relevance scores, or are better suited to equal representation. To address fairness in these settings, we present a new plug-and-play fairness-aware fusion strategy: WISE fusion. WISE works in fusion settings where we have closed-box access to a score-powered rank fusion (SRF) method, making it possible to fairness-enhance existing fusion pipelines with little added cost. WISE uses existing evaluations of candidates from an as-is SRF method to achieve proportional or equal rank fairness in the final fused ranking. Our experimental study demonstrates that WISE beats the fairness and utility performance of state-of-the-art methods applied to these new fair rank fusion settings.|排名融合是一种将多个排名合并为单一综合排名的技术，常见于高风险应用中。例如，在招聘决策中，一个融合后的排名可能会将来自不同求职网站的候选人评估整合成一个名单。理想情况下，这种融合后的排名应该是公平的，即它们不会剥夺边缘化候选人群体的机会或资源，即使待融合的排名本身可能存在偏见。先前的工作在公平地聚合排名方面，仅限于在合并包含相同候选人项目的排名列表时确保比例（而非平等）公平性。然而，现实世界的融合任务通常涉及合并不同候选人集合的排名，可能还包含相关性评分，或者更适合于平等代表性。为了在这些情境中解决公平性问题，我们提出了一种新的即插即用公平感知融合策略：WISE融合。WISE适用于我们对基于分数的排名融合（SRF）方法有封闭式访问权限的融合场景，使得我们能够以较低的额外成本增强现有融合流程的公平性。WISE利用现有SRF方法对候选人的评估，以在最终融合排名中实现比例或平等的排名公平性。我们的实验研究表明，WISE在这些新的公平排名融合设置中，其公平性和效用性能优于最先进的方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Wise+Fusion:+Group+Fairness+Enhanced+Rank+Fusion)|0| -|[FCS-HGNN: Flexible Multi-type Community Search in Heterogeneous Information Networks](https://doi.org/10.1145/3627673.3679696)|Guoxin Chen, Fangda Guo, Yongqing Wang, Yanghao Liu, Peiying Yu, Huawei Shen, Xueqi Cheng|; University of Chinese Academy of Sciences Institute of Computing Technology; Chinese Academy of Sciences Institute of Computing Technology; Soochow University Institute of Computing Technology|Community search is a personalized community discovery problem designed to identify densely connected subgraphs containing the query node. Recently, community search in heterogeneous information networks (HINs) has received considerable attention. Existing methods typically focus on modeling relationships in HINs through predefined meta-paths or user-specified relational constraints. However, metapath-based methods are primarily designed to identify single-type communities with nodes of the same type rather than multi-type communities involving nodes of different types. Constraint-based methods require users to have a good understanding of community patterns to define a suitable set of relational constraints, which increases the burden on users. In this paper, we propose FCS-HGNN, a novel method for flexibly identifying both single-type and multi-type communities in HINs. Specifically, FCS-HGNN extracts complementary information from different views and dynamically considers the contribution of each relation instead of treating them equally, thereby capturing more fine-grained heterogeneous information. Furthermore, to improve efficiency on large-scale graphs, we further propose LS-FCS-HGNN, which incorporates i) the neighbor sampling strategy to improve training efficiency, and ii) the depth-based heuristic search strategy to improve query efficiency. We conducted extensive experiments to demonstrate the superiority of our proposed methods over state-of-the-art methods, achieving average improvements of 14.3% and 11.1% on single-type and multi-type communities, respectively.|社区搜索是一个个性化的社区发现问题，旨在识别包含查询节点的密集连接子图。近来，异构信息网络（HINs）中的社区搜索引起了广泛关注。现有方法通常通过预定义的元路径或用户指定的关系约束来建模HINs中的关系。然而，基于元路径的方法主要设计用于识别单一类型的社区，其中节点属于同一类型，而不是涉及不同类型节点的多类型社区。基于约束的方法要求用户对社区模式有良好的理解，以定义合适的关系约束集合，这增加了用户的负担。在本文中，我们提出了FCS-HGNN，一种新颖的方法，用于在HINs中灵活识别单一类型和多类型社区。具体来说，FCS-HGNN从不同视角提取互补信息，并动态考虑每种关系的贡献，而不是平等对待它们，从而捕捉到更细粒度的异构信息。此外，为了提高大规模图上的效率，我们进一步提出了LS-FCS-HGNN，它结合了：i）邻居采样策略以提高训练效率，以及ii）基于深度的启发式搜索策略以提高查询效率。我们进行了广泛的实验，以展示我们提出的方法相对于最先进方法的优越性，在单一类型和多类型社区上分别实现了14.3%和11.1%的平均改进。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=FCS-HGNN:+Flexible+Multi-type+Community+Search+in+Heterogeneous+Information+Networks)|0| +|[FCS-HGNN: Flexible Multi-type Community Search in Heterogeneous Information Networks](https://doi.org/10.1145/3627673.3679696)|Guoxin Chen, Fangda Guo, Yongqing Wang, Yanghao Liu, Peiying Yu, Huawei Shen, Xueqi Cheng|; Soochow University Institute of Computing Technology; Chinese Academy of Sciences Institute of Computing Technology; University of Chinese Academy of Sciences Institute of Computing Technology|Community search is a personalized community discovery problem designed to identify densely connected subgraphs containing the query node. Recently, community search in heterogeneous information networks (HINs) has received considerable attention. Existing methods typically focus on modeling relationships in HINs through predefined meta-paths or user-specified relational constraints. However, metapath-based methods are primarily designed to identify single-type communities with nodes of the same type rather than multi-type communities involving nodes of different types. Constraint-based methods require users to have a good understanding of community patterns to define a suitable set of relational constraints, which increases the burden on users. In this paper, we propose FCS-HGNN, a novel method for flexibly identifying both single-type and multi-type communities in HINs. Specifically, FCS-HGNN extracts complementary information from different views and dynamically considers the contribution of each relation instead of treating them equally, thereby capturing more fine-grained heterogeneous information. Furthermore, to improve efficiency on large-scale graphs, we further propose LS-FCS-HGNN, which incorporates i) the neighbor sampling strategy to improve training efficiency, and ii) the depth-based heuristic search strategy to improve query efficiency. We conducted extensive experiments to demonstrate the superiority of our proposed methods over state-of-the-art methods, achieving average improvements of 14.3% and 11.1% on single-type and multi-type communities, respectively.|社区搜索是一个个性化的社区发现问题，旨在识别包含查询节点的密集连接子图。近来，异构信息网络（HINs）中的社区搜索引起了广泛关注。现有方法通常通过预定义的元路径或用户指定的关系约束来建模HINs中的关系。然而，基于元路径的方法主要设计用于识别单一类型的社区，其中节点属于同一类型，而不是涉及不同类型节点的多类型社区。基于约束的方法要求用户对社区模式有良好的理解，以定义合适的关系约束集合，这增加了用户的负担。在本文中，我们提出了FCS-HGNN，一种新颖的方法，用于在HINs中灵活识别单一类型和多类型社区。具体来说，FCS-HGNN从不同视角提取互补信息，并动态考虑每种关系的贡献，而不是平等对待它们，从而捕捉到更细粒度的异构信息。此外，为了提高大规模图上的效率，我们进一步提出了LS-FCS-HGNN，它结合了：i）邻居采样策略以提高训练效率，以及ii）基于深度的启发式搜索策略以提高查询效率。我们进行了广泛的实验，以展示我们提出的方法相对于最先进方法的优越性，在单一类型和多类型社区上分别实现了14.3%和11.1%的平均改进。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=FCS-HGNN:+Flexible+Multi-type+Community+Search+in+Heterogeneous+Information+Networks)|0| |[ELCoRec: Enhance Language Understanding with Co-Propagation of Numerical and Categorical Features for Recommendation](https://doi.org/10.1145/3627673.3679789)|Jizheng Chen, Kounianhua Du, Jianghao Lin, Bo Chen, Ruiming Tang, Weinan Zhang, Yong Yu||Large language models have been flourishing in the natural language processing (NLP) domain, and their potential for recommendation has been paid much attention to. Despite the intelligence shown by the recommendation-oriented finetuned models, LLMs struggle to fully understand the user behavior patterns due to their innate weakness in interpreting numerical features and the overhead for long context, where the temporal relations among user behaviors, subtle quantitative signals among different ratings, and various side features of items are not well explored. Existing works only fine-tune a sole LLM on given text data without introducing that important information to it, leaving these problems unsolved. In this paper, we propose ELCoRec to Enhance Language understanding with CoPropagation of numerical and categorical features for Recommendation. Concretely, we propose to inject the preference understanding capability into LLM via a GAT expert model where the user preference is better encoded by parallelly propagating the temporal relations, and rating signals as well as various side information of historical items. The parallel propagation mechanism could stabilize heterogeneous features and offer an informative user preference encoding, which is then injected into the language models via soft prompting at the cost of a single token embedding. To further obtain the user's recent interests, we proposed a novel Recent interaction Augmented Prompt (RAP) template. Experiment results over three datasets against strong baselines validate the effectiveness of ELCoRec. The code is available at https://anonymous.4open.science/r/CIKM_Code_Repo-E6F5/README.md.|大型语言模型在自然语言处理（NLP）领域蓬勃发展，其在推荐系统中的潜力也备受关注。尽管面向推荐的微调模型展现了智能性，但大型语言模型（LLMs）由于其固有的对数值特征解释的弱点以及长上下文处理的负担，难以完全理解用户行为模式。这些模式包括用户行为之间的时间关系、不同评分之间的细微数量信号以及项目各种侧边特征等，都未得到充分探索。现有工作仅在给定文本数据上对单一LLM进行微调，而未引入这些重要信息，导致这些问题未得到解决。本文提出ELCoRec，旨在通过数值和类别特征的协同传播来增强语言理解，以提升推荐效果。具体而言，我们提出通过一个图注意力网络（GAT）专家模型将偏好理解能力注入LLM，其中用户偏好通过并行传播时间关系、评分信号以及历史项目的各种侧信息得到更好编码。这种并行传播机制能够稳定异构特征，并提供信息丰富的用户偏好编码，随后通过单个令牌嵌入的软提示方式注入语言模型。为进一步捕捉用户的近期兴趣，我们提出了新颖的近期交互增强提示（RAP）模板。在三个数据集上与强基线方法的实验结果验证了ELCoRec的有效性。代码可访问https://anonymous.4open.science/r/CIKM_Code_Repo-E6F5/README.md获取。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=ELCoRec:+Enhance+Language+Understanding+with+Co-Propagation+of+Numerical+and+Categorical+Features+for+Recommendation)|0| |[Social Influence Learning for Recommendation Systems](https://doi.org/10.1145/3627673.3679598)|Ximing Chen, Pui Ieng Lei, Yijun Sheng, Yanyan Liu, Zhiguo Gong|University of Macau, Macau, China|Social recommendation systems leverage the social relations among users to deal with the inherent cold-start problem in user-item interactions. However, previous models only treat the social graph as the static auxiliary to the user-item interaction graph, rather than dig out the hidden essentials and optimize them for better recommendations. Thus, the potential of social influence is still under-explored. In this paper, we will fill this gap by proposing a novel model for social influence learning to derive the essential influence patterns within the user relationships. Our model views the social influence from the perspectives of (1) the diversity of neighborhood's influence on the users, (2) the disentanglement of neighborhood's influence on the users, and (3) the exploration of underlying implicit social influence. To this end, we first employ a novel layerwise graph-enhanced variational autoencoder for the reconstruction of neighborhoods' representations, which aims to learn the pattern of social influence as well as simulate the social profile of each user for overcoming the sparsity issue in social relation data. Meanwhile, we introduce a layerwise graph attentive network for capturing the most influential scope of neighborhood. Finally, we adopt a dual sampling process to generate new social relations for enhancing the social recommendation. Extensive experiments have been conducted on three widely-used benchmark datasets, verifying the superiority of our proposed model compared with the representative approaches.|社交推荐系统利用用户之间的社交关系来应对用户-物品交互中固有的冷启动问题。然而，以往的模型仅将社交图视为用户-物品交互图的静态辅助，而未深入挖掘隐藏的基本要素并对其进行优化以实现更好的推荐。因此，社交影响力的潜力仍未得到充分探索。本文通过提出一种新的社交影响力学习模型来填补这一空白，旨在从用户关系中提取基本的影响模式。我们的模型从以下三个角度看待社交影响力：（1）邻域对用户影响的多样性，（2）邻域对用户影响的解耦，以及（3）潜在隐性社交影响力的探索。为此，我们首先采用了一种新颖的分层图增强变分自编码器来重构邻域的表示，旨在学习社交影响力的模式并模拟每个用户的社交概况，以克服社交关系数据中的稀疏性问题。同时，我们引入了一种分层图注意力网络来捕捉邻域中最具影响力的范围。最后，我们采用双重采样过程来生成新的社交关系，以增强社交推荐。我们在三个广泛使用的基准数据集上进行了大量实验，验证了我们提出的模型相对于代表性方法的优越性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Social+Influence+Learning+for+Recommendation+Systems)|0| -|[Enhancing Deep Entity Resolution with Integrated Blocker-Matcher Training: Balancing Consensus and Discrepancy](https://doi.org/10.1145/3627673.3679843)|Wenzhou Dou, Derong Shen, Xiangmin Zhou, Hui Bai, Yue Kou, Tiezheng Nie, Hang Cui, Ge Yu|Northeastern University, Shenyang, China; University of Illinois at Urbana-Champaign, Urbana, USA; RMIT University, Melbourne, Australia|Deep entity resolution (ER) identifies matching entities across data sources using techniques based on deep learning. It involves two steps: a blocker for identifying the potential matches to generate the candidate pairs, and a matcher for accurately distinguishing the matches and non-matches among these candidate pairs. Recent deep ER approaches utilize pretrained language models (PLMs) to extract similarity features for blocking and matching, achieving state-of-the-art performance. However, they often fail to balance the consensus and discrepancy between the blocker and matcher, emphasizing the consensus while neglecting the discrepancy. This paper proposes MutualER, a deep entity resolution framework that integrates and jointly trains the blocker and matcher, balancing both the consensus and discrepancy between them. Specifically, we firstly introduce a lightweight PLM in siamese structure for the blocker and a heavier PLM in cross structure or an autoregressive large language model (LLM) for the matcher. Two optimization techniques named Mutual Sample Selection (MSS) and Similarity Knowledge Transferring (SKT) are designed to jointly train the blocker and matcher. MSS enables the blocker and matcher to mutually select the customized training samples for each other to maintain the discrepancy, while SKT allows them to share the similarity knowledge for improving their blocking and matching capabilities respectively to maintain the consensus. Extensive experiments on five datasets demonstrate that MutualER significantly outperforms existing PLM-based and LLM-based approaches, achieving leading performance in both effectiveness and efficiency.|深度实体解析（ER）利用基于深度学习的技术，识别跨数据源的匹配实体。它包括两个步骤：用于识别潜在匹配以生成候选对的阻塞器，以及用于在这些候选对中准确区分匹配与非匹配的匹配器。最近的深度ER方法利用预训练语言模型（PLMs）来提取用于阻塞和匹配的相似性特征，从而达到最先进的性能。然而，这些方法往往未能平衡阻塞器和匹配器之间的共识与差异，过于强调共识而忽视了差异。本文提出了MutualER，这是一个深度实体解析框架，它整合并联合训练阻塞器和匹配器，平衡两者之间的共识与差异。具体来说，我们首先在阻塞器中引入一个轻量级的PLM，采用孪生结构，而在匹配器中使用更重的PLM，采用交叉结构或自回归大型语言模型（LLM）。我们设计了两种优化技术，名为互样本选择（MSS）和相似性知识转移（SKT），以联合训练阻塞器和匹配器。MSS使阻塞器和匹配器能够相互选择定制的训练样本，以保持差异，而SKT则允许它们共享相似性知识，以分别提高各自的阻塞和匹配能力，从而保持共识。在五个数据集上的广泛实验表明，MutualER显著优于现有的基于PLM和LLM的方法，在效果和效率方面均达到了领先水平。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Enhancing+Deep+Entity+Resolution+with+Integrated+Blocker-Matcher+Training:+Balancing+Consensus+and+Discrepancy)|0| +|[Enhancing Deep Entity Resolution with Integrated Blocker-Matcher Training: Balancing Consensus and Discrepancy](https://doi.org/10.1145/3627673.3679843)|Wenzhou Dou, Derong Shen, Xiangmin Zhou, Hui Bai, Yue Kou, Tiezheng Nie, Hang Cui, Ge Yu|University of Illinois at Urbana-Champaign, Urbana, USA; Northeastern University, Shenyang, China; RMIT University, Melbourne, Australia|Deep entity resolution (ER) identifies matching entities across data sources using techniques based on deep learning. It involves two steps: a blocker for identifying the potential matches to generate the candidate pairs, and a matcher for accurately distinguishing the matches and non-matches among these candidate pairs. Recent deep ER approaches utilize pretrained language models (PLMs) to extract similarity features for blocking and matching, achieving state-of-the-art performance. However, they often fail to balance the consensus and discrepancy between the blocker and matcher, emphasizing the consensus while neglecting the discrepancy. This paper proposes MutualER, a deep entity resolution framework that integrates and jointly trains the blocker and matcher, balancing both the consensus and discrepancy between them. Specifically, we firstly introduce a lightweight PLM in siamese structure for the blocker and a heavier PLM in cross structure or an autoregressive large language model (LLM) for the matcher. Two optimization techniques named Mutual Sample Selection (MSS) and Similarity Knowledge Transferring (SKT) are designed to jointly train the blocker and matcher. MSS enables the blocker and matcher to mutually select the customized training samples for each other to maintain the discrepancy, while SKT allows them to share the similarity knowledge for improving their blocking and matching capabilities respectively to maintain the consensus. Extensive experiments on five datasets demonstrate that MutualER significantly outperforms existing PLM-based and LLM-based approaches, achieving leading performance in both effectiveness and efficiency.|深度实体解析（ER）利用基于深度学习的技术，识别跨数据源的匹配实体。它包括两个步骤：用于识别潜在匹配以生成候选对的阻塞器，以及用于在这些候选对中准确区分匹配与非匹配的匹配器。最近的深度ER方法利用预训练语言模型（PLMs）来提取用于阻塞和匹配的相似性特征，从而达到最先进的性能。然而，这些方法往往未能平衡阻塞器和匹配器之间的共识与差异，过于强调共识而忽视了差异。本文提出了MutualER，这是一个深度实体解析框架，它整合并联合训练阻塞器和匹配器，平衡两者之间的共识与差异。具体来说，我们首先在阻塞器中引入一个轻量级的PLM，采用孪生结构，而在匹配器中使用更重的PLM，采用交叉结构或自回归大型语言模型（LLM）。我们设计了两种优化技术，名为互样本选择（MSS）和相似性知识转移（SKT），以联合训练阻塞器和匹配器。MSS使阻塞器和匹配器能够相互选择定制的训练样本，以保持差异，而SKT则允许它们共享相似性知识，以分别提高各自的阻塞和匹配能力，从而保持共识。在五个数据集上的广泛实验表明，MutualER显著优于现有的基于PLM和LLM的方法，在效果和效率方面均达到了领先水平。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Enhancing+Deep+Entity+Resolution+with+Integrated+Blocker-Matcher+Training:+Balancing+Consensus+and+Discrepancy)|0| |[CHDAER: Consistent Hashing-based Data Allocation for Efficient Recommendation in Edge Environment](https://doi.org/10.1145/3627673.3679809)|Zhikang Feng, Chao Yan, Rong Jiang, Xiaolong Xu, Xuyun Zhang, Xiaokang Zhou, Wanchun Dou, Lianyong Qi||||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=CHDAER:+Consistent+Hashing-based+Data+Allocation+for+Efficient+Recommendation+in+Edge+Environment)|0| -|[HierRec: Scenario-Aware Hierarchical Modeling for Multi-scenario Recommendations](https://doi.org/10.1145/3627673.3679615)|Jingtong Gao, Bo Chen, Menghui Zhu, Xiangyu Zhao, Xiaopeng Li, Yuhao Wang, Yichao Wang, Huifeng Guo, Ruiming Tang|City University of Hong Kong, Hong Kong, Hong Kong; Huawei Noah's Ark Lab, Shenzhen, China|Click-Through Rate (CTR) prediction is a fundamental technique in recommendation and advertising systems. Recent studies have shown that implementing multi-scenario recommendations contributes to strengthening information sharing and improving overall performance. However, existing multi-scenario models only consider coarse-grained explicit scenario modeling that depends on pre-defined scenario identification from manual prior rules, which is biased and sub-optimal. To address these limitations, we propose a Scenario-Aware Hierarchical Dynamic Network for Multi-Scenario Recommendations (HierRec), which perceives implicit patterns adaptively, and conducts explicit and implicit scenario modeling jointly. In particular, HierRec designs a basic scenario-oriented module based on the dynamic weight to capture scenario-specific representations. Then the hierarchical explicit and implicit scenario-aware modules are proposed to model hybrid-grained scenario information, where the multi-head implicit modeling design contributes to perceiving distinctive patterns from different perspectives. Our experiments on two public datasets and real-world industrial applications on a mainstream online advertising platform demonstrate that HierRec outperforms existing models significantly. The implementation code is available for reproducibility.|点击率（CTR）预测是推荐和广告系统中的基础技术。近期的研究表明，实施多场景推荐有助于增强信息共享并提升整体性能。然而，现有的多场景模型仅考虑基于预定义场景识别的粗粒度显式场景建模，这种识别依赖于人工先验规则，存在偏差且效果不佳。为解决这些局限，我们提出了面向多场景推荐的场景感知分层动态网络（HierRec），该网络能够自适应地感知隐式模式，并联合进行显式和隐式场景建模。具体而言，HierRec设计了一个基于动态权重的基本场景导向模块，以捕捉特定场景的表示。随后，提出了分层的显式和隐式场景感知模块，用于建模混合粒度的场景信息，其中多头的隐式建模设计有助于从不同角度感知独特的模式。我们在两个公开数据集以及主流在线广告平台的实际工业应用中的实验表明，HierRec显著优于现有的模型。实现代码已公开，便于复现。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=HierRec:+Scenario-Aware+Hierarchical+Modeling+for+Multi-scenario+Recommendations)|0| +|[HierRec: Scenario-Aware Hierarchical Modeling for Multi-scenario Recommendations](https://doi.org/10.1145/3627673.3679615)|Jingtong Gao, Bo Chen, Menghui Zhu, Xiangyu Zhao, Xiaopeng Li, Yuhao Wang, Yichao Wang, Huifeng Guo, Ruiming Tang|Huawei Noah's Ark Lab, Shenzhen, China; City University of Hong Kong, Hong Kong, Hong Kong|Click-Through Rate (CTR) prediction is a fundamental technique in recommendation and advertising systems. Recent studies have shown that implementing multi-scenario recommendations contributes to strengthening information sharing and improving overall performance. However, existing multi-scenario models only consider coarse-grained explicit scenario modeling that depends on pre-defined scenario identification from manual prior rules, which is biased and sub-optimal. To address these limitations, we propose a Scenario-Aware Hierarchical Dynamic Network for Multi-Scenario Recommendations (HierRec), which perceives implicit patterns adaptively, and conducts explicit and implicit scenario modeling jointly. In particular, HierRec designs a basic scenario-oriented module based on the dynamic weight to capture scenario-specific representations. Then the hierarchical explicit and implicit scenario-aware modules are proposed to model hybrid-grained scenario information, where the multi-head implicit modeling design contributes to perceiving distinctive patterns from different perspectives. Our experiments on two public datasets and real-world industrial applications on a mainstream online advertising platform demonstrate that HierRec outperforms existing models significantly. The implementation code is available for reproducibility.|点击率（CTR）预测是推荐和广告系统中的基础技术。近期的研究表明，实施多场景推荐有助于增强信息共享并提升整体性能。然而，现有的多场景模型仅考虑基于预定义场景识别的粗粒度显式场景建模，这种识别依赖于人工先验规则，存在偏差且效果不佳。为解决这些局限，我们提出了面向多场景推荐的场景感知分层动态网络（HierRec），该网络能够自适应地感知隐式模式，并联合进行显式和隐式场景建模。具体而言，HierRec设计了一个基于动态权重的基本场景导向模块，以捕捉特定场景的表示。随后，提出了分层的显式和隐式场景感知模块，用于建模混合粒度的场景信息，其中多头的隐式建模设计有助于从不同角度感知独特的模式。我们在两个公开数据集以及主流在线广告平台的实际工业应用中的实验表明，HierRec显著优于现有的模型。实现代码已公开，便于复现。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=HierRec:+Scenario-Aware+Hierarchical+Modeling+for+Multi-scenario+Recommendations)|0| |[Information Retrieval Optimization for Non-Exemplar Class Incremental Learning](https://doi.org/10.1145/3627673.3679631)|Shuai Guo, Yang Gu, Yuan Ma, Yingwei Zhang, Weining Weng, Jun Liu, Weiwei Dai, Yiqiang Chen||Existing non-example class-incremental learning (NECIL) methods usually utilize a combination strategy of replay mechanism and knowledge distillation. However, this combination strategy only focuses on the preservation of old information quantitatively, ignoring the preservation quality. When the old knowledge has wrong redundant information, catastrophic forgetting is more likely to occur. Therefore, obtaining adequate information without impurities as much as possible and removing invalid or even harmful information has become an effective solution to improve the performance of NECIL. This process is consistent with the information bottleneck (IB) theory. Thus, we propose a new NECIL method based on the IB framework. By using the different information obtained from the new and old class samples and the implicit knowledge in the teacher model training process, the error of harmful redundant information learned is eliminated. Specifically, we propose two optimization strategies that align with the two optimization processes of the information bottleneck. Firstly, we employ a pseudo-prototype selection mechanism that selectively incorporates pseudo-samples into the learning process of new and old categories, thus enhancing the distinction between new and old categories and diminishing the mutual information between the input and intermediate features. Secondly, we introduce an attention-based feature distillation method that regulates the distillation strength between feature pairs based on their similarity, thereby augmenting the mutual information between intermediate features and output prediction. Extensive experiments on three benchmarks demonstrate that the proposed method exhibits significant incremental performance improvements over existing methods.|现有的非示例类增量学习（NECIL）方法通常采用回放机制和知识蒸馏的组合策略。然而，这种组合策略仅关注旧信息的定量保存，忽略了保存的质量。当旧知识包含错误的冗余信息时，灾难性遗忘更容易发生。因此，尽可能获取无杂质的充足信息并去除无效甚至有害信息，已成为提升NECIL性能的有效解决方案。这一过程与信息瓶颈（IB）理论相一致。因此，我们提出了一种基于IB框架的新NECIL方法。通过利用新旧类别样本获取的不同信息以及教师模型训练过程中蕴含的隐性知识，消除了有害冗余信息学习的错误。具体而言，我们提出了两种优化策略，分别对应信息瓶颈的两个优化过程。首先，我们采用伪原型选择机制，有选择地将伪样本纳入新旧类别的学习过程，从而增强新旧类别之间的区分度，并减少输入与中间特征之间的互信息。其次，我们引入了一种基于注意力的特征蒸馏方法，根据特征对之间的相似性调节蒸馏强度，从而增强中间特征与输出预测之间的互信息。在三个基准上的大量实验表明，所提出的方法相较于现有方法显著提升了增量学习性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Information+Retrieval+Optimization+for+Non-Exemplar+Class+Incremental+Learning)|0| |[Fragment Allocations for Partially Replicated Databases Considering Data Modifications and Changing Workloads](https://doi.org/10.1145/3627673.3679767)|Stefan Halfpap, Rainer Schlosser|BIFOLD, TU Berlin, Berlin, Germany; Hasso Plattner Institute, Potsdam, Germany|Columnar database systems can process complex mixed workloads on a single node. In case of increasing and peak analytical processing demand, we can offload read-only queries to replicas. Partial replication, i.e., duplicating only data subsets to additional nodes, is more cost-efficient than full replication for two primary reasons: (i) Partial replicas require less storage and can be set up faster. (ii) Partial replicas must synchronize only stored data subsets, allowing better scalability. However, determining which queries to offload is challenging for larger workloads because queries access overlapping data subsets and cause synchronization costs. This paper shows how to calculate optimized replica configurations that consider reallocation and data modification costs using integer linear programming (ILP) techniques. While ILP is effective for solving assignment problems, it does not scale well. For larger problems, users often fall back to simple heuristics, which can lose optimization potential. This paper demonstrates that scalable heuristics can be built on ILP, preserving its strengths. The three proposed approaches for reducing the calculation time allow trading solution quality flexibly. Our evaluations using TPC-H, TPC-DS, and a large real-world accounting workload show that our approach outperforms state-of-the-art solutions, often reducing reallocated data by more than 80% and halving modification costs. At the same time, the new allocations reduce the storage consumption by over 30%, with solutions computed in just a few seconds.|列式数据库系统能够在单个节点上处理复杂的混合工作负载。在分析处理需求增加和达到峰值的情况下，我们可以将只读查询卸载到副本上。部分复制，即仅将数据子集复制到附加节点，相较于全复制更为成本高效，主要原因有两点：(i) 部分副本所需的存储较少，且设置速度更快；(ii) 部分副本仅需同步存储的数据子集，从而实现更好的可扩展性。然而，对于较大的工作负载，确定哪些查询应被卸载是一个挑战，因为查询访问重叠的数据子集，并导致同步成本增加。本文展示了如何使用整数线性规划（ILP）技术计算优化的副本配置，考虑了重新分配和数据修改成本。尽管ILP在解决分配问题方面有效，但其扩展性不佳。对于较大的问题，用户通常会回退到简单的启发式方法，这可能会失去优化的潜力。本文证明，可扩展的启发式方法可以建立在ILP基础上，保留其优势。所提出的三种减少计算时间的方法允许灵活地权衡解决方案的质量。我们使用TPC-H、TPC-DS和大规模真实会计工作负载的评估显示，我们的方法优于最先进的解决方案，通常将重新分配的数据减少超过80%，并将修改成本减半。同时，新的分配方案将存储消耗减少了30%以上，且解决方案在几秒钟内即可计算完成。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Fragment+Allocations+for+Partially+Replicated+Databases+Considering+Data+Modifications+and+Changing+Workloads)|0| |[Practical and Robust Safety Guarantees for Advanced Counterfactual Learning to Rank](https://doi.org/10.1145/3627673.3679531)|Shashank Gupta, Harrie Oosterhuis, Maarten de Rijke||Counterfactual learning to rank (CLTR ) can be risky; various circumstances can cause it to produce sub-optimal models that hurt performance when deployed. Safe CLTR was introduced to mitigate these risks when using inverse propensity scoring to correct for position bias. However, the existing safety measure for CLTR is not applicable to state-of-the-art CLTR, it cannot handle trust bias, and its guarantees rely on specific assumptions about user behavior. Our contributions are two-fold. First, we generalize the existing safe CLTR approach to make it applicable to state-of-the-art doubly robust (DR) CLTR and trust bias. Second, we propose a novel approach, proximal ranking policy optimization (PRPO ), that provides safety in deployment without assumptions about user behavior. PRPO removes incentives for learning ranking behavior that is too dissimilar to a safe ranking model. Thereby, PRPO imposes a limit on how much learned models can degrade performance metrics, without relying on any specific user assumptions. Our experiments show that both our novel safe doubly robust method and PRPO provide higher performance than the existing safe inverse propensity scoring approach. However, when circumstances are unexpected, the safe doubly robust approach can become unsafe and bring detrimental performance. In contrast, PRPO always maintains safety, even in maximally adversarial situations. By avoiding assumptions, PRPO is the first method with unconditional safety in deployment that translates to robust safety for real-world applications.|反事实学习排序（CLTR）存在风险；多种情况可能导致其生成次优模型，从而在部署时损害性能。安全CLTR被引入以在使用逆倾向评分来纠正位置偏差时减轻这些风险。然而，现有的CLTR安全措施不适用于最先进的CLTR，无法处理信任偏差，并且其安全保证依赖于对用户行为的特定假设。我们的贡献有两方面。首先，我们将现有的安全CLTR方法推广到适用于最先进的双重稳健（DR）CLTR和信任偏差。其次，我们提出了一种新颖的方法，即近端排序策略优化（PRPO），该方法在部署时提供安全性，而无需对用户行为做出任何假设。PRPO消除了学习与安全排序模型过于不同的排序行为的激励。因此，PRPO在不依赖任何特定用户假设的情况下，对学习模型可能降低性能指标的程度施加了限制。我们的实验表明，我们的新颖安全双重稳健方法和PRPO都比现有的安全逆倾向评分方法提供了更高的性能。然而，在遇到意外情况时，安全双重稳健方法可能会变得不安全并带来有害的性能。相比之下，PRPO始终保持安全，即使在最恶劣的对抗情况下也是如此。通过避免假设，PRPO是第一种在部署时具有无条件安全性的方法，这为实际应用带来了强大的安全性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Practical+and+Robust+Safety+Guarantees+for+Advanced+Counterfactual+Learning+to+Rank)|0| @@ -149,13 +149,13 @@ |[Understanding GNNs for Boolean Satisfiability through Approximation Algorithms](https://doi.org/10.1145/3627673.3679813)|Jan Hula, David Mojzísek, Mikolás Janota||The paper deals with the interpretability of Graph Neural Networks in the context of Boolean Satisfiability. The goal is to demystify the internal workings of these models and provide insightful perspectives into their decision-making processes. This is done by uncovering connections to two approximation algorithms studied in the domain of Boolean Satisfiability: Belief Propagation and Semidefinite Programming Relaxations. Revealing these connections has empowered us to introduce a suite of impactful enhancements. The first significant enhancement is a curriculum training procedure, which incrementally increases the problem complexity in the training set, together with increasing the number of message passing iterations of the Graph Neural Network. We show that the curriculum, together with several other optimizations, reduces the training time by more than an order of magnitude compared to the baseline without the curriculum. Furthermore, we apply decimation and sampling of initial embeddings, which significantly increase the percentage of solved problems.|本文探讨了在布尔可满足性（Boolean Satisfiability）背景下图神经网络（Graph Neural Networks, GNN）的可解释性问题。其目标在于揭秘这些模型的内部运作机制，并深入洞察其决策过程。通过揭示与布尔可满足性领域内两种近似算法——信念传播（Belief Propagation）和半定规划松弛（Semidefinite Programming Relaxations）之间的联系，我们得以引入一系列具有影响力的改进措施。首先，一个显著的改进是课程训练程序，该程序在训练集中逐步增加问题复杂度，同时提升图神经网络的消息传递迭代次数。研究表明，结合课程训练与其他多项优化措施，相较于无课程训练的基线模型，训练时间减少了超过一个数量级。此外，我们还采用了初始嵌入的降维和采样技术，这显著提高了问题解决的百分比。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Understanding+GNNs+for+Boolean+Satisfiability+through+Approximation+Algorithms)|0| |[HiQuE: Hierarchical Question Embedding Network for Multimodal Depression Detection](https://doi.org/10.1145/3627673.3679797)|Juho Jung, Chaewon Kang, Jeewoo Yoon, Seungbae Kim, Jinyoung Han||The utilization of automated depression detection significantly enhances early intervention for individuals experiencing depression. Despite numerous proposals on automated depression detection using recorded clinical interview videos, limited attention has been paid to considering the hierarchical structure of the interview questions. In clinical interviews for diagnosing depression, clinicians use a structured questionnaire that includes routine baseline questions and follow-up questions to assess the interviewee's condition. This paper introduces HiQuE (Hierarchical Question Embedding network), a novel depression detection framework that leverages the hierarchical relationship between primary and follow-up questions in clinical interviews. HiQuE can effectively capture the importance of each question in diagnosing depression by learning mutual information across multiple modalities. We conduct extensive experiments on the widely-used clinical interview data, DAIC-WOZ, where our model outperforms other state-of-the-art multimodal depression detection models and emotion recognition models, showcasing its clinical utility in depression detection.|自动化抑郁症检测的利用显著增强了针对抑郁症患者的早期干预。尽管已有众多关于使用录制的临床访谈视频进行自动化抑郁症检测的提议，但很少有研究考虑到访谈问题之间的层次结构。在诊断抑郁症的临床访谈中，临床医生使用包含常规基线问题和后续问题的结构化问卷来评估受访者的状况。本文介绍了HiQuE（分层问题嵌入网络），这是一种利用临床访谈中主要问题与后续问题之间层次关系的新型抑郁症检测框架。HiQuE通过跨多种模态学习互信息，能够有效捕捉每个问题在抑郁症诊断中的重要性。我们在广泛使用的临床访谈数据DAIC-WOZ上进行了大量实验，结果表明，我们的模型在多模态抑郁症检测和情感识别模型中表现优于其他最先进的模型，展示了其在抑郁症检测中的临床实用性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=HiQuE:+Hierarchical+Question+Embedding+Network+for+Multimodal+Depression+Detection)|0| |[Embedding Knowledge Graphs in Function Spaces](https://doi.org/10.1145/3627673.3679819)|Louis Mozart Kamdem Teyou, Caglar Demir, AxelCyrille Ngonga Ngomo||We introduce a novel embedding method diverging from conventional approaches by operating within function spaces of finite dimension rather than finite vector space, thus departing significantly from standard knowledge graph embedding techniques. Initially employing polynomial functions to compute embeddings, we progress to more intricate representations using neural networks with varying layer complexities. We argue that employing functions for embedding computation enhances expressiveness and allows for more degrees of freedom, enabling operations such as composition, derivatives and primitive of entities representation. Additionally, we meticulously outline the step-by-step construction of our approach and provide code for reproducibility, thereby facilitating further exploration and application in the field.|我们提出了一种新颖的嵌入方法，该方法与传统方法不同，它在有限维函数空间中进行操作，而非有限维向量空间，从而显著区别于标准的知识图谱嵌入技术。初始阶段使用多项式函数来计算嵌入，随后我们采用具有不同层复杂度的神经网络来实现更为复杂的表示。我们认为，使用函数进行嵌入计算能够增强表达能力，并提供更多的自由度，使得实体表示的组合、导数和原函数等操作成为可能。此外，我们详细描述了该方法的逐步构建过程，并提供了可重复使用的代码，从而促进该领域内的进一步探索和应用。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Embedding+Knowledge+Graphs+in+Function+Spaces)|0| -|[Federated Deep Equilibrium Learning: Harnessing Compact Global Representations to Enhance Personalization](https://doi.org/10.1145/3627673.3679752)|Long Tan Le, Tuan Dung Nguyen, TungAnh Nguyen, Choong Seon Hong, Suranga Seneviratne, Wei Bao, Nguyen H. Tran|The University of Sydney, Sydney, NSW, Australia; Kyung Hee University, Yongin-si, Republic of Korea; The University of Pennsylvania, Philadelphia, PA, USA|Federated Learning (FL) has emerged as a groundbreaking distributed learning paradigm enabling clients to train a global model collaboratively without exchanging data. Despite enhancing privacy and efficiency in information retrieval and knowledge management contexts, training and deploying FL models confront significant challenges such as communication bottlenecks, data heterogeneity, and memory limitations. To comprehensively address these challenges, we introduce FeDEQ, a novel FL framework that incorporates deep equilibrium learning and consensus optimization to harness compact global data representations for efficient personalization. Specifically, we design a unique model structure featuring an equilibrium layer for global representation extraction, followed by explicit layers tailored for local personalization. We then propose a novel FL algorithm rooted in the alternating directions method of multipliers (ADMM), which enables the joint optimization of a shared equilibrium layer and individual personalized layers across distributed datasets. Our theoretical analysis confirms that FeDEQ converges to a stationary point, achieving both compact global representations and optimal personalized parameters for each client. Extensive experiments on various benchmarks demonstrate that FeDEQ matches the performance of state-of-the-art personalized FL methods, while significantly reducing communication size by up to 4 times and memory footprint by 1.5 times during training.|联邦学习（Federated Learning, FL）作为一种突破性的分布式学习范式，使得客户端能够在不交换数据的情况下协作训练全局模型。尽管在信息检索和知识管理领域中增强了隐私和效率，但训练和部署FL模型仍面临重大挑战，如通信瓶颈、数据异质性和内存限制。为了全面应对这些挑战，我们提出了FeDEQ，这是一种新颖的FL框架，它结合了深度平衡学习和共识优化，以利用紧凑的全局数据表示实现高效个性化。具体而言，我们设计了一种独特的模型结构，包括一个用于提取全局表示的平衡层，随后是专门为本地个性化定制的显式层。接着，我们提出了一种基于交替方向乘子法（ADMM）的新型FL算法，该算法能够联合优化分布式数据集中的共享平衡层和各个个性化层。我们的理论分析证实，FeDEQ能够收敛到一个平稳点，为每个客户端实现紧凑的全局表示和最优的个性化参数。在多个基准上的广泛实验表明，FeDEQ与最先进的个性化FL方法性能相当，同时在训练过程中将通信量减少了高达4倍，内存占用减少了1.5倍。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Federated+Deep+Equilibrium+Learning:+Harnessing+Compact+Global+Representations+to+Enhance+Personalization)|0| -|[Privacy-preserving Spatial Dataset Search in Cloud](https://doi.org/10.1145/3627673.3679733)|Pengyue Li, Hua Dai, Sheng Wang, Wenzhe Yang, Geng Yang|School of Computer Science, Wuhan University, Wuhan, China; School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing, China|The development of cloud computing has met the growing demand for dataset search in the era of massive data. In the field of spatial dataset search, the high prevalence of sensitive information in spatial datasets underscores the necessity of privacy-preserving search processing in the cloud. However, existing spatial dataset search schemes are designed on plaintext datasets and do not consider privacy protection in search processing. In this paper, we first propose a privacy-preserving spatial dataset search scheme. The density distribution-based similarity model is proposed to measure the similarity between spatial datasets, and then the order-preserving encrypted similarity is designed to achieve secure similarity calculation. With the above idea, the baseline search scheme (PriDAS) is proposed. To improve the search efficiency, a two-layer index is designed to filter candidate datasets and accelerate the similarity calculation between datasets. By using the index, the optimized search scheme (PriDAS+) is proposed. To analyze the security of the proposed schemes, the game simulation-based proof is presented. Experimental results on three real-world spatial data repositories with 100,000 spatial datasets show that PriDAS+ only needs less than 0.4 seconds to accomplish the search processing.|云计算的发展满足了大数据时代对数据集搜索日益增长的需求。在空间数据集搜索领域，空间数据集中敏感信息的高普及率突显了在云环境中进行隐私保护搜索处理的必要性。然而，现有的空间数据集搜索方案设计基于明文数据集，并未考虑搜索处理中的隐私保护。本文首先提出了一种隐私保护的空间数据集搜索方案。基于密度分布的相似性模型被提出用于衡量空间数据集之间的相似性，然后设计了顺序保持加密的相似性以实现安全的相似性计算。基于上述思路，提出了基线搜索方案（PriDAS）。为了提高搜索效率，设计了一个两层索引以过滤候选数据集并加速数据集之间的相似性计算。通过使用该索引，提出了优化的搜索方案（PriDAS+）。为了分析所提出方案的安全性，提出了基于博弈模拟的证明。在包含10万个空间数据集的三个真实世界空间数据存储库上的实验结果表明，PriDAS+仅需不到0.4秒即可完成搜索处理。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Privacy-preserving+Spatial+Dataset+Search+in+Cloud)|0| +|[Federated Deep Equilibrium Learning: Harnessing Compact Global Representations to Enhance Personalization](https://doi.org/10.1145/3627673.3679752)|Long Tan Le, Tuan Dung Nguyen, TungAnh Nguyen, Choong Seon Hong, Suranga Seneviratne, Wei Bao, Nguyen H. Tran|The University of Pennsylvania, Philadelphia, PA, USA; Kyung Hee University, Yongin-si, Republic of Korea; The University of Sydney, Sydney, NSW, Australia|Federated Learning (FL) has emerged as a groundbreaking distributed learning paradigm enabling clients to train a global model collaboratively without exchanging data. Despite enhancing privacy and efficiency in information retrieval and knowledge management contexts, training and deploying FL models confront significant challenges such as communication bottlenecks, data heterogeneity, and memory limitations. To comprehensively address these challenges, we introduce FeDEQ, a novel FL framework that incorporates deep equilibrium learning and consensus optimization to harness compact global data representations for efficient personalization. Specifically, we design a unique model structure featuring an equilibrium layer for global representation extraction, followed by explicit layers tailored for local personalization. We then propose a novel FL algorithm rooted in the alternating directions method of multipliers (ADMM), which enables the joint optimization of a shared equilibrium layer and individual personalized layers across distributed datasets. Our theoretical analysis confirms that FeDEQ converges to a stationary point, achieving both compact global representations and optimal personalized parameters for each client. Extensive experiments on various benchmarks demonstrate that FeDEQ matches the performance of state-of-the-art personalized FL methods, while significantly reducing communication size by up to 4 times and memory footprint by 1.5 times during training.|联邦学习（Federated Learning, FL）作为一种突破性的分布式学习范式，使得客户端能够在不交换数据的情况下协作训练全局模型。尽管在信息检索和知识管理领域中增强了隐私和效率，但训练和部署FL模型仍面临重大挑战，如通信瓶颈、数据异质性和内存限制。为了全面应对这些挑战，我们提出了FeDEQ，这是一种新颖的FL框架，它结合了深度平衡学习和共识优化，以利用紧凑的全局数据表示实现高效个性化。具体而言，我们设计了一种独特的模型结构，包括一个用于提取全局表示的平衡层，随后是专门为本地个性化定制的显式层。接着，我们提出了一种基于交替方向乘子法（ADMM）的新型FL算法，该算法能够联合优化分布式数据集中的共享平衡层和各个个性化层。我们的理论分析证实，FeDEQ能够收敛到一个平稳点，为每个客户端实现紧凑的全局表示和最优的个性化参数。在多个基准上的广泛实验表明，FeDEQ与最先进的个性化FL方法性能相当，同时在训练过程中将通信量减少了高达4倍，内存占用减少了1.5倍。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Federated+Deep+Equilibrium+Learning:+Harnessing+Compact+Global+Representations+to+Enhance+Personalization)|0| +|[Privacy-preserving Spatial Dataset Search in Cloud](https://doi.org/10.1145/3627673.3679733)|Pengyue Li, Hua Dai, Sheng Wang, Wenzhe Yang, Geng Yang|School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing, China; School of Computer Science, Wuhan University, Wuhan, China|The development of cloud computing has met the growing demand for dataset search in the era of massive data. In the field of spatial dataset search, the high prevalence of sensitive information in spatial datasets underscores the necessity of privacy-preserving search processing in the cloud. However, existing spatial dataset search schemes are designed on plaintext datasets and do not consider privacy protection in search processing. In this paper, we first propose a privacy-preserving spatial dataset search scheme. The density distribution-based similarity model is proposed to measure the similarity between spatial datasets, and then the order-preserving encrypted similarity is designed to achieve secure similarity calculation. With the above idea, the baseline search scheme (PriDAS) is proposed. To improve the search efficiency, a two-layer index is designed to filter candidate datasets and accelerate the similarity calculation between datasets. By using the index, the optimized search scheme (PriDAS+) is proposed. To analyze the security of the proposed schemes, the game simulation-based proof is presented. Experimental results on three real-world spatial data repositories with 100,000 spatial datasets show that PriDAS+ only needs less than 0.4 seconds to accomplish the search processing.|云计算的发展满足了大数据时代对数据集搜索日益增长的需求。在空间数据集搜索领域，空间数据集中敏感信息的高普及率突显了在云环境中进行隐私保护搜索处理的必要性。然而，现有的空间数据集搜索方案设计基于明文数据集，并未考虑搜索处理中的隐私保护。本文首先提出了一种隐私保护的空间数据集搜索方案。基于密度分布的相似性模型被提出用于衡量空间数据集之间的相似性，然后设计了顺序保持加密的相似性以实现安全的相似性计算。基于上述思路，提出了基线搜索方案（PriDAS）。为了提高搜索效率，设计了一个两层索引以过滤候选数据集并加速数据集之间的相似性计算。通过使用该索引，提出了优化的搜索方案（PriDAS+）。为了分析所提出方案的安全性，提出了基于博弈模拟的证明。在包含10万个空间数据集的三个真实世界空间数据存储库上的实验结果表明，PriDAS+仅需不到0.4秒即可完成搜索处理。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Privacy-preserving+Spatial+Dataset+Search+in+Cloud)|0| |[Privacy-Preserving Graph Embedding based on Local Differential Privacy](https://doi.org/10.1145/3627673.3679759)|Zening Li, RongHua Li, Meihao Liao, Fusheng Jin, Guoren Wang|Beijing Institute of Technology, Beijing, China|Graph embedding has become a powerful tool for learning latent representations of nodes in a graph. Despite its superior performance in various graph-based machine learning tasks, serious privacy concerns arise when the graph data contains personal or sensitive information. To address this issue, we investigate and develop graph embedding algorithms that satisfy local differential privacy (LDP). We introduce a novel privacy-preserving graph embedding framework, named PrivGE, to protect node data privacy. Specifically, we propose an LDP mechanism to obfuscate node data and utilize personalized PageRank as the proximity measure to learn node representations. Furthermore, we provide a theoretical analysis of the privacy guarantees and utility offered by the PrivGE framework. Extensive experiments on several real-world graph datasets demonstrate that PrivGE achieves an optimal balance between privacy and utility, and significantly outperforms existing methods in node classification and link prediction tasks.|图嵌入已成为学习图中节点潜在表示的有力工具。尽管在各种基于图的机器学习任务中表现优异，但当图数据包含个人信息或敏感信息时，严重的隐私问题也随之产生。为解决这一问题，我们研究并开发了满足局部差分隐私（LDP）的图嵌入算法。我们引入了一种新的隐私保护图嵌入框架，命名为PrivGE，以保护节点数据隐私。具体而言，我们提出了一种LDP机制来混淆节点数据，并利用个性化PageRank作为接近度度量来学习节点表示。此外，我们对PrivGE框架提供的隐私保障和效用进行了理论分析。在多个真实世界图数据集上的广泛实验表明，PrivGE在隐私与效用之间实现了最佳平衡，并在节点分类和链接预测任务中显著优于现有方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Privacy-Preserving+Graph+Embedding+based+on+Local+Differential+Privacy)|0| -|[On Evaluation Metrics for Diversity-enhanced Recommendations](https://doi.org/10.1145/3627673.3679629)|Xueqi Li, Gao Cong, Guoqing Xiao, Yang Xu, Wenjun Jiang, Kenli Li|College of Computer Science and Electronic Engineering, Hunan University, Changsha, China; Nanyang Technological University, Singapore, Singapore|Diversity is increasingly recognized as a crucial factor in recommendation systems for enhancing user satisfaction. However, existing studies on diversity-enhanced recommendation systems primarily focus on designing recommendation strategies, often overlooking the development of evaluation metrics. Widely used diversity metrics such as CC, ILAD, and ILMD are typically assessed independently of accuracy. This separation leads to a critical limitation: existing diversity measures are unable to distinguish between diversity improvements from effective recommendations and those from in effective recommendations. Our evaluations reveal that the diversity improvements are primarily contributed by ineffective recommendations, which often do not positively contribute to user satisfaction. Furthermore, existing diversity metrics disregard the feature distribution of ground-truth items, potentially skewing the assessment of diversity performance. To address these limitations, we design three new accuracy-aware metrics: DCC, FDCC, and DILAD, and conduct a re-evaluation using these metrics. Surprisingly, our results illustrate that the diversity improvements of existing diversity-enhanced approaches are limited and even negative compared to those of accurate recommendations. This finding underscores the need to explore more sophisticated diversity-enhanced techniques for improving the diversity within effective recommendations.|多样性在提升用户满意度的推荐系统中日益被视为一个关键因素。然而，现有关于增强多样性的推荐系统的研究主要集中在设计推荐策略上，往往忽视了评估指标的开发。广泛使用的多样性指标如CC、ILAD和ILMD通常独立于准确性进行评估。这种分离导致了一个关键的局限性：现有的多样性测量无法区分多样性改进是来自有效的推荐还是无效的推荐。我们的评估显示，多样性改进主要由无效推荐贡献，这些推荐往往对用户满意度没有积极贡献。此外，现有的多样性指标忽略了真实物品的特征分布，这可能扭曲了对多样性表现的评估。为了解决这些限制，我们设计了三种新的关注准确性的指标：DCC、FDCC和DILAD，并使用这些指标进行了重新评估。令人惊讶的是，我们的结果表明，现有增强多样性的方法在多样性改进方面是有限的，甚至相比于准确推荐是负面的。这一发现强调了需要探索更复杂的多样性增强技术，以提高有效推荐中的多样性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=On+Evaluation+Metrics+for+Diversity-enhanced+Recommendations)|0| +|[On Evaluation Metrics for Diversity-enhanced Recommendations](https://doi.org/10.1145/3627673.3679629)|Xueqi Li, Gao Cong, Guoqing Xiao, Yang Xu, Wenjun Jiang, Kenli Li|Nanyang Technological University, Singapore, Singapore; College of Computer Science and Electronic Engineering, Hunan University, Changsha, China|Diversity is increasingly recognized as a crucial factor in recommendation systems for enhancing user satisfaction. However, existing studies on diversity-enhanced recommendation systems primarily focus on designing recommendation strategies, often overlooking the development of evaluation metrics. Widely used diversity metrics such as CC, ILAD, and ILMD are typically assessed independently of accuracy. This separation leads to a critical limitation: existing diversity measures are unable to distinguish between diversity improvements from effective recommendations and those from in effective recommendations. Our evaluations reveal that the diversity improvements are primarily contributed by ineffective recommendations, which often do not positively contribute to user satisfaction. Furthermore, existing diversity metrics disregard the feature distribution of ground-truth items, potentially skewing the assessment of diversity performance. To address these limitations, we design three new accuracy-aware metrics: DCC, FDCC, and DILAD, and conduct a re-evaluation using these metrics. Surprisingly, our results illustrate that the diversity improvements of existing diversity-enhanced approaches are limited and even negative compared to those of accurate recommendations. This finding underscores the need to explore more sophisticated diversity-enhanced techniques for improving the diversity within effective recommendations.|多样性在提升用户满意度的推荐系统中日益被视为一个关键因素。然而，现有关于增强多样性的推荐系统的研究主要集中在设计推荐策略上，往往忽视了评估指标的开发。广泛使用的多样性指标如CC、ILAD和ILMD通常独立于准确性进行评估。这种分离导致了一个关键的局限性：现有的多样性测量无法区分多样性改进是来自有效的推荐还是无效的推荐。我们的评估显示，多样性改进主要由无效推荐贡献，这些推荐往往对用户满意度没有积极贡献。此外，现有的多样性指标忽略了真实物品的特征分布，这可能扭曲了对多样性表现的评估。为了解决这些限制，我们设计了三种新的关注准确性的指标：DCC、FDCC和DILAD，并使用这些指标进行了重新评估。令人惊讶的是，我们的结果表明，现有增强多样性的方法在多样性改进方面是有限的，甚至相比于准确推荐是负面的。这一发现强调了需要探索更复杂的多样性增强技术，以提高有效推荐中的多样性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=On+Evaluation+Metrics+for+Diversity-enhanced+Recommendations)|0| |[RecDiff: Diffusion Model for Social Recommendation](https://doi.org/10.1145/3627673.3679630)|Zongwei Li, Lianghao Xia, Chao Huang||Social recommendation has emerged as a powerful approach to enhancepersonalized recommendations by leveraging the social connections among users,such as following and friend relations observed in online social platforms. Thefundamental assumption of social recommendation is that socially-connectedusers exhibit homophily in their preference patterns. This means that usersconnected by social ties tend to have similar tastes in user-item activities,such as rating and purchasing. However, this assumption is not always valid dueto the presence of irrelevant and false social ties, which can contaminate userembeddings and adversely affect recommendation accuracy. To address thischallenge, we propose a novel diffusion-based social denoising framework forrecommendation (RecDiff). Our approach utilizes a simple yet effectivehidden-space diffusion paradigm to alleivate the noisy effect in the compressedand dense representation space. By performing multi-step noise diffusion andremoval, RecDiff possesses a robust ability to identify and eliminate noisefrom the encoded user representations, even when the noise levels vary. Thediffusion module is optimized in a downstream task-aware manner, therebymaximizing its ability to enhance the recommendation process. We conductedextensive experiments to evaluate the efficacy of our framework, and theresults demonstrate its superiority in terms of recommendation accuracy,training efficiency, and denoising effectiveness. The source code for the modelimplementation is publicly available at: https://github.com/HKUDS/RecDiff.|社交推荐作为一种利用用户间社交关系（如在线社交平台中的关注和好友关系）来增强个性化推荐的方法，已经崭露头角。社交推荐的基本假设是，通过社交关系连接的用户在偏好模式上表现出同质性。这意味着通过社交纽带连接的用户在用户-项目活动（如评分和购买）中往往具有相似的品味。然而，由于存在无关和虚假的社交关系，这一假设并不总是成立，这些关系可能会污染用户嵌入，从而对推荐准确性产生负面影响。为了应对这一挑战，我们提出了一种基于扩散的社交去噪推荐框架（RecDiff）。我们的方法采用了一种简单而有效的隐空间扩散范式，以减轻压缩和密集表示空间中的噪声影响。通过执行多步噪声扩散和去除，RecDiff具有强大的能力来识别和消除编码用户表示中的噪声，即使在噪声水平变化的情况下也是如此。扩散模块以任务感知的方式进行优化，从而最大化其增强推荐过程的能力。我们进行了广泛的实验来评估我们框架的有效性，结果表明它在推荐准确性、训练效率和去噪效果方面具有优越性。该模型的实现代码已在以下公开可用：https://github.com/HKUDS/RecDiff。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=RecDiff:+Diffusion+Model+for+Social+Recommendation)|0| -|[Efficient and Robust Regularized Federated Recommendation](https://doi.org/10.1145/3627673.3679682)|Langming Liu, Wanyu Wang, Xiangyu Zhao, Zijian Zhang, Chunxu Zhang, Shanru Lin, Yiqi Wang, Lixin Zou, Zitao Liu, Xuetao Wei, Hongzhi Yin, Qing Li|Michigan State University, East Lansing, USA; Jinan University, Guangzhou, China; Southern University of Science and Technology, Shenzhen, China; The University of Queensland, Brisbane, Australia; Jilin University, Changchun, China; Wuhan University, Wuhan, China; City University of Hong Kong, Hong Kong, China; The Hong Kong Polytechnic University, Hong Kong, China|Recommender systems play a pivotal role across practical scenarios, showcasing remarkable capabilities in user preference modeling. However, the centralized learning paradigm predominantly used raises serious privacy concerns. The federated recommender system (FedRS) addresses this by updating models on clients, while a central server orchestrates training without accessing private data. Existing FedRS approaches, however, face unresolved challenges, including non-convex optimization, vulnerability, potential privacy leakage risk, and communication inefficiency. This paper addresses these challenges by reformulating the federated recommendation problem as a convex optimization issue, ensuring convergence to the global optimum. Based on this, we devise a novel method, RFRec, to tackle this optimization problem efficiently. In addition, we propose RFRecF, a highly efficient version that incorporates non-uniform stochastic gradient descent to improve communication efficiency. In user preference modeling, both methods learn local and global models, collaboratively learning users' common and personalized interests under the federated learning setting. Moreover, both methods significantly enhance communication efficiency, robustness, and privacy protection, with theoretical support. Comprehensive evaluations on four benchmark datasets demonstrate RFRec and RFRecF's superior performance compared to diverse baselines.|推荐系统在实际应用场景中扮演着关键角色，展现了在用户偏好建模方面的显著能力。然而，目前主要采用的集中式学习模式引发了严重的隐私问题。联邦推荐系统（FedRS）通过在客户端更新模型来解决这一问题，同时中央服务器在不访问私人数据的情况下协调训练。尽管如此，现有的FedRS方法仍面临一些未解决的挑战，包括非凸优化、易受攻击性、潜在的隐私泄露风险以及通信效率低下。本文通过将联邦推荐问题重新表述为凸优化问题，确保了全局最优解的收敛性，从而应对这些挑战。基于此，我们设计了一种新型方法RFRec，以高效解决这一优化问题。此外，我们还提出了RFRecF，这是一种高效版本，结合了非均匀随机梯度下降以提高通信效率。在用户偏好建模方面，这两种方法都学习局部和全局模型，在联邦学习设置下协同学习用户的共同和个性化兴趣。此外，这两种方法在理论支持下显著提升了通信效率、鲁棒性和隐私保护。对四个基准数据集的综合评估表明，RFRec和RFRecF相较于多种基线方法表现更为优越。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Efficient+and+Robust+Regularized+Federated+Recommendation)|0| -|[Two Heads are Better than One: Zero-shot Cognitive Reasoning via Multi-LLM Knowledge Fusion](https://doi.org/10.1145/3627673.3679744)|Liang Liu, Dong Zhang, Shoushan Li, Guodong Zhou, Erik Cambria|School of Computer Science and Technology, Soochow University, Suzhou, China; College of Computing and Data Science, Nanyang Technological University, Singapore, Singapore|Cognitive reasoning holds a significant place within Natural Language Processing (NLP). Yet, the exploration of zero-shot scenarios, which align more closely with real-life situations than supervised scenarios, has been relatively limited. While a few studies have employed Large Language Models (LLMs) to tackle zero-shot cognitive reasoning tasks, they still grapple with two key challenges: 1) Traditional approaches rely on the chain-of-thought (CoT) mechanism, wherein LLMs are provided with a "Let's think step by step'' prompt. However, this schema may not accurately understand the meaning of a given question and ignores the possible learned knowledge (e.g., background or commonsense) of the LLMs about the questions, leading to incorrect answers. 2) Previous CoT methods normally exploit a single Large Language Model (LLM) and design many strategies to augment this LLM. We argue that the power of a single LLM is typically finite since it may not have learned some relevant knowledge about the question. To address these issues, we propose a Multi-LLM Knowledge Fusion (MLKF) approach, which resorts to heterogeneous knowledge emerging from multiple LLMs, for zero-shot cognitive reasoning tasks. Through extensive experiments and detailed analysis, we demonstrate that our MLKF can outperform the existing zero-shot or unsupervised state-of-the-art methods on four kinds of zero-shot tasks: aspect sentiment analysis, named entity recognition, question answering, and mathematical reasoning. Our code is available at https://github.com/trueBatty/MLKF|认知推理在自然语言处理（NLP）中占有重要地位。然而，零样本场景的研究相对较少，这些场景比监督场景更接近现实情况。尽管一些研究已经使用大型语言模型（LLMs）来解决零样本认知推理任务，但它们仍然面临两个关键挑战：1）传统方法依赖于思维链（CoT）机制，其中LLMs通过“让我们一步一步地思考”提示进行推理。然而，这种模式可能无法准确理解给定问题的含义，并且忽略了LLMs关于问题的潜在学习知识（例如背景或常识），导致答案错误。2）之前的CoT方法通常利用单一的大型语言模型（LLM），并设计多种策略来增强该LLM。我们认为，单一LLM的能力通常是有限的，因为它可能没有学习到与问题相关的某些知识。为了解决这些问题，我们提出了一种多LLM知识融合（MLKF）方法，该方法利用多个LLMs中涌现的异构知识来进行零样本认知推理任务。通过广泛的实验和详细分析，我们证明了我们的MLKF在四种零样本任务（方面情感分析、命名实体识别、问答和数学推理）上可以超越现有的零样本或无监督的最先进方法。我们的代码可在https://github.com/trueBatty/MLKF获取。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Two+Heads+are+Better+than+One:+Zero-shot+Cognitive+Reasoning+via+Multi-LLM+Knowledge+Fusion)|0| +|[Efficient and Robust Regularized Federated Recommendation](https://doi.org/10.1145/3627673.3679682)|Langming Liu, Wanyu Wang, Xiangyu Zhao, Zijian Zhang, Chunxu Zhang, Shanru Lin, Yiqi Wang, Lixin Zou, Zitao Liu, Xuetao Wei, Hongzhi Yin, Qing Li|The University of Queensland, Brisbane, Australia; Jilin University, Changchun, China; The Hong Kong Polytechnic University, Hong Kong, China; City University of Hong Kong, Hong Kong, China; Michigan State University, East Lansing, USA; Jinan University, Guangzhou, China; Southern University of Science and Technology, Shenzhen, China; Wuhan University, Wuhan, China|Recommender systems play a pivotal role across practical scenarios, showcasing remarkable capabilities in user preference modeling. However, the centralized learning paradigm predominantly used raises serious privacy concerns. The federated recommender system (FedRS) addresses this by updating models on clients, while a central server orchestrates training without accessing private data. Existing FedRS approaches, however, face unresolved challenges, including non-convex optimization, vulnerability, potential privacy leakage risk, and communication inefficiency. This paper addresses these challenges by reformulating the federated recommendation problem as a convex optimization issue, ensuring convergence to the global optimum. Based on this, we devise a novel method, RFRec, to tackle this optimization problem efficiently. In addition, we propose RFRecF, a highly efficient version that incorporates non-uniform stochastic gradient descent to improve communication efficiency. In user preference modeling, both methods learn local and global models, collaboratively learning users' common and personalized interests under the federated learning setting. Moreover, both methods significantly enhance communication efficiency, robustness, and privacy protection, with theoretical support. Comprehensive evaluations on four benchmark datasets demonstrate RFRec and RFRecF's superior performance compared to diverse baselines.|推荐系统在实际应用场景中扮演着关键角色，展现了在用户偏好建模方面的显著能力。然而，目前主要采用的集中式学习模式引发了严重的隐私问题。联邦推荐系统（FedRS）通过在客户端更新模型来解决这一问题，同时中央服务器在不访问私人数据的情况下协调训练。尽管如此，现有的FedRS方法仍面临一些未解决的挑战，包括非凸优化、易受攻击性、潜在的隐私泄露风险以及通信效率低下。本文通过将联邦推荐问题重新表述为凸优化问题，确保了全局最优解的收敛性，从而应对这些挑战。基于此，我们设计了一种新型方法RFRec，以高效解决这一优化问题。此外，我们还提出了RFRecF，这是一种高效版本，结合了非均匀随机梯度下降以提高通信效率。在用户偏好建模方面，这两种方法都学习局部和全局模型，在联邦学习设置下协同学习用户的共同和个性化兴趣。此外，这两种方法在理论支持下显著提升了通信效率、鲁棒性和隐私保护。对四个基准数据集的综合评估表明，RFRec和RFRecF相较于多种基线方法表现更为优越。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Efficient+and+Robust+Regularized+Federated+Recommendation)|0| +|[Two Heads are Better than One: Zero-shot Cognitive Reasoning via Multi-LLM Knowledge Fusion](https://doi.org/10.1145/3627673.3679744)|Liang Liu, Dong Zhang, Shoushan Li, Guodong Zhou, Erik Cambria|College of Computing and Data Science, Nanyang Technological University, Singapore, Singapore; School of Computer Science and Technology, Soochow University, Suzhou, China|Cognitive reasoning holds a significant place within Natural Language Processing (NLP). Yet, the exploration of zero-shot scenarios, which align more closely with real-life situations than supervised scenarios, has been relatively limited. While a few studies have employed Large Language Models (LLMs) to tackle zero-shot cognitive reasoning tasks, they still grapple with two key challenges: 1) Traditional approaches rely on the chain-of-thought (CoT) mechanism, wherein LLMs are provided with a "Let's think step by step'' prompt. However, this schema may not accurately understand the meaning of a given question and ignores the possible learned knowledge (e.g., background or commonsense) of the LLMs about the questions, leading to incorrect answers. 2) Previous CoT methods normally exploit a single Large Language Model (LLM) and design many strategies to augment this LLM. We argue that the power of a single LLM is typically finite since it may not have learned some relevant knowledge about the question. To address these issues, we propose a Multi-LLM Knowledge Fusion (MLKF) approach, which resorts to heterogeneous knowledge emerging from multiple LLMs, for zero-shot cognitive reasoning tasks. Through extensive experiments and detailed analysis, we demonstrate that our MLKF can outperform the existing zero-shot or unsupervised state-of-the-art methods on four kinds of zero-shot tasks: aspect sentiment analysis, named entity recognition, question answering, and mathematical reasoning. Our code is available at https://github.com/trueBatty/MLKF|认知推理在自然语言处理（NLP）中占有重要地位。然而，零样本场景的研究相对较少，这些场景比监督场景更接近现实情况。尽管一些研究已经使用大型语言模型（LLMs）来解决零样本认知推理任务，但它们仍然面临两个关键挑战：1）传统方法依赖于思维链（CoT）机制，其中LLMs通过“让我们一步一步地思考”提示进行推理。然而，这种模式可能无法准确理解给定问题的含义，并且忽略了LLMs关于问题的潜在学习知识（例如背景或常识），导致答案错误。2）之前的CoT方法通常利用单一的大型语言模型（LLM），并设计多种策略来增强该LLM。我们认为，单一LLM的能力通常是有限的，因为它可能没有学习到与问题相关的某些知识。为了解决这些问题，我们提出了一种多LLM知识融合（MLKF）方法，该方法利用多个LLMs中涌现的异构知识来进行零样本认知推理任务。通过广泛的实验和详细分析，我们证明了我们的MLKF在四种零样本任务（方面情感分析、命名实体识别、问答和数学推理）上可以超越现有的零样本或无监督的最先进方法。我们的代码可在https://github.com/trueBatty/MLKF获取。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Two+Heads+are+Better+than+One:+Zero-shot+Cognitive+Reasoning+via+Multi-LLM+Knowledge+Fusion)|0| |[Collaborative Fraud Detection on Large Scale Graph Using Secure Multi-Party Computation](https://doi.org/10.1145/3627673.3679863)|Xin Liu, Xiaoyu Fan, Rong Ma, Kun Chen, Yi Li, Guosai Wang, Wei Xu|Tsinghua University, Beijing, China; Independent Researcher, Beijing, China; Ant Group, Beijing, China; Tsingjiao Information Technology Co. Ltd., Beijing, China|Enabling various parties to share data enhances online fraud detection capabilities considering fraudsters tend to reuse resources attacking multiple platforms. Multi-party computation (MPC) techniques, such as secret sharing, offer potential privacy-preserving solutions but face efficiency challenges when handling large-scale data. This paper presents a novel approach, SecureFD (Secure Fraud Detector), aimed at detecting fraud in multi-party graph data, ensuring privacy, accuracy, and scalability. We propose a graph neural network EPR-GNN, which is MPC-friendly, as the base detector. Then we design a framework that allows multiple parties to train EPR-GNN collaboratively on secure sparse graphs in a privacy- preserving manner. The oblivious node embedding sharing protocol in the collaborative training procedure achieves up to a 45× speed-up, supporting over four million users compared to the naive solution. Additionally, we further reduce secure computation by locally pruning a significant number of non-suspicious users and selecting only the most valuable resources for sharing. Experiments on real datasets demonstrate that by securely integrating data from different parties, SecureFD achieves superior detection performance compared to state-of-the-art local detectors. And the local pruning greatly improves the scalability without compromising detection accuracies.|使各方能够共享数据增强了在线欺诈检测能力，因为欺诈者倾向于重复使用资源攻击多个平台。多方计算（MPC）技术，如秘密共享，提供了潜在的隐私保护解决方案，但在处理大规模数据时面临效率挑战。本文提出了一种新方法，SecureFD（安全欺诈检测器），旨在检测多方图数据中的欺诈行为，确保隐私、准确性和可扩展性。我们提出了一种图神经网络EPR-GNN，它对MPC友好，作为基础检测器。然后，我们设计了一个框架，允许多方在隐私保护的方式下，在安全稀疏图上协作训练EPR-GNN。协作训练过程中的不经意节点嵌入共享协议实现了高达45倍的速度提升，相比朴素解决方案，支持超过四百万用户。此外，我们通过本地修剪大量非可疑用户并仅选择最有价值的资源进行共享，进一步减少了安全计算。在真实数据集上的实验表明，通过安全地整合来自不同方的数据，SecureFD相比最先进的本地检测器实现了更优越的检测性能。而本地修剪极大地提高了可扩展性，且不影响检测准确性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Collaborative+Fraud+Detection+on+Large+Scale+Graph+Using+Secure+Multi-Party+Computation)|0| |[AlignRec: Aligning and Training in Multimodal Recommendations](https://doi.org/10.1145/3627673.3679626)|Yifan Liu, Kangning Zhang, Xiangyuan Ren, Yanhua Huang, Jiarui Jin, Yingjie Qin, Ruilong Su, Ruiwen Xu, Yong Yu, Weinan Zhang||With the development of multimedia systems, multimodal recommendations are playing an essential role, as they can leverage rich contexts beyond interactions. Existing methods mainly regard multimodal information as an auxiliary, using them to help learn ID features; However, there exist semantic gaps among multimodal content features and ID-based features, for which directly using multimodal information as an auxiliary would lead to misalignment in representations of users and items. In this paper, we first systematically investigate the misalignment issue in multimodal recommendations, and propose a solution named AlignRec. In AlignRec, the recommendation objective is decomposed into three alignments, namely alignment within contents, alignment between content and categorical ID, and alignment between users and items. Each alignment is characterized by a specific objective function and is integrated into our multimodal recommendation framework. To effectively train AlignRec, we propose starting from pre-training the first alignment to obtain unified multimodal features and subsequently training the following two alignments together with these features as input. As it is essential to analyze whether each multimodal feature helps in training and accelerate the iteration cycle of recommendation models, we design three new classes of metrics to evaluate intermediate performance. Our extensive experiments on three real-world datasets consistently verify the superiority of AlignRec compared to nine baselines. We also find that the multimodal features generated by AlignRec are better than currently used ones, which are to be open-sourced in our repository https://github.com/sjtulyf123/AlignRec_CIKM24.|随着多媒体系统的发展，多模态推荐系统正发挥着至关重要的作用，因为它们能够利用超越交互的丰富上下文信息。现有的方法主要将多模态信息视为辅助手段，利用它们来帮助学习ID特征；然而，多模态内容特征与基于ID的特征之间存在语义鸿沟，直接将多模态信息作为辅助使用会导致用户和物品表示之间的对齐错误。在本文中，我们首先系统地研究了多模态推荐中的对齐问题，并提出了一种名为AlignRec的解决方案。在AlignRec中，推荐目标被分解为三种对齐方式，即内容内部对齐、内容与类别ID之间的对齐，以及用户与物品之间的对齐。每种对齐方式都由一个特定的目标函数表征，并被整合到我们的多模态推荐框架中。为了有效训练AlignRec，我们提出从预训练第一个对齐开始，以获得统一的多模态特征，随后将这些特征作为输入，同时训练后续的两个对齐。由于分析每个多模态特征是否有助于训练并加速推荐模型的迭代周期至关重要，我们设计了三类新的指标来评估中间性能。我们在三个真实世界数据集上的广泛实验一致验证了AlignRec相对于九个基线的优越性。我们还发现，由AlignRec生成的多模态特征优于当前使用的特征，这些特征将在我们的代码库https://github.com/sjtulyf123/AlignRec_CIKM24中开源。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=AlignRec:+Aligning+and+Training+in+Multimodal+Recommendations)|0| |[A Universal Sets-level Optimization Framework for Next Set Recommendation](https://doi.org/10.1145/3627673.3679610)|Yuli Liu, Min Liu, Christian Walder, Lexing Xie|; Google DeepMind, Montreal, Canada; Australian National University, Canberra, Australia; Qinghai University & Australian National University, Xining, China|Next Set Recommendation (NSRec), encompassing related tasks such as next basket recommendation and temporal sets prediction, stands as a trending research topic. Although numerous attempts have been made on this topic, there are certain drawbacks: (i) Existing studies are still confined to utilizing objective functions commonly found in Next Item Recommendation (NIRec), such as binary cross entropy and BPR, which are calculated based on individual item comparisons; (ii) They place emphasis on building sophisticated learning models to capture intricate dependency relationships across sequential sets, but frequently overlook pivotal dependency in their objective functions; (iii) Diversity factor within sequential sets is frequently overlooked. In this research, we endeavor to unveil a universal and Sets-level optimization framework for Next Set Recommendation (SNSRec), offering a holistic fusion of diversity distribution and intricate dependency relationships within temporal sets. To realize this, the following contributions are made: (i) We directly model the temporal set in a sequence as a cohesive entity, leveraging the Structured Determinantal Point Process (SDPP), wherein the probabilistic DPP distribution prioritizes collections of structures (sequential sets) instead of individual items; (ii) We introduce a co-occurrence representation to discern and acknowledge the importance of different sets; (iii) We propose a sets-level optimization criterion, which integrates the diversity distribution and dependency relations across the entire sequence of sets, guiding the model to recommend relevant and diversified set. Extensive experiments on real-world datasets show that our approach consistently outperforms previous methods on both relevance and diversity.|下一集合推荐（NSRec），包括下一篮子推荐和时间集合预测等相关的任务，已经成为一个热门的研究课题。尽管在这个领域已经有很多尝试，但仍存在一些不足：（i）现有的研究仍然局限于使用在下一项目推荐（NIRec）中常见的目标函数，如二元交叉熵和BPR，这些函数是基于单个项目的比较计算的；（ii）它们注重构建复杂的学习模型来捕捉序列集合之间的复杂依赖关系，但往往忽略了目标函数中的关键依赖关系；（iii）序列集合内的多样性因素经常被忽视。在本研究中，我们努力揭示一个通用的、集合级别的优化框架，用于下一集合推荐（SNSRec），提供了一个将多样性分布和时间集合内的复杂依赖关系全面融合的方案。为了实现这一点，我们做出了以下贡献：（i）我们将时间序列集合直接建模为一个有凝聚力的实体，利用结构化行列式点过程（SDPP），其中概率DPP分布优先考虑结构集合（序列集合）而不是单个项目；（ii）我们引入了一个共现表示来识别和承认不同集合的重要性；（iii）我们提出了一种集合级别的优化标准，该标准整合了整个序列集合的多样性分布和依赖关系，指导模型推荐相关且多样化的集合。在真实世界数据集上的广泛实验表明，我们的方法在相关性和多样性方面始终优于以前的方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Universal+Sets-level+Optimization+Framework+for+Next+Set+Recommendation)|0| @@ -164,15 +164,15 @@ |[No Query Left Behind: Query Refinement via Backtranslation](https://doi.org/10.1145/3627673.3679729)|Delaram Rajaei, Zahra Taheri, Hossein Fani|School of Computer Science, University of Windsor, Windsor, ON., Canada|Query refinement is to enhance the relevance of search results by modifying users' original queries to refined versions. State-of-the-art query refinement models have been trained on web query logs, which are predisposed to topic drifts. To fill the gap, little work has been proposed to generate benchmark datasets of (query ’ refined query) pairs through an overwhelming application of unsupervised or supervised modifications to the original query while controlling topic drifts. In this paper, however, we propose leveraging natural language backtranslation, a round-trip translation of a query from a source language via target languages, as a simple yet effective unsupervised approach to scale up generating gold-standard benchmark datasets. Backtranslation can (1) uncover terms that are omitted in a query for being commonly understood in a source language, but may not be known in a target language (e.g., 'figs'’(tamil) 'in a target language (e.g., ‘figs’→(tamil) ‘அத்திமரங்கள்’→‘the fig trees’), (2) augment a query with context-aware synonyms in a target language (e.g., ‘italian nobel prize winners’→(farsi) ’برنده های ایتالیایی جایزه نوبل‘ →‘italian nobel laureates’, and (3) help with the semantic disambiguation of polysemous terms and collocations (e.g., 'custer's last stand'’(malay)`pertahan terakhir custer'’`custer's last defence'. Our experiments across 5 query sets with different query lengths and topics and 10 languages from 7 language families using 2 neural machine translators validated the effectiveness of query backtranslation in generating a more extensive gold-standard dataset for query refinement. We open-sourced our research at https://github.com/fani-lab/RePair/tree/nqlb.|查询精炼是通过修改用户原始查询为精炼版本，以增强搜索结果的相关性。目前最先进的查询精炼模型已经在网络查询日志上进行了训练，这些日志容易出现主题偏移。为了填补这一空白，很少有研究提出通过广泛应用无监督或监督的修改方法来生成（查询-精炼查询）对的基准数据集，同时控制主题偏移。然而，本文提出利用自然语言回译（一种通过目标语言进行源语言查询的往返翻译）作为一种简单而有效的无监督方法，来扩展生成黄金标准的基准数据集。回译可以（1）揭示在源语言中因常见而被省略但在目标语言中可能不为人知的术语（例如，‘figs’→(tamil) ‘அத்திமரங்கள்’→‘the fig trees’），（2）通过目标语言中的上下文相关同义词来增强查询（例如，‘italian nobel prize winners’→(farsi) ’برنده های ایتالیایی جایزه نوبل‘ →‘italian nobel laureates’），以及（3）帮助消除多义词和搭配的语义歧义（例如，‘custer's last stand’→(malay) ‘pertahan terakhir custer’→‘custer's last defence’）。我们在5个不同查询长度和主题的查询集以及来自7个语系的10种语言上进行的实验，使用了2种神经机器翻译器，验证了查询回译在生成更广泛的查询精炼黄金标准数据集方面的有效性。我们在https://github.com/fani-lab/RePair/tree/nqlb 开源了我们的研究。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=No+Query+Left+Behind:+Query+Refinement+via+Backtranslation)|0| |[Retrieval-enhanced Knowledge Editing in Language Models for Multi-Hop Question Answering](https://doi.org/10.1145/3627673.3679722)|Yucheng Shi, Qiaoyu Tan, Xuansheng Wu, Shaochen Zhong, Kaixiong Zhou, Ninghao Liu||Large Language Models (LLMs) have shown proficiency in question-answering tasks but often struggle to integrate real-time knowledge, leading to potentially outdated or inaccurate responses. This problem becomes even more challenging when dealing with multi-hop questions, since they require LLMs to update and integrate multiple knowledge pieces relevant to the questions. To tackle the problem, we propose the Retrieval-Augmented model Editing (RAE) framework for multi-hop question answering. RAE first retrieves edited facts and then refines the language model through in-context learning. Specifically, our retrieval approach, based on mutual information maximization, leverages the reasoning abilities of LLMs to identify chain facts that traditional similarity-based searches might miss. In addition, our framework includes a pruning strategy to eliminate redundant information from the retrieved facts, which enhances the editing accuracy and mitigates the hallucination problem. Our framework is supported by theoretical justification for its fact retrieval efficacy. Finally, comprehensive evaluation across various LLMs validates RAE's ability in providing accurate answers with updated knowledge. Our code is available at: https://github.com/sycny/RAE.|大型语言模型（LLMs）在问答任务中表现出色，但往往难以整合实时知识，导致可能提供过时或不准确的信息。在处理多跳问题时，这一问题变得更加复杂，因为这要求LLMs更新并整合与问题相关的多个知识片段。为了解决这一问题，我们提出了多跳问答的检索增强模型编辑（RAE）框架。RAE首先检索编辑后的事实，然后通过上下文学习对语言模型进行精炼。具体而言，我们的检索方法基于互信息最大化，利用LLMs的推理能力来识别传统基于相似性搜索可能遗漏的链式事实。此外，我们的框架还包括一种剪枝策略，以消除检索事实中的冗余信息，从而提高编辑的准确性并缓解幻觉问题。我们的框架得到了理论上的支持，证明了其在事实检索中的有效性。最后，通过对多种LLMs的综合评估，验证了RAE在提供更新知识的基础上准确回答问题的能力。我们的代码可在以下链接获取：https://github.com/sycny/RAE。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Retrieval-enhanced+Knowledge+Editing+in+Language+Models+for+Multi-Hop+Question+Answering)|0| |[Large Language Models Enhanced Collaborative Filtering](https://doi.org/10.1145/3627673.3679558)|Zhongxiang Sun, Zihua Si, Xiaoxue Zang, Kai Zheng, Yang Song, Xiao Zhang, Jun Xu|Kuaishou Technology Co., Ltd; Renmin University of China Gaoling School of Artificial Intelligence|Recent advancements in Large Language Models (LLMs) have attractedconsiderable interest among researchers to leverage these models to enhanceRecommender Systems (RSs). Existing work predominantly utilizes LLMs togenerate knowledge-rich texts or utilizes LLM-derived embeddings as features toimprove RSs. Al- though the extensive world knowledge embedded in LLMsgenerally benefits RSs, the application can only take limited number of usersand items as inputs, without adequately exploiting collaborative filteringinformation. Considering its crucial role in RSs, one key challenge inenhancing RSs with LLMs lies in providing better collaborative filteringinformation through LLMs. In this paper, drawing inspiration from thein-context learning and chain of thought reasoning in LLMs, we propose theLarge Language Models enhanced Collaborative Filtering (LLM-CF) framework,which distils the world knowledge and reasoning capabilities of LLMs intocollaborative filtering. We also explored a concise and efficientinstruction-tuning method, which improves the recommendation capabilities ofLLMs while preserving their general functionalities (e.g., not decreasing onthe LLM benchmark). Comprehensive experiments on three real-world datasetsdemonstrate that LLM-CF significantly enhances several backbone recommendationmodels and consistently outperforms competitive baselines, showcasing itseffectiveness in distilling the world knowledge and reasoning capabilities ofLLM into collaborative filtering.|近期大型语言模型（LLMs）的进展引起了研究者的广泛关注，他们试图利用这些模型来提升推荐系统（RSs）的性能。现有研究主要利用LLMs生成知识丰富的文本，或使用从LLM派生的嵌入作为特征来改进推荐系统。尽管LLMs嵌入的广泛世界知识通常对推荐系统有益，但这些应用只能处理有限数量的用户和物品作为输入，未能充分挖掘协同过滤信息。考虑到协同过滤在推荐系统中的关键作用，利用LLMs提升推荐系统的一个主要挑战在于通过LLMs提供更好的协同过滤信息。本文受LLMs中的上下文学习和思维链推理的启发，提出了大型语言模型增强的协同过滤（LLM-CF）框架，该框架将LLMs的世界知识和推理能力提炼到协同过滤中。我们还探索了一种简洁高效的指令调优方法，该方法在保留LLMs通用功能（如在LLM基准测试中不降低性能）的同时，提升了其推荐能力。在三个真实世界数据集上的综合实验表明，LLM-CF显著增强了多个骨干推荐模型，并持续优于竞争基线，展示了其将LLM的世界知识和推理能力提炼到协同过滤中的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Large+Language+Models+Enhanced+Collaborative+Filtering)|0| -|[Natural Language-Assisted Multi-modal Medication Recommendation](https://doi.org/10.1145/3627673.3679529)|Jie Tan, Yu Rong, Kangfei Zhao, Tian Bian, Tingyang Xu, Junzhou Huang, Hong Cheng, Helen Meng|The Chinese University of Hong Kong, Hong Kong, China; DAMO Academy, Alibaba Group, Hupan Lab, Hangzhou, China; The Chinese University of Hong Kong, HongKong, China; Beijing Institute of Technology, Beijing, China; University of Texas at Arlington, Arlington, TX, USA|Combinatorial medication recommendation (CMR) is a fundamental task of healthcare, which offers opportunities for clinical physicians to provide more precise prescriptions for patients with intricate health conditions, particularly in the scenarios of long-term medical care. Previous research efforts have sought to extract meaningful information from electronic health records (EHRs) to facilitate combinatorial medication recommendations. Existing learning-based approaches further consider the chemical structures of medications, but ignore the textual medication descriptions in which the functionalities are clearly described. Furthermore, the textual knowledge derived from the EHRs of patients remains largely underutilized. To address these issues, we introduce the Natural Language-Assisted Multi-modal Medication Recommendation (NLA-MMR), a multimodal alignment framework designed to learn knowledge from the patient view and medication view jointly. Specifically, NLA-MMR formulates CMR as an alignment problem from patient and medication modalities. In this vein, we employ pretrained language models (PLMs) to extract in-domain knowledge regarding patients and medications, serving as the foundational representation for both modalities. In the medication modality, we exploit both chemical structures and textual descriptions to create medication representations. In the patient modality, we generate the patient representations based on textual descriptions of diagnosis, procedure, and symptom. Extensive experiments conducted on three publicly accessible datasets demonstrate that NLA-MMR achieves new state-of-the-art performance, with a notable average improvement of 4.72% in Jaccard score.|组合药物推荐（CMR）是医疗保健中的一个基本任务，它为临床医生提供了为病情复杂的患者提供更精确处方的机会，特别是在长期医疗护理的情景中。以往的研究致力于从电子健康记录（EHRs）中提取有意义的信息，以促进组合药物推荐。现有的基于学习的方法进一步考虑了药物的化学结构，但忽略了药物描述文本，这些文本中清楚地描述了药物的功能。此外，从患者EHRs中提取的文本知识在很大程度上未被充分利用。为了解决这些问题，我们引入了自然语言辅助的多模态药物推荐（NLA-MMR），这是一个多模态对齐框架，旨在从患者视角和药物视角共同学习知识。具体来说，NLA-MMR将CMR表述为从患者和药物模态出发的对齐问题。为此，我们采用预训练语言模型（PLMs）来提取关于患者和药物的领域内知识，作为两种模态的基础表示。在药物模态中，我们利用化学结构和文本描述来创建药物表示。在患者模态中，我们基于诊断、治疗程序和症状的文本描述生成患者表示。在三个公开可用的数据集上进行的广泛实验表明，NLA-MMR达到了新的最先进性能，Jaccard分数平均提高了4.72%。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Natural+Language-Assisted+Multi-modal+Medication+Recommendation)|0| -|[LAMRec: Label-aware Multi-view Drug Recommendation](https://doi.org/10.1145/3627673.3679656)|Yunsen Tang, Ning Liu, Haitao Yuan, Yonghe Yan, Lei Liu, Weixing Tan, Lizhen Cui|Shandong University, Jinan, China; Shandong Research Institute of Industrial Technology, Jinan, China; Nanyang Technological University, Singapore, Singapore|The drug recommendation task aims to predict safe and effective drug prescriptions based on the patients' historical electronic health records (EHRs). However, existing drug recommendation models generally have two limitations. First, they neglect the inherent characteristics of multiple views existing in patients' clinical data (e.g., diagnoses and procedures), leading to fragmented and inconsistent patient representations. Second, they do not fully exploit drug label information. Most models do not explicitly establish a mapping relationship between drug labels and patients' historical visits. To address these two problems, we proposed a label-aware multi-view drug recommendation model named LAMRec. In particular, LAMRec uses a cross-attention module to fuse information from the diagnosis and procedure views, and increases the mutual information of patient multi-view representations through multi-view contrastive loss; the label-wise attention mechanism fully explores drug label information by constructing an adaptive mapping of drug-visit to generate personalized representations that are aware of the drug-related visit information. Experiments on three real world medical datasets demonstrated the superiority of LAMRec, with a relative reduction of 5.25% in DDI compared to the optimal baseline, a relative improvement of 4.20% in Jaccard similarity scores, and a relative improvement of 3.10% in F1 scores. We released the code online at: https://github.com/Tyunsen/LAMRec.|药物推荐任务旨在根据患者的电子健康记录（EHR）历史数据预测安全有效的药物处方。然而，现有的药物推荐模型普遍存在两个局限性。首先，它们忽略了患者临床数据中多视图（如诊断和手术）的固有特性，导致患者表示碎片化和不一致。其次，它们未能充分利用药物标签信息。大多数模型没有明确建立药物标签与患者历史就诊之间的映射关系。为了解决这两个问题，我们提出了一种名为LAMRec的标签感知多视图药物推荐模型。具体而言，LAMRec通过交叉注意力模块融合诊断和手术视图的信息，并通过多视图对比损失增加患者多视图表示的互信息；标签感知注意力机制通过构建药物-就诊的自适应映射，充分挖掘药物标签信息，生成包含药物相关就诊信息的个性化表示。在三个真实世界的医疗数据集上的实验表明，LAMRec具有优越性，与最优基线相比，DDI相对减少5.25%，Jaccard相似度分数相对提高4.20%，F1分数相对提高3.10%。我们在网上发布了代码：https://github.com/Tyunsen/LAMRec。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=LAMRec:+Label-aware+Multi-view+Drug+Recommendation)|0| +|[Natural Language-Assisted Multi-modal Medication Recommendation](https://doi.org/10.1145/3627673.3679529)|Jie Tan, Yu Rong, Kangfei Zhao, Tian Bian, Tingyang Xu, Junzhou Huang, Hong Cheng, Helen Meng|The Chinese University of Hong Kong, HongKong, China; DAMO Academy, Alibaba Group, Hupan Lab, Hangzhou, China; The Chinese University of Hong Kong, Hong Kong, China; Beijing Institute of Technology, Beijing, China; University of Texas at Arlington, Arlington, TX, USA|Combinatorial medication recommendation (CMR) is a fundamental task of healthcare, which offers opportunities for clinical physicians to provide more precise prescriptions for patients with intricate health conditions, particularly in the scenarios of long-term medical care. Previous research efforts have sought to extract meaningful information from electronic health records (EHRs) to facilitate combinatorial medication recommendations. Existing learning-based approaches further consider the chemical structures of medications, but ignore the textual medication descriptions in which the functionalities are clearly described. Furthermore, the textual knowledge derived from the EHRs of patients remains largely underutilized. To address these issues, we introduce the Natural Language-Assisted Multi-modal Medication Recommendation (NLA-MMR), a multimodal alignment framework designed to learn knowledge from the patient view and medication view jointly. Specifically, NLA-MMR formulates CMR as an alignment problem from patient and medication modalities. In this vein, we employ pretrained language models (PLMs) to extract in-domain knowledge regarding patients and medications, serving as the foundational representation for both modalities. In the medication modality, we exploit both chemical structures and textual descriptions to create medication representations. In the patient modality, we generate the patient representations based on textual descriptions of diagnosis, procedure, and symptom. Extensive experiments conducted on three publicly accessible datasets demonstrate that NLA-MMR achieves new state-of-the-art performance, with a notable average improvement of 4.72% in Jaccard score.|组合药物推荐（CMR）是医疗保健中的一个基本任务，它为临床医生提供了为病情复杂的患者提供更精确处方的机会，特别是在长期医疗护理的情景中。以往的研究致力于从电子健康记录（EHRs）中提取有意义的信息，以促进组合药物推荐。现有的基于学习的方法进一步考虑了药物的化学结构，但忽略了药物描述文本，这些文本中清楚地描述了药物的功能。此外，从患者EHRs中提取的文本知识在很大程度上未被充分利用。为了解决这些问题，我们引入了自然语言辅助的多模态药物推荐（NLA-MMR），这是一个多模态对齐框架，旨在从患者视角和药物视角共同学习知识。具体来说，NLA-MMR将CMR表述为从患者和药物模态出发的对齐问题。为此，我们采用预训练语言模型（PLMs）来提取关于患者和药物的领域内知识，作为两种模态的基础表示。在药物模态中，我们利用化学结构和文本描述来创建药物表示。在患者模态中，我们基于诊断、治疗程序和症状的文本描述生成患者表示。在三个公开可用的数据集上进行的广泛实验表明，NLA-MMR达到了新的最先进性能，Jaccard分数平均提高了4.72%。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Natural+Language-Assisted+Multi-modal+Medication+Recommendation)|0| +|[LAMRec: Label-aware Multi-view Drug Recommendation](https://doi.org/10.1145/3627673.3679656)|Yunsen Tang, Ning Liu, Haitao Yuan, Yonghe Yan, Lei Liu, Weixing Tan, Lizhen Cui|Shandong University, Jinan, China; Nanyang Technological University, Singapore, Singapore; Shandong Research Institute of Industrial Technology, Jinan, China|The drug recommendation task aims to predict safe and effective drug prescriptions based on the patients' historical electronic health records (EHRs). However, existing drug recommendation models generally have two limitations. First, they neglect the inherent characteristics of multiple views existing in patients' clinical data (e.g., diagnoses and procedures), leading to fragmented and inconsistent patient representations. Second, they do not fully exploit drug label information. Most models do not explicitly establish a mapping relationship between drug labels and patients' historical visits. To address these two problems, we proposed a label-aware multi-view drug recommendation model named LAMRec. In particular, LAMRec uses a cross-attention module to fuse information from the diagnosis and procedure views, and increases the mutual information of patient multi-view representations through multi-view contrastive loss; the label-wise attention mechanism fully explores drug label information by constructing an adaptive mapping of drug-visit to generate personalized representations that are aware of the drug-related visit information. Experiments on three real world medical datasets demonstrated the superiority of LAMRec, with a relative reduction of 5.25% in DDI compared to the optimal baseline, a relative improvement of 4.20% in Jaccard similarity scores, and a relative improvement of 3.10% in F1 scores. We released the code online at: https://github.com/Tyunsen/LAMRec.|药物推荐任务旨在根据患者的电子健康记录（EHR）历史数据预测安全有效的药物处方。然而，现有的药物推荐模型普遍存在两个局限性。首先，它们忽略了患者临床数据中多视图（如诊断和手术）的固有特性，导致患者表示碎片化和不一致。其次，它们未能充分利用药物标签信息。大多数模型没有明确建立药物标签与患者历史就诊之间的映射关系。为了解决这两个问题，我们提出了一种名为LAMRec的标签感知多视图药物推荐模型。具体而言，LAMRec通过交叉注意力模块融合诊断和手术视图的信息，并通过多视图对比损失增加患者多视图表示的互信息；标签感知注意力机制通过构建药物-就诊的自适应映射，充分挖掘药物标签信息，生成包含药物相关就诊信息的个性化表示。在三个真实世界的医疗数据集上的实验表明，LAMRec具有优越性，与最优基线相比，DDI相对减少5.25%，Jaccard相似度分数相对提高4.20%，F1分数相对提高3.10%。我们在网上发布了代码：https://github.com/Tyunsen/LAMRec。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=LAMRec:+Label-aware+Multi-view+Drug+Recommendation)|0| |[Retrieval Augmented Deep Anomaly Detection for Tabular Data](https://doi.org/10.1145/3627673.3679559)|Hugo Thimonier, Fabrice Popineau, Arpad Rimmel, BichLiên Doan||Deep learning for tabular data has garnered increasing attention in recent years, yet employing deep models for structured data remains challenging. While these models excel with unstructured data, their efficacy with structured data has been limited. Recent research has introduced retrieval-augmented models to address this gap, demonstrating promising results in supervised tasks such as classification and regression. In this work, we investigate using retrieval-augmented models for anomaly detection on tabular data. We propose a reconstruction-based approach in which a transformer model learns to reconstruct masked features of normal samples. We test the effectiveness of KNN-based and attention-based modules to select relevant samples to help in the reconstruction process of the target sample. Our experiments on a benchmark of 31 tabular datasets reveal that augmenting this reconstruction-based anomaly detection (AD) method with sample-sample dependencies via retrieval modules significantly boosts performance. The present work supports the idea that retrieval module are useful to augment any deep AD method to enhance anomaly detection on tabular data.|近年来，深度学习在表格数据处理领域引起了越来越多的关注，然而，将深度模型应用于结构化数据仍然充满挑战。尽管这些模型在非结构化数据上表现出色，但它们在结构化数据上的有效性却受到限制。最近的研究引入了检索增强模型来填补这一空白，在分类和回归等监督任务中展示了有前景的结果。在这项工作中，我们探讨了使用检索增强模型进行表格数据异常检测的方法。我们提出了一种基于重构的方法，其中变压器模型学习重构正常样本的掩码特征。我们测试了基于KNN和基于注意力模块的有效性，以选择相关样本来辅助目标样本的重构过程。我们在31个表格数据集的基准测试中进行的实验表明，通过检索模块增强这种基于重构的异常检测（AD）方法，利用样本间的依赖关系，显著提升了性能。本研究支持了这样一个观点：检索模块对于增强任何深度AD方法，以提高表格数据上的异常检测效能是有益的。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Retrieval+Augmented+Deep+Anomaly+Detection+for+Tabular+Data)|0| |[On Causally Disentangled State Representation Learning for Reinforcement Learning based Recommender Systems](https://doi.org/10.1145/3627673.3679674)|Siyu Wang, Xiaocong Chen, Lina Yao||In Reinforcement Learning-based Recommender Systems (RLRS), the complexity and dynamism of user interactions often result in high-dimensional and noisy state spaces, making it challenging to discern which aspects of the state are truly influential in driving the decision-making process. This issue is exacerbated by the evolving nature of user preferences and behaviors, requiring the recommender system to adaptively focus on the most relevant information for decision-making while preserving generaliability. To tackle this problem, we introduce an innovative causal approach for decomposing the state and extracting \textbf{C}ausal-\textbf{I}n\textbf{D}ispensable \textbf{S}tate Representations (CIDS) in RLRS. Our method concentrates on identifying the \textbf{D}irectly \textbf{A}ction-\textbf{I}nfluenced \textbf{S}tate Variables (DAIS) and \textbf{A}ction-\textbf{I}nfluence \textbf{A}ncestors (AIA), which are essential for making effective recommendations. By leveraging conditional mutual information, we develop a framework that not only discerns the causal relationships within the generative process but also isolates critical state variables from the typically dense and high-dimensional state representations. We provide theoretical evidence for the identifiability of these variables. Then, by making use of the identified causal relationship, we construct causal-indispensable state representations, enabling the training of policies over a more advantageous subset of the agent's state space. We demonstrate the efficacy of our approach through extensive experiments, showcasing our method outperforms state-of-the-art methods.|在基于强化学习的推荐系统（RLRS）中，用户交互的复杂性和动态性常常导致高维度和噪声状态空间，使得难以辨别哪些状态方面真正影响决策过程。这一问题因用户偏好和行为的不断演变而加剧，要求推荐系统在保持泛化能力的同时，自适应地专注于决策中最相关的信息。为解决此问题，我们引入了一种创新的因果分解方法，用于在RLRS中提取\textbf{C}ausal-\textbf{I}n\textbf{D}ispensable \textbf{S}tate Representations（CIDS）。我们的方法专注于识别\textbf{D}irectly \textbf{A}ction-\textbf{I}nfluenced \textbf{S}tate Variables（DAIS）和\textbf{A}ction-\textbf{I}nfluence \textbf{A}ncestors（AIA），这些变量对于做出有效推荐至关重要。通过利用条件互信息，我们开发了一个框架，不仅能够辨别生成过程中的因果关系，还能从通常密集且高维的状态表示中隔离出关键状态变量。我们提供了这些变量可识别性的理论证据。随后，通过利用识别出的因果关系，我们构建了因果不可或缺的状态表示，使得在代理状态空间中更具优势的子集上训练策略成为可能。我们通过广泛的实验展示了我们方法的有效性，证明其优于现有最先进的方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=On+Causally+Disentangled+State+Representation+Learning+for+Reinforcement+Learning+based+Recommender+Systems)|0| |[Topology-aware Retrieval Augmentation for Text Generation](https://doi.org/10.1145/3627673.3679746)|Yu Wang, Nedim Lipka, Ruiyi Zhang, Alexa F. Siu, Yuying Zhao, Bo Ni, Xin Wang, Ryan A. Rossi, Tyler Derr||Despite the impressive advancements of Large Language Models (LLMs) ingenerating text, they are often limited by the knowledge contained in the inputand prone to producing inaccurate or hallucinated content. To tackle theseissues, Retrieval-augmented Generation (RAG) is employed as an effectivestrategy to enhance the available knowledge base and anchor the responses inreality by pulling additional texts from external databases. In real-worldapplications, texts are often linked through entities within a graph, such ascitations in academic papers or comments in social networks. This paperexploits these topological relationships to guide the retrieval process in RAG.Specifically, we explore two kinds of topological connections: proximity-based,focusing on closely connected nodes, and role-based, which looks at nodessharing similar subgraph structures. Our empirical research confirms theirrelevance to text relationships, leading us to develop a Topology-awareRetrieval-augmented Generation framework. This framework includes a retrievalmodule that selects texts based on their topological relationships and anaggregation module that integrates these texts into prompts to stimulate LLMsfor text generation. We have curated established text-attributed networks andconducted comprehensive experiments to validate the effectiveness of thisframework, demonstrating its potential to enhance RAG with topologicalawareness.|尽管大型语言模型（LLM）在生成文本方面取得了显著进展，但它们通常受限于输入中的知识，容易产生不准确或虚构的内容。为了解决这些问题，检索增强生成（RAG）被用作一种有效的策略，通过从外部数据库中提取额外的文本来增强可用知识库，并将响应锚定在现实世界中。在实际应用中，文本通常通过图中的实体相互关联，例如学术论文中的引用或社交网络中的评论。本文利用这些拓扑关系来指导RAG中的检索过程。具体而言，我们探索了两类拓扑连接：基于接近度的连接，关注紧密连接的节点；以及基于角色的连接，关注共享相似子图结构的节点。我们的实证研究表明它们与文本关系的相关性，从而开发了一种拓扑感知检索增强生成框架。该框架包括一个基于拓扑关系选择文本的检索模块和一个将这些文本整合到提示中以刺激LLM进行文本生成的聚合模块。我们精心构建了现有的文本属性网络，并进行了全面的实验以验证该框架的有效性，展示了其通过拓扑感知增强RAG的潜力。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Topology-aware+Retrieval+Augmentation+for+Text+Generation)|0| |[LLM4MSR: An LLM-Enhanced Paradigm for Multi-Scenario Recommendation](https://doi.org/10.1145/3627673.3679743)|Yuhao Wang, Yichao Wang, Zichuan Fu, Xiangyang Li, Wanyu Wang, Yuyang Ye, Xiangyu Zhao, Huifeng Guo, Ruiming Tang||As the demand for more personalized recommendation grows and a dramatic boomin commercial scenarios arises, the study on multi-scenario recommendation(MSR) has attracted much attention, which uses the data from all scenarios tosimultaneously improve their recommendation performance. However, existingmethods tend to integrate insufficient scenario knowledge and neglect learningpersonalized cross-scenario preferences, thus leading to suboptimal performanceand inadequate interpretability. Meanwhile, though large language model (LLM)has shown great capability of reasoning and capturing semantic information, thehigh inference latency and high computation cost of tuning hinder itsimplementation in industrial recommender systems. To fill these gaps, wepropose an effective efficient interpretable LLM-enhanced paradigm LLM4MSR inthis work. Specifically, we first leverage LLM to uncover multi-level knowledgeincluding scenario correlations and users' cross-scenario interests from thedesigned scenario- and user-level prompt without fine-tuning the LLM, thenadopt hierarchical meta networks to generate multi-level meta layers toexplicitly improves the scenario-aware and personalized recommendationcapability. Our experiments on KuaiSAR-small, KuaiSAR, and Amazon datasetsvalidate two significant advantages of LLM4MSR: (i) the effectiveness andcompatibility with different multi-scenario backbone models (achieving 1.51deployability on industrial recommender systems, and (iii) improvedinterpretability. The implemented code and data is available to easereproduction.|随着对更个性化推荐的需求日益增长以及商业场景中的显著繁荣，多场景推荐（MSR）研究引起了广泛关注。该研究利用所有场景的数据，旨在同时提升各场景的推荐性能。然而，现有方法往往整合不足的场景知识，忽视了跨场景个性化偏好的学习，导致性能次优且解释性不足。同时，尽管大型语言模型（LLM）在推理和捕捉语义信息方面展现出强大能力，但其高推理延迟和高计算成本的调优阻碍了其在工业推荐系统中的应用。 为填补这些空白，本文提出了一种高效的、可解释的LLM增强范式LLM4MSR。具体而言，我们首先利用LLM在不进行微调的情况下，通过设计场景级和用户级提示，揭示包括场景关联和用户跨场景兴趣在内的多层次知识。随后，采用分层元网络生成多层次元层，以显式提升场景感知和个性化推荐能力。我们在KuaiSAR-small、KuaiSAR和Amazon数据集上的实验验证了LLM4MSR的两个显著优势：（i）有效性和与不同多场景骨干模型的兼容性（在工业推荐系统中实现1.51的部署性），以及（iii）提升的解释性。我们提供的实现代码和数据将有助于复现研究成果。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=LLM4MSR:+An+LLM-Enhanced+Paradigm+for+Multi-Scenario+Recommendation)|0| -|[Time-Sensitve Retrieval-Augmented Generation for Question Answering](https://doi.org/10.1145/3627673.3679800)|Feifan Wu, Lingyuan Liu, Wentao He, Ziqi Liu, Zhiqiang Zhang, Haofen Wang, Meng Wang|Southeast University & XAI Lab, Tongji University, Nanjing, China; Southeast University, Nanjing, China; Ant Group, Hangzhou, China; College of Design and Innovation, Tongji University, Shanghai, China|Retrieval-augmented generation (RAG) enhances large language models (LLMs) by accessing external data sources, offering a promising way to improve accuracy and reliability. Despite its potential, conventional retrievers encounter bias and flaws with time-sensitive queries. In this paper, a benchmark query dataset is constructed to retrieve documents containing time-evolving facts, and the results show that current embedding-based similarity-matching methods struggle to handle queries with explicit temporal constraints. Therefore, we propose a novel approach that integrates supervised contrastive learning with tailored negative sample pairs for temporal constraints to train the retriever of an RAG system, along with query-side fine-tuning and routing techniques. Experimental results show that our approach significantly enhances the retriever performance of time-sensitive queries while ensuring the effectiveness of general queries. We will make the code and dataset publicly available at https://github.com/suzhou-22/TS-Retriever.|检索增强生成（RAG）通过访问外部数据源，增强了大型语言模型（LLMs）的能力，为提高准确性和可靠性提供了一种有前景的方式。尽管其潜力巨大，传统的检索器在处理与时间敏感的查询时仍面临偏差和缺陷。本文构建了一个基准查询数据集，用于检索包含时间演化事实的文档，结果显示当前基于嵌入的相似性匹配方法在处理带有明确时间约束的查询时表现不佳。因此，我们提出了一种新方法，将监督对比学习与针对时间约束定制的负样本对相结合，用于训练RAG系统的检索器，同时结合查询端的微调和路由技术。实验结果表明，我们的方法显著提升了对时间敏感查询的检索器性能，同时确保了通用查询的有效性。我们将在https://github.com/suzhou-22/TS-Retriever公开代码和数据集。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Time-Sensitve+Retrieval-Augmented+Generation+for+Question+Answering)|0| +|[Time-Sensitve Retrieval-Augmented Generation for Question Answering](https://doi.org/10.1145/3627673.3679800)|Feifan Wu, Lingyuan Liu, Wentao He, Ziqi Liu, Zhiqiang Zhang, Haofen Wang, Meng Wang|Southeast University, Nanjing, China; Ant Group, Hangzhou, China; Southeast University & XAI Lab, Tongji University, Nanjing, China; College of Design and Innovation, Tongji University, Shanghai, China|Retrieval-augmented generation (RAG) enhances large language models (LLMs) by accessing external data sources, offering a promising way to improve accuracy and reliability. Despite its potential, conventional retrievers encounter bias and flaws with time-sensitive queries. In this paper, a benchmark query dataset is constructed to retrieve documents containing time-evolving facts, and the results show that current embedding-based similarity-matching methods struggle to handle queries with explicit temporal constraints. Therefore, we propose a novel approach that integrates supervised contrastive learning with tailored negative sample pairs for temporal constraints to train the retriever of an RAG system, along with query-side fine-tuning and routing techniques. Experimental results show that our approach significantly enhances the retriever performance of time-sensitive queries while ensuring the effectiveness of general queries. We will make the code and dataset publicly available at https://github.com/suzhou-22/TS-Retriever.|检索增强生成（RAG）通过访问外部数据源，增强了大型语言模型（LLMs）的能力，为提高准确性和可靠性提供了一种有前景的方式。尽管其潜力巨大，传统的检索器在处理与时间敏感的查询时仍面临偏差和缺陷。本文构建了一个基准查询数据集，用于检索包含时间演化事实的文档，结果显示当前基于嵌入的相似性匹配方法在处理带有明确时间约束的查询时表现不佳。因此，我们提出了一种新方法，将监督对比学习与针对时间约束定制的负样本对相结合，用于训练RAG系统的检索器，同时结合查询端的微调和路由技术。实验结果表明，我们的方法显著提升了对时间敏感查询的检索器性能，同时确保了通用查询的有效性。我们将在https://github.com/suzhou-22/TS-Retriever公开代码和数据集。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Time-Sensitve+Retrieval-Augmented+Generation+for+Question+Answering)|0| |[Bridge the Gap between Past and Future: Siamese Model Optimization for Context-Aware Document Ranking](https://doi.org/10.1145/3627673.3679661)|Songhao Wu, Quan Tu, Mingjie Zhong, Hong Liu, Jia Xu, Jinjie Gu, Rui Yan|Ant Group, Hangzhou, China; Gaoling School of Artificial Intelligence, Renmin University of China, Beijing, China|In the realm of information retrieval, users often engage in multi-turn interactions with search engines to acquire information, leading to the formation of sequences of user feedback behaviors. Leveraging the session context has proven to be beneficial for inferring user search intent and document ranking. A multitude of approaches have been proposed to exploit in-session context for improved document ranking. Despite these advances, the limitation of historical session data for capturing evolving user intent remains a challenge. In this work, we explore the integration of future contextual information into the session context to enhance document ranking. We present the siamese model optimization framework, comprising a history-conditioned model and a future-aware model. The former processes only the historical behavior sequence, while the latter integrates both historical and anticipated future behaviors. Both models are trained collaboratively using the supervised labels and pseudo labels predicted by the other. The history-conditioned model, referred to as ForeRanker, progressively learns future-relevant information to enhance ranking, while it singly uses historical session at inference time. To mitigate inconsistencies during training, we introduce the peer knowledge distillation method with a dynamic gating mechanism, allowing models to selectively incorporate contextual information. Experimental results on benchmark datasets demonstrate the effectiveness of our ForeRanker, showcasing its superior performance compared to existing methods.|在信息检索领域，用户通常通过与搜索引擎的多轮交互来获取信息，从而形成一系列用户反馈行为。利用会话上下文已被证明有助于推断用户搜索意图和文档排序。已经提出了多种方法来利用会话内上下文以改进文档排序。尽管取得了这些进展，但捕捉用户意图演变的历史会话数据的局限性仍然是一个挑战。在这项工作中，我们探讨了将会话上下文与未来上下文信息相结合以增强文档排序的方法。我们提出了孪生模型优化框架，包括一个历史条件模型和一个未来感知模型。前者仅处理历史行为序列，而后者则结合了历史和预期的未来行为。两个模型通过监督标签和另一个模型预测的伪标签进行协同训练。历史条件模型，称为ForeRanker，逐步学习与未来相关的信息以提升排序，而在推理时单独使用历史会话。为了减少训练过程中的不一致性，我们引入了具有动态门控机制的同行知识蒸馏方法，使模型能够选择性地整合上下文信息。在基准数据集上的实验结果证明了我们ForeRanker的有效性，展示了其相对于现有方法的优越性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Bridge+the+Gap+between+Past+and+Future:+Siamese+Model+Optimization+for+Context-Aware+Document+Ranking)|0| |[Federated Node Classification over Distributed Ego-Networks with Secure Contrastive Embedding Sharing](https://doi.org/10.1145/3627673.3679834)|Han Xie, Li Xiong, Carl Yang|Emory University, Atlanta, GA, USA|Federated learning on graphs (a.k.a., federated graph learning - FGL) has recently received increasing attention due to its capacity to enable collaborative learning over distributed graph datasets without compromising local clients' data privacy. In previous works, clients of FGL typically represent institutes or organizations that possess sets of entire graphs (e.g., molecule graphs in biochemical research) or parts of a larger graph (e.g., sub-user networks of e-commerce platforms). However, another natural paradigm exists where clients act as remote devices retaining the graph structures of local neighborhoods centered around the device owners (i.e., ego-networks), which can be modeled for specific graph applications such as user profiling on social ego-networks and infection prediction on contact ego-networks. FGL in such novel yet realistic ego-network settings faces the unique challenge of incomplete neighborhood information for non-ego local nodes since they likely appear and have different sets of neighbors in multiple ego-networks. To address this challenge, we propose an FGL method for distributed ego-networks in which clients obtain complete neighborhood information of local nodes through sharing node embeddings with other clients. A contrastive learning mechanism is proposed to bridge the gap between local and global node embeddings and stabilize the local training of graph neural network models, while a secure embedding sharing protocol is employed to protect individual node identity and embedding privacy against the server and other clients. Comprehensive experiments on various distributed ego-network datasets successfully demonstrate the effectiveness of our proposed embedding sharing method on top of different federated model sharing frameworks, and we also provide discussions on the potential efficiency and privacy drawbacks of the method as well as their future mitigation.|图上的联邦学习（又称联邦图学习 - FGL）近年来因其能够在不损害本地客户端数据隐私的情况下实现分布式图数据集上的协作学习而受到越来越多的关注。在以往的研究中，FGL的客户端通常代表拥有完整图集（例如，生物化学研究中的分子图）或大型图的一部分（例如，电子商务平台的子用户网络）的机构或组织。然而，还存在另一种自然范式，其中客户端作为远程设备，保留以设备所有者为中心的本地邻域的图结构（即自我网络），这些图结构可以用于特定的图应用，例如社交自我网络上的用户画像和接触自我网络上的感染预测。在这种新颖且现实的自我网络设置中，FGL面临一个独特的挑战，即非自我本地节点的邻域信息不完整，因为它们可能出现在多个自我网络中并具有不同的邻居集合。为了应对这一挑战，我们提出了一种针对分布式自我网络的FGL方法，其中客户端通过与其他客户端共享节点嵌入来获取本地节点的完整邻域信息。我们提出了一种对比学习机制，以弥合本地和全局节点嵌入之间的差距，并稳定图神经网络模型的本地训练，同时采用了一种安全的嵌入共享协议，以保护服务器和其他客户端对个体节点身份和嵌入隐私的访问。在各种分布式自我网络数据集上的综合实验成功证明了我们提出的嵌入共享方法在不同联邦模型共享框架上的有效性，并且我们还讨论了该方法潜在的效率和隐私缺陷及其未来的缓解措施。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Federated+Node+Classification+over+Distributed+Ego-Networks+with+Secure+Contrastive+Embedding+Sharing)|0| |[UniMPC: Towards a Unified Framework for Multi-Party Conversations](https://doi.org/10.1145/3627673.3679864)|Yunhe Xie, Chengjie Sun, Yifan Liu, Zhenzhou Ji, Bingquan Liu|Faculty of Computing, Harbin Institute of Technology, Harbin, China|The Multi-Party Conversation (MPC) system has gained attention for its relevance in modern communication. Recent work has focused on developing specialized models for different MPC subtasks, improving state-of-the-art (SOTA) performance. However, since MPC demands often arise collaboratively, managing multiple specialized models is impractical. Additionally, dialogue evolves through diverse meta-information, where knowledge from specific subtasks can influence others. To address this, we propose UniMPC, a unified framework that consolidates common MPC subtasks. UniMPC uses a graph network with utterance nodes, a global node for combined local and global information, and two adaptable free nodes. It also incorporates discourse parsing to enhance model updates. We introduce MPCEval, a new benchmark for evaluating MPC systems. Experiments show UniMPC achieves over 95% of SOTA performance across all subtasks, with some surpassing existing SOTA, highlighting the effectiveness of the global node, free nodes, and dynamic discourse-aware graphs.|多方对话（Multi-Party Conversation, MPC）系统因其与现代通信的相关性而受到关注。近期研究致力于为不同的MPC子任务开发专用模型，以提升最先进（SOTA）的性能。然而，由于MPC需求通常是协同产生的，管理多个专用模型并不现实。此外，对话通过多样化的元信息演变，特定子任务的知识可以影响其他子任务。为了解决这一问题，我们提出了UniMPC，这是一个整合常见MPC子任务的统一框架。UniMPC采用了一个图网络，其中包括话语节点、一个用于结合局部和全局信息的全局节点以及两个可适应的自由节点。它还集成了话语解析以增强模型更新。我们引入了MPCEval，一个新的用于评估MPC系统的基准。实验表明，UniMPC在所有子任务上达到了超过95%的SOTA性能，其中一些子任务超过了现有的SOTA，突显了全局节点、自由节点和动态话语感知图的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=UniMPC:+Towards+a+Unified+Framework+for+Multi-Party+Conversations)|0| @@ -181,18 +181,18 @@ |[Topological Anonymous Walk Embedding: A New Structural Node Embedding Approach](https://doi.org/10.1145/3627673.3679565)|Yuchen Yan, Yongyi Hu, Qinghai Zhou, Shurang Wu, Dingsu Wang, Hanghang Tong|Shanghai Jiao Tong University, Minhang, Shanghai, China; University of Science and Technology of China, Hefei, Anhui, China; University of Illinois at Urbana-Champaign, Urbana, IL, USA|Network embedding is a commonly used technique in graph mining and plays an important role in a variety of applications. Most network embedding works can be categorized into positional node embedding methods and target at capturing the proximity/relative position of node pairs. Recently, structural node embedding has attracted tremendous research interest, which is intended to perceive the local structural information of node, i.e., nodes can share similar local structures in different positions of graphs. Although numerous structural node embedding methods are designed to encode such structural information, most, if not all, of these methods cannot simultaneously achieve the following three desired properties: (1) bijective mapping between embedding and local structure of node; (2) inductive capability; and (3) good interpretability of node embedding. To address this challenge, in this paper, we propose a novel structural node embedding algorithm named topological anonymous walk embedding (TAWE). Specifically, TAWE creatively integrates anonymous walk and breadth-first search (BFS) to construct the bijective mapping between node embedding and local structure of node. In addition, TAWE possesses inductive capability and good interpretability of node embedding. Experimental results on both synthetic and real-world datasets demonstrate the effectiveness of the proposed TAWE algorithm in both structural node classification task and structural node clustering task.|网络嵌入是图挖掘中常用的一种技术，在多种应用中发挥着重要作用。大多数网络嵌入工作可以归类为位置节点嵌入方法，旨在捕捉节点对的接近度/相对位置。最近，结构节点嵌入引起了极大的研究兴趣，其目的是感知节点的局部结构信息，即节点可以在图的不同位置共享相似的局部结构。尽管设计了大量结构节点嵌入方法来编码这种结构信息，但大多数（如果不是全部）这些方法无法同时实现以下三个期望属性：（1）嵌入与节点局部结构之间的双射映射；（2）归纳能力；（3）节点嵌入的良好可解释性。为了解决这一挑战，本文提出了一种名为拓扑匿名游走嵌入（Topological Anonymous Walk Embedding, TAWE）的新型结构节点嵌入算法。具体来说，TAWE创新地将匿名游走与广度优先搜索（BFS）相结合，构建了节点嵌入与其局部结构之间的双射映射。此外，TAWE具有归纳能力和良好的节点嵌入可解释性。在合成数据集和真实世界数据集上的实验结果表明，所提出的TAWE算法在结构节点分类任务和结构节点聚类任务中均表现出了有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Topological+Anonymous+Walk+Embedding:+A+New+Structural+Node+Embedding+Approach)|0| |[Spectral-Aware Augmentation for Enhanced Graph Representation Learning](https://doi.org/10.1145/3627673.3679762)|Kaiqi Yang, Haoyu Han, Wei Jin, Hui Liu|Michigan State University; Emory University|Graph Contrastive Learning (GCL) has demonstrated remarkable effectiveness in learning representations on graphs in recent years. To generate ideal augmentation views, the augmentation generation methods should preserve essential information while discarding less relevant details for downstream tasks. However, current augmentation methods usually involve random topology corruption in the spatial domain, which fails to adequately address information spread across different frequencies in the spectral domain. Our preliminary study highlights this issue, demonstrating that spatial random perturbations impact all frequency bands almost uniformly. Given that task-relevant information typically resides in specific spectral regions that vary across graphs, this one-size-fits-all approach can pose challenges. We argue that indiscriminate spatial random perturbation might unintentionally weaken task-relevant information, reducing its effectiveness. To tackle this challenge, we propose applying perturbations selectively, focusing on information specific to different frequencies across diverse graphs. In this paper, we present GASSER, a model that applies tailored perturbations to specific frequencies of graph structures in the spectral domain, guided by spectral hints. Through extensive experimentation and theoretical analysis, we demonstrate that the augmentation views generated by GASSER are adaptive, controllable, and intuitively aligned with the homophily ratios and spectrum of graph structures.|图对比学习（GCL）近年来在图的表示学习中展示了显著的有效性。为了生成理想的增强视图，增强生成方法应在保留关键信息的同时，去除与下游任务不相关的细节。然而，当前的增强方法通常涉及空间域中的随机拓扑破坏，这未能充分解决频谱域中不同频率上的信息分布问题。我们的初步研究表明了这一问题，表明空间随机扰动几乎均匀地影响所有频段。鉴于任务相关的信息通常位于特定图谱区域，这些区域在不同图中有所不同，这种一刀切的方法可能会带来挑战。我们认为，不分青红皂白的空间随机扰动可能会无意中削弱与任务相关的信息，从而降低其有效性。为了应对这一挑战，我们提出有选择地应用扰动，专注于不同频率上特定于不同图的信息。本文中，我们介绍了GASSER模型，该模型在频谱域中根据频谱提示对图结构的特定频率应用定制的扰动。通过广泛的实验和理论分析，我们证明GASSER生成的增强视图具有自适应性、可控性，并且直观地与图结构的同质性比率和频谱相一致。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Spectral-Aware+Augmentation+for+Enhanced+Graph+Representation+Learning)|0| |[Efficient Pruned Top-K Subgraph Matching with Topology-Aware Bounds](https://doi.org/10.1145/3627673.3679790)|Linglin Yang, Yuqi Zhou, Yue Pang, Lei Zou|Peking University, Beijing, China|Given a query graph, top-k subgraph matching finds up to k matches in a data graph with the highest scores according to a user-defined scoring function. It has wide applications across many fields, including knowledge graphs and social networks. Due to the enormous search space, existing methods are not efficient enough on large graphs. In this paper, we propose PTAB, an efficient algorithm for top-k subgraph matching. It traverses an efficiently pruned search space by topology-aware sub-space score upper bounds computed from a novel hop index, which stores the range of node properties in a constrained multi-hop neighborhood of each node. Additionally, PTAB integrates a cost-aware root selection strategy, which chooses query nodes leading to a search process that utilizes the pruning power of the hop index as much as possible. Furthermore, we use a novel edge-cut strategy to handle general query graphs with cycles. Experimental results on real and synthetic datasets demonstrate that our method outperforms existing methods.|给定一个查询图，top-k子图匹配任务是在数据图中找到至多k个匹配度最高的子图，匹配度由用户定义的评分函数决定。该任务在多个领域中有广泛应用，包括知识图谱和社会网络。由于搜索空间巨大，现有方法在大规模图上效率不足。本文提出了PTAB，一种高效的top-k子图匹配算法。它通过一种新颖的跳跃索引计算出的拓扑感知子空间评分上界，遍历经过高效剪枝的搜索空间，该跳跃索引存储了每个节点在受限多跳邻域内节点属性的范围。此外，PTAB集成了一个成本感知的根节点选择策略，选择能够引导搜索过程尽可能利用跳跃索引剪枝能力的查询节点。我们还采用了一种新颖的边切策略来处理包含循环的一般查询图。在真实和合成数据集上的实验结果表明，我们的方法优于现有方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Efficient+Pruned+Top-K+Subgraph+Matching+with+Topology-Aware+Bounds)|0| -|[A New Framework for Evaluating Faithfulness of Video Moment Retrieval against Multiple Distractors](https://doi.org/10.1145/3627673.3679838)|Nakyeong Yang, Minsung Kim, Seunghyun Yoon, Joongbo Shin, Kyomin Jung|LG AI Research; Seoul National University; Adobe Research|With the explosion of multimedia content, video moment retrieval (VMR), which aims to detect a video moment that matches a given text query from a video, has been studied intensively as a critical problem. However, the existing VMR framework evaluates video moment retrieval performance, assuming that a video is given, which may not reveal whether the models exhibit overconfidence in the falsely given video. In this paper, we propose the MVMR (Massive Videos Moment Retrieval for Faithfulness Evaluation) task that aims to retrieve video moments within a massive video set, including multiple distractors, to evaluate the faithfulness of VMR models. For this task, we suggest an automated massive video pool construction framework to categorize negative (distractors) and positive (false-negative) video sets using textual and visual semantic distance verification methods. We extend existing VMR datasets using these methods and newly construct three practical MVMR datasets. To solve the task, we further propose a strong informative sample-weighted learning method, CroCs, which employs two contrastive learning mechanisms: (1) weakly-supervised potential negative learning and (2) cross-directional hard-negative learning. Experimental results on the MVMR datasets reveal that existing VMR models are easily distracted by the misinformation (distractors), whereas our model shows significantly robust performance, demonstrating that CroCs is essential to distinguishing positive moments against distractors. Our code and datasets are publicly available: https://github.com/yny0506/Massive-Videos-Moment-Retrieval.|随着多媒体内容的激增，视频时刻检索（VMR）——旨在从视频中检测与给定文本查询匹配的视频片段——已被广泛研究为一个关键问题。然而，现有的VMR框架在评估视频时刻检索性能时，假设视频是已知的，这可能无法揭示模型是否在错误提供的视频上表现出过度自信。在本文中，我们提出了MVMR（大规模视频时刻检索以评估模型忠实度）任务，该任务旨在从包含多个干扰项的大规模视频集中检索视频片段，以评估VMR模型的忠实度。为此任务，我们建议了一种自动大规模视频池构建框架，通过文本和视觉语义距离验证方法来分类负样本（干扰项）和正样本（假负例）视频集。我们使用这些方法扩展了现有的VMR数据集，并新构建了三个实用的MVMR数据集。为了解决该任务，我们进一步提出了一种强信息样本加权学习方法CroCs，该方法采用两种对比学习机制：（1）弱监督潜在负样本学习；（2）跨方向硬负样本学习。在MVMR数据集上的实验结果表明，现有VMR模型容易被错误信息（干扰项）所迷惑，而我们的模型表现出显著的鲁棒性能，证明了CroCs在区分正样本时刻与干扰项方面的重要性。我们的代码和数据集已公开发布：https://github.com/yny0506/Massive-Videos-Moment-Retrieval。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+New+Framework+for+Evaluating+Faithfulness+of+Video+Moment+Retrieval+against+Multiple+Distractors)|0| +|[A New Framework for Evaluating Faithfulness of Video Moment Retrieval against Multiple Distractors](https://doi.org/10.1145/3627673.3679838)|Nakyeong Yang, Minsung Kim, Seunghyun Yoon, Joongbo Shin, Kyomin Jung|Seoul National University; Adobe Research; LG AI Research|With the explosion of multimedia content, video moment retrieval (VMR), which aims to detect a video moment that matches a given text query from a video, has been studied intensively as a critical problem. However, the existing VMR framework evaluates video moment retrieval performance, assuming that a video is given, which may not reveal whether the models exhibit overconfidence in the falsely given video. In this paper, we propose the MVMR (Massive Videos Moment Retrieval for Faithfulness Evaluation) task that aims to retrieve video moments within a massive video set, including multiple distractors, to evaluate the faithfulness of VMR models. For this task, we suggest an automated massive video pool construction framework to categorize negative (distractors) and positive (false-negative) video sets using textual and visual semantic distance verification methods. We extend existing VMR datasets using these methods and newly construct three practical MVMR datasets. To solve the task, we further propose a strong informative sample-weighted learning method, CroCs, which employs two contrastive learning mechanisms: (1) weakly-supervised potential negative learning and (2) cross-directional hard-negative learning. Experimental results on the MVMR datasets reveal that existing VMR models are easily distracted by the misinformation (distractors), whereas our model shows significantly robust performance, demonstrating that CroCs is essential to distinguishing positive moments against distractors. Our code and datasets are publicly available: https://github.com/yny0506/Massive-Videos-Moment-Retrieval.|随着多媒体内容的激增，视频时刻检索（VMR）——旨在从视频中检测与给定文本查询匹配的视频片段——已被广泛研究为一个关键问题。然而，现有的VMR框架在评估视频时刻检索性能时，假设视频是已知的，这可能无法揭示模型是否在错误提供的视频上表现出过度自信。在本文中，我们提出了MVMR（大规模视频时刻检索以评估模型忠实度）任务，该任务旨在从包含多个干扰项的大规模视频集中检索视频片段，以评估VMR模型的忠实度。为此任务，我们建议了一种自动大规模视频池构建框架，通过文本和视觉语义距离验证方法来分类负样本（干扰项）和正样本（假负例）视频集。我们使用这些方法扩展了现有的VMR数据集，并新构建了三个实用的MVMR数据集。为了解决该任务，我们进一步提出了一种强信息样本加权学习方法CroCs，该方法采用两种对比学习机制：（1）弱监督潜在负样本学习；（2）跨方向硬负样本学习。在MVMR数据集上的实验结果表明，现有VMR模型容易被错误信息（干扰项）所迷惑，而我们的模型表现出显著的鲁棒性能，证明了CroCs在区分正样本时刻与干扰项方面的重要性。我们的代码和数据集已公开发布：https://github.com/yny0506/Massive-Videos-Moment-Retrieval。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+New+Framework+for+Evaluating+Faithfulness+of+Video+Moment+Retrieval+against+Multiple+Distractors)|0| |[Attacking Visually-aware Recommender Systems with Transferable and Imperceptible Adversarial Styles](https://doi.org/10.1145/3627673.3679828)|Shiyi Yang, Chen Wang, Xiwei Xu, Liming Zhu, Lina Yao|Data61, CSIRO & The University of New South Wales, Eveleigh, Australia; The University of New South Wales & Data61, CSIRO, Sydney, Australia|The inclusion of the images opens up a security vulnerability of visually-aware recommender systems (VARSs). It can be exploited by unscrupulous parties to upload well-crafted adversarial images for certain malicious purposes (e.g., promoting their own products for profits). Some studies have focused on attacking VARSs to gain insights into their robustness, while they are still far from practical, i.e., the attacks often 1) lack diversity in perturbations, 2) are easily perceived and 3) have limited transferability, which may lead to overestimation of defenses in practice. To tackle the problems, we propose to perturb the style of the product, which is an unnoticeable but important property of visual recommendations. Specifically, we propose a novel Style perturbation-based Practical Attack Framework (SPAF). Unlike existing attacks that change pixels within l∞ -norm constraints, SPAF interferes with styles in latent feature space so that the attack becomes unbounded in the pixel space to reflect possible actual perturbations. SPAF formulates attack objectives as an optimization problem and adopts an adaptive adversarial style transfer network to solve it so that transferable and imperceptible attacks can be generated. Comprehensive experiments on real-world datasets demonstrate that SPAF significantly outperforms state-of-the-art attacks.|图像的引入为视觉感知推荐系统（VARSs）带来了一个安全漏洞。不法分子可以利用这一漏洞上传精心制作的对抗性图像以达到某些恶意目的（例如，推广自己的产品以获取利润）。一些研究专注于攻击VARSs以了解其鲁棒性，但这些攻击方法在实际应用中仍存在不足，主要表现为：1) 扰动缺乏多样性，2) 容易被察觉，3) 转移性有限，这可能导致对防御措施的实际效果产生高估。为解决这些问题，我们提出对产品风格进行扰动，这是一种不易察觉但影响视觉推荐的重要属性。具体而言，我们提出了一种基于风格扰动的实用攻击框架（SPAF）。与现有在l∞范数约束下改变像素的攻击方法不同，SPAF在潜在特征空间中干扰风格，使得攻击在像素空间中变得无边界，以反映可能的实际扰动。SPAF将攻击目标形式化为一个优化问题，并采用自适应对抗风格转移网络来解决该问题，从而生成可转移且不可察觉的攻击。在真实世界数据集上的全面实验表明，SPAF显著优于现有的最先进攻击方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Attacking+Visually-aware+Recommender+Systems+with+Transferable+and+Imperceptible+Adversarial+Styles)|0| |[A Cause-Focused Query Optimizer Alert System](https://doi.org/10.1145/3627673.3679771)|Runfan Ye, Zibo Liang, Xu Chen, Shuncheng Liu, Kai Zheng||A series of studies apply machine learning to assist cost-based query optimizers in DBMS, emphasizing incorporating uncertainty predictions to guide decision-making. While these approaches have demonstrated advancement in some benchmarks, their drawbacks, such as unstable performance, stem from the inherent challenges of using machine learning models to predict the cost of execution plans and the lack of exploration of the intrinsic characteristics of suboptimal plans. In this paper, we introduce an alert system for query optimization, which is built upon cost models to reduce the selection of regressed plans. The key insight is that there are differences in the predictive uncertainty that lead to query optimization and the regression of execution plans. We investigate the causes of these differences in uncertainty and design a discriminator to filter out execution plans with higher risks of regression. The alert system can be integrated with various cost models, enhancing the robustness of query optimizers. In our experiments, the system further reduces execution time by 20% compared to learned optimizers. Meanwhile, the proportion of optimized queries reduced by the alert system is just 15% of the proportion of regressed queries diminished.|一系列研究将机器学习应用于数据库管理系统（DBMS）中的基于成本的查询优化器，强调了将不确定性预测纳入决策过程的重要性。尽管这些方法在一些基准测试中展示了进步，但其缺点，如性能不稳定，源于使用机器学习模型预测执行计划成本的固有挑战以及对次优计划内在特性的探索不足。本文介绍了一种查询优化预警系统，该系统基于成本模型来减少选择退化的执行计划。关键见解在于，导致查询优化和执行计划退化的预测不确定性之间存在差异。我们研究了这些不确定性差异的原因，并设计了一个判别器来筛选出更有可能退化的执行计划。该预警系统可以与各种成本模型集成，增强了查询优化器的鲁棒性。在我们的实验中，该系统相比学习型优化器进一步减少了20%的执行时间。同时，预警系统减少的优化查询比例仅为减少的退化查询比例的15%。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Cause-Focused+Query+Optimizer+Alert+System)|0| |[DAMe: Personalized Federated Social Event Detection with Dual Aggregation Mechanism](https://doi.org/10.1145/3627673.3679551)|Xiaoyan Yu, Yifan Wei, Pu Li, Shuaishuai Zhou, Hao Peng, Li Sun, Liehuang Zhu, Philip S. Yu||Training social event detection models through federated learning (FedSED) aims to improve participants' performance on the task. However, existing federated learning paradigms are inadequate for achieving FedSED's objective and exhibit limitations in handling the inherent heterogeneity in social data. This paper proposes a personalized federated learning framework with a dual aggregation mechanism for social event detection, namely DAMe. We present a novel local aggregation strategy utilizing Bayesian optimization to incorporate global knowledge while retaining local characteristics. Moreover, we introduce a global aggregation strategy to provide clients with maximum external knowledge of their preferences. In addition, we incorporate a global-local event-centric constraint to prevent local overfitting and “client-drift”. Experiments within a realistic simulation of a natural federated setting, utilizing six social event datasets spanning six languages and two social media platforms, along with an ablation study, have demonstrated the effectiveness of the proposed framework. Further robustness analyses have shown that DAMe is resistant to injection attacks.|通过联邦学习（FedSED）训练社交事件检测模型的目的是提高参与者在任务中的表现。然而，现有的联邦学习范式不足以实现FedSED的目标，并且在处理社交数据固有的异质性方面存在局限性。本文提出了一种具有双重聚合机制的个性化联邦学习框架，用于社交事件检测，即DAMe。我们提出了一种新颖的局部聚合策略，利用贝叶斯优化来融合全局知识同时保留局部特征。此外，我们引入了一种全局聚合策略，以向客户端提供与其偏好相关的最大外部知识。此外，我们结合了一个全局-局部以事件为中心的约束，以防止局部过拟合和“客户端漂移”。在一个现实的联邦设置模拟实验中，使用了跨越六种语言和两个社交媒体平台的六个社交事件数据集，以及一项消融研究，已证明了所提出框架的有效性。进一步的鲁棒性分析表明，DAMe对注入攻击具有抵抗力。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=DAMe:+Personalized+Federated+Social+Event+Detection+with+Dual+Aggregation+Mechanism)|0| -|[Transformer Based Bayesian Network Embedding for Efficient Multiple Probabilistic Inferences](https://doi.org/10.1145/3627673.3679860)|Kun Yue, Zhiwei Qi, Liang Duan|Kunming Univ Sci & Technol, Fac Informat Engn & Automat, Kunming, Yunnan, Peoples R China; Chengdu Univ Informat Technol, Sch Software Engn, Chengdu, Peoples R China; Yunnan Univ, Sch Informat Sci & Engn, Kunming, Yunnan, Peoples R China|Bayesian network (BN) is a well adopted framework for representing and inferring uncertain knowledge. By the existing methods, multiple probabilistic inferences on the same BN are often fulfilled one by one via repeated searches and calculations of probabilities. However, lots of intermediate results of probability calculations cannot be shared and reused among different probabilistic inferences. It is necessary to improve the overall efficiency of multiple probabilistic inferences on the same BN by incorporating an easy-to-calculate representation of BN and an easy-to-reuse technique for common calculations in multiple inferences. In this paper, we first propose the method of Bayesian network embedding to generate the easy-to-reuse node embeddings. Specifically, we transform BN into the point mutual information (PMI) matrix to simultaneously preserve the directed acyclic graph (DAG) and conditional probability tables (CPTs). Then, we give the singular value decomposition (SVD) based method to factorize the PMI matrix for generating node embeddings. Secondly, we propose a novel method of random sampling to make multiple probabilistic inferences via similarity calculation between node embeddings. Experimental results show that the runtime of our proposed BNERS performing 10 times of inferences is 30% faster than Gibbs sampling (GS) and 50% faster than forward sampling (FS) on LINK BN (very large network), while maintaining almost the same results as GS and FS.|贝叶斯网络（BN）是一个广泛采用的框架，用于表示和推断不确定的知识。现有的方法通常通过重复搜索和概率计算来逐一完成同一贝叶斯网络上的多个概率推断。然而，不同概率推断之间无法共享和重用大量的概率计算中间结果。为了提高同一贝叶斯网络上多个概率推断的整体效率，有必要结合易于计算的贝叶斯网络表示和易于在多次推断中重用的通用计算技术。本文首先提出了贝叶斯网络嵌入方法，以生成易于重用的节点嵌入。具体而言，我们将贝叶斯网络转换为点互信息（PMI）矩阵，以同时保留有向无环图（DAG）和条件概率表（CPTs）。接着，我们给出了基于奇异值分解（SVD）的方法，用于对PMI矩阵进行分解以生成节点嵌入。其次，我们提出了一种新的随机采样方法，通过节点嵌入之间的相似度计算来进行多个概率推断。实验结果表明，在我们提出的BNERS方法中，进行10次推断的运行时间比吉布斯采样（GS）快30%，比前向采样（FS）快50%，并且在LINK BN（非常大的网络）上保持了与GS和FS几乎相同的结果。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Transformer+Based+Bayesian+Network+Embedding+for+Efficient+Multiple+Probabilistic+Inferences)|0| +|[Transformer Based Bayesian Network Embedding for Efficient Multiple Probabilistic Inferences](https://doi.org/10.1145/3627673.3679860)|Kun Yue, Zhiwei Qi, Liang Duan|Chengdu Univ Informat Technol, Sch Software Engn, Chengdu, Peoples R China; Kunming Univ Sci & Technol, Fac Informat Engn & Automat, Kunming, Yunnan, Peoples R China; Yunnan Univ, Sch Informat Sci & Engn, Kunming, Yunnan, Peoples R China|Bayesian network (BN) is a well adopted framework for representing and inferring uncertain knowledge. By the existing methods, multiple probabilistic inferences on the same BN are often fulfilled one by one via repeated searches and calculations of probabilities. However, lots of intermediate results of probability calculations cannot be shared and reused among different probabilistic inferences. It is necessary to improve the overall efficiency of multiple probabilistic inferences on the same BN by incorporating an easy-to-calculate representation of BN and an easy-to-reuse technique for common calculations in multiple inferences. In this paper, we first propose the method of Bayesian network embedding to generate the easy-to-reuse node embeddings. Specifically, we transform BN into the point mutual information (PMI) matrix to simultaneously preserve the directed acyclic graph (DAG) and conditional probability tables (CPTs). Then, we give the singular value decomposition (SVD) based method to factorize the PMI matrix for generating node embeddings. Secondly, we propose a novel method of random sampling to make multiple probabilistic inferences via similarity calculation between node embeddings. Experimental results show that the runtime of our proposed BNERS performing 10 times of inferences is 30% faster than Gibbs sampling (GS) and 50% faster than forward sampling (FS) on LINK BN (very large network), while maintaining almost the same results as GS and FS.|贝叶斯网络（BN）是一个广泛采用的框架，用于表示和推断不确定的知识。现有的方法通常通过重复搜索和概率计算来逐一完成同一贝叶斯网络上的多个概率推断。然而，不同概率推断之间无法共享和重用大量的概率计算中间结果。为了提高同一贝叶斯网络上多个概率推断的整体效率，有必要结合易于计算的贝叶斯网络表示和易于在多次推断中重用的通用计算技术。本文首先提出了贝叶斯网络嵌入方法，以生成易于重用的节点嵌入。具体而言，我们将贝叶斯网络转换为点互信息（PMI）矩阵，以同时保留有向无环图（DAG）和条件概率表（CPTs）。接着，我们给出了基于奇异值分解（SVD）的方法，用于对PMI矩阵进行分解以生成节点嵌入。其次，我们提出了一种新的随机采样方法，通过节点嵌入之间的相似度计算来进行多个概率推断。实验结果表明，在我们提出的BNERS方法中，进行10次推断的运行时间比吉布斯采样（GS）快30%，比前向采样（FS）快50%，并且在LINK BN（非常大的网络）上保持了与GS和FS几乎相同的结果。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Transformer+Based+Bayesian+Network+Embedding+for+Efficient+Multiple+Probabilistic+Inferences)|0| |[Do We Really Need Graph Convolution During Training? Light Post-Training Graph-ODE for Efficient Recommendation](https://doi.org/10.1145/3627673.3679773)|Weizhi Zhang, Liangwei Yang, Zihe Song, Henry Peng Zou, Ke Xu, Liancheng Fang, Philip S. Yu||The efficiency and scalability of graph convolution networks (GCNs) in training recommender systems (RecSys) have been persistent concerns, hindering their deployment in real-world applications. This paper presents a critical examination of the necessity of graph convolutions during the training phase and introduces an innovative alternative: the Light Post-Training Graph Ordinary-Differential-Equation (LightGODE). Our investigation reveals that the benefits of GCNs are more pronounced during testing rather than training. Motivated by this, LightGODE utilizes a novel post-training graph convolution method that bypasses the computation-intensive message passing of GCNs and employs a non-parametric continuous graph ordinary-differential-equation (ODE) to dynamically model node representations. This approach drastically reduces training time while achieving fine-grained post-training graph convolution to avoid the distortion of the original training embedding space, termed the embedding discrepancy issue. We validate our model across several real-world datasets of different scales, demonstrating that LightGODE not only outperforms GCN-based models in terms of efficiency and effectiveness but also significantly mitigates the embedding discrepancy commonly associated with deeper graph convolution layers. Our LightGODE challenges the prevailing paradigms in RecSys training and suggests re-evaluating the role of graph convolutions, potentially guiding future developments of efficient large-scale graph-based RecSys.|图卷积网络（GCNs）在训练推荐系统（RecSys）中的效率和可扩展性一直是持续关注的问题，阻碍了其在实际应用中的部署。本文对训练阶段图卷积的必要性进行了批判性审视，并提出了一种创新替代方案：轻量级后训练图常微分方程（LightGODE）。我们的研究揭示，GCNs的优势在测试阶段比在训练阶段更为显著。基于此，LightGODE采用了一种新颖的后训练图卷积方法，该方法绕过了GCNs计算密集的消息传递过程，并采用非参数连续图常微分方程（ODE）来动态建模节点表示。这种方法大幅减少了训练时间，同时实现了细粒度的后训练图卷积，以避免原始训练嵌入空间的扭曲，即所谓的嵌入差异问题。我们在多个不同规模的实际数据集上验证了模型的有效性，结果表明LightGODE不仅在效率和效果上优于基于GCN的模型，而且显著缓解了深层图卷积层常见的嵌入差异问题。我们的LightGODE挑战了当前推荐系统训练的主流范式，并建议重新评估图卷积的作用，可能为未来高效大规模基于图的推荐系统的发展提供指导。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Do+We+Really+Need+Graph+Convolution+During+Training?+Light+Post-Training+Graph-ODE+for+Efficient+Recommendation)|0| |[ROLeR: Effective Reward Shaping in Offline Reinforcement Learning for Recommender Systems](https://doi.org/10.1145/3627673.3679633)|Yi Zhang, Ruihong Qiu, Jiajun Liu, Sen Wang||Offline reinforcement learning (RL) is an effective tool for real-world recommender systems with its capacity to model the dynamic interest of users and its interactive nature. Most existing offline RL recommender systems focus on model-based RL through learning a world model from offline data and building the recommendation policy by interacting with this model. Although these methods have made progress in the recommendation performance, the effectiveness of model-based offline RL methods is often constrained by the accuracy of the estimation of the reward model and the model uncertainties, primarily due to the extreme discrepancy between offline logged data and real-world data in user interactions with online platforms. To fill this gap, a more accurate reward model and uncertainty estimation are needed for the model-based RL methods. In this paper, a novel model-based Reward Shaping in Offline Reinforcement Learning for Recommender Systems, ROLeR, is proposed for reward and uncertainty estimation in recommendation systems. Specifically, a non-parametric reward shaping method is designed to refine the reward model. In addition, a flexible and more representative uncertainty penalty is designed to fit the needs of recommendation systems. Extensive experiments conducted on four benchmark datasets showcase that ROLeR achieves state-of-the-art performance compared with existing baselines. The source code can be downloaded at https://github.com/ArronDZhang/ROLeR.|离线强化学习（RL）凭借其对用户动态兴趣的建模能力和交互特性，成为现实世界推荐系统的有效工具。现有的多数离线RL推荐系统侧重于基于模型的RL方法，即通过从离线数据中学习世界模型，并通过与该模型交互来构建推荐策略。尽管这些方法在推荐性能上取得了进展，但基于模型的离线RL方法的有效性往往受限于奖励模型估计的准确性和模型不确定性，主要原因在于离线日志数据与用户在在线平台上的真实交互数据之间存在极大的差异。为填补这一差距，基于模型的RL方法需要更准确的奖励模型和不确定性估计。本文提出了一种新颖的基于模型的离线强化学习推荐系统奖励塑造方法——ROLeR，用于推荐系统中的奖励和不确定性估计。具体而言，设计了一种非参数的奖励塑造方法来优化奖励模型。此外，还设计了一种灵活且更具代表性的不确定性惩罚机制，以满足推荐系统的需求。在四个基准数据集上进行的大量实验表明，ROLeR相较于现有的基线方法，实现了最先进的性能。源代码可在https://github.com/ArronDZhang/ROLeR下载。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=ROLeR:+Effective+Reward+Shaping+in+Offline+Reinforcement+Learning+for+Recommender+Systems)|0| |[Aligning Explanations for Recommendation with Rating and Feature via Maximizing Mutual Information](https://doi.org/10.1145/3627673.3679663)|Yurou Zhao, Yiding Sun, Ruidong Han, Fei Jiang, Lu Guan, Xiang Li, Wei Lin, Weizhi Ma, Jiaxin Mao||Providing natural language-based explanations to justify recommendations helps to improve users' satisfaction and gain users' trust. However, as current explanation generation methods are commonly trained with an objective to mimic existing user reviews, the generated explanations are often not aligned with the predicted ratings or some important features of the recommended items, and thus, are suboptimal in helping users make informed decision on the recommendation platform. To tackle this problem, we propose a flexible model-agnostic method named MMI (Maximizing Mutual Information) framework to enhance the alignment between the generated natural language explanations and the predicted rating/important item features. Specifically, we propose to use mutual information (MI) as a measure for the alignment and train a neural MI estimator. Then, we treat a well-trained explanation generation model as the backbone model and further fine-tune it through reinforcement learning with guidance from the MI estimator, which rewards a generated explanation that is more aligned with the predicted rating or a pre-defined feature of the recommended item. Experiments on three datasets demonstrate that our MMI framework can boost different backbone models, enabling them to outperform existing baselines in terms of alignment with predicted ratings and item features. Additionally, user studies verify that MI-enhanced explanations indeed facilitate users' decisions and are favorable compared with other baselines due to their better alignment properties.|提供基于自然语言的解释以证明推荐理由，有助于提升用户的满意度并赢得用户的信任。然而，当前的解释生成方法通常以模仿现有用户评论为目标进行训练，导致生成的解释往往与预测的评分或推荐项目的重要特征不一致，从而在帮助用户在推荐平台上做出明智决策方面表现不佳。为解决这一问题，我们提出了一种灵活的、与模型无关的方法，名为MMI（最大化互信息）框架，以增强生成的自然语言解释与预测评分或重要项目特征之间的一致性。具体而言，我们建议使用互信息（MI）作为一致性的度量，并训练一个神经互信息估计器。随后，我们将一个训练良好的解释生成模型作为基础模型，并通过强化学习对其进行进一步微调，强化学习的指导来自互信息估计器，该估计器奖励那些与预测评分或预定义的项目特征更一致的生成解释。在三个数据集上的实验表明，我们的MMI框架能够提升不同的基础模型，使其在预测评分和项目特征的一致性方面优于现有的基线模型。此外，用户研究表明，经过互信息增强的解释确实有助于用户做出决策，并且由于其更好的对齐特性，相比其他基线方法更受用户青睐。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Aligning+Explanations+for+Recommendation+with+Rating+and+Feature+via+Maximizing+Mutual+Information)|0| |[Interaction-level Membership Inference Attack against Recommender Systems with Long-tailed Distribution](https://doi.org/10.1145/3627673.3679804)|Da Zhong, Xiuling Wang, Zhichao Xu, Jun Xu, Wendy Hui Wang|Stevens Institute of Technology, Hoboken, NJ, USA; University of Utah, Salt Lake City, UT, USA|Recommender systems (RSs) are susceptible to Interaction-level Membership Inference Attacks (IMIAs), which aim to determine whether specific user-item interactions are present in the training data of the target RS. However, existing IMIAs struggle with inferring the membership of tail interactions, i.e., the interactions involving tail items, due to the limited information available about these items. This paper introduces MINER, a new IMIA designed to enhance attack performance against RSs with long-tailed item distribution. MINER addresses the information scarcity of tail items at both the feature and sample levels. At the feature level, MINER leverages the Knowledge Graphs (KGs) to obtain the auxiliary knowledge of tail items. At the sample level, MINER designs a Bilateral-Branch Network (BBN) as the attack model. The BBN trains two branches independently, with one branch trained on interaction samples with the original long-tailed item distribution and the other on interaction samples with a more balanced item distribution. The outputs of the two branches are aggregated using a cumulative learning component. Our experimental results demonstrate that MINER significantly enhances the attack accuracy of IMIA, especially for tail interactions. Beyond attack design, we design a defense mechanism named RGL to defend against MINER. Empirical evaluations demonstrate that RGL effectively mitigates the privacy risks posed by MINER while preserving recommendation accuracy. Our code is available at https://github.com/dzhong2/MINER.|推荐系统（RSs）容易受到交互级别成员推断攻击（IMIA）的影响，这种攻击旨在确定特定用户-项目交互是否存在于目标推荐系统的训练数据中。然而，现有的IMIA在推断尾部交互（即涉及尾部项目的交互）的成员身份时遇到困难，因为这些项目的信息有限。本文介绍了MINER，这是一种新的IMIA，旨在提高对具有长尾项目分布的推荐系统的攻击性能。MINER在特征和样本两个层面上解决了尾部项目的信息稀缺问题。在特征层面上，MINER利用知识图谱（KGs）获取尾部项目的辅助知识。在样本层面上，MINER设计了一个双分支网络（BBN）作为攻击模型。BBN独立训练两个分支，其中一个分支在具有原始长尾项目分布的交互样本上训练，另一个分支在具有更平衡项目分布的交互样本上训练。两个分支的输出通过累积学习组件进行聚合。我们的实验结果表明，MINER显著提高了IMIA的攻击准确性，尤其是对尾部交互的攻击。除了攻击设计，我们还设计了一种名为RGL的防御机制来抵御MINER。实证评估表明，RGL在保持推荐准确性的同时，有效减轻了MINER带来的隐私风险。我们的代码可在https://github.com/dzhong2/MINER获取。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Interaction-level+Membership+Inference+Attack+against+Recommender+Systems+with+Long-tailed+Distribution)|0| |[A Power Method to Alleviate Over-smoothing for Recommendation](https://doi.org/10.1145/3627673.3679553)|Peng Zhou, Yachao Cui, Han Cao|Shaanxi Normal University, Xi'an, Shaanxi, China|In recent years, graph convolution networks (GCNs) have been widely used in recommender systems due to high-order node information propagation and aggregation mechanisms. However, existing GCN-based recommender systems drop sharply in performance as the depth of the network increases. This phenomenon is called over-smoothing, which refers to the fact that the embeddings of all nodes become more similar and indistinguishable. Previous works have rarely explored over-smoothing from characteristics of the recommendation field. Specifically, we found experimentally that too many layers can lead to such large loss values that they are difficult to decrease. After theoretical analysis, we can effectively solve the problem of difficulty in decreasing the loss value by adding only a hyperparameter, called "power". This hyperparameter can effectively control the smoothness and alleviate the over-smoothing problem. Experiments on four public datasets demonstrate that this hyperparameter can effectively improve performance.|近年来，图卷积网络（GCN）由于其高阶节点信息传播和聚合机制，在推荐系统中得到了广泛应用。然而，现有的基于GCN的推荐系统在网络深度增加时性能急剧下降。这种现象被称为过平滑，即所有节点的嵌入变得更为相似且难以区分。以往的研究很少从推荐领域的特性出发探讨过平滑问题。具体来说，我们通过实验发现，过多的层数会导致损失值变得如此之大，以至于难以进一步降低。经过理论分析，我们可以通过仅添加一个称为“幂”的超参数来有效解决损失值难以降低的问题。这一超参数能有效控制平滑度并缓解过平滑问题。在四个公开数据集上的实验结果表明，该超参数能有效提升性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Power+Method+to+Alleviate+Over-smoothing+for+Recommendation)|0| -|[Not All Negatives are Equally Negative: Soft Contrastive Learning for Unsupervised Sentence Representations](https://doi.org/10.1145/3627673.3679745)|Haojie Zhuang, Wei Emma Zhang, Jian Yang, Weitong Chen, Quan Z. Sheng|Macquarie University, Sydney, Australia; The University of Adelaide, Adelaide, Australia|Contrastive learning has been extensively studied in sentence representation learning as it demonstrates effectiveness in various downstream applications, where the same sentence with different dropout masks (or other augmentation methods) is considered as positive pair while taking other sentences in the same mini-batch as negative pairs. However, these methods mostly treat all negative examples equally and overlook the different similarities between the negative examples and the anchors, which thus fail to capture the fine-grained semantic information of the sentences. To address this issue, we explicitly differentiate the negative examples by their similarities with the anchor, and thus propose a simple yet effective method SoftCSE that individualizes either the weight or temperature of each negative pair in the standard InfoNCE loss according to the similarities of the negative examples and the anchors. We further provide the theoretical analysis of our methods to show why and how SoftCSE works, including the optimal solution, gradient analysis and the connection with other loss. Empirically, we conduct extensive experiments on semantic textual similarity (STS) and transfer (TR) tasks, as well as text retrieval and reranking, where we observe significant performance improvements compared to strong baseline models.|对比学习在句子表征学习中得到了广泛研究，因为它在各种下游应用中展示了有效性，其中同一个句子在不同的dropout掩码（或其他增强方法）下被视为正样本对，而同一小批次中的其他句子则被视为负样本对。然而，这些方法大多将所有负样本平等对待，忽略了负样本与锚点之间的不同相似性，从而未能捕捉到句子的细粒度语义信息。为了解决这一问题，我们根据负样本与锚点的相似性显式区分负样本，并提出了一种简单而有效的方法SoftCSE，该方法根据负样本与锚点的相似性，在标准的InfoNCE损失中个性化地调整每个负样本对的权重或温度。我们进一步提供了方法的理论分析，以展示SoftCSE为何及如何工作，包括最优解、梯度分析以及与其他损失函数的联系。在实验上，我们在语义文本相似性（STS）和迁移（TR）任务以及文本检索和重排序任务中进行了广泛的实验，观察到与强基线模型相比显著的性能提升。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Not+All+Negatives+are+Equally+Negative:+Soft+Contrastive+Learning+for+Unsupervised+Sentence+Representations)|0| -|[Professionalism-Aware Pre-Finetuning for Profitability Ranking](https://doi.org/10.1145/3627673.3679981)|ChungChi Chen, Hiroya Takamura, Ichiro Kobayashi, Yusuke Miyao|National Institute of Advanced Industrial Science and Technology, Tokyo, Japan; Ochanomizu University, Tokyo, Japan; The University of Tokyo, Tokyo, Japan|Opinion mining, specifically in the investment sector, has experienced a significant increase in interest over recent years. This paper presents a novel approach to overcome current limitations in assessing and ranking investor opinions based on profitability. The study introduces a pre-finetuning scheme to improve language models' capacity to distinguish professionalism, thus enabling ranking of all available opinions. Furthermore, the paper evaluates ranking results using traditional metrics and suggests the use of a pairwise setting for better performances over a regression setting. Lastly, our method is shown to be effective across various investor opinion tasks, encompassing both professional and amateur investors. The results indicate that this approach significantly enhances the efficiency and accuracy of opinion mining in the investment sector.|观点挖掘，特别是在投资领域，近年来引起了极大的关注。本文提出了一种新颖的方法，以克服当前在根据盈利能力评估和排序投资者观点方面的局限性。研究引入了一种预微调方案，以提高语言模型区分专业性的能力，从而实现对所有可用观点的排序。此外，本文使用传统指标评估排序结果，并建议采用成对设置以在回归设置中获得更好的性能。最后，我们的方法在各种投资者观点任务中显示出有效性，涵盖了专业和业余投资者。结果表明，这种方法显著提高了投资领域观点挖掘的效率和准确性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Professionalism-Aware+Pre-Finetuning+for+Profitability+Ranking)|0| +|[Not All Negatives are Equally Negative: Soft Contrastive Learning for Unsupervised Sentence Representations](https://doi.org/10.1145/3627673.3679745)|Haojie Zhuang, Wei Emma Zhang, Jian Yang, Weitong Chen, Quan Z. Sheng|The University of Adelaide, Adelaide, Australia; Macquarie University, Sydney, Australia|Contrastive learning has been extensively studied in sentence representation learning as it demonstrates effectiveness in various downstream applications, where the same sentence with different dropout masks (or other augmentation methods) is considered as positive pair while taking other sentences in the same mini-batch as negative pairs. However, these methods mostly treat all negative examples equally and overlook the different similarities between the negative examples and the anchors, which thus fail to capture the fine-grained semantic information of the sentences. To address this issue, we explicitly differentiate the negative examples by their similarities with the anchor, and thus propose a simple yet effective method SoftCSE that individualizes either the weight or temperature of each negative pair in the standard InfoNCE loss according to the similarities of the negative examples and the anchors. We further provide the theoretical analysis of our methods to show why and how SoftCSE works, including the optimal solution, gradient analysis and the connection with other loss. Empirically, we conduct extensive experiments on semantic textual similarity (STS) and transfer (TR) tasks, as well as text retrieval and reranking, where we observe significant performance improvements compared to strong baseline models.|对比学习在句子表征学习中得到了广泛研究，因为它在各种下游应用中展示了有效性，其中同一个句子在不同的dropout掩码（或其他增强方法）下被视为正样本对，而同一小批次中的其他句子则被视为负样本对。然而，这些方法大多将所有负样本平等对待，忽略了负样本与锚点之间的不同相似性，从而未能捕捉到句子的细粒度语义信息。为了解决这一问题，我们根据负样本与锚点的相似性显式区分负样本，并提出了一种简单而有效的方法SoftCSE，该方法根据负样本与锚点的相似性，在标准的InfoNCE损失中个性化地调整每个负样本对的权重或温度。我们进一步提供了方法的理论分析，以展示SoftCSE为何及如何工作，包括最优解、梯度分析以及与其他损失函数的联系。在实验上，我们在语义文本相似性（STS）和迁移（TR）任务以及文本检索和重排序任务中进行了广泛的实验，观察到与强基线模型相比显著的性能提升。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Not+All+Negatives+are+Equally+Negative:+Soft+Contrastive+Learning+for+Unsupervised+Sentence+Representations)|0| +|[Professionalism-Aware Pre-Finetuning for Profitability Ranking](https://doi.org/10.1145/3627673.3679981)|ChungChi Chen, Hiroya Takamura, Ichiro Kobayashi, Yusuke Miyao|The University of Tokyo, Tokyo, Japan; National Institute of Advanced Industrial Science and Technology, Tokyo, Japan; Ochanomizu University, Tokyo, Japan|Opinion mining, specifically in the investment sector, has experienced a significant increase in interest over recent years. This paper presents a novel approach to overcome current limitations in assessing and ranking investor opinions based on profitability. The study introduces a pre-finetuning scheme to improve language models' capacity to distinguish professionalism, thus enabling ranking of all available opinions. Furthermore, the paper evaluates ranking results using traditional metrics and suggests the use of a pairwise setting for better performances over a regression setting. Lastly, our method is shown to be effective across various investor opinion tasks, encompassing both professional and amateur investors. The results indicate that this approach significantly enhances the efficiency and accuracy of opinion mining in the investment sector.|观点挖掘，特别是在投资领域，近年来引起了极大的关注。本文提出了一种新颖的方法，以克服当前在根据盈利能力评估和排序投资者观点方面的局限性。研究引入了一种预微调方案，以提高语言模型区分专业性的能力，从而实现对所有可用观点的排序。此外，本文使用传统指标评估排序结果，并建议采用成对设置以在回归设置中获得更好的性能。最后，我们的方法在各种投资者观点任务中显示出有效性，涵盖了专业和业余投资者。结果表明，这种方法显著提高了投资领域观点挖掘的效率和准确性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Professionalism-Aware+Pre-Finetuning+for+Profitability+Ranking)|0| |[Boosting Large Language Models with Socratic Method for Conversational Mathematics Teaching](https://doi.org/10.1145/3627673.3679881)|Yuyang Ding, Hanglei Hu, Jie Zhou, Qin Chen, Bo Jiang, Liang He||With the introduction of large language models (LLMs), automatic math reasoning has seen tremendous success. However, current methods primarily focus on providing solutions or using techniques like Chain-of-Thought to enhance problem-solving accuracy. In this paper, we focus on improving the capability of mathematics teaching via a Socratic teaching-based LLM (), which guides learners toward profound thinking with clarity and self-discovery via conversation. We collect and release a high-quality mathematical teaching dataset, named , which provides Socratic-style conversations of problems with extra knowledge. Also, we propose a knowledge-enhanced LLM as a strong baseline to generate reliable responses with review, guidance/heuristic, rectification, and summarization. Experimental results show the great advantages of by comparing it with several strong generative models. The codes and datasets are available on .|随着大型语言模型（LLMs）的引入，自动数学推理取得了显著的成功。然而，当前的方法主要集中在提供解决方案或使用链式思维（Chain-of-Thought）等技术来提高问题解决的准确性。在本文中，我们专注于通过基于苏格拉底教学法的LLM（）来提升数学教学能力，该模型通过对话引导学习者进行清晰且自我发现的深刻思考。我们收集并发布了一个高质量的数学教学数据集，命名为，该数据集提供了包含额外知识的苏格拉底式问题对话。此外，我们提出了一个知识增强的LLM作为强基线模型，以生成包含审查、指导/启发、纠正和总结的可靠响应。实验结果表明，通过与几个强大的生成模型进行比较，显示出显著的优势。代码和数据集可在上获取。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Boosting+Large+Language+Models+with+Socratic+Method+for+Conversational+Mathematics+Teaching)|0| |[Towards Better Utilization of Multiple Views for Bundle Recommendation](https://doi.org/10.1145/3627673.3680003)|Kyungho Kim, Sunwoo Kim, Geon Lee, Kijung Shin|KAIST, Seoul, Republic of Korea|Bundle recommender systems aim to recommend suitable collections (i.e., bundles) of items to each user, meeting their diverse needs with all-in-one convenience. Typically, they utilize three distinct types of information: user-bundle purchase interactions (U-B view), user-item purchase interactions (U-I view), and bundle-item affiliations (B-I view). Our focus is on better integrating these three perspectives (i.e., views) to deliver more accurate bundle recommendations. Our examination of different role (main or sub-views) combinations of the views reveals two key observations: (1) the best combination varies across target users (i.e., who receive recommendations), and (2) the U-I view is relatively weak as the main role. Driven by these observations, we propose PET, which synergizes the three views through (1) personalized view weighting, (2) U-I view enhancement, and (3) two-pronged contrastive learning. Our extensive experiments demonstrate that PET significantly outperforms existing methods in all popular benchmark datasets. Our code and datasets are available at https://github.com/K-Kyungho/PET.|捆绑推荐系统旨在向每位用户推荐合适的物品集合（即捆绑包），以一站式服务的便利性满足他们的多样化需求。通常，这些系统利用三种不同类型的信息：用户-捆绑包购买交互（U-B视图）、用户-物品购买交互（U-I视图）以及捆绑包-物品关联（B-I视图）。我们的重点是更好地整合这三种视角（即视图），以提供更准确的捆绑推荐。我们对不同角色（主视图或子视图）组合的视图进行了考察，发现了两个关键观察结果：（1）最佳组合因目标用户（即接收推荐的用户）而异，（2）U-I视图作为主角色时相对较弱。基于这些观察，我们提出了PET，它通过以下方式协同整合三种视图：（1）个性化视图加权，（2）U-I视图增强，以及（3）双管齐下的对比学习。我们的广泛实验表明，PET在所有流行的基准数据集上显著优于现有方法。我们的代码和数据集可在https://github.com/K-Kyungho/PET获取。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Towards+Better+Utilization+of+Multiple+Views+for+Bundle+Recommendation)|0| |[Improving Prompt-based News Recommendation with Individual Template and Customized Answer](https://doi.org/10.1145/3627673.3679945)|Yijiang Li, Jun Wu||Prompt learning plays a key role in aligning the task of news recommendation (NR) with the Pre-trained Language Models (PLMs). However, current prompt-based NR methods utilize fixed templates and answer words, ignoring the personalization of user's demand and the diversity between news topics. To this end, we propose an Automatic Prompt based NR (AutoPNR) scheme, which automatically generates individual templates for users according to their potential interests, and customized answer words w.r.t. the topics of candidate news. Concretely, such an individual template utilizes several specific tokens to encode a user's interest extracted from her/his reading history, while a pair of customized answer words are retrieved from a large vocabulary (often existing alongside PLMs) based on the topic of candidate news. Through extensive experiments on the real-world datasets, we show that our AutoPNR works well with different PLMs, and considerably outperforms state-of-the-art NR techniques.|提示学习在将新闻推荐（NR）任务与预训练语言模型（PLMs）对齐方面起着关键作用。然而，当前基于提示的NR方法使用固定的模板和答案词，忽视了用户需求的个性化以及新闻主题之间的多样性。为此，我们提出了一种基于自动提示的NR（AutoPNR）方案，该方案根据用户的潜在兴趣自动生成个性化的模板，并根据候选新闻的主题定制答案词。具体而言，这种个性化模板使用多个特定标记来编码从用户的阅读历史中提取的兴趣，而一对定制的答案词则根据候选新闻的主题从大型词汇表（通常与PLMs一起存在）中检索。通过对真实世界数据集的广泛实验，我们展示了AutoPNR在不同PLMs上的良好表现，并显著优于现有的最先进NR技术。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Improving+Prompt-based+News+Recommendation+with+Individual+Template+and+Customized+Answer)|0| @@ -200,9 +200,9 @@ |[Enhanced Privacy Bound for Shuffle Model with Personalized Privacy](https://doi.org/10.1145/3627673.3679911)|Yixuan Liu, Yuhan Liu, Li Xiong, Yujie Gu, Hong Chen||The shuffle model of Differential Privacy (DP) is an enhanced privacy protocol which introduces an intermediate trusted server between local users and a central data curator. It significantly amplifies the central DP guarantee by anonymizing and shuffling the local randomized data. Yet, deriving a tight privacy bound is challenging due to its complicated randomization protocol. While most existing work are focused on unified local privacy settings, this work focuses on deriving the central privacy bound for a more practical setting where personalized local privacy is required by each user. To bound the privacy after shuffling, we first need to capture the probability of each user generating clones of the neighboring data points. Second, we need to quantify the indistinguishability between two distributions of the number of clones on neighboring datasets. Existing works either inaccurately capture the probability, or underestimate the indistinguishability between neighboring datasets. Motivated by this, we develop a more precise analysis, which yields a general and tighter bound for arbitrary DP mechanisms. Firstly, we derive the clone-generating probability by hypothesis testing perspective, which leads to a more accurate characterization of the probability. Secondly, we analyze the indistinguishability in the context of f-DP, where the convexity of the distributions is leveraged to achieve a tighter privacy bound. Theoretical and numerical results demonstrate that our bound remarkably outperforms the existing results in the literature.|差分隐私（DP）的洗牌模型是一种增强隐私协议，它在本地用户和中央数据管理员之间引入了一个中间的可信服务器。通过匿名化和洗牌本地随机化的数据，它显著增强了中央DP的保障。然而，由于其复杂的随机化协议，推导出一个紧密的隐私界限是具有挑战性的。尽管大多数现有工作集中在统一的本地隐私设置上，但本文关注的是为每个用户需要个性化本地隐私的更实际设置推导中央隐私界限。为了在洗牌后界定隐私，我们首先需要捕捉每个用户生成相邻数据点副本的概率。其次，我们需要量化相邻数据集上副本数量的两个分布之间的不可区分性。现有工作要么不准确地捕捉概率，要么低估了相邻数据集之间的不可区分性。受此启发，我们开发了一种更精确的分析方法，为任意DP机制提供了一个更通用且更紧密的界限。首先，我们通过假设检验的角度推导出副本生成的概率，从而更准确地描述了概率。其次，我们在f-DP的背景下分析不可区分性，利用分布的凸性来实现更紧密的隐私界限。理论和数值结果表明，我们的界限在文献中显著优于现有结果。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Enhanced+Privacy+Bound+for+Shuffle+Model+with+Personalized+Privacy)|0| |[Channel-Aware Low-Rank Adaptation in Time Series Forecasting](https://doi.org/10.1145/3627673.3679884)|Tong Nie, Yuewen Mei, Guoyang Qin, Jian Sun, Wei Ma||The balance between model capacity and generalization has been a key focus of recent discussions in long-term time series forecasting. Two representative channel strategies are closely associated with model expressivity and robustness, including channel independence (CI) and channel dependence (CD). The former adopts individual channel treatment and has been shown to be more robust to distribution shifts, but lacks sufficient capacity to model meaningful channel interactions. The latter is more expressive for representing complex cross-channel dependencies, but is prone to overfitting. To balance the two strategies, we present a channel-aware low-rank adaptation method to condition CD models on identity-aware individual components. As a plug-in solution, it is adaptable for a wide range of backbone architectures. Extensive experiments show that it can consistently and significantly improve the performance of both CI and CD models with demonstrated efficiency and flexibility. The code is available at https://github.com/tongnie/C-LoRA.|在长期时间序列预测中，模型容量与泛化能力的平衡一直是近期讨论的焦点。两种具有代表性的通道策略与模型的表达能力和鲁棒性密切相关，分别是通道独立性（CI）和通道依赖性（CD）。前者采用独立通道处理，已被证明对分布偏移更具鲁棒性，但缺乏足够的容量来建模有意义的通道交互。后者在表示复杂的跨通道依赖方面更具表达力，但容易过拟合。为了平衡这两种策略，我们提出了一种通道感知的低秩适应方法，将CD模型条件化为具有身份感知的独立组件。作为一种即插即用的解决方案，它适用于广泛的主干架构。大量实验表明，该方法能够持续且显著地提升CI和CD模型的性能，并展现出高效性和灵活性。代码可在https://github.com/tongnie/C-LoRA获取。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Channel-Aware+Low-Rank+Adaptation+in+Time+Series+Forecasting)|0| |[Learning Links for Adaptable and Explainable Retrieval](https://doi.org/10.1145/3627673.3679953)|Jianqiang Shen, Yuchin Juan, Ping Liu, Wen Pu, Shaobo Zhang, Qianqi Shen, Liangjie Hong, Wenjing Zhang|LinkedIn, Mountain View, CA, USA|Web-scale search systems typically tackle the scalability challenge with a two-step paradigm: retrieval and ranking. The retrieval step, also known as candidate selection, often involves extracting entities, creating an inverted index, and performing term matching for retrieval. Such traditional methods require manual and time-consuming development of retrieval models. In this paper, we propose a framework for constructing a graph that integrates human knowledge with user activity data analysis. The learned links are utilized for retrieval purposes. The model is easy to explain, debug, and tune. The system implementation is straightforward and can directly leverage existing inverted index systems. We applied this retrieval framework to enhance the job search and recommendation systems on a large professional networking portal, resulting in significant performance improvements.|网络规模的搜索引擎通常采用两步范式来应对可扩展性挑战：检索和排序。检索步骤，也称为候选选择，通常涉及提取实体、创建倒排索引以及执行术语匹配以进行检索。这些传统方法需要手动且耗时的检索模型开发。在本文中，我们提出了一种构建图的框架，该框架将人类知识与用户活动数据分析相结合。学习到的链接用于检索目的。该模型易于解释、调试和调整。系统实现简单直接，可以直接利用现有的倒排索引系统。我们将此检索框架应用于大型专业社交门户网站上的职位搜索和推荐系统，显著提升了性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Learning+Links+for+Adaptable+and+Explainable+Retrieval)|0| -|[Preliminary Study on Incremental Learning for Large Language Model-based Recommender Systems](https://doi.org/10.1145/3627673.3679922)|Tianhao Shi, Yang Zhang, Zhijian Xu, Chong Chen, Fuli Feng, Xiangnan He, Qi Tian|Huawei Cloud BU; University of Science and Technology of China Hefei|Adapting Large Language Models for Recommendation (LLM4Rec) has shown promising results. However, the challenges of deploying LLM4Rec in real-world scenarios remain largely unexplored. In particular, recommender models need incremental adaptation to evolving user preferences, while the suitability of traditional incremental learning methods within LLM4Rec remains ambiguous due to the unique characteristics of Large Language Models (LLMs). In this study, we empirically evaluate two commonly employed incremental learning strategies (full retraining and fine-tuning) for LLM4Rec. Surprisingly, neither approach shows significant improvements in the performance of LLM4Rec. Instead of dismissing the role of incremental learning, we attribute the lack of anticipated performance enhancement to a mismatch between the LLM4Rec architecture and incremental learning: LLM4Rec employs a single adaptation module for learning recommendations, limiting its ability to simultaneously capture long-term and short-term user preferences in the incremental learning context. To test this speculation, we introduce a Long- and Short-term Adaptation-aware Tuning (LSAT) framework for incremental learning in LLM4Rec. Unlike the single adaptation module approach, LSAT utilizes two distinct adaptation modules to independently learn long-term and short-term user preferences. Empirical results verify that LSAT enhances performance, thereby validating our speculation. We release our code at: https://github.com/TianhaoShi2001/LSAT.|将大型语言模型（LLM）应用于推荐系统（LLM4Rec）已显示出良好的前景。然而，在实际场景中部署LLM4Rec的挑战仍未得到充分探索。特别是，推荐模型需要对不断变化的用户偏好进行增量适应，而由于大型语言模型的独特特性，传统增量学习方法在LLM4Rec中的适用性尚不明确。在本研究中，我们实证评估了两种常用的增量学习策略（全量重新训练和微调）在LLM4Rec中的应用。令人意外的是，这两种方法均未显著提升LLM4Rec的性能。我们并未因此否定增量学习的作用，而是认为预期的性能提升未能实现的原因在于LLM4Rec架构与增量学习之间的不匹配：LLM4Rec采用单一适应模块进行推荐学习，这限制了其在增量学习情境下同时捕捉长期和短期用户偏好的能力。为验证这一推测，我们引入了长短期适应感知调优（Long- and Short-term Adaptation-aware Tuning, LSAT）框架，用于LLM4Rec中的增量学习。与单一适应模块方法不同，LSAT使用两个独立的适应模块分别学习长期和短期用户偏好。实证结果验证了LSAT能够提升性能，从而证实了我们的推测。我们已在以下链接公开了代码：https://github.com/TianhaoShi2001/LSAT。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Preliminary+Study+on+Incremental+Learning+for+Large+Language+Model-based+Recommender+Systems)|0| +|[Preliminary Study on Incremental Learning for Large Language Model-based Recommender Systems](https://doi.org/10.1145/3627673.3679922)|Tianhao Shi, Yang Zhang, Zhijian Xu, Chong Chen, Fuli Feng, Xiangnan He, Qi Tian|University of Science and Technology of China Hefei; Huawei Cloud BU|Adapting Large Language Models for Recommendation (LLM4Rec) has shown promising results. However, the challenges of deploying LLM4Rec in real-world scenarios remain largely unexplored. In particular, recommender models need incremental adaptation to evolving user preferences, while the suitability of traditional incremental learning methods within LLM4Rec remains ambiguous due to the unique characteristics of Large Language Models (LLMs). In this study, we empirically evaluate two commonly employed incremental learning strategies (full retraining and fine-tuning) for LLM4Rec. Surprisingly, neither approach shows significant improvements in the performance of LLM4Rec. Instead of dismissing the role of incremental learning, we attribute the lack of anticipated performance enhancement to a mismatch between the LLM4Rec architecture and incremental learning: LLM4Rec employs a single adaptation module for learning recommendations, limiting its ability to simultaneously capture long-term and short-term user preferences in the incremental learning context. To test this speculation, we introduce a Long- and Short-term Adaptation-aware Tuning (LSAT) framework for incremental learning in LLM4Rec. Unlike the single adaptation module approach, LSAT utilizes two distinct adaptation modules to independently learn long-term and short-term user preferences. Empirical results verify that LSAT enhances performance, thereby validating our speculation. We release our code at: https://github.com/TianhaoShi2001/LSAT.|将大型语言模型（LLM）应用于推荐系统（LLM4Rec）已显示出良好的前景。然而，在实际场景中部署LLM4Rec的挑战仍未得到充分探索。特别是，推荐模型需要对不断变化的用户偏好进行增量适应，而由于大型语言模型的独特特性，传统增量学习方法在LLM4Rec中的适用性尚不明确。在本研究中，我们实证评估了两种常用的增量学习策略（全量重新训练和微调）在LLM4Rec中的应用。令人意外的是，这两种方法均未显著提升LLM4Rec的性能。我们并未因此否定增量学习的作用，而是认为预期的性能提升未能实现的原因在于LLM4Rec架构与增量学习之间的不匹配：LLM4Rec采用单一适应模块进行推荐学习，这限制了其在增量学习情境下同时捕捉长期和短期用户偏好的能力。为验证这一推测，我们引入了长短期适应感知调优（Long- and Short-term Adaptation-aware Tuning, LSAT）框架，用于LLM4Rec中的增量学习。与单一适应模块方法不同，LSAT使用两个独立的适应模块分别学习长期和短期用户偏好。实证结果验证了LSAT能够提升性能，从而证实了我们的推测。我们已在以下链接公开了代码：https://github.com/TianhaoShi2001/LSAT。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Preliminary+Study+on+Incremental+Learning+for+Large+Language+Model-based+Recommender+Systems)|0| |[ Tabularis Revilio: Converting Text to Tables](https://doi.org/10.1145/3627673.3680000)|Mukul Singh, Gust Verbruggen, Vu Le, Sumit Gulwani|Microsoft, Redmond, WA, USA; Microsoft, Keerbergen, Belgium|Copying tables from documents and applications without proper tabular support, like PDF documents, web pages or images, surprisingly remains a challenge. In this paper, we present Revilio, a novel neurosymbolic system for reconstructing tables when their column boundaries have been lost. Revilio addresses this task by detecting headers, generating an initial table sketch using a large language model, and using that sketch as a guiding representation during an enumerate-and-test strategy that evaluates syntactic and semantic table structures. We evaluate Revilio on a diverse set of datasets, demonstrating significant improvements over existing table parsing methods. Revilio outperforms traditional techniques in both accuracy and scalability, handling large tables with over 100,000 rows. Our experiments find an increase in reconstruction accuracy by 5.8-11.3% over both neural and symbolic baseline systems|从缺乏适当表格支持的文档和应用程序（如PDF文档、网页或图像）中复制表格，依然是一个出乎意料的挑战。本文介绍了Revilio，这是一种新颖的神经符号系统，用于在表格的列边界丢失时重建表格。Revilio通过检测表头、利用大型语言模型生成初始表格草图，并在此草图的指导下，采用枚举与测试策略来评估句法和语义表格结构，从而解决这一任务。我们在多个数据集上对Revilio进行了评估，结果显示其显著优于现有的表格解析方法。Revilio在准确性和可扩展性方面均超越了传统技术，能够处理包含超过100,000行的大型表格。我们的实验发现，与神经和符号基线系统相比，Revilio的重建准确性提高了5.8%至11.3%。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=+Tabularis+Revilio:++Converting+Text+to+Tables)|0| -|[STAR: Sparse Text Approach for Recommendation](https://doi.org/10.1145/3627673.3679999)|Anna Tigunova, Ghazaleh Haratinezhad Torbati, Andrew Yates, Gerhard Weikum|Amazon, Berlin, Germany; Max Planck Institute for Informatics, Saarbrücken, Germany; University of Amsterdam, Amsterdam, Netherlands|In this work we propose to adapt Learned Sparse Retrieval, an emerging approach in IR, to text-centric content-based recommendations, leveraging the strengths of transformer models for an efficient and interpretable user-item matching. We conduct extensive experiments, showing that our LSR-based recommender, dubbed STAR, outperforms existing dense bi-encoder baselines on three recommendation domains. The obtained word-level representations of users and items are easy to examine and result in over 10x more compact indexes.|在这项工作中，我们提出将新兴的信息检索（IR）方法——学习稀疏检索（Learned Sparse Retrieval, LSR），应用于以文本为中心的内容推荐系统中，利用转换器模型的优势实现高效且可解释的用户-物品匹配。我们进行了广泛的实验，结果表明，基于LSR的推荐系统（称为STAR）在三个推荐领域中均优于现有的密集双编码器基线。所获得的用户和物品的词级表示易于检查，并且生成的索引比传统方法紧凑10倍以上。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=STAR:+Sparse+Text+Approach+for+Recommendation)|0| +|[STAR: Sparse Text Approach for Recommendation](https://doi.org/10.1145/3627673.3679999)|Anna Tigunova, Ghazaleh Haratinezhad Torbati, Andrew Yates, Gerhard Weikum|Max Planck Institute for Informatics, Saarbrücken, Germany; Amazon, Berlin, Germany; University of Amsterdam, Amsterdam, Netherlands|In this work we propose to adapt Learned Sparse Retrieval, an emerging approach in IR, to text-centric content-based recommendations, leveraging the strengths of transformer models for an efficient and interpretable user-item matching. We conduct extensive experiments, showing that our LSR-based recommender, dubbed STAR, outperforms existing dense bi-encoder baselines on three recommendation domains. The obtained word-level representations of users and items are easy to examine and result in over 10x more compact indexes.|在这项工作中，我们提出将新兴的信息检索（IR）方法——学习稀疏检索（Learned Sparse Retrieval, LSR），应用于以文本为中心的内容推荐系统中，利用转换器模型的优势实现高效且可解释的用户-物品匹配。我们进行了广泛的实验，结果表明，基于LSR的推荐系统（称为STAR）在三个推荐领域中均优于现有的密集双编码器基线。所获得的用户和物品的词级表示易于检查，并且生成的索引比传统方法紧凑10倍以上。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=STAR:+Sparse+Text+Approach+for+Recommendation)|0| |[Harnessing Empathy and Ethics for Relevance Detection and Information Categorization in Climate and COVID-19 Tweets](https://doi.org/10.1145/3627673.3679937)|Apoorva Upadhyaya, Wolfgang Nejdl, Marco Fisichella|L3S Research Center, Hannover, Germany|In this work, we aim to understand the general public perception of societal issues related to the current climate crisis and the COVID-19 pandemic on Twitter (X). Social media discussions on such matters often lead to misleading information, resulting in delays in initiatives proposed by governments or policymakers. Hence, we focus on extracting relevant information from the conversations on climate change and COVID that could be useful for authorities to curb the spread of potentially biased information by proposing the classification tasks of relevance detection (RD) and information categorization (IC). We first curate the datasets for the RD and IC tasks for the climate domain and extend the COVID-19 benchmark attention-worthy Twitter dataset for the IC task through manual annotation. We initially conduct experiments with LLMs and observe that LLMs can extract the relevant information in zero and few-shot settings based on multi-perspective reasoning in the form of cognitive empathy and ethical standards, but still perform worse than fine-tuned small language models. Based on the initial findings, we conclude that LLMs may not be the best extractor of relevant information, but induce cognitive empathy and ethical reasonings that can intuitively guide supervised models. To achieve this idea, we develop a cognitive empathy and ethical reasoning-based multi-tasking pipelined network for RD and IC tasks. Our proposed approach provides valuable insights that could be useful in real-world scenarios for governments, policymakers, and other researchers to decode the overall public outlook on societal issues.|在这项工作中，我们的目标是理解公众在Twitter（X）上对当前气候危机和COVID-19大流行相关社会问题的看法。社交媒体上关于这些话题的讨论往往会导致误导性信息的传播，从而延误政府或政策制定者提出的倡议。因此，我们专注于从气候变化和COVID的对话中提取相关信息，这些信息对当局来说可能有助于遏制潜在偏见信息的传播，具体通过提出相关性检测（RD）和信息分类（IC）的分类任务来实现。我们首先为气候领域的RD和IC任务策划数据集，并通过手动注释扩展了COVID-19基准的值得关注的Twitter数据集以用于IC任务。我们最初使用大型语言模型（LLMs）进行实验，观察到LLMs在零样本和少样本设置下能够基于认知共情和伦理标准的多角度推理提取相关信息，但仍然表现不如经过微调的小型语言模型。基于初步发现，我们得出结论，LLMs可能不是相关信息的最佳提取器，但能引发认知共情和伦理推理，这些可以直观地指导监督模型。为了实现这一想法，我们开发了一个基于认知共情和伦理推理的多任务流水线网络，用于RD和IC任务。我们提出的方法提供了有价值的见解，这些见解在现实世界中对政府、政策制定者和其他研究人员解读公众对社会问题的整体看法时可能非常有用。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Harnessing+Empathy+and+Ethics+for+Relevance+Detection+and+Information+Categorization+in+Climate+and+COVID-19+Tweets)|0| |[FashionLOGO: Prompting Multimodal Large Language Models for Fashion Logo Embeddings](https://doi.org/10.1145/3627673.3679926)|Zhen Wang, Da Li, Yulin Su, Min Yang, Minghui Qiu, Walton Wang||Logo embedding models convert the product logos in images into vectors, enabling their utilization for logo recognition and detection within e-commerce platforms. This facilitates the enforcement of intellectual property rights and enhances product search capabilities. However, current methods treat logo embedding as a purely visual problem. A noteworthy issue is that visual models capture features more than logos. Instead, we view this as a multimodal task, using text as auxiliary information to facilitate the visual model's understanding of the logo. The emerging Multimodal Large Language Models (MLLMs) have demonstrated remarkable capabilities in both visual and textual understanding. Inspired by this, we propose an approach, FashionLOGO, to explore how to prompt MLLMs to generate appropriate text for product images, which can help visual models achieve better logo embeddings. We adopt a cross-attention transformer block that enables visual embedding to automatically learn supplementary knowledge from textual embedding. Our extensive experiments on real-world datasets prove that FashionLOGO is capable of generating generic and robust logo embeddings, achieving state-of-the-art performance in all benchmarks.|Logo嵌入模型将图像中的产品Logo转换为向量，从而使其能够在电子商务平台中用于Logo识别和检测。这有助于知识产权的保护并提升产品搜索能力。然而，现有方法将Logo嵌入视为纯粹的视觉问题。一个值得注意的问题是，视觉模型捕捉到的特征往往超出Logo本身。相反，我们将其视为一个多模态任务，利用文本作为辅助信息来帮助视觉模型更好地理解Logo。新兴的多模态大型语言模型（MLLMs）在视觉和文本理解方面展示出卓越的能力。受此启发，我们提出了一种名为FashionLOGO的方法，探索如何引导MLLMs为产品图像生成适当的文本，这有助于视觉模型实现更好的Logo嵌入。我们采用了一个交叉注意力转换器块，使视觉嵌入能够自动从文本嵌入中学习补充知识。我们在真实世界数据集上的广泛实验证明，FashionLOGO能够生成通用且鲁棒的Logo嵌入，在所有基准测试中均达到了最先进的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=FashionLOGO:+Prompting+Multimodal+Large+Language+Models+for+Fashion+Logo+Embeddings)|0| |[CrossPred: A Cross-City Mobility Prediction Framework for Long-Distance Travelers via POI Feature Matching](https://doi.org/10.1145/3627673.3679893)|Shuai Xu, Donghai Guan|Nanjing University of Aeronautics and Astronautics, Nanjing, China|Current studies mainly rely on overlapping users (who leave trajectories in both cities) as a medium to learn travelers' preference in the target city, however it is unrealistic to find overlapping users when two cities are far apart, thus a severe data scarcity issue exists for this problem. Besides, due to the mixture of mobility pattern from both cities, directly applying the model trained in the source city may lead to negative transfer in the target city. To tackle these issues, in this paper, we conceive and implement a novel framework called CrossPred to predict the cross-city mobility of long-distance travelers in the target city. Specifically, POI features including popularity, textual description, spatial distribution as well as sequential pattern are considered for cross-city POI matching, which further acts as a vital link for jointly modeling native user mobility preference in both source and target cities. Maximum Mean Discrepancy (MMD) is adopted to strengthen the shared POI features among cities and weaken the unique POI features, thereby promoting cross-city POI feature matching. Extensive experiments on real-world datasets demonstrate the effectiveness and superiority of the proposed framework.|当前的研究主要依赖于重叠用户（在两个城市都留下轨迹的用户）作为媒介来学习目标城市中旅行者的偏好，然而当两个城市相距甚远时，找到重叠用户是不现实的，因此这个问题存在严重的数据稀缺问题。此外，由于两个城市的移动模式混合，直接应用在源城市训练的模型可能会导致在目标城市中出现负迁移。为了解决这些问题，本文构思并实现了一个名为CrossPred的新框架，用于预测目标城市中长途旅行者的跨城市移动。具体而言，POI特征包括流行度、文本描述、空间分布以及序列模式被考虑用于跨城市POI匹配，这进一步作为联合建模源城市和目标城市中本地用户移动偏好的关键环节。最大均值差异（MMD）被采用来加强城市间的共享POI特征并削弱独特的POI特征，从而促进跨城市POI特征匹配。在真实世界数据集上的广泛实验证明了所提出框架的有效性和优越性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=CrossPred:+A+Cross-City+Mobility+Prediction+Framework+for+Long-Distance+Travelers+via+POI+Feature+Matching)|0| @@ -210,73 +210,73 @@ |[Learn From Mistakes: Guidance on Zero-shot Conversational Text-to-SQL](https://doi.org/10.1145/3627673.3679951)|Wenshuo Zhai, Xiang Zhao, Jinzhi Liao, Ziyang Chen||Large language models (LLMs) possess powerful contextual comprehension capabilities and have demonstrated remarkable success in conversational tasks. However, existing works that apply LLMs to conversational text-to-SQL task have the problem of repetitive mistakes, which results in the failure to bring out the performance of LLMs. In this paper, we propose a novel approach that provides guidance through learning from mistakes. Specifically, the guidance offered by our approach includes tailored suggestions, corrective feedback, and personalized strategies aimed at improving learning outcomes. Furthermore, we employ chain-of-thought (CoT) to utilize guidance that is not suitable directly as prompts. Our method rigorously analyzes actual errors and strategizes on how to utilize the derived guidance effectively. Experimental results demonstrate that our approach improves the state-of-the-art (SOTA) performance metrics, increasing QEX performance from 66.3% to 70.9% (an absolute improvement of 4.6%) and IEX performance from 37.4% to 45.1% (an absolute improvement of 7.7%) on the CoSQL dataset.|大型语言模型（LLMs）具备强大的上下文理解能力，并在对话任务中展示了显著的成功。然而，现有将LLMs应用于对话式文本到SQL任务的研究存在重复错误的问题，导致无法充分发挥LLMs的性能。本文提出了一种通过从错误中学习来提供指导的新方法。具体而言，我们的方法提供的指导包括定制建议、纠正反馈和个性化策略，旨在提升学习效果。此外，我们采用思维链（Chain-of-Thought, CoT）来利用不适合直接作为提示的指导。我们的方法严格分析实际错误，并策略性地规划如何有效利用所得到的指导。实验结果表明，我们的方法提升了最先进（SOTA）的性能指标，在CoSQL数据集上，QEX性能从66.3%提高到70.9%（绝对提升4.6%），IEX性能从37.4%提高到45.1%（绝对提升7.7%）。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Learn+From+Mistakes:+Guidance+on+Zero-shot+Conversational+Text-to-SQL)|0| |[Mamba Retriever: Utilizing Mamba for Effective and Efficient Dense Retrieval](https://doi.org/10.1145/3627673.3679959)|Hanqi Zhang, Chong Chen, Lang Mei, Qi Liu, Jiaxin Mao||In the information retrieval (IR) area, dense retrieval (DR) models use deep learning techniques to encode queries and passages into embedding space to compute their semantic relations. It is important for DR models to balance both efficiency and effectiveness. Pre-trained language models (PLMs), especially Transformer-based PLMs, have been proven to be effective encoders of DR models. However, the self-attention component in Transformer-based PLM results in a computational complexity that grows quadratically with sequence length, and thus exhibits a slow inference speed for long-text retrieval. Some recently proposed non-Transformer PLMs, especially the Mamba architecture PLMs, have demonstrated not only comparable effectiveness to Transformer-based PLMs on generative language tasks but also better efficiency due to linear time scaling in sequence length. This paper implements the Mamba Retriever to explore whether Mamba can serve as an effective and efficient encoder of DR model for IR tasks. We fine-tune the Mamba Retriever on the classic short-text MS MARCO passage ranking dataset and the long-text LoCoV0 dataset. Experimental results show that (1) on the MS MARCO passage ranking dataset and BEIR, the Mamba Retriever achieves comparable or better effectiveness compared to Transformer-based retrieval models, and the effectiveness grows with the size of the Mamba model; (2) on the long-text LoCoV0 dataset, the Mamba Retriever can extend to longer text length than its pre-trained length after fine-tuning on retrieval task, and it has comparable or better effectiveness compared to other long-text retrieval models; (3) the Mamba Retriever has superior inference speed for long-text retrieval. In conclusion, Mamba Retriever is both effective and efficient, making it a practical model, especially for long-text retrieval.|在信息检索（IR）领域，密集检索（DR）模型利用深度学习技术将查询和文档编码到嵌入空间中，以计算它们的语义关系。对于DR模型来说，平衡效率和效果至关重要。预训练语言模型（PLMs），特别是基于Transformer的PLMs，已被证明是有效的DR模型编码器。然而，基于Transformer的PLM中的自注意力组件导致了计算复杂度随序列长度呈二次增长的特性，因此在长文本检索中表现出较慢的推理速度。最近提出的一些非Transformer的PLMs，特别是Mamba架构的PLMs，不仅在生成性语言任务上展示了与基于Transformer的PLMs相当的有效性，而且由于序列长度线性时间缩放的特性，还表现出更高的效率。本文实现了Mamba检索器，以探讨Mamba是否可以作为IR任务中DR模型的有效且高效的编码器。我们在经典的短文本MS MARCO文档排序数据集和长文本LoCoV0数据集上对Mamba检索器进行了微调。实验结果表明：（1）在MS MARCO文档排序数据集和BEIR上，Mamba检索器与基于Transformer的检索模型相比，达到了相当或更好的效果，并且效果随着Mamba模型规模的增大而提升；（2）在长文本LoCoV0数据集上，Mamba检索器在检索任务上微调后，可以扩展到比预训练时更长的文本长度，并且与其他长文本检索模型相比，具有相当或更好的效果；（3）Mamba检索器在长文本检索中具有优越的推理速度。综上所述，Mamba检索器既有效又高效，尤其适用于长文本检索，是一个实用的模型。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Mamba+Retriever:+Utilizing+Mamba+for+Effective+and+Efficient+Dense+Retrieval)|0| |[Generating Cross-model Analytics Workloads Using LLMs](https://doi.org/10.1145/3627673.3679932)|Xiuwen Zheng, Arun Kumar, Amarnath Gupta|University of California, San Diego, La Jolla, USA|Data analytics applications today often require processing heterogeneous data from different data models, including relational, graph, and text data, for more holistic analytics. While query optimization for single data models, especially relational data, has been studied for decades, there is surprisingly little work on query optimization for cross-model data analytics. Cross-model query optimization can benefit from the long line of prior work in query optimization in the relational realm, wherein cost-based and/or machine learning-based (ML-based) optimizers are common. Both approaches require a large and diverse set of query workloads to measure, tune, and evaluate a query optimizer. To the best of our knowledge, there are still no large public cross-model benchmark workloads, a significant obstacle for systems researchers in this space. In this paper, we take a step toward filling this research gap by generating new query workloads spanning relational and graph data, which are ubiquitous in analytics applications. Our approach leverages large language models (LLMs) via different prompting strategies to generate queries and proposes new rule-based post-processing methods to ensure query correctness. We evaluate the pros and cons of each strategy and perform an in-depth analysis by categorizing the syntactic and semantic errors of the generated queries. So far, we have produced over 4000 correct cross-model queries, the largest set ever. Our code, prompts, data, and query workloads will all be released publicly.|当今的数据分析应用通常需要处理来自不同数据模型的异构数据，包括关系型数据、图数据和文本数据，以实现更全面的分析。尽管针对单一数据模型的查询优化，尤其是关系型数据，已经研究了几十年，但关于跨模型数据分析的查询优化研究却出乎意料地少。跨模型查询优化可以借鉴关系领域中丰富的查询优化先前工作，其中基于成本和/或基于机器学习（ML-based）的优化器是常见的。这两种方法都需要大量且多样化的查询工作负载来测量、调整和评估查询优化器。据我们所知，目前仍没有大型公共的跨模型基准工作负载，这对该领域的系统研究人员来说是一个重大障碍。在本文中，我们朝着填补这一研究空白迈出了一步，生成了涵盖关系型和图数据的新查询工作负载，这些数据在分析应用中非常普遍。我们的方法利用大型语言模型（LLMs）通过不同的提示策略生成查询，并提出了新的基于规则的后处理方法以确保查询的正确性。我们评估了每种策略的优缺点，并通过分类生成的查询的句法和语义错误进行了深入分析。到目前为止，我们已经生成了超过4000个正确的跨模型查询，这是迄今为止最大的集合。我们的代码、提示、数据和查询工作负载将全部公开发布。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Generating+Cross-model+Analytics+Workloads+Using+LLMs)|0| -|[Deep Journey Hierarchical Attention Networks for Conversion Predictions in Digital Marketing](https://doi.org/10.1145/3627673.3680066)|Girim Ban, Hyeonseok Yun, Banseok Lee, David Sung, Simon S. Woo|Sungkyunkwan University, Suwon, Republic of Korea; Korea Telecom (KT) NexR, Seoul, Republic of Korea|In digital marketing, precise audience targeting is crucial for campaign efficiency. However, digital marketing agencies often struggle with incomplete user profiles and interaction details from Advertising Identifier (ADID) data in user behavior modeling. To address this, we introduce the Deep Journey Hierarchical Attention Networks (DJHAN). This novel method enhances conversion predictions by leveraging heterogeneous action sequences associated with ADIDs and encapsulating these interactions into structured journeys. These journeys are hierarchically aggregated to effectively represent ADID's behavioral attributes. Moreover, DJHAN incorporates three specialized attention mechanisms: temporal attention for time-sensitive contexts, action attention for emphasizing key behaviors, and journety attention for highlighting influential journeys in the purchase conversion process. Emprically, DJHAN surpasses state-of-the-art (SOTA) models across three diverse datasets, including real-world data from NasMedia, a leading media representative in Asia. In backtesting simulations with three advertisers, DJHAN outperforms existing baselines, achieving the highest improvements in Conversion Rate (CVR) and Return on Ad Spend (ROAS) across three advertisers, demonstrating its practical potential in digital marketing.|在数字营销中，精准的受众定位对于提升广告活动效率至关重要。然而，数字营销机构在用户行为建模中常常面临用户资料不完整以及从广告标识符（ADID）数据中获取的互动细节不足的问题。为解决这一问题，我们提出了深度旅程分层注意力网络（DJHAN）。这一创新方法通过利用与ADID相关的异构行为序列，并将这些互动封装成结构化的旅程，从而提高转化预测的准确性。这些旅程被分层聚合，以有效表示ADID的行为属性。此外，DJHAN结合了三种专门的注意力机制：时间注意力用于处理时间敏感的上下文，行为注意力用于强调关键行为，以及旅程注意力用于突出购买转化过程中具有影响力的旅程。实验证明，DJHAN在三个不同数据集上均超越了当前最先进（SOTA）的模型，其中包括来自亚洲领先媒体代表NasMedia的真实世界数据。在针对三家广告商的回测模拟中，DJHAN优于现有的基准模型，在转化率（CVR）和广告支出回报率（ROAS）方面均实现了最高的提升，展示了其在数字营销中的实际应用潜力。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Deep+Journey+Hierarchical+Attention+Networks+for+Conversion+Predictions+in+Digital+Marketing)|0| +|[Deep Journey Hierarchical Attention Networks for Conversion Predictions in Digital Marketing](https://doi.org/10.1145/3627673.3680066)|Girim Ban, Hyeonseok Yun, Banseok Lee, David Sung, Simon S. Woo|Korea Telecom (KT) NexR, Seoul, Republic of Korea; Sungkyunkwan University, Suwon, Republic of Korea|In digital marketing, precise audience targeting is crucial for campaign efficiency. However, digital marketing agencies often struggle with incomplete user profiles and interaction details from Advertising Identifier (ADID) data in user behavior modeling. To address this, we introduce the Deep Journey Hierarchical Attention Networks (DJHAN). This novel method enhances conversion predictions by leveraging heterogeneous action sequences associated with ADIDs and encapsulating these interactions into structured journeys. These journeys are hierarchically aggregated to effectively represent ADID's behavioral attributes. Moreover, DJHAN incorporates three specialized attention mechanisms: temporal attention for time-sensitive contexts, action attention for emphasizing key behaviors, and journety attention for highlighting influential journeys in the purchase conversion process. Emprically, DJHAN surpasses state-of-the-art (SOTA) models across three diverse datasets, including real-world data from NasMedia, a leading media representative in Asia. In backtesting simulations with three advertisers, DJHAN outperforms existing baselines, achieving the highest improvements in Conversion Rate (CVR) and Return on Ad Spend (ROAS) across three advertisers, demonstrating its practical potential in digital marketing.|在数字营销中，精准的受众定位对于提升广告活动效率至关重要。然而，数字营销机构在用户行为建模中常常面临用户资料不完整以及从广告标识符（ADID）数据中获取的互动细节不足的问题。为解决这一问题，我们提出了深度旅程分层注意力网络（DJHAN）。这一创新方法通过利用与ADID相关的异构行为序列，并将这些互动封装成结构化的旅程，从而提高转化预测的准确性。这些旅程被分层聚合，以有效表示ADID的行为属性。此外，DJHAN结合了三种专门的注意力机制：时间注意力用于处理时间敏感的上下文，行为注意力用于强调关键行为，以及旅程注意力用于突出购买转化过程中具有影响力的旅程。实验证明，DJHAN在三个不同数据集上均超越了当前最先进（SOTA）的模型，其中包括来自亚洲领先媒体代表NasMedia的真实世界数据。在针对三家广告商的回测模拟中，DJHAN优于现有的基准模型，在转化率（CVR）和广告支出回报率（ROAS）方面均实现了最高的提升，展示了其在数字营销中的实际应用潜力。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Deep+Journey+Hierarchical+Attention+Networks+for+Conversion+Predictions+in+Digital+Marketing)|0| |[LiNR: Model Based Neural Retrieval on GPUs at LinkedIn](https://doi.org/10.1145/3627673.3680091)|Fedor Borisyuk, Qingquan Song, Mingzhou Zhou, Ganesh Parameswaran, Madhu Arun, Siva Popuri, Tugrul Bingol, Zhuotao Pei, KuangHsuan Lee, Lu Zheng, Qizhan Shao, Ali Naqvi, Sen Zhou, Aman Gupta||This paper introduces LiNR, LinkedIn's large-scale, GPU-based retrieval system. LiNR supports a billion-sized index on GPU models. We discuss our experiences and challenges in creating scalable, differentiable search indexes using TensorFlow and PyTorch at production scale. In LiNR, both items and model weights are integrated into the model binary. Viewing index construction as a form of model training, we describe scaling our system for large indexes, incorporating full scans and efficient filtering. A key focus is on enabling attribute-based pre-filtering for exhaustive GPU searches, addressing the common challenge of post-filtering in KNN searches that often reduces system quality. We further provide multi-embedding retrieval algorithms and strategies for tackling cold start issues in retrieval. Our advancements in supporting larger indexes through quantization are also discussed. We believe LiNR represents one of the industry's first Live-updated model-based retrieval indexes. Applied to out-of-network post recommendations on LinkedIn Feed, LiNR has contributed to a 3% relative increase in professional daily active users. We envisage LiNR as a step towards integrating retrieval and ranking into a single GPU model, simplifying complex infrastructures and enabling end-to-end optimization of the entire differentiable infrastructure through gradient descent.|本文介绍了LiNR，即LinkedIn基于GPU的大规模检索系统。LiNR支持在GPU模型上处理十亿级索引。我们探讨了在生产规模下使用TensorFlow和PyTorch创建可扩展、可微分搜索索引的经验和挑战。在LiNR中，项目和模型权重都被整合到模型二进制文件中。将索引构建视为一种模型训练的形式，我们描述了如何扩展系统以处理大规模索引，包括全扫描和高效过滤。一个关键重点是启用基于属性的预过滤，以支持全面的GPU搜索，解决KNN搜索中常见的后过滤问题，这些问题通常会降低系统质量。此外，我们还提供了多嵌入检索算法和策略，用于解决检索中的冷启动问题。我们还在通过量化支持更大索引方面取得了进展。我们相信，LiNR代表了业界首批实时更新的基于模型的检索索引之一。应用于LinkedIn Feed的非网络帖子推荐，LiNR已为专业每日活跃用户带来了3%的相对增长。我们设想LiNR是朝着将检索和排序整合到单一GPU模型中的方向迈出的一步，简化了复杂的基础设施，并通过梯度下降实现了整个可微分基础设施的端到端优化。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=LiNR:+Model+Based+Neural+Retrieval+on+GPUs+at+LinkedIn)|0| |[Personalized Video Summarization by Multimodal Video Understanding](https://doi.org/10.1145/3627673.3680011)|Brian Y. Chen, Xiangyuan Zhao, Yingnan Zhu|VDIL, Samsung Research America, Irvine, CA, USA; VDIL, Samsung Research America, irvine, CA, USA|Video summarization techniques have been proven to improve the overall user experience when it comes to accessing and comprehending video content. If the user's preference is known, video summarization can identify significant information or relevant content from an input video, aiding them in obtaining the necessary information or determining their interest in watching the original video. Adapting video summarization to various types of video and user preferences requires significant training data and expensive human labeling. To facilitate such research, we proposed a new benchmark for video summarization that captures various user preferences. Also, we present a pipeline called Video Summarization with Language (VSL) for user-preferred video summarization that is based on pre-trained visual language models (VLMs) to avoid the need to train a video summarization system on a large training dataset. The pipeline takes both video and closed captioning as input and performs semantic analysis at the scene level by converting video frames into text. Subsequently, the user's genre preference was used as the basis for selecting the pertinent textual scenes. The experimental results demonstrate that our proposed pipeline outperforms current state-of-the-art unsupervised video summarization models. We show that our method is more adaptable across different datasets compared to supervised query-based video summarization models. In the end, the runtime analysis demonstrates that our pipeline is more suitable for practical use when scaling up the number of user preferences and videos.|视频摘要技术已被证明在用户访问和理解视频内容时能够提升整体用户体验。如果用户的偏好已知，视频摘要可以从输入视频中识别出重要信息或相关内容，帮助用户获取必要信息或判断是否对观看原视频感兴趣。将视频摘要适应于各种类型的视频和用户偏好需要大量的训练数据和昂贵的人工标注。为了促进此类研究，我们提出了一种新的视频摘要基准，该基准捕捉了多种用户偏好。此外，我们提出了一种名为Video Summarization with Language（VSL）的管道，用于基于预训练的视觉语言模型（VLMs）的用户偏好视频摘要，以避免在大规模训练数据集上训练视频摘要系统的需求。该管道接受视频和封闭字幕作为输入，并通过将视频帧转换为文本来在场景级别进行语义分析。随后，用户的类型偏好被用作选择相关文本场景的基础。实验结果表明，我们提出的管道优于当前最先进的无监督视频摘要模型。我们展示了与基于监督查询的视频摘要模型相比，我们的方法在不同数据集上更具适应性。最后，运行时分析表明，当扩展用户偏好和视频数量时，我们的管道更适合实际应用。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Personalized+Video+Summarization+by+Multimodal+Video+Understanding)|0| |[Blind-Match: Efficient Homomorphic Encryption-Based 1: N Matching for Privacy-Preserving Biometric Identification](https://doi.org/10.1145/3627673.3680017)|Hyunmin Choi, Jiwon Kim, Chiyoung Song, Simon S. Woo, Hyoungshick Kim||We present Blind-Match, a novel biometric identification system that leverages homomorphic encryption (HE) for efficient and privacy-preserving 1:N matching. Blind-Match introduces a HE-optimized cosine similarity computation method, where the key idea is to divide the feature vector into smaller parts for processing rather than computing the entire vector at once. By optimizing the number of these parts, Blind-Match minimizes execution time while ensuring data privacy through HE. Blind-Match achieves superior performance compared to state-of-the-art methods across various biometric datasets. On the LFW face dataset, Blind-Match attains a 99.63 feature vector, demonstrating its robustness in face recognition tasks. For fingerprint identification, Blind-Match achieves a remarkable 99.55 accuracy on the PolyU dataset, even with a compact 16-dimensional feature vector, significantly outperforming the state-of-the-art method, Blind-Touch, which achieves only 59.17 efficiency in large-scale biometric identification scenarios, such as Naver Cloud's FaceSign, by processing 6,144 biometric samples in 0.74 seconds using a 128-dimensional feature vector.|我们提出了Blind-Match，这是一种新颖的生物识别身份验证系统，利用同态加密（HE）实现高效且隐私保护的1:N匹配。Blind-Match引入了一种HE优化的余弦相似度计算方法，其关键思想是将特征向量分成较小的部分进行处理，而不是一次性计算整个向量。通过优化这些部分的数量，Blind-Match在确保通过HE保护数据隐私的同时，最小化了执行时间。与现有最先进的方法相比，Blind-Match在各种生物识别数据集上表现出色。在LFW人脸数据集上，Blind-Match实现了99.63的特征向量识别率，展示了其在人脸识别任务中的鲁棒性。对于指纹识别，Blind-Match在PolyU数据集上达到了惊人的99.55的准确率，即使使用的是紧凑的16维特征向量，也显著优于最先进的方法Blind-Touch，后者在大规模生物识别身份验证场景中，如Naver Cloud的FaceSign，仅实现了59.17的效率，通过处理6,144个生物识别样本在0.74秒内使用128维特征向量。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Blind-Match:++Efficient+Homomorphic+Encryption-Based+1:+N+Matching+for+Privacy-Preserving+Biometric+Identification)|0| -|[Automated Contrastive Learning Strategy Search for Time Series](https://doi.org/10.1145/3627673.3680086)|Baoyu Jing, Yansen Wang, Guoxin Sui, Jing Hong, Jingrui He, Yuqing Yang, Dongsheng Li, Kan Ren|ShanghaiTech University; Ruijin Hospital; Microsoft Research Asia; University of Illinois at Urbana-Champaign|In recent years, Contrastive Learning (CL) has become a predominantrepresentation learning paradigm for time series. Most existing methods in theliterature focus on manually building specific Contrastive Learning Strategies(CLS) by human heuristics for certain datasets and tasks. However, manuallydeveloping CLS usually require excessive prior knowledge about the datasets andtasks, e.g., professional cognition of the medical time series in healthcare,as well as huge human labor and massive experiments to determine the detailedlearning configurations. In this paper, we present an Automated MachineLearning (AutoML) practice at Microsoft, which automatically learns tocontrastively learn representations for various time series datasets and tasks,namely Automated Contrastive Learning (AutoCL). We first construct a principleduniversal search space of size over 3x1012, covering data augmentation,embedding transformation, contrastive pair construction and contrastive losses.Further, we introduce an efficient reinforcement learning algorithm, whichoptimizes CLS from the performance on the validation tasks, to obtain moreeffective CLS within the space. Experimental results on various real-worldtasks and datasets demonstrate that AutoCL could automatically find thesuitable CLS for a given dataset and task. From the candidate CLS found byAutoCL on several public datasets/tasks, we compose a transferable GenerallyGood Strategy (GGS), which has a strong performance for other datasets. We alsoprovide empirical analysis as a guidance for future design of CLS.|近年来，对比学习（Contrastive Learning, CL）已成为时间序列的主要表示学习范式。现有文献中的大多数方法侧重于通过人类启发式方法为特定数据集和任务手动构建特定的对比学习策略（Contrastive Learning Strategy, CLS）。然而，手动开发CLS通常需要对数据集和任务有大量的先验知识，例如医疗保健领域中对医疗时间序列的专业认知，以及大量的人力劳动和实验来确定详细的学习配置。在本文中，我们介绍了微软在自动化机器学习（Automated Machine Learning, AutoML）方面的一项实践，该实践能够自动学习为各种时间序列数据集和任务进行对比学习表示，即自动化对比学习（Automated Contrastive Learning, AutoCL）。我们首先构建了一个原则性的通用搜索空间，其大小超过3x10^12，涵盖了数据增强、嵌入变换、对比对构建和对比损失。此外，我们引入了一种高效的强化学习算法，该算法从验证任务的性能出发优化CLS，以在空间内获得更有效的CLS。在各种真实世界任务和数据集上的实验结果表明，AutoCL能够自动找到适合给定数据集和任务的CLS。从AutoCL在几个公共数据集/任务上找到的候选CLS中，我们组合了一个可迁移的通用良好策略（Generally Good Strategy, GGS），该策略在其他数据集上表现出色。我们还提供了经验分析，作为未来设计CLS的指导。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Automated+Contrastive+Learning+Strategy+Search+for+Time+Series)|0| +|[Automated Contrastive Learning Strategy Search for Time Series](https://doi.org/10.1145/3627673.3680086)|Baoyu Jing, Yansen Wang, Guoxin Sui, Jing Hong, Jingrui He, Yuqing Yang, Dongsheng Li, Kan Ren|Microsoft Research Asia; ShanghaiTech University; University of Illinois at Urbana-Champaign; Ruijin Hospital|In recent years, Contrastive Learning (CL) has become a predominantrepresentation learning paradigm for time series. Most existing methods in theliterature focus on manually building specific Contrastive Learning Strategies(CLS) by human heuristics for certain datasets and tasks. However, manuallydeveloping CLS usually require excessive prior knowledge about the datasets andtasks, e.g., professional cognition of the medical time series in healthcare,as well as huge human labor and massive experiments to determine the detailedlearning configurations. In this paper, we present an Automated MachineLearning (AutoML) practice at Microsoft, which automatically learns tocontrastively learn representations for various time series datasets and tasks,namely Automated Contrastive Learning (AutoCL). We first construct a principleduniversal search space of size over 3x1012, covering data augmentation,embedding transformation, contrastive pair construction and contrastive losses.Further, we introduce an efficient reinforcement learning algorithm, whichoptimizes CLS from the performance on the validation tasks, to obtain moreeffective CLS within the space. Experimental results on various real-worldtasks and datasets demonstrate that AutoCL could automatically find thesuitable CLS for a given dataset and task. From the candidate CLS found byAutoCL on several public datasets/tasks, we compose a transferable GenerallyGood Strategy (GGS), which has a strong performance for other datasets. We alsoprovide empirical analysis as a guidance for future design of CLS.|近年来，对比学习（Contrastive Learning, CL）已成为时间序列的主要表示学习范式。现有文献中的大多数方法侧重于通过人类启发式方法为特定数据集和任务手动构建特定的对比学习策略（Contrastive Learning Strategy, CLS）。然而，手动开发CLS通常需要对数据集和任务有大量的先验知识，例如医疗保健领域中对医疗时间序列的专业认知，以及大量的人力劳动和实验来确定详细的学习配置。在本文中，我们介绍了微软在自动化机器学习（Automated Machine Learning, AutoML）方面的一项实践，该实践能够自动学习为各种时间序列数据集和任务进行对比学习表示，即自动化对比学习（Automated Contrastive Learning, AutoCL）。我们首先构建了一个原则性的通用搜索空间，其大小超过3x10^12，涵盖了数据增强、嵌入变换、对比对构建和对比损失。此外，我们引入了一种高效的强化学习算法，该算法从验证任务的性能出发优化CLS，以在空间内获得更有效的CLS。在各种真实世界任务和数据集上的实验结果表明，AutoCL能够自动找到适合给定数据集和任务的CLS。从AutoCL在几个公共数据集/任务上找到的候选CLS中，我们组合了一个可迁移的通用良好策略（Generally Good Strategy, GGS），该策略在其他数据集上表现出色。我们还提供了经验分析，作为未来设计CLS的指导。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Automated+Contrastive+Learning+Strategy+Search+for+Time+Series)|0| |[REAPER: Reasoning based Retrieval Planning for Complex RAG Systems](https://doi.org/10.1145/3627673.3680087)|Ashutosh Joshi, Sheikh Muhammad Sarwar, Samarth Varshney, Sreyashi Nag, Shrivats Agrawal, Juhi Naik||Complex dialog systems often use retrieved evidence to facilitate factual responses. Such RAG (Retrieval Augmented Generation) systems retrieve from massive heterogeneous data stores that are usually architected as multiple indexes or APIs instead of a single monolithic source. For a given query, relevant evidence needs to be retrieved from one or a small subset of possible retrieval sources. Complex queries can even require multi-step retrieval. For example, a conversational agent on a retail site answering customer questions about past orders will need to retrieve the appropriate customer order first and then the evidence relevant to the customer's question in the context of the ordered product. Most RAG Agents handle such Chain-of-Thought (CoT) tasks by interleaving reasoning and retrieval steps. However, each reasoning step directly adds to the latency of the system. For large models (>100B parameters) this latency cost is significant – in the order of multiple seconds. Multi-agent systems may classify the query to a single Agent associated with a retrieval source, though this means that a (small) classification model dictates the performance of a large language model. In this work we present REAPER (REAsoning-based PlannER) - an LLM based planner to generate retrieval plans in conversational systems. We show significant gains in latency over Agent-based systems and are able to scale easily to new and unseen use cases as compared to classification-based planning. Though our method can be applied to any RAG system, we show our results in the context of Rufus – Amazon's conversational shopping assistant.|复杂的对话系统通常使用检索到的证据来支持事实性回答。这类RAG（检索增强生成）系统从通常架构为多个索引或API而非单一整体源的庞大异构数据存储中进行检索。对于给定的查询，相关证据需要从一个或少数几个可能的检索源中检索出来。复杂的查询甚至可能需要多步检索。例如，一个零售网站上的对话代理在回答关于过去订单的客户问题时，首先需要检索到适当的客户订单，然后在此订单产品的上下文中检索与客户问题相关的证据。大多数RAG代理通过交错推理和检索步骤来处理此类思维链（Chain-of-Thought, CoT）任务。然而，每个推理步骤都会直接增加系统的延迟。对于大型模型（超过1000亿参数），这种延迟成本是显著的——通常在几秒的量级。多代理系统可能会将查询分类到一个与检索源相关的单一代理，尽管这意味着一个（小型）分类模型决定了大型语言模型的性能。在这项工作中，我们提出了REAPER（基于推理的计划器）——一个基于LLM的计划器，用于在对话系统中生成检索计划。我们展示了在基于代理的系统中显著的延迟改进，并且能够轻松扩展到新的和未见过的用例，相比于基于分类的计划。虽然我们的方法可以应用于任何RAG系统，但我们展示了在Rufus——亚马逊的对话购物助手——中的结果。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=REAPER:+Reasoning+based+Retrieval+Planning+for+Complex+RAG+Systems)|0| -|[RL-ISLAP: A Reinforcement Learning Framework for Industrial-Scale Linear Assignment Problems at Alipay](https://doi.org/10.1145/3627673.3680108)|Hanjie Li, Yue Ning, Yang Bao, Changsheng Li, Boxiao Chen, Xingyu Lu, Ye Yuan, Guoren Wang|Independent Researcher, Shanghai, China; Independent Researcher, Hangzhou, China; Beijing Institute of Technology, Beijing, China|Industrial-scale linear assignment problems (LAPs) are frequently encountered in various industrial scenarios, e.g., asset allocation within the domain of credit management. However, optimization algorithms for such problems (e.g., PJ-ADMM) are highly sensitive to hyper-parameters. Existing solving systems rely on empirical parameter selection, which is challenging to achieve convergence and extremely time-consuming. Additionally, the resulting parameter rules are often inefficient. To alleviate this issue, we propose RL-ISLAP, an efficient and lightweight Reinforcement Learning framework for Industrial-Scale Linear Assignment Problems. We formulate the hyper-parameter selection for PJ-ADMM as a sequential decision problem and leverage reinforcement learning to enhance its convergence. Addressing the sparse reward challenge inherent in learning policies for such problems, we devise auxiliary rewards to provide dense signals for policy optimization, and present a rollback mechanism to prevent divergence in the solving process. Experiments on OR-Library benchmark demonstrate that our method is competitive to SOTA stand-alone solvers. Furthermore, the scale-independent design of observations enables us to transfer the acquired hyper-parameter policy to a scenario of LAPs in varying scales. On two real-world industrial-scale LAPs with up to 10 millions of decision variables, our proposed RL-ISLAP achieves solutions of comparable quality in 2/3 of the time when compared to the SOTA distributed solving system employing fine-tuned empirical parameter rules.|在各种工业场景中，例如信用管理领域的资产分配，经常会遇到大规模的线性分配问题（LAPs）。然而，针对此类问题的优化算法（例如PJ-ADMM）对超参数非常敏感。现有的求解系统依赖于经验参数选择，这不仅难以实现收敛，而且极其耗时。此外，由此产生的参数规则往往效率低下。为了缓解这一问题，我们提出了RL-ISLAP，一个针对工业规模线性分配问题的高效且轻量级的强化学习框架。我们将PJ-ADMM的超参数选择问题形式化为一个序列决策问题，并利用强化学习来增强其收敛性。针对此类问题中固有的稀疏奖励挑战，我们设计了辅助奖励以提供密集的信号用于策略优化，并提出了一种回滚机制以防止求解过程中的发散。在OR-Library基准测试中的实验表明，我们的方法与最先进的独立求解器具有竞争力。此外，观察结果的规模无关设计使我们能够将获得的超参数策略迁移到不同规模的LAP场景中。在两个具有多达1000万个决策变量的真实工业规模LAP问题上，与使用精细调参的经验参数规则的最先进分布式求解系统相比，我们提出的RL-ISLAP在2/3的时间内实现了同等质量的解决方案。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=RL-ISLAP:+A+Reinforcement+Learning+Framework+for+Industrial-Scale+Linear+Assignment+Problems+at+Alipay)|0| +|[RL-ISLAP: A Reinforcement Learning Framework for Industrial-Scale Linear Assignment Problems at Alipay](https://doi.org/10.1145/3627673.3680108)|Hanjie Li, Yue Ning, Yang Bao, Changsheng Li, Boxiao Chen, Xingyu Lu, Ye Yuan, Guoren Wang|Beijing Institute of Technology, Beijing, China; Independent Researcher, Shanghai, China; Independent Researcher, Hangzhou, China|Industrial-scale linear assignment problems (LAPs) are frequently encountered in various industrial scenarios, e.g., asset allocation within the domain of credit management. However, optimization algorithms for such problems (e.g., PJ-ADMM) are highly sensitive to hyper-parameters. Existing solving systems rely on empirical parameter selection, which is challenging to achieve convergence and extremely time-consuming. Additionally, the resulting parameter rules are often inefficient. To alleviate this issue, we propose RL-ISLAP, an efficient and lightweight Reinforcement Learning framework for Industrial-Scale Linear Assignment Problems. We formulate the hyper-parameter selection for PJ-ADMM as a sequential decision problem and leverage reinforcement learning to enhance its convergence. Addressing the sparse reward challenge inherent in learning policies for such problems, we devise auxiliary rewards to provide dense signals for policy optimization, and present a rollback mechanism to prevent divergence in the solving process. Experiments on OR-Library benchmark demonstrate that our method is competitive to SOTA stand-alone solvers. Furthermore, the scale-independent design of observations enables us to transfer the acquired hyper-parameter policy to a scenario of LAPs in varying scales. On two real-world industrial-scale LAPs with up to 10 millions of decision variables, our proposed RL-ISLAP achieves solutions of comparable quality in 2/3 of the time when compared to the SOTA distributed solving system employing fine-tuned empirical parameter rules.|在各种工业场景中，例如信用管理领域的资产分配，经常会遇到大规模的线性分配问题（LAPs）。然而，针对此类问题的优化算法（例如PJ-ADMM）对超参数非常敏感。现有的求解系统依赖于经验参数选择，这不仅难以实现收敛，而且极其耗时。此外，由此产生的参数规则往往效率低下。为了缓解这一问题，我们提出了RL-ISLAP，一个针对工业规模线性分配问题的高效且轻量级的强化学习框架。我们将PJ-ADMM的超参数选择问题形式化为一个序列决策问题，并利用强化学习来增强其收敛性。针对此类问题中固有的稀疏奖励挑战，我们设计了辅助奖励以提供密集的信号用于策略优化，并提出了一种回滚机制以防止求解过程中的发散。在OR-Library基准测试中的实验表明，我们的方法与最先进的独立求解器具有竞争力。此外，观察结果的规模无关设计使我们能够将获得的超参数策略迁移到不同规模的LAP场景中。在两个具有多达1000万个决策变量的真实工业规模LAP问题上，与使用精细调参的经验参数规则的最先进分布式求解系统相比，我们提出的RL-ISLAP在2/3的时间内实现了同等质量的解决方案。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=RL-ISLAP:+A+Reinforcement+Learning+Framework+for+Industrial-Scale+Linear+Assignment+Problems+at+Alipay)|0| |[Explainable and Coherent Complement Recommendation Based on Large Language Models](https://doi.org/10.1145/3627673.3680028)|Zelong Li, Yan Liang, Ming Wang, Sungro Yoon, Jiaying Shi, Xin Shen, Xiang He, Chenwei Zhang, Wenyi Wu, Hanbo Wang, Jin Li, Jim Chan, Yongfeng Zhang|Amazon.com, Seattle, WA, USA; Rutgers University, New Brunswick, NJ, USA|A complementary item is an item that pairs well with another item when consumed together. In the context of e-commerce, providing recommendations for complementary items is essential for both customers and stores. Current models for suggesting complementary items often rely heavily on user behavior data, such as co-purchase relationships. However, just because two items are frequently bought together does not necessarily mean they are truly complementary. Relying solely on co-purchase data may not align perfectly with the goal of making meaningful complementary recommendations. In this paper, we introduce the concept of "coherent complement recommendation", where "coherent" implies that recommended item pairs are compatible and relevant. Our approach builds upon complementary item pairs, with a focus on ensuring that recommended items are well used together and contextually relevant. To enhance the explainability and coherence of our complement recommendations, we fine-tune the Large Language Model (LLM) with coherent complement recommendation and explanation generation tasks since LLM has strong natural language explanation generation ability and multi-task fine-tuning enhances task understanding. Experimental results indicate that our model can provide more coherent complementary recommendations than existing state-of-the-art methods, and human evaluation validates that our approach achieves up to a 48% increase in the coherent rate of complement recommendations.|互补商品是指在共同消费时能够良好搭配的商品。在电子商务领域，为顾客和商家提供互补商品的推荐至关重要。当前推荐互补商品的模型通常严重依赖用户行为数据，如共同购买关系。然而，仅仅因为两种商品经常被一起购买并不一定意味着它们真正具有互补性。仅依赖共同购买数据可能无法完全实现提供有意义的互补推荐的目标。本文中，我们引入了“连贯互补推荐”的概念，其中“连贯”意味着推荐的商品对是兼容且相关的。我们的方法基于互补商品对，重点确保推荐的商品在使用时能够良好搭配并具有上下文相关性。为了增强互补推荐的解释性和连贯性，我们通过连贯互补推荐和解释生成任务对大型语言模型（LLM）进行微调，因为LLM具有强大的自然语言解释生成能力和多任务微调可以增强任务理解。实验结果表明，我们的模型能够提供比现有最先进方法更为连贯的互补推荐，而人工评估验证了我们的方法在互补推荐连贯率上实现了高达48%的提升。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Explainable+and+Coherent+Complement+Recommendation+Based+on+Large+Language+Models)|0| |[Boosting LLM-based Relevance Modeling with Distribution-Aware Robust Learning](https://doi.org/10.1145/3627673.3680052)|Hong Liu, Saisai Gong, Yixin Ji, Kaixin Wu, Jia Xu, Jinjie Gu|Ant Group, Hangzhou, China|Relevance modeling plays a crucial role in e-commerce search engines, striving to identify the utmost pertinent items corresponding to a given search query. With the rapid advancement of pre-trained large language models (LLMs), recent endeavors have leveraged the capabilities of LLMs in relevance modeling, resulting in enhanced performance. This is usually done through the process of fine-tuning LLMs on specifically annotated datasets to determine the relevance between queries and items. However, there are two limitations when LLMs are naively employed for relevance modeling through fine-tuning and inference. First, it is not inherently efficient for performing nuanced tasks beyond simple yes or no answers, such as assessing search relevance. It may therefore tend to be overconfident and struggle to distinguish fine-grained degrees of relevance (e.g., strong relevance, weak relevance, irrelevance) used in search engines. Second, it exhibits significant performance degradation when confronted with data distribution shift in real-world scenarios. In this paper, we propose a novel Distribution-Aware Robust Learning framework (DaRL) for relevance modeling in Alipay Search. Specifically, we design an effective loss function to enhance the discriminability of LLM-based relevance modeling across various fine-grained degrees of query-item relevance. To improve the generalizability of LLM-based relevance modeling, we first propose the Distribution-Aware Sample Augmentation (DASA) module. This module utilizes out-of-distribution (OOD) detection techniques to actively select appropriate samples that are not well covered by the original training set for model fine-tuning. Furthermore, we adopt a multi-stage fine-tuning strategy to simultaneously improve in-distribution (ID) and OOD performance, bridging the performance gap between them. DaRL has been deployed online to serve the Alipay's insurance product search. Both offline experiments on real-world industry data and online A/B testing show that DaRL effectively improves the performance of relevance modeling.|相关性建模在电子商务搜索引擎中扮演着至关重要的角色，旨在识别与给定搜索查询最相关的商品。随着预训练大型语言模型（LLMs）的快速发展，近期研究已利用LLMs在相关性建模中的能力，从而提升了性能。这通常通过在专门标注的数据集上微调LLMs来实现，以确定查询与商品之间的相关性。然而，当LLMs通过微调和推理简单地用于相关性建模时，存在两个局限性。首先，LLMs并不擅长执行超出简单是或否回答的细微任务，例如评估搜索相关性。因此，它们可能倾向于过度自信，难以区分搜索引擎中使用的细粒度相关性程度（如强相关、弱相关、不相关）。其次，在面对现实场景中的数据分布偏移时，LLMs表现出显著的性能下降。 本文提出了一个名为分布感知鲁棒学习框架（DaRL）的新方法，用于支付宝搜索中的相关性建模。具体而言，我们设计了一种有效的损失函数，以增强基于LLM的相关性建模在不同细粒度查询-商品相关性上的区分能力。为了提高基于LLM的相关性建模的泛化能力，我们首先提出了分布感知样本增强（DASA）模块。该模块利用分布外（OOD）检测技术，主动选择原始训练集未充分覆盖的适当样本进行模型微调。此外，我们采用多阶段微调策略，以同时提升分布内（ID）和分布外（OOD）性能，缩小两者之间的性能差距。DaRL已部署上线，服务于支付宝的保险产品搜索。基于真实行业数据的线下实验和在线A/B测试均表明，DaRL有效提升了相关性建模的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Boosting+LLM-based+Relevance+Modeling+with+Distribution-Aware+Robust+Learning)|0| -|[A Self-Adaptive Fairness Constraint Framework for Industrial Recommender System](https://doi.org/10.1145/3627673.3680099)|Zhiqiang Liu, Xiaoxiao Xu, Jiaqi Yu, Han Xu, Lantao Hu, Han Li, Kun Gai|Unaffiliated, Beijing, China; Kuaishou Technology, BeiJing, China; Kuaishou Technology, Beijing, China|Achieving fairness among different individuals or groups is an essential task for industrial recommender systems. Due to the group's personalized selection tendencies and the non-uniform population distributions, existing industrial recommenders tend to make unfair predictions towards the preferences of minority groups. To alleviate this unfairness, we propose a model-agnostic self-adaptive fairness constraint framework (SaFair) based on the posterior preferences of different groups. We construct group-level and individual-level fairness constraints. The former measures consistency between group-level posterior preferences and predicted interests, and the latter relies on the degree of consistency in interests between a user and their associated group to perform self-adaptive constraints. In particular, to balance effectiveness and fairness, we utilize uncertainty estimation to adjust the intensity of constraints according to the model's learning status called self-adaptive constraints. Extensive offline experiments and online A/B Testing are conducted and the results validate the superiority of our proposed method over the baselines. SaFair has been successfully deployed in Kuaishou, one of China's most popular short-video streaming platforms with hundreds of millions of active users.|在工业推荐系统中，实现不同个体或群体之间的公平性是一项至关重要的任务。由于群体的个性化选择倾向和非均匀的人口分布，现有的工业推荐系统往往对少数群体的偏好做出不公平的预测。为了缓解这种不公平性，我们提出了一个基于不同群体后验偏好的模型无关的自适应公平约束框架（SaFair）。我们构建了群体级别和个人级别的公平约束。前者衡量群体级别后验偏好与预测兴趣之间的一致性，而后者则依赖于用户与其所属群体之间兴趣一致性的程度来执行自适应约束。特别地，为了平衡有效性和公平性，我们利用不确定性估计根据模型的学习状态调整约束的强度，称为自适应约束。我们进行了广泛的离线实验和在线A/B测试，结果验证了我们提出的方法相对于基线的优越性。SaFair已成功部署在中国最受欢迎的短视频流媒体平台之一——快手，该平台拥有数亿活跃用户。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Self-Adaptive+Fairness+Constraint+Framework+for+Industrial+Recommender+System)|0| +|[A Self-Adaptive Fairness Constraint Framework for Industrial Recommender System](https://doi.org/10.1145/3627673.3680099)|Zhiqiang Liu, Xiaoxiao Xu, Jiaqi Yu, Han Xu, Lantao Hu, Han Li, Kun Gai|Kuaishou Technology, BeiJing, China; Unaffiliated, Beijing, China; Kuaishou Technology, Beijing, China|Achieving fairness among different individuals or groups is an essential task for industrial recommender systems. Due to the group's personalized selection tendencies and the non-uniform population distributions, existing industrial recommenders tend to make unfair predictions towards the preferences of minority groups. To alleviate this unfairness, we propose a model-agnostic self-adaptive fairness constraint framework (SaFair) based on the posterior preferences of different groups. We construct group-level and individual-level fairness constraints. The former measures consistency between group-level posterior preferences and predicted interests, and the latter relies on the degree of consistency in interests between a user and their associated group to perform self-adaptive constraints. In particular, to balance effectiveness and fairness, we utilize uncertainty estimation to adjust the intensity of constraints according to the model's learning status called self-adaptive constraints. Extensive offline experiments and online A/B Testing are conducted and the results validate the superiority of our proposed method over the baselines. SaFair has been successfully deployed in Kuaishou, one of China's most popular short-video streaming platforms with hundreds of millions of active users.|在工业推荐系统中，实现不同个体或群体之间的公平性是一项至关重要的任务。由于群体的个性化选择倾向和非均匀的人口分布，现有的工业推荐系统往往对少数群体的偏好做出不公平的预测。为了缓解这种不公平性，我们提出了一个基于不同群体后验偏好的模型无关的自适应公平约束框架（SaFair）。我们构建了群体级别和个人级别的公平约束。前者衡量群体级别后验偏好与预测兴趣之间的一致性，而后者则依赖于用户与其所属群体之间兴趣一致性的程度来执行自适应约束。特别地，为了平衡有效性和公平性，我们利用不确定性估计根据模型的学习状态调整约束的强度，称为自适应约束。我们进行了广泛的离线实验和在线A/B测试，结果验证了我们提出的方法相对于基线的优越性。SaFair已成功部署在中国最受欢迎的短视频流媒体平台之一——快手，该平台拥有数亿活跃用户。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Self-Adaptive+Fairness+Constraint+Framework+for+Industrial+Recommender+System)|0| |[GLaD: Synergizing Molecular Graphs and Language Descriptors for Enhanced Power Conversion Efficiency Prediction in Organic Photovoltaic Devices](https://doi.org/10.1145/3627673.3680103)|Thao Nguyen, Tiara TorresFlores, Changhyun Hwang, Carl Edwards, Ying Diao, Heng Ji|University of Illinois Urbana-Champaign Siebel School of Computing and Data Science; University of Illinois Urbana-Champaign Department of Chemical & Biomolecular Engineering|This paper presents a novel approach for predicting Power ConversionEfficiency (PCE) of Organic Photovoltaic (OPV) devices, called GLaD:synergizing molecular Graphs and Language Descriptors for enhanced PCEprediction. Due to the lack of high-quality experimental data, we collect adataset consisting of 500 pairs of OPV donor and acceptor molecules along withtheir corresponding PCE values, which we utilize as the training data for ourpredictive model. In this low-data regime, GLaD leverages properties learnedfrom large language models (LLMs) pretrained on extensive scientific literatureto enrich molecular structural representations, allowing for a multimodalrepresentation of molecules. GLaD achieves precise predictions of PCE, therebyfacilitating the synthesis of new OPV molecules with improved efficiency.Furthermore, GLaD showcases versatility, as it applies to a range of molecularproperty prediction tasks (BBBP, BACE, ClinTox, and SIDER), not limited tothose concerning OPV materials. Especially, GLaD proves valuable for tasks inlow-data regimes within the chemical space, as it enriches molecularrepresentations by incorporating molecular property descriptions learned fromlarge-scale pretraining. This capability is significant in real-worldscientific endeavors like drug and material discovery, where access tocomprehensive data is crucial for informed decision-making and efficientexploration of the chemical space.|本文提出了一种名为GLaD（结合分子图和语言描述符以增强功率转换效率预测）的新方法，用于预测有机光伏（OPV）器件的功率转换效率（PCE）。由于缺乏高质量的实验数据，我们收集了一个包含500对OPV供体和受体分子及其相应PCE值的数据集，并将其用作预测模型的训练数据。在这种数据量较少的情况下，GLaD利用从广泛科学文献预训练的大型语言模型（LLMs）中学习到的属性来丰富分子结构表示，从而实现分子的多模态表示。GLaD能够精确预测PCE，从而促进合成效率更高的新OPV分子。此外，GLaD展示了其多功能性，适用于一系列分子属性预测任务（BBBP、BACE、ClinTox和SIDER），不仅限于与OPV材料相关的任务。特别是在化学空间中的低数据任务中，GLaD通过整合从大规模预训练中学习到的分子属性描述来丰富分子表示，这被证明具有重要价值。这一能力在药物和材料发现等实际科学工作中至关重要，因为全面的数据对于明智的决策和化学空间的有效探索至关重要。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=GLaD:+Synergizing+Molecular+Graphs+and+Language+Descriptors+for+Enhanced+Power+Conversion+Efficiency+Prediction+in+Organic+Photovoltaic+Devices)|0| -|[Cross-contextual Sequential Optimization via Deep Reinforcement Learning for Algorithmic Trading](https://doi.org/10.1145/3627673.3680101)|Kaiming Pan, Yifan Hu, Li Han, Haoyu Sun, Dawei Cheng, Yuqi Liang|Software Engineering Institute, East China Normal University, Shanghai, China; Department of Computer Science, Tongji University, Shanghai, China; Seek Data Group, Emoney Inc., Shanghai, China|High-frequency algorithmic trading has consistently attracted attention in both academic and industrial fields, which is formally modeled as a near real-time sequential decision problem. DRL methods are treated as a promising direction compared with the traditional approaches, as they have shown great potential in chasing maximum accumulative return. However, the financial data gathered from volatile market change rapidly, which makes it dramatically difficult to grasp crucial factors for effective decision-making. Existing works mainly focus on capturing temporal relations while ignoring deriving essential factors across features. Therefore, we propose a DRL-based cross-contextual sequential optimization (CCSO) method for algorithmic trading. In particular, we employ a convolution module in the first stage to derive latent factors via inter-sequence aggregation and apply a well-designed self-attention module in the second stage to capture market dynamics by aggregating temporal intra-sequence details. With the two-stage extractor as encoder and a RNN-based decision-maker as decoder, an Encoder-Decoder module is established as the policy network to conduct potent feature analysis and suggest action plans. Then, we design a dynamic programming based learning method to address the challenge of complex network updates in reinforcement learning, leading to considerable enhancement in learning stability and efficiency. To the best of our knowledge, this is the first work that solves the sequential optimization problem by joint representation of trading data across time and features in the DRL framework. Extensive experiments demonstrate the superior performance of our method compared to other state-of-the-art algorithmic trading approaches in various widely-used metrics.|高频算法交易在学术界和工业界持续受到关注，其正式模型被视为一种近实时序列决策问题。与传统方法相比，深度强化学习（DRL）方法被视为一个有前景的方向，因为它们在追求最大累积回报方面展现了巨大潜力。然而，从市场波动中收集的金融数据变化迅速，这使得捕捉有效决策的关键因素变得极其困难。现有研究主要集中在捕捉时间关系，而忽视了跨特征推导重要因素。因此，我们提出了一种基于DRL的跨上下文序列优化（CCSO）方法用于算法交易。具体而言，我们在第一阶段采用卷积模块通过序列间聚合推导潜在因素，并在第二阶段应用设计良好的自注意力模块通过聚合时间序列内细节来捕捉市场动态。通过将两阶段提取器作为编码器和基于RNN的决策者作为解码器，构建了一个编码器-解码器模块作为策略网络，以进行强大的特征分析并提出行动计划。随后，我们设计了一种基于动态规划的学习方法来应对强化学习中复杂网络更新的挑战，从而显著提升了学习稳定性和效率。据我们所知，这是首次在DRL框架中通过时间与特征的联合表示来解决序列优化问题的工作。广泛的实验证明，我们的方法在各种广泛使用的指标上优于其他最先进的算法交易方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Cross-contextual+Sequential+Optimization+via+Deep+Reinforcement+Learning+for+Algorithmic+Trading)|0| +|[Cross-contextual Sequential Optimization via Deep Reinforcement Learning for Algorithmic Trading](https://doi.org/10.1145/3627673.3680101)|Kaiming Pan, Yifan Hu, Li Han, Haoyu Sun, Dawei Cheng, Yuqi Liang|Seek Data Group, Emoney Inc., Shanghai, China; Software Engineering Institute, East China Normal University, Shanghai, China; Department of Computer Science, Tongji University, Shanghai, China|High-frequency algorithmic trading has consistently attracted attention in both academic and industrial fields, which is formally modeled as a near real-time sequential decision problem. DRL methods are treated as a promising direction compared with the traditional approaches, as they have shown great potential in chasing maximum accumulative return. However, the financial data gathered from volatile market change rapidly, which makes it dramatically difficult to grasp crucial factors for effective decision-making. Existing works mainly focus on capturing temporal relations while ignoring deriving essential factors across features. Therefore, we propose a DRL-based cross-contextual sequential optimization (CCSO) method for algorithmic trading. In particular, we employ a convolution module in the first stage to derive latent factors via inter-sequence aggregation and apply a well-designed self-attention module in the second stage to capture market dynamics by aggregating temporal intra-sequence details. With the two-stage extractor as encoder and a RNN-based decision-maker as decoder, an Encoder-Decoder module is established as the policy network to conduct potent feature analysis and suggest action plans. Then, we design a dynamic programming based learning method to address the challenge of complex network updates in reinforcement learning, leading to considerable enhancement in learning stability and efficiency. To the best of our knowledge, this is the first work that solves the sequential optimization problem by joint representation of trading data across time and features in the DRL framework. Extensive experiments demonstrate the superior performance of our method compared to other state-of-the-art algorithmic trading approaches in various widely-used metrics.|高频算法交易在学术界和工业界持续受到关注，其正式模型被视为一种近实时序列决策问题。与传统方法相比，深度强化学习（DRL）方法被视为一个有前景的方向，因为它们在追求最大累积回报方面展现了巨大潜力。然而，从市场波动中收集的金融数据变化迅速，这使得捕捉有效决策的关键因素变得极其困难。现有研究主要集中在捕捉时间关系，而忽视了跨特征推导重要因素。因此，我们提出了一种基于DRL的跨上下文序列优化（CCSO）方法用于算法交易。具体而言，我们在第一阶段采用卷积模块通过序列间聚合推导潜在因素，并在第二阶段应用设计良好的自注意力模块通过聚合时间序列内细节来捕捉市场动态。通过将两阶段提取器作为编码器和基于RNN的决策者作为解码器，构建了一个编码器-解码器模块作为策略网络，以进行强大的特征分析并提出行动计划。随后，我们设计了一种基于动态规划的学习方法来应对强化学习中复杂网络更新的挑战，从而显著提升了学习稳定性和效率。据我们所知，这是首次在DRL框架中通过时间与特征的联合表示来解决序列优化问题的工作。广泛的实验证明，我们的方法在各种广泛使用的指标上优于其他最先进的算法交易方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Cross-contextual+Sequential+Optimization+via+Deep+Reinforcement+Learning+for+Algorithmic+Trading)|0| |[STIR: Siamese Transformer for Image Retrieval Postprocessing](https://doi.org/10.1145/3627673.3680075)|Aleksei Shabanov, Aleksei Tarasov, Sergey I. Nikolenko||Current metric learning approaches for image retrieval are usually based on learning a space of informative latent representations where simple approaches such as the cosine distance will work well. Recent state of the art methods such as HypViT move to more complex embedding spaces that may yield better results but are harder to scale to production environments. In this work, we first construct a simpler model based on triplet loss with hard negatives mining that performs at the state of the art level but does not have these drawbacks. Second, we introduce a novel approach for image retrieval postprocessing called Siamese Transformer for Image Retrieval (STIR) that reranks several top outputs in a single forward pass. Unlike previously proposed Reranking Transformers, STIR does not rely on global/local feature extraction and directly compares a query image and a retrieved candidate on pixel level with the usage of attention mechanism. The resulting approach defines a new state of the art on standard image retrieval datasets: Stanford Online Products and DeepFashion In-shop. We also release the source code at https://github.com/OML-Team/open-metric-learning/tree/main/pipelines/postprocessing/ and an interactive demo of our approach at https://dapladoc-oml-postprocessing-demo-srcappmain-pfh2g0.streamlit.app/|当前用于图像检索的度量学习方法通常基于学习一个信息丰富的潜在表示空间，其中简单的度量方法（如余弦距离）表现良好。最近的最先进方法，如HypViT，转向了更复杂的嵌入空间，可能带来更好的结果，但更难以扩展到生产环境中。在这项工作中，我们首先构建了一个基于三元组损失与难负样本挖掘的简单模型，该模型达到了最先进的性能水平，但没有这些缺点。其次，我们引入了一种名为Siamese Transformer for Image Retrieval（STIR）的图像检索后处理新方法，通过一次前向传播对多个顶部输出进行重新排序。与之前提出的重新排序变压器不同，STIR不依赖于全局/局部特征提取，而是直接在像素级别上使用注意力机制比较查询图像和检索到的候选图像。该方法在标准图像检索数据集上定义了新的最先进水平：Stanford Online Products和DeepFashion In-shop。我们还发布了源代码，链接为https://github.com/OML-Team/open-metric-learning/tree/main/pipelines/postprocessing/，并提供了一个交互式演示，链接为https://dapladoc-oml-postprocessing-demo-srcappmain-pfh2g0.streamlit.app/。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=STIR:+Siamese+Transformer+for+Image+Retrieval+Postprocessing)|0| |[Enhancing Taobao Display Advertising with Multimodal Representations: Challenges, Approaches and Insights](https://doi.org/10.1145/3627673.3680068)|XiangRong Sheng, Feifan Yang, Litong Gong, Biao Wang, Zhangming Chan, Yujing Zhang, Yueyao Cheng, YongNan Zhu, Tiezheng Ge, Han Zhu, Yuning Jiang, Jian Xu, Bo Zheng||Despite the recognized potential of multimodal data to improve model accuracy, many large-scale industrial recommendation systems, including Taobao display advertising system, predominantly depend on sparse ID features in their models. In this work, we explore approaches to leverage multimodal data to enhance the recommendation accuracy. We start from identifying the key challenges in adopting multimodal data in a manner that is both effective and cost-efficient for industrial systems. To address these challenges, we introduce a two-phase framework, including: 1) the pre-training of multimodal representations to capture semantic similarity, and 2) the integration of these representations with existing ID-based models. Furthermore, we detail the architecture of our production system, which is designed to facilitate the deployment of multimodal representations. Since the integration of multimodal representations in mid-2023, we have observed significant performance improvements in Taobao display advertising system. We believe that the insights we have gathered will serve as a valuable resource for practitioners seeking to leverage multimodal data in their systems.|尽管多模态数据被认为具有提高模型准确性的潜力，但许多大规模工业推荐系统，包括淘宝展示广告系统，主要依赖于模型中的稀疏ID特征。在这项工作中，我们探索了利用多模态数据来增强推荐准确性的方法。我们从识别在工业系统中有效且成本高效地采用多模态数据的关键挑战开始。为了解决这些挑战，我们引入了一个两阶段框架，包括：1）多模态表示的预训练以捕捉语义相似性，以及2）将这些表示与现有的基于ID的模型进行整合。此外，我们详细介绍了我们的生产系统架构，该架构设计用于促进多模态表示的部署。自2023年年中多模态表示整合以来，我们在淘宝展示广告系统中观察到了显著的性能提升。我们相信，我们获得的这些见解将为寻求在其系统中利用多模态数据的从业者提供宝贵的资源。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Enhancing+Taobao+Display+Advertising+with+Multimodal+Representations:+Challenges,+Approaches+and+Insights)|0| -|[LLM-based Automated Web Retrieval and Text Classification of Food Sharing Initiatives](https://doi.org/10.1145/3627673.3680090)|Hao Wu, Hyunji Cho, Anna R. Davies, Gareth J. F. Jones|Geography, School of Natural Sciences, Trinity College Dublin, Dublin, Ireland; ADAPT Centre, School of Computing, Dublin City University, Dublin, Ireland|Urban and peri-urban (UPU) food systems encounter challenges in sustainability and are fragile and vulnerable to shocks. Addressing these issues is one of the key drivers of food sharing initiatives (FSIs) which focus on collective acts around food across the food system. FSIs range from seed sharing and surplus food redistribution to community composting. We describe our development and deployment of web retrieval and content classification tools designed to provide automated mapping of FSIs at scale to populate databases of FSIs within cities. We present our novel automated system tailored for retrieving, identifying, categorizing and real-time monitoring of FSIs in over 200 European cities. Developed within the European CULTIVATE project, this system not only aids in comprehending the complex dynamics of the food sharing economy, but also enhances its visibility and operational efficiency. The automation of these processes plays a vital role in supporting the goals of the CULTIVATE project, notably in promoting sustainable food practices and resilient local food networks. Our system integrates web search using queries constructed automatically using domain-specific vocabulary resources with Large Language Model (LLM) query writing and classification methods. Experimental results using a collection of data derived from real online FSI content underscore the potential of digital automation to make significant contributions to innovative digital solutions to contemporary sustainability challenges. As such, the findings of this work pave the way for future research and implementation in similar contexts.|城市和城郊（UPU）食品系统在可持续性方面面临挑战，且容易受到冲击的影响，表现出脆弱性。解决这些问题是食品共享倡议（FSIs）的关键驱动力之一，这些倡议侧重于整个食品系统中的集体食品行为。FSIs的范围从种子共享和剩余食品再分配到社区堆肥。我们描述了开发和部署的网络检索和内容分类工具，这些工具旨在大规模自动绘制FSIs地图，以填充城市内的FSIs数据库。我们展示了一种专为在200多个欧洲城市中检索、识别、分类和实时监控FSIs而设计的自动化系统。该系统是在欧洲CULTIVATE项目中开发的，不仅有助于理解食品共享经济的复杂动态，还增强了其可见性和运营效率。这些过程的自动化对支持CULTIVATE项目的目标起着至关重要的作用，特别是在促进可持续食品实践和弹性本地食品网络方面。我们的系统集成了利用领域特定词汇资源自动构建查询的网络搜索与大型语言模型（LLM）查询编写和分类方法。使用从真实在线FSI内容中提取的数据集进行的实验结果，突显了数字自动化对当代可持续性挑战创新数字解决方案的潜在贡献。因此，这项工作的发现为未来在类似情境中的研究和实施铺平了道路。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=LLM-based+Automated+Web+Retrieval+and+Text+Classification+of+Food+Sharing+Initiatives)|0| -|[Breaking the Barrier: Utilizing Large Language Models for Industrial Recommendation Systems through an Inferential Knowledge Graph](https://doi.org/10.1145/3627673.3680022)|Qian Zhao, Hao Qian, Ziqi Liu, GongDuo Zhang, Lihong Gu|; Utilizing Large Language Models for Industrial Recom-mendation Systems through an Inferential Knowledge Graph; Ant Group Hangzhou|Recommendation systems are widely used in e-commerce websites and onlineplatforms to address information overload. However, existing systems primarilyrely on historical data and user feedback, making it difficult to capture userintent transitions. Recently, Knowledge Base (KB)-based models are proposed toincorporate expert knowledge, but it struggle to adapt to new items and theevolving e-commerce environment. To address these challenges, we propose anovel Large Language Model based Complementary Knowledge EnhancedRecommendation System (LLM-KERec). It introduces an entity extractor thatextracts unified concept terms from item and user information. To providecost-effective and reliable prior knowledge, entity pairs are generated basedon entity popularity and specific strategies. The large language modeldetermines complementary relationships in each entity pair, constructing acomplementary knowledge graph. Furthermore, a new complementary recall moduleand an Entity-Entity-Item (E-E-I) weight decision model refine the scoring ofthe ranking model using real complementary exposure-click samples. Extensiveexperiments conducted on three industry datasets demonstrate the significantperformance improvement of our model compared to existing approaches.Additionally, detailed analysis shows that LLM-KERec enhances users' enthusiasmfor consumption by recommending complementary items. In summary, LLM-KERecaddresses the limitations of traditional recommendation systems byincorporating complementary knowledge and utilizing a large language model tocapture user intent transitions, adapt to new items, and enhance recommendationefficiency in the evolving e-commerce landscape.|推荐系统在电子商务网站和在线平台上被广泛使用，以应对信息过载问题。然而，现有系统主要依赖历史数据和用户反馈，难以捕捉用户意图的转变。最近，基于知识库（KB）的模型被提出以整合专家知识，但它们难以适应新商品和不断变化的电子商务环境。为解决这些挑战，我们提出了一种新颖的大语言模型（LLM）增强的补充知识推荐系统（LLM-KERec）。该系统引入了一个实体提取器，从商品和用户信息中提取统一的概念术语。为了提供成本效益高且可靠的先验知识，实体对基于实体流行度和特定策略生成。大语言模型确定每个实体对中的补充关系，构建一个补充知识图。此外，一个新的补充召回模块和一个实体-实体-商品（E-E-I）权重决策模型使用真实的补充曝光-点击样本来优化排序模型的评分。在三个行业数据集上进行的大量实验表明，我们的模型相较于现有方法显著提升了性能。详细分析显示，LLM-KERec通过推荐补充商品增强了用户的消费热情。总之，LLM-KERec通过整合补充知识并利用大语言模型捕捉用户意图转变，适应新商品，并在不断变化的电子商务环境中提升推荐效率，从而解决了传统推荐系统的局限性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Breaking+the+Barrier:+Utilizing+Large+Language+Models+for+Industrial+Recommendation+Systems+through+an+Inferential+Knowledge+Graph)|0| +|[LLM-based Automated Web Retrieval and Text Classification of Food Sharing Initiatives](https://doi.org/10.1145/3627673.3680090)|Hao Wu, Hyunji Cho, Anna R. Davies, Gareth J. F. Jones|ADAPT Centre, School of Computing, Dublin City University, Dublin, Ireland; Geography, School of Natural Sciences, Trinity College Dublin, Dublin, Ireland|Urban and peri-urban (UPU) food systems encounter challenges in sustainability and are fragile and vulnerable to shocks. Addressing these issues is one of the key drivers of food sharing initiatives (FSIs) which focus on collective acts around food across the food system. FSIs range from seed sharing and surplus food redistribution to community composting. We describe our development and deployment of web retrieval and content classification tools designed to provide automated mapping of FSIs at scale to populate databases of FSIs within cities. We present our novel automated system tailored for retrieving, identifying, categorizing and real-time monitoring of FSIs in over 200 European cities. Developed within the European CULTIVATE project, this system not only aids in comprehending the complex dynamics of the food sharing economy, but also enhances its visibility and operational efficiency. The automation of these processes plays a vital role in supporting the goals of the CULTIVATE project, notably in promoting sustainable food practices and resilient local food networks. Our system integrates web search using queries constructed automatically using domain-specific vocabulary resources with Large Language Model (LLM) query writing and classification methods. Experimental results using a collection of data derived from real online FSI content underscore the potential of digital automation to make significant contributions to innovative digital solutions to contemporary sustainability challenges. As such, the findings of this work pave the way for future research and implementation in similar contexts.|城市和城郊（UPU）食品系统在可持续性方面面临挑战，且容易受到冲击的影响，表现出脆弱性。解决这些问题是食品共享倡议（FSIs）的关键驱动力之一，这些倡议侧重于整个食品系统中的集体食品行为。FSIs的范围从种子共享和剩余食品再分配到社区堆肥。我们描述了开发和部署的网络检索和内容分类工具，这些工具旨在大规模自动绘制FSIs地图，以填充城市内的FSIs数据库。我们展示了一种专为在200多个欧洲城市中检索、识别、分类和实时监控FSIs而设计的自动化系统。该系统是在欧洲CULTIVATE项目中开发的，不仅有助于理解食品共享经济的复杂动态，还增强了其可见性和运营效率。这些过程的自动化对支持CULTIVATE项目的目标起着至关重要的作用，特别是在促进可持续食品实践和弹性本地食品网络方面。我们的系统集成了利用领域特定词汇资源自动构建查询的网络搜索与大型语言模型（LLM）查询编写和分类方法。使用从真实在线FSI内容中提取的数据集进行的实验结果，突显了数字自动化对当代可持续性挑战创新数字解决方案的潜在贡献。因此，这项工作的发现为未来在类似情境中的研究和实施铺平了道路。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=LLM-based+Automated+Web+Retrieval+and+Text+Classification+of+Food+Sharing+Initiatives)|0| +|[Breaking the Barrier: Utilizing Large Language Models for Industrial Recommendation Systems through an Inferential Knowledge Graph](https://doi.org/10.1145/3627673.3680022)|Qian Zhao, Hao Qian, Ziqi Liu, GongDuo Zhang, Lihong Gu|; Ant Group Hangzhou; Utilizing Large Language Models for Industrial Recom-mendation Systems through an Inferential Knowledge Graph|Recommendation systems are widely used in e-commerce websites and onlineplatforms to address information overload. However, existing systems primarilyrely on historical data and user feedback, making it difficult to capture userintent transitions. Recently, Knowledge Base (KB)-based models are proposed toincorporate expert knowledge, but it struggle to adapt to new items and theevolving e-commerce environment. To address these challenges, we propose anovel Large Language Model based Complementary Knowledge EnhancedRecommendation System (LLM-KERec). It introduces an entity extractor thatextracts unified concept terms from item and user information. To providecost-effective and reliable prior knowledge, entity pairs are generated basedon entity popularity and specific strategies. The large language modeldetermines complementary relationships in each entity pair, constructing acomplementary knowledge graph. Furthermore, a new complementary recall moduleand an Entity-Entity-Item (E-E-I) weight decision model refine the scoring ofthe ranking model using real complementary exposure-click samples. Extensiveexperiments conducted on three industry datasets demonstrate the significantperformance improvement of our model compared to existing approaches.Additionally, detailed analysis shows that LLM-KERec enhances users' enthusiasmfor consumption by recommending complementary items. In summary, LLM-KERecaddresses the limitations of traditional recommendation systems byincorporating complementary knowledge and utilizing a large language model tocapture user intent transitions, adapt to new items, and enhance recommendationefficiency in the evolving e-commerce landscape.|推荐系统在电子商务网站和在线平台上被广泛使用，以应对信息过载问题。然而，现有系统主要依赖历史数据和用户反馈，难以捕捉用户意图的转变。最近，基于知识库（KB）的模型被提出以整合专家知识，但它们难以适应新商品和不断变化的电子商务环境。为解决这些挑战，我们提出了一种新颖的大语言模型（LLM）增强的补充知识推荐系统（LLM-KERec）。该系统引入了一个实体提取器，从商品和用户信息中提取统一的概念术语。为了提供成本效益高且可靠的先验知识，实体对基于实体流行度和特定策略生成。大语言模型确定每个实体对中的补充关系，构建一个补充知识图。此外，一个新的补充召回模块和一个实体-实体-商品（E-E-I）权重决策模型使用真实的补充曝光-点击样本来优化排序模型的评分。在三个行业数据集上进行的大量实验表明，我们的模型相较于现有方法显著提升了性能。详细分析显示，LLM-KERec通过推荐补充商品增强了用户的消费热情。总之，LLM-KERec通过整合补充知识并利用大语言模型捕捉用户意图转变，适应新商品，并在不断变化的电子商务环境中提升推荐效率，从而解决了传统推荐系统的局限性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Breaking+the+Barrier:+Utilizing+Large+Language+Models+for+Industrial+Recommendation+Systems+through+an+Inferential+Knowledge+Graph)|0| |[STaR: Space and Time-aware Statistic Query Answering](https://doi.org/10.1145/3627673.3679209)|Oana Balalau, Simon Ebel, Helena Galhardas, Théo Galizzi, Ioana Manolescu|INESC-ID & IST, Universidade Lisboa, Lisbon, Portugal; Inria & Institut Polytechnique de Paris, Palaiseau, France|High-quality data is essential for informed public debate. High-quality statistical data sources provide valuable reference information for verifying claims. To assist journalists and fact-checkers, user queries about specific claims should be automatically answered using statistical tables. However, the large number and variety of these sources make this task challenging. We propose to demonstrate STaR, a novel method for Space and Time-aware STatistic Retrieval, based on a user natural language query. STaR is deployed within our system StatCheck, which we developed and shared with fact-checking journalists. STaR improves the quality of statistic fact retrieval by treating space and time separately from the other parts of the statistics dataset. Specifically, we use them as dimensions of the data (and the query), and focus the linguistic part of our dataset search on the rich, varied language present in the data. Our demonstration uses statistic datasets from France, Europe, and a few beyond, allowing users to query and explore along space and time dimensions.|高质量的数据对于公众辩论至关重要。高质量的统计数据源为验证声明提供了宝贵的参考信息。为了协助记者和事实核查人员，应能够自动使用统计表格回答用户关于特定声明的查询。然而，这些来源的数量和多样性使得这项任务颇具挑战性。我们提出了STaR，一种基于用户自然语言查询的空间和时间感知统计检索新方法。STaR被部署在我们的系统StatCheck中，该系统是我们开发并与事实核查记者共享的。STaR通过将空间和时间与统计数据集的其他部分分开处理，提高了统计事实检索的质量。具体而言，我们将它们用作数据的维度（以及查询的维度），并将数据集中搜索的语言部分聚焦于数据中丰富多样的语言。我们的演示使用了来自法国、欧洲及其它几个地区的统计数据集，允许用户沿空间和时间维度进行查询和探索。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=STaR:+Space+and+Time-aware+Statistic+Query+Answering)|0| |[FairRankTune: A Python Toolkit for Fair Ranking Tasks](https://doi.org/10.1145/3627673.3679238)|Kathleen Cachel, Elke A. Rundensteiner|Worcester Polytechnic Institute, Worcester, MA, USA|We present FairRankTune, a multi-purpose open-source Python toolkit offering three primary services: quantifying fairness-related harms, leveraging bias mitigation algorithms, and constructing custom fairness-relevant datasets. FairRankTune provides researchers and practitioners with a self-contained resource for fairness auditing, experimentation, and advancing research. The central piece of FairRankTune is a novel fairness-tunable ranked data generator, RankTune, that streamlines the creation of custom fairness-relevant ranked datasets. FairRankTune also offers numerous fair ranking metrics and fairness-aware ranking algorithms within the same plug-and-play package. We demonstrate the key innovations of FairRankTune, focusing on features that are valuable to stakeholders via use cases highlighting workflows in the end-to-end process of mitigating bias in ranking systems. FairRankTune addresses the gap of limited publicly available datasets, auditing tools, and implementations for fair ranking.|我们介绍了FairRankTune，这是一个多用途的开源Python工具包，提供三项主要服务：量化与公平性相关的损害、运用偏差缓解算法以及构建自定义的与公平性相关的数据集。FairRankTune为研究人员和实践者提供了一个自包含的资源，用于公平性审计、实验和推动研究进展。该工具包的核心是一个新颖的可调公平性的排名数据生成器RankTune，它简化了自定义公平相关排名数据集的创建过程。FairRankTune还在同一即插即用包中提供了众多公平排名指标和公平意识排名算法。我们展示了FairRankTune的关键创新，重点介绍了通过端到端流程中的用例突出显示的工作流，这些工作流对利益相关者具有重要价值，特别是在缓解排名系统中的偏差方面。FairRankTune解决了公开可用数据集、审计工具和公平排名实现有限的缺口问题。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=FairRankTune:+A+Python+Toolkit+for+Fair+Ranking+Tasks)|0| |[LLM-PQA: LLM-enhanced Prediction Query Answering](https://doi.org/10.1145/3627673.3679210)|Ziyu Li, Wenjie Zhao, Asterios Katsifodimos, Rihan Hai||The advent of Large Language Models (LLMs) provides an opportunity to change the way queries are processed, moving beyond the constraints of conventional SQL-based database systems. However, using an LLM to answer a prediction query is still challenging, since an external ML model has to be employed and inference has to be performed in order to provide an answer. This paper introduces LLM-PQA, a novel tool that addresses prediction queries formulated in natural language. LLM-PQA is the first to combine the capabilities of LLMs and retrieval-augmented mechanism for the needs of prediction queries by integrating data lakes and model zoos. This integration provides users with access to a vast spectrum of heterogeneous data and diverse ML models, facilitating dynamic prediction query answering. In addition, LLM-PQA can dynamically train models on demand, based on specific query requirements, ensuring reliable and relevant results even when no pre-trained model in a model zoo, available for the task.|大规模语言模型（LLMs）的出现为我们提供了一个改变查询处理方式的契机，使其超越了传统基于SQL的数据库系统的限制。然而，使用LLM来回答预测性查询仍然具有挑战性，因为需要借助外部机器学习模型并进行推理才能提供答案。本文介绍了LLM-PQA，这是一种新颖的工具，专门用于处理以自然语言表达的预测性查询。LLM-PQA首次将LLM的能力与检索增强机制相结合，以满足预测查询的需求，通过整合数据湖和模型库来实现这一目标。这种整合使用户能够访问广泛且异构的数据集以及多样化的机器学习模型，从而促进动态预测查询的解答。此外，LLM-PQA能够根据特定查询需求动态训练模型，即使在模型库中没有适用于该任务的预训练模型时，也能确保结果的可靠性和相关性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=LLM-PQA:+LLM-enhanced+Prediction+Query+Answering)|0| |[Unified Argument Retrieval System from German News Articles Using Large Language Models](https://doi.org/10.1145/3627673.3679232)|Piriyakorn Piriyatamwong, Saikishore Kalloori, Fabio Zünd|ETH Zürich, Zürich, Switzerland|The rapid growth in the number of news articles published daily can create challenges for users to explore specific topics and gather different perspectives around the topics to make neutral and unbiased conclusions. The system's ability to intelligently cluster news articles from multiple sources and retrieve concise (pro/con) relevant arguments is necessary for users' well-informed decision-making. In this paper, we introduce our unified argument retrieval system that uses our clustering model to cluster news articles and subsequently extracts the core arguments from news articles using the argument prediction model. We conducted a user study to understand the system's usability and users' satisfaction with the quality of clusters and arguments extracted.|随着每日发布的新闻文章数量迅速增长，用户在探索特定话题并收集不同观点以做出中立和无偏见的结论方面面临挑战。系统能够智能地从多个来源聚类新闻文章并检索简明的（支持/反对）相关论点，这对于用户做出明智决策是必要的。在本文中，我们介绍了一个统一的论点检索系统，该系统使用我们的聚类模型对新闻文章进行聚类，并随后使用论点预测模型从新闻文章中提取核心论点。我们进行了一项用户研究，以了解系统的可用性以及用户对聚类和提取论点质量的满意度。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Unified+Argument+Retrieval+System+from+German+News+Articles+Using+Large+Language+Models)|0| -|[Empowering Shoppers with Event-focused Search](https://doi.org/10.1145/3627673.3679235)|Austin R. Ward, Omar Alonso|Amazon, Seattle, WA, USA; Amazon, Palo Alto, CA, USA|We present Event-focused Search, an automated and scalable pipeline designed to facilitate event discovery and enhance event-based search. This is done by leveraging large language models (LLMs) to populate event datasets, perform temporal search based on selected dates, and aggregate search results based on appropriate events based on those searches. We illustrate this pipeline through proof-of-concept interfaces in an e-commerce context, though such a framework is applicable to different types of search scenarios (e.g., sports, entertainment).|我们提出了事件聚焦搜索（Event-focused Search），这是一个自动化且可扩展的流程，旨在促进事件发现并增强基于事件的搜索。通过利用大型语言模型（LLMs）来填充事件数据集，基于选定日期执行时间搜索，并根据这些搜索结果聚合相关事件的搜索结果，我们实现了这一目标。我们通过在电子商务环境中展示概念验证界面来说明这一流程，尽管该框架同样适用于不同类型搜索场景（例如，体育、娱乐）。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Empowering+Shoppers+with+Event-focused+Search)|0| -|[Multi-turn Classroom Dialogue Dataset: Assessing Student Performance from One-on-one Conversations](https://doi.org/10.1145/3627673.3679108)|Jiahao Chen, Zitao Liu, Mingliang Hou, Xiangyu Zhao, Weiqi Luo|School of Data Science, City University of Hong Kong, Hong Kong, China; TAL Education Group, Beijing, China; Guangdong Institute of Smart Education, Jinan University, Guangzhou, China|Accurately judging student on-going performance is crucial for adaptive teaching. In this work, we focus on the task of automatically predicting students' levels of mastery of math questions from teacher-student classroom dialogue data in online one-on-one classes. As a step toward this direction, we introduce the Multi-turn Classroom Dialogue (MCD) dataset as a benchmark testing the capabilities of machine learning models in classroom conversation understanding of student performance judgment. Our dataset contains aligned multi-turn spoken language of 5000+ unique samples of solving grade-8 math questions collected from 500+ hours' worth of online one-on-one tutoring classes. In our experiments, we assess various state-of-the-art models on the MCD dataset, highlighting the importance of understanding multi-turn dialogues and handling noisy ASR transcriptions. Our findings demonstrate the dataset's utility in advancing research on automated student performance assessment. To encourage reproducible research, we make our data publicly available at https://github.com/ai4ed/MCD.|准确判断学生的实时表现对于适应性教学至关重要。本研究聚焦于从在线一对一课堂中的师生对话数据中自动预测学生对数学问题的掌握程度。为此，我们引入了多轮课堂对话（Multi-turn Classroom Dialogue, MCD）数据集，作为评估机器学习模型在课堂对话理解与学生表现判断能力方面的基准。该数据集包含了5000多个独特样本的多轮口语对话，这些样本来自500多小时的在线一对一辅导课程，内容涉及八年级数学问题的解答。在实验中，我们评估了多种最先进的模型在MCD数据集上的表现，强调了理解多轮对话和处理噪声ASR（自动语音识别）转录文本的重要性。研究结果表明，该数据集在推动自动化学生表现评估研究方面具有重要价值。为促进可重复研究，我们公开了数据集，访问地址为：https://github.com/ai4ed/MCD。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Multi-turn+Classroom+Dialogue+Dataset:+Assessing+Student+Performance+from+One-on-one+Conversations)|0| +|[Empowering Shoppers with Event-focused Search](https://doi.org/10.1145/3627673.3679235)|Austin R. Ward, Omar Alonso|Amazon, Palo Alto, CA, USA; Amazon, Seattle, WA, USA|We present Event-focused Search, an automated and scalable pipeline designed to facilitate event discovery and enhance event-based search. This is done by leveraging large language models (LLMs) to populate event datasets, perform temporal search based on selected dates, and aggregate search results based on appropriate events based on those searches. We illustrate this pipeline through proof-of-concept interfaces in an e-commerce context, though such a framework is applicable to different types of search scenarios (e.g., sports, entertainment).|我们提出了事件聚焦搜索（Event-focused Search），这是一个自动化且可扩展的流程，旨在促进事件发现并增强基于事件的搜索。通过利用大型语言模型（LLMs）来填充事件数据集，基于选定日期执行时间搜索，并根据这些搜索结果聚合相关事件的搜索结果，我们实现了这一目标。我们通过在电子商务环境中展示概念验证界面来说明这一流程，尽管该框架同样适用于不同类型搜索场景（例如，体育、娱乐）。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Empowering+Shoppers+with+Event-focused+Search)|0| +|[Multi-turn Classroom Dialogue Dataset: Assessing Student Performance from One-on-one Conversations](https://doi.org/10.1145/3627673.3679108)|Jiahao Chen, Zitao Liu, Mingliang Hou, Xiangyu Zhao, Weiqi Luo|Guangdong Institute of Smart Education, Jinan University, Guangzhou, China; TAL Education Group, Beijing, China; School of Data Science, City University of Hong Kong, Hong Kong, China|Accurately judging student on-going performance is crucial for adaptive teaching. In this work, we focus on the task of automatically predicting students' levels of mastery of math questions from teacher-student classroom dialogue data in online one-on-one classes. As a step toward this direction, we introduce the Multi-turn Classroom Dialogue (MCD) dataset as a benchmark testing the capabilities of machine learning models in classroom conversation understanding of student performance judgment. Our dataset contains aligned multi-turn spoken language of 5000+ unique samples of solving grade-8 math questions collected from 500+ hours' worth of online one-on-one tutoring classes. In our experiments, we assess various state-of-the-art models on the MCD dataset, highlighting the importance of understanding multi-turn dialogues and handling noisy ASR transcriptions. Our findings demonstrate the dataset's utility in advancing research on automated student performance assessment. To encourage reproducible research, we make our data publicly available at https://github.com/ai4ed/MCD.|准确判断学生的实时表现对于适应性教学至关重要。本研究聚焦于从在线一对一课堂中的师生对话数据中自动预测学生对数学问题的掌握程度。为此，我们引入了多轮课堂对话（Multi-turn Classroom Dialogue, MCD）数据集，作为评估机器学习模型在课堂对话理解与学生表现判断能力方面的基准。该数据集包含了5000多个独特样本的多轮口语对话，这些样本来自500多小时的在线一对一辅导课程，内容涉及八年级数学问题的解答。在实验中，我们评估了多种最先进的模型在MCD数据集上的表现，强调了理解多轮对话和处理噪声ASR（自动语音识别）转录文本的重要性。研究结果表明，该数据集在推动自动化学生表现评估研究方面具有重要价值。为促进可重复研究，我们公开了数据集，访问地址为：https://github.com/ai4ed/MCD。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Multi-turn+Classroom+Dialogue+Dataset:+Assessing+Student+Performance+from+One-on-one+Conversations)|0| |[An Evaluation Framework for Attributed Information Retrieval using Large Language Models](https://doi.org/10.1145/3627673.3679172)|Hanane Djeddal, Pierre Erbacher, Raouf Toukal, Laure Soulier, Karen PinelSauvagnat, Sophia Katrenko, Lynda Tamine||With the growing success of Large Language models (LLMs) in information-seeking scenarios, search engines are now adopting generative approaches to provide answers along with in-line citations as attribution. While existing work focuses mainly on attributed question answering, in this paper, we target information-seeking scenarios which are often more challenging due to the open-ended nature of the queries and the size of the label space in terms of the diversity of candidate-attributed answers per query. We propose a reproducible framework to evaluate and benchmark attributed information seeking, using any backbone LLM, and different architectural designs: (1) Generate (2) Retrieve then Generate, and (3) Generate then Retrieve. Experiments using HAGRID, an attributed information-seeking dataset, show the impact of different scenarios on both the correctness and attributability of answers.|随着大型语言模型（LLMs）在信息检索场景中的应用日益成功，搜索引擎正采用生成式方法来提供带有内联引用作为归属的答案。尽管现有研究主要集中在归属问答上，本文则针对更具挑战性的信息检索场景，这类场景由于查询的开放性及每个查询对应的候选归属答案多样性导致的标签空间庞大而显得尤为复杂。我们提出了一种可复现的框架，用于评估和基准测试归属信息检索，使用任何骨干LLM，并结合不同的架构设计：（1）生成式（2）检索后生成，以及（3）生成后检索。通过使用HAGRID这一归属信息检索数据集进行的实验，展示了不同场景对答案正确性和归属性的影响。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=An+Evaluation+Framework+for+Attributed+Information+Retrieval+using+Large+Language+Models)|0| |[AnnoRank: A Comprehensive Web-Based Framework for Collecting Annotations and Assessing Rankings](https://doi.org/10.1145/3627673.3679174)|Clara Rus, Gabrielle Poerwawinata, Andrew Yates, Maarten de Rijke|University of Amsterdam, Amsterdam, Netherlands|We present AnnoRank, a web-based user interface (UI) framework designed to facilitate collecting crowdsource annotations in the context of information retrieval. AnnoRank enables the collection of explicit and implicit annotations for a specified query and a single or multiple documents, allowing for the observation of user-selected items and the assignment of relevance judgments. Furthermore, AnnoRank allows for ranking comparisons, allowing for the visualization and evaluation of a ranked list generated by different fairness interventions, along with its utility and fairness metrics. Fairness interventions in the annotation pipeline are necessary to prevent the propagation of bias when a user selects the top-k items in a ranked list. With the widespread use of ranking systems, the application supports multimodality through text and image document formats. We also support the assessment of agreement between annotators to ensure the quality of the annotations. AnnoRank is integrated with the Ranklib library, offering a vast range of ranking models that can be applied to the data and displayed in the UI. AnnoRank is designed to be flexible, configurable, and easy to deploy to meet diverse annotation needs in information retrieval. AnnoRank is publicly available as open-source software, together with detailed documentation at https://github.com/ClaraRus/AnnoRank.|我们提出了AnnoRank，这是一个基于网络的用户界面（UI）框架，旨在促进在信息检索背景下收集众包标注。AnnoRank能够收集针对指定查询和一个或多个文档的显式和隐式标注，使用户能够观察到用户选择的项目并进行相关性判断。此外，AnnoRank支持排序比较，允许可视化和评估由不同公平性干预措施生成的排序列表，以及其效用和公平性指标。在标注流程中实施公平性干预是必要的，以防止用户在选择排序列表中的前k个项目时偏差的传播。随着排序系统的广泛应用，该系统通过文本和图像文档格式支持多模态。我们还支持评估标注者之间的一致性，以确保标注的质量。AnnoRank与Ranklib库集成，提供了一系列广泛的排序模型，这些模型可以应用于数据并在UI中展示。AnnoRank设计灵活、可配置，并且易于部署，以满足信息检索中多样化的标注需求。AnnoRank作为开源软件公开发布，详细文档可在https://github.com/ClaraRus/AnnoRank获取。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=AnnoRank:+A+Comprehensive+Web-Based+Framework+for+Collecting+Annotations+and+Assessing+Rankings)|0| |[Advancing Misinformation Awareness in Recommender Systems for Social Media Information Integrity](https://doi.org/10.1145/3627673.3680259)|Royal Pathak|Boise State University, Boise, ID, USA|Recommender systems play an essential role in determining the content users encounter on social media platforms and in uncovering relevant news. However, they also present significant risks, such as reinforcing biases, over-personalizing content, fostering filter bubbles, and inadvertently promoting misinformation. The spread of false information is rampant across various online platforms, such as Twitter (now X), Meta, and TikTok, especially noticeable during events like the COVID-19 pandemic and the US Presidential elections. These instances underscore the critical necessity for transparency and regulatory oversight in the development of recommender systems. Given the challenge of balancing free speech with the risks of outright removal of fake news, this paper aims to address the spread of misinformation from algorithmic biases in recommender systems using a social science perspective.|推荐系统在决定用户在社交媒体平台上接触的内容以及揭示相关新闻方面发挥着至关重要的作用。然而，它们也带来了重大风险，例如强化偏见、过度个性化内容、助长信息茧房以及无意中推广虚假信息。虚假信息的传播在各种在线平台上十分猖獗，如Twitter（现为X）、Meta和TikTok，尤其在COVID-19疫情和美国大选等事件期间更为明显。这些情况凸显了在推荐系统开发中透明度和监管监督的迫切需要。鉴于在平衡言论自由与彻底删除虚假新闻的风险方面的挑战，本文旨在从社会科学的角度探讨推荐系统中的算法偏差如何导致虚假信息的传播。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Advancing+Misinformation+Awareness+in+Recommender+Systems+for+Social+Media+Information+Integrity)|0| |[Multi-Granularity Modeling in Recommendation: from the Multi-Scenario Perspective](https://doi.org/10.1145/3627673.3680264)|Yuhao Wang|City University of Hong Kong, Hong Kong, Hong Kong|In today's digital landscape, Deep Recommender Systems (DRS) play a crucial role in navigating and customizing online content for individual preferences. However, conventional methods, which mainly depend on single recommendation task, scenario, data modality and user behavior, are increasingly seen as insufficient due to their inability to accurately reflect users' complex and changing preferences. This gap underscores the need for multi-granularity modeling, which are central to overcoming these limitations by integrating diverse tasks, scenarios, modalities, and behaviors in the recommendation process, thus promising significant enhancements in recommendation precision, efficiency, and customization. In this paper, from the multi-scenario perspective, we illustrate our existing explorations and present results. Ultimately, we wish to highlight our multi-granularity approach sheds light on building the next generation of recommender system1 .|在当今的数字环境中，深度推荐系统（DRS）在根据个人偏好导航和定制在线内容方面发挥着关键作用。然而，传统方法主要依赖于单一推荐任务、场景、数据模态和用户行为，由于无法准确反映用户复杂且多变的偏好，这些方法的局限性日益凸显。这一差距突显了多粒度建模的必要性，该方法通过在推荐过程中整合多种任务、场景、模态和行为，有望显著提升推荐的准确性、效率和个性化水平。本文从多场景的角度，展示了我们现有的探索成果，并介绍了相关结果。最终，我们希望强调我们的多粒度方法为构建下一代推荐系统提供了重要启示。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Multi-Granularity+Modeling+in+Recommendation:+from+the+Multi-Scenario+Perspective)|0| -|[Unifying Spectral and Spatial Graph Neural Networks](https://doi.org/10.1145/3627673.3679088)|Zhiqian Chen, Lei Zhang, Liang Zhao|Mississippi State University, Starkville, MS, USA; Emory University, Atlanta, GA, USA; Northern Illinois University, Dekalb, IL, USA|In recent years, Graph Neural Networks (GNNs) have attracted considerable attention. However, the rapid emergence of diverse GNN models, each grounded in different theoretical foundations, complicates the model selection process, as these models are not easily understood within a unified framework. Initial GNNs were constructed using spectral theory, while others were developed based on spatial theory. This theoretical divergence makes direct comparisons difficult. Furthermore, the variety of models within each theoretical domain further complicates their evaluation. In this tutorial, we explore state-of-the-art GNNs and present a comprehensive framework that bridges the spatial and spectral domains, clarifying their interrelationship. This framework deepens our understanding of GNN operations. The tutorial delves into key paradigms, such as spatial and spectral methods, through a synthesis of spectral graph theory and approximation theory. We conduct an in-depth analysis of recent research advancements, addressing emerging issues like over-smoothing, using well-established GNN models to illustrate the universality of our framework.|近年来，图神经网络（GNNs）引起了广泛关注。然而，由于不同理论基础支撑的多样化GNN模型的快速涌现，模型选择过程变得复杂，因为这些模型难以在一个统一的框架内被理解。最初的GNN模型是基于谱理论构建的，而其他模型则基于空间理论发展。这种理论上的分歧使得直接比较变得困难。此外，每个理论领域内模型的多样性进一步增加了评估的复杂性。在本教程中，我们探讨了最先进的GNN，并提出了一个综合框架，该框架连接了空间和谱域，阐明了它们之间的相互关系。这一框架加深了我们对GNN操作的理解。教程深入探讨了关键范式，如空间和谱方法，通过结合谱图理论和近似理论的合成来进行分析。我们对最近的研究进展进行了深入分析，并使用成熟的GNN模型来解决新兴问题，如过平滑问题，以说明我们框架的普遍性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Unifying+Spectral+and+Spatial+Graph+Neural+Networks)|0| -|[Tutorial on Landing Generative AI in Industrial Social and E-commerce Recsys](https://doi.org/10.1145/3627673.3679099)|Da Xu, Danqing Zhang, Lingling Zheng, Bo Yang, Guangyu Yang, Shuyuan Xu, Cindy Liang|TikTok, Santa Clara, California, USA; Amazon, Palo Alto, California, USA; Microsoft, Redmond, Washington, USA; TikTok, San Jose, California, USA; LinkedIn, Sunnyvale, California, USA|Over the past two years, GAI has evolved rapidly, influencing various fields including social and e-commerce Recsys. Despite exciting advances, landing these innovations in real-world Recsys remains challenging due to the sophistication of modern industrial product and systems. Our tutorial begins with a brief overview of building industrial Recsys and GAI fundamentals, followed by the ongoing efforts and opportunities to enhance personalized recommendations with foundation models. We then explore the integration of curation capabilities into Recsys, such as repurposing raw content, incorporating external knowledge, and generating personalized insights/explanations to foster transparency and trust. Next, the tutorial illustrates how AI agents can transform Recsys through interactive reasoning and action loops, shifting away from traditional passive feedback models. Finally, we shed insights on real-world solutions for human-AI alignment and responsible GAI practices. A critical component of the tutorial is detailing the AI, Infrastructure, LLMOps, and Product roadmap (including the evaluation and responsible AI practices) derived from the production solutions in LinkedIn, Amazon, TikTok, and Microsoft. While GAI in Recsys is still in its early stages, this tutorial provides valuable insights and practical solutions for the Recsys and GAI communities.|在过去两年中，生成式人工智能（GAI）迅速发展，影响了包括社交和电子商务推荐系统（Recsys）在内的多个领域。尽管取得了令人振奋的进展，但将这些创新应用于实际的推荐系统仍然面临挑战，这主要是由于现代工业产品和系统的复杂性。我们的教程首先简要概述了构建工业级推荐系统的基础知识以及生成式人工智能的基本原理，随后探讨了利用基础模型提升个性化推荐的努力和机遇。接着，我们探讨了如何将内容策划能力整合到推荐系统中，例如重新利用原始内容、整合外部知识，以及生成个性化的见解和解释，以促进透明度和用户信任。然后，教程展示了人工智能代理如何通过交互式推理和行动循环来转变推荐系统，从而摆脱传统的被动反馈模式。最后，我们分享了关于人机协作和负责任的生成式人工智能实践的实际解决方案。教程的一个重要部分是详细介绍了从LinkedIn、Amazon、TikTok和Microsoft的生产解决方案中提炼出的AI、基础设施、LLMOps和产品路线图（包括评估和负责任的AI实践）。尽管生成式人工智能在推荐系统中的应用仍处于早期阶段，但本教程为推荐系统和生成式人工智能社区提供了宝贵的见解和实用的解决方案。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Tutorial+on+Landing+Generative+AI+in+Industrial+Social+and+E-commerce+Recsys)|0| +|[Unifying Spectral and Spatial Graph Neural Networks](https://doi.org/10.1145/3627673.3679088)|Zhiqian Chen, Lei Zhang, Liang Zhao|Emory University, Atlanta, GA, USA; Mississippi State University, Starkville, MS, USA; Northern Illinois University, Dekalb, IL, USA|In recent years, Graph Neural Networks (GNNs) have attracted considerable attention. However, the rapid emergence of diverse GNN models, each grounded in different theoretical foundations, complicates the model selection process, as these models are not easily understood within a unified framework. Initial GNNs were constructed using spectral theory, while others were developed based on spatial theory. This theoretical divergence makes direct comparisons difficult. Furthermore, the variety of models within each theoretical domain further complicates their evaluation. In this tutorial, we explore state-of-the-art GNNs and present a comprehensive framework that bridges the spatial and spectral domains, clarifying their interrelationship. This framework deepens our understanding of GNN operations. The tutorial delves into key paradigms, such as spatial and spectral methods, through a synthesis of spectral graph theory and approximation theory. We conduct an in-depth analysis of recent research advancements, addressing emerging issues like over-smoothing, using well-established GNN models to illustrate the universality of our framework.|近年来，图神经网络（GNNs）引起了广泛关注。然而，由于不同理论基础支撑的多样化GNN模型的快速涌现，模型选择过程变得复杂，因为这些模型难以在一个统一的框架内被理解。最初的GNN模型是基于谱理论构建的，而其他模型则基于空间理论发展。这种理论上的分歧使得直接比较变得困难。此外，每个理论领域内模型的多样性进一步增加了评估的复杂性。在本教程中，我们探讨了最先进的GNN，并提出了一个综合框架，该框架连接了空间和谱域，阐明了它们之间的相互关系。这一框架加深了我们对GNN操作的理解。教程深入探讨了关键范式，如空间和谱方法，通过结合谱图理论和近似理论的合成来进行分析。我们对最近的研究进展进行了深入分析，并使用成熟的GNN模型来解决新兴问题，如过平滑问题，以说明我们框架的普遍性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Unifying+Spectral+and+Spatial+Graph+Neural+Networks)|0| +|[Tutorial on Landing Generative AI in Industrial Social and E-commerce Recsys](https://doi.org/10.1145/3627673.3679099)|Da Xu, Danqing Zhang, Lingling Zheng, Bo Yang, Guangyu Yang, Shuyuan Xu, Cindy Liang|LinkedIn, Sunnyvale, California, USA; TikTok, San Jose, California, USA; Microsoft, Redmond, Washington, USA; TikTok, Santa Clara, California, USA; Amazon, Palo Alto, California, USA|Over the past two years, GAI has evolved rapidly, influencing various fields including social and e-commerce Recsys. Despite exciting advances, landing these innovations in real-world Recsys remains challenging due to the sophistication of modern industrial product and systems. Our tutorial begins with a brief overview of building industrial Recsys and GAI fundamentals, followed by the ongoing efforts and opportunities to enhance personalized recommendations with foundation models. We then explore the integration of curation capabilities into Recsys, such as repurposing raw content, incorporating external knowledge, and generating personalized insights/explanations to foster transparency and trust. Next, the tutorial illustrates how AI agents can transform Recsys through interactive reasoning and action loops, shifting away from traditional passive feedback models. Finally, we shed insights on real-world solutions for human-AI alignment and responsible GAI practices. A critical component of the tutorial is detailing the AI, Infrastructure, LLMOps, and Product roadmap (including the evaluation and responsible AI practices) derived from the production solutions in LinkedIn, Amazon, TikTok, and Microsoft. While GAI in Recsys is still in its early stages, this tutorial provides valuable insights and practical solutions for the Recsys and GAI communities.|在过去两年中，生成式人工智能（GAI）迅速发展，影响了包括社交和电子商务推荐系统（Recsys）在内的多个领域。尽管取得了令人振奋的进展，但将这些创新应用于实际的推荐系统仍然面临挑战，这主要是由于现代工业产品和系统的复杂性。我们的教程首先简要概述了构建工业级推荐系统的基础知识以及生成式人工智能的基本原理，随后探讨了利用基础模型提升个性化推荐的努力和机遇。接着，我们探讨了如何将内容策划能力整合到推荐系统中，例如重新利用原始内容、整合外部知识，以及生成个性化的见解和解释，以促进透明度和用户信任。然后，教程展示了人工智能代理如何通过交互式推理和行动循环来转变推荐系统，从而摆脱传统的被动反馈模式。最后，我们分享了关于人机协作和负责任的生成式人工智能实践的实际解决方案。教程的一个重要部分是详细介绍了从LinkedIn、Amazon、TikTok和Microsoft的生产解决方案中提炼出的AI、基础设施、LLMOps和产品路线图（包括评估和负责任的AI实践）。尽管生成式人工智能在推荐系统中的应用仍处于早期阶段，但本教程为推荐系统和生成式人工智能社区提供了宝贵的见解和实用的解决方案。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Tutorial+on+Landing+Generative+AI+in+Industrial+Social+and+E-commerce+Recsys)|0| |[Reviewerly: Modeling the Reviewer Assignment Task as an Information Retrieval Problem](https://doi.org/10.1145/3627673.3679081)|Negar Arabzadeh, Sajad Ebrahimi, Sara Salamat, Mahdi Bashari, Ebrahim Bagheri|Reviewerly, Guelph, ON, Canada; Reviewerly, Toronto, ON, Canada|The peer review process is a fundamental aspect of academic publishing, ensuring the quality and credibility of scholarly work. In this talk, we will explore the critical challenges associated specifically with the assignment of reviewers to submitted papers. We will introduce Reviewerly, our innovative solution designed to enhance the efficiency and effectiveness of reviewer assignments by leveraging data from diverse sources, including OpenAlex, PubMed, and DBLP. By modeling the reviewer assignment problem as an information retrieval task, we focus on retrieving a pool of relevant and diverse reviewers for each paper.|同行评审过程是学术出版的一个基本环节，确保学术工作的质量和可信度。在本次演讲中，我们将探讨与提交论文的审稿人分配相关的关键挑战。我们将介绍Reviewerly，这是我们的一项创新解决方案，旨在通过利用来自OpenAlex、PubMed和DBLP等多种来源的数据，提高审稿人分配的效率和效果。通过将审稿人分配问题建模为信息检索任务，我们专注于为每篇论文检索一组相关且多样化的审稿人。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Reviewerly:+Modeling+the+Reviewer+Assignment+Task+as+an+Information+Retrieval+Problem)|0| -|[AI-safe Autocompletion with RAG and Relevance Curation](https://doi.org/10.1145/3627673.3679078)|Kilian Merkelbach, Ksenia Riabinova, Arnab Dutta|eBay Inc., Dreilinden, Germany; eBay Inc., Aachen, Germany|In search, autocomplete (AC) is an essential tool that provides suggestions for each keystroke, functioning well with token-based queries. However, it is challenging to handle at scale when input queries are conversational and semantically rich. Identifying relevant queries for sub-tokens requires efficient lookup strategies, real-time ranking, and relevance in the results. This work integrates Retrieval-Augmented Generation (RAG), AI safety, and relevance ranking to produce autocomplete suggestions for conversational queries in a production system. RAG-based responses ensure a high hit ratio for popular AC inputs and maintain a very low risk category by not triggering any critical AI safety concerns.|在搜索领域，自动补全（Autocomplete，简称AC）是一项关键工具，它能够针对每个按键提供建议，与基于词汇的查询配合良好。然而，当输入的查询是会话式的且语义丰富时，大规模处理这些查询变得极具挑战性。为子词汇识别相关查询需要高效的查找策略、实时排序以及结果的相关性。本研究将检索增强生成（Retrieval-Augmented Generation，简称RAG）、人工智能安全性和相关性排序相结合，以在生产系统中为会话式查询生成自动补全建议。基于RAG的响应确保了对常见AC输入的高命中比率，并通过不触发任何关键的人工智能安全问题，保持了极低的风险类别。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=AI-safe+Autocompletion+with+RAG+and+Relevance+Curation)|0| -|[Towards Real-Time and Personalized Code Generation](https://doi.org/10.1145/3627673.3679071)|Han Xu, Xingyuan Wang, Haipeng Chen|William & Mary, Williamsburg, VA, USA; Meta Platforms Inc., Seattle, WA, USA; University of Illinois Urbana-Champaign, Urbana, IL, USA|Large language models (LLMs) have transformed automated code generation. However, their high computational demands often lead to server overload and increased latency in SaaS deployments. To address this, we present SpeCoder, a framework that accelerates server-side code generation using speculative sampling (SpS) and supervised fine-tuning (SFT). SpS allows lower latency in the code generation, whereas SFT enables more personalized code generation tailored to the user's needs.|大型语言模型（LLMs）已经彻底改变了自动化代码生成的领域。然而，其高计算需求常常导致服务器过载，并在软件即服务（SaaS）部署中增加了延迟。为此，我们提出了SpeCoder框架，该框架通过使用推测采样（SpS）和监督微调（SFT）来加速服务器端代码生成。SpS降低了代码生成的延迟，而SFT则使代码生成更加个性化，以满足用户的特定需求。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Towards+Real-Time+and+Personalized+Code+Generation)|0| +|[AI-safe Autocompletion with RAG and Relevance Curation](https://doi.org/10.1145/3627673.3679078)|Kilian Merkelbach, Ksenia Riabinova, Arnab Dutta|eBay Inc., Aachen, Germany; eBay Inc., Dreilinden, Germany|In search, autocomplete (AC) is an essential tool that provides suggestions for each keystroke, functioning well with token-based queries. However, it is challenging to handle at scale when input queries are conversational and semantically rich. Identifying relevant queries for sub-tokens requires efficient lookup strategies, real-time ranking, and relevance in the results. This work integrates Retrieval-Augmented Generation (RAG), AI safety, and relevance ranking to produce autocomplete suggestions for conversational queries in a production system. RAG-based responses ensure a high hit ratio for popular AC inputs and maintain a very low risk category by not triggering any critical AI safety concerns.|在搜索领域，自动补全（Autocomplete，简称AC）是一项关键工具，它能够针对每个按键提供建议，与基于词汇的查询配合良好。然而，当输入的查询是会话式的且语义丰富时，大规模处理这些查询变得极具挑战性。为子词汇识别相关查询需要高效的查找策略、实时排序以及结果的相关性。本研究将检索增强生成（Retrieval-Augmented Generation，简称RAG）、人工智能安全性和相关性排序相结合，以在生产系统中为会话式查询生成自动补全建议。基于RAG的响应确保了对常见AC输入的高命中比率，并通过不触发任何关键的人工智能安全问题，保持了极低的风险类别。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=AI-safe+Autocompletion+with+RAG+and+Relevance+Curation)|0| +|[Towards Real-Time and Personalized Code Generation](https://doi.org/10.1145/3627673.3679071)|Han Xu, Xingyuan Wang, Haipeng Chen|University of Illinois Urbana-Champaign, Urbana, IL, USA; William & Mary, Williamsburg, VA, USA; Meta Platforms Inc., Seattle, WA, USA|Large language models (LLMs) have transformed automated code generation. However, their high computational demands often lead to server overload and increased latency in SaaS deployments. To address this, we present SpeCoder, a framework that accelerates server-side code generation using speculative sampling (SpS) and supervised fine-tuning (SFT). SpS allows lower latency in the code generation, whereas SFT enables more personalized code generation tailored to the user's needs.|大型语言模型（LLMs）已经彻底改变了自动化代码生成的领域。然而，其高计算需求常常导致服务器过载，并在软件即服务（SaaS）部署中增加了延迟。为此，我们提出了SpeCoder框架，该框架通过使用推测采样（SpS）和监督微调（SFT）来加速服务器端代码生成。SpS降低了代码生成的延迟，而SFT则使代码生成更加个性化，以满足用户的特定需求。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Towards+Real-Time+and+Personalized+Code+Generation)|0| |[Advertiser Content Understanding via LLMs for Google Ads Safety](https://doi.org/10.1145/3627673.3679077)|Joseph Wallace, Tushar Dogra, Wei Qiao, Yuan Wang||Ads Content Safety at Google requires classifying billions of ads for Google Ads content policies. Consistent and accurate policy enforcement is important for advertiser experience and user safety and it is a challenging problem, so there is a lot of value for improving it for advertisers and users. Inconsistent policy enforcement causes increased policy friction and poor experience with good advertisers, and bad advertisers exploit the inconsistency by creating multiple similar ads in the hope that some will get through our defenses. This study proposes a method to understand advertiser's intent for content policy violations, using Large Language Models (LLMs). We focus on identifying good advertisers to reduce content over-flagging and improve advertiser experience, though the approach can easily be extended to classify bad advertisers too. We generate advertiser's content profile based on multiple signals from their ads, domains, targeting info, etc. We then use LLMs to classify the advertiser content profile, along with relying on any knowledge the LLM has of the advertiser, their products or brand, to understand whether they are likely to violate a certain policy or not. After minimal prompt tuning our method was able to reach 95% accuracy on a small test set.|谷歌的广告内容安全工作要求对数十亿条广告进行分类，以符合谷歌广告的内容政策。一致且准确的政策执行对于广告主体验和用户安全至关重要，这也是一个具有挑战性的问题，因此改进这一工作对广告主和用户都有很大价值。政策执行的不一致会导致政策摩擦增加，并对遵守规则的广告主带来不良体验，而违规广告主则利用这种不一致性，通过创建多个相似广告，寄希望于其中部分广告能够绕过我们的防御机制。本研究提出了一种利用大型语言模型（LLMs）来理解广告主内容政策违规意图的方法。我们专注于识别遵守规则的广告主，以减少内容过度标记并提升广告主体验，尽管该方法同样可以轻松扩展用于分类违规广告主。我们基于广告主的广告、域名、定向信息等多个信号生成其内容画像，然后利用LLMs对广告主内容画像进行分类，并结合LLMs对广告主、其产品或品牌的已有知识，判断其是否可能违反特定政策。在经过最小化的提示调优后，我们的方法在小型测试集上达到了95%的准确率。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Advertiser+Content+Understanding+via+LLMs+for+Google+Ads+Safety)|0| -|[Generative AI and Retrieval-Augmented Generation (RAG) Systems for Enterprise](https://doi.org/10.1145/3627673.3680117)|Anbang Xu, Tan Yu, Min Du, Pritam Gundecha, Yufan Guo, Xinliang Zhu, May Wang, Ping Li, Xinyun Chen|NVIDIA, Santa Clara, California, USA; Amazon, Seattle, Washington, USA; Google Brain, Mountain View, California, USA; VECML, Seattle, Washington, USA; Amazon, Palo Alto, Washington, USA; Palo Alto Networks, Santa Clara, California, USA|This workshop introduces generative AI applications for enterprise, with a focus on retrieval-augmented generation (RAG) systems. Generative AI is a field of artificial intelligence that can create new content and solve complex problems. RAG systems are a novel generative AI technique that combines information retrieval with text generation to generate rich and diverse responses. RAG systems can leverage enterprise data, which is often specific, structured, and dynamic, to provide customized solutions for various domains. However, enterprise data also poses challenges such as scalability, security, and data quality. This workshop convenes researchers and practitioners to explore RAG and other generative AI systems in real-world enterprise scenarios, fostering knowledge exchange, collaboration, and identification of future directions. Relevant to the CIKM community, the workshop intersects with core areas of data science and machine learning, offering potential benefits across various domains.|本次研讨会介绍了生成式AI在企业中的应用，重点聚焦于检索增强生成（Retrieval-Augmented Generation，RAG）系统。生成式AI是人工智能领域的一个分支，能够创建新内容并解决复杂问题。RAG系统是一种新颖的生成式AI技术，它将信息检索与文本生成相结合，以生成丰富多样的响应。RAG系统能够利用企业数据，这些数据通常具有特定性、结构性和动态性，从而为各个领域提供定制化的解决方案。然而，企业数据也带来了诸如可扩展性、安全性和数据质量等方面的挑战。本次研讨会汇聚了研究人员和实践者，共同探讨RAG及其他生成式AI系统在实际企业场景中的应用，促进知识交流、合作以及未来发展方向的识别。研讨会与CIKM社区相关，涉及数据科学和机器学习的核心领域，为多个领域带来了潜在的益处。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Generative+AI+and+Retrieval-Augmented+Generation+(RAG)+Systems+for+Enterprise)|0| +|[Generative AI and Retrieval-Augmented Generation (RAG) Systems for Enterprise](https://doi.org/10.1145/3627673.3680117)|Anbang Xu, Tan Yu, Min Du, Pritam Gundecha, Yufan Guo, Xinliang Zhu, May Wang, Ping Li, Xinyun Chen|Palo Alto Networks, Santa Clara, California, USA; Amazon, Seattle, Washington, USA; VECML, Seattle, Washington, USA; Amazon, Palo Alto, Washington, USA; Google Brain, Mountain View, California, USA; NVIDIA, Santa Clara, California, USA|This workshop introduces generative AI applications for enterprise, with a focus on retrieval-augmented generation (RAG) systems. Generative AI is a field of artificial intelligence that can create new content and solve complex problems. RAG systems are a novel generative AI technique that combines information retrieval with text generation to generate rich and diverse responses. RAG systems can leverage enterprise data, which is often specific, structured, and dynamic, to provide customized solutions for various domains. However, enterprise data also poses challenges such as scalability, security, and data quality. This workshop convenes researchers and practitioners to explore RAG and other generative AI systems in real-world enterprise scenarios, fostering knowledge exchange, collaboration, and identification of future directions. Relevant to the CIKM community, the workshop intersects with core areas of data science and machine learning, offering potential benefits across various domains.|本次研讨会介绍了生成式AI在企业中的应用，重点聚焦于检索增强生成（Retrieval-Augmented Generation，RAG）系统。生成式AI是人工智能领域的一个分支，能够创建新内容并解决复杂问题。RAG系统是一种新颖的生成式AI技术，它将信息检索与文本生成相结合，以生成丰富多样的响应。RAG系统能够利用企业数据，这些数据通常具有特定性、结构性和动态性，从而为各个领域提供定制化的解决方案。然而，企业数据也带来了诸如可扩展性、安全性和数据质量等方面的挑战。本次研讨会汇聚了研究人员和实践者，共同探讨RAG及其他生成式AI系统在实际企业场景中的应用，促进知识交流、合作以及未来发展方向的识别。研讨会与CIKM社区相关，涉及数据科学和机器学习的核心领域，为多个领域带来了潜在的益处。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Generative+AI+and+Retrieval-Augmented+Generation+(RAG)+Systems+for+Enterprise)|0| |[A Bayesian Multi-Armed Bandit Algorithm for Bid Shading in Online Display Advertising](https://doi.org/10.1145/3627673.3680107)|Mengzhuo Guo, Wuqi Zhang, Congde Yuan, Binfeng Jia, Guoqing Song, Hua Hua, Shuangyang Wang, Qingpeng Zhang|The University of Hong Kong, Hong Kong, Hong Kong; Sichuan University, Chengdu, Sichuan, China; Tencent, Shenzhen, Guangdong, China|In real-time bidding systems, ad exchanges and supply-side platforms (SSP) are switching from the second-price auction (SPA) to the first-price auction (FPA), where the advertisers should pay what they bid if they win the auction. To avoid overpaying, advertisers are motivated to conceal their truthful evaluations of impression opportunities through bid shading methods. However, advertisers are consistently facing a trade-off between the probability and cost-saving of winning, due to the information asymmetry, where advertisers lack knowledge about their competitors' bids in the market. To address this challenge, we propose a Bayes ian Multi-Armed Bandit (BayesMAB) algorithm for bid shading when the winning price is unknown to advertisers who lose the impression opportunity. BayesMAB incorporates the mechanism of FPA to infer each price interval's winning rate by progressively updating the market price hidden by SSP. In this way, BayesMAB better approximates the winning rates of price intervals and thus is able to derive the optimal shaded bid that balances the trade-off between the probability and cost-saving of winning the impression opportunity. We conducted large-scale A/B tests on Tencent's online display advertising platform. The cost-per-mile (CPM) and cost-per-action (CPA) decreased by 13.06% and 11.90%, respectively, whereas the return on investment (ROI) increased by 12.31% with only 2.7% sacrifice of the winning rate. We also validated BayesMAB's superior performance in an offline semi-simulated experiment with SPA data sets. BayesMAB has been deployed online and is impacting billions of traffic every day. Codes are available at https://github.com/BayesMAB/BayesMAB.|在实时竞价系统中，广告交易平台和供应方平台（SSP）正从第二价格拍卖（SPA）转向第一价格拍卖（FPA），在这种拍卖中，广告主如果赢得竞价，则需支付其出价金额。为了避免支付过高，广告主倾向于通过出价遮蔽（bid shading）方法隐藏其对展示机会的真实评估。然而，由于信息不对称——广告主缺乏对市场上竞争对手出价的了解——他们始终面临着一个在赢得竞价概率与成本节约之间的权衡。为应对这一挑战，我们提出了一种贝叶斯多臂赌博机（BayesMAB）算法，用于在广告主未能赢得展示机会且无法知晓获胜价格时进行出价遮蔽。BayesMAB结合了FPA机制，通过逐步更新SSP隐藏的市场价格来推断每个价格区间的获胜率。通过这种方式，BayesMAB能够更好地逼近价格区间的获胜率，从而得出能够平衡赢得展示机会概率与成本节约之间权衡的最优遮蔽出价。我们在腾讯的在线展示广告平台上进行了大规模A/B测试。结果显示，每千次展示成本（CPM）和每次行动成本（CPA）分别下降了13.06%和11.90%，而投资回报率（ROI）提升了12.31%，同时仅牺牲了2.7%的获胜率。我们还在基于SPA数据集的线下半模拟实验中验证了BayesMAB的优越性能。BayesMAB已在线部署，每天影响数十亿流量。代码可在https://github.com/BayesMAB/BayesMAB获取。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Bayesian+Multi-Armed+Bandit+Algorithm+for+Bid+Shading+in+Online+Display+Advertising)|0| -|[SGFL-Attack: A Similarity-Guidance Strategy for Hard-Label Textual Adversarial Attack Based on Feedback Learning](https://doi.org/10.1145/3627673.3679639)|Panjia Qiu, Guanghao Zhou, Mingyuan Fan, Cen Chen, Yaliang Li, Wenming Zhou|Alibaba Group, Hangzhou, China; East China Normal University, ShangHai, China; East China Normal University, Shanghai, China|Hard-label black-box textual adversarial attack presents a challenging task where only the predictions of the victim model are available. Moreover, several constraints further complicate the task of launching such attacks, including the inherent discrete and non-differentiable nature of text data and the need to introduce subtle perturbations that remain imperceptible to humans while preserving semantic similarity. Despite the considerable research efforts dedicated to this problem, existing methods still suffer from several limitations. For example, algorithms based on complex heuristic searches necessitate extensive querying, rendering them computationally expensive. The introduction of continuous gradient strategies into discrete text spaces often leads to estimation errors. Meanwhile, geometry-based strategies are prone to falling into local optima. To address these limitations, in this paper, we introduce SGFL-Attack, a novel approach that leverages a Similarity-Guidance strategy based on Feedback Learning for hard-label textual adversarial attack, with limited query budget. Specifically, the proposed SGFL-Attack utilizes word embedding vectors to assess the importance of words and positions in text sequences, and employs a feedback learning mechanism to determine reward or punishment based on changes in predicted labels caused by replacing words. In each iteration, SGFL-Attack guides the search based on knowledge acquired from the feedback learning mechanism, generating more similar samples while maintaining low perturbations. Moreover, to reduce the query budget, we incorporate local hash mapping to avoid redundant queries during the search process. Extensive experiments on seven widely used datasets show that the proposed SGFL-Attack method significantly outperforms state-of-the-art baselines and defenses over multiple language models.|硬标签黑箱文本对抗攻击是一项具有挑战性的任务，其中仅能获取受害模型的预测结果。此外，多种约束条件进一步增加了实施此类攻击的难度，包括文本数据固有的离散性和不可微性，以及需要在保持语义相似性的同时引入细微扰动且不被人察觉。尽管针对这一问题已有大量研究工作，现有方法仍存在诸多局限性。例如，基于复杂启发式搜索的算法需要大量查询，导致计算成本高昂；将连续梯度策略引入离散文本空间往往导致估计误差；而基于几何的策略则容易陷入局部最优。为解决这些局限性，本文提出了SGFL-Attack，这是一种基于反馈学习的相似性引导策略的新型硬标签文本对抗攻击方法，旨在有限的查询预算下实现攻击。具体而言，所提出的SGFL-Attack方法利用词嵌入向量评估文本序列中词语及其位置的重要性，并通过反馈学习机制根据替换词语导致的预测标签变化来决定奖励或惩罚。在每次迭代中，SGFL-Attack根据反馈学习机制获取的知识引导搜索，生成相似度更高的样本同时保持较低的扰动。此外，为减少查询预算，我们引入了局部哈希映射以避免搜索过程中的冗余查询。在七个广泛使用的数据集上的大量实验表明，所提出的SGFL-Attack方法在多种语言模型上显著优于当前最先进的基线和防御方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=SGFL-Attack:+A+Similarity-Guidance+Strategy+for+Hard-Label+Textual+Adversarial+Attack+Based+on+Feedback+Learning)|0| -|[Factor Model-Based Large Covariance Estimation from Streaming Data Using a Knowledge-Based Sketch Matrix](https://doi.org/10.1145/3627673.3679820)|Xiao Tan, Zhaoyang Wang, Hao Qian, Jun Zhou, Peibo Duan, Dian Shen, Meng Wang, Beilun Wang|Monash University, Melbourne, Australia; Southeast University, Nanjing, China; Ant Group, Hangzhou, China; Tongji University, Shanghai, China|Covariance matrix estimation is an important problem in statistics, with wide applications in finance, neuroscience, meteorology, oceanography, and other fields. However, when the data are high-dimensional and constantly generated and updated in a streaming fashion, the covariance matrix estimation faces huge challenges, including the curse of dimensionality and limited memory space. The existing methods either assume sparsity, ignoring any possible common factor among the variables, or obtain poor performance in recovering the covariance matrix directly from sketched data. To address these issues, we propose a novel method - KEEF: Knowledge-based Time and Memory Efficient Covariance Estimator in Factor Model and its extended variation. Our method leverages historical data to train a knowledge-based sketch matrix, which is used to accelerate the factor analysis of streaming data and directly estimates the covariance matrix from the sketched data. We provide theoretical guarantees, showing the advantages of our method in terms of time and space complexity, as well as accuracy. We conduct extensive experiments on synthetic and real-world data, comparing KEEF with several state-of-the-art methods, demonstrating the superior performance of our method.|协方差矩阵估计是统计学中的一个重要问题，广泛应用于金融、神经科学、气象学、海洋学等多个领域。然而，当数据具有高维度且以流式方式持续生成和更新时，协方差矩阵估计面临巨大挑战，包括维度灾难和有限的内存空间。现有的方法要么假设数据稀疏，忽视了变量之间可能存在的共同因子，要么在从草图数据中直接恢复协方差矩阵时表现不佳。为解决这些问题，我们提出了一种新颖的方法——KEEF：基于知识的时-空高效协方差估计器，适用于因子模型及其扩展变体。我们的方法利用历史数据训练一个基于知识的草图矩阵，该矩阵用于加速流数据的因子分析，并直接从草图数据中估计协方差矩阵。我们提供了理论保证，证明了该方法在时间和空间复杂度以及准确性方面的优势。我们在合成数据和真实数据上进行了广泛的实验，将KEEF与几种最先进的方法进行了比较，展示了我们方法的优越性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Factor+Model-Based+Large+Covariance+Estimation+from+Streaming+Data+Using+a+Knowledge-Based+Sketch+Matrix)|0| +|[SGFL-Attack: A Similarity-Guidance Strategy for Hard-Label Textual Adversarial Attack Based on Feedback Learning](https://doi.org/10.1145/3627673.3679639)|Panjia Qiu, Guanghao Zhou, Mingyuan Fan, Cen Chen, Yaliang Li, Wenming Zhou|East China Normal University, ShangHai, China; Alibaba Group, Hangzhou, China; East China Normal University, Shanghai, China|Hard-label black-box textual adversarial attack presents a challenging task where only the predictions of the victim model are available. Moreover, several constraints further complicate the task of launching such attacks, including the inherent discrete and non-differentiable nature of text data and the need to introduce subtle perturbations that remain imperceptible to humans while preserving semantic similarity. Despite the considerable research efforts dedicated to this problem, existing methods still suffer from several limitations. For example, algorithms based on complex heuristic searches necessitate extensive querying, rendering them computationally expensive. The introduction of continuous gradient strategies into discrete text spaces often leads to estimation errors. Meanwhile, geometry-based strategies are prone to falling into local optima. To address these limitations, in this paper, we introduce SGFL-Attack, a novel approach that leverages a Similarity-Guidance strategy based on Feedback Learning for hard-label textual adversarial attack, with limited query budget. Specifically, the proposed SGFL-Attack utilizes word embedding vectors to assess the importance of words and positions in text sequences, and employs a feedback learning mechanism to determine reward or punishment based on changes in predicted labels caused by replacing words. In each iteration, SGFL-Attack guides the search based on knowledge acquired from the feedback learning mechanism, generating more similar samples while maintaining low perturbations. Moreover, to reduce the query budget, we incorporate local hash mapping to avoid redundant queries during the search process. Extensive experiments on seven widely used datasets show that the proposed SGFL-Attack method significantly outperforms state-of-the-art baselines and defenses over multiple language models.|硬标签黑箱文本对抗攻击是一项具有挑战性的任务，其中仅能获取受害模型的预测结果。此外，多种约束条件进一步增加了实施此类攻击的难度，包括文本数据固有的离散性和不可微性，以及需要在保持语义相似性的同时引入细微扰动且不被人察觉。尽管针对这一问题已有大量研究工作，现有方法仍存在诸多局限性。例如，基于复杂启发式搜索的算法需要大量查询，导致计算成本高昂；将连续梯度策略引入离散文本空间往往导致估计误差；而基于几何的策略则容易陷入局部最优。为解决这些局限性，本文提出了SGFL-Attack，这是一种基于反馈学习的相似性引导策略的新型硬标签文本对抗攻击方法，旨在有限的查询预算下实现攻击。具体而言，所提出的SGFL-Attack方法利用词嵌入向量评估文本序列中词语及其位置的重要性，并通过反馈学习机制根据替换词语导致的预测标签变化来决定奖励或惩罚。在每次迭代中，SGFL-Attack根据反馈学习机制获取的知识引导搜索，生成相似度更高的样本同时保持较低的扰动。此外，为减少查询预算，我们引入了局部哈希映射以避免搜索过程中的冗余查询。在七个广泛使用的数据集上的大量实验表明，所提出的SGFL-Attack方法在多种语言模型上显著优于当前最先进的基线和防御方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=SGFL-Attack:+A+Similarity-Guidance+Strategy+for+Hard-Label+Textual+Adversarial+Attack+Based+on+Feedback+Learning)|0| +|[Factor Model-Based Large Covariance Estimation from Streaming Data Using a Knowledge-Based Sketch Matrix](https://doi.org/10.1145/3627673.3679820)|Xiao Tan, Zhaoyang Wang, Hao Qian, Jun Zhou, Peibo Duan, Dian Shen, Meng Wang, Beilun Wang|Southeast University, Nanjing, China; Ant Group, Hangzhou, China; Tongji University, Shanghai, China; Monash University, Melbourne, Australia|Covariance matrix estimation is an important problem in statistics, with wide applications in finance, neuroscience, meteorology, oceanography, and other fields. However, when the data are high-dimensional and constantly generated and updated in a streaming fashion, the covariance matrix estimation faces huge challenges, including the curse of dimensionality and limited memory space. The existing methods either assume sparsity, ignoring any possible common factor among the variables, or obtain poor performance in recovering the covariance matrix directly from sketched data. To address these issues, we propose a novel method - KEEF: Knowledge-based Time and Memory Efficient Covariance Estimator in Factor Model and its extended variation. Our method leverages historical data to train a knowledge-based sketch matrix, which is used to accelerate the factor analysis of streaming data and directly estimates the covariance matrix from the sketched data. We provide theoretical guarantees, showing the advantages of our method in terms of time and space complexity, as well as accuracy. We conduct extensive experiments on synthetic and real-world data, comparing KEEF with several state-of-the-art methods, demonstrating the superior performance of our method.|协方差矩阵估计是统计学中的一个重要问题，广泛应用于金融、神经科学、气象学、海洋学等多个领域。然而，当数据具有高维度且以流式方式持续生成和更新时，协方差矩阵估计面临巨大挑战，包括维度灾难和有限的内存空间。现有的方法要么假设数据稀疏，忽视了变量之间可能存在的共同因子，要么在从草图数据中直接恢复协方差矩阵时表现不佳。为解决这些问题，我们提出了一种新颖的方法——KEEF：基于知识的时-空高效协方差估计器，适用于因子模型及其扩展变体。我们的方法利用历史数据训练一个基于知识的草图矩阵，该矩阵用于加速流数据的因子分析，并直接从草图数据中估计协方差矩阵。我们提供了理论保证，证明了该方法在时间和空间复杂度以及准确性方面的优势。我们在合成数据和真实数据上进行了广泛的实验，将KEEF与几种最先进的方法进行了比较，展示了我们方法的优越性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Factor+Model-Based+Large+Covariance+Estimation+from+Streaming+Data+Using+a+Knowledge-Based+Sketch+Matrix)|0| |[Teach Harder, Learn Poorer: Rethinking Hard Sample Distillation for GNN-to-MLP Knowledge Distillation](https://doi.org/10.1145/3627673.3679586)|Lirong Wu, Yunfan Liu, Haitao Lin, Yufei Huang, Stan Z. Li||To bridge the gaps between powerful Graph Neural Networks (GNNs) and lightweight Multi-Layer Perceptron (MLPs), GNN-to-MLP Knowledge Distillation (KD) proposes to distill knowledge from a well-trained teacher GNN into a student MLP. In this paper, we revisit the knowledge samples (nodes) in teacher GNNs from the perspective of hardness, and identify that hard sample distillation may be a major performance bottleneck of existing graph KD algorithms. The GNN-to-MLP KD involves two different types of hardness, one student-free knowledge hardness describing the inherent complexity of GNN knowledge, and the other student-dependent distillation hardness describing the difficulty of teacher-to-student distillation. However, most of the existing work focuses on only one of these aspects or regards them as one thing. This paper proposes a simple yet effective Hardness-aware GNN-to-MLP Distillation (HGMD) framework, which decouples the two hardnesses and estimates them using a non-parametric approach. Finally, two hardness-aware distillation schemes (i.e., HGMD-weight and HGMD-mixup) are further proposed to distill hardness-aware knowledge from teacher GNNs into the corresponding nodes of student MLPs. As non-parametric distillation, HGMD does not involve any additional learnable parameters beyond the student MLPs, but it still outperforms most of the state-of-the-art competitors. HGMD-mixup improves over the vanilla MLPs by 12.95 over seven real-world datasets.|为了弥合强大的图神经网络（GNN）与轻量级多层感知器（MLP）之间的差距，GNN-to-MLP知识蒸馏（KD）提出将经过良好训练的教师GNN的知识蒸馏到学生MLP中。本文从难度的角度重新审视教师GNN中的知识样本（节点），并识别出硬样本蒸馏可能是现有图KD算法的主要性能瓶颈。GNN-to-MLP KD涉及两种不同类型的难度：一种是描述GNN知识固有复杂性的学生无关知识难度，另一种是描述教师到学生蒸馏难度的学生依赖蒸馏难度。然而，现有的大部分工作只关注其中一个方面，或将两者视为一体。本文提出了一种简单而有效的硬度感知GNN-to-MLP蒸馏（HGMD）框架，该框架将两种难度解耦，并使用非参数方法进行估计。最后，进一步提出了两种硬度感知蒸馏方案（即HGMD-weight和HGMD-mixup），以将硬度感知的知识从教师GNN蒸馏到学生MLP的相应节点中。作为一种非参数蒸馏方法，HGMD除了学生MLP外不涉及任何额外的可学习参数，但仍优于大多数最先进的竞争对手。在七个真实世界数据集上，HGMD-mixup相较于普通MLP提升了12.95%。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Teach+Harder,+Learn+Poorer:+Rethinking+Hard+Sample+Distillation+for+GNN-to-MLP+Knowledge+Distillation)|0| -|[Correcting Biases of Shapley Value Attributions for Informative Machine Learning Model Explanations](https://doi.org/10.1145/3627673.3679846)|Ningsheng Zhao, Jia Yuan Yu, Trang Bui, Krzysztof Dzieciolowski|Concordia University, Montreal, Quebec, Canada; Concordia University and Daesys Inc., Montreal, Quebec, Canada; University of Waterloo, Waterloo, Ontario, Canada|Shapley value attribution (SVA) is an increasingly popular Explainable AI (XAI) approach that has been widely used in many recent applied studies to gain new insights into the underlying information systems. However, most existing SVA methods are error-prone, providing biased or unreliable explanations that fail to correctly capture the informational dependencies between features and model outputs. These explanation errors can be decomposed into two components: 1) observation bias which stems from data sparsity and leads to over-informativeness; and 2) structural bias which stems from distributional assumptions and leads to under-informativeness. To alleviate these biases, in this paper, we propose a series of refinement methods that combine out-of-distribution (OOD) detection and importance sampling. In essence, our methods aim to rectify the distribution drift caused by distributional assumptions. We apply our refinement methods to two popular SVAs: the marginal SVA and the surrogate model-based SVA. Our extensive experiments show that the proposed methods significantly enhance the informativeness of both local and global Shapley value-based explanations.|Shapley值归因（SVA）是一种日益流行的可解释人工智能（XAI）方法，近年来在许多应用研究中被广泛使用，以深入了解底层信息系统。然而，现有的SVA方法大多存在误差，提供有偏或不可靠的解释，未能正确捕捉特征与模型输出之间的信息依赖关系。这些解释误差可以分解为两个部分：1）观测偏差，源于数据稀疏性，导致过度信息量；2）结构偏差，源于分布假设，导致信息量不足。为了缓解这些偏差，本文提出了一系列结合分布外（OOD）检测和重要性采样的改进方法。本质上，我们的方法旨在纠正由分布假设引起的分布偏移。我们将这些改进方法应用于两种流行的SVA方法：边际SVA和基于代理模型的SVA。大量实验表明，所提出的方法显著提高了基于Shapley值的局部和全局解释的信息量。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Correcting+Biases+of+Shapley+Value+Attributions+for+Informative+Machine+Learning+Model+Explanations)|0| -|[HiMTM: Hierarchical Multi-Scale Masked Time Series Modeling with Self-Distillation for Long-Term Forecasting](https://doi.org/10.1145/3627673.3679741)|Shubao Zhao, Ming Jin, Zhaoxiang Hou, Chengyi Yang, Zengxiang Li, Qingsong Wen, Yi Wang|; Digital Research Institute of ENN Group; Monash University; The University of Hong Kong|Time series forecasting is a critical and challenging task in practical application. Recent advancements in pre-trained foundation models for time series forecasting have gained significant interest. However, current methods often overlook the multi-scale nature of time series, which is essential for accurate forecasting. To address this, we propose HiMTM, a hierarchical multi-scale masked time series modeling with self-distillation for long-term forecasting. HiMTM integrates four key components: (1) hierarchical multi-scale transformer (HMT) to capture temporal information at different scales; (2) decoupled encoder-decoder (DED) that directs the encoder towards feature extraction while the decoder focuses on pretext tasks; (3) hierarchical self-distillation (HSD) for multi-stage feature-level supervision signals during pre-training; and (4) cross-scale attention fine-tuning (CSA-FT) to capture dependencies between different scales for downstream tasks. These components collectively enhance multi-scale feature extraction in masked time series modeling, improving forecasting accuracy. Extensive experiments on seven mainstream datasets show that HiMTM surpasses state-of-the-art self-supervised and end-to-end learning methods by a considerable margin of 3.16-68.54%. Additionally, HiMTM outperforms the latest robust self-supervised learning method, PatchTST, in cross-domain forecasting by a significant margin of 2.3%. The effectiveness of HiMTM is further demonstrated through its application in natural gas demand forecasting.|时间序列预测在实际应用中是一项重要且具有挑战性的任务。近年来，针对时间序列预测的预训练基础模型取得了显著进展，并引起了广泛关注。然而，现有方法往往忽视了时间序列的多尺度特性，而这一特性对于精确预测至关重要。为此，我们提出了HiMTM，一种用于长期预测的分层多尺度掩码时间序列建模方法，结合了自蒸馏机制。HiMTM整合了四个关键组件：（1）分层多尺度Transformer（HMT），用于捕捉不同尺度的时间信息；（2）解耦编码器-解码器（DED），使编码器专注于特征提取，而解码器则聚焦于前置任务；（3）分层自蒸馏（HSD），在预训练过程中提供多阶段的特征级监督信号；（4）跨尺度注意力微调（CSA-FT），用于捕捉下游任务中不同尺度之间的依赖关系。这些组件共同增强了掩码时间序列建模中的多尺度特征提取能力，从而提高了预测精度。在七个主流数据集上的广泛实验表明，HiMTM在自监督和端到端学习方法上显著超越了现有最先进的方法，提升幅度达3.16%至68.54%。此外，HiMTM在跨领域预测中显著优于最新的鲁棒自监督学习方法PatchTST，提升幅度为2.3%。通过在天然气需求预测中的应用，进一步证明了HiMTM的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=HiMTM:+Hierarchical+Multi-Scale+Masked+Time+Series+Modeling+with+Self-Distillation+for+Long-Term+Forecasting)|0| -|[GLFNet: Global and Local Frequency-domain Network for Long-term Time Series Forecasting](https://doi.org/10.1145/3627673.3679579)|Xucheng Zhou, Yuwen Liu, Lianyong Qi, Xiaolong Xu, Wanchun Dou, Xuyun Zhang, Yang Zhang, Xiaokang Zhou|Nanjing University of Information Science and Technology, Nanjing, China; China University of Petroleum (East China), Qingdao, China; Macquarie University, Sydney, Australia; Kansai University, Osaka, Japan; China University of Petroleum (East China) & Qufu Normal University, Qingdao, China; Nanjing University, Nanjing, China|Recently, patch-based transformer methods have demonstrated strong effectiveness in time series forecasting. However, the complexity of self-attention imposes demands on memory and compute resources. In addition, though patches can capture comprehensive temporal information while preserving locality, temporal information within patches remains important for time series prediction. The existing methods mainly focus on modeling long-term dependencies across patches, while paying little attention to the short-term dependencies within patches. In this paper, we propose the Global and Local Frequency-domain Network (GLFNet), a novel architecture that efficiently learns global time dependencies and local time relationships in the frequency domain. Specifically, we design a frequency filtering layer to learn the temporal interactions instead of self-attention. Then we devise a dual filtering block consisting of global filter block and local filter block which learns the global dependencies across patches and local dependencies within patches. Experiments on seven benchmark datasets demonstrate that our approach achieve superior performance with improved efficiency.|近年来，基于分块的Transformer方法在时间序列预测中展现了强大的有效性。然而，自注意力机制的复杂性对内存和计算资源提出了较高要求。此外，尽管分块方法能够在保留局部性的同时捕捉全面的时间信息，但分块内部的时间信息对于时间序列预测仍然至关重要。现有方法主要关注跨分块的长程依赖建模，而对分块内部短程依赖的关注较少。本文提出了一种新颖的架构——全局与局部频域网络（GLFNet），该架构能够在频域中高效学习全局时间依赖关系和局部时间关系。具体而言，我们设计了一个频域滤波层来学习时间交互，而非使用自注意力机制。随后，我们提出了一种双滤波块结构，由全局滤波块和局部滤波块组成，分别学习跨分块的全局依赖关系和分块内部的局部依赖关系。在七个基准数据集上的实验表明，我们的方法在提升效率的同时实现了卓越的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=GLFNet:+Global+and+Local+Frequency-domain+Network+for+Long-term+Time+Series+Forecasting)|0| +|[Correcting Biases of Shapley Value Attributions for Informative Machine Learning Model Explanations](https://doi.org/10.1145/3627673.3679846)|Ningsheng Zhao, Jia Yuan Yu, Trang Bui, Krzysztof Dzieciolowski|Concordia University, Montreal, Quebec, Canada; University of Waterloo, Waterloo, Ontario, Canada; Concordia University and Daesys Inc., Montreal, Quebec, Canada|Shapley value attribution (SVA) is an increasingly popular Explainable AI (XAI) approach that has been widely used in many recent applied studies to gain new insights into the underlying information systems. However, most existing SVA methods are error-prone, providing biased or unreliable explanations that fail to correctly capture the informational dependencies between features and model outputs. These explanation errors can be decomposed into two components: 1) observation bias which stems from data sparsity and leads to over-informativeness; and 2) structural bias which stems from distributional assumptions and leads to under-informativeness. To alleviate these biases, in this paper, we propose a series of refinement methods that combine out-of-distribution (OOD) detection and importance sampling. In essence, our methods aim to rectify the distribution drift caused by distributional assumptions. We apply our refinement methods to two popular SVAs: the marginal SVA and the surrogate model-based SVA. Our extensive experiments show that the proposed methods significantly enhance the informativeness of both local and global Shapley value-based explanations.|Shapley值归因（SVA）是一种日益流行的可解释人工智能（XAI）方法，近年来在许多应用研究中被广泛使用，以深入了解底层信息系统。然而，现有的SVA方法大多存在误差，提供有偏或不可靠的解释，未能正确捕捉特征与模型输出之间的信息依赖关系。这些解释误差可以分解为两个部分：1）观测偏差，源于数据稀疏性，导致过度信息量；2）结构偏差，源于分布假设，导致信息量不足。为了缓解这些偏差，本文提出了一系列结合分布外（OOD）检测和重要性采样的改进方法。本质上，我们的方法旨在纠正由分布假设引起的分布偏移。我们将这些改进方法应用于两种流行的SVA方法：边际SVA和基于代理模型的SVA。大量实验表明，所提出的方法显著提高了基于Shapley值的局部和全局解释的信息量。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Correcting+Biases+of+Shapley+Value+Attributions+for+Informative+Machine+Learning+Model+Explanations)|0| +|[HiMTM: Hierarchical Multi-Scale Masked Time Series Modeling with Self-Distillation for Long-Term Forecasting](https://doi.org/10.1145/3627673.3679741)|Shubao Zhao, Ming Jin, Zhaoxiang Hou, Chengyi Yang, Zengxiang Li, Qingsong Wen, Yi Wang|Monash University; Digital Research Institute of ENN Group; The University of Hong Kong; |Time series forecasting is a critical and challenging task in practical application. Recent advancements in pre-trained foundation models for time series forecasting have gained significant interest. However, current methods often overlook the multi-scale nature of time series, which is essential for accurate forecasting. To address this, we propose HiMTM, a hierarchical multi-scale masked time series modeling with self-distillation for long-term forecasting. HiMTM integrates four key components: (1) hierarchical multi-scale transformer (HMT) to capture temporal information at different scales; (2) decoupled encoder-decoder (DED) that directs the encoder towards feature extraction while the decoder focuses on pretext tasks; (3) hierarchical self-distillation (HSD) for multi-stage feature-level supervision signals during pre-training; and (4) cross-scale attention fine-tuning (CSA-FT) to capture dependencies between different scales for downstream tasks. These components collectively enhance multi-scale feature extraction in masked time series modeling, improving forecasting accuracy. Extensive experiments on seven mainstream datasets show that HiMTM surpasses state-of-the-art self-supervised and end-to-end learning methods by a considerable margin of 3.16-68.54%. Additionally, HiMTM outperforms the latest robust self-supervised learning method, PatchTST, in cross-domain forecasting by a significant margin of 2.3%. The effectiveness of HiMTM is further demonstrated through its application in natural gas demand forecasting.|时间序列预测在实际应用中是一项重要且具有挑战性的任务。近年来，针对时间序列预测的预训练基础模型取得了显著进展，并引起了广泛关注。然而，现有方法往往忽视了时间序列的多尺度特性，而这一特性对于精确预测至关重要。为此，我们提出了HiMTM，一种用于长期预测的分层多尺度掩码时间序列建模方法，结合了自蒸馏机制。HiMTM整合了四个关键组件：（1）分层多尺度Transformer（HMT），用于捕捉不同尺度的时间信息；（2）解耦编码器-解码器（DED），使编码器专注于特征提取，而解码器则聚焦于前置任务；（3）分层自蒸馏（HSD），在预训练过程中提供多阶段的特征级监督信号；（4）跨尺度注意力微调（CSA-FT），用于捕捉下游任务中不同尺度之间的依赖关系。这些组件共同增强了掩码时间序列建模中的多尺度特征提取能力，从而提高了预测精度。在七个主流数据集上的广泛实验表明，HiMTM在自监督和端到端学习方法上显著超越了现有最先进的方法，提升幅度达3.16%至68.54%。此外，HiMTM在跨领域预测中显著优于最新的鲁棒自监督学习方法PatchTST，提升幅度为2.3%。通过在天然气需求预测中的应用，进一步证明了HiMTM的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=HiMTM:+Hierarchical+Multi-Scale+Masked+Time+Series+Modeling+with+Self-Distillation+for+Long-Term+Forecasting)|0| +|[GLFNet: Global and Local Frequency-domain Network for Long-term Time Series Forecasting](https://doi.org/10.1145/3627673.3679579)|Xucheng Zhou, Yuwen Liu, Lianyong Qi, Xiaolong Xu, Wanchun Dou, Xuyun Zhang, Yang Zhang, Xiaokang Zhou|Nanjing University of Information Science and Technology, Nanjing, China; Macquarie University, Sydney, Australia; Nanjing University, Nanjing, China; Kansai University, Osaka, Japan; China University of Petroleum (East China), Qingdao, China; China University of Petroleum (East China) & Qufu Normal University, Qingdao, China|Recently, patch-based transformer methods have demonstrated strong effectiveness in time series forecasting. However, the complexity of self-attention imposes demands on memory and compute resources. In addition, though patches can capture comprehensive temporal information while preserving locality, temporal information within patches remains important for time series prediction. The existing methods mainly focus on modeling long-term dependencies across patches, while paying little attention to the short-term dependencies within patches. In this paper, we propose the Global and Local Frequency-domain Network (GLFNet), a novel architecture that efficiently learns global time dependencies and local time relationships in the frequency domain. Specifically, we design a frequency filtering layer to learn the temporal interactions instead of self-attention. Then we devise a dual filtering block consisting of global filter block and local filter block which learns the global dependencies across patches and local dependencies within patches. Experiments on seven benchmark datasets demonstrate that our approach achieve superior performance with improved efficiency.|近年来，基于分块的Transformer方法在时间序列预测中展现了强大的有效性。然而，自注意力机制的复杂性对内存和计算资源提出了较高要求。此外，尽管分块方法能够在保留局部性的同时捕捉全面的时间信息，但分块内部的时间信息对于时间序列预测仍然至关重要。现有方法主要关注跨分块的长程依赖建模，而对分块内部短程依赖的关注较少。本文提出了一种新颖的架构——全局与局部频域网络（GLFNet），该架构能够在频域中高效学习全局时间依赖关系和局部时间关系。具体而言，我们设计了一个频域滤波层来学习时间交互，而非使用自注意力机制。随后，我们提出了一种双滤波块结构，由全局滤波块和局部滤波块组成，分别学习跨分块的全局依赖关系和分块内部的局部依赖关系。在七个基准数据集上的实验表明，我们的方法在提升效率的同时实现了卓越的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=GLFNet:+Global+and+Local+Frequency-domain+Network+for+Long-term+Time+Series+Forecasting)|0| |[Facets of Disparate Impact: Evaluating Legally Consistent Bias in Machine Learning](https://doi.org/10.1145/3627673.3679925)|Jarren Briscoe, Assefaw H. Gebremedhin|Washington State University, Pullman, WA, USA|Leveraging current legal standards, we define bias through the lens of marginal benefits and objective testing with the novel metric "Objective Fairness Index". This index combines the contextual nuances of objective testing with metric stability, providing a legally consistent and reliable measure. Utilizing the Objective Fairness Index, we provide fresh insights into sensitive machine learning applications, such as COMPAS (recidivism prediction), highlighting the metric's practical and theoretical significance. The Objective Fairness Index allows one to differentiate between discriminatory tests and systemic disparities.|借助当前的法律标准，我们通过边际效益和客观测试的视角定义了偏见，并提出了一个新的指标——“客观公平指数”。该指数结合了客观测试的上下文细微差别与指标稳定性，提供了一种法律上一致且可靠的衡量标准。利用客观公平指数，我们对敏感的机器学习应用（如COMPAS再犯预测）进行了深入分析，突显了该指标在实际应用和理论上的重要性。客观公平指数能够区分歧视性测试与系统性差异。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Facets+of+Disparate+Impact:+Evaluating+Legally+Consistent+Bias+in+Machine+Learning)|0| -|[Bubble Sketch: A High-performance and Memory-efficient Sketch for Finding Top-k Items in Data Streams](https://doi.org/10.1145/3627673.3679882)|Lu Cao, Qilong Shi, Yuxi Liu, Hanyue Zheng, Yao Xin, Wenjun Li, Tong Yang, Yangyang Wang, Yang Xu, Weizhe Zhang, Mingwei Xu|Harbin Institute of Technology, Harbin, China; Guangzhou University, Guangzhou, China; Peking University, Beijing, China; Harbin Institute of Technology, Shenzhen, China; Tsinghua University, Beijing, China; Fudan University, Shanghai, China; Pengcheng Laboratory, Shenzhen, China|Sketch algorithms are crucial for identifying top-k items in large-scale data streams. Existing methods often compromise between performance and accuracy, unable to efficiently handle increasing data volumes with limited memory. We present Bubble Sketch, a compact algorithm that excels in both performance and accuracy. Bubble Sketch achieves this by (1) Recording only full keys of hot items, significantly reducing memory usage, and (2) Using threshold relocation to resolve conflicts, enhancing detection accuracy. Unlike traditional methods, Bubble Sketch eliminates the need for a Min-Heap, ensuring fast processing speeds. Experiments show Bubble Sketch outperforms the other seven algorithms compared, with the highest throughput and precision, and surpasses HeavyKeeper in accuracy by up to two orders of magnitude.|草图算法在识别大规模数据流中的前k个项目方面至关重要。现有方法通常在性能和准确性之间做出妥协，无法在有限内存下高效处理日益增长的数据量。我们提出了Bubble Sketch，这是一种在性能和准确性方面表现出色的紧凑型算法。Bubble Sketch通过以下方式实现这一目标：(1) 仅记录热门项目的完整键，显著减少内存使用；(2) 使用阈值重定位来解决冲突，提高检测准确性。与传统方法不同，Bubble Sketch无需使用Min-Heap，从而确保了快速的处理速度。实验表明，Bubble Sketch在与其他七种算法相比时，具有最高的吞吐量和精确度，并且在准确性上超越了HeavyKeeper多达两个数量级。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Bubble+Sketch:+A+High-performance+and+Memory-efficient+Sketch+for+Finding+Top-k+Items+in+Data+Streams)|0| +|[Bubble Sketch: A High-performance and Memory-efficient Sketch for Finding Top-k Items in Data Streams](https://doi.org/10.1145/3627673.3679882)|Lu Cao, Qilong Shi, Yuxi Liu, Hanyue Zheng, Yao Xin, Wenjun Li, Tong Yang, Yangyang Wang, Yang Xu, Weizhe Zhang, Mingwei Xu|Peking University, Beijing, China; Pengcheng Laboratory, Shenzhen, China; Tsinghua University, Beijing, China; Harbin Institute of Technology, Harbin, China; Guangzhou University, Guangzhou, China; Harbin Institute of Technology, Shenzhen, China; Fudan University, Shanghai, China|Sketch algorithms are crucial for identifying top-k items in large-scale data streams. Existing methods often compromise between performance and accuracy, unable to efficiently handle increasing data volumes with limited memory. We present Bubble Sketch, a compact algorithm that excels in both performance and accuracy. Bubble Sketch achieves this by (1) Recording only full keys of hot items, significantly reducing memory usage, and (2) Using threshold relocation to resolve conflicts, enhancing detection accuracy. Unlike traditional methods, Bubble Sketch eliminates the need for a Min-Heap, ensuring fast processing speeds. Experiments show Bubble Sketch outperforms the other seven algorithms compared, with the highest throughput and precision, and surpasses HeavyKeeper in accuracy by up to two orders of magnitude.|草图算法在识别大规模数据流中的前k个项目方面至关重要。现有方法通常在性能和准确性之间做出妥协，无法在有限内存下高效处理日益增长的数据量。我们提出了Bubble Sketch，这是一种在性能和准确性方面表现出色的紧凑型算法。Bubble Sketch通过以下方式实现这一目标：(1) 仅记录热门项目的完整键，显著减少内存使用；(2) 使用阈值重定位来解决冲突，提高检测准确性。与传统方法不同，Bubble Sketch无需使用Min-Heap，从而确保了快速的处理速度。实验表明，Bubble Sketch在与其他七种算法相比时，具有最高的吞吐量和精确度，并且在准确性上超越了HeavyKeeper多达两个数量级。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Bubble+Sketch:+A+High-performance+and+Memory-efficient+Sketch+for+Finding+Top-k+Items+in+Data+Streams)|0| |[Optimizing Numerical Estimation and Operational Efficiency in the Legal Domain through Large Language Models](https://doi.org/10.1145/3627673.3680025)|JiaHong Huang, ChaoChun Yang, Yixian Shen, Alessio M. Pacces, Evangelos Kanoulas||The legal landscape encompasses a wide array of lawsuit types, presenting lawyers with challenges in delivering timely and accurate information to clients, particularly concerning critical aspects like potential imprisonment duration or financial repercussions. Compounded by the scarcity of legal experts, there's an urgent need to enhance the efficiency of traditional legal workflows. Recent advances in deep learning, especially Large Language Models (LLMs), offer promising solutions to this challenge. Leveraging LLMs' mathematical reasoning capabilities, we propose a novel approach integrating LLM-based methodologies with specially designed prompts to address precision requirements in legal Artificial Intelligence (LegalAI) applications. The proposed work seeks to bridge the gap between traditional legal practices and modern technological advancements, paving the way for a more accessible, efficient, and equitable legal system. To validate this method, we introduce a curated dataset tailored to precision-oriented LegalAI tasks, serving as a benchmark for evaluating LLM-based approaches. Extensive experimentation confirms the efficacy of our methodology in generating accurate numerical estimates within the legal domain, emphasizing the role of LLMs in streamlining legal processes and meeting the evolving demands of LegalAI.|法律领域的诉讼类型繁多，给律师在向客户提供及时、准确的信息方面带来了挑战，尤其是在涉及潜在监禁时间或财务影响等关键问题上。加之法律专家的稀缺，提升传统法律工作流程的效率显得尤为迫切。近年来，深度学习的进展，特别是大型语言模型（LLMs）的发展，为这一挑战提供了有前景的解决方案。利用LLMs的数学推理能力，我们提出了一种将基于LLM的方法与专门设计的提示相结合的新方法，以满足法律人工智能（LegalAI）应用中的精确性要求。该研究旨在弥合传统法律实践与现代技术进步之间的差距，为构建更加便捷、高效和公平的法律体系铺平道路。为验证这一方法，我们引入了一个针对精确导向的LegalAI任务定制的数据集，作为评估基于LLM方法的基准。广泛的实验证实了我们的方法在法律领域内生成准确数值估计的有效性，突显了LLMs在简化法律流程和满足LegalAI不断变化需求中的重要作用。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Optimizing+Numerical+Estimation+and+Operational+Efficiency+in+the+Legal+Domain+through+Large+Language+Models)|0| -|[A Multi-Node Multi-GPU Distributed GNN Training Framework for Large-Scale Online Advertising](https://doi.org/10.1145/3627673.3680018)|Xuewu Jiao, Xinsheng Luo, Miao Li, Jiang Bian, Junchao Yang, Wei Hu, Mingqing Hu, Weipeng Lu, Shikun Feng, Danlei Feng, Dongxu Yang, Haoyi Xiong, Shuanglong Li, Lin Liu|NVIDIA, CA, USA; Baidu Inc., Beijing, China; Big Data Lab, Baidu Inc., Beijing, China|Graph Neural Networks (GNNs) have become critical in various domains such as online advertising but face scalability challenges due to the growing size of graph data, leading to the needs for advanced distributed GPU computation strategies across multiple nodes. This paper presents PGLBox-Cluster, a robust distributed graph learning framework constructed atop the PaddlePaddle platform, implemented to efficiently process graphs comprising billions of nodes and edges. Through strategic partitioning of the model, node attributes, and graph data and leveraging industrial-grade RPC and NCCL for communication, PGLBox-Cluster facilitates effective distributed computation. The extensive experimental results confirm that PGLBox-Cluster achieves a 1.94x to 2.93x speedup over the single-node configuration, significantly elevating graph neural network scalability and efficiency by handling datasets exceeding 3 billion nodes and 120 billion edges with its novel asynchronous communication and graph partitioning techniques. The repository is released at This Link.|图神经网络（GNN）在在线广告等多个领域中已成为关键技术，但由于图数据规模的不断增长，面临着可扩展性挑战，这促使需要跨多个节点的先进分布式GPU计算策略。本文介绍了PGLBox-Cluster，这是一个构建在PaddlePaddle平台之上的强大分布式图学习框架，旨在高效处理包含数十亿节点和边的图数据。通过策略性地对模型、节点属性和图数据进行分区，并利用工业级的RPC和NCCL进行通信，PGLBox-Cluster促进了有效的分布式计算。广泛的实验结果证实，PGLBox-Cluster相较于单节点配置实现了1.94倍至2.93倍的加速，通过其新颖的异步通信和图分区技术，显著提升了图神经网络的可扩展性和效率，能够处理超过30亿节点和1200亿条边的数据集。该框架的代码库已在此链接发布。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Multi-Node+Multi-GPU+Distributed+GNN+Training+Framework+for+Large-Scale+Online+Advertising)|0| +|[A Multi-Node Multi-GPU Distributed GNN Training Framework for Large-Scale Online Advertising](https://doi.org/10.1145/3627673.3680018)|Xuewu Jiao, Xinsheng Luo, Miao Li, Jiang Bian, Junchao Yang, Wei Hu, Mingqing Hu, Weipeng Lu, Shikun Feng, Danlei Feng, Dongxu Yang, Haoyi Xiong, Shuanglong Li, Lin Liu|NVIDIA, CA, USA; Big Data Lab, Baidu Inc., Beijing, China; Baidu Inc., Beijing, China|Graph Neural Networks (GNNs) have become critical in various domains such as online advertising but face scalability challenges due to the growing size of graph data, leading to the needs for advanced distributed GPU computation strategies across multiple nodes. This paper presents PGLBox-Cluster, a robust distributed graph learning framework constructed atop the PaddlePaddle platform, implemented to efficiently process graphs comprising billions of nodes and edges. Through strategic partitioning of the model, node attributes, and graph data and leveraging industrial-grade RPC and NCCL for communication, PGLBox-Cluster facilitates effective distributed computation. The extensive experimental results confirm that PGLBox-Cluster achieves a 1.94x to 2.93x speedup over the single-node configuration, significantly elevating graph neural network scalability and efficiency by handling datasets exceeding 3 billion nodes and 120 billion edges with its novel asynchronous communication and graph partitioning techniques. The repository is released at This Link.|图神经网络（GNN）在在线广告等多个领域中已成为关键技术，但由于图数据规模的不断增长，面临着可扩展性挑战，这促使需要跨多个节点的先进分布式GPU计算策略。本文介绍了PGLBox-Cluster，这是一个构建在PaddlePaddle平台之上的强大分布式图学习框架，旨在高效处理包含数十亿节点和边的图数据。通过策略性地对模型、节点属性和图数据进行分区，并利用工业级的RPC和NCCL进行通信，PGLBox-Cluster促进了有效的分布式计算。广泛的实验结果证实，PGLBox-Cluster相较于单节点配置实现了1.94倍至2.93倍的加速，通过其新颖的异步通信和图分区技术，显著提升了图神经网络的可扩展性和效率，能够处理超过30亿节点和1200亿条边的数据集。该框架的代码库已在此链接发布。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Multi-Node+Multi-GPU+Distributed+GNN+Training+Framework+for+Large-Scale+Online+Advertising)|0| |[3M-Health: Multimodal Multi-Teacher Knowledge Distillation for Mental Health Detection](https://doi.org/10.1145/3627673.3679635)|Rina Carines Cabral, Siwen Luo, Josiah Poon, Soyeon Caren Han||The significance of mental health classification is paramount in contemporary society, where digital platforms serve as crucial sources for monitoring individuals' well-being. However, existing social media mental health datasets primarily consist of text-only samples, potentially limiting the efficacy of models trained on such data. Recognising that humans utilise cross-modal information to comprehend complex situations or issues, we present a novel approach to address the limitations of current methodologies. In this work, we introduce a Multimodal and Multi-Teacher Knowledge Distillation model for Mental Health Classification, leveraging insights from cross-modal human understanding. Unlike conventional approaches that often rely on simple concatenation to integrate diverse features, our model addresses the challenge of appropriately representing inputs of varying natures (e.g., texts and sounds). To mitigate the computational complexity associated with integrating all features into a single model, we employ a multimodal and multi-teacher architecture. By distributing the learning process across multiple teachers, each specialising in a particular feature extraction aspect, we enhance the overall mental health classification performance. Through experimental validation, we demonstrate the efficacy of our model in achieving improved performance.|心理健康分类在当代社会中具有至关重要的意义，数字平台作为监测个体健康状况的关键来源。然而，现有的社交媒体心理健康数据集主要由纯文本样本组成，这可能限制了基于此类数据训练的模型的有效性。认识到人类利用跨模态信息来理解复杂情境或问题，我们提出了一种新颖的方法来解决当前方法的局限性。在这项工作中，我们引入了一种多模态和多教师知识蒸馏模型用于心理健康分类，借鉴了跨模态人类理解的见解。与传统方法通常依赖简单拼接来整合不同特征不同，我们的模型解决了适当表示不同性质输入（如文本和声音）的挑战。为了减轻将所有特征整合到单一模型中带来的计算复杂性，我们采用了多模态和多教师架构。通过将学习过程分布到多个教师中，每个教师专门负责特定的特征提取方面，我们提升了整体心理健康分类的性能。通过实验验证，我们展示了该模型在提高性能方面的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=3M-Health:+Multimodal+Multi-Teacher+Knowledge+Distillation+for+Mental+Health+Detection)|0| |[Hypergraph Hash Learning for Efficient Trajectory Similarity Computation](https://doi.org/10.1145/3627673.3679555)|Yuan Cao, Lei Li, Xiangru Chen, Xue Xu, Zuojin Huang, Yanwei Yu|Computer Science and Technology, Ocean University of China, Qingdao, Shandong, China|Trajectory similarity computation is a fundamental problem in various applications (e.g., transportation optimization, behavioral study). Recent researches learn trajectory representations instead of point matching to realize more accurate and efficient trajectory similarity computation. However, these methods can still not be scaled to large datasets due to high computational cost. In this paper, we propose a novel hash learning method to encode the trajectories into binary hash codes and compute trajectory similarities by Hamming distances which is much more efficient. To the best of our knowledge, this is the first work to conduct hash learning for trajectory similarity computation. Furthermore, unlike the Word2Vec model based on random walk strategy, we utilize hypergraph neural networks for the first time to learn the representations for the grids by constructing the hyperedges according to the real-life trajectories, resulting in more representative grid embeddings. In addition, we design a residual network into the multi-layer GRU to learn more discriminative trajectory representations. The proposed Hypergraph Hash Learning for Trajectory similarity commutation is an end-to-end framework and named HHL-Traj. Experimental results on two real-world trajectory datasets (i.e., Porto and Beijing) demonstrate that the proposed framework achieves up to 6.23% and 15.42% accuracy gains compared with state-of-the-art baselines in unhashed and hashed cases, respectively. The efficiency of trajectory similarity computation based on hash codes is also verified. Our code is available at https://github.com/caoyuan57/HHL-Traj.|轨迹相似度计算是众多应用中的一个基础问题（例如，交通优化、行为研究）。近期的研究通过学习轨迹表示而非点匹配来实现更精确和高效的轨迹相似度计算。然而，这些方法由于高计算成本仍无法扩展到大规模数据集。本文提出了一种新颖的哈希学习方法，将轨迹编码为二进制哈希码，并通过汉明距离计算轨迹相似度，从而显著提高效率。据我们所知，这是首次将哈希学习应用于轨迹相似度计算的工作。此外，与基于随机游走策略的Word2Vec模型不同，我们首次利用超图神经网络，根据现实生活中的轨迹构建超边，从而学习更具代表性的网格表示。此外，我们将残差网络设计融入多层GRU中，以学习更具辨别力的轨迹表示。所提出的超图哈希学习框架用于轨迹相似度计算，是一个端到端的框架，命名为HHL-Traj。在两个真实世界的轨迹数据集（即波尔图和北京）上的实验结果表明，与未哈希和哈希情况下的最先进基线相比，所提出的框架分别实现了高达6.23%和15.42%的准确性提升。基于哈希码的轨迹相似度计算效率也得到了验证。我们的代码可在https://github.com/caoyuan57/HHL-Traj获取。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Hypergraph+Hash+Learning+for+Efficient+Trajectory+Similarity+Computation)|0| |[Towards Online and Safe Configuration Tuning with Semi-supervised Anomaly Detection](https://doi.org/10.1145/3627673.3679700)|Haitian Chen, Xu Chen, Zibo Liang, Xiushi Feng, Jiandong Xie, Han Su, Kai Zheng|; Huawei Technologies Co., Ltd., Chengdu, China|The performance of modern database management systems highly relies on hundreds of adjustable knobs. Traditionally, these knobs are manually adjusted by database administrators, a process that is both inefficient and ineffective for tuning large-scale databases in cloud environments. Recent research has explored the use of machine learning techniques to enable the automatic tuning of database configurations. Although most existing learning-based methods achieve satisfactory results on static workloads, they often experience performance degradation and low sampling efficiency in real-world environments. According to our study, this is primarily due to a lack of safety guarantees during the configuration sampling process. To address the aforementioned issues, we propose SafeTune, an online tuning system that adapts to dynamic workloads. Our core idea is to filter out a large number of configurations with potential risks during the configuration sampling process. We employ a two-stage filtering approach: The first stage utilizes a semi-supervised outlier ensemble with feature learning to achieve high-quality feature representation. The second stage employs a ranking-based classifier to refine the filtering process. In addition, to alleviate the cold-start problem, we leverage the historical tuning experience to provide high-quality initial samples during the initialization phase. We conducted comprehensive evaluations on static and dynamic workloads. In comparison to offline baseline methods, SafeTune reduces 95.6%-98.6% unsafe configuration suggestions. In contrast with state-of-the-art methods, SafeTune has improved cumulative performance by 10.5%-46.6% and tuning speed by 15.1%-35.4%.|现代数据库管理系统的表现高度依赖于数百个可调节的参数。传统上，这些参数由数据库管理员手动调整，这种方法在云环境中对大规模数据库进行调优时既不高效也不有效。最近的研究探索了使用机器学习技术来自动调整数据库配置。尽管大多数现有的基于学习的方法在静态工作负载上取得了令人满意的结果，但它们在现实环境中往往会出现性能下降和采样效率低下的问题。根据我们的研究，这主要是由于在配置采样过程中缺乏安全保障。为了解决上述问题，我们提出了SafeTune，一个适应动态工作负载的在线调优系统。我们的核心思想是在配置采样过程中过滤掉大量可能存在风险的配置。我们采用两阶段过滤方法：第一阶段利用带有特征学习的半监督异常值集成来实现高质量的特征表示；第二阶段采用基于排名的分类器来优化过滤过程。此外，为了缓解冷启动问题，我们利用历史调优经验在初始化阶段提供高质量的初始样本。我们对静态和动态工作负载进行了全面的评估。与离线基线方法相比，SafeTune减少了95.6%-98.6%的不安全配置建议。与最先进的方法相比，SafeTune的累计性能提高了10.5%-46.6%，调优速度提高了15.1%-35.4%。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Towards+Online+and+Safe+Configuration+Tuning+with+Semi-supervised+Anomaly+Detection)|0| |[Urban Traffic Accident Risk Prediction Revisited: Regionality, Proximity, Similarity and Sparsity](https://doi.org/10.1145/3627673.3679567)|Minxiao Chen, Haitao Yuan, Nan Jiang, Zhifeng Bao, Shangguang Wang||Traffic accidents pose a significant risk to human health and property safety. Therefore, to prevent traffic accidents, predicting their risks has garnered growing interest. We argue that a desired prediction solution should demonstrate resilience to the complexity of traffic accidents. In particular, it should adequately consider the regional background, accurately capture both spatial proximity and semantic similarity, and effectively address the sparsity of traffic accidents. However, these factors are often overlooked or difficult to incorporate. In this paper, we propose a novel multi-granularity hierarchical spatio-temporal network. Initially, we innovate by incorporating remote sensing data, facilitating the creation of hierarchical multi-granularity structure and the comprehension of regional background. We construct multiple high-level risk prediction tasks to enhance model's ability to cope with sparsity. Subsequently, to capture both spatial proximity and semantic similarity, region feature and multi-view graph undergo encoding processes to distill effective representations. Additionally, we propose message passing and adaptive temporal attention module that bridges different granularities and dynamically captures time correlations inherent in traffic accident patterns. At last, a multivariate hierarchical loss function is devised considering the complexity of the prediction purpose. Extensive experiments on two real datasets verify the superiority of our model against the state-of-the-art methods.|交通事故对人类健康和财产安全构成重大威胁。因此，为预防交通事故，预测其风险的重要性日益凸显。我们认为，理想的预测方案应能应对交通事故的复杂性。具体而言，它应充分考虑区域背景，准确捕捉空间邻近性和语义相似性，并有效处理交通事故数据的稀疏性。然而，这些因素往往被忽视或难以整合。本文提出了一种新颖的多粒度层次时空网络。首先，我们创新性地引入遥感数据，有助于构建层次化的多粒度结构并理解区域背景。我们构建了多个高层风险预测任务，以增强模型应对稀疏性的能力。接着，为捕捉空间邻近性和语义相似性，我们对区域特征和多视角图进行编码，提炼出有效的表示。此外，我们提出了消息传递和自适应时间注意力模块，这些模块连接不同粒度，并动态捕捉交通事故模式中的时间相关性。最后，针对预测目的的复杂性，我们设计了一种多元层次损失函数。在两个真实数据集上的广泛实验验证了我们的模型相较于现有最先进方法的优越性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Urban+Traffic+Accident+Risk+Prediction+Revisited:+Regionality,+Proximity,+Similarity+and+Sparsity)|0| -|[Hyperedge Importance Estimation via Identity-aware Hypergraph Attention Network](https://doi.org/10.1145/3627673.3679685)|Yin Chen, Xiaoyang Wang, Chen Chen|University of Wollongong, Wollongong, Australia; University of New South Wales, Sydney, Australia; Zhejiang Gongshang University, Hangzhou, China|Hypergraphs provide a more flexible representation for group interactions in complex systems compared to ordinary graphs, where each hyperedge can connect any number of nodes. In practice, data modeled as hypergraphs often contain hyperedge importance values, which indicate the influence or popularity of the group collaborations. For example, in a co-authorship hypergraph, a paper (hyperedge) is co-authored by multiple authors (nodes). The number of citations a paper receives can be regarded as the importance value of its corresponding hyperedge, reflecting its academic influence and significance. In this work, we introduce hyperedge importance estimation as a new problem in hypergraph learning. The flexibility of hyperedges enables hypergraph modeling to capture high-order relationships between entities, which has attracted widespread attention. The importance value of hyperedge has also been proven to be highly valuable in many applications. To address this problem, we propose the Identity-aware Hypergraph Attention Network (ID-HAN) for efficient hyperedge importance estimation. ID-HAN~employs a special attention mechanism to model the importance contribution of each node within the hyperedge, which injects identity information according to the hyperedge-dependent node labels. Additionally, a centrality-aware positional encoding module generates learnable positional embeddings of nodes and hyperedges based on the relative order of degree centrality and identity information, thereby enhancing the consistency between message passing and importance propagation. Extensive experiments on four real-world datasets demonstrate that ID-HAN~significantly outperforms the state-of-the-art hypergraph neural networks on the hyperedge importance estimation task.|相较于普通图，超图在复杂系统中为群体交互提供了更为灵活的表示方式，其中每条超边可以连接任意数量的节点。在实际应用中，以超图形式建模的数据通常包含超边的重要性值，这些值反映了群体协作的影响力或受欢迎程度。例如，在合著超图中，一篇论文（超边）由多位作者（节点）共同撰写。一篇论文的引用次数可以视为其对应超边的重要性值，体现了其学术影响力和重要性。在本研究中，我们引入了超边重要性估计作为超图学习中的一个新问题。超边的灵活性使得超图建模能够捕捉实体间的高阶关系，这一特性受到了广泛关注。超边的重要性值在许多应用中也已被证明具有极高的价值。为解决这一问题，我们提出了身份感知超图注意力网络（Identity-aware Hypergraph Attention Network, ID-HAN），用于高效的超边重要性估计。ID-HAN采用了一种特殊的注意力机制来建模超边内每个节点的重要性贡献，该机制根据超边依赖的节点标签注入了身份信息。此外，一个中心性感知的位置编码模块基于节点的度中心性和身份信息的相对顺序，生成了可学习的节点和超边位置嵌入，从而增强了消息传递与重要性传播之间的一致性。在四个真实世界数据集上的广泛实验表明，ID-HAN在超边重要性估计任务上显著优于当前最先进的超图神经网络。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Hyperedge+Importance+Estimation+via+Identity-aware+Hypergraph+Attention+Network)|0| -|[PIXEL: Prompt-based Zero-shot Hashing via Visual and Textual Semantic Alignment](https://doi.org/10.1145/3627673.3679747)|Zeyu Dong, Qingqing Long, Yihang Zhou, Pengfei Wang, Zhihong Zhu, Xiao Luo, Yidong Wang, Pengyang Wang, Yuanchun Zhou|; University of California, Los Angeles, Los Angeles, USA; Peking University, Beijing, China; University of Macau, Macau, China|Zero-Shot Hashing (ZSH) has aroused significant attention due to its efficiency and generalizability in multi-modal retrieval scenarios, which aims to encode semantic information into hash codes without needing unseen labeled training samples. In addition to commonly used visual images as visual semantics and class labels as global semantics, the corresponding attribute descriptions contain critical local semantics with detailed information. However, most existing methods focus on leveraging the extracted attribute numerical values, without exploring the textual semantics in attribute descriptions. To bridge this gap, in this paper, we propose Prompt-based zero-shot hashing via vIsual and teXtual sEmantic aLignment, namely PIXEL. Concretely, we design the attribute prompt template depending on attribute descriptions to make the model capture the corresponding local semantics. Then, achieving the textual embedding and visual embedding, we proposed an alignment module to model the intra- and inter-class contrastive distances. In addition, the attribute-wise constraint and class-wise constraint are utilized to collaboratively learn the hash code, image representation, and visual attributes more effectively. Finally, extensive experimental results demonstrate the superiority of PIXEL.|零样本哈希（Zero-Shot Hashing, ZSH）因其在大规模多模态检索场景中的高效性和通用性而备受关注，其目标是将语义信息编码为哈希码，而无需使用未见过的标注训练样本。除了常用的视觉图像作为视觉语义和类别标签作为全局语义外，相应的属性描述还包含了具有详细信息的局部语义。然而，现有的大多数方法主要利用提取的属性数值，而未探索属性描述中的文本语义。为了填补这一空白，本文提出了一种基于提示的零样本哈希方法，通过视觉与文本语义对齐来实现，命名为PIXEL。具体而言，我们根据属性描述设计了属性提示模板，以使模型能够捕捉相应的局部语义。随后，在获得文本嵌入和视觉嵌入后，我们提出了一种对齐模块，用于建模类内和类间对比距离。此外，利用属性级约束和类别级约束来协同学习哈希码、图像表示和视觉属性，从而更有效地实现目标。最后，广泛的实验结果证明了PIXEL的优越性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=PIXEL:+Prompt-based+Zero-shot+Hashing+via+Visual+and+Textual+Semantic+Alignment)|0| +|[Hyperedge Importance Estimation via Identity-aware Hypergraph Attention Network](https://doi.org/10.1145/3627673.3679685)|Yin Chen, Xiaoyang Wang, Chen Chen|Zhejiang Gongshang University, Hangzhou, China; University of New South Wales, Sydney, Australia; University of Wollongong, Wollongong, Australia|Hypergraphs provide a more flexible representation for group interactions in complex systems compared to ordinary graphs, where each hyperedge can connect any number of nodes. In practice, data modeled as hypergraphs often contain hyperedge importance values, which indicate the influence or popularity of the group collaborations. For example, in a co-authorship hypergraph, a paper (hyperedge) is co-authored by multiple authors (nodes). The number of citations a paper receives can be regarded as the importance value of its corresponding hyperedge, reflecting its academic influence and significance. In this work, we introduce hyperedge importance estimation as a new problem in hypergraph learning. The flexibility of hyperedges enables hypergraph modeling to capture high-order relationships between entities, which has attracted widespread attention. The importance value of hyperedge has also been proven to be highly valuable in many applications. To address this problem, we propose the Identity-aware Hypergraph Attention Network (ID-HAN) for efficient hyperedge importance estimation. ID-HAN~employs a special attention mechanism to model the importance contribution of each node within the hyperedge, which injects identity information according to the hyperedge-dependent node labels. Additionally, a centrality-aware positional encoding module generates learnable positional embeddings of nodes and hyperedges based on the relative order of degree centrality and identity information, thereby enhancing the consistency between message passing and importance propagation. Extensive experiments on four real-world datasets demonstrate that ID-HAN~significantly outperforms the state-of-the-art hypergraph neural networks on the hyperedge importance estimation task.|相较于普通图，超图在复杂系统中为群体交互提供了更为灵活的表示方式，其中每条超边可以连接任意数量的节点。在实际应用中，以超图形式建模的数据通常包含超边的重要性值，这些值反映了群体协作的影响力或受欢迎程度。例如，在合著超图中，一篇论文（超边）由多位作者（节点）共同撰写。一篇论文的引用次数可以视为其对应超边的重要性值，体现了其学术影响力和重要性。在本研究中，我们引入了超边重要性估计作为超图学习中的一个新问题。超边的灵活性使得超图建模能够捕捉实体间的高阶关系，这一特性受到了广泛关注。超边的重要性值在许多应用中也已被证明具有极高的价值。为解决这一问题，我们提出了身份感知超图注意力网络（Identity-aware Hypergraph Attention Network, ID-HAN），用于高效的超边重要性估计。ID-HAN采用了一种特殊的注意力机制来建模超边内每个节点的重要性贡献，该机制根据超边依赖的节点标签注入了身份信息。此外，一个中心性感知的位置编码模块基于节点的度中心性和身份信息的相对顺序，生成了可学习的节点和超边位置嵌入，从而增强了消息传递与重要性传播之间的一致性。在四个真实世界数据集上的广泛实验表明，ID-HAN在超边重要性估计任务上显著优于当前最先进的超图神经网络。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Hyperedge+Importance+Estimation+via+Identity-aware+Hypergraph+Attention+Network)|0| +|[PIXEL: Prompt-based Zero-shot Hashing via Visual and Textual Semantic Alignment](https://doi.org/10.1145/3627673.3679747)|Zeyu Dong, Qingqing Long, Yihang Zhou, Pengfei Wang, Zhihong Zhu, Xiao Luo, Yidong Wang, Pengyang Wang, Yuanchun Zhou|; University of Macau, Macau, China; Peking University, Beijing, China; University of California, Los Angeles, Los Angeles, USA|Zero-Shot Hashing (ZSH) has aroused significant attention due to its efficiency and generalizability in multi-modal retrieval scenarios, which aims to encode semantic information into hash codes without needing unseen labeled training samples. In addition to commonly used visual images as visual semantics and class labels as global semantics, the corresponding attribute descriptions contain critical local semantics with detailed information. However, most existing methods focus on leveraging the extracted attribute numerical values, without exploring the textual semantics in attribute descriptions. To bridge this gap, in this paper, we propose Prompt-based zero-shot hashing via vIsual and teXtual sEmantic aLignment, namely PIXEL. Concretely, we design the attribute prompt template depending on attribute descriptions to make the model capture the corresponding local semantics. Then, achieving the textual embedding and visual embedding, we proposed an alignment module to model the intra- and inter-class contrastive distances. In addition, the attribute-wise constraint and class-wise constraint are utilized to collaboratively learn the hash code, image representation, and visual attributes more effectively. Finally, extensive experimental results demonstrate the superiority of PIXEL.|零样本哈希（Zero-Shot Hashing, ZSH）因其在大规模多模态检索场景中的高效性和通用性而备受关注，其目标是将语义信息编码为哈希码，而无需使用未见过的标注训练样本。除了常用的视觉图像作为视觉语义和类别标签作为全局语义外，相应的属性描述还包含了具有详细信息的局部语义。然而，现有的大多数方法主要利用提取的属性数值，而未探索属性描述中的文本语义。为了填补这一空白，本文提出了一种基于提示的零样本哈希方法，通过视觉与文本语义对齐来实现，命名为PIXEL。具体而言，我们根据属性描述设计了属性提示模板，以使模型能够捕捉相应的局部语义。随后，在获得文本嵌入和视觉嵌入后，我们提出了一种对齐模块，用于建模类内和类间对比距离。此外，利用属性级约束和类别级约束来协同学习哈希码、图像表示和视觉属性，从而更有效地实现目标。最后，广泛的实验结果证明了PIXEL的优越性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=PIXEL:+Prompt-based+Zero-shot+Hashing+via+Visual+and+Textual+Semantic+Alignment)|0| |[Progressive Multimodal Pivot Learning: Towards Semantic Discordance Understanding as Humans](https://doi.org/10.1145/3627673.3679524)|Junlin Fang, Wenya Wang, Tianze Luo, Yanyong Huang, Fengmao Lv|; School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu, China; School of Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore|Multimodal recognition can achieve enhanced performance by leveraging the complementary information from different modalities. However, in real-world scenarios, multimodal samples often express discordant semantic meanings across modalities, lacking evident complementary information. Unlike humans who can easily understand the intrinsic semantic information of these semantically discordant samples, existing multimodal recognition models show poor performance on them. With the motivation of improving the robustness of multimodal recognition models in practical scenarios, this work poses a new challenge in multimodal recognition, which is coined as Semantic Discordance Understanding. Unlike existing works only focusing on detecting semantically discordant samples as noisy data, this new challenge requires deep models to follow humans' ability in understanding the inherent semantic meanings of semantically discordant samples. To address this challenge, we further propose the Progressive Multimodal Pivot Learning (PMPL) approach by introducing a learnable pivot memory to explore the inherent semantics meaning hidden under discordant modalities. To this end, our approach inserts Pivot Memory Learning (PML) modules into multiple layers of unimodal foundation models to progressively trade-off the conflict information across modalities. By introducing the multimodal pivot learning paradigm for multimodal recognition, the proposed PMPL approach can alleviate the negative effect of semantic discordance caused by the cross-modal information exchange mechanism of existing multimodal recognition models. Experiments on different benchmarks validate the superiority of our approach. Code is available at https://github.com/tiggers23/PMPL.|多模态识别通过利用不同模态间的互补信息，能够实现性能的提升。然而，在现实场景中，多模态样本往往在不同模态间表现出不一致的语义含义，缺乏明显的互补信息。与人类能够轻松理解这些语义不一致样本的内在语义信息不同，现有的多模态识别模型在这些样本上的表现较差。为了提高多模态识别模型在实际场景中的鲁棒性，本研究提出了一项新的多模态识别挑战，称为“语义不一致理解”。与现有工作仅关注将语义不一致样本检测为噪声数据不同，这一新挑战要求深度模型具备像人类一样理解语义不一致样本内在语义信息的能力。为应对这一挑战，我们进一步提出了渐进式多模态枢纽学习（Progressive Multimodal Pivot Learning, PMPL）方法，通过引入可学习的枢纽记忆体来探索隐藏在不一致模态下的内在语义信息。为此，我们的方法在单模态基础模型的多个层中插入了枢纽记忆学习（Pivot Memory Learning, PML）模块，以逐步权衡模态间的冲突信息。通过引入多模态枢纽学习范式，所提出的PMPL方法能够缓解现有多模态识别模型中跨模态信息交换机制导致的语义不一致的负面影响。在不同基准上的实验验证了我们方法的优越性。代码可在https://github.com/tiggers23/PMPL获取。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Progressive+Multimodal+Pivot+Learning:+Towards+Semantic+Discordance+Understanding+as+Humans)|0| |[Precision Meets Resilience: Cross-Database Generalization with Uncertainty Quantification for Robust Cost Estimation](https://doi.org/10.1145/3627673.3679632)|Shuhuan Fan, Mengshu Hou, Rui Xi, Wenwen Ma|; University of Electronic Science and Technology of China, Chengdu, China|Learning-based models have shown promise in addressing query optimization challenges in the database field, where the learned cost model plays a central role. While these models outperform traditional optimizers on static datasets, their resilience and reliability in real-world applications remain a concern, limiting their widespread adoption. In this paper, we take a step towards a practical cost estimation model, named Tosure, which can quantify the uncerT ainty for cost estimation and generalizes to unseen databases accurately and efficiently. It consists primarily of two modules: a Cross-Database Representation (CDR) module and a Cost Estimation with Uncertainty (CEU) module. The CDR module captures the transferable features by focusing the minimal set based on deep-learning network, thereby enhancing the model's generalization capabilities. The CEU module introduces a novel Neural Network Gaussian Process (NNGP) to quantify the uncertainty in cost estimation, ensuring more robust estimations with an upper bound. To improve the model's performance, we perform pre-training on diverse large-scale datasets. Furthermore, we implement the model and integrate it with traditional query optimizer to validate its usability and effectiveness in real-world scenarios. Extensive experimentation demonstrates that Tosure outperforms state-of-the-art methods, achieving a 20% improvement in cost estimation accuracy and twice of the robustness.|基于学习的模型在解决数据库领域中的查询优化挑战方面展现了潜力，其中学习到的成本模型起着核心作用。尽管这些模型在静态数据集上优于传统优化器，但它们在实际应用中的韧性和可靠性仍然是一个问题，限制了它们的广泛采用。本文中，我们朝着实现一个实用的成本估算模型迈出了一步，该模型名为Tosure，能够量化成本估算的不确定性，并能够准确高效地推广到未见过的数据库。它主要由两个模块组成：跨数据库表示（CDR）模块和带不确定性的成本估算（CEU）模块。CDR模块通过聚焦基于深度学习网络的最小特征集来捕捉可迁移的特征，从而增强模型的泛化能力。CEU模块引入了一种新颖的神经网络高斯过程（NNGP）来量化成本估算中的不确定性，确保更鲁棒的估算并带有上限。为了提升模型的性能，我们在多样的大规模数据集上进行预训练。此外，我们还实现了该模型并将其与传统查询优化器集成，以验证其在实际场景中的可用性和有效性。广泛的实验表明，Tosure优于最先进的方法，成本估算准确性提高了20%，并且鲁棒性提升了两倍。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Precision+Meets+Resilience:+Cross-Database+Generalization+with+Uncertainty+Quantification+for+Robust+Cost+Estimation)|0| |[ACDM: An Effective and Scalable Active Clustering with Pairwise Constraint](https://doi.org/10.1145/3627673.3679601)|Xun Fu, WenBo Xie, Bin Chen, Tao Deng, Tian Zou, Xin Wang|School of Computer Science and Software Engineering, Southwest Petroleum University, Chengdu, China|Clustering is fundamentally a subjective task: a single dataset can be validly clustered in various ways, and without further information, clustering systems cannot determine the appropriate clustering to perform. This underscores the importance of integrating constraints into clustering, enabling users to convey their preferences to the system. Active constraint-based clustering approaches prioritize the identification of the most valuable constraints to inquire about, striving to achieve effective clustering with the minimal number of constraints needed. We propose an A ctive C lustering with D iffusion M odel (ACDM). ACDM applies the nearest-neighbor technique to construct a diffusion graph, and utilizes an online framework to refine the clustering result iteratively. In each iteration, (a) nodes with high uncertainty and representativeness are selected in batch mode, (b) then a novel neighborhood-set-based query is used for categorizing the selected nodes, using pairwise constraints, and (c) the categorized nodes are used as source nodes in the diffusion model for cluster refinement. We experimentally demonstrate that ACDM outperforms state-of-the-art methods in terms of clustering quality and scalability.|聚类本质上是一项主观任务：单一数据集可以通过多种方式进行合理聚类，而在缺乏进一步信息的情况下，聚类系统无法确定应执行的适当聚类方式。这突显了将约束条件整合到聚类过程中的重要性，使用户能够向系统传达其偏好。基于主动约束的聚类方法优先识别最有价值的约束条件以进行询问，力求以最少数量的约束实现有效的聚类。我们提出了一种基于扩散模型的主动聚类方法（Active Clustering with Diffusion Model，简称ACDM）。ACDM采用最近邻技术构建扩散图，并利用在线框架迭代优化聚类结果。在每次迭代中，（a）以批处理模式选择具有高不确定性和代表性的节点，（b）然后使用基于邻域集的新型查询方法，通过成对约束对所选节点进行分类，（c）将分类后的节点作为扩散模型中的源节点，用于进一步的聚类优化。实验结果表明，ACDM在聚类质量和可扩展性方面均优于当前最先进的聚类方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=ACDM:+An+Effective+and+Scalable+Active+Clustering+with+Pairwise+Constraint)|0| |[Compositional and Hierarchical Semantic Learning Model for Hospital Readmission Prediction](https://doi.org/10.1145/3627673.3679814)|Weiting Gao, Xiangyu Gao, Yi Chen|New Jersey Institute of Technology, Newark, NJ, USA|Clinical notes provide a wealth of patient information that is valuable for predicting clinical outcomes. In particular, predicting hospital 30-day readmission is important to improve healthcare outcomes and reduce cost. Previous works on outcome prediction using clinical notes overlook complex semantic compositions and syntactic structure when learning the note level embedding, which may fail to capture the note semantics and make accurate predictions. To address these limitations, we propose a Compositional and Hierarchical Semantic Learning Model (CHSLM). It formulates the semantic learning of clinical notes into three hierarchies: word, composition, and note, and aggregates the semantics in a bottom-up manner. To aggregate the semantics from words to compositions, we construct heterogeneous medical-composition graphs to represent word interactions within and between medical compositions and use Graph Neural Networks to learn the composition embedding. To aggregate the semantics from composition- to note-level, we incorporate a mutual BiAffine transformation process. The experimental results on 30-day readmission prediction using two types of clinical notes demonstrate the effectiveness of our method over the state-of-the-art clinical prediction models.|临床记录提供了丰富的患者信息，这些信息对于预测临床结果具有重要价值。特别是，预测患者在出院后30天内再次入院的情况对于改善医疗结果和降低成本至关重要。以往利用临床记录进行结果预测的研究在学习笔记级别的嵌入时，忽视了复杂的语义组合和句法结构，这可能导致无法准确捕捉笔记的语义并做出精确的预测。为解决这些局限性，我们提出了一种组合与层次语义学习模型（CHSLM）。该模型将临床记录的语义学习划分为三个层次：词、组合和笔记，并以自底向上的方式聚合语义。为了从词到组合聚合语义，我们构建了异构的医疗组合图，以表示医疗组合内外的词交互，并使用图神经网络来学习组合嵌入。为了从组合层级到笔记层级聚合语义，我们引入了一个双向BiAffine转换过程。在利用两种类型的临床记录进行30天再入院预测的实验中，我们的方法展示了其优于现有最先进临床预测模型的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Compositional+and+Hierarchical+Semantic+Learning+Model+for+Hospital+Readmission+Prediction)|0| |[Mitigating Cold-Start Problems in Knowledge Tracing with Large Language Models: An Attribute-aware Approach](https://doi.org/10.1145/3627673.3679664)|Yuxiang Guo, Shuanghong Shen, Qi Liu, Zhenya Huang, Linbo Zhu, Yu Su, Enhong Chen|; Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, China|Knowledge Tracing (KT) is a crucial research task for dynamically monitoring students' knowledge states, particularly in online education systems. Recently, knowledge tracing has gained significant attention and in-depth research. Most existing methods rely on students' response data for question understanding and modeling, which helps better updating students' knowledge states. Meanwhile, question ID is utilized to indicate and represent questions. However, this presents a challenge when transitioning to new, cold-start questions that few students has answered before. Also, prior work has overlooked the semantic modeling of questions, which could better assist in modeling the transfer of students' knowledge states. In this paper, we explore leveraging the power of Large Language Models (LLMs) to help understand questions for knowledge tracing, which benefits mitigating cold-start and sparse problems and modeling the transfer of students' knowledge states in a sophisticated manner. Specifically, we first design an attribute estimation module to estimate the attribute of the questions (e.g., difficulty, ability requirements, expected response time) by prompting Large Language Models. Subsequently, we have developed a question embedding module that incorporates graph attention network to effectively utilizing these attributes. Extensive experiments on various datasets demonstrate that our model outperforms existing state-of-the-art models and effectively addresses the problems of cold-start and sparsity. In addition, due to the estimation of multiple attributes of the questions, our model exhibits superior interpretability.|知识追踪（Knowledge Tracing, KT）是动态监测学生知识状态的关键研究任务，尤其在在线教育系统中具有重要意义。近年来，知识追踪受到了广泛关注并进行了深入研究。现有的大多数方法依赖于学生的回答数据来理解问题并进行建模，从而更好地更新学生的知识状态。同时，问题ID被用来指示和表示问题。然而，这种方法在面对新出现的“冷启动”问题时存在挑战，尤其是那些之前很少有学生回答过的问题。此外，先前的研究忽视了问题的语义建模，这本可以更好地辅助建模学生知识状态的转移。本文探讨了利用大语言模型（Large Language Models, LLMs）来帮助理解问题，以进行知识追踪，这有助于缓解冷启动和数据稀疏问题，并以更精细的方式建模学生知识状态的转移。具体而言，我们首先设计了一个属性估计模块，通过提示大语言模型来估计问题的属性（如难度、能力要求、预期回答时间）。随后，我们开发了一个问题嵌入模块，结合图注意力网络，以有效利用这些属性。在多个数据集上的广泛实验表明，我们的模型优于现有的最先进模型，并有效地解决了冷启动和稀疏性问题。此外，由于对问题多个属性的估计，我们的模型展现出更强的可解释性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Mitigating+Cold-Start+Problems+in+Knowledge+Tracing+with+Large+Language+Models:+An+Attribute-aware+Approach)|0| -|[HeckmanCD: Exploiting Selection Bias in Cognitive Diagnosis](https://doi.org/10.1145/3627673.3679648)|Dongxuan Han, Qi Liu, Siqi Lei, Shiwei Tong, Wei Huang||||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=HeckmanCD:+Exploiting+Selection+Bias+in+Cognitive+Diagnosis)|0| -|[Spatio-Temporal Transformer Network with Physical Knowledge Distillation for Weather Forecasting](https://doi.org/10.1145/3627673.3679841)|Jing He, Junzhong Ji, Minglong Lei||||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Spatio-Temporal+Transformer+Network+with+Physical+Knowledge+Distillation+for+Weather+Forecasting)|0| -|[New Localization Frameworks: User-centric Approaches to Source Localization in Real-world Propagation Scenarios](https://doi.org/10.1145/3627673.3679796)|Dongpeng Hou, Yuchen Wang, Chao Gao, Xianghua Li, Zhen Wang||||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=New+Localization+Frameworks:+User-centric+Approaches+to+Source+Localization+in+Real-world+Propagation+Scenarios)|0| -|[Physics-guided Active Sample Reweighting for Urban Flow Prediction](https://doi.org/10.1145/3627673.3679738)|Wei Jiang, Tong Chen, Guanhua Ye, Wentao Zhang, Lizhen Cui, Zi Huang, Hongzhi Yin||||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Physics-guided+Active+Sample+Reweighting+for+Urban+Flow+Prediction)|0| -|[Federated Heterogeneous Contrastive Distillation for Molecular Representation Learning](https://doi.org/10.1145/3627673.3679725)|Jinjia Feng, Zhen Wang, Zhewei Wei, Yaliang Li, Bolin Ding, Hongteng Xu||||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Federated+Heterogeneous+Contrastive+Distillation+for+Molecular+Representation+Learning)|0| -|[Discrepancy-guided Channel Dropout for Domain Generalization](https://doi.org/10.1145/3627673.3679539)|Seonggyeom Kim, Byeongtae Park, Harim Lee, DongKyu Chae||||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Discrepancy-guided+Channel+Dropout+for+Domain+Generalization)|0| -|[Efficient and Secure Contribution Estimation in Vertical Federated Learning](https://doi.org/10.1145/3627673.3679613)|Juan Li, Rui Deng, Tianzi Zang, Mingqi Kong, Kun Zhu||||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Efficient+and+Secure+Contribution+Estimation+in+Vertical+Federated+Learning)|0| -|[MoTTo: Scalable Motif Counting with Time-aware Topology Constraint for Large-scale Temporal Graphs](https://doi.org/10.1145/3627673.3679694)|Jiantao Li, Jianpeng Qi, Yueling Huang, Lei Cao, Yanwei Yu, Junyu Dong||||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=MoTTo:+Scalable+Motif+Counting+with+Time-aware+Topology+Constraint+for+Large-scale+Temporal+Graphs)|0| -|[LagCNN: A Fast yet Effective Model for Multivariate Long-term Time Series Forecasting](https://doi.org/10.1145/3627673.3679672)|Linsen Li, Chunfei Jian, Feng Wan, Dongdong Geng, Ziquan Fang, Lu Chen, Yunjun Gao, Weihao Jiang, Jiang Zhu||||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=LagCNN:+A+Fast+yet+Effective+Model+for+Multivariate+Long-term+Time+Series+Forecasting)|0| -|[Noise-Resilient Unsupervised Graph Representation Learning via Multi-Hop Feature Quality Estimation](https://doi.org/10.1145/3627673.3679758)|Shiyuan Li, Yixin Liu, Qingfeng Chen, Geoffrey I. Webb, Shirui Pan||||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Noise-Resilient+Unsupervised+Graph+Representation+Learning+via+Multi-Hop+Feature+Quality+Estimation)|0| +|[HeckmanCD: Exploiting Selection Bias in Cognitive Diagnosis](https://doi.org/10.1145/3627673.3679648)|Dongxuan Han, Qi Liu, Siqi Lei, Shiwei Tong, Wei Huang|; Tencent Company, Shenzhen, China; Item Bank Department, National Education Examinations Authority, Beijing, China|Cognitive diagnosis, a fundamental task in education assessments, aims to quantify the students' proficiency level based on the historical test logs. However, the interactions between students and exercises are incomplete and even sparse, which means that only a few exercise scores of a specific student are observed. A key finding is that the pattern of this missingness is non-random, which could induce bias in the estimated proficiency value. To this end, we formulate cognitive diagnosis with a sample selection problem where observations are sampled through non-random probabilities that correlate with both the student's response correctness and the features of the student and exercise. We proposed a simple but effective method called HeckmanCD, adapting the Heckman two-stage approach to mitigate this endogeneity issue. We first employ an interaction model to predict the occurrence probability of a specific student-exercise pair. After that, a selection variable, derived from this interaction model, is incorporated as a controlled independent variable in the cognitive diagnosis framework. Our analysis reveals that the vanilla estimations of the item response theory model are inherently biased in the existence of confounders, and our method can correct this bias by capturing the covariance. The proposed HeckmanCD can be applied to most existing cognitive diagnosis models, including deep models, and the empirical evaluation demonstrates the effectiveness of our method while no other auxiliary information is required such as textual descriptions of exercises.|认知诊断，作为教育评估中的基础任务，旨在基于学生的历史测试记录量化其熟练度水平。然而，学生与练习之间的互动往往是不完整的，甚至是稀疏的，这意味着只能观察到特定学生的少数练习成绩。一个关键发现是，这种缺失的模式并非随机，可能会导致估计的熟练度值出现偏差。为此，我们将认知诊断问题形式化为一个样本选择问题，其中观察值是通过与学生回答正确性和学生及练习特征相关的非随机概率进行采样的。我们提出了一种简单但有效的方法，称为HeckmanCD，该方法采用Heckman两阶段方法来缓解这种内生性问题。首先，我们使用一个交互模型来预测特定学生-练习对的发生概率。随后，从该交互模型中得出的选择变量被纳入认知诊断框架中，作为受控的自变量。我们的分析表明，在存在混杂因素的情况下，项目反应理论模型的朴素估计本质上是有偏的，而我们的方法通过捕捉协方差可以纠正这种偏差。所提出的HeckmanCD方法可以应用于大多数现有的认知诊断模型，包括深度模型，并且实证评估显示了该方法的有效性，而无需其他辅助信息，如练习的文本描述。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=HeckmanCD:+Exploiting+Selection+Bias+in+Cognitive+Diagnosis)|0| +|[Spatio-Temporal Transformer Network with Physical Knowledge Distillation for Weather Forecasting](https://doi.org/10.1145/3627673.3679841)|Jing He, Junzhong Ji, Minglong Lei|College of Computer Science, Beijing University of Technology, Beijing, China|Weather forecasting has become a popular research topic recently, which mainly benefits from the development of spatio-temporal neural networks to effectively extract useful patterns from weather data. Generally, the weather changes in the meteorological system are governed by physical principles. However, it is challenging for spatio-temporal methods to capture the physical knowledge of meteorological dynamics. To address this problem, we propose in this paper a spatio-temporal Transformer network with physical knowledge distillation (PKD-STTN) for weather forecasting. First, the teacher network is implemented by a differential equation network that models weather changes by the potential energy in the atmosphere to reveal the physical mechanism of atmospheric movements. Second, the student network uses a spatio-temporal Transformer that concurrently utilizes three attention modules to comprehensively capture the semantic spatial correlation, geographical spatial correlation, and temporal correlation from weather data. Finally, the physical knowledge of the teacher network is transferred to the student network by inserting a distillation position encoding into the Transformer. Notice that the output of the teacher network is distilled to the position encoding rather than the output of the student network, which can largely utilize physical knowledge without influencing the feature extraction process of Transformers. Experiments on benchmark datasets show that the proposed method can effectively utilize physical principles of weather changes and has obvious performance advantages compared with several strong baselines.|天气预报近年来成为热门研究课题，主要得益于时空神经网络的发展，能够有效从天气数据中提取有用模式。通常，气象系统中的天气变化受物理原理支配。然而，时空方法难以捕捉气象动力学的物理知识。为解决此问题，本文提出一种融入物理知识蒸馏的时空Transformer网络（PKD-STTN）用于天气预报。首先，教师网络采用微分方程网络，通过大气中的势能建模天气变化，揭示大气运动的物理机制。其次，学生网络使用时空Transformer，同时利用三个注意力模块全面捕捉天气数据中的语义空间相关性、地理空间相关性和时间相关性。最后，通过插入蒸馏位置编码，将教师网络的物理知识传递给学生网络。注意，教师网络的输出被蒸馏至位置编码而非学生网络的输出，这样可以在不影响Transformer特征提取过程的情况下充分利用物理知识。在基准数据集上的实验表明，所提方法能有效利用天气变化的物理原理，相比多个强基线方法具有明显的性能优势。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Spatio-Temporal+Transformer+Network+with+Physical+Knowledge+Distillation+for+Weather+Forecasting)|0| +|[New Localization Frameworks: User-centric Approaches to Source Localization in Real-world Propagation Scenarios](https://doi.org/10.1145/3627673.3679796)|Dongpeng Hou, Yuchen Wang, Chao Gao, Xianghua Li, Zhen Wang|Northwestern Polytechnical University, Xi'an, Shaanxi, China|Source localization in social platforms is critical for managing and controlling the misinformation spreading. Despite all the recent advancements, existing methods do not consider the dynamic and heterogeneous propagation behaviors of users and are developed based on simulated data with strong model assumptions, limiting the application in real-world scenarios. This research addresses this limitation by presenting a novel framework for source localization, grounded in real-world propagation cascades from platforms like Weibo and Twitter. What's more, recognizing the user-driven nature of users in information spread, we systematically crawl and integrate user-specific profiles, offering a realistic understanding of user-driven propagation dynamics. In summary, by developing datasets derived from real-world propagation cascades, we set a precedent in enhancing the authenticity and practice of source identification for social media. Our comprehensive experiments not only validate the feasibility and rationale of our novel user-centric localization approaches but also emphasize the significance of considering user profiles in real-world propagation scenarios. The code is available at https://github.com/cgao-comp/NFSL.|社交平台中的信息源定位对于管理和控制虚假信息的传播至关重要。尽管近年来取得了诸多进展，但现有方法并未考虑用户传播行为的动态性和异质性，并且这些方法基于具有强假设的模拟数据开发，限制了其在现实场景中的应用。本研究通过提出一种基于微博和推特等平台真实传播链的全新源定位框架，解决了这一局限性。此外，鉴于信息传播中用户驱动的特性，我们系统地爬取并整合了用户特定的个人资料，从而提供了对用户驱动传播动态的真实理解。总之，通过开发源自真实传播链的数据集，我们为增强社交媒体源识别的真实性和实用性树立了先例。我们的全面实验不仅验证了我们以用户为中心的新型定位方法的可行性和合理性，还强调了在现实传播场景中考虑用户个人资料的重要性。代码已公开，详见 https://github.com/cgao-comp/NFSL。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=New+Localization+Frameworks:+User-centric+Approaches+to+Source+Localization+in+Real-world+Propagation+Scenarios)|0| +|[Physics-guided Active Sample Reweighting for Urban Flow Prediction](https://doi.org/10.1145/3627673.3679738)|Wei Jiang, Tong Chen, Guanhua Ye, Wentao Zhang, Lizhen Cui, Zi Huang, Hongzhi Yin||Urban flow prediction is a spatio-temporal modeling task that estimates the throughput of transportation services like buses, taxis, and ride-sharing, where data-driven models have become the most popular solution in the past decade. Meanwhile, the implicitly learned mapping between historical observations to the prediction targets tend to over-simplify the dynamics of real-world urban flows, leading to suboptimal predictions. Some recent spatio-temporal prediction solutions bring remedies with the notion of physics-guided machine learning (PGML), which describes spatio-temporal data with nuanced and principled physics laws, thus enhancing both the prediction accuracy and interpretability. However, these spatio-temporal PGML methods are built upon a strong assumption that the observed data fully conforms to the differential equations that define the physical system, which can quickly become ill-posed in urban flow prediction tasks. The observed urban flow data, especially when sliced into time-dependent snapshots to facilitate predictions, is typically incomplete and sparse, and prone to inherent noise incurred in the collection process. As a result, such physical inconsistency between the data and PGML model significantly limits the predictive power and robustness of the solution. Moreover, due to the interval-based predictions and intermittent nature of data filing in many transportation services, the instantaneous dynamics of urban flows can hardly be captured, rendering differential equation-based continuous modeling a loose fit for this setting. To overcome the challenges, we develop a discretized physics-guided network (PN), and propose a data-aware framework Physics-guided Active Sample Reweighting (P-GASR) to enhance PN. Experimental results in four real-world datasets demonstrate that our method achieves state-of-the-art performance with a demonstrable improvement in robustness.|城市流量预测是一项时空建模任务，旨在估算公交车、出租车和共享出行等交通服务的吞吐量，其中数据驱动模型在过去十年中已成为最受欢迎的解决方案。然而，隐式学习的历史观测与预测目标之间的映射关系往往过于简化现实世界中城市流量的动态特性，导致预测效果不佳。近期，一些时空预测解决方案引入了物理引导机器学习（PGML）的概念，通过利用细致且基于物理定律的描述来增强预测的准确性和可解释性。然而，这些时空PGML方法基于一个强假设，即观测数据完全符合定义物理系统的微分方程，这在城市流量预测任务中往往难以成立。观测到的城市流量数据，尤其是为了便于预测而分割成时间依赖的快照时，通常是不完整且稀疏的，并且容易受到采集过程中固有噪声的影响。因此，数据与PGML模型之间的物理不一致性显著限制了该解决方案的预测能力和鲁棒性。此外，由于许多交通服务中的数据记录是基于时间间隔的，并且具有间歇性，难以捕捉城市流量的瞬时动态，使得基于微分方程的连续建模方法在此场景下并不适用。为应对这些挑战，我们开发了一种离散化的物理引导网络（PN），并提出了一种数据感知框架——物理引导主动样本重加权（P-GASR），以增强PN的性能。在四个真实世界数据集上的实验结果表明，我们的方法在实现最先进性能的同时，显著提升了模型的鲁棒性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Physics-guided+Active+Sample+Reweighting+for+Urban+Flow+Prediction)|0| +|[Federated Heterogeneous Contrastive Distillation for Molecular Representation Learning](https://doi.org/10.1145/3627673.3679725)|Jinjia Feng, Zhen Wang, Zhewei Wei, Yaliang Li, Bolin Ding, Hongteng Xu|Sun Yat-sen University, Guangzhou, China; Peng Cheng Laboratory & Renmin University of China, Shenzhen, China; Renmin University of China, Beijing, China; Alibaba Group, Bellevue, WA, USA; Alibaba Group, Bellevue, CA, USA|With the increasing application of deep learning to solve scientific problems in biochemistry, molecular federated learning has become popular due to its ability to offer distributed privacy-preserving solutions. However, most existing molecular federated learning methods rely on joint training with public datasets, which are difficult to obtain in practice. These methods also fail to leverage multi-modal molecular representations effectively. To address the above issues, we propose a novel framework, Federated Heterogeneous Contrastive Distillation (FedHCD), which enables to jointly train global models from clients with heterogeneous data modalities, learning tasks, and molecular models. To aggregate data representations of different modalities in a data-free manner, we design a global multi-modal contrastive strategy to align the representation of clients without public dataset. Utilizing intrinsic characteristics of molecular data in different modalities, we tackle the exacerbation of local model drift and data Non-IIDness caused by multi-modal clients. We introduce a multi-view contrastive knowledge transfer to extract features from atoms, substructures, and molecules, solving the issue of information distillation failure due to dimensional biases in different data modalities. Our evaluations on eight real-world molecular datasets and ablation experiments show that FedHCD outperforms other state-of-the-art FL methods, irrespective of whether or not they use public datasets.|随着深度学习在解决生物化学领域科学问题中的应用日益增多，分子联邦学习因其能够提供分布式隐私保护解决方案而受到广泛关注。然而，现有的大多数分子联邦学习方法依赖于与公共数据集的联合训练，这在实际中难以获取。此外，这些方法未能有效利用多模态分子表示。为解决上述问题，我们提出了一种新颖的框架——联邦异构对比蒸馏（Federated Heterogeneous Contrastive Distillation, FedHCD），该框架能够从具有异构数据模态、学习任务和分子模型的客户端中联合训练全局模型。为了在无需数据的情况下聚合不同模态的数据表示，我们设计了一种全局多模态对比策略，以对齐客户端的表示，而不依赖于公共数据集。利用不同模态分子数据的内禀特性，我们解决了多模态客户端导致的局部模型漂移和数据非独立同分布（Non-IID）问题。我们引入了一种多视角对比知识迁移方法，从原子、子结构和分子中提取特征，解决了由于不同数据模态间的维度偏差导致的信息蒸馏失败问题。我们在八个真实世界的分子数据集上进行了评估和消融实验，结果表明，无论是否使用公共数据集，FedHCD均优于其他最先进的联邦学习方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Federated+Heterogeneous+Contrastive+Distillation+for+Molecular+Representation+Learning)|0| +|[Discrepancy-guided Channel Dropout for Domain Generalization](https://doi.org/10.1145/3627673.3679539)|Seonggyeom Kim, Byeongtae Park, Harim Lee, DongKyu Chae|Hanyang University, Seoul, Republic of Korea|Deep Neural Networks (DNNs) tend to perform poorly on unseen domains due to domain shifts. Domain Generalization (DG) aims to improve the performance on such scenarios by minimizing the distribution discrepancy between source domains. Among many studies, dropout-based DG approaches which remove domain-specific features have gained attention. However, they are limited in minimizing the upper bound of generalization risk because they do not explicitly consider the distribution discrepancy when discarding features. In this paper, we propose a novel Discrepancy-guided Channel Dropout (DgCD) for DG that explicitly derives the discrepancy between domains and drops the channels with significant distribution discrepancy. Given a training batch, we perform two ways of standardization: (1) based on the variance/mean of the batch (i.e., sampled from all source domains) and (2) based on the variance/mean of domain-wise samples in the batch. With the two normal distributions, we explicitly derive the discrepancy using KL-divergence and backpropagate it towards each channel. A channel with a higher contribution to the discrepancy is more likely to be dropped. Experimental results show the superiority of DgCD over the state-of-the-art DG baselines, demonstrating the effectiveness of our dropout strategy which is directly coupled to reducing the domain discrepancy. Our code is available at: https://github.com/gyeomo/DgCD|深度神经网络（DNNs）在面对未见过的领域时往往表现不佳，这是由于领域转移造成的。领域泛化（Domain Generalization, DG）旨在通过最小化源领域之间的分布差异来提升在这些场景下的性能。在众多研究中，基于dropout的DG方法因其能够去除领域特定特征而受到关注。然而，这些方法在最小化泛化风险的上界方面存在局限，因为它们在丢弃特征时并未明确考虑分布差异。本文提出了一种新颖的差异引导通道dropout（Discrepancy-guided Channel Dropout, DgCD）方法，用于领域泛化，该方法明确地推导出领域间的差异，并丢弃具有显著分布差异的通道。在给定的训练批次中，我们执行两种标准化方式：（1）基于批次的方差/均值（即从所有源领域中采样）；（2）基于批次中各领域样本的方差/均值。通过这两个正态分布，我们使用KL散度明确地推导出差异，并将其反向传播到每个通道。对差异贡献较大的通道更有可能被丢弃。实验结果表明，DgCD在性能上优于当前最先进的领域泛化基线方法，证明了我们的dropout策略能直接减少领域差异的有效性。我们的代码已公开，地址为：https://github.com/gyeomo/DgCD。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Discrepancy-guided+Channel+Dropout+for+Domain+Generalization)|0| +|[Efficient and Secure Contribution Estimation in Vertical Federated Learning](https://doi.org/10.1145/3627673.3679613)|Juan Li, Rui Deng, Tianzi Zang, Mingqi Kong, Kun Zhu|Nanjing University of Aeronautics and Astronautics, Nanjing, China|As necessary information about whether cooperation can be reached, rewards should be determined in advance in Vertical Federated Learning (VFL). To determine reasonable rewards, participant contributions should be estimated precisely. We propose a Vertically Federated Contribution Estimation (VF-CE) method. VF-CE calculates Mutual Information (MI) between distributed features and the label using a neural network trained via VFL itself. Note that compensation for CE is low as it only covers computation costs, and reward for real VFL training is high as it needs to cover training costs as well as participants' contributions to model performance and the resulting business benefits. Because MI presents a strong positive correlation with the final model performance, contributions to model performance can be estimated based on contributions to MI. We integrate a scalar-level attention mechanism in MI neural network. The attention weights of participants are treated as their contributions. We find that attention weights can effectively measure contribution redundancy, as its Spearman correlation coefficient with Shapley value is as high as 0.963. We demonstrate that VF-CE also satisfies properties of balance, zero element, and symmetry concerning fairness, which are hallmark properties of Shapley value. Compared with existing work, we consider contribution redundancy precisely, efficiently output approximated Shapley values through one MI calculation instead of 2 n where n is the number of participants, and introduce no extra privacy risk except the inherent risk in VFL, i.e., gradient transmission.|在纵向联邦学习（Vertical Federated Learning, VFL）中，为了确定合作是否能够达成，奖励应提前设定。为了设定合理的奖励，参与者的贡献应被精确估计。我们提出了一种纵向联邦贡献估计（Vertically Federated Contribution Estimation, VF-CE）方法。VF-CE通过使用VFL自身训练的神经网络计算分布式特征与标签之间的互信息（Mutual Information, MI）。需要注意的是，CE的补偿较低，因为它仅涵盖计算成本，而实际VFL训练的奖励较高，因为它需要涵盖训练成本以及参与者对模型性能和由此产生的业务效益的贡献。由于MI与最终模型性能呈现强正相关，因此可以通过MI的贡献来估计对模型性能的贡献。我们在MI神经网络中集成了一个标量级别的注意力机制，将参与者的注意力权重视为他们的贡献。我们发现，注意力权重能够有效衡量贡献冗余，其与Shapley值的Spearman相关系数高达0.963。我们证明，VF-CE在公平性方面也满足平衡性、零元素和对称性等特性，这些是Shapley值的标志性特性。与现有工作相比，我们精确考虑了贡献冗余，通过一次MI计算高效输出近似的Shapley值，而不是2^n次计算（其中n是参与者的数量），并且除了VFL固有的梯度传输隐私风险外，不引入额外的隐私风险。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Efficient+and+Secure+Contribution+Estimation+in+Vertical+Federated+Learning)|0| +|[MoTTo: Scalable Motif Counting with Time-aware Topology Constraint for Large-scale Temporal Graphs](https://doi.org/10.1145/3627673.3679694)|Jiantao Li, Jianpeng Qi, Yueling Huang, Lei Cao, Yanwei Yu, Junyu Dong|Ocean University of China, Qingdao, Shandong, China; University of Arizona, Tucson, USA; Ocean University of China, Qingdao, China|Temporal motifs are recurring subgraph patterns in temporal graphs, and are present in various domains such as social networks, fraud detection, and biological networks. Despite their significance, counting temporal motifs efficiently remains a challenge, particularly on moderately sized datasets with millions of motif instances. To address this challenge, we propose a novel algorithm called Scalable Motif Counting with Time-aware Topology Constraint (MoTTo). MoTTo focuses on accurately counting temporal motifs with up to three nodes and three edges. It first utilizes a topology constraint-based pruning strategy to eliminate nodes that cannot participate in forming temporal motifs before the counting process. Then, it adopts a time-aware topology constraint-based pruning strategy to split large-scale datasets into independent partitions and filter out the unrelated ones, ensuring that the counting results remain unaffected. By investigating the second pruning strategy, we also find that MoTTo can be implemented in a multi-thread manner, further accelerating the counting process significantly. Experimental results on several real-world datasets of varying sizes demonstrate that MoTTo outperforms state-of-the-art methods in terms of efficiency, achieving up to a nine-fold improvement in total temporal motif counting. Specifically, the efficiency of counting triangular temporal motifs is enhanced by up to 31 times compared to state-of-the-art baselines.|时态模体是在时态图中重复出现的子图模式，广泛存在于社交网络、欺诈检测和生物网络等多个领域。尽管它们具有重要意义，但高效地统计时态模体仍然是一个挑战，尤其是在包含数百万模体实例的中等规模数据集上。为了应对这一挑战，我们提出了一种名为“带有时态拓扑约束的可扩展模体计数”（MoTTo）的新算法。MoTTo专注于精确统计包含最多三个节点和三条边的时态模体。首先，它利用基于拓扑约束的剪枝策略，在计数过程之前排除无法参与形成时态模体的节点。接着，它采用基于时态拓扑约束的剪枝策略，将大规模数据集分割成独立的分区并过滤掉无关部分，从而确保计数结果不受影响。通过研究第二种剪枝策略，我们还发现MoTTo可以实现多线程处理，进一步显著加速计数过程。在多个不同规模的实际数据集上的实验结果表明，MoTTo在效率方面优于当前最先进的方法，总时态模体计数效率提升高达九倍。具体而言，与最先进的基线方法相比，三角形时态模体的计数效率提高了多达31倍。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=MoTTo:+Scalable+Motif+Counting+with+Time-aware+Topology+Constraint+for+Large-scale+Temporal+Graphs)|0| +|[LagCNN: A Fast yet Effective Model for Multivariate Long-term Time Series Forecasting](https://doi.org/10.1145/3627673.3679672)|Linsen Li, Chunfei Jian, Feng Wan, Dongdong Geng, Ziquan Fang, Lu Chen, Yunjun Gao, Weihao Jiang, Jiang Zhu|Zhejiang University, Hangzhou, China; Hikvision Research Institute, Hangzhou, China; Zhejiang University & Hikvision Research Institute, Hangzhou, China|Long-term time series forecasting has gained significant attention in recent years due to its widely-application in various fields. Transformer-based models have gained popularity for the ability to capture long-sequence interactions. However, these models are limited in real-world use because of the memory consumption and computation explosion. The CNN-based models are also one of the main models used for time series prediction, but their performance has always been inferior to the transformer-based models in previous works. We have reconsidered the role of CNN components and redefined the way CNN basic components are used for time series prediction. In addition, the time lags information between periods in the time series is important. Unfortunately, existing works lack consideration of this classic but important information. Motivated by these factors, we propose a fast yet effective CNN model with time lags for multivariate long-term time series forecasting, named LagCNN. Specifically, the time series is transformed into lag-patches to capture the correlation between periods. Then, a fast CNN model is performed in the feature dimension rather than the time dimension like most previous works do. Meanwhile, information aggregation is performed in the time dimension to extract complex temporal patterns. LagCNN significantly outperforms state-of-the-art on multiple publicly available datasets. One step further, LagCNN exhibits significant efficiency advantages over the most efficient Transformer model (PatchTST), resulting in a significant reduction in memory usage (4.4×) and runtime (10.7×).|长期时间序列预测近年来因其广泛的应用而备受关注。基于Transformer的模型因其捕捉长序列交互的能力而受到欢迎。然而，这些模型在实际应用中受到限制，因为它们存在内存消耗和计算爆炸的问题。基于CNN的模型也是用于时间序列预测的主要模型之一，但它们在以往的研究中的表现一直不如基于Transformer的模型。我们重新考虑了CNN组件的作用，并重新定义了用于时间序列预测的CNN基本组件的使用方式。此外，时间序列中各周期之间的时间滞后信息非常重要。遗憾的是，现有研究缺乏对这一经典但重要信息的考虑。受这些因素的启发，我们提出了一种快速且有效的CNN模型，用于多元长期时间序列预测，命名为LagCNN。具体来说，时间序列被转换为滞后补丁，以捕捉周期之间的相关性。然后，在特征维度而不是像大多数先前的工作那样在时间维度上执行快速CNN模型。同时，在时间维度上进行信息聚合，以提取复杂的时间模式。LagCNN在多个公开可用的数据集上显著优于最先进的方法。进一步地，LagCNN在与最有效的Transformer模型（PatchTST）相比时表现出显著的效率优势，导致内存使用量（4.4倍）和运行时间（10.7倍）大幅减少。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=LagCNN:+A+Fast+yet+Effective+Model+for+Multivariate+Long-term+Time+Series+Forecasting)|0| +|[Noise-Resilient Unsupervised Graph Representation Learning via Multi-Hop Feature Quality Estimation](https://doi.org/10.1145/3627673.3679758)|Shiyuan Li, Yixin Liu, Qingfeng Chen, Geoffrey I. Webb, Shirui Pan||Unsupervised graph representation learning (UGRL) based on graph neural networks (GNNs), has received increasing attention owing to its efficacy in handling graph-structured data. However, existing UGRL methods ideally assume that the node features are noise-free, which makes them fail to distinguish between useful information and noise when applied to real data with noisy features, thus affecting the quality of learned representations. This urges us to take node noisy features into account in real-world UGRL. With empirical analysis, we reveal that feature propagation, the essential operation in GNNs, acts as a "double-edged sword" in handling noisy features - it can both denoise and diffuse noise, leading to varying feature quality across nodes, even within the same node at different hops. Building on this insight, we propose a novel UGRL method based on Multi-hop feature Quality Estimation (MQE for short). Unlike most UGRL models that directly utilize propagation-based GNNs to generate representations, our approach aims to learn representations through estimating the quality of propagated features at different hops. Specifically, we introduce a Gaussian model that utilizes a learnable "meta-representation" as a condition to estimate the expectation and variance of multi-hop propagated features via neural networks. In this way, the "meta representation" captures the semantic and structural information underlying multiple propagated features but is naturally less susceptible to interference by noise, thereby serving as high-quality node representations beneficial for downstream tasks. Extensive experiments on multiple real-world datasets demonstrate that MQE in learning reliable node representations in scenarios with diverse types of feature noise.|基于图神经网络（GNN）的无监督图表示学习（UGRL）因其有效处理图结构数据的能力而受到越来越多的关注。然而，现有的UGRL方法理想化地假设节点特征是无噪声的，这使得它们在应用于具有噪声特征的真实数据时，无法区分有用信息和噪声，从而影响学习到的表示质量。这促使我们在现实世界的UGRL中考虑节点噪声特征。通过实证分析，我们揭示了特征传播——GNN中的基本操作——在处理噪声特征时起到了“双刃剑”的作用——它既能去噪又能扩散噪声，导致不同节点之间的特征质量不同，甚至在同一节点的不同跳数之间也存在差异。基于这一见解，我们提出了一种基于多跳特征质量估计（MQE）的新型UGRL方法。与大多数直接利用基于传播的GNN生成表示的UGRL模型不同，我们的方法旨在通过估计不同跳数传播特征的质量来学习表示。具体来说，我们引入了一个高斯模型，该模型利用可学习的“元表示”作为条件，通过神经网络估计多跳传播特征的期望和方差。通过这种方式，“元表示”捕捉了多个传播特征背后的语义和结构信息，但自然较少受到噪声的干扰，从而作为高质量的节点表示，有利于下游任务。在多个真实世界数据集上的广泛实验表明，MQE在处理具有多种特征噪声的场景中能够学习到可靠的节点表示。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Noise-Resilient+Unsupervised+Graph+Representation+Learning+via+Multi-Hop+Feature+Quality+Estimation)|0| |[Aligning Large Language Models to a Domain-specific Graph Database for NL2GQL](https://doi.org/10.1145/3627673.3679713)|Yuanyuan Liang, Keren Tan, Tingyu Xie, Wenbiao Tao, Siyuan Wang, Yunshi Lan, Weining Qian||||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Aligning+Large+Language+Models+to+a+Domain-specific+Graph+Database+for+NL2GQL)|0| |[ITIU: Intention Understanding via Interactive Table in Large Language Models](https://doi.org/10.1145/3627673.3679688)|Zenghua Liao, Jinzhi Liao, Xiang Zhao||||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=ITIU:+Intention+Understanding+via+Interactive+Table+in+Large+Language+Models)|0| |[Unveiling Intellectual Property Vulnerabilities of GAN-Based Distributed Machine Learning through Model Extraction Attacks](https://doi.org/10.1145/3627673.3679850)|Mengyao Ma, Shuofeng Liu, M. A. P. Chamikara, Mohan Baruwal Chhetri, Guangdong Bai||||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Unveiling+Intellectual+Property+Vulnerabilities+of+GAN-Based+Distributed+Machine+Learning+through+Model+Extraction+Attacks)|0| diff --git a/papers/ecir/ecir2024.md b/papers/ecir/ecir2024.md index 861dc0ee..7e1fdc17 100644 --- a/papers/ecir/ecir2024.md +++ b/papers/ecir/ecir2024.md @@ -8,7 +8,7 @@ |[Incorporating Query Recommendation for Improving In-Car Conversational Search](https://doi.org/10.1007/978-3-031-56069-9_36)|Md. Rashad Al Hasan Rony, Soumya Ranjan Sahoo, Abbas Goher Khan, Ken E. Friedl, Viju Sudhi, Christian Süß||||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Incorporating+Query+Recommendation+for+Improving+In-Car+Conversational+Search)|0| |[ChatGPT Goes Shopping: LLMs Can Predict Relevance in eCommerce Search](https://doi.org/10.1007/978-3-031-56066-8_1)|Beatriz Soviero, Daniel Kuhn, Alexandre Salle, Viviane Pereira Moreira||||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=ChatGPT+Goes+Shopping:+LLMs+Can+Predict+Relevance+in+eCommerce+Search)|0| |[Lottery4CVR: Neuron-Connection Level Sharing for Multi-task Learning in Video Conversion Rate Prediction](https://doi.org/10.1007/978-3-031-56069-9_31)|Xuanji Xiao, Jimmy Chen, Yuzhen Liu, Xing Yao, Pei Liu, Chaosheng Fan||||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Lottery4CVR:+Neuron-Connection+Level+Sharing+for+Multi-task+Learning+in+Video+Conversion+Rate+Prediction)|0| -|[Utilizing Low-Dimensional Molecular Embeddings for Rapid Chemical Similarity Search](https://doi.org/10.1007/978-3-031-56060-6_3)|Kathryn E. Kirchoff, James Wellnitz, Joshua E. Hochuli, Travis Maxfield, Konstantin I. Popov, Shawn M. Gomez, Alexander Tropsha|Department of Computer Science, UNC Chapel Hill.; Eshelman School of Pharmacy, UNC Chapel Hill.; Department of Pharmacology, UNC Chapel Hill.|Nearest neighbor-based similarity searching is a common task in chemistry, with notable use cases in drug discovery. Yet, some of the most commonly used approaches for this task still leverage a brute-force approach. In practice this can be computationally costly and overly time-consuming, due in part to the sheer size of modern chemical databases. Previous computational advancements for this task have generally relied on improvements to hardware or dataset-specific tricks that lack generalizability. Approaches that leverage lower-complexity searching algorithms remain relatively underexplored. However, many of these algorithms are approximate solutions and/or struggle with typical high-dimensional chemical embeddings. Here we evaluate whether a combination of low-dimensional chemical embeddings and a k-d tree data structure can achieve fast nearest neighbor queries while maintaining performance on standard chemical similarity search benchmarks. We examine different dimensionality reductions of standard chemical embeddings as well as a learned, structurally-aware embedding-SmallSA-for this task. With this framework, searches on over one billion chemicals execute in less than a second on a single CPU core, five orders of magnitude faster than the brute-force approach. We also demonstrate that SmallSA achieves competitive performance on chemical similarity benchmarks.|基于最近邻的相似性搜索是化学中的一个常见任务，在药物发现中有着显著的应用案例。然而，这项任务中一些最常用的方法仍然使用蛮力方法。在实践中，由于现代化学品数据库的庞大规模，这可能会造成计算成本高昂和过度耗时。此任务之前的计算改进通常依赖于对缺乏普遍性的硬件或数据集特定技巧的改进。利用低复杂度搜索算法的方法仍然相对缺乏探索。然而，许多这些算法是近似解决方案和/或与典型的高维化学嵌入斗争。在这里，我们评估是否结合低维化学嵌入和 k-d 树数据结构可以实现快速最近邻查询，同时保持标准化学相似性搜索基准的性能。我们考察了不同维度的标准化学嵌入降低以及一个学习，结构意识的嵌入-SmallSA-为这项任务。有了这个框架，超过十亿种化学物质的搜索在不到一秒钟的时间内在一个 CPU 核心上执行，比蛮力搜索数量级快5倍。我们亦证明 SmallSA 在化学相似性基准方面取得具竞争力的表现。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Utilizing+Low-Dimensional+Molecular+Embeddings+for+Rapid+Chemical+Similarity+Search)|0| +|[Utilizing Low-Dimensional Molecular Embeddings for Rapid Chemical Similarity Search](https://doi.org/10.1007/978-3-031-56060-6_3)|Kathryn E. Kirchoff, James Wellnitz, Joshua E. Hochuli, Travis Maxfield, Konstantin I. Popov, Shawn M. Gomez, Alexander Tropsha|Eshelman School of Pharmacy, UNC Chapel Hill.; Department of Pharmacology, UNC Chapel Hill.; Department of Computer Science, UNC Chapel Hill.|Nearest neighbor-based similarity searching is a common task in chemistry, with notable use cases in drug discovery. Yet, some of the most commonly used approaches for this task still leverage a brute-force approach. In practice this can be computationally costly and overly time-consuming, due in part to the sheer size of modern chemical databases. Previous computational advancements for this task have generally relied on improvements to hardware or dataset-specific tricks that lack generalizability. Approaches that leverage lower-complexity searching algorithms remain relatively underexplored. However, many of these algorithms are approximate solutions and/or struggle with typical high-dimensional chemical embeddings. Here we evaluate whether a combination of low-dimensional chemical embeddings and a k-d tree data structure can achieve fast nearest neighbor queries while maintaining performance on standard chemical similarity search benchmarks. We examine different dimensionality reductions of standard chemical embeddings as well as a learned, structurally-aware embedding-SmallSA-for this task. With this framework, searches on over one billion chemicals execute in less than a second on a single CPU core, five orders of magnitude faster than the brute-force approach. We also demonstrate that SmallSA achieves competitive performance on chemical similarity benchmarks.|基于最近邻的相似性搜索是化学中的一个常见任务，在药物发现中有着显著的应用案例。然而，这项任务中一些最常用的方法仍然使用蛮力方法。在实践中，由于现代化学品数据库的庞大规模，这可能会造成计算成本高昂和过度耗时。此任务之前的计算改进通常依赖于对缺乏普遍性的硬件或数据集特定技巧的改进。利用低复杂度搜索算法的方法仍然相对缺乏探索。然而，许多这些算法是近似解决方案和/或与典型的高维化学嵌入斗争。在这里，我们评估是否结合低维化学嵌入和 k-d 树数据结构可以实现快速最近邻查询，同时保持标准化学相似性搜索基准的性能。我们考察了不同维度的标准化学嵌入降低以及一个学习，结构意识的嵌入-SmallSA-为这项任务。有了这个框架，超过十亿种化学物质的搜索在不到一秒钟的时间内在一个 CPU 核心上执行，比蛮力搜索数量级快5倍。我们亦证明 SmallSA 在化学相似性基准方面取得具竞争力的表现。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Utilizing+Low-Dimensional+Molecular+Embeddings+for+Rapid+Chemical+Similarity+Search)|0| |[Evaluating the Impact of Content Deletion on Tabular Data Similarity and Retrieval Using Contextual Word Embeddings](https://doi.org/10.1007/978-3-031-56060-6_28)|Alberto Berenguer, David Tomás, JoseNorberto Mazón||||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Evaluating+the+Impact+of+Content+Deletion+on+Tabular+Data+Similarity+and+Retrieval+Using+Contextual+Word+Embeddings)|0| |[RIGHT: Retrieval-Augmented Generation for Mainstream Hashtag Recommendation](https://doi.org/10.1007/978-3-031-56027-9_3)|RunZe Fan, Yixing Fan, Jiangui Chen, Jiafeng Guo, Ruqing Zhang, Xueqi Cheng||Automatic mainstream hashtag recommendation aims to accurately provide users with concise and popular topical hashtags before publication. Generally, mainstream hashtag recommendation faces challenges in the comprehensive difficulty of newly posted tweets in response to new topics, and the accurate identification of mainstream hashtags beyond semantic correctness. However, previous retrieval-based methods based on a fixed predefined mainstream hashtag list excel in producing mainstream hashtags, but fail to understand the constant flow of up-to-date information. Conversely, generation-based methods demonstrate a superior ability to comprehend newly posted tweets, but their capacity is constrained to identifying mainstream hashtags without additional features. Inspired by the recent success of the retrieval-augmented technique, in this work, we attempt to adopt this framework to combine the advantages of both approaches. Meantime, with the help of the generator component, we could rethink how to further improve the quality of the retriever component at a low cost. Therefore, we propose RetrIeval-augmented Generative Mainstream HashTag Recommender (RIGHT), which consists of three components: 1) a retriever seeks relevant hashtags from the entire tweet-hashtags set; 2) a selector enhances mainstream identification by introducing global signals; and 3) a generator incorporates input tweets and selected hashtags to directly generate the desired hashtags. The experimental results show that our method achieves significant improvements over state-of-the-art baselines. Moreover, RIGHT can be easily integrated into large language models, improving the performance of ChatGPT by more than 10%.|自动主流话题标签推荐的目的是准确地为用户提供简洁和流行的话题标签发布前。一般来说，主流话题标签推荐面临的挑战包括: 新发布的推文在回应新话题方面的综合难度，以及在语义正确性之外对主流话题标签的准确识别。然而，以往基于固定预定义主流标签列表的检索方法在生成主流标签方面表现出色，但不能理解不断更新的信息流。相反，基于生成的方法展示了理解新发布的 tweet 的优越能力，但它们的能力仅限于识别主流标签，而没有其他特性。受近年来检索增强技术的成功启发，本文尝试采用这一框架将两种方法的优点结合起来。同时，借助于发生器组件，我们可以重新思考如何以较低的成本进一步提高检索器组件的质量。因此，我们提出了 RetrIeval 增强的生成主流 HashTag 推荐器(RIGHT) ，它由三个组成部分组成: 1)检索器从整个 tweet-HashTag 集中寻找相关的 HashTag; 2)选择器通过引入全局信号增强主流识别; 3)生成器结合输入 tweet 和选定的 HashTag 直接生成所需的 HashTag。实验结果表明，我们的方法取得了显着的改进，在最先进的基线。此外，可以很容易地将权限集成到大型语言模型中，使 ChatGPT 的性能提高10% 以上。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=RIGHT:+Retrieval-Augmented+Generation+for+Mainstream+Hashtag+Recommendation)|0| |[Exploring the Nexus Between Retrievability and Query Generation Strategies](https://doi.org/10.1007/978-3-031-56066-8_16)|Aman Sinha, Priyanshu Raj Mall, Dwaipayan Roy||Quantifying bias in retrieval functions through document retrievability scores is vital for assessing recall-oriented retrieval systems. However, many studies investigating retrieval model bias lack validation of their query generation methods as accurate representations of retrievability for real users and their queries. This limitation results from the absence of established criteria for query generation in retrievability assessments. Typically, researchers resort to using frequent collocations from document corpora when no query log is available. In this study, we address the issue of reproducibility and seek to validate query generation methods by comparing retrievability scores generated from artificially generated queries to those derived from query logs. Our findings demonstrate a minimal or negligible correlation between retrievability scores from artificial queries and those from query logs. This suggests that artificially generated queries may not accurately reflect retrievability scores as derived from query logs. We further explore alternative query generation techniques, uncovering a variation that exhibits the highest correlation. This alternative approach holds promise for improving reproducibility when query logs are unavailable.|通过文档可检索性评分量化检索功能中的偏差对于评估面向回忆的检索系统至关重要。然而，许多研究检索模型偏倚的研究缺乏验证其查询生成方法作为准确表示的可检索性的真实用户和他们的查询。这种局限性是由于在可检索性评估中缺乏确定的查询生成标准造成的。通常，当没有查询日志可用时，研究人员会使用文档语料库中的频繁搭配。在这项研究中，我们解决了重复性的问题，并寻求验证查询生成方法，通过比较从人工生成的查询和从查询日志得到的查询可检索性得分。我们的研究结果表明，人工查询和查询日志的可检索性得分之间的相关性很小，甚至可以忽略不计。这表明人工生成的查询可能不能准确地反映从查询日志中获得的可检索性得分。我们进一步探索替代的查询生成技术，发现具有最高相关性的变体。这种替代方法有望在查询日志不可用时提高可重复性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Exploring+the+Nexus+Between+Retrievability+and+Query+Generation+Strategies)|0| @@ -42,7 +42,7 @@ |[A Conversational Search Framework for Multimedia Archives](https://doi.org/10.1007/978-3-031-56069-9_25)|Anastasia Potyagalova, Gareth J. F. Jones||||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Conversational+Search+Framework+for+Multimedia+Archives)|0| |[Effective and Efficient Transformer Models for Sequential Recommendation](https://doi.org/10.1007/978-3-031-56069-9_39)|Aleksandr V. Petrov||||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Effective+and+Efficient+Transformer+Models+for+Sequential+Recommendation)|0| |[Quantum Computing for Information Retrieval and Recommender Systems](https://doi.org/10.1007/978-3-031-56069-9_47)|Maurizio Ferrari Dacrema, Andrea Pasin, Paolo Cremonesi, Nicola Ferro||||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Quantum+Computing+for+Information+Retrieval+and+Recommender+Systems)|0| -|[Transformers for Sequential Recommendation](https://doi.org/10.1007/978-3-031-56069-9_49)|Aleksandr V. Petrov, Craig Macdonald|Ocean University of China, Qingdao, China; University of Hong Kong, Hong Kong, China; Wuhan University, Wuhan, China; National University of Singapore, Singapore, Singapore|Learning dynamic user preference has become an increasingly important component for many online platforms (e.g., video-sharing sites, e-commerce systems) to make sequential recommendations. Previous works have made many efforts to model item-item transitions over user interaction sequences, based on various architectures, e.g., recurrent neural networks and self-attention mechanism. Recently emerged graph neural networks also serve as useful backbone models to capture item dependencies in sequential recommendation scenarios. Despite their effectiveness, existing methods have far focused on item sequence representation with singular type of interactions, and thus are limited to capture dynamic heterogeneous relational structures between users and items (e.g., page view, add-to-favorite, purchase). To tackle this challenge, we design a Multi-Behavior Hypergraph-enhanced T ransformer framework (MBHT) to capture both short-term and long-term cross-type behavior dependencies. Specifically, a multi-scale Transformer is equipped with low-rank self-attention to jointly encode behavior-aware sequential patterns from fine-grained and coarse-grained levels. Additionally,we incorporate the global multi-behavior dependency into the hypergraph neural architecture to capture the hierarchical long-range item correlations in a customized manner. Experimental results demonstrate the superiority of our MBHT over various state-of- the-art recommendation solutions across different settings. Further ablation studies validate the effectiveness of our model design and benefits of the new MBHT framework. Our implementation code is released at: https://github.com/yuh-yang/MBHT-KDD22.|学习动态用户偏好已经成为许多在线平台(如视频分享网站、电子商务系统)提供连续推荐的一个越来越重要的组成部分。以往的研究基于多种体系结构，如递归神经网络和自我注意机制，对用户交互序列上的项目-项目转换进行了大量的研究。最近出现的图形神经网络也可以作为有用的骨干模型，以捕获项目依赖的顺序推荐场景。尽管现有的方法很有效，但是现有的方法都集中在单一交互类型的项目序列表示上，因此仅限于捕获用户和项目之间的动态异构关系结构(例如，页面查看、添加到收藏夹、购买)。为了应对这一挑战，我们设计了一个多行为超图增强型 T 变换器框架(MBHT)来捕获短期和长期的跨类型行为依赖。具体而言，多尺度变压器配备低级自注意，以从细粒度和粗粒度级别联合编码行为感知的序列模式。此外，我们将全局多行为依赖引入到超图神经结构中，以自定义的方式获取层次化的长期项目相关性。实验结果表明，我们的 MBHT 优于不同设置的各种最先进的推荐解决方案。进一步的消融研究验证了我们的模型设计的有效性和新的 MBHT 框架的好处。我们的实施代码在以下 https://github.com/yuh-yang/mbht-kdd22发布:。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Transformers+for+Sequential+Recommendation)|0| +|[Transformers for Sequential Recommendation](https://doi.org/10.1007/978-3-031-56069-9_49)|Aleksandr V. Petrov, Craig Macdonald|Ocean University of China, Qingdao, China; National University of Singapore, Singapore, Singapore; Wuhan University, Wuhan, China; University of Hong Kong, Hong Kong, China|Learning dynamic user preference has become an increasingly important component for many online platforms (e.g., video-sharing sites, e-commerce systems) to make sequential recommendations. Previous works have made many efforts to model item-item transitions over user interaction sequences, based on various architectures, e.g., recurrent neural networks and self-attention mechanism. Recently emerged graph neural networks also serve as useful backbone models to capture item dependencies in sequential recommendation scenarios. Despite their effectiveness, existing methods have far focused on item sequence representation with singular type of interactions, and thus are limited to capture dynamic heterogeneous relational structures between users and items (e.g., page view, add-to-favorite, purchase). To tackle this challenge, we design a Multi-Behavior Hypergraph-enhanced T ransformer framework (MBHT) to capture both short-term and long-term cross-type behavior dependencies. Specifically, a multi-scale Transformer is equipped with low-rank self-attention to jointly encode behavior-aware sequential patterns from fine-grained and coarse-grained levels. Additionally,we incorporate the global multi-behavior dependency into the hypergraph neural architecture to capture the hierarchical long-range item correlations in a customized manner. Experimental results demonstrate the superiority of our MBHT over various state-of- the-art recommendation solutions across different settings. Further ablation studies validate the effectiveness of our model design and benefits of the new MBHT framework. Our implementation code is released at: https://github.com/yuh-yang/MBHT-KDD22.|学习动态用户偏好已经成为许多在线平台(如视频分享网站、电子商务系统)提供连续推荐的一个越来越重要的组成部分。以往的研究基于多种体系结构，如递归神经网络和自我注意机制，对用户交互序列上的项目-项目转换进行了大量的研究。最近出现的图形神经网络也可以作为有用的骨干模型，以捕获项目依赖的顺序推荐场景。尽管现有的方法很有效，但是现有的方法都集中在单一交互类型的项目序列表示上，因此仅限于捕获用户和项目之间的动态异构关系结构(例如，页面查看、添加到收藏夹、购买)。为了应对这一挑战，我们设计了一个多行为超图增强型 T 变换器框架(MBHT)来捕获短期和长期的跨类型行为依赖。具体而言，多尺度变压器配备低级自注意，以从细粒度和粗粒度级别联合编码行为感知的序列模式。此外，我们将全局多行为依赖引入到超图神经结构中，以自定义的方式获取层次化的长期项目相关性。实验结果表明，我们的 MBHT 优于不同设置的各种最先进的推荐解决方案。进一步的消融研究验证了我们的模型设计的有效性和新的 MBHT 框架的好处。我们的实施代码在以下 https://github.com/yuh-yang/mbht-kdd22发布:。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Transformers+for+Sequential+Recommendation)|0| |[Context-Aware Query Term Difficulty Estimation for Performance Prediction](https://doi.org/10.1007/978-3-031-56066-8_4)|Abbas Saleminezhad, Negar Arabzadeh, Soosan Beheshti, Ebrahim Bagheri||||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Context-Aware+Query+Term+Difficulty+Estimation+for+Performance+Prediction)|0| |[Navigating the Thin Line: Examining User Behavior in Search to Detect Engagement and Backfire Effects](https://doi.org/10.1007/978-3-031-56066-8_30)|Federico Maria Cau, Nava Tintarev||Opinionated users often seek information that aligns with their preexisting beliefs while dismissing contradictory evidence due to confirmation bias. This conduct hinders their ability to consider alternative stances when searching the web. Despite this, few studies have analyzed how the diversification of search results on disputed topics influences the search behavior of highly opinionated users. To this end, we present a preregistered user study (n = 257) investigating whether different levels (low and high) of bias metrics and search results presentation (with or without AI-predicted stances labels) can affect the stance diversity consumption and search behavior of opinionated users on three debated topics (i.e., atheism, intellectual property rights, and school uniforms). Our results show that exposing participants to (counter-attitudinally) biased search results increases their consumption of attitude-opposing content, but we also found that bias was associated with a trend toward overall fewer interactions within the search page. We also found that 19 any search results. When we removed these participants in a post-hoc analysis, we found that stance labels increased the diversity of stances consumed by users, particularly when the search results were biased. Our findings highlight the need for future research to explore distinct search scenario settings to gain insight into opinionated users' behavior.|固执己见的用户往往寻求与他们先前存在的信念相一致的信息，而由于确认偏见而排除相互矛盾的证据。这种行为妨碍了他们在搜索网页时考虑其他立场的能力。尽管如此，很少有研究分析有争议话题的搜索结果的多样化如何影响高度固执己见的用户的搜索行为。为此，我们提出了一项预先注册的用户研究(n = 257) ，调查不同水平(低和高)的偏倚指标和搜索结果表示(有或没有 AI 预测的立场标签)是否会影响立场多样性消费和搜索行为有意见的用户在三个有争议的话题(即无神论，知识产权和校服)。我们的研究结果显示，将参与者暴露在(反态度的)有偏见的搜索结果中，会增加他们对与态度相反的内容的消费，但是我们也发现，偏见与搜索页面内的整体互动减少的趋势有关。我们还发现19任何搜索结果。当我们在一个事后比较中移除这些参与者时，我们发现立场标签增加了用户使用的立场的多样性，特别是当搜索结果有偏见时。我们的研究结果强调了未来研究探索不同搜索场景设置的必要性，以深入了解固执己见的用户的行为。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Navigating+the+Thin+Line:+Examining+User+Behavior+in+Search+to+Detect+Engagement+and+Backfire+Effects)|0| |[Measuring Bias in a Ranked List Using Term-Based Representations](https://doi.org/10.1007/978-3-031-56069-9_1)|Amin Abolghasemi, Leif Azzopardi, Arian Askari, Maarten de Rijke, Suzan Verberne||In most recent studies, gender bias in document ranking is evaluated with the NFaiRR metric, which measures bias in a ranked list based on an aggregation over the unbiasedness scores of each ranked document. This perspective in measuring the bias of a ranked list has a key limitation: individual documents of a ranked list might be biased while the ranked list as a whole balances the groups' representations. To address this issue, we propose a novel metric called TExFAIR (term exposure-based fairness), which is based on two new extensions to a generic fairness evaluation framework, attention-weighted ranking fairness (AWRF). TExFAIR assesses fairness based on the term-based representation of groups in a ranked list: (i) an explicit definition of associating documents to groups based on probabilistic term-level associations, and (ii) a rank-biased discounting factor (RBDF) for counting non-representative documents towards the measurement of the fairness of a ranked list. We assess TExFAIR on the task of measuring gender bias in passage ranking, and study the relationship between TExFAIR and NFaiRR. Our experiments show that there is no strong correlation between TExFAIR and NFaiRR, which indicates that TExFAIR measures a different dimension of fairness than NFaiRR. With TExFAIR, we extend the AWRF framework to allow for the evaluation of fairness in settings with term-based representations of groups in documents in a ranked list.|在最近的大多数研究中，文档排名中的性别偏见是通过 NFaiRR 度量来评估的，该度量基于每个排名文档的无偏评分的聚合来衡量排名列表中的偏见。这种测量排名表偏差的视角有一个关键的局限性: 排名表的个别文档可能有偏差，而排名表作为一个整体平衡各组的表示。为了解决这个问题，我们提出了一种新的度量方法 TExFAIR (术语暴露公平性) ，它基于通用公平性评估框架的两个新的扩展，即注意力加权排序公平性(AWRF)。TExFAIR 基于排名列表中基于术语的群体表示来评估公平性: (i)基于概率术语水平关联的关联文档与群体的明确定义，以及(ii)用于计数非代表性文档的排名折扣因子(RBDF)对排名列表的公平性进行测量。我们通过测量文章排序中的性别偏见来评估 TExFAIR，并研究 TExFAIR 和 NFaiRR 之间的关系。我们的实验表明，TExFAIR 和 NFaiRR 之间没有很强的相关性，这表明 TExFAIR 测量的公平性维度不同于 NFaiRR。通过 TExFAIR，我们扩展了 AWRF 框架，允许在排名列表中的文档中使用基于术语的群组表示来评估设置中的公平性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Measuring+Bias+in+a+Ranked+List+Using+Term-Based+Representations)|0| diff --git a/papers/kdd/kdd2022.md b/papers/kdd/kdd2022.md index 2e44ff38..2dae9c13 100644 --- a/papers/kdd/kdd2022.md +++ b/papers/kdd/kdd2022.md @@ -12,7 +12,7 @@ |[MSDR: Multi-Step Dependency Relation Networks for Spatial Temporal Forecasting](https://doi.org/10.1145/3534678.3539397)|Dachuan Liu, Jin Wang, Shuo Shang, Peng Han||||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=MSDR:+Multi-Step+Dependency+Relation+Networks+for+Spatial+Temporal+Forecasting)|7| |[Joint Knowledge Graph Completion and Question Answering](https://doi.org/10.1145/3534678.3539289)|Lihui Liu, Boxin Du, Jiejun Xu, Yinglong Xia, Hanghang Tong||||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Joint+Knowledge+Graph+Completion+and+Question+Answering)|7| |[Graph Neural Networks for Multimodal Single-Cell Data Integration](https://doi.org/10.1145/3534678.3539213)|Hongzhi Wen, Jiayuan Ding, Wei Jin, Yiqi Wang, Yuying Xie, Jiliang Tang||||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Graph+Neural+Networks+for+Multimodal+Single-Cell+Data+Integration)|7| -|[Multi-Behavior Hypergraph-Enhanced Transformer for Sequential Recommendation](https://doi.org/10.1145/3534678.3539342)|Yuhao Yang, Chao Huang, Lianghao Xia, Yuxuan Liang, Yanwei Yu, Chenliang Li|Ocean University of China, Qingdao, China; University of Hong Kong, Hong Kong, China; Wuhan University, Wuhan, China; National University of Singapore, Singapore, Singapore|Learning dynamic user preference has become an increasingly important component for many online platforms (e.g., video-sharing sites, e-commerce systems) to make sequential recommendations. Previous works have made many efforts to model item-item transitions over user interaction sequences, based on various architectures, e.g., recurrent neural networks and self-attention mechanism. Recently emerged graph neural networks also serve as useful backbone models to capture item dependencies in sequential recommendation scenarios. Despite their effectiveness, existing methods have far focused on item sequence representation with singular type of interactions, and thus are limited to capture dynamic heterogeneous relational structures between users and items (e.g., page view, add-to-favorite, purchase). To tackle this challenge, we design a Multi-Behavior Hypergraph-enhanced T ransformer framework (MBHT) to capture both short-term and long-term cross-type behavior dependencies. Specifically, a multi-scale Transformer is equipped with low-rank self-attention to jointly encode behavior-aware sequential patterns from fine-grained and coarse-grained levels. Additionally,we incorporate the global multi-behavior dependency into the hypergraph neural architecture to capture the hierarchical long-range item correlations in a customized manner. Experimental results demonstrate the superiority of our MBHT over various state-of- the-art recommendation solutions across different settings. Further ablation studies validate the effectiveness of our model design and benefits of the new MBHT framework. Our implementation code is released at: https://github.com/yuh-yang/MBHT-KDD22.|学习动态用户偏好已经成为许多在线平台(如视频分享网站、电子商务系统)提供顺序推荐的一个越来越重要的组成部分。以往的研究基于多种体系结构，如递归神经网络和自我注意机制，对用户交互序列上的项目-项目转换进行了大量的研究。最近出现的图形神经网络也可以作为有用的骨干模型，以捕获项目依赖的顺序推荐场景。尽管现有的方法很有效，但是现有的方法都集中在单一交互类型的项目序列表示上，因此仅限于捕获用户和项目之间的动态异构关系结构(例如，页面查看、添加到收藏夹、购买)。为了应对这一挑战，我们设计了一个多行为超图增强型 T 变换器框架(MBHT)来捕获短期和长期的跨类型行为依赖。具体而言，多尺度变压器配备低级自注意，以从细粒度和粗粒度级别联合编码行为感知的序列模式。此外，我们将全局多行为依赖引入到超图神经结构中，以自定义的方式获取层次化的远程项目相关性。实验结果表明，我们的 MBHT 优于不同设置的各种最先进的推荐解决方案。进一步的消融研究验证了我们的模型设计的有效性和新的 MBHT 框架的好处。我们的实施代码在以下 https://github.com/yuh-yang/mbht-kdd22发布:。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Multi-Behavior+Hypergraph-Enhanced+Transformer+for+Sequential+Recommendation)|6| +|[Multi-Behavior Hypergraph-Enhanced Transformer for Sequential Recommendation](https://doi.org/10.1145/3534678.3539342)|Yuhao Yang, Chao Huang, Lianghao Xia, Yuxuan Liang, Yanwei Yu, Chenliang Li|Ocean University of China, Qingdao, China; National University of Singapore, Singapore, Singapore; Wuhan University, Wuhan, China; University of Hong Kong, Hong Kong, China|Learning dynamic user preference has become an increasingly important component for many online platforms (e.g., video-sharing sites, e-commerce systems) to make sequential recommendations. Previous works have made many efforts to model item-item transitions over user interaction sequences, based on various architectures, e.g., recurrent neural networks and self-attention mechanism. Recently emerged graph neural networks also serve as useful backbone models to capture item dependencies in sequential recommendation scenarios. Despite their effectiveness, existing methods have far focused on item sequence representation with singular type of interactions, and thus are limited to capture dynamic heterogeneous relational structures between users and items (e.g., page view, add-to-favorite, purchase). To tackle this challenge, we design a Multi-Behavior Hypergraph-enhanced T ransformer framework (MBHT) to capture both short-term and long-term cross-type behavior dependencies. Specifically, a multi-scale Transformer is equipped with low-rank self-attention to jointly encode behavior-aware sequential patterns from fine-grained and coarse-grained levels. Additionally,we incorporate the global multi-behavior dependency into the hypergraph neural architecture to capture the hierarchical long-range item correlations in a customized manner. Experimental results demonstrate the superiority of our MBHT over various state-of- the-art recommendation solutions across different settings. Further ablation studies validate the effectiveness of our model design and benefits of the new MBHT framework. Our implementation code is released at: https://github.com/yuh-yang/MBHT-KDD22.|学习动态用户偏好已经成为许多在线平台(如视频分享网站、电子商务系统)提供顺序推荐的一个越来越重要的组成部分。以往的研究基于多种体系结构，如递归神经网络和自我注意机制，对用户交互序列上的项目-项目转换进行了大量的研究。最近出现的图形神经网络也可以作为有用的骨干模型，以捕获项目依赖的顺序推荐场景。尽管现有的方法很有效，但是现有的方法都集中在单一交互类型的项目序列表示上，因此仅限于捕获用户和项目之间的动态异构关系结构(例如，页面查看、添加到收藏夹、购买)。为了应对这一挑战，我们设计了一个多行为超图增强型 T 变换器框架(MBHT)来捕获短期和长期的跨类型行为依赖。具体而言，多尺度变压器配备低级自注意，以从细粒度和粗粒度级别联合编码行为感知的序列模式。此外，我们将全局多行为依赖引入到超图神经结构中，以自定义的方式获取层次化的远程项目相关性。实验结果表明，我们的 MBHT 优于不同设置的各种最先进的推荐解决方案。进一步的消融研究验证了我们的模型设计的有效性和新的 MBHT 框架的好处。我们的实施代码在以下 https://github.com/yuh-yang/mbht-kdd22发布:。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Multi-Behavior+Hypergraph-Enhanced+Transformer+for+Sequential+Recommendation)|6| |[CommerceMM: Large-Scale Commerce MultiModal Representation Learning with Omni Retrieval](https://doi.org/10.1145/3534678.3539151)|Licheng Yu, Jun Chen, Animesh Sinha, Mengjiao Wang, Yu Chen, Tamara L. Berg, Ning Zhang|Meta AI, Menlo Park, CA, USA|We introduce CommerceMM - a multimodal model capable of providing a diverse and granular understanding of commerce topics associated to the given piece of content (image, text, image+text), and having the capability to generalize to a wide range of tasks, including Multimodal Categorization, Image-Text Retrieval, Query-to-Product Retrieval, Image-to-Product Retrieval, etc. We follow the pre-training + fine-tuning training regime and present 5 effective pre-training tasks on image-text pairs. To embrace more common and diverse commerce data with text-to-multimodal, image-to-multimodal, and multimodal-to-multimodal mapping, we propose another 9 novel cross-modal and cross-pair retrieval tasks, called Omni-Retrieval pre-training. We also propose a novel approach of modality randomization to dynamically adjust our model under different efficiency constraints. The pre-training is conducted in an efficient manner with only two forward/backward updates for the combined 14 tasks. Extensive experiments and analysis show the effectiveness of each task. When combining all pre-training tasks, our model achieves state-of-the-art performance on 7 commerce-related downstream tasks after fine-tuning.|我们介绍 CommerceMM ——一个多模态模型，它能够提供对与给定内容(图像、文本、图像 + 文本)相关的商业主题的多样化和细粒度的理解，并且能够泛化到广泛的任务，包括多模态分类、图像-文本检索、查询到产品检索、图像到产品检索等。我们遵循预先训练 + 微调训练制度，提出了5个有效的图像-文本对预先训练任务。为了使用文本到多模式、图像到多模式以及多模式到多模式映射来接受更多常见和多样化的商业数据，我们提出了另外9个新的跨模式和交叉对检索任务，称为 Omni-Retrieval pre-training。提出了一种新的模态随机化方法，在不同的效率约束下动态调整模型。预先培训是在一个有效的方式进行，只有两个向前/向后更新的合并14个任务。大量的实验和分析表明了每个任务的有效性。当结合所有的预训练任务时，我们的模型在经过微调后在7个与商业相关的下游任务上达到了最先进的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=CommerceMM:+Large-Scale+Commerce+MultiModal+Representation+Learning+with+Omni+Retrieval)|6| |[Learning to Rotate: Quaternion Transformer for Complicated Periodical Time Series Forecasting](https://doi.org/10.1145/3534678.3539234)|Weiqi Chen, Wenwei Wang, Bingqing Peng, Qingsong Wen, Tian Zhou, Liang Sun||||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Learning+to+Rotate:+Quaternion+Transformer+for+Complicated+Periodical+Time+Series+Forecasting)|6| |[FederatedScope-GNN: Towards a Unified, Comprehensive and Efficient Package for Federated Graph Learning](https://doi.org/10.1145/3534678.3539112)|Zhen Wang, Weirui Kuang, Yuexiang Xie, Liuyi Yao, Yaliang Li, Bolin Ding, Jingren Zhou||||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=FederatedScope-GNN:+Towards+a+Unified,+Comprehensive+and+Efficient+Package+for+Federated+Graph+Learning)|6| @@ -24,10 +24,10 @@ |[ERNIE-GeoL: A Geography-and-Language Pre-trained Model and its Applications in Baidu Maps](https://doi.org/10.1145/3534678.3539021)|Jizhou Huang, Haifeng Wang, Yibo Sun, Yunsheng Shi, Zhengjie Huang, An Zhuo, Shikun Feng||||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=ERNIE-GeoL:+A+Geography-and-Language+Pre-trained+Model+and+its+Applications+in+Baidu+Maps)|5| |[ChemicalX: A Deep Learning Library for Drug Pair Scoring](https://doi.org/10.1145/3534678.3539023)|Benedek Rozemberczki, Charles Tapley Hoyt, Anna Gogleva, Piotr Grabowski, Klas Karis, Andrej Lamov, Andriy Nikolov, Sebastian Nilsson, Michaël Ughetto, Yu Wang, Tyler Derr, Benjamin M. Gyori||||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=ChemicalX:+A+Deep+Learning+Library+for+Drug+Pair+Scoring)|5| |[DuARE: Automatic Road Extraction with Aerial Images and Trajectory Data at Baidu Maps](https://doi.org/10.1145/3534678.3539029)|Jianzhong Yang, Xiaoqing Ye, Bin Wu, Yanlei Gu, Ziyu Wang, Deguo Xia, Jizhou Huang||||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=DuARE:+Automatic+Road+Extraction+with+Aerial+Images+and+Trajectory+Data+at+Baidu+Maps)|5| -|[TwHIN: Embedding the Twitter Heterogeneous Information Network for Personalized Recommendation](https://doi.org/10.1145/3534678.3539080)|Ahmed ElKishky, Thomas Markovich, Serim Park, Chetan Verma, Baekjin Kim, Ramy Eskander, Yury Malkov, Frank Portman, Sofía Samaniego, Ying Xiao, Aria Haghighi|Twitter Cortex, Boston, MA, USA; Twitter, San Francisco, CA, USA; Twitter Cortex, New York, NY, USA; Twitter Cortex, Seattle, WA, USA; Twitter Cortex, San Francisco, CA, USA|Social networks, such as Twitter, form a heterogeneous information network (HIN) where nodes represent domain entities (e.g., user, content, advertiser, etc.) and edges represent one of many entity interactions (e.g, a user re-sharing content or "following" another). Interactions from multiple relation types can encode valuable information about social network entities not fully captured by a single relation; for instance, a user's preference for accounts to follow may depend on both user-content engagement interactions and the other users they follow. In this work, we investigate knowledge-graph embeddings for entities in the Twitter HIN (TwHIN); we show that these pretrained representations yield significant offline and online improvement for a diverse range of downstream recommendation and classification tasks: personalized ads rankings, account follow-recommendation, offensive content detection, and search ranking. We discuss design choices and practical challenges of deploying industry-scale HIN embeddings, including compressing them to reduce end-to-end model latency and handling parameter drift across versions.|社交网络，如 Twitter，形成了一个异构的信息网络(HIN) ，其中节点代表领域实体(例如，用户，内容，广告商等) ，边缘代表许多实体交互之一(例如，用户重新分享内容或“关注”另一个)。来自多种关系类型的交互可以编码关于社交网络实体的有价值的信息，而这些信息并没有被单个关系完全捕获; 例如，用户对账户的偏好可能同时取决于用户内容参与交互和他们所关注的其他用户。在这项工作中，我们调查了知识图表嵌入实体在 Twitter HIN (TwHIN) ; 我们表明，这些预先训练的表示产生了显着的离线和在线改善的下游推荐和分类任务的范围: 个性化广告排名，帐户跟踪推荐，攻击性内容检测和搜索排名。我们讨论了部署行业规模的 HIN 嵌入的设计选择和实际挑战，包括压缩它们以减少端到端模型延迟和处理跨版本的参数漂移。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=TwHIN:+Embedding+the+Twitter+Heterogeneous+Information+Network+for+Personalized+Recommendation)|4| +|[TwHIN: Embedding the Twitter Heterogeneous Information Network for Personalized Recommendation](https://doi.org/10.1145/3534678.3539080)|Ahmed ElKishky, Thomas Markovich, Serim Park, Chetan Verma, Baekjin Kim, Ramy Eskander, Yury Malkov, Frank Portman, Sofía Samaniego, Ying Xiao, Aria Haghighi|Twitter Cortex, Seattle, WA, USA; Twitter, San Francisco, CA, USA; Twitter Cortex, New York, NY, USA; Twitter Cortex, San Francisco, CA, USA; Twitter Cortex, Boston, MA, USA|Social networks, such as Twitter, form a heterogeneous information network (HIN) where nodes represent domain entities (e.g., user, content, advertiser, etc.) and edges represent one of many entity interactions (e.g, a user re-sharing content or "following" another). Interactions from multiple relation types can encode valuable information about social network entities not fully captured by a single relation; for instance, a user's preference for accounts to follow may depend on both user-content engagement interactions and the other users they follow. In this work, we investigate knowledge-graph embeddings for entities in the Twitter HIN (TwHIN); we show that these pretrained representations yield significant offline and online improvement for a diverse range of downstream recommendation and classification tasks: personalized ads rankings, account follow-recommendation, offensive content detection, and search ranking. We discuss design choices and practical challenges of deploying industry-scale HIN embeddings, including compressing them to reduce end-to-end model latency and handling parameter drift across versions.|社交网络，如 Twitter，形成了一个异构的信息网络(HIN) ，其中节点代表领域实体(例如，用户，内容，广告商等) ，边缘代表许多实体交互之一(例如，用户重新分享内容或“关注”另一个)。来自多种关系类型的交互可以编码关于社交网络实体的有价值的信息，而这些信息并没有被单个关系完全捕获; 例如，用户对账户的偏好可能同时取决于用户内容参与交互和他们所关注的其他用户。在这项工作中，我们调查了知识图表嵌入实体在 Twitter HIN (TwHIN) ; 我们表明，这些预先训练的表示产生了显着的离线和在线改善的下游推荐和分类任务的范围: 个性化广告排名，帐户跟踪推荐，攻击性内容检测和搜索排名。我们讨论了部署行业规模的 HIN 嵌入的设计选择和实际挑战，包括压缩它们以减少端到端模型延迟和处理跨版本的参数漂移。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=TwHIN:+Embedding+the+Twitter+Heterogeneous+Information+Network+for+Personalized+Recommendation)|4| |[Multi-Task Fusion via Reinforcement Learning for Long-Term User Satisfaction in Recommender Systems](https://doi.org/10.1145/3534678.3539040)|Qihua Zhang, Junning Liu, Yuzhuo Dai, Yiyan Qi, Yifan Yuan, Kunlun Zheng, Fan Huang, Xianfeng Tan|Tencent, Beijing, China; Tencent, Shenzhen, China|Recommender System (RS) is an important online application that affects billions of users every day. The mainstream RS ranking framework is composed of two parts: a Multi-Task Learning model (MTL) that predicts various user feedback, i.e., clicks, likes, sharings, and a Multi-Task Fusion model (MTF) that combines the multi-task outputs into one final ranking score with respect to user satisfaction. There has not been much research on the fusion model while it has great impact on the final recommendation as the last crucial process of the ranking. To optimize long-term user satisfaction rather than obtain instant returns greedily, we formulate MTF task as Markov Decision Process (MDP) within a recommendation session and propose a Batch Reinforcement Learning (RL) based Multi-Task Fusion framework (BatchRL-MTF) that includes a Batch RL framework and an online exploration. The former exploits Batch RL to learn an optimal recommendation policy from the fixed batch data offline for long-term user satisfaction, while the latter explores potential high-value actions online to break through the local optimal dilemma. With a comprehensive investigation on user behaviors, we model the user satisfaction reward with subtle heuristics from two aspects of user stickiness and user activeness. Finally, we conduct extensive experiments on a billion-sample level real-world dataset to show the effectiveness of our model. We propose a conservative offline policy estimator (Conservative-OPEstimator) to test our model offline. Furthermore, we take online experiments in a real recommendation environment to compare performance of different models. As one of few Batch RL researches applied in MTF task successfully, our model has also been deployed on a large-scale industrial short video platform, serving hundreds of millions of users.|推荐系统(RS)是一个重要的在线应用程序，每天影响数十亿用户。RS 的主流排名框架由两部分组成: 一个是多任务学习模型(Multi-Task Learning model，MTL) ，它预测用户的各种反馈，即点击、喜欢、分享; 另一个是多任务融合模型(Multi-Task Fusion model，MTF) ，它将多任务输出结合成一个用户满意度的最终排名得分。融合模型作为排名的最后一个关键过程，对最终推荐有着重要的影响。为了优化长期用户满意度，而不是贪婪地获得即时回报，我们在一个推荐会话中将 MTF 任务制定为马可夫决策过程(mDP) ，并提出了一个基于批处理强化学习(RL)的多任务融合框架(BatchRL-MTF) ，其中包括一个批处理强化学习框架和一个在线探索。前者利用批量 RL 从离线的固定批量数据中学习最优推荐策略以获得长期用户满意度，后者利用在线的潜在高价值行为来突破局部最优困境。通过对用户行为的全面调查，从用户粘性和用户主动性两个方面采用微妙的启发式方法建立了用户满意奖励模型。最后，我们在十亿个样本级别的真实世界数据集上进行了广泛的实验，以显示我们的模型的有效性。我们提出了一个保守的离线策略估计(保守-最优估计)来测试我们的模型离线。此外，我们在一个真实的推荐环境中进行在线实验，比较不同模型的性能。作为少数几个成功应用于 MTF 任务的批量 RL 研究之一，我们的模型也已经部署在一个大型工业短视频平台上，为数亿用户服务。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Multi-Task+Fusion+via+Reinforcement+Learning+for+Long-Term+User+Satisfaction+in+Recommender+Systems)|4| |[Feature-aware Diversified Re-ranking with Disentangled Representations for Relevant Recommendation](https://doi.org/10.1145/3534678.3539130)|Zihan Lin, Hui Wang, Jingshu Mao, Wayne Xin Zhao, Cheng Wang, Peng Jiang, JiRong Wen|Renmin University of China, Beijing, China; Renmin University of China, Beijing Key Laboratory of Big Data Management and Analysis Methods, & Beijing Academy of Artificial Intelligence, Beijing, China; Kuaishou Inc., Beijing, China|Relevant recommendation is a special recommendation scenario which provides relevant items when users express interests on one target item (e.g., click, like and purchase). Besides considering the relevance between recommendations and trigger item, the recommendations should also be diversified to avoid information cocoons. However, existing diversified recommendation methods mainly focus on item-level diversity which is insufficient when the recommended items are all relevant to the target item. Moreover, redundant or noisy item features might affect the performance of simple feature-aware recommendation approaches. Faced with these issues, we propose a Feature Disentanglement Self-Balancing Re-ranking framework (FDSB) to capture feature- aware diversity. The framework consists of two major modules, namely disentangled attention encoder (DAE) and self-balanced multi-aspect ranker. In DAE, we use multi-head attention to learn disentangled aspects from rich item features. In the ranker, we develop an aspect-specific ranking mechanism that is able to adaptively balance the relevance and diversity for each aspect. In experiments, we conduct offline evaluation on the collected dataset and deploy FDSB on KuaiShou app for online ??/?? test on the function of relevant recommendation. The significant improvements on both recommendation quality and user experience verify the effectiveness of our approach.|相关推荐是一种特殊的推荐场景，当用户对一个目标项目表示兴趣时(例如，点击、喜欢和购买) ，它会提供相关的项目。除了考虑建议与触发项目之间的相关性之外，建议还应当多样化，以避免信息茧。然而，现有的多样化推荐方法主要侧重于项目层次的多样性，当推荐项目都与目标项目相关时，这种多样性是不够的。此外，冗余或嘈杂的项目特征可能会影响简单的特征感知推荐方法的性能。针对这些问题，我们提出了一种特征分离自平衡重排框架(FDSB)来捕获特征感知的多样性。该框架包括两个主要模块，即分离注意编码器(DAE)和自平衡多方面排序器。在 DAE 中，我们使用多头注意从丰富的项目特征中学习分离的方面。在排名中，我们开发了一个方面特定的排名机制，能够自适应地平衡每个方面的相关性和多样性。在实验中，我们对收集到的数据集进行离线评估，并在快手应用上部署 FDSB 以实现在线? ? ？/?？检验有关推荐的作用。在推荐质量和用户体验方面的重大改进验证了我们方法的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Feature-aware+Diversified+Re-ranking+with+Disentangled+Representations+for+Relevant+Recommendation)|4| -|[Counteracting User Attention Bias in Music Streaming Recommendation via Reward Modification](https://doi.org/10.1145/3534678.3539393)|Xiao Zhang, Sunhao Dai, Jun Xu, Zhenhua Dong, Quanyu Dai, JiRong Wen|Renmin University of China, Beijing, China; Huawei Noah's Ark Lab, Shenzhen, China|In streaming media applications, like music Apps, songs are recommended in a continuous way in users' daily life. The recommended songs are played automatically although users may not pay any attention to them, posing a challenge of user attention bias in training recommendation models, i.e., the training instances contain a large number of false-positive labels (users' feedback). Existing approaches either directly use the auto-feedbacks or heuristically delete the potential false-positive labels. Both of the approaches lead to biased results because the false-positive labels cause the shift of training data distribution, hurting the accuracy of the recommendation models. In this paper, we propose a learning-based counterfactual approach to adjusting the user auto-feedbacks and learning the recommendation models using Neural Dueling Bandit algorithm, called NDB. Specifically, NDB maintains two neural networks: a user attention network for computing the importance weights that are used for modifying the original rewards, and another random network trained with dueling bandit for conducting online recommendations based on the modified rewards. Theoretical analysis showed that the modified rewards are statistically unbiased, and the learned bandit policy enjoys a sub-linear regret bound. Experimental results demonstrated that NDB can significantly outperform the state-of-the-art baselines.|在流媒体应用程序中，比如音乐应用程序，歌曲被持续推荐到用户的日常生活中。虽然用户可能没有注意到这些歌曲，但推荐的歌曲会自动播放，这对训练推荐模型中的用户注意偏差提出了挑战，即训练实例中包含大量假阳性标签(用户反馈)。现有的方法要么直接使用自动反馈，要么启发性地删除潜在的假阳性标签。这两种方法都会导致结果偏差，因为假阳性标签会引起训练数据分布的变化，从而影响推荐模型的准确性。在本文中，我们提出了一种基于学习的反事实方法来调整用户自动反馈和学习推荐模型的神经决斗盗贼算法，称为 NDB。具体来说，新开发银行维护两个神经网络: 一个是用户注意力网络，用于计算用于修改原始奖励的重要性权重，另一个是与决斗强盗一起训练的随机网络，用于根据修改后的奖励进行在线推荐。理论分析表明，修正后的奖励具有统计上的无偏性，学会的土匪政策具有亚线性后悔界限。实验结果表明，新数据库的性能明显优于最先进的基线。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Counteracting+User+Attention+Bias+in+Music+Streaming+Recommendation+via+Reward+Modification)|4| +|[Counteracting User Attention Bias in Music Streaming Recommendation via Reward Modification](https://doi.org/10.1145/3534678.3539393)|Xiao Zhang, Sunhao Dai, Jun Xu, Zhenhua Dong, Quanyu Dai, JiRong Wen|Huawei Noah's Ark Lab, Shenzhen, China; Renmin University of China, Beijing, China|In streaming media applications, like music Apps, songs are recommended in a continuous way in users' daily life. The recommended songs are played automatically although users may not pay any attention to them, posing a challenge of user attention bias in training recommendation models, i.e., the training instances contain a large number of false-positive labels (users' feedback). Existing approaches either directly use the auto-feedbacks or heuristically delete the potential false-positive labels. Both of the approaches lead to biased results because the false-positive labels cause the shift of training data distribution, hurting the accuracy of the recommendation models. In this paper, we propose a learning-based counterfactual approach to adjusting the user auto-feedbacks and learning the recommendation models using Neural Dueling Bandit algorithm, called NDB. Specifically, NDB maintains two neural networks: a user attention network for computing the importance weights that are used for modifying the original rewards, and another random network trained with dueling bandit for conducting online recommendations based on the modified rewards. Theoretical analysis showed that the modified rewards are statistically unbiased, and the learned bandit policy enjoys a sub-linear regret bound. Experimental results demonstrated that NDB can significantly outperform the state-of-the-art baselines.|在流媒体应用程序中，比如音乐应用程序，歌曲被持续推荐到用户的日常生活中。虽然用户可能没有注意到这些歌曲，但推荐的歌曲会自动播放，这对训练推荐模型中的用户注意偏差提出了挑战，即训练实例中包含大量假阳性标签(用户反馈)。现有的方法要么直接使用自动反馈，要么启发性地删除潜在的假阳性标签。这两种方法都会导致结果偏差，因为假阳性标签会引起训练数据分布的变化，从而影响推荐模型的准确性。在本文中，我们提出了一种基于学习的反事实方法来调整用户自动反馈和学习推荐模型的神经决斗盗贼算法，称为 NDB。具体来说，新开发银行维护两个神经网络: 一个是用户注意力网络，用于计算用于修改原始奖励的重要性权重，另一个是与决斗强盗一起训练的随机网络，用于根据修改后的奖励进行在线推荐。理论分析表明，修正后的奖励具有统计上的无偏性，学会的土匪政策具有亚线性后悔界限。实验结果表明，新数据库的性能明显优于最先进的基线。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Counteracting+User+Attention+Bias+in+Music+Streaming+Recommendation+via+Reward+Modification)|4| |[Knowledge-enhanced Black-box Attacks for Recommendations](https://doi.org/10.1145/3534678.3539359)|Jingfan Chen, Wenqi Fan, Guanghui Zhu, Xiangyu Zhao, Chunfeng Yuan, Qing Li, Yihua Huang||||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Knowledge-enhanced+Black-box+Attacks+for+Recommendations)|4| |[Towards Universal Sequence Representation Learning for Recommender Systems](https://doi.org/10.1145/3534678.3539381)|Yupeng Hou, Shanlei Mu, Wayne Xin Zhao, Yaliang Li, Bolin Ding, JiRong Wen||||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Towards+Universal+Sequence+Representation+Learning+for+Recommender+Systems)|4| |[On Structural Explanation of Bias in Graph Neural Networks](https://doi.org/10.1145/3534678.3539319)|Yushun Dong, Song Wang, Yu Wang, Tyler Derr, Jundong Li||||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=On+Structural+Explanation+of+Bias+in+Graph+Neural+Networks)|4| @@ -49,7 +49,7 @@ |[Variational Flow Graphical Model](https://doi.org/10.1145/3534678.3539450)|Shaogang Ren, Belhal Karimi, Dingcheng Li, Ping Li||||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Variational+Flow+Graphical+Model)|3| |[Interpretability, Then What? Editing Machine Learning Models to Reflect Human Knowledge and Values](https://doi.org/10.1145/3534678.3539074)|Zijie J. Wang, Alex Kale, Harsha Nori, Peter Stella, Mark E. Nunnally, Duen Horng Chau, Mihaela Vorvoreanu, Jennifer Wortman Vaughan, Rich Caruana||||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Interpretability,+Then+What?+Editing+Machine+Learning+Models+to+Reflect+Human+Knowledge+and+Values)|3| |[GBPNet: Universal Geometric Representation Learning on Protein Structures](https://doi.org/10.1145/3534678.3539441)|Sarp Aykent, Tian Xia|Auburn University, Auburn, AL, USA|Representation learning of protein 3D structures is challenging and essential for applications, e.g., computational protein design or protein engineering. Recently, geometric deep learning has achieved great success in non-Euclidean domains. Although protein can be represented as a graph naturally, it remains under-explored mainly due to the significant challenges in modeling the complex representations and capturing the inherent correlation in the 3D structure modeling. Several challenges include: 1) It is challenging to extract and preserve multi-level rotation and translation equivariant information during learning. 2) Difficulty in developing appropriate tools to effectively leverage the input spatial representations to capture complex geometries across the spatial dimension. 3) Difficulty in incorporating various geometric features and preserving the inherent structural relations. In this work, we introduce geometric bottleneck perceptron, and a general SO(3)-equivariant message passing neural network built on top of it for protein structure representation learning. The proposed geometric bottleneck perceptron can be incorporated into diverse network architecture backbones to process geometric data in different domains. This research shed new light on geometric deep learning in 3D structure studies. Empirically, we demonstrate the strength of our proposed approach on three core downstream tasks, where our model achieves significant improvements and outperforms existing benchmarks. The implementation is available at https://github.com/sarpaykent/GBPNet.|蛋白质三维结构的表示学习是具有挑战性和必要的应用，例如，计算蛋白质设计或蛋白质工程。近年来，几何深度学习在非欧几里德领域取得了巨大的成功。虽然蛋白质可以自然地表示为一个图形，但是它仍然没有得到充分的开发，主要是由于在建模复杂的表示和捕获三维结构建模中的内在关联方面的重大挑战。这些挑战包括: 1)在学习过程中提取和保存多层次旋转和翻译等变信息是一个挑战。2)难以开发合适的工具来有效地利用输入空间表示来捕获跨空间维度的复杂几何图形。3)难以结合各种几何特征和保持固有的结构关系。本文介绍了几何瓶颈感知器，并在此基础上构建了一个通用的 SO (3)等变信息传递神经网络，用于蛋白质结构表示学习。提出的几何瓶颈感知器可以整合到不同的网络结构骨架中，用于处理不同领域的几何数据。本研究为三维结构研究中的几何深度学习提供了新的思路。实际上，我们在三个核心的下游任务中展示了我们提议的方法的优势，在这些任务中，我们的模型实现了显著的改进，并优于现有的基准测试。有关实施方案可于 https://github.com/sarpaykent/gbpnet 索取。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=GBPNet:+Universal+Geometric+Representation+Learning+on+Protein+Structures)|3| -|[Motif Prediction with Graph Neural Networks](https://doi.org/10.1145/3534678.3539343)|Maciej Besta, Raphael Grob, Cesare Miglioli, Nicola Bernold, Grzegorz Kwasniewski, Gabriel Gjini, Raghavendra Kanakagiri, Saleh Ashkboos, Lukas Gianinazzi, Nikoli Dryden, Torsten Hoefler|ETH Zürich, Zurich, Switzerland; ETH Zurich, Zurich, Switzerland; University of Illinois at Urbana-Champaign, Urbana-Champaign, IL, USA; University of Geneva, Geneva, Switzerland|Link prediction is one of the central problems in graph mining. However, recent studies highlight the importance of higher-order network analysis, where complex structures called motifs are the first-class citizens. We first show that existing link prediction schemes fail to effectively predict motifs. To alleviate this, we establish a general motif prediction problem and we propose several heuristics that assess the chances for a specified motif to appear. To make the scores realistic, our heuristics consider - among others - correlations between links, i.e., the potential impact of some arriving links on the appearance of other links in a given motif. Finally, for highest accuracy, we develop a graph neural network (GNN) architecture for motif prediction. Our architecture offers vertex features and sampling schemes that capture the rich structural properties of motifs. While our heuristics are fast and do not need any training, GNNs ensure highest accuracy of predicting motifs, both for dense (e.g., k-cliques) and for sparse ones (e.g., k-stars). We consistently outperform the best available competitor by more than 10% on average and up to 32% in area under the curve. Importantly, the advantages of our approach over schemes based on uncorrelated link prediction increase with the increasing motif size and complexity. We also successfully apply our architecture for predicting more arbitrary clusters and communities, illustrating its potential for graph mining beyond motif analysis.|链接预测是图挖掘的核心问题之一。然而，最近的研究强调了高阶网络分析的重要性，在这种网络分析中，被称为图案的复杂结构是一等公民。我们首先证明了现有的链路预测方案不能有效地预测图案。为了解决这个问题，我们建立了一个通用的主题预测问题，并提出了几种启发式算法来评估特定主题出现的可能性。为了使得分数更加真实，我们的启发式方法考虑了链接之间的相关性，也就是说，一些到达的链接对给定主题中其他链接的外观的潜在影响。最后，为了获得最高的精度，我们开发了一个图形神经网络(GNN)结构用于模体预测。我们的体系结构提供了顶点特征和抽样方案，这些特征和抽样方案捕获了图案丰富的结构属性。虽然我们的启发式算法是快速的，不需要任何训练，GNN 确保预测图案的最高准确性，无论是对于密集的(例如，k- 团)和稀疏的(例如，k- 星)。我们始终超越最好的竞争对手超过10% 的平均水平和高达32% 的面积下的曲线。重要的是，与基于不相关链路预测的方案相比，我们的方法的优势随着基序大小和复杂度的增加而增加。我们还成功地应用了我们的体系结构来预测更多的任意集群和社区，说明了它在图形挖掘方面的潜力超越了主题分析。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Motif+Prediction+with+Graph+Neural+Networks)|3| +|[Motif Prediction with Graph Neural Networks](https://doi.org/10.1145/3534678.3539343)|Maciej Besta, Raphael Grob, Cesare Miglioli, Nicola Bernold, Grzegorz Kwasniewski, Gabriel Gjini, Raghavendra Kanakagiri, Saleh Ashkboos, Lukas Gianinazzi, Nikoli Dryden, Torsten Hoefler|ETH Zürich, Zurich, Switzerland; ETH Zurich, Zurich, Switzerland; University of Geneva, Geneva, Switzerland; University of Illinois at Urbana-Champaign, Urbana-Champaign, IL, USA|Link prediction is one of the central problems in graph mining. However, recent studies highlight the importance of higher-order network analysis, where complex structures called motifs are the first-class citizens. We first show that existing link prediction schemes fail to effectively predict motifs. To alleviate this, we establish a general motif prediction problem and we propose several heuristics that assess the chances for a specified motif to appear. To make the scores realistic, our heuristics consider - among others - correlations between links, i.e., the potential impact of some arriving links on the appearance of other links in a given motif. Finally, for highest accuracy, we develop a graph neural network (GNN) architecture for motif prediction. Our architecture offers vertex features and sampling schemes that capture the rich structural properties of motifs. While our heuristics are fast and do not need any training, GNNs ensure highest accuracy of predicting motifs, both for dense (e.g., k-cliques) and for sparse ones (e.g., k-stars). We consistently outperform the best available competitor by more than 10% on average and up to 32% in area under the curve. Importantly, the advantages of our approach over schemes based on uncorrelated link prediction increase with the increasing motif size and complexity. We also successfully apply our architecture for predicting more arbitrary clusters and communities, illustrating its potential for graph mining beyond motif analysis.|链接预测是图挖掘的核心问题之一。然而，最近的研究强调了高阶网络分析的重要性，在这种网络分析中，被称为图案的复杂结构是一等公民。我们首先证明了现有的链路预测方案不能有效地预测图案。为了解决这个问题，我们建立了一个通用的主题预测问题，并提出了几种启发式算法来评估特定主题出现的可能性。为了使得分数更加真实，我们的启发式方法考虑了链接之间的相关性，也就是说，一些到达的链接对给定主题中其他链接的外观的潜在影响。最后，为了获得最高的精度，我们开发了一个图形神经网络(GNN)结构用于模体预测。我们的体系结构提供了顶点特征和抽样方案，这些特征和抽样方案捕获了图案丰富的结构属性。虽然我们的启发式算法是快速的，不需要任何训练，GNN 确保预测图案的最高准确性，无论是对于密集的(例如，k- 团)和稀疏的(例如，k- 星)。我们始终超越最好的竞争对手超过10% 的平均水平和高达32% 的面积下的曲线。重要的是，与基于不相关链路预测的方案相比，我们的方法的优势随着基序大小和复杂度的增加而增加。我们还成功地应用了我们的体系结构来预测更多的任意集群和社区，说明了它在图形挖掘方面的潜力超越了主题分析。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Motif+Prediction+with+Graph+Neural+Networks)|3| |[Efficient Orthogonal Multi-view Subspace Clustering](https://doi.org/10.1145/3534678.3539282)|Mansheng Chen, ChangDong Wang, Dong Huang, JianHuang Lai, Philip S. Yu||||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Efficient+Orthogonal+Multi-view+Subspace+Clustering)|3| |[Local Evaluation of Time Series Anomaly Detection Algorithms](https://doi.org/10.1145/3534678.3539339)|Alexis Huet, José Manuel Navarro, Dario Rossi||||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Local+Evaluation+of+Time+Series+Anomaly+Detection+Algorithms)|3| |[Feature Overcorrelation in Deep Graph Neural Networks: A New Perspective](https://doi.org/10.1145/3534678.3539445)|Wei Jin, Xiaorui Liu, Yao Ma, Charu C. Aggarwal, Jiliang Tang||||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Feature+Overcorrelation+in+Deep+Graph+Neural+Networks:+A+New+Perspective)|3| @@ -68,10 +68,10 @@ |[Graph Attention Multi-Layer Perceptron](https://doi.org/10.1145/3534678.3539121)|Wentao Zhang, Ziqi Yin, Zeang Sheng, Yang Li, Wen Ouyang, Xiaosen Li, Yangyu Tao, Zhi Yang, Bin Cui||||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Graph+Attention+Multi-Layer+Perceptron)|3| |[ItemSage: Learning Product Embeddings for Shopping Recommendations at Pinterest](https://doi.org/10.1145/3534678.3539170)|Paul Baltescu, Haoyu Chen, Nikil Pancha, Andrew Zhai, Jure Leskovec, Charles Rosenberg|Pinterest, San Francisco, CA, USA|Learned embeddings for products are an important building block for web-scale e-commerce recommendation systems. At Pinterest, we build a single set of product embeddings called ItemSage to provide relevant recommendations in all shopping use cases including user, image and search based recommendations. This approach has led to significant improvements in engagement and conversion metrics, while reducing both infrastructure and maintenance cost. While most prior work focuses on building product embeddings from features coming from a single modality, we introduce a transformer-based architecture capable of aggregating information from both text and image modalities and show that it significantly outperforms single modality baselines. We also utilize multi-task learning to make ItemSage optimized for several engagement types, leading to a candidate generation system that is efficient for all of the engagement objectives of the end-to-end recommendation system. Extensive offline experiments are conducted to illustrate the effectiveness of our approach and results from online A/B experiments show substantial gains in key business metrics (up to +7% gross merchandise value/user and +11% click volume).|产品学习嵌入是网络电子商务推荐系统的重要组成部分。在 Pinterest，我们构建了一套名为 ItemSage 的产品嵌入，在所有购物用例中提供相关推荐，包括用户、图片和基于搜索的推荐。这种方法显著改善了参与度和转换度量，同时降低了基础设施和维护成本。虽然大多数先前的工作集中于构建来自单一模式的特征的产品嵌入，但是我们引入了一个基于转换器的体系结构，该体系结构能够聚合来自文本和图像模式的信息，并表明它明显优于单一模式基线。我们还利用多任务学习来使 ItemSage 针对几种参与类型进行优化，从而产生一个对端到端推荐系统的所有参与目标都有效的候选人生成系统。为了说明我们的方法的有效性，我们进行了大量的离线实验，在线 A/B 实验的结果显示，在关键的商业指标方面取得了实质性的进展(高达7% 的商品总价值/用户和 + 11% 的点击量)。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=ItemSage:+Learning+Product+Embeddings+for+Shopping+Recommendations+at+Pinterest)|2| |[Towards Unified Conversational Recommender Systems via Knowledge-Enhanced Prompt Learning](https://doi.org/10.1145/3534678.3539382)|Xiaolei Wang, Kun Zhou, JiRong Wen, Wayne Xin Zhao|Renmin University of China, Beijing, China|Conversational recommender systems (CRS) aim to proactively elicit user preference and recommend high-quality items through natural language conversations. Typically, a CRS consists of a recommendation module to predict preferred items for users and a conversation module to generate appropriate responses. To develop an effective CRS, it is essential to seamlessly integrate the two modules. Existing works either design semantic alignment strategies, or share knowledge resources and representations between the two modules. However, these approaches still rely on different architectures or techniques to develop the two modules, making it difficult for effective module integration. To address this problem, we propose a unified CRS model named UniCRS based on knowledge-enhanced prompt learning. Our approach unifies the recommendation and conversation subtasks into the prompt learning paradigm, and utilizes knowledge-enhanced prompts based on a fixed pre-trained language model (PLM) to fulfill both subtasks in a unified approach. In the prompt design, we include fused knowledge representations, task-specific soft tokens, and the dialogue context, which can provide sufficient contextual information to adapt the PLM for the CRS task. Besides, for the recommendation subtask, we also incorporate the generated response template as an important part of the prompt, to enhance the information interaction between the two subtasks. Extensive experiments on two public CRS datasets have demonstrated the effectiveness of our approach. Our code is publicly available at the link: https://github.com/RUCAIBox/UniCRS.|会话推荐系统(CRS)的目标是通过自然语言的对话主动地引导用户偏好，并推荐高质量的项目。通常，CRS 由一个推荐模块和一个会话模块组成，前者用于预测用户的首选项，后者用于生成适当的响应。为了开发一个有效的 CRS 系统，必须将这两个模块无缝地结合起来。现有的工作或者设计语义对齐策略，或者在两个模块之间共享知识资源和表示。然而，这些方法仍然依赖于不同的体系结构或技术来开发这两个模块，这使得有效的模块集成变得困难。针对这一问题，提出了一种基于知识增强的快速学习的统一 CRS 模型 UniCRS。该方法将推荐子任务和会话子任务统一到快速学习范式中，并利用基于固定预训练语言模型(PLM)的知识增强提示来统一实现推荐子任务和会话子任务。在快速设计中，我们包括融合知识表示、任务特定的软标记和对话上下文，它们可以提供足够的上下文信息来使 PLM 适应 CRS 任务。此外，对于推荐子任务，我们还将生成的响应模板作为提示的重要组成部分，以增强两个子任务之间的信息交互。在两个公共 CRS 数据集上的大量实验已经证明了我们方法的有效性。我们的代码可在以下 https://github.com/rucaibox/unicrs 公开获得:。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Towards+Unified+Conversational+Recommender+Systems+via+Knowledge-Enhanced+Prompt+Learning)|2| -|[Device-cloud Collaborative Recommendation via Meta Controller](https://doi.org/10.1145/3534678.3539181)|Jiangchao Yao, Feng Wang, Xichen Ding, Shaohu Chen, Bo Han, Jingren Zhou, Hongxia Yang|Hong Kong Baptist University, Hong Kong, China; CMIC, Shanghai Jiao Tong University, Shanghai, China; Ant Group, Beijing, China; DAMO Academy, Alibaba Group, Hangzhou, China|On-device machine learning enables the lightweight deployment of recommendation models in local clients, which reduces the burden of the cloud-based recommenders and simultaneously incorporates more real-time user features. Nevertheless, the cloud-based recommendation in the industry is still very important considering its powerful model capacity and the efficient candidate generation from the billion-scale item pool. Previous attempts to integrate the merits of both paradigms mainly resort to a sequential mechanism, which builds the on-device recommender on top of the cloud-based recommendation. However, such a design is inflexible when user interests dramatically change: the on-device model is stuck by the limited item cache while the cloud-based recommendation based on the large item pool do not respond without the new re-fresh feedback. To overcome this issue, we propose a meta controller to dynamically manage the collaboration between the on-device recommender and the cloud-based recommender, and introduce a novel efficient sample construction from the causal perspective to solve the dataset absence issue of meta controller. On the basis of the counterfactual samples and the extended training, extensive experiments in the industrial recommendation scenarios show the promise of meta controller in the device-cloud collaboration.|设备上的机器学习支持在本地客户机中轻量级部署推荐模型，这减轻了基于云的推荐模型的负担，同时包含了更多的实时用户特性。尽管如此，考虑到其强大的模型容量和从数十亿规模的项目库中有效地生成候选项，基于云的推荐在业界仍然非常重要。以前整合这两种模式优点的尝试主要依赖于一种顺序机制，这种机制在基于云的推荐之上构建设备上的推荐。然而，当用户的兴趣发生巨大变化时，这样的设计是不灵活的: 设备上的模型被有限的项目缓存卡住了，而基于大型项目池的基于云的推荐在没有新的更新反馈的情况下不会响应。针对这一问题，本文提出了一种元控制器来动态管理设备上的推荐器和基于云的推荐器之间的协作，并从因果关系的角度提出了一种新的有效的样本结构来解决元控制器数据集缺失的问题。在反事实样本和扩展训练的基础上，在工业推荐场景中的大量实验显示了元控制器在设备-云协作中的应用前景。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Device-cloud+Collaborative+Recommendation+via+Meta+Controller)|2| -|[MolSearch: Search-based Multi-objective Molecular Generation and Property Optimization](https://doi.org/10.1145/3534678.3542676)|Mengying Sun, Jing Xing, Han Meng, Huijun Wang, Bin Chen, Jiayu Zhou|Michigan State University, East Lansing, MI, USA; Michigan State University, Grand Rapids, MI, USA; Agios Pharmaceuticals, Cambridge, MA, USA|Leveraging computational methods to generate small molecules with desired properties has been an active research area in the drug discovery field. Towards real-world applications, however, efficient generation of molecules that satisfy multiple property requirements simultaneously remains a key challenge. In this paper, we tackle this challenge using a search-based approach and propose a simple yet effective framework called MolSearch for multi-objective molecular generation (optimization).We show that given proper design and sufficient domain information, search-based methods can achieve performance comparable or even better than deep learning methods while being computationally efficient. Such efficiency enables massive exploration of chemical space given constrained computational resources. In particular, MolSearch starts with existing molecules and uses a two-stage search strategy to gradually modify them into new ones, based on transformation rules derived systematically and exhaustively from large compound libraries. We evaluate MolSearch in multiple benchmark generation settings and demonstrate its effectiveness and efficiency.|利用计算方法生成具有期望特性的小分子已经成为药物发现领域的一个活跃的研究领域。然而，对于实际应用来说，同时满足多种性能要求的高效生产分子仍然是一个关键的挑战。在本文中，我们使用基于搜索的方法来解决这一挑战，并提出了一个简单而有效的框架，称为 MolSearch 的多目标分子生成(优化)。我们表明，给定适当的设计和充分的领域信息，基于搜索的方法可以实现性能可比，甚至比深度学习方法更好，同时计算效率。这样的效率使得在计算资源有限的情况下对化学空间进行大规模探索成为可能。特别是，MolSearch 从现有的分子开始，使用一个两阶段的搜索策略，逐渐修改成新的，基于转换规则，系统地和详尽地从大型化合物库。我们评估了 MolSearch 在多个基准测试生成环境中的性能，并证明了它的有效性和效率。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=MolSearch:+Search-based+Multi-objective+Molecular+Generation+and+Property+Optimization)|2| -|[Invariant Preference Learning for General Debiasing in Recommendation](https://doi.org/10.1145/3534678.3539439)|Zimu Wang, Yue He, Jiashuo Liu, Wenchao Zou, Philip S. Yu, Peng Cui|Tsinghua University, Beijing, China; University of Illinois at Chicago, Chicago, IL, USA; Siemens China, Shanghai, China|Current recommender systems have achieved great successes in online services, such as E-commerce and social media. However, they still suffer from the performance degradation in real scenarios, because various biases always occur in the generation process of user behaviors. Despite the recent development of addressing some specific type of bias, a variety of data bias, some of which are even unknown, are often mixed up in real applications. Although the uniform (or unbiased) data may help for the purpose of general debiasing, such data can either be hardly available or induce high experimental cost. In this paper, we consider a more practical setting where we aim to conduct general debiasing with the biased observational data alone. We assume that the observational user behaviors are determined by invariant preference (i.e. a user's true preference) and the variant preference (affected by some unobserved confounders). We propose a novel recommendation framework called InvPref which iteratively decomposes the invariant preference and variant preference from biased observational user behaviors by estimating heterogeneous environments corresponding to different types of latent bias. Extensive experiments, including the settings of general debiasing and specific debiasing, verify the advantages of our method.|现有的推荐系统在电子商务和社交媒体等在线服务领域取得了巨大的成功。然而，在实际场景中，它们仍然会受到性能下降的影响，因为在用户行为的生成过程中总是会出现各种偏差。尽管最近的发展解决一些特定类型的偏差，各种各样的数据偏差，其中一些甚至是未知的，往往是混合在实际应用。虽然统一(或无偏)的数据可能有助于一般的去偏目的，这样的数据可能难以获得或诱导高实验成本。在本文中，我们考虑一个更实际的设置，其中我们的目的是进行一般的去偏与有偏的观测数据单独。我们假设观察用户行为是由不变偏好(即用户的真实偏好)和变异偏好(受一些未观察到的混杂因素的影响)决定的。提出了一种新的推荐框架 InvPref，该框架通过估计不同类型潜在偏差对应的异质环境，迭代分解有偏差的观察用户行为的不变偏好和变异偏好。广泛的实验，包括一般消偏和具体消偏的设置，验证了我们的方法的优点。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Invariant+Preference+Learning+for+General+Debiasing+in+Recommendation)|2| -|[Automatic Controllable Product Copywriting for E-Commerce](https://doi.org/10.1145/3534678.3539171)|Xiaojie Guo, Qingkai Zeng, Meng Jiang, Yun Xiao, Bo Long, Lingfei Wu|University of Notre Dame, Notre Dame, IN, USA; JD.COM Silicon Valley Research Center, Mountain View, CA, USA; JD.COM, Beijing, China|Automatic product description generation for e-commerce has witnessed significant advancement in the past decade. Product copy- writing aims to attract users' interest and improve user experience by highlighting product characteristics with textual descriptions. As the services provided by e-commerce platforms become diverse, it is necessary to adapt the patterns of automatically-generated descriptions dynamically. In this paper, we report our experience in deploying an E-commerce Prefix-based Controllable Copywriting Generation (EPCCG) system into the JD.com e-commerce product recommendation platform. The development of the system contains two main components: 1) copywriting aspect extraction; 2) weakly supervised aspect labelling; 3) text generation with a prefix-based language model; and 4) copywriting quality control. We conduct experiments to validate the effectiveness of the proposed EPCCG. In addition, we introduce the deployed architecture which cooperates the EPCCG into the real-time JD.com e-commerce recommendation platform and the significant payoff since deployment. The codes for implementation are provided at https://github.com/xguo7/Automatic-Controllable-Product-Copywriting-for-E-Commerce.git.|电子商务中的产品描述自动生成技术在过去的十年中取得了长足的进步。产品文案的目的是通过文字描述突出产品特征，吸引用户的兴趣，提高用户体验。随着电子商务平台提供的服务变得多样化，有必要动态调整自动生成描述的模式。在本文中，我们报告了在京东电子商务产品推荐平台上部署基于前缀的可控文案生成(ECCG)系统的经验。该系统的开发包括两个主要组成部分: 1)文案方面提取; 2)弱监督方面标注; 3)基于前缀语言模型的文本生成; 4)文案质量控制。我们进行了实验，以验证所提出的心电图的有效性。此外，我们还将 EPCCG 协同的已部署体系结构引入到实时 JD.com 电子商务推荐平台中，并且从部署以来获得了显著的回报。实施守则载于 https://github.com/xguo7/automatic-controllable-product-copywriting-for-e-commerce.git。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Automatic+Controllable+Product+Copywriting+for+E-Commerce)|2| +|[Device-cloud Collaborative Recommendation via Meta Controller](https://doi.org/10.1145/3534678.3539181)|Jiangchao Yao, Feng Wang, Xichen Ding, Shaohu Chen, Bo Han, Jingren Zhou, Hongxia Yang|DAMO Academy, Alibaba Group, Hangzhou, China; Ant Group, Beijing, China; CMIC, Shanghai Jiao Tong University, Shanghai, China; Hong Kong Baptist University, Hong Kong, China|On-device machine learning enables the lightweight deployment of recommendation models in local clients, which reduces the burden of the cloud-based recommenders and simultaneously incorporates more real-time user features. Nevertheless, the cloud-based recommendation in the industry is still very important considering its powerful model capacity and the efficient candidate generation from the billion-scale item pool. Previous attempts to integrate the merits of both paradigms mainly resort to a sequential mechanism, which builds the on-device recommender on top of the cloud-based recommendation. However, such a design is inflexible when user interests dramatically change: the on-device model is stuck by the limited item cache while the cloud-based recommendation based on the large item pool do not respond without the new re-fresh feedback. To overcome this issue, we propose a meta controller to dynamically manage the collaboration between the on-device recommender and the cloud-based recommender, and introduce a novel efficient sample construction from the causal perspective to solve the dataset absence issue of meta controller. On the basis of the counterfactual samples and the extended training, extensive experiments in the industrial recommendation scenarios show the promise of meta controller in the device-cloud collaboration.|设备上的机器学习支持在本地客户机中轻量级部署推荐模型，这减轻了基于云的推荐模型的负担，同时包含了更多的实时用户特性。尽管如此，考虑到其强大的模型容量和从数十亿规模的项目库中有效地生成候选项，基于云的推荐在业界仍然非常重要。以前整合这两种模式优点的尝试主要依赖于一种顺序机制，这种机制在基于云的推荐之上构建设备上的推荐。然而，当用户的兴趣发生巨大变化时，这样的设计是不灵活的: 设备上的模型被有限的项目缓存卡住了，而基于大型项目池的基于云的推荐在没有新的更新反馈的情况下不会响应。针对这一问题，本文提出了一种元控制器来动态管理设备上的推荐器和基于云的推荐器之间的协作，并从因果关系的角度提出了一种新的有效的样本结构来解决元控制器数据集缺失的问题。在反事实样本和扩展训练的基础上，在工业推荐场景中的大量实验显示了元控制器在设备-云协作中的应用前景。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Device-cloud+Collaborative+Recommendation+via+Meta+Controller)|2| +|[MolSearch: Search-based Multi-objective Molecular Generation and Property Optimization](https://doi.org/10.1145/3534678.3542676)|Mengying Sun, Jing Xing, Han Meng, Huijun Wang, Bin Chen, Jiayu Zhou|Agios Pharmaceuticals, Cambridge, MA, USA; Michigan State University, Grand Rapids, MI, USA; Michigan State University, East Lansing, MI, USA|Leveraging computational methods to generate small molecules with desired properties has been an active research area in the drug discovery field. Towards real-world applications, however, efficient generation of molecules that satisfy multiple property requirements simultaneously remains a key challenge. In this paper, we tackle this challenge using a search-based approach and propose a simple yet effective framework called MolSearch for multi-objective molecular generation (optimization).We show that given proper design and sufficient domain information, search-based methods can achieve performance comparable or even better than deep learning methods while being computationally efficient. Such efficiency enables massive exploration of chemical space given constrained computational resources. In particular, MolSearch starts with existing molecules and uses a two-stage search strategy to gradually modify them into new ones, based on transformation rules derived systematically and exhaustively from large compound libraries. We evaluate MolSearch in multiple benchmark generation settings and demonstrate its effectiveness and efficiency.|利用计算方法生成具有期望特性的小分子已经成为药物发现领域的一个活跃的研究领域。然而，对于实际应用来说，同时满足多种性能要求的高效生产分子仍然是一个关键的挑战。在本文中，我们使用基于搜索的方法来解决这一挑战，并提出了一个简单而有效的框架，称为 MolSearch 的多目标分子生成(优化)。我们表明，给定适当的设计和充分的领域信息，基于搜索的方法可以实现性能可比，甚至比深度学习方法更好，同时计算效率。这样的效率使得在计算资源有限的情况下对化学空间进行大规模探索成为可能。特别是，MolSearch 从现有的分子开始，使用一个两阶段的搜索策略，逐渐修改成新的，基于转换规则，系统地和详尽地从大型化合物库。我们评估了 MolSearch 在多个基准测试生成环境中的性能，并证明了它的有效性和效率。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=MolSearch:+Search-based+Multi-objective+Molecular+Generation+and+Property+Optimization)|2| +|[Invariant Preference Learning for General Debiasing in Recommendation](https://doi.org/10.1145/3534678.3539439)|Zimu Wang, Yue He, Jiashuo Liu, Wenchao Zou, Philip S. Yu, Peng Cui|University of Illinois at Chicago, Chicago, IL, USA; Tsinghua University, Beijing, China; Siemens China, Shanghai, China|Current recommender systems have achieved great successes in online services, such as E-commerce and social media. However, they still suffer from the performance degradation in real scenarios, because various biases always occur in the generation process of user behaviors. Despite the recent development of addressing some specific type of bias, a variety of data bias, some of which are even unknown, are often mixed up in real applications. Although the uniform (or unbiased) data may help for the purpose of general debiasing, such data can either be hardly available or induce high experimental cost. In this paper, we consider a more practical setting where we aim to conduct general debiasing with the biased observational data alone. We assume that the observational user behaviors are determined by invariant preference (i.e. a user's true preference) and the variant preference (affected by some unobserved confounders). We propose a novel recommendation framework called InvPref which iteratively decomposes the invariant preference and variant preference from biased observational user behaviors by estimating heterogeneous environments corresponding to different types of latent bias. Extensive experiments, including the settings of general debiasing and specific debiasing, verify the advantages of our method.|现有的推荐系统在电子商务和社交媒体等在线服务领域取得了巨大的成功。然而，在实际场景中，它们仍然会受到性能下降的影响，因为在用户行为的生成过程中总是会出现各种偏差。尽管最近的发展解决一些特定类型的偏差，各种各样的数据偏差，其中一些甚至是未知的，往往是混合在实际应用。虽然统一(或无偏)的数据可能有助于一般的去偏目的，这样的数据可能难以获得或诱导高实验成本。在本文中，我们考虑一个更实际的设置，其中我们的目的是进行一般的去偏与有偏的观测数据单独。我们假设观察用户行为是由不变偏好(即用户的真实偏好)和变异偏好(受一些未观察到的混杂因素的影响)决定的。提出了一种新的推荐框架 InvPref，该框架通过估计不同类型潜在偏差对应的异质环境，迭代分解有偏差的观察用户行为的不变偏好和变异偏好。广泛的实验，包括一般消偏和具体消偏的设置，验证了我们的方法的优点。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Invariant+Preference+Learning+for+General+Debiasing+in+Recommendation)|2| +|[Automatic Controllable Product Copywriting for E-Commerce](https://doi.org/10.1145/3534678.3539171)|Xiaojie Guo, Qingkai Zeng, Meng Jiang, Yun Xiao, Bo Long, Lingfei Wu|JD.COM Silicon Valley Research Center, Mountain View, CA, USA; University of Notre Dame, Notre Dame, IN, USA; JD.COM, Beijing, China|Automatic product description generation for e-commerce has witnessed significant advancement in the past decade. Product copy- writing aims to attract users' interest and improve user experience by highlighting product characteristics with textual descriptions. As the services provided by e-commerce platforms become diverse, it is necessary to adapt the patterns of automatically-generated descriptions dynamically. In this paper, we report our experience in deploying an E-commerce Prefix-based Controllable Copywriting Generation (EPCCG) system into the JD.com e-commerce product recommendation platform. The development of the system contains two main components: 1) copywriting aspect extraction; 2) weakly supervised aspect labelling; 3) text generation with a prefix-based language model; and 4) copywriting quality control. We conduct experiments to validate the effectiveness of the proposed EPCCG. In addition, we introduce the deployed architecture which cooperates the EPCCG into the real-time JD.com e-commerce recommendation platform and the significant payoff since deployment. The codes for implementation are provided at https://github.com/xguo7/Automatic-Controllable-Product-Copywriting-for-E-Commerce.git.|电子商务中的产品描述自动生成技术在过去的十年中取得了长足的进步。产品文案的目的是通过文字描述突出产品特征，吸引用户的兴趣，提高用户体验。随着电子商务平台提供的服务变得多样化，有必要动态调整自动生成描述的模式。在本文中，我们报告了在京东电子商务产品推荐平台上部署基于前缀的可控文案生成(ECCG)系统的经验。该系统的开发包括两个主要组成部分: 1)文案方面提取; 2)弱监督方面标注; 3)基于前缀语言模型的文本生成; 4)文案质量控制。我们进行了实验，以验证所提出的心电图的有效性。此外，我们还将 EPCCG 协同的已部署体系结构引入到实时 JD.com 电子商务推荐平台中，并且从部署以来获得了显著的回报。实施守则载于 https://github.com/xguo7/automatic-controllable-product-copywriting-for-e-commerce.git。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Automatic+Controllable+Product+Copywriting+for+E-Commerce)|2| |[Multi-task Hierarchical Classification for Disk Failure Prediction in Online Service Systems](https://doi.org/10.1145/3534678.3539176)|Yudong Liu, Hailan Yang, Pu Zhao, Minghua Ma, Chengwu Wen, Hongyu Zhang, Chuan Luo, Qingwei Lin, Chang Yi, Jiaojian Wang, Chenjian Zhang, Paul Wang, Yingnong Dang, Saravan Rajmohan, Dongmei Zhang||||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Multi-task+Hierarchical+Classification+for+Disk+Failure+Prediction+in+Online+Service+Systems)|2| |[EGM: Enhanced Graph-based Model for Large-scale Video Advertisement Search](https://doi.org/10.1145/3534678.3539061)|Tan Yu, Jie Liu, Yi Yang, Yi Li, Hongliang Fei, Ping Li||||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=EGM:+Enhanced+Graph-based+Model+for+Large-scale+Video+Advertisement+Search)|2| |[Saliency-Regularized Deep Multi-Task Learning](https://doi.org/10.1145/3534678.3539442)|Guangji Bai, Liang Zhao|Emory University, Atlanta, GA, USA|Multi-task learning (MTL) is a framework that enforces multiple learning tasks to share their knowledge to improve their generalization abilities. While shallow multi-task learning can learn task relations, it can only handle pre-defined features. Modern deep multi-task learning can jointly learn latent features and task sharing, but they are obscure in task relation. Also, they pre-define which layers and neurons should share across tasks and cannot learn adaptively. To address these challenges, this paper proposes a new multi-task learning framework that jointly learns latent features and explicit task relations by complementing the strength of existing shallow and deep multitask learning scenarios. Specifically, we propose to model the task relation as the similarity between tasks' input gradients, with a theoretical analysis of their equivalency. In addition, we innovatively propose a multi-task learning objective that explicitly learns task relations by a new regularizer. Theoretical analysis shows that the generalizability error has been reduced thanks to the proposed regularizer. Extensive experiments on several multi-task learning and image classification benchmarks demonstrate the proposed method's effectiveness, efficiency as well as reasonableness in the learned task relation patterns.|多任务学习(MTL)是一种强制多任务共享知识以提高其泛化能力的学习框架。浅层多任务学习虽然可以学习任务关系，但只能处理预定义的特征。现代深度多任务学习可以联合学习任务的潜在特征和任务共享，但在任务关系方面较为模糊。此外，他们预先定义了哪些层和神经元应该跨任务共享，而不能自适应地学习。针对这些挑战，本文提出了一种新的多任务学习框架，通过补充现有浅层和深层多任务学习场景的优势，联合学习潜在特征和显性任务关系。具体来说，我们提出将任务关系建模为任务输入梯度之间的相似性，并对其等效性进行了理论分析。此外，我们创新地提出了一个多任务学习目标，通过一个新的正则化器显式学习任务关系。理论分析表明，该正则化器可以减小泛化误差。通过对多个多任务学习和图像分类基准的大量实验，证明了该方法在学习任务关系模式方面的有效性、高效性和合理性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Saliency-Regularized+Deep+Multi-Task+Learning)|2| @@ -112,14 +112,14 @@ |[Robust Time Series Analysis and Applications: An Industrial Perspective](https://doi.org/10.1145/3534678.3542612)|Qingsong Wen, Linxiao Yang, Tian Zhou, Liang Sun||||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Robust+Time+Series+Analysis+and+Applications:+An+Industrial+Perspective)|2| |[PECOS: Prediction for Enormous and Correlated Output Spaces](https://doi.org/10.1145/3534678.3542629)|HsiangFu Yu, Jiong Zhang, WeiCheng Chang, JyunYu Jiang, Wei Li, ChoJui Hsieh||||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=PECOS:+Prediction+for+Enormous+and+Correlated+Output+Spaces)|2| |[Extracting Relevant Information from User's Utterances in Conversational Search and Recommendation](https://doi.org/10.1145/3534678.3539471)|Ali Montazeralghaem, James Allan|University of Massachusetts Amherst, Amherst, MA, USA|Conversational search and recommendation systems can ask clarifying questions through the conversation and collect valuable information from users. However, an important question remains: how can we extract relevant information from the user's utterances and use it in the retrieval or recommendation in the next turn of the conversation? Utilizing relevant information from users' utterances leads the system to better results at the end of the conversation. In this paper, we propose a model based on reinforcement learning, namely RelInCo, which takes the user's utterances and the context of the conversation and classifies each word in the user's utterances as belonging to the relevant or non-relevant class. RelInCo uses two Actors: 1) Arrangement-Actor, which finds the most relevant order of words in user's utterances, and 2) Selector-Actor, which determines which words, in the order provided by the arrangement Actor, can bring the system closer to the target of the conversation. In this way, we can find relevant information in the user's utterance and use it in the conversation. The objective function in our model is designed in such a way that it can maximize any desired retrieval and recommendation metrics (i.e., the ultimate|会话搜索和推荐系统可以通过会话提出澄清问题，并从用户那里收集有价值的信息。然而，一个重要的问题仍然存在: 我们如何从用户的话语中提取相关信息，并将其用于下一轮对话中的检索或推荐？利用用户话语中的相关信息，可以使系统在对话结束时获得更好的结果。在本文中，我们提出了一个基于强化学习的模型，即 RelinCo，该模型根据用户的话语和对话的上下文，将用户话语中的每个单词归类为相关或非相关类别。RelInCo 使用了两个参与者: 1)安排-参与者，它找到用户话语中最相关的词语顺序; 2)选择-参与者，它根据安排-参与者提供的顺序决定哪些词语可以使系统更接近对话的目标。通过这种方式，我们可以在用户的话语中找到相关信息，并在对话中加以利用。我们模型中的目标函数是这样设计的，它可以最大化任何所需的检索和推荐指标(即，最终的|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Extracting+Relevant+Information+from+User's+Utterances+in+Conversational+Search+and+Recommendation)|1| -|[Uni-Retriever: Towards Learning the Unified Embedding Based Retriever in Bing Sponsored Search](https://doi.org/10.1145/3534678.3539212)|Jianjin Zhang, Zheng Liu, Weihao Han, Shitao Xiao, Ruicheng Zheng, Yingxia Shao, Hao Sun, Hanqing Zhu, Premkumar Srinivasan, Weiwei Deng, Qi Zhang, Xing Xie|Beijing University of Posts and Telecommunications, Beijing, China; Microsoft, Seattle, DC, USA; Microsoft, Newark, NJ, USA; Microsoft, Beijing, China|Embedding based retrieval (EBR) is a fundamental building block in many web applications. However, EBR in sponsored search is distinguished from other generic scenarios and technically challenging due to the need of serving multiple retrieval purposes: firstly, it has to retrieve high-relevance ads, which may exactly serve user's search intent; secondly, it needs to retrieve high-CTR ads so as to maximize the overall user clicks. In this paper, we present a novel representation learning framework Uni-Retriever developed for Bing Search, which unifies two different training modes knowledge distillation and contrastive learning to realize both required objectives. On one hand, the capability of making high-relevance retrieval is established by distilling knowledge from the "relevance teacher model''. On the other hand, the capability of making high-CTR retrieval is optimized by learning to discriminate user's clicked ads from the entire corpus. The two training modes are jointly performed as a multi-objective learning process, such that the ads of high relevance and CTR can be favored by the generated embeddings. Besides the learning strategy, we also elaborate our solution for EBR serving pipeline built upon the substantially optimized DiskANN, where massive-scale EBR can be performed with competitive time and memory efficiency, and accomplished in high-quality. We make comprehensive offline and online experiments to evaluate the proposed techniques, whose findings may provide useful insights for the future development of EBR systems. Uni-Retriever has been mainstreamed as the major retrieval path in Bing's production thanks to the notable improvements on the representation and EBR serving quality.|嵌入式基于检索(EBR)是许多 Web 应用程序的基础构件。然而，由于需要服务于多种检索目的，赞助商搜索中的 EBR 不同于其他一般情况，在技术上具有挑战性: 首先，它必须检索高相关度的广告，这可能恰好服务于用户的搜索意图; 其次，它需要检索高点击率的广告，以最大限度地提高用户的总体点击率。本文提出了一种新的面向 Bing 搜索的 Uni-Retriever 表示学习框架，该框架将两种不同的训练模式知识提取和对比学习相结合，实现了两种不同的目标。一方面，从“关联教师模型”中提取知识，建立高关联检索能力;。另一方面，通过学习从整个语料库中区分用户点击广告，优化了高点击率检索的能力。这两种训练模式作为一个多目标学习过程共同执行，使得嵌入生成的广告更有利于高关联度和点击率的广告。除了学习策略，我们还详细阐述了我们的解决方案，EBR 服务流水线的基础上大幅度优化的 DiskANN，其中大规模的 EBR 可以执行竞争时间和内存效率，并完成在高质量。我们进行了全面的离线和在线实验来评估所提出的技术，其结果可能为未来 EBR 系统的发展提供有用的见解。统一检索已成为主流的检索路径在必应的生产显着改善的表示和 EBR 服务质量。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Uni-Retriever:+Towards+Learning+the+Unified+Embedding+Based+Retriever+in+Bing+Sponsored+Search)|1| +|[Uni-Retriever: Towards Learning the Unified Embedding Based Retriever in Bing Sponsored Search](https://doi.org/10.1145/3534678.3539212)|Jianjin Zhang, Zheng Liu, Weihao Han, Shitao Xiao, Ruicheng Zheng, Yingxia Shao, Hao Sun, Hanqing Zhu, Premkumar Srinivasan, Weiwei Deng, Qi Zhang, Xing Xie|Beijing University of Posts and Telecommunications, Beijing, China; Microsoft, Seattle, DC, USA; Microsoft, Beijing, China; Microsoft, Newark, NJ, USA|Embedding based retrieval (EBR) is a fundamental building block in many web applications. However, EBR in sponsored search is distinguished from other generic scenarios and technically challenging due to the need of serving multiple retrieval purposes: firstly, it has to retrieve high-relevance ads, which may exactly serve user's search intent; secondly, it needs to retrieve high-CTR ads so as to maximize the overall user clicks. In this paper, we present a novel representation learning framework Uni-Retriever developed for Bing Search, which unifies two different training modes knowledge distillation and contrastive learning to realize both required objectives. On one hand, the capability of making high-relevance retrieval is established by distilling knowledge from the "relevance teacher model''. On the other hand, the capability of making high-CTR retrieval is optimized by learning to discriminate user's clicked ads from the entire corpus. The two training modes are jointly performed as a multi-objective learning process, such that the ads of high relevance and CTR can be favored by the generated embeddings. Besides the learning strategy, we also elaborate our solution for EBR serving pipeline built upon the substantially optimized DiskANN, where massive-scale EBR can be performed with competitive time and memory efficiency, and accomplished in high-quality. We make comprehensive offline and online experiments to evaluate the proposed techniques, whose findings may provide useful insights for the future development of EBR systems. Uni-Retriever has been mainstreamed as the major retrieval path in Bing's production thanks to the notable improvements on the representation and EBR serving quality.|嵌入式基于检索(EBR)是许多 Web 应用程序的基础构件。然而，由于需要服务于多种检索目的，赞助商搜索中的 EBR 不同于其他一般情况，在技术上具有挑战性: 首先，它必须检索高相关度的广告，这可能恰好服务于用户的搜索意图; 其次，它需要检索高点击率的广告，以最大限度地提高用户的总体点击率。本文提出了一种新的面向 Bing 搜索的 Uni-Retriever 表示学习框架，该框架将两种不同的训练模式知识提取和对比学习相结合，实现了两种不同的目标。一方面，从“关联教师模型”中提取知识，建立高关联检索能力;。另一方面，通过学习从整个语料库中区分用户点击广告，优化了高点击率检索的能力。这两种训练模式作为一个多目标学习过程共同执行，使得嵌入生成的广告更有利于高关联度和点击率的广告。除了学习策略，我们还详细阐述了我们的解决方案，EBR 服务流水线的基础上大幅度优化的 DiskANN，其中大规模的 EBR 可以执行竞争时间和内存效率，并完成在高质量。我们进行了全面的离线和在线实验来评估所提出的技术，其结果可能为未来 EBR 系统的发展提供有用的见解。统一检索已成为主流的检索路径在必应的生产显着改善的表示和 EBR 服务质量。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Uni-Retriever:+Towards+Learning+the+Unified+Embedding+Based+Retriever+in+Bing+Sponsored+Search)|1| |[An Online Multi-task Learning Framework for Google Feed Ads Auction Models](https://doi.org/10.1145/3534678.3539055)|Ning Ma, Mustafa Ispir, Yuan Li, Yongpeng Yang, Zhe Chen, Derek Zhiyuan Cheng, Lan Nie, Kishor Barman|Google Inc., Mountain View, CA, USA|In this paper, we introduce a large scale online multi-task deep learning framework for modeling multiple feed ads auction prediction tasks on an industry-scale feed ads recommendation platform. Multiple prediction tasks are combined into one single model which is continuously trained on real time new ads data. Multi-tasking ads auction models in real-time faces many real-world challenges. For example, each task may be trained on different set of training data; the labels of different tasks may have different arrival time due to label delay; different tasks will interact with each other; combining the losses of each task is non-trivial. We tackle these challenges using practical and novel techniques such as multi-stage training for handling label delay, Multi-gate Mixture-of-Experts (MMoE) to optimize model interaction and an auto-parameter learning algorithm to optimize the loss weights of different tasks. We demonstrate that our proposed techniques can lead to quality improvements and substantial resource saving compared to modeling each single task independently.|本文介绍了一个大规模的在线多任务深度学习框架，在一个行业规模的推荐平台上对多种推广广告拍卖预测任务进行建模。将多个预测任务组合成一个单独的模型，对实时的新广告数据进行连续的训练。实时多任务广告拍卖模型在现实生活中面临着许多挑战。例如，每个任务可以在不同的训练数据集上进行训练; 由于标签延迟，不同任务的标签可能有不同的到达时间; 不同的任务将相互作用;。针对这些问题，我们采用了多阶段训练来处理标签延迟，多门专家混合(MMoE)来优化模型交互，以及自动参数学习算法来优化不同任务的损失权重。我们证明，与独立建模每个单独的任务相比，我们提出的技术可以导致质量改进和大量资源节省。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=An+Online+Multi-task+Learning+Framework+for+Google+Feed+Ads+Auction+Models)|1| |[NxtPost: User To Post Recommendations In Facebook Groups](https://doi.org/10.1145/3534678.3539042)|Kaushik Rangadurai, Yiqun Liu, Siddarth Malreddy, Xiaoyi Liu, Piyush Maheshwari, Vishwanath Sangale, Fedor Borisyuk|Meta Platforms Inc., Menlo Park, CA, USA|In this paper, we present NxtPost, a deployed user-to-post content based sequential recommender system for Facebook Groups. Inspired by recent advances in NLP, we have adapted a Transformer based model to the domain of sequential recommendation. We explore causal masked multi-head attention that optimizes both short and long-term user interests. From a user's past activities validated by defined safety process, NxtPost seeks to learn a representation for the user's dynamic content preference and to predict the next post user may be interested in. In contrast to previous Transformer based methods, we do not assume that the recommendable posts have a fixed corpus. Accordingly, we use an external item/token embedding to extend a sequence-based approach to a large vocabulary. We achieve 49% abs. improvement in offline evaluation. As a result of NxtPost deployment, 0.6% more users are meeting new people, engaging with the community, sharing knowledge and getting support. The paper shares our experience in developing a personalized sequential recommender system, lessons deploying the model for cold start users, how to deal with freshness, and tuning strategies to reach higher efficiency in online A/B experiments.|在本文中，我们介绍了 NxtPost，这是一个为 Facebook group 部署的基于用户到发布内容的顺序推荐系统。受自然语言处理最新进展的启发，我们将一个基于 Transform- 的模型应用于顺序推荐领域。我们探索因果掩盖多头注意，优化短期和长期用户的兴趣。通过定义的安全过程验证用户过去的活动，NxtPost 试图学习用户动态内容偏好的表示，并预测下一个帖子用户可能感兴趣的内容。与以前基于 former 的方法相比，我们不假定推荐的帖子具有固定的语料库。因此，我们使用外部项/令牌嵌入来将基于序列的方法扩展到大型词汇表。我们有49% 的腹肌。离线评估的改进。作为 NxtPost 部署的结果，0.6% 的用户正在结识新朋友，参与社区活动，分享知识并获得支持。本文分享了我们在开发个性化连续推荐系统的经验、为冷启动用户部署模型的教训、如何处理新鲜感，以及在线 A/B 实验中为提高效率而调整策略的经验。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=NxtPost:+User+To+Post+Recommendations+In+Facebook+Groups)|1| |[ReprBERT: Distilling BERT to an Efficient Representation-Based Relevance Model for E-Commerce](https://doi.org/10.1145/3534678.3539090)|Shaowei Yao, Jiwei Tan, Xi Chen, Juhao Zhang, Xiaoyi Zeng, Keping Yang|Alibaba Group, Hangzhou, China|Text relevance or text matching of query and product is an essential technique for e-commerce search engine, which helps users find the desirable products and is also crucial to ensuring user experience. A major difficulty for e-commerce text relevance is the severe vocabulary gap between query and product. Recently, neural networks have been the mainstream for the text matching task owing to the better performance for semantic matching. Practical e-commerce relevance models are usually representation-based architecture, which can pre-compute representations offline and are therefore online efficient. Interaction-based models, although can achieve better performance, are mostly time-consuming and hard to be deployed online. Recently BERT has achieved significant progress on many NLP tasks including text matching, and it is of great value but also big challenge to deploy BERT to the e-commerce relevance task. To realize this goal, we propose ReprBERT, which has the advantages of both excellent performance and low latency, by distilling the interaction-based BERT model to a representation-based architecture. To reduce the performance decline, we investigate the key reasons and propose two novel interaction strategies to resolve the absence of representation interaction and low-level semantic interaction. Finally, ReprBERT can achieve only about 1.5% AUC loss from the interaction-based BERT, but has more than 10% AUC improvement compared to previous state-of-the-art representation-based models. ReprBERT has already been deployed on the search engine of Taobao and serving the entire search traffic, achieving significant gain of user experience and business profit.|查询和产品的文本相关性或文本匹配是电子商务搜索引擎的关键技术，它可以帮助用户找到想要的产品，也是保证用户体验的关键。电子商务文本相关性的一个主要困难是查询和产品之间严重的词汇差距。近年来，神经网络以其较好的语义匹配性能成为文本匹配的主流。实用的电子商务相关性模型通常是基于表示的体系结构，它可以离线预先计算表示，因此具有在线效率。基于交互的模型，尽管可以获得更好的性能，但是大部分都是耗时的，并且很难在线部署。近年来，BERT 在包括文本匹配在内的许多自然语言处理任务中取得了显著的进展，将 BERT 部署到电子商务相关任务中具有很大的价值，但也面临很大的挑战。为了实现这一目标，我们提出了 ReprBERT，它具有良好的性能和低延迟的优点，通过提炼基于交互的 BERT 模型到一个基于表示的体系结构。为了减少表征交互和低层次语义交互的缺失，本文研究了表征交互和低层次语义交互的关键原因，并提出了两种新的交互策略来解决表征交互和低层次语义交互的缺失问题。最后，ReprBERT 只能从基于交互的 BERT 中获得约1.5% 的 AUC 损失，但与以前的基于最先进表示的模型相比，具有超过10% 的 AUC 改善。ReprBERT 已经部署在淘宝的搜索引擎上，服务于整个搜索流量，取得了显著的用户体验和商业利润收益。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=ReprBERT:+Distilling+BERT+to+an+Efficient+Representation-Based+Relevance+Model+for+E-Commerce)|1| -|[Learning Supplementary NLP Features for CTR Prediction in Sponsored Search](https://doi.org/10.1145/3534678.3539064)|Dong Wang, Shaoguang Yan, Yunqing Xia, Kavé Salamatian, Weiwei Deng, Qi Zhang|University of Savoie & Tallinn University of Technology, Annecy, France; Microsoft Corporation, Beijing, China|In sponsored search engines, pre-trained language models have shown promising performance improvements on Click-Through-Rate (CTR) prediction. A widely used approach for utilizing pre-trained language models in CTR prediction consists of fine-tuning the language models with click labels and early stopping on peak value of the obtained Area Under the ROC Curve (AUC). Thereafter the output of these fine-tuned models, i.e., the final score or intermediate embedding generated by language model, is used as a new Natural Language Processing (NLP) feature into CTR prediction baseline. This cascade approach avoids complicating the CTR prediction baseline, while keeping flexibility and agility. However, we show in this work that calibrating separately the language model based on the peak single model AUC does not always yield NLP features that give the best performance in CTR prediction model ultimately. Our analysis reveals that the misalignment is due to overlap and redundancy between the new NLP features and the existing features in CTR prediction baseline. In other words, the NLP features can improve CTR prediction better if such overlap can be reduced. For this purpose, we introduce a simple and general joint-training framework for fine-tuning of language models, combined with the already existing features in CTR prediction baseline, to extract supplementary knowledge for NLP feature. Moreover, we develop an efficient Supplementary Knowledge Distillation (SuKD) that transfers the supplementary knowledge learned by a heavy language model to a light and serviceable model. Comprehensive experiments on both public data and commercial data presented in this work demonstrate that the new NLP features resulting from the joint-training framework can outperform significantly the ones from the independent fine-tuning based on click labels. we also show that the light model distilled with SuKD can provide obvious AUC improvement in CTR prediction over the traditional feature-based knowledge distillation.|在赞助商搜索引擎中，预先训练好的语言模型在点击率(Click-Through-Rate，CTR)预测方面显示出有希望的性能改进。一个广泛使用的方法，利用预先训练的语言模型在点击率预测包括微调的语言模型与点击标签和早期停止在 ROC 曲线下面积(AUC)峰值获得。然后，这些微调模型的输出，即语言模型生成的最终分数或中间嵌入，被用作 CTR 预测基线的一个新的自然语言处理(NLP)特征。这种级联方法避免了使 CTR 预测基线复杂化，同时保持了灵活性和敏捷性。然而，我们的工作表明，基于峰值单模型 AUC 分别标定语言模型并不总是产生 NLP 特征，最终给出 CTR 预测模型的最佳性能。我们的分析表明，失调是由于重叠和冗余之间的新 NLP 特征和现有的特征在 CTR 预测基线。换句话说，如果能够减少这种重叠，NLP 特征能够更好地提高 CTR 预测。为此，本文提出了一种简单通用的语言模型微调联合训练框架，结合 CTR 预测基线中已有的特征，提取 NLP 特征的补充知识。此外，我们开发了一个有效的补充知识提取(SuKD) ，将重语言模型所学到的补充知识转化为一个简单易用的模型。对公共数据和商业数据的综合实验表明，联合训练框架所产生的新的自然语言处理特征可以显著优于基于点击标签的独立微调。与传统的基于特征的知识提取方法相比，用 SuKD 提取的光模型在 CTR 预测方面可以提供明显的 AUC 改进。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Learning+Supplementary+NLP+Features+for+CTR+Prediction+in+Sponsored+Search)|1| -|[AutoShard: Automated Embedding Table Sharding for Recommender Systems](https://doi.org/10.1145/3534678.3539034)|Daochen Zha, Louis Feng, Bhargav Bhushanam, Dhruv Choudhary, Jade Nie, Yuandong Tian, Jay Chae, Yinbin Ma, Arun Kejariwal, Xia Hu|Meta Platforms, Inc., Menlo Park, CA, USA; Rice University, Houston, TX, USA|Embedding learning is an important technique in deep recommendation models to map categorical features to dense vectors. However, the embedding tables often demand an extremely large number of parameters, which become the storage and efficiency bottlenecks. Distributed training solutions have been adopted to partition the embedding tables into multiple devices. However, the embedding tables can easily lead to imbalances if not carefully partitioned. This is a significant design challenge of distributed systems named embedding table sharding, i.e., how we should partition the embedding tables to balance the costs across devices, which is a non-trivial task because 1) it is hard to efficiently and precisely measure the cost, and 2) the partition problem is known to be NP-hard. In this work, we introduce our novel practice in Meta, namely AutoShard, which uses a neural cost model to directly predict the multi-table costs and leverages deep reinforcement learning to solve the partition problem. Experimental results on an open-sourced large-scale synthetic dataset and Meta's production dataset demonstrate the superiority of AutoShard over the heuristics. Moreover, the learned policy of AutoShard can transfer to sharding tasks with various numbers of tables and different ratios of the unseen tables without any fine-tuning. Furthermore, AutoShard can efficiently shard hundreds of tables in seconds. The effectiveness, transferability, and efficiency of AutoShard make it desirable for production use. Our algorithms have been deployed in Meta production environment. A prototype is available at https://github.com/daochenzha/autoshard|嵌入式学习是深度推荐模型中将分类特征映射到密集向量的一项重要技术。然而，嵌入式表往往需要大量的参数，成为存储和效率的瓶颈。采用分布式训练解决方案将嵌入表划分为多个设备。然而，如果不仔细分区，嵌入表很容易导致不平衡。这是分布式系统嵌入表分片的一个重大设计挑战，即我们应该如何划分嵌入表来平衡设备之间的成本，这是一个非常重要的任务，因为1)很难有效和精确地度量成本，2)划分问题是已知的 NP 难题。在这项工作中，我们介绍了我们在 Meta 中的新实践，即 AutoShard，它使用一个神经成本模型来直接预测多表成本，并利用深度强化学习来解决分区问题。在一个开源的大规模合成数据集和 Meta 生产数据集上的实验结果证明了 AutoShard 相对于启发式算法的优越性。此外，AutoShard 的学习策略可以转换为使用不同数量的表和看不见的表的不同比例的分片任务，而不需要进行任何微调。此外，AutoShard 可以在几秒钟内高效地切分数百个表。AutoShard 的有效性、可转移性和效率使其适合生产使用。我们的算法已经部署在元生产环境中。Https://github.com/daochenzha/autoshard 上有一个原型|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=AutoShard:+Automated+Embedding+Table+Sharding+for+Recommender+Systems)|1| -|[On-Device Learning for Model Personalization with Large-Scale Cloud-Coordinated Domain Adaption](https://doi.org/10.1145/3534678.3539263)|Yikai Yan, Chaoyue Niu, Renjie Gu, Fan Wu, Shaojie Tang, Lifeng Hua, Chengfei Lyu, Guihai Chen|Alibaba Group, Hangzhou, China; University of Texas at Dallas, Richardson, TX, USA; Shanghai Jiao Tong University, Shanghai, China|Cloud-based learning is currently the mainstream in both academia and industry. However, the global data distribution, as a mixture of all the users' data distributions, for training a global model may deviate from each user's local distribution for inference, making the global model non-optimal for each individual user. To mitigate distribution discrepancy, on-device training over local data for model personalization is a potential solution, but suffers from serious overfitting. In this work, we propose a new device-cloud collaborative learning framework under the paradigm of domain adaption, called MPDA, to break the dilemmas of purely cloud-based learning and on-device training. From the perspective of a certain user, the general idea of MPDA is to retrieve some similar data from the cloud's global pool, which functions as large-scale source domains, to augment the user's local data as the target domain. The key principle of choosing which outside data depends on whether the model trained over these data can generalize well over the local data. We theoretically analyze that MPDA can reduce distribution discrepancy and overfitting risk. We also extensively evaluate over the public MovieLens 20M and Amazon Electronics datasets, as well as an industrial dataset collected from Mobile Taobao over a period of 30 days. We finally build a device-tunnel-cloud system pipeline, deploy MPDA in the icon area of Mobile Taobao for click-through rate prediction, and conduct online A/B testing. Both offline and online results demonstrate that MPDA outperforms the baselines of cloud-based learning and on-device training only over local data, from multiple offline and online metrics.|基于云的学习是目前学术界和工业界的主流。然而，全局数据分布作为所有用户数据分布的混合，用于训练全局模型可能偏离每个用户的局部分布进行推理，使得全局模型对于每个用户不是最优的。为了缓解分布差异，对模型个性化的本地数据进行设备上的训练是一个潜在的解决方案，但是存在严重的过拟合问题。在这项工作中，我们提出了一个新的设备-云计算合作学习框架，在领域适应的范例下称为 MPDA，以打破纯粹基于云的学习和设备上培训的困境。从某个用户的角度来看，MPDA 的总体思想是从作为大规模源域的云的全局池中检索一些类似的数据，以增加用户的本地数据作为目标域。选择哪些外部数据的关键原则取决于对这些数据进行训练的模型是否能够比本地数据更好地推广。从理论上分析了 MPDA 可以降低分布差异和过拟合风险。我们还广泛评估了公开的 MovieLens 20M 和亚马逊电子数据集，以及在30天内从移动淘宝收集的工业数据集。最后，我们建立了设备-隧道-云系统流水线，在移动淘宝的图标区域部署 MPDA 进行点进率预测，并进行在线 A/B 测试。离线和在线结果都表明，MPDA 仅在多个离线和在线指标的本地数据上优于基于云的学习和设备上培训的基线。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=On-Device+Learning+for+Model+Personalization+with+Large-Scale+Cloud-Coordinated+Domain+Adaption)|1| -|[Debiasing Learning for Membership Inference Attacks Against Recommender Systems](https://doi.org/10.1145/3534678.3539392)|Zihan Wang, Na Huang, Fei Sun, Pengjie Ren, Zhumin Chen, Hengliang Luo, Maarten de Rijke, Zhaochun Ren|Alibaba Group, Beijing, China; Shandong University, Qingdao, China; Meituan, Beijing, China; University of Amsterdam, Amsterdam, Netherlands|Learned recommender systems may inadvertently leak information about their training data, leading to privacy violations. We investigate privacy threats faced by recommender systems through the lens of membership inference. In such attacks, an adversary aims to infer whether a user's data is used to train the target recommender. To achieve this, previous work has used a shadow recommender to derive training data for the attack model, and then predicts the membership by calculating difference vectors between users' historical interactions and recommended items. State-of-the-art methods face two challenging problems: (i) training data for the attack model is biased due to the gap between shadow and target recommenders, and (ii) hidden states in recommenders are not observational, resulting in inaccurate estimations of difference vectors. To address the above limitations, we propose a Debiasing Learning for Membership Inference Attacks against recommender systems (DL-MIA) framework that has four main components: (i) a difference vector generator, (ii) a disentangled encoder, (iii) a weight estimator, and (iv) an attack model. To mitigate the gap between recommenders, a variational auto-encoder (VAE) based disentangled encoder is devised to identify recommender invariant and specific features. To reduce the estimation bias, we design a weight estimator, assigning a truth-level score for each difference vector to indicate estimation accuracy. We evaluate DL-MIA against both general recommenders and sequential recommenders on three real-world datasets. Experimental results show that DL-MIA effectively alleviates training and estimation biases simultaneously, and Íachieves state-of-the-art attack performance.|经验丰富的推荐系统可能无意中泄露有关其培训数据的信息，从而导致侵犯隐私。我们通过成员推理的视角来研究推荐系统所面临的隐私威胁。在这种攻击中，对手的目的是推断用户的数据是否被用来训练目标推荐器。为了实现这一目标，以前的工作是使用阴影推荐来获取攻击模型的训练数据，然后通过计算用户历史交互和推荐项目之间的差异向量来预测成员关系。最先进的方法面临两个具有挑战性的问题: (i)攻击模型的训练数据由于阴影和目标推荐器之间的差距而有偏差，以及(ii)推荐器中的隐藏状态不是观察性的，导致差异向量的估计不准确。为了解决上述局限性，我们提出了针对推荐系统(DL-MIA)的成员推断攻击的去偏学习框架，其具有四个主要组成部分: (i)差分矢量生成器，(ii)分离编码器，(iii)权重估计器和(iv)攻击模型。为了缩小推荐器之间的差距，设计了一种基于变分自动编码器(VAE)的解纠缠编码器来识别推荐器的不变性和特定特征。为了减少估计偏差，我们设计了一个权重估计器，为每个差异向量指定一个真值水平分数来表示估计的准确性。我们在三个真实世界的数据集上评估 DL-MIA 与通用推荐和顺序推荐的对比。实验结果表明，DL-MIA 同时有效地减小了训练偏差和估计偏差，并取得了一流的攻击性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Debiasing+Learning+for+Membership+Inference+Attacks+Against+Recommender+Systems)|1| +|[Learning Supplementary NLP Features for CTR Prediction in Sponsored Search](https://doi.org/10.1145/3534678.3539064)|Dong Wang, Shaoguang Yan, Yunqing Xia, Kavé Salamatian, Weiwei Deng, Qi Zhang|Microsoft Corporation, Beijing, China; University of Savoie & Tallinn University of Technology, Annecy, France|In sponsored search engines, pre-trained language models have shown promising performance improvements on Click-Through-Rate (CTR) prediction. A widely used approach for utilizing pre-trained language models in CTR prediction consists of fine-tuning the language models with click labels and early stopping on peak value of the obtained Area Under the ROC Curve (AUC). Thereafter the output of these fine-tuned models, i.e., the final score or intermediate embedding generated by language model, is used as a new Natural Language Processing (NLP) feature into CTR prediction baseline. This cascade approach avoids complicating the CTR prediction baseline, while keeping flexibility and agility. However, we show in this work that calibrating separately the language model based on the peak single model AUC does not always yield NLP features that give the best performance in CTR prediction model ultimately. Our analysis reveals that the misalignment is due to overlap and redundancy between the new NLP features and the existing features in CTR prediction baseline. In other words, the NLP features can improve CTR prediction better if such overlap can be reduced. For this purpose, we introduce a simple and general joint-training framework for fine-tuning of language models, combined with the already existing features in CTR prediction baseline, to extract supplementary knowledge for NLP feature. Moreover, we develop an efficient Supplementary Knowledge Distillation (SuKD) that transfers the supplementary knowledge learned by a heavy language model to a light and serviceable model. Comprehensive experiments on both public data and commercial data presented in this work demonstrate that the new NLP features resulting from the joint-training framework can outperform significantly the ones from the independent fine-tuning based on click labels. we also show that the light model distilled with SuKD can provide obvious AUC improvement in CTR prediction over the traditional feature-based knowledge distillation.|在赞助商搜索引擎中，预先训练好的语言模型在点击率(Click-Through-Rate，CTR)预测方面显示出有希望的性能改进。一个广泛使用的方法，利用预先训练的语言模型在点击率预测包括微调的语言模型与点击标签和早期停止在 ROC 曲线下面积(AUC)峰值获得。然后，这些微调模型的输出，即语言模型生成的最终分数或中间嵌入，被用作 CTR 预测基线的一个新的自然语言处理(NLP)特征。这种级联方法避免了使 CTR 预测基线复杂化，同时保持了灵活性和敏捷性。然而，我们的工作表明，基于峰值单模型 AUC 分别标定语言模型并不总是产生 NLP 特征，最终给出 CTR 预测模型的最佳性能。我们的分析表明，失调是由于重叠和冗余之间的新 NLP 特征和现有的特征在 CTR 预测基线。换句话说，如果能够减少这种重叠，NLP 特征能够更好地提高 CTR 预测。为此，本文提出了一种简单通用的语言模型微调联合训练框架，结合 CTR 预测基线中已有的特征，提取 NLP 特征的补充知识。此外，我们开发了一个有效的补充知识提取(SuKD) ，将重语言模型所学到的补充知识转化为一个简单易用的模型。对公共数据和商业数据的综合实验表明，联合训练框架所产生的新的自然语言处理特征可以显著优于基于点击标签的独立微调。与传统的基于特征的知识提取方法相比，用 SuKD 提取的光模型在 CTR 预测方面可以提供明显的 AUC 改进。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Learning+Supplementary+NLP+Features+for+CTR+Prediction+in+Sponsored+Search)|1| +|[AutoShard: Automated Embedding Table Sharding for Recommender Systems](https://doi.org/10.1145/3534678.3539034)|Daochen Zha, Louis Feng, Bhargav Bhushanam, Dhruv Choudhary, Jade Nie, Yuandong Tian, Jay Chae, Yinbin Ma, Arun Kejariwal, Xia Hu|Rice University, Houston, TX, USA; Meta Platforms, Inc., Menlo Park, CA, USA|Embedding learning is an important technique in deep recommendation models to map categorical features to dense vectors. However, the embedding tables often demand an extremely large number of parameters, which become the storage and efficiency bottlenecks. Distributed training solutions have been adopted to partition the embedding tables into multiple devices. However, the embedding tables can easily lead to imbalances if not carefully partitioned. This is a significant design challenge of distributed systems named embedding table sharding, i.e., how we should partition the embedding tables to balance the costs across devices, which is a non-trivial task because 1) it is hard to efficiently and precisely measure the cost, and 2) the partition problem is known to be NP-hard. In this work, we introduce our novel practice in Meta, namely AutoShard, which uses a neural cost model to directly predict the multi-table costs and leverages deep reinforcement learning to solve the partition problem. Experimental results on an open-sourced large-scale synthetic dataset and Meta's production dataset demonstrate the superiority of AutoShard over the heuristics. Moreover, the learned policy of AutoShard can transfer to sharding tasks with various numbers of tables and different ratios of the unseen tables without any fine-tuning. Furthermore, AutoShard can efficiently shard hundreds of tables in seconds. The effectiveness, transferability, and efficiency of AutoShard make it desirable for production use. Our algorithms have been deployed in Meta production environment. A prototype is available at https://github.com/daochenzha/autoshard|嵌入式学习是深度推荐模型中将分类特征映射到密集向量的一项重要技术。然而，嵌入式表往往需要大量的参数，成为存储和效率的瓶颈。采用分布式训练解决方案将嵌入表划分为多个设备。然而，如果不仔细分区，嵌入表很容易导致不平衡。这是分布式系统嵌入表分片的一个重大设计挑战，即我们应该如何划分嵌入表来平衡设备之间的成本，这是一个非常重要的任务，因为1)很难有效和精确地度量成本，2)划分问题是已知的 NP 难题。在这项工作中，我们介绍了我们在 Meta 中的新实践，即 AutoShard，它使用一个神经成本模型来直接预测多表成本，并利用深度强化学习来解决分区问题。在一个开源的大规模合成数据集和 Meta 生产数据集上的实验结果证明了 AutoShard 相对于启发式算法的优越性。此外，AutoShard 的学习策略可以转换为使用不同数量的表和看不见的表的不同比例的分片任务，而不需要进行任何微调。此外，AutoShard 可以在几秒钟内高效地切分数百个表。AutoShard 的有效性、可转移性和效率使其适合生产使用。我们的算法已经部署在元生产环境中。Https://github.com/daochenzha/autoshard 上有一个原型|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=AutoShard:+Automated+Embedding+Table+Sharding+for+Recommender+Systems)|1| +|[On-Device Learning for Model Personalization with Large-Scale Cloud-Coordinated Domain Adaption](https://doi.org/10.1145/3534678.3539263)|Yikai Yan, Chaoyue Niu, Renjie Gu, Fan Wu, Shaojie Tang, Lifeng Hua, Chengfei Lyu, Guihai Chen|Alibaba Group, Hangzhou, China; Shanghai Jiao Tong University, Shanghai, China; University of Texas at Dallas, Richardson, TX, USA|Cloud-based learning is currently the mainstream in both academia and industry. However, the global data distribution, as a mixture of all the users' data distributions, for training a global model may deviate from each user's local distribution for inference, making the global model non-optimal for each individual user. To mitigate distribution discrepancy, on-device training over local data for model personalization is a potential solution, but suffers from serious overfitting. In this work, we propose a new device-cloud collaborative learning framework under the paradigm of domain adaption, called MPDA, to break the dilemmas of purely cloud-based learning and on-device training. From the perspective of a certain user, the general idea of MPDA is to retrieve some similar data from the cloud's global pool, which functions as large-scale source domains, to augment the user's local data as the target domain. The key principle of choosing which outside data depends on whether the model trained over these data can generalize well over the local data. We theoretically analyze that MPDA can reduce distribution discrepancy and overfitting risk. We also extensively evaluate over the public MovieLens 20M and Amazon Electronics datasets, as well as an industrial dataset collected from Mobile Taobao over a period of 30 days. We finally build a device-tunnel-cloud system pipeline, deploy MPDA in the icon area of Mobile Taobao for click-through rate prediction, and conduct online A/B testing. Both offline and online results demonstrate that MPDA outperforms the baselines of cloud-based learning and on-device training only over local data, from multiple offline and online metrics.|基于云的学习是目前学术界和工业界的主流。然而，全局数据分布作为所有用户数据分布的混合，用于训练全局模型可能偏离每个用户的局部分布进行推理，使得全局模型对于每个用户不是最优的。为了缓解分布差异，对模型个性化的本地数据进行设备上的训练是一个潜在的解决方案，但是存在严重的过拟合问题。在这项工作中，我们提出了一个新的设备-云计算合作学习框架，在领域适应的范例下称为 MPDA，以打破纯粹基于云的学习和设备上培训的困境。从某个用户的角度来看，MPDA 的总体思想是从作为大规模源域的云的全局池中检索一些类似的数据，以增加用户的本地数据作为目标域。选择哪些外部数据的关键原则取决于对这些数据进行训练的模型是否能够比本地数据更好地推广。从理论上分析了 MPDA 可以降低分布差异和过拟合风险。我们还广泛评估了公开的 MovieLens 20M 和亚马逊电子数据集，以及在30天内从移动淘宝收集的工业数据集。最后，我们建立了设备-隧道-云系统流水线，在移动淘宝的图标区域部署 MPDA 进行点进率预测，并进行在线 A/B 测试。离线和在线结果都表明，MPDA 仅在多个离线和在线指标的本地数据上优于基于云的学习和设备上培训的基线。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=On-Device+Learning+for+Model+Personalization+with+Large-Scale+Cloud-Coordinated+Domain+Adaption)|1| +|[Debiasing Learning for Membership Inference Attacks Against Recommender Systems](https://doi.org/10.1145/3534678.3539392)|Zihan Wang, Na Huang, Fei Sun, Pengjie Ren, Zhumin Chen, Hengliang Luo, Maarten de Rijke, Zhaochun Ren|Alibaba Group, Beijing, China; Meituan, Beijing, China; Shandong University, Qingdao, China; University of Amsterdam, Amsterdam, Netherlands|Learned recommender systems may inadvertently leak information about their training data, leading to privacy violations. We investigate privacy threats faced by recommender systems through the lens of membership inference. In such attacks, an adversary aims to infer whether a user's data is used to train the target recommender. To achieve this, previous work has used a shadow recommender to derive training data for the attack model, and then predicts the membership by calculating difference vectors between users' historical interactions and recommended items. State-of-the-art methods face two challenging problems: (i) training data for the attack model is biased due to the gap between shadow and target recommenders, and (ii) hidden states in recommenders are not observational, resulting in inaccurate estimations of difference vectors. To address the above limitations, we propose a Debiasing Learning for Membership Inference Attacks against recommender systems (DL-MIA) framework that has four main components: (i) a difference vector generator, (ii) a disentangled encoder, (iii) a weight estimator, and (iv) an attack model. To mitigate the gap between recommenders, a variational auto-encoder (VAE) based disentangled encoder is devised to identify recommender invariant and specific features. To reduce the estimation bias, we design a weight estimator, assigning a truth-level score for each difference vector to indicate estimation accuracy. We evaluate DL-MIA against both general recommenders and sequential recommenders on three real-world datasets. Experimental results show that DL-MIA effectively alleviates training and estimation biases simultaneously, and Íachieves state-of-the-art attack performance.|经验丰富的推荐系统可能无意中泄露有关其培训数据的信息，从而导致侵犯隐私。我们通过成员推理的视角来研究推荐系统所面临的隐私威胁。在这种攻击中，对手的目的是推断用户的数据是否被用来训练目标推荐器。为了实现这一目标，以前的工作是使用阴影推荐来获取攻击模型的训练数据，然后通过计算用户历史交互和推荐项目之间的差异向量来预测成员关系。最先进的方法面临两个具有挑战性的问题: (i)攻击模型的训练数据由于阴影和目标推荐器之间的差距而有偏差，以及(ii)推荐器中的隐藏状态不是观察性的，导致差异向量的估计不准确。为了解决上述局限性，我们提出了针对推荐系统(DL-MIA)的成员推断攻击的去偏学习框架，其具有四个主要组成部分: (i)差分矢量生成器，(ii)分离编码器，(iii)权重估计器和(iv)攻击模型。为了缩小推荐器之间的差距，设计了一种基于变分自动编码器(VAE)的解纠缠编码器来识别推荐器的不变性和特定特征。为了减少估计偏差，我们设计了一个权重估计器，为每个差异向量指定一个真值水平分数来表示估计的准确性。我们在三个真实世界的数据集上评估 DL-MIA 与通用推荐和顺序推荐的对比。实验结果表明，DL-MIA 同时有效地减小了训练偏差和估计偏差，并取得了一流的攻击性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Debiasing+Learning+for+Membership+Inference+Attacks+Against+Recommender+Systems)|1| |[Automatic Generation of Product-Image Sequence in E-commerce](https://doi.org/10.1145/3534678.3539149)|Xiaochuan Fan, Chi Zhang, Yong Yang, Yue Shang, Xueying Zhang, Zhen He, Yun Xiao, Bo Long, Lingfei Wu|JD.COM, Beijing, UNK, China; JD.COM Research, Mountain View, CA, USA|Product images are essential for providing desirable user experience in an e-commerce platform. For a platform with billions of products, it is extremely time-costly and labor-expensive to manually pick and organize qualified images. Furthermore, there are the numerous and complicated image rules that a product image needs to comply in order to be generated/selected. To address these challenges, in this paper, we present a new learning framework in order to achieve Automatic Generation of Product-Image Sequence (AGPIS) in e-commerce. To this end, we propose a Multi-modality Unified Image-sequence Classifier (MUIsC), which is able to simultaneously detect all categories of rule violations through learning. MUIsC leverages textual review feedback as the additional training target and utilizes product textual description to provide extra semantic information. %Without using prior knowledge or manually-crafted task, a single MUIsC model is able to learn the holistic knowledge of image reviewing and detect all categories of rule violations simultaneously. Based on offline evaluations, we show that the proposed MUIsC significantly outperforms various baselines. Besides MUIsC, we also integrate some other important modules in the proposed framework, such as primary image selection, non-compliant content detection, and image deduplication. With all these modules, our framework works effectively and efficiently in JD.com recommendation platform. By Dec 2021, our AGPIS framework has generated high-standard images for about 1.5 million products and achieves 13.6% in reject rate. Code of this work is available at https://github.com/efan3000/muisc.|在电子商务平台中，产品图像对于提供理想的用户体验至关重要。对于一个拥有数十亿产品的平台来说，手动挑选和组织合格的图像是非常耗费时间和人力的。此外，还有许多复杂的图像规则，产品图像需要遵守这些规则才能生成/选择。针对这些挑战，本文提出了一种新的学习框架，以实现电子商务中产品图像序列(AGPIS)的自动生成。为此，我们提出了一种多模态统一图像序列分类器(MUIsC) ，它能够通过学习同时检测所有类别的违规行为。MUisC 利用文本评论反馈作为额外的培训目标，并利用产品文本描述提供额外的语义信息。% 在不使用先前知识或手工制作任务的情况下，单一的 MUIsC 模型能够学习图像审查的整体知识，并同时发现所有类别的违规行为。基于离线评估，我们表明所提出的 MUIsC 明显优于各种基线。除了 MUIsC，我们还整合了一些其他的重要模块，如初始图像选择、不兼容的内容检测和图像去重。通过所有这些模块，我们的框架在 JD.com 推荐平台上高效地工作。到2021年12月，我们的 AGPIS 框架已经为大约150万个产品生成了高标准的图像，并且实现了13.6% 的拒绝率。这项工作的代码可在 https://github.com/efan3000/muisc 查阅。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Automatic+Generation+of+Product-Image+Sequence+in+E-commerce)|1| |[Semantic Retrieval at Walmart](https://doi.org/10.1145/3534678.3539164)|Alessandro Magnani, Feng Liu, Suthee Chaidaroon, Sachin Yadav, Praveen Reddy Suram, Ajit Puthenputhussery, Sijie Chen, Min Xie, Anirudh Kashi, Tony Lee, Ciya Liao||||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Semantic+Retrieval+at+Walmart)|1| |[Training Large-Scale News Recommenders with Pretrained Language Models in the Loop](https://doi.org/10.1145/3534678.3539120)|Shitao Xiao, Zheng Liu, Yingxia Shao, Tao Di, Bhuvan Middha, Fangzhao Wu, Xing Xie||||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Training+Large-Scale+News+Recommenders+with+Pretrained+Language+Models+in+the+Loop)|1| @@ -223,13 +223,13 @@ |[Epidemic Forecasting with a Data-Centric Lens](https://doi.org/10.1145/3534678.3542620)|Alexander Rodríguez, Harshavardhan Kamarthi, B. Aditya Prakash||||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Epidemic+Forecasting+with+a+Data-Centric+Lens)|1| |[EXTR: Click-Through Rate Prediction with Externalities in E-Commerce Sponsored Search](https://doi.org/10.1145/3534678.3539053)|Chi Chen, Hui Chen, Kangzhi Zhao, Junsheng Zhou, Li He, Hongbo Deng, Jian Xu, Bo Zheng, Yong Zhang, Chunxiao Xing|Tsinghua University, BeiJing, China; Alibaba Group, BeiJing, China|Click-Through Rate (CTR) prediction, estimating the probability of a user clicking on items, plays a key fundamental role in sponsored search. E-commerce platforms display organic search results and advertisements (ads), collectively called items, together as a mixed list. The items displayed around the predicted ad, i.e. external items, may affect the user clicking on the predicted. Previous CTR models assume the user click only relies on the ad itself, which overlooks the effects of external items, referred to as external effects, or externalities. During the advertising prediction, the organic results have been generated by the organic system, while the final displayed ads on multiple ad slots have not been figured out, which leads to two challenges: 1) the predicted (target) ad may win any ad slot, bringing about diverse externalities. 2) external ads are undetermined, resulting in incomplete externalities. Facing the above challenges, inspired by the Transformer, we propose EXternality TRansformer (EXTR) which regards target ad with all slots as query and external items as key&value to model externalities in all exposure situations in parallel. Furthermore, we design a Potential Allocation Generator (PAG) for EXTR, to learn the allocation of potential external ads to complete the externalities. Extensive experimental results on Alibaba datasets demonstrate the effectiveness of externalities in the task of CTR prediction and illustrate that our proposed approach can bring significant profits to the real-world e-commerce platform. EXTR now has been successfully deployed in the online search advertising system in Alibaba, serving the main traffic.|点进率(ctrl)预测，估计用户点击项目的概率，在赞助商搜索中起着关键的基础作用。电子商务平台显示有机搜索结果和广告(广告) ，统称项目，一起作为一个混合清单。在预测广告周围显示的项目，即外部项目，可能会影响用户点击预测广告。以前的 CTR 模型假设用户的点击只依赖于广告本身，它忽略了外部项目的影响，称为外部影响，或外部性。在广告预测过程中，有机结果是由有机系统产生的，而最终在多个广告时段上显示的广告还没有计算出来，这就带来了两个挑战: 1)预测的(目标)广告可能赢得任何一个广告时段，带来不同的外部性。2)外部广告不确定性，导致外部性不完全。面对上述挑战，我们提出外部性变压器(EXTR)的启发，以所有时隙为查询目标广告和外部项目为关键和价值模型的外部性在所有曝光情况下并行。此外，我们还为 EXTR 设计了一个潜在分配生成器(PAG) ，学习如何分配潜在的外部广告来完成外部性。对阿里巴巴数据集的大量实验结果显示了外部性在点击率预测任务中的有效性，并说明我们建议的方法可以为现实世界的电子商务平台带来显著的利润。EXTR 现已成功应用于阿里巴巴的在线搜索广告系统，为主要流量提供服务。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=EXTR:+Click-Through+Rate+Prediction+with+Externalities+in+E-Commerce+Sponsored+Search)|0| |[PARSRec: Explainable Personalized Attention-fused Recurrent Sequential Recommendation Using Session Partial Actions](https://doi.org/10.1145/3534678.3539432)|Ehsan Gholami, Mohammad Motamedi, Ashwin Aravindakshan|University of California, Davis, Davis, CA, USA|The emerging meta- and multi-verse landscape is yet another step towards the more prevalent use of already ubiquitous online markets. In such markets, recommender systems play critical roles by offering items of interest to the users, thereby narrowing down a vast search space that comprises hundreds of thousands of products. Recommender systems are usually designed to learn common user behaviors and rely on them for inference. This approach, while effective, is oblivious to subtle idiosyncrasies that differentiate humans from each other. Focusing on this observation, we propose an architecture that relies on common patterns as well as individual behaviors to tailor its recommendations for each person. Simulations under a controlled environment show that our proposed model learns interpretable personalized user behaviors. Our empirical results on Nielsen Consumer Panel dataset indicate that the proposed approach achieves up to 27.9% performance improvement compared to the state-of-the-art.|新兴的元和多元宇宙景观是朝着更普遍地使用已经无处不在的在线市场迈出的又一步。在这样的市场中，推荐系统通过向用户提供感兴趣的项目发挥着关键作用，从而缩小了由成千上万个产品组成的巨大搜索空间。推荐系统通常被设计用来学习常见的用户行为，并依赖它们进行推理。这种方法虽然有效，却忽略了区分人与人之间的微妙特质。基于这一观察，我们提出了一个依赖于公共模式和个人行为的体系结构，以便为每个人量身定制其建议。在受控环境下的仿真表明，我们提出的模型学习可解释的个性化用户行为。我们对 AC尼尔森面板数据集的实验结果表明，与最先进的技术相比，提出的方法实现了高达27.9% 的性能改进。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=PARSRec:+Explainable+Personalized+Attention-fused+Recurrent+Sequential+Recommendation+Using+Session+Partial+Actions)|0| -|[Pretraining Representations of Multi-modal Multi-query E-commerce Search](https://doi.org/10.1145/3534678.3539200)|Xinyi Liu, Wanxian Guan, Lianyun Li, Hui Li, Chen Lin, Xubin Li, Si Chen, Jian Xu, Hongbo Deng, Bo Zheng|Alibaba Group, Hangzhou, China; Xiamen University, Xiamen, China|The importance of modeling contextual information within a search session has been widely acknowledged. However, learning representations of multi-query multi-modal (MM) search, in which Mobile Taobao users repeatedly submit textual and visual queries, remains unexplored in literature. Previous work which learns task-specific representations of textual query sessions fails to capture diverse query types and correlations in MM search sessions. This paper presents to represent MM search sessions by heterogeneous graph neural network (HGN). A multi-view contrastive learning framework is proposed to pretrain the HGN, with two views to model different intra-query, inter-query, and inter-modality information diffusion in MM search. Extensive experiments demonstrate that, the pretrained session representation can benefit state-of-the-art baselines on various downstream tasks, such as personalized click prediction, query suggestion, and intent classification.|在搜索会话中建模上下文信息的重要性已经得到了广泛的认可。然而，多查询多模态(MM)搜索的学习表征，其中移动淘宝用户重复提交文本和视觉查询，仍然没有文献探索。前面的工作学习了文本查询会话的特定任务表示，但未能在 MM 搜索会话中捕获不同的查询类型和相关性。本文提出用异构图神经网络(HGN)来表示 MM 搜索会话。提出了一种多视图对比学习框架对 HGN 进行预训练，使用两种视图对 MM 搜索中不同的查询内、查询间和模态间信息扩散进行建模。大量的实验表明，预先训练的会话表示可以使各种下游任务的最先进的基线受益，例如个性化的点击预测、查询建议和意图分类。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Pretraining+Representations+of+Multi-modal+Multi-query+E-commerce+Search)|0| -|[Deep Search Relevance Ranking in Practice](https://doi.org/10.1145/3534678.3542632)|Linsey Pang, Wei Liu, Kenghao Chang, Xue Li, Moumita Bhattacharya, Xianjing Liu, Stephen Guo|Twitter, San Jose , CA, USA; Salesforce, San Francisco, CA, USA; Microsoft, Mountain View, CA, USA; Walmart Global Tech, Sunnyvale, CA, USA; University of Technology Sydney, Sydney, Australia; Netflix, Los Gatos, CA, USA|Machine learning techniques for developing industry-scale search engines have long been a prominent part of most domains and their online products. Search relevance algorithms are key components of products across different fields, including e-commerce, streaming services, and social networks. In this tutorial, we give an introduction to such large-scale search ranking systems, specifically focusing on deep learning techniques in this area. The topics we cover are the following: (1) Overview of search ranking systems in practice, including classical and machine learning techniques; (2) Introduction to sequential and language models in the context of search ranking; and (3) Knowledge distillation approaches for this area. For each of the aforementioned sessions, we first give an introductory talk and then go over an hands-on tutorial to really hone in on the concepts. We cover fundamental concepts using demos, case studies, and hands-on examples, including the latest Deep Learning methods that have achieved state-of-the-art results in generating the most relevant search results. Moreover, we show example implementations of these methods in python, leveraging a variety of open-source machine-learning/deep-learning libraries as well as real industrial data or open-source data.|用于开发行业规模搜索引擎的机器学习技术长期以来一直是大多数领域及其在线产品的重要组成部分。搜索相关算法是不同领域产品的关键组成部分，包括电子商务、流媒体服务和社交网络。在本教程中，我们将介绍这种大规模的搜索排名系统，特别关注这一领域的深度学习技术。我们讨论的主题如下: (1)搜索排名系统在实践中的概述，包括经典的和机器学习技术; (2)在搜索排名的背景下序列和语言模型的介绍; 和(3)这个领域的知识提取方法。对于前面提到的每一个会议，我们首先做一个介绍性的演讲，然后通过一个实践教程来真正地深入理解这些概念。我们使用演示、案例研究和实践例子介绍基本概念，包括最新的深度学习方法，这些方法在生成最相关的搜索结果时取得了最先进的结果。此外，我们还展示了这些方法在 python 中的实现示例，利用了各种开源机器学习/深度学习库以及真实的工业数据或开源数据。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Deep+Search+Relevance+Ranking+in+Practice)|0| +|[Pretraining Representations of Multi-modal Multi-query E-commerce Search](https://doi.org/10.1145/3534678.3539200)|Xinyi Liu, Wanxian Guan, Lianyun Li, Hui Li, Chen Lin, Xubin Li, Si Chen, Jian Xu, Hongbo Deng, Bo Zheng|Xiamen University, Xiamen, China; Alibaba Group, Hangzhou, China|The importance of modeling contextual information within a search session has been widely acknowledged. However, learning representations of multi-query multi-modal (MM) search, in which Mobile Taobao users repeatedly submit textual and visual queries, remains unexplored in literature. Previous work which learns task-specific representations of textual query sessions fails to capture diverse query types and correlations in MM search sessions. This paper presents to represent MM search sessions by heterogeneous graph neural network (HGN). A multi-view contrastive learning framework is proposed to pretrain the HGN, with two views to model different intra-query, inter-query, and inter-modality information diffusion in MM search. Extensive experiments demonstrate that, the pretrained session representation can benefit state-of-the-art baselines on various downstream tasks, such as personalized click prediction, query suggestion, and intent classification.|在搜索会话中建模上下文信息的重要性已经得到了广泛的认可。然而，多查询多模态(MM)搜索的学习表征，其中移动淘宝用户重复提交文本和视觉查询，仍然没有文献探索。前面的工作学习了文本查询会话的特定任务表示，但未能在 MM 搜索会话中捕获不同的查询类型和相关性。本文提出用异构图神经网络(HGN)来表示 MM 搜索会话。提出了一种多视图对比学习框架对 HGN 进行预训练，使用两种视图对 MM 搜索中不同的查询内、查询间和模态间信息扩散进行建模。大量的实验表明，预先训练的会话表示可以使各种下游任务的最先进的基线受益，例如个性化的点击预测、查询建议和意图分类。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Pretraining+Representations+of+Multi-modal+Multi-query+E-commerce+Search)|0| +|[Deep Search Relevance Ranking in Practice](https://doi.org/10.1145/3534678.3542632)|Linsey Pang, Wei Liu, Kenghao Chang, Xue Li, Moumita Bhattacharya, Xianjing Liu, Stephen Guo|Walmart Global Tech, Sunnyvale, CA, USA; University of Technology Sydney, Sydney, Australia; Salesforce, San Francisco, CA, USA; Netflix, Los Gatos, CA, USA; Microsoft, Mountain View, CA, USA; Twitter, San Jose , CA, USA|Machine learning techniques for developing industry-scale search engines have long been a prominent part of most domains and their online products. Search relevance algorithms are key components of products across different fields, including e-commerce, streaming services, and social networks. In this tutorial, we give an introduction to such large-scale search ranking systems, specifically focusing on deep learning techniques in this area. The topics we cover are the following: (1) Overview of search ranking systems in practice, including classical and machine learning techniques; (2) Introduction to sequential and language models in the context of search ranking; and (3) Knowledge distillation approaches for this area. For each of the aforementioned sessions, we first give an introductory talk and then go over an hands-on tutorial to really hone in on the concepts. We cover fundamental concepts using demos, case studies, and hands-on examples, including the latest Deep Learning methods that have achieved state-of-the-art results in generating the most relevant search results. Moreover, we show example implementations of these methods in python, leveraging a variety of open-source machine-learning/deep-learning libraries as well as real industrial data or open-source data.|用于开发行业规模搜索引擎的机器学习技术长期以来一直是大多数领域及其在线产品的重要组成部分。搜索相关算法是不同领域产品的关键组成部分，包括电子商务、流媒体服务和社交网络。在本教程中，我们将介绍这种大规模的搜索排名系统，特别关注这一领域的深度学习技术。我们讨论的主题如下: (1)搜索排名系统在实践中的概述，包括经典的和机器学习技术; (2)在搜索排名的背景下序列和语言模型的介绍; 和(3)这个领域的知识提取方法。对于前面提到的每一个会议，我们首先做一个介绍性的演讲，然后通过一个实践教程来真正地深入理解这些概念。我们使用演示、案例研究和实践例子介绍基本概念，包括最新的深度学习方法，这些方法在生成最相关的搜索结果时取得了最先进的结果。此外，我们还展示了这些方法在 python 中的实现示例，利用了各种开源机器学习/深度学习库以及真实的工业数据或开源数据。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Deep+Search+Relevance+Ranking+in+Practice)|0| |[Debiasing the Cloze Task in Sequential Recommendation with Bidirectional Transformers](https://doi.org/10.1145/3534678.3539430)|Khalil Damak, Sami Khenissi, Olfa Nasraoui|University of Louisville, Louisville, KY, USA|Bidirectional Transformer architectures are state-of-the-art sequential recommendation models that use a bi-directional representation capacity based on the Cloze task, a.k.a. Masked Language Modeling. The latter aims to predict randomly masked items within the sequence. Because they assume that the true interacted item is the most relevant one, an exposure bias results, where non-interacted items with low exposure propensities are assumed to be irrelevant. The most common approach to mitigating exposure bias in recommendation has been Inverse Propensity Scoring (IPS), which consists of down-weighting the interacted predictions in the loss function in proportion to their propensities of exposure, yielding a theoretically unbiased learning. In this work, we argue and prove that IPS does not extend to sequential recommendation because it fails to account for the temporal nature of the problem. We then propose a novel propensity scoring mechanism, which can theoretically debias the Cloze task in sequential recommendation. Finally we empirically demonstrate the debiasing capabilities of our proposed approach and its robustness to the severity of exposure bias.|双向转换器体系结构是最先进的顺序推荐模型，它使用基于完形填空任务的双向表示能力，也就是掩码语言建模。后者旨在预测序列中随机掩盖的项目。因为他们假设真正的相互作用的项目是最相关的一个，暴露偏差的结果，其中没有相互作用的项目低暴露倾向被认为是无关紧要的。减轻推荐中暴露偏倚的最常见方法是逆倾向评分(IPS) ，其包括按照暴露倾向的比例降低损失函数中的相互作用预测的权重，从而产生理论上无偏倚的学习。在这项工作中，我们争论和证明 IPS 没有扩展到顺序推荐，因为它没有考虑到问题的时间性质。然后，我们提出了一种新的倾向评分机制，它可以在理论上降低完形填空任务的顺序推荐。最后，我们通过实证证明了我们提出的方法的去偏能力及其对暴露偏差严重程度的鲁棒性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Debiasing+the+Cloze+Task+in+Sequential+Recommendation+with+Bidirectional+Transformers)|0| -|[A Generalized Doubly Robust Learning Framework for Debiasing Post-Click Conversion Rate Prediction](https://doi.org/10.1145/3534678.3539270)|Quanyu Dai, Haoxuan Li, Peng Wu, Zhenhua Dong, XiaoHua Zhou, Rui Zhang, Rui Zhang, Jie Sun|Beijing Technology and Business University, Beijing, China; Huawei Noah's Ark Lab, Shenzhen, China; ruizhang.info, Shenzhen, China; Peking University, Beijing, China; Huawei Hong Kong Theory Lab, Hong Kong, China|Post-click conversion rate (CVR) prediction is an essential task for discovering user interests and increasing platform revenues in a range of industrial applications. One of the most challenging problems of this task is the existence of severe selection bias caused by the inherent self-selection behavior of users and the item selection process of systems. Currently, doubly robust (DR) learning approaches achieve the state-of-the-art performance for debiasing CVR prediction. However, in this paper, by theoretically analyzing the bias, variance and generalization bounds of DR methods, we find that existing DR approaches may have poor generalization caused by inaccurate estimation of propensity scores and imputation errors, which often occur in practice. Motivated by such analysis, we propose a generalized learning framework that not only unifies existing DR methods, but also provides a valuable opportunity to develop a series of new debiasing techniques to accommodate different application scenarios. Based on the framework, we propose two new DR methods, namely DR-BIAS and DR-MSE. DR-BIAS directly controls the bias of DR loss, while DR-MSE balances the bias and variance flexibly, which achieves better generalization performance. In addition, we propose a novel tri-level joint learning optimization method for DR-MSE in CVR prediction, and an efficient training algorithm correspondingly. We conduct extensive experiments on both real-world and semi-synthetic datasets, which validate the effectiveness of our proposed methods.|点击后转换率(CVR)预测是发现用户兴趣和增加平台收入的一个重要任务，在一系列的工业应用。这项任务最具挑战性的问题之一是由于用户固有的自我选择行为和系统的项目选择过程所引起的严重选择偏差的存在。目前，双鲁棒(DR)学习方法在降低 CVR 预测偏差方面取得了最好的效果。然而，通过对 DR 方法的偏差、方差和泛化界限的理论分析，我们发现现有的 DR 方法可能由于在实际应用中经常出现的倾向分数估计不准确和插补错误而导致泛化能力较差。基于这样的分析，我们提出了一个通用的学习框架，它不仅统一了现有的 DR 方法，而且为开发一系列新的去偏技术以适应不同的应用场景提供了宝贵的机会。在此基础上，提出了两种新的 DR 方法: DR-BIAS 和 DR-MSE。DR-BIAS 直接控制 DR 损失的偏差，而 DR-MSE 灵活地平衡偏差和方差，从而获得更好的泛化性能。此外，本文还提出了一种新的基于 DR-MSE 的 CVR 预测三层联合学习优化方法，并给出了相应的训练算法。我们在真实世界和半合成数据集上进行了广泛的实验，验证了我们提出的方法的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Generalized+Doubly+Robust+Learning+Framework+for+Debiasing+Post-Click+Conversion+Rate+Prediction)|0| -|[User-Event Graph Embedding Learning for Context-Aware Recommendation](https://doi.org/10.1145/3534678.3539458)|Dugang Liu, Mingkai He, Jinwei Luo, Jiangxu Lin, Meng Wang, Xiaolian Zhang, Weike Pan, Zhong Ming|Huawei Technologies Co Ltd, Shenzhen, China; Southeast University, Nanjing, China; Shenzhen University & Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ), Shenzhen, China; Shenzhen University, Shenzhen, China|Most methods for context-aware recommendation focus on improving the feature interaction layer, but overlook the embedding layer. However, an embedding layer with random initialization often suffers in practice from the sparsity of the contextual features, as well as the interactions between the users (or items) and context. In this paper, we propose a novel user-event graph embedding learning (UEG-EL) framework to address these two sparsity challenges. Specifically, our UEG-EL contains three modules: 1) a graph construction module is used to obtain a user-event graph containing nodes for users, intents and items, where the intent nodes are generated by applying intent node attention (INA) on nodes of the contextual features; 2) a user-event collaborative graph convolution module is designed to obtain the refined embeddings of all features by executing a new convolution strategy on the user-event graph, where each intent node acts as a hub to efficiently propagate the information among different features; 3) a recommendation module is equipped to integrate some existing context-aware recommendation model, where the feature embeddings are directly initialized with the obtained refined embeddings. Moreover, we identify a unique challenge of the basic framework, that is, the contextual features associated with too many instances may suffer from noise when aggregating the information. We thus further propose a simple but effective variant, i.e., UEG-EL-V, in order to prune the information propagation of the contextual features. Finally, we conduct extensive experiments on three public datasets to verify the effectiveness and compatibility of our UEG-EL and its variant.|大多数上下文感知的推荐方法侧重于改进特征交互层，而忽略了嵌入层。然而，具有随机初始化的嵌入层在实践中经常受到上下文特征稀疏性以及用户(或项目)与上下文之间交互的影响。本文提出了一种新的用户事件图嵌入学习(UEG-EL)框架来解决这两个稀疏性问题。具体来说，我们的 UEG-EL 包含三个模块: 1)一个图形构造模块用于获得一个包含用户、意图和项目节点的用户事件图，其中意图节点是通过在上下文特征的节点上应用意图节点注意力(INA)来生成的; 2)一个用户事件协作图卷积模块用于通过在用户事件图上执行一个新的卷积策略来获得所有特征的精细嵌入，其中每个意图节点作为一个中心来有效地传播不同特征之间的信息; 3)一个推荐模块用于集成一些现有的上下文感知的推荐模型，其中特征嵌入是直接初始化。此外，我们发现了基本框架的一个独特的挑战，即与太多实例相关的上下文特征在聚合信息时可能会受到噪声的影响。因此，我们进一步提出了一个简单而有效的变体，即 UEG-EL-V，以修剪信息传播的上下文特征。最后，我们在三个公共数据集上进行了广泛的实验，以验证我们的 UEG-EL 及其变体的有效性和兼容性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=User-Event+Graph+Embedding+Learning+for+Context-Aware+Recommendation)|0| +|[A Generalized Doubly Robust Learning Framework for Debiasing Post-Click Conversion Rate Prediction](https://doi.org/10.1145/3534678.3539270)|Quanyu Dai, Haoxuan Li, Peng Wu, Zhenhua Dong, XiaoHua Zhou, Rui Zhang, Rui Zhang, Jie Sun|Huawei Noah's Ark Lab, Shenzhen, China; Peking University, Beijing, China; ruizhang.info, Shenzhen, China; Huawei Hong Kong Theory Lab, Hong Kong, China; Beijing Technology and Business University, Beijing, China|Post-click conversion rate (CVR) prediction is an essential task for discovering user interests and increasing platform revenues in a range of industrial applications. One of the most challenging problems of this task is the existence of severe selection bias caused by the inherent self-selection behavior of users and the item selection process of systems. Currently, doubly robust (DR) learning approaches achieve the state-of-the-art performance for debiasing CVR prediction. However, in this paper, by theoretically analyzing the bias, variance and generalization bounds of DR methods, we find that existing DR approaches may have poor generalization caused by inaccurate estimation of propensity scores and imputation errors, which often occur in practice. Motivated by such analysis, we propose a generalized learning framework that not only unifies existing DR methods, but also provides a valuable opportunity to develop a series of new debiasing techniques to accommodate different application scenarios. Based on the framework, we propose two new DR methods, namely DR-BIAS and DR-MSE. DR-BIAS directly controls the bias of DR loss, while DR-MSE balances the bias and variance flexibly, which achieves better generalization performance. In addition, we propose a novel tri-level joint learning optimization method for DR-MSE in CVR prediction, and an efficient training algorithm correspondingly. We conduct extensive experiments on both real-world and semi-synthetic datasets, which validate the effectiveness of our proposed methods.|点击后转换率(CVR)预测是发现用户兴趣和增加平台收入的一个重要任务，在一系列的工业应用。这项任务最具挑战性的问题之一是由于用户固有的自我选择行为和系统的项目选择过程所引起的严重选择偏差的存在。目前，双鲁棒(DR)学习方法在降低 CVR 预测偏差方面取得了最好的效果。然而，通过对 DR 方法的偏差、方差和泛化界限的理论分析，我们发现现有的 DR 方法可能由于在实际应用中经常出现的倾向分数估计不准确和插补错误而导致泛化能力较差。基于这样的分析，我们提出了一个通用的学习框架，它不仅统一了现有的 DR 方法，而且为开发一系列新的去偏技术以适应不同的应用场景提供了宝贵的机会。在此基础上，提出了两种新的 DR 方法: DR-BIAS 和 DR-MSE。DR-BIAS 直接控制 DR 损失的偏差，而 DR-MSE 灵活地平衡偏差和方差，从而获得更好的泛化性能。此外，本文还提出了一种新的基于 DR-MSE 的 CVR 预测三层联合学习优化方法，并给出了相应的训练算法。我们在真实世界和半合成数据集上进行了广泛的实验，验证了我们提出的方法的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Generalized+Doubly+Robust+Learning+Framework+for+Debiasing+Post-Click+Conversion+Rate+Prediction)|0| +|[User-Event Graph Embedding Learning for Context-Aware Recommendation](https://doi.org/10.1145/3534678.3539458)|Dugang Liu, Mingkai He, Jinwei Luo, Jiangxu Lin, Meng Wang, Xiaolian Zhang, Weike Pan, Zhong Ming|Shenzhen University, Shenzhen, China; Huawei Technologies Co Ltd, Shenzhen, China; Southeast University, Nanjing, China; Shenzhen University & Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ), Shenzhen, China|Most methods for context-aware recommendation focus on improving the feature interaction layer, but overlook the embedding layer. However, an embedding layer with random initialization often suffers in practice from the sparsity of the contextual features, as well as the interactions between the users (or items) and context. In this paper, we propose a novel user-event graph embedding learning (UEG-EL) framework to address these two sparsity challenges. Specifically, our UEG-EL contains three modules: 1) a graph construction module is used to obtain a user-event graph containing nodes for users, intents and items, where the intent nodes are generated by applying intent node attention (INA) on nodes of the contextual features; 2) a user-event collaborative graph convolution module is designed to obtain the refined embeddings of all features by executing a new convolution strategy on the user-event graph, where each intent node acts as a hub to efficiently propagate the information among different features; 3) a recommendation module is equipped to integrate some existing context-aware recommendation model, where the feature embeddings are directly initialized with the obtained refined embeddings. Moreover, we identify a unique challenge of the basic framework, that is, the contextual features associated with too many instances may suffer from noise when aggregating the information. We thus further propose a simple but effective variant, i.e., UEG-EL-V, in order to prune the information propagation of the contextual features. Finally, we conduct extensive experiments on three public datasets to verify the effectiveness and compatibility of our UEG-EL and its variant.|大多数上下文感知的推荐方法侧重于改进特征交互层，而忽略了嵌入层。然而，具有随机初始化的嵌入层在实践中经常受到上下文特征稀疏性以及用户(或项目)与上下文之间交互的影响。本文提出了一种新的用户事件图嵌入学习(UEG-EL)框架来解决这两个稀疏性问题。具体来说，我们的 UEG-EL 包含三个模块: 1)一个图形构造模块用于获得一个包含用户、意图和项目节点的用户事件图，其中意图节点是通过在上下文特征的节点上应用意图节点注意力(INA)来生成的; 2)一个用户事件协作图卷积模块用于通过在用户事件图上执行一个新的卷积策略来获得所有特征的精细嵌入，其中每个意图节点作为一个中心来有效地传播不同特征之间的信息; 3)一个推荐模块用于集成一些现有的上下文感知的推荐模型，其中特征嵌入是直接初始化。此外，我们发现了基本框架的一个独特的挑战，即与太多实例相关的上下文特征在聚合信息时可能会受到噪声的影响。因此，我们进一步提出了一个简单而有效的变体，即 UEG-EL-V，以修剪信息传播的上下文特征。最后，我们在三个公共数据集上进行了广泛的实验，以验证我们的 UEG-EL 及其变体的有效性和兼容性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=User-Event+Graph+Embedding+Learning+for+Context-Aware+Recommendation)|0| |[Adversarial Gradient Driven Exploration for Deep Click-Through Rate Prediction](https://doi.org/10.1145/3534678.3539461)|Kailun Wu, Weijie Bian, Zhangming Chan, Lejian Ren, Shiming Xiang, Shuguang Han, Hongbo Deng, Bo Zheng|Alibaba Group, Beijing, China; Institute of Automation, Chinese Academy of Sciences, Beijing, China|Exploration-Exploitation (E& E) algorithms are commonly adopted to deal with the feedback-loop issue in large-scale online recommender systems. Most of existing studies believe that high uncertainty can be a good indicator of potential reward, and thus primarily focus on the estimation of model uncertainty. We argue that such an approach overlooks the subsequent effect of exploration on model training. From the perspective of online learning, the adoption of an exploration strategy would also affect the collecting of training data, which further influences model learning. To understand the interaction between exploration and training, we design a Pseudo-Exploration module that simulates the model updating process after a certain item is explored and the corresponding feedback is received. We further show that such a process is equivalent to adding an adversarial perturbation to the model input, and thereby name our proposed approach as an the Adversarial Gradient Driven Exploration (AGE). For production deployment, we propose a dynamic gating unit to pre-determine the utility of an exploration. This enables us to utilize the limited amount of resources for exploration, and avoid wasting pageview resources on ineffective exploration. The effectiveness of AGE was firstly examined through an extensive number of ablation studies on an academic dataset. Meanwhile, AGE has also been deployed to one of the world-leading display advertising platforms, and we observe significant improvements on various top-line evaluation metrics.|在大规模在线推荐系统中，探索-开发(E & E)算法是处理反馈回路问题的常用算法。大多数已有的研究认为高不确定性可以作为潜在报酬的一个很好的指标，因此主要集中在模型不确定性的估计上。我们认为这种方法忽视了探索对模型训练的后续影响。从在线学习的角度来看，探索策略的采用也会影响训练数据的收集，从而进一步影响模型学习。为了理解探索与训练的相互作用，我们设计了一个拟探索模块，模拟探索某一项目并收到相应反馈后的模型更新过程。我们进一步表明，这样一个过程是相当于添加一个对抗扰动的模型输入，从而命名我们提出的方法作为一个对抗梯度驱动探索(AGE)。对于生产部署，我们提出了一个动态门控单元来预先确定勘探的效用。这使我们能够利用有限的资源进行探索，避免在无效探索上浪费页面浏览资源。AGE 的有效性首先通过一个学术数据集上的大量消融研究进行了检验。与此同时，AGE 也被部署到世界领先的展示广告平台之一，我们观察到各种顶线评估指标的显著改进。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Adversarial+Gradient+Driven+Exploration+for+Deep+Click-Through+Rate+Prediction)|0| -|[Graph-based Multilingual Language Model: Leveraging Product Relations for Search Relevance](https://doi.org/10.1145/3534678.3539158)|Nurendra Choudhary, Nikhil Rao, Karthik Subbian, Chandan K. Reddy|Virginia Tech, Arlington, VA, USA; Amazon, Palo Alto, CA, USA|The large-scale nature of product catalog and the changing demands of customer queries makes product search a challenging problem. The customer queries are ambiguous and implicit. They may be looking for an exact match of their query, or a functional equivalent (i.e., substitute), or an accessory to go with it (i.e., complement). It is important to distinguish these three categories from merely classifying an item for a customer query as relevant or not. This information can help direct the customer and improve search applications to understand the customer mission. In this paper, we formulate search relevance as a multi-class classification problem and propose a graph-based solution to classify a given query-item pair as exact, substitute, complement, or irrelevant (ESCI). The customer engagement (clicks, add-to-cart, and purchases) between query and items serve as a crucial information for this problem. However, existing approaches rely purely on the textual information (such as BERT) and do not sufficiently focus on the structural relationships. Another challenge in including the structural information is the sparsity of such data in some regions. We propose Structure-Aware multilingual LAnguage Model (SALAM), that utilizes a language model along with a graph neural network, to extract region-specific semantics as well as relational information for the classification of query-product pairs. Our model is first pre-trained on a large region-agnostic dataset and behavioral graph data and then fine-tuned on region-specific versions to address the sparsity. We show in our experiments that SALAM significantly outperforms the current matching frameworks on the ESCI classification task in several regions. We also demonstrate the effectiveness of using a two-phased training setup (i.e., pre-training and fine-tuning) in capturing region-specific information. Also, we provide various challenges and solutions for using the model in an industrial setting and outline its contribution to the e-commerce engine.|产品目录的大规模性和客户查询需求的变化使得产品搜索成为一个具有挑战性的问题。客户查询是模糊和隐式的。他们可能在寻找与他们的查询完全匹配的查询，或者功能等价的查询(即替代查询) ，或者附属查询(即补充查询)。区分这三个类别与仅仅为客户查询分类一个项目是否相关是很重要的。这些信息可以帮助指导客户并改进搜索应用程序，以理解客户的使命。本文将搜索相关性表述为一个多类分类问题，并提出了一种基于图的解决方案，将给定的查询项对分类为精确、替代、补充或不相关(ESCI)。查询和项目之间的客户参与(单击、添加到购物车和购买)是解决此问题的关键信息。然而，现有的方法仅仅依赖于文本信息(比如 BERT) ，并没有充分关注结构关系。在纳入结构信息方面的另一个挑战是，一些区域的此类数据稀少。提出了一种基于结构感知的多语言语言模型(SALAM) ，该模型利用语言模型和图神经网络提取区域特定的语义和关系信息，用于查询产品对的分类。我们的模型首先在大型区域不可知数据集和行为图数据上进行预训练，然后在区域特定版本上进行微调，以解决稀疏性问题。我们的实验表明，在 ESCI 分类任务中，SALAM 在几个地区的性能明显优于目前的匹配框架。我们还演示了使用两阶段的训练设置(即预训练和微调)在捕获特定区域的信息方面的有效性。此外，我们提供了在工业环境中使用该模型的各种挑战和解决方案，并概述了其对电子商务引擎的贡献。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Graph-based+Multilingual+Language+Model:+Leveraging+Product+Relations+for+Search+Relevance)|0| +|[Graph-based Multilingual Language Model: Leveraging Product Relations for Search Relevance](https://doi.org/10.1145/3534678.3539158)|Nurendra Choudhary, Nikhil Rao, Karthik Subbian, Chandan K. Reddy|Amazon, Palo Alto, CA, USA; Virginia Tech, Arlington, VA, USA|The large-scale nature of product catalog and the changing demands of customer queries makes product search a challenging problem. The customer queries are ambiguous and implicit. They may be looking for an exact match of their query, or a functional equivalent (i.e., substitute), or an accessory to go with it (i.e., complement). It is important to distinguish these three categories from merely classifying an item for a customer query as relevant or not. This information can help direct the customer and improve search applications to understand the customer mission. In this paper, we formulate search relevance as a multi-class classification problem and propose a graph-based solution to classify a given query-item pair as exact, substitute, complement, or irrelevant (ESCI). The customer engagement (clicks, add-to-cart, and purchases) between query and items serve as a crucial information for this problem. However, existing approaches rely purely on the textual information (such as BERT) and do not sufficiently focus on the structural relationships. Another challenge in including the structural information is the sparsity of such data in some regions. We propose Structure-Aware multilingual LAnguage Model (SALAM), that utilizes a language model along with a graph neural network, to extract region-specific semantics as well as relational information for the classification of query-product pairs. Our model is first pre-trained on a large region-agnostic dataset and behavioral graph data and then fine-tuned on region-specific versions to address the sparsity. We show in our experiments that SALAM significantly outperforms the current matching frameworks on the ESCI classification task in several regions. We also demonstrate the effectiveness of using a two-phased training setup (i.e., pre-training and fine-tuning) in capturing region-specific information. Also, we provide various challenges and solutions for using the model in an industrial setting and outline its contribution to the e-commerce engine.|产品目录的大规模性和客户查询需求的变化使得产品搜索成为一个具有挑战性的问题。客户查询是模糊和隐式的。他们可能在寻找与他们的查询完全匹配的查询，或者功能等价的查询(即替代查询) ，或者附属查询(即补充查询)。区分这三个类别与仅仅为客户查询分类一个项目是否相关是很重要的。这些信息可以帮助指导客户并改进搜索应用程序，以理解客户的使命。本文将搜索相关性表述为一个多类分类问题，并提出了一种基于图的解决方案，将给定的查询项对分类为精确、替代、补充或不相关(ESCI)。查询和项目之间的客户参与(单击、添加到购物车和购买)是解决此问题的关键信息。然而，现有的方法仅仅依赖于文本信息(比如 BERT) ，并没有充分关注结构关系。在纳入结构信息方面的另一个挑战是，一些区域的此类数据稀少。提出了一种基于结构感知的多语言语言模型(SALAM) ，该模型利用语言模型和图神经网络提取区域特定的语义和关系信息，用于查询产品对的分类。我们的模型首先在大型区域不可知数据集和行为图数据上进行预训练，然后在区域特定版本上进行微调，以解决稀疏性问题。我们的实验表明，在 ESCI 分类任务中，SALAM 在几个地区的性能明显优于目前的匹配框架。我们还演示了使用两阶段的训练设置(即预训练和微调)在捕获特定区域的信息方面的有效性。此外，我们提供了在工业环境中使用该模型的各种挑战和解决方案，并概述了其对电子商务引擎的贡献。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Graph-based+Multilingual+Language+Model:+Leveraging+Product+Relations+for+Search+Relevance)|0| |[ASPIRE: Air Shipping Recommendation for E-commerce Products via Causal Inference Framework](https://doi.org/10.1145/3534678.3539197)|Abhirup Mondal, Anirban Majumder, Vineet Chaoji|Amazon, Bengaluru, India|Speed of delivery is critical for the success of e-commerce platforms. Faster delivery promise to the customer results in increased conversion and revenue. There are typically two mechanisms to control the delivery speed - a) replication of products across warehouses, and b) air-shipping the product. In this paper, we present a machine learning based framework to recommend air-shipping eligibility for products. Specifically, we develop a causal inference framework (referred to as Air Shipping Recommendation or ASPIRE) that balances the trade-off between revenue or conversion and delivery cost to decide whether a product should be shipped via air. We propose a doubly-robust estimation technique followed by an optimization algorithm to determine air eligibility of products and calculate the uplift in revenue and shipping cost. We ran extensive experiments (both offline and online) to demonstrate the superiority of our technique as compared to the incumbent policies and baseline approaches. ASPIRE resulted in a lift of +79 bps of revenue as measured through an A/B experiment in an emerging marketplace on Amazon.|交付速度对电子商务平台的成功至关重要。更快的交付承诺给客户的结果增加转换和收入。通常有两种机制来控制交付速度: a)在仓库之间复制产品，b)空运产品。在本文中，我们提出了一个基于机器学习的框架来推荐产品的空运资格。具体来说，我们开发了一个因果推理框架(称为航空运输建议书或 ASPIRE) ，平衡收入或转换和交付成本之间的权衡，以决定是否应该通过空运运输产品。我们提出了一个双稳健估计技术和一个优化算法来确定产品的空气合格性，并计算收入和运输成本的提高。我们进行了大量的实验(线下和线上) ，以证明我们的技术相对于现有的策略和基线方法的优越性。通过在亚马逊新兴市场的 A/B 实验，ASPIRE 的收入提高了79个基点。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=ASPIRE:+Air+Shipping+Recommendation+for+E-commerce+Products+via+Causal+Inference+Framework)|0| |[Improving Relevance Modeling via Heterogeneous Behavior Graph Learning in Bing Ads](https://doi.org/10.1145/3534678.3539128)|Bochen Pang, Chaozhuo Li, Yuming Liu, Jianxun Lian, Jianan Zhao, Hao Sun, Weiwei Deng, Xing Xie, Qi Zhang|University of Notre Dame, Indiana, IN, USA; Microsoft Research Asia, Beijing, China; Microsoft, Beijing, China|As the fundamental basis of sponsored search, relevance modeling measures the closeness between the input queries and the candidate ads. Conventional relevance models solely rely on the textual data, which suffer from the scarce semantic signals within the short queries. Recently, user historical click behaviors are incorporated in the format of click graphs to provide additional correlations beyond pure textual semantics, which contributes to advancing the relevance modeling performance. However, user behaviors are usually arbitrary and unpredictable, leading to the noisy and sparse graph topology. In addition, there exist other types of user behaviors besides clicks, which may also provide complementary information. In this paper, we study the novel problem of heterogeneous behavior graph learning to facilitate relevance modeling task. Our motivation lies in learning an optimal and task-relevant heterogeneous behavior graph consisting of multiple types of user behaviors. We further propose a novel HBGLR model to learn the behavior graph structure by mining the sophisticated correlations between node semantics and graph topology, and encode the textual semantics and structural heterogeneity into the learned representations. Our proposal is evaluated over real-world industry datasets, and has been mainstreamed in the Bing ads. Both offline and online experimental results demonstrate its superiority.|作为赞助商搜索的基础，相关性建模测量了输入查询和候选广告之间的密切程度。传统的关联模型仅仅依赖于文本数据，而文本数据受到短查询中语义信号稀缺的影响。近年来，用户的历史点击行为被整合到点击图的格式中，提供了超越纯文本语义的额外相关性，这有助于提高相关性建模的性能。然而，用户行为通常是任意和不可预测的，导致噪声和稀疏图拓扑。此外，除了点击之外，还存在其他类型的用户行为，这些行为也可能提供补充信息。本文研究了异构行为图学习的新问题，以促进相关建模任务的完成。我们的动机在于学习一个由多种类型的用户行为组成的最优的和与任务相关的异构行为图。我们进一步提出了一种新的 HBGLR 模型，通过挖掘节点语义和图拓扑之间复杂的相关性来学习行为图结构，并将文本语义和结构异质性编码到所学习的表示中。我们的建议是评估在现实世界的行业数据集，并已成为主流的必应广告。离线和在线实验结果都证明了该方法的优越性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Improving+Relevance+Modeling+via+Heterogeneous+Behavior+Graph+Learning+in+Bing+Ads)|0| |[Type Linking for Query Understanding and Semantic Search](https://doi.org/10.1145/3534678.3539067)|Giorgos Stoilos, Nikos Papasarantopoulos, Pavlos Vougiouklis, Patrik Bansky|Huawei Technologies, Edinburgh, United Kingdom|Huawei is currently undertaking an effort to build map and web search services using query understanding and semantic search techniques. We present our efforts to built a low-latency type mention detection and linking service for map search. In addition to latency challenges, we only had access to low quality and biased training data plus we had to support 13 languages. Consequently, our service is based mostly on unsupervised term- and vector-based methods. Nevertheless, we trained a Transformer-based query tagger which we integrated with the rest of the pipeline using a reward and penalisation approach. We present techniques that we designed in order to address challenges with the type dictionary, incompatibilities in scoring between the term-based and vector-based methods as well as over-segmentation issues in Thai, Chinese, and Japanese. We have evaluated our approach on the Huawei map search use case as well as on community Question Answering benchmarks.|华为目前正致力于利用查询理解和语义搜索技术建立地图和网络搜索服务。我们介绍了我们的努力，建立一个低延迟类型提及检测和地图搜索链接服务。除了延迟挑战，我们只能访问低质量和有偏见的培训数据，加上我们必须支持13种语言。因此，我们的服务主要是基于无监督的术语和向量方法。尽管如此，我们还是训练了一个基于 Transformer 的查询标记器，并使用奖励和惩罚方法将其与管道的其他部分集成在一起。我们提出的技术，我们设计的目的是为了解决类型字典的挑战，在评分之间的基于术语和基于向量的方法以及在泰国，中国和日本的过分割问题。我们评估了华为地图搜索用例和社区问答基准的方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Type+Linking+for+Query+Understanding+and+Semantic+Search)|0| @@ -239,14 +239,14 @@ |[Personalized Chit-Chat Generation for Recommendation Using External Chat Corpora](https://doi.org/10.1145/3534678.3539215)|Changyu Chen, Xiting Wang, Xiaoyuan Yi, Fangzhao Wu, Xing Xie, Rui Yan|Renmin University of China, Beijing, China; Microsoft Research Asia, Beijing, China|Chit-chat has been shown effective in engaging users in human-computer interaction. We find with a user study that generating appropriate chit-chat for news articles can help expand user interest and increase the probability that a user reads a recommended news article. Based on this observation, we propose a method to generate personalized chit-chat for news recommendation. Different from existing methods for personalized text generation, our method only requires an external chat corpus obtained from an online forum, which can be disconnected from the recommendation dataset from both the user and item (news) perspectives. This is achieved by designing a weak supervision method for estimating users' personalized interest in a chit-chat post by transferring knowledge learned by a news recommendation model. Based on the method for estimating user interest, a reinforcement learning framework is proposed to generate personalized chit-chat. Extensive experiments, including the automatic offline evaluation and user studies, demonstrate the effectiveness of our method.|聊天已被证明能有效地吸引用户参与人机交互。我们通过用户研究发现，为新闻文章产生适当的闲聊可以帮助扩大用户的兴趣，并增加用户阅读推荐新闻文章的可能性。在此基础上，本文提出了一种新闻推荐个性化聊天的生成方法。与现有的个性化文本生成方法不同，该方法只需要一个从在线论坛获得的外部聊天语料库，该语料库可以从用户和项目(新闻)的角度与推荐数据集分离。这是通过设计一种弱监督方法，通过传递新闻推荐模型中学到的知识来估计用户在闲聊帖子中的个性化兴趣来实现的。基于评估用户兴趣的方法，提出了一个强化学习框架来生成个性化的聊天。广泛的实验，包括自动离线评估和用户研究，证明了我们的方法的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Personalized+Chit-Chat+Generation+for+Recommendation+Using+External+Chat+Corpora)|0| |[G2NET: A General Geography-Aware Representation Network for Hotel Search Ranking](https://doi.org/10.1145/3534678.3539025)|Jia Xu, Fei Xiong, Zulong Chen, Mingyuan Tao, Liangyue Li, Quan Lu|Alibaba Group, Hangzhou, China; Guangxi University, Nanning, China|Hotel search ranking is the core function of Online Travel Platforms (OTPs), while geography information of location entities involved in it plays a critically important role in guaranteeing its ranking quality. The closest line of works to the hotel search ranking problem is thus the next POI (or location) recommendation problem, which has extensive works but fails to cope with two new challenges, i.e., consideration of two more location entities and effective utilization of geographical information, in a hotel search ranking scenario. To this end, we propose a General Geography-aware representation NETwork (G2NET for short) to better represent geography information of location entities so as to optimize the hotel search ranking. In G2NET, to address the first challenge, we first propose the concept of Geography Interaction Schema (GIS) which is a meta template for representing the arbitrary number of location entity types and their interactions. Then, a novel geography interaction encoder is devised providing general representation ability for an instance of GIS, followed by an attentive operation that aggregates representations of instances corresponding to all historically interacted hotels of a user in a weighted manner. The second challenge is handled by the combined application of three proposed geography embedding modules in G2NET, each of which focuses on computing embeddings of location entities based on a certain aspect of geographical information of location entities. Moreover, a self-attention layer is deployed in G2NET, to capture correlations among historically interacted hotels of a user which provides non-trivial functionality of understanding the user's behaviors. Both offline and online experiments show that G2NET outperforms the state-of-the-art methods. G2NET has now been successfully deployed to provide the high-quality hotel search ranking service at Fliggy, one of the most popular OTPs in China, serving tens of millions of users.|酒店搜索排名是在线旅游平台(OTP)的核心功能，而位置实体的地理信息对于保证其排名质量起着至关重要的作用。因此，与酒店搜索排名问题最接近的工作是下一个 POI (或位置)推荐问题，这个问题有大量的工作，但未能应对两个新的挑战，即在一个酒店搜索排名场景中考虑另外两个位置实体和有效利用地理信息。为此，我们提出了一个通用地理感知表示网络(G2NET) ，以更好地表示位置实体的地理信息，从而优化酒店搜索排名。在 G2NET 中，为了应对第一个挑战，我们首先提出了地理交互模式(GIS)的概念，它是一个元模板，用于表示任意数量的位置实体类型及其交互。然后，设计了一种新颖的地理交互编码器，提供了 GIS 实例的一般表示能力，然后进行了注意操作，以加权的方式聚合了对应于用户的所有历史交互酒店的实例表示。第二个挑战是通过在 G2NET 中联合应用三个地理嵌入模块来解决，每个模块的重点都是基于位置实体的地理信息的某一方面来计算位置实体的嵌入。此外，在 G2NET 中部署了一个自我关注层，以捕获用户在历史上交互的酒店之间的相关性，从而提供了理解用户行为的重要功能。离线和在线实验都表明，G2NET 的性能优于最先进的方法。目前，g2NET 已成功部署到 Fliggy，为数千万用户提供高质量的酒店搜索排名服务。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=G2NET:+A+General+Geography-Aware+Representation+Network+for+Hotel+Search+Ranking)|0| |[Avoiding Biases due to Similarity Assumptions in Node Embeddings](https://doi.org/10.1145/3534678.3539287)|Deepayan Chakrabarti|University of Texas at Austin, Austin, TX, USA|Node embeddings are vectors, one per node, that capture a graph's structure. The basic structure is the adjacency matrix of the graph. Recent methods also make assumptions about the similarity of unlinked nodes. However, such assumptions can lead to unintentional but systematic biases against groups of nodes. Calculating similarities between far-off nodes is also difficult under privacy constraints and in dynamic graphs. Our proposed embedding, called NEWS, makes no similarity assumptions, avoiding potential risks to privacy and fairness. NEWS is parameter-free, enables fast link prediction, and has linear complexity. These gains from avoiding assumptions do not significantly affect accuracy, as we show via comparisons against several existing methods on $21$ real-world networks. Code is available at https://github.com/deepayan12/news.|节点嵌入是向量，每个节点一个，它捕获图的结构。基本结构是图形的邻接矩阵。最近的方法也对未链接节点的相似性做了假设。然而，这样的假设可能会导致对节点群的无意的但是系统性的偏见。在隐私约束和动态图中，计算远程节点之间的相似度也很困难。我们提出的嵌入，称为新闻，没有相似的假设，避免了隐私和公平的潜在风险。NEWS 是无参数的，支持快速链路预测，具有线性复杂度。这些从避免假设中获得的收益不会显著影响准确性，正如我们通过比较现有的几种方法在 $21 $真实世界的网络上所显示的。密码可于 https://github.com/deepayan12/news 索取。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Avoiding+Biases+due+to+Similarity+Assumptions+in+Node+Embeddings)|0| -|[Task-optimized User Clustering based on Mobile App Usage for Cold-start Recommendations](https://doi.org/10.1145/3534678.3539105)|Bulou Liu, Bing Bai, Weibang Xie, Yiwen Guo, Hao Chen|Independent Researcher, Beijing, China; Tencent Security Big Data Lab, Beijing, China; University of California, Davis, Davis, CA, USA; Tencent Inc., Guangzhou, China|This paper reports our recent practice of recommending articles to cold-start users at Tencent. Transferring knowledge from information-rich domains to help user modeling is an effective way to address the user-side cold-start problem. Our previous work demonstrated that general-purpose user embeddings based on mobile app usage helped article recommendations. However, high-dimensional embeddings are cumbersome for online usage, thus limiting the adoption. On the other hand, user clustering, which partitions users into several groups, can provide a lightweight, online-friendly, and explainable way to help recommendations. Effective user clustering for article recommendations based on mobile app usage faces unique challenges, including (1) the gap between an active user's behavior of mobile app usage and article reading, and (2) the gap between mobile app usage patterns of active and cold-start users. To address the challenges, we propose a tailored Dual Alignment User Clustering (DAUC) model, which applies a sample-wise contrastive alignment to eliminate the gap between active users' mobile app usage and article reading behavior, and a distribution-wise adversarial alignment to eliminate the gap between active users' and cold-start users' app usage behavior. With DAUC, cold-start recommendation-optimized user clustering based on mobile app usage can be achieved. On top of the user clusters, we further build candidate generation strategies, real-time features, and corresponding ranking models without much engineering difficulty. Both online and offline experiments demonstrate the effectiveness of our work.|本文报道了我们最近向腾讯的冷启动用户推荐文章的做法。从信息丰富的领域转移知识以帮助用户建模是解决用户端冷启动问题的有效途径。我们以前的工作表明，基于移动应用程序使用的通用用户嵌入有助于文章推荐。但是，高维嵌入对于在线使用来说很麻烦，因此限制了采用。另一方面，用户集群(将用户划分为几个组)可以提供一种轻量级的、在线友好的、可解释的方式来帮助推荐。基于移动应用使用的文章推荐的有效用户聚类面临独特的挑战，包括(1)活跃用户的移动应用使用行为和文章阅读之间的差距，以及(2)活跃用户和冷启动用户的移动应用使用模式之间的差距。为了应对这些挑战，我们提出了一个定制的双对齐用户聚类(DAUC)模型，该模型应用样本对比对齐来消除活跃用户的移动应用程序使用和文章阅读行为之间的差距，以及分布式对抗对齐来消除活跃用户和冷启动用户的应用程序使用行为之间的差距。利用 DAUC，可以实现基于移动应用使用情况的冷启动推荐优化用户聚类。在用户集群的基础上，我们进一步构建了候选生成策略、实时特征以及相应的排序模型，这些都不需要很大的工程难度。这两个在线和离线实验都证明了我们工作的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Task-optimized+User+Clustering+based+on+Mobile+App+Usage+for+Cold-start+Recommendations)|0| +|[Task-optimized User Clustering based on Mobile App Usage for Cold-start Recommendations](https://doi.org/10.1145/3534678.3539105)|Bulou Liu, Bing Bai, Weibang Xie, Yiwen Guo, Hao Chen|Tencent Security Big Data Lab, Beijing, China; Tencent Inc., Guangzhou, China; University of California, Davis, Davis, CA, USA; Independent Researcher, Beijing, China|This paper reports our recent practice of recommending articles to cold-start users at Tencent. Transferring knowledge from information-rich domains to help user modeling is an effective way to address the user-side cold-start problem. Our previous work demonstrated that general-purpose user embeddings based on mobile app usage helped article recommendations. However, high-dimensional embeddings are cumbersome for online usage, thus limiting the adoption. On the other hand, user clustering, which partitions users into several groups, can provide a lightweight, online-friendly, and explainable way to help recommendations. Effective user clustering for article recommendations based on mobile app usage faces unique challenges, including (1) the gap between an active user's behavior of mobile app usage and article reading, and (2) the gap between mobile app usage patterns of active and cold-start users. To address the challenges, we propose a tailored Dual Alignment User Clustering (DAUC) model, which applies a sample-wise contrastive alignment to eliminate the gap between active users' mobile app usage and article reading behavior, and a distribution-wise adversarial alignment to eliminate the gap between active users' and cold-start users' app usage behavior. With DAUC, cold-start recommendation-optimized user clustering based on mobile app usage can be achieved. On top of the user clusters, we further build candidate generation strategies, real-time features, and corresponding ranking models without much engineering difficulty. Both online and offline experiments demonstrate the effectiveness of our work.|本文报道了我们最近向腾讯的冷启动用户推荐文章的做法。从信息丰富的领域转移知识以帮助用户建模是解决用户端冷启动问题的有效途径。我们以前的工作表明，基于移动应用程序使用的通用用户嵌入有助于文章推荐。但是，高维嵌入对于在线使用来说很麻烦，因此限制了采用。另一方面，用户集群(将用户划分为几个组)可以提供一种轻量级的、在线友好的、可解释的方式来帮助推荐。基于移动应用使用的文章推荐的有效用户聚类面临独特的挑战，包括(1)活跃用户的移动应用使用行为和文章阅读之间的差距，以及(2)活跃用户和冷启动用户的移动应用使用模式之间的差距。为了应对这些挑战，我们提出了一个定制的双对齐用户聚类(DAUC)模型，该模型应用样本对比对齐来消除活跃用户的移动应用程序使用和文章阅读行为之间的差距，以及分布式对抗对齐来消除活跃用户和冷启动用户的应用程序使用行为之间的差距。利用 DAUC，可以实现基于移动应用使用情况的冷启动推荐优化用户聚类。在用户集群的基础上，我们进一步构建了候选生成策略、实时特征以及相应的排序模型，这些都不需要很大的工程难度。这两个在线和离线实验都证明了我们工作的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Task-optimized+User+Clustering+based+on+Mobile+App+Usage+for+Cold-start+Recommendations)|0| |[Promotheus: An End-to-End Machine Learning Framework for Optimizing Markdown in Online Fashion E-commerce](https://doi.org/10.1145/3534678.3539148)|Eleanor Loh, Jalaj Khandelwal, Brian Regan, Duncan A. Little|ASOS.com, London, United Kingdom|Managing discount promotional events ("markdown") is a significant part of running an e-commerce business, and inefficiencies here can significantly hamper a retailer's profitability. Traditional approaches for tackling this problem rely heavily on price elasticity modelling. However, the partial information nature of price elasticity modelling, together with the non-negotiable responsibility for protecting profitability, mean that machine learning practitioners must often go through great lengths to define strategies for measuring offline model quality. In the face of this, many retailers fall back on rule-based methods, thus forgoing significant gains in profitability that can be captured by machine learning. In this paper, we introduce two novel end-to-end markdown management systems for optimising markdown at different stages of a retailer's journey. The first system, "Ithax," enacts a rational supply-side pricing strategy without demand estimation, and can be usefully deployed as a "cold start" solution to collect markdown data while maintaining revenue control. The second system, "Promotheus," presents a full framework for markdown optimization with price elasticity. We describe in detail the specific modelling and validation procedures that, within our experience, have been crucial to building a system that performs robustly in the real world. Both markdown systems achieve superior profitability compared to decisions made by our experienced operations teams in a controlled online test, with improvements of 86% (Promotheus) and 79% (Ithax) relative to manual strategies. These systems have been deployed to manage markdown at ASOS.com, and both systems can be fruitfully deployed for price optimization across a wide variety of retail e-commerce settings.|管理折扣促销活动(“降价”)是经营电子商务业务的一个重要组成部分，这里的低效率会严重阻碍零售商的盈利能力。解决这一问题的传统方法在很大程度上依赖于价格弹性模型。然而，价格弹性建模的部分信息性质，加上保护盈利能力的不可协商的责任，意味着机器学习从业人员必须经常花费大量的时间来确定衡量离线模型质量的策略。面对这种情况，许多零售商退回到基于规则的方法，因此放弃了可以通过机器学习获得的利润率的显著增长。在本文中，我们介绍了两个新颖的端到端降价管理系统优化降价在不同阶段的零售商的旅程。第一个系统，“ Ithax”，制定了一个合理的供应侧定价策略，没有需求估计，可以作为一个有用的“冷启动”解决方案，收集降价数据，同时保持收入控制。第二个系统，“茂德修斯”，提出了一个完整的框架降价优化与价格弹性。我们详细描述了具体的建模和验证程序，根据我们的经验，这些程序对于建立一个在现实世界中运行良好的系统至关重要。与我们经验丰富的运营团队在受控的在线测试中做出的决策相比，这两种降价系统都实现了更高的盈利能力，相对于手工策略，降价系统的改进率分别为86% 和79% 。这些系统已经部署到 ASOS.com 管理降价，这两个系统都可以在各种零售电子商务环境中进行价格优化，从而取得丰硕成果。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Promotheus:+An+End-to-End+Machine+Learning+Framework+for+Optimizing+Markdown+in+Online+Fashion+E-commerce)|0| -|[Scalar is Not Enough: Vectorization-based Unbiased Learning to Rank](https://doi.org/10.1145/3534678.3539468)|Mouxiang Chen, Chenghao Liu, Zemin Liu, Jianling Sun|Singapore Management University, Singapore, Singapore; Salesforce Research Area, Singapore, Singapore; Zhejiang University & Alibaba-Zhejiang University Joint Institute of Frontier Technologies, Hangzhou, China|Unbiased learning to rank (ULTR) aims to train an unbiased ranking model from biased user click logs. Most of the current ULTR methods are based on the examination hypothesis (EH), which assumes that the click probability can be factorized into two scalar functions, one related to ranking features and the other related to bias factors. Unfortunately, the interactions among features, bias factors and clicks are complicated in practice, and usually cannot be factorized in this independent way. Fitting click data with EH could lead to model misspecification and bring the approximation error. In this paper, we propose a vector-based EH and formulate the click probability as a dot product of two vector functions. This solution is complete due to its universality in fitting arbitrary click functions. Based on it, we propose a novel model named Vectorization to adaptively learn the relevance embeddings and sort documents by projecting embeddings onto a base vector. Extensive experiments show that our method significantly outperforms the state-of-the-art ULTR methods on complex real clicks as well as simple simulated clicks.|无偏学习排名(ULTR)的目的是从有偏见的用户点击日志中训练一个无偏见的排名模型。目前的 ULTR 方法大多基于检验假设(EH) ，假设点击概率可以分解为两个标量函数，一个与排序特征有关，另一个与偏差因子有关。遗憾的是，特征、偏差因素和点击之间的相互作用在实践中是复杂的，通常不能以这种独立的方式进行因子分解。将 click 数据与 EH 进行匹配可能导致模型错误说明，并带来逼近误差。本文提出了一种基于向量的 EH，并将点击概率表示为两个向量函数的点乘。该解决方案是完整的，因为它在拟合任意点击函数的通用性。在此基础上，提出了一种新的向量化模型，通过向基向量投影来自适应地学习相关嵌入和排序文档。大量的实验表明，我们的方法在复杂的真实点击和简单的模拟点击方面明显优于最先进的 ULTR 方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Scalar+is+Not+Enough:+Vectorization-based+Unbiased+Learning+to+Rank)|0| +|[Scalar is Not Enough: Vectorization-based Unbiased Learning to Rank](https://doi.org/10.1145/3534678.3539468)|Mouxiang Chen, Chenghao Liu, Zemin Liu, Jianling Sun|Salesforce Research Area, Singapore, Singapore; Zhejiang University & Alibaba-Zhejiang University Joint Institute of Frontier Technologies, Hangzhou, China; Singapore Management University, Singapore, Singapore|Unbiased learning to rank (ULTR) aims to train an unbiased ranking model from biased user click logs. Most of the current ULTR methods are based on the examination hypothesis (EH), which assumes that the click probability can be factorized into two scalar functions, one related to ranking features and the other related to bias factors. Unfortunately, the interactions among features, bias factors and clicks are complicated in practice, and usually cannot be factorized in this independent way. Fitting click data with EH could lead to model misspecification and bring the approximation error. In this paper, we propose a vector-based EH and formulate the click probability as a dot product of two vector functions. This solution is complete due to its universality in fitting arbitrary click functions. Based on it, we propose a novel model named Vectorization to adaptively learn the relevance embeddings and sort documents by projecting embeddings onto a base vector. Extensive experiments show that our method significantly outperforms the state-of-the-art ULTR methods on complex real clicks as well as simple simulated clicks.|无偏学习排名(ULTR)的目的是从有偏见的用户点击日志中训练一个无偏见的排名模型。目前的 ULTR 方法大多基于检验假设(EH) ，假设点击概率可以分解为两个标量函数，一个与排序特征有关，另一个与偏差因子有关。遗憾的是，特征、偏差因素和点击之间的相互作用在实践中是复杂的，通常不能以这种独立的方式进行因子分解。将 click 数据与 EH 进行匹配可能导致模型错误说明，并带来逼近误差。本文提出了一种基于向量的 EH，并将点击概率表示为两个向量函数的点乘。该解决方案是完整的，因为它在拟合任意点击函数的通用性。在此基础上，提出了一种新的向量化模型，通过向基向量投影来自适应地学习相关嵌入和排序文档。大量的实验表明，我们的方法在复杂的真实点击和简单的模拟点击方面明显优于最先进的 ULTR 方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Scalar+is+Not+Enough:+Vectorization-based+Unbiased+Learning+to+Rank)|0| |[Efficient Approximate Algorithms for Empirical Variance with Hashed Block Sampling](https://doi.org/10.1145/3534678.3539377)|Xingguang Chen, Fangyuan Zhang, Sibo Wang|The Chinese University of Hong Kong, Hong Kong, China|Empirical variance is a fundamental concept widely used in data management and data analytics, e.g., query optimization, approximate query processing, and feature selection. A direct solution to derive the empirical variance is scanning the whole data table, which is expensive when the data size is huge. Hence, most current works focus on approximate answers by sampling. For results with approximation guarantees, the samples usually need to be uniformly independent random, incurring high cache miss rates especially in compact columnar style layouts. An alternative uses block sampling to avoid this issue, which directly samples a block of consecutive records fitting page sizes instead of sampling one record each time. However, this provides no theoretical guarantee. Existing studies show that the practical estimations can be inaccurate as the records within a block can be correlated. Motivated by this, we investigate how to provide approximation guarantees for empirical variances with block sampling from a theoretical perspective. Our results shows that if the records stored in a table are 4-wise independent to each other according to keys, a slightly modified block sampling can provide the same approximation guarantee with the same asymptotic sampling cost as that of independent random sampling. In practice, storing records via hash clusters or hash organized tables are typical scenarios in modern commercial database systems. Thus, for data analysis on tables in the data lake or OLAP stores that are exported from such hash-based storage, our strategy can be easily integrated to improve the sampling efficiency. Based on our sampling strategy, we present an approximate algorithm for empirical variance and an approximate top-k algorithm to return the k columns with the highest empirical variance scores. Extensive experiments show that our solutions outperform existing solutions by up to an order of magnitude.|经验方差是数据管理和数据分析中广泛使用的一个基本概念，如查询优化、近似查询处理和特征选择。推导经验方差的直接方法是对整个数据表进行扫描，当数据量很大时，扫描成本很高。因此，目前大多数的工作集中在抽样近似答案。对于具有近似保证的结果，样本通常需要是一致独立的随机的，特别是在紧凑的柱状样式布局中，会导致高缓存错过率。另一种方法是使用块抽样来避免这个问题，即直接抽样一个连续的记录块来适应页面大小，而不是每次抽样一个记录。然而，这并不能提供理论上的保证。现有的研究表明，实际的估计可能是不准确的，因为一个区块内的记录可以相关。在此基础上，我们从理论的角度研究了如何为区组抽样的经验方差提供近似保证。结果表明，如果存储在表中的记录按键相互独立，稍加修改的块抽样可以提供与独立随机抽样相同的渐近抽样代价的近似保证。在实践中，通过散列集群或散列组织表存储记录是现代商业数据库系统中的典型场景。因此，对于从这种基于散列的存储器导出的数据湖或 OLAP 存储器中的表的数据分析，可以很容易地将我们的策略集成起来以提高采样效率。基于我们的抽样策略，我们提出了一个经验方差的近似算法和一个近似 top-k 算法来返回经验方差得分最高的 k 列。大量的实验表明，我们的解决方案比现有解决方案的性能高出一个数量级。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Efficient+Approximate+Algorithms+for+Empirical+Variance+with+Hashed+Block+Sampling)|0| |[Towards a Native Quantum Paradigm for Graph Representation Learning: A Sampling-based Recurrent Embedding Approach](https://doi.org/10.1145/3534678.3539327)|Ge Yan, Yehui Tang, Junchi Yan|Shanghai Jiao Tong University, Shanghai, China|Graph representation learning has been extensively studied, and recent models can well incorporate both node features and graph structures. Despite these progress, the inherent scalability challenge for classical computers of processing graph data and solving the downstream tasks (many are NP-hard) is still a bottleneck for existing classical graph learning models. On the other hand, quantum computing is known a promising direction for its theoretically verified scalability as well as the increasing evidence for the access to physical quantum machine in near-term. Different from many existing classical-quantum hybrid machine learning models on graphs, in this paper we take a more aggressive initiative for developing a native quantum paradigm for (attributed) graph representation learning, which to our best knowledge, has not been fulfilled in literature yet. Specifically, our model adopts the well-established theory and technique in quantum computing e.g. quantum random walk, and adapt it to the attributed graph. Then the node attribute quantum state sequence is fed into a quantum recurrent network to obtain the final node embedding. Experimental results on three public datasets show the effectiveness of our quantum model which also outperforms a classical learning approach GraphRNA notably in terms of efficiency even on a classical computer. Though it is still restricted to the classical loss-based learning paradigm with gradient descent for model parameter training, while our computing scheme is compatible with quantum computing without involving classical computers. This is in fact largely in contrast to many hybrid quantum graph learning models which often involve many steps and modules having to be performed on classical computers.|图表示学习已经得到了广泛的研究，现有的模型能够很好地结合节点特征和图结构。尽管取得了这些进展，经典计算机在处理图形数据和解决下游任务(许多是 NP 难的)方面固有的可伸缩性挑战仍然是现有经典图形学习模型的瓶颈。另一方面，量子计算因其在理论上被证实的可扩展性以及近期越来越多的物理量子计算的证据而被认为是一个有前途的方向。与许多现有的经典-量子混合机器学习模型不同，本文采取了更积极的主动性，开发了一个本土的量子范式(属性)图表示学习，据我们所知，这尚未在文献中得到实现。具体地说，我们的模型采用了量子计算中已经成熟的理论和技术，例如量子随机游走，并将其适用于属性图。然后将节点属性量子状态序列输入到量子递归网络中，得到最终的节点嵌入。在三个公共数据集上的实验结果表明了量子模型的有效性，即使在经典的计算机上，量子模型的效率也明显优于经典的学习方法 GraphRNA。虽然我们的计算机系统仍然局限于传统的以损失为基础的学习范式，并且只有模型参数训练的梯度下降法，但我们的计算机系统可以与量子计算兼容，而不需要使用传统的计算机。这实际上在很大程度上与许多混合量子图学习模型形成对比，这些模型通常涉及许多步骤和模块，必须在经典计算机上执行。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Towards+a+Native+Quantum+Paradigm+for+Graph+Representation+Learning:+A+Sampling-based+Recurrent+Embedding+Approach)|0| -|[Toward Real-life Dialogue State Tracking Involving Negative Feedback Utterances](https://doi.org/10.1145/3534678.3539385)|Puhai Yang, Heyan Huang, Wei Wei, XianLing Mao|Beijing Institute of Technology & Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications, Beijing, China; Huazhong University of Science and Technology, Wuhan, China; Beijing Institute of Technology, Beijing, China|Recently, the research of dialogue systems has been widely concerned, especially task-oriented dialogue systems, which have received increased attention due to their wide application prospect. As a core component, dialogue state tracking (DST) plays a key role in task-oriented dialogue systems, and its function is to parse natural language dialogues into dialogue state formed by slot-value pairs. It is well known that dialogue state tracking has been well studied and explored on current benchmark datasets such as the MultiWOZ. However, almost all current research completely ignores the user negative feedback utterances that exist in real-life conversations when a system error occurs, which often contains user-provided corrective information for the system error. Obviously, user negative feedback utterances can be used to correct the inevitable errors in automatic speech recognition and model generalization. Thus, in this paper, we will explore the role of negative feedback utterances in dialogue state tracking in detail through simulated negative feedback utterances. Specifically, due to the lack of dataset involving negative feedback utterances, first, we have to define the schema of user negative feedback utterances and propose a joint modeling method for feedback utterance generation and filtering. Then, we explore three aspects of interaction mechanism that should be considered in real-life conversations involving negative feedback utterances and propose evaluation metrics related to negative feedback utterances. Finally, on WOZ2.0 and MultiWOZ2.1 datasets, by constructing simulated negative feedback utterances in training and testing, we not only verify the important role of negative feedback utterances in dialogue state tracking, but also analyze the advantages and disadvantages of different interaction mechanisms involving negative feedback utterances, lighting future research on negative feedback utterances.|近年来，对话系统的研究受到了广泛的关注，尤其是面向任务的对话系统，由于其广阔的应用前景而受到越来越多的关注。对话状态跟踪(DST)是任务导向对话系统的核心组成部分，其功能是将自然语言对话解析为由插槽值对形成的对话状态。众所周知，对话状态跟踪已经在当前的基准数据集(如 MultiWOZ)上得到了很好的研究和探索。然而，目前几乎所有的研究都完全忽视了系统错误发生时用户在现实交谈中的负面反馈语，其中往往包含用户提供的系统错误纠正信息。显然，用户负反馈话语可以用来纠正语音自动识别和模型推广中不可避免的错误。因此，本文将通过模拟负反馈话语来详细探讨负反馈话语在对话状态跟踪中的作用。具体来说，由于缺乏涉及负反馈话语的数据集，首先，我们必须定义用户负反馈话语的模式，并提出一种联合建模的方法来生成和过滤反馈话语。然后，从三个方面探讨了负反馈话语在现实会话中应该考虑的互动机制，并提出了与负反馈话语相关的评价指标。最后，在 WOZ2.0和 MultiWOZ2.1数据集上，通过构建训练和测试中的模拟负反馈话语，不仅验证了负反馈话语在对话状态跟踪中的重要作用，而且分析了负反馈话语不同交互机制的优缺点，为进一步研究负反馈话语提供参考。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Toward+Real-life+Dialogue+State+Tracking+Involving+Negative+Feedback+Utterances)|0| -|[M-Mix: Generating Hard Negatives via Multi-sample Mixing for Contrastive Learning](https://doi.org/10.1145/3534678.3539248)|Shaofeng Zhang, Meng Liu, Junchi Yan, Hengrui Zhang, Lingxiao Huang, Xiaokang Yang, Pinyan Lu|Huawei TCS Lab, Shanghai, China; Shanghai University of Finance and Economics & Huawei TCS Lab, Shanghai, China; Shanghai Jiao Tong University, Shanghai, China|Negative pairs, especially hard negatives as combined with common negatives (easy to discriminate), are essential in contrastive learning, which plays a role of avoiding degenerate solutions in the sense of constant representation across different instances. Inspired by recent hard negative mining methods via pairwise mixup operation in vision, we propose M-Mix, which dynamically generates a sequence of hard negatives. Compared with previous methods, M-Mix mainly has three features: 1) adaptively choose samples to mix; 2) simultaneously mix multiple samples; 3) automatically assign different mixing weights to the selected samples. We evaluate our method on two image datasets (CIFAR-10, CIFAR-100), five node classification datasets (PPI, DBLP, Pubmed, etc), five graph classification datasets (IMDB, PTC_MR, etc), and two downstream combinatorial tasks (graph edit distance and node clustering). Results show that it achieves state-of-the-art performance under self-supervised settings. Code is available at: https://github.com/Sherrylone/m-mix.|否定对，尤其是硬否定与普通否定(容易区分)的结合，在对比学习中是必不可少的，对比学习的作用是避免退化的解决方案在不同情况下的持续表征。受当前视觉硬负片挖掘方法的启发，提出了 M-Mix 算法，该算法动态生成硬负片序列。与以往的混合方法相比，M-Mix 方法主要有三个特点: 1)自适应地选择混合样本; 2)同时混合多个样本; 3)自动分配不同的混合权重给选定的样本。我们在两个图像数据集(CIFAR-10，CIFAR-100) ，五个节点分类数据集(PPI，DBLP，Pubmed 等) ，五个图形分类数据集(IMDB，PTC _ MR 等)和两个下游组合任务(图形编辑距离和节点聚类)上评估我们的方法。结果表明，该算法在自监督设置下达到了最佳性能。密码可于以下 https://github.com/sherrylone/m-mix 索取:。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=M-Mix:+Generating+Hard+Negatives+via+Multi-sample+Mixing+for+Contrastive+Learning)|0| -|[Modeling Persuasion Factor of User Decision for Recommendation](https://doi.org/10.1145/3534678.3539114)|Chang Liu, Chen Gao, Yuan Yuan, Chen Bai, Lingrui Luo, Xiaoyi Du, Xinlei Shi, Hengliang Luo, Depeng Jin, Yong Li|Tsinghua University, Beijing, China; Meituan Inc., Beijing, China|In online information systems, users make decisions based on factors of several specific aspects, such as brand, price, etc. Existing recommendation engines ignore the explicit modeling of these factors, leading to sub-optimal recommendation performance. In this paper, we focus on the real-world scenario where these factors can be explicitly captured (the users are exposed with decision factor-based persuasion texts, i.e., persuasion factors). Although it allows us for explicit modeling of user-decision process, there are critical challenges including the persuasion factor's representation learning and effect estimation, along with the data-sparsity problem. To address them, in this work, we present our POEM (short for Persuasion factOr Effect Modeling) system. We first propose the persuasion-factor graph convolutional layers for encoding and learning representations from the persuasion-aware interaction data. Then we develop a prediction layer that fully considers the user sensitivity to the persuasion factors. Finally, to address the data-sparsity issue, we propose a counterfactual learning-based data augmentation method to enhance the supervision signal. Real-world experiments demonstrate the effectiveness of our proposed framework of modeling the effect of persuasion factors.|在网络信息系统中，用户根据品牌、价格等几个具体方面的因素进行决策。现有的推荐引擎忽略了这些因素的显式建模，导致推荐性能不理想。在本文中，我们关注的是真实世界中这些因素可以被明确地捕获的场景(用户暴露在基于决策因素的说服文本中，即，说服因素)。尽管它允许我们对用户决策过程进行明确的建模，但是仍然存在一些关键的挑战，包括说服因子的表示学习和效果估计，以及数据稀疏问题。为了解决这些问题，在本文中，我们提出了我们的 POEM (劝导因素效果建模的缩写)系统。我们首先提出了说服因子图卷积层，用于从感知说服的交互数据中编码和学习表示。然后我们开发了一个预测层，充分考虑了用户对说服因素的敏感性。最后，针对数据稀疏问题，提出了一种基于反事实学习的数据增强方法来增强监控信号。现实世界的实验证明了我们提出的说服因素效应建模框架的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Modeling+Persuasion+Factor+of+User+Decision+for+Recommendation)|0| +|[Toward Real-life Dialogue State Tracking Involving Negative Feedback Utterances](https://doi.org/10.1145/3534678.3539385)|Puhai Yang, Heyan Huang, Wei Wei, XianLing Mao|Beijing Institute of Technology, Beijing, China; Huazhong University of Science and Technology, Wuhan, China; Beijing Institute of Technology & Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications, Beijing, China|Recently, the research of dialogue systems has been widely concerned, especially task-oriented dialogue systems, which have received increased attention due to their wide application prospect. As a core component, dialogue state tracking (DST) plays a key role in task-oriented dialogue systems, and its function is to parse natural language dialogues into dialogue state formed by slot-value pairs. It is well known that dialogue state tracking has been well studied and explored on current benchmark datasets such as the MultiWOZ. However, almost all current research completely ignores the user negative feedback utterances that exist in real-life conversations when a system error occurs, which often contains user-provided corrective information for the system error. Obviously, user negative feedback utterances can be used to correct the inevitable errors in automatic speech recognition and model generalization. Thus, in this paper, we will explore the role of negative feedback utterances in dialogue state tracking in detail through simulated negative feedback utterances. Specifically, due to the lack of dataset involving negative feedback utterances, first, we have to define the schema of user negative feedback utterances and propose a joint modeling method for feedback utterance generation and filtering. Then, we explore three aspects of interaction mechanism that should be considered in real-life conversations involving negative feedback utterances and propose evaluation metrics related to negative feedback utterances. Finally, on WOZ2.0 and MultiWOZ2.1 datasets, by constructing simulated negative feedback utterances in training and testing, we not only verify the important role of negative feedback utterances in dialogue state tracking, but also analyze the advantages and disadvantages of different interaction mechanisms involving negative feedback utterances, lighting future research on negative feedback utterances.|近年来，对话系统的研究受到了广泛的关注，尤其是面向任务的对话系统，由于其广阔的应用前景而受到越来越多的关注。对话状态跟踪(DST)是任务导向对话系统的核心组成部分，其功能是将自然语言对话解析为由插槽值对形成的对话状态。众所周知，对话状态跟踪已经在当前的基准数据集(如 MultiWOZ)上得到了很好的研究和探索。然而，目前几乎所有的研究都完全忽视了系统错误发生时用户在现实交谈中的负面反馈语，其中往往包含用户提供的系统错误纠正信息。显然，用户负反馈话语可以用来纠正语音自动识别和模型推广中不可避免的错误。因此，本文将通过模拟负反馈话语来详细探讨负反馈话语在对话状态跟踪中的作用。具体来说，由于缺乏涉及负反馈话语的数据集，首先，我们必须定义用户负反馈话语的模式，并提出一种联合建模的方法来生成和过滤反馈话语。然后，从三个方面探讨了负反馈话语在现实会话中应该考虑的互动机制，并提出了与负反馈话语相关的评价指标。最后，在 WOZ2.0和 MultiWOZ2.1数据集上，通过构建训练和测试中的模拟负反馈话语，不仅验证了负反馈话语在对话状态跟踪中的重要作用，而且分析了负反馈话语不同交互机制的优缺点，为进一步研究负反馈话语提供参考。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Toward+Real-life+Dialogue+State+Tracking+Involving+Negative+Feedback+Utterances)|0| +|[M-Mix: Generating Hard Negatives via Multi-sample Mixing for Contrastive Learning](https://doi.org/10.1145/3534678.3539248)|Shaofeng Zhang, Meng Liu, Junchi Yan, Hengrui Zhang, Lingxiao Huang, Xiaokang Yang, Pinyan Lu|Shanghai Jiao Tong University, Shanghai, China; Shanghai University of Finance and Economics & Huawei TCS Lab, Shanghai, China; Huawei TCS Lab, Shanghai, China|Negative pairs, especially hard negatives as combined with common negatives (easy to discriminate), are essential in contrastive learning, which plays a role of avoiding degenerate solutions in the sense of constant representation across different instances. Inspired by recent hard negative mining methods via pairwise mixup operation in vision, we propose M-Mix, which dynamically generates a sequence of hard negatives. Compared with previous methods, M-Mix mainly has three features: 1) adaptively choose samples to mix; 2) simultaneously mix multiple samples; 3) automatically assign different mixing weights to the selected samples. We evaluate our method on two image datasets (CIFAR-10, CIFAR-100), five node classification datasets (PPI, DBLP, Pubmed, etc), five graph classification datasets (IMDB, PTC_MR, etc), and two downstream combinatorial tasks (graph edit distance and node clustering). Results show that it achieves state-of-the-art performance under self-supervised settings. Code is available at: https://github.com/Sherrylone/m-mix.|否定对，尤其是硬否定与普通否定(容易区分)的结合，在对比学习中是必不可少的，对比学习的作用是避免退化的解决方案在不同情况下的持续表征。受当前视觉硬负片挖掘方法的启发，提出了 M-Mix 算法，该算法动态生成硬负片序列。与以往的混合方法相比，M-Mix 方法主要有三个特点: 1)自适应地选择混合样本; 2)同时混合多个样本; 3)自动分配不同的混合权重给选定的样本。我们在两个图像数据集(CIFAR-10，CIFAR-100) ，五个节点分类数据集(PPI，DBLP，Pubmed 等) ，五个图形分类数据集(IMDB，PTC _ MR 等)和两个下游组合任务(图形编辑距离和节点聚类)上评估我们的方法。结果表明，该算法在自监督设置下达到了最佳性能。密码可于以下 https://github.com/sherrylone/m-mix 索取:。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=M-Mix:+Generating+Hard+Negatives+via+Multi-sample+Mixing+for+Contrastive+Learning)|0| +|[Modeling Persuasion Factor of User Decision for Recommendation](https://doi.org/10.1145/3534678.3539114)|Chang Liu, Chen Gao, Yuan Yuan, Chen Bai, Lingrui Luo, Xiaoyi Du, Xinlei Shi, Hengliang Luo, Depeng Jin, Yong Li|Meituan Inc., Beijing, China; Tsinghua University, Beijing, China|In online information systems, users make decisions based on factors of several specific aspects, such as brand, price, etc. Existing recommendation engines ignore the explicit modeling of these factors, leading to sub-optimal recommendation performance. In this paper, we focus on the real-world scenario where these factors can be explicitly captured (the users are exposed with decision factor-based persuasion texts, i.e., persuasion factors). Although it allows us for explicit modeling of user-decision process, there are critical challenges including the persuasion factor's representation learning and effect estimation, along with the data-sparsity problem. To address them, in this work, we present our POEM (short for Persuasion factOr Effect Modeling) system. We first propose the persuasion-factor graph convolutional layers for encoding and learning representations from the persuasion-aware interaction data. Then we develop a prediction layer that fully considers the user sensitivity to the persuasion factors. Finally, to address the data-sparsity issue, we propose a counterfactual learning-based data augmentation method to enhance the supervision signal. Real-world experiments demonstrate the effectiveness of our proposed framework of modeling the effect of persuasion factors.|在网络信息系统中，用户根据品牌、价格等几个具体方面的因素进行决策。现有的推荐引擎忽略了这些因素的显式建模，导致推荐性能不理想。在本文中，我们关注的是真实世界中这些因素可以被明确地捕获的场景(用户暴露在基于决策因素的说服文本中，即，说服因素)。尽管它允许我们对用户决策过程进行明确的建模，但是仍然存在一些关键的挑战，包括说服因子的表示学习和效果估计，以及数据稀疏问题。为了解决这些问题，在本文中，我们提出了我们的 POEM (劝导因素效果建模的缩写)系统。我们首先提出了说服因子图卷积层，用于从感知说服的交互数据中编码和学习表示。然后我们开发了一个预测层，充分考虑了用户对说服因素的敏感性。最后，针对数据稀疏问题，提出了一种基于反事实学习的数据增强方法来增强监控信号。现实世界的实验证明了我们提出的说服因素效应建模框架的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Modeling+Persuasion+Factor+of+User+Decision+for+Recommendation)|0| |[Lion: A GPU-Accelerated Online Serving System for Web-Scale Recommendation at Baidu](https://doi.org/10.1145/3534678.3539058)|Hao Liu, Qian Gao, Xiaochao Liao, Guangxing Chen, Hao Xiong, Silin Ren, Guobao Yang, Zhiwei Zha|Baidu, Inc., Beijing, China; HKUST(GZ), HKUST, Guangzhou, China|Deep Neural Network (DNN) based recommendation systems are widely used in the modern internet industry for a variety of services. However, the rapid expansion of application scenarios and the explosive global internet traffic growth have caused the industry to face increasing challenges to serve the complicated recommendation workflow regarding online recommendation efficiency and compute resource overhead. In this paper, we present a GPU-accelerated online serving system, namely Lion, which consists of the staged event-driven heterogeneous pipeline, unified memory manager, and automatic execution optimizer to handle web-scale traffic in a real-time and cost-effective way. Moreover, Lion provides a heterogeneous template library to enable fast development and migration for diverse in-house web-scale recommendation systems without requiring knowledge of heterogeneous programming. The system is currently deployed at Baidu, supporting over twenty recommendation services, including news feed, short video clips, and the search engine. Extensive experimental studies on five real-world deployed online recommendation services demonstrate the superiority of the proposed GPU-accelerated online serving system. Since launched in early 2020, Lion has answered billions of recommendation requests per day, and has helped Baidu successfully save millions of U.S. dollars in hardware and utility costs per year.|基于深度神经网络(DNN)的推荐系统广泛应用于现代互联网行业的各种服务。然而，应用场景的快速扩展和全球互联网流量的爆炸性增长，使得业界面临着越来越多的挑战，以服务复杂的推荐工作流，包括在线推荐效率和计算资源开销。本文提出了一种基于 GPU 加速的在线服务系统 Lion，该系统由分级事件驱动的异构流水线、统一内存管理器和自动执行优化器组成，能够实时、高效地处理网络流量。此外，Lion 还提供了一个异构模板库，可以在不需要异构编程知识的情况下快速开发和迁移各种内部 Web 规模的推荐系统。该系统目前部署在百度，支持超过20种推荐服务，包括新闻馈送、短视频剪辑和搜索引擎。通过对五个实际部署的在线推荐服务的大量实验研究，证明了所提出的 GPU 加速在线服务系统的优越性。自2020年初推出以来，Lion 每天回应了数十亿的推荐请求，并帮助百度每年成功节省了数百万美元的硬件和公用事业成本。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Lion:+A+GPU-Accelerated+Online+Serving+System+for+Web-Scale+Recommendation+at+Baidu)|0| |[CognitionNet: A Collaborative Neural Network for Play Style Discovery in Online Skill Gaming Platform](https://doi.org/10.1145/3534678.3539179)|Rukma Talwadker, Surajit Chakrabarty, Aditya Pareek, Tridib Mukherjee, Deepak Saini||||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=CognitionNet:+A+Collaborative+Neural+Network+for+Play+Style+Discovery+in+Online+Skill+Gaming+Platform)|0| |[FedAttack: Effective and Covert Poisoning Attack on Federated Recommendation via Hard Sampling](https://doi.org/10.1145/3534678.3539119)|Chuhan Wu, Fangzhao Wu, Tao Qi, Yongfeng Huang, Xing Xie||||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=FedAttack:+Effective+and+Covert+Poisoning+Attack+on+Federated+Recommendation+via+Hard+Sampling)|0| diff --git a/papers/kdd/kdd2023.md b/papers/kdd/kdd2023.md index 81efc305..893b328d 100644 --- a/papers/kdd/kdd2023.md +++ b/papers/kdd/kdd2023.md @@ -17,116 +17,116 @@ |[An Empirical Study of Selection Bias in Pinterest Ads Retrieval](https://doi.org/10.1145/3580305.3599771)|Yuan Wang, Peifeng Yin, Zhiqiang Tao, Hari Venkatesan, Jin Lai, Yi Fang, PJ Xiao||Data selection bias has been a long-lasting challenge in the machine learning domain, especially in multi-stage recommendation systems, where the distribution of labeled items for model training is very different from that of the actual candidates during inference time. This distribution shift is even more prominent in the context of online advertising where the user base is diverse and the platform contains a wide range of contents. In this paper, we first investigate the data selection bias in the upper funnel (Ads Retrieval) of Pinterest's multi-cascade ads ranking system. We then conduct comprehensive experiments to assess the performance of various state-of-the-art methods, including transfer learning, adversarial learning, and unsupervised domain adaptation. Moreover, we further introduce some modifications into the unsupervised domain adaptation and evaluate the performance of different variants of this modified method. Our online A/B experiments show that the modified version of unsupervised domain adaptation (MUDA) could provide the largest improvements to the performance of Pinterest's advertisement ranking system compared with other methods and the one used in current production.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=An+Empirical+Study+of+Selection+Bias+in+Pinterest+Ads+Retrieval)|0| |[PIER: Permutation-Level Interest-Based End-to-End Re-ranking Framework in E-commerce](https://doi.org/10.1145/3580305.3599886)|Xiaowen Shi, Fan Yang, Ze Wang, Xiaoxu Wu, Muzhi Guan, Guogang Liao, Yongkang Wang, Xingxing Wang, Dong Wang|Meituan|Re-ranking draws increased attention on both academics and industries, which rearranges the ranking list by modeling the mutual influence among items to better meet users' demands. Many existing re-ranking methods directly take the initial ranking list as input, and generate the optimal permutation through a well-designed context-wise model, which brings the evaluation-before-reranking problem. Meanwhile, evaluating all candidate permutations brings unacceptable computational costs in practice. Thus, to better balance efficiency and effectiveness, online systems usually use a two-stage architecture which uses some heuristic methods such as beam-search to generate a suitable amount of candidate permutations firstly, which are then fed into the evaluation model to get the optimal permutation. However, existing methods in both stages can be improved through the following aspects. As for generation stage, heuristic methods only use point-wise prediction scores and lack an effective judgment. As for evaluation stage, most existing context-wise evaluation models only consider the item context and lack more fine-grained feature context modeling. This paper presents a novel end-to-end re-ranking framework named PIER to tackle the above challenges which still follows the two-stage architecture and contains two mainly modules named FPSM and OCPM. We apply SimHash in FPSM to select top-K candidates from the full permutation based on user's permutation-level interest in an efficient way. Then we design a novel omnidirectional attention mechanism in OCPM to capture the context information in the permutation. Finally, we jointly train these two modules end-to-end by introducing a comparative learning loss. Offline experiment results demonstrate that PIER outperforms baseline models on both public and industrial datasets, and we have successfully deployed PIER on Meituan food delivery platform.|重新排名吸引了越来越多的学术界和行业的关注，它们通过建立项目之间的相互影响模型来重新排列排名列表，以更好地满足用户的需求。许多现有的重新排序方法直接以初始排序列表为输入，通过设计良好的上下文智能模型生成最优排序，从而产生重新排序前的评价问题。同时，评估所有候选排列在实践中带来不可接受的计算成本。因此，为了更好地平衡效率和有效性，在线系统通常采用两阶段的体系结构，使用一些启发式的方法，如束搜索，生成适当数量的候选排列，然后反馈到评估模型，以获得最优的排列。然而，这两个阶段的现有方法可以通过以下几个方面进行改进。对于生成阶段，启发式方法只使用逐点预测得分，缺乏有效的判断。在评价阶段，现有的基于上下文的评价模型大多只考虑项目上下文，缺乏更细粒度的特征上下文建模。本文提出了一种新的端到端重新排序框架 PIER，以解决上述挑战，该框架仍然遵循两阶段的体系结构，包含两个主要模块: FPSM 和 OCPM。将模拟哈希算法应用于基于用户兴趣排列的 FSM 中，有效地从完全排列中选择出最优 K 候选算法。然后在 OCPM 中设计了一种新的全方位注意机制来捕获排列中的上下文信息。最后，通过引入比较学习损失，对这两个模块进行了端到端的联合训练。离线实验结果显示 PIER 在公共和工业数据集上都优于基线模型，我们已经成功地在美团食品配送平台上部署 PIER。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=PIER:+Permutation-Level+Interest-Based+End-to-End+Re-ranking+Framework+in+E-commerce)|0| |[Exploiting Intent Evolution in E-commercial Query Recommendation](https://doi.org/10.1145/3580305.3599821)|Yu Wang, Zhengyang Wang, Hengrui Zhang, Qingyu Yin, Xianfeng Tang, Yinghan Wang, Danqing Zhang, Limeng Cui, Monica Cheng, Bing Yin, Suhang Wang, Philip S. Yu||Aiming at a better understanding of the search goals in the user search sessions, recent query recommender systems explicitly model the reformulations of queries, which hopes to estimate the intents behind these reformulations and thus benefit the next-query recommendation. However, in real-world e-commercial search scenarios, user intents are much more complicated and may evolve dynamically. Existing methods merely consider trivial reformulation intents from semantic aspects and fail to model dynamic reformulation intent flows in search sessions, leading to sub-optimal capacities to recommend desired queries. To deal with these limitations, we first explicitly define six types of query reformulation intents according to the desired products of two consecutive queries. We then apply two self-attentive encoders on top of two pre-trained large language models to learn the transition dynamics from semantic query and intent reformulation sequences, respectively. We develop an intent-aware query decoder to utilize the predicted intents for suggesting the next queries. We instantiate such a framework with an Intent-aware Variational AutoEncoder (IVAE) under deployment at Amazon. We conduct comprehensive experiments on two real-world e-commercial datasets from Amazon and one public dataset from BestBuy. Specifically, IVAE improves the Recall@15 by 25.44% and 60.47% on two Amazon datasets and 13.91% on BestBuy, respectively.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Exploiting+Intent+Evolution+in+E-commercial+Query+Recommendation)|0| -|[QUERT: Continual Pre-training of Language Model for Query Understanding in Travel Domain Search](https://doi.org/10.1145/3580305.3599891)|Jian Xie, Yidan Liang, Jingping Liu, Yanghua Xiao, Baohua Wu, Shenghua Ni|School of Information Science and Engineering, East China University of Science and Technology; Shanghai Key Laboratory of Data Science, School of Computer Science, Fudan University; Alibaba Group|In light of the success of the pre-trained language models (PLMs), continual pre-training of generic PLMs has been the paradigm of domain adaption. In this paper, we propose QUERT, A Continual Pre-trained Language Model for QUERy Understanding in Travel Domain Search. QUERT is jointly trained on four tailored pre-training tasks to the characteristics of query in travel domain search: Geography-aware Mask Prediction, Geohash Code Prediction, User Click Behavior Learning, and Phrase and Token Order Prediction. Performance improvement of downstream tasks and ablation experiment demonstrate the effectiveness of our proposed pre-training tasks. To be specific, the average performance of downstream tasks increases by 2.02% and 30.93% in supervised and unsupervised settings, respectively. To check on the improvement of QUERT to online business, we deploy QUERT and perform A/B testing on Fliggy APP. The feedback results show that QUERT increases the Unique Click-Through Rate and Page Click-Through Rate by 0.89% and 1.03% when applying QUERT as the encoder. Our code and downstream task data will be released for future research.|鉴于预训练语言模型(PLM)的成功，通用 PLM 的连续预训练已经成为领域适应的范例。本文提出了一种连续预训练语言模型 QUERT，用于旅游领域搜索中的查询理解。QUERT 针对旅游领域搜索中查询的特点，共同接受了四项量身定制的预先培训任务: 地理感知掩码预测、 Geohash 代码预测、用户点击行为学习以及短语和令牌顺序预测。下游任务的性能改进和烧蚀实验验证了我们提出的预训练任务的有效性。具体来说，在监督和非监督环境下，下游任务的平均性能分别提高了2.02% 和30.93% 。为了检查 QUERT 对在线业务的改进，我们部署 QUERT 并在 Fliggy APP 上进行 A/B 测试。反馈结果显示，当应用 QUERT 作为编码器时，QUERT 增加了0.89% 和1.03% 的唯一点进率和页面点进率。我们的代码和下游任务数据将被公布，以供未来研究使用。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=QUERT:+Continual+Pre-training+of+Language+Model+for+Query+Understanding+in+Travel+Domain+Search)|0| +|[QUERT: Continual Pre-training of Language Model for Query Understanding in Travel Domain Search](https://doi.org/10.1145/3580305.3599891)|Jian Xie, Yidan Liang, Jingping Liu, Yanghua Xiao, Baohua Wu, Shenghua Ni|Alibaba Group; Shanghai Key Laboratory of Data Science, School of Computer Science, Fudan University; School of Information Science and Engineering, East China University of Science and Technology|In light of the success of the pre-trained language models (PLMs), continual pre-training of generic PLMs has been the paradigm of domain adaption. In this paper, we propose QUERT, A Continual Pre-trained Language Model for QUERy Understanding in Travel Domain Search. QUERT is jointly trained on four tailored pre-training tasks to the characteristics of query in travel domain search: Geography-aware Mask Prediction, Geohash Code Prediction, User Click Behavior Learning, and Phrase and Token Order Prediction. Performance improvement of downstream tasks and ablation experiment demonstrate the effectiveness of our proposed pre-training tasks. To be specific, the average performance of downstream tasks increases by 2.02% and 30.93% in supervised and unsupervised settings, respectively. To check on the improvement of QUERT to online business, we deploy QUERT and perform A/B testing on Fliggy APP. The feedback results show that QUERT increases the Unique Click-Through Rate and Page Click-Through Rate by 0.89% and 1.03% when applying QUERT as the encoder. Our code and downstream task data will be released for future research.|鉴于预训练语言模型(PLM)的成功，通用 PLM 的连续预训练已经成为领域适应的范例。本文提出了一种连续预训练语言模型 QUERT，用于旅游领域搜索中的查询理解。QUERT 针对旅游领域搜索中查询的特点，共同接受了四项量身定制的预先培训任务: 地理感知掩码预测、 Geohash 代码预测、用户点击行为学习以及短语和令牌顺序预测。下游任务的性能改进和烧蚀实验验证了我们提出的预训练任务的有效性。具体来说，在监督和非监督环境下，下游任务的平均性能分别提高了2.02% 和30.93% 。为了检查 QUERT 对在线业务的改进，我们部署 QUERT 并在 Fliggy APP 上进行 A/B 测试。反馈结果显示，当应用 QUERT 作为编码器时，QUERT 增加了0.89% 和1.03% 的唯一点进率和页面点进率。我们的代码和下游任务数据将被公布，以供未来研究使用。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=QUERT:+Continual+Pre-training+of+Language+Model+for+Query+Understanding+in+Travel+Domain+Search)|0| |[A Collaborative Transfer Learning Framework for Cross-domain Recommendation](https://doi.org/10.1145/3580305.3599758)|Wei Zhang, Pengye Zhang, Bo Zhang, Xingxing Wang, Dong Wang|Meituan|In the recommendation systems, there are multiple business domains to meet the diverse interests and needs of users, and the click-through rate(CTR) of each domain can be quite different, which leads to the demand for CTR prediction modeling for different business domains. The industry solution is to use domain-specific models or transfer learning techniques for each domain. The disadvantage of the former is that the data from other domains is not utilized by a single domain model, while the latter leverage all the data from different domains, but the fine-tuned model of transfer learning may trap the model in a local optimum of the source domain, making it difficult to fit the target domain. Meanwhile, significant differences in data quantity and feature schemas between different domains, known as domain shift, may lead to negative transfer in the process of transferring. To overcome these challenges, we propose the Collaborative Cross-Domain Transfer Learning Framework (CCTL). CCTL evaluates the information gain of the source domain on the target domain using a symmetric companion network and adjusts the information transfer weight of each source domain sample using the information flow network. This approach enables full utilization of other domain data while avoiding negative migration. Additionally, a representation enhancement network is used as an auxiliary task to preserve domain-specific features. Comprehensive experiments on both public and real-world industrial datasets, CCTL achieved SOTA score on offline metrics. At the same time, the CCTL algorithm has been deployed in Meituan, bringing 4.37% CTR and 5.43% GMV lift, which is significant to the business.|在推荐系统中，有多个业务领域可以满足用户的不同兴趣和需求，而每个领域的点进率可能有很大差异，因此需要为不同的业务领域建立点击率预测模型。行业解决方案是对每个领域使用特定于领域的模型或转移学习技术。前者的缺点是其他领域的数据不能被单一的领域模型所利用，而后者则利用来自不同领域的所有数据，但是经过微调的迁移学习模型可能使模型陷入源领域的局部最优，从而难以适应目标领域。同时，不同领域间数据量和特征模式的显著差异，称为领域移位，可能导致传递过程中的负迁移。为了克服这些挑战，我们提出了协作跨域转移学习框架(CCTL)。CCTL 使用对称伴侣网络对源域在目标域上的信息增益进行评估，并使用信息流网络调整每个源域样本的信息传输权重。这种方法可以充分利用其他域数据，同时避免负迁移。此外，表示增强网络用作辅助任务，以保持特定领域的特征。CCTL 在公共和现实世界的工业数据集上进行了全面的实验，在离线指标上取得了 SOTA 评分。与此同时，CCTL 算法已经在美团中部署，带来了4.37% 的点击率和5.43% 的 GMV 提升，这对业务具有重要意义。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Collaborative+Transfer+Learning+Framework+for+Cross-domain+Recommendation)|0| -|[Towards Disentangling Relevance and Bias in Unbiased Learning to Rank](https://doi.org/10.1145/3580305.3599914)|Yunan Zhang, Le Yan, Zhen Qin, Honglei Zhuang, Jiaming Shen, Xuanhui Wang, Michael Bendersky, Marc Najork|Google; University of Illinois at Urbana-Champaign; Google Research|Unbiased learning to rank (ULTR) studies the problem of mitigating various biases from implicit user feedback data such as clicks, and has been receiving considerable attention recently. A popular ULTR approach for real-world applications uses a two-tower architecture, where click modeling is factorized into a relevance tower with regular input features, and a bias tower with bias-relevant inputs such as the position of a document. A successful factorization will allow the relevance tower to be exempt from biases. In this work, we identify a critical issue that existing ULTR methods ignored - the bias tower can be confounded with the relevance tower via the underlying true relevance. In particular, the positions were determined by the logging policy, i.e., the previous production model, which would possess relevance information. We give both theoretical analysis and empirical results to show the negative effects on relevance tower due to such a correlation. We then propose three methods to mitigate the negative confounding effects by better disentangling relevance and bias. Empirical results on both controlled public datasets and a large-scale industry dataset show the effectiveness of the proposed approaches.|无偏学习排序(ULTR)研究的是如何减轻隐性用户反馈数据(如点击)中的各种偏差，近年来受到了广泛的关注。一种流行的 ULTR 方法用于现实世界的应用程序使用一个双塔架构，其中点击建模被分解为一个具有常规输入特征的相关塔，以及一个具有偏倚相关输入(如文档的位置)的偏倚塔。一个成功的因子分解将使相关塔免于偏见。在这项工作中，我们确定了一个关键问题，现有的 ULTR 方法忽略-偏倚塔可以混淆与相关塔通过潜在的真实相关性。具体来说，位置是由测井策略决定的，即先前的生产模型，它将拥有相关信息。我们给出了理论分析和实证结果来说明这种相关性对关联塔的负面影响。然后，我们提出了三种方法，通过更好地分离相关性和偏倚来减轻负面混杂效应。对受控公共数据集和大规模行业数据集的实证结果表明了该方法的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Towards+Disentangling+Relevance+and+Bias+in+Unbiased+Learning+to+Rank)|0| +|[Towards Disentangling Relevance and Bias in Unbiased Learning to Rank](https://doi.org/10.1145/3580305.3599914)|Yunan Zhang, Le Yan, Zhen Qin, Honglei Zhuang, Jiaming Shen, Xuanhui Wang, Michael Bendersky, Marc Najork|Google Research; University of Illinois at Urbana-Champaign; Google|Unbiased learning to rank (ULTR) studies the problem of mitigating various biases from implicit user feedback data such as clicks, and has been receiving considerable attention recently. A popular ULTR approach for real-world applications uses a two-tower architecture, where click modeling is factorized into a relevance tower with regular input features, and a bias tower with bias-relevant inputs such as the position of a document. A successful factorization will allow the relevance tower to be exempt from biases. In this work, we identify a critical issue that existing ULTR methods ignored - the bias tower can be confounded with the relevance tower via the underlying true relevance. In particular, the positions were determined by the logging policy, i.e., the previous production model, which would possess relevance information. We give both theoretical analysis and empirical results to show the negative effects on relevance tower due to such a correlation. We then propose three methods to mitigate the negative confounding effects by better disentangling relevance and bias. Empirical results on both controlled public datasets and a large-scale industry dataset show the effectiveness of the proposed approaches.|无偏学习排序(ULTR)研究的是如何减轻隐性用户反馈数据(如点击)中的各种偏差，近年来受到了广泛的关注。一种流行的 ULTR 方法用于现实世界的应用程序使用一个双塔架构，其中点击建模被分解为一个具有常规输入特征的相关塔，以及一个具有偏倚相关输入(如文档的位置)的偏倚塔。一个成功的因子分解将使相关塔免于偏见。在这项工作中，我们确定了一个关键问题，现有的 ULTR 方法忽略-偏倚塔可以混淆与相关塔通过潜在的真实相关性。具体来说，位置是由测井策略决定的，即先前的生产模型，它将拥有相关信息。我们给出了理论分析和实证结果来说明这种相关性对关联塔的负面影响。然后，我们提出了三种方法，通过更好地分离相关性和偏倚来减轻负面混杂效应。对受控公共数据集和大规模行业数据集的实证结果表明了该方法的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Towards+Disentangling+Relevance+and+Bias+in+Unbiased+Learning+to+Rank)|0| |[M5: Multi-Modal Multi-Interest Multi-Scenario Matching for Over-the-Top Recommendation](https://doi.org/10.1145/3580305.3599863)|Pengyu Zhao, Xin Gao, Chunxu Xu, Liang Chen||Matching preferred shows to the subscribers is extremely important in the Over-the-Top (OTT) platforms. The existing methods did not adequately consider the characteristics of the OTT services, i.e., rich meta information, diverse user interests, and mixed recommendation scenarios, leading to sub-optimal performance. This paper introduces the Multi-Modal Multi-Interest Multi-Scenario Matching (M5) for the OTT recommendation to fully exploit these attributes. A multi-modal embedding layer is first introduced to transform the show IDs into both ID embeddings initialized randomly and content graph (CG) embeddings derived from the node representations pre-trained on a metagraph. To segregate the semantics between ID and CG embeddings, M5 exploits the mirrored two-tower modeling in the subsequent layers for efficiency and effectiveness. Specifically, a multi-interest extraction layer is proposed separately on ID and CG behaviors to model users' coarse-grained and fine-grained interests through behavioral categorization, subsidiary decoration, masked-language-modeling augmented self-attention modeling and subsidiary-intensity interest calibration. Facing the inherent diverse scenarios, M5 distinguishes the scenario differences at both feature and model levels, which crosses features with the scenario indicators and employs Split Mixture-of-Experts to generate the ID, and CG user embeddings. Finally, a weighted candidate matching layer is established to calculate the ID- and CG-oriented user-item preferences and then merge into a hybrid score with dynamic weighting. The extensive online and offline experiments over two real-world OTT platforms Hulu and Disney+ reveal that M5 significantly outperforms the previous state-of-the-art and online matching algorithms over various scenarios, indicating the effectiveness and robustness of the proposed method. M5 has been fully deployed on the main traffic of the most popular "For You'' sets of both platforms, continuously enhancing the user experience for hundreds of millions of subscribers every day and steadily increasing business revenue.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=M5:+Multi-Modal+Multi-Interest+Multi-Scenario+Matching+for+Over-the-Top+Recommendation)|0| -|[Accelerating Personalized PageRank Vector Computation](https://doi.org/10.1145/3580305.3599251)|Zhen Chen, Xingzhi Guo, Baojian Zhou, Deqing Yang, Steven Skiena|Fudan University; State University of New York at Stony Brook|Personalized PageRank Vectors are widely used as fundamental graph-learning tools for detecting anomalous spammers, learning graph embeddings, and training graph neural networks. The well-known local FwdPush algorithm approximates PPVs and has a sublinear rate of $O\big(\frac{1}{\alpha\epsilon}\big)$. A recent study found that when high precision is required, FwdPush is similar to the power iteration method, and its run time is pessimistically bounded by $O\big(\frac{m}{\alpha} \log\frac{1}{\epsilon}\big)$. This paper looks closely at calculating PPVs for both directed and undirected graphs. By leveraging the linear invariant property, we show that FwdPush is a variant of Gauss-Seidel and propose a Successive Over-Relaxation based method, FwdPushSOR to speed it up by slightly modifying FwdPush. Additionally, we prove FwdPush has local linear convergence rate $O\big(\tfrac{\text{vol}(S)}{\alpha} \log\tfrac{1}{\epsilon}\big)$ enjoying advantages of two existing bounds. We also design a new local heuristic push method that reduces the number of operations by 10-50 percent compared to FwdPush. For undirected graphs, we propose two momentum-based acceleration methods that can be expressed as one-line updates and speed up non-acceleration methods by$\mathcal{O}\big(\tfrac{1}{\sqrt{\alpha}}\big)$. Our experiments on six real-world graph datasets confirm the efficiency of FwdPushSOR and the acceleration methods for directed and undirected graphs, respectively.|个性化 PageRank 向量广泛用作基本的图形学习工具，用于检测异常垃圾邮件发送者、学习图形嵌入和训练图形神经网络。著名的局部 FwdPush 算法近似于 PPV，其次线性速率为 $O big (frac {1}{ alpha epsilon } big) $。最近的一项研究发现，当需要高精度时，FwdPush 类似于幂迭代法，其运行时间悲观地受到 $O big (frac { m }{ alpha } log frac {1}{ epsilon } big) $的限制。本文主要研究有向图和无向图的 PPV 的计算。通过利用线性不变性，我们证明了 FwdPush 是 Gauss-Seidel 的一个变体，并提出了一个基于逐次超松驰法的方法，FwdPushSOR，通过稍微修改 FwdPush 来加速它。另外，我们证明了 FwdPush 具有局部线性收敛速度 $O big (tfrac { text { vol }(S)}{ alpha } log tfrac {1}{ epsilon } big) $具有两个现有界的优点。我们还设计了一种新的局部启发式推送方法，与 FwdPush 相比减少了10-50% 的操作次数。对于无向图，我们提出了两种基于动量的加速方法，它们可以表示为一行更新，并且可以通过 $mathcal { O } big (tfrac {1}{ sqrt { alpha }} big) $来加速非加速方法。我们在六个实际图形数据集上的实验分别证实了 FwdPushSOR 和有向图和无向图加速方法的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Accelerating+Personalized+PageRank+Vector+Computation)|0| +|[Accelerating Personalized PageRank Vector Computation](https://doi.org/10.1145/3580305.3599251)|Zhen Chen, Xingzhi Guo, Baojian Zhou, Deqing Yang, Steven Skiena|State University of New York at Stony Brook; Fudan University|Personalized PageRank Vectors are widely used as fundamental graph-learning tools for detecting anomalous spammers, learning graph embeddings, and training graph neural networks. The well-known local FwdPush algorithm approximates PPVs and has a sublinear rate of $O\big(\frac{1}{\alpha\epsilon}\big)$. A recent study found that when high precision is required, FwdPush is similar to the power iteration method, and its run time is pessimistically bounded by $O\big(\frac{m}{\alpha} \log\frac{1}{\epsilon}\big)$. This paper looks closely at calculating PPVs for both directed and undirected graphs. By leveraging the linear invariant property, we show that FwdPush is a variant of Gauss-Seidel and propose a Successive Over-Relaxation based method, FwdPushSOR to speed it up by slightly modifying FwdPush. Additionally, we prove FwdPush has local linear convergence rate $O\big(\tfrac{\text{vol}(S)}{\alpha} \log\tfrac{1}{\epsilon}\big)$ enjoying advantages of two existing bounds. We also design a new local heuristic push method that reduces the number of operations by 10-50 percent compared to FwdPush. For undirected graphs, we propose two momentum-based acceleration methods that can be expressed as one-line updates and speed up non-acceleration methods by$\mathcal{O}\big(\tfrac{1}{\sqrt{\alpha}}\big)$. Our experiments on six real-world graph datasets confirm the efficiency of FwdPushSOR and the acceleration methods for directed and undirected graphs, respectively.|个性化 PageRank 向量广泛用作基本的图形学习工具，用于检测异常垃圾邮件发送者、学习图形嵌入和训练图形神经网络。著名的局部 FwdPush 算法近似于 PPV，其次线性速率为 $O big (frac {1}{ alpha epsilon } big) $。最近的一项研究发现，当需要高精度时，FwdPush 类似于幂迭代法，其运行时间悲观地受到 $O big (frac { m }{ alpha } log frac {1}{ epsilon } big) $的限制。本文主要研究有向图和无向图的 PPV 的计算。通过利用线性不变性，我们证明了 FwdPush 是 Gauss-Seidel 的一个变体，并提出了一个基于逐次超松驰法的方法，FwdPushSOR，通过稍微修改 FwdPush 来加速它。另外，我们证明了 FwdPush 具有局部线性收敛速度 $O big (tfrac { text { vol }(S)}{ alpha } log tfrac {1}{ epsilon } big) $具有两个现有界的优点。我们还设计了一种新的局部启发式推送方法，与 FwdPush 相比减少了10-50% 的操作次数。对于无向图，我们提出了两种基于动量的加速方法，它们可以表示为一行更新，并且可以通过 $mathcal { O } big (tfrac {1}{ sqrt { alpha }} big) $来加速非加速方法。我们在六个实际图形数据集上的实验分别证实了 FwdPushSOR 和有向图和无向图加速方法的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Accelerating+Personalized+PageRank+Vector+Computation)|0| |[Text Is All You Need: Learning Language Representations for Sequential Recommendation](https://doi.org/10.1145/3580305.3599519)|Jiacheng Li, Ming Wang, Jin Li, Jinmiao Fu, Xin Shen, Jingbo Shang, Julian J. McAuley|Amazon; University of California, San Diego|Sequential recommendation aims to model dynamic user behavior from historical interactions. Existing methods rely on either explicit item IDs or general textual features for sequence modeling to understand user preferences. While promising, these approaches still struggle to model cold-start items or transfer knowledge to new datasets. In this paper, we propose to model user preferences and item features as language representations that can be generalized to new items and datasets. To this end, we present a novel framework, named Recformer, which effectively learns language representations for sequential recommendation. Specifically, we propose to formulate an item as a "sentence" (word sequence) by flattening item key-value attributes described by text so that an item sequence for a user becomes a sequence of sentences. For recommendation, Recformer is trained to understand the "sentence" sequence and retrieve the next "sentence". To encode item sequences, we design a bi-directional Transformer similar to the model Longformer but with different embedding layers for sequential recommendation. For effective representation learning, we propose novel pretraining and finetuning methods which combine language understanding and recommendation tasks. Therefore, Recformer can effectively recommend the next item based on language representations. Extensive experiments conducted on six datasets demonstrate the effectiveness of Recformer for sequential recommendation, especially in low-resource and cold-start settings.|顺序推荐旨在从历史交互中建立动态用户行为模型。现有的方法依赖于显式的项 ID 或一般的文本特性来进行序列建模，以理解用户的首选项。尽管这些方法很有前途，但它们仍然难以对冷启动项目进行建模或将知识转移到新的数据集中。本文提出将用户偏好和项目特征建模为语言表示，并将其推广到新的项目和数据集。为此，我们提出了一个新的框架，称为 Recformer，它有效地学习语言表示顺序推荐。具体来说，我们建议通过将文本所描述的项目键值属性扁平化，将项目表述为“句子”(单词序列) ，从而使用户的项目序列成为一个句子序列。为了便于推荐，Recformer 接受了理解“句子”序列和检索下一个“句子”的训练。为了对项目序列进行编码，我们设计了一个类似于 Longform 模型但具有不同嵌入层的双向变压器用于顺序推荐。为了有效地进行表征学习，我们提出了一种新的预训练和微调方法，将语言理解和推荐任务结合起来。因此，Recformer 可以根据语言表示有效地推荐下一个项目。在六个数据集上进行的大量实验证明了 Recformer 对于顺序推荐的有效性，特别是在低资源和冷启动环境下。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Text+Is+All+You+Need:+Learning+Language+Representations+for+Sequential+Recommendation)|0| -|[MAP: A Model-agnostic Pretraining Framework for Click-through Rate Prediction](https://doi.org/10.1145/3580305.3599422)|Jianghao Lin, Yanru Qu, Wei Guo, Xinyi Dai, Ruiming Tang, Yong Yu, Weinan Zhang|Shanghai Jiao Tong University; Huawei NoahÊäØ Ark Lab|With the widespread application of personalized online services, click-through rate (CTR) prediction has received more and more attention and research. The most prominent features of CTR prediction are its multi-field categorical data format, and vast and daily-growing data volume. The large capacity of neural models helps digest such massive amounts of data under the supervised learning paradigm, yet they fail to utilize the substantial data to its full potential, since the 1-bit click signal is not sufficient to guide the model to learn capable representations of features and instances. The self-supervised learning paradigm provides a more promising pretrain-finetune solution to better exploit the large amount of user click logs, and learn more generalized and effective representations. However, self-supervised learning for CTR prediction is still an open question, since current works on this line are only preliminary and rudimentary. To this end, we propose a Model-agnostic pretraining (MAP) framework that applies feature corruption and recovery on multi-field categorical data, and more specifically, we derive two practical algorithms: masked feature prediction (MFP) and replaced feature detection (RFD). MFP digs into feature interactions within each instance through masking and predicting a small portion of input features, and introduces noise contrastive estimation (NCE) to handle large feature spaces. RFD further turns MFP into a binary classification mode through replacing and detecting changes in input features, making it even simpler and more effective for CTR pretraining. Our extensive experiments on two real-world large-scale datasets (i.e., Avazu, Criteo) demonstrate the advantages of these two methods on several strong backbones (e.g., DCNv2, DeepFM), and achieve new state-of-the-art performance in terms of both effectiveness and efficiency for CTR prediction.|随着个性化网上服务的广泛应用，点进率预测越来越受到重视和研究。CTR 预测最突出的特点是它的多领域分类数据格式，以及海量和日益增长的数据量。神经模型的巨大容量有助于在监督式学习范式下消化如此大量的数据，但它们未能充分利用大量的数据，因为1位点击信号不足以指导模型学习特征和实例的能力表示。自监督学习范式为更好地利用大量的用户点击日志，学习更广泛和有效的表示提供了一种更有前途的预训练-微调解决方案。然而，自我监督学习的 CTR 预测仍然是一个悬而未决的问题，因为目前在这方面的工作只是初步和基础。为此，我们提出了一个模型无关预训练(model-agnotic pretraining，MAP)框架，该框架将特征损坏和恢复应用于多领域分类数据，更具体地说，我们推导出两种实用算法: 掩盖特征预测(mFP)和替换特征提取(RFD)。MFP 通过屏蔽和预测一小部分输入特征，深入挖掘每个实例中的特征交互，并引入噪声对比估计(NCE)来处理较大的特征空间。RFD 通过替换和检测输入特征的变化，进一步将 MFP 转化为二进制分类模式，使 CTR 预训练更加简单有效。我们在两个真实世界的大规模数据集(例如，Avazu，Criteo)上的广泛实验证明了这两种方法在几个强骨干(例如，dCNv2，DeepFM)上的优势，并在有效性和效率方面实现了新的最先进的 CTR 预测性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=MAP:+A+Model-agnostic+Pretraining+Framework+for+Click-through+Rate+Prediction)|0| -|[Learning to Relate to Previous Turns in Conversational Search](https://doi.org/10.1145/3580305.3599411)|Fengran Mo, JianYun Nie, Kaiyu Huang, Kelong Mao, Yutao Zhu, Peng Li, Yang Liu|Renmin University of China; Tsinghua University; University of Montreal|Conversational search allows a user to interact with a search system in multiple turns. A query is strongly dependent on the conversation context. An effective way to improve retrieval effectiveness is to expand the current query with historical queries. However, not all the previous queries are related to, and useful for expanding the current query. In this paper, we propose a new method to select relevant historical queries that are useful for the current query. To cope with the lack of labeled training data, we use a pseudo-labeling approach to annotate useful historical queries based on their impact on the retrieval results. The pseudo-labeled data are used to train a selection model. We further propose a multi-task learning framework to jointly train the selector and the retriever during fine-tuning, allowing us to mitigate the possible inconsistency between the pseudo labels and the changed retriever. Extensive experiments on four conversational search datasets demonstrate the effectiveness and broad applicability of our method compared with several strong baselines.|会话搜索允许用户多次与搜索系统交互。查询强烈依赖于会话上下文。提高检索效率的一个有效方法是使用历史查询扩展当前查询。但是，并非所有以前的查询都与之相关，并且对于展开当前查询非常有用。本文提出了一种新的方法来选择对当前查询有用的相关历史查询。为了解决缺乏标记训练数据的问题，我们使用伪标记方法根据有用的历史查询对检索结果的影响来注释它们。利用伪标记数据训练选择模型。我们进一步提出了一个多任务学习框架，在微调过程中联合训练选择器和检索器，使我们能够减轻伪标签和更改后的检索器之间可能的不一致性。通过对四个会话搜索数据集的大量实验，证明了该方法的有效性和广泛的适用性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Learning+to+Relate+to+Previous+Turns+in+Conversational+Search)|0| +|[MAP: A Model-agnostic Pretraining Framework for Click-through Rate Prediction](https://doi.org/10.1145/3580305.3599422)|Jianghao Lin, Yanru Qu, Wei Guo, Xinyi Dai, Ruiming Tang, Yong Yu, Weinan Zhang|Huawei NoahÊäØ Ark Lab; Shanghai Jiao Tong University|With the widespread application of personalized online services, click-through rate (CTR) prediction has received more and more attention and research. The most prominent features of CTR prediction are its multi-field categorical data format, and vast and daily-growing data volume. The large capacity of neural models helps digest such massive amounts of data under the supervised learning paradigm, yet they fail to utilize the substantial data to its full potential, since the 1-bit click signal is not sufficient to guide the model to learn capable representations of features and instances. The self-supervised learning paradigm provides a more promising pretrain-finetune solution to better exploit the large amount of user click logs, and learn more generalized and effective representations. However, self-supervised learning for CTR prediction is still an open question, since current works on this line are only preliminary and rudimentary. To this end, we propose a Model-agnostic pretraining (MAP) framework that applies feature corruption and recovery on multi-field categorical data, and more specifically, we derive two practical algorithms: masked feature prediction (MFP) and replaced feature detection (RFD). MFP digs into feature interactions within each instance through masking and predicting a small portion of input features, and introduces noise contrastive estimation (NCE) to handle large feature spaces. RFD further turns MFP into a binary classification mode through replacing and detecting changes in input features, making it even simpler and more effective for CTR pretraining. Our extensive experiments on two real-world large-scale datasets (i.e., Avazu, Criteo) demonstrate the advantages of these two methods on several strong backbones (e.g., DCNv2, DeepFM), and achieve new state-of-the-art performance in terms of both effectiveness and efficiency for CTR prediction.|随着个性化网上服务的广泛应用，点进率预测越来越受到重视和研究。CTR 预测最突出的特点是它的多领域分类数据格式，以及海量和日益增长的数据量。神经模型的巨大容量有助于在监督式学习范式下消化如此大量的数据，但它们未能充分利用大量的数据，因为1位点击信号不足以指导模型学习特征和实例的能力表示。自监督学习范式为更好地利用大量的用户点击日志，学习更广泛和有效的表示提供了一种更有前途的预训练-微调解决方案。然而，自我监督学习的 CTR 预测仍然是一个悬而未决的问题，因为目前在这方面的工作只是初步和基础。为此，我们提出了一个模型无关预训练(model-agnotic pretraining，MAP)框架，该框架将特征损坏和恢复应用于多领域分类数据，更具体地说，我们推导出两种实用算法: 掩盖特征预测(mFP)和替换特征提取(RFD)。MFP 通过屏蔽和预测一小部分输入特征，深入挖掘每个实例中的特征交互，并引入噪声对比估计(NCE)来处理较大的特征空间。RFD 通过替换和检测输入特征的变化，进一步将 MFP 转化为二进制分类模式，使 CTR 预训练更加简单有效。我们在两个真实世界的大规模数据集(例如，Avazu，Criteo)上的广泛实验证明了这两种方法在几个强骨干(例如，dCNv2，DeepFM)上的优势，并在有效性和效率方面实现了新的最先进的 CTR 预测性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=MAP:+A+Model-agnostic+Pretraining+Framework+for+Click-through+Rate+Prediction)|0| +|[Learning to Relate to Previous Turns in Conversational Search](https://doi.org/10.1145/3580305.3599411)|Fengran Mo, JianYun Nie, Kaiyu Huang, Kelong Mao, Yutao Zhu, Peng Li, Yang Liu|Renmin University of China; University of Montreal; Tsinghua University|Conversational search allows a user to interact with a search system in multiple turns. A query is strongly dependent on the conversation context. An effective way to improve retrieval effectiveness is to expand the current query with historical queries. However, not all the previous queries are related to, and useful for expanding the current query. In this paper, we propose a new method to select relevant historical queries that are useful for the current query. To cope with the lack of labeled training data, we use a pseudo-labeling approach to annotate useful historical queries based on their impact on the retrieval results. The pseudo-labeled data are used to train a selection model. We further propose a multi-task learning framework to jointly train the selector and the retriever during fine-tuning, allowing us to mitigate the possible inconsistency between the pseudo labels and the changed retriever. Extensive experiments on four conversational search datasets demonstrate the effectiveness and broad applicability of our method compared with several strong baselines.|会话搜索允许用户多次与搜索系统交互。查询强烈依赖于会话上下文。提高检索效率的一个有效方法是使用历史查询扩展当前查询。但是，并非所有以前的查询都与之相关，并且对于展开当前查询非常有用。本文提出了一种新的方法来选择对当前查询有用的相关历史查询。为了解决缺乏标记训练数据的问题，我们使用伪标记方法根据有用的历史查询对检索结果的影响来注释它们。利用伪标记数据训练选择模型。我们进一步提出了一个多任务学习框架，在微调过程中联合训练选择器和检索器，使我们能够减轻伪标签和更改后的检索器之间可能的不一致性。通过对四个会话搜索数据集的大量实验，证明了该方法的有效性和广泛的适用性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Learning+to+Relate+to+Previous+Turns+in+Conversational+Search)|0| |[PSLOG: Pretraining with Search Logs for Document Ranking](https://doi.org/10.1145/3580305.3599477)|Zhan Su, Zhicheng Dou, Yujia Zhou, Ziyuan Zhao, JiRong Wen||Recently, pretrained models have achieved remarkable performance not only in natural language processing but also in information retrieval (IR). Previous studies show that IR-oriented pretraining tasks can achieve better performance than only finetuning pretrained language models in IR datasets. Besides, the massive search log data obtained from mainstream search engines can be used in IR pretraining, for it contains users' implicit judgments of document relevance under a concrete query. However, existing methods mainly use direct query-document click signals to pretrain models. The potential supervision signals from search logs are far from being well explored. In this paper, we propose to comprehensively leverage four query-document relevance relations, including co-interaction and multi-hop relations, to pretrain ranking models in IR. Specifically, we focus on the user's click behavior and construct an Interaction Graph to represent the global relevance relations between queries and documents from all search logs. With the graph, we can consider the co-interaction and multi-hop q-d relationships through their neighbor nodes. Based on the relations extracted from the interaction graph, we propose four strategies to generate contrastive positive and negative q-d pairs and use these data to pretrain ranking models. Experimental results on both industrial and academic datasets demonstrate the effectiveness of our method.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=PSLOG:+Pretraining+with+Search+Logs+for+Document+Ranking)|0| |[Improving Conversational Recommendation Systems via Counterfactual Data Simulation](https://doi.org/10.1145/3580305.3599387)|Xiaolei Wang, Kun Zhou, Xinyu Tang, Wayne Xin Zhao, Fan Pan, Zhao Cao, JiRong Wen|Renmin University of China; Huawei Poisson Lab|Conversational recommender systems (CRSs) aim to provide recommendation services via natural language conversations. Although a number of approaches have been proposed for developing capable CRSs, they typically rely on sufficient training data for training. Since it is difficult to annotate recommendation-oriented dialogue datasets, existing CRS approaches often suffer from the issue of insufficient training due to the scarcity of training data. To address this issue, in this paper, we propose a CounterFactual data simulation approach for CRS, named CFCRS, to alleviate the issue of data scarcity in CRSs. Our approach is developed based on the framework of counterfactual data augmentation, which gradually incorporates the rewriting to the user preference from a real dialogue without interfering with the entire conversation flow. To develop our approach, we characterize user preference and organize the conversation flow by the entities involved in the dialogue, and design a multi-stage recommendation dialogue simulator based on a conversation flow language model. Under the guidance of the learned user preference and dialogue schema, the flow language model can produce reasonable, coherent conversation flows, which can be further realized into complete dialogues. Based on the simulator, we perform the intervention at the representations of the interacted entities of target users, and design an adversarial training method with a curriculum schedule that can gradually optimize the data augmentation strategy. Extensive experiments show that our approach can consistently boost the performance of several competitive CRSs, and outperform other data augmentation methods, especially when the training data is limited. Our code is publicly available at https://github.com/RUCAIBox/CFCRS.|会话推荐系统(CRS)旨在通过自然语言对话提供推荐服务。虽然已经提出了一些开发有能力的 CRS 的方法，但它们通常依赖于足够的培训数据进行培训。由于很难对面向建议的对话数据集进行注释，现有的 CRS 方法往往因缺乏培训数据而面临培训不足的问题。为了解决这一问题，本文提出了一种 CRS 的 CounterFact 数据模拟方法 CFCRS，以缓解 CRS 中的数据稀缺问题。我们的方法是在反事实数据增强框架的基础上发展起来的，该框架在不干扰整个会话流程的情况下，逐渐将真实对话中的用户偏好重写纳入其中。为了开发这种方法，我们描述了用户偏好的特征，并根据对话所涉及的实体组织了对话流程，设计了一个基于对话流程语言模型的多阶段推荐对话模拟器。在用户偏好和对话模式的指导下，流语言模型可以产生合理、连贯的会话流，进一步实现完整的对话。在该模拟器的基础上，对目标用户交互实体的表示进行干预，设计了一种基于课程表的对抗性训练方法，可以逐步优化数据增强策略。大量实验表明，该方法可以持续提高多个竞争性 CRS 的性能，并且优于其他数据增强方法，特别是在训练数据有限的情况下。我们的代码可以在 https://github.com/rucaibox/cfcrs 上公开获取。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Improving+Conversational+Recommendation+Systems+via+Counterfactual+Data+Simulation)|0| -|[Efficient and Joint Hyperparameter and Architecture Search for Collaborative Filtering](https://doi.org/10.1145/3580305.3599322)|Yan Wen, Chen Gao, Lingling Yi, Liwei Qiu, Yaqing Wang, Yong Li|Baidu Inc.; Tsinghua University; Tencent Inc.|Automated Machine Learning (AutoML) techniques have recently been introduced to design Collaborative Filtering (CF) models in a data-specific manner. However, existing works either search architectures or hyperparameters while ignoring the fact they are intrinsically related and should be considered together. This motivates us to consider a joint hyperparameter and architecture search method to design CF models. However, this is not easy because of the large search space and high evaluation cost. To solve these challenges, we reduce the space by screening out usefulness yperparameter choices through a comprehensive understanding of individual hyperparameters. Next, we propose a two-stage search algorithm to find proper configurations from the reduced space. In the first stage, we leverage knowledge from subsampled datasets to reduce evaluation costs; in the second stage, we efficiently fine-tune top candidate models on the whole dataset. Extensive experiments on real-world datasets show better performance can be achieved compared with both hand-designed and previous searched models. Besides, ablation and case studies demonstrate the effectiveness of our search framework.|自动机器学习(AutoML)技术最近被引入到设计特定数据的协同过滤模型(CF)中。然而，现有的工作要么搜索体系结构或超参数，而忽略了这些内在联系的事实，应该一起考虑。这促使我们考虑联合使用超参数和体系结构搜索方法来设计 CF 模型。然而，这并不容易，因为大搜索空间和高评价成本。为了解决这些挑战，我们通过全面理解各个超参数筛选出有用的超参数选择来减少空间。接下来，我们提出了一个两阶段的搜索算法，以找到适当的配置从缩减的空间。在第一阶段，我们利用次采样数据集的知识来降低评估成本; 在第二阶段，我们有效地微调整整个数据集上的顶级候选模型。在真实世界数据集上的大量实验表明，与手工设计和以前的搜索模型相比，该算法可以获得更好的性能。此外，消融和案例研究证明了我们的搜索框架的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Efficient+and+Joint+Hyperparameter+and+Architecture+Search+for+Collaborative+Filtering)|0| +|[Efficient and Joint Hyperparameter and Architecture Search for Collaborative Filtering](https://doi.org/10.1145/3580305.3599322)|Yan Wen, Chen Gao, Lingling Yi, Liwei Qiu, Yaqing Wang, Yong Li|Tsinghua University; Baidu Inc.; Tencent Inc.|Automated Machine Learning (AutoML) techniques have recently been introduced to design Collaborative Filtering (CF) models in a data-specific manner. However, existing works either search architectures or hyperparameters while ignoring the fact they are intrinsically related and should be considered together. This motivates us to consider a joint hyperparameter and architecture search method to design CF models. However, this is not easy because of the large search space and high evaluation cost. To solve these challenges, we reduce the space by screening out usefulness yperparameter choices through a comprehensive understanding of individual hyperparameters. Next, we propose a two-stage search algorithm to find proper configurations from the reduced space. In the first stage, we leverage knowledge from subsampled datasets to reduce evaluation costs; in the second stage, we efficiently fine-tune top candidate models on the whole dataset. Extensive experiments on real-world datasets show better performance can be achieved compared with both hand-designed and previous searched models. Besides, ablation and case studies demonstrate the effectiveness of our search framework.|自动机器学习(AutoML)技术最近被引入到设计特定数据的协同过滤模型(CF)中。然而，现有的工作要么搜索体系结构或超参数，而忽略了这些内在联系的事实，应该一起考虑。这促使我们考虑联合使用超参数和体系结构搜索方法来设计 CF 模型。然而，这并不容易，因为大搜索空间和高评价成本。为了解决这些挑战，我们通过全面理解各个超参数筛选出有用的超参数选择来减少空间。接下来，我们提出了一个两阶段的搜索算法，以找到适当的配置从缩减的空间。在第一阶段，我们利用次采样数据集的知识来降低评估成本; 在第二阶段，我们有效地微调整整个数据集上的顶级候选模型。在真实世界数据集上的大量实验表明，与手工设计和以前的搜索模型相比，该算法可以获得更好的性能。此外，消融和案例研究证明了我们的搜索框架的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Efficient+and+Joint+Hyperparameter+and+Architecture+Search+for+Collaborative+Filtering)|0| |[Efficient Single-Source SimRank Query by Path Aggregation](https://doi.org/10.1145/3580305.3599328)|Mingxi Zhang, Yanghua Xiao, Wei Wang|University of Shanghai for Science and Technology; Fudan University|Single-source SimRank query calculates the similarity between a query node and every node in a graph, which traverses the paths starting from the query node for similarity computation. However, the scale of the paths increases exponentially as path length increases, which decreases the computation efficiency. Sampling-based algorithms reduce computational cost by path sampling, but they need to sample sufficient paths to ensure the accuracy, and the performance might be affected by the large scale of paths. In this paper, we propose VecSim for efficient single-source SimRank query by path aggregation. VecSim first aggregates the paths starting from query node with common arrived nodes step by step to obtain the hitting probabilities, and then aggregates the paths starting from the arrived nodes reversely to obtain the first-meeting probabilities in a similar way, in which only several vectors are maintained. The extra-meeting probabilities are excluded from each step, and an efficient sampling-based algorithm is designed, which estimates the extra-meeting probabilities by sampling paths within a specified length. For further speeding up query processing, we propose a threshold-sieved algorithm, which prunes the entries with small values that contribute little to the final similarity scores by setting a threshold. Extensive experiments are done on four small and four large graphs, which demonstrate that VecSim outperforms the competitors in terms of time and space costs on a comparable accuracy. In particular, VecSim achieves an empirical error of 10 -4 level in under 0.1 second over all of these graphs.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Efficient+Single-Source+SimRank+Query+by+Path+Aggregation)|0| |[Adaptive Disentangled Transformer for Sequential Recommendation](https://doi.org/10.1145/3580305.3599253)|Yipeng Zhang, Xin Wang, Hong Chen, Wenwu Zhu|Tsinghua University, Tsinghua University|Sequential recommendation aims at mining time-aware user interests through modeling sequential behaviors. Transformer, as an effective architecture designed to process sequential input data, has shown its superiority in capturing sequential relations for recommendation. Nevertheless, existing Transformer architectures lack explicit regularization for layer-wise disentanglement, which fails to take advantage of disentangled representation in recommendation and leads to suboptimal performance. In this paper, we study the problem of layer-wise disentanglement for Transformer architectures and propose the Adaptive Disentangled Transformer (ADT) framework, which is able to adaptively determine the optimal degree of disentanglement of attention heads within different layers. Concretely, we propose to encourage disentanglement by requiring the independence constraint via mutual information estimation over attention heads and employing auxiliary objectives to prevent the information from collapsing into useless noise. We further propose a progressive scheduler to adaptively adjust the weights controlling the degree of disentanglement via an evolutionary process. Extensive experiments on various real-world datasets demonstrate the effectiveness of our proposed ADT framework.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Adaptive+Disentangled+Transformer+for+Sequential+Recommendation)|0| |[CADENCE: Offline Category Constrained and Diverse Query Generation for E-commerce Autosuggest](https://doi.org/10.1145/3580305.3599787)|Abhinav Anand, Surender Kumar, Nandeesh Kumar, Samir Shah||Query AutoComplete (QAC) or AutoSuggest is the first place of user interaction with an e-commerce search engine. It is critical for the QAC system to suggest relevant and well-formed queries for multiple possible user intents. Suggesting only the historical user queries fails in the case of infrequent or new prefixes. Much of the recent works generate synthetic candidates using models trained on user queries and thus have these issues: a) cold start problem as new products in the catalogue fail to get visibility due to lack of representation in user queries b) poor quality of generated candidates due to concept drift and c) low diversity/coverage of attributes such as brand, color & other facets in generated candidates. In this paper, we propose an offline neural query generation framework - CADENCE - to address these challenges by a) using both user queries and noisy product titles to train two separate neural language models using self-attention memory networks, b) adding category constraints during the training and query generation process to prevent concept drift c) implementing customized dynamic beam search to generate more diverse candidates for a given prefix. Besides solving for cold start and rare/unseen prefix coverage, CADENCE also increases the coverage of the existing query prefixes through a higher number of relevant and diverse query suggestions. We generated ~700K new offline queries, which have resulted in significant improvement in recall, reduction in product cold start, and increased coverage of attributes. Online A/B tests also show a significant impact on QAC usage, downstream search click-through rates, and product conversion.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=CADENCE:+Offline+Category+Constrained+and+Diverse+Query+Generation+for+E-commerce+Autosuggest)|0| |[PEPNet: Parameter and Embedding Personalized Network for Infusing with Personalized Prior Information](https://doi.org/10.1145/3580305.3599884)|Jianxin Chang, Chenbin Zhang, Yiqun Hui, Dewei Leng, Yanan Niu, Yang Song, Kun Gai|Kuaishou Technology; Unaffiliated|With the increase of content pages and display styles in online services such as online-shopping and video-watching websites, industrial-scale recommender systems face challenges in multi-domain and multi-task recommendations. The core of multi-task and multi-domain recommendation is to accurately capture user interests in different domains given different user behaviors. In this paper, we propose a plug-and-play \textit{\textbf{P}arameter and \textbf{E}mbedding \textbf{P}ersonalized \textbf{Net}work (\textbf{PEPNet})} for multi-task recommendation in the multi-domain setting. PEPNet takes features with strong biases as input and dynamically scales the bottom-layer embeddings and the top-layer DNN hidden units in the model through a gate mechanism. By mapping personalized priors to scaling weights ranging from 0 to 2, PEPNet introduces both parameter personalization and embedding personalization. Embedding Personalized Network (EPNet) selects and aligns embeddings with different semantics under multiple domains. Parameter Personalized Network (PPNet) influences DNN parameters to balance interdependent targets in multiple tasks. We have made a series of special engineering optimizations combining the Kuaishou training framework and the online deployment environment. We have deployed the model in Kuaishou apps, serving over 300 million daily users. Both online and offline experiments have demonstrated substantial improvements in multiple metrics. In particular, we have seen a more than 1\% online increase in three major scenarios.|随着在线购物和视频观看网站等在线服务内容页面和显示方式的增加，工业规模的推荐系统面临着多领域、多任务推荐的挑战。多任务、多领域推荐的核心是根据不同的用户行为准确捕获不同领域的用户兴趣。本文针对多领域环境下的多任务推荐问题，提出了一种即插即用的文本参数{ textbf { P }参数和 textbf { E }嵌入式 textbf { P }个性化 textbf { Net } work (textbf { PEPNet })}。PEPNet 以具有强偏差的特征作为输入，通过门机制动态扩展模型中的底层嵌入和顶层 DNN 隐藏单元。通过将个性化前期映射到0到2之间的权重，PEPNet 引入了参数个性化和嵌入个性化。嵌入式个性化网络(EPNet)在多个域下选择和对齐具有不同语义的嵌入式。参数个性化网络(PPNet)影响 DNN 参数以平衡多任务中相互依赖的目标。我们结合快手培训框架和在线部署环境，进行了一系列特殊的工程优化。我们在 Kuaishou 的应用程序中采用了这种模式，每天为超过3亿用户提供服务。这两个在线和离线的实验都显示了在多个指标方面的重大改进。特别是，我们已经看到在三种主要情况下在线增长超过1% 。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=PEPNet:+Parameter+and+Embedding+Personalized+Network+for+Infusing+with+Personalized+Prior+Information)|0| |[Controllable Multi-Objective Re-ranking with Policy Hypernetworks](https://doi.org/10.1145/3580305.3599796)|Sirui Chen, Yuan Wang, Zijing Wen, Zhiyu Li, Changshuo Zhang, Xiao Zhang, Quan Lin, Cheng Zhu, Jun Xu||Multi-stage ranking pipelines have become widely used strategies in modern recommender systems, where the final stage aims to return a ranked list of items that balances a number of requirements such as user preference, diversity, novelty etc. Linear scalarization is arguably the most widely used technique to merge multiple requirements into one optimization objective, by summing up the requirements with certain preference weights. Existing final-stage ranking methods often adopt a static model where the preference weights are determined during offline training and kept unchanged during online serving. Whenever a modification of the preference weights is needed, the model has to be re-trained, which is time and resources inefficient. Meanwhile, the most appropriate weights may vary greatly for different groups of targeting users or at different time periods (e.g., during holiday promotions). In this paper, we propose a framework called controllable multi-objective re-ranking (CMR) which incorporates a hypernetwork to generate parameters for a re-ranking model according to different preference weights. In this way, CMR is enabled to adapt the preference weights according to the environment changes in an online manner, without retraining the models. Moreover, we classify practical business-oriented tasks into four main categories and seamlessly incorporate them in a new proposed re-ranking model based on an Actor-Evaluator framework, which serves as a reliable real-world testbed for CMR. Offline experiments based on the dataset collected from Taobao App showed that CMR improved several popular re-ranking models by using them as underlying models. Online A/B tests also demonstrated the effectiveness and trustworthiness of CMR.|多阶段排名管道已经成为现代推荐系统中广泛使用的策略，最后阶段的目标是返回一个项目的排名列表，平衡用户偏好、多样性、新颖性等要求。线性标量可以说是最广泛使用的技术合并多个需求到一个优化目标，通过总结需求与一定的偏好权重。现有的最后阶段排序方法通常采用静态模型，在离线训练时确定偏好权重，在线服务时保持不变。当需要修改偏好权重时，模型必须重新训练，这是时间和资源效率低下的。与此同时，最合适的权重可能会因不同的目标用户群体或在不同的时间段(例如，在假日促销期间)而有很大差异。本文提出了一种可控的多目标重排序(CMR)框架，该框架结合了一个超网络，根据不同的偏好权重为重排序模型生成参数。通过这种方式，CMR 能够根据环境变化在线调整偏好权重，而不需要重新训练模型。此外，我们将面向业务的实际任务分为四个主要类别，并将它们无缝地纳入一个新提出的重新排序模型，该模型基于一个演员-评估者框架，作为一个可靠的现实世界的 CMR 测试平台。基于从淘宝应用收集的数据集的离线实验表明，CMR 通过使用它们作为基础模型改进了几个流行的重新排名模型。在线 A/B 测试也证明了 CMR 的有效性和可信性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Controllable+Multi-Objective+Re-ranking+with+Policy+Hypernetworks)|0| |[CT4Rec: Simple yet Effective Consistency Training for Sequential Recommendation](https://doi.org/10.1145/3580305.3599798)|Liu Chong, Xiaoyang Liu, Rongqin Zheng, Lixin Zhang, Xiaobo Liang, Juntao Li, Lijun Wu, Min Zhang, Leyu Lin||Sequential recommendation methods are increasingly important in cutting-edge recommender systems. Through leveraging historical records, the systems can capture user interests and perform recommendations accordingly. State-of-the-art sequential recommendation models proposed very recently combine contrastive learning techniques for obtaining high-quality user representations. Though effective and performing well, the models based on contrastive learning require careful selection of data augmentation methods and pretext tasks, efficient negative sampling strategies, and massive hyper-parameters validation. In this paper, we propose an ultra-simple alternative for obtaining better user representations and improving sequential recommendation performance. Specifically, we present a simple yet effective Consistency T braining method for sequential Recommendation (CT4Rec) in which only two extra training objectives are utilized without any structural modifications and data augmentation. Experiments on three benchmark datasets and one large newly crawled industrial corpus demonstrate that our proposed method outperforms SOTA models by a large margin and with much less training time than these based on contrastive learning. Online evaluation on real-world content recommendation system also achieves 2.717% improvement on the click-through rate and 3.679% increase on the average click number per capita. Further exploration reveals that such a simple method has great potential for CTR prediction. Our code is available at https://github.com/ct4rec/CT4Rec.git.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=CT4Rec:+Simple+yet+Effective+Consistency+Training+for+Sequential+Recommendation)|0| -|[S2phere: Semi-Supervised Pre-training for Web Search over Heterogeneous Learning to Rank Data](https://doi.org/10.1145/3580305.3599935)|Yuchen Li, Haoyi Xiong, Linghe Kong, Qingzhong Wang, Shuaiqiang Wang, Guihai Chen, Dawei Yin|Baidu Inc., Beijing, China; Shanghai Jiao Tong University, Shanghai, China|While Learning to Rank (LTR) models on top of transformers have been widely adopted to achieve decent performance, it is still challenging to train the model with sufficient data as only an extremely small number of query-webpage pairs could be annotated versus trillions of webpages available online and billions of web search queries everyday. In the meanwhile, industry research communities have released a number of open-source LTR datasets with well annotations but incorporating different designs of LTR features/labels (i.e., heterogeneous domains). In this work, inspired by the recent progress in pre-training transformers for performance advantages, we study the problem of pre-training LTR models using both labeled and unlabeled samples, especially we focus on the use of well-annotated samples in heterogeneous open-source LTR datasets to boost the performance of pre-training. Hereby, we propose S 2 phere-Semi-Supervised Pre-training with Heterogeneous LTR data strategies for LTR models using both unlabeled and labeled query-webpage pairs across heterogeneous LTR datasets. S 2 phere consists of a three-step approach: (1) Semi-supervised Feature Extraction Pre-training via Perturbed Contrastive Loss, (2) Cross-domain Ranker Pre-training over Heterogeneous LTR Datasets and (3) End-to-end LTR Fine-tuning via Modular Network Composition. Specifically, given an LTR model composed of a backbone (the feature extractor), a neck (the module to reason the orders) and a head (the predictor of ranking scores), S 2 phere uses unlabeled/labeled data from the search engine to pre-train the backbone in Step (1) via semi-supervised learning; then Step (2) incorporates multiple open-source heterogeneous LTR datasets to improve pre-training of the neck module as shared parameters of cross-domain learning; and finally, S2phere in Step (3) composes the backbone and neck with a randomly-initialized head into a whole LTR model and fine-tunes the model using search engine data with various learning strategies. Extensive experiments have been done with both offline experiments and online A/B Test on top of Baidu search engine. The comparisons against numbers of baseline algorithms confirmed the advantages of S 2 phere in producing high-performance LTR models for web-scale search.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=S2phere:+Semi-Supervised+Pre-training+for+Web+Search+over+Heterogeneous+Learning+to+Rank+Data)|0| +|[S2phere: Semi-Supervised Pre-training for Web Search over Heterogeneous Learning to Rank Data](https://doi.org/10.1145/3580305.3599935)|Yuchen Li, Haoyi Xiong, Linghe Kong, Qingzhong Wang, Shuaiqiang Wang, Guihai Chen, Dawei Yin|Shanghai Jiao Tong University, Shanghai, China; Baidu Inc., Beijing, China|While Learning to Rank (LTR) models on top of transformers have been widely adopted to achieve decent performance, it is still challenging to train the model with sufficient data as only an extremely small number of query-webpage pairs could be annotated versus trillions of webpages available online and billions of web search queries everyday. In the meanwhile, industry research communities have released a number of open-source LTR datasets with well annotations but incorporating different designs of LTR features/labels (i.e., heterogeneous domains). In this work, inspired by the recent progress in pre-training transformers for performance advantages, we study the problem of pre-training LTR models using both labeled and unlabeled samples, especially we focus on the use of well-annotated samples in heterogeneous open-source LTR datasets to boost the performance of pre-training. Hereby, we propose S 2 phere-Semi-Supervised Pre-training with Heterogeneous LTR data strategies for LTR models using both unlabeled and labeled query-webpage pairs across heterogeneous LTR datasets. S 2 phere consists of a three-step approach: (1) Semi-supervised Feature Extraction Pre-training via Perturbed Contrastive Loss, (2) Cross-domain Ranker Pre-training over Heterogeneous LTR Datasets and (3) End-to-end LTR Fine-tuning via Modular Network Composition. Specifically, given an LTR model composed of a backbone (the feature extractor), a neck (the module to reason the orders) and a head (the predictor of ranking scores), S 2 phere uses unlabeled/labeled data from the search engine to pre-train the backbone in Step (1) via semi-supervised learning; then Step (2) incorporates multiple open-source heterogeneous LTR datasets to improve pre-training of the neck module as shared parameters of cross-domain learning; and finally, S2phere in Step (3) composes the backbone and neck with a randomly-initialized head into a whole LTR model and fine-tunes the model using search engine data with various learning strategies. Extensive experiments have been done with both offline experiments and online A/B Test on top of Baidu search engine. The comparisons against numbers of baseline algorithms confirmed the advantages of S 2 phere in producing high-performance LTR models for web-scale search.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=S2phere:+Semi-Supervised+Pre-training+for+Web+Search+over+Heterogeneous+Learning+to+Rank+Data)|0| |[Multi-Label Learning to Rank through Multi-Objective Optimization](https://doi.org/10.1145/3580305.3599870)|Debabrata Mahapatra, Chaosheng Dong, Yetian Chen, Michinari Momma||Learning to Rank (LTR) technique is ubiquitous in the Information Retrieval system nowadays, especially in the Search Ranking application. The query-item relevance labels typically used to train the ranking model are often noisy measurements of human behavior, e.g., product rating for product search. The coarse measurements make the ground truth ranking non-unique with respect to a single relevance criterion. To resolve ambiguity, it is desirable to train a model using many relevance criteria, giving rise to Multi-Label LTR (MLLTR). Moreover, it formulates multiple goals that may be conflicting yet important to optimize for simultaneously, e.g., in product search, a ranking model can be trained based on product quality and purchase likelihood to increase revenue. In this research, we leverage the Multi-Objective Optimization (MOO) aspect of the MLLTR problem and employ recently developed MOO algorithms to solve it. Specifically, we propose a general framework where the information from labels can be combined in a variety of ways to meaningfully characterize the trade-off among the goals. Our framework allows for any gradient based MOO algorithm to be used for solving the MLLTR problem. We test the proposed framework on two publicly available LTR datasets and one e-commerce dataset to show its efficacy.|学习排名(LTR)技术在当今的信息检索系统中无处不在，特别是在搜索排名应用程序中。通常用于训练排名模型的查询条目相关标签通常是对人类行为的嘈杂测量，例如，产品搜索的产品评级。粗测量使得地面真实度排序相对于单一的相关准则是非唯一的。为了解决模糊问题，需要使用多个相关准则来训练模型，从而产生多标签 LTR (MLLTR)。此外，它制定了多个目标，可能是冲突的，但重要的优化同时进行，例如，在产品搜索，排名模型可以训练基于产品质量和购买可能性，以增加收入。在这项研究中，我们利用多目标优化(MOO)方面的 MLLTR 问题，并采用最近开发的 MOO 算法来解决它。具体而言，我们提出了一个总体框架，在这个框架中，可以通过各种方式组合来自标签的信息，以有意义地描述目标之间的权衡。我们的框架允许使用任何基于梯度的 MOO 算法来解决 MLLTR 问题。我们在两个公开的 LTR 数据集和一个电子商务数据集上测试了该框架，以验证其有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Multi-Label+Learning+to+Rank+through+Multi-Objective+Optimization)|0| |[Entity-aware Multi-task Learning for Query Understanding at Walmart](https://doi.org/10.1145/3580305.3599816)|Zhiyuan Peng, Vachik Dave, Nicole McNabb, Rahul Sharnagat, Alessandro Magnani, Ciya Liao, Yi Fang, Sravanthi Rajanala||Query Understanding (QU) is a fundamental process in E-commerce search engines by extracting the shopping intents of customers. It usually includes a set of different tasks such as named entity recognization and query classification. Traditional approaches often tackle each task separately by its own network, which leads to excessive workload for development and maintenance as well as increased latency and resource usage in large-scale E-commerce platforms. To tackle these challenges, this paper presents a multi-task learning approach to query understanding at Walmart. We experimented with several state-of-the-art multi-task learning architectures including MTDNN, MMoE, and PLE. Furthermore, we propose a novel large-scale entity-aware multi-task learning model (EAMT) 1 by retrieving entities from engagement data as query context to augment the query representation. To the best of our knowledge, there exists no prior work on multi-task learning for E-commerce query understanding. Comprehensive offline experiments are conducted on industry-scale datasets (up to 965M queries) to illustrate the effectiveness of our approach. The results from online experiments show substantial gains in key accuracy and latency metrics. https://github.com/zhiyuanpeng/KDD2023-EAMT|查询理解(QU)是电子商务搜索引擎中通过提取顾客购物意图的一个基本过程。它通常包括一组不同的任务，如命名实体识别和查询分类。传统的方法往往通过自己的网络分别处理每一项任务，这导致大规模电子商务平台的开发和维护工作量过大，延迟和资源使用增加。为了应对这些挑战，本文提出了一个多任务学习方法来查询理解沃尔玛。我们试验了几种最先进的多任务学习架构，包括 MTDNN、 MMoE 和 PLE。在此基础上，提出了一种新的大规模实体感知多任务学习模型(EAMT)1，该模型通过从参与数据中检索实体作为查询上下文来增强查询表示。据我们所知，目前还没有关于电子商务查询理解的多任务学习的研究。在工业规模的数据集上进行了全面的离线实验(多达965M 个查询) ，以说明我们的方法的有效性。在线实验的结果显示，在关键准确性和延迟指标方面取得了实质性的进展。Https://github.com/zhiyuanpeng/kdd2023-eamt|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Entity-aware+Multi-task+Learning+for+Query+Understanding+at+Walmart)|0| -|[Improving Training Stability for Multitask Ranking Models in Recommender Systems](https://doi.org/10.1145/3580305.3599846)|Jiaxi Tang, Yoel Drori, Daryl Chang, Maheswaran Sathiamoorthy, Justin Gilmer, Li Wei, Xinyang Yi, Lichan Hong, Ed H. Chi|Google Inc; Google Deepmind; Google Research|Recommender systems play an important role in many content platforms. While most recommendation research is dedicated to designing better models to improve user experience, we found that research on stabilizing the training for such models is severely under-explored. As recommendation models become larger and more sophisticated, they are more susceptible to training instability issues, \emph{i.e.}, loss divergence, which can make the model unusable, waste significant resources and block model developments. In this paper, we share our findings and best practices we learned for improving the training stability of a real-world multitask ranking model for YouTube recommendations. We show some properties of the model that lead to unstable training and conjecture on the causes. Furthermore, based on our observations of training dynamics near the point of training instability, we hypothesize why existing solutions would fail, and propose a new algorithm to mitigate the limitations of existing solutions. Our experiments on YouTube production dataset show the proposed algorithm can significantly improve training stability while not compromising convergence, comparing with several commonly used baseline methods.|推荐系统在许多内容平台中发挥着重要作用。虽然大多数推荐研究致力于设计更好的模型来改善用户体验，但是我们发现，关于稳定此类模型的训练的研究严重不足。随着推荐模型的不断扩大和复杂化，它们更容易受到训练不稳定性问题的影响，如损失发散等，这些问题会导致模型无法使用，浪费大量资源和阻塞模型的发展。在本文中，我们分享了我们的发现和最佳实践，我们学到了提高训练的稳定性的一个真实世界的多任务排名模型 YouTube 的建议。我们给出了模型的一些性质，这些性质导致了训练的不稳定性和对原因的猜测。此外，基于我们对训练不稳定点附近的训练动力学的观察，我们假设为什么现有的解决方案会失败，并提出了一个新的算法来减轻现有解决方案的局限性。我们在 YouTube 生产数据集上的实验表明，与几种常用的基线方法相比，该算法能够在不影响收敛性的前提下显著提高训练的稳定性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Improving+Training+Stability+for+Multitask+Ranking+Models+in+Recommender+Systems)|0| +|[Improving Training Stability for Multitask Ranking Models in Recommender Systems](https://doi.org/10.1145/3580305.3599846)|Jiaxi Tang, Yoel Drori, Daryl Chang, Maheswaran Sathiamoorthy, Justin Gilmer, Li Wei, Xinyang Yi, Lichan Hong, Ed H. Chi|Google Deepmind; Google Inc; Google Research|Recommender systems play an important role in many content platforms. While most recommendation research is dedicated to designing better models to improve user experience, we found that research on stabilizing the training for such models is severely under-explored. As recommendation models become larger and more sophisticated, they are more susceptible to training instability issues, \emph{i.e.}, loss divergence, which can make the model unusable, waste significant resources and block model developments. In this paper, we share our findings and best practices we learned for improving the training stability of a real-world multitask ranking model for YouTube recommendations. We show some properties of the model that lead to unstable training and conjecture on the causes. Furthermore, based on our observations of training dynamics near the point of training instability, we hypothesize why existing solutions would fail, and propose a new algorithm to mitigate the limitations of existing solutions. Our experiments on YouTube production dataset show the proposed algorithm can significantly improve training stability while not compromising convergence, comparing with several commonly used baseline methods.|推荐系统在许多内容平台中发挥着重要作用。虽然大多数推荐研究致力于设计更好的模型来改善用户体验，但是我们发现，关于稳定此类模型的训练的研究严重不足。随着推荐模型的不断扩大和复杂化，它们更容易受到训练不稳定性问题的影响，如损失发散等，这些问题会导致模型无法使用，浪费大量资源和阻塞模型的发展。在本文中，我们分享了我们的发现和最佳实践，我们学到了提高训练的稳定性的一个真实世界的多任务排名模型 YouTube 的建议。我们给出了模型的一些性质，这些性质导致了训练的不稳定性和对原因的猜测。此外，基于我们对训练不稳定点附近的训练动力学的观察，我们假设为什么现有的解决方案会失败，并提出了一个新的算法来减轻现有解决方案的局限性。我们在 YouTube 生产数据集上的实验表明，与几种常用的基线方法相比，该算法能够在不影响收敛性的前提下显著提高训练的稳定性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Improving+Training+Stability+for+Multitask+Ranking+Models+in+Recommender+Systems)|0| |[PASS: Personalized Advertiser-aware Sponsored Search](https://doi.org/10.1145/3580305.3599882)|Zhoujin Tian, Chaozhuo Li, Zhiqiang Zuo, Zengxuan Wen, Lichao Sun, Xinyue Hu, Wen Zhang, Haizhen Huang, Senzhang Wang, Weiwei Deng, Xing Xie, Qi Zhang||The nucleus of online sponsored search systems lies in measuring the relevance between the search intents of users and the advertising purposes of advertisers. Existing conventional doublet-based (query-keyword) relevance models solely rely on short queries and keywords to uncover such intents, which ignore the diverse and personalized preferences of participants (i.e., users and advertisers), resulting in undesirable advertising performance. In this paper, we investigate the novel problem of Personalized A dvertiser-aware Sponsored Search (PASS). Our motivation lies in incorporating the portraits of users and advertisers into relevance models to facilitate the modeling of intrinsic search intents and advertising purposes, leading to a quadruple-based (i.e., user-query-keyword-advertiser) task. Various types of historical behaviors are explored in the format of hypergraphs to provide abundant signals on identifying the preferences of participants. A novel heterogeneous textual hypergraph transformer is further proposed to deeply fuse the textual semantics and the high-order hypergraph topology. Our proposal is extensively evaluated over real industry datasets, and experimental results demonstrate its superiority.|在线赞助搜索系统的核心在于衡量用户的搜索意图与广告商的广告目的之间的相关性。现有的传统的基于对偶(查询-关键词)的相关性模型仅仅依赖于短查询和关键词来揭示这些意图，这忽略了参与者(即用户和广告商)的多样化和个性化偏好，导致不良的广告表现。在本文中，我们研究了个性化广告主意识支持搜索(PASS)的新问题。我们的动机在于将用户和广告商的肖像融入相关性模型，以促进内在搜索意图和广告目的的建模，从而导致一个基于四重(即，用户-查询-关键字-广告商)的任务。以超图的形式探讨了不同类型的历史行为，为识别参与者的偏好提供了丰富的信号。进一步提出了一种新的异构文本超图转换器，将文本语义和高阶超图拓扑深度融合。我们的方案在实际工业数据集上进行了广泛的评估，实验结果证明了其优越性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=PASS:+Personalized+Advertiser-aware+Sponsored+Search)|0| |[Towards Fairness in Personalized Ads Using Impression Variance Aware Reinforcement Learning](https://doi.org/10.1145/3580305.3599916)|Aditya Srinivas Timmaraju, Mehdi Mashayekhi, Mingliang Chen, Qi Zeng, Quintin Fettes, Wesley Cheung, Yihan Xiao, Manojkumar Rangasamy Kannadasan, Pushkar Tripathi, Sean Gahagan, Miranda Bogen, Rob Roudani|Meta|Variances in ad impression outcomes across demographic groups are increasingly considered to be potentially indicative of algorithmic bias in personalized ads systems. While there are many definitions of fairness that could be applicable in the context of personalized systems, we present a framework which we call the Variance Reduction System (VRS) for achieving more equitable outcomes in Meta's ads systems. VRS seeks to achieve a distribution of impressions with respect to selected protected class (PC) attributes that more closely aligns the demographics of an ad's eligible audience (a function of advertiser targeting criteria) with the audience who sees that ad, in a privacy-preserving manner. We first define metrics to quantify fairness gaps in terms of ad impression variances with respect to PC attributes including gender and estimated race. We then present the VRS for re-ranking ads in an impression variance-aware manner. We evaluate VRS via extensive simulations over different parameter choices and study the effect of the VRS on the chosen fairness metric. We finally present online A/B testing results from applying VRS to Meta's ads systems, concluding with a discussion of future work. We have deployed the VRS to all users in the US for housing ads, resulting in significant improvement in our fairness metric. VRS is the first large-scale deployed framework for pursuing fairness for multiple PC attributes in online advertising.|不同人群的广告印象结果的差异越来越被认为是个性化广告系统中算法偏差的潜在指示。虽然有许多公平的定义，可以适用于个性化系统的背景下，我们提出了一个框架，我们称之为方差减少系统(VRS) ，以实现更公平的结果在元数据的广告系统。VRS 试图通过选定的受保护类别(PC)属性来实现印象的分布，从而以保护隐私的方式将广告合格受众的人口统计数据(广告客户定位标准的功能)与看到该广告的受众的人口统计数据更紧密地联系起来。我们首先定义指标来量化广告印象差异的公平性差距方面的个人电脑属性，包括性别和估计的种族。然后，我们提出了一个印象方差感知的方式重新排名广告的 VRS。我们通过对不同参数选择的大量仿真来评估 VRS，并研究 VRS 对选择的公平性度量的影响。最后给出了 VRS 应用于 Meta 广告系统的在线 A/B 测试结果，并对今后的工作进行了讨论。我们已在美国所有用户的住房广告中部署了 VRS，从而显著改善了我们的公平性指标。VRS 是第一个大规模部署的框架，以追求公平的多个个人电脑属性在网上广告。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Towards+Fairness+in+Personalized+Ads+Using+Impression+Variance+Aware+Reinforcement+Learning)|0| |[PlanRanker: Towards Personalized Ranking of Train Transfer Plans](https://doi.org/10.1145/3580305.3599887)|Jia Xu, Wanjie Tao, Zulong Chen, Jin Huang, Huihui Liu, Hong Wen, Shenghua Ni, Qun Dai, Yu Gu||Train transfer plan ranking has become the core business of online travel platforms (OTPs), due to the flourish development of high- speed rail technology and convenience of booking trains online. Currently, mainstream OTPs adopt rule-based or simple preference- based strategies to rank train transfer plans. However, the insuf- ficient emphasis on the costs of plans and the negligence of con- sidering reference transfer plans make these existing strategies less effective in solving the personalized ranking problem of train transfer plans. To this end, a novel personalized deep network (Plan- Ranker) is presented in this paper to better address the problem. In PlanRanker, a personalized learning component is first proposed to capture both of the query semantics and the target transfer plan- relevant personalized interests of a user over the user's behavior log data. Then, we present a cost learning component, where both of the price cost and the time cost of a target transfer plan are emphasized and learned. Finally, a reference transfer plan learning component is designed to enable the whole framework of PlanRanker to learn from reference transfer plans which are pieced together by plat- form users and thus reflect the wisdom of crowd. PlanRanker is now successfully deployed at Alibaba Fliggy, one of the largest OTPs in China, serving millions of users every day for train ticket reservation. Offline experiments on two production datasets and a country-scale online A/B test at Fliggy both demonstrate the superiority of the proposed PlanRanker over baselines.|列车换乘计划排序已成为在线旅游平台(OTP)的核心业务，由于高速铁路技术的蓬勃发展和在线预订列车的便利性。目前，主流 OTP 采用基于规则或简单偏好的策略对列车换乘计划进行排序。然而，由于对列车换乘计划成本的重视不够，而忽视了对参考换乘计划的考虑，使得现有的换乘策略在解决列车换乘计划个性化排序问题时效率较低。为此，本文提出了一种新的个性化深度网络(Plan-Ranker)来更好地解决这一问题。在 PlanRanker 中，首先提出了一个个性化学习组件，用于在用户行为日志数据上捕获用户的查询语义和与目标传输计划相关的个性化兴趣。然后，我们提出了一个成本学习组件，其中的价格成本和时间成本的目标转移计划都强调和学习。最后，设计了一个参考转移计划学习组件，使得 PlanRanker 的整个框架能够从平台用户拼凑起来的参考转移计划中学习，从而反映出群体的智慧。PlanRanker 现已成功部署在中国最大的在线旅行社之一的阿里巴巴，每天为数百万用户提供火车票预订服务。在两个生产数据集上的离线实验和在 Fliggy 进行的国家级在线 A/B 测试都证明了提议的 PlanRanker 比基线更优越。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=PlanRanker:+Towards+Personalized+Ranking+of+Train+Transfer+Plans)|0| -|[Multi-factor Sequential Re-ranking with Perception-Aware Diversification](https://doi.org/10.1145/3580305.3599869)|Yue Xu, Hao Chen, Zefan Wang, Jianwen Yin, Qijie Shen, Dimin Wang, Feiran Huang, Lixiang Lai, Tao Zhuang, Junfeng Ge, Xia Hu|The Hong Kong Polytechnic University; Jinan University; Rice University; Alibaba Group|Feed recommendation systems, which recommend a sequence of items for users to browse and interact with, have gained significant popularity in practical applications. In feed products, users tend to browse a large number of items in succession, so the previously viewed items have a significant impact on users' behavior towards the following items. Therefore, traditional methods that mainly focus on improving the accuracy of recommended items are suboptimal for feed recommendations because they may recommend highly similar items. For feed recommendation, it is crucial to consider both the accuracy and diversity of the recommended item sequences in order to satisfy users' evolving interest when consecutively viewing items. To this end, this work proposes a general re-ranking framework named Multi-factor Sequential Re-ranking with Perception-Aware Diversification (MPAD) to jointly optimize accuracy and diversity for feed recommendation in a sequential manner. Specifically, MPAD first extracts users' different scales of interests from their behavior sequences through graph clustering-based aggregations. Then, MPAD proposes two sub-models to respectively evaluate the accuracy and diversity of a given item by capturing users' evolving interest due to the ever-changing context and users' personal perception of diversity from an item sequence perspective. This is consistent with the browsing nature of the feed scenario. Finally, MPAD generates the return list by sequentially selecting optimal items from the candidate set to maximize the joint benefits of accuracy and diversity of the entire list. MPAD has been implemented in Taobao's homepage feed to serve the main traffic and provide services to recommend billions of items to hundreds of millions of users every day.|提要推荐系统为用户推荐了一系列可供浏览和交互的条目，在实际应用中得到了广泛的应用。在提要产品中，用户倾向于连续浏览大量条目，因此以前查看的条目对用户对下列条目的行为有显著影响。因此，主要侧重于提高推荐项目准确性的传统方法对于饲料推荐是次优的，因为它们可能推荐高度相似的项目。为了满足用户在连续查看条目时不断变化的兴趣，对推荐条目序列的准确性和多样性进行考虑是至关重要的。为此，本文提出了一种基于感知多样化的多因素序贯推荐(MPAD)的通用推荐框架，该框架以序贯方式对推荐的准确性和多样性进行联合优化。具体来说，MPAD 首先通过基于图聚类的聚合从用户的行为序列中提取出用户不同尺度的兴趣。然后，MPAD 提出了两个子模型，分别从项目序列的角度通过捕获不断变化的用户兴趣和用户个人对多样性的感知来评价项目的准确性和多样性。这与提要场景的浏览特性一致。最后，MPAD 通过从候选集中依次选择最优项目来生成返回列表，以最大限度地提高整个列表的准确性和多样性。MPAD 已经在淘宝网的主页 feed 中实现，为主流流量提供服务，每天向数亿用户推荐数十亿个项目。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Multi-factor+Sequential+Re-ranking+with+Perception-Aware+Diversification)|0| +|[Multi-factor Sequential Re-ranking with Perception-Aware Diversification](https://doi.org/10.1145/3580305.3599869)|Yue Xu, Hao Chen, Zefan Wang, Jianwen Yin, Qijie Shen, Dimin Wang, Feiran Huang, Lixiang Lai, Tao Zhuang, Junfeng Ge, Xia Hu|Alibaba Group; The Hong Kong Polytechnic University; Rice University; Jinan University|Feed recommendation systems, which recommend a sequence of items for users to browse and interact with, have gained significant popularity in practical applications. In feed products, users tend to browse a large number of items in succession, so the previously viewed items have a significant impact on users' behavior towards the following items. Therefore, traditional methods that mainly focus on improving the accuracy of recommended items are suboptimal for feed recommendations because they may recommend highly similar items. For feed recommendation, it is crucial to consider both the accuracy and diversity of the recommended item sequences in order to satisfy users' evolving interest when consecutively viewing items. To this end, this work proposes a general re-ranking framework named Multi-factor Sequential Re-ranking with Perception-Aware Diversification (MPAD) to jointly optimize accuracy and diversity for feed recommendation in a sequential manner. Specifically, MPAD first extracts users' different scales of interests from their behavior sequences through graph clustering-based aggregations. Then, MPAD proposes two sub-models to respectively evaluate the accuracy and diversity of a given item by capturing users' evolving interest due to the ever-changing context and users' personal perception of diversity from an item sequence perspective. This is consistent with the browsing nature of the feed scenario. Finally, MPAD generates the return list by sequentially selecting optimal items from the candidate set to maximize the joint benefits of accuracy and diversity of the entire list. MPAD has been implemented in Taobao's homepage feed to serve the main traffic and provide services to recommend billions of items to hundreds of millions of users every day.|提要推荐系统为用户推荐了一系列可供浏览和交互的条目，在实际应用中得到了广泛的应用。在提要产品中，用户倾向于连续浏览大量条目，因此以前查看的条目对用户对下列条目的行为有显著影响。因此，主要侧重于提高推荐项目准确性的传统方法对于饲料推荐是次优的，因为它们可能推荐高度相似的项目。为了满足用户在连续查看条目时不断变化的兴趣，对推荐条目序列的准确性和多样性进行考虑是至关重要的。为此，本文提出了一种基于感知多样化的多因素序贯推荐(MPAD)的通用推荐框架，该框架以序贯方式对推荐的准确性和多样性进行联合优化。具体来说，MPAD 首先通过基于图聚类的聚合从用户的行为序列中提取出用户不同尺度的兴趣。然后，MPAD 提出了两个子模型，分别从项目序列的角度通过捕获不断变化的用户兴趣和用户个人对多样性的感知来评价项目的准确性和多样性。这与提要场景的浏览特性一致。最后，MPAD 通过从候选集中依次选择最优项目来生成返回列表，以最大限度地提高整个列表的准确性和多样性。MPAD 已经在淘宝网的主页 feed 中实现，为主流流量提供服务，每天向数亿用户推荐数十亿个项目。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Multi-factor+Sequential+Re-ranking+with+Perception-Aware+Diversification)|0| |[TWIN: TWo-stage Interest Network for Lifelong User Behavior Modeling in CTR Prediction at Kuaishou](https://doi.org/10.1145/3580305.3599922)|Jianxin Chang, Chenbin Zhang, Zhiyi Fu, Xiaoxue Zang, Lin Guan, Jing Lu, Yiqun Hui, Dewei Leng, Yanan Niu, Yang Song, Kun Gai|Kuaishou Technology; Unaffiliated|Life-long user behavior modeling, i.e., extracting a user's hidden interests from rich historical behaviors in months or even years, plays a central role in modern CTR prediction systems. Conventional algorithms mostly follow two cascading stages: a simple General Search Unit (GSU) for fast and coarse search over tens of thousands of long-term behaviors and an Exact Search Unit (ESU) for effective Target Attention (TA) over the small number of finalists from GSU. Although efficient, existing algorithms mostly suffer from a crucial limitation: the \textit{inconsistent} target-behavior relevance metrics between GSU and ESU. As a result, their GSU usually misses highly relevant behaviors but retrieves ones considered irrelevant by ESU. In such case, the TA in ESU, no matter how attention is allocated, mostly deviates from the real user interests and thus degrades the overall CTR prediction accuracy. To address such inconsistency, we propose \textbf{TWo-stage Interest Network (TWIN)}, where our Consistency-Preserved GSU (CP-GSU) adopts the identical target-behavior relevance metric as the TA in ESU, making the two stages twins. Specifically, to break TA's computational bottleneck and extend it from ESU to GSU, or namely from behavior length $10^2$ to length $10^4-10^5$, we build a novel attention mechanism by behavior feature splitting. For the video inherent features of a behavior, we calculate their linear projection by efficient pre-computing \& caching strategies. And for the user-item cross features, we compress each into a one-dimentional bias term in the attention score calculation to save the computational cost. The consistency between two stages, together with the effective TA-based relevance metric in CP-GSU, contributes to significant performance gain in CTR prediction.|终身用户行为建模，即在数月甚至数年内从丰富的历史行为中提取用户隐藏的兴趣，在现代 CTR 预测系统中起着核心作用。传统的算法大多遵循两个级联阶段: 一个简单的通用搜索单元(GSU)用于快速和粗略搜索成千上万的长期行为和一个精确搜索单元(ESU)用于有效的目标注意(TA)在少数决赛选手从 GSU。虽然有效，但现有的算法大多受到一个关键的限制: 文本{不一致}目标行为相关度量 GSU 和 ESU 之间。因此，他们的 GSU 通常会错过高度相关的行为，但检索被 ESU 认为无关的行为。在这种情况下，ESU 中的 TA，无论如何分配注意力，大多偏离了真实用户的兴趣，从而降低了整体 CTR 预测的准确性。为了解决这种不一致性，我们提出 textbf { TWo-stage Interest Network (TWIN)} ，其中我们的保持一致性的 GSU (CP-GSU)采用与 ESU 中的 TA 相同的目标行为相关度量，使两个阶段成为孪生的。具体来说，为了打破 TA 的计算瓶颈，将其从 ESU 扩展到 GSU，或者说从行为长度 $10 ^ 2 $扩展到长度 $10 ^ 4-10 ^ 5 $，我们通过行为特征分裂构建了一种新的注意机制。对于视频行为的固有特征，我们通过有效的预计算和缓存策略来计算它们的线性投影。对于用户-项目交叉特征，在注意得分计算中将每个特征压缩为一维偏差项，以节省计算成本。两个阶段之间的一致性，加上 CP-GSU 中有效的基于 TA 的相关度量，有助于提高 CTR 预测的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=TWIN:+TWo-stage+Interest+Network+for+Lifelong+User+Behavior+Modeling+in+CTR+Prediction+at+Kuaishou)|0| |[On Manipulating Signals of User-Item Graph: A Jacobi Polynomial-based Graph Collaborative Filtering](https://doi.org/10.1145/3580305.3599450)|Jiayan Guo, Lun Du, Xu Chen, Xiaojun Ma, Qiang Fu, Shi Han, Dongmei Zhang, Yan Zhang|Peking University; Microsoft|Collaborative filtering (CF) is an important research direction in recommender systems that aims to make recommendations given the information on user-item interactions. Graph CF has attracted more and more attention in recent years due to its effectiveness in leveraging high-order information in the user-item bipartite graph for better recommendations. Specifically, recent studies show the success of graph neural networks (GNN) for CF is attributed to its low-pass filtering effects. However, current researches lack a study of how different signal components contributes to recommendations, and how to design strategies to properly use them well. To this end, from the view of spectral transformation, we analyze the important factors that a graph filter should consider to achieve better performance. Based on the discoveries, we design JGCF, an efficient and effective method for CF based on Jacobi polynomial bases and frequency decomposition strategies. Extensive experiments on four widely used public datasets show the effectiveness and efficiency of the proposed methods, which brings at most 27.06% performance gain on Alibaba-iFashion. Besides, the experimental results also show that JGCF is better at handling sparse datasets, which shows potential in making recommendations for cold-start users.|协同过滤(CF)是推荐系统的一个重要研究方向，其目的是根据用户项目交互的信息提供推荐。近年来，Graph CF 由于能够有效地利用用户-项目双向图中的高阶信息来获得更好的建议而引起了越来越多的关注。具体来说，最近的研究表明，图神经网络(GNN)对 CF 的成功归功于其低通滤波效果。然而，目前的研究缺乏研究不同的信号成分如何有助于推荐，以及如何设计策略，以适当地使用它们。为此，本文从谱变换的角度出发，分析了图形滤波器要获得更好的性能所应考虑的重要因素。基于这些发现，我们设计了一种基于 Jacobi 多项式基和频率分解策略的高效率和有效的协同过滤方法。在四个广泛使用的公共数据集上进行的大量实验表明了该方法的有效性和效率，在阿里巴巴-iFashion 平台上最多获得27.06% 的性能增益。此外，实验结果还表明，JGCF 在处理稀疏数据集方面有较好的表现，可以为冷启动用户提供建议。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=On+Manipulating+Signals+of+User-Item+Graph:+A+Jacobi+Polynomial-based+Graph+Collaborative+Filtering)|0| -|[Off-Policy Evaluation of Ranking Policies under Diverse User Behavior](https://doi.org/10.1145/3580305.3599447)|Haruka Kiyohara, Masatoshi Uehara, Yusuke Narita, Nobuyuki Shimizu, Yasuo Yamamoto, Yuta Saito|Cornell University; Hanjuku-Kaso Co., Ltd.; Yale University; Yahoo Japan Corporation|Ranking interfaces are everywhere in online platforms. There is thus an ever growing interest in their Off-Policy Evaluation (OPE), aiming towards an accurate performance evaluation of ranking policies using logged data. A de-facto approach for OPE is Inverse Propensity Scoring (IPS), which provides an unbiased and consistent value estimate. However, it becomes extremely inaccurate in the ranking setup due to its high variance under large action spaces. To deal with this problem, previous studies assume either independent or cascade user behavior, resulting in some ranking versions of IPS. While these estimators are somewhat effective in reducing the variance, all existing estimators apply a single universal assumption to every user, causing excessive bias and variance. Therefore, this work explores a far more general formulation where user behavior is diverse and can vary depending on the user context. We show that the resulting estimator, which we call Adaptive IPS (AIPS), can be unbiased under any complex user behavior. Moreover, AIPS achieves the minimum variance among all unbiased estimators based on IPS. We further develop a procedure to identify the appropriate user behavior model to minimize the mean squared error (MSE) of AIPS in a data-driven fashion. Extensive experiments demonstrate that the empirical accuracy improvement can be significant, enabling effective OPE of ranking systems even under diverse user behavior.|在线平台中，排序界面无处不在。因此，人们对非策略评估(OPE)越来越感兴趣，其目标是使用日志数据对策略进行准确的性能评估。OPE 的一个事实上的方法是反倾向评分(IPS) ，它提供了一个无偏和一致的价值估计。然而，它变得非常不准确的排名设置，由于其高方差下的大行动空间。为了解决这个问题，以前的研究假设独立或级联用户行为，导致一些排名版本的 IPS。虽然这些估计量在减少方差方面有一定的效果，但是所有现有的估计量都对每个用户适用一个统一的假设，从而导致过度的偏差和方差。因此，这项工作探索了一个更一般的公式，其中用户行为是多样的，可以根据用户上下文而变化。我们证明了所得到的估计量，我们称之为自适应 IPS (AIPS) ，在任何复杂的用户行为下都是无偏的。此外，AIPS 在所有基于 IPS 的无偏估计量之间实现了最小方差。我们进一步开发了一个程序，以确定适当的用户行为模型，从而以数据驱动的方式最大限度地减少 AIPS 的均方差。大量的实验表明，经验的准确性改善可以是显着的，使有效的排名系统的 OPE 即使在不同的用户行为。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Off-Policy+Evaluation+of+Ranking+Policies+under+Diverse+User+Behavior)|0| +|[Off-Policy Evaluation of Ranking Policies under Diverse User Behavior](https://doi.org/10.1145/3580305.3599447)|Haruka Kiyohara, Masatoshi Uehara, Yusuke Narita, Nobuyuki Shimizu, Yasuo Yamamoto, Yuta Saito|Yahoo Japan Corporation; Hanjuku-Kaso Co., Ltd.; Yale University; Cornell University|Ranking interfaces are everywhere in online platforms. There is thus an ever growing interest in their Off-Policy Evaluation (OPE), aiming towards an accurate performance evaluation of ranking policies using logged data. A de-facto approach for OPE is Inverse Propensity Scoring (IPS), which provides an unbiased and consistent value estimate. However, it becomes extremely inaccurate in the ranking setup due to its high variance under large action spaces. To deal with this problem, previous studies assume either independent or cascade user behavior, resulting in some ranking versions of IPS. While these estimators are somewhat effective in reducing the variance, all existing estimators apply a single universal assumption to every user, causing excessive bias and variance. Therefore, this work explores a far more general formulation where user behavior is diverse and can vary depending on the user context. We show that the resulting estimator, which we call Adaptive IPS (AIPS), can be unbiased under any complex user behavior. Moreover, AIPS achieves the minimum variance among all unbiased estimators based on IPS. We further develop a procedure to identify the appropriate user behavior model to minimize the mean squared error (MSE) of AIPS in a data-driven fashion. Extensive experiments demonstrate that the empirical accuracy improvement can be significant, enabling effective OPE of ranking systems even under diverse user behavior.|在线平台中，排序界面无处不在。因此，人们对非策略评估(OPE)越来越感兴趣，其目标是使用日志数据对策略进行准确的性能评估。OPE 的一个事实上的方法是反倾向评分(IPS) ，它提供了一个无偏和一致的价值估计。然而，它变得非常不准确的排名设置，由于其高方差下的大行动空间。为了解决这个问题，以前的研究假设独立或级联用户行为，导致一些排名版本的 IPS。虽然这些估计量在减少方差方面有一定的效果，但是所有现有的估计量都对每个用户适用一个统一的假设，从而导致过度的偏差和方差。因此，这项工作探索了一个更一般的公式，其中用户行为是多样的，可以根据用户上下文而变化。我们证明了所得到的估计量，我们称之为自适应 IPS (AIPS) ，在任何复杂的用户行为下都是无偏的。此外，AIPS 在所有基于 IPS 的无偏估计量之间实现了最小方差。我们进一步开发了一个程序，以确定适当的用户行为模型，从而以数据驱动的方式最大限度地减少 AIPS 的均方差。大量的实验表明，经验的准确性改善可以是显着的，使有效的排名系统的 OPE 即使在不同的用户行为。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Off-Policy+Evaluation+of+Ranking+Policies+under+Diverse+User+Behavior)|0| |[Impatient Bandits: Optimizing Recommendations for the Long-Term Without Delay](https://doi.org/10.1145/3580305.3599386)|Thomas M. McDonald, Lucas Maystre, Mounia Lalmas, Daniel Russo, Kamil Ciosek|; Photon Spot (United States); University of Manchester|Recommender systems are a ubiquitous feature of online platforms. Increasingly, they are explicitly tasked with increasing users' long-term satisfaction. In this context, we study a content exploration task, which we formalize as a multi-armed bandit problem with delayed rewards. We observe that there is an apparent trade-off in choosing the learning signal: Waiting for the full reward to become available might take several weeks, hurting the rate at which learning happens, whereas measuring short-term proxy rewards reflects the actual long-term goal only imperfectly. We address this challenge in two steps. First, we develop a predictive model of delayed rewards that incorporates all information obtained to date. Full observations as well as partial (short or medium-term) outcomes are combined through a Bayesian filter to obtain a probabilistic belief. Second, we devise a bandit algorithm that takes advantage of this new predictive model. The algorithm quickly learns to identify content aligned with long-term success by carefully balancing exploration and exploitation. We apply our approach to a podcast recommendation problem, where we seek to identify shows that users engage with repeatedly over two months. We empirically validate that our approach results in substantially better performance compared to approaches that either optimize for short-term proxies, or wait for the long-term outcome to be fully realized.|推荐系统是在线平台的一个普遍特征。他们越来越明确地被赋予了提高用户长期满意度的任务。在这个背景下，我们研究了一个内容探索任务，我们将其形式化为一个延迟奖励的多臂老虎机问题。我们观察到，在选择学习信号时有一个明显的权衡: 等待完整的奖励变得可用可能需要几个星期，损害了学习发生的速度，而衡量短期代理奖励只能不完美地反映实际的长期目标。我们用两个步骤来应对这个挑战。首先，我们开发了一个延迟奖励的预测模型，其中包含了迄今为止获得的所有信息。通过贝叶斯滤波器将全部观测结果以及部分(短期或中期)结果组合起来，得到概率信念。其次，我们设计了一个土匪算法，利用这个新的预测模型的优势。该算法通过仔细平衡探索和开发，很快学会识别与长期成功相一致的内容。我们将我们的方法应用于播客推荐问题，我们试图确定用户在两个月内反复参与的节目。我们经验性地验证了我们的方法与那些为短期代理优化或者等待长期结果完全实现的方法相比，能够获得更好的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Impatient+Bandits:+Optimizing+Recommendations+for+the+Long-Term+Without+Delay)|0| -|[Unbiased Delayed Feedback Label Correction for Conversion Rate Prediction](https://doi.org/10.1145/3580305.3599536)|Yifan Wang, Peijie Sun, Min Zhang, Qinglin Jia, Jingjie Li, Shaoping Ma|Noah’s Ark Lab, Huawei; Tsinghua University|Conversion rate prediction is critical to many online applications such as digital display advertising. To capture dynamic data distribution, industrial systems often require retraining models on recent data daily or weekly. However, the delay of conversion behavior usually leads to incorrect labeling, which is called delayed feedback problem. Existing work may fail to introduce the correct information about false negative samples due to data sparsity and dynamic data distribution. To directly introduce the correct feedback label information, we propose an Unbiased delayed feedback Label Correction framework (ULC), which uses an auxiliary model to correct labels for observed negative feedback samples. Firstly, we theoretically prove that the label-corrected loss is an unbiased estimate of the oracle loss using true labels. Then, as there are no ready training data for label correction, counterfactual labeling is used to construct artificial training data. Furthermore, since counterfactual labeling utilizes only partial training data, we design an embedding-based alternative training method to enhance performance. Comparative experiments on both public and private datasets and detailed analyses show that our proposed approach effectively alleviates the delayed feedback problem and consistently outperforms the previous state-of-the-art methods.|转化率预测是许多在线应用程序，如数字显示广告的关键。为了捕获动态数据分布，工业系统通常需要每天或每周对最近的数据进行再训练。然而，转换行为的延迟通常会导致不正确的标记，这就是所谓的延迟反馈问题。由于数据稀疏和数据分布的动态性，现有的工作可能无法引入正确的假阴性样本信息。为了直接引入正确的反馈标签信息，我们提出了一种无偏的延迟反馈标签校正框架(ULC) ，它使用一个辅助模型对观测到的负反馈样本进行标签校正。首先，我们从理论上证明了标签校正损失是使用真实标签对甲骨文损失进行的无偏估计。然后，由于没有现成的训练数据用于标签校正，采用反事实标注来构造人工训练数据。此外，由于反事实标注只利用部分训练数据，我们设计了一个基于嵌入的替代训练方法来提高性能。对公共和私人数据集的比较实验和详细的分析表明，我们提出的方法有效地缓解了延迟反馈问题，并始终优于以前的最先进的方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Unbiased+Delayed+Feedback+Label+Correction+for+Conversion+Rate+Prediction)|0| +|[Unbiased Delayed Feedback Label Correction for Conversion Rate Prediction](https://doi.org/10.1145/3580305.3599536)|Yifan Wang, Peijie Sun, Min Zhang, Qinglin Jia, Jingjie Li, Shaoping Ma|Tsinghua University; Noah’s Ark Lab, Huawei|Conversion rate prediction is critical to many online applications such as digital display advertising. To capture dynamic data distribution, industrial systems often require retraining models on recent data daily or weekly. However, the delay of conversion behavior usually leads to incorrect labeling, which is called delayed feedback problem. Existing work may fail to introduce the correct information about false negative samples due to data sparsity and dynamic data distribution. To directly introduce the correct feedback label information, we propose an Unbiased delayed feedback Label Correction framework (ULC), which uses an auxiliary model to correct labels for observed negative feedback samples. Firstly, we theoretically prove that the label-corrected loss is an unbiased estimate of the oracle loss using true labels. Then, as there are no ready training data for label correction, counterfactual labeling is used to construct artificial training data. Furthermore, since counterfactual labeling utilizes only partial training data, we design an embedding-based alternative training method to enhance performance. Comparative experiments on both public and private datasets and detailed analyses show that our proposed approach effectively alleviates the delayed feedback problem and consistently outperforms the previous state-of-the-art methods.|转化率预测是许多在线应用程序，如数字显示广告的关键。为了捕获动态数据分布，工业系统通常需要每天或每周对最近的数据进行再训练。然而，转换行为的延迟通常会导致不正确的标记，这就是所谓的延迟反馈问题。由于数据稀疏和数据分布的动态性，现有的工作可能无法引入正确的假阴性样本信息。为了直接引入正确的反馈标签信息，我们提出了一种无偏的延迟反馈标签校正框架(ULC) ，它使用一个辅助模型对观测到的负反馈样本进行标签校正。首先，我们从理论上证明了标签校正损失是使用真实标签对甲骨文损失进行的无偏估计。然后，由于没有现成的训练数据用于标签校正，采用反事实标注来构造人工训练数据。此外，由于反事实标注只利用部分训练数据，我们设计了一个基于嵌入的替代训练方法来提高性能。对公共和私人数据集的比较实验和详细的分析表明，我们提出的方法有效地缓解了延迟反馈问题，并始终优于以前的最先进的方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Unbiased+Delayed+Feedback+Label+Correction+for+Conversion+Rate+Prediction)|0| |[PrefRec: Recommender Systems with Human Preferences for Reinforcing Long-term User Engagement](https://doi.org/10.1145/3580305.3599473)|Wanqi Xue, Qingpeng Cai, Zhenghai Xue, Shuo Sun, Shuchang Liu, Dong Zheng, Peng Jiang, Kun Gai, Bo An||Current advances in recommender systems have been remarkably successful in optimizing immediate engagement. However, long-term user engagement, a more desirable performance metric, remains difficult to improve. Meanwhile, recent reinforcement learning (RL) algorithms have shown their effectiveness in a variety of long-term goal optimization tasks. For this reason, RL is widely considered as a promising framework for optimizing long-term user engagement in recommendation. Though promising, the application of RL heavily relies on well-designed rewards, but designing rewards related to long-term user engagement is quite difficult. To mitigate the problem, we propose a novel paradigm, recommender systems with human preferences (or Preference-based Recommender systems), which allows RL recommender systems to learn from preferences about users' historical behaviors rather than explicitly defined rewards. Such preferences are easily accessible through techniques such as crowdsourcing, as they do not require any expert knowledge. With PrefRec, we can fully exploit the advantages of RL in optimizing long-term goals, while avoiding complex reward engineering. PrefRec uses the preferences to automatically train a reward function in an end-to-end manner. The reward function is then used to generate learning signals to train the recommendation policy. Furthermore, we design an effective optimization method for PrefRec, which uses an additional value function, expectile regression and reward model pre-training to improve the performance. We conduct experiments on a variety of long-term user engagement optimization tasks. The results show that PrefRec significantly outperforms previous state-of-the-art methods in all the tasks.|目前在推荐系统方面取得的进展在优化即时接触方面取得了显著的成功。然而，长期用户参与(一个更理想的性能指标)仍然难以改进。与此同时，最近的强化学习算法已经证明了它们在各种长期目标优化任务中的有效性。由于这个原因，RL 被广泛认为是一个优化长期用户参与推荐的有前途的框架。尽管 RL 的应用前景看好，但它严重依赖于精心设计的奖励，但设计与长期用户参与相关的奖励却相当困难。为了缓解这一问题，我们提出了一种新的范式，即具有人类偏好的推荐系统(或基于偏好的推荐系统) ，它允许 RL 推荐系统从用户的历史行为偏好中学习，而不是明确定义奖励。这些偏好通过诸如众包等技术很容易获得，因为它们不需要任何专业知识。使用 PrefRec，我们可以充分发挥 RL 在优化长期目标方面的优势，同时避免复杂的报酬工程。PrefRec 使用首选项以端到端的方式自动训练奖励函数。然后利用奖励函数生成学习信号对推荐策略进行训练。在此基础上，设计了一种有效的 PrefRec 优化方法，该方法利用附加值函数、期望回归和奖励模型预训练来提高系统的性能。我们对各种长期的用户参与优化任务进行了实验。结果表明，PrefRec 在所有任务中都明显优于以前的最先进的方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=PrefRec:+Recommender+Systems+with+Human+Preferences+for+Reinforcing+Long-term+User+Engagement)|0| |[Sequence As Genes: An User Behavior Modeling Framework for Fraud Transaction Detection in E-commerce](https://doi.org/10.1145/3580305.3599905)|Ziming Wang, Qianru Wu, Baolin Zheng, Junjie Wang, Kaiyu Huang, Yanjie Shi||With the explosive growth of e-commerce, detecting fraudulent transactions in real-world scenarios is becoming increasingly important for e-commerce platforms. Recently, several supervised approaches have been proposed to use user behavior sequences, which record the user's track on platforms and contain rich information for fraud transaction detection. Nevertheless, these methods always suffer from the scarcity of labeled data in real-world scenarios. The recent remarkable pre-training methods in Natural Language Processing (NLP) and Computer Vision (CV) domains offered glimmers of light. However, user behavior sequences differ intrinsically from text, images, and videos. In this paper, we propose a novel and general user behavior pre-training framework, named Sequence As GEnes (SAGE), which provides a new perspective for user behavior modeling. Following the inspiration of treating sequences as genes, we carefully designed the user behavior data organization paradigm and pre-training scheme. Specifically, we propose an efficient data organization paradigm inspired by the nature of DNA expression, which decouples the length of behavior sequences and the corresponding time spans. Also inspired by the natural mechanisms in genetics, we propose two pre-training tasks, namely sequential mutation and sequential recombination, to improve the robustness and consistency of user behavior representations in complicated real-world scenes. Extensive experiments on four differentiated fraud transaction detection real scenarios demonstrate the effectiveness of our proposed framework.|随着电子商务的爆炸式增长，在现实世界中检测欺诈交易对于电子商务平台变得越来越重要。最近，人们提出了几种监督方法来利用用户行为序列来记录用户在平台上的行为轨迹，并包含丰富的欺诈交易检测信息。然而，这些方法总是受到缺乏标记的数据在现实世界的情况下。最近在自然语言处理(NLP)和计算机视觉(CV)领域显著的预训练方法提供了一线曙光。然而，用户行为序列与文本、图像和视频本质上是不同的。本文提出了一种新的通用的用户行为预训练框架，即序列作为基因(SAGE) ，为用户行为建模提供了一个新的视角。在将序列视为基因的启示下，我们精心设计了用户行为数据组织范式和预训练方案。具体来说，我们提出了一种受 DNA 表达本质启发的高效数据组织范式，它解耦了行为序列的长度和相应的时间跨度。受遗传学中自然机制的启发，我们提出了两个预训练任务，即序列变异和序列重组，以提高用户行为表征在复杂现实场景中的鲁棒性和一致性。在四种不同的欺诈交易检测实际场景中进行的大量实验证明了我们提出的框架的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Sequence+As+Genes:+An+User+Behavior+Modeling+Framework+for+Fraud+Transaction+Detection+in+E-commerce)|0| |[TransAct: Transformer-based Realtime User Action Model for Recommendation at Pinterest](https://doi.org/10.1145/3580305.3599918)|Xue Xia, Pong Eksombatchai, Nikil Pancha, Dhruvil Deven Badani, PoWei Wang, Neng Gu, Saurabh Vishwas Joshi, Nazanin Farahpour, Zhiyuan Zhang, Andrew Zhai|Pinterest|Sequential models that encode user activity for next action prediction have become a popular design choice for building web-scale personalized recommendation systems. Traditional methods of sequential recommendation either utilize end-to-end learning on realtime user actions, or learn user representations separately in an offline batch-generated manner. This paper (1) presents Pinterest's ranking architecture for Homefeed, our personalized recommendation product and the largest engagement surface; (2) proposes TransAct, a sequential model that extracts users' short-term preferences from their realtime activities; (3) describes our hybrid approach to ranking, which combines end-to-end sequential modeling via TransAct with batch-generated user embeddings. The hybrid approach allows us to combine the advantages of responsiveness from learning directly on realtime user activity with the cost-effectiveness of batch user representations learned over a longer time period. We describe the results of ablation studies, the challenges we faced during productionization, and the outcome of an online A/B experiment, which validates the effectiveness of our hybrid ranking model. We further demonstrate the effectiveness of TransAct on other surfaces such as contextual recommendations and search. Our model has been deployed to production in Homefeed, Related Pins, Notifications, and Search at Pinterest.|为下一步行动预测编码用户活动的序列模型已成为建立网络规模个性化推荐系统的流行设计选择。传统的顺序推荐方法要么利用实时用户操作的端到端学习，要么以离线批量生成的方式单独学习用户表示。本文(1)介绍了 Pinterest 针对 Homefeed 的排名体系结构，这是我们的个性化推荐产品，也是最大的参与表面; (2)提出了 TransAct，一个从用户的实时活动中提取用户短期偏好的顺序模型; (3)描述了我们的混合排名方法，它结合了通过 TransAct 的端到端顺序建模和批量生成的用户嵌入。这种混合方法使我们能够将直接学习实时用户活动的响应性优势与长期学习的批量用户表示的成本效益结合起来。我们描述了烧蚀研究的结果，我们在生产过程中面临的挑战，以及一个在线 A/B 实验的结果，它验证了我们的混合排序模型的有效性。我们进一步展示了 TransAct 在上下文推荐和搜索等其他表面上的有效性。我们的模型已经部署到 Homefeed 的生产环境中，相关的 Pins，通知，和在 Pinterest 上的搜索。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=TransAct:+Transformer-based+Realtime+User+Action+Model+for+Recommendation+at+Pinterest)|0| |[A Personalized Automated Bidding Framework for Fairness-aware Online Advertising](https://doi.org/10.1145/3580305.3599765)|Haoqi Zhang, Lvyin Niu, Zhenzhe Zheng, Zhilin Zhang, Shan Gu, Fan Wu, Chuan Yu, Jian Xu, Guihai Chen, Bo Zheng||Powered by machine learning techniques, online advertising platforms have launched various automated bidding strategy services to facilitate intelligent decision-making for advertisers. However, advertisers experience heterogeneous advertising environments, and thus the unified bidding strategies widely used in both academia and industry suffer from severe unfairness issues, resulting in significant ad performance disparity among advertisers. In this work, to resolve the unfairness issue and improve the overall system performance, we propose a personalized automated bidding framework, namely PerBid, shifting the classical automated bidding strategy with a unified agent to multiple context-aware agents corresponding to different advertiser clusters. Specifically, we first design an ad campaign profiling network to model dynamic advertising environments. By clustering the advertisers with similar profiles and generating context-aware automated bidding agents for each cluster, we can match advertisers with personalized automated bidding strategies. Experiments conducted on the real-world dataset and online A/B test on Alibaba display advertising platform demonstrate the effectiveness of PerBid in improving overall ad performance and guaranteeing fairness among heterogeneous advertisers.|在机器学习技术的推动下，在线广告平台推出了各种自动化投标策略服务，以促进广告商的智能决策。然而，广告主所处的广告环境是异质的，因此学术界和业界广泛采用的统一投标策略存在着严重的不公平问题，造成了广告主之间广告绩效的显著差异。为了解决不公平问题，提高系统的整体性能，本文提出了一个个性化的自动投标框架 PerBid，将传统的统一代理的自动投标策略转化为对应于不同广告客户集群的多上下文感知代理的自动投标策略。具体来说，我们首先设计一个广告活动分析网络来模拟动态的广告环境。通过对具有相似特征的广告主进行聚类，并为每个聚类生成上下文感知的自动投标代理，我们可以将广告主与个性化的自动投标策略进行匹配。在阿里巴巴展示广告平台上对现实世界的数据集和在线 A/B 测试进行的实验表明，perBid 在提高整体广告效果和保证不同广告商之间的公平性方面是有效的。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Personalized+Automated+Bidding+Framework+for+Fairness-aware+Online+Advertising)|0| |[Privacy Matters: Vertical Federated Linear Contextual Bandits for Privacy Protected Recommendation](https://doi.org/10.1145/3580305.3599475)|Zeyu Cao, Zhipeng Liang, Bingzhe Wu, Shu Zhang, Hangyu Li, Ouyang Wen, Yu Rong, Peilin Zhao||Recent awareness of privacy protection and compliance requirement resulted in a controversial view of recommendation system due to personal data usage. Therefore, privacy-protected recommendation emerges as a novel research direction. In this paper, we first formulate this problem as a vertical federated learning problem, i.e., features are vertically distributed over different departments. We study a contextual bandit learning problem for recommendation in the vertical federated setting. To this end, we carefully design a customized encryption scheme named orthogonal matrix-based mask mechanism (O3M). O3M mechanism, a tailored component for contextual bandits by carefully exploiting their shared structure, can ensure privacy protection while avoiding expensive conventional cryptographic techniques. We further apply the mechanism to two commonly-used bandit algorithms, LinUCB and LinTS, and instantiate two practical protocols for online recommendation. The proposed protocols can perfectly recover the service quality of centralized bandit algorithms while achieving a satisfactory runtime efficiency, which is theoretically proved and analysed in this paper. By conducting extensive experiments on both synthetic and real-world datasets, we show the superiority of the proposed method in terms of privacy protection and recommendation performance.|由于个人数据的使用，最近对隐私保护和遵守要求的认识导致对推荐系统的看法存在争议。因此，隐私保护推荐成为一个新的研究方向。在本文中，我们首先将这个问题表述为一个垂直联合学习问题，即特征在不同部门之间垂直分布。本文研究了垂直联邦环境下的上下文强盗学习推荐问题。为此，我们精心设计了一个定制的加密方案——基于正交矩阵的掩码机制(O3M)。O3M 机制是一种为上下文盗版者量身定制的组件，通过仔细利用它们的共享结构，可以在避免昂贵的传统密码技术的同时确保隐私保护。我们进一步将该机制应用于两个常用的盗贼算法 LinUCB 和 LinTS，并实例化了两个实用的在线推荐协议。本文从理论上证明和分析了所提出的协议能够很好地恢复集中式抢劫算法的服务质量，同时获得满意的运行效率。通过对合成数据集和真实数据集的大量实验，我们证明了该方法在隐私保护和推荐性能方面的优越性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Privacy+Matters:+Vertical+Federated+Linear+Contextual+Bandits+for+Privacy+Protected+Recommendation)|0| |[Approximation Algorithms for Size-Constrained Non-Monotone Submodular Maximization in Deterministic Linear Time](https://doi.org/10.1145/3580305.3599259)|Yixin Chen, Alan Kuhnle||In this work, we study the problem of finding the maximum value of a non-negative submodular function subject to a limit on the number of items selected, a ubiquitous problem that appears in many applications, such as data summarization and nonlinear regression. We provide the first deterministic, linear-time approximation algorithms for this problem that do not assume the objective is monotone. We present three deterministic, linear-time algorithms: a single-pass streaming algorithm with a ratio of 23.313 + ε, which is the first linear-time streaming algorithm; a simpler deterministic linear-time algorithm with a ratio of 11.657; and a (4 + O(ε))-approximation algorithm. Finally, we present a deterministic algorithm that obtains ratio of e + ε in O_ε (n log(n)) time, close to the best known expected ratio of e - 0.121 in polynomial time.|在这项工作中，我们研究的问题是找到一个非负子模函数的最大值受到选择的项目数量的限制，这是一个普遍存在的问题，在许多应用中出现，如数据汇总和非线性回归。我们提供了这个问题的第一个确定性，线性时间近似算法，不假设目标是单调的。我们提出了三个确定性的线性时间算法: 一个比率为23.313 + ε 的单通道流式算法，这是第一个线性时间流式算法; 一个比率为11.657的简单的确定性线性时间算法; 和一个(4 + o (ε))-近似演算法。最后，我们给出了一个确定性算法，在 O _ ε (n log (n))时间内得到 e + ε 的比率，接近于多项式时间内 e-0.121的最佳期望比率。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Approximation+Algorithms+for+Size-Constrained+Non-Monotone+Submodular+Maximization+in+Deterministic+Linear+Time)|0| -|[Constraint-aware and Ranking-distilled Token Pruning for Efficient Transformer Inference](https://doi.org/10.1145/3580305.3599284)|Junyan Li, Li Lyna Zhang, Jiahang Xu, Yujing Wang, Shaoguang Yan, Yunqing Xia, Yuqing Yang, Ting Cao, Hao Sun, Weiwei Deng, Qi Zhang, Mao Yang|Zhejiang University; Microsoft Research; Microsoft|Deploying pre-trained transformer models like BERT on downstream tasks in resource-constrained scenarios is challenging due to their high inference cost, which grows rapidly with input sequence length. In this work, we propose a constraint-aware and ranking-distilled token pruning method ToP, which selectively removes unnecessary tokens as input sequence passes through layers, allowing the model to improve online inference speed while preserving accuracy. ToP overcomes the limitation of inaccurate token importance ranking in the conventional self-attention mechanism through a ranking-distilled token distillation technique, which distills effective token rankings from the final layer of unpruned models to early layers of pruned models. Then, ToP introduces a coarse-to-fine pruning approach that automatically selects the optimal subset of transformer layers and optimizes token pruning decisions within these layers through improved $L_0$ regularization. Extensive experiments on GLUE benchmark and SQuAD tasks demonstrate that ToP outperforms state-of-the-art token pruning and model compression methods with improved accuracy and speedups. ToP reduces the average FLOPs of BERT by 8.1x while achieving competitive accuracy on GLUE, and provides a real latency speedup of up to 7.4x on an Intel CPU.|在资源受限的情况下，在下游任务中部署像 BERT 这样的预先训练的变压器模型是具有挑战性的，因为它们的推理成本很高，并且随着输入序列长度的增长而迅速增长。本文提出了一种基于约束和排序的令牌剪枝方法 TOP，该方法在输入序列通过层的同时选择性地去除不必要的令牌，使模型在保持精度的同时提高了在线推理速度。TOP 通过排序-提取令牌精馏技术克服了传统自注意机制中不准确的令牌重要性排序的局限性，该技术将有效的令牌排序从未修剪模型的最后一层提取到修剪模型的早期层。然后，TOP 引入了一种从粗到精的剪枝方法，该方法自动选择变压器层的最优子集，并通过改进的 $L _ 0 $正则化来优化这些变压器层内的令牌剪枝决策。在 GLUE 基准测试和 SQuAD 任务上的大量实验表明，ToP 优于最先进的令牌剪枝和模型压缩方法，具有更高的准确性和加速性。在 GLUE 上，TOP 减少了 BERT 的平均 FLOP 8.1 x，同时实现了具有竞争力的准确性，并且在 Intel CPU 上提供了高达7.4 x 的实际延迟加速。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Constraint-aware+and+Ranking-distilled+Token+Pruning+for+Efficient+Transformer+Inference)|0| -|[Learning Balanced Tree Indexes for Large-Scale Vector Retrieval](https://doi.org/10.1145/3580305.3599406)|Wuchao Li, Chao Feng, Defu Lian, Yuxin Xie, Haifeng Liu, Yong Ge, Enhong Chen|University of Arizona; Guangdong OPPO Mobile Telecommunications Corp., Ltd; University of Science and Technology of China|Vector retrieval focuses on finding the k-nearest neighbors from a bunch of data points, and is widely used in a diverse set of areas such as information retrieval and recommender system. The current state-of-the-art methods represented by HNSW usually generate indexes with a big memory footprint, restricting the scale of data they can handle, except resorting to a hybrid index with external storage. The space-partitioning learned indexes, which only occupy a small memory, have made great breakthroughs in recent years. However, these methods rely on a large amount of labeled data for supervised learning, so model complexity affects the generalization. To this end, we propose a lightweight learnable hierarchical space partitioning index based on a balanced K-ary tree, called BAlanced Tree Learner (BATL), where the same bucket of data points are represented by a path from the root to the corresponding leaf. Instead of mapping each query into a bucket, BATL classifies it into a sequence of branches (i.e. a path), which drastically reduces the number of classes and potentially improves generalization. BATL updates the classifier and the balanced tree in an alternating way. When updating the classifier, we innovatively leverage the sequence-to-sequence learning paradigm for learning to route each query into the ground-truth leaf on the balanced tree. Retrieval is then boiled down into a sequence (i.e. path) generation task, which can be simply achieved by beam search on the encoder-decoder. When updating a balanced tree, we apply the classifier for navigating each data point into the tree nodes layer by layer under the balance constraints. We finally evaluate BATL with several large-scale vector datasets, where the experimental results show the superiority of the proposed method to the SOTA baselines in the tradeoff among latency, accuracy, and memory cost.|矢量检索主要从一系列数据点中寻找 k 个最近的邻居，广泛应用于信息检索和推荐系统等不同的领域。以 HNSW 为代表的当前最先进的方法通常生成内存占用量很大的索引，这限制了它们可以处理的数据规模，只能求助于带有外部存储的混合索引。空间划分学习指标只占用很小的内存，近年来取得了很大的突破。然而，这些方法依赖于大量的标记数据作为监督式学习，所以模型的复杂性会影响泛化。为此，我们提出了一个轻量级的可学习的层次化空间分割索引，它基于一个平衡的 k 元树，称为平衡树学习器(balancedtree Learner，BATL) ，其中相同的一桶数据点由一条从根到相应叶子的路径表示。BATL 没有将每个查询映射到一个 bucket 中，而是将其分类到一个分支序列(即路径)中，这极大地减少了类的数量，并有可能提高泛化能力。BATL 以交替的方式更新分类器和平衡树。在更新分类器时，我们创新地利用序列到序列学习范式来学习将每个查询路由到平衡树上的地面真相叶子。然后，检索被归结为一个序列(即路径)生成任务，这可以简单地通过在编码器-解码器上进行波束搜索来实现。在更新平衡树时，我们应用分类器在平衡约束下将每个数据点逐层导航到树节点。最后利用几个大规模矢量数据集对 BATL 进行了评估，实验结果表明该方法在时延、准确性和内存消耗等方面优于 SOTA 基线。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Learning+Balanced+Tree+Indexes+for+Large-Scale+Vector+Retrieval)|0| -|[Generative Flow Network for Listwise Recommendation](https://doi.org/10.1145/3580305.3599364)|Shuchang Liu, Qingpeng Cai, Zhankui He, Bowen Sun, Julian J. McAuley, Dong Zheng, Peng Jiang, Kun Gai|Kuaishou Technology; Peking University; Unaffiliated; University of California, San Diego|Personalized recommender systems fulfill the daily demands of customers and boost online businesses. The goal is to learn a policy that can generate a list of items that matches the user's demand or interest. While most existing methods learn a pointwise scoring model that predicts the ranking score of each individual item, recent research shows that the listwise approach can further improve the recommendation quality by modeling the intra-list correlations of items that are exposed together. This has motivated the recent list reranking and generative recommendation approaches that optimize the overall utility of the entire list. However, it is challenging to explore the combinatorial space of list actions and existing methods that use cross-entropy loss may suffer from low diversity issues. In this work, we aim to learn a policy that can generate sufficiently diverse item lists for users while maintaining high recommendation quality. The proposed solution, GFN4Rec, is a generative method that takes the insight of the flow network to ensure the alignment between list generation probability and its reward. The key advantages of our solution are the log scale reward matching loss that intrinsically improves the generation diversity and the autoregressive item selection model that captures the item mutual influences while capturing future reward of the list. As validation of our method's effectiveness and its superior diversity during active exploration, we conduct experiments on simulated online environments as well as an offline evaluation framework for two real-world datasets.|个性化推荐系统满足了客户的日常需求，促进了在线业务的发展。目标是学习一种策略，该策略可以生成符合用户需求或兴趣的项目列表。虽然大多数现有的方法学习点式评分模型，预测每个单独的项目的排名得分，最近的研究表明，列表式方法可以进一步提高推荐质量的建模内列表相关性的项目是暴露在一起。这推动了最近的名单重新排名和生成性建议方法，优化了整个名单的总体效用。然而，探索列表行为的组合空间是一个挑战，现有的使用交叉熵损失的方法可能会遇到低多样性问题。在这项工作中，我们的目标是学习一个政策，可以产生足够多样化的项目清单的用户，同时保持高质量的推荐。所提出的解决方案 GFN4Rec 是一种生成方法，它利用流网络的洞察力来确保列表生成概率与其报酬之间的一致性。该解决方案的主要优点是对数规模的奖励匹配损失，本质上改善了生成多样性和自回归项选择模型，捕捉项目的相互影响，同时捕捉未来的奖励列表。为了验证我们的方法在主动勘探过程中的有效性和优越的多样性，我们在模拟的在线环境中进行了实验，并对两个真实世界的数据集进行了离线评估框架。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Generative+Flow+Network+for+Listwise+Recommendation)|0| +|[Constraint-aware and Ranking-distilled Token Pruning for Efficient Transformer Inference](https://doi.org/10.1145/3580305.3599284)|Junyan Li, Li Lyna Zhang, Jiahang Xu, Yujing Wang, Shaoguang Yan, Yunqing Xia, Yuqing Yang, Ting Cao, Hao Sun, Weiwei Deng, Qi Zhang, Mao Yang|Microsoft; Microsoft Research; Zhejiang University|Deploying pre-trained transformer models like BERT on downstream tasks in resource-constrained scenarios is challenging due to their high inference cost, which grows rapidly with input sequence length. In this work, we propose a constraint-aware and ranking-distilled token pruning method ToP, which selectively removes unnecessary tokens as input sequence passes through layers, allowing the model to improve online inference speed while preserving accuracy. ToP overcomes the limitation of inaccurate token importance ranking in the conventional self-attention mechanism through a ranking-distilled token distillation technique, which distills effective token rankings from the final layer of unpruned models to early layers of pruned models. Then, ToP introduces a coarse-to-fine pruning approach that automatically selects the optimal subset of transformer layers and optimizes token pruning decisions within these layers through improved $L_0$ regularization. Extensive experiments on GLUE benchmark and SQuAD tasks demonstrate that ToP outperforms state-of-the-art token pruning and model compression methods with improved accuracy and speedups. ToP reduces the average FLOPs of BERT by 8.1x while achieving competitive accuracy on GLUE, and provides a real latency speedup of up to 7.4x on an Intel CPU.|在资源受限的情况下，在下游任务中部署像 BERT 这样的预先训练的变压器模型是具有挑战性的，因为它们的推理成本很高，并且随着输入序列长度的增长而迅速增长。本文提出了一种基于约束和排序的令牌剪枝方法 TOP，该方法在输入序列通过层的同时选择性地去除不必要的令牌，使模型在保持精度的同时提高了在线推理速度。TOP 通过排序-提取令牌精馏技术克服了传统自注意机制中不准确的令牌重要性排序的局限性，该技术将有效的令牌排序从未修剪模型的最后一层提取到修剪模型的早期层。然后，TOP 引入了一种从粗到精的剪枝方法，该方法自动选择变压器层的最优子集，并通过改进的 $L _ 0 $正则化来优化这些变压器层内的令牌剪枝决策。在 GLUE 基准测试和 SQuAD 任务上的大量实验表明，ToP 优于最先进的令牌剪枝和模型压缩方法，具有更高的准确性和加速性。在 GLUE 上，TOP 减少了 BERT 的平均 FLOP 8.1 x，同时实现了具有竞争力的准确性，并且在 Intel CPU 上提供了高达7.4 x 的实际延迟加速。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Constraint-aware+and+Ranking-distilled+Token+Pruning+for+Efficient+Transformer+Inference)|0| +|[Learning Balanced Tree Indexes for Large-Scale Vector Retrieval](https://doi.org/10.1145/3580305.3599406)|Wuchao Li, Chao Feng, Defu Lian, Yuxin Xie, Haifeng Liu, Yong Ge, Enhong Chen|University of Science and Technology of China; Guangdong OPPO Mobile Telecommunications Corp., Ltd; University of Arizona|Vector retrieval focuses on finding the k-nearest neighbors from a bunch of data points, and is widely used in a diverse set of areas such as information retrieval and recommender system. The current state-of-the-art methods represented by HNSW usually generate indexes with a big memory footprint, restricting the scale of data they can handle, except resorting to a hybrid index with external storage. The space-partitioning learned indexes, which only occupy a small memory, have made great breakthroughs in recent years. However, these methods rely on a large amount of labeled data for supervised learning, so model complexity affects the generalization. To this end, we propose a lightweight learnable hierarchical space partitioning index based on a balanced K-ary tree, called BAlanced Tree Learner (BATL), where the same bucket of data points are represented by a path from the root to the corresponding leaf. Instead of mapping each query into a bucket, BATL classifies it into a sequence of branches (i.e. a path), which drastically reduces the number of classes and potentially improves generalization. BATL updates the classifier and the balanced tree in an alternating way. When updating the classifier, we innovatively leverage the sequence-to-sequence learning paradigm for learning to route each query into the ground-truth leaf on the balanced tree. Retrieval is then boiled down into a sequence (i.e. path) generation task, which can be simply achieved by beam search on the encoder-decoder. When updating a balanced tree, we apply the classifier for navigating each data point into the tree nodes layer by layer under the balance constraints. We finally evaluate BATL with several large-scale vector datasets, where the experimental results show the superiority of the proposed method to the SOTA baselines in the tradeoff among latency, accuracy, and memory cost.|矢量检索主要从一系列数据点中寻找 k 个最近的邻居，广泛应用于信息检索和推荐系统等不同的领域。以 HNSW 为代表的当前最先进的方法通常生成内存占用量很大的索引，这限制了它们可以处理的数据规模，只能求助于带有外部存储的混合索引。空间划分学习指标只占用很小的内存，近年来取得了很大的突破。然而，这些方法依赖于大量的标记数据作为监督式学习，所以模型的复杂性会影响泛化。为此，我们提出了一个轻量级的可学习的层次化空间分割索引，它基于一个平衡的 k 元树，称为平衡树学习器(balancedtree Learner，BATL) ，其中相同的一桶数据点由一条从根到相应叶子的路径表示。BATL 没有将每个查询映射到一个 bucket 中，而是将其分类到一个分支序列(即路径)中，这极大地减少了类的数量，并有可能提高泛化能力。BATL 以交替的方式更新分类器和平衡树。在更新分类器时，我们创新地利用序列到序列学习范式来学习将每个查询路由到平衡树上的地面真相叶子。然后，检索被归结为一个序列(即路径)生成任务，这可以简单地通过在编码器-解码器上进行波束搜索来实现。在更新平衡树时，我们应用分类器在平衡约束下将每个数据点逐层导航到树节点。最后利用几个大规模矢量数据集对 BATL 进行了评估，实验结果表明该方法在时延、准确性和内存消耗等方面优于 SOTA 基线。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Learning+Balanced+Tree+Indexes+for+Large-Scale+Vector+Retrieval)|0| +|[Generative Flow Network for Listwise Recommendation](https://doi.org/10.1145/3580305.3599364)|Shuchang Liu, Qingpeng Cai, Zhankui He, Bowen Sun, Julian J. McAuley, Dong Zheng, Peng Jiang, Kun Gai|Unaffiliated; Kuaishou Technology; Peking University; University of California, San Diego|Personalized recommender systems fulfill the daily demands of customers and boost online businesses. The goal is to learn a policy that can generate a list of items that matches the user's demand or interest. While most existing methods learn a pointwise scoring model that predicts the ranking score of each individual item, recent research shows that the listwise approach can further improve the recommendation quality by modeling the intra-list correlations of items that are exposed together. This has motivated the recent list reranking and generative recommendation approaches that optimize the overall utility of the entire list. However, it is challenging to explore the combinatorial space of list actions and existing methods that use cross-entropy loss may suffer from low diversity issues. In this work, we aim to learn a policy that can generate sufficiently diverse item lists for users while maintaining high recommendation quality. The proposed solution, GFN4Rec, is a generative method that takes the insight of the flow network to ensure the alignment between list generation probability and its reward. The key advantages of our solution are the log scale reward matching loss that intrinsically improves the generation diversity and the autoregressive item selection model that captures the item mutual influences while capturing future reward of the list. As validation of our method's effectiveness and its superior diversity during active exploration, we conduct experiments on simulated online environments as well as an offline evaluation framework for two real-world datasets.|个性化推荐系统满足了客户的日常需求，促进了在线业务的发展。目标是学习一种策略，该策略可以生成符合用户需求或兴趣的项目列表。虽然大多数现有的方法学习点式评分模型，预测每个单独的项目的排名得分，最近的研究表明，列表式方法可以进一步提高推荐质量的建模内列表相关性的项目是暴露在一起。这推动了最近的名单重新排名和生成性建议方法，优化了整个名单的总体效用。然而，探索列表行为的组合空间是一个挑战，现有的使用交叉熵损失的方法可能会遇到低多样性问题。在这项工作中，我们的目标是学习一个政策，可以产生足够多样化的项目清单的用户，同时保持高质量的推荐。所提出的解决方案 GFN4Rec 是一种生成方法，它利用流网络的洞察力来确保列表生成概率与其报酬之间的一致性。该解决方案的主要优点是对数规模的奖励匹配损失，本质上改善了生成多样性和自回归项选择模型，捕捉项目的相互影响，同时捕捉未来的奖励列表。为了验证我们的方法在主动勘探过程中的有效性和优越的多样性，我们在模拟的在线环境中进行了实验，并对两个真实世界的数据集进行了离线评估框架。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Generative+Flow+Network+for+Listwise+Recommendation)|0| |[Hyper-USS: Answering Subset Query Over Multi-Attribute Data Stream](https://doi.org/10.1145/3580305.3599383)|Ruijie Miao, Yiyao Zhang, Guanyu Qu, Kaicheng Yang, Tong Yang, Bin Cui||Sketching algorithms are considered as promising solutions for answering approximate query on massive data stream. In real scenarios, a large number of problems can be abstracted as subset query over multiple attributes. Existing sketches are designed for query on single attributes, and therefore are inefficient for query on multiple attributes. In this work, we propose Hyper-USS, an innovative sketching algorithm that supports subset query over multiple attributes accurately and efficiently. To the best of our knowledge, this work is the first sketching algorithm designed to answer approximate query over multi-attribute data stream. We utilize the key technique, Joint Variance Optimization, to guarantee high estimation accuracy on all attributes. Experiment results show that, compared with the state-of-the-art (SOTA) sketches that support subset query on single attributes, Hyper-USS improves the accuracy by 16.67X and the throughput by 8.54X. The code is open-sourced at Github.|草图算法被认为是解决海量数据流中近似查询问题的有效方法。在实际场景中，大量的问题可以抽象为对多个属性的子集查询。现有的草图是为单个属性的查询而设计的，因此对于多个属性的查询效率很低。在这项工作中，我们提出了一个新颖的草图算法 Hyper-USS，它能够准确而有效地支持多属性的子集查询。据我们所知，本文是第一个针对多属性数据流的近似查询而设计的草图算法。我们利用关键技术，联合方差优化，以保证高估计精度的所有属性。实验结果表明，与支持单属性子集查询的 SOTA 草图相比，Hyper-USS 算法的精度提高了16.67 X，吞吐量提高了8.54 X。这些代码在 Github 是开源的。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Hyper-USS:+Answering+Subset+Query+Over+Multi-Attribute+Data+Stream)|0| |[Reconsidering Learning Objectives in Unbiased Recommendation: A Distribution Shift Perspective](https://doi.org/10.1145/3580305.3599487)|Teng Xiao, Zhengyu Chen, Suhang Wang||This work studies the problem of learning unbiased algorithms from biased feedback for recommendation. We address this problem from a novel distribution shift perspective. Recent works in unbiased recommendation have advanced the state-of-the-art with various techniques such as re-weighting, multi-task learning, and meta-learning. Despite their empirical successes, most of them lack theoretical guarantees, forming non-negligible gaps between theories and recent algorithms. In this paper, we propose a theoretical understanding of why existing unbiased learning objectives work for unbiased recommendation. We establish a close connection between unbiased recommendation and distribution shift, which shows that existing unbiased learning objectives implicitly align biased training and unbiased test distributions. Built upon this connection, we develop two generalization bounds for existing unbiased learning methods and analyze their learning behavior. Besides, as a result of the distribution shift, we further propose a principled framework, Adversarial Self-Training (AST), for unbiased recommendation. Extensive experiments on real-world and semi-synthetic datasets demonstrate the effectiveness of AST.|本文研究了从有偏反馈中学习无偏推荐算法的问题。我们从一个新的分布转移的角度来解决这个问题。最近的作品在无偏见的推荐已经推进了国家的最先进的各种技术，如重新加权，多任务学习，元学习。尽管他们在经验上取得了成功，但大多数缺乏理论保证，在理论和最近的算法之间形成了不容忽视的差距。在本文中，我们提出了一个理论上的理解，为什么现有的无偏学习目标工作的无偏推荐。我们建立了无偏见的推荐和分布转移之间的密切联系，这表明现有的无偏见的学习目标隐含地调整有偏见的培训和无偏见的测试分布。在此基础上，我们对现有的无偏学习方法进行了两种推广，并分析了它们的学习行为。此外，由于分布转移的结果，我们进一步提出了一个原则性的框架，对抗性自我训练(AST) ，为无偏见的建议。在真实世界和半合成数据集上的大量实验证明了 AST 的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Reconsidering+Learning+Objectives+in+Unbiased+Recommendation:+A+Distribution+Shift+Perspective)|0| |[VQNE: Variational Quantum Network Embedding with Application to Network Alignment](https://doi.org/10.1145/3580305.3599542)|Xinyu Ye, Ge Yan, Junchi Yan|Shanghai Jiao Tong University|Learning of network embedding with vector-based node representation has attracted wide attention over the decade. It differs from the general setting of graph node embedding whereby the node attributes are also considered and yet may incur privacy issues. In this paper, we depart from the classic CPU/GPU architecture to consider the well-established network alignment problem based on network embedding, and develop a quantum machine learning approach with a low qubit cost for its near-future applicability on Noisy Intermediate-Scale Quantum (NISQ) devices. Specifically, our model adopts the discrete-time quantum walk (QW) and conducts the QW on the tailored merged network to extract structure information from the two aligning networks without the need for quantum state preparation which otherwise requires high quantum gate cost. Then the quantum states from QW are fed to a quantum embedding ansatz (i.e., parameterized circuit) to learn the latent representation of each node. The key part of our approach is to connect these two quantum modules to achieve a pure quantum paradigm without involving classical modules. To our best knowledge, there has not been any classic-quantum hybrid approach to network embedding, let alone a pure quantum paradigm being free from the bottleneck of communication between classic devices and quantum devices, which is still an open problem. Experimental results on two real-world datasets show the effectiveness of our quantum embedding approach in comparison with classical embedding approaches. Our model is readily and efficiently implemented in Python with a full-amplitude simulation of the QW and the quantum circuit. Therefore, our model can be readily deployed on an existing NISQ device with all the circuits provided, and only 13 qubits are needed in the experiments, which is rarely attained in existing quantum graph learning works.|基于矢量节点表示的网络嵌入学习近年来受到广泛关注。它不同于图节点嵌入的一般设置，即也考虑节点属性，但可能会引起隐私问题。本文从传统的 CPU/GPU 体系结构出发，考虑了基于网络嵌入的网络对准问题，提出了一种低量子比特代价的量子机器学习方法，并将其应用于噪声中尺度量子(NISQ)器件。具体地说，我们的模型采用离散时间量子行走(QW) ，在量子化的合并网络上进行量子行走，从两个对齐的网络中提取结构信息，而不需要进行量子态准备，否则需要很高的量子门开销。然后将量子态反馈给量子嵌入回路(即参数化电路) ，学习每个节点的潜在表示。我们的方法的关键部分是连接这两个量子模块，以实现一个纯量子范式，而不涉及经典模块。据我们所知，目前还没有任何经典的量子混合方法来实现网络嵌入，更不用说一个纯量子范式能够摆脱经典设备和量子设备之间通信的瓶颈，这仍然是一个悬而未决的问题。在两个实际数据集上的实验结果表明了量子嵌入方法与经典嵌入方法相比的有效性。我们的模型是容易和有效地实现在 Python 与全振幅模拟量子波和量子电路。因此，我们的模型可以很容易地部署在现有的 NISQ 器件上，所提供的所有电路，只需要13个量子位的实验，这是很少达到现有的量子图学习工作。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=VQNE:+Variational+Quantum+Network+Embedding+with+Application+to+Network+Alignment)|0| -|[Debiasing Recommendation by Learning Identifiable Latent Confounders](https://doi.org/10.1145/3580305.3599296)|Qing Zhang, Xiaoying Zhang, Yang Liu, Hongning Wang, Min Gao, Jiheng Zhang, Ruocheng Guo|ByteDance Research; Chongqing University; University of Virginia; Hong Kong University of Science and Technology|Recommendation systems aim to predict users' feedback on items not exposed to them. Confounding bias arises due to the presence of unmeasured variables (e.g., the socio-economic status of a user) that can affect both a user's exposure and feedback. Existing methods either (1) make untenable assumptions about these unmeasured variables or (2) directly infer latent confounders from users' exposure. However, they cannot guarantee the identification of counterfactual feedback, which can lead to biased predictions. In this work, we propose a novel method, i.e., identifiable deconfounder (iDCF), which leverages a set of proxy variables (e.g., observed user features) to resolve the aforementioned non-identification issue. The proposed iDCF is a general deconfounded recommendation framework that applies proximal causal inference to infer the unmeasured confounders and identify the counterfactual feedback with theoretical guarantees. Extensive experiments on various real-world and synthetic datasets verify the proposed method's effectiveness and robustness.|推荐系统旨在预测用户对未接触到的项目的反馈。由于存在不可测量的变量(例如，用户的社会经济地位) ，可以影响用户的曝光和反馈，混淆偏见就会产生。现有的方法要么(1)对这些未测量的变量做出不可靠的假设，要么(2)直接从用户的暴露中推断出潜在的混杂因素。然而，他们不能保证识别反事实反馈，这可能导致偏见的预测。在这项工作中，我们提出了一种新的方法，即可识别的解构者(iDCF) ，它利用一组代理变量(例如，观察到的用户特征)来解决上述非识别问题。提出的 iDCF 是一个通用的解构推荐框架，它应用近因推理来推断不可测量的混杂因素，并用理论保证来识别反事实反馈。在各种真实世界和合成数据集上的大量实验验证了该方法的有效性和鲁棒性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Debiasing+Recommendation+by+Learning+Identifiable+Latent+Confounders)|0| +|[Debiasing Recommendation by Learning Identifiable Latent Confounders](https://doi.org/10.1145/3580305.3599296)|Qing Zhang, Xiaoying Zhang, Yang Liu, Hongning Wang, Min Gao, Jiheng Zhang, Ruocheng Guo|Chongqing University; Hong Kong University of Science and Technology; ByteDance Research; University of Virginia|Recommendation systems aim to predict users' feedback on items not exposed to them. Confounding bias arises due to the presence of unmeasured variables (e.g., the socio-economic status of a user) that can affect both a user's exposure and feedback. Existing methods either (1) make untenable assumptions about these unmeasured variables or (2) directly infer latent confounders from users' exposure. However, they cannot guarantee the identification of counterfactual feedback, which can lead to biased predictions. In this work, we propose a novel method, i.e., identifiable deconfounder (iDCF), which leverages a set of proxy variables (e.g., observed user features) to resolve the aforementioned non-identification issue. The proposed iDCF is a general deconfounded recommendation framework that applies proximal causal inference to infer the unmeasured confounders and identify the counterfactual feedback with theoretical guarantees. Extensive experiments on various real-world and synthetic datasets verify the proposed method's effectiveness and robustness.|推荐系统旨在预测用户对未接触到的项目的反馈。由于存在不可测量的变量(例如，用户的社会经济地位) ，可以影响用户的曝光和反馈，混淆偏见就会产生。现有的方法要么(1)对这些未测量的变量做出不可靠的假设，要么(2)直接从用户的暴露中推断出潜在的混杂因素。然而，他们不能保证识别反事实反馈，这可能导致偏见的预测。在这项工作中，我们提出了一种新的方法，即可识别的解构者(iDCF) ，它利用一组代理变量(例如，观察到的用户特征)来解决上述非识别问题。提出的 iDCF 是一个通用的解构推荐框架，它应用近因推理来推断不可测量的混杂因素，并用理论保证来识别反事实反馈。在各种真实世界和合成数据集上的大量实验验证了该方法的有效性和鲁棒性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Debiasing+Recommendation+by+Learning+Identifiable+Latent+Confounders)|0| |[Hierarchical Invariant Learning for Domain Generalization Recommendation](https://doi.org/10.1145/3580305.3599377)|Zeyu Zhang, Heyang Gao, Hao Yang, Xu Chen||Most cross-domain recommenders require samples on target domains or source-target overlaps to carry out domain adaptation. However, in many real-world situations, target domains are lack of such knowledge. Few works discuss this problem, whose essence is domain generalization recommendation. In this paper, we figure out domain generalization recommendation with a clear symbolized definition and propose corresponding models. Moreover, we illustrate its strong connection with zero-shot recommendation, pretrained recommendation and cold-start recommendation, distinguishing it from content-based recommendation. By analyzing its properties, we propose HIRL^+ and a series of heuristic methods to solve this problem. We propose hierarchical invariant learning to expel the specific patterns in both domain-level and environment-level, and find the common patterns in generalization space. To make the division of environments flexible, fine-grained and balanced, we put forward a learnable environment assignment method. To improve the robustness against distribution shifts inside domain generalization, we present an adversarial environment refinement method. In addition, we conduct experiments on real-word datasets to verify the effectiveness of our models, and carry out further studies on the domain distance and domain diversity. To benefit the research community and promote this direction, we discuss the future of this field.|大多数跨域推荐需要在目标域或源-目标重叠上采样来执行域适配。然而，在许多现实情况下，目标领域缺乏这样的知识。很少有文献讨论这个问题，其实质是领域推广推荐。在本文中，我们给出了一个明确的符号化定义的域泛化推荐，并提出了相应的模型。此外，我们还说明了它与零拍推荐、预训练推荐和冷启动推荐之间的紧密联系，区别于基于内容的推荐。通过分析其性质，提出了 HIRL ^ + 及一系列启发式方法来解决这一问题。我们提出了分层不变式学习来去除领域层和环境层的特定模式，并在泛化空间中找到共同的模式。为了实现环境划分的灵活性、细粒度和均衡性，提出了一种可学习的环境分配方法。为了提高对域内分布移位的鲁棒性，我们提出了一种对抗性环境细化方法。此外，我们还对实际数据集进行了实验，验证了模型的有效性，并对领域距离和领域多样性进行了进一步的研究。为了有利于研究界和促进这一方向，我们讨论了这一领域的未来。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Hierarchical+Invariant+Learning+for+Domain+Generalization+Recommendation)|0| -|[Narrow the Input Mismatch in Deep Graph Neural Network Distillation](https://doi.org/10.1145/3580305.3599442)|Qiqi Zhou, Yanyan Shen, Lei Chen|Shanghai Jiao Tong University; Hong Kong University of Science and Technology|Graph neural networks (GNNs) have been widely studied for modeling graph-structured data. Thanks to the over-parameterization and large receptive field of deep GNNs, "deep" is a promising direction to develop GNNs further and has shown some superior performances. However, the over-stacked structures of deep architectures incur high inference cost in deployment. To compress deep GNNs, we can use knowledge distillation (KD) to make shallow student GNNs mimic teacher GNNs. Existing KD methods in graph domain focus on constructing diverse supervision on embedding or prediction produced by student GNNs, but overlook the gap of the receptive field (i.e., input information) between student and teacher, which brings difficulties to KD. We call this gap "input mismatch". To alleviate this problem, we propose a lightweight stochastic extended module to provide an estimation for missing input information for student GNNs. The estimator models the distribution of missing information. Specifically, we model the missing information as an independent distribution from graph level and a conditional distribution from node level (given the condition of observable input). These two estimates are optimized using a Bayesian methodology and combined into a balanced estimate as additional input to student GNNs. To the best of our knowledge, we are the first to address the "input mismatch" problem in deep GNNs distillation. Experiments on extensive benchmarks demonstrate that our method outperforms existing KD methods for GNNs in distillation performance, which confirms that the estimations are reasonable and effective.|图形神经网络(GNN)在图形结构数据建模中得到了广泛的研究。由于深层 GNN 的过度参数化和较大的接收场，“深层”是 GNN 进一步发展的一个有希望的方向，并已显示出一些优越的性能。但是，深层体系结构的过堆叠结构在部署过程中会产生很高的推理成本。为了压缩深层 GNN，我们可以使用知识提取(KD)来使浅层学生 GNN 模拟教师 GNN。现有的图形领域的 KD 方法侧重于构建对学生 GNN 嵌入或预测的多样化监控，但忽视了学生与教师之间接受域(即输入信息)的差距，给 KD 带来了困难。我们称这种差距为“输入不匹配”。为了解决这个问题，我们提出了一个轻量级的随机扩展模块来估计学生 GNN 丢失的输入信息。估计器模拟缺失信息的分布。具体来说，我们将缺失信息建模为独立于图层的分布和从节点层的条件分布(给定可观测输入的条件)。这两个估计是优化使用贝叶斯方法，并结合成一个平衡的估计作为额外的投入学生 GNN。据我们所知，我们是第一个解决深层 GNN 精馏中的“输入不匹配”问题的。在大量基准上的实验表明，该方法在精馏性能方面优于现有的 KD 方法，证明了该方法的合理性和有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Narrow+the+Input+Mismatch+in+Deep+Graph+Neural+Network+Distillation)|0| -|[Robust Positive-Unlabeled Learning via Noise Negative Sample Self-correction](https://doi.org/10.1145/3580305.3599491)|Zhangchi Zhu, Lu Wang, Pu Zhao, Chao Du, Wei Zhang, Hang Dong, Bo Qiao, Qingwei Lin, Saravan Rajmohan, Dongmei Zhang|Microsoft 365; Microsoft Research; East China Normal University|Learning from positive and unlabeled data is known as positive-unlabeled (PU) learning in literature and has attracted much attention in recent years. One common approach in PU learning is to sample a set of pseudo-negatives from the unlabeled data using ad-hoc thresholds so that conventional supervised methods can be applied with both positive and negative samples. Owing to the label uncertainty among the unlabeled data, errors of misclassifying unlabeled positive samples as negative samples inevitably appear and may even accumulate during the training processes. Those errors often lead to performance degradation and model instability. To mitigate the impact of label uncertainty and improve the robustness of learning with positive and unlabeled data, we propose a new robust PU learning method with a training strategy motivated by the nature of human learning: easy cases should be learned first. Similar intuition has been utilized in curriculum learning to only use easier cases in the early stage of training before introducing more complex cases. Specifically, we utilize a novel ``hardness'' measure to distinguish unlabeled samples with a high chance of being negative from unlabeled samples with large label noise. An iterative training strategy is then implemented to fine-tune the selection of negative samples during the training process in an iterative manner to include more ``easy'' samples in the early stage of training. Extensive experimental validations over a wide range of learning tasks show that this approach can effectively improve the accuracy and stability of learning with positive and unlabeled data. Our code is available at https://github.com/woriazzc/Robust-PU|从阳性和未标记数据中学习被称为阳性-未标记(PU)学习，近年来引起了人们的广泛关注。PU 学习中常用的一种方法是使用自组织阈值从未标记的数据中抽取一组伪阴性样本，这样传统的监督方法就可以同时应用于正样本和负样本。由于未标记数据之间存在标记不确定性，训练过程中不可避免地会出现将未标记阳性样本错误分类为阴性样本的错误，甚至可能累积。这些错误经常导致性能下降和模型不稳定。为了减轻标签不确定性的影响，提高正数和未标签数据学习的鲁棒性，我们提出了一种新的鲁棒性 PU 学习方法，其训练策略受人类学习的本质驱动: 应该首先学习简单的情况。在课程学习中也使用了类似的直觉，即在培训的早期阶段只使用较容易的案例，然后再引入更复杂的案例。具体来说，我们利用一种新的“硬度”测量方法来区分未标记样品与具有较大标记噪声的未标记样品。然后采用迭代训练策略，以迭代的方式对训练过程中的负样本选择进行微调，以便在训练的早期阶段包含更多的“简单”样本。通过对大量学习任务的大量实验验证表明，该方法能够有效地提高正数和未标记数据学习的准确性和稳定性。我们的代码可以在 https://github.com/woriazzc/robust-pu 找到|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Robust+Positive-Unlabeled+Learning+via+Noise+Negative+Sample+Self-correction)|0| +|[Narrow the Input Mismatch in Deep Graph Neural Network Distillation](https://doi.org/10.1145/3580305.3599442)|Qiqi Zhou, Yanyan Shen, Lei Chen|Hong Kong University of Science and Technology; Shanghai Jiao Tong University|Graph neural networks (GNNs) have been widely studied for modeling graph-structured data. Thanks to the over-parameterization and large receptive field of deep GNNs, "deep" is a promising direction to develop GNNs further and has shown some superior performances. However, the over-stacked structures of deep architectures incur high inference cost in deployment. To compress deep GNNs, we can use knowledge distillation (KD) to make shallow student GNNs mimic teacher GNNs. Existing KD methods in graph domain focus on constructing diverse supervision on embedding or prediction produced by student GNNs, but overlook the gap of the receptive field (i.e., input information) between student and teacher, which brings difficulties to KD. We call this gap "input mismatch". To alleviate this problem, we propose a lightweight stochastic extended module to provide an estimation for missing input information for student GNNs. The estimator models the distribution of missing information. Specifically, we model the missing information as an independent distribution from graph level and a conditional distribution from node level (given the condition of observable input). These two estimates are optimized using a Bayesian methodology and combined into a balanced estimate as additional input to student GNNs. To the best of our knowledge, we are the first to address the "input mismatch" problem in deep GNNs distillation. Experiments on extensive benchmarks demonstrate that our method outperforms existing KD methods for GNNs in distillation performance, which confirms that the estimations are reasonable and effective.|图形神经网络(GNN)在图形结构数据建模中得到了广泛的研究。由于深层 GNN 的过度参数化和较大的接收场，“深层”是 GNN 进一步发展的一个有希望的方向，并已显示出一些优越的性能。但是，深层体系结构的过堆叠结构在部署过程中会产生很高的推理成本。为了压缩深层 GNN，我们可以使用知识提取(KD)来使浅层学生 GNN 模拟教师 GNN。现有的图形领域的 KD 方法侧重于构建对学生 GNN 嵌入或预测的多样化监控，但忽视了学生与教师之间接受域(即输入信息)的差距，给 KD 带来了困难。我们称这种差距为“输入不匹配”。为了解决这个问题，我们提出了一个轻量级的随机扩展模块来估计学生 GNN 丢失的输入信息。估计器模拟缺失信息的分布。具体来说，我们将缺失信息建模为独立于图层的分布和从节点层的条件分布(给定可观测输入的条件)。这两个估计是优化使用贝叶斯方法，并结合成一个平衡的估计作为额外的投入学生 GNN。据我们所知，我们是第一个解决深层 GNN 精馏中的“输入不匹配”问题的。在大量基准上的实验表明，该方法在精馏性能方面优于现有的 KD 方法，证明了该方法的合理性和有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Narrow+the+Input+Mismatch+in+Deep+Graph+Neural+Network+Distillation)|0| +|[Robust Positive-Unlabeled Learning via Noise Negative Sample Self-correction](https://doi.org/10.1145/3580305.3599491)|Zhangchi Zhu, Lu Wang, Pu Zhao, Chao Du, Wei Zhang, Hang Dong, Bo Qiao, Qingwei Lin, Saravan Rajmohan, Dongmei Zhang|East China Normal University; Microsoft Research; Microsoft 365|Learning from positive and unlabeled data is known as positive-unlabeled (PU) learning in literature and has attracted much attention in recent years. One common approach in PU learning is to sample a set of pseudo-negatives from the unlabeled data using ad-hoc thresholds so that conventional supervised methods can be applied with both positive and negative samples. Owing to the label uncertainty among the unlabeled data, errors of misclassifying unlabeled positive samples as negative samples inevitably appear and may even accumulate during the training processes. Those errors often lead to performance degradation and model instability. To mitigate the impact of label uncertainty and improve the robustness of learning with positive and unlabeled data, we propose a new robust PU learning method with a training strategy motivated by the nature of human learning: easy cases should be learned first. Similar intuition has been utilized in curriculum learning to only use easier cases in the early stage of training before introducing more complex cases. Specifically, we utilize a novel ``hardness'' measure to distinguish unlabeled samples with a high chance of being negative from unlabeled samples with large label noise. An iterative training strategy is then implemented to fine-tune the selection of negative samples during the training process in an iterative manner to include more ``easy'' samples in the early stage of training. Extensive experimental validations over a wide range of learning tasks show that this approach can effectively improve the accuracy and stability of learning with positive and unlabeled data. Our code is available at https://github.com/woriazzc/Robust-PU|从阳性和未标记数据中学习被称为阳性-未标记(PU)学习，近年来引起了人们的广泛关注。PU 学习中常用的一种方法是使用自组织阈值从未标记的数据中抽取一组伪阴性样本，这样传统的监督方法就可以同时应用于正样本和负样本。由于未标记数据之间存在标记不确定性，训练过程中不可避免地会出现将未标记阳性样本错误分类为阴性样本的错误，甚至可能累积。这些错误经常导致性能下降和模型不稳定。为了减轻标签不确定性的影响，提高正数和未标签数据学习的鲁棒性，我们提出了一种新的鲁棒性 PU 学习方法，其训练策略受人类学习的本质驱动: 应该首先学习简单的情况。在课程学习中也使用了类似的直觉，即在培训的早期阶段只使用较容易的案例，然后再引入更复杂的案例。具体来说，我们利用一种新的“硬度”测量方法来区分未标记样品与具有较大标记噪声的未标记样品。然后采用迭代训练策略，以迭代的方式对训练过程中的负样本选择进行微调，以便在训练的早期阶段包含更多的“简单”样本。通过对大量学习任务的大量实验验证表明，该方法能够有效地提高正数和未标记数据学习的准确性和稳定性。我们的代码可以在 https://github.com/woriazzc/robust-pu 找到|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Robust+Positive-Unlabeled+Learning+via+Noise+Negative+Sample+Self-correction)|0| |[RankFormer: Listwise Learning-to-Rank Using Listwide Labels](https://doi.org/10.1145/3580305.3599892)|Maarten Buyl, Paul Missault, PierreAntoine Sondag|Amazon|Web applications where users are presented with a limited selection of items have long employed ranking models to put the most relevant results first. Any feedback received from users is typically assumed to reflect a relative judgement on the utility of items, e.g. a user clicking on an item only implies it is better than items not clicked in the same ranked list. Hence, the objectives optimized in Learning-to-Rank (LTR) tend to be pairwise or listwise. Yet, by only viewing feedback as relative, we neglect the user's absolute feedback on the list's overall quality, e.g. when no items in the selection are clicked. We thus reconsider the standard LTR paradigm and argue the benefits of learning from this listwide signal. To this end, we propose the RankFormer as an architecture that, with a Transformer at its core, can jointly optimize a novel listwide assessment objective and a traditional listwise LTR objective. We simulate implicit feedback on public datasets and observe that the RankFormer succeeds in benefitting from listwide signals. Additionally, we conduct experiments in e-commerce on Amazon Search data and find the RankFormer to be superior to all baselines offline. An online experiment shows that knowledge distillation can be used to find immediate practical use for the RankFormer.|在 Web 应用程序中，用户只能看到有限的条目，这种情况长期以来一直采用排名模型，将最相关的结果放在第一位。从用户收到的任何反馈通常被认为反映了对项目效用的相对判断，例如，用户点击一个项目只意味着它比没有在同一排名列表中点击的项目要好。因此，学习排名(Learning-to-Rank，LTR)中优化的目标往往是成对的或列表的。然而，由于只把反馈看作是相对的，我们忽略了用户对列表总体质量的绝对反馈，例如，当选择中没有项被点击的时候。因此，我们重新考虑标准的 LTR 范式，并讨论从这个列表范围的信号中学习的好处。为此，我们提出 RankForm 作为一种体系结构，其核心是一个 Transformer，可以联合优化一个新的列表范围评估目标和一个传统的列表式 LTR 目标。我们模拟公共数据集上的隐式反馈，并观察到 RankForm 成功地从列表宽信号中受益。此外，我们在亚马逊搜索数据上进行电子商务实验，发现排名前优于所有离线基线。一个在线实验表明，知识提取可以用来找到直接的实际应用的秩次前。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=RankFormer:+Listwise+Learning-to-Rank+Using+Listwide+Labels)|0| -|[Graph-Based Model-Agnostic Data Subsampling for Recommendation Systems](https://doi.org/10.1145/3580305.3599834)|Xiaohui Chen, Jiankai Sun, Taiqing Wang, Ruocheng Guo, LiPing Liu, Aonan Zhang|Apple Inc.; ByteDance Inc.; ByteDance Research; Tufts University|Data subsampling is widely used to speed up the training of large-scale recommendation systems. Most subsampling methods are model-based and often require a pre-trained pilot model to measure data importance via e.g. sample hardness. However, when the pilot model is misspecified, model-based subsampling methods deteriorate. Since model misspecification is persistent in real recommendation systems, we instead propose model-agnostic data subsampling methods by only exploring input data structure represented by graphs. Specifically, we study the topology of the user-item graph to estimate the importance of each user-item interaction (an edge in the user-item graph) via graph conductance, followed by a propagation step on the network to smooth out the estimated importance value. Since our proposed method is model-agnostic, we can marry the merits of both model-agnostic and model-based subsampling methods. Empirically, we show that combing the two consistently improves over any single method on the used datasets. Experimental results on KuaiRec and MIND datasets demonstrate that our proposed methods achieve superior results compared to baseline approaches.|数据子采样被广泛用于加速大规模推荐系统的训练。大多数次抽样方法是基于模型的，通常需要一个预先训练的试点模型来通过例如样本硬度来测量数据的重要性。然而，当导频模型被错误指定时，基于模型的子抽样方法就会变质。由于模型错误说明在实际推荐系统中一直存在，因此我们提出了模型无关的数据子抽样方法，只是探讨了用图表示的输入数据结构。具体来说，我们研究了用户项目图的拓扑结构，通过图电导来估计每个用户项目交互(用户项目图中的一条边)的重要性，然后通过网络上的传播步骤来平滑估计的重要性值。由于我们提出的方法是模型不可知的，我们可以结合模型不可知和基于模型的子抽样方法的优点。经验上，我们表明，结合使用这两种方法比使用的数据集上的任何单一方法都要好。在 KuaiRec 和 MIND 数据集上的实验结果表明，与基线方法相比，我们提出的方法取得了更好的结果。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Graph-Based+Model-Agnostic+Data+Subsampling+for+Recommendation+Systems)|0| +|[Graph-Based Model-Agnostic Data Subsampling for Recommendation Systems](https://doi.org/10.1145/3580305.3599834)|Xiaohui Chen, Jiankai Sun, Taiqing Wang, Ruocheng Guo, LiPing Liu, Aonan Zhang|ByteDance Inc.; Tufts University; ByteDance Research; Apple Inc.|Data subsampling is widely used to speed up the training of large-scale recommendation systems. Most subsampling methods are model-based and often require a pre-trained pilot model to measure data importance via e.g. sample hardness. However, when the pilot model is misspecified, model-based subsampling methods deteriorate. Since model misspecification is persistent in real recommendation systems, we instead propose model-agnostic data subsampling methods by only exploring input data structure represented by graphs. Specifically, we study the topology of the user-item graph to estimate the importance of each user-item interaction (an edge in the user-item graph) via graph conductance, followed by a propagation step on the network to smooth out the estimated importance value. Since our proposed method is model-agnostic, we can marry the merits of both model-agnostic and model-based subsampling methods. Empirically, we show that combing the two consistently improves over any single method on the used datasets. Experimental results on KuaiRec and MIND datasets demonstrate that our proposed methods achieve superior results compared to baseline approaches.|数据子采样被广泛用于加速大规模推荐系统的训练。大多数次抽样方法是基于模型的，通常需要一个预先训练的试点模型来通过例如样本硬度来测量数据的重要性。然而，当导频模型被错误指定时，基于模型的子抽样方法就会变质。由于模型错误说明在实际推荐系统中一直存在，因此我们提出了模型无关的数据子抽样方法，只是探讨了用图表示的输入数据结构。具体来说，我们研究了用户项目图的拓扑结构，通过图电导来估计每个用户项目交互(用户项目图中的一条边)的重要性，然后通过网络上的传播步骤来平滑估计的重要性值。由于我们提出的方法是模型不可知的，我们可以结合模型不可知和基于模型的子抽样方法的优点。经验上，我们表明，结合使用这两种方法比使用的数据集上的任何单一方法都要好。在 KuaiRec 和 MIND 数据集上的实验结果表明，与基线方法相比，我们提出的方法取得了更好的结果。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Graph-Based+Model-Agnostic+Data+Subsampling+for+Recommendation+Systems)|0| |[BOSS: A Bilateral Occupational-Suitability-Aware Recommender System for Online Recruitment](https://doi.org/10.1145/3580305.3599783)|Xiao Hu, Yuan Cheng, Zhi Zheng, Yue Wang, Xinxin Chi, Hengshu Zhu||With the rapid development of online recruitment platforms, a variety of emerging recommendation services have been witnessed for benefiting both job seekers and recruiters. While many researchers have studied the problem of reciprocal recommendation in two- sided markets (e.g., marriage market and real estate market), there is still a lack of in-depth understanding of the bilateral occupational preferences of different participants in the online recruitment market. To this end, in this paper, we propose a Bilateral Occupational-Suitability-aware recommender System (BOSS) for online recruitment, in consideration of the reciprocal, bilateral, and sequential properties of realistic recruitment scenarios simultaneously. To be specific, in BOSS, we first propose a multi-group-based mixture-of-experts (MoE) module to independently learn the preference representations of job seekers and recruiters. Then, with a specially-designed multi-task learning module, BOSS can progressively model the action sequence of recruitment process through a bilateral probabilistic manner. As a result, the reciprocal recommendations can be efficiently implemented by leveraging the product of different action probabilities of job seekers and recruiters. Finally, we have conducted extensive experiments on 5 real-world large-scale datasets as well as the online environment. Both online A/B test and offline experimental results clearly validate that our recommender system BOSS can outperform other state-of-the-art baselines with a significant margin.|随着在线招聘平台的迅速发展，各种新出现的推荐服务使求职者和招聘人员都受益。虽然许多研究者对双边市场(如婚姻市场和房地产市场)中的互惠推荐问题进行了研究，但对于网络招聘市场中不同参与者的双边职业偏好仍缺乏深入的理解。为此，在本文中，我们提出了一个双边职业适合性意识在线招聘推荐系统(BOSS) ，同时考虑到现实招聘场景的互惠性、双边性和顺序性。具体来说，在 BOSS 系统中，我们首先提出了一个基于多群体的专家混合模型(MoE)来独立学习求职者和招聘者的偏好表示。然后，通过专门设计的多任务学习模块，BOSS 可以通过双边概率的方式逐步建模招聘过程的行为序列。因此，通过利用求职者和招聘人员不同行动概率的结果，可以有效地执行互惠建议。最后，我们在5个真实世界的大规模数据集上以及在线环境下进行了广泛的实验。线上和线下的实验结果都清楚地证明了我们的推荐系统老板能够以显著的优势超越其他最先进的基准线。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=BOSS:+A+Bilateral+Occupational-Suitability-Aware+Recommender+System+for+Online+Recruitment)|0| |[Real Time Index and Search Across Large Quantities of GNN Experts for Low Latency Online Learning](https://doi.org/10.1145/3580305.3599893)|Johan Kok Zhi Kang, Sien Yi Tan, Bingsheng He, Zhen Zhang|National University of Singapore; GrabTaxi Holdings|Online learning is a powerful technique that allows models to adjust to concept drift in dynamically changing graphs. This approach is crucial for large mobility-based companies like Grab, where batch-learning methods fail to keep up with the large amount of training data. Our work focuses on scaling graph neural network mixture of expert (MoE) models for real-time traffic speed prediction on road networks, while meeting high accuracy and low latency requirements. Conventional spatio-temporal and incremental MoE frameworks struggle with poor inference accuracy and linear time complexity when scaling experts, for the latter, leading to prohibitively high latency in model updates. To address this issue, we introduce the Indexed Router, a novel method that categorizes experts into a structured hierarchy called the indexed tree. This approach reduces the time to scale and search N number of experts from O(N) to O(log N), making it ideal for online learning under tight service level agreements. Our experiments show that these time savings do not compromise inference accuracy, and our Indexed Router outperforms state-of-the-art spatio-temporal and incremental MoE models in terms of traffic speed prediction accuracy on real-life GPS traces from Grab's database and publicly available records. In summary, the Indexed Router enables MoE models to scale across large numbers of experts with low latency, while accurately identifying the relevant experts for inference.|在线学习是一种强大的技术，它允许模型根据动态变化的图形中的概念漂移进行调整。这种方法对于大型的基于移动性的公司来说是至关重要的，比如 Grab 公司，批量学习方法无法跟上大量的培训数据。我们的工作集中在缩放图神经网络混合专家(MoE)模型的实时交通速度预测道路网络，同时满足高精度和低延迟的要求。传统的时空和增量式 MoE 框架在缩放专家(对于后者而言)面临推断精度和线性时间复杂性较差的问题，导致模型更新延迟过高。为了解决这个问题，我们引入了索引路由器(Indexed Router) ，这是一种将专家分类到一个称为索引树(Indexed tree)的结构化层次结构中的新方法。这种方法减少了从 O (N)到 O (log N)的缩放和搜索 N 个专家的时间，使其成为严格服务水平协议下在线学习的理想选择。我们的实验表明，这些节省的时间不会影响推断的准确性，我们的 Indexed 路由器在时空和增量 MoE 模型方面优于最先进的交通速度预测精度，这些预测精度来自 Grab 数据库和公开可用记录的实际 GPS 跟踪。总之，索引路由器使 MoE 模型能够以较低的延迟跨越大量的专家，同时准确地确定相关的专家进行推理。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Real+Time+Index+and+Search+Across+Large+Quantities+of+GNN+Experts+for+Low+Latency+Online+Learning)|0| |[A Preference-aware Meta-optimization Framework for Personalized Vehicle Energy Consumption Estimation](https://doi.org/10.1145/3580305.3599767)|Siqi Lai, Weijia Zhang, Hao Liu|The Hong Kong University of Science and Technology (Guangzhou)|Vehicle Energy Consumption (VEC) estimation aims to predict the total energy required for a given trip before it starts, which is of great importance to trip planning and transportation sustainability. Existing approaches mainly focus on extracting statistically significant factors from typical trips to improve the VEC estimation. However, the energy consumption of each vehicle may diverge widely due to the personalized driving behavior under varying travel contexts. To this end, this paper proposes a preference-aware meta-optimization framework Meta-Pec for personalized vehicle energy consumption estimation. Specifically, we first propose a spatiotemporal behavior learning module to capture the latent driver preference hidden in historical trips. Moreover, based on the memorization of driver preference, we devise a selection-based driving behavior prediction module to infer driver-specific driving patterns on a given route, which provides additional basis and supervision signals for VEC estimation. Besides, a driver-specific meta-optimization scheme is proposed to enable fast model adaption by learning and sharing transferable knowledge globally. Extensive experiments on two real-world datasets show the superiority of our proposed framework against ten numerical and data-driven machine learning baselines. The source code is available at https://github.com/usail-hkust/Meta-Pec.|车辆能耗(VEC)估算的目的是在出行前预测出行所需的总能量，这对出行规划和交通可持续性有重要意义。现有的方法主要集中在从典型行程中提取统计学显著因子，以改善 VEC 估计。然而，在不同的出行环境下，由于个性化驾驶行为的影响，每辆车的能源消耗可能会有很大的差异。为此，本文提出了一个基于偏好感知的元优化框架 Meta-Pec，用于个性化车辆能耗估算。具体来说，我们首先提出了一个时空行为学习模块来捕捉隐藏在历史行程中的潜在驱动偏好。此外，基于驾驶员偏好的记忆，我们设计了一个基于选择的驾驶行为预测模块，以推断特定路线上驾驶员的驾驶模式，为 VEC 估计提供额外的依据和监控信号。此外，提出了一种特定于驱动程序的元优化方案，通过全局学习和共享可转移知识来实现模型的快速自适应。在两个实际数据集上的大量实验表明，我们提出的框架对于十个数字和数据驱动的机器学习基线具有优越性。源代码可在 https://github.com/usail-hkust/meta-pec 下载。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Preference-aware+Meta-optimization+Framework+for+Personalized+Vehicle+Energy+Consumption+Estimation)|0| -|[MUSER: A MUlti-Step Evidence Retrieval Enhancement Framework for Fake News Detection](https://doi.org/10.1145/3580305.3599873)|Hao Liao, Jiahao Peng, Zhanyi Huang, Wei Zhang, Guanghua Li, Kai Shu, Xing Xie|Illinois Institute of Technology; Microsoft Research Asia; Shenzhen University|The ease of spreading false information online enables individuals with malicious intent to manipulate public opinion and destabilize social stability. Recently, fake news detection based on evidence retrieval has gained popularity in an effort to identify fake news reliably and reduce its impact. Evidence retrieval-based methods can improve the reliability of fake news detection by computing the textual consistency between the evidence and the claim in the news. In this paper, we propose a framework for fake news detection based on MUlti-Step Evidence Retrieval enhancement (MUSER), which simulates the steps of human beings in the process of reading news, summarizing, consulting materials, and inferring whether the news is true or fake. Our model can explicitly model dependencies among multiple pieces of evidence, and perform multi-step associations for the evidence required for news verification through multi-step retrieval. In addition, our model is able to automatically collect existing evidence through paragraph retrieval and key evidence selection, which can save the tedious process of manual evidence collection. We conducted extensive experiments on real-world datasets in different languages, and the results demonstrate that our proposed model outperforms state-of-the-art baseline methods for detecting fake news by at least 3% in F1-Macro and 4% in F1-Micro. Furthermore, it provides interpretable evidence for end users.|在网上传播虚假信息的便利使得有恶意的个人能够操纵公众舆论，破坏社会稳定。近年来，基于证据检索的假新闻检测技术在可靠识别假新闻、减少假新闻影响等方面得到了广泛的应用。基于证据检索的方法通过计算新闻中证据与索赔之间的文本一致性，提高了假新闻检测的可靠性。本文提出了一种基于多步证据检索增强(MUSER)的假新闻检测框架，该框架模拟了人类在阅读新闻、总结新闻、查阅资料、推断新闻是真是假的过程中的步骤。该模型可以显式地对多个证据之间的依赖关系进行建模，并通过多步检索对新闻验证所需的证据进行多步关联。此外，该模型通过段落检索和关键证据选择，能够自动收集现有证据，节省了繁琐的人工证据收集过程。我们在不同语言的真实世界数据集上进行了广泛的实验，结果表明，我们提出的模型比最先进的基线方法在 F1-Macro 中检测假新闻的性能至少高出3% ，在 F1-Micro 中高出4% 。此外，它还为最终用户提供了可解释的证据。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=MUSER:+A+MUlti-Step+Evidence+Retrieval+Enhancement+Framework+for+Fake+News+Detection)|0| +|[MUSER: A MUlti-Step Evidence Retrieval Enhancement Framework for Fake News Detection](https://doi.org/10.1145/3580305.3599873)|Hao Liao, Jiahao Peng, Zhanyi Huang, Wei Zhang, Guanghua Li, Kai Shu, Xing Xie|Shenzhen University; Microsoft Research Asia; Illinois Institute of Technology|The ease of spreading false information online enables individuals with malicious intent to manipulate public opinion and destabilize social stability. Recently, fake news detection based on evidence retrieval has gained popularity in an effort to identify fake news reliably and reduce its impact. Evidence retrieval-based methods can improve the reliability of fake news detection by computing the textual consistency between the evidence and the claim in the news. In this paper, we propose a framework for fake news detection based on MUlti-Step Evidence Retrieval enhancement (MUSER), which simulates the steps of human beings in the process of reading news, summarizing, consulting materials, and inferring whether the news is true or fake. Our model can explicitly model dependencies among multiple pieces of evidence, and perform multi-step associations for the evidence required for news verification through multi-step retrieval. In addition, our model is able to automatically collect existing evidence through paragraph retrieval and key evidence selection, which can save the tedious process of manual evidence collection. We conducted extensive experiments on real-world datasets in different languages, and the results demonstrate that our proposed model outperforms state-of-the-art baseline methods for detecting fake news by at least 3% in F1-Macro and 4% in F1-Micro. Furthermore, it provides interpretable evidence for end users.|在网上传播虚假信息的便利使得有恶意的个人能够操纵公众舆论，破坏社会稳定。近年来，基于证据检索的假新闻检测技术在可靠识别假新闻、减少假新闻影响等方面得到了广泛的应用。基于证据检索的方法通过计算新闻中证据与索赔之间的文本一致性，提高了假新闻检测的可靠性。本文提出了一种基于多步证据检索增强(MUSER)的假新闻检测框架，该框架模拟了人类在阅读新闻、总结新闻、查阅资料、推断新闻是真是假的过程中的步骤。该模型可以显式地对多个证据之间的依赖关系进行建模，并通过多步检索对新闻验证所需的证据进行多步关联。此外，该模型通过段落检索和关键证据选择，能够自动收集现有证据，节省了繁琐的人工证据收集过程。我们在不同语言的真实世界数据集上进行了广泛的实验，结果表明，我们提出的模型比最先进的基线方法在 F1-Macro 中检测假新闻的性能至少高出3% ，在 F1-Micro 中高出4% 。此外，它还为最终用户提供了可解释的证据。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=MUSER:+A+MUlti-Step+Evidence+Retrieval+Enhancement+Framework+for+Fake+News+Detection)|0| |[PrivateRec: Differentially Private Model Training and Online Serving for Federated News Recommendation](https://doi.org/10.1145/3580305.3599889)|Ruixuan Liu, Yang Cao, Yanlin Wang, Lingjuan Lyu, Yun Chen, Hong Chen||Federated recommendation can potentially alleviate the privacy concerns in collecting sensitive and personal data for training personalized recommendation systems. However, it suffers from a low recommendation quality when a local serving is inapplicable due to the local resource limitation and the data privacy of querying clients is required in online serving. Furthermore, a theoretically private solution in both the training and serving of federated recommendation is essential but still lacking. Naively applying differential privacy (DP) to the two stages in federated recommendation would fail to achieve a satisfactory trade-off between privacy and utility due to the high-dimensional characteristics of model gradients and hidden representations. In this work, we propose a federated news recommendation method for achieving better utility in model training and online serving under a DP guarantee. We first clarify the DP definition over behavior data for each round in the pipeline of federated recommendation systems. Next, we propose a privacy-preserving online serving mechanism under this definition based on the idea of decomposing user embeddings with public basic vectors and perturbing the lower-dimensional combination coefficients. We apply a random behavior padding mechanism to reduce the required noise intensity for better utility. Besides, we design a federated recommendation model training method, which can generate effective and public basic vectors for serving while providing DP for training participants. We avoid the dimension-dependent noise for large models via label permutation and differentially private attention modules. Experiments on real-world news recommendation datasets validate that our method achieves superior utility under a DP guarantee in both training and serving of federated news recommendations.|联合推荐可以减轻收集敏感数据和个人数据以培训个性化推荐系统时对隐私的担忧。然而，当本地服务由于本地资源的限制而不适用，并且在线服务需要查询客户端的数据隐私时，该算法的推荐质量较低。此外，在联合推荐的培训和服务中，理论上的私有解决方案是必不可少的，但仍然缺乏。由于模型梯度和隐藏表示的高维特性，在联邦推荐的两个阶段天真地应用差分隐私(DP)将无法在隐私和实用性之间达成令人满意的平衡。在本研究中，我们提出一个联邦新闻推荐的方法，以达到更好的效用模型训练和在线服务下的 DP 保证。我们首先阐明联邦推荐系统管道中每一轮行为数据的 DP 定义。其次，基于公共基本向量分解用户嵌入和扰动低维组合系数的思想，在此定义下提出了一种保护隐私的在线服务机制。我们应用一个随机行为填充机制，以降低所需的噪声强度，以获得更好的效用。此外，我们还设计了一种联邦推荐模型训练方法，该方法可以生成有效的、公开的基本向量，为训练参与者提供 DP 服务。对于大型模型，我们通过标签置换和差分私有注意模块来避免与维数相关的噪声。在实际的新闻推荐数据集上进行的实验验证了该方法在联邦新闻推荐的培训和服务方面，在 DP 保证下取得了较好的效用。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=PrivateRec:+Differentially+Private+Model+Training+and+Online+Serving+for+Federated+News+Recommendation)|0| |[Hierarchical Projection Enhanced Multi-behavior Recommendation](https://doi.org/10.1145/3580305.3599838)|Chang Meng, Hengyu Zhang, Wei Guo, Huifeng Guo, Haotian Liu, Yingxue Zhang, Hongkun Zheng, Ruiming Tang, Xiu Li, Rui Zhang||Various types of user behaviors are recorded in most real-world recommendation scenarios. To fully utilize the multi-behavior information, the exploration of multiplex interaction among them is essential. Many multi-task learning based multi-behavior methods are proposed recently to use multiple types of supervision signals and perform information transfer among them. Despite the great successes, these methods fail to design prediction tasks comprehensively, leading to insufficient utilization of multi-behavior correlative information. Besides, these methods are either based on the weighting of expert information extracted from the coupled input or modeling of information transfer between multiple behavior levels through task-specific extractors, which are usually accompanied by negative transfer phenomenon 1 . To address the above problems, we propose a multi-behavior recommendation framework, called Hierarchical Projection Enhanced Multi-behavior Recommendation (HPMR). The key module, Projection-based Transfer Network (PTN), uses the projection mechanism to "explicitly" model the correlations of upstream and downstream behaviors, refines the upstream behavior representations, and fully uses the refined representations to enhance the learning of downstream tasks. Offline experiments on public and industrial datasets and online A/B test further verify the effectiveness of HPMR in modeling the associations from upstream to downstream and alleviating the negative transfer. The source code and datasets are available at https://github.com/MC-CV/HPMR.|在大多数真实世界的推荐场景中记录了各种类型的用户行为。为了充分利用多行为信息，探索它们之间的多元互动是必不可少的。近年来出现了许多基于多任务学习的多行为方法，它们利用多种监控信号进行信息传递。这些方法虽然取得了很大的成功，但是未能全面地设计预测任务，导致多行为相关信息利用不足。此外，这些方法要么是基于从耦合输入中提取的专家信息的权重，要么是通过任务特定的提取器建立多个行为水平之间的信息传递模型，通常伴随着负迁移现象1。为了解决上述问题，我们提出了一个多行为推荐框架，称为层次投影增强多行为推荐(HPMR)。关键模块，基于投影的传输网络(PTN) ，使用投影机制来“显式”建模上游或下游行为的相关性，提炼上游行为表示，并充分利用提炼表示来增强下游任务的学习。在公共和工业数据集上的离线实验和在线 A/B 检验进一步验证了 HPMR 模型在建立上下游关联模型和减轻负迁移方面的有效性。源代码和数据集可在 https://github.com/mc-cv/hpmr 下载。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Hierarchical+Projection+Enhanced+Multi-behavior+Recommendation)|0| |[End-to-End Query Term Weighting](https://doi.org/10.1145/3580305.3599815)|Karan Samel, Cheng Li, Weize Kong, Tao Chen, Mingyang Zhang, Shaleen Kumar Gupta, Swaraj Khadanga, Wensong Xu, Xingyu Wang, Kashyap Kolipaka, Michael Bendersky, Marc Najork||Bag-of-words based lexical retrieval systems are still the most commonly used methods for real-world search applications. Recently deep learning methods have shown promising results to improve this retrieval performance but are expensive to run in an online fashion, non-trivial to integrate into existing production systems, and might not generalize well in out-of-domain retrieval scenarios. Instead, we build on top of lexical retrievers by proposing a Term Weighting BERT (TW-BERT) model. TW-BERT learns to predict the weight for individual n-gram (e.g., uni-grams and bi-grams) query input terms. These inferred weights and terms can be used directly by a retrieval system to perform a query search. To optimize these term weights, TW-BERT incorporates the scoring function used by the search engine, such as BM25, to score query-document pairs. Given sample query-document pairs we can compute a ranking loss over these matching scores, optimizing the learned query term weights in an end-to-end fashion. Aligning TW-BERT with search engine scorers minimizes the changes needed to integrate it into existing production applications, whereas existing deep learning based search methods would require further infrastructure optimization and hardware requirements. The learned weights can be easily utilized by standard lexical retrievers and by other retrieval techniques such as query expansion. We show that TW-BERT improves retrieval performance over strong term weighting baselines within MSMARCO and in out-of-domain retrieval on TREC datasets.|基于词包的词汇检索系统仍然是实际搜索应用中最常用的方法。近年来，深度学习方法在提高检索性能方面取得了一些有希望的成果，但是在在线方式下运行成本较高，与现有生产系统的集成也不容易，而且在域外检索场景中可能无法得到很好的推广。相反，我们通过提出一个词汇加权 BERT (TW-BERT)模型，在词汇检索器的基础上构建。TW-BERT 学习预测单个 n-gram (例如，un- gram 和 bi-gram)查询输入项的权重。检索系统可以直接使用这些推断的权重和术语来执行查询搜索。为了优化这些术语权重，TW-BERT 结合了搜索引擎(如 BM25)使用的评分函数，以对查询文档对进行评分。给定样本查询-文档对，我们可以计算这些匹配得分的排名损失，以端到端的方式优化学习的查询术语权重。将 TW-BERT 与搜索引擎评分器结合起来，可以最大限度地减少将其集成到现有生产应用程序所需的更改，而现有的基于深度学习的搜索方法将需要进一步的基础设施优化和硬件要求。标准词法检索器和查询扩展等其他检索技术可以很容易地利用学习权重。我们表明，TW-BERT 提高检索性能的强项权重基线内 MSMARCO 和域外的 TREC 数据集检索。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=End-to-End+Query+Term+Weighting)|0| |[UnifieR: A Unified Retriever for Large-Scale Retrieval](https://doi.org/10.1145/3580305.3599927)|Tao Shen, Xiubo Geng, Chongyang Tao, Can Xu, Guodong Long, Kai Zhang, Daxin Jiang||Large-scale retrieval is to recall relevant documents from a huge collection given a query. It relies on representation learning to embed documents and queries into a common semantic encoding space. According to the encoding space, recent retrieval methods based on pre-trained language models (PLM) can be coarsely categorized into either dense-vector or lexicon-based paradigms. These two paradigms unveil the PLMs' representation capability in different granularities, i.e., global sequence-level compression and local word-level contexts, respectively. Inspired by their complementary global-local contextualization and distinct representing views, we propose a new learning framework, UnifieR, which unifies dense-vector and lexicon-based retrieval in one model with a dual-representing capability. Experiments on passage retrieval benchmarks verify its effectiveness in both paradigms. A uni-retrieval scheme is further presented with even better retrieval quality. We lastly evaluate the model on BEIR benchmark to verify its transferability.|大规模检索是从给定查询的大量集合中回收相关文档。它依靠表示学习将文档和查询嵌入到一个公共的语义编码空间中。根据编码空间的不同，现有的基于预训练语言模型(PLM)的检索方法可以粗略地分为基于密集向量的检索方法和基于词典的检索方法。这两种范式分别揭示了 PLM 在不同粒度上的表示能力，即全局序列级压缩和局部词级上下文。受到它们互补的全局-局部上下文化和不同表示视图的启发，我们提出了一种新的学习框架 UnifieR，它将密集向量检索和基于词典的检索结合在一个具有双重表示能力的模型中。文章检索基准的实验结果验证了该方法在两种范式下的有效性。进一步提出了一种单一检索方案，检索效果更好。最后通过对 BEIR 基准测试模型的评估，验证了该模型的可推广性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=UnifieR:+A+Unified+Retriever+for+Large-Scale+Retrieval)|0| |[Counterfactual Video Recommendation for Duration Debiasing](https://doi.org/10.1145/3580305.3599797)|Shisong Tang, Qing Li, Dingmin Wang, Ci Gao, Wentao Xiao, Dan Zhao, Yong Jiang, Qian Ma, Aoyang Zhang||Duration bias widely exists in video recommendations, where models tend to recommend short videos for the higher ratio of finish playing and thus possibly fail to capture users' true interests. In this paper, we eliminate the duration bias from both data and model. First, based on the extensive data analysis, we observe that play completion rate of videos with the same duration presents a bimodal distribution. Hence, we propose to perform threshold division to construct binary labels as training labels for alleviating the drawback of finish playing labels overly biased towards short videos. Algorithmically, we resort to causal inference, which enables us to inspect causal relationships of video recommendations with a causal graph. We identify that duration has two kinds of effect on prediction: direct and indirect. Duration bias lies in the direct effect, while the indirect effect benefits prediction. To this end, we design a model-agnostic Counterfactual Video Recommendation for Duration Debiasing (CVRDD) framework, which incorporates multi-task learning to estimate different causal effect during training. In the inference phase, we perform counterfactual inference to remove the direct effect of duration for unbiased prediction. We conduct experiments on two industrial datasets, and in addition to achieving highly promising results on traditional top-k recommendation metrics, CVRDD also improves the user watch time.|持续时间偏差广泛存在于视频推荐中，其中模型倾向于推荐较高比例的完成播放的短视频，因此可能无法捕捉用户的真实兴趣。本文从数据和模型两方面消除了持续时间偏差。首先，基于广泛的数据分析，我们观察到在相同持续时间的视频中，播放完成率呈现出一个双峰分布。因此，我们建议使用阈值分割来构造二进制标签作为训练标签，以减轻完成播放标签过于偏向短视频的缺点。在算法上，我们求助于因果推理，它使我们能够检查因果图视频推荐的因果关系。我们发现持续时间对预测有两种影响: 直接影响和间接影响。持续时间偏差在于直接效应，而间接效应有利于预测。为此，我们设计了一个模型无关的持续时间消偏反事实视频推荐(CVRDD)框架，该框架结合多任务学习来估计训练过程中不同的因果效应。在推理阶段，我们通过反事实推理消除持续时间对无偏预测的直接影响。我们在两个工业数据集上进行了实验，除了在传统的 top-k 推荐指标上取得了非常有前景的结果之外，CVRDD 还提高了用户的观看时间。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Counterfactual+Video+Recommendation+for+Duration+Debiasing)|0| -|[Semantic-Enhanced Differentiable Search Index Inspired by Learning Strategies](https://doi.org/10.1145/3580305.3599903)|Yubao Tang, Ruqing Zhang, Jiafeng Guo, Jiangui Chen, Zuowei Zhu, Shuaiqiang Wang, Dawei Yin, Xueqi Cheng|Baidu Inc.; ICT, CAS|Recently, a new paradigm called Differentiable Search Index (DSI) has been proposed for document retrieval, wherein a sequence-to-sequence model is learned to directly map queries to relevant document identifiers. The key idea behind DSI is to fully parameterize traditional ``index-retrieve'' pipelines within a single neural model, by encoding all documents in the corpus into the model parameters. In essence, DSI needs to resolve two major questions: (1) how to assign an identifier to each document, and (2) how to learn the associations between a document and its identifier. In this work, we propose a Semantic-Enhanced DSI model (SE-DSI) motivated by Learning Strategies in the area of Cognitive Psychology. Our approach advances original DSI in two ways: (1) For the document identifier, we take inspiration from Elaboration Strategies in human learning. Specifically, we assign each document an Elaborative Description based on the query generation technique, which is more meaningful than a string of integers in the original DSI; and (2) For the associations between a document and its identifier, we take inspiration from Rehearsal Strategies in human learning. Specifically, we select fine-grained semantic features from a document as Rehearsal Contents to improve document memorization. Both the offline and online experiments show improved retrieval performance over prevailing baselines.|最近，一个新的范例被称为微分搜索索引(DSI)已被提出的文献检索，其中一个序列到序列模型学习直接映射查询到相关的文档标识符。DSI 的关键思想是将传统的“索引-检索”流水线完全参数化在一个单一的神经模型中，将语料库中的所有文档编码到模型参数中。本质上，DSI 需要解决两个主要问题: (1)如何为每个文档分配标识符，(2)如何学习文档与其标识符之间的关联。在本研究中，我们在认知心理学领域提出了一个以学习策略为动机的语义增强型 DSI 模型(SE-DSI)。我们的方法从两个方面对原始 DSI 进行了改进: (1)对于文档标识符，我们从人类学习中的精化策略中得到了启发。具体来说，我们基于查询生成技术为每个文档分配一个精细描述，这比原始 DSI 中的一串整数更有意义; (2)对于文档及其标识符之间的关联，我们从人类学习中的排练策略中获得灵感。具体来说，我们从文档中选择细粒度的语义特征作为预演内容，以提高文档的记忆能力。离线和在线实验都表明，相对于主流基线，检索性能有所提高。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Semantic-Enhanced+Differentiable+Search+Index+Inspired+by+Learning+Strategies)|0| +|[Semantic-Enhanced Differentiable Search Index Inspired by Learning Strategies](https://doi.org/10.1145/3580305.3599903)|Yubao Tang, Ruqing Zhang, Jiafeng Guo, Jiangui Chen, Zuowei Zhu, Shuaiqiang Wang, Dawei Yin, Xueqi Cheng|ICT, CAS; Baidu Inc.|Recently, a new paradigm called Differentiable Search Index (DSI) has been proposed for document retrieval, wherein a sequence-to-sequence model is learned to directly map queries to relevant document identifiers. The key idea behind DSI is to fully parameterize traditional ``index-retrieve'' pipelines within a single neural model, by encoding all documents in the corpus into the model parameters. In essence, DSI needs to resolve two major questions: (1) how to assign an identifier to each document, and (2) how to learn the associations between a document and its identifier. In this work, we propose a Semantic-Enhanced DSI model (SE-DSI) motivated by Learning Strategies in the area of Cognitive Psychology. Our approach advances original DSI in two ways: (1) For the document identifier, we take inspiration from Elaboration Strategies in human learning. Specifically, we assign each document an Elaborative Description based on the query generation technique, which is more meaningful than a string of integers in the original DSI; and (2) For the associations between a document and its identifier, we take inspiration from Rehearsal Strategies in human learning. Specifically, we select fine-grained semantic features from a document as Rehearsal Contents to improve document memorization. Both the offline and online experiments show improved retrieval performance over prevailing baselines.|最近，一个新的范例被称为微分搜索索引(DSI)已被提出的文献检索，其中一个序列到序列模型学习直接映射查询到相关的文档标识符。DSI 的关键思想是将传统的“索引-检索”流水线完全参数化在一个单一的神经模型中，将语料库中的所有文档编码到模型参数中。本质上，DSI 需要解决两个主要问题: (1)如何为每个文档分配标识符，(2)如何学习文档与其标识符之间的关联。在本研究中，我们在认知心理学领域提出了一个以学习策略为动机的语义增强型 DSI 模型(SE-DSI)。我们的方法从两个方面对原始 DSI 进行了改进: (1)对于文档标识符，我们从人类学习中的精化策略中得到了启发。具体来说，我们基于查询生成技术为每个文档分配一个精细描述，这比原始 DSI 中的一串整数更有意义; (2)对于文档及其标识符之间的关联，我们从人类学习中的排练策略中获得灵感。具体来说，我们从文档中选择细粒度的语义特征作为预演内容，以提高文档的记忆能力。离线和在线实验都表明，相对于主流基线，检索性能有所提高。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Semantic-Enhanced+Differentiable+Search+Index+Inspired+by+Learning+Strategies)|0| |[Doctor Specific Tag Recommendation for Online Medical Record Management](https://doi.org/10.1145/3580305.3599810)|Yejing Wang, Shen Ge, Xiangyu Zhao, Xian Wu, Tong Xu, Chen Ma, Zhi Zheng||With the rapid growth of online medical platforms, more and more doctors are willing to manage and communicate with patients via online services. Considering the large volume and various patient conditions, identifying and classifying patients' medical records has become a crucial problem. To efficiently index these records, a common practice is to annotate them with semantically meaningful tags. However, manual labeling tags by doctors is impractical due to the possibility of thousands of tag candidates, which necessitates a tag recommender system. Due to the long tail distribution of tags and the dominance of low-activity doctors, as well as the unique uploaded medical records, this task is rather challenging. This paper proposes an efficient doctor specific tag recommendation framework for improved medical record management without side information. Specifically, we first utilize effective language models to learn the text representation. Then, we construct a doctor embedding learning module to enhance the recommendation quality by integrating implicit information within text representations and considering latent tag correlations to make more accurate predictions. Extensive experiment results demonstrate the effectiveness of our framework from the viewpoints of all doctors (20% improvement) or low-activity doctors (10% improvement).|随着在线医疗平台的快速发展，越来越多的医生愿意通过在线服务与患者进行管理和沟通。鉴于病案数量庞大，病人情况多种多样，对病案进行识别和分类已成为一个关键问题。为了有效地对这些记录进行索引，通常的做法是使用有语义意义的标记对它们进行注释。然而，由医生手动标签是不切实际的，因为可能有数千个标签候选人，这就需要一个标签推荐系统。由于标签的长尾分布和低活跃度医生的主导地位，以及独特的上传医疗记录，这项任务是相当具有挑战性的。本文提出了一个有效的医生特定标签推荐框架，改进了病案管理中的无侧信息。具体来说，我们首先利用有效的语言模型来学习文本表征。然后，通过整合文本表征中的隐含信息，考虑潜在的标签相关性，构建医生嵌入式学习模块，提高推荐质量。广泛的实验结果表明，我们的框架从所有医生的观点(20% 的改善)或低活动医生(10% 的改善)的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Doctor+Specific+Tag+Recommendation+for+Online+Medical+Record+Management)|0| |[On-device Integrated Re-ranking with Heterogeneous Behavior Modeling](https://doi.org/10.1145/3580305.3599878)|Yunjia Xi, Weiwen Liu, Yang Wang, Ruiming Tang, Weinan Zhang, Yue Zhu, Rui Zhang, Yong Yu||As an emerging field driven by industrial applications, integrated re-ranking combines lists from upstream sources into a single list, and presents it to the user. The quality of integrated re-ranking is especially sensitive to real-time user behaviors and preferences. However, existing methods are all built on the cloud-to-edge framework, where mixed lists are generated by the cloud model and then sent to the devices. Despite its effectiveness, such a framework fails to capture users' real-time preferences due to the network bandwidth and latency. Hence, we propose to place the integrated re-ranking model on devices, allowing for the full exploitation of real-time behaviors. To achieve this, we need to address two key issues: first, how to extract users' preferences for different sources from heterogeneous and imbalanced user behaviors; second, how to explore the correlation between the extracted personalized preferences and the candidate items. In this work, we present the first on-Device Integrated Re-ranking framework, DIR, to avoid delays in processing real-time user behaviors. DIR includes a multi-sequence behavior modeling module to extract the user's source-level preferences, and a preference-adaptive re-ranking module to incorporate personalized source-level preferences into the re-ranking of candidate items. Besides, we design exposure loss and utility loss to jointly optimize exposure fairness and overall utility. Extensive experiments on three datasets show that DIR significantly outperforms the state-of-the-art baselines in utility-based and fairness-based metrics.|作为一个以工业应用为驱动力的新兴领域，集成重排将来自上游源的列表合并为一个单独的列表，并将其呈现给用户。集成重新排序的质量对实时用户行为和偏好特别敏感。但是，现有的方法都构建在云到边缘的框架上，在这个框架中，云模型生成混合列表，然后发送给设备。尽管这种框架很有效，但由于网络带宽和延迟，它无法捕获用户的实时偏好。因此，我们建议将集成的重新排序模型放置在设备上，以便充分利用实时行为。为了实现这一目标，我们需要解决两个关键问题: 第一，如何从异构和不平衡的用户行为中提取用户对不同来源的偏好; 第二，如何探索提取的个性化偏好与候选项之间的相关性。在这项工作中，我们提出了第一个设备集成重新排序框架 DIR，以避免处理实时用户行为的延迟。DIR 包括用于提取用户源级偏好的多序列行为建模模块和用于将个性化源级偏好合并到候选项重新排序中的偏好自适应重新排序模块。此外，我们还设计了曝光损失和效用损失，以共同优化曝光公平性和整体效用。对三个数据集的大量实验表明，DIR 在基于效用和基于公平的指标方面明显优于最先进的基线。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=On-device+Integrated+Re-ranking+with+Heterogeneous+Behavior+Modeling)|0| |[Empowering Long-tail Item Recommendation through Cross Decoupling Network (CDN)](https://doi.org/10.1145/3580305.3599814)|Yin Zhang, Ruoxi Wang, Derek Zhiyuan Cheng, Tiansheng Yao, Xinyang Yi, Lichan Hong, James Caverlee, Ed H. Chi|Google Research, Brain Team; Texas AM University|Recommenders provide personalized content recommendations to users. They often suffer from highly skewed long-tail item distributions, with a small fraction of the items receiving most of the user feedback. This hurts model quality especially for the slices without much supervision. Existing work in both academia and industry mainly focuses on re-balancing strategies (e.g., up-sampling and up-weighting), leveraging content features, and transfer learning. However, there still lacks of a deeper understanding of how the long-tail distribution influences the recommendation performance. In this work, we theoretically demonstrate that the prediction of user preference is biased under the long-tail distributions. This bias comes from the discrepancy of both the prior and conditional probabilities between training data and test data. Most existing methods mainly attempt to reduce the bias from the prior perspective, which ignores the discrepancy in the conditional probability. This leads to a severe forgetting issue and results in suboptimal performance. To address the problem, we design a novel Cross Decoupling Network (CDN) to reduce the differences in both prior and conditional probabilities. Specifically, CDN (i) decouples the learning process of memorization and generalization on the item side through a mixture-of-expert structure; (ii) decouples the user samples from different distributions through a regularized bilateral branch network. Finally, a novel adapter is introduced to aggregate the decoupled vectors, and softly shift the training attention to tail items. Extensive experimental results show that CDN significantly outperforms state-of-the-art approaches on popular benchmark datasets, leading to an improvement in HR@50 (hit ratio) of 8.7\% for overall recommendation and 12.4\% for tail items.|推荐程序向用户提供个性化内容推荐。他们经常受到高度扭曲的长尾条目分布的影响，其中一小部分条目接受了大部分用户反馈。这会损害模型的质量，特别是对于没有很多监督的切片。学术界和业界现有的工作主要集中在重新平衡策略(例如，上调样本和上调权重)、利用内容特性和转移学习。然而，对于长尾分布是如何影响推荐性能的，目前还缺乏更深入的理解。本文从理论上证明了在长尾分布下，用户偏好的预测是有偏差的。这种偏差来自于训练数据和测试数据之间先验概率和条件概率的差异。大多数现有的方法主要试图从先验的角度减少偏差，而忽略了条件概率的差异。这会导致严重的遗忘问题，并导致次优性能。为了解决这一问题，我们设计了一种新的交叉解耦网络(CDN) ，以减少先验概率和条件概率的差异。具体来说，CDN (i)通过混合专家结构解耦项目侧记忆和概括的学习过程; (ii)通过正则化的双边分支网络解耦来自不同分布的用户样本。最后，引入一种新的适配器对解耦后的向量进行聚合，并将训练注意力柔和地转移到尾项上。广泛的实验结果表明，CDN 在流行的基准数据集上显着优于最先进的方法，导致总体推荐的 HR@50(命中率)改善为8.7% ，尾部项目的改善为12.4% 。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Empowering+Long-tail+Item+Recommendation+through+Cross+Decoupling+Network+(CDN))|0| -|[PDAS: A Practical Distributed ADMM System for Large-Scale Linear Programming Problems at Alipay](https://doi.org/10.1145/3580305.3599883)|Jun Zhou, Yang Bao, Daohong Jian, Hua Wu|Zhejiang University; Ant Group|Linear programming (LP) is arguably the most common optimization problem encountered in practical settings. Important examples include machine learning systems optimization, resource allocation, and other decision-making scenarios. However, even with state-of-the-art (SOTA) solvers, it is extremely challenging to solve large-scale problems arising in industry settings, which could have up to billions of decision variables and require solutions within a time limit to meet business demands. This paper proposes PDAS, a Practical Distributed ADMM System to solve such problems with a variant of the Alternating Direction Method of Multipliers (ADMM) algorithm. PDAS offers user-friendly interfaces and provides near-linear speedup thanks to its high scalability and excellent performance. It also comes with a failover mechanism to ensure the stability of the iterative process. The convergence, feasibility, and optimality of PDAS have been verified on two real-world data-sets, resulting in a 10 -4 average relative deviation from Gurobi. Although SOTA solvers do have advantages if only considering the solving time when tested on five small and medium-sized public data-sets, PDAS is more promising after including the modeling time. Moreover, when used to solve large-scale LP problems with up to 10 9 decision variables and 10 4 constraints in three real-world scenarios, PDAS achieves at least 2x speedups, well beyond the capabilities of SOTA.|线性规划(LP)可以说是在实际环境中遇到的最常见的最佳化问题。重要的例子包括机器学习系统优化、资源分配和其他决策场景。然而，即使使用最先进的(SOTA)解决方案，解决产业环境中出现的大规模问题也是极具挑战性的，这些问题可能有数十亿个决策变量，需要在一定时限内找到解决方案以满足业务需求。本文提出了一种实用的分布式 ADMM 系统 PDAS，采用改进的交替方向乘法器(ADMM)算法来解决这类问题。PDAS 提供用户友好的界面，并提供近线性的加速，由于其高可伸缩性和优异的性能。它还提供了故障转移机制，以确保迭代过程的稳定性。PDAS 的收敛性、可行性和最优性已在两个现实世界的数据集上得到验证，结果与 Gurobi 的平均相对偏差为10-4。虽然 SOTA 求解器在五个中小型公共数据集上测试时只考虑求解时间的优势，但在考虑建模时间的情况下，PDAS 更有前途。此外，当在三个实际场景中用于解决具有多达10个9个决策变量和10个4个约束的大规模 LP 问题时，PDAS 至少可以实现2倍的加速，远远超过 SOTA 的能力。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=PDAS:+A+Practical+Distributed+ADMM+System+for+Large-Scale+Linear+Programming+Problems+at+Alipay)|0| -|[Practical Design of Performant Recommender Systems using Large-scale Linear Programming-based Global Inference](https://doi.org/10.1145/3580305.3599183)|Aman Gupta, S. Sathiya Keerthi, Ayan Acharya, Miao Cheng, Borja Ocejo Elizondo, Rohan Ramanath, Rahul Mazumder, Kinjal Basu, J. Kenneth Tay, Rupesh Gupta|Children's Hospital, Harvard Medical School, USA.; Alzheimer's Association, USA.; Norton Healthcare, University of Kentucky, USA.; University of Colorado at Denver, USA.; Baylor College of Medicine, USA.; The Hastings Center, USA.; Burke Rehabilitation Hospital, USA.; Banyan Biomarkers, USA.; University of Virginia School of Medicine, USA.; Safe Kids Worldwide, Inc., USA.; Alzheimer's Drug Discovery Foundation, 57 West 57th Street, Suite 904, New York, NY 10019, USA.; Icahn School of Medicine at Mount Sinai, USA.; CrowdOptic, Inc., USA.; Metro Orthopedics & Sports Therapy, USA.; George Washington School of Medicine, USA.; Novant Health Sports Medicine, USA.; University of California, USA.; Boston University Medical Center, USA.; Stanford Center on Longevity, USA.; National Collegiate Athletic Association, USA.; Andrews Institute for Orthopaedics and Sports Medicine, USA.|Sports-related concussions and repetitive subconcussive exposure are increasingly recognized as potential dangers to paediatric populations, but much remains unknown about the short-term and long-term consequences of these events, including potential cognitive impairment and risk of later-life dementia. This Expert Consensus Document is the result of a 1-day meeting convened by Safe Kids Worldwide, the Alzheimer's Drug Discovery Foundation, and the Andrews Institute for Orthopaedics and Sports Medicine. The goal is to highlight knowledge gaps and areas of critically needed research in the areas of concussion science, dementia, genetics, diagnostic and prognostic biomarkers, neuroimaging, sports injury surveillance, and information sharing. For each of these areas, we propose clear and achievable paths to improve the understanding, treatment and prevention of youth sports-related concussions. In 2009, around 250,000 nonfatal traumatic brain injuries (TBIs) were recorded among individuals aged <19 years in the USA.1 The Centers for Disease Control and Prevention estimate that young people aged 5–18 years sustain 65% of all sports-related concussions.2 Despite recent advances in diagnostic brain imaging and in our understanding of the physics of concussion, long-term cognitive outcomes remain poorly understood. As the physical, cognitive and emotional consequences of concussion gain wider public attention, our incomplete knowledge of how to prevent, diagnose and treat such injuries endangers the health of our children in general and the health of their brains in particular. This Expert Consensus Document is the result of a 1-day meeting of experts in the fields of paediatric and adult TBI, Alzheimer disease (AD) research, genetics, epidemiology, bioethics and sports medicine (Box 1), which was convened in November 2013 by Safe Kids Worldwide, the Alzheimer's Drug Discovery Foundation and the Andrews Institute for Orthopaedics and Sports Medicine. Our primary goal is to highlight critical gaps in our knowledge of child and adolescent concussion. We emphasize areas where research is needed, such as development of diagnostic and predictive biomarkers, elucidation of genetic risk factors, and prediction of short-term and long-term outcomes. In our conclusions, we suggest paths toward improving our understanding of the long-term consequences of sports-related paediatric concussion. The term 'concussion' is often used interchangeably with the term 'mild TBI' (mTBI), a potentially misleading practice considering the possible extent of brain damage and potential for chronic neuropsychological dysfunction following concussion. We should stress, however, that most concussions resolve without sequelae. The American Congress of Rehabilitative Medicine defines mTBI as a Glasgow Coma Scale3 score of 13–15, with loss of consciousness for <30 min and post-traumatic amnesia lasting <24 h.4 Concussion describes a heterogeneous mixture of injury phenotypes that depends on many factors, including the magnitude, location and direction of head impact. Despite a lack of macroscopic structural findings, concussive brain injury involves primary neuronal injury caused by linear and rotational shear forces that disrupt axonal and membrane function (diffuse axonal injury,5 ionic flux and glutamate excitotoxicity), followed by secondary pathophysiological effects including mitochondrial oxidative stress, disruption of cerebral blood flow, compromised blood–brain barrier (BBB) integrity, synaptic dysfunction, and neuroinflammation.6, 7 Lasting neuropsychological post-concussion symptoms (post-concussion syndrome) comprise mood disorders (for example, depression), difficulty concentrating, and memory problems (Box 2).8 Both physical and physiological components of concussive injury can damage the developing brain, putting youths engaged in impact sports at particular risk. The necks and torsos of young athletes are weaker than those of older individuals and, consequently, less force is required to cause brain injury. The developing brain might also be particularly vulnerable to axonal damage caused by the shearing forces of head trauma, which, in youth American football, can exceed linear acceleration forces of 100 g.9 However, the average forces sustained in youth sports will generally be smaller than at higher levels of sport. Proper synaptic development is critical to cognitive and behavioural health.10, 11, 12, 13, 14, 15 Processes such as neurogenesis, competitive synaptic elimination ('pruning'), myelination, and axonal and dendritic arborization continue from prenatal development throughout the lifespan.14 The frontal and temporal lobes are the last areas to mature, and humans experience pruning in these regions into their early 20s,16 so damage to these still-developing areas may have pathophysiological effects on the brain that increase the potential for neuropsychological problems later in life.17 Axonal myelination continues through adolescence into the early 20s, and is susceptible to disruption by injury.10, 18, 19, 20, 21, 22 Early results from the Professional Fighters Brain Health Study, a 5-year longitudinal study of boxers and mixed martial arts fighters, who experienced repetitive subconcussive injuries as well as concussions, indicate that earlier age of first exposure to competitive boxing correlates with greater loss of caudate volume and greater axonal damage in the frontal lobe.23, 24 The young brain also has features that contribute to its resilience. Increased neuroplasticity in this age group has been shown to contribute to better outcomes after focal injuries.25 In addition, developing animals display a shorter window of glucose metabolic impairment in response to repeat TBI than do adult animals.26 Overall, the developing brain shows both vulnerability and resilience after TBI. These interwoven factors are likely to account for differences in the effects of concussion and repeat mTBI on young versus adult brains. A conservative approach to concussion risk and greater efforts to investigate these developmental differences should be given high priority. Most people—both young and old—recover fully from concussions. In children, factors potentially influencing recovery include age and history of concussions.27, 28 In one study, approximately 90% of young adult male athletes experienced symptomatic recovery within 21 days.29 However, in an emergency department study of patients aged 11–22 years (including all causes of concussion, not just sports-related), 15% of the sample still exhibited post-concussion symptoms, including headache, dizziness, 'mental fogginess' and depression, 90 days after injury.30 Several studies suggest that high school American football players are slower to recover from concussion than are college31, 32 and professional players.33 No direct comparisons with adolescents below high school age have yet been published, although a recent study that included a pre-adolescent age group (11–12 years) suggested that post-concussion recovery duration may not exhibit a linear relationship with age,30 as adolescents in this sample took longer to recover than did the pre-adolescent children. These findings, taken together, imply a unique risk of lengthier recovery in the male adolescent age group. Further studies of younger children and females would add greatly to our ability to assess and mitigate risk across the full paediatric and adolescent age span. Youths who sustained one or more concussions within 1 year prior to a new concussion reported more-prolonged symptoms,30 suggesting a possible 'window of vulnerability', and placing previously injured youths at higher risk of protracted recovery. Adolescents aged 11–18 years were nearly 80% more likely to develop post-concussion syndrome after presenting in emergency rooms than were children aged 5–10 years; similarly, presentation with headache doubled the risk of post-concussion syndrome in both children and adolescents.34 Among children treated in an emergency room after mTBI, those aged >6 years reported higher rates of persistent symptoms 3 months post injury than did those aged <6 years.35 Of course, the ability to acquire accurate information about concussion symptoms in children <6 years of age may be limited by a lack of self-awareness of symptoms and the necessary verbal skills to effectively communicate those symptoms. Also, direct comparison of injury severity is not possible from these reports; in fact, the physical heterogeneity of various injuries, taken together with the individual's innate capacity to recover from concussion, makes such comparisons highly challenging. 'Smart helmets' are being used in some speciality research centres to standardize the physical force and angular acceleration that accompanies head hits, and the utility of these helmets to measure and predict impacts that may result in concussion is currently under investigation.36, 37 Young people recovering from concussion can experience important challenges, including altered social and academic development,38, 39, 40 lower scores on general intelligence tests, and decreased school performance (measured by grade-point average).39 Lower levels of parental education and child academic achievement both correlate with poorer concussion recovery.41 Personality traits also play a part; for example, pre-injury anxiety is a risk factor for prolonged recovery periods after sports-related concussion.42 Young athletes of both sexes are at risk of concussion, but girls report higher concussion rates than boys, particularly in high school and college soccer, basketball, and baseball or softball.28, 43, 44, 45 The factors that account for these differences remain uncertain, but might include quality of protective gear, recognition and reporting of concussion symptoms, and neck length and neck muscle strength.46 Differences in recovery trajectories between males and females are also poorly understood. However, one recent study suggested that progesterone levels in females influence post-concussion recovery.47 Hormonal changes during puberty that contribute to migraine headaches might also contribute to sex differences in concussion recovery. Migraine headaches are up to fourfold more common in females than in males after puberty,48, 49 and some evidence suggests that migraineurs recover more slowly after concussion.50, 51 Research is warranted to further delineate sex differences in concussion risk and recovery. In general, adult concussive brain injury is much better understood than its counterpart in children and adolescents. Several points are important to note. First, concussion has multiple, non-harmonized definitions. Second, concussion diagnosis is an imperfect art. Last, in the absence of rapid and inexpensive objective diagnostic measures, concussion remains a clinical diagnosis that is subject to variability—including different thresholds for diagnosis across various subspecialities and across individual physicians, neuropsychologists and athletic trainers—and under-reporting by coaches, parents and young athletes. Without validated diagnostics, concussion will remain a nebulous and under-reported entity, and the accuracy of incidence estimates will continue to be tainted by the differential application of inexact criteria. Repetitive subconcussive trauma can result in structural and functional brain changes.52 White matter abnormalities detected by diffusion tensor imaging (DTI) have been reported in professional soccer players even in the absence of any obvious history of concussions. Compared with swimmers, male professional soccer players showed DTI signal changes suggestive of decreased white matter integrity in several brain regions, which might indicate loss of axonal myelination, similar to changes seen in individuals with mTBI.53 Collegiate ice hockey players exhibited similar white matter changes over the course of a season.54, 55, 56, 57 In addition, repetitive subconcussive head impacts in collegiate American football players have been linked, in a dose-dependent manner, to deficits in BBB integrity, potential loss of white matter integrity, and cognitive dysfunction.58 These findings probably reflect some level of risk for youths who sustain repetitive subconcussive head impacts, although little research has been devoted specifically to this topic. A metric to track head impacts—that is, a 'hit count'—has been proposed,59 and could serve as one factor to determine cumulative risk exposure. One challenge of this approach is to accurately define the parameters of a 'hit', but improved biosensors show some promise in this regard. Similar to a 'pitch count' in baseball, this concept has also recently been proposed for boxers.24 No evidence is currently available to show a causal link between repetitive subconcussive head impacts in youth and dementia later in life, and such metrics could prove invaluable if validated by future studies correlating head impacts with subsequent neuropsychological dysfunction. In adults, TBI, including concussion,60, 61, 62 might increase an individual's risk of developing neurodegenerative disease,63, 64 including AD and chronic traumatic encephalopathy (CTE), a disease associated exclusively with repetitive head trauma.65, 66 TBI may also increase the risk of developing Parkinson disease (PD),67 although the relationship between mTBI and PD risk remains uncertain.68 In paediatric populations, particularly young athletes, the effects of single or repetitive concussions on the risk of later-life neurodegeneration and dementia are unknown. CTE was first described symptomatically in the late 1920s as 'punch-drunk' dementia in boxers,69 was later described as 'dementia pugilistica',70 and was first described pathologically in 1973.71 Since the identification of CTE in a former professional American football player in 2005,72 and additional intensive pathological studies, this condition has gained widespread public attention, and has now been identified in brains of former ice hockey, baseball, rugby and soccer players,73 wrestlers,74 and military veterans.75, 76 The prevalence and incidence of CTE in amateur and professional athletes is still unknown, adding to difficulties in discussing its epidemiology and population risks for athletes. Although CTE is primarily considered to be a neurodegenerative disease that sometimes results from a career of either collegiate or professional contact sports, cases of CTE have been reported in high school athletes.77 This finding suggests that long sporting careers are not required for CTE development, and that youth athletes represent an at-risk population. Emerging evidence suggests that clinical CTE symptoms can be grouped into two common presentations: cognitive and mood–behavioural.78, 79 Subjective memory complaints such as anterograde amnesia are common, as are mood disorders including anxiety or depression,79 and reduced executive function, which can result in disinhibition and impaired decision-making skills.80 These clinical symptoms define disease severity.81 The neurodegenerative pathophysiology of CTE is complex, and the neurological sequelae are poorly understood. In severe cases, the cerebral cortex and medial temporal lobes seem most profoundly affected,81, 82 with pathology characterized by neurofibrillary tangles composed of phosphorylated tau79 and, in some cases, TAR DNA-binding protein 43 pathology.83 CTE is also associated with marked atrophy, notably in the frontal cortex and medial temporal lobe, as well as in the mammillary bodies, thalamus and hypothalamus.79 Confirmed clinical diagnosis of CTE remains autopsy-based.84 Given the uncertainty over whether the tauopathy described in CTE is causative of the clinical phenotype, and the fact that most professional and collegiate athletes do not develop CTE, it is vital to understand whether early exposure to concussion is associated with other forms of neurodegeneration and cognitive dysfunction, including chronic neurocognitive impairment (CNI). Important clinical distinctions exist between CTE and CNI,28, 51 some of which make direct comparisons difficult. CTE is an emerging clinical and pathological condition that involves progressive deterioration of neurological and cognitive function in multiple domains, and is diagnosed primarily at autopsy. Conversely, the CNI phenotype is not necessarily progressive, and is characterized by functional decline from group averages or baseline functioning established before TBI. CNI can be diagnosed clinically through neuropsychological testing. No causal link between CNI and head trauma has yet been confirmed, but a dose-dependent risk has consistently been found in professional athletes.28 In addition, almost half of the studies conducted in amateur athletes have found an elevated risk of CNI.28 Whether similar risk associations are present in younger populations remains to be determined. One hypothesis is that CNI represents a prodromal—but not inevitable—step toward CTE, analogous to the relationship between mild cognitive impairment (MCI) and AD.85, 86 Alternatively, CNI may represent static impairment without degeneration. Our current lack of understanding of the basic biological underpinnings of CNI and CTE underscores the need for more research. Increased knowledge of the biology of both conditions, as well as early detection of CNI in athletes (in particular, youth athletes), may drive interventions to stem the development of further cognitive impairment, and could also aid validation of putative biomarkers. Assessment of CNI via tau imaging may help determine the likelihood of progression to CTE. The field of concussion genetics, especially in paediatric populations, is still in its infancy. Although repetitive head impacts seem necessary for the development of CTE, other factors, including genetics, are likely to have an important role, as most concussed athletes do not develop CTE.87 The genetic risk factors for CTE probably overlap with those that influence susceptibility to and recovery from concussion, and genetic risk factors for AD are providing important clues to the identity of these factors. The ε4 allele of apolipoprotein E (APOE ε4), the most important genetic risk factor for AD identified to date,88 critically affects the CNS injury response,89 in particular, amyloid-β (Aβ) clearance from the brain. The three alleles of APOE confer varying degrees of AD risk: APOE ε2 reduces the risk, APOE ε3, the most common allele, represents baseline risk with which other variants are compared, and APOE ε4 increases the risk.90, 91 Studies suggest an interaction between APOE ε4 and sex, such that APOE ε4-related risk of AD is more prominent in women than in men.92, 93 The APOE genotype acts synergistically with TBI in increasing the risk of AD,94 although its hypothesized risk association with CTE as an outcome of repetitive mTBI requires more study.95 No consensus has yet been reached on the effects of APOE isotype on the outcome of paediatric TBI, but data from adults suggest that APOE ε4 negatively influences concussion outcomes. Several studies indicate that possession of at least one APOE ε4 allele is associated with poorer cognition and lasting neuropsychological impairment after concussion in professional American football players,96 boxers95 and other adults,97, 98, 99, 100 although other studies found no such association.101, 102 Some evidence points to polymorphisms in both the APOE gene and its promoter as contributory factors to concussion risk in college athletes.103, 104 Another study did not identify a role for APOE ε4 in concussion risk,105 although this allele might increase the risk of dementia following midlife or late-life mTBI.106 Drawing conclusions from these conflicting studies is difficult, owing to small sample sizes and differing methodologies. In children, little is known about the relationship between APOE ε4 and neuropsychological outcomes after concussion, and APOE ε4 testing is not routine in paediatric TBI studies. In 2012, Kurowski reviewed the few existing studies and combined the results of three studies107, 108, 109 that used the Glasgow Outcome Scale.110 In the combined sample (252 children), the risk of poor clinical outcomes after 6–12 months was over twofold higher in APOE ε4 carriers than in noncarriers (19% versus 9%). However, these studies included a broad developmental range of children with heterogeneous injuries, and did not account for a possible interaction between age and genotype. In addition, the interaction between APOE and sex has not been studied in the context of concussion. Improved prospective studies are warranted to clarify these connections. Incorporation of genetics into paediatric concussion research is fraught with complicated challenges, including acquisition of parental consent and informed consent for a child, perceived stigmatization of clinical study participants, the actionability of the genetic knowledge obtained, and potential concerns regarding insurability (particularly long-term care insurance). Studies of adults who learn of their APOE ε4+ status demonstrate that many are willing to make lifestyle modifications, including increased exercise and improved medication management,111 as well as increased purchases of health and long-term care insurance.112, 113 Education about new genetic knowledge and corresponding disease risk is essential, as demonstrated by the substantial discordance between an individual's personal feelings about the implications of the acquired knowledge and the actual consequences of increased dementia risk.114 The effects of APOE genetic knowledge on children, their families and decision-making processes regarding participation in impact sports remain unclear. The influence of APOE genotype on concussion risk and recovery in this age group also needs further elucidation. If future studies find that, for any particular level of impact, children with APOE ε4+ status are at greater risk of concussion or poor recovery than are their APOE ε4− peers, consideration should be given to genetic testing of school-age athletes before participation in impact sports. Careful studies of high school and younger athletes are required to fully understand the nuances of genetic influences. Future research into youth concussion outcomes, including cognitive outcomes and risk of dementia, should include APOE genotyping wherever possible. New APOE studies should standardize research methodologies and reporting measures, including the collection of 'common data elements', to ensure valid comparison across studies.110, 115 The APOE genotype is not necessarily a non-modifiable risk factor for concussion recovery: therapies being developed for AD include drugs that modify the interaction between the ApoE4 protein and Aβ, which might also be applicable to paediatric concussion.116, 117 The Val66Met polymorphism in the gene encoding brain-derived neurotrophic factor has been linked to better outcomes after mTBI,118 but worse outcomes after focal penetrating brain injury.119 Polymorphisms in genes involved in dopaminergic signalling may also help to account for the wide range of TBI outcomes.120 In addition, the Rep1 polymorphism in the promoter region of the α-synuclein gene might increase the risk of PD after head injury.121 To advance our understanding of concussion risk and management, large, prospective, population-based genome-wide association studies (GWAS) and whole-genome sequencing studies should be conducted to identify other genetic variants—possibly of low frequency or low penetrance—that modify the risk of prolonged recovery, poor cognitive outcomes or dementia.122 Such studies will require large-scale data sharing, and must address issues of ethics, privacy, and potential implications for insurability and employability. Despite progress in identifying possible cerebrospinal fluid (CSF) and blood-based biomarkers that might be applied to adult TBI management, no clinically validated biomarkers are available for either the adult or the paediatric population. Paediatric concussions present with even greater clinical variability than do adult concussions; therefore, biomarkers have special potential for improving concussion diagnosis in children. Of note, most TBI biomarkers have been studied in the context of moderate to severe TBI, leaving us with obvious gaps in our knowledge of mTBI biomarkers, especially in children. Biomarker development has been critical to the advancement of AD therapeutics. CSF-based biomarkers are already being employed to identify at-risk patients and to improve the design of both epidemiological studies and clinical trials.123 New PET radioligands, such as amyloid-labelling agents (three of which are now FDA-approved), can be used both diagnostically and to improve neuropathology-based patient stratification for clinical trials. Several tau imaging agents are also in human trials, and their utility in tauopathies, including CTE, is rapidly being established. As with fluid-based biomarkers, there are currently no neuroimaging biomarkers sensitive or specific enough to diagnose concussion or CTE in either adults or children. No TBI diagnostic or therapeutic agents have yet been approved by the FDA, and validation of concussion biomarkers could accelerate the development of such agents. Efforts must be made, however, to ensure the cost-effectiveness and wide availability of clinical biomarker testing. Also, given the risks associated with lumbar puncture, ethical concerns regarding sampling of CSF from concussed youths for biomarker research should be addressed. Promising findings in adult fluid-based biomarker research must be explored in paediatric populations. Putative concussion biomarkers have emerged sporadically in the scientific literature over the past few decades, the most prominent being S100 calcium-binding protein B (S100B), a nonspecific marker of astrocyte activation. The presence of S100B in serum may indicate loss of BBB integrity. Elevated serum and CSF levels of S100B have been observed in adult boxers after matches, and correlate positively with the number and severity of head impacts.124, 125 Increased serum S100B levels have also been observed in concussed professional ice hockey players,126 with levels measured 1 h post-concussion predicting symptomatic recovery time. However, S100B levels were also raised after controlled play where no concussions occurred, indicating that this marker is not injury-specific.126 Indeed, S100B serum levels are elevated in adult trauma patients without head injury.127, 128, 129 Other research suggests that initial post-concussion S100B levels are poor predictors of recovery.130 As with all biomarkers, the role of S100B in TBI management in children is even less clear,131 with some arguing that this marker has little diagnostic or prognostic utility in paediatric populations.132 In a study of children with TBI aged ≤15 years, those <5 years or >9 years of age had higher serum levels of S100B than did those aged 5–9 years.133 S100B may, therefore, be an inadequate marker to distinguish between symptomatic and asymptomatic children with concussion,133 and the utility of S100B in diagnostics and outcome prognosis is questionable.134, 135, 136 Neuron-specific enolase (NSE) is a marker of neuronal injury, but its usefulness as a serum or CSF biomarker remains uncertain.133, 134, 135, 136, 137 Elevated serum NSE levels have been observed after head impacts in boxers,124 but were also seen in ice hockey players after a match where no concussions occurred.126 Serum NSE levels failed to predict recovery time after concussion,126 and might not correlate with injury severity in children.133 In children aged ≤15 years, serum NSE levels correlate inversely with age.133 Once released into the blood, NSE has slow elimination kinetics, making it difficult to distinguish primary from secondary neuronal injuries on the basis of NSE levels.138, 139 Neurofilament light chain and glial fibrillary acidic protein (GFAP) are CSF neuron-specific and glial-specific damage markers, respectively, and are both elevated in CSF in adult boxers after fights.125, 137, 140 Little is known about either marker in the context of paediatric concussion, but a preliminary study in children and young adults suggested that serum GFAP levels within 72 h after concussion correlate with symptom burden up to 1 month post injury.141 The neuron-specific protein UCH-L1 (ubiquitin carboxyl-terminal hydrolase isozyme L1) was first linked to neurodegenerative pathology through its involvement in PD,142 and its presence in serum was later identified as a biomarker for severe TBI.143, 144, 145 Serum levels of UCH-L1 may have diagnostic utility in concussion,146 but recent evidence suggests a lack of correlation between elevated serum levels and subconcussive hits.147 The clinical utility of UCH-L1 in paediatric populations warrants further study. Perhaps the most promising advances in adult fluid-based TBI biomarkers concern tau protein. Serum or CSF tau levels are thought to indicate axonal damage, as tau normally resides in axons, where it stabilizes microtubules. Serum tau is proteolytically cleaved,148 and in patients with AD, levels of cleaved tau in CSF might correlate with cognitive function.149 Tau levels in CSF and blood are elevated in boxers after a match, and CSF tau levels correlate with the quality and quantity of head impacts.125, 150 Recent evidence suggests that tau levels are elevated in the blood of ice hockey players after concussion, and may be useful in predicting recovery time.126 Questions remain, however, with several studies reporting little or no value of serum cleaved tau for predicting post-concussion syndrome or long-term outcomes.130, 151 The potential of tau as a biomarker in children remains unclear, with no studies conducted to date. In fact, the reliability of serum tau as a biomarker has not yet been established for any indication. The likelihood is that no single biomarker will suffice to diagnose paediatric concussion or predict outcomes. In addition, few studies have examined the interactions between genetic make-up and putative biomarkers. As our understanding of the relationships of biomarkers to injury severity and to each other increases, development of biomarker panels, perhaps incorporating inflammatory and oxidative markers,152 should be considered. Future studies should attempt to further define these relationships and establish the clinical value of biomarker panels, factoring in commercial cost and practical feasibility. Recent advances in metabolomics, lipidomics and proteomics—in particular, the search for metabolomic and lipidomic markers for AD—might inform future research into biomarkers for concussion and subconcussive injuries. Several recent studies propose altered metabolite and lipid profiles associated with MCI and AD.153, 154, 155, 156 Data from animal models suggest that lipid and metabolite changes accompany both acute and chronic post-concussion periods, and could be useful for predicting recovery trajectory,157, 158 but these findings have yet to be validated in humans. Expanding the biomarker search beyond blood and CSF to saliva and urine159 might improve the ability to obtain measurements rapidly and noninvasively, particularly from children. Sampling of CSF from children, particularly when rapid assessment is desirable, is largely impractical. Mondello et al. proposed a set of useful criteria for evaluating TBI biomarkers that should allow more-streamlined development and validation.137 Any validated biomarker panel must, inevitably, be a component of a larger, multimodal diagnostic suite that may include structural and functional imaging and neuropsychological testing. When designing future biomarker studies, the potential for FDA approval should be considered, in order to expedite approval for clinical use. Although concussion remains a clinical diagnosis, neuroimaging techniques are improving our understanding of the structural and functional consequences in adults. Neuroimaging in paediatric populations may be limited by several factors; for example, measurements of longitudinal changes after concussion are complicated by the background of a dynamic, immature brain. No imaging techniques have been validated as diagnostic tools for concussion, and the correlation between imaging findings and clinically measurable cognitive or behavioural functions is variable. Tools such as volumetric imaging, DTI and functional MRI (fMRI)—in particular, arterial spin labelling—are currently being explored.160, 161 Fractional anisotropy (FA), as measured by DTI, allows inference of the structural integrity of white matter tracts, which are commonly disrupted after TBI. The clinical implications of FA change remain controversial, as both increased and decreased FA has been observed in concussion studies.162, 163, 164, 165, 166 These discrepancies may be due, in part, to the considerable spatial heterogeneity in the brain areas examined,167 as well as differences in the post-injury interval. FA may still have prognostic value, with evidence suggesting that the direction and magnitude of change correlates with clinical outcomes;166, 168 however, this idea awaits validation in both paediatric and adult populations. FA might lack the necessary sensitivity to fully appreciate changes in white matter tract integrity following brain injury, and measures of diffusivity may be more appropriate.169 The DTI field would benefit greatly from the development of normative data sets against which to gauge observed changes. Pre-game versus post-game and season-long studies of young athletes could employ serial DTI imaging to establish normative data for a particular individual, but the utility of the data when pooled is unclear. The scarcity of normative paediatric data severely limits the clinical usefulness of neuroimaging techniques, including DTI. Studies of 'return-to-baseline' neuroimaging after paediatric concussion are also needed, as they could greatly improve prediction of recovery. Although automation has increased reproducibility, DTI measurements remain sensitive to the hardware and software specifics, acquisition parameters and analysis software, which limit reproducibility, standardization and comparison between centres and across studies. Efforts to standardize DTI across imaging centres are underway.170 MRI has been particularly successful in mapping the brain's 'connectome'—the collection of structural and functional neural connectivity networks and their respective focal nodes—and for studying how concussion affects these networks. Focal or diffuse TBI can disrupt the brain's functional connectivity, resulting in dysfunction of multiple networks including the default mode and salience networks, which have been implicated in memory, emotion and mood.171 Network dysfunction might have a stronger influence on recovery than does lesion location,171, 172, 173 but the long-term implications for brain development and cognitive function remain unclear.26, 174 Further studies of network connectivity dysfunction in children after concussion will be critical to improve injury prognostication and management. Radiotracers for PET imaging have the potential to advance the diagnosis and treatment of concussion and CTE, but their use in paediatric populations is purely investigational at present. Three FDA-approved radiolabelled imaging agents are currently available for detecting brain amyloid in patients with suspected AD.175 In adults, some cases of concussion are associated with acute Aβ pathology. PET scanning could enable paediatric patients to be monitored for the presence and persistence of acute post-concussion amyloid, and to determine whether scan positivity and negativity predict different outcomes.176, 177 Other PET imaging agents with potential utility in paediatric populations include new tracers that bind neurofibrillary tangles composed of tau. Early imaging results with 18F-T807, 18F-T808 and 18F-THK5105 are proving to be useful in confirming the presence of tauopathy in various clinical situations, including AD.178, 179, 180 In a recent AD study, the magnitude of tau tracer signal correlated positively with the stage of disease and severity of cognitive impairment.180 A third tau PET tracer, 11C-PBB3, has been tested in healthy individuals and patients with AD, and may be able to detect non-AD conformations of tau.181 In addition, a recent report contains the first description of tauopathy imaging in a living person with suspected sports-associated CTE.177 Given the extent of chronic tau pathology in concussion, repetitive subconcussive injury and CTE, tau tracers may be useful as diagnostic and prognostic biomarkers (for example, to distinguish CNI from CTE). Studies with these tracers in adults with CTE are underway, but their use in paediatric populations will depend on future research to determine whether tau pathology is present in young patients after TBI or concussion. A PET tracer for the microglial cholesterol transporter protein might be useful for imaging of neuroinflammation associated with TBI.182 New PET ligands to image brain microglia, which are being developed with potential utility in neurodegenerative diseases, may also prove useful in concussion and CTE management. Exploration of these PET ligands in paediatric populations with concussion and TBI would be informative, but risk–benefit analyses must be performed before embarking on studies involving radiotracers in this age group. The ultimate utility of any PET imaging agent will depend on its diagnostic and prognostic value as part of a multimodal panel of biomarkers and neuroimaging techniques. Noninvasive techniques such as transcranial magnetic stimulation (TMS) have also uncovered changes in synaptic plasticity following TBI and concussion,183 particularly in asymptomatic individuals.184, 185, 186 Several small TMS studies of young athletes in their early 20s with a history of concussion suggest imbalances in γ-aminobutyric acid and/or glutamate neurotransmission in the motor cortex that are associated with deficits in synaptic long-term potentiation and depression.184, 185, 187, 188 TMS has also revealed that concussion-related impairments in synaptic plasticity can impair aspects of motor learning,188 and that these deficits are detectable decades after an individual's last concussion.189 Another crucial noninvasive tool for detecting neurochemical dysfunction associated with concussion is proton magnetic resonance spectroscopy (MRS). Reports specifically addressing the use of spectroscopy following sports-related concussion suggest various abnormalities consistent with neurochemical alterations.190 In younger (high school) athletes, increased glutamate and glutamine levels were detected by MRS at post-season versus pre-season evaluation, even in players who had not experienced clinically significant concussion during the season.191 Such findings suggest that even subconcussive head impacts can result in the activation of glutamate pathways, implying cellular injury or neuronal death, despite the absence of symptoms. Levels of creatinine and myoinositol (an organic osmolyte located in astrocytes192, 193) were also significantly altered in a subset of the participants in the aforementioned study. In a rare longitudinal study utilizing MRS,194 individuals who sustained a single sports-related concussion exhibited significantly reduced levels of N-acetylaspartate (NAA, a marker of neuronal and axonal health, integrity and functioning195) in the brain 3 days after injury. Levels were increased at 15 days post injury, and reverted to control values at 30 days post injury. By contrast, participants who sustained a second concussion 10–13 days after their initial concussion displayed a prolonged reduction in NAA levels, which had not normalized even 45 days post injury. These results suggest that repeated injury within a short time frame increases the likelihood of protracted or incomplete recovery. In addition to the acute and subacute alterations detected by MRS, other studies of the long-term effects of concussion have disclosed increased myoinositol (associated with glial proliferation) and decreased choline (associated with membrane turnover195) levels in the medial temporal lobe in otherwise healthy former athletes who sustained their last concussion more than three decades prior to testing.196 Another recent study examined a cohort of symptomatic retired National Football League players, using an advanced MRS method called correlated spectroscopy (COSY), which can measure additional metabolites.197 The authors identified increased choline and glutamate–glutamine levels (indicative of diffuse axonal injury and excitotoxicity, respectively), consistent with previous mTBI MRS studies, as well as additional cerebral metabolites that were indicative of neuroinflammatory changes. These metabolic changes may provide insight into mechanisms of injury, such as excitotoxicity and/or inflammation, which could underlie the reported structural changes. Overall, the available data support the use of MRS as a research tool to identify altered neurophysiology and monitor recovery in adult athletes, even following resolution of post-concussive symptoms. At present, MRS-detected biochemical alterations may enhance our understanding of the underlying pathophysiology, but do not yet provide specific diagnostic information. Larger cross-sectional, prospective and longitudinal studies are needed to determine the sensitivity and prognostic value of MRS within the field of sports-related concussion.190 Because the interpretation of MRS in the immature brain requires certain developmental considerations, appropriate comparison samples will be needed for future work in children. MRS techniques with greater spectral resolution, including COSY, might provide additional biochemical specificity.197 Other advances in spatial resolution, such as 3D chemical shift imaging, may also provide greater specificity by allowing the investigation of metabolic alterations throughout the brain rather than in specific regions of interest. Finally, MRS could have a role in measurement of treatment effects, such as those induced by transcranial direct current stimulation198 and TMS.199 The mechanisms and surveillance infrastructure for sports-related injury measurement, reporting, tracking and data sharing are insufficient for current needs and objectives. Concussion research and clinical efforts are hindered by a lack of concussion data across sports and playing levels. A 2014 Institute of Medicine report identified only three national sports injury surveillance systems: the National Electronic Injury Surveillance System—All Injury Program (NEISS-AIP), the National Collegiate Athletic Association Injury Surveillance System (NCAA ISS), and the High School Reporting Injury Online (RIO™).1 These systems can be supplemented with clinical data (for example, from emergency departments, hospitalized inpatients and sports clinics), but these data are biased toward more-severe injuries and patients of higher socioeconomic status. Indeed, schools in rural areas or communities with lower socioeconomic status often have limited access to sports medicine care professionals and facilities. Several emerging programmes may improve surveillance. Regional efforts such as Clinical Outcomes Research Education for Athletic Trainers (CORE-AT) and national efforts such as the National Athletic Trainers' Association National Athletic Treatment, Injury and Outcomes Network (NATA NATION™) attempt to integrate injury tracking with treatment and outcomes data at the high school and collegiate levels. However, none of these systems specifically capture injuries to younger athletes, those participating in non-school sponsored sports, or those at schools without athletic trainers. Sports injury databases also rarely account for demographic factors including socioeconomic status, race or ethnicity, and health-care coverage. Currently, no effective mechanisms exist to consistently and inexpensively link various surveillance data sets, or to follow up individual athletes across sports, tracking systems or the age continuum. There is a considerable need for a system that tracks individual athletes through their playing careers and beyond. Each individual should be tracked for several decades to establish if, when and how a given burden of TBI evolves into CTE, and to assess all the possible negative health outcomes associated with concussion. Such a system would also provide more-accurate descriptions of concussion history and exposure to risk factors, and could capture both short-term and long-term outcomes, including measures of physical and mental health, academic and career success, quality of life and social connectivity, and evolving socioeconomic status. Such efforts are challenged by a variety of issues, including a lack of mandatory reporting of concussion at any level. Mandatory concussion reporting, funding for surveillance efforts, and provision of training to data reporters (for example, coaches and athletic trainers) would greatly improve epidemiological research. However, mandatory reporting will not provide meaningful results without validated, consensus definitions for concussions, and development of a universal data repository and a global unique identifier (GUID) system. Data sets from standardized surveillance efforts could then be linked, thereby improving data sharing for research and clinical care. Coupling of surveillance data with standardized collection, storage and curation infrastructures for biobanking of tissue and fluid samples could dramatically improve injury and outcomes research.200 These efforts might be catalyzed by funding from public–private partnerships, and made actionable by setting realistic short-term and long-term goals to create a multi-year plan. However, in the USA at least, such efforts are currently hampered by misunderstanding of Health Insurance Portability and Accountability Act (HIPAA) regulations and general concerns for athlete confidentiality. Wider use of computerized neurocognitive testing (CNT) for athletes could improve concussion surveillance, as well as diagnosis and management. However, several important challenges must be overcome before CNT becomes routine. These challenges include a lack of standardized administration protocols, the potential for technological errors arising from different computer hardware, limits in the types of cognitive functions assessed, and a lack of qualified test administrators and data interpreters.201 Despite these shortcomings, however, CNT is already used by approximately 40% of US high schools that employ athletic trainers.202 Though not affordable for all schools, CNT could enhance ground-level data collection and aid risk-exposure estimation and post-concussion recovery tracking, as well as increasing the quality of data reported to sports injury surveillance networks. CNT may be also useful in evaluating and tracking post-concussion cognitive improvement or decline, and could have utility in predicting outcomes.203, 204 Whether CNT data collected in the school setting will reach the validation and reproducibility standards achieved by CNT conducted by a clinical research team remains to be seen. Importantly, CNT needs standardization and guidelines for determining 'return to play' and 'return to learn' for athletes who show recovery in one domain but are still symptomatic in others. More research is required on the utility of CNT, both in the clinic and for concussion surveillance and management of youth athletes. In several critical areas, incomplete knowledge hampers meaningful advances in the field of paediatric concussion. At the molecular and cellular levels, research that focuses on axonal damage after concussion and repetitive subconcussive injury is urgently needed to elucidate changes in axonal trafficking and repair, and to better define the role of transient Aβ accumulation as a potential driver of downstream and/or future pathology. Concussion researchers may need to identify more-suitable animal models to study molecular pathology, including tau and its contribution to post-concussion and CTE pathologies, as the structure and organization of the brain differs dramatically in rodents and humans. Without a clearer understanding of how TBI changes the young, still-developing brain, and what pathological events happen in the weeks, months and years following injury, we are left to speculate about the underlying biological bases of such changes. Head impact data collection and risk assessment in youth sports might be improved through use of sensor technologies that record linear and rotational forces. Such commercially available devices, if validated, could determine levels of cumulative head impact forces during games and across seasons of play, and the findings could be linked to neuroimaging data and functional outcome assessments. Combined with 'hit-count' metrics, sensor data may improve knowledge of short-term and long-term neuropsychological outcomes of repetitive subconcussive impacts. Our knowledge of CTE might be improved by understanding baseline rates in the general population, in injured athletes, among uninjured athletes matched by sport and playing positions, and in 'control' athletes in low-risk sports. Improved knowledge of risk exposures could lead to prevention efforts, including practice and competition rule changes. A decades-long, prospective, longitudinal study, following youth athletes through their sporting careers and beyond, would provide more-definitive knowledge of cumulative head impacts and risks of long-term neuropsychological dysfunction and dementia. Such a study is underway in NCAA alumni, who were first studied in 2003 and were re-assessed in 2013.29, 205 Studies in other populations, especially if NIH-funded, would probably begin with a 5-year study that could be renewed in further 5-year increments. Public–private partnerships are likely to be required to secure enough funding to involve multiple study centres. The NCAA has provided partial sponsorship for the 10-year re-assessment of over 100 athletes, but further funding from the NIH, the US Department of Defense (DoD), and private philanthropic sources will be required to extend the range of assessment from neuropsychology, through MRI, to molecular imaging for amyloid, tau and/or inflammation. Ideally, the longitudinal study design should combine epidemiological and interventional trial methodologies and utilize multiple control groups, including non-contact athletes and uninjured impact sport athletes. A longitudinal study would also shed light on the role of cognitive reserve. A precedent for such studies has been established by the late-life dementia research community, using NIH funds and public–private partnerships involving pharmaceutical companies and foundations. For such studies to be successful, additional surveillance systems and data repositories must first be established. Efforts would be accelerated if athletes participating in impact sports had universal access to athletic trainers, who could act as reliable data reporters while promoting safety and providing basic care. In addition, any longitudinal studies must include postmortem analyses to better understand the influence of childhood and young-adult concussions on the development of neurodegenerative pathology and dementia in later life. 'Return-to-play' guidelines are currently hampered by a lack of rigorous epidemiological evidence, and could be greatly improved by long-term safety data from longitudinal studies.206 Longitudinal research could also include studies to determine whether those athletes who fail to follow guidelines experience any negative health effects, such as lingering symptoms or altered risk of incurring a second concussion. The infrastructure for a long-term prospective study might be created through the formation of a research consortium modelled after the Alzheimer's Disease Neuroimaging Initiative (ADNI). ADNI has set standards for data collection, dissemination agreements, testing methodologies, and biomarker collection and analysis. A version of ADNI currently underway with participation of the DoD (ADNI-DoD) is focused on blast-related TBI research in military populations.207 In May 2014, in addition to the NCAA Concussion Study, the NCAA and the DoD announced the launch of the largest prospective sports-related concussion study to date, which will monitor approximately 37,000 NCAA athletes over 3 years. One can envision how this study's infrastructure may eventually be extended to study younger athletes over an extended longitudinal range. Many gaps remain in our knowledge of the biology of TBI, which limit our ability to develop effective drugs. These gaps must be filled if we are to tackle the underlying disease pathology and move beyond treating the symptoms. However, much can be accomplished while research into fundamental TBI biology continues. Drug repurposing involves testing of existing FDA-approved drugs for new indications, and can reduce expense and shorten the path for drug approval. Current repurposing trials include methylphenidate for pain and mental fatigue,208 the dopamine receptor agonist bromocriptine for working memory,209 and the antidepressant sertraline for mood and anxiety, the most frequent neuropsychological complications that influence long-term outcomes after concussion.210 Larger randomized clinical trials should be conducted before these drugs can be introduced into clinical practice for these new indications. In addition, the recent failure of the PROTECT phase III trial of progesterone to improve outcomes after acute TBI211 may serve as a reminder of the need for more research to better understand the fundamental biology underlying TBI. Although many drug repurposing efforts are designed primarily to address concussion symptoms, the drugs may also influence injury pathology and progression. Research on established drugs can also lead to new drug discovery efforts and, potentially, new preventive or management therapeutics. New drugs are urgently needed for TBI and concussions that do not resolve. Drug discovery efforts in the areas of neuroprotection and anti-inflammation are especially relevant because of their potential cross-applicability to neurodegenerative diseases such as AD. Similarly, drugs currently in development for other neurodegenerative diseases might be repositioned for testing in patients with TBI or nonresolving concussion symptoms. As is often the case in medical research, recent advances in concussion research raise as many questions as they answer. Evidence exists for long-term neuropsychological dysfunction and later-life dementia after concussions or repetitive subconcussive head impacts, and more work is needed to better understand the implications and outcomes of youth participation in impact sports. As outlined in this Expert Consensus Document, there is a path forward, but achieving the goals outlined here will require public and private sector cooperation. While recommendations can be improved with increased knowledge, the available evidence can still inform individual decision-making when considering youth sport participation, as well as practice policies and competition rules. With an ageing population and a looming epidemic of dementia, we must learn more about potential early-life risk factors, including sports-related concussion. The choices made by parents, coaches, school boards and children will be better informed when the critical gaps in scientific knowledge of concussion are filled. Download references|与运动相关的脑震荡和重复性亚震荡暴露越来越被认为是儿科人群的潜在危险，但是对于这些事件的短期和长期后果，包括潜在的认知障碍和晚年痴呆的风险，仍然知之甚少。这份专家共识文件是由全球安全儿童、阿尔茨海默氏症药物发现基金会和安德鲁斯矫形外科和运动医学研究所召集的为期一天的会议的结果。目标是强调在脑震荡科学、痴呆症、遗传学、诊断和预后生物标志物、神经影像学、运动损伤监测和信息共享等领域的知识差距和亟需研究的领域。针对这些领域，我们提出了明确和可实现的途径，以提高对青少年体育相关脑震荡的理解、治疗和预防。2009年，美国年龄 < 19岁的个体中记录了约250,000例非致命性创伤性脑损伤(TBI)。1疾病控制和预防中心估计，5-18岁的年轻人维持着所有运动相关脑震荡的65% 。2尽管最近在诊断性脑成像方面取得了进展，并且在我们对脑震荡物理学的理解方面，长期的认知结果仍然知之甚少。由于脑震荡的身体、认知和情感后果引起了公众的广泛关注，我们对如何预防、诊断和治疗这种伤害的不完整知识危及我们儿童的总体健康，特别是他们的大脑健康。这份专家共识文件是儿科和成人创伤性脑损伤、阿兹海默病(AD)研究、遗传学、流行病学、生物伦理学和运动医学领域专家为期一天的会议的结果(专栏1) ，该会议于2013年11月由全球安全儿童、阿尔茨海默氏症药物发现基金会和安德鲁斯矫形外科和运动医学研究所召集。我们的主要目标是强调我们在儿童和青少年脑震荡知识方面的重大差距。我们强调需要进行研究的领域，如开发诊断和预测性生物标志物，阐明遗传风险因素，以及预测短期和长期结果。在我们的结论中，我们提出了提高我们对与运动相关的儿童脑震荡的长期后果的理解的途径。术语“脑震荡”经常与术语“轻度 TBI”(mTBI)交替使用，考虑到脑震荡后可能的脑损伤程度和慢性神经心理功能障碍的潜在可能性，这是一种潜在的误导性做法。然而，我们应该强调的是，大多数脑震荡不会产生后遗症。美国康复医学会将 mTBI 定义为格拉斯哥昏迷量表3评分为13-15分，意识丧失 < 30分钟，创伤后遗忘持续时间 < 24小时。脑震荡描述了损伤表型的异质混合物，取决于许多因素，包括头部撞击的大小，位置和方向。尽管缺乏宏观结构发现，脑震荡损伤涉及由线性和旋转剪切力破坏轴突和膜功能(弥漫性轴突损伤，5离子通量和谷氨酸兴奋毒性)引起的原发性神经元损伤，随后是继发性病理生理效应，包括线粒体氧化应激，脑血流中断，血脑屏障(BBB)完整性受损，突触功能障碍和神经炎症。持续的神经心理学脑震荡后症状(脑震盪症候群)包括情绪障碍(例如抑郁症) ，难以集中和记忆问题(方框2)。年轻运动员的脖子和躯干比老年人的脖子和躯干更弱，因此，造成脑损伤所需的力量更少。发育中的大脑也可能特别容易受到由头部创伤的剪切力引起的轴突损伤，这在美国青年足球中可以超过100g 的线性加速力。然而，青年运动中持续的平均力量通常会小于较高水平的运动。正确的突触发育对认知和行为健康至关重要。神经发生、竞争性突触消除(“修剪”)、髓鞘形成、轴突和树突树枝化等过程在产前发育的整个生命周期中持续进行。额叶和颞叶是最后成熟的区域，人类在20岁出头的时候经历了这些区域的修剪[16] ，因此这些仍在发育的区域的损伤可能对大脑产生病理生理效应，增加了以后生活中出现神经心理问题的可能性。轴突髓鞘形成在青春期持续到20岁出头，易受损伤的影响。职业拳击手大脑健康研究的早期结果表明，第一次接触拳击比赛的年龄越早，尾状核体积损失越大，额叶轴突损伤越严重。这项研究对拳击手和追踪研究综合格斗拳击手进行了5年的研究，他们都经历过重复性的脑震荡和脑震荡。23,24年轻的大脑也有一些有助于恢复的特征。已经显示，这个年龄组的神经可塑性增加有助于局灶性损伤后更好的结果[25]。此外，发育中的动物对重复 TBI 的葡萄糖代谢障碍的窗口比成年动物更短[26]。总的来说，发育中的大脑在 TBI 后显示出脆弱性和恢复力。这些相互交织的因素可能解释了脑震荡和重复 mTBI 对年轻人和成年人大脑影响的差异。应高度重视对脑震荡风险采取保守的方法，并加大努力调查这些发育差异。大多数人ーー无论老少ーー从脑震荡中完全恢复过来。在儿童中，可能影响康复的因素包括年龄和脑震荡史。27,28在一项研究中，大约90% 的年轻成年男运动员在21天内经历了症状恢复。然而，在一项针对11-22岁患者(包括所有脑震荡原因，而不仅仅是运动相关)的急诊科研究中，15% 的样本在受伤后90天仍然表现出脑震荡后症状，包括头痛，头晕，“精神模糊”和抑郁。一些研究表明，美国高中橄榄球运动员从脑震荡中恢复的速度比大学运动员和职业运动员要慢。尽管最近一项包括青春期前年龄组(11-12岁)的研究表明，脑震荡后恢复持续时间可能与年龄没有线性关系，但与高中以下青少年的直接比较尚未发表[30] ，因为这个样本中的青少年恢复时间比青春期前的儿童更长。这些发现加在一起，意味着男性青春期年龄组的恢复时间较长的独特风险。对年幼儿童和女性的进一步研究将大大提高我们评估和减轻整个儿科和青少年年龄段风险的能力。在新的脑震荡发生前1年内遭受一次或多次脑震荡的青少年报告出现更长时间的症状，30表明可能存在“脆弱性窗口”，并将先前受伤的青少年置于更高的长期恢复风险中。11-18岁的青少年在急诊室出现脑震荡后发生脑震盪症候群的可能性比5-10岁的儿童高出近80% ，同样，伴有头痛的儿童和青少年出现脑震盪症候群的风险增加了一倍。在 mtBI 后在急诊室接受治疗的儿童中，6岁以上的儿童在受伤后3个月报告持续症状的发生率高于6岁以下的儿童。当然，获得 < 6岁儿童脑震荡症状的准确信息的能力可能受到缺乏症状自我意识和有效沟通这些症状的必要语言技能的限制。此外，从这些报告中不可能直接比较损伤的严重程度; 事实上，各种损伤的身体异质性，加上个体从脑震荡中恢复的先天能力，使得这种比较具有高度挑战性。一些专业研究中心正在使用“智能头盔”来标准化头部撞击产生的体力和角加速度，目前正在研究这些头盔用于测量和预测可能导致脑震荡的影响。36,37从脑震荡中恢复的年轻人可能会经历重大挑战，包括社会和学术发展的改变，38,39,40在一般智力测试中得分较低，以及学校表现下降(以年级平均分衡量)。39较低的父母教育水平和儿童学业成绩都与较差的脑震荡恢复相关。人格特质也起到了一定的作用，例如，伤前焦虑是运动性脑震荡后长时间恢复的一个危险因素。42年轻的男女运动员都有脑震荡的危险，但是女孩的脑震荡发生率高于男孩，特别是在高中和大学的足球、篮球、棒球或垒球比赛中。28,43,44,45解释这些差异的因素仍然不确定，但可能包括保护装备的质量，脑震荡症状的识别和报告，以及颈部长度和颈部肌肉力量。46男女之间在恢复轨迹方面的差异也知之甚少。然而，最近的一项研究表明，女性黄体酮水平影响脑震荡后的恢复。47青春期激素变化导致偏头痛，也可能导致脑震荡后恢复的性别差异。在青春期后，女性偏头痛的发病率是男性的四倍[48,49] ，一些证据表明，偏头痛患者在脑震荡后恢复较慢[50,51]。有必要进一步研究脑震荡风险和恢复的性别差异。一般来说，成人脑震荡比儿童和青少年脑震荡更容易理解。有几点值得注意。首先，脑震荡有多种非协调的定义。其次，脑震荡诊断是一门不完善的艺术。最后，在缺乏快速和廉价的客观诊断措施的情况下，脑震荡仍然是一种临床诊断，受到变异性的影响，包括不同亚专业和个体医生、神经心理学家和运动训练员的诊断阈值不同，以及教练、家长和年轻运动员报告不足。如果没有经过验证的诊断，脑震荡将仍然是一个模糊和报告不足的实体，发病率估计的准确性将继续受到不确切标准的差别应用的影响。重复性次级脑震荡可导致大脑结构和功能的改变。52弥散张量成像(DTI)检测到的白质异常在职业足球运动员中已有报道，即使没有任何明显的脑震荡史。与游泳运动员相比，男性职业足球运动员表现出 DTI 信号改变，提示几个大脑区域的白质完整性降低，这可能表明轴突髓鞘形成的丧失，类似于 mTBI 患者的改变。53名大学冰球运动员在一个赛季中表现出类似的白质变化。54,55,56,57此外，美国大学生橄榄球运动员重复性亚震荡性头部撞击已经以剂量依赖性方式与 BBB 完整性缺陷，白质完整性潜在丧失和认知功能障碍有关。58这些研究结果可能反映了持续遭受重复性次生脑震荡撞击的青少年的某种程度的风险，尽管很少有专门针对这一主题的研究。一个跟踪头部影响的指标ーー即“命中次数”ーー已经提出，59可以作为确定累积风险敞口的一个因素。这种方法的一个挑战是准确定义“命中”的参数，但改进的生物传感器在这方面显示出一些希望。与棒球中的“投球次数”类似，这个概念最近也被提出用于拳击运动员。24目前没有证据表明青少年重复性脑震荡冲击与晚年痴呆之间的因果关系，如果未来的研究将头部冲击与随后的神经心理功能障碍相关联，这些指标可能被证明是无价的。在成年人中，包括脑震荡在内的脑外伤可能会增加个体发生神经退行性疾病的风险，包括 AD 和 CTE (CTE) ，这是一种仅与重复性头部创伤相关的疾病[65,66]。尽管 mTBI 和 PD 风险之间的关系仍然不确定，但 TBI 也可能增加发生帕金森氏症的风险[67]。在儿科人群，特别是年轻运动员中，单次或重复性脑震荡对晚年神经退行性疾病和痴呆风险的影响是未知的。CTE 在20世纪20年代后期首次被症状性描述为拳击运动员的“拳击醉”痴呆，69后来被描述为“痴呆拳击”[70] ，并在1973年首次被病理学描述[71]。自2005年在一名前职业美式足球运动员身上发现 CTE 以来,这种病症已经引起了公众的广泛关注，目前已经在前冰球、棒球、橄榄球和足球运动员、73名摔跤运动员、74名退伍军人的大脑中发现。75,76业余和职业运动员慢性创伤性脑病的患病率和发病率仍然是未知的，这增加了讨论其流行病学和运动员的人口风险的困难。虽然慢性创伤性脑病主要被认为是一种神经退行性疾病，有时是由大学或专业接触性运动的职业生涯造成的，但在高中运动员中也有慢性创伤性脑病的报道。这一发现表明，慢性创伤性脑病的发展并不需要长期的运动生涯，青年运动员代表着高危人群。新出现的证据表明，临床的慢性创伤性脑病症状可以分为认知和情绪行为两种常见表现[78,79]。主观记忆症状如顺行性遗忘症是常见的，包括焦虑或抑郁在内的情绪障碍也是常见的[79] ，并且执行功能降低，这可能导致去抑制和决策技能受损[80]。这些临床症状定义了疾病的严重程度[81]。慢性创伤性脑病的神经退行性病理生理学是复杂的，对神经系统后遗症的了解很少。在严重的情况下，大脑皮层和内侧颞叶似乎受到最深刻的影响，81,82与病理学拥有属性由磷酸化 tau79组成的神经原纤维缠结，在某些情况下，TAR DNA 结合蛋白43病理学。CTE 也与明显的萎缩有关，特别是在额叶皮层和内侧颞叶，以及在乳头体，丘脑和下丘脑。79确诊的 CTE 临床诊断仍以尸检为基础。鉴于慢性脑震荡中描述的重复病变是否引起临床表型的不确定性，以及大多数专业和大学运动员不发展慢性脑震荡的事实，了解早期暴露于脑震荡是否与其他形式的神经退行性疾病和认知功能障碍(包括慢性神经认知障碍(CNI))相关至关重要。CTE 和 CNI 之间存在重要的临床区别，其中一些使得直接比较困难。CTE 是一种新出现的临床和病理状况，涉及多个领域的神经和认知功能的进行性恶化，主要在尸检中诊断。相反，CNI 表型并不一定是进行性的，而是拥有属性功能从组平均值或基线功能下降到创伤性脑损伤之前的水平。CNI 可以通过神经心理测试进行临床诊断。CNI 与头部创伤之间的因果关系尚未得到证实，但在专业运动员中一直发现剂量依赖性风险。此外，在业余运动员中进行的几乎一半的研究发现 CNI 的风险升高。年轻人群中是否存在类似的风险关联仍有待确定。一个假设是 CNI 代表了慢性创伤性脑病的前驱症状，但并非不可避免，类似于轻微认知障碍和 AD 之间的关系。另外，CNI 可能代表静态损伤而不退化。我们目前对 CNI 和 CTE 的基本生物学基础缺乏了解，这强调了进一步研究的必要性。对这两种情况的生物学知识的增加以及运动员(特别是青年运动员) CNI 的早期检测可能会推动干预措施以阻止进一步认知障碍的发展，并且还可能有助于验证推定的生物标志物。通过 tau 成像评估 CNI 可能有助于确定进展为 CTE 的可能性。脑震荡遗传学领域，特别是在儿科人群中，仍然处于起步阶段。尽管重复的头部撞击似乎对于 CTE 的发展是必要的，但是包括遗传学在内的其他因素可能具有重要作用，因为大多数脑震荡运动员不发展 CTE.87 CTE 的遗传危险因素可能与影响脑震荡易感性和恢复的因素重叠，AD 的遗传危险因素为这些因素的身份提供了重要的线索。E型载脂蛋白质的 ε4等位基因(APOEε4)是迄今为止发现的 AD 最重要的遗传危险因素，它严重影响中枢神经系统的损伤反应，特别是从大脑中清除淀粉样蛋白 -β (Aβ)。APOE 的三个等位基因赋予不同程度的 AD 风险: APOEε2降低风险，APOEε3是最常见的等位基因，代表与其他变体进行比较的基线风险，APOEε4增加风险。90,91研究表明 APOEε4与性别之间存在相互作用，因此 APOEε4相关的 AD 风险在女性中比在男性中更为突出。92,93 APOE 基因型与 TBI 协同作用增加 AD 的风险[94] ，尽管其与 CTE 作为重复 mTBI 的结果的假设风险相关性需要更多的研究。关于 APOE 同种型对儿童 TBI 结果的影响尚未达成共识，但来自成年人的数据表明 APOEε4对脑震荡结果有负面影响。一些研究表明，拥有至少一个 APOEε4等位基因与美国职业橄榄球运动员，96名拳击运动员95和其他成年人97,98,99,100的脑震荡后认知较差和持续的神经心理障碍有关，尽管其他研究没有发现这种关联。101,102一些证据表明 APOE 基因及其启动子的多态性是大学生运动员脑震荡危险的促成因素。另一项研究没有确定 APOEε4在脑震荡风险中的作用[105] ，尽管这个等位基因可能增加中年或晚年 mTBI 后痴呆的风险。106由于样本量小，方法不同，很难从这些相互矛盾的研究中得出结论。在儿童中，对于 APOEε4与脑震荡后神经心理学结果之间的关系知之甚少，而且 APOEε4测试在儿科 TBI 研究中并不常规。2012年，Kurowski 回顾了少数现有的研究，并结合了使用格拉斯哥结果量表的三项研究的结果[107,108,109]。在合并样本(252名儿童)中，6-12个月后不良临床结果的风险在 APOEε4携带者中高于非携带者(19% 比9%)。然而，这些研究包括了广泛的异质性损伤儿童的发育范围，并没有考虑到年龄和基因型之间可能的相互作用。此外，APOE 与性别之间的相互作用尚未在脑震荡的背景下进行研究。改进的前瞻性研究有助于澄清这些联系。将遗传学纳入儿科脑震荡研究充满了复杂的挑战，包括获得父母同意和儿童的知情同意，临床研究参与者的感知耻辱，获得的遗传知识的可行性以及关于可保性(特别是长期护理保险)的潜在担忧。对了解 APOEε4 + 状态的成年人的研究表明，许多人愿意改变生活方式，包括增加运动和改善药物管理[111] ，以及增加购买健康和长期护理保险[112,113]。关于新的遗传知识和相应的疾病风险的教育是必不可少的，正如个人对获得的知识的影响的个人感觉与痴呆风险增加的实际后果之间的实质性不一致所证明的那样.114 APOE 遗传知识对儿童，其家庭和参与影响性体育的决策过程的影响尚不清楚。APOE 基因型对该年龄组脑震荡风险和恢复的影响也需要进一步阐明。如果未来的研究发现，对于任何特定水平的影响，具有 APOEε4 + 状态的儿童比其 APOEε4同龄人具有更大的脑震荡或恢复不良的风险，则应考虑在参加影响性运动之前对学龄运动员进行基因检测。要充分理解基因影响的细微差别，就需要对高中和年轻运动员进行仔细研究。未来对青少年脑震荡结果(包括认知结果和痴呆风险)的研究应尽可能包括 APOE 基因分型。新的 APOE 研究应标准化研究方法和报告措施，包括收集“共同数据元素”，以确保有效的比较研究。110,115 APOE 基因型不一定是脑震荡恢复的不可改变的危险因素: 正在开发的 AD 治疗包括改变 ApoE4蛋白和 Aβ 之间相互作用的药物，这也可能适用于儿科脑震荡。编码脑源性神经营养因子的基因中的 Val66Met 多态性与 mtBI 后更好的结果有关，但与局灶性穿透性脑损伤后更差的结果有关。参与多巴胺能信号传导的基因多态性也可能有助于解释广泛的 TBI 结果。120此外，α-synuclein 基因启动子区的 Rep1多态性可能增加头部损伤后帕金森病的风险。为了提高我们对脑震荡风险和管理的理解，应该进行大型的前瞻性基于人群的全基因组关联研究(GWAS)和全基因组测序研究，以确定其他遗传变异(可能是低频率或低外显率) ，这些变异可以改变长期恢复，认知结果差或痴呆的风险。122这样的研究将需要大规模的数据共享，并且必须解决道德、隐私以及对可保性和可雇佣性的潜在影响等问题。尽管在确定可能应用于成人创伤性脑损伤治疗的可能的脑嵴液(CSF)和血液生物标志物方面取得了进展，但成人或儿科人群都没有经过临床验证的生物标志物。与成人脑震荡相比，儿童脑震荡的临床变异性更大; 因此，生物标志物在改善儿童脑震荡诊断方面具有特殊的潜力。值得注意的是，大多数 TBI 生物标志物已经在中度至重度 TBI 的背景下进行了研究，这使我们在 mTBI 生物标志物的知识方面存在明显的差距，特别是在儿童中。生物标志物的发展对 AD 治疗的进步至关重要。基于脑脊液的生物标志物已经被用于识别高危患者，并改善流行病学研究和临床试验的设计。123新的 PET 放射性配体，如淀粉样蛋白标记剂(其中三种现在是 FDA 批准的) ，可以用于诊断和改善基于神经病理学的患者临床试验分层。一些 tau 成像剂也在人体试验中，它们在包括 CTE 在内的 tau 病中的应用正在迅速建立。与基于液体的生物标志物一样，目前还没有足够敏感或特异的神经影像生物标志物来诊断成人或儿童的脑震荡或 CTE。目前 FDA 尚未批准任何创伤性脑损伤的诊断或治疗药物，而脑震荡生物标志物的验证可以加速这类药物的开发。然而，必须努力确保临床生物标志物检测的成本效益和广泛可用性。此外，考虑到与腰椎穿刺相关的风险，对脑震荡青少年脑脊液取样用于生物标志物研究的伦理问题应该得到解决。在成人体液为基础的生物标志物研究中有希望的发现必须在儿科人群中探索。过去数十年，推定脑震荡的生物标志物在科学文献中零星出现，其中最突出的是星形胶质细胞活化的非特异性标志物 S100钙结合蛋白 B (S100B)。血清中 S100B 的存在可能提示血脑屏障完整性的丧失。在成年拳击手比赛后观察到血清和脑脊液 S100B 水平升高，并且与头部撞击的数量和严重程度呈正相关。在脑震荡的职业冰球运动员中也观察到血清 S100B 水平升高，126在脑震荡后1小时测量的水平预测症状恢复时间。然而，S100B 的水平也提高后，控制发挥，没有发生脑震荡，表明这一标志物是不伤害特异性。事实上，没有头部损伤的成年创伤患者血清 S100B 水平升高。127,128,129其他研究表明，脑震荡后最初的 S100B 水平对于恢复不能很好地预测。与所有生物标志物一样，S100B 在儿童 TBI 管理中的作用甚至更不清楚[131] ，一些人认为这种标志物在儿科人群中几乎没有诊断或预后效用。132在一项关于≤15岁 TBI 患儿的研究中，5岁以下或9岁以上儿童的血清 S100B 水平高于5-9岁儿童。因此，S100B 可能不足以区分有症状和无症状的脑震荡儿童[133] ，S100B 在诊断和预后预后方面的效用是值得怀疑的。134,135,136神经元特异性烯醇化酶(NSE)是神经元损伤的标志物，但其作为血清或脑脊液生物标志物的用途仍不确定。拳击手头部撞击后观察到血清 NSE 水平升高[133,134,135,136,137] ，但在没有发生脑震荡的比赛后，冰球运动员也观察到 NSE 水平升高。血清 NSE 水平无法预测脑震荡后的恢复时间，可能与儿童损伤严重程度无关。133在≤15岁的儿童中，血清 NSE 水平与年龄呈负相关。一旦释放到血液中，NSE 具有缓慢的消除动力学，使得难以根据 NSE 水平区分原发性和继发性神经元损伤。神经丝轻链和胶质纤维酸性蛋白(GFAP)分别是 CSF 神经元特异性和胶质特异性损伤标志物，并且在成年拳击手打斗后 CSF 均升高。125,137,140在儿科脑震荡的情况下，对任何一种标志物都知之甚少，但对儿童和年轻成年人的初步研究表明，脑震荡后72小时内的血清 GFAP 水平与损伤后1个月的症状负担相关。神经元特异性蛋白 UCH-L1(泛素羧基末端水解酶同工酶 L1)首先通过参与 PD 与神经退行性病理学相关[142] ，其在血清中的存在后来被确定为严重 TBI 的生物标志物。血清 UCH-L1水平可能对脑震荡有诊断价值[146] ，但最近的证据表明血清水平升高与脑震荡次数之间缺乏相关性。UCH-L1在儿科人群中的临床应用值得进一步研究。也许最有希望的进展成人液基 TBI 生物标志物涉及 tau 蛋白。血清或脑脊液 tau 蛋白水平被认为表明轴突损伤，因为 tau 蛋白通常存在于轴突中，稳定微管。在 AD 患者中，脑脊液中切割的 tau 蛋白水解水平可能与认知功能相关。拳击手在比赛后脑脊液和血液中的 Tau 水平升高，脑脊液 Tau 水平与头部撞击的质量和数量相关。125,150最近的证据表明，脑震荡后冰球运动员血液中的 tau 水平升高，可能有助于预测恢复时间。然而，问题依然存在，一些研究报道血清切割 tau 对预测脑震盪症候群或长期结果的价值很小或没有价值。130,151 tau 作为儿童生物标志物的潜力尚不清楚，至今没有进行研究。事实上，血清 tau 作为一种生物标志物的可靠性尚未被确定为任何适应症。这种可能性是没有单一的生物标志物将足以诊断儿童脑震荡或预测结果。此外，很少有研究调查遗传组成和推定的生物标志物之间的相互作用。随着我们对生物标志物与损伤严重程度及其相互关系的理解的增加，生物标志物小组的发展，可能包括炎症和氧化标志物，152应该被考虑。未来的研究应试图进一步确定这些关系，建立生物标志物小组的临床价值，考虑到商业成本和实际可行性。代谢组学、脂质组学和蛋白质组学的最新进展ーー特别是寻找 AD 的代谢组学和脂质组学标志物ーー可能为今后研究脑震荡和脑震荡下损伤的生物标志物提供参考。最近的一些研究提出了与 MCI 和 AD 相关的代谢物和脂质谱的改变.153,154,155,156来自动物模型的数据表明，脂质和代谢物变化伴随着急性和慢性脑震荡后期，并且可能有助于预测恢复轨迹，157,158但是这些发现尚未在人类中得到验证。将生物标志物的搜索范围从血液和脑脊液扩展到唾液和尿液159，可能会提高快速和非侵入性测量的能力，特别是从儿童身上。从儿童抽取脑脊液样本，特别是在需要快速评估的情况下，在很大程度上是不切实际的。Mondello 等人提出了一套评估 TBI 生物标志物的有用标准，这些标准应该允许更精简的开发和验证.137任何经过验证的生物标志物小组必然是更大的多模式诊断套件的组成部分，其中可能包括结构和功能成像以及神经心理学测试。在设计未来的生物标志物研究时，应考虑 FDA 批准的可能性，以加快批准临床使用。虽然脑震荡仍然是一种临床诊断，但神经影像学技术正在提高我们对成人脑结构和功能后果的认识。儿科人群的神经影像学可能受到几个因素的限制，例如，脑震荡后纵向变化的测量由于动态的、未成熟的大脑的背景而变得复杂。没有成像技术被证实为脑震荡的诊断工具，成像结果与临床可测量的认知或行为功能之间的相关性是可变的。目前正在研究容积成像、 DTI 和功能磁共振成像(fMRI)等工具，特别是动脉自旋标记。通过 DTI 测量的分数各向异性(FA)可以推断白质束的结构完整性，TBI 后白质束通常被破坏。FA 变化的临床意义仍然存在争议，因为在脑震荡研究中观察到 FA 增加和减少[162,163,164,165,166]。这些差异可能部分是由于所检查的脑区域的相当大的空间异质性[167]以及损伤后间隔的差异。FA 可能仍然具有预后价值，有证据表明变化的方向和幅度与临床结果相关; 然而，这个想法等待在儿科和成人人群中验证。FA 可能缺乏必要的敏感性来充分评估脑损伤后白质束完整性的变化，扩散率的测量可能更合适。169 DTI 领域将大大受益于规范数据集的开发，以衡量观察到的变化。年轻运动员的赛前、赛后和赛季研究可以采用连续 DTI 成像技术为特定个体建立规范的数据，但数据汇总后的效用尚不清楚。儿科标准数据的缺乏严重限制了包括 DTI 在内的神经影像技术的临床应用。儿童脑震荡后的“回归基线”神经影像学研究也是必要的，因为它们可以极大地改善恢复的预测。尽管自动化提高了重复性，但 DTI 测量仍然对硬件和软件特异性，采集参数和分析软件敏感，这限制了重复性，标准化和中心之间以及跨研究之间的比较。标准化 DTI 成像中心的努力正在进行中。170 MRI 在绘制大脑的“连接体”(结构和功能神经连接网络及其各自的焦点节点的集合)以及研究脑震荡如何影响这些网络方面特别成功。局灶性或弥漫性 TBI 可以破坏大脑的功能连接，导致多个网络的功能障碍，包括默认模式和显着网络，这与记忆，情绪和情绪有关[171]。网络功能障碍对恢复的影响可能比病变部位更强[171,172,173] ，但对大脑发育和认知功能的长期影响尚不清楚[26,174]。脑震荡后儿童网络连接功能障碍的进一步研究对于改善损伤预后和管理至关重要。用于 PET 成像的放射性示踪剂有可能推进脑震荡和 CTE 的诊断和治疗，但目前它们在儿科人群中的应用纯粹是研究性的。三种 FDA 批准的放射性标记成像剂目前可用于检测疑似 AD 患者的脑淀粉样蛋白。175在成年人中，一些脑震荡病例与急性 Aβ 病理有关。PET 扫描可以使儿科患者监测急性脑震荡后淀粉样蛋白的存在和持续性，并确定扫描阳性和阴性是否预测不同的结果.176,177在儿科人群中具有潜在用途的其他 PET 成像剂包括结合由 tau 组成的神经原纤维缠结的新示踪剂。用18F-T807,18F-T808和18F-THK5105进行的早期成像结果证明对于确认包括 AD 在内的各种临床情况下存在共病是有用的。178,179,180在最近的一项 AD 研究中，tau 示踪信号的大小与疾病的分期和认知障碍的严重程度呈正相关。第三种 tau PET 示踪剂11C-PBB3已经在健康个体和 AD 患者中进行了测试，并且可能能够检测 tau 的非 AD 构象。181此外，最近的一份报告首次描述了疑似与运动相关的慢性创伤性脑病(CTE)在活人中的重病影像学表现。鉴于脑震荡，重复性亚震荡损伤和 CTE 中慢性 tau 病理学的程度，tau 示踪剂可用作诊断和预后生物标志物(例如，区分 CNI 和 CTE)。目前正在对 CTE 成人进行这些示踪剂的研究，但它们在儿科人群中的应用将取决于未来的研究，以确定 TBI 或脑震荡后年轻患者是否存在 tau 病理学。小胶质细胞胆固醇转运蛋白的 PET 示踪剂可能有助于成像与创伤性脑损伤相关的神经炎症。182正在开发的新型 PET 配体可以成像脑小胶质细胞，对神经退行性疾病具有潜在的应用价值，也可能证明对脑震荡和慢性创伤性脑病的治疗有用。在脑震荡和 TBI 的儿科人群中探索这些 PET 配体将是有益的，但是在开始进行涉及该年龄组放射性示踪剂的研究之前必须进行风险-效益分析。任何 PET 成像剂的最终效用将取决于其作为多模式生物标志物和神经影像技术小组的一部分的诊断和预后价值。非侵入性技术如经颅磁力刺激(tMS)也发现了创伤性脑损伤和脑震荡后突触可塑性的变化，特别是在无症状的个体中。对20多岁有脑震荡史的年轻运动员进行的几项小型 TMS 研究表明，运动皮层中 γ-氨基丁酸和/或谷氨酸神经传导的不平衡与突触长时程增强作用和抑郁症的缺陷有关。184,185,187,188经颅磁刺激还显示，脑震荡相关的突触可塑性损伤可以损害运动学习的各个方面，这些缺陷在个体最后一次脑震荡几十年后仍然可以检测到。另一个检测与脑震荡相关的神经化学功能障碍的关键非侵入性工具是质子磁共振谱(MRS)。专门针对运动相关脑震荡后使用光谱学的报告表明，与神经化学改变一致的各种异常。在年轻(高中)运动员中，MRS 在赛季后与赛季前评估中检测到谷氨酸和谷氨酰胺水平增加，即使在赛季期间没有经历临床显着脑震荡的运动员中也是如此。这些发现表明，即使是次震荡性头部撞击也可能导致谷氨酸途径的激活，意味着细胞损伤或神经元死亡，尽管没有症状。在上述研究中，一部分参与者的肌酐和肌醇水平(位于星形胶质细胞中的有机渗透液192,193)也发生了显著变化。在一项使用 MRS 的罕见追踪研究中，194名持续单次运动相关脑震荡的个体在受伤后3天在大脑中表现出显着降低的 N- 乙酰天冬氨酸(NAA，神经元和轴突健康，完整性和功能的标志物195)水平。损伤后15天水平升高，损伤后30天恢复到对照值。相比之下，在第一次脑震荡后10-13天再次受到脑震荡的参与者表现出 NAA 水平的长时间下降，即使在受伤后45天也没有恢复正常。这些结果表明，在短时间内反复受伤增加了延长或不完全恢复的可能性。除了 MRS 检测到的急性和亚急性改变之外，其他关于脑震荡长期影响的研究已经揭示了在其他健康的前运动员中，内侧颞叶中肌醇(与胶质增殖相关)增加和胆碱(与膜转换相关195)水平降低在测试之前持续最后一次脑震荡超过三十年。196最近的另一项研究使用一种叫做相关光谱学(COSY)的先进的 MRS 方法，检测了一组有症状的退役国家橄榄球联盟球员，这种方法可以测量额外的代谢物。作者发现胆碱和谷氨酸-谷氨酰胺水平升高(分别表明弥漫性轴突损伤和兴奋性毒性) ，与之前的 mtBI MRS 研究一致，以及额外的大脑代谢物表明神经炎症的变化。这些新陈代谢的变化可能提供了损伤机制的洞察力，如兴奋性毒性和/或炎症，这可能是所报道的结构变化的基础。总的来说，现有的数据支持使用 MRS 作为一种研究工具，以确定改变的神经生理学和监测恢复成年运动员，即使在解决后脑震荡症状。目前，MRS 检测到的生化改变可以增强我们对潜在病理生理学的理解，但尚不能提供具体的诊断信息。需要更大的横断面，前瞻性和纵向研究来确定 MRS 在运动相关脑震荡领域内的敏感性和预后价值.190由于未成熟大脑中 MRS 的解释需要某些发育方面的考虑，因此将来在儿童中的工作将需要适当的比较样本。具有更高光谱分辨率的 MRS 技术，包括 COSY，可能提供额外的生化特异性。空间分辨率的其他进展，如3D 化学位移成像，也可以通过允许调查整个大脑的代谢改变而不是在特定的感兴趣的区域，提供更大的特异性。最后，MRS 可以在测量治疗效果方面发挥作用，例如经颅直流电刺激198和 TMS.199。体育相关伤害测量，报告，跟踪和数据共享的机制和监测基础设施不足以满足目前的需求和目标。脑震荡的研究和临床工作受到缺乏运动和运动水平的脑震荡数据的阻碍。2014年美国医学研究所的一份报告只确定了三个国家运动伤害监测系统: 国家电子伤害监测系统ーー所有伤害项目(NEISS-AIP)、全美大学体育协会伤害监测系统(NCAA ISS)和高中伤害在线报告系统(rIOTM)。1这些系统可以补充临床数据(例如，来自急诊科、住院病人和体育诊所) ，但这些数据偏向于更严重的伤害和社会经济地位更高的病人。事实上，农村地区或社会经济地位较低的社区的学校往往很难获得运动医疗专业人员和设施。一些新出现的项目可能会改善监督。区域性的努力，如运动训练员临床结果研究教育(CORE-AT)和全国性的努力，如全国运动训练员协会全国运动治疗，伤害和结果网络(NATA NATIONTM)试图将伤害跟踪与高中和大学水平的治疗和结果数据结合起来。然而，这些系统中没有一个专门针对年轻运动员、那些参加非学校赞助体育项目的运动员或那些在没有运动教练的学校的运动员。运动损伤数据库也很少考虑人口统计因素，包括社会经济地位、种族或民族以及医疗保健覆盖率。目前，还没有有效的机制来连贯和廉价地将各种监测数据集联系起来，或者跨越体育、跟踪系统或年龄连续体跟踪个别运动员。现在相当需要一个系统来追踪个人运动员的运动生涯和其他方面。应该对每个人进行数十年的跟踪，以确定 TBI 的负担是否、何时以及如何演变为 CTE，并评估与脑震荡相关的所有可能的负面健康结果。这种系统还可以更准确地描述脑震荡病史和风险因素，并可以捕捉短期和长期的结果，包括身体和心理健康、学业和职业成功、生活质量和社会联系以及不断变化的社会经济地位。这种努力受到各种问题的挑战，包括缺乏任何级别的脑震荡强制性报告。强制性脑震荡报告、为监测工作提供资金以及为数据记者(例如教练和运动员培训员)提供培训将极大地改善流行病学研究。然而，如果没有经过验证的、对脑震荡的共识定义，以及通用数据库和全球唯一标识符(GUID)系统的开发，强制性报告将无法提供有意义的结果。然后可以将标准化监测工作的数据集联系起来，从而改善研究和临床护理的数据共享。将监测数据与组织和液体样本生物库的标准化收集、储存和管理基础设施耦合起来，可以大大改善损伤和结果研究。200这些努力可以通过公私伙伴关系的资金来催化，并通过制定现实的短期和长期目标来实现，以创建一个多年计划。然而，至少在美国，这些努力目前受到对健康保险便利和责任法案(HIPAA)规定的误解和对运动员保密的普遍关注的阻碍。运动员更广泛地使用计算机神经认知测试(CNT)可以改善脑震荡的监测，以及诊断和管理。然而，在 CNT 成为常规手术之前，必须克服几个重要的挑战。这些挑战包括缺乏标准化的管理协议，不同计算机硬件引起的技术错误的可能性，评估的认知功能类型的限制，以及缺乏合格的测试管理员和数据解释员.201尽管存在这些缺陷，但是，CNT 已经被大约40% 的美国高中雇用运动教练员.202虽然不是所有学校都负担得起，但是 CNT 可以加强地面数据收集，帮助风险暴露估计和脑震荡后恢复跟踪，以及提高向运动损伤监测网络报告的数据质量。CNT 也可能有助于评估和跟踪脑震荡后认知改善或下降，并可能有助于预测结果.203,204在学校环境中收集的 CNT 数据是否将达到由临床研究小组进行的 CNT 所达到的验证和重复性标准仍有待观察。重要的是，CNT 需要标准化和指导方针，以确定“返回运动”和“返回学习”的运动员在一个领域表现出恢复，但在其他领域仍然有症状。在临床和青少年运动员脑震荡监测和管理方面，需要对 CNT 的应用进行更多的研究。在一些关键领域，不完整的知识阻碍了儿科脑震荡领域有意义的进展。在分子和细胞水平上，迫切需要重点研究脑震荡和重复性亚震荡损伤后的轴突损伤，以阐明轴突运输和修复的变化，并更好地定义瞬时 Aβ 积累作为下游和/或未来病理学的潜在驱动因素的作用。脑震荡研究人员可能需要确定更合适的动物模型来研究分子病理学，包括 tau 蛋白及其对脑震荡后和慢性创伤脑炎病理学的贡献，因为啮齿动物和人类的大脑结构和组织大不相同。如果不能更清楚地了解创伤性脑损伤如何改变年轻、仍在发育中的大脑，以及在损伤后的数周、数月和数年内会发生什么样的病理事件，我们就只能推测这种改变的潜在生物学基础。通过使用记录线性和旋转力的传感器技术，可以改进青年体育运动中头部影响数据的收集和风险评估。这种商业上可用的设备，如果经过验证，可以确定在比赛期间和整个比赛季节中头部累积冲击力的水平，并且研究结果可以与神经影像学数据和功能结果评估联系起来。结合“击中计数”指标，传感器数据可以提高对重复性次生震荡影响的短期和长期神经心理学结果的认识。我们对慢性创伤性脑病的认识可以通过了解一般人群、受伤运动员、运动和运动位置匹配的未受伤运动员以及低风险运动中的“控制”运动员的基线率来改善。提高对风险暴露的认识可导致预防努力，包括改变做法和竞争规则。一项长达数十年的前瞻性追踪研究，追踪青年运动员的运动生涯及以后的发展，将提供有关累积性头部撞击以及长期神经心理功能障碍和痴呆风险的更确切知识。这样的研究正在 NCAA 校友中进行，他们于2003年首次接受研究，并于2013年重新评估。其他人群的研究，特别是如果 NIH 资助的话，可能会从5年的研究开始，可以进一步延长5年的增量。可能需要建立公私伙伴关系，以获得足够的资金，使多个研究中心参与进来。NCAA 已经为100多名运动员的10年重新评估提供了部分赞助，但需要来自 NIH，美国国防部(DoD)和私人慈善来源的进一步资助，以扩大评估范围，从神经心理学，通过 MRI，淀粉样蛋白，tau 和/或炎症的分子成像。理想情况下，追踪研究设计应结合流行病学和介入试验方法，并利用多个对照组，包括非接触运动员和未受伤的撞击运动员。追踪研究还将阐明认知储备的作用。老年痴呆症研究团体利用国家卫生研究院的资金以及涉及制药公司和基金会的公私伙伴关系，开创了这类研究的先例。为了使这类研究取得成功，必须首先建立更多的监测系统和数据库。如果参加影响力体育运动的运动员能够普遍获得运动员训练员的帮助，这些训练员能够在促进安全和提供基本护理的同时充当可靠的数据报告员，那么将加快努力。此外，任何纵向研究都必须包括死后分析，以便更好地了解儿童和青少年脑震荡对今后生活中神经退行性病理和痴呆发展的影响。由于缺乏严格的流行病学证据，“重返赛场”的指导方针目前受到阻碍，纵向研究的长期安全数据可能会大大改善这一点。纵向研究还可以包括确定那些未能遵循指导方针的运动员是否会经历任何负面健康影响的研究，例如持续的症状或改变发生第二次脑震荡的风险。长期前瞻性研究的基础设施可以通过建立一个以阿尔茨海默氏病神经影像学倡议(ADNI)为模型的研究联盟来创建。ADNI 为数据收集、传播协议、测试方法和生物标志物收集和分析制定了标准。目前正在国防部参与的一个版本的 ADNI (ADNI-DoD)专注于军事人群中与爆炸相关的 TBI 研究。2072014年5月，除了 NCAA 脑震荡研究，NCAA 和国防部宣布启动迄今为止最大的前瞻性运动相关脑震荡研究，该研究将在3年内监测大约37,000名 NCAA 运动员。我们可以想象，这项研究的基础设施可能最终扩展到研究年轻运动员在一个延长的纵向范围。我们对创伤性脑损伤的生物学知识仍然存在许多差距，这限制了我们开发有效药物的能力。如果我们要解决潜在的疾病病理，并超越治疗症状，就必须填补这些空白。然而，当基础创伤性脑损伤生物学的研究继续进行时，许多工作可以完成。药物再利用包括测试现有 FDA 批准的新适应症药物，可以减少费用和缩短药物批准的路径。目前的再利用试验包括哌醋甲酯治疗疼痛和精神疲劳，多巴胺受体激动剂溴隐亭治疗工作记忆，舍曲林治疗情绪和焦虑，这是最常见的影响脑震荡后长期结果的神经心理并发症。此外，黄体酮的 PROTECT III 期临床试验最近未能改善急性 TBI211后的结局，这可能提醒人们需要更多的研究来更好地理解 TBI 的基础生物学。虽然许多药物重新利用的努力主要是为了解决脑震荡症状，药物也可能影响损伤病理学和进展。对现有药物的研究也可能导致新的药物发现努力，并可能导致新的预防或管理治疗。急需新的药物治疗创伤性脑损伤和无法消除的脑震荡。在神经保护和抗炎领域的药物发现努力是特别相关的，因为它们潜在的交叉适用于神经退行性疾病，如 AD。同样，目前正在开发的治疗其他神经退行性疾病的药物可能会被重新定位，用于 TBI 或无脑震荡症状患者的检测。正如医学研究中经常出现的情况一样，脑震荡研究的最新进展提出的问题和回答的问题一样多。有证据表明脑震荡或重复性次生脑震荡后长期神经心理功能障碍和晚年痴呆，需要更多的工作来更好地理解青年参与影响性运动的含义和结果。正如本专家共识文件所概述的那样，有一条前进的道路，但实现这里概述的目标将需要公共和私营部门的合作。虽然可以通过增加知识来改进建议，但现有证据仍然可以在考虑青年参与体育运动以及实践政策和竞赛规则时为个人决策提供信息。随着人口老龄化和痴呆症的流行，我们必须更多地了解潜在的早期生活风险因素，包括与运动有关的脑震荡。家长、教练、学校董事会和孩子们做出的选择将在脑震荡科学知识的关键差距得到填补时得到更好的信息。下载参考资料|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Practical+Design+of+Performant+Recommender+Systems+using+Large-scale+Linear+Programming-based+Global+Inference)|0| +|[PDAS: A Practical Distributed ADMM System for Large-Scale Linear Programming Problems at Alipay](https://doi.org/10.1145/3580305.3599883)|Jun Zhou, Yang Bao, Daohong Jian, Hua Wu|Ant Group; Zhejiang University|Linear programming (LP) is arguably the most common optimization problem encountered in practical settings. Important examples include machine learning systems optimization, resource allocation, and other decision-making scenarios. However, even with state-of-the-art (SOTA) solvers, it is extremely challenging to solve large-scale problems arising in industry settings, which could have up to billions of decision variables and require solutions within a time limit to meet business demands. This paper proposes PDAS, a Practical Distributed ADMM System to solve such problems with a variant of the Alternating Direction Method of Multipliers (ADMM) algorithm. PDAS offers user-friendly interfaces and provides near-linear speedup thanks to its high scalability and excellent performance. It also comes with a failover mechanism to ensure the stability of the iterative process. The convergence, feasibility, and optimality of PDAS have been verified on two real-world data-sets, resulting in a 10 -4 average relative deviation from Gurobi. Although SOTA solvers do have advantages if only considering the solving time when tested on five small and medium-sized public data-sets, PDAS is more promising after including the modeling time. Moreover, when used to solve large-scale LP problems with up to 10 9 decision variables and 10 4 constraints in three real-world scenarios, PDAS achieves at least 2x speedups, well beyond the capabilities of SOTA.|线性规划(LP)可以说是在实际环境中遇到的最常见的最佳化问题。重要的例子包括机器学习系统优化、资源分配和其他决策场景。然而，即使使用最先进的(SOTA)解决方案，解决产业环境中出现的大规模问题也是极具挑战性的，这些问题可能有数十亿个决策变量，需要在一定时限内找到解决方案以满足业务需求。本文提出了一种实用的分布式 ADMM 系统 PDAS，采用改进的交替方向乘法器(ADMM)算法来解决这类问题。PDAS 提供用户友好的界面，并提供近线性的加速，由于其高可伸缩性和优异的性能。它还提供了故障转移机制，以确保迭代过程的稳定性。PDAS 的收敛性、可行性和最优性已在两个现实世界的数据集上得到验证，结果与 Gurobi 的平均相对偏差为10-4。虽然 SOTA 求解器在五个中小型公共数据集上测试时只考虑求解时间的优势，但在考虑建模时间的情况下，PDAS 更有前途。此外，当在三个实际场景中用于解决具有多达10个9个决策变量和10个4个约束的大规模 LP 问题时，PDAS 至少可以实现2倍的加速，远远超过 SOTA 的能力。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=PDAS:+A+Practical+Distributed+ADMM+System+for+Large-Scale+Linear+Programming+Problems+at+Alipay)|0| +|[Practical Design of Performant Recommender Systems using Large-scale Linear Programming-based Global Inference](https://doi.org/10.1145/3580305.3599183)|Aman Gupta, S. Sathiya Keerthi, Ayan Acharya, Miao Cheng, Borja Ocejo Elizondo, Rohan Ramanath, Rahul Mazumder, Kinjal Basu, J. Kenneth Tay, Rupesh Gupta|Metro Orthopedics & Sports Therapy, USA.; Baylor College of Medicine, USA.; Andrews Institute for Orthopaedics and Sports Medicine, USA.; George Washington School of Medicine, USA.; Children's Hospital, Harvard Medical School, USA.; Stanford Center on Longevity, USA.; University of Colorado at Denver, USA.; Burke Rehabilitation Hospital, USA.; Icahn School of Medicine at Mount Sinai, USA.; Novant Health Sports Medicine, USA.; Boston University Medical Center, USA.; CrowdOptic, Inc., USA.; The Hastings Center, USA.; Norton Healthcare, University of Kentucky, USA.; Banyan Biomarkers, USA.; Alzheimer's Drug Discovery Foundation, 57 West 57th Street, Suite 904, New York, NY 10019, USA.; Safe Kids Worldwide, Inc., USA.; National Collegiate Athletic Association, USA.; University of Virginia School of Medicine, USA.; University of California, USA.; Alzheimer's Association, USA.|Sports-related concussions and repetitive subconcussive exposure are increasingly recognized as potential dangers to paediatric populations, but much remains unknown about the short-term and long-term consequences of these events, including potential cognitive impairment and risk of later-life dementia. This Expert Consensus Document is the result of a 1-day meeting convened by Safe Kids Worldwide, the Alzheimer's Drug Discovery Foundation, and the Andrews Institute for Orthopaedics and Sports Medicine. The goal is to highlight knowledge gaps and areas of critically needed research in the areas of concussion science, dementia, genetics, diagnostic and prognostic biomarkers, neuroimaging, sports injury surveillance, and information sharing. For each of these areas, we propose clear and achievable paths to improve the understanding, treatment and prevention of youth sports-related concussions. In 2009, around 250,000 nonfatal traumatic brain injuries (TBIs) were recorded among individuals aged <19 years in the USA.1 The Centers for Disease Control and Prevention estimate that young people aged 5–18 years sustain 65% of all sports-related concussions.2 Despite recent advances in diagnostic brain imaging and in our understanding of the physics of concussion, long-term cognitive outcomes remain poorly understood. As the physical, cognitive and emotional consequences of concussion gain wider public attention, our incomplete knowledge of how to prevent, diagnose and treat such injuries endangers the health of our children in general and the health of their brains in particular. This Expert Consensus Document is the result of a 1-day meeting of experts in the fields of paediatric and adult TBI, Alzheimer disease (AD) research, genetics, epidemiology, bioethics and sports medicine (Box 1), which was convened in November 2013 by Safe Kids Worldwide, the Alzheimer's Drug Discovery Foundation and the Andrews Institute for Orthopaedics and Sports Medicine. Our primary goal is to highlight critical gaps in our knowledge of child and adolescent concussion. We emphasize areas where research is needed, such as development of diagnostic and predictive biomarkers, elucidation of genetic risk factors, and prediction of short-term and long-term outcomes. In our conclusions, we suggest paths toward improving our understanding of the long-term consequences of sports-related paediatric concussion. The term 'concussion' is often used interchangeably with the term 'mild TBI' (mTBI), a potentially misleading practice considering the possible extent of brain damage and potential for chronic neuropsychological dysfunction following concussion. We should stress, however, that most concussions resolve without sequelae. The American Congress of Rehabilitative Medicine defines mTBI as a Glasgow Coma Scale3 score of 13–15, with loss of consciousness for <30 min and post-traumatic amnesia lasting <24 h.4 Concussion describes a heterogeneous mixture of injury phenotypes that depends on many factors, including the magnitude, location and direction of head impact. Despite a lack of macroscopic structural findings, concussive brain injury involves primary neuronal injury caused by linear and rotational shear forces that disrupt axonal and membrane function (diffuse axonal injury,5 ionic flux and glutamate excitotoxicity), followed by secondary pathophysiological effects including mitochondrial oxidative stress, disruption of cerebral blood flow, compromised blood–brain barrier (BBB) integrity, synaptic dysfunction, and neuroinflammation.6, 7 Lasting neuropsychological post-concussion symptoms (post-concussion syndrome) comprise mood disorders (for example, depression), difficulty concentrating, and memory problems (Box 2).8 Both physical and physiological components of concussive injury can damage the developing brain, putting youths engaged in impact sports at particular risk. The necks and torsos of young athletes are weaker than those of older individuals and, consequently, less force is required to cause brain injury. The developing brain might also be particularly vulnerable to axonal damage caused by the shearing forces of head trauma, which, in youth American football, can exceed linear acceleration forces of 100 g.9 However, the average forces sustained in youth sports will generally be smaller than at higher levels of sport. Proper synaptic development is critical to cognitive and behavioural health.10, 11, 12, 13, 14, 15 Processes such as neurogenesis, competitive synaptic elimination ('pruning'), myelination, and axonal and dendritic arborization continue from prenatal development throughout the lifespan.14 The frontal and temporal lobes are the last areas to mature, and humans experience pruning in these regions into their early 20s,16 so damage to these still-developing areas may have pathophysiological effects on the brain that increase the potential for neuropsychological problems later in life.17 Axonal myelination continues through adolescence into the early 20s, and is susceptible to disruption by injury.10, 18, 19, 20, 21, 22 Early results from the Professional Fighters Brain Health Study, a 5-year longitudinal study of boxers and mixed martial arts fighters, who experienced repetitive subconcussive injuries as well as concussions, indicate that earlier age of first exposure to competitive boxing correlates with greater loss of caudate volume and greater axonal damage in the frontal lobe.23, 24 The young brain also has features that contribute to its resilience. Increased neuroplasticity in this age group has been shown to contribute to better outcomes after focal injuries.25 In addition, developing animals display a shorter window of glucose metabolic impairment in response to repeat TBI than do adult animals.26 Overall, the developing brain shows both vulnerability and resilience after TBI. These interwoven factors are likely to account for differences in the effects of concussion and repeat mTBI on young versus adult brains. A conservative approach to concussion risk and greater efforts to investigate these developmental differences should be given high priority. Most people—both young and old—recover fully from concussions. In children, factors potentially influencing recovery include age and history of concussions.27, 28 In one study, approximately 90% of young adult male athletes experienced symptomatic recovery within 21 days.29 However, in an emergency department study of patients aged 11–22 years (including all causes of concussion, not just sports-related), 15% of the sample still exhibited post-concussion symptoms, including headache, dizziness, 'mental fogginess' and depression, 90 days after injury.30 Several studies suggest that high school American football players are slower to recover from concussion than are college31, 32 and professional players.33 No direct comparisons with adolescents below high school age have yet been published, although a recent study that included a pre-adolescent age group (11–12 years) suggested that post-concussion recovery duration may not exhibit a linear relationship with age,30 as adolescents in this sample took longer to recover than did the pre-adolescent children. These findings, taken together, imply a unique risk of lengthier recovery in the male adolescent age group. Further studies of younger children and females would add greatly to our ability to assess and mitigate risk across the full paediatric and adolescent age span. Youths who sustained one or more concussions within 1 year prior to a new concussion reported more-prolonged symptoms,30 suggesting a possible 'window of vulnerability', and placing previously injured youths at higher risk of protracted recovery. Adolescents aged 11–18 years were nearly 80% more likely to develop post-concussion syndrome after presenting in emergency rooms than were children aged 5–10 years; similarly, presentation with headache doubled the risk of post-concussion syndrome in both children and adolescents.34 Among children treated in an emergency room after mTBI, those aged >6 years reported higher rates of persistent symptoms 3 months post injury than did those aged <6 years.35 Of course, the ability to acquire accurate information about concussion symptoms in children <6 years of age may be limited by a lack of self-awareness of symptoms and the necessary verbal skills to effectively communicate those symptoms. Also, direct comparison of injury severity is not possible from these reports; in fact, the physical heterogeneity of various injuries, taken together with the individual's innate capacity to recover from concussion, makes such comparisons highly challenging. 'Smart helmets' are being used in some speciality research centres to standardize the physical force and angular acceleration that accompanies head hits, and the utility of these helmets to measure and predict impacts that may result in concussion is currently under investigation.36, 37 Young people recovering from concussion can experience important challenges, including altered social and academic development,38, 39, 40 lower scores on general intelligence tests, and decreased school performance (measured by grade-point average).39 Lower levels of parental education and child academic achievement both correlate with poorer concussion recovery.41 Personality traits also play a part; for example, pre-injury anxiety is a risk factor for prolonged recovery periods after sports-related concussion.42 Young athletes of both sexes are at risk of concussion, but girls report higher concussion rates than boys, particularly in high school and college soccer, basketball, and baseball or softball.28, 43, 44, 45 The factors that account for these differences remain uncertain, but might include quality of protective gear, recognition and reporting of concussion symptoms, and neck length and neck muscle strength.46 Differences in recovery trajectories between males and females are also poorly understood. However, one recent study suggested that progesterone levels in females influence post-concussion recovery.47 Hormonal changes during puberty that contribute to migraine headaches might also contribute to sex differences in concussion recovery. Migraine headaches are up to fourfold more common in females than in males after puberty,48, 49 and some evidence suggests that migraineurs recover more slowly after concussion.50, 51 Research is warranted to further delineate sex differences in concussion risk and recovery. In general, adult concussive brain injury is much better understood than its counterpart in children and adolescents. Several points are important to note. First, concussion has multiple, non-harmonized definitions. Second, concussion diagnosis is an imperfect art. Last, in the absence of rapid and inexpensive objective diagnostic measures, concussion remains a clinical diagnosis that is subject to variability—including different thresholds for diagnosis across various subspecialities and across individual physicians, neuropsychologists and athletic trainers—and under-reporting by coaches, parents and young athletes. Without validated diagnostics, concussion will remain a nebulous and under-reported entity, and the accuracy of incidence estimates will continue to be tainted by the differential application of inexact criteria. Repetitive subconcussive trauma can result in structural and functional brain changes.52 White matter abnormalities detected by diffusion tensor imaging (DTI) have been reported in professional soccer players even in the absence of any obvious history of concussions. Compared with swimmers, male professional soccer players showed DTI signal changes suggestive of decreased white matter integrity in several brain regions, which might indicate loss of axonal myelination, similar to changes seen in individuals with mTBI.53 Collegiate ice hockey players exhibited similar white matter changes over the course of a season.54, 55, 56, 57 In addition, repetitive subconcussive head impacts in collegiate American football players have been linked, in a dose-dependent manner, to deficits in BBB integrity, potential loss of white matter integrity, and cognitive dysfunction.58 These findings probably reflect some level of risk for youths who sustain repetitive subconcussive head impacts, although little research has been devoted specifically to this topic. A metric to track head impacts—that is, a 'hit count'—has been proposed,59 and could serve as one factor to determine cumulative risk exposure. One challenge of this approach is to accurately define the parameters of a 'hit', but improved biosensors show some promise in this regard. Similar to a 'pitch count' in baseball, this concept has also recently been proposed for boxers.24 No evidence is currently available to show a causal link between repetitive subconcussive head impacts in youth and dementia later in life, and such metrics could prove invaluable if validated by future studies correlating head impacts with subsequent neuropsychological dysfunction. In adults, TBI, including concussion,60, 61, 62 might increase an individual's risk of developing neurodegenerative disease,63, 64 including AD and chronic traumatic encephalopathy (CTE), a disease associated exclusively with repetitive head trauma.65, 66 TBI may also increase the risk of developing Parkinson disease (PD),67 although the relationship between mTBI and PD risk remains uncertain.68 In paediatric populations, particularly young athletes, the effects of single or repetitive concussions on the risk of later-life neurodegeneration and dementia are unknown. CTE was first described symptomatically in the late 1920s as 'punch-drunk' dementia in boxers,69 was later described as 'dementia pugilistica',70 and was first described pathologically in 1973.71 Since the identification of CTE in a former professional American football player in 2005,72 and additional intensive pathological studies, this condition has gained widespread public attention, and has now been identified in brains of former ice hockey, baseball, rugby and soccer players,73 wrestlers,74 and military veterans.75, 76 The prevalence and incidence of CTE in amateur and professional athletes is still unknown, adding to difficulties in discussing its epidemiology and population risks for athletes. Although CTE is primarily considered to be a neurodegenerative disease that sometimes results from a career of either collegiate or professional contact sports, cases of CTE have been reported in high school athletes.77 This finding suggests that long sporting careers are not required for CTE development, and that youth athletes represent an at-risk population. Emerging evidence suggests that clinical CTE symptoms can be grouped into two common presentations: cognitive and mood–behavioural.78, 79 Subjective memory complaints such as anterograde amnesia are common, as are mood disorders including anxiety or depression,79 and reduced executive function, which can result in disinhibition and impaired decision-making skills.80 These clinical symptoms define disease severity.81 The neurodegenerative pathophysiology of CTE is complex, and the neurological sequelae are poorly understood. In severe cases, the cerebral cortex and medial temporal lobes seem most profoundly affected,81, 82 with pathology characterized by neurofibrillary tangles composed of phosphorylated tau79 and, in some cases, TAR DNA-binding protein 43 pathology.83 CTE is also associated with marked atrophy, notably in the frontal cortex and medial temporal lobe, as well as in the mammillary bodies, thalamus and hypothalamus.79 Confirmed clinical diagnosis of CTE remains autopsy-based.84 Given the uncertainty over whether the tauopathy described in CTE is causative of the clinical phenotype, and the fact that most professional and collegiate athletes do not develop CTE, it is vital to understand whether early exposure to concussion is associated with other forms of neurodegeneration and cognitive dysfunction, including chronic neurocognitive impairment (CNI). Important clinical distinctions exist between CTE and CNI,28, 51 some of which make direct comparisons difficult. CTE is an emerging clinical and pathological condition that involves progressive deterioration of neurological and cognitive function in multiple domains, and is diagnosed primarily at autopsy. Conversely, the CNI phenotype is not necessarily progressive, and is characterized by functional decline from group averages or baseline functioning established before TBI. CNI can be diagnosed clinically through neuropsychological testing. No causal link between CNI and head trauma has yet been confirmed, but a dose-dependent risk has consistently been found in professional athletes.28 In addition, almost half of the studies conducted in amateur athletes have found an elevated risk of CNI.28 Whether similar risk associations are present in younger populations remains to be determined. One hypothesis is that CNI represents a prodromal—but not inevitable—step toward CTE, analogous to the relationship between mild cognitive impairment (MCI) and AD.85, 86 Alternatively, CNI may represent static impairment without degeneration. Our current lack of understanding of the basic biological underpinnings of CNI and CTE underscores the need for more research. Increased knowledge of the biology of both conditions, as well as early detection of CNI in athletes (in particular, youth athletes), may drive interventions to stem the development of further cognitive impairment, and could also aid validation of putative biomarkers. Assessment of CNI via tau imaging may help determine the likelihood of progression to CTE. The field of concussion genetics, especially in paediatric populations, is still in its infancy. Although repetitive head impacts seem necessary for the development of CTE, other factors, including genetics, are likely to have an important role, as most concussed athletes do not develop CTE.87 The genetic risk factors for CTE probably overlap with those that influence susceptibility to and recovery from concussion, and genetic risk factors for AD are providing important clues to the identity of these factors. The ε4 allele of apolipoprotein E (APOE ε4), the most important genetic risk factor for AD identified to date,88 critically affects the CNS injury response,89 in particular, amyloid-β (Aβ) clearance from the brain. The three alleles of APOE confer varying degrees of AD risk: APOE ε2 reduces the risk, APOE ε3, the most common allele, represents baseline risk with which other variants are compared, and APOE ε4 increases the risk.90, 91 Studies suggest an interaction between APOE ε4 and sex, such that APOE ε4-related risk of AD is more prominent in women than in men.92, 93 The APOE genotype acts synergistically with TBI in increasing the risk of AD,94 although its hypothesized risk association with CTE as an outcome of repetitive mTBI requires more study.95 No consensus has yet been reached on the effects of APOE isotype on the outcome of paediatric TBI, but data from adults suggest that APOE ε4 negatively influences concussion outcomes. Several studies indicate that possession of at least one APOE ε4 allele is associated with poorer cognition and lasting neuropsychological impairment after concussion in professional American football players,96 boxers95 and other adults,97, 98, 99, 100 although other studies found no such association.101, 102 Some evidence points to polymorphisms in both the APOE gene and its promoter as contributory factors to concussion risk in college athletes.103, 104 Another study did not identify a role for APOE ε4 in concussion risk,105 although this allele might increase the risk of dementia following midlife or late-life mTBI.106 Drawing conclusions from these conflicting studies is difficult, owing to small sample sizes and differing methodologies. In children, little is known about the relationship between APOE ε4 and neuropsychological outcomes after concussion, and APOE ε4 testing is not routine in paediatric TBI studies. In 2012, Kurowski reviewed the few existing studies and combined the results of three studies107, 108, 109 that used the Glasgow Outcome Scale.110 In the combined sample (252 children), the risk of poor clinical outcomes after 6–12 months was over twofold higher in APOE ε4 carriers than in noncarriers (19% versus 9%). However, these studies included a broad developmental range of children with heterogeneous injuries, and did not account for a possible interaction between age and genotype. In addition, the interaction between APOE and sex has not been studied in the context of concussion. Improved prospective studies are warranted to clarify these connections. Incorporation of genetics into paediatric concussion research is fraught with complicated challenges, including acquisition of parental consent and informed consent for a child, perceived stigmatization of clinical study participants, the actionability of the genetic knowledge obtained, and potential concerns regarding insurability (particularly long-term care insurance). Studies of adults who learn of their APOE ε4+ status demonstrate that many are willing to make lifestyle modifications, including increased exercise and improved medication management,111 as well as increased purchases of health and long-term care insurance.112, 113 Education about new genetic knowledge and corresponding disease risk is essential, as demonstrated by the substantial discordance between an individual's personal feelings about the implications of the acquired knowledge and the actual consequences of increased dementia risk.114 The effects of APOE genetic knowledge on children, their families and decision-making processes regarding participation in impact sports remain unclear. The influence of APOE genotype on concussion risk and recovery in this age group also needs further elucidation. If future studies find that, for any particular level of impact, children with APOE ε4+ status are at greater risk of concussion or poor recovery than are their APOE ε4− peers, consideration should be given to genetic testing of school-age athletes before participation in impact sports. Careful studies of high school and younger athletes are required to fully understand the nuances of genetic influences. Future research into youth concussion outcomes, including cognitive outcomes and risk of dementia, should include APOE genotyping wherever possible. New APOE studies should standardize research methodologies and reporting measures, including the collection of 'common data elements', to ensure valid comparison across studies.110, 115 The APOE genotype is not necessarily a non-modifiable risk factor for concussion recovery: therapies being developed for AD include drugs that modify the interaction between the ApoE4 protein and Aβ, which might also be applicable to paediatric concussion.116, 117 The Val66Met polymorphism in the gene encoding brain-derived neurotrophic factor has been linked to better outcomes after mTBI,118 but worse outcomes after focal penetrating brain injury.119 Polymorphisms in genes involved in dopaminergic signalling may also help to account for the wide range of TBI outcomes.120 In addition, the Rep1 polymorphism in the promoter region of the α-synuclein gene might increase the risk of PD after head injury.121 To advance our understanding of concussion risk and management, large, prospective, population-based genome-wide association studies (GWAS) and whole-genome sequencing studies should be conducted to identify other genetic variants—possibly of low frequency or low penetrance—that modify the risk of prolonged recovery, poor cognitive outcomes or dementia.122 Such studies will require large-scale data sharing, and must address issues of ethics, privacy, and potential implications for insurability and employability. Despite progress in identifying possible cerebrospinal fluid (CSF) and blood-based biomarkers that might be applied to adult TBI management, no clinically validated biomarkers are available for either the adult or the paediatric population. Paediatric concussions present with even greater clinical variability than do adult concussions; therefore, biomarkers have special potential for improving concussion diagnosis in children. Of note, most TBI biomarkers have been studied in the context of moderate to severe TBI, leaving us with obvious gaps in our knowledge of mTBI biomarkers, especially in children. Biomarker development has been critical to the advancement of AD therapeutics. CSF-based biomarkers are already being employed to identify at-risk patients and to improve the design of both epidemiological studies and clinical trials.123 New PET radioligands, such as amyloid-labelling agents (three of which are now FDA-approved), can be used both diagnostically and to improve neuropathology-based patient stratification for clinical trials. Several tau imaging agents are also in human trials, and their utility in tauopathies, including CTE, is rapidly being established. As with fluid-based biomarkers, there are currently no neuroimaging biomarkers sensitive or specific enough to diagnose concussion or CTE in either adults or children. No TBI diagnostic or therapeutic agents have yet been approved by the FDA, and validation of concussion biomarkers could accelerate the development of such agents. Efforts must be made, however, to ensure the cost-effectiveness and wide availability of clinical biomarker testing. Also, given the risks associated with lumbar puncture, ethical concerns regarding sampling of CSF from concussed youths for biomarker research should be addressed. Promising findings in adult fluid-based biomarker research must be explored in paediatric populations. Putative concussion biomarkers have emerged sporadically in the scientific literature over the past few decades, the most prominent being S100 calcium-binding protein B (S100B), a nonspecific marker of astrocyte activation. The presence of S100B in serum may indicate loss of BBB integrity. Elevated serum and CSF levels of S100B have been observed in adult boxers after matches, and correlate positively with the number and severity of head impacts.124, 125 Increased serum S100B levels have also been observed in concussed professional ice hockey players,126 with levels measured 1 h post-concussion predicting symptomatic recovery time. However, S100B levels were also raised after controlled play where no concussions occurred, indicating that this marker is not injury-specific.126 Indeed, S100B serum levels are elevated in adult trauma patients without head injury.127, 128, 129 Other research suggests that initial post-concussion S100B levels are poor predictors of recovery.130 As with all biomarkers, the role of S100B in TBI management in children is even less clear,131 with some arguing that this marker has little diagnostic or prognostic utility in paediatric populations.132 In a study of children with TBI aged ≤15 years, those <5 years or >9 years of age had higher serum levels of S100B than did those aged 5–9 years.133 S100B may, therefore, be an inadequate marker to distinguish between symptomatic and asymptomatic children with concussion,133 and the utility of S100B in diagnostics and outcome prognosis is questionable.134, 135, 136 Neuron-specific enolase (NSE) is a marker of neuronal injury, but its usefulness as a serum or CSF biomarker remains uncertain.133, 134, 135, 136, 137 Elevated serum NSE levels have been observed after head impacts in boxers,124 but were also seen in ice hockey players after a match where no concussions occurred.126 Serum NSE levels failed to predict recovery time after concussion,126 and might not correlate with injury severity in children.133 In children aged ≤15 years, serum NSE levels correlate inversely with age.133 Once released into the blood, NSE has slow elimination kinetics, making it difficult to distinguish primary from secondary neuronal injuries on the basis of NSE levels.138, 139 Neurofilament light chain and glial fibrillary acidic protein (GFAP) are CSF neuron-specific and glial-specific damage markers, respectively, and are both elevated in CSF in adult boxers after fights.125, 137, 140 Little is known about either marker in the context of paediatric concussion, but a preliminary study in children and young adults suggested that serum GFAP levels within 72 h after concussion correlate with symptom burden up to 1 month post injury.141 The neuron-specific protein UCH-L1 (ubiquitin carboxyl-terminal hydrolase isozyme L1) was first linked to neurodegenerative pathology through its involvement in PD,142 and its presence in serum was later identified as a biomarker for severe TBI.143, 144, 145 Serum levels of UCH-L1 may have diagnostic utility in concussion,146 but recent evidence suggests a lack of correlation between elevated serum levels and subconcussive hits.147 The clinical utility of UCH-L1 in paediatric populations warrants further study. Perhaps the most promising advances in adult fluid-based TBI biomarkers concern tau protein. Serum or CSF tau levels are thought to indicate axonal damage, as tau normally resides in axons, where it stabilizes microtubules. Serum tau is proteolytically cleaved,148 and in patients with AD, levels of cleaved tau in CSF might correlate with cognitive function.149 Tau levels in CSF and blood are elevated in boxers after a match, and CSF tau levels correlate with the quality and quantity of head impacts.125, 150 Recent evidence suggests that tau levels are elevated in the blood of ice hockey players after concussion, and may be useful in predicting recovery time.126 Questions remain, however, with several studies reporting little or no value of serum cleaved tau for predicting post-concussion syndrome or long-term outcomes.130, 151 The potential of tau as a biomarker in children remains unclear, with no studies conducted to date. In fact, the reliability of serum tau as a biomarker has not yet been established for any indication. The likelihood is that no single biomarker will suffice to diagnose paediatric concussion or predict outcomes. In addition, few studies have examined the interactions between genetic make-up and putative biomarkers. As our understanding of the relationships of biomarkers to injury severity and to each other increases, development of biomarker panels, perhaps incorporating inflammatory and oxidative markers,152 should be considered. Future studies should attempt to further define these relationships and establish the clinical value of biomarker panels, factoring in commercial cost and practical feasibility. Recent advances in metabolomics, lipidomics and proteomics—in particular, the search for metabolomic and lipidomic markers for AD—might inform future research into biomarkers for concussion and subconcussive injuries. Several recent studies propose altered metabolite and lipid profiles associated with MCI and AD.153, 154, 155, 156 Data from animal models suggest that lipid and metabolite changes accompany both acute and chronic post-concussion periods, and could be useful for predicting recovery trajectory,157, 158 but these findings have yet to be validated in humans. Expanding the biomarker search beyond blood and CSF to saliva and urine159 might improve the ability to obtain measurements rapidly and noninvasively, particularly from children. Sampling of CSF from children, particularly when rapid assessment is desirable, is largely impractical. Mondello et al. proposed a set of useful criteria for evaluating TBI biomarkers that should allow more-streamlined development and validation.137 Any validated biomarker panel must, inevitably, be a component of a larger, multimodal diagnostic suite that may include structural and functional imaging and neuropsychological testing. When designing future biomarker studies, the potential for FDA approval should be considered, in order to expedite approval for clinical use. Although concussion remains a clinical diagnosis, neuroimaging techniques are improving our understanding of the structural and functional consequences in adults. Neuroimaging in paediatric populations may be limited by several factors; for example, measurements of longitudinal changes after concussion are complicated by the background of a dynamic, immature brain. No imaging techniques have been validated as diagnostic tools for concussion, and the correlation between imaging findings and clinically measurable cognitive or behavioural functions is variable. Tools such as volumetric imaging, DTI and functional MRI (fMRI)—in particular, arterial spin labelling—are currently being explored.160, 161 Fractional anisotropy (FA), as measured by DTI, allows inference of the structural integrity of white matter tracts, which are commonly disrupted after TBI. The clinical implications of FA change remain controversial, as both increased and decreased FA has been observed in concussion studies.162, 163, 164, 165, 166 These discrepancies may be due, in part, to the considerable spatial heterogeneity in the brain areas examined,167 as well as differences in the post-injury interval. FA may still have prognostic value, with evidence suggesting that the direction and magnitude of change correlates with clinical outcomes;166, 168 however, this idea awaits validation in both paediatric and adult populations. FA might lack the necessary sensitivity to fully appreciate changes in white matter tract integrity following brain injury, and measures of diffusivity may be more appropriate.169 The DTI field would benefit greatly from the development of normative data sets against which to gauge observed changes. Pre-game versus post-game and season-long studies of young athletes could employ serial DTI imaging to establish normative data for a particular individual, but the utility of the data when pooled is unclear. The scarcity of normative paediatric data severely limits the clinical usefulness of neuroimaging techniques, including DTI. Studies of 'return-to-baseline' neuroimaging after paediatric concussion are also needed, as they could greatly improve prediction of recovery. Although automation has increased reproducibility, DTI measurements remain sensitive to the hardware and software specifics, acquisition parameters and analysis software, which limit reproducibility, standardization and comparison between centres and across studies. Efforts to standardize DTI across imaging centres are underway.170 MRI has been particularly successful in mapping the brain's 'connectome'—the collection of structural and functional neural connectivity networks and their respective focal nodes—and for studying how concussion affects these networks. Focal or diffuse TBI can disrupt the brain's functional connectivity, resulting in dysfunction of multiple networks including the default mode and salience networks, which have been implicated in memory, emotion and mood.171 Network dysfunction might have a stronger influence on recovery than does lesion location,171, 172, 173 but the long-term implications for brain development and cognitive function remain unclear.26, 174 Further studies of network connectivity dysfunction in children after concussion will be critical to improve injury prognostication and management. Radiotracers for PET imaging have the potential to advance the diagnosis and treatment of concussion and CTE, but their use in paediatric populations is purely investigational at present. Three FDA-approved radiolabelled imaging agents are currently available for detecting brain amyloid in patients with suspected AD.175 In adults, some cases of concussion are associated with acute Aβ pathology. PET scanning could enable paediatric patients to be monitored for the presence and persistence of acute post-concussion amyloid, and to determine whether scan positivity and negativity predict different outcomes.176, 177 Other PET imaging agents with potential utility in paediatric populations include new tracers that bind neurofibrillary tangles composed of tau. Early imaging results with 18F-T807, 18F-T808 and 18F-THK5105 are proving to be useful in confirming the presence of tauopathy in various clinical situations, including AD.178, 179, 180 In a recent AD study, the magnitude of tau tracer signal correlated positively with the stage of disease and severity of cognitive impairment.180 A third tau PET tracer, 11C-PBB3, has been tested in healthy individuals and patients with AD, and may be able to detect non-AD conformations of tau.181 In addition, a recent report contains the first description of tauopathy imaging in a living person with suspected sports-associated CTE.177 Given the extent of chronic tau pathology in concussion, repetitive subconcussive injury and CTE, tau tracers may be useful as diagnostic and prognostic biomarkers (for example, to distinguish CNI from CTE). Studies with these tracers in adults with CTE are underway, but their use in paediatric populations will depend on future research to determine whether tau pathology is present in young patients after TBI or concussion. A PET tracer for the microglial cholesterol transporter protein might be useful for imaging of neuroinflammation associated with TBI.182 New PET ligands to image brain microglia, which are being developed with potential utility in neurodegenerative diseases, may also prove useful in concussion and CTE management. Exploration of these PET ligands in paediatric populations with concussion and TBI would be informative, but risk–benefit analyses must be performed before embarking on studies involving radiotracers in this age group. The ultimate utility of any PET imaging agent will depend on its diagnostic and prognostic value as part of a multimodal panel of biomarkers and neuroimaging techniques. Noninvasive techniques such as transcranial magnetic stimulation (TMS) have also uncovered changes in synaptic plasticity following TBI and concussion,183 particularly in asymptomatic individuals.184, 185, 186 Several small TMS studies of young athletes in their early 20s with a history of concussion suggest imbalances in γ-aminobutyric acid and/or glutamate neurotransmission in the motor cortex that are associated with deficits in synaptic long-term potentiation and depression.184, 185, 187, 188 TMS has also revealed that concussion-related impairments in synaptic plasticity can impair aspects of motor learning,188 and that these deficits are detectable decades after an individual's last concussion.189 Another crucial noninvasive tool for detecting neurochemical dysfunction associated with concussion is proton magnetic resonance spectroscopy (MRS). Reports specifically addressing the use of spectroscopy following sports-related concussion suggest various abnormalities consistent with neurochemical alterations.190 In younger (high school) athletes, increased glutamate and glutamine levels were detected by MRS at post-season versus pre-season evaluation, even in players who had not experienced clinically significant concussion during the season.191 Such findings suggest that even subconcussive head impacts can result in the activation of glutamate pathways, implying cellular injury or neuronal death, despite the absence of symptoms. Levels of creatinine and myoinositol (an organic osmolyte located in astrocytes192, 193) were also significantly altered in a subset of the participants in the aforementioned study. In a rare longitudinal study utilizing MRS,194 individuals who sustained a single sports-related concussion exhibited significantly reduced levels of N-acetylaspartate (NAA, a marker of neuronal and axonal health, integrity and functioning195) in the brain 3 days after injury. Levels were increased at 15 days post injury, and reverted to control values at 30 days post injury. By contrast, participants who sustained a second concussion 10–13 days after their initial concussion displayed a prolonged reduction in NAA levels, which had not normalized even 45 days post injury. These results suggest that repeated injury within a short time frame increases the likelihood of protracted or incomplete recovery. In addition to the acute and subacute alterations detected by MRS, other studies of the long-term effects of concussion have disclosed increased myoinositol (associated with glial proliferation) and decreased choline (associated with membrane turnover195) levels in the medial temporal lobe in otherwise healthy former athletes who sustained their last concussion more than three decades prior to testing.196 Another recent study examined a cohort of symptomatic retired National Football League players, using an advanced MRS method called correlated spectroscopy (COSY), which can measure additional metabolites.197 The authors identified increased choline and glutamate–glutamine levels (indicative of diffuse axonal injury and excitotoxicity, respectively), consistent with previous mTBI MRS studies, as well as additional cerebral metabolites that were indicative of neuroinflammatory changes. These metabolic changes may provide insight into mechanisms of injury, such as excitotoxicity and/or inflammation, which could underlie the reported structural changes. Overall, the available data support the use of MRS as a research tool to identify altered neurophysiology and monitor recovery in adult athletes, even following resolution of post-concussive symptoms. At present, MRS-detected biochemical alterations may enhance our understanding of the underlying pathophysiology, but do not yet provide specific diagnostic information. Larger cross-sectional, prospective and longitudinal studies are needed to determine the sensitivity and prognostic value of MRS within the field of sports-related concussion.190 Because the interpretation of MRS in the immature brain requires certain developmental considerations, appropriate comparison samples will be needed for future work in children. MRS techniques with greater spectral resolution, including COSY, might provide additional biochemical specificity.197 Other advances in spatial resolution, such as 3D chemical shift imaging, may also provide greater specificity by allowing the investigation of metabolic alterations throughout the brain rather than in specific regions of interest. Finally, MRS could have a role in measurement of treatment effects, such as those induced by transcranial direct current stimulation198 and TMS.199 The mechanisms and surveillance infrastructure for sports-related injury measurement, reporting, tracking and data sharing are insufficient for current needs and objectives. Concussion research and clinical efforts are hindered by a lack of concussion data across sports and playing levels. A 2014 Institute of Medicine report identified only three national sports injury surveillance systems: the National Electronic Injury Surveillance System—All Injury Program (NEISS-AIP), the National Collegiate Athletic Association Injury Surveillance System (NCAA ISS), and the High School Reporting Injury Online (RIO™).1 These systems can be supplemented with clinical data (for example, from emergency departments, hospitalized inpatients and sports clinics), but these data are biased toward more-severe injuries and patients of higher socioeconomic status. Indeed, schools in rural areas or communities with lower socioeconomic status often have limited access to sports medicine care professionals and facilities. Several emerging programmes may improve surveillance. Regional efforts such as Clinical Outcomes Research Education for Athletic Trainers (CORE-AT) and national efforts such as the National Athletic Trainers' Association National Athletic Treatment, Injury and Outcomes Network (NATA NATION™) attempt to integrate injury tracking with treatment and outcomes data at the high school and collegiate levels. However, none of these systems specifically capture injuries to younger athletes, those participating in non-school sponsored sports, or those at schools without athletic trainers. Sports injury databases also rarely account for demographic factors including socioeconomic status, race or ethnicity, and health-care coverage. Currently, no effective mechanisms exist to consistently and inexpensively link various surveillance data sets, or to follow up individual athletes across sports, tracking systems or the age continuum. There is a considerable need for a system that tracks individual athletes through their playing careers and beyond. Each individual should be tracked for several decades to establish if, when and how a given burden of TBI evolves into CTE, and to assess all the possible negative health outcomes associated with concussion. Such a system would also provide more-accurate descriptions of concussion history and exposure to risk factors, and could capture both short-term and long-term outcomes, including measures of physical and mental health, academic and career success, quality of life and social connectivity, and evolving socioeconomic status. Such efforts are challenged by a variety of issues, including a lack of mandatory reporting of concussion at any level. Mandatory concussion reporting, funding for surveillance efforts, and provision of training to data reporters (for example, coaches and athletic trainers) would greatly improve epidemiological research. However, mandatory reporting will not provide meaningful results without validated, consensus definitions for concussions, and development of a universal data repository and a global unique identifier (GUID) system. Data sets from standardized surveillance efforts could then be linked, thereby improving data sharing for research and clinical care. Coupling of surveillance data with standardized collection, storage and curation infrastructures for biobanking of tissue and fluid samples could dramatically improve injury and outcomes research.200 These efforts might be catalyzed by funding from public–private partnerships, and made actionable by setting realistic short-term and long-term goals to create a multi-year plan. However, in the USA at least, such efforts are currently hampered by misunderstanding of Health Insurance Portability and Accountability Act (HIPAA) regulations and general concerns for athlete confidentiality. Wider use of computerized neurocognitive testing (CNT) for athletes could improve concussion surveillance, as well as diagnosis and management. However, several important challenges must be overcome before CNT becomes routine. These challenges include a lack of standardized administration protocols, the potential for technological errors arising from different computer hardware, limits in the types of cognitive functions assessed, and a lack of qualified test administrators and data interpreters.201 Despite these shortcomings, however, CNT is already used by approximately 40% of US high schools that employ athletic trainers.202 Though not affordable for all schools, CNT could enhance ground-level data collection and aid risk-exposure estimation and post-concussion recovery tracking, as well as increasing the quality of data reported to sports injury surveillance networks. CNT may be also useful in evaluating and tracking post-concussion cognitive improvement or decline, and could have utility in predicting outcomes.203, 204 Whether CNT data collected in the school setting will reach the validation and reproducibility standards achieved by CNT conducted by a clinical research team remains to be seen. Importantly, CNT needs standardization and guidelines for determining 'return to play' and 'return to learn' for athletes who show recovery in one domain but are still symptomatic in others. More research is required on the utility of CNT, both in the clinic and for concussion surveillance and management of youth athletes. In several critical areas, incomplete knowledge hampers meaningful advances in the field of paediatric concussion. At the molecular and cellular levels, research that focuses on axonal damage after concussion and repetitive subconcussive injury is urgently needed to elucidate changes in axonal trafficking and repair, and to better define the role of transient Aβ accumulation as a potential driver of downstream and/or future pathology. Concussion researchers may need to identify more-suitable animal models to study molecular pathology, including tau and its contribution to post-concussion and CTE pathologies, as the structure and organization of the brain differs dramatically in rodents and humans. Without a clearer understanding of how TBI changes the young, still-developing brain, and what pathological events happen in the weeks, months and years following injury, we are left to speculate about the underlying biological bases of such changes. Head impact data collection and risk assessment in youth sports might be improved through use of sensor technologies that record linear and rotational forces. Such commercially available devices, if validated, could determine levels of cumulative head impact forces during games and across seasons of play, and the findings could be linked to neuroimaging data and functional outcome assessments. Combined with 'hit-count' metrics, sensor data may improve knowledge of short-term and long-term neuropsychological outcomes of repetitive subconcussive impacts. Our knowledge of CTE might be improved by understanding baseline rates in the general population, in injured athletes, among uninjured athletes matched by sport and playing positions, and in 'control' athletes in low-risk sports. Improved knowledge of risk exposures could lead to prevention efforts, including practice and competition rule changes. A decades-long, prospective, longitudinal study, following youth athletes through their sporting careers and beyond, would provide more-definitive knowledge of cumulative head impacts and risks of long-term neuropsychological dysfunction and dementia. Such a study is underway in NCAA alumni, who were first studied in 2003 and were re-assessed in 2013.29, 205 Studies in other populations, especially if NIH-funded, would probably begin with a 5-year study that could be renewed in further 5-year increments. Public–private partnerships are likely to be required to secure enough funding to involve multiple study centres. The NCAA has provided partial sponsorship for the 10-year re-assessment of over 100 athletes, but further funding from the NIH, the US Department of Defense (DoD), and private philanthropic sources will be required to extend the range of assessment from neuropsychology, through MRI, to molecular imaging for amyloid, tau and/or inflammation. Ideally, the longitudinal study design should combine epidemiological and interventional trial methodologies and utilize multiple control groups, including non-contact athletes and uninjured impact sport athletes. A longitudinal study would also shed light on the role of cognitive reserve. A precedent for such studies has been established by the late-life dementia research community, using NIH funds and public–private partnerships involving pharmaceutical companies and foundations. For such studies to be successful, additional surveillance systems and data repositories must first be established. Efforts would be accelerated if athletes participating in impact sports had universal access to athletic trainers, who could act as reliable data reporters while promoting safety and providing basic care. In addition, any longitudinal studies must include postmortem analyses to better understand the influence of childhood and young-adult concussions on the development of neurodegenerative pathology and dementia in later life. 'Return-to-play' guidelines are currently hampered by a lack of rigorous epidemiological evidence, and could be greatly improved by long-term safety data from longitudinal studies.206 Longitudinal research could also include studies to determine whether those athletes who fail to follow guidelines experience any negative health effects, such as lingering symptoms or altered risk of incurring a second concussion. The infrastructure for a long-term prospective study might be created through the formation of a research consortium modelled after the Alzheimer's Disease Neuroimaging Initiative (ADNI). ADNI has set standards for data collection, dissemination agreements, testing methodologies, and biomarker collection and analysis. A version of ADNI currently underway with participation of the DoD (ADNI-DoD) is focused on blast-related TBI research in military populations.207 In May 2014, in addition to the NCAA Concussion Study, the NCAA and the DoD announced the launch of the largest prospective sports-related concussion study to date, which will monitor approximately 37,000 NCAA athletes over 3 years. One can envision how this study's infrastructure may eventually be extended to study younger athletes over an extended longitudinal range. Many gaps remain in our knowledge of the biology of TBI, which limit our ability to develop effective drugs. These gaps must be filled if we are to tackle the underlying disease pathology and move beyond treating the symptoms. However, much can be accomplished while research into fundamental TBI biology continues. Drug repurposing involves testing of existing FDA-approved drugs for new indications, and can reduce expense and shorten the path for drug approval. Current repurposing trials include methylphenidate for pain and mental fatigue,208 the dopamine receptor agonist bromocriptine for working memory,209 and the antidepressant sertraline for mood and anxiety, the most frequent neuropsychological complications that influence long-term outcomes after concussion.210 Larger randomized clinical trials should be conducted before these drugs can be introduced into clinical practice for these new indications. In addition, the recent failure of the PROTECT phase III trial of progesterone to improve outcomes after acute TBI211 may serve as a reminder of the need for more research to better understand the fundamental biology underlying TBI. Although many drug repurposing efforts are designed primarily to address concussion symptoms, the drugs may also influence injury pathology and progression. Research on established drugs can also lead to new drug discovery efforts and, potentially, new preventive or management therapeutics. New drugs are urgently needed for TBI and concussions that do not resolve. Drug discovery efforts in the areas of neuroprotection and anti-inflammation are especially relevant because of their potential cross-applicability to neurodegenerative diseases such as AD. Similarly, drugs currently in development for other neurodegenerative diseases might be repositioned for testing in patients with TBI or nonresolving concussion symptoms. As is often the case in medical research, recent advances in concussion research raise as many questions as they answer. Evidence exists for long-term neuropsychological dysfunction and later-life dementia after concussions or repetitive subconcussive head impacts, and more work is needed to better understand the implications and outcomes of youth participation in impact sports. As outlined in this Expert Consensus Document, there is a path forward, but achieving the goals outlined here will require public and private sector cooperation. While recommendations can be improved with increased knowledge, the available evidence can still inform individual decision-making when considering youth sport participation, as well as practice policies and competition rules. With an ageing population and a looming epidemic of dementia, we must learn more about potential early-life risk factors, including sports-related concussion. The choices made by parents, coaches, school boards and children will be better informed when the critical gaps in scientific knowledge of concussion are filled. Download references|与运动相关的脑震荡和重复性亚震荡暴露越来越被认为是儿科人群的潜在危险，但是对于这些事件的短期和长期后果，包括潜在的认知障碍和晚年痴呆的风险，仍然知之甚少。这份专家共识文件是由全球安全儿童、阿尔茨海默氏症药物发现基金会和安德鲁斯矫形外科和运动医学研究所召集的为期一天的会议的结果。目标是强调在脑震荡科学、痴呆症、遗传学、诊断和预后生物标志物、神经影像学、运动损伤监测和信息共享等领域的知识差距和亟需研究的领域。针对这些领域，我们提出了明确和可实现的途径，以提高对青少年体育相关脑震荡的理解、治疗和预防。2009年，美国年龄 < 19岁的个体中记录了约250,000例非致命性创伤性脑损伤(TBI)。1疾病控制和预防中心估计，5-18岁的年轻人维持着所有运动相关脑震荡的65% 。2尽管最近在诊断性脑成像方面取得了进展，并且在我们对脑震荡物理学的理解方面，长期的认知结果仍然知之甚少。由于脑震荡的身体、认知和情感后果引起了公众的广泛关注，我们对如何预防、诊断和治疗这种伤害的不完整知识危及我们儿童的总体健康，特别是他们的大脑健康。这份专家共识文件是儿科和成人创伤性脑损伤、阿兹海默病(AD)研究、遗传学、流行病学、生物伦理学和运动医学领域专家为期一天的会议的结果(专栏1) ，该会议于2013年11月由全球安全儿童、阿尔茨海默氏症药物发现基金会和安德鲁斯矫形外科和运动医学研究所召集。我们的主要目标是强调我们在儿童和青少年脑震荡知识方面的重大差距。我们强调需要进行研究的领域，如开发诊断和预测性生物标志物，阐明遗传风险因素，以及预测短期和长期结果。在我们的结论中，我们提出了提高我们对与运动相关的儿童脑震荡的长期后果的理解的途径。术语“脑震荡”经常与术语“轻度 TBI”(mTBI)交替使用，考虑到脑震荡后可能的脑损伤程度和慢性神经心理功能障碍的潜在可能性，这是一种潜在的误导性做法。然而，我们应该强调的是，大多数脑震荡不会产生后遗症。美国康复医学会将 mTBI 定义为格拉斯哥昏迷量表3评分为13-15分，意识丧失 < 30分钟，创伤后遗忘持续时间 < 24小时。脑震荡描述了损伤表型的异质混合物，取决于许多因素，包括头部撞击的大小，位置和方向。尽管缺乏宏观结构发现，脑震荡损伤涉及由线性和旋转剪切力破坏轴突和膜功能(弥漫性轴突损伤，5离子通量和谷氨酸兴奋毒性)引起的原发性神经元损伤，随后是继发性病理生理效应，包括线粒体氧化应激，脑血流中断，血脑屏障(BBB)完整性受损，突触功能障碍和神经炎症。持续的神经心理学脑震荡后症状(脑震盪症候群)包括情绪障碍(例如抑郁症) ，难以集中和记忆问题(方框2)。年轻运动员的脖子和躯干比老年人的脖子和躯干更弱，因此，造成脑损伤所需的力量更少。发育中的大脑也可能特别容易受到由头部创伤的剪切力引起的轴突损伤，这在美国青年足球中可以超过100g 的线性加速力。然而，青年运动中持续的平均力量通常会小于较高水平的运动。正确的突触发育对认知和行为健康至关重要。神经发生、竞争性突触消除(“修剪”)、髓鞘形成、轴突和树突树枝化等过程在产前发育的整个生命周期中持续进行。额叶和颞叶是最后成熟的区域，人类在20岁出头的时候经历了这些区域的修剪[16] ，因此这些仍在发育的区域的损伤可能对大脑产生病理生理效应，增加了以后生活中出现神经心理问题的可能性。轴突髓鞘形成在青春期持续到20岁出头，易受损伤的影响。职业拳击手大脑健康研究的早期结果表明，第一次接触拳击比赛的年龄越早，尾状核体积损失越大，额叶轴突损伤越严重。这项研究对拳击手和追踪研究综合格斗拳击手进行了5年的研究，他们都经历过重复性的脑震荡和脑震荡。23,24年轻的大脑也有一些有助于恢复的特征。已经显示，这个年龄组的神经可塑性增加有助于局灶性损伤后更好的结果[25]。此外，发育中的动物对重复 TBI 的葡萄糖代谢障碍的窗口比成年动物更短[26]。总的来说，发育中的大脑在 TBI 后显示出脆弱性和恢复力。这些相互交织的因素可能解释了脑震荡和重复 mTBI 对年轻人和成年人大脑影响的差异。应高度重视对脑震荡风险采取保守的方法，并加大努力调查这些发育差异。大多数人ーー无论老少ーー从脑震荡中完全恢复过来。在儿童中，可能影响康复的因素包括年龄和脑震荡史。27,28在一项研究中，大约90% 的年轻成年男运动员在21天内经历了症状恢复。然而，在一项针对11-22岁患者(包括所有脑震荡原因，而不仅仅是运动相关)的急诊科研究中，15% 的样本在受伤后90天仍然表现出脑震荡后症状，包括头痛，头晕，“精神模糊”和抑郁。一些研究表明，美国高中橄榄球运动员从脑震荡中恢复的速度比大学运动员和职业运动员要慢。尽管最近一项包括青春期前年龄组(11-12岁)的研究表明，脑震荡后恢复持续时间可能与年龄没有线性关系，但与高中以下青少年的直接比较尚未发表[30] ，因为这个样本中的青少年恢复时间比青春期前的儿童更长。这些发现加在一起，意味着男性青春期年龄组的恢复时间较长的独特风险。对年幼儿童和女性的进一步研究将大大提高我们评估和减轻整个儿科和青少年年龄段风险的能力。在新的脑震荡发生前1年内遭受一次或多次脑震荡的青少年报告出现更长时间的症状，30表明可能存在“脆弱性窗口”，并将先前受伤的青少年置于更高的长期恢复风险中。11-18岁的青少年在急诊室出现脑震荡后发生脑震盪症候群的可能性比5-10岁的儿童高出近80% ，同样，伴有头痛的儿童和青少年出现脑震盪症候群的风险增加了一倍。在 mtBI 后在急诊室接受治疗的儿童中，6岁以上的儿童在受伤后3个月报告持续症状的发生率高于6岁以下的儿童。当然，获得 < 6岁儿童脑震荡症状的准确信息的能力可能受到缺乏症状自我意识和有效沟通这些症状的必要语言技能的限制。此外，从这些报告中不可能直接比较损伤的严重程度; 事实上，各种损伤的身体异质性，加上个体从脑震荡中恢复的先天能力，使得这种比较具有高度挑战性。一些专业研究中心正在使用“智能头盔”来标准化头部撞击产生的体力和角加速度，目前正在研究这些头盔用于测量和预测可能导致脑震荡的影响。36,37从脑震荡中恢复的年轻人可能会经历重大挑战，包括社会和学术发展的改变，38,39,40在一般智力测试中得分较低，以及学校表现下降(以年级平均分衡量)。39较低的父母教育水平和儿童学业成绩都与较差的脑震荡恢复相关。人格特质也起到了一定的作用，例如，伤前焦虑是运动性脑震荡后长时间恢复的一个危险因素。42年轻的男女运动员都有脑震荡的危险，但是女孩的脑震荡发生率高于男孩，特别是在高中和大学的足球、篮球、棒球或垒球比赛中。28,43,44,45解释这些差异的因素仍然不确定，但可能包括保护装备的质量，脑震荡症状的识别和报告，以及颈部长度和颈部肌肉力量。46男女之间在恢复轨迹方面的差异也知之甚少。然而，最近的一项研究表明，女性黄体酮水平影响脑震荡后的恢复。47青春期激素变化导致偏头痛，也可能导致脑震荡后恢复的性别差异。在青春期后，女性偏头痛的发病率是男性的四倍[48,49] ，一些证据表明，偏头痛患者在脑震荡后恢复较慢[50,51]。有必要进一步研究脑震荡风险和恢复的性别差异。一般来说，成人脑震荡比儿童和青少年脑震荡更容易理解。有几点值得注意。首先，脑震荡有多种非协调的定义。其次，脑震荡诊断是一门不完善的艺术。最后，在缺乏快速和廉价的客观诊断措施的情况下，脑震荡仍然是一种临床诊断，受到变异性的影响，包括不同亚专业和个体医生、神经心理学家和运动训练员的诊断阈值不同，以及教练、家长和年轻运动员报告不足。如果没有经过验证的诊断，脑震荡将仍然是一个模糊和报告不足的实体，发病率估计的准确性将继续受到不确切标准的差别应用的影响。重复性次级脑震荡可导致大脑结构和功能的改变。52弥散张量成像(DTI)检测到的白质异常在职业足球运动员中已有报道，即使没有任何明显的脑震荡史。与游泳运动员相比，男性职业足球运动员表现出 DTI 信号改变，提示几个大脑区域的白质完整性降低，这可能表明轴突髓鞘形成的丧失，类似于 mTBI 患者的改变。53名大学冰球运动员在一个赛季中表现出类似的白质变化。54,55,56,57此外，美国大学生橄榄球运动员重复性亚震荡性头部撞击已经以剂量依赖性方式与 BBB 完整性缺陷，白质完整性潜在丧失和认知功能障碍有关。58这些研究结果可能反映了持续遭受重复性次生脑震荡撞击的青少年的某种程度的风险，尽管很少有专门针对这一主题的研究。一个跟踪头部影响的指标ーー即“命中次数”ーー已经提出，59可以作为确定累积风险敞口的一个因素。这种方法的一个挑战是准确定义“命中”的参数，但改进的生物传感器在这方面显示出一些希望。与棒球中的“投球次数”类似，这个概念最近也被提出用于拳击运动员。24目前没有证据表明青少年重复性脑震荡冲击与晚年痴呆之间的因果关系，如果未来的研究将头部冲击与随后的神经心理功能障碍相关联，这些指标可能被证明是无价的。在成年人中，包括脑震荡在内的脑外伤可能会增加个体发生神经退行性疾病的风险，包括 AD 和 CTE (CTE) ，这是一种仅与重复性头部创伤相关的疾病[65,66]。尽管 mTBI 和 PD 风险之间的关系仍然不确定，但 TBI 也可能增加发生帕金森氏症的风险[67]。在儿科人群，特别是年轻运动员中，单次或重复性脑震荡对晚年神经退行性疾病和痴呆风险的影响是未知的。CTE 在20世纪20年代后期首次被症状性描述为拳击运动员的“拳击醉”痴呆，69后来被描述为“痴呆拳击”[70] ，并在1973年首次被病理学描述[71]。自2005年在一名前职业美式足球运动员身上发现 CTE 以来,这种病症已经引起了公众的广泛关注，目前已经在前冰球、棒球、橄榄球和足球运动员、73名摔跤运动员、74名退伍军人的大脑中发现。75,76业余和职业运动员慢性创伤性脑病的患病率和发病率仍然是未知的，这增加了讨论其流行病学和运动员的人口风险的困难。虽然慢性创伤性脑病主要被认为是一种神经退行性疾病，有时是由大学或专业接触性运动的职业生涯造成的，但在高中运动员中也有慢性创伤性脑病的报道。这一发现表明，慢性创伤性脑病的发展并不需要长期的运动生涯，青年运动员代表着高危人群。新出现的证据表明，临床的慢性创伤性脑病症状可以分为认知和情绪行为两种常见表现[78,79]。主观记忆症状如顺行性遗忘症是常见的，包括焦虑或抑郁在内的情绪障碍也是常见的[79] ，并且执行功能降低，这可能导致去抑制和决策技能受损[80]。这些临床症状定义了疾病的严重程度[81]。慢性创伤性脑病的神经退行性病理生理学是复杂的，对神经系统后遗症的了解很少。在严重的情况下，大脑皮层和内侧颞叶似乎受到最深刻的影响，81,82与病理学拥有属性由磷酸化 tau79组成的神经原纤维缠结，在某些情况下，TAR DNA 结合蛋白43病理学。CTE 也与明显的萎缩有关，特别是在额叶皮层和内侧颞叶，以及在乳头体，丘脑和下丘脑。79确诊的 CTE 临床诊断仍以尸检为基础。鉴于慢性脑震荡中描述的重复病变是否引起临床表型的不确定性，以及大多数专业和大学运动员不发展慢性脑震荡的事实，了解早期暴露于脑震荡是否与其他形式的神经退行性疾病和认知功能障碍(包括慢性神经认知障碍(CNI))相关至关重要。CTE 和 CNI 之间存在重要的临床区别，其中一些使得直接比较困难。CTE 是一种新出现的临床和病理状况，涉及多个领域的神经和认知功能的进行性恶化，主要在尸检中诊断。相反，CNI 表型并不一定是进行性的，而是拥有属性功能从组平均值或基线功能下降到创伤性脑损伤之前的水平。CNI 可以通过神经心理测试进行临床诊断。CNI 与头部创伤之间的因果关系尚未得到证实，但在专业运动员中一直发现剂量依赖性风险。此外，在业余运动员中进行的几乎一半的研究发现 CNI 的风险升高。年轻人群中是否存在类似的风险关联仍有待确定。一个假设是 CNI 代表了慢性创伤性脑病的前驱症状，但并非不可避免，类似于轻微认知障碍和 AD 之间的关系。另外，CNI 可能代表静态损伤而不退化。我们目前对 CNI 和 CTE 的基本生物学基础缺乏了解，这强调了进一步研究的必要性。对这两种情况的生物学知识的增加以及运动员(特别是青年运动员) CNI 的早期检测可能会推动干预措施以阻止进一步认知障碍的发展，并且还可能有助于验证推定的生物标志物。通过 tau 成像评估 CNI 可能有助于确定进展为 CTE 的可能性。脑震荡遗传学领域，特别是在儿科人群中，仍然处于起步阶段。尽管重复的头部撞击似乎对于 CTE 的发展是必要的，但是包括遗传学在内的其他因素可能具有重要作用，因为大多数脑震荡运动员不发展 CTE.87 CTE 的遗传危险因素可能与影响脑震荡易感性和恢复的因素重叠，AD 的遗传危险因素为这些因素的身份提供了重要的线索。E型载脂蛋白质的 ε4等位基因(APOEε4)是迄今为止发现的 AD 最重要的遗传危险因素，它严重影响中枢神经系统的损伤反应，特别是从大脑中清除淀粉样蛋白 -β (Aβ)。APOE 的三个等位基因赋予不同程度的 AD 风险: APOEε2降低风险，APOEε3是最常见的等位基因，代表与其他变体进行比较的基线风险，APOEε4增加风险。90,91研究表明 APOEε4与性别之间存在相互作用，因此 APOEε4相关的 AD 风险在女性中比在男性中更为突出。92,93 APOE 基因型与 TBI 协同作用增加 AD 的风险[94] ，尽管其与 CTE 作为重复 mTBI 的结果的假设风险相关性需要更多的研究。关于 APOE 同种型对儿童 TBI 结果的影响尚未达成共识，但来自成年人的数据表明 APOEε4对脑震荡结果有负面影响。一些研究表明，拥有至少一个 APOEε4等位基因与美国职业橄榄球运动员，96名拳击运动员95和其他成年人97,98,99,100的脑震荡后认知较差和持续的神经心理障碍有关，尽管其他研究没有发现这种关联。101,102一些证据表明 APOE 基因及其启动子的多态性是大学生运动员脑震荡危险的促成因素。另一项研究没有确定 APOEε4在脑震荡风险中的作用[105] ，尽管这个等位基因可能增加中年或晚年 mTBI 后痴呆的风险。106由于样本量小，方法不同，很难从这些相互矛盾的研究中得出结论。在儿童中，对于 APOEε4与脑震荡后神经心理学结果之间的关系知之甚少，而且 APOEε4测试在儿科 TBI 研究中并不常规。2012年，Kurowski 回顾了少数现有的研究，并结合了使用格拉斯哥结果量表的三项研究的结果[107,108,109]。在合并样本(252名儿童)中，6-12个月后不良临床结果的风险在 APOEε4携带者中高于非携带者(19% 比9%)。然而，这些研究包括了广泛的异质性损伤儿童的发育范围，并没有考虑到年龄和基因型之间可能的相互作用。此外，APOE 与性别之间的相互作用尚未在脑震荡的背景下进行研究。改进的前瞻性研究有助于澄清这些联系。将遗传学纳入儿科脑震荡研究充满了复杂的挑战，包括获得父母同意和儿童的知情同意，临床研究参与者的感知耻辱，获得的遗传知识的可行性以及关于可保性(特别是长期护理保险)的潜在担忧。对了解 APOEε4 + 状态的成年人的研究表明，许多人愿意改变生活方式，包括增加运动和改善药物管理[111] ，以及增加购买健康和长期护理保险[112,113]。关于新的遗传知识和相应的疾病风险的教育是必不可少的，正如个人对获得的知识的影响的个人感觉与痴呆风险增加的实际后果之间的实质性不一致所证明的那样.114 APOE 遗传知识对儿童，其家庭和参与影响性体育的决策过程的影响尚不清楚。APOE 基因型对该年龄组脑震荡风险和恢复的影响也需要进一步阐明。如果未来的研究发现，对于任何特定水平的影响，具有 APOEε4 + 状态的儿童比其 APOEε4同龄人具有更大的脑震荡或恢复不良的风险，则应考虑在参加影响性运动之前对学龄运动员进行基因检测。要充分理解基因影响的细微差别，就需要对高中和年轻运动员进行仔细研究。未来对青少年脑震荡结果(包括认知结果和痴呆风险)的研究应尽可能包括 APOE 基因分型。新的 APOE 研究应标准化研究方法和报告措施，包括收集“共同数据元素”，以确保有效的比较研究。110,115 APOE 基因型不一定是脑震荡恢复的不可改变的危险因素: 正在开发的 AD 治疗包括改变 ApoE4蛋白和 Aβ 之间相互作用的药物，这也可能适用于儿科脑震荡。编码脑源性神经营养因子的基因中的 Val66Met 多态性与 mtBI 后更好的结果有关，但与局灶性穿透性脑损伤后更差的结果有关。参与多巴胺能信号传导的基因多态性也可能有助于解释广泛的 TBI 结果。120此外，α-synuclein 基因启动子区的 Rep1多态性可能增加头部损伤后帕金森病的风险。为了提高我们对脑震荡风险和管理的理解，应该进行大型的前瞻性基于人群的全基因组关联研究(GWAS)和全基因组测序研究，以确定其他遗传变异(可能是低频率或低外显率) ，这些变异可以改变长期恢复，认知结果差或痴呆的风险。122这样的研究将需要大规模的数据共享，并且必须解决道德、隐私以及对可保性和可雇佣性的潜在影响等问题。尽管在确定可能应用于成人创伤性脑损伤治疗的可能的脑嵴液(CSF)和血液生物标志物方面取得了进展，但成人或儿科人群都没有经过临床验证的生物标志物。与成人脑震荡相比，儿童脑震荡的临床变异性更大; 因此，生物标志物在改善儿童脑震荡诊断方面具有特殊的潜力。值得注意的是，大多数 TBI 生物标志物已经在中度至重度 TBI 的背景下进行了研究，这使我们在 mTBI 生物标志物的知识方面存在明显的差距，特别是在儿童中。生物标志物的发展对 AD 治疗的进步至关重要。基于脑脊液的生物标志物已经被用于识别高危患者，并改善流行病学研究和临床试验的设计。123新的 PET 放射性配体，如淀粉样蛋白标记剂(其中三种现在是 FDA 批准的) ，可以用于诊断和改善基于神经病理学的患者临床试验分层。一些 tau 成像剂也在人体试验中，它们在包括 CTE 在内的 tau 病中的应用正在迅速建立。与基于液体的生物标志物一样，目前还没有足够敏感或特异的神经影像生物标志物来诊断成人或儿童的脑震荡或 CTE。目前 FDA 尚未批准任何创伤性脑损伤的诊断或治疗药物，而脑震荡生物标志物的验证可以加速这类药物的开发。然而，必须努力确保临床生物标志物检测的成本效益和广泛可用性。此外，考虑到与腰椎穿刺相关的风险，对脑震荡青少年脑脊液取样用于生物标志物研究的伦理问题应该得到解决。在成人体液为基础的生物标志物研究中有希望的发现必须在儿科人群中探索。过去数十年，推定脑震荡的生物标志物在科学文献中零星出现，其中最突出的是星形胶质细胞活化的非特异性标志物 S100钙结合蛋白 B (S100B)。血清中 S100B 的存在可能提示血脑屏障完整性的丧失。在成年拳击手比赛后观察到血清和脑脊液 S100B 水平升高，并且与头部撞击的数量和严重程度呈正相关。在脑震荡的职业冰球运动员中也观察到血清 S100B 水平升高，126在脑震荡后1小时测量的水平预测症状恢复时间。然而，S100B 的水平也提高后，控制发挥，没有发生脑震荡，表明这一标志物是不伤害特异性。事实上，没有头部损伤的成年创伤患者血清 S100B 水平升高。127,128,129其他研究表明，脑震荡后最初的 S100B 水平对于恢复不能很好地预测。与所有生物标志物一样，S100B 在儿童 TBI 管理中的作用甚至更不清楚[131] ，一些人认为这种标志物在儿科人群中几乎没有诊断或预后效用。132在一项关于≤15岁 TBI 患儿的研究中，5岁以下或9岁以上儿童的血清 S100B 水平高于5-9岁儿童。因此，S100B 可能不足以区分有症状和无症状的脑震荡儿童[133] ，S100B 在诊断和预后预后方面的效用是值得怀疑的。134,135,136神经元特异性烯醇化酶(NSE)是神经元损伤的标志物，但其作为血清或脑脊液生物标志物的用途仍不确定。拳击手头部撞击后观察到血清 NSE 水平升高[133,134,135,136,137] ，但在没有发生脑震荡的比赛后，冰球运动员也观察到 NSE 水平升高。血清 NSE 水平无法预测脑震荡后的恢复时间，可能与儿童损伤严重程度无关。133在≤15岁的儿童中，血清 NSE 水平与年龄呈负相关。一旦释放到血液中，NSE 具有缓慢的消除动力学，使得难以根据 NSE 水平区分原发性和继发性神经元损伤。神经丝轻链和胶质纤维酸性蛋白(GFAP)分别是 CSF 神经元特异性和胶质特异性损伤标志物，并且在成年拳击手打斗后 CSF 均升高。125,137,140在儿科脑震荡的情况下，对任何一种标志物都知之甚少，但对儿童和年轻成年人的初步研究表明，脑震荡后72小时内的血清 GFAP 水平与损伤后1个月的症状负担相关。神经元特异性蛋白 UCH-L1(泛素羧基末端水解酶同工酶 L1)首先通过参与 PD 与神经退行性病理学相关[142] ，其在血清中的存在后来被确定为严重 TBI 的生物标志物。血清 UCH-L1水平可能对脑震荡有诊断价值[146] ，但最近的证据表明血清水平升高与脑震荡次数之间缺乏相关性。UCH-L1在儿科人群中的临床应用值得进一步研究。也许最有希望的进展成人液基 TBI 生物标志物涉及 tau 蛋白。血清或脑脊液 tau 蛋白水平被认为表明轴突损伤，因为 tau 蛋白通常存在于轴突中，稳定微管。在 AD 患者中，脑脊液中切割的 tau 蛋白水解水平可能与认知功能相关。拳击手在比赛后脑脊液和血液中的 Tau 水平升高，脑脊液 Tau 水平与头部撞击的质量和数量相关。125,150最近的证据表明，脑震荡后冰球运动员血液中的 tau 水平升高，可能有助于预测恢复时间。然而，问题依然存在，一些研究报道血清切割 tau 对预测脑震盪症候群或长期结果的价值很小或没有价值。130,151 tau 作为儿童生物标志物的潜力尚不清楚，至今没有进行研究。事实上，血清 tau 作为一种生物标志物的可靠性尚未被确定为任何适应症。这种可能性是没有单一的生物标志物将足以诊断儿童脑震荡或预测结果。此外，很少有研究调查遗传组成和推定的生物标志物之间的相互作用。随着我们对生物标志物与损伤严重程度及其相互关系的理解的增加，生物标志物小组的发展，可能包括炎症和氧化标志物，152应该被考虑。未来的研究应试图进一步确定这些关系，建立生物标志物小组的临床价值，考虑到商业成本和实际可行性。代谢组学、脂质组学和蛋白质组学的最新进展ーー特别是寻找 AD 的代谢组学和脂质组学标志物ーー可能为今后研究脑震荡和脑震荡下损伤的生物标志物提供参考。最近的一些研究提出了与 MCI 和 AD 相关的代谢物和脂质谱的改变.153,154,155,156来自动物模型的数据表明，脂质和代谢物变化伴随着急性和慢性脑震荡后期，并且可能有助于预测恢复轨迹，157,158但是这些发现尚未在人类中得到验证。将生物标志物的搜索范围从血液和脑脊液扩展到唾液和尿液159，可能会提高快速和非侵入性测量的能力，特别是从儿童身上。从儿童抽取脑脊液样本，特别是在需要快速评估的情况下，在很大程度上是不切实际的。Mondello 等人提出了一套评估 TBI 生物标志物的有用标准，这些标准应该允许更精简的开发和验证.137任何经过验证的生物标志物小组必然是更大的多模式诊断套件的组成部分，其中可能包括结构和功能成像以及神经心理学测试。在设计未来的生物标志物研究时，应考虑 FDA 批准的可能性，以加快批准临床使用。虽然脑震荡仍然是一种临床诊断，但神经影像学技术正在提高我们对成人脑结构和功能后果的认识。儿科人群的神经影像学可能受到几个因素的限制，例如，脑震荡后纵向变化的测量由于动态的、未成熟的大脑的背景而变得复杂。没有成像技术被证实为脑震荡的诊断工具，成像结果与临床可测量的认知或行为功能之间的相关性是可变的。目前正在研究容积成像、 DTI 和功能磁共振成像(fMRI)等工具，特别是动脉自旋标记。通过 DTI 测量的分数各向异性(FA)可以推断白质束的结构完整性，TBI 后白质束通常被破坏。FA 变化的临床意义仍然存在争议，因为在脑震荡研究中观察到 FA 增加和减少[162,163,164,165,166]。这些差异可能部分是由于所检查的脑区域的相当大的空间异质性[167]以及损伤后间隔的差异。FA 可能仍然具有预后价值，有证据表明变化的方向和幅度与临床结果相关; 然而，这个想法等待在儿科和成人人群中验证。FA 可能缺乏必要的敏感性来充分评估脑损伤后白质束完整性的变化，扩散率的测量可能更合适。169 DTI 领域将大大受益于规范数据集的开发，以衡量观察到的变化。年轻运动员的赛前、赛后和赛季研究可以采用连续 DTI 成像技术为特定个体建立规范的数据，但数据汇总后的效用尚不清楚。儿科标准数据的缺乏严重限制了包括 DTI 在内的神经影像技术的临床应用。儿童脑震荡后的“回归基线”神经影像学研究也是必要的，因为它们可以极大地改善恢复的预测。尽管自动化提高了重复性，但 DTI 测量仍然对硬件和软件特异性，采集参数和分析软件敏感，这限制了重复性，标准化和中心之间以及跨研究之间的比较。标准化 DTI 成像中心的努力正在进行中。170 MRI 在绘制大脑的“连接体”(结构和功能神经连接网络及其各自的焦点节点的集合)以及研究脑震荡如何影响这些网络方面特别成功。局灶性或弥漫性 TBI 可以破坏大脑的功能连接，导致多个网络的功能障碍，包括默认模式和显着网络，这与记忆，情绪和情绪有关[171]。网络功能障碍对恢复的影响可能比病变部位更强[171,172,173] ，但对大脑发育和认知功能的长期影响尚不清楚[26,174]。脑震荡后儿童网络连接功能障碍的进一步研究对于改善损伤预后和管理至关重要。用于 PET 成像的放射性示踪剂有可能推进脑震荡和 CTE 的诊断和治疗，但目前它们在儿科人群中的应用纯粹是研究性的。三种 FDA 批准的放射性标记成像剂目前可用于检测疑似 AD 患者的脑淀粉样蛋白。175在成年人中，一些脑震荡病例与急性 Aβ 病理有关。PET 扫描可以使儿科患者监测急性脑震荡后淀粉样蛋白的存在和持续性，并确定扫描阳性和阴性是否预测不同的结果.176,177在儿科人群中具有潜在用途的其他 PET 成像剂包括结合由 tau 组成的神经原纤维缠结的新示踪剂。用18F-T807,18F-T808和18F-THK5105进行的早期成像结果证明对于确认包括 AD 在内的各种临床情况下存在共病是有用的。178,179,180在最近的一项 AD 研究中，tau 示踪信号的大小与疾病的分期和认知障碍的严重程度呈正相关。第三种 tau PET 示踪剂11C-PBB3已经在健康个体和 AD 患者中进行了测试，并且可能能够检测 tau 的非 AD 构象。181此外，最近的一份报告首次描述了疑似与运动相关的慢性创伤性脑病(CTE)在活人中的重病影像学表现。鉴于脑震荡，重复性亚震荡损伤和 CTE 中慢性 tau 病理学的程度，tau 示踪剂可用作诊断和预后生物标志物(例如，区分 CNI 和 CTE)。目前正在对 CTE 成人进行这些示踪剂的研究，但它们在儿科人群中的应用将取决于未来的研究，以确定 TBI 或脑震荡后年轻患者是否存在 tau 病理学。小胶质细胞胆固醇转运蛋白的 PET 示踪剂可能有助于成像与创伤性脑损伤相关的神经炎症。182正在开发的新型 PET 配体可以成像脑小胶质细胞，对神经退行性疾病具有潜在的应用价值，也可能证明对脑震荡和慢性创伤性脑病的治疗有用。在脑震荡和 TBI 的儿科人群中探索这些 PET 配体将是有益的，但是在开始进行涉及该年龄组放射性示踪剂的研究之前必须进行风险-效益分析。任何 PET 成像剂的最终效用将取决于其作为多模式生物标志物和神经影像技术小组的一部分的诊断和预后价值。非侵入性技术如经颅磁力刺激(tMS)也发现了创伤性脑损伤和脑震荡后突触可塑性的变化，特别是在无症状的个体中。对20多岁有脑震荡史的年轻运动员进行的几项小型 TMS 研究表明，运动皮层中 γ-氨基丁酸和/或谷氨酸神经传导的不平衡与突触长时程增强作用和抑郁症的缺陷有关。184,185,187,188经颅磁刺激还显示，脑震荡相关的突触可塑性损伤可以损害运动学习的各个方面，这些缺陷在个体最后一次脑震荡几十年后仍然可以检测到。另一个检测与脑震荡相关的神经化学功能障碍的关键非侵入性工具是质子磁共振谱(MRS)。专门针对运动相关脑震荡后使用光谱学的报告表明，与神经化学改变一致的各种异常。在年轻(高中)运动员中，MRS 在赛季后与赛季前评估中检测到谷氨酸和谷氨酰胺水平增加，即使在赛季期间没有经历临床显着脑震荡的运动员中也是如此。这些发现表明，即使是次震荡性头部撞击也可能导致谷氨酸途径的激活，意味着细胞损伤或神经元死亡，尽管没有症状。在上述研究中，一部分参与者的肌酐和肌醇水平(位于星形胶质细胞中的有机渗透液192,193)也发生了显著变化。在一项使用 MRS 的罕见追踪研究中，194名持续单次运动相关脑震荡的个体在受伤后3天在大脑中表现出显着降低的 N- 乙酰天冬氨酸(NAA，神经元和轴突健康，完整性和功能的标志物195)水平。损伤后15天水平升高，损伤后30天恢复到对照值。相比之下，在第一次脑震荡后10-13天再次受到脑震荡的参与者表现出 NAA 水平的长时间下降，即使在受伤后45天也没有恢复正常。这些结果表明，在短时间内反复受伤增加了延长或不完全恢复的可能性。除了 MRS 检测到的急性和亚急性改变之外，其他关于脑震荡长期影响的研究已经揭示了在其他健康的前运动员中，内侧颞叶中肌醇(与胶质增殖相关)增加和胆碱(与膜转换相关195)水平降低在测试之前持续最后一次脑震荡超过三十年。196最近的另一项研究使用一种叫做相关光谱学(COSY)的先进的 MRS 方法，检测了一组有症状的退役国家橄榄球联盟球员，这种方法可以测量额外的代谢物。作者发现胆碱和谷氨酸-谷氨酰胺水平升高(分别表明弥漫性轴突损伤和兴奋性毒性) ，与之前的 mtBI MRS 研究一致，以及额外的大脑代谢物表明神经炎症的变化。这些新陈代谢的变化可能提供了损伤机制的洞察力，如兴奋性毒性和/或炎症，这可能是所报道的结构变化的基础。总的来说，现有的数据支持使用 MRS 作为一种研究工具，以确定改变的神经生理学和监测恢复成年运动员，即使在解决后脑震荡症状。目前，MRS 检测到的生化改变可以增强我们对潜在病理生理学的理解，但尚不能提供具体的诊断信息。需要更大的横断面，前瞻性和纵向研究来确定 MRS 在运动相关脑震荡领域内的敏感性和预后价值.190由于未成熟大脑中 MRS 的解释需要某些发育方面的考虑，因此将来在儿童中的工作将需要适当的比较样本。具有更高光谱分辨率的 MRS 技术，包括 COSY，可能提供额外的生化特异性。空间分辨率的其他进展，如3D 化学位移成像，也可以通过允许调查整个大脑的代谢改变而不是在特定的感兴趣的区域，提供更大的特异性。最后，MRS 可以在测量治疗效果方面发挥作用，例如经颅直流电刺激198和 TMS.199。体育相关伤害测量，报告，跟踪和数据共享的机制和监测基础设施不足以满足目前的需求和目标。脑震荡的研究和临床工作受到缺乏运动和运动水平的脑震荡数据的阻碍。2014年美国医学研究所的一份报告只确定了三个国家运动伤害监测系统: 国家电子伤害监测系统ーー所有伤害项目(NEISS-AIP)、全美大学体育协会伤害监测系统(NCAA ISS)和高中伤害在线报告系统(rIOTM)。1这些系统可以补充临床数据(例如，来自急诊科、住院病人和体育诊所) ，但这些数据偏向于更严重的伤害和社会经济地位更高的病人。事实上，农村地区或社会经济地位较低的社区的学校往往很难获得运动医疗专业人员和设施。一些新出现的项目可能会改善监督。区域性的努力，如运动训练员临床结果研究教育(CORE-AT)和全国性的努力，如全国运动训练员协会全国运动治疗，伤害和结果网络(NATA NATIONTM)试图将伤害跟踪与高中和大学水平的治疗和结果数据结合起来。然而，这些系统中没有一个专门针对年轻运动员、那些参加非学校赞助体育项目的运动员或那些在没有运动教练的学校的运动员。运动损伤数据库也很少考虑人口统计因素，包括社会经济地位、种族或民族以及医疗保健覆盖率。目前，还没有有效的机制来连贯和廉价地将各种监测数据集联系起来，或者跨越体育、跟踪系统或年龄连续体跟踪个别运动员。现在相当需要一个系统来追踪个人运动员的运动生涯和其他方面。应该对每个人进行数十年的跟踪，以确定 TBI 的负担是否、何时以及如何演变为 CTE，并评估与脑震荡相关的所有可能的负面健康结果。这种系统还可以更准确地描述脑震荡病史和风险因素，并可以捕捉短期和长期的结果，包括身体和心理健康、学业和职业成功、生活质量和社会联系以及不断变化的社会经济地位。这种努力受到各种问题的挑战，包括缺乏任何级别的脑震荡强制性报告。强制性脑震荡报告、为监测工作提供资金以及为数据记者(例如教练和运动员培训员)提供培训将极大地改善流行病学研究。然而，如果没有经过验证的、对脑震荡的共识定义，以及通用数据库和全球唯一标识符(GUID)系统的开发，强制性报告将无法提供有意义的结果。然后可以将标准化监测工作的数据集联系起来，从而改善研究和临床护理的数据共享。将监测数据与组织和液体样本生物库的标准化收集、储存和管理基础设施耦合起来，可以大大改善损伤和结果研究。200这些努力可以通过公私伙伴关系的资金来催化，并通过制定现实的短期和长期目标来实现，以创建一个多年计划。然而，至少在美国，这些努力目前受到对健康保险便利和责任法案(HIPAA)规定的误解和对运动员保密的普遍关注的阻碍。运动员更广泛地使用计算机神经认知测试(CNT)可以改善脑震荡的监测，以及诊断和管理。然而，在 CNT 成为常规手术之前，必须克服几个重要的挑战。这些挑战包括缺乏标准化的管理协议，不同计算机硬件引起的技术错误的可能性，评估的认知功能类型的限制，以及缺乏合格的测试管理员和数据解释员.201尽管存在这些缺陷，但是，CNT 已经被大约40% 的美国高中雇用运动教练员.202虽然不是所有学校都负担得起，但是 CNT 可以加强地面数据收集，帮助风险暴露估计和脑震荡后恢复跟踪，以及提高向运动损伤监测网络报告的数据质量。CNT 也可能有助于评估和跟踪脑震荡后认知改善或下降，并可能有助于预测结果.203,204在学校环境中收集的 CNT 数据是否将达到由临床研究小组进行的 CNT 所达到的验证和重复性标准仍有待观察。重要的是，CNT 需要标准化和指导方针，以确定“返回运动”和“返回学习”的运动员在一个领域表现出恢复，但在其他领域仍然有症状。在临床和青少年运动员脑震荡监测和管理方面，需要对 CNT 的应用进行更多的研究。在一些关键领域，不完整的知识阻碍了儿科脑震荡领域有意义的进展。在分子和细胞水平上，迫切需要重点研究脑震荡和重复性亚震荡损伤后的轴突损伤，以阐明轴突运输和修复的变化，并更好地定义瞬时 Aβ 积累作为下游和/或未来病理学的潜在驱动因素的作用。脑震荡研究人员可能需要确定更合适的动物模型来研究分子病理学，包括 tau 蛋白及其对脑震荡后和慢性创伤脑炎病理学的贡献，因为啮齿动物和人类的大脑结构和组织大不相同。如果不能更清楚地了解创伤性脑损伤如何改变年轻、仍在发育中的大脑，以及在损伤后的数周、数月和数年内会发生什么样的病理事件，我们就只能推测这种改变的潜在生物学基础。通过使用记录线性和旋转力的传感器技术，可以改进青年体育运动中头部影响数据的收集和风险评估。这种商业上可用的设备，如果经过验证，可以确定在比赛期间和整个比赛季节中头部累积冲击力的水平，并且研究结果可以与神经影像学数据和功能结果评估联系起来。结合“击中计数”指标，传感器数据可以提高对重复性次生震荡影响的短期和长期神经心理学结果的认识。我们对慢性创伤性脑病的认识可以通过了解一般人群、受伤运动员、运动和运动位置匹配的未受伤运动员以及低风险运动中的“控制”运动员的基线率来改善。提高对风险暴露的认识可导致预防努力，包括改变做法和竞争规则。一项长达数十年的前瞻性追踪研究，追踪青年运动员的运动生涯及以后的发展，将提供有关累积性头部撞击以及长期神经心理功能障碍和痴呆风险的更确切知识。这样的研究正在 NCAA 校友中进行，他们于2003年首次接受研究，并于2013年重新评估。其他人群的研究，特别是如果 NIH 资助的话，可能会从5年的研究开始，可以进一步延长5年的增量。可能需要建立公私伙伴关系，以获得足够的资金，使多个研究中心参与进来。NCAA 已经为100多名运动员的10年重新评估提供了部分赞助，但需要来自 NIH，美国国防部(DoD)和私人慈善来源的进一步资助，以扩大评估范围，从神经心理学，通过 MRI，淀粉样蛋白，tau 和/或炎症的分子成像。理想情况下，追踪研究设计应结合流行病学和介入试验方法，并利用多个对照组，包括非接触运动员和未受伤的撞击运动员。追踪研究还将阐明认知储备的作用。老年痴呆症研究团体利用国家卫生研究院的资金以及涉及制药公司和基金会的公私伙伴关系，开创了这类研究的先例。为了使这类研究取得成功，必须首先建立更多的监测系统和数据库。如果参加影响力体育运动的运动员能够普遍获得运动员训练员的帮助，这些训练员能够在促进安全和提供基本护理的同时充当可靠的数据报告员，那么将加快努力。此外，任何纵向研究都必须包括死后分析，以便更好地了解儿童和青少年脑震荡对今后生活中神经退行性病理和痴呆发展的影响。由于缺乏严格的流行病学证据，“重返赛场”的指导方针目前受到阻碍，纵向研究的长期安全数据可能会大大改善这一点。纵向研究还可以包括确定那些未能遵循指导方针的运动员是否会经历任何负面健康影响的研究，例如持续的症状或改变发生第二次脑震荡的风险。长期前瞻性研究的基础设施可以通过建立一个以阿尔茨海默氏病神经影像学倡议(ADNI)为模型的研究联盟来创建。ADNI 为数据收集、传播协议、测试方法和生物标志物收集和分析制定了标准。目前正在国防部参与的一个版本的 ADNI (ADNI-DoD)专注于军事人群中与爆炸相关的 TBI 研究。2072014年5月，除了 NCAA 脑震荡研究，NCAA 和国防部宣布启动迄今为止最大的前瞻性运动相关脑震荡研究，该研究将在3年内监测大约37,000名 NCAA 运动员。我们可以想象，这项研究的基础设施可能最终扩展到研究年轻运动员在一个延长的纵向范围。我们对创伤性脑损伤的生物学知识仍然存在许多差距，这限制了我们开发有效药物的能力。如果我们要解决潜在的疾病病理，并超越治疗症状，就必须填补这些空白。然而，当基础创伤性脑损伤生物学的研究继续进行时，许多工作可以完成。药物再利用包括测试现有 FDA 批准的新适应症药物，可以减少费用和缩短药物批准的路径。目前的再利用试验包括哌醋甲酯治疗疼痛和精神疲劳，多巴胺受体激动剂溴隐亭治疗工作记忆，舍曲林治疗情绪和焦虑，这是最常见的影响脑震荡后长期结果的神经心理并发症。此外，黄体酮的 PROTECT III 期临床试验最近未能改善急性 TBI211后的结局，这可能提醒人们需要更多的研究来更好地理解 TBI 的基础生物学。虽然许多药物重新利用的努力主要是为了解决脑震荡症状，药物也可能影响损伤病理学和进展。对现有药物的研究也可能导致新的药物发现努力，并可能导致新的预防或管理治疗。急需新的药物治疗创伤性脑损伤和无法消除的脑震荡。在神经保护和抗炎领域的药物发现努力是特别相关的，因为它们潜在的交叉适用于神经退行性疾病，如 AD。同样，目前正在开发的治疗其他神经退行性疾病的药物可能会被重新定位，用于 TBI 或无脑震荡症状患者的检测。正如医学研究中经常出现的情况一样，脑震荡研究的最新进展提出的问题和回答的问题一样多。有证据表明脑震荡或重复性次生脑震荡后长期神经心理功能障碍和晚年痴呆，需要更多的工作来更好地理解青年参与影响性运动的含义和结果。正如本专家共识文件所概述的那样，有一条前进的道路，但实现这里概述的目标将需要公共和私营部门的合作。虽然可以通过增加知识来改进建议，但现有证据仍然可以在考虑青年参与体育运动以及实践政策和竞赛规则时为个人决策提供信息。随着人口老龄化和痴呆症的流行，我们必须更多地了解潜在的早期生活风险因素，包括与运动有关的脑震荡。家长、教练、学校董事会和孩子们做出的选择将在脑震荡科学知识的关键差距得到填补时得到更好的信息。下载参考资料|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Practical+Design+of+Performant+Recommender+Systems+using+Large-scale+Linear+Programming-based+Global+Inference)|0| |[Rank-heterogeneous Preference Models for School Choice](https://doi.org/10.1145/3580305.3599484)|Amel Awadelkarim, Arjun Seshadri, Itai Ashlagi, Irene Lo, Johan Ugander|Stanford University; Amazon|School choice mechanism designers use discrete choice models to understand and predict families' preferences. The most widely-used choice model, the multinomial logit (MNL), is linear in school and/or household attributes. While the model is simple and interpretable, it assumes the ranked preference lists arise from a choice process that is uniform throughout the ranking, from top to bottom. In this work, we introduce two strategies for rank-heterogeneous choice modeling tailored for school choice. First, we adapt a context-dependent random utility model (CDM), considering down-rank choices as occurring in the context of earlier up-rank choices. Second, we consider stratifying the choice modeling by rank, regularizing rank-adjacent models towards one another when appropriate. Using data on household preferences from the San Francisco Unified School District (SFUSD) across multiple years, we show that the contextual models considerably improve our out-of-sample evaluation metrics across all rank positions over the non-contextual models in the literature. Meanwhile, stratifying the model by rank can yield more accurate first-choice predictions while down-rank predictions are relatively unimproved. These models provide performance upgrades that school choice researchers can adopt to improve predictions and counterfactual analyses.|学校选择机制的设计者使用离散选择模型来理解和预测家庭的偏好。最广泛使用的选择模型，多项式 logit (MNL) ，在学校和/或家庭属性中是线性的。虽然这个模型是简单和可解释的，但是它假设排名的偏好列表来自于一个从上到下在整个排名过程中是统一的选择过程。本文介绍了两种适用于学校选择的秩异质选择模型的建模策略。首先，我们采用了一个上下文相关的随机效用模型(CDM) ，考虑了在早期上层选择的情况下发生的下层选择。其次，我们考虑根据等级对选择模型进行分层，在适当的时候将相邻等级的模型相互调整。使用来自旧金山联合校区多年的家庭偏好数据，我们发现相对于文献中的非上下文模型，上下文模型大大提高了我们在所有排名位置的外部评估指标。同时，按等级对模型进行分层可以得到更准确的第一选择预测，而低等级预测相对来说没有改进。这些模型提供了学校选择研究人员可以用来改进预测和反事实分析的绩效提升。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Rank-heterogeneous+Preference+Models+for+School+Choice)|0| |[Connecting the Dots - Density-Connectivity Distance unifies DBSCAN, k-Center and Spectral Clustering](https://doi.org/10.1145/3580305.3599283)|Anna Beer, Andrew Draganov, Ellen Hohma, Philipp Jahn, Christian M. M. Frey, Ira Assent||Despite the popularity of density-based clustering, its procedural definition makes it difficult to analyze compared to clustering methods that minimize a loss function. In this paper, we reformulate DBSCAN through a clean objective function by introducing the density-connectivity distance (dc-dist), which captures the essence of density-based clusters by endowing the minimax distance with the concept of density. This novel ultrametric allows us to show that DBSCAN, k-center, and spectral clustering are equivalent in the space given by the dc-dist, despite these algorithms being perceived as fundamentally different in their respective literatures. We also verify that finding the pairwise dc-dists gives DBSCAN clusterings across all epsilon-values, simplifying the problem of parameterizing density-based clustering. We conclude by thoroughly analyzing density-connectivity and its properties -- a task that has been elusive thus far in the literature due to the lack of formal tools. Our code recreates every experiment below: https://github.com/Andrew-Draganov/dc_dist|尽管基于密度的聚类方法很流行，但是它的过程定义使得它很难与损失函数最小化的聚类方法进行比较。本文通过引入密度连通度距离(dc-dist) ，通过一个简洁的目标函数对 DBSCAN 进行了重新构造，该目标函数赋予极大极小距离密度的概念，从而抓住了基于密度的聚类的本质。这种新颖的超测量法使我们能够展示 DBSCAN、 k-center 和 SVD 在 dc-dist 给出的空间中是等价的，尽管这些算法在各自的文献中被认为是根本不同的。我们还验证了找到成对的 dc-dists 可以给出跨所有 ε 值的 DBSCAN 聚类，从而简化了基于密度的聚类的参数化问题。最后，我们通过深入分析密度连通性及其性质得出结论——由于缺乏形式工具，这项任务在文献中迄今为止一直难以实现。我们的代码重现了下面的每一个实验: https://github.com/andrew-draganov/dc_dist|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Connecting+the+Dots+-+Density-Connectivity+Distance+unifies+DBSCAN,+k-Center+and+Spectral+Clustering)|0| |[Shilling Black-box Review-based Recommender Systems through Fake Review Generation](https://doi.org/10.1145/3580305.3599502)|HungYun Chiang, YiSyuan Chen, YunZhu Song, HongHan Shuai, Jason S. Chang|National Tsing Hua University; National Yang Ming Chiao Tung University|Review-Based Recommender Systems (RBRS) have attracted increasing research interest due to their ability to alleviate well-known cold-start problems. RBRS utilizes reviews to construct the user and items representations. However, in this paper, we argue that such a reliance on reviews may instead expose systems to the risk of being shilled. To explore this possibility, in this paper, we propose the first generation-based model for shilling attacks against RBRSs. Specifically, we learn a fake review generator through reinforcement learning, which maliciously promotes items by forcing prediction shifts after adding generated reviews to the system. By introducing the auxiliary rewards to increase text fluency and diversity with the aid of pre-trained language models and aspect predictors, the generated reviews can be effective for shilling with high fidelity. Experimental results demonstrate that the proposed framework can successfully attack three different kinds of RBRSs on the Amazon corpus with three domains and Yelp corpus. Furthermore, human studies also show that the generated reviews are fluent and informative. Finally, equipped with Attack Review Generators (ARGs), RBRSs with adversarial training are much more robust to malicious reviews.|基于评论的推荐系统(RBRS)由于其缓解众所周知的冷启动问题的能力而引起了越来越多的研究兴趣。RBRS 利用评论来构建用户和项目表示。然而，在本文中，我们认为，这种对审查的依赖反而可能使系统面临被托儿的风险。为了探索这种可能性，本文提出了第一代基于先令攻击的 RBRS 模型。具体来说，我们通过强化学习学习一个虚假的评论生成器，它在向系统添加生成的评论之后，通过强制预测变化来恶意推销项目。通过引入辅助奖励，以提高文本流畅性和多样性的帮助下，预先训练的语言模型和方面预测，生成的评论可以有效的先令与高保真度。实验结果表明，该框架能够成功地利用三个域和 Yelp 语料库对亚马逊语料库中的三种不同类型的 RBRS 进行攻击。此外，人类研究也表明，生成的评论是流畅和信息。最后，配备了攻击评论生成器(ARGs) ，具有对抗性训练的 RBRS 对恶意评论更加有力。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Shilling+Black-box+Review-based+Recommender+Systems+through+Fake+Review+Generation)|0| |[Below the Surface: Summarizing Event Sequences with Generalized Sequential Patterns](https://doi.org/10.1145/3580305.3599264)|Joscha Cüppers, Jilles Vreeken||We study the problem of succinctly summarizing a database of event sequences in terms of generalized sequential patterns. That is, we are interested in patterns that are not exclusively defined over observed surface-level events, as is usual, but rather may additionally include generalized events that can match a set of events. To avoid spurious and redundant results we define the problem in terms of the Minimum Description Length principle, by which we are after that set of patterns and generalizations that together best compress the data without loss. The resulting optimization problem does not lend itself for exact search, which is why we propose the heuristic Flock algorithm to efficiently find high-quality models in practice. Extensive experiments on synthetic and real-world data show that Flock results in compact and easily interpretable models that accurately recover the ground truth, including rare instances of generalized patterns. Additionally Flock recovers how generalized events within patterns depend on each other, and overall provides clearer insight into the data-generating process than using state of the art algorithms that only consider surface-level patterns.|我们研究了用广义序列模式简洁地汇总事件序列数据库的问题。也就是说，我们感兴趣的模式并不像通常那样专门定义在观察到的表面事件上，而是可能另外包括能够匹配一组事件的广义事件。为了避免虚假和冗余的结果，我们根据最小描述长度原则来定义问题，根据这个原则，我们追求的是一组模式和泛化，这些模式和泛化一起最好地压缩数据而不会丢失。由此产生的最佳化问题无法进行精确的搜索，这就是为什么我们提出启发式的 Flock 算法，以便在实践中有效地找到高质量的模型。对合成和真实世界数据的大量实验表明，Flock 产生了紧凑且易于解释的模型，准确地恢复了地面真相，包括罕见的广义模式实例。此外，Flock 还回顾了模式中的广义事件是如何相互依赖的，并且总体上提供了对数据生成过程的更清晰的洞察，而不是使用只考虑表面层模式的最新算法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Below+the+Surface:+Summarizing+Event+Sequences+with+Generalized+Sequential+Patterns)|0| -|[Generalized Matrix Local Low Rank Representation by Random Projection and Submatrix Propagation](https://doi.org/10.1145/3580305.3599361)|Pengtao Dang, Haiqi Zhu, Tingbo Guo, Changlin Wan, Tong Zhao, Paul Salama, Yijie Wang, Sha Cao, Chi Zhang|; Genentech; Indiana University, Bloomington; Indiana University, School of Medicine; Purdue University; Amazon; Indiana University|Detecting distinct submatrices of low rank property is a highly desirable matrix representation learning technique for the ease of data interpretation, called the matrix local low rank representation (MLLRR). Based on different mathematical assumptions of the local pattern, the MLLRR problem could be categorized into two sub-problems, namely local constant variation (LCV) and local linear low rank (LLR). Existing solutions on MLLRR only focused on the LCV problem, which misses a substantial amount of true and interesting patterns. In this work, we develop a novel matrix computational framework called RPSP (Random Probing based submatrix Propagation) that provides an effective solution for both of the LCV and LLR problems. RPSP detects local low rank patterns that grow from small submatrices of low rank property, which are determined by a random projection approach. RPSP is supported by theories of random projection. Experiments on synthetic data demonstrate that RPSP outperforms all state-of-the-art methods, with the capacity to robustly and correctly identify the low rank matrices under both LCV and LLR settings. On real-world datasets, RPSP also demonstrates its effectiveness in identifying interpretable local low rank matrices.|矩阵局部低秩表示(MLLRR)是一种非常理想的矩阵表示学习技术，它可以检测出具有低秩性质的不同子矩阵。根据对局部模式的不同数学假设，MLLRR 问题可以分为局部常变(LCV)和局部线性低秩(LLR)两个子问题。MLLRR 上的现有解决方案只关注 LCV 问题，而这个问题忽略了大量真实而有趣的模式。在这项工作中，我们开发了一个新的矩阵计算框架称为 RPSP (随机探测为基础的子矩阵传播) ，提供了一个有效的解决方案，这两个 LCV 和 LLR 问题。RPSP 检测由低秩性质的小子矩阵生成的局部低秩模式，这些小子矩阵由随机投影方法确定。RPSP 得到了随机投影理论的支持。对合成数据的实验表明，RPSP 算法优于所有的最新方法，在 LCV 和 LLR 设置下都具有鲁棒性和正确识别低秩矩阵的能力。在实际数据集上，RPSP 也证明了其识别可解释的局部低秩矩阵的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Generalized+Matrix+Local+Low+Rank+Representation+by+Random+Projection+and+Submatrix+Propagation)|0| +|[Generalized Matrix Local Low Rank Representation by Random Projection and Submatrix Propagation](https://doi.org/10.1145/3580305.3599361)|Pengtao Dang, Haiqi Zhu, Tingbo Guo, Changlin Wan, Tong Zhao, Paul Salama, Yijie Wang, Sha Cao, Chi Zhang|; Indiana University; Amazon; Purdue University; Indiana University, School of Medicine; Genentech; Indiana University, Bloomington|Detecting distinct submatrices of low rank property is a highly desirable matrix representation learning technique for the ease of data interpretation, called the matrix local low rank representation (MLLRR). Based on different mathematical assumptions of the local pattern, the MLLRR problem could be categorized into two sub-problems, namely local constant variation (LCV) and local linear low rank (LLR). Existing solutions on MLLRR only focused on the LCV problem, which misses a substantial amount of true and interesting patterns. In this work, we develop a novel matrix computational framework called RPSP (Random Probing based submatrix Propagation) that provides an effective solution for both of the LCV and LLR problems. RPSP detects local low rank patterns that grow from small submatrices of low rank property, which are determined by a random projection approach. RPSP is supported by theories of random projection. Experiments on synthetic data demonstrate that RPSP outperforms all state-of-the-art methods, with the capacity to robustly and correctly identify the low rank matrices under both LCV and LLR settings. On real-world datasets, RPSP also demonstrates its effectiveness in identifying interpretable local low rank matrices.|矩阵局部低秩表示(MLLRR)是一种非常理想的矩阵表示学习技术，它可以检测出具有低秩性质的不同子矩阵。根据对局部模式的不同数学假设，MLLRR 问题可以分为局部常变(LCV)和局部线性低秩(LLR)两个子问题。MLLRR 上的现有解决方案只关注 LCV 问题，而这个问题忽略了大量真实而有趣的模式。在这项工作中，我们开发了一个新的矩阵计算框架称为 RPSP (随机探测为基础的子矩阵传播) ，提供了一个有效的解决方案，这两个 LCV 和 LLR 问题。RPSP 检测由低秩性质的小子矩阵生成的局部低秩模式，这些小子矩阵由随机投影方法确定。RPSP 得到了随机投影理论的支持。对合成数据的实验表明，RPSP 算法优于所有的最新方法，在 LCV 和 LLR 设置下都具有鲁棒性和正确识别低秩矩阵的能力。在实际数据集上，RPSP 也证明了其识别可解释的局部低秩矩阵的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Generalized+Matrix+Local+Low+Rank+Representation+by+Random+Projection+and+Submatrix+Propagation)|0| |[TWIN: Personalized Clinical Trial Digital Twin Generation](https://doi.org/10.1145/3580305.3599534)|Trisha Das, Zifeng Wang, Jimeng Sun||Clinical trial digital twins are virtual patients that reflect personal characteristics in a high degree of granularity and can be used to simulate various patient outcomes under different conditions. With the growth of clinical trial databases captured by Electronic Data Capture (EDC) systems, there is a growing interest in using machine learning models to generate digital twins. This can benefit the drug development process by reducing the sample size required for participant recruitment, improving patient outcome predictive modeling, and mitigating privacy risks when sharing synthetic clinical trial data. However, prior research has mainly focused on generating Electronic Healthcare Records (EHRs), which often assume large training data and do not account for personalized synthetic patient record generation. In this paper, we propose a sample-efficient method TWIN for generating personalized clinical trial digital twins. TWIN can produce digital twins of patient-level clinical trial records with high fidelity to the targeting participant's record and preserves the temporal relations across visits and events. We compare our method with various baselines for generating real-world patient-level clinical trial data. The results show that TWIN generates synthetic trial data with high fidelity to facilitate patient outcome predictions in low-data scenarios and strong privacy protection against real patients from the trials.|临床试验数字双胞胎是反映个人特征的高度粒度虚拟患者，可用于模拟不同条件下的各种患者结果。随着电子数据采集(EDC)系统所采集的临床试验数据库的增长，利用机器学习模型生成数字双胞胎的兴趣日益增长。这可以通过减少参与者招募所需的样本量，改善患者结果预测建模，以及在共享合成临床试验数据时减少隐私风险，从而有利于药物开发过程。然而，以前的研究主要集中在生成电子医疗记录(EHRs) ，这往往假设大量的训练数据，并没有考虑到个性化的合成病人记录的生成。在本文中，我们提出了一个样本效率的方法 TWIN 生成个性化的临床试验数字双胞胎。TWIN 可以生成患者级临床试验记录的数字双胞胎，对目标参与者的记录具有高保真度，并保存访问和事件之间的时间关系。我们比较我们的方法与各种基线生成真实世界的患者水平的临床试验数据。结果表明，TWIN 生成的合成试验数据具有高保真度，以便于在低数据情景下预测患者的结果，并对来自试验的真实患者提供强烈的隐私保护。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=TWIN:+Personalized+Clinical+Trial+Digital+Twin+Generation)|0| -|[Accelerating Dynamic Network Embedding with Billions of Parameter Updates to Milliseconds](https://doi.org/10.1145/3580305.3599250)|Haoran Deng, Yang Yang, Jiahe Li, Haoyang Cai, Shiliang Pu, Weihao Jiang|Zhejiang University; Carnegie Mellon University; Hikvision Research Institute|Network embedding, a graph representation learning method illustrating network topology by mapping nodes into lower-dimension vectors, is challenging to accommodate the ever-changing dynamic graphs in practice. Existing research is mainly based on node-by-node embedding modifications, which falls into the dilemma of efficient calculation and accuracy. Observing that the embedding dimensions are usually much smaller than the number of nodes, we break this dilemma with a novel dynamic network embedding paradigm that rotates and scales the axes of embedding space instead of a node-by-node update. Specifically, we propose the Dynamic Adjacency Matrix Factorization (DAMF) algorithm, which achieves an efficient and accurate dynamic network embedding by rotating and scaling the coordinate system where the network embedding resides with no more than the number of edge modifications changes of node embeddings. Moreover, a dynamic Personalized PageRank is applied to the obtained network embeddings to enhance node embeddings and capture higher-order neighbor information dynamically. Experiments of node classification, link prediction, and graph reconstruction on different-sized dynamic graphs suggest that DAMF advances dynamic network embedding. Further, we unprecedentedly expand dynamic network embedding experiments to billion-edge graphs, where DAMF updates billion-level parameters in less than 10ms.|网络嵌入是一种通过将节点映射为低维向量来表示网络拓扑的图形表示学习方法，在实际应用中很难适应不断变化的动态图形。现有的研究主要是基于逐个节点的嵌入修改，这种方法陷入了计算效率和精度的两难境地。针对嵌入维数通常远小于节点数的问题，提出了一种新的动态网络嵌入方法，该方法不需要逐个节点更新，而是通过对嵌入空间的轴线进行旋转和缩放来解决这一问题。具体来说，我们提出了动态邻接矩阵分解(dAMF)算法，该算法通过旋转和缩放网络嵌入所在的坐标系，在不超过节点嵌入的边修改变化量的情况下，实现了一个高效、准确的动态网络嵌入。此外，将动态个性化 PageRank 应用于所获得的网络嵌入，以增强节点的嵌入，并动态捕获高阶邻居信息。对不同大小的动态图进行节点分类、链路预测和图重构的实验表明，DAMF 推进了动态网络嵌入。进一步，我们前所未有地将动态网络嵌入实验扩展到十亿边图，其中 DAMF 在不到10ms 的时间内更新十亿级参数。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Accelerating+Dynamic+Network+Embedding+with+Billions+of+Parameter+Updates+to+Milliseconds)|0| +|[Accelerating Dynamic Network Embedding with Billions of Parameter Updates to Milliseconds](https://doi.org/10.1145/3580305.3599250)|Haoran Deng, Yang Yang, Jiahe Li, Haoyang Cai, Shiliang Pu, Weihao Jiang|Carnegie Mellon University; Hikvision Research Institute; Zhejiang University|Network embedding, a graph representation learning method illustrating network topology by mapping nodes into lower-dimension vectors, is challenging to accommodate the ever-changing dynamic graphs in practice. Existing research is mainly based on node-by-node embedding modifications, which falls into the dilemma of efficient calculation and accuracy. Observing that the embedding dimensions are usually much smaller than the number of nodes, we break this dilemma with a novel dynamic network embedding paradigm that rotates and scales the axes of embedding space instead of a node-by-node update. Specifically, we propose the Dynamic Adjacency Matrix Factorization (DAMF) algorithm, which achieves an efficient and accurate dynamic network embedding by rotating and scaling the coordinate system where the network embedding resides with no more than the number of edge modifications changes of node embeddings. Moreover, a dynamic Personalized PageRank is applied to the obtained network embeddings to enhance node embeddings and capture higher-order neighbor information dynamically. Experiments of node classification, link prediction, and graph reconstruction on different-sized dynamic graphs suggest that DAMF advances dynamic network embedding. Further, we unprecedentedly expand dynamic network embedding experiments to billion-edge graphs, where DAMF updates billion-level parameters in less than 10ms.|网络嵌入是一种通过将节点映射为低维向量来表示网络拓扑的图形表示学习方法，在实际应用中很难适应不断变化的动态图形。现有的研究主要是基于逐个节点的嵌入修改，这种方法陷入了计算效率和精度的两难境地。针对嵌入维数通常远小于节点数的问题，提出了一种新的动态网络嵌入方法，该方法不需要逐个节点更新，而是通过对嵌入空间的轴线进行旋转和缩放来解决这一问题。具体来说，我们提出了动态邻接矩阵分解(dAMF)算法，该算法通过旋转和缩放网络嵌入所在的坐标系，在不超过节点嵌入的边修改变化量的情况下，实现了一个高效、准确的动态网络嵌入。此外，将动态个性化 PageRank 应用于所获得的网络嵌入，以增强节点的嵌入，并动态捕获高阶邻居信息。对不同大小的动态图进行节点分类、链路预测和图重构的实验表明，DAMF 推进了动态网络嵌入。进一步，我们前所未有地将动态网络嵌入实验扩展到十亿边图，其中 DAMF 在不到10ms 的时间内更新十亿级参数。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Accelerating+Dynamic+Network+Embedding+with+Billions+of+Parameter+Updates+to+Milliseconds)|0| |[MetricPrompt: Prompting Model as a Relevance Metric for Few-shot Text Classification](https://doi.org/10.1145/3580305.3599430)|Hongyuan Dong, Weinan Zhang, Wanxiang Che|Harbin Institute of Technology|Prompting methods have shown impressive performance in a variety of text mining tasks and applications, especially few-shot ones. Despite the promising prospects, the performance of prompting model largely depends on the design of prompt template and verbalizer. In this work, we propose MetricPrompt, which eases verbalizer design difficulty by reformulating few-shot text classification task into text pair relevance estimation task. MetricPrompt adopts prompting model as the relevance metric, further bridging the gap between Pre-trained Language Model's (PLM) pre-training objective and text classification task, making possible PLM's smooth adaption. Taking a training sample and a query one simultaneously, MetricPrompt captures cross-sample relevance information for accurate relevance estimation. We conduct experiments on three widely used text classification datasets across four few-shot settings. Results show that MetricPrompt outperforms manual verbalizer and other automatic verbalizer design methods across all few-shot settings, achieving new state-of-the-art (SOTA) performance.|提示方法已经在各种文本挖掘任务和应用程序中显示出了令人印象深刻的性能，特别是那些很少使用的方法。尽管激励模式前景广阔，但其性能在很大程度上取决于激励模板和语言表达器的设计。在这项工作中，我们提出了 MetricPrompt，它通过将少镜头文本分类任务重构为文本对相关性估计任务，从而减轻了语言表达器的设计难度。MetricPrompt 采用提示模型作为相关度量，进一步缩小了预训练语言模型(Pre-training Language Model，PLM)的预训练目标与文本分类任务之间的差距，使得 PLM 的顺利适应成为可能。同时采用训练样本和查询样本，MetricPrompt 捕获跨样本的相关性信息以进行准确的相关性估计。我们在三个广泛使用的文本分类数据集上通过四个少镜头设置进行实验。结果表明，MetricPrompt 在所有短镜头设置中都优于手动语音表达器和其他自动语音表达器设计方法，实现了新的最新(SOTA)性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=MetricPrompt:+Prompting+Model+as+a+Relevance+Metric+for+Few-shot+Text+Classification)|0| |[Delving into Global Dialogue Structures: Structure Planning Augmented Response Selection for Multi-turn Conversations](https://doi.org/10.1145/3580305.3599304)|Tingchen Fu, Xueliang Zhao, Rui Yan||Retrieval-based dialogue systems are a crucial component of natural language processing, employing information retrieval techniques to select responses from a predefined pool of candidates. The advent of pre-trained language models (PLMs) has significantly advanced the field, with a prevailing paradigm that involves post-training PLMs on specific dialogue corpora, followed by fine-tuning for the response selection (RS) task. This post-training process aims to capture dialogue-specific features, as most PLMs are originally trained on plain text. However, prior approaches predominantly rely on self-supervised tasks or session-level graph neural networks during post-training, focusing on capturing underlying patterns of coherent dialogues without explicitly refining the global pattern across the entire dialogue corpus. Consequently, the learned knowledge for organizing coherent dialogues remains isolated, heavily reliant on specific contexts. Additionally, interpreting or visualizing the implicit knowledge acquired through self-supervised tasks proves challenging. In this study, we address these limitations by explicitly refining the knowledge required for response selection and structuring it into a coherent global flow, known as "dialogue structure." This structure captures the inter-dependency of utterances and topic shifts, thereby enhancing the response selection task. To achieve this, we propose a novel structure model comprising a state recognizer and a structure planner. This model effectively captures the flow within the utterance history and plans the trajectory of future utterances. Importantly, the structure model operates orthogonally to the retrieval model, enabling seamless integration with existing retrieval models and facilitating collaborative training. Extensive experiments conducted on three benchmark datasets demonstrate the superior performance of our method over a wide range of competitive baselines, establishing a new state-of-the-art in the field.|基于检索的对话系统是自然语言处理的重要组成部分，它使用信息检索技术从预先确定的候选人中选择答案。预训练语言模型的出现极大地推动了该领域的发展，其主要范式包括在特定对话语料库中对预训练语言模型进行后训练，然后对响应选择(RS)任务进行微调。这个培训后的过程旨在捕捉对话的特定功能，因为大多数 PLM 最初都是纯文本培训。然而，先前的方法主要依赖于自我监督的任务或会话级图形神经网络在训练后，侧重于捕获连贯对话的潜在模式，而不明确完善整个对话语料库的全局模式。因此，组织连贯对话的所学知识仍然是孤立的，严重依赖于特定的背景。此外，解释或可视化通过自我监督任务获得的内隐知识证明是具有挑战性的。在本研究中，我们通过明确提炼响应选择所需的知识，并将其组织成一个连贯的全球流，即所谓的“对话结构”，来解决这些局限性这种结构捕获了话语和话题转移的相互依赖性，从而增强了回答选择任务。为了实现这一目标，我们提出了一种新的结构模型，包括状态识别器和结构规划器。该模型有效地捕捉了话语历史中的流动，并规划了未来话语的发展轨迹。重要的是，结构模型与检索模型正交操作，能够与现有检索模型无缝集成，并促进协作培训。在三个基准数据集上进行的大量实验表明，我们的方法在广泛的竞争基线上具有优越的性能，建立了该领域的一个新的最先进的状态。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Delving+into+Global+Dialogue+Structures:+Structure+Planning+Augmented+Response+Selection+for+Multi-turn+Conversations)|0| -|[Partial-label Learning with Mixed Closed-set and Open-set Out-of-candidate Examples](https://doi.org/10.1145/3580305.3599460)|Shuo He, Lei Feng, Guowu Yang|University of Electronic Science and Technology of China; Nanyang Technological University|Partial-label learning (PLL) relies on a key assumption that the true label of each training example must be in the candidate label set. This restrictive assumption may be violated in complex real-world scenarios, and thus the true label of some collected examples could be unexpectedly outside the assigned candidate label set. In this paper, we term the examples whose true label is outside the candidate label set OOC (out-of-candidate) examples, and pioneer a new PLL study to learn with OOC examples. We consider two types of OOC examples in reality, i.e., the closed-set/open-set OOC examples whose true label is inside/outside the known label space. To solve this new PLL problem, we first calculate the wooden cross-entropy loss from candidate and non-candidate labels respectively, and dynamically differentiate the two types of OOC examples based on specially designed criteria. Then, for closed-set OOC examples, we conduct reversed label disambiguation in the non-candidate label set; for open-set OOC examples, we leverage them for training by utilizing an effective regularization strategy that dynamically assigns random candidate labels from the candidate label set. In this way, the two types of OOC examples can be differentiated and further leveraged for model training. Extensive experiments demonstrate that our proposed method outperforms state-of-the-art PLL methods.|部分标签学习(PLL)依赖于一个关键的假设，即每个训练样本的真实标签必须在候选标签集中。在复杂的现实场景中，这种限制性假设可能会被违反，因此，一些收集的示例的真实标签可能意外地位于分配的候选标签集之外。在本文中，我们将真实标签在候选标签集外的例子称为候选标签集外的例子，并且开创了一种新的 PLL 研究方法来学习候选标签集外的例子。我们在实际中考虑两种类型的 OOC 示例，即闭集/开集 OOC 示例，它们的真实标签位于已知标签空间的内部或外部。为了解决这个新的锁相环问题，我们首先分别计算候选标签和非候选标签的木质交叉熵损失，并根据特定的准则动态区分两种类型的 OOC 实例。然后，对于闭集 OOC 例子，我们在非候选标签集中进行反向标签消歧; 对于开集 OOC 例子，我们利用它们进行训练，利用一种有效的正则化策略，从候选标签集中动态分配随机候选标签。通过这种方式，两种类型的 OOC 示例可以区分并进一步用于模型培训。大量的实验表明，我们提出的方法优于最先进的锁相环方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Partial-label+Learning+with+Mixed+Closed-set+and+Open-set+Out-of-candidate+Examples)|0| -|[COMET: Learning Cardinality Constrained Mixture of Experts with Trees and Local Search](https://doi.org/10.1145/3580305.3599278)|Shibal Ibrahim, Wenyu Chen, Hussein Hazimeh, Natalia Ponomareva, Zhe Zhao, Rahul Mazumder|Google DeepMind; Massachusetts Institute of Technology; Google Research|The sparse Mixture-of-Experts (Sparse-MoE) framework efficiently scales up model capacity in various domains, such as natural language processing and vision. Sparse-MoEs select a subset of the "experts" (thus, only a portion of the overall network) for each input sample using a sparse, trainable gate. Existing sparse gates are prone to convergence and performance issues when training with first-order optimization methods. In this paper, we introduce two improvements to current MoE approaches. First, we propose a new sparse gate: COMET, which relies on a novel tree-based mechanism. COMET is differentiable, can exploit sparsity to speed up computation, and outperforms state-of-the-art gates. Second, due to the challenging combinatorial nature of sparse expert selection, first-order methods are typically prone to low-quality solutions. To deal with this challenge, we propose a novel, permutation-based local search method that can complement first-order methods in training any sparse gate, e.g., Hash routing, Top-k, DSelect-k, and COMET. We show that local search can help networks escape bad initializations or solutions. We performed large-scale experiments on various domains, including recommender systems, vision, and natural language processing. On standard vision and recommender systems benchmarks, COMET+ (COMET with local search) achieves up to 13% improvement in ROC AUC over popular gates, e.g., Hash routing and Top-k, and up to 9% over prior differentiable gates e.g., DSelect-k. When Top-k and Hash gates are combined with local search, we see up to $100\times$ reduction in the budget needed for hyperparameter tuning. Moreover, for language modeling, our approach improves over the state-of-the-art MoEBERT model for distilling BERT on 5/7 GLUE benchmarks as well as SQuAD dataset.|稀疏混合专家(Sparse-MoE)框架有效地扩展了各种领域的模型容量，例如自然语言处理和视觉。稀疏-MoEs 使用稀疏的、可训练的门为每个输入样本选择一个“专家”子集(因此，只是整个网络的一部分)。现有的稀疏门在用一阶优化方法进行训练时容易出现收敛和性能问题。在本文中，我们介绍了两个改进的目前的教育方法。首先，我们提出了一种新的稀疏门: COMET，它依赖于一种新的基于树的机制。COMET 是可微的，可以利用稀疏性来加速计算，并且性能优于最先进的门。其次，由于稀疏专家选择具有挑战性的组合性质，一阶方法通常倾向于低质量的解决方案。为了应对这一挑战，我们提出了一种新颖的基于置换的局部搜索方法，可以补充一阶方法训练任何稀疏门，例如，散列路由，Top-k，DSelect-k 和 COMET。我们展示了本地搜索可以帮助网络逃避糟糕的初始化或解决方案。我们在不同的领域进行了大规模的实验，包括推荐系统、视觉和自然语言处理。在标准愿景和推荐系统基准上，COMET + (本地搜索的 COMET)在 ROC AUC 比流行的门(如散列路由和 Top-k)提高了13% ，比以前的可微分门(如 DSelect-k)提高了9% 。当 Top-k 和 Hash 门与本地搜索相结合时，我们看到超参数调优所需的预算减少了100倍。此外，对于语言建模，我们的方法改进了最先进的 MoEBERT 模型，用于提取5/7 GLUE 基准测试和 SQuAD 数据集上的 BERT。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=COMET:+Learning+Cardinality+Constrained+Mixture+of+Experts+with+Trees+and+Local+Search)|0| +|[Partial-label Learning with Mixed Closed-set and Open-set Out-of-candidate Examples](https://doi.org/10.1145/3580305.3599460)|Shuo He, Lei Feng, Guowu Yang|Nanyang Technological University; University of Electronic Science and Technology of China|Partial-label learning (PLL) relies on a key assumption that the true label of each training example must be in the candidate label set. This restrictive assumption may be violated in complex real-world scenarios, and thus the true label of some collected examples could be unexpectedly outside the assigned candidate label set. In this paper, we term the examples whose true label is outside the candidate label set OOC (out-of-candidate) examples, and pioneer a new PLL study to learn with OOC examples. We consider two types of OOC examples in reality, i.e., the closed-set/open-set OOC examples whose true label is inside/outside the known label space. To solve this new PLL problem, we first calculate the wooden cross-entropy loss from candidate and non-candidate labels respectively, and dynamically differentiate the two types of OOC examples based on specially designed criteria. Then, for closed-set OOC examples, we conduct reversed label disambiguation in the non-candidate label set; for open-set OOC examples, we leverage them for training by utilizing an effective regularization strategy that dynamically assigns random candidate labels from the candidate label set. In this way, the two types of OOC examples can be differentiated and further leveraged for model training. Extensive experiments demonstrate that our proposed method outperforms state-of-the-art PLL methods.|部分标签学习(PLL)依赖于一个关键的假设，即每个训练样本的真实标签必须在候选标签集中。在复杂的现实场景中，这种限制性假设可能会被违反，因此，一些收集的示例的真实标签可能意外地位于分配的候选标签集之外。在本文中，我们将真实标签在候选标签集外的例子称为候选标签集外的例子，并且开创了一种新的 PLL 研究方法来学习候选标签集外的例子。我们在实际中考虑两种类型的 OOC 示例，即闭集/开集 OOC 示例，它们的真实标签位于已知标签空间的内部或外部。为了解决这个新的锁相环问题，我们首先分别计算候选标签和非候选标签的木质交叉熵损失，并根据特定的准则动态区分两种类型的 OOC 实例。然后，对于闭集 OOC 例子，我们在非候选标签集中进行反向标签消歧; 对于开集 OOC 例子，我们利用它们进行训练，利用一种有效的正则化策略，从候选标签集中动态分配随机候选标签。通过这种方式，两种类型的 OOC 示例可以区分并进一步用于模型培训。大量的实验表明，我们提出的方法优于最先进的锁相环方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Partial-label+Learning+with+Mixed+Closed-set+and+Open-set+Out-of-candidate+Examples)|0| +|[COMET: Learning Cardinality Constrained Mixture of Experts with Trees and Local Search](https://doi.org/10.1145/3580305.3599278)|Shibal Ibrahim, Wenyu Chen, Hussein Hazimeh, Natalia Ponomareva, Zhe Zhao, Rahul Mazumder|Google Research; Massachusetts Institute of Technology; Google DeepMind|The sparse Mixture-of-Experts (Sparse-MoE) framework efficiently scales up model capacity in various domains, such as natural language processing and vision. Sparse-MoEs select a subset of the "experts" (thus, only a portion of the overall network) for each input sample using a sparse, trainable gate. Existing sparse gates are prone to convergence and performance issues when training with first-order optimization methods. In this paper, we introduce two improvements to current MoE approaches. First, we propose a new sparse gate: COMET, which relies on a novel tree-based mechanism. COMET is differentiable, can exploit sparsity to speed up computation, and outperforms state-of-the-art gates. Second, due to the challenging combinatorial nature of sparse expert selection, first-order methods are typically prone to low-quality solutions. To deal with this challenge, we propose a novel, permutation-based local search method that can complement first-order methods in training any sparse gate, e.g., Hash routing, Top-k, DSelect-k, and COMET. We show that local search can help networks escape bad initializations or solutions. We performed large-scale experiments on various domains, including recommender systems, vision, and natural language processing. On standard vision and recommender systems benchmarks, COMET+ (COMET with local search) achieves up to 13% improvement in ROC AUC over popular gates, e.g., Hash routing and Top-k, and up to 9% over prior differentiable gates e.g., DSelect-k. When Top-k and Hash gates are combined with local search, we see up to $100\times$ reduction in the budget needed for hyperparameter tuning. Moreover, for language modeling, our approach improves over the state-of-the-art MoEBERT model for distilling BERT on 5/7 GLUE benchmarks as well as SQuAD dataset.|稀疏混合专家(Sparse-MoE)框架有效地扩展了各种领域的模型容量，例如自然语言处理和视觉。稀疏-MoEs 使用稀疏的、可训练的门为每个输入样本选择一个“专家”子集(因此，只是整个网络的一部分)。现有的稀疏门在用一阶优化方法进行训练时容易出现收敛和性能问题。在本文中，我们介绍了两个改进的目前的教育方法。首先，我们提出了一种新的稀疏门: COMET，它依赖于一种新的基于树的机制。COMET 是可微的，可以利用稀疏性来加速计算，并且性能优于最先进的门。其次，由于稀疏专家选择具有挑战性的组合性质，一阶方法通常倾向于低质量的解决方案。为了应对这一挑战，我们提出了一种新颖的基于置换的局部搜索方法，可以补充一阶方法训练任何稀疏门，例如，散列路由，Top-k，DSelect-k 和 COMET。我们展示了本地搜索可以帮助网络逃避糟糕的初始化或解决方案。我们在不同的领域进行了大规模的实验，包括推荐系统、视觉和自然语言处理。在标准愿景和推荐系统基准上，COMET + (本地搜索的 COMET)在 ROC AUC 比流行的门(如散列路由和 Top-k)提高了13% ，比以前的可微分门(如 DSelect-k)提高了9% 。当 Top-k 和 Hash 门与本地搜索相结合时，我们看到超参数调优所需的预算减少了100倍。此外，对于语言建模，我们的方法改进了最先进的 MoEBERT 模型，用于提取5/7 GLUE 基准测试和 SQuAD 数据集上的 BERT。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=COMET:+Learning+Cardinality+Constrained+Mixture+of+Experts+with+Trees+and+Local+Search)|0| |[Exploiting Relation-aware Attribute Representation Learning in Knowledge Graph Embedding for Numerical Reasoning](https://doi.org/10.1145/3580305.3599338)|Gayeong Kim, Sookyung Kim, Ko Keun Kim, Suchan Park, Heesoo Jung, Hogun Park||Numerical reasoning is an essential task for supporting machine learning applications, such as recommendation and information retrieval. The reasoning task aims to compare two items and infer new facts (e.g., is taller than) by leveraging existing relational information and numerical attributes (e.g., the height of an entity) in knowledge graphs. However, most existing methods rely on leveraging attribute encoders or additional loss functions to predict numerical relations. Therefore, the prediction performance is often not robust in cases when attributes are sparsely observed. In this paper, we propose a Relation-AAware attribute representation learning-based Knowledge Graph Embedding method for numerical reasoning tasks, which we call RAKGE. RAKGE incorporates a newly proposed attribute representation learning mechanism, which can leverage the association between relations and their corresponding numerical attributes. In addition, we introduce a robust self-supervised learning method to generate unseen positive and negative examples, thereby making our approach more reliable when numerical attributes are sparsely available. In the evaluation of three real-world datasets, our proposed model outperformed state-of-the-art methods, achieving an improvement of up to 65.1% in Hits@1 and up to 52.6% in MRR compared to the best competitor. Our implementation code is available at https://github.com/learndatalab/RAKGE.|数值推理是支持机器学习应用(如推荐和信息检索)的基本任务。推理任务旨在通过利用知识图表中现有的关系信息和数值属性(例如实体的高度)来比较两个项目并推断出新的事实(例如高于)。然而，大多数现有的方法依赖于利用属性编码器或额外的损失函数来预测数值关系。因此，当属性被稀疏地观察到时，预测性能往往是不稳健的。针对数值推理任务，提出了一种基于关系 AAware 属性表示学习的知识图嵌入方法—— RAKGE。RAKGE 引入了一种新的属性表示学习机制，该机制可以利用关系与其相应的数值属性之间的关联。此外，我们还引入了一种鲁棒的自监督学习方法来生成看不见的正例和负例，从而使我们的方法在数值属性稀疏可用的情况下更加可靠。在对三个真实世界数据集的评估中，我们提出的模型优于最先进的方法，与最好的竞争对手相比，Hits@1的改善高达65.1% ，MRR 的改善高达52.6% 。我们的实施守则可于 https://github.com/learndatalab/rakge 索取。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Exploiting+Relation-aware+Attribute+Representation+Learning+in+Knowledge+Graph+Embedding+for+Numerical+Reasoning)|0| |[Efficient Distributed Approximate k-Nearest Neighbor Graph Construction by Multiway Random Division Forest](https://doi.org/10.1145/3580305.3599327)|SangHong Kim, HaMyung Park||k-nearest neighbor graphs, shortly k-NN graphs, are widely used in many data mining applications like recommendation, information retrieval, and similarity search. Approximate k-NN graph construction has been getting a lot of attention, and most researches focus on developing algorithms that operate efficiently and quickly on a single machine. A few pioneering studies propose distributed algorithms to increase the size of data that can be processed to billions. However, we notice that the distributed algorithms don't perform well enough due to the problems of graph fragmentation and massive data exchange. In this paper, we propose MRDF (Multiway Random Division Forest), a scalable distributed algorithm that constructs highly accurate k-NN graph from numerous high-dimensional vectors quickly. MRDF resolves the problems that the existing distributed algorithms suffer from, through coarse-grained partitioning based on tree path annotation. Experimental results on real-world datasets show that MRDF outperforms the state-of-the-art distributed algorithms with up to 7.6 times faster speed and up to 56%p better accuracy than the second best results.|K- 最近邻图，即短 k- 近邻图，广泛应用于许多数据挖掘应用程序，如推荐、信息检索和最近邻搜索。近似 k-NN 图的构造一直受到人们的广泛关注，目前的研究主要集中在单机高效快速的算法开发上。一些开创性的研究提出了分布式算法，以增加数据的大小，可以处理数十亿。然而，我们注意到由于图的碎片化和大量数据交换的问题，分布式算法的性能不够好。在这篇文章中，我们提出了多路随机分割森林，这是一个可扩展的分散式演算法，它可以从大量的高维向量中快速构造出高精度的 k-NN 图。MRDF 通过基于树路径标注的粗粒度划分解决了现有分布式算法存在的问题。在实际数据集上的实验结果表明，MRDF 算法的速度比最先进的分布式算法快7.6倍，准确率比次优算法高56% 。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Efficient+Distributed+Approximate+k-Nearest+Neighbor+Graph+Construction+by+Multiway+Random+Division+Forest)|0| -|[MM-DAG: Multi-task DAG Learning for Multi-modal Data - with Application for Traffic Congestion Analysis](https://doi.org/10.1145/3580305.3599436)|Tian Lan, Ziyue Li, Zhishuai Li, Lei Bai, Man Li, Fugee Tsung, Wolfgang Ketter, Rui Zhao, Chen Zhang|SenseTime Research; The Hong Kong University of Science and Technology; University of Cologne; The Hong Kong University of Science and Technology (Guangzhou); Tsinghua University; Shanghai AI Laboratory|This paper proposes to learn Multi-task, Multi-modal Direct Acyclic Graphs (MM-DAGs), which are commonly observed in complex systems, e.g., traffic, manufacturing, and weather systems, whose variables are multi-modal with scalars, vectors, and functions. This paper takes the traffic congestion analysis as a concrete case, where a traffic intersection is usually regarded as a DAG. In a road network of multiple intersections, different intersections can only have some overlapping and distinct variables observed. For example, a signalized intersection has traffic light-related variables, whereas unsignalized ones do not. This encourages the multi-task design: with each DAG as a task, the MM-DAG tries to learn the multiple DAGs jointly so that their consensus and consistency are maximized. To this end, we innovatively propose a multi-modal regression for linear causal relationship description of different variables. Then we develop a novel Causality Difference (CD) measure and its differentiable approximator. Compared with existing SOTA measures, CD can penalize the causal structural difference among DAGs with distinct nodes and can better consider the uncertainty of causal orders. We rigidly prove our design's topological interpretation and consistency properties. We conduct thorough simulations and one case study to show the effectiveness of our MM-DAG. The code is available under https://github.com/Lantian72/MM-DAG|本文提出学习多任务、多模态直接无环图(MM-DAGs) ，这是在交通、制造、天气等复杂系统中常见的图形，其变量是多模态的，包括标量、向量和函数。本文以交通堵塞分析作为一个具体案例，其中交通十字路口通常被视为一个 DAG。在一个多交叉口的道路网络中，不同的交叉口只能观察到一些重叠的、不同的变量。例如，信号交叉口有与交通灯相关的变量，而无信号交叉口没有。这鼓励了多任务设计: 将每个 DAG 作为一个任务，MM-DAG 试图联合学习多个 DAG，以便最大化它们的一致性和一致性。为此，我们创新性地提出了一种多模态回归方法来描述不同变量之间的线性因果关系。然后我们发展了一个新的因果差分(CD)测度及其可微逼近器。与现有的 SOTA 方法相比，CD 方法能够更好地考虑因果顺序的不确定性，并且能够惩罚具有不同节点的 DAGs 之间的因果结构差异。我们严格证明了我们的设计的拓扑解释和一致性性质。我们进行了彻底的模拟和一个案例研究，以显示我们的 MM-DAG 的有效性。代码可在 https://github.com/lantian72/mm-dag 下查阅|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=MM-DAG:+Multi-task+DAG+Learning+for+Multi-modal+Data+-+with+Application+for+Traffic+Congestion+Analysis)|0| +|[MM-DAG: Multi-task DAG Learning for Multi-modal Data - with Application for Traffic Congestion Analysis](https://doi.org/10.1145/3580305.3599436)|Tian Lan, Ziyue Li, Zhishuai Li, Lei Bai, Man Li, Fugee Tsung, Wolfgang Ketter, Rui Zhao, Chen Zhang|Shanghai AI Laboratory; The Hong Kong University of Science and Technology (Guangzhou); The Hong Kong University of Science and Technology; SenseTime Research; Tsinghua University; University of Cologne|This paper proposes to learn Multi-task, Multi-modal Direct Acyclic Graphs (MM-DAGs), which are commonly observed in complex systems, e.g., traffic, manufacturing, and weather systems, whose variables are multi-modal with scalars, vectors, and functions. This paper takes the traffic congestion analysis as a concrete case, where a traffic intersection is usually regarded as a DAG. In a road network of multiple intersections, different intersections can only have some overlapping and distinct variables observed. For example, a signalized intersection has traffic light-related variables, whereas unsignalized ones do not. This encourages the multi-task design: with each DAG as a task, the MM-DAG tries to learn the multiple DAGs jointly so that their consensus and consistency are maximized. To this end, we innovatively propose a multi-modal regression for linear causal relationship description of different variables. Then we develop a novel Causality Difference (CD) measure and its differentiable approximator. Compared with existing SOTA measures, CD can penalize the causal structural difference among DAGs with distinct nodes and can better consider the uncertainty of causal orders. We rigidly prove our design's topological interpretation and consistency properties. We conduct thorough simulations and one case study to show the effectiveness of our MM-DAG. The code is available under https://github.com/Lantian72/MM-DAG|本文提出学习多任务、多模态直接无环图(MM-DAGs) ，这是在交通、制造、天气等复杂系统中常见的图形，其变量是多模态的，包括标量、向量和函数。本文以交通堵塞分析作为一个具体案例，其中交通十字路口通常被视为一个 DAG。在一个多交叉口的道路网络中，不同的交叉口只能观察到一些重叠的、不同的变量。例如，信号交叉口有与交通灯相关的变量，而无信号交叉口没有。这鼓励了多任务设计: 将每个 DAG 作为一个任务，MM-DAG 试图联合学习多个 DAG，以便最大化它们的一致性和一致性。为此，我们创新性地提出了一种多模态回归方法来描述不同变量之间的线性因果关系。然后我们发展了一个新的因果差分(CD)测度及其可微逼近器。与现有的 SOTA 方法相比，CD 方法能够更好地考虑因果顺序的不确定性，并且能够惩罚具有不同节点的 DAGs 之间的因果结构差异。我们严格证明了我们的设计的拓扑解释和一致性性质。我们进行了彻底的模拟和一个案例研究，以显示我们的 MM-DAG 的有效性。代码可在 https://github.com/lantian72/mm-dag 下查阅|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=MM-DAG:+Multi-task+DAG+Learning+for+Multi-modal+Data+-+with+Application+for+Traffic+Congestion+Analysis)|0| |[Who Should Be Given Incentives? Counterfactual Optimal Treatment Regimes Learning for Recommendation](https://doi.org/10.1145/3580305.3599550)|Haoxuan Li, Chunyuan Zheng, Peng Wu, Kun Kuang, Yue Liu, Peng Cui||Effective personalized incentives can improve user experience and increase platform revenue, resulting in a win-win situation between users and e-commerce companies. Previous studies have used uplift modeling methods to estimate the conditional average treatment effects of users' incentives, and then placed the incentives by maximizing the sum of estimated treatment effects under a limited budget. However, some users will always buy whether incentives are given or not, and they will actively collect and use incentives if provided, named "Always Buyers". Identifying and predicting these "Always Buyers" and reducing incentive delivery to them can lead to a more rational incentive allocation. In this paper, we first divide users into five strata from an individual counterfactual perspective, and reveal the failure of previous uplift modeling methods to identify and predict the "Always Buyers". Then, we propose principled counterfactual identification and estimation methods and prove their unbiasedness. We further propose a counterfactual entire-space multi-task learning approach to accurately perform personalized incentive policy learning with a limited budget. We also theoretically derive a lower bound on the reward of the learned policy. Extensive experiments are conducted on three real-world datasets with two common incentive scenarios, and the results demonstrate the effectiveness of the proposed approaches.|有效的个性化激励可以改善用户体验，增加平台收入，从而实现用户与电子商务公司之间的双赢。以往的研究采用提升模型方法来估计使用者激励的条件平均治疗效果，然后在有限的预算下通过最大化估计治疗效果的总和来放置激励。然而，一些用户总是会购买无论是否给予奖励，他们将积极收集和使用奖励，如果提供，命名为“总是买家”。识别和预测这些“永远的买家”，减少对他们的激励，可以导致更合理的激励分配。本文首先从个体反事实的角度将用户划分为五个层次，揭示了以往的提升建模方法在识别和预测“总是买家”方面的失败。然后，提出了有原则的反事实识别和估计方法，并证明了它们的无偏性。我们进一步提出了一个反事实的全空间多任务学习方法，以准确地执行有限预算的个性化激励政策学习。我们还从理论上导出了学习策略的报酬下界。在三个具有两种常见激励情景的真实世界数据集上进行了广泛的实验，实验结果表明了所提方法的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Who+Should+Be+Given+Incentives?+Counterfactual+Optimal+Treatment+Regimes+Learning+for+Recommendation)|0| |[UCEpic: Unifying Aspect Planning and Lexical Constraints for Generating Explanations in Recommendation](https://doi.org/10.1145/3580305.3599535)|Jiacheng Li, Zhankui He, Jingbo Shang, Julian J. McAuley|University of California, San Diego|Personalized natural language generation for explainable recommendations plays a key role in justifying why a recommendation might match a user's interests. Existing models usually control the generation process by aspect planning. While promising, these aspect-planning methods struggle to generate specific information correctly, which prevents generated explanations from being convincing. In this paper, we claim that introducing lexical constraints can alleviate the above issues. We propose a model, UCEpic, that generates high-quality personalized explanations for recommendation results by unifying aspect planning and lexical constraints in an insertion-based generation manner. Methodologically, to ensure text generation quality and robustness to various lexical constraints, we pre-train a non-personalized text generator via our proposed robust insertion process. Then, to obtain personalized explanations under this framework of insertion-based generation, we design a method of incorporating aspect planning and personalized references into the insertion process. Hence, UCEpic unifies aspect planning and lexical constraints into one framework and generates explanations for recommendations under different settings. Compared to previous recommendation explanation generators controlled by only aspects, UCEpic incorporates specific information from keyphrases and then largely improves the diversity and informativeness of generated explanations for recommendations on datasets such as RateBeer and Yelp.|为可解释的推荐生成个性化的自然语言在证明为什么推荐可能符合用户的兴趣方面起着关键作用。现有模型通常通过方面规划来控制生成过程。尽管这些方面规划方法很有前途，但它们难以正确地生成特定的信息，这使得生成的解释无法令人信服。在本文中，我们认为引入词汇约束可以缓解上述问题。我们提出了一个模型，UCEic，通过统一的方面规划和词法约束在一个基于插入的生成方式，为推荐结果生成高质量的个性化解释。在方法上，为了保证文本生成的质量和对各种词汇约束的鲁棒性，我们通过提出的鲁棒插入过程预训练了一个非个性化的文本生成器。然后，为了在基于插入的生成框架下获得个性化的解释，我们设计了一种将方面规划和个性化参考引用融入到插入过程中的方法。因此，UCEic 将方面规划和词法约束统一到一个框架中，并在不同的设置下生成对建议的解释。与以前只受方面控制的推荐解释生成器相比，UCEic 整合了来自关键词的特定信息，然后在很大程度上提高了 RateBeer 和 Yelp 等数据集上生成的推荐解释的多样性和信息性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=UCEpic:+Unifying+Aspect+Planning+and+Lexical+Constraints+for+Generating+Explanations+in+Recommendation)|0| |[Learning-Based Ad Auction Design with Externalities: The Framework and A Matching-Based Approach](https://doi.org/10.1145/3580305.3599403)|Ningyuan Li, Yunxuan Ma, Yang Zhao, Zhijian Duan, Yurong Chen, Zhilin Zhang, Jian Xu, Bo Zheng, Xiaotie Deng||Learning-based ad auctions have increasingly been adopted in online advertising. However, existing approaches neglect externalities, such as the interaction between ads and organic items. In this paper, we propose a general framework, namely Score-Weighted VCG, for designing learning-based ad auctions that account for externalities. The framework decomposes the optimal auction design into two parts: designing a monotone score function and an allocation algorithm, which facilitates data-driven implementation. Theoretical results demonstrate that this framework produces the optimal incentive-compatible and individually rational ad auction under various externality-aware CTR models while being data-efficient and robust. Moreover, we present an approach to implement the proposed framework with a matching-based allocation algorithm. Experiment results on both real-world and synthetic data illustrate the effectiveness of the proposed approach.|基于学习的广告拍卖越来越多地被用于在线广告。然而，现有的方法忽视了外部性，如广告与有机项目之间的相互作用。在本文中，我们提出了一个通用的框架，即得分加权 VCG，设计基于学习的广告拍卖，考虑到外部性。该框架将最优拍卖设计分解为两部分: 设计单调得分函数和分配算法，便于数据驱动实现。理论结果表明，该框架在不同的外部性感知的 CTR 模型下产生了最优的激励相容和个体理性的广告拍卖，同时具有数据效率和鲁棒性。此外，我们还提出了一种基于匹配的分配算法实现该框架的方法。对实际数据和合成数据的实验结果表明了该方法的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Learning-Based+Ad+Auction+Design+with+Externalities:+The+Framework+and+A+Matching-Based+Approach)|0| -|[Communication Efficient Distributed Newton Method with Fast Convergence Rates](https://doi.org/10.1145/3580305.3599280)|Chengchang Liu, Lesi Chen, Luo Luo, John C. S. Lui|The Chinese University of Hong Kong; Chinese University of Hong Kong; Fudan University|We propose a communication and computation efficient second-order method for distributed optimization. For each iteration, our method only requires $\mathcal{O}(d)$ communication complexity, where $d$ is the problem dimension. We also provide theoretical analysis to show the proposed method has the similar convergence rate as the classical second-order optimization algorithms. Concretely, our method can find~$\big(\epsilon, \sqrt{dL\epsilon}\,\big)$-second-order stationary points for nonconvex problem by $\mathcal{O}\big(\sqrt{dL}\,\epsilon^{-3/2}\big)$ iterations, where $L$ is the Lipschitz constant of Hessian. Moreover, it enjoys a local superlinear convergence under the strongly-convex assumption. Experiments on both convex and nonconvex problems show that our proposed method performs significantly better than baselines.|提出了一种分布式优化的通信和计算有效的二阶方法。对于每个迭代，我们的方法只需要 $mathcal { O }(d) $通信复杂性，其中 $d $是问题维度。理论分析表明，该方法与经典的二阶优化算法具有相似的收敛速度。具体地说，我们的方法可以通过数学上的{ O } big (sqrt { dL } ，epsilon ^ {-3/2} big)迭代找到非凸问题的 ~ $big (epsilon，sqrt { dL epsilon } ，big) $- 二阶驻点，其中 $L $是 Hessian 的 Lipschitz 常数。在强凸假设下，该算法具有局部超线性收敛性。对凸问题和非凸问题的实验表明，该方法的性能明显优于基线方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Communication+Efficient+Distributed+Newton+Method+with+Fast+Convergence+Rates)|0| +|[Communication Efficient Distributed Newton Method with Fast Convergence Rates](https://doi.org/10.1145/3580305.3599280)|Chengchang Liu, Lesi Chen, Luo Luo, John C. S. Lui|Fudan University; The Chinese University of Hong Kong; Chinese University of Hong Kong|We propose a communication and computation efficient second-order method for distributed optimization. For each iteration, our method only requires $\mathcal{O}(d)$ communication complexity, where $d$ is the problem dimension. We also provide theoretical analysis to show the proposed method has the similar convergence rate as the classical second-order optimization algorithms. Concretely, our method can find~$\big(\epsilon, \sqrt{dL\epsilon}\,\big)$-second-order stationary points for nonconvex problem by $\mathcal{O}\big(\sqrt{dL}\,\epsilon^{-3/2}\big)$ iterations, where $L$ is the Lipschitz constant of Hessian. Moreover, it enjoys a local superlinear convergence under the strongly-convex assumption. Experiments on both convex and nonconvex problems show that our proposed method performs significantly better than baselines.|提出了一种分布式优化的通信和计算有效的二阶方法。对于每个迭代，我们的方法只需要 $mathcal { O }(d) $通信复杂性，其中 $d $是问题维度。理论分析表明，该方法与经典的二阶优化算法具有相似的收敛速度。具体地说，我们的方法可以通过数学上的{ O } big (sqrt { dL } ，epsilon ^ {-3/2} big)迭代找到非凸问题的 ~ $big (epsilon，sqrt { dL epsilon } ，big) $- 二阶驻点，其中 $L $是 Hessian 的 Lipschitz 常数。在强凸假设下，该算法具有局部超线性收敛性。对凸问题和非凸问题的实验表明，该方法的性能明显优于基线方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Communication+Efficient+Distributed+Newton+Method+with+Fast+Convergence+Rates)|0| |[Meta Multi-agent Exercise Recommendation: A Game Application Perspective](https://doi.org/10.1145/3580305.3599429)|Fei Liu, Xuegang Hu, Shuochen Liu, Chenyang Bu, Le Wu||Exercise recommendation is a fundamental and important task in the E-learning system, facilitating students' personalized learning. Most existing exercise recommendation algorithms design a scoring criterion (e.g., weakest mastery, lowest historical correctness) in conjunction with experience, and then recommend the recommended knowledge concepts (KCs). These algorithms rely entirely on the scoring criteria by treating exercise recommendations as a centralized system. However, it is a complex problem for the centralized system to choose a limited number of exercises in a period of time to consolidate and learn the KCs efficiently. Moreover, different groups of students (e.g., different countries, schools, or classes) have different solutions for the same group of KCs according to their own situations, in the spirit of competency-based instructing. Therefore, we propose Meta Multi-Agent Exercise Recommendation (MMER). Specifically, we design the multi-agent exercise recommendation module, in which the KCs involved in exercises are considered agents with competition and cooperation among them. And the meta-training stage is designed to learn a robust recommendation module for new student groups. Extensive experiments on real-world datasets validate the satisfactory performance of the proposed model. Furthermore, the effectiveness of the multi-agent and meta-training part is demonstrated for the model in recommendation applications.|作业推荐是网络学习系统中一项基础性的重要任务，可以促进学生的个性化学习。大多数现有的练习推荐算法结合经验设计一个评分标准(例如，最弱的掌握能力，最低的历史正确率) ，然后推荐推荐的知识概念(KCs)。这些算法完全依赖于评分标准，将练习推荐视为一个集中的系统。然而，中央系统在一段时间内选择有限数量的练习来有效地巩固和学习知识中心是一个复杂的问题。此外，不同类别的学生(例如不同国家、学校或班级)会因应个别情况，本着能力为本的教学精神，为同一类别的知识型中心提供不同的解决方案。因此，我们提出了元多 Agent 练习推荐(MMER)。具体来说，我们设计了多智能体演练推荐模块，将演练中涉及的知识中心视为具有竞争和合作的智能体。元培训阶段的设计目的是为新生群体学习一个健壮的推荐模块。在实际数据集上的大量实验验证了该模型的良好性能。此外，在推荐应用中验证了该模型的多智能体和元训练部分的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Meta+Multi-agent+Exercise+Recommendation:+A+Game+Application+Perspective)|0| |[Criteria Tell You More than Ratings: Criteria Preference-Aware Light Graph Convolution for Effective Multi-Criteria Recommendation](https://doi.org/10.1145/3580305.3599292)|JinDuk Park, Siqing Li, Xin Cao, WonYong Shin|Yonsei University; The University of New South Wales|The multi-criteria (MC) recommender system, which leverages MC rating information in a wide range of e-commerce areas, is ubiquitous nowadays. Surprisingly, although graph neural networks (GNNs) have been widely applied to develop various recommender systems due to GNN's high expressive capability in learning graph representations, it has been still unexplored how to design MC recommender systems with GNNs. In light of this, we make the first attempt towards designing a GNN-aided MC recommender system. Specifically, rather than straightforwardly adopting existing GNN-based recommendation methods, we devise a novel criteria preference-aware light graph convolution CPA-LGC method, which is capable of precisely capturing the criteria preference of users as well as the collaborative signal in complex high-order connectivities. To this end, we first construct an MC expansion graph that transforms user--item MC ratings into an expanded bipartite graph to potentially learn from the collaborative signal in MC ratings. Next, to strengthen the capability of criteria preference awareness, CPA-LGC incorporates newly characterized embeddings, including user-specific criteria-preference embeddings and item-specific criterion embeddings, into our graph convolution model. Through comprehensive evaluations using four real-world datasets, we demonstrate (a) the superiority over benchmark MC recommendation methods and benchmark recommendation methods using GNNs with tremendous gains, (b) the effectiveness of core components in CPA-LGC, and (c) the computational efficiency.|多准则推荐系统在电子商贸领域广泛应用，充分利用多准则评级信息。令人惊讶的是，尽管图神经网络(GNN)由于其在学习图表示方面的高度表达能力而被广泛应用于开发各种推荐系统，但是如何利用 GNN 设计 MC 推荐系统仍然是一个未知数。有鉴于此，我们首次尝试设计一个 GNN 辅助的 MC 推荐系统。具体而言，我们不直接采用现有的基于 GNN 的推荐方法，而是设计了一种新的标准偏好感知光图卷积 CPA-LGC 方法，该方法能够精确地捕获用户的标准偏好以及复杂高阶连接中的协作信号。为此，我们首先构建一个 MC 扩展图，将用户-项目 MC 评分转换为一个扩展的二分图，以便潜在地学习 MC 评分中的协作信号。接下来，为了加强标准偏好意识的能力，CPA-LGC 将新的特征嵌入，包括用户特定的标准偏好嵌入和项目特定的标准嵌入，纳入我们的图卷积模型。通过使用四个实际数据集的综合评估，我们证明了(a)使用 GNN 的基准 MC 推荐方法和基准推荐方法的优越性，(b) CPA-LGC 中核心组件的有效性，以及(c)计算效率。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Criteria+Tell+You+More+than+Ratings:+Criteria+Preference-Aware+Light+Graph+Convolution+for+Effective+Multi-Criteria+Recommendation)|0| |[Locality Sensitive Hashing for Optimizing Subgraph Query Processing in Parallel Computing Systems](https://doi.org/10.1145/3580305.3599419)|Peng Peng, Shengyi Ji, Zhen Tian, Hongbo Jiang, Weiguo Zheng, Xuecang Zhang||This paper explores parallel computing systems for efficient subgraph query processing in large graphs. We investigate how to take advantage of the inherent parallelism of parallel computing systems for both intraquery and interquery optimization during subgraph query processing. Rather than relying on widely-used hash-based methods, we utilize and extend locality sensitive hashing methods. For intraquery optimization, we use the structures of both the data graph and subgraph query to design a query-constraint locality sensitive hashing method named QCMH, which can be used to merge multiple tasks during a single subgraph query processing. For interquery optimization, we propose a query locality sensitive hashing method named QMH, which can be used to detect common subgraphs among different subgraph queries, thereby merging multiple subgraph queries. Our proposed methods can reduce the redundant computation among multiple tasks duringa single subgraph query processing or multiple queries. Extensive experimental studies on large real and synthetic graphs show that our proposed methods can improve query performance compared to state-of-the-art methods by 10% to 50%.|本文探讨了大型图中子图查询处理的并行计算系统。我们研究在子图查询处理过程中如何利用并行计算系统固有的并行性进行查询内和查询间优化。我们不依赖于广泛使用的基于哈希的方法，而是利用和扩展了区域敏感的哈希方法。对于查询内优化，我们利用数据图和子图查询的结构设计了一种查询约束局部敏感的哈希方法 QCMH，该方法可以在单个子图查询处理过程中合并多个任务。针对查询间优化问题，提出了一种查询位置敏感的哈希方法 QMH，该方法可以检测不同子图查询之间的公共子图，从而实现多个子图查询的合并。该方法可以减少单个子图查询处理或多个查询过程中多个任务之间的冗余计算。对大型实图和合成图的大量实验研究表明，与最先进的方法相比，我们提出的方法可以提高10% 到50% 的查询性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Locality+Sensitive+Hashing+for+Optimizing+Subgraph+Query+Processing+in+Parallel+Computing+Systems)|0| |[Deep Pipeline Embeddings for AutoML](https://doi.org/10.1145/3580305.3599303)|Sebastian PinedaArango, Josif Grabocka|University of Freiburg|Automated Machine Learning (AutoML) is a promising direction for democratizing AI by automatically deploying Machine Learning systems with minimal human expertise. The core technical challenge behind AutoML is optimizing the pipelines of Machine Learning systems (e.g. the choice of preprocessing, augmentations, models, optimizers, etc.). Existing Pipeline Optimization techniques fail to explore deep interactions between pipeline stages/components. As a remedy, this paper proposes a novel neural architecture that captures the deep interaction between the components of a Machine Learning pipeline. We propose embedding pipelines into a latent representation through a novel per-component encoder mechanism. To search for optimal pipelines, such pipeline embeddings are used within deep-kernel Gaussian Process surrogates inside a Bayesian Optimization setup. Furthermore, we meta-learn the parameters of the pipeline embedding network using existing evaluations of pipelines on diverse collections of related datasets (a.k.a. meta-datasets). Through extensive experiments on three large-scale meta-datasets, we demonstrate that pipeline embeddings yield state-of-the-art results in Pipeline Optimization.|自动机器学习(AutoML)是通过自动部署具有最少人类专业知识的机器学习系统来实现人工智能大众化的一个有前途的方向。AutoML 背后的核心技术挑战是优化机器学习系统的管道(例如，预处理、扩展、模型、优化器等的选择)。现有的流水线优化技术无法探索流水线阶段/组件之间的深层交互。作为补救措施，本文提出了一种新颖的神经网络结构，该结构能够捕捉机器学习流水线各组件之间的深层交互。我们提出了一种新的每组件编码机制，将管道嵌入到潜在表示中。为了寻找最佳管道，这种管道嵌入在贝叶斯优化设置内的深核高斯过程代理中使用。此外，我们使用现有的对不同相关数据集(也称为元数据集)上的管道的评估来元学习管道嵌入网络的参数。通过在三个大规模元数据集上的大量实验，我们证明了流水线嵌入在流水线优化中产生了最先进的结果。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Deep+Pipeline+Embeddings+for+AutoML)|0| |[FedAPEN: Personalized Cross-silo Federated Learning with Adaptability to Statistical Heterogeneity](https://doi.org/10.1145/3580305.3599344)|Zhen Qin, Shuiguang Deng, Mingyu Zhao, Xueqiang Yan||In cross-silo federated learning (FL), the data among clients are usually statistically heterogeneous (aka not independent and identically distributed, non-IID) due to diversified data sources, lowering the accuracy of FL. Although many personalized FL (PFL) approaches have been proposed to address this issue, they are only suitable for data with specific degrees of statistical heterogeneity. In the real world, the heterogeneity of data among clients is often immeasurable due to privacy concern, making the targeted selection of PFL approaches difficult. Besides, in cross-silo FL, clients are usually from different organizations, tending to hold architecturally different private models. In this work, we propose a novel FL framework, FedAPEN, which combines mutual learning and ensemble learning to take the advantages of private and shared global models while allowing heterogeneous models. Within FedAPEN, we propose two mechanisms to coordinate and promote model ensemble such that FedAPEN achieves excellent accuracy on various data distributions without prior knowledge of data heterogeneity, and thus, obtains the adaptability to data heterogeneity. We conduct extensive experiments on four real-world datasets, including: 1) Fashion MNIST, CIFAR-10, and CIFAR-100, each with ten different types and degrees of label distribution skew; and 2) eICU with feature distribution skew. The experiments demonstrate that FedAPEN almost obtains superior accuracy on data with varying types and degrees of heterogeneity compared with baselines.|在跨竖井联邦学习(FL)中，由于数据来源的多样性，客户之间的数据通常具有统计异构性(即非独立、同分布、非 IID) ，从而降低了 FL 的准确性。虽然许多个性化的 FL (PFL)方法已被提出来解决这个问题，他们只适用于具有特定程度的统计异质性的数据。在现实世界中，由于对隐私的关注，客户之间的数据异构性往往是不可测量的，这使得有针对性地选择 PFL 方法变得困难。此外，在跨竖井 FL 中，客户通常来自不同的组织，倾向于持有架构上不同的私有模型。在这项工作中，我们提出了一个新的 FL 框架，FedapEN，它结合了相互学习和集成学习，利用私有和共享的全球模型的优势，同时允许异构模型。在 FedAPEN 中，我们提出了两种协调和促进模型集成的机制，使得 FedAPEN 在不知道数据异构性的情况下，在各种数据分布上获得优异的准确性，从而获得对数据异构性的适应性。我们在四个真实世界的数据集上进行了广泛的实验，包括: 1)时尚 MNIST，CIFAR-10和 CIFAR-100，每个都有十种不同的类型和标签分布倾斜程度; 2)特征分布倾斜的 eICU。实验表明，与基线数据相比，FedAPEN 在不同类型和不同程度的异质性数据上几乎获得了更高的准确度。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=FedAPEN:+Personalized+Cross-silo+Federated+Learning+with+Adaptability+to+Statistical+Heterogeneity)|0| -|[All in One: Multi-Task Prompting for Graph Neural Networks](https://doi.org/10.1145/3580305.3599256)|Xiangguo Sun, Hong Cheng, Jia Li, Bo Liu, Jihong Guan|Southeast University; The Chinese University of Hong Kong; The Hong Kong University of Science and Technology (Guangzhou); Tongji University|Recently, ''pre-training and fine-tuning'' has been adopted as a standard workflow for many graph tasks since it can take general graph knowledge to relieve the lack of graph annotations from each application. However, graph tasks with node level, edge level, and graph level are far diversified, making the pre-training pretext often incompatible with these multiple tasks. This gap may even cause a ''negative transfer'' to the specific application, leading to poor results. Inspired by the prompt learning in natural language processing (NLP), which has presented significant effectiveness in leveraging prior knowledge for various NLP tasks, we study the prompting topic for graphs with the motivation of filling the gap between pre-trained models and various graph tasks. In this paper, we propose a novel multi-task prompting method for graph models. Specifically, we first unify the format of graph prompts and language prompts with the prompt token, token structure, and inserting pattern. In this way, the prompting idea from NLP can be seamlessly introduced to the graph area. Then, to further narrow the gap between various graph tasks and state-of-the-art pre-training strategies, we further study the task space of various graph applications and reformulate downstream problems to the graph-level task. Afterward, we introduce meta-learning to efficiently learn a better initialization for the multi-task prompt of graphs so that our prompting framework can be more reliable and general for different tasks. We conduct extensive experiments, results from which demonstrate the superiority of our method.|近年来，“预训练和微调”已经成为许多图形任务的标准工作流，因为它需要一般的图形知识来解决每个应用程序缺乏图形注释的问题。然而，具有节点级、边级和图级的图形任务种类繁多，使得预训练的借口往往与这些多任务不相容。这种差距甚至可能导致特定应用程序的“负转移”，从而导致较差的结果。自然语言处理中的快速学习在利用先验知识完成各种自然语言处理任务方面表现出了显著的效果，受此启发，我们研究了图形的提示主题，以填补预先训练的模型和各种图形任务之间的空白。本文提出了一种新的图模型多任务提示方法。具体来说，我们首先将图形提示符和语言提示符的格式与提示符标记、标记结构和插入模式统一起来。通过这种方式，可以将自然语言处理中的提示思想无缝地引入到图区域中。然后，为了进一步缩小各种图形任务与最先进的预训练策略之间的差距，我们进一步研究了各种图形应用的任务空间，并将下游问题重新表述为图形级任务。在此基础上，引入元学习，有效地学习图形的多任务提示的初始化，使得提示框架对于不同的任务具有更高的可靠性和通用性。我们进行了广泛的实验，实验结果证明了我们方法的优越性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=All+in+One:+Multi-Task+Prompting+for+Graph+Neural+Networks)|0| +|[All in One: Multi-Task Prompting for Graph Neural Networks](https://doi.org/10.1145/3580305.3599256)|Xiangguo Sun, Hong Cheng, Jia Li, Bo Liu, Jihong Guan|Southeast University; The Hong Kong University of Science and Technology (Guangzhou); The Chinese University of Hong Kong; Tongji University|Recently, ''pre-training and fine-tuning'' has been adopted as a standard workflow for many graph tasks since it can take general graph knowledge to relieve the lack of graph annotations from each application. However, graph tasks with node level, edge level, and graph level are far diversified, making the pre-training pretext often incompatible with these multiple tasks. This gap may even cause a ''negative transfer'' to the specific application, leading to poor results. Inspired by the prompt learning in natural language processing (NLP), which has presented significant effectiveness in leveraging prior knowledge for various NLP tasks, we study the prompting topic for graphs with the motivation of filling the gap between pre-trained models and various graph tasks. In this paper, we propose a novel multi-task prompting method for graph models. Specifically, we first unify the format of graph prompts and language prompts with the prompt token, token structure, and inserting pattern. In this way, the prompting idea from NLP can be seamlessly introduced to the graph area. Then, to further narrow the gap between various graph tasks and state-of-the-art pre-training strategies, we further study the task space of various graph applications and reformulate downstream problems to the graph-level task. Afterward, we introduce meta-learning to efficiently learn a better initialization for the multi-task prompt of graphs so that our prompting framework can be more reliable and general for different tasks. We conduct extensive experiments, results from which demonstrate the superiority of our method.|近年来，“预训练和微调”已经成为许多图形任务的标准工作流，因为它需要一般的图形知识来解决每个应用程序缺乏图形注释的问题。然而，具有节点级、边级和图级的图形任务种类繁多，使得预训练的借口往往与这些多任务不相容。这种差距甚至可能导致特定应用程序的“负转移”，从而导致较差的结果。自然语言处理中的快速学习在利用先验知识完成各种自然语言处理任务方面表现出了显著的效果，受此启发，我们研究了图形的提示主题，以填补预先训练的模型和各种图形任务之间的空白。本文提出了一种新的图模型多任务提示方法。具体来说，我们首先将图形提示符和语言提示符的格式与提示符标记、标记结构和插入模式统一起来。通过这种方式，可以将自然语言处理中的提示思想无缝地引入到图区域中。然后，为了进一步缩小各种图形任务与最先进的预训练策略之间的差距，我们进一步研究了各种图形应用的任务空间，并将下游问题重新表述为图形级任务。在此基础上，引入元学习，有效地学习图形的多任务提示的初始化，使得提示框架对于不同的任务具有更高的可靠性和通用性。我们进行了广泛的实验，实验结果证明了我们方法的优越性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=All+in+One:+Multi-Task+Prompting+for+Graph+Neural+Networks)|0| |[GMOCAT: A Graph-Enhanced Multi-Objective Method for Computerized Adaptive Testing](https://doi.org/10.1145/3580305.3599367)|Hangyu Wang, Ting Long, Liang Yin, Weinan Zhang, Wei Xia, Qichen Hong, Dingyin Xia, Ruiming Tang, Yong Yu||Computerized Adaptive Testing(CAT) refers to an online system that adaptively selects the best-suited question for students with various abilities based on their historical response records. Most CAT methods only focus on the quality objective of predicting the student ability accurately, but neglect concept diversity or question exposure control, which are important considerations in ensuring the performance and validity of CAT. Besides, the students' response records contain valuable relational information between questions and knowledge concepts. The previous methods ignore this relational information, resulting in the selection of sub-optimal test questions. To address these challenges, we propose a Graph-Enhanced Multi-Objective method for CAT (GMOCAT). Firstly, three objectives, namely quality, diversity and novelty, are introduced into the Scalarized Multi-Objective Reinforcement Learning framework of CAT, which respectively correspond to improving the prediction accuracy, increasing the concept diversity and reducing the question exposure. We use an Actor-Critic Recommender to select questions and optimize three objectives simultaneously by the scalarization function. Secondly, we utilize the graph neural network to learn relation-aware embeddings of questions and concepts. These embeddings are able to aggregate neighborhood information in the relation graphs between questions and concepts. We conduct experiments on three real-world educational datasets, and show that GMOCAT not only outperforms the state-of-the-art methods in the ability prediction, but also achieve superior performance in improving the concept diversity and alleviating the question exposure. Our code is available at https://github.com/justarter/GMOCAT.|计算机自适应测试(CAT)是指根据学生的历史回答记录，自适应地为不同能力的学生选择最适合的问题的在线系统。大多数计算机辅助测试(CAT)方法只着眼于准确预测学生能力的质量目标，而忽视了概念多样性或问题暴露控制，这是保证计算机辅助测试(CAT)成绩和有效性的重要因素。此外，学生的回答记录还包含了问题与知识概念之间有价值的关系信息。以前的方法忽略了这些关系信息，导致了次优测试题的选择。为了应对这些挑战，我们提出了一种基于图增强的多目标 CAT (GMOCAT)方法。首先，在计算机辅助测试的标量化多目标强化学习框架中引入质量、多样性和新颖性三个目标，分别对应于提高预测精度、增加概念多样性和减少问题暴露。我们使用一个行为者-批评者推荐系统来选择问题，并通过标量化函数同时优化三个目标。其次，利用图神经网络学习问题和概念的关系感知嵌入。这些嵌入能够在问题和概念之间的关系图中聚合邻域信息。我们在三个实际教育数据集上进行了实验，结果表明，GMOCAT 不仅在能力预测方面优于最先进的方法，而且在提高概念多样性和减少问题暴露方面也取得了较好的效果。我们的代码可以在 https://github.com/justarter/gmocat 找到。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=GMOCAT:+A+Graph-Enhanced+Multi-Objective+Method+for+Computerized+Adaptive+Testing)|0| |[Theoretical Convergence Guaranteed Resource-Adaptive Federated Learning with Mixed Heterogeneity](https://doi.org/10.1145/3580305.3599521)|Yangyang Wang, Xiao Zhang, Mingyi Li, Tian Lan, Huashan Chen, Hui Xiong, Xiuzhen Cheng, Dongxiao Yu||In this paper, we propose an adaptive learning paradigm for resource-constrained cross-device federated learning, in which heterogeneous local submodels with varying resources can be jointly trained to produce a global model. Different from existing studies, the submodel structures of different clients are formed by arbitrarily assigned neurons according to their local resources. Along this line, we first design a general resource-adaptive federated learning algorithm, namely RA-Fed, and rigorously prove its convergence with asymptotically optimal rate O(1/√Γ*TQ) under loose assumptions. Furthermore, to address both submodels heterogeneity and data heterogeneity challenges under non-uniform training, we come up with a new server aggregation mechanism RAM-Fed with the same theoretically proved convergence rate. Moreover, we shed light on several key factors impacting convergence, such as minimum coverage rate, data heterogeneity level, submodel induced noises. Finally, we conduct extensive experiments on two types of tasks with three widely used datasets under different experimental settings. Compared with the state-of-the-arts, our methods improve the accuracy up to 10% on average. Particularly, when submodels jointly train with 50% parameters, RAM-Fed achieves comparable accuracy to FedAvg trained with the full model.|在本文中，我们提出了一个资源受限的跨设备联邦学习的在线机机器学习范式，在这个范式中，具有不同资源的异构本地子模型可以被联合训练以产生一个全局模型。与已有的研究不同，不同客户端的子模型结构是由任意分配的神经元根据其局部资源形成的。在此基础上，我们首先设计了一个通用的资源自适应联邦学习算法，即 RA-Fed，并在松散假设下严格证明了它与渐近最优速率 O (1/√ Γ * TQ)的收敛性。此外，为了解决非均匀训练下子模型异构性和数据异构性的问题，我们提出了一种新的服务器聚合机制 RAM-FED，其收敛速度在理论上得到了相同的证明。此外，本文还分析了影响收敛性的几个关键因素，如最小覆盖率、数据异构程度、子模型引起的噪声。最后，在不同的实验环境下，我们利用三个广泛使用的数据集对两类任务进行了广泛的实验。与最先进的方法相比，我们的方法平均提高了10% 的准确度。特别是，当子模型与50% 的参数联合训练时，RAM-Fed 达到了与 FedAvg 训练相当的精度与完整模型。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Theoretical+Convergence+Guaranteed+Resource-Adaptive+Federated+Learning+with+Mixed+Heterogeneity)|0| |[Efficient Bi-Level Optimization for Recommendation Denoising](https://doi.org/10.1145/3580305.3599324)|Zongwei Wang, Min Gao, Wentao Li, Junliang Yu, Linxin Guo, Hongzhi Yin||The acquisition of explicit user feedback (e.g., ratings) in real-world recommender systems is often hindered by the need for active user involvement. To mitigate this issue, implicit feedback (e.g., clicks) generated during user browsing is exploited as a viable substitute. However, implicit feedback possesses a high degree of noise, which significantly undermines recommendation quality. While many methods have been proposed to address this issue by assigning varying weights to implicit feedback, two shortcomings persist: (1) the weight calculation in these methods is iteration-independent, without considering the influence of weights in previous iterations, and (2) the weight calculation often relies on prior knowledge, which may not always be readily available or universally applicable. To overcome these two limitations, we model recommendation denoising as a bi-level optimization problem. The inner optimization aims to derive an effective model for the recommendation, as well as guiding the weight determination, thereby eliminating the need for prior knowledge. The outer optimization leverages gradients of the inner optimization and adjusts the weights in a manner considering the impact of previous weights. To efficiently solve this bi-level optimization problem, we employ a weight generator to avoid the storage of weights and a one-step gradient-matching-based loss to significantly reduce computational time. The experimental results on three benchmark datasets demonstrate that our proposed approach outperforms both state-of-the-art general and denoising recommendation models. The code is available at https://github.com/CoderWZW/BOD.|在现实世界的推荐系统中，获得明确的用户反馈(例如评分)常常受到需要积极的用户参与的阻碍。为了缓解这个问题，在用户浏览期间产生的隐式反馈(例如点击)被用作一个可行的替代品。然而，隐式反馈具有高度的噪声，严重影响了推荐质量。虽然已经提出了许多方法来解决这个问题，通过赋予不同的权重隐式反馈，两个缺点仍然存在: (1)这些方法的权重计算是迭代无关的，没有考虑权重的影响在以前的迭代，和(2)权重计算往往依赖于先验知识，这可能并不总是容易获得或普遍适用。为了克服这两个限制，我们将建议去噪建模为双层最佳化问题。内部优化的目的是为推荐建立一个有效的模型，并指导权重的确定，从而消除了对先验知识的需要。外部优化利用了内部优化的梯度，并在考虑以前权重影响的情况下调整权重。为了有效地解决这种双层最佳化问题，我们使用了权重生成器来避免权重的存储，并使用了一步梯度匹配丢失来显著地减少计算时间。在三个基准数据集上的实验结果表明，我们提出的方法优于最先进的一般和去噪推荐模型。密码可在 https://github.com/coderwzw/bod 查阅。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Efficient+Bi-Level+Optimization+for+Recommendation+Denoising)|0| |[Meta Graph Learning for Long-tail Recommendation](https://doi.org/10.1145/3580305.3599428)|Chunyu Wei, Jian Liang, Di Liu, Zehui Dai, Mang Li, Fei Wang||Highly skewed long-tail item distribution commonly hurts model performance on tail items in recommendation systems, especially for graph-based recommendation models. We propose a novel idea to learn relations among items as an auxiliary graph to enhance the graph-based representation learning and make recommendations collectively in a coupled framework. This raises two challenges, 1) the long-tail downstream information may also bias the auxiliary graph learning, and 2) the learned auxiliary graph may cause negative transfer to the original user-item bipartite graph. We innovatively propose a novel Meta Graph Learning framework for long-tail recommendation (MGL) for solving both challenges. The meta-learning strategy is introduced to the learning of an edge generator, which is first tuned to reconstruct a debiased item co-occurrence matrix, and then virtually evaluated on generating item relations for recommendation. Moreover, we propose a popularity-aware contrastive learning strategy to prevent negative transfer by aligning the confident head item representations with those of the learned auxiliary graph. Experiments on public datasets demonstrate that our proposed model significantly outperforms strong baselines for tail items without compromising the overall performance.|在推荐系统中，高度倾斜的长尾条目分布通常会损害模型在尾条目上的性能，特别是对于基于图的推荐模型。我们提出了一个新的概念来学习项目之间的关系作为一个辅助图，以提高基于图的表示学习和建议集体在一个耦合的框架。这就提出了两个挑战: 1)长尾下游信息也可能使辅助图学习产生偏差; 2)学习的辅助图可能导致原始用户项二部图的负迁移。我们创新地提出了一个新的元图学习框架，用于长尾推荐(MGL) ，以解决这两个挑战。将元学习策略引入到边缘生成器的学习中，首先对边缘生成器进行调整以重建去偏项目共现矩阵，然后对边缘生成器生成推荐项目关系进行虚拟评估。此外，我们还提出了一种基于知名度的对比学习策略，通过将自信头项表示与学习辅助图的表示对齐来防止负迁移。在公共数据集上的实验表明，我们提出的模型在不影响整体性能的情况下，显著优于尾部项目的强基线。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Meta+Graph+Learning+for+Long-tail+Recommendation)|0| |[Personalized Federated Learning with Parameter Propagation](https://doi.org/10.1145/3580305.3599464)|Jun Wu, Wenxuan Bao, Elizabeth A. Ainsworth, Jingrui He||With decentralized data collected from diverse clients, a personalized federated learning paradigm has been proposed for training machine learning models without exchanging raw data from local clients. We dive into personalized federated learning from the perspective of privacy-preserving transfer learning, and identify the limitations of previous personalized federated learning algorithms. First, previous works suffer from negative knowledge transferability for some clients, when focusing more on the overall performance of all clients. Second, high communication costs are required to explicitly learn statistical task relatedness among clients. Third, it is computationally expensive to generalize the learned knowledge from experienced clients to new clients. To solve these problems, in this paper, we propose a novel federated parameter propagation (FEDORA) framework for personalized federated learning. Specifically, we reformulate the standard personalized federated learning as a privacy-preserving transfer learning problem, with the goal of improving the generalization performance for every client. The crucial idea behind FEDORA is to learn how to transfer and whether to transfer simultaneously, including (1) adaptive parameter propagation: one client is enforced to adaptively propagate its parameters to others based on their task relatedness (e.g., explicitly measured by distribution similarity), and (2) selective regularization: each client would regularize its local personalized model with received parameters, only when those parameters are positively correlated with the generalization performance of its local model. The experiments on a variety of federated learning benchmarks demonstrate the effectiveness of the proposed FEDORA framework over state-of-the-art personalized federated learning baselines.|通过从不同的客户端收集分散的数据，提出了一种个性化的联邦学习范式，用于训练机器学习模型，而不需要交换来自本地客户端的原始数据。本文从保护隐私的传递学习的角度对个性化联邦学习进行了深入研究，指出了以往个性化联邦学习算法的局限性。首先，以前的作品在更多地关注所有客户的整体表现时，对一些客户来说存在负面的知识可转移性。第二，显式学习客户之间的统计任务相关性需要较高的沟通成本。第三，将从有经验的客户那里学到的知识推广到新的客户那里是计算代价高昂的。为了解决这些问题，本文提出了一种新的个性化联邦学习的联邦参数传播(FEDORA)框架。具体地说，我们将标准的个性化联邦学习重新定义为一个保护隐私的迁移学习问题，目标是提高每个客户端的泛化性能。FEDORA 背后的关键思想是学习如何传输和是否同时传输，包括(1)自适应参数传播: 一个客户端被强制根据其任务相关性(例如，通过分布相似性明确测量)自适应传播其参数到其他客户端，和(2)选择性正则化: 每个客户端将正则化其本地个性化模型与接收参数，只有当这些参数与其本地模型的泛化性能正相关。在各种联邦学习基准上的实验证明了所提出的 FEDORA 框架在最先进的个性化联邦学习基准上的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Personalized+Federated+Learning+with+Parameter+Propagation)|0| |[Serverless Federated AUPRC Optimization for Multi-Party Collaborative Imbalanced Data Mining](https://doi.org/10.1145/3580305.3599499)|Xidong Wu, Zhengmian Hu, Jian Pei, Heng Huang|University of Pittsburgh; Duke University|Multi-party collaborative training, such as distributed learning and federated learning, is used to address the big data challenges. However, traditional multi-party collaborative training algorithms were mainly designed for balanced data mining tasks and are intended to optimize accuracy (\emph{e.g.}, cross-entropy). The data distribution in many real-world applications is skewed and classifiers, which are trained to improve accuracy, perform poorly when applied to imbalanced data tasks since models could be significantly biased toward the primary class. Therefore, the Area Under Precision-Recall Curve (AUPRC) was introduced as an effective metric. Although single-machine AUPRC maximization methods have been designed, multi-party collaborative algorithm has never been studied. The change from the single-machine to the multi-party setting poses critical challenges. To address the above challenge, we study the serverless multi-party collaborative AUPRC maximization problem since serverless multi-party collaborative training can cut down the communications cost by avoiding the server node bottleneck, and reformulate it as a conditional stochastic optimization problem in a serverless multi-party collaborative learning setting and propose a new ServerLess biAsed sTochastic gradiEnt (SLATE) algorithm to directly optimize the AUPRC. After that, we use the variance reduction technique and propose ServerLess biAsed sTochastic gradiEnt with Momentum-based variance reduction (SLATE-M) algorithm to improve the convergence rate, which matches the best theoretical convergence result reached by the single-machine online method. To the best of our knowledge, this is the first work to solve the multi-party collaborative AUPRC maximization problem.|多方协作培训，如分布式学习和联合学习，被用来解决大数据的挑战。然而，传统的多方协同训练算法主要是针对平衡的数据挖掘任务而设计的，其目的是优化精度(例如: 交叉熵)。许多实际应用中的数据分布是倾斜的，分类器经过训练以提高准确性，但在应用于不平衡的数据任务时表现不佳，因为模型可能明显偏向于主类。因此，引入精确回忆曲线下面积(AUPRC)作为一个有效的度量指标。虽然单机 AUPRC 最大化方法已经设计出来，但是多方协作算法还没有得到研究。从单一机器设置到多方设置的变化提出了关键的挑战。为了解决上述问题，我们研究了无服务器多方协作的 AUPRC 最大化问题，因为无服务器多方协作培训可以通过避免服务器节点瓶颈来降低通信成本，并将其重新表述为无服务器多方协作环境中的条件随机最佳化问题，提出了一种新的无服务器偏置 sTo侯机梯度(slATE)算法来直接优化 AUPRC，该算法可以用于解决无服务器多方协作的合作学习。在此基础上，利用方差减少技术，提出了基于动量方差减少(SLATE-M)的 ServerLess 偏向随机梯度算法，提高了算法的收敛速度，达到了单机在线算法的最佳理论收敛效果。据我们所知，这是第一个解决多方协作 AUPRC 最大化问题的工作。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Serverless+Federated+AUPRC+Optimization+for+Multi-Party+Collaborative+Imbalanced+Data+Mining)|0| -|[MSSRNet: Manipulating Sequential Style Representation for Unsupervised Text Style Transfer](https://doi.org/10.1145/3580305.3599438)|Yazheng Yang, Zhou Zhao, Qi Liu|Zhejiang University; The University of Hong Kong|Unsupervised text style transfer task aims to rewrite a text into target style while preserving its main content. Traditional methods rely on the use of a fixed-sized vector to regulate text style, which is difficult to accurately convey the style strength for each individual token. In fact, each token of a text contains different style intensity and makes different contribution to the overall style. Our proposed method addresses this issue by assigning individual style vector to each token in a text, allowing for fine-grained control and manipulation of the style strength. Additionally, an adversarial training framework integrated with teacher-student learning is introduced to enhance training stability and reduce the complexity of high-dimensional optimization. The results of our experiments demonstrate the efficacy of our method in terms of clearly improved style transfer accuracy and content preservation in both two-style transfer and multi-style transfer settings.|无监督文本样式转换任务的目标是在保留文本主要内容的同时将文本重写成目标样式。传统的方法依赖于使用固定大小的向量来调整文本样式，这很难准确地表达每个单独标记的样式强度。事实上，文本的每一个标记都包含着不同的风格强度，并对整体风格做出不同的贡献。我们提出的方法通过为文本中的每个标记分配单独的样式向量来解决这个问题，从而允许对样式强度进行细粒度控制和操作。此外，为了提高训练的稳定性，降低高维优化的复杂性，提出了一种结合师生学习的对抗性训练框架。实验结果表明，该方法在两种类型和多种类型的转移设置下，均能明显提高文体转移的准确性和内容保存率。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=MSSRNet:+Manipulating+Sequential+Style+Representation+for+Unsupervised+Text+Style+Transfer)|0| -|[Knowledge Graph Self-Supervised Rationalization for Recommendation](https://doi.org/10.1145/3580305.3599400)|Yuhao Yang, Chao Huang, Lianghao Xia, Chunzhen Huang|Tencent; The University of Hong Kong|In this paper, we introduce a new self-supervised rationalization method, called KGRec, for knowledge-aware recommender systems. To effectively identify informative knowledge connections, we propose an attentive knowledge rationalization mechanism that generates rational scores for knowledge triplets. With these scores, KGRec integrates generative and contrastive self-supervised tasks for recommendation through rational masking. To highlight rationales in the knowledge graph, we design a novel generative task in the form of masking-reconstructing. By masking important knowledge with high rational scores, KGRec is trained to rebuild and highlight useful knowledge connections that serve as rationales. To further rationalize the effect of collaborative interactions on knowledge graph learning, we introduce a contrastive learning task that aligns signals from knowledge and user-item interaction views. To ensure noise-resistant contrasting, potential noisy edges in both graphs judged by the rational scores are masked. Extensive experiments on three real-world datasets demonstrate that KGRec outperforms state-of-the-art methods. We also provide the implementation codes for our approach at https://github.com/HKUDS/KGRec.|本文针对知识感知推荐系统，提出了一种新的自监督合理化方法 KGRec。为了有效地识别信息知识连接，我们提出了一种注意的知识合理化机制，为知识三元组生成合理的分数。根据这些分数，KGRec 通过合理的掩蔽将生成性和对比性自我监督任务集成到推荐系统中。为了突出知识图中的基本原理，我们设计了一个新的生成任务，即掩蔽-重构。通过用高理性分数掩盖重要的知识，KGRec 被训练重建和突出作为基本原理的有用的知识联系。为了进一步合理化协作交互对知识图学习的影响，我们引入了一个对比学习任务，该任务从知识和用户项目交互视图中调整信号。为了确保抗噪声的对比，在两个图的潜在噪声边缘判断有理分数被掩盖。在三个真实世界数据集上的大量实验表明，KGRec 的性能优于最先进的方法。我们亦会在 https://github.com/hkuds/kgrec 为我们的方法提供实施守则。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Knowledge+Graph+Self-Supervised+Rationalization+for+Recommendation)|0| -|[FedCP: Separating Feature Information for Personalized Federated Learning via Conditional Policy](https://doi.org/10.1145/3580305.3599345)|Jianqing Zhang, Yang Hua, Hao Wang, Tao Song, Zhengui Xue, Ruhui Ma, Haibing Guan|Shanghai Jiao Tong University; Queen’s University Belfast; Louisiana State University|Recently, personalized federated learning (pFL) has attracted increasing attention in privacy protection, collaborative learning, and tackling statistical heterogeneity among clients, e.g., hospitals, mobile smartphones, etc. Most existing pFL methods focus on exploiting the global information and personalized information in the client-level model parameters while neglecting that data is the source of these two kinds of information. To address this, we propose the Federated Conditional Policy (FedCP) method, which generates a conditional policy for each sample to separate the global information and personalized information in its features and then processes them by a global head and a personalized head, respectively. FedCP is more fine-grained to consider personalization in a sample-specific manner than existing pFL methods. Extensive experiments in computer vision and natural language processing domains show that FedCP outperforms eleven state-of-the-art methods by up to 6.69%. Furthermore, FedCP maintains its superiority when some clients accidentally drop out, which frequently happens in mobile settings. Our code is public at https://github.com/TsingZ0/FedCP.|近年来，个性化联邦学习(pFL)在保护个人隐私、合作学习以及处理客户之间的统计异质性等方面受到越来越多的关注，例如医院、移动智能手机等。现有的 pFL 方法大多侧重于利用客户端模型参数中的全局信息和个性化信息，而忽视了数据是这两类信息的来源。为了解决这个问题，我们提出了联邦条件策略(FedCP)方法，该方法为每个样本生成一个条件策略来分离其特征中的全局信息和个性化信息，然后分别通过一个全局头和一个个性化头来处理它们。与现有的 pFL 方法相比，FedCP 更加细粒度地以特定于样本的方式考虑个性化。在计算机视觉和自然语言处理领域的大量实验表明，FedCP 比11种最先进的方法的性能提高了6.69% 。此外，当一些客户端意外退出时，FedCP 仍然保持其优势，这种情况在移动设置中经常发生。我们的代码在 https://github.com/tsingz0/fedcp 是公开的。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=FedCP:+Separating+Feature+Information+for+Personalized+Federated+Learning+via+Conditional+Policy)|0| +|[MSSRNet: Manipulating Sequential Style Representation for Unsupervised Text Style Transfer](https://doi.org/10.1145/3580305.3599438)|Yazheng Yang, Zhou Zhao, Qi Liu|The University of Hong Kong; Zhejiang University|Unsupervised text style transfer task aims to rewrite a text into target style while preserving its main content. Traditional methods rely on the use of a fixed-sized vector to regulate text style, which is difficult to accurately convey the style strength for each individual token. In fact, each token of a text contains different style intensity and makes different contribution to the overall style. Our proposed method addresses this issue by assigning individual style vector to each token in a text, allowing for fine-grained control and manipulation of the style strength. Additionally, an adversarial training framework integrated with teacher-student learning is introduced to enhance training stability and reduce the complexity of high-dimensional optimization. The results of our experiments demonstrate the efficacy of our method in terms of clearly improved style transfer accuracy and content preservation in both two-style transfer and multi-style transfer settings.|无监督文本样式转换任务的目标是在保留文本主要内容的同时将文本重写成目标样式。传统的方法依赖于使用固定大小的向量来调整文本样式，这很难准确地表达每个单独标记的样式强度。事实上，文本的每一个标记都包含着不同的风格强度，并对整体风格做出不同的贡献。我们提出的方法通过为文本中的每个标记分配单独的样式向量来解决这个问题，从而允许对样式强度进行细粒度控制和操作。此外，为了提高训练的稳定性，降低高维优化的复杂性，提出了一种结合师生学习的对抗性训练框架。实验结果表明，该方法在两种类型和多种类型的转移设置下，均能明显提高文体转移的准确性和内容保存率。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=MSSRNet:+Manipulating+Sequential+Style+Representation+for+Unsupervised+Text+Style+Transfer)|0| +|[Knowledge Graph Self-Supervised Rationalization for Recommendation](https://doi.org/10.1145/3580305.3599400)|Yuhao Yang, Chao Huang, Lianghao Xia, Chunzhen Huang|The University of Hong Kong; Tencent|In this paper, we introduce a new self-supervised rationalization method, called KGRec, for knowledge-aware recommender systems. To effectively identify informative knowledge connections, we propose an attentive knowledge rationalization mechanism that generates rational scores for knowledge triplets. With these scores, KGRec integrates generative and contrastive self-supervised tasks for recommendation through rational masking. To highlight rationales in the knowledge graph, we design a novel generative task in the form of masking-reconstructing. By masking important knowledge with high rational scores, KGRec is trained to rebuild and highlight useful knowledge connections that serve as rationales. To further rationalize the effect of collaborative interactions on knowledge graph learning, we introduce a contrastive learning task that aligns signals from knowledge and user-item interaction views. To ensure noise-resistant contrasting, potential noisy edges in both graphs judged by the rational scores are masked. Extensive experiments on three real-world datasets demonstrate that KGRec outperforms state-of-the-art methods. We also provide the implementation codes for our approach at https://github.com/HKUDS/KGRec.|本文针对知识感知推荐系统，提出了一种新的自监督合理化方法 KGRec。为了有效地识别信息知识连接，我们提出了一种注意的知识合理化机制，为知识三元组生成合理的分数。根据这些分数，KGRec 通过合理的掩蔽将生成性和对比性自我监督任务集成到推荐系统中。为了突出知识图中的基本原理，我们设计了一个新的生成任务，即掩蔽-重构。通过用高理性分数掩盖重要的知识，KGRec 被训练重建和突出作为基本原理的有用的知识联系。为了进一步合理化协作交互对知识图学习的影响，我们引入了一个对比学习任务，该任务从知识和用户项目交互视图中调整信号。为了确保抗噪声的对比，在两个图的潜在噪声边缘判断有理分数被掩盖。在三个真实世界数据集上的大量实验表明，KGRec 的性能优于最先进的方法。我们亦会在 https://github.com/hkuds/kgrec 为我们的方法提供实施守则。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Knowledge+Graph+Self-Supervised+Rationalization+for+Recommendation)|0| +|[FedCP: Separating Feature Information for Personalized Federated Learning via Conditional Policy](https://doi.org/10.1145/3580305.3599345)|Jianqing Zhang, Yang Hua, Hao Wang, Tao Song, Zhengui Xue, Ruhui Ma, Haibing Guan|Queen’s University Belfast; Shanghai Jiao Tong University; Louisiana State University|Recently, personalized federated learning (pFL) has attracted increasing attention in privacy protection, collaborative learning, and tackling statistical heterogeneity among clients, e.g., hospitals, mobile smartphones, etc. Most existing pFL methods focus on exploiting the global information and personalized information in the client-level model parameters while neglecting that data is the source of these two kinds of information. To address this, we propose the Federated Conditional Policy (FedCP) method, which generates a conditional policy for each sample to separate the global information and personalized information in its features and then processes them by a global head and a personalized head, respectively. FedCP is more fine-grained to consider personalization in a sample-specific manner than existing pFL methods. Extensive experiments in computer vision and natural language processing domains show that FedCP outperforms eleven state-of-the-art methods by up to 6.69%. Furthermore, FedCP maintains its superiority when some clients accidentally drop out, which frequently happens in mobile settings. Our code is public at https://github.com/TsingZ0/FedCP.|近年来，个性化联邦学习(pFL)在保护个人隐私、合作学习以及处理客户之间的统计异质性等方面受到越来越多的关注，例如医院、移动智能手机等。现有的 pFL 方法大多侧重于利用客户端模型参数中的全局信息和个性化信息，而忽视了数据是这两类信息的来源。为了解决这个问题，我们提出了联邦条件策略(FedCP)方法，该方法为每个样本生成一个条件策略来分离其特征中的全局信息和个性化信息，然后分别通过一个全局头和一个个性化头来处理它们。与现有的 pFL 方法相比，FedCP 更加细粒度地以特定于样本的方式考虑个性化。在计算机视觉和自然语言处理领域的大量实验表明，FedCP 比11种最先进的方法的性能提高了6.69% 。此外，当一些客户端意外退出时，FedCP 仍然保持其优势，这种情况在移动设置中经常发生。我们的代码在 https://github.com/tsingz0/fedcp 是公开的。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=FedCP:+Separating+Feature+Information+for+Personalized+Federated+Learning+via+Conditional+Policy)|0| |[CFGL-LCR: A Counterfactual Graph Learning Framework for Legal Case Retrieval](https://doi.org/10.1145/3580305.3599273)|Kun Zhang, Chong Chen, Yuanzhuo Wang, Qi Tian, Long Bai||Legal case retrieval, which aims to find relevant cases based on a short case description, serves as an important part of modern legal systems. Despite the success of existing retrieval methods based on Pretrained Language Models, there are still two issues in legal case retrieval that have not been well considered before. First, existing methods underestimate the semantics associations among legal elements, e.g., law articles and crimes, which played an essential role in legal case retrieval. These methods only adopt the pre-training language model to encode the whole legal case, instead of distinguishing different legal elements in the legal case. They randomly split a legal case into different segments, which may break the completeness of each legal element. Second, due to the difficulty in annotating the relevant labels of similar cases, legal case retrieval inevitably faces the problem of lacking training data. In this paper, we propose a counterfactual graph learning framework for legal case retrieval. Concretely, to overcome the above challenges, we transform the legal case document into a graph and model the semantics of the legal elements through a graph neural network. To alleviate the low resource and learn the causal relationship between the semantics of legal elements and relevance, a counterfactual data generator is designed to augment counterfactual data and enhance legal case representation. Extensive experiments based on two publicly available legal benchmarks demonstrate that our CFGL-LCR can significantly outperform previous state-of-the-art methods in legal case retrieval.|法律案例检索是现代法律体系的重要组成部分，其目的是在简短的案例描述的基础上发现相关案例。尽管现有的基于预训练语言模型的检索方法取得了成功，但在法律案例检索中仍然存在两个以前没有得到很好考虑的问题。首先，现有的方法低估了法律要素之间的语义联系，如法律条文和犯罪，这些语义联系在法律案例检索中发挥了重要作用。这些方法只采用预训练语言模式对整个法律案件进行编码，而没有区分法律案件中的不同法律要素。他们随机地将一个法律案件分成不同的部分，这可能会打破每个法律要素的完整性。其次，由于类似案件相关标注的困难，法律案件检索不可避免地面临缺乏训练数据的问题。本文提出了一个用于法律案例检索的反事实图学习框架。具体来说，为了克服上述挑战，我们将法律案例文档转化为一个图形，并通过一个图神经网络对法律要素的语义进行建模。为了解决法律要素语义与相关性之间的因果关系，减轻资源不足的问题，设计了一个反事实数据生成器，以增强反事实数据，提高法律案件的表述能力。基于两个公开可用的法律基准的大量实验表明，我们的 CFGL-LCR 在法律案件检索方面可以显著优于以前的最先进的方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=CFGL-LCR:+A+Counterfactual+Graph+Learning+Framework+for+Legal+Case+Retrieval)|0| |[DM-PFL: Hitchhiking Generic Federated Learning for Efficient Shift-Robust Personalization](https://doi.org/10.1145/3580305.3599311)|Wenhao Zhang, Zimu Zhou, Yansheng Wang, Yongxin Tong||Personalized federated learning collaboratively trains client-specific models, which holds potential for various mobile and IoT applications with heterogeneous data. However, existing solutions are vulnerable to distribution shifts between training and test data, and involve high training workloads on local devices. These two shortcomings hinder the practical usage of personalized federated learning on real-world mobile applications. To overcome these drawbacks, we explore efficient shift-robust personalization for federated learning. The principle is to hitchhike the global model to improve the shift-robustness of personalized models with minimal extra training overhead. To this end, we present DM-PFL, a novel framework that utilizes a dual masking mechanism to train both global and personalized models with weight-level parameter sharing and end-to-end sparse training. Evaluations on various datasets show that our methods not only improve the test accuracy in presence of test-time distribution shifts but also save the communication and computation costs compared to state-of-the-art personalized federated learning schemes.|个性化联邦学习协同培训特定于客户端的模型，这为各种具有异构数据的移动和物联网应用提供了潜力。然而，现有的解决方案很容易受到培训和测试数据之间分布转移的影响，并且需要在本地设备上进行高负荷的培训。这两个缺点阻碍了个性化联邦学习在实际移动应用中的实际应用。为了克服这些缺点，我们探索了联邦学习中有效的移位鲁棒个性化方法。其原理是搭便车全局模型，以最小的额外训练开销提高个性化模型的移位鲁棒性。为此，我们提出了 DM-PFL，一个新的框架，利用双掩蔽机制训练全局和个性化的模型与权重水平参数共享和端到端稀疏训练。对各种数据集的评估表明，与现有的个性化联邦学习方案相比，该方法不仅提高了测试时间分布偏移情况下的测试精度，而且节省了通信和计算成本。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=DM-PFL:+Hitchhiking+Generic+Federated+Learning+for+Efficient+Shift-Robust+Personalization)|0| -|[Efficient Approximation Algorithms for Spanning Centrality](https://doi.org/10.1145/3580305.3599323)|Shiqi Zhang, Renchi Yang, Jing Tang, Xiaokui Xiao, Bo Tang|Hong Kong Baptist University; The Hong Kong University of Science and Technology (Guangzhou); National University of Singapore; Southern University of Science and Technology|Given a graph $\mathcal{G}$, the spanning centrality (SC) of an edge $e$ measures the importance of $e$ for $\mathcal{G}$ to be connected. In practice, SC has seen extensive applications in computational biology, electrical networks, and combinatorial optimization. However, it is highly challenging to compute the SC of all edges (AESC) on large graphs. Existing techniques fail to deal with such graphs, as they either suffer from expensive matrix operations or require sampling numerous long random walks. To circumvent these issues, this paper proposes TGT and its enhanced version TGT+, two algorithms for AESC computation that offers rigorous theoretical approximation guarantees. In particular, TGT remedies the deficiencies of previous solutions by conducting deterministic graph traversals with carefully-crafted truncated lengths. TGT+ further advances TGT in terms of both empirical efficiency and asymptotic performance while retaining result quality, based on the combination of TGT with random walks and several additional heuristic optimizations. We experimentally evaluate TGT+ against recent competitors for AESC using a variety of real datasets. The experimental outcomes authenticate that TGT+ outperforms the state of the arts often by over one order of magnitude speedup without degrading the accuracy.|给定一个图 $mathal { G } $，边 $e $的生成中心性(SC)度量 $e $对于要连接的 $mathal { G } $的重要性。在实践中，SC 已经在计算生物学、电力网络和组合优化等领域得到了广泛的应用。然而，计算大图上所有边的 SC (AESC)是一个非常具有挑战性的问题。现有的技术无法处理这样的图，因为它们要么需要进行昂贵的矩阵运算，要么需要对大量的长随机游动进行采样。为了解决这些问题，本文提出了 TGT 及其改进版本 TGT + ，这两种 AESC 计算算法提供了严格的理论近似保证。特别是，TGT 通过使用精心设计的截断长度进行确定性图遍历，弥补了以前解决方案的缺陷。TGT + 基于 TGT 与随机游动的结合以及几个附加的启发式优化，在保持结果质量的同时，进一步提高了 TGT 的经验有效性和渐近性能。我们使用各种实际数据集对 AESC 的最近竞争对手进行了 TGT + 的实验评估。实验结果表明，在不降低准确性的情况下，TGT + 的性能通常比现有技术水平高出一个数量级。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Efficient+Approximation+Algorithms+for+Spanning+Centrality)|0| +|[Efficient Approximation Algorithms for Spanning Centrality](https://doi.org/10.1145/3580305.3599323)|Shiqi Zhang, Renchi Yang, Jing Tang, Xiaokui Xiao, Bo Tang|National University of Singapore; Southern University of Science and Technology; Hong Kong Baptist University; The Hong Kong University of Science and Technology (Guangzhou)|Given a graph $\mathcal{G}$, the spanning centrality (SC) of an edge $e$ measures the importance of $e$ for $\mathcal{G}$ to be connected. In practice, SC has seen extensive applications in computational biology, electrical networks, and combinatorial optimization. However, it is highly challenging to compute the SC of all edges (AESC) on large graphs. Existing techniques fail to deal with such graphs, as they either suffer from expensive matrix operations or require sampling numerous long random walks. To circumvent these issues, this paper proposes TGT and its enhanced version TGT+, two algorithms for AESC computation that offers rigorous theoretical approximation guarantees. In particular, TGT remedies the deficiencies of previous solutions by conducting deterministic graph traversals with carefully-crafted truncated lengths. TGT+ further advances TGT in terms of both empirical efficiency and asymptotic performance while retaining result quality, based on the combination of TGT with random walks and several additional heuristic optimizations. We experimentally evaluate TGT+ against recent competitors for AESC using a variety of real datasets. The experimental outcomes authenticate that TGT+ outperforms the state of the arts often by over one order of magnitude speedup without degrading the accuracy.|给定一个图 $mathal { G } $，边 $e $的生成中心性(SC)度量 $e $对于要连接的 $mathal { G } $的重要性。在实践中，SC 已经在计算生物学、电力网络和组合优化等领域得到了广泛的应用。然而，计算大图上所有边的 SC (AESC)是一个非常具有挑战性的问题。现有的技术无法处理这样的图，因为它们要么需要进行昂贵的矩阵运算，要么需要对大量的长随机游动进行采样。为了解决这些问题，本文提出了 TGT 及其改进版本 TGT + ，这两种 AESC 计算算法提供了严格的理论近似保证。特别是，TGT 通过使用精心设计的截断长度进行确定性图遍历，弥补了以前解决方案的缺陷。TGT + 基于 TGT 与随机游动的结合以及几个附加的启发式优化，在保持结果质量的同时，进一步提高了 TGT 的经验有效性和渐近性能。我们使用各种实际数据集对 AESC 的最近竞争对手进行了 TGT + 的实验评估。实验结果表明，在不降低准确性的情况下，TGT + 的性能通常比现有技术水平高出一个数量级。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Efficient+Approximation+Algorithms+for+Spanning+Centrality)|0| |[Improving Search Clarification with Structured Information Extracted from Search Results](https://doi.org/10.1145/3580305.3599389)|Ziliang Zhao, Zhicheng Dou, Yu Guo, Zhao Cao, Xiaohua Cheng||Search clarification in conversational search systems exhibits a clarification pane composed of several candidate aspect items and a clarifying question. To generate a pane, existing studies usually rely on unstructured document texts. However, important structured information in search results is not effectively considered, making the generated panes inaccurate in some cases. In this paper, we emphasize the importance of structured information in search results for improving search clarification. We propose enhancing unstructured documents with two kinds of structured information: one is "In-List'' relation obtained from HTML list structures, which helps extract groups of high-quality items with abundant parallel information. Another is "Is-A'' relation extracted from knowledge bases, which is helpful to generate good questions with explicit prompts. To avoid introducing excessive noises, we design a relation selection process to filter out ineffective relations. We further design a BART-based model for generating clarification panes. The experimental results show that the structured information is good supplement for generating high-quality clarification panes.|会话搜索系统中的搜索澄清显示一个由几个候选方面项和一个澄清问题组成的澄清窗格。要生成窗格，现有的研究通常依赖于非结构化文档文本。但是，搜索结果中的重要结构化信息没有得到有效考虑，使得生成的窗格在某些情况下不准确。在本文中，我们强调结构化信息在搜索结果中对提高搜索清晰度的重要性。我们提出了利用两种结构化信息来增强非结构化文档: 一种是从 HTML 列表结构中获取的“ In-List”关系，它有助于提取具有大量并行信息的高质量项目组。另一种是从知识库中提取的“是-A”关系，它有助于在明确的提示下产生好的问题。为了避免引入过多的噪声，我们设计了一个关系选择过程来滤除无效关系。我们进一步设计了一个基于 BART 的生成澄清窗格的模型。实验结果表明，结构化信息对生成高质量的澄清面板有很好的补充作用。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Improving+Search+Clarification+with+Structured+Information+Extracted+from+Search+Results)|0| |[Dense Representation Learning and Retrieval for Tabular Data Prediction](https://doi.org/10.1145/3580305.3599305)|Lei Zheng, Ning Li, Xianyu Chen, Quan Gan, Weinan Zhang||Data science is concerned with mining data patterns from a database, which is assembled by tabular data. As the routine of machine learning, most of the previous work mining the tabular data's pattern based on a single instance. However, they neglect the similar tabular data instances that could help make the label prediction of the target data instance. Recently, some retrieval-based methods for tabular data label prediction have been proposed, which, however, treat the data as sparse vectors to perform the retrieval, which fails to make use of the semantic information of the tabular data. To address such a problem, in this paper, we propose a novel framework of dense retrieval on tabular data (DERT) to support flexible data representation learning and effective label prediction on tabular data. DERT consists of two major components: (i) the encoder that makes the tabular data as embeddings, which could be trained by flexible neural networks and auxiliary loss functions; (ii) the retrieval and prediction component, which makes use of similar rows in the table to make label prediction of the target row. We test DERT on two tasks based on five real-world datasets and experimental results show that DERT achieves consistent improvements over the state-of-the-art and various baselines.|数据科学涉及从数据库中挖掘数据模式，数据库由表格数据组装而成。作为机器学习的例程，以往的大部分工作都是基于单个实例挖掘表格数据的模式。但是，它们忽略了可以帮助对目标数据实例进行标签预测的类似表格数据实例。最近，人们提出了一些基于检索的表格数据标签预测方法，但这些方法将数据作为稀疏向量进行检索，未能充分利用表格数据的语义信息。为了解决这一问题，本文提出了一种新的表格数据密集检索框架(DERT) ，以支持灵活的表格数据表示学习和有效的标签预测。DERT 包括两个主要部分: (i)将表格数据作为嵌入的编码器，可以通过灵活的神经网络和辅助损失函数进行训练; (ii)检索和预测部分，利用表格中的相似行对目标行进行标签预测。我们基于五个真实世界的数据集对两个任务进行了 DERT 测试，实验结果表明 DERT 在最先进的和不同的基线上取得了一致的改进。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Dense+Representation+Learning+and+Retrieval+for+Tabular+Data+Prediction)|0| |[A Sublinear Time Algorithm for Opinion Optimization in Directed Social Networks via Edge Recommendation](https://doi.org/10.1145/3580305.3599247)|Xiaotian Zhou, Liwang Zhu, Wei Li, Zhongzhi Zhang||In this paper, we study the opinion maximization problem for the leader-follower DeGroot model of opinion dynamics in a social network modelled by a directed graph with n nodes, where a small number of nodes are competing leader nodes with binary opposing opinions 0 or 1, and the rest are follower nodes. We address the problem of maximizing the overall opinion by adding k ⇐ n new edges, where each edge is incident to a 1-leader and a follower. We prove that the objective function is monotone and submodular, and then propose a deterministic greedy algorithm with an approximation ratio (1-1 over e) and O(n 3 ) running time. We then develop a fast sampling algorithm based on l-truncated absorbing random walks and sample-materialization techniques, which has sublinear time complexity O(kn 1/2 log 3/2 n/ε 3 ) for any error parameter ε > 0. We provide extensive experiments on real networks to evaluate the performance of our algorithms. The results show that for undirected graphs our fast sampling algorithm outperforms the state-of-the-art method in terms of efficiency and effectiveness. While for directed graphs our fast sampling algorithm is as effective as our deterministic greedy algorithm, both of which are much better than the baseline strategies. Moreover, our fast algorithm is scalable to large directed graphs with over 41 million nodes.|本文研究了一类社会网络中的领导者-跟随者 DeGroot 意见动力学模型的意见最大化问题，该模型由 n 个节点的有向图建模，其中少数节点为竞争的领导者节点，其余为跟随者节点。我们通过添加 kn 个新边来解决整体意见最大化的问题，其中每个边都与一个1-领导者和一个跟随者相关。证明了目标函数是单调的、子模的，提出了一种具有逼近比(1-1/e)和运行时间为 O (n3)的确定性贪婪算法。然后提出了一种基于 l 截断吸收随机游动和样本物化技术的快速采样算法，该算法对于任意误差参数 ε > 0，具有次线性时间复杂度 O (kn1/2 log 3/2 n/ε3)。我们提供广泛的实验在真实的网络，以评估我们的算法的性能。结果表明，对于无向图，我们的快速抽样算法在效率和有效性方面优于现有的方法。而对于有向图，我们的快速采样算法和确定性贪婪算法一样有效，两者都比基线策略好得多。此外，我们的快速算法可扩展到超过4100万个节点的大型有向图。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Sublinear+Time+Algorithm+for+Opinion+Optimization+in+Directed+Social+Networks+via+Edge+Recommendation)|0| |[Path-Specific Counterfactual Fairness for Recommender Systems](https://doi.org/10.1145/3580305.3599462)|Yaochen Zhu, Jing Ma, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li|LinkedIn Inc.; University of Virginia|Recommender systems (RSs) have become an indispensable part of online platforms. With the growing concerns of algorithmic fairness, RSs are not only expected to deliver high-quality personalized content, but are also demanded not to discriminate against users based on their demographic information. However, existing RSs could capture undesirable correlations between sensitive features and observed user behaviors, leading to biased recommendations. Most fair RSs tackle this problem by completely blocking the influences of sensitive features on recommendations. But since sensitive features may also affect user interests in a fair manner (e.g., race on culture-based preferences), indiscriminately eliminating all the influences of sensitive features inevitably degenerate the recommendations quality and necessary diversities. To address this challenge, we propose a path-specific fair RS (PSF-RS) for recommendations. Specifically, we summarize all fair and unfair correlations between sensitive features and observed ratings into two latent proxy mediators, where the concept of path-specific bias (PS-Bias) is defined based on path-specific counterfactual inference. Inspired by Pearl's minimal change principle, we address the PS-Bias by minimally transforming the biased factual world into a hypothetically fair world, where a fair RS model can be learned accordingly by solving a constrained optimization problem. For the technical part, we propose a feasible implementation of PSF-RS, i.e., PSF-VAE, with weakly-supervised variational inference, which robustly infers the latent mediators such that unfairness can be mitigated while necessary recommendation diversities can be maximally preserved simultaneously. Experiments conducted on semi-simulated and real-world datasets demonstrate the effectiveness of PSF-RS.|推荐系统已经成为在线平台不可或缺的一部分。随着对算法公平性的日益关注，RSS 不仅被期望提供高质量的个性化内容，而且被要求不因用户的人口统计信息而歧视用户。然而，现有的 RSS 可能捕获敏感特性和观察到的用户行为之间不希望看到的相关性，从而导致有偏见的推荐。大多数公平的 RSS 通过完全屏蔽敏感特性对推荐的影响来解决这个问题。但是，由于敏感特性也可能以公平的方式影响用户的兴趣(例如，基于文化的偏好的种族) ，不加区分地消除敏感特性的所有影响必然会降低推荐的质量和必要的多样性。为了应对这一挑战，我们提出了一个路径特定公平 RS (PSF-RS)的建议。具体而言，我们将敏感特征和观察评分之间的所有公平和不公平的相关性总结为两个潜在的代理中介，其中路径特异性偏倚(PS-Bias)的概念是基于路径特异性反事实推断定义的。受珀尔的最小改变原则的启发，我们通过最小化地将有偏见的现实世界转化为一个假设的公平世界，在这个假设的公平世界中，可以通过解决一个受限制的最佳化问题来相应地学习一个公平的遥感模型，从而解决偏差问题。在技术部分，我们提出了一种可行的 PSF-RS 实现方法，即 PSF-VAE，该方法利用弱监督变分推理强有力地推导出潜在的中介因子，从而在最大限度地保留必要的推荐多样性的同时减少不公平性。在半模拟和真实数据集上进行的实验证明了 PSF-RS 算法的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Path-Specific+Counterfactual+Fairness+for+Recommender+Systems)|0| -|[Capturing Conversion Rate Fluctuation during Sales Promotions: A Novel Historical Data Reuse Approach](https://doi.org/10.1145/3580305.3599788)|Zhangming Chan, Yu Zhang, Shuguang Han, Yong Bai, XiangRong Sheng, Siyuan Lou, Jiacen Hu, Baolin Liu, Yuning Jiang, Jian Xu, Bo Zheng|University of Science and Technology Beijing; Nanjing University; Alibaba Group|Conversion rate (CVR) prediction is one of the core components in online recommender systems, and various approaches have been proposed to obtain accurate and well-calibrated CVR estimation. However, we observe that a well-trained CVR prediction model often performs sub-optimally during sales promotions. This can be largely ascribed to the problem of the data distribution shift, in which the conventional methods no longer work. To this end, we seek to develop alternative modeling techniques for CVR prediction. Observing similar purchase patterns across different promotions, we propose reusing the historical promotion data to capture the promotional conversion patterns. Herein, we propose a novel \textbf{H}istorical \textbf{D}ata \textbf{R}euse (\textbf{HDR}) approach that first retrieves historically similar promotion data and then fine-tunes the CVR prediction model with the acquired data for better adaptation to the promotion mode. HDR consists of three components: an automated data retrieval module that seeks similar data from historical promotions, a distribution shift correction module that re-weights the retrieved data for better aligning with the target promotion, and a TransBlock module that quickly fine-tunes the original model for better adaptation to the promotion mode. Experiments conducted with real-world data demonstrate the effectiveness of HDR, as it improves both ranking and calibration metrics to a large extent. HDR has also been deployed on the display advertising system in Alibaba, bringing a lift of $9\%$ RPM and $16\%$ CVR during Double 11 Sales in 2022.|转化率(CVR)预测是在线推荐系统的核心组成部分之一，为了获得准确、标定良好的 CVR 估计，人们提出了各种方法。然而，我们观察到，训练有素的 CVR 预测模型在促销期间往往表现不佳。这在很大程度上归因于数据分布偏移的问题，在这个问题中，传统的方法不再起作用。为此，我们寻求发展可替代的 CVR 预测建模技术。通过观察不同促销活动中相似的购买模式，我们建议重用历史促销数据来捕获促销转换模式。在这里，我们提出了一种新的 textbf { H }历史 textbf { D } ata textbf { R } euse (textbf { HDR })方法，首先检索历史上相似的促销数据，然后用所获得的数据对 CVR 预测模型进行微调，以更好地适应促销模式。人类发展报告由三个组成部分组成: 一个自动数据检索模块，从历史促销活动中寻找类似数据; 一个分配转移校正模块，重新加权检索的数据，以便更好地与目标促销活动保持一致; 一个 TransBlock 模块，快速微调原始模型，以便更好地适应促销模式。利用实际数据进行的实验证明了 HDR 的有效性，因为它在很大程度上改善了排序和校准指标。HDR 也已经部署在阿里巴巴的显示广告系统上，在2022年双11销售期间，带来了9% 的每分钟转速和16% 的 CVR。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Capturing+Conversion+Rate+Fluctuation+during+Sales+Promotions:+A+Novel+Historical+Data+Reuse+Approach)|0| +|[Capturing Conversion Rate Fluctuation during Sales Promotions: A Novel Historical Data Reuse Approach](https://doi.org/10.1145/3580305.3599788)|Zhangming Chan, Yu Zhang, Shuguang Han, Yong Bai, XiangRong Sheng, Siyuan Lou, Jiacen Hu, Baolin Liu, Yuning Jiang, Jian Xu, Bo Zheng|Alibaba Group; University of Science and Technology Beijing; Nanjing University|Conversion rate (CVR) prediction is one of the core components in online recommender systems, and various approaches have been proposed to obtain accurate and well-calibrated CVR estimation. However, we observe that a well-trained CVR prediction model often performs sub-optimally during sales promotions. This can be largely ascribed to the problem of the data distribution shift, in which the conventional methods no longer work. To this end, we seek to develop alternative modeling techniques for CVR prediction. Observing similar purchase patterns across different promotions, we propose reusing the historical promotion data to capture the promotional conversion patterns. Herein, we propose a novel \textbf{H}istorical \textbf{D}ata \textbf{R}euse (\textbf{HDR}) approach that first retrieves historically similar promotion data and then fine-tunes the CVR prediction model with the acquired data for better adaptation to the promotion mode. HDR consists of three components: an automated data retrieval module that seeks similar data from historical promotions, a distribution shift correction module that re-weights the retrieved data for better aligning with the target promotion, and a TransBlock module that quickly fine-tunes the original model for better adaptation to the promotion mode. Experiments conducted with real-world data demonstrate the effectiveness of HDR, as it improves both ranking and calibration metrics to a large extent. HDR has also been deployed on the display advertising system in Alibaba, bringing a lift of $9\%$ RPM and $16\%$ CVR during Double 11 Sales in 2022.|转化率(CVR)预测是在线推荐系统的核心组成部分之一，为了获得准确、标定良好的 CVR 估计，人们提出了各种方法。然而，我们观察到，训练有素的 CVR 预测模型在促销期间往往表现不佳。这在很大程度上归因于数据分布偏移的问题，在这个问题中，传统的方法不再起作用。为此，我们寻求发展可替代的 CVR 预测建模技术。通过观察不同促销活动中相似的购买模式，我们建议重用历史促销数据来捕获促销转换模式。在这里，我们提出了一种新的 textbf { H }历史 textbf { D } ata textbf { R } euse (textbf { HDR })方法，首先检索历史上相似的促销数据，然后用所获得的数据对 CVR 预测模型进行微调，以更好地适应促销模式。人类发展报告由三个组成部分组成: 一个自动数据检索模块，从历史促销活动中寻找类似数据; 一个分配转移校正模块，重新加权检索的数据，以便更好地与目标促销活动保持一致; 一个 TransBlock 模块，快速微调原始模型，以便更好地适应促销模式。利用实际数据进行的实验证明了 HDR 的有效性，因为它在很大程度上改善了排序和校准指标。HDR 也已经部署在阿里巴巴的显示广告系统上，在2022年双11销售期间，带来了9% 的每分钟转速和16% 的 CVR。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Capturing+Conversion+Rate+Fluctuation+during+Sales+Promotions:+A+Novel+Historical+Data+Reuse+Approach)|0| |[SAMD: An Industrial Framework for Heterogeneous Multi-Scenario Recommendation](https://doi.org/10.1145/3580305.3599955)|Zhaoxin Huan, Ang Li, Xiaolu Zhang, Xu Min, Jieyu Yang, Yong He, Jun Zhou||Industrial recommender systems usually need to serve multiple scenarios at the same time. In practice, there are various heterogeneous scenarios, since users frequently engage in scenarios with varying intentions and the items within each scenario typically belong to diverse categories. Existing works of multi-scenario recommendation mainly focus on modeling homogeneous scenarios which have similar data distributions. They equally transfer knowledge to each scenario without considering the diversity of heterogeneous scenarios. In this paper, we argue that the heterogeneity in multi-scenario recommendations is a key problem that needs to be solved. To this end, we propose an industrial framework named Scenario-Aware Model-Agnostic Meta Distillation (SAMD) for the multi-scenario recommendation. SAMD aims to provide scenario-aware and model-agnostic knowledge sharing across heterogeneous scenarios by modeling scenarios' relationship and conducting heterogeneous knowledge distillation. Specifically, SAMD first measures the comprehensive representation of each scenario and then proposes a novel meta distillation paradigm to conduct scenario-aware knowledge sharing. The meta network first establishes the potential scenarios' relationships and generates the strategies of knowledge sharing for each scenario. Then the heterogeneous knowledge distillation utilizes scenario-aware strategies to share knowledge across heterogeneous scenarios through intermediate features distillation without the restriction of the model architecture. In this way, SAMD shares knowledge across heterogeneous scenarios in a scenario-aware and model-agnostic manner, which addresses the problem of heterogeneity. Compared with other state-of-the-art methods, extensive offline experiments, and online A/B testing demonstrate the superior performance of the proposed SAMD framework, especially in heterogeneous scenarios.|工业推荐系统通常需要同时服务于多个场景。在实践中，存在各种不同的场景，因为用户经常参与具有不同意图的场景，并且每个场景中的项目通常属于不同的类别。现有的多场景推荐主要针对具有相似数据分布的同类场景进行建模。它们同样地将知识转移到每个场景，而不考虑异构场景的多样性。在本文中，我们认为多情景推荐中的异构性是一个需要解决的关键问题。为此，我们提出了一个名为场景感知模型-不可知元精馏(SAMD)的工业框架，用于多场景推荐。SAMD 旨在通过对异构场景关系建模和异构知识提取，实现异构场景间的场景感知和模型无关知识共享。具体来说，SAMD 首先度量每个场景的全面表示，然后提出一种新的元精馏范式来进行场景感知的知识共享。元网络首先建立潜在场景之间的关系，并为每个场景生成知识共享策略。然后在不受模型体系结构限制的前提下，通过中间特征提取，利用场景感知策略实现异构场景间的知识共享。通过这种方式，SAMD 以场景感知和模型无关的方式在异构场景之间共享知识，这解决了异构性问题。与其他最先进的方法相比，大量的离线实验和在线 A/B 测试证明了所提出的 SAMD 框架的优越性能，特别是在异构场景中。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=SAMD:+An+Industrial+Framework+for+Heterogeneous+Multi-Scenario+Recommendation)|0| |[Learning Discrete Document Representations in Web Search](https://doi.org/10.1145/3580305.3599854)|Rong Huang, Danfeng Zhang, Weixue Lu, Han Li, Meng Wang, Daiting Shi, Jun Fan, Zhicong Cheng, Simiu Gu, Dawei Yin||Product quantization (PQ) has been usually applied to dense retrieval (DR) of documents thanks to its competitive time, memory efficiency and compatibility with other approximate nearest search (ANN) methods. Originally, PQ was learned to minimize the reconstruction loss, i.e., the distortions between the original dense embeddings and the reconstructed embeddings after quantization. Unfortunately, such an objective is inconsistent with the goal of selecting ground-truth documents for the input query, which may cause a severe loss of retrieval quality. Recent research has primarily concentrated on jointly training the biencoders and PQ to ensure consistency for improved performance. However, it is still difficult to design an approach that can cope with challenges like discrete representation collapse, mining informative negatives, and deploying effective embedding-based retrieval (EBR) systems in a real search engine. In this paper, we propose a Two-stage Multi-task Joint training technique (TMJ) to learn discrete document representations, which is simple and effective for real-world practical applications. In the first stage, the PQ centroid embeddings are regularized by the dense retrieval loss, which ensures the distinguishability of the quantized vectors and preserves the retrieval quality of dense embeddings. In the second stage, a PQ-oriented sample mining strategy is introduced to explore more informative negatives and further improve the performance. Offline evaluations are performed on a public benchmark (MS MARCO) and two private real-world web search datasets, where our method notably outperforms the SOTA PQ methods both in Recall and Mean Reciprocal Ranking (MRR). Besides, online experiments are conducted to validate that our technique can significantly provide high-quality vector quantization. Moreover, our joint training framework has been successfully applied to a billion-scale web search system.|产品量化(PQ)由于其具有竞争时间、存储效率以及与其他近似最近搜索(ANN)方法的兼容性等优点，被广泛应用于文档的密集检索(DR)。最初，PQ 学会了尽量减少重建损失，即原始的密集嵌入和量化后的重建嵌入之间的扭曲。遗憾的是，这样的目标不符合为输入查询选择地面真实文档的目标，这可能导致检索质量的严重损失。最近的研究主要集中在联合培训双向编码器和 PQ，以确保一致性，提高性能。然而，设计一种能够应对离散表示崩溃、挖掘信息否定性以及在实际搜索引擎中部署有效的嵌入式检索(EBR)系统等挑战的方法仍然十分困难。本文提出了一种两阶段多任务联合训练技术(TMJ)来学习离散文档表示，该方法简单有效，适用于实际应用。在第一阶段，PQ 质心嵌入通过密集检索损失进行正则化，保证了量化向量的可区分性，同时保持了密集嵌入的检索质量。在第二阶段，引入了一个面向 PQ 的样本挖掘策略，以探索更多的负信息，并进一步提高性能。离线评估是在一个公共基准(MS MARCO)和两个私有的真实世界网络搜索数据集上进行的，其中我们的方法明显优于召回和平均互惠排名(MRR)中的 SOTA PQ 方法。此外，还进行了在线实验，以验证我们的技术能够显著提供高质量的向量量化。此外，我们的联合培训框架已经成功地应用于一个十亿规模的网络搜索系统。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Learning+Discrete+Document+Representations+in+Web+Search)|0| |[AdSEE: Investigating the Impact of Image Style Editing on Advertisement Attractiveness](https://doi.org/10.1145/3580305.3599770)|Liyao Jiang, Chenglin Li, Haolan Chen, Xiaodong Gao, Xinwang Zhong, Yang Qiu, Shani Ye, Di Niu|University of Alberta; Platform and Content Group, Tencent|Online advertisements are important elements in e-commerce sites, social media platforms, and search engines. With the increasing popularity of mobile browsing, many online ads are displayed with visual information in the form of a cover image in addition to text descriptions to grab the attention of users. Various recent studies have focused on predicting the click rates of online advertisements aware of visual features or composing optimal advertisement elements to enhance visibility. In this paper, we propose Advertisement Style Editing and Attractiveness Enhancement (AdSEE), which explores whether semantic editing to ads images can affect or alter the popularity of online advertisements. We introduce StyleGAN-based facial semantic editing and inversion to ads images and train a click rate predictor attributing GAN-based face latent representations in addition to traditional visual and textual features to click rates. Through a large collected dataset named QQ-AD, containing 20,527 online ads, we perform extensive offline tests to study how different semantic directions and their edit coefficients may impact click rates. We further design a Genetic Advertisement Editor to efficiently search for the optimal edit directions and intensity given an input ad cover image to enhance its projected click rates. Online A/B tests performed over a period of 5 days have verified the increased click-through rates of AdSEE-edited samples as compared to a control group of original ads, verifying the relation between image styles and ad popularity. We open source the code for AdSEE research at https://github.com/LiyaoJiang1998/adsee.|在线广告是电子商务网站、社交媒体平台和搜索引擎的重要组成部分。随着手机浏览的日益普及，许多在线广告除了文字描述外，还以封面图像的形式显示视觉信息，以吸引用户的注意力。最近的各种研究侧重于预测了解视觉特征的在线广告的点击率或构成最佳的广告元素以提高可见度。本文提出了广告风格编辑与吸引力增强(AdSee)的概念，探讨了对广告图像进行语义编辑是否会影响或改变网络广告的受欢迎程度。我们将基于 StyleGAN 的人脸语义编辑和反转技术引入到广告图像中，除了传统的视觉和文本特征外，我们还训练了一个基于 GAN 的人脸潜在表征的点击率预测器来提高点击率。通过一个名为 QQ-AD 的大型数据集，包含20,527个在线广告，我们进行了大量的离线测试来研究不同的语义方向及其编辑系数如何影响点击率。我们进一步设计了一个遗传广告编辑器，以有效地搜索最佳的编辑方向和强度给予输入广告封面图像，以提高其预计点击率。为期5天的在线 A/B 测试已经证实，与对照组的原始广告相比，广告编辑样本的点击率有所提高，验证了图像风格与广告流行度之间的关系。我们在 https://github.com/liyaojiang1998/AdSEE 上开源了 AdSEE 研究的代码。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=AdSEE:+Investigating+the+Impact+of+Image+Style+Editing+on+Advertisement+Attractiveness)|0| |[Adaptive Graph Contrastive Learning for Recommendation](https://doi.org/10.1145/3580305.3599768)|Yangqin Jiang, Chao Huang, Lianghao Huang|University of Hong Kong|Recently, graph neural networks (GNNs) have been successfully applied to recommender systems as an effective collaborative filtering (CF) approach. The key idea of GNN-based recommender system is to recursively perform the message passing along the user-item interaction edge for refining the encoded embeddings, relying on sufficient and high-quality training data. Since user behavior data in practical recommendation scenarios is often noisy and exhibits skewed distribution, some recommendation approaches, e.g., SGL and SimGCL, leverage self-supervised learning to improve user representations against the above issues. Despite their effectiveness, however, they conduct self-supervised learning through creating contrastvie views, depending on the exploration of data augmentations with the problem of tedious trial-and-error selection of augmentation methods. In this paper, we propose a novel Adaptive Graph Contrastive Learning (AdaptiveGCL) framework which conducts graph contrastive learning with two adaptive contrastive view generators to better empower CF paradigm. Specifically, we use two trainable view generators, which are a graph generative model and a graph denoising model respectively, to create contrastive views. Two generators are able to create adaptive contrastive views, addressing the problem of model collapse and achieving adaptive contrastive learning. With two adaptive contrasive views, more additionally high-quality training signals will be introduced into the CF paradigm and help to alleviate the data sparsity and noise issues. Extensive experiments on three benchmark datasets demonstrate the superiority of our model over various state-of-the-art recommendation methods. Further visual analysis intuitively explains why our AdaptiveGCL outperforms existing contrastive learning approaches based on selected data augmentation methods.|最近，图形神经网络(GNN)已成功应用于推荐系统，作为一种有效的协同过滤(CF)方法。基于 GNN 的推荐系统的关键思想是依靠充分和高质量的训练数据，递归地执行沿用户项目交互边缘传递的消息，以完善编码的嵌入。由于实际推荐场景中的用户行为数据通常是有噪音的，并且呈现出偏态分布，因此一些推荐方法，如 SGL 和 SimGCL，利用自监督学习来改善用户对上述问题的表示。然而，尽管他们的有效性，他们进行自我监督学习通过创建对比观点，依赖于探索数据增强与繁琐的试错选择增强方法的问题。本文提出了一种新的自适应图形对比学习(AdaptiveGCL)框架，该框架使用两个自适应对比视图生成器进行图形对比学习，以更好地支持 CF 范式。具体来说，我们使用两个可训练的视图生成器，分别是一个图形生成模型和一个图形去噪模型，来创建对比视图。两个生成器能够创建自适应对比视图，解决模型崩溃问题，实现自适应对比学习。通过两个自适应对立视图，在 CF 范式中引入更多高质量的训练信号，有助于缓解数据稀疏和噪声问题。在三个基准数据集上的大量实验证明了我们的模型优于各种最先进的推荐方法。进一步的可视化分析直观地解释了为什么我们的 AdaptiveGCL 优于基于所选数据增强方法的现有对比学习方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Adaptive+Graph+Contrastive+Learning+for+Recommendation)|0| |[PGLBox: Multi-GPU Graph Learning Framework for Web-Scale Recommendation](https://doi.org/10.1145/3580305.3599885)|Xuewu Jiao, Weibin Li, Xinxuan Wu, Wei Hu, Miao Li, Jiang Bian, Siming Dai, Xinsheng Luo, Mingqing Hu, Zhengjie Huang, Danlei Feng, Junchao Yang, Shikun Feng, Haoyi Xiong, Dianhai Yu, Shuanglong Li, Jingzhou He, Yanjun Ma, Lin Liu||While having been used widely for large-scale recommendation and online advertising, the Graph Neural Network (GNN) has demonstrated its representation learning capacity to extract embeddings of nodes and edges through passing, transforming, and aggregating information over the graph. In this work, we propose PGLBox 1 - a multi-GPU graph learning framework based on PaddlePaddle [24], incorporating with optimized storage, computation, and communication strategies, to train deep GNNs based on web-scale graphs for the recommendation. Specifically, PGLBox adopts a hierarchical storage system with three layers to facilitate I/O, where graphs and embeddings are stored in the HBMs and SSDs, respectively, with MEMs as the cache. To fully utilize multi-GPUs and I/O bandwidth, PGLBox proposes an asynchronous pipeline with three stages - it first samples the subgraphs from the input graph, then pulls & updates embeddings and trains GNNs on the subgraph with parameters updating queued at the end of the pipeline. Thanks to the capacity of PGLBox in handling web-scale graphs, it becomes feasible to unify the view of GNN-based recommendation tasks for multiple advertising verticals and fuse all these graphs into a unified yet huge one. We evaluate PGLBox using a bucket of realistic GNN training tasks for the recommendation, and compare the performance of PGLBox on top of a multi-GPU server (Tesla A100×8) and the legacy training system based on a 40-node MPI cluster at Baidu. The overall comparisons show that PGLBox could save up to 55% monetary cost for training GNN models, and achieve up to 14× training speedup with the same accuracy as the legacy trainer. The open-source implementation of PGLBox is available at https://github.com/PaddlePaddle/PGL/tree/main/apps/PGLBox.|在广泛应用于大规模推荐和在线广告的同时，图形神经网络(GNN)通过在图上传递、转换和聚合信息，展示了其表示学习能力，能够提取节点和边的嵌入。在这项工作中，我们提出了 PGLBox 1-一个基于 PaddlePaddle [24]的多 GPU 图形学习框架，结合优化的存储，计算和通信策略，训练基于网络尺度图的深度 GNN 用于推荐。具体来说，PGLBox 采用了一个具有三层的层次化存储系统来实现 I/O，其中图形和嵌入分别存储在 HBM 和 SSD 中，并以 MEM 作为缓存。为了充分利用多 GPU 和 I/O 带宽，PGLBox 提出了一种异步流水线模型，该模型分为三个阶段: 首先从输入图中采样子图，然后提取和更新嵌入，并在子图上训练 GNN，在流水线末端排队更新参数。由于 PGLBox 在处理网络图形方面的能力，将基于 GNN 的多个垂直广告推荐任务视图统一起来，并将所有这些图形融合成一个统一而庞大的图形变得可行。我们使用一桶现实的 GNN 培训任务来评估 PGLBox 的推荐，并比较基于多 GPU 服务器(Tesla A100 × 8)的 PGLBox 和基于百度40节点 MPI 集群的遗留培训系统的性能。总体比较表明，PGLBox 可以节省高达55% 的训练 GNN 模型的资金成本，并实现高达14倍的训练加速与遗留教练机同样的准确性。PGLBox 的开源实现可在 https://github.com/paddlepaddle/pgl/tree/main/apps/PGLBox 获得。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=PGLBox:+Multi-GPU+Graph+Learning+Framework+for+Web-Scale+Recommendation)|0| -|[IGB: Addressing The Gaps In Labeling, Features, Heterogeneity, and Size of Public Graph Datasets for Deep Learning Research](https://doi.org/10.1145/3580305.3599843)|Arpandeep Khatua, Vikram Sharma Mailthody, Bhagyashree Taleka, Tengfei Ma, Xiang Song, WenMei Hwu|IBM Research; NVIDIA; UIUC; AWS AI|Graph neural networks (GNNs) have shown high potential for a variety of real-world, challenging applications, but one of the major obstacles in GNN research is the lack of large-scale flexible datasets. Most existing public datasets for GNNs are relatively small, which limits the ability of GNNs to generalize to unseen data. The few existing large-scale graph datasets provide very limited labeled data. This makes it difficult to determine if the GNN model's low accuracy for unseen data is inherently due to insufficient training data or if the model failed to generalize. Additionally, datasets used to train GNNs need to offer flexibility to enable a thorough study of the impact of various factors while training GNN models. In this work, we introduce the Illinois Graph Benchmark (IGB), a research dataset tool that the developers can use to train, scrutinize and systematically evaluate GNN models with high fidelity. IGB includes both homogeneous and heterogeneous graphs of enormous sizes, with more than 40% of their nodes labeled. Compared to the largest graph datasets publicly available, the IGB provides over 162X more labeled data for deep learning practitioners and developers to create and evaluate models with higher accuracy. The IGB dataset is designed to be flexible, enabling the study of various GNN architectures, embedding generation techniques, and analyzing system performance issues. IGB is open-sourced, supports DGL and PyG frameworks, and comes with releases of the raw text that we believe foster emerging language models and GNN research projects. An early public version of IGB is available at https://github.com/IllinoisGraphBenchmark/IGB-Datasets.|图形神经网络(GNN)在现实世界中具有很大的应用潜力，但是缺乏大规模的灵活数据集是 GNN 研究的主要障碍之一。大多数现有的 GNN 公共数据集相对较小，这限制了 GNN 推广到未见数据的能力。少数现有的大规模图形数据集提供非常有限的标记数据。这使得很难确定 GNN 模型对于不可见数据的低精度是否本质上是由于训练数据不足或者模型没有推广。此外，用于训练 GNN 的数据集需要提供灵活性，以便在训练 GNN 模型时能够对各种因素的影响进行彻底的研究。在这项工作中，我们介绍了伊利诺伊图基准(IGB) ，一个研究数据集工具，开发人员可以用来训练，审查和系统地评估 GNN 模型的高保真度。IGB 包括大型的同质和异质图，其中超过40% 的节点被标记。与公开发布的最大的图形数据集相比，IGB 为深度学习从业者和开发者提供了超过162倍的标记数据，以创建和评估更高精度的模型。IGB 数据集的设计是灵活的，能够研究各种 GNN 体系结构、嵌入生成技术和分析系统性能问题。IGB 是开源的，支持 DGL 和 PyG 框架，并且附带了原始文本的发布，我们相信这些原始文本可以促进新兴语言模型和 GNN 研究项目的发展。IGB 的早期公开版本可在 https://github.com/illinoisgraphbenchmark/IGB-datasets 下载。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=IGB:+Addressing+The+Gaps+In+Labeling,+Features,+Heterogeneity,+and+Size+of+Public+Graph+Datasets+for+Deep+Learning+Research)|0| +|[IGB: Addressing The Gaps In Labeling, Features, Heterogeneity, and Size of Public Graph Datasets for Deep Learning Research](https://doi.org/10.1145/3580305.3599843)|Arpandeep Khatua, Vikram Sharma Mailthody, Bhagyashree Taleka, Tengfei Ma, Xiang Song, WenMei Hwu|NVIDIA; AWS AI; UIUC; IBM Research|Graph neural networks (GNNs) have shown high potential for a variety of real-world, challenging applications, but one of the major obstacles in GNN research is the lack of large-scale flexible datasets. Most existing public datasets for GNNs are relatively small, which limits the ability of GNNs to generalize to unseen data. The few existing large-scale graph datasets provide very limited labeled data. This makes it difficult to determine if the GNN model's low accuracy for unseen data is inherently due to insufficient training data or if the model failed to generalize. Additionally, datasets used to train GNNs need to offer flexibility to enable a thorough study of the impact of various factors while training GNN models. In this work, we introduce the Illinois Graph Benchmark (IGB), a research dataset tool that the developers can use to train, scrutinize and systematically evaluate GNN models with high fidelity. IGB includes both homogeneous and heterogeneous graphs of enormous sizes, with more than 40% of their nodes labeled. Compared to the largest graph datasets publicly available, the IGB provides over 162X more labeled data for deep learning practitioners and developers to create and evaluate models with higher accuracy. The IGB dataset is designed to be flexible, enabling the study of various GNN architectures, embedding generation techniques, and analyzing system performance issues. IGB is open-sourced, supports DGL and PyG frameworks, and comes with releases of the raw text that we believe foster emerging language models and GNN research projects. An early public version of IGB is available at https://github.com/IllinoisGraphBenchmark/IGB-Datasets.|图形神经网络(GNN)在现实世界中具有很大的应用潜力，但是缺乏大规模的灵活数据集是 GNN 研究的主要障碍之一。大多数现有的 GNN 公共数据集相对较小，这限制了 GNN 推广到未见数据的能力。少数现有的大规模图形数据集提供非常有限的标记数据。这使得很难确定 GNN 模型对于不可见数据的低精度是否本质上是由于训练数据不足或者模型没有推广。此外，用于训练 GNN 的数据集需要提供灵活性，以便在训练 GNN 模型时能够对各种因素的影响进行彻底的研究。在这项工作中，我们介绍了伊利诺伊图基准(IGB) ，一个研究数据集工具，开发人员可以用来训练，审查和系统地评估 GNN 模型的高保真度。IGB 包括大型的同质和异质图，其中超过40% 的节点被标记。与公开发布的最大的图形数据集相比，IGB 为深度学习从业者和开发者提供了超过162倍的标记数据，以创建和评估更高精度的模型。IGB 数据集的设计是灵活的，能够研究各种 GNN 体系结构、嵌入生成技术和分析系统性能问题。IGB 是开源的，支持 DGL 和 PyG 框架，并且附带了原始文本的发布，我们相信这些原始文本可以促进新兴语言模型和 GNN 研究项目的发展。IGB 的早期公开版本可在 https://github.com/illinoisgraphbenchmark/IGB-datasets 下载。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=IGB:+Addressing+The+Gaps+In+Labeling,+Features,+Heterogeneity,+and+Size+of+Public+Graph+Datasets+for+Deep+Learning+Research)|0| |[AdaTT: Adaptive Task-to-Task Fusion Network for Multitask Learning in Recommendations](https://doi.org/10.1145/3580305.3599769)|Danwei Li, Zhengyu Zhang, Siyang Yuan, Mingze Gao, Weilin Zhang, Chaofei Yang, Xi Liu, Jiyan Yang|Meta Platforms, Inc.; Meta AI|Multi-task learning (MTL) aims at enhancing the performance and efficiency of machine learning models by training them on multiple tasks simultaneously. However, MTL research faces two challenges: 1) modeling the relationships between tasks to effectively share knowledge between them, and 2) jointly learning task-specific and shared knowledge. In this paper, we present a novel model Adaptive Task-to-Task Fusion Network (AdaTT) to address both challenges. AdaTT is a deep fusion network built with task specific and optional shared fusion units at multiple levels. By leveraging a residual mechanism and gating mechanism for task-to-task fusion, these units adaptively learn shared knowledge and task specific knowledge. To evaluate the performance of AdaTT, we conduct experiments on a public benchmark and an industrial recommendation dataset using various task groups. Results demonstrate AdaTT can significantly outperform existing state-of-the-art baselines.|多任务学习(MTL)旨在通过同时对多任务进行训练来提高机器学习模型的性能和效率。然而，MTL 研究面临着两个挑战: 1)建立任务之间的关系以有效地分享它们之间的知识，2)联合学习任务特定的和共享的知识。在本文中，我们提出了一个新的模型自适应任务到任务融合网络(AdaTT) ，以解决这两个挑战。AdaTT 是一个深度融合网络，由多个级别的任务特定的和可选的共享融合单元构成。通过利用剩余机制和门控机制进行任务-任务融合，这些单元自适应地学习共享知识和任务特定知识。为了评估 AdaTT 的性能，我们使用不同的任务组在一个公共基准和一个工业推荐数据集上进行了实验。结果表明，AdaTT 可以显著优于现有的最先进的基线。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=AdaTT:+Adaptive+Task-to-Task+Fusion+Network+for+Multitask+Learning+in+Recommendations)|0| |[Stationary Algorithmic Balancing For Dynamic Email Re-Ranking Problem](https://doi.org/10.1145/3580305.3599909)|Jiayi Liu, Jennifer Neville|Purdue University|Email platforms need to generate personalized rankings of emails that satisfy user preferences, which may vary over time. We approach this as a recommendation problem based on three criteria: closeness (how relevant the sender and topic are to the user), timeliness (how recent the email is), and conciseness (how brief the email is). We propose MOSR (Multi-Objective Stationary Recommender), a novel online algorithm that uses an adaptive control model to dynamically balance these criteria and adapt to preference changes. We evaluate MOSR on the Enron Email Dataset, a large collection of real emails, and compare it with other baselines. The results show that MOSR achieves better performance, especially under non-stationary preferences, where users value different criteria more or less over time. We also test MOSR's robustness on a smaller down-sampled dataset that exhibits high variance in email characteristics, and show that it maintains stable rankings across different samples. Our work offers novel insights into how to design email re-ranking systems that account for multiple objectives impacting user satisfaction.|电子邮件平台需要生成个性化的电子邮件排名，以满足用户的喜好，这可能随着时间的推移而变化。我们基于三个标准来处理这个推荐问题: 亲密性(发送者和主题与用户的相关程度)、及时性(邮件发送时间有多近)和简洁性(邮件有多简短)。我们提出了一种新的在线算法——多目标平稳推荐(MOSR) ，它使用自适应控制模型来动态平衡这些标准，并适应偏好的变化。我们评估 MOSR 的安然电子邮件数据集，一大批真实的电子邮件，并比较它与其他基线。结果表明，在非平稳偏好条件下，特别是在用户随着时间的推移或多或少评价不同标准的情况下，MOSR 可以获得更好的性能。我们还测试了 MOSR 的稳健性较小的下采样数据集，表现出高的电子邮件特征方差，并表明它保持稳定的排名在不同的样本。我们的工作提供了新颖的见解，如何设计电子邮件重新排序系统的帐户多个目标影响用户的满意度。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Stationary+Algorithmic+Balancing+For+Dynamic+Email+Re-Ranking+Problem)|0| |[Tree based Progressive Regression Model for Watch-Time Prediction in Short-video Recommendation](https://doi.org/10.1145/3580305.3599919)|Xiao Lin, Xiaokai Chen, Linfeng Song, Jingwei Liu, Biao Li, Peng Jiang|Kuaishou Technology|An accurate prediction of watch time has been of vital importance to enhance user engagement in video recommender systems. To achieve this, there are four properties that a watch time prediction framework should satisfy: first, despite its continuous value, watch time is also an ordinal variable and the relative ordering between its values reflects the differences in user preferences. Therefore the ordinal relations should be reflected in watch time predictions. Second, the conditional dependence between the video-watching behaviors should be captured in the model. For instance, one has to watch half of the video before he/she finishes watching the whole video. Third, modeling watch time with a point estimation ignores the fact that models might give results with high uncertainty and this could cause bad cases in recommender systems. Therefore the framework should be aware of prediction uncertainty. Forth, the real-life recommender systems suffer from severe bias amplifications thus an estimation without bias amplification is expected. Therefore we propose TPM for watch time prediction. Specifically, the ordinal ranks of watch time are introduced into TPM and the problem is decomposed into a series of conditional dependent classification tasks which are organized into a tree structure. The expectation of watch time can be generated by traversing the tree and the variance of watch time predictions is explicitly introduced into the objective function as a measurement for uncertainty. Moreover, we illustrate that backdoor adjustment can be seamlessly incorporated into TPM, which alleviates bias amplifications. Extensive offline evaluations have been conducted in public datasets and TPM have been deployed in a real-world video app Kuaishou with over 300 million DAUs. The results indicate that TPM outperforms state-of-the-art approaches and indeed improves video consumption significantly.|准确预测观看时间对于提高用户在视频推荐系统中的参与度至关重要。为了实现这一点，手表时间预测框架应该满足四个特性: 第一，尽管手表时间是连续的，但它也是一个有序变量，其值之间的相对排序反映了用户偏好的差异。因此，序数关系应反映在手表时间预测中。其次，在模型中要捕捉视频观看行为之间的条件依赖关系。例如，一个人必须看完一半的视频才能看完整个视频。第三，使用点估计对手表时间进行建模忽略了这样一个事实，即模型可能会给出高度不确定性的结果，这可能会导致推荐系统出现问题。因此，框架应该意识到预测的不确定性。第四，现实生活中的推荐系统遭受严重的偏差放大，因此估计没有偏差放大的预期。因此，我们提出 TPM 来预测手表时间。在 TPM 中引入了观察时间序列，并将问题分解为一系列条件相关的分类任务，这些任务被组织成一个树形结构。通过遍历该树可以产生观察时间的期望值，并且在目标函数中明确地引入观察时间预测的方差作为不确定性的度量。此外，我们说明后门调整可以无缝地纳入 TPM，从而减轻偏差放大。在公共数据集中已经进行了广泛的离线评估，TPM 已经部署在一个现实世界的视频应用快手中，有超过3亿 DAU。结果表明，TPM 优于最先进的方法，确实显著提高了视频消费。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Tree+based+Progressive+Regression+Model+for+Watch-Time+Prediction+in+Short-video+Recommendation)|0| @@ -143,34 +143,34 @@ |[RLTP: Reinforcement Learning to Pace for Delayed Impression Modeling in Preloaded Ads](https://doi.org/10.1145/3580305.3599900)|Penghui Wei, Yongqiang Chen, Shaoguo Liu, Liang Wang, Bo Zheng|Alibaba Group|To increase brand awareness, many advertisers conclude contracts with advertising platforms to purchase traffic and then deliver advertisements to target audiences. In a whole delivery period, advertisers usually desire a certain impression count for the ads, and they also expect that the delivery performance is as good as possible (e.g., obtaining high click-through rate). Advertising platforms employ pacing algorithms to satisfy the demands via adjusting the selection probabilities to traffic requests in real-time. However, the delivery procedure is also affected by the strategies from publishers, which cannot be controlled by advertising platforms. Preloading is a widely used strategy for many types of ads (e.g., video ads) to make sure that the response time for displaying after a traffic request is legitimate, which results in delayed impression phenomenon. Traditional pacing algorithms cannot handle the preloading nature well because they rely on immediate feedback signals, and may fail to guarantee the demands from advertisers. In this paper, we focus on a new research problem of impression pacing for preloaded ads, and propose a Reinforcement Learning To Pace framework RLTP. It learns a pacing agent that sequentially produces selection probabilities in the whole delivery period. To jointly optimize the two objectives of impression count and delivery performance, RLTP employs tailored reward estimator to satisfy the guaranteed impression count, penalize the over-delivery and maximize the traffic value. Experiments on large-scale industrial datasets verify that RLTP outperforms baseline pacing algorithms by a large margin. We have deployed the RLTP framework online to our advertising platform, and results show that it achieves significant uplift to core metrics including delivery completion rate and click-through rate.|为了提高品牌知名度，许多广告商与广告平台签订合同，购买流量，然后向目标受众投放广告。在整个投放期间，广告商通常希望广告能给人留下一定的印象，而且他们也希望投放的效果尽可能好(例如，获得较高的点进率)。广告平台采用节奏算法，通过实时调整流量请求的选择概率来满足需求。然而，传递过程也受到出版商策略的影响，而出版商策略又不受广告平台的控制。预加载是一种广泛使用的策略，许多类型的广告(如视频广告) ，以确保响应时间显示后的流量请求是合法的，这导致了延迟印象现象。传统的节奏算法不能很好地处理预载性质，因为它们依赖于即时反馈信号，可能无法保证来自广告商的需求。在这篇文章中，我们关注一个新的研究问题——预装广告的印象节奏，并提出了一个强化学习到节奏的框架 RLTP。它学习一种起搏剂，该起搏剂在整个交付期间依次产生选择概率。为了共同优化印象计数和传递性能这两个目标，RLTP 使用定制的报酬估计器来满足保证的印象计数，惩罚超额传递和最大化流量价值。在大规模工业数据集上的实验证明，RLTP 算法的性能优于基线起搏算法。我们已经在我们的广告平台上部署了 RLTP 框架，结果显示它实现了包括交付完成率和点进率在内的核心指标的显著提升。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=RLTP:+Reinforcement+Learning+to+Pace+for+Delayed+Impression+Modeling+in+Preloaded+Ads)|0| |[Multi-channel Integrated Recommendation with Exposure Constraints](https://doi.org/10.1145/3580305.3599868)|Yue Xu, Qijie Shen, Jianwen Yin, Zengde Deng, Dimin Wang, Hao Chen, Lixiang Lai, Tao Zhuang, Junfeng Ge|Cainiao Network.; The Hong Kong Polytechnic University.; Alibaba Group.|Integrated recommendation, which aims at jointly recommending heterogeneous items from different channels in a main feed, has been widely applied to various online platforms. Though attractive, integrated recommendation requires the ranking methods to migrate from conventional user-item models to the new user-channel-item paradigm in order to better capture users' preferences on both item and channel levels. Moreover, practical feed recommendation systems usually impose exposure constraints on different channels to ensure user experience. This leads to greater difficulty in the joint ranking of heterogeneous items. In this paper, we investigate the integrated recommendation task with exposure constraints in practical recommender systems. Our contribution is forth-fold. First, we formulate this task as a binary online linear programming problem and propose a two-layer framework named Multi-channel Integrated Recommendation with Exposure Constraints (MIREC) to obtain the optimal solution. Second, we propose an efficient online allocation algorithm to determine the optimal exposure assignment of different channels from a global view of all user requests over the entire time horizon. We prove that this algorithm reaches the optimal point under a regret bound of $ \mathcal{O}(\sqrt{T}) $ with linear complexity. Third, we propose a series of collaborative models to determine the optimal layout of heterogeneous items at each user request. The joint modeling of user interests, cross-channel correlation, and page context in our models aligns more with the browsing nature of feed products than existing models. Finally, we conduct extensive experiments on both offline datasets and online A/B tests to verify the effectiveness of MIREC. The proposed framework has now been implemented on the homepage of Taobao to serve the main traffic.|综合推荐是指在一个主要的推送平台上联合推荐来自不同渠道的异构项目，已广泛应用于各种在线平台。虽然综合推荐具有吸引力，但是它需要排名方法从传统的用户项目模型迁移到新的用户渠道项目范式，以便更好地捕捉用户在项目和渠道级别上的偏好。此外，实际的饲料推荐系统通常对不同的渠道施加暴露约束，以确保用户体验。这导致了异构项目联合排序的更大困难。本文研究了实际推荐系统中具有曝光约束的集成推荐任务。我们的贡献是四倍。首先，我们将这个任务表述为一个二进制在线线性规划问题，并提出一个名为多通道暴露约束综合推荐(MIREC)的两层架构来获得最优解。其次，我们提出了一个有效的在线分配算法，从全局的角度来确定不同信道在整个时间范围内的最佳曝光分配。证明了该算法在线性复杂度为 $数学{ O }(sqrt { T }) $的遗憾界下达到最优点。第三，我们提出了一系列的协作模型，以确定在每个用户请求的异构项目的最佳布局。在我们的模型中，用户兴趣、跨通道相关性和页面上下文的联合建模比现有模型更符合饲料产品的浏览特性。最后，我们对离线数据集和在线 A/B 测试进行了广泛的实验，以验证 MIREC 的有效性。这个建议框架现已在淘宝网的主页上实施，以服务于主要流量。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Multi-channel+Integrated+Recommendation+with+Exposure+Constraints)|0| |[Interactive Generalized Additive Model and Its Applications in Electric Load Forecasting](https://doi.org/10.1145/3580305.3599848)|Linxiao Yang, Rui Ren, Xinyue Gu, Liang Sun|DAMO Academy, Alibaba Group|Electric load forecasting is an indispensable component of electric power system planning and management. Inaccurate load forecasting may lead to the threat of outages or a waste of energy. Accurate electric load forecasting is challenging when there is limited data or even no data, such as load forecasting in holiday, or under extreme weather conditions. As high-stakes decision-making usually follows after load forecasting, model interpretability is crucial for the adoption of forecasting models. In this paper, we propose an interactive GAM which is not only interpretable but also can incorporate specific domain knowledge in electric power industry for improved performance. This boosting-based GAM leverages piecewise linear functions and can be learned through our efficient algorithm. In both public benchmark and electricity datasets, our interactive GAM outperforms current state-of-the-art methods and demonstrates good generalization ability in the cases of extreme weather events. We launched a user-friendly web-based tool based on interactive GAM and already incorporated it into our eForecaster product, a unified AI platform for electricity forecasting.|电力负荷预测是电力系统规划和管理中不可缺少的组成部分。不准确的负荷预测可能导致停电或能源浪费的威胁。准确的电力负荷预测是具有挑战性的时候，有限的数据，甚至没有数据，如负荷预测在假日，或在极端天气条件下。由于高风险决策通常在负荷预测之后进行，因此模型的可解释性对于采用预测模型至关重要。本文提出了一种交互式 GAM 模型，它不仅具有可解释性，而且能够融合电力行业的特定领域知识，以提高性能。这种基于升压的 GAM 利用分段线性函数，可以通过我们的高效算法学习。在公共基准和电力数据集中，我们的交互式 GAM 优于目前最先进的方法，并且在极端天气事件中展示了良好的推广能力。我们推出了一个基于交互式 GAM 的用户友好的网络工具，并已经将其纳入我们的电子预报产品，一个用于电力预报的统一人工智能平台。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Interactive+Generalized+Additive+Model+and+Its+Applications+in+Electric+Load+Forecasting)|0| -|[UA-FedRec: Untargeted Attack on Federated News Recommendation](https://doi.org/10.1145/3580305.3599923)|Jingwei Yi, Fangzhao Wu, Bin Zhu, Jing Yao, Zhulin Tao, Guangzhong Sun, Xing Xie|; University of Science and Technology of China; Microsoft Research Asia|News recommendation is critical for personalized news distribution. Federated news recommendation enables collaborative model learning from many clients without sharing their raw data. It is promising for privacy-preserving news recommendation. However, the security of federated news recommendation is still unclear. In this paper, we study this problem by proposing an untargeted attack called UA-FedRec. By exploiting the prior knowledge of news recommendation and federated learning, UA-FedRec can effectively degrade the model performance with a small percentage of malicious clients. First, the effectiveness of news recommendation highly depends on user modeling and news modeling. We design a news similarity perturbation method to make representations of similar news farther and those of dissimilar news closer to interrupt news modeling, and propose a user model perturbation method to make malicious user updates in opposite directions of benign updates to interrupt user modeling. Second, updates from different clients are typically aggregated by weighted-averaging based on their sample sizes. We propose a quantity perturbation method to enlarge sample sizes of malicious clients in a reasonable range to amplify the impact of malicious updates. Extensive experiments on two real-world datasets show that UA-FedRec can effectively degrade the accuracy of existing federated news recommendation methods, even when defense is applied. Our study reveals a critical security issue in existing federated news recommendation systems and calls for research efforts to address the issue.|新闻推荐对个性化新闻发布至关重要。联合新闻推荐使得许多客户能够在不共享原始数据的情况下进行协作模型学习。它对于保护隐私的新闻推荐来说是很有前途的。然而，联邦新闻推荐的安全性仍不清楚。在本文中，我们通过提出一种称为 UA-FedRec 的非目标攻击来研究这个问题。通过利用新闻推荐和联邦学习的先验知识，UA-FedRec 能够有效地降低小比例恶意客户端的模型性能。首先，新闻推荐的有效性很大程度上取决于用户建模和新闻建模。设计了一种新闻相似性摄动方法，使相似新闻和不同新闻的表示更接近于中断新闻建模，提出了一种用户模型摄动方法，使恶意用户在良性更新的相反方向更新，以中断用户建模。其次，来自不同客户端的更新通常根据样本大小进行加权平均。我们提出了一种数量扰动方法，在合理的范围内扩大恶意客户端的样本量，以放大恶意更新的影响。在两个实际数据集上的大量实验表明，UA-FedRec 能够有效地降低现有联邦新闻推荐方法的准确性，即使在采用防御策略的情况下也是如此。我们的研究揭示了现有联邦新闻推荐系统中的一个关键安全问题，并呼吁研究人员努力解决这个问题。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=UA-FedRec:+Untargeted+Attack+on+Federated+News+Recommendation)|0| +|[UA-FedRec: Untargeted Attack on Federated News Recommendation](https://doi.org/10.1145/3580305.3599923)|Jingwei Yi, Fangzhao Wu, Bin Zhu, Jing Yao, Zhulin Tao, Guangzhong Sun, Xing Xie|University of Science and Technology of China; Microsoft Research Asia; |News recommendation is critical for personalized news distribution. Federated news recommendation enables collaborative model learning from many clients without sharing their raw data. It is promising for privacy-preserving news recommendation. However, the security of federated news recommendation is still unclear. In this paper, we study this problem by proposing an untargeted attack called UA-FedRec. By exploiting the prior knowledge of news recommendation and federated learning, UA-FedRec can effectively degrade the model performance with a small percentage of malicious clients. First, the effectiveness of news recommendation highly depends on user modeling and news modeling. We design a news similarity perturbation method to make representations of similar news farther and those of dissimilar news closer to interrupt news modeling, and propose a user model perturbation method to make malicious user updates in opposite directions of benign updates to interrupt user modeling. Second, updates from different clients are typically aggregated by weighted-averaging based on their sample sizes. We propose a quantity perturbation method to enlarge sample sizes of malicious clients in a reasonable range to amplify the impact of malicious updates. Extensive experiments on two real-world datasets show that UA-FedRec can effectively degrade the accuracy of existing federated news recommendation methods, even when defense is applied. Our study reveals a critical security issue in existing federated news recommendation systems and calls for research efforts to address the issue.|新闻推荐对个性化新闻发布至关重要。联合新闻推荐使得许多客户能够在不共享原始数据的情况下进行协作模型学习。它对于保护隐私的新闻推荐来说是很有前途的。然而，联邦新闻推荐的安全性仍不清楚。在本文中，我们通过提出一种称为 UA-FedRec 的非目标攻击来研究这个问题。通过利用新闻推荐和联邦学习的先验知识，UA-FedRec 能够有效地降低小比例恶意客户端的模型性能。首先，新闻推荐的有效性很大程度上取决于用户建模和新闻建模。设计了一种新闻相似性摄动方法，使相似新闻和不同新闻的表示更接近于中断新闻建模，提出了一种用户模型摄动方法，使恶意用户在良性更新的相反方向更新，以中断用户建模。其次，来自不同客户端的更新通常根据样本大小进行加权平均。我们提出了一种数量扰动方法，在合理的范围内扩大恶意客户端的样本量，以放大恶意更新的影响。在两个实际数据集上的大量实验表明，UA-FedRec 能够有效地降低现有联邦新闻推荐方法的准确性，即使在采用防御策略的情况下也是如此。我们的研究揭示了现有联邦新闻推荐系统中的一个关键安全问题，并呼吁研究人员努力解决这个问题。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=UA-FedRec:+Untargeted+Attack+on+Federated+News+Recommendation)|0| |[Group-based Fraud Detection Network on e-Commerce Platforms](https://doi.org/10.1145/3580305.3599836)|Jianke Yu, Hanchen Wang, Xiaoyang Wang, Zhao Li, Lu Qin, Wenjie Zhang, Jian Liao, Ying Zhang||Along with the rapid technological and commercial innovation on the e-commerce platforms, there are an increasing number of frauds that bring great harm to these platforms. Many frauds are conducted by organized groups of fraudsters for higher efficiency and lower costs, which are also known as group-based frauds. Despite the high concealment and strong destructiveness of group-based fraud, there is no existing research work that can thoroughly exploit the information within the transaction networks of e-commerce platforms for group-based fraud detection. In this work, we analyze and summarize the characteristics of group-based frauds, based on which we propose a novel end-to-end semi-supervised Group-based Fraud Detection Network (GFDN) to support such fraud detection in real-world applications. Experimental results on large-scale e-commerce datasets from Taobao and Bitcoin trading datasets show the superior effectiveness and efficiency of our proposed model for group-based fraud detection on bipartite graphs.|随着电子商务平台上技术和商业的快速创新，给这些平台带来巨大危害的欺诈案件日益增多。许多欺诈是由有组织的欺诈者集团为了提高效率和降低成本而进行的，这也被称为集团欺诈。尽管基于群体的欺诈具有高度的隐蔽性和强大的破坏性，但目前还没有一项研究工作能够彻底利用电子商务平台交易网络内的信息进行基于群体的欺诈侦查。本文分析和总结了基于群体的欺诈行为的特点，在此基础上提出了一种新的端到端半监督的基于群体的欺诈检测网络(GFDN) ，以支持现实应用中的欺诈检测。对淘宝和比特币交易数据集的大规模电子商务数据集的实验结果表明，本文提出的基于二分图的群欺诈检测模型具有较好的有效性和高效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Group-based+Fraud+Detection+Network+on+e-Commerce+Platforms)|0| |[Commonsense Knowledge Graph towards Super APP and Its Applications in Alipay](https://doi.org/10.1145/3580305.3599791)|Xiaoling Zang, Binbin Hu, Jun Chu, Zhiqiang Zhang, Guannan Zhang, Jun Zhou, Wenliang Zhong|Ant Group, Hang Zhou, China|The recently explosive growth of Super Apps brings great convenience to people's daily life by providing a wide variety of services through mini-programs, including online shopping, travel, finance, and so on. Due to the considerable gap between various scenarios, the restriction of effective information transfer and sharing severely blocks the efficient delivery of online services, potentially affecting the user's app experience. To deeply understand users' needs, we propose SupKG, a commonsense knowledge graph towards Super APP to help comprehensively characterize user behaviors across different business scenarios. In particular, our SupKG is carefully established from multiplex and heterogeneous data source in Alipay (a well-known Super App in China), which also emphasize abundant spatiotemporal relations and intent-related entities to answer the fundamental question in life service ''which service do users need at what time and where''. On the hand, the successful application of SupKG hinges on the effective form of network representation ie Knowledge Graph Embedding (KGE). However, a series of unsatisfying issues still need to be carefully considered in the industrial environment: i) bridging language representations with knowledge structure in a unified manner, ii) alleviating the skewed data distribution in SupKG, and iii) effectively characterizing hierarchical structures in SupKG. With these motivations, we develop a novel knowledge graph representation learning framework for SupKG, enabling various downstream applications to benefit from learned representations of entities and relations. Extensive experiments on the standard knowledge graph completion task demonstrate the consistent and significant performance improvement of our representation learning framework, which also greatly benefits the supplementation of potential knowledge of SupKG. Towards real-world applications in Alipay, our SupKG and learned representations show the potential superiority of integrating global behaviors in cold-start scenarios and providing high-quality knowledge for warming up the graph-based ranking.|最近超级应用程序的爆炸式增长为人们的日常生活带来了极大的便利，通过小程序提供各种各样的服务，包括在线购物，旅游，金融等。由于不同场景之间的巨大差距，有效信息传递和共享的限制严重阻碍了在线服务的有效传递，潜在地影响了用户的应用体验。为了深入了解用户的需求，我们提出了 SupKG，一个面向 Super APP 的常识性知识图，帮助全面描述不同业务场景中的用户行为。特别是，我们的 SupKG 是从支付宝(中国著名的超级应用程序)中的多路异构数据源精心建立起来的，它还强调丰富的时空关系和意图相关实体，以回答生活服务中的基本问题“用户在什么时间和地点需要什么服务”。另一方面，SupKG 的成功应用取决于网络表示的有效形式，即知识图嵌入(KGE)。然而，在工业环境中仍然需要仔细考虑一系列不令人满意的问题: i)以统一的方式将语言表示与知识结构连接起来，ii)缓解 SupKG 中的偏斜数据分布，以及 iii)有效表征 SupKG 中的层次结构。基于这些动机，我们为 SupKG 开发了一个新的知识图表示学习框架，使各种下游应用程序能够从实体和关系的学习表示中受益。通过对标准知识图完成任务的大量实验，证明了我们的表示学习框架在性能上的一致性和显著性改进，这也极大地有利于对 SupKG 潜在知识的补充。对于支付宝中的实际应用，我们的 SupKG 和学习表示显示了在冷启动场景中整合全球行为的潜在优势，并为预热基于图表的排名提供高质量的知识。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Commonsense+Knowledge+Graph+towards+Super+APP+and+Its+Applications+in+Alipay)|0| |[Revisiting Neural Retrieval on Accelerators](https://doi.org/10.1145/3580305.3599897)|Jiaqi Zhai, Zhaojie Gong, Yueming Wang, Xiao Sun, Zheng Yan, Fu Li, Xing Liu|Meta Platforms, Inc.|Retrieval finds a small number of relevant candidates from a large corpus for information retrieval and recommendation applications. A key component of retrieval is to model (user, item) similarity, which is commonly represented as the dot product of two learned embeddings. This formulation permits efficient inference, commonly known as Maximum Inner Product Search (MIPS). Despite its popularity, dot products cannot capture complex user-item interactions, which are multifaceted and likely high rank. We hence examine non-dot-product retrieval settings on accelerators, and propose \textit{mixture of logits} (MoL), which models (user, item) similarity as an adaptive composition of elementary similarity functions. This new formulation is expressive, capable of modeling high rank (user, item) interactions, and further generalizes to the long tail. When combined with a hierarchical retrieval strategy, \textit{h-indexer}, we are able to scale up MoL to 100M corpus on a single GPU with latency comparable to MIPS baselines. On public datasets, our approach leads to uplifts of up to 77.3\% in hit rate (HR). Experiments on a large recommendation surface at Meta showed strong metric gains and reduced popularity bias, validating the proposed approach's performance and improved generalization.|Retrieval 从一个大型语料库中为信息检索和推荐应用程序找到少量相关的候选人。检索的一个关键组成部分是模型(用户，项目)的相似性，这是通常表示为点积的两个学习嵌入。这个公式允许有效的推理，通常称为最大内积搜索(MIPS)。尽管广受欢迎，点产品不能捕捉复杂的用户项目交互，这是多方面的，可能排名很高。因此，我们研究了加速器上的非点积检索设置，并提出了 text { mix of logits }(MoL) ，它将(用户，项目)相似度建模为基本相似度函数的自适应组合。这个新的公式是有表现力的，能够建模高级别(用户，项目)的交互，并进一步推广到长尾。当结合分层检索策略 texttit { h-indexer }时，我们能够在单个 GPU 上扩展 MoL 到100M 语料库，延迟与 MIPS 基线相当。在公共数据集上，我们的方法导致命中率(HR)提高高达77.3% 。在 Meta 的一个大型推荐面上进行的实验表明，该方法具有很强的度量增益和较小的普及偏差，验证了该方法的性能和改进的泛化能力。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Revisiting+Neural+Retrieval+on+Accelerators)|0| |[Constrained Social Community Recommendation](https://doi.org/10.1145/3580305.3599793)|Xingyi Zhang, Shuliang Xu, Wenqing Lin, Sibo Wang||In online social networks, users with similar interests tend to come together, forming social communities. Nowadays, user-defined communities become a prominent part of online social platforms as people who have joined such communities tend to be more active in social networks. Therefore, recommending explicit communities to users provides great potential to advance online services. In this paper, we focus on the constrained social community recommendation problem in real applications, where each user can only join at most one community. Previous attempts at community recommendation mostly adopt collaborative filtering approaches or random walk-based approaches, while ignoring social relationships between users as well as the local structure of each community. Therefore, they only derive an extremely sparse affinity matrix, which degrades the model performances. To tackle this issue, we propose ComRec which simultaneously captures both global and local information on the extended graph during pre-computation, speeding up the training process on real-world large graphs. In addition, we present a labeling component to improve the expressiveness of our framework. We conduct experiments on three Tencent mobile games to evaluate our proposed method. Extensive experimental results show that our ComRec consistently outperforms other competitors by up to 12.80% and 6.61% in the corresponding evaluation metrics of offline and online experiments, respectively.|在线社交网络中，兴趣相似的用户往往聚集在一起，形成社交社区。如今，用户定义的社区成为在线社交平台的重要组成部分，因为加入这些社区的人往往在社交网络中更加活跃。因此，向用户推荐明确的社区为推进在线服务提供了巨大的潜力。在本文中，我们主要研究实际应用中的受限社区推荐问题，其中每个用户最多只能加入一个社区。以前的社区推荐尝试大多采用协同过滤推荐或者基于随机漫步的方法，而忽略了用户之间的社会关系以及每个社区的本地结构。因此，他们只得到一个极其稀疏的亲和矩阵，这降低了模型的性能。为了解决这一问题，我们提出了 ComRec 算法，该算法在预计算过程中同时捕获扩展图上的全局和局部信息，加快了对真实世界大图的训练过程。此外，我们提出了一个标签组件，以提高我们的框架的表达能力。我们在三款腾讯手机游戏上进行实验，以评估我们提出的方法。广泛的实验结果表明，我们的 ComRec 在相应的离线和在线实验评价指标方面始终优于其他竞争对手，分别高达12.80% 和6.61% 。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Constrained+Social+Community+Recommendation)|0| -|[Modeling Dual Period-Varying Preferences for Takeaway Recommendation](https://doi.org/10.1145/3580305.3599866)|Yuting Zhang, Yiqing Wu, Ran Le, Yongchun Zhu, Fuzhen Zhuang, Ruidong Han, Xiang Li, Wei Lin, Zhulin An, Yongjun Xu|Institute of Computing Technology, Chinese Academy of Sciences; Meituan; Institute of Artificial Intelligence, Beihang University; Unaffiliated|Takeaway recommender systems, which aim to accurately provide stores that offer foods meeting users' interests, have served billions of users in our daily life. Different from traditional recommendation, takeaway recommendation faces two main challenges: (1) Dual Interaction-Aware Preference Modeling. Traditional recommendation commonly focuses on users' single preferences for items while takeaway recommendation needs to comprehensively consider users' dual preferences for stores and foods. (2) Period-Varying Preference Modeling. Conventional recommendation generally models continuous changes in users' preferences from a session-level or day-level perspective. However, in practical takeaway systems, users' preferences vary significantly during the morning, noon, night, and late night periods of the day. To address these challenges, we propose a Dual Period-Varying Preference modeling (DPVP) for takeaway recommendation. Specifically, we design a dual interaction-aware module, aiming to capture users' dual preferences based on their interactions with stores and foods. Moreover, to model various preferences in different time periods of the day, we propose a time-based decomposition module as well as a time-aware gating mechanism. Extensive offline and online experiments demonstrate that our model outperforms state-of-the-art methods on real-world datasets and it is capable of modeling the dual period-varying preferences. Moreover, our model has been deployed online on Meituan Takeaway platform, leading to an average improvement in GMV (Gross Merchandise Value) of 0.70%.|外卖推荐系统，旨在准确地提供商店，提供符合用户兴趣的食品，已服务于数十亿用户在我们的日常生活。与传统的推荐不同，外卖推荐面临着两个主要挑战: (1)双交互感知偏好建模。传统的推荐方式通常侧重于用户对商品的单一偏好，而外卖推荐方式则需要全面考虑用户对商店和食品的双重偏好。(变周期偏好模型。传统的推荐通常从会话级或日级的角度模拟用户偏好的持续变化。然而，在实际的外卖系统中，用户的偏好在白天的早上、中午、晚上和深夜各不相同。为了应对这些挑战，我们提出了一个外卖推荐的双周期变化偏好模型(DPVP)。具体来说，我们设计了一个双交互感知模块，旨在根据用户与商店和食物的交互来捕捉他们的双重偏好。此外，为了模拟一天中不同时段的不同偏好，我们提出了一个基于时间的分解模块以及一个时间感知的门控机制。大量的离线和在线实验表明，我们的模型优于现实世界数据集的最先进的方法，它能够建模的双周期变化的偏好。此外，我们的模型已经在美团外卖平台上进行了在线部署，导致平均商品总值(GMV)提高了0.70% 。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Modeling+Dual+Period-Varying+Preferences+for+Takeaway+Recommendation)|0| -|[JiuZhang 2.0: A Unified Chinese Pre-trained Language Model for Multi-task Mathematical Problem Solving](https://doi.org/10.1145/3580305.3599850)|Xin Zhao, Kun Zhou, Beichen Zhang, Zheng Gong, Zhipeng Chen, Yuanhang Zhou, JiRong Wen, Jing Sha, Shijin Wang, Cong Liu, Guoping Hu|iFLYTEK Research; iFLYTEK AI Research (Central China; Gaoling School of Artificial Intelligence, Renmin University of China; School of Information, Renmin University of China; iFLYTEK Research, State Key Laboratory of Cognitive Intelligence|Although pre-trained language models~(PLMs) have recently advanced the research progress in mathematical reasoning, they are not specially designed as a capable multi-task solver, suffering from high cost for multi-task deployment (\eg a model copy for a task) and inferior performance on complex mathematical problems in practical applications. To address these issues, in this paper, we propose \textbf{JiuZhang~2.0}, a unified Chinese PLM specially for multi-task mathematical problem solving. Our idea is to maintain a moderate-sized model and employ the \emph{cross-task knowledge sharing} to improve the model capacity in a multi-task setting. Specially, we construct a Mixture-of-Experts~(MoE) architecture for modeling mathematical text, so as to capture the common mathematical knowledge across tasks. For optimizing the MoE architecture, we design \emph{multi-task continual pre-training} and \emph{multi-task fine-tuning} strategies for multi-task adaptation. These training strategies can effectively decompose the knowledge from the task data and establish the cross-task sharing via expert networks. In order to further improve the general capacity of solving different complex tasks, we leverage large language models~(LLMs) as complementary models to iteratively refine the generated solution by our PLM, via in-context learning. Extensive experiments have demonstrated the effectiveness of our model.|尽管预先训练的语言模型 ~ (PLM)最近已经推动了数学推理的研究进展，但是它们并没有被特别设计成一个有能力的多任务解决者，因为多任务部署的高成本(例如一个任务的模型拷贝)和在实际应用中复杂数学问题的低表现。为了解决这些问题，本文提出了一个专门用于多任务数学问题求解的统一中文 PLM textbf {旧掌 ~ 2.0}。我们的想法是维持一个中等规模的模型，并采用跨任务知识共享的方法来提高模型在多任务环境下的能力。特别地，我们构建了一个专家混合模型，用于数学文本建模，以便跨任务获取常见的数学知识。为了优化教学体系结构，我们设计了多任务连续预训练和多任务微调策略来实现多任务自适应。这些训练策略可以有效地分解任务数据中的知识，并通过专家网络建立跨任务共享。为了进一步提高解决不同复杂任务的能力，我们利用大语言模型 ~ (LLM)作为补充模型，通过上下文学习的方法，迭代地完善 PLM 生成的解决方案。大量的实验证明了我们模型的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=JiuZhang+2.0:+A+Unified+Chinese+Pre-trained+Language+Model+for+Multi-task+Mathematical+Problem+Solving)|0| +|[Modeling Dual Period-Varying Preferences for Takeaway Recommendation](https://doi.org/10.1145/3580305.3599866)|Yuting Zhang, Yiqing Wu, Ran Le, Yongchun Zhu, Fuzhen Zhuang, Ruidong Han, Xiang Li, Wei Lin, Zhulin An, Yongjun Xu|Institute of Artificial Intelligence, Beihang University; Institute of Computing Technology, Chinese Academy of Sciences; Meituan; Unaffiliated|Takeaway recommender systems, which aim to accurately provide stores that offer foods meeting users' interests, have served billions of users in our daily life. Different from traditional recommendation, takeaway recommendation faces two main challenges: (1) Dual Interaction-Aware Preference Modeling. Traditional recommendation commonly focuses on users' single preferences for items while takeaway recommendation needs to comprehensively consider users' dual preferences for stores and foods. (2) Period-Varying Preference Modeling. Conventional recommendation generally models continuous changes in users' preferences from a session-level or day-level perspective. However, in practical takeaway systems, users' preferences vary significantly during the morning, noon, night, and late night periods of the day. To address these challenges, we propose a Dual Period-Varying Preference modeling (DPVP) for takeaway recommendation. Specifically, we design a dual interaction-aware module, aiming to capture users' dual preferences based on their interactions with stores and foods. Moreover, to model various preferences in different time periods of the day, we propose a time-based decomposition module as well as a time-aware gating mechanism. Extensive offline and online experiments demonstrate that our model outperforms state-of-the-art methods on real-world datasets and it is capable of modeling the dual period-varying preferences. Moreover, our model has been deployed online on Meituan Takeaway platform, leading to an average improvement in GMV (Gross Merchandise Value) of 0.70%.|外卖推荐系统，旨在准确地提供商店，提供符合用户兴趣的食品，已服务于数十亿用户在我们的日常生活。与传统的推荐不同，外卖推荐面临着两个主要挑战: (1)双交互感知偏好建模。传统的推荐方式通常侧重于用户对商品的单一偏好，而外卖推荐方式则需要全面考虑用户对商店和食品的双重偏好。(变周期偏好模型。传统的推荐通常从会话级或日级的角度模拟用户偏好的持续变化。然而，在实际的外卖系统中，用户的偏好在白天的早上、中午、晚上和深夜各不相同。为了应对这些挑战，我们提出了一个外卖推荐的双周期变化偏好模型(DPVP)。具体来说，我们设计了一个双交互感知模块，旨在根据用户与商店和食物的交互来捕捉他们的双重偏好。此外，为了模拟一天中不同时段的不同偏好，我们提出了一个基于时间的分解模块以及一个时间感知的门控机制。大量的离线和在线实验表明，我们的模型优于现实世界数据集的最先进的方法，它能够建模的双周期变化的偏好。此外，我们的模型已经在美团外卖平台上进行了在线部署，导致平均商品总值(GMV)提高了0.70% 。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Modeling+Dual+Period-Varying+Preferences+for+Takeaway+Recommendation)|0| +|[JiuZhang 2.0: A Unified Chinese Pre-trained Language Model for Multi-task Mathematical Problem Solving](https://doi.org/10.1145/3580305.3599850)|Xin Zhao, Kun Zhou, Beichen Zhang, Zheng Gong, Zhipeng Chen, Yuanhang Zhou, JiRong Wen, Jing Sha, Shijin Wang, Cong Liu, Guoping Hu|iFLYTEK AI Research (Central China; Gaoling School of Artificial Intelligence, Renmin University of China; School of Information, Renmin University of China; iFLYTEK Research, State Key Laboratory of Cognitive Intelligence; iFLYTEK Research|Although pre-trained language models~(PLMs) have recently advanced the research progress in mathematical reasoning, they are not specially designed as a capable multi-task solver, suffering from high cost for multi-task deployment (\eg a model copy for a task) and inferior performance on complex mathematical problems in practical applications. To address these issues, in this paper, we propose \textbf{JiuZhang~2.0}, a unified Chinese PLM specially for multi-task mathematical problem solving. Our idea is to maintain a moderate-sized model and employ the \emph{cross-task knowledge sharing} to improve the model capacity in a multi-task setting. Specially, we construct a Mixture-of-Experts~(MoE) architecture for modeling mathematical text, so as to capture the common mathematical knowledge across tasks. For optimizing the MoE architecture, we design \emph{multi-task continual pre-training} and \emph{multi-task fine-tuning} strategies for multi-task adaptation. These training strategies can effectively decompose the knowledge from the task data and establish the cross-task sharing via expert networks. In order to further improve the general capacity of solving different complex tasks, we leverage large language models~(LLMs) as complementary models to iteratively refine the generated solution by our PLM, via in-context learning. Extensive experiments have demonstrated the effectiveness of our model.|尽管预先训练的语言模型 ~ (PLM)最近已经推动了数学推理的研究进展，但是它们并没有被特别设计成一个有能力的多任务解决者，因为多任务部署的高成本(例如一个任务的模型拷贝)和在实际应用中复杂数学问题的低表现。为了解决这些问题，本文提出了一个专门用于多任务数学问题求解的统一中文 PLM textbf {旧掌 ~ 2.0}。我们的想法是维持一个中等规模的模型，并采用跨任务知识共享的方法来提高模型在多任务环境下的能力。特别地，我们构建了一个专家混合模型，用于数学文本建模，以便跨任务获取常见的数学知识。为了优化教学体系结构，我们设计了多任务连续预训练和多任务微调策略来实现多任务自适应。这些训练策略可以有效地分解任务数据中的知识，并通过专家网络建立跨任务共享。为了进一步提高解决不同复杂任务的能力，我们利用大语言模型 ~ (LLM)作为补充模型，通过上下文学习的方法，迭代地完善 PLM 生成的解决方案。大量的实验证明了我们模型的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=JiuZhang+2.0:+A+Unified+Chinese+Pre-trained+Language+Model+for+Multi-task+Mathematical+Problem+Solving)|0| |[ReLoop2: Building Self-Adaptive Recommendation Models via Responsive Error Compensation Loop](https://doi.org/10.1145/3580305.3599785)|Jieming Zhu, Guohao Cai, Junjie Huang, Zhenhua Dong, Ruiming Tang, Weinan Zhang|Shanghai Jiao Tong University; Huawei Noah’s Ark Lab|Industrial recommender systems face the challenge of operating in non-stationary environments, where data distribution shifts arise from evolving user behaviors over time. To tackle this challenge, a common approach is to periodically re-train or incrementally update deployed deep models with newly observed data, resulting in a continual training process. However, the conventional learning paradigm of neural networks relies on iterative gradient-based updates with a small learning rate, making it slow for large recommendation models to adapt. In this paper, we introduce ReLoop2, a self-correcting learning loop that facilitates fast model adaptation in online recommender systems through responsive error compensation. Inspired by the slow-fast complementary learning system observed in human brains, we propose an error memory module that directly stores error samples from incoming data streams. These stored samples are subsequently leveraged to compensate for model prediction errors during testing, particularly under distribution shifts. The error memory module is designed with fast access capabilities and undergoes continual refreshing with newly observed data samples during the model serving phase to support fast model adaptation. We evaluate the effectiveness of ReLoop2 on three open benchmark datasets as well as a real-world production dataset. The results demonstrate the potential of ReLoop2 in enhancing the responsiveness and adaptiveness of recommender systems operating in non-stationary environments.|工业推荐系统面临着在非平稳环境下运行的挑战，数据分布随着时间的推移而发生变化。为了应对这一挑战，一种常见的方法是定期用新观测数据重新训练或增量更新已部署的深度模型，从而形成持续的训练过程。然而，传统的神经网络学习范式依赖于迭代的基于梯度的更新，学习速度很小，使得大型推荐模型的适应速度变慢。本文介绍了 ReLoop2，一种通过响应误差补偿实现在线推荐系统中模型快速自适应的自校正学习循环。受到在人脑中观察到的慢-快互补学习系统的启发，我们提出了一个错误记忆模块，它直接存储来自输入数据流的错误样本。这些存储的样本随后被用来补偿测试期间的模型预测错误，特别是在分布变化的情况下。错误存储模块设计具有快速访问能力，并在模型服务阶段不断刷新新观察到的数据样本，以支持快速模型适应。我们评估了 ReLoop2在三个开放基准数据集和一个真实生产数据集上的有效性。结果表明，ReLoop2在提高非平稳环境中运行的推荐系统的响应能力和适应能力方面具有潜力。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=ReLoop2:+Building+Self-Adaptive+Recommendation+Models+via+Responsive+Error+Compensation+Loop)|0| -|[Trustworthy Recommender Systems: Foundations and Frontiers](https://doi.org/10.1145/3580305.3599575)|Wenqi Fan, Xiangyu Zhao, Lin Wang, Xiao Chen, Jingtong Gao, Qidong Liu, Shijie Wang|City University of Hong Kong & Xi'an Jiaotong University, Hong Kong SAR, China; The Hong Kong Polytechnic University, Hong Kong SAR, China; The Hong Kong Polytechnic University & Innovation, Chinese Academy of Sciences, Hong Kong SAR, China; City University of Hong Kong, Hong Kong SAR, China|Recommender systems aim to provide personalized suggestions to users, helping them make effective decisions. However, recent evidence has revealed the untrustworthy aspects of advanced recommender systems, leading to harmful effects in safety-critical areas like finance and healthcare. This tutorial will offer a comprehensive overview of achieving trustworthy recommender systems. It will cover six important aspects: Safety & Robustness, Non-discrimination & Fairness, Explainability, Privacy, Environmental Well-being, and Accountability & Auditability. Each aspect will be defined and categorized, followed by a discussion of the latest research progress and notable works. Additionally, potential interactions among these aspects and future research directions for trustworthy recommender systems will be explored.|推荐系统旨在为用户提供个性化的建议，帮助他们做出有效的决策。然而，最近的证据显示，先进的推荐系统的不可信方面，导致有害影响的安全关键领域，如金融和医疗保健。本教程将全面介绍如何实现值得信赖的推荐系统。它将涵盖六个重要方面: 安全与稳健、非歧视与公平、可解释性、隐私、环境福祉和问责与审核。每个方面将被定义和分类，然后讨论最新的研究进展和著名的工作。此外，这些方面之间的潜在交互作用和未来的研究方向值得信赖的推荐系统将被探讨。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Trustworthy+Recommender+Systems:+Foundations+and+Frontiers)|0| +|[Trustworthy Recommender Systems: Foundations and Frontiers](https://doi.org/10.1145/3580305.3599575)|Wenqi Fan, Xiangyu Zhao, Lin Wang, Xiao Chen, Jingtong Gao, Qidong Liu, Shijie Wang|City University of Hong Kong, Hong Kong SAR, China; The Hong Kong Polytechnic University, Hong Kong SAR, China; The Hong Kong Polytechnic University & Innovation, Chinese Academy of Sciences, Hong Kong SAR, China; City University of Hong Kong & Xi'an Jiaotong University, Hong Kong SAR, China|Recommender systems aim to provide personalized suggestions to users, helping them make effective decisions. However, recent evidence has revealed the untrustworthy aspects of advanced recommender systems, leading to harmful effects in safety-critical areas like finance and healthcare. This tutorial will offer a comprehensive overview of achieving trustworthy recommender systems. It will cover six important aspects: Safety & Robustness, Non-discrimination & Fairness, Explainability, Privacy, Environmental Well-being, and Accountability & Auditability. Each aspect will be defined and categorized, followed by a discussion of the latest research progress and notable works. Additionally, potential interactions among these aspects and future research directions for trustworthy recommender systems will be explored.|推荐系统旨在为用户提供个性化的建议，帮助他们做出有效的决策。然而，最近的证据显示，先进的推荐系统的不可信方面，导致有害影响的安全关键领域，如金融和医疗保健。本教程将全面介绍如何实现值得信赖的推荐系统。它将涵盖六个重要方面: 安全与稳健、非歧视与公平、可解释性、隐私、环境福祉和问责与审核。每个方面将被定义和分类，然后讨论最新的研究进展和著名的工作。此外，这些方面之间的潜在交互作用和未来的研究方向值得信赖的推荐系统将被探讨。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Trustworthy+Recommender+Systems:+Foundations+and+Frontiers)|0| |[Mining Electronic Health Records for Real-World Evidence](https://doi.org/10.1145/3580305.3599566)|Chengxi Zang, Weishen Pan, Fei Wang|Department of Clinical Pharmacy and Toxicology, Leiden University Medical Center, Leiden, the Netherlands.|Real-world evidence can close the inferential gap between marketing authorization studies and clinical practice. However, the current standard for real-world data extraction from electronic health records (EHRs) for treatment evaluation is manual review (MR), which is time-consuming and laborious. Clinical Data Collector (CDC) is a novel natural language processing and text mining software tool for both structured and unstructured EHR data and only shows relevant EHR sections improving efficiency. We investigated CDC as a real-world data (RWD) collection method, through application of CDC queries for patient inclusion and information extraction on a cohort of patients with metastatic renal cell carcinoma (RCC) receiving systemic drug treatment. Baseline patient characteristics, disease characteristics, and treatment outcomes were extracted and these were compared with MR for validation. One hundred patients receiving 175 treatments were included using CDC, which corresponded to 99% with MR. Calculated median overall survival was 21.7 months (95% confidence interval (CI) 18.7-24.8) vs. 21.7 months (95% CI 18.6-24.8) and progression-free survival 8.9 months (95% CI 5.4-12.4) vs. 7.6 months (95% CI 5.7-9.4) for CDC vs. MR, respectively. Highest F1-score was found for cancer-related variables (88.1-100), followed by comorbidities (71.5-90.4) and adverse drug events (53.3-74.5), with most diverse scores on international metastatic RCC database criteria (51.4-100). Mean data collection time was 12 minutes (CDC) vs. 86 minutes (MR). In conclusion, CDC is a promising tool for retrieving RWD from EHRs because the correct patient population can be identified as well as relevant outcome data, such as overall survival and progression-free survival.|真实世界的证据可以缩小上市许可研究和临床实践之间的推断差距。然而，目前从电子健康记录(EHRs)中提取真实数据用于治疗评估的标准是人工审查(MR) ，这是一项费时费力的工作。临床数据采集器(CDC)是一种新型的自然语言处理和文本挖掘软件工具，用于结构化和非结构化 EHR 数据，只显示相关的 EHR 部分提高效率。我们研究了 CDC 作为一种现实世界数据(RWD)收集方法，通过应用 CDC 查询对患者进行纳入，并对接受全身药物治疗的转移性信息抽取(rCC)患者队列进行肾细胞癌分析。提取基线患者特征、疾病特征和治疗结果，并与 MR 进行比较验证。接受175次治疗的100名患者使用 CDC，其中99% 为 MR。计算的中位总生存期分别为21.7个月(95% 置信区间(CI)18.7-24.8)和21.7个月(95% CI 18.6-24.8)和无进展生存期分别为 CDC 和 MR 的8.9个月(95% CI 5.4-12.4)和7.6个月(95% CI 5.7-9.4)。发现癌症相关变量(88.1-100)的 F1评分最高，其次是合并症(71.5-90.4)和不良药物事件(53.3-74.5) ，国际转移性 RCC 数据库标准(51.4-100)。平均数据收集时间为12分钟(CDC)比86分钟(MR)。总之，CDC 是从 EHR 中检索 RWD 的有希望的工具，因为可以确定正确的患者人群以及相关的结果数据，如总生存期和无进展生存期。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Mining+Electronic+Health+Records+for+Real-World+Evidence)|0| |[EvalRS 2023: Well-Rounded Recommender Systems for Real-World Deployments](https://doi.org/10.1145/3580305.3599222)|Federico Bianchi, Patrick John Chia, Jacopo Tagliabue, Ciro Greco, Gabriel de Souza P. Moreira, Davide Eynard, Fahd Husain, Claudio Pomo||EvalRS aims to bring together practitioners from industry and academia to foster a debate on rounded evaluation of recommender systems, with a focus on real-world impact across a multitude of deployment scenarios. Recommender systems are often evaluated only through accuracy metrics, which fall short of fully characterizing their generalization capabilities and miss important aspects, such as fairness, bias, usefulness, informativeness. This workshop builds on the success of last year's workshop at CIKM, but with a broader scope and an interactive format.|EvalRS 旨在汇集来自行业和学术界的从业人员，促进关于全面评估推荐系统的辩论，重点是在多种部署情景下的现实世界影响。推荐系统往往只能通过精度指标进行评估，这些指标不能充分表征推荐系统的泛化能力，而且忽略了公平性、偏差性、有用性、信息性等重要方面。这个研讨会建立在去年 CIKM 研讨会的成功基础之上，但是范围更广，而且采用了交互式的形式。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=EvalRS+2023:+Well-Rounded+Recommender+Systems+for+Real-World+Deployments)|0| |[A Multi-stage Framework for Online Bonus Allocation Based on Constrained User Intent Detection](https://doi.org/10.1145/3580305.3599764)|Chao Wang, Xiaowei Shi, Shuai Xu, Zhe Wang, Zhiqiang Fan, Yan Feng, An You, Yu Chen||With the explosive development of e-commerce for service, tens of millions of orders are generated every day on the Meituan platform. By allocating bonuses to new customers when they pay, the Meituan platform encourages them to use its own payment service for a better experience in the future. It can be formulated as a multi-choice knapsack problem (MCKP), and the mainstream solution is usually a two-stage method. The first stage is user intent detection, predicting the effect for each bonus treatment. Then, it serves as the objective of the MCKP, and the problem is solved in the second stage to obtain the optimal allocation strategy. However, this solution usually faces the following challenges: (1) In the user intent detection stage, due to the sparsity of interaction and noise, the traditional multi-treatment effect estimation methods lack interpretability, which may violate the domain knowledge that the marginal gain is non-negative with the increase of the bonus amount in economic theory. (2) There is an optimality gap between the two stages, which limits the upper bound of the optimal value obtained in the second stage. (3) Due to changes in the distribution of orders online, the actual cost consumption often violates the given budget limit. To solve the above challenges, we propose a framework that consists of three modules, i.e., User Intent Detection Module, Online Allocation Module, and Feedback Control Module. In the User Intent Detection Module, we implicitly model the treatment increment based on deep representation learning and constrain it to be non-negative to achieve monotonicity constraints. Then, in order to reduce the optimality gap, we further propose a convex constrained model to increase the upper bound of the optimal value. For the third challenge, to cope with the fluctuation of online bonus consumption, we leverage a feedback control strategy in the framework to make the actual cost more accurately approach the given budget limit. Finally, we conduct extensive offline and online experiments, demonstrating the superiority of our proposed framework, which reduced customer acquisition costs by 5.07% and is still running online.|随着服务性电子商务的迅猛发展，美团平台每天产生数以千万计的订单。通过在新客户付款时向他们发放奖金，美团平台鼓励他们在未来使用自己的支付服务，以获得更好的体验。它可以表述为一个多选择背包问题(MCKP) ，而主流的解决方案通常是一个两阶段的方法。第一个阶段是用户意图检测，预测每个奖金处理的效果。然后以此作为 MCKP 的目标，在第二阶段对问题进行求解，得到最优分配策略。(1)在用户意图检测阶段，由于交互作用和噪声的稀疏性，传统的多处理效应估计方法缺乏可解释性，这可能违反了经济学理论中边际收益随着奖金数额的增加而非负的领域知识。(2)两阶段之间存在一个最优性差距，限制了第二阶段得到的最优值的上界。(3)由于在线订单分布的变化，实际成本消耗往往违反给定的预算限额。为了解决上述问题，我们提出了一个由三个模块组成的框架，即用户意图检测模块、在线分配模块和反馈控制模块。在用户意图检测模块中，我们对基于深度表示学习的治疗增量进行隐式建模，并将其约束为非负值以达到单调性约束。然后，为了减少最优性间隔，我们进一步提出了一个凸约束模型来增加最优值的上界。对于第三个挑战，为了应对在线奖金消费的波动，我们利用框架中的反馈控制策略，使实际成本更准确地接近给定的预算限额。最后，我们进行了广泛的离线和在线实验，证明了我们提出的框架的优越性，它降低了5.07% 的客户获取成本，并仍然在线运行。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Multi-stage+Framework+for+Online+Bonus+Allocation+Based+on+Constrained+User+Intent+Detection)|0| |[LEA: Improving Sentence Similarity Robustness to Typos Using Lexical Attention Bias](https://doi.org/10.1145/3580305.3599402)|Mario Almagro, Emilio J. Almazán, Diego Ortego, David Jiménez|NielsenIQ Innovation, Madrid, Spain|Textual noise, such as typos or abbreviations, is a well-known issue that penalizes vanilla Transformers for most downstream tasks. We show that this is also the case for sentence similarity, a fundamental task in multiple domains, e.g. matching, retrieval or paraphrasing. Sentence similarity can be approached using cross-encoders, where the two sentences are concatenated in the input allowing the model to exploit the inter-relations between them. Previous works addressing the noise issue mainly rely on data augmentation strategies, showing improved robustness when dealing with corrupted samples that are similar to the ones used for training. However, all these methods still suffer from the token distribution shift induced by typos. In this work, we propose to tackle textual noise by equipping cross-encoders with a novel LExical-aware Attention module (LEA) that incorporates lexical similarities between words in both sentences. By using raw text similarities, our approach avoids the tokenization shift problem obtaining improved robustness. We demonstrate that the attention bias introduced by LEA helps cross-encoders to tackle complex scenarios with textual noise, specially in domains with short-text descriptions and limited context. Experiments using three popular Transformer encoders in five e-commerce datasets for product matching show that LEA consistently boosts performance under the presence of noise, while remaining competitive on the original (clean) splits. We also evaluate our approach in two datasets for textual entailment and paraphrasing showing that LEA is robust to typos in domains with longer sentences and more natural context. Additionally, we thoroughly analyze several design choices in our approach, providing insights about the impact of the decisions made and fostering future research in cross-encoders dealing with typos.|文本噪音，如输入错误或缩写，是一个众所周知的问题，惩罚大多数下游任务的普通变形金刚。我们证明了句子相似性也是如此，这是多领域的基本任务，例如匹配、检索或释义。句子相似性可以使用交叉编码器进行处理，其中两个句子在输入中连接，使模型能够利用它们之间的相互关系。以往的研究主要依靠数据增强策略来解决噪声问题，在处理类似于训练样本的损坏样本时显示出了更好的鲁棒性。但是，所有这些方法仍然受到输入错误引起的令牌分布偏移的影响。在这项工作中，我们提出了解决文本噪声的交叉编码器装备一个新颖的词汇感知注意模块(LEA) ，其中包括词汇之间的相似性在两个句子。通过使用原始文本的相似性，我们的方法避免了标记转移问题，获得了更好的鲁棒性。我们证明了 LEA 引入的注意偏差有助于交叉编码器处理文本噪声的复杂场景，特别是在短文本描述和有限上下文的领域。在五个电子商务数据集中使用三种流行的变压器编码器进行产品匹配的实验表明，LEA 在噪声存在的情况下始终提高性能，同时在原始(干净)分割上保持竞争力。我们还评估了我们的方法在两个数据集的文字蕴涵和释义表明，LEA 是健壮的字体错误的领域与较长的句子和更自然的上下文。此外，我们彻底分析了我们的方法中的几种设计选择，提供了关于所做决定的影响的见解，并促进了交叉编码器处理输入错误的未来研究。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=LEA:+Improving+Sentence+Similarity+Robustness+to+Typos+Using+Lexical+Attention+Bias)|0| -|[IPOC: An Adaptive Interval Prediction Model based on Online Chasing and Conformal Inference for Large-Scale Systems](https://doi.org/10.1145/3580305.3599396)|Jiadong Chen, Yang Luo, Xiuqi Huang, Fuxin Jiang, Yangguang Shi, Tieying Zhang, Xiaofeng Gao|Shanghai Jiao Tong University; Bytedance Inc.; Shandong University|In large-scale systems, due to system complexity and demand volatility, diverse and dynamic workloads make accurate predictions difficult. In this work, we address an online interval prediction problem (OnPred-Int) and adopt ensemble learning to solve it. We depict that the ensemble learning for OnPred-Int is a dynamic deterministic Markov Decision Process (Dd-MDP) and convert it into a stateful online learning task. Then we propose IPOC, a lightweight and flexible model able to produce effective confidence intervals, adapting the dynamics of real-time workload streams. At each time, IPOC selects a target model and executes chasing for it by a designed chasing oracle, during which process IPOC produces accurate confidence intervals. The effectiveness of IPOCis theoretically validated through sublinear regret analysis and satisfaction of confidence interval requirements. Besides, we conduct extensive experiments on 4 real-world datasets comparing with 19 baselines. To the best of our knowledge, we are the first to apply the frontier theory of online learning to time series prediction tasks.|在大型系统中，由于系统的复杂性和需求的不稳定性，多样化和动态的工作负载使得准确的预测变得困难。在这项工作中，我们解决了一个在线时间间隔预测问题(OnPred-Int) ，并采用集成学习来解决它。我们描述了 Onpred-Int 的集成学习是一个动态的确定性马可夫决策过程(dd-mDP) ，并将其转换为一个有状态的在线学习任务。然后我们提出了 IPOC 模型，这是一个轻量级的、灵活的模型，能够产生有效的置信区间，适应实时工作流的动态性。每次，IPOC 选择一个目标模型并通过一个设计的追踪预言执行追踪，在这个过程中 IPOC 产生准确的置信区间。通过次线性遗憾分析和满足置信区间要求，从理论上验证了 IPoc 的有效性。此外，我们对4个真实世界的数据集进行了广泛的实验，比较了19个基线。据我们所知，我们是第一个将在线学习的前沿理论应用到时间序列预测任务中的人。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=IPOC:+An+Adaptive+Interval+Prediction+Model+based+on+Online+Chasing+and+Conformal+Inference+for+Large-Scale+Systems)|0| +|[IPOC: An Adaptive Interval Prediction Model based on Online Chasing and Conformal Inference for Large-Scale Systems](https://doi.org/10.1145/3580305.3599396)|Jiadong Chen, Yang Luo, Xiuqi Huang, Fuxin Jiang, Yangguang Shi, Tieying Zhang, Xiaofeng Gao|Shandong University; Bytedance Inc.; Shanghai Jiao Tong University|In large-scale systems, due to system complexity and demand volatility, diverse and dynamic workloads make accurate predictions difficult. In this work, we address an online interval prediction problem (OnPred-Int) and adopt ensemble learning to solve it. We depict that the ensemble learning for OnPred-Int is a dynamic deterministic Markov Decision Process (Dd-MDP) and convert it into a stateful online learning task. Then we propose IPOC, a lightweight and flexible model able to produce effective confidence intervals, adapting the dynamics of real-time workload streams. At each time, IPOC selects a target model and executes chasing for it by a designed chasing oracle, during which process IPOC produces accurate confidence intervals. The effectiveness of IPOCis theoretically validated through sublinear regret analysis and satisfaction of confidence interval requirements. Besides, we conduct extensive experiments on 4 real-world datasets comparing with 19 baselines. To the best of our knowledge, we are the first to apply the frontier theory of online learning to time series prediction tasks.|在大型系统中，由于系统的复杂性和需求的不稳定性，多样化和动态的工作负载使得准确的预测变得困难。在这项工作中，我们解决了一个在线时间间隔预测问题(OnPred-Int) ，并采用集成学习来解决它。我们描述了 Onpred-Int 的集成学习是一个动态的确定性马可夫决策过程(dd-mDP) ，并将其转换为一个有状态的在线学习任务。然后我们提出了 IPOC 模型，这是一个轻量级的、灵活的模型，能够产生有效的置信区间，适应实时工作流的动态性。每次，IPOC 选择一个目标模型并通过一个设计的追踪预言执行追踪，在这个过程中 IPOC 产生准确的置信区间。通过次线性遗憾分析和满足置信区间要求，从理论上验证了 IPoc 的有效性。此外，我们对4个真实世界的数据集进行了广泛的实验，比较了19个基线。据我们所知，我们是第一个将在线学习的前沿理论应用到时间序列预测任务中的人。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=IPOC:+An+Adaptive+Interval+Prediction+Model+based+on+Online+Chasing+and+Conformal+Inference+for+Large-Scale+Systems)|0| |[SketchPolymer: Estimate Per-item Tail Quantile Using One Sketch](https://doi.org/10.1145/3580305.3599505)|Jiarui Guo, Yisen Hong, Yuhan Wu, Yunfei Liu, Tong Yang, Bin Cui||1 Estimating the quantile of distribution, especially tail distribution, is an interesting topic in data stream models, and has obtained extensive interest from many researchers. In this paper, we propose a novel sketch, namely SketchPolymer to accurately estimate per-item tail quantile. SketchPolymer uses a technique called Early Filtration to filter infrequent items, and another technique called VSS to reduce error. Our experimental results show that the accuracy of SketchPolymer is on average 32.67 times better than state-of-the-art techniques. We also implement our SketchPolymer on P4 and FPGA platforms to verify its deployment flexibility. All our codes are available at GitHub.[1]|1分布的分位数估计，特别是尾分布的估计，是数据流模型中的一个有趣的课题，已经引起了许多研究者的广泛兴趣。在本文中，我们提出了一个新的草图，即素描聚合物，以准确估计每个项目的尾分位数。素描聚合物使用一种称为早期过滤的技术来过滤不常见的项目，另一种称为 VSS 的技术来减少错误。我们的实验结果表明，素描聚合物的准确性平均是32.67倍以上的国家最先进的技术。我们还在 P4和 FPGA 平台上实现了我们的 SketchPP，以验证其部署灵活性。我们所有的代码都可以在 GitHub 上找到。[1]|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=SketchPolymer:+Estimate+Per-item+Tail+Quantile+Using+One+Sketch)|0| |[Unbiased Locally Private Estimator for Polynomials of Laplacian Variables](https://doi.org/10.1145/3580305.3599537)|Quentin Hillebrand, Vorapong Suppakitpaisarn, Tetsuo Shibuya||This work presents a mechanism to debias polynomial functions computed from locally differentially private data. Local differential privacy is a widely used privacy notion where users add Laplacian noise to their information before submitting it to a central server. That, however, causes bias when we calculate non-linear functions based on those noisy information. Our proposed recursive algorithm debiases these functions, with a calculation time of O(r n log n), where r is the polynomial degree and n is the number of users. We evaluate our method on the problems of k-star counting and variance estimation, comparing results with state-of-the-art algorithms. The results show that our method not only eliminates bias, but also provides at least 100 times more accuracy than previous works.|本文提出了一种从局部差分私有数据中去偏多项式函数的机制。本地差分隐私是一个广泛使用的隐私概念，用户在将信息提交到中央服务器之前，会在其中添加拉普拉斯噪音。然而，当我们基于这些噪声信息计算非线性函数时，这会导致偏差。我们提出的递归算法将这些函数的计算时间缩短为 O (r n logn) ，其中 r 是多项式次数，n 是用户数。我们评估了我们的方法在 k 星计数和方差估计问题，比较结果与国家的最新算法。结果表明，我们的方法不仅消除了偏差，而且提供了至少100倍以上的精度比以往的工作。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Unbiased+Locally+Private+Estimator+for+Polynomials+of+Laplacian+Variables)|0| |[Semantic Dissimilarity Guided Locality Preserving Projections for Partial Label Dimensionality Reduction](https://doi.org/10.1145/3580305.3599496)|Yuheng Jia, Jiahao Jiang, Yongheng Wang||Partial label learning (PLL) is a significant weakly supervised learning framework, where each training example corresponds to a set of candidate labels among which only one is the ground-truth label. Existing works on partial label dimensionality reduction only exploit the disambiguated labels, but overlook the available semantic dissimilarity relationship hidden in the disambiguated labeling confidence, i.e., the smaller the inner product of the labeling confidences of two instances, the less likely they have the same ground-truth label. By combining such global dissimilarity relationship with local neighborhood information, we propose a novel partial label dimensionality reduction method named SDLPP, which employs an alternating procedure including candidate label disambiguation, semantic dissimilarity generation and dimensionality reduction. The labeling confidences of candidate labels and semantic dissimilarity relationship are constantly updated through the alternating procedure, where the processes in each iteration are based on the low-dimensional data obtained in the previous iteration. After the alternating procedure, SDLPP maps the original data to a pre-specified low-dimensional feature space. Comprehensive experiments on both synthetic and real-world data sets validate that SDLPP can improve the generalization performance of different PLL algorithms, and outperform state-of-the-art partial label dimensionality reduction methods. The codes can be publicly accessible on the link https://github.com/jhjiangSEU/SDLPP.|部分标签学习(PLL)是一个重要的弱监督式学习框架，其中每个训练例子对应一组候选标签，其中只有一个是地面真相标签。现有的部分标签降维只利用了消除歧义的标签，但忽略了隐藏在消除歧义标签置信度中的可用语义差异关系，即，两个实例的标签置信度的内积越小，它们具有相同的基本事实标签的可能性就越小。通过将这种全局不相似关系与局部邻域信息相结合，提出了一种新的部分标签降维方法 SDLPP，该方法采用了候选标签消歧、语义不相似生成和降维的交替过程。候选标签的标注置信度和语义不相似关系通过交替过程不断更新，其中每次迭代的过程都基于前一次迭代获得的低维数据。在交替过程之后，SDLPP 将原始数据映射到预先指定的低维特征空间。对合成和真实数据集的综合实验验证了 SDLPP 可以提高不同 PLL 算法的泛化性能，并优于最先进的部分标签降维方法。这些代码可在连结 https://github.com/jhjiangseu/sdlpp 上公开查阅。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Semantic+Dissimilarity+Guided+Locality+Preserving+Projections+for+Partial+Label+Dimensionality+Reduction)|0| -|[B2-Sampling: Fusing Balanced and Biased Sampling for Graph Contrastive Learning](https://doi.org/10.1145/3580305.3599262)|Mengyue Liu, Yun Lin, Jun Liu, Bohao Liu, Qinghua Zheng, Jin Song Dong|Shanghai Jiao Tong University; National University of Singapore; Xi’an Jiaotong University|Graph contrastive learning (GCL), aiming for an embedding space where semantically similar nodes are closer, has been widely applied in graph-structured data. Researchers have proposed many approaches to define positive and negative pairs (i.e., semantically similar and dissimilar pairs) on the graph, serving as labels to learn their embedding distances. Despite the effectiveness, those approaches usually suffer from two typical learning challenges. First, the number of candidate negative pairs is enormous. Thus, it is non-trivial to select representative ones to train the model in a more effective way. Second, the heuristics (e.g., graph views or meta-path patterns) to define positive and negative pairs are sometimes less reliable, causing considerable noise for both "labelled" positive and negative pairs. In this work, we propose a novel sampling approach B-2-Sampling to address the above challenges in a unified way. On the one hand, we use balanced sampling to select the most representative negative pairs regarding both the topological and embedding diversities. On the other hand, we use biased sampling to learn and correct the labels of the most error-prone negative pairs during the training. The balanced and biased samplings can be applied iteratively for discriminating and correcting training pairs, boosting the performance of GCL models. B-2-Sampling is designed as a framework to support many known GCL models. Our extensive experiments on node classification, node clustering, and graph classification tasks show that B-2-Sampling significantly improves the performance of GCL models with acceptable run-time overhead. Our website [11] provides access to our codes and additional experiment results.|图形对比学习(GCL)是一种针对语义相似节点更接近的嵌入空间的学习方法，在图结构化数据中得到了广泛的应用。研究人员提出了许多方法来定义图上的正对和负对(即语义相似和不相似的对) ，作为标签来学习它们的嵌入距离。尽管有效，这些方法通常会遇到两个典型的学习挑战。首先，候选负对的数量是巨大的。因此，选择具有代表性的模型对模型进行更有效的训练是非常重要的。其次，用于定义正面和负面对的启发式(例如，图形视图或元路径模式)有时不太可靠，对“标记的”正面和负面对造成相当大的噪音。在这项工作中，我们提出了一种新颖的抽样方法 B-2-抽样，以解决上述挑战在一个统一的方式。一方面，我们采用平衡抽样的方法，从拓扑和嵌入差异两方面选择最具代表性的负对。另一方面，在训练过程中，我们使用偏向抽样来学习和纠正最容易出错的否定对的标签。平衡和偏置抽样可以迭代地应用于训练样本的判别和校正，提高了 GCL 模型的性能。B-2-Sampling 被设计成一个支持许多已知 GCL 模型的框架。我们在节点分类、节点聚类和图分类任务上的大量实验表明，B-2采样能够在可接受的运行时开销下显著提高 GCL 模型的性能。我们的网站[11]提供了我们的代码和额外的实验结果。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=B2-Sampling:+Fusing+Balanced+and+Biased+Sampling+for+Graph+Contrastive+Learning)|0| +|[B2-Sampling: Fusing Balanced and Biased Sampling for Graph Contrastive Learning](https://doi.org/10.1145/3580305.3599262)|Mengyue Liu, Yun Lin, Jun Liu, Bohao Liu, Qinghua Zheng, Jin Song Dong|Xi’an Jiaotong University; National University of Singapore; Shanghai Jiao Tong University|Graph contrastive learning (GCL), aiming for an embedding space where semantically similar nodes are closer, has been widely applied in graph-structured data. Researchers have proposed many approaches to define positive and negative pairs (i.e., semantically similar and dissimilar pairs) on the graph, serving as labels to learn their embedding distances. Despite the effectiveness, those approaches usually suffer from two typical learning challenges. First, the number of candidate negative pairs is enormous. Thus, it is non-trivial to select representative ones to train the model in a more effective way. Second, the heuristics (e.g., graph views or meta-path patterns) to define positive and negative pairs are sometimes less reliable, causing considerable noise for both "labelled" positive and negative pairs. In this work, we propose a novel sampling approach B-2-Sampling to address the above challenges in a unified way. On the one hand, we use balanced sampling to select the most representative negative pairs regarding both the topological and embedding diversities. On the other hand, we use biased sampling to learn and correct the labels of the most error-prone negative pairs during the training. The balanced and biased samplings can be applied iteratively for discriminating and correcting training pairs, boosting the performance of GCL models. B-2-Sampling is designed as a framework to support many known GCL models. Our extensive experiments on node classification, node clustering, and graph classification tasks show that B-2-Sampling significantly improves the performance of GCL models with acceptable run-time overhead. Our website [11] provides access to our codes and additional experiment results.|图形对比学习(GCL)是一种针对语义相似节点更接近的嵌入空间的学习方法，在图结构化数据中得到了广泛的应用。研究人员提出了许多方法来定义图上的正对和负对(即语义相似和不相似的对) ，作为标签来学习它们的嵌入距离。尽管有效，这些方法通常会遇到两个典型的学习挑战。首先，候选负对的数量是巨大的。因此，选择具有代表性的模型对模型进行更有效的训练是非常重要的。其次，用于定义正面和负面对的启发式(例如，图形视图或元路径模式)有时不太可靠，对“标记的”正面和负面对造成相当大的噪音。在这项工作中，我们提出了一种新颖的抽样方法 B-2-抽样，以解决上述挑战在一个统一的方式。一方面，我们采用平衡抽样的方法，从拓扑和嵌入差异两方面选择最具代表性的负对。另一方面，在训练过程中，我们使用偏向抽样来学习和纠正最容易出错的否定对的标签。平衡和偏置抽样可以迭代地应用于训练样本的判别和校正，提高了 GCL 模型的性能。B-2-Sampling 被设计成一个支持许多已知 GCL 模型的框架。我们在节点分类、节点聚类和图分类任务上的大量实验表明，B-2采样能够在可接受的运行时开销下显著提高 GCL 模型的性能。我们的网站[11]提供了我们的代码和额外的实验结果。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=B2-Sampling:+Fusing+Balanced+and+Biased+Sampling+for+Graph+Contrastive+Learning)|0| |[DotHash: Estimating Set Similarity Metrics for Link Prediction and Document Deduplication](https://doi.org/10.1145/3580305.3599314)|Igor Nunes, Mike Heddes, Pere Vergés, Danny Abraham, Alexander V. Veidenbaum, Alex Nicolau, Tony Givargis|University of California, Irvine|Metrics for set similarity are a core aspect of several data mining tasks. To remove duplicate results in a Web search, for example, a common approach looks at the Jaccard index between all pairs of pages. In social network analysis, a much-celebrated metric is the Adamic-Adar index, widely used to compare node neighborhood sets in the important problem of predicting links. However, with the increasing amount of data to be processed, calculating the exact similarity between all pairs can be intractable. The challenge of working at this scale has motivated research into efficient estimators for set similarity metrics. The two most popular estimators, MinHash and SimHash, are indeed used in applications such as document deduplication and recommender systems where large volumes of data need to be processed. Given the importance of these tasks, the demand for advancing estimators is evident. We propose DotHash, an unbiased estimator for the intersection size of two sets. DotHash can be used to estimate the Jaccard index and, to the best of our knowledge, is the first method that can also estimate the Adamic-Adar index and a family of related metrics. We formally define this family of metrics, provide theoretical bounds on the probability of estimate errors, and analyze its empirical performance. Our experimental results indicate that DotHash is more accurate than the other estimators in link prediction and detecting duplicate documents with the same complexity and similar comparison time.|集合相似性度量是数据挖掘任务的一个核心方面。例如，为了删除 Web 搜索中的重复结果，通常的方法是查看所有页对之间的 Jaccard 索引。在社会网络分析中，一个著名的度量是阿达姆-阿达尔指数，广泛用于比较节点邻域集在预测链路的重要问题。然而，随着需要处理的数据量的增加，计算所有对之间的精确相似度是很困难的。在这种规模下工作的挑战促使人们研究集合相似度量的有效估计器。MinHash 和 SimHash 这两个最流行的估计器确实用于需要处理大量数据的应用程序，如文档删除重复数据和推荐系统。鉴于这些任务的重要性，提前估算的需求是显而易见的。我们提出了 DotHash，一个两个集合的交集大小的无偏估计。DotHash 可以用来估计 Jaccard 指数，据我们所知，DotHash 是第一种也可以估计 Adam-Adar 指数和一系列相关指标的方法。我们正式地定义了这个度量族，给出了估计误差概率的理论界限，并分析了它的经验性能。实验结果表明，在相同复杂度和相似比较时间的链路预测和重复文档检测方面，DotHash 比其他估计器具有更高的精度。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=DotHash:+Estimating+Set+Similarity+Metrics+for+Link+Prediction+and+Document+Deduplication)|0| |[Domain-Guided Spatio-Temporal Self-Attention for Egocentric 3D Pose Estimation](https://doi.org/10.1145/3580305.3599312)|Jinman Park, Kimathi Kaai, Saad Hossain, Norikatsu Sumi, Sirisha Rambhatla, Paul W. Fieguth||Vision-based ego-centric 3D human pose estimation (ego-HPE) is essential to support critical applications of xR-technologies. However, severe self-occlusions and strong distortion introduced by the fish-eye view from the head mounted camera, make ego-HPE extremely challenging. To address these challenges, we propose a domain-guided spatio-temporal transformer model that leverages information specific to ego-views. Powered by this domain-guided transformer, we build Egocentric Spatio-Temporal Self-Attention Network (Ego-STAN), which uses 2D image representations and spatio-temporal attention to address both distortions and self-occlusions in ego-HPE. Additionally, we introduce a spatial concept called feature map tokens (FMT) which endows Ego-STAN with the ability to draw complex spatio-temporal information encoded in ego-centric videos. Our quantitative evaluation on the contemporary xR-EgoPose dataset, achieves a 38.2% improvement on the highest error joints against the SOTA ego-HPE model, while accomplishing a 22% decrease in the number of parameters. Finally, we also demonstrate the generalization capabilities of our model to real-world HPE tasks beyond ego-views achieving 7.7% improvement on 2D human pose estimation with the Human3.6M dataset. Our code is also made available at: https://github.com/jmpark0808/Ego-STAN|基于视觉的以自我为中心的三维人体姿态估计(ego-HPE)对于支持 xR 技术的关键应用至关重要。然而，严重的自闭和强烈的扭曲所引入的鱼眼视图从头部安装的相机，使自我 HPE 极具挑战性。为了应对这些挑战，我们提出了一个领域引导的时空转换器模型，该模型利用特定于自我视图的信息。在此基础上，构建了以自我为中心的时空自注意网络(Ego-STAN) ，该网络利用二维图像表示和时空注意来解决自我 HPE 中的失真和自我遮挡问题。此外，我们还引入了一个称为特征映射标记(FMT)的空间概念，它赋予自我 STAN 提取以自我为中心的视频中编码的复杂时空信息的能力。我们对当代 xR-EgoPose 数据集的定量评估，对 SOTA ego-HPE 模型的最高误差关节实现了38.2% 的改进，同时实现了22% 的参数数量减少。最后，我们还展示了我们的模型对现实世界 HPE 任务的泛化能力，超越了自我视图，使用 Human3.6 M 数据集对二维人体姿态估计的改进达到了7.7% 。我们的代码也可以在以下 https://github.com/jmpark0808/ego-stan 找到|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Domain-Guided+Spatio-Temporal+Self-Attention+for+Egocentric+3D+Pose+Estimation)|0| -|[Quantitatively Measuring and Contrastively Exploring Heterogeneity for Domain Generalization](https://doi.org/10.1145/3580305.3599481)|Yunze Tong, Junkun Yuan, Min Zhang, Didi Zhu, Keli Zhang, Fei Wu, Kun Kuang|Zhejiang University; Noah’s Ark Lab, Huawei Technologies|Domain generalization (DG) is a prevalent problem in real-world applications, which aims to train well-generalized models for unseen target domains by utilizing several source domains. Since domain labels, i.e., which domain each data point is sampled from, naturally exist, most DG algorithms treat them as a kind of supervision information to improve the generalization performance. However, the original domain labels may not be the optimal supervision signal due to the lack of domain heterogeneity, i.e., the diversity among domains. For example, a sample in one domain may be closer to another domain, its original label thus can be the noise to disturb the generalization learning. Although some methods try to solve it by re-dividing domains and applying the newly generated dividing pattern, the pattern they choose may not be the most heterogeneous due to the lack of the metric for heterogeneity. In this paper, we point out that domain heterogeneity mainly lies in variant features under the invariant learning framework. With contrastive learning, we propose a learning potential-guided metric for domain heterogeneity by promoting learning variant features. Then we notice the differences between seeking variance-based heterogeneity and training invariance-based generalizable model. We thus propose a novel method called Heterogeneity-based Two-stage Contrastive Learning (HTCL) for the DG task. In the first stage, we generate the most heterogeneous dividing pattern with our contrastive metric. In the second stage, we employ an invariance-aimed contrastive learning by re-building pairs with the stable relation hinted by domains and classes, which better utilizes generated domain labels for generalization learning. Extensive experiments show HTCL better digs heterogeneity and yields great generalization performance.|领域广义化(DG)是现实应用中的一个普遍问题，其目的是利用多个源域来训练未知目标域的广义模型。由于领域标签(即每个数据点从哪个领域采样)的自然存在，大多数 DG 算法都将它们视为一种监督信息，以提高泛化性能。然而，由于缺乏领域异质性，即领域之间的差异性，原始的领域标签可能不是最佳的监督信号。例如，一个领域中的样本可能更接近另一个领域，其原始标签因此可能是噪声干扰推广学习。尽管有些方法试图通过重新划分域并应用新生成的划分模式来解决这个问题，但是由于缺乏对异构性的度量，所选择的模式可能不是最异构的。本文指出，在不变学习框架下，领域异质性主要表现在变异特征上。在对比学习的基础上，提出了一种基于学习势引导的领域异构度量方法。然后我们注意到基于方差的异质性寻求和基于训练不变性的可推广模型之间的区别。因此，我们提出了一种新的方法称为异质性为基础的两阶段对比学习(HTCL)的 DG 任务。在第一阶段，我们使用对比度量生成最不均匀的分割模式。在第二阶段，我们采用不变性对比学习方法，通过重新构建由领域和类提示的稳定关系的对，更好地利用生成的领域标签进行泛化学习。大量实验表明，HTCL 能够更好地挖掘异构性，并产生很好的泛化性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Quantitatively+Measuring+and+Contrastively+Exploring+Heterogeneity+for+Domain+Generalization)|0| -|[Grace: Graph Self-Distillation and Completion to Mitigate Degree-Related Biases](https://doi.org/10.1145/3580305.3599368)|Hui Xu, Liyao Xiang, Femke Huang, Yuting Weng, Ruijie Xu, Xinbing Wang, Chenghu Zhou|Chinese Academy of Sciences, Beijing, China; Shanghai Jiao Tong University, Shanghai, China|Due to the universality of graph data, node classification shows its great importance in a wide range of real-world applications. Despite the successes of Graph Neural Networks (GNNs), GNN based methods rely heavily on rich connections and perform poorly on low-degree nodes. Since many real-world graphs follow a long-tailed distribution in node degrees, they suffer from a substantial performance bottleneck as a significant fraction of nodes is of low degree. In this paper, we point out that under-represented self-representations and low neighborhood homophily ratio of low-degree nodes are two main culprits. Based on that, we propose a novel method Grace which improves the node representation by self-distillation, and increases neighborhood homophily ratio of low-degree nodes by graph completion. To avoid error propagation of graph completion, label propagation is further leveraged. Experimental evidence has shown that our method well supports real-world graphs, and is superior in balancing degree-related bias and overall performance on node classification tasks.|由于图形数据的通用性，节点分类在实际应用中显得非常重要。尽管图形神经网络(GNN)取得了成功，但是基于 GNN 的方法在很大程度上依赖于丰富的连接，在低度节点上表现不佳。由于现实世界中的许多图都遵循节点度的长尾分布，因此，由于很大一部分节点的度较低，它们受到了严重的性能瓶颈问题的困扰。本文指出低度节点的自我表征不足和低邻域同调比是造成这种现象的两个主要原因。在此基础上，提出了一种新的格雷斯方法，该方法通过自提取改善了节点的表示，并通过图完成提高了低度节点的邻域同调率。为了避免图完成的错误传播，进一步利用了标签传播。实验结果表明，该方法能够很好地支持实际图形，并且在平衡度相关偏差和节点分类任务的整体性能方面具有优势。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Grace:+Graph+Self-Distillation+and+Completion+to+Mitigate+Degree-Related+Biases)|0| +|[Quantitatively Measuring and Contrastively Exploring Heterogeneity for Domain Generalization](https://doi.org/10.1145/3580305.3599481)|Yunze Tong, Junkun Yuan, Min Zhang, Didi Zhu, Keli Zhang, Fei Wu, Kun Kuang|Noah’s Ark Lab, Huawei Technologies; Zhejiang University|Domain generalization (DG) is a prevalent problem in real-world applications, which aims to train well-generalized models for unseen target domains by utilizing several source domains. Since domain labels, i.e., which domain each data point is sampled from, naturally exist, most DG algorithms treat them as a kind of supervision information to improve the generalization performance. However, the original domain labels may not be the optimal supervision signal due to the lack of domain heterogeneity, i.e., the diversity among domains. For example, a sample in one domain may be closer to another domain, its original label thus can be the noise to disturb the generalization learning. Although some methods try to solve it by re-dividing domains and applying the newly generated dividing pattern, the pattern they choose may not be the most heterogeneous due to the lack of the metric for heterogeneity. In this paper, we point out that domain heterogeneity mainly lies in variant features under the invariant learning framework. With contrastive learning, we propose a learning potential-guided metric for domain heterogeneity by promoting learning variant features. Then we notice the differences between seeking variance-based heterogeneity and training invariance-based generalizable model. We thus propose a novel method called Heterogeneity-based Two-stage Contrastive Learning (HTCL) for the DG task. In the first stage, we generate the most heterogeneous dividing pattern with our contrastive metric. In the second stage, we employ an invariance-aimed contrastive learning by re-building pairs with the stable relation hinted by domains and classes, which better utilizes generated domain labels for generalization learning. Extensive experiments show HTCL better digs heterogeneity and yields great generalization performance.|领域广义化(DG)是现实应用中的一个普遍问题，其目的是利用多个源域来训练未知目标域的广义模型。由于领域标签(即每个数据点从哪个领域采样)的自然存在，大多数 DG 算法都将它们视为一种监督信息，以提高泛化性能。然而，由于缺乏领域异质性，即领域之间的差异性，原始的领域标签可能不是最佳的监督信号。例如，一个领域中的样本可能更接近另一个领域，其原始标签因此可能是噪声干扰推广学习。尽管有些方法试图通过重新划分域并应用新生成的划分模式来解决这个问题，但是由于缺乏对异构性的度量，所选择的模式可能不是最异构的。本文指出，在不变学习框架下，领域异质性主要表现在变异特征上。在对比学习的基础上，提出了一种基于学习势引导的领域异构度量方法。然后我们注意到基于方差的异质性寻求和基于训练不变性的可推广模型之间的区别。因此，我们提出了一种新的方法称为异质性为基础的两阶段对比学习(HTCL)的 DG 任务。在第一阶段，我们使用对比度量生成最不均匀的分割模式。在第二阶段，我们采用不变性对比学习方法，通过重新构建由领域和类提示的稳定关系的对，更好地利用生成的领域标签进行泛化学习。大量实验表明，HTCL 能够更好地挖掘异构性，并产生很好的泛化性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Quantitatively+Measuring+and+Contrastively+Exploring+Heterogeneity+for+Domain+Generalization)|0| +|[Grace: Graph Self-Distillation and Completion to Mitigate Degree-Related Biases](https://doi.org/10.1145/3580305.3599368)|Hui Xu, Liyao Xiang, Femke Huang, Yuting Weng, Ruijie Xu, Xinbing Wang, Chenghu Zhou|Shanghai Jiao Tong University, Shanghai, China; Chinese Academy of Sciences, Beijing, China|Due to the universality of graph data, node classification shows its great importance in a wide range of real-world applications. Despite the successes of Graph Neural Networks (GNNs), GNN based methods rely heavily on rich connections and perform poorly on low-degree nodes. Since many real-world graphs follow a long-tailed distribution in node degrees, they suffer from a substantial performance bottleneck as a significant fraction of nodes is of low degree. In this paper, we point out that under-represented self-representations and low neighborhood homophily ratio of low-degree nodes are two main culprits. Based on that, we propose a novel method Grace which improves the node representation by self-distillation, and increases neighborhood homophily ratio of low-degree nodes by graph completion. To avoid error propagation of graph completion, label propagation is further leveraged. Experimental evidence has shown that our method well supports real-world graphs, and is superior in balancing degree-related bias and overall performance on node classification tasks.|由于图形数据的通用性，节点分类在实际应用中显得非常重要。尽管图形神经网络(GNN)取得了成功，但是基于 GNN 的方法在很大程度上依赖于丰富的连接，在低度节点上表现不佳。由于现实世界中的许多图都遵循节点度的长尾分布，因此，由于很大一部分节点的度较低，它们受到了严重的性能瓶颈问题的困扰。本文指出低度节点的自我表征不足和低邻域同调比是造成这种现象的两个主要原因。在此基础上，提出了一种新的格雷斯方法，该方法通过自提取改善了节点的表示，并通过图完成提高了低度节点的邻域同调率。为了避免图完成的错误传播，进一步利用了标签传播。实验结果表明，该方法能够很好地支持实际图形，并且在平衡度相关偏差和节点分类任务的整体性能方面具有优势。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Grace:+Graph+Self-Distillation+and+Completion+to+Mitigate+Degree-Related+Biases)|0| |[DisasterNet: Causal Bayesian Networks with Normalizing Flows for Cascading Hazards Estimation from Satellite Imagery](https://doi.org/10.1145/3580305.3599807)|Xuechun Li, Paula M. Bürgi, Wei Ma, Hae Young Noh, David Jay Wald, Susu Xu||Sudden-onset hazards like earthquakes often induce cascading secondary hazards (e.g., landslides, liquefaction, debris flows, etc.) and subsequent impacts (e.g., building and infrastructure damage) that cause catastrophic human and economic losses. Rapid and accurate estimates of these hazards and impacts are critical for timely and effective post-disaster responses. Emerging remote sensing techniques provide pre- and post-event satellite images for rapid hazard estimation. However, hazards and damage often co-occur or colocate with underlying complex cascading geophysical processes, making it challenging to directly differentiate multiple hazards and impacts from satellite imagery using existing single-hazard models. We introduce DisasterNet, a novel family of causal Bayesian networks to model processes that a major hazard triggers cascading hazards and impacts and further jointly induces signal changes in remotely sensed observations. We integrate normalizing flows to effectively model the highly complex causal dependencies in this cascading process. A triplet loss is further designed to leverage prior geophysical knowledge to enhance the identifiability of our highly expressive Bayesian networks. Moreover, a novel stochastic variational inference with normalizing flows is derived to jointly approximate posteriors of multiple unobserved hazards and impacts from noisy remote sensing observations. Integrating with the USGS Prompt Assessment of Global Earthquakes for Response (PAGER) system, our framework is evaluated in recent global earthquake events. Evaluation results show that DisasterNet significantly improves multiple hazard and impact estimation compared to existing USGS products.|像地震这样的突发性灾害通常会引发连锁的次级灾害(如山体滑坡、液化、泥石流等)和随后的影响(如建筑物和基础设施的破坏) ，从而造成灾难性的人员和经济损失。迅速和准确地估计这些灾害和影响对及时和有效的灾后反应至关重要。新兴的遥感技术提供事件发生前后的卫星图像，用于快速评估灾害。然而，危害和损害往往与潜在的复杂的级联地球物理过程同时发生或共同存在，这使得利用现有的单一危害模型直接区分多种危害和影响具有挑战性卫星地图。我们引入灾难网络，一个新的因果贝叶斯网络家族模型的过程中，一个主要的危险触发级联危险和影响，并进一步共同诱导信号变化的遥感观测。我们整合了规范化流程来有效地模拟这个级联过程中高度复杂的因果依赖关系。进一步设计了三元组损失，以利用先前的地球物理知识，提高我们的高度表达贝叶斯网络的可识别性。此外，一个新的随机变分推断与归一化流动推导出联合近似后多个未观测的危险和影响的噪声遥感观测。结合美国地质调查局全球地震快速反应评估(PAGER)系统，对我们的框架在最近的全球地震事件中进行了评估。评估结果表明，与现有的 USGS 产品相比，灾难网络显著改善了多重危害和影响的估计。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=DisasterNet:+Causal+Bayesian+Networks+with+Normalizing+Flows+for+Cascading+Hazards+Estimation+from+Satellite+Imagery)|0| -|[Explicit Feature Interaction-aware Uplift Network for Online Marketing](https://doi.org/10.1145/3580305.3599820)|Dugang Liu, Xing Tang, Han Gao, Fuyuan Lyu, Xiuqiang He|FiT, Tencent; Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ; McGill University|As a key component in online marketing, uplift modeling aims to accurately capture the degree to which different treatments motivate different users, such as coupons or discounts, also known as the estimation of individual treatment effect (ITE). In an actual business scenario, the options for treatment may be numerous and complex, and there may be correlations between different treatments. In addition, each marketing instance may also have rich user and contextual features. However, existing methods still fall short in both fully exploiting treatment information and mining features that are sensitive to a particular treatment. In this paper, we propose an explicit feature interaction-aware uplift network (EFIN) to address these two problems. Our EFIN includes four customized modules: 1) a feature encoding module encodes not only the user and contextual features, but also the treatment features; 2) a self-interaction module aims to accurately model the user's natural response with all but the treatment features; 3) a treatment-aware interaction module accurately models the degree to which a particular treatment motivates a user through interactions between the treatment features and other features, i.e., ITE; and 4) an intervention constraint module is used to balance the ITE distribution of users between the control and treatment groups so that the model would still achieve a accurate uplift ranking on data collected from a non-random intervention marketing scenario. We conduct extensive experiments on two public datasets and one product dataset to verify the effectiveness of our EFIN. In addition, our EFIN has been deployed in a credit card bill payment scenario of a large online financial platform with a significant improvement.|作为在线营销的一个关键组成部分，提升模型旨在准确地捕捉不同的治疗激励不同的用户的程度，如优惠券或折扣，也被称为个体治疗效果(ITE)的估计。在实际的业务场景中，治疗的选择可能是多种多样和复杂的，不同治疗之间可能存在相关性。此外，每个营销实例还可能具有丰富的用户和上下文特性。然而，现有方法仍然不能充分利用对特定治疗敏感的治疗信息和挖掘特征。针对这两个问题，本文提出了一种显式的特征交互感知提升网络(EFIN)。我们的 EFIN 包括四个定制模块: 1)特征编码模块不仅编码用户和上下文特征，而且还编码治疗特征; 2)自我交互模块旨在准确地模拟除治疗特征以外的所有用户的自然反应; 3)治疗感知交互模块准确地模拟特定治疗通过治疗特征和其他特征之间的交互激励用户的程度，即 ITE; 和4)干预约束模块用于平衡控制和治疗组之间的用户 ITE 分布，以便该模型仍然能够实现从非随机干预营销场景收集的数据的准确提升排名。我们对两个公共数据集和一个产品数据集进行了广泛的实验，以验证我们的 EFIN 的有效性。此外，我们的 EFIN 已经部署在一个大型在线金融平台的信用卡账单支付场景中，并得到了显著的改进。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Explicit+Feature+Interaction-aware+Uplift+Network+for+Online+Marketing)|0| +|[Explicit Feature Interaction-aware Uplift Network for Online Marketing](https://doi.org/10.1145/3580305.3599820)|Dugang Liu, Xing Tang, Han Gao, Fuyuan Lyu, Xiuqiang He|Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ; FiT, Tencent; McGill University|As a key component in online marketing, uplift modeling aims to accurately capture the degree to which different treatments motivate different users, such as coupons or discounts, also known as the estimation of individual treatment effect (ITE). In an actual business scenario, the options for treatment may be numerous and complex, and there may be correlations between different treatments. In addition, each marketing instance may also have rich user and contextual features. However, existing methods still fall short in both fully exploiting treatment information and mining features that are sensitive to a particular treatment. In this paper, we propose an explicit feature interaction-aware uplift network (EFIN) to address these two problems. Our EFIN includes four customized modules: 1) a feature encoding module encodes not only the user and contextual features, but also the treatment features; 2) a self-interaction module aims to accurately model the user's natural response with all but the treatment features; 3) a treatment-aware interaction module accurately models the degree to which a particular treatment motivates a user through interactions between the treatment features and other features, i.e., ITE; and 4) an intervention constraint module is used to balance the ITE distribution of users between the control and treatment groups so that the model would still achieve a accurate uplift ranking on data collected from a non-random intervention marketing scenario. We conduct extensive experiments on two public datasets and one product dataset to verify the effectiveness of our EFIN. In addition, our EFIN has been deployed in a credit card bill payment scenario of a large online financial platform with a significant improvement.|作为在线营销的一个关键组成部分，提升模型旨在准确地捕捉不同的治疗激励不同的用户的程度，如优惠券或折扣，也被称为个体治疗效果(ITE)的估计。在实际的业务场景中，治疗的选择可能是多种多样和复杂的，不同治疗之间可能存在相关性。此外，每个营销实例还可能具有丰富的用户和上下文特性。然而，现有方法仍然不能充分利用对特定治疗敏感的治疗信息和挖掘特征。针对这两个问题，本文提出了一种显式的特征交互感知提升网络(EFIN)。我们的 EFIN 包括四个定制模块: 1)特征编码模块不仅编码用户和上下文特征，而且还编码治疗特征; 2)自我交互模块旨在准确地模拟除治疗特征以外的所有用户的自然反应; 3)治疗感知交互模块准确地模拟特定治疗通过治疗特征和其他特征之间的交互激励用户的程度，即 ITE; 和4)干预约束模块用于平衡控制和治疗组之间的用户 ITE 分布，以便该模型仍然能够实现从非随机干预营销场景收集的数据的准确提升排名。我们对两个公共数据集和一个产品数据集进行了广泛的实验，以验证我们的 EFIN 的有效性。此外，我们的 EFIN 已经部署在一个大型在线金融平台的信用卡账单支付场景中，并得到了显著的改进。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Explicit+Feature+Interaction-aware+Uplift+Network+for+Online+Marketing)|0| |[Online Quality Prediction in Windshield Manufacturing using Data-Efficient Machine Learning](https://doi.org/10.1145/3580305.3599880)|Hasan Tercan, Tobias Meisen||The digitization of manufacturing processes opens up the possibility of using machine learning methods on process data to predict future product quality. Based on the model predictions, quality improvement actions can be taken at an early stage. However, significant challenges must be overcome to successfully implement the predictions. Production lines are subject to hardware and memory limitations and are characterized by constant changes in quality influencing factors. In this paper, we address these challenges and present an online prediction approach for real-world manufacturing processes. On the one hand, it includes methods for feature extraction and selection from multimodal process and sensor data. On the other hand, a continual learning method based on memory-aware synapses is developed to efficiently train an artificial neural network over process changes. We deploy and evaluate the approach in a windshield production process. Our experimental evaluation shows that the model can accurately predict windshield quality and achieve significant process improvement. By comparing with other learning strategies such as transfer learning, we also show that the continual learning method both prevents catastrophic forgetting of the model and maintains its data efficiency.|制造过程的数字化为利用机器学习方法对过程数据进行预测未来产品质量提供了可能性。根据模型预测，可以在早期阶段采取质量改进措施。然而，要成功实现这些预测，必须克服重大挑战。生产线受到硬件和内存的限制，拥有属性的质量影响因素不断变化。在本文中，我们解决了这些挑战，并提出了一个在线预测方法的真实世界的制造过程。一方面，它包括从多模态过程和传感器数据中提取和选择特征的方法。另一方面，提出了一种基于记忆感知突触的连续学习方法，以有效地训练人工神经网络的过程变化。我们部署和评估的方法在一个挡风玻璃生产过程。实验结果表明，该模型能够准确地预测挡风玻璃的质量，实现了工艺的显著改进。通过与其他学习策略(如迁移学习)的比较，我们还发现连续学习方法不仅防止了模型的灾难性遗忘，而且保持了数据的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Online+Quality+Prediction+in+Windshield+Manufacturing+using+Data-Efficient+Machine+Learning)|0| |[C-AOI: Contour-based Instance Segmentation for High-Quality Areas-of-Interest in Online Food Delivery Platform](https://doi.org/10.1145/3580305.3599786)|Yida Zhu, Liying Chen, Daping Xiong, Shuiping Chen, Fangxiao Du, Jinghua Hao, Renqing He, Zhizhao Sun||Online food delivery (OFD) services have become popular globally, serving people's daily needs. Precise area-of-interest (AOI) boundaries help OFD platforms determine customers' exact locations, which is crucial for maintaining consistency in delivery difficulty and providing a uniform customer experience within an AOI. Existing AOI generation methods primarily rely on predefined shapes or density-based clustering, which limits the quality of the contours. Recently, Meituan has treated the AOI contours as a binary semantic segmentation problem. Their approach involves a multi-step post-process to address the issues with boundary breaks caused by semantic segmentation models, leading to decreased quality and inefficiency in the learning process. In this paper, we propose a novel method for AOI contour generation called C-AOI (Contour-based Area-of-Interest). C-AOI is an instance segmentation model that focuses on generating high-quality AOI contours. Unlike the former method, which relies on pixel-by-pixel classification, C-AOI starts from the center point of the AOI and regresses the boundary. This approach results in a higher-quality boundary and is less computationally intensive. C-AOI first corrects errors on the contour using a local aggregation mechanism. Then, we propose a novel deforming module called the contour transformer, which captures the global geometry of the object. To enhance the positional relationship among vertices, we introduce a learnable cyclic positional encoding applied to the contour transformer. Finally, to improve the boundary details, we propose the Adaptive Matching Loss (AML) that eliminates over-smoothed boundaries and promotes optimized convergence pathways. Experimental results on real-world datasets collected from Meituan have demonstrated that C-AOI significantly improves the mask and boundary quality compared to Meituan's previous work. Moreover, Its inference speed is comparable to that of E2EC, a state-of-the-art real-time contour-based method. It is noteworthy that C-AOI has been deployed in the Meituan platform for producing AOIs.|在线送餐(OFD)服务已经在全球范围内流行起来，满足人们的日常需求。精确的感兴趣区域(AOI)边界有助于 OFD 平台确定客户的确切位置，这对于保持交付困难的一致性和在 AOI 中提供统一的客户体验至关重要。现有的 AOI 生成方法主要依赖于预定义的形状或基于密度的聚类，这限制了轮廓的质量。最近，美团已经将 AOI 轮廓作为一个二进制语义分割问题来处理。他们的方法涉及到一个多步骤的后处理，以解决由于语义分割模型造成的边界断裂问题，导致学习过程中质量下降和效率低下。本文提出了一种新的 AOI 轮廓生成方法，称为基于轮廓的感兴趣区域(C- AOI)。C-AOI 是一个实例分割模型，侧重于生成高质量的 AOI 轮廓。不像前一种方法依赖于像素分类，C-AOI 从 AOI 的中心点开始并回归边界。这种方法会产生一个更高质量的边界，并且计算量更小。C-AOI 首先使用本地聚合机制纠正轮廓上的错误。然后，我们提出了一种新颖的变形模块称为轮廓变换器，它捕捉物体的全局几何形状。为了增强顶点之间的位置关系，我们引入了一种可学习的循环位置编码应用于轮廓变换器。最后，为了改善边界细节，我们提出了自适应匹配损失(AML) ，消除了过于平滑的边界，促进优化收敛路径。实验结果表明，与美团美团以前的工作相比，C-AOI 显著改善了掩模和边界质量。此外，它的推理速度相当于 E2EC，一个国家的最先进的实时轮廓为基础的方法。值得注意的是，C-AOI 已被部署在生产 AOI 的美团平台上。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=C-AOI:+Contour-based+Instance+Segmentation+for+High-Quality+Areas-of-Interest+in+Online+Food+Delivery+Platform)|0| -|[Addressing Bias and Fairness in Machine Learning: A Practical Guide and Hands-on Tutorial](https://doi.org/10.1145/3580305.3599180)|Rayid Ghani, Kit T. Rodolfa, Pedro Saleiro, Sérgio M. Jesus|Carnegie Mellon University, Pittsburgh, PA, USA; Stanford University, Stanford, CA, USA; Feedzai, Porto, Portugal; Feedzai & Universidade do Porto, Porto, Portugal|As data science and machine learning (ML) increasingly shape our society, the importance of developing fair algorithmic decision-making systems becomes paramount. There is a pressing need to train data scientists and practitioners on handling bias and fairness in real-world scenarios, from early stages of a data science project to maintaining ML systems in production. Existing resources are mostly academic and cover the ML training and optimization aspects of bias mitigation, leaving practitioners without comprehensive frameworks for making decisions throughout a real-world project lifecycle. This tutorial aims to bridge the gap between research and practice, providing an in-depth exploration of algorithmic fairness, encompassing metrics and definitions, practical case studies, data bias understanding, bias mitigation and model fairness audits using the Aequitas toolkit. Participants will be equipped to engage in conversations about bias, assist decision-makers in understanding options and trade-offs, evaluate project scoping aspects influencing fairness outcomes, and define actions and interventions based on model predictions. They will also learn to identify cohorts, target variables, evaluation metrics, and establish bias and fairness goals for different groups. Moreover, participants will gain insights into auditing and mitigating model bias, and implementing continuous monitoring to assess retraining needs. The tutorial addresses the current lack of practical training materials, methodologies, and tools for researchers and developers working on real-world algorithmic decision-making systems. By the conclusion of this hands-on tutorial, attendees will be well-versed in navigating bias-related issues, selecting appropriate metrics, and applying bias audit and mitigation frameworks and tools for informed design decisions in real-world data science systems.|随着数据科学和机器学习(ML)对我们社会的影响越来越大，开发公平的算法决策系统变得至关重要。从数据科学项目的早期阶段到维护生产中的机器学习系统，迫切需要培训数据科学家和从业人员如何在现实世界中处理偏见和公平问题。现有的资源大部分是学术性的，涵盖了机器学习培训和减少偏差的优化方面，使得从业者在整个现实世界的项目生命周期中没有全面的框架来做出决策。本教程旨在弥合研究和实践之间的差距，提供对算法公平性的深入探索，包括度量和定义，实际案例研究，数据偏差理解，偏差缓解和模型公平性审计使用 Aequitas 工具包。与会者将有能力参与关于偏见的对话，协助决策者了解备选方案和权衡，评估影响公平结果的项目范围，并根据模型预测确定行动和干预措施。他们还将学习识别队列、目标变量、评估指标，并为不同群体建立偏见和公平目标。此外，参与者将获得审计和减轻模型偏差的见解，并实施持续监测，以评估再培训需求。本教程针对研究人员和开发人员在现实世界中的算法决策系统中缺乏实用的培训材料、方法和工具的问题。通过这个实践教程的结论，与会者将精通导航偏差相关的问题，选择适当的度量标准，并应用偏差审计和缓解框架和工具，在现实世界的数据科学系统的知情设计决策。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Addressing+Bias+and+Fairness+in+Machine+Learning:+A+Practical+Guide+and+Hands-on+Tutorial)|0| -|[Causal Inference and Machine Learning in Practice: Use Cases for Product, Brand, Policy and Beyond](https://doi.org/10.1145/3580305.3599221)|JeongYoon Lee, Yifeng Wu, Keith Battocchi, Fabio Vera, Zhenyu Zhao, Totte Harinen, Jing Pan, Huigang Chen, Zeyu Zheng, Chu Wang, Yingfei Wang, Xinwei Ma|Snap, Los Angeles, CA, USA; Tencent, Palo Alto, CA, USA; Amazon, Seattle, WA, USA; Microsoft Research, Cambridge, MA, USA; University of California, San Diego, San Diego, CA, USA; Meta, Los Angeles, CA, USA; University of California, Berkeley, Berkeley, CA, USA; Uber Technologies, Inc., San Francisco, CA, USA; AirBnB, San Francisco, CA, USA; University of Washington, Seattle, WA, USA; Uber Technologies, Inc., Los Angeles, CA, USA|The increasing demand for data-driven decision-making has led to the rapid growth of machine learning applications in various industries. However, the ability to draw causal inferences from observational data remains a crucial challenge. In recent years, causal inference has emerged as a powerful tool for understanding the effects of interventions in complex systems. Combining causal inference with machine learning has the potential to provide a deeper understanding of the underlying mechanisms and to develop more effective solutions to real-world problems. This workshop aims to bring together researchers and practitioners from academia and industry to share their experiences and insights on applying causal inference and machine learning techniques to real-world problems in the areas of product, brand, policy, and beyond. The workshop welcomes original research that covers machine learning theory, deep learning, causal inference, and online learning. Additionally, the workshop encourages topics that address scalable system design, algorithm bias, and interpretability. Through keynote talks, panel discussions, and contributed talks and posters, the workshop will provide a forum for discussing the latest advances and challenges in applying causal inference and machine learning to real-world problems. The workshop will also offer opportunities for networking and collaboration among researchers and practitioners working in industry, government, and academia.|对数据驱动决策的需求日益增长，导致了机器学习应用在各个行业的迅速增长。然而，从观测数据中得出因果推论的能力仍然是一个关键的挑战。近年来，因果推理已经成为理解复杂系统干预效果的有力工具。将因果推理与机器学习相结合，有可能提供对潜在机制的更深入的理解，并为现实世界的问题找到更有效的解决方案。这个研讨会的目的是聚集来自学术界和工业界的研究人员和从业人员，分享他们的经验和见解，应用因果推理和机器学习技术的现实世界的问题，在产品，品牌，政策和以外的领域。研讨会欢迎包括机器学习理论、深度学习、因果推理和在线学习在内的原创性研究。此外，研讨会鼓励讨论可伸缩系统设计、算法偏差和可解释性的主题。通过主题演讲、小组讨论、贡献的演讲和海报，研讨会将提供一个论坛，讨论在将因果推理和机器学习应用于现实世界问题方面的最新进展和挑战。研讨会还将提供在工业、政府和学术界工作的研究人员和从业人员之间建立联系和开展合作的机会。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Causal+Inference+and+Machine+Learning+in+Practice:+Use+Cases+for+Product,+Brand,+Policy+and+Beyond)|0| +|[Addressing Bias and Fairness in Machine Learning: A Practical Guide and Hands-on Tutorial](https://doi.org/10.1145/3580305.3599180)|Rayid Ghani, Kit T. Rodolfa, Pedro Saleiro, Sérgio M. Jesus|Stanford University, Stanford, CA, USA; Feedzai, Porto, Portugal; Carnegie Mellon University, Pittsburgh, PA, USA; Feedzai & Universidade do Porto, Porto, Portugal|As data science and machine learning (ML) increasingly shape our society, the importance of developing fair algorithmic decision-making systems becomes paramount. There is a pressing need to train data scientists and practitioners on handling bias and fairness in real-world scenarios, from early stages of a data science project to maintaining ML systems in production. Existing resources are mostly academic and cover the ML training and optimization aspects of bias mitigation, leaving practitioners without comprehensive frameworks for making decisions throughout a real-world project lifecycle. This tutorial aims to bridge the gap between research and practice, providing an in-depth exploration of algorithmic fairness, encompassing metrics and definitions, practical case studies, data bias understanding, bias mitigation and model fairness audits using the Aequitas toolkit. Participants will be equipped to engage in conversations about bias, assist decision-makers in understanding options and trade-offs, evaluate project scoping aspects influencing fairness outcomes, and define actions and interventions based on model predictions. They will also learn to identify cohorts, target variables, evaluation metrics, and establish bias and fairness goals for different groups. Moreover, participants will gain insights into auditing and mitigating model bias, and implementing continuous monitoring to assess retraining needs. The tutorial addresses the current lack of practical training materials, methodologies, and tools for researchers and developers working on real-world algorithmic decision-making systems. By the conclusion of this hands-on tutorial, attendees will be well-versed in navigating bias-related issues, selecting appropriate metrics, and applying bias audit and mitigation frameworks and tools for informed design decisions in real-world data science systems.|随着数据科学和机器学习(ML)对我们社会的影响越来越大，开发公平的算法决策系统变得至关重要。从数据科学项目的早期阶段到维护生产中的机器学习系统，迫切需要培训数据科学家和从业人员如何在现实世界中处理偏见和公平问题。现有的资源大部分是学术性的，涵盖了机器学习培训和减少偏差的优化方面，使得从业者在整个现实世界的项目生命周期中没有全面的框架来做出决策。本教程旨在弥合研究和实践之间的差距，提供对算法公平性的深入探索，包括度量和定义，实际案例研究，数据偏差理解，偏差缓解和模型公平性审计使用 Aequitas 工具包。与会者将有能力参与关于偏见的对话，协助决策者了解备选方案和权衡，评估影响公平结果的项目范围，并根据模型预测确定行动和干预措施。他们还将学习识别队列、目标变量、评估指标，并为不同群体建立偏见和公平目标。此外，参与者将获得审计和减轻模型偏差的见解，并实施持续监测，以评估再培训需求。本教程针对研究人员和开发人员在现实世界中的算法决策系统中缺乏实用的培训材料、方法和工具的问题。通过这个实践教程的结论，与会者将精通导航偏差相关的问题，选择适当的度量标准，并应用偏差审计和缓解框架和工具，在现实世界的数据科学系统的知情设计决策。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Addressing+Bias+and+Fairness+in+Machine+Learning:+A+Practical+Guide+and+Hands-on+Tutorial)|0| +|[Causal Inference and Machine Learning in Practice: Use Cases for Product, Brand, Policy and Beyond](https://doi.org/10.1145/3580305.3599221)|JeongYoon Lee, Yifeng Wu, Keith Battocchi, Fabio Vera, Zhenyu Zhao, Totte Harinen, Jing Pan, Huigang Chen, Zeyu Zheng, Chu Wang, Yingfei Wang, Xinwei Ma|Snap, Los Angeles, CA, USA; University of Washington, Seattle, WA, USA; University of California, San Diego, San Diego, CA, USA; Uber Technologies, Inc., San Francisco, CA, USA; Microsoft Research, Cambridge, MA, USA; AirBnB, San Francisco, CA, USA; Amazon, Seattle, WA, USA; Tencent, Palo Alto, CA, USA; University of California, Berkeley, Berkeley, CA, USA; Uber Technologies, Inc., Los Angeles, CA, USA; Meta, Los Angeles, CA, USA|The increasing demand for data-driven decision-making has led to the rapid growth of machine learning applications in various industries. However, the ability to draw causal inferences from observational data remains a crucial challenge. In recent years, causal inference has emerged as a powerful tool for understanding the effects of interventions in complex systems. Combining causal inference with machine learning has the potential to provide a deeper understanding of the underlying mechanisms and to develop more effective solutions to real-world problems. This workshop aims to bring together researchers and practitioners from academia and industry to share their experiences and insights on applying causal inference and machine learning techniques to real-world problems in the areas of product, brand, policy, and beyond. The workshop welcomes original research that covers machine learning theory, deep learning, causal inference, and online learning. Additionally, the workshop encourages topics that address scalable system design, algorithm bias, and interpretability. Through keynote talks, panel discussions, and contributed talks and posters, the workshop will provide a forum for discussing the latest advances and challenges in applying causal inference and machine learning to real-world problems. The workshop will also offer opportunities for networking and collaboration among researchers and practitioners working in industry, government, and academia.|对数据驱动决策的需求日益增长，导致了机器学习应用在各个行业的迅速增长。然而，从观测数据中得出因果推论的能力仍然是一个关键的挑战。近年来，因果推理已经成为理解复杂系统干预效果的有力工具。将因果推理与机器学习相结合，有可能提供对潜在机制的更深入的理解，并为现实世界的问题找到更有效的解决方案。这个研讨会的目的是聚集来自学术界和工业界的研究人员和从业人员，分享他们的经验和见解，应用因果推理和机器学习技术的现实世界的问题，在产品，品牌，政策和以外的领域。研讨会欢迎包括机器学习理论、深度学习、因果推理和在线学习在内的原创性研究。此外，研讨会鼓励讨论可伸缩系统设计、算法偏差和可解释性的主题。通过主题演讲、小组讨论、贡献的演讲和海报，研讨会将提供一个论坛，讨论在将因果推理和机器学习应用于现实世界问题方面的最新进展和挑战。研讨会还将提供在工业、政府和学术界工作的研究人员和从业人员之间建立联系和开展合作的机会。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Causal+Inference+and+Machine+Learning+in+Practice:+Use+Cases+for+Product,+Brand,+Policy+and+Beyond)|0| |[Sketch-Based Anomaly Detection in Streaming Graphs](https://doi.org/10.1145/3580305.3599504)|Siddharth Bhatia, Mohit Wadhwa, Kenji Kawaguchi, Neil Shah, Philip S. Yu, Bryan Hooi||Given a stream of graph edges from a dynamic graph, how can we assign anomaly scores to edges and subgraphs in an online manner, for the purpose of detecting unusual behavior, using constant time and memory? For example, in intrusion detection, existing work seeks to detect either anomalous edges or anomalous subgraphs, but not both. In this paper, we first extend the count-min sketch data structure to a higher-order sketch. This higher-order sketch has the useful property of preserving the dense subgraph structure (dense subgraphs in the input turn into dense submatrices in the data structure). We then propose 4 online algorithms that utilize this enhanced data structure, which (a) detect both edge and graph anomalies; (b) process each edge and graph in constant memory and constant update time per newly arriving edge, and; (c) outperform state-of-the-art baselines on 4 real-world datasets. Our method is the first streaming approach that incorporates dense subgraph search to detect graph anomalies in constant memory and time.|给定一个动态图的图边流，我们如何以一种在线的方式为边和子图分配异常评分，以便使用恒定的时间和内存来检测异常行为？例如，在入侵检测中，现有的工作寻求检测异常边缘或异常子图，但不是两者兼而有之。在本文中，我们首先将 count-min 草图数据结构扩展到一个高阶草图。这种高阶素描具有保持稠密子图结构的有用性质(输入中的稠密子图变成数据结构中的稠密子矩阵)。然后，我们提出了4个在线算法，利用这种增强的数据结构，其中(a)检测边缘和图形异常; (b)处理每个边缘和图形的恒定记忆和每个新到达的边缘的恒定更新时间，以及(c)在4个真实世界的数据集优于最先进的基线。我们的方法是第一个流的方法，结合密集子图搜索检测图的异常在恒定的记忆和时间。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Sketch-Based+Anomaly+Detection+in+Streaming+Graphs)|0| |[On Hierarchical Disentanglement of Interactive Behaviors for Multimodal Spatiotemporal Data with Incompleteness](https://doi.org/10.1145/3580305.3599448)|Jiayi Chen, Aidong Zhang||Multimodal spatiotemporal data (MST) consists of multiple simultaneous spatiotemporal modalities that interact with each other in a dynamic manner. Due to the complexity of MST and the recent desire for the explainability of artificial intelligent systems, disentangled representation learning for MST (DisentMST) has become a significant task, which aims to learn disentangled representations that can expose the underlying spatial semantics, temporal dynamic patterns, and inter-modality interaction modes of the complex MST. One limitation of existing approaches is that they might fail to tolerate the real-world incomplete MST data, where missing information might break the cross-modal spatiotemporal dynamics and bring noise and ambiguity to the learning process. Another limitation is that no existing work systematically reveals the structure of different types of disentangled information. To tackle the two limitations, we define a novel two-level hierarchically structured disentanglement task for MST, which reveals informative and structured disentangled representations for MST as well as digests the real-world MST with incompleteness. We propose a new framework, BiDisentMST, which leverages Gaussian Processes and Graph Factorization on the latent space to achieve our purposes. The experimental results demonstrate the effectiveness of our proposed framework compared with baselines with respect to disentanglement and imputation results.|多模态时空数据(MST)由多个同时的时空模式组成，它们以动态的方式相互作用。由于 MST 的复杂性和对人工智能系统可解释性的需求，MST 的分离表示学习(DisentMST)已成为一项重要的任务，其目标是学习能够揭示复杂 MST 的潜在空间语义、时间动态模式和模态间交互模式的分离表示。现有方法的一个局限性是它们可能无法容忍现实世界中不完整的 MST 数据，其中缺失的信息可能会破坏跨模态时空动力学，并给学习过程带来噪声和模糊性。另一个限制是现有的工作没有系统地揭示不同类型的分离信息的结构。为了解决这两个局限性，我们定义了一个新的两级分层结构的 MST 解纠缠任务，它揭示了 MST 的信息化和结构化的解纠缠表示，并对现实世界的 MST 进行了不完全消化。我们提出了一个新的框架，BiDisentMST，它利用高斯过程和图因子分解的潜在空间，以实现我们的目的。实验结果表明，与基线相比，我们提出的框架在解缠和插补结果方面的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=On+Hierarchical+Disentanglement+of+Interactive+Behaviors+for+Multimodal+Spatiotemporal+Data+with+Incompleteness)|0| |[Multiplex Heterogeneous Graph Neural Network with Behavior Pattern Modeling](https://doi.org/10.1145/3580305.3599441)|Chaofan Fu, Guanjie Zheng, Chao Huang, Yanwei Yu, Junyu Dong||Heterogeneous graph neural networks have gained great popularity in tackling various network analysis tasks on heterogeneous network data. However, most existing works mainly focus on general heterogeneous networks, and assume that there is only one type of edge between two nodes, while ignoring the multiplex characteristics between multi-typed nodes in multiplex heterogeneous networks and the different importance of multiplex structures among nodes for node embedding. In addition, the over-smoothing issue of graph neural networks limits existing models to only capturing local structure signals but hardly learning the global relevant information of the network. To tackle these challenges, this work proposes a model called Behavior Pattern based Heterogeneous Graph Neural Network (BPHGNN) for multiplex heterogeneous network embedding. Specifically, BPHGNN can collaboratively learn node representations across different multiplex structures among nodes with adaptive importance learning from local and global perspectives in multiplex heterogeneous networks through depth behavior pattern aggregation and breadth behavior pattern aggregation. Extensive experiments on six real-world networks with various network analytical tasks demonstrate the significant superiority of BPHGNN against state-of-the-art approaches in terms of various evaluation metrics.|异构图形神经网络在处理异质网路数据的各种网络分析任务方面得到了广泛的应用。然而，现有的大多数研究主要集中在一般的异构网络上，假设两个节点之间只有一种类型的边，而忽略了多类型异构网络中多类型节点之间的多重特性以及节点之间的多重结构对于节点嵌入的不同重要性。此外，图神经网络的过度平滑问题限制了现有的模型只能捕获局部结构信号，而很难学习网络的全局相关信息。为了应对这些挑战，这项工作提出了一个叫做基于行为模式的异构图神经网络(bphGNN)的模型，用于多路异质网路嵌入。具体来说，BPHGNN 可以通过深度行为模式聚合和广度行为模式聚合，在多路异构网络中从局部和全局角度自适应重要性学习，协同学习不同多路结构之间的节点表示。在具有各种网络分析任务的六个现实世界网络上进行的大量实验表明，BPHGNN 在各种评估指标方面明显优于最先进的方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Multiplex+Heterogeneous+Graph+Neural+Network+with+Behavior+Pattern+Modeling)|0| @@ -181,50 +181,50 @@ |[Similarity Preserving Adversarial Graph Contrastive Learning](https://doi.org/10.1145/3580305.3599503)|Yeonjun In, Kanghoon Yoon, Chanyoung Park|KAIST|Recent works demonstrate that GNN models are vulnerable to adversarial attacks, which refer to imperceptible perturbation on the graph structure and node features. Among various GNN models, graph contrastive learning (GCL) based methods specifically suffer from adversarial attacks due to their inherent design that highly depends on the self-supervision signals derived from the original graph, which however already contains noise when the graph is attacked. To achieve adversarial robustness against such attacks, existing methods adopt adversarial training (AT) to the GCL framework, which considers the attacked graph as an augmentation under the GCL framework. However, we find that existing adversarially trained GCL methods achieve robustness at the expense of not being able to preserve the node feature similarity. In this paper, we propose a similarity-preserving adversarial graph contrastive learning (SP-AGCL) framework that contrasts the clean graph with two auxiliary views of different properties (i.e., the node similarity-preserving view and the adversarial view). Extensive experiments demonstrate that SP-AGCL achieves a competitive performance on several downstream tasks, and shows its effectiveness in various scenarios, e.g., a network with adversarial attacks, noisy labels, and heterophilous neighbors. Our code is available at https://github.com/yeonjun-in/torch-SP-AGCL.|最近的工作表明，GNN 模型是脆弱的对手攻击，这是指不可察觉的扰动图结构和节点特征。在各种 GNN 模型中，基于图形对比学习(GCL)的方法由于其固有的设计，高度依赖于来自原始图形的自我监督信号，而这些信号在图形受到攻击时已经含有噪声，因此特别容易受到攻击。为了实现对抗这种攻击的鲁棒性，现有的方法对 GCL 框架采用了对抗训练(AT) ，该框架将被攻击图视为 GCL 框架下的一种增强。然而，我们发现现有的对抗训练的 GCL 方法在不能保持节点特征相似性的前提下达到了鲁棒性。本文提出了一个保持相似性的对抗图对比学习(SP-AGCL)框架，该框架将干净图与具有不同性质的两个辅助视图(即节点相似性保持视图和对抗视图)进行对比。广泛的实验表明，SP-AGCL 在几个下游任务上取得了有竞争力的性能，并且在多种情况下显示了其有效性，例如，一个具有对抗性攻击、噪声标签和异质邻居的网络。我们的代码可以在 https://github.com/yeonjun-in/torch-sp-agcl 找到。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Similarity+Preserving+Adversarial+Graph+Contrastive+Learning)|0| |[Fast and Accurate Dual-Way Streaming PARAFAC2 for Irregular Tensors - Algorithm and Application](https://doi.org/10.1145/3580305.3599342)|JunGi Jang, Jeongyoung Lee, Yongchan Park, U Kang|Seoul National University|How can we efficiently and accurately analyze an irregular tensor in a dual-way streaming setting where the sizes of two dimensions of the tensor increase over time? What types of anomalies are there in the dual-way streaming setting? An irregular tensor is a collection of matrices whose column lengths are the same while their row lengths are different. In a dual-way streaming setting, both new rows of existing matrices and new matrices arrive over time. PARAFAC2 decomposition is a crucial tool for analyzing irregular tensors. Although real-time analysis is necessary in the dual-way streaming, static PARAFAC2 decomposition methods fail to efficiently work in this setting since they perform PARAFAC2 decomposition for accumulated tensors whenever new data arrive. Existing streaming PARAFAC2 decomposition methods work in a limited setting and fail to handle new rows of matrices efficiently. In this paper, we propose Dash, an efficient and accurate PARAFAC2 decomposition method working in the dual-way streaming setting. When new data are given, Dash efficiently performs PARAFAC2 decomposition by carefully dividing the terms related to old and new data and avoiding naive computations involved with old data. Furthermore, applying a forgetting factor makes Dash follow recent movements. Extensive experiments show that Dash achieves up to 14.0x faster speed than existing PARAFAC2 decomposition methods for newly arrived data. We also provide discoveries for detecting anomalies in real-world datasets, including Subprime Mortgage Crisis and COVID-19.|我们如何才能有效和准确地分析一个不规则张量在双向流设置，其中张量的二维尺寸随着时间的推移增加？在双向流设置中有哪些类型的异常？不规则张量是列长相同而行长不同的矩阵集合。在双向流设置中，现有矩阵的新行和新矩阵都会随着时间的推移到达。PARAFAC2分解是分析不规则张量的重要工具。尽管实时分析在双向流中是必需的，但是静态 PARAFAC2分解方法在这种情况下无法有效工作，因为每当新数据到达时，它们都会对累积的张量执行 PARAFAC2分解。现有的流 PARAFAC2分解方法在有限的设置下工作，无法有效地处理新的矩阵行。本文提出了一种高效、准确的双向流设置 PARAFAC2分解方法 Dash。当给定新数据时，Dash 通过仔细划分与旧数据和新数据相关的术语并避免涉及旧数据的幼稚计算，有效地执行 PARAFAC2分解。此外，应用遗忘因子使达什跟随最近的动作。大量的实验表明，Dash 对新到达的数据的分解速度比现有的 PARAFAC2分解方法快14.0倍。我们还提供发现，以检测现实世界数据集中的异常，包括次贷危机和2019冠状病毒疾病。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Fast+and+Accurate+Dual-Way+Streaming+PARAFAC2+for+Irregular+Tensors+-+Algorithm+and+Application)|0| |[Predicting Information Pathways Across Online Communities](https://doi.org/10.1145/3580305.3599470)|Yiqiao Jin, YeonChang Lee, Kartik Sharma, Meng Ye, Karan Sikka, Ajay Divakaran, Srijan Kumar|Georgia Institute of Technology; SRI International|The problem of community-level information pathway prediction (CLIPP) aims at predicting the transmission trajectory of content across online communities. A successful solution to CLIPP holds significance as it facilitates the distribution of valuable information to a larger audience and prevents the proliferation of misinformation. Notably, solving CLIPP is non-trivial as inter-community relationships and influence are unknown, information spread is multi-modal, and new content and new communities appear over time. In this work, we address CLIPP by collecting large-scale, multi-modal datasets to examine the diffusion of online YouTube videos on Reddit. We analyze these datasets to construct community influence graphs (CIGs) and develop a novel dynamic graph framework, INPAC (Information Pathway Across Online Communities), which incorporates CIGs to capture the temporal variability and multi-modal nature of video propagation across communities. Experimental results in both warm-start and cold-start scenarios show that INPAC outperforms seven baselines in CLIPP.|社区层面的信息路径预测(CLIPP)问题旨在预测内容在网络社区之间的传播轨迹。CLIPP 的成功解决方案具有重要意义，因为它有助于向更多的受众传播有价值的信息，并防止错误信息的扩散。值得注意的是，解决 CLIPP 是非常重要的，因为社区之间的关系和影响是未知的，信息传播是多模式的，新的内容和新的社区随着时间的推移出现。在这项工作中，我们通过收集大规模的多模态数据集来检查在线 YouTube 视频在 Reddit 上的传播，从而解决 CLIPP 问题。我们分析这些数据集来构建社区影响图(CIGs) ，并开发一种新的动态图框架 INPAC (在线社区信息路径) ，其结合 CIGs 来捕获跨社区视频传播的时间变异性和多模态性质。在热启动和冷启动两种情况下的实验结果表明，INPAC 的性能优于 CLIPP 中的七个基线。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Predicting+Information+Pathways+Across+Online+Communities)|0| -|[Task Relation-aware Continual User Representation Learning](https://doi.org/10.1145/3580305.3599516)|Sein Kim, Namkyeong Lee, Donghyun Kim, MinChul Yang, Chanyoung Park|NAVER Corporation; KAIST|User modeling, which learns to represent users into a low-dimensional representation space based on their past behaviors, got a surge of interest from the industry for providing personalized services to users. Previous efforts in user modeling mainly focus on learning a task-specific user representation that is designed for a single task. However, since learning task-specific user representations for every task is infeasible, recent studies introduce the concept of universal user representation, which is a more generalized representation of a user that is relevant to a variety of tasks. Despite their effectiveness, existing approaches for learning universal user representations are impractical in real-world applications due to the data requirement, catastrophic forgetting and the limited learning capability for continually added tasks. In this paper, we propose a novel continual user representation learning method, called TERACON, whose learning capability is not limited as the number of learned tasks increases while capturing the relationship between the tasks. The main idea is to introduce an embedding for each task, i.e., task embedding, which is utilized to generate task-specific soft masks that not only allow the entire model parameters to be updated until the end of training sequence, but also facilitate the relationship between the tasks to be captured. Moreover, we introduce a novel knowledge retention module with pseudo-labeling strategy that successfully alleviates the long-standing problem of continual learning, i.e., catastrophic forgetting. Extensive experiments on public and proprietary real-world datasets demonstrate the superiority and practicality of TERACON. Our code is available at https://github.com/Sein-Kim/TERACON.|用户建模是根据用户过去的行为学习如何将用户表示成一个低维的表示空间，因此为用户提供个性化的服务引起了业界的极大兴趣。以前的用户建模工作主要集中在学习为单个任务设计的特定于任务的用户表示。然而，由于学习任务特定的每个任务的用户表示是不可行的，最近的研究引入了通用用户表示的概念，这是一个更广泛的用户表示相关的各种任务。尽管现有的学习通用用户表示的方法很有效，但是由于数据需求、灾难性遗忘以及对不断增加的任务的学习能力有限，这些方法在实际应用中是不切实际的。在本文中，我们提出了一种新的持续用户表征学习方法，称为 TERACON，它的学习能力不受任务数量增加的限制，同时捕捉任务之间的关系。其主要思想是为每个任务引入一个嵌入，即任务嵌入，用于生成任务特定的软掩码，不仅允许整个模型参数更新直到训练序列结束，而且有利于任务之间的关系被捕获。此外，我们还引入了一个新的知识保留模块，该模块采用伪标记策略，成功地解决了长期以来存在的连续学习问题，即灾难性遗忘问题。在公共和专有的真实世界数据集上的大量实验证明了 TERACON 的优越性和实用性。我们的代码可以在 https://github.com/sein-kim/teracon 找到。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Task+Relation-aware+Continual+User+Representation+Learning)|0| -|[GraphSHA: Synthesizing Harder Samples for Class-Imbalanced Node Classification](https://doi.org/10.1145/3580305.3599374)|WenZhi Li, ChangDong Wang, Hui Xiong, JianHuang Lai|Sun Yat-sen University; The Hong Kong University of Science and Technology (Guangzhou)|Class imbalance is the phenomenon that some classes have much fewer instances than others, which is ubiquitous in real-world graph-structured scenarios. Recent studies find that off-the-shelf Graph Neural Networks (GNNs) would under-represent minor class samples. We investigate this phenomenon and discover that the subspaces of minor classes being squeezed by those of the major ones in the latent space is the main cause of this failure. We are naturally inspired to enlarge the decision boundaries of minor classes and propose a general framework GraphSHA by Synthesizing HArder minor samples. Furthermore, to avoid the enlarged minor boundary violating the subspaces of neighbor classes, we also propose a module called SemiMixup to transmit enlarged boundary information to the interior of the minor classes while blocking information propagation from minor classes to neighbor classes. Empirically, GraphSHA shows its effectiveness in enlarging the decision boundaries of minor classes, as it outperforms various baseline methods in class-imbalanced node classification with different GNN backbone encoders over seven public benchmark datasets. Code is avilable at https://github.com/wenzhilics/GraphSHA.|类不平衡是一些类的实例比其他类少得多的现象，这种现象在真实世界的图形结构场景中普遍存在。最近的研究发现，现成的图形神经网络(GNN)会低估次要类样本。我们研究了这一现象，发现潜空间中次类的子空间被主类的子空间挤压是导致这一失败的主要原因。我们很自然地受到了扩大次类决策边界的启发，并通过综合 HArder 次样本提出了一个通用的 GraphSHA 框架。此外，为了避免扩大的次边界侵犯邻居类的子空间，我们还提出了一个称为 SemiMixup 的模块来传输扩大的边界信息到次类的内部，同时阻止信息从次类传播到邻居类。实验表明，GraphSHA 在扩大次类的决策边界方面是有效的，因为它在七个公共基准数据集上使用不同的 GNN 骨干编码器进行类不平衡节点分类时优于各种基准方法。代码可在 https://github.com/wenzhilics/graphsha 下载。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=GraphSHA:+Synthesizing+Harder+Samples+for+Class-Imbalanced+Node+Classification)|0| +|[Task Relation-aware Continual User Representation Learning](https://doi.org/10.1145/3580305.3599516)|Sein Kim, Namkyeong Lee, Donghyun Kim, MinChul Yang, Chanyoung Park|KAIST; NAVER Corporation|User modeling, which learns to represent users into a low-dimensional representation space based on their past behaviors, got a surge of interest from the industry for providing personalized services to users. Previous efforts in user modeling mainly focus on learning a task-specific user representation that is designed for a single task. However, since learning task-specific user representations for every task is infeasible, recent studies introduce the concept of universal user representation, which is a more generalized representation of a user that is relevant to a variety of tasks. Despite their effectiveness, existing approaches for learning universal user representations are impractical in real-world applications due to the data requirement, catastrophic forgetting and the limited learning capability for continually added tasks. In this paper, we propose a novel continual user representation learning method, called TERACON, whose learning capability is not limited as the number of learned tasks increases while capturing the relationship between the tasks. The main idea is to introduce an embedding for each task, i.e., task embedding, which is utilized to generate task-specific soft masks that not only allow the entire model parameters to be updated until the end of training sequence, but also facilitate the relationship between the tasks to be captured. Moreover, we introduce a novel knowledge retention module with pseudo-labeling strategy that successfully alleviates the long-standing problem of continual learning, i.e., catastrophic forgetting. Extensive experiments on public and proprietary real-world datasets demonstrate the superiority and practicality of TERACON. Our code is available at https://github.com/Sein-Kim/TERACON.|用户建模是根据用户过去的行为学习如何将用户表示成一个低维的表示空间，因此为用户提供个性化的服务引起了业界的极大兴趣。以前的用户建模工作主要集中在学习为单个任务设计的特定于任务的用户表示。然而，由于学习任务特定的每个任务的用户表示是不可行的，最近的研究引入了通用用户表示的概念，这是一个更广泛的用户表示相关的各种任务。尽管现有的学习通用用户表示的方法很有效，但是由于数据需求、灾难性遗忘以及对不断增加的任务的学习能力有限，这些方法在实际应用中是不切实际的。在本文中，我们提出了一种新的持续用户表征学习方法，称为 TERACON，它的学习能力不受任务数量增加的限制，同时捕捉任务之间的关系。其主要思想是为每个任务引入一个嵌入，即任务嵌入，用于生成任务特定的软掩码，不仅允许整个模型参数更新直到训练序列结束，而且有利于任务之间的关系被捕获。此外，我们还引入了一个新的知识保留模块，该模块采用伪标记策略，成功地解决了长期以来存在的连续学习问题，即灾难性遗忘问题。在公共和专有的真实世界数据集上的大量实验证明了 TERACON 的优越性和实用性。我们的代码可以在 https://github.com/sein-kim/teracon 找到。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Task+Relation-aware+Continual+User+Representation+Learning)|0| +|[GraphSHA: Synthesizing Harder Samples for Class-Imbalanced Node Classification](https://doi.org/10.1145/3580305.3599374)|WenZhi Li, ChangDong Wang, Hui Xiong, JianHuang Lai|The Hong Kong University of Science and Technology (Guangzhou); Sun Yat-sen University|Class imbalance is the phenomenon that some classes have much fewer instances than others, which is ubiquitous in real-world graph-structured scenarios. Recent studies find that off-the-shelf Graph Neural Networks (GNNs) would under-represent minor class samples. We investigate this phenomenon and discover that the subspaces of minor classes being squeezed by those of the major ones in the latent space is the main cause of this failure. We are naturally inspired to enlarge the decision boundaries of minor classes and propose a general framework GraphSHA by Synthesizing HArder minor samples. Furthermore, to avoid the enlarged minor boundary violating the subspaces of neighbor classes, we also propose a module called SemiMixup to transmit enlarged boundary information to the interior of the minor classes while blocking information propagation from minor classes to neighbor classes. Empirically, GraphSHA shows its effectiveness in enlarging the decision boundaries of minor classes, as it outperforms various baseline methods in class-imbalanced node classification with different GNN backbone encoders over seven public benchmark datasets. Code is avilable at https://github.com/wenzhilics/GraphSHA.|类不平衡是一些类的实例比其他类少得多的现象，这种现象在真实世界的图形结构场景中普遍存在。最近的研究发现，现成的图形神经网络(GNN)会低估次要类样本。我们研究了这一现象，发现潜空间中次类的子空间被主类的子空间挤压是导致这一失败的主要原因。我们很自然地受到了扩大次类决策边界的启发，并通过综合 HArder 次样本提出了一个通用的 GraphSHA 框架。此外，为了避免扩大的次边界侵犯邻居类的子空间，我们还提出了一个称为 SemiMixup 的模块来传输扩大的边界信息到次类的内部，同时阻止信息从次类传播到邻居类。实验表明，GraphSHA 在扩大次类的决策边界方面是有效的，因为它在七个公共基准数据集上使用不同的 GNN 骨干编码器进行类不平衡节点分类时优于各种基准方法。代码可在 https://github.com/wenzhilics/graphsha 下载。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=GraphSHA:+Synthesizing+Harder+Samples+for+Class-Imbalanced+Node+Classification)|0| |[Physics-Guided Discovery of Highly Nonlinear Parametric Partial Differential Equations](https://doi.org/10.1145/3580305.3599466)|Yingtao Luo, Qiang Liu, Yuntian Chen, Wenbo Hu, Tian Tian, Jun Zhu||Partial differential equations (PDEs) fitting scientific data can represent physical laws with explainable mechanisms for various mathematically-oriented subjects. The data-driven discovery of PDEs from scientific data thrives as a new attempt to model complex phenomena in nature, but the effectiveness of current practice is typically limited by the scarcity of data and the complexity of phenomena. Especially, the discovery of PDEs with highly nonlinear coefficients from low-quality data remains largely under-addressed. To deal with this challenge, we propose a novel physics-guided learning method, which can not only encode observation knowledge such as initial and boundary conditions but also incorporate the basic physical principles and laws to guide the model optimization. We empirically demonstrate that the proposed method is more robust against data noise and sparsity, and can reduce the estimation error by a large margin; moreover, for the first time we are able to discover PDEs with highly nonlinear coefficients. With the promising performance, the proposed method pushes forward the boundary of the PDEs that can be found by machine learning models for scientific discovery.|拟合科学数据的偏微分方程(PDE)可以表示各种数学导向学科的具有可解释机制的物理规律。数据驱动发现偏微分方程的科学数据蓬勃发展，作为一种新的尝试模拟自然界中的复杂现象，但目前的做法的有效性通常受到数据稀缺和现象复杂性的限制。特别是，从低质量数据中发现具有高度非线性系数的偏微分方程仍然是一个很大的问题。为了解决这一问题，我们提出了一种新的物理导向学习方法，它不仅可以对初始条件和边界条件等观测知识进行编码，而且可以结合基本的物理原理和规律来指导模型的优化。实验结果表明，该方法对数据噪声和稀疏性具有较强的鲁棒性，能够大幅度地减小估计误差，并且首次发现了具有高度非线性系数的偏微分方程。该方法具有良好的性能，为科学发现推进了机器学习模型所能找到的偏微分方程的边界。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Physics-Guided+Discovery+of+Highly+Nonlinear+Parametric+Partial+Differential+Equations)|0| |[Online Fairness Auditing through Iterative Refinement](https://doi.org/10.1145/3580305.3599454)|Pranav Maneriker, Codi Burley, Srinivasan Parthasarathy||A sizable proportion of deployed machine learning models make their decisions in a black-box manner. Such decision-making procedures are susceptible to intrinsic biases, which has led to a call for accountability in deployed decision systems. In this work, we investigate mechanisms that help audit claimed mathematical guarantees of the fairness of such systems. We construct AVOIR, a system that reduces the number of observations required for the runtime monitoring of probabilistic assertions over fairness metrics specified on decision functions associated with black-box AI models. AVOIR provides an adaptive process that automates the inference of probabilistic guarantees associated with estimating a wide range of fairness metrics. In addition, AVOIR enables the exploration of fairness violations aligned with governance and regulatory requirements. We conduct case studies with fairness metrics on three different datasets and demonstrate how AVOIR can help detect and localize fairness violations and ameliorate the issues with faulty fairness metric design.|相当一部分已部署的机器学习模型以黑盒方式作出决策。这种决策程序容易产生内在的偏见，导致要求在已部署的决策系统中实行问责制。在这项工作中，我们研究的机制，有助于审计声称的数学保证，这些系统的公平性。我们构建了 AVOIR 系统，该系统减少了运行时监测与黑盒 AI 模型相关的决策函数中指定的公平性指标的概率断言所需的观测数量。AVOIR 提供了一个自适应过程，自动推断与估计广泛的公平性度量相关的概率保证。此外，AVOIR 还支持探索与治理和监管需求相一致的违反公平性的行为。我们在三个不同的数据集上进行了公平性度量的案例研究，并演示了 AVOIR 如何帮助检测和定位违反公平性的行为，以及如何改善错误公平性度量设计的问题。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Online+Fairness+Auditing+through+Iterative+Refinement)|0| |[Online Level-wise Hierarchical Clustering](https://doi.org/10.1145/3580305.3599455)|Nicholas Monath, Manzil Zaheer, Andrew McCallum||Online hierarchical clustering algorithms, compared to their scalable batch setting counterparts, typically provide more limited accuracy and efficiency performance. Yet, when data is incrementally arriving, a crucial setting in many clustering applications (e.g., entity resolution and concept discovery), these batch setting algorithms do not apply. This paper presents a family of new algorithms for online hierarchical clustering that combine high quality trees and fast per-point insertion time--made possible through a limited number of parallel non-greedy tree re-arrangements. We analyze our methods under assumptions about the data and the separability of clusters. Empirically, we find that our proposed algorithms yield state-of-the-art results in hierarchical clustering dendrogram purity and in building compressed prototypes for a k-nearest representative classifier.|在线分层聚类算法与其可扩展的批处理设置算法相比，通常提供更有限的准确性和效率性能。然而，当数据以递增方式到达时，这是许多集群应用程序(例如，实体解析和概念发现)中的关键设置，这些批处理设置算法并不适用。本文提出了一系列新的在线层次聚类算法，它们结合了高质量的树和快速的每点插入时间——通过有限数量的并行非贪婪树重排实现。我们分析了我们的方法在假设的数据和集群的可分性。经验上，我们发现我们提出的算法在分层聚类树状图纯度和建立 k- 最近代表性分类器的压缩原型方面取得了最先进的结果。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Online+Level-wise+Hierarchical+Clustering)|0| |[Cracking White-box DNN Watermarks via Invariant Neuron Transforms](https://doi.org/10.1145/3580305.3599291)|Xudong Pan, Mi Zhang, Yifan Yan, Yining Wang, Min Yang|; Fudan University|Recently, how to protect the Intellectual Property (IP) of deep neural networks (DNN) becomes a major concern for the AI industry. To combat potential model piracy, recent works explore various watermarking strategies to embed secret identity messages into the prediction behaviors or the internals (e.g., weights and neuron activation) of the target model. Sacrificing less functionality and involving more knowledge about the target model, the latter branch of watermarking schemes (i.e., white-box model watermarking) is claimed to be accurate, credible and secure against most known watermark removal attacks, with emerging research efforts and applications in the industry. In this paper, we present the first effective removal attack which cracks almost all the existing white-box watermarking schemes with provably no performance overhead and no required prior knowledge. By analyzing these IP protection mechanisms at the granularity of neurons, we for the first time discover their common dependence on a set of fragile features of a local neuron group, all of which can be arbitrarily tampered by our proposed chain of invariant neuron transforms. On $9$ state-of-the-art white-box watermarking schemes and a broad set of industry-level DNN architectures, our attack for the first time reduces the embedded identity message in the protected models to be almost random. Meanwhile, unlike known removal attacks, our attack requires no prior knowledge on the training data distribution or the adopted watermark algorithms, and leaves model functionality intact.|近年来，如何保护深层神经网络(DNN)的知识产权成为人工智能产业关注的主要问题。为了打击潜在的盗版模型，最近的研究探索了各种水印策略，将秘密身份信息嵌入到目标模型的预测行为或内部(如权重和神经元激活)中。水印技术的后一个分支(即白盒模型水印)牺牲了较少的功能，涉及更多关于目标模型的知识，被认为是准确、可靠和安全的，能够抵御大多数已知的水印去除攻击，并且在业界中得到了新的研究和应用。在本文中，我们提出了第一个有效的移除攻击，这个攻击破坏了几乎所有现有的白盒水印方案，并且可以证明没有性能开销和不需要先验知识。通过分析神经元粒度上的这些 IP 保护机制，我们首次发现它们对局部神经元群的一组脆弱特征的共同依赖性，所有这些特征都可以被我们提出的不变神经元变换链任意篡改。在9美元的国家最先进的白盒水印方案和广泛的行业级 DNN 架构，我们的攻击第一次减少嵌入的身份信息在受保护的模型几乎是随机的。同时，与已知的移除攻击不同，我们的攻击不需要关于训练数据分布或所采用的水印算法的先验知识，并且保留了模型的功能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Cracking+White-box+DNN+Watermarks+via+Invariant+Neuron+Transforms)|0| -|[Graph Neural Bandits](https://doi.org/10.1145/3580305.3599371)|Yunzhe Qi, Yikun Ban, Jingrui He|University of Illinois at Urbana-Champaign; University of Illinois, Urbana Champaign|Contextual bandits aim to choose the optimal arm with the highest reward out of a set of candidates based on their contextual information, and various bandit algorithms have been applied to personalized recommendation due to their ability of solving the exploitation-exploration dilemma. Motivated by online recommendation scenarios, in this paper, we propose a framework named Graph Neural Bandits (GNB) to leverage the collaborative nature among users empowered by graph neural networks (GNNs). Instead of estimating rigid user clusters, we model the "fine-grained'' collaborative effects through estimated user graphs in terms of exploitation and exploration individually. Then, to refine the recommendation strategy, we utilize separate GNN-based models on estimated user graphs for exploitation and adaptive exploration. Theoretical analysis and experimental results on multiple real data sets in comparison with state-of-the-art baselines are provided to demonstrate the effectiveness of our proposed framework.|关联强盗的目标是根据候选人的关联信息从一组候选人中选择报酬最高的最优组合，各种强盗算法因其解决开发-探索两难问题的能力而被应用于个性化推荐。受在线推荐场景的启发，本文提出了一种基于图形神经网络的用户协作框架 GNB。我们没有对刚性用户集群进行估计，而是根据开发和探索的不同，通过估计的用户图对“细粒度”的协作效果进行建模。然后，为了完善推荐策略，我们利用基于 GNN 的分离模型对估计用户图进行开发和自适应探索。通过对多个实际数据集的理论分析和实验结果与最新基线的比较，证明了我们提出的框架的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Graph+Neural+Bandits)|0| +|[Graph Neural Bandits](https://doi.org/10.1145/3580305.3599371)|Yunzhe Qi, Yikun Ban, Jingrui He|University of Illinois, Urbana Champaign; University of Illinois at Urbana-Champaign|Contextual bandits aim to choose the optimal arm with the highest reward out of a set of candidates based on their contextual information, and various bandit algorithms have been applied to personalized recommendation due to their ability of solving the exploitation-exploration dilemma. Motivated by online recommendation scenarios, in this paper, we propose a framework named Graph Neural Bandits (GNB) to leverage the collaborative nature among users empowered by graph neural networks (GNNs). Instead of estimating rigid user clusters, we model the "fine-grained'' collaborative effects through estimated user graphs in terms of exploitation and exploration individually. Then, to refine the recommendation strategy, we utilize separate GNN-based models on estimated user graphs for exploitation and adaptive exploration. Theoretical analysis and experimental results on multiple real data sets in comparison with state-of-the-art baselines are provided to demonstrate the effectiveness of our proposed framework.|关联强盗的目标是根据候选人的关联信息从一组候选人中选择报酬最高的最优组合，各种强盗算法因其解决开发-探索两难问题的能力而被应用于个性化推荐。受在线推荐场景的启发，本文提出了一种基于图形神经网络的用户协作框架 GNB。我们没有对刚性用户集群进行估计，而是根据开发和探索的不同，通过估计的用户图对“细粒度”的协作效果进行建模。然后，为了完善推荐策略，我们利用基于 GNN 的分离模型对估计用户图进行开发和自适应探索。通过对多个实际数据集的理论分析和实验结果与最新基线的比较，证明了我们提出的框架的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Graph+Neural+Bandits)|0| |[Source-Free Domain Adaptation with Temporal Imputation for Time Series Data](https://doi.org/10.1145/3580305.3599507)|Mohamed Ragab, Emadeldeen Eldele, Min Wu, ChuanSheng Foo, Xiaoli Li, Zhenghua Chen|Institute for Infocomm Research, Agency for Science Technology and Research (A*STAR); Center for Frontier AI Research, Agency for Science and Technology and Research (A*STAR)|Source-free domain adaptation (SFDA) aims to adapt a pretrained model from a labeled source domain to an unlabeled target domain without access to the source domain data, preserving source domain privacy. Despite its prevalence in visual applications, SFDA is largely unexplored in time series applications. The existing SFDA methods that are mainly designed for visual applications may fail to handle the temporal dynamics in time series, leading to impaired adaptation performance. To address this challenge, this paper presents a simple yet effective approach for source-free domain adaptation on time series data, namely MAsk and imPUte (MAPU). First, to capture temporal information of the source domain, our method performs random masking on the time series signals while leveraging a novel temporal imputer to recover the original signal from a masked version in the embedding space. Second, in the adaptation step, the imputer network is leveraged to guide the target model to produce target features that are temporally consistent with the source features. To this end, our MAPU can explicitly account for temporal dependency during the adaptation while avoiding the imputation in the noisy input space. Our method is the first to handle temporal consistency in SFDA for time series data and can be seamlessly equipped with other existing SFDA methods. Extensive experiments conducted on three real-world time series datasets demonstrate that our MAPU achieves significant performance gain over existing methods. Our code is available at \url{https://github.com/mohamedr002/MAPU_SFDA_TS}.|无源域适应(SFDA)的目标是在不访问源域数据的情况下，将预先训练好的模型从标记的源域适应到未标记的目标域，从而保护源域的隐私。尽管 SFDA 在视觉应用方面很流行，但在时间序列应用方面却很大程度上没有得到探索。现有的 SFDA 方法主要是为视觉应用而设计的，可能无法处理时间序列中的时间动态，导致适应性能受损。为了解决这一问题，本文提出了一种简单而有效的时间序列数据无源域自适应方法，即 MAsk 和 imPUte (MAPU)。首先，为了获取源域的时间信息，该方法对时间序列信号进行随机掩蔽，同时利用一种新的时间计算机在嵌入空间中从掩蔽版本中恢复原始信号。其次，在适应步骤中，利用计算机网络引导目标模型产生与源特征在时间上一致的目标特征。为此，我们的 MAPU 可以明确地说明在适应期间的时间依赖性，同时避免了在噪声输入空间的插补。我们的方法是第一个处理时间序列数据时间一致性的 SFDA 方法，可以与其他现有的 SFDA 方法无缝配备。在三个实际时间序列数据集上进行的大量实验表明，我们的 MAPU 比现有的方法获得了显著的性能提高。我们的代码可以在 url { https://github.com/mohamedr002/mapu_sfda_ts }找到。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Source-Free+Domain+Adaptation+with+Temporal+Imputation+for+Time+Series+Data)|0| |[Causal Effect Estimation on Hierarchical Spatial Graph Data](https://doi.org/10.1145/3580305.3599269)|Koh Takeuchi, Ryo Nishida, Hisashi Kashima, Masaki Onishi||Estimating individual treatment effects from observational data is a fundamental problem in causal inference. To accurately estimate treatment effects in the spatial domain, we need to address certain aspects such as how to use the spatial coordinates of covariates and treatments and how the covariates and the treatments interact spatially. We introduce a new problem of predicting treatment effects on time series outcomes from spatial graph data with a hierarchical structure. To address this problem, we propose a spatial intervention neural network (SINet) that leverages the hierarchical structure of spatial graphs to learn a rich representation of the covariates and the treatments and exploits this representation to predict a time series of treatment outcome. Using a multi-agent simulator, we synthesized a crowd movement guidance dataset and conduct experiments to estimate the conditional average treatment effect, where we considered the initial locations of the crowds as covariates, route guidance as a treatment, and number of agents reaching a goal at each time stamp as the outcome. We employed state-of-the-art spatio-temporal graph neural networks and neural network-based causal inference methods as baselines, and show that our proposed method outperformed baselines both quantitatively and qualitatively.|根据观测数据估计个体治疗效果是因果推断中的一个基本问题。为了准确地估计空间域中的治疗效果，我们需要解决某些方面的问题，例如如何使用协变量和治疗的空间坐标，以及协变量和治疗如何在空间上相互作用。我们提出了一个新的问题，预测治疗效果的时间序列结果的空间图数据与层次结构。为了解决这个问题，我们提出了一种空间干预神经网络(SINet) ，它利用空间图的层次结构来学习协变量和治疗的丰富表示，并利用这种表示来预测治疗结果的时间序列。使用多智能体模拟器，我们合成了一个人群运动指导数据集，并进行实验来估计条件平均治疗效果，其中我们考虑了人群的初始位置作为协变量，路径指导作为治疗，以及在每个时间戳达到目标的代理人数量作为结果。我们采用了最先进的时空图神经网络和基于神经网络的因果推理方法作为基线，并表明我们提出的方法在定量和定性上都优于基线。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Causal+Effect+Estimation+on+Hierarchical+Spatial+Graph+Data)|0| |[Networked Time Series Imputation via Position-aware Graph Enhanced Variational Autoencoders](https://doi.org/10.1145/3580305.3599444)|Dingsu Wang, Yuchen Yan, Ruizhong Qiu, Yada Zhu, Kaiyu Guan, Andrew Margenot, Hanghang Tong|IBM Research; University of Illinois at Urbana-Champaign|Multivariate time series (MTS) imputation is a widely studied problem in recent years. Existing methods can be divided into two main groups, including (1) deep recurrent or generative models that primarily focus on time series features, and (2) graph neural networks (GNNs) based models that utilize the topological information from the inherent graph structure of MTS as relational inductive bias for imputation. Nevertheless, these methods either neglect topological information or assume the graph structure is fixed and accurately known. Thus, they fail to fully utilize the graph dynamics for precise imputation in more challenging MTS data such as networked time series (NTS), where the underlying graph is constantly changing and might have missing edges. In this paper, we propose a novel approach to overcome these limitations. First, we define the problem of imputation over NTS which contains missing values in both node time series features and graph structures. Then, we design a new model named PoGeVon which leverages variational autoencoder (VAE) to predict missing values over both node time series features and graph structures. In particular, we propose a new node position embedding based on random walk with restart (RWR) in the encoder with provable higher expressive power compared with message-passing based graph neural networks (GNNs). We further design a decoder with 3-stage predictions from the perspective of multi-task learning to impute missing values in both time series and graph structures reciprocally. Experiment results demonstrate the effectiveness of our model over baselines.|多变量时间序列(MTS)插补是近年来研究较多的一个问题。现有的方法可以分为两大类，包括(1)主要关注时间序列特征的深度递归或生成模型，和(2)基于图神经网络(GNN)的模型，这些模型利用 MTS 固有图结构的拓扑信息作为关系归纳偏差进行插补。然而，这些方法或者忽略了拓扑信息，或者假设图的结构是固定的并且已知的。因此，他们未能充分利用图动力学精确插补更具挑战性的 MTS 数据，如网络时间序列(NTS) ，其中底层图是不断变化的，可能有缺失的边。在本文中，我们提出了一种新的方法来克服这些限制。首先，我们定义了包含节点时间序列特征和图结构缺失值的 NTS 插补问题。然后，我们设计了一个新的模型 PoGeVon，它利用变分自动编码器(VAE)来预测节点时间序列特征和图结构上的缺失值。特别地，我们提出了一种新的基于重启随机游走(RWR)的节点位置嵌入编码器，与基于消息传递的图形神经网络(GNN)相比，具有可证明的更高的表达能力。我们进一步从多任务学习的角度设计了一个具有三阶段预测的解码器来相互推算时间序列和图结构中的缺失值。实验结果证明了该模型在基线上的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Networked+Time+Series+Imputation+via+Position-aware+Graph+Enhanced+Variational+Autoencoders)|0| -|[Incremental Causal Graph Learning for Online Root Cause Analysis](https://doi.org/10.1145/3580305.3599392)|Dongjie Wang, Zhengzhang Chen, Yanjie Fu, Yanchi Liu, Haifeng Chen|University of Central Florida; |The task of root cause analysis (RCA) is to identify the root causes of system faults/failures by analyzing system monitoring data. Efficient RCA can greatly accelerate system failure recovery and mitigate system damages or financial losses. However, previous research has mostly focused on developing offline RCA algorithms, which often require manually initiating the RCA process, a significant amount of time and data to train a robust model, and then being retrained from scratch for a new system fault. In this paper, we propose CORAL, a novel online RCA framework that can automatically trigger the RCA process and incrementally update the RCA model. CORAL consists of Trigger Point Detection, Incremental Disentangled Causal Graph Learning, and Network Propagation-based Root Cause Localization. The Trigger Point Detection component aims to detect system state transitions automatically and in near-real-time. To achieve this, we develop an online trigger point detection approach based on multivariate singular spectrum analysis and cumulative sum statistics. To efficiently update the RCA model, we propose an incremental disentangled causal graph learning approach to decouple the state-invariant and state-dependent information. After that, CORAL applies a random walk with restarts to the updated causal graph to accurately identify root causes. The online RCA process terminates when the causal graph and the generated root cause list converge. Extensive experiments on three real-world datasets demonstrate the effectiveness and superiority of the proposed framework.|根本原因分析的任务是透过分析系统监察数据，找出系统故障/失效的根本原因。有效的 RCA 可以大大加速系统故障恢复，减轻系统损坏或财务损失。然而，以往的研究主要集中在开发离线 RCA 算法，这往往需要人工启动 RCA 过程，需要大量的时间和数据来训练鲁棒模型，然后从头开始对新的系统故障进行再训练。在本文中，我们提出了一个新的在线 RCA 框架 CORAL，它可以自动触发 RCA 过程，并逐步更新 RCA 模型。CORAL 包括触发点检测、增量分离因果图学习和基于网络传播的根源定位。触发点检测组件的目的是自动检测系统状态转换并接近实时。为此，提出了一种基于多元奇异谱分析和累积和统计量的在线触发点检测方法。为了有效地更新 RCA 模型，我们提出了一种增量式解纠缠因果图学习方法来解耦状态不变和状态相关的信息。之后，CORAL 对更新的因果图应用随机游走并重新启动，以准确识别根本原因。当因果图和生成的根原因列表收敛时，在线 RCA 进程终止。在三个实际数据集上的大量实验表明了该框架的有效性和优越性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Incremental+Causal+Graph+Learning+for+Online+Root+Cause+Analysis)|0| +|[Incremental Causal Graph Learning for Online Root Cause Analysis](https://doi.org/10.1145/3580305.3599392)|Dongjie Wang, Zhengzhang Chen, Yanjie Fu, Yanchi Liu, Haifeng Chen|; University of Central Florida|The task of root cause analysis (RCA) is to identify the root causes of system faults/failures by analyzing system monitoring data. Efficient RCA can greatly accelerate system failure recovery and mitigate system damages or financial losses. However, previous research has mostly focused on developing offline RCA algorithms, which often require manually initiating the RCA process, a significant amount of time and data to train a robust model, and then being retrained from scratch for a new system fault. In this paper, we propose CORAL, a novel online RCA framework that can automatically trigger the RCA process and incrementally update the RCA model. CORAL consists of Trigger Point Detection, Incremental Disentangled Causal Graph Learning, and Network Propagation-based Root Cause Localization. The Trigger Point Detection component aims to detect system state transitions automatically and in near-real-time. To achieve this, we develop an online trigger point detection approach based on multivariate singular spectrum analysis and cumulative sum statistics. To efficiently update the RCA model, we propose an incremental disentangled causal graph learning approach to decouple the state-invariant and state-dependent information. After that, CORAL applies a random walk with restarts to the updated causal graph to accurately identify root causes. The online RCA process terminates when the causal graph and the generated root cause list converge. Extensive experiments on three real-world datasets demonstrate the effectiveness and superiority of the proposed framework.|根本原因分析的任务是透过分析系统监察数据，找出系统故障/失效的根本原因。有效的 RCA 可以大大加速系统故障恢复，减轻系统损坏或财务损失。然而，以往的研究主要集中在开发离线 RCA 算法，这往往需要人工启动 RCA 过程，需要大量的时间和数据来训练鲁棒模型，然后从头开始对新的系统故障进行再训练。在本文中，我们提出了一个新的在线 RCA 框架 CORAL，它可以自动触发 RCA 过程，并逐步更新 RCA 模型。CORAL 包括触发点检测、增量分离因果图学习和基于网络传播的根源定位。触发点检测组件的目的是自动检测系统状态转换并接近实时。为此，提出了一种基于多元奇异谱分析和累积和统计量的在线触发点检测方法。为了有效地更新 RCA 模型，我们提出了一种增量式解纠缠因果图学习方法来解耦状态不变和状态相关的信息。之后，CORAL 对更新的因果图应用随机游走并重新启动，以准确识别根本原因。当因果图和生成的根原因列表收敛时，在线 RCA 进程终止。在三个实际数据集上的大量实验表明了该框架的有效性和优越性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Incremental+Causal+Graph+Learning+for+Online+Root+Cause+Analysis)|0| |[Treatment Effect Estimation with Adjustment Feature Selection](https://doi.org/10.1145/3580305.3599531)|Haotian Wang, Kun Kuang, Haoang Chi, Longqi Yang, Mingyang Geng, Wanrong Huang, Wenjing Yang||In causal inference, it is common to select a subset of observed covariates, named the adjustment features, to be adjusted for estimating the treatment effect. For real-world applications, the abundant covariates are usually observed, which contain extra variables partially correlating to the treatment (treatment-only variables, e.g., instrumental variables) or the outcome (outcome-only variables, e.g., precision variables) besides the confounders (variables that affect both the treatment and outcome). In principle, unbiased treatment effect estimation is achieved once the adjustment features contain all the confounders. However, the performance of empirical estimations varies a lot with different extra variables. To solve this issue, variable separation/selection for treatment effect estimation has received growing attention when the extra variables contain instrumental variables and precision variables. In this paper, assuming no mediator variables exist, we consider a more general setting by allowing for the existence of post-treatment and post-outcome variables rather than instrumental and precision variables in observed covariates. Our target is to separate the treatment-only variables from the adjustment features. To this end, we establish a metric named Optimal Adjustment Features(OAF), which empirically measures the asymptotic variance of the estimation. Theoretically, we show that our OAF metric is minimized if and only if adjustment features consist of the confounders and outcome-only variables, i.e., the treatment-only variables are perfectly separated. As optimizing the OAF metric is a combinatorial optimization problem, we introduce Reinforcement Learning (RL) and adopt the policy gradient to search for the optimal adjustment set. Empirical results on both synthetic and real-world datasets demonstrate that (a) our method successfully searches the optimal adjustment features and (b) the searched adjustment features achieve a more precise estimation of the treatment effect.|在因果推断中，通常选择一个观察到的协变量子集，命名为调整特征，进行调整以估计治疗效果。对于真实世界的应用，通常观察到丰富的协变量，其包含与治疗(仅治疗变量，例如工具变量)或结果(仅结果变量，例如精度变量)部分相关的额外变量除了混杂因素(影响治疗和结果的变量)。原则上，一旦调整特征包含所有的混杂因素，就可以实现无偏的治疗效果估计。然而，不同的额外变量对经验估计的表现有很大的影响。为了解决这个问题，当额外变量包含工具变量和精度变量时，用于治疗效果估计的变量分离/选择越来越受到关注。在本文中，假设不存在中介变量，我们考虑一个更一般的设置，允许存在治疗后和结果后变量，而不是观察到的协变量中的工具和精度变量。我们的目标是从调整特征中分离出仅用于治疗的变量。为此，我们建立了一个称为最优平差特征(OAF)的度量，它经验地测量估计的渐近方差。理论上，我们表明，我们的 OAF 度量是最小化的，当且仅当调整特征由混杂因素和仅结果变量组成，即，仅治疗变量是完全分离的。由于优化经营成本调整幅度是一个组合优化问题，我们引入强化学习，并采用政策梯度来寻找最佳的调整集合。在合成数据集和实际数据集上的实验结果表明: (a)我们的方法成功地搜索到了最优的平差特征; (b)搜索到的平差特征实现了对治疗效果的更精确的估计。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Treatment+Effect+Estimation+with+Adjustment+Feature+Selection)|0| |[Adversarial Constrained Bidding via Minimax Regret Optimization with Causality-Aware Reinforcement Learning](https://doi.org/10.1145/3580305.3599254)|Haozhe Wang, Chao Du, Panyan Fang, Li He, Liang Wang, Bo Zheng|Alibaba Group|The proliferation of the Internet has led to the emergence of online advertising, driven by the mechanics of online auctions. In these repeated auctions, software agents participate on behalf of aggregated advertisers to optimize for their long-term utility. To fulfill the diverse demands, bidding strategies are employed to optimize advertising objectives subject to different spending constraints. Existing approaches on constrained bidding typically rely on i.i.d. train and test conditions, which contradicts the adversarial nature of online ad markets where different parties possess potentially conflicting objectives. In this regard, we explore the problem of constrained bidding in adversarial bidding environments, which assumes no knowledge about the adversarial factors. Instead of relying on the i.i.d. assumption, our insight is to align the train distribution of environments with the potential test distribution meanwhile minimizing policy regret. Based on this insight, we propose a practical Minimax Regret Optimization (MiRO) approach that interleaves between a teacher finding adversarial environments for tutoring and a learner meta-learning its policy over the given distribution of environments. In addition, we pioneer to incorporate expert demonstrations for learning bidding strategies. Through a causality-aware policy design, we improve upon MiRO by distilling knowledge from the experts. Extensive experiments on both industrial data and synthetic data show that our method, MiRO with Causality-aware reinforcement Learning (MiROCL), outperforms prior methods by over 30%.|互联网的扩散导致了在线广告的出现，这是由在线拍卖的机制所驱动的。在这些重复的拍卖中，软件代理商代表广告主集合参与，以优化他们的长期效用。为了满足不同的需求，投标策略被用来优化受不同支出约束的广告目标。现有的限制性投标方法通常依赖于身份证培训和测试条件，这与在线广告市场的对抗性质相矛盾，因为在线广告市场中，不同的当事人拥有潜在的相互冲突的目标。在这方面，我们探讨了在不考虑竞争因素的情况下，在竞争性投标环境下的约束投标问题。我们的洞察力不是依赖于内部识别假设，而是使环境的列车分布与潜在的测试分布保持一致，同时最大限度地减少政策遗憾。基于这种观点，我们提出了一种实用的极大极小遗憾优化(Miniax Regret Optimation，MiRO)方法，该方法在教师寻找对抗性的辅导环境和学习者元学习策略之间进行交叉。此外，我们率先采用专家演示学习投标策略。通过一个因果关系感知策略设计，我们从专家那里提取知识来改进 MiRO。对工业数据和合成数据的大量实验表明，我们的方法，带有因果感知强化学习(miROCL)的 miRO，比之前的方法性能高出30% 以上。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Adversarial+Constrained+Bidding+via+Minimax+Regret+Optimization+with+Causality-Aware+Reinforcement+Learning)|0| |[Efficient Sparse Linear Bandits under High Dimensional Data](https://doi.org/10.1145/3580305.3599329)|Xue Wang, Mike Mingcheng Wei, Tao Yao||We propose a computationally efficient Lasso Random Project Bandit (LRP-Bandit) algorithm for sparse linear bandit problems under high-dimensional settings with limited samples. LRP-Bandit bridges Lasso and Random Projection as feature selection and dimension reduction techniques to alleviate the computational complexity and improve the regret performance. We demonstrate that for the total feature dimension d, the significant feature dimension s, and the sample size T, the expected cumulative regret under LRP-Bandit is upper bounded by Õ (T 2 over 3 s 3 over 2 log 7 over 6 d), where Õ suppresses the logarithmic dependence on T. Further, we show that when available samples are larger than a problem-dependent threshold, the regret upper bound for LRP-Bandit can be further improved to Õ (s√T log d). These regret upper bounds on T for both data-poor and data-rich regimes match the theoretical minimax lower bounds up to logarithmic factors. Through experiments, we show that LRP-Bandit is computationally efficient and outperforms other benchmarks on the expected cumulative regret.|针对有限样本条件下高维环境下的稀疏线性匪问题，提出了一种计算效率较高的套索随机投影匪(LRP-Bandit)算法。LRP-Bandit 桥接套索和随机投影作为特征选择和维度减化技术，以减轻计算复杂性和提高后悔性能。我们证明了对于总特征维数 d，显著特征维数 s 和样本大小 T，LRP-Bandit 下的预期累积遗憾上界为 Õ (T2/3s3/2log7/6d) ，其中 Õ 抑制了对 T 的对数依赖性。此外，我们还证明了当可用样本大于问题相关阈值时，LRP-Bandit 的遗憾上界可以进一步改进为 Õ (s √ T log d)。这些遗憾的上界 T 的数据贫乏和数据丰富的制度匹配的理论极大极小下界直到对数因子。实验结果表明，LRP-Bandit 算法具有较高的计算效率，在预期累积遗憾方面优于其他基准。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Efficient+Sparse+Linear+Bandits+under+High+Dimensional+Data)|0| |[MicroscopeSketch: Accurate Sliding Estimation Using Adaptive Zooming](https://doi.org/10.1145/3580305.3599432)|Yuhan Wu, Shiqi Jiang, Siyuan Dong, Zheng Zhong, Jiale Chen, Yutong Hu, Tong Yang, Steve Uhlig, Bin Cui||High-accuracy real-time data stream estimations are critical for various applications, and sliding-window-based techniques have attracted wide attention. However, existing solutions struggle to achieve high accuracy, generality, and low memory usage simultaneously. To overcome these limitations, we present MicroscopeSketch, a high-accuracy sketch framework. Our key technique, called adaptive zooming, dynamically adjusts the granularity of counters to maximize accuracy while minimizing memory usage. By applying MicroscopeSketch to three specific tasks---frequency estimation, top-k frequent items discovery, and top-k heavy changes identification-we demonstrate substantial improvements over existing methods, reducing errors by roughly 4 times for frequency estimation and 3 times for identifying top-k items. The relevant source code is available in a GitHub repository.|高精度的实时数据流估计是各种应用的关键，基于滑动窗口的数据流估计技术已经引起了人们的广泛关注。然而，现有的解决方案难以同时实现高精度、通用性和低内存使用率。为了克服这些限制，我们提出了一个高精度的草图框架 MicrocopeSketch。我们的关键技术，称为自适应缩放，动态调整计数器的粒度，以最大限度地提高准确性，同时最大限度地减少内存使用。通过将 Microscope Sketch 应用于三个特定的任务——频率估计、 top-k 频繁项目发现和 top-k 重大变化识别——我们证明了对现有方法的实质性改进，频率估计的误差减少了大约4倍，识别 top-k 项目的误差减少了3倍。相关的源代码可以在 GitHub 存储库中找到。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=MicroscopeSketch:+Accurate+Sliding+Estimation+Using+Adaptive+Zooming)|0| -|[Learning Behavior-oriented Knowledge Tracing](https://doi.org/10.1145/3580305.3599407)|Bihan Xu, Zhenya Huang, Jiayu Liu, Shuanghong Shen, Qi Liu, Enhong Chen, Jinze Wu, Shijin Wang|School of Data Science, University of Science and Technology of China; School of Computer Science and Technology, University of Science and Technology of China; State Key Laboratory of Cognitive Intelligence; Anhui Province Key Laboratory of Big Data Analysis and Application, University of Science and Technology of China; iFLYTEK Research, iFLYTEK Co., Ltd|Exploring how learners' knowledge states evolve during the learning activities is a critical task in online learning systems, which can facilitate personalized services downstream, such as course recommendation. Most of existing methods have devoted great efforts to analyzing learners' knowledge states according to their responses (i.e., right or wrong) to different questions. However, the significant effect of learners' learning behaviors (e.g., answering speed, the number of attempts) is omitted, which can reflect their knowledge acquisition deeper and ensure the reliability of the response. In this paper, we propose a Learning Behavior-oriented Knowledge Tracing (LBKT) model, with the goal of explicitly exploring the learning behavior effects on learners' knowledge states. Specifically, we first analyze and summarize several dominated learning behaviors including Speed, Attempts and Hints in the learning process. As the characteristics of different learning behaviors vary greatly, we separately estimate their various effects on learners' knowledge acquisition in a quantitative manner. Then, considering that different learning behaviors are closely dependent with each other, we assess the fused effect of multiple learning behaviors by capturing their complex dependent patterns. Finally, we integrate the forgetting factor with learners' knowledge acquisition to comprehensively update their changing knowledge states in learning. Extensive experimental results on several public datasets demonstrate that our model generates better performance prediction for learners against existing methods. Moreover, LBKT shows good interpretability in tracking learners' knowledge state by incorporating the learning behavior effects. Our codes are available at https://github.com/xbh0720/LBKT.|研究学习者的知识状态在学习活动中如何演变是在线学习系统的一个关键任务，它可以促进个性化的下游服务，如课程推荐。现有的大多数方法都致力于根据学习者对不同问题的反应(即对或错)来分析学习者的知识状态。然而，学习者的学习行为(如答题速度、尝试次数)的显著影响被忽略了，这可以更深刻地反映他们的知识获得，并保证反应的可靠性。本文提出了一个面向学习行为的知识追踪模型(LBKT) ，旨在明确探讨学习行为对学习者知识状态的影响。具体来说，我们首先分析和总结了学习过程中的几种主导性学习行为，包括速度、尝试和提示。由于不同学习行为的特点差异很大，我们分别以定量的方式估计了它们对学习者知识习得的不同影响。然后，考虑到不同学习行为之间的相互依赖性，我们通过捕捉多个学习行为的复杂依赖模式来评估它们的融合效应。最后，将遗忘因素与学习者的知识习得相结合，全面更新学习过程中不断变化的知识状态。在几个公共数据集上的大量实验结果表明，与现有的方法相比，我们的模型能够为学习者提供更好的性能预测。LBKT 结合学习行为效应对学习者的知识状态进行跟踪，表现出良好的可解释性。我们的代码可以在 https://github.com/xbh0720/lbkt 找到。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Learning+Behavior-oriented+Knowledge+Tracing)|0| +|[Learning Behavior-oriented Knowledge Tracing](https://doi.org/10.1145/3580305.3599407)|Bihan Xu, Zhenya Huang, Jiayu Liu, Shuanghong Shen, Qi Liu, Enhong Chen, Jinze Wu, Shijin Wang|School of Computer Science and Technology, University of Science and Technology of China; State Key Laboratory of Cognitive Intelligence; Anhui Province Key Laboratory of Big Data Analysis and Application, University of Science and Technology of China; School of Data Science, University of Science and Technology of China; iFLYTEK Research, iFLYTEK Co., Ltd|Exploring how learners' knowledge states evolve during the learning activities is a critical task in online learning systems, which can facilitate personalized services downstream, such as course recommendation. Most of existing methods have devoted great efforts to analyzing learners' knowledge states according to their responses (i.e., right or wrong) to different questions. However, the significant effect of learners' learning behaviors (e.g., answering speed, the number of attempts) is omitted, which can reflect their knowledge acquisition deeper and ensure the reliability of the response. In this paper, we propose a Learning Behavior-oriented Knowledge Tracing (LBKT) model, with the goal of explicitly exploring the learning behavior effects on learners' knowledge states. Specifically, we first analyze and summarize several dominated learning behaviors including Speed, Attempts and Hints in the learning process. As the characteristics of different learning behaviors vary greatly, we separately estimate their various effects on learners' knowledge acquisition in a quantitative manner. Then, considering that different learning behaviors are closely dependent with each other, we assess the fused effect of multiple learning behaviors by capturing their complex dependent patterns. Finally, we integrate the forgetting factor with learners' knowledge acquisition to comprehensively update their changing knowledge states in learning. Extensive experimental results on several public datasets demonstrate that our model generates better performance prediction for learners against existing methods. Moreover, LBKT shows good interpretability in tracking learners' knowledge state by incorporating the learning behavior effects. Our codes are available at https://github.com/xbh0720/LBKT.|研究学习者的知识状态在学习活动中如何演变是在线学习系统的一个关键任务，它可以促进个性化的下游服务，如课程推荐。现有的大多数方法都致力于根据学习者对不同问题的反应(即对或错)来分析学习者的知识状态。然而，学习者的学习行为(如答题速度、尝试次数)的显著影响被忽略了，这可以更深刻地反映他们的知识获得，并保证反应的可靠性。本文提出了一个面向学习行为的知识追踪模型(LBKT) ，旨在明确探讨学习行为对学习者知识状态的影响。具体来说，我们首先分析和总结了学习过程中的几种主导性学习行为，包括速度、尝试和提示。由于不同学习行为的特点差异很大，我们分别以定量的方式估计了它们对学习者知识习得的不同影响。然后，考虑到不同学习行为之间的相互依赖性，我们通过捕捉多个学习行为的复杂依赖模式来评估它们的融合效应。最后，将遗忘因素与学习者的知识习得相结合，全面更新学习过程中不断变化的知识状态。在几个公共数据集上的大量实验结果表明，与现有的方法相比，我们的模型能够为学习者提供更好的性能预测。LBKT 结合学习行为效应对学习者的知识状态进行跟踪，表现出良好的可解释性。我们的代码可以在 https://github.com/xbh0720/lbkt 找到。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Learning+Behavior-oriented+Knowledge+Tracing)|0| |[MimoSketch: A Framework to Mine Item Frequency on Multiple Nodes with Sketches](https://doi.org/10.1145/3580305.3599433)|Yuchen Xu, Wenfei Wu, Bohan Zhao, Tong Yang, Yikai Zhao||We abstract a MIMO scenario in distributed data stream mining, where a stream of multiple items is mined by multiple nodes. We design a framework named MimoSketch for the MIMO-specific scenario, which improves the fundamental mining task of item frequency estimation. MimoSketch consists of an algorithm design and a policy to schedule items to nodes. MimoSketch's algorithm applies random counting to preserve a mathematically proven unbiasedness property, which makes it friendly to the aggregate query on multiple nodes; its memory layout is dynamically adaptive to the runtime item size distribution, which maximizes the estimation accuracy by storing more items. MimoSketch's scheduling policy balances items among nodes, avoiding nodes being overloaded or underloaded, which improves the overall mining accuracy. Our prototype and evaluation show that our algorithm can improve the item frequency estimation accuracy by an order of magnitude compared with the state-of-the-art solutions, and the scheduling policy further promotes the performance in MIMO scenarios.|在分布式数据流挖掘中，我们抽象出一个 MIMO 场景，其中多个节点挖掘多个项目流。针对 MIMO 特定场景设计了一个 MimoSketch 框架，改进了项目频率估计的基本挖掘任务。MimoSketch 由算法设计和节点调度策略组成。MimoSketch 算法采用随机计数的方法，保留了经过数学证明的无偏性质，对多节点的聚合查询更加友好; 其内存布局动态适应运行时项目大小分布，通过存储更多项目来最大化估计精度。MimoSketch 的调度策略平衡了节点之间的项目，避免了节点的过载或过载，提高了整体挖掘的准确性。我们的原型和评估表明，与最先进的解决方案相比，我们的算法可以提高项目频率估计的准确性数量级，并且调度策略进一步提高了在 MIMO 场景中的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=MimoSketch:+A+Framework+to+Mine+Item+Frequency+on+Multiple+Nodes+with+Sketches)|0| |[Kernel Ridge Regression-Based Graph Dataset Distillation](https://doi.org/10.1145/3580305.3599398)|Zhe Xu, Yuzhong Chen, Menghai Pan, Huiyuan Chen, Mahashweta Das, Hao Yang, Hanghang Tong||The huge volume of emerging graph datasets has become a double-bladed sword for graph machine learning. On the one hand, it empowers the success of a myriad of graph neural networks (GNNs) with strong empirical performance. On the other hand, training modern graph neural networks on huge graph data is computationally expensive. How to distill the given graph dataset while retaining most of the trained models' performance is a challenging problem. Existing efforts try to approach this problem by solving meta-learning-based bilevel optimization objectives. A major hurdle lies in that the exact solutions of these methods are computationally intensive and thus, most, if not all, of them are solved by approximate strategies which in turn hurt the distillation performance. In this paper, inspired by the recent advances in neural network kernel methods, we adopt a kernel ridge regression-based meta-learning objective which has a feasible exact solution. However, the computation of graph neural tangent kernel is very expensive, especially in the context of dataset distillation. As a response, we design a graph kernel, named LiteGNTK, tailored for the dataset distillation problem which is closely related to the classic random walk graph kernel. An effective model named Kernel rıdge regression-based graph Dataset Distillation (KIDD) and its variants are proposed. KIDD shows nice efficiency in both the forward and backward propagation processes. At the same time, KIDD shows strong empirical performance over 7 real-world datasets compared with the state-of-the-art distillation methods. Thanks to the ability to find the exact solution of the distillation objective, the learned training graphs by KIDD can sometimes even outperform the original whole training set with as few as 1.65% training graphs.|海量的新兴图形数据集已经成为图机学习的双刃剑。一方面，它赋予了无数图形神经网络(GNN)以强大的经验性能的成功。另一方面，在海量图形数据上训练现代图形神经网络计算量很大。如何提取给定的图形数据集，同时保留训练模型的大部分性能是一个具有挑战性的问题。现有的研究试图通过解决基于元学习的双层优化目标来解决这个问题。一个主要的障碍在于这些方法的精确解是计算密集型的，因此，如果不是全部的话，它们中的大多数是通过近似策略来解决的，这反过来又会损害精馏性能。本文受神经网络核方法的启发，采用了一种基于核岭回归的元学习目标，该目标具有可行的精确解。然而，图神经切核的计算是非常昂贵的，特别是在数据集精馏的背景下。作为响应，我们针对与经典随机游走图核密切相关的数据集精馏问题，设计了一个图核 LiteGNTK。提出了一种有效的基于核边缘回归的图形数据集提取(KIDD)模型及其变体。KIDD 在正向和反向传播过程中都表现出良好的效率。同时，与现有的精馏方法相比，KIDD 算法在7个实际数据集上表现出了较强的经验性能。由于 KIDD 能够找到精馏目标的精确解，所学到的训练图有时甚至能够以1.65% 的训练图优于原来的整个训练集。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Kernel+Ridge+Regression-Based+Graph+Dataset+Distillation)|0| |[BatchSampler: Sampling Mini-Batches for Contrastive Learning in Vision, Language, and Graphs](https://doi.org/10.1145/3580305.3599263)|Zhen Yang, Tinglin Huang, Ming Ding, Yuxiao Dong, Rex Ying, Yukuo Cen, Yangliao Geng, Jie Tang|Yale University; Tsinghua University|In-Batch contrastive learning is a state-of-the-art self-supervised method that brings semantically-similar instances close while pushing dissimilar instances apart within a mini-batch. Its key to success is the negative sharing strategy, in which every instance serves as a negative for the others within the mini-batch. Recent studies aim to improve performance by sampling hard negatives \textit{within the current mini-batch}, whose quality is bounded by the mini-batch itself. In this work, we propose to improve contrastive learning by sampling mini-batches from the input data. We present BatchSampler\footnote{The code is available at \url{https://github.com/THUDM/BatchSampler}} to sample mini-batches of hard-to-distinguish (i.e., hard and true negatives to each other) instances. To make each mini-batch have fewer false negatives, we design the proximity graph of randomly-selected instances. To form the mini-batch, we leverage random walk with restart on the proximity graph to help sample hard-to-distinguish instances. BatchSampler is a simple and general technique that can be directly plugged into existing contrastive learning models in vision, language, and graphs. Extensive experiments on datasets of three modalities show that BatchSampler can consistently improve the performance of powerful contrastive models, as shown by significant improvements of SimCLR on ImageNet-100, SimCSE on STS (language), and GraphCL and MVGRL on graph datasets.|批内对比学习是一种最先进的自我监督方法，它可以使语义相似的实例关闭，同时在小批内将不同的实例分开。其成功的关键是消极分享策略，在这种策略中，每一个实例对于小批次中的其他实例都是消极的。最近的研究旨在提高性能抽样硬负面文本{在当前的小批量} ，其质量是有界的小批量本身。在这项工作中，我们提出改善对比学习的抽样小批量的输入数据。我们提供 BatchSampler 脚注{该代码可在 url { https://github.com/thudm/BatchSampler }}获得，用于对难以区分(即彼此之间的硬负片和真负片)的迷你批次实例进行抽样。为了使每个小批量产品具有较少的假阴性，我们设计了随机选择实例的接近图。为了形成迷你批处理，我们利用在接近图上重新启动的随机游动来帮助抽样难以区分的实例。BatchSampler 是一种简单而通用的技术，可以直接插入到视觉、语言和图形中现有的对比学习模型中。对三种模式的数据集进行的广泛实验表明，BatchSampler 可以持续改善强大的对比模型的性能，如 ImageNet-100上的 SimCLR，STS (语言)上的 SimCSE 以及图形数据集上的 GraphCL 和 MVGRL 的显着改进所示。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=BatchSampler:+Sampling+Mini-Batches+for+Contrastive+Learning+in+Vision,+Language,+and+Graphs)|0| |[Web-based Long-term Spine Treatment Outcome Forecasting](https://doi.org/10.1145/3580305.3599545)|Hangting Ye, Zhining Liu, Wei Cao, Amir M. Amiri, Jiang Bian, Yi Chang, Jon D. Lurie, Jim Weinstein, TieYan Liu||The aging of global population is witnessing increasing prevalence of spinal disorders. According to latest statistics, nearly five percent of the global population is suffering from spinal disorders. To relieve the pain, many spine patients tend to choose surgeries. However, recent evidences reveal that some spine patients can self-heal over time with nonoperative treatment and even surgeries may not ease the pain for some others, which raises a critical question regarding the appropriateness of such surgeries. Furthermore, the complex and time-consuming diagnostic process places a great burden on both clinicians and patients. Due to the development of web technology, it is possible for spine patients to obtain decision making suggestions on the Internet. The uniqueness of web technology, including its popularity, convenience, and immediacy, makes intelligent healthcare techniques, especially Treatment Outcome Forecasting (TOF), able to support clinical decision-making for doctors and healthcare providers. Despite a few machine-learning-based methods have been proposed for TOF, their performance and feasibility are mostly unsatisfactory due to the neglect of a few practical challenges (caused by applying on the Internet), including biased data selection, noisy supervision, and patient noncompliance. In light of this, we propose DeepTOF, a novel end-to-end deep learning model to cope with the unique challenges in web-based long-term continuous spine TOF. In particular, we combine different patient groups and train a unified predictive model to eliminate the data selection bias. Towards robust learning, we further take advantage of indirect but fine-grained supervision signals to mutually calibrate with the noisy training labels. Additionally, a feature selector was co-trained with DeepTOF to select the most important features (i.e., answers/indicators that need to be collected) for inference, thus easing the use of DeepTOF during web-based real-world application. The proposed DeepTOF could bring great benefits to the rehabilitation of spine patients. Comprehensive experiments and analysis show that DeepTOF outperforms conventional solutions by a large margin.|随着全球人口的老龄化，脊柱疾病的发病率不断上升。根据最新的统计数据，全球近5% 的人口患有脊柱疾病。为了减轻疼痛，许多脊柱病人倾向于选择手术。然而，最近的证据表明，随着时间的推移，一些脊柱患者可以通过非手术治疗自我愈合，甚至手术可能不会减轻其他一些人的疼痛，这就提出了关于这种手术的适当性的关键问题。此外，复杂而耗时的诊断过程给临床医生和患者都带来了很大的负担。随着网络技术的发展，脊柱疾病患者可以在因特网上获得决策建议。网络技术的独特性，包括它的普及性、方便性和即时性，使得智能医疗技术，特别是治疗结果预测(TOF) ，能够支持医生和医疗保健提供者的临床决策。尽管已经提出了一些基于机器学习的 TOF 方法，但是由于忽视了一些实际挑战(由于在互联网上的应用) ，包括有偏见的数据选择，嘈杂的监督和患者不遵从性，其性能和可行性大多不令人满意。鉴于此，我们提出了 DeepTOF，一种新颖的端到端深度学习模型，以应对基于网络的长期连续脊柱 TOF 的独特挑战。特别是，我们结合不同的患者组，并训练一个统一的预测模型，以消除数据选择偏倚。在稳健学习方面，我们进一步利用间接但细粒度的监督信号与噪声训练标签进行相互校准。此外，特征选择器与 DeepTOF 共同训练，以选择最重要的特征(即需要收集的答案/指标)进行推断，从而简化了 DeepTOF 在基于网络的现实世界应用中的使用。提出的 DeepTOF 可以为脊柱病人的康复带来巨大的好处。综合实验和分析表明，DeepTOF 的性能大大优于传统的解决方案。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Web-based+Long-term+Spine+Treatment+Outcome+Forecasting)|0| |[Optimal Dynamic Subset Sampling: Theory and Applications](https://doi.org/10.1145/3580305.3599458)|Lu Yi, Hanzhi Wang, Zhewei Wei|Renmin University of China|We study the fundamental problem of sampling independent events, called subset sampling. Specifically, consider a set of $n$ events $S=\{x_1, \ldots, x_n\}$, where each event $x_i$ has an associated probability $p(x_i)$. The subset sampling problem aims to sample a subset $T \subseteq S$, such that every $x_i$ is independently included in $S$ with probability $p_i$. A naive solution is to flip a coin for each event, which takes $O(n)$ time. However, the specific goal is to develop data structures that allow drawing a sample in time proportional to the expected output size $\mu=\sum_{i=1}^n p(x_i)$, which can be significantly smaller than $n$ in many applications. The subset sampling problem serves as an important building block in many tasks and has been the subject of various research for more than a decade. However, most of the existing subset sampling approaches are conducted in a static setting, where the events or their associated probability in set $S$ is not allowed to be changed over time. These algorithms incur either large query time or update time in a dynamic setting despite the ubiquitous time-evolving events with changing probability in real life. Therefore, it is a pressing need, but still, an open problem, to design efficient dynamic subset sampling algorithms. In this paper, we propose ODSS, the first optimal dynamic subset sampling algorithm. The expected query time and update time of ODSS are both optimal, matching the lower bounds of the subset sampling problem. We present a nontrivial theoretical analysis to demonstrate the superiority of ODSS. We also conduct comprehensive experiments to empirically evaluate the performance of ODSS. Moreover, we apply ODSS to a concrete application: influence maximization. We empirically show that our ODSS can improve the complexities of existing influence maximization algorithms on large real-world evolving social networks.|我们研究抽样独立事件的基本问题，称为子集抽样。具体来说，考虑一组 $n $事件 $S = { x _ 1，ldot，x _ n } $，其中每个事件 $x _ i $具有相关的概率 $p (x _ i) $。子集抽样问题的目标是抽样一个子集 $T 子集 S $，这样每个 $x _ i $都独立地包含在 $S $中，概率为 $p _ i $。一个天真的解决方案是为每个事件抛硬币，这需要花费 $O (n) $时间。然而，我们的具体目标是开发一种数据结构，它允许在与预期输出大小成正比的时间内绘制样本 $mu = sum _ { i = 1} ^ n p (x _ i) $，在许多应用程序中，它可以明显小于 $n $。子集抽样问题是许多工作中的一个重要组成部分，也是近十多年来各种研究的主题。但是，大多数现有的子集抽样方法都是在静态环境中进行的，其中不允许随着时间的推移更改集 $S $中的事件或其相关概率。尽管在现实生活中随时间演化的事件随概率的变化无处不在，但这些算法在动态环境中会产生大量的查询时间或更新时间。因此，设计高效的动态子集采样算法是一个迫切而又尚未解决的问题。本文提出了第一种最优动态子集抽样算法 ODSS。ODSS 的期望查询时间和更新时间均为最优，与子集抽样问题的下界相匹配。我们提出了一个非平凡的理论分析，以证明 ODSS 的优越性。我们还进行了综合性的实验，对 ODSS 的性能进行了实证评估。此外，我们将 ODSS 应用于一个具体的应用: 影响最大化。我们的实验表明，我们的 ODSS 可以改善现有的影响最大化算法在大型真实世界演化的社会网络上的复杂性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Optimal+Dynamic+Subset+Sampling:+Theory+and+Applications)|0| |[Sharpness-Aware Minimization Revisited: Weighted Sharpness as a Regularization Term](https://doi.org/10.1145/3580305.3599501)|Yun Yue, Jiadi Jiang, Zhiling Ye, Ning Gao, Yongchao Liu, Ke Zhang|Ant Group|Deep Neural Networks (DNNs) generalization is known to be closely related to the flatness of minima, leading to the development of Sharpness-Aware Minimization (SAM) for seeking flatter minima and better generalization. In this paper, we revisit the loss of SAM and propose a more general method, called WSAM, by incorporating sharpness as a regularization term. We prove its generalization bound through the combination of PAC and Bayes-PAC techniques, and evaluate its performance on various public datasets. The results demonstrate that WSAM achieves improved generalization, or is at least highly competitive, compared to the vanilla optimizer, SAM and its variants. The code is available at https://github.com/intelligent-machine-learning/dlrover/tree/master/atorch/atorch/optimizers.|深度神经网络(DNN)泛化与最小值的平坦性密切相关，导致锐度感知最小化(SAM)的发展，以寻求更平坦的最小值和更好的泛化。在本文中，我们重新审视了 SAM 的损失，并提出了一种更一般的方法，称为 WSAM，通过合并锐度作为一个正则项。通过结合 PAC 和 Bayes-PAC 技术证明了其泛化界，并对其在各种公共数据集上的性能进行了评估。结果表明，与普通的优化器 SAM 及其变体相比，WSAM 实现了改进的泛化，或者至少具有很强的竞争力。密码可在 https://github.com/intelligent-machine-learning/dlrover/tree/master/atorch/atorch/optimizers 查阅。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Sharpness-Aware+Minimization+Revisited:+Weighted+Sharpness+as+a+Regularization+Term)|0| -|[Doubly Robust AUC Optimization against Noisy and Adversarial Samples](https://doi.org/10.1145/3580305.3599316)|Chenkang Zhang, Wanli Shi, Lei Luo, Bin Gu|Nanjing University of Science and Technology; Nanjing University of Information Science and Technology|Area under the ROC curve (AUC) is an important and widely used metric in machine learning especially for imbalanced datasets. In current practical learning problems, not only adversarial samples but also noisy samples seriously threaten the performance of learning models. Nowadays, there have been a lot of research works proposed to defend the adversarial samples and noisy samples separately. Unfortunately, to the best of our knowledge, none of them with AUC optimization can secure against the two kinds of harmful samples simultaneously. To fill this gap and also address the challenge, in this paper, we propose a novel doubly robust dAUC optimization (DRAUC) algorithm. Specifically, we first exploit the deep integration of self-paced learning and adversarial training under the framework of AUC optimization, and provide a statistical upper bound to the AUC adversarial risk. Inspired by the statistical upper bound, we propose our optimization objective followed by an efficient alternatively stochastic descent algorithm, which can effectively improve the performance of learning models by guarding against adversarial samples and noisy samples. Experimental results on several standard datasets demonstrate that our DRAUC algorithm has better noise robustness and adversarial robustness than the state-of-the-art algorithms.|ROC 曲线下面积(aUC)是机器学习中广泛使用的一个重要指标，特别是对于不平衡的数据集。在当前的实际学习问题中，不仅对手样本严重威胁着学习模型的性能，而且噪声样本也严重威胁着学习模型的性能。目前，已经有很多研究工作提出将对抗样本和噪声样本分别进行辩护。不幸的是，据我们所知，没有一个 AUC 优化能够同时抵抗这两种有害样品。为了填补这一空白，并解决这一挑战，本文提出了一种新的双鲁棒 dAUC 优化(DRAUC)算法。具体来说，我们首先利用 AUC 优化框架下自主学习和对抗性训练的深度整合，并提供了 AUC 对抗性风险的统计上界。受统计上界的启发，我们提出了优化目标和一个有效的交替随机下降算法，它可以有效地提高学习模型的性能，防止对手样本和噪声样本。在几个标准数据集上的实验结果表明，我们的 DRAUC 算法具有比现有算法更好的噪声鲁棒性和对抗鲁棒性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Doubly+Robust+AUC+Optimization+against+Noisy+and+Adversarial+Samples)|0| +|[Doubly Robust AUC Optimization against Noisy and Adversarial Samples](https://doi.org/10.1145/3580305.3599316)|Chenkang Zhang, Wanli Shi, Lei Luo, Bin Gu|Nanjing University of Information Science and Technology; Nanjing University of Science and Technology|Area under the ROC curve (AUC) is an important and widely used metric in machine learning especially for imbalanced datasets. In current practical learning problems, not only adversarial samples but also noisy samples seriously threaten the performance of learning models. Nowadays, there have been a lot of research works proposed to defend the adversarial samples and noisy samples separately. Unfortunately, to the best of our knowledge, none of them with AUC optimization can secure against the two kinds of harmful samples simultaneously. To fill this gap and also address the challenge, in this paper, we propose a novel doubly robust dAUC optimization (DRAUC) algorithm. Specifically, we first exploit the deep integration of self-paced learning and adversarial training under the framework of AUC optimization, and provide a statistical upper bound to the AUC adversarial risk. Inspired by the statistical upper bound, we propose our optimization objective followed by an efficient alternatively stochastic descent algorithm, which can effectively improve the performance of learning models by guarding against adversarial samples and noisy samples. Experimental results on several standard datasets demonstrate that our DRAUC algorithm has better noise robustness and adversarial robustness than the state-of-the-art algorithms.|ROC 曲线下面积(aUC)是机器学习中广泛使用的一个重要指标，特别是对于不平衡的数据集。在当前的实际学习问题中，不仅对手样本严重威胁着学习模型的性能，而且噪声样本也严重威胁着学习模型的性能。目前，已经有很多研究工作提出将对抗样本和噪声样本分别进行辩护。不幸的是，据我们所知，没有一个 AUC 优化能够同时抵抗这两种有害样品。为了填补这一空白，并解决这一挑战，本文提出了一种新的双鲁棒 dAUC 优化(DRAUC)算法。具体来说，我们首先利用 AUC 优化框架下自主学习和对抗性训练的深度整合，并提供了 AUC 对抗性风险的统计上界。受统计上界的启发，我们提出了优化目标和一个有效的交替随机下降算法，它可以有效地提高学习模型的性能，防止对手样本和噪声样本。在几个标准数据集上的实验结果表明，我们的 DRAUC 算法具有比现有算法更好的噪声鲁棒性和对抗鲁棒性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Doubly+Robust+AUC+Optimization+against+Noisy+and+Adversarial+Samples)|0| |[Finding Favourite Tuples on Data Streams with Provably Few Comparisons](https://doi.org/10.1145/3580305.3599352)|Guangyi Zhang, Nikolaj Tatti, Aristides Gionis|HIIT, University of Helsinki; Shenzhen Institute of Computing Sciences; KTH Royal Institute of Technology|One of the most fundamental tasks in data science is to assist a user with unknown preferences in finding high-utility tuples within a large database. To accurately elicit the unknown user preferences, a widely-adopted way is by asking the user to compare pairs of tuples. In this paper, we study the problem of identifying one or more high-utility tuples by adaptively receiving user input on a minimum number of pairwise comparisons. We devise a single-pass streaming algorithm, which processes each tuple in the stream at most once, while ensuring that the memory size and the number of requested comparisons are in the worst case logarithmic in $n$, where $n$ is the number of all tuples. An important variant of the problem, which can help to reduce human error in comparisons, is to allow users to declare ties when confronted with pairs of tuples of nearly equal utility. We show that the theoretical guarantees of our method can be maintained for this important problem variant. In addition, we show how to enhance existing pruning techniques in the literature by leveraging powerful tools from mathematical programming. Finally, we systematically evaluate all proposed algorithms over both synthetic and real-life datasets, examine their scalability, and demonstrate their superior performance over existing methods.|数据科学中最基本的任务之一是帮助具有未知偏好的用户在大型数据库中查找高效用元组。为了准确地获得未知的用户首选项，一种被广泛采用的方法是要求用户比较元组对。在本文中，我们研究识别一个或多个高效用元组的问题，通过自适应接收用户输入的最小数目的成对比较。我们设计了一个单通道流式算法，它最多处理流中的每个元组一次，同时确保内存大小和请求比较的数量在最坏的情况下是以 $n $为对数的，其中 $n $是所有元组的数量。该问题的一个重要变体是允许用户在遇到效用几乎相等的元组对时声明关系，这有助于减少比较中的人为错误。我们表明，对于这个重要的问题变量，我们方法的理论保证是可以保持的。此外，我们还展示了如何通过利用数学编程中的强大工具来增强文献中现有的剪枝技术。最后，我们系统地评估了所有提出的算法在合成和实际数据集上的性能，检验了它们的可伸缩性，并证明了它们优于现有方法的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Finding+Favourite+Tuples+on+Data+Streams+with+Provably+Few+Comparisons)|0| |[Domain-Specific Risk Minimization for Domain Generalization](https://doi.org/10.1145/3580305.3599313)|YiFan Zhang, Jindong Wang, Jian Liang, Zhang Zhang, Baosheng Yu, Liang Wang, Dacheng Tao, Xing Xie||Domain generalization (DG) approaches typically use the hypothesis learned on source domains for inference on the unseen target domain. However, such a hypothesis can be arbitrarily far from the optimal one for the target domain, induced by a gap termed ''adaptivity gap.'' Without exploiting the domain information from the unseen test samples, adaptivity gap estimation and minimization are intractable, which hinders us to robustify a model to any unknown distribution. In this paper, we first establish a generalization bound that explicitly considers the adaptivity gap. Our bound motivates two strategies to reduce the gap: the first one is ensembling multiple classifiers to enrich the hypothesis space, then we propose effective gap estimation methods for guiding the selection of a better hypothesis for the target. The other method is minimizing the gap directly by adapting model parameters using online target samples. We thus propose Domain-specific Risk Minimization (DRM). During training, DRM models the distributions of different source domains separately; for inference, DRM performs online model steering using the source hypothesis for each arriving target sample. Extensive experiments demonstrate the effectiveness of the proposed DRM for domain generalization. Code is available at: https://github.com/yfzhang114/AdaNPC.|域泛化(DG)方法通常使用在源域上学到的假设来推断未知的目标域。然而，这样的假设可以任意地远离目标领域的最佳假设，由一个称为“适应性缺口”的缺口引起如果不利用未知测试样本的域信息，自适应间隙估计和最小化是难以解决的问题，这阻碍了我们将模型鲁棒化到任何未知分布。在本文中，我们首先建立一个显式考虑自适应差距的泛化界。本文提出了两种缩小差距的策略: 第一种是将多个分类器集成在一起来丰富假设空间，然后提出了有效的差距估计方法来指导目标选择更好的假设;。另一种方法是利用在线目标样本自适应模型参数，直接最小化间隙。因此，我们提出领域特定风险最小化(DRM)。在训练过程中，DRM 分别对不同源域的分布进行建模; 为了推理，DRM 使用每个到达目标样本的源假设进行在线模型导向。大量的实验证明了该方法的有效性。密码可于以下 https://github.com/yfzhang114/adanpc 索取:。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Domain-Specific+Risk+Minimization+for+Domain+Generalization)|0| -|[Towards Fair Disentangled Online Learning for Changing Environments](https://doi.org/10.1145/3580305.3599523)|Chen Zhao, Feng Mi, Xintao Wu, Kai Jiang, Latifur Khan, Christan Grant, Feng Chen|University of Texas at Dallas; University of Arkansas; Baylor University; The University of Texas at Dallas; University of Florida|In the problem of online learning for changing environments, data are sequentially received one after another over time, and their distribution assumptions may vary frequently. Although existing methods demonstrate the effectiveness of their learning algorithms by providing a tight bound on either dynamic regret or adaptive regret, most of them completely ignore learning with model fairness, defined as the statistical parity across different sub-population (e.g., race and gender). Another drawback is that when adapting to a new environment, an online learner needs to update model parameters with a global change, which is costly and inefficient. Inspired by the sparse mechanism shift hypothesis, we claim that changing environments in online learning can be attributed to partial changes in learned parameters that are specific to environments and the rest remain invariant to changing environments. To this end, in this paper, we propose a novel algorithm under the assumption that data collected at each time can be disentangled with two representations, an environment-invariant semantic factor and an environment-specific variation factor. The semantic factor is further used for fair prediction under a group fairness constraint. To evaluate the sequence of model parameters generated by the learner, a novel regret is proposed in which it takes a mixed form of dynamic and static regret metrics followed by a fairness-aware long-term constraint. The detailed analysis provides theoretical guarantees for loss regret and violation of cumulative fairness constraints. Empirical evaluations on real-world datasets demonstrate our proposed method sequentially outperforms baseline methods in model accuracy and fairness.|在变化环境下的在线学习问题中，数据随着时间的推移依次接收，其分布假设可能会频繁变化。尽管现有的方法通过提供动态后悔或适应性后悔的紧密界限来证明其学习算法的有效性，但大多数方法完全忽略了模型公平性的学习，这种模型公平性被定义为不同子群(例如种族和性别)的统计平价。另一个缺点是，在适应新环境时，在线学习者需要根据全局变化更新模型参数，这样做成本高，效率低。受稀疏机制转移假说的启发，我们认为在线学习中不断变化的环境可以归因于特定于环境的学习参数的部分变化，而其余的参数对不断变化的环境保持不变。为此，本文提出了一种新的算法，该算法假设每次采集的数据可以分解为两种表示: 环境不变的语义因子和环境特定的变化因子。语义因子进一步用于群体公平约束下的公平预测。为了评估学习者生成的模型参数序列，提出了一种新的遗憾度量方法，该方法采用动态和静态遗憾度量的混合形式，并且具有公平意识的长期约束。详细的分析为损失后悔和违反累积公平约束提供了理论保证。对实际数据集的实证分析表明，该方法在模型精度和公平性方面均优于基线方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Towards+Fair+Disentangled+Online+Learning+for+Changing+Environments)|0| +|[Towards Fair Disentangled Online Learning for Changing Environments](https://doi.org/10.1145/3580305.3599523)|Chen Zhao, Feng Mi, Xintao Wu, Kai Jiang, Latifur Khan, Christan Grant, Feng Chen|University of Texas at Dallas; University of Florida; Baylor University; University of Arkansas; The University of Texas at Dallas|In the problem of online learning for changing environments, data are sequentially received one after another over time, and their distribution assumptions may vary frequently. Although existing methods demonstrate the effectiveness of their learning algorithms by providing a tight bound on either dynamic regret or adaptive regret, most of them completely ignore learning with model fairness, defined as the statistical parity across different sub-population (e.g., race and gender). Another drawback is that when adapting to a new environment, an online learner needs to update model parameters with a global change, which is costly and inefficient. Inspired by the sparse mechanism shift hypothesis, we claim that changing environments in online learning can be attributed to partial changes in learned parameters that are specific to environments and the rest remain invariant to changing environments. To this end, in this paper, we propose a novel algorithm under the assumption that data collected at each time can be disentangled with two representations, an environment-invariant semantic factor and an environment-specific variation factor. The semantic factor is further used for fair prediction under a group fairness constraint. To evaluate the sequence of model parameters generated by the learner, a novel regret is proposed in which it takes a mixed form of dynamic and static regret metrics followed by a fairness-aware long-term constraint. The detailed analysis provides theoretical guarantees for loss regret and violation of cumulative fairness constraints. Empirical evaluations on real-world datasets demonstrate our proposed method sequentially outperforms baseline methods in model accuracy and fairness.|在变化环境下的在线学习问题中，数据随着时间的推移依次接收，其分布假设可能会频繁变化。尽管现有的方法通过提供动态后悔或适应性后悔的紧密界限来证明其学习算法的有效性，但大多数方法完全忽略了模型公平性的学习，这种模型公平性被定义为不同子群(例如种族和性别)的统计平价。另一个缺点是，在适应新环境时，在线学习者需要根据全局变化更新模型参数，这样做成本高，效率低。受稀疏机制转移假说的启发，我们认为在线学习中不断变化的环境可以归因于特定于环境的学习参数的部分变化，而其余的参数对不断变化的环境保持不变。为此，本文提出了一种新的算法，该算法假设每次采集的数据可以分解为两种表示: 环境不变的语义因子和环境特定的变化因子。语义因子进一步用于群体公平约束下的公平预测。为了评估学习者生成的模型参数序列，提出了一种新的遗憾度量方法，该方法采用动态和静态遗憾度量的混合形式，并且具有公平意识的长期约束。详细的分析为损失后悔和违反累积公平约束提供了理论保证。对实际数据集的实证分析表明，该方法在模型精度和公平性方面均优于基线方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Towards+Fair+Disentangled+Online+Learning+for+Changing+Environments)|0| |[SMILE: Evaluation and Domain Adaptation for Social Media Language Understanding](https://doi.org/10.1145/3580305.3599907)|Vasilisa Bashlovkina, Riley Matthews, Zhaobin Kuang, Simon Baumgartner, Michael Bendersky|Google Research|We study the ability of transformer-based language models (LMs) to understand social media language. Social media (SM) language is distinct from standard written language, yet existing benchmarks fall short of capturing LM performance in this socially, economically, and politically important domain. We quantify the degree to which social media language differs from conventional language and conclude that the difference is significant both in terms of token distribution and rate of linguistic shift. Next, we introduce a new benchmark for Social MedIa Language Evaluation (SMILE) that covers four SM platforms and eleven tasks. Finally, we show that learning a tokenizer and pretraining on a mix of social media and conventional language yields an LM that outperforms the best similar-sized alternative by 4.2 points on the overall SMILE score.|我们研究了基于转换器的语言模型(LM)理解社交媒体语言的能力。社会媒体语言(SM)与标准的书面语言不同，然而现有的基准在这个社会、经济和政治重要的领域还不足以捕捉 LM 的表现。我们量化了社交媒体语言与传统语言的差异程度，并得出结论: 社交媒体语言与传统语言的差异在表征分布和语言转换率方面都是显著的。接下来，我们将为社会媒体语言评估(SMILE)引入一个新的基准，它涵盖了四个 SM 平台和十一个任务。最后，我们表明，学习一个标记器和预训练的社会媒体和传统语言的混合产生了一个 LM 的表现最好的类似大小的选择4.2分的总体 SMILE 得分。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=SMILE:+Evaluation+and+Domain+Adaptation+for+Social+Media+Language+Understanding)|0| -|[Augmenting Rule-based DNS Censorship Detection at Scale with Machine Learning](https://doi.org/10.1145/3580305.3599775)|Jacob Alexander Markson Brown, Xi Jiang, Van Tran, Arjun Nitin Bhagoji, Nguyen Phong Hoang, Nick Feamster, Prateek Mittal, Vinod Yegneswaran|SRI International; Princeton University; University of Chicago|The proliferation of global censorship has led to the development of a plethora of measurement platforms to monitor and expose it. Censorship of the domain name system (DNS) is a key mechanism used across different countries. It is currently detected by applying heuristics to samples of DNS queries and responses (probes) for specific destinations. These heuristics, however, are both platform-specific and have been found to be brittle when censors change their blocking behavior, necessitating a more reliable automated process for detecting censorship. In this paper, we explore how machine learning (ML) models can (1) help streamline the detection process, (2) improve the usability of large-scale datasets for censorship detection, and (3) discover new censorship instances and blocking signatures missed by existing heuristic methods. Our study shows that supervised models, trained using expert-derived labels on instances of known anomalies and possible censorship, can learn the detection heuristics employed by different measurement platforms. More crucially, we find that unsupervised models, trained solely on uncensored instances, can identify new instances and variations of censorship missed by existing heuristics. Moreover, both methods demonstrate the capability to uncover a substantial number of new DNS blocking signatures, i.e., injected fake IP addresses overlooked by existing heuristics. These results are underpinned by an important methodological finding: comparing the outputs of models trained using the same probes but with labels arising from independent processes allows us to more reliably detect cases of censorship in the absence of ground-truth labels of censorship.|全球审查制度的扩散导致了监测和揭露它的大量测量平台的发展。域名系统(DNS)的审查是各国使用的一个关键机制。目前，通过对特定目的地的 DNS 查询和响应(探测)样本应用启发式方法来检测它。然而，这些启发式方法都是针对特定平台的，当审查者改变他们的拦截行为时，这些方法被发现是脆弱的，这就需要一个更可靠的自动化过程来检测审查。本文探讨了机器学习(ML)模型在以下几个方面的作用: (1)简化检测过程; (2)提高大规模数据集在检测中的可用性; (3)发现新的检测实例和现有启发式方法遗漏的阻塞签名。我们的研究表明，监督模型，训练使用专家派生的标签对已知的异常和可能的检查的实例，可以学习检测启发采用不同的测量平台。更重要的是，我们发现，无监督模型，仅仅训练未经审查的实例，可以识别新的实例和变化的审查错过了现有的启发。此外，这两种方法都证明了能够发现大量新的 DNS 阻塞签名，即注入的假 IP 地址被现有的启发式方法忽略。这些结果得到了一个重要的方法论发现的支持: 比较使用相同探针训练的模型的输出，但是与独立过程产生的标签进行比较，使我们能够更可靠地检测在没有审查的地面真相标签的情况下的审查情况。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Augmenting+Rule-based+DNS+Censorship+Detection+at+Scale+with+Machine+Learning)|0| +|[Augmenting Rule-based DNS Censorship Detection at Scale with Machine Learning](https://doi.org/10.1145/3580305.3599775)|Jacob Alexander Markson Brown, Xi Jiang, Van Tran, Arjun Nitin Bhagoji, Nguyen Phong Hoang, Nick Feamster, Prateek Mittal, Vinod Yegneswaran|University of Chicago; SRI International; Princeton University|The proliferation of global censorship has led to the development of a plethora of measurement platforms to monitor and expose it. Censorship of the domain name system (DNS) is a key mechanism used across different countries. It is currently detected by applying heuristics to samples of DNS queries and responses (probes) for specific destinations. These heuristics, however, are both platform-specific and have been found to be brittle when censors change their blocking behavior, necessitating a more reliable automated process for detecting censorship. In this paper, we explore how machine learning (ML) models can (1) help streamline the detection process, (2) improve the usability of large-scale datasets for censorship detection, and (3) discover new censorship instances and blocking signatures missed by existing heuristic methods. Our study shows that supervised models, trained using expert-derived labels on instances of known anomalies and possible censorship, can learn the detection heuristics employed by different measurement platforms. More crucially, we find that unsupervised models, trained solely on uncensored instances, can identify new instances and variations of censorship missed by existing heuristics. Moreover, both methods demonstrate the capability to uncover a substantial number of new DNS blocking signatures, i.e., injected fake IP addresses overlooked by existing heuristics. These results are underpinned by an important methodological finding: comparing the outputs of models trained using the same probes but with labels arising from independent processes allows us to more reliably detect cases of censorship in the absence of ground-truth labels of censorship.|全球审查制度的扩散导致了监测和揭露它的大量测量平台的发展。域名系统(DNS)的审查是各国使用的一个关键机制。目前，通过对特定目的地的 DNS 查询和响应(探测)样本应用启发式方法来检测它。然而，这些启发式方法都是针对特定平台的，当审查者改变他们的拦截行为时，这些方法被发现是脆弱的，这就需要一个更可靠的自动化过程来检测审查。本文探讨了机器学习(ML)模型在以下几个方面的作用: (1)简化检测过程; (2)提高大规模数据集在检测中的可用性; (3)发现新的检测实例和现有启发式方法遗漏的阻塞签名。我们的研究表明，监督模型，训练使用专家派生的标签对已知的异常和可能的检查的实例，可以学习检测启发采用不同的测量平台。更重要的是，我们发现，无监督模型，仅仅训练未经审查的实例，可以识别新的实例和变化的审查错过了现有的启发。此外，这两种方法都证明了能够发现大量新的 DNS 阻塞签名，即注入的假 IP 地址被现有的启发式方法忽略。这些结果得到了一个重要的方法论发现的支持: 比较使用相同探针训练的模型的输出，但是与独立过程产生的标签进行比较，使我们能够更可靠地检测在没有审查的地面真相标签的情况下的审查情况。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Augmenting+Rule-based+DNS+Censorship+Detection+at+Scale+with+Machine+Learning)|0| |[Taming the Domain Shift in Multi-source Learning for Energy Disaggregation](https://doi.org/10.1145/3580305.3599910)|Xiaomin Chang, Wei Li, Yunchuan Shi, Albert Y. Zomaya||Non-intrusive load monitoring (NILM) is a cost-effective energy disaggregation means to estimate the energy consumption of individual appliances from a central load reading. Learning-based methods are the new trends in NILM implementations but require large labeled data to work properly at end-user premises. We first formulate an unsupervised multi-source domain adaptation problem to address this challenge by leveraging rich public datasets for building the NILM model. Then, we prove a new generalization bound for the target domain under multi-source settings. A hybrid loss-driven multi-source domain adversarial network (HLD-MDAN) is developed by approximating and optimizing the bound to tackle the domain shift between source and target domains. We conduct extensive experiments on three real-world residential energy datasets to evaluate the effectiveness of HLD-MDAN, showing that it is superior to other methods in single-source and multi-source learning scenarios.|非侵入性负荷监测(NILM)是一种具有成本效益的能源分解方法，可以从中央负荷读数估计单个电器的能源消耗。基于学习的方法是 NILM 实现的新趋势，但是需要大量的标记数据才能在最终用户前提下正常工作。我们首先提出一个无监督的多源域自适应问题来解决这个挑战，利用丰富的公共数据集来建立 NILM 模型。然后，在多源设置下证明了目标域的一个新的泛化界。针对源域和目标域之间的域漂移问题，提出了一种混合损耗驱动的多源域对抗网络(HLD-MDAN)。我们在三个真实的住宅能源数据集上进行了广泛的实验来评估 HLD-MDAN 的有效性，结果表明它在单源和多源学习情景下优于其他方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Taming+the+Domain+Shift+in+Multi-source+Learning+for+Energy+Disaggregation)|0| |[Variance Reduction Using In-Experiment Data: Efficient and Targeted Online Measurement for Sparse and Delayed Outcomes](https://doi.org/10.1145/3580305.3599928)|Alex Deng, Michelle Du, Anna Matlin, Qing Zhang||Improving statistical power is a common challenge for online experimentation platforms so that more hypotheses can be tested and lower effect sizes can be detected. To increase the power without increasing the sample size, it is necessary to consider the variance of experimental outcome metrics. Variance reduction was previously applied to online experimentation based on the idea of using pre-experiment covariate data to account for noise in the final metrics. Since this method relies on correlations between pre-experiment covariates and experiment outcomes, its effectiveness can be limited when testing features for specific product surfaces. We were also motivated by the challenge of attributing sparse, delayed binary outcomes to individual user-product interactions. We present two novel methods for variance reduction that rely exclusively on in-experiment data. The first method is a framework for a model-based leading indicator metric which continually estimates progress toward a delayed binary outcome. The second method is a counterfactual treatment exposure index that quantifies the amount that a user is impacted by the treatment. We applied these methods to past experiments and found that both can achieve variance reduction of 50% or more compared to the delayed outcome metric. The substantial reduction in variance afforded by the two methods presented in this paper has enabled Airbnb's experimentation platform to become more agile and innovative.|提高统计能力是在线实验平台面临的一个共同挑战，这样就可以检验更多的假设，检测更低的效应大小。为了在不增加样本量的情况下增加功率，有必要考虑实验结果指标的方差。基于使用预实验协变量数据来解决最终指标中的噪声问题的思想，方差降低已经被应用到在线实验中。由于该方法依赖于实验前协变量与实验结果之间的相关性，因此在测试特定产品表面的特征时，其有效性可能受到限制。我们还受到将稀疏、延迟的二进制结果归因于个人用户产品交互的挑战的激励。我们提出了两种新的方差减少的方法，完全依赖于实验中的数据。第一种方法是一个基于模型的领先指标度量的框架，它不断地估计延迟的二进制结果的进展。第二种方法是反事实治疗暴露指数，它量化了使用者受治疗影响的数量。我们将这些方法应用于以往的实验，发现与延迟结果指标相比，两者都可以实现50% 或更多的方差减少。本文提出的两种方法大大减少了方差，使 Airbnb 的实验平台变得更加灵活和创新。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Variance+Reduction+Using+In-Experiment+Data:+Efficient+and+Targeted+Online+Measurement+for+Sparse+and+Delayed+Outcomes)|0| |[Modelling Delayed Redemption with Importance Sampling and Pre-Redemption Engagement](https://doi.org/10.1145/3580305.3599867)|Samik Datta, Anshuman Mourya, Anirban Majumder, Vineet Chaoji||Rewards-based programs are popular within e-commerce online stores, with the goal of providing serendipitous incentives to delight customers. These rewards (or incentives) could be in the form of cashback, free-shipping or discount coupons on purchases within specific categories. The success of such programs relies on their ability to identify relevant rewards for customers, from a wide variety of incentives available on the online store. Estimating the likelihood of a customer redeeming an incentive is challenging due to 1) data sparsity: relatively rare occurrence of coupon redemptions as compared to issuances, and 2) delayed feedback: customers taking time to redeem, resulting in inaccurate model refresh, compounded by data drift due to new customers and coupons. To overcome these challenges, we present a novel framework, DRESS (Delayed Redemption Entire Space Sampling), that jointly models the effect of data sparsity and delayed feedback on redemptions. Our solution entails an architecture based on the recently proposed Entire Space Model ([12]), where we leverage pre-redemption engagement of customers (e.g. clipping of coupon) to overcome the sparsity challenge. The effect of delayed feedback is mitigated via a novel importance sampling mechanism, whose efficacy we formally analyze via a novel application of Influence Function ([10]). Experimental evaluation suggests that DRESS achieves significant lift in offline metric in comparison to state-of-the-art alternatives. Additionally, a live A/B test with DRESS resulted in a lift of 10 basis points in the redemption rate.|基于奖励的项目在电子商务网店中很流行，其目标是提供意外的奖励来取悦顾客。这些奖励(或激励)可以是现金返还、免运费或特定类别购物的折扣券。这些方案的成功取决于它们能够从网上商店提供的各种各样的激励措施中为顾客确定相关的奖励。估计客户兑现激励的可能性是具有挑战性的，因为1)数据稀少: 与发行相比，优惠券兑现的发生相对较少，2)延迟反馈: 客户需要时间来兑现，导致不准确的模型刷新，由于新客户和优惠券导致的数据漂移。为了克服这些挑战，我们提出了一个新的框架 DRESS (延迟赎回整个空间抽样) ，它共同模拟了数据稀疏和延迟反馈对赎回的影响。我们的解决方案需要一个基于最近提出的整体空间模型([12])的体系结构，在这个体系结构中，我们利用客户的提前赎回约定(例如削减优惠券)来克服稀缺性挑战。延迟反馈的影响通过一种新的重要性抽样机制得到缓解，我们通过一种新的影响函数的应用正式分析了这种机制的功效([10])。实验评估表明，与最先进的替代方案相比，DRESS 在离线测量方面取得了显著的提升。此外，通过 DRESS 的 A/B 测试，赎回率上升了10个基点。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Modelling+Delayed+Redemption+with+Importance+Sampling+and+Pre-Redemption+Engagement)|0| |[From Human Days to Machine Seconds: Automatically Answering and Generating Machine Learning Final Exams](https://doi.org/10.1145/3580305.3599827)|Iddo Drori, Sarah J. Zhang, Reece Shuttleworth, Sarah Zhang, Keith Tyser, Zad Chin, Pedro Lantigua, Saisamrit Surbehera, Gregory Hunter, Derek Austin, Leonard Tang, Yann Hicke, Sage Simhon, Sathwik Karnik, Darnell Granberry, Madeleine Udell||A final exam in machine learning at a top institution such as MIT, Harvard, or Cornell typically takes faculty days to write, and students hours to solve. We demonstrate that large language models pass machine learning finals at a human level on finals available online and automatically generate new human-quality final exam questions in seconds. Previous work has developed program synthesis and few-shot learning methods to solve university-level problem set questions in mathematics and STEM courses. In this work, we develop and compare methods that solve final exams, which differ from problem sets in several ways: the questions are longer, have multiple parts, are more complicated, and span a broader set of topics. We curate a dataset and benchmark of questions from machine learning final exams available online and code for answering these questions and generating new questions. We show how to generate new questions from other questions and course notes. For reproducibility and future research on this final exam benchmark, we use automatic checkers for multiple-choice, numeric, and questions with expression answers. A student survey comparing the quality, appropriateness, and difficulty of machine-generated questions with human-written questions shows that across multiple aspects, machine-generated questions are indistinguishable from human-generated questions and are suitable for final exams. We perform ablation studies comparing zero-shot learning with few-shot learning and chain-of-thought prompting using GPT-3, OPT, Codex, and ChatGPT across machine learning topics and find that few-shot learning methods perform best. We highlight the transformative potential of language models to streamline the writing and solution of large-scale assessments, significantly reducing the workload from human days to mere machine seconds. Our results suggest that rather than banning large language models such as ChatGPT in class, instructors should teach students to harness them by asking students meta-questions about correctness, completeness, and originality of the responses generated, encouraging critical thinking in academic studies.|麻省理工学院、哈佛大学或康奈尔大学等顶尖学府的机器学习期末考试，通常需要教师花几天时间写作，而学生则需要几个小时来解答。我们证明了大型语言模型通过期末考试在人类水平上的机器学习期末考试可在线获得，并自动生成新的人类质量期末考试问题在几秒钟内。以前的工作已经开发了程序综合和少拍学习方法来解决大学水平的数学和 STEM 课程中的问题集问题。在这项工作中，我们开发和比较解决期末考试的方法，它不同于问题集在几个方面: 问题更长，有多个部分，更复杂，并跨越更广泛的主题集。我们策划了一个数据集和基准的问题，从机器学习期末考试可在线和代码回答这些问题和产生新的问题。我们展示了如何从其他问题和课程笔记中生成新的问题。为了重复性和这个期末考试基准的未来研究，我们使用自动检查器来检查多项选择、数字和带有表达式答案的问题。一项学生调查比较了机器生成的问题和人写的问题的质量、适当性和难度，结果显示，在多个方面，机器生成的问题与人写的问题没有什么区别，适合期末考试。我们使用 GPT-3，OPT，Codex 和 ChatGPT 对机器学习主题进行比较零镜头学习和少镜头学习以及思维链激励的消融研究，发现少镜头学习方法表现最好。我们强调语言模型在简化大规模评估的写作和解决方案方面的变革潜力，大大减少了工作量，从人类的日子减少到仅仅是机器秒。我们的研究结果表明，与其在课堂上禁止像 ChatGPT 这样的大型语言模式，教师应该通过问学生关于回答的正确性、完整性和原创性的元问题来教会学生利用这些模式，鼓励学术研究中的批判性思维。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=From+Human+Days+to+Machine+Seconds:+Automatically+Answering+and+Generating+Machine+Learning+Final+Exams)|0| -|[Learning Multi-Agent Intention-Aware Communication for Optimal Multi-Order Execution in Finance](https://doi.org/10.1145/3580305.3599856)|Yuchen Fang, Zhenggang Tang, Kan Ren, Weiqing Liu, Li Zhao, Jiang Bian, Dongsheng Li, Weinan Zhang, Yong Yu, TieYan Liu|Shanghai Jiao Tong University; Microsoft Research Asia; Microsoft; University of Illinois Urbana-Champaign|Order execution is a fundamental task in quantitative finance, aiming at finishing acquisition or liquidation for a number of trading orders of the specific assets. Recent advance in model-free reinforcement learning (RL) provides a data-driven solution to the order execution problem. However, the existing works always optimize execution for an individual order, overlooking the practice that multiple orders are specified to execute simultaneously, resulting in suboptimality and bias. In this paper, we first present a multi-agent RL (MARL) method for multi-order execution considering practical constraints. Specifically, we treat every agent as an individual operator to trade one specific order, while keeping communicating with each other and collaborating for maximizing the overall profits. Nevertheless, the existing MARL algorithms often incorporate communication among agents by exchanging only the information of their partial observations, which is inefficient in complicated financial market. To improve collaboration, we then propose a learnable multi-round communication protocol, for the agents communicating the intended actions with each other and refining accordingly. It is optimized through a novel action value attribution method which is provably consistent with the original learning objective yet more efficient. The experiments on the data from two real-world markets have illustrated superior performance with significantly better collaboration effectiveness achieved by our method.|定单执行是定量金融的一项基本任务，其目的是完成对特定资产的多个交易定单的收购或清算。无模型强化学习的最新进展为订单执行问题提供了一个数据驱动的解决方案。然而，现有的工作总是优化单个订单的执行，忽视了多个订单指定同时执行的做法，导致次优性和偏差。本文首先提出了一种考虑实际约束的多代理 RL (MARL)多订单执行方法。具体来说，我们把每个代理当作一个独立的经营者来交易一个特定的订单，同时保持相互之间的沟通和合作，以实现总体利润的最大化。然而，现有的 MARL 算法往往只交换代理人的部分观测信息，而不考虑代理人之间的通信，在复杂的金融市场中效率低下。为了改进协作，我们提出了一个可学习的多轮通信协议，用于代理之间相互通信预期的操作并相应地进行细化。通过一种新的行为价值归因方法对其进行优化，该方法与原有的学习目标一致，但效率更高。对两个实际市场的数据进行的实验表明，该方法具有更好的协作效率和更好的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Learning+Multi-Agent+Intention-Aware+Communication+for+Optimal+Multi-Order+Execution+in+Finance)|0| +|[Learning Multi-Agent Intention-Aware Communication for Optimal Multi-Order Execution in Finance](https://doi.org/10.1145/3580305.3599856)|Yuchen Fang, Zhenggang Tang, Kan Ren, Weiqing Liu, Li Zhao, Jiang Bian, Dongsheng Li, Weinan Zhang, Yong Yu, TieYan Liu|Microsoft Research Asia; University of Illinois Urbana-Champaign; Microsoft; Shanghai Jiao Tong University|Order execution is a fundamental task in quantitative finance, aiming at finishing acquisition or liquidation for a number of trading orders of the specific assets. Recent advance in model-free reinforcement learning (RL) provides a data-driven solution to the order execution problem. However, the existing works always optimize execution for an individual order, overlooking the practice that multiple orders are specified to execute simultaneously, resulting in suboptimality and bias. In this paper, we first present a multi-agent RL (MARL) method for multi-order execution considering practical constraints. Specifically, we treat every agent as an individual operator to trade one specific order, while keeping communicating with each other and collaborating for maximizing the overall profits. Nevertheless, the existing MARL algorithms often incorporate communication among agents by exchanging only the information of their partial observations, which is inefficient in complicated financial market. To improve collaboration, we then propose a learnable multi-round communication protocol, for the agents communicating the intended actions with each other and refining accordingly. It is optimized through a novel action value attribution method which is provably consistent with the original learning objective yet more efficient. The experiments on the data from two real-world markets have illustrated superior performance with significantly better collaboration effectiveness achieved by our method.|定单执行是定量金融的一项基本任务，其目的是完成对特定资产的多个交易定单的收购或清算。无模型强化学习的最新进展为订单执行问题提供了一个数据驱动的解决方案。然而，现有的工作总是优化单个订单的执行，忽视了多个订单指定同时执行的做法，导致次优性和偏差。本文首先提出了一种考虑实际约束的多代理 RL (MARL)多订单执行方法。具体来说，我们把每个代理当作一个独立的经营者来交易一个特定的订单，同时保持相互之间的沟通和合作，以实现总体利润的最大化。然而，现有的 MARL 算法往往只交换代理人的部分观测信息，而不考虑代理人之间的通信，在复杂的金融市场中效率低下。为了改进协作，我们提出了一个可学习的多轮通信协议，用于代理之间相互通信预期的操作并相应地进行细化。通过一种新的行为价值归因方法对其进行优化，该方法与原有的学习目标一致，但效率更高。对两个实际市场的数据进行的实验表明，该方法具有更好的协作效率和更好的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Learning+Multi-Agent+Intention-Aware+Communication+for+Optimal+Multi-Order+Execution+in+Finance)|0| |[iETA: A Robust and Scalable Incremental Learning Framework for Time-of-Arrival Estimation](https://doi.org/10.1145/3580305.3599842)|Jindong Han, Hao Liu, Shui Liu, Xi Chen, Naiqiang Tan, Hua Chai, Hui Xiong||Time-of-arrival estimation or Estimated Time of Arrival (ETA) has become an indispensable building block of modern intelligent transportation systems. While many efforts have been made for time-of-arrival estimation, most of them have scalability and robustness issues when dealing with real-world large-scale ETA scenarios, where billions of vehicle trajectories and ETA requests have been continuously generating every day. To this end, in this paper, we propose a robust and scalable incremental ETA learning framework, iETA, to continuously exploit spatio-temporal traffic patterns from massive floating-car data and thus achieve better estimation performances. Specifically, we first build an incremental travel time predictor that can be incrementally updated based on newly generated traffic data. The incremental travel time predictor not only reduces the overall learning overhead but also improves the model's robustness toward urban traffic distribution shifts. Then, we propose a historical traffic knowledge consolidation module to preserve critical spatio-temporal knowledge from previous ETA predictors under the incremental learning setting. Moreover, to reduce interference induced by low-quality traffic data, we propose an adversarial training module to improve the learning robustness by proactively mitigating and resisting traffic noise perturbations. Finally, extensive experiments demonstrate the effectiveness and efficiency of the proposed system against state-of-the-art baselines in large-scale ETA scenarios. Most importantly, iETA has been deployed on the Didi Chuxing platform, handling real-time billions of ETA queries every day, and substantially improves the prediction accuracy.|到达时间估计(ETA)已成为现代智能交通系统不可或缺的组成部分。虽然在到达时间估计方面作出了许多努力，但其中大多数在处理现实世界中的大规模预计到达时间情景时都存在可扩展性和稳健性问题，在这些情景中，每天不断产生数十亿车辆轨迹和预计到达时间请求。为此，本文提出了一个鲁棒的、可扩展的增量式 ETA 学习框架 iETA，它可以从大量的浮动车数据中不断地利用时空交通模式，从而获得更好的估计性能。具体来说，我们首先构建一个增量旅行时间预测器，它可以根据新生成的交通数据进行增量更新。增量式行程时间预测器不仅降低了整体学习开销，而且提高了模型对城市交通分布变化的鲁棒性。然后，我们提出了一个历史交通知识整合模块，以保存关键的时空知识从以前的预测 ETA 的在线机机器学习设置。此外，为了减少低质量交通数据引起的干扰，我们提出了一个对抗训练模块，通过主动减轻和抵抗交通噪声扰动来提高学习的鲁棒性。最后，大量的实验证明了该系统在大规模 ETA 场景中对抗最先进基线的有效性和效率。最重要的是，iETA 已经部署在滴滴出行平台上，每天处理数十亿的实时 ETA 查询，大大提高了预测的准确性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=iETA:+A+Robust+and+Scalable+Incremental+Learning+Framework+for+Time-of-Arrival+Estimation)|0| |[Identifying Complicated Contagion Scenarios from Cascade Data](https://doi.org/10.1145/3580305.3599841)|Galen Harrison, Amro Alabsi Aljundi, Jiangzhuo Chen, S. S. Ravi, Anil Kumar S. Vullikanti, Madhav V. Marathe, Abhijin Adiga|University of Virginia|We consider the setting of cascades that result from contagion dynamics on large realistic contact networks. We address the question of whether the structural properties of a (partially) observed cascade can characterize the contagion scenario and identify the interventions that might be in effect. Using epidemic spread as a concrete example, we study how social interventions such as compliance in social distancing, extent (and efficacy) of vaccination, and the transmissibility of disease can be inferred. The techniques developed are more generally applicable to other contagions as well. Our approach involves the use of large realistic social contact networks of certain regions of USA and an agent-based model (ABM) to simulate spread under two interventions, namely vaccination and generic social distancing (GSD). Through a machine learning approach, coupled with parameter significance analysis, our experimental results show that subgraph counts of the graph induced by the cascade can be used effectively to characterize the contagion scenario even during the initial stages of the epidemic, when traditional information such as case counts alone are not adequate for this task. Further, we show that our approach performs well even for partially observed cascades. These results demonstrate that cascade data collected from digital tracing applications under poor digital penetration and privacy constraints can provide valuable information about the contagion scenario.|我们考虑在大型现实接触网络上由传染动力学产生的级联的设置。我们解决的问题是，一个(部分)观察级联的结构特性是否可以表征传染情景，并确定可能有效的干预措施。以流行病传播为具体例子，我们研究如何推断社会干预措施，如社会距离的依从性、疫苗接种的范围(和有效性)以及疾病的传播性。所开发的技术也更普遍地适用于其他传染病。我们的方法包括使用美国某些地区的大型现实社会接触网络和个体为本模型(ABM)来模拟两种干预措施下的传播，即疫苗接种和通用社会距离(gSD)。通过机器学习方法，结合参数显著性分析，我们的实验结果表明，即使在传染病流行的初始阶段，当病例计数等传统信息不足以完成这项任务时，级联诱导的图的子图计数也可以有效地用于描述传染病情景。此外，我们还展示了我们的方法即使在部分观察到的级联情况下也表现良好。这些结果表明，从数字追踪应用程序收集的级联数据在不良的数字渗透和隐私约束下，可以提供有价值的信息传染情况。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Identifying+Complicated+Contagion+Scenarios+from+Cascade+Data)|0| |[Large-scale Urban Cellular Traffic Generation via Knowledge-Enhanced GANs with Multi-Periodic Patterns](https://doi.org/10.1145/3580305.3599853)|Shuodi Hui, Huandong Wang, Tong Li, Xinghao Yang, Xing Wang, Junlan Feng, Lin Zhu, Chao Deng, Pan Hui, Depeng Jin, Yong Li||With the rapid development of the cellular network, network planning is increasingly important. Generating large-scale urban cellular traffic contributes to network planning via simulating the behaviors of the planned network. Existing methods fail in simulating the long-term temporal behaviors of cellular traffic while cannot model the influences of the urban environment on the cellular networks. We propose a knowledge-enhanced GAN with multi-periodic patterns to generate large-scale cellular traffic based on the urban environment. First, we design a GAN model to simulate the multi-periodic patterns and long-term aperiodic temporal dynamics of cellular traffic via learning the daily patterns, weekly patterns, and residual traffic between long-term traffic and periodic patterns step by step. Then, we leverage urban knowledge to enhance traffic generation via constructing a knowledge graph containing multiple factors affecting cellular traffic in the surrounding urban environment. Finally, we evaluate our model on a real cellular traffic dataset. Our proposed model outperforms three state-of-art generation models by over 32.77%, and the urban knowledge enhancement improves the performance of our model by 4.71%. Moreover, our model achieves good generalization and robustness in generating traffic for urban cellular networks without training data in the surrounding areas.|随着蜂窝网络的迅速发展，网络规划变得越来越重要。生成大规模的城市蜂窝网络流量有助于通过模拟规划网络的行为进行网络规划。现有的方法不能模拟蜂窝网络的长期时间行为，也不能模拟城市环境对蜂窝网络的影响。提出了一种基于知识增强的多周期模式 GAN，用于产生基于城市环境的大规模蜂窝业务。首先，我们设计了一个 GAN 模型，通过逐步学习长期流量和周期流量之间的日流量、周流量和剩余流量，来模拟蜂窝网络流量的多周期模式和长期非周期时间动态。然后，我们利用城市知识，通过构建一个包含多个因素影响周围城市环境中蜂窝式交通的知识图来提高交通生成。最后，我们在一个真实的蜂窝网络流量数据集上评估我们的模型。我们提出的模型比三种最先进的生成模型的性能提高了32.77% 以上，城市知识增强使模型的性能提高了4.71% 。此外，该模型在不需要周边训练数据的情况下，对于城市蜂窝网络的流量生成具有良好的泛化性和鲁棒性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Large-scale+Urban+Cellular+Traffic+Generation+via+Knowledge-Enhanced+GANs+with+Multi-Periodic+Patterns)|0| -|[SentiGOLD: A Large Bangla Gold Standard Multi-Domain Sentiment Analysis Dataset and Its Evaluation](https://doi.org/10.1145/3580305.3599904)|Md. Ekramul Islam, Labib Chowdhury, Faisal Ahamed Khan, Shazzad Hossain, Md. Sourave Hossain, Mohammad Mamun Or Rashid, Nabeel Mohammed, Mohammad Ruhul Amin|Fordham University; Giga Tech Limited; North South University; Bangladesh Computer Council|This study introduces SentiGOLD, a Bangla multi-domain sentiment analysis dataset. Comprising 70,000 samples, it was created from diverse sources and annotated by a gender-balanced team of linguists. SentiGOLD adheres to established linguistic conventions agreed upon by the Government of Bangladesh and a Bangla linguistics committee. Unlike English and other languages, Bangla lacks standard sentiment analysis datasets due to the absence of a national linguistics framework. The dataset incorporates data from online video comments, social media posts, blogs, news, and other sources while maintaining domain and class distribution rigorously. It spans 30 domains (e.g., politics, entertainment, sports) and includes 5 sentiment classes (strongly negative, weakly negative, neutral, and strongly positive). The annotation scheme, approved by the national linguistics committee, ensures a robust Inter Annotator Agreement (IAA) with a Fleiss' kappa score of 0.88. Intra- and cross-dataset evaluation protocols are applied to establish a standard classification system. Cross-dataset evaluation on the noisy SentNoB dataset presents a challenging test scenario. Additionally, zero-shot experiments demonstrate the generalizability of SentiGOLD. The top model achieves a macro f1 score of 0.62 (intra-dataset) across 5 classes, setting a benchmark, and 0.61 (cross-dataset from SentNoB) across 3 classes, comparable to the state-of-the-art. Fine-tuned sentiment analysis model can be accessed at https://sentiment.bangla.gov.bd.|本文介绍了孟加拉语多领域情感分析数据集 SentiGOLD。它包括70,000个样本，由不同的来源创建，并由一个性别平衡的语言学家团队进行注释。SentiGOLD 遵守孟加拉国政府和孟加拉语言学委员会商定的既定语言公约。与英语和其他语言不同，由于缺乏国家语言学框架，孟加拉语缺乏标准的情感分析数据集。该数据集合并了来自在线视频评论、社交媒体帖子、博客、新闻和其他来源的数据，同时严格维护了域和类的分布。它跨越30个领域(例如，政治，娱乐，体育) ，包括5个情绪类(强烈消极，弱消极，中立，和强烈积极)。由国家语言学委员会批准的注释方案确保了一个强有力的内部注释协议(IAA) ，Fleiss 的 kappa 得分为0.88。数据集内和数据集间的评估协议被用来建立一个标准的分类方案。对噪声 SentNoB 数据集进行跨数据集评估是一个具有挑战性的测试场景。此外，零拍实验证明了 SentiGOLD 的通用性。顶级模型在5个类别中实现了0.62(数据集内)的宏观 f1评分，设定了基准，并在3个类别中实现了0.61(来自 SentNoB 的跨数据集) ，与最先进的技术相当。微调的情绪分析模型可以在 https://sentiment.bangla.gov.bd 访问。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=SentiGOLD:+A+Large+Bangla+Gold+Standard+Multi-Domain+Sentiment+Analysis+Dataset+and+Its+Evaluation)|0| +|[SentiGOLD: A Large Bangla Gold Standard Multi-Domain Sentiment Analysis Dataset and Its Evaluation](https://doi.org/10.1145/3580305.3599904)|Md. Ekramul Islam, Labib Chowdhury, Faisal Ahamed Khan, Shazzad Hossain, Md. Sourave Hossain, Mohammad Mamun Or Rashid, Nabeel Mohammed, Mohammad Ruhul Amin|Fordham University; Bangladesh Computer Council; Giga Tech Limited; North South University|This study introduces SentiGOLD, a Bangla multi-domain sentiment analysis dataset. Comprising 70,000 samples, it was created from diverse sources and annotated by a gender-balanced team of linguists. SentiGOLD adheres to established linguistic conventions agreed upon by the Government of Bangladesh and a Bangla linguistics committee. Unlike English and other languages, Bangla lacks standard sentiment analysis datasets due to the absence of a national linguistics framework. The dataset incorporates data from online video comments, social media posts, blogs, news, and other sources while maintaining domain and class distribution rigorously. It spans 30 domains (e.g., politics, entertainment, sports) and includes 5 sentiment classes (strongly negative, weakly negative, neutral, and strongly positive). The annotation scheme, approved by the national linguistics committee, ensures a robust Inter Annotator Agreement (IAA) with a Fleiss' kappa score of 0.88. Intra- and cross-dataset evaluation protocols are applied to establish a standard classification system. Cross-dataset evaluation on the noisy SentNoB dataset presents a challenging test scenario. Additionally, zero-shot experiments demonstrate the generalizability of SentiGOLD. The top model achieves a macro f1 score of 0.62 (intra-dataset) across 5 classes, setting a benchmark, and 0.61 (cross-dataset from SentNoB) across 3 classes, comparable to the state-of-the-art. Fine-tuned sentiment analysis model can be accessed at https://sentiment.bangla.gov.bd.|本文介绍了孟加拉语多领域情感分析数据集 SentiGOLD。它包括70,000个样本，由不同的来源创建，并由一个性别平衡的语言学家团队进行注释。SentiGOLD 遵守孟加拉国政府和孟加拉语言学委员会商定的既定语言公约。与英语和其他语言不同，由于缺乏国家语言学框架，孟加拉语缺乏标准的情感分析数据集。该数据集合并了来自在线视频评论、社交媒体帖子、博客、新闻和其他来源的数据，同时严格维护了域和类的分布。它跨越30个领域(例如，政治，娱乐，体育) ，包括5个情绪类(强烈消极，弱消极，中立，和强烈积极)。由国家语言学委员会批准的注释方案确保了一个强有力的内部注释协议(IAA) ，Fleiss 的 kappa 得分为0.88。数据集内和数据集间的评估协议被用来建立一个标准的分类方案。对噪声 SentNoB 数据集进行跨数据集评估是一个具有挑战性的测试场景。此外，零拍实验证明了 SentiGOLD 的通用性。顶级模型在5个类别中实现了0.62(数据集内)的宏观 f1评分，设定了基准，并在3个类别中实现了0.61(来自 SentNoB 的跨数据集) ，与最先进的技术相当。微调的情绪分析模型可以在 https://sentiment.bangla.gov.bd 访问。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=SentiGOLD:+A+Large+Bangla+Gold+Standard+Multi-Domain+Sentiment+Analysis+Dataset+and+Its+Evaluation)|0| |[Off-Policy Learning-to-Bid with AuctionGym](https://doi.org/10.1145/3580305.3599877)|Olivier Jeunen, Sean Murphy, Ben Allison||Online advertising opportunities are sold through auctions, billions of times every day across the web. Advertisers who participate in those auctions need to decide on a bidding strategy: how much they are willing to bid for a given impression opportunity. Deciding on such a strategy is not a straightforward task, because of the interactive and reactive nature of the repeated auction mechanism. Indeed, an advertiser does not observe counterfactual outcomes of bid amounts that were not submitted, and successful advertisers will adapt their own strategies based on bids placed by competitors. These characteristics complicate effective learning and evaluation of bidding strategies based on logged data alone. The interactive and reactive nature of the bidding problem lends itself to a bandit or reinforcement learning formulation, where a bidding strategy can be optimised to maximise cumulative rewards. Several design choices then need to be made regarding parameterisation, model-based or model-free approaches, and the formulation of the objective function. This work provides a unified framework for such "learning to bid'' methods, showing how many existing approaches fall under the value-based paradigm. We then introduce novel policy-based and doubly robust formulations of the bidding problem. To allow for reliable and reproducible offline validation of such methods without relying on sensitive proprietary data, we introduce AuctionGym: a simulation environment that enables the use of bandit learning for bidding strategies in online advertising auctions. We present results from a suite of experiments under varying environmental conditions, unveiling insights that can guide practitioners who need to decide on a model class. Empirical observations highlight the effectiveness of our newly proposed methods. AuctionGym is released under an open-source license, and we expect the research community to benefit from this tool.|在线广告机会是通过拍卖出售的，每天在网络上出售数十亿次。参与这些拍卖的广告商需要决定一个投标策略: 他们愿意为给定的印象机会出多少价。由于重复拍卖机制的互动性和反应性，决定这样一种战略不是一项直截了当的任务。事实上，广告客户不会观察到没有提交的出价金额的反事实结果，成功的广告客户将根据竞争对手的出价调整自己的策略。这些特点使得单独基于日志数据的投标策略的有效学习和评价复杂化。招标问题的互动性和反应性，使其成为一种强盗或强化学习的表述，在这种表述中，可以优化招标策略，以实现累积回报的最大化。然后，需要对参量化、基于模型或无模型的方法以及目标函数的表述做出几种设计选择。这项工作为这种“学习投标”方法提供了一个统一的框架，显示了有多少现有的方法属于基于价值的范式。然后，我们引入新的基于策略和双稳健公式的投标问题。为了能够在不依赖敏感的专有数据的情况下对这些方法进行可靠和可重复的离线验证，我们介绍 AuctionGym: 一个模拟环境，它能够在在线广告拍卖中使用强盗学习竞价策略。我们展示了在不同环境条件下的一系列实验结果，揭示了可以指导从业者决定模型课程的见解。经验观察突出了我们新提出的方法的有效性。AuctionGym 是在开源许可下发布的，我们希望研究社区能从这个工具中获益。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Off-Policy+Learning-to-Bid+with+AuctionGym)|0| |[FairCod: A Fairness-aware Concurrent Dispatch System for Large-scale Instant Delivery Services](https://doi.org/10.1145/3580305.3599824)|Lin Jiang, Shuai Wang, Baoshen Guo, Hai Wang, Desheng Zhang, Guang Wang||In recent years, we have been witnessing a rapid prevalence of instant delivery services (e,g., UberEats, Instacart, and Eleme) due to their convenience and timeliness. A unique characteristic of instant delivery services is the concurrent dispatch mode, where (i) one courier usually simultaneously delivers multiple orders, especially during rush hours, and (ii) couriers can receive new orders when delivering existing orders. Most existing concurrent dispatch systems are efficiency-oriented, which means they usually dispatch a group of orders that have a similar delivery route to a courier. Although this strategy may achieve high overall efficiency, it also potentially causes a huge disparity of earnings between different couriers. To address the problem, in this paper, we design a Fairness-aware Concurrent dispatch system called FairCod, which aims to optimize the overall operation efficiency and individual fairness at the same time. Specifically, in FairCod, we design a Dynamic Advantage Actor-Critic algorithm with Fairness constrain (DA2CF). The basic idea is that it includes an Actor network to make dispatch decisions based on dynamic action space and a Critic network to evaluate the dispatch decisions from the fairness perspective. More importantly, we extensively evaluate our FairCod system based on one-month real-world data consisting of 36.38 million orders from 42,000 couriers collected by one of the largest instant delivery companies in China. Experimental results show that our FairCod improves courier fairness by 30.3% without sacrificing the overall system benefit compared to state-of-the-art baselines.|近年来，我们见证了即时递送服务(例如，g. ，UberEats，Instacart 和饿了么)的迅速普及，这是由于它们的方便性和及时性。即时递送服务的一个独特特征是并发分发模式，其中(i)一个快递员通常同时递送多个订单，特别是在高峰时间，以及(ii)快递员可以在递送现有订单时接收新订单。现有的大多数并发调度系统都是以效率为导向的，这意味着它们通常将一组具有相似配送路线的订单调度给快递员。虽然这种策略可以达到很高的整体效率，但它也有可能造成不同快递公司之间收入的巨大差距。为了解决这个问题，本文设计了一个基于公平感知的并发调度系统 FairCod，目的是同时优化整体运行效率和个体公平性。具体来说，在 FairCod，我们设计了一个带有公平约束的动态优势行为者批判算法(da2CF)。其基本思想是包括一个基于动态行为空间的调度决策参与者网络和一个从公平角度评价调度决策的批判网络。更重要的是，我们广泛评估我们的 FairCod 系统的基础上，一个月的现实世界的数据，包括3638万订单从42,000快递公司收集在中国最大的快递公司之一。实验结果表明，与最先进的基准相比，我们的 FairCod 在不牺牲系统整体效益的情况下，提高了30.3% 的信使公平性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=FairCod:+A+Fairness-aware+Concurrent+Dispatch+System+for+Large-scale+Instant+Delivery+Services)|0| |[CBLab: Supporting the Training of Large-scale Traffic Control Policies with Scalable Traffic Simulation](https://doi.org/10.1145/3580305.3599789)|Chumeng Liang, Zherui Huang, Yicheng Liu, Zhanyu Liu, Guanjie Zheng, Hanyuan Shi, Kan Wu, Yuhao Du, Fuliang Li, Zhenhui Jessie Li||Traffic simulation provides interactive data for the optimization of traffic control policies. However, existing traffic simulators are limited by their lack of scalability and shortage in input data, which prevents them from generating interactive data from traffic simulation in the scenarios of real large-scale city road networks. In this paper, we present City Brain Lab, a toolkit for scalable traffic simulation. CBLab consists of three components: CBEngine, CBData, and CBScenario. CBEngine is a highly efficient simulator supporting large-scale traffic simulation. CBData includes a traffic dataset with road network data of 100 cities all around the world. We also develop a pipeline to conduct a one-click transformation from raw road networks to input data of our traffic simulation. Combining CBEngine and CBData allows researchers to run scalable traffic simulations in the road network of real large-scale cities. Based on that, CBScenario implements an interactive environment and a benchmark for two scenarios of traffic control policies respectively, with which traffic control policies adaptable for large-scale urban traffic can be trained and tuned. To the best of our knowledge, CBLab is the first infrastructure supporting traffic control policy optimization in large-scale urban scenarios. CBLab has supported the City Brain Challenge @ KDD CUP 2021. The project is available on GitHub:~https://github.com/CityBrainLab/CityBrainLab.git.|交通仿真为交通控制策略的优化提供了交互数据。然而，现有的交通模拟器由于缺乏可扩展性和输入数据的不足，使得它们无法在真实的大规模城市道路网络场景中通过交通模拟生成交互式数据。在本文中，我们介绍了城市大脑实验室，一个可扩展的交通仿真工具包。CBLab 由三个组件组成: CBEngine、 CBData 和 CBScenario。CBEngine 是一个支持大规模交通仿真的高效仿真器。CBData 包括一个包含全球100个城市道路网数据的交通数据集。我们还开发了一个管道，从原始道路网络进行一键转换，以输入我们的交通模拟数据。CBEngine 和 CBData 的结合使得研究人员能够在真实的大规模城市的道路网络中运行可扩展的交通模拟。在此基础上，CBScenario 分别为两个交通控制策略场景实现了一个交互式环境和一个基准，用于训练和调整适应大规模城市交通的交通控制策略。据我们所知，CBLab 是支持大规模城市场景中交通控制政策优化的第一个基础设施。CBLab 支持城市大脑挑战@KDD CUP 2021。这个项目可以在 gitHub 上找到: ~ https://GitHub.com/citybrainlab/citybrainlab.git。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=CBLab:+Supporting+the+Training+of+Large-scale+Traffic+Control+Policies+with+Scalable+Traffic+Simulation)|0| -|[Practical Synthetic Human Trajectories Generation Based on Variational Point Processes](https://doi.org/10.1145/3580305.3599888)|Qingyue Long, Huandong Wang, Tong Li, Lisi Huang, Kun Wang, Qiong Wu, Guangyu Li, Yanping Liang, Li Yu, Yong Li|China Mobile Research Institute; Department of Electronic Engineering, Tsinghua University|Human trajectories, reflecting people's travel patterns and the range of activities, are crucial for the applications like urban planning and epidemic control. However, the real-world human trajectory data tends to be limited by user privacy or device acquisition issues, leading to its insufficient quality to support the above applications. Hence, generating human trajectory data is a crucial but challenging task, which suffers from the following two critical challenges: 1) how to capture the user distribution in human trajectories (group view), and 2) how to model the complex mobility patterns of each user trajectory (individual view). In this paper, we propose a novel human trajectories generator (named VOLUNTEER), consisting of a user VAE and a trajectory VAE, to address the above challenges. Specifically, in the user VAE, we propose to learn the user distribution with all human trajectories from a group view. In the trajectory VAE, from the individual view, we model the complex mobility patterns by decoupling travel time and dwell time to accurately simulate individual trajectories. Extensive experiments on two real-world datasets show the superiority of our model over the state-of-the-art baselines. Further application analysis in the industrial system also demonstrates the effectiveness of our model.|反映人们旅行模式和活动范围的人类轨迹对于城市规划和流行病控制等应用至关重要。然而，现实世界中的人类轨迹数据往往受到用户隐私或设备获取问题的限制，导致其质量不足以支持上述应用程序。因此，生成人类轨迹数据是一项至关重要但具有挑战性的任务，它面临以下两个关键挑战: 1)如何捕获人类轨迹中的用户分布(组视图) ，以及2)如何建模每个用户轨迹的复杂移动模式(个人视图)。为了解决上述问题，本文提出了一种新的人体轨迹生成器(VOLUNTEER) ，它由用户 VAE 和轨迹 VAE 组成。具体来说，在用户 VAE 中，我们建议从组视图中了解具有所有人类轨迹的用户分布。在轨迹 VAE 中，从个体的角度出发，通过解耦行程时间和停留时间来建立复杂的移动模式，以准确地模拟个体的轨迹。在两个真实世界数据集上的大量实验表明，我们的模型优于最先进的基线。在工业系统中的进一步应用分析也证明了该模型的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Practical+Synthetic+Human+Trajectories+Generation+Based+on+Variational+Point+Processes)|0| +|[Practical Synthetic Human Trajectories Generation Based on Variational Point Processes](https://doi.org/10.1145/3580305.3599888)|Qingyue Long, Huandong Wang, Tong Li, Lisi Huang, Kun Wang, Qiong Wu, Guangyu Li, Yanping Liang, Li Yu, Yong Li|Department of Electronic Engineering, Tsinghua University; China Mobile Research Institute|Human trajectories, reflecting people's travel patterns and the range of activities, are crucial for the applications like urban planning and epidemic control. However, the real-world human trajectory data tends to be limited by user privacy or device acquisition issues, leading to its insufficient quality to support the above applications. Hence, generating human trajectory data is a crucial but challenging task, which suffers from the following two critical challenges: 1) how to capture the user distribution in human trajectories (group view), and 2) how to model the complex mobility patterns of each user trajectory (individual view). In this paper, we propose a novel human trajectories generator (named VOLUNTEER), consisting of a user VAE and a trajectory VAE, to address the above challenges. Specifically, in the user VAE, we propose to learn the user distribution with all human trajectories from a group view. In the trajectory VAE, from the individual view, we model the complex mobility patterns by decoupling travel time and dwell time to accurately simulate individual trajectories. Extensive experiments on two real-world datasets show the superiority of our model over the state-of-the-art baselines. Further application analysis in the industrial system also demonstrates the effectiveness of our model.|反映人们旅行模式和活动范围的人类轨迹对于城市规划和流行病控制等应用至关重要。然而，现实世界中的人类轨迹数据往往受到用户隐私或设备获取问题的限制，导致其质量不足以支持上述应用程序。因此，生成人类轨迹数据是一项至关重要但具有挑战性的任务，它面临以下两个关键挑战: 1)如何捕获人类轨迹中的用户分布(组视图) ，以及2)如何建模每个用户轨迹的复杂移动模式(个人视图)。为了解决上述问题，本文提出了一种新的人体轨迹生成器(VOLUNTEER) ，它由用户 VAE 和轨迹 VAE 组成。具体来说，在用户 VAE 中，我们建议从组视图中了解具有所有人类轨迹的用户分布。在轨迹 VAE 中，从个体的角度出发，通过解耦行程时间和停留时间来建立复杂的移动模式，以准确地模拟个体的轨迹。在两个真实世界数据集上的大量实验表明，我们的模型优于最先进的基线。在工业系统中的进一步应用分析也证明了该模型的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Practical+Synthetic+Human+Trajectories+Generation+Based+on+Variational+Point+Processes)|0| |[Deep Landscape Forecasting in Multi-Slot Real-Time Bidding](https://doi.org/10.1145/3580305.3599799)|Weitong Ou, Bo Chen, Yingxuan Yang, Xinyi Dai, Weiwen Liu, Weinan Zhang, Ruiming Tang, Yong Yu||Real-Time Bidding (RTB) has shown remarkable success in display advertising and has been employed in other advertising scenarios, e.g., sponsored search advertising with multiple ad slots. Many current RTB techniques built for single-slot display advertising are thus no longer applicable, especially in the bid landscape forecasting. Landscape forecasting predicts market competition, including the highest bid price and winning probability, which is preliminary and crucial for the subsequent bidding strategy design. In the multi-slot advertising, predicting the winning prices for each position requires a more precise differentiation of bids among top advertisers. Furthermore, defining the winning probability and addressing censorship issues are not as straightforward as in the case of a single slot. In view of these challenges, how to forecast the bidding landscape in the multi-slot environment remains open. In this work, we are the first to study the landscape forecasting problem in multi-slot RTB, considering the correlation between ad slots in the same pageview. Specifically, we formulate the research topic into two subproblems: predicting the distribution of the winning price and predicting the winning probability of the bid price for each position. Based on the observation from the production data and survival analysis techniques, we propose a deep recurrent model to predict the distribution of the winning price as well as the winning probability for each position. A comprehensive loss function is proposed to learn from the censoring data. Experiments on two public semi-synthetic datasets and one private industrial dataset demonstrate the effectiveness of our method.|实时竞价(RTB)在展示广告方面取得了显著的成功，并被用于其他广告场景，例如，具有多个广告时段的赞助商搜索广告。因此，目前许多为单插槽显示广告建立的 RTB 技术已不再适用，特别是在投标前景预测方面。景观预测预测市场竞争，包括最高投标价格和中标概率，是后续投标策略设计的初步和关键。在多插槽广告中，要预测每个广告位的中标价格，就需要对顶级广告商的出价进行更精确的区分。此外，确定获胜的可能性和解决审查问题并不像单一时段那样简单。鉴于这些挑战，如何预测多时段环境下的投标景观仍然是开放的。本文首次研究了多时隙 RTB 的景观预测问题，考虑了同一页面视图中广告时隙之间的相关性。具体来说，我们将研究课题分为两个子问题: 预测中标价格的分布和预测每个位置的中标价格的中标概率。基于对生产数据的观察和生存分析技术，我们提出了一个深度递归模型来预测中标价格的分布以及每个位置的中标概率。提出了一种综合损失函数，以便从截尾数据中学习。在两个公共半合成数据集和一个私有工业数据集上的实验表明了该方法的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Deep+Landscape+Forecasting+in+Multi-Slot+Real-Time+Bidding)|0| |[NFT-Based Data Marketplace with Digital Watermarking](https://doi.org/10.1145/3580305.3599876)|Saeed Ranjbar Alvar, Mohammad Akbari, David (Ming Xuan) Yue, Yong Zhang|Huawei Technologies Canada Co., Ltd.; Ming Xuan) Yue (Huawei Technologies Canada Co., Ltd.|In today's digital world, enterprises and individuals are generating massive data that is potentially useful for many data consumers with data driven applications. The emergence of data marketplaces is a step toward helping the data owners to monetize their digital assets and get connected to the potential buyers. The current data marketplaces cannot handle the challenges related to data ownership claims, illegal redistribution, and data ownership traceability. To overcome these problems in a general-purpose market, we propose a marketplace based on watermarking and Non-Fungible Token (NFT) technologies. In the proposed NFT-based marketplace, the owner's data is stored as an NFT where the underlying content of the NFT holds the watermarked data. The watermarked data is obtained by embedding some information about the owners and the buyers into the original data. The embedded information can later be extracted to identify the owner and the buyer of the traded data. Furthermore, the transactions corresponding to the NFT provide verifiable ownership proof and traceable ownership history. A Proof-Of-Concept (POC) implementation of the proposed marketplace that will be integrated within AI-Gallery Data Marketplace service in Huawei Cloud is presented for trading image data. An extensive set of experiments to measure the gas consumption on the blockchain and evaluate the robustness of the watermarked assets against 51 attacks are performed. Finally, a method based on error correction codes is proposed for improving the watermarking robustness in the implemented marketplace. The link for the codes and the POC demo is provided in the appendix.|在当今的数字世界中，企业和个人正在生成大量数据，这些数据对于许多使用数据驱动应用程序的数据消费者来说可能非常有用。数据市场的出现是帮助数据所有者将其数字资产货币化并与潜在买家建立联系的一个步骤。当前的数据市场无法处理与数据所有权声明、非法重新分配和数据所有权可追踪性相关的挑战。为了克服这些问题，在一个通用市场，我们提出了一个基于水印和非可替换令牌(NFT)技术的市场。在提议的基于 NFT 的市场中，所有者的数据作为 NFT 存储，其中 NFT 的基础内容保存有水印数据。水印数据是通过在原始数据中嵌入所有者和购买者的信息来获得的。随后可以提取嵌入的信息以识别交易数据的所有者和买家。此外，与 NFT 相对应的交易提供了可验证的所有权证明和可追踪的所有权历史。为了交易图像数据，华为云计算的人工智能画廊数据市场服务(AI-Gallery Data Marketplace service)将集成一个拟议市场的概念验证(POC)实现。为了测量区块链上的耗气量并评估水印资产对51种攻击的鲁棒性，进行了一系列广泛的实验。最后，提出了一种基于纠错编码的水印鲁棒性提高方法。附录中提供了代码和 POC 演示的链接。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=NFT-Based+Data+Marketplace+with+Digital+Watermarking)|0| -|[Rover: An Online Spark SQL Tuning Service via Generalized Transfer Learning](https://doi.org/10.1145/3580305.3599953)|Yu Shen, Xinyuyang Ren, Yupeng Lu, Huaijun Jiang, Huanyong Xu, Di Peng, Yang Li, Wentao Zhang, Bin Cui|Peking University; Mila – Québec AI Institute; ByteDance Inc.|Distributed data analytic engines like Spark are common choices to process massive data in industry. However, the performance of Spark SQL highly depends on the choice of configurations, where the optimal ones vary with the executed workloads. Among various alternatives for Spark SQL tuning, Bayesian optimization (BO) is a popular framework that finds near-optimal configurations given sufficient budget, but it suffers from the re-optimization issue and is not practical in real production. When applying transfer learning to accelerate the tuning process, we notice two domain-specific challenges: 1) most previous work focus on transferring tuning history, while expert knowledge from Spark engineers is of great potential to improve the tuning performance but is not well studied so far; 2) history tasks should be carefully utilized, where using dissimilar ones lead to a deteriorated performance in production. In this paper, we present Rover, a deployed online Spark SQL tuning service for efficient and safe search on industrial workloads. To address the challenges, we propose generalized transfer learning to boost the tuning performance based on external knowledge, including expert-assisted Bayesian optimization and controlled history transfer. Experiments on public benchmarks and real-world tasks show the superiority of Rover over competitive baselines. Notably, Rover saves an average of 50.1% of the memory cost on 12k real-world Spark SQL tasks in 20 iterations, among which 76.2% of the tasks achieve a significant memory reduction of over 60%.|像 Spark 这样的分布式数据分析引擎是工业中处理海量数据的常见选择。然而，Spark SQL 的性能在很大程度上取决于配置的选择，其中最佳配置随执行的工作负载而变化。在各种 Spark SQL 调优方案中，贝叶斯优化(BO)是一种流行的框架，它能够在预算充足的情况下找到接近最优的配置，但是它存在重新优化的问题，在实际生产中并不实用。当应用转移学习来加速调优过程时，我们注意到两个领域特有的挑战: 1)大多数以前的工作集中在转移调优历史，而来自 Spark 工程师的专家知识对于提高调优性能具有巨大的潜力，但是目前还没有得到很好的研究; 2)历史任务应该被仔细地利用，在使用不同的任务导致生产性能恶化的情况下。在本文中，我们介绍了 Rover，一个已部署的在线 Spark SQL 调优服务，用于在工业工作负载上进行高效和安全的搜索。针对这一挑战，我们提出了基于外部知识的广义迁移学习来提高调优性能，包括专家辅助的贝叶斯优化和受控历史迁移。在公共基准测试和实际任务上的实验表明，Rover 优于竞争基准测试。值得注意的是，在20次迭代中，Rover 为12k 实际 Spark SQL 任务平均节省了50.1% 的内存成本，其中76.2% 的任务实现了超过60% 的显著内存减少。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Rover:+An+Online+Spark+SQL+Tuning+Service+via+Generalized+Transfer+Learning)|0| +|[Rover: An Online Spark SQL Tuning Service via Generalized Transfer Learning](https://doi.org/10.1145/3580305.3599953)|Yu Shen, Xinyuyang Ren, Yupeng Lu, Huaijun Jiang, Huanyong Xu, Di Peng, Yang Li, Wentao Zhang, Bin Cui|Mila – Québec AI Institute; ByteDance Inc.; Peking University|Distributed data analytic engines like Spark are common choices to process massive data in industry. However, the performance of Spark SQL highly depends on the choice of configurations, where the optimal ones vary with the executed workloads. Among various alternatives for Spark SQL tuning, Bayesian optimization (BO) is a popular framework that finds near-optimal configurations given sufficient budget, but it suffers from the re-optimization issue and is not practical in real production. When applying transfer learning to accelerate the tuning process, we notice two domain-specific challenges: 1) most previous work focus on transferring tuning history, while expert knowledge from Spark engineers is of great potential to improve the tuning performance but is not well studied so far; 2) history tasks should be carefully utilized, where using dissimilar ones lead to a deteriorated performance in production. In this paper, we present Rover, a deployed online Spark SQL tuning service for efficient and safe search on industrial workloads. To address the challenges, we propose generalized transfer learning to boost the tuning performance based on external knowledge, including expert-assisted Bayesian optimization and controlled history transfer. Experiments on public benchmarks and real-world tasks show the superiority of Rover over competitive baselines. Notably, Rover saves an average of 50.1% of the memory cost on 12k real-world Spark SQL tasks in 20 iterations, among which 76.2% of the tasks achieve a significant memory reduction of over 60%.|像 Spark 这样的分布式数据分析引擎是工业中处理海量数据的常见选择。然而，Spark SQL 的性能在很大程度上取决于配置的选择，其中最佳配置随执行的工作负载而变化。在各种 Spark SQL 调优方案中，贝叶斯优化(BO)是一种流行的框架，它能够在预算充足的情况下找到接近最优的配置，但是它存在重新优化的问题，在实际生产中并不实用。当应用转移学习来加速调优过程时，我们注意到两个领域特有的挑战: 1)大多数以前的工作集中在转移调优历史，而来自 Spark 工程师的专家知识对于提高调优性能具有巨大的潜力，但是目前还没有得到很好的研究; 2)历史任务应该被仔细地利用，在使用不同的任务导致生产性能恶化的情况下。在本文中，我们介绍了 Rover，一个已部署的在线 Spark SQL 调优服务，用于在工业工作负载上进行高效和安全的搜索。针对这一挑战，我们提出了基于外部知识的广义迁移学习来提高调优性能，包括专家辅助的贝叶斯优化和受控历史迁移。在公共基准测试和实际任务上的实验表明，Rover 优于竞争基准测试。值得注意的是，在20次迭代中，Rover 为12k 实际 Spark SQL 任务平均节省了50.1% 的内存成本，其中76.2% 的任务实现了超过60% 的显著内存减少。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Rover:+An+Online+Spark+SQL+Tuning+Service+via+Generalized+Transfer+Learning)|0| |[Root Cause Analysis for Microservice Systems via Hierarchical Reinforcement Learning from Human Feedback](https://doi.org/10.1145/3580305.3599934)|Lu Wang, Chaoyun Zhang, Ruomeng Ding, Yong Xu, Qihang Chen, Wentao Zou, Qingjun Chen, Meng Zhang, Xuedong Gao, Hao Fan, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang||In microservice systems, the identification of root causes of anomalies is imperative for service reliability and business impact. This process is typically divided into two phases: (i)constructing a service dependency graph that outlines the sequence and structure of system components that are invoked, and (ii) localizing the root cause components using the graph, traces, logs, and Key Performance Indicators (KPIs) such as latency. However, both phases are not straightforward due to the highly dynamic and complex nature of the system, particularly in large-scale commercial architectures like Microsoft Exchange. In this paper, we propose a new framework that employs Hierarchical Reinforcement Learning from Human Feedback (HRLHF) to address these challenges. Our framework leverages the static topology of the microservice system and efficiently employs the feedback of engineers to reduce uncertainty in the discovery of the service dependency graph. The framework utilizes reinforcement learning to reduce the number of queries required from O(N 2 ) to O(1), enabling the construction of the dependency graph with high accuracy and minimal human effort. Additionally, we extend the discovered dependency graphs to window causal graphs that capture the characteristics of time series over a specified time period, resulting in improved root cause analysis accuracy and robustness. Evaluations on both real datasets from Microsoft Exchange and synthetic datasets with injected anomalies demonstrate superior performance on various metrics compared to state-of-the-art methods. It is worth mentioning that, our framework has been integrated as a crucial component in Microsoft M365 Exchange service.|在微服务系统中，识别异常的根本原因对于服务的可靠性和业务影响是必不可少的。这个过程通常分为两个阶段: (i)构建一个服务依赖关系图，概述被调用的系统组件的顺序和结构; (ii)使用图、跟踪、日志和关键性能指标(KPI)(比如延迟)本地化根源组件。然而，由于系统的高度动态性和复杂性，这两个阶段并不简单，特别是在 Microsoft Exchange 这样的大型商业体系结构中。在本文中，我们提出了一个新的框架，利用人类反馈(HRLHF)的分层强化学习来解决这些挑战。我们的框架利用了微服务系统的静态拓扑结构，并有效地利用了工程师的反馈来减少服务依赖图发现中的不确定性。该框架利用强化学习来减少从 O (n2)到 O (1)所需的查询数量，从而能够以高精度和最小的人工成本构建依赖关系图。此外，我们将发现的依赖关系图扩展到窗口因果关系图，以捕获特定时间段内时间序列的特征，从而提高根本原因分析的准确性和鲁棒性。对来自 MicrosoftExchange 的实际数据集和注入异常的合成数据集的评估表明，与最先进的方法相比，在各种指标上具有更好的性能。值得一提的是，我们的框架已经集成为 MicrosoftM365Exchange 服务中的一个关键组件。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Root+Cause+Analysis+for+Microservice+Systems+via+Hierarchical+Reinforcement+Learning+from+Human+Feedback)|0| |[Knowledge Based Prohibited Item Detection on Heterogeneous Risk Graphs](https://doi.org/10.1145/3580305.3599852)|Tingyan Xiang, Ao Li, Yugang Ji, Dong Li||With the popularity of online shopping in recent years, various prohibited items are continuously attacking e-commerce portals. Searching and deleting such risk items online has played a fundamental role in protecting the health of e-commerce trades. To mitigate negative impact of limited supervision and adversarial behaviors of malicious sellers, current state-of-the-art work mainly introduces heterogeneous graph neural network with further improvements such as graph structure learning, pairwise training mechanism, etc. However, performance of these models is highly limited since domain knowledge is indispensable for identifying prohibited items but ignored by these methods. In this paper, we propose a novel Knowledge Based Prohibited item Detection system (named KBPD) to break through this limitation. To make full use of rich risk knowledge, the proposed method introduces the Risk-Domain Knowledge Graph (named RDKG), which is encoded by a path-based graph neural network method. Furthermore, to utilize information from both the RDKG and the Heterogeneous Risk Graph (named HRG), an interactive fusion framework is proposed and further improves the detection performance. We collect real-world datasets from the largest Chinese second-hand commodity trading platform, Xianyu. Both offline and online experimental results consistently demonstrate that KBPD outperforms the state-of-the-art baselines. The improvement over the second-best method is up to 22.67% in the AP metric.|随着近年来网上购物的普及，各种违禁商品不断攻击电子商务门户网站。在网上搜索和删除此类风险项目，对保护电子商务行业的健康发挥了根本性作用。为了减轻有限监管和恶意卖方对抗行为的负面影响，当前的研究主要是引入异构图神经网络，并对其进行了图结构学习、成对训练机制等改进。然而，这些模型的性能是高度有限的，因为领域知识是必不可少的，以确定违禁项目，但忽视了这些方法。为了突破这一局限，本文提出了一种新的基于知识的违禁品检测系统(KBPD)。为了充分利用丰富的风险知识，该方法引入了风险领域知识图(RDKG) ，采用基于路径的图神经网络方法对其进行编码。此外，为了同时利用 RDKG 和异构风险图(HRG)的信息，提出了一种交互式融合框架，进一步提高了检测性能。我们从中国最大的二手商品交易平台 Xianyu 收集真实世界的数据集。离线和在线实验结果一致表明，KBPD 优于最先进的基线。在 AP 指标中，相对于次优方法的改进率高达22.67% 。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Knowledge+Based+Prohibited+Item+Detection+on+Heterogeneous+Risk+Graphs)|0| |[A Data-Driven Decision Support Framework for Player Churn Analysis in Online Games](https://doi.org/10.1145/3580305.3599759)|Yu Xiong, Runze Wu, Shiwei Zhao, Jianrong Tao, Xudong Shen, Tangjie Lyu, Changjie Fan, Peng Cui||Faced with saturated market and fierce competition of online games, it is of great value to analyze the causes of the player churn for improving the game product, maintaining the player retention. A large number of research efforts on churn analysis have been made into churn prediction, which can achieve a sound accuracy benefiting from the booming of AI technologies. However, game publishers are usually unable to apply high-accuracy prediction methods in practice for preventing or relieving the churn due to the lack of the specific decision support (e.g., why they leave and what to do next). In this study, we fully exploit the expertise in online games and propose a comprehensive data-driven decision support framework for addressing game player churn. We first define the churn analysis in online games from a commercial perspective and elaborate the core demands of game publishers for churn analysis. Then we employ and improve the cutting-edge eXplainable AI (XAI) methods to predict player churn and analyze the potential churn causes. The possible churn causes can finally guide game publishers to make specific decisions of revision or intervention in our designed procedure. We demonstrate the effectiveness and high practical value of the framework by conducting extensive experiments on a real-world large-scale online game, Justice PC. The whole decision support framework, bringing interesting and valuable insights, also receives quite positive reviews from the game product and operation teams. Notably, the whole pipeline is readily transplanted to other online systems for decision support to address similar issues.|面对网络游戏市场的饱和和激烈竞争，分析网络游戏玩家流失的原因，对于提高网络游戏产品质量，保持网络游戏玩家的持久性具有重要的参考价值。人工智能技术的蓬勃发展，使得人工智能的预测精度得到了很大的提高。然而，由于缺乏具体的决策支持(例如，他们为什么离开以及下一步做什么) ，游戏发行商通常无法在实践中应用高精度的预测方法来防止或缓解流失。在这项研究中，我们充分利用在线游戏的专业知识，并提出了一个全面的数据驱动的决策支持框架，以解决游戏玩家流失。我们首先从商业角度定义了在线游戏的流失分析，并阐述了游戏发行商对流失分析的核心要求。然后采用改进的可解释人工智能(XAI)方法对球员流失进行预测，分析潜在的流失原因。可能的变动原因，最终可以指导游戏发行商作出具体的决定，修订或干预我们的设计程序。通过在现实大型网络游戏 Justice PC 上进行广泛的实验，验证了该框架的有效性和较高的实用价值。整个决策支持框架，带来了有趣和有价值的见解，也从游戏产品和运营团队得到了相当积极的评价。值得注意的是，整个流水线很容易移植到其他在线系统中，以便为解决类似问题提供决策支持。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Data-Driven+Decision+Support+Framework+for+Player+Churn+Analysis+in+Online+Games)|0| @@ -233,41 +233,41 @@ |[TwHIN-BERT: A Socially-Enriched Pre-trained Language Model for Multilingual Tweet Representations at Twitter](https://doi.org/10.1145/3580305.3599921)|Xinyang Zhang, Yury Malkov, Omar Florez, Serim Park, Brian McWilliams, Jiawei Han, Ahmed ElKishky|Univ Illinois, Urbana, IL 61801 USA; Twitter Cortex, San Francisco, CA 94103 USA|Pre-trained language models (PLMs) are fundamental for natural language processing applications. Most existing PLMs are not tailored to the noisy user-generated text on social media, and the pre-training does not factor in the valuable social engagement logs available in a social network. We present TwHIN-BERT, a multilingual language model productionized at Twitter, trained on indomain data from the popular social network. TwHIN-BERT differs from prior pre-trained language-models as it is trained with not only text-based self-supervision but also with a social objective based on the rich social engagements within a Twitter heterogeneous information network (TwHIN). Our model is trained on 7 billion tweets covering over 100 distinct languages, providing a valuable representation to model short, noisy, user-generated text. We evaluate our model on various multilingual social recommendation and semantic understanding tasks and demonstrate significant metric improvement over established pre-trained language models. We open-source TwHIN-BERT and our curated hashtag prediction and social engagement benchmark datasets to the research community(1).|预训练语言模型(PLM)是自然语言处理应用程序的基础。大多数现有的 PLM 都没有针对社交媒体上用户生成的嘈杂文本进行调整，而且预先培训也没有考虑到社交网络中可用的有价值的社交参与日志。我们介绍 TwHIN-BERT，一个在 Twitter 上生产的多语言模型，它使用流行社交网络的域名数据进行训练。TwHIN-BERT 不同于先前预先训练的语言模型，因为它不仅受到基于文本的自我监督的训练，而且还受到基于 Twitter 异构信息网络(TwHIN)内丰富的社会参与的社会目标的训练。我们的模型是在涵盖100多种不同语言的70亿条 tweet 上进行训练的，为建立简短、嘈杂、用户生成的文本模型提供了有价值的表示。我们评估了我们的模型在各种多语言社会推荐和语义理解任务上的表现，并证明了在已建立的预先训练的语言模型上有显著的度量改进。我们开源的 TwHIN-BERT 和我们策划的标签预测和社会参与基准数据集提供给研究界(1)。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=TwHIN-BERT:+A+Socially-Enriched+Pre-trained+Language+Model+for+Multilingual+Tweet+Representations+at+Twitter)|0| |[Online Few-Shot Time Series Classification for Aftershock Detection](https://doi.org/10.1145/3580305.3599879)|Sheng Zhong, Vinicius M. A. Souza, Glenn Eli Baker, Abdullah Mueen||Seismic monitoring systems sift through seismograms in real-time, searching for target events, such as underground explosions. In this monitoring system, a burst of aftershocks (minor earthquakes occur after a major earthquake over days or even years) can be a source of confounding signals. Such a burst of aftershock signals can overload the human analysts of the monitoring system. To alleviate this burden at the onset of a sequence of events (e.g., aftershocks), a human analyst can label the first few of these events and start an online classifier to filter out subsequent aftershock events. We propose an online few-shot classification model FewSig for time series data for the above use case. The framework of FewSig consists of a selective model to identify the high-confidence positive events which are used for updating the models and a general classifier to label the remaining events. Our specific technique uses a %two-level decision tree selective model based on sliding DTW distance and a general classifier model based on distance metric learning with Neighborhood Component Analysis (NCA). The algorithm demonstrates surprising robustness when tested on univariate datasets from the UEA/UCR archive. Furthermore, we show two real-world earthquake events where the FewSig reduces the human effort in monitoring applications by filtering out the aftershock events.|地震监测系统通过实时筛选地震图，搜索目标事件，如地下爆炸。在这个监测系统中，一连串的余震(大地震发生几天甚至几年后的小地震)可能是混淆信号的来源。这种余震信号的爆发会使监测系统的人类分析员超负荷工作。为了减轻一系列事件(例如余震)发生时的这种负担，人类分析师可以标记前几个事件，并启动在线分类器来过滤后续的余震事件。针对上述用例，我们提出了一种针对时间序列数据的在线少镜头分类模型 FewSig。FewSig 的框架包括一个选择性模型来识别用于更新模型的高置信度正事件，以及一个通用分类器来标记剩余事件。我们的具体技术使用了基于滑动 DTW 距离的% 两级决策树选择模型和基于邻域分量分析(NCA)距离度量学习的通用分类器模型。该算法在 UEA/UCR 存档的单变量数据集上进行测试时表现出惊人的鲁棒性。此外，我们展示了两个真实世界的地震事件，其中 FewSig 通过过滤掉余震事件减少了人们监测应用程序的工作量。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Online+Few-Shot+Time+Series+Classification+for+Aftershock+Detection)|0| |[A Feature-Based Coalition Game Framework with Privileged Knowledge Transfer for User-tag Profile Modeling](https://doi.org/10.1145/3580305.3599761)|Xianghui Zhu, Peng Du, Shuo Shao, Chenxu Zhu, Weinan Zhang, Yang Wang, Yang Cao||User-tag profiling is an effective way of mining user attributes in modern recommender systems. However, prior researches fail to extract users' precise preferences for tags in the items due to their incomplete feature-input patterns. To convert user-item interactions to user-tag preferences, we propose a novel feature-based framework named Coalition Tag Multi-View Mapping (CTMVM), which identifies and investigates two special features, Coalition Feature and Privileged Feature. The former indicates decisive tags in each click where relationships between tags in one item are treated as a coalition game. The latter represents highly informative features that only occur during training. For the coalition feature, we adopt Shapley Value based Empowerment (SVE) to model the tags in items with a game-theoretic paradigm and charge the network to straight master user preferences for essential tags. For the privileged feature, we present Privileged Knowledge Mapping (PKM) to explicitly distill privileged feature knowledge for each tag into one single embedding, which assists the model in predicting user-tag preferences at a more fine-grained level. However, the barren capacity of single embeddings limits the diverse relations between each tag and different privileged features. Therefore, we further propose Adaptive Multi-View Mapping (AMVM) model to enhance effect by handling multiple mapping networks. Excellent offline experiment results on two public and one private datasets show the out-standing performance of CTMVM. After the deployment on Alibaba large-scale recommendation systems, CTMVM achieved improvement by 10.81% and 6.74% in terms of Theme-CTR and Item-CTR respectively, which validates the effectiveness of taking in the two particular features for training.|用户标签剖析是现代推荐系统中挖掘用户属性的一种有效方法。然而，由于特征输入模式的不完整性，以往的研究未能提取出用户对标签的精确偏好。为了将用户交互转化为用户标签偏好，提出了一种基于特征的联盟标签多视图映射(Coalition Tag Multi-View Mapping，CTMVM)框架。前者表示每次点击决定性的标签，其中一个项目中的标签之间的关系被视为一个联盟游戏。后者代表了只有在训练期间才会出现的高度信息化的特征。对于联盟功能，我们采用基于 Shapley 值的授权(SVE)模型的项目中的标签与博弈论的范式和收费网络直接主人的用户偏好的重要标签。对于特权特征，我们提出了特权知识映射(PKM) ，将每个标签的特权知识显式地提取为一个单独的嵌入，这有助于模型在更细粒度的层次上预测用户标签的偏好。然而，单个嵌入容量的不足限制了每个标签和不同特权特性之间的不同关系。因此，我们进一步提出自适应多视点映射(AMVM)模型，通过处理多个映射网络来增强效果。在两个公共数据集和一个私有数据集上的优秀离线实验结果表明，CTMVM 具有出色的性能。在使用阿里巴巴大型推荐系统后，CTMVM 的主题-点击率和项目-点击率分别提高了10.81% 和6.74% ，验证了采用这两个特定功能进行培训的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Feature-Based+Coalition+Game+Framework+with+Privileged+Knowledge+Transfer+for+User-tag+Profile+Modeling)|0| -|[Fairness in Graph Machine Learning: Recent Advances and Future Prospectives](https://doi.org/10.1145/3580305.3599555)|Yushun Dong, Oyku Deniz Kose, Yanning Shen, Jundong Li|; vu university amsterdam; vienna university of economics and business; university of reading; university of cambridge; polish academy of sciences; netherlands environmental assessment agency; university of grenoble; potsdam institute for climate impact research|Scenarios are used to explore the consequences of different adaptation and mitigation strategies under uncertainty. In this paper, two scenarios are used to explore developments with (1) no mitigation leading to an increase of global mean temperature of 4 °C by 2100 and (2) an ambitious mitigation strategy leading to 2 °C increase by 2100. For the second scenario, uncertainties in the climate system imply that a global mean temperature increase of 3 °C or more cannot be ruled out. Our analysis shows that, in many cases, adaptation and mitigation are not trade-offs but supplements. For example, the number of people exposed to increased water resource stress due to climate change can be substantially reduced in the mitigation scenario, but adaptation will still be required for the remaining large numbers of people exposed to increased stress. Another example is sea level rise, for which, from a global and purely monetary perspective, adaptation (up to 2100) seems more effective than mitigation. From the perspective of poorer and small island countries, however, stringent mitigation is necessary to keep risks at manageable levels. For agriculture, only a scenario based on a combination of adaptation and mitigation is able to avoid serious climate change impacts. Keywords Scenarios Integrated assessment Climate change Mitigation Adaptation Climate impacts 1 Introduction Scenario analysis forms a very important tool in the assessment of climate change and climate change policy, allowing analysts to explore the complex and uncertain future interactions between factors like economic development, greenhouse gas (GHG) emissions, climate and ecosystems. Together these factors determine the need and the possibilities for mitigation and adaptation policy. Scenarios can also act as a means to harmonize assumptions across very different research communities that are involved in the fields of climate research, allowing a better comparison of their results. As such, scenarios have been used extensively in both mitigation and adaptation studies (see Metz et al., 2007; Parry et al., 2007 ) (especially the scenarios from Special Report on Emission Scenarios (SRES) ( Nakicenovic et al., 2000 )). Moss et al. (2010) point out that since the SRES information requirements from scenario analysis are changing. First, there is an increasing interest in exploring the relationships between adaptation and mitigation. As indicated by Moss et al. (2010) , this would require a further integration of information across the different analytical traditions involved in climate research. Secondly, there is also an increased interest in scenarios that explicitly explore the impact of climate policies in addition to the climate policy-free scenarios explored so far. Specifically, there is a strong interest in being able to evaluate the “costs” and “benefits” of long-term climate goals vis-à-vis the situation without climate policy. In this paper, we follow this line of thought and explore how scenario analysis can contribute to a joint assessment of future adaptation and mitigation strategies. Such a joint assessment can be useful for several reasons: (1) the preferred mitigation strategy depends on expected climate impacts and adaptation costs, (2) it takes account of the limitations of adaptation to climate change, (3) some adaptation and mitigation strategies may interact and (4) finally, impacts of climate change may have important feedbacks that need to be taken into account. Such analysis is most useful at a strategic level, and not for individual adaptation (or mitigation) decisions. Given this purpose, we discuss in the paper two main scenarios that include elements of adaptation and mitigation strategies (see further in this paper), resulting in an increase of global mean temperature of 4 °C and 2 °C by the end of this century. These two temperature levels have started to become iconic numbers, representing a potential outcome in the situation without mitigation policy (4 °C) and the temperature target of international climate negotiations (2 °C) ( Copenhagen Accord, 2009 ). Arguably, understanding the implications of these two temperature levels is essential if political leaders are to make informed choices about the balance between mitigation, adaptation and climate impacts ( Environmental Change Institute, 2009 ). Integrated assessment of mitigation and adaptation strategies is hampered by methodological differences. Integrated assessment models have difficulties describing adaptation processes given the importance of local circumstances ( Patt et al., 2010 ). A practical problem is that to date a considerable part of the impact literature has concentrated on impacts under no-policy scenarios (exceptions include Arnell et al., 2002; Bakkenes et al., 2006; Hayashi et al., 2010; Krol et al., 1997; Nicholls and Lowe, 2004 ). This paper therefore presents a generalised scenario assessment based on coupled pieces of information – but without pretending to be complete or to be fully integrated. As a learning-by-doing exercise, the paper intends to show important differences between a 4 °C and a 2 °C world, but also to identify some of the practical issues involved in performing integrated scenario analysis. This implies that the most important advancement compared to existing literature is that we present a multi-sector analysis based on consistent scenarios. Given the state-of-the-art of current integrated assessment models, the experiments have been done using several loosely coupled models. As a result, several important linkages could not be addressed such as between the adaptation responses for agriculture, which may involve irrigation (see Section 5.3 ) and water demand (Section 5.4 ). In fact, an important question raised in the paper is whether a fully integrated analysis is needed or whether partial integration is sufficient. The paper is organized as follows: we first discuss some of the methodological complications in developing scenarios that can provide information for both adaptation and mitigation policy decisions. Next, we discuss the differences between the two main scenarios in terms of socio-economic drivers (Sections 3 and 4 ). In Section 5 we explore the potential consequences of adaptation and mitigation strategies on various impacts of climate change. 2 Assessment of climate strategies and scenario development (theory and methods) 2.1 Different strategies in response to climate change Climate change and the responses to it can lead to three forms of costs (not necessarily monetary): (1) the (residual) costs of climate impacts, (2) the costs of adaptation and (3) the costs of mitigation. At least theoretically, this corresponds to three different strategies: (1) “laissez faire” (accept climate change), (2) focus on adaptation and (3) focus on mitigation as illustrated conceptually in Fig. 1 (see also Klein et al., 2007 ). While Fig. 1 suggests that the costs and benefits of mitigation, adaptation and residual damages can be traded-off against each other, there are conceptual and analytical problems that complicate such an approach. These relate to spatial and temporal scales, and risks and uncertainty ( Swart and Raes, 2007 ). Mitigation and adaptation are processes that take place at different spatial s cales. While mitigation action is often taken at the national or local scale, the benefits are shared globally. As a result, a critical factor in the success and costs of climate policy is the degree of international cooperation ( Barker et al., 2009; Clarke et al., 2010; van Vliet et al., 2009; van Vuuren et al., 2009 ). For adaptation, in contrast, both costs and benefits occur on multiple scales from local to national and even international. An enabling environment at a larger scale can still enhance adaptation at a smaller scale (e.g. local capacity-building funded by international financing mechanisms). For these kinds of reasons, assessment of mitigation tend to concentrate on the global level, while by contrast, adaptation research is mostly focusing at the local scale. The dynamics over time of mitigation and adaptation is also an important factor. Stringent mitigation scenarios typically require strong, early reduction of emissions. Climate change impacts of these scenarios, however, will in the short-term (first decades) hardly differ from those in scenarios without climate change policy due to the large inertia within the climate system. In contrast, some associated impacts (e.g. co-benefits in reduced local air pollution) may be realized at a much faster pace. Adaptation measures are likely to yield private and social benefits over the near-term. For instance, simple adaptation measures such as air conditioning can bring clear short-term benefits. Some important exceptions exist which may require decades to implement, such as changes in spatial planning or large-scale engineering works for flood protection (see Hallegatte, 2009 ). Other important factors are risk and uncertainty . Our understanding of climate change faces many uncertainties. Key uncertainties to be identified comprise epistemic, data, model, and ontic uncertainties ( Schneider and Kuntz-Duriseti, 2002; van Vuuren et al., 2008a ). Examples of factors that involve uncertainty are (i) future emissions, (ii) the climate system, (iii) future vulnerability and exposure to climate risks and (iv) mitigation costs. Taking mitigative action reduces some uncertainties, since it reduces the originating sources of climate change and reveals the actual mitigation costs ( Barker, 2003; Piani et al., 2005 ). Mitigation may, however, also add to risks. For example, bio-energy, if implemented unsustainably, may offset one set of risks (climate change) while creating another set of different risks (biodiversity loss and reduced food security). One way of dealing with risks is to include assessments of probabilities. This is often done using past evidence, extrapolated to cover specific future circumstances. Other uncertainties (for instance unknowable shocks and surprises) are more difficult to deal with in quantitative sense, but justify acknowledgement of ignorance. Scenarios can be used to explore the potential for extreme events and the robustness of various policy portfolios but this is not often done ( Berkhout et al., 2002 ). Traditionally, the disciplines involved in mitigation research and adaptation research have different ways of describing uncertainty. While mitigation research often uses quantitative methods and concentrates on mean estimates, adaptation research often focuses more on qualitative descriptions of uncertainty and concentrates on the risks of hazardous events even if these have a low probability of occurrence. These different perceptions of uncertainty may complicate an integrated assessment of different strategies ( Swart et al., 2009 ). 2.2 Types of scenarios We can characterize scenarios into different classes based on the considerations about mitigation and adaptation. First, we define a baseline scenario, as a trajectory of events assuming no major feedbacks from climate change and no specific policy efforts on either mitigation or adaptation (such a scenario may still include many actions that indirectly influence the ability to mitigate or adapt to climate change; for instance, increasing income levels can be expected to coincide with greater investment in health services reducing the risks of climate-related diseases such as malaria). The main purpose of this type of scenario is analytical, serving as a point of reference for other scenarios. Second, adaptation scenarios describe a world in which societies are responding to climate change impacts. Their purpose is to explore the type of technologies and policies required to adapt to climate change, the avoided damage and the associated costs. Adaptation includes so-called autonomous adaptation (i.e. actions that occur without specific government action) and planned adaptation. Third, mitigation scenarios describe a world including policies aiming to limit climate change. Their purpose is to explore the type of technologies and policies required to minimize climate change and the associated costs. As there will always be remaining impacts, the fourth set, adaptation and mitigation scenarios combine both types of responses to climate change. Possibly, this fourth category of scenarios could re-order policy options according to the synergies that might exists between adaptation and mitigation options, e.g. for some re-afforestation options. Each of these scenarios is connected to a broader social, political and cultural context in which they are assumed to arise. In exploring a preferred mix of mitigation, adaptation and residual damage, two main approaches exist: (i) the impact and risk-based approach that describes potential impacts as function of global mean temperature increase (and thus mitigation), and (ii) the cost–benefit analysis, which identifies monetary costs and benefits in order to maximize welfare (see for instance Nordhaus, 2008; Tol, 2002c ). In both cases, we believe it to be more useful and reflective of the issue to describe the relationships between different response strategies than to seek to determine an optimum. Given the complexities and uncertainties laid out in Section 2.1 , we believe no optimal mitigation, adaptation or combined strategy can be pursued in reality. 2.3 Integrated analysis An integrated analysis of mitigation and adaptation can be achieved in different ways: e.g., by using one single, so-called integrated assessment model, or by exchanging information between different models and disciplines, assessing available literature and making results comparable. Both methods are organized around the cause–effect chain of climate change, i.e. describing the relationship between economic activities (income, energy use, agriculture, etc.), emissions, climate change and impacts – and the related feedbacks ( Fig. 2 ). The scheme in fact also forms the backbone of information flows around scenarios for the IPCC reports ( Moss et al., 2010 ). Scenarios are developed first by integrated assessment and emission modelers (focusing on economic driving forces, energy and land use and GHG emissions (IPCC “Working Group III”)). Subsequently, the emission trajectories are used in climate models to assess the impacts of climate change (IPCC “Working Group I”). Finally, the scenarios are used for impact, adaptation and vulnerability analyses (IPCC “Working Group II”). The involvement of different research disciplines and working groups implies that it is difficult to account for feedbacks between the different areas. Integrated Assessment models capture only a limited number of the possible feedbacks (frequently omitted feedbacks include the impact of food and water security on population and economic drivers; relationships between water scarcity and food production, impact of climate change on energy use, etc.). Ignoring (some of) these feedbacks may be reasonable if they are not substantial enough to significantly influence the system. For analytical reasons, there are major advantages to organizing scenario development within disciplinary fields and consider a limited number of feedbacks. It allows researchers to focus on elements of the chain that they understand well and to add the required amount of detail, without being confronted with the complications of interlinkages. However, this may change in a situation of increased focus on integrated analysis of mitigation and adaptation strategies. Some examples of why an integrated approach may be necessary are: i. Climate impacts, such as those triggered by extreme events, may be so severe that they undermine the economic assumptions of the original scenario; ii. Climate impacts could be substantial in agriculture so that estimates of land-use related emissions not taking impacts into account might be wrong, and the mitigation potential of bio-energy may be affected; and iii. There may be competing claims for land areas attractive for both mitigation and adaptation purposes. Thus, an interesting question is whether the need for more integrated analysis is so urgent that more complex modes of integration are needed (interactive coupling of models; one complex model), or whether the impacts can be handled separately simplifying the analysis framework. The time horizon and the decision focus may also be important here, e.g. whether potential tipping points are taken into account ( Lenton et al., 2008 ). The few available studies that have looked into this question seem to suggest that in most sectors the adaptation implications of any mitigation project are small as well as the emissions generated by most adaptation activities ( Klein et al., 2007 ). The most integrated analyses to date come from the cost–benefit oriented integrated assessment models like FUND, DICE and MERGE ( Manne and Richels, 2005; Nordhaus, 2008; Tol, 2002c ) – but these models typically aggregated climate impacts into a limited amount of rather abstract damage functions. We believe that over time, with growing intensity of both mitigation and adaptation measures across many sectors, the need for joint assessment with sufficient detail will intensify. The scenarios presented here, based on the current state of the art in modeling and scenario development, take a first step. The same scenarios are used in one assessment for mitigation and impact assessment and we explicitly address mitigation and adaptation strategies (either as part of the scenarios or within the models used for the different impacts). However, many feedbacks are not accounted for. We come back at the end of the paper to the role of more integrated (but also more complex) scenarios. 2.4 Methods used in this paper As described above, several types of scenarios can be identified: baseline, mitigation, adaptation and adaptation–mitigation scenarios. These scenario types are also presented in this paper. For the baseline/adaptation scenario, we assume intermediate assumptions for most socio-economic drivers. Scenarios assumptions are described in Sections 3 and 4 . The scenarios do not include mitigation, leading to a global mean temperature increase of 4 °C above pre-industrial levels by 2100. While we describe possible impacts and adaptation in these scenarios, we do not include feedbacks on the original drivers. In the mitigation scenarios, stringent mitigation efforts are included leading to a global mean temperature increase of 2 °C. Using the median value for climate sensitivity given by IPCC of 3 °C ( Meehl et al., 2007 ), this translates into a stabilization level of around 450 ppm CO 2 -equivalent (CO 2 -equiv.). The impacts of climate policy on economic drivers are not accounted for – but several other relationships are coupled (e.g. land use). In most of the paper, we thus ignore potential impacts of climate change and climate policy on the economic assumptions. In Section 5.8 , however, we discuss their impacts within a simple, economic model (FAIR) to provide some insight in the possible size of the economic consequences on the global scale. Several model tools are used. The scenarios are mainly developed using the IMAGE integrated assessment model ( Bouwman et al., 2006 ). The IMAGE model describes developments in energy and land use in the 21st century based on assumptions for population and the world economy, combined with assumptions for technology development and consumption patterns. The model projects climate change (as indexed by global mean temperature change and sea level rise) at the global scale, and constructs spatial scenarios for change in monthly temperature and rainfall at a 0.5° × 0.5° grid by pattern-scaling downscaled climate model patterns. The output of IMAGE is used in the model DIVA to describe sea-level rise; in the global hydrology model Mac-PDM to estimate consequences for water stress; in the TIMER energy model to estimate implications for heating and cooling demand; in the MARA/ARMA malaria suitability model for impacts on malaria and in the FAIR model for a monetary cost–benefit analysis. Moreover, we discuss more generally the implications for agriculture (based on IPCC AR4) and extreme events. Appendix A provides a brief description of all models used. In our descriptions, we focus on the global level (in view of the limited space). Clearly, this leads to limitations in our discussion of adaptation. The experiments depend on the design each model and thus the number of scenarios that can be presented differs between different impacts. This implies that the study should be interpreted as a first illustration of an integrated assessment, and not as a holistic study on adaptation and its limits. 3 Results: socio-economic trends in the baseline scenario 3.1 Population development and economic growth We assume that population follows medium-fertility variant of the 2004 revision of the World Population Projections ( UN, 2005 ) up to 2050, and the UN's long-range medium projections up to 2100 ( Fig. 3 ). This implies that the global population steadily increases to almost 9.1 billion people by 2050 and stabilizes at about 9.2 billion people over the subsequent 50 years up to 2100. The scenario takes a middle ground within the range of population forecasting (see Fig. 3 ). For economic growth up to 2050, the scenario follows projections linked to the Cambridge model E3MG ( Barker and Scrieciu, 2010; Barker et al., 2008 ). The scenario was extended beyond 2050 using the economic growth projections of the SRES-based B2 scenario ( IMAGE-team, 2001 ). Quantitatively, the scenario is a medium to high economic growth scenario, which is mainly the result of optimistic growth assumptions for China and India. The OECD economies are projected to remain the richest in the world in per capita terms, but in terms of total economic activity the importance of developing regions grows rapidly. The growth of GDP per capita is between 0 and 2% per annum in Africa, the Middle East and Latin America. In Asia, it falls from the current high levels to 3% per annum in 2050. 3.2 Energy use and greenhouse gas emissions for the baseline scenario Energy use in the baseline scenario is made consistent with a baseline published by the European Commission ( EC, 2006 ). Despite a further decrease of energy intensity, world energy consumption more than doubles in the 2000–2050 period and increases by another 25% in the 2050–2100 period ( Fig. 4 ). Over the whole century, energy supply remains dominated by fossil fuels. While oil and natural gas production peak and decline during the century, the use of coal increases during the whole scenario period. Also non-fossil energy production increases rapidly. Nuclear energy use increases by a factor of two to three to 76 EJ over the period until 2100, the use of biomass increases strongly, while hydro-electricity production increases by about 60–80%. The largest relative increase is that of wind and solar energy; this rises from less than 1% of all non-fossil energy to between 10 and 14% in 2050. Total renewable energy use in 2050 is 120–140 EJ, and 190 EJ in 2100. The trends described above imply that emissions of CO 2 from energy activities more than double in the period to 2050, and rise by another third between 2050 and 2100 (see Fig. 3 ). As such, the scenario forms an intermediate baseline scenario within the literature range ( Fisher et al., 2007 ). Non-CO 2 GHGs (in particular methane) increase steadily in the period 2000–2050, but at a slower rate than CO 2 (as their driver, agriculture, is expected to grow more slowly than the energy sector). CO 2 emissions from land-use fall back to zero during the first half of the century. The area of agricultural land lies within the range of similar scenarios that have recently been published, although at the low end of the range ( Rose et al., 2007 ). 4 Results for the mitigation scenario and climate scenarios 4.1 Energy use and greenhouse gas emissions The mitigation scenario aims at stabilising GHGs at around 450 ppm CO 2 -equiv. (see also van Vuuren et al., 2007, 2010 ). The scenario allows for an initial overshoot of concentration to about 510 ppm CO 2 -equiv. Den Elzen and van Vuuren (2007) have shown earlier that a limited overshoot of concentration allows for meeting similar climate targets at lower costs. Emission reductions are achieved in various ways. One element is to increase energy efficiency, which reduces the total amount of energy use (a 20% reduction in 2050 compared to baseline) (see Fig. 4 ). The scenario also shows an increasing use of energy from non-fossil sources, which account for most of the growth in total energy use. Non-fossil energy use increases from about 15% of total primary energy use in 2010 to more than 30% in 2050 and is over 40% of the total by the end of the century. Most of this growth is due to an increase in bio-energy use. Carbon capture and storage is applied in most remaining stationary uses of fossil fuels. Finally, also non-carbon dioxide greenhouse gas emissions are reduced. As a result, global emissions peak around 2020, and reduce further with time. Emissions are reduced by more than 70% compared to the baseline in 2050 and more than 80% by 2100. The consequences of mitigation policies affect not only the energy sector, but also land use. Substantial additional land areas are used for afforestation and bio-energy (see Fig. 5 ). Model comparison studies show that the mitigation scenarios presented here are consistent with the current literature, although models show significant differences in the contribution of various reduction measures ( Clarke et al., 2010; Edenhofer et al., 2010 ). According to the IMAGE model calculations, the abatement costs of the emission reductions are in the order of 1–2% of GDP (i.e. the annual additional expenditures which can be compared to the current expenditure of around 1.5% of GDP on environmental policy in OECD countries) ( Fig. 6 ). The literature range of comparable scenarios is in the order 0.5–5.5% in 2100. Most studies agree that these additional expenditures would lead to a reduction of GDP. We discuss this further in Section 5.8 . 4.2 Climate change under the baseline and mitigation scenario The atmospheric GHG concentration and associated mean global temperature change resulting from the emissions of the two scenarios is shown in Fig. 7 (solid lines indicate best-guess values), based on the IMAGE model calculations. The IMAGE model uses the MAGICC model to calculate changes in global mean temperature. The MAGICC model was used earlier for similar IMAGE scenarios by van Vuuren et al. (2008b) to calculate trajectories for greenhouse gas concentration and temperature including uncertainty ranges. Here, the uncertainty ranges used for the MAGICC calculations were based on existing runs of more complex carbon cycle and climate models. We have used the implications for ranges in greenhouse gas concentration and temperature outcomes to also depict the uncertainty ranges here as is indicated by the shaded areas in this graph. For temperature, the wider shaded area indicates the uncertainty as result of uncertainty in the carbon cycle and climate sensitivity. For the baseline scenario, global mean temperature increases almost linearly to 2.1 °C above the pre-industrial levels in 2050 and to 3.7 °C in 2100 (uncertainty range 3–5 °C). In the mitigation scenario, the global mean temperature increase by 2100 is limited to 1.9 °C. Again, there is considerable uncertainty. Fig. 7 indicates that by the end of the century the mitigation case could also lead to a temperature increase of 2.6 °C compared to pre-industrial levels. As the mitigation scenario presented here is among the most stringent in the scientific literature (cf. Clarke et al., 2010; Edenhofer et al., 2010; Fisher et al., 2007 ), two important conclusions can be drawn. First, the analysis indicates that global warming can be moderated but not halted. Second, the observation that a stringent scenario could also lead to considerably greater climate change than 2 °C may imply that hedging adaptation policies against more warming might have considerable value. For example, such policies may be to ‘… aim for 2 °C, but prepare for 3 °C’. In the assessment of impacts below, we focus on the central climate change projections. Changes in mean monthly temperature and precipitation across the globe at the 0.5° × 0.5° scale, associated with the global average temperature changes, have been constructed by rescaling patterns derived from the HadCM2 climate model ( Fig. 8 ). These patterns show that the change in annual mean temperature is larger at high latitudes than at low latitudes, and show considerable spatial variation in change in rainfall. Considerable disagreement about the expected patterns of climate change exists, especially for precipitation: the impact results presented in this paper therefore represent only one possible outcome. 5 Results: impacts and adaptation in the different scenarios 5.1 Introduction IPCC's Fourth Assessment Report ( IPCC, 2007 ) gives an overview of climate impacts. Some of these impacts result from changes in average climate, but other impacts may result from changes in extreme events. Table 1 summarizes some of the impacts, for health, agriculture, water availability, coastal flooding, urban areas and energy system, and large-scale disruptions of the climate system (in contrast, biodiversity and ecosystem services have not been included). As noted earlier, most of the literature has treated climate change as “a gradual phenomena” ( Agrawala and Fankhauser, 2008 ). This is problematic for impacts characterized by low probabilities coupled with high impacts (see below). In this exploratory analysis, we sketch some of the impacts and adaptation requirements. We aimed to cover several key impacts mentioned in Table 1 , but the assessment was limited by the availability of models that could easily be coupled. Therefore, rather than intending to be exhaustive, the descriptions provide some indication of the magnitude of some impacts and key adaptation challenges. In presenting our results, we have used several new model runs based on the scenario discussed above (e.g. for malaria, water resources, sea-level rise, heating and cooling demand). We have, however, also assessed existing information from IPCC 4th Assessment Report in the context of the two scenarios presented here (temperature-related mortality, agriculture and extreme events). 5.2 Human health: temperature-related mortality and malaria Health impacts of climate change need to be seen in the context of other, more important drivers of human health, including lifestyle-related factors ( Hilderink et al., 2008 ). We focus here on temperature-related mortality and malaria. 5.2.1 Temperature-related mortality Temperature-related mortality impacts may occur via changes in extreme temperatures, changes in average temperatures, or in seasonal variation of temperatures, with the literature showing varying results. McMichael et al. (1996) made an estimation of temperature-related mortality using relative risk ratios, showing that there is an optimum temperature at which the death rate is lowest (also know as the U-shaped dose–response relation). If temperature increases, heat stress-related mortality increases, but cold-related mortality decreases. Tol (2002a) concluded that in monetary terms the reduction in cold-related mortality due to climate change outnumbers the increase in heat-related mortality. This conclusion is however, influenced by the approach used to value a life and also subject to the large uncertainties with respect to the relationships between average and regional temperatures and temperature and health. Adaptation may occur both by the adjustment of the human physiology to higher temperatures ( McMichael et al., 1996 ), changes in behavior and an increase of air conditioning use ( Kinney et al., 2008 ). Given the complexities in using dose–response relationships between temperature and mortality, we have not attempted to quantify these here. 5.2.2 Malaria Considerable attention has been paid to the relationship between malaria and climate change. In this paper, we also focus on climate-induced changes in malaria risks. Annually more than one million people, mostly African children, die from malaria, a vector-born infectious disease. The anopheles mosquitoes (the vector which spreads the malaria infection) can only survive in climates with high average temperatures, no frost and sufficient precipitation. The MARA/ARMA malaria suitability model ( Craig et al., 1999 ) incorporates these factors to determine climatically suitable areas. Mortality due to malaria is, however, also heavily influenced by factors such as access to preventative measures (including indoor spraying and insecticide-treated bed nets) and access to health care. In the MARA/ARMA model these factors are linked to income and urbanization. Fig. 9 shows the results of this model for the scenarios of this paper. The impact of autonomous adaptation (as function of rising income) reduces malaria deaths by around 50%, especially in Africa (mainly due to better provision of health care). In contrast, the impacts of climate – and especially the difference between the mitigation scenario and the baseline case are much smaller. Mitigation reduces malaria health risks by about 2% (2050). Adaptation, therefore, has a much more decisive influence on malaria control than mitigation (this finding seems to be robust with available literature). 5.3 Agriculture: impacts on yields Easterling et al. (2007) have synthesized a large amount of research on the impacts of climate change on crop growth, with and without adaptation. The results were summarized as a function of global mean temperature increase, although in reality changes in temperature and precipitation patterns and CO 2 fertilisation all play a role. For instance, the impacts of CO 2 fertilisation partly offset the impact of climate change. The results can be used to assess the climate impacts for our scenarios by using the best-fit polynomials from Easterling et al. (2007) , that indicate the impact on yield as a function of mean temperature change. 1 1 We have in each case taken the global mean temperature change for a scenario and used that as an indication of the average local temperature change to be expected. This means that our impact estimates are likely to be conservative, as temperature increase is likely to be stronger the global average over many land areas. We looked at the impacts for the baseline (4 °C) and mitigation (2 °C) scenario, with and without adaptation, for maize, wheat and rice (see Fig. 10 ; results are presented for tropical and temperate zones in 2100; these impacts are additional to the yield increases as a result of other factors than climate change). Although the results are very uncertain, some conclusions seem to be possible. First, the baseline scenario (no adaptation) causes a very substantial decrease in yields (relative to the situation without climate change) for all cases shown: Climate change impacts may reduce yields for the aggregated regions shown by 10–35% for the crops studied (2050). Second, engaging in either mitigation or adaptation limits the decrease in yields. In the tropics, however, impacts remain negative and typically in the order of a 10% loss. Third, the combination of mitigation and adaptation may result in an improvement from today's situation. Agricultural impacts may be more positive for temperate regions, but only if the advantages of higher temperature are not offset by impacts of extreme weather. These results underline the need to look at both mitigation and adaptation. The results presented are based on the IPCC assessment and represent a wide range of models. The results can also be illustrated by individual studies. Tubiello and Fischer (2007) , for instance, found that a mitigation scenario could reduce the global costs of climate change in agriculture significantly. Similarly, Fischer et al. (2007) illustrated the importance of adaptation for water irrigation requirements. They found that mitigation reduced agricultural water requirements by about 40%, leaving 60% of the impacts requiring adaptation. When dealing with impacts on agriculture both drought and heat wave stress play important roles. Fig. 11 shows, for Europe, the impact of drought and heat wave stress on crop yields for a 2 °C warming scenario, assuming various forms of adaptation ( Mechler et al., 2010; Moriondo et al., 2010 ). 2 2 Calculations were done using the Cropsyst model on the basis of the HADCM3 climate model for the 2030–2060 time slice. Winter and summer crop yields were simulated for spring wheat with today's and future crop management practices. Adaptation options considered comprised shifting the sowing date by a few days and using cultivars with a longer/shorter growth cycle. Results show that Southern Europe and parts of France are today already particularly exposed to drought and heat stress, and this situation is expected to worsen even under the 2 °C (mitigation) scenario ( Fig. 11 panel A). When considering the two adaptation strategies in combination with mitigation ( Fig. 11 panels B and C), many regions in Europe may actually benefit. Northern Europe, in particular, could exploit the advantage of higher precipitation by using crop varieties with a longer growing cycle. In contrast, in Southern Europe the same adaptation options would result in an added negative impact, since crop development would shift towards summer when longer dry spells and heat waves may significantly affect crop growth. Also, the results show that while there are some region-specific limits to adaptation, overall adaptation would effectively reduce impacts on the agricultural sector in Europe. 5.4 Water resources: potential water availability The effects of the two scenarios on exposure to changes in water resources stress are assessed using a global-scale water resources impact model ( Arnell, 2003 ). Fig. 12 shows the percentage change in average annual runoff by 2100 (relative to the 1961–1990 mean) under the baseline scenario and the mitigation scenario (with the HadCM2 climate model pattern). We define watersheds to be in a water-stressed condition if average annual runoff is less than 1000 m 3 /capita/year (other definitions are also used in the literature). The effect of climate change is indexed by summing (i) the populations living in water-stressed watersheds where runoff decreases (increases) significantly (typically by more than 5–10%) and (ii) the population living in watersheds that become water-stressed (cease to be water-stressed) due to climate change. The number of people exposed to an increase or decrease in water stress due to climate change have not been summed for two reasons: (i) the adverse effects of having less water are greater than the beneficial effects of having more water in a water-stressed catchment, and (ii) the regions with an increase and decrease in exposure to water resources stress are widely separated, and “surpluses” in one area do not offset “deficits” in another. The results show substantial differences in exposure to increased water resource stress in 2050, 2080 and 2100 between the mitigation and baseline scenarios. In 2020, there is little difference in runoff between the two scenarios. Fig. 13 shows the numbers of people exposed to an increase or decrease in water resource stress due to climate change under the two scenarios. In both the baseline and the mitigation scenario, the numbers of people living in water-stressed watersheds who apparently benefit from increased water availability is larger than the numbers exposed to a reduction in runoff, but – as outlined above – we do not focus on the net effect. The numbers of people exposed to change in water resources stresses are sensitive to the assumed pattern of climate change. Compared to the baseline, the mitigation scenario reduces the numbers exposed to an increase in water resources stress by 135 million (reducing impacts by 12%), 281 million (20% reduction) and 457 (30% reduction) million in 2050, 2080 and 2100 respectively. At the same time, however, there are also people benefiting from climate change. The relative size of the groups with positive and negative impacts depends on the climate model used (here only the Hadley pattern has been used). Clearly, mitigation also decreases the number of people benefiting from climate change. It is also clear that mitigation does not eliminate water supply impacts of climate change, and adaptation will be required for the remaining billion people exposed to increased water resource stress due to climate change. Adaptation may include measures to increase water storage, transport of water, or reduction of water demand by increasing efficiency. Underlying results show that the effects of mitigation vary significantly by region. In fact, in some regions mitigation may even increase the numbers of people exposed to increased stress. Specific uncertainty analysis shows that results are highly dependent on the uncertainty in the changes in the precipitation pattern due to climate change. 5.5 Sea level rise Another important impact of climate change is rising sea levels. Global mean sea-level rise has been projected for both scenarios using the MAGICC component of the IMAGE model. Due to the delayed response of sea-level to global warming, the projections mainly diverge in the second part of the century: sea level rise is 35 and 31 cm in 2050 in the 4 °C and 2 °C scenario, respectively and 71 and 49 cm in 2100. These projections do not include a potential accelerated contribution of the ice sheets of Greenland and Antarctica, which could lead to higher sea-level rises but the underlying processes are insufficiently understood and are currently not included in climate models ( Meehl et al., 2007; Nicholls et al., 2010; Vermeer and Rahmstorf, 2009 ). We use the DIVA model to assess both damage and adaptation costs of sea-level rise, associated storm surges and socio-economic development under the two scenarios taking into account coastal erosion (both direct and indirect), forced migration, coastal flooding (including rivers) and salinity intrusion into deltas and estuaries. For each scenario the model is run first without and then with adaptation in terms of raising dikes and nourishing beaches ( DINAS-COAST Consortium, 2006; Hinkel and Klein, 2009 ). Further impacts such as salinity intrusion in coastal aquifers, loss of coastal wetlands and biodiversity as well as further adaptation options such as salinity intrusion barriers, port upgrade, set-back zones and ecosystem-based protection could not be included due to the unavailability of global data and general models of these processes. Fig. 14 shows that independent of the level of mitigation, adaptation reduces global overall costs rather effectively, which illustrates the necessity for engaging in adaptation even under ambitious mitigation. At the aggregated scale more damages can be avoided through an adaptation-only strategy than through a mitigation-only strategy, although a combination of the two has the strongest positive impact. From the perspective of poorer and small island countries, however, stringent mitigation is necessary to keep risks at manageable levels. Even without sea-level rise, adaptation would be cost-effective in order to protect the assets situated in the floodplain, which increase due to socio-economic development alone. While this would involve substantial investment flows (tens of billions of US$ worldwide), they are a relatively small fraction of global GDP, even for sea level rise at the level of the baseline scenario. However, for individual countries or regions (particularly small island states) these costs can be a much larger fraction of GDP, including the risk of a complete loss. 5.6 Heating and cooling demand (settlements and society) Climate change is likely to influence the demand for space cooling and heating. Therefore, we have developed a set of simple relationships to describe heating and air conditioning demand in the residential sector and explored the impacts of climate change on this simulated energy demand ( Isaac and van Vuuren, 2009 ). Clearly, changes in population and income are projected to lead to a considerable growth in the energy demand for heating and air conditioning in the coming century (see Fig. 15 , no climate change case). Driven by climate, changes in cooling and heating practices are examples of autonomous adaptation (i.e. without policy intervention). Adaptation is not universal, however, since the population will not always be able to respond. Unfulfilled demand for heating and cooling can lead to health impacts (as described in Section 5.2 ) and to loss of labour productivity. In addition to these effects, there is reduced comfort when indoor temperatures rise above a given level. Fig. 15 shows that, globally, the autonomous increase in energy demand without taking climate change into account due to increasing income and wealth is much larger than the difference between the energy demand in the baseline scenario and the mitigation scenario ( Isaac and van Vuuren (2009) show this a robust result also for other baselines). The effect of climate change on combined energy demand is also smaller than the effect on heating and air conditioning separately, since increases in air conditioning compensate decreases in heating. On the regional and country level, impacts can be far more significant: for example, in India we project a large increase in energy demand due to increased cooling, while in Western Europe and the USA, we project a substantial decrease due to reduced heating. 5.7 Extreme events Climate change is expected to lead to changes in the frequency and intensity of some weather-related extreme events ( Parry et al., 2007 ). Extremes like floods, droughts, heat waves and storm surges could become more frequent and intense, while cold-extremes, such as cold spells, are likely to become less frequent and weaker. Assessing risks of climate change based on changes in average conditions-only runs the risk that changes in extreme event risks are averaged out. A more risk-based, geographically explicit method is therefore preferable. However, knowledge on disaster impacts is complex and contested. To date, there are only a limited number of national level studies taking a probabilistic approach to projecting future risk in the presence of climate change, mostly focusing on flood risk ( Mechler et al., 2010 ). One such study on the pan-European scale by Feyen et al. (2009) computed that expected annual damages would triple under a baseline scenario. A key constraint to quantitative risk approaches is the uncertainty in the climate projections. For precipitation, for instance, models often disagree on the sign of changes at the local scale. This is especially important for studies looking for instance flood risk. While the Mechler et al. (2010) study aimed to project future risk, they found future projection to be so uncertain that the authors refrained from projecting future flood risk based on an estimate of today's flood impacts. Current models and data, however, seem to be sufficient to assess the combined risk of drought and heat wave stress on agriculture with a relatively high level of certainty (slower phenomena). Some examples of work in the context of the 2 °C and 4 °C scenarios are provided here. Several studies looked into flood-affected people at the global scale ( Hirabayashi and Kanae, 2009; Kundzewicz et al., 2010 ). Regression of samples shows that the average global number of people affected by 100-year floods per year for the mitigation scenario (2 °C) is projected to be 211 million compared to 544 million for the baseline (4 °C). Mirza et al. (2003) showed that for Bangladesh, a flood-vulnerable country, even the 2 °C scenario is expected to increase the projected flooded area by at least 23–29%. It should be noted, however, that the uncertainties about exposure, vulnerability and adaptation still lead to a wide range of estimates for the costs of future flood damage. With respect to drought, the projections for the 2090s made by Burke et al. (2006) show that the number of extreme drought events per 100 years and mean drought duration are likely to increase by factors of two and six, respectively, for the baseline scenario by the 2090s. Evidence suggests that damage of weather and climate related impacts has already increased in the present-day, but these are mainly due to the wealth and population increases ( Bouwer, 2010 ). However, climate change is expected to increase over time, and is likely to become a more significant contributor to rising damages in the future. The most recent IPCC report indicates that the costs of major events are expected to range from several percent of annual regional GDP and income in very large regions with very strong economies, to more than 25% in smaller areas ( Parry et al., 2007 ). Disaster losses for highly exposed small island states in the past have in fact exceeded annual GDP ( Cummins and Mahul, 2009 ). 5.8 Economic evaluation of impacts Cost–benefit analysis (CBA) is used to express the costs and benefits of climate change of different strategies in terms of a common monetary unit. We use the CBA module of the FAIR model (see model Appendix A ) here to obtain some idea of impacts at a more aggregated scale. For mitigation costs, the FAIR model uses the information of the IMAGE model presented earlier. The climate damage and adaptation cost functions used in FAIR are derived from the AD-DICE model ( De Bruin et al., 2009a; Hof et al., 2009a ). In short, AD-DICE estimates adaptation costs based on the damage function of the DICE model ( Nordhaus and Boyer, 2000 ). The AD-DICE separates these functions into a damage cost function and residual damage function based on an assessment of each impact category described in the DICE model – agriculture, coastal zones, health, settlements, non-market time use, other vulnerable markets and catastrophic impacts. For this study, we assumed an optimal adaptation response to climate change (i.e. given a level of temperature change the model minimizes the sum of adaptation costs and residual impacts). The impact estimates used in DICE (and thus FAIR) include: (i) real, measurable, economic costs (so-called market costs); and (ii) other, intangible losses (non-market losses), which are monetized using the willingness-to-pay concept. The damage functions are not directly related to the physical or economic damages described earlier in this section, as they are derived from a separate source. It has been shown earlier that the FAIR results of adaptation costs are consistent with the range of values reported in the literature ( Hof et al., 2009a ). Under default settings of the FAIR model and a discount rate of 2.5%, the discounted costs as a share of global GDP due to climate change impacts for the period 2005–2200 amount to nearly 4.5% in the baseline ( Fig. 16 ). These costs may seem higher than suggested by the limited set of sectoral analyses presented above, but include more sectors and also the impacts of possible catastrophic events ( Nordhaus and Boyer, 2000 ). Annual costs rise sharply over time, reaching 17% in 2200 (note that impact estimates are very uncertain and both higher and lower values can be found in the literature ( Parry et al., 2007; Stern, 2006; Tol, 2002b )). Scenarios with only adaptation or mitigation reduce discounted costs substantially to around 2.5% ( Fig. 16 ). Hof et al. (2008) have shown that the results of CBA of climate change are very sensitive to model assumptions, with the discount rate playing the most important role. The discount rate is especially important due to the different costs function over time related to the adaptation only and mitigation only scenarios. 3 3 A discount rate of 5% leads to discounted costs of 0.8% and 1.9% for the adaptation-only scenario and mitigation-only scenario, respectively. If a discount rate of 1.4% is used (equal to the discount rate used by Stern (2006) ), the discounted costs are 3.2% and 2.5% for the adaptation-only scenario and mitigation-only scenario, respectively. With our discount rate of 2.5%, the combination of mitigation and adaptation leads to the lowest discounted costs, namely 2% of GDP. Consistent with literature, the adaptation investments are assessed to be smaller than mitigation investments and residual damages. However, they are very important in limiting residual damages. Some important caveats need to be mentioned. First, calculations cannot be regarded as reliable for the extreme tails of risks (i.e. low probability, high impact events). As a subjective assessment on how to handle such risks is involved, Weitzman (2008) questioned the usefulness of CBA for policymakers. Secondly, the value of the discount rate to account for time preference and risk is currently heavily debated, with arguments relating to subjective time preference and risk perception ( Nordhaus, 2008; Price, 2005; Stern, 2006 ). As mentioned above, the value of the discount rate can have a large effect on the results. Finally, non-market impacts need subjective quantification of damages; while it is difficult to monetize these impacts, in general, it is even more difficult for irreversible changes, for example a warming of the oceans leading to the loss of coral reefs ( Ackerman and Heinzerling, 2004 ). 5.9 Uncertainties in climate change, impacts and adaptation There are many sources of uncertainty in projections of future climate change and its impacts. Uncertainties are associated with every step in the causal chain: emissions, climatic drivers (e.g. the carbon cycle), climate (mainly climate sensitivity and pattern of climate change), and impacts (including adaptive capacity). As a result, different studies might give very different results for the same emission scenario. In fact, these differences are often larger than those arising in a particular model under different emission scenarios. For example, for precipitation changes at the end of the century, the multi-model ensemble mean exceeds the inter-model standard deviation only at high latitudes ( Kundzewicz et al., 2007 ). Uncertainties in climate change projections increase with the length of the time horizon. In the near term (e.g., the 2020s), climate model uncertainties play the most important role; while over longer time horizons (e.g. the 2090s), uncertainties due to the selection of emissions scenario become increasingly significant ( Jenkins and Lowe, 2003 ). The impact of future climate change on extreme events is particularly uncertain. This is partly due to a mismatch between the larger spatial and temporal scale of coarse-resolution climate models, and the local occurrence and short life of some weather extremes (e.g. cloudburst precipitation and flash floods). As impacts and adaptation take place at the local scale, detailed information is needed – which implies an increase in uncertainty. The large uncertainty ranges suggests that planning for adaptation should not be based on a single scenarios, but that a large range of projections need to be account for. 6 Conclusions In this paper, we have discussed how scenario analysis may contribute to the assessment of mitigation and adaptation strategies. We have also presented two integrated scenarios as a starting point for analysis. The scenarios have explicitly treated mitigation and adaptation action for several indicators – and cover several important linkages and feedbacks between socio-economic development and impacts (e.g. the impacts of climate change on land use and mitigation are accounted for). We specified impacts in those scenarios for a selected number of indicators, focusing mainly on mean climate changes. Based on our work, we draw the following conclusions: • By describing two contrasting sets of possible climate change trajectories for the world, we have created the basis for a more integrated analysis of the interaction between mitigation, adaptation and climate impacts. The first scenario (no mitigation) is expected to lead to a global mean temperature increase by the end of the century of around 4 °C (for the most likely values for climate parameters, and current economic trends). This scenario has high adaptation needs as has been shown in some of our analyses. The second scenario assumes stringent mitigation and limits global mean temperature change to 2 °C, with a probability of 50%. Even under this scenario, substantial adaptation measures will be needed. • Integrated scenario analysis as presented here can form a good basis for exploring the different consequences of policy choices (including uncertainties); it is not feasible, given uncertainties to determine an optimal mix between mitigation, adaptation and residual damages. As discussed in this paper, the weighing of the consequences of climate change and the various policy responses is complicated by large differences in scale, space and time; large uncertainties; and clear differences in interest between actors (whether they are perpetrators or victims of climate change, for instance). As a result, subjective interpretation of risks will always play an important role. Still, scenario analysis can provide a description of possible consequences and risks. At this stage, the monetary assessment of cost and benefits (Section 5.8 ) could not be linked to the description of physical change in the preceding sections. • Effective climate policy includes both adaptation and mitigation. Model calculations show that mitigation scenarios can be designed that lead to an increase of global mean temperature increase 2 °C for a best-guess climate sensitivity. However, even these stringent scenarios can still also result in a global mean temperature increase of more than 2.5 °C (and at best a temperature increase of 1.5 °C) and regional temperature change which is far greater. The need for a combination of mitigation and adaptation has been shown for most of the impacts explored in this paper. For example, adaptation can be more effective than mitigation in dealing with sea-level rise (at least during the 21st century), but mitigation still has a role to play in reducing damages and costs of adaptation. Agriculture presents an example where adaptation and mitigation are both clearly necessary. Crop yields in agriculture are projected to suffer negative impacts in many regions due to climate change in the absence of both adaptation and mitigation action. Without stringent mitigation, adaptation could limit negative impacts, but not remove them. An advantage of mitigation is that it affects all impact categories, while adaptation needs to be tailored to impacts and contexts. • While impacts of climate change can be severe and, depending on subjective choices, may warrant stringent climate policy, the impacts assessed in this study (given the state of the art) are likely to remain secondary influences of population change and economic growth at a global scale. Yet important caveats apply (see below). While climate change may have an impact on millions of people, other challenges are likely to influence people and governance more significantly. It should be noted, however, that we have covered only a limited set of impacts and focused mostly on mean estimates of gradual climate change and, for instance, not on catastrophic, very high-impact, extremely low-probability events ( Weitzman, 2008 ). Such events in fact may be so severe that the conclusion above no longer holds. If costs at a global scale remain relatively low, there is less need for global analysis to include all feedbacks on main drivers based on the consistency of the storylines. Clearly, at the local scale the situation is likely to be very different; impacts for individual countries can be far more substantial than at the global scale. For example, sea level rise is very important for some low-lying island states and countries that could be significantly affected by either large adaptation costs and/or damages (up to complete destruction). For agriculture, positive and negative impacts are projected to occur in different places and at different times – with low-income countries often experiencing relatively more negative impacts. Agriculture in temperate regions, where it is currently temperature-limited, could benefit. All in all, we believe that it useful to pursue further the development of integrated scenarios specifying these further on a regional scale. While this paper presents a useful first step, it also has left many feedbacks still unaccounted for. • The overall mitigation costs in this study are estimated to be in the order of 1–2% of GDP for the 2 °C scenario. The mitigation scenario reduces the risks of climate change. There are several types of benefits of investments in mitigation. First, climate-related damages and the costs of adaptation are reduced. Second, also uncertainty is reduced, which is important given the risks involved. While we argue there can be no optimal trade-off between mitigation and adaptation at a global level, we have shown that over the longer-run the costs and benefits of mitigation and adaptation are of an equivalent magnitude. • Important foci for further analysis include the linkages between assessment of physical changes and monetary impact analysis, variability and changes in extreme events, the potential role of large scale disruptions and governance. In our and other assessments, the focus has mostly been on changes in mean values, yet there is considerable concern about extreme events (resulting in natural disasters) associated with climate variability, but also in large scale disruptions (such as the disintegration of the West Antarctic Ice Shield), which are not accurately described by average values. Projections of changes in climate variability have been highly uncertain, and to date often hinder analyses from robustly predicting future extreme event risk. The role of different actors is another issue; some forms of adaptation require active governmental involvement; other forms are likely to be implemented by private investors, such as installation of space cooling systems. The differences between these two adaptation protagonists are relevant for future scenario development. Acknowledgements The research presented in this paper was performed as part of the EU-funded ADAM research project. An earlier version of this paper was published as part of the book “Making Climate Change work for us” edited by Hulme and Neufeld and published by Cambridge University Press in 2010. Appendix A Model descriptions A.1 IMAGE 2.4 The IMAGE 2.4 Integrated Assessment model ( Bouwman et al., 2006 ) consists of a set of linked and integrated models that together describe important elements of the long-term dynamics of global environmental change, such as air pollution, climate change, and land-use change. As part of IMAGE, the global energy model TIMER ( van Vuuren et al., 2006 ) describes the long-term dynamics of demand and production of primary and secondary energy and the related emissions of greenhouse gases and regional air pollutants. The model behavior is mainly determined by substitution processes of various technologies on the basis of long-term prices and fuel-preferences. The agricultural model of IMAGE models the productivity of 7 crop groups and 5 animal categories ( Leemans and Born, 1994 ). The regional production of agricultural goods is distributed spatially (at 0.5° × 0.5°) on the basis of a set of allocation rules ( Alcamo et al., 1998 ). Both the land use change maps and the agricultural activity data are used to model emissions from land use (change). The emissions of GHGs are used by the MAGICC model to calculate global mean temperature change ( Wigley and Raper, 2001 ). Patterns of temperature change are obtained by making a link to climate change patterns generated by a general circulation models (GCM). Limitations : IMAGE is provides a physically oriented description of human activities (use of tons of oil, production of tons of cereals, etc.). A fuller macro-economic description only emerges from cooperation with other models. The broad coverage of IMAGE as Integrated Assessment Model implies that many critical uncertainties influence the model outcomes. In this context, use of a single baseline (as in the ADAM project) does not do fully justice to the fundament uncertainties involved. A.2 FAIR The climate policy model FAIR ( Den Elzen et al., 2008 ) is used in conjunction with the IMAGE model to determine the reduction rates across different emission sources. Global climate calculations make use of the simple climate model, MAGICC 4.1 ( Wigley, 2003; Wigley and Raper, 2001 ). Required global emission reductions are derived by taking the difference between the baseline and a global emission pathway. The FAIR cost model distributes these between the regions following a least-cost approach using regional marginal abatement costs curves (MACs) for the different emissions sources. Recently, the FAIR model has been extended with damage and adaptation costs curves (based on the AD-DICE model ( De Bruin et al., 2009b ) and the ability to estimate macro-economic impacts on GDP growth ( Hof et al., 2008 )). This allows the model to explore the economic impacts of combined mitigation and adaptation strategies. Limitations : In its aim to be flexible, the FAIR model does not include a sectoral macro-economic model or an energy model. The model thus works from a partial equilibrium approach – and more underlying consequences of climate policy can only be studied by forwarding the FAIR results to other (linked) models. A.3 DIVA DIVA (Dynamic and Interactive Vulnerability Assessment) is an integrated model of coastal systems that was developed, together with its proper coastal database, within the EU-funded project DINAS-COAST 4 4 Dynamic and Interactive Assessment of National, Regional and Global Vulnerability of Coastal Zones to Sea-Level Rise; http://www.pik-potsdam.de/dinas-coast/ . ( DINAS-COAST Consortium, 2006; Hinkel and Klein, 2009 ). DIVA produces quantitative information on a range of ecological, social and economic coastal vulnerability indicators from sub-national to global scales, covering all coastal nations. The model consists of a number of modules developed by experts from various engineering, natural and social science disciplines. Based on climatic and socio-economic scenarios, the model assesses coastal erosion (both direct and indirect), coastal flooding (including rivers), wetland change and salinity intrusion into deltas and estuaries. DIVA also considers coastal adaptation in terms of raising dikes and nourishing beaches and includes several predefined adaption strategies such as no protection, full protection or optimal protection. Limitations : DIVA excludes the following processes that are likely to affect coastal impacts, but can currently not be modeled with confidence: changes in storm frequency and intensity, local distribution of GDP and population growth due to rapid coastal development and urbanization, and salinity intrusion into coastal aquifers. Further important uncertainties arise due to the coarse resolution and accuracy of elevation data. A.4 TIMER-cooling/heating energy demand The TIMER cooling/heating energy demand model ( Isaac and van Vuuren, 2009 ) describes the energy use for cooling and heating as a function of several factors, including population levels, changing income levels and climate. For both heating and cooling, empirical data is used to calibrate a set of system-dynamic demand functions. Climate (cooling and heating degree days) plays an important role. The model is able to account for the impacts of climate change. Limitations : The empirical basis on which the model is calibrated is relatively poor for developing countries. The model does not contain a description of different ways cooling and heating demand can be supplied and the costs involved in substituting one technology for the other. A.5 Water resources impact model The water resources impact model ( Arnell, 2003, 2004 ) has two components. The first simulates river runoff across the entire global land surface (at 0.5° × 0.5°) using the macro-scale hydrological model Mac-PDM, and the second determines indicators of water resources stress at the watershed level by calculating per capita water resource availability. A watershed is assumed to be exposed to water resources stress if it has an annual average runoff equivalent to less than 1000 m 3 /capita/year, a semi-arbitrary threshold widely used to identify water-stressed regions. Climate change leads to an increase in exposure to water resources stress if it causes runoff in a water-stressed watershed to decrease significantly, or causes the watershed to fall below the threshold. Climate change leads to an apparent reduction in exposure for the opposite trends. These changes cannot be directly compared; whilst a reduction in runoff (and an increase in exposure) is highly likely to be adverse, an increase in runoff (and apparent decrease in exposure) may not be beneficial if the additional water cannot be stored or if it occurs during high flow seasons as increased flooding. The number of people living in watersheds exposed to an increase in water resources stress can be used as an indicator of exposure to climate change. The actual impacts (in terms of real water shortages) will depend on water management structures in place. Limitations : The hydrological model does not simulate perfectly the volume of river runoff, and in particular tends to overestimate runoff in semi-arid regions. The water resources indicator is a measure of exposure to impact, not actual impact; it can be seen as a surrogate for the demand for adaptation. A.6 Malaria risks Malaria vectors, the mosquitoes spreading the infection, can only survive in suitable climates with high average temperatures, no frost and enough precipitation. The MARA/ARMA malaria suitability model ( Craig et al., 1999 ) incorporates these climatic factors to determine climatic suitable areas. The climatic levels required for the maximum suitability of 1, and for the minimum suitability of 0, are shown in Table A.1 . For indicators with levels between those required for 0 or 1 suitability a level is calculated using s simple function ( Craig et al., 1999 ). All these factors are calculated at half by half degree grid level, making use of the output from the IMAGE-model ( Bouwman et al., 2006 ). Total climatic malaria suitability for each grid cell is determined by the lowest of these three indices. Limitations : The MARA/ARMA model describes suitability for malaria vectors. It does not provide a process description of the spread of mosquitos, nor does it explicitly describe how people may react to increased risk levels. References Ackerman and Heinzerling, 2004 F. Ackerman L. Heinzerling Priceless: On Knowing the Price of Everything and the Value of Nothing 2004 The New Press New York Agrawala and Fankhauser, 2008 S. Agrawala S. Fankhauser Economic Aspects of Adaptation to Climate Change. Costs, Benefits and Policy Instruments 2008 OECD Paris Alcamo et al., 1998 J. Alcamo E. Kreileman M. Krol R. Leemans J. Bollen J.V. Minnen M. Schaeffer S. Toet B. de Vries Global modelling of environmental change: an overview of IMAGE 2.1 J. Alcamo R. Leemans E. Kreileman Global Change Scenarios of the 21st Century. Results from the IMAGE 2.1 Model 1998 Elsevier Science Ltd. Oxford 3 94 Arnell, 2003 N. Arnell Effects of IPCC SRES emissions scenarios on river runoff: a global perspective Hydrology and Earth System Sciences 7 5 2003 619 641 Arnell, 2004 N. Arnell Climate change and global water resources: SRES emissions and socio-economic scenarios Global Environmental Change 14 1 2004 31 52 Arnell et al., 2002 N.W. Arnell M.G.R. Cannell M. Hulme R.S. Kovats J.F.B. Mitchell R.J. Nicholls M.L. Parry M.J.L. Livermore A. White The consequences of CO 2 stabilisation for the impacts of climate change Climatic Change 53 4 2002 413 446 Bakkenes et al., 2006 M. Bakkenes B. Eickhout R. Alkemade Impacts of different climate stabilisation scenarios on plant species in Europe Global Environmental Change 16 1 2006 19 28 Barker, 2003 T. Barker Representing global climate change, adaptation and mitigation Global Environmental Change 13 2003 1 6 Barker et al., 2009 Barker, T., Kenber, M., Scrieciu, S., Ryan, D., 2009. Breaking the Climate Deadlock. Cutting the Cost: The Economic Benefits of Collaborative Climate Action. The Climate Group, The Office of Tony Blair, 4CMR – University of Cambridge and Cambridge Econometrics. Barker and Scrieciu, 2010 T. Barker S.S. Scrieciu Modelling low stabilisation with E3MG: towards a ‘New Economics’ approach to simulating energy-environment-economy system dynamics The Energy Journal 31 Special issue 1 2010 137 164 Barker et al., 2008 T. Barker S.S. Scrieciu T. Foxon Achieving the G8 50% target: modelling induced and accelerated technological change using the macro-econometric model E3MG Climate Policy 8 2008 S30 S45 Berkhout et al., 2002 F. Berkhout J. Hertin A. Jordan Socio-economic futures in climate change impact assessment: using scenarios as ‘learning machines’ Global Environmental Change 12 2 2002 83 95 Bouwer, 2010 L.M. Bouwer Have disaster losses increased due to anthropogenic climate change? Bulletin of the American Meteorological Society 2010 10.1175/2010BAMS3092.1 Bouwman et al., 2006 A.F. Bouwman T. Kram K. Klein Goldewijk Integrated Modelling of Global Environmental Change. An Overview of IMAGE 2.4 2006 Netherlands Environmental Assessment Agency Bilthoven 228 pp. (Publication 500110002/2006) Burke et al., 2006 E.J. Burke S.J. Brown N. Christidis Modelling the recent evolution of global drought and projections for the 21st century with the Hadley Centre climate model Journal of Hydrometeorology 7 2006 1113 1125 Clarke et al., 2010 L. Clarke J. Edmonds V. Krey R. Richels S. Rose M. Tavoni International climate policy architectures: overview of the EMF 22 international scenarios Energy Economics 31 Suppl. 2 2010 S64 S81 Copenhagen Accord, 2009 Copenhagen Accord, 2009. (Copenhagen Accord of 18 December 2009). United Nations Climate Change Conference 2009, Copenhagen. Craig et al., 1999 M.H. Craig R.W. Snow D. le Sueur A climate-based distribution model of malaria transmission in Africa Parasitology Today 15 3 1999 105 111 Cummins and Mahul, 2009 J.D. Cummins O. Mahul Catastrophe Risk Financing in Developing Countries: Principles for Public Intervention 2009 The World Bank Washington, DC De Bruin et al., 2009a K.C. De Bruin R.B. Dellink S. Agrawala Economic Aspects of Adaptation to Climate Change: Integrated Assessment Modelling of Adaptation Costs and Benefits 2009 OECD Paris De Bruin et al., 2009b K.C. De Bruin R.B. Dellink R.S.J. Tol AD-DICE: an implementation of adaptation in the DICE model Climatic Change 95 1–2 2009 63 81 Den Elzen et al., 2008 M.G.J. Den Elzen P.L. Lucas D.P. van Vuuren Regional abatement action and costs under allocation schemes for emission allowances for achieving low CO 2 -equivalent concentrations Climatic Change 90 3 2008 243 268 Den Elzen and van Vuuren, 2007 M.G.J. Den Elzen D.P. van Vuuren Peaking profiles for achieving long-term temperature targets with more likelihood at lower costs Proceedings of the National Academy of Sciences of the United States of America 104 46 2007 17931 17936 DINAS-COAST Consortium, 2006 DINAS-COAST Consortium, 2006. DIVA 1.5.5 CD-ROM, Potsdam Institute for Climate Impact Research, Potsdam, Germany. Easterling et al., 2007 W. Easterling P. Aggarwal P. Batima K. Brander L. Erda M. Howden A. Kirilenko J. Morton J.-F. Soussana J. Schmidhuber F.N. Tubiello Food, fibre and forest products M.L. Parry O.F. Canziani J.P. Palutikof P.J. van der Linden C.E. Hanson Climate Change 2007: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change 2007 Cambridge University Press Cambridge, UK EC, 2006 EC World Energy Technology Outlook 2050 (WETO H2) 2006 European Commission Brussels Edenhofer et al., 2010 O. Edenhofer B. Knopf T. Barker L. Baumstark E. Bellevrat B. Chateau P. Criqui M. Isaac A. Kitous S. Kypreos M. Leimbach K. Lessmann B. Magné S. Scrieciu H. Turton D.P. van Vuuren The economics of low stabilization: model comparison of mitigation strategies and costs The Energy Journal 31 SI-1 2010 11 48 Environmental Change Institute, 2009 Environmental Change Institute, 2009. International Climate Conference – 4 Degrees and Beyond. Environmental Change Institute, Oxford University, 28–30 September, Oxford, UK. Feyen et al., 2009 L. Feyen J.I. Barredo R. Dankers Implications of global warming and urban land use change on flooding in Europe J. Feyen K. Shannon M. Neville Water and Urban Development Paradigms. Towards an Integration of Engineering, Design and Management Approaches 2009 Taylor and Francis Group London Fischer et al., 2007 G. Fischer F.N. Tubiello H. van Velthuizen D.A. Wiberg Climate change impacts on irrigation water requirements: effects of mitigation, 1990–2080 Technological Forecasting and Social Change 74 7 2007 1083 1107 Fisher et al., 2007 B. Fisher N. Nakicenovic K. Alfsen J. Corfee Morlot F. de la Chesnaye J.-C. Hourcade K. Jiang M. Kainuma E. La Rovere A. Matysek A. Rana K. Riahi R. Richels S. Rose D. van Vuuren R. Warren P. Ambrosi F. Birol D. Bouille C. Clapp B. Eickhout T. Hanaoka M.D. Mastrandrea Y. Matsuoko B. O’Neill H. Pitcher S. Rao F. Toth Issues related to mitigation in the long-term context B. Metz O. Davidson P. Bosch R. Dave L. Meyer Climate Change 2007. Mitigation of Climate Change. Contribution of Working Group III to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change 2007 Cambridge University Press New York 169 250 Hallegatte, 2009 S. Hallegatte Strategies to adapt to an uncertain climate change Global Environmental Change 19 2009 240 247 Hayashi et al., 2010 A. Hayashi K. Akimoto F. Sano S. Mori T. Tomoda Evaluation of global warming impacts for different levels of stabilization as a step toward determination of the long-term stabilization target Climatic Change 98 2010 87 112 Hilderink et al., 2008 H. Hilderink P.L. Lucas A. ten Hove M. Kok M. de Vos P. Janssen J. Meijer A. Faber A. Ignaciuk A. Petersen H.J.M. de Vries Towards a Global Integrated Sustainability Model 2008 Netherlands Environmental Assessment Agency Bilthoven Hinkel and Klein, 2009 J. Hinkel R.J.T. Klein Integrating knowledge to assess coastal vulnerability to sea-level rise: the development of the DIVA tool Global Environmental Change 19 3 2009 384 395 Hirabayashi and Kanae, 2009 Y. Hirabayashi S. Kanae First estimate of the future global population at risk of flooding Hydrological Research Letters 3 2009 6 9 Hof et al., 2009a A.F. Hof K.C. de Bruin R.B. Dellink M.G.J. den Elzen D.P. van Vuuren The effect of different mitigation strategies on international financing of adaptation Environmental Science and Policy 12 7 2009 832 843 Hof et al., 2009b A.F. Hof K. de Bruin R. Dellink M.G.J. den Elzen D.P. van Vuuren Costs, benefits and inter-linkages between adaptation and mitigation F. Biermann P. Pattberg F. Zelli Global Climate Governance After 2012: Architecture, Agency and Adaptation 2009 Cambridge University Press Cambridge Hof et al., 2008 A.F. Hof M.G.J. den Elzen D.P. van Vuuren Analysing the costs and benefits of climate policy: value judgements and scientific uncertainties Global Environmental Change 18 3 2008 412 424 IMAGE-team, 2001 IMAGE-team, 2001. The IMAGE 2.2 implementation of the IPCC SRES scenarios. A comprehensive analysis of emissions, climate change and impacts in the 21st century. RIVM CD-ROM publication 481508018, National Institute for Public Health and the Environment, Bilthoven, the Netherlands. IPCC, 2007 IPCC (Ed.), 2007. Climate Change 2007: Synthesis Report. Contribution of Working Groups I, II and III to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. IPCC, Geneva, 104 pp. Isaac and van Vuuren, 2009 M. Isaac D.P. van Vuuren Modeling global residential sector energy demand for heating and air conditioning in the context of climate change Energy Policy 37 2 2009 507 521 Jenkins and Lowe, 2003 Jenkins, G., Lowe, J., 2003. Handling uncertainties in the UKCIP02 scenarios of climate change. Hadley Centre Technical Note 44, Met Office, Exeter. Kinney et al., 2008 P.L. Kinney M.S. O’Neill M.L. Bell J. Schwartz Approaches for estimating effects of climate change on heat-related deaths: challenges and opportunities Environmental Science and Policy 11 87 2008 Klein et al., 2007 R.J.T. Klein S. Huq F. Denton T.E. Downing R.G. Richels J.B. Robinson F.L. Toth Inter-relationships between Adaptation and Mitigation. Climate Change 2007. Impacts, Adaptation and Vulnerability. Contribution of Working Group II. Report of the Intergovernmental Panel on Climate Change 2007 Cambridge University Press Cambridge 745 777 Krol et al., 1997 M. Krol J. Alcamo R. Leemans Global and regional impacts of stabilizing atmospheric CO 2 Mitigation and Adaptation Strategies for Global Change 1 1997 341 361 Kundzewicz et al., 2010 Z.W. Kundzewicz Y. Hirabayashi S. Kanae River floods in the changing climate – observations and projections Water Resources Management 2010 10.1007/s11269-009-9571-6 Kundzewicz et al., 2007 Z.W. Kundzewicz L.J. Mata N. Arnell P. Döll P. Kabat B. Jiménez K. Miller T. Oki Z. Şen I. Shiklomanov Freshwater resources and their management M.L. Parry O.F. Canziani J.P. Palutikof C.E. Hanson P.J. van der Linden Climate Change 2007: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change 2007 Cambridge University Press Cambridge, UK Leemans and Born, 1994 R. Leemans G.J.v.d. Born Determining the potential global distribution of natural vegetation, crops and agricultural productivity Water, Air and Soil Pollution 76 1994 133 161 Lenton et al., 2008 T.M. Lenton H. Held E. Kriegler J.W. Hall W. Lucht S. Rahmstorf H.J. Schellnhuber Tipping elements in the Earth's climate system Proceedings of the National Academy of Sciences of the United States of America 105 6 2008 1786 1793 Manne and Richels, 2005 A.S. Manne R.G. Richels Merge: an integrated assessment model for global climate change R. Loulou J.-P. Waaub G. Zaccour Energy and Environment 2005 Springer USA McMichael et al., 1996 A. McMichael A. Haines R. Slooff S. Kovats Climate Change and Human Health 1996 World Health Organization Geneva Mechler et al., 2010 R. Mechler S. Hochrainer A. Aaheim Z. Kundzewicz N. Lugeri M. Moriondo H. Salen M. Bindi I. Banaszak A. Chorynski E. Genovese H. Kalirai J. Linnerooth-Bayer C. Lavalle D. McEvoy P. Matczak M. Radziejewski D. Rübbelke M.-J. Schelhaas M. Szwed A. Wreford Risk management approach for assessing adaptation to changing flood and drought risks in Europe M. Hulme H. Neufeldt Making Climate Change Work for Us: European Perspectives on Adaptation and Mitigation Strategies 2010 Cambridge University Cambridge, UK Meehl et al., 2007 G.A. Meehl T.F. Stocker W.D. Collins P. Friedlingstein A.T. Gaye J.M. Gregory A. Kitoh R. Knutti J.M. Murphy A. Noda S.C.B. Raper I.G. Watterson A.J. Weaver Z.-C. Zhao Global climate projections S. Solomon Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change 2007 Cambridge University Press Cambridge Metz et al., 2007 B. Metz O.R. Davidson P.R. Bosch R. Dave L.A. Meyer Climate Change. Mitigation of Climate Change. Contribution of Working Group III to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change 2007 Cambridge University Press Cambridge, United Kingdom Mirza et al., 2003 M.M.Q. Mirza R.A. Warrick N.J. Ericksen The implications of climate change on floods of the Ganges, Brahmaputra and Meghna Rrivers in Bangladesh Climatic Change 57 2003 287 318 Moriondo et al., 2010 M. Moriondo M. Bindi Z.W. Kundzewicz M. Szwed A. Chorynski P. Matczak M. Radziejewski D. McEvoy A. Wreford Impact and adaptation opportunities for European agriculture in response to climatic change and variability Mitigation and Adaptation Strategies for Global Change 15 7 2010 657 679 Moss et al., 2010 R.H. Moss J.A. Edmonds K.A. Hibbard M.R. Manning S.K. Rose D.P. van Vuuren T.R. Carter S. Emori M. Kainuma T. Kram G.A. Meehl J.F.B. Mitchell N. Nakicenovic K. Riahi S.J. Smith R.J. Stouffer A.M. Thomson J.P. Weyant T.J. Wilbanks The next generation of scenarios for climate change research and assessment Nature 2010 10.1038/nature08823 Nakicenovic et al., 2000 N. Nakicenovic Special Report on Emissions Scenarios (SRES) 2000 Cambridge University Press Cambridge, UK Nakicenovic et al., 2006 N. Nakicenovic P. Kolp K. Riahi M. Kainuma T. Hanaoka Assessment of Emissions Scenarios Revisited Environmental Economics and Policy Studies 7 3 2006 137 173 Nicholls and Lowe, 2004 R.J. Nicholls J.A. Lowe Benefits of mitigation of climate change for coastal areas Global Environmental Change 14 3 2004 229 244 Nicholls et al., 2010 R.J. Nicholls N. Marinova J.A. Lowe S. Brown P. Vellinga D. de Gusmao J. Hinkel R.S.J. Tol Sea-level rise and its possible impacts given a “beyond 4 degree world” in the 21st century Philosophical Transactions of the Royal Society 2010 10.1098/rsta.2010.029 Nordhaus and Boyer, 2000 W.D. Nordhaus J. Boyer Warming the World: Economic Models for Global Warming 2000 MIT Press Cambridge, MA pp. 315–328 Nordhaus, 2008 W.D. Nordhaus A Question of Balance Weighing the Options on Global Warming Policies 2008 Yale University Press New Haven and London Parry et al., 2007 M.L. Parry O.F. Canziani J.P. Palutikof P.J. van der Linden C.E. Hanson Climate Change 2007: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change 2007 Cambridge University Press Cambridge Patt et al., 2010 A.G. Patt D.P. van Vuuren F. Berkhout A. Aaheim A.F. Hof M. Isaac R. Mechler Adaptation in integrated assessment modeling: Where do we stand? Climatic Change 99 3 2010 383 402 Piani et al., 2005 C. Piani D.J. Frame D.A. Stainforth M.R. Allen Constraints on climate change from a multi-thousand member ensemble of simulations Geophysical Research Letters 32 2005 L23825 Price, 2005 C. Price An intergenerational perspective on effects of environmental changes: discounting the future's viewpoint J.L. Innes G.M. Hickey H.F. Hoen Forestry and Environmental Change: Socioeconomic and Political Dimensions 2005 International Union on Forestry Research Organisations (IUFRO) Vienna Rose et al., 2007 Rose, S., Ahammad, H., Eickhout, B., Fisher, B., Kurosawa, A., Rao, S., Riahi, K., van Vuuren, D. 2007. Land in climate stabilization modeling: initial observations Energy Modeling Forum Report. Stanford University. Schneider and Kuntz-Duriseti, 2002 S.H. Schneider K. Kuntz-Duriseti Uncertainty and climate change policy S.H. Schneider A. Rosencranz O. Niles Climate Change Policy: A Survey 2002 Island Press Washington, DC Stern, 2006 N. Stern Stern Review on the Economics of Climate Change 2006 Cambridge University Press Cambridge Swart et al., 2009 R. Swart L. Bernstein M. Ha-Duong A. Petersen Agreeing to disagree: uncertainty management in assessing climate change, impacts and responses by the IPCC Climatic Change 92 2009 1 29 Swart and Raes, 2007 R. Swart F. Raes Making integration of adaptation and mitigation work: mainstreaming into sustainable development policies? Climate Policy 7 4 2007 288 303 Tol, 2002a R. Tol Estimates of the damage costs of climate change. Part II. Dynamic estimates Environmental and Resource Economics 21 2 2002 135 160 Tol, 2002b R.S.J. Tol Estimates of the damage costs of climate change. Part 1. Benchmark estimates Environmental and Resource Economics 21 1 2002 47 73 Tol, 2002c R.S.J. Tol Welfare specifications and optimal control of climate change: an application of fund Energy Economics 24 4 2002 367 376 Tubiello and Fischer, 2007 F.N. Tubiello G. Fischer Reducing climate change impacts on agriculture: global and regional effects of mitigation, 2000–2080 Technological Forecasting and Social Change 74 7 2007 1030 1056 UN, 2005 UN, 2005. World Population Prospects: The 2004 Revision. CD-ROM Edition – Extended Dataset. United Nations publications, Sales No. E.05.XIII.12, United Nations, Department of Economic and Social Affairs, Population Division. van Vliet et al., 2009 J. van Vliet M.G.J. den Elzen D.P. van Vuuren Meeting radiative forcing targets under delayed participation Energy Economics 31 Suppl. 2 2009 S152 S162 van Vuuren et al., 2006 D.P. van Vuuren B. van Ruijven M. Hoogwijk M. Isaac B. De Vries TIMER 2: model description and application L. Bouwman T. Kram K. Klein-Goldewijk Integrated Modelling of Global Environmental Change. An overview of IMAGE 2.4 2006 MNP - Netherlands Environmental Assessment Agency Bilthoven van Vuuren et al., 2007 D.P. van Vuuren M.G.J. Den Elzen P.L. Lucas B. Eickhout B.J. Strengers B. Van Ruijven S. Wonink R. Van Houdt Stabilizing greenhouse gas concentrations at low levels: an assessment of reduction strategies and costs Climatic Change 81 2 2007 119 159 van Vuuren et al., 2008a D.P. van Vuuren B. De Vries A. Beusen P.S.C. Heuberger Conditional probabilistic estimates of 21st century greenhouse gas emissions based on the storylines of the IPCC-SRES scenarios Global Environmental Change 18 4 2008 635 654 van Vuuren et al., 2008b D.P. van Vuuren M. Meinshausen G.K. Plattner F. Joos K.M. Strassmann S.J. Smith T.M.L. Wigley S.C.B. Raper K. Riahi F. De La Chesnaye M.G.J. Den Elzen J. Fujino K. Jiang N. Nakicenovic S. Paltsev J.M. Reilly Temperature increase of 21st century mitigation scenarios Proceedings of the National Academy of Sciences of the United States of America 105 40 2008 15258 15262 van Vuuren et al., 2009 D.P. van Vuuren M.G.J. den Elzen J. van Vliet T. Kram P. Lucas M. Isaac Comparison of different climate regimes: the impact of broadening participation Energy Policy 37 12 2009 5351 5362 van Vuuren et al., 2010 D.P. van Vuuren E. Stehfest M.G.J. den Elzen J. Van Vliet M. Isaac Exploring scenarios that keep greenhouse gas radiative forcing below 3 W/m 2 in 2100 Energy Economics 31 Special Issue 1 2010 165 192 Vermeer and Rahmstorf, 2009 M. Vermeer S. Rahmstorf Global sea level linked to global temperature Proceedings of the National Academy of Sciences of the United States of America 106 2009 21527 21532 Weitzman, 2008 Weitzman, M.L., 2008. On modeling and interpreting the economics of catastrophic climate change. Wigley, 2003 T.M.L. Wigley MAGICC/SCENGEN 4.1: Technical Manual 2003 UCAR - Climate and Global Dynamics Division Boulder, CO Wigley and Raper, 2001 T.M.L. Wigley S.C.B. Raper Interpretation of high projections for global-mean warming Science 293 2001 451 454|设想情景用于探讨不确定情况下不同适应和缓解战略的后果。在本文中，我们使用了两种情景来探讨发展: (1)没有缓解措施导致全球平均气温到2100年上升4摄氏度; (2)一个雄心勃勃的缓解策略导致到2100年上升2摄氏度。就第二种情况而言，气候系统的不确定性意味着不能排除全球平均气温上升3摄氏度或更多的可能性。我们的分析表明，在许多情况下，适应和减缓不是权衡，而是补充。例如，在缓解设想方案中，因气候变化而面临更大水资源压力的人数可以大幅度减少，但仍然需要对面临更大压力的其余大量人口进行适应。另一个例子是海平面上升，从全球和纯货币的角度来看，适应(直到2100年)似乎比缓解更有效。然而，从较贫穷和小岛屿国家的角度来看，严格的缓解措施对于将风险保持在可控水平是必要的。就农业而言，只有基于适应和缓解相结合的设想方案才能避免严重的气候变化影响。关键词情景综合评估气候变化缓解适应气候影响1引言情景分析是评估气候变化和气候变化政策的一个非常重要的工具，它使分析人员能够探索经济发展、温室气体排放、气候和生态系统等因素之间复杂而不确定的未来相互作用。这些因素共同决定了缓解和适应政策的必要性和可能性。设想情景还可以作为一种手段，协调参与气候研究领域的各种不同研究群体的假设，从而更好地比较其结果。因此，情景在缓解和适应研究中得到了广泛的应用(参见 Metz 等，2007; Parry 等，2007)(特别是来自排放情景特别报告(SRES)的情景(Nakicenovic 等，2000))。Moss 等人(2010)指出，由于 SRES 对场景分析的信息需求正在发生变化。首先，人们对探索适应与缓解之间的关系越来越感兴趣。正如 Moss 等人(2010)所指出的，这将需要进一步整合气候研究中涉及的不同分析传统的信息。第二，除了迄今为止探讨的无气候政策情景之外，人们对明确探讨气候政策影响的情景也越来越感兴趣。具体而言，在没有气候政策的情况下，能够评估长期气候目标的“成本”和“收益”是非常有意义的。在本文中，我们遵循这一思路，探讨情景分析如何能够促进对未来适应和缓解战略的联合评估。这样的联合评估有以下几个原因: (1)首选的缓解策略取决于预期的气候影响和适应成本; (2)考虑到适应气候变化的局限性; (3)一些适应和缓解策略可能相互作用; (4)最后，气候变化的影响可能有需要考虑的重要反馈。这种分析在战略层面上是最有用的，而不是针对个人的适应(或缓解)决策。鉴于这一目的，我们在本文中讨论了两个主要的情景，其中包括适应和减缓战略的要素(见本文的进一步内容) ，导致本世纪末全球平均气温上升4摄氏度和2摄氏度。这两个温度水平已经开始成为标志性的数字，代表着在没有减缓政策(4摄氏度)和国际气候谈判的温度目标(2摄氏度)(2009年哥本哈根协议)的情况下的潜在结果。可以说，如果政治领导人要在减缓、适应和气候影响之间做出明智的选择，了解这两个温度水平的影响是至关重要的(环境变化研究所，2009)。缓解和适应战略的综合评估由于方法上的差异而受到阻碍。考虑到当地环境的重要性，综合评估模型很难描述适应过程(Patt et al。 ，2010)。一个实际问题是，迄今为止，影响文献的相当一部分集中在非政策情景下的影响(例外包括 Arnell 等，2002; Bakkenes 等，2006; Hayashi 等，2010; Krol 等，1997; Nicholls 和 Lowe，2004)。因此，本文提出了一个基于耦合信息的广义情景评估——但没有假装是完整的或完全集成的。作为一项边做边学的活动，本文件打算说明4摄氏度和2摄氏度世界之间的重要区别，但也要确定进行综合情景分析所涉及的一些实际问题。这意味着，与现有文献相比，最重要的进步是我们提出了一个基于一致情景的多部门分析。鉴于目前综合评估模型的先进水平，已经使用几个松散耦合模型进行了试验。因此，一些重要的联系无法得到解决，如农业的适应性反应，这可能涉及灌溉(见第5.3节)和水需求(第5.4节)。事实上，本文提出的一个重要问题是，是否需要进行全面综合分析，或者部分综合是否足够。本文的内容安排如下: 我们首先讨论在开发能够为适应和缓解政策决策提供信息的设想方案时所遇到的一些方法上的复杂问题。接下来，我们讨论两种主要情景在社会经济驱动因素方面的差异(第3和第4部分)。在第5节中，我们探讨了适应和减缓战略对气候变化各种影响的潜在后果。2评估气候战略和情景发展(理论和方法)2.1应对气候变化的不同战略气候变化及其响应可能导致三种形式的成本(不一定是货币) : (1)气候影响的(剩余)成本，(2)适应的成本和(3)缓解的成本。至少在理论上，这对应于三种不同的策略: (1)“自由放任”(接受气候变化) ，(2)关注适应，(3)关注缓解，如图1所示(另见 Klein 等，2007)。虽然图1表明，缓解、适应和剩余损害的成本和收益可以相互交换，但存在一些概念和分析问题，使这种办法复杂化。这些与空间和时间尺度、风险和不确定性有关(SwartandRaes，2007)。缓解和适应是在不同空间尺度上发生的过程。虽然缓解行动通常是在国家或地方范围内采取的，但好处是全球共享的。因此，气候政策成功和成本的关键因素是国际合作的程度(Barker 等，2009; Clarke 等，2010; van Vliet 等，2009; van Vuuren 等，2009)。相比之下，对于适应而言，成本和收益在从地方到国家乃至国际的多个尺度上都存在。较大规模的扶持性环境仍然可以在较小规模上加强适应(例如，由国际融资机制资助的地方能力建设)。由于这些原因，缓解评估往往集中在全球一级，而相比之下，适应研究大多集中在地方一级。随着时间的推移，缓解和适应的动态也是一个重要因素。严格的缓解方案通常需要强有力的早期减排。然而，由于气候系统内部的巨大惯性，这些假设情景的气候变化影响在短期(前几十年)与没有气候变化政策的假设情景几乎没有差别。相比之下，一些相关的影响(例如减少当地空气污染的共同利益)可以以更快的速度实现。适应措施可能在短期内产生私人和社会效益。例如，空气调节等简单的适应措施可以带来明显的短期效益。一些重要的例外存在，可能需要几十年的实施，如空间规划的变化或大规模的工程工程防洪(见哈勒盖特，2009年)。其他重要因素是风险和不确定性。我们对气候变化的理解面临许多不确定性。要确定的关键不确定性包括认知、数据、模型和实体不确定性(施奈德和 Kuntz-Duriseti，2002; van Vuuren 等，2008a)。涉及不确定因素的例子有: (i)未来的排放量，(ii)气候系统，(iii)未来的脆弱性和对气候风险的暴露，以及(iv)缓解成本。采取缓解行动减少了一些不确定性，因为它减少了气候变化的源头，并揭示了实际的缓解成本(Barker，2003; Piani 等，2005)。然而，缓解措施也可能增加风险。例如，如果以不可持续的方式实施生物能源，可能会抵消一组风险(气候变化) ，同时产生另一组不同的风险(生物多样性丧失和粮食安全下降)。处理风险的一种方法是包括概率评估。这通常是使用过去的证据，推断以涵盖特定的未来情况。其他不确定性(例如不可知的冲击和意外)在量化意义上更难处理，但它们证明了承认无知的合理性。情景可以用来探索极端事件的可能性和各种政策组合的稳健性，但这并不常见(Berkhout et al。 ，2002)。传统上，涉及缓解研究和适应研究的学科对不确定性有不同的描述方式。虽然缓解研究往往使用定量方法并侧重于平均估计，但适应研究往往更侧重于对不确定性的定性描述，并侧重于危险事件的风险，即使这些事件发生的概率很低。这些不同的不确定性感知可能会使不同策略的综合评估复杂化(Swartet al。 ，2009)。2.2场景的类型我们可以根据缓解和适应的考虑将场景分为不同的类别。首先，我们将基线情景定义为一个事件的轨迹，假设没有来自气候变化的重大反馈，也没有关于缓解或适应的具体政策努力(这种情景可能仍然包括许多间接影响缓解或适应气候变化能力的行动; 例如，可以预期收入水平的增加与对减少疟疾等气候相关疾病风险的卫生服务的更大投资相一致)。这种类型的场景的主要目的是进行分析，作为其他场景的参考点。其次，适应情景描述了一个社会正在应对气候变化影响的世界。其目的是探讨适应气候变化所需的技术和政策类型、避免的损害和相关费用。适应包括所谓的自主适应(即在没有特定政府行动的情况下发生的行动)和有计划的适应。第三，缓解方案描述了一个包括旨在限制气候变化的政策的世界。其目的是探讨最大限度地减少气候变化及相关成本所需的技术和政策类型。由于总是存在剩余的影响，第四组，适应和缓解情景综合了两种类型的气候变化应对措施。可能的话，这第四类情景可以根据适应和缓解备选办法之间可能存在的协同作用，例如对于一些重新造林备选办法，重新排列政策备选办法。每一种情况都与更广泛的社会、政治和文化背景有关，在这种背景下，它们被认为会出现。在探索缓解、适应和残余损害的优选组合时，存在两种主要方法: (i)将潜在影响描述为全球平均气温上升(从而缓解)的功能的影响和基于风险的方法，以及(ii)成本效益分析，其中确定货币成本和收益，以最大限度地提高福利(例如，参见 Nordhaus，2008; Tol，2002c)。在这两种情况下，我们认为，描述不同应对战略之间的关系比寻求确定最佳办法更有用，也更能反映问题。鉴于第2.1节所列出的复杂性和不确定性，我们认为在现实中不可能采取任何最佳的缓解、适应或联合策略。2.3综合分析缓解和适应的综合分析可以通过不同方式实现: 例如，使用单一的所谓综合评估模型，或在不同模型和学科之间交流信息，评估现有文献并使结果具有可比性。这两种方法都是围绕气候变化的因果链进行组织的，即描述经济活动(收入、能源使用、农业等)、排放、气候变化和影响之间的关系——以及相关的反馈(图2)。实际上，该方案也构成了 IPCC 报告情景信息流的主干(Moss et al。 ，2010)。情景首先由综合评估和排放模型制定(侧重于经济驱动力、能源和土地使用以及温室气体排放(IPCC“工作组 III”))。随后，排放轨迹在气候模型中被用来评估气候变化的影响(IPCC“工作组 I”)。最后，这些情景被用于影响、适应和脆弱性分析(IPCC“工作组 II”)。不同研究学科和工作组的参与意味着很难说明不同领域之间的反馈意见。综合评估模型只能获取有限数量的可能反馈(经常被忽略的反馈包括粮食和水安全对人口和经济驱动因素的影响; 水资源短缺与粮食生产之间的关系; 气候变化对能源使用的影响等)。如果这些反馈不足以对系统产生重大影响，忽略(其中一些)可能是合理的。出于分析原因，在学科领域内组织场景开发并考虑有限数量的反馈有很大的优势。它使研究人员能够专注于他们很好地理解的链条要素，并增加所需的细节数量，而不必面对相互联系的复杂性。然而，在更加注重对缓解和适应战略进行综合分析的情况下，这种情况可能会改变。关于为什么需要采取综合办法的一些例子是: 一、气候影响，例如极端事件引发的影响，可能非常严重，破坏了原先设想的经济假设; 二。气候影响可能对农业产生重大影响，因此，对未考虑影响的土地使用相关排放量的估计可能是错误的，生物能源的缓解潜力可能受到影响;。对于对缓解和适应都有吸引力的土地面积，可能存在相互竞争的权利主张。因此，一个有趣的问题是，是否需要更加集成的分析是如此迫切，以至于需要更加复杂的集成模式(模型的交互耦合; 一个复杂的模型) ，或者是否可以单独处理影响，简化分析框架。时间范围和决策焦点在这里可能也很重要，例如是否考虑到潜在的临界点(Lenton et al。 ，2008)。研究这个问题的少数现有研究似乎表明，在大多数部门，任何缓解项目的适应影响都很小，大多数适应活动产生的排放量也很小(Klein et al。 ，2007)。迄今为止，最完整的分析来自以成本效益为导向的综合评估模型，如基金、 DICE 和 MERGE (Manne and Richels，2005; Nordhaus，2008; Tol，2002c) ，但这些模型通常将气候影响聚合为有限数量的相当抽象的损害函数。我们认为，随着时间的推移，随着许多部门减缓和适应措施的力度不断加大，将更加需要进行足够详细的联合评估。这里提供的场景基于建模和场景开发的当前技术状态，迈出了第一步。缓解和影响评估的一项评估使用了相同的设想方案，我们明确提出了缓解和适应战略(作为设想方案的一部分或在用于不同影响的模型中)。然而，许多反馈没有被考虑进去。在本文的最后，我们回到了更加集成(但也更加复杂)的场景的作用。2.4本文件使用的方法如上所述，可以确定几种情景: 基线情景、缓解情景、适应情景和适应-缓解情景。本文还介绍了这些场景类型。对于基准/适应情景，我们假设大多数社会经济驱动因素的中间假设。情景假设在第3和第4节中描述。这些设想方案不包括缓解措施，导致到2100年全球平均气温比工业化前水平上升4摄氏度。虽然我们描述了在这些情况下可能产生的影响和适应，但我们没有包括对原始驱动因素的反馈。在缓解设想方案中，包括严格的缓解努力，导致全球平均气温上升2摄氏度。使用 IPCC 给出的3 ° C 的气候敏感性中值(Meehl 等，2007) ，这意味着稳定水平约为450 ppm CO2当量(CO2当量).气候政策对经济驱动因素的影响没有被考虑在内——但是其他几个关系是耦合的(例如土地使用)。因此，在大多数文章中，我们忽略了气候变化和气候政策对经济假设的潜在影响。然而，在第5.8节中，我们用一个简单的经济模型(FAIR)来讨论它们的影响，以提供对全球范围内经济后果的可能规模的一些见解。使用了几个模型工具。这些情景主要是使用 IMAGE 综合评估模型开发的(Bouwman 等，2006)。IMAGE 模型根据对人口和世界经济的假设，结合对技术发展和消费模式的假设，描述了21世纪能源和土地使用的发展情况。该模型预测了全球范围内的气候变化(以全球平均温度变化和海平面上升为指标) ，并通过模式缩放缩小的气候模式模式构建了0.5 ° × 0.5 ° 网格上月气温和降雨量变化的空间场景。IMAGE 的产出用于描述海平面上升的 DIVA 模型; 用于估计水资源紧张后果的 Mac-PDM 全球水文模型; 用于估计加热和降温需求影响的 TIMER 能源模型; 用于疟疾对疟疾影响的 MARA/ARMA 适宜性模型和用于货币成本效益分析的 FAIR 模型。此外，我们更一般地讨论对农业的影响(基于 IPCC AR4)和极端事件。附录 A 提供了所有使用模型的简要描述。在我们的描述中，我们关注于全局级别(考虑到空间有限)。显然，这导致了我们讨论适应的局限性。实验取决于每个模型的设计，因此，不同影响之间可以提出的假设情景的数量是不同的。这意味着研究报告应被解释为综合评估的第一个例证，而不是关于适应及其限制的全面研究。3结果: 基线情景中的社会经济趋势3.1人口发展和经济增长我们假设人口遵循2004年世界人口预测修订版(UN，2005)到2050年的中等生育率变量，以及联合国到2100年的长期中等预测(图3)。这意味着，到2050年，全球人口将稳步增至近91亿，并在随后的50年中稳定在约92亿人，直至2100年。这种情况在人口预测范围内采取中间立场(见图3)。对于直到2050年的经济增长，这种情况遵循与剑桥模型 E3MG 相关的预测(巴克和 Scrieciu，2010; 巴克等人，2008)。利用基于 SRES 的 B2情景(IMAGE-team，2001)的经济增长预测，该情景延伸到2050年以后。从数量上看，这是一个中高速经济增长的情景，主要是对中国和印度经济增长的乐观假设的结果。按人均计算，经合组织经济体预计仍将是世界上最富有的经济体，但就经济活动总量而言，发展中区域的重要性迅速增加。在非洲、中东和拉丁美洲，人均国内生产总值的年增长率在0% 到2% 之间。在亚洲，到2050年，这一比例将从目前的高水平降至每年3% 。3.2基线情景下的能源使用和温室气体排放基线情景下的能源使用与欧盟委员会(EC，2006)公布的基线保持一致。尽管能源强度进一步降低，世界能源消费在2000-2050年期间增加了一倍以上，在2050-2100年期间又增加了25% (图4)。整个世纪以来，能源供应仍然以化石燃料为主。当石油和天然气的生产在本世纪达到高峰和下降时，煤炭的使用在整个情景期间增加。此外，非化石能源的产量也在迅速增长。在截至2100年的这段时间里，核能使用量增加了2到3倍，达到76 EJ，生物质使用量强劲增加，而水力发电产量增加了大约60% 到80% 。相对增长最大的是风能和太阳能; 到2050年，风能和太阳能在所有非化石能源中的比例将从不到1% 上升到10% 至14% 。2050年可再生能源总使用量为120-140 EJ，2100年为190 EJ。上述趋势表明，能源活动的二氧化碳排放量在2050年之前增加了一倍以上，在2050年至2100年之间又增加了三分之一(见图3)。因此，该方案在文献范围内形成了一个中间基线方案(Fisher et al。 ，2007)。非二氧化碳温室气体(尤其是甲烷)在2000-2050年期间稳步增长，但增长速度低于二氧化碳(作为其驱动因素，农业的增长速度预计将低于能源部门)。本世纪上半叶，土地利用产生的二氧化碳排放量回落至零。农业用地面积位于最近公布的类似情景的范围内，尽管处于该范围的低端(Rose et al。 ，2007)。4缓解方案和气候方案的结果4.1能源使用和温室气体排放缓解方案旨在将温室气体稳定在大约450 ppm CO2-当量。(参见 van Vuuren 等人，2007,2010)。这种情况允许初始浓度超调至大约510 ppm 二氧化碳当量。DenElzen 和 van Vuuren (2007)早些时候已经表明，有限的过度集中可以以较低的成本达到类似的气候目标。减少排放的方法有很多种。一个要素是提高能源效率，从而减少能源使用总量(2050年与基准相比减少20%)(见图4)。该设想方案还显示，非化石能源的使用日益增加，占能源使用总量增长的大部分。非化石能源的使用从2010年占一次能源使用总量的15% 增加到2050年的30% 以上，到本世纪末占总量的40% 以上。这种增长主要是由于生物能源使用的增加。碳捕获和储存应用于化石燃料的大多数固定用途。最后，还减少了非二氧化碳温室气体的排放。其结果是，全球排放量在2020年左右达到峰值，并随着时间的推移进一步减少。与2050年的基准相比，排放量减少了70% 以上，到2100年减少了80% 以上。减缓政策的后果不仅影响能源部门，而且影响土地使用。大量额外的土地用于造林和生物能源(见图5)。模型比较研究表明，这里提出的缓解方案与目前的文献是一致的，尽管模型显示各种减排措施的贡献有显着差异(Clarke 等，2010; Edenhofer 等，2010)。根据 IMAGE 模型计算，减排成本约为 GDP 的1-2% (即每年的额外支出，可与经合组织国家目前约占 GDP 1.5% 的环境政策支出相比较)(图6)。可比情景的文献范围在2100年的0.5.5% 左右。大多数研究都认为，这些额外的支出将导致国内生产总值的减少。我们将在第5.8节进一步讨论这个问题。4.2基线和缓解情景下的气候变化基于 IMAGE 模型计算，大气温室气体浓度和由这两种情景排放引起的相关平均全球温度变化如图7所示(实线表示最佳猜测值)。IMAGE 模型使用 MAGICC 模型来计算全球平均温度的变化。早期，van Vuuren 等人(2008b)将 MAGICC 模型用于类似的 IMAGE 场景，以计算温室气体浓度和温度(包括不确定范围)的轨迹。在这里，用于 MAGICC 计算的不确定性范围是基于现有的更复杂的碳循环和气候模型。我们使用了温室气体浓度范围和温度结果的含义来描述这里的不确定性范围，如图中阴影区域所示。对于温度，较宽的阴影区域表示由于碳循环和气候敏感性的不确定性而产生的不确定性。对于基线情景，全球平均气温在2050年几乎与工业化前水平线性上升至2.1摄氏度，在2100年上升至3.7摄氏度(不确定性范围为3-5摄氏度)。在缓解方案中，到2100年全球平均气温上升幅度限制在1.9摄氏度。同样，存在相当大的不确定性。图7表明，到本世纪末，与工业化前水平相比，减缓气候变化的情况也可能导致气温上升2.6摄氏度。由于这里提出的缓解方案是科学文献中最严格的方案之一(参考 Clarke 等，2010; Edenhofer 等，2010; Fisher 等，2007) ，可以得出两个重要的结论。首先，分析表明，全球变暖可以得到缓解，但不能停止。第二，严格的设想也可能导致气候变化大大超过2摄氏度这一观察结果可能意味着，对冲适应政策以防止气候变暖加剧可能具有相当大的价值。例如，这些政策可能是“ ... ... 目标是2摄氏度，但准备3摄氏度”。在下面的影响评估中，我们关注的是中央气候变化预测。通过源自 HadCM2气候模型的重新尺度模式(图8)构建了与全球平均温度变化相关的全球0.5 ° × 0.5 ° 尺度上的月平均温度和降水量的变化。结果表明，高纬度地区年平均气温变化大于低纬度地区，降水量变化具有明显的空间变化特征。关于气候变化的预期模式，特别是降水的模式，存在着相当大的分歧: 因此，本文件提出的影响结果只代表一种可能的结果。5结果: 不同情景下的影响和适应5.1导言 IPCC 的第四次评估报告(IPCC，2007)概述了气候影响。其中一些影响来自平均气候的变化，但其他影响可能来自极端事件的变化。表1总结了一些影响，包括健康、农业、水资源供应、沿海洪水、城市地区和能源系统，以及气候系统的大规模破坏(相比之下，生物多样性和生态系统服务没有包括在内)。如前所述，大多数文献都将气候变化视为“渐进现象”(Agrawala and Fankhauser，2008)。这对于低概率和高拥有属性的影响是有问题的(见下文)。在这个探索性的分析中，我们描绘了一些影响和适应需求。我们的目标是涵盖表1中提到的几个关键影响，但是评估受到可以很容易耦合的模型的限制。因此，这些描述并不打算详尽无遗，而是在一定程度上说明了一些影响的严重程度和关键的适应挑战。在介绍我们的结果时，我们使用了几个基于上述情景的新模型运行(例如疟疾、水资源、海平面上升、加热和降温需求)。然而，我们也根据这里提出的两种情景(与温度相关的死亡率、农业和极端事件)评估了 IPCC 第四次评估报告中的现有信息。5.2人类健康: 与温度相关的死亡率和疟疾气候变化的健康影响需要在其他更重要的人类健康驱动因素的背景下看待，包括与生活方式相关的因素(Hilderink 等，2008)。我们在这里关注与温度有关的死亡率和疟疾。5.2.1与温度有关的死亡率与温度有关的死亡率影响可能通过极端温度的变化、平均温度的变化或温度的季节性变化而发生，文献显示的结果各不相同。McMichael 等(1996)使用相对风险比对温度相关死亡率进行了估计，表明存在死亡率最低的最佳温度(也称为 U 形剂量-反应关系)。如果温度升高，热应激相关的死亡率增加，但寒冷相关的死亡率下降。Tol (2002a)的结论是，从货币角度来看，由于气候变化导致的与寒冷有关的死亡率的下降超过与热有关的死亡率的增加。然而，这一结论受到用于评估生命价值的方法的影响，也受到平均和区域温度以及温度和健康之间关系的巨大不确定性的影响。适应可能发生在人体生理学适应更高的温度(麦克迈克尔等人，1996) ，行为的改变和空气调节使用的增加(Kinney 等人，2008)。考虑到使用温度和死亡率之间的剂量-反应关系的复杂性，我们还没有尝试在这里量化这些。5.2.2疟疾疟疾与气候变化之间的关系引起了人们的极大关注。在本文中，我们还重点讨论了气候引起的疟疾风险变化。每年有100多万人死于疟疾，其中大多数是非洲儿童。疟疾是一种由病媒传播的传染病。按蚊(传播疟疾感染的媒介)只能在平均温度高、没有霜冻和降水充足的气候中生存。MARA/ARMA 疟疾适宜性模型(Craig et al。 ，1999)综合了这些因素来确定气候适宜的地区。然而，疟疾造成的死亡率也受到诸如获得预防措施(包括室内喷洒和经杀虫剂处理的蚊帐)和获得医疗保健等因素的严重影响。在 MARA/ARMA 模型中，这些因素与收入和城市化有关。图9显示了这个模型在本文情景下的结果。自主适应的影响(作为收入增加的功能)减少了约50% 的疟疾死亡，特别是在非洲(主要是由于更好地提供卫生保健)。相比之下，气候的影响——特别是缓解设想方案与基准设想方案之间的差异要小得多。减缓措施将疟疾健康风险降低约2% (2050年)。因此，适应对疟疾控制的影响比缓解更具决定性(这一发现在现有文献中似乎很有说服力)。5.3农业: 对产量的影响伊斯特林等人(2007)综合了大量关于气候变化对作物生长的影响的研究，包括适应和不适应。结果总结为全球平均气温升高的函数，尽管实际上气温和降水模式的变化以及 CO2施肥都起作用。例如，二氧化碳施肥的影响部分抵消了气候变化的影响。结果可以用来评估我们的情景的气候影响使用最佳拟合多项式从伊斯特林等人(2007年) ，这表明产量的影响作为平均温度变化的函数。我们在每种情况下都采用全球平均气温变化作为一种假设，并将其作为预期的局部平均气温变化的指示。这意味着我们的影响估计可能是保守的，因为在许多陆地地区，气温上升可能比全球平均水平更强。我们研究了基线(4摄氏度)和缓解(2摄氏度)情景对玉米、小麦和水稻的影响，包括适应和不适应(见图10; 2100年热带和温带地区的结果; 这些影响是由于气候变化以外的其他因素导致的产量增加的额外影响)。虽然结果很不确定，但有些结论似乎是可能的。首先，基准情景(无适应性)导致所有情况下的产量(相对于没有气候变化的情况)大幅度下降: 气候变化影响可能会使所研究作物(2050年)的总体产量下降10-35% 。其次，无论是采取缓解还是适应措施，都会限制产量的下降。然而，在热带地区，影响仍然是负面的，通常在10% 左右的损失。第三，缓解和适应相结合可能会导致从今天的情况得到改善。对温带地区的农业影响可能更为积极，但前提是气温较高的优势不被极端天气的影响所抵消。这些结果突出表明，需要同时考虑缓解和适应问题。所提出的结果以气专委的评估为基础，代表了范围广泛的模型。结果也可以通过个别研究来说明。比如，Tubiello 和 Fischer (2007)发现，缓解方案可以显著降低全球农业气候变化的成本。同样，Fischer 等人(2007)阐述了适应水灌溉需求的重要性。他们发现，缓解措施减少了约40% 的农业用水需求，剩下60% 的影响需要适应。在处理对农业的影响时，干旱和热浪胁迫都起着重要作用。图11显示了在欧洲，假设各种形式的适应(Mechler 等，2010; Moriondo 等，2010) ，干旱和热浪胁迫对2 °C 升温情景下作物产量的影响。22在 HADCM3气候模式的基础上，利用 Cropsyst 模式对2030-2060年的时间片进行了计算。利用当前和未来的作物管理措施，模拟了春小麦的冬夏季作物产量。所考虑的适应选择包括将播种日期推迟几天和使用生长周期较长/较短的品种。结果显示，南欧和法国部分地区如今已经特别容易受到干旱和热应激的影响，即使在2摄氏度(缓解)的情况下，这种情况预计也会恶化(图11图 A)。当考虑两种适应策略与缓解措施相结合时(图11图 B 和 C) ，欧洲的许多地区实际上可能会受益。尤其是北欧，可以利用降水量较高的优势，使用生长周期较长的作物品种。相比之下，在南欧，同样的适应办法将产生额外的负面影响，因为作物发展将转向夏季，而夏季较长的干旱期和热浪可能会严重影响作物生长。此外，结果表明，虽然适应存在一些区域特有的限制，但整体适应将有效减少对欧洲农业部门的影响。5.4水资源: 潜在的水资源可利用性使用全球范围的水资源影响模型(Arnell，2003)评估了这两种情景对水资源压力变化的影响。图12显示了在基线情景和缓解情景(使用 HadCM2气候模型模式)下，到2100年(相对于1961-1990年的平均值)平均年径流量的百分比变化。如果年平均径流量小于1000 m3/人均/年，我们将流域定义为处于水资源紧张状态(文献中也使用了其他定义)。气候变化的影响是通过总结(i)生活在径流显着减少(增加)的水资源紧张流域的人口(通常超过5-10%)和(ii)生活在由于气候变化而变得水资源紧张(不再水资源紧张)的流域的人口。因气候变化而面对水资源压力上升或下降的人数没有计算在内，原因有二: (i)水资源压力下降的负面影响大于水资源压力下降的有利影响; (ii)面对水资源压力上升或下降的地区分布广泛，一个地区的“盈余”不能抵消另一个地区的“亏损”。结果显示，在2050年、2080年和2100年，缓解和基线假设情景之间，水资源紧张程度增加的风险暴露存在显著差异。到2020年，这两种情况的径流量几乎没有差别。图13显示了在这两种情景下，由于气候变化而面临水资源压力增加或减少的人数。在基线和缓解方案中，生活在缺水流域的人们显然从增加的可用水量中受益，这个数字大于暴露于径流减少的人数，但是，如上所述，我们并不关注净效应。面临水资源压力变化的人数对假定的气候变化模式很敏感。与基线相比，缓解方案在2050年、2080年和2100年分别减少了1.35亿(减少12% 的影响)、2.81亿(减少20%)和4.57亿(减少30%)面临水资源压力增加的人口。然而，与此同时，也有人从气候变化中受益。具有积极和消极影响的群体的相对规模取决于所使用的气候模型(这里只使用了哈德利模式)。显然，减缓气候变化也减少了从气候变化中受益的人数。同样显而易见的是，缓解并不能消除气候变化对供水的影响，因此需要对气候变化而面临更大水资源压力的其余10亿人进行适应。适应措施可以包括增加水的储存、水的运输或通过提高效率来减少水的需求。基本结果表明，缓解效果因地区而异。事实上，在一些地区，缓解甚至可能增加暴露于压力增加的人数。具体的不确定性分析表明，结果高度依赖于气候变化引起的降水模式变化的不确定性。5.5海平面上升气候变化的另一个重要影响是海平面上升。利用 IMAGE 模型的 MAGICC 组成部分，预测了这两种情况下的全球平均海平面上升。由于海平面对全球变暖的反应迟缓，预测主要在本世纪下半叶出现分歧: 在4摄氏度和2摄氏度的情况下，2050年海平面上升分别为35厘米和31厘米，在2100年分别为71厘米和49厘米。这些预测不包括格陵兰岛和南极洲冰盖的潜在加速贡献，这可能导致更高的海平面上升，但潜在的过程没有得到充分的理解，目前不包括在气候模型中(Meehl 等，2007; Nicholls 等，2010; Vermeer 和 Rahmstorf，2009)。我们使用 DIVA 模型来评估海平面上升、相关风暴潮和社会经济发展在这两种情况下的损害和适应成本，同时考虑到海岸侵蚀(直接和间接)、强迫迁移、沿海洪水(包括河流)以及盐度入侵三角洲和河口。对于每种情况，模型首先在没有堤坝的情况下运行，然后根据提高堤坝和滋养海滩的情况进行适应(DINAS-COAST Consortium，2006; Hinkel and Klein，2009)。由于缺乏全球数据和这些过程的一般模型，无法列入诸如沿海含水层盐度入侵、沿海湿地和生物多样性丧失等进一步影响，以及诸如盐度入侵屏障、港口升级、倒退区和基于生态系统的保护等进一步适应办法。图14显示，与缓解水平无关的是，适应相当有效地降低了全球总体成本，这说明即使在目标远大的缓解情况下也必须进行适应。在总体规模上，仅通过适应战略比仅通过缓解战略可以避免更多的损害，尽管两者结合起来产生的积极影响最大。然而，从较贫穷和小岛屿国家的角度来看，严格的缓解措施对于将风险保持在可控水平是必要的。即使没有海平面上升，为了保护仅由于社会经济发展而增加的泛滥平原资产，适应措施也是具有成本效益的。虽然这将涉及大量的投资流动(全球数百亿美元) ，但它们在全球 GDP 中所占的比例相对较小，即使对于基线情景下的海平面上升而言也是如此。然而，对于个别国家或地区(特别是小岛屿国家)来说，这些成本可能占 GDP 的比例要大得多，包括完全丧失的风险。5.6供热和供冷需求(居住区和社会)气候变化可能会影响空间供冷和供热的需求。因此，我们建立了一套简单的关系来描述住宅部门的供暖和空气调节需求，并探索了气候变化对这种模拟能源需求的影响(Isaac and van Vuuren，2009)。显然，人口和收入的变化预计将导致下个世纪取暖和空气调节的能源需求大幅增长(见图15，没有气候变化的例子)。在气候的驱动下，制冷和制热实践的变化是自主适应的例子(即没有政策干预)。然而，适应并不是普遍的，因为人们并不总是能够做出反应。供暖和制冷需求得不到满足可能导致健康影响(如第5.2节所述)和劳动生产率的损失。除了这些影响，当室内温度超过一定水平时，舒适度也会降低。图15显示，在全球范围内，由于收入和财富的增加而不考虑气候变化的能源需求的自主增长远远大于基线情景和缓解情景中的能源需求之间的差异(Isaac 和 van Vuuren (2009)显示，这对其他基线也是一个强有力的结果)。气候变化对综合能源需求的影响也小于单独对供暖和空气调节的影响，因为空气调节的增加弥补了供暖的减少。在区域和国家层面，影响可能更为显著: 例如，在印度，我们预计由于冷却增加，能源需求将大幅增加，而在西欧和美国，我们预计由于供暖减少，能源需求将大幅减少。5.7极端事件气候变化预计将导致一些与天气有关的极端事件的频率和强度发生变化(Parry et al。 ，2007)。像洪水、干旱、热浪和风暴潮这样的极端天气可能会变得更加频繁和强烈，而寒冷的极端天气，如寒潮，可能会变得不那么频繁和弱化。根据平均条件的变化来评估气候变化的风险——只有极端事件风险的变化被平均化的风险。因此，一种更基于风险、更明确地理位置的方法更可取。然而，关于灾害影响的知识是复杂和有争议的。迄今为止，只有有限的国家级研究采用概率方法预测气候变化存在的未来风险，主要集中在洪水风险(Mechler et al。 ，2010)。Feyen 等人(2009)在泛欧范围内进行的一项研究计算出，在基线情景下，预期的年度损失将增加三倍。定量风险方法的一个关键制约因素是气候预测的不确定性。对于降水，例如，模型往往不同意在局部尺度的变化迹象。这对于寻找例如洪水风险的研究尤其重要。虽然 Mechler 等人(2010)的研究旨在预测未来的风险，但他们发现未来的预测是如此的不确定，以至于作者没有根据对当今洪水影响的估计来预测未来的洪水风险。然而，目前的模型和数据似乎足以以相对较高的确定性(较慢的现象)评估干旱和热浪对农业造成的综合风险。这里提供了在2 °C 和4 °C 情景下的一些工作实例。一些研究调查了全球范围内受洪水影响的人们(Hirabayashi 和 Kanae，2009; Kundzewicz 等，2010)。样本回归分析显示，在缓解方案(2摄氏度)中，全球每年受100年洪灾影响的人口平均为2.11亿人，而基线(4摄氏度)为5.44亿人。Mirza 等人(2003)指出，对于孟加拉国这样一个易受水灾影响的国家来说，即使是2摄氏度的情况，预计也会使预计的洪水泛滥面积至少增加23-29% 。然而，应当指出的是，由于暴露、脆弱性和适应方面的不确定性，对未来洪灾损失的估计范围仍然很广。关于干旱，Burke 等人(2006)对2090年代的预测表明，对于2090年代的基线情景，每100年的极端干旱事件数量和平均干旱持续时间可能分别增加2倍和6倍。有证据表明，天气和气候相关影响造成的损害在当今已经增加，但这主要是由于财富和人口的增加(Bouwer，2010)。然而，气候变化预计将随着时间的推移而增加，并可能成为未来损害增加的一个更重要的因素。IPCC 最近的报告指出，重大事件的成本预计从占地区年 GDP 和收入的几个百分点到经济强劲的大地区的25% 不等(Parry et al。 ，2007)。过去受灾严重的小岛屿国家的灾害损失实际上已经超过了年度 GDP (Cummins and Mahul，2009)。5.8影响的经济评估成本-收益分析(CBA)是用一个共同的货币单位来表示不同战略的气候变化的成本和收益。我们在这里使用 FAIR 模型的 CBA 模块(参见模型附录 A)来获得一些关于更加聚合规模的影响的概念。对于缓解成本，FAIR 模型使用了前面介绍的 IMAGE 模型的信息。FAIR 中使用的气候损害和适应成本函数是从 AD-DICE 模型中推导出来的(De Bbu 等，2009a; Hof 等，2009a)。简而言之，AD-DICE 根据 DICE 模型的损伤函数估计适应成本(Nordhaus and Boyer，2000)。AD-DICE 将这些功能分为损害成本函数和残余损害函数，基于 DICE 模型中描述的每个影响类别的评估-农业，沿海地区，健康，住区，非市场时间使用，其他脆弱市场和灾难性影响。在这项研究中，我们假设了对气候变化的最佳适应响应(即给定一个温度变化水平，模型将适应成本和剩余影响的总和最小化)。DICE (因此 FAIR)中使用的影响估计包括: (i)实际的，可测量的经济成本(所谓的市场成本) ; 和(ii)其他的，无形的损失(非市场损失) ，使用支付意愿概念货币化。损害函数与本节前面所述的物理或经济损害没有直接关系，因为它们来自单独的来源。早先已经表明，适应成本的 FAIR 结果与文献中报道的值范围一致(Hof et al。 ，2009a)。在 FAIR 模型的默认设置和2.5% 的贴现率下，2005-2200年期间气候变化影响造成的贴现成本占全球 GDP 的比例在基线水平上接近4.5% (图16)。这些成本可能看起来高于上面提到的有限的部门分析，但是包括更多的部门和可能的灾难性事件的影响(Nordhaus and Boyer，2000)。年度成本随着时间的推移急剧上升，到2200年达到17% (注意，影响估计非常不确定，文献中可以找到更高和更低的值(Parry 等，2007; Stern，2006; Tol，2002b))。只有适应或缓解的情况下，折扣成本大幅降低到2.5% 左右(图16)。Hof 等人(2008)的研究表明，气候变化的 CBA 结果对模型假设非常敏感，其中折现率起着最重要的作用。贴现率特别重要，因为随着时间的推移，与仅适应和仅缓解设想相关的成本函数不同。3.5% 的贴现率导致仅适应情景和仅缓解情景的贴现成本分别为0.8% 和1.9% 。如果使用1.4% 的贴现率(相当于 Stern (2006)使用的贴现率) ，仅适应情景和仅缓解情景的贴现成本分别为3.2% 和2.5% 。在贴现率为2.5% 的情况下，缓解与适应相结合，贴现成本最低，即占 GDP 的2% 。与文献资料一致，适应性投资被评估为小于缓解投资和剩余损害。然而，它们在限制残余损伤方面是非常重要的。需要提及一些重要的警告。首先，对于风险的极端尾部(即低概率、高影响事件) ，计算不能被视为可靠。作为对如何处理这些风险的主观评估，Weitzman (2008)质疑 CBA 对决策者的有用性。其次，考虑到时间偏好和风险的贴现率的价值目前正在激烈争论，争论涉及主观时间偏好和风险感知(Nordhaus，2008; Price，2005; Stern，2006)。如上所述，贴现率的价值可以对结果产生很大的影响。最后，非市场影响需要对损害进行主观量化; 虽然这些影响很难货币化，但一般来说，不可逆转的变化更加困难，例如导致珊瑚礁丧失的海洋变暖(Ackerman and Heinzerling，2004)。5.9气候变化、影响和适应方面的不确定性对未来气候变化及其影响的预测有许多不确定性的来源。不确定性与因果链中的每一步都有关联: 排放、气候驱动因素(如碳循环)、气候(主要是气候敏感性和气候变化模式)以及影响(包括适应能力)。因此，对于同样的排放情景，不同的研究可能会给出非常不同的结果。事实上，这些差异往往大于不同排放情景下某一特定模型产生的差异。例如，对于本世纪末的降水变化，多模式集合平均值仅在高纬度地区超过模式间的标准差(Kundzewicz et al。 ，2007)。气候变化预测的不确定性随着时间的推移而增加。在近期(例如2020年代) ，气候模型的不确定性起着最重要的作用; 而在较长的时期(例如2090年代) ，由于排放情景的选择而产生的不确定性变得越来越重要(Jenkins and Lowe，2003)。未来气候变化对极端事件的影响尤其不确定。这部分是由于粗分辨率气候模型的较大空间和时间尺度与某些极端天气(如暴雨降水和山洪)的局部发生和短期生命之间的不匹配。由于影响和适应是在当地范围内发生的，因此需要详细的信息——这意味着不确定性的增加。较大的不确定性范围表明，适应规划不应基于单一的假设情景，而是需要考虑到大范围的预测。6结论在本文中，我们讨论了情景分析如何有助于评估缓解和适应战略。我们还提出了两个集成的场景作为分析的起点。这些设想方案明确地将缓解和适应行动纳入了几个指标，并涵盖了社会经济发展与影响之间的几个重要联系和反馈(例如，考虑了气候变化对土地利用和缓解的影响)。我们为选定的一些指标确定了这些设想方案的影响，主要侧重于平均气候变化。基于我们的工作，我们得出以下结论: •通过描述两套对比鲜明的世界可能的气候变化轨迹，我们为对减缓、适应和气候影响之间的相互作用进行更加综合的分析奠定了基础。第一种情况(不采取缓解措施)预计将导致全球平均气温在本世纪末上升4摄氏度左右(气候参数和当前经济趋势的最有可能值)。正如我们的一些分析所显示的，这种情况有很高的适应需求。第二种设想假设有严格的缓解措施，并将全球平均气温变化限制在2 °C，概率为50% 。即使在这种情况下，也需要大量的适应措施。•这里提出的综合情景分析可以为探讨政策选择的不同后果(包括不确定性)奠定良好基础; 鉴于不确定性，确定缓解、适应和剩余损害之间的最佳组合是不可行的。正如本文所讨论的那样，衡量气候变化的后果和各种政策回应的复杂性在于规模、空间和时间上的巨大差异; 巨大的不确定性; 以及行为者之间利益的明显差异(例如，他们是气候变化的肇事者还是受害者)。因此，对风险的主观解释将始终发挥重要作用。尽管如此，情景分析可以提供对可能结果和风险的描述。在这个阶段，成本和收益的货币评估(第5.8节)不能与前面章节中对物理变化的描述联系起来。有效的气候政策包括适应和减缓。模型计算表明，可以设计缓解设想方案，使全球平均气温上升2摄氏度，从而达到对气候敏感性的最佳猜测。然而，即使是这些严格的情况也可能导致全球平均气温上升超过2.5摄氏度(最多上升1.5摄氏度)和区域气温变化更大。本文件探讨的大多数影响都表明需要将缓解和适应结合起来。例如，在应对海平面上升(至少在21世纪)方面，适应措施可能比缓解措施更有效，但缓解措施在减少损害和降低适应成本方面仍然可以发挥作用。农业提供了一个明显需要适应和缓解的例子。如果不采取适应和减缓行动，预计许多区域的农作物产量将因气候变化而受到不利影响。如果没有严格的缓解措施，适应只能限制负面影响，而不能消除它们。缓解的一个好处是，它影响到所有影响类别，而适应需要根据影响和环境进行调整。•尽管气候变化的影响可能很严重，而且根据主观选择，可能需要制定严格的气候政策，但本研究评估的影响(鉴于目前的技术水平)可能仍然是全球范围内人口变化和经济增长的次要影响。然而，需要注意的是(见下文)。虽然气候变化可能对数百万人产生影响，但其他挑战可能对人民和治理产生更大的影响。然而，应该指出的是，我们只涉及了有限的一组影响，并且主要集中在对逐渐变化的气候的平均估计上，例如，没有涉及灾难性的、影响非常大的、极低概率的事件(Weitzman，2008)。这些事件实际上可能非常严重，以至于上述结论不再成立。如果全球范围的成本仍然相对较低，就不太需要进行全球分析，以便根据故事情节的一致性纳入对主要驱动因素的所有反馈。显然，在地方一级，情况可能大不相同; 对个别国家的影响可能远远大于全球一级的影响。例如，海平面上升对一些低洼岛国和国家非常重要，这些国家可能会受到巨大的适应成本和/或损害(直至完全毁灭)的显著影响。对农业而言，预计积极和消极影响将在不同地方和不同时间发生，低收入国家往往受到相对较为负面的影响。目前温度受到限制的温带地区的农业可能会受益。总之，我们认为，进一步制定在区域范围内进一步具体说明这些情况的综合设想是有益的。虽然本文提出了一个有用的第一步，它也留下了许多反馈意见仍然没有说明。本研究中的总体缓解成本估计为2 °C 情景下国内生产总值的1-2% 左右。缓解方案降低了气候变化的风险。在缓解方面的投资有几种类型的好处。首先，与气候相关的损害和适应成本得到降低。其次，不确定性也会减少，考虑到所涉及的风险，这一点很重要。虽然我们认为，在全球一级，缓解和适应之间不可能存在最佳的平衡，但我们已经表明，从长远来看，缓解和适应的成本和收益是相当的。进一步分析的重点包括评估实际变化与货币影响分析之间的联系、极端事件的可变性和变化、大规模干扰和治理的潜在作用。在我们和其他评估中，主要关注的是平均值的变化，然而，人们对与气候变化有关的极端事件(导致自然灾害) ，以及大规模的破坏(如西南极冰盾的解体)有相当大的关注，这些破坏并没有被平均值准确地描述。对气候变异性变化的预测高度不确定，迄今为止常常妨碍分析有力地预测未来的极端事件风险。不同行为者的作用是另一个问题; 某些形式的适应需要政府的积极参与; 其他形式的适应可能由私人投资者实施，例如安装空间冷却系统。这两个适应主体之间的差异与未来的情景发展有关。本文中提出的研究是作为欧盟资助的 ADAM 研究项目的一部分进行的。这篇论文的早期版本是作为《让气候变化为我们服务》一书的一部分出版的，该书由休姆和纽菲尔德编辑，剑桥大学出版社于2010年出版。附录 A.模型描述 A.1 IMAGE 2.4 IMAGE 2.4综合评估模型(Bouwman et al。 ，2006)由一系列相互关联的综合模型组成，这些模型共同描述了全球环境变化长期动态的重要因素，如空气污染、气候变化和土地使用变化。作为 IMAGE 的一部分，全球能源模型 TIMER (van Vuuren et al。 ，2006)描述了一次能源和二次能源需求和生产的长期动态以及温室气体和区域空气污染物的相关排放。模型行为主要是由各种技术的替代过程决定的，基于长期价格和燃料偏好。IMAGE 的农业模型模拟了7个作物类别和5个动物类别的生产力(Leemans 和 Born，1994)。根据一套分配规则，农产品的区域生产在空间上(0.5 ° × 0.5 °)分布(Alcamo et al。土地利用变化图和农业活动数据都被用来模拟土地利用(变化)产生的排放。MAGICC 模型利用温室气体排放量来计算全球平均气温变化(Wigley and Raper，2001)。温度变化模式是通过与大气环流模式(GCM)产生的气候变化模式联系而获得的。局限性: IMAGE 提供了对人类活动的物理描述(使用成吨的石油，生产成吨的谷物等)。更全面的宏观经济描述只能通过与其他模型的合作得到。IMAGE 作为综合评估模型的广泛覆盖范围意味着许多关键的不确定性影响模型的结果。在这种情况下，使用单一的基线(如在 ADAM 项目中)并不能完全满足所涉及的基本不确定性。A. 2 FAIR 气候政策模型 FAIR (Den Elzen 等，2008)与 IMAGE 模型一起用于确定不同排放源的减排率。全球气候计算利用了简单的气候模型 MAGICC 4.1(Wigley，2003; Wigley and Raper，2001)。所要求的全球减排量是通过计算基准和全球排放路径之间的差额得出的。FAIR 成本模型使用不同排放源的区域边际减排成本曲线(MAC) ，采用最小成本方法，在各区域之间进行分配。最近，FAIR 模型已经扩展到损害和适应成本曲线(基于 AD-DICE 模型(De Bbu 等，2009b)和估计宏观经济对 GDP 增长的影响的能力(Hof 等，2008))。这使模型能够探讨减缓和适应综合战略的经济影响。限制: 为了灵活起见，公平竞争模式不包括部门宏观经济模式或能源模式。因此，该模型从一个局部均衡的方法工作-和更深层次的后果气候政策只能通过转发公平的结果到其他(相关)模型研究。A.3 DIVA DIVA (动态和互动脆弱性评估)是在欧盟资助的项目 DINAS-COAST 44中开发的一个沿海系统的综合模型，连同其适当的沿海数据库，对沿海地区对海平面上升的国家、区域和全球脆弱性进行动态和互动评估; http://www.pik-potsdam.de/DINAS-COAST/。(DINAS-COAST Consortium，2006; Hinkel and Klein，2009).《综合发展战略》提供了一系列生态、社会和经济沿海脆弱性指标的定量信息，从国家以下各级到全球各级，涵盖所有沿海国家。该模型由来自各种工程、自然和社会科学学科的专家开发的若干模块组成。根据气候和社会经济情景，该模型评估了海岸侵蚀(直接和间接)、海岸洪水(包括河流)、湿地变化和盐度入侵三角洲和河口。家庭影响评估还从提高堤坝和滋养海滩的角度考虑沿海适应问题，并包括一些预先确定的适应战略，例如不予保护、充分保护或最佳保护。限制因素: 《综合可持续发展战略》排除了可能影响沿海影响的下列进程，但目前无法有把握地建立模型: 风暴频率和强度的变化、沿海快速发展和城市化导致的国内生产总值的地方分布和人口增长，以及盐度侵入沿海含水层。由于高程数据的粗分辨率和精度，还会产生更多的重要不确定性。A. 4 TIMER ——冷却/加热能源需求 TIMER 冷却/加热能源需求模型(Isaac and van Vuuren，2009)描述了冷却和加热能源的使用是几个因素的函数，包括人口水平、不断变化的收入水平和气候。对于加热和冷却，经验数据被用来校准一组系统-动态需求函数。气候(降温和升温度日)起着重要作用。该模型能够解释气候变化的影响。局限性: 对发展中国家而言，校准模型的经验基础相对较差。该模型没有描述可以提供冷却和加热需求的不同方式以及用一种技术替代另一种技术所涉及的成本。水资源影响模型水资源影响模型(Arnell，2003,2004)有两个组成部分。第一阶段采用宏观尺度水文模型 Mac-PDM 模拟全球地表(0.5 ° × 0.5 °)的径流，第二阶段通过计算人均水资源可利用率确定流域水资源压力指标。如果一个流域的年平均径流量低于每人每年1000立方米，则假定该流域面临水资源压力，这是一个半任意的阈值，广泛用于确定水资源压力区域。如果气候变化导致缺水流域的径流量显著减少，或者导致流域降低到阈值以下，那么气候变化将导致水资源压力暴露的增加。气候变化导致对相反趋势的暴露明显减少。这些变化不能直接比较; 虽然径流量的减少(和暴露量的增加)极有可能是不利的，但如果额外的水不能储存，或者在洪水增加的高流量季节发生，则径流量的增加(和暴露量的明显减少)可能不是有益的。生活在水资源压力增加的流域的人口数量可以作为暴露于气候变化的一个指标。实际影响(就真正的水资源短缺而言)将取决于现有的水资源管理结构。局限性: 水文模型不能完全模拟河流径流量，特别是在半干旱地区倾向于高估径流量。水资源指标是衡量受影响程度的指标，而不是实际影响; 它可以被视为适应需求的替代指标。答.6疟疾的危险疟疾媒介，蚊子传播感染，只能生存在适当的气候与高平均温度，没有霜冻和足够的降水。MARA/ARMA 疟疾适宜性模型(Craig et al。 ，1999)综合了这些气候因素来确定气候适宜区域。最大适合度为1和最小适合度为0所需的气候水平见表 A.1。对于水平介于0或1适合性所需水平之间的指标，使用简单函数计算水平(Craig et al。 ，1999)。利用 IMAGE 模型的输出结果，所有这些因子都是在半乘以半度的网格水平上计算的(Bouwman et al。 ，2006)。每个栅格细胞的总气候疟疾适应性是由这三个指数中的最低值决定的。局限性: MARA/ARMA 模型描述了对疟疾病媒的适用性。它没有提供蚊子传播的过程说明，也没有明确说明人们可能对增加的风险水平作出何种反应。参考文献 Ackerman and Heinzerling，2004 F。 Ackerman L。 Heinzerling 无价之宝: 关于了解一切事物的价格和一无所有的价值2004 The New Press 纽约 Agrawala and Fankhauser，2008 S。 Agrawala S。 Fankhauser 适应气候变化的经济方面。成本、收益和政策工具2008经合组织巴黎 Alcamo 等人，1998年 J。 Alcamo E。 Krol R。 Leemans J。 Bolen J。 Minnen M。 Schaeffer S。 Toet B。 Vries 全球环境变化模型: IMAGE 2.1 J。IMAGE 2.1型号1998爱思唯尔科技有限公司测试结果。Arnell，2004 N.Arnell 气候变化与全球水资源: 气候变化与社会经济情景全球环境变化14120043152 Arnell 等，2002 N.W。Arnell M.G.R.Cannell M. Hulme R.S.Kovats J.F.B.Mitchell R.J.Nicholls M.L.Parry M.J.L.利弗莫尔 · A · 怀特二氧化碳稳定化对气候变化影响的后果气候变化5342002413446 Bakkenes 等，2006 M。 Bakkenes B. Eickhout R. Alkemade 不同气候稳定化情景对欧洲植物物种的影响全球环境变化16120061928,2003年代表全球气候变化、适应和减缓全球环境变化132003年16巴克等人，2009年巴克，t。 ，肯伯，M。 ，Scrieciu，S。打破气候僵局。降低成本: 合作气候行动的经济效益。气候小组，托尼 · 布莱尔办公室，4CMR-剑桥大学和 Cambridge Econometrics。Barker and Scrieciu，2010 T Barker SS.Scrieciu 用 E3MG 模拟低稳定性: 走向模拟能源-环境-经济系统动力学的“新经济学”方法能源期刊31特刊12010137164 Barker 等，2008 T。Scrieciu T. Foxon 实现八国集团50% 的目标: 使用宏观经济计量模型 E3MG 气候政策82008 S30 S45 Berkhout 等,2002 F. Berkhout J. Hertin A. Jordan 气候变化影响评估中的社会经济未来: 使用情景作为“学习机器”全球环境变化12220028395 Bouwer，2010 L.M。人为气候变化造成的灾害损失增加了吗？2010年美国气象学会简报10.1175/2010BAMS 3092.1 Bouwman 等人，2006年 A.F. Bouwman T. Kram K. Klein Goldewijk 全球环境变化综合模拟。2006年荷兰环境评估机构 Bilthoven 228页。(出版物500110002/2006) Burke 等人，2006 E.J。 Burke S.J。 Brown N. Christidis 利用哈德利中心气候模型对21世纪全球干旱的近期演变和预测进行建模。《水文气象学杂志》2006年7月1113日1125 Clarke 等人，2010年 L。 Clarke J. Edmonds V. Krey R. Richels S. Rose M. Tavoni 国际气候政策架构: EMF 22国际情景的概述能源经济学31号补编。2010年 S64/s81哥本哈根协议，2009年哥本哈根协议，2009年。(2009年12月18日哥本哈根协议)。2009年联合国气候变化会议，哥本哈根。Craig 等人，1999 M.H。Craig R.W.基于气候的疟疾在非洲传播的分布模型寄生虫学今天1531999105111康明斯和马胡尔，2009 J.D。发展中国家巨灾风险融资: 公共干预原则，2009年世界银行华盛顿，DC De Bbu 等，2009a K.C。德布鲁姆 R.B。Dellink S. Agrawala 适应气候变化的经济方面: 适应成本和效益综合评估模型，2009年经合组织巴黎德布鲁恩等，2009 b。德布鲁姆 R.B。Dellink R.S.J.Tol AD-DICE: DICE 模型气候变化951-220096381 Den Elzen 等，2008 M.G。J.登伊尔森私人侦探社。Lucas D.P.为实现低二氧化碳当量浓度而采取的区域减排行动和分配办法下的排放限额费用气候变化9032008243268 Den Elzen 和 van Vuuren，2007 M.G。J.Den Elzen D.P.104-46/2007/17931/17936 DINAS-COAST 财团，2006年 DINAS-COAST 财团，2006年，更有可能以更低的成本实现长期温度目标的范维伦峰值美国国家科学院院刊。DIVA 1.5.5光盘，德国波茨坦气候影响研究所。伊斯特林等，2007 W。伊斯特林 P。阿加瓦尔 P。巴蒂玛 K。布兰德 L。埃尔达 M。霍登 A。基里连科 J。莫顿 J。Soussana J. Schmidhuber F.N. Tubiello 食品、纤维和森林产品 M.L. Parry O.F. Canziani J.P. Palutikof P.J. van der Linden C.E. Hanson 气候变化2007: 影响、适应和脆弱性。第二工作组对政府间气候变化专门委员会2007年剑桥大学出版社第四次评估报告的贡献剑桥，英国欧共体，2006年欧共体2050年世界能源技术展望(WETO H2)2006年欧盟委员会布鲁塞尔埃登霍费尔等人,2010 O。 Edenhofer B。 Knopf T。 Barker L。 Baumstart E。 Bellevrat B。 Chateau P。 Criqui M。 Isaac A。 Kitous S。 Kypreos M。 Leimbach K。 Lessmann B。 Magné S。 Scrieciu H。 Turton D。Van Vuuren 低稳定性的经济学: 减缓战略和成本的模型比较能源杂志31 SI-120101148环境变化研究所，2009环境变化研究所，2009。国际气候会议-4度及以上。英国牛津大学环境变化研究所，9月28日至30日。Feyen J.I. Barredo R. Dankers 全球变暖和城市土地利用变化对欧洲洪水的影响。迈向工程、设计和管理方法的一体化2009 Taylor and Francis Group London Fischer et al。 ，2007 G. Fischer F.N. Tubiello H. van Velthuizen D.Wiberg 气候变化对灌溉用水需求的影响: 缓解的影响，1990-2080技术预测和社会变化747200710831107 Fisher et al。时间序列: K.Jiang M.Kainuma E. La Rovere A. Matysek A. Rana K. Riahi R. Richels S. Rose D. van Vuuren R. Warren P. Ambrosi F. Birol D. Bouille C. Clapp B. Eickhout T. Hanaoka M.D. Mastrandrea Y.Matsuoko B.O’Neill H. Pitcher S. Rao F. Toth 与长期减缓有关的问题:。减缓气候变化。第三工作组对2007年政府间气候变化专门委员会剑桥大学出版社第四次评估报告的贡献,2010年 A. Hayashi K. Akimoto F. Sano S. Mori T. Tomoda 评估全球变暖对不同稳定水平的影响，作为确定长期稳定目标气候变化的一个步骤98201087112 Hilderink et al。 ，2008 H. Hilderink P.L。卢卡斯 · A · 滕霍夫 · 科克 · M · 德沃斯 · P · 詹森 · J · 梅耶尔 · A · 费伯尔 · A · 伊格纳修克 · A · 彼得森 · H · J。M.2008年荷兰环境评估机构 Bilthoven Hinkel 和 Klein，2009年 J。T.Klein 整合知识以评估海平面上升对沿海脆弱性的影响: DIVA 工具全球环境变化的发展1932009384395 Hirabayashi and Kanae，2009 Y. Hirabayashi S. Kanae 第一次估计未来全球人口面临洪水风险水文研究快报3200969 Hof 等，2009a A.F。霍夫 K.C。德布鲁姆 R.B。Dellink M.G.J.Elzen D.P.不同减缓战略对国际适应融资的影响环境科学和政策1272009832843 Hof 等，2009b A.F。霍夫・德・布鲁姆・ R ・德林克・ M.G。J.Elzen D.P.2012年后的全球气候治理: 架构、机构和适应2009年剑桥大学出版社剑桥霍夫等人，2008年 A.F。Hof M.G.J.Elzen D.P.范维伦分析气候政策的成本和收益: 价值判断和科学不确定性全球环境变化1832008412424 IMAGE-team，2001 IMAGE-team，2001。IPCC SRES 情景的图像2.2实现。全面分析21世纪的排放、气候变化和影响。RIVM 光盘出版物481508018，比尔特霍芬国家公共卫生与环境研究所。政府间气候变化专门委员会，2007年，2007年。2007年气候变化: 综合报告。第一、第二和第三工作组对政府间气候变化专门委员会第四次评估报告的贡献。政府间气候变化专门委员会，日内瓦，104页。能源政策背景下的全球住宅部门供暖和空气调节的能源需求建模。处理 UKCIP02气候变化情景中的不确定性。埃克塞特气象局哈德利中心技术说明44。Kinney et al。 ，2008 P.L。 Kinney M.S. O’Neill M.L。 Bell J. Schwartz 用于估计气候变化对与热有关的死亡的影响的方法: 挑战和机会环境科学和政策11872008 Klein et al。 ，2007 R.J.T. Klein S. Huq F. Denton T.E. Downing R.G. Richels J.B. Robinson F.L. 适应和缓解之间的相互关系。2007年气候变化。影响、适应和脆弱性。第二工作组的贡献。2007年政府间气候变化专门委员会报告剑桥大学出版社剑桥745777 Krol 等人，1997年 M. Krol J. Alcamo R. Leemans 稳定大气中二氧化碳的全球和区域影响全球变化的缓解和适应策略11997年341361 Kundzewicz 等人，2010 Z.W。Kundzewicz y. Hirabayashi 气候变化中的 S. Kanae River 洪水——水资源管理的观察和预测2010年10.1007/s11269-009-9571-6 Kundzewicz 等，2007 z.W。Kundzewicz L.J.Mata N. Arnell P. Döll P. Kabat B. Jiménez K. Miller T. Oki Z. en I. Shiklomanov 淡水资源及其管理。招架。坎齐亚尼 J.P。普鲁提克。Hanson P.J.2007年范德林登气候变化: 影响、适应和脆弱性。第二工作组对2007年政府间气候变化专门委员会剑桥大学出版社第四次评估报告的贡献。翻译。D.确定自然植被、作物和农业生产力的潜在全球分布水、空气和土壤污染761994133161 Lenton 等人，2008 T.M。Lenton H 拘留了 E. Kriegler J.W。鲁赫特 · S · 拉姆斯托夫 · H · J 大厅。Schellnhuber 地球气候系统中的倾斜元素美国国家科学院院刊1056200817861793。Manne R.G.Richels Merge: 全球气候变化综合评估模型。Zaccour Energy and Environment 2005 Springer USA McMichael et al。 ，1996 A. McMichael A. Haines R. Sloff S. Kovats Climate Change and Human Health 1996 World Health Organization Geneva Mechler et al。 ，2010 R. Mechler S. Hochrainer A. Aaheim Z. Kundzewicz N. Lugeri M. Moriondo H. Salen M. Bindi I. Banaszak A. Chorynski E. Genovese H. Kalirai J. Linnerooth-Bayer C. Lavalle D. McEvoy P. Matczak M. RadzieJewski D. Rübbelke M.-J。评估欧洲适应不断变化的洪水和干旱风险的风险管理方法 M.Hulme H. Neufeldt 让气候变化为我们服务: 关于适应和减缓战略的欧洲观点2010年剑桥大学，英国 Meehl 等人，2007年 G.A. Meehl T.F. Stocker W.D. Collins Friedlingstein A.T. Gaye J.M. Gregory A. Kitoh R. Knutti J.M. Murphy A. Noda S.C.B. Raper I.G. Watterson A.J. Weaver Z- C。赵全球气候预测2007所罗门气候变化: 物理科学基础。第一工作组对2007年剑桥大学出版社第四次评估报告的政府间气候变化专门委员会。减缓气候变化。第三工作组对2007年政府间气候变化专门委员会剑桥大学出版社第四次评估报告的贡献剑桥，United Kingdom Mirza 等，2003 M.M。问:。Mirza R.A.Warrick N.J.气候变化对恒河、 Brahmaputra 和梅格纳河洪水的影响孟加拉国气候变化572003287318 Moriondo et al。欧洲农业应对气候变化和可变性的影响和适应机会全球变化的缓解和适应战略1572010657679 Moss 等，2010 R.H。Moss J.A.Edmonds K.A.Hibbard M.R.Manning S.K.Rose D.P.Van Vuuren T.R.Kainuma T. Kram G.A.Meehl J.F.B.Mitchell N. Nakicenovic K. Riahi S.J.史密斯 R.J。早上吃饱。汤姆森 J.P。Weyant T.J.Nakicenovic 等，2000 N。 Nakicenovic 排放情景特别报告(SRES)2000剑桥大学出版社剑桥，英国 Nakicenovic 等，2000,nakicenovic P. Kolp K. Riahi M. Kainuma T. Hanaoka 排放情景评估重新审视环境经济学和政策研究732006137173 Nicholls and Lowe，2004 R.J。Nicholls J.A.全球环境变化1432004229244 Nicholls 等，2010 R.J。Nicholls N. Marinova J.A.劳 · S · 布朗 · P · 维林加 · D · 德 · 古斯芒 · J · 欣克尔。J.21世纪英国皇家学会哲学汇刊2010年10月10日。2010.029 Nordhaus and Boyer，2000 W.D.诺德豪斯 · J · 博耶(Nordhaus J. Boyer)《全球变暖: 2000年全球变暖的经济模型》麻省理工学院出版社，剑桥，马萨诸塞州，第10页。315-328 Nordhaus，2008 W.D.平衡的问题衡量全球变暖政策的选择2008年纽黑文和伦敦帕里等人的耶鲁大学出版社，2007年。招架。坎齐亚尼 J.P。PJ 的 Palutikof。Van der Linden C.E.2007年汉森气候变化: 影响、适应和脆弱性。返回文章页面第二工作组对2007年剑桥大学出版社第四次评估报告的贡献政府间气候变化专门委员会:？气候变化9932010383402 Piani 等人，2005 C. Piani D.J。陷害地方检察官。Stainforth M.R.艾伦对气候变化的约束来自数千名成员的模拟地球物理研究通讯322005 L23825 Price，2005 c 价格关于环境变化影响的代际视角: 折现未来的观点 J.L。Innes 通用汽车。Hickey H.F.2005年国际林业研究组织联合会(国际林研组织)维也纳罗斯等人，2007年，Ahammad，h,a，Rao，S. ，Riahi，K. ，van Vuuren，D. 2007.土地在气候稳定模拟中的作用: 初步观测能源模拟论坛报告。斯坦福大学。Schneider 和 Kuntz-Duriseti，2002 S.H。不确定性与气候变化政策。奈尔斯气候变化政策: 一项调查2002年岛屿出版社华盛顿特区斯特恩，2006年 N。斯特恩气候变化经济学评论2006年剑桥大学出版社剑桥斯沃特等人,斯瓦特 · L · 伯恩斯坦 · M · Ha-Duong A. Petersen 同意不同意: 政府间气候变化专门委员会评估气候变化、影响和应对措施的不确定性管理922009129斯瓦特和雷斯，2007 R · 斯瓦特 · F · 雷斯将适应和缓解工作结合起来: 纳入可持续发展政策的主流？气候政策742007288303 Tol，2002a。第二部分。环境和资源经济学2122002135160托尔，2002年 b。第一部分。基准估计环境和资源经济学21120024773 Tol，2002 c R.S.J. Tol 福利规格和气候变化的最佳控制: 基金的应用能源经济学2442002367376 Tubiello and Fischer，2007 F.N. Tubiello G. Fischer 减少气候变化对农业的影响: 减缓的全球和区域影响，2000-2080年技术预测和社会变化747200710301056联合国，2005年。世界人口前景: 2004年修订本。光盘版-扩展数据集。联合国出版物。E.05.十三.12，联合国，经济和社会事务部，人口司。Van Vliet et al。 ，2009 J.van Vliet M.G.J.den Elzen D.P. van Vuuren 会议根据延迟参与的能源经济31号补充辐射效应制定了目标。22009 S152 S162 van Vuuren et al。 ，2006 D.P. van Vuuren B。 van Ruijven M。 Hoogwijk M。 Isaac B。 De Vries TIMER 2: 模型描述和应用 L。 Bouwman T。 Kram K。 Klein-Goldewijk 全球环境变化综合模型。IMAGE 2.42006 MNP 概述-荷兰环境评估机构 Bilthoven van Vuuren 等，2007 D.P。Van Vuuren M.G.J.登伊尔森私人侦探社。Lucas B. Eickhout B.J.稳定低水平的温室气体浓度: 减少战略和成本的评估气候变化8122007119159 Van Vuuren 等，2008 a D.P。Van Vuuren B. De Vries A. Beusen P.S.C.21世纪温室气体排放的有条件概率估计基于 IPCC-SRES 设想的全球环境变化1842008635654 van Vuuren 等，2008b D.P。Van Vuuren M Meinshausen G.K.普拉特纳 · F · 乔斯 · K.M。斯特拉斯曼 S.J。Smith T.M.L.Wigley S.C.B.Raper K. Riahi F. De La Chesnaye M.G.J.登藤野 K。江 N。 Nakicenovic S。 Paltsev J.M。21世纪减缓美国国家科学院院刊的温度上升。Van Vuuren M.G.J.艾萨克不同气候制度的比较: 扩大参与的影响能源政策3712200953515362 van Vuuren 等，2010 D.P。Van Vuuren E. Stehfest M.G.J.艾萨克探索将温室气体辐射效应控制在3瓦/平方米以下的情景能源经济学31特刊12010165192维梅尔和拉姆斯托夫,2009年 M. Vermeer S. Rahmstorf 与全球气温美国国家科学院院刊相关的全球海平面10620092152721532 Weitzman，2008 Weitzman，M.L。，2008年。灾难性气候变化的经济学模型与解释。Wigley，2003 T.M.L. Wigley MAGICC/SCENGEN 4.1: 技术手册2003 UCAR-气候和全球动力学分部博尔德，CO Wigley and Raper，2001 T.M.L. Wigley S.C.B. Raper 全球平均变暖科学高预测的解释2932001451454|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Fairness+in+Graph+Machine+Learning:+Recent+Advances+and+Future+Prospectives)|0| -|[Socially Responsible Machine Learning: A Causal Perspective](https://doi.org/10.1145/3580305.3599571)|Raha Moraffah, AmirHossein Karimi, Adrienne Raglin, Huan Liu|Shanghai Lixin Univ Accounting & Finance, Shanghai, Peoples R China; Pukyong Natl Univ, Grad Sch Management Technol, Busan, South Korea|The underlying assumption of using investor sentiment to predict stock prices, stock market returns, and liquidity is that of synergy between stock prices and investor sentiment. However, this synergistic relationship has received little attention in the literature. This paper investigates the synergistic pattern between stock prices and investor sentiment using social media messages from stock market investors and natural language processing techniques. At the macro level, we reveal extremely significant positive synergy between investor sentiment and stock prices. That is, when a stock price rises, investor sentiment rises, and when a stock price falls, investor sentiment falls. However, this synergy may be reversed or even disappear over a specific time period. Through a segmented measurement of the synergy between stock prices and investor sentiment over the course of a day, we also find that investor sentiment on social media is forward looking. This provides theoretical support for using investor sentiment in stock price prediction. We also examine the effect of lockdowns, the most draconian response to COVID-19, on synergy between stock prices and investor sentiment through causal inference machine learning. Our analysis shows that external anxiety can significantly affect synergy between stock prices and investor sentiment, but this effect can promote either positive or negative synergy. This paper offers a new perspective on stock price forecasting, investor sentiment, behavioral finance, and the impact of COVID-19 on the stock markets. Copyright (c) 2022 Borsa Istanbul Anonim S, irketi. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).|利用投资者情绪来预测股价、股市回报和流动性的基本假设是股价和投资者情绪之间的协同作用。然而，这种协同关系在文献中很少受到关注。本文利用来自股市投资者的社交媒体信息和自然语言处理技术，研究了股票价格与投资者情绪之间的协同关系。在宏观层面，我们发现投资者情绪与股价之间存在极其显著的创建力量。也就是说，当股价上涨时，投资者情绪上升，当股价下跌时，投资者情绪下降。然而，这种协同作用可能会逆转，甚至在特定的时间段内消失。通过对股票价格和投资者情绪在一天内的协同效应进行分段测量，我们还发现，社交媒体上的投资者情绪具有前瞻性。这为利用投资者情绪进行股价预测提供了理论支持。我们亦会透过因果推理机器学习，研究封锁对股价与投资者情绪之间的协同效应的影响。封锁是对2019冠状病毒疾病最严厉的回应。我们的分析表明，外部焦虑可以显著影响股票价格和投资者情绪之间的协同效应，但这种效应可以促进正面或负面的协同效应。本文提供了一个新的视角股票价格预测，投资者情绪，行为金融学，以及2019冠状病毒疾病对股票市场的影响。版权所有(c)2022伊斯坦布尔博尔萨 Anonim S，irketi。由 Elsevier B.V 出版。这是 CC BY-NC-nd 许可证下的一篇开放存取文章( http://creativecommons.org/licenses/BY-NC-ND/4.0/)。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Socially+Responsible+Machine+Learning:+A+Causal+Perspective)|0| -|[Training Large-scale Foundation Models on Emerging AI Chips](https://doi.org/10.1145/3580305.3599573)|Aashiq Muhamed, Christian Bock, Rahul Solanki, Youngsuk Park, Yida Wang, Jun Huan|AWS AI Labs, Munich, Germany; AWS AIRE, Santa Clara, CA, USA; AWS AI Labs, Santa Clara, CA, USA; AWS Neuron, Cupertino, CA, USA|Foundation models such as ChatGPT and GPT-4 have garnered significant interest from both academia and industry due to their emergent capabilities, such as few-shot prompting, multi-step reasoning, instruction following, and model calibration. Such capabilities were previously only attainable with specially designed models, such as those using knowledge graphs, but can now be achieved on a much larger scale with foundation models. As the capabilities of foundation models have increased, so too have their sizes at a rate much faster than Moore's law. For example, the BERT large model was initially released as a 334M model in 2018, and by 2023, the largest GPT-4 models are estimated to range between 200-300B, representing an increase of three orders of magnitude in just five years. The training of foundation models requires massive computing power. For instance, training a BERT model on a single state-of-the-art GPU machine with multi-A100 chips can take several days, while training GPT-3 models on a large multi-instance GPU cluster can take several months to complete the estimated 3 X 10 23 flops. This tutorial provides an overview of the latest progress in supporting foundation model training and inference with new AI chips. It reviews progress on the modeling side, with an emphasis on the transformer architecture, and presents the system architecture supporting training and serving foundation models. This includes programming language frameworks such as PyTorch and Tensorflow, graph compilers, 3D parallelisms, and accelerators such as the GPU H100, TPU, and Trainium. Finally, the tutorial presents our experience of training foundation models using different systems.|基础模型，如 ChatGPT 和 GPT-4，由于它们的突现能力，如小镜头提示、多步推理、指令跟踪和模型校准，已经引起了学术界和工业界的极大兴趣。这种能力以前只能通过特别设计的模型来实现，例如使用知识图表的模型，但现在可以通过基础模型在更大规模上实现。随着基础模型能力的提高，它们的大小也以比摩尔定律快得多的速度增长。例如，BERT 大型机型最初于2018年作为334M 型号发布，到2023年，最大的 GPT-4型号估计在200-300b 之间，这意味着在短短五年内增加了三个数量级。基础模型的训练需要大量的计算能力。例如，在一台配有多个 A100芯片的最先进的 GPU 机器上训练 BERT 模型可能需要几天时间，而在一个大型多实例 GPU 集群上训练 GPT-3模型可能需要几个月才能完成估计的3 × 1023次失败。本教程概述了用新的 AI 芯片支持基础模型训练和推理的最新进展。在建模方面回顾了进展，重点介绍了变压器体系结构，并提出了支持培训和服务的系统体系结构基础模型。这包括编程语言框架，如 PyTorch 和 Tensorflow，图形编译器，3 d 并行，以及加速器，如 GPU H100，TPU 和 Trainium。最后，本教程介绍了使用不同系统培训基础模型的经验。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Training+Large-scale+Foundation+Models+on+Emerging+AI+Chips)|0| -|[How to DP-fy ML: A Practical Tutorial to Machine Learning with Differential Privacy](https://doi.org/10.1145/3580305.3599561)|Natalia Ponomareva, Sergei Vassilvitskii, Zheng Xu, Brendan McMahan, Alexey Kurakin, Chiyaun Zhang|Google Research, Mountain View, USA; Google Research, New York, USA; Google Research, Seattle, USA|Machine Learning (ML) models are ubiquitous in real world applications and are a constant focus of research. At the same time, the community has started to realize the importance of protecting the privacy of models' training data. Differential Privacy (DP) has become a gold standard for making formal statements about data anonymization. However, while some adoption of DP has happened in industry, attempts to apply DP to real world ML models are still few and far between. The adoption of DP is hindered by limited practical guidance of what DP protection entails, what privacy guarantees to aim for, and the difficulty of achieving good privacy-utility-computation trade-offs for ML models. Tricks for tuning and maximizing performance are scattered among papers or stored in the heads of practitioners. Furthermore, the literature seems to present conflicting evidence on how and whether to apply architectural adjustments and which components are "safe'' to use with DP. The question of hyperparameter-tuning for DP models is also often overlooked. Even in academic work, best practices for rigorous reporting of privacy guarantees, like the privacy cost of any data touches and amplification by sampling peculiarities, are often glanced over. In this tutorial, we guide the attendees through in-depth overview of the field of DP ML models. We present information about achieving the best possible DP ML model with rigorous privacy guarantees. People interested in applying DP to their ML models will benefit from a clear overview of current advances and areas for improvement. We also highlight important topics such as privacy accounting and its assumptions, as well as convergence. Additionally we provide an overview of how architectural decisions affect privacy and utility of the ML models. Finally, we have a write-up survey paper that covers all these topics and can serve as further reading material for interested parties.|机器学习(ML)模型在实际应用中无处不在，一直是研究的热点。与此同时，社区已经开始意识到保护模特训练数据隐私的重要性。数据差分隐私(DP)已经成为正式声明数据匿名化的黄金标准。然而，尽管工业界已经出现了一些使用 DP 的情况，但是将 DP 应用于实际机器学习模型的尝试仍然很少。DP 的采用受到实际指导有限的阻碍，这些指导包括 DP 保护意味着什么，隐私保护的目标是什么，以及在机器学习模型中实现良好的隐私-效用-计算权衡的困难。调优和最大化性能的技巧分散在各种论文中，或存储在从业者的头脑中。此外，关于如何以及是否应用体系结构调整，以及哪些组件与 DP 一起使用是“安全”的，文献似乎提供了相互矛盾的证据。DP 模型的超参数调整问题也经常被忽视。即使在学术工作中，严格报告隐私保障的最佳实践，例如任何数据接触的隐私成本和通过采样特性放大，也经常被粗略地浏览。在本教程中，我们通过对 DP ML 模型领域的深入概述来引导与会者。我们提供的信息，以实现最好的可能的 DP 机器学习模型与严格的隐私保障。对将 DP 应用于机器学习模型感兴趣的人们将从当前进展和需要改进的领域的清晰概述中受益。我们还强调了重要的主题，如隐私会计及其假设，以及趋同。此外，我们还提供了架构决策如何影响机器学习模型的私密性和实用性的概述。最后，我们有一份书面调查报告，涵盖所有这些主题，可以作为感兴趣的各方的进一步阅读材料。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=How+to+DP-fy+ML:+A+Practical+Tutorial+to+Machine+Learning+with+Differential+Privacy)|0| -|[Trustworthy Machine Learning: Robustness, Generalization, and Interpretability](https://doi.org/10.1145/3580305.3599574)|Jindong Wang, Haoliang Li, Haohan Wang, Sinno Jialin Pan, Xing Xie|Google, Mountain View, CA USA; Univ Wisconsin, Madison, WI 53706 USA|An emerging problem in trustworthy machine learning is to train models that produce robust interpretations for their predictions. We take a step towards solving this problem through the lens of axiomatic attribution of neural networks. Our theory is grounded in the recent work, Integrated Gradients (IG) [STY17], in axiomatically attributing a neural network's output change to its input change. We propose training objectives in classic robust optimization models to achieve robust IG attributions. Our objectives give principled generalizations of previous objectives designed for robust predictions, and they naturally degenerate to classic soft-margin training for one-layer neural networks. We also generalize previous theory and prove that the objectives for different robust optimization models are closely related. Experiments demonstrate the effectiveness of our method, and also point to intriguing problems which hint at the need for better optimization techniques or better neural network architectures for robust attribution training.|值得信赖的机器学习中一个新出现的问题是训练模型，为它们的预测产生可靠的解释。我们通过神经网络的公理属性透镜来解决这个问题。我们的理论是基于最近的工作，综合梯度(IG)[ STY17] ，在公理归因于一个神经网络的输出变化的输入变化。在经典的鲁棒优化模型中，我们提出训练目标来获得鲁棒 IG 属性。我们的目标提供了原则性的概括以前的目标设计的稳健预测，他们自然退化到经典的软边际训练的一层神经网络。我们还推广了以往的理论，证明了不同鲁棒优化模型的目标是密切相关的。实验证明了我们的方法的有效性，也指出了有趣的问题，暗示需要更好的优化技术或更好的神经网络架构的鲁棒性归因训练。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Trustworthy+Machine+Learning:+Robustness,+Generalization,+and+Interpretability)|0| -|[Large-Scale Graph Neural Networks: The Past and New Frontiers](https://doi.org/10.1145/3580305.3599565)|Rui Xue, Haoyu Han, Tong Zhao, Neil Shah, Jiliang Tang, Xiaorui Liu|Graduate School of Agricultural and Life Sciences, The University of Tokyo, Yayoi 1-1-1, Bunkyo-ku, Tokyo 113-8657, Japan; Maekawa Manufacturing Co., Ltd., Tatsuzawa 2000, Moriya-shi, Ibaraki-Prefecture 302-0118, Japan; College of Biosystems Engineering and Food Science, Zhejiang University, Huajiachi Campus, Hangzhou, PR China|Highlights ► A real-time detection method by UV–Vis spectroscopy was developed for monitoring of ATP and viable cells on meat surface. ► The linear relationship was observed between the ATP amount and plate count with the determination coefficient of 0.95. ► The 2nd derivative of reflectance spectra gave a high correlation for the first 48 h with both ATP amount and viable cell count at 318 nm. Abstract Cleanliness monitoring at slaughterhouses depend on traditional methods, e.g. , visual inspection or swabbing. The visual inspection is not always accurate. Swabbing requires skilled workers and further plate count or ATP bioluminescence technique. To solve these problems, a rapid technique based on non-destructive UV–Vis reflectance was developed to monitor the ATP and viable cells. Samples were lean part of pork loin. The samples stored at 15 °C were analyzed at 0, 24, 48, 72, 84 and 96 h for ATP, plate count and UV–Vis reflectance. The reflectance spectra were measured from 240 to 540 nm at 20 °C, and then the area of 40 × 40 mm 2 of the sample surface was swabbed for the determination of plate count and ATP amount. The plate count on the sample surface increased from the initial count of 29 to 3.2 × 10 7 CFU/cm 2 after 84 h. The ATP amount also increased with time from the initial amount of 9.2 × 10 −15 to 2.8 × 10 −10 mol/cm 2 after 84 h. The linear relationship was observed between the ATP amount and plate count with the determination coefficient of 0.95. The 2nd derivative of raw spectra gave a high correlation for the first 48 h with both ATP amount and viable cell count showing the determination coefficient of 0.89 and 0.83, respectively at 318 nm. The results strongly suggested that the UV–Vis reflectance spectrum analysis could be used as the real-time monitoring of ATP and/or plate count on meat surface with the optimal wavelength. Keywords ATP Sanitation Real-time monitoring Non-destructive detection Pork Spectroscopy Plate count Absorbance Reflectance Meat Quality 1 Introduction Muscle foods that include both meat and poultry are an integral part of the human diet and have been so for several thousand years. However, within the past two decades public concern, as well as awareness, has been raised due to high profile food safety issues such as the BSE and foot and mouth epidemics ( Fox, 2001; Pickrell and Enserink, 2001 ). These outbreaks, along with concerns over specific pathogenic bacteria within meats have illustrated the requirement for a rapid and accurate detection system for microbial spoilage of meats within what is a large-scale production industry whose turn over is billions of £ and $ per annum ( Ellis and Goodacre, 2001 ). The major role of microorganisms in the spoilage of food and the role of food as a vector for the transmission of microbes responsible for food-borne disease are well recognized. At a slaughterhouse of poultry, pork and beef, monitoring of cleanliness depends mainly on traditional methods of visual inspection, swabbing and subsequent viable cell count or ATP bioluminescence technique ( Hawronskyj and Holah, 1997 ). This is especially important for microbial hazards associated with food process. In the case of poultry, pork and beef processing, verification of the efficacy of preventive measures to reduce or eliminate microbial hazards may be achieved by routine carcass analysis using cultural method, i.e., classical Standard Plate Count ( Bautista et al., 1997 ). However, the development of more rapid methods on a ‘real time basis’ for microbiological quality control has been in the interest of scientists ever since routine microbiological analysis was applied to foods. Rapid detection methods based on the detection of whole cells or their metabolites can be divided into two main classes: direct methods are based on the detection of cells with or without incubation and indirect methods are based on the measurement of metabolic products or other changes caused by the cell growth ( Vanne et al., 1996 ). Although rapid detection methods have been under development, conventional methods for microbial monitoring are used on the job site of slaughterhouse. However, such methods usually require operator’s skill, long analysis time and high expenses. Moreover, visual inspection is not always accurate, the swabbing requires skilled worker and further plate count analysis which usually requires 24–48 h. The conventional microbiological approach to food sampling has changed over the last half century and it has been estimated that there are currently in excess of 40 methods to measure and detect bacterial spoilage in meats ( Jay, 2005; Nychas et al., 1988 ). The development of rapid microbiological test procedures over the last two decades can be divided into two main groups: enumeration and presence–absence tests. Several commercial presence–absence (P-A) test kits area available and were evaluated over a 6-month period in 1990 by using the Ontario Ministry of the Environment P-A test for comparison by Clark and El-Shaarawi (1993) . Current rapid enumeration methods are generally based on microscopy, ATP bioluminescence or the measurement of electrical phenomena ( Ellis and Goodacre, 2001 ). The use of ATP bioluminescence assay is a logical approach and relies on the fact that all living cells contain adenosine 5’-triphosphate (ATP), which is a universal energy donor for metabolism ( Bautista et al., 1997 ). Detection of the high-energy molecule adenosine triphosphate (ATP) extracted from cells is a widely used indirect assay method. The ATP amount is measured as the light energy released by the luciferin–luciferase system in the presence of magnesium ions ( Stanley, 1989 ). The assay is rapid, only a few seconds in hygiene monitoring applications and less than an hour of most other samples. Previously, it was thought that this technology has limitations – because of the fact that ATP is present in all viable cells. Therefore, intrinsic ATP originating from the target cells must be removed enzymatically before the assay ( Vanne et al., 1996 ). Siragusa et al. (1996) stated that the major challenge in using microbial ATP as a means of determining total microbial populations in food samples is the separation of nonmicrobial ATP from microbial ATP. The basis of their described Rapid-microbial ATP assay was the use of a filtration device in which somatic ATP was extracted: then within the same device, extraction of bacterial ATP was followed by its quantification. In the case of microscopic methods sophisticated techniques have been developed where microorganisms are stained with fluorescent dyes and viewed with an epifluorescent microscope. ATP bioluminescence acts by measuring ATP levels in bacterial cells in culture in order to calculate the number of cells present in that culture ( Champiat et al., 2001; de Boer and Beurmer, 1999; D’Souza, 2001; Siragusa et al., 1996 ). The problem with this method is that ATP is the primary energy source of all living cells and the food samples themselves will also contain large amounts of this chemical which have to be destroyed before microbial ATP can be measured. Consequently, the measurement of ATP bioluminescence is probably the best suited to detection of contaminated surfaces on equipment and machinery associated with food production and preparation ( Ellis and Goodacre, 2001 ). In the case that the viable cells should only be detected, the above-mentioned limitation of ATP bioluminescence technology and drawback of microscopic method have to be taken into account. However, the total amount of ATP originating from both meat and viable cell has sufficient importance in the cleanliness evaluation, because the ATP of meat origin acts as a nutrient source for the bacteria leading to bacterial spoilage. Due to the advantages of nondestructive, free of chemical preparation and fast inspection speed, spectroscopy has been studied extensively for determining properties of agricultural products, but less for meat products as compared to plant materials ( Chan et al., 2002 ).According to the literature, VIS/NIRS technology has been used in pork to determine intramuscular fat ( Hoving-Bolink et al., 2005; Savenije et al., 2006 ), fatty acid composition ( Fernandez-Cabanas et al., 2007; Gonzalez-Martin et al., 2003, 2005 ), color ( Cozzoliono et al., 2003 ), water-holding capacity ( Brondum et al., 2000 ), presence of RN − genetic allele ( Josell et al., 2000 ), and Doroc and Iberian porl neural network classification ( del Moral et al., 2009 ), but it has not been applied for the direct qualitative classification of meats of varied quality and price ( del Moral et al., 2009 ). Moreover, only a few reports are available for determination of quality of food products by using reflectance data. From these current conditions, the objective of this study was to develop a real-time detection method for monitoring of ATP and viable cells on meat surface by using reflectance spectra that could be used for sanitation management. 2 Materials and methods 2.1 Meat samples The lean part of pork loin samples sliced in 5-mm thick was obtained from a retailer. It was slaughtered 3 days ago and kept in the marketing conditions at the retailer shop. A total of 24-sliced samples were cut into pieces of about 6 × 6 cm 2 , and were individually placed in sterilized Petri dishes. 2.2 Experimental setup The samples were separated into six groups with four samples of each and were stored in a constant temperature chamber at 15 °C. The storage temperature was selected as the highest temperature in a working room of a slaughterhouse, where the temperature is usually controlled from 10 to 15 °C in consideration of worker’s health, according to our conversation with the slaughterhouse management. Measurements were conducted after 0, 24, 48, 72, 84 and 96 h of storage. The each value shown is a mean of four pieces. The experiment was repeated thrice to validate the results. Similar results were obtained in all the repeated experiments. Here, for simplicity, results of only one experiment are shown. 2.3 UV–Vis reflectance spectrum A dual beam spectrometer (UV-3600, Shimadzu Co., Kyoto, Japan) equipped with an integrating sphere setup was used for recording reflectance spectrum from a surface of meat sample (9 × 20 mm 2 ). Measured range of wave length was 240–1200 nm with the resolution of 2 nm; however, the results from 240 to 540 nm are only shown in the Section 3 . In order to confirm the maximum absorption wavelength of ATP, the transmittance of serial dilutions of ATP standard solution (LL-100-1, TOYO B-Net Co., Tokyo, Japan) was obtained with 10 mm quartz cells. 2.4 Spectral data pre-treatment Spectral data are often pre-processed to reduce undesirable systemtic noise, such as baseline variation, light scattering, path length differences and so on, and enhance the contribution of the chemical composition (Tigabu and Oden, 2002). In this study, two types of pre-processing were employed: Savitzky–Golay 1st and 2nd derivative. In our case, the possible sources of systematic variation could be due to the path length slight difference arising from the positioning of individual meat samples with slight different sizes during scanning. 2.5 Sampling protocol and microbiological analysis 2.5.1 Sampling protocol Sampling of materials on pork meat surface (40 × 40 mm 2 ) covering the area for spectroscopic measurement was carried out using a swab technique. To ensure adequate sampling, the sample was swabbed in a horizontal pattern and again in a vertical pattern being rotated between the index finger and the thumb in a back and forth motion according to Bautista et al. (1997) . The end of cotton bud used for swabbing was cut into 9 ml of sterilized water and then the swab sample was stirred well for the further examination for plate count and ATP determination. 2.5.2 Plate count Serial dilutions of the swab sample were prepared from the phosphate buffer solution in which the swab was immersed and 1 ml of the dilution was dispensed onto Petrifilms™ (AC plate, Sumitomo 3M Ltd., Tokyo, Japan) for total aerobic counts. The Petrifilms™ were incubated for 48 h at 35 °C. 2.5.3 ATP bioluminescence assay One hundred microliters of the swab sample (phosphate buffer solution in which the swab was immersed) was injected into a fresh cuvette placed in a luminometer (Luminescenser MCA, Atto Corporation, Tokyo, Japan), and then, 100 μl of Extractant (LL-100-2, Toyo B-Net Co. Ltd., Tokyo, Japan) was added into it. After 10 s, 100 μl of Luciferin–luciferase complex (LL-100-1, Toyo B-Net Co. Ltd., Tokyo, Japan) was added, and the light output was measured. From each swab, two measurements were taken and means were calculated to determine relative light units (RLU). The RLU was then converted into the amount of ATP by a standard curve constructed with ATP standard solution (LL-100-1, Toyo B-Net Co. Ltd., Tokyo, Japan) in the range of 10 −16 –10 −11 mole/100 microliters. 2.6 Statistical analysis The samples of four pieces of pork meat were selected at random for the storage time period. The regression analysis was carried out to know the relationship between ATP contents and plate count. The raw data had background information; therefore, it was converted by using the 1st and the 2nd derivatives, and the best one was selected. 3 Results 3.1 Plate count The plate count on the sample meat surface increased with the storage time period. At the outset of the experiment, the initial count was 29 CFU/cm 2 and 84 h after storage it was 3.2 × 10 7 CFU/cm 2 . 3.2 ATP content The amount of ATP increased with storage time period from the initial amount of 9.2 × 10 −5 to 2.8 × 10 −10 mol/cm 2 (84 h after storage). A linear relationship was observed between the amount of ATP and the plate count with the determination coefficient (R 2 ) of 0.95 as shown in Fig. 1 . 3.3 Absorption maximum of pure ATP The transmittance of ATP solutions of different concentration from 1 × 10 −4 to 5.85 × 10 −6 M are shown in Fig. 2 . It shows that the transmittance decreased with an increase in the ATP concentration, and spectra taken for all samples of different ATP concentrations showed that the maximum absorbance related to the decrease in transmittance was at 260 nm ( Fig. 2 ). 3.4 Estimation of ATP and plate count from reflectance The reflectance spectra obtained at 0–84 h of storage are shown in Fig. 3 in the UV–Vis range (from 240 to 540 nm). There was a very little difference between the reflectance at 0 h and that at 24 h. The reflectance of samples taken at 48, 72 and 84 h, however, showed a decreasing trend with increase in the storage time period. The 2nd derivative of reflectance data selected as the best between 1st and 2nd derivatives of reflectance is shown in Fig. 4 . Many upward and downward peaks were observed and the analysis of correlation of peaks at 298, 318, 344 and 374 nm in UV range was conducted. Fig. 5 shows the correlation coefficient between the 2nd derivative of reflectance and log (ATP). This gave a high correlation between the values of the 2nd derivative and log (ATP). Considering bathochromic shift, any of these four wave lengths could be taken as the maximum absorption of ATP. 4 Discussion Spectroscopic methods have gained importance in the evaluation of food quality attributes during the last decades (Nadai, 1983; Nadai and Mihalyi-Kengyel, 1984). Although NIR spectra reflect several parameters relating to complex quality of food (Williams and Norris, 2001), the information on ATP and/or microorganisms can not be detected in the range of NIR. Therefore, in the present study, UV–Vis was applied ranging from 240 to 540 nm. 4.1 Plate count In this study, samples were evaluated as fresh until that time when bacterial counts crossed the boundary line of 107 CFU/g and no putrid odor could be perceived. After 72 h, the plate count reached the order of 107 CFU/g and samples gave off a faint putrid odor. These samples were in the initial stage of spoilage and would be regarded as unacceptable. Plate count is a fundamental index of meat spoilage, and count of 10 7 CFU/g in meat is regarded as unacceptable ( Brown, 1982 ). Detection of the order of 10 6 CFU/g is important as this is achieved just before the meat reaches the unacceptable stage. Fresh meats generally have a pH range between 5.5 and 5.9 and contain sufficient glucose and other simple carbohydrates to support approximately 10 9 CFU/cm 2 . The organisms that grow the fastest and utilize glucose at refrigeration temperatures are the pseudomonas ( Gill and Newton, 1977; Jay, 2005; Seymour et al., 1994 ). At levels of 10 7 CFU/cm 2 off-odors may become evident in the form of a faint ‘dairy’ type aroma and once the surface population of bacteria has reached 10 8 CFU/cm 2 the supply of simple carbohydrates has been exhausted and recognizable off-odors develop leading to what is known as ‘sensory’ spoilage ( Jackson et al., 1997; Jay, 2005; Stanbridge and Davies, 1998 ). The development of off-odors is dependent upon the extent to which free amino acid utilization has occurred and these odors have been variously described as dairy/buttery/fatty/cheesy at 10 7 CFU/cm 2 through to a sickly sweet/fruity aroma at 10 8 CFU/cm 2 and finally putrid odor at 10 9 CFU/cm 2 ( Adams and Moss, 2007; Dainty et al., 1985 ). 4.2 ATP content Fig. 1 shows the linear relationship between log 10 ATP and log 10 plate count. From this figure both the ATP analysis and the plate count methods were able to assess the hygienence of pork meat samples. The ATP analysis provides only an estimation of the total bacterial count, and cannot differentiate between bacteria ( Baumgart, 1993 ). Theoretically, ATP amounts as low as 100 fg (10 −13 g) can be measured, corresponding to about 100 bacterial cells. Under practical conditions the sensitivity is about 1000 fg (10 −12 g), which corresponds to about 1000 bacterial cells or one to two yeast cells ( Heeschen et al., 1991 ). Stressed cells and cells in the stationary growth phase contain less ATP, which also affects the results ( Bulte and Reuter, 1985 ). On the other hand, however, the amount of ATP in a sample provides an estimate of the active microbial population, which is important when considering the shelf life of the product. Stressed cells can also be allowed to resuscitate before the ATP assay ( Graumlich, 1985 ). The enzyme, luciferase, converts the chemical energy provided by ATP into light by a stochiometric reaction. Thus, the amount of light produced is proportional to the concentration of ATP present, which in turn, is directly related to the number of cells in the sample ( Bautista et al., 1997 ). ATP bioluminescence is also useful for monitoring microbial contamination in scalding and chilling tanks within a meat processing operation. In the ATP bioluminescence assays for carcass contamination and process water quality, microbial cells are removed by filtration before they are lysed to release intracellular ATP. To simplify the method, it would be desirable if the step could be eliminated to allow direct detection of ATP on swabs of the carcass surface, in much the same way as for the ATP bioluminescence hygiene monitoring tests ( Griffiths, 1996 ). However, there would be no way of differentiating ATP from microbial and non-microbial sources using a swab assay, but results would be obtained within 2 min, as opposed to the 10–15 min required when a filtration step is incorporated ( Bautista et al., 1997 ). Siragusa et al. (1996) developed segmented-model statistical approach to determine the lower limits of assay sensitivity and by using this model analyzed in-plant data. According to them, the rapid microbial-ATP test responded in a linear fashion to levels of microbial contamination of >log 10 3.2 aerobic CFU/cm 2 for pork carcasses. 4.3 Absorption maximum of pure ATP As shown in Fig. 2 , the transmittance decreased with an increase in ATP concentration, and different spectra showed the minimum absorbance at 260 nm. The wave length of 260 nm was in accordance with the maximum absorbance of ATP (259 nm) as previously reported by Bagshaw (2001) . 4.4 Estimation of ATP and plate count from reflectance Reflectance ( Fig. 3 ) showed a decreasing trend with time in the UV–Vis range, although there was a very little difference between the reflectance at 0 h and that at 24 h. To remove the background effect the raw data was transformed by using the 1st and the 2nd derivates. However, the 2nd derivative was chosen because the effect was more clearer in it. The 2nd derivative technique is often used to process NIR data. It helps to separate overlapping absorption bands, remove baseline shifts and increase apparent spectral resolution (Lin et al., 2004), although the derivatives are notoriously sensitive to noise (Tsai and Philpot, 1998). Many upward and downward peaks were observed when the 2nd derivative of raw reflectance spectra for all storage time periods (from 0 to 96 h) was taken ( Fig. 4 ). The analysis of correlation of peaks at 298, 318, 344 and 374 nm was conducted. These selected wavelengths were in the UV range, i.e., less than 400 nm. The greatest differences were obtained for all selected wavelengths, between time 0 and 96 h. The maximum differences between time 0 and 96 h were in the range of 318 nm. This wavelength range could mainly differentiate between samples at 0 and 96 h. Fig. 5 shows the correlation coefficient between the 2nd derivative of reflectance and log (ATP). This gave a high correlation between the value of the 2nd derivative and log (ATP). Considering bathochromic shift, any of these four wave lengths could be taken as the maximum absorption of ATP. On the other hand, it is widely known that the spectral absorption by ATP is usually masked by protein absorbance and cannot be exploited in spectroscopic studies ( Bagshaw, 2001 ). However, the graph of correlation coefficient between the 2nd derivative of reflectance and log (plate count) shown in Fig. 6 became very similar in shape to Fig. 5 . This indicated that the 2nd derivative of reflectance involved the information of ATP in viable cells. The understanding of this is also supported by the result that the amount of ATP corresponded to the plate count ( Fig. 1 ). From these considerations, the wave length of 318 nm showing the highest correlation coefficient was selected. The linear relationship between the value of the 2nd derivative and log (ATP) for the first 48 h at 318 nm is shown in Fig. 7 with the determination coefficient of 0.89. The similar relationship was also observed between the value of the 2nd derivative and log (plate counts) for the first 48 h at 318 nm with the determination coefficient of 0.83. The duration of the first 48 h chosen here means that pork meat samples were fresh. From these results, it is expected that the selection of appropriate wave length could give the real-time monitoring of ATP and/or viable cell count on meat surface by the use of reflectance information. The plate count gives an estimate of microbial contamination whereas the ATP bioluminescence method used in this study measures total ATP, from both microbial and non-microbial sources, and may be a better measure of the overall cleanliness of the carcass. Therefore, an exact relationship between the two methods should not be expected and results obtained from the two assay systems should be interpreted separately. Multiple linear regression analysis using more than one reflectance at different wave length is a powerful tool in estimating ATP and/or viable cell count on meat surface, and can lead to higher predictive power. However, such paradigm may lead to overfitting. Accordingly, in this study, only one wave length (i.e., 318 nm) was selected for the prediction of ATP. 5 Conclusions A real-time detection method for monitoring of ATP and viable cells on meat surface by using reflectance spectra was developed. The data showed that the plate count on the sample meat surface increased and it corresponded exactly to the increase in the amount of ATP during 84 h storage at 15 °C. The linear relationship between the amount of ATP and plate count was supported by its determination coefficient of 0.95. Reflectance showed a decreasing trend with time in UV–Vis range and at the peak of 318 nm, 2nd derivative of reflectance gave a high correlation with log (ATP). As a similar high correlation was also observed between the 2nd derivative of reflectance and log (plate count), it is suggested that the 2nd derivative of reflectance involved the information of ATP in viable cells. From these observations, a linear relationship was given for the estimation of the amount of microbialy-derived ATP on the basis of reflectance analysis of meat surface. Hence, the developed technique can give a powerful way for monitoring of cleanness at a slaughterhouse. Acknowledgements This research was partly funded by the Japan Society for the Promotion of Science (JSPS) Grant No. 19:07178 . References Adams and Moss, 2007 Adams, M.R., Moss, M.O., 2007. Food Microbiology, third ed. The Royal Society of Chemistry, Cambridge, pp. 138. Bagshaw, 2001 C.R. Bagshaw ATP analogues at a glance Journal of Cell Science 114 2001 459 460 Baumgart, 1993 J. Baumgart Lebensmitteluberwachung und–qualitatssicherung Mikrobiologisch- hygienische Schnellverfahren Fleischwirtschaft 73 1993 292 396 Bautista et al., 1997 D.A. Bautista D.W. Sprung S. Barbut M.W. Griffiths A sampling regime based on an ATP bioluminescence assay to assess the quality of poultry carcasses at critical control points during processing Food Research International 30 1997 803 809 Brondum et al., 2000 J. Brondum L. Munck P. Henckel A. Karlsson E. Tornberg S.B. Engelsen Prediction and water-holding capacity and composition of porcine meat by comparative spectroscopy Meat Science 55 2000 177 185 Brown, 1982 Brown, M. H. (1982). Meat microbiology. (p. 410). New York: Applied Science Publications. Bulte and Reuter, 1985 M. Bulte G. Reuter The bioluminescence as a rapid method for the determination of the microflora of meat International Journal of Food Microbiology 2 1985 371 381 Champiat et al., 2001 D. Champiat N. Matas B. Monofort H. Fraass Applications of bioluminescence to HACCP Luminescence 16 2001 193 198 Chan et al., 2002 D.E. Chan P.N. Walker E.W. Mills Prediction of pork quality characteristics using visible and NIR spectroscopy Transactions of ASAE 45 2002 1519 1527 Clark and El-Shaarawi, 1993 J.A. Clark A.H. El-Shaarawi Evaluation of commercial presence-absence test kits for detection of total coliforms, Escherichia coli, and other indicator bacteria Applied and Environmental Microbiology 59 2 1993 380 388 Cozzoliono et al., 2003 D. Cozzoliono N. Barlocco A. Vadell F. Ballesteros G. Gallieta The use of visible and near-infrared reflectance spectroscopy to predict colour on both intact and homogenized pork muscle LWT-Food Science and Technology 36 2003 195 202 de Boer and Beurmer, 1999 E. de Boer R.R. Beurmer Methodology for detection and typing of food borne microorganisms International Journal of Food Microbiology 50 1999 119 130 del Moral et al., 2009 F.G. del Moral A. Guillen K.G. del Moral F. O’Valle L. Martinez R.G. del Moral Duroc and Iberian porl neural network classification by visible and near infrared reflectance spectroscopy Journal of Food Engineering 90 2009 540 547 Dainty et al., 1985 R.H. Dainty R.A. Edwards C.M. Hibbard Time course of volatile compound formation during refrigerated storage of naturally contaminated beef in air Journal of Applied Bacteriology 59 1985 303 309 D’Souza, 2001 S.F. D’Souza Microbial biosensors Biosensors and Bioelectronics 16 2001 337 353 Ellis and Goodacre, 2001 D.I. Ellis R. Goodacre Rapid and quantitative detection of the microbial spoilage of muscle foods: Current status and future trends Trends in Food Science & Technology. 12 2001 414 424 Fernandez-Cabanas et al., 2007 V.M. Fernandez-Cabanas A. Garrido-Varo J. Gracia-Olmo E. De Pedro P. Dardenne Optimisation of the spectral pre-treatments used for Iberian pig fat NIR calibration Chemometrics and Intelligent Laboratory System 87 2007 104 112 Fox, 2001 S. Fox WHO to convene on worldwide risk of BSE and CJD Infections in Medicine. 18 2001 69 Gonzalez-Martin et al., 2003 I. Gonzalez-Martin C. Gonzalez-Perez J. Hernandez-Menderz N. Alvarez-Gracia Determination of fatty acids in the subcutaneous fat of Iberian breed swine by near infrared spectroscopy (NIRS) with a fiber-optic probe Meat Science 65 2003 713 719 Gonzalez-Martin et al., 2005 I. Gonzalez-Martin C. Gonzalez-Perez N. Alvarez-Gracia J.M. Gonzalez-Cabrera On-line determination of fatty acids composition in intramuscular fat of Iberian pork loin by NIRS with a remote reflectance fibre optic probe Meat Science 65 2005 713 719 Gill and Newton, 1977 C.O. Gill K.G. Newton The development of aerobic spoilage flora on meat stored at chill temperatures Journal of Applied Bacteriology 43 1977 189 195 Graumlich, 1985 T.R. Graumlich Estimation of microbial populations in orange juice by bioluminescence Journal of Food Science 50 1985 116 117, 124 Griffiths, 1996 M.W. Griffiths The role of ATP bioluminescence in the food industry: new light on old problems Food Technology 50 6 1996 62 72 Hawronskyj and Holah, 1997 J.M. Hawronskyj J. Holah ATP: A universal hygiene monitor Trends in Food Science & Technology 8 1997 79 84 Heeschen et al., 1991 W.H. Heeschen G. Suhren G. Hahn Rapid methods in the dairy industry A. Vaheri R.C. Tilton A. Balows Rapid methods and Automation in Microbiology and Immunology 1991 Springer-Verlag Berlin Heidelberg 520 532 Hoving-Bolink et al., 2005 A.H. Hoving-Bolink H.W. Vedder J.W.M. Merks W.J.H. de Klein H.G.M. Reimert R. Frankhuizen W.H.A.M. van den Broek enE. Lambooji Perspective of NIRS measurements early post mortem for prediction of pork quality Meat Science 69 2005 417 423 Jackson et al., 1997 T.C. Jackson G.R. Acuff J.S. Dickson Meat, poultry, and seafood M.P. Doyle L.R. Beuchat T.J. Montville Food microbiology: fundamentals and frontiers 1997 ASM Press Washington DC 83 100 Jay, 2005 J.M. Jay Modern food microbiology sixth ed. 2005 Aspen Publishers Maryland Josell et al., 2000 A. Josell L. Martinsson C. Borggaard J.R. Anderson E. Tornberg Determination of RN - phenotype in pigs at slaughter-line using visual and near-infrared spectroscopy Meat Science 55 2000 273 278 Nychas et al., 1988 G.J. Nychas V.M. Dillon R.G. Board Glucose, the key substrate in the microbiological changes occurring in meat and certain meat products Biotechnology and applied biochemistry 10 1988 203 231 Pickrell and Enserink, 2001 J. Pickrell M. Enserink Foot-and-mouth disease – UK outbreak is latest in global epidemic Science 291 2001 1677 Savenije et al., 2006 B. Savenije G.H. Geesink J.G.P. van der Palen G. Hemke Prediction of pork quality using visible/near infrared reflectance spectroscopy Meat Science 73 2006 181 184 Seymour et al., 1994 I.J. Seymour M.B. Cole P.J. Coote A substrate-mediated assay of bacterial proton efflux/influx to predict the degree of spoilage of beef mince stored at chill temperatures Journal of Applied Bacteriology 76 1994 608 615 Siragusa et al., 1996 G.R. Siragusa W.J. Dorsa C.N. Cutter Perino L.J. Kooh-maraie Use of a newly developed rapid microbial ATP bioluminescence assay to detect microbial contamination on poultry carcasses Journal of Bioluminescence and Chemilumoscence 11 1996 297 301 Stanbridge and Davies, 1998 L.H. Stanbridge A.R. Davies The microbiology of chill-stored meat Davies R. Board The microbiology of meat and poultry 1998 Blackie Academic & Professional London 174 219 Stanley, 1989 P.E. Stanley A review of bioluminescent ATP techniques in rapid microbiology Journal of Bioluminescence and Chemiluminescence 4 1989 375 380 Vanne et al., 1996 L. Vanne M. Karwoski S. Karppinen A.M. Sjoberg HACCP-based food quality control and rapid detection methods for microorganisms Food Control 7 1996 263 276|建立了一种紫外-可见光谱实时检测肉表面 ATP 和活细胞的方法。ATP 含量与平板计数呈线性关系，测定系数为0.95。反射光谱的二阶导数与前48h 的 ATP 含量和318nm 的活细胞计数均有较高的相关性。摘要屠宰场的清洁度监测依赖于传统的方法，例如目视检查或擦拭。目视检查并不总是准确的。采样需要熟练的工人和进一步的平板计数或 ATP 生物发光技术。为了解决这些问题，开发了一种基于无损紫外-可见光反射的快速检测技术来监测 ATP 和活细胞。样本为猪腰瘦肉。分别在0、24、48、72、84和96h 分析15 °C 保存的样品的 ATP、平板计数和紫外-可见光反射率。测定了样品在20 °C 下240 ~ 540nm 范围内的反射光谱，然后采集样品表面40 × 40mm2的面积，测定平板计数和 ATP 含量。84h 后，样品表面的平板计数由最初的29个增加到3.2 × 107CFU/cm2。84h 后，ATP 含量从最初的9.2 × 10 ~ (-15) mol/cm2增加到2.8 × 10 ~ (-10) mol/cm2。ATP 含量与平板计数呈线性关系，测定系数为0.95。原始光谱的二阶导数与 ATP 含量和活细胞计数在318nm 处的相关系数分别为0.89和0.83。实验结果表明，紫外-可见光反射光谱分析可以用于实时监测肉品表面 ATP 和/或平板计数，并且具有最佳波长。关键词 ATP 卫生实时监测无损检测猪肉光谱板计数吸光度反射率肉类质量1简介包括肉类和家禽的肌肉食品是人类饮食的一个组成部分，并已经存在了几千年。然而，在过去的二十年中，由于诸如疯牛病和口蹄疫等引人注目的食品安全问题(Fox，2001; Pickrell and Enserink，2001) ，公众的关注和意识已经提高。这些疾病的爆发，以及对肉类中特定病原菌的担忧，说明了在这个大规模生产行业中，对肉类微生物腐败的快速和准确检测系统的要求，这个行业每年的营业额达数十亿美元(Ellis and Goodacre，2001)。微生物在食物变质中的主要作用以及食物作为传播导致食源性疾病的微生物的媒介的作用已得到公认。在家禽、猪肉和牛肉的屠宰场，清洁度的监测主要依赖于传统的目视检查、拭子和随后的活细胞计数或 ATP 生物发光技术(Hawronskyj and Holah，1997)。这对于与食物加工过程有关的微生物危害尤其重要。就家禽、猪肉和牛肉加工而言，可以通过使用培养方法(即经典的标准盘计数法)进行常规屠体分析，来验证预防措施减少或消除微生物危害的有效性(Bautista et al。 ，1997)。然而，自从常规微生物分析应用于食品以来，科学家一直对发展更快速的“实时”微生物质量控制方法感兴趣。基于检测整个细胞或其代谢物的快速检测方法可以分为两大类: 直接方法基于检测有或没有孵育的细胞，间接方法基于测量代谢产物或由细胞生长引起的其他变化(Vanne 等，1996)。虽然快速检测方法正在发展中，传统的微生物监测方法在屠宰场的工作现场使用。然而，这些方法往往需要操作人员的技能，分析时间长，费用高。此外，目测检查并不总是准确的，擦拭需要熟练的工人和进一步的板计数分析，通常需要24-48小时。在过去的半个世纪中，传统的食品采样微生物学方法已经发生了变化，据估计，目前有超过40种方法来测量和检测肉类中的细菌变质(Jay，2005; Nychas 等，1988)。近二十年来微生物快速检测技术的发展可分为两大类: 计数检测和存在-缺失检测。1990年，通过使用安大略省环境部的 P-A 测试，Clark 和 El-Shaarawi (1993)比较了几种商业存在缺失(P-A)测试试剂盒区域，并在6个月内进行了评估。目前的快速计数方法一般基于显微镜、 ATP 生物发光或电现象的测量(Ellis and Goodacre，2001)。ATP 生物发光测定的使用是一种合乎逻辑的方法，并且依赖于所有活细胞都含有腺苷5’-三磷酸(ATP)的事实，ATP 是代谢的通用能量供体(Bautista 等，1997)。检测从细胞中提取的高能分子三磷酸腺苷(ATP)是一种广泛使用的间接测定方法。三磷酸腺苷(ATP)量是以镁离子存在下荧光素-荧光素酶系统释放的光能量来测量的(Stanley，1989)。该分析是快速的，只有几秒钟的卫生监测应用和不到一个小时的大多数其他样品。以前，人们认为这种技术有局限性，因为 ATP 存在于所有活细胞中。因此，来自靶细胞的内源性 ATP 必须在测定前被酶去除(Vanne 等，1996)。Siragusa 等(1996)指出，使用微生物 ATP 作为确定食品样品中微生物总数的手段的主要挑战是从微生物 ATP 中分离非微生物 ATP。他们描述的快速微生物 ATP 测定的基础是使用过滤装置提取体细胞 ATP: 然后在同一装置内，提取细菌 ATP 后进行定量。在显微镜方法的情况下，复杂的技术已经开发出来，其中微生物被荧光染料染色，并用荧光显微镜观察。ATP 生物发光通过测量培养细菌细胞中的 ATP 水平来计算该培养物中存在的细胞数量(Champiat 等，2001; de Boer 和 Beurmer，1999; D’Souza，2001; Siragusa 等，1996)。这种方法的问题在于，ATP 是所有活细胞的主要能量来源，而且食品样本本身也含有大量这种化学物质，在测量微生物 ATP 之前，必须将其销毁。因此，ATP 生物发光的测量可能最适合于检测与食品生产和制备相关的设备和机械的污染表面(Ellis and Goodacre，2001)。在只检测活细胞的情况下，必须考虑 ATP 生物发光技术的上述局限性和显微技术的缺陷。然而，来源于肉类和活细胞的 ATP 总量在清洁度评估中具有足够的重要性，因为来源于肉类的 ATP 是导致细菌腐败的营养来源。由于无损、无化学制备和快速检测的优点，光谱学已被广泛研究用于确定农产品的性质，但与植物材料相比，肉类产品的性质较少(Chan et al。 ，2002)。根据文献，VIS/NIRS 技术已应用于猪肉中以测定肌间脂肪(Hoving-Bolink 等，2005; Savenije 等，2006) ，脂肪酸组成(Fernandez-Cabanas 等，2007; Gonzalez-Martin 等，2003,2005) ，颜色(Cozzoliono 等，2003) ，持水能力(Brondum 等，2000)(Josell et al。 ，2000) ，以及 Doroc 和伊比利亚 porl 神经网络分类(del Moral。 ，2009) ，但是它还没有被应用于不同质量和价格的肉类的直接定性分类(del Moral。 ，2009)。此外，利用反射率数据测定食品质量的报告屈指可数。从目前的情况来看，这项研究的目的是开发一种实时检测方法，以监测 ATP 和活细胞在肉表面的反射光谱，可用于卫生管理。2材料及方法2.1肉类样本2.1从零售商取得切成5毫米厚的猪腰样本的瘦肉部分。3天前被宰杀，在零售商店保存在市场条件下。共有24个切片的样品被切成约6 × 6cm2的块，并分别放置在消毒的培养皿中。2.2实验装置样品分为6组，每组4个样品，在15 °C 恒温箱中保存。根据我们与屠宰场管理人员的谈话，我们选择了屠宰场工作室的最高温度作为储存温度，考虑到工人的健康，通常将温度控制在10 °C 至15 °C 之间。在储存0,24,48,72,84和96小时后进行测量。显示的每个值是四个部分的平均值。实验重复三次以验证结果。在所有的重复实验中都得到了相似的结果。在这里，为了简单起见，只显示了一个实验的结果。2.3 UV-Vis 反射光谱日本京都岛津公司的 UV-3600双光束光谱仪配备了积分球装置，用于记录肉样表面(9 × 20mm2)的反射光谱。测量的波长范围为240-1200纳米，分辨率为2纳米; 然而，从240-540纳米的结果只显示在第3节。为了确定 ATP 的最大吸收波长，用10mm 石英电池测定了 ATP 标准溶液(LL-100-1，日本东京东洋 B-Net 公司)系列稀释液的透过率。2.4光谱数据预处理光谱数据经常被预处理，以减少不良的系统噪声，如基线变化、光散射、路径长度差异等，并增强化学成份的贡献(Tigabu 和 Oden，2002)。在这项研究中，两种类型的预处理采用: 萨维茨基-戈雷一阶和二阶导数。在我们的案例中，系统变化的可能来源可能是由于路径长度的微小差异产生的定位个别肉类样本与轻微不同的大小在扫描过程中。2.5采样方案和微生物分析2.5.1采样方案采用拭子技术对覆盖光谱测量区域的猪肉表面(40 × 40mm2)材料进行采样。为了确保足够的采样，根据 Bautista 等人(1997) ，以水平模式擦拭样品，并在食指和拇指之间来回旋转的垂直模式中再次擦拭样品。将棉签末端切入9ml 无菌水中，搅拌均匀，进一步检测棉签平板计数和 ATP 含量。2.5.2平板计数从浸入拭子的磷酸盐缓冲溶液中制备拭子样品的连续稀释液，并将1ml 稀释液分配到 Petri ilmsTM (AC 板，Sumitomo 3M Ltd. ，Tokyo，Japan)上以进行总有氧计数。在35 °C 下培养48小时。2.5.3 ATP 生物发光测定将100微升拭子样品(将拭子浸入其中的磷酸盐缓冲溶液)注射到置于发光计(Luminescenser MCA，Atto Corporation，Tokyo，Japan)中的新鲜试管中，然后将100μl 萃取剂(LL-100-2，Toyo B-Net Co. Ltd. ，Tokyo，Japan)加入其中。10秒后，加入100μl 萤光素-荧光素酶复合物(LL-100-1，日本东京东洋 B-Net Co. Ltd. ，Tokyo，Japan) ，并测量光输出。从每个拭子，采取两个测量和手段计算确定相对光单位(RLU)。然后通过用 ATP 标准溶液(LL-100-1，Toyo B-Net Co. Ltd. ，Tokyo，Japan)在10-16-10-11摩尔/100微升范围内构建的标准曲线将 RLU 转化为 ATP 的量。2.6统计分析在贮存期间，随机抽取四块猪肉样本作统计分析。研究人员进行回归分析测试，以了解三磷酸腺苷(ATP)含量与盘子数量的关系。由于原始数据具有背景信息，因此采用一阶导数和二阶导数对原始数据进行转换，从中选出最优的一个。3结果3.1平板计数样品肉表面平板计数随贮藏时间延长而增加。试验开始时，初始计数为29CFU/cm2，贮藏84h 后为3.2 × 107CFU/cm2。3.2 ATP 含量随着贮藏时间的延长，ATP 含量由最初的9.2 × 10-5增加到2.8 × 10-10mol/cm2(贮藏后84h)。ATP 含量与平板计数呈线性关系，测定系数(R2)为0.95，如图1所示。3.3纯 ATP 的最大吸收量不同浓度(1 × 10-4 ~ 5.85 × 10-6M)的 ATP 溶液的透过率如图2所示。它表明，透过率随着 ATP 浓度的增加而降低，并且对不同 ATP 浓度的所有样品进行的光谱显示与透过率降低相关的最大吸光度在260nm 处(图2)。3.4根据反射率估计 ATP 和平板计数储存0-84小时获得的反射光谱如图3所示，在 UV-Vis 范围内(从240到540nm)。0小时和24小时的反射率差别很小。在48、72和84小时采集的样品的反射率随着储存时间的延长呈下降趋势。反射率数据的二阶导数被选为反射率一阶导数和二阶导数之间的最佳值，如图4所示。在紫外光谱范围内，观察到多个向上和向下的峰，并对298,318,344和374nm 的峰进行了相关性分析。图5显示了反射率二阶导数与对数(ATP)之间的相关系数。这给出了二阶导数和对数(ATP)之间的高度相关性。考虑到暗色移动，这四个波长中的任何一个都可以作为 ATP 的最大吸收。4在过去的几十年中，光谱方法在食品质量属性的评价中获得了重要性(Nadai，1983; Nadai 和 Mihalyi-Kengyel，1984)。尽管近红外光谱反映了与食品复杂质量有关的几个参数(Williams 和 Norris，2001) ，但是在近红外光谱范围内不能检测到 ATP 和/或微生物的信息。因此，在本研究中，UV-Vis 的应用范围从240到540纳米。4.1平板计数在这项研究中，样品被评估为新鲜，直到当细菌计数超过107CFU/g 的边界线，没有腐臭气味可以察觉。72小时后，平板计数达到107 CFU/g，样品散发出微弱的腐臭气味。这些样品处于变质的初始阶段，将被视为不可接受。盘子计数是肉类腐败的一个基本指标，肉类中107CFU/g 的计数被认为是不可接受的(Brown，1982)。检测106 CFU/g 的量级很重要，因为这是在肉类达到不可接受阶段之前完成的。新鲜肉类的 pH 值一般在5.5至5.9之间，并含有足够的葡萄糖和其他简单碳水化合物，以支持大约109 CFU/cm2。在冷藏温度下生长最快并利用葡萄糖的生物是假单胞菌(Gill and Newton，1977; Jay，2005; Seymour et al。 ，1994)。在107 CFU/cm2的水平下，异味可能会以淡淡的“奶制品”香味的形式变得明显，一旦表面细菌数量达到108 CFU/cm2，简单碳水化合物的供应已经耗尽，可识别的异味发展导致所谓的“感官”腐败(Jackson 等，1997; Jay，2005; Stanbridge and Davies，1998)。异味的发展取决于游离氨基酸利用发生的程度，这些气味已经被不同地描述为107CFU/cm2的乳制品/黄油/脂肪/奶酪，直到108CFU/cm2的病态甜味/水果香味，最后是109CFU/cm2的腐臭气味(Adams 和 Moss，2007; Dainty 等，1985)。4.2 ATP 含量图1显示了对数10 ATP 和对数10平板计数之间的线性关系。根据这个数字，ATP 分析法和平板计数法都能够评估猪肉样本的卫生情况。ATP 分析只能提供总细菌计数的估计，不能区分细菌(Baumgart，1993)。理论上，ATP 含量低至100fg (10-13g) ，相当于约100个细菌细胞。在实际条件下，灵敏度约为1000fg (10-12g) ，相当于约1000个细菌细胞或一至两个酵母细胞(Heeschen 等，1991)。应激细胞和处于静止生长阶段的细胞含有较少的 ATP，这也影响了结果(Bulte 和 Reuter，1985)。然而，另一方面，样品中 ATP 的含量提供了对活性微生物种群的估计，这在考虑产品的货架期时是很重要的。应激细胞也可以在 ATP 测定前复苏(Graumlich，1985)。这种酶，萤光素酶，通过化学计量反应将 ATP 提供的化学能转化为光。因此，产生的光量与存在的 ATP 浓度成正比，而 ATP 浓度又与样品中细胞的数量直接相关(Bautista 等，1997)。ATP 生物发光也可用于监测肉类加工过程中烫伤和冷却罐中的微生物污染。在对屠体污染和处理水质的 ATP 生物发光测定中，微生物细胞在裂解释放细胞内 ATP 之前通过过滤除去。为了简化这种方法，如果能够消除这一步骤以允许在屠体表面的拭子上直接检测 ATP，就像 ATP 生物发光卫生监测测试(Griffiths，1996)一样是可取的。然而，使用拭子测定将无法区分 ATP 与微生物和非微生物来源，但是结果将在2分钟内获得，而不是当纳入过滤步骤时所需的10-15分钟(Bautista 等，1997)。Siragusa 等(1996)开发了分段模型统计方法来确定检测灵敏度的下限，并使用该模型分析植物内数据。根据他们的研究，快速微生物 ATP 测试以线性方式响应猪肉胴体 > log 103.2需氧 CFU/cm2的微生物污染水平。4.3纯 ATP 的吸收峰如图2所示，透过率随 ATP 浓度的增加而降低，不同的光谱在260nm 处表现出最小的吸光度。260nm 的波长与之前 Bagshaw (2001)报道的 ATP (259nm)的最大吸光度一致。4.4从反射反射率估计 ATP 和平板计数(图3)在 UV-Vis 范围内随着时间的推移显示出减少的趋势，尽管在0小时和24小时的反射率之间差异很小。为了消除背景效应原始数据通过使用第一和第二导数进行转换。然而，选择二阶导数是因为它的效果更加明显。二阶导数技术是近红外数据处理的常用方法。它有助于分离重叠吸收带，消除基线偏移和增加明显的光谱分辨率(Lin et al。 ，2004) ，尽管衍生物是众所周知的对噪声敏感(Tsai 和 Philpot，1998)。在所有贮存时间段(0-96小时)的原始反射光谱二阶导数测量时，观察到许多向上和向下的峰值(图4)。对298、318、344和374nm 波段的峰进行了相关性分析。这些选定的波长在紫外线范围内，即小于400纳米。最大的差异获得所有选定的波长，在时间0和96小时之间。时间0 ~ 96h 的最大差异在318nm 范围内。这个波长范围主要可以区分0和96小时的样品。图5显示了反射率的二阶导数与对数(ATP)之间的相关系数。这给出了二阶导数的值与对数(ATP)之间的高度相关性。考虑到暗色移动，这四个波长中的任何一个都可以作为 ATP 的最大吸收。另一方面，众所周知，ATP 的光谱吸收通常被蛋白质吸收所掩盖，在光谱学研究中不能被利用(Bagshaw，2001)。然而，图6所示的反射率二阶导数与对数(平板计数)之间的相关系数图在形状上与图5非常相似。这表明反射率的二阶导数涉及活细胞内 ATP 的信息。对这一点的理解也得到了 ATP 含量与平板计数相对应的结果的支持(图1)。从这些考虑，波长318纳米显示最高的相关系数被选中。在318nm 处的前48h，二阶导数的值与对数(ATP)之间的线性关系如图7所示，测定系数为0.89。在318nm 处测定前48h，二阶导数值与对数(平板计数)之间也存在相似的关系，测定系数为0.83。这里选择的前48小时的持续时间意味着猪肉样本是新鲜的。从这些结果可以看出，选择合适的波长可以利用反射率信息实时监测肉表面 ATP 和/或活细胞计数。平板计数给出了微生物污染的估计值，而本研究中使用的 ATP 生物发光法测量来自微生物和非微生物来源的总 ATP，可能是对屠体整体清洁度的更好测量。因此，不应期望两种方法之间有确切的关系，从两种测定系统获得的结果应该分开解释。在不同波长下使用多个反射率的多个线性回归分析是估计肉表面 ATP 和/或存活细胞数量的有力工具，并且可以导致更高的预测能力。然而，这样的范式可能会导致过度拟合。因此，在这项研究中，只有一个波长(即，318纳米)被选择用于 ATP 的预测。5结论建立了一种利用反射光谱实时监测肉表面 ATP 和活细胞的方法。结果表明，在15 °C 下贮藏84h，样品表面的平板数量增加，与 ATP 含量的增加完全一致。其测定系数为0.95，支持 ATP 含量与平板计数之间的线性关系。在紫外-可见光范围内，反射率随时间呈下降趋势，在318nm 的峰值，反射率的二阶导数与对数(ATP)呈高度相关。由于反射率的二阶导数与对数(平板计数)之间也存在相似的高相关性，提示反射率的二阶导数涉及活细胞中 ATP 的信息。根据这些观察结果，在肉表面反射率分析的基础上，给出了估算微生物源性 ATP 含量的线性关系。因此，开发的技术可以提供一个强有力的方式监测清洁度在屠宰场。这项研究部分由日本科学促进会(JSPS)拨款19:07178资助。参考文献 Adams and Moss 2007 Adams M.R. Moss 作案手法2007。食品微生物学。英国皇家化学学会，剑桥，第138页。巴格肖，2001年产。Bagshaw ATP 类似物一目了然细胞科学杂志1142001459460 Baumgart，1993 J. Baumgart Lebensmitteluberwachung und-qualitatssecherung Mikrobiologch-hygienische Schnellverfahren Fleischwirtschaft 731993292396 Bautista 等，1997 D.A。Bautista D.W.Sprung S. Barbut M.W.Griffiths 基于 ATP 生物发光测定的采样制度，用于评估食品研究国际组织在加工过程中关键控制点的家禽尸体的质量。301997803809 Brondum et al。 ，2000 J. Brondum L. Munck P. Henckel A. Karlsson E. Tornberg S.B。利用比较光谱法对猪肉的恩格尔森预测和持水性及其组成的研究[55]。肉类微生物学。(p. 410).纽约: 应用科学出版物。生物发光法作为一种快速测定肉类微生物区系的方法，国际食品微生物学杂志，1985年2月37日，381 Champiat 等人,2001 D. Champiat N. Matas B. Monofort H. Fraass 生物发光在 HACCP 发光中的应用162001193198 Chan 等，2002 D.E。陈 PN。Walker E.W.利用 ASAE 45200215191527 Clark 和 El-Shaarawi 的可见光和近红外光谱法预测猪肉品质特性。Clark A.H.用于检测总大肠菌群、大肠桿菌和其他指示细菌应用与环境微生物学的商业存在-缺失试剂盒的评估5921993380388 Cozzoliono 等人，2003 D. Cozzoliono N. Barlocco A. Vadell F. Ballesteros G. Gallieta 使用可见光和近红外反射光谱来预测完整和均化猪肉的颜色 LWT-Food Science and Technology 362003195202 de Boer and Beurmer，1999E.De Boer R.R.Beurmer 食源性微生物检测和分型方法国际食品微生物学杂志501999119130 del Moral 等，2009 F.G。德尔 · 道德 · A · 吉伦 · KG。马丁内斯 R.G。可见光和近红外反射光谱学对杜洛克和伊比利亚 Porl 神经网络的分类食品工程杂志902009540547 Dainty 等，1985 R.H。小巧的 R.A。爱德华兹 C.M。空气中自然污染牛肉冷藏过程中挥发性化合物形成的时间过程应用细菌学杂志591985303309 d’Souza，2001 S.F. 。D’Souza 微生物生物传感器生物传感器和生物电子学162001337353 Ellis and Goodacre，2001 D。肌肉食品微生物腐败的快速定量检测: 食品科学与技术的现状与未来趋势。122001414424 Fernandez-Cabanas 等，2007 V.M. Fernandez-Cabanas A. Garrido-Varo J. Gracia-Olmo E. De Pedro P. Dardenne 优化用于伊比利亚猪脂肪近红外校准的光谱预处理化学计量学和智能实验室系统872007104112 Fox，2001 S.Fox 世界卫生组织召开关于世界范围内疯牛病和 CJD 医学感染风险的会议。冈萨雷斯-马丁等人，2003 I. 冈萨雷斯-马丁冈萨雷斯-佩雷斯 · 埃尔南德斯 · 门德斯 · N · 阿尔瓦雷斯 · 格拉西亚用光纤探针近红外光谱学(NIRS)测定伊比利亚种猪皮下脂肪中的脂肪酸。肉类科学652003713719冈萨雷斯-马丁等人,2005年 I. Gonzalez-Martin C. Gonzalez-Perez N. Alvarez-Gracia J.M。冈萨雷斯-卡布雷拉利用远程反射光纤探针在线测定伊比利亚猪腰肌间脂肪脂肪酸组成。Gill K.G.牛顿冷藏肉类上有氧腐败菌群的发展应用细菌学杂志431977189195 Graumlich，1985 T.R。《食品科学生物发光杂志》1985116117,124 Griffiths，1996 M.W 对橙汁中微生物种群的 Graumlich 估算。三磷酸腺苷生物发光在食品工业中的作用: 对旧问题的新认识食品技术50619966272 Hawronskyj and Holah，1997 J.M。Hawronskyj J. Holah ATP: 食品科学与技术通用卫生监测器趋势819977984 Heeschen 等，1991 W.H。乳品工业中的快速方法。《微生物学和免疫学中的快速方法和自动化》 ，1991年，施普林格-出版社，Berlin Heidelberg 520532霍文-博林克等，2005年。霍温-博林克 H.W。Vedder J.W.M.Merks W.J.H.De Klein H.G.M.Reimert R. Frankhuizen W.H.A.M.范登布鲁克。返回文章页面兰布吉对近红外光谱仪测量结果的透视译者: pestwave 肉类科学692005417423杰克逊等人，1997年杰克逊 G.R. Acuff J.S. 迪克森肉类，家禽和海鲜 M.P. 道尔 L.R. 博伊查特 T.J. 蒙特维尔食品微生物学: 基础和前沿1997年美国广播公司出版社华盛顿 DC 83100杰伊，2005 J.M. 杰伊现代食品微生物学第六版。2005年 Aspen 出版社 Maryland Josell 等人，2000年 A. Josell L. Martinsson C. Borggaard J.R。利用视觉和近红外光谱技术肉类科学552000273278尼查斯等，1988 G.J。Nychas V.M.Dillon R.G.O < o < o < o < o < o < o < o < o < o < o < o < o < o < o < o < o < o < o < o < o < o < o < o < o < o < o log 10 3.2 aerobic CFU/cm 2 for pork carcasses. 4.3 Absorption maximum of pure ATP As shown in Fig. 2 , the transmittance decreased with an increase in ATP concentration, and different spectra showed the minimum absorbance at 260 nm. The wave length of 260 nm was in accordance with the maximum absorbance of ATP (259 nm) as previously reported by Bagshaw (2001) . 4.4 Estimation of ATP and plate count from reflectance Reflectance ( Fig. 3 ) showed a decreasing trend with time in the UV–Vis range, although there was a very little difference between the reflectance at 0 h and that at 24 h. To remove the background effect the raw data was transformed by using the 1st and the 2nd derivates. However, the 2nd derivative was chosen because the effect was more clearer in it. The 2nd derivative technique is often used to process NIR data. It helps to separate overlapping absorption bands, remove baseline shifts and increase apparent spectral resolution (Lin et al., 2004), although the derivatives are notoriously sensitive to noise (Tsai and Philpot, 1998). Many upward and downward peaks were observed when the 2nd derivative of raw reflectance spectra for all storage time periods (from 0 to 96 h) was taken ( Fig. 4 ). The analysis of correlation of peaks at 298, 318, 344 and 374 nm was conducted. These selected wavelengths were in the UV range, i.e., less than 400 nm. The greatest differences were obtained for all selected wavelengths, between time 0 and 96 h. The maximum differences between time 0 and 96 h were in the range of 318 nm. This wavelength range could mainly differentiate between samples at 0 and 96 h. Fig. 5 shows the correlation coefficient between the 2nd derivative of reflectance and log (ATP). This gave a high correlation between the value of the 2nd derivative and log (ATP). Considering bathochromic shift, any of these four wave lengths could be taken as the maximum absorption of ATP. On the other hand, it is widely known that the spectral absorption by ATP is usually masked by protein absorbance and cannot be exploited in spectroscopic studies ( Bagshaw, 2001 ). However, the graph of correlation coefficient between the 2nd derivative of reflectance and log (plate count) shown in Fig. 6 became very similar in shape to Fig. 5 . This indicated that the 2nd derivative of reflectance involved the information of ATP in viable cells. The understanding of this is also supported by the result that the amount of ATP corresponded to the plate count ( Fig. 1 ). From these considerations, the wave length of 318 nm showing the highest correlation coefficient was selected. The linear relationship between the value of the 2nd derivative and log (ATP) for the first 48 h at 318 nm is shown in Fig. 7 with the determination coefficient of 0.89. The similar relationship was also observed between the value of the 2nd derivative and log (plate counts) for the first 48 h at 318 nm with the determination coefficient of 0.83. The duration of the first 48 h chosen here means that pork meat samples were fresh. From these results, it is expected that the selection of appropriate wave length could give the real-time monitoring of ATP and/or viable cell count on meat surface by the use of reflectance information. The plate count gives an estimate of microbial contamination whereas the ATP bioluminescence method used in this study measures total ATP, from both microbial and non-microbial sources, and may be a better measure of the overall cleanliness of the carcass. Therefore, an exact relationship between the two methods should not be expected and results obtained from the two assay systems should be interpreted separately. Multiple linear regression analysis using more than one reflectance at different wave length is a powerful tool in estimating ATP and/or viable cell count on meat surface, and can lead to higher predictive power. However, such paradigm may lead to overfitting. Accordingly, in this study, only one wave length (i.e., 318 nm) was selected for the prediction of ATP. 5 Conclusions A real-time detection method for monitoring of ATP and viable cells on meat surface by using reflectance spectra was developed. The data showed that the plate count on the sample meat surface increased and it corresponded exactly to the increase in the amount of ATP during 84 h storage at 15 °C. The linear relationship between the amount of ATP and plate count was supported by its determination coefficient of 0.95. Reflectance showed a decreasing trend with time in UV–Vis range and at the peak of 318 nm, 2nd derivative of reflectance gave a high correlation with log (ATP). As a similar high correlation was also observed between the 2nd derivative of reflectance and log (plate count), it is suggested that the 2nd derivative of reflectance involved the information of ATP in viable cells. From these observations, a linear relationship was given for the estimation of the amount of microbialy-derived ATP on the basis of reflectance analysis of meat surface. Hence, the developed technique can give a powerful way for monitoring of cleanness at a slaughterhouse. Acknowledgements This research was partly funded by the Japan Society for the Promotion of Science (JSPS) Grant No. 19:07178 . References Adams and Moss, 2007 Adams, M.R., Moss, M.O., 2007. Food Microbiology, third ed. The Royal Society of Chemistry, Cambridge, pp. 138. Bagshaw, 2001 C.R. Bagshaw ATP analogues at a glance Journal of Cell Science 114 2001 459 460 Baumgart, 1993 J. Baumgart Lebensmitteluberwachung und–qualitatssicherung Mikrobiologisch- hygienische Schnellverfahren Fleischwirtschaft 73 1993 292 396 Bautista et al., 1997 D.A. Bautista D.W. Sprung S. Barbut M.W. Griffiths A sampling regime based on an ATP bioluminescence assay to assess the quality of poultry carcasses at critical control points during processing Food Research International 30 1997 803 809 Brondum et al., 2000 J. Brondum L. Munck P. Henckel A. Karlsson E. Tornberg S.B. Engelsen Prediction and water-holding capacity and composition of porcine meat by comparative spectroscopy Meat Science 55 2000 177 185 Brown, 1982 Brown, M. H. (1982). Meat microbiology. (p. 410). New York: Applied Science Publications. Bulte and Reuter, 1985 M. Bulte G. Reuter The bioluminescence as a rapid method for the determination of the microflora of meat International Journal of Food Microbiology 2 1985 371 381 Champiat et al., 2001 D. Champiat N. Matas B. Monofort H. Fraass Applications of bioluminescence to HACCP Luminescence 16 2001 193 198 Chan et al., 2002 D.E. Chan P.N. Walker E.W. Mills Prediction of pork quality characteristics using visible and NIR spectroscopy Transactions of ASAE 45 2002 1519 1527 Clark and El-Shaarawi, 1993 J.A. Clark A.H. El-Shaarawi Evaluation of commercial presence-absence test kits for detection of total coliforms, Escherichia coli, and other indicator bacteria Applied and Environmental Microbiology 59 2 1993 380 388 Cozzoliono et al., 2003 D. Cozzoliono N. Barlocco A. Vadell F. Ballesteros G. Gallieta The use of visible and near-infrared reflectance spectroscopy to predict colour on both intact and homogenized pork muscle LWT-Food Science and Technology 36 2003 195 202 de Boer and Beurmer, 1999 E. de Boer R.R. Beurmer Methodology for detection and typing of food borne microorganisms International Journal of Food Microbiology 50 1999 119 130 del Moral et al., 2009 F.G. del Moral A. Guillen K.G. del Moral F. O’Valle L. Martinez R.G. del Moral Duroc and Iberian porl neural network classification by visible and near infrared reflectance spectroscopy Journal of Food Engineering 90 2009 540 547 Dainty et al., 1985 R.H. Dainty R.A. Edwards C.M. Hibbard Time course of volatile compound formation during refrigerated storage of naturally contaminated beef in air Journal of Applied Bacteriology 59 1985 303 309 D’Souza, 2001 S.F. D’Souza Microbial biosensors Biosensors and Bioelectronics 16 2001 337 353 Ellis and Goodacre, 2001 D.I. Ellis R. Goodacre Rapid and quantitative detection of the microbial spoilage of muscle foods: Current status and future trends Trends in Food Science & Technology. 12 2001 414 424 Fernandez-Cabanas et al., 2007 V.M. Fernandez-Cabanas A. Garrido-Varo J. Gracia-Olmo E. De Pedro P. Dardenne Optimisation of the spectral pre-treatments used for Iberian pig fat NIR calibration Chemometrics and Intelligent Laboratory System 87 2007 104 112 Fox, 2001 S. Fox WHO to convene on worldwide risk of BSE and CJD Infections in Medicine. 18 2001 69 Gonzalez-Martin et al., 2003 I. Gonzalez-Martin C. Gonzalez-Perez J. Hernandez-Menderz N. Alvarez-Gracia Determination of fatty acids in the subcutaneous fat of Iberian breed swine by near infrared spectroscopy (NIRS) with a fiber-optic probe Meat Science 65 2003 713 719 Gonzalez-Martin et al., 2005 I. Gonzalez-Martin C. Gonzalez-Perez N. Alvarez-Gracia J.M. Gonzalez-Cabrera On-line determination of fatty acids composition in intramuscular fat of Iberian pork loin by NIRS with a remote reflectance fibre optic probe Meat Science 65 2005 713 719 Gill and Newton, 1977 C.O. Gill K.G. Newton The development of aerobic spoilage flora on meat stored at chill temperatures Journal of Applied Bacteriology 43 1977 189 195 Graumlich, 1985 T.R. Graumlich Estimation of microbial populations in orange juice by bioluminescence Journal of Food Science 50 1985 116 117, 124 Griffiths, 1996 M.W. Griffiths The role of ATP bioluminescence in the food industry: new light on old problems Food Technology 50 6 1996 62 72 Hawronskyj and Holah, 1997 J.M. Hawronskyj J. Holah ATP: A universal hygiene monitor Trends in Food Science & Technology 8 1997 79 84 Heeschen et al., 1991 W.H. Heeschen G. Suhren G. Hahn Rapid methods in the dairy industry A. Vaheri R.C. Tilton A. Balows Rapid methods and Automation in Microbiology and Immunology 1991 Springer-Verlag Berlin Heidelberg 520 532 Hoving-Bolink et al., 2005 A.H. Hoving-Bolink H.W. Vedder J.W.M. Merks W.J.H. de Klein H.G.M. Reimert R. Frankhuizen W.H.A.M. van den Broek enE. Lambooji Perspective of NIRS measurements early post mortem for prediction of pork quality Meat Science 69 2005 417 423 Jackson et al., 1997 T.C. Jackson G.R. Acuff J.S. Dickson Meat, poultry, and seafood M.P. Doyle L.R. Beuchat T.J. Montville Food microbiology: fundamentals and frontiers 1997 ASM Press Washington DC 83 100 Jay, 2005 J.M. Jay Modern food microbiology sixth ed. 2005 Aspen Publishers Maryland Josell et al., 2000 A. Josell L. Martinsson C. Borggaard J.R. Anderson E. Tornberg Determination of RN - phenotype in pigs at slaughter-line using visual and near-infrared spectroscopy Meat Science 55 2000 273 278 Nychas et al., 1988 G.J. Nychas V.M. Dillon R.G. Board Glucose, the key substrate in the microbiological changes occurring in meat and certain meat products Biotechnology and applied biochemistry 10 1988 203 231 Pickrell and Enserink, 2001 J. Pickrell M. Enserink Foot-and-mouth disease – UK outbreak is latest in global epidemic Science 291 2001 1677 Savenije et al., 2006 B. Savenije G.H. Geesink J.G.P. van der Palen G. Hemke Prediction of pork quality using visible/near infrared reflectance spectroscopy Meat Science 73 2006 181 184 Seymour et al., 1994 I.J. Seymour M.B. Cole P.J. Coote A substrate-mediated assay of bacterial proton efflux/influx to predict the degree of spoilage of beef mince stored at chill temperatures Journal of Applied Bacteriology 76 1994 608 615 Siragusa et al., 1996 G.R. Siragusa W.J. Dorsa C.N. Cutter Perino L.J. Kooh-maraie Use of a newly developed rapid microbial ATP bioluminescence assay to detect microbial contamination on poultry carcasses Journal of Bioluminescence and Chemilumoscence 11 1996 297 301 Stanbridge and Davies, 1998 L.H. Stanbridge A.R. Davies The microbiology of chill-stored meat Davies R. Board The microbiology of meat and poultry 1998 Blackie Academic & Professional London 174 219 Stanley, 1989 P.E. Stanley A review of bioluminescent ATP techniques in rapid microbiology Journal of Bioluminescence and Chemiluminescence 4 1989 375 380 Vanne et al., 1996 L. Vanne M. Karwoski S. Karppinen A.M. Sjoberg HACCP-based food quality control and rapid detection methods for microorganisms Food Control 7 1996 263 276|建立了一种紫外-可见光谱实时检测肉表面 ATP 和活细胞的方法。ATP 含量与平板计数呈线性关系，测定系数为0.95。反射光谱的二阶导数与前48h 的 ATP 含量和318nm 的活细胞计数均有较高的相关性。摘要屠宰场的清洁度监测依赖于传统的方法，例如目视检查或擦拭。目视检查并不总是准确的。采样需要熟练的工人和进一步的平板计数或 ATP 生物发光技术。为了解决这些问题，开发了一种基于无损紫外-可见光反射的快速检测技术来监测 ATP 和活细胞。样本为猪腰瘦肉。分别在0、24、48、72、84和96h 分析15 °C 保存的样品的 ATP、平板计数和紫外-可见光反射率。测定了样品在20 °C 下240 ~ 540nm 范围内的反射光谱，然后采集样品表面40 × 40mm2的面积，测定平板计数和 ATP 含量。84h 后，样品表面的平板计数由最初的29个增加到3.2 × 107CFU/cm2。84h 后，ATP 含量从最初的9.2 × 10 ~ (-15) mol/cm2增加到2.8 × 10 ~ (-10) mol/cm2。ATP 含量与平板计数呈线性关系，测定系数为0.95。原始光谱的二阶导数与 ATP 含量和活细胞计数在318nm 处的相关系数分别为0.89和0.83。实验结果表明，紫外-可见光反射光谱分析可以用于实时监测肉品表面 ATP 和/或平板计数，并且具有最佳波长。关键词 ATP 卫生实时监测无损检测猪肉光谱板计数吸光度反射率肉类质量1简介包括肉类和家禽的肌肉食品是人类饮食的一个组成部分，并已经存在了几千年。然而，在过去的二十年中，由于诸如疯牛病和口蹄疫等引人注目的食品安全问题(Fox，2001; Pickrell and Enserink，2001) ，公众的关注和意识已经提高。这些疾病的爆发，以及对肉类中特定病原菌的担忧，说明了在这个大规模生产行业中，对肉类微生物腐败的快速和准确检测系统的要求，这个行业每年的营业额达数十亿美元(Ellis and Goodacre，2001)。微生物在食物变质中的主要作用以及食物作为传播导致食源性疾病的微生物的媒介的作用已得到公认。在家禽、猪肉和牛肉的屠宰场，清洁度的监测主要依赖于传统的目视检查、拭子和随后的活细胞计数或 ATP 生物发光技术(Hawronskyj and Holah，1997)。这对于与食物加工过程有关的微生物危害尤其重要。就家禽、猪肉和牛肉加工而言，可以通过使用培养方法(即经典的标准盘计数法)进行常规屠体分析，来验证预防措施减少或消除微生物危害的有效性(Bautista et al。 ，1997)。然而，自从常规微生物分析应用于食品以来，科学家一直对发展更快速的“实时”微生物质量控制方法感兴趣。基于检测整个细胞或其代谢物的快速检测方法可以分为两大类: 直接方法基于检测有或没有孵育的细胞，间接方法基于测量代谢产物或由细胞生长引起的其他变化(Vanne 等，1996)。虽然快速检测方法正在发展中，传统的微生物监测方法在屠宰场的工作现场使用。然而，这些方法往往需要操作人员的技能，分析时间长，费用高。此外，目测检查并不总是准确的，擦拭需要熟练的工人和进一步的板计数分析，通常需要24-48小时。在过去的半个世纪中，传统的食品采样微生物学方法已经发生了变化，据估计，目前有超过40种方法来测量和检测肉类中的细菌变质(Jay，2005; Nychas 等，1988)。近二十年来微生物快速检测技术的发展可分为两大类: 计数检测和存在-缺失检测。1990年，通过使用安大略省环境部的 P-A 测试，Clark 和 El-Shaarawi (1993)比较了几种商业存在缺失(P-A)测试试剂盒区域，并在6个月内进行了评估。目前的快速计数方法一般基于显微镜、 ATP 生物发光或电现象的测量(Ellis and Goodacre，2001)。ATP 生物发光测定的使用是一种合乎逻辑的方法，并且依赖于所有活细胞都含有腺苷5’-三磷酸(ATP)的事实，ATP 是代谢的通用能量供体(Bautista 等，1997)。检测从细胞中提取的高能分子三磷酸腺苷(ATP)是一种广泛使用的间接测定方法。三磷酸腺苷(ATP)量是以镁离子存在下荧光素-荧光素酶系统释放的光能量来测量的(Stanley，1989)。该分析是快速的，只有几秒钟的卫生监测应用和不到一个小时的大多数其他样品。以前，人们认为这种技术有局限性，因为 ATP 存在于所有活细胞中。因此，来自靶细胞的内源性 ATP 必须在测定前被酶去除(Vanne 等，1996)。Siragusa 等(1996)指出，使用微生物 ATP 作为确定食品样品中微生物总数的手段的主要挑战是从微生物 ATP 中分离非微生物 ATP。他们描述的快速微生物 ATP 测定的基础是使用过滤装置提取体细胞 ATP: 然后在同一装置内，提取细菌 ATP 后进行定量。在显微镜方法的情况下，复杂的技术已经开发出来，其中微生物被荧光染料染色，并用荧光显微镜观察。ATP 生物发光通过测量培养细菌细胞中的 ATP 水平来计算该培养物中存在的细胞数量(Champiat 等，2001; de Boer 和 Beurmer，1999; D’Souza，2001; Siragusa 等，1996)。这种方法的问题在于，ATP 是所有活细胞的主要能量来源，而且食品样本本身也含有大量这种化学物质，在测量微生物 ATP 之前，必须将其销毁。因此，ATP 生物发光的测量可能最适合于检测与食品生产和制备相关的设备和机械的污染表面(Ellis and Goodacre，2001)。在只检测活细胞的情况下，必须考虑 ATP 生物发光技术的上述局限性和显微技术的缺陷。然而，来源于肉类和活细胞的 ATP 总量在清洁度评估中具有足够的重要性，因为来源于肉类的 ATP 是导致细菌腐败的营养来源。由于无损、无化学制备和快速检测的优点，光谱学已被广泛研究用于确定农产品的性质，但与植物材料相比，肉类产品的性质较少(Chan et al。 ，2002)。根据文献，VIS/NIRS 技术已应用于猪肉中以测定肌间脂肪(Hoving-Bolink 等，2005; Savenije 等，2006) ，脂肪酸组成(Fernandez-Cabanas 等，2007; Gonzalez-Martin 等，2003,2005) ，颜色(Cozzoliono 等，2003) ，持水能力(Brondum 等，2000)(Josell et al。 ，2000) ，以及 Doroc 和伊比利亚 porl 神经网络分类(del Moral。 ，2009) ，但是它还没有被应用于不同质量和价格的肉类的直接定性分类(del Moral。 ，2009)。此外，利用反射率数据测定食品质量的报告屈指可数。从目前的情况来看，这项研究的目的是开发一种实时检测方法，以监测 ATP 和活细胞在肉表面的反射光谱，可用于卫生管理。2材料及方法2.1肉类样本2.1从零售商取得切成5毫米厚的猪腰样本的瘦肉部分。3天前被宰杀，在零售商店保存在市场条件下。共有24个切片的样品被切成约6 × 6cm2的块，并分别放置在消毒的培养皿中。2.2实验装置样品分为6组，每组4个样品，在15 °C 恒温箱中保存。根据我们与屠宰场管理人员的谈话，我们选择了屠宰场工作室的最高温度作为储存温度，考虑到工人的健康，通常将温度控制在10 °C 至15 °C 之间。在储存0,24,48,72,84和96小时后进行测量。显示的每个值是四个部分的平均值。实验重复三次以验证结果。在所有的重复实验中都得到了相似的结果。在这里，为了简单起见，只显示了一个实验的结果。2.3 UV-Vis 反射光谱日本京都岛津公司的 UV-3600双光束光谱仪配备了积分球装置，用于记录肉样表面(9 × 20mm2)的反射光谱。测量的波长范围为240-1200纳米，分辨率为2纳米; 然而，从240-540纳米的结果只显示在第3节。为了确定 ATP 的最大吸收波长，用10mm 石英电池测定了 ATP 标准溶液(LL-100-1，日本东京东洋 B-Net 公司)系列稀释液的透过率。2.4光谱数据预处理光谱数据经常被预处理，以减少不良的系统噪声，如基线变化、光散射、路径长度差异等，并增强化学成份的贡献(Tigabu 和 Oden，2002)。在这项研究中，两种类型的预处理采用: 萨维茨基-戈雷一阶和二阶导数。在我们的案例中，系统变化的可能来源可能是由于路径长度的微小差异产生的定位个别肉类样本与轻微不同的大小在扫描过程中。2.5采样方案和微生物分析2.5.1采样方案采用拭子技术对覆盖光谱测量区域的猪肉表面(40 × 40mm2)材料进行采样。为了确保足够的采样，根据 Bautista 等人(1997) ，以水平模式擦拭样品，并在食指和拇指之间来回旋转的垂直模式中再次擦拭样品。将棉签末端切入9ml 无菌水中，搅拌均匀，进一步检测棉签平板计数和 ATP 含量。2.5.2平板计数从浸入拭子的磷酸盐缓冲溶液中制备拭子样品的连续稀释液，并将1ml 稀释液分配到 Petri ilmsTM (AC 板，Sumitomo 3M Ltd. ，Tokyo，Japan)上以进行总有氧计数。在35 °C 下培养48小时。2.5.3 ATP 生物发光测定将100微升拭子样品(将拭子浸入其中的磷酸盐缓冲溶液)注射到置于发光计(Luminescenser MCA，Atto Corporation，Tokyo，Japan)中的新鲜试管中，然后将100μl 萃取剂(LL-100-2，Toyo B-Net Co. Ltd. ，Tokyo，Japan)加入其中。10秒后，加入100μl 萤光素-荧光素酶复合物(LL-100-1，日本东京东洋 B-Net Co. Ltd. ，Tokyo，Japan) ，并测量光输出。从每个拭子，采取两个测量和手段计算确定相对光单位(RLU)。然后通过用 ATP 标准溶液(LL-100-1，Toyo B-Net Co. Ltd. ，Tokyo，Japan)在10-16-10-11摩尔/100微升范围内构建的标准曲线将 RLU 转化为 ATP 的量。2.6统计分析在贮存期间，随机抽取四块猪肉样本作统计分析。研究人员进行回归分析测试，以了解三磷酸腺苷(ATP)含量与盘子数量的关系。由于原始数据具有背景信息，因此采用一阶导数和二阶导数对原始数据进行转换，从中选出最优的一个。3结果3.1平板计数样品肉表面平板计数随贮藏时间延长而增加。试验开始时，初始计数为29CFU/cm2，贮藏84h 后为3.2 × 107CFU/cm2。3.2 ATP 含量随着贮藏时间的延长，ATP 含量由最初的9.2 × 10-5增加到2.8 × 10-10mol/cm2(贮藏后84h)。ATP 含量与平板计数呈线性关系，测定系数(R2)为0.95，如图1所示。3.3纯 ATP 的最大吸收量不同浓度(1 × 10-4 ~ 5.85 × 10-6M)的 ATP 溶液的透过率如图2所示。它表明，透过率随着 ATP 浓度的增加而降低，并且对不同 ATP 浓度的所有样品进行的光谱显示与透过率降低相关的最大吸光度在260nm 处(图2)。3.4根据反射率估计 ATP 和平板计数储存0-84小时获得的反射光谱如图3所示，在 UV-Vis 范围内(从240到540nm)。0小时和24小时的反射率差别很小。在48、72和84小时采集的样品的反射率随着储存时间的延长呈下降趋势。反射率数据的二阶导数被选为反射率一阶导数和二阶导数之间的最佳值，如图4所示。在紫外光谱范围内，观察到多个向上和向下的峰，并对298,318,344和374nm 的峰进行了相关性分析。图5显示了反射率二阶导数与对数(ATP)之间的相关系数。这给出了二阶导数和对数(ATP)之间的高度相关性。考虑到暗色移动，这四个波长中的任何一个都可以作为 ATP 的最大吸收。4在过去的几十年中，光谱方法在食品质量属性的评价中获得了重要性(Nadai，1983; Nadai 和 Mihalyi-Kengyel，1984)。尽管近红外光谱反映了与食品复杂质量有关的几个参数(Williams 和 Norris，2001) ，但是在近红外光谱范围内不能检测到 ATP 和/或微生物的信息。因此，在本研究中，UV-Vis 的应用范围从240到540纳米。4.1平板计数在这项研究中，样品被评估为新鲜，直到当细菌计数超过107CFU/g 的边界线，没有腐臭气味可以察觉。72小时后，平板计数达到107 CFU/g，样品散发出微弱的腐臭气味。这些样品处于变质的初始阶段，将被视为不可接受。盘子计数是肉类腐败的一个基本指标，肉类中107CFU/g 的计数被认为是不可接受的(Brown，1982)。检测106 CFU/g 的量级很重要，因为这是在肉类达到不可接受阶段之前完成的。新鲜肉类的 pH 值一般在5.5至5.9之间，并含有足够的葡萄糖和其他简单碳水化合物，以支持大约109 CFU/cm2。在冷藏温度下生长最快并利用葡萄糖的生物是假单胞菌(Gill and Newton，1977; Jay，2005; Seymour et al。 ，1994)。在107 CFU/cm2的水平下，异味可能会以淡淡的“奶制品”香味的形式变得明显，一旦表面细菌数量达到108 CFU/cm2，简单碳水化合物的供应已经耗尽，可识别的异味发展导致所谓的“感官”腐败(Jackson 等，1997; Jay，2005; Stanbridge and Davies，1998)。异味的发展取决于游离氨基酸利用发生的程度，这些气味已经被不同地描述为107CFU/cm2的乳制品/黄油/脂肪/奶酪，直到108CFU/cm2的病态甜味/水果香味，最后是109CFU/cm2的腐臭气味(Adams 和 Moss，2007; Dainty 等，1985)。4.2 ATP 含量图1显示了对数10 ATP 和对数10平板计数之间的线性关系。根据这个数字，ATP 分析法和平板计数法都能够评估猪肉样本的卫生情况。ATP 分析只能提供总细菌计数的估计，不能区分细菌(Baumgart，1993)。理论上，ATP 含量低至100fg (10-13g) ，相当于约100个细菌细胞。在实际条件下，灵敏度约为1000fg (10-12g) ，相当于约1000个细菌细胞或一至两个酵母细胞(Heeschen 等，1991)。应激细胞和处于静止生长阶段的细胞含有较少的 ATP，这也影响了结果(Bulte 和 Reuter，1985)。然而，另一方面，样品中 ATP 的含量提供了对活性微生物种群的估计，这在考虑产品的货架期时是很重要的。应激细胞也可以在 ATP 测定前复苏(Graumlich，1985)。这种酶，萤光素酶，通过化学计量反应将 ATP 提供的化学能转化为光。因此，产生的光量与存在的 ATP 浓度成正比，而 ATP 浓度又与样品中细胞的数量直接相关(Bautista 等，1997)。ATP 生物发光也可用于监测肉类加工过程中烫伤和冷却罐中的微生物污染。在对屠体污染和处理水质的 ATP 生物发光测定中，微生物细胞在裂解释放细胞内 ATP 之前通过过滤除去。为了简化这种方法，如果能够消除这一步骤以允许在屠体表面的拭子上直接检测 ATP，就像 ATP 生物发光卫生监测测试(Griffiths，1996)一样是可取的。然而，使用拭子测定将无法区分 ATP 与微生物和非微生物来源，但是结果将在2分钟内获得，而不是当纳入过滤步骤时所需的10-15分钟(Bautista 等，1997)。Siragusa 等(1996)开发了分段模型统计方法来确定检测灵敏度的下限，并使用该模型分析植物内数据。根据他们的研究，快速微生物 ATP 测试以线性方式响应猪肉胴体 > log 103.2需氧 CFU/cm2的微生物污染水平。4.3纯 ATP 的吸收峰如图2所示，透过率随 ATP 浓度的增加而降低，不同的光谱在260nm 处表现出最小的吸光度。260nm 的波长与之前 Bagshaw (2001)报道的 ATP (259nm)的最大吸光度一致。4.4从反射反射率估计 ATP 和平板计数(图3)在 UV-Vis 范围内随着时间的推移显示出减少的趋势，尽管在0小时和24小时的反射率之间差异很小。为了消除背景效应原始数据通过使用第一和第二导数进行转换。然而，选择二阶导数是因为它的效果更加明显。二阶导数技术是近红外数据处理的常用方法。它有助于分离重叠吸收带，消除基线偏移和增加明显的光谱分辨率(Lin et al。 ，2004) ，尽管衍生物是众所周知的对噪声敏感(Tsai 和 Philpot，1998)。在所有贮存时间段(0-96小时)的原始反射光谱二阶导数测量时，观察到许多向上和向下的峰值(图4)。对298、318、344和374nm 波段的峰进行了相关性分析。这些选定的波长在紫外线范围内，即小于400纳米。最大的差异获得所有选定的波长，在时间0和96小时之间。时间0 ~ 96h 的最大差异在318nm 范围内。这个波长范围主要可以区分0和96小时的样品。图5显示了反射率的二阶导数与对数(ATP)之间的相关系数。这给出了二阶导数的值与对数(ATP)之间的高度相关性。考虑到暗色移动，这四个波长中的任何一个都可以作为 ATP 的最大吸收。另一方面，众所周知，ATP 的光谱吸收通常被蛋白质吸收所掩盖，在光谱学研究中不能被利用(Bagshaw，2001)。然而，图6所示的反射率二阶导数与对数(平板计数)之间的相关系数图在形状上与图5非常相似。这表明反射率的二阶导数涉及活细胞内 ATP 的信息。对这一点的理解也得到了 ATP 含量与平板计数相对应的结果的支持(图1)。从这些考虑，波长318纳米显示最高的相关系数被选中。在318nm 处的前48h，二阶导数的值与对数(ATP)之间的线性关系如图7所示，测定系数为0.89。在318nm 处测定前48h，二阶导数值与对数(平板计数)之间也存在相似的关系，测定系数为0.83。这里选择的前48小时的持续时间意味着猪肉样本是新鲜的。从这些结果可以看出，选择合适的波长可以利用反射率信息实时监测肉表面 ATP 和/或活细胞计数。平板计数给出了微生物污染的估计值，而本研究中使用的 ATP 生物发光法测量来自微生物和非微生物来源的总 ATP，可能是对屠体整体清洁度的更好测量。因此，不应期望两种方法之间有确切的关系，从两种测定系统获得的结果应该分开解释。在不同波长下使用多个反射率的多个线性回归分析是估计肉表面 ATP 和/或存活细胞数量的有力工具，并且可以导致更高的预测能力。然而，这样的范式可能会导致过度拟合。因此，在这项研究中，只有一个波长(即，318纳米)被选择用于 ATP 的预测。5结论建立了一种利用反射光谱实时监测肉表面 ATP 和活细胞的方法。结果表明，在15 °C 下贮藏84h，样品表面的平板数量增加，与 ATP 含量的增加完全一致。其测定系数为0.95，支持 ATP 含量与平板计数之间的线性关系。在紫外-可见光范围内，反射率随时间呈下降趋势，在318nm 的峰值，反射率的二阶导数与对数(ATP)呈高度相关。由于反射率的二阶导数与对数(平板计数)之间也存在相似的高相关性，提示反射率的二阶导数涉及活细胞中 ATP 的信息。根据这些观察结果，在肉表面反射率分析的基础上，给出了估算微生物源性 ATP 含量的线性关系。因此，开发的技术可以提供一个强有力的方式监测清洁度在屠宰场。这项研究部分由日本科学促进会(JSPS)拨款19:07178资助。参考文献 Adams and Moss 2007 Adams M.R. Moss 作案手法2007。食品微生物学。英国皇家化学学会，剑桥，第138页。巴格肖，2001年产。Bagshaw ATP 类似物一目了然细胞科学杂志1142001459460 Baumgart，1993 J. Baumgart Lebensmitteluberwachung und-qualitatssecherung Mikrobiologch-hygienische Schnellverfahren Fleischwirtschaft 731993292396 Bautista 等，1997 D.A。Bautista D.W.Sprung S. Barbut M.W.Griffiths 基于 ATP 生物发光测定的采样制度，用于评估食品研究国际组织在加工过程中关键控制点的家禽尸体的质量。301997803809 Brondum et al。 ，2000 J. Brondum L. Munck P. Henckel A. Karlsson E. Tornberg S.B。利用比较光谱法对猪肉的恩格尔森预测和持水性及其组成的研究[55]。肉类微生物学。(p. 410).纽约: 应用科学出版物。生物发光法作为一种快速测定肉类微生物区系的方法，国际食品微生物学杂志，1985年2月37日，381 Champiat 等人,2001 D. Champiat N. Matas B. Monofort H. Fraass 生物发光在 HACCP 发光中的应用162001193198 Chan 等，2002 D.E。陈 PN。Walker E.W.利用 ASAE 45200215191527 Clark 和 El-Shaarawi 的可见光和近红外光谱法预测猪肉品质特性。Clark A.H.用于检测总大肠菌群、大肠桿菌和其他指示细菌应用与环境微生物学的商业存在-缺失试剂盒的评估5921993380388 Cozzoliono 等人，2003 D. Cozzoliono N. Barlocco A. Vadell F. Ballesteros G. Gallieta 使用可见光和近红外反射光谱来预测完整和均化猪肉的颜色 LWT-Food Science and Technology 362003195202 de Boer and Beurmer，1999E.De Boer R.R.Beurmer 食源性微生物检测和分型方法国际食品微生物学杂志501999119130 del Moral 等，2009 F.G。德尔 · 道德 · A · 吉伦 · KG。马丁内斯 R.G。可见光和近红外反射光谱学对杜洛克和伊比利亚 Porl 神经网络的分类食品工程杂志902009540547 Dainty 等，1985 R.H。小巧的 R.A。爱德华兹 C.M。空气中自然污染牛肉冷藏过程中挥发性化合物形成的时间过程应用细菌学杂志591985303309 d’Souza，2001 S.F. 。D’Souza 微生物生物传感器生物传感器和生物电子学162001337353 Ellis and Goodacre，2001 D。肌肉食品微生物腐败的快速定量检测: 食品科学与技术的现状与未来趋势。122001414424 Fernandez-Cabanas 等，2007 V.M. Fernandez-Cabanas A. Garrido-Varo J. Gracia-Olmo E. De Pedro P. Dardenne 优化用于伊比利亚猪脂肪近红外校准的光谱预处理化学计量学和智能实验室系统872007104112 Fox，2001 S.Fox 世界卫生组织召开关于世界范围内疯牛病和 CJD 医学感染风险的会议。冈萨雷斯-马丁等人，2003 I. 冈萨雷斯-马丁冈萨雷斯-佩雷斯 · 埃尔南德斯 · 门德斯 · N · 阿尔瓦雷斯 · 格拉西亚用光纤探针近红外光谱学(NIRS)测定伊比利亚种猪皮下脂肪中的脂肪酸。肉类科学652003713719冈萨雷斯-马丁等人,2005年 I. Gonzalez-Martin C. Gonzalez-Perez N. Alvarez-Gracia J.M。冈萨雷斯-卡布雷拉利用远程反射光纤探针在线测定伊比利亚猪腰肌间脂肪脂肪酸组成。Gill K.G.牛顿冷藏肉类上有氧腐败菌群的发展应用细菌学杂志431977189195 Graumlich，1985 T.R。《食品科学生物发光杂志》1985116117,124 Griffiths，1996 M.W 对橙汁中微生物种群的 Graumlich 估算。三磷酸腺苷生物发光在食品工业中的作用: 对旧问题的新认识食品技术50619966272 Hawronskyj and Holah，1997 J.M。Hawronskyj J. Holah ATP: 食品科学与技术通用卫生监测器趋势819977984 Heeschen 等，1991 W.H。乳品工业中的快速方法。《微生物学和免疫学中的快速方法和自动化》 ，1991年，施普林格-出版社，Berlin Heidelberg 520532霍文-博林克等，2005年。霍温-博林克 H.W。Vedder J.W.M.Merks W.J.H.De Klein H.G.M.Reimert R. Frankhuizen W.H.A.M.范登布鲁克。返回文章页面兰布吉对近红外光谱仪测量结果的透视译者: pestwave 肉类科学692005417423杰克逊等人，1997年杰克逊 G.R. Acuff J.S. 迪克森肉类，家禽和海鲜 M.P. 道尔 L.R. 博伊查特 T.J. 蒙特维尔食品微生物学: 基础和前沿1997年美国广播公司出版社华盛顿 DC 83100杰伊，2005 J.M. 杰伊现代食品微生物学第六版。2005年 Aspen 出版社 Maryland Josell 等人，2000年 A. Josell L. Martinsson C. Borggaard J.R。利用视觉和近红外光谱技术肉类科学552000273278尼查斯等，1988 G.J。Nychas V.M.Dillon R.G.O < o < o < o < o < o < o < o < o < o < o < o < o < o < o < o < o < o < o < o < o < o < o < o < o < o < o D which are mapped to decisions through a known mapping function. For content moderation, such labels can correspond to violation reasons (e.g. nudity, violence), while the space of decisions is binary: remove the content or keep it up. In this setting, it is more important to make the right decision rather than estimating the correct underlying label. Existing methods typically separate out the labels to decisions mapping from the modeling of annotators, leading to sub-optimal statistical inference efficiency and excessive computation complexity. We propose a novel confusion matrix model for each annotator that leverages this mapping. Our model is parameterized in a hierarchical manner with both population parameters shared across annotators to model shared confusions and individual parameters to admit heterogeneity among annotators. With extensive numerical experiments, we demonstrate that the proposed model substantially improves accuracy over existing methods and scales well for moderate and large L. In a real-world application on content moderation at Meta, the proposed method offers a 13% improvement in AUC over prior methods, including Meta's existing model in production.|在线平台通常依赖人工注释器为内容审核等任务做出实时操作决策。虽然众包模型被提议用于聚合噪音标签，但是当注释者在一个大空间中生成一个标签(例如，从复杂的评论树生成)时，它们不能很好地推广。我们研究了一个新的众包设置与 D 可能的操作决策或结果，但注释者产生的标签在一个更大的空间大小 L > D 映射到决策通过一个已知的映射函数。对于内容审查，这样的标签可以对应违规原因(如裸体，暴力) ，而空间的决定是二元的: 删除内容或保持它。在这种情况下，更重要的是做出正确的决定，而不是估计正确的基础标签。现有的方法通常将标签和决策映射从注释器的建模中分离出来，导致推论统计学效率不够理想，计算复杂度过高。我们为每个注释者提供一个新的混淆矩阵模型来利用这个映射。我们的模型以分层的方式参数化，在注释者之间共享群体参数来模拟共享的混淆，以及个体参数来承认注释者之间的异质性。通过大量的数值实验，我们证明了所提出的模型比现有的方法和尺度有很大的提高精度，适用于中等大小的 L。在 Meta 内容审核的实际应用中，提出的方法比以前的方法(包括 Meta 现有的生产模型)提供了13% 的 AUC 改进。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=From+Labels+to+Decisions:+A+Mapping-Aware+Annotator+Model)|0| -|[M3PT: A Multi-Modal Model for POI Tagging](https://doi.org/10.1145/3580305.3599862)|Jingsong Yang, Guanzhou Han, Deqing Yang, Jingping Liu, Yanghua Xiao, Xiang Xu, Baohua Wu, Shenghua Ni|East China University of Science and Technology; Fudan University; Alibaba Group|POI tagging aims to annotate a point of interest (POI) with some informative tags, which facilitates many services related to POIs, including search, recommendation, and so on. Most of the existing solutions neglect the significance of POI images and seldom fuse the textual and visual features of POIs, resulting in suboptimal tagging performance. In this paper, we propose a novel Multi-Modal Model for POI Tagging, namely M3PT, which achieves enhanced POI tagging through fusing the target POI's textual and visual features, and the precise matching between the multi-modal representations. Specifically, we first devise a domain-adaptive image encoder (DIE) to obtain the image embeddings aligned to their gold tags' semantics. Then, in M3PT's text-image fusion module (TIF), the textual and visual representations are fully fused into the POIs' content embeddings for the subsequent matching. In addition, we adopt a contrastive learning strategy to further bridge the gap between the representations of different modalities. To evaluate the tagging models' performance, we have constructed two high-quality POI tagging datasets from the real-world business scenario of Ali Fliggy. Upon the datasets, we conducted the extensive experiments to demonstrate our model's advantage over the baselines of uni-modality and multi-modality, and verify the effectiveness of important components in M3PT, including DIE, TIF and the contrastive learning strategy.|POI 标记的目的是用一些信息性标记来注释兴趣点(POI) ，这有利于许多与 POI 相关的服务，包括搜索、推荐等。现有的解决方案大多忽略了 POI 图像的重要性，很少融合 POI 图像的文本特征和视觉特征，导致标注性能不理想。本文提出了一种新的多模态 POI 标注模型 M3PT，通过融合目标 POI 的文本特征和视觉特征，以及多模态表示之间的精确匹配，实现了 POI 标注的增强。具体来说，我们首先设计一个域自适应图像编码器(DIE)来获得与其金标记语义对齐的图像嵌入。然后，在 M3PT 的文本-图像融合模块(TIF)中，将文本和可视化表示完全融合到 POI 的内容嵌入中，进行后续匹配。此外，我们采用对比学习策略，进一步缩小不同模式表征之间的差距。为了评估标签模型的性能，我们从 Ali Fliggy 的实际业务场景中构建了两个高质量的 POI 标签数据集。在数据集上，我们进行了广泛的实验，以验证我们的模型在单模态和多模态基线上的优势，并验证了 M3PT 中的重要组件，包括 DIE、 TIF 和对比学习策略的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=M3PT:+A+Multi-Modal+Model+for+POI+Tagging)|0| +|[M3PT: A Multi-Modal Model for POI Tagging](https://doi.org/10.1145/3580305.3599862)|Jingsong Yang, Guanzhou Han, Deqing Yang, Jingping Liu, Yanghua Xiao, Xiang Xu, Baohua Wu, Shenghua Ni|Alibaba Group; East China University of Science and Technology; Fudan University|POI tagging aims to annotate a point of interest (POI) with some informative tags, which facilitates many services related to POIs, including search, recommendation, and so on. Most of the existing solutions neglect the significance of POI images and seldom fuse the textual and visual features of POIs, resulting in suboptimal tagging performance. In this paper, we propose a novel Multi-Modal Model for POI Tagging, namely M3PT, which achieves enhanced POI tagging through fusing the target POI's textual and visual features, and the precise matching between the multi-modal representations. Specifically, we first devise a domain-adaptive image encoder (DIE) to obtain the image embeddings aligned to their gold tags' semantics. Then, in M3PT's text-image fusion module (TIF), the textual and visual representations are fully fused into the POIs' content embeddings for the subsequent matching. In addition, we adopt a contrastive learning strategy to further bridge the gap between the representations of different modalities. To evaluate the tagging models' performance, we have constructed two high-quality POI tagging datasets from the real-world business scenario of Ali Fliggy. Upon the datasets, we conducted the extensive experiments to demonstrate our model's advantage over the baselines of uni-modality and multi-modality, and verify the effectiveness of important components in M3PT, including DIE, TIF and the contrastive learning strategy.|POI 标记的目的是用一些信息性标记来注释兴趣点(POI) ，这有利于许多与 POI 相关的服务，包括搜索、推荐等。现有的解决方案大多忽略了 POI 图像的重要性，很少融合 POI 图像的文本特征和视觉特征，导致标注性能不理想。本文提出了一种新的多模态 POI 标注模型 M3PT，通过融合目标 POI 的文本特征和视觉特征，以及多模态表示之间的精确匹配，实现了 POI 标注的增强。具体来说，我们首先设计一个域自适应图像编码器(DIE)来获得与其金标记语义对齐的图像嵌入。然后，在 M3PT 的文本-图像融合模块(TIF)中，将文本和可视化表示完全融合到 POI 的内容嵌入中，进行后续匹配。此外，我们采用对比学习策略，进一步缩小不同模式表征之间的差距。为了评估标签模型的性能，我们从 Ali Fliggy 的实际业务场景中构建了两个高质量的 POI 标签数据集。在数据集上，我们进行了广泛的实验，以验证我们的模型在单模态和多模态基线上的优势，并验证了 M3PT 中的重要组件，包括 DIE、 TIF 和对比学习策略的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=M3PT:+A+Multi-Modal+Model+for+POI+Tagging)|0| |[Self-supervised Classification of Clinical Multivariate Time Series using Time Series Dynamics](https://doi.org/10.1145/3580305.3599954)|Yakir Yehuda, Daniel Freedman, Kira Radinsky||To improve the accuracy of clinical multivariate time series (MTS) classification (such as EEG and ECG) by a novel self-supervised paradigm that directly captures the dynamics between the different time series learned together to optimize the classification task. Labels in clinical datasets are very often insufficient. One way to address this challenge is leveraging self-supervision. This paradigm attempts to identify a supervisory signal inherent within a dataset to serve as a surrogate label. We present a novel form of self-supervision: dynamics of clinical MTS. Unlike other self-supervision methods, such as masking, that are intuitive but still heuristic, we suggest to learn a representation justified by Koopman theory. The latter was shown useful for representing clinical time series and can be used as a form of surrogate task to improve the clinical MTS classification. In the ECG task, we show that our proposed framework achieved higher sensitivity and specificity than the state-of-the-art (SOTA) baseline over numerous common diagnoses. For EEG abnormality classification, our proposed framework also achieved higher sensitivity and specificity than the SOTA baseline. All results are statistically significant. Our technique yields reliable clinical diagnosis in an empirical study employing signals from thousands of patients in multiple clinical tasks employing two types of clinical-grade sensors (ECG and EEG) as compared to the state-of-the-art machine learning. Leveraging time-series-dynamics self-supervision can help mitigate the lack of labels in clinical datasets used for training machine learning algorithms and significantly improve their performance. Specifically, the ECG system presented in this work is being trialed in hospitals, used by top cardiologists for patient diagnosis and treatment. We believe that the deployment of such cutting-edge technology will significantly improve the accuracy and speed of cardiac assessments.|为了提高临床多变量时间序列(MTS)分类(如 EEG 和 ECG)的准确性，通过一种新的自我监督范式，直接捕获共同学习的不同时间序列之间的动态，以优化分类任务。临床数据集中的标签通常是不够的。应对这一挑战的一种方法是利用自我监督。这个范例试图识别数据集内部固有的监督信号，以作为代理标签。我们提出了一种新的自我监督形式: 临床 MTS 的动力学。不像其他自我监督的方法，如掩蔽，这是直观的，但仍然是启发式的，我们建议学习一个表示证明库普曼理论。后者显示有用的代表临床时间序列，可作为一种形式的替代任务，以改善临床 MTS 分类。在心电图任务中，我们展示了我们提出的框架在许多常见诊断中取得了比最先进的(SOTA)基线更高的灵敏度和特异度。对于脑电图异常分类，我们提出的框架也获得了比 SOTA 基线更高的灵敏度和特异度。所有的结果都有统计学意义。与最先进的机器学习相比，我们的技术在一项实验研究中产生了可靠的临床诊断，该研究使用了来自数千名患者的信号，采用了两种临床等级传感器(ECG 和 EEG)进行多种临床任务。利用时间序列动力学自我监控可以帮助减少用于训练机器学习算法的临床数据集中缺乏标签的情况，并显著提高其性能。具体来说，在这项工作中提出的心电图系统正在医院进行试验，由顶级心脏病专家用于病人的诊断和治疗。我们相信，这种尖端技术的部署将大大提高心脏评估的准确性和速度。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Self-supervised+Classification+of+Clinical+Multivariate+Time+Series+using+Time+Series+Dynamics)|0| |[DGI: An Easy and Efficient Framework for GNN Model Evaluation](https://doi.org/10.1145/3580305.3599805)|Peiqi Yin, Xiao Yan, Jinjing Zhou, Qiang Fu, Zhenkun Cai, James Cheng, Bo Tang, Minjie Wang||While many systems have been developed to train graph neural networks (GNNs), efficient model evaluation, which computes node embedding according to a given model, remains to be addressed. For instance, using the widely adopted node-wise approach, model evaluation can account for over 90% of the time in the end-to-end training process due to neighbor explosion, which means that a node accesses its multi-hop neighbors. The layer-wise approach avoids neighbor explosion by conducting computation layer by layer in GNN models. However, layer-wise model evaluation takes considerable implementation efforts because users need to manually decompose the GNN model into layers, and different implementations are required for GNN models with different structures. In this paper, we present DGI -a framework for easy and efficient GNN model evaluation, which automatically translates the training code of a GNN model for layer-wise evaluation to minimize user effort. DGI is general for different GNN models and evaluation requests (e.g., computing embedding for all or some of the nodes), and supports out-of-core execution on large graphs that cannot fit in CPU memory. Under the hood, DGI traces the computation graph of GNN model, partitions the computation graph into layers that are suitable for layer-wise evaluation according to tailored rules, and executes each layer efficiently by reordering the computation tasks and managing device memory consumption. Experiment results show that DGI matches hand-written implementations of layer-wise evaluation in efficiency and consistently outperforms node-wise evaluation across different datasets and hardware settings, and the speedup can be over 1,000x.|虽然已经开发了许多系统来训练图形神经网络(GNN) ，但是根据给定的模型计算节点嵌入的有效模型评估仍然有待解决。例如，使用广泛采用的节点分析方法，模型评估可以在端到端训练过程中占用90% 以上的时间，由于邻居爆炸，这意味着一个节点访问它的多跳邻居。在 GNN 模型中，采用分层方法逐层进行计算，避免了相邻爆炸。然而，层次模型评估需要大量的实现工作，因为用户需要手动地将 GNN 模型分解成层，并且具有不同结构的 GNN 模型需要不同的实现。本文提出了一个简单有效的 GNN 模型评估框架 DGI，它自动翻译 GNN 模型的训练代码进行分层评估，以最小化用户的工作量。DGI 通用于不同的 GNN 模型和求值请求(例如，对所有或部分节点进行计算嵌入) ，并支持在 CPU 内存无法容纳的大型图表上执行核外操作。DGI 通过对 GNN 模型的计算图进行跟踪，根据裁剪规则将计算图划分为适合分层评估的层次，并通过重新排序计算任务和管理设备内存消耗来有效地执行每一层。实验结果表明，DGI 在效率上与手写的分层评估实现相匹配，并且在不同数据集和硬件设置下的性能一致地优于节点评估，加速比可达1000倍以上。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=DGI:+An+Easy+and+Efficient+Framework+for+GNN+Model+Evaluation)|0| |[Learning Multivariate Hawkes Process via Graph Recurrent Neural Network](https://doi.org/10.1145/3580305.3599857)|Kanghoon Yoon, Youngjun Im, Jingyu Choi, Taehwan Jeong, Jinkyoo Park||This paper presents a novel approach for modeling and predicting patterns of events in time-series learning, named graph recurrent temporal point process (GRTPP). Prior research has focused on using deep learning techniques, such as recurrent neural networks (RNNs) or attention-based sequential data embedding, on modeling the time-varying intensity of events. However, these models were typically limited to modeling a single intensity function capturing the event occurrence of all event types simultaneously. GRTPP addresses this issue by encoding multivariate event sequences into a sequence of graphs, where each node contains information about the event occurrence and time. The sequence of graphs is then embedded into node embeddings for each event type, taking into account the relationships between the event types. By integrating the estimated intensity functions, GRTPP predicts the event type and the timing of the next event. The proposed GRTPP model offers improved effectiveness and explainability compared to previous models, as demonstrated through empirical evaluations on five real-world datasets and the actual credit card transaction dataset. The code is available at https://github.com/im0j/GRTPP https://github.com/im0j/GRTPP.|本文提出了一种新的时间序列学习中事件模式的建模和预测方法——图回归时间点过程(GRTPP)。先前的研究主要集中在使用深度学习技术，如递归神经网络(RNN)或基于注意力的序列数据嵌入，对事件的时变强度进行建模。然而，这些模型通常仅限于建模一个单一的强度函数，捕获同时发生的所有事件类型。GRTPP 通过将多变量事件序列编码为图序列来解决这个问题，其中每个节点包含关于事件发生和时间的信息。然后，考虑到事件类型之间的关系，将图序列嵌入到每个事件类型的节点嵌入中。通过积分估计的强度函数，GRTPP 预测下一个事件的类型和时间。通过对五个实际数据集和实际信用卡交易数据集的实证评估，所提出的 GRTPP 模型与以前的模型相比，提高了有效性和可解释性。密码可在 https://github.com/im0j/grtpp https://github.com/im0j/grtpp 查阅。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Learning+Multivariate+Hawkes+Process+via+Graph+Recurrent+Neural+Network)|0| -|[Generating Synergistic Formulaic Alpha Collections via Reinforcement Learning](https://doi.org/10.1145/3580305.3599831)|Shuo Yu, Hongyan Xue, Xiang Ao, Feiyang Pan, Jia He, Dandan Tu, Qing He|Institute of Computing Technology, Chinese Academy of Sciences; Huawei EI Innovation Lab|In the field of quantitative trading, it is common practice to transform raw historical stock data into indicative signals for the market trend. Such signals are called alpha factors. Alphas in formula forms are more interpretable and thus favored by practitioners concerned with risk. In practice, a set of formulaic alphas is often used together for better modeling precision, so we need to find synergistic formulaic alpha sets that work well together. However, most traditional alpha generators mine alphas one by one separately, overlooking the fact that the alphas would be combined later. In this paper, we propose a new alpha-mining framework that prioritizes mining a synergistic set of alphas, i.e., it directly uses the performance of the downstream combination model to optimize the alpha generator. Our framework also leverages the strong exploratory capabilities of reinforcement learning~(RL) to better explore the vast search space of formulaic alphas. The contribution to the combination models' performance is assigned to be the return used in the RL process, driving the alpha generator to find better alphas that improve upon the current set. Experimental evaluations on real-world stock market data demonstrate both the effectiveness and the efficiency of our framework for stock trend forecasting. The investment simulation results show that our framework is able to achieve higher returns compared to previous approaches.|在定量交易领域，将原始历史股票数据转化为市场趋势的指示性信号是一种常见的做法。这种信号被称为阿尔法因子。公式形式的阿尔法更易于解释，因此受到关注风险的从业人员的青睐。在实践中，一组公式化 alpha 通常一起使用以获得更好的建模精度，因此我们需要找到协同的公式化 alpha 集，以便能够很好地协同工作。然而，大多数传统的 alpha 生成器都是分别挖掘一个又一个的 alpha，忽略了以后会合并 alpha 的事实。在本文中，我们提出了一个新的阿尔法挖掘框架，优先挖掘一个协同的阿尔法集，即它直接使用下游组合模型的性能来优化阿尔法生成器。我们的框架还利用了强化学习 ~ (RL)强大的探索能力来更好地探索公式化 alpha 的广阔搜索空间。对组合模型性能的贡献被指定为 RL 过程中使用的返回值，从而驱动 alpha 生成器在当前集合的基础上寻找更好的 alpha。通过对实际股票市场数据的实验评估，验证了本文提出的股票趋势预测框架的有效性和有效性。投资模拟结果表明，与以往的方法相比，我们的框架能够获得更高的收益。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Generating+Synergistic+Formulaic+Alpha+Collections+via+Reinforcement+Learning)|0| -|[LibAUC: A Deep Learning Library for X-Risk Optimization](https://doi.org/10.1145/3580305.3599861)|Zhuoning Yuan, Dixian Zhu, ZiHao Qiu, Gang Li, Xuanhui Wang, Tianbao Yang|University of Iowa; Texas AM University; Nanjing University; Google Research|This paper introduces the award-winning deep learning (DL) library called LibAUC for implementing state-of-the-art algorithms towards optimizing a family of risk functions named X-risks. X-risks refer to a family of compositional functions in which the loss function of each data point is defined in a way that contrasts the data point with a large number of others. They have broad applications in AI for solving classical and emerging problems, including but not limited to classification for imbalanced data (CID), learning to rank (LTR), and contrastive learning of representations (CLR). The motivation of developing LibAUC is to address the convergence issues of existing libraries for solving these problems. In particular, existing libraries may not converge or require very large mini-batch sizes in order to attain good performance for these problems, due to the usage of the standard mini-batch technique in the empirical risk minimization (ERM) framework. Our library is for deep X-risk optimization (DXO) that has achieved great success in solving a variety of tasks for CID, LTR and CLR. The contributions of this paper include: (1) It introduces a new mini-batch based pipeline for implementing DXO algorithms, which differs from existing DL pipeline in the design of controlled data samplers and dynamic mini-batch losses; (2) It provides extensive benchmarking experiments for ablation studies and comparison with existing libraries. The LibAUC library features scalable performance for millions of items to be contrasted, faster and better convergence than existing libraries for optimizing X-risks, seamless PyTorch deployment and versatile APIs for various loss optimization. Our library is available to the open source community at https://github.com/Optimization-AI/LibAUC, to facilitate further academic research and industrial applications.|本文介绍了获奖的深度学习(DL)库 LibAUC，该库用于实现最先进的算法，以优化一系列名为 X-risk 的风险函数。X 风险是指一系列复合函数，其中每个数据点的损失函数的定义方式是将该数据点与大量其他数据点进行对比。它们在人工智能中广泛应用于解决经典问题和新兴问题，包括但不限于不平衡数据分类(CID)、学习排序(LTR)和对比表征学习(CLR)。发展 LibAUC 的动机是解决现有图书馆的融合问题，以解决这些问题。特别是，由于在经验风险最小化(ERM)框架中使用了标准的迷你批处理技术，现有的库可能不会收敛或需要非常大的迷你批处理大小来获得良好的性能。我们的库是用于深度 X 风险优化(DXO)的，它在解决 CID、 LTR 和 CLR 的各种任务方面取得了巨大的成功。本文的主要贡献包括: (1)介绍了一种新的基于小批量的 DXO 算法实现流水线，该流水线不同于现有的 DL 流水线，在设计受控数据采样器和动态小批量损失方面有所不同; (2)为烧蚀研究提供了广泛的基准实验，并与现有的数据库进行了比较。LibAUC 库具有可扩展性能，可以对数百万个项目进行对比，比现有库更快、更好地收敛，可以优化 X 风险，可以无缝地部署 PyTorch，还可以使用多种 API 进行各种损失优化。我们的图书馆可供开放源码社区 https://github.com/optimization-ai/libauc 使用，以促进进一步的学术研究和工业应用。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=LibAUC:+A+Deep+Learning+Library+for+X-Risk+Optimization)|0| +|[Generating Synergistic Formulaic Alpha Collections via Reinforcement Learning](https://doi.org/10.1145/3580305.3599831)|Shuo Yu, Hongyan Xue, Xiang Ao, Feiyang Pan, Jia He, Dandan Tu, Qing He|Huawei EI Innovation Lab; Institute of Computing Technology, Chinese Academy of Sciences|In the field of quantitative trading, it is common practice to transform raw historical stock data into indicative signals for the market trend. Such signals are called alpha factors. Alphas in formula forms are more interpretable and thus favored by practitioners concerned with risk. In practice, a set of formulaic alphas is often used together for better modeling precision, so we need to find synergistic formulaic alpha sets that work well together. However, most traditional alpha generators mine alphas one by one separately, overlooking the fact that the alphas would be combined later. In this paper, we propose a new alpha-mining framework that prioritizes mining a synergistic set of alphas, i.e., it directly uses the performance of the downstream combination model to optimize the alpha generator. Our framework also leverages the strong exploratory capabilities of reinforcement learning~(RL) to better explore the vast search space of formulaic alphas. The contribution to the combination models' performance is assigned to be the return used in the RL process, driving the alpha generator to find better alphas that improve upon the current set. Experimental evaluations on real-world stock market data demonstrate both the effectiveness and the efficiency of our framework for stock trend forecasting. The investment simulation results show that our framework is able to achieve higher returns compared to previous approaches.|在定量交易领域，将原始历史股票数据转化为市场趋势的指示性信号是一种常见的做法。这种信号被称为阿尔法因子。公式形式的阿尔法更易于解释，因此受到关注风险的从业人员的青睐。在实践中，一组公式化 alpha 通常一起使用以获得更好的建模精度，因此我们需要找到协同的公式化 alpha 集，以便能够很好地协同工作。然而，大多数传统的 alpha 生成器都是分别挖掘一个又一个的 alpha，忽略了以后会合并 alpha 的事实。在本文中，我们提出了一个新的阿尔法挖掘框架，优先挖掘一个协同的阿尔法集，即它直接使用下游组合模型的性能来优化阿尔法生成器。我们的框架还利用了强化学习 ~ (RL)强大的探索能力来更好地探索公式化 alpha 的广阔搜索空间。对组合模型性能的贡献被指定为 RL 过程中使用的返回值，从而驱动 alpha 生成器在当前集合的基础上寻找更好的 alpha。通过对实际股票市场数据的实验评估，验证了本文提出的股票趋势预测框架的有效性和有效性。投资模拟结果表明，与以往的方法相比，我们的框架能够获得更高的收益。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Generating+Synergistic+Formulaic+Alpha+Collections+via+Reinforcement+Learning)|0| +|[LibAUC: A Deep Learning Library for X-Risk Optimization](https://doi.org/10.1145/3580305.3599861)|Zhuoning Yuan, Dixian Zhu, ZiHao Qiu, Gang Li, Xuanhui Wang, Tianbao Yang|Google Research; Nanjing University; University of Iowa; Texas AM University|This paper introduces the award-winning deep learning (DL) library called LibAUC for implementing state-of-the-art algorithms towards optimizing a family of risk functions named X-risks. X-risks refer to a family of compositional functions in which the loss function of each data point is defined in a way that contrasts the data point with a large number of others. They have broad applications in AI for solving classical and emerging problems, including but not limited to classification for imbalanced data (CID), learning to rank (LTR), and contrastive learning of representations (CLR). The motivation of developing LibAUC is to address the convergence issues of existing libraries for solving these problems. In particular, existing libraries may not converge or require very large mini-batch sizes in order to attain good performance for these problems, due to the usage of the standard mini-batch technique in the empirical risk minimization (ERM) framework. Our library is for deep X-risk optimization (DXO) that has achieved great success in solving a variety of tasks for CID, LTR and CLR. The contributions of this paper include: (1) It introduces a new mini-batch based pipeline for implementing DXO algorithms, which differs from existing DL pipeline in the design of controlled data samplers and dynamic mini-batch losses; (2) It provides extensive benchmarking experiments for ablation studies and comparison with existing libraries. The LibAUC library features scalable performance for millions of items to be contrasted, faster and better convergence than existing libraries for optimizing X-risks, seamless PyTorch deployment and versatile APIs for various loss optimization. Our library is available to the open source community at https://github.com/Optimization-AI/LibAUC, to facilitate further academic research and industrial applications.|本文介绍了获奖的深度学习(DL)库 LibAUC，该库用于实现最先进的算法，以优化一系列名为 X-risk 的风险函数。X 风险是指一系列复合函数，其中每个数据点的损失函数的定义方式是将该数据点与大量其他数据点进行对比。它们在人工智能中广泛应用于解决经典问题和新兴问题，包括但不限于不平衡数据分类(CID)、学习排序(LTR)和对比表征学习(CLR)。发展 LibAUC 的动机是解决现有图书馆的融合问题，以解决这些问题。特别是，由于在经验风险最小化(ERM)框架中使用了标准的迷你批处理技术，现有的库可能不会收敛或需要非常大的迷你批处理大小来获得良好的性能。我们的库是用于深度 X 风险优化(DXO)的，它在解决 CID、 LTR 和 CLR 的各种任务方面取得了巨大的成功。本文的主要贡献包括: (1)介绍了一种新的基于小批量的 DXO 算法实现流水线，该流水线不同于现有的 DL 流水线，在设计受控数据采样器和动态小批量损失方面有所不同; (2)为烧蚀研究提供了广泛的基准实验，并与现有的数据库进行了比较。LibAUC 库具有可扩展性能，可以对数百万个项目进行对比，比现有库更快、更好地收敛，可以优化 X 风险，可以无缝地部署 PyTorch，还可以使用多种 API 进行各种损失优化。我们的图书馆可供开放源码社区 https://github.com/optimization-ai/libauc 使用，以促进进一步的学术研究和工业应用。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=LibAUC:+A+Deep+Learning+Library+for+X-Risk+Optimization)|0| |[Towards a Generic Framework for Mechanism-guided Deep Learning for Manufacturing Applications](https://doi.org/10.1145/3580305.3599913)|Hanbo Zhang, Jiangxin Li, Shen Liang, Peng Wang, Themis Palpanas, Chen Wang, Wei Wang, Haoxuan Zhou, Jianwei Song, Wen Lu||Manufacturing data analytics tasks are traditionally undertaken with Mechanism Models (MMs), which are domain-specific mathematical equations modeling the underlying physical or chemical processes of the tasks. Recently, Deep Learning (DL) has been increasingly applied to manufacturing. MMs and DL have their individual pros and cons, motivating the development of Mechanism-guided Deep Learning Models (MDLMs) that combine the two. Existing MDLMs are often tailored to specific tasks or types of MMs, and can fail to effectively 1) utilize interconnections of multiple input examples, 2) adaptively self-correct prediction errors with error bounding, and 3) ensemble multiple MMs. In this work, we propose a generic, task-agnostic MDLM framework that can embed one or more MMs in deep networks, and address the 3 aforementioned issues. We present 2 diverse use cases where we experimentally demonstrate the effectiveness and efficiency of our models.|制造业数据分析任务传统上是用机制模型(MM)进行的，它是特定领域的数学方程，模拟任务的基本物理或化学过程。近年来，深度学习(DL)在制造业中的应用越来越广泛。MM 和 DL 各有利弊，促进了机制引导的深度学习模型(MDLM)的发展，将两者结合起来。现有的 MDLM 通常针对特定的任务或 MM 类型进行定制，并且可能无法有效地1)利用多个输入示例的互连，2)具有误差边界的自适应自校正预测错误，以及3)集成多个 MM。在这项工作中，我们提出了一个通用的，任务无关的 MDLM 框架，可以嵌入一个或多个深层网络 MM，并解决上述3个问题。我们提出了2个不同的用例，在这些用例中我们实验性地证明了我们的模型的有效性和效率。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Towards+a+Generic+Framework+for+Mechanism-guided+Deep+Learning+for+Manufacturing+Applications)|0| -|[GLM-Dialog: Noise-tolerant Pre-training for Knowledge-grounded Dialogue Generation](https://doi.org/10.1145/3580305.3599832)|Jing Zhang, Xiaokang Zhang, Daniel ZhangLi, Jifan Yu, Zijun Yao, Zeyao Ma, Yiqi Xu, Haohua Wang, Xiaohan Zhang, Nianyi Lin, Sunrui Lu, Juanzi Li, Jie Tang|Renmin University of China; ZHIPU.AI; Tsinghua University; Beijing University of Posts and Telecommunications|We present GLM-Dialog, a large-scale language model (LLM) with 10B parameters capable of knowledge-grounded conversation in Chinese using a search engine to access the Internet knowledge. GLM-Dialog offers a series of applicable techniques for exploiting various external knowledge including both helpful and noisy knowledge, enabling the creation of robust knowledge-grounded dialogue LLMs with limited proper datasets. To evaluate the GLM-Dialog more fairly, we also propose a novel evaluation method to allow humans to converse with multiple deployed bots simultaneously and compare their performance implicitly instead of explicitly rating using multidimensional metrics.Comprehensive evaluations from automatic to human perspective demonstrate the advantages of GLM-Dialog comparing with existing open source Chinese dialogue models. We release both the model checkpoint and source code, and also deploy it as a WeChat application to interact with users. We offer our evaluation platform online in an effort to prompt the development of open source models and reliable dialogue evaluation systems. The additional easy-to-use toolkit that consists of short text entity linking, query generation, and helpful knowledge classification is also released to enable diverse applications. All the source code is available on Github.|本文提出了一种大规模语言模型 GLM-Dialog，该模型具有10B 参数，能够通过搜索引擎访问互联网上的知识，实现基于知识的汉语会话。GLM-Dialog 提供了一系列适用的技术来开发各种外部知识，包括有用的和有噪音的知识，使得能够创建具有有限适当数据集的健壮的基于知识的对话 LLM。为了更公平地评估 GLM-Dialog，我们还提出了一种新的评估方法，允许人类同时与多个部署的机器人进行对话，并隐式地比较它们的性能，而不是使用多维度度量进行明确的评估。从自动化到人性化的综合评价体现了 GLM-Dialog 与现有开源中文对话模型相比的优势。我们同时发布了模型检查点和源代码，并将其作为微信应用程序部署，以便与用户进行交互。我们提供在线评估平台，以促进开放源码模型和可靠的对话评估系统的开发。还发布了其他易于使用的工具包，包括短文本实体链接、查询生成和有用的知识分类，以支持不同的应用程序。所有的源代码都可以在 Github 上找到。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=GLM-Dialog:+Noise-tolerant+Pre-training+for+Knowledge-grounded+Dialogue+Generation)|0| -|[Robust Multimodal Failure Detection for Microservice Systems](https://doi.org/10.1145/3580305.3599902)|Chenyu Zhao, Minghua Ma, Zhenyu Zhong, Shenglin Zhang, Zhiyuan Tan, Xiao Xiong, LuLu Yu, Jiayi Feng, Yongqian Sun, Yuzhi Zhang, Dan Pei, Qingwei Lin, Dongmei Zhang|Tsinghua University; Microsoft; Nankai University|Proactive failure detection of instances is vitally essential to microservice systems because an instance failure can propagate to the whole system and degrade the system's performance. Over the years, many single-modal (i.e., metrics, logs, or traces) data-based nomaly detection methods have been proposed. However, they tend to miss a large number of failures and generate numerous false alarms because they ignore the correlation of multimodal data. In this work, we propose AnoFusion, an unsupervised failure detection approach, to proactively detect instance failures through multimodal data for microservice systems. It applies a Graph Transformer Network (GTN) to learn the correlation of the heterogeneous multimodal data and integrates a Graph Attention Network (GAT) with Gated Recurrent Unit (GRU) to address the challenges introduced by dynamically changing multimodal data. We evaluate the performance of AnoFusion through two datasets, demonstrating that it achieves the F1-score of 0.857 and 0.922, respectively, outperforming the state-of-the-art failure detection approaches.|实例的主动故障检测对于微服务系统至关重要，因为实例故障可以传播到整个系统并降低系统的性能。多年来，已经提出了许多基于单模态(即度量、日志或跟踪)数据的异常检测方法。然而，由于它们忽略了多模态数据的相关性，它们往往会错过大量的故障并产生大量的虚警。在这项工作中，我们提出了一种无监督的故障检测方法 AnoFusion，主动检测实例故障通过多模态数据的微服务系统。它利用图形转换网络(GTN)来学习异构多模态数据的相关性，并将图形注意网络(GAT)与门限回归单元(GRU)相结合，以解决多模态数据动态变化所带来的挑战。我们通过两个数据集评估 AnoFusion 的性能，证明其分别达到0.857和0.922的 F1评分，优于最先进的故障检测方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Robust+Multimodal+Failure+Detection+for+Microservice+Systems)|0| +|[GLM-Dialog: Noise-tolerant Pre-training for Knowledge-grounded Dialogue Generation](https://doi.org/10.1145/3580305.3599832)|Jing Zhang, Xiaokang Zhang, Daniel ZhangLi, Jifan Yu, Zijun Yao, Zeyao Ma, Yiqi Xu, Haohua Wang, Xiaohan Zhang, Nianyi Lin, Sunrui Lu, Juanzi Li, Jie Tang|Beijing University of Posts and Telecommunications; Renmin University of China; Tsinghua University; ZHIPU.AI|We present GLM-Dialog, a large-scale language model (LLM) with 10B parameters capable of knowledge-grounded conversation in Chinese using a search engine to access the Internet knowledge. GLM-Dialog offers a series of applicable techniques for exploiting various external knowledge including both helpful and noisy knowledge, enabling the creation of robust knowledge-grounded dialogue LLMs with limited proper datasets. To evaluate the GLM-Dialog more fairly, we also propose a novel evaluation method to allow humans to converse with multiple deployed bots simultaneously and compare their performance implicitly instead of explicitly rating using multidimensional metrics.Comprehensive evaluations from automatic to human perspective demonstrate the advantages of GLM-Dialog comparing with existing open source Chinese dialogue models. We release both the model checkpoint and source code, and also deploy it as a WeChat application to interact with users. We offer our evaluation platform online in an effort to prompt the development of open source models and reliable dialogue evaluation systems. The additional easy-to-use toolkit that consists of short text entity linking, query generation, and helpful knowledge classification is also released to enable diverse applications. All the source code is available on Github.|本文提出了一种大规模语言模型 GLM-Dialog，该模型具有10B 参数，能够通过搜索引擎访问互联网上的知识，实现基于知识的汉语会话。GLM-Dialog 提供了一系列适用的技术来开发各种外部知识，包括有用的和有噪音的知识，使得能够创建具有有限适当数据集的健壮的基于知识的对话 LLM。为了更公平地评估 GLM-Dialog，我们还提出了一种新的评估方法，允许人类同时与多个部署的机器人进行对话，并隐式地比较它们的性能，而不是使用多维度度量进行明确的评估。从自动化到人性化的综合评价体现了 GLM-Dialog 与现有开源中文对话模型相比的优势。我们同时发布了模型检查点和源代码，并将其作为微信应用程序部署，以便与用户进行交互。我们提供在线评估平台，以促进开放源码模型和可靠的对话评估系统的开发。还发布了其他易于使用的工具包，包括短文本实体链接、查询生成和有用的知识分类，以支持不同的应用程序。所有的源代码都可以在 Github 上找到。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=GLM-Dialog:+Noise-tolerant+Pre-training+for+Knowledge-grounded+Dialogue+Generation)|0| +|[Robust Multimodal Failure Detection for Microservice Systems](https://doi.org/10.1145/3580305.3599902)|Chenyu Zhao, Minghua Ma, Zhenyu Zhong, Shenglin Zhang, Zhiyuan Tan, Xiao Xiong, LuLu Yu, Jiayi Feng, Yongqian Sun, Yuzhi Zhang, Dan Pei, Qingwei Lin, Dongmei Zhang|Nankai University; Tsinghua University; Microsoft|Proactive failure detection of instances is vitally essential to microservice systems because an instance failure can propagate to the whole system and degrade the system's performance. Over the years, many single-modal (i.e., metrics, logs, or traces) data-based nomaly detection methods have been proposed. However, they tend to miss a large number of failures and generate numerous false alarms because they ignore the correlation of multimodal data. In this work, we propose AnoFusion, an unsupervised failure detection approach, to proactively detect instance failures through multimodal data for microservice systems. It applies a Graph Transformer Network (GTN) to learn the correlation of the heterogeneous multimodal data and integrates a Graph Attention Network (GAT) with Gated Recurrent Unit (GRU) to address the challenges introduced by dynamically changing multimodal data. We evaluate the performance of AnoFusion through two datasets, demonstrating that it achieves the F1-score of 0.857 and 0.922, respectively, outperforming the state-of-the-art failure detection approaches.|实例的主动故障检测对于微服务系统至关重要，因为实例故障可以传播到整个系统并降低系统的性能。多年来，已经提出了许多基于单模态(即度量、日志或跟踪)数据的异常检测方法。然而，由于它们忽略了多模态数据的相关性，它们往往会错过大量的故障并产生大量的虚警。在这项工作中，我们提出了一种无监督的故障检测方法 AnoFusion，主动检测实例故障通过多模态数据的微服务系统。它利用图形转换网络(GTN)来学习异构多模态数据的相关性，并将图形注意网络(GAT)与门限回归单元(GRU)相结合，以解决多模态数据动态变化所带来的挑战。我们通过两个数据集评估 AnoFusion 的性能，证明其分别达到0.857和0.922的 F1评分，优于最先进的故障检测方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Robust+Multimodal+Failure+Detection+for+Microservice+Systems)|0| |[CodeGeeX: A Pre-Trained Model for Code Generation with Multilingual Benchmarking on HumanEval-X](https://doi.org/10.1145/3580305.3599790)|Qinkai Zheng, Xiao Xia, Xu Zou, Yuxiao Dong, Shan Wang, Yufei Xue, Lei Shen, Zihan Wang, Andi Wang, Yang Li, Teng Su, Zhilin Yang, Jie Tang||Large pre-trained code generation models, such as OpenAI Codex, can generate syntax-and function-correct code, making the coding of programmers more productive. In this paper, we introduce CodeGeeX, a multilingual model with 13 billion parameters for code generation. CodeGeeX is pre-trained on 850 billion tokens of 23 programming languages as of June 2022. Our extensive experiments suggest that CodeGeeX outperforms multilingual code models of similar scale for both the tasks of code generation and translation on HumanEval-X. Building upon HumanEval (Python only), we develop the HumanEval-X benchmark for evaluating multilingual models by hand-writing the solutions in C++, Java, JavaScript, and Go. In addition, we build CodeGeeX-based extensions on Visual Studio Code, JetBrains, and Cloud Studio, generating 8 billion tokens for tens of thousands of active users per week. Our user study demonstrates that CodeGeeX can help to increase coding efficiency for 83.4% of its users. Finally, CodeGeeX is publicly accessible since Sep. 2022, we open-sourced its code, model weights, API, extensions, and HumanEval-X at https://github.com/THUDM/CodeGeeX.|大型预先训练好的代码生成模型(如 OpenAI Codex)可以生成语法和函数正确的代码，从而提高编程人员的工作效率。在本文中，我们介绍了 CodeGeeX，这是一个具有130亿个代码生成参数的多语言模型。截至2022年6月，CodeGeeX 预先接受了23种编程语言的8,500亿个令牌的培训。我们的大量实验表明，CodeGeeX 在 HumanEval-X 上的代码生成和翻译任务都优于同等规模的多语言代码模型。基于 HumanEval (仅用于 Python) ，我们开发了 HumanEval-X 基准，通过用 C + + 、 Java、 JavaScript 和 Go 手工编写解决方案来评估多语言模型。此外，我们在 Visual Studio Code、 JetBrains 和 Cloud Studio 上构建基于 CodeGeeX 的扩展，每周为成千上万的活跃用户生成80亿令牌。我们的用户研究表明，CodeGeeX 可以帮助83.4% 的用户提高编码效率。最后，CodeGeeX 自2022年9月开始向公众开放，我们开放了它的代码、模型权重、 API、扩展和 HumanEval-X 的 https://github.com/thudm/CodeGeeX。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=CodeGeeX:+A+Pre-Trained+Model+for+Code+Generation+with+Multilingual+Benchmarking+on+HumanEval-X)|0| -|[MIDLG: Mutual Information based Dual Level GNN for Transaction Fraud Complaint Verification](https://doi.org/10.1145/3580305.3599865)|Wen Zheng, Bingbing Xu, Emiao Lu, Yang Li, Qi Cao, Xuan Zong, Huawei Shen|Institute of Computing Technology, Chinese Academy of Sciences; Wechat Pay, Tencent; Institute of Computing Technology, University of Chinese Academy of Sciences|"Transaction fraud" complaint verification, i.e., verifying whether a transaction corresponding to a complaint is fraudulent, is particularly critical to prevent economic loss. Compared with traditional fraud pre-transaction detection, complaint verification puts forward higher requirements: 1)an individual tends to exhibit different identities in different complaints, e.g., complainant or respondent, requiring the model to capture identity-related representations corresponding to the complaint; 2)the fraud ways evolve frequently to confront detection, requiring the model to perform stably under different fraud ways. Previous methods mainly focused on fraud pre-transaction detection, utilizing the historical information of users or conduct message passing based GNNs on relationship networks. However, they rarely consider capturing various identity-related representations and ignore the evolution of fraud ways, leading to failure in complaint verification. To address the above challenges, we propose the mutual information based dual level graph neural network, namely MIDLG, which defines a complaint as a super-node consisting of involved individuals, and characterizes the individual over node-level and super-node-level. Furthermore, the mutual information minimization objective is proposed based on "complaint verification-causal graph" to decouple the model prediction from relying on specific fraud ways, and thus achieve stability. MIDLG achieves SOTA results through extensive experiments in complaint verification on WeChat Finance, one online payment service serving more than 600 million users in China.|“交易欺诈”投诉核实，即核实与投诉相应的交易是否属于欺诈，对于防止经济损失尤为重要。与传统的交易前欺诈检测相比，投诉验证提出了更高的要求: 1)在不同的投诉中，个体倾向于表现出不同的身份，如投诉人或被投诉人，要求模型捕捉与投诉相应的身份相关表征; 2)欺诈方式频繁演变以面对检测，要求模型在不同的欺诈方式下表现稳定。以往的方法主要集中在欺诈的交易前检测，利用用户的历史信息或进行信息传递的 GNN 的关系网络。然而，他们很少考虑捕获各种与身份相关的表示，并忽视欺诈方式的演变，导致投诉验证失败。为了应对上述挑战，我们提出了基于互信息的双层图形神经网络，即 MIDLG，它将投诉定义为一个由相关个体组成的超级节点，并在节点级和超级节点级表征个体。在此基础上，提出了基于“投诉验证-因果图”的互信息最小化目标，使模型预测与特定的欺诈方式解耦，从而实现稳定性。MIDLG 通过在微信金融上进行广泛的投诉验证实验，取得了 SOTA 结果。微信金融是一家为中国6亿多用户服务的在线支付服务公司。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=MIDLG:+Mutual+Information+based+Dual+Level+GNN+for+Transaction+Fraud+Complaint+Verification)|0| +|[MIDLG: Mutual Information based Dual Level GNN for Transaction Fraud Complaint Verification](https://doi.org/10.1145/3580305.3599865)|Wen Zheng, Bingbing Xu, Emiao Lu, Yang Li, Qi Cao, Xuan Zong, Huawei Shen|Institute of Computing Technology, University of Chinese Academy of Sciences; Institute of Computing Technology, Chinese Academy of Sciences; Wechat Pay, Tencent|"Transaction fraud" complaint verification, i.e., verifying whether a transaction corresponding to a complaint is fraudulent, is particularly critical to prevent economic loss. Compared with traditional fraud pre-transaction detection, complaint verification puts forward higher requirements: 1)an individual tends to exhibit different identities in different complaints, e.g., complainant or respondent, requiring the model to capture identity-related representations corresponding to the complaint; 2)the fraud ways evolve frequently to confront detection, requiring the model to perform stably under different fraud ways. Previous methods mainly focused on fraud pre-transaction detection, utilizing the historical information of users or conduct message passing based GNNs on relationship networks. However, they rarely consider capturing various identity-related representations and ignore the evolution of fraud ways, leading to failure in complaint verification. To address the above challenges, we propose the mutual information based dual level graph neural network, namely MIDLG, which defines a complaint as a super-node consisting of involved individuals, and characterizes the individual over node-level and super-node-level. Furthermore, the mutual information minimization objective is proposed based on "complaint verification-causal graph" to decouple the model prediction from relying on specific fraud ways, and thus achieve stability. MIDLG achieves SOTA results through extensive experiments in complaint verification on WeChat Finance, one online payment service serving more than 600 million users in China.|“交易欺诈”投诉核实，即核实与投诉相应的交易是否属于欺诈，对于防止经济损失尤为重要。与传统的交易前欺诈检测相比，投诉验证提出了更高的要求: 1)在不同的投诉中，个体倾向于表现出不同的身份，如投诉人或被投诉人，要求模型捕捉与投诉相应的身份相关表征; 2)欺诈方式频繁演变以面对检测，要求模型在不同的欺诈方式下表现稳定。以往的方法主要集中在欺诈的交易前检测，利用用户的历史信息或进行信息传递的 GNN 的关系网络。然而，他们很少考虑捕获各种与身份相关的表示，并忽视欺诈方式的演变，导致投诉验证失败。为了应对上述挑战，我们提出了基于互信息的双层图形神经网络，即 MIDLG，它将投诉定义为一个由相关个体组成的超级节点，并在节点级和超级节点级表征个体。在此基础上，提出了基于“投诉验证-因果图”的互信息最小化目标，使模型预测与特定的欺诈方式解耦，从而实现稳定性。MIDLG 通过在微信金融上进行广泛的投诉验证实验，取得了 SOTA 结果。微信金融是一家为中国6亿多用户服务的在线支付服务公司。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=MIDLG:+Mutual+Information+based+Dual+Level+GNN+for+Transaction+Fraud+Complaint+Verification)|0| |[Road Planning for Slums via Deep Reinforcement Learning](https://doi.org/10.1145/3580305.3599901)|Yu Zheng, Hongyuan Su, Jingtao Ding, Depeng Jin, Yong Li|Department of Electronic Engineering, BNRist, Tsinghua University|Millions of slum dwellers suffer from poor accessibility to urban services due to inadequate road infrastructure within slums, and road planning for slums is critical to the sustainable development of cities. Existing re-blocking or heuristic methods are either time-consuming which cannot generalize to different slums, or yield sub-optimal road plans in terms of accessibility and construction costs. In this paper, we present a deep reinforcement learning based approach to automatically layout roads for slums. We propose a generic graph model to capture the topological structure of a slum, and devise a novel graph neural network to select locations for the planned roads. Through masked policy optimization, our model can generate road plans that connect places in a slum at minimal construction costs. Extensive experiments on real-world slums in different countries verify the effectiveness of our model, which can significantly improve accessibility by 14.3% against existing baseline methods. Further investigations on transferring across different tasks demonstrate that our model can master road planning skills in simple scenarios and adapt them to much more complicated ones, indicating the potential of applying our model in real-world slum upgrading.|由于贫民窟内道路基础设施不足，数百万贫民窟居民难以获得城市服务，贫民窟道路规划对于城市的可持续发展至关重要。现有的重新封锁或启发式方法要么耗费时间，无法推广到不同的贫民窟，要么在可达性和建设成本方面产生次优的道路规划。在这篇文章中，我们提出了一个基于深度强化学习的方法来自动规划贫民窟的道路。我们提出了一个通用的图模型来捕捉贫民窟的拓扑结构，并设计了一个新颖的图神经网络来选择规划道路的位置。通过隐蔽的政策优化，我们的模型可以生成道路计划，以最低的建设成本连接贫民窟的地方。在不同国家对现实世界中的贫民窟进行的大量实验验证了我们模型的有效性，与现有的基准方法相比，该模型可以显著提高14.3% 的可达性。关于跨不同任务转移的进一步调查表明，我们的模型能够在简单的情景中掌握道路规划技能，并使之适应更为复杂的情景，这表明我们的模型在现实世界贫民窟改造中的应用潜力。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Road+Planning+for+Slums+via+Deep+Reinforcement+Learning)|0| -|[AI Explainability 360 Toolkit for Time-Series and Industrial Use Cases](https://doi.org/10.1145/3580305.3599182)|Giridhar Ganapavarapu, Sumanta Mukherjee, Natalia Martinez Gil, Kanthi K. Sarpatwar, Amaresh Rajasekharan, Amit Dhurandhar, Vijay Arya, Roman Vaculín|IBM Research, Bengaluru, India; IBM Research, Yorktown Heights, NY, USA; IBM Research, Gurugram, India; IBM Software, Poughkeepsie, NY, USA|With the growing adoption of AI, trust and explainability have become critical which has attracted a lot of research attention over the past decade and has led to the development of many popular AI explainability libraries such as AIX360, Alibi, OmniXAI, etc. Despite that, applying explainability techniques in practice often poses challenges such as lack of consistency between explainers, semantically incorrect explanations, or scalability. Furthermore, one of the key modalities that has been less explored, both from the algorithmic and practice point of view, is time-series. Several application domains involve time-series including Industry 4.0, asset monitoring, supply chain or finance to name a few. The AIX360 library (https://github.com/Trusted-AI/AIX360) has been incubated by the Linux Foundation AI & Data open-source projects and it has gained significant popularity: its public GitHub repository has over 1.3K stars and has been broadly adopted in the academic and applied settings. Motivated by industrial applications, large scale client projects and deployments in software products in the areas of IoT, asset management or supply chain, the AIX360 library has been recently expanded significantly to address the above challenges. AIX360 now includes new techniques including support for time-series modality introducing time series based explainers such as TS-LIME, TS Saliency explainer, TS-ICE and TS-SHAP. It also introduces improvements in generating model agnostic, consistent, diverse, and scalable explanations, and new algorithms for tabular data. In this hands-on tutorial, we provide an overview of the library with the focus on the latest additions, time series explainers and use cases such as forecasting, time series anomaly detection or classification, and hands-on demonstrations based on industrial use-cases selected to demonstrate practical challenges and how they are addressed. The audience will be able to evaluate different types of explanations with a focus on practical aspects motivated by real deployments.|随着人工智能的日益普及，信任和可解释性已经成为一个关键问题，近十年来引起了人们的广泛关注，并导致了 AIX360、 Alibi、 OmniXAI 等许多流行的人工智能可解释性库的发展。尽管如此，在实践中应用可解释性技术往往会带来诸如解释者之间缺乏一致性、语义不正确的解释或可伸缩性等挑战。此外，从算法和实践的角度来看，时间序列是较少被探索的关键模式之一。有几个应用领域涉及时间序列，包括行业4.0、资产监控、供应链或金融等等。AIx360库( https://GitHub.com/trusted-AI/AIX360)是由 Linux 基金会的人工智能和数据开源项目孵化出来的，它已经变得非常受欢迎: 它的公共 GitHub 库已经超过1.3 k 星级，已经被学术界和应用界广泛采用。受到工业应用、大规模客户项目以及物联网、资产管理或供应链领域软件产品部署的推动，AIX360库最近得到了显著扩展，以应对上述挑战。AIX360现在包括新的技术，包括支持时间序列模态引入基于时间序列的解释器，如 TS-LIME，TS 显著性解释器，TS-ICE 和 TS-SHAP。它还介绍了在生成模型不可知性、一致性、多样性和可扩展性解释方面的改进，以及表格数据的新算法。在这个实践教程中，我们提供了一个图书馆的概述，重点是最新的补充，时间序列解释和用例，如预测，时间序列异常检测或分类，以及基于工业用例的实践演示，选择了展示实际挑战和如何解决这些挑战。听众将能够评估不同类型的解释，重点放在由实际部署引起的实际方面。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=AI+Explainability+360+Toolkit+for+Time-Series+and+Industrial+Use+Cases)|0| -|[Hands-on Tutorial: "Explanations in AI: Methods, Stakeholders and Pitfalls"](https://doi.org/10.1145/3580305.3599181)|Mia C. Mayer, Muhammad Bilal Zafar, Luca Franceschi, Huzefa Rangwala|Amazon Web Services, Arlington, VA, USA; Amazon Web Services, Berlin, Germany; Amazon Web Services, Seattle, WA, USA|While using vast amounts of training data and sophisticated models has enhanced the predictive performance of Machine Learning (ML) and Artificial Intelligence (AI) solutions, it has also led to an increased difficulty in comprehending their predictions. The ability to explain predictions is often one of the primary desiderata for adopting AI and ML solutions [6, 13]. The desire for explainability has led to a rapidly growing body of literature on explainable AI (XAI) and has also resulted in the development of hundreds of XAI methods targeting different domains (e.g., finance, healthcare), applications (e.g., model debugging, actionable recourse), data modalities (e.g., tabular data, images), models (e.g., transformers, convolutional neural networks) and stakeholders (e.g., end-users, regulatory authorities, data scientists). The goal of this tutorial is to present a comprehensive overview of the XAI field to the participants. As a hands-on tutorial, we will showcase state-of-the-art methods that can be used for different data modalities and contexts to extract the right abstractions for interpretation. We will also cover common pitfalls when using explanations, e.g., misrepresentation, and lack of robustness of explanations.|虽然使用大量的训练数据和复杂的模型提高了机器学习(ML)和人工智能(AI)解决方案的预测性能，但它也导致理解它们的预测变得更加困难。解释预测的能力通常是采用人工智能和机器学习解决方案的首要条件之一[6,13]。对可解释性的渴望导致了关于可解释 AI (XAI)的文献的迅速增长，并且还导致了针对不同领域(例如，金融，医疗) ，应用(例如，模型调试，可操作的追索权) ，数据模式(例如，表格数据，图像) ，模型(例如，变压器，卷积神经网络)和利益相关者(例如，最终用户，监管当局，数据科学家)的数百种 XAI 方法的发展。本教程的目标是向参与者展示 XAI 字段的全面概述。作为一个实践教程，我们将展示最先进的方法，这些方法可用于不同的数据模式和上下文，以提取正确的抽象进行解释。我们也会介绍使用解释时常见的缺陷，例如不正当手法引诱，以及缺乏可靠的解释。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Hands-on+Tutorial:+"Explanations+in+AI:+Methods,+Stakeholders+and+Pitfalls")|0| +|[AI Explainability 360 Toolkit for Time-Series and Industrial Use Cases](https://doi.org/10.1145/3580305.3599182)|Giridhar Ganapavarapu, Sumanta Mukherjee, Natalia Martinez Gil, Kanthi K. Sarpatwar, Amaresh Rajasekharan, Amit Dhurandhar, Vijay Arya, Roman Vaculín|IBM Software, Poughkeepsie, NY, USA; IBM Research, Gurugram, India; IBM Research, Bengaluru, India; IBM Research, Yorktown Heights, NY, USA|With the growing adoption of AI, trust and explainability have become critical which has attracted a lot of research attention over the past decade and has led to the development of many popular AI explainability libraries such as AIX360, Alibi, OmniXAI, etc. Despite that, applying explainability techniques in practice often poses challenges such as lack of consistency between explainers, semantically incorrect explanations, or scalability. Furthermore, one of the key modalities that has been less explored, both from the algorithmic and practice point of view, is time-series. Several application domains involve time-series including Industry 4.0, asset monitoring, supply chain or finance to name a few. The AIX360 library (https://github.com/Trusted-AI/AIX360) has been incubated by the Linux Foundation AI & Data open-source projects and it has gained significant popularity: its public GitHub repository has over 1.3K stars and has been broadly adopted in the academic and applied settings. Motivated by industrial applications, large scale client projects and deployments in software products in the areas of IoT, asset management or supply chain, the AIX360 library has been recently expanded significantly to address the above challenges. AIX360 now includes new techniques including support for time-series modality introducing time series based explainers such as TS-LIME, TS Saliency explainer, TS-ICE and TS-SHAP. It also introduces improvements in generating model agnostic, consistent, diverse, and scalable explanations, and new algorithms for tabular data. In this hands-on tutorial, we provide an overview of the library with the focus on the latest additions, time series explainers and use cases such as forecasting, time series anomaly detection or classification, and hands-on demonstrations based on industrial use-cases selected to demonstrate practical challenges and how they are addressed. The audience will be able to evaluate different types of explanations with a focus on practical aspects motivated by real deployments.|随着人工智能的日益普及，信任和可解释性已经成为一个关键问题，近十年来引起了人们的广泛关注，并导致了 AIX360、 Alibi、 OmniXAI 等许多流行的人工智能可解释性库的发展。尽管如此，在实践中应用可解释性技术往往会带来诸如解释者之间缺乏一致性、语义不正确的解释或可伸缩性等挑战。此外，从算法和实践的角度来看，时间序列是较少被探索的关键模式之一。有几个应用领域涉及时间序列，包括行业4.0、资产监控、供应链或金融等等。AIx360库( https://GitHub.com/trusted-AI/AIX360)是由 Linux 基金会的人工智能和数据开源项目孵化出来的，它已经变得非常受欢迎: 它的公共 GitHub 库已经超过1.3 k 星级，已经被学术界和应用界广泛采用。受到工业应用、大规模客户项目以及物联网、资产管理或供应链领域软件产品部署的推动，AIX360库最近得到了显著扩展，以应对上述挑战。AIX360现在包括新的技术，包括支持时间序列模态引入基于时间序列的解释器，如 TS-LIME，TS 显著性解释器，TS-ICE 和 TS-SHAP。它还介绍了在生成模型不可知性、一致性、多样性和可扩展性解释方面的改进，以及表格数据的新算法。在这个实践教程中，我们提供了一个图书馆的概述，重点是最新的补充，时间序列解释和用例，如预测，时间序列异常检测或分类，以及基于工业用例的实践演示，选择了展示实际挑战和如何解决这些挑战。听众将能够评估不同类型的解释，重点放在由实际部署引起的实际方面。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=AI+Explainability+360+Toolkit+for+Time-Series+and+Industrial+Use+Cases)|0| +|[Hands-on Tutorial: "Explanations in AI: Methods, Stakeholders and Pitfalls"](https://doi.org/10.1145/3580305.3599181)|Mia C. Mayer, Muhammad Bilal Zafar, Luca Franceschi, Huzefa Rangwala|Amazon Web Services, Berlin, Germany; Amazon Web Services, Seattle, WA, USA; Amazon Web Services, Arlington, VA, USA|While using vast amounts of training data and sophisticated models has enhanced the predictive performance of Machine Learning (ML) and Artificial Intelligence (AI) solutions, it has also led to an increased difficulty in comprehending their predictions. The ability to explain predictions is often one of the primary desiderata for adopting AI and ML solutions [6, 13]. The desire for explainability has led to a rapidly growing body of literature on explainable AI (XAI) and has also resulted in the development of hundreds of XAI methods targeting different domains (e.g., finance, healthcare), applications (e.g., model debugging, actionable recourse), data modalities (e.g., tabular data, images), models (e.g., transformers, convolutional neural networks) and stakeholders (e.g., end-users, regulatory authorities, data scientists). The goal of this tutorial is to present a comprehensive overview of the XAI field to the participants. As a hands-on tutorial, we will showcase state-of-the-art methods that can be used for different data modalities and contexts to extract the right abstractions for interpretation. We will also cover common pitfalls when using explanations, e.g., misrepresentation, and lack of robustness of explanations.|虽然使用大量的训练数据和复杂的模型提高了机器学习(ML)和人工智能(AI)解决方案的预测性能，但它也导致理解它们的预测变得更加困难。解释预测的能力通常是采用人工智能和机器学习解决方案的首要条件之一[6,13]。对可解释性的渴望导致了关于可解释 AI (XAI)的文献的迅速增长，并且还导致了针对不同领域(例如，金融，医疗) ，应用(例如，模型调试，可操作的追索权) ，数据模式(例如，表格数据，图像) ，模型(例如，变压器，卷积神经网络)和利益相关者(例如，最终用户，监管当局，数据科学家)的数百种 XAI 方法的发展。本教程的目标是向参与者展示 XAI 字段的全面概述。作为一个实践教程，我们将展示最先进的方法，这些方法可用于不同的数据模式和上下文，以提取正确的抽象进行解释。我们也会介绍使用解释时常见的缺陷，例如不正当手法引诱，以及缺乏可靠的解释。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Hands-on+Tutorial:+"Explanations+in+AI:+Methods,+Stakeholders+and+Pitfalls")|0| |[Graph Neural Networks in TensorFlow](https://doi.org/10.1145/3580305.3599177)|Bryan Perozzi, Sami AbuElHaija, Anton Tsitsulin|Electronics, Information, and Bioengineering, Politecnico di Milano, Milan, Italy; Informatics, Universita della Svizzera Italiana, Lugano, Switzerland|In this paper we present Spektral, an open-source Python library for building graph neural networks with TensorFlow and the Keras application programming interface. Spektral implements a large set of methods for deep learning on graphs, including message-passing and pooling operators, as well as utilities for processing graphs and loading popular benchmark datasets. The purpose of this library is to provide the essential building blocks for creating graph neural networks, focusing on the guiding principles of user-friendliness and quick prototyping on which Keras is based. Spektral is, therefore, suitable for absolute beginners and expert deep learning practitioners alike. In this work, we present an overview of Spektral's features and report the performance of the methods implemented by the library in scenarios of node classification, graph classification, and graph regression.|本文介绍了 Spektral，这是一个开源的 Python 库，用于构建具有 TensorFlow 和 Kera 应用程序编程接口的图形神经网络。Spektral 实现了大量用于图表深度学习的方法，包括消息传递和池操作符，以及用于处理图表和加载流行基准数据集的实用程序。这个图书馆的目的是为建立图形神经网络提供基本的组成部分，着重于方便用户和快速原型的指导原则，这是 Keras 的基础。因此，Spektral 适合于绝对初学者和专家深入学习实践者一样。在这项工作中，我们提出了 Spektral 的特点概述，并报告了该库实现的方法在节点分类、图分类和图回归场景中的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Graph+Neural+Networks+in+TensorFlow)|0| -|[PyHealth: A Deep Learning Toolkit for Healthcare Applications](https://doi.org/10.1145/3580305.3599178)|Chaoqi Yang, Zhenbang Wu, Patrick Jiang, Zhen Lin, Junyi Gao, Benjamin P. Danek, Jimeng Sun|University of Edinburgh, Health Data Research UK, Edinburgh, United Kingdom; University of Illinois Urbana-Champaign, Urbana, IL, USA|Deep learning (DL) has emerged as a promising tool in healthcare applications. However, the reproducibility of many studies in this field is limited by the lack of accessible code implementations and standard benchmarks. To address the issue, we create PyHealth, a comprehensive library to build, deploy, and validate DL pipelines for healthcare applications. PyHealth supports various data modalities, including electronic health records (EHRs), physiological signals, medical images, and clinical text. It offers various advanced DL models and maintains comprehensive medical knowledge systems. The library is designed to support both DL researchers and clinical data scientists. Upon the time of writing, PyHealth has received 633 stars, 130 forks, and 15k+ downloads in total on GitHub. This tutorial will provide an overview of PyHealth, present different modules, and showcase their functionality through hands-on demos. Participants can follow along and gain hands-on experience on the Google Colab platform during the session.|深度学习(DL)已经成为医疗保健应用中一个很有前途的工具。然而，由于缺乏可访问的代码实现和标准基准，该领域的许多研究的可重复性受到了限制。为了解决这个问题，我们创建了 PyHealth，这是一个用于为医疗保健应用程序构建、部署和验证 DL 管道的综合库。PyHealth 支持各种数据模式，包括电子健康记录(EHR)、生理信号、医学图像和临床文本。它提供各种先进的 DL 模型，并维护全面的医学知识体系。该图书馆旨在支持 DL 研究人员和临床数据科学家。在撰写本文之时，PyHealth 已经在 GitHub 上获得了633颗星、130个 fork 和总共15k + 的下载。本教程将提供 PyHealth 的概述，展示不同的模块，并通过实践演示展示它们的功能。与会者可以跟随并在会议期间在 GoogleColab 平台上获得实践经验。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=PyHealth:+A+Deep+Learning+Toolkit+for+Healthcare+Applications)|0| +|[PyHealth: A Deep Learning Toolkit for Healthcare Applications](https://doi.org/10.1145/3580305.3599178)|Chaoqi Yang, Zhenbang Wu, Patrick Jiang, Zhen Lin, Junyi Gao, Benjamin P. Danek, Jimeng Sun|University of Illinois Urbana-Champaign, Urbana, IL, USA; University of Edinburgh, Health Data Research UK, Edinburgh, United Kingdom|Deep learning (DL) has emerged as a promising tool in healthcare applications. However, the reproducibility of many studies in this field is limited by the lack of accessible code implementations and standard benchmarks. To address the issue, we create PyHealth, a comprehensive library to build, deploy, and validate DL pipelines for healthcare applications. PyHealth supports various data modalities, including electronic health records (EHRs), physiological signals, medical images, and clinical text. It offers various advanced DL models and maintains comprehensive medical knowledge systems. The library is designed to support both DL researchers and clinical data scientists. Upon the time of writing, PyHealth has received 633 stars, 130 forks, and 15k+ downloads in total on GitHub. This tutorial will provide an overview of PyHealth, present different modules, and showcase their functionality through hands-on demos. Participants can follow along and gain hands-on experience on the Google Colab platform during the session.|深度学习(DL)已经成为医疗保健应用中一个很有前途的工具。然而，由于缺乏可访问的代码实现和标准基准，该领域的许多研究的可重复性受到了限制。为了解决这个问题，我们创建了 PyHealth，这是一个用于为医疗保健应用程序构建、部署和验证 DL 管道的综合库。PyHealth 支持各种数据模式，包括电子健康记录(EHR)、生理信号、医学图像和临床文本。它提供各种先进的 DL 模型，并维护全面的医学知识体系。该图书馆旨在支持 DL 研究人员和临床数据科学家。在撰写本文之时，PyHealth 已经在 GitHub 上获得了633颗星、130个 fork 和总共15k + 的下载。本教程将提供 PyHealth 的概述，展示不同的模块，并通过实践演示展示它们的功能。与会者可以跟随并在会议期间在 GoogleColab 平台上获得实践经验。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=PyHealth:+A+Deep+Learning+Toolkit+for+Healthcare+Applications)|0| |[GraphStorm an Easy-to-use and Scalable Graph Neural Network Framework: From Beginners to Heroes](https://doi.org/10.1145/3580305.3599179)|Jian Zhang, Da Zheng, Xiang Song, Theodore Vasiloudis, Israt Nisa, Jim Lu||||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=GraphStorm+an+Easy-to-use+and+Scalable+Graph+Neural+Network+Framework:+From+Beginners+to+Heroes)|0| -|[Towards Next-Generation Intelligent Assistants Leveraging LLM Techniques](https://doi.org/10.1145/3580305.3599572)|Xin Luna Dong, Seungwhan Moon, Yifan Ethan Xu, Kshitiz Malik, Zhou Yu|Meta Reality Labs, Menlo Park, CA, USA; Meta Reality Labs, Austin, TX, USA; Columbia University, New York City, NY, USA; Meta Reality Labs, Redmond, WA, USA|Virtual Intelligent Assistants take user requests in the voice form, perform actions such as setting an alarm, turning on a light, and answering a question, and provide answers or confirmations in the voice form or through other channels such as a screen. Assistants have become prevalent in the past decade, and users have been taking services from assistants like Amazon Alexa, Apple Siri, Google Assistant, and Microsoft Cortana. The emergence of AR/VR devices raised many new challenges for building intelligent assistants. The unique requirements have inspired new research directions such as (a) understanding users' situated multi-modal contexts (e.g. vision, sensor signals) as well as language-oriented conversational contexts, (b) personalizing the assistant services by grounding interactions on growing public and personal knowledge graphs and online search engines, and (c) on- device model inference and training techniques that satisfy strict resource and privacy constraints. In this tutorial, we will provide an in-depth walk-through of techniques in the afore-mentioned areas in the recent literature. We aim to introduce techniques for researchers and practitioners who are building intelligent assistants, and inspire research that will bring us one step closer to realizing the dream of building an all-day accompanying assistant. Additionally, we will highlight the significant role that Large Language Models (LLMs) play in enhancing these strategies, underscoring their potential to reshape the future landscape of intelligent assistance.|虚拟智能助理以语音形式接受用户请求，执行诸如设置闹钟、打开灯光和回答问题等动作，并以语音形式或通过屏幕等其他渠道提供答案或确认。在过去的十年里，助理服务变得越来越普遍，用户使用的服务来自亚马逊的 Alexa、苹果的 Siri、谷歌助理和 Cortana。AR/VR 设备的出现为构建智能助手提出了许多新的挑战。独特的需求激发了新的研究方向，例如(a)理解用户所处的多模态环境(例如视觉，传感器信号)以及面向语言的会话环境，(b)通过基于不断增长的公共和个人知识图表和在线搜索引擎的交互使辅助服务个性化，以及(c)满足严格的资源和隐私约束的设备上模型推断和培训技术。在本教程中，我们将在最近的文献中提供上述领域中的技术的深入演练。我们的目标是向研究人员和从业人员介绍建立智能助手的技术，并激发研究，将使我们更接近实现建立一个全天陪伴助手的梦想。此外，我们将强调大型语言模型(LLM)在增强这些策略方面发挥的重要作用，强调它们重塑未来智能辅助领域的潜力。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Towards+Next-Generation+Intelligent+Assistants+Leveraging+LLM+Techniques)|0| -|[XAI for Predictive Maintenance](https://doi.org/10.1145/3580305.3599578)|João Gama, Slawomir Nowaczyk, Sepideh Pashami, Rita P. Ribeiro, Grzegorz J. Nalepa, Bruno Veloso|eKare Inc, Fairfax, VA 22031 USA; Old Dominion Univ, Dept Engn Technol, Norfolk, VA 23529 USA; Norwegian Univ Sci & Technol, Dept Elect Power Engn, N-7491 Trondheim, Norway; Univ N Carolina, Dept Elect & Comp Engn, Charlotte, NC 28223 USA|Over the last two decades, Artificial Intelligence (AI) approaches have been applied to various applications of the smart grid, such as demand response, predictive maintenance, and load forecasting. However, AI is still considered to be a "black-box" due to its lack of explainability and transparency, especially for something like solar photovoltaic (PV) forecasts that involves many parameters. Explainable Artificial Intelligence (XAI) has become an emerging research field in the smart grid domain since it addresses this gap and helps understand why the AI system made a forecast decision. This article presents several use cases of solar PV energy forecasting using XAI tools, such as LIME, SHAP, and ELI5, which can contribute to adopting XAI tools for smart grid applications. Understanding the inner workings of a prediction model based on AI can give insights into the application field. Such insight can provide improvements to the solar PV forecasting models and point out relevant parameters.|在过去的二十年中，人工智能(AI)方法已经被应用到智能电网的各种应用中，如需求响应、预测维护和负荷预测。然而，人工智能仍然被认为是一个“黑盒子”，因为它缺乏解释性和透明度，特别是对于像太阳能光伏(PV)预测这样涉及许多参数。可解释人工智能(XAI)是智能电网领域的一个新兴研究领域，它弥补了这一空白，有助于理解为什么人工智能系统会做出预测决策。本文介绍了使用 XAI 工具(如 LIME、 SHAP 和 ELI5)进行太阳能光伏发电量预测的几个用例，这些工具有助于将 XAI 工具用于智能电网应用。了解基于人工智能的预测模型的内部工作原理，可以深入了解其应用领域。这种洞察力可以为太阳能光伏预测模型提供改进，并指出相关参数。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=XAI+for+Predictive+Maintenance)|0| +|[Towards Next-Generation Intelligent Assistants Leveraging LLM Techniques](https://doi.org/10.1145/3580305.3599572)|Xin Luna Dong, Seungwhan Moon, Yifan Ethan Xu, Kshitiz Malik, Zhou Yu|Columbia University, New York City, NY, USA; Meta Reality Labs, Austin, TX, USA; Meta Reality Labs, Menlo Park, CA, USA; Meta Reality Labs, Redmond, WA, USA|Virtual Intelligent Assistants take user requests in the voice form, perform actions such as setting an alarm, turning on a light, and answering a question, and provide answers or confirmations in the voice form or through other channels such as a screen. Assistants have become prevalent in the past decade, and users have been taking services from assistants like Amazon Alexa, Apple Siri, Google Assistant, and Microsoft Cortana. The emergence of AR/VR devices raised many new challenges for building intelligent assistants. The unique requirements have inspired new research directions such as (a) understanding users' situated multi-modal contexts (e.g. vision, sensor signals) as well as language-oriented conversational contexts, (b) personalizing the assistant services by grounding interactions on growing public and personal knowledge graphs and online search engines, and (c) on- device model inference and training techniques that satisfy strict resource and privacy constraints. In this tutorial, we will provide an in-depth walk-through of techniques in the afore-mentioned areas in the recent literature. We aim to introduce techniques for researchers and practitioners who are building intelligent assistants, and inspire research that will bring us one step closer to realizing the dream of building an all-day accompanying assistant. Additionally, we will highlight the significant role that Large Language Models (LLMs) play in enhancing these strategies, underscoring their potential to reshape the future landscape of intelligent assistance.|虚拟智能助理以语音形式接受用户请求，执行诸如设置闹钟、打开灯光和回答问题等动作，并以语音形式或通过屏幕等其他渠道提供答案或确认。在过去的十年里，助理服务变得越来越普遍，用户使用的服务来自亚马逊的 Alexa、苹果的 Siri、谷歌助理和 Cortana。AR/VR 设备的出现为构建智能助手提出了许多新的挑战。独特的需求激发了新的研究方向，例如(a)理解用户所处的多模态环境(例如视觉，传感器信号)以及面向语言的会话环境，(b)通过基于不断增长的公共和个人知识图表和在线搜索引擎的交互使辅助服务个性化，以及(c)满足严格的资源和隐私约束的设备上模型推断和培训技术。在本教程中，我们将在最近的文献中提供上述领域中的技术的深入演练。我们的目标是向研究人员和从业人员介绍建立智能助手的技术，并激发研究，将使我们更接近实现建立一个全天陪伴助手的梦想。此外，我们将强调大型语言模型(LLM)在增强这些策略方面发挥的重要作用，强调它们重塑未来智能辅助领域的潜力。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Towards+Next-Generation+Intelligent+Assistants+Leveraging+LLM+Techniques)|0| +|[XAI for Predictive Maintenance](https://doi.org/10.1145/3580305.3599578)|João Gama, Slawomir Nowaczyk, Sepideh Pashami, Rita P. Ribeiro, Grzegorz J. Nalepa, Bruno Veloso|Univ N Carolina, Dept Elect & Comp Engn, Charlotte, NC 28223 USA; eKare Inc, Fairfax, VA 22031 USA; Norwegian Univ Sci & Technol, Dept Elect Power Engn, N-7491 Trondheim, Norway; Old Dominion Univ, Dept Engn Technol, Norfolk, VA 23529 USA|Over the last two decades, Artificial Intelligence (AI) approaches have been applied to various applications of the smart grid, such as demand response, predictive maintenance, and load forecasting. However, AI is still considered to be a "black-box" due to its lack of explainability and transparency, especially for something like solar photovoltaic (PV) forecasts that involves many parameters. Explainable Artificial Intelligence (XAI) has become an emerging research field in the smart grid domain since it addresses this gap and helps understand why the AI system made a forecast decision. This article presents several use cases of solar PV energy forecasting using XAI tools, such as LIME, SHAP, and ELI5, which can contribute to adopting XAI tools for smart grid applications. Understanding the inner workings of a prediction model based on AI can give insights into the application field. Such insight can provide improvements to the solar PV forecasting models and point out relevant parameters.|在过去的二十年中，人工智能(AI)方法已经被应用到智能电网的各种应用中，如需求响应、预测维护和负荷预测。然而，人工智能仍然被认为是一个“黑盒子”，因为它缺乏解释性和透明度，特别是对于像太阳能光伏(PV)预测这样涉及许多参数。可解释人工智能(XAI)是智能电网领域的一个新兴研究领域，它弥补了这一空白，有助于理解为什么人工智能系统会做出预测决策。本文介绍了使用 XAI 工具(如 LIME、 SHAP 和 ELI5)进行太阳能光伏发电量预测的几个用例，这些工具有助于将 XAI 工具用于智能电网应用。了解基于人工智能的预测模型的内部工作原理，可以深入了解其应用领域。这种洞察力可以为太阳能光伏预测模型提供改进，并指出相关参数。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=XAI+for+Predictive+Maintenance)|0| |[Distributed Optimization for Big Data Analytics: Beyond Minimization](https://doi.org/10.1145/3580305.3599554)|Hongchang Gao, Xinwen Zhang|Temple University, Philadelphia, PA, USA|The traditional machine learning model can be formulated as an empirical risk minimization problem, which is typically optimized via stochastic gradient descent (SGD). With the emergence of big data, distributed optimization, e.g., distributed SGD, has been attracting increasing attention to facilitate machine learning models for big data analytics. However, existing distributed optimization mainly focuses on the standard empirical risk minimization problem, failing to deal with the emerging machine learning models that are beyond that category. Thus, of particular interest of this tutorial includes the stochastic minimax optimization, stochastic bilevel optimization, and stochastic compositional optimization, which covers a wide range of emerging machine learning models, e.g., model-agnostic meta-learning models, adversarially robust machine learning models, imbalanced data classification models, etc. Since these models have been widely used in big data analytics, it is necessary to provide a comprehensive introduction about the new distributed optimization algorithms designed for these models. Therefore, the goal of this tutorial is to present the state-of-the-art and recent advances in distributed minimax optimization, distributed bilevel optimization, and distributed compositional optimization. In particular, we will introduce the typical applications in each category and discuss the corresponding distributed optimization algorithms in both centralized and decentralized settings. Through this tutorial, the researchers will be exposed to the fundamental algorithmic design and basic convergence theories, and the practitioners will be able to benefit from this tutorial to apply these algorithms to real-world data mining applications.|传统的机器学习模型可以表述为一个经验风险最小化问题，通常通过随机梯度下降(SGD)进行优化。随着大数据的出现，分布式优化(如分布式 SGD)越来越受到人们的关注，以方便大数据分析的机器学习模型的建立。然而，现有的分布式优化主要集中在标准的经验风险最小化问题，未能处理新兴的机器学习模型超出这一范畴。因此，本教程特别感兴趣的包括随机极大极小优化，随机双层优化和随机组合优化，其中涵盖了广泛的新兴机器学习模型，如模型无关元学习模型，对抗鲁棒性机器学习模型，不平衡数据分类模型等。由于这些模型在大数据分析中得到了广泛的应用，因此有必要对为这些模型设计的新的分布式优化算法进行全面的介绍。因此，本教程的目标是介绍分布式极大极小优化、分布式两级优化和分布式组合优化方面的最新进展。特别地，我们将介绍每个类别中的典型应用，并讨论相应的集中和分散环境下的分布式优化算法。通过本教程，研究人员将了解基本的算法设计和基本收敛理论，从业人员将能够从本教程中受益，将这些算法应用到现实世界的数据挖掘应用程序。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Distributed+Optimization+for+Big+Data+Analytics:+Beyond+Minimization)|0| -|[Privacy in Advertising: Analytics and Modeling](https://doi.org/10.1145/3580305.3599570)|Badih Ghazi, Ravi Kumar, Pasin Manurangsi|Google, Mountain View, CA, USA; Google, Bangkok, Thailand|Privacy in general, and differential privacy (DP) in particular, have become important topics in data mining and machine learning. Digital advertising is a critical component of the internet and is powered by large-scale data analytics and machine learning models; privacy concerns around these are on the rise. Despite the central importance of private ad analytics and training privacy-preserving ad prediction models, there has been relatively little exposure of this subject to the broader KDD community. In the past three years, the interest in privacy and the interest in online advertising have been steadily growing in KDD. The aim of this tutorial is to provide KDD researchers with an introduction to the problems that arise in private analytics and modeling in advertising, survey recent results, and describe the main research challenges in the space.|一般而言，私隐，特别是差分隐私(DP) ，已成为数据挖掘和机器学习的重要课题。数字广告是互联网的重要组成部分，由大规模的数据分析和机器学习模型提供动力; 围绕这些的隐私问题正在上升。尽管私人广告分析和培训保护隐私的广告预测模型至关重要，但是这个主题在更广泛的 KDD 社区中的曝光率相对较低。在过去的三年中，KDD 对隐私和在线广告的兴趣一直在稳步增长。本教程的目的是为 KDD 研究人员提供一个介绍在私人分析和广告建模中出现的问题，调查最近的结果，并描述在空间中的主要研究挑战。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Privacy+in+Advertising:+Analytics+and+Modeling)|0| -|[Causal Discovery from Temporal Data](https://doi.org/10.1145/3580305.3599552)|Chang Gong, Di Yao, Chuzhe Zhang, Wenbin Li, Jingping Bi, Lun Du, Jin Wang|Microsoft, Beijing, China; Fudan University, Shanghai, China; Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China; Megagon Labs, Mountain View, CA, USA; Institute of Computing Technology, Chinese Academy of Sciences & University of Chinese Academy of Sciences, Beijing, China|Temporal data representing chronological observations of complex systems can be ubiquitously collected in smart industry, medicine, finance and etc. In the last decade, many tasks have been studied for mining temporal data and offered significant value for various applications. Among these tasks, causal discovery aims to understand the underlying generation mechanism of temporal data and has attracted much research attention. According to whether the data is calibrated, existing causal discovery approaches can be divided into two subtasks, i.e., multivariate time-series causal discovery, and event sequence causal discovery. Previous tutorials or surveys have primarily focused on causal discovery from time-series data and disregarded the second ones. In this tutorial, we elucidate the correlation between the two subtasks and provide a comprehensive review of the existing solutions. Moreover, we offer some potential applications and summarize new perspectives for discovering causal relations from temporal data. We hope the audiences can obtain a systematic overview of this topic and inspire some new ideas for their own research.|代表复杂系统时间观测的时间数据可以在智能工业、医学、金融等领域广泛收集。近十年来，人们对时态数据挖掘进行了大量的研究，为各种应用提供了重要的价值。在这些任务中，因果发现旨在理解时间数据的潜在生成机制，引起了研究者的广泛关注。根据数据是否被校准，现有的因果发现方法可以分为两个子任务，即多变量时间序列因果发现和事件序列因果发现。以前的教程或调查主要侧重于从时间序列数据中发现因果关系，而忽略了第二个。在本教程中，我们阐明了这两个子任务之间的关系，并对现有的解决方案进行了全面的回顾。此外，我们提供了一些潜在的应用，并总结了从时间数据中发现因果关系的新视角。我们希望读者能够对这一课题有一个系统的了解，并为自己的研究提供一些新的思路。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Causal+Discovery+from+Temporal+Data)|0| -|[Generative AI meets Responsible AI: Practical Challenges and Opportunities](https://doi.org/10.1145/3580305.3599557)|Krishnaram Kenthapadi, Himabindu Lakkaraju, Nazneen Rajani|Harvard University, Cambridge, MA, USA; Fiddler AI, Palo Alto, CA, USA; Hugging Face, Palo Alto, CA, USA|Generative AI models and applications are being rapidly developed and deployed across a wide spectrum of industries and applications ranging from writing and email assistants to graphic design and art generation to educational assistants to coding to drug discovery. However, there are several ethical and social considerations associated with generative AI models and applications. These concerns include lack of interpretability, bias and discrimination, privacy, lack of model robustness, fake and misleading content, copyright implications, plagiarism, and environmental impact associated with training and inference of generative AI models. In this tutorial, we first motivate the need for adopting responsible AI principles when developing and deploying large language models (LLMs) and other generative AI models, as part of a broader AI model governance and responsible AI framework, from societal, legal, user, and model developer perspectives, and provide a roadmap for thinking about responsible AI for generative AI in practice. We provide a brief technical overview of text and image generation models, and highlight the key responsible AI desiderata associated with these models. We then describe the technical considerations and challenges associated with realizing the above desiderata in practice. We focus on real-world generative AI use cases spanning domains such as media generation, writing assistants, copywriting, code generation, and conversational assistants, present practical solution approaches / guidelines for applying responsible AI techniques effectively, discuss lessons learned from deploying responsible AI approaches for generative AI applications in practice, and highlight the key open research problems. We hope that our tutorial will inform both researchers and practitioners, stimulate further research on responsible AI in the context of generative AI, and pave the way for building more reliable and trustworthy generative AI applications in the future.|生成性人工智能模型和应用程序正在迅速开发和应用于广泛的行业和应用程序，从写作和电子邮件助理到图形设计和艺术生成，从教育助理到编码到药物发现。然而，与生成性人工智能模型和应用相关的一些伦理和社会因素。这些担忧包括缺乏可解释性，偏见和歧视，隐私，缺乏模型的稳健性，虚假和误导内容，版权影响，剽窃，以及与培训和推断生成 AI 模型相关的环境影响。在本教程中，我们首先从社会，法律，用户和模型开发者的角度，激发了在开发和部署大型语言模型(LLM)和其他生成性人工智能模型时采用负责任的人工智能原则的需要，作为更广泛的人工智能模型治理和负责任的人工智能框架的一部分，并提供了一个路线图，用于在实践中思考负责任的人工智能生成性人工智能。我们提供了一个简短的文本和图像生成模型的技术概述，并突出了与这些模型相关的关键负责人工智能急需数据。然后，我们描述了与在实践中实现上述目标相关的技术考虑和挑战。我们关注跨越媒体生成，写作助手，文案，代码生成和会话助手等领域的现实世界生成 AI 用例，提出有效应用负责任 AI 技术的实用解决方案/指南，讨论在实践中为生成 AI 应用部署负责任 AI 方法的经验教训，并强调关键的开放研究问题。我们希望，我们的教程将告知研究人员和从业人员，刺激在生成性人工智能背景下负责任的人工智能的进一步研究，并为未来建立更可靠和可信赖的生成性人工智能应用铺平道路。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Generative+AI+meets+Responsible+AI:+Practical+Challenges+and+Opportunities)|0| +|[Privacy in Advertising: Analytics and Modeling](https://doi.org/10.1145/3580305.3599570)|Badih Ghazi, Ravi Kumar, Pasin Manurangsi|Google, Bangkok, Thailand; Google, Mountain View, CA, USA|Privacy in general, and differential privacy (DP) in particular, have become important topics in data mining and machine learning. Digital advertising is a critical component of the internet and is powered by large-scale data analytics and machine learning models; privacy concerns around these are on the rise. Despite the central importance of private ad analytics and training privacy-preserving ad prediction models, there has been relatively little exposure of this subject to the broader KDD community. In the past three years, the interest in privacy and the interest in online advertising have been steadily growing in KDD. The aim of this tutorial is to provide KDD researchers with an introduction to the problems that arise in private analytics and modeling in advertising, survey recent results, and describe the main research challenges in the space.|一般而言，私隐，特别是差分隐私(DP) ，已成为数据挖掘和机器学习的重要课题。数字广告是互联网的重要组成部分，由大规模的数据分析和机器学习模型提供动力; 围绕这些的隐私问题正在上升。尽管私人广告分析和培训保护隐私的广告预测模型至关重要，但是这个主题在更广泛的 KDD 社区中的曝光率相对较低。在过去的三年中，KDD 对隐私和在线广告的兴趣一直在稳步增长。本教程的目的是为 KDD 研究人员提供一个介绍在私人分析和广告建模中出现的问题，调查最近的结果，并描述在空间中的主要研究挑战。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Privacy+in+Advertising:+Analytics+and+Modeling)|0| +|[Causal Discovery from Temporal Data](https://doi.org/10.1145/3580305.3599552)|Chang Gong, Di Yao, Chuzhe Zhang, Wenbin Li, Jingping Bi, Lun Du, Jin Wang|Microsoft, Beijing, China; Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China; Megagon Labs, Mountain View, CA, USA; Fudan University, Shanghai, China; Institute of Computing Technology, Chinese Academy of Sciences & University of Chinese Academy of Sciences, Beijing, China|Temporal data representing chronological observations of complex systems can be ubiquitously collected in smart industry, medicine, finance and etc. In the last decade, many tasks have been studied for mining temporal data and offered significant value for various applications. Among these tasks, causal discovery aims to understand the underlying generation mechanism of temporal data and has attracted much research attention. According to whether the data is calibrated, existing causal discovery approaches can be divided into two subtasks, i.e., multivariate time-series causal discovery, and event sequence causal discovery. Previous tutorials or surveys have primarily focused on causal discovery from time-series data and disregarded the second ones. In this tutorial, we elucidate the correlation between the two subtasks and provide a comprehensive review of the existing solutions. Moreover, we offer some potential applications and summarize new perspectives for discovering causal relations from temporal data. We hope the audiences can obtain a systematic overview of this topic and inspire some new ideas for their own research.|代表复杂系统时间观测的时间数据可以在智能工业、医学、金融等领域广泛收集。近十年来，人们对时态数据挖掘进行了大量的研究，为各种应用提供了重要的价值。在这些任务中，因果发现旨在理解时间数据的潜在生成机制，引起了研究者的广泛关注。根据数据是否被校准，现有的因果发现方法可以分为两个子任务，即多变量时间序列因果发现和事件序列因果发现。以前的教程或调查主要侧重于从时间序列数据中发现因果关系，而忽略了第二个。在本教程中，我们阐明了这两个子任务之间的关系，并对现有的解决方案进行了全面的回顾。此外，我们提供了一些潜在的应用，并总结了从时间数据中发现因果关系的新视角。我们希望读者能够对这一课题有一个系统的了解，并为自己的研究提供一些新的思路。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Causal+Discovery+from+Temporal+Data)|0| +|[Generative AI meets Responsible AI: Practical Challenges and Opportunities](https://doi.org/10.1145/3580305.3599557)|Krishnaram Kenthapadi, Himabindu Lakkaraju, Nazneen Rajani|Hugging Face, Palo Alto, CA, USA; Harvard University, Cambridge, MA, USA; Fiddler AI, Palo Alto, CA, USA|Generative AI models and applications are being rapidly developed and deployed across a wide spectrum of industries and applications ranging from writing and email assistants to graphic design and art generation to educational assistants to coding to drug discovery. However, there are several ethical and social considerations associated with generative AI models and applications. These concerns include lack of interpretability, bias and discrimination, privacy, lack of model robustness, fake and misleading content, copyright implications, plagiarism, and environmental impact associated with training and inference of generative AI models. In this tutorial, we first motivate the need for adopting responsible AI principles when developing and deploying large language models (LLMs) and other generative AI models, as part of a broader AI model governance and responsible AI framework, from societal, legal, user, and model developer perspectives, and provide a roadmap for thinking about responsible AI for generative AI in practice. We provide a brief technical overview of text and image generation models, and highlight the key responsible AI desiderata associated with these models. We then describe the technical considerations and challenges associated with realizing the above desiderata in practice. We focus on real-world generative AI use cases spanning domains such as media generation, writing assistants, copywriting, code generation, and conversational assistants, present practical solution approaches / guidelines for applying responsible AI techniques effectively, discuss lessons learned from deploying responsible AI approaches for generative AI applications in practice, and highlight the key open research problems. We hope that our tutorial will inform both researchers and practitioners, stimulate further research on responsible AI in the context of generative AI, and pave the way for building more reliable and trustworthy generative AI applications in the future.|生成性人工智能模型和应用程序正在迅速开发和应用于广泛的行业和应用程序，从写作和电子邮件助理到图形设计和艺术生成，从教育助理到编码到药物发现。然而，与生成性人工智能模型和应用相关的一些伦理和社会因素。这些担忧包括缺乏可解释性，偏见和歧视，隐私，缺乏模型的稳健性，虚假和误导内容，版权影响，剽窃，以及与培训和推断生成 AI 模型相关的环境影响。在本教程中，我们首先从社会，法律，用户和模型开发者的角度，激发了在开发和部署大型语言模型(LLM)和其他生成性人工智能模型时采用负责任的人工智能原则的需要，作为更广泛的人工智能模型治理和负责任的人工智能框架的一部分，并提供了一个路线图，用于在实践中思考负责任的人工智能生成性人工智能。我们提供了一个简短的文本和图像生成模型的技术概述，并突出了与这些模型相关的关键负责人工智能急需数据。然后，我们描述了与在实践中实现上述目标相关的技术考虑和挑战。我们关注跨越媒体生成，写作助手，文案，代码生成和会话助手等领域的现实世界生成 AI 用例，提出有效应用负责任 AI 技术的实用解决方案/指南，讨论在实践中为生成 AI 应用部署负责任 AI 方法的经验教训，并强调关键的开放研究问题。我们希望，我们的教程将告知研究人员和从业人员，刺激在生成性人工智能背景下负责任的人工智能的进一步研究，并为未来建立更可靠和可信赖的生成性人工智能应用铺平道路。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Generative+AI+meets+Responsible+AI:+Practical+Challenges+and+Opportunities)|0| |[Getting an h-Index of 100 in 20 Years or Less!](https://doi.org/10.1145/3580305.3599558)|Eamonn Keogh|University of California, Riverside, Riverside, CA, USA|The title of this tutorial is clickbait! However, a high h-index (relative to the stage of your career), will make you advisor, chair, dean and chancellor happy, and it can help you get a job or get promoted. Moreover, I will show that h-indices are highly susceptible to the Matthew Effect, a high h-index may help you get funding and attract strong collaborators/students, which in turn will likely further increase your h-index! Thus, "kickstarting" your h-index early in your career can pay huge dividends. It goes without saying that optimizing h-index is not the same as optimizing your scientific impact. Note that John Clauser, the 2022 Nobel prize winner in physics has an h-index of just 29. However, a high h-index does mean that you have been prolific at publishing papers, and it also means that the community does read and cite these papers. In this tutorial, I show that beyond pure quality of your research, there are many "tricks" you can do to increase both your paper's chance of acceptance, and increase its number of citations, thus optimizing your h-index. Please note that there are ways to increase your h-index that are clearly disingenuous and bad for the community (citation cartels, ghost authorships etc.). However, I believe that all the ideas presented in this tutorial are legal, moral, and are generally helpful for the entire community.|本教程的标题是“点击诱饵”！然而，高 h 指数(相对于你的职业生涯阶段)会让你的顾问、主席、院长和校长感到高兴，它可以帮助你得到一份工作或得到晋升。此外，我将说明 h 指数高度容易受到马太效应的影响，高 h 指数可以帮助你获得资金和吸引强大的合作者/学生，这反过来可能会进一步增加你的 h 指数！因此，在你职业生涯的早期“启动”你的 h 指数可以带来巨大的回报。不言而喻，优化 h 指数并不等同于优化你的科学影响力。请注意，2022年诺贝尔物理学奖得主约翰•克劳泽(John Clauser)的 h 指数仅为29。然而，高 h 指数确实意味着你在发表论文方面很多产，这也意味着社区确实阅读并引用了这些论文。在本教程中，我将向您展示除了纯粹的研究质量之外，还有许多“技巧”可以用来增加论文被接受的机会，并增加其引用次数，从而优化您的 h-index。请注意，增加 h 索引的方法显然是虚伪的，对社区有害的(引用卡特尔，幽灵作者等)。但是，我相信本教程中提出的所有想法都是合法的、道德的，并且对整个社区都有帮助。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Getting+an+h-Index+of+100+in+20+Years+or+Less!)|0| -|[Uncertainty Quantification in Deep Learning](https://doi.org/10.1145/3580305.3599577)|Lingkai Kong, Harshavardhan Kamarthi, Peng Chen, B. Aditya Prakash, Chao Zhang|Dibrugarh Univ, Dibrugarh, Assam, India; ; Univ Waterloo, Dept Syst Design Engn, Waterloo, ON, Canada; Deakin Univ, Inst Intelligent Syst Res & Innovat IISRI, Geelong, Vic, Australia; Swinburne Univ Technol, Dept Comp Sci & Software Engn, Melbourne, Vic, Australia; Univ Oulu, Ctr Machine Vis & Signal Anal, Oulu, Finland; Google Research, Google, USA; Shenzhen Univ, Coll Math & Stat, Guangdong Key Lab Intelligent Informat Proc, Shenzhen, Peoples R China|Uncertainty quantification (UQ) methods play a pivotal role in reducing the impact of uncertainties during both optimization and decision making processes. They have been applied to solve a variety of real-world problems in science and engineering. Bayesian approximation and ensemble learning techniques are two widely-used types of uncertainty quantification (UQ) methods. In this regard, researchers have proposed different UQ methods and examined their performance in a variety of applications such as computer vision (e.g., self-driving cars and object detection), image processing (e.g., image restoration), medical image analysis (e.g., medical image classification and segmentation), natural language processing (e.g., text classification, social media texts and recidivism risk-scoring), bioinformatics, etc. This study reviews recent advances in UQ methods used in deep learning, investigates the application of these methods in reinforcement learning, and highlights fundamental research challenges and directions associated with UQ.|不确定性量化(UQ)方法在优化和决策过程中对减少不确定性的影响起着关键作用。它们已被应用于解决各种现实世界的科学和工程问题。贝叶斯近似和集成学习技术是两种广泛使用的不确定性量化方法。在这方面，研究人员提出了不同的智商测试方法，并检验了它们在各种应用中的表现，例如计算机视觉(例如自动驾驶汽车和目标检测)、图像处理(例如影像复原)、医学图像分析(例如医学图像分类和分割)、自然语言处理(例如文本分类、社交媒体文本和累犯风险评分)、生物信息学等。本研究回顾了深度学习中使用的智商方法的最新进展，调查了这些方法在强化学习中的应用，并强调了与智商相关的基础研究挑战和方向。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Uncertainty+Quantification+in+Deep+Learning)|0| +|[Uncertainty Quantification in Deep Learning](https://doi.org/10.1145/3580305.3599577)|Lingkai Kong, Harshavardhan Kamarthi, Peng Chen, B. Aditya Prakash, Chao Zhang|; Shenzhen Univ, Coll Math & Stat, Guangdong Key Lab Intelligent Informat Proc, Shenzhen, Peoples R China; Univ Waterloo, Dept Syst Design Engn, Waterloo, ON, Canada; Swinburne Univ Technol, Dept Comp Sci & Software Engn, Melbourne, Vic, Australia; Deakin Univ, Inst Intelligent Syst Res & Innovat IISRI, Geelong, Vic, Australia; Dibrugarh Univ, Dibrugarh, Assam, India; Google Research, Google, USA; Univ Oulu, Ctr Machine Vis & Signal Anal, Oulu, Finland|Uncertainty quantification (UQ) methods play a pivotal role in reducing the impact of uncertainties during both optimization and decision making processes. They have been applied to solve a variety of real-world problems in science and engineering. Bayesian approximation and ensemble learning techniques are two widely-used types of uncertainty quantification (UQ) methods. In this regard, researchers have proposed different UQ methods and examined their performance in a variety of applications such as computer vision (e.g., self-driving cars and object detection), image processing (e.g., image restoration), medical image analysis (e.g., medical image classification and segmentation), natural language processing (e.g., text classification, social media texts and recidivism risk-scoring), bioinformatics, etc. This study reviews recent advances in UQ methods used in deep learning, investigates the application of these methods in reinforcement learning, and highlights fundamental research challenges and directions associated with UQ.|不确定性量化(UQ)方法在优化和决策过程中对减少不确定性的影响起着关键作用。它们已被应用于解决各种现实世界的科学和工程问题。贝叶斯近似和集成学习技术是两种广泛使用的不确定性量化方法。在这方面，研究人员提出了不同的智商测试方法，并检验了它们在各种应用中的表现，例如计算机视觉(例如自动驾驶汽车和目标检测)、图像处理(例如影像复原)、医学图像分析(例如医学图像分类和分割)、自然语言处理(例如文本分类、社交媒体文本和累犯风险评分)、生物信息学等。本研究回顾了深度学习中使用的智商方法的最新进展，调查了这些方法在强化学习中的应用，并强调了与智商相关的基础研究挑战和方向。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Uncertainty+Quantification+in+Deep+Learning)|0| |[Mining of Real-world Hypergraphs: Patterns, Tools, and Generators](https://doi.org/10.1145/3580305.3599567)|Geon Lee, Jaemin Yoo, Kijung Shin|Carnegie Mellon University, Pittsburgh, PA, USA; KAIST, Seoul, South Korea|ABSTRACTGroup interactions are prevalent in various complex systems (e.g., collaborations of researchers and group discussions on online Q&A sites), and they are commonly modeled as hypergraphs. Hyperedges, which compose a hypergraph, are non-empty subsets of any number of nodes, and thus each hyperedge naturally represents a group interaction among entities. The higher-order nature of hypergraphs brings about unique structural properties that have not been considered in ordinary pairwise graphs. In this tutorial, we offer a comprehensive overview of a new research topic called hypergraph mining. We first present recently revealed structural properties of real-world hypergraphs, including (a) static and dynamic patterns, (b) global and local patterns, and (c) connectivity and overlapping patterns. Together with the patterns, we describe advanced data mining tools used for their discovery. Lastly, we introduce simple yet realistic hypergraph generative models that provide an explanation of the structural properties.|在各种复杂的系统中(例如，研究人员的合作和在线问答网站上的小组讨论) ，小组之间的交互非常普遍，它们通常被建模为超图。组成超图的超边是任意数量节点的非空子集，因此每个超边自然地表示实体之间的群交互。超图的高阶性质带来了普通成对图所没有考虑到的独特的结构性质。在本教程中，我们将提供一个称为超图挖掘的新研究主题的全面概述。我们首先介绍了现实世界超图的结构特征，包括(a)静态和动态模式，(b)全局和局部模式，以及(c)连通性和重叠模式。结合这些模式，我们描述了用于发现这些模式的高级数据挖掘工具。最后，我们介绍了一个简单而实际的超图生成模型，它解释了超图的结构性质。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Mining+of+Real-world+Hypergraphs:+Patterns,+Tools,+and+Generators)|0| |[Knowledge Graph Reasoning and Its Applications](https://doi.org/10.1145/3580305.3599564)|Lihui Liu, Hanghang Tong|University of Illinois at Urbana-Champaign, Urbana, USA|The use of knowledge graphs has gained significant traction in a wide variety of applications, ranging from recommender systems and question answering to fact checking. By leveraging the wealth of information contained within knowledge graphs, it is possible to greatly enhance various downstream tasks, making reasoning over knowledge graphs an area of increasing interest. However, despite its popularity, knowledge graph reasoning remains a challenging problem. The first major challenge of knowledge graph reasoning lies in the nature of knowledge graphs themselves. Most knowledge graphs are incomplete, meaning that they may not capture all the relevant knowledge required for reasoning. As a result, reasoning on incomplete knowledge graphs can be difficult. Additionally, real-world knowledge graphs often evolve over time, which presents an additional challenge. The second challenge of knowledge graph reasoning pertains to the input data. In some KG reasoning applications, users may be unfamiliar with the background knowledge graph, leading to the possibility of asking ambiguous questions that can make KG reasoning tasks more challenging. Moreover, some applications require iterative reasoning, where users ask several related questions in sequence, further increasing the complexity of the task. The third challenge of knowledge graph reasoning concerns the algorithmic aspect. Due to the varied properties of relations in knowledge graphs, such as transitivity, symmetry, and asymmetry, designing an all-round KG reasoning model that fits all these properties can be challenging. Furthermore, most KG reasoning models tend to focus on solving a specific problem, lacking the generalization ability required to apply to other tasks. This tutorial aims to comprehensively review different aspects of knowledge graph reasoning applications and highlight open challenges and future directions. It is intended to benefit researchers and practitioners in the fields of data mining, artificial intelligence, and social science.|知识图表的使用在推荐系统、问题回答和事实核查等广泛的应用中获得了巨大的推动力。通过利用包含在知识图表中的丰富信息，有可能极大地增强各种下游任务，使知识图表的推理成为一个日益感兴趣的领域。然而，尽管知识图推理很流行，它仍然是一个具有挑战性的问题。知识图推理的第一个主要挑战在于知识图本身的性质。大多数知识图表是不完整的，这意味着它们可能无法捕获推理所需的所有相关知识。因此，对不完全知识图的推理是困难的。此外，真实世界的知识图经常随着时间的推移而发展，这带来了额外的挑战。知识图推理的第二个挑战与输入数据有关。在一些 KG 推理应用程序中，用户可能不熟悉背景知识图，导致提出模棱两可的问题的可能性，使 KG 推理任务更具挑战性。此外，一些应用程序需要迭代推理，其中用户按顺序提出几个相关的问题，进一步增加了任务的复杂性。知识图推理的第三个挑战涉及到算法方面。由于知识图中的关系具有传递性、对称性和不对称性等多种属性，因此设计一个能够满足这些属性的全方位 KG 推理模型具有一定的挑战性。此外，大多数 KG 推理模型往往侧重于解决特定问题，缺乏应用于其他任务所需的泛化能力。本教程旨在全面回顾知识图推理应用程序的不同方面，并强调开放的挑战和未来的方向。它旨在使数据挖掘、人工智能和社会科学领域的研究人员和从业人员受益。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Knowledge+Graph+Reasoning+and+Its+Applications)|0| -|[Fast Text Generation with Text-Editing Models](https://doi.org/10.1145/3580305.3599579)|Eric Malmi, Yue Dong, Jonathan Mallinson, Aleksandr Chuklin, Jakub Adámek, Daniil Mirylenka, Felix Stahlberg, Sebastian Krause, Shankar Kumar, Aliaksei Severyn|Google, Zurich, Switzerland; University of California, Riverside, Riverside, CA, USA; Google, Berlin, Germany; Google, New York, NY, USA|Text-editing models have recently become a prominent alternative to seq2seq models for monolingual text-generation tasks such as grammatical error correction, simplification, and style transfer. These tasks share a common trait -- they exhibit a large amount of textual overlap between the source and target texts. Text-editing models take advantage of this observation and learn to generate the output by predicting edit operations applied to the source sequence. In contrast, seq2seq models generate outputs word-by-word from scratch thus making them slow at inference time. Text-editing models provide several benefits over seq2seq models including faster inference speed, higher sample efficiency, and better control and explainability of the outputs. This tutorial provides a comprehensive overview of text-editing models and discusses how they can be used to mitigate hallucination and bias, both pressing challenges in the field of text generation. Finally, we discuss how to optimize latency of large language models via distillation to text-editing models and other means.|文本编辑模型最近已成为 seq2seq 模型的一个突出的替代方案，用于单语文本生成任务，如语法错误纠正、简化和样式转换。这些任务有一个共同的特点——它们在源文本和目标文本之间表现出大量的文本重叠。文本编辑模型利用这种观察，并学习通过预测应用于源序列的编辑操作来生成输出。相比之下，seq2seq 模型从头开始逐字生成输出，从而使它们在推理时变慢。与 seq2seq 模型相比，文本编辑模型提供了几个好处，包括更快的推理速度、更高的采样效率以及更好的输出控制和可解释性。本教程提供了一个文本编辑模型的全面概述，并讨论了如何使用它们来减轻幻觉和偏见，这两个紧迫的挑战在文本生成领域。最后，通过对文本编辑模型和其他方法的提取，讨论了如何优化大型语言模型的延迟。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Fast+Text+Generation+with+Text-Editing+Models)|0| +|[Fast Text Generation with Text-Editing Models](https://doi.org/10.1145/3580305.3599579)|Eric Malmi, Yue Dong, Jonathan Mallinson, Aleksandr Chuklin, Jakub Adámek, Daniil Mirylenka, Felix Stahlberg, Sebastian Krause, Shankar Kumar, Aliaksei Severyn|University of California, Riverside, Riverside, CA, USA; Google, New York, NY, USA; Google, Zurich, Switzerland; Google, Berlin, Germany|Text-editing models have recently become a prominent alternative to seq2seq models for monolingual text-generation tasks such as grammatical error correction, simplification, and style transfer. These tasks share a common trait -- they exhibit a large amount of textual overlap between the source and target texts. Text-editing models take advantage of this observation and learn to generate the output by predicting edit operations applied to the source sequence. In contrast, seq2seq models generate outputs word-by-word from scratch thus making them slow at inference time. Text-editing models provide several benefits over seq2seq models including faster inference speed, higher sample efficiency, and better control and explainability of the outputs. This tutorial provides a comprehensive overview of text-editing models and discusses how they can be used to mitigate hallucination and bias, both pressing challenges in the field of text generation. Finally, we discuss how to optimize latency of large language models via distillation to text-editing models and other means.|文本编辑模型最近已成为 seq2seq 模型的一个突出的替代方案，用于单语文本生成任务，如语法错误纠正、简化和样式转换。这些任务有一个共同的特点——它们在源文本和目标文本之间表现出大量的文本重叠。文本编辑模型利用这种观察，并学习通过预测应用于源序列的编辑操作来生成输出。相比之下，seq2seq 模型从头开始逐字生成输出，从而使它们在推理时变慢。与 seq2seq 模型相比，文本编辑模型提供了几个好处，包括更快的推理速度、更高的采样效率以及更好的输出控制和可解释性。本教程提供了一个文本编辑模型的全面概述，并讨论了如何使用它们来减轻幻觉和偏见，这两个紧迫的挑战在文本生成领域。最后，通过对文本编辑模型和其他方法的提取，讨论了如何优化大型语言模型的延迟。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Fast+Text+Generation+with+Text-Editing+Models)|0| |[Pretrained Language Representations for Text Understanding: A Weakly-Supervised Perspective](https://doi.org/10.1145/3580305.3599569)|Yu Meng, Jiaxin Huang, Yu Zhang, Yunyi Zhang, Jiawei Han|UIUC, Urbana, USA|Language representations pretrained on general-domain corpora and adapted to downstream task data have achieved enormous success in building natural language understanding (NLU) systems. While the standard supervised fine-tuning of pretrained language models (PLMs) has proven an effective approach for superior NLU performance, it often necessitates a large quantity of costly human-annotated training data. For example, the enormous success of ChatGPT and GPT-4 can be largely credited to their supervised fine-tuning with massive manually-labeled prompt-response training pairs. Unfortunately, obtaining large-scale human annotations is in general infeasible for most practitioners. To broaden the applicability of PLMs to various tasks and settings, weakly-supervised learning offers a promising direction to minimize the annotation requirements for PLM adaptions. In this tutorial, we cover the recent advancements in pretraining language models and adaptation methods for a wide range of NLU tasks. Our tutorial has a particular focus on weakly-supervised approaches that do not require massive human annotations. We will introduce the following topics in this tutorial: (1) pretraining language representation models that serve as the fundamentals for various NLU tasks, (2) extracting entities and hierarchical relations from unlabeled texts, (3) discovering topical structures from massive text corpora for text organization, and (4) understanding documents and sentences with weakly-supervised techniques.|在通用领域语料库上预先训练并适应下游任务数据的语言表示在构建自然语言理解(NLU)系统方面取得了巨大的成功。预训练语言模型(PLM)的标准监督微调已被证明是提高 NLU 性能的有效方法，但它往往需要大量昂贵的人工注释训练数据。例如，ChatGPT 和 GPT-4的巨大成功在很大程度上归功于它们通过大量手工标记的提示-响应训练对进行的监督微调。不幸的是，对于大多数实践者来说，获得大规模的人工注释通常是不可行的。为了扩大 PLM 在各种任务和设置中的适用性，弱监督学习为最小化 PLM 适配的注释需求提供了一个有希望的方向。在本教程中，我们将介绍针对大量 NLU 任务的预训练语言模型和适应方法的最新进展。我们的教程特别关注不需要大量人工注释的弱监督方法。在本教程中，我们将介绍以下主题: (1)作为各种 NLU 任务基础的预训练语言表示模型，(2)从未标记的文本中提取实体和层次关系，(3)从大量文本语料库中发现用于文本组织的主题结构，(4)用弱监督技术理解文档和句子。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Pretrained+Language+Representations+for+Text+Understanding:+A+Weakly-Supervised+Perspective)|0| |[Precision Health in the Age of Large Language Models](https://doi.org/10.1145/3580305.3599568)|Hoifung Poon, Tristan Naumann, Sheng Zhang, Javier González Hernández|Microsoft Research, Redmond, USA; Microsoft Research, Cambridge, United Kingdom|Medicine today is imprecise. Among the top 20 drugs in the U.S., up to 80% of patients are non-responders. The goal of precision health is to provide the right intervention for the right people at the right time. The key to realize this dream is to develop a data-driven, learning system that can instantly incorporate new health information to optimize care delivery and accelerate biomedical discovery. In reality, however, the health ecosystem is mired in overwhelming unstructured data and excruciating manual processing. For example, in cancer, standard of care often fails, and clinical trials are the last hope. Yet less than 3% of patients could find a matching trial, whereas 40% of trial failures simply stem from insufficient recruitment. Discovery is painfully slow as a new drug may take billions of dollars and over a decade to develop. In this tutorial, we will explore how large language models (LLMs) can serve as a universal structuring tool to democratize biomedical knowledge work and usher in an intelligence revolution in precision health. We first review background for precision health and give a broad overview of the AI revolution that culminated in the development of large language models, highlighting key technical innovations and prominent trends such as consolidation of AI methods across modalities. We then give an in-depth review of biomedical LLMs and precision health applications, with a particular focus on scaling real-world evidence generation and drug discovery. To conclude, we discuss key technical challenges (e.g., bias, hallucination, cost), societal ramifications (e.g., privacy, regulation), as well as exciting research frontiers such as prompt programming, knowledge distillation, multi-modal learning, causal discovery.|今天的医学是不精确的。在美国排名前20的药物中，高达80% 的患者无反应。精确健康的目标是在正确的时间为正确的人提供正确的干预。实现这一梦想的关键是开发一个数据驱动的学习系统，该系统可以即时整合新的健康信息，以优化护理服务，加速生物医学的发现。然而，在现实中，健康生态系统陷入了压倒性的非结构化数据和痛苦的手工处理的泥潭。例如，在癌症治疗中，标准治疗常常失败，临床试验是最后的希望。然而，只有不到3% 的患者能够找到匹配的试验，而40% 的试验失败仅仅是由于招募不足。由于新药的研发可能需要数十亿美元和十年以上的时间，因此发现速度非常缓慢。在本教程中，我们将探索如何大语言模型(LLM)可以作为一个通用的结构化工具，民主化生物医学知识工作，并在精确健康的智能革命。我们首先回顾了精确健康的背景，并对人工智能革命进行了广泛的概述，这场革命最终导致了大型语言模型的发展，突出了关键的技术创新和突出的趋势，例如跨模式的人工智能方法的整合。然后，我们给出了生物医学 LLM 和精确健康应用的深入评论，特别是重点放在规模的现实世界的证据生成和药物发现。最后，我们讨论了关键的技术挑战(例如，偏见，幻觉，成本) ，社会后果(例如，隐私，监管) ，以及令人兴奋的研究前沿，如快速编程，知识提取，多模态学习，因果发现。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Precision+Health+in+the+Age+of+Large+Language+Models)|0| |[Trustworthy Transfer Learning: Transferability and Trustworthiness](https://doi.org/10.1145/3580305.3599576)|Jun Wu, Jingrui He|University of Illinois at Urbana-Champaign, Champaign, IL, USA|Deep transfer learning investigates the transfer of knowledge or information from a source domain to a relevant target domain via deep neural networks. In this tutorial, we dive into understanding deep transfer learning from the perspective of knowledge transferability and trustworthiness (e.g., privacy, robustness, fairness, transparency, etc.). To this end, we provide a comprehensive review of state-of-the-art theoretical analysis and algorithms for deep transfer learning. To be specific, we start by introducing the concepts of transferability and trustworthiness in the context of deep transfer learning. Then we summarize recent theories and algorithms for understanding knowledge transferability from two aspects: (1) IID transferability: the samples within each domain are independent and identically distributed (e.g., individual images), and (2) non-IID transferability: The samples within each domain are interdependent (e.g., connected nodes within a graph). In addition to knowledge transferability, we also review the impact of trustworthiness on deep transfer learning, e.g., whether the transferred knowledge is adversarially robust or algorithmically fair, how to transfer the knowledge under privacy-preserving constraints, etc. Finally, we highlight the open questions and future directions for understanding deep transfer learning in real-world applications. We believe this tutorial can benefit researchers and practitioners by rethinking the trade-off between knowledge transferability and trustworthiness in developing trustworthy transfer learning systems.|深度迁移学习研究的是通过深度神经网络将知识或信息从源领域转移到相关目标领域。在本教程中，我们将从知识可转移性和可信度(例如，隐私、健壮性、公平性、透明度等)的角度深入理解深度迁移学习。为此，我们提供了一个全面的综述国家的最新理论分析和算法的深度迁移学习。具体来说，我们首先在深度迁移学习的背景下介绍可迁移性和可信度的概念。然后，我们从两个方面总结了理解知识可转移性的最新理论和算法: (1) IID 可转移性: 每个领域内的样本是独立和同分布的(例如，单个图像) ，和(2)非 IID 可转移性: 每个领域内的样本是相互依赖的(例如，图中的连接节点)。除了知识可转移性之外，我们还回顾了可信性对深度转移学习的影响，例如，转移的知识是否具有对抗鲁棒性或算法公平性，如何在保护隐私的约束下转移知识等。最后，我们强调了在实际应用中理解深度迁移学习的开放性问题和未来方向。我们相信本教程可以通过重新思考在开发可信赖的转移学习系统中知识可转移性和可信赖性之间的权衡，使研究人员和从业人员受益。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Trustworthy+Transfer+Learning:+Transferability+and+Trustworthiness)|0| -|[Graph Neural Networks: Foundation, Frontiers and Applications](https://doi.org/10.1145/3580305.3599560)|Lingfei Wu, Peng Cui, Jian Pei, Liang Zhao, Xiaojie Guo|Emory University, Atlantic, USA; IBM T.J. Watson Research Center, Yorktown Height, USA; Tsinghua University, Beijing, USA; Pinterest, San Francisco, USA; Duke University, Durham, USA|The field of graph neural networks (GNNs) has seen rapid and incredible strides over the recent years. Graph neural networks, also known as deep learning on graphs, graph representation learning, or geometric deep learning, have become one of the fastest-growing research topics in machine learning, especially deep learning. This wave of research at the intersection of graph theory and deep learning has also influenced other fields of science, including recommendation systems, computer vision, natural language processing, inductive logic programming, program synthesis, software mining, automated planning, cybersecurity, and intelligent transportation. However, as the field rapidly grows, it has been extremely challenging to gain a global perspective of the developments of GNNs. Therefore, we feel the urgency to bridge the above gap and have a comprehensive tutorial on this fast-growing yet challenging topic. This tutorial of Graph Neural Networks (GNNs): Foundation, Frontiers and Applications will cover a broad range of topics in graph neural networks, by reviewing and introducing the fundamental concepts and algorithms of GNNs, new research frontiers of GNNs, and broad and emerging applications with GNNs. In addition, rich tutorial materials will be included and introduced to help the audience gain a systematic understanding by using our recently published book-Graph Neural Networks (GNN): Foundation, Frontiers, and Applications [12], which can easily be accessed at https://graph-neural-networks.github.io/index.html.|近年来，图形神经网络(GNN)领域取得了令人难以置信的快速发展。图形神经网络，又称图形深度学习、图形表示学习或几何深度学习，已成为机器学习，尤其是深度学习中发展最快的研究课题之一。图论和深度学习交叉领域的研究热潮也影响了其他科学领域，包括推荐系统、计算机视觉、自然语言处理、归纳逻辑编程、程序综合、软件挖掘、自动规划、网络安全和智能交通。然而，随着该领域的迅速发展，要从全球的角度了解 GNN 的发展变得极具挑战性。因此，我们感到紧迫的桥梁以上的差距，并有一个全面的教程对这个快速增长但具有挑战性的主题。本教程的图形神经网络(GNNs) : 基础，前沿和应用将涵盖在图形神经网络的广泛的主题，通过审查和介绍 GNNs 的基本概念和算法，GNNs 的新的研究前沿，广泛和新兴的应用与 GNNs。此外，丰富的教学材料将包括和介绍，以帮助观众获得一个系统的理解，使用我们最近出版的书-图形神经网络(GNN) : 基础，前沿，和应用[12] ，这可以很容易地在 https://graph-Neural-Networks.github.io/index.html 访问。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Graph+Neural+Networks:+Foundation,+Frontiers+and+Applications)|0| -|[Graph and Geometry Generative Modeling for Drug Discovery](https://doi.org/10.1145/3580305.3599559)|Minkai Xu, Meng Liu, Wengong Jin, Shuiwang Ji, Jure Leskovec, Stefano Ermon|Broad Institute of MIT & Harvard University, Boston, MA, USA; Stanford University, Palo Alto, CA, USA; Texas A&M University, College Station, TX, USA|With the recent progress in geometric deep learning, generative modeling, and the availability of large-scale biological datasets, molecular graph and geometry generative modeling have emerged as a highly promising direction for scientific discovery such as drug design. These generative methods enable efficient chemical space exploration and potential drug candidate generation. However, by representing molecules as 2D graphs or 3D geometries, there exist many both fundamental and challenging problems for modeling the distribution of these irregular and complex relational data. In this tutorial, we will introduce participants to the latest key developments in this field, covering important topics including 2D molecular graph generation, 3D molecular geometry generation, 2D graph to 3D geometry generation, and conditional 3D molecular geometry generation. We further include antibody generation, where we particularly consider large-size antibody molecules. For each topic, we will outline the underlying problem characteristics, summarize key challenges, present unified views of the representative approaches, and highlight future research direction and potential impacts. We anticipate this lecture-style tutorial would attract a broad audience of researchers and practitioners.|近年来，随着几何深度学习、生成建模以及大规模生物数据集的出现，分子图和几何生成建模已经成为药物设计等科学发现的一个非常有前途的方向。这些生成方法使有效的化学空间探索和潜在的候选药物生成成为可能。然而，通过将分子表示为二维图形或三维几何图形，对于这些不规则和复杂的关系数据的分布建模存在许多基本的和具有挑战性的问题。在本教程中，我们将向学员介绍这一领域的最新发展，包括二维分子图生成、三维分子结构生成、二维图到三维几何生成以及条件三维分子结构生成等重要主题。我们进一步包括抗体生成，其中我们特别考虑大型抗体分子。对于每个主题，我们将概述潜在的问题特征，总结关键挑战，提出代表性方法的统一观点，并强调未来的研究方向和潜在影响。我们预计这种讲座式的教学将吸引广大的研究人员和从业人员的观众。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Graph+and+Geometry+Generative+Modeling+for+Drug+Discovery)|0| -|[Data-centric AI: Techniques and Future Perspectives](https://doi.org/10.1145/3580305.3599553)|Daochen Zha, KweiHerng Lai, Fan Yang, Na Zou, Huiji Gao, Xia Hu|Rice University, Houston, TX, USA; Texas A&M University, College Station, TX, USA; Wake Forest University, Winston-Salem, NC, USA; Airbnb, Inc., San Francisco, CA, USA|The role of data in AI has been significantly magnified by the emerging concept of data-centric AI. In contrast to the traditional model-centric paradigm, which focuses on developing more effective models given fixed datasets, data-centric AI emphasizes the systematic engineering of data in building AI systems. However, as a new concept, many critical aspects of data-centric AI remain ambiguous, such as its definitions, associated tasks, algorithms, challenges, and benchmarks. This tutorial aims to review and discuss this emerging field, with a particular focus on the three general data-centric AI goals: training data development, inference data development, and data maintenance. The objective of this tutorial is threefold: (1) to formally categorize the field of data-centric AI using a goal-driven taxonomy and discuss the needs and challenges of each goal, (2) to comprehensively review the state-of-the-art techniques, and (3) to discuss the future perspectives and open research directions to inspire further innovations in this field.|以数据为中心的人工智能概念的出现大大放大了数据在人工智能中的作用。与传统的以模型为中心的模式不同，以数据为中心的人工智能强调在构建人工智能系统时对数据进行系统化处理。然而，作为一个新的概念，以数据为中心的人工智能的许多关键方面仍然模糊不清，例如它的定义、相关的任务、算法、挑战和基准。本教程旨在回顾和讨论这个新兴领域，特别关注三个以数据为中心的人工智能目标: 培训数据开发、推断数据开发和数据维护。本教程的目标有三个: (1)使用目标驱动的分类法正式对以数据为中心的 AI 领域进行分类，并讨论每个目标的需求和挑战，(2)全面回顾最先进的技术，(3)讨论未来的观点和开放的研究方向，以激励该领域的进一步创新。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Data-centric+AI:+Techniques+and+Future+Perspectives)|0| -|[Hyperbolic Graph Neural Networks: A Tutorial on Methods and Applications](https://doi.org/10.1145/3580305.3599562)|Min Zhou, Menglin Yang, Bo Xiong, Hui Xiong, Irwin King|University of Stuttgart, Stuttgart, Germany; The Chinese University of Hong Kong, Hong Kong, Hong Kong; Huawei Cloud, Shenzhen, China; Hong Kong University of Science and Technology (Guangzhou), Guangzhou, Hong Kong|Graph Neural Networks (GNNs) generalize conventional neural networks to graph-structured data and have received considerable attention owing to their impressive performance. In spite of the notable successes, the performance of Euclidean models is inherently bounded and limited by the representation ability of Euclidean geometry, especially when it comes to datasets with highly non-Euclidean latent anatomy. Recently, hyperbolic spaces have emerged as a promising alternative for processing graph data with tree-like structure or power-law distribution and a surge of works on either methods or novel applications have been seen. Unlike Euclidean space, which expands polynomially, hyperbolic space grows exponentially with its radius, making it more suitable for modeling complex real-world data. Hence, it gains natural advantages in abstracting tree-like graphs with a hierarchical organization or power-law distribution. To support the burgeoning interest in Hyperbolic Graph Neural Networks (HGNNs), the primary goal of this tutorial is to give a systematical review of the methods, applications, and challenges in this fast-growing and vibrant area, with the express purpose of being accessible to all audiences.More specifically, we will first give a brief introduction to graph neural networks as well as some preliminary of Riemannian manifold and hyperbolic geometry. We then will comprehensively revisit the technical details of the developed HGNNs, by unifying them into a general framework and summarizing the variants of each component. Besides, we will introduce applications deployed in a variety of fields. Finally, we will discuss several challenges and present the potential solutions to address them, including some initial attempts of our own, which potentially paves the path for the further flourishing of the research community.|图形神经网络(GNN)将传统的神经网络推广到图形结构的数据，由于其令人印象深刻的性能而受到广泛的关注。尽管取得了显著的成功，但欧几里德模型的性能本质上受到欧几里得几何表示能力的限制，尤其是当涉及到具有高度非欧几里德潜在解剖结构的数据集时。近年来，双曲空间已成为处理具有树状结构或幂律分布的图形数据的一种有前途的替代方法。不像欧几里得空间，它是多项式展开的，双曲空间随着半径呈指数增长，这使得它更适合于建模复杂的现实世界数据。因此，它在用分层组织分布或幂律分布抽象树状图方面获得了天然的优势。为了支持对双曲图形神经网络(HGNNs)的兴趣，本教程的主要目标是对这个快速增长和充满活力的领域中的方法、应用和挑战进行系统的回顾，明确的目的是让所有的受众都可以访问。更具体地说，我们将首先简要介绍图形神经网络，以及一些初步的黎曼流形和双曲几何。然后，我们将全面重新审视已开发的 HGNN 的技术细节，将它们统一到一个总体框架中，并总结每个组件的变体。此外，我们还将介绍部署在各个领域的应用程序。最后，我们将讨论几个挑战，并提出解决这些问题的潜在解决方案，包括我们自己的一些初步尝试，这有可能为研究界的进一步繁荣铺平道路。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Hyperbolic+Graph+Neural+Networks:+A+Tutorial+on+Methods+and+Applications)|0| -|[Fragile Earth: AI for Climate Sustainability - From Wildfire Disaster Management to Public Health and Beyond](https://doi.org/10.1145/3580305.3599217)|Naoki Abe, Kathleen Buckingham, Yuzhou Chen, Bistra Dilkina, Emre Eftelioglu, Auroop R. Ganguly, Yulia R. Gel, James Hodson, Ramakrishnan Kannan, Huikyo Lee, Jiafu Mao, Rose Yu|Amazon, Inc., Seattle, WA, USA; Temple University, Philadelphia, PA, USA; Oak Ridge National Laboratory, Oak Ridge, CA, USA; AI for Good Foundation, San Francisco, CA, USA; University of Southern California, Los Angeles, CA, USA; Veritree, Washington, DC, USA; University of California, San Diego, La Jolla, CA, USA; IBM Research, Yorktown Heights, NY, USA; University of Texas at Dallas, Richardson, TX, USA; Northeastern University, Boston, MA, USA; Jet Propulsion Laboratory, Pasadena, CA, USA|The Fragile Earth Workshop is a recurring event in ACM's KDD Conference on research in knowledge discovery and data mining that gathers the research community to find and explore how data science can measure and progress climate and social issues, fol- lowing the United Nations Sustainable Development Goals (SDGs) framework.|脆弱地球研讨会是 ACM 关于知识发现和数据挖掘研究的 KDD 会议上的一个经常性活动，该会议聚集了研究界，探索数据科学如何在联合国可持续发展目标(SDGs)框架下衡量和推进气候和社会问题。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Fragile+Earth:+AI+for+Climate+Sustainability+-+From+Wildfire+Disaster+Management+to+Public+Health+and+Beyond)|0| -|[AdKDD 2023](https://doi.org/10.1145/3580305.3599582)|Abraham Bagherjeiran, Nemanja Djuric, KuangChih Lee, Linsey Pang, Vladan Radosavljevic, Suju Rajan|Salesforce, San Francisco, CA, USA; Amazon, Palo Alto, CA, USA; eBay, Inc., San Jose, CA, USA; Spotify, New York City, NY, USA; Walmart, Sunnyvale, CA, USA; Aurora Innovation, Inc., Pittsburgh, PA, USA|The digital advertising field has always had challenging ML problems, learning from petabytes of data that is highly imbalanced, reactivity times in the milliseconds, and more recently compounded with the complex user's path to purchase across devices, across platforms, and even online/real-world behavior. The AdKDD workshop continues to be a forum for researchers in advertising, during and after KDD. Our website which hosts slides and abstracts receives approximately 2,000 monthly visits and 1,800 active users during the KDD 2021. In surveys during AdKDD 2019 and 2020, over 60% agreed that AdKDD is the reason they attended KDD, and over 90% indicated they would attend next year. The 2023 edition is particularly timely because of the increasing application of Graph-based NN and Generative AI models in advertising. Coupled with privacy-preserving initiatives enforced by GDPR, CCPA the future of computational advertising is at an interesting crossroads. For this edition, we plan to solicit papers that span the spectrum of deep user understanding while remaining privacy-preserving. In addition, we will seek papers that discuss fairness in the context of advertising, to what extent does hyper-personalization work, and whether the ad industry as a whole needs to think through more effective business models such as incrementality. We have hosted several academic and industry luminaries as keynote speakers and have found our invited speaker series hosting expert practitioners to be an audience favorite. We will continue fielding a diverse set of keynote speakers and invited talks for this edition as well. As with past editions, we hope to motivate researchers in this space to think not only about the ML aspects but also to spark conversations about the societal impact of online advertising.|数字广告领域一直存在机器学习的挑战性问题，从高度不平衡的千兆字节数据中学习，以毫秒为单位的反应时间，以及最近复杂的用户跨设备、跨平台、甚至在线/现实世界行为的购买路径。AdKDD 研讨会仍然是广告研究人员的论坛，在 KDD 期间和之后。我们的网站提供幻灯片和摘要，在2021年的 KDD 期间，每月大约有2000次访问和1800个活跃用户。在2019年和2020年的调查中，超过60% 的人同意 AdKDD 是他们参加 KDD 的原因，超过90% 的人表示他们明年会参加。2023年的版本是特别及时的，因为越来越多的应用基于图的神经网络和生成人工智能模型在广告。加上 GDPR 实施的保护隐私的措施，CCPA 计算机广告的未来正处于一个有趣的十字路口。对于这个版本，我们计划征集论文，跨越深刻的用户理解范围，同时保留隐私。此外，我们将寻找那些讨论广告背景下的公平性的论文，超个性化在多大程度上起作用，以及广告业作为一个整体是否需要考虑更有效的商业模式，比如增量。我们已经主持了几个学术和行业的杰出人物作为主题演讲者，并发现我们的邀请演讲者系列主持专家从业人员是观众的最爱。我们将继续安排各种各样的主题演讲者，并邀请他们在本届会议上发表演讲。与过去的版本一样，我们希望激励这一领域的研究人员不仅要思考机器学习方面的问题，而且要激发关于在线广告的社会影响的讨论。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=AdKDD+2023)|0| -|[Robust NLP for Finance (RobustFin)](https://doi.org/10.1145/3580305.3599211)|Sameena Shah, Xiaodan Zhu, Gerard de Melo, Armineh Nourbakhsh, Xiaomo Liu, Zhiqiang Ma, Charese Smiley, Zhiyu Chen|Meta, Redmond, USA; Queen's University, Kingston, Canada; HPI / University of Potsdam, Potsdam, Germany; JPMorgan AI Research, New York, USA; JPMorgan AI Research, Chicago, USA|Natural language processing (NLP) technologies have been widely applied in business domains such as e-commerce and customer service, but their adoption in the financial sector has been constrained by industry-specific performance standards and regulatory restrictions. This challenge has created new opportunities for core research in related areas. Recent advancements in NLP, such as the advent of large language models, has encouraged adoption in the finance sector. However, compared to other domains, finance has stricter requirements for robustness, explainability, and generalizability. Given this background, we propose to organize the first Robust NLP for Finance (RobustFin) workshop at KDD '23 to encourage the study of and research on robustness and explainability technologies with regard to financial NLP. The goal of the workshop is to extend the applications of NLP in finance, while motivating further research in robust NLP.|自然语言处理(NLP)技术已被广泛应用于商业领域，如电子商务和客户服务，但它们在金融部门的采用受到行业特定的性能标准和监管限制。这一挑战为相关领域的核心研究创造了新的机遇。自然语言处理(NLP)的最新进展，例如大型语言模型的出现，鼓励了金融业的采用。然而，与其他领域相比，金融对健壮性、可解释性和普遍性有更严格的要求。在这种背景下，我们建议在 KDD’23组织第一个健壮的金融自然语言处理(RobustFin)研讨会，以鼓励对金融自然语言处理方面的健壮性和可解释性技术的研究。研讨会的目的是扩大自然语言处理在金融领域的应用，同时激励进一步研究健壮的自然语言处理。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Robust+NLP+for+Finance+(RobustFin))|0| -|[From Innovation to Scale (I2S) - Discuss and Learn How to Successfully Build, Commercialize, and Scale AI Innovations in Challenging Market Conditions](https://doi.org/10.1145/3580305.3599580)|Ankur M. Teredesai, Michael Zeller, Shenghua Bao, Wee Hyong Tok, Linsey Pang|CueZen Inc., & University of Washington, Seattle, WA, USA; Temasek, San Diego, CA, USA; Amazon, Cupertino, CA, USA; Salesforce, San Francisco, CA, USA; Microsoft, Seattle, WA, USA|In recent years, the AI community has witnessed an exciting acceleration in innovation across foundation models, deep learning, new AI applications across numerous verticals, and more. In addition, AI innovations driven by both academic and industry research labs have rapidly been adopted by big tech companies and startups to deliver value-differentiated products and services. For many machine learning researchers looking at commercializing their work, one of the frequently wondered questions is - "How do I kickstart a startup that can commercialize my research innovations?". For many ML practitioners in the KDD community, there is always curiosity on how big tech and startups take AI research and innovations, and scale it to be used by millions of users. This interactive workshop aims to achieve two goals: First, the workshop will bring together invited AI thought leaders working in academia, big tech as well as startups to share their perspective on the next big AI ideas that will change the world, and deliver impact. Second, the workshop will invite startup founders (from both academia and industry) to share their journey of acquiring customers, building a team, pitching for initial funding, and commercializing their research into successful enterprises|近年来，人工智能领域在基础模型、深度学习、新的人工智能应用等领域的创新都有了令人兴奋的加速发展。此外，由学术和行业研究实验室推动的人工智能创新已被大型科技公司和初创企业迅速采用，以提供价值差异化的产品和服务。对于许多机器学习研究人员来说，看着商业化他们的工作，一个经常想知道的问题是-“我如何启动一个初创公司，可以商业化我的研究创新?”.对于 KDD 社区的许多机器学习从业者来说，对于大型科技公司和创业公司如何利用人工智能的研究和创新，并将其扩展到数百万用户来使用总是充满好奇。这个互动研讨会旨在实现两个目标: 首先，研讨会将邀请来自学术界、大型科技公司以及初创企业的人工智能思想领袖参加，分享他们对下一个将改变世界并产生影响的大型人工智能想法的看法。其次，研讨会将邀请创业公司的创始人(来自学术界和行业)分享他们获得客户、建立团队、争取初始资金以及将他们的研究成果商业化成为成功企业的过程|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=From+Innovation+to+Scale+(I2S)+-+Discuss+and+Learn+How+to+Successfully+Build,+Commercialize,+and+Scale+AI+Innovations+in+Challenging+Market+Conditions)|0| -|[Deep Learning on Graphs: Methods and Applications (DLG-KDD2023)](https://doi.org/10.1145/3580305.3599207)|Lingfei Wu, Jian Pei, Jiliang Tang, Yinglong Xia, Xiaojie Guo|Michigan State University, East Lansing, USA; IBM T.J.Watson Research Center, Yorktown Height, USA; Meta AI, Menlo Park, USA; Pinterest, San Francisco, USA; Duke University, Durham, USA|Deep Learning models are at the core of research in Artificial Intelligence research today. A tide in research for deep learning on graphs or graph neural networks. This wave of research at the intersection of graph theory and deep learning has also influenced other fields of science, including computer vision, natural language processing, program synthesis and analysis, financial security, Drug Discovery and so on. However, there are still many challenges regarding a broad range of the topics in deep learning on graphs, from methodologies to applications, and from foundations to the new frontiers of GNNs. This international workshop on "Deep Learning on Graphs: Method and Applications (DLG-KDD'23)" aims to bring together both academic researchers and industrial practitioners from different backgrounds and perspectives to above challenges.|深度学习模型是当今人工智能研究的核心。图或图神经网络深度学习的研究热点。这股图论与深度学习交叉的研究热潮也影响了其他科学领域，包括计算机视觉、自然语言处理、程序综合与分析、金融安全、药物发现等。然而，从方法论到应用，从基础到 GNN 的新前沿，在图形深度学习的广泛课题方面仍然存在许多挑战。这个名为“图表的深度学习: 方法与应用”的国际研讨会旨在将来自不同背景和视角的学术研究人员和工业实践者聚集在一起，共同应对上述挑战。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Deep+Learning+on+Graphs:+Methods+and+Applications+(DLG-KDD2023))|0| -|[Scenario-Adaptive Feature Interaction for Click-Through Rate Prediction](https://doi.org/10.1145/3580305.3599936)|Erxue Min, Da Luo, Kangyi Lin, Chunzhen Huang, Yang Liu|The Hong Kong University of Science and Technology; Independent Researcher; Weixin Open Platform, Tencent|Traditional Click-Through Rate (CTR) prediction models are usually trained and deployed in a single scenario. However, large-scale commercial platforms usually contain multiple recommendation scenarios, the traffic characteristics of which may be significantly different. Recent studies have proved that learning a unified model to serve multiple scenarios is effective in improving the overall performance. However, most existing approaches suffer from various limitations respectively, such as insufficient distinction modeling, inefficiency with the increase of scenarios, and lack of interpretability. More importantly, as far as we know, none of existing Multi-Scenario Modeling approaches takes explicit feature interaction into consideration when modeling scenario distinctions, which limits the expressive power of the network and thus impairs the performance. In this paper, we propose a novel Scenario-Adaptive Feature Interaction framework named SATrans, which models scenario discrepancy as the distinction of patterns in feature correlations. Specifically, SATrans is built on a Transformer architecture to learn high-order feature interaction and involves the scenario information in the modeling of self-attention to capture distribution shifts across scenarios. We provide various implementations of our framework to boost the performance, and experiments on both public and industrial datasets show that SATrans 1) significantly outperforms existing state-of-the-art approaches for prediction, 2) is parameter-efficient as the space complexity grows marginally with the increase of scenarios, 3) offers good interpretability in both instance-level and scenario-level. We have deployed the model in WeChat Official Account Platform and have seen more than 2.84% online CTR increase on average in three major scenarios.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Scenario-Adaptive+Feature+Interaction+for+Click-Through+Rate+Prediction)|-1| +|[Graph Neural Networks: Foundation, Frontiers and Applications](https://doi.org/10.1145/3580305.3599560)|Lingfei Wu, Peng Cui, Jian Pei, Liang Zhao, Xiaojie Guo|Emory University, Atlantic, USA; IBM T.J. Watson Research Center, Yorktown Height, USA; Duke University, Durham, USA; Pinterest, San Francisco, USA; Tsinghua University, Beijing, USA|The field of graph neural networks (GNNs) has seen rapid and incredible strides over the recent years. Graph neural networks, also known as deep learning on graphs, graph representation learning, or geometric deep learning, have become one of the fastest-growing research topics in machine learning, especially deep learning. This wave of research at the intersection of graph theory and deep learning has also influenced other fields of science, including recommendation systems, computer vision, natural language processing, inductive logic programming, program synthesis, software mining, automated planning, cybersecurity, and intelligent transportation. However, as the field rapidly grows, it has been extremely challenging to gain a global perspective of the developments of GNNs. Therefore, we feel the urgency to bridge the above gap and have a comprehensive tutorial on this fast-growing yet challenging topic. This tutorial of Graph Neural Networks (GNNs): Foundation, Frontiers and Applications will cover a broad range of topics in graph neural networks, by reviewing and introducing the fundamental concepts and algorithms of GNNs, new research frontiers of GNNs, and broad and emerging applications with GNNs. In addition, rich tutorial materials will be included and introduced to help the audience gain a systematic understanding by using our recently published book-Graph Neural Networks (GNN): Foundation, Frontiers, and Applications [12], which can easily be accessed at https://graph-neural-networks.github.io/index.html.|近年来，图形神经网络(GNN)领域取得了令人难以置信的快速发展。图形神经网络，又称图形深度学习、图形表示学习或几何深度学习，已成为机器学习，尤其是深度学习中发展最快的研究课题之一。图论和深度学习交叉领域的研究热潮也影响了其他科学领域，包括推荐系统、计算机视觉、自然语言处理、归纳逻辑编程、程序综合、软件挖掘、自动规划、网络安全和智能交通。然而，随着该领域的迅速发展，要从全球的角度了解 GNN 的发展变得极具挑战性。因此，我们感到紧迫的桥梁以上的差距，并有一个全面的教程对这个快速增长但具有挑战性的主题。本教程的图形神经网络(GNNs) : 基础，前沿和应用将涵盖在图形神经网络的广泛的主题，通过审查和介绍 GNNs 的基本概念和算法，GNNs 的新的研究前沿，广泛和新兴的应用与 GNNs。此外，丰富的教学材料将包括和介绍，以帮助观众获得一个系统的理解，使用我们最近出版的书-图形神经网络(GNN) : 基础，前沿，和应用[12] ，这可以很容易地在 https://graph-Neural-Networks.github.io/index.html 访问。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Graph+Neural+Networks:+Foundation,+Frontiers+and+Applications)|0| +|[Graph and Geometry Generative Modeling for Drug Discovery](https://doi.org/10.1145/3580305.3599559)|Minkai Xu, Meng Liu, Wengong Jin, Shuiwang Ji, Jure Leskovec, Stefano Ermon|Texas A&M University, College Station, TX, USA; Broad Institute of MIT & Harvard University, Boston, MA, USA; Stanford University, Palo Alto, CA, USA|With the recent progress in geometric deep learning, generative modeling, and the availability of large-scale biological datasets, molecular graph and geometry generative modeling have emerged as a highly promising direction for scientific discovery such as drug design. These generative methods enable efficient chemical space exploration and potential drug candidate generation. However, by representing molecules as 2D graphs or 3D geometries, there exist many both fundamental and challenging problems for modeling the distribution of these irregular and complex relational data. In this tutorial, we will introduce participants to the latest key developments in this field, covering important topics including 2D molecular graph generation, 3D molecular geometry generation, 2D graph to 3D geometry generation, and conditional 3D molecular geometry generation. We further include antibody generation, where we particularly consider large-size antibody molecules. For each topic, we will outline the underlying problem characteristics, summarize key challenges, present unified views of the representative approaches, and highlight future research direction and potential impacts. We anticipate this lecture-style tutorial would attract a broad audience of researchers and practitioners.|近年来，随着几何深度学习、生成建模以及大规模生物数据集的出现，分子图和几何生成建模已经成为药物设计等科学发现的一个非常有前途的方向。这些生成方法使有效的化学空间探索和潜在的候选药物生成成为可能。然而，通过将分子表示为二维图形或三维几何图形，对于这些不规则和复杂的关系数据的分布建模存在许多基本的和具有挑战性的问题。在本教程中，我们将向学员介绍这一领域的最新发展，包括二维分子图生成、三维分子结构生成、二维图到三维几何生成以及条件三维分子结构生成等重要主题。我们进一步包括抗体生成，其中我们特别考虑大型抗体分子。对于每个主题，我们将概述潜在的问题特征，总结关键挑战，提出代表性方法的统一观点，并强调未来的研究方向和潜在影响。我们预计这种讲座式的教学将吸引广大的研究人员和从业人员的观众。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Graph+and+Geometry+Generative+Modeling+for+Drug+Discovery)|0| +|[Data-centric AI: Techniques and Future Perspectives](https://doi.org/10.1145/3580305.3599553)|Daochen Zha, KweiHerng Lai, Fan Yang, Na Zou, Huiji Gao, Xia Hu|Texas A&M University, College Station, TX, USA; Rice University, Houston, TX, USA; Wake Forest University, Winston-Salem, NC, USA; Airbnb, Inc., San Francisco, CA, USA|The role of data in AI has been significantly magnified by the emerging concept of data-centric AI. In contrast to the traditional model-centric paradigm, which focuses on developing more effective models given fixed datasets, data-centric AI emphasizes the systematic engineering of data in building AI systems. However, as a new concept, many critical aspects of data-centric AI remain ambiguous, such as its definitions, associated tasks, algorithms, challenges, and benchmarks. This tutorial aims to review and discuss this emerging field, with a particular focus on the three general data-centric AI goals: training data development, inference data development, and data maintenance. The objective of this tutorial is threefold: (1) to formally categorize the field of data-centric AI using a goal-driven taxonomy and discuss the needs and challenges of each goal, (2) to comprehensively review the state-of-the-art techniques, and (3) to discuss the future perspectives and open research directions to inspire further innovations in this field.|以数据为中心的人工智能概念的出现大大放大了数据在人工智能中的作用。与传统的以模型为中心的模式不同，以数据为中心的人工智能强调在构建人工智能系统时对数据进行系统化处理。然而，作为一个新的概念，以数据为中心的人工智能的许多关键方面仍然模糊不清，例如它的定义、相关的任务、算法、挑战和基准。本教程旨在回顾和讨论这个新兴领域，特别关注三个以数据为中心的人工智能目标: 培训数据开发、推断数据开发和数据维护。本教程的目标有三个: (1)使用目标驱动的分类法正式对以数据为中心的 AI 领域进行分类，并讨论每个目标的需求和挑战，(2)全面回顾最先进的技术，(3)讨论未来的观点和开放的研究方向，以激励该领域的进一步创新。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Data-centric+AI:+Techniques+and+Future+Perspectives)|0| +|[Hyperbolic Graph Neural Networks: A Tutorial on Methods and Applications](https://doi.org/10.1145/3580305.3599562)|Min Zhou, Menglin Yang, Bo Xiong, Hui Xiong, Irwin King|Huawei Cloud, Shenzhen, China; University of Stuttgart, Stuttgart, Germany; Hong Kong University of Science and Technology (Guangzhou), Guangzhou, Hong Kong; The Chinese University of Hong Kong, Hong Kong, Hong Kong|Graph Neural Networks (GNNs) generalize conventional neural networks to graph-structured data and have received considerable attention owing to their impressive performance. In spite of the notable successes, the performance of Euclidean models is inherently bounded and limited by the representation ability of Euclidean geometry, especially when it comes to datasets with highly non-Euclidean latent anatomy. Recently, hyperbolic spaces have emerged as a promising alternative for processing graph data with tree-like structure or power-law distribution and a surge of works on either methods or novel applications have been seen. Unlike Euclidean space, which expands polynomially, hyperbolic space grows exponentially with its radius, making it more suitable for modeling complex real-world data. Hence, it gains natural advantages in abstracting tree-like graphs with a hierarchical organization or power-law distribution. To support the burgeoning interest in Hyperbolic Graph Neural Networks (HGNNs), the primary goal of this tutorial is to give a systematical review of the methods, applications, and challenges in this fast-growing and vibrant area, with the express purpose of being accessible to all audiences.More specifically, we will first give a brief introduction to graph neural networks as well as some preliminary of Riemannian manifold and hyperbolic geometry. We then will comprehensively revisit the technical details of the developed HGNNs, by unifying them into a general framework and summarizing the variants of each component. Besides, we will introduce applications deployed in a variety of fields. Finally, we will discuss several challenges and present the potential solutions to address them, including some initial attempts of our own, which potentially paves the path for the further flourishing of the research community.|图形神经网络(GNN)将传统的神经网络推广到图形结构的数据，由于其令人印象深刻的性能而受到广泛的关注。尽管取得了显著的成功，但欧几里德模型的性能本质上受到欧几里得几何表示能力的限制，尤其是当涉及到具有高度非欧几里德潜在解剖结构的数据集时。近年来，双曲空间已成为处理具有树状结构或幂律分布的图形数据的一种有前途的替代方法。不像欧几里得空间，它是多项式展开的，双曲空间随着半径呈指数增长，这使得它更适合于建模复杂的现实世界数据。因此，它在用分层组织分布或幂律分布抽象树状图方面获得了天然的优势。为了支持对双曲图形神经网络(HGNNs)的兴趣，本教程的主要目标是对这个快速增长和充满活力的领域中的方法、应用和挑战进行系统的回顾，明确的目的是让所有的受众都可以访问。更具体地说，我们将首先简要介绍图形神经网络，以及一些初步的黎曼流形和双曲几何。然后，我们将全面重新审视已开发的 HGNN 的技术细节，将它们统一到一个总体框架中，并总结每个组件的变体。此外，我们还将介绍部署在各个领域的应用程序。最后，我们将讨论几个挑战，并提出解决这些问题的潜在解决方案，包括我们自己的一些初步尝试，这有可能为研究界的进一步繁荣铺平道路。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Hyperbolic+Graph+Neural+Networks:+A+Tutorial+on+Methods+and+Applications)|0| +|[Fragile Earth: AI for Climate Sustainability - From Wildfire Disaster Management to Public Health and Beyond](https://doi.org/10.1145/3580305.3599217)|Naoki Abe, Kathleen Buckingham, Yuzhou Chen, Bistra Dilkina, Emre Eftelioglu, Auroop R. Ganguly, Yulia R. Gel, James Hodson, Ramakrishnan Kannan, Huikyo Lee, Jiafu Mao, Rose Yu|Northeastern University, Boston, MA, USA; Veritree, Washington, DC, USA; University of Southern California, Los Angeles, CA, USA; AI for Good Foundation, San Francisco, CA, USA; Oak Ridge National Laboratory, Oak Ridge, CA, USA; Jet Propulsion Laboratory, Pasadena, CA, USA; University of California, San Diego, La Jolla, CA, USA; Temple University, Philadelphia, PA, USA; Amazon, Inc., Seattle, WA, USA; IBM Research, Yorktown Heights, NY, USA; University of Texas at Dallas, Richardson, TX, USA|The Fragile Earth Workshop is a recurring event in ACM's KDD Conference on research in knowledge discovery and data mining that gathers the research community to find and explore how data science can measure and progress climate and social issues, fol- lowing the United Nations Sustainable Development Goals (SDGs) framework.|脆弱地球研讨会是 ACM 关于知识发现和数据挖掘研究的 KDD 会议上的一个经常性活动，该会议聚集了研究界，探索数据科学如何在联合国可持续发展目标(SDGs)框架下衡量和推进气候和社会问题。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Fragile+Earth:+AI+for+Climate+Sustainability+-+From+Wildfire+Disaster+Management+to+Public+Health+and+Beyond)|0| +|[AdKDD 2023](https://doi.org/10.1145/3580305.3599582)|Abraham Bagherjeiran, Nemanja Djuric, KuangChih Lee, Linsey Pang, Vladan Radosavljevic, Suju Rajan|Spotify, New York City, NY, USA; eBay, Inc., San Jose, CA, USA; Aurora Innovation, Inc., Pittsburgh, PA, USA; Salesforce, San Francisco, CA, USA; Walmart, Sunnyvale, CA, USA; Amazon, Palo Alto, CA, USA|The digital advertising field has always had challenging ML problems, learning from petabytes of data that is highly imbalanced, reactivity times in the milliseconds, and more recently compounded with the complex user's path to purchase across devices, across platforms, and even online/real-world behavior. The AdKDD workshop continues to be a forum for researchers in advertising, during and after KDD. Our website which hosts slides and abstracts receives approximately 2,000 monthly visits and 1,800 active users during the KDD 2021. In surveys during AdKDD 2019 and 2020, over 60% agreed that AdKDD is the reason they attended KDD, and over 90% indicated they would attend next year. The 2023 edition is particularly timely because of the increasing application of Graph-based NN and Generative AI models in advertising. Coupled with privacy-preserving initiatives enforced by GDPR, CCPA the future of computational advertising is at an interesting crossroads. For this edition, we plan to solicit papers that span the spectrum of deep user understanding while remaining privacy-preserving. In addition, we will seek papers that discuss fairness in the context of advertising, to what extent does hyper-personalization work, and whether the ad industry as a whole needs to think through more effective business models such as incrementality. We have hosted several academic and industry luminaries as keynote speakers and have found our invited speaker series hosting expert practitioners to be an audience favorite. We will continue fielding a diverse set of keynote speakers and invited talks for this edition as well. As with past editions, we hope to motivate researchers in this space to think not only about the ML aspects but also to spark conversations about the societal impact of online advertising.|数字广告领域一直存在机器学习的挑战性问题，从高度不平衡的千兆字节数据中学习，以毫秒为单位的反应时间，以及最近复杂的用户跨设备、跨平台、甚至在线/现实世界行为的购买路径。AdKDD 研讨会仍然是广告研究人员的论坛，在 KDD 期间和之后。我们的网站提供幻灯片和摘要，在2021年的 KDD 期间，每月大约有2000次访问和1800个活跃用户。在2019年和2020年的调查中，超过60% 的人同意 AdKDD 是他们参加 KDD 的原因，超过90% 的人表示他们明年会参加。2023年的版本是特别及时的，因为越来越多的应用基于图的神经网络和生成人工智能模型在广告。加上 GDPR 实施的保护隐私的措施，CCPA 计算机广告的未来正处于一个有趣的十字路口。对于这个版本，我们计划征集论文，跨越深刻的用户理解范围，同时保留隐私。此外，我们将寻找那些讨论广告背景下的公平性的论文，超个性化在多大程度上起作用，以及广告业作为一个整体是否需要考虑更有效的商业模式，比如增量。我们已经主持了几个学术和行业的杰出人物作为主题演讲者，并发现我们的邀请演讲者系列主持专家从业人员是观众的最爱。我们将继续安排各种各样的主题演讲者，并邀请他们在本届会议上发表演讲。与过去的版本一样，我们希望激励这一领域的研究人员不仅要思考机器学习方面的问题，而且要激发关于在线广告的社会影响的讨论。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=AdKDD+2023)|0| +|[Robust NLP for Finance (RobustFin)](https://doi.org/10.1145/3580305.3599211)|Sameena Shah, Xiaodan Zhu, Gerard de Melo, Armineh Nourbakhsh, Xiaomo Liu, Zhiqiang Ma, Charese Smiley, Zhiyu Chen|Queen's University, Kingston, Canada; Meta, Redmond, USA; JPMorgan AI Research, Chicago, USA; JPMorgan AI Research, New York, USA; HPI / University of Potsdam, Potsdam, Germany|Natural language processing (NLP) technologies have been widely applied in business domains such as e-commerce and customer service, but their adoption in the financial sector has been constrained by industry-specific performance standards and regulatory restrictions. This challenge has created new opportunities for core research in related areas. Recent advancements in NLP, such as the advent of large language models, has encouraged adoption in the finance sector. However, compared to other domains, finance has stricter requirements for robustness, explainability, and generalizability. Given this background, we propose to organize the first Robust NLP for Finance (RobustFin) workshop at KDD '23 to encourage the study of and research on robustness and explainability technologies with regard to financial NLP. The goal of the workshop is to extend the applications of NLP in finance, while motivating further research in robust NLP.|自然语言处理(NLP)技术已被广泛应用于商业领域，如电子商务和客户服务，但它们在金融部门的采用受到行业特定的性能标准和监管限制。这一挑战为相关领域的核心研究创造了新的机遇。自然语言处理(NLP)的最新进展，例如大型语言模型的出现，鼓励了金融业的采用。然而，与其他领域相比，金融对健壮性、可解释性和普遍性有更严格的要求。在这种背景下，我们建议在 KDD’23组织第一个健壮的金融自然语言处理(RobustFin)研讨会，以鼓励对金融自然语言处理方面的健壮性和可解释性技术的研究。研讨会的目的是扩大自然语言处理在金融领域的应用，同时激励进一步研究健壮的自然语言处理。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Robust+NLP+for+Finance+(RobustFin))|0| +|[From Innovation to Scale (I2S) - Discuss and Learn How to Successfully Build, Commercialize, and Scale AI Innovations in Challenging Market Conditions](https://doi.org/10.1145/3580305.3599580)|Ankur M. Teredesai, Michael Zeller, Shenghua Bao, Wee Hyong Tok, Linsey Pang|Temasek, San Diego, CA, USA; Microsoft, Seattle, WA, USA; Amazon, Cupertino, CA, USA; Salesforce, San Francisco, CA, USA; CueZen Inc., & University of Washington, Seattle, WA, USA|In recent years, the AI community has witnessed an exciting acceleration in innovation across foundation models, deep learning, new AI applications across numerous verticals, and more. In addition, AI innovations driven by both academic and industry research labs have rapidly been adopted by big tech companies and startups to deliver value-differentiated products and services. For many machine learning researchers looking at commercializing their work, one of the frequently wondered questions is - "How do I kickstart a startup that can commercialize my research innovations?". For many ML practitioners in the KDD community, there is always curiosity on how big tech and startups take AI research and innovations, and scale it to be used by millions of users. This interactive workshop aims to achieve two goals: First, the workshop will bring together invited AI thought leaders working in academia, big tech as well as startups to share their perspective on the next big AI ideas that will change the world, and deliver impact. Second, the workshop will invite startup founders (from both academia and industry) to share their journey of acquiring customers, building a team, pitching for initial funding, and commercializing their research into successful enterprises|近年来，人工智能领域在基础模型、深度学习、新的人工智能应用等领域的创新都有了令人兴奋的加速发展。此外，由学术和行业研究实验室推动的人工智能创新已被大型科技公司和初创企业迅速采用，以提供价值差异化的产品和服务。对于许多机器学习研究人员来说，看着商业化他们的工作，一个经常想知道的问题是-“我如何启动一个初创公司，可以商业化我的研究创新?”.对于 KDD 社区的许多机器学习从业者来说，对于大型科技公司和创业公司如何利用人工智能的研究和创新，并将其扩展到数百万用户来使用总是充满好奇。这个互动研讨会旨在实现两个目标: 首先，研讨会将邀请来自学术界、大型科技公司以及初创企业的人工智能思想领袖参加，分享他们对下一个将改变世界并产生影响的大型人工智能想法的看法。其次，研讨会将邀请创业公司的创始人(来自学术界和行业)分享他们获得客户、建立团队、争取初始资金以及将他们的研究成果商业化成为成功企业的过程|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=From+Innovation+to+Scale+(I2S)+-+Discuss+and+Learn+How+to+Successfully+Build,+Commercialize,+and+Scale+AI+Innovations+in+Challenging+Market+Conditions)|0| +|[Deep Learning on Graphs: Methods and Applications (DLG-KDD2023)](https://doi.org/10.1145/3580305.3599207)|Lingfei Wu, Jian Pei, Jiliang Tang, Yinglong Xia, Xiaojie Guo|IBM T.J.Watson Research Center, Yorktown Height, USA; Duke University, Durham, USA; Meta AI, Menlo Park, USA; Pinterest, San Francisco, USA; Michigan State University, East Lansing, USA|Deep Learning models are at the core of research in Artificial Intelligence research today. A tide in research for deep learning on graphs or graph neural networks. This wave of research at the intersection of graph theory and deep learning has also influenced other fields of science, including computer vision, natural language processing, program synthesis and analysis, financial security, Drug Discovery and so on. However, there are still many challenges regarding a broad range of the topics in deep learning on graphs, from methodologies to applications, and from foundations to the new frontiers of GNNs. This international workshop on "Deep Learning on Graphs: Method and Applications (DLG-KDD'23)" aims to bring together both academic researchers and industrial practitioners from different backgrounds and perspectives to above challenges.|深度学习模型是当今人工智能研究的核心。图或图神经网络深度学习的研究热点。这股图论与深度学习交叉的研究热潮也影响了其他科学领域，包括计算机视觉、自然语言处理、程序综合与分析、金融安全、药物发现等。然而，从方法论到应用，从基础到 GNN 的新前沿，在图形深度学习的广泛课题方面仍然存在许多挑战。这个名为“图表的深度学习: 方法与应用”的国际研讨会旨在将来自不同背景和视角的学术研究人员和工业实践者聚集在一起，共同应对上述挑战。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Deep+Learning+on+Graphs:+Methods+and+Applications+(DLG-KDD2023))|0| +|[Scenario-Adaptive Feature Interaction for Click-Through Rate Prediction](https://doi.org/10.1145/3580305.3599936)|Erxue Min, Da Luo, Kangyi Lin, Chunzhen Huang, Yang Liu|Independent Researcher; Weixin Open Platform, Tencent; The Hong Kong University of Science and Technology|Traditional Click-Through Rate (CTR) prediction models are usually trained and deployed in a single scenario. However, large-scale commercial platforms usually contain multiple recommendation scenarios, the traffic characteristics of which may be significantly different. Recent studies have proved that learning a unified model to serve multiple scenarios is effective in improving the overall performance. However, most existing approaches suffer from various limitations respectively, such as insufficient distinction modeling, inefficiency with the increase of scenarios, and lack of interpretability. More importantly, as far as we know, none of existing Multi-Scenario Modeling approaches takes explicit feature interaction into consideration when modeling scenario distinctions, which limits the expressive power of the network and thus impairs the performance. In this paper, we propose a novel Scenario-Adaptive Feature Interaction framework named SATrans, which models scenario discrepancy as the distinction of patterns in feature correlations. Specifically, SATrans is built on a Transformer architecture to learn high-order feature interaction and involves the scenario information in the modeling of self-attention to capture distribution shifts across scenarios. We provide various implementations of our framework to boost the performance, and experiments on both public and industrial datasets show that SATrans 1) significantly outperforms existing state-of-the-art approaches for prediction, 2) is parameter-efficient as the space complexity grows marginally with the increase of scenarios, 3) offers good interpretability in both instance-level and scenario-level. We have deployed the model in WeChat Official Account Platform and have seen more than 2.84% online CTR increase on average in three major scenarios.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Scenario-Adaptive+Feature+Interaction+for+Click-Through+Rate+Prediction)|-1| |[Sequential Learning Algorithms for Contextual Model-Free Influence Maximization](https://doi.org/10.1145/3580305.3599498)|Alexandra Iacob, Bogdan Cautis, Silviu Maniu||The online influence maximization (OIM) problem aims to learn sequentially an optimal policy for selecting seed nodes which maximize the cumulative spread of information (influence) in a diffusion medium, throughout a multi-round diffusion campaign. We consider the sub-class of OIM problems where (i) the reward of a given round of the ongoing campaign consists of only the new activations(not observed at previous rounds), and (ii) the round's context and the historical data from previous rounds can be exploited to learn the best policy. This problem is directly motivated by the real-world scenarios of information diffusion in influencer marketing, where (i) only a target user's first / unique activation is of interest (and this activation will persist as an acquired, latent one throughout the campaign), and (ii) valuable side-information is available to the learning agent. We call this OIM formulation Episodic Contextual Influence Maximization with Persistence (in short, ECIMP). We propose the algorithm LSVI-GT-UCB, which implements the optimism in the face of uncertainty principle for episodic reinforcement learning with linear approximation. The learning agent estimates for each seed node its remaining potential with a Good-Turing estimator, modified by an estimated Q-function. The algorithm is empirically proven to perform better than state-of-the-art methods on two real-world datasets and a synthetically generated one.|在线影响最大化(OIM)问题的目的是依次学习在多轮扩散过程中最大化信息在扩散介质中累积传播(影响)的种子节点选择的最优策略。我们考虑 OIM 问题的子类，其中(i)正在进行的一轮活动的奖励仅包括新的激活(在前几轮中没有观察到) ，以及(ii)可以利用前几轮的背景和历史数据来学习最佳策略。这个问题直接受到影响者营销中信息传播的现实世界场景的激发，其中(i)只有目标用户的第一次/唯一激活是有意义的(并且这种激活将在整个活动中作为获得的潜在的一种持续存在) ，以及(ii)有价值的侧面信息可用于学习代理。我们称这种 OIM 公式为持久性情景影响最大化(简称 ECIMP)。我们提出了 LSVI-gT-UCB 算法，该算法在具有强化学习的情节线性近似的不确定性原理面前实现了乐观。每个种子节点的学习代理用一个 Good-Turing 估计器估计其剩余潜力，并用一个估计的 Q 函数进行修正。经验证明，该算法在两个实际数据集和一个综合生成的数据集上的性能优于最先进的方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Sequential+Learning+Algorithms+for+Contextual+Model-Free+Influence+Maximization)|-1| |[LightToken: A Task and Model-agnostic Lightweight Token Embedding Framework for Pre-trained Language Models](https://doi.org/10.1145/3580305.3599416)|Haoyu Wang, Ruirui Li, Haoming Jiang, Zhengyang Wang, Xianfeng Tang, Bin Bi, Monica Xiao Cheng, Bing Yin, Yaqing Wang, Tuo Zhao, Jing Gao||Pre-trained language models~(PLMs) such as BERT, RoBERTa, and DeBERTa have achieved state-of-the-art performance on various downstream tasks. The enormous sizes of PLMs hinder their deployment in resource-constrained scenarios, e.g., on edge and mobile devices. To address this issue, many model compression approaches have been proposed to reduce the number of model parameters. This paper focuses on compressing the token embedding matrices of PLMs, which typically make up a large proportion~(around 20-30%) of the entire model parameters. Existing efforts to compress token embedding usually require the introduction of customized compression architectures or the optimization of model compression processes for individual downstream tasks, limiting their applicability in both model and task dimensions. To overcome these limitations and adhere to the principle of "one-for-all", we propose a lightweight token embedding framework named LightToken, which is able to produce compressed token embedding in a task and model-agnostic fashion. LightToken is generally compatible with different architectures and applicable to any downstream task. Specifically, through an integration of low-rank approximation, novel residual binary autoencoder, and a new compression loss function, LightToken can significantly improve the model compression ratio. To demonstrate the effectiveness of LightToken, we conduct comprehensive experiments on natural language understanding and question answering tasks. In particular, LightToken improves the state-of-the-art token embedding compression ratio from 5 to 25 and outperforms the existing token embedding compression approaches by 11% and 5% on GLUE and SQuAD v1.1 benchmarks, respectively.|像 BERT、 RoBERTa 和 DeBERTa 这样的预训练语言模型已经在各种下游任务中取得了最先进的性能。PLM 的巨大规模阻碍了它们在资源受限的场景中的部署，例如在边缘和移动设备上。为了解决这个问题，人们提出了许多模型压缩方法来减少模型参数的数量。本文主要研究 PLM 中的令牌嵌入矩阵的压缩问题，这些令牌嵌入矩阵在整个模型参数中占有很大的比例(约20-30%)。现有的压缩令牌嵌入的工作通常需要引入定制的压缩体系结构，或者为单个下游任务优化模型压缩过程，从而限制了它们在模型和任务维度上的适用性。为了克服这些限制并坚持“一个对所有人”的原则，我们提出了一个名为 LightToken 的轻量级令牌嵌入框架，它能够以任务和模型无关的方式产生压缩令牌嵌入。LightToken 通常与不同的体系结构兼容，并适用于任何下游任务。具体来说，通过集成低秩近似、新颖的剩余二进制自动编码器和新的压缩损耗函数，LightToken 可以显著改善模型的压缩比。为了证明 LightToken 的有效性，我们对自然语言理解和问答任务进行了全面的实验。特别值得一提的是，LightToken 将最先进的令牌嵌入压缩比从5提高到了25，并且在 GLUE 和 SQUAD v1.1基准上分别比现有的令牌嵌入压缩方法提高了11% 和5% 。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=LightToken:+A+Task+and+Model-agnostic+Lightweight+Token+Embedding+Framework+for+Pre-trained+Language+Models)|-1| |[Automatic Temporal Relation in Multi-Task Learning](https://doi.org/10.1145/3580305.3599261)|Menghui Zhou, Po Yang|The University of Sheffield|Multi-task learning with temporal relation is a common prediction method for modelling the evolution of a wide range of systems. Considering the inherent relations between multiple time points, many works apply multi-task learning to jointly analyse all time points, with each time point corresponding to a prediction task. The most difficult challenge is determining how to fully explore and thus exploit the shared valuable temporal information between tasks to improve the generalization performance and robustness of the model. Existing works are classified as temporal smoothness and mean temporal relations. Both approaches, however, utilize a predefined and symmetric task relation structure that is too rigid and insufficient to adequately capture the intricate temporal relations between tasks. Instead, we propose a novel mechanism named Automatic Temporal Relation (AutoTR) for directly and automatically learning the temporal relation from any given dataset. To solve the biconvex objective function, we adopt the alternating optimization and show that the two related sub-optimization problems are amenable to closed-form computation of the proximal operator. To solve the two problems efficiently, the accelerated proximal gradient method is used, which has the fastest convergence rate of any first-order method. We have preprocessed six public real-life datasets and conducted extensive experiments to fully demonstrate the superiority of AutoTR. The results show that AutoTR outperforms several baseline methods on almost all datasets with different training ratios, in terms of overall model performance and every individual task performance. Furthermore, our findings verify that the temporal relation between tasks is asymmetrical, which has not been considered in previous works. The implementation source can be found at https://github.com/menghui-zhou/AutoTR.|具有时间关系的多任务学习是一种常用的模拟系统演化的预测方法。考虑到多个时间点之间的内在联系，许多工作采用多任务学习的方法对所有时间点进行联合分析，每个时间点对应一个预测任务。最困难的挑战是确定如何充分探索并利用任务之间共享的有价值的时间信息，以提高模型的泛化性能和鲁棒性。现有的作品分为时间平滑性和平均时间关系。然而，这两种方法都使用了一种预定义的对称任务关系结构，这种结构过于严格，不足以充分捕获任务之间错综复杂的时间关系。相反，我们提出了一种新的机制称为自动时间关系(AutoTR)直接和自动学习的时间关系从任何给定的数据集。为了求解双凸目标函数，我们采用了交替优化的方法，并且证明了两个相关的子优化问题适合于近似算子的闭式计算。为了有效地解决这两个问题，采用了加速近似梯度法，它的收敛速度是任何一阶方法中最快的。为了充分展示 AutoTR 的优越性，我们对六个公共现实数据集进行了预处理，并进行了广泛的实验。结果表明，AutoTR 在不同训练比例的数据集上，在整体模型性能和各个任务性能方面，优于几乎所有的基线方法。此外，我们的研究结果证实了任务之间的时间关系是不对称的，这是以前的研究没有考虑到的。实施来源可参阅 https://github.com/menghui-zhou/autotr。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Automatic+Temporal+Relation+in+Multi-Task+Learning)|-1| |[Empowering General-purpose User Representation with Full-life Cycle Behavior Modeling](https://doi.org/10.1145/3580305.3599331)|Bei Yang, Jie Gu, Ke Liu, Xiaoxiao Xu, Renjun Xu, Qinghui Sun, Hong Liu||User Modeling plays an essential role in industry. In this field, task-agnostic approaches, which generate general-purpose representation applicable to diverse downstream user cognition tasks, is a promising direction being more valuable and economical than task-specific representation learning. With the rapid development of Internet service platforms, user behaviors have been accumulated continuously. However, existing general-purpose user representation researches have little ability for full-life cycle modeling on extremely long behavior sequences since user registration. In this study, we propose a novel framework called full- Life cycle User Representation Model (LURM) to tackle this challenge. Specifically, LURM consists of two cascaded sub-models: (\romannumeral1) Bag-of-Interests (BoI) encodes user behaviors in any time period into a sparse vector with super-high dimension (\textite. \textitg. , 10^5 ); (\romannumeral2) Self-supervised Multi-anchor Encoder Network (SMEN) maps sequences of BoI features to multiple low-dimensional user representations. Specially, SMEN achieves almost lossless dimensionality reduction, benefiting from a novel multi-anchor module which can learn different aspects of user interests. Experiments on several benchmark datasets show that our approach outperforms state-of-the-art general-purpose representation methods.|用户建模在工业中起着至关重要的作用。在这一领域，任务无关方法产生适用于不同下游用户认知任务的通用表示，是一个比任务特定表示学习更有价值和更经济的方向。随着互联网服务平台的飞速发展，用户行为不断积累。然而，现有的通用用户表示研究对于用户注册后的极长行为序列的全生命周期建模能力不足。在这项研究中，我们提出了一个全生命周期用户表示模型(LURM)的新框架来应对这一挑战。具体来说，LURM 由两个级联子模型组成: 利益包(BoI)将用户在任意时间段的行为编码成一个超高维稀疏向量(textite)。短信。自监督多锚编码器网络(SMN)将 BoI 特征序列映射到多个低维用户表示。特别值得一提的是，采用新颖的多锚模块，可以了解用户兴趣的不同方面，实现了几乎无损的降维。在几个基准数据集上的实验表明，我们的方法优于最先进的通用表示方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Empowering+General-purpose+User+Representation+with+Full-life+Cycle+Behavior+Modeling)|-1| |[Towards Understanding and Enhancing Robustness of Deep Learning Models against Malicious Unlearning Attacks](https://doi.org/10.1145/3580305.3599526)|Wei Qian, Chenxu Zhao, Wei Le, Meiyi Ma, Mengdi Huai||Given the availability of abundant data, deep learning models have been advanced and become ubiquitous in the past decade. In practice, due to many different reasons (e.g., privacy, usability, and fidelity), individuals also want the trained deep models to forget some specific data. Motivated by this, machine unlearning (also known as selective data forgetting) has been intensively studied, which aims at removing the influence that any particular training sample had on the trained model during the unlearning process. However, people usually employ machine unlearning methods as trusted basic tools and rarely have any doubt about their reliability. In fact, the increasingly critical role of machine unlearning makes deep learning models susceptible to the risk of being maliciously attacked. To well understand the performance of deep learning models in malicious environments, we believe that it is critical to study the robustness of deep learning models to malicious unlearning attacks, which happen during the unlearning process. To bridge this gap, in this paper, we first demonstrate that malicious unlearning attacks pose immense threats to the security of deep learning systems. Specifically, we present a broad class of malicious unlearning attacks wherein maliciously crafted unlearning requests trigger deep learning models to misbehave on target samples in a highly controllable and predictable manner. In addition, to improve the robustness of deep learning models, we also present a general defense mechanism, which aims to identify and unlearn effective malicious unlearning requests based on their gradient influence on the unlearned models. Further, theoretical analyses are conducted to analyze the proposed methods. Extensive experiments on real-world datasets validate the vulnerabilities of deep learning models to malicious unlearning attacks and the effectiveness of the introduced defense mechanism.|由于有大量的数据可用，深度学习模型在过去十年中得到了发展并变得无处不在。在实践中，由于许多不同的原因(例如，隐私、可用性和保真度) ，个人也希望训练有素的深度模型忘记一些特定的数据。基于此，机器学习(也称为选择性数据遗忘)被广泛研究，旨在消除任何特定训练样本在遗忘过程中对训练模型的影响。然而，人们通常使用机器忘却方法作为可信赖的基本工具，很少怀疑它们的可靠性。事实上，机器忘记的作用越来越重要，使得深度学习模型容易受到恶意攻击的风险。为了更好地理解深度学习模型在恶意环境中的性能，我们认为研究深度学习模型对于恶意忘却攻击的鲁棒性是至关重要的。为了弥补这一差距，本文首先论证了恶意忘记攻击对深度学习系统的安全性构成了巨大的威胁。具体来说，我们提出了一类广泛的恶意忘却攻击，其中恶意编写的忘却请求触发深度学习模型，以高度可控和可预测的方式对目标样本进行错误行为。此外，为了提高深度学习模型的鲁棒性，我们还提出了一种通用的防御机制，该机制旨在根据有效的恶意忘却请求对未学习模型的梯度影响来识别和忘却这些恶意忘却请求。进一步，对提出的方法进行了理论分析。在实际数据集上的大量实验验证了深度学习模型对恶意忘却攻击的脆弱性以及所引入的防御机制的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Towards+Understanding+and+Enhancing+Robustness+of+Deep+Learning+Models+against+Malicious+Unlearning+Attacks)|-1| |[Virtual Node Tuning for Few-shot Node Classification](https://doi.org/10.1145/3580305.3599541)|Zhen Tan, Ruocheng Guo, Kaize Ding, Huan Liu|Arizona State University|Few-shot Node Classification (FSNC) is a challenge in graph representation learning where only a few labeled nodes per class are available for training. To tackle this issue, meta-learning has been proposed to transfer structural knowledge from base classes with abundant labels to target novel classes. However, existing solutions become ineffective or inapplicable when base classes have no or limited labeled nodes. To address this challenge, we propose an innovative method dubbed Virtual Node Tuning (VNT). Our approach utilizes a pretrained graph transformer as the encoder and injects virtual nodes as soft prompts in the embedding space, which can be optimized with few-shot labels in novel classes to modulate node embeddings for each specific FSNC task. A unique feature of VNT is that, by incorporating a Graph-based Pseudo Prompt Evolution (GPPE) module, VNT-GPPE can handle scenarios with sparse labels in base classes. Experimental results on four datasets demonstrate the superiority of the proposed approach in addressing FSNC with unlabeled or sparsely labeled base classes, outperforming existing state-of-the-art methods and even fully supervised baselines.|少镜头节点分类(FSNC)是图表示学习中的一个挑战，每个类只有少量标记节点可用于训练。为了解决这个问题，元学习已经被提出来将结构化知识从有大量标签的基类转移到新类中。但是，当基类没有或只有有限的标记节点时，现有的解决方案将变得无效或不适用。为了应对这一挑战，我们提出了一种称为虚拟节点优化(Virtual Node Tuning，VNT)的创新方法。该方法利用预先训练好的图形变换器作为编码器，在嵌入空间中注入虚拟节点作为软提示，并通过新类中的少镜头标签优化，调节节点嵌入，以适应每个特定的 FSNC 任务。VNT 的一个独特特性是，通过合并一个基于图的伪提示演化(PseudoPrompt Evolution，GPPE)模块，VNT-GPPE 可以处理基类中带有稀疏标签的场景。在四个数据集上的实验结果表明，该方法在处理未标记或稀疏标记基类的 FSNC 问题上具有优越性，其性能优于现有的最先进的方法，甚至优于完全监督的基线。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Virtual+Node+Tuning+for+Few-shot+Node+Classification)|-1| -|[Select and Trade: Towards Unified Pair Trading with Hierarchical Reinforcement Learning](https://doi.org/10.1145/3580305.3599951)|Weiguang Han, Boyi Zhang, Qianqian Xie, Min Peng, Yanzhao Lai, Jimin Huang|Southwest Jiaotong University; Wuhan University; Chancefocus AMC|Pair trading is one of the most effective statistical arbitrage strategies which seeks a neutral profit by hedging a pair of selected assets. Existing methods generally decompose the task into two separate steps: pair selection and trading. However, the decoupling of two closely related subtasks can block information propagation and lead to limited overall performance. For pair selection, ignoring the trading performance results in the wrong assets being selected with irrelevant price movements, while the agent trained for trading can overfit to the selected assets without any historical information of other assets. To address it, in this paper, we propose a paradigm for automatic pair trading as a unified task rather than a two-step pipeline. We design a hierarchical reinforcement learning framework to jointly learn and optimize two subtasks. A high-level policy would select two assets from all possible combinations and a low-level policy would then perform a series of trading actions. Experimental results on real-world stock data demonstrate the effectiveness of our method on pair trading compared with both existing pair selection and trading methods.|成对交易是最有效的统计套利策略之一，它通过对选定的资产进行套期保值来寻求中性利润。现有的方法通常将任务分解为两个独立的步骤: 配对选择和交易。然而，两个紧密相关的子任务的解耦会阻碍信息传播，并导致有限的总体性能。对于配对选择，忽略交易绩效会导致错误的资产选择和不相关的价格变动，而受过交易培训的代理人可能在没有任何其他资产历史信息的情况下过度适应选定的资产。为了解决这个问题，本文提出了一个自动配对交易的范例，它是一个统一的任务，而不是一个两步的流水线。我们设计了一个层次化的强化学习框架来联合学习和优化两个子任务。高级策略将从所有可能的组合中选择两种资产，然后低级策略将执行一系列交易操作。对实际股票数据的实验结果表明，与现有的成对选择和成对交易方法相比，本文提出的方法是有效的。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Select+and+Trade:+Towards+Unified+Pair+Trading+with+Hierarchical+Reinforcement+Learning)|-1| +|[Select and Trade: Towards Unified Pair Trading with Hierarchical Reinforcement Learning](https://doi.org/10.1145/3580305.3599951)|Weiguang Han, Boyi Zhang, Qianqian Xie, Min Peng, Yanzhao Lai, Jimin Huang|Chancefocus AMC; Southwest Jiaotong University; Wuhan University|Pair trading is one of the most effective statistical arbitrage strategies which seeks a neutral profit by hedging a pair of selected assets. Existing methods generally decompose the task into two separate steps: pair selection and trading. However, the decoupling of two closely related subtasks can block information propagation and lead to limited overall performance. For pair selection, ignoring the trading performance results in the wrong assets being selected with irrelevant price movements, while the agent trained for trading can overfit to the selected assets without any historical information of other assets. To address it, in this paper, we propose a paradigm for automatic pair trading as a unified task rather than a two-step pipeline. We design a hierarchical reinforcement learning framework to jointly learn and optimize two subtasks. A high-level policy would select two assets from all possible combinations and a low-level policy would then perform a series of trading actions. Experimental results on real-world stock data demonstrate the effectiveness of our method on pair trading compared with both existing pair selection and trading methods.|成对交易是最有效的统计套利策略之一，它通过对选定的资产进行套期保值来寻求中性利润。现有的方法通常将任务分解为两个独立的步骤: 配对选择和交易。然而，两个紧密相关的子任务的解耦会阻碍信息传播，并导致有限的总体性能。对于配对选择，忽略交易绩效会导致错误的资产选择和不相关的价格变动，而受过交易培训的代理人可能在没有任何其他资产历史信息的情况下过度适应选定的资产。为了解决这个问题，本文提出了一个自动配对交易的范例，它是一个统一的任务，而不是一个两步的流水线。我们设计了一个层次化的强化学习框架来联合学习和优化两个子任务。高级策略将从所有可能的组合中选择两种资产，然后低级策略将执行一系列交易操作。对实际股票数据的实验结果表明，与现有的成对选择和成对交易方法相比，本文提出的方法是有效的。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Select+and+Trade:+Towards+Unified+Pair+Trading+with+Hierarchical+Reinforcement+Learning)|-1| diff --git a/papers/kdd/kdd2024.md b/papers/kdd/kdd2024.md index 31d4aed4..905b4089 100644 --- a/papers/kdd/kdd2024.md +++ b/papers/kdd/kdd2024.md @@ -2,405 +2,405 @@ |论文|作者|组织|摘要|翻译|代码|引用数| |---|---|---|---|---|---|---| -|[On the Convergence of Zeroth-Order Federated Tuning for Large Language Models](https://doi.org/10.1145/3637528.3671865)|Zhenqing Ling, Daoyuan Chen, Liuyi Yao, Yaliang Li, Ying Shen|Sun Yat-sen University, Shenzhen, Guangdong, China; Alibaba Group, Bellevue, Washington, USA; Sun Yat-sen University & Pazhou Lab, Shenzhen, Guangdong, China; Alibaba Group, Hangzhou, Zhejiang, China|The confluence of Federated Learning (FL) and Large Language Models (LLMs) isushering in a new era in privacy-preserving natural language processing.However, the intensive memory requirements for fine-tuning LLMs posesignificant challenges, especially when deploying on clients with limitedcomputational resources. To circumvent this, we explore the novel integrationof Memory-efficient Zeroth-Order Optimization within a federated setting, asynergy we term as FedMeZO. Our study is the first to examine the theoreticalunderpinnings of FedMeZO in the context of LLMs, tackling key questionsregarding the influence of large parameter spaces on optimization behavior, theestablishment of convergence properties, and the identification of criticalparameters for convergence to inform personalized federated strategies. Ourextensive empirical evidence supports the theory, showing that FedMeZO not onlyconverges faster than traditional first-order methods such as FedAvg but alsosignificantly reduces GPU memory usage during training to levels comparable tothose during inference. Moreover, the proposed personalized FL strategy that isbuilt upon the theoretical insights to customize the client-wise learning ratecan effectively accelerate loss reduction. We hope our work can help to bridgetheoretical and practical aspects of federated fine-tuning for LLMs, therebystimulating further advancements and research in this area.|联邦学习(FL)和大语言模型(LLM)的融合开创了保护隐私的自然语言处理的新纪元。然而，微调 LLM 所需的大量内存带来了巨大的挑战，特别是在部署到计算资源有限的客户机上时。为了规避这个问题，我们探索了一种新的集成内存高效的零阶优化在一个联邦设置，我们称之为 FedMeZO 的不协调。我们的研究首次在 LLM 的背景下检验了 FedMeZO 的理论基础，解决了大参数空间对优化行为的影响，建立收敛性质，以及识别收敛的关键参数以通知个性化的联邦策略等关键问题。我们广泛的经验证明支持这一理论，表明 FedmeZO 不仅比传统的一阶方法(如 FedAvg)收敛得更快，而且在训练过程中显著降低了 GPU 内存的使用，与推理过程中的使用水平相当。此外，提出的个性化 FL 策略是建立在理论的洞察力，定制客户明智的学习率，可以有效地加速减少损失。我们希望我们的工作能够有助于联合微调 LLM 的理论和实践方面的桥梁，从而促进该领域的进一步发展和研究。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=On+the+Convergence+of+Zeroth-Order+Federated+Tuning+for+Large+Language+Models)|2| -|[LLM4DyG: Can Large Language Models Solve Spatial-Temporal Problems on Dynamic Graphs?](https://doi.org/10.1145/3637528.3671709)|Zeyang Zhang, Xin Wang, Ziwei Zhang, Haoyang Li, Yijian Qin, Wenwu Zhu|DCST, BNRist, Tsinghua University, Beijing, China; DCST, Tsinghua University, Beijing, China|In an era marked by the increasing adoption of Large Language Models (LLMs)for various tasks, there is a growing focus on exploring LLMs' capabilities inhandling web data, particularly graph data. Dynamic graphs, which capturetemporal network evolution patterns, are ubiquitous in real-world web data.Evaluating LLMs' competence in understanding spatial-temporal information ondynamic graphs is essential for their adoption in web applications, whichremains unexplored in the literature. In this paper, we bridge the gap viaproposing to evaluate LLMs' spatial-temporal understanding abilities on dynamicgraphs, to the best of our knowledge, for the first time. Specifically, wepropose the LLM4DyG benchmark, which includes nine specially designed tasksconsidering the capability evaluation of LLMs from both temporal and spatialdimensions. Then, we conduct extensive experiments to analyze the impacts ofdifferent data generators, data statistics, prompting techniques, and LLMs onthe model performance. Finally, we propose Disentangled Spatial-TemporalThoughts (DST2) for LLMs on dynamic graphs to enhance LLMs' spatial-temporalunderstanding abilities. Our main observations are: 1) LLMs have preliminaryspatial-temporal understanding abilities on dynamic graphs, 2) Dynamic graphtasks show increasing difficulties for LLMs as the graph size and densityincrease, while not sensitive to the time span and data generation mechanism,3) the proposed DST2 prompting method can help to improve LLMs'spatial-temporal understanding abilities on dynamic graphs for most tasks. Thedata and codes will be open-sourced at publication time.|在一个为各种任务越来越多地采用大语言模型(LLM)的时代，人们越来越关注探索 LLM 处理 Web 数据(尤其是图形数据)的能力。动态图是现实网络数据中普遍存在的一种捕捉网络演化模式的图形。评估 LLM 理解动态图中时空信息的能力对于它们在 Web 应用程序中的应用至关重要，这在文献中尚未得到探索。本文首次提出了利用动态图来评价 LLM 的时空理解能力。具体来说，我们提出 LLM4DyG 基准，其中包括九个特别设计的任务，考虑到时间和空间维度的 LLM 的能力评估。然后，我们进行了广泛的实验来分析不同的数据生成器、数据统计、提示技术和 LLM 对模型性能的影响。最后，我们提出了动态图上 LLM 的时空分离思想(DST2) ，以提高 LLM 的时空理解能力。我们的主要观察结果是: 1) LLM 对动态图具有初步的时空理解能力; 2)随着图的大小和密度的增加，动态图任务对 LLM 的理解难度增加，而对时间跨度和数据生成机制不敏感; 3)所提出的 DST2提示方法有助于提高 LLM 对大多数任务的动态图的时空理解能力。数据和代码将在出版时公开来源。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=LLM4DyG:+Can+Large+Language+Models+Solve+Spatial-Temporal+Problems+on+Dynamic+Graphs?)|2| -|[FederatedScope-LLM: A Comprehensive Package for Fine-tuning Large Language Models in Federated Learning](https://doi.org/10.1145/3637528.3671573)|Weirui Kuang, Bingchen Qian, Zitao Li, Daoyuan Chen, Dawei Gao, Xuchen Pan, Yuexiang Xie, Yaliang Li, Bolin Ding, Jingren Zhou|Alibaba Group, Beijing, China; Alibaba Group, Hangzhou, China; Alibaba Group, Bellevue, USA|Large language models (LLMs) have demonstrated great capabilities in various natural language understanding and generation tasks. These pre-trained LLMs can be further improved for specific downstream tasks by fine-tuning. However, the adoption of LLM in real-world applications can be hindered by privacy concerns and the resource-intensive nature of model training and fine-tuning. When multiple entities have similar interested tasks but cannot directly share their local data due to privacy regulations, federated learning (FL) is a mainstream solution to leverage the data of different entities. Besides avoiding direct data sharing, FL can also achieve rigorous data privacy protection, model intelligent property protection, and model customization via composition with different techniques. Despite the aforementioned advantages of FL, fine-tuning LLMs in FL settings still lacks adequate support from the existing frameworks and, therefore, faces challenges in optimizing the consumption of significant communication and computational resources, preparing various data for different tasks, and satisfying diverse information protection demands. In this paper, we discuss these challenges and introduce our package FederatedScope-LLM (FS-LLM) as a main contribution, which consists: (1) We build a complete end-to-end benchmarking pipeline under real-world scenarios, automizing the processes of dataset preprocessing, federated fine-tuning execution or simulation, and performance evaluation; (2) We provide comprehensive and off-the-shelf federated parameter-efficient fine-tuning (PEFT) algorithm implementations and versatile programming interfaces for future extension, enhancing the capabilities of LLMs in FL scenarios with low communication and computation costs, even without accessing the full model; (3) We adopt several accelerating and resource-efficient operators, and provide flexible pluggable sub-routines for interdisciplinary study. We conduct extensive and reproducible experiments to show the effectiveness of FS-LLM and benchmark advanced LLMs with PEFT algorithms in FL. We release FS-LLM at https://github.com/alibaba/FederatedScope/tree/llm.|大型语言模型(LLM)在各种自然语言理解和生成任务中表现出了很强的能力。通过微调，这些预先训练好的 LLM 可以针对特定的下游任务进一步改进。然而，在实际应用中采用 LLM 可能会受到隐私问题以及模型训练和微调的资源密集性的阻碍。当多个实体具有相似的感兴趣任务，但由于隐私规则的限制而不能直接共享本地数据时，联邦学习(FL)是利用不同实体数据的主流解决方案。除了避免直接数据共享，FL 还可以实现严格的数据隐私保护、模型智能财产保护和通过不同技术组合的模型定制。尽管 FL 具有上述优势，但 FL 环境中的微调 LLM 仍然缺乏现有框架的足够支持，因此在优化重要通信和计算资源的消耗，为不同任务准备各种数据以及满足不同的信息保护需求方面面临挑战。本文讨论了这些挑战，并介绍了我们的软件包 FederatedScope-LLM (FS-LLM) ，它的主要贡献包括: (1)构建了一个完整的现实场景下端到端基准测试流水线，实现了数据集预处理、联邦微调执行或仿真、性能评估等过程的自动化;(2)我们提供全面和现成的联邦参数高效微调(PEFT)算法实现和多功能的编程接口，为未来的扩展提供支持，提高 LLM 在低通信和计算成本的 FL 场景中的能力，即使不访问完整的模型; (3)我们采用多个加速和资源高效的运算符，并为跨学科研究提供灵活的可插入子例程。我们进行了广泛和可重复的实验，以证明 FS-LLM 和基准高级 LLM 与 PEFT 算法在 FL 的有效性。我们在 https://github.com/alibaba/federatedscope/tree/llm 发布 FS-LLM。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=FederatedScope-LLM:+A+Comprehensive+Package+for+Fine-tuning+Large+Language+Models+in+Federated+Learning)|2| +|[On the Convergence of Zeroth-Order Federated Tuning for Large Language Models](https://doi.org/10.1145/3637528.3671865)|Zhenqing Ling, Daoyuan Chen, Liuyi Yao, Yaliang Li, Ying Shen|Sun Yat-sen University & Pazhou Lab, Shenzhen, Guangdong, China; Alibaba Group, Bellevue, Washington, USA; Alibaba Group, Hangzhou, Zhejiang, China; Sun Yat-sen University, Shenzhen, Guangdong, China|The confluence of Federated Learning (FL) and Large Language Models (LLMs) isushering in a new era in privacy-preserving natural language processing.However, the intensive memory requirements for fine-tuning LLMs posesignificant challenges, especially when deploying on clients with limitedcomputational resources. To circumvent this, we explore the novel integrationof Memory-efficient Zeroth-Order Optimization within a federated setting, asynergy we term as FedMeZO. Our study is the first to examine the theoreticalunderpinnings of FedMeZO in the context of LLMs, tackling key questionsregarding the influence of large parameter spaces on optimization behavior, theestablishment of convergence properties, and the identification of criticalparameters for convergence to inform personalized federated strategies. Ourextensive empirical evidence supports the theory, showing that FedMeZO not onlyconverges faster than traditional first-order methods such as FedAvg but alsosignificantly reduces GPU memory usage during training to levels comparable tothose during inference. Moreover, the proposed personalized FL strategy that isbuilt upon the theoretical insights to customize the client-wise learning ratecan effectively accelerate loss reduction. We hope our work can help to bridgetheoretical and practical aspects of federated fine-tuning for LLMs, therebystimulating further advancements and research in this area.|联邦学习(FL)和大语言模型(LLM)的融合开创了保护隐私的自然语言处理的新纪元。然而，微调 LLM 所需的大量内存带来了巨大的挑战，特别是在部署到计算资源有限的客户机上时。为了规避这个问题，我们探索了一种新的集成内存高效的零阶优化在一个联邦设置，我们称之为 FedMeZO 的不协调。我们的研究首次在 LLM 的背景下检验了 FedMeZO 的理论基础，解决了大参数空间对优化行为的影响，建立收敛性质，以及识别收敛的关键参数以通知个性化的联邦策略等关键问题。我们广泛的经验证明支持这一理论，表明 FedmeZO 不仅比传统的一阶方法(如 FedAvg)收敛得更快，而且在训练过程中显著降低了 GPU 内存的使用，与推理过程中的使用水平相当。此外，提出的个性化 FL 策略是建立在理论的洞察力，定制客户明智的学习率，可以有效地加速减少损失。我们希望我们的工作能够有助于联合微调 LLM 的理论和实践方面的桥梁，从而促进该领域的进一步发展和研究。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=On+the+Convergence+of+Zeroth-Order+Federated+Tuning+for+Large+Language+Models)|2| +|[LLM4DyG: Can Large Language Models Solve Spatial-Temporal Problems on Dynamic Graphs?](https://doi.org/10.1145/3637528.3671709)|Zeyang Zhang, Xin Wang, Ziwei Zhang, Haoyang Li, Yijian Qin, Wenwu Zhu|DCST, Tsinghua University, Beijing, China; DCST, BNRist, Tsinghua University, Beijing, China|In an era marked by the increasing adoption of Large Language Models (LLMs)for various tasks, there is a growing focus on exploring LLMs' capabilities inhandling web data, particularly graph data. Dynamic graphs, which capturetemporal network evolution patterns, are ubiquitous in real-world web data.Evaluating LLMs' competence in understanding spatial-temporal information ondynamic graphs is essential for their adoption in web applications, whichremains unexplored in the literature. In this paper, we bridge the gap viaproposing to evaluate LLMs' spatial-temporal understanding abilities on dynamicgraphs, to the best of our knowledge, for the first time. Specifically, wepropose the LLM4DyG benchmark, which includes nine specially designed tasksconsidering the capability evaluation of LLMs from both temporal and spatialdimensions. Then, we conduct extensive experiments to analyze the impacts ofdifferent data generators, data statistics, prompting techniques, and LLMs onthe model performance. Finally, we propose Disentangled Spatial-TemporalThoughts (DST2) for LLMs on dynamic graphs to enhance LLMs' spatial-temporalunderstanding abilities. Our main observations are: 1) LLMs have preliminaryspatial-temporal understanding abilities on dynamic graphs, 2) Dynamic graphtasks show increasing difficulties for LLMs as the graph size and densityincrease, while not sensitive to the time span and data generation mechanism,3) the proposed DST2 prompting method can help to improve LLMs'spatial-temporal understanding abilities on dynamic graphs for most tasks. Thedata and codes will be open-sourced at publication time.|在一个为各种任务越来越多地采用大语言模型(LLM)的时代，人们越来越关注探索 LLM 处理 Web 数据(尤其是图形数据)的能力。动态图是现实网络数据中普遍存在的一种捕捉网络演化模式的图形。评估 LLM 理解动态图中时空信息的能力对于它们在 Web 应用程序中的应用至关重要，这在文献中尚未得到探索。本文首次提出了利用动态图来评价 LLM 的时空理解能力。具体来说，我们提出 LLM4DyG 基准，其中包括九个特别设计的任务，考虑到时间和空间维度的 LLM 的能力评估。然后，我们进行了广泛的实验来分析不同的数据生成器、数据统计、提示技术和 LLM 对模型性能的影响。最后，我们提出了动态图上 LLM 的时空分离思想(DST2) ，以提高 LLM 的时空理解能力。我们的主要观察结果是: 1) LLM 对动态图具有初步的时空理解能力; 2)随着图的大小和密度的增加，动态图任务对 LLM 的理解难度增加，而对时间跨度和数据生成机制不敏感; 3)所提出的 DST2提示方法有助于提高 LLM 对大多数任务的动态图的时空理解能力。数据和代码将在出版时公开来源。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=LLM4DyG:+Can+Large+Language+Models+Solve+Spatial-Temporal+Problems+on+Dynamic+Graphs?)|2| +|[FederatedScope-LLM: A Comprehensive Package for Fine-tuning Large Language Models in Federated Learning](https://doi.org/10.1145/3637528.3671573)|Weirui Kuang, Bingchen Qian, Zitao Li, Daoyuan Chen, Dawei Gao, Xuchen Pan, Yuexiang Xie, Yaliang Li, Bolin Ding, Jingren Zhou|Alibaba Group, Bellevue, USA; Alibaba Group, Hangzhou, China; Alibaba Group, Beijing, China|Large language models (LLMs) have demonstrated great capabilities in various natural language understanding and generation tasks. These pre-trained LLMs can be further improved for specific downstream tasks by fine-tuning. However, the adoption of LLM in real-world applications can be hindered by privacy concerns and the resource-intensive nature of model training and fine-tuning. When multiple entities have similar interested tasks but cannot directly share their local data due to privacy regulations, federated learning (FL) is a mainstream solution to leverage the data of different entities. Besides avoiding direct data sharing, FL can also achieve rigorous data privacy protection, model intelligent property protection, and model customization via composition with different techniques. Despite the aforementioned advantages of FL, fine-tuning LLMs in FL settings still lacks adequate support from the existing frameworks and, therefore, faces challenges in optimizing the consumption of significant communication and computational resources, preparing various data for different tasks, and satisfying diverse information protection demands. In this paper, we discuss these challenges and introduce our package FederatedScope-LLM (FS-LLM) as a main contribution, which consists: (1) We build a complete end-to-end benchmarking pipeline under real-world scenarios, automizing the processes of dataset preprocessing, federated fine-tuning execution or simulation, and performance evaluation; (2) We provide comprehensive and off-the-shelf federated parameter-efficient fine-tuning (PEFT) algorithm implementations and versatile programming interfaces for future extension, enhancing the capabilities of LLMs in FL scenarios with low communication and computation costs, even without accessing the full model; (3) We adopt several accelerating and resource-efficient operators, and provide flexible pluggable sub-routines for interdisciplinary study. We conduct extensive and reproducible experiments to show the effectiveness of FS-LLM and benchmark advanced LLMs with PEFT algorithms in FL. We release FS-LLM at https://github.com/alibaba/FederatedScope/tree/llm.|大型语言模型(LLM)在各种自然语言理解和生成任务中表现出了很强的能力。通过微调，这些预先训练好的 LLM 可以针对特定的下游任务进一步改进。然而，在实际应用中采用 LLM 可能会受到隐私问题以及模型训练和微调的资源密集性的阻碍。当多个实体具有相似的感兴趣任务，但由于隐私规则的限制而不能直接共享本地数据时，联邦学习(FL)是利用不同实体数据的主流解决方案。除了避免直接数据共享，FL 还可以实现严格的数据隐私保护、模型智能财产保护和通过不同技术组合的模型定制。尽管 FL 具有上述优势，但 FL 环境中的微调 LLM 仍然缺乏现有框架的足够支持，因此在优化重要通信和计算资源的消耗，为不同任务准备各种数据以及满足不同的信息保护需求方面面临挑战。本文讨论了这些挑战，并介绍了我们的软件包 FederatedScope-LLM (FS-LLM) ，它的主要贡献包括: (1)构建了一个完整的现实场景下端到端基准测试流水线，实现了数据集预处理、联邦微调执行或仿真、性能评估等过程的自动化;(2)我们提供全面和现成的联邦参数高效微调(PEFT)算法实现和多功能的编程接口，为未来的扩展提供支持，提高 LLM 在低通信和计算成本的 FL 场景中的能力，即使不访问完整的模型; (3)我们采用多个加速和资源高效的运算符，并为跨学科研究提供灵活的可插入子例程。我们进行了广泛和可重复的实验，以证明 FS-LLM 和基准高级 LLM 与 PEFT 算法在 FL 的有效性。我们在 https://github.com/alibaba/federatedscope/tree/llm 发布 FS-LLM。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=FederatedScope-LLM:+A+Comprehensive+Package+for+Fine-tuning+Large+Language+Models+in+Federated+Learning)|2| |[Ads Recommendation in a Collapsed and Entangled World](https://doi.org/10.1145/3637528.3671607)|Junwei Pan, Wei Xue, Ximei Wang, Haibin Yu, Xun Liu, Shijie Quan, Xueming Qiu, Dapeng Liu, Lei Xiao, Jie Jiang|Tencent Inc., Shenzhen, China|We present Tencent's ads recommendation system and examine the challenges and practices of learning appropriate recommendation representations. Our study begins by showcasing our approaches to preserving prior knowledge when encoding features of diverse types into embedding representations. We specifically address sequence features, numeric features, and pre-trained embedding features. Subsequently, we delve into two crucial challenges related to feature representation: the dimensional collapse of embeddings and the interest entanglement across different tasks or scenarios. We propose several practical approaches to address these challenges that result in robust and disentangled recommendation representations. We then explore several training techniques to facilitate model optimization, reduce bias, and enhance exploration. Additionally, we introduce three analysis tools that enable us to study feature correlation, dimensional collapse, and interest entanglement. This work builds upon the continuous efforts of Tencent's ads recommendation team over the past decade. It summarizes general design principles and presents a series of readily applicable solutions and analysis tools. The reported performance is based on our online advertising platform, which handles hundreds of billions of requests daily and serves millions of ads to billions of users.|我们将介绍腾讯的广告推荐系统，并探讨学习适当的推荐表达的挑战和实践。我们的研究首先展示了我们在将不同类型的特征编码到嵌入表示中时保留先验知识的方法。我们专门讨论序列特征、数字特征和预先训练的嵌入特征。随后，我们深入研究了与特征表示相关的两个关键挑战: 嵌入的维度崩溃和不同任务或场景之间的兴趣纠缠。我们提出了几个实用的方法来解决这些挑战，导致健壮的和分离的建议表示。然后，我们探讨了几种训练技术，以促进模型优化，减少偏差，并加强探索。此外，我们还介绍了三种分析工具，使我们能够研究特征相关性、维度折叠和利益纠缠。这项工作建立在腾讯广告推荐团队过去十年不断努力的基础上。它总结了一般的设计原则，并提出了一系列容易适用的解决方案和分析工具。报告的性能是基于我们的在线广告平台，该平台每天处理数千亿个请求，为数十亿用户提供数百万个广告。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Ads+Recommendation+in+a+Collapsed+and+Entangled+World)|1| -|[EAGER: Two-Stream Generative Recommender with Behavior-Semantic Collaboration](https://doi.org/10.1145/3637528.3671775)|Ye Wang, Jiahao Xun, Minjie Hong, Jieming Zhu, Tao Jin, Wang Lin, Haoyuan Li, Linjun Li, Yan Xia, Zhou Zhao, Zhenhua Dong|Zhejiang University, Hangzhou, Zhejiang, China; Zhejiang University, Hangzhou, China; Huawei Noah's Ark Lab, Shenzhen, China|Generative retrieval has recently emerged as a promising approach to sequential recommendation, framing candidate item retrieval as an autoregressive sequence generation problem. However, existing generative methods typically focus solely on either behavioral or semantic aspects of item information, neglecting their complementary nature and thus resulting in limited effectiveness. To address this limitation, we introduce EAGER, a novel generative recommendation framework that seamlessly integrates both behavioral and semantic information. Specifically, we identify three key challenges in combining these two types of information: a unified generative architecture capable of handling two feature types, ensuring sufficient and independent learning for each type, and fostering subtle interactions that enhance collaborative information utilization. To achieve these goals, we propose (1) a two-stream generation architecture leveraging a shared encoder and two separate decoders to decode behavior tokens and semantic tokens with a confidence-based ranking strategy; (2) a global contrastive task with summary tokens to achieve discriminative decoding for each type of information; and (3) a semantic-guided transfer task designed to implicitly promote cross-interactions through reconstruction and estimation objectives. We validate the effectiveness of EAGER on four public benchmarks, demonstrating its superior performance compared to existing methods. Our source code will be publicly available on PapersWithCode.com.|生成性检索是近年来出现的一种有前途的序列推荐方法，它将候选项检索框架为一个自回归序列生成问题。然而，现有的生成方法通常只关注项目信息的行为方面或语义方面，忽视了它们的互补性，因此效果有限。为了解决这个问题，我们引入了一个新的生成推荐框架 EAGER，它能够无缝地整合行为推荐和语义信息推荐。具体来说，我们确定了将这两种类型的信息结合起来的三个关键挑战: 一个能够处理两种特征类型的统一生成体系结构，确保对每种类型进行充分和独立的学习，以及培养能够提高协作信息利用率的微妙交互。为了实现这些目标，我们提出(1)利用共享编码器和两个单独的解码器的两流生成架构，以基于置信度的排序策略解码行为标记和语义标记; (2)具有摘要标记的全局对比任务，以实现每种类型的信息的区分性解码; 和(3)语义指导的转移任务，旨在通过重建和评估目标隐式促进交叉互动。我们在四个公共基准上验证了 EAGER 算法的有效性，证明了与现有方法相比，EAGER 算法具有更好的性能。我们的源代码将在 paperswithcode.com 上公开。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=EAGER:+Two-Stream+Generative+Recommender+with+Behavior-Semantic+Collaboration)|1| -|[Debiased Recommendation with Noisy Feedback](https://doi.org/10.1145/3637528.3671915)|Haoxuan Li, Chunyuan Zheng, Wenjie Wang, Hao Wang, Fuli Feng, XiaoHua Zhou|Peking University, Beijing, China; National University of Singapore, Singapore, Singapore; University of Science and Technology of China, Hefei, China; Zhejiang University, Hangzhou, China; University of California, San Diego, Beijing, China|Ratings of a user to most items in recommender systems are usually missing not at random (MNAR), largely because users are free to choose which items to rate. To achieve unbiased learning of the prediction model under MNAR data, three typical solutions have been proposed, including error-imputation-based (EIB), inverse-propensity-scoring (IPS), and doubly robust (DR) methods. However, these methods ignore an alternative form of bias caused by the inconsistency between the observed ratings and the users' true preferences, also known as noisy feedback or outcome measurement errors (OME), e.g., due to public opinion or low-quality data collection process. In this work, we study intersectional threats to the unbiased learning of the prediction model from data MNAR and OME in the collected data. First, we design OME-EIB, OME-IPS, and OME-DR estimators, which largely extend the existing estimators to combat OME in real-world recommendation scenarios. Next, we theoretically prove the unbiasedness and generalization bound of the proposed estimators. We further propose an alternate denoising training approach to achieve unbiased learning of the prediction model under MNAR data with OME. Extensive experiments are conducted on three real-world datasets and one semi-synthetic dataset to show the effectiveness of our proposed approaches. The code is available at https://github.com/haoxuanli-pku/KDD24-OME-DR.|在推荐系统中，用户对大多数项目的评分通常不是随机丢失的(MNAR) ，主要是因为用户可以自由选择对哪些项目进行评分。为了实现 MNAR 数据下预测模型的无偏学习，提出了三种典型的解决方案，包括基于错误插补(EIB)、逆倾向评分(IPS)和双鲁棒(DR)方法。然而，这些方法忽略了由观察到的评分和用户的真实偏好之间的不一致引起的另一种形式的偏差，也称为噪声反馈或结果测量错误(OME) ，例如由于公众意见或低质量的数据收集过程。在这项工作中，我们研究交叉威胁的预测模型无偏学习的数据 MNAR 和 OME 收集的数据。首先，我们设计了 OME-EIB、 OME-IPS 和 OME-DR 估计器，它们在很大程度上扩展了现有的估计器，以便在实际推荐场景中对抗 OME。接着，我们从理论上证明了所提出的估计量的无偏性和广义界。我们进一步提出了一种交替去噪训练方法，用 OME 实现 MNAR 数据下预测模型的无偏学习。在三个实际数据集和一个半合成数据集上进行了广泛的实验，以证明我们提出的方法的有效性。密码可在 https://github.com/haoxuanli-pku/kdd24-ome-dr 查阅。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Debiased+Recommendation+with+Noisy+Feedback)|1| -|[Harm Mitigation in Recommender Systems under User Preference Dynamics](https://doi.org/10.1145/3637528.3671925)|Jerry Chee, Shankar Kalyanaraman, Sindhu Kiranmai Ernala, Udi Weinsberg, Sarah Dean, Stratis Ioannidis|Meta, Menlo Park, CA, USA; Cornell University, Ithaca, NY, USA; Northeastern University, Boston, MA, USA|We consider a recommender system that takes into account the interplaybetween recommendations, the evolution of user interests, and harmful content.We model the impact of recommendations on user behavior, particularly thetendency to consume harmful content. We seek recommendation policies thatestablish a tradeoff between maximizing click-through rate (CTR) and mitigatingharm. We establish conditions under which the user profile dynamics have astationary point, and propose algorithms for finding an optimal recommendationpolicy at stationarity. We experiment on a semi-synthetic movie recommendationsetting initialized with real data and observe that our policies outperformbaselines at simultaneously maximizing CTR and mitigating harm.|我们考虑建议之间的相互作用，用户兴趣的演变和有害内容的推荐系统。我们模拟建议对用户行为的影响，特别是消费有害内容的趋势。我们寻求建议政策，在最大化点进率和减少伤害之间建立平衡。我们建立了用户轮廓动态具有平稳点的条件，并提出了在平稳点寻找最优推荐策略的算法。我们在一个半合成的电影推荐设置上进行了实验，初始化为真实数据，并观察到我们的策略在同时最大化点击率和减少危害方面优于基线。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Harm+Mitigation+in+Recommender+Systems+under+User+Preference+Dynamics)|1| -|[Face4Rag: Factual Consistency Evaluation for Retrieval Augmented Generation in Chinese](https://doi.org/10.1145/3637528.3671656)|Yunqi Xu, Tianchi Cai, Jiyan Jiang, Xierui Song|Ant Group, Shanghai, China; Ant Group, Hangzhou, China; Tsinghua University, Beijing, China|The prevailing issue of factual inconsistency errors in conventional Retrieval Augmented Generation (RAG) motivates the study of Factual Consistency Evaluation (FCE). Despite the various FCE methods proposed earlier, these methods are evaluated on datasets generated by specific Large Language Models (LLMs). Without a comprehensive benchmark, it remains unexplored how these FCE methods perform on other LLMs with different error distributions or even unseen error types, as these methods may fail to detect the error types generated by other LLMs. To fill this gap, in this paper, we propose the first comprehensive FCE benchmark Face4RAG for RAG independent of the underlying LLM. Our benchmark consists of a synthetic dataset built upon a carefully designed typology for factuality inconsistency error and a real-world dataset constructed from six commonly used LLMs, enabling evaluation of FCE methods on specific error types or real-world error distributions. On the proposed benchmark, we discover the failure of existing FCE methods to detect the logical fallacy, which refers to a mismatch of logic structures between the answer and the retrieved reference. To fix this issue, we further propose a new method called L-Face4RAG with two novel designs of logic-preserving answer decomposition and fact-logic FCE. Extensive experiments show L-Face4RAG substantially outperforms previous methods for factual inconsistency detection on a wide range of tasks, notably beyond the RAG task from which it is originally motivated. Both the benchmark and our proposed method are publicly available. https://huggingface.co/datasets/yq27/Face4RAG|传统检索增强生成(RAG)中事实不一致性错误的普遍问题激发了事实一致性评价(FCE)的研究。尽管之前提出了各种 FCE 方法，但是这些方法是在特定的大语言模型(LLM)生成的数据集上进行评估的。由于没有一个全面的基准测试，这些 FCE 方法在其他具有不同错误分布甚至不可见错误类型的 LLM 上的表现仍然是未知的，因为这些方法可能无法检测其他 LLM 产生的错误类型。为了填补这个空白，在本文中，我们提出了独立于底层 LLM 的 RAG 的第一个全面的 FCE 基准 Face4RAG。我们的基准包括一个基于事实不一致性错误类型精心设计的合成数据集，以及一个由六个常用 LLM 构建的现实世界数据集，从而能够对特定错误类型或现实世界错误分布的 FCE 方法进行评估。在所提出的基准上，我们发现现有的 FCE 方法在检测逻辑谬误方面的失败，这是指答案和检索到的引用之间的逻辑结构不匹配。为了解决这一问题，我们进一步提出了一种新的方法，称为 L-Face4RAG 与两个新颖的设计保持逻辑的答案分解和事实逻辑 FCE。大量的实验表明，L-Face4RAG 在广泛的任务范围内大大优于以前的事实不一致性检测方法，特别是在 RAG 任务之外。基准测试和我们提出的方法都是公开的。Https://huggingface.co/datasets/yq27/face4rag|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Face4Rag:+Factual+Consistency+Evaluation+for+Retrieval+Augmented+Generation+in+Chinese)|1| -|[A Review of Modern Recommender Systems Using Generative Models (Gen-RecSys)](https://doi.org/10.1145/3637528.3671474)|Yashar Deldjoo, Zhankui He, Julian McAuley, Anton Korikov, Scott Sanner, Arnau Ramisa, René Vidal, Maheswaran Sathiamoorthy, Atoosa Kasirzadeh, Silvia Milano|University of California, La Jolla, CA, USA; University of Edinburgh, Edinburgh, UK; Bespoke Labs, Santa Clara, CA, USA; Amazon, Palo Alto, CA, USA; Polytechnic University of Bari, Bari, Italy; University of Exeter and LMU Munich, Munich, Germany; University of Toronto, Toronto, ON, Canada|Traditional recommender systems typically use user-item rating histories as their main data source. However, deep generative models now have the capability to model and sample from complex data distributions, including user-item interactions, text, images, and videos, enabling novel recommendation tasks. This comprehensive, multidisciplinary survey connects key advancements in RS using Generative Models (Gen-RecSys), covering: interaction-driven generative models; the use of large language models (LLM) and textual data for natural language recommendation; and the integration of multimodal models for generating and processing images/videos in RS. Our work highlights necessary paradigms for evaluating the impact and harm of Gen-RecSys and identifies open challenges. This survey accompanies a "tutorial" presented at ACM KDD'24, with supporting materials provided at: https://encr.pw/vDhLq.|传统的推荐系统通常使用用户项评分历史作为其主要数据源。然而，深层生成模型现在能够对复杂的数据分布进行建模和采样，包括用户项交互、文本、图像和视频，从而实现新的推荐任务。这个全面的，多学科的调查连接了 RS 使用生成模型(Gen-RecSys)的关键进步，包括: 交互驱动的生成模型; 使用大语言模型(LLM)和文本数据进行自然语言推荐; 以及集成多模态模型用于生成和处理 RS 中的图像/视频。我们的工作突出了评估 Gen-RecSys 的影响和危害的必要范式，并确定了公开的挑战。本调查附有在 ACM kDD’24会议上提出的“教程”，辅助材料提供在以下 https://encr.pw/vdhlq。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Review+of+Modern+Recommender+Systems+Using+Generative+Models+(Gen-RecSys))|1| -|[A Population-to-individual Tuning Framework for Adapting Pretrained LM to On-device User Intent Prediction](https://doi.org/10.1145/3637528.3671984)|Jiahui Gong, Jingtao Ding, Fanjin Meng, Guilong Chen, Hong Chen, Shen Zhao, Haisheng Lu, Yong Li|Department of Electronic Engineering, BNRist, Tsinghua University, Beijing, China; Honor Device Co., Ltd., Shenzhen, China|Mobile devices, especially smartphones, can support rich functions and have developed into indispensable tools in daily life. With the rise of generative AI services, smartphones can potentially transform into personalized assistants, anticipating user needs and scheduling services accordingly. Predicting user intents on smartphones, and reflecting anticipated activities based on past interactions and context, remains a pivotal step towards this vision. Existing research predominantly focuses on specific domains, neglecting the challenge of modeling diverse event sequences across dynamic contexts. Leveraging pre-trained language models (PLMs) offers a promising avenue, yet adapting PLMs to on-device user intent prediction presents significant challenges. To address these challenges, we propose PITuning, a Population-to-Individual Tuning framework. PITuning enhances common pattern extraction through dynamic event-to-intent transition modeling and addresses long-tailed preferences via adaptive unlearning strategies. Experimental results on real-world datasets demonstrate PITuning's superior intent prediction performance, highlighting its ability to capture long-tailed preferences and its practicality for on-device prediction scenarios.|移动设备，尤其是智能手机，可以支持丰富的功能，并已发展成为日常生活中不可或缺的工具。随着产生式人工智能服务的兴起，智能手机有可能转变为个性化的助手，预测用户需求并相应地安排服务。在智能手机上预测用户意图，并根据过去的交互和上下文反映预期活动，仍然是实现这一愿景的关键一步。现有的研究主要集中在特定领域，忽视了在动态背景下建模不同事件序列的挑战。利用预先训练的语言模型(PLM)提供了一个有前途的途径，然而使 PLM 适应设备上的用户意图预测提出了重大挑战。为了应对这些挑战，我们提出 PITuning，一个人口到个人的调整框架。PITuning 通过动态事件-意图转换模型增强了公共模式提取，并通过自适应忘却策略解决了长尾偏好。在真实世界数据集上的实验结果表明，PITuning 具有优越的意图预测性能，突出了其捕获长尾偏好的能力以及在设备上预测场景的实用性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Population-to-individual+Tuning+Framework+for+Adapting+Pretrained+LM+to+On-device+User+Intent+Prediction)|1| -|[Efficient Exploration of the Rashomon Set of Rule-Set Models](https://doi.org/10.1145/3637528.3671818)|Martino Ciaperoni, Han Xiao, Aristides Gionis|The Upright Project, Helsinki, Uusimaa, Finland; KTH Royal Institute of Technology, Stockholm, Sweden; Aalto University, Espoo, Uusimaa, Finland|Today, as increasingly complex predictive models are developed, simple rule sets remain a crucial tool to obtain interpretable predictions and drive high-stakes decision making. However, a single rule set provides a partial representation of a learning task. An emerging paradigm in interpretable machine learning aims at exploring the Rashomon set of all models exhibiting near-optimal performance. Existing work on Rashomon-set exploration focuses on exhaustive search of the Rashomon set for particular classes of models, which can be a computationally challenging task. On the other hand, exhaustive enumeration leads to redundancy that often is not necessary, and a representative sample or an estimate of the size of the Rashomon set is sufficient for many applications. In this work, we propose, for the first time, efficient methods to explore the Rashomon set of rule set models with or without exhaustive search. Extensive experiments demonstrate the effectiveness of the proposed methods in a variety of scenarios.|今天，随着越来越复杂的预测模型的发展，简单的规则集仍然是获得可解释的预测和推动高风险决策的关键工具。但是，单个规则集提供了学习任务的部分表示。可解释机器学习的一个新兴范式旨在探索所有表现出接近最佳性能的罗生门模型集。现有的罗生门集探索工作侧重于对罗生门集进行详尽的搜索，寻找特定类别的模型，这可能是一项具有计算挑战性的任务。另一方面，穷举导致冗余，往往是不必要的，一个代表性的样本或罗生门集大小的估计是足够的许多应用程序。在这项工作中，我们首次提出了有效的方法来探索罗生门集的规则集模型有或没有穷举搜索。大量的实验证明了该方法在各种场景下的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Efficient+Exploration+of+the+Rashomon+Set+of+Rule-Set+Models)|1| -|[Learning the Covariance of Treatment Effects Across Many Weak Experiments](https://doi.org/10.1145/3637528.3672034)|Aurélien Bibaut, Winston Chou, Simon Ejdemyr, Nathan Kallus|Netflix & Cornell University, Los Gatos, CA, USA; Netflix, Los Gatos, CA, USA|When primary objectives are insensitive or delayed, experimenters may instead focus on proxy metrics derived from secondary outcomes. For example, technology companies often infer the long-term impacts of product interventions from their effects on short-term user engagement signals. We consider the meta-analysis of many historical experiments to learn the covariance of treatment effects on these outcomes, which can support the construction of such proxies. Even when experiments are plentiful, if treatment effects are weak, the covariance of estimated treatment effects across experiments can be highly biased. We overcome this with techniques inspired by weak instrumental variable analysis. We show that Limited Information Maximum Likelihood (LIML) learns a parameter equivalent to fitting total least squares to a transformation of the scatterplot of treatment effects, and that Jackknife Instrumental Variables Estimation (JIVE) learns another parameter computable from the average of Jackknifed covariance matrices across experiments. We also present a total covariance estimator for the latter estimand under homoskedasticity, which is equivalent to a k-class estimator. We show how these parameters can be used to construct unbiased proxy metrics under various structural models. Lastly, we discuss the real-world application of our methods at Netflix.|当主要目标不敏感或延迟时，实验者可能会转而关注从次要结果衍生出的代理指标。例如，技术公司往往从产品干预对短期用户参与信号的影响中推断出产品干预的长期影响。我们考虑了许多历史实验的荟萃分析，以了解治疗效果对这些结果的协方差，这可以支持构建这样的代理。即使实验数量充足，如果治疗效果较弱，跨实验的估计治疗效果的协方差可能会有很大的偏差。我们用受弱工具变量分析启发的技术克服了这个问题。我们表明，有限信息最大似然(LIML)学习了一个等价于将总最小二乘拟合到治疗效果的散点图变换的参数，并且杰克刀仪器变量估计(JIVE)学习了另一个可以从杰克刀协方差矩阵的平均值跨实验计算的参数。我们还给出了后一种估计在同方差下的总协方差估计，它等价于一个 k 类估计。我们展示了如何使用这些参数在各种结构模型下构建无偏代理度量。最后，我们讨论了我们的方法在 Netflix 上的实际应用。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Learning+the+Covariance+of+Treatment+Effects+Across+Many+Weak+Experiments)|1| +|[EAGER: Two-Stream Generative Recommender with Behavior-Semantic Collaboration](https://doi.org/10.1145/3637528.3671775)|Ye Wang, Jiahao Xun, Minjie Hong, Jieming Zhu, Tao Jin, Wang Lin, Haoyuan Li, Linjun Li, Yan Xia, Zhou Zhao, Zhenhua Dong|Zhejiang University, Hangzhou, Zhejiang, China; Huawei Noah's Ark Lab, Shenzhen, China; Zhejiang University, Hangzhou, China|Generative retrieval has recently emerged as a promising approach to sequential recommendation, framing candidate item retrieval as an autoregressive sequence generation problem. However, existing generative methods typically focus solely on either behavioral or semantic aspects of item information, neglecting their complementary nature and thus resulting in limited effectiveness. To address this limitation, we introduce EAGER, a novel generative recommendation framework that seamlessly integrates both behavioral and semantic information. Specifically, we identify three key challenges in combining these two types of information: a unified generative architecture capable of handling two feature types, ensuring sufficient and independent learning for each type, and fostering subtle interactions that enhance collaborative information utilization. To achieve these goals, we propose (1) a two-stream generation architecture leveraging a shared encoder and two separate decoders to decode behavior tokens and semantic tokens with a confidence-based ranking strategy; (2) a global contrastive task with summary tokens to achieve discriminative decoding for each type of information; and (3) a semantic-guided transfer task designed to implicitly promote cross-interactions through reconstruction and estimation objectives. We validate the effectiveness of EAGER on four public benchmarks, demonstrating its superior performance compared to existing methods. Our source code will be publicly available on PapersWithCode.com.|生成性检索是近年来出现的一种有前途的序列推荐方法，它将候选项检索框架为一个自回归序列生成问题。然而，现有的生成方法通常只关注项目信息的行为方面或语义方面，忽视了它们的互补性，因此效果有限。为了解决这个问题，我们引入了一个新的生成推荐框架 EAGER，它能够无缝地整合行为推荐和语义信息推荐。具体来说，我们确定了将这两种类型的信息结合起来的三个关键挑战: 一个能够处理两种特征类型的统一生成体系结构，确保对每种类型进行充分和独立的学习，以及培养能够提高协作信息利用率的微妙交互。为了实现这些目标，我们提出(1)利用共享编码器和两个单独的解码器的两流生成架构，以基于置信度的排序策略解码行为标记和语义标记; (2)具有摘要标记的全局对比任务，以实现每种类型的信息的区分性解码; 和(3)语义指导的转移任务，旨在通过重建和评估目标隐式促进交叉互动。我们在四个公共基准上验证了 EAGER 算法的有效性，证明了与现有方法相比，EAGER 算法具有更好的性能。我们的源代码将在 paperswithcode.com 上公开。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=EAGER:+Two-Stream+Generative+Recommender+with+Behavior-Semantic+Collaboration)|1| +|[Debiased Recommendation with Noisy Feedback](https://doi.org/10.1145/3637528.3671915)|Haoxuan Li, Chunyuan Zheng, Wenjie Wang, Hao Wang, Fuli Feng, XiaoHua Zhou|National University of Singapore, Singapore, Singapore; Peking University, Beijing, China; Zhejiang University, Hangzhou, China; University of California, San Diego, Beijing, China; University of Science and Technology of China, Hefei, China|Ratings of a user to most items in recommender systems are usually missing not at random (MNAR), largely because users are free to choose which items to rate. To achieve unbiased learning of the prediction model under MNAR data, three typical solutions have been proposed, including error-imputation-based (EIB), inverse-propensity-scoring (IPS), and doubly robust (DR) methods. However, these methods ignore an alternative form of bias caused by the inconsistency between the observed ratings and the users' true preferences, also known as noisy feedback or outcome measurement errors (OME), e.g., due to public opinion or low-quality data collection process. In this work, we study intersectional threats to the unbiased learning of the prediction model from data MNAR and OME in the collected data. First, we design OME-EIB, OME-IPS, and OME-DR estimators, which largely extend the existing estimators to combat OME in real-world recommendation scenarios. Next, we theoretically prove the unbiasedness and generalization bound of the proposed estimators. We further propose an alternate denoising training approach to achieve unbiased learning of the prediction model under MNAR data with OME. Extensive experiments are conducted on three real-world datasets and one semi-synthetic dataset to show the effectiveness of our proposed approaches. The code is available at https://github.com/haoxuanli-pku/KDD24-OME-DR.|在推荐系统中，用户对大多数项目的评分通常不是随机丢失的(MNAR) ，主要是因为用户可以自由选择对哪些项目进行评分。为了实现 MNAR 数据下预测模型的无偏学习，提出了三种典型的解决方案，包括基于错误插补(EIB)、逆倾向评分(IPS)和双鲁棒(DR)方法。然而，这些方法忽略了由观察到的评分和用户的真实偏好之间的不一致引起的另一种形式的偏差，也称为噪声反馈或结果测量错误(OME) ，例如由于公众意见或低质量的数据收集过程。在这项工作中，我们研究交叉威胁的预测模型无偏学习的数据 MNAR 和 OME 收集的数据。首先，我们设计了 OME-EIB、 OME-IPS 和 OME-DR 估计器，它们在很大程度上扩展了现有的估计器，以便在实际推荐场景中对抗 OME。接着，我们从理论上证明了所提出的估计量的无偏性和广义界。我们进一步提出了一种交替去噪训练方法，用 OME 实现 MNAR 数据下预测模型的无偏学习。在三个实际数据集和一个半合成数据集上进行了广泛的实验，以证明我们提出的方法的有效性。密码可在 https://github.com/haoxuanli-pku/kdd24-ome-dr 查阅。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Debiased+Recommendation+with+Noisy+Feedback)|1| +|[Harm Mitigation in Recommender Systems under User Preference Dynamics](https://doi.org/10.1145/3637528.3671925)|Jerry Chee, Shankar Kalyanaraman, Sindhu Kiranmai Ernala, Udi Weinsberg, Sarah Dean, Stratis Ioannidis|Northeastern University, Boston, MA, USA; Cornell University, Ithaca, NY, USA; Meta, Menlo Park, CA, USA|We consider a recommender system that takes into account the interplaybetween recommendations, the evolution of user interests, and harmful content.We model the impact of recommendations on user behavior, particularly thetendency to consume harmful content. We seek recommendation policies thatestablish a tradeoff between maximizing click-through rate (CTR) and mitigatingharm. We establish conditions under which the user profile dynamics have astationary point, and propose algorithms for finding an optimal recommendationpolicy at stationarity. We experiment on a semi-synthetic movie recommendationsetting initialized with real data and observe that our policies outperformbaselines at simultaneously maximizing CTR and mitigating harm.|我们考虑建议之间的相互作用，用户兴趣的演变和有害内容的推荐系统。我们模拟建议对用户行为的影响，特别是消费有害内容的趋势。我们寻求建议政策，在最大化点进率和减少伤害之间建立平衡。我们建立了用户轮廓动态具有平稳点的条件，并提出了在平稳点寻找最优推荐策略的算法。我们在一个半合成的电影推荐设置上进行了实验，初始化为真实数据，并观察到我们的策略在同时最大化点击率和减少危害方面优于基线。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Harm+Mitigation+in+Recommender+Systems+under+User+Preference+Dynamics)|1| +|[Face4Rag: Factual Consistency Evaluation for Retrieval Augmented Generation in Chinese](https://doi.org/10.1145/3637528.3671656)|Yunqi Xu, Tianchi Cai, Jiyan Jiang, Xierui Song|Ant Group, Hangzhou, China; Tsinghua University, Beijing, China; Ant Group, Shanghai, China|The prevailing issue of factual inconsistency errors in conventional Retrieval Augmented Generation (RAG) motivates the study of Factual Consistency Evaluation (FCE). Despite the various FCE methods proposed earlier, these methods are evaluated on datasets generated by specific Large Language Models (LLMs). Without a comprehensive benchmark, it remains unexplored how these FCE methods perform on other LLMs with different error distributions or even unseen error types, as these methods may fail to detect the error types generated by other LLMs. To fill this gap, in this paper, we propose the first comprehensive FCE benchmark Face4RAG for RAG independent of the underlying LLM. Our benchmark consists of a synthetic dataset built upon a carefully designed typology for factuality inconsistency error and a real-world dataset constructed from six commonly used LLMs, enabling evaluation of FCE methods on specific error types or real-world error distributions. On the proposed benchmark, we discover the failure of existing FCE methods to detect the logical fallacy, which refers to a mismatch of logic structures between the answer and the retrieved reference. To fix this issue, we further propose a new method called L-Face4RAG with two novel designs of logic-preserving answer decomposition and fact-logic FCE. Extensive experiments show L-Face4RAG substantially outperforms previous methods for factual inconsistency detection on a wide range of tasks, notably beyond the RAG task from which it is originally motivated. Both the benchmark and our proposed method are publicly available. https://huggingface.co/datasets/yq27/Face4RAG|传统检索增强生成(RAG)中事实不一致性错误的普遍问题激发了事实一致性评价(FCE)的研究。尽管之前提出了各种 FCE 方法，但是这些方法是在特定的大语言模型(LLM)生成的数据集上进行评估的。由于没有一个全面的基准测试，这些 FCE 方法在其他具有不同错误分布甚至不可见错误类型的 LLM 上的表现仍然是未知的，因为这些方法可能无法检测其他 LLM 产生的错误类型。为了填补这个空白，在本文中，我们提出了独立于底层 LLM 的 RAG 的第一个全面的 FCE 基准 Face4RAG。我们的基准包括一个基于事实不一致性错误类型精心设计的合成数据集，以及一个由六个常用 LLM 构建的现实世界数据集，从而能够对特定错误类型或现实世界错误分布的 FCE 方法进行评估。在所提出的基准上，我们发现现有的 FCE 方法在检测逻辑谬误方面的失败，这是指答案和检索到的引用之间的逻辑结构不匹配。为了解决这一问题，我们进一步提出了一种新的方法，称为 L-Face4RAG 与两个新颖的设计保持逻辑的答案分解和事实逻辑 FCE。大量的实验表明，L-Face4RAG 在广泛的任务范围内大大优于以前的事实不一致性检测方法，特别是在 RAG 任务之外。基准测试和我们提出的方法都是公开的。Https://huggingface.co/datasets/yq27/face4rag|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Face4Rag:+Factual+Consistency+Evaluation+for+Retrieval+Augmented+Generation+in+Chinese)|1| +|[A Review of Modern Recommender Systems Using Generative Models (Gen-RecSys)](https://doi.org/10.1145/3637528.3671474)|Yashar Deldjoo, Zhankui He, Julian McAuley, Anton Korikov, Scott Sanner, Arnau Ramisa, René Vidal, Maheswaran Sathiamoorthy, Atoosa Kasirzadeh, Silvia Milano|University of California, La Jolla, CA, USA; University of Edinburgh, Edinburgh, UK; University of Toronto, Toronto, ON, Canada; University of Exeter and LMU Munich, Munich, Germany; Polytechnic University of Bari, Bari, Italy; Bespoke Labs, Santa Clara, CA, USA; Amazon, Palo Alto, CA, USA|Traditional recommender systems typically use user-item rating histories as their main data source. However, deep generative models now have the capability to model and sample from complex data distributions, including user-item interactions, text, images, and videos, enabling novel recommendation tasks. This comprehensive, multidisciplinary survey connects key advancements in RS using Generative Models (Gen-RecSys), covering: interaction-driven generative models; the use of large language models (LLM) and textual data for natural language recommendation; and the integration of multimodal models for generating and processing images/videos in RS. Our work highlights necessary paradigms for evaluating the impact and harm of Gen-RecSys and identifies open challenges. This survey accompanies a "tutorial" presented at ACM KDD'24, with supporting materials provided at: https://encr.pw/vDhLq.|传统的推荐系统通常使用用户项评分历史作为其主要数据源。然而，深层生成模型现在能够对复杂的数据分布进行建模和采样，包括用户项交互、文本、图像和视频，从而实现新的推荐任务。这个全面的，多学科的调查连接了 RS 使用生成模型(Gen-RecSys)的关键进步，包括: 交互驱动的生成模型; 使用大语言模型(LLM)和文本数据进行自然语言推荐; 以及集成多模态模型用于生成和处理 RS 中的图像/视频。我们的工作突出了评估 Gen-RecSys 的影响和危害的必要范式，并确定了公开的挑战。本调查附有在 ACM kDD’24会议上提出的“教程”，辅助材料提供在以下 https://encr.pw/vdhlq。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Review+of+Modern+Recommender+Systems+Using+Generative+Models+(Gen-RecSys))|1| +|[A Population-to-individual Tuning Framework for Adapting Pretrained LM to On-device User Intent Prediction](https://doi.org/10.1145/3637528.3671984)|Jiahui Gong, Jingtao Ding, Fanjin Meng, Guilong Chen, Hong Chen, Shen Zhao, Haisheng Lu, Yong Li|Honor Device Co., Ltd., Shenzhen, China; Department of Electronic Engineering, BNRist, Tsinghua University, Beijing, China|Mobile devices, especially smartphones, can support rich functions and have developed into indispensable tools in daily life. With the rise of generative AI services, smartphones can potentially transform into personalized assistants, anticipating user needs and scheduling services accordingly. Predicting user intents on smartphones, and reflecting anticipated activities based on past interactions and context, remains a pivotal step towards this vision. Existing research predominantly focuses on specific domains, neglecting the challenge of modeling diverse event sequences across dynamic contexts. Leveraging pre-trained language models (PLMs) offers a promising avenue, yet adapting PLMs to on-device user intent prediction presents significant challenges. To address these challenges, we propose PITuning, a Population-to-Individual Tuning framework. PITuning enhances common pattern extraction through dynamic event-to-intent transition modeling and addresses long-tailed preferences via adaptive unlearning strategies. Experimental results on real-world datasets demonstrate PITuning's superior intent prediction performance, highlighting its ability to capture long-tailed preferences and its practicality for on-device prediction scenarios.|移动设备，尤其是智能手机，可以支持丰富的功能，并已发展成为日常生活中不可或缺的工具。随着产生式人工智能服务的兴起，智能手机有可能转变为个性化的助手，预测用户需求并相应地安排服务。在智能手机上预测用户意图，并根据过去的交互和上下文反映预期活动，仍然是实现这一愿景的关键一步。现有的研究主要集中在特定领域，忽视了在动态背景下建模不同事件序列的挑战。利用预先训练的语言模型(PLM)提供了一个有前途的途径，然而使 PLM 适应设备上的用户意图预测提出了重大挑战。为了应对这些挑战，我们提出 PITuning，一个人口到个人的调整框架。PITuning 通过动态事件-意图转换模型增强了公共模式提取，并通过自适应忘却策略解决了长尾偏好。在真实世界数据集上的实验结果表明，PITuning 具有优越的意图预测性能，突出了其捕获长尾偏好的能力以及在设备上预测场景的实用性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Population-to-individual+Tuning+Framework+for+Adapting+Pretrained+LM+to+On-device+User+Intent+Prediction)|1| +|[Efficient Exploration of the Rashomon Set of Rule-Set Models](https://doi.org/10.1145/3637528.3671818)|Martino Ciaperoni, Han Xiao, Aristides Gionis|Aalto University, Espoo, Uusimaa, Finland; The Upright Project, Helsinki, Uusimaa, Finland; KTH Royal Institute of Technology, Stockholm, Sweden|Today, as increasingly complex predictive models are developed, simple rule sets remain a crucial tool to obtain interpretable predictions and drive high-stakes decision making. However, a single rule set provides a partial representation of a learning task. An emerging paradigm in interpretable machine learning aims at exploring the Rashomon set of all models exhibiting near-optimal performance. Existing work on Rashomon-set exploration focuses on exhaustive search of the Rashomon set for particular classes of models, which can be a computationally challenging task. On the other hand, exhaustive enumeration leads to redundancy that often is not necessary, and a representative sample or an estimate of the size of the Rashomon set is sufficient for many applications. In this work, we propose, for the first time, efficient methods to explore the Rashomon set of rule set models with or without exhaustive search. Extensive experiments demonstrate the effectiveness of the proposed methods in a variety of scenarios.|今天，随着越来越复杂的预测模型的发展，简单的规则集仍然是获得可解释的预测和推动高风险决策的关键工具。但是，单个规则集提供了学习任务的部分表示。可解释机器学习的一个新兴范式旨在探索所有表现出接近最佳性能的罗生门模型集。现有的罗生门集探索工作侧重于对罗生门集进行详尽的搜索，寻找特定类别的模型，这可能是一项具有计算挑战性的任务。另一方面，穷举导致冗余，往往是不必要的，一个代表性的样本或罗生门集大小的估计是足够的许多应用程序。在这项工作中，我们首次提出了有效的方法来探索罗生门集的规则集模型有或没有穷举搜索。大量的实验证明了该方法在各种场景下的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Efficient+Exploration+of+the+Rashomon+Set+of+Rule-Set+Models)|1| +|[Learning the Covariance of Treatment Effects Across Many Weak Experiments](https://doi.org/10.1145/3637528.3672034)|Aurélien Bibaut, Winston Chou, Simon Ejdemyr, Nathan Kallus|Netflix, Los Gatos, CA, USA; Netflix & Cornell University, Los Gatos, CA, USA|When primary objectives are insensitive or delayed, experimenters may instead focus on proxy metrics derived from secondary outcomes. For example, technology companies often infer the long-term impacts of product interventions from their effects on short-term user engagement signals. We consider the meta-analysis of many historical experiments to learn the covariance of treatment effects on these outcomes, which can support the construction of such proxies. Even when experiments are plentiful, if treatment effects are weak, the covariance of estimated treatment effects across experiments can be highly biased. We overcome this with techniques inspired by weak instrumental variable analysis. We show that Limited Information Maximum Likelihood (LIML) learns a parameter equivalent to fitting total least squares to a transformation of the scatterplot of treatment effects, and that Jackknife Instrumental Variables Estimation (JIVE) learns another parameter computable from the average of Jackknifed covariance matrices across experiments. We also present a total covariance estimator for the latter estimand under homoskedasticity, which is equivalent to a k-class estimator. We show how these parameters can be used to construct unbiased proxy metrics under various structural models. Lastly, we discuss the real-world application of our methods at Netflix.|当主要目标不敏感或延迟时，实验者可能会转而关注从次要结果衍生出的代理指标。例如，技术公司往往从产品干预对短期用户参与信号的影响中推断出产品干预的长期影响。我们考虑了许多历史实验的荟萃分析，以了解治疗效果对这些结果的协方差，这可以支持构建这样的代理。即使实验数量充足，如果治疗效果较弱，跨实验的估计治疗效果的协方差可能会有很大的偏差。我们用受弱工具变量分析启发的技术克服了这个问题。我们表明，有限信息最大似然(LIML)学习了一个等价于将总最小二乘拟合到治疗效果的散点图变换的参数，并且杰克刀仪器变量估计(JIVE)学习了另一个可以从杰克刀协方差矩阵的平均值跨实验计算的参数。我们还给出了后一种估计在同方差下的总协方差估计，它等价于一个 k 类估计。我们展示了如何使用这些参数在各种结构模型下构建无偏代理度量。最后，我们讨论了我们的方法在 Netflix 上的实际应用。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Learning+the+Covariance+of+Treatment+Effects+Across+Many+Weak+Experiments)|1| |[Compact Decomposition of Irregular Tensors for Data Compression: From Sparse to Dense to High-Order Tensors](https://doi.org/10.1145/3637528.3671846)|Taehyung Kwon, Jihoon Ko, Jinhong Jung, JunGi Jang, Kijung Shin|KAIST, Seoul, Republic of Korea; UIUC, Champaign, IL, USA; Soongsil University, Seoul, Republic of Korea|An irregular tensor is a collection of matrices with different numbers of rows. Real-world data from diverse domains, including medical and stock data, are effectively represented as irregular tensors due to the inherent variations in data length. For their analysis, various tensor decomposition methods (e.g., PARAFAC2) have been devised. While they are expected to be effective in compressing large-scale irregular tensors, akin to regular tensor decomposition methods, our analysis reveals that their compression performance is limited due to the larger number of first mode factor matrices. In this work, we propose accurate and compact decomposition methods for lossy compression of irregular tensors. First, we propose Light-IT, which unifies all first mode factor matrices into a single matrix, dramatically reducing the size of compressed outputs. Second, motivated by the success of Tucker decomposition in regular tensor compression, we extend Light-IT to Light-IT++ to enhance its expressive power and thus reduce compression error. Finally, we generalize both methods to handle irregular tensors of any order and leverage the sparsity of tensors for acceleration. Extensive experiments on 6 real-world datasets demonstrate that our methods are (a) Compact: their compressed output is up to 37× smaller than that of the most concise baseline, (b) Accurate: our methods are up to 5× more accurate, with smaller compressed output, than the most accurate baseline, and (c) Versatile: our methods are effective for sparse, dense, and higher-order tensors.|不规则张量是具有不同行数的矩阵的集合。来自不同领域的真实世界数据，包括医疗和股票数据，由于数据长度的固有变化，有效地表示为不规则张量。对于它们的分析，各种张量分解方法(例如，PARAFAC2)已被设计出来。虽然它们在压缩大规模不规则张量方面是有效的，类似于正则张量分解方法，但我们的分析表明，由于第一模因子矩阵数量较多，它们的压缩性能受到限制。在这项工作中，我们提出了精确和紧凑的分解方法对不规则张量的有损数据压缩。首先，我们提出了光信息技术，它将所有的第一模因子矩阵统一到一个单一的矩阵，大大减少了压缩输出的大小。其次，由于 Tucker 分解在正则张量压缩中的成功，我们将 Light-IT 扩展到 Light-IT + + ，以增强其表达能力，从而减少压缩误差。最后，我们将这两种方法推广到处理任意阶的不规则张量，并利用张量的稀疏性来加速。在6个真实世界数据集上的大量实验表明，我们的方法是(a)紧凑的: 其压缩输出比最简洁的基线小37倍; (b)精确: 我们的方法比最精确的基线精确5倍，压缩输出更小; (c)多功能: 我们的方法对稀疏，密集和高阶张量是有效的。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Compact+Decomposition+of+Irregular+Tensors+for+Data+Compression:+From+Sparse+to+Dense+to+High-Order+Tensors)|1| |[TDNetGen: Empowering Complex Network Resilience Prediction with Generative Augmentation of Topology and Dynamics](https://doi.org/10.1145/3637528.3671934)|Chang Liu, Jingtao Ding, Yiwen Song, Yong Li|Department of Electronic Engineering, BNRist, Tsinghua University, Beijing, China; Shenzhen International Graduate School, Tsinghua University, Shenzhen, Guangdong, China|Predicting the resilience of complex networks, which represents the ability to retain fundamental functionality amidst external perturbations or internal failures, plays a critical role in understanding and improving real-world complex systems. Traditional theoretical approaches grounded in nonlinear dynamical systems rely on prior knowledge of network dynamics. On the other hand, data-driven approaches frequently encounter the challenge of insufficient labeled data, a predicament commonly observed in real-world scenarios. In this paper, we introduce a novel resilience prediction framework for complex networks, designed to tackle this issue through generative data augmentation of network topology and dynamics. The core idea is the strategic utilization of the inherent joint distribution present in unlabeled network data, facilitating the learning process of the resilience predictor by illuminating the relationship between network topology and dynamics. Experiment results on three network datasets demonstrate that our proposed framework TDNetGen can achieve high prediction accuracy up to 85%-95%. Furthermore, the framework still demonstrates a pronounced augmentation capability in extreme low-data regimes, thereby underscoring its utility and robustness in enhancing the prediction of network resilience. We have open-sourced our code in the following link, https://github.com/tsinghua-fib-lab/TDNetGen.|预测复杂网络的恢复能力，代表了在外部扰动或内部故障中保持基本功能的能力，在理解和改善现实世界中的复杂系统方面起着关键作用。基于非线性动力系统的传统理论方法依赖于网络动力学的先验知识。另一方面，数据驱动的方法经常遇到标记数据不足的挑战，这是在现实场景中常见的困境。在这篇文章中，我们介绍了一个新的复杂网络弹性预测框架，旨在解决这个问题，通过网络拓扑和动态的生成数据增强。其核心思想是战略性地利用未标记网络数据中固有的联合分布，通过阐明网络拓扑和动态之间的关系，促进弹性预测器的学习过程。在三个网络数据集上的实验结果表明，我们提出的框架 TDNetGen 能够实现高达85% -95% 的预测准确率。此外，该框架在极端低数据情况下仍然显示出明显的增强能力，从而突出了其在加强网络复原力预测方面的效用和稳健性。我们已在以下连结开放我们的程式码供 https://github.com/tsinghua-fib-lab/tdnetgen 使用。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=TDNetGen:+Empowering+Complex+Network+Resilience+Prediction+with+Generative+Augmentation+of+Topology+and+Dynamics)|1| |[Scalable Temporal Motif Densest Subnetwork Discovery](https://doi.org/10.1145/3637528.3671889)|Ilie Sarpe, Fabio Vandin, Aristides Gionis|KTH Royal Institute of Technology, Stockholm, Sweden; University of Padova, Padova, Italy|Finding dense subnetworks, with density based on edges or more complex structures, such as subgraphs or k-cliques, is a fundamental algorithmic problem with many applications. While the problem has been studied extensively in static networks, much remains to be explored for temporal networks. In this work we introduce the novel problem of identifying the temporal motif densest subnetwork, i.e., the densest subnetwork with respect to temporal motifs, which are high-order patterns characterizing temporal networks. Identifying temporal motifs is an extremely challenging task, and thus, efficient methods are required. To address this challenge, we design two novel randomized approximation algorithms with rigorous probabilistic guarantees that provide high-quality solutions. We perform extensive experiments showing that our methods outperform baselines. Furthermore, our algorithms scale on networks with up to billions of temporal edges, while baselines cannot handle such large networks. We use our techniques to analyze a financial network and show that our formulation reveals important network structures, such as bursty temporal events and communities of users with similar interests.|寻找基于边或更复杂结构(如子图或 k 团)的密集子网络是许多应用中的一个基本计算问题。虽然这个问题已经在静态网络中得到了广泛的研究，但对于时态网络还有许多有待探索的地方。在本文中，我们介绍了一个新的时间模式最密集子网络的识别问题，即时间模式方面的最密集子网络，这是高阶模式的特点时间网络。识别时间模式是一项极具挑战性的任务，因此，需要有效的方法。为了解决这个问题，我们设计了两种新的随机近似算法，它们具有严格的概率保证，能够提供高质量的解决方案。我们进行了大量的实验，表明我们的方法比基线更有效。此外，我们的算法扩展到具有数十亿时间边缘的网络上，而基线不能处理如此大的网络。我们使用我们的技术来分析一个金融网络，并表明我们的公式揭示了重要的网络结构，如突发的时间事件和具有相似兴趣的用户群体。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Scalable+Temporal+Motif+Densest+Subnetwork+Discovery)|1| |[UniST: A Prompt-Empowered Universal Model for Urban Spatio-Temporal Prediction](https://doi.org/10.1145/3637528.3671662)|Yuan Yuan, Jingtao Ding, Jie Feng, Depeng Jin, Yong Li|Department of Electronic Engineering, BNRist, Tsinghua University, Beijing, China|Urban spatio-temporal prediction is crucial for informed decision-making, such as traffic management, resource optimization, and emergence response. Despite remarkable breakthroughs in pretrained natural language models that enable one model to handle diverse tasks, a universal solution for spatio-temporal prediction remains challenging. Existing prediction approaches are typically tailored for specific spatio-temporal scenarios, requiring task-specific model designs and extensive domain-specific training data. In this study, we introduce UniST, a universal model designed for general urban spatio-temporal prediction across a wide range of scenarios. Inspired by large language models, UniST achieves success through: (i) utilizing diverse spatio-temporal data, (ii) effective pre-training to capture complex spatio-temporal relationships, (iii) spatio-temporal knowledge-guided prompts to enhance generalization capabilities. These designs together unlock the potential of building a universal model for various scenarios. Extensive experiments on more than 20 spatio-temporal scenarios demonstrate UniST's efficacy in advancing state-of-the-art performance, especially in few-shot and zero-shot prediction. The datasets and code implementation are released on https://github.com/tsinghua-fib-lab/UniST.|城市时空预测对于交通管理、资源优化和应急响应等知情决策至关重要。尽管在预先训练的自然语言模型中取得了显著的突破，使得一个模型能够处理不同的任务，但时空预测的通用解决方案仍然具有挑战性。现有的预测方法通常针对特定的时空场景，需要特定于任务的模型设计和大量特定于领域的训练数据。在这项研究中，我们介绍了 UniST，一个通用的模型设计的一般城市时空预测在广泛的情景。受到大型语言模型的启发，UniST 通过以下方式取得成功: (i)利用不同的时空数据，(ii)有效的预训练以捕获复杂的时空关系，(iii)时空知识引导的提示以增强泛化能力。这些设计一起释放了为各种场景构建通用模型的潜力。在20多个时空场景上的大量实验证明了 UniST 在提高最先进性能方面的功效，特别是在小镜头和零镜头预测方面。数据集和代码实现在 https://github.com/tsinghua-fib-lab/unist 上发布。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=UniST:+A+Prompt-Empowered+Universal+Model+for+Urban+Spatio-Temporal+Prediction)|1| -|[UrbanGPT: Spatio-Temporal Large Language Models](https://doi.org/10.1145/3637528.3671578)|Zhonghang Li, Lianghao Xia, Jiabin Tang, Yong Xu, Lei Shi, Long Xia, Dawei Yin, Chao Huang|South China University of Technology & The University of Hong Kong, Guangzhou, China; Baidu Inc., Beijing, China; The University of Hong Kong, Hong Kong SAR, China; South China University of Technology, Guangzhou, China|Spatio-temporal prediction aims to forecast and gain insights into the ever-changing dynamics of urban environments across both time and space. Its purpose is to anticipate future patterns, trends, and events in diverse facets of urban life, including transportation, population movement, and crime rates. Although numerous efforts have been dedicated to developing neural network techniques for accurate predictions on spatio-temporal data, it is important to note that many of these methods heavily depend on having sufficient labeled data to generate precise spatio-temporal representations. Unfortunately, the issue of data scarcity is pervasive in practical urban sensing scenarios. In certain cases, it becomes challenging to collect any labeled data from downstream scenarios, intensifying the problem further. Consequently, it becomes necessary to build a spatio-temporal model that can exhibit strong generalization capabilities across diverse spatio-temporal learning scenarios. Taking inspiration from the remarkable achievements of large language models (LLMs), our objective is to create a spatio-temporal LLM that can exhibit exceptional generalization capabilities across a wide range of downstream urban tasks. To achieve this objective, we present the UrbanGPT, which seamlessly integrates a spatio-temporal dependency encoder with the instruction-tuning paradigm. This integration enables LLMs to comprehend the complex inter-dependencies across time and space, facilitating more comprehensive and accurate predictions under data scarcity. To validate the effectiveness of our approach, we conduct extensive experiments on various public datasets, covering different spatio-temporal prediction tasks. The results consistently demonstrate that our UrbanGPT, with its carefully designed architecture, consistently outperforms state-of-the-art baselines. These findings highlight the potential of building large language models for spatio-temporal learning, particularly in zero-shot scenarios where labeled data is scarce. The code and data are available at: https://github.com/HKUDS/UrbanGPT.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=UrbanGPT:+Spatio-Temporal+Large+Language+Models)|1| +|[UrbanGPT: Spatio-Temporal Large Language Models](https://doi.org/10.1145/3637528.3671578)|Zhonghang Li, Lianghao Xia, Jiabin Tang, Yong Xu, Lei Shi, Long Xia, Dawei Yin, Chao Huang|South China University of Technology, Guangzhou, China; South China University of Technology & The University of Hong Kong, Guangzhou, China; The University of Hong Kong, Hong Kong SAR, China; Baidu Inc., Beijing, China|Spatio-temporal prediction aims to forecast and gain insights into the ever-changing dynamics of urban environments across both time and space. Its purpose is to anticipate future patterns, trends, and events in diverse facets of urban life, including transportation, population movement, and crime rates. Although numerous efforts have been dedicated to developing neural network techniques for accurate predictions on spatio-temporal data, it is important to note that many of these methods heavily depend on having sufficient labeled data to generate precise spatio-temporal representations. Unfortunately, the issue of data scarcity is pervasive in practical urban sensing scenarios. In certain cases, it becomes challenging to collect any labeled data from downstream scenarios, intensifying the problem further. Consequently, it becomes necessary to build a spatio-temporal model that can exhibit strong generalization capabilities across diverse spatio-temporal learning scenarios. Taking inspiration from the remarkable achievements of large language models (LLMs), our objective is to create a spatio-temporal LLM that can exhibit exceptional generalization capabilities across a wide range of downstream urban tasks. To achieve this objective, we present the UrbanGPT, which seamlessly integrates a spatio-temporal dependency encoder with the instruction-tuning paradigm. This integration enables LLMs to comprehend the complex inter-dependencies across time and space, facilitating more comprehensive and accurate predictions under data scarcity. To validate the effectiveness of our approach, we conduct extensive experiments on various public datasets, covering different spatio-temporal prediction tasks. The results consistently demonstrate that our UrbanGPT, with its carefully designed architecture, consistently outperforms state-of-the-art baselines. These findings highlight the potential of building large language models for spatio-temporal learning, particularly in zero-shot scenarios where labeled data is scarce. The code and data are available at: https://github.com/HKUDS/UrbanGPT.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=UrbanGPT:+Spatio-Temporal+Large+Language+Models)|1| |[Choosing a Proxy Metric from Past Experiments](https://doi.org/10.1145/3637528.3671543)|Nilesh Tripuraneni, Lee Richardson, Alexander D'Amour, Jacopo Soriano, Steve Yadlowsky||In many randomized experiments, the treatment effect of the long-term metric (i.e. the primary outcome of interest) is often difficult or infeasible to measure. Such long-term metrics are often slow to react to changes and sufficiently noisy they are challenging to faithfully estimate in short-horizon experiments. A common alternative is to measure several short-term proxy metrics in the hope they closely track the long-term metric -- so they can be used to effectively guide decision-making in the near-term. We introduce a new statistical framework to both define and construct an optimal proxy metric for use in a homogeneous population of randomized experiments. Our procedure first reduces the construction of an optimal proxy metric in a given experiment to a portfolio optimization problem which depends on the true latent treatment effects and noise level of experiment under consideration. We then denoise the observed treatment effects of the long-term metric and a set of proxies in a historical corpus of randomized experiments to extract estimates of the latent treatment effects for use in the optimization problem. One key insight derived from our approach is that the optimal proxy metric for a given experiment is not apriori fixed; rather it should depend on the sample size (or effective noise level) of the randomized experiment for which it is deployed. To instantiate and evaluate our framework, we employ our methodology in a large corpus of randomized experiments from an industrial recommendation system and construct proxy metrics that perform favorably relative to several baselines.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Choosing+a+Proxy+Metric+from+Past+Experiments)|1| |[A Review of Graph Neural Networks in Epidemic Modeling](https://doi.org/10.1145/3637528.3671455)|Zewen Liu, Guancheng Wan, B. Aditya Prakash, Max S. Y. Lau, Wei Jin|Emory University, Atlanta, GA, USA; Georgia Institute of Technology, Atlanta, GA, USA|Since the onset of the COVID-19 pandemic, there has been a growing interestin studying epidemiological models. Traditional mechanistic modelsmathematically describe the transmission mechanisms of infectious diseases.However, they often fall short when confronted with the growing challenges oftoday. Consequently, Graph Neural Networks (GNNs) have emerged as aprogressively popular tool in epidemic research. In this paper, we endeavor tofurnish a comprehensive review of GNNs in epidemic tasks and highlightpotential future directions. To accomplish this objective, we introducehierarchical taxonomies for both epidemic tasks and methodologies, offering atrajectory of development within this domain. For epidemic tasks, we establisha taxonomy akin to those typically employed within the epidemic domain. Formethodology, we categorize existing work into Neural Models andHybrid Models. Following this, we perform an exhaustive and systematicexamination of the methodologies, encompassing both the tasks and theirtechnical details. Furthermore, we discuss the limitations of existing methodsfrom diverse perspectives and systematically propose future researchdirections. This survey aims to bridge literature gaps and promote theprogression of this promising field. We hope that it will facilitate synergiesbetween the communities of GNNs and epidemiology, and contribute to theircollective progress.|自2019冠状病毒疾病大流行开始以来，人们对研究流行病学模型的兴趣日益浓厚。传统的机械模型从数学上描述了传染病的传导机制。然而，当他们面对今天日益增长的挑战时，他们往往力不从心。因此，图形神经网络(GNN)已经成为流行病学研究中一种日益流行的工具。在本文中，我们努力提供一个全面的回顾 GNN 在流行病的任务和突出潜在的未来方向。为了实现这一目标，我们引入了流行病任务和方法的层次分类法，提供了这一领域的发展轨迹。对于传染病任务，我们建立了类似于传染病领域中典型使用的分类法。在方法论上，我们将现有的工作分为神经模型和混合模型。接下来，我们对方法论进行了详尽而系统的检查，包括任务和技术细节。此外，从不同角度讨论了现有方法的局限性，并系统地提出了未来的研究方向。这项调查旨在弥补文献差距，促进这个有前途的领域的进展。我们希望这将促进 GNN 和流行病学社区之间的协同作用，并为他们的共同进步做出贡献。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Review+of+Graph+Neural+Networks+in+Epidemic+Modeling)|1| -|[Cross-Domain LifeLong Sequential Modeling for Online Click-Through Rate Prediction](https://doi.org/10.1145/3637528.3671601)|Ruijie Hou, Zhaoyang Yang, Ming Yu, Hongyu Lu, Zhuobin Zheng, Yu Chen, Qinsong Zeng, Ming Chen|Wechat, Tencent, Beijing, China; Wechat, Tencent, Guangzhou, China|Lifelong sequential modeling (LSM) has significantly advanced recommendation systems on social media platforms. Diverging from single-domain LSM, cross-domain LSM involves modeling lifelong behavior sequences from a source domain to a different target domain. In this paper, we propose the Lifelong Cross Network (LCN), a novel approach for cross-domain LSM. LCN features a Cross Representation Production (CRP) module that utilizes contrastive loss to improve the learning of item embeddings, effectively bridging items across domains. This is important for enhancing the retrieval of relevant items in cross-domain lifelong sequences. Furthermore, we propose the Lifelong Attention Pyramid (LAP) module, which contains three cascading attention levels. By adding an intermediate level and integrating the results from all three levels, the LAP module can capture a broad spectrum of user interests and ensure gradient propagation throughout the sequence. The proposed LAP can also achieve remarkable consistency across attention levels, making it possible to further narrow the candidate item pool of the top level. This allows for the use of advanced attention techniques to effectively mitigate the impact of the noise in cross-domain sequences and improve the non-linearity of the representation, all while maintaining computational efficiency. Extensive experiments conducted on both a public dataset and an industrial dataset from the WeChat Channels platform reveal that the LCN outperforms current methods in terms of prediction accuracy and online performance metrics.|终身顺序建模(LSM)在社交媒体平台上拥有非常先进的推荐系统。与单域 LSM 不同，跨域 LSM 涉及从源域到不同目标域的终身行为序列建模。本文提出了一种新的跨域 LSM 方法——终身交叉网络(LCN)。LCN 提供了一个交叉表示生成(CRP)模块，该模块利用对比度损失来改进项目嵌入的学习，有效地跨域连接项目。这对于提高跨域终身序列中相关项目的检索是非常重要的。此外，我们提出了终身注意金字塔(LAP)模块，它包含三个级联注意水平。通过增加一个中间层，并整合来自所有三个层次的结果，LAP 模块可以捕获广泛的用户兴趣，并确保梯度传播整个序列。提出的 LAP 还可以在不同的注意水平上实现显著的一致性，从而有可能进一步缩小最高水平的候选项库。这允许使用先进的注意力技术，以有效地减轻跨域序列中噪声的影响，并改善非线性表示，同时保持计算效率。在公共数据集和来自微信频道平台的工业数据集上进行的大量实验表明，LCN 在预测准确性和在线性能指标方面优于目前的方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Cross-Domain+LifeLong+Sequential+Modeling+for+Online+Click-Through+Rate+Prediction)|0| +|[Cross-Domain LifeLong Sequential Modeling for Online Click-Through Rate Prediction](https://doi.org/10.1145/3637528.3671601)|Ruijie Hou, Zhaoyang Yang, Ming Yu, Hongyu Lu, Zhuobin Zheng, Yu Chen, Qinsong Zeng, Ming Chen|Wechat, Tencent, Guangzhou, China; Wechat, Tencent, Beijing, China|Lifelong sequential modeling (LSM) has significantly advanced recommendation systems on social media platforms. Diverging from single-domain LSM, cross-domain LSM involves modeling lifelong behavior sequences from a source domain to a different target domain. In this paper, we propose the Lifelong Cross Network (LCN), a novel approach for cross-domain LSM. LCN features a Cross Representation Production (CRP) module that utilizes contrastive loss to improve the learning of item embeddings, effectively bridging items across domains. This is important for enhancing the retrieval of relevant items in cross-domain lifelong sequences. Furthermore, we propose the Lifelong Attention Pyramid (LAP) module, which contains three cascading attention levels. By adding an intermediate level and integrating the results from all three levels, the LAP module can capture a broad spectrum of user interests and ensure gradient propagation throughout the sequence. The proposed LAP can also achieve remarkable consistency across attention levels, making it possible to further narrow the candidate item pool of the top level. This allows for the use of advanced attention techniques to effectively mitigate the impact of the noise in cross-domain sequences and improve the non-linearity of the representation, all while maintaining computational efficiency. Extensive experiments conducted on both a public dataset and an industrial dataset from the WeChat Channels platform reveal that the LCN outperforms current methods in terms of prediction accuracy and online performance metrics.|终身顺序建模(LSM)在社交媒体平台上拥有非常先进的推荐系统。与单域 LSM 不同，跨域 LSM 涉及从源域到不同目标域的终身行为序列建模。本文提出了一种新的跨域 LSM 方法——终身交叉网络(LCN)。LCN 提供了一个交叉表示生成(CRP)模块，该模块利用对比度损失来改进项目嵌入的学习，有效地跨域连接项目。这对于提高跨域终身序列中相关项目的检索是非常重要的。此外，我们提出了终身注意金字塔(LAP)模块，它包含三个级联注意水平。通过增加一个中间层，并整合来自所有三个层次的结果，LAP 模块可以捕获广泛的用户兴趣，并确保梯度传播整个序列。提出的 LAP 还可以在不同的注意水平上实现显著的一致性，从而有可能进一步缩小最高水平的候选项库。这允许使用先进的注意力技术，以有效地减轻跨域序列中噪声的影响，并改善非线性表示，同时保持计算效率。在公共数据集和来自微信频道平台的工业数据集上进行的大量实验表明，LCN 在预测准确性和在线性能指标方面优于目前的方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Cross-Domain+LifeLong+Sequential+Modeling+for+Online+Click-Through+Rate+Prediction)|0| |[Mitigating Pooling Bias in E-commerce Search via False Negative Estimation](https://doi.org/10.1145/3637528.3671630)|Xiaochen Wang, Xiao Xiao, Ruhan Zhang, Xuan Zhang, Taesik Na, Tejaswi Tenneti, Haixun Wang, Fenglong Ma|The Pennsylvania State University, University Park, PA, USA; Instacart, San Francisco, CA, USA|Efficient and accurate product relevance assessment is critical for user experiences and business success. Training a proficient relevance assessment model requires high-quality query-product pairs, often obtained through negative sampling strategies. Unfortunately, current methods introduce pooling bias by mistakenly sampling false negatives, diminishing performance and business impact. To address this, we present Bias-mitigating Hard Negative Sampling (BHNS), a novel negative sampling strategy tailored to identify and adjust for false negatives, building upon our original False Negative Estimation algorithm. Our experiments in the Instacart search setting confirm BHNS as effective for practical e-commerce use. Furthermore, comparative analyses on public dataset showcase its domain-agnostic potential for diverse applications.|高效、准确的产品相关性评估对于用户体验和商业成功至关重要。训练一个熟练的相关性评估模型需要高质量的查询产品对，通常通过负抽样策略获得。不幸的是，目前的方法通过错误地采样错误的否定，减少性能和业务影响引入汇集偏见。为了解决这个问题，我们提出了消除偏差的硬负采样(BHNS) ，一种新的负采样策略，专门用于识别和调整假阴性，建立在我们原来的假阴性估计算法的基础上。我们在 Instacart 搜索设置中的实验证实了 BHNS 对于实际电子商务的使用是有效的。此外，对公共数据集的比较分析表明，其领域不可知的潜力适用于不同的应用。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Mitigating+Pooling+Bias+in+E-commerce+Search+via+False+Negative+Estimation)|0| |[Automatic Multi-Task Learning Framework with Neural Architecture Search in Recommendations](https://doi.org/10.1145/3637528.3671715)|Shen Jiang, Guanghui Zhu, Yue Wang, Chunfeng Yuan, Yihua Huang|State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China|Multi-task learning (MTL), which aims to make full use of knowledge contained in multiple tasks to enhance overall performance and efficiency, has been broadly applied in recommendations. The main challenge for MTL models is negative transfer. Existing MTL models, mainly built on the Mixture-of-Experts (MoE) structure, seek enhancements in performance through feature selection and specific expert sharing mode design. However, one expert sharing mode may not be universally applicable due to the complex correlations and diverse demands among various tasks. Additionally, homogeneous expert architectures in such models further limit their performance. To address these issues, in this paper, we propose an innovative automatic MTL framework, AutoMTL, leveraging neural architecture search (NAS) to design optimal expert architectures and sharing modes. The Dual-level Expert Sharing mode and Architecture Navigator (DESAN) search space of AutoMTL can not only efficiently explore expert sharing modes and feature selection schemes but also focus on the architectures of expert subnetworks. Along with this, we introduce an efficient Progressively Discretizing Differentiable Architecture Search (PD-DARTS) algorithm for search space exploration. Extensive experiments demonstrate that AutoMTL can consistently outperform state-of-the-art, human-crafted MTL models. Moreover, the insights obtained from the discovered architectures provide valuable guidance for building new multi-task recommendation models.|多任务学习(Multi-Task Learning，MTL)旨在充分利用多任务中包含的知识，提高整体绩效和效率，已被广泛应用于建议学习中。MTL 模型的主要挑战是负迁移。现有的 MTL 模型主要建立在专家混合(MoE)结构的基础上，通过特征选择和专家共享模式设计来提高性能。然而，由于各种任务之间复杂的相关性和不同的需求，一种专家共享模式并不能普遍适用。此外，这些模型中的同类专家体系结构进一步限制了它们的性能。为了解决这些问题，本文提出了一种创新的自动 MTL 框架 AutoMTL，利用神经结构搜索(NAS)来设计最优的专家结构和共享模式。AutoMTL 的双层专家共享模式和体系结构导航器(DESAN)搜索空间不仅可以有效地探索专家共享模式和特征选择方案，而且可以集中研究专家子网的体系结构。在此基础上，提出了一种高效的逐次离散可微体系结构搜索(PD-DARTS)算法。大量的实验表明，AutoMTL 可以持续优于最先进的，人工制作的 MTL 模型。此外，从所发现的体系结构中获得的见解为构建新的多任务推荐模型提供了有价值的指导。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Automatic+Multi-Task+Learning+Framework+with+Neural+Architecture+Search+in+Recommendations)|0| -|[CoRAL: Collaborative Retrieval-Augmented Large Language Models Improve Long-tail Recommendation](https://doi.org/10.1145/3637528.3671901)|Junda Wu, ChengChun Chang, Tong Yu, Zhankui He, Jianing Wang, Yupeng Hou, Julian J. McAuley|Adobe Research, San Jose, CA, USA; Columbia University, New York, NY, USA; University of California San Diego, La Jolla, CA, USA|The long-tail recommendation is a challenging task for traditionalrecommender systems, due to data sparsity and data imbalance issues. The recentdevelopment of large language models (LLMs) has shown their abilities incomplex reasoning, which can help to deduce users' preferences based on veryfew previous interactions. However, since most LLM-based systems rely on items'semantic meaning as the sole evidence for reasoning, the collaborativeinformation of user-item interactions is neglected, which can cause the LLM'sreasoning to be misaligned with task-specific collaborative information of thedataset. To further align LLMs' reasoning to task-specific user-iteminteraction knowledge, we introduce collaborative retrieval-augmented LLMs,CoRAL, which directly incorporate collaborative evidence into the prompts.Based on the retrieved user-item interactions, the LLM can analyze shared anddistinct preferences among users, and summarize the patterns indicating whichtypes of users would be attracted by certain items. The retrieved collaborativeevidence prompts the LLM to align its reasoning with the user-item interactionpatterns in the dataset. However, since the capacity of the input prompt islimited, finding the minimally-sufficient collaborative information forrecommendation tasks can be challenging. We propose to find the optimalinteraction set through a sequential decision-making process and develop aretrieval policy learned through a reinforcement learning (RL) framework,CoRAL. Our experimental results show that CoRAL can significantly improve LLMs'reasoning abilities on specific recommendation tasks. Our analysis also revealsthat CoRAL can more efficiently explore collaborative information throughreinforcement learning.|由于数据稀疏和数据不平衡的问题，长尾推荐对于传统的推荐系统来说是一项具有挑战性的任务。大型语言模型(LLM)的最新发展已经显示出它们在复杂推理方面的能力，这种能力可以帮助推断用户基于极少数以前的交互的偏好。然而，由于大多数基于 LLM 的系统依赖于项目的语义作为推理的唯一证据，用户-项目交互的协作信息被忽视，这可能导致 LLM 的推理与数据集的特定任务的协作信息不一致。为了进一步将 LLM 的推理与特定于任务的用户项目交互知识结合起来，我们引入了协作检索增强 LLM，CoRAL，它直接将协作证据合并到提示中。基于检索到的用户-项目交互，LLM 可以分析用户之间的共享和不同偏好，并总结模式，指出哪些类型的用户会被某些项目吸引。检索到的协作证据提示 LLM 使其推理与数据集中的用户项交互模式保持一致。然而，由于输入提示的能力是有限的，找到最低限度-足够的协作信息的推荐任务可能是具有挑战性的。我们建议通过一个连续的决策过程来寻找最佳的交互集合，并通过一个强化学习(RL)框架 CoRAL 来发展检索策略。实验结果表明，CoRAL 可以显著提高 LLM 对特定推荐任务的推理能力。我们的分析还显示，通过强化学习，CoRAL 可以更有效地探索协作信息。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=CoRAL:+Collaborative+Retrieval-Augmented+Large+Language+Models+Improve+Long-tail+Recommendation)|0| +|[CoRAL: Collaborative Retrieval-Augmented Large Language Models Improve Long-tail Recommendation](https://doi.org/10.1145/3637528.3671901)|Junda Wu, ChengChun Chang, Tong Yu, Zhankui He, Jianing Wang, Yupeng Hou, Julian J. McAuley|Columbia University, New York, NY, USA; University of California San Diego, La Jolla, CA, USA; Adobe Research, San Jose, CA, USA|The long-tail recommendation is a challenging task for traditionalrecommender systems, due to data sparsity and data imbalance issues. The recentdevelopment of large language models (LLMs) has shown their abilities incomplex reasoning, which can help to deduce users' preferences based on veryfew previous interactions. However, since most LLM-based systems rely on items'semantic meaning as the sole evidence for reasoning, the collaborativeinformation of user-item interactions is neglected, which can cause the LLM'sreasoning to be misaligned with task-specific collaborative information of thedataset. To further align LLMs' reasoning to task-specific user-iteminteraction knowledge, we introduce collaborative retrieval-augmented LLMs,CoRAL, which directly incorporate collaborative evidence into the prompts.Based on the retrieved user-item interactions, the LLM can analyze shared anddistinct preferences among users, and summarize the patterns indicating whichtypes of users would be attracted by certain items. The retrieved collaborativeevidence prompts the LLM to align its reasoning with the user-item interactionpatterns in the dataset. However, since the capacity of the input prompt islimited, finding the minimally-sufficient collaborative information forrecommendation tasks can be challenging. We propose to find the optimalinteraction set through a sequential decision-making process and develop aretrieval policy learned through a reinforcement learning (RL) framework,CoRAL. Our experimental results show that CoRAL can significantly improve LLMs'reasoning abilities on specific recommendation tasks. Our analysis also revealsthat CoRAL can more efficiently explore collaborative information throughreinforcement learning.|由于数据稀疏和数据不平衡的问题，长尾推荐对于传统的推荐系统来说是一项具有挑战性的任务。大型语言模型(LLM)的最新发展已经显示出它们在复杂推理方面的能力，这种能力可以帮助推断用户基于极少数以前的交互的偏好。然而，由于大多数基于 LLM 的系统依赖于项目的语义作为推理的唯一证据，用户-项目交互的协作信息被忽视，这可能导致 LLM 的推理与数据集的特定任务的协作信息不一致。为了进一步将 LLM 的推理与特定于任务的用户项目交互知识结合起来，我们引入了协作检索增强 LLM，CoRAL，它直接将协作证据合并到提示中。基于检索到的用户-项目交互，LLM 可以分析用户之间的共享和不同偏好，并总结模式，指出哪些类型的用户会被某些项目吸引。检索到的协作证据提示 LLM 使其推理与数据集中的用户项交互模式保持一致。然而，由于输入提示的能力是有限的，找到最低限度-足够的协作信息的推荐任务可能是具有挑战性的。我们建议通过一个连续的决策过程来寻找最佳的交互集合，并通过一个强化学习(RL)框架 CoRAL 来发展检索策略。实验结果表明，CoRAL 可以显著提高 LLM 对特定推荐任务的推理能力。我们的分析还显示，通过强化学习，CoRAL 可以更有效地探索协作信息。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=CoRAL:+Collaborative+Retrieval-Augmented+Large+Language+Models+Improve+Long-tail+Recommendation)|0| |[Text Matching Indexers in Taobao Search](https://doi.org/10.1145/3637528.3671654)|Sen Li, Fuyu Lv, Ruqing Zhang, Dan Ou, Zhixuan Zhang, Maarten de Rijke|CAS Key Lab of Network Data Science and Technology, ICT, CAS, Beijing, China; Alibaba Group, Hangzhou, China; University of Amsterdam, Amsterdam, Netherlands|Product search is an important service on Taobao, the largest e-commerce platform in China. Through this service, users can easily find products relevant to their specific needs. Coping with billion-size query loads, Taobao product search has traditionally relied on classical term-based retrieval models due to their powerful and interpretable indexes. In essence, efficient retrieval hinges on the proper storage of the inverted index. Recent successes involve reducing the size (pruning) of the inverted index but the construction and deployment of lossless static index pruning in practical product search still pose non-trivial challenges. In this work, we introduce a novel SM art INDexing (SMIND) solution in Taobao product search. SMIND is designed to reduce information loss during the static pruning process by incorporating user search preferences. Specifically, we first construct "user-query-item'' hypergraphs for four different search preferences, namely purchase, click, exposure, and relevance. Then, we develop an efficient TermRank algorithm applied to these hypergraphs, to preserve relevant items based on specific user preferences during the pruning of the inverted indexer. Our approach offers fresh insights into the field of product search, emphasizing that term dependencies in user search preferences go beyond mere text relevance. Moreover, to address the vocabulary mismatch problem inherent in term-based models, we also incorporate an multi-granularity semantic retrieval model to facilitate semantic matching. Empirical results from both offline evaluation and online A/B tests showcase the superiority of SMIND over state-of-the-art methods, especially in commerce metrics with significant improvements of 1.34% in Pay Order Count and 1.50% in Gross Merchandise Value. Besides, SMIND effectively mitigates the Matthew effect of user queries and has been in service for hundreds of millions of daily users since November 2022.|产品搜索是中国最大的电子商务平台淘宝上的一项重要服务。通过这项服务，用户可以很容易地找到与他们的具体需求相关的产品。为了应对数十亿大小的查询负载，淘宝产品搜索传统上依赖于传统的基于词汇的检索模型，因为它们的索引功能强大且易于解释。实质上，有效的检索取决于适当存储倒排索引。最近的成功包括减少了反向索引的大小(修剪) ，但是在实际的产品搜索中构建和部署无损静态索引修剪仍然带来了不小的挑战。在本文中，我们介绍了一个新颖的 SM 艺术索引(SMIND)解决方案在淘宝产品搜索。SMIND 的目的是通过合并用户搜索偏好，减少静态修剪过程中的信息损失。具体来说，我们首先为四种不同的搜索偏好(即购买、点击、曝光和相关性)构建“用户查询项目”超图。然后，我们开发了一个有效的 TermRank 算法应用于这些超图，以保留相关的项目基于特定的用户喜好在反向索引器的修剪过程中。我们的方法为产品搜索领域提供了新的视角，强调用户搜索偏好中的术语依赖性不仅仅是文本相关性。此外，为了解决基于词汇的模型所固有的词汇不匹配问题，我们还引入了一个多粒度的语义检索模型来促进语义匹配。线下评估和在线 A/B 测试的实证结果表明，SMIND 相对于最先进的方法具有优势，尤其是在商业指标方面，支付订单计数和商品总价值分别显著提高了1.34% 和1.50% 。此外，SMIND 有效地减轻了用户查询的“马太效应”，自2022年11月以来已为数亿日常用户提供服务。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Text+Matching+Indexers+in+Taobao+Search)|0| -|[Unified Low-rank Compression Framework for Click-through Rate Prediction](https://doi.org/10.1145/3637528.3671520)|Hao Yu, Minghao Fu, Jiandong Ding, Yusheng Zhou, Jianxin Wu|Nanjing University, Nanjing, Jiangsu, China; Researcher, Shanghai, China|Deep Click-Through Rate (CTR) prediction models play an important role in modern industrial recommendation scenarios. However, high memory overhead and computational costs limit their deployment in resource-constrained environments. Low-rank approximation is an effective method for computer vision and natural language processing models, but its application in compressing CTR prediction models has been less explored. Due to the limited memory and computing resources, compression of CTR prediction models often confronts three fundamental challenges, i.e., (1). How to reduce the model sizes to adapt to edge devices? (2). How to speed up CTR prediction model inference? (3). How to retain the capabilities of original models after compression? Previous low-rank compression research mostly uses tensor decomposition, which can achieve a high parameter compression ratio, but brings in AUC degradation and additional computing overhead. To address these challenges, we propose a unified low-rank decomposition framework for compressing CTR prediction models. We find that even with the most classic matrix decomposition SVD method, our framework can achieve better performance than the original model. To further improve the effectiveness of our framework, we locally compress the output features instead of compressing the model weights. Our unified low-rank compression framework can be applied to embedding tables and MLP layers in various CTR prediction models. Extensive experiments on two academic datasets and one real industrial benchmark demonstrate that, with 3--5× model size reduction, our compressed models can achieve both faster inference and higher AUC than the uncompressed original models. Our code is at https://github.com/yuhao318/Atomic_Feature_Mimicking.|深点进率(ctrl)预测模型在现代工业推荐方案中扮演着重要角色。但是，较高的内存开销和计算成本限制了它们在资源受限环境中的部署。低秩近似是计算机视觉和自然语言处理模型的一种有效方法，但其在压缩 CTR 预测模型方面的应用研究较少。由于有限的内存和计算资源，CTR 预测模型的压缩往往面临三个基本挑战，即(1)。如何减小模型尺寸以适应边缘设备？(2).如何加快 CTR 预测模型的推导？(3).如何保留原始模型压缩后的能力？先前的低秩压缩研究大多使用张量分解，这可以实现高参数的压缩比，但是带来了 AUC 降解和额外的计算开销。为了应对这些挑战，我们提出了一个统一的低秩分解框架来压缩 CTR 预测模型。我们发现，即使使用最经典的矩阵分解奇异值分解方法，我们的框架也能取得比原始模型更好的性能。为了进一步提高框架的有效性，我们对输出特征进行了局部压缩，而不是对模型权重进行压缩。我们统一的低秩压缩框架可以应用于各种 CTR 预测模型中的表和 MLP 层的嵌入。在两个学术数据集和一个实际工业基准上的大量实验表明，与未压缩的原始模型相比，通过3-5 × 模型尺寸的缩减，我们的压缩模型可以实现更快的推理和更高的 AUC。我们的代码是 https://github.com/yuhao318/atomic_feature_mimicking。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Unified+Low-rank+Compression+Framework+for+Click-through+Rate+Prediction)|0| -|[Optimizing Smartphone App Usage Prediction: A Click-Through Rate Ranking Approach](https://doi.org/10.1145/3637528.3671567)|Yuqi Zhang, Meiying Kang, Xiucheng Li, Yu Qiu, Zhijun Li|Harbin Institute of Technology, Harbin, China; Independent, Chengdu, China; Soochow University, Suzhou, China; Harbin Institute of Technology, Shenzhen, China|Over the past decade, smartphones have become indispensable personal mobile devices, experiencing a remarkable surge in software apps. These apps empower users to seamlessly connect with various internet services, such as social communication and online shopping. Accurately predicting smartphone app usage can effectively improve user experience and optimize resource utilization. However, existing models often treat app usage prediction as a classification problem, which suffers from issues of app usage imbalance and out-of-distribution (OOD) during deployment. To address these challenges, this paper proposes a novel click-through rate (CTR) ranking-based method for predicting app usage. By transforming the classification problem into a CTR problem, we can eliminate the negative impact of the app usage imbalance issue. To address the OOD issue during deployment, we generate the app click sequence and three types of discriminative features, which enable generalization on unseen apps. The app click sequence and the three types of features serve as inputs for training a CTR estimation model in the cloud, and the trained model is then deployed on the user's smartphone to predict the CTR for each installed app. The decision-making process involves ranking these CTR values and selecting the app with the highest CTR as the final prediction. Our method has been extensively tested with large-scale app usage data. The results demonstrate that our approach is able to outperform state-of-the-art methods, with improvements over 4.93% in top-3 accuracy and 6.64% in top-5 accuracy. It achieves approximately twice the accuracy in predicting apps with low usage frequencies in comparison to baseline methods. Our method has been successfully deployed on the app recommendation system of a leading smartphone manufacturer.|在过去十年里，智能手机已经成为不可或缺的个人移动设备，软件应用程序出现了惊人的增长。这些应用程序使用户能够无缝连接各种互联网服务，如社会交流和网上购物。准确预测智能手机应用程序的使用情况可以有效地改善用户体验和优化资源利用。然而，现有的模型往往将应用程序使用预测视为一个分类问题，在部署过程中存在应用程序使用不平衡和分布不均衡(OOD)的问题。为了应对这些挑战，本文提出了一种新的基于点进率排名(ctrr)的方法来预测应用程序的使用情况。通过将分类问题转化为 CTR 问题，我们可以消除应用程序使用不平衡问题的负面影响。为了解决部署过程中的 OOD 问题，我们生成了应用程序的点击序列和三种类型的区分特性，这些特性可以对看不见的应用程序进行泛化。应用程序点击序列和三种类型的功能作为输入，用于在云中训练一个点击率估计模型，然后将训练好的模型部署到用户的智能手机上，以预测每个安装的应用程序的点击率。决策过程包括对这些点击率值进行排序，并选择点击率最高的应用程序作为最终预测。我们的方法已经通过大规模的应用程序使用数据进行了广泛的测试。结果表明，我们的方法能够优于国家的最先进的方法，提高了4.93% 以上的前3名的准确性和6.64% 以上的前5名的准确性。与基线方法相比，它在预测使用频率较低的应用程序方面达到了大约两倍的准确性。我们的方法已成功应用于一家领先的智能手机制造商的应用程序推荐系统。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Optimizing+Smartphone+App+Usage+Prediction:+A+Click-Through+Rate+Ranking+Approach)|0| -|[Relevance Meets Diversity: A User-Centric Framework for Knowledge Exploration Through Recommendations](https://doi.org/10.1145/3637528.3671949)|Erica Coppolillo, Giuseppe Manco, Aristides Gionis|Division of Theoretical Computer Science, KTH Royal Institute of Technology, Stockholm, Sweden; ICAR-CNR, Rende, Italy; Department of Computer Science, University of Calabria & ICAR-CNR, Rende, Italy|Providing recommendations that are both relevant and diverse is a key consideration of modern recommender systems. Optimizing both of these measures presents a fundamental trade-off, as higher diversity typically comes at the cost of relevance, resulting in lower user engagement. Existing recommendation algorithms try to resolve this trade-off by combining the two measures, relevance and diversity, into one aim and then seeking recommendations that optimize the combined objective, for a given number of items. Traditional approaches, however, do not consider the user interaction with the suggested items. In this paper, we put the user at the central stage, and build on the interplay between relevance, diversity, and user behavior. In contrast to applications where the goal is solely to maximize engagement, we focus on scenarios aiming at maximizing the total amount of knowledge encountered by the user. We use diversity as a surrogate for the amount of knowledge obtained by the user while interacting with the system, and we seek to maximize diversity. We propose a probabilistic user-behavior model in which users keep interacting with the recommender system as long as they receive relevant suggestions, but they may stop if the relevance of the recommended items drops. Thus, for a recommender system to achieve a high-diversity measure, it will need to produce recommendations that are both relevant and diverse. Finally, we propose a novel recommendation strategy that combines relevance and diversity by a copula function. We conduct an extensive evaluation of the proposed methodology over multiple datasets, and we show that our strategy outperforms several state-of-the-art competitors. Our implementation is publicly available at https://github.com/EricaCoppolillo/EXPLORE.|提供相关和多样化的建议是现代推荐系统的一个关键考虑因素。优化这两个措施提出了一个基本的权衡，因为更高的多样性通常以相关性为代价，导致用户参与度降低。现有的推荐算法试图通过将相关性和多样性这两个衡量标准结合为一个目标来解决这种权衡，然后寻求建议，优化合并目标，以满足给定数量的项目。但是，传统方法不考虑用户与建议项的交互。在本文中，我们把用户放在中心阶段，并建立在相关性，多样性和用户行为之间的相互作用。与目标仅仅是最大化参与的应用程序不同，我们关注的场景旨在最大化用户遇到的知识总量。我们使用多样性作为用户在与系统交互时获得的知识量的替代指标，并寻求最大化多样性。我们提出了一个概率用户行为模型，在这个模型中，用户只要收到相关的建议，就会与推荐系统保持互动，但是如果推荐项目的相关性下降，他们可能会停止。因此，推荐系统要实现高度多样化的措施，就需要提出相关且多样化的建议。最后，我们提出了一个新的推荐策略，通过一个 Copula 函数结合相关性和多样性。我们在多个数据集上对提出的方法进行了广泛的评估，我们表明我们的策略优于几个最先进的竞争对手。我们的实施 https://github.com/ericacoppolillo/explore 公开发布。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Relevance+Meets+Diversity:+A+User-Centric+Framework+for+Knowledge+Exploration+Through+Recommendations)|0| +|[Unified Low-rank Compression Framework for Click-through Rate Prediction](https://doi.org/10.1145/3637528.3671520)|Hao Yu, Minghao Fu, Jiandong Ding, Yusheng Zhou, Jianxin Wu|Researcher, Shanghai, China; Nanjing University, Nanjing, Jiangsu, China|Deep Click-Through Rate (CTR) prediction models play an important role in modern industrial recommendation scenarios. However, high memory overhead and computational costs limit their deployment in resource-constrained environments. Low-rank approximation is an effective method for computer vision and natural language processing models, but its application in compressing CTR prediction models has been less explored. Due to the limited memory and computing resources, compression of CTR prediction models often confronts three fundamental challenges, i.e., (1). How to reduce the model sizes to adapt to edge devices? (2). How to speed up CTR prediction model inference? (3). How to retain the capabilities of original models after compression? Previous low-rank compression research mostly uses tensor decomposition, which can achieve a high parameter compression ratio, but brings in AUC degradation and additional computing overhead. To address these challenges, we propose a unified low-rank decomposition framework for compressing CTR prediction models. We find that even with the most classic matrix decomposition SVD method, our framework can achieve better performance than the original model. To further improve the effectiveness of our framework, we locally compress the output features instead of compressing the model weights. Our unified low-rank compression framework can be applied to embedding tables and MLP layers in various CTR prediction models. Extensive experiments on two academic datasets and one real industrial benchmark demonstrate that, with 3--5× model size reduction, our compressed models can achieve both faster inference and higher AUC than the uncompressed original models. Our code is at https://github.com/yuhao318/Atomic_Feature_Mimicking.|深点进率(ctrl)预测模型在现代工业推荐方案中扮演着重要角色。但是，较高的内存开销和计算成本限制了它们在资源受限环境中的部署。低秩近似是计算机视觉和自然语言处理模型的一种有效方法，但其在压缩 CTR 预测模型方面的应用研究较少。由于有限的内存和计算资源，CTR 预测模型的压缩往往面临三个基本挑战，即(1)。如何减小模型尺寸以适应边缘设备？(2).如何加快 CTR 预测模型的推导？(3).如何保留原始模型压缩后的能力？先前的低秩压缩研究大多使用张量分解，这可以实现高参数的压缩比，但是带来了 AUC 降解和额外的计算开销。为了应对这些挑战，我们提出了一个统一的低秩分解框架来压缩 CTR 预测模型。我们发现，即使使用最经典的矩阵分解奇异值分解方法，我们的框架也能取得比原始模型更好的性能。为了进一步提高框架的有效性，我们对输出特征进行了局部压缩，而不是对模型权重进行压缩。我们统一的低秩压缩框架可以应用于各种 CTR 预测模型中的表和 MLP 层的嵌入。在两个学术数据集和一个实际工业基准上的大量实验表明，与未压缩的原始模型相比，通过3-5 × 模型尺寸的缩减，我们的压缩模型可以实现更快的推理和更高的 AUC。我们的代码是 https://github.com/yuhao318/atomic_feature_mimicking。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Unified+Low-rank+Compression+Framework+for+Click-through+Rate+Prediction)|0| +|[Optimizing Smartphone App Usage Prediction: A Click-Through Rate Ranking Approach](https://doi.org/10.1145/3637528.3671567)|Yuqi Zhang, Meiying Kang, Xiucheng Li, Yu Qiu, Zhijun Li|Harbin Institute of Technology, Harbin, China; Independent, Chengdu, China; Harbin Institute of Technology, Shenzhen, China; Soochow University, Suzhou, China|Over the past decade, smartphones have become indispensable personal mobile devices, experiencing a remarkable surge in software apps. These apps empower users to seamlessly connect with various internet services, such as social communication and online shopping. Accurately predicting smartphone app usage can effectively improve user experience and optimize resource utilization. However, existing models often treat app usage prediction as a classification problem, which suffers from issues of app usage imbalance and out-of-distribution (OOD) during deployment. To address these challenges, this paper proposes a novel click-through rate (CTR) ranking-based method for predicting app usage. By transforming the classification problem into a CTR problem, we can eliminate the negative impact of the app usage imbalance issue. To address the OOD issue during deployment, we generate the app click sequence and three types of discriminative features, which enable generalization on unseen apps. The app click sequence and the three types of features serve as inputs for training a CTR estimation model in the cloud, and the trained model is then deployed on the user's smartphone to predict the CTR for each installed app. The decision-making process involves ranking these CTR values and selecting the app with the highest CTR as the final prediction. Our method has been extensively tested with large-scale app usage data. The results demonstrate that our approach is able to outperform state-of-the-art methods, with improvements over 4.93% in top-3 accuracy and 6.64% in top-5 accuracy. It achieves approximately twice the accuracy in predicting apps with low usage frequencies in comparison to baseline methods. Our method has been successfully deployed on the app recommendation system of a leading smartphone manufacturer.|在过去十年里，智能手机已经成为不可或缺的个人移动设备，软件应用程序出现了惊人的增长。这些应用程序使用户能够无缝连接各种互联网服务，如社会交流和网上购物。准确预测智能手机应用程序的使用情况可以有效地改善用户体验和优化资源利用。然而，现有的模型往往将应用程序使用预测视为一个分类问题，在部署过程中存在应用程序使用不平衡和分布不均衡(OOD)的问题。为了应对这些挑战，本文提出了一种新的基于点进率排名(ctrr)的方法来预测应用程序的使用情况。通过将分类问题转化为 CTR 问题，我们可以消除应用程序使用不平衡问题的负面影响。为了解决部署过程中的 OOD 问题，我们生成了应用程序的点击序列和三种类型的区分特性，这些特性可以对看不见的应用程序进行泛化。应用程序点击序列和三种类型的功能作为输入，用于在云中训练一个点击率估计模型，然后将训练好的模型部署到用户的智能手机上，以预测每个安装的应用程序的点击率。决策过程包括对这些点击率值进行排序，并选择点击率最高的应用程序作为最终预测。我们的方法已经通过大规模的应用程序使用数据进行了广泛的测试。结果表明，我们的方法能够优于国家的最先进的方法，提高了4.93% 以上的前3名的准确性和6.64% 以上的前5名的准确性。与基线方法相比，它在预测使用频率较低的应用程序方面达到了大约两倍的准确性。我们的方法已成功应用于一家领先的智能手机制造商的应用程序推荐系统。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Optimizing+Smartphone+App+Usage+Prediction:+A+Click-Through+Rate+Ranking+Approach)|0| +|[Relevance Meets Diversity: A User-Centric Framework for Knowledge Exploration Through Recommendations](https://doi.org/10.1145/3637528.3671949)|Erica Coppolillo, Giuseppe Manco, Aristides Gionis|Department of Computer Science, University of Calabria & ICAR-CNR, Rende, Italy; ICAR-CNR, Rende, Italy; Division of Theoretical Computer Science, KTH Royal Institute of Technology, Stockholm, Sweden|Providing recommendations that are both relevant and diverse is a key consideration of modern recommender systems. Optimizing both of these measures presents a fundamental trade-off, as higher diversity typically comes at the cost of relevance, resulting in lower user engagement. Existing recommendation algorithms try to resolve this trade-off by combining the two measures, relevance and diversity, into one aim and then seeking recommendations that optimize the combined objective, for a given number of items. Traditional approaches, however, do not consider the user interaction with the suggested items. In this paper, we put the user at the central stage, and build on the interplay between relevance, diversity, and user behavior. In contrast to applications where the goal is solely to maximize engagement, we focus on scenarios aiming at maximizing the total amount of knowledge encountered by the user. We use diversity as a surrogate for the amount of knowledge obtained by the user while interacting with the system, and we seek to maximize diversity. We propose a probabilistic user-behavior model in which users keep interacting with the recommender system as long as they receive relevant suggestions, but they may stop if the relevance of the recommended items drops. Thus, for a recommender system to achieve a high-diversity measure, it will need to produce recommendations that are both relevant and diverse. Finally, we propose a novel recommendation strategy that combines relevance and diversity by a copula function. We conduct an extensive evaluation of the proposed methodology over multiple datasets, and we show that our strategy outperforms several state-of-the-art competitors. Our implementation is publicly available at https://github.com/EricaCoppolillo/EXPLORE.|提供相关和多样化的建议是现代推荐系统的一个关键考虑因素。优化这两个措施提出了一个基本的权衡，因为更高的多样性通常以相关性为代价，导致用户参与度降低。现有的推荐算法试图通过将相关性和多样性这两个衡量标准结合为一个目标来解决这种权衡，然后寻求建议，优化合并目标，以满足给定数量的项目。但是，传统方法不考虑用户与建议项的交互。在本文中，我们把用户放在中心阶段，并建立在相关性，多样性和用户行为之间的相互作用。与目标仅仅是最大化参与的应用程序不同，我们关注的场景旨在最大化用户遇到的知识总量。我们使用多样性作为用户在与系统交互时获得的知识量的替代指标，并寻求最大化多样性。我们提出了一个概率用户行为模型，在这个模型中，用户只要收到相关的建议，就会与推荐系统保持互动，但是如果推荐项目的相关性下降，他们可能会停止。因此，推荐系统要实现高度多样化的措施，就需要提出相关且多样化的建议。最后，我们提出了一个新的推荐策略，通过一个 Copula 函数结合相关性和多样性。我们在多个数据集上对提出的方法进行了广泛的评估，我们表明我们的策略优于几个最先进的竞争对手。我们的实施 https://github.com/ericacoppolillo/explore 公开发布。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Relevance+Meets+Diversity:+A+User-Centric+Framework+for+Knowledge+Exploration+Through+Recommendations)|0| |[Understanding the Ranking Loss for Recommendation with Sparse User Feedback](https://doi.org/10.1145/3637528.3671565)|Zhutian Lin, Junwei Pan, Shangyu Zhang, Ximei Wang, Xi Xiao, Shudong Huang, Lei Xiao, Jie Jiang|Tencent Inc., Shenzhen, China; Shenzhen International Graduate School, Tsinghua University, Shenzhen, Guangdong, China; Shenzhen International Graduate School, Tsinghua University, Shenzhen, China|Click-through rate (CTR) prediction is a crucial area of research in online advertising. While binary cross entropy (BCE) has been widely used as the optimization objective for treating CTR prediction as a binary classification problem, recent advancements have shown that combining BCE loss with an auxiliary ranking loss can significantly improve performance. However, the full effectiveness of this combination loss is not yet fully understood. In this paper, we uncover a new challenge associated with the BCE loss in scenarios where positive feedback is sparse: the issue of gradient vanishing for negative samples. We introduce a novel perspective on the effectiveness of the auxiliary ranking loss in CTR prediction: it generates larger gradients on negative samples, thereby mitigating the optimization difficulties when using the BCE loss only and resulting in improved classification ability. To validate our perspective, we conduct theoretical analysis and extensive empirical evaluations on public datasets. Additionally, we successfully integrate the ranking loss into Tencent's online advertising system, achieving notable lifts of 0.70% and 1.26% in Gross Merchandise Value (GMV) for two main scenarios. The code is openly accessible at: https://github.com/SkylerLinn/Understanding-the-Ranking-Loss.|点进率预测是在线广告研究的一个关键领域。二进制交叉熵(BCE)已被广泛用作将 CTR 预测作为二进制分类问题处理的优化目标，但最近的研究表明，将 BCE 损失与辅助排序损失相结合可以显著提高性能。然而，这种组合损失的全部有效性尚未完全了解。在本文中，我们揭示了一个新的挑战与 BCE 损失相关的情况下，正反馈是稀疏的: 问题的梯度消失的负样本。我们介绍了一种新的视角辅助排序损失在 CTR 预测中的有效性: 它在负样本上产生较大的梯度，从而减少了优化时仅使用 BCE 损失的困难，并导致分类能力的提高。为了验证我们的观点，我们对公共数据集进行了理论分析和广泛的实证评估。此外，我们成功地将排名损失纳入腾讯的在线广告系统，在两个主要情况下，商品总值(GMV)分别显著提高了0.70% 和1.26% 。该守则可在以下 https://github.com/skylerlinn/understanding-The-ranking-loss 公开查阅:。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Understanding+the+Ranking+Loss+for+Recommendation+with+Sparse+User+Feedback)|0| -|[Multi-Task Neural Linear Bandit for Exploration in Recommender Systems](https://doi.org/10.1145/3637528.3671649)|Yi Su, Haokai Lu, Yuening Li, Liang Liu, Shuchao Bi, Ed H. Chi, Minmin Chen|Google, Mountain View, CA, USA; Google Deepmind, Mountain View, CA, USA|Exposure bias and its induced feedback loop effect are well-known problems in recommender systems. Exploration is believed to be the key to break such feedback loops. While classical contextual bandit algorithms such as Upper-Confidence-Bound and Thompson Sampling have been successful in addressing the exploration-exploitation trade-off in the single-task settings with one clear reward signal, modern recommender systems often leverage multiple rich sources of feedback such as clicks, likes, dislikes, shares, satisfaction survey responses, and employ multi-task learning in practice. It is unclear how one can incorporate exploration in the multi-task setup with different objectives. In this paper, we study an efficient bandit algorithm tailored to multi-task recommender systems, named Multi-task Neural Linear Bandit (mtNLB). In particular, we investigate efficient feature embeddings in the multi-task setups that could be used as contextual features in the Neural Linear Bandit, a contextual bandit algorithm that nicely combines the representation power from DNN and simplicity in uncertainty calculation from linear models. We further study cost-effective approximations of the uncertainty estimate and principled ways to incorporate uncertainty into the multi-task scoring of items. To showcase the efficacy of our proposed method, we conduct live experiments on a large-scale commercial recommendation platform that serves billions of users. We evaluate the quality of the uncertainty estimate and demonstrate its ability to improve exploration across the different dimensions of the reward signals in comparison to baseline approaches.|在推荐系统中，曝光偏差及其诱导反馈回路效应是一个众所周知的问题。勘探被认为是打破这种反馈循环的关键。尽管传统的情境强盗算法如 Upper-Confidence-Bound 和 Thompson Sampling 已经成功地解决了单任务环境中的探索-开发权衡问题，但是现代推荐系统往往利用多种丰富的反馈来源，如点击，喜欢，不喜欢，分享，满意度调查反馈，并在实践中采用多任务学习。目前还不清楚如何将探索结合到具有不同目标的多任务设置中。本文研究了一种适用于多任务推荐系统的高效盗贼算法——多任务神经网络线性盗贼(mtNLB)。特别地，我们研究了在多任务设置中有效的特征嵌入，这些特征可以作为神经网络线性匪徒算法中的上下文特征，这种上下文匪徒算法很好地结合了 DNN 的表示能力和线性模型的不确定性计算的简单性。我们进一步研究了不确定性估计的成本效益近似，以及将不确定性纳入多任务项目评分的原则方法。为了展示我们提出的方法的有效性，我们在一个服务于数十亿用户的大规模商业推荐平台上进行了现场实验。我们评估不确定性估计的质量，并证明其能力，以改善探索的不同维度的奖励信号相比，基线方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Multi-Task+Neural+Linear+Bandit+for+Exploration+in+Recommender+Systems)|0| +|[Multi-Task Neural Linear Bandit for Exploration in Recommender Systems](https://doi.org/10.1145/3637528.3671649)|Yi Su, Haokai Lu, Yuening Li, Liang Liu, Shuchao Bi, Ed H. Chi, Minmin Chen|Google Deepmind, Mountain View, CA, USA; Google, Mountain View, CA, USA|Exposure bias and its induced feedback loop effect are well-known problems in recommender systems. Exploration is believed to be the key to break such feedback loops. While classical contextual bandit algorithms such as Upper-Confidence-Bound and Thompson Sampling have been successful in addressing the exploration-exploitation trade-off in the single-task settings with one clear reward signal, modern recommender systems often leverage multiple rich sources of feedback such as clicks, likes, dislikes, shares, satisfaction survey responses, and employ multi-task learning in practice. It is unclear how one can incorporate exploration in the multi-task setup with different objectives. In this paper, we study an efficient bandit algorithm tailored to multi-task recommender systems, named Multi-task Neural Linear Bandit (mtNLB). In particular, we investigate efficient feature embeddings in the multi-task setups that could be used as contextual features in the Neural Linear Bandit, a contextual bandit algorithm that nicely combines the representation power from DNN and simplicity in uncertainty calculation from linear models. We further study cost-effective approximations of the uncertainty estimate and principled ways to incorporate uncertainty into the multi-task scoring of items. To showcase the efficacy of our proposed method, we conduct live experiments on a large-scale commercial recommendation platform that serves billions of users. We evaluate the quality of the uncertainty estimate and demonstrate its ability to improve exploration across the different dimensions of the reward signals in comparison to baseline approaches.|在推荐系统中，曝光偏差及其诱导反馈回路效应是一个众所周知的问题。勘探被认为是打破这种反馈循环的关键。尽管传统的情境强盗算法如 Upper-Confidence-Bound 和 Thompson Sampling 已经成功地解决了单任务环境中的探索-开发权衡问题，但是现代推荐系统往往利用多种丰富的反馈来源，如点击，喜欢，不喜欢，分享，满意度调查反馈，并在实践中采用多任务学习。目前还不清楚如何将探索结合到具有不同目标的多任务设置中。本文研究了一种适用于多任务推荐系统的高效盗贼算法——多任务神经网络线性盗贼(mtNLB)。特别地，我们研究了在多任务设置中有效的特征嵌入，这些特征可以作为神经网络线性匪徒算法中的上下文特征，这种上下文匪徒算法很好地结合了 DNN 的表示能力和线性模型的不确定性计算的简单性。我们进一步研究了不确定性估计的成本效益近似，以及将不确定性纳入多任务项目评分的原则方法。为了展示我们提出的方法的有效性，我们在一个服务于数十亿用户的大规模商业推荐平台上进行了现场实验。我们评估不确定性估计的质量，并证明其能力，以改善探索的不同维度的奖励信号相比，基线方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Multi-Task+Neural+Linear+Bandit+for+Exploration+in+Recommender+Systems)|0| |[Enhancing Pre-Ranking Performance: Tackling Intermediary Challenges in Multi-Stage Cascading Recommendation Systems](https://doi.org/10.1145/3637528.3671580)|Jianping Wei, Yujie Zhou, Zhengwei Wu, Ziqi Liu|Ant Group, HangZhou, China|Large-scale search engines and recommendation systems utilize a three-stage cascading architecture-recall, pre-ranking, and ranking-to deliver relevant results within stringent latency limits. The pre-ranking stage is crucial for filtering a large number of recalled items into a manageable set for the ranking stage, greatly affecting the system's performance. Pre-ranking faces two intermediary challenges: Sample Selection Bias (SSB) arises when training is based on ranking stage feedback but the evaluation is on a broader recall dataset. Also, compared to the ranking stage, simpler pre-rank models may perform worse and less consistently. Traditional methods to tackle SSB issues include using all recall results and treating unexposed portions as negatives for training, which can be costly and noisy. To boost performance and consistency, some pre-ranking feature interaction enhancers don't fully fix consistency issues, while methods like knowledge distillation in ranking models ignore exposure bias. Our proposed framework targets these issues with three integral modules: Sample Selection, Domain Adaptation, and Unbiased Distillation. Sample Selection filters recall results to mitigate SSB and compute costs. Domain Adaptation enhances model robustness by assigning pseudo-labels to unexposed samples. Unbiased Distillation uses exposure-independent scores from Domain Adaptation to implement unbiased distillation for the pre-ranking model. The framework focuses on optimizing pre-ranking while maintaining training efficiency. We introduce new metrics for pre-ranking evaluation, while experiments confirm the effectiveness of our framework. Our framework is also deployed in real industrial systems.|大型搜索引擎和推荐系统利用三阶段级联架构——召回、预先排序和排序——在严格的延迟限制内交付相关结果。预排序阶段对于将大量被召回的项目过滤到一个可管理的集合中以进行排序至关重要，这极大地影响了系统的性能。预排序面临两个中间挑战: 样本选择偏差(SSB)出现时，训练是基于排序阶段的反馈，但评估是在一个更广泛的召回数据集。此外，与排名阶段相比，更简单的预排名模型可能表现得更差，更不一致。解决 SSB 问题的传统方法包括使用所有的召回结果，并将未暴露的部分作为培训的负面因素，这可能是昂贵和嘈杂的。为了提高性能和一致性，一些预排序特征交互增强器不能完全解决一致性问题，而排序模型中的知识提取等方法忽略了暴露偏差。我们提出的框架通过三个整体模块来解决这些问题: 样本选择、领域适应和无偏提取。示例选择过滤器召回结果以减少 SSB 和计算成本。领域自适应通过为未暴露的样本分配伪标签来增强模型的鲁棒性。无偏蒸馏使用领域适应的暴露无关分数实现预排序模型的无偏蒸馏。该框架的重点是优化预排序，同时保持培训效率。在实验的基础上，我们引入了新的指标来进行预排序评价，并验证了该框架的有效性。我们的框架也部署在真实的工业系统中。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Enhancing+Pre-Ranking+Performance:+Tackling+Intermediary+Challenges+in+Multi-Stage+Cascading+Recommendation+Systems)|0| -|[Explicit and Implicit Modeling via Dual-Path Transformer for Behavior Set-informed Sequential Recommendation](https://doi.org/10.1145/3637528.3671755)|Ming Chen, Weike Pan, Zhong Ming|; Shenzhen University & Shenzhen Technology University, Shenzhen, China; College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China|Sequential recommendation (SR) and multi-behavior sequential recommendation (MBSR) both come from real-world scenarios. Compared with SR, MBSR takes into account the dependencies of different behaviors. We find that most existing works on MBSR are studied in the context of e-commerce scenarios. In terms of the data format of the behavior types, we observe that the conventional label-formatted data carries limited information and is inadequate for scenarios like social media. With this observation, we introducebehavior set and extend MBSR to behavior set-informed sequential recommendation (BSSR). In BSSR, behavior dependencies become more complex and personalized, and user interest arousal may lack explicit contextual associations. To delve into the dynamics inhered within a behavior set and adaptively tailor recommendation lists upon its variability, we propose a novel solution called Explicit and Implicit modeling via Dual-Path Transformer (EIDP) for BSSR. Our EIDP adopts a dual-path architecture, distinguishing between explicit modeling path (EMP) and implicit modeling path (IMP) based on whether to directly incorporate the behavior representations. EMP features the personalized behavior set-wise transition pattern extractor (PBS-TPE) as its core component. It couples behavioral representations with both the items and positions to explore intra-behavior dynamics within a behavior set at a fine granularity. IMP utilizes light multi-head self-attention blocks (L-MSAB) as encoders under specific behavior types. The obtained multi-view representations are then aggregated by cross-behavior attention fusion (CBAF), using the behavior set of the next time step as a guidance to extract collaborative semantics at the behavioral level. Extensive experiments on two real-world datasets demonstrate the effectiveness of our EIDP. We release the implementation code at: https://github.com/OshiNoCSMA/EIDP.|序贯推荐(SR)和多行为序贯推荐(MBSR)都来自于现实场景。与 SR 相比，MBSR 考虑了不同行为的依赖性。我们发现现有的大多数 MBSR 的研究工作都是在电子商务环境下进行的。就行为类型的数据格式而言，我们观察到传统的标签格式的数据携带有限的信息，对于像社交媒体这样的场景来说是不够的。在此基础上，我们引入了行为集合，并将 MBSR 扩展到行为集合知情序列推荐(BSSR)。在 BSSR，行为依赖变得更加复杂和个性化，用户兴趣唤起可能缺乏明确的上下文关联。为了深入研究行为集内部的动态性，并根据其可变性自适应调整推荐列表，我们提出了一种新的解决方案，即通过双路径转换器(EIDP)对 BSSR 进行显式和隐式建模。我们的 EIDP 采用双路径结构，根据是否直接合并行为表示，区分显式建模路径(EMP)和隐式建模路径(IMP)。EMP 以个性化行为集合过渡模式提取器(PBS-TPE)为核心组件。它将行为表示与项目和位置耦合起来，以便在一个细粒度的行为集内探索行为内动态。IMP 利用轻型多头自我注意块(L-MSAB)作为特定行为类型下的编码器。然后通过交叉行为注意融合(CBAF)对所获得的多视图表示进行聚合，利用下一个时间步骤的行为集作为指导，从行为层面提取协作语义。在两个实际数据集上的大量实验证明了我们的 EIDP 算法的有效性。我们在以下 https://github.com/oshinocsma/eidp 发布实现代码:。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Explicit+and+Implicit+Modeling+via+Dual-Path+Transformer+for+Behavior+Set-informed+Sequential+Recommendation)|0| +|[Explicit and Implicit Modeling via Dual-Path Transformer for Behavior Set-informed Sequential Recommendation](https://doi.org/10.1145/3637528.3671755)|Ming Chen, Weike Pan, Zhong Ming|; College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China; Shenzhen University & Shenzhen Technology University, Shenzhen, China|Sequential recommendation (SR) and multi-behavior sequential recommendation (MBSR) both come from real-world scenarios. Compared with SR, MBSR takes into account the dependencies of different behaviors. We find that most existing works on MBSR are studied in the context of e-commerce scenarios. In terms of the data format of the behavior types, we observe that the conventional label-formatted data carries limited information and is inadequate for scenarios like social media. With this observation, we introducebehavior set and extend MBSR to behavior set-informed sequential recommendation (BSSR). In BSSR, behavior dependencies become more complex and personalized, and user interest arousal may lack explicit contextual associations. To delve into the dynamics inhered within a behavior set and adaptively tailor recommendation lists upon its variability, we propose a novel solution called Explicit and Implicit modeling via Dual-Path Transformer (EIDP) for BSSR. Our EIDP adopts a dual-path architecture, distinguishing between explicit modeling path (EMP) and implicit modeling path (IMP) based on whether to directly incorporate the behavior representations. EMP features the personalized behavior set-wise transition pattern extractor (PBS-TPE) as its core component. It couples behavioral representations with both the items and positions to explore intra-behavior dynamics within a behavior set at a fine granularity. IMP utilizes light multi-head self-attention blocks (L-MSAB) as encoders under specific behavior types. The obtained multi-view representations are then aggregated by cross-behavior attention fusion (CBAF), using the behavior set of the next time step as a guidance to extract collaborative semantics at the behavioral level. Extensive experiments on two real-world datasets demonstrate the effectiveness of our EIDP. We release the implementation code at: https://github.com/OshiNoCSMA/EIDP.|序贯推荐(SR)和多行为序贯推荐(MBSR)都来自于现实场景。与 SR 相比，MBSR 考虑了不同行为的依赖性。我们发现现有的大多数 MBSR 的研究工作都是在电子商务环境下进行的。就行为类型的数据格式而言，我们观察到传统的标签格式的数据携带有限的信息，对于像社交媒体这样的场景来说是不够的。在此基础上，我们引入了行为集合，并将 MBSR 扩展到行为集合知情序列推荐(BSSR)。在 BSSR，行为依赖变得更加复杂和个性化，用户兴趣唤起可能缺乏明确的上下文关联。为了深入研究行为集内部的动态性，并根据其可变性自适应调整推荐列表，我们提出了一种新的解决方案，即通过双路径转换器(EIDP)对 BSSR 进行显式和隐式建模。我们的 EIDP 采用双路径结构，根据是否直接合并行为表示，区分显式建模路径(EMP)和隐式建模路径(IMP)。EMP 以个性化行为集合过渡模式提取器(PBS-TPE)为核心组件。它将行为表示与项目和位置耦合起来，以便在一个细粒度的行为集内探索行为内动态。IMP 利用轻型多头自我注意块(L-MSAB)作为特定行为类型下的编码器。然后通过交叉行为注意融合(CBAF)对所获得的多视图表示进行聚合，利用下一个时间步骤的行为集作为指导，从行为层面提取协作语义。在两个实际数据集上的大量实验证明了我们的 EIDP 算法的有效性。我们在以下 https://github.com/oshinocsma/eidp 发布实现代码:。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Explicit+and+Implicit+Modeling+via+Dual-Path+Transformer+for+Behavior+Set-informed+Sequential+Recommendation)|0| |[Disentangled Multi-interest Representation Learning for Sequential Recommendation](https://doi.org/10.1145/3637528.3671800)|Yingpeng Du, Ziyan Wang, Zhu Sun, Yining Ma, Hongzhi Liu, Jie Zhang|Singapore University of Technology and Design, Singapore, Singapore; Peking University, Beijing, China; Nanyang Technological University, Singapore, Singapore|Recently, much effort has been devoted to modeling users' multi-interests (aka multi-faceted preferences) based on their behaviors, aiming to accurately capture users' complex preferences. Existing methods attempt to model each interest of users through a distinct representation, but these multi-interest representations easily collapse into similar ones due to a lack of effective guidance. In this paper, we propose a generic multi-interest method for sequential recommendation, achieving disentangled representation learning of diverse interests technically and theoretically. To alleviate the collapse issue of multi-interests, we propose to conduct item partition guided by their likelihood of being co-purchased in a global view. It can encourage items in each group to focus on a discriminated interest, thus achieving effective disentangled learning of multi-interests. Specifically, we first prove the theoretical connection between item partition and spectral clustering, demonstrating its effectiveness in alleviating item-level and facet-level collapse issues that hinder existing disentangled methods. To efficiently optimize this problem, we then propose a Markov Random Field (MRF)-based method that samples small-scale sub-graphs from two separate MRFs, thus it can be approximated with a cross-entropy loss and optimized through contrastive learning. Finally, we perform multi-task learning to seamlessly align item partition learning with multi-interest modeling for more accurate recommendation. Experiments on three real-world datasets show that our method significantly outperforms state-of-the-art methods and can flexibly integrate with existing multi-interest models as a plugin to enhance their performances.|近年来，人们致力于根据用户的行为建立用户的多重兴趣(即多方面偏好)模型，以准确捕捉用户的复杂偏好。现有的方法试图通过一个不同的表示来对用户的每个兴趣进行建模，但是由于缺乏有效的指导，这些多兴趣表示很容易崩溃成为相似的表示。本文提出了一种通用的多兴趣序列推荐方法，从理论和技术上实现了不同兴趣的分离表示学习。为了缓解多重利益的崩溃问题，我们提出在全局视角下，以多重利益共同购买的可能性为指导进行项目分割。它可以鼓励项目在每个组集中在一个歧视性的兴趣，从而实现有效的多兴趣分离学习。具体来说，我们首先证明了项目划分和 SVD 之间的理论联系，证明了它在缓解阻碍现有分离方法的项目级和面级崩溃问题方面的有效性。为了有效地优化这个问题，我们提出了一个基于马尔可夫网络(MRF)的方法，从两个不同的 MRF 中抽取小规模子图，因此它可以用交叉熵损失近似，并通过对比学习进行优化。最后，我们进行多任务学习，使项目划分学习与多兴趣模型无缝对齐，以获得更准确的推荐。在三个实际数据集上的实验表明，该方法的性能明显优于最新的方法，并且可以灵活地与现有的多兴趣模型集成，作为一个插件来提高它们的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Disentangled+Multi-interest+Representation+Learning+for+Sequential+Recommendation)|0| |[Continual Collaborative Distillation for Recommender System](https://doi.org/10.1145/3637528.3671924)|Gyuseok Lee, SeongKu Kang, Wonbin Kweon, Hwanjo Yu|University of Illinois Urbana-Champaign, Champaign, Illinois, USA; Pohang University of Science and Technology, Pohang, Gyeongsangbuk-do, Republic of Korea|Knowledge distillation (KD) has emerged as a promising technique foraddressing the computational challenges associated with deploying large-scalerecommender systems. KD transfers the knowledge of a massive teacher system toa compact student model, to reduce the huge computational burdens for inferencewhile retaining high accuracy. The existing KD studies primarily focus onone-time distillation in static environments, leaving a substantial gap intheir applicability to real-world scenarios dealing with continuously incomingusers, items, and their interactions. In this work, we delve into a systematicapproach to operating the teacher-student KD in a non-stationary data stream.Our goal is to enable efficient deployment through a compact student, whichpreserves the high performance of the massive teacher, while effectivelyadapting to continuously incoming data. We propose Continual CollaborativeDistillation (CCD) framework, where both the teacher and the studentcontinually and collaboratively evolve along the data stream. CCD facilitatesthe student in effectively adapting to new data, while also enabling theteacher to fully leverage accumulated knowledge. We validate the effectivenessof CCD through extensive quantitative, ablative, and exploratory experiments ontwo real-world datasets. We expect this research direction to contribute tonarrowing the gap between existing KD studies and practical applications,thereby enhancing the applicability of KD in real-world systems.|知识精馏(KD)已经成为解决部署大规模推荐系统所面临的计算挑战的一种有前途的技术。KD 将大规模教师系统的知识转移到一个紧凑的学生模型，以减少推理的巨大计算负担，同时保持高精度。现有的 KD 研究主要集中在静态环境中的一次性提取，在处理不断进入的用户、项目及其交互的实际场景的适用性方面留下了很大的空白。在这项工作中，我们深入探讨了一个系统的方法来运行师生知识发展在一个非平稳的数据流。我们的目标是通过一个紧凑的学生实现有效的部署，这样可以保持大规模教师的高性能，同时有效地适应不断传入的数据。我们提出了持续协作蒸馏(CCD)框架，在这个框架中，教师和学生沿着数据流不断地、协作地发展。CCD 帮助学生有效地适应新的数据，同时也使教师能够充分利用积累的知识。我们通过在两个实际数据集上进行广泛的定量、消融和探索性实验，验证了 CCD 的有效性。我们期望这一研究方向能够缩小现有 KD 研究与实际应用之间的差距，从而提高 KD 在现实世界系统中的适用性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Continual+Collaborative+Distillation+for+Recommender+System)|0| |[Mitigating Negative Transfer in Cross-Domain Recommendation via Knowledge Transferability Enhancement](https://doi.org/10.1145/3637528.3671799)|Zijian Song, Wenhan Zhang, Lifang Deng, Jiandong Zhang, Zhihua Wu, Kaigui Bian, Bin Cui|; Lazada Group, Beijing, China|Cross-Domain Recommendation (CDR) is a promising technique to alleviate data sparsity by transferring knowledge across domains. However, the negative transfer issue in the presence of numerous domains has received limited attention. Most existing methods transfer all information from source domains to the target domain without distinction. This introduces harmful noise and irrelevant features, resulting in suboptimal performance. Although some methods decompose user features into domain-specific and domain-shared components, they fail to consider other causes of negative transfer. Worse still, we argue that simple feature decomposition is insufficient for multi-domain scenarios. To bridge this gap, we propose TrineCDR, the TRIple-level kNowledge transferability Enhanced model for multi-target CDR. Unlike previous methods, TrineCDR captures single domain and targeted cross-domain embeddings to serve multi-domain recommendation. For the latter, we identify three fundamental causes of negative transfer, ranging from micro to macro perspectives, and correspondingly enhance knowledge transferability at three different levels: the feature level, the interaction level, and the domain level. Through these efforts, TrineCDR effectively filters out noise and irrelevant information from source domains, leading to more comprehensive and accurate representations in the target domain. We extensively evaluate the proposed model on real-world datasets, sampled from Amazon and Douban, under both dual-target and multi-target scenarios. The experimental results demonstrate the superiority of TrineCDR over state-of-the-art cross-domain recommendation methods.|跨域推荐(CDR)是一种通过跨域传输知识来缓解数据稀疏性的有前途的技术。然而，在众多领域存在的负迁移问题受到的关注有限。大多数现有的方法不加区别地将所有信息从源域传输到目标域。这会引入有害的噪音和不相关的特征，导致性能不理想。尽管有些方法将用户特性分解为特定于领域和共享领域的组件，但它们没有考虑到负迁移的其他原因。更糟糕的是，我们认为简单的特征分解对于多领域场景是不够的。为了弥补这一差距，我们提出了 TrineCDR，一种针对多目标 CDR 的 TRIple-level 知识可转移性增强模型。与以前的方法不同，TrineCDR 捕获单个域和目标跨域嵌入，以服务于多域推荐。对于后者，我们从微观到宏观的角度找出了负迁移的三个基本原因，并相应地在三个不同的层面上提高了知识的可迁移性: 特征层面、交互层面和领域层面。通过这些努力，TrineCDR 有效地过滤掉源域中的噪声和不相关信息，从而在目标域中实现更全面、更准确的表示。在双目标和多目标情景下，我们对亚马逊和豆瓣采样的真实世界数据集上提出的模型进行了广泛的评估。实验结果表明 TrineCDR 方法优于目前最先进的跨域推荐方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Mitigating+Negative+Transfer+in+Cross-Domain+Recommendation+via+Knowledge+Transferability+Enhancement)|0| -|[Controllable Multi-Behavior Recommendation for In-Game Skins with Large Sequential Model](https://doi.org/10.1145/3637528.3671572)|Yanjie Gou, Yuanzhou Yao, Zhao Zhang, Yiqing Wu, Yi Hu, Fuzhen Zhuang, Jiangming Liu, Yongjun Xu|; School of Information Science and Engineering, Yunnan University, Kunming, China; Common Data Platform, Tencent, Shenzhen, China; Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China; Institute of Artificial Intelligence, Beihang University & Zhongguancun Laboratory, Beijing, China|Online games often house virtual shops where players can acquire character skins. Our task is centered on tailoring skin recommendations across diverse scenarios by analyzing historical interactions such as clicks, usage, and purchases. Traditional multi-behavior recommendation models employed for this task are limited. They either only predict skins based on a single type of behavior or merely recommend skins for target behavior type/task. These models lack the ability to control predictions of skins that are associated with different scenarios and behaviors. To overcome these limitations, we utilize the pretraining capabilities of Large Sequential Models (LSMs) coupled with a novel stimulus prompt mechanism and build a controllable multi-behavior recommendation (CMBR) model. In our approach, the pretraining ability is used to encapsulate users' multi-behavioral sequences into the representation of users' general interests. Subsequently, our designed stimulus prompt mechanism stimulates the model to extract scenario-related interests, thus generating potential skin purchases (or clicks and other interactions) for users. To the best of our knowledge, this is the first work to provide controlled multi-behavior recommendations, and also the first to apply the pretraining capabilities of LSMs in game domain. Through offline experiments and online A/B tests, we validate our method significantly outperforms baseline models, exhibiting about a tenfold improvement on various metrics during the offline test.|在线游戏通常会有虚拟商店，玩家可以在那里获得角色皮肤。我们的任务是通过分析点击、使用和购买等历史交互，跨不同场景裁剪皮肤推荐。传统的用于此任务的多行为推荐模型是有限的。它们要么只根据单一类型的行为预测皮肤，要么只为目标行为类型/任务推荐皮肤。这些模型缺乏控制与不同场景和行为相关联的皮肤预测的能力。为了克服这些局限性，我们利用大序列模型(LSM)的预训练能力，结合一种新颖的刺激提示机制，建立了一个可控的多行为推荐(CMBR)模型。该方法利用预训练能力将用户的多行为序列封装成用户兴趣的表示。随后，我们设计的刺激提示机制刺激模型以提取场景相关的兴趣，从而为用户产生潜在的皮肤购买(或点击和其他交互)。据我们所知，这是第一个提供可控的多行为建议的工作，也是第一个应用在游戏领域的 LSM 的预训练能力。通过离线实验和在线 A/B 测试，我们验证了我们的方法明显优于基线模型，在离线测试期间在各种指标上表现出大约10倍的改进。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Controllable+Multi-Behavior+Recommendation+for+In-Game+Skins+with+Large+Sequential+Model)|0| +|[Controllable Multi-Behavior Recommendation for In-Game Skins with Large Sequential Model](https://doi.org/10.1145/3637528.3671572)|Yanjie Gou, Yuanzhou Yao, Zhao Zhang, Yiqing Wu, Yi Hu, Fuzhen Zhuang, Jiangming Liu, Yongjun Xu|; School of Information Science and Engineering, Yunnan University, Kunming, China; Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China; Institute of Artificial Intelligence, Beihang University & Zhongguancun Laboratory, Beijing, China; Common Data Platform, Tencent, Shenzhen, China|Online games often house virtual shops where players can acquire character skins. Our task is centered on tailoring skin recommendations across diverse scenarios by analyzing historical interactions such as clicks, usage, and purchases. Traditional multi-behavior recommendation models employed for this task are limited. They either only predict skins based on a single type of behavior or merely recommend skins for target behavior type/task. These models lack the ability to control predictions of skins that are associated with different scenarios and behaviors. To overcome these limitations, we utilize the pretraining capabilities of Large Sequential Models (LSMs) coupled with a novel stimulus prompt mechanism and build a controllable multi-behavior recommendation (CMBR) model. In our approach, the pretraining ability is used to encapsulate users' multi-behavioral sequences into the representation of users' general interests. Subsequently, our designed stimulus prompt mechanism stimulates the model to extract scenario-related interests, thus generating potential skin purchases (or clicks and other interactions) for users. To the best of our knowledge, this is the first work to provide controlled multi-behavior recommendations, and also the first to apply the pretraining capabilities of LSMs in game domain. Through offline experiments and online A/B tests, we validate our method significantly outperforms baseline models, exhibiting about a tenfold improvement on various metrics during the offline test.|在线游戏通常会有虚拟商店，玩家可以在那里获得角色皮肤。我们的任务是通过分析点击、使用和购买等历史交互，跨不同场景裁剪皮肤推荐。传统的用于此任务的多行为推荐模型是有限的。它们要么只根据单一类型的行为预测皮肤，要么只为目标行为类型/任务推荐皮肤。这些模型缺乏控制与不同场景和行为相关联的皮肤预测的能力。为了克服这些局限性，我们利用大序列模型(LSM)的预训练能力，结合一种新颖的刺激提示机制，建立了一个可控的多行为推荐(CMBR)模型。该方法利用预训练能力将用户的多行为序列封装成用户兴趣的表示。随后，我们设计的刺激提示机制刺激模型以提取场景相关的兴趣，从而为用户产生潜在的皮肤购买(或点击和其他交互)。据我们所知，这是第一个提供可控的多行为建议的工作，也是第一个应用在游戏领域的 LSM 的预训练能力。通过离线实验和在线 A/B 测试，我们验证了我们的方法明显优于基线模型，在离线测试期间在各种指标上表现出大约10倍的改进。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Controllable+Multi-Behavior+Recommendation+for+In-Game+Skins+with+Large+Sequential+Model)|0| |[Multi-objective Learning to Rank by Model Distillation](https://doi.org/10.1145/3637528.3671597)|Jie Tang, Huiji Gao, Liwei He, Sanjeev Katariya|Airbnb, San Francisco, CA, USA|In online marketplaces, search ranking's objective is not only to purchase or conversion (primary objective), but to also the purchase outcomes(secondary objectives), e.g. order cancellation(or return), review rating, customer service inquiries, platform long term growth. Multi-objective learning to rank has been widely studied to balance primary and secondary objectives. But traditional approaches in industry face some challenges including expensive parameter tuning leads to sub-optimal solution, suffering from imbalanced data sparsity issue, and being not compatible with ad-hoc objective. In this paper, we propose a distillation-based ranking solution for multi-objective ranking, which optimizes the end-to-end ranking system at Airbnb across multiple ranking models on different objectives along with various considerations to optimize training and serving efficiency to meet industry standards. We found it performs much better than traditional approaches, it doesn't only significantly increases primary objective by a large margin but also meet secondary objectives constraints and improve model stability. We also demonstrated the proposed system could be further simplified by model self-distillation. Besides this, we did additional simulations to show that this approach could also help us efficiently inject ad-hoc non-differentiable business objective into the ranking system while enabling us to balance our optimization objectives.|在在线市场中，搜索排名的目标不仅仅是购买或转换(主要目标) ，还包括购买结果(次要目标) ，例如取消订单(或返回) ，评论等级，客户服务查询，平台长期增长。多目标排序学习已被广泛研究，以平衡小学和中学的目标。但是，传统的方法在工业上面临着一些挑战，包括参数调整费用昂贵、数据稀疏性不平衡、与特定目标不兼容等问题。针对多目标排序问题，提出了一种基于精馏的排序方法，该方法在不同目标的多个排序模型上对 Airbnb 的端到端排序系统进行优化，同时考虑各种因素，优化培训和服务效率，以满足行业标准。我们发现它比传统的方法有更好的性能，它不仅大幅度增加了主要目标，而且满足次要目标的约束，提高了模型的稳定性。我们还证明了模型自蒸馏可以进一步简化所提出的体系。除此之外，我们做了额外的模拟，以表明这种方法也可以帮助我们有效地注入临时不可微的业务目标到排名系统，同时使我们能够平衡我们的优化目标。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Multi-objective+Learning+to+Rank+by+Model+Distillation)|0| -|[Unified Dual-Intent Translation for Joint Modeling of Search and Recommendation](https://doi.org/10.1145/3637528.3671519)|Yuting Zhang, Yiqing Wu, Ruidong Han, Ying Sun, Yongchun Zhu, Xiang Li, Wei Lin, Fuzhen Zhuang, Zhulin An, Yongjun Xu|; Meituan, Beijing, China; Institute of Artificial Intelligence, Beihang University & Zhongguancun Laboratory, Beijing, China; Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China|Recommendation systems, which assist users in discovering their preferred items among numerous options, have served billions of users across various online platforms. Intuitively, users' interactions with items are highly driven by their unchanging inherent intents (e.g., always preferring high-quality items) and changing demand intents (e.g., wanting a T-shirt in summer but a down jacket in winter). However, both types of intents are implicitly expressed in recommendation scenario, posing challenges in leveraging them for accurate intent-aware recommendations. Fortunately, in search scenario, often found alongside recommendation on the same online platform, users express their demand intents explicitly through their query words. Intuitively, in both scenarios, a user shares the same inherent intent and his/her interactions may be influenced by the same demand intent. It is therefore feasible to utilize the interaction data from both scenarios to reinforce the dual intents for joint intent-aware modeling. But the joint modeling should deal with two problems: (1) accurately modeling users' implicit demand intents in recommendation; (2) modeling the relation between the dual intents and the interactive items. To address these problems, we propose a novel model named Unified Dual-Intents Translation for joint modeling of Search and Recommendation (UDITSR). To accurately simulate users' demand intents in recommendation, we utilize real queries from search data as supervision information to guide its generation. To explicitly model the relation among the triplet , we propose a dual-intent translation propagation mechanism to learn the triplet in the same semantic space via embedding translations. Extensive experiments demonstrate that UDITSR outperforms SOTA baselines both in search and recommendation tasks. Moreover, our model has been deployed online on Meituan Waimai platform, leading to an average improvement in GMV (Gross Merchandise Value) of 1.46% and CTR(Click-Through Rate) of 0.77% over one month.|推荐系统帮助用户在众多选项中发现他们喜欢的项目，已经在各种在线平台上为数十亿用户服务。直觉上，用户与物品的互动高度受到他们不变的内在意图(例如，总是喜欢高质量的物品)和不断变化的需求意图(例如，夏天想要一件 T 恤，冬天想要一件羽绒服)的驱动。然而，这两种类型的意图都隐式地在推荐场景中表达，在利用它们获得准确的意图感知建议方面提出了挑战。幸运的是，在搜索场景中，用户通过他们的查询词明确地表达他们的需求意图，这种情况经常在同一个在线平台的推荐旁边发现。直观地说，在这两种情况下，用户共享相同的内在意图，他/她的交互可能受到相同的需求意图的影响。因此，利用来自两个场景的交互数据来加强联合意图感知建模的双重意图是可行的。但是联合建模需要解决两个问题: (1)准确地建立推荐中用户隐含需求意图的模型; (2)建立双重意图与交互项目之间的关系模型。针对这些问题，本文提出了一种新的联合建模模型——统一双意图翻译模型(UDITSR)。为了准确地模拟推荐中用户的需求意图，我们利用搜索数据中的实际查询作为监督信息来指导推荐的生成。为了明确地模拟三联体之间的关系，我们提出了一种双意图翻译传播机制，通过嵌入翻译来学习同一语义空间中的三联体。大量的实验表明，UDITSR 在搜索和推荐任务中都优于 SOTA 基线。此外，我们的模型已经在 Waimai 的美团平台上使用，导致一个月内平均商品总值(GMV)提高了1.46% ，点击率(点进率)提高了0.77% 。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Unified+Dual-Intent+Translation+for+Joint+Modeling+of+Search+and+Recommendation)|0| +|[Unified Dual-Intent Translation for Joint Modeling of Search and Recommendation](https://doi.org/10.1145/3637528.3671519)|Yuting Zhang, Yiqing Wu, Ruidong Han, Ying Sun, Yongchun Zhu, Xiang Li, Wei Lin, Fuzhen Zhuang, Zhulin An, Yongjun Xu|; Institute of Artificial Intelligence, Beihang University & Zhongguancun Laboratory, Beijing, China; Meituan, Beijing, China; Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China|Recommendation systems, which assist users in discovering their preferred items among numerous options, have served billions of users across various online platforms. Intuitively, users' interactions with items are highly driven by their unchanging inherent intents (e.g., always preferring high-quality items) and changing demand intents (e.g., wanting a T-shirt in summer but a down jacket in winter). However, both types of intents are implicitly expressed in recommendation scenario, posing challenges in leveraging them for accurate intent-aware recommendations. Fortunately, in search scenario, often found alongside recommendation on the same online platform, users express their demand intents explicitly through their query words. Intuitively, in both scenarios, a user shares the same inherent intent and his/her interactions may be influenced by the same demand intent. It is therefore feasible to utilize the interaction data from both scenarios to reinforce the dual intents for joint intent-aware modeling. But the joint modeling should deal with two problems: (1) accurately modeling users' implicit demand intents in recommendation; (2) modeling the relation between the dual intents and the interactive items. To address these problems, we propose a novel model named Unified Dual-Intents Translation for joint modeling of Search and Recommendation (UDITSR). To accurately simulate users' demand intents in recommendation, we utilize real queries from search data as supervision information to guide its generation. To explicitly model the relation among the triplet , we propose a dual-intent translation propagation mechanism to learn the triplet in the same semantic space via embedding translations. Extensive experiments demonstrate that UDITSR outperforms SOTA baselines both in search and recommendation tasks. Moreover, our model has been deployed online on Meituan Waimai platform, leading to an average improvement in GMV (Gross Merchandise Value) of 1.46% and CTR(Click-Through Rate) of 0.77% over one month.|推荐系统帮助用户在众多选项中发现他们喜欢的项目，已经在各种在线平台上为数十亿用户服务。直觉上，用户与物品的互动高度受到他们不变的内在意图(例如，总是喜欢高质量的物品)和不断变化的需求意图(例如，夏天想要一件 T 恤，冬天想要一件羽绒服)的驱动。然而，这两种类型的意图都隐式地在推荐场景中表达，在利用它们获得准确的意图感知建议方面提出了挑战。幸运的是，在搜索场景中，用户通过他们的查询词明确地表达他们的需求意图，这种情况经常在同一个在线平台的推荐旁边发现。直观地说，在这两种情况下，用户共享相同的内在意图，他/她的交互可能受到相同的需求意图的影响。因此，利用来自两个场景的交互数据来加强联合意图感知建模的双重意图是可行的。但是联合建模需要解决两个问题: (1)准确地建立推荐中用户隐含需求意图的模型; (2)建立双重意图与交互项目之间的关系模型。针对这些问题，本文提出了一种新的联合建模模型——统一双意图翻译模型(UDITSR)。为了准确地模拟推荐中用户的需求意图，我们利用搜索数据中的实际查询作为监督信息来指导推荐的生成。为了明确地模拟三联体之间的关系，我们提出了一种双意图翻译传播机制，通过嵌入翻译来学习同一语义空间中的三联体。大量的实验表明，UDITSR 在搜索和推荐任务中都优于 SOTA 基线。此外，我们的模型已经在 Waimai 的美团平台上使用，导致一个月内平均商品总值(GMV)提高了1.46% ，点击率(点进率)提高了0.77% 。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Unified+Dual-Intent+Translation+for+Joint+Modeling+of+Search+and+Recommendation)|0| |[Shopping Trajectory Representation Learning with Pre-training for E-commerce Customer Understanding and Recommendation](https://doi.org/10.1145/3637528.3671747)|Yankai Chen, QuocTuan Truong, Xin Shen, Jin Li, Irwin King|The Chinese University of Hong Kong, Hong Kong, China; Amazon, Seattle, WA, USA|Understanding customer behavior is crucial for improving service quality in large-scale E-commerce. This paper proposes C-STAR, a new framework that learns compact representations from customer shopping journeys, with good versatility to fuel multiple downstream customer-centric tasks. We define the notion of shopping trajectory that encompasses customer interactions at the level of product categories, capturing the overall flow of their browsing and purchase activities. C-STAR excels at modeling both inter-trajectory distribution similarity-the structural similarities between different trajectories, and intra-trajectory semantic correlation-the semantic relationships within individual ones. This coarse-to-fine approach ensures informative trajectory embeddings for representing customers. To enhance embedding quality, we introduce a pre-training strategy that captures two intrinsic properties within the pre-training data. Extensive evaluation on large-scale industrial and public datasets demonstrates the effectiveness of C-STAR across three diverse customer-centric tasks. These tasks empower customer profiling and recommendation services for enhancing personalized shopping experiences on our E-commerce platform.|了解顾客行为是提高大规模电子商务服务质量的关键。本文提出了一个新的框架 C-STAR，它从顾客购物旅程中学习紧凑的表示，具有良好的通用性，可以为多个下游顾客中心任务提供支持。我们定义了购物轨迹的概念，它包含了产品类别层面的客户交互，捕捉了他们浏览和购买活动的总体流程。C-STAR 在建立轨迹间分布相似性(不同轨迹之间的结构相似性)和轨迹内语义相关性(各个轨迹之间的语义关系)两方面都表现出色。这种从粗到细的方法确保为代表客户嵌入信息轨迹。为了提高嵌入质量，我们引入了一种预训练策略，捕获预训练数据的两个内在属性。对大规模工业和公共数据集的广泛评估证明了 C-STAR 在三种不同的以客户为中心的任务中的有效性。这些任务授权客户档案和推荐服务，以提高我们的电子商务平台上的个性化购物体验。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Shopping+Trajectory+Representation+Learning+with+Pre-training+for+E-commerce+Customer+Understanding+and+Recommendation)|0| |[DIET: Customized Slimming for Incompatible Networks in Sequential Recommendation](https://doi.org/10.1145/3637528.3671669)|Kairui Fu, Shengyu Zhang, Zheqi Lv, Jingyuan Chen, Jiwei Li|Zhejiang University & Shanghai Institute for Advanced Study of Zhejiang University, Hangzhou, China; Zhejiang University, Hangzhou, China|Due to the continuously improving capabilities of mobile edges, recommendersystems start to deploy models on edges to alleviate network congestion causedby frequent mobile requests. Several studies have leveraged the proximity ofedge-side to real-time data, fine-tuning them to create edge-specific models.Despite their significant progress, these methods require substantial on-edgecomputational resources and frequent network transfers to keep the model up todate. The former may disrupt other processes on the edge to acquirecomputational resources, while the latter consumes network bandwidth, leadingto a decrease in user satisfaction. In response to these challenges, we proposea customizeD slImming framework for incompatiblE neTworks(DIET). DIET deploysthe same generic backbone (potentially incompatible for a specific edge) to alldevices. To minimize frequent bandwidth usage and storage consumption inpersonalization, DIET tailors specific subnets for each edge based on its pastinteractions, learning to generate slimming subnets(diets) within incompatiblenetworks for efficient transfer. It also takes the inter-layer relationshipsinto account, empirically reducing inference time while obtaining more suitablediets. We further explore the repeated modules within networks and propose amore storage-efficient framework, DIETING, which utilizes a single layer ofparameters to represent the entire network, achieving comparably excellentperformance. The experiments across four state-of-the-art datasets and twowidely used models demonstrate the superior accuracy in recommendation andefficiency in transmission and storage of our framework.|由于移动边缘功能的不断改进，推荐系统开始在边缘部署模型，以缓解频繁的移动请求造成的拥塞控制。一些研究已经利用边缘接近实时数据，微调它们以创建边缘特定的模型。尽管这些方法取得了显著的进展，但它们需要大量的边缘计算资源和频繁的网络传输来保持模型的最新性。前者可能破坏边缘的其他进程以获取计算资源，而后者消耗网络带宽，导致用户满意度下降。为了应对这些挑战，我们提出了针对不兼容网络(DIET)的定制减肥框架。DIET 为所有设备部署相同的通用主干网(对于特定的边缘可能不兼容)。为了尽量减少频繁的带宽使用和个性化存储消耗，DIET 根据其过去的相互作用为每个边裁剪特定的子网，学习在不兼容的网络中生成减肥子网(饮食)以便有效地传输。它还考虑到层间关系，通过实验减少推断时间，同时获得更合适的饮食。我们进一步探讨了网络中的重复模块，并提出了一种存储效率更高的框架—— DIETING，它利用一个单一的参数层来表示整个网络，取得了较好的性能。通过四个最先进的数据集和两个广泛使用的模型进行的实验表明，我们的框架在传输和存储方面具有优越的推荐准确性和效率。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=DIET:+Customized+Slimming+for+Incompatible+Networks+in+Sequential+Recommendation)|0| |[Large Language Models meet Collaborative Filtering: An Efficient All-round LLM-based Recommender System](https://doi.org/10.1145/3637528.3671931)|Sein Kim, Hongseok Kang, Seungyoon Choi, Donghyun Kim, MinChul Yang, Chanyoung Park|KAIST, Daejeon, Republic of Korea; NAVER Corporation, Seongnam, Republic of Korea|Collaborative filtering recommender systems (CF-RecSys) have shown successiveresults in enhancing the user experience on social media and e-commerceplatforms. However, as CF-RecSys struggles under cold scenarios with sparseuser-item interactions, recent strategies have focused on leveraging modalityinformation of user/items (e.g., text or images) based on pre-trained modalityencoders and Large Language Models (LLMs). Despite their effectiveness undercold scenarios, we observe that they underperform simple traditionalcollaborative filtering models under warm scenarios due to the lack ofcollaborative knowledge. In this work, we propose an efficient All-roundLLM-based Recommender system, called A-LLMRec, that excels not only in the coldscenario but also in the warm scenario. Our main idea is to enable an LLM todirectly leverage the collaborative knowledge contained in a pre-trainedstate-of-the-art CF-RecSys so that the emergent ability of the LLM as well asthe high-quality user/item embeddings that are already trained by thestate-of-the-art CF-RecSys can be jointly exploited. This approach yields twoadvantages: (1) model-agnostic, allowing for integration with various existingCF-RecSys, and (2) efficiency, eliminating the extensive fine-tuning typicallyrequired for LLM-based recommenders. Our extensive experiments on variousreal-world datasets demonstrate the superiority of A-LLMRec in variousscenarios, including cold/warm, few-shot, cold user, and cross-domainscenarios. Beyond the recommendation task, we also show the potential ofA-LLMRec in generating natural language outputs based on the understanding ofthe collaborative knowledge by performing a favorite genre prediction task. Ourcode is available at https://github.com/ghdtjr/A-LLMRec .|协同过滤推荐系统(CF-recsys)在增强社交媒体和电子商务平台的用户体验方面取得了成功。然而，由于 CF-RecSys 在冷场景下与稀疏用户-项目交互的斗争，最近的策略集中在基于预先训练的 modalityencoders 和 Large Language Model (LLM)利用用户/项目(例如文本或图像)的模态信息。尽管它们在低温情景下有效，但是我们观察到，由于缺乏协作知识，它们在温暖情景下表现不如传统的简单协作过滤模型。在这项工作中，我们提出了一个高效的基于全局 LLM 的推荐系统，称为 A-LLmrec，它不仅在冷场景中表现出色，而且在暖场景中也表现出色。我们的主要想法是使 LLM 能够直接利用包含在预先培训的最先进的 CF-RecSys 中的协作知识，以便 LLM 的应急能力以及已经由最先进的 CF-RecSys 培训的高质量用户/项目嵌入能够被共同利用。这种方法有两个优点: (1)模型无关，允许与各种现有的 CF-RecSys 集成; (2)效率，消除了基于 LLM 的推荐程序通常需要的广泛的微调。我们在各种真实世界数据集上的广泛实验证明了 A-LLMRec 在各种场景下的优越性，包括冷/温、少拍摄、冷用户和跨域场景。除了推荐任务，我们还展示了 A-LLMRec 的潜力，通过执行一个喜欢的体裁预测任务，基于对协作知识的理解来生成自然语言输出。我们的代码可以在 https://github.com/ghdtjr/a-llmrec 下载。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Large+Language+Models+meet+Collaborative+Filtering:+An+Efficient+All-round+LLM-based+Recommender+System)|0| -|[Probabilistic Attention for Sequential Recommendation](https://doi.org/10.1145/3637528.3671733)|Yuli Liu, Christian Walder, Lexing Xie, Yiqun Liu|; Australian National University & Data61 CSIRO, Canberra, Australia; Google Research, Brain Team, Montreal, Canada|Sequential Recommendation (SR) navigates users' dynamic preferences through modeling their historical interactions. The incorporation of the popular Transformer framework, which captures long relationships through pairwise dot products, has notably benefited SR. However, prevailing research in this domain faces three significant challenges: (i) Existing studies directly adopt the primary component of Transformer (i.e., the self-attention mechanism), without a clear explanation or tailored definition for its specific role in SR; (ii) The predominant focus on pairwise computations overlooks the global context or relative prevalence of item pairs within the overall sequence; (iii) Transformer primarily pursues relevance-dominated relationships, neglecting another essential objective in recommendation, i.e., diversity. In response, this work introduces a fresh perspective to elucidate the attention mechanism in SR. Here, attention is defined as dependency interactions among items, quantitatively determined under a global probabilistic model by observing the probabilities of corresponding item subsets. This viewpoint offers a precise and context-specific definition of attention, leading to the design of a distinctive attention mechanism tailored for SR. Specifically, we transmute the well-formulated global, repulsive interactions in Determinantal Point Processes (DPPs) to effectively model dependency interactions. Guided by the repulsive interactions, a theoretically and practically feasible DPP kernel is designed, enabling our attention mechanism to directly consider category/topic distribution for enhancing diversity. Consequently, the Probabilistic Attention mechanism (PAtt) for sequential recommendation is developed. Experimental results demonstrate the excellent scalability and adaptability of our attention mechanism, which significantly improves recommendation performance in terms of both relevance and diversity.|顺序推荐(SR)通过建模用户的历史交互来导航用户的动态偏好。通过成对点产品捕获长关系的流行的 Transformer 框架的结合，使 SR 受益匪浅。然而，这个领域的主流研究面临三个重大挑战: (i)现有的研究直接采用 Transformer 的主要组成部分(即自我注意机制) ，没有明确的解释或量身定制的定义其在 SR 中的具体作用; (ii)成对计算的主要重点忽视了整体序列中项目对的全局上下文或相对流行程度; (iii) Transformer 主要追求相关性主导的关系，忽视了推荐中的另一个基本目标，即多样性。作为回应，本文引入了一个新的视角来阐明注意机制。这里，注意被定义为项目之间的依赖交互作用，在全局概率模型下通过观察相应项目子集的概率来定量确定。这一观点提供了一个精确的和具体的上下文关注的定义，导致了一个独特的注意机制的设计专门为 SR。具体来说，我们转换了确定性点过程(DPP)中的良好制定的全局排斥交互作用，以有效地建模依赖交互作用。在排斥交互作用的指导下，设计了一个理论上和实际上可行的 DPP 核，使我们的注意机制能够直接考虑类别/主题分布，从而增强多样性。为此，提出了序贯推荐的概率注意机制。实验结果表明，该注意机制具有良好的可扩展性和适应性，在相关性和多样性方面显著提高了推荐性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Probabilistic+Attention+for+Sequential+Recommendation)|0| +|[Probabilistic Attention for Sequential Recommendation](https://doi.org/10.1145/3637528.3671733)|Yuli Liu, Christian Walder, Lexing Xie, Yiqun Liu|; Google Research, Brain Team, Montreal, Canada; Australian National University & Data61 CSIRO, Canberra, Australia|Sequential Recommendation (SR) navigates users' dynamic preferences through modeling their historical interactions. The incorporation of the popular Transformer framework, which captures long relationships through pairwise dot products, has notably benefited SR. However, prevailing research in this domain faces three significant challenges: (i) Existing studies directly adopt the primary component of Transformer (i.e., the self-attention mechanism), without a clear explanation or tailored definition for its specific role in SR; (ii) The predominant focus on pairwise computations overlooks the global context or relative prevalence of item pairs within the overall sequence; (iii) Transformer primarily pursues relevance-dominated relationships, neglecting another essential objective in recommendation, i.e., diversity. In response, this work introduces a fresh perspective to elucidate the attention mechanism in SR. Here, attention is defined as dependency interactions among items, quantitatively determined under a global probabilistic model by observing the probabilities of corresponding item subsets. This viewpoint offers a precise and context-specific definition of attention, leading to the design of a distinctive attention mechanism tailored for SR. Specifically, we transmute the well-formulated global, repulsive interactions in Determinantal Point Processes (DPPs) to effectively model dependency interactions. Guided by the repulsive interactions, a theoretically and practically feasible DPP kernel is designed, enabling our attention mechanism to directly consider category/topic distribution for enhancing diversity. Consequently, the Probabilistic Attention mechanism (PAtt) for sequential recommendation is developed. Experimental results demonstrate the excellent scalability and adaptability of our attention mechanism, which significantly improves recommendation performance in terms of both relevance and diversity.|顺序推荐(SR)通过建模用户的历史交互来导航用户的动态偏好。通过成对点产品捕获长关系的流行的 Transformer 框架的结合，使 SR 受益匪浅。然而，这个领域的主流研究面临三个重大挑战: (i)现有的研究直接采用 Transformer 的主要组成部分(即自我注意机制) ，没有明确的解释或量身定制的定义其在 SR 中的具体作用; (ii)成对计算的主要重点忽视了整体序列中项目对的全局上下文或相对流行程度; (iii) Transformer 主要追求相关性主导的关系，忽视了推荐中的另一个基本目标，即多样性。作为回应，本文引入了一个新的视角来阐明注意机制。这里，注意被定义为项目之间的依赖交互作用，在全局概率模型下通过观察相应项目子集的概率来定量确定。这一观点提供了一个精确的和具体的上下文关注的定义，导致了一个独特的注意机制的设计专门为 SR。具体来说，我们转换了确定性点过程(DPP)中的良好制定的全局排斥交互作用，以有效地建模依赖交互作用。在排斥交互作用的指导下，设计了一个理论上和实际上可行的 DPP 核，使我们的注意机制能够直接考虑类别/主题分布，从而增强多样性。为此，提出了序贯推荐的概率注意机制。实验结果表明，该注意机制具有良好的可扩展性和适应性，在相关性和多样性方面显著提高了推荐性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Probabilistic+Attention+for+Sequential+Recommendation)|0| |[Diffusion-Based Cloud-Edge-Device Collaborative Learning for Next POI Recommendations](https://doi.org/10.1145/3637528.3671743)|Jing Long, Guanhua Ye, Tong Chen, Yang Wang, Meng Wang, Hongzhi Yin|The University of Queensland, Brisbane, Australia; Hefei University of Technology, Hefei, China; Beijing University of Posts and Telecommunications, BeiJing, China|The rapid expansion of Location-Based Social Networks (LBSNs) has highlightedthe importance of effective next Point-of-Interest (POI) recommendations, whichleverage historical check-in data to predict users' next POIs to visit.Traditional centralized deep neural networks (DNNs) offer impressive POIrecommendation performance but face challenges due to privacy concerns andlimited timeliness. In response, on-device POI recommendations have beenintroduced, utilizing federated learning (FL) and decentralized approaches toensure privacy and recommendation timeliness. However, these methods oftensuffer from computational strain on devices and struggle to adapt to new usersand regions. This paper introduces a novel collaborative learning framework,Diffusion-Based Cloud-Edge-Device Collaborative Learning for Next POIRecommendations (DCPR), leveraging the diffusion model known for its successacross various domains. DCPR operates with a cloud-edge-device architecture tooffer region-specific and highly personalized POI recommendations whilereducing on-device computational burdens. DCPR minimizes on-devicecomputational demands through a unique blend of global and local learningprocesses. Our evaluation with two real-world datasets demonstrates DCPR'ssuperior performance in recommendation accuracy, efficiency, and adaptabilityto new users and regions, marking a significant step forward in on-device POIrecommendation technology.|基于位置的社交网络(LBSNs)的快速扩张突出了有效的下一个兴趣点(POI)建议的重要性，这些建议利用历史签入数据来预测用户下一个访问的 POI。传统的集中式深层神经网络(DNN)提供了令人印象深刻的 POI 推荐性能，但由于隐私问题和有限的时间面临挑战。作为回应，在设备上的 POI 推荐已经被引入，利用联邦学习(FL)和分散的方法来确保隐私和推荐的及时性。然而，这些方法经常受到设备计算压力的影响，难以适应新的用户和地区。本文介绍了一个新的合作学习框架，基于扩散的云端设备合作学习，用于下一个 POI 建议(DCPR) ，利用扩散模型在各个领域的成功。DCPR 采用云端设备架构，可以提供区域特定的高度个性化的 POI 建议，同时减少设备上的计算负担。DCPR 通过独特的全球和本地学习过程的混合，最大限度地减少了设备上的计算需求。我们用两个真实世界的数据集进行的评估表明，DCPR 在推荐准确性、效率以及对新用户和地区的适应性方面具有优越的性能，这标志着在设备 POI 推荐技术方面向前迈进了一大步。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Diffusion-Based+Cloud-Edge-Device+Collaborative+Learning+for+Next+POI+Recommendations)|0| -|[Certified Robustness on Visual Graph Matching via Searching Optimal Smoothing Range](https://doi.org/10.1145/3637528.3671852)|Huaqing Shao, Lanjun Wang, Yongwei Wang, Qibing Ren, Junchi Yan|School of AI and Department of CSE, Shanghai Jiao Tong University, Shanghai, China; SNMC, Tianjin University, Tianjin, China; SIAS and College of Computer Science, Zhejiang University, Hangzhou, China; Department of CSE and MoE Key Lab of AI, Shanghai Jiao Tong University, Shanghai, China; Department of CSE, Shanghai Jiao Tong University, Shanghai, China|Deep visual graph matching (GM) is a challenging combinatorial task that involves finding a permutation matrix that indicates the correspondence between keypoints from a pair of images. Like many learning systems, empirical studies have shown that visual GM is susceptible to adversarial attacks, with reliability issues in downstream applications. To the best of our knowledge, certifying robustness for deep visual GM remains an open challenge with two main difficulties: how to handle the paired inputs together with the heavily non-linear permutation output space (especially at large scale), and how to balance the trade-off between certified robustness and matching performance. Inspired by the randomized smoothing (RS) technique, we propose the Certified Robustness based on the Optimal Smoothing Range Search (CR-OSRS) technique to fulfill the robustness guarantee for deep visual GM. First, unlike conventional RS methods that use isotropic Gaussian distributions for smoothing, we build the smoothed model with paired joint Gaussian distributions, which capture the structural information among keypoints, and mitigate the performance degradation caused by smoothing. For the vast space of the permutation output, we devise a similarity-based partitioning method that can lower the computational complexity and certification difficulty. We then derive a stringent robustness guarantee that links the certified space of inputs to their corresponding fixed outputs. Second, we design a global optimization method to search for optimal joint Gaussian distributions and facilitate a larger certified space and better performance. Third, we apply data augmentation and a similarity-based regularizer in training to enhance smoothed model performance. Lastly, for the high-dimensional and multivariable nature of the certified space, we propose two methods (sampling and marginal radii) to evaluate it. Experimental results on public benchmarks show that our method achieves state-of-the-art certified robustness.|深度视觉图形匹配(GM)是一项具有挑战性的组合任务，包括从一对图像中找到一个指示关键点之间对应关系的置换矩阵。与许多学习系统一样，经验研究表明，视觉 GM 易受敌对攻击，在下游应用中存在可靠性问题。据我们所知，深度视觉 GM 的鲁棒性认证仍然是一个公开的挑战，有两个主要的困难: 如何处理配对输入和严重的非线性排列输出空间(特别是在大规模) ，以及如何平衡之间的权衡认证鲁棒性和匹配性能。受随机平滑(RS)技术的启发，我们提出了基于最优平滑范围搜索(CR-OSRS)技术的认证鲁棒性，以实现深层视觉 GM 的鲁棒性保证。首先，不同于传统的 RS 方法使用各向同性高斯分布进行平滑，我们建立了平滑模型与配对联合高斯分布，捕捉关键点之间的结构信息，并减轻平滑造成的性能下降。针对排列输出的巨大空间，提出了一种基于相似度的划分方法，降低了计算复杂度和认证难度。然后，我们推导出一个严格的鲁棒性保证，将经过验证的输入空间与它们相应的固定输出联系起来。其次，我们设计了一个全局优化方法来寻找最佳的联合高斯分布，使得更大的认证空间和更好的性能。第三，在训练中应用数据增强和基于相似性的正则化方法来提高平滑模型的性能。最后，针对证明空间的高维性和多变量性，提出了两种评价方法(抽样法和边缘半径法)。对公共基准测试的实验结果表明，该方法具有较好的鲁棒性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Certified+Robustness+on+Visual+Graph+Matching+via+Searching+Optimal+Smoothing+Range)|0| -|[Pre-Training with Transferable Attention for Addressing Market Shifts in Cross-Market Sequential Recommendation](https://doi.org/10.1145/3637528.3671698)|Chen Wang, Ziwei Fan, Liangwei Yang, Mingdai Yang, Xiaolong Liu, Zhiwei Liu, Philip S. Yu|Salesforce AI Research, Palo Alto, CA, USA; The University of Chicago, Chicago, IL, USA; Tsinghua University, Beijing, China; Amazon, Santa Clara, CA, USA; University of Illinois Chicago, Chicago, IL, USA|Cross-market recommendation (CMR) involves selling the same set of items across multiple nations or regions within a transfer learning framework. However, CMR's distinctive characteristics, including limited data sharing due to privacy policies, absence of user overlap, and a shared item set between markets present challenges for traditional recommendation methods. Moreover, CMR experiences market shifts, leading to differences in item popularity and user preferences among different markets. This study focuses on cross-market sequential recommendation (CMSR) and proposes the Cross-market Attention Transferring with Sequential Recommendation (CAT-SR) framework to address these challenges and market shifts. CAT-SR incorporates a pre-training strategy emphasizing item-item correlation, selective self-attention transferring for effective transfer learning, and query and key adapters for market-specific user preferences. Experimental results on real-world cross-market datasets demonstrate the superiority of CAT-SR, and ablation studies validate the benefits of its components across different geographical continents. CAT-SR offers a robust and adaptable solution for cross-market sequential recommendation. The code is available at https://github.com/ChenMetanoia/CATSR-KDD/.|跨市场推荐(CMR)涉及在一个迁移学习框架内在多个国家或地区销售同一套产品。然而，CMR 的显著特点，包括由于隐私政策导致的有限数据共享、用户重叠的缺失以及市场之间的共享项目集，对传统的推荐方法提出了挑战。此外，CMR 经历了市场变化，导致不同市场之间的产品流行度和用户偏好的差异。本研究以跨市场序贯推荐(CMSR)为研究对象，提出了基于序贯推荐的跨市场注意力转移(CAT-SR)框架，以解决这些挑战和市场转移问题。CAT-SR 包括一个强调项目-项目相关性的预训练策略，选择性自我注意转移以有效转移学习，以及针对特定市场用户偏好的查询和关键适配器。在现实世界跨市场数据集上的实验结果证明了 CAT-SR 的优越性，并且消融研究验证了其组件在不同地理大陆上的优势。CAT-SR 为跨市场连续推荐提供了一个健壮的、适应性强的解决方案。密码可在 https://github.com/chenmetanoia/catsr-kdd/查阅。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Pre-Training+with+Transferable+Attention+for+Addressing+Market+Shifts+in+Cross-Market+Sequential+Recommendation)|0| +|[Certified Robustness on Visual Graph Matching via Searching Optimal Smoothing Range](https://doi.org/10.1145/3637528.3671852)|Huaqing Shao, Lanjun Wang, Yongwei Wang, Qibing Ren, Junchi Yan|School of AI and Department of CSE, Shanghai Jiao Tong University, Shanghai, China; Department of CSE and MoE Key Lab of AI, Shanghai Jiao Tong University, Shanghai, China; Department of CSE, Shanghai Jiao Tong University, Shanghai, China; SNMC, Tianjin University, Tianjin, China; SIAS and College of Computer Science, Zhejiang University, Hangzhou, China|Deep visual graph matching (GM) is a challenging combinatorial task that involves finding a permutation matrix that indicates the correspondence between keypoints from a pair of images. Like many learning systems, empirical studies have shown that visual GM is susceptible to adversarial attacks, with reliability issues in downstream applications. To the best of our knowledge, certifying robustness for deep visual GM remains an open challenge with two main difficulties: how to handle the paired inputs together with the heavily non-linear permutation output space (especially at large scale), and how to balance the trade-off between certified robustness and matching performance. Inspired by the randomized smoothing (RS) technique, we propose the Certified Robustness based on the Optimal Smoothing Range Search (CR-OSRS) technique to fulfill the robustness guarantee for deep visual GM. First, unlike conventional RS methods that use isotropic Gaussian distributions for smoothing, we build the smoothed model with paired joint Gaussian distributions, which capture the structural information among keypoints, and mitigate the performance degradation caused by smoothing. For the vast space of the permutation output, we devise a similarity-based partitioning method that can lower the computational complexity and certification difficulty. We then derive a stringent robustness guarantee that links the certified space of inputs to their corresponding fixed outputs. Second, we design a global optimization method to search for optimal joint Gaussian distributions and facilitate a larger certified space and better performance. Third, we apply data augmentation and a similarity-based regularizer in training to enhance smoothed model performance. Lastly, for the high-dimensional and multivariable nature of the certified space, we propose two methods (sampling and marginal radii) to evaluate it. Experimental results on public benchmarks show that our method achieves state-of-the-art certified robustness.|深度视觉图形匹配(GM)是一项具有挑战性的组合任务，包括从一对图像中找到一个指示关键点之间对应关系的置换矩阵。与许多学习系统一样，经验研究表明，视觉 GM 易受敌对攻击，在下游应用中存在可靠性问题。据我们所知，深度视觉 GM 的鲁棒性认证仍然是一个公开的挑战，有两个主要的困难: 如何处理配对输入和严重的非线性排列输出空间(特别是在大规模) ，以及如何平衡之间的权衡认证鲁棒性和匹配性能。受随机平滑(RS)技术的启发，我们提出了基于最优平滑范围搜索(CR-OSRS)技术的认证鲁棒性，以实现深层视觉 GM 的鲁棒性保证。首先，不同于传统的 RS 方法使用各向同性高斯分布进行平滑，我们建立了平滑模型与配对联合高斯分布，捕捉关键点之间的结构信息，并减轻平滑造成的性能下降。针对排列输出的巨大空间，提出了一种基于相似度的划分方法，降低了计算复杂度和认证难度。然后，我们推导出一个严格的鲁棒性保证，将经过验证的输入空间与它们相应的固定输出联系起来。其次，我们设计了一个全局优化方法来寻找最佳的联合高斯分布，使得更大的认证空间和更好的性能。第三，在训练中应用数据增强和基于相似性的正则化方法来提高平滑模型的性能。最后，针对证明空间的高维性和多变量性，提出了两种评价方法(抽样法和边缘半径法)。对公共基准测试的实验结果表明，该方法具有较好的鲁棒性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Certified+Robustness+on+Visual+Graph+Matching+via+Searching+Optimal+Smoothing+Range)|0| +|[Pre-Training with Transferable Attention for Addressing Market Shifts in Cross-Market Sequential Recommendation](https://doi.org/10.1145/3637528.3671698)|Chen Wang, Ziwei Fan, Liangwei Yang, Mingdai Yang, Xiaolong Liu, Zhiwei Liu, Philip S. Yu|Amazon, Santa Clara, CA, USA; Tsinghua University, Beijing, China; University of Illinois Chicago, Chicago, IL, USA; Salesforce AI Research, Palo Alto, CA, USA; The University of Chicago, Chicago, IL, USA|Cross-market recommendation (CMR) involves selling the same set of items across multiple nations or regions within a transfer learning framework. However, CMR's distinctive characteristics, including limited data sharing due to privacy policies, absence of user overlap, and a shared item set between markets present challenges for traditional recommendation methods. Moreover, CMR experiences market shifts, leading to differences in item popularity and user preferences among different markets. This study focuses on cross-market sequential recommendation (CMSR) and proposes the Cross-market Attention Transferring with Sequential Recommendation (CAT-SR) framework to address these challenges and market shifts. CAT-SR incorporates a pre-training strategy emphasizing item-item correlation, selective self-attention transferring for effective transfer learning, and query and key adapters for market-specific user preferences. Experimental results on real-world cross-market datasets demonstrate the superiority of CAT-SR, and ablation studies validate the benefits of its components across different geographical continents. CAT-SR offers a robust and adaptable solution for cross-market sequential recommendation. The code is available at https://github.com/ChenMetanoia/CATSR-KDD/.|跨市场推荐(CMR)涉及在一个迁移学习框架内在多个国家或地区销售同一套产品。然而，CMR 的显著特点，包括由于隐私政策导致的有限数据共享、用户重叠的缺失以及市场之间的共享项目集，对传统的推荐方法提出了挑战。此外，CMR 经历了市场变化，导致不同市场之间的产品流行度和用户偏好的差异。本研究以跨市场序贯推荐(CMSR)为研究对象，提出了基于序贯推荐的跨市场注意力转移(CAT-SR)框架，以解决这些挑战和市场转移问题。CAT-SR 包括一个强调项目-项目相关性的预训练策略，选择性自我注意转移以有效转移学习，以及针对特定市场用户偏好的查询和关键适配器。在现实世界跨市场数据集上的实验结果证明了 CAT-SR 的优越性，并且消融研究验证了其组件在不同地理大陆上的优势。CAT-SR 为跨市场连续推荐提供了一个健壮的、适应性强的解决方案。密码可在 https://github.com/chenmetanoia/catsr-kdd/查阅。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Pre-Training+with+Transferable+Attention+for+Addressing+Market+Shifts+in+Cross-Market+Sequential+Recommendation)|0| |[Dataset Regeneration for Sequential Recommendation](https://doi.org/10.1145/3637528.3671841)|Mingjia Yin, Hao Wang, Wei Guo, Yong Liu, Suojuan Zhang, Sirui Zhao, Defu Lian, Enhong Chen|; Huawei Singapore Research Center, Singapore, Singapore|The sequential recommender (SR) system is a crucial component of modern recommender systems, as it aims to capture the evolving preferences of users. Significant efforts have been made to enhance the capabilities of SR systems. These methods typically follow the model-centric paradigm, which involves developing effective models based on fixed datasets. However, this approach often overlooks potential quality issues and flaws inherent in the data. Driven by the potential of data-centric AI, we propose a novel data-centric paradigm for developing an ideal training dataset using a model-agnostic dataset regeneration framework called DR4SR. This framework enables the regeneration of a dataset with exceptional cross-architecture generalizability. Additionally, we introduce the DR4SR+ framework, which incorporates a model-aware dataset personalizer to tailor the regenerated dataset specifically for a target model. To demonstrate the effectiveness of the data-centric paradigm, we integrate our framework with various model-centric methods and observe significant performance improvements across four widely adopted datasets. Furthermore, we conduct in-depth analyses to explore the potential of the data-centric paradigm and provide valuable insights. The code can be found at https://github.com/USTC-StarTeam/DR4SR.|顺序推荐(SR)系统是现代推荐系统的重要组成部分，因为它旨在捕获用户不断变化的偏好。为提高 SR 系统的性能已经做出了重大努力。这些方法通常遵循以模型为中心的范式，其中包括基于固定数据集开发有效的模型。然而，这种方法常常忽略数据中潜在的质量问题和固有缺陷。在以数据为中心的人工智能的潜力驱动下，我们提出了一种新的以数据为中心的范式，用于开发一个理想的训练数据集，使用一个模型无关的数据集再生框架 DR4SR。这个框架使得数据集的再生具有异常的跨架构通用性。此外，我们还介绍了 DR4SR + 框架，该框架结合了一个模型感知的数据集个性化工具，可以专门为目标模型定制重新生成的数据集。为了证明以数据为中心的范式的有效性，我们将我们的框架与各种以模型为中心的方法集成在一起，并观察四个广泛采用的数据集的显著性能改进。此外，我们进行深入的分析，以探索潜在的数据为中心的范式，并提供有价值的见解。密码可以在 https://github.com/ustc-starteam/dr4sr 找到。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Dataset+Regeneration+for+Sequential+Recommendation)|0| |[GPFedRec: Graph-Guided Personalization for Federated Recommendation](https://doi.org/10.1145/3637528.3671702)|Chunxu Zhang, Guodong Long, Tianyi Zhou, Zijian Zhang, Peng Yan, Bo Yang|; Computer Science and UMIACS, University of Maryland, Maryland, USA|The federated recommendation system is an emerging AI service architecture that provides recommendation services in a privacy-preserving manner. Using user-relation graphs to enhance federated recommendations is a promising topic. However, it is still an open challenge to construct the user-relation graph while preserving data locality-based privacy protection in federated settings. Inspired by a simple motivation, similar users share a similar vision (embeddings) to the same item set, this paper proposes a novel Graph-guided Personalization for Federated Recommendation (GPFedRec). The proposed method constructs a user-relation graph from user-specific personalized item embeddings at the server without accessing the users' interaction records. The personalized item embedding is locally fine-tuned on each device, and then a user-relation graph will be constructed by measuring the similarity among client-specific item embeddings. Without accessing users' historical interactions, we embody the data locality-based privacy protection of vanilla federated learning. Furthermore, a graph-guided aggregation mechanism is designed to leverage the user-relation graph and federated optimization framework simultaneously. Extensive experiments on five benchmark datasets demonstrate GPFedRec's superior performance. The in-depth study validates that GPFedRec can generally improve existing federated recommendation methods as a plugin while keeping user privacy safe. Code is available https://github.com/Zhangcx19/GPFedRec|联邦推荐系统是一种新兴的人工智能服务架构，它以保护隐私的方式提供推荐服务。使用用户关系图来增强联邦推荐是一个很有前途的课题。然而，在联邦环境中保护基于数据位置的隐私保护的同时构建用户关系图仍然是一个公开的挑战。受简单动机的启发，相似用户对同一条目集有着相似的愿景(嵌入) ，本文提出了一种新的基于图的联邦推荐个性化(GPFedRec)方法。该方法在不访问用户交互记录的情况下，通过在服务器上嵌入用户特定的个性化项目来构造用户关系图。在每个设备上对个性化项目嵌入进行局部微调，然后通过测量客户特定项目嵌入之间的相似度来构造用户关系图。在不访问用户历史交互的情况下，体现了基于数据位置的普通联邦学习的隐私保护。此外，设计了一种图引导的聚合机制，同时利用用户关系图和联邦优化框架。在五个基准数据集上的大量实验证明了 GPFedRec 的优越性能。深入的研究证实，GPFedRec 可以作为一个插件改进现有的联邦推荐方法，同时保护用户隐私安全。密码 https://github.com/zhangcx19/gpfedrec|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=GPFedRec:+Graph-Guided+Personalization+for+Federated+Recommendation)|0| -|[GradCraft: Elevating Multi-task Recommendations through Holistic Gradient Crafting](https://doi.org/10.1145/3637528.3671585)|Yimeng Bai, Yang Zhang, Fuli Feng, Jing Lu, Xiaoxue Zang, Chenyi Lei, Yang Song|Kuaishou Technology, Beijing, China; University of Science and Technology of China, Hefei, China; University of Science and Technology of China & USTC Beijing Research Institute, Hefei, China|Recommender systems require the simultaneous optimization of multiple objectives to accurately model user interests, necessitating the application of multi-task learning methods. However, existing multi-task learning methods in recommendations overlook the specific characteristics of recommendation scenarios, falling short in achieving proper gradient balance. To address this challenge, we set the target of multi-task learning as attaining the appropriate magnitude balance and the global direction balance, and propose an innovative methodology named GradCraft in response. GradCraft dynamically adjusts gradient magnitudes to align with the maximum gradient norm, mitigating interference from gradient magnitudes for subsequent manipulation. It then employs projections to eliminate gradient conflicts in directions while considering all conflicting tasks simultaneously, theoretically guaranteeing the global resolution of direction conflicts. GradCraft ensures the concurrent achievement of appropriate magnitude balance and global direction balance, aligning with the inherent characteristics of recommendation scenarios. Both offline and online experiments attest to the efficacy of GradCraft in enhancing multi-task performance in recommendations. The source code for GradCraft can be accessed at https://github.com/baiyimeng/GradCraft.|推荐系统需要同时对多个目标进行优化，以准确地建立用户兴趣模型，这就需要应用多任务学习方法。然而，现有的多任务推荐学习方法忽视了推荐场景的特殊性，未能实现适当的梯度平衡。为了应对这一挑战，我们将多任务学习的目标设定为实现适当的量级平衡和全局方向平衡，并提出了一种名为“毕业设计”的创新方法。梯度工艺动态调整梯度大小，以符合最大梯度范数，减少干扰梯度大小，以便随后的操作。然后利用预测消除方向梯度冲突，同时考虑所有冲突任务，从理论上保证了方向冲突的全局解决。Gracraft 确保同时实现适当的规模平衡和全球方向平衡，与建议方案的固有特点保持一致。线下和线上的实验都证明了 GradCraft 在提高推荐中的多任务表现方面的有效性。你可透过 https://github.com/baiyimeng/GradCraft 查阅葛拉夫特的源代码。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=GradCraft:+Elevating+Multi-task+Recommendations+through+Holistic+Gradient+Crafting)|0| +|[GradCraft: Elevating Multi-task Recommendations through Holistic Gradient Crafting](https://doi.org/10.1145/3637528.3671585)|Yimeng Bai, Yang Zhang, Fuli Feng, Jing Lu, Xiaoxue Zang, Chenyi Lei, Yang Song|University of Science and Technology of China & USTC Beijing Research Institute, Hefei, China; University of Science and Technology of China, Hefei, China; Kuaishou Technology, Beijing, China|Recommender systems require the simultaneous optimization of multiple objectives to accurately model user interests, necessitating the application of multi-task learning methods. However, existing multi-task learning methods in recommendations overlook the specific characteristics of recommendation scenarios, falling short in achieving proper gradient balance. To address this challenge, we set the target of multi-task learning as attaining the appropriate magnitude balance and the global direction balance, and propose an innovative methodology named GradCraft in response. GradCraft dynamically adjusts gradient magnitudes to align with the maximum gradient norm, mitigating interference from gradient magnitudes for subsequent manipulation. It then employs projections to eliminate gradient conflicts in directions while considering all conflicting tasks simultaneously, theoretically guaranteeing the global resolution of direction conflicts. GradCraft ensures the concurrent achievement of appropriate magnitude balance and global direction balance, aligning with the inherent characteristics of recommendation scenarios. Both offline and online experiments attest to the efficacy of GradCraft in enhancing multi-task performance in recommendations. The source code for GradCraft can be accessed at https://github.com/baiyimeng/GradCraft.|推荐系统需要同时对多个目标进行优化，以准确地建立用户兴趣模型，这就需要应用多任务学习方法。然而，现有的多任务推荐学习方法忽视了推荐场景的特殊性，未能实现适当的梯度平衡。为了应对这一挑战，我们将多任务学习的目标设定为实现适当的量级平衡和全局方向平衡，并提出了一种名为“毕业设计”的创新方法。梯度工艺动态调整梯度大小，以符合最大梯度范数，减少干扰梯度大小，以便随后的操作。然后利用预测消除方向梯度冲突，同时考虑所有冲突任务，从理论上保证了方向冲突的全局解决。Gracraft 确保同时实现适当的规模平衡和全球方向平衡，与建议方案的固有特点保持一致。线下和线上的实验都证明了 GradCraft 在提高推荐中的多任务表现方面的有效性。你可透过 https://github.com/baiyimeng/GradCraft 查阅葛拉夫特的源代码。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=GradCraft:+Elevating+Multi-task+Recommendations+through+Holistic+Gradient+Crafting)|0| |[NudgeRank: Digital Algorithmic Nudging for Personalized Health](https://doi.org/10.1145/3637528.3671562)|Jodi Chiam, Aloysius Lim, Ankur Teredesai|CueZen, Inc. & University of Washington, Seattle, WA, USA; CueZen, Inc., Singapore, Singapore|In this paper we describe NudgeRankTM, an innovative digital algorithmic nudging system designed to foster positive health behaviors on a population-wide scale. Utilizing a novel combination of Graph Neural Networks augmented with an extensible Knowledge Graph, this Recommender System is operational in production, delivering personalized and context-aware nudges to over 1.1 million care recipients daily. This enterprise deployment marks one of the largest AI-driven health behavior change initiatives, accommodating diverse health conditions and wearable devices. Rigorous evaluation reveals statistically significant improvements in health outcomes, including a 6.17% increase in daily steps and 7.61% more exercise minutes. Moreover, user engagement and program enrollment surged, with a 13.1% open rate compared to baseline systems' 4%. Demonstrating scalability and reliability, NudgeRankTM operates efficiently on commodity compute resources while maintaining automation and observability standards essential for production systems.|在本文中，我们描述了 NudgeRankTM，一个创新的数字算法推动系统，旨在培养积极的健康行为在全人口范围内。利用图形神经网络与可扩展的知识图表相结合的新颖组合，这个推荐系统在生产中运作，每天向超过110万名护理接受者提供个性化和上下文感知的推动。这个企业部署标志着一个最大的人工智能驱动的健康行为改变倡议，适应不同的健康条件和可穿戴设备。严格的评估显示，健康结果在统计学上有显著的改善，包括每日步数增加6.17% ，运动时间增加7.61% 。此外，用户参与度和程序注册率也大幅上升，开放率为13.1% ，而基准系统的开放率为4% 。NudgeRankTM 展示了可伸缩性和可靠性，它可以有效地运行商品计算资源，同时维护生产系统必不可少的自动化和可观测性标准。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=NudgeRank:+Digital+Algorithmic+Nudging+for+Personalized+Health)|0| -|[Achieving a Better Tradeoff in Multi-stage Recommender Systems through Personalization](https://doi.org/10.1145/3637528.3671593)|Ariel Evnine, Stratis Ioannidis, Dimitris Kalimeris, Shankar Kalyanaraman, Weiwei Li, Israel Nir, Wei Sun, Udi Weinsberg|Meta, Menlo Park, CA, USA; Northeastern University, Boston, MA, USA|Recommender systems in social media websites provide value to their communities by recommending engaging content and meaningful connections. Scaling high-quality recommendations to billions of users in real-time requires sophisticated ranking models operating on a vast number of potential items to recommend, becoming prohibitively expensive computationally. A common technique "funnels'' these items through progressively complex models ("multi-stage''), each ranking fewer items but at higher computational cost for greater accuracy. This architecture introduces a trade-off between the cost of ranking items and providing users with the best recommendations. A key observation we make in this paper is that, all else equal, ranking more items indeed improves the overall objective but has diminishing returns. Following this observation, we provide a rigorous formulation through the framework of DR-submodularity, and argue that for a certain class of objectives (reward functions), it is possible to improve the trade-off between performance and computational cost in multi-stage ranking systems with strong theoretical guarantees. We show that this class of reward functions that provide this guarantee is large and robust to various noise models. Finally, we describe extensive experimentation of our method on three real-world recommender systems in Facebook, achieving 8.8% reduction in overall compute resources with no significant impact on recommendation quality, compared to a 0.8% quality loss in a non-personalized budget allocation.|社交媒体网站的推荐系统通过推荐参与内容和有意义的联系，为社区提供价值。实时将高质量推荐扩展到数十亿用户需要对大量潜在项目进行复杂的排名模型，计算成本高得令人望而却步。一种常见的技术是通过逐步复杂的模型(“多阶段”)“漏斗”这些项目，每个项目的排名较少，但计算成本较高，以获得更高的准确性。这种体系结构在对项目排序的成本和为用户提供最佳建议之间进行了权衡。我们在本文中的一个关键观察结果是，在其他条件相同的情况下，对更多项目进行排序确实会提高整体目标，但具有报酬递减。在此基础上，我们通过 DR- 子模块化的框架提供了一个严格的公式，并认为对于一定类型的目标(奖励函数) ，在具有强理论保证的多阶段排序系统中，可以改善性能和计算成本之间的权衡。我们证明了这类提供这种保证的奖励函数对各种噪声模型是大的和鲁棒的。最后，我们描述了我们的方法在 Facebook 的三个现实世界推荐系统上的广泛实验，实现了总体计算资源减少8.8% ，对推荐质量没有显着影响，而非个性化预算分配中的质量损失为0.8% 。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Achieving+a+Better+Tradeoff+in+Multi-stage+Recommender+Systems+through+Personalization)|0| -|[Residual Multi-Task Learner for Applied Ranking](https://doi.org/10.1145/3637528.3671523)|Cong Fu, Kun Wang, Jiahua Wu, Yizhou Chen, Guangda Huzhang, Yabo Ni, Anxiang Zeng, Zhiming Zhou|Shopee Pte. Ltd., Shanghai, China; Shopee Pte. Ltd., Singapore, Singapore; ECONCS, Shanghai University of Finance and Economics, Shanghai, China; SCSE, Nanyang Technological University, Singapore, Singapore; Nanyang Technological University, Singapore, Singapore|Modern e-commerce platforms rely heavily on modeling diverse user feedback to provide personalized services. Consequently, multi-task learning has become an integral part of their ranking systems. However, existing multi-task learning methods encounter two main challenges: some lack explicit modeling of task relationships, resulting in inferior performance, while others have limited applicability due to being computationally intensive, having scalability issues, or relying on strong assumptions. To address these limitations and better fit our real-world scenario, pre-rank in Shopee Search, we introduce in this paper ResFlow, a lightweight multi-task learning framework that enables efficient cross-task information sharing via residual connections between corresponding layers of task networks. Extensive experiments on datasets from various scenarios and modalities demonstrate its superior performance and adaptability over state-of-the-art methods. The online A/B tests in Shopee Search showcase its practical value in large-scale industrial applications, evidenced by a 1.29% increase in OPU (order-per-user) without additional system latency. ResFlow is now fully deployed in the pre-rank module of Shopee Search. To facilitate efficient online deployment, we propose a novel offline metric Weighted Recall@K, which aligns well with our online metric OPU, addressing the longstanding online-offline metric misalignment issue. Besides, we propose to fuse scores from the multiple tasks additively when ranking items, which outperforms traditional multiplicative fusion.|现代电子商务平台在很大程度上依赖于建模不同的用户反馈来提供个性化服务。因此，多任务学习已成为其排名系统的一个组成部分。然而，现有的多任务学习方法遇到了两个主要的挑战: 一些方法缺乏对任务关系的明确建模，导致性能较差，而另一些方法由于计算密集、存在可伸缩性问题或依赖于强大的假设而适用性有限。为了解决这些局限性，并更好地适应我们的现实世界场景，在 Shopee 搜索中的预排序，本文介绍了 ResFlow，一个轻量级的多任务学习框架，使得有效的跨任务信息共享通过相应的任务网络层之间的剩余连接。对来自不同场景和模式的数据集进行的大量实验表明，它比最先进的方法具有更好的性能和适应性。Shopee Search 的在线 A/B 测试展示了其在大规模工业应用中的实用价值，在不增加系统延迟的情况下，OPU (每用户订单)增长了1.29% 。ResFlow 现在完全部署在 Shopee Search 的 pre-rank 模块中。为了促进有效的在线部署，我们提出了一种新的离线度量加权召回@K，它与我们的在线度量 OPU 很好地一致，解决了长期存在的在线-离线度量失调问题。此外，本文还提出了在项目排序时对多个任务的得分进行累加融合，这种融合方法的性能优于传统的乘法融合方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Residual+Multi-Task+Learner+for+Applied+Ranking)|0| -|[Multi-task Conditional Attention Network for Conversion Prediction in Logistics Advertising](https://doi.org/10.1145/3637528.3671549)|Baoshen Guo, Xining Song, Shuai Wang, Wei Gong, Tian He, Xue Liu|Southeast University, Nanjing, China; Southeast University & JD Logistics, Nanjing, China; University of Science and Technology of China, Hefei, China; JD Logistics, Beijing, China; McGill University, Montréal, Canada|Logistics advertising is an emerging task in online-to-offline logistics systems, where logistics companies expand parcel shipping services to new users through advertisements on shopping websites. Compared to existing online e-commerce advertising, logistics advertising has two significant new characteristics: (i) the complex factors in logistics advertising considering both users' offline logistics preference and online purchasing profiles; and (ii) data sparsity and mutual relations among multiple steps due to longer advertising conversion processes. To address these challenges, we design MCAC, a Multi-task Conditional Attention network-based logistics advertising Conversion prediction framework, which consists of (i) an offline shipping preference extraction model to extract the user's offline logistics preference from historical shipping records, and (ii) a multi-task conditional attention-based conversion rate prediction module to model mutual relations among multiple steps in logistics advertising conversion processes. We evaluate and deploy MCAC on one of the largest e-commerce platforms in China for logistics advertising. Extensive offline experiments show that our method outperforms state-of-the-art baselines in various metrics. Moreover, the conversion rate prediction results of large-scale online A/B testing show that MCAC achieves a 15.22% improvement compared to existing industrial practices, which demonstrates the effectiveness of the proposed framework.|物流广告是线上到线下物流系统中的一项新兴任务，物流公司通过在购物网站上投放广告，向新用户提供包裹运输服务。与现有的在线电子商务广告相比，物流广告具有两个重要的新特点: (1)考虑用户线下物流偏好和线上购买概况的物流广告复杂因素; (2)由于广告转换过程较长，数据稀疏和多步骤之间的相互关系。针对这些挑战，我们设计了基于多任务条件注意网络的物流广告转化预测框架 MCAC，该框架包括: (1)离线运输偏好提取模型，从历史运输记录中提取用户的离线物流偏好; (2)基于多任务条件注意的转化率预测模型，模拟物流广告转化过程中多个步骤之间的相互关系。我们评估和部署 MCAC 在中国最大的电子商务平台之一的物流广告。大量的离线实验表明，我们的方法在各种指标上都优于最先进的基线。此外，大规模在线 A/B 测试的转换率预测结果表明，与现有工业实践相比，MCAC 的转换率提高了15.22% ，证明了该框架的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Multi-task+Conditional+Attention+Network+for+Conversion+Prediction+in+Logistics+Advertising)|0| +|[Achieving a Better Tradeoff in Multi-stage Recommender Systems through Personalization](https://doi.org/10.1145/3637528.3671593)|Ariel Evnine, Stratis Ioannidis, Dimitris Kalimeris, Shankar Kalyanaraman, Weiwei Li, Israel Nir, Wei Sun, Udi Weinsberg|Northeastern University, Boston, MA, USA; Meta, Menlo Park, CA, USA|Recommender systems in social media websites provide value to their communities by recommending engaging content and meaningful connections. Scaling high-quality recommendations to billions of users in real-time requires sophisticated ranking models operating on a vast number of potential items to recommend, becoming prohibitively expensive computationally. A common technique "funnels'' these items through progressively complex models ("multi-stage''), each ranking fewer items but at higher computational cost for greater accuracy. This architecture introduces a trade-off between the cost of ranking items and providing users with the best recommendations. A key observation we make in this paper is that, all else equal, ranking more items indeed improves the overall objective but has diminishing returns. Following this observation, we provide a rigorous formulation through the framework of DR-submodularity, and argue that for a certain class of objectives (reward functions), it is possible to improve the trade-off between performance and computational cost in multi-stage ranking systems with strong theoretical guarantees. We show that this class of reward functions that provide this guarantee is large and robust to various noise models. Finally, we describe extensive experimentation of our method on three real-world recommender systems in Facebook, achieving 8.8% reduction in overall compute resources with no significant impact on recommendation quality, compared to a 0.8% quality loss in a non-personalized budget allocation.|社交媒体网站的推荐系统通过推荐参与内容和有意义的联系，为社区提供价值。实时将高质量推荐扩展到数十亿用户需要对大量潜在项目进行复杂的排名模型，计算成本高得令人望而却步。一种常见的技术是通过逐步复杂的模型(“多阶段”)“漏斗”这些项目，每个项目的排名较少，但计算成本较高，以获得更高的准确性。这种体系结构在对项目排序的成本和为用户提供最佳建议之间进行了权衡。我们在本文中的一个关键观察结果是，在其他条件相同的情况下，对更多项目进行排序确实会提高整体目标，但具有报酬递减。在此基础上，我们通过 DR- 子模块化的框架提供了一个严格的公式，并认为对于一定类型的目标(奖励函数) ，在具有强理论保证的多阶段排序系统中，可以改善性能和计算成本之间的权衡。我们证明了这类提供这种保证的奖励函数对各种噪声模型是大的和鲁棒的。最后，我们描述了我们的方法在 Facebook 的三个现实世界推荐系统上的广泛实验，实现了总体计算资源减少8.8% ，对推荐质量没有显着影响，而非个性化预算分配中的质量损失为0.8% 。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Achieving+a+Better+Tradeoff+in+Multi-stage+Recommender+Systems+through+Personalization)|0| +|[Residual Multi-Task Learner for Applied Ranking](https://doi.org/10.1145/3637528.3671523)|Cong Fu, Kun Wang, Jiahua Wu, Yizhou Chen, Guangda Huzhang, Yabo Ni, Anxiang Zeng, Zhiming Zhou|Shopee Pte. Ltd., Singapore, Singapore; SCSE, Nanyang Technological University, Singapore, Singapore; Nanyang Technological University, Singapore, Singapore; ECONCS, Shanghai University of Finance and Economics, Shanghai, China; Shopee Pte. Ltd., Shanghai, China|Modern e-commerce platforms rely heavily on modeling diverse user feedback to provide personalized services. Consequently, multi-task learning has become an integral part of their ranking systems. However, existing multi-task learning methods encounter two main challenges: some lack explicit modeling of task relationships, resulting in inferior performance, while others have limited applicability due to being computationally intensive, having scalability issues, or relying on strong assumptions. To address these limitations and better fit our real-world scenario, pre-rank in Shopee Search, we introduce in this paper ResFlow, a lightweight multi-task learning framework that enables efficient cross-task information sharing via residual connections between corresponding layers of task networks. Extensive experiments on datasets from various scenarios and modalities demonstrate its superior performance and adaptability over state-of-the-art methods. The online A/B tests in Shopee Search showcase its practical value in large-scale industrial applications, evidenced by a 1.29% increase in OPU (order-per-user) without additional system latency. ResFlow is now fully deployed in the pre-rank module of Shopee Search. To facilitate efficient online deployment, we propose a novel offline metric Weighted Recall@K, which aligns well with our online metric OPU, addressing the longstanding online-offline metric misalignment issue. Besides, we propose to fuse scores from the multiple tasks additively when ranking items, which outperforms traditional multiplicative fusion.|现代电子商务平台在很大程度上依赖于建模不同的用户反馈来提供个性化服务。因此，多任务学习已成为其排名系统的一个组成部分。然而，现有的多任务学习方法遇到了两个主要的挑战: 一些方法缺乏对任务关系的明确建模，导致性能较差，而另一些方法由于计算密集、存在可伸缩性问题或依赖于强大的假设而适用性有限。为了解决这些局限性，并更好地适应我们的现实世界场景，在 Shopee 搜索中的预排序，本文介绍了 ResFlow，一个轻量级的多任务学习框架，使得有效的跨任务信息共享通过相应的任务网络层之间的剩余连接。对来自不同场景和模式的数据集进行的大量实验表明，它比最先进的方法具有更好的性能和适应性。Shopee Search 的在线 A/B 测试展示了其在大规模工业应用中的实用价值，在不增加系统延迟的情况下，OPU (每用户订单)增长了1.29% 。ResFlow 现在完全部署在 Shopee Search 的 pre-rank 模块中。为了促进有效的在线部署，我们提出了一种新的离线度量加权召回@K，它与我们的在线度量 OPU 很好地一致，解决了长期存在的在线-离线度量失调问题。此外，本文还提出了在项目排序时对多个任务的得分进行累加融合，这种融合方法的性能优于传统的乘法融合方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Residual+Multi-Task+Learner+for+Applied+Ranking)|0| +|[Multi-task Conditional Attention Network for Conversion Prediction in Logistics Advertising](https://doi.org/10.1145/3637528.3671549)|Baoshen Guo, Xining Song, Shuai Wang, Wei Gong, Tian He, Xue Liu|Southeast University, Nanjing, China; Southeast University & JD Logistics, Nanjing, China; McGill University, Montréal, Canada; JD Logistics, Beijing, China; University of Science and Technology of China, Hefei, China|Logistics advertising is an emerging task in online-to-offline logistics systems, where logistics companies expand parcel shipping services to new users through advertisements on shopping websites. Compared to existing online e-commerce advertising, logistics advertising has two significant new characteristics: (i) the complex factors in logistics advertising considering both users' offline logistics preference and online purchasing profiles; and (ii) data sparsity and mutual relations among multiple steps due to longer advertising conversion processes. To address these challenges, we design MCAC, a Multi-task Conditional Attention network-based logistics advertising Conversion prediction framework, which consists of (i) an offline shipping preference extraction model to extract the user's offline logistics preference from historical shipping records, and (ii) a multi-task conditional attention-based conversion rate prediction module to model mutual relations among multiple steps in logistics advertising conversion processes. We evaluate and deploy MCAC on one of the largest e-commerce platforms in China for logistics advertising. Extensive offline experiments show that our method outperforms state-of-the-art baselines in various metrics. Moreover, the conversion rate prediction results of large-scale online A/B testing show that MCAC achieves a 15.22% improvement compared to existing industrial practices, which demonstrates the effectiveness of the proposed framework.|物流广告是线上到线下物流系统中的一项新兴任务，物流公司通过在购物网站上投放广告，向新用户提供包裹运输服务。与现有的在线电子商务广告相比，物流广告具有两个重要的新特点: (1)考虑用户线下物流偏好和线上购买概况的物流广告复杂因素; (2)由于广告转换过程较长，数据稀疏和多步骤之间的相互关系。针对这些挑战，我们设计了基于多任务条件注意网络的物流广告转化预测框架 MCAC，该框架包括: (1)离线运输偏好提取模型，从历史运输记录中提取用户的离线物流偏好; (2)基于多任务条件注意的转化率预测模型，模拟物流广告转化过程中多个步骤之间的相互关系。我们评估和部署 MCAC 在中国最大的电子商务平台之一的物流广告。大量的离线实验表明，我们的方法在各种指标上都优于最先进的基线。此外，大规模在线 A/B 测试的转换率预测结果表明，与现有工业实践相比，MCAC 的转换率提高了15.22% ，证明了该框架的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Multi-task+Conditional+Attention+Network+for+Conversion+Prediction+in+Logistics+Advertising)|0| |[Learning to Rank for Maps at Airbnb](https://doi.org/10.1145/3637528.3671648)|Malay Haldar, Hongwei Zhang, Kedar Bellare, Sherry Chen, Soumyadip Banerjee, Xiaotang Wang, Mustafa Abdool, Huiji Gao, Pavan Tapadia, Liwei He, Sanjeev Katariya|Airbnb, Inc., San Francisco, WA, USA; Airbnb, Inc., San Francisco, CA, USA|As a two-sided marketplace, Airbnb brings together hosts who own listings for rent with prospective guests from around the globe. Results from a guest's search for listings are displayed primarily through two interfaces: (1) as a list of rectangular cards that contain on them the listing image, price, rating, and other details, referred to as list-results (2) as oval pins on a map showing the listing price, called map-results. Both these interfaces, since their inception, have used the same ranking algorithm that orders listings by their booking probabilities and selects the top listings for display. But some of the basic assumptions underlying ranking, built for a world where search results are presented as lists, simply break down for maps. This paper describes how we rebuilt ranking for maps by revising the mathematical foundations of how users interact with search results. Our iterative and experiment-driven approach led us through a path full of twists and turns, ending in a unified theory for the two interfaces. Our journey shows how assumptions taken for granted when designing machine learning algorithms may not apply equally across all user interfaces, and how they can be adapted. The net impact was one of the largest improvements in user experience for Airbnb which we discuss as a series of experimental validations.|作为一个双向的市场，Airbnb 将拥有房源的房东和来自世界各地的潜在客人聚集在一起。客人对列表的搜索结果主要通过两个界面显示: (1)作为一个矩形卡片列表，其中包含列表图像、价格、评级和其他细节，称为列表结果(2)作为椭圆形针在地图上显示列表价格，称为地图结果。这两个界面从一开始就使用相同的排名算法，根据预订概率对列表进行排序，并选择最上面的列表进行显示。但是，一些基本的排名假设，建立在一个世界里，搜索结果显示为列表，只是分解为地图。本文描述了我们如何通过修改用户与搜索结果交互的数学基础来重建地图的排名。我们的迭代和实验驱动的方法带领我们走过了一条充满曲折的道路，最终形成了两个接口的统一理论。我们的旅程表明，当设计机器学习算法时，假设是理所当然的，可能不会平等地适用于所有用户界面，以及它们是如何适应的。净影响是 Airbnb 用户体验的最大改进之一，我们将其作为一系列实验验证进行讨论。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Learning+to+Rank+for+Maps+at+Airbnb)|0| |[Deep Bag-of-Words Model: An Efficient and Interpretable Relevance Architecture for Chinese E-Commerce](https://doi.org/10.1145/3637528.3671559)|Zhe Lin, Jiwei Tan, Dan Ou, Xi Chen, Shaowei Yao, Bo Zheng|Alibaba Group, HangZhou, China|Text relevance or text matching of query and product is an essential technique for the e-commerce search system to ensure that the displayed products can match the intent of the query. Many studies focus on improving the performance of the relevance model in search system. Recently, pre-trained language models like BERT have achieved promising performance on the text relevance task. While these models perform well on the offline test dataset, there are still obstacles to deploy the pre-trained language model to the online system as their high latency. The two-tower model is extensively employed in industrial scenarios, owing to its ability to harmonize performance with computational efficiency. Regrettably, such models present an opaque ''black box'' nature, which prevents developers from making special optimizations. In this paper, we raise deep Bag-o f-Words (DeepBoW) model, an efficient and interpretable relevance architecture for Chinese e-commerce. Our approach proposes to encode the query and the product into the sparse BoW representation, which is a set of word-weight pairs. The weight means the important or the relevant score between the corresponding word and the raw text. The relevance score is measured by the accumulation of the matched word between the sparse BoW representation of the query and the product. Compared to popular dense distributed representation that usually suffers from the drawback of black-box, the most advantage of the proposed representation model is highly explainable and interventionable, which is a superior advantage to the deployment and operation of online search engines. Moreover, the online efficiency of the proposed model is even better than the most efficient inner product form of dense representation. The proposed model is experimented on three different datasets for learning the sparse BoW representations, including the human-annotation set, the search-log set and the click-through set. Then the models are evaluated by experienced human annotators. Both the auto metrics and the online evaluations show our DeepBoW model achieves competitive performance while the online inference is much more efficient than the other models. Our DeepBoW model has already deployed to the biggest Chinese e-commerce search engine Taobao and served the entire search traffic for over 6 months.|查询与产品的文本相关性或文本匹配是电子商务搜索系统中保证所显示的产品与查询意图相匹配的关键技术。许多研究集中在提高搜索系统中相关性模型的性能。近年来，像 BERT 这样的预训练语言模型在文本相关性任务中取得了良好的效果。尽管这些模型在离线测试数据集上表现良好，但仍然存在将预先训练的语言模型部署到在线系统的障碍，因为它们具有较高的延迟。由于双塔模型能够协调性能和计算效率，因此在工业场景中得到了广泛的应用。遗憾的是，这样的模型呈现出一种不透明的“黑盒”特性，这阻止了开发人员进行特殊的优化。本文提出了一种面向中国电子商务的高效、易解释的关联结构——深层 Bag-o-Words 模型。我们的方法建议将查询和产品编码成稀疏的 BW 表示，这是一组字权重对。加权表示相应单词和原始文本之间的重要或相关得分。相关性得分是通过查询的稀疏弓形表示和产品之间匹配词的累积来衡量的。该模型的最大优点是可解释性强、可干预性强，优于在线搜索引擎的部署和运行。此外，该模型的在线效率甚至优于最有效的密集表示内积形式。该模型在三个不同的数据集上进行了实验，包括人工注释集、搜索日志集和点击通过集。然后由经验丰富的人工注释者对模型进行评估。自动度量和在线评估都表明，我们的 DeepBW 模型达到了竞争性能，而在线推理是更有效的比其他模型。我们的 DeepBow 模式已经部署到中国最大的电子商务搜索引擎淘宝，并服务于整个搜索流量超过6个月。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Deep+Bag-of-Words+Model:+An+Efficient+and+Interpretable+Relevance+Architecture+for+Chinese+E-Commerce)|0| |[GRAM: Generative Retrieval Augmented Matching of Data Schemas in the Context of Data Security](https://doi.org/10.1145/3637528.3671602)|Xuanqing Liu, Runhui Wang, Yang Song, Luyang Kong|Amazon Web Services, Seattle, WA, USA|Schema matching constitutes a pivotal phase in the data ingestion process forcontemporary database systems. Its objective is to discern pairwisesimilarities between two sets of attributes, each associated with a distinctdata table. This challenge emerges at the initial stages of data analytics,such as when incorporating a third-party table into existing databases toinform business insights. Given its significance in the realm of databasesystems, schema matching has been under investigation since the 2000s. Thisstudy revisits this foundational problem within the context of large languagemodels. Adhering to increasingly stringent data security policies, our focuslies on the zero-shot and few-shot scenarios: the model should analyze only aminimal amount of customer data to execute the matching task, contrasting withthe conventional approach of scrutinizing the entire data table. We emphasizethat the zero-shot or few-shot assumption is imperative to safeguard theidentity and privacy of customer data, even at the potential cost of accuracy.The capability to accurately match attributes under such stringent requirementsdistinguishes our work from previous literature in this domain.|模式匹配是现代数据库系统数据摄取过程中的一个关键阶段。它的目标是识别两组属性之间的成对相似性，每组属性都与一个不同的数据表相关联。这一挑战出现在数据分析的初始阶段，例如将第三方表合并到现有数据库中以提供业务见解。鉴于模式匹配在数据库领域的重要性，自2000年以来，模式匹配一直在研究之中。本研究在大型语言模型的背景下重新审视这个基本问题。坚持越来越严格的数据安全策略，我们的重点是零射击和少射击场景: 模型应该只分析最小数量的客户数据来执行匹配任务，与审查整个数据表的传统方法形成对比。我们强调，零拍摄或少拍摄假设是必要的，以保护客户数据的身份和隐私，即使在潜在的准确性成本。在如此严格的要求下精确匹配属性的能力使我们的工作区别于这个领域以前的文献。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=GRAM:+Generative+Retrieval+Augmented+Matching+of+Data+Schemas+in+the+Context+of+Data+Security)|0| |[Non-autoregressive Generative Models for Reranking Recommendation](https://doi.org/10.1145/3637528.3671645)|Yuxin Ren, Qiya Yang, Yichun Wu, Wei Xu, Yalong Wang, Zhiqiang Zhang|Tsinghua University, Beijing, China; Peking University, Beijing, China; Kuaishou Technology, Beijing, China|Contemporary recommendation systems are designed to meet users' needs by delivering tailored lists of items that align with their specific demands or interests. In a multi-stage recommendation system, reranking plays a crucial role by modeling the intra-list correlations among items. The key challenge of reranking lies in the exploration of optimal sequences within the combinatorial space of permutations. Recent research proposes a generator-evaluator learning paradigm, where the generator generates multiple feasible sequences and the evaluator picks out the best sequence based on the estimated listwise score. The generator is of vital importance, and generative models are well-suited for the generator function. Current generative models employ an autoregressive strategy for sequence generation. However, deploying autoregressive models in real-time industrial systems is challenging. Firstly, the generator can only generate the target items one by one and hence suffers from slow inference. Secondly, the discrepancy between training and inference brings an error accumulation. Lastly, the left-to-right generation overlooks information from succeeding items, leading to suboptimal performance. To address these issues, we propose a Non-AutoRegressive generative model for reranking Recommendation (NAR4Rec) designed to enhance efficiency and effectiveness. To tackle challenges such as sparse training samples and dynamic candidates, we introduce a matching model. Considering the diverse nature of user feedback, we employ a sequence-level unlikelihood training objective to differentiate feasible sequences from unfeasible ones. Additionally, to overcome the lack of dependency modeling in non-autoregressive models regarding target items, we introduce contrastive decoding to capture correlations among these items. Extensive offline experiments validate the superior performance of NAR4Rec over state-of-the-art reranking methods. Online A/B tests reveal that NAR4Rec significantly enhances the user experience. Furthermore, NAR4Rec has been fully deployed in a popular video app Kuaishou with over 300 million daily active users.|当代的推荐系统通过提供符合用户特定需求或兴趣的量身定制的项目列表来满足用户的需求。在多阶段推荐系统中，重新排序通过建立项目之间的列表内相关性起着至关重要的作用。重新排序的关键挑战在于在排列的组合空间中探索最优序列。最近的研究提出了生成器-评估器学习范式，其中生成器生成多个可行序列，评估器根据估计的列表分数挑选出最佳序列。生成器是至关重要的，生成模型非常适合于生成器函数。当前的生成模型采用自回归策略进行序列生成。然而，在实时工业系统中部署自回归模型是具有挑战性的。首先，生成器只能生成一个个目标项，因此存在推理速度慢的问题。其次，训练与推理的差异带来了错误的积累。最后，从左到右的生成会忽略来自后续项的信息，从而导致性能不理想。为了解决这些问题，我们提出了一个非自动回归的重新排名建议(NAR4rec)生成模型，旨在提高效率和效力。为了解决稀疏训练样本和动态候选人等问题，我们引入了一个匹配模型。考虑到用户反馈的多样性，我们采用序列级不似然训练目标来区分可行序列和不可行序列。此外，为了克服非自回归模型中缺乏对目标项的依赖建模，我们引入对比解码来捕获这些项之间的相关性。大量的离线实验验证了 NAR4Rec 优于最先进的重新排序方法的性能。在线 A/B 测试显示 NAR4Rec 显著提高了用户体验。此外，NAR4Rec 已经完全部署在一个流行的视频应用快手中，每天有超过3亿的活跃用户。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Non-autoregressive+Generative+Models+for+Reranking+Recommendation)|0| |[Chaining Text-to-Image and Large Language Model: A Novel Approach for Generating Personalized e-commerce Banners](https://doi.org/10.1145/3637528.3671636)|Shanu Vashishtha, Abhinav Prakash, Lalitesh Morishetti, Kaushiki Nag, Yokila Arora, Sushant Kumar, Kannan Achan|Walmart Global Tech, Sunnyvale, CA, USA|Text-to-image models such as stable diffusion have opened a plethora ofopportunities for generating art. Recent literature has surveyed the use oftext-to-image models for enhancing the work of many creative artists. Manye-commerce platforms employ a manual process to generate the banners, which istime-consuming and has limitations of scalability. In this work, we demonstratethe use of text-to-image models for generating personalized web banners withdynamic content for online shoppers based on their interactions. The novelty inthis approach lies in converting users' interaction data to meaningful promptswithout human intervention. To this end, we utilize a large language model(LLM) to systematically extract a tuple of attributes from itemmeta-information. The attributes are then passed to a text-to-image model viaprompt engineering to generate images for the banner. Our results show that theproposed approach can create high-quality personalized banners for users.|文本到图像的模型，例如稳定的扩散，已经为艺术的生成提供了大量的机会。最近的文献调查了文本到图像模型的使用，以提高许多创造性的艺术家的工作。许多电子商务平台使用手工过程来生成横幅，这非常耗时并且具有可伸缩性的限制。在这项工作中，我们演示了使用文本到图像的模型来生成个性化的网络横幅与动态内容的在线购物者基于他们的交互。这种方法的新颖之处在于无需人工干预就能将用户的交互数据转换为有意义的提示。为此，我们利用大型语言模型(LLM)系统地从 itemmeta 信息中提取属性元组。然后通过提示工程将属性传递给文本到图像模型，以生成横幅的图像。结果表明，该方法可以为用户创建高质量的个性化横幅。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Chaining+Text-to-Image+and+Large+Language+Model:+A+Novel+Approach+for+Generating+Personalized+e-commerce+Banners)|0| -|[LiMAML: Personalization of Deep Recommender Models via Meta Learning](https://doi.org/10.1145/3637528.3671599)|Ruofan Wang, Prakruthi Prabhakar, Gaurav Srivastava, Tianqi Wang, Zeinab S. Jalali, Varun Bharill, Yunbo Ouyang, Aastha Nigam, Divya Venugopalan, Aman Gupta, Fedor Borisyuk, S. Sathiya Keerthi, Ajith Muralidharan|LinkedIn Corporation, Sunnyvale, CA, USA; Aliveo AI Corp, Sunnyvale, CA, USA|In the realm of recommender systems, the ubiquitous adoption of deep neural networks has emerged as a dominant paradigm for modeling diverse business objectives. As user bases continue to expand, the necessity of personalization and frequent model updates have assumed paramount significance to ensure the delivery of relevant and refreshed experiences to a diverse array of members. In this work, we introduce an innovative meta-learning solution tailored to the personalization of models for individual members and other entities, coupled with the frequent updates based on the latest user interaction signals. Specifically, we leverage the Model-Agnostic Meta Learning (MAML) algorithm to adapt per-task sub-networks using recent user interaction data. Given the near infeasibility of productionizing original MAML-based models in online recommendation systems, we propose an efficient strategy to operationalize meta-learned sub-networks in production, which involves transforming them into fixed-sized vectors, termed meta embeddings, thereby enabling the seamless deployment of models with hundreds of billions of parameters for online serving. Through extensive experimentation on production data drawn from various applications at LinkedIn, we demonstrate that the proposed solution consistently outperforms the best performing baseline models of those applications, including strong baselines such as using wide-and-deep ID based personalization approach. Our approach has enabled the deployment of a range of highly personalized AI models across diverse LinkedIn applications, leading to substantial improvements in business metrics as well as refreshed experience for our members.|在推荐系统领域，深层神经网络的普遍采用已经成为建模不同业务目标的主要范例。随着用户基础的不断扩大，个性化和频繁更新模型的必要性对于确保向各类成员提供相关的最新经验具有至关重要的意义。在这项工作中，我们介绍了一个创新的元学习解决方案，专为个人成员和其他实体的模型个性化，加上基于最新用户交互信号的频繁更新。具体来说，我们利用模型不可知元学习(MAML)算法来使用最近的用户交互数据来适应每个任务的子网络。鉴于在在线推荐系统中生产原始的基于 MAML 的模型几乎是不可行的，我们提出了一个有效的策略来操作生产中的元学习子网络，包括将它们转换成固定大小的向量，称为元嵌入，从而能够无缝部署具有数千亿参数的在线服务模型。通过对来自 LinkedIn 各种应用程序的生产数据进行广泛的实验，我们证明了所提出的解决方案始终优于这些应用程序的最佳性能基线模型，包括强大的基线，例如使用基于广泛和深度 ID 的个性化方法。我们的方法使得一系列高度个性化的人工智能模型能够在不同的 LinkedIn 应用程序中部署，从而大大改善了业务指标，并为我们的会员带来了全新的体验。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=LiMAML:+Personalization+of+Deep+Recommender+Models+via+Meta+Learning)|0| +|[LiMAML: Personalization of Deep Recommender Models via Meta Learning](https://doi.org/10.1145/3637528.3671599)|Ruofan Wang, Prakruthi Prabhakar, Gaurav Srivastava, Tianqi Wang, Zeinab S. Jalali, Varun Bharill, Yunbo Ouyang, Aastha Nigam, Divya Venugopalan, Aman Gupta, Fedor Borisyuk, S. Sathiya Keerthi, Ajith Muralidharan|Aliveo AI Corp, Sunnyvale, CA, USA; LinkedIn Corporation, Sunnyvale, CA, USA|In the realm of recommender systems, the ubiquitous adoption of deep neural networks has emerged as a dominant paradigm for modeling diverse business objectives. As user bases continue to expand, the necessity of personalization and frequent model updates have assumed paramount significance to ensure the delivery of relevant and refreshed experiences to a diverse array of members. In this work, we introduce an innovative meta-learning solution tailored to the personalization of models for individual members and other entities, coupled with the frequent updates based on the latest user interaction signals. Specifically, we leverage the Model-Agnostic Meta Learning (MAML) algorithm to adapt per-task sub-networks using recent user interaction data. Given the near infeasibility of productionizing original MAML-based models in online recommendation systems, we propose an efficient strategy to operationalize meta-learned sub-networks in production, which involves transforming them into fixed-sized vectors, termed meta embeddings, thereby enabling the seamless deployment of models with hundreds of billions of parameters for online serving. Through extensive experimentation on production data drawn from various applications at LinkedIn, we demonstrate that the proposed solution consistently outperforms the best performing baseline models of those applications, including strong baselines such as using wide-and-deep ID based personalization approach. Our approach has enabled the deployment of a range of highly personalized AI models across diverse LinkedIn applications, leading to substantial improvements in business metrics as well as refreshed experience for our members.|在推荐系统领域，深层神经网络的普遍采用已经成为建模不同业务目标的主要范例。随着用户基础的不断扩大，个性化和频繁更新模型的必要性对于确保向各类成员提供相关的最新经验具有至关重要的意义。在这项工作中，我们介绍了一个创新的元学习解决方案，专为个人成员和其他实体的模型个性化，加上基于最新用户交互信号的频繁更新。具体来说，我们利用模型不可知元学习(MAML)算法来使用最近的用户交互数据来适应每个任务的子网络。鉴于在在线推荐系统中生产原始的基于 MAML 的模型几乎是不可行的，我们提出了一个有效的策略来操作生产中的元学习子网络，包括将它们转换成固定大小的向量，称为元嵌入，从而能够无缝部署具有数千亿参数的在线服务模型。通过对来自 LinkedIn 各种应用程序的生产数据进行广泛的实验，我们证明了所提出的解决方案始终优于这些应用程序的最佳性能基线模型，包括强大的基线，例如使用基于广泛和深度 ID 的个性化方法。我们的方法使得一系列高度个性化的人工智能模型能够在不同的 LinkedIn 应用程序中部署，从而大大改善了业务指标，并为我们的会员带来了全新的体验。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=LiMAML:+Personalization+of+Deep+Recommender+Models+via+Meta+Learning)|0| |[Enhancing Asymmetric Web Search through Question-Answer Generation and Ranking](https://doi.org/10.1145/3637528.3671517)|Dezhi Ye, Jie Liu, Jiabin Fan, Bowen Tian, Tianhua Zhou, Xiang Chen, Jin Ma|Tencent PCG, Beijing, China|This paper addresses the challenge of the semantic gap between user queries and web content, commonly referred to as asymmetric text matching, within the domain of web search. By leveraging BERT for reading comprehension, current algorithms enable significant advancements in query understanding, but still encounter limitations in effectively resolving the asymmetrical ranking problem due to model comprehension and summarization constraints. To tackle this issue, we propose the QAGR (Question-Answer Generation and Ranking) method, comprising an offline module called QAGeneration and an online module called QARanking. The QAGeneration module utilizes large language models (LLMs) to generate high-quality question-answering pairs for each web page. This process involves two steps: generating question-answer pairs and performing verification to eliminate irrelevant questions, resulting in high-quality questions associated with their respective documents. The QARanking module combines and ranks the generated questions and web page content. To ensure efficient online inference, we design the QARanking model as a homogeneous dual-tower model, incorporating query intent to drive score fusion while balancing keyword matching and asymmetric matching. Additionally, we conduct a preliminary screening of questions for each document, selecting only the top-N relevant questions for further relevance calculation. Empirical results demonstrate the substantial performance improvement of our proposed method in web search. We achieve over 8.7% relative offline relevance improvement and over 8.5% online engagement gain compared to the state-of-the-art web search system. Furthermore, we deploy QAGR to online web search engines and share our deployment experience, including production considerations and ablation experiments. This research contributes to advancing the field of asymmetric web search and provides valuable insights for enhancing search engine performance.|本文讨论了在网络搜索领域中，用户查询和网页内容之间的语义差异(通常称为非对称文本匹配)所带来的挑战。通过利用 BERT 的阅读理解，当前的算法在查询理解方面取得了显著的进步，但由于模型理解和摘要约束，在有效解决非对称排序问题方面仍然存在局限性。为了解决这个问题，我们提出了 QAGR (问答生成和排序)方法，包括一个称为 QAGeneration 的离线模块和一个称为 QARanking 的在线模块。QAGeneration 模块利用大语言模型(LLM)为每个网页生成高质量的问答对。这个过程包括两个步骤: 生成问题-答案对和进行验证，以消除不相关的问题，从而产生与各自文件相关的高质量问题。QARanking 模块将生成的问题和网页内容进行组合和排序。为了保证在线推理的有效性，我们将 QARanking 模型设计成一个均匀的双塔模型，在平衡关键字匹配和非对称匹配的同时，结合查询意图驱动得分融合。此外，我们对每个文档的问题进行初步筛选，只选择排名前 N 位的相关问题进行进一步的相关性计算。实验结果表明，本文提出的方法在网络搜索中性能得到了显著提高。与最先进的网络搜索系统相比，我们实现了超过8.7% 的相对离线相关性改善和超过8.5% 的在线参与收益。此外，我们部署 QAGR 到在线网络搜索引擎和分享我们的部署经验，包括生产考虑和烧蚀实验。本文的研究有助于推进非对称网络搜索领域的发展，为提高搜索引擎性能提供了有价值的见解。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Enhancing+Asymmetric+Web+Search+through+Question-Answer+Generation+and+Ranking)|0| |[Unsupervised Ranking Ensemble Model for Recommendation](https://doi.org/10.1145/3637528.3671598)|Wenhui Yu, Bingqi Liu, Bin Xia, Xiaoxiao Xu, Ying Chen, Yongchang Li, Lantao Hu|Kuaishou Technology, Beijing, China|When visiting an online platform, a user generates various actions, such as clicks, long views, likes, comments, etc. To capture user preferences in these aspects, we learn these objectives and return multiple rankings of candidate items for each user. We need to aggregate them into one to truncate the candidate set, and ranking ensemble model is proposed for this task. However, there is a critical issue: though we input abundant information, what model learns depends on the supervision. Unfortunately, the existing supervision is poorly designed, leading to serious information loss issue. To address this issue, we designed an unsupervised loss to compel the ranking ensemble model to learn all information of input rankings, including sequential and numerical information. (1) For sequential information, we design a distance measure between two rankings, and train the ensemble ranking to have similar order with all input rankings by minimizing the distance. (2) For numerical information, we design a decoder to reconstruct values of original rankings from the hidden layer of the model, to guarantee that the model captures as much input information as possible. Our unsupervised loss is compatible with all ranking ensemble models. We optimize several widely-used structures to propose unsupervised ranking ensemble models. We devise comprehensive experiments on two real-world datasets to demonstrate the effectiveness of the proposed models. We also apply our model in a short video platform with billions of users, and achieve significant improvement.|当访问在线平台时，用户生成各种各样的动作，如点击、长视图、喜欢、评论等。为了捕获这些方面的用户偏好，我们学习这些目标，并返回每个用户的候选项的多个排名。为了截断候选集，我们需要将它们聚合成一个集合，并提出了排序集成模型。然而，有一个关键的问题: 虽然我们输入了大量的信息，但是模型学到了什么取决于监督。然而，现有的监管体系设计不当，导致了严重的信息流失问题。为了解决这个问题，我们设计了一个无监督的损失，以迫使排名集成模型学习所有的输入排名信息，包括顺序和数字信息。(1)对于序列信息，我们设计了两个排名之间的距离度量，并通过最小化距离训练集合排名与所有输入排名具有相似的顺序。(2)对于数值信息，我们设计了一个解码器，从模型的隐层重建原始排名值，以保证模型捕获尽可能多的输入信息。我们的无监督损失与所有等级集合模型兼容。我们优化了几个广泛使用的结构，提出了无监督排序集成模型。我们设计了两个实际数据集的综合实验来验证所提出模型的有效性。在一个拥有数十亿用户的短视频平台上应用了该模型，并取得了显著的改进。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Unsupervised+Ranking+Ensemble+Model+for+Recommendation)|0| -|[Adapting Job Recommendations to User Preference Drift with Behavioral-Semantic Fusion Learning](https://doi.org/10.1145/3637528.3671759)|Xiao Han, Chen Zhu, Xiao Hu, Chuan Qin, Xiangyu Zhao, Hengshu Zhu|Career Science Lab, BOSS Zhipin, University of Science and Technology of China, Beijing, China; Career Science Lab, BOSS Zhipin, Beijing, China; City University of Hong Kong, Hong Kong, China|Job recommender systems are crucial for aligning job opportunities with job-seekers in online job-seeking. However, users tend to adjust their job preferences to secure employment opportunities continually, which limits the performance of job recommendations. The inherent frequency of preference drift poses a challenge to promptly and precisely capture user preferences. To address this issue, we propose a novel session-based framework, BISTRO, to timely model user preference through fusion learning of semantic and behavioral information. Specifically, BISTRO is composed of three stages: 1) coarse-grained semantic clustering, 2) fine-grained job preference extraction, and 3) personalized top-k job recommendation. Initially, BISTRO segments the user interaction sequence into sessions and leverages session-based semantic clustering to achieve broad identification of person-job matching. Subsequently, we design a hypergraph wavelet learning method to capture the nuanced job preference drift. To mitigate the effect of noise in interactions caused by frequent preference drift, we innovatively propose an adaptive wavelet filtering technique to remove noisy interaction. Finally, a recurrent neural network is utilized to analyze session-based interaction for inferring personalized preferences. Extensive experiments on three real-world offline recruitment datasets demonstrate the significant performances of our framework. Significantly, BISTRO also excels in online experiments, affirming its effectiveness in live recruitment settings. This dual success underscores the robustness and adaptability of BISTRO. The source code is available at https://github.com/Applied-Machine-Learning-Lab/BISTRO.|在网上求职中，职位推荐系统对于将求职机会与求职者联系起来至关重要。然而，用户倾向于不断调整自己的工作偏好以获得就业机会，这就限制了工作推荐的效果。偏好漂移的固有频率对及时、准确地捕捉用户偏好提出了挑战。为了解决这个问题，我们提出了一个新的基于会话的框架 BISTRO，通过语义和行为信息的融合学习来及时建模用户偏好。具体来说，BISTRO 由三个阶段组成: 1)粗粒度语义聚类，2)细粒度工作偏好提取，3)个性化的 top-k 工作推荐。最初，BISTRO 将用户交互序列分割成会话，并利用基于会话的语义聚类实现人-工匹配的广泛识别。随后，我们设计了一种超图小波学习方法来捕捉细微的工作偏好漂移。为了消除频繁偏好漂移引起的相互作用中的噪声影响，本文创新性地提出了一种自适应小波滤波技术来去除噪声相互作用。最后，一个递归神经网络被用来分析基于会话的交互来推断个性化的偏好。在三个真实世界的离线招聘数据集上的大量实验证明了我们的框架的显著性能。值得注意的是，BISTRO 还擅长在线实验，确认其在现场招聘设置的有效性。这种双重成功突出了 BISTRO 的稳健性和适应性。源代码可在 https://github.com/applied-machine-learning-lab/bistro 下载。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Adapting+Job+Recommendations+to+User+Preference+Drift+with+Behavioral-Semantic+Fusion+Learning)|0| +|[Adapting Job Recommendations to User Preference Drift with Behavioral-Semantic Fusion Learning](https://doi.org/10.1145/3637528.3671759)|Xiao Han, Chen Zhu, Xiao Hu, Chuan Qin, Xiangyu Zhao, Hengshu Zhu|Career Science Lab, BOSS Zhipin, University of Science and Technology of China, Beijing, China; City University of Hong Kong, Hong Kong, China; Career Science Lab, BOSS Zhipin, Beijing, China|Job recommender systems are crucial for aligning job opportunities with job-seekers in online job-seeking. However, users tend to adjust their job preferences to secure employment opportunities continually, which limits the performance of job recommendations. The inherent frequency of preference drift poses a challenge to promptly and precisely capture user preferences. To address this issue, we propose a novel session-based framework, BISTRO, to timely model user preference through fusion learning of semantic and behavioral information. Specifically, BISTRO is composed of three stages: 1) coarse-grained semantic clustering, 2) fine-grained job preference extraction, and 3) personalized top-k job recommendation. Initially, BISTRO segments the user interaction sequence into sessions and leverages session-based semantic clustering to achieve broad identification of person-job matching. Subsequently, we design a hypergraph wavelet learning method to capture the nuanced job preference drift. To mitigate the effect of noise in interactions caused by frequent preference drift, we innovatively propose an adaptive wavelet filtering technique to remove noisy interaction. Finally, a recurrent neural network is utilized to analyze session-based interaction for inferring personalized preferences. Extensive experiments on three real-world offline recruitment datasets demonstrate the significant performances of our framework. Significantly, BISTRO also excels in online experiments, affirming its effectiveness in live recruitment settings. This dual success underscores the robustness and adaptability of BISTRO. The source code is available at https://github.com/Applied-Machine-Learning-Lab/BISTRO.|在网上求职中，职位推荐系统对于将求职机会与求职者联系起来至关重要。然而，用户倾向于不断调整自己的工作偏好以获得就业机会，这就限制了工作推荐的效果。偏好漂移的固有频率对及时、准确地捕捉用户偏好提出了挑战。为了解决这个问题，我们提出了一个新的基于会话的框架 BISTRO，通过语义和行为信息的融合学习来及时建模用户偏好。具体来说，BISTRO 由三个阶段组成: 1)粗粒度语义聚类，2)细粒度工作偏好提取，3)个性化的 top-k 工作推荐。最初，BISTRO 将用户交互序列分割成会话，并利用基于会话的语义聚类实现人-工匹配的广泛识别。随后，我们设计了一种超图小波学习方法来捕捉细微的工作偏好漂移。为了消除频繁偏好漂移引起的相互作用中的噪声影响，本文创新性地提出了一种自适应小波滤波技术来去除噪声相互作用。最后，一个递归神经网络被用来分析基于会话的交互来推断个性化的偏好。在三个真实世界的离线招聘数据集上的大量实验证明了我们的框架的显著性能。值得注意的是，BISTRO 还擅长在线实验，确认其在现场招聘设置的有效性。这种双重成功突出了 BISTRO 的稳健性和适应性。源代码可在 https://github.com/applied-machine-learning-lab/bistro 下载。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Adapting+Job+Recommendations+to+User+Preference+Drift+with+Behavioral-Semantic+Fusion+Learning)|0| |[Learning to Bid the Interest Rate in Online Unsecured Personal Loans](https://doi.org/10.1145/3637528.3671584)|Dong Jun Jee, Seung Jung Jin, JiHoon Yoo, Byunggyu Ahn|PFC Technologies, Seoul, Republic of Korea|The unsecured personal loan (UPL) market is a multi-billion dollar market where numerous financial institutions compete. Due to the development of online banking, loan applicants start to compare numerous loan products. They aim for high loan limits and low interest rates. Since loan applicants have a desired loan amount, institutions instead focus on adjusting interest rates. Despite the importance of determining optimal interest strategies, institutions have traditionally relied on heuristic methods by human experts to set interest rates. This is done by adding a target return on assets (ROA) to the applicant's expected default probability predicted by a credit scoring system (CSS) such as the FICO score. We conceptualize the UPL market dynamics as a repeated auction scenario, where loan applicants (akin to sellers) seek the lowest interest rates, while financial institutions (akin to bidders) aim to maximize profits through higher interest rates. To the best of our knowledge, this is the first time anyone has approached the UPL market through the viewpoint of a repeated auction. While there are several research done in learning to bid in repeated auctions, those works cannot be directly applied to the UPL market due to the lack of any feedback about other bidders' strategies and the need to satisfy the bidder's target loan volume and profit variance. We present an algorithm named AutoInterest, which is a modification of the dual gradient descent algorithm. In addition, we provide a framework to evaluate interest rate bidding strategies on a benchmark dataset and the credit bureau dataset of actual loan applicants in South Korea. We evaluate AutoInterest on this framework and show higher cumulative profit compared to other common online algorithms and the current fixed strategy used by real institutions.|无担保个人贷款(UPL)市场是一个数十亿美元的市场，众多金融机构在其中展开竞争。由于网上银行的发展，贷款申请者开始比较众多的贷款产品。他们的目标是高贷款限额和低利率。由于贷款申请人有一个理想的贷款数额，机构反而把重点放在调整利率。尽管确定最优利率策略很重要，但机构历来依赖人类专家的启发式方法来设定利率。这是通过将目标资产收益率(ROA)添加到由信用评分系统(CSS)(例如 FICO 评分)预测的申请者的预期违约概率来完成的。我们将 UPL 市场动态概念化为一个重复的拍卖场景，其中贷款申请者(类似于卖方)寻求最低的利率，而金融机构(类似于投标者)旨在通过更高的利率实现利润最大化。据我们所知，这是第一次有人通过重复拍卖的观点进入 UPL 市场。虽然在重复拍卖中学习投标已经有了一些研究，但是由于缺乏对其他投标者策略的反馈，以及需要满足投标者的目标贷款量和利润差异，这些工作不能直接应用于 UPL 市场。我们提出了一个名为“自动兴趣”的算法，它是对双梯度下降法算法的修改。此外，我们还提供了一个基于基准数据集和韩国实际贷款申请者信用局数据集评估利率投标策略的框架。我们在这个框架下评估了自动收益，并显示了比其他常见的在线算法和当前实际机构使用的固定策略更高的累积利润。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Learning+to+Bid+the+Interest+Rate+in+Online+Unsecured+Personal+Loans)|0| -|[A Hierarchical and Disentangling Interest Learning Framework for Unbiased and True News Recommendation](https://doi.org/10.1145/3637528.3671944)|Shoujin Wang, Wentao Wang, Xiuzhen Zhang, Yan Wang, Huan Liu, Fang Chen|Macquarie University, Sydney, Australia; University of Technology Sydney, Sydney, Australia; Arizona State University, Tempe, USA; RMIT University, Melbourne, Australia|In the era of information explosion, news recommender systems are crucial for users to effectively and efficiently discover their interested news. However, most of the existing news recommender systems face two major issues, hampering recommendation quality. Firstly, they often oversimplify users' reading interests, neglecting their hierarchical nature, spanning from high-level event (e.g., US Election) related interests to low-level news article-specifc interests. Secondly, existing work often assumes a simplistic context, disregarding the prevalence of fake news and political bias under the real-world context. This oversight leads to recommendations of biased or fake news, posing risks to individuals and society. To this end, this paper addresses these gaps by introducing a novel framework, the Hierarchical and Disentangling Interest learning framework (HDInt). HDInt incorporates a hierarchical interest learning module and a disentangling interest learning module. The former captures users' high- and low-level interests, enhancing next-news recommendation accuracy. The latter effectively separates polarity and veracity information from news contents and model them more specifcally, promoting fairness- and truth-aware reading interest learning for unbiased and true news recommendations. Extensive experiments on two real-world datasets demonstrate HDInt's superiority over state-of-the-art news recommender systems in delivering accurate, unbiased, and true news recommendations.|在信息爆炸时代，新闻推荐系统对于用户有效发现感兴趣的新闻至关重要。然而，大多数现有的新闻推荐系统面临两个主要问题，阻碍了推荐质量。首先，他们往往过分简化用户的阅读兴趣，忽视了他们的等级性质，从高水平的事件(如美国大选)相关兴趣低水平的新闻文章的具体兴趣。其次，现有作品往往假设一个简单化的语境，忽视了现实语境下假新闻和政治偏见的盛行。这种疏忽导致了有偏见或假新闻的建议，给个人和社会带来风险。为此，本文提出了一种新的兴趣学习框架——分层分离式兴趣学习框架(HDInt)。HDInt 集成了分层兴趣学习模块和分离兴趣学习模块。前者捕捉用户的高层次和低层次兴趣，提高下一新闻推荐的准确性。后者有效地将极性和准确性信息从新闻内容中分离出来，并对其进行更具体的建模，促进公平和真实意识的阅读兴趣学习，以获得无偏见和真实的新闻推荐。在两个真实世界数据集上的大量实验表明，HDInt 在提供准确、公正和真实的新闻推荐方面优于最先进的新闻推荐系统。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Hierarchical+and+Disentangling+Interest+Learning+Framework+for+Unbiased+and+True+News+Recommendation)|0| -|[Warming Up Cold-Start CTR Prediction by Learning Item-Specific Feature Interactions](https://doi.org/10.1145/3637528.3671784)|Yaqing Wang, Hongming Piao, Daxiang Dong, Quanming Yao, Jingbo Zhou|Baidu Research, Baidu Inc., Beijing, China; Baidu AI Cloud, Baidu Inc., Beijing, China; Department of Computer Science, City University of Hong Kong, Hong Kong, Hong Kong; Department of Electronic Engineering, Tsinghua University, Beijing, China|In recommendation systems, new items are continuously introduced, initially lacking interaction records but gradually accumulating them over time. Accurately predicting the click-through rate (CTR) for these items is crucial for enhancing both revenue and user experience. While existing methods focus on enhancing item ID embeddings for new items within general CTR models, they tend to adopt a global feature interaction approach, often overshadowing new items with sparse data by those with abundant interactions. Addressing this, our work introduces EmerG, a novel approach that warms up cold-start CTR prediction by learning item-specific feature interaction patterns. EmerG utilizes hypernetworks to generate an item-specific feature graph based on item characteristics, which is then processed by a Graph Neural Network (GNN). This GNN is specially tailored to provably capture feature interactions at any order through a customized message passing mechanism. We further design a meta learning strategy that optimizes parameters of hypernetworks and GNN across various item CTR prediction tasks, while only adjusting a minimal set of item-specific parameters within each task. This strategy effectively reduces the risk of overfitting when dealing with limited data. Extensive experiments on benchmark datasets validate that EmerG consistently performs the best given no, a few and sufficient instances of new items.|在推荐系统中，不断引入新的项目，最初缺乏交互记录，但随着时间的推移逐渐积累。准确地预测这些项目的点进率对于提高收入和用户体验至关重要。虽然现有的方法侧重于在一般的 CTR 模型中增强新项目的项目 ID 嵌入，但它们倾向于采用全局特征交互方法，往往使那些具有丰富交互的数据稀疏的新项目黯然失色。为了解决这个问题，我们的工作介绍了 EmerG，这是一种通过学习项目特定的特征交互模式来预测冷启动 CTR 的新方法。EmerG 利用超网络生成基于项目特征的项目特征图，然后通过图神经网络(GNN)对其进行处理。这个 GNN 是专门定制的，可以通过定制的消息传递机制以任何顺序捕获特性交互。我们进一步设计了一个元学习策略，在不同的项目 CTR 预测任务中优化超级网络和 GNN 的参数，同时在每个任务中只调整最小的项目特定参数集。这种策略有效地降低了处理有限数据时过度拟合的风险。在基准数据集上的大量实验验证了 EmerG 始终如一地表现出最好的特性——没有、少数和充分的新项目实例。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Warming+Up+Cold-Start+CTR+Prediction+by+Learning+Item-Specific+Feature+Interactions)|0| -|[Improving Multi-modal Recommender Systems by Denoising and Aligning Multi-modal Content and User Feedback](https://doi.org/10.1145/3637528.3671703)|Guipeng Xv, Xinyu Li, Ruobing Xie, Chen Lin, Chong Liu, Feng Xia, Zhanhui Kang, Leyu Lin|School of Informatics, Xiamen University, Xiamen, Fujian, China; Tencent, Beijing, China|Multi-modal recommender systems (MRSs) are pivotal in diverse online web platforms and have garnered considerable attention in recent years. However, previous studies overlook the challenges of (1)noisy multi-modal content, (2) noisy user feedback, and (3) aligning multi-modal content and user feedback. To tackle these challenges, we propose Denoising and Aligning Multi-modal Recommender System (DA-MRS). To mitigate noise in multi-modal content, DA-MRS first constructs item-item graphs determined by consistent content similarity across modalities. To denoise user feedback, DA-MRS associates the probability of observed feedback with multi-modal content and devises a denoised BPR loss. Furthermore, DA-MRS implements Alignment guided by User preference to enhance task-specific item representation and Alignment guided by graded Item relations to provide finer-grained alignment. Extensive experiments verify that DA-MRS is a plug-and-play framework and achieves significant and consistent improvements across various datasets, backbone models, and noisy scenarios.|多模态推荐系统(MRS)是各种在线网络平台中的关键技术，近年来得到了广泛的关注。然而，以往的研究忽略了以下挑战: (1)噪声多模态内容，(2)噪声用户反馈，和(3)调整多模态内容和用户反馈。为了应对这些挑战，我们提出了去噪和对齐多模态推荐系统(DA-MRS)。为了减轻多模态内容中的噪声，DA-MRS 首先构建由不同模态间一致的内容相似性确定的项目-项目图。为了去除用户反馈的噪声，DA-MRS 将观测反馈的概率与多模态内容联系起来，设计了一种去除 BPR 损失的方法。此外，DA-MRS 还实现了用户偏好引导的对齐，增强了任务特定项目的表示和分级项目关系引导的对齐，提供了更细粒度的对齐。大量的实验验证了 DA-MRS 是一个即插即用的框架，并在各种数据集、骨干模型和噪声场景中实现了显著和一致的改进。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Improving+Multi-modal+Recommender+Systems+by+Denoising+and+Aligning+Multi-modal+Content+and+User+Feedback)|0| +|[A Hierarchical and Disentangling Interest Learning Framework for Unbiased and True News Recommendation](https://doi.org/10.1145/3637528.3671944)|Shoujin Wang, Wentao Wang, Xiuzhen Zhang, Yan Wang, Huan Liu, Fang Chen|University of Technology Sydney, Sydney, Australia; Arizona State University, Tempe, USA; RMIT University, Melbourne, Australia; Macquarie University, Sydney, Australia|In the era of information explosion, news recommender systems are crucial for users to effectively and efficiently discover their interested news. However, most of the existing news recommender systems face two major issues, hampering recommendation quality. Firstly, they often oversimplify users' reading interests, neglecting their hierarchical nature, spanning from high-level event (e.g., US Election) related interests to low-level news article-specifc interests. Secondly, existing work often assumes a simplistic context, disregarding the prevalence of fake news and political bias under the real-world context. This oversight leads to recommendations of biased or fake news, posing risks to individuals and society. To this end, this paper addresses these gaps by introducing a novel framework, the Hierarchical and Disentangling Interest learning framework (HDInt). HDInt incorporates a hierarchical interest learning module and a disentangling interest learning module. The former captures users' high- and low-level interests, enhancing next-news recommendation accuracy. The latter effectively separates polarity and veracity information from news contents and model them more specifcally, promoting fairness- and truth-aware reading interest learning for unbiased and true news recommendations. Extensive experiments on two real-world datasets demonstrate HDInt's superiority over state-of-the-art news recommender systems in delivering accurate, unbiased, and true news recommendations.|在信息爆炸时代，新闻推荐系统对于用户有效发现感兴趣的新闻至关重要。然而，大多数现有的新闻推荐系统面临两个主要问题，阻碍了推荐质量。首先，他们往往过分简化用户的阅读兴趣，忽视了他们的等级性质，从高水平的事件(如美国大选)相关兴趣低水平的新闻文章的具体兴趣。其次，现有作品往往假设一个简单化的语境，忽视了现实语境下假新闻和政治偏见的盛行。这种疏忽导致了有偏见或假新闻的建议，给个人和社会带来风险。为此，本文提出了一种新的兴趣学习框架——分层分离式兴趣学习框架(HDInt)。HDInt 集成了分层兴趣学习模块和分离兴趣学习模块。前者捕捉用户的高层次和低层次兴趣，提高下一新闻推荐的准确性。后者有效地将极性和准确性信息从新闻内容中分离出来，并对其进行更具体的建模，促进公平和真实意识的阅读兴趣学习，以获得无偏见和真实的新闻推荐。在两个真实世界数据集上的大量实验表明，HDInt 在提供准确、公正和真实的新闻推荐方面优于最先进的新闻推荐系统。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Hierarchical+and+Disentangling+Interest+Learning+Framework+for+Unbiased+and+True+News+Recommendation)|0| +|[Warming Up Cold-Start CTR Prediction by Learning Item-Specific Feature Interactions](https://doi.org/10.1145/3637528.3671784)|Yaqing Wang, Hongming Piao, Daxiang Dong, Quanming Yao, Jingbo Zhou|Baidu Research, Baidu Inc., Beijing, China; Department of Electronic Engineering, Tsinghua University, Beijing, China; Baidu AI Cloud, Baidu Inc., Beijing, China; Department of Computer Science, City University of Hong Kong, Hong Kong, Hong Kong|In recommendation systems, new items are continuously introduced, initially lacking interaction records but gradually accumulating them over time. Accurately predicting the click-through rate (CTR) for these items is crucial for enhancing both revenue and user experience. While existing methods focus on enhancing item ID embeddings for new items within general CTR models, they tend to adopt a global feature interaction approach, often overshadowing new items with sparse data by those with abundant interactions. Addressing this, our work introduces EmerG, a novel approach that warms up cold-start CTR prediction by learning item-specific feature interaction patterns. EmerG utilizes hypernetworks to generate an item-specific feature graph based on item characteristics, which is then processed by a Graph Neural Network (GNN). This GNN is specially tailored to provably capture feature interactions at any order through a customized message passing mechanism. We further design a meta learning strategy that optimizes parameters of hypernetworks and GNN across various item CTR prediction tasks, while only adjusting a minimal set of item-specific parameters within each task. This strategy effectively reduces the risk of overfitting when dealing with limited data. Extensive experiments on benchmark datasets validate that EmerG consistently performs the best given no, a few and sufficient instances of new items.|在推荐系统中，不断引入新的项目，最初缺乏交互记录，但随着时间的推移逐渐积累。准确地预测这些项目的点进率对于提高收入和用户体验至关重要。虽然现有的方法侧重于在一般的 CTR 模型中增强新项目的项目 ID 嵌入，但它们倾向于采用全局特征交互方法，往往使那些具有丰富交互的数据稀疏的新项目黯然失色。为了解决这个问题，我们的工作介绍了 EmerG，这是一种通过学习项目特定的特征交互模式来预测冷启动 CTR 的新方法。EmerG 利用超网络生成基于项目特征的项目特征图，然后通过图神经网络(GNN)对其进行处理。这个 GNN 是专门定制的，可以通过定制的消息传递机制以任何顺序捕获特性交互。我们进一步设计了一个元学习策略，在不同的项目 CTR 预测任务中优化超级网络和 GNN 的参数，同时在每个任务中只调整最小的项目特定参数集。这种策略有效地降低了处理有限数据时过度拟合的风险。在基准数据集上的大量实验验证了 EmerG 始终如一地表现出最好的特性——没有、少数和充分的新项目实例。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Warming+Up+Cold-Start+CTR+Prediction+by+Learning+Item-Specific+Feature+Interactions)|0| +|[Improving Multi-modal Recommender Systems by Denoising and Aligning Multi-modal Content and User Feedback](https://doi.org/10.1145/3637528.3671703)|Guipeng Xv, Xinyu Li, Ruobing Xie, Chen Lin, Chong Liu, Feng Xia, Zhanhui Kang, Leyu Lin|Tencent, Beijing, China; School of Informatics, Xiamen University, Xiamen, Fujian, China|Multi-modal recommender systems (MRSs) are pivotal in diverse online web platforms and have garnered considerable attention in recent years. However, previous studies overlook the challenges of (1)noisy multi-modal content, (2) noisy user feedback, and (3) aligning multi-modal content and user feedback. To tackle these challenges, we propose Denoising and Aligning Multi-modal Recommender System (DA-MRS). To mitigate noise in multi-modal content, DA-MRS first constructs item-item graphs determined by consistent content similarity across modalities. To denoise user feedback, DA-MRS associates the probability of observed feedback with multi-modal content and devises a denoised BPR loss. Furthermore, DA-MRS implements Alignment guided by User preference to enhance task-specific item representation and Alignment guided by graded Item relations to provide finer-grained alignment. Extensive experiments verify that DA-MRS is a plug-and-play framework and achieves significant and consistent improvements across various datasets, backbone models, and noisy scenarios.|多模态推荐系统(MRS)是各种在线网络平台中的关键技术，近年来得到了广泛的关注。然而，以往的研究忽略了以下挑战: (1)噪声多模态内容，(2)噪声用户反馈，和(3)调整多模态内容和用户反馈。为了应对这些挑战，我们提出了去噪和对齐多模态推荐系统(DA-MRS)。为了减轻多模态内容中的噪声，DA-MRS 首先构建由不同模态间一致的内容相似性确定的项目-项目图。为了去除用户反馈的噪声，DA-MRS 将观测反馈的概率与多模态内容联系起来，设计了一种去除 BPR 损失的方法。此外，DA-MRS 还实现了用户偏好引导的对齐，增强了任务特定项目的表示和分级项目关系引导的对齐，提供了更细粒度的对齐。大量的实验验证了 DA-MRS 是一个即插即用的框架，并在各种数据集、骨干模型和噪声场景中实现了显著和一致的改进。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Improving+Multi-modal+Recommender+Systems+by+Denoising+and+Aligning+Multi-modal+Content+and+User+Feedback)|0| |[DDCDR: A Disentangle-based Distillation Framework for Cross-Domain Recommendation](https://doi.org/10.1145/3637528.3671605)|Zhicheng An, Zhexu Gu, Li Yu, Ke Tu, Zhengwei Wu, Binbin Hu, Zhiqiang Zhang, Lihong Gu, Jinjie Gu|Ant Group, Hangzhou, Zhejiang, China|Modern recommendation platforms frequently encompass multiple domains to cater to the varied preferences of users. Recently, cross-domain learning has gained traction as a significant paradigm within the context of recommendation systems, enabling the leveraging of rich information from a well-endowed source domain to enhance a target domain, often limited by inadequate data resources. A primary concern in cross-domain recommendation is the mitigation of negative transfer-ensuring the selective transference of pertinent knowledge from the source (domain-shared knowledge) while maintaining the integrity of domain-unique insights within the target domain (domain-specific knowledge). In this paper, we propose a novel Disentangle-based Distillation Framework for Cross-Domain Recommendation (DDCDR), designed to operate at the representational level and rooted in the established teacher-student knowledge distillation paradigm. Our methodology begins with the development of a cross-domain teacher model, trained adversarially alongside a domain discriminator. This is followed by the creation of a target domain-specific student model. By employing the trained domain discriminator, we successfully segregate domain-shared from domain-specific representations. The teacher model guides the learning of domain-shared features, while domain-specific features are enhanced via contrastive learning methods. Experiments conducted on both public datasets and an industrial dataset demonstrate DDCDR achieves a new state-of-the-art performance. The implementation within Ant Group's platform further confirms its online efficacy, manifesting relative improvements of 0.33% and 0.45% in Unique Visitor Click-Through Rate (UVCTR) across two distinct recommendation scenarios, compared to baseline performances.|现代推荐平台经常包含多个域，以满足用户的不同偏好。最近，跨领域学习作为推荐系统范围内的一个重要范式已经获得了广泛的关注，使得能够利用来自资源丰富的源领域的丰富信息来增强目标领域，而这往往受到数据资源不足的限制。跨领域推荐的主要关注点是减轻负面转移-确保从源(领域共享知识)选择性转移相关知识，同时保持目标领域(领域特定知识)内领域独特见解的完整性。本文提出了一种新的基于分离角度的跨域推荐精馏框架(DDCDR) ，该框架基于已建立的师生知识精馏范式，设计在表示层次上进行操作。我们的方法开始于开发一个跨领域的教师模型，与领域鉴别器一起进行对抗性的培训。然后创建特定于目标领域的学生模型。通过使用训练有素的领域鉴别器，我们成功地将领域共享与领域特定的表示隔离开来。教师模型指导领域共享特征的学习，而领域特定特征通过对比学习方法得到增强。在公共数据集和工业数据集上进行的实验表明，DDCDR 实现了一种新的最先进的性能。蚂蚁集团平台的实施进一步证实了其在线功效，与基线表现相比，在两个不同的推荐场景中，Unique Visitor 点进率(UVCTR)的相对改善率分别为0.33% 和0.45% 。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=DDCDR:+A+Disentangle-based+Distillation+Framework+for+Cross-Domain+Recommendation)|0| -|[Rankability-enhanced Revenue Uplift Modeling Framework for Online Marketing](https://doi.org/10.1145/3637528.3671516)|Bowei He, Yunpeng Weng, Xing Tang, Ziqiang Cui, Zexu Sun, Liang Chen, Xiuqiang He, Chen Ma|Renmin University of China, Beijing, China; City University of Hong Kong, Hong Kong, Hong Kong; FiT, Tencent, Shenzhen, China|Uplift modeling has been widely employed in online marketing by predicting the response difference between the treatment and control groups, so as to identify the sensitive individuals toward interventions like coupons or discounts. Compared with traditional conversion uplift modeling,revenue uplift modeling exhibits higher potential due to its direct connection with the corporate income. However, previous works can hardly handle the continuous long-tail response distribution in revenue uplift modeling. Moreover, they have neglected to optimize the uplift ranking among different individuals, which is actually the core of uplift modeling. To address such issues, in this paper, we first utilize the zero-inflated lognormal (ZILN) loss to regress the responses and customize the corresponding modeling network, which can be adapted to different existing uplift models. Then, we study the ranking-related uplift modeling error from the theoretical perspective and propose two tighter error bounds as the additional loss terms to the conventional response regression loss. Finally, we directly model the uplift ranking error for the entire population with a listwise uplift ranking loss. The experiment results on offline public and industrial datasets validate the effectiveness of our method for revenue uplift modeling. Furthermore, we conduct large-scale experiments on a prominent online fintech marketing platform, Tencent FiT, which further demonstrates the superiority of our method in real-world applications.|提升模型通过预测治疗组与对照组之间的反应差异，以识别对优惠券或折扣等干预措施敏感的个体，在网络营销中得到了广泛的应用。与传统的转换提升模型相比，收入提升模型由于与企业收入直接相关，因此具有更大的潜力。然而，以往的工作难以处理连续的长尾响应分布的收入提升模型。而且，他们忽略了优化不同个体之间的提升排序，这实际上是提升模型的核心。为了解决这些问题，本文首先利用零膨胀对数正态(ZILN)损失对响应进行回归，并定制相应的模型网络，以适应不同的现有抬升模型。然后，从理论角度研究了与排名相关的提升模型误差，提出了两个更严格的误差界作为常规响应回归损失的附加损失项。最后，我们直接模型的提升排名误差的整个人口与列表提升排名损失。在离线公共数据集和工业数据集上的实验结果验证了该方法对收入提升模型的有效性。此外，我们在一个著名的网上金融科技营销平台腾讯 FiT 上进行了大规模的实验，这进一步证明了我们的方法在实际应用中的优越性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Rankability-enhanced+Revenue+Uplift+Modeling+Framework+for+Online+Marketing)|0| +|[Rankability-enhanced Revenue Uplift Modeling Framework for Online Marketing](https://doi.org/10.1145/3637528.3671516)|Bowei He, Yunpeng Weng, Xing Tang, Ziqiang Cui, Zexu Sun, Liang Chen, Xiuqiang He, Chen Ma|Renmin University of China, Beijing, China; FiT, Tencent, Shenzhen, China; City University of Hong Kong, Hong Kong, Hong Kong|Uplift modeling has been widely employed in online marketing by predicting the response difference between the treatment and control groups, so as to identify the sensitive individuals toward interventions like coupons or discounts. Compared with traditional conversion uplift modeling,revenue uplift modeling exhibits higher potential due to its direct connection with the corporate income. However, previous works can hardly handle the continuous long-tail response distribution in revenue uplift modeling. Moreover, they have neglected to optimize the uplift ranking among different individuals, which is actually the core of uplift modeling. To address such issues, in this paper, we first utilize the zero-inflated lognormal (ZILN) loss to regress the responses and customize the corresponding modeling network, which can be adapted to different existing uplift models. Then, we study the ranking-related uplift modeling error from the theoretical perspective and propose two tighter error bounds as the additional loss terms to the conventional response regression loss. Finally, we directly model the uplift ranking error for the entire population with a listwise uplift ranking loss. The experiment results on offline public and industrial datasets validate the effectiveness of our method for revenue uplift modeling. Furthermore, we conduct large-scale experiments on a prominent online fintech marketing platform, Tencent FiT, which further demonstrates the superiority of our method in real-world applications.|提升模型通过预测治疗组与对照组之间的反应差异，以识别对优惠券或折扣等干预措施敏感的个体，在网络营销中得到了广泛的应用。与传统的转换提升模型相比，收入提升模型由于与企业收入直接相关，因此具有更大的潜力。然而，以往的工作难以处理连续的长尾响应分布的收入提升模型。而且，他们忽略了优化不同个体之间的提升排序，这实际上是提升模型的核心。为了解决这些问题，本文首先利用零膨胀对数正态(ZILN)损失对响应进行回归，并定制相应的模型网络，以适应不同的现有抬升模型。然后，从理论角度研究了与排名相关的提升模型误差，提出了两个更严格的误差界作为常规响应回归损失的附加损失项。最后，我们直接模型的提升排名误差的整个人口与列表提升排名损失。在离线公共数据集和工业数据集上的实验结果验证了该方法对收入提升模型的有效性。此外，我们在一个著名的网上金融科技营销平台腾讯 FiT 上进行了大规模的实验，这进一步证明了我们的方法在实际应用中的优越性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Rankability-enhanced+Revenue+Uplift+Modeling+Framework+for+Online+Marketing)|0| |[Personalized Product Assortment with Real-time 3D Perception and Bayesian Payoff Estimation](https://doi.org/10.1145/3637528.3671518)|Porter Jenkins, Michael Selander, J. Stockton Jenkins, Andrew Merrill, Kyle Armstrong|Delicious AI, Lehi, UT, USA; Department of Computer Science, Brigham Young University, Provo, UT, USA|Product assortment selection is a critical challenge facing physical retailers. Effectively aligning inventory with the preferences of shoppers can increase sales and decrease out-of-stocks. However, in real-world settings the problem is challenging due to the combinatorial explosion of product assortment possibilities. Consumer preferences are typically heterogeneous across space and time, making inventory-preference alignment challenging. Additionally, existing strategies rely on syndicated data, which tends to be aggregated, low resolution, and suffer from high latency. To solve these challenges, we introduce a real-time recommendation system, which we call EdgeRec3D. Our system utilizes recent advances in 3D computer vision for perception and automatic, fine grained sales estimation. These perceptual components run on the edge of the network and facilitate real-time reward signals. Additionally, we develop a Bayesian payoff model to account for noisy estimates from 3D LIDAR data. We rely on spatial clustering to allow the system to adapt to heterogeneous consumer preferences, and a graph-based candidate generation algorithm to address the combinatorial search problem. We test our system in real-world stores across two, 6-8 week A/B tests with beverage products and demonstrate a 35% and 27% increase in sales respectively. Finally, we monitor the deployed system for a period of 28 weeks with an observational study and show a 9.4% increase in sales.|产品分类选择是实体零售商面临的一个关键挑战。有效地调整库存与购物者的偏好可以增加销售和减少缺货。然而，在现实世界中，这个问题是具有挑战性的，因为产品分类的可能性是组合爆炸的。消费者的偏好在空间和时间上具有典型的异质性，这使得库存偏好的调整具有挑战性。此外，现有的策略依赖于聚合数据，这些数据往往是聚合的、低分辨率的，并且存在高延迟。为了解决这些问题，我们引入了一个实时推荐系统，我们称之为 EdgeRec3D。我们的系统利用三维计算机视觉的最新进展来进行感知和自动、细粒度的销售估算。这些感知成分运行在网络的边缘，便于实时奖励信号。此外，我们开发了贝叶斯支付模型，以考虑噪声估计从三维激光雷达数据。我们依靠空间聚类来使系统能够适应不同的消费者偏好，以及一个基于图的候选人生成算法来解决组合搜索问题。我们测试我们的系统在现实世界的商店两个，6-8周的 A/B 测试与饮料产品，并证明了35% 和27% 的销售分别增长。最后，我们对已部署的系统进行了为期28周的观察性研究监控，结果显示销售额增长了9.4% 。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Personalized+Product+Assortment+with+Real-time+3D+Perception+and+Bayesian+Payoff+Estimation)|0| -|[Where Have You Been? A Study of Privacy Risk for Point-of-Interest Recommendation](https://doi.org/10.1145/3637528.3671758)|Kunlin Cai, Jinghuai Zhang, Zhiqing Hong, William Shand, Guang Wang, Desheng Zhang, Jianfeng Chi, Yuan Tian|Florida State University, Tallahassee, FL, USA; Meta, New York, NY, USA; University of California, Los Angeles, Los Angeles, CA, USA; Rutgers University, New Brunswick, NJ, USA|As location-based services (LBS) have grown in popularity, more human mobility data has been collected. The collected data can be used to build machine learning (ML) models for LBS to enhance their performance and improve overall experience for users. However, the convenience comes with the risk of privacy leakage since this type of data might contain sensitive information related to user identities, such as home/work locations. Prior work focuses on protecting mobility data privacy during transmission or prior to release, lacking the privacy risk evaluation of mobility data-based ML models. To better understand and quantify the privacy leakage in mobility data-based ML models, we design a privacy attack suite containing data extraction and membership inference attacks tailored for point-of-interest (POI) recommendation models, one of the most widely used mobility data-based ML models. These attacks in our attack suite assume different adversary knowledge and aim to extract different types of sensitive information from mobility data, providing a holistic privacy risk assessment for POI recommendation models. Our experimental evaluation using two real-world mobility datasets demonstrates that current POI recommendation models are vulnerable to our attacks. We also present unique findings to understand what types of mobility data are more susceptible to privacy attacks. Finally, we evaluate defenses against these attacks and highlight future directions and challenges.|随着基于位置的服务(LBS)越来越流行，越来越多的移动性数据被收集。所收集的数据可以用来为 LBS 建立机器学习(ML)模型，以提高它们的性能和改善用户的整体体验。然而，这种便利伴随着隐私泄露的风险，因为这类数据可能包含与用户身份有关的敏感信息，例如家庭/工作地点。此前的工作重点是保护移动数据在传输过程中或发布之前的隐私，缺乏基于移动数据的机器学习模型的隐私风险评估。为了更好地理解和量化基于移动性数据的机器学习模型中的隐私泄漏，我们设计了一个隐私攻击套件，其中包含针对感兴趣点(POI)推荐模型的数据提取和成员推断攻击，这是最广泛使用的基于移动性数据的机器学习模型之一。我们的攻击套件中的这些攻击假设不同的敌人知识，目的是从移动数据中提取不同类型的敏感信息，为 POI 推荐模型提供一个全面的隐私风险评估。我们的实验评估使用两个真实世界的移动性数据集表明，目前的 POI 推荐模型是脆弱的，我们的攻击。我们还提出了独特的发现，以了解哪些类型的移动数据更容易受到隐私攻击。最后，我们将评估针对这些攻击的防御措施，并强调未来的方向和挑战。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Where+Have+You+Been?+A+Study+of+Privacy+Risk+for+Point-of-Interest+Recommendation)|0| +|[Where Have You Been? A Study of Privacy Risk for Point-of-Interest Recommendation](https://doi.org/10.1145/3637528.3671758)|Kunlin Cai, Jinghuai Zhang, Zhiqing Hong, William Shand, Guang Wang, Desheng Zhang, Jianfeng Chi, Yuan Tian|Florida State University, Tallahassee, FL, USA; University of California, Los Angeles, Los Angeles, CA, USA; Meta, New York, NY, USA; Rutgers University, New Brunswick, NJ, USA|As location-based services (LBS) have grown in popularity, more human mobility data has been collected. The collected data can be used to build machine learning (ML) models for LBS to enhance their performance and improve overall experience for users. However, the convenience comes with the risk of privacy leakage since this type of data might contain sensitive information related to user identities, such as home/work locations. Prior work focuses on protecting mobility data privacy during transmission or prior to release, lacking the privacy risk evaluation of mobility data-based ML models. To better understand and quantify the privacy leakage in mobility data-based ML models, we design a privacy attack suite containing data extraction and membership inference attacks tailored for point-of-interest (POI) recommendation models, one of the most widely used mobility data-based ML models. These attacks in our attack suite assume different adversary knowledge and aim to extract different types of sensitive information from mobility data, providing a holistic privacy risk assessment for POI recommendation models. Our experimental evaluation using two real-world mobility datasets demonstrates that current POI recommendation models are vulnerable to our attacks. We also present unique findings to understand what types of mobility data are more susceptible to privacy attacks. Finally, we evaluate defenses against these attacks and highlight future directions and challenges.|随着基于位置的服务(LBS)越来越流行，越来越多的移动性数据被收集。所收集的数据可以用来为 LBS 建立机器学习(ML)模型，以提高它们的性能和改善用户的整体体验。然而，这种便利伴随着隐私泄露的风险，因为这类数据可能包含与用户身份有关的敏感信息，例如家庭/工作地点。此前的工作重点是保护移动数据在传输过程中或发布之前的隐私，缺乏基于移动数据的机器学习模型的隐私风险评估。为了更好地理解和量化基于移动性数据的机器学习模型中的隐私泄漏，我们设计了一个隐私攻击套件，其中包含针对感兴趣点(POI)推荐模型的数据提取和成员推断攻击，这是最广泛使用的基于移动性数据的机器学习模型之一。我们的攻击套件中的这些攻击假设不同的敌人知识，目的是从移动数据中提取不同类型的敏感信息，为 POI 推荐模型提供一个全面的隐私风险评估。我们的实验评估使用两个真实世界的移动性数据集表明，目前的 POI 推荐模型是脆弱的，我们的攻击。我们还提出了独特的发现，以了解哪些类型的移动数据更容易受到隐私攻击。最后，我们将评估针对这些攻击的防御措施，并强调未来的方向和挑战。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Where+Have+You+Been?+A+Study+of+Privacy+Risk+for+Point-of-Interest+Recommendation)|0| |[Neural Retrievers are Biased Towards LLM-Generated Content](https://doi.org/10.1145/3637528.3671882)|Sunhao Dai, Yuqi Zhou, Liang Pang, Weihao Liu, Xiaolin Hu, Yong Liu, Xiao Zhang, Gang Wang, Jun Xu|; Noah's Ark Lab, Huawei, Shenzhen, China; Gaoling School of Artificial Intelligence, Renmin University of China, Beijing, China|Recently, the emergence of large language models (LLMs) has revolutionized the paradigm of information retrieval (IR) applications, especially in web search, by generating vast amounts of human-like texts on the Internet. As a result, IR systems in the LLM era are facing a new challenge: the indexed documents are now not only written by human beings but also automatically generated by the LLMs. How these LLM-generated documents influence the IR systems is a pressing and still unexplored question. In this work, we conduct a quantitative evaluation of IR models in scenarios where both human-written and LLM-generated texts are involved. Surprisingly, our findings indicate that neural retrieval models tend to rank LLM-generated documents higher. We refer to this category of biases in neural retrievers towards the LLM-generated content as the source bias. Moreover, we discover that this bias is not confined to the first-stage neural retrievers, but extends to the second-stage neural re-rankers. Then, in-depth analyses from the perspective of text compression indicate that LLM-generated texts exhibit more focused semantics with less noise, making it easier for neural retrieval models to semantic match. To mitigate the source bias, we also propose a plug-and-play debiased constraint for the optimization objective, and experimental results show its effectiveness. Finally, we discuss the potential severe concerns stemming from the observed source bias and hope our findings can serve as a critical wake-up call to the IR community and beyond. To facilitate future explorations of IR in the LLM era, the constructed two new benchmarks are available at https://github.com/KID-22/Source-Bias.|最近，大语言模型(LLMs)的出现彻底改变了信息检索(IR)应用的范式，特别是在网络搜索中，通过在互联网上生成大量类似人类的文本。因此，LLM 时代的信息检索系统面临着新的挑战: 索引文档不仅由人工编写，而且由 LLM 自动生成。这些 LLM 生成的文档如何影响 IR 系统是一个紧迫的、尚未探索的问题。在这项工作中，我们进行了定量评估的情况下，国际关系模型的人写和 LLM 生成的文本都涉及。令人惊讶的是，我们的研究结果表明，神经检索模型往往排名 LLM 生成的文档更高。我们将神经检索器对 LLM 生成的内容的这类偏差称为源偏差。此外，我们发现这种偏差并不局限于第一阶段的神经检索，而是延伸到第二阶段的神经重新排序。然后，从文本压缩的角度进行深入分析，结果表明 LLM 生成的文本具有更集中的语义和更少的噪声，使得神经检索模型更容易进行语义匹配。为了减小源偏差，我们还提出了一个即插即用的去偏约束优化目标，实验结果表明其有效性。最后，我们讨论了由观察到的来源偏差引起的潜在的严重关切，并希望我们的发现可以作为对 IR 社区和其他方面的一个关键的警告。为了促进 LLM 时代对信息检索的未来探索，构建了两个新的基准 https://github.com/kid-22/source-bias。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Neural+Retrievers+are+Biased+Towards+LLM-Generated+Content)|0| |[DisCo: Towards Harmonious Disentanglement and Collaboration between Tabular and Semantic Space for Recommendation](https://doi.org/10.1145/3637528.3672008)|Kounianhua Du, Jizheng Chen, Jianghao Lin, Yunjia Xi, Hangyu Wang, Xinyi Dai, Bo Chen, Ruiming Tang, Weinan Zhang|Huawei Noah's Ark Lab, Shenzhen, China; Huawei Noah's Ark Lab, Shanghai, China; Shanghai Jiao Tong University, Shanghai, China|Recommender systems play important roles in various applications such ase-commerce, social media, etc. Conventional recommendation methods usuallymodel the collaborative signals within the tabular representation space.Despite the personalization modeling and the efficiency, the latent semanticdependencies are omitted. Methods that introduce semantics into recommendationthen emerge, injecting knowledge from the semantic representation space wherethe general language understanding are compressed. However, existingsemantic-enhanced recommendation methods focus on aligning the two spaces,during which the representations of the two spaces tend to get close while theunique patterns are discarded and not well explored. In this paper, we proposeDisCo to Disentangle the unique patterns from the two representation spaces andCollaborate the two spaces for recommendation enhancement, where both thespecificity and the consistency of the two spaces are captured. Concretely, wepropose 1) a dual-side attentive network to capture the intra-domain patternsand the inter-domain patterns, 2) a sufficiency constraint to preserve thetask-relevant information of each representation space and filter out thenoise, and 3) a disentanglement constraint to avoid the model from discardingthe unique information. These modules strike a balance between disentanglementand collaboration of the two representation spaces to produce informativepattern vectors, which could serve as extra features and be appended toarbitrary recommendation backbones for enhancement. Experiment results validatethe superiority of our method against different models and the compatibility ofDisCo over different backbones. Various ablation studies and efficiencyanalysis are also conducted to justify each model component.|推荐系统在电子商务、社交媒体等各种应用中发挥着重要作用。传统的推荐方法通常是在表格表示空间中对协作信号进行建模。尽管个性化建模和效率，潜在的语义依赖性被忽略。然后出现将语义引入推荐的方法，从语义表示空间注入知识，在这个空间中一般语言理解被压缩。然而，现有的语义增强推荐方法侧重于对齐这两个空间，在此期间，两个空间的表示趋于接近，而唯一的模式被丢弃，没有得到很好的探索。在本文中，我们提出了 DisCo 从两个表示空间中分离出唯一的模式，并协作两个空间进行推荐增强，同时捕获两个空间的特殊性和一致性。具体来说，我们提出了两种方案: 1)双侧注意网络捕获域内模式和域间模式; 2)充分约束保留每个表示空间的任务相关信息并过滤掉噪声; 3)解缠约束避免模型丢弃唯一信息。这些模块在两个表示空间的分离和协作之间取得了平衡，从而产生了信息模式向量，这些向量可以作为额外的特征，并附加到任意的推荐主干上进行增强。实验结果验证了该方法对不同模型的优越性以及 DisCo 在不同骨架上的兼容性。各种消融研究和效率分析也进行了验证每个模型组件。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=DisCo:+Towards+Harmonious+Disentanglement+and+Collaboration+between+Tabular+and+Semantic+Space+for+Recommendation)|0| |[Label Shift Correction via Bidirectional Marginal Distribution Matching](https://doi.org/10.1145/3637528.3671867)|Ruidong Fan, Xiao Ouyang, Hong Tao, Chenping Hou|National University of Defense Technology, Changsha, Hunan, China|Due to the timeliness and uncertainty of data acquisition, label shift, which assumes that the source (training) and target (test) label distributions differ, occurs with the changing environment and reduces the generalization ability of traditional models. To correct the label shift, existing methods estimate the true label distribution by prediction of target data from a source classifier, which results in high variance, especially with large label shift. In this paper, we tackle this problem by proposing a novel approach termed as Label Shift Correction via Bidirectional Marginal Distribution Matching (BMDM). Our approach matchs the label and feature marginal distributions simultaneously to ensure the stability of estimated class proportions. We prove theoretically that there is a unique optimal solution, i.e., true target label distribution, for our approach under mild conditions, and an efficient optimization strategy is also proposed. On this basis, in multi-shot scenario where label distribution changes continuously, we extend BMDM by designing a new distribution matching mechanism and constructing a regularization term that constrains the direction of label distribution change. Extensive experimental results validate the effectiveness of our approach over existing state-of-the-arts methods.|由于数据采集的及时性和不确定性，假设源(训练)和目标(测试)标签分布不同的标签偏移随着环境的变化而发生，降低了传统模型的泛化能力。为了校正标签偏移，现有的方法通过预测源分类器的目标数据来估计真实的标签分布，这导致了很大的方差，尤其是标签偏移。在本文中，我们提出了一种新的方法来解决这个问题，这种方法被称为双向边缘分布匹配的标签偏移校正(bMDM)。我们的方法同时匹配标签和特征的边际分布，以确保估计的类比例的稳定性。从理论上证明了该方法在温和条件下存在唯一的最优解，即真实目标标签分布，并提出了一种有效的优化策略。在此基础上，在标签分布不断变化的多镜头场景下，通过设计一种新的分布匹配机制，构造一个约束标签分布变化方向的正则项，对 BMDM 进行了扩展。大量的实验结果验证了我们的方法比现有的最先进的方法更有效。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Label+Shift+Correction+via+Bidirectional+Marginal+Distribution+Matching)|0| -|[On (Normalised) Discounted Cumulative Gain as an Off-Policy Evaluation Metric for Top-n Recommendation](https://doi.org/10.1145/3637528.3671687)|Olivier Jeunen, Ivan Potapov, Aleksei Ustimenko|ShareChat, London, United Kingdom; ShareChat, Edinburgh, United Kingdom|Approaches to recommendation are typically evaluated in one of two ways: (1)via a (simulated) online experiment, often seen as the gold standard, or (2)via some offline evaluation procedure, where the goal is to approximate theoutcome of an online experiment. Several offline evaluation metrics have beenadopted in the literature, inspired by ranking metrics prevalent in the fieldof Information Retrieval. (Normalised) Discounted Cumulative Gain (nDCG) is onesuch metric that has seen widespread adoption in empirical studies, and higher(n)DCG values have been used to present new methods as the state-of-the-art intop-n recommendation for many years. Our work takes a critical look at this approach, and investigates when we canexpect such metrics to approximate the gold standard outcome of an onlineexperiment. We formally present the assumptions that are necessary to considerDCG an unbiased estimator of online reward and provide a derivation for thismetric from first principles, highlighting where we deviate from itstraditional uses in IR. Importantly, we show that normalising the metricrenders it inconsistent, in that even when DCG is unbiased, ranking competingmethods by their normalised DCG can invert their relative order. Through acorrelation analysis between off- and on-line experiments conducted on alarge-scale recommendation platform, we show that our unbiased DCG estimatesstrongly correlate with online reward, even when some of the metric's inherentassumptions are violated. This statement no longer holds for its normalisedvariant, suggesting that nDCG's practical utility may be limited.|推荐方法通常以两种方式之一进行评估: (1)通过(模拟)在线实验，通常被视为黄金标准，或者(2)通过一些离线评估程序，其目标是近似在线实验的结果。文献中已经采用了一些线下评估指标，这些指标的灵感来自于信息检索领域中流行的排名指标。(标准化)贴现累积增益(nDCG)是在实证研究中得到广泛采用的一种度量标准，较高的(n) DCG 值已被用于表示新方法作为最先进的推荐方法多年。我们的工作对这种方法进行了批判性的研究，并且调查了我们什么时候可以期望这些指标接近在线实验的黄金标准结果。我们正式提出的假设是必要的，认为 DCG 是一个公正的估计在线奖励，并提供了从第一原则这一度量的推导，突出了我们偏离其传统用途在 IR。重要的是，我们表明，规范化的度量呈现它不一致，即使当 DCG 是无偏见的，排名竞争方法的规范化 DCG 可以颠倒他们的相对顺序。通过在大规模推荐平台上进行的离线和在线实验之间的相关性分析，我们表明我们的无偏 DCG 估计与在线奖励强烈相关，即使一些度量的固有假设被违背。这种说法不再适用于它的正常化变体，表明 nDCG 的实际用途可能是有限的。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=On+(Normalised)+Discounted+Cumulative+Gain+as+an+Off-Policy+Evaluation+Metric+for+Top-n+Recommendation)|0| -|[FairMatch: Promoting Partial Label Learning by Unlabeled Samples](https://doi.org/10.1145/3637528.3671685)|Jiahao Jiang, Yuheng Jia, Hui Liu, Junhui Hou|College of Software Engineering, Southeast University, Nanjing, China; School of Computing & Information Sciences, Saint Francis University, HongKong, China; Department of Computer Science, City University of Hong Kong, HongKong, China; School of Computer Science and Engineering, Southeast University, Nanjing, China|This paper studies the semi-supervised partial label learning (SSPLL) problem, which aims to improve the partial label learning (PLL) by leveraging unlabeled samples. Both the existing SSPLL methods and the semi-supervised learning methods exploit the information in unlabeled samples by selecting high-confidence unlabeled samples as the pseudo labels based on the maximum value of the model output. However, the scarcity of labeled samples and the ambiguity from partial labels skew this strategy towards an unfair selection of high-confidence samples on each class, most notably during the initial phases of training, resulting in slower training and performance degradation. In this paper, we propose a novel method FairMatch, which adopts a learning state aware self-adaptive threshold for selecting the same number of high-confidence samples on each class, and uses augmentation consistency to incorporate the unlabeled samples to promote PLL. In addition, we adopt the candidate label disambiguation to utilize the partial labeled samples and mix up the partial labeled samples and the selected high-confidence unlabeled samples to prevent the model from overfitting on partial label samples. FairMatch can achieve maximum accuracy improvements of 9.53%, 4.9%, and 16.45% on CIFAR-10, CIFAR-100, and CIFAR-100H, respectively. The codes can be found at https://github.com/jhjiangSEU/FairMatch.|本文研究了半监督部分标记学习(SSPLL)问题，旨在利用未标记样本改进部分标记学习(PLL)。现有的 SSPLL 方法和半监督学习方法都是根据模型输出的最大值，选择高置信度的未标记样本作为伪标签，从而利用未标记样本的信息。然而，标记样本的稀缺性和部分标记的模糊性使该策略倾向于在每个类别上不公平地选择高置信度样本，最显着的是在训练的初始阶段，导致训练较慢和性能下降。本文提出了一种新的 FairMatch 方法，该方法采用一种学习状态感知的自适应阈值来选择每类中相同数量的高置信度样本，并使用增强一致性来合并未标记的样本来提升锁相环。此外，我们采用候选标签消歧的方法，利用部分标签样本，混合部分标签样本和选择的高置信度未标签样本，以防止模型对部分标签样本的过度拟合。FairMatch 可以分别在 CIFAR-10，CIFAR-100和 CIFAR-100H 上实现9.53% ，4.9% 和16.45% 的最大准确性改进。密码可以在 https://github.com/jhjiangseu/fairmatch 找到。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=FairMatch:+Promoting+Partial+Label+Learning+by+Unlabeled+Samples)|0| +|[On (Normalised) Discounted Cumulative Gain as an Off-Policy Evaluation Metric for Top-n Recommendation](https://doi.org/10.1145/3637528.3671687)|Olivier Jeunen, Ivan Potapov, Aleksei Ustimenko|ShareChat, Edinburgh, United Kingdom; ShareChat, London, United Kingdom|Approaches to recommendation are typically evaluated in one of two ways: (1)via a (simulated) online experiment, often seen as the gold standard, or (2)via some offline evaluation procedure, where the goal is to approximate theoutcome of an online experiment. Several offline evaluation metrics have beenadopted in the literature, inspired by ranking metrics prevalent in the fieldof Information Retrieval. (Normalised) Discounted Cumulative Gain (nDCG) is onesuch metric that has seen widespread adoption in empirical studies, and higher(n)DCG values have been used to present new methods as the state-of-the-art intop-n recommendation for many years. Our work takes a critical look at this approach, and investigates when we canexpect such metrics to approximate the gold standard outcome of an onlineexperiment. We formally present the assumptions that are necessary to considerDCG an unbiased estimator of online reward and provide a derivation for thismetric from first principles, highlighting where we deviate from itstraditional uses in IR. Importantly, we show that normalising the metricrenders it inconsistent, in that even when DCG is unbiased, ranking competingmethods by their normalised DCG can invert their relative order. Through acorrelation analysis between off- and on-line experiments conducted on alarge-scale recommendation platform, we show that our unbiased DCG estimatesstrongly correlate with online reward, even when some of the metric's inherentassumptions are violated. This statement no longer holds for its normalisedvariant, suggesting that nDCG's practical utility may be limited.|推荐方法通常以两种方式之一进行评估: (1)通过(模拟)在线实验，通常被视为黄金标准，或者(2)通过一些离线评估程序，其目标是近似在线实验的结果。文献中已经采用了一些线下评估指标，这些指标的灵感来自于信息检索领域中流行的排名指标。(标准化)贴现累积增益(nDCG)是在实证研究中得到广泛采用的一种度量标准，较高的(n) DCG 值已被用于表示新方法作为最先进的推荐方法多年。我们的工作对这种方法进行了批判性的研究，并且调查了我们什么时候可以期望这些指标接近在线实验的黄金标准结果。我们正式提出的假设是必要的，认为 DCG 是一个公正的估计在线奖励，并提供了从第一原则这一度量的推导，突出了我们偏离其传统用途在 IR。重要的是，我们表明，规范化的度量呈现它不一致，即使当 DCG 是无偏见的，排名竞争方法的规范化 DCG 可以颠倒他们的相对顺序。通过在大规模推荐平台上进行的离线和在线实验之间的相关性分析，我们表明我们的无偏 DCG 估计与在线奖励强烈相关，即使一些度量的固有假设被违背。这种说法不再适用于它的正常化变体，表明 nDCG 的实际用途可能是有限的。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=On+(Normalised)+Discounted+Cumulative+Gain+as+an+Off-Policy+Evaluation+Metric+for+Top-n+Recommendation)|0| +|[FairMatch: Promoting Partial Label Learning by Unlabeled Samples](https://doi.org/10.1145/3637528.3671685)|Jiahao Jiang, Yuheng Jia, Hui Liu, Junhui Hou|Department of Computer Science, City University of Hong Kong, HongKong, China; School of Computing & Information Sciences, Saint Francis University, HongKong, China; School of Computer Science and Engineering, Southeast University, Nanjing, China; College of Software Engineering, Southeast University, Nanjing, China|This paper studies the semi-supervised partial label learning (SSPLL) problem, which aims to improve the partial label learning (PLL) by leveraging unlabeled samples. Both the existing SSPLL methods and the semi-supervised learning methods exploit the information in unlabeled samples by selecting high-confidence unlabeled samples as the pseudo labels based on the maximum value of the model output. However, the scarcity of labeled samples and the ambiguity from partial labels skew this strategy towards an unfair selection of high-confidence samples on each class, most notably during the initial phases of training, resulting in slower training and performance degradation. In this paper, we propose a novel method FairMatch, which adopts a learning state aware self-adaptive threshold for selecting the same number of high-confidence samples on each class, and uses augmentation consistency to incorporate the unlabeled samples to promote PLL. In addition, we adopt the candidate label disambiguation to utilize the partial labeled samples and mix up the partial labeled samples and the selected high-confidence unlabeled samples to prevent the model from overfitting on partial label samples. FairMatch can achieve maximum accuracy improvements of 9.53%, 4.9%, and 16.45% on CIFAR-10, CIFAR-100, and CIFAR-100H, respectively. The codes can be found at https://github.com/jhjiangSEU/FairMatch.|本文研究了半监督部分标记学习(SSPLL)问题，旨在利用未标记样本改进部分标记学习(PLL)。现有的 SSPLL 方法和半监督学习方法都是根据模型输出的最大值，选择高置信度的未标记样本作为伪标签，从而利用未标记样本的信息。然而，标记样本的稀缺性和部分标记的模糊性使该策略倾向于在每个类别上不公平地选择高置信度样本，最显着的是在训练的初始阶段，导致训练较慢和性能下降。本文提出了一种新的 FairMatch 方法，该方法采用一种学习状态感知的自适应阈值来选择每类中相同数量的高置信度样本，并使用增强一致性来合并未标记的样本来提升锁相环。此外，我们采用候选标签消歧的方法，利用部分标签样本，混合部分标签样本和选择的高置信度未标签样本，以防止模型对部分标签样本的过度拟合。FairMatch 可以分别在 CIFAR-10，CIFAR-100和 CIFAR-100H 上实现9.53% ，4.9% 和16.45% 的最大准确性改进。密码可以在 https://github.com/jhjiangseu/fairmatch 找到。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=FairMatch:+Promoting+Partial+Label+Learning+by+Unlabeled+Samples)|0| |[Privileged Knowledge State Distillation for Reinforcement Learning-based Educational Path Recommendation](https://doi.org/10.1145/3637528.3671872)|Qingyao Li, Wei Xia, Li'ang Yin, Jiarui Jin, Yong Yu|Huawei Noah's Ark Lab, Shenzhen, China; Shanghai Jiao Tong University, Shanghai, China|Educational recommendation seeks to suggest knowledge concepts that match a learner's ability, thus facilitating a personalized learning experience. In recent years, reinforcement learning (RL) methods have achieved considerable results by taking the encoding of the learner's exercise log as the state and employing an RL-based agent to make suitable recommendations. However, these approaches suffer from handling the diverse and dynamic learner's knowledge states. In this paper, we introduce the privileged feature distillation technique and propose the P rivileged K nowledge S tate D istillation (PKSD ) framework, allowing the RL agent to leverage the "actual'' knowledge state as privileged information in the state encoding to help tailor recommendations to meet individual needs. Concretely, our PKSD takes the privileged knowledge states together with the representations of the exercise log for the state representations during training. And through distillation, we transfer the ability to adapt to learners to aknowledge state adapter. During inference, theknowledge state adapter would serve as the estimated privileged knowledge states instead of the real one since it is not accessible. Considering that there are strong connections among the knowledge concepts in education, we further propose to collaborate the graph structure learning for concepts into our PKSD framework. This new approach is termed GEPKSD (Graph-Enhanced PKSD). As our method is model-agnostic, we evaluate PKSD and GEPKSD by integrating them with five different RL bases on four public simulators, respectively. Our results verify that PKSD can consistently improve the recommendation performance with various RL methods, and our GEPKSD could further enhance the effectiveness of PKSD in all the simulations.|教育推荐旨在建议符合学习者能力的知识概念，从而促进个性化的学习体验。近年来，以学习者运动日志的编码为状态，并使用基于强化学习的代理来提出合适的建议，这些方法已经取得了相当大的成果。然而，这些方法在处理多样化和动态学习者的知识状态时存在缺陷。本文介绍了特权特征提取技术，提出了 P 特权 K 知识 S 状态 D 提取(PKSD)框架，允许 RL 代理利用“实际”知识状态作为状态编码中的特权信息，帮助定制推荐以满足个体需求。具体地说，我们的 PKSD 将特权知识状态与训练日志的表示一起提取，用于训练过程中的状态表示。并通过升华，将学习者的适应能力转化为知识状态适应能力。在推理过程中，知识状态适配器将作为估计的特权知识状态，而不是实际知识状态，因为它是不可访问的。考虑到教育中知识概念之间的紧密联系，我们进一步提出将概念的图形结构学习协同到 PKSD 框架中。这种新的方法被称为 GEPKSD (图形增强 PKSD)。由于我们的方法是模型无关的，所以我们分别在四个公共模拟器上将 PKSD 和 GEPKSD 与五个不同的 RL 基地集成在一起进行评估。实验结果表明，PKSD 能够持续改善各种 RL 方法的推荐性能，而 GEPKSD 能够进一步提高 PKSD 在所有仿真中的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Privileged+Knowledge+State+Distillation+for+Reinforcement+Learning-based+Educational+Path+Recommendation)|0| |[Toward Structure Fairness in Dynamic Graph Embedding: A Trend-aware Dual Debiasing Approach](https://doi.org/10.1145/3637528.3671848)|Yicong Li, Yu Yang, Jiannong Cao, Shuaiqi Liu, Haoran Tang, Guandong Xu|The Education University of Hong Kong & University of Technology Sydney, Hong Kong, Hong Kong; The Hong Kong Polytechnic University, Hong Kong, Hong Kong|Recent studies successfully learned static graph embeddings that arestructurally fair by preventing the effectiveness disparity of high- andlow-degree vertex groups in downstream graph mining tasks. However, achievingstructure fairness in dynamic graph embedding remains an open problem.Neglecting degree changes in dynamic graphs will significantly impair embeddingeffectiveness without notably improving structure fairness. This is because theembedding performance of high-degree and low-to-high-degree vertices willsignificantly drop close to the generally poorer embedding performance of mostslightly changed vertices in the long-tail part of the power-law distribution.We first identify biased structural evolutions in a dynamic graph based on theevolving trend of vertex degree and then propose FairDGE, the firststructurally Fair Dynamic Graph Embedding algorithm. FairDGE learns biasedstructural evolutions by jointly embedding the connection changes amongvertices and the long-short-term evolutionary trend of vertex degrees.Furthermore, a novel dual debiasing approach is devised to encode fairembeddings contrastively, customizing debiasing strategies for different biasedstructural evolutions. This innovative debiasing strategy breaks theeffectiveness bottleneck of embeddings without notable fairness loss. Extensiveexperiments demonstrate that FairDGE achieves simultaneous improvement in theeffectiveness and fairness of embeddings.|最近的研究成功地学习了静态图嵌入，通过防止有效性差异的高度和低度顶点组在下游图挖掘任务。然而，在动态图嵌入中实现结构公平性仍然是一个悬而未决的问题。忽略动态图的度变化会显著降低嵌入效率，而不能显著提高结构的公平性。这是因为在幂律分布的长尾部分，高度顶点和低到高度顶点的嵌入性能会明显下降，接近于最微小变化顶点的嵌入性能普遍较差。我们首先根据顶点度的演化趋势在动态图中识别有偏的结构演化，然后提出 FairDGE，第一个结构公平的动态图嵌入算法。FairDGE 通过联合嵌入顶点之间的联系变化和顶点度的长期短期演化趋势来学习有偏的结构演化。此外，设计了一种新的双重消偏方法来对比编码公平层合，定制消偏策略以适应不同的有偏结构演化。这种创新的去偏策略打破了嵌入的有效性瓶颈，没有明显的公平性损失。大量实验表明，FairDGE 算法同时提高了嵌入的有效性和公平性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Toward+Structure+Fairness+in+Dynamic+Graph+Embedding:+A+Trend-aware+Dual+Debiasing+Approach)|0| -|[Bridging Items and Language: A Transition Paradigm for Large Language Model-Based Recommendation](https://doi.org/10.1145/3637528.3671884)|Xinyu Lin, Wenjie Wang, Yongqi Li, Fuli Feng, SeeKiong Ng, TatSeng Chua|University of Science and Technology of China, Hefei, China; The Hong Kong Polytechnic University, Hong Kong SAR, China; National University of Singapore, Singapore, Singapore|Harnessing Large Language Models (LLMs) for recommendation is rapidly emerging, which relies on two fundamental steps to bridge the recommendation item space and the language space: 1) item indexing utilizes identifiers to represent items in the language space, and 2) generation grounding associates LLMs' generated token sequences to in-corpus items. However, previous methods exhibit inherent limitations in the two steps. Existing ID-based identifiers (e.g., numeric IDs) and description-based identifiers (e.g., titles) either lose semantics or lack adequate distinctiveness. Moreover, prior generation grounding methods might generate invalid identifiers, thus misaligning with in-corpus items. To address these issues, we propose a novel Transition paradigm for LLM-based Recommender (named TransRec) to bridge items and language. Specifically, TransRec presents multi-facet identifiers, which simultaneously incorporate ID, title, and attribute for item indexing to pursue both distinctiveness and semantics. Additionally, we introduce a specialized data structure for TransRec to ensure generating valid identifiers only and utilize substring indexing to encourage LLMs to generate from any position of identifiers. Lastly, TransRec presents an aggregated grounding module to leverage generated multi-facet identifiers to rank in-corpus items efficiently. We instantiate TransRec on two backbone models, BART-large and LLaMA-7B. Extensive results on three real-world datasets under diverse settings validate the superiority of TransRec.|利用大型语言模型(LLM)进行推荐正在迅速兴起，这依赖于两个基本步骤来连接推荐项空间和语言空间: 1)项索引利用标识符来表示语言空间中的项目，2)生成基础将 LLM 生成的令牌序列与语料库中的项目相关联。然而，以前的方法在这两个步骤中表现出固有的局限性。现有的基于 ID 的标识符(例如，数字 ID)和基于描述的标识符(例如，标题)要么失去语义，要么缺乏足够的区别性。此外，上一代接地方法可能会产生无效的标识符，从而与语料库中的项目不一致。为了解决这些问题，我们提出了一种新的基于 LLM 的传输范式(称为 TransRec) ，以连接项目和语言。具体来说，TransRec 提供了多方面标识符，这些标识符同时合并 ID、 title 和属性用于项目索引，以实现独特性和语义。此外，我们还为 TransRec 引入了专门的数据结构，以确保只生成有效的标识符，并利用子字符串索引鼓励 LLM 从标识符的任何位置生成标识符。最后，TransRec 提出了一个聚合的接地模块，利用生成的多方面标识符对语料库中的项目进行有效排序。我们在两个骨干模型上实例化 TransRec，BART-large 和 LLaMA-7B。在三个不同设置的真实世界数据集上的广泛结果验证了 TransRec 的优越性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Bridging+Items+and+Language:+A+Transition+Paradigm+for+Large+Language+Model-Based+Recommendation)|0| -|[BadSampler: Harnessing the Power of Catastrophic Forgetting to Poison Byzantine-robust Federated Learning](https://doi.org/10.1145/3637528.3671879)|Yi Liu, Cong Wang, Xingliang Yuan|The University of Melbourne, Melbourne, Australia; City University of Hong Kong, Hong Kong, China|Federated Learning (FL) is susceptible to poisoning attacks, wherein compromised clients manipulate the global model by modifying local datasets or sending manipulated model updates. Experienced defenders can readily detect and mitigate the poisoning effects of malicious behaviors using Byzantine-robust aggregation rules. However, the exploration of poisoning attacks in scenarios where such behaviors are absent remains largely unexplored for Byzantine-robust FL. This paper addresses the challenging problem of poisoning Byzantine-robust FL by introducing catastrophic forgetting. To fill this gap, we first formally define generalization error and establish its connection to catastrophic forgetting, paving the way for the development of a clean-label data poisoning attack named BadSampler. This attack leverages only clean-label data (i.e., without poisoned data) to poison Byzantine-robust FL and requires the adversary to selectively sample training data with high loss to feed model training and maximize the model's generalization error. We formulate the attack as an optimization problem and present two elegant adversarial sampling strategies, Top-k sampling, and meta-sampling, to approximately solve it. Additionally, our formal error upper bound and time complexity analysis demonstrate that our design can preserve attack utility with high efficiency. Extensive evaluations on two real-world datasets illustrate the effectiveness and performance of our proposed attacks.|联邦学习(FL)容易受到中毒攻击，其中受损的客户端通过修改本地数据集或发送受控模型更新来操纵全局模型。经验丰富的防御者可以很容易地使用拜占庭稳健的聚合规则检测和减轻恶意行为的毒害效应。然而，在没有这种行为的情况下，对中毒攻击的探索对于拜占庭-鲁棒 FL 来说仍然很大程度上是未知的。本文通过引入灾难遗忘，解决了对拜占庭-鲁棒 FL 中毒的挑战性问题。为了填补这一空白，我们首先正式定义了泛化误差，并建立了它与灾难性遗忘之间的联系，为名为 BadSampler 的清洁标签数据中毒攻击的开发铺平了道路。这种攻击只利用干净的标签数据(即，没有中毒的数据)来毒害拜占庭-鲁棒的 FL，并要求对手有选择地采样高损失的训练数据来提供模型训练，并最大限度地提高模型的泛化误差。我们把这种攻击作为一种最佳化问题，并提出了两种优雅的对抗性抽样策略: Top-k 抽样和 meta 抽样，来近似地解决这个问题。此外，我们的形式误差上限和时间复杂度分析表明，我们的设计可以保持攻击效用的高效率。对两个真实世界数据集的广泛评估说明了我们提出的攻击的有效性和性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=BadSampler:++Harnessing+the+Power+of+Catastrophic+Forgetting+to+Poison+Byzantine-robust+Federated+Learning)|0| -|[Dataset Condensation for Time Series Classification via Dual Domain Matching](https://doi.org/10.1145/3637528.3671675)|Zhanyu Liu, Ke Hao, Guanjie Zheng, Yanwei Yu|Ocean University of China, Qingdao, China; Shanghai Jiao Tong University, Shanghai, China|Time series data has been demonstrated to be crucial in various researchfields. The management of large quantities of time series data presentschallenges in terms of deep learning tasks, particularly for training a deepneural network. Recently, a technique named Dataset Condensation hasemerged as a solution to this problem. This technique generates a smallersynthetic dataset that has comparable performance to the full real dataset indownstream tasks such as classification. However, previous methods areprimarily designed for image and graph datasets, and directly adapting them tothe time series dataset leads to suboptimal performance due to their inabilityto effectively leverage the rich information inherent in time series data,particularly in the frequency domain. In this paper, we propose a novelframework named Dataset Condensation forTime SeriesClassification via Dual Domain Matching (CondTSC)which focuses on the time series classification dataset condensation task.Different from previous methods, our proposed framework aims to generate acondensed dataset that matches the surrogate objectives in both the time andfrequency domains. Specifically, CondTSC incorporates multi-view dataaugmentation, dual domain training, and dual surrogate objectives to enhancethe dataset condensation process in the time and frequency domains. Throughextensive experiments, we demonstrate the effectiveness of our proposedframework, which outperforms other baselines and learns a condensed syntheticdataset that exhibits desirable characteristics such as conforming to thedistribution of the original data.|时间序列数据已被证明在各个研究领域都是至关重要的。大量时间序列数据的管理在深度学习任务方面面临挑战，特别是在训练深度神经网络方面。最近，一种名为“数据集压缩”的技术被用来解决这个问题。该技术生成一个较小的合成数据集，其性能与完整的实际数据集的下游任务(如分类)具有可比性。然而，以前的方法主要是为图像和图形数据集设计的，直接将它们适应于时间序列数据集会导致次优性能，因为它们无法有效地利用时间序列数据中固有的丰富信息，特别是在频率域。本文提出了一种基于双域匹配的时间序列分类数据集压缩框架(CondTSC) ，该框架主要针对时间序列分类数据集压缩任务。与以往的方法不同，我们提出的框架旨在生成在时间和频率领域匹配替代目标的浓缩数据集。具体来说，CondTSC 结合了多视图数据增强、双域训练和双代理目标来增强数据集在时间和频率域的缩聚过程。通过大量的实验，我们证明了我们提出的框架的有效性，它的性能优于其他基线，并学习了一个浓缩的合成数据集，这个数据集展示了令人满意的特征，例如符合原始数据的分布。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Dataset+Condensation+for+Time+Series+Classification+via+Dual+Domain+Matching)|0| +|[Bridging Items and Language: A Transition Paradigm for Large Language Model-Based Recommendation](https://doi.org/10.1145/3637528.3671884)|Xinyu Lin, Wenjie Wang, Yongqi Li, Fuli Feng, SeeKiong Ng, TatSeng Chua|The Hong Kong Polytechnic University, Hong Kong SAR, China; National University of Singapore, Singapore, Singapore; University of Science and Technology of China, Hefei, China|Harnessing Large Language Models (LLMs) for recommendation is rapidly emerging, which relies on two fundamental steps to bridge the recommendation item space and the language space: 1) item indexing utilizes identifiers to represent items in the language space, and 2) generation grounding associates LLMs' generated token sequences to in-corpus items. However, previous methods exhibit inherent limitations in the two steps. Existing ID-based identifiers (e.g., numeric IDs) and description-based identifiers (e.g., titles) either lose semantics or lack adequate distinctiveness. Moreover, prior generation grounding methods might generate invalid identifiers, thus misaligning with in-corpus items. To address these issues, we propose a novel Transition paradigm for LLM-based Recommender (named TransRec) to bridge items and language. Specifically, TransRec presents multi-facet identifiers, which simultaneously incorporate ID, title, and attribute for item indexing to pursue both distinctiveness and semantics. Additionally, we introduce a specialized data structure for TransRec to ensure generating valid identifiers only and utilize substring indexing to encourage LLMs to generate from any position of identifiers. Lastly, TransRec presents an aggregated grounding module to leverage generated multi-facet identifiers to rank in-corpus items efficiently. We instantiate TransRec on two backbone models, BART-large and LLaMA-7B. Extensive results on three real-world datasets under diverse settings validate the superiority of TransRec.|利用大型语言模型(LLM)进行推荐正在迅速兴起，这依赖于两个基本步骤来连接推荐项空间和语言空间: 1)项索引利用标识符来表示语言空间中的项目，2)生成基础将 LLM 生成的令牌序列与语料库中的项目相关联。然而，以前的方法在这两个步骤中表现出固有的局限性。现有的基于 ID 的标识符(例如，数字 ID)和基于描述的标识符(例如，标题)要么失去语义，要么缺乏足够的区别性。此外，上一代接地方法可能会产生无效的标识符，从而与语料库中的项目不一致。为了解决这些问题，我们提出了一种新的基于 LLM 的传输范式(称为 TransRec) ，以连接项目和语言。具体来说，TransRec 提供了多方面标识符，这些标识符同时合并 ID、 title 和属性用于项目索引，以实现独特性和语义。此外，我们还为 TransRec 引入了专门的数据结构，以确保只生成有效的标识符，并利用子字符串索引鼓励 LLM 从标识符的任何位置生成标识符。最后，TransRec 提出了一个聚合的接地模块，利用生成的多方面标识符对语料库中的项目进行有效排序。我们在两个骨干模型上实例化 TransRec，BART-large 和 LLaMA-7B。在三个不同设置的真实世界数据集上的广泛结果验证了 TransRec 的优越性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Bridging+Items+and+Language:+A+Transition+Paradigm+for+Large+Language+Model-Based+Recommendation)|0| +|[BadSampler: Harnessing the Power of Catastrophic Forgetting to Poison Byzantine-robust Federated Learning](https://doi.org/10.1145/3637528.3671879)|Yi Liu, Cong Wang, Xingliang Yuan|City University of Hong Kong, Hong Kong, China; The University of Melbourne, Melbourne, Australia|Federated Learning (FL) is susceptible to poisoning attacks, wherein compromised clients manipulate the global model by modifying local datasets or sending manipulated model updates. Experienced defenders can readily detect and mitigate the poisoning effects of malicious behaviors using Byzantine-robust aggregation rules. However, the exploration of poisoning attacks in scenarios where such behaviors are absent remains largely unexplored for Byzantine-robust FL. This paper addresses the challenging problem of poisoning Byzantine-robust FL by introducing catastrophic forgetting. To fill this gap, we first formally define generalization error and establish its connection to catastrophic forgetting, paving the way for the development of a clean-label data poisoning attack named BadSampler. This attack leverages only clean-label data (i.e., without poisoned data) to poison Byzantine-robust FL and requires the adversary to selectively sample training data with high loss to feed model training and maximize the model's generalization error. We formulate the attack as an optimization problem and present two elegant adversarial sampling strategies, Top-k sampling, and meta-sampling, to approximately solve it. Additionally, our formal error upper bound and time complexity analysis demonstrate that our design can preserve attack utility with high efficiency. Extensive evaluations on two real-world datasets illustrate the effectiveness and performance of our proposed attacks.|联邦学习(FL)容易受到中毒攻击，其中受损的客户端通过修改本地数据集或发送受控模型更新来操纵全局模型。经验丰富的防御者可以很容易地使用拜占庭稳健的聚合规则检测和减轻恶意行为的毒害效应。然而，在没有这种行为的情况下，对中毒攻击的探索对于拜占庭-鲁棒 FL 来说仍然很大程度上是未知的。本文通过引入灾难遗忘，解决了对拜占庭-鲁棒 FL 中毒的挑战性问题。为了填补这一空白，我们首先正式定义了泛化误差，并建立了它与灾难性遗忘之间的联系，为名为 BadSampler 的清洁标签数据中毒攻击的开发铺平了道路。这种攻击只利用干净的标签数据(即，没有中毒的数据)来毒害拜占庭-鲁棒的 FL，并要求对手有选择地采样高损失的训练数据来提供模型训练，并最大限度地提高模型的泛化误差。我们把这种攻击作为一种最佳化问题，并提出了两种优雅的对抗性抽样策略: Top-k 抽样和 meta 抽样，来近似地解决这个问题。此外，我们的形式误差上限和时间复杂度分析表明，我们的设计可以保持攻击效用的高效率。对两个真实世界数据集的广泛评估说明了我们提出的攻击的有效性和性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=BadSampler:++Harnessing+the+Power+of+Catastrophic+Forgetting+to+Poison+Byzantine-robust+Federated+Learning)|0| +|[Dataset Condensation for Time Series Classification via Dual Domain Matching](https://doi.org/10.1145/3637528.3671675)|Zhanyu Liu, Ke Hao, Guanjie Zheng, Yanwei Yu|Shanghai Jiao Tong University, Shanghai, China; Ocean University of China, Qingdao, China|Time series data has been demonstrated to be crucial in various researchfields. The management of large quantities of time series data presentschallenges in terms of deep learning tasks, particularly for training a deepneural network. Recently, a technique named Dataset Condensation hasemerged as a solution to this problem. This technique generates a smallersynthetic dataset that has comparable performance to the full real dataset indownstream tasks such as classification. However, previous methods areprimarily designed for image and graph datasets, and directly adapting them tothe time series dataset leads to suboptimal performance due to their inabilityto effectively leverage the rich information inherent in time series data,particularly in the frequency domain. In this paper, we propose a novelframework named Dataset Condensation forTime SeriesClassification via Dual Domain Matching (CondTSC)which focuses on the time series classification dataset condensation task.Different from previous methods, our proposed framework aims to generate acondensed dataset that matches the surrogate objectives in both the time andfrequency domains. Specifically, CondTSC incorporates multi-view dataaugmentation, dual domain training, and dual surrogate objectives to enhancethe dataset condensation process in the time and frequency domains. Throughextensive experiments, we demonstrate the effectiveness of our proposedframework, which outperforms other baselines and learns a condensed syntheticdataset that exhibits desirable characteristics such as conforming to thedistribution of the original data.|时间序列数据已被证明在各个研究领域都是至关重要的。大量时间序列数据的管理在深度学习任务方面面临挑战，特别是在训练深度神经网络方面。最近，一种名为“数据集压缩”的技术被用来解决这个问题。该技术生成一个较小的合成数据集，其性能与完整的实际数据集的下游任务(如分类)具有可比性。然而，以前的方法主要是为图像和图形数据集设计的，直接将它们适应于时间序列数据集会导致次优性能，因为它们无法有效地利用时间序列数据中固有的丰富信息，特别是在频率域。本文提出了一种基于双域匹配的时间序列分类数据集压缩框架(CondTSC) ，该框架主要针对时间序列分类数据集压缩任务。与以往的方法不同，我们提出的框架旨在生成在时间和频率领域匹配替代目标的浓缩数据集。具体来说，CondTSC 结合了多视图数据增强、双域训练和双代理目标来增强数据集在时间和频率域的缩聚过程。通过大量的实验，我们证明了我们提出的框架的有效性，它的性能优于其他基线，并学习了一个浓缩的合成数据集，这个数据集展示了令人满意的特征，例如符合原始数据的分布。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Dataset+Condensation+for+Time+Series+Classification+via+Dual+Domain+Matching)|0| |[Self-Supervised Denoising through Independent Cascade Graph Augmentation for Robust Social Recommendation](https://doi.org/10.1145/3637528.3671958)|Youchen Sun, Zhu Sun, Yingpeng Du, Jie Zhang, Yew Soon Ong|; ASTAR Centre for Frontier AI Research & Nanyang Technological University, Singapore, Singapore; Nanyang Technological University, Singapore, Singapore|Social Recommendation (SR) typically exploits neighborhood influence in the social network to enhance user preference modeling. However, users' intricate social behaviors may introduce noisy social connections for user modeling and harm the models' robustness. Existing solutions to alleviate social noise either filter out the noisy connections or generate new potential social connections. Due to the absence of labels, the former approaches may retain uncertain connections for user preference modeling while the latter methods may introduce additional social noise. Through data analysis, we discover that (1) social noise likely comes from the connected users with low preference similarity; and (2) Opinion Leaders (OLs) play a pivotal role in influence dissemination, surpassing high-similarity neighbors, regardless of their preference similarity with trusting peers. Guided by these observations, we propose a novel Self-Supervised Denoising approach through Independent Cascade Graph Augmentation, for more robust SR. Specifically, we employ the independent cascade diffusion model to generate an augmented graph view, which traverses the social graph and activates the edges in sequence to simulate the cascading influence spread. To steer the augmentation towards a denoised social graph, we (1) introduce a hierarchical contrastive loss to prioritize the activation of OLs first, followed by high-similarity neighbors, while weakening the low-similarity neighbors; and (2) integrate an information bottleneck based contrastive loss, aiming to minimize mutual information between original and augmented graphs yet preserve sufficient information for improved SR. Experiments conducted on two public datasets demonstrate that our model outperforms the state-of-the-art while also exhibiting higher robustness to different extents of social noise.|社交推荐(SR)通常利用社交网络中的邻域影响来增强用户偏好建模。然而，用户错综复杂的社会行为可能为用户建模引入噪声社会关系，损害模型的鲁棒性。现有的减轻社会噪音的解决方案要么过滤掉噪音连接，要么产生新的潜在社会连接。由于标签的缺失，前一种方法在用户偏好建模时可能会保留不确定的联系，而后一种方法可能会引入额外的社会噪声。通过数据分析，我们发现: (1)社交噪声可能来自偏好相似度较低的关联用户; (2)意见领袖(OLs)在影响力传播中发挥着关键作用，超过了高相似度的邻居，而不管他们与信任同伴的偏好相似度如何。在这些观测结果的指导下，我们提出了一种新的自我监督去噪方法，通过独立级联图增强，更健壮的 SR。具体来说，我们采用独立的级联扩散模型来生成一个扩展图视图，该视图横穿社会图并依次激活边界，以模拟级联影响的传播。为了将扩展引向去噪的社会图，我们(1)引入分层对比损失来优先激活 OLs，然后是高相似性邻居，同时弱化低相似性邻居; (2)整合基于对比损失的信息瓶颈，旨在最小化原始图和扩展图之间的相互信息，同时保留足够的信息以改善 SR。在两个公共数据集上进行的实验表明，我们的模型优于最先进的水平，同时也表现出对不同程度的社会噪声更高的鲁棒性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Self-Supervised+Denoising+through+Independent+Cascade+Graph+Augmentation+for+Robust+Social+Recommendation)|0| |[Revisiting Local PageRank Estimation on Undirected Graphs: Simple and Optimal](https://doi.org/10.1145/3637528.3671820)|Hanzhi Wang|Renmin University of China, Beijing, China|We propose a simple and optimal algorithm, BackMC, for local PageRank estimation in undirected graphs: given an arbitrary target node t in an undirected graph G comprising n nodes and m edges, BackMC accurately estimates the PageRank score of node t while assuring a small relative error and a high success probability. The worst-case computational complexity of BackMC is upper bounded by O(1/dmin ⋅ min(dt, m1/2)), where dmin denotes the minimum degree of G, and dt denotes the degree of t, respectively. Compared to the previously best upper bound of O(log n ⋅ min(dt, m1/2)) (VLDB '23), which is derived from a significantly more complex algorithm and analysis, our BackMC improves the computational complexity for this problem by a factor of Θ(log n/dmin) with a much simpler algorithm. Furthermore, we establish a matching lower bound of Ω(1/dmin ⋅ min(dt, m1/2)) for any algorithm that attempts to solve the problem of local PageRank estimation, demonstrating the theoretical optimality of our BackMC. We conduct extensive experiments on various large-scale real-world and synthetic graphs, where BackMC consistently shows superior performance.|针对无向图的局部 PageRank 估计问题，提出了一种简单而优化的 BackMC 算法: 在包含 n 个节点和 m 条边的无向图 G 中，给定一个任意目标节点 t，BackMC 在保证较小的相对误差和较高的成功概率的情况下，精确地估计节点 t 的 PageRank 得分。最坏情况下 BackMC 的计算复杂度上界为 O (1/dmin ≥ min (dt，m1/2)) ，其中 dmin 表示 G 的最小度，dt 表示 t 的最小度。相比之前的最佳上界 O (log n  (dt，m1/2))(VLDB’23) ，它是由一个更加复杂的算法和分析得到的，我们的 BackMC 用一个更加简单的算法提高了 Θ (log n/dmin)的一个因子，从而提高了这个问题的计算复杂度。进一步，我们建立了任何试图解决局部 PageRank 估计问题的算法的匹配下界 Ω (1/dmin min (dt，m1/2)) ，证明了我们的 BackMC 算法的理论最优性。我们在各种大规模的真实世界和合成图上进行广泛的实验，其中 BackMC 始终显示出优越的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Revisiting+Local+PageRank+Estimation+on+Undirected+Graphs:+Simple+and+Optimal)|0| -|[Performative Debias with Fair-exposure Optimization Driven by Strategic Agents in Recommender Systems](https://doi.org/10.1145/3637528.3671786)|Zhichen Xiang, Hongke Zhao, Chuang Zhao, Ming He, Jianping Fan|; The Hong Kong University of Science and Technology, Hong Kong, China; AI Lab at Lenovo Research, Beijing, China|Data bias, e.g., popularity impairs the dynamics of two-sided markets within recommender systems. This overshadows the less visible but potentially intriguing long-tail items that could capture user interest. Despite the abundance of research surrounding this issue, it still poses challenges and remains a hot topic in academic circles. Along this line, in this paper, we developed a re-ranking approach in dynamic settings with fair-exposure optimization driven by strategic agents. Designed for the producer side, the execution of agents assumes content creators can modify item features based on strategic incentives to maximize their exposure. This iterative process entails an end-to-end optimization, employing differentiable ranking operators that simultaneously target accuracy and fairness. Joint objectives ensure the performance of recommendations while enhancing the visibility of tail items. We also leveraged the performativity nature of predictions to illustrate how strategic learning influences content creators to shift towards fairness efficiently, thereby incentivizing features of tail items. Through comprehensive experiments on both public and industrial datasets, we have substantiated the effectiveness and dominance of the proposed method especially on unveiling the potential of tail items.|数据偏差，例如，受欢迎程度会损害推荐系统中双边市场的动态性。这掩盖了不太可见但可能引起用户兴趣的长尾项目。尽管围绕这一问题进行了大量的研究，但它仍然是学术界面临的挑战和热点问题。沿着这条路线，本文开发了一种在动态环境下的重新排序方法，其中公平曝光优化是由战略代理驱动的。代理的执行是为生产者设计的，它假定内容创造者可以基于战略激励来修改项目特征，以最大限度地提高他们的曝光率。这个迭代过程需要一个端到端的优化，使用可微排名运算符，同时目标的准确性和公平性。联合目标确保建议的执行，同时提高尾部项目的可见性。我们还利用预测的执行性质来说明战略学习如何影响内容创建者有效地转向公平，从而激励尾部项目的特性。通过对公共数据集和工业数据集的综合实验，验证了该方法的有效性和优越性，特别是在揭示尾部项目的潜力方面。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Performative+Debias+with+Fair-exposure+Optimization+Driven+by+Strategic+Agents+in+Recommender+Systems)|0| +|[Performative Debias with Fair-exposure Optimization Driven by Strategic Agents in Recommender Systems](https://doi.org/10.1145/3637528.3671786)|Zhichen Xiang, Hongke Zhao, Chuang Zhao, Ming He, Jianping Fan|; AI Lab at Lenovo Research, Beijing, China; The Hong Kong University of Science and Technology, Hong Kong, China|Data bias, e.g., popularity impairs the dynamics of two-sided markets within recommender systems. This overshadows the less visible but potentially intriguing long-tail items that could capture user interest. Despite the abundance of research surrounding this issue, it still poses challenges and remains a hot topic in academic circles. Along this line, in this paper, we developed a re-ranking approach in dynamic settings with fair-exposure optimization driven by strategic agents. Designed for the producer side, the execution of agents assumes content creators can modify item features based on strategic incentives to maximize their exposure. This iterative process entails an end-to-end optimization, employing differentiable ranking operators that simultaneously target accuracy and fairness. Joint objectives ensure the performance of recommendations while enhancing the visibility of tail items. We also leveraged the performativity nature of predictions to illustrate how strategic learning influences content creators to shift towards fairness efficiently, thereby incentivizing features of tail items. Through comprehensive experiments on both public and industrial datasets, we have substantiated the effectiveness and dominance of the proposed method especially on unveiling the potential of tail items.|数据偏差，例如，受欢迎程度会损害推荐系统中双边市场的动态性。这掩盖了不太可见但可能引起用户兴趣的长尾项目。尽管围绕这一问题进行了大量的研究，但它仍然是学术界面临的挑战和热点问题。沿着这条路线，本文开发了一种在动态环境下的重新排序方法，其中公平曝光优化是由战略代理驱动的。代理的执行是为生产者设计的，它假定内容创造者可以基于战略激励来修改项目特征，以最大限度地提高他们的曝光率。这个迭代过程需要一个端到端的优化，使用可微排名运算符，同时目标的准确性和公平性。联合目标确保建议的执行，同时提高尾部项目的可见性。我们还利用预测的执行性质来说明战略学习如何影响内容创建者有效地转向公平，从而激励尾部项目的特性。通过对公共数据集和工业数据集的综合实验，验证了该方法的有效性和优越性，特别是在揭示尾部项目的潜力方面。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Performative+Debias+with+Fair-exposure+Optimization+Driven+by+Strategic+Agents+in+Recommender+Systems)|0| |[Preventing Strategic Behaviors in Collaborative Inference for Vertical Federated Learning](https://doi.org/10.1145/3637528.3671663)|Yidan Xing, Zhenzhe Zheng, Fan Wu|Shanghai Jiao Tong University, Shanghai, China|Vertical federated learning (VFL) is an emerging collaborative machine learning paradigm to facilitate the utilization of private features distributed across multiple parties. During the inference process of VFL, the involved parties need to upload their local embeddings to be aggregated for the final prediction. Despite its remarkable performances, the inference process of the current VFL system is vulnerable to the strategic behavior of involved parties, as they could easily change the uploaded local embeddings to exert direct influences on the prediction result. In a representative case study of federated recommendation, we find the allocation of display opportunities to be severely disrupted due to the parties' preferences in display content. In order to elicit the true local embeddings for VFL system, we propose a distribution-based penalty mechanism to detect and penalize the strategic behaviors in collaborative inference. As the key motivation of our design, we theoretically prove the power of constraining the distribution of uploaded embeddings in preventing the dishonest parties from achieving higher utility. Our mechanism leverages statistical two-sample tests to distinguish whether the distribution of uploaded embeddings is reasonable, and penalize the dishonest party through deactivating her uploaded embeddings. The resulted mechanism could be shown to admit truth-telling to converge to a Bayesian Nash equilibrium asymptotically under mild conditions. The experimental results further demonstrate the effectiveness of the proposed mechanism to reduce the dishonest utility increase of strategic behaviors and promote the truthful uploading of local embeddings in inferences.|垂直联邦学习(VFL)是一种新兴的协作机器学习范式，它有助于利用分布在多个方面的私有特性。在 VFL 的推理过程中，各参与方需要上传自己的局部嵌入信息进行聚合，才能得到最终的预测结果。当前 VFL 系统的推理过程虽然具有显著的性能，但容易受到相关各方策略行为的影响，因为它们很容易改变上传的局部嵌入，从而直接影响预测结果。在联邦推荐的一个典型案例中，我们发现由于各方对显示内容的偏好，显示机会的分配会受到严重干扰。为了在 VFL 系统中实现真正的局部嵌入，我们提出了一种基于分布的惩罚机制来检测和惩罚协同推理中的策略行为。作为我们设计的关键动机，我们从理论上证明了约束上传嵌入分布的力量，防止不诚实的当事人获得更高的效用。我们的机制利用统计双样本检验来判断上传嵌入的分布是否合理，并通过停用不诚实方的上传嵌入来惩罚不诚实方。结果显示，在温和的条件下，该机制能够承认说实话，并渐近地收敛到贝叶斯纳什均衡点。实验结果进一步证明了该机制在减少策略行为的不诚实效用增加和促进推理中局部嵌入的真实上传方面的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Preventing+Strategic+Behaviors+in+Collaborative+Inference+for+Vertical+Federated+Learning)|0| |[Extreme Meta-Classification for Large-Scale Zero-Shot Retrieval](https://doi.org/10.1145/3637528.3672046)|Sachin Yadav, Deepak Saini, Anirudh Buvanesh, Bhawna Paliwal, Kunal Dahiya, Siddarth Asokan, Yashoteja Prabhu, Jian Jiao, Manik Varma|Microsoft, Redmond, WA, USA; Microsoft Research, Bangalore, India; Indian Institute of Technology, Delhi, India|We develop accurate and efficient solutions for large-scale retrieval tasks where novel (zero-shot) items can arrive continuously at a rapid pace. Conventional Siamese-style approaches embed both queries and items through a small encoder and retrieve the items lying closest to the query. While this approach allows efficient addition and retrieval of novel items, the small encoder lacks sufficient capacity for the necessary world knowledge in complex retrieval tasks. The extreme classification approaches have addressed this by learning a separate classifier for each item observed in the training set which significantly increases the representation capacity of the model. Such classifiers outperform Siamese approaches on observed items, but cannot be trained for novel items due to data and latency constraints. To bridge these gaps, this paper develops: (1) A new algorithmic framework, EMMETT, which efficiently synthesizes classifiers on-the-fly for novel items, by relying on the readily available classifiers for observed items; (2) A new algorithm, IRENE, which is a simple and effective instance of EMMETT that is specifically suited for large-scale deployments, and (3) A new theoretical framework for analyzing the generalization performance in large-scale zero-shot retrieval which guides our algorithm and training related design decisions. Comprehensive experiments are conducted on a wide range of retrieval tasks which demonstrate that IRENE improves the zero-shot retrieval accuracy by up to 15% points in Recall@10 when added on top of leading encoders. Additionally, on an online A/B test in a large-scale ad retrieval task in a major search engine, IRENE improved the ad click-through rate by 4.2%. Lastly, we validate our design choices through extensive ablative experiments. The source code for IRENE is available at https://aka.ms/irene.|我们开发准确和有效的解决方案，大规模的检索任务，新的(零射击)项目可以连续到达快速的步伐。传统的暹罗式方法通过一个小编码器嵌入查询和项，并检索与查询最接近的项。虽然这种方法可以有效地增加和检索新的项目，小编码器缺乏足够的能力，必要的世界知识在复杂的检索任务。极端分类方法通过为训练集中观察到的每个项目学习一个单独的分类器来解决这个问题，这大大提高了模型的表示能力。这种分类器在观察项目上的表现优于暹罗方法，但是由于数据和延迟限制，不能对新项目进行训练。为了弥补这些差距，本文开发了: (1)一种新的算法框架—— EMMETT，该算法依靠现有的分类器对观察到的项目进行高效的动态综合分类; (2)一种新的算法—— IRENE，它是 EMMETT 的一个简单而有效的实例，特别适合于大规模部署; (3)一种新的理论框架，用于分析大规模零拍检索中的泛化性能，指导我们的算法和训练相关的设计决策。实验结果表明，在前置编码器的基础上加入 IRENE 后，Recall@10的零镜头检索精度提高了15% 。此外，在一个主要搜索引擎的大规模广告检索任务的在线 A/B 测试中，iRENE 将广告点进率提高了4.2% 。最后，我们通过广泛的烧蚀实验验证了我们的设计选择。IRENe 的源代码可在 https://aka.ms/IRENE 下载。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Extreme+Meta-Classification+for+Large-Scale+Zero-Shot+Retrieval)|0| -|[Conversational Dueling Bandits in Generalized Linear Models](https://doi.org/10.1145/3637528.3671892)|Shuhua Yang, Hui Yuan, Xiaoying Zhang, Mengdi Wang, Hong Zhang, Huazheng Wang|Oregon State University, Corvallis, OR, USA; Princeton University, Princeton, NJ, USA; University of Science and Technology of China, Hefei, China; ByteDance, Beijing, China|Conversational recommendation systems elicit user preferences by interacting with users to obtain their feedback on recommended commodities. Such systems utilize a multi-armed bandit framework to learn user preferences in an online manner and have received great success in recent years. However, existing conversational bandit methods have several limitations. First, they only enable users to provide explicit binary feedback on the recommended items or categories, leading to ambiguity in interpretation. In practice, users are usually faced with more than one choice. Relative feedback, known for its informativeness, has gained increasing popularity in recommendation system design. Moreover, current contextual bandit methods mainly work under linear reward assumptions, ignoring practical non-linear reward structures in generalized linear models. Therefore, in this paper, we introduce relative feedback-based conversations into conversational recommendation systems through the integration of dueling bandits in generalized linear models (GLM) and propose a novel conversational dueling bandit algorithm called ConDuel. Theoretical analyses of regret upper bounds and empirical validations on synthetic and real-world data underscore ConDuel's efficacy. We also demonstrate the potential to extend our algorithm to multinomial logit bandits with theoretical and experimental guarantees, which further proves the applicability of the proposed framework.|对话式推荐系统通过与用户进行交互以获得他们对推荐商品的反馈，从而引起用户的偏好。这类系统利用多臂老虎机框架，以在线方式学习用户偏好，近年来取得了巨大成功。然而，现有的会话强盗方法有一些局限性。首先，它们只允许用户对推荐的项目或类别提供明确的二进制反馈，从而导致解释上的歧义。实际上，用户通常面临不止一种选择。相对反馈以信息量大而闻名，在推荐系统设计中越来越受欢迎。此外，目前的情境强盗方法主要在线性报酬假设下工作，忽略了广义线性模型中实际的非线性报酬结构。因此，本文通过广义线性模型(GLM)中对决斗强盗的集成，将基于相对反馈的会话引入到会话推荐系统中，提出了一种新的会话决斗强盗算法 ConDuel。对遗憾上限的理论分析以及对合成数据和现实数据的经验验证强调了 ConDuel 的有效性。在理论和实验的基础上，证明了该算法在多项式 Logit 强盗问题上的可行性，进一步证明了该算法的适用性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Conversational+Dueling+Bandits+in+Generalized+Linear+Models)|0| -|[User Welfare Optimization in Recommender Systems with Competing Content Creators](https://doi.org/10.1145/3637528.3672021)|Fan Yao, Yiming Liao, Mingzhe Wu, Chuanhao Li, Yan Zhu, James Yang, Jingzhou Liu, Qifan Wang, Haifeng Xu, Hongning Wang|University of Chicago, Chicago, USA; Google, Mountain View, USA; Meta Platforms, Inc., Menlo Park, USA; Meta Platforms, Inc., New York, USA; Yale University, New Haven, USA; University of Virginia, Charlottesville, USA; University of Southern California, Los Angeles, USA|Driven by the new economic opportunities created by the creator economy, an increasing number of content creators rely on and compete for revenue generated from online content recommendation platforms. This burgeoning competition reshapes the dynamics of content distribution and profoundly impacts long-term user welfare on the platform. However, the absence of a comprehensive picture of global user preference distribution often traps the competition, especially the creators, in states that yield sub-optimal user welfare. To encourage creators to best serve a broad user population with relevant content, it becomes the platform's responsibility to leverage its information advantage regarding user preference distribution to accurately signal creators. In this study, we perform system-side user welfare optimization under a competitive game setting among content creators. We propose an algorithmic solution for the platform, which dynamically computes a sequence of weights for each user based on their satisfaction of the recommended content. These weights are then utilized to design mechanisms that adjust the recommendation policy or the post-recommendation rewards, thereby influencing creators' content production strategies. To validate the effectiveness of our proposed method, we report our findings from a series of experiments, including: 1. a proof-of-concept negative example illustrating how creators' strategies converge towards sub-optimal states without platform intervention; 2. offline experiments employing our proposed intervention mechanisms on diverse datasets; and 3. results from a three-week online experiment conducted on Instagram Reels short-video recommendation platform.|在创作者经济带来的新经济机遇的驱动下，越来越多的内容创作者依赖并竞争在线内容推荐平台产生的收入。这种蓬勃发展的竞争重塑了内容分发的动态，并深刻影响了平台上的长期用户福利。然而，缺乏全球用户偏好分布的全面图像，往往会使竞争，尤其是创造者陷入产生次优用户福利的状态。为了鼓励创作者用相关内容最好地服务于广泛的用户群体，平台有责任利用其在用户偏好分布方面的信息优势来准确地向创作者发出信号。在这项研究中，我们在内容创作者之间的竞争博弈环境下进行系统端用户福利优化。我们提出了一个平台的算法解决方案，该方案根据每个用户对推荐内容的满意度动态计算每个用户的权重序列。然后利用这些权重来设计调整推荐策略或推荐后奖励的机制，从而影响创作者的内容生产策略。为了验证我们提出的方法的有效性，我们报告了一系列的实验结果，包括: 1。一个概念证明的否定例子，说明创造者的策略如何在没有平台干预的情况下收敛到次优状态;。在不同的数据集上使用我们提出的干预机制的离线实验;。在 Instagram Reels 短视频推荐平台上进行了为期三周的在线实验。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=User+Welfare+Optimization+in+Recommender+Systems+with+Competing+Content+Creators)|0| -|[Embedding Two-View Knowledge Graphs with Class Inheritance and Structural Similarity](https://doi.org/10.1145/3637528.3671941)|Kyuhwan Yeom, Hyeongjun Yang, Gayeon Park, Myeongheon Jeon, Yunjeong Ko, Byungkook Oh, KyongHo Lee|Computer Science, Yonsei University, Seoul, Republic of Korea; Artificial Intelligence, Yonsei University, Seoul, Republic of Korea; Computer Science and Engineering, Konkuk University, Seoul, Republic of Korea|Numerous large-scale knowledge graphs (KGs) fundamentally represent two-view KGs: an ontology-view KG with abstract classes in ontology and an instance-view KG with specific collections of entities instantiated from ontology classes. Two-view KG embedding aims to jointly learn continuous vector representations of entities and relations in the aforementioned two-view KGs. In essence, an ontology schema exhibits a tree-like structure guided by class hierarchies, which leads classes to form inheritance hierarchies. However, existing two-view KG embedding models neglect those hierarchies, which provides the necessity to reflect class inheritance. On the other hand, KG is constructed based on a pre-defined ontology schema that includes heterogeneous relations between classes. Furthermore, these relations are defined within the scope of those among classes since instances inherit all the properties of their corresponding classes, which reveals structural similarity between two multi-relational networks. Despite the consideration to bridge the gap among two-view KG representations, existing methods ignore the existence of structural similarity between two-view KGs. To address these issues, we propose a novel two-view KG embedding model, CISS, considering Class Inheritance and Structural Similarity between two-view KGs. To deal with class inheritance, we utilize class sets, each of which is composed of sibling classes, to learn fine-grained class representations. In addition, we configure virtual instance-view KG from clustered instances and compare subgraph representations of two-view KGs to enhance structural similarity between them. Experimental results show our superior performance compared to existing models.|许多大规模的知识图(KG)从根本上表示两个视图 KG: 一个本体视图 KG 具有本体中的抽象类，一个实例视图 KG 具有从本体类实例化的实体的特定集合。双视图幼儿园嵌入的目的是联合学习上述两视图幼儿园中实体和关系的连续向量表示。从本质上讲，本体模式表现出一种由类层次结构引导的树状结构，这种结构引导类形成继承层次结构。然而，现有的双视图 KG 嵌入模型忽略了这些层次结构，这就需要反映类继承。另一方面，KG 是基于一个预定义的本体模式构建的，该模式包含类之间的异构关系。此外，这些关系是在类之间的范围内定义的，因为实例继承了相应类的所有属性，这揭示了两个多关系网络之间的结构相似性。尽管现行方法已考虑填补双视角幼稚园表现形式之间的差距，但却忽略了双视角幼稚园之间是否存在结构相似性。为了解决这些问题，我们提出了一个新的双视图幼儿园嵌入模型 CISS，该模型考虑了类继承和双视图幼儿园之间的结构相似性。为了处理类继承，我们利用类集(每个类集都由兄弟类组成)来学习细粒度的类表示。此外，我们从集群实例配置虚拟实例视图幼稚园，并比较两个视图幼稚园的子图表示，以增强它们之间的结构相似性。实验结果表明，我们的性能优于现有的模型。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Embedding+Two-View+Knowledge+Graphs+with+Class+Inheritance+and+Structural+Similarity)|0| -|[Item-Difficulty-Aware Learning Path Recommendation: From a Real Walking Perspective](https://doi.org/10.1145/3637528.3671947)|Haotian Zhang, Shuanghong Shen, Bihan Xu, Zhenya Huang, Jinze Wu, Jing Sha, Shijin Wang|; iFLYTEK AI Research, Hefei, China; State Key Laboratory of Cognitive Intelligence & iFLYTEK AI Research, Hefei, China|Learning path recommendation aims to provide learners with a reasonable order of items to achieve their learning goals. Intuitively, the learning process on the learning path can be metaphorically likened to walking. Despite extensive efforts in this area, most previous methods mainly focus on the relationship among items but overlook the difficulty of items, which may raise two issues from a real walking perspective: (1) The path may be rough: When learners tread the path without considering item difficulty, it's akin to walking a dark, uneven road, making learning harder and dampening interest. (2) The path may be inefficient: Allowing learners only a few attempts on very challenging items before switching, or persisting with a difficult item despite numerous attempts without mastery, can result in inefficiencies in the learning journey. To conquer the above limitations, we propose a novel method named Difficulty-constrained Learning Path Recommendation (DLPR), which is aware of item difficulty. Specifically, we first explicitly categorize items into learning items and practice items, then construct a hierarchical graph to model and leverage item difficulty adequately. Then we design a Difficulty-driven Hierarchical Reinforcement Learning (DHRL) framework to facilitate learning paths with efficiency and smoothness. Finally, extensive experiments on three different simulators demonstrate our framework achieves state-of-the-art performance.|学习路径推荐的目的是为学习者提供一个合理的项目顺序，以实现他们的学习目标。直观地说，学习过程中的学习路径可以比喻为行走。尽管在这个领域做了大量的努力，以前的大多数方法主要关注项目之间的关系，但是忽略了项目的难度，这可能会从一个真正的行走的角度提出两个问题: (1)路径可能是粗糙的: 当学习者在不考虑项目难度的情况下行走在路径上，这就像走在一条黑暗的、不平坦的路上，使学习更加困难和抑制兴趣。(2)路径可能是低效的: 允许学习者在转换之前只尝试几次非常具有挑战性的项目，或者尽管尝试了很多次但没有掌握，仍然坚持一个困难的项目，可能会导致学习过程中的低效。为了克服上述限制，本文提出了一种新的学习路径推荐方法——难度约束学习路径推荐(DLPR) ，该方法能够识别项目的难度。具体来说，我们首先明确地将项目分类为学习项目和实践项目，然后构建一个层次图来充分地建模和利用项目难度。然后，我们设计了一个难度驱动的层次强化学习(dHRL)框架，以促进学习路径的有效性和顺畅性。最后，在三个不同的模拟器上进行了广泛的实验，证明了我们的框架实现了最先进的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Item-Difficulty-Aware+Learning+Path+Recommendation:+From+a+Real+Walking+Perspective)|0| +|[Conversational Dueling Bandits in Generalized Linear Models](https://doi.org/10.1145/3637528.3671892)|Shuhua Yang, Hui Yuan, Xiaoying Zhang, Mengdi Wang, Hong Zhang, Huazheng Wang|ByteDance, Beijing, China; Princeton University, Princeton, NJ, USA; University of Science and Technology of China, Hefei, China; Oregon State University, Corvallis, OR, USA|Conversational recommendation systems elicit user preferences by interacting with users to obtain their feedback on recommended commodities. Such systems utilize a multi-armed bandit framework to learn user preferences in an online manner and have received great success in recent years. However, existing conversational bandit methods have several limitations. First, they only enable users to provide explicit binary feedback on the recommended items or categories, leading to ambiguity in interpretation. In practice, users are usually faced with more than one choice. Relative feedback, known for its informativeness, has gained increasing popularity in recommendation system design. Moreover, current contextual bandit methods mainly work under linear reward assumptions, ignoring practical non-linear reward structures in generalized linear models. Therefore, in this paper, we introduce relative feedback-based conversations into conversational recommendation systems through the integration of dueling bandits in generalized linear models (GLM) and propose a novel conversational dueling bandit algorithm called ConDuel. Theoretical analyses of regret upper bounds and empirical validations on synthetic and real-world data underscore ConDuel's efficacy. We also demonstrate the potential to extend our algorithm to multinomial logit bandits with theoretical and experimental guarantees, which further proves the applicability of the proposed framework.|对话式推荐系统通过与用户进行交互以获得他们对推荐商品的反馈，从而引起用户的偏好。这类系统利用多臂老虎机框架，以在线方式学习用户偏好，近年来取得了巨大成功。然而，现有的会话强盗方法有一些局限性。首先，它们只允许用户对推荐的项目或类别提供明确的二进制反馈，从而导致解释上的歧义。实际上，用户通常面临不止一种选择。相对反馈以信息量大而闻名，在推荐系统设计中越来越受欢迎。此外，目前的情境强盗方法主要在线性报酬假设下工作，忽略了广义线性模型中实际的非线性报酬结构。因此，本文通过广义线性模型(GLM)中对决斗强盗的集成，将基于相对反馈的会话引入到会话推荐系统中，提出了一种新的会话决斗强盗算法 ConDuel。对遗憾上限的理论分析以及对合成数据和现实数据的经验验证强调了 ConDuel 的有效性。在理论和实验的基础上，证明了该算法在多项式 Logit 强盗问题上的可行性，进一步证明了该算法的适用性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Conversational+Dueling+Bandits+in+Generalized+Linear+Models)|0| +|[User Welfare Optimization in Recommender Systems with Competing Content Creators](https://doi.org/10.1145/3637528.3672021)|Fan Yao, Yiming Liao, Mingzhe Wu, Chuanhao Li, Yan Zhu, James Yang, Jingzhou Liu, Qifan Wang, Haifeng Xu, Hongning Wang|University of Virginia, Charlottesville, USA; Meta Platforms, Inc., Menlo Park, USA; Google, Mountain View, USA; University of Chicago, Chicago, USA; Yale University, New Haven, USA; University of Southern California, Los Angeles, USA; Meta Platforms, Inc., New York, USA|Driven by the new economic opportunities created by the creator economy, an increasing number of content creators rely on and compete for revenue generated from online content recommendation platforms. This burgeoning competition reshapes the dynamics of content distribution and profoundly impacts long-term user welfare on the platform. However, the absence of a comprehensive picture of global user preference distribution often traps the competition, especially the creators, in states that yield sub-optimal user welfare. To encourage creators to best serve a broad user population with relevant content, it becomes the platform's responsibility to leverage its information advantage regarding user preference distribution to accurately signal creators. In this study, we perform system-side user welfare optimization under a competitive game setting among content creators. We propose an algorithmic solution for the platform, which dynamically computes a sequence of weights for each user based on their satisfaction of the recommended content. These weights are then utilized to design mechanisms that adjust the recommendation policy or the post-recommendation rewards, thereby influencing creators' content production strategies. To validate the effectiveness of our proposed method, we report our findings from a series of experiments, including: 1. a proof-of-concept negative example illustrating how creators' strategies converge towards sub-optimal states without platform intervention; 2. offline experiments employing our proposed intervention mechanisms on diverse datasets; and 3. results from a three-week online experiment conducted on Instagram Reels short-video recommendation platform.|在创作者经济带来的新经济机遇的驱动下，越来越多的内容创作者依赖并竞争在线内容推荐平台产生的收入。这种蓬勃发展的竞争重塑了内容分发的动态，并深刻影响了平台上的长期用户福利。然而，缺乏全球用户偏好分布的全面图像，往往会使竞争，尤其是创造者陷入产生次优用户福利的状态。为了鼓励创作者用相关内容最好地服务于广泛的用户群体，平台有责任利用其在用户偏好分布方面的信息优势来准确地向创作者发出信号。在这项研究中，我们在内容创作者之间的竞争博弈环境下进行系统端用户福利优化。我们提出了一个平台的算法解决方案，该方案根据每个用户对推荐内容的满意度动态计算每个用户的权重序列。然后利用这些权重来设计调整推荐策略或推荐后奖励的机制，从而影响创作者的内容生产策略。为了验证我们提出的方法的有效性，我们报告了一系列的实验结果，包括: 1。一个概念证明的否定例子，说明创造者的策略如何在没有平台干预的情况下收敛到次优状态;。在不同的数据集上使用我们提出的干预机制的离线实验;。在 Instagram Reels 短视频推荐平台上进行了为期三周的在线实验。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=User+Welfare+Optimization+in+Recommender+Systems+with+Competing+Content+Creators)|0| +|[Embedding Two-View Knowledge Graphs with Class Inheritance and Structural Similarity](https://doi.org/10.1145/3637528.3671941)|Kyuhwan Yeom, Hyeongjun Yang, Gayeon Park, Myeongheon Jeon, Yunjeong Ko, Byungkook Oh, KyongHo Lee|Computer Science and Engineering, Konkuk University, Seoul, Republic of Korea; Computer Science, Yonsei University, Seoul, Republic of Korea; Artificial Intelligence, Yonsei University, Seoul, Republic of Korea|Numerous large-scale knowledge graphs (KGs) fundamentally represent two-view KGs: an ontology-view KG with abstract classes in ontology and an instance-view KG with specific collections of entities instantiated from ontology classes. Two-view KG embedding aims to jointly learn continuous vector representations of entities and relations in the aforementioned two-view KGs. In essence, an ontology schema exhibits a tree-like structure guided by class hierarchies, which leads classes to form inheritance hierarchies. However, existing two-view KG embedding models neglect those hierarchies, which provides the necessity to reflect class inheritance. On the other hand, KG is constructed based on a pre-defined ontology schema that includes heterogeneous relations between classes. Furthermore, these relations are defined within the scope of those among classes since instances inherit all the properties of their corresponding classes, which reveals structural similarity between two multi-relational networks. Despite the consideration to bridge the gap among two-view KG representations, existing methods ignore the existence of structural similarity between two-view KGs. To address these issues, we propose a novel two-view KG embedding model, CISS, considering Class Inheritance and Structural Similarity between two-view KGs. To deal with class inheritance, we utilize class sets, each of which is composed of sibling classes, to learn fine-grained class representations. In addition, we configure virtual instance-view KG from clustered instances and compare subgraph representations of two-view KGs to enhance structural similarity between them. Experimental results show our superior performance compared to existing models.|许多大规模的知识图(KG)从根本上表示两个视图 KG: 一个本体视图 KG 具有本体中的抽象类，一个实例视图 KG 具有从本体类实例化的实体的特定集合。双视图幼儿园嵌入的目的是联合学习上述两视图幼儿园中实体和关系的连续向量表示。从本质上讲，本体模式表现出一种由类层次结构引导的树状结构，这种结构引导类形成继承层次结构。然而，现有的双视图 KG 嵌入模型忽略了这些层次结构，这就需要反映类继承。另一方面，KG 是基于一个预定义的本体模式构建的，该模式包含类之间的异构关系。此外，这些关系是在类之间的范围内定义的，因为实例继承了相应类的所有属性，这揭示了两个多关系网络之间的结构相似性。尽管现行方法已考虑填补双视角幼稚园表现形式之间的差距，但却忽略了双视角幼稚园之间是否存在结构相似性。为了解决这些问题，我们提出了一个新的双视图幼儿园嵌入模型 CISS，该模型考虑了类继承和双视图幼儿园之间的结构相似性。为了处理类继承，我们利用类集(每个类集都由兄弟类组成)来学习细粒度的类表示。此外，我们从集群实例配置虚拟实例视图幼稚园，并比较两个视图幼稚园的子图表示，以增强它们之间的结构相似性。实验结果表明，我们的性能优于现有的模型。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Embedding+Two-View+Knowledge+Graphs+with+Class+Inheritance+and+Structural+Similarity)|0| +|[Item-Difficulty-Aware Learning Path Recommendation: From a Real Walking Perspective](https://doi.org/10.1145/3637528.3671947)|Haotian Zhang, Shuanghong Shen, Bihan Xu, Zhenya Huang, Jinze Wu, Jing Sha, Shijin Wang|; State Key Laboratory of Cognitive Intelligence & iFLYTEK AI Research, Hefei, China; iFLYTEK AI Research, Hefei, China|Learning path recommendation aims to provide learners with a reasonable order of items to achieve their learning goals. Intuitively, the learning process on the learning path can be metaphorically likened to walking. Despite extensive efforts in this area, most previous methods mainly focus on the relationship among items but overlook the difficulty of items, which may raise two issues from a real walking perspective: (1) The path may be rough: When learners tread the path without considering item difficulty, it's akin to walking a dark, uneven road, making learning harder and dampening interest. (2) The path may be inefficient: Allowing learners only a few attempts on very challenging items before switching, or persisting with a difficult item despite numerous attempts without mastery, can result in inefficiencies in the learning journey. To conquer the above limitations, we propose a novel method named Difficulty-constrained Learning Path Recommendation (DLPR), which is aware of item difficulty. Specifically, we first explicitly categorize items into learning items and practice items, then construct a hierarchical graph to model and leverage item difficulty adequately. Then we design a Difficulty-driven Hierarchical Reinforcement Learning (DHRL) framework to facilitate learning paths with efficiency and smoothness. Finally, extensive experiments on three different simulators demonstrate our framework achieves state-of-the-art performance.|学习路径推荐的目的是为学习者提供一个合理的项目顺序，以实现他们的学习目标。直观地说，学习过程中的学习路径可以比喻为行走。尽管在这个领域做了大量的努力，以前的大多数方法主要关注项目之间的关系，但是忽略了项目的难度，这可能会从一个真正的行走的角度提出两个问题: (1)路径可能是粗糙的: 当学习者在不考虑项目难度的情况下行走在路径上，这就像走在一条黑暗的、不平坦的路上，使学习更加困难和抑制兴趣。(2)路径可能是低效的: 允许学习者在转换之前只尝试几次非常具有挑战性的项目，或者尽管尝试了很多次但没有掌握，仍然坚持一个困难的项目，可能会导致学习过程中的低效。为了克服上述限制，本文提出了一种新的学习路径推荐方法——难度约束学习路径推荐(DLPR) ，该方法能够识别项目的难度。具体来说，我们首先明确地将项目分类为学习项目和实践项目，然后构建一个层次图来充分地建模和利用项目难度。然后，我们设计了一个难度驱动的层次强化学习(dHRL)框架，以促进学习路径的有效性和顺畅性。最后，在三个不同的模拟器上进行了广泛的实验，证明了我们的框架实现了最先进的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Item-Difficulty-Aware+Learning+Path+Recommendation:+From+a+Real+Walking+Perspective)|0| |[Optimized Cost Per Click in Online Advertising: A Theoretical Analysis](https://doi.org/10.1145/3637528.3671767)|Kaichen Zhang, Zixuan Yuan, Hui Xiong||In recent years, Optimized Cost Per Click (OCPC) and Optimized Cost Per Mille(OCPM) have emerged as the most widely adopted pricing models in the onlineadvertising industry. However, the existing literature has yet to identify thespecific conditions under which these models outperform traditional pricingmodels like Cost Per Click (CPC) and Cost Per Action (CPA). To fill the gap,this paper builds an economic model that compares OCPC with CPC and CPAtheoretically, which incorporates out-site scenarios and outside options as twokey factors. Our analysis reveals that OCPC can effectively replace CPA bytackling the problem of advertisers strategically manipulating conversionreporting in out-site scenarios where conversions occur outside the advertisingplatform. Furthermore, OCPC exhibits the potential to surpass CPC in platformpayoffs by providing higher advertiser payoffs and consequently attracting moreadvertisers. However, if advertisers have less competitive outside options andconsistently stay in the focal platform, the platform may achieve higherpayoffs using CPC. Our findings deliver valuable insights for onlineadvertising platforms in selecting optimal pricing models, and providerecommendations for further enhancing their payoffs. To the best of ourknowledge, this is the first study to analyze OCPC from an economicperspective. Moreover, our analysis can be applied to the OCPM model as well.|近年来，优化每点击成本(OCPC)和优化每公里成本(OCPM)已经成为在线广告行业最广泛采用的定价模型。然而，现有文献尚未确定这些模型优于传统定价模型如每次点击成本(CPC)和每次行动成本(CPA)的具体条件。为了填补这一空白，本文从理论上建立了一个比较 OCPC 与 CPC 和 CPA 的经济模型，该模型将场外情景和场外选择作为两个关键因素。我们的分析表明，OCPC 可以有效地替代 CPA，解决广告商在转化发生在广告平台之外的外部场景中策略性地操纵转化报告的问题。此外，OCPC 通过提供更高的广告客户收益，从而吸引更多的广告客户，在平台收益方面显示出超过 CPC 的潜力。然而，如果广告商没有那么多竞争性的外部选择，并且一直呆在焦点平台上，那么该平台可能会利用 CPC 获得更高的回报。我们的研究结果为在线广告平台选择最佳定价模型提供了有价值的见解，并为进一步提高其收益提供了建议。据我们所知，这是第一个从经济学角度分析 OCPC 的研究。此外，我们的分析也适用于 OCPM 模型。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Optimized+Cost+Per+Click+in+Online+Advertising:+A+Theoretical+Analysis)|0| -|[Counteracting Duration Bias in Video Recommendation via Counterfactual Watch Time](https://doi.org/10.1145/3637528.3671817)|Haiyuan Zhao, Guohao Cai, Jieming Zhu, Zhenhua Dong, Jun Xu, JiRong Wen|School of Information, Renmin University of China, Beijing, China; Noah's Ark Lab, Huawei, Shenzhen, China; Gaoling School of Artificial Intelligence, Renmin University of China, Beijing, China|In video recommendation, an ongoing effort is to satisfy users' personalizedinformation needs by leveraging their logged watch time. However, watch timeprediction suffers from duration bias, hindering its ability to reflect users'interests accurately. Existing label-correction approaches attempt to uncoveruser interests through grouping and normalizing observed watch time accordingto video duration. Although effective to some extent, we found that theseapproaches regard completely played records (i.e., a user watches the entirevideo) as equally high interest, which deviates from what we observed on realdatasets: users have varied explicit feedback proportion when completelyplaying videos. In this paper, we introduce the counterfactual watch time(CWT),the potential watch time a user would spend on the video if its duration issufficiently long. Analysis shows that the duration bias is caused by thetruncation of CWT due to the video duration limitation, which usually occurs onthose completely played records. Besides, a Counterfactual Watch Model (CWM) isproposed, revealing that CWT equals the time users get the maximum benefit fromvideo recommender systems. Moreover, a cost-based transform function is definedto transform the CWT into the estimation of user interest, and the model can belearned by optimizing a counterfactual likelihood function defined overobserved user watch times. Extensive experiments on three real videorecommendation datasets and online A/B testing demonstrated that CWMeffectively enhanced video recommendation accuracy and counteracted theduration bias.|在视频推荐中，一个持续的努力是通过利用用户的观看时间来满足用户的个性化信息需求。然而，手表时间预测存在持续时间偏差，影响了其准确反映用户兴趣的能力。现有的标签校正方法试图通过根据视频持续时间对观看时间进行分组和标准化来揭示用户的兴趣。虽然在某种程度上有效，但是我们发现这些方法把完全播放的记录(例如，用户观看整个视频)视为同样高的兴趣，这偏离了我们在真实数据集上观察到的: 当完全播放视频时，用户有不同的显式反馈比例。本文介绍了反事实观看时间(CWT) ，即当视频持续时间过长时，用户可能花在视频上的观看时间。分析表明，持续时间偏差是由于视频持续时间受到限制而导致的连续小波变换(CWT)截断所引起的，这种情况通常发生在完全播放的记录上。此外，提出了一种反事实观察模型(CWM) ，揭示了 CWT 等于用户从视频推荐系统中获得最大收益的时间。此外，定义了一个基于代价的转换函数，将连续小波变换转换为用户兴趣的估计，该模型可以通过优化一个反事实似然函数来定义过度观察的用户观察时间。在三个真实视频推荐数据集上的大量实验和在线 A/B 测试表明，CWM 有效地提高了视频推荐的准确性，抵消了持续时间偏差。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Counteracting+Duration+Bias+in+Video+Recommendation+via+Counterfactual+Watch+Time)|0| -|[MMBee: Live Streaming Gift-Sending Recommendations via Multi-Modal Fusion and Behaviour Expansion](https://doi.org/10.1145/3637528.3671511)|Jiaxin Deng, Shiyao Wang, Yuchen Wang, Jiansong Qi, Liqin Zhao, Guorui Zhou, Gaofeng Meng|; Institute of Automation, Beijing, China; KuaiShou Inc., Beijing, China|Live streaming services are becoming increasingly popular due to real-time interactions and entertainment. Viewers can chat and send comments or virtual gifts to express their preferences for the streamers. Accurately modeling the gifting interaction not only enhances users' experience but also increases streamers' revenue. Previous studies on live streaming gifting prediction treat this task as a conventional recommendation problem, and model users' preferences using categorical data and observed historical behaviors. However, it is challenging to precisely describe the real-time content changes in live streaming using limited categorical information. Moreover, due to the sparsity of gifting behaviors, capturing the preferences and intentions of users is quite difficult. In this work, we propose MMBee based on real-time Multi-Modal Fusion and Behaviour Expansion to address these issues. Specifically, we first present a Multi-modal Fusion Module with Learnable Query (MFQ) to perceive the dynamic content of streaming segments and process complex multi-modal interactions, including images, text comments and speech. To alleviate the sparsity issue of gifting behaviors, we present a novel Graph-guided Interest Expansion (GIE) approach that learns both user and streamer representations on large-scale gifting graphs with multi-modal attributes. It consists of two main parts: graph node representations pre-training and metapath-based behavior expansion, all of which help model jump out of the specific historical gifting behaviors for exploration and largely enrich the behavior representations. Comprehensive experiment results show that MMBee achieves significant performance improvements on both public datasets and Kuaishou real-world streaming datasets and the effectiveness has been further validated through online A/B experiments. MMBee has been deployed and is serving hundreds of millions of users at Kuaishou.|由于实时交互和娱乐，流媒体直播服务变得越来越流行。观众可以聊天和发送评论或虚拟礼物来表达他们对主播的喜好。精确建模礼物互动不仅提高了用户的体验，而且增加了主播的收入。以往的流媒体直播礼物预测研究将这一任务视为一个传统的推荐问题，并利用分类数据和观察到的历史行为对用户的偏好进行建模。然而，使用有限的分类信息来精确描述直播流中的实时内容变化是一个挑战。此外，由于送礼行为的稀少性，要捕捉用户的喜好和意图是相当困难的。在这项工作中，我们提出了基于实时多模态融合和行为扩展的 MMBee 来解决这些问题。具体来说，我们首先提出了一种基于可学习查询(Learnable Query，MFQ)的多模态融合模块来感知流媒体片段的动态内容，并处理复杂的多模态交互，包括图像、文本注释和语音。为了缓解送礼行为的稀疏性问题，我们提出了一种新的图引导兴趣扩展(GIE)方法，该方法同时学习用户和流媒体在具有多模态属性的大规模送礼图上的表示。它包括两个主要部分: 图节点表示预训练和基于元路径的行为扩展，所有这些都有助于模型跳出具体的历史馈赠行为去探索，并大大丰富了行为表示。综合实验结果表明，mMBee 在公共数据集和 Kuaishou 真实世界流数据集上都取得了显著的性能改善，并通过在线 A/B 实验进一步验证了其有效性。MMBee 已经部署完毕，目前正在 Kuaishou 为数亿用户服务。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=MMBee:+Live+Streaming+Gift-Sending+Recommendations+via+Multi-Modal+Fusion+and+Behaviour+Expansion)|0| -|[Contextual Distillation Model for Diversified Recommendation](https://doi.org/10.1145/3637528.3671514)|Fan Li, Xu Si, Shisong Tang, Dingmin Wang, Kunyan Han, Bing Han, Guorui Zhou, Yang Song, Hechang Chen|Kuaishou Inc. & Tsinghua University, Beijing, China; Tsinghua University, Beijing, China; University of Oxford, Oxford, United Kingdom; University of Science and Technology of China, Hefei, China; Jilin University, Changchun, China; Kuaishou Inc., Beijing, China|The diversity of recommendation is equally crucial as accuracy in improvinguser experience. Existing studies, e.g., Determinantal Point Process (DPP) andMaximal Marginal Relevance (MMR), employ a greedy paradigm to iterativelyselect items that optimize both accuracy and diversity. However, prior methodstypically exhibit quadratic complexity, limiting their applications to there-ranking stage and are not applicable to other recommendation stages with alarger pool of candidate items, such as the pre-ranking and ranking stages. Inthis paper, we propose Contextual Distillation Model (CDM), an efficientrecommendation model that addresses diversification, suitable for thedeployment in all stages of industrial recommendation pipelines. Specifically,CDM utilizes the candidate items in the same user request as context to enhancethe diversification of the results. We propose a contrastive context encoderthat employs attention mechanisms to model both positive and negative contexts.For the training of CDM, we compare each target item with its context embeddingand utilize the knowledge distillation framework to learn the win probabilityof each target item under the MMR algorithm, where the teacher is derived fromMMR outputs. During inference, ranking is performed through a linearcombination of the recommendation and student model scores, ensuring bothdiversity and efficiency. We perform offline evaluations on two industrialdatasets and conduct online A/B test of CDM on the short-video platformKuaiShou. The considerable enhancements observed in both recommendation qualityand diversity, as shown by metrics, provide strong superiority for theeffectiveness of CDM.|推荐的多样性与提高用户体验的准确性同样重要。现有的研究，例如行列式点过程(DPP)和最大边际相关性(MMR) ，采用贪婪的范式迭代选择项目，优化准确性和多样性。然而，先验方法通常表现出二次复杂性，将其应用限制在三阶段排名阶段，不适用于其他候选项目较多的推荐阶段，如预先排名阶段和排名阶段。本文提出了一种适用于行业推荐流程各个阶段的高效多样化推荐模型——上下文精馏模型(CDM)。具体来说，CDM 利用同一用户请求中的候选项作为上下文，以增强结果的多样化。我们提出了一个对比语境编码器，它使用注意机制来模拟积极和消极的语境。对于 CDM 的培训，我们将每个目标项目与其上下文嵌入进行比较，并利用知识提取框架在 MMR 算法下学习每个目标项目的获胜概率，其中教师是从 MMR 输出中获得的。在推理过程中，排名是通过推荐和学生模型得分的线性组合来完成的，从而保证了多样性和效率。我们对两个工业数据集进行离线评估，并在快手短视频平台上对 CDM 进行在线 A/B 测试。指标表明，在推荐质量和多样性方面观察到的显著增强为 CDM 的有效性提供了强大的优势。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Contextual+Distillation+Model+for+Diversified+Recommendation)|0| +|[Counteracting Duration Bias in Video Recommendation via Counterfactual Watch Time](https://doi.org/10.1145/3637528.3671817)|Haiyuan Zhao, Guohao Cai, Jieming Zhu, Zhenhua Dong, Jun Xu, JiRong Wen|Noah's Ark Lab, Huawei, Shenzhen, China; School of Information, Renmin University of China, Beijing, China; Gaoling School of Artificial Intelligence, Renmin University of China, Beijing, China|In video recommendation, an ongoing effort is to satisfy users' personalizedinformation needs by leveraging their logged watch time. However, watch timeprediction suffers from duration bias, hindering its ability to reflect users'interests accurately. Existing label-correction approaches attempt to uncoveruser interests through grouping and normalizing observed watch time accordingto video duration. Although effective to some extent, we found that theseapproaches regard completely played records (i.e., a user watches the entirevideo) as equally high interest, which deviates from what we observed on realdatasets: users have varied explicit feedback proportion when completelyplaying videos. In this paper, we introduce the counterfactual watch time(CWT),the potential watch time a user would spend on the video if its duration issufficiently long. Analysis shows that the duration bias is caused by thetruncation of CWT due to the video duration limitation, which usually occurs onthose completely played records. Besides, a Counterfactual Watch Model (CWM) isproposed, revealing that CWT equals the time users get the maximum benefit fromvideo recommender systems. Moreover, a cost-based transform function is definedto transform the CWT into the estimation of user interest, and the model can belearned by optimizing a counterfactual likelihood function defined overobserved user watch times. Extensive experiments on three real videorecommendation datasets and online A/B testing demonstrated that CWMeffectively enhanced video recommendation accuracy and counteracted theduration bias.|在视频推荐中，一个持续的努力是通过利用用户的观看时间来满足用户的个性化信息需求。然而，手表时间预测存在持续时间偏差，影响了其准确反映用户兴趣的能力。现有的标签校正方法试图通过根据视频持续时间对观看时间进行分组和标准化来揭示用户的兴趣。虽然在某种程度上有效，但是我们发现这些方法把完全播放的记录(例如，用户观看整个视频)视为同样高的兴趣，这偏离了我们在真实数据集上观察到的: 当完全播放视频时，用户有不同的显式反馈比例。本文介绍了反事实观看时间(CWT) ，即当视频持续时间过长时，用户可能花在视频上的观看时间。分析表明，持续时间偏差是由于视频持续时间受到限制而导致的连续小波变换(CWT)截断所引起的，这种情况通常发生在完全播放的记录上。此外，提出了一种反事实观察模型(CWM) ，揭示了 CWT 等于用户从视频推荐系统中获得最大收益的时间。此外，定义了一个基于代价的转换函数，将连续小波变换转换为用户兴趣的估计，该模型可以通过优化一个反事实似然函数来定义过度观察的用户观察时间。在三个真实视频推荐数据集上的大量实验和在线 A/B 测试表明，CWM 有效地提高了视频推荐的准确性，抵消了持续时间偏差。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Counteracting+Duration+Bias+in+Video+Recommendation+via+Counterfactual+Watch+Time)|0| +|[MMBee: Live Streaming Gift-Sending Recommendations via Multi-Modal Fusion and Behaviour Expansion](https://doi.org/10.1145/3637528.3671511)|Jiaxin Deng, Shiyao Wang, Yuchen Wang, Jiansong Qi, Liqin Zhao, Guorui Zhou, Gaofeng Meng|; KuaiShou Inc., Beijing, China; Institute of Automation, Beijing, China|Live streaming services are becoming increasingly popular due to real-time interactions and entertainment. Viewers can chat and send comments or virtual gifts to express their preferences for the streamers. Accurately modeling the gifting interaction not only enhances users' experience but also increases streamers' revenue. Previous studies on live streaming gifting prediction treat this task as a conventional recommendation problem, and model users' preferences using categorical data and observed historical behaviors. However, it is challenging to precisely describe the real-time content changes in live streaming using limited categorical information. Moreover, due to the sparsity of gifting behaviors, capturing the preferences and intentions of users is quite difficult. In this work, we propose MMBee based on real-time Multi-Modal Fusion and Behaviour Expansion to address these issues. Specifically, we first present a Multi-modal Fusion Module with Learnable Query (MFQ) to perceive the dynamic content of streaming segments and process complex multi-modal interactions, including images, text comments and speech. To alleviate the sparsity issue of gifting behaviors, we present a novel Graph-guided Interest Expansion (GIE) approach that learns both user and streamer representations on large-scale gifting graphs with multi-modal attributes. It consists of two main parts: graph node representations pre-training and metapath-based behavior expansion, all of which help model jump out of the specific historical gifting behaviors for exploration and largely enrich the behavior representations. Comprehensive experiment results show that MMBee achieves significant performance improvements on both public datasets and Kuaishou real-world streaming datasets and the effectiveness has been further validated through online A/B experiments. MMBee has been deployed and is serving hundreds of millions of users at Kuaishou.|由于实时交互和娱乐，流媒体直播服务变得越来越流行。观众可以聊天和发送评论或虚拟礼物来表达他们对主播的喜好。精确建模礼物互动不仅提高了用户的体验，而且增加了主播的收入。以往的流媒体直播礼物预测研究将这一任务视为一个传统的推荐问题，并利用分类数据和观察到的历史行为对用户的偏好进行建模。然而，使用有限的分类信息来精确描述直播流中的实时内容变化是一个挑战。此外，由于送礼行为的稀少性，要捕捉用户的喜好和意图是相当困难的。在这项工作中，我们提出了基于实时多模态融合和行为扩展的 MMBee 来解决这些问题。具体来说，我们首先提出了一种基于可学习查询(Learnable Query，MFQ)的多模态融合模块来感知流媒体片段的动态内容，并处理复杂的多模态交互，包括图像、文本注释和语音。为了缓解送礼行为的稀疏性问题，我们提出了一种新的图引导兴趣扩展(GIE)方法，该方法同时学习用户和流媒体在具有多模态属性的大规模送礼图上的表示。它包括两个主要部分: 图节点表示预训练和基于元路径的行为扩展，所有这些都有助于模型跳出具体的历史馈赠行为去探索，并大大丰富了行为表示。综合实验结果表明，mMBee 在公共数据集和 Kuaishou 真实世界流数据集上都取得了显著的性能改善，并通过在线 A/B 实验进一步验证了其有效性。MMBee 已经部署完毕，目前正在 Kuaishou 为数亿用户服务。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=MMBee:+Live+Streaming+Gift-Sending+Recommendations+via+Multi-Modal+Fusion+and+Behaviour+Expansion)|0| +|[Contextual Distillation Model for Diversified Recommendation](https://doi.org/10.1145/3637528.3671514)|Fan Li, Xu Si, Shisong Tang, Dingmin Wang, Kunyan Han, Bing Han, Guorui Zhou, Yang Song, Hechang Chen|Kuaishou Inc. & Tsinghua University, Beijing, China; Jilin University, Changchun, China; Tsinghua University, Beijing, China; Kuaishou Inc., Beijing, China; University of Oxford, Oxford, United Kingdom; University of Science and Technology of China, Hefei, China|The diversity of recommendation is equally crucial as accuracy in improvinguser experience. Existing studies, e.g., Determinantal Point Process (DPP) andMaximal Marginal Relevance (MMR), employ a greedy paradigm to iterativelyselect items that optimize both accuracy and diversity. However, prior methodstypically exhibit quadratic complexity, limiting their applications to there-ranking stage and are not applicable to other recommendation stages with alarger pool of candidate items, such as the pre-ranking and ranking stages. Inthis paper, we propose Contextual Distillation Model (CDM), an efficientrecommendation model that addresses diversification, suitable for thedeployment in all stages of industrial recommendation pipelines. Specifically,CDM utilizes the candidate items in the same user request as context to enhancethe diversification of the results. We propose a contrastive context encoderthat employs attention mechanisms to model both positive and negative contexts.For the training of CDM, we compare each target item with its context embeddingand utilize the knowledge distillation framework to learn the win probabilityof each target item under the MMR algorithm, where the teacher is derived fromMMR outputs. During inference, ranking is performed through a linearcombination of the recommendation and student model scores, ensuring bothdiversity and efficiency. We perform offline evaluations on two industrialdatasets and conduct online A/B test of CDM on the short-video platformKuaiShou. The considerable enhancements observed in both recommendation qualityand diversity, as shown by metrics, provide strong superiority for theeffectiveness of CDM.|推荐的多样性与提高用户体验的准确性同样重要。现有的研究，例如行列式点过程(DPP)和最大边际相关性(MMR) ，采用贪婪的范式迭代选择项目，优化准确性和多样性。然而，先验方法通常表现出二次复杂性，将其应用限制在三阶段排名阶段，不适用于其他候选项目较多的推荐阶段，如预先排名阶段和排名阶段。本文提出了一种适用于行业推荐流程各个阶段的高效多样化推荐模型——上下文精馏模型(CDM)。具体来说，CDM 利用同一用户请求中的候选项作为上下文，以增强结果的多样化。我们提出了一个对比语境编码器，它使用注意机制来模拟积极和消极的语境。对于 CDM 的培训，我们将每个目标项目与其上下文嵌入进行比较，并利用知识提取框架在 MMR 算法下学习每个目标项目的获胜概率，其中教师是从 MMR 输出中获得的。在推理过程中，排名是通过推荐和学生模型得分的线性组合来完成的，从而保证了多样性和效率。我们对两个工业数据集进行离线评估，并在快手短视频平台上对 CDM 进行在线 A/B 测试。指标表明，在推荐质量和多样性方面观察到的显著增强为 CDM 的有效性提供了强大的优势。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Contextual+Distillation+Model+for+Diversified+Recommendation)|0| |[MGMatch: Fast Matchmaking with Nonlinear Objective and Constraints via Multimodal Deep Graph Learning](https://doi.org/10.1145/3637528.3671553)|Yu Sun, Kai Wang, Zhipeng Hu, Runze Wu, Yaoxin Wu, Wen Song, Xudong Shen, Tangjie Lv, Changjie Fan|Shandong University, Qingdao, Shandong, China; Fuxi AI Lab, NetEase Inc., Hangzhou, Zhejiang, China; Eindhoven University of Technology, Eindhoven, Netherlands|As a core problem of online games, matchmaking is to assign players into multiple teams to maximize their gaming experience. With the rapid development of game industry, it is increasingly difficulty to explicitly model players' experiences as linear functions. Instead, it is often modeled in a data-driven way by training a neural network. Meanwhile, complex rules must be satisfied to ensure the robustness of matchmaking, which are often described using logical operators. Therefore, matchmaking in practical scenarios is a challenging combinatorial optimization problem with nonlinear objective, linear constraints and logical constraints, which receives much less attention in previous research. In this paper, we propose a novel deep learning method for high-quality matchmaking in real-time. We first cast the problem as standard mixed-integer programming (MIP) by linearizing ReLU networks and logical constraints. Then, based on supervised learning, we design and train a multi-modal graph learning architecture to predict optimal solutions end-to-end from instance data, and solve a surrogate problem to efficiently obtain feasible solutions. Evaluation results on real industry datasets show that our method can deliver near-optimal solutions within 100ms.|作为网络游戏的一个核心问题，匹配是将玩家分配到多个团队中，以最大限度地提高他们的游戏体验。随着游戏产业的快速发展，将玩家的体验明确地建模为线性函数变得越来越困难。相反，它通常是通过训练神经网络以数据驱动的方式建模的。同时，为了保证匹配的鲁棒性，必须满足复杂的规则，这些规则通常用逻辑运算符来描述。因此，实际场景中的匹配问题是一个具有非线性目标、线性约束和逻辑约束的组合优化问题，在以往的研究中受到的关注较少。本文提出了一种新的高质量实时匹配的深度学习方法。首先通过线性化 ReLU 网络和逻辑约束将问题转化为标准的混合整数规划(MIP)问题。然后，在监督式学习的基础上，我们设计并训练了一个多模态图学习架构来从实例数据中预测端到端的最优解，并解决一个代理问题来有效地获得可行的解。对实际工业数据集的评估结果表明，该方法可以在100ms 内提供接近最优的解决方案。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=MGMatch:+Fast+Matchmaking+with+Nonlinear+Objective+and+Constraints+via+Multimodal+Deep+Graph+Learning)|0| -|[R-Eval: A Unified Toolkit for Evaluating Domain Knowledge of Retrieval Augmented Large Language Models](https://doi.org/10.1145/3637528.3671564)|Shangqing Tu, Yuanchun Wang, Jifan Yu, Yuyang Xie, Yaran Shi, Xiaozhi Wang, Jing Zhang, Lei Hou, Juanzi Li|SoI, Renmin University of China, Beijing, China; BNRist, DCST, Tsinghua University, Beijing, China; SIOE, Beihang University, Beijing, China; DCST, Tsinghua University, Beijing, China|Large language models have achieved remarkable success on general NLP tasks,but they may fall short for domain-specific problems. Recently, variousRetrieval-Augmented Large Language Models (RALLMs) are proposed to address thisshortcoming. However, existing evaluation tools only provide a few baselinesand evaluate them on various domains without mining the depth of domainknowledge. In this paper, we address the challenges of evaluating RALLMs byintroducing the R-Eval toolkit, a Python toolkit designed to streamline theevaluation of different RAG workflows in conjunction with LLMs. Our toolkit,which supports popular built-in RAG workflows and allows for the incorporationof customized testing data on the specific domain, is designed to beuser-friendly, modular, and extensible. We conduct an evaluation of 21 RALLMsacross three task levels and two representative domains, revealing significantvariations in the effectiveness of RALLMs across different tasks and domains.Our analysis emphasizes the importance of considering both task and domainrequirements when choosing a RAG workflow and LLM combination. We are committedto continuously maintaining our platform at https://github.com/THU-KEG/R-Evalto facilitate both the industry and the researchers.|大型语言模型已经在一般的 NLP 任务上取得了显著的成功，但是它们可能在特定领域的问题上有所不足。最近，各种检索增强大型语言模型(RALLMs)被提出来解决这个问题。然而，现有的评估工具只提供了一些基线，并在不同的领域进行评估，而没有挖掘领域知识的深度。在本文中，我们通过引入 R-Eval 工具包来解决评估 RAG 工作流的挑战，这是一个 Python 工具包，旨在与 LLM 一起简化不同 RAG 工作流的评估。我们的工具包，支持流行的内置 RAG 工作流程，并允许在特定领域集成定制的测试数据，被设计成用户友好的、模块化的和可扩展的。我们在三个任务级别和两个代表性领域对21个 RALLM 进行了评估，揭示了 RALLM 在不同任务和领域的有效性的显著差异。我们的分析强调了在选择 RAG 工作流和 LLM 组合时同时考虑任务和领域需求的重要性。我们致力于不断维护我们的平台， https://github.com/thu-keg/r-evalto 为业界和研究人员提供便利。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=R-Eval:+A+Unified+Toolkit+for+Evaluating+Domain+Knowledge+of+Retrieval+Augmented+Large+Language+Models)|0| -|[ADSNet: Cross-Domain LTV Prediction with an Adaptive Siamese Network in Advertising](https://doi.org/10.1145/3637528.3671612)|Ruize Wang, Hui Xu, Ying Cheng, Qi He, Xing Zhou, Rui Feng, Wei Xu, Lei Huang, Jie Jiang|Tencent Inc., Shenzhen, China; Tencent Inc., Shanghai, China; School of Computer Science, Fudan University, Shanghai, China|Advertising platforms have evolved in estimating Lifetime Value (LTV) to better align with advertisers' true performance metric which considers cumulative sum of purchases a customer contributes over a period. Accurate LTV estimation is crucial for the precision of the advertising system and the effectiveness of advertisements. However, the sparsity of real-world LTV data presents a significant challenge to LTV predictive model(i.e., pLTV), severely limiting the their capabilities. Therefore, we propose to utilize external data, in addition to the internal data of advertising platform, to expand the size of purchase samples and enhance the LTV prediction model of the advertising platform. To tackle the issue of data distribution shift between internal and external platforms, we introduce an Adaptive Difference Siamese Network (ADSNet), which employs cross-domain transfer learning to prevent negative transfer. Specifically, ADSNet is designed to learn information that is beneficial to the target domain. We introduce a gain evaluation strategy to calculate information gain, aiding the model in learning helpful information for the target domain and providing the ability to reject noisy samples, thus avoiding negative transfer. Additionally, we also design a Domain Adaptation Module as a bridge to connect different domains, reduce the distribution distance between them, and enhance the consistency of representation space distribution. We conduct extensive offline experiments and online A/B tests on a real advertising platform. Our proposed ADSNet method outperforms other methods, improving GINI by 2%. The ablation study highlights the importance of the gain evaluation strategy in negative gain sample rejection and improving model performance. Additionally, ADSNet significantly improves long-tail prediction. The online A/B tests confirm ADSNet's efficacy, increasing online LTV by 3.47% and GMV by 3.89%.|广告平台已经发展到估算终身价值(LTV) ，以便更好地与广告商的真实业绩指标保持一致，后者考虑的是消费者在一段时间内贡献的累计购买总额。准确的 LTV 估计对于广告系统的准确性和广告的有效性至关重要。然而，实际 LTV 数据的稀疏性对 LTV 预测模型(即 pLTV)提出了严峻的挑战，严重限制了它们的能力。因此，我们建议利用外部数据，除了广告平台的内部数据外，扩大购买样本的规模，提高广告平台的 LTV 预测模型。为了解决内部平台和外部平台之间数据分布转移的问题，我们引入了自适应差分暹罗网(ADSNet) ，该网络采用跨域传输学习来防止负向传输。具体来说，ADSNet 是为了学习对目标域有益的信息而设计的。我们引入一个增益评估策略来计算信息增益，帮助模型学习目标域的有用信息，并提供拒绝噪声样本的能力，从而避免负迁移。此外，我们还设计了一个领域适应模块作为连接不同领域的桥梁，减少它们之间的分布距离，提高表示空间分布的一致性。我们在一个真实的广告平台上进行广泛的离线实验和在线 A/B 测试。我们提出的 ADSNet 方法优于其他方法，GINI 提高了2% 。烧蚀研究突出了增益评价策略在抑制负增益样本和改善模型性能中的重要性。此外，ADSNet 显著改善了长尾预测。在线 A/B 测试证实了 ADSNet 的有效性，在线 LTV 增加了3.47% ，GMV 增加了3.89% 。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=ADSNet:+Cross-Domain+LTV+Prediction+with+an+Adaptive+Siamese+Network+in+Advertising)|0| -|[Multi-Behavior Collaborative Filtering with Partial Order Graph Convolutional Networks](https://doi.org/10.1145/3637528.3671569)|Yijie Zhang, Yuanchen Bei, Hao Chen, Qijie Shen, Zheng Yuan, Huan Gong, Senzhang Wang, Feiran Huang, Xiao Huang|Jinan University, Guangzhou, China; Central South University, Hunan, Changsha, China; Alibaba Group, Zhejiang, Hangzhou, China; Zhejiang University, Zhejiang, Hangzhou, China; National University of Defense Technology, Changsha, China; The Hong Kong Polytechnic University, Hong Kong, China|Representing information of multiple behaviors in the single graph collaborative filtering (CF) vector has been a long-standing challenge. This is because different behaviors naturally form separate behavior graphs and learn separate CF embeddings. Existing models merge the separate embeddings by appointing the CF embeddings for some behaviors as the primary embedding and utilizing other auxiliaries to enhance the primary embedding. However, this approach often results in the joint embedding performing well on the main tasks but poorly on the auxiliary ones. To address the problem arising from the separate behavior graphs, we propose the concept of Partial Order Recommendation Graphs (POG). POG defines the partial order relation of multiple behaviors and models behavior combinations as weighted edges to merge separate behavior graphs into a joint POG. Theoretical proof verifies that POG can be generalized to any given set of multiple behaviors. Based on POG, we propose the tailored Partial Order Graph Convolutional Networks (POGCN) that convolute neighbors' information while considering the behavior relations between users and items. POGCN also introduces a partial-order BPR sampling strategy for efficient and effective multiple-behavior CF training. POGCN has been successfully deployed on the homepage of Alibaba for two months, providing recommendation services for over one billion users. Extensive offline experiments conducted on three public benchmark datasets demonstrate that POGCN outperforms state-of-the-art multi-behavior baselines across all types of behaviors. Furthermore, online A/B tests confirm the superiority of POGCN in billion-scale recommender systems.|在单一图形协同过滤(CF)向量中表示多种行为的信息一直是一个长期的挑战。这是因为不同的行为自然地形成独立的行为图，并学习独立的 CF 嵌入。现有的模型通过指定一些行为的 CF 嵌入作为主嵌入，并利用其他辅助来增强主嵌入，从而将分离的嵌入进行合并。然而，这种方法往往导致联合嵌入在主要任务上表现良好，但在辅助任务上表现不佳。为了解决分离的行为图所带来的问题，我们提出了偏序推荐图(POG)的概念。POG 将多个行为和模型行为组合的偏序关系定义为加权边，以便将单独的行为图合并到一个联合 POG 中。理论证明了 POG 可以推广到任意给定的一组多行为。在 POG 的基础上，提出了一种考虑用户与项目之间行为关系的卷积邻居信息的剪裁偏序图卷积网络(POGCN)。POGCN 还引入了一种偏序 BPR 抽样策略，用于有效的多行为 CF 训练。POgcn 已成功登入阿里巴巴网页两个月，为超过十亿用户提供推荐服务。在三个公共基准数据集上进行的大量离线实验表明，POGCN 在所有类型的行为上都优于最先进的多行为基准。此外，在线 A/B 测试证实了 POGCN 在亿万规模推荐系统中的优越性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Multi-Behavior+Collaborative+Filtering+with+Partial+Order+Graph+Convolutional+Networks)|0| +|[R-Eval: A Unified Toolkit for Evaluating Domain Knowledge of Retrieval Augmented Large Language Models](https://doi.org/10.1145/3637528.3671564)|Shangqing Tu, Yuanchun Wang, Jifan Yu, Yuyang Xie, Yaran Shi, Xiaozhi Wang, Jing Zhang, Lei Hou, Juanzi Li|DCST, Tsinghua University, Beijing, China; SoI, Renmin University of China, Beijing, China; BNRist, DCST, Tsinghua University, Beijing, China; SIOE, Beihang University, Beijing, China|Large language models have achieved remarkable success on general NLP tasks,but they may fall short for domain-specific problems. Recently, variousRetrieval-Augmented Large Language Models (RALLMs) are proposed to address thisshortcoming. However, existing evaluation tools only provide a few baselinesand evaluate them on various domains without mining the depth of domainknowledge. In this paper, we address the challenges of evaluating RALLMs byintroducing the R-Eval toolkit, a Python toolkit designed to streamline theevaluation of different RAG workflows in conjunction with LLMs. Our toolkit,which supports popular built-in RAG workflows and allows for the incorporationof customized testing data on the specific domain, is designed to beuser-friendly, modular, and extensible. We conduct an evaluation of 21 RALLMsacross three task levels and two representative domains, revealing significantvariations in the effectiveness of RALLMs across different tasks and domains.Our analysis emphasizes the importance of considering both task and domainrequirements when choosing a RAG workflow and LLM combination. We are committedto continuously maintaining our platform at https://github.com/THU-KEG/R-Evalto facilitate both the industry and the researchers.|大型语言模型已经在一般的 NLP 任务上取得了显著的成功，但是它们可能在特定领域的问题上有所不足。最近，各种检索增强大型语言模型(RALLMs)被提出来解决这个问题。然而，现有的评估工具只提供了一些基线，并在不同的领域进行评估，而没有挖掘领域知识的深度。在本文中，我们通过引入 R-Eval 工具包来解决评估 RAG 工作流的挑战，这是一个 Python 工具包，旨在与 LLM 一起简化不同 RAG 工作流的评估。我们的工具包，支持流行的内置 RAG 工作流程，并允许在特定领域集成定制的测试数据，被设计成用户友好的、模块化的和可扩展的。我们在三个任务级别和两个代表性领域对21个 RALLM 进行了评估，揭示了 RALLM 在不同任务和领域的有效性的显著差异。我们的分析强调了在选择 RAG 工作流和 LLM 组合时同时考虑任务和领域需求的重要性。我们致力于不断维护我们的平台， https://github.com/thu-keg/r-evalto 为业界和研究人员提供便利。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=R-Eval:+A+Unified+Toolkit+for+Evaluating+Domain+Knowledge+of+Retrieval+Augmented+Large+Language+Models)|0| +|[ADSNet: Cross-Domain LTV Prediction with an Adaptive Siamese Network in Advertising](https://doi.org/10.1145/3637528.3671612)|Ruize Wang, Hui Xu, Ying Cheng, Qi He, Xing Zhou, Rui Feng, Wei Xu, Lei Huang, Jie Jiang|School of Computer Science, Fudan University, Shanghai, China; Tencent Inc., Shenzhen, China; Tencent Inc., Shanghai, China|Advertising platforms have evolved in estimating Lifetime Value (LTV) to better align with advertisers' true performance metric which considers cumulative sum of purchases a customer contributes over a period. Accurate LTV estimation is crucial for the precision of the advertising system and the effectiveness of advertisements. However, the sparsity of real-world LTV data presents a significant challenge to LTV predictive model(i.e., pLTV), severely limiting the their capabilities. Therefore, we propose to utilize external data, in addition to the internal data of advertising platform, to expand the size of purchase samples and enhance the LTV prediction model of the advertising platform. To tackle the issue of data distribution shift between internal and external platforms, we introduce an Adaptive Difference Siamese Network (ADSNet), which employs cross-domain transfer learning to prevent negative transfer. Specifically, ADSNet is designed to learn information that is beneficial to the target domain. We introduce a gain evaluation strategy to calculate information gain, aiding the model in learning helpful information for the target domain and providing the ability to reject noisy samples, thus avoiding negative transfer. Additionally, we also design a Domain Adaptation Module as a bridge to connect different domains, reduce the distribution distance between them, and enhance the consistency of representation space distribution. We conduct extensive offline experiments and online A/B tests on a real advertising platform. Our proposed ADSNet method outperforms other methods, improving GINI by 2%. The ablation study highlights the importance of the gain evaluation strategy in negative gain sample rejection and improving model performance. Additionally, ADSNet significantly improves long-tail prediction. The online A/B tests confirm ADSNet's efficacy, increasing online LTV by 3.47% and GMV by 3.89%.|广告平台已经发展到估算终身价值(LTV) ，以便更好地与广告商的真实业绩指标保持一致，后者考虑的是消费者在一段时间内贡献的累计购买总额。准确的 LTV 估计对于广告系统的准确性和广告的有效性至关重要。然而，实际 LTV 数据的稀疏性对 LTV 预测模型(即 pLTV)提出了严峻的挑战，严重限制了它们的能力。因此，我们建议利用外部数据，除了广告平台的内部数据外，扩大购买样本的规模，提高广告平台的 LTV 预测模型。为了解决内部平台和外部平台之间数据分布转移的问题，我们引入了自适应差分暹罗网(ADSNet) ，该网络采用跨域传输学习来防止负向传输。具体来说，ADSNet 是为了学习对目标域有益的信息而设计的。我们引入一个增益评估策略来计算信息增益，帮助模型学习目标域的有用信息，并提供拒绝噪声样本的能力，从而避免负迁移。此外，我们还设计了一个领域适应模块作为连接不同领域的桥梁，减少它们之间的分布距离，提高表示空间分布的一致性。我们在一个真实的广告平台上进行广泛的离线实验和在线 A/B 测试。我们提出的 ADSNet 方法优于其他方法，GINI 提高了2% 。烧蚀研究突出了增益评价策略在抑制负增益样本和改善模型性能中的重要性。此外，ADSNet 显著改善了长尾预测。在线 A/B 测试证实了 ADSNet 的有效性，在线 LTV 增加了3.47% ，GMV 增加了3.89% 。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=ADSNet:+Cross-Domain+LTV+Prediction+with+an+Adaptive+Siamese+Network+in+Advertising)|0| +|[Multi-Behavior Collaborative Filtering with Partial Order Graph Convolutional Networks](https://doi.org/10.1145/3637528.3671569)|Yijie Zhang, Yuanchen Bei, Hao Chen, Qijie Shen, Zheng Yuan, Huan Gong, Senzhang Wang, Feiran Huang, Xiao Huang|The Hong Kong Polytechnic University, Hong Kong, China; Alibaba Group, Zhejiang, Hangzhou, China; Zhejiang University, Zhejiang, Hangzhou, China; Central South University, Hunan, Changsha, China; National University of Defense Technology, Changsha, China; Jinan University, Guangzhou, China|Representing information of multiple behaviors in the single graph collaborative filtering (CF) vector has been a long-standing challenge. This is because different behaviors naturally form separate behavior graphs and learn separate CF embeddings. Existing models merge the separate embeddings by appointing the CF embeddings for some behaviors as the primary embedding and utilizing other auxiliaries to enhance the primary embedding. However, this approach often results in the joint embedding performing well on the main tasks but poorly on the auxiliary ones. To address the problem arising from the separate behavior graphs, we propose the concept of Partial Order Recommendation Graphs (POG). POG defines the partial order relation of multiple behaviors and models behavior combinations as weighted edges to merge separate behavior graphs into a joint POG. Theoretical proof verifies that POG can be generalized to any given set of multiple behaviors. Based on POG, we propose the tailored Partial Order Graph Convolutional Networks (POGCN) that convolute neighbors' information while considering the behavior relations between users and items. POGCN also introduces a partial-order BPR sampling strategy for efficient and effective multiple-behavior CF training. POGCN has been successfully deployed on the homepage of Alibaba for two months, providing recommendation services for over one billion users. Extensive offline experiments conducted on three public benchmark datasets demonstrate that POGCN outperforms state-of-the-art multi-behavior baselines across all types of behaviors. Furthermore, online A/B tests confirm the superiority of POGCN in billion-scale recommender systems.|在单一图形协同过滤(CF)向量中表示多种行为的信息一直是一个长期的挑战。这是因为不同的行为自然地形成独立的行为图，并学习独立的 CF 嵌入。现有的模型通过指定一些行为的 CF 嵌入作为主嵌入，并利用其他辅助来增强主嵌入，从而将分离的嵌入进行合并。然而，这种方法往往导致联合嵌入在主要任务上表现良好，但在辅助任务上表现不佳。为了解决分离的行为图所带来的问题，我们提出了偏序推荐图(POG)的概念。POG 将多个行为和模型行为组合的偏序关系定义为加权边，以便将单独的行为图合并到一个联合 POG 中。理论证明了 POG 可以推广到任意给定的一组多行为。在 POG 的基础上，提出了一种考虑用户与项目之间行为关系的卷积邻居信息的剪裁偏序图卷积网络(POGCN)。POGCN 还引入了一种偏序 BPR 抽样策略，用于有效的多行为 CF 训练。POgcn 已成功登入阿里巴巴网页两个月，为超过十亿用户提供推荐服务。在三个公共基准数据集上进行的大量离线实验表明，POGCN 在所有类型的行为上都优于最先进的多行为基准。此外，在线 A/B 测试证实了 POGCN 在亿万规模推荐系统中的优越性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Multi-Behavior+Collaborative+Filtering+with+Partial+Order+Graph+Convolutional+Networks)|0| |[Inductive Modeling for Realtime Cold Start Recommendations](https://doi.org/10.1145/3637528.3671588)|Chandler Zuo, Jonathan Castaldo, Hanqing Zhu, Haoyu Zhang, Ji Liu, Yangpeng Ou, Xiao Kong|Meta, Menlo Park, CA, USA|In recommendation systems, the timely delivery of new content to their relevant audiences is critical for generating a growing and high quality collection of content for all users. The nature of this problem requires retrieval models to be able to make inferences in real time and with high relevance. There are two specific challenges for cold start contents. First, the information loss problem in a standard Two Tower model, due to the limited feature interactions between the user and item towers, is exacerbated for cold start items due to training data sparsity. Second, the huge volume of user-generated content in industry applications today poses a big bottleneck in the end-to-end latency of recommending new content. To overcome the two challenges, we propose a novel architecture, the Item History Model (IHM). IHM directly injects user-interaction information into the item tower to overcome information loss. In addition, IHM incorporates an inductive structure using attention-based pooling to eliminate the need for recurring training, a key bottleneck for the real-timeness. On both public and industry datasets, we demonstrate that IHM can not only outperform baselines in recommending cold start contents, but also achieves SoTA real-timeness in industry applications.|在推荐系统中，及时向相关受众提供新内容对于为所有用户收集越来越多的高质量内容至关重要。这个问题的性质要求检索模型能够实时地进行推理，并且具有高度的相关性。对于冷启动内容有两个具体的挑战。首先，标准双塔模型中的信息丢失问题，由于用户和项目塔之间的特征交互有限，由于训练数据稀疏而加剧了冷启动项目的信息丢失问题。其次，当今行业应用程序中的大量用户生成内容对推荐新内容的端到端延迟造成了巨大的瓶颈。为了克服这两个挑战，我们提出了一个新的体系结构，项目历史模型(IHM)。IHM 直接将用户交互信息注入到项目塔中以克服信息丢失。此外，IHM 采用了一种归纳结构，使用基于注意力的汇集来消除重复训练的需要，这是实时性的一个关键瓶颈。在公共数据集和工业数据集上，我们证明 IHM 不仅在推荐冷启动内容方面优于基线，而且在工业应用中实现了 SoTA 的实时性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Inductive+Modeling+for+Realtime+Cold+Start+Recommendations)|0| |[Bias and Unfairness in Information Retrieval Systems: New Challenges in the LLM Era](https://doi.org/10.1145/3637528.3671458)|Sunhao Dai, Chen Xu, Shicheng Xu, Liang Pang, Zhenhua Dong, Jun Xu|; Huawei Noah's Ark Lab, Shenzhen, China; Gaoling School of Artificial Intelligence, Renmin University of China, Beijing, China|With the rapid advancements of large language models (LLMs), information retrieval (IR) systems, such as search engines and recommender systems, have undergone a significant paradigm shift. This evolution, while heralding new opportunities, introduces emerging challenges, particularly in terms of biases and unfairness, which may threaten the information ecosystem. In this paper, we present a comprehensive survey of existing works on emerging and pressing bias and unfairness issues in IR systems when the integration of LLMs. We first unify bias and unfairness issues as distribution mismatch problems, providing a groundwork for categorizing various mitigation strategies through distribution alignment. Subsequently, we systematically delve into the specific bias and unfairness issues arising from three critical stages of LLMs integration into IR systems: data collection, model development, and result evaluation. In doing so, we meticulously review and analyze recent literature, focusing on the definitions, characteristics, and corresponding mitigation strategies associated with these issues. Finally, we identify and highlight some open problems and challenges for future work, aiming to inspire researchers and stakeholders in the IR field and beyond to better understand and mitigate bias and unfairness issues of IR in this LLM era. We also consistently maintain a GitHub repository for the relevant papers and resources in this rising direction at https://github.com/KID-22/LLM-IR-Bias-Fairness-Survey.|随着大型语言模型(LLM)的快速发展，搜索引擎和推荐系统等信息检索(IR)系统经历了一个重大的范式转变。这种演变虽然预示着新的机遇，但也带来了新的挑战，特别是偏见和不公平方面的挑战，可能威胁到信息生态系统。在本文中，我们提出了一个综合调查的现有工作中出现的和紧迫的偏见和不公平问题的国际关系系统时，一体化的 LLM。我们首先将偏见和不公平问题统一为分布不匹配问题，为通过分布对齐来分类各种缓解策略提供了基础。随后，我们系统地研究了 LLM 集成到 IR 系统的三个关键阶段所引起的具体的偏见和不公平问题: 数据收集、模型开发和结果评估。在这样做的时候，我们仔细审查和分析最近的文献，重点是定义，特点和相应的缓解策略与这些问题有关。最后，我们确定并强调了一些开放的问题和未来工作的挑战，旨在激励研究人员和利益相关者在国际关系领域和以外更好地理解和减轻在这个 LLM 时代的国际关系的偏见和不公平问题。我们也一直保持着一个 GitHub 资源库，用来存放这个不断增长的 https://GitHub.com/kid-22/llm-ir-bias-fairness-survey 的相关文章和资源。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Bias+and+Unfairness+in+Information+Retrieval+Systems:+New+Challenges+in+the+LLM+Era)|0| -|[Approximating Memorization Using Loss Surface Geometry for Dataset Pruning and Summarization](https://doi.org/10.1145/3637528.3671985)|Andrea Agiollo, Young In Kim, Rajiv Khanna|University of Bologna, Bologna, Italy; Purdue University, West Lafayette, IN, USA|The sustainable training of modern neural network models represents an open challenge. Several existing methods approach this issue by identifying a subset of relevant data samples from the full training data to be used in model optimization with the goal of matching the performance of the full data training with that of the subset data training. Our work explores using memorization scores to find representative and atypical samples. We demonstrate that memorization-aware dataset summarization improves the subset construction performance. However, computing memorization scores is notably resource-intensive. To this end, we propose a novel method that leverages the discrepancy between sharpness-aware minimization and stochastic gradient descent to capture data points atypicality. We evaluate our metric over several efficient approximation functions for memorization scores - namely proxies -, empirically showing superior correlation and effectiveness. We explore the causes behind our approximation quality, highlighting how typical data points trigger a flatter loss landscape compared to atypical ones. Extensive experiments confirm the effectiveness of our proxy for dataset pruning and summarization tasks, surpassing state-of-the-art approaches both on canonical setups - where atypical data points benefit performance - and few-shot learning scenarios-where atypical data points can be detrimental.|现代神经网络模型的可持续训练是一个开放的挑战。一些现有的方法通过从完整的训练数据中确定一个相关数据样本子集来解决这个问题，这些数据样本将用于模型优化，目标是使完整数据训练的性能与子集数据训练的性能相匹配。我们的工作探索使用记忆分数来寻找代表性和非典型样本。我们证明了记忆感知的数据集摘要能够提高幂集构造的性能。然而，计算记忆分数是显著的资源密集型。为此，我们提出了一种新的方法，利用锐度感知最小化和随机梯度下降之间的差异来捕捉数据点的非典型性。我们评估我们的度量在几个有效的近似函数的记忆分数-即代理-，经验表明优越的相关性和有效性。我们探索了近似质量背后的原因，强调了典型数据点与非典型数据点相比如何触发更平坦的损失景观。大量的实验证实了我们的代理对于数据集裁剪和总结任务的有效性，超越了在规范设置(非典型数据点有利于性能)和少数学习场景(非典型数据点可能是有害的)上的最新方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Approximating+Memorization+Using+Loss+Surface+Geometry+for+Dataset+Pruning+and+Summarization)|0| +|[Approximating Memorization Using Loss Surface Geometry for Dataset Pruning and Summarization](https://doi.org/10.1145/3637528.3671985)|Andrea Agiollo, Young In Kim, Rajiv Khanna|Purdue University, West Lafayette, IN, USA; University of Bologna, Bologna, Italy|The sustainable training of modern neural network models represents an open challenge. Several existing methods approach this issue by identifying a subset of relevant data samples from the full training data to be used in model optimization with the goal of matching the performance of the full data training with that of the subset data training. Our work explores using memorization scores to find representative and atypical samples. We demonstrate that memorization-aware dataset summarization improves the subset construction performance. However, computing memorization scores is notably resource-intensive. To this end, we propose a novel method that leverages the discrepancy between sharpness-aware minimization and stochastic gradient descent to capture data points atypicality. We evaluate our metric over several efficient approximation functions for memorization scores - namely proxies -, empirically showing superior correlation and effectiveness. We explore the causes behind our approximation quality, highlighting how typical data points trigger a flatter loss landscape compared to atypical ones. Extensive experiments confirm the effectiveness of our proxy for dataset pruning and summarization tasks, surpassing state-of-the-art approaches both on canonical setups - where atypical data points benefit performance - and few-shot learning scenarios-where atypical data points can be detrimental.|现代神经网络模型的可持续训练是一个开放的挑战。一些现有的方法通过从完整的训练数据中确定一个相关数据样本子集来解决这个问题，这些数据样本将用于模型优化，目标是使完整数据训练的性能与子集数据训练的性能相匹配。我们的工作探索使用记忆分数来寻找代表性和非典型样本。我们证明了记忆感知的数据集摘要能够提高幂集构造的性能。然而，计算记忆分数是显著的资源密集型。为此，我们提出了一种新的方法，利用锐度感知最小化和随机梯度下降之间的差异来捕捉数据点的非典型性。我们评估我们的度量在几个有效的近似函数的记忆分数-即代理-，经验表明优越的相关性和有效性。我们探索了近似质量背后的原因，强调了典型数据点与非典型数据点相比如何触发更平坦的损失景观。大量的实验证实了我们的代理对于数据集裁剪和总结任务的有效性，超越了在规范设置(非典型数据点有利于性能)和少数学习场景(非典型数据点可能是有害的)上的最新方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Approximating+Memorization+Using+Loss+Surface+Geometry+for+Dataset+Pruning+and+Summarization)|0| |[Evading Community Detection via Counterfactual Neighborhood Search](https://doi.org/10.1145/3637528.3671896)|Andrea Bernini, Fabrizio Silvestri, Gabriele Tolomei|Sapienza University of Rome, Rome, Italy|Community detection techniques are useful for social media platforms todiscover tightly connected groups of users who share common interests. However,this functionality often comes at the expense of potentially exposingindividuals to privacy breaches by inadvertently revealing their tastes orpreferences. Therefore, some users may wish to preserve their anonymity and optout of community detection for various reasons, such as affiliation withpolitical or religious organizations, without leaving the platform. In thisstudy, we address the challenge of community membership hiding, which involvesstrategically altering the structural properties of a network graph to preventone or more nodes from being identified by a given community detectionalgorithm. We tackle this problem by formulating it as a constrainedcounterfactual graph objective, and we solve it via deep reinforcementlearning. Extensive experiments demonstrate that our method outperformsexisting baselines, striking the best balance between accuracy and cost.|社区检测技术对于社交媒体平台发现有共同兴趣的紧密联系的用户群非常有用。然而，这种功能往往是以牺牲个人隐私被侵犯的潜在风险为代价的，因为它无意中暴露了个人的品味或偏好。因此，一些用户可能希望保持匿名，并出于各种原因，例如与政治或宗教组织的联系，而不离开平台，拒绝进行社区检测。在这项研究中，我们解决了社区成员隐藏的挑战，这涉及到策略性地改变网络图的结构特性，以防止一个或多个节点被给定的社区检测算法识别。我们把这个问题表述为一个有约束的反事实图目标，并通过深度强化学习来解决它。大量的实验表明，我们的方法性能优于现有的基线，在准确性和成本之间达到了最佳的平衡。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Evading+Community+Detection+via+Counterfactual+Neighborhood+Search)|0| -|[FoRAG: Factuality-optimized Retrieval Augmented Generation for Web-enhanced Long-form Question Answering](https://doi.org/10.1145/3637528.3672065)|Tianchi Cai, Zhiwen Tan, Xierui Song, Tao Sun, Jiyan Jiang, Yunqi Xu, Yinger Zhang, Jinjie Gu|Tsinghua University, Beijing, China; Ant Group, Hangzhou, China|Retrieval Augmented Generation (RAG) has become prevalent in question-answering (QA) tasks due to its ability of utilizing search engine to enhance the quality of long-form question-answering (LFQA). Despite the emergence of various open source methods and web-enhanced commercial systems such as Bing Chat, two critical problems remain unsolved, i.e., the lack of factuality and clear logic in the generated long-form answers. In this paper, we remedy these issues via a systematic study on answer generation in web-enhanced LFQA. Specifically, we first propose a novel outline-enhanced generator to achieve clear logic in the generation of multifaceted answers and construct two datasets accordingly. Then we propose a factuality optimization method based on a carefully designed doubly fine-grained RLHF framework, which contains automatic evaluation and reward modeling in different levels of granularity. Our generic framework comprises conventional fine-grained RLHF methods as special cases. Extensive experiments verify the superiority of our proposed Factuality-optimized RAG (FoRAG) method on both English and Chinese benchmarks. In particular, when applying our method to Llama2-7B-chat, the derived model FoRAG-L-7B outperforms WebGPT-175B in terms of three commonly used metrics (i.e., coherence, helpfulness, and factuality), while the number of parameters is much smaller (only 1/24 of that of WebGPT-175B). Our datasets and models are made publicly available for better reproducibility.https://huggingface.co/forag łabelfootnote_dataset_url|检索增强生成技术(RAG)由于能够利用搜索引擎提高长形式问答(LFQA)的质量，在问答(QA)任务中得到了广泛的应用。尽管出现了各种开源方法和网络增强的商业系统，如必应聊天，两个关键问题仍然没有得到解决，即缺乏事实性和清晰的逻辑生成的长形式的答案。本文通过对网络增强型 LFQA 中问题生成的系统研究，解决了这些问题。具体来说，我们首先提出了一种新的轮廓增强生成器，以实现清晰的逻辑生成多方面的答案，并相应地构造两个数据集。然后提出了一种基于精心设计的双细粒度 RLHF 框架的事实优化方法，该框架包含了不同粒度级别的自动评价和奖励建模。我们的通用框架包括传统的细粒度 RLHF 方法作为特殊情况。大量的实验验证了我们提出的基于事实优化的 RAG (FoRAG)方法在中英文基准测试中的优越性。特别是，当将我们的方法应用于 Llama2-7B-chat 时，衍生模型 FoRAG-L-7B 在三个常用指标(即一致性，有益性和真实性)方面优于 WebGPT-175B，而参数的数量要小得多(只有 WebGPT-175B 的1/24)。我们的数据集和模型公开发布，以便更好地重现。 https://huggingface.co/forag abelfoonote _ datset _ url|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=FoRAG:+Factuality-optimized+Retrieval+Augmented+Generation+for+Web-enhanced+Long-form+Question+Answering)|0| +|[FoRAG: Factuality-optimized Retrieval Augmented Generation for Web-enhanced Long-form Question Answering](https://doi.org/10.1145/3637528.3672065)|Tianchi Cai, Zhiwen Tan, Xierui Song, Tao Sun, Jiyan Jiang, Yunqi Xu, Yinger Zhang, Jinjie Gu|Ant Group, Hangzhou, China; Tsinghua University, Beijing, China|Retrieval Augmented Generation (RAG) has become prevalent in question-answering (QA) tasks due to its ability of utilizing search engine to enhance the quality of long-form question-answering (LFQA). Despite the emergence of various open source methods and web-enhanced commercial systems such as Bing Chat, two critical problems remain unsolved, i.e., the lack of factuality and clear logic in the generated long-form answers. In this paper, we remedy these issues via a systematic study on answer generation in web-enhanced LFQA. Specifically, we first propose a novel outline-enhanced generator to achieve clear logic in the generation of multifaceted answers and construct two datasets accordingly. Then we propose a factuality optimization method based on a carefully designed doubly fine-grained RLHF framework, which contains automatic evaluation and reward modeling in different levels of granularity. Our generic framework comprises conventional fine-grained RLHF methods as special cases. Extensive experiments verify the superiority of our proposed Factuality-optimized RAG (FoRAG) method on both English and Chinese benchmarks. In particular, when applying our method to Llama2-7B-chat, the derived model FoRAG-L-7B outperforms WebGPT-175B in terms of three commonly used metrics (i.e., coherence, helpfulness, and factuality), while the number of parameters is much smaller (only 1/24 of that of WebGPT-175B). Our datasets and models are made publicly available for better reproducibility.https://huggingface.co/forag łabelfootnote_dataset_url|检索增强生成技术(RAG)由于能够利用搜索引擎提高长形式问答(LFQA)的质量，在问答(QA)任务中得到了广泛的应用。尽管出现了各种开源方法和网络增强的商业系统，如必应聊天，两个关键问题仍然没有得到解决，即缺乏事实性和清晰的逻辑生成的长形式的答案。本文通过对网络增强型 LFQA 中问题生成的系统研究，解决了这些问题。具体来说，我们首先提出了一种新的轮廓增强生成器，以实现清晰的逻辑生成多方面的答案，并相应地构造两个数据集。然后提出了一种基于精心设计的双细粒度 RLHF 框架的事实优化方法，该框架包含了不同粒度级别的自动评价和奖励建模。我们的通用框架包括传统的细粒度 RLHF 方法作为特殊情况。大量的实验验证了我们提出的基于事实优化的 RAG (FoRAG)方法在中英文基准测试中的优越性。特别是，当将我们的方法应用于 Llama2-7B-chat 时，衍生模型 FoRAG-L-7B 在三个常用指标(即一致性，有益性和真实性)方面优于 WebGPT-175B，而参数的数量要小得多(只有 WebGPT-175B 的1/24)。我们的数据集和模型公开发布，以便更好地重现。 https://huggingface.co/forag abelfoonote _ datset _ url|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=FoRAG:+Factuality-optimized+Retrieval+Augmented+Generation+for+Web-enhanced+Long-form+Question+Answering)|0| |[A Hierarchical Context Augmentation Method to Improve Retrieval-Augmented LLMs on Scientific Papers](https://doi.org/10.1145/3637528.3671847)|TianYi Che, XianLing Mao, Tian Lan, Heyan Huang|Beijing Institute of Technology, Beijing, China|Scientific papers of a large scale on the Internet encompass a wealth of data and knowledge, attracting the attention of numerous researchers. To fully utilize these knowledge, Retrieval-Augmented Large Language Models (LLMs) usually leverage large-scale scientific corpus to train and then retrieve relevant passages from external memory to improve generation, which have demonstrated outstanding performance. However, existing methods can only capture one-dimension fragmented textual information without incorporating hierarchical structural knowledge, eg. the deduction relationship of abstract and main body, which makes it difficult to grasp the central thought of papers. To tackle this problem, we propose a hierarchical context augmentation method, which helps Retrieval-Augmented LLMs to autoregressively learn the structure knowledge of scientific papers. Specifically, we utilize the document tree to represent the hierarchical relationship of a paper and enhance the structure information of scientific context from three aspects: scale, format and global information. First, we think each top-bottom path of document tree is a logical independent context, which can be used to largely increase the scale of extracted structural corpus. Second, we propose a novel label-based format to represent the structure of context in textual sequences, unified between training and inference. Third, we introduce the global information of retrieved passages to further enhance the structure of context. Extensive experiments on three scientific tasks show that the proposed method significantly improves the performance of Retrieval-Augmented LLMs on all tasks. Besides, our method achieves start-of-art performance in Question Answer task and outperforms ChatGPT. Moreover, it also brings considerate gains with irrelevant retrieval passages, illustrating its effectiveness on practical application scenarios.|互联网上的大量科学论文包含了丰富的数据和知识，吸引了众多研究者的关注。为了充分利用这些知识，检索增强型大语言模型通常利用大规模的科学语料库来训练和检索外部记忆中的相关段落，以提高生成能力。然而，现有的方法只能捕捉一维零碎的文本信息，没有结合层次结构知识，如抽象与主体的演绎关系，难以把握论文的中心思想。针对这一问题，提出了一种层次上下文增强方法，该方法可以帮助检索增强 LLM 自回归地学习科技论文的结构知识。具体来说，我们利用文档树来表示论文的层次关系，并从规模、格式和全局信息三个方面增强科学语境的结构信息。首先，我们认为文档树的每一条顶底路径都是一个逻辑独立的上下文，可以用来大幅度增加提取结构化语料的规模。其次，我们提出了一种新的基于标签的格式来表示文本序列中的上下文结构，统一于训练和推理。第三，引入检索段落的全局信息，进一步增强语境结构。在三个科学任务上的大量实验表明，该方法可以显著提高检索增强 LLM 在所有任务上的性能。此外，该方法在问答任务中取得了较好的启动性能，优于 ChatGPT。此外，它也带来了相当的收益与不相关的检索段落，说明其在实际应用场景的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Hierarchical+Context+Augmentation+Method+to+Improve+Retrieval-Augmented+LLMs+on+Scientific+Papers)|0| -|[Maximum-Entropy Regularized Decision Transformer with Reward Relabelling for Dynamic Recommendation](https://doi.org/10.1145/3637528.3671750)|Xiaocong Chen, Siyu Wang, Lina Yao|Data 61, CSIRO, Eveleigh, Australia; The University of New South Wales, Sydney, Australia; Data 61, CSIRO & The University of New South Wales, Eveleigh, Australia|Reinforcement learning-based recommender systems have recently gainedpopularity. However, due to the typical limitations of simulation environments(e.g., data inefficiency), most of the work cannot be broadly applied in alldomains. To counter these challenges, recent advancements have leveragedoffline reinforcement learning methods, notable for their data-driven approachutilizing offline datasets. A prominent example of this is the DecisionTransformer. Despite its popularity, the Decision Transformer approach hasinherent drawbacks, particularly evident in recommendation methods based on it.This paper identifies two key shortcomings in existing DecisionTransformer-based methods: a lack of stitching capability and limitedeffectiveness in online adoption. In response, we introduce a novel methodologynamed Max-Entropy enhanced Decision Transformer with Reward Relabeling forOffline RLRS (EDT4Rec). Our approach begins with a max entropy perspective,leading to the development of a max entropy enhanced exploration strategy. Thisstrategy is designed to facilitate more effective exploration in onlineenvironments. Additionally, to augment the model's capability to stitchsub-optimal trajectories, we incorporate a unique reward relabeling technique.To validate the effectiveness and superiority of EDT4Rec, we have conductedcomprehensive experiments across six real-world offline datasets and in anonline simulator.|基于强化学习的推荐系统最近变得流行起来。然而，由于模拟环境的典型局限性(例如，数据效率低下) ，大多数工作不能广泛应用于所有领域。为了应对这些挑战，最近的进步已经利用了离线强化学习方法，值得注意的是它们利用离线数据集的数据驱动方法。这方面的一个突出例子是决策转换器。尽管决策转换器方法很流行，但是它有固有的缺点，在基于它的推荐方法中尤其明显。本文指出了现有基于决策变压器的方法存在的两个关键缺陷: 缺乏拼接能力和在线采用的有效性有限。作为回应，我们介绍了一种新的方法，最大熵增强决策变压器与奖励重新标记离线 RLRS (EDT4Rec)。我们的方法从最大熵的角度出发，导致了最大熵增强勘探策略的发展。该策略旨在促进在线环境中更有效的探索。此外，为了增强模型缝合次优轨迹的能力，我们纳入了独特的奖励重新标记技术。为了验证 EDT4Rec 的有效性和优越性，我们在六个真实世界的离线数据集和在线模拟器中进行了全面的实验。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Maximum-Entropy+Regularized+Decision+Transformer+with+Reward+Relabelling+for+Dynamic+Recommendation)|0| -|[Retrieval-Augmented Hypergraph for Multimodal Social Media Popularity Prediction](https://doi.org/10.1145/3637528.3672041)|Zhangtao Cheng, Jienan Zhang, Xovee Xu, Goce Trajcevski, Ting Zhong, Fan Zhou|Iowa State University, Ames, Iowa, USA; ; University of Electronic Science and Technology of China, Chengdu, Sichuan, China|Accurately predicting the popularity of multimodal user-generated content (UGC) is fundamental for many real-world applications such as online advertising and recommendation. Existing approaches generally focus on limited contextual information within individual UGCs, yet overlook the potential benefit of exploiting meaningful knowledge in relevant UGCs. In this work, we propose RAGTrans, an aspect-aware retrieval-augmented multi-modal hypergraph transformer that retrieves pertinent knowledge from a multi-modal memory bank and enhances UGC representations via neighborhood knowledge aggregation on multi-model hypergraphs. In particular, we initially retrieve relevant multimedia instances from a large corpus of UGCs via the aspect information and construct a knowledge-enhanced hypergraph based on retrieved relevant instances. This allows capturing meaningful contextual information across the data. We then design a novel bootstrapping hypergraph transformer on multimodal hypergraphs to strengthen UGC representations across modalities via customizing a propagation algorithm to effectively diffuse information across nodes and edges. Additionally, we propose a user-aware attention-based fusion module to comprise the enriched UGC representations for popularity prediction. Extensive experiments on real-world social media datasets demonstrate that RAGTrans outperforms state-of-the-art popularity prediction models across settings.|准确预测多模式用户生成内容的流行程度对于许多现实世界的应用程序(如在线广告和推荐)至关重要。现有的方法一般集中于个别教资会内有限的上下文资料，但却忽略了利用相关教资会内有意义的知识的潜在好处。在这项工作中，我们提出了 RAGTrans，一个方面感知检索增强型多模态超图转换器，它可以从多模态记忆库中检索相关知识，并通过多模态超图上的邻域知识聚合来增强 UGC 表示。特别地，我们首先通过方面信息从一个大型的用户教学资源库中检索相关的多媒体实例，然后在检索到的相关实例的基础上构造一个知识增强的超图。这允许跨数据捕获有意义的上下文信息。然后在多模态超图上设计了一种新的自举超图转换器，通过定制传播算法有效地在节点和边上传播信息，从而增强跨模态的 UGC 表示。此外，我们还提出了一个基于用户感知的注意力融合模块来构成用户用户生成表示，用于流行度预测。在真实世界的社交媒体数据集上进行的大量实验表明，RAGTrans 在不同设置下的流行程度预测模型优于最先进的模型。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Retrieval-Augmented+Hypergraph+for+Multimodal+Social+Media+Popularity+Prediction)|0| -|[ROTAN: A Rotation-based Temporal Attention Network for Time-Specific Next POI Recommendation](https://doi.org/10.1145/3637528.3671809)|Shanshan Feng, Feiyu Meng, Lisi Chen, Shuo Shang, Yew Soon Ong|; Centre for Frontier AI Research, ASTAR & Nanyang Technological University, Singapore, Singapore; University of Electronic Science and Technology of China, Chengdu, China; University of Electronic Science and Technology of China, Chengdu, Sichuan, China|The next Point-of-interest recommendation has attracted extensive research interest recently, which predicts users' subsequent movements. The main challenge is how to effectively capture users' personalized sequential transitions in check-in trajectory, and various methods have been developed. However, most existing studies ignore the temporal information when conducting the next POI recommendation. To fill this gap, we investigate a time-specific next POI recommendation task, which additionally incorporates the target time information. We propose a brand new Time2Rotation technique to capture the temporal information. Different from conventional methods, we represent timeslots as rotation vectors and then perform the rotation operations. Based on the Time2Rotation technique, we propose a novel rotation-based temporal attention network, namely ROTAN, for the time-specific next POI recommendation task. The ROTAN begins by building a collaborative POI transition graph, capturing the asymmetric temporal influence in sequential transitions. After that, it incorporates temporal information into the modeling of individual check-in trajectories, extracting separate representations for user preference and POI influence to reflect their distinct temporal patterns. Lastly, the target time is integrated to generate recommendations. Extensive experiments are conducted on three real-world datasets, which demonstrates the advantages of the proposed Time2Rotation technique and ROTAN recommendation model.|最近，下一个兴趣点推荐引起了广泛的研究兴趣，它可以预测用户随后的活动。如何有效地捕获用户在签入轨迹中的个性化顺序转换是其面临的主要挑战，各种方法已经被开发出来。然而，大多数现有的研究忽略了时间信息进行下一个 POI 建议。为了填补这个空白，我们研究了一个特定于时间的下一个 POI 推荐任务，它还包含了目标时间信息。我们提出了一种全新的 Time2旋转技术来捕获时间信息。与传统的方法不同，我们将时隙表示为旋转向量，然后进行旋转运算。基于 Time2旋转技术，我们提出了一个新的基于旋转的时间注意网络，即 ROTAN，用于特定时间的下一个 POI 推荐任务。ROTAN 从建立一个合作的 POI 转换图开始，捕捉连续转换中不对称的时间影响。然后，将时间信息融入到单个签入轨迹的建模中，提取用户偏好和 POI 影响的独立表示，以反映它们不同的时间模式。最后，整合目标时间来生成建议。在三个真实世界的数据集上进行了大量的实验，这些实验证明了提出的 Time2旋转技术和 ROTAN 推荐模型的优势。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=ROTAN:+A+Rotation-based+Temporal+Attention+Network+for+Time-Specific+Next+POI+Recommendation)|0| +|[Maximum-Entropy Regularized Decision Transformer with Reward Relabelling for Dynamic Recommendation](https://doi.org/10.1145/3637528.3671750)|Xiaocong Chen, Siyu Wang, Lina Yao|Data 61, CSIRO, Eveleigh, Australia; Data 61, CSIRO & The University of New South Wales, Eveleigh, Australia; The University of New South Wales, Sydney, Australia|Reinforcement learning-based recommender systems have recently gainedpopularity. However, due to the typical limitations of simulation environments(e.g., data inefficiency), most of the work cannot be broadly applied in alldomains. To counter these challenges, recent advancements have leveragedoffline reinforcement learning methods, notable for their data-driven approachutilizing offline datasets. A prominent example of this is the DecisionTransformer. Despite its popularity, the Decision Transformer approach hasinherent drawbacks, particularly evident in recommendation methods based on it.This paper identifies two key shortcomings in existing DecisionTransformer-based methods: a lack of stitching capability and limitedeffectiveness in online adoption. In response, we introduce a novel methodologynamed Max-Entropy enhanced Decision Transformer with Reward Relabeling forOffline RLRS (EDT4Rec). Our approach begins with a max entropy perspective,leading to the development of a max entropy enhanced exploration strategy. Thisstrategy is designed to facilitate more effective exploration in onlineenvironments. Additionally, to augment the model's capability to stitchsub-optimal trajectories, we incorporate a unique reward relabeling technique.To validate the effectiveness and superiority of EDT4Rec, we have conductedcomprehensive experiments across six real-world offline datasets and in anonline simulator.|基于强化学习的推荐系统最近变得流行起来。然而，由于模拟环境的典型局限性(例如，数据效率低下) ，大多数工作不能广泛应用于所有领域。为了应对这些挑战，最近的进步已经利用了离线强化学习方法，值得注意的是它们利用离线数据集的数据驱动方法。这方面的一个突出例子是决策转换器。尽管决策转换器方法很流行，但是它有固有的缺点，在基于它的推荐方法中尤其明显。本文指出了现有基于决策变压器的方法存在的两个关键缺陷: 缺乏拼接能力和在线采用的有效性有限。作为回应，我们介绍了一种新的方法，最大熵增强决策变压器与奖励重新标记离线 RLRS (EDT4Rec)。我们的方法从最大熵的角度出发，导致了最大熵增强勘探策略的发展。该策略旨在促进在线环境中更有效的探索。此外，为了增强模型缝合次优轨迹的能力，我们纳入了独特的奖励重新标记技术。为了验证 EDT4Rec 的有效性和优越性，我们在六个真实世界的离线数据集和在线模拟器中进行了全面的实验。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Maximum-Entropy+Regularized+Decision+Transformer+with+Reward+Relabelling+for+Dynamic+Recommendation)|0| +|[Retrieval-Augmented Hypergraph for Multimodal Social Media Popularity Prediction](https://doi.org/10.1145/3637528.3672041)|Zhangtao Cheng, Jienan Zhang, Xovee Xu, Goce Trajcevski, Ting Zhong, Fan Zhou|University of Electronic Science and Technology of China, Chengdu, Sichuan, China; ; Iowa State University, Ames, Iowa, USA|Accurately predicting the popularity of multimodal user-generated content (UGC) is fundamental for many real-world applications such as online advertising and recommendation. Existing approaches generally focus on limited contextual information within individual UGCs, yet overlook the potential benefit of exploiting meaningful knowledge in relevant UGCs. In this work, we propose RAGTrans, an aspect-aware retrieval-augmented multi-modal hypergraph transformer that retrieves pertinent knowledge from a multi-modal memory bank and enhances UGC representations via neighborhood knowledge aggregation on multi-model hypergraphs. In particular, we initially retrieve relevant multimedia instances from a large corpus of UGCs via the aspect information and construct a knowledge-enhanced hypergraph based on retrieved relevant instances. This allows capturing meaningful contextual information across the data. We then design a novel bootstrapping hypergraph transformer on multimodal hypergraphs to strengthen UGC representations across modalities via customizing a propagation algorithm to effectively diffuse information across nodes and edges. Additionally, we propose a user-aware attention-based fusion module to comprise the enriched UGC representations for popularity prediction. Extensive experiments on real-world social media datasets demonstrate that RAGTrans outperforms state-of-the-art popularity prediction models across settings.|准确预测多模式用户生成内容的流行程度对于许多现实世界的应用程序(如在线广告和推荐)至关重要。现有的方法一般集中于个别教资会内有限的上下文资料，但却忽略了利用相关教资会内有意义的知识的潜在好处。在这项工作中，我们提出了 RAGTrans，一个方面感知检索增强型多模态超图转换器，它可以从多模态记忆库中检索相关知识，并通过多模态超图上的邻域知识聚合来增强 UGC 表示。特别地，我们首先通过方面信息从一个大型的用户教学资源库中检索相关的多媒体实例，然后在检索到的相关实例的基础上构造一个知识增强的超图。这允许跨数据捕获有意义的上下文信息。然后在多模态超图上设计了一种新的自举超图转换器，通过定制传播算法有效地在节点和边上传播信息，从而增强跨模态的 UGC 表示。此外，我们还提出了一个基于用户感知的注意力融合模块来构成用户用户生成表示，用于流行度预测。在真实世界的社交媒体数据集上进行的大量实验表明，RAGTrans 在不同设置下的流行程度预测模型优于最先进的模型。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Retrieval-Augmented+Hypergraph+for+Multimodal+Social+Media+Popularity+Prediction)|0| +|[ROTAN: A Rotation-based Temporal Attention Network for Time-Specific Next POI Recommendation](https://doi.org/10.1145/3637528.3671809)|Shanshan Feng, Feiyu Meng, Lisi Chen, Shuo Shang, Yew Soon Ong|; University of Electronic Science and Technology of China, Chengdu, Sichuan, China; University of Electronic Science and Technology of China, Chengdu, China; Centre for Frontier AI Research, ASTAR & Nanyang Technological University, Singapore, Singapore|The next Point-of-interest recommendation has attracted extensive research interest recently, which predicts users' subsequent movements. The main challenge is how to effectively capture users' personalized sequential transitions in check-in trajectory, and various methods have been developed. However, most existing studies ignore the temporal information when conducting the next POI recommendation. To fill this gap, we investigate a time-specific next POI recommendation task, which additionally incorporates the target time information. We propose a brand new Time2Rotation technique to capture the temporal information. Different from conventional methods, we represent timeslots as rotation vectors and then perform the rotation operations. Based on the Time2Rotation technique, we propose a novel rotation-based temporal attention network, namely ROTAN, for the time-specific next POI recommendation task. The ROTAN begins by building a collaborative POI transition graph, capturing the asymmetric temporal influence in sequential transitions. After that, it incorporates temporal information into the modeling of individual check-in trajectories, extracting separate representations for user preference and POI influence to reflect their distinct temporal patterns. Lastly, the target time is integrated to generate recommendations. Extensive experiments are conducted on three real-world datasets, which demonstrates the advantages of the proposed Time2Rotation technique and ROTAN recommendation model.|最近，下一个兴趣点推荐引起了广泛的研究兴趣，它可以预测用户随后的活动。如何有效地捕获用户在签入轨迹中的个性化顺序转换是其面临的主要挑战，各种方法已经被开发出来。然而，大多数现有的研究忽略了时间信息进行下一个 POI 建议。为了填补这个空白，我们研究了一个特定于时间的下一个 POI 推荐任务，它还包含了目标时间信息。我们提出了一种全新的 Time2旋转技术来捕获时间信息。与传统的方法不同，我们将时隙表示为旋转向量，然后进行旋转运算。基于 Time2旋转技术，我们提出了一个新的基于旋转的时间注意网络，即 ROTAN，用于特定时间的下一个 POI 推荐任务。ROTAN 从建立一个合作的 POI 转换图开始，捕捉连续转换中不对称的时间影响。然后，将时间信息融入到单个签入轨迹的建模中，提取用户偏好和 POI 影响的独立表示，以反映它们不同的时间模式。最后，整合目标时间来生成建议。在三个真实世界的数据集上进行了大量的实验，这些实验证明了提出的 Time2旋转技术和 ROTAN 推荐模型的优势。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=ROTAN:+A+Rotation-based+Temporal+Attention+Network+for+Time-Specific+Next+POI+Recommendation)|0| |[AutoXPCR: Automated Multi-Objective Model Selection for Time Series Forecasting](https://doi.org/10.1145/3637528.3672057)|Raphael Fischer, Amal Saadallah||Automated machine learning (AutoML) streamlines the creation of ML models, but few specialized methods have approached the challenging domain of time series forecasting. Deep neural networks (DNNs) often deliver state-of-the-art predictive performance for forecasting data, however these models are also criticized for being computationally intensive black boxes. As a result, when searching for the "best" model, it is crucial to also acknowledge other aspects, such as interpretability and resource consumption. In this paper, we propose AutoXPCR - a novel method that produces DNNs for forecasting under consideration of multiple objectives in an automated and explainable fashion. Our approach leverages meta-learning to estimate any model's performance along PCR criteria, which encompass (P)redictive error, (C)omplexity, and (R)esource demand. Explainability is addressed on multiple levels, as AutoXPCR pro-vides by-product explanations of recommendations and allows to interactively control the desired PCR criteria importance and trade-offs. We demonstrate the practical feasibility AutoXPCR across 108 forecasting data sets from various domains. Notably, our method outperforms competing AutoML approaches - on average, it only requires 20% of computation costs for recommending highly efficient models with 85% of the empirical best quality.|自动机器学习(AutoML)简化了机器学习模型的创建，但很少有专门的方法能够接近时间序列预测这一具有挑战性的领域。深度神经网络(DNN)往往提供最先进的预测性能的预测数据，但这些模型也被批评为计算密集型黑盒。因此，在寻找“最佳”模型时，关键是还要承认其他方面，如可解释性和资源消耗。在本文中，我们提出 AutoXPCR-一种新的方法，产生 DNN 的预测考虑多个目标，在一个自动化和可解释的方式。我们的方法利用元学习来估计任何模型沿 PCR 标准的性能，其中包括(P)预测误差，(C)复杂性和(R)资源需求。可解释性是在多个层面上解决的，因为 AutoXPCR 提供建议的副产品解释，并允许交互式地控制所需的 PCR 标准的重要性和权衡。我们论证了 AutoXPCR 在108个不同领域的预测数据集中的实际可行性。值得注意的是，我们的方法优于竞争对手 AutoML 方法-平均而言，它只需要20% 的计算成本推荐高效率的模型与85% 的经验最佳质量。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=AutoXPCR:+Automated+Multi-Objective+Model+Selection+for+Time+Series+Forecasting)|0| |[Topology-Driven Multi-View Clustering via Tensorial Refined Sigmoid Rank Minimization](https://doi.org/10.1145/3637528.3672070)|Zhibin Gu, Zhendong Li, Songhe Feng||Benefiting from the effective exploitation of the high-order correlations across multiple views, tensor-based multi-view clustering (TMVC) has garnered considerable attention in recent years. Nevertheless, prior TMVC techniques commonly involve assembling multiple view-specific spatial similarity graphs into a three-dimensional tensor, overlooking the intrinsic topological structure essential for precise clustering of data within a manifold. Additionally, mainstream techniques are constrained by equally shrinking all singular values to recover a low-rank tensor, limiting their capacity to distinguish significant variations among different singular values. In this investigation, we present an innovative TMVC framework termed toPology-driven multi-view clustering viA refined teNsorial sigmoiD rAnk minimization (PANDA ). Specifically, PANDA extracts view-specific topological structures from Euclidean graphs and intricately integrates them into a low-rank three-dimensional tensor, facilitating the concurrent utilization of intra-view topological connectivity and inter-view high-order correlations. Moreover, we develop a refined sigmoid function as the tighter surrogate to tensor rank, enabling the exploration of significant information of heterogeneous singular values. Meanwhile, the topological structures are merged into a unified structure with varying weights, associated with a connectivity constraint, empowering the significant divergence among views and the explicit cluster structure of the target graph are simultaneously leveraged. Extensive experiments demonstrate the superiority of PANDA, outperforming SOTA methods.|基于张量的多视图聚类算法(TMVC)得益于多视图高阶相关性的有效利用，近年来引起了人们的广泛关注。然而，先前的 TMVC 技术通常涉及将多个视图特定的空间相似性图组装成一个三维张量，忽略了流形中数据精确聚类所必需的内在拓扑结构。此外，主流技术受到同样缩小所有奇异值以恢复低秩张量的限制，从而限制了它们区分不同奇异值之间显著变化的能力。在这项研究中，我们提出了一个创新的 TMVC 框架，称为拓扑驱动的多视图聚类通过一个精化的 teNsorial sigmoiD rRank 最小化(PANDA)。具体而言，PANDA 从欧几里德图中提取视图特定的拓扑结构，并将其复杂地集成到一个低秩三维张量中，促进视图内拓扑连通性和视图间高阶相关性的并发利用。此外，我们发展了一个精确的 S形函数，作为张量级更紧密的替代品，使探索异质奇异值的重要信息成为可能。同时，将拓扑结构合并为一个具有不同权重的统一结构，并结合连通性约束，赋予视图之间的显著差异，同时利用目标图的显式聚类结构。大量的实验证明了 PANDA 方法的优越性，优于 SOTA 方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Topology-Driven+Multi-View+Clustering+via+Tensorial+Refined+Sigmoid+Rank+Minimization)|0| |[Ranking with Slot Constraints](https://doi.org/10.1145/3637528.3672000)|Wentao Guo, Andrew Wang, Bradon Thymes, Thorsten Joachims||We introduce the problem of ranking with slot constraints, which can be used to model a wide range of application problems -- from college admission with limited slots for different majors, to composing a stratified cohort of eligible participants in a medical trial. We show that the conventional Probability Ranking Principle (PRP) can be highly sub-optimal for slot-constrained ranking problems, and we devise a new ranking algorithm, called MatchRank. The goal of MatchRank is to produce rankings that maximize the number of filled slots if candidates are evaluated by a human decision maker in the order of the ranking. In this way, MatchRank generalizes the PRP, and it subsumes the PRP as a special case when there are no slot constraints. Our theoretical analysis shows that MatchRank has a strong approximation guarantee without any independence assumptions between slots or candidates. Furthermore, we show how MatchRank can be implemented efficiently. Beyond the theoretical guarantees, empirical evaluations show that MatchRank can provide substantial improvements over a range of synthetic and real-world tasks.|我们介绍了有时间限制的排名问题，这个问题可以用来模拟范围广泛的申请问题——从不同专业有时间限制的大学录取，到在医学试验中合格参与者的分层队列组成。我们证明了传统的概率排序原则(PRP)对于时隙约束排序问题可能是高度次优的，并且我们设计了一种新的排序算法，称为 MatchRank。MatchRank 的目标是，如果候选人是由人类决策者按照排名顺序进行评估的，那么它将产生最大化已填补空缺数量的排名。通过这种方式，MatchRank 对 PRP 进行泛化，当没有时隙约束时，它将 PRP 包含为特殊情况。我们的理论分析表明，MatchRank 有一个强大的近似保证，没有任何独立的假设之间的插槽或候选人。此外，我们还展示了如何有效地实现 MatchRank。除了理论上的保证，经验性的评估表明，MatchRank 可以在一系列合成和现实世界的任务上提供实质性的改进。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Ranking+with+Slot+Constraints)|0| -|[Consistency and Discrepancy-Based Contrastive Tripartite Graph Learning for Recommendations](https://doi.org/10.1145/3637528.3672056)|Linxin Guo, Yaochen Zhu, Min Gao, Yinghui Tao, Junliang Yu, Chen Chen|University of Virginia, Charlottesville, VA, USA; Chongqing University, Chongqing, China; the University of Queensland, Queensland, Australia; Institute of Guizhou Aerospace Measuring and Testing Technology, Guiyang, China|Tripartite graph-based recommender systems markedly diverge from traditional models by recommending unique combinations such as user groups and item bundles. Despite their effectiveness, these systems exacerbate the long-standing cold-start problem in traditional recommender systems, because any number of user groups or item bundles can be formed among users or items. To address this issue, we introduce a Consistency and Discrepancy-based graph contrastive learning method for tripartite graph-based Recommendation (CDR). This approach leverages two novel meta-path-based metrics-consistency and discrepancy-to capture nuanced, implicit associations between the recommended objects and the recommendees. These metrics, indicative of high-order similarities, can be efficiently calculated with infinite graph convolutional networks (GCN) layers under a multi-objective optimization framework, using the limit theory of GCN. Additionally, we introduce a novel Contrastive Divergence (CD) loss, which can seamlessly integrate the consistency and discrepancy metrics into the contrastive objective as the positive and contrastive supervision signals to learn node representations, enhancing the pairwise ranking of recommended objects and proving particularly valuable in severe cold-start scenarios. Extensive experiments demonstrate the effectiveness of the proposed CDR. The code is released at https://github.com/foodfaust/CDR.|基于三方图表的推荐系统通过推荐独特的组合，如用户组和项目包，明显地区别于传统模型。尽管这些系统很有效，但它们加剧了传统推荐系统中长期存在的“冷启动”问题，因为在用户或项目之间可以形成任意数量的用户组或项目包。为了解决这个问题，我们提出了一种基于一致性和差异性的图形对比学习方法。这种方法利用两种新颖的基于元路径的度量——一致性和差异性——来捕获被推荐对象和被推荐对象之间微妙的、隐含的关联。在多目标优化框架下，利用无穷图卷积网络(GCN)的极限理论，可以有效地计算这些指标，它们表示高阶相似性。此外，我们引入了一种新的对比发散(CD)损失，它可以无缝地将一致性和差异度量集成到对比目标中，作为正向和对比监督信号来学习节点表示，提高推荐对象的成对排序，并证明在严重的冷启动情况下特别有价值。大量的实验证明了所提出的 CDR 算法的有效性。密码在 https://github.com/foodfaust/cdr 发布。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Consistency+and+Discrepancy-Based+Contrastive+Tripartite+Graph+Learning+for+Recommendations)|0| -|[Binder: Hierarchical Concept Representation through Order Embedding of Binary Vectors](https://doi.org/10.1145/3637528.3671793)|Croix Gyurek, Niloy Talukder, Mohammad Al Hasan|Indiana University at Indianapolis, Indianapolis, IN, USA; University of Waterloo, Waterloo, Ontario, Canada|For natural language understanding and generation, embedding concepts using an order-based representation is an essential task. Unlike traditional point vector based representation, an order-based representation imposes geometric constraints on the representation vectors for explicitly capturing various semantic relationships that may exist between a pair of concepts. In existing literature, several approaches on order-based embedding have been proposed, mostly focusing on capturing hierarchical relationships; examples include vectors in Euclidean space, complex, Hyperbolic, order, and Box Embedding. Box embedding creates region-based rich representation of concepts, but along the process it sacrifices simplicity, requiring a custom-made optimization scheme for learning the representation. Hyperbolic embedding improves embedding quality by exploiting the ever-expanding property of Hyperbolic space, but it also suffers from the same fate as box embedding as gradient descent like optimization is not simple in the Hyperbolic space. In this work, we propose Binder, a novel approach for order-based representation. Binder uses binary vectors for embedding, so the embedding vectors are compact with an order of magnitude smaller footprint than other methods. Binder uses a simple and efficient optimization scheme for learning representation vectors with a linear time complexity. Our comprehensive experimental results show that Binder is very accurate, yielding competitive results on the representation task. But Binder stands out from its competitors on the transitive closure link prediction task as it can learn concept embeddings just from the direct edges, whereas all existing order-based approaches rely on the indirect edges. In particular, Binder achieves a whopping 70% higher F1-score than the second best method (98.6% vs 29%) in our largest dataset, WordNet Nouns (743,241 edges), when using only direct edges during training.|对于自然语言的理解和生成，使用基于顺序的表示来嵌入概念是一个基本的任务。与传统的基于点向量的表示不同，基于顺序的表示对表示向量施加几何约束，以显式地捕获可能存在于一对概念之间的各种语义关系。在现有的文献中，已经提出了几种基于顺序的嵌入方法，主要集中在捕获层次关系，例如欧氏空间中的向量，复数，双曲，顺序和盒嵌入。框嵌入创建了基于区域的概念丰富表示，但是在这个过程中它牺牲了简单性，需要一个定制的优化方案来学习表示。双曲嵌入通过利用双曲空间不断扩展的特性来提高嵌入质量，但是它也遭受着与盒子嵌入相同的命运，因为像优化这样的梯度下降法在双曲空间中并不简单。在这项工作中，我们提出了一种新的基于顺序的表示方法 Binder。Binder 使用二进制向量进行嵌入，因此嵌入向量比其他方法更紧凑，数量级更小。Binder 使用一种简单有效的优化方法来学习具有线性时间复杂度的表示向量。我们的综合实验结果表明，粘合剂是非常准确的，产生了竞争结果的表示任务。但是 Binder 在传递闭包链接预测任务中脱颖而出，因为它只能从直接边缘学习概念嵌入，而现有的基于顺序的方法都依赖于间接边缘。特别是，在我们最大的数据集 WordNet 名词(743,241条边)中，当仅在训练期间使用直接边时，Binder 比第二最佳方法(98.6% 比29%)高出70% 。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Binder:+Hierarchical+Concept+Representation+through+Order+Embedding+of+Binary+Vectors)|0| +|[Consistency and Discrepancy-Based Contrastive Tripartite Graph Learning for Recommendations](https://doi.org/10.1145/3637528.3672056)|Linxin Guo, Yaochen Zhu, Min Gao, Yinghui Tao, Junliang Yu, Chen Chen|University of Virginia, Charlottesville, VA, USA; the University of Queensland, Queensland, Australia; Institute of Guizhou Aerospace Measuring and Testing Technology, Guiyang, China; Chongqing University, Chongqing, China|Tripartite graph-based recommender systems markedly diverge from traditional models by recommending unique combinations such as user groups and item bundles. Despite their effectiveness, these systems exacerbate the long-standing cold-start problem in traditional recommender systems, because any number of user groups or item bundles can be formed among users or items. To address this issue, we introduce a Consistency and Discrepancy-based graph contrastive learning method for tripartite graph-based Recommendation (CDR). This approach leverages two novel meta-path-based metrics-consistency and discrepancy-to capture nuanced, implicit associations between the recommended objects and the recommendees. These metrics, indicative of high-order similarities, can be efficiently calculated with infinite graph convolutional networks (GCN) layers under a multi-objective optimization framework, using the limit theory of GCN. Additionally, we introduce a novel Contrastive Divergence (CD) loss, which can seamlessly integrate the consistency and discrepancy metrics into the contrastive objective as the positive and contrastive supervision signals to learn node representations, enhancing the pairwise ranking of recommended objects and proving particularly valuable in severe cold-start scenarios. Extensive experiments demonstrate the effectiveness of the proposed CDR. The code is released at https://github.com/foodfaust/CDR.|基于三方图表的推荐系统通过推荐独特的组合，如用户组和项目包，明显地区别于传统模型。尽管这些系统很有效，但它们加剧了传统推荐系统中长期存在的“冷启动”问题，因为在用户或项目之间可以形成任意数量的用户组或项目包。为了解决这个问题，我们提出了一种基于一致性和差异性的图形对比学习方法。这种方法利用两种新颖的基于元路径的度量——一致性和差异性——来捕获被推荐对象和被推荐对象之间微妙的、隐含的关联。在多目标优化框架下，利用无穷图卷积网络(GCN)的极限理论，可以有效地计算这些指标，它们表示高阶相似性。此外，我们引入了一种新的对比发散(CD)损失，它可以无缝地将一致性和差异度量集成到对比目标中，作为正向和对比监督信号来学习节点表示，提高推荐对象的成对排序，并证明在严重的冷启动情况下特别有价值。大量的实验证明了所提出的 CDR 算法的有效性。密码在 https://github.com/foodfaust/cdr 发布。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Consistency+and+Discrepancy-Based+Contrastive+Tripartite+Graph+Learning+for+Recommendations)|0| +|[Binder: Hierarchical Concept Representation through Order Embedding of Binary Vectors](https://doi.org/10.1145/3637528.3671793)|Croix Gyurek, Niloy Talukder, Mohammad Al Hasan|University of Waterloo, Waterloo, Ontario, Canada; Indiana University at Indianapolis, Indianapolis, IN, USA|For natural language understanding and generation, embedding concepts using an order-based representation is an essential task. Unlike traditional point vector based representation, an order-based representation imposes geometric constraints on the representation vectors for explicitly capturing various semantic relationships that may exist between a pair of concepts. In existing literature, several approaches on order-based embedding have been proposed, mostly focusing on capturing hierarchical relationships; examples include vectors in Euclidean space, complex, Hyperbolic, order, and Box Embedding. Box embedding creates region-based rich representation of concepts, but along the process it sacrifices simplicity, requiring a custom-made optimization scheme for learning the representation. Hyperbolic embedding improves embedding quality by exploiting the ever-expanding property of Hyperbolic space, but it also suffers from the same fate as box embedding as gradient descent like optimization is not simple in the Hyperbolic space. In this work, we propose Binder, a novel approach for order-based representation. Binder uses binary vectors for embedding, so the embedding vectors are compact with an order of magnitude smaller footprint than other methods. Binder uses a simple and efficient optimization scheme for learning representation vectors with a linear time complexity. Our comprehensive experimental results show that Binder is very accurate, yielding competitive results on the representation task. But Binder stands out from its competitors on the transitive closure link prediction task as it can learn concept embeddings just from the direct edges, whereas all existing order-based approaches rely on the indirect edges. In particular, Binder achieves a whopping 70% higher F1-score than the second best method (98.6% vs 29%) in our largest dataset, WordNet Nouns (743,241 edges), when using only direct edges during training.|对于自然语言的理解和生成，使用基于顺序的表示来嵌入概念是一个基本的任务。与传统的基于点向量的表示不同，基于顺序的表示对表示向量施加几何约束，以显式地捕获可能存在于一对概念之间的各种语义关系。在现有的文献中，已经提出了几种基于顺序的嵌入方法，主要集中在捕获层次关系，例如欧氏空间中的向量，复数，双曲，顺序和盒嵌入。框嵌入创建了基于区域的概念丰富表示，但是在这个过程中它牺牲了简单性，需要一个定制的优化方案来学习表示。双曲嵌入通过利用双曲空间不断扩展的特性来提高嵌入质量，但是它也遭受着与盒子嵌入相同的命运，因为像优化这样的梯度下降法在双曲空间中并不简单。在这项工作中，我们提出了一种新的基于顺序的表示方法 Binder。Binder 使用二进制向量进行嵌入，因此嵌入向量比其他方法更紧凑，数量级更小。Binder 使用一种简单有效的优化方法来学习具有线性时间复杂度的表示向量。我们的综合实验结果表明，粘合剂是非常准确的，产生了竞争结果的表示任务。但是 Binder 在传递闭包链接预测任务中脱颖而出，因为它只能从直接边缘学习概念嵌入，而现有的基于顺序的方法都依赖于间接边缘。特别是，在我们最大的数据集 WordNet 名词(743,241条边)中，当仅在训练期间使用直接边时，Binder 比第二最佳方法(98.6% 比29%)高出70% 。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Binder:+Hierarchical+Concept+Representation+through+Order+Embedding+of+Binary+Vectors)|0| |[An Efficient Local Search Algorithm for Large GD Advertising Inventory Allocation with Multilinear Constraints](https://doi.org/10.1145/3637528.3671811)|Xiang He, Wuyang Mao, Zhenghang Xu, Yuanzhe Gu, Yundu Huang, Zhonglin Zu, Liang Wang, Mengyu Zhao, Mengchuan Zou|; Alibaba Group, Beijing, China; Alibaba Group, Hangzhou, China|The Guaranteed Delivery (GD) advertising is a crucial component of the online advertising industry, and the allocation of inventory in GD advertising is an important procedure that influences directly the ability of the publisher to fulfill the requirements and increase its revenues. Nowadays, as the requirements of advertisers become more and more diverse and fine-grained, the focus ratio requirement, which states that the portion of allocated impressions of a designated contract on focus media among all possible media should be greater than another contract, often appears in business scenarios. However, taking these requirements into account brings hardness for the GD advertising inventory allocation as the focus ratio requirements involve non-convex multilinear constraints. Existing methods which rely on the convex properties are not suitable for processing this problem, while mathematical programming or constraint-based heuristic solvers are unable to produce high-quality solutions within the time limit. Therefore, we propose a local search framework to address this challenge. It incorporates four new operators designed for handling multilinear constraints and a two-mode algorithmic architecture. Experimental results demonstrate that our algorithm is able to compute high-quality allocations with better business metrics compared to the state-of-the-art mathematical programming or constraint based heuristic solvers. Moreover, our algorithm is able to handle the general multilinear constraints and we hope it could be used to solve other problems in GD advertising with similar requirements.|保送广告是网络广告业的重要组成部分，而保送广告的库存分配是直接影响发行商满足需求和增加收入的重要环节。如今，随着广告商的要求变得越来越多样化和细化，焦点比率要求往往出现在商业场景中。然而，考虑到这些要求，GD 广告库存分配的困难，因为焦点比率的要求涉及非凸多线性约束。现有的依赖于凸性质的方法不适合处理这个问题，而数学规划或基于约束的启发式求解器不能在时间限制内产生高质量的解。因此，我们提出了一个本地搜索框架来应对这一挑战。它包括四个新的运算符设计处理多线性约束和一个双模算法体系结构。实验结果表明，与最先进的数学规划或基于约束的启发式求解器相比，该算法能够以更好的业务指标计算高质量的分配。此外，我们的算法能够处理一般的多线性约束，我们希望它可以用来解决其他问题广东广告类似的要求。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=An+Efficient+Local+Search+Algorithm+for+Large+GD+Advertising+Inventory+Allocation+with+Multilinear+Constraints)|0| -|[Double Correction Framework for Denoising Recommendation](https://doi.org/10.1145/3637528.3671692)|Zhuangzhuang He, Yifan Wang, Yonghui Yang, Peijie Sun, Le Wu, Haoyue Bai, Jinqi Gong, Richang Hong, Min Zhang|Tsinghua University, Beijing, China; ; Hefei University of Technology, Hefei, China; University of Macau, Macau, China|As its availability and generality in online services, implicit feedback ismore commonly used in recommender systems. However, implicit feedback usuallypresents noisy samples in real-world recommendation scenarios (such asmisclicks or non-preferential behaviors), which will affect precise userpreference learning. To overcome the noisy samples problem, a popular solutionis based on dropping noisy samples in the model training phase, which followsthe observation that noisy samples have higher training losses than cleansamples. Despite the effectiveness, we argue that this solution still haslimits. (1) High training losses can result from model optimization instabilityor hard samples, not just noisy samples. (2) Completely dropping of noisysamples will aggravate the data sparsity, which lacks full data exploitation.To tackle the above limitations, we propose a Double Correction Framework forDenoising Recommendation (DCF), which contains two correction components fromviews of more precise sample dropping and avoiding more sparse data. In thesample dropping correction component, we use the loss value of the samples overtime to determine whether it is noise or not, increasing dropping stability.Instead of averaging directly, we use the damping function to reduce the biaseffect of outliers. Furthermore, due to the higher variance exhibited by hardsamples, we derive a lower bound for the loss through concentration inequalityto identify and reuse hard samples. In progressive label correction, weiteratively re-label highly deterministic noisy samples and retrain them tofurther improve performance. Finally, extensive experimental results on threedatasets and four backbones demonstrate the effectiveness and generalization ofour proposed framework.|由于在线服务的可用性和普遍性，隐式反馈在推荐系统中得到了广泛的应用。然而，在现实推荐场景中，隐式反馈通常会产生噪声样本(如错误点击或非优先行为) ，从而影响精确的用户偏好学习。为了克服噪声样本问题，提出了一种在模型训练阶段丢弃噪声样本的方法，该方法通过观察噪声样本比清洗样本具有更高的训练损失。尽管有效，我们认为这个解决方案仍然有局限性。(1)模型优化的不稳定性或硬样本会导致高训练损失，而不仅仅是噪声样本。(2)完全丢弃噪声样本会加剧数据稀疏性，而数据稀疏性缺乏充分的数据利用。针对上述限制，我们建议采用双修正框架去噪建议(DCF) ，其中包含两个修正组成部分，从更精确的丢弃样本和避免更稀疏的数据的角度出发。在样本掉落校正分量中，我们利用样本的损失值来判断样本是否为噪声，增加了掉落的稳定性。我们使用阻尼函数来减少异常值的偏差效应，而不是直接求平均值。此外，由于硬样本具有较高的方差，我们通过浓度不等式得到了一个损失的下界来识别和重用硬样本。在渐进式标记校正中，反复重新标记高确定性噪声样本，并对它们进行再训练以进一步提高性能。最后，在三个数据集和四个骨干上的大量实验结果证明了该框架的有效性和通用性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Double+Correction+Framework+for+Denoising+Recommendation)|0| -|[Prompt Perturbation in Retrieval-Augmented Generation based Large Language Models](https://doi.org/10.1145/3637528.3671932)|Zhibo Hu, Chen Wang, Yanfeng Shu, HyeYoung Paik, Liming Zhu|University of New South Wales, Sydney, NSW, Australia; CSIRO Data61 & University of New South Wales, Sydney, NSW, Australia; CSIRO Data61, Hobart, Tasmania, Australia; The University of New South Wales & CSIRO Data61, Sydney, NSW, Australia; CSIRO Data61 & The University of New South Wales, Sydney, NSW, Australia|The robustness of large language models (LLMs) becomes increasingly importantas their use rapidly grows in a wide range of domains. Retrieval-AugmentedGeneration (RAG) is considered as a means to improve the trustworthiness oftext generation from LLMs. However, how the outputs from RAG-based LLMs areaffected by slightly different inputs is not well studied. In this work, wefind that the insertion of even a short prefix to the prompt leads to thegeneration of outputs far away from factually correct answers. Wesystematically evaluate the effect of such prefixes on RAG by introducing anovel optimization technique called Gradient Guided Prompt Perturbation (GGPP).GGPP achieves a high success rate in steering outputs of RAG-based LLMs totargeted wrong answers. It can also cope with instructions in the promptsrequesting to ignore irrelevant context. We also exploit LLMs' neuronactivation difference between prompts with and without GGPP perturbations togive a method that improves the robustness of RAG-based LLMs through a highlyeffective detector trained on neuron activation triggered by GGPP generatedprompts. Our evaluation on open-sourced LLMs demonstrates the effectiveness ofour methods.|随着大型语言模型在广泛领域的应用迅速增长，其健壮性变得越来越重要。检索-增强生成(RAG)被认为是提高 LLM 文本生成可信度的一种手段。然而，基于 RAG 的 LLM 的输出如何受到稍微不同的输入的影响还没有得到很好的研究。在这项工作中，我们发现，即使在提示符中插入一个短的前缀，也会导致输出远离事实上正确的答案。通过引入梯度引导提示扰动(GGPP)这一新的优化技术，系统地评价了这些前缀对 RAG 的影响。GGPP 在引导基于 RAG 的 LLM 的输出定位错误答案方面取得了很高的成功率。它还可以处理提示中要求忽略不相关上下文的指令。我们还利用具有和不具有 GGPP 扰动的提示之间的 LLM 的神经元激活差异，通过对由 GGPP 产生的提示触发的神经元激活进行训练的高效检测器来提高基于 RAG 的 LLM 的鲁棒性。我们对开源 LLM 的评估证明了我们方法的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Prompt+Perturbation+in+Retrieval-Augmented+Generation+based+Large+Language+Models)|0| +|[Double Correction Framework for Denoising Recommendation](https://doi.org/10.1145/3637528.3671692)|Zhuangzhuang He, Yifan Wang, Yonghui Yang, Peijie Sun, Le Wu, Haoyue Bai, Jinqi Gong, Richang Hong, Min Zhang|; Tsinghua University, Beijing, China; Hefei University of Technology, Hefei, China; University of Macau, Macau, China|As its availability and generality in online services, implicit feedback ismore commonly used in recommender systems. However, implicit feedback usuallypresents noisy samples in real-world recommendation scenarios (such asmisclicks or non-preferential behaviors), which will affect precise userpreference learning. To overcome the noisy samples problem, a popular solutionis based on dropping noisy samples in the model training phase, which followsthe observation that noisy samples have higher training losses than cleansamples. Despite the effectiveness, we argue that this solution still haslimits. (1) High training losses can result from model optimization instabilityor hard samples, not just noisy samples. (2) Completely dropping of noisysamples will aggravate the data sparsity, which lacks full data exploitation.To tackle the above limitations, we propose a Double Correction Framework forDenoising Recommendation (DCF), which contains two correction components fromviews of more precise sample dropping and avoiding more sparse data. In thesample dropping correction component, we use the loss value of the samples overtime to determine whether it is noise or not, increasing dropping stability.Instead of averaging directly, we use the damping function to reduce the biaseffect of outliers. Furthermore, due to the higher variance exhibited by hardsamples, we derive a lower bound for the loss through concentration inequalityto identify and reuse hard samples. In progressive label correction, weiteratively re-label highly deterministic noisy samples and retrain them tofurther improve performance. Finally, extensive experimental results on threedatasets and four backbones demonstrate the effectiveness and generalization ofour proposed framework.|由于在线服务的可用性和普遍性，隐式反馈在推荐系统中得到了广泛的应用。然而，在现实推荐场景中，隐式反馈通常会产生噪声样本(如错误点击或非优先行为) ，从而影响精确的用户偏好学习。为了克服噪声样本问题，提出了一种在模型训练阶段丢弃噪声样本的方法，该方法通过观察噪声样本比清洗样本具有更高的训练损失。尽管有效，我们认为这个解决方案仍然有局限性。(1)模型优化的不稳定性或硬样本会导致高训练损失，而不仅仅是噪声样本。(2)完全丢弃噪声样本会加剧数据稀疏性，而数据稀疏性缺乏充分的数据利用。针对上述限制，我们建议采用双修正框架去噪建议(DCF) ，其中包含两个修正组成部分，从更精确的丢弃样本和避免更稀疏的数据的角度出发。在样本掉落校正分量中，我们利用样本的损失值来判断样本是否为噪声，增加了掉落的稳定性。我们使用阻尼函数来减少异常值的偏差效应，而不是直接求平均值。此外，由于硬样本具有较高的方差，我们通过浓度不等式得到了一个损失的下界来识别和重用硬样本。在渐进式标记校正中，反复重新标记高确定性噪声样本，并对它们进行再训练以进一步提高性能。最后，在三个数据集和四个骨干上的大量实验结果证明了该框架的有效性和通用性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Double+Correction+Framework+for+Denoising+Recommendation)|0| +|[Prompt Perturbation in Retrieval-Augmented Generation based Large Language Models](https://doi.org/10.1145/3637528.3671932)|Zhibo Hu, Chen Wang, Yanfeng Shu, HyeYoung Paik, Liming Zhu|University of New South Wales, Sydney, NSW, Australia; CSIRO Data61 & University of New South Wales, Sydney, NSW, Australia; CSIRO Data61, Hobart, Tasmania, Australia; CSIRO Data61 & The University of New South Wales, Sydney, NSW, Australia; The University of New South Wales & CSIRO Data61, Sydney, NSW, Australia|The robustness of large language models (LLMs) becomes increasingly importantas their use rapidly grows in a wide range of domains. Retrieval-AugmentedGeneration (RAG) is considered as a means to improve the trustworthiness oftext generation from LLMs. However, how the outputs from RAG-based LLMs areaffected by slightly different inputs is not well studied. In this work, wefind that the insertion of even a short prefix to the prompt leads to thegeneration of outputs far away from factually correct answers. Wesystematically evaluate the effect of such prefixes on RAG by introducing anovel optimization technique called Gradient Guided Prompt Perturbation (GGPP).GGPP achieves a high success rate in steering outputs of RAG-based LLMs totargeted wrong answers. It can also cope with instructions in the promptsrequesting to ignore irrelevant context. We also exploit LLMs' neuronactivation difference between prompts with and without GGPP perturbations togive a method that improves the robustness of RAG-based LLMs through a highlyeffective detector trained on neuron activation triggered by GGPP generatedprompts. Our evaluation on open-sourced LLMs demonstrates the effectiveness ofour methods.|随着大型语言模型在广泛领域的应用迅速增长，其健壮性变得越来越重要。检索-增强生成(RAG)被认为是提高 LLM 文本生成可信度的一种手段。然而，基于 RAG 的 LLM 的输出如何受到稍微不同的输入的影响还没有得到很好的研究。在这项工作中，我们发现，即使在提示符中插入一个短的前缀，也会导致输出远离事实上正确的答案。通过引入梯度引导提示扰动(GGPP)这一新的优化技术，系统地评价了这些前缀对 RAG 的影响。GGPP 在引导基于 RAG 的 LLM 的输出定位错误答案方面取得了很高的成功率。它还可以处理提示中要求忽略不相关上下文的指令。我们还利用具有和不具有 GGPP 扰动的提示之间的 LLM 的神经元激活差异，通过对由 GGPP 产生的提示触发的神经元激活进行训练的高效检测器来提高基于 RAG 的 LLM 的鲁棒性。我们对开源 LLM 的评估证明了我们方法的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Prompt+Perturbation+in+Retrieval-Augmented+Generation+based+Large+Language+Models)|0| |[Dynamic Neural Dowker Network: Approximating Persistent Homology in Dynamic Directed Graphs](https://doi.org/10.1145/3637528.3671980)|Hao Li, Hao Jiang, Jiajun Fan, Dongsheng Ye, Liang Du|Electronic Information School, Wuhan University, Wuhan, Hubei, China|Persistent homology, a fundamental technique within Topological Data Analysis (TDA), captures structural and shape characteristics of graphs, yet encounters computational difficulties when applied to dynamic directed graphs. This paper introduces the Dynamic Neural Dowker Network (DNDN), a novel framework specifically designed to approximate the results of dynamic Dowker filtration, aiming to capture the high-order topological features of dynamic directed graphs. Our approach creatively uses line graph transformations to produce both source and sink line graphs, highlighting the shared neighbor structures that Dowker complexes focus on. The DNDN incorporates a Source-Sink Line Graph Neural Network (SSLGNN) layer to effectively capture the neighborhood relationships among dynamic edges. Additionally, we introduce an innovative duality edge fusion mechanism, ensuring that the results for both the sink and source line graphs adhere to the duality principle intrinsic to Dowker complexes. Our approach is validated through comprehensive experiments on real-world datasets, demonstrating DNDN's capability not only to effectively approximate dynamic Dowker filtration results but also to perform exceptionally in dynamic graph classification tasks.|持久同调是拓扑数据分析(TDA)中的一项基本技术，它捕获图的结构和形状特征，但在应用于动态有向图时遇到了计算困难。本文介绍了动态神经道克网络(DNDN) ，这是一种专门设计来逼近动态道克滤波结果的新框架，旨在捕获动态有向图的高阶拓扑特征。我们的方法创造性地使用线图转换来生成源和汇线图，突出道克复合体关注的共享邻居结构。DNDN 结合源-汇线图神经网络(SSLGNN)层，有效地捕获动态边之间的邻域关系。此外，我们还引入了一种创新的对偶边融合机制，确保汇和源线图的结果都坚持道克复合体固有的对偶原则。通过对实际数据集的全面实验验证了该方法的有效性，证明了 DNDN 不仅能有效逼近动态 Dowker 滤波结果，而且能在动态图形分类任务中表现优异。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Dynamic+Neural+Dowker+Network:+Approximating+Persistent+Homology+in+Dynamic+Directed+Graphs)|0| |[RecExplainer: Aligning Large Language Models for Explaining Recommendation Models](https://doi.org/10.1145/3637528.3671802)|Yuxuan Lei, Jianxun Lian, Jing Yao, Xu Huang, Defu Lian, Xing Xie|University of Science and Technology of China, Hefei, China; Microsoft Research Asia, Beijing, China|Recommender systems are widely used in online services, with embedding-based models being particularly popular due to their expressiveness in representing complex signals. However, these models often function as a black box, making them less transparent and reliable for both users and developers. Recently, large language models (LLMs) have demonstrated remarkable intelligence in understanding, reasoning, and instruction following. This paper presents the initial exploration of using LLMs as surrogate models to explaining black-box recommender models. The primary concept involves training LLMs to comprehend and emulate the behavior of target recommender models. By leveraging LLMs' own extensive world knowledge and multi-step reasoning abilities, these aligned LLMs can serve as advanced surrogates, capable of reasoning about observations. Moreover, employing natural language as an interface allows for the creation of customizable explanations that can be adapted to individual user preferences. To facilitate an effective alignment, we introduce three methods: behavior alignment, intention alignment, and hybrid alignment. Behavior alignment operates in the language space, representing user preferences and item information as text to mimic the target model's behavior; intention alignment works in the latent space of the recommendation model, using user and item representations to understand the model's behavior; hybrid alignment combines both language and latent spaces. Comprehensive experiments conducted on three public datasets show that our approach yields promising results in understanding and mimicking target models, producing high-quality, high-fidelity, and distinct explanations. Our code is available at https://github.com/microsoft/RecAI.|推荐系统在在线服务中得到了广泛的应用，基于嵌入的推荐系统模型因其表达复杂信号的能力而受到人们的青睐。然而，这些模型通常起到黑匣子的作用，使它们对用户和开发人员来说都不那么透明和可靠。近年来，大型语言模型(LLM)在理解、推理和指令跟随方面显示出非凡的智力。本文介绍了利用 LLM 作为代理模型来解释黑盒推荐模型的初步探索。主要概念包括训练 LLM 理解和仿真目标推荐模型的行为。通过利用 LLM 自身丰富的世界知识和多步推理能力，这些对齐的 LLM 可以作为高级代理，能够对观测进行推理。此外，使用自然语言作为界面允许创建可定制的解释，可以适应个人用户的喜好。为了实现有效的校准，我们介绍了三种方法: 行为校准、意图校准和混合校准。行为对齐操作在语言空间中，将用户偏好和项目信息表示为文本，以模仿目标模型的行为; 意图对齐工作在推荐模型的潜在空间中，使用用户和项目表示来理解模型的行为; 混合对齐结合了语言和潜在空间。在三个公共数据集上进行的综合实验表明，我们的方法在理解和模仿目标模型方面产生了有希望的结果，产生了高质量、高保真度和独特的解释。我们的代码可以在 https://github.com/microsoft/recai 找到。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=RecExplainer:+Aligning+Large+Language+Models+for+Explaining+Recommendation+Models)|0| -|[Customizing Graph Neural Network for CAD Assembly Recommendation](https://doi.org/10.1145/3637528.3671788)|Fengqi Liang, Huan Zhao, Yuhan Quan, Wei Fang, Chuan Shi|Beijing University of Post and Telecommunication, Beijing, China; 4Paradigm Inc., Beijing, China|CAD assembly modeling, which refers to using CAD software to design new products from a catalog of existing machine components, is important in the industrial field. The graph neural network (GNN) based recommender system for CAD assembly modeling can help designers make decisions and speed up the design process by recommending the next required component based on the existing components in CAD software. These components can be represented as a graph naturally. However, present recommender systems for CAD assembly modeling adopt fixed GNN architectures, which may be sub-optimal for different manufacturers with different data distribution. Therefore, to customize a well-suited recommender system for different manufacturers, we propose a novel neural architecture search (NAS) framework, dubbed CusGNN, which can design data-specific GNN automatically. Specifically, we design a search space from three dimensions (i.e., aggregation, fusion, and readout functions), which contains a wide variety of GNN architectures. Then, we develop an effective differentiable search algorithm to search high-performing GNN from the search space. Experimental results show that the customized GNNs achieve 1.5-5.1% higher top-10 accuracy compared to previous manual designed methods, demonstrating the superiority of the proposed approach. Code and data are available at https://github.com/BUPT-GAMMA/CusGNN.|CAD 装配建模是指利用 CAD 软件从现有机械零部件目录中设计新产品，在工业领域具有重要意义。基于图形神经网络(GNN)的 CAD 装配建模推荐系统可以帮助设计人员根据 CAD 软件中现有的组件推荐下一个需要的组件，从而帮助设计人员做出决策并加快设计过程。这些组件可以自然地表示为一个图形。然而，现有的 CAD 装配建模推荐系统采用固定的 GNN 体系结构，对于具有不同数据分布的不同制造商可能是次优的。因此，为了为不同的制造商定制一个非常适合的推荐系统，我们提出了一个新的神经结构搜索(NAS)框架，称为 CusGNN，它可以自动设计数据特定的 GNN。具体来说，我们从三个维度(即聚合、融合和读出函数)设计一个搜索空间，其中包含多种 GNN 体系结构。然后，我们开发了一个有效的可微搜索算法来搜索高性能的 GNN 从搜索空间。实验结果表明，与以往的手工设计方法相比，自定义 GNN 的前10位精度提高了1.5 -5.1% ，证明了该方法的优越性。代码和数据可在 https://github.com/bupt-gamma/cusgnn 查阅。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Customizing+Graph+Neural+Network+for+CAD+Assembly+Recommendation)|0| -|[When Box Meets Graph Neural Network in Tag-aware Recommendation](https://doi.org/10.1145/3637528.3671973)|Fake Lin, Ziwei Zhao, Xi Zhu, Da Zhang, Shitian Shen, Xueying Li, Tong Xu, Suojuan Zhang, Enhong Chen|Alibaba Group, Hangzhou, China; Alibaba Group, Hangzhou, NC, USA; University of Science and Technology of China, Hefei, China; Army Engineering University of PLA, Nanjing, China|Last year has witnessed the re-flourishment of tag-aware recommender systems supported by the LLM-enriched tags. Unfortunately, though large efforts have been made, current solutions may fail to describe the diversity and uncertainty inherent in user preferences with only tag-driven profiles. Recently, with the development of geometry-based techniques, e.g., box embeddings, the diversity of user preferences now could be fully modeled as the range within a box in high dimension space. However, defect still exists as these approaches are incapable of capturing high-order neighbor signals, i.e., semantic-rich multi-hop relations within the user-tag-item tripartite graph, which severely limits the effectiveness of user modeling. To deal with this challenge, in this paper, we propose a novel framework, called BoxGNN, to perform message aggregation via combinations of logical operations, thereby incorporating high-order signals. Specifically, we first embed users, items, and tags as hyper-boxes rather than simple points in the representation space, and define two logical operations, i.e., union and intersection, to facilitate the subsequent process. Next, we perform the message aggregation mechanism via the combination of logical operations, to obtain the corresponding high-order box representations. Finally, we adopt a volume-based learning objective with Gumbel smoothing techniques to refine the representation of boxes. Extensive experiments on two publicly available datasets and one LLM-enhanced e-commerce dataset have validated the superiority of BoxGNN compared with various state-of-the-art baselines. The code is released online: https://github.com/critical88/BoxGNN.|去年见证了标签感知推荐系统的重新繁荣，这些系统由 LLM 丰富的标签支持。不幸的是，尽管已经做出了巨大的努力，目前的解决方案可能无法描述用户偏好中固有的多样性和不确定性，只能使用标签驱动的配置文件。近年来，随着几何技术的发展，如盒子嵌入，用户偏好的多样性现在可以完全模拟为高维空间中盒子内的范围。然而，由于这些方法不能捕获高阶相邻信号，即用户标签项三部分图中语义丰富的多跳关系，这严重限制了用户建模的有效性。为了应对这一挑战，在本文中，我们提出了一种新的框架，称为 BoxGNN，通过逻辑操作的组合来执行消息聚合，从而合并高阶信号。具体来说，我们首先将用户、项目和标记作为超盒而不是表示空间中的简单点嵌入，并定义两个逻辑操作，即联合和交集，以方便后续处理。接下来，我们通过逻辑操作的组合执行消息聚合机制，以获得相应的高阶框表示。最后，我们采用基于体积的学习目标和 Gumbel 平滑技术来改善盒子的表示。在两个公开可用的数据集和一个 LLM 增强的电子商务数据集上的大量实验验证了 BoxGNN 相对于各种最新基线的优越性。代码在网上发布: https://github.com/critical88/boxgnn。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=When+Box+Meets+Graph+Neural+Network+in+Tag-aware+Recommendation)|0| -|[Fast Query of Biharmonic Distance in Networks](https://doi.org/10.1145/3637528.3671856)|Changan Liu, Ahad N. Zehmakan, Zhongzhi Zhang|Fudan University, Shanghai, China; Australian National University, Canberra, Australia|Thebiharmonic distance (BD) is a fundamental metric that measures the distance of two nodes in a graph. It has found applications in network coherence, machine learning, and computational graphics, among others. In spite of BD's importance, efficient algorithms for the exact computation or approximation of this metric on large graphs remain notably absent. In this work, we provide several algorithms to estimate BD, building on a novel formulation of this metric. These algorithms enjoy locality property (that is, they only read a small portion of the input graph) and at the same time possess provable performance guarantees. In particular, our main algorithms approximate the BD between any node pair with an arbitrarily small additive error ε in time O(1/ε2 poly(log n/ε)). Furthermore, we perform an extensive empirical study on several benchmark networks, validating the performance and accuracy of our algorithms.|双调和距离(BD)是度量图中两个节点之间距离的基本度量。它在网络一致性、机器学习和计算图形学等领域都有应用。尽管 BD 的重要性，有效的算法精确计算或逼近这一度量在大图仍然明显缺乏。在这项工作中，我们提供了几种算法来估计 BD，建立在一个新的公式的这个度量。这些算法具有局部性(也就是说，它们只读取输入图的一小部分) ，同时具有可证明的性能保证。特别地，我们的主要算法在时间 O (1/ε2多边形(log n/ε))上具有任意小的加性误差 ε 的任意节点对之间逼近 BD。此外，我们对几个基准网络进行了广泛的实证研究，验证了我们的算法的性能和准确性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Fast+Query+of+Biharmonic+Distance+in+Networks)|0| -|[Multi-Task Learning for Routing Problem with Cross-Problem Zero-Shot Generalization](https://doi.org/10.1145/3637528.3672040)|Fei Liu, Xi Lin, Zhenkun Wang, Qingfu Zhang, Tong Xialiang, Mingxuan Yuan|Huawei Technologies Ltd., Hong Kong, China; Huawei Technologies Ltd., Shenzhen, China; Southern University of Science and Technology, Shenzhen, China; City University of Hong Kong, Hong Kong, China|Vehicle routing problems (VRP) are very important in many real-world applications and has been studied for several decades. Recently, neural combinatorial optimization (NCO) has attracted growing research effort. NCO is to train a neural network model to solve an optimization problem in question. However, existing NCO methods often build a different model for each routing problem, which significantly hinders their application in some areas where there are many different VRP variants to solve. In this work, we make a first attempt to tackle the crucial challenge of cross-problem generalization in NCO. We formulate VRPs as different combinations of a set of shared underlying attributes and solve them simultaneously via a single model through attribute composition. In this way, our proposed model can successfully solve VRPs with unseen attribute combinations in a zero-shot generalization manner. In our experiments, the neural model is trained on five VRP variants and its performance is tested on eleven VRP variants. The experimental results show that the model demonstrates superior performance on these eleven VRP variants, reducing the average gap to around 5% from over 20% and achieving a notable performance boost on both benchmark datasets and real-world logistics scenarios.|车辆路径问题(VRP)是车辆路径问题的一个重要研究方向。最近，神经组合优化(NCO)已经吸引了越来越多的研究人员。神经网络模型来解决最佳化问题问题。然而，现有的 NCO 方法往往为每个路由问题建立不同的模型，这严重阻碍了它们在有许多不同的 VRP 变量需要解决的一些领域的应用。在这项工作中，我们首次尝试解决 NCO 中跨问题泛化的关键挑战。我们将 VRP 描述为一组共享的底层属性的不同组合，并通过属性组合的方法通过单一模型同时求解。通过这种方法，我们提出的模型可以成功地解决具有不可见属性组合的 VRP 问题。在我们的实验中，神经模型被训练在五个 VRP 变体上，它的性能被测试在十一个 VRP 变体上。实验结果显示，该模型在这十一种 VRP 变种上表现卓越，将平均差距从超过20% 缩小至约5% ，并在基准数据集和现实世界物流场景上均取得显著的性能提升。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Multi-Task+Learning+for+Routing+Problem+with+Cross-Problem+Zero-Shot+Generalization)|0| +|[Customizing Graph Neural Network for CAD Assembly Recommendation](https://doi.org/10.1145/3637528.3671788)|Fengqi Liang, Huan Zhao, Yuhan Quan, Wei Fang, Chuan Shi|4Paradigm Inc., Beijing, China; Beijing University of Post and Telecommunication, Beijing, China|CAD assembly modeling, which refers to using CAD software to design new products from a catalog of existing machine components, is important in the industrial field. The graph neural network (GNN) based recommender system for CAD assembly modeling can help designers make decisions and speed up the design process by recommending the next required component based on the existing components in CAD software. These components can be represented as a graph naturally. However, present recommender systems for CAD assembly modeling adopt fixed GNN architectures, which may be sub-optimal for different manufacturers with different data distribution. Therefore, to customize a well-suited recommender system for different manufacturers, we propose a novel neural architecture search (NAS) framework, dubbed CusGNN, which can design data-specific GNN automatically. Specifically, we design a search space from three dimensions (i.e., aggregation, fusion, and readout functions), which contains a wide variety of GNN architectures. Then, we develop an effective differentiable search algorithm to search high-performing GNN from the search space. Experimental results show that the customized GNNs achieve 1.5-5.1% higher top-10 accuracy compared to previous manual designed methods, demonstrating the superiority of the proposed approach. Code and data are available at https://github.com/BUPT-GAMMA/CusGNN.|CAD 装配建模是指利用 CAD 软件从现有机械零部件目录中设计新产品，在工业领域具有重要意义。基于图形神经网络(GNN)的 CAD 装配建模推荐系统可以帮助设计人员根据 CAD 软件中现有的组件推荐下一个需要的组件，从而帮助设计人员做出决策并加快设计过程。这些组件可以自然地表示为一个图形。然而，现有的 CAD 装配建模推荐系统采用固定的 GNN 体系结构，对于具有不同数据分布的不同制造商可能是次优的。因此，为了为不同的制造商定制一个非常适合的推荐系统，我们提出了一个新的神经结构搜索(NAS)框架，称为 CusGNN，它可以自动设计数据特定的 GNN。具体来说，我们从三个维度(即聚合、融合和读出函数)设计一个搜索空间，其中包含多种 GNN 体系结构。然后，我们开发了一个有效的可微搜索算法来搜索高性能的 GNN 从搜索空间。实验结果表明，与以往的手工设计方法相比，自定义 GNN 的前10位精度提高了1.5 -5.1% ，证明了该方法的优越性。代码和数据可在 https://github.com/bupt-gamma/cusgnn 查阅。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Customizing+Graph+Neural+Network+for+CAD+Assembly+Recommendation)|0| +|[When Box Meets Graph Neural Network in Tag-aware Recommendation](https://doi.org/10.1145/3637528.3671973)|Fake Lin, Ziwei Zhao, Xi Zhu, Da Zhang, Shitian Shen, Xueying Li, Tong Xu, Suojuan Zhang, Enhong Chen|Army Engineering University of PLA, Nanjing, China; Alibaba Group, Hangzhou, China; University of Science and Technology of China, Hefei, China; Alibaba Group, Hangzhou, NC, USA|Last year has witnessed the re-flourishment of tag-aware recommender systems supported by the LLM-enriched tags. Unfortunately, though large efforts have been made, current solutions may fail to describe the diversity and uncertainty inherent in user preferences with only tag-driven profiles. Recently, with the development of geometry-based techniques, e.g., box embeddings, the diversity of user preferences now could be fully modeled as the range within a box in high dimension space. However, defect still exists as these approaches are incapable of capturing high-order neighbor signals, i.e., semantic-rich multi-hop relations within the user-tag-item tripartite graph, which severely limits the effectiveness of user modeling. To deal with this challenge, in this paper, we propose a novel framework, called BoxGNN, to perform message aggregation via combinations of logical operations, thereby incorporating high-order signals. Specifically, we first embed users, items, and tags as hyper-boxes rather than simple points in the representation space, and define two logical operations, i.e., union and intersection, to facilitate the subsequent process. Next, we perform the message aggregation mechanism via the combination of logical operations, to obtain the corresponding high-order box representations. Finally, we adopt a volume-based learning objective with Gumbel smoothing techniques to refine the representation of boxes. Extensive experiments on two publicly available datasets and one LLM-enhanced e-commerce dataset have validated the superiority of BoxGNN compared with various state-of-the-art baselines. The code is released online: https://github.com/critical88/BoxGNN.|去年见证了标签感知推荐系统的重新繁荣，这些系统由 LLM 丰富的标签支持。不幸的是，尽管已经做出了巨大的努力，目前的解决方案可能无法描述用户偏好中固有的多样性和不确定性，只能使用标签驱动的配置文件。近年来，随着几何技术的发展，如盒子嵌入，用户偏好的多样性现在可以完全模拟为高维空间中盒子内的范围。然而，由于这些方法不能捕获高阶相邻信号，即用户标签项三部分图中语义丰富的多跳关系，这严重限制了用户建模的有效性。为了应对这一挑战，在本文中，我们提出了一种新的框架，称为 BoxGNN，通过逻辑操作的组合来执行消息聚合，从而合并高阶信号。具体来说，我们首先将用户、项目和标记作为超盒而不是表示空间中的简单点嵌入，并定义两个逻辑操作，即联合和交集，以方便后续处理。接下来，我们通过逻辑操作的组合执行消息聚合机制，以获得相应的高阶框表示。最后，我们采用基于体积的学习目标和 Gumbel 平滑技术来改善盒子的表示。在两个公开可用的数据集和一个 LLM 增强的电子商务数据集上的大量实验验证了 BoxGNN 相对于各种最新基线的优越性。代码在网上发布: https://github.com/critical88/boxgnn。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=When+Box+Meets+Graph+Neural+Network+in+Tag-aware+Recommendation)|0| +|[Fast Query of Biharmonic Distance in Networks](https://doi.org/10.1145/3637528.3671856)|Changan Liu, Ahad N. Zehmakan, Zhongzhi Zhang|Australian National University, Canberra, Australia; Fudan University, Shanghai, China|Thebiharmonic distance (BD) is a fundamental metric that measures the distance of two nodes in a graph. It has found applications in network coherence, machine learning, and computational graphics, among others. In spite of BD's importance, efficient algorithms for the exact computation or approximation of this metric on large graphs remain notably absent. In this work, we provide several algorithms to estimate BD, building on a novel formulation of this metric. These algorithms enjoy locality property (that is, they only read a small portion of the input graph) and at the same time possess provable performance guarantees. In particular, our main algorithms approximate the BD between any node pair with an arbitrarily small additive error ε in time O(1/ε2 poly(log n/ε)). Furthermore, we perform an extensive empirical study on several benchmark networks, validating the performance and accuracy of our algorithms.|双调和距离(BD)是度量图中两个节点之间距离的基本度量。它在网络一致性、机器学习和计算图形学等领域都有应用。尽管 BD 的重要性，有效的算法精确计算或逼近这一度量在大图仍然明显缺乏。在这项工作中，我们提供了几种算法来估计 BD，建立在一个新的公式的这个度量。这些算法具有局部性(也就是说，它们只读取输入图的一小部分) ，同时具有可证明的性能保证。特别地，我们的主要算法在时间 O (1/ε2多边形(log n/ε))上具有任意小的加性误差 ε 的任意节点对之间逼近 BD。此外，我们对几个基准网络进行了广泛的实证研究，验证了我们的算法的性能和准确性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Fast+Query+of+Biharmonic+Distance+in+Networks)|0| +|[Multi-Task Learning for Routing Problem with Cross-Problem Zero-Shot Generalization](https://doi.org/10.1145/3637528.3672040)|Fei Liu, Xi Lin, Zhenkun Wang, Qingfu Zhang, Tong Xialiang, Mingxuan Yuan|Huawei Technologies Ltd., Hong Kong, China; City University of Hong Kong, Hong Kong, China; Southern University of Science and Technology, Shenzhen, China; Huawei Technologies Ltd., Shenzhen, China|Vehicle routing problems (VRP) are very important in many real-world applications and has been studied for several decades. Recently, neural combinatorial optimization (NCO) has attracted growing research effort. NCO is to train a neural network model to solve an optimization problem in question. However, existing NCO methods often build a different model for each routing problem, which significantly hinders their application in some areas where there are many different VRP variants to solve. In this work, we make a first attempt to tackle the crucial challenge of cross-problem generalization in NCO. We formulate VRPs as different combinations of a set of shared underlying attributes and solve them simultaneously via a single model through attribute composition. In this way, our proposed model can successfully solve VRPs with unseen attribute combinations in a zero-shot generalization manner. In our experiments, the neural model is trained on five VRP variants and its performance is tested on eleven VRP variants. The experimental results show that the model demonstrates superior performance on these eleven VRP variants, reducing the average gap to around 5% from over 20% and achieving a notable performance boost on both benchmark datasets and real-world logistics scenarios.|车辆路径问题(VRP)是车辆路径问题的一个重要研究方向。最近，神经组合优化(NCO)已经吸引了越来越多的研究人员。神经网络模型来解决最佳化问题问题。然而，现有的 NCO 方法往往为每个路由问题建立不同的模型，这严重阻碍了它们在有许多不同的 VRP 变量需要解决的一些领域的应用。在这项工作中，我们首次尝试解决 NCO 中跨问题泛化的关键挑战。我们将 VRP 描述为一组共享的底层属性的不同组合，并通过属性组合的方法通过单一模型同时求解。通过这种方法，我们提出的模型可以成功地解决具有不可见属性组合的 VRP 问题。在我们的实验中，神经模型被训练在五个 VRP 变体上，它的性能被测试在十一个 VRP 变体上。实验结果显示，该模型在这十一种 VRP 变种上表现卓越，将平均差距从超过20% 缩小至约5% ，并在基准数据集和现实世界物流场景上均取得显著的性能提升。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Multi-Task+Learning+for+Routing+Problem+with+Cross-Problem+Zero-Shot+Generalization)|0| |[Low Rank Multi-Dictionary Selection at Scale](https://doi.org/10.1145/3637528.3671723)|Boya Ma, Maxwell McNeil, Abram Magner, Petko Bogdanov|Department of Computer Science, University at Albany, State University of New York, Albany, NY, USA|The sparse dictionary coding framework represents signals as a linear combination of a few predefined dictionary atoms. It has been employed for images, time series, graph signals and recently for 2-way (or 2D) spatio-temporal data employing jointly temporal and spatial dictionaries. Large and over-complete dictionaries enable high-quality models, but also pose scalability challenges which are exacerbated in multi-dictionary settings. Hence, an important problem that we address in this paper is: How to scale multi-dictionary coding for large dictionaries and datasets? We propose a multi-dictionary atom selection technique for low-rank sparse coding named LRMDS. To enable scalability to large dictionaries and datasets, it progressively selects groups of row-column atom pairs based on their alignment with the data and performs convex relaxation coding via the corresponding sub-dictionaries. We demonstrate both theoretically and experimentally that when the data has a low-rank encoding with a sparse subset of the atoms, LRMDS is able to select them with strong guarantees under mild assumptions. Furthermore, we demonstrate the scalability and quality of LRMDS in both synthetic and real-world datasets and for a range of coding dictionaries. It achieves 3 times to 10 times speed-up compared to baselines, while obtaining up to two orders of magnitude improvement in representation quality on some of the real world datasets given a fixed target number of atoms.|稀疏字典编码框架将信号表示为几个预定义字典原子的线性组合。它已经应用于图像，时间序列，图形信号和最近的2路(或2D)时空数据使用联合时间和空间字典。大型和过于完整的字典使高质量的模型成为可能，但也带来了可伸缩性方面的挑战，这些挑战在多字典设置中会加剧。因此，本文要解决的一个重要问题是: 如何对大型字典和数据集进行多字典编码？提出了一种低秩稀疏编码的多字典原子选择技术 LRMDS。为了使大型字典和数据集具有可伸缩性，它逐步根据行-列原子对与数据的对齐情况选择它们的组，并通过相应的子字典执行凸松弛编码。我们在理论和实验上都证明了，当数据具有一个低秩编码和一个稀疏的原子子集时，LRMDS 能够在温和的假设条件下选择它们，并且具有很强的保证性。此外，我们证明了 LRMDS 在合成和真实世界数据集和一系列编码字典中的可伸缩性和质量。与基线相比，它的速度提高了3到10倍，同时在给定固定目标原子数量的情况下，一些真实世界数据集的表示质量提高了两个数量级。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Low+Rank+Multi-Dictionary+Selection+at+Scale)|0| -|[ImputeFormer: Low Rankness-Induced Transformers for Generalizable Spatiotemporal Imputation](https://doi.org/10.1145/3637528.3671751)|Tong Nie, Guoyang Qin, Wei Ma, Yuewen Mei, Jian Sun|The Hong Kong Polytechnic University, Hong Kong SAR, China; Tongji University, Shanghai, China|Missing data is a pervasive issue in both scientific and engineering tasks, especially for the modeling of spatiotemporal data. Existing imputation solutions mainly include low-rank models and deep learning models. The former assumes general structural priors but has limited model capacity. The latter possesses salient expressivity, but lacks prior knowledge of the underlying spatiotemporal structures. Leveraging the strengths of both two paradigms, we demonstrate a low rankness-induced Transformer to achieve a balance between strong inductive bias and high expressivity. The exploitation of the inherent structures of spatiotemporal data enables our model to learn balanced signal-noise representations, making it generalizable for a variety of imputation tasks. We demonstrate its superiority in terms of accuracy, efficiency, and versatility in heterogeneous datasets, including traffic flow, solar energy, smart meters, and air quality. Promising empirical results provide strong conviction that incorporating time series primitives, such as low-rankness, can substantially facilitate the development of a generalizable model to approach a wide range of spatiotemporal imputation problems.|缺失数据是科学和工程任务中普遍存在的问题，特别是对于时空数据的建模。现有的归责解决方案主要包括低阶模型和深度学习模型。前者假设一般结构先验，但模型容量有限。后者具有显著的表现力，但缺乏对潜在时空结构的先验知识。利用这两种模式的优势，我们展示了一个低等级感应变压器，以实现强感应偏置和高表达性之间的平衡。利用时空数据的固有结构，使我们的模型能够学习平衡的信号-噪声表示，使它可以推广到各种插补任务。我们证明了它在准确性、效率和异构数据集的通用性方面的优势，包括交通流量、太阳能、智能仪表和空气质量。有希望的经验结果提供了强有力的信念，结合时间序列原语，如低秩，可以大大促进发展一个可推广的模型来处理广泛的时空插补问题。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=ImputeFormer:+Low+Rankness-Induced+Transformers+for+Generalizable+Spatiotemporal+Imputation)|0| +|[ImputeFormer: Low Rankness-Induced Transformers for Generalizable Spatiotemporal Imputation](https://doi.org/10.1145/3637528.3671751)|Tong Nie, Guoyang Qin, Wei Ma, Yuewen Mei, Jian Sun|Tongji University, Shanghai, China; The Hong Kong Polytechnic University, Hong Kong SAR, China|Missing data is a pervasive issue in both scientific and engineering tasks, especially for the modeling of spatiotemporal data. Existing imputation solutions mainly include low-rank models and deep learning models. The former assumes general structural priors but has limited model capacity. The latter possesses salient expressivity, but lacks prior knowledge of the underlying spatiotemporal structures. Leveraging the strengths of both two paradigms, we demonstrate a low rankness-induced Transformer to achieve a balance between strong inductive bias and high expressivity. The exploitation of the inherent structures of spatiotemporal data enables our model to learn balanced signal-noise representations, making it generalizable for a variety of imputation tasks. We demonstrate its superiority in terms of accuracy, efficiency, and versatility in heterogeneous datasets, including traffic flow, solar energy, smart meters, and air quality. Promising empirical results provide strong conviction that incorporating time series primitives, such as low-rankness, can substantially facilitate the development of a generalizable model to approach a wide range of spatiotemporal imputation problems.|缺失数据是科学和工程任务中普遍存在的问题，特别是对于时空数据的建模。现有的归责解决方案主要包括低阶模型和深度学习模型。前者假设一般结构先验，但模型容量有限。后者具有显著的表现力，但缺乏对潜在时空结构的先验知识。利用这两种模式的优势，我们展示了一个低等级感应变压器，以实现强感应偏置和高表达性之间的平衡。利用时空数据的固有结构，使我们的模型能够学习平衡的信号-噪声表示，使它可以推广到各种插补任务。我们证明了它在准确性、效率和异构数据集的通用性方面的优势，包括交通流量、太阳能、智能仪表和空气质量。有希望的经验结果提供了强有力的信念，结合时间序列原语，如低秩，可以大大促进发展一个可推广的模型来处理广泛的时空插补问题。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=ImputeFormer:+Low+Rankness-Induced+Transformers+for+Generalizable+Spatiotemporal+Imputation)|0| |[Improving the Consistency in Cross-Lingual Cross-Modal Retrieval with 1-to-K Contrastive Learning](https://doi.org/10.1145/3637528.3671787)|Zhijie Nie, Richong Zhang, Zhangchi Feng, Hailang Huang, Xudong Liu|CCSE, Beihang University, Beijing, China|Cross-lingual Cross-modal Retrieval (CCR) is an essential task in web search, which aims to break the barriers between modality and language simultaneously and achieves image-text retrieval in the multi-lingual scenario with a single model. In recent years, excellent progress has been made based on cross-lingual cross-modal pre-training; particularly, the methods based on contrastive learning on large-scale data have significantly improved retrieval tasks. However, these methods directly follow the existing pre-training methods in the cross-lingual or cross-modal domain, leading to two problems of inconsistency in CCR: The methods with cross-lingual style suffer from the intra-modal error propagation, resulting in inconsistent recall performance across languages in the whole dataset. The methods with cross-modal style suffer from the inter-modal optimization direction bias, resulting in inconsistent rank across languages within each instance, which cannot be reflected by Recall@K. To solve these problems, we propose a simple but effective 1-to-K contrastive learning method, which treats each language equally and eliminates error propagation and optimization bias. In addition, we propose a new evaluation metric, Mean Rank Variance (MRV), to reflect the rank inconsistency across languages within each instance. Extensive experiments on four CCR datasets show that our method improves both recall rates and MRV with smaller-scale pre-trained data, achieving the new state-of-art.|跨语言交叉模式检索(CCR)是网络搜索中的一项重要任务，其目的是同时打破语言和情态之间的界限，以单一的模型实现多语种情景下的图像-文本检索。近年来，基于跨语言跨模式预训练的检索方法取得了显著的进展，尤其是基于大规模数据的对比学习方法显著改善了检索任务。然而，这些方法直接遵循现有的跨语言或跨模式领域的预训练方法，导致 CCR 中的两个不一致问题: 跨语言风格的方法受到模式内错误传播的影响，导致整个数据集中跨语言的召回性能不一致。具有跨模态风格的方法存在多模态优化方向偏差，导致每个实例中语言间的排名不一致，Recall@K 不能反映这一点。为了解决这些问题，我们提出了一种简单而有效的1-To-K 对比学习方法，该方法对每种语言一视同仁，消除了错误传播和优化偏差。此外，我们提出了一个新的评估指标，平均秩方差(MRV) ，以反映秩不一致的语言在每个实例。在四个 CCR 数据集上的大量实验表明，我们的方法利用较小规模的预训练数据提高了召回率和 MRV，实现了新的技术水平。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Improving+the+Consistency+in+Cross-Lingual+Cross-Modal+Retrieval+with+1-to-K+Contrastive+Learning)|0| |[CheatAgent: Attacking LLM-Empowered Recommender Systems via LLM Agent](https://doi.org/10.1145/3637528.3671837)|LiangBo Ning, Shijie Wang, Wenqi Fan, Qing Li, Xin Xu, Hao Chen, Feiran Huang|; Jinan University, Guangzhou, China; The Hong Kong Polytechnic University, Hong Kong, China|Recently, Large Language Model (LLM)-empowered recommender systems (RecSys) have brought significant advances in personalized user experience and have attracted considerable attention. Despite the impressive progress, the research question regarding the safety vulnerability of LLM-empowered RecSys still remains largely under-investigated. Given the security and privacy concerns, it is more practical to focus on attacking the black-box RecSys, where attackers can only observe the system's inputs and outputs. However, traditional attack approaches employing reinforcement learning (RL) agents are not effective for attacking LLM-empowered RecSys due to the limited capabilities in processing complex textual inputs, planning, and reasoning. On the other hand, LLMs provide unprecedented opportunities to serve as attack agents to attack RecSys because of their impressive capability in simulating human-like decision-making processes. Therefore, in this paper, we propose a novel attack framework called CheatAgent by harnessing the human-like capabilities of LLMs, where an LLM-based agent is developed to attack LLM-Empowered RecSys. Specifically, our method first identifies the insertion position for maximum impact with minimal input modification. After that, the LLM agent is designed to generate adversarial perturbations to insert at target positions. To further improve the quality of generated perturbations, we utilize the prompt tuning technique to improve attacking strategies via feedback from the victim RecSys iteratively. Extensive experiments across three real-world datasets demonstrate the effectiveness of our proposed attacking method.|近年来，大语言模型(LLM)授权的推荐系统(RecSys)在个性化用户体验方面取得了重大进展，引起了人们的广泛关注。尽管取得了令人印象深刻的进展，关于 LLM 授权的 RecSys 的安全漏洞的研究问题仍然在很大程度上没有得到充分的研究。考虑到安全和隐私问题，更实际的做法是集中攻击黑匣子 RecSys，攻击者只能观察系统的输入和输出。然而，传统的使用强化学习代理的攻击方法并不能有效地攻击具有 LLM 授权的 RecSys，因为它在处理复杂的文本输入、规划和推理方面的能力有限。另一方面，LLM 提供了前所未有的机会作为攻击代理攻击 RecSys，因为它们在模拟类人决策过程方面具有令人印象深刻的能力。因此，本文提出了一种新的攻击框架——欺骗代理(CheatAgent) ，利用 LLM 的类人功能，开发了一种基于 LLM 的代理来攻击 LLM 授权的 RecSys。具体来说，我们的方法首先确定插入位置，以便在最小的输入修改下获得最大的影响。在此之后，LLM 代理被设计为产生对抗性扰动插入到目标位置。为了进一步提高生成扰动的质量，我们利用快速调整技术，通过迭代地从受害者 RecSys 反馈来改进攻击策略。通过对三个实际数据集的大量实验证明了我们提出的攻击方法的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=CheatAgent:+Attacking+LLM-Empowered+Recommender+Systems+via+LLM+Agent)|0| -|[Reliable Confidence Intervals for Information Retrieval Evaluation Using Generative A.I](https://doi.org/10.1145/3637528.3671883)|Harrie Oosterhuis, Rolf Jagerman, Zhen Qin, Xuanhui Wang, Michael Bendersky|Google Research & Radboud University, Amsterdam, Netherlands; Google Research, Mountain View, USA; Google Research, New York City, USA; Google Research, Amsterdam, Netherlands|The traditional evaluation of information retrieval (IR) systems is generally very costly as it requires manual relevance annotation from human experts. Recent advancements in generative artificial intelligence -specifically large language models (LLMs)- can generate relevance annotations at an enormous scale with relatively small computational costs. Potentially, this could alleviate the costs traditionally associated with IR evaluation and make it applicable to numerous low-resource applications. However, generated relevance annotations are not immune to (systematic) errors, and as a result, directly using them for evaluation produces unreliable results. In this work, we propose two methods based on prediction-powered inference and conformal risk control that utilize computer-generated relevance annotations to place reliable confidence intervals (CIs) around IR evaluation metrics. Our proposed methods require a small number of reliable annotations from which the methods can statistically analyze the errors in the generated annotations. Using this information, we can place CIs around evaluation metrics with strong theoretical guarantees. Unlike existing approaches, our conformal risk control method is specifically designed for ranking metrics and can vary its CIs per query and document. Our experimental results show that our CIs accurately capture both the variance and bias in evaluation based on LLM annotations, better than the typical empirical bootstrapping estimates. We hope our contributions bring reliable evaluation to the many IR applications where this was traditionally infeasible.|传统的信息检索系统评估通常是非常昂贵的，因为它需要人类专家的手工相关注释。生成性人工智能的最新进展——特别是大语言模型(LLM)——能够以相对较小的计算成本产生大规模的相关注释。这可能会减轻传统上与 IR 评估相关的成本，并使其适用于许多低资源的应用程序。然而，生成的相关性注释不能免疫(系统)错误，因此，直接使用它们进行评估会产生不可靠的结果。在这项工作中，我们提出了两种基于预测推理和保形风险控制的方法，利用计算机生成的相关性注释，将可靠的置信区间(CI)放置在 IR 评估指标周围。我们提出的方法需要少量可靠的注释，这些方法可以从中统计分析生成的注释中的错误。利用这些信息，我们可以将 CI 放在具有强大理论保证的评估指标周围。与现有的方法不同，我们的适形风险控制方法是专门为排序指标而设计的，并且可以根据查询和文档改变其 CI。我们的实验结果表明，我们的 CI 能够准确地捕获基于 LLM 注释的评估中的方差和偏差，比典型的经验自举估计更好。我们希望我们的贡献能够为许多传统上不可行的 IR 应用带来可靠的评估。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Reliable+Confidence+Intervals+for+Information+Retrieval+Evaluation+Using+Generative+A.I)|0| -|[How Powerful is Graph Filtering for Recommendation](https://doi.org/10.1145/3637528.3671789)|Shaowen Peng, Xin Liu, Kazunari Sugiyama, Tsunenori Mine|Osaka Seikei University Osaka, Japan; National Institute of Advanced Industrial Science and Technology Tokyo, Japan; Kyushu University Fukuoka, Japan; NARA Institute of Science and Technology, Nara, Japan|It has been shown that the effectiveness of graph convolutional network (GCN) for recommendation is attributed to the spectral graph filtering. Most GCN-based methods consist of a graph filter or followed by a low-rank mapping optimized based on supervised training. However, we show two limitations suppressing the power of graph filtering: (1) Lack of generality. Due to the varied noise distribution, graph filters fail to denoise sparse data where noise is scattered across all frequencies, while supervised training results in worse performance on dense data where noise is concentrated in middle frequencies that can be removed by graph filters without training. (2) Lack of expressive power. We theoretically show that linear GCN (LGCN) that is effective on collaborative filtering (CF) cannot generate arbitrary embeddings, implying the possibility that optimal data representation might be unreachable. To tackle the first limitation, we show close relation between noise distribution and the sharpness of spectrum where a sharper spectral distribution is more desirable causing data noise to be separable from important features without training. Based on this observation, we propose a generalized graph normalization (G2N) with hyperparameters adjusting the sharpness of spectral distribution in order to redistribute data noise to assure that it can be removed by graph filtering without training. As for the second limitation, we propose an individualized graph filter (IGF) adapting to the different confidence levels of the user preference that interactions can reflect, which is proved to be able to generate arbitrary embeddings. By simplifying LGCN, we further propose a simplified graph filtering for CF (SGFCF) which only requires the top-K singular values for recommendation. Finally, experimental results on four datasets with different density settings demonstrate the effectiveness and efficiency of our proposed methods.|研究表明，图卷积网络(GCN)的推荐有效性归功于谱图滤波。大多数基于 GCN 的方法包括一个图滤波器或后跟一个基于监督训练优化的低秩映射。然而，我们发现了抑制图滤波能力的两个限制: (1)缺乏通用性。由于噪声分布的多样性，图形滤波器在噪声散布于所有频率的稀疏数据中无法去噪，而监督训练在噪声集中于中间频率的稠密数据中效果较差，图形滤波器不需要训练就可以去除噪声。(2)缺乏表达能力。我们从理论上证明了对协同过滤有效的线性 GCN (LGCN)不能产生任意的嵌入，这意味着最佳数据表示可能无法实现。为了解决第一个局限性，我们展示了噪声分布和频谱清晰度之间的密切关系，其中更清晰的频谱分布更可取，使得数据噪声可以不经训练地从重要特征中分离出来。在此基础上，我们提出了一种带有超参数的广义图归一化算法(G2N) ，该算法通过调整光谱分布的清晰度来重新分布数据噪声，以保证不需要训练就可以通过图滤波去除噪声。针对第二个限制，我们提出了一种个性化图形滤波器(IGF) ，它能够适应交互所反映的用户偏好的不同置信水平，并且能够产生任意的嵌入。通过简化 LGCN，我们进一步提出了一种简化的 CF (SGFCF)图形滤波器，它只需要最高 K 的奇异值作为推荐值。最后，在四个不同密度设置的数据集上的实验结果表明了本文方法的有效性和高效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=How+Powerful+is+Graph+Filtering+for+Recommendation)|0| -|[STEMO: Early Spatio-temporal Forecasting with Multi-Objective Reinforcement Learning](https://doi.org/10.1145/3637528.3671922)|Wei Shao, Yufan Kang, Ziyan Peng, Xiao Xiao, Lei Wang, Yuhui Yang, Flora D. Salim|Data61, CSIRO, Clayton, Victoria, Australia; Xidian University, Xi'an, China; RMIT University, Melbourne, Victoria, Australia; University of New South Wales, Sydney, Australia; Zhejiang University, Hangzhou, China|Accuracy and timeliness are indeed often conflicting goals in predictiontasks. Premature predictions may yield a higher rate of false alarms, whereasdelaying predictions to gather more information can render them too late to beuseful. In applications such as wildfires, crimes, and traffic jams, timelyforecasting are vital for safeguarding human life and property. Consequently,finding a balance between accuracy and timeliness is crucial. In this paper, wepropose an early spatio-temporal forecasting model based on Multi-Objectivereinforcement learning that can either implement an optimal policy given apreference or infer the preference based on a small number of samples. Themodel addresses two primary challenges: 1) enhancing the accuracy of earlyforecasting and 2) providing the optimal policy for determining the mostsuitable prediction time for each area. Our method demonstrates superiorperformance on three large-scale real-world datasets, surpassing existingmethods in early spatio-temporal forecasting tasks.|在预测任务中，准确性和及时性往往是矛盾的目标。过早的预测可能会产生更高的错误警报率，而为了收集更多信息而推迟预测可能会使预测太晚而无法发挥作用。在野火、犯罪和交通堵塞等应用中，及时预报对保护人类生命和财产至关重要。因此，在准确性和及时性之间找到一个平衡点是至关重要的。本文提出了一种基于多目标强化学习的早期时空预测模型，该模型既可以实现给定偏好的最优策略，也可以基于少量样本进行偏好推断。该模型解决了两个主要的挑战: 1)提高早期预测的准确性和2)为确定每个地区最合适的预测时间提供最优策略。该方法在三个大规模真实世界数据集上表现出优越的性能，在早期时空预测任务中优于现有方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=STEMO:+Early+Spatio-temporal+Forecasting+with+Multi-Objective+Reinforcement+Learning)|0| -|[Marrying Dialogue Systems with Data Visualization: Interactive Data Visualization Generation from Natural Language Conversations](https://doi.org/10.1145/3637528.3671935)|Yuanfeng Song, Xuefang Zhao, Raymond ChiWing Wong|AI Group, WeBank Co., Ltd., Shenzhen, China; The Hong Kong University of Science and Technology, Hong Kong, China|Data visualization (DV) has become the prevailing tool in the market due to its effectiveness into illustrating insights in vast amounts of data. To lower the barrier of using DVs, automatic DV tasks, such as natural language question (NLQ) to visualization translation (formally called text-to-vis), have been investigated in the research community. However, text-to-vis assumes the NLQ to be well-organized and expressed in a single sentence. However, in real-world settings, complex DV is needed through consecutive exchanges between the DV system and the users. In this paper, we propose a new task named CoVis, short for Conversational text-to-Visualization, aiming at constructing DVs through a series of interactions between users and the system. Since it is the task which has not been studied in the literature, we first build a benchmark dataset named Dial-NVBench, including dialogue sessions with a sequence of queries from a user and responses from the system. The ultimate goal of each dialogue session is to create a suitable DV. However, this process can contain diverse dialogue queries, such as seeking information about the dataset, manipulating parts of the data, and visualizing the data. Then, we propose a multi-modal neural network named MMCoVisNet to answer these DV-related queries. In particular, MMCoVisNet first fully understands the dialogue context and determines the corresponding responses. Then, it uses adaptive decoders to provide the appropriate replies: (i) a straightforward text decoder is used to produce general responses, (ii) an SQL-form decoder is applied to synthesize data querying responses, and (iii) a DV-form decoder tries to construct the appropriate DVs. We comparatively evaluate MMCoVisNet with other baselines over our proposed benchmark dataset. Experimental results validate that MMCoVisNet performs better than existing baselines and achieves a state-of-the-art performance.|数据可视化(DV)已成为市场上流行的工具，因为它能有效地用大量数据来说明见解。为了降低数字视频的使用障碍，自动数字视频任务，如自然语言问题(NLQ)到可视化翻译(正式称为文本到视觉) ，已经在研究界进行了研究。然而，文本-视觉假设 NLQ 是组织良好的，并表达在一个单一的句子。然而，在现实世界中，复杂的 DV 需要通过 DV 系统与用户之间的连续交换。本文提出了一个新的任务 CoVis，即会话文本到可视化(Conversational text-to-Visualization) ，旨在通过用户与系统之间的一系列交互来构建 DVs。本文首先构建了一个基准数据集 Dial-NVBench，包括用户的对话会话和系统的响应。每个对话会议的最终目标是创建一个合适的 DV。但是，这个过程可以包含不同的对话查询，例如查找有关数据集的信息、操作部分数据和可视化数据。然后，提出了一种多模态神经网络 MMCoVisNet 来回答这些与 DV 相关的查询。具体来说，MMCoVisNet 首先完全理解对话语境并确定相应的响应。然后，它使用自适应解码器提供适当的答复: (i)一个简单的文本解码器用于产生一般的响应，(ii)一个 SQL 形式的解码器用于合成数据查询响应，和(iii)一个 DV 形式的解码器试图构造适当的 DVs。我们比较评估 MMCoVisNet 与其他基线在我们提出的基准数据集。实验结果验证了 MMCoVisNet 的性能优于现有的基准线，达到了最先进的性能水平。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Marrying+Dialogue+Systems+with+Data+Visualization:+Interactive+Data+Visualization+Generation+from+Natural+Language+Conversations)|0| -|[Towards Robust Recommendation via Decision Boundary-aware Graph Contrastive Learning](https://doi.org/10.1145/3637528.3671661)|Jiakai Tang, Sunhao Dai, Zexu Sun, Xu Chen, Jun Xu, Wenhui Yu, Lantao Hu, Peng Jiang, Han Li|Gaoling School of Artificial Intelligence, Renmin University of China, Beijing, China; Kuaishou Technology, Beijing, China|In recent years, graph contrastive learning (GCL) has received increasing attention in recommender systems due to its effectiveness in reducing bias caused by data sparsity. However, most existing GCL models rely on heuristic approaches and usually assume entity independence when constructing contrastive views. We argue that these methods struggle to strike a balance between semantic invariance and view hardness across the dynamic training process, both of which are critical factors in graph contrastive learning. To address the above issues, we propose a novel GCL-based recommendation framework RGCL, which effectively maintains the semantic invariance of contrastive pairs and dynamically adapts as the model capability evolves through the training process. Specifically, RGCL first introduces decision boundary-aware adversarial perturbations to constrain the exploration space of contrastive augmented views, avoiding the decrease of task-specific information. Furthermore, to incorporate global user-user and item-item collaboration relationships for guiding on the generation of hard contrastive views, we propose an adversarial-contrastive learning objective to construct a relation-aware view-generator. Besides, considering that unsupervised GCL could potentially narrower margins between data points and the decision boundary, resulting in decreased model robustness, we introduce the adversarial examples based on maximum perturbations to achieve margin maximization. We also provide theoretical analyses on the effectiveness of our designs. Through extensive experiments on five public datasets, we demonstrate the superiority of RGCL compared against twelve baseline models.|近年来，图形对比学习(GCL)由于能够有效地减少数据稀疏带来的偏差，在推荐系统中得到了越来越多的关注。然而，大多数现有的 GCL 模型依赖于启发式方法，并且在构造对比视图时通常假设实体独立。我们认为这些方法在动态训练过程中很难在语义不变性和视图硬度之间取得平衡，这两者都是图形对比学习的关键因素。为了解决上述问题，我们提出了一种新的基于 GCL 的推荐框架 RGCL，该框架有效地保持了对比对的语义不变性，并随着模型能力在训练过程中的发展而动态适应。具体来说，RGCL 首先引入了决策边界感知的对抗扰动来约束对比增广视图的探索空间，避免了任务特定信息的减少。此外，为了整合全局用户-用户和项目-项目协作关系来指导硬对比视图的生成，我们提出了一个对抗对比学习目标来构造一个关系感知视图生成器。此外，考虑到无监督的 GCL 可能会缩小数据点与决策边界之间的利润率，从而降低模型的稳健性，我们引入了基于最大扰动的对抗性例子，以实现利润率最大化。我们还对我们的设计的有效性提供了理论分析。通过对5个公共数据集的大量实验，我们证明了 RgCL 相对于十二个基线模型的优越性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Towards+Robust+Recommendation+via+Decision+Boundary-aware+Graph+Contrastive+Learning)|0| +|[Reliable Confidence Intervals for Information Retrieval Evaluation Using Generative A.I](https://doi.org/10.1145/3637528.3671883)|Harrie Oosterhuis, Rolf Jagerman, Zhen Qin, Xuanhui Wang, Michael Bendersky|Google Research, New York City, USA; Google Research, Amsterdam, Netherlands; Google Research & Radboud University, Amsterdam, Netherlands; Google Research, Mountain View, USA|The traditional evaluation of information retrieval (IR) systems is generally very costly as it requires manual relevance annotation from human experts. Recent advancements in generative artificial intelligence -specifically large language models (LLMs)- can generate relevance annotations at an enormous scale with relatively small computational costs. Potentially, this could alleviate the costs traditionally associated with IR evaluation and make it applicable to numerous low-resource applications. However, generated relevance annotations are not immune to (systematic) errors, and as a result, directly using them for evaluation produces unreliable results. In this work, we propose two methods based on prediction-powered inference and conformal risk control that utilize computer-generated relevance annotations to place reliable confidence intervals (CIs) around IR evaluation metrics. Our proposed methods require a small number of reliable annotations from which the methods can statistically analyze the errors in the generated annotations. Using this information, we can place CIs around evaluation metrics with strong theoretical guarantees. Unlike existing approaches, our conformal risk control method is specifically designed for ranking metrics and can vary its CIs per query and document. Our experimental results show that our CIs accurately capture both the variance and bias in evaluation based on LLM annotations, better than the typical empirical bootstrapping estimates. We hope our contributions bring reliable evaluation to the many IR applications where this was traditionally infeasible.|传统的信息检索系统评估通常是非常昂贵的，因为它需要人类专家的手工相关注释。生成性人工智能的最新进展——特别是大语言模型(LLM)——能够以相对较小的计算成本产生大规模的相关注释。这可能会减轻传统上与 IR 评估相关的成本，并使其适用于许多低资源的应用程序。然而，生成的相关性注释不能免疫(系统)错误，因此，直接使用它们进行评估会产生不可靠的结果。在这项工作中，我们提出了两种基于预测推理和保形风险控制的方法，利用计算机生成的相关性注释，将可靠的置信区间(CI)放置在 IR 评估指标周围。我们提出的方法需要少量可靠的注释，这些方法可以从中统计分析生成的注释中的错误。利用这些信息，我们可以将 CI 放在具有强大理论保证的评估指标周围。与现有的方法不同，我们的适形风险控制方法是专门为排序指标而设计的，并且可以根据查询和文档改变其 CI。我们的实验结果表明，我们的 CI 能够准确地捕获基于 LLM 注释的评估中的方差和偏差，比典型的经验自举估计更好。我们希望我们的贡献能够为许多传统上不可行的 IR 应用带来可靠的评估。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Reliable+Confidence+Intervals+for+Information+Retrieval+Evaluation+Using+Generative+A.I)|0| +|[How Powerful is Graph Filtering for Recommendation](https://doi.org/10.1145/3637528.3671789)|Shaowen Peng, Xin Liu, Kazunari Sugiyama, Tsunenori Mine|Osaka Seikei University Osaka, Japan; National Institute of Advanced Industrial Science and Technology Tokyo, Japan; NARA Institute of Science and Technology, Nara, Japan; Kyushu University Fukuoka, Japan|It has been shown that the effectiveness of graph convolutional network (GCN) for recommendation is attributed to the spectral graph filtering. Most GCN-based methods consist of a graph filter or followed by a low-rank mapping optimized based on supervised training. However, we show two limitations suppressing the power of graph filtering: (1) Lack of generality. Due to the varied noise distribution, graph filters fail to denoise sparse data where noise is scattered across all frequencies, while supervised training results in worse performance on dense data where noise is concentrated in middle frequencies that can be removed by graph filters without training. (2) Lack of expressive power. We theoretically show that linear GCN (LGCN) that is effective on collaborative filtering (CF) cannot generate arbitrary embeddings, implying the possibility that optimal data representation might be unreachable. To tackle the first limitation, we show close relation between noise distribution and the sharpness of spectrum where a sharper spectral distribution is more desirable causing data noise to be separable from important features without training. Based on this observation, we propose a generalized graph normalization (G2N) with hyperparameters adjusting the sharpness of spectral distribution in order to redistribute data noise to assure that it can be removed by graph filtering without training. As for the second limitation, we propose an individualized graph filter (IGF) adapting to the different confidence levels of the user preference that interactions can reflect, which is proved to be able to generate arbitrary embeddings. By simplifying LGCN, we further propose a simplified graph filtering for CF (SGFCF) which only requires the top-K singular values for recommendation. Finally, experimental results on four datasets with different density settings demonstrate the effectiveness and efficiency of our proposed methods.|研究表明，图卷积网络(GCN)的推荐有效性归功于谱图滤波。大多数基于 GCN 的方法包括一个图滤波器或后跟一个基于监督训练优化的低秩映射。然而，我们发现了抑制图滤波能力的两个限制: (1)缺乏通用性。由于噪声分布的多样性，图形滤波器在噪声散布于所有频率的稀疏数据中无法去噪，而监督训练在噪声集中于中间频率的稠密数据中效果较差，图形滤波器不需要训练就可以去除噪声。(2)缺乏表达能力。我们从理论上证明了对协同过滤有效的线性 GCN (LGCN)不能产生任意的嵌入，这意味着最佳数据表示可能无法实现。为了解决第一个局限性，我们展示了噪声分布和频谱清晰度之间的密切关系，其中更清晰的频谱分布更可取，使得数据噪声可以不经训练地从重要特征中分离出来。在此基础上，我们提出了一种带有超参数的广义图归一化算法(G2N) ，该算法通过调整光谱分布的清晰度来重新分布数据噪声，以保证不需要训练就可以通过图滤波去除噪声。针对第二个限制，我们提出了一种个性化图形滤波器(IGF) ，它能够适应交互所反映的用户偏好的不同置信水平，并且能够产生任意的嵌入。通过简化 LGCN，我们进一步提出了一种简化的 CF (SGFCF)图形滤波器，它只需要最高 K 的奇异值作为推荐值。最后，在四个不同密度设置的数据集上的实验结果表明了本文方法的有效性和高效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=How+Powerful+is+Graph+Filtering+for+Recommendation)|0| +|[STEMO: Early Spatio-temporal Forecasting with Multi-Objective Reinforcement Learning](https://doi.org/10.1145/3637528.3671922)|Wei Shao, Yufan Kang, Ziyan Peng, Xiao Xiao, Lei Wang, Yuhui Yang, Flora D. Salim|University of New South Wales, Sydney, Australia; Data61, CSIRO, Clayton, Victoria, Australia; Zhejiang University, Hangzhou, China; Xidian University, Xi'an, China; RMIT University, Melbourne, Victoria, Australia|Accuracy and timeliness are indeed often conflicting goals in predictiontasks. Premature predictions may yield a higher rate of false alarms, whereasdelaying predictions to gather more information can render them too late to beuseful. In applications such as wildfires, crimes, and traffic jams, timelyforecasting are vital for safeguarding human life and property. Consequently,finding a balance between accuracy and timeliness is crucial. In this paper, wepropose an early spatio-temporal forecasting model based on Multi-Objectivereinforcement learning that can either implement an optimal policy given apreference or infer the preference based on a small number of samples. Themodel addresses two primary challenges: 1) enhancing the accuracy of earlyforecasting and 2) providing the optimal policy for determining the mostsuitable prediction time for each area. Our method demonstrates superiorperformance on three large-scale real-world datasets, surpassing existingmethods in early spatio-temporal forecasting tasks.|在预测任务中，准确性和及时性往往是矛盾的目标。过早的预测可能会产生更高的错误警报率，而为了收集更多信息而推迟预测可能会使预测太晚而无法发挥作用。在野火、犯罪和交通堵塞等应用中，及时预报对保护人类生命和财产至关重要。因此，在准确性和及时性之间找到一个平衡点是至关重要的。本文提出了一种基于多目标强化学习的早期时空预测模型，该模型既可以实现给定偏好的最优策略，也可以基于少量样本进行偏好推断。该模型解决了两个主要的挑战: 1)提高早期预测的准确性和2)为确定每个地区最合适的预测时间提供最优策略。该方法在三个大规模真实世界数据集上表现出优越的性能，在早期时空预测任务中优于现有方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=STEMO:+Early+Spatio-temporal+Forecasting+with+Multi-Objective+Reinforcement+Learning)|0| +|[Marrying Dialogue Systems with Data Visualization: Interactive Data Visualization Generation from Natural Language Conversations](https://doi.org/10.1145/3637528.3671935)|Yuanfeng Song, Xuefang Zhao, Raymond ChiWing Wong|The Hong Kong University of Science and Technology, Hong Kong, China; AI Group, WeBank Co., Ltd., Shenzhen, China|Data visualization (DV) has become the prevailing tool in the market due to its effectiveness into illustrating insights in vast amounts of data. To lower the barrier of using DVs, automatic DV tasks, such as natural language question (NLQ) to visualization translation (formally called text-to-vis), have been investigated in the research community. However, text-to-vis assumes the NLQ to be well-organized and expressed in a single sentence. However, in real-world settings, complex DV is needed through consecutive exchanges between the DV system and the users. In this paper, we propose a new task named CoVis, short for Conversational text-to-Visualization, aiming at constructing DVs through a series of interactions between users and the system. Since it is the task which has not been studied in the literature, we first build a benchmark dataset named Dial-NVBench, including dialogue sessions with a sequence of queries from a user and responses from the system. The ultimate goal of each dialogue session is to create a suitable DV. However, this process can contain diverse dialogue queries, such as seeking information about the dataset, manipulating parts of the data, and visualizing the data. Then, we propose a multi-modal neural network named MMCoVisNet to answer these DV-related queries. In particular, MMCoVisNet first fully understands the dialogue context and determines the corresponding responses. Then, it uses adaptive decoders to provide the appropriate replies: (i) a straightforward text decoder is used to produce general responses, (ii) an SQL-form decoder is applied to synthesize data querying responses, and (iii) a DV-form decoder tries to construct the appropriate DVs. We comparatively evaluate MMCoVisNet with other baselines over our proposed benchmark dataset. Experimental results validate that MMCoVisNet performs better than existing baselines and achieves a state-of-the-art performance.|数据可视化(DV)已成为市场上流行的工具，因为它能有效地用大量数据来说明见解。为了降低数字视频的使用障碍，自动数字视频任务，如自然语言问题(NLQ)到可视化翻译(正式称为文本到视觉) ，已经在研究界进行了研究。然而，文本-视觉假设 NLQ 是组织良好的，并表达在一个单一的句子。然而，在现实世界中，复杂的 DV 需要通过 DV 系统与用户之间的连续交换。本文提出了一个新的任务 CoVis，即会话文本到可视化(Conversational text-to-Visualization) ，旨在通过用户与系统之间的一系列交互来构建 DVs。本文首先构建了一个基准数据集 Dial-NVBench，包括用户的对话会话和系统的响应。每个对话会议的最终目标是创建一个合适的 DV。但是，这个过程可以包含不同的对话查询，例如查找有关数据集的信息、操作部分数据和可视化数据。然后，提出了一种多模态神经网络 MMCoVisNet 来回答这些与 DV 相关的查询。具体来说，MMCoVisNet 首先完全理解对话语境并确定相应的响应。然后，它使用自适应解码器提供适当的答复: (i)一个简单的文本解码器用于产生一般的响应，(ii)一个 SQL 形式的解码器用于合成数据查询响应，和(iii)一个 DV 形式的解码器试图构造适当的 DVs。我们比较评估 MMCoVisNet 与其他基线在我们提出的基准数据集。实验结果验证了 MMCoVisNet 的性能优于现有的基准线，达到了最先进的性能水平。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Marrying+Dialogue+Systems+with+Data+Visualization:+Interactive+Data+Visualization+Generation+from+Natural+Language+Conversations)|0| +|[Towards Robust Recommendation via Decision Boundary-aware Graph Contrastive Learning](https://doi.org/10.1145/3637528.3671661)|Jiakai Tang, Sunhao Dai, Zexu Sun, Xu Chen, Jun Xu, Wenhui Yu, Lantao Hu, Peng Jiang, Han Li|Kuaishou Technology, Beijing, China; Gaoling School of Artificial Intelligence, Renmin University of China, Beijing, China|In recent years, graph contrastive learning (GCL) has received increasing attention in recommender systems due to its effectiveness in reducing bias caused by data sparsity. However, most existing GCL models rely on heuristic approaches and usually assume entity independence when constructing contrastive views. We argue that these methods struggle to strike a balance between semantic invariance and view hardness across the dynamic training process, both of which are critical factors in graph contrastive learning. To address the above issues, we propose a novel GCL-based recommendation framework RGCL, which effectively maintains the semantic invariance of contrastive pairs and dynamically adapts as the model capability evolves through the training process. Specifically, RGCL first introduces decision boundary-aware adversarial perturbations to constrain the exploration space of contrastive augmented views, avoiding the decrease of task-specific information. Furthermore, to incorporate global user-user and item-item collaboration relationships for guiding on the generation of hard contrastive views, we propose an adversarial-contrastive learning objective to construct a relation-aware view-generator. Besides, considering that unsupervised GCL could potentially narrower margins between data points and the decision boundary, resulting in decreased model robustness, we introduce the adversarial examples based on maximum perturbations to achieve margin maximization. We also provide theoretical analyses on the effectiveness of our designs. Through extensive experiments on five public datasets, we demonstrate the superiority of RGCL compared against twelve baseline models.|近年来，图形对比学习(GCL)由于能够有效地减少数据稀疏带来的偏差，在推荐系统中得到了越来越多的关注。然而，大多数现有的 GCL 模型依赖于启发式方法，并且在构造对比视图时通常假设实体独立。我们认为这些方法在动态训练过程中很难在语义不变性和视图硬度之间取得平衡，这两者都是图形对比学习的关键因素。为了解决上述问题，我们提出了一种新的基于 GCL 的推荐框架 RGCL，该框架有效地保持了对比对的语义不变性，并随着模型能力在训练过程中的发展而动态适应。具体来说，RGCL 首先引入了决策边界感知的对抗扰动来约束对比增广视图的探索空间，避免了任务特定信息的减少。此外，为了整合全局用户-用户和项目-项目协作关系来指导硬对比视图的生成，我们提出了一个对抗对比学习目标来构造一个关系感知视图生成器。此外，考虑到无监督的 GCL 可能会缩小数据点与决策边界之间的利润率，从而降低模型的稳健性，我们引入了基于最大扰动的对抗性例子，以实现利润率最大化。我们还对我们的设计的有效性提供了理论分析。通过对5个公共数据集的大量实验，我们证明了 RgCL 相对于十二个基线模型的优越性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Towards+Robust+Recommendation+via+Decision+Boundary-aware+Graph+Contrastive+Learning)|0| |[Reinforced Compressive Neural Architecture Search for Versatile Adversarial Robustness](https://doi.org/10.1145/3637528.3672009)|Dingrong Wang, Hitesh Sapkota, Zhiqiang Tao, Qi Yu|Rochester Institute of Technology, Rochester, NY, USA; Amazon Inc., Sunnyvale, CA, USA|Prior research on neural architecture search (NAS) for adversarial robustness has revealed that a lightweight and adversarially robust sub-network could exist in a non-robust large teacher network. Such a sub-network is generally discovered based on heuristic rules to perform neural architecture search. However, heuristic rules are inadequate to handle diverse adversarial attacks and different "teacher" network capacity. To address this key challenge, we propose Reinforced Compressive Neural Architecture Search (RC-NAS), aiming to achieve Versatile Adversarial Robustness. Specifically, we define novel task settings that compose datasets, adversarial attacks, and teacher network configuration. Given diverse tasks, we develop an innovative dual-level training paradigm that consists of a meta-training and a fine-tuning phase to effectively expose the RL agent to diverse attack scenarios (in meta-training), and make it adapt quickly to locate an optimal sub-network (in fine-tuning) for previously unseen scenarios. Experiments show that our framework could achieve adaptive compression towards different initial teacher networks, datasets, and adversarial attacks, resulting in more lightweight and adversarially robust architectures. We also provide a theoretical analysis to explain why the reinforcement learning (RL)-guided adversarial architectural search helps adversarial robustness over standard adversarial training methods.|针对对抗性鲁棒性的神经网络结构搜索(NAS)研究表明，在非鲁棒的大型教师网络中存在一个轻量级的对抗性鲁棒子网络。这样的子网络通常是基于启发式规则发现的，用于执行神经结构搜索。然而，启发式规则不足以处理不同的对手攻击和不同的“教师”网络容量。为了解决这一关键问题，我们提出了增强压缩神经结构搜索(RC-NAS) ，旨在实现通用的对抗鲁棒性。具体来说，我们定义了组成数据集、对抗性攻击和教师网络配置的新任务设置。考虑到不同的任务，我们开发了一个创新的双级训练范例，其中包括元训练和微调阶段，以有效地将 RL 代理暴露于不同的攻击场景(在元训练中) ，并使其快速适应定位最佳子网络(在微调中)以前看不见的场景。实验表明，我们的框架能够针对不同的初始教师网络、数据集和对抗性攻击实现自适应压缩，从而产生更轻量级和对抗性更强的体系结构。我们还提供了一个理论分析来解释为什么强化学习指导的对抗架构搜索比标准的对抗训练方法有助于对抗的稳健性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Reinforced+Compressive+Neural+Architecture+Search+for+Versatile+Adversarial+Robustness)|0| |[Routing Evidence for Unseen Actions in Video Moment Retrieval](https://doi.org/10.1145/3637528.3671693)|Guolong Wang, Xun Wu, Zheng Qin, Liangliang Shi|; Tsinghua University, Beijing, China|Video moment retrieval (VMR) is a cutting-edge vision-language task locating a segment in a video according to the query. Though the methods have achieved significant performance, they assume that training and testing samples share the same action types, hindering real-world application. In this paper, we specifically consider a new problem: video moment retrieval by queries with unseen actions. We propose a plug-and-play structure, Routing Evidence (RE), with multiple evidence-learning heads and dynamically route one to locate a sentence with an unseen action. Each evidence-learning head estimates the uncertainty while regressing timestamps. We formulate the evidence distribution by a Normal-Inverse Gamma function and design a router to select the most appropriate distribution for a sample. Empirically, we study the efficacy of RE on three updated databases where training and testing samples contain different action types. We find that RE outperforms other state-of-the-art methods with a more robust predictor. Code and data will be available at https://github.com/dieuroi/Routing-Evidence.|视频矩检索(VMR)是一种根据查询对视频片段进行定位的前沿视觉语言任务。尽管这些方法已经取得了显著的性能，但是它们假设训练和测试样本共享相同的动作类型，从而阻碍了真实世界的应用。在本文中，我们特别考虑了一个新的问题: 视频时刻检索的查询与看不见的行动。我们提出了一种即插即用的结构，路由证据(RE) ，具有多个证据学习头，并动态路由其中一个来定位一个看不见动作的句子。每个证据学习负责人在回归时间戳时估计不确定性。我们利用一个正反伽玛函数来表达证据分布，并设计一个路由器来选择最适合样本的分布。通过实证研究，我们在三个更新的训练和测试样本包含不同行为类型的数据库上研究了 RE 的效能。我们发现 RE 比其他最先进的方法具有更强大的预测器。代码和数据将在 https://github.com/dieuroi/routing-evidence 公布。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Routing+Evidence+for+Unseen+Actions+in+Video+Moment+Retrieval)|0| -|[Unveiling Vulnerabilities of Contrastive Recommender Systems to Poisoning Attacks](https://doi.org/10.1145/3637528.3671795)|Zongwei Wang, Junliang Yu, Min Gao, Hongzhi Yin, Bin Cui, Shazia W. Sadiq|Chongqing University, Chongqing, China; Peking University, Beijing, China; The University of Queensland, Brisbane, Australia|Contrastive learning (CL) has recently gained prominence in the domain of recommender systems due to its great ability to enhance recommendation accuracy and improve model robustness. Despite its advantages, this paper identifies a vulnerability of CL-based recommender systems that they are more susceptible to poisoning attacks aiming to promote individual items. Our analysis indicates that this vulnerability is attributed to the uniform spread of representations caused by the InfoNCE loss. Furthermore, theoretical and empirical evidence shows that optimizing this loss favors smooth spectral values of representations. This finding suggests that attackers could facilitate this optimization process of CL by encouraging a more uniform distribution of spectral values, thereby enhancing the degree of representation dispersion. With these insights, we attempt to reveal a potential poisoning attack against CL-based recommender systems, which encompasses a dual-objective framework: one that induces a smoother spectral value distribution to amplify the InfoNCE loss's inherent dispersion effect, named dispersion promotion; and the other that directly elevates the visibility of target items, named rank promotion. We validate the threats of our attack model through extensive experimentation on four datasets. By shedding light on these vulnerabilities, our goal is to advance the development of more robust CL-based recommender systems. The code is available at https://github.com/CoderWZW/ARLib.|对比学习由于具有提高推荐精度和增强模型鲁棒性的能力，近年来在推荐系统领域得到了广泛的应用。本文认为基于 CL 的推荐系统虽然具有一定的优势，但是它们更容易受到旨在推广个别项目的中毒攻击。我们的分析表明，这一漏洞是由于表示的统一传播造成的信息 NCE 的损失。此外，理论和经验证明表明，优化这种损失有利于表示的平滑光谱值。这一发现表明，攻击者可以通过鼓励谱值的更均匀分布来促进 CL 的优化过程，从而提高表示离散度。有了这些见解，我们试图揭示一种针对基于 CL 的推荐系统的潜在中毒攻击，其中包含一个双重目标框架: 一个是诱导更平滑的光谱值分布，以放大 InfoNCE 损失的固有分散效应，称为分散促进; 另一个是直接提高目标项目的可见性，称为等级促进。我们通过在四个数据集上的大量实验验证了我们的攻击模型的威胁性。通过阐明这些漏洞，我们的目标是推动开发更健壮的基于 CL 的推荐系统。密码可在 https://github.com/coderwzw/arlib 查阅。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Unveiling+Vulnerabilities+of+Contrastive+Recommender+Systems+to+Poisoning+Attacks)|0| +|[Unveiling Vulnerabilities of Contrastive Recommender Systems to Poisoning Attacks](https://doi.org/10.1145/3637528.3671795)|Zongwei Wang, Junliang Yu, Min Gao, Hongzhi Yin, Bin Cui, Shazia W. Sadiq|The University of Queensland, Brisbane, Australia; Peking University, Beijing, China; Chongqing University, Chongqing, China|Contrastive learning (CL) has recently gained prominence in the domain of recommender systems due to its great ability to enhance recommendation accuracy and improve model robustness. Despite its advantages, this paper identifies a vulnerability of CL-based recommender systems that they are more susceptible to poisoning attacks aiming to promote individual items. Our analysis indicates that this vulnerability is attributed to the uniform spread of representations caused by the InfoNCE loss. Furthermore, theoretical and empirical evidence shows that optimizing this loss favors smooth spectral values of representations. This finding suggests that attackers could facilitate this optimization process of CL by encouraging a more uniform distribution of spectral values, thereby enhancing the degree of representation dispersion. With these insights, we attempt to reveal a potential poisoning attack against CL-based recommender systems, which encompasses a dual-objective framework: one that induces a smoother spectral value distribution to amplify the InfoNCE loss's inherent dispersion effect, named dispersion promotion; and the other that directly elevates the visibility of target items, named rank promotion. We validate the threats of our attack model through extensive experimentation on four datasets. By shedding light on these vulnerabilities, our goal is to advance the development of more robust CL-based recommender systems. The code is available at https://github.com/CoderWZW/ARLib.|对比学习由于具有提高推荐精度和增强模型鲁棒性的能力，近年来在推荐系统领域得到了广泛的应用。本文认为基于 CL 的推荐系统虽然具有一定的优势，但是它们更容易受到旨在推广个别项目的中毒攻击。我们的分析表明，这一漏洞是由于表示的统一传播造成的信息 NCE 的损失。此外，理论和经验证明表明，优化这种损失有利于表示的平滑光谱值。这一发现表明，攻击者可以通过鼓励谱值的更均匀分布来促进 CL 的优化过程，从而提高表示离散度。有了这些见解，我们试图揭示一种针对基于 CL 的推荐系统的潜在中毒攻击，其中包含一个双重目标框架: 一个是诱导更平滑的光谱值分布，以放大 InfoNCE 损失的固有分散效应，称为分散促进; 另一个是直接提高目标项目的可见性，称为等级促进。我们通过在四个数据集上的大量实验验证了我们的攻击模型的威胁性。通过阐明这些漏洞，我们的目标是推动开发更健壮的基于 CL 的推荐系统。密码可在 https://github.com/coderwzw/arlib 查阅。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Unveiling+Vulnerabilities+of+Contrastive+Recommender+Systems+to+Poisoning+Attacks)|0| |[FedSAC: Dynamic Submodel Allocation for Collaborative Fairness in Federated Learning](https://doi.org/10.1145/3637528.3671748)|Zihui Wang, Zheng Wang, Lingjuan Lyu, Zhaopeng Peng, Zhicheng Yang, Chenglu Wen, Rongshan Yu, Cheng Wang, Xiaoliang Fan|; Sony AI, Zurich, Swaziland|Collaborative fairness stands as an essential element in federated learningto encourage client participation by equitably distributing rewards based onindividual contributions. Existing methods primarily focus on adjustinggradient allocations among clients to achieve collaborative fairness. However,they frequently overlook crucial factors such as maintaining consistency acrosslocal models and catering to the diverse requirements of high-contributingclients. This oversight inevitably decreases both fairness and model accuracyin practice. To address these issues, we propose FedSAC, a novel Federatedlearning framework with dynamic Submodel Allocation for Collaborative fairness,backed by a theoretical convergence guarantee. First, we present the concept of"bounded collaborative fairness (BCF)", which ensures fairness by tailoringrewards to individual clients based on their contributions. Second, toimplement the BCF, we design a submodel allocation module with a theoreticalguarantee of fairness. This module incentivizes high-contributing clients withhigh-performance submodels containing a diverse range of crucial neurons,thereby preserving consistency across local models. Third, we further develop adynamic aggregation module to adaptively aggregate submodels, ensuring theequitable treatment of low-frequency neurons and consequently enhancing overallmodel accuracy. Extensive experiments conducted on three public benchmarksdemonstrate that FedSAC outperforms all baseline methods in both fairness andmodel accuracy. We see this work as a significant step towards incentivizingbroader client participation in federated learning. The source code isavailable at https://github.com/wangzihuixmu/FedSAC.|协作公平是联合学习的一个重要组成部分，通过公平分配基于个人贡献的奖励来鼓励客户参与。现有的方法主要集中在调整客户间的梯度分配以实现协作公平。然而，他们经常忽略一些关键因素，例如保持当地模式的一致性以及满足高贡献客户的不同需求。这种疏忽不可避免地降低了实践中的公平性和模型的准确性。为了解决这些问题，我们提出了 FedSAC，这是一个新的联邦学习框架，具有动态子模型分配的协作公平性，在理论趋同保证的支持下。首先，我们提出了“有限协作公平(BCF)”的概念，它通过根据客户的贡献量身定制奖励来确保公平性。其次，为了实现 BCF，我们设计了一个具有公平性理论保证的子模型分配模块。该模块通过包含多种关键神经元的高性能子模型激励高贡献客户，从而保持局部模型之间的一致性。第三，我们进一步发展动态聚集模块，以自适应聚集子模型，确保公平的治疗低频神经元，从而提高整体模型的准确性。在三个公共基准上进行的大量实验表明，FedSAC 在公平性和模型准确性方面优于所有基准方法。我们认为这项工作是激励更广泛的客户参与联合学习的重要一步。源代码可以在 https://github.com/wangzihuixmu/fedsac 找到。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=FedSAC:+Dynamic+Submodel+Allocation+for+Collaborative+Fairness+in+Federated+Learning)|0| -|[Unifying Graph Convolution and Contrastive Learning in Collaborative Filtering](https://doi.org/10.1145/3637528.3671840)|Yihong Wu, Le Zhang, Fengran Mo, Tianyu Zhu, Weizhi Ma, JianYun Nie|MIIT Key Laboratory of Data Intelligence and Management, Beihang University, Beijing, China; Institute for AI Industry Research (AIR), Tsinghua University, Beijing, China; Université de Montréal, Montréal, Canada; Mila - Quebec AI Institute, Montréal, Canada|Graph-based models and contrastive learning have emerged as prominent methods in Collaborative Filtering (CF). While many existing models in CF incorporate these methods in their design, there seems to be a limited depth of analysis regarding the foundational principles behind them. This paper bridges graph convolution, a pivotal element of graph-based models, with contrastive learning through a theoretical framework. By examining the learning dynamics and equilibrium of the contrastive loss, we offer a fresh lens to understand contrastive learning via graph theory, emphasizing its capability to capture high-order connectivity. Building on this analysis, we further show that the graph convolutional layers often used in graph-based models are not essential for high-order connectivity modeling and might contribute to the risk of oversmoothing. Stemming from our findings, we introduce Simple Contrastive Collaborative Filtering (SCCF), a simple and effective algorithm based on a naive embedding model and a modified contrastive loss. The efficacy of the algorithm is demonstrated through extensive experiments across four public datasets. The experiment code is available at https://github.com/wu1hong/SCCF.|基于图表的模型和对比学习已经成为协同过滤研究的主要方法。虽然 CF 中的许多现有模型在其设计中包含了这些方法，但似乎对其背后的基本原则的分析深度有限。本文通过一个理论框架，将图卷积这一基于图的模型的关键要素与对比学习结合起来。通过研究对比损失的学习动力学和均衡性，我们提供了一个新的视角，通过图论来理解对比学习，强调其捕捉高阶连通性的能力。在此分析的基础上，我们进一步表明，在基于图的模型中经常使用的图卷积层并不是高阶连通性建模所必需的，而且可能会导致过度平滑的风险。根据我们的发现，我们介绍了简单对比协同过滤(SCCF) ，这是一个简单而有效的算法，基于一个幼稚的嵌入模型和一个修正的对比度损失。通过对四个公共数据集的大量实验，验证了算法的有效性。实验代码可在 https://github.com/wu1hong/sccf 下载。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Unifying+Graph+Convolution+and+Contrastive+Learning+in+Collaborative+Filtering)|0| -|[Towards Lightweight Graph Neural Network Search with Curriculum Graph Sparsification](https://doi.org/10.1145/3637528.3671706)|Beini Xie, Heng Chang, Ziwei Zhang, Zeyang Zhang, Simin Wu, Xin Wang, Yuan Meng, Wenwu Zhu|DCST, BNRist, Tsinghua University, Beijing, China; DCST, Tsinghua University, Beijing, China; Lanzhou University, Lanzhou, China|Graph Neural Architecture Search (GNAS) has achieved superior performance on various graph-structured tasks. However, existing GNAS studies overlook the applications of GNAS in resource-constrained scenarios. This paper proposes to design a joint graph data and architecture mechanism, which identifies important sub-architectures via the valuable graph data. To search for optimal lightweight Graph Neural Networks (GNNs), we propose a Lightweight Graph Neural Architecture Search with Graph SparsIfication and Network Pruning (GASSIP) method. In particular, GASSIP comprises an operation-pruned architecture search module to enable efficient lightweight GNN search. Meanwhile, we design a novel curriculum graph data sparsification module with an architecture-aware edge-removing difficulty measurement to help select optimal sub-architectures. With the aid of two differentiable masks, we iteratively optimize these two modules to efficiently search for the optimal lightweight architecture. Extensive experiments on five benchmarks demonstrate the effectiveness of GASSIP. Particularly, our method achieves on-par or even higher node classification performance with half or fewer model parameters of searched GNNs and a sparser graph.|图形神经结构搜索(GNAS)已经在各种图形结构的任务中取得了优异的性能。然而，现有的 GNAS 研究忽视了 GNAS 在资源受限情况下的应用。本文提出了一种联合图数据和体系结构机制，通过有价值的图数据来识别重要的子体系结构。为了寻找最优的轻量级图神经网络(GNN) ，提出了一种基于图稀疏化和网络剪枝的轻量级图神经网络体系结构搜索(GASSIP)方法。特别是，GASSIP 包含一个操作修剪的体系结构搜索模块，以支持高效的轻量级 GNN 搜索。同时，我们设计了一个新颖的课程图数据稀疏化模块，该模块具有体系结构感知的边缘去除困难度度量，以帮助选择最佳的子体系结构。借助于两个可微掩模，我们对这两个模块进行迭代优化，以有效地寻找最优的轻量级体系结构。在五个基准上的大量实验证明了 GASSIP 的有效性。特别地，我们的方法只需要搜索到的 GNN 的一半或更少的模型参数和一个更稀疏的图，就可以获得同等甚至更高的节点分类性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Towards+Lightweight+Graph+Neural+Network+Search+with+Curriculum+Graph+Sparsification)|0| -|[Revisiting Reciprocal Recommender Systems: Metrics, Formulation, and Method](https://doi.org/10.1145/3637528.3671734)|Chen Yang, Sunhao Dai, Yupeng Hou, Wayne Xin Zhao, Jun Xu, Yang Song, Hengshu Zhu|; University of California, San Diego, La Jolla, USA; Career Science Lab, BOSS Zhipin, Beijing, China; Gaoling School of Artificial Intelligence, Renmin University of China, Beijing, China; Nanbeige Lab, BOSS Zhipin, Beijing, China|Reciprocal recommender systems (RRS), conducting bilateral recommendations between two involved parties, have gained increasing attention for enhancing matching efficiency. However, the majority of existing methods in the literature still reuse conventional ranking metrics to separately assess the performance on each side of the recommendation process. These methods overlook the fact that the ranking outcomes of both sides collectively influence the effectiveness of the RRS, neglecting the necessity of a more holistic evaluation and a capable systemic solution. In this paper, we systemically revisit the task of reciprocal recommendation, by introducing the new metrics, formulation, and method. Firstly, we propose five new evaluation metrics that comprehensively and accurately assess the performance of RRS from three distinct perspectives: overall coverage, bilateral stability, and balanced ranking. These metrics provide a more holistic understanding of the system's effectiveness and enable a comprehensive evaluation. Furthermore, we formulate the RRS from a causal perspective, formulating recommendations as bilateral interventions, which can better model the decoupled effects of potential influencing factors. By utilizing the potential outcome framework, we further develop a model-agnostic causal reciprocal recommendation method that considers the causal effects of recommendations. Additionally, we introduce a reranking strategy to maximize matching outcomes, as measured by the proposed metrics. Extensive experiments on two real-world datasets from recruitment and dating scenarios demonstrate the effectiveness of our proposed metrics and approach. The code and dataset are available at: https://github.com/RUCAIBox/CRRS.|互惠推荐系统(RRS)在两个相关方之间进行双边推荐，在提高匹配效率方面受到越来越多的关注。然而，文献中的大多数现有方法仍然重用传统的排名指标来分别评估推荐过程的各个方面的性能。这些方法忽略了这样一个事实，即双方的排名结果共同影响区域资源规划的有效性，忽视了更加全面的评估和有能力的系统解决方案的必要性。在本文中，我们系统地重新审视互惠推荐的任务，通过介绍新的指标，公式和方法。首先，我们提出了五个新的评价指标，全面和准确地评估 RRS 的绩效从三个不同的角度: 整体覆盖，双边稳定性和平衡排名。这些指标提供了对系统有效性的更全面的理解，并能够进行全面的评估。此外，我们从因果关系的角度制定 RRS，提出建议作为双边干预措施，可以更好地模拟潜在影响因素的解耦效应。通过利用潜在的结果框架，我们进一步开发了一个模型无关的因果互惠推荐方法，考虑了推荐的因果效应。此外，我们引入了一个重新排序策略，以最大限度地匹配结果，由提议的度量衡量。来自招聘和约会场景的两个真实世界数据集的大量实验证明了我们提出的指标和方法的有效性。代码和数据集可在以下 https://github.com/rucaibox/crrs 获得:。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Revisiting+Reciprocal+Recommender+Systems:+Metrics,+Formulation,+and+Method)|0| +|[Unifying Graph Convolution and Contrastive Learning in Collaborative Filtering](https://doi.org/10.1145/3637528.3671840)|Yihong Wu, Le Zhang, Fengran Mo, Tianyu Zhu, Weizhi Ma, JianYun Nie|Mila - Quebec AI Institute, Montréal, Canada; Institute for AI Industry Research (AIR), Tsinghua University, Beijing, China; MIIT Key Laboratory of Data Intelligence and Management, Beihang University, Beijing, China; Université de Montréal, Montréal, Canada|Graph-based models and contrastive learning have emerged as prominent methods in Collaborative Filtering (CF). While many existing models in CF incorporate these methods in their design, there seems to be a limited depth of analysis regarding the foundational principles behind them. This paper bridges graph convolution, a pivotal element of graph-based models, with contrastive learning through a theoretical framework. By examining the learning dynamics and equilibrium of the contrastive loss, we offer a fresh lens to understand contrastive learning via graph theory, emphasizing its capability to capture high-order connectivity. Building on this analysis, we further show that the graph convolutional layers often used in graph-based models are not essential for high-order connectivity modeling and might contribute to the risk of oversmoothing. Stemming from our findings, we introduce Simple Contrastive Collaborative Filtering (SCCF), a simple and effective algorithm based on a naive embedding model and a modified contrastive loss. The efficacy of the algorithm is demonstrated through extensive experiments across four public datasets. The experiment code is available at https://github.com/wu1hong/SCCF.|基于图表的模型和对比学习已经成为协同过滤研究的主要方法。虽然 CF 中的许多现有模型在其设计中包含了这些方法，但似乎对其背后的基本原则的分析深度有限。本文通过一个理论框架，将图卷积这一基于图的模型的关键要素与对比学习结合起来。通过研究对比损失的学习动力学和均衡性，我们提供了一个新的视角，通过图论来理解对比学习，强调其捕捉高阶连通性的能力。在此分析的基础上，我们进一步表明，在基于图的模型中经常使用的图卷积层并不是高阶连通性建模所必需的，而且可能会导致过度平滑的风险。根据我们的发现，我们介绍了简单对比协同过滤(SCCF) ，这是一个简单而有效的算法，基于一个幼稚的嵌入模型和一个修正的对比度损失。通过对四个公共数据集的大量实验，验证了算法的有效性。实验代码可在 https://github.com/wu1hong/sccf 下载。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Unifying+Graph+Convolution+and+Contrastive+Learning+in+Collaborative+Filtering)|0| +|[Towards Lightweight Graph Neural Network Search with Curriculum Graph Sparsification](https://doi.org/10.1145/3637528.3671706)|Beini Xie, Heng Chang, Ziwei Zhang, Zeyang Zhang, Simin Wu, Xin Wang, Yuan Meng, Wenwu Zhu|DCST, Tsinghua University, Beijing, China; Lanzhou University, Lanzhou, China; DCST, BNRist, Tsinghua University, Beijing, China|Graph Neural Architecture Search (GNAS) has achieved superior performance on various graph-structured tasks. However, existing GNAS studies overlook the applications of GNAS in resource-constrained scenarios. This paper proposes to design a joint graph data and architecture mechanism, which identifies important sub-architectures via the valuable graph data. To search for optimal lightweight Graph Neural Networks (GNNs), we propose a Lightweight Graph Neural Architecture Search with Graph SparsIfication and Network Pruning (GASSIP) method. In particular, GASSIP comprises an operation-pruned architecture search module to enable efficient lightweight GNN search. Meanwhile, we design a novel curriculum graph data sparsification module with an architecture-aware edge-removing difficulty measurement to help select optimal sub-architectures. With the aid of two differentiable masks, we iteratively optimize these two modules to efficiently search for the optimal lightweight architecture. Extensive experiments on five benchmarks demonstrate the effectiveness of GASSIP. Particularly, our method achieves on-par or even higher node classification performance with half or fewer model parameters of searched GNNs and a sparser graph.|图形神经结构搜索(GNAS)已经在各种图形结构的任务中取得了优异的性能。然而，现有的 GNAS 研究忽视了 GNAS 在资源受限情况下的应用。本文提出了一种联合图数据和体系结构机制，通过有价值的图数据来识别重要的子体系结构。为了寻找最优的轻量级图神经网络(GNN) ，提出了一种基于图稀疏化和网络剪枝的轻量级图神经网络体系结构搜索(GASSIP)方法。特别是，GASSIP 包含一个操作修剪的体系结构搜索模块，以支持高效的轻量级 GNN 搜索。同时，我们设计了一个新颖的课程图数据稀疏化模块，该模块具有体系结构感知的边缘去除困难度度量，以帮助选择最佳的子体系结构。借助于两个可微掩模，我们对这两个模块进行迭代优化，以有效地寻找最优的轻量级体系结构。在五个基准上的大量实验证明了 GASSIP 的有效性。特别地，我们的方法只需要搜索到的 GNN 的一半或更少的模型参数和一个更稀疏的图，就可以获得同等甚至更高的节点分类性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Towards+Lightweight+Graph+Neural+Network+Search+with+Curriculum+Graph+Sparsification)|0| +|[Revisiting Reciprocal Recommender Systems: Metrics, Formulation, and Method](https://doi.org/10.1145/3637528.3671734)|Chen Yang, Sunhao Dai, Yupeng Hou, Wayne Xin Zhao, Jun Xu, Yang Song, Hengshu Zhu|; Nanbeige Lab, BOSS Zhipin, Beijing, China; Gaoling School of Artificial Intelligence, Renmin University of China, Beijing, China; University of California, San Diego, La Jolla, USA; Career Science Lab, BOSS Zhipin, Beijing, China|Reciprocal recommender systems (RRS), conducting bilateral recommendations between two involved parties, have gained increasing attention for enhancing matching efficiency. However, the majority of existing methods in the literature still reuse conventional ranking metrics to separately assess the performance on each side of the recommendation process. These methods overlook the fact that the ranking outcomes of both sides collectively influence the effectiveness of the RRS, neglecting the necessity of a more holistic evaluation and a capable systemic solution. In this paper, we systemically revisit the task of reciprocal recommendation, by introducing the new metrics, formulation, and method. Firstly, we propose five new evaluation metrics that comprehensively and accurately assess the performance of RRS from three distinct perspectives: overall coverage, bilateral stability, and balanced ranking. These metrics provide a more holistic understanding of the system's effectiveness and enable a comprehensive evaluation. Furthermore, we formulate the RRS from a causal perspective, formulating recommendations as bilateral interventions, which can better model the decoupled effects of potential influencing factors. By utilizing the potential outcome framework, we further develop a model-agnostic causal reciprocal recommendation method that considers the causal effects of recommendations. Additionally, we introduce a reranking strategy to maximize matching outcomes, as measured by the proposed metrics. Extensive experiments on two real-world datasets from recruitment and dating scenarios demonstrate the effectiveness of our proposed metrics and approach. The code and dataset are available at: https://github.com/RUCAIBox/CRRS.|互惠推荐系统(RRS)在两个相关方之间进行双边推荐，在提高匹配效率方面受到越来越多的关注。然而，文献中的大多数现有方法仍然重用传统的排名指标来分别评估推荐过程的各个方面的性能。这些方法忽略了这样一个事实，即双方的排名结果共同影响区域资源规划的有效性，忽视了更加全面的评估和有能力的系统解决方案的必要性。在本文中，我们系统地重新审视互惠推荐的任务，通过介绍新的指标，公式和方法。首先，我们提出了五个新的评价指标，全面和准确地评估 RRS 的绩效从三个不同的角度: 整体覆盖，双边稳定性和平衡排名。这些指标提供了对系统有效性的更全面的理解，并能够进行全面的评估。此外，我们从因果关系的角度制定 RRS，提出建议作为双边干预措施，可以更好地模拟潜在影响因素的解耦效应。通过利用潜在的结果框架，我们进一步开发了一个模型无关的因果互惠推荐方法，考虑了推荐的因果效应。此外，我们引入了一个重新排序策略，以最大限度地匹配结果，由提议的度量衡量。来自招聘和约会场景的两个真实世界数据集的大量实验证明了我们提出的指标和方法的有效性。代码和数据集可在以下 https://github.com/rucaibox/crrs 获得:。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Revisiting+Reciprocal+Recommender+Systems:+Metrics,+Formulation,+and+Method)|0| |[Graph Bottlenecked Social Recommendation](https://doi.org/10.1145/3637528.3671807)|Yonghui Yang, Le Wu, Zihan Wang, Zhuangzhuang He, Richang Hong, Meng Wang|Hefei University of Technology, Hefei, China|With the emergence of social networks, social recommendation has become an essential technique for personalized services. Recently, graph-based social recommendations have shown promising results by capturing the high-order social influence. Most empirical studies of graph-based social recommendations directly take the observed social networks into formulation, and produce user preferences based on social homogeneity. Despite the effectiveness, we argue that social networks in the real-world are inevitably noisy~(existing redundant social relations), which may obstruct precise user preference characterization. Nevertheless, identifying and removing redundant social relations is challenging due to a lack of labels. In this paper, we focus on learning the denoised social structure to facilitate recommendation tasks from an information bottleneck perspective. Specifically, we propose a novel Graph Bottlenecked Social Recommendation (GBSR) framework to tackle the social noise issue. GBSR is a model-agnostic social denoising framework, that aims to maximize the mutual information between the denoised social graph and recommendation labels, meanwhile minimizing it between the denoised social graph and the original one. This enables GBSR to learn the minimal yet sufficient social structure, effectively reducing redundant social relations and enhancing social recommendations. Technically, GBSR consists of two elaborate components, preference-guided social graph refinement, and HSIC-based bottleneck learning. Extensive experimental results demonstrate the superiority of the proposed GBSR, including high performances and good generality combined with various backbones. Our code is available at: https://github.com/yimutianyang/KDD24-GBSR.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Graph+Bottlenecked+Social+Recommendation)|0| -|[Efficient and Effective Anchored Densest Subgraph Search: A Convex-programming based Approach](https://doi.org/10.1145/3637528.3671727)|Xiaowei Ye, RongHua Li, Lei Liang, Zhizhen Liu, Longlong Lin, Guoren Wang|; Southwest University, Chongqing, China; Ant Group, Hangzhou, China; Beijing Institute of Technology, Beijing, China|The quest to identify local dense communities closely connected to predetermined seed nodes is vital across numerous applications. Given the seed nodes R, the R-subgraph density of a subgraph S is defined as traditional graph density of S with penalties on the nodes in S / R. The state-of-the-art (SOTA) anchored densest subgraph model, which is based on R-subgraph density, is designed to address the community search problem. However, it often struggles to efficiently uncover truly dense communities. To eliminate this issue, we propose a novel NR-subgraph density metric, a nuanced measure that identifies communities intimately linked to seed nodes and also exhibiting overall high graph density. We redefine the anchored densest subgraph search problem through the lens of NR-subgraph density and cast it as a Linear Programming (LP) problem. This allows us to transition into a dual problem, tapping into the efficiency and effectiveness of convex programming-based iterative algorithm. To solve this redefined problem, we propose two algorithms: FDP, an iterative method that swiftly attains near-optimal solutions, and FDPE, an exact approach that ensures full convergence. We perform extensive experiments on 12 real-world networks. The results show that our proposed algorithms not only outperform the SOTA methods by 3.6~14.1 times in terms of running time, but also produce subgraphs with superior internal quality.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Efficient+and+Effective+Anchored+Densest+Subgraph+Search:+A+Convex-programming+based+Approach)|0| +|[Efficient and Effective Anchored Densest Subgraph Search: A Convex-programming based Approach](https://doi.org/10.1145/3637528.3671727)|Xiaowei Ye, RongHua Li, Lei Liang, Zhizhen Liu, Longlong Lin, Guoren Wang|Beijing Institute of Technology, Beijing, China; ; Southwest University, Chongqing, China; Ant Group, Hangzhou, China|The quest to identify local dense communities closely connected to predetermined seed nodes is vital across numerous applications. Given the seed nodes R, the R-subgraph density of a subgraph S is defined as traditional graph density of S with penalties on the nodes in S / R. The state-of-the-art (SOTA) anchored densest subgraph model, which is based on R-subgraph density, is designed to address the community search problem. However, it often struggles to efficiently uncover truly dense communities. To eliminate this issue, we propose a novel NR-subgraph density metric, a nuanced measure that identifies communities intimately linked to seed nodes and also exhibiting overall high graph density. We redefine the anchored densest subgraph search problem through the lens of NR-subgraph density and cast it as a Linear Programming (LP) problem. This allows us to transition into a dual problem, tapping into the efficiency and effectiveness of convex programming-based iterative algorithm. To solve this redefined problem, we propose two algorithms: FDP, an iterative method that swiftly attains near-optimal solutions, and FDPE, an exact approach that ensures full convergence. We perform extensive experiments on 12 real-world networks. The results show that our proposed algorithms not only outperform the SOTA methods by 3.6~14.1 times in terms of running time, but also produce subgraphs with superior internal quality.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Efficient+and+Effective+Anchored+Densest+Subgraph+Search:+A+Convex-programming+based+Approach)|0| |[Approximate Matrix Multiplication over Sliding Windows](https://doi.org/10.1145/3637528.3671819)|Ziqi Yao, Lianzhi Li, Mingsong Chen, Xian Wei, Cheng Chen|East China Normal University, Shanghai, China|Large-scale streaming matrix multiplication is very common in various applications, sparking significant interest in develop efficient algorithms for approximate matrix multiplication (AMM) over streams. In addition, many practical scenarios require to process time-sensitive data and aim to compute matrix multiplication for most recent columns of the data matrices rather than the entire matrices, which motivated us to study efficient AMM algorithms over sliding windows. In this paper, we present two novel deterministic algorithms for this problem and provide corresponding error guarantees. We further reduce the space and time costs of our methods for sparse matrices by performing an approximate singular value decomposition which can utilize the sparsity of matrices. Extensive experimental results on both synthetic and real-world datasets validate our theoretical analysis and highlight the efficiency of our methods.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Approximate+Matrix+Multiplication+over+Sliding+Windows)|0| -|[Unsupervised Generative Feature Transformation via Graph Contrastive Pre-training and Multi-objective Fine-tuning](https://doi.org/10.1145/3637528.3672015)|Wangyang Ying, Dongjie Wang, Xuanming Hu, Yuanchun Zhou, Charu C. Aggarwal, Yanjie Fu|Arizona State University, Tempe, AZ, USA; International Business Machines T. J. Watson Research Center, Yorktown Heights, USA; Computer Network Information Center, Chinese Academy of Sciences, Beijing, China; The University of Kansas, Lawrence, KS, USA|Feature transformation is to derive a new feature set from original features to augment the AI power of data. In many science domains such as material performance screening, while feature transformation can model material formula interactions and compositions and discover performance drivers, supervised labels are collected from expensive and lengthy experiments. This issue motivates an Unsupervised Feature Transformation Learning (UFTL) problem. Prior literature, such as manual transformation, supervised feedback guided search, and PCA, either relies on domain knowledge or expensive supervised feedback, or suffers from large search space, or overlooks non-linear feature-feature interactions. UFTL imposes a major challenge on existing methods: how to design a new unsupervised paradigm that captures complex feature interactions and avoids large search space? To fill this gap, we connect graph, contrastive, and generative learning to develop a measurement-pretrain-finetune paradigm for UFTL. For unsupervised feature set utility measurement, we propose a feature value consistency preservation perspective and develop a mean discounted cumulative gain like unsupervised metric to evaluate feature set utility. For unsupervised feature set representation pretraining, we regard a feature set as a feature-feature interaction graph, and develop an unsupervised graph contrastive learning encoder to embed feature sets into vectors. For generative transformation finetuning, we regard a feature set as a feature cross sequence and feature transformation as sequential generation. We develop a deep generative feature transformation model that coordinates the pretrained feature set encoder and the gradient information extracted from a feature set utility evaluator to optimize a transformed feature generator. Finally, we conduct extensive experiments to demonstrate the effectiveness, efficiency, traceability, and explicitness of our framework.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Unsupervised+Generative+Feature+Transformation+via+Graph+Contrastive+Pre-training+and+Multi-objective+Fine-tuning)|0| -|[Personalized Federated Continual Learning via Multi-Granularity Prompt](https://doi.org/10.1145/3637528.3671948)|Hao Yu, Xin Yang, Xin Gao, Yan Kang, Hao Wang, Junbo Zhang, Tianrui Li|; JD Intelligent Cities Research & JD iCity, JD Technology, Beijing, China; School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu, China; Webank, Shenzhen, China; College of Computer Science, Sichuan University, Chengdu, China|Personalized Federated Continual Learning (PFCL) is a new practical scenario that poses greater challenges in sharing and personalizing knowledge. PFCL not only relies on knowledge fusion for server aggregation at the global spatial-temporal perspective but also needs model improvement for each client according to the local requirements. Existing methods, whether in Personalized Federated Learning (PFL) or Federated Continual Learning (FCL), have overlooked the multi-granularity representation of knowledge, which can be utilized to overcome Spatial-Temporal Catastrophic Forgetting (STCF) and adopt generalized knowledge to itself by coarse-to-fine human cognitive mechanisms. Moreover, it allows more effectively to personalized shared knowledge, thus serving its own purpose. To this end, we propose a novel concept called multi-granularity prompt, i.e., coarse-grained global prompt acquired through the common model learning process, and fine-grained local prompt used to personalize the generalized representation. The former focuses on efficiently transferring shared global knowledge without spatial forgetting, and the latter emphasizes specific learning of personalized local knowledge to overcome temporal forgetting. In addition, we design a selective prompt fusion mechanism for aggregating knowledge of global prompts distilled from different clients. By the exclusive fusion of coarse-grained knowledge, we achieve the transmission and refinement of common knowledge among clients, further enhancing the performance of personalization. Extensive experiments demonstrate the effectiveness of the proposed method in addressing STCF as well as improving personalized performance.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Personalized+Federated+Continual+Learning+via+Multi-Granularity+Prompt)|0| +|[Unsupervised Generative Feature Transformation via Graph Contrastive Pre-training and Multi-objective Fine-tuning](https://doi.org/10.1145/3637528.3672015)|Wangyang Ying, Dongjie Wang, Xuanming Hu, Yuanchun Zhou, Charu C. Aggarwal, Yanjie Fu|Arizona State University, Tempe, AZ, USA; Computer Network Information Center, Chinese Academy of Sciences, Beijing, China; The University of Kansas, Lawrence, KS, USA; International Business Machines T. J. Watson Research Center, Yorktown Heights, USA|Feature transformation is to derive a new feature set from original features to augment the AI power of data. In many science domains such as material performance screening, while feature transformation can model material formula interactions and compositions and discover performance drivers, supervised labels are collected from expensive and lengthy experiments. This issue motivates an Unsupervised Feature Transformation Learning (UFTL) problem. Prior literature, such as manual transformation, supervised feedback guided search, and PCA, either relies on domain knowledge or expensive supervised feedback, or suffers from large search space, or overlooks non-linear feature-feature interactions. UFTL imposes a major challenge on existing methods: how to design a new unsupervised paradigm that captures complex feature interactions and avoids large search space? To fill this gap, we connect graph, contrastive, and generative learning to develop a measurement-pretrain-finetune paradigm for UFTL. For unsupervised feature set utility measurement, we propose a feature value consistency preservation perspective and develop a mean discounted cumulative gain like unsupervised metric to evaluate feature set utility. For unsupervised feature set representation pretraining, we regard a feature set as a feature-feature interaction graph, and develop an unsupervised graph contrastive learning encoder to embed feature sets into vectors. For generative transformation finetuning, we regard a feature set as a feature cross sequence and feature transformation as sequential generation. We develop a deep generative feature transformation model that coordinates the pretrained feature set encoder and the gradient information extracted from a feature set utility evaluator to optimize a transformed feature generator. Finally, we conduct extensive experiments to demonstrate the effectiveness, efficiency, traceability, and explicitness of our framework.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Unsupervised+Generative+Feature+Transformation+via+Graph+Contrastive+Pre-training+and+Multi-objective+Fine-tuning)|0| +|[Personalized Federated Continual Learning via Multi-Granularity Prompt](https://doi.org/10.1145/3637528.3671948)|Hao Yu, Xin Yang, Xin Gao, Yan Kang, Hao Wang, Junbo Zhang, Tianrui Li|; School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu, China; Webank, Shenzhen, China; College of Computer Science, Sichuan University, Chengdu, China; JD Intelligent Cities Research & JD iCity, JD Technology, Beijing, China|Personalized Federated Continual Learning (PFCL) is a new practical scenario that poses greater challenges in sharing and personalizing knowledge. PFCL not only relies on knowledge fusion for server aggregation at the global spatial-temporal perspective but also needs model improvement for each client according to the local requirements. Existing methods, whether in Personalized Federated Learning (PFL) or Federated Continual Learning (FCL), have overlooked the multi-granularity representation of knowledge, which can be utilized to overcome Spatial-Temporal Catastrophic Forgetting (STCF) and adopt generalized knowledge to itself by coarse-to-fine human cognitive mechanisms. Moreover, it allows more effectively to personalized shared knowledge, thus serving its own purpose. To this end, we propose a novel concept called multi-granularity prompt, i.e., coarse-grained global prompt acquired through the common model learning process, and fine-grained local prompt used to personalize the generalized representation. The former focuses on efficiently transferring shared global knowledge without spatial forgetting, and the latter emphasizes specific learning of personalized local knowledge to overcome temporal forgetting. In addition, we design a selective prompt fusion mechanism for aggregating knowledge of global prompts distilled from different clients. By the exclusive fusion of coarse-grained knowledge, we achieve the transmission and refinement of common knowledge among clients, further enhancing the performance of personalization. Extensive experiments demonstrate the effectiveness of the proposed method in addressing STCF as well as improving personalized performance.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Personalized+Federated+Continual+Learning+via+Multi-Granularity+Prompt)|0| |[DipDNN: Preserving Inverse Consistency and Approximation Efficiency for Invertible Learning](https://doi.org/10.1145/3637528.3672036)|Jingyi Yuan, Yang Weng, Erik Blasch|Arizona State University, Tempe, AZ, USA; Air Force Research Lab, Arlington, VA, USA|Consistent bi-directional inferences are the key for many machine learning applications. Without consistency, inverse learning-based inferences can cause fuzzy images, erroneous control signals, and cascading failure in SCADA systems. Since standard deep neural networks (DNNs) are not inherently invertible to offer consistency, some past methods reconstruct DNN architecture analytically for one-to-one correspondence but compromise key features such as universal approximation. Other work maintains the capability of universal approximation in DNNs via iterative numerical approximation. However, these methods limit their applications significantly due to Lipschitz conditions and issues of numerical convergence. The dilemma of the analytical and numerical methods is the incompatibility between nonlinear layer compositions and bijective function construction for inverse modeling. Based on the observation, we propose decomposed-invertible-pathway DNNs (DipDNN). It relaxes the redundant reconstruction of nested DNN in the former methods and eases the Lipschitz constraint. As a result, we strictly guarantee the consistency of global inverse modeling without harming DNN's capability for universal approximation. As numerical stability and generalizability are keys for controlling critical infrastructures, we integrate contractive property with a parallel structure for inductive biases, leading to stable performance. Numerical results show that DipDNN performs significantly better than past methods, thanks to its enforcement of inverse consistency, numerical stability, and physical regularization.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=DipDNN:+Preserving+Inverse+Consistency+and+Approximation+Efficiency+for+Invertible+Learning)|0| -|[Conditional Logical Message Passing Transformer for Complex Query Answering](https://doi.org/10.1145/3637528.3671869)|Chongzhi Zhang, Zhiping Peng, Junhao Zheng, Qianli Ma|Guangdong University of Petrochemical Technology & Jiangmen Polytechnic, Maoming, China; South China University of Technology, Guangzhou, China|Complex Query Answering (CQA) over Knowledge Graphs (KGs) is a challenging task. Given that KGs are usually incomplete, neural models are proposed to solve CQA by performing multi-hop logical reasoning. However, most of them cannot perform well on both one-hop and multi-hop queries simultaneously. Recent work proposes a logical message passing mechanism based on the pre-trained neural link predictors. While effective on both one-hop and multi-hop queries, it ignores the difference between the constant and variable nodes in a query graph. In addition, during the node embedding update stage, this mechanism cannot dynamically measure the importance of different messages, and whether it can capture the implicit logical dependencies related to a node and received messages remains unclear. In this paper, we propose Conditional Logical Message Passing Transformer (CLMPT), which considers the difference between constants and variables in the case of using pre-trained neural link predictors and performs message passing conditionally on the node type. We empirically verified that this approach can reduce computational costs without affecting performance. Furthermore, CLMPT uses the transformer to aggregate received messages and update the corresponding node embedding. Through the self-attention mechanism, CLMPT can assign adaptive weights to elements in an input set consisting of received messages and the corresponding node and explicitly model logical dependencies between various elements. Experimental results show that CLMPT is a new state-of-the-art neural CQA model. https://github.com/qianlima-lab/CLMPT.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Conditional+Logical+Message+Passing+Transformer+for+Complex+Query+Answering)|0| -|[Natural Language Explainable Recommendation with Robustness Enhancement](https://doi.org/10.1145/3637528.3671781)|Jingsen Zhang, Jiakai Tang, Xu Chen, Wenhui Yu, Lantao Hu, Peng Jiang, Han Li|Gaoling School of Artificial Intelligence, Renmin University of China, Beijing, China; Kuaishou Technology, Beijing, China|Natural language explainable recommendation has become a promising direction to facilitate more efficient and informed user decisions. Previous models mostly focus on how to enhance the explanation accuracy. However, the robustness problem has been largely ignored, which requires the explanations generated for similar user-item pairs should not be too much different. Different from traditional classification problems, improving the robustness of natural languages has two unique characteristics: (1) Different token importances, that is, different tokens play various roles in representing the complete sentence, and the robustness requirements for predicting them should also be different. (2) Continuous token semantics, that is, the similarity of the output should be judged based on semantics, and the sequences without any token-level overlap may also be highly similar. Based on these characteristics, we formulate and solve a novel problem in the recommendation domain, that is, robust natural language explainable recommendation. To the best of our knowledge, it is the first time in this field. Specifically, we base our modeling on adversarial robust optimization and design four types of heuristic methods to modify the adversarial outputs with weighted token probabilities and synonym replacements. Furthermore, to consider the mutual influence between the above characteristics, we regard language generation as a decision-making problem and design a dual-policy reinforcement learning framework to improve the robustness of the generated languages. We conduct extensive experiments to demonstrate the effectiveness of our framework.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Natural+Language+Explainable+Recommendation+with+Robustness+Enhancement)|0| -|[Enabling Collaborative Test-Time Adaptation in Dynamic Environment via Federated Learning](https://doi.org/10.1145/3637528.3671908)|Jiayuan Zhang, Xuefeng Liu, Yukang Zhang, Guogang Zhu, Jianwei Niu, Shaojie Tang|; Shenzhen International Graduate School, Tsinghua University, Beijing, China; Jindal School of Management, The University of Texas at Dallas, Richardson, TX, USA|Deep learning models often suffer performance degradation when test data diverges from training data. Test-Time Adaptation (TTA) aims to adapt a trained model to the test data distribution using unlabeled test data streams. In many real-world applications, it is quite common for the trained model to be deployed across multiple devices simultaneously. Although each device can execute TTA independently, it fails to leverage information from the test data of other devices. To address this problem, we introduce Federated Learning (FL) to TTA to facilitate on-the-fly collaboration among devices during test time. The workflow involves clients (i.e., the devices) executing TTA locally, uploading their updated models to a central server for aggregation, and downloading the aggregated model for inference. However, implementing FL in TTA presents many challenges, especially in establishing inter-client collaboration in dynamic environment, where the test data distribution on different clients changes over time in different manners. To tackle these challenges, we propose a server-side Temporal-Spatial Aggregation (TSA) method. TSA utilizes a temporal-spatial attention module to capture intra-client temporal correlations and inter-client spatial correlations. To further improve robustness against temporal-spatial heterogeneity, we propose a heterogeneity-aware augmentation method and optimize the module using a self-supervised approach. More importantly, TSA can be implemented as a plug-in to TTA methods in distributed environments. Experiments on multiple datasets demonstrate that TSA outperforms existing methods and exhibits robustness across various levels of heterogeneity. The code is available at https://github.com/ZhangJiayuan-BUAA/FedTSA.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Enabling+Collaborative+Test-Time+Adaptation+in+Dynamic+Environment+via+Federated+Learning)|0| -|[Topology-aware Embedding Memory for Continual Learning on Expanding Networks](https://doi.org/10.1145/3637528.3671732)|Xikun Zhang, Dongjin Song, Yixin Chen, Dacheng Tao|The University of Sydney, Sydney, NSW, Australia; Washington University, Saint Louis, St. Louis, MO, USA; University of Connecticut, Storrs, CT, USA|Memory replay based techniques have shown great success for continual learning with incrementally accumulated Euclidean data. Directly applying them to continually expanding networks, however, leads to the potential memory explosion problem due to the need to buffer representative nodes and their associated topological neighborhood structures. To this end, we systematically analyze the key challenges in the memory explosion problem, and present a general framework,i.e., Parameter Decoupled Graph Neural Networks (PDGNNs) with Topology-aware Embedding Memory (TEM), to tackle this issue. The proposed framework not only reduces the memory space complexity from O (ndL) to O (n)1: memory budget, d: average node degree, L: the radius of the GNN receptive field, but also fully utilizes the topological information for memory replay. Specifically, PDGNNs decouple trainable parameters from the computation ego-subnetwork viaTopology-aware Embeddings (TEs), which compress ego-subnetworks into compact vectors (i.e., TEs) to reduce the memory consumption. Based on this framework, we discover a unique pseudo-training effect in continual learning on expanding networks and this effect motivates us to develop a novel coverage maximization sampling strategy that can enhance the performance with a tight memory budget. Thorough empirical studies demonstrate that, by tackling the memory explosion problem and incorporating topological information into memory replay, PDGNNs with TEM significantly outperform state-of-the-art techniques, especially in the challenging class-incremental setting.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Topology-aware+Embedding+Memory+for+Continual+Learning+on+Expanding+Networks)|0| -|[Urban-Focused Multi-Task Offline Reinforcement Learning with Contrastive Data Sharing](https://doi.org/10.1145/3637528.3671823)|Xinbo Zhao, Yingxue Zhang, Xin Zhang, Yu Yang, Yiqun Xie, Yanhua Li, Jun Luo|Worcester Polytechnic Institute, Worcester, MA, USA; Binghamton University, Binghamton, NY, USA; University of Maryland, College Park, College Park, MD, USA; Logistics and Supply Chain MultiTech R&D Centre, Hong Kong, Hong Kong; Lehigh University, Bethlehem, PA, USA; San Diego State University, San Diego, CA, USA|Enhancing diverse human decision-making processes in an urban environment is a critical issue across various applications, including ride-sharing vehicle dispatching, public transportation management, and autonomous driving. Offline reinforcement learning (RL) is a promising approach to learn and optimize human urban strategies (or policies) from pre-collected human-generated spatial-temporal urban data. However, standard offline RL faces two significant challenges: (1) data scarcity and data heterogeneity, and (2) distributional shift. In this paper, we introduce MODA - a Multi-Task Offline Reinforcement Learning with Contrastive Data Sharing approach. MODA addresses the challenges of data scarcity and heterogeneity in a multi-task urban setting through Contrastive Data Sharing among tasks. This technique involves extracting latent representations of human behaviors by contrasting positive and negative data pairs. It then shares data presenting similar representations with the target task, facilitating data augmentation for each task. Moreover, MODA develops a novel model-based multi-task offline RL algorithm. This algorithm constructs a robust Markov Decision Process (MDP) by integrating a dynamics model with a Generative Adversarial Network (GAN). Once the robust MDP is established, any online RL or planning algorithm can be applied. Extensive experiments conducted in a real-world multi-task urban setting validate the effectiveness of MODA. The results demonstrate that MODA exhibits significant improvements compared to state-of-the-art baselines, showcasing its capability in advancing urban decision-making processes. We also made our code available to the research community.|在城市环境中增强多样化的人类决策过程是一个跨越多种应用的关键问题，包括拼车调度、公共交通管理和自主驾驶。离线强化学习是从预先收集的人类生成的时空城市数据中学习和优化人类城市策略(或政策)的一种很有前途的方法。然而，标准的离线 RL 面临两个重大挑战: (1)数据稀缺性和数据异构性，以及(2)分布式转移。这篇文章介绍了 MODA-一个基于对比数据共享的多任务脱机强化学习。MODA 通过任务之间的对比数据共享，解决了多任务城市环境中数据稀缺性和异质性的挑战。这种技术包括通过对比正负数据对来提取人类行为的潜在表征。然后，它与目标任务共享呈现类似表示的数据，这有助于为每个任务增强数据。此外，MODA 开发了一种新的基于模型的多任务离线 RL 算法。该算法通过将动力学模型与生成对抗网络(gAN)相结合来构建一个鲁棒的马可夫决策过程(mDP)。一旦稳健的 MDP 建立，任何在线 RL 或规划算法都可以应用。在现实世界多任务城市环境中进行的大量实验验证了 MODA 的有效性。结果表明，与最先进的基线相比，MODA 显示出显著的改进，显示了其推进城市决策进程的能力。我们还向研究团体提供了我们的代码。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Urban-Focused+Multi-Task+Offline+Reinforcement+Learning+with+Contrastive+Data+Sharing)|0| -|[Synthesizing Multimodal Electronic Health Records via Predictive Diffusion Models](https://doi.org/10.1145/3637528.3671836)|Yuan Zhong, Xiaochen Wang, Jiaqi Wang, Xiaokun Zhang, Yaqing Wang, Mengdi Huai, Cao Xiao, Fenglong Ma|GE Healthcare, Seattle, WA, USA; Dalian University of Technology, Dalian, Liaoning, China; The Penn State University, University Park, PA, USA; Iowa State University, Ames, IA, USA; Purdue University, West Lafayette, IN, USA; The Pennsylvania State University, University Park, PA, USA|Synthesizing electronic health records (EHR) data has become a preferredstrategy to address data scarcity, improve data quality, and model fairness inhealthcare. However, existing approaches for EHR data generation predominantlyrely on state-of-the-art generative techniques like generative adversarialnetworks, variational autoencoders, and language models. These methodstypically replicate input visits, resulting in inadequate modeling of temporaldependencies between visits and overlooking the generation of time information,a crucial element in EHR data. Moreover, their ability to learn visitrepresentations is limited due to simple linear mapping functions, thuscompromising generation quality. To address these limitations, we propose anovel EHR data generation model called EHRPD. It is a diffusion-based modeldesigned to predict the next visit based on the current one while alsoincorporating time interval estimation. To enhance generation quality anddiversity, we introduce a novel time-aware visit embedding module and apioneering predictive denoising diffusion probabilistic model (PDDPM).Additionally, we devise a predictive U-Net (PU-Net) to optimize P-DDPM.Weconduct experiments on two public datasets and evaluate EHRPD from fidelity,privacy, and utility perspectives. The experimental results demonstrate theefficacy and utility of the proposed EHRPD in addressing the aforementionedlimitations and advancing EHR data generation.|合成电子健康记录(EHR)数据已经成为解决数据稀缺性、提高数据质量和医疗保健公平性的首选策略。然而，现有的 EHR 数据生成方法主要依赖于最先进的生成技术，如生成对抗网络、变分自动编码器和语言模型。这些方法通常重复输入访问，导致访问之间的时间依赖性建模不足，并忽略了时间信息的生成，这是电子健康记录数据中的一个关键要素。此外，由于简单的线性映射函数，他们学习访问表示的能力受到限制，从而影响了生成质量。为了解决这些局限性，我们提出了一种新的 EHR 数据生成模型 EHRPD。这是一个基于扩散的模型，旨在预测下一次访问的基础上，当前的一个，同时也结合了时间间隔估计。为了提高产生的质量和多样性，我们引入了一种新的时间感知访问嵌入模块和先锋预测去噪扩散概率模型(PDDPM)。此外，我们设计了一个预测 U 网(PU-Net)来优化 P-DDPM。我们在两个公共数据集上进行实验，并从保真度、隐私和效用的角度评估 EHRPD。实验结果证明了提出的 EHRPD 在解决上述局限性和推进 EHR 数据生成方面的有效性和实用性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Synthesizing+Multimodal+Electronic+Health+Records+via+Predictive+Diffusion+Models)|0| +|[Conditional Logical Message Passing Transformer for Complex Query Answering](https://doi.org/10.1145/3637528.3671869)|Chongzhi Zhang, Zhiping Peng, Junhao Zheng, Qianli Ma|South China University of Technology, Guangzhou, China; Guangdong University of Petrochemical Technology & Jiangmen Polytechnic, Maoming, China|Complex Query Answering (CQA) over Knowledge Graphs (KGs) is a challenging task. Given that KGs are usually incomplete, neural models are proposed to solve CQA by performing multi-hop logical reasoning. However, most of them cannot perform well on both one-hop and multi-hop queries simultaneously. Recent work proposes a logical message passing mechanism based on the pre-trained neural link predictors. While effective on both one-hop and multi-hop queries, it ignores the difference between the constant and variable nodes in a query graph. In addition, during the node embedding update stage, this mechanism cannot dynamically measure the importance of different messages, and whether it can capture the implicit logical dependencies related to a node and received messages remains unclear. In this paper, we propose Conditional Logical Message Passing Transformer (CLMPT), which considers the difference between constants and variables in the case of using pre-trained neural link predictors and performs message passing conditionally on the node type. We empirically verified that this approach can reduce computational costs without affecting performance. Furthermore, CLMPT uses the transformer to aggregate received messages and update the corresponding node embedding. Through the self-attention mechanism, CLMPT can assign adaptive weights to elements in an input set consisting of received messages and the corresponding node and explicitly model logical dependencies between various elements. Experimental results show that CLMPT is a new state-of-the-art neural CQA model. https://github.com/qianlima-lab/CLMPT.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Conditional+Logical+Message+Passing+Transformer+for+Complex+Query+Answering)|0| +|[Natural Language Explainable Recommendation with Robustness Enhancement](https://doi.org/10.1145/3637528.3671781)|Jingsen Zhang, Jiakai Tang, Xu Chen, Wenhui Yu, Lantao Hu, Peng Jiang, Han Li|Kuaishou Technology, Beijing, China; Gaoling School of Artificial Intelligence, Renmin University of China, Beijing, China|Natural language explainable recommendation has become a promising direction to facilitate more efficient and informed user decisions. Previous models mostly focus on how to enhance the explanation accuracy. However, the robustness problem has been largely ignored, which requires the explanations generated for similar user-item pairs should not be too much different. Different from traditional classification problems, improving the robustness of natural languages has two unique characteristics: (1) Different token importances, that is, different tokens play various roles in representing the complete sentence, and the robustness requirements for predicting them should also be different. (2) Continuous token semantics, that is, the similarity of the output should be judged based on semantics, and the sequences without any token-level overlap may also be highly similar. Based on these characteristics, we formulate and solve a novel problem in the recommendation domain, that is, robust natural language explainable recommendation. To the best of our knowledge, it is the first time in this field. Specifically, we base our modeling on adversarial robust optimization and design four types of heuristic methods to modify the adversarial outputs with weighted token probabilities and synonym replacements. Furthermore, to consider the mutual influence between the above characteristics, we regard language generation as a decision-making problem and design a dual-policy reinforcement learning framework to improve the robustness of the generated languages. We conduct extensive experiments to demonstrate the effectiveness of our framework.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Natural+Language+Explainable+Recommendation+with+Robustness+Enhancement)|0| +|[Enabling Collaborative Test-Time Adaptation in Dynamic Environment via Federated Learning](https://doi.org/10.1145/3637528.3671908)|Jiayuan Zhang, Xuefeng Liu, Yukang Zhang, Guogang Zhu, Jianwei Niu, Shaojie Tang|; Jindal School of Management, The University of Texas at Dallas, Richardson, TX, USA; Shenzhen International Graduate School, Tsinghua University, Beijing, China|Deep learning models often suffer performance degradation when test data diverges from training data. Test-Time Adaptation (TTA) aims to adapt a trained model to the test data distribution using unlabeled test data streams. In many real-world applications, it is quite common for the trained model to be deployed across multiple devices simultaneously. Although each device can execute TTA independently, it fails to leverage information from the test data of other devices. To address this problem, we introduce Federated Learning (FL) to TTA to facilitate on-the-fly collaboration among devices during test time. The workflow involves clients (i.e., the devices) executing TTA locally, uploading their updated models to a central server for aggregation, and downloading the aggregated model for inference. However, implementing FL in TTA presents many challenges, especially in establishing inter-client collaboration in dynamic environment, where the test data distribution on different clients changes over time in different manners. To tackle these challenges, we propose a server-side Temporal-Spatial Aggregation (TSA) method. TSA utilizes a temporal-spatial attention module to capture intra-client temporal correlations and inter-client spatial correlations. To further improve robustness against temporal-spatial heterogeneity, we propose a heterogeneity-aware augmentation method and optimize the module using a self-supervised approach. More importantly, TSA can be implemented as a plug-in to TTA methods in distributed environments. Experiments on multiple datasets demonstrate that TSA outperforms existing methods and exhibits robustness across various levels of heterogeneity. The code is available at https://github.com/ZhangJiayuan-BUAA/FedTSA.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Enabling+Collaborative+Test-Time+Adaptation+in+Dynamic+Environment+via+Federated+Learning)|0| +|[Topology-aware Embedding Memory for Continual Learning on Expanding Networks](https://doi.org/10.1145/3637528.3671732)|Xikun Zhang, Dongjin Song, Yixin Chen, Dacheng Tao|Washington University, Saint Louis, St. Louis, MO, USA; The University of Sydney, Sydney, NSW, Australia; University of Connecticut, Storrs, CT, USA|Memory replay based techniques have shown great success for continual learning with incrementally accumulated Euclidean data. Directly applying them to continually expanding networks, however, leads to the potential memory explosion problem due to the need to buffer representative nodes and their associated topological neighborhood structures. To this end, we systematically analyze the key challenges in the memory explosion problem, and present a general framework,i.e., Parameter Decoupled Graph Neural Networks (PDGNNs) with Topology-aware Embedding Memory (TEM), to tackle this issue. The proposed framework not only reduces the memory space complexity from O (ndL) to O (n)1: memory budget, d: average node degree, L: the radius of the GNN receptive field, but also fully utilizes the topological information for memory replay. Specifically, PDGNNs decouple trainable parameters from the computation ego-subnetwork viaTopology-aware Embeddings (TEs), which compress ego-subnetworks into compact vectors (i.e., TEs) to reduce the memory consumption. Based on this framework, we discover a unique pseudo-training effect in continual learning on expanding networks and this effect motivates us to develop a novel coverage maximization sampling strategy that can enhance the performance with a tight memory budget. Thorough empirical studies demonstrate that, by tackling the memory explosion problem and incorporating topological information into memory replay, PDGNNs with TEM significantly outperform state-of-the-art techniques, especially in the challenging class-incremental setting.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Topology-aware+Embedding+Memory+for+Continual+Learning+on+Expanding+Networks)|0| +|[Urban-Focused Multi-Task Offline Reinforcement Learning with Contrastive Data Sharing](https://doi.org/10.1145/3637528.3671823)|Xinbo Zhao, Yingxue Zhang, Xin Zhang, Yu Yang, Yiqun Xie, Yanhua Li, Jun Luo|San Diego State University, San Diego, CA, USA; Worcester Polytechnic Institute, Worcester, MA, USA; Logistics and Supply Chain MultiTech R&D Centre, Hong Kong, Hong Kong; University of Maryland, College Park, College Park, MD, USA; Lehigh University, Bethlehem, PA, USA; Binghamton University, Binghamton, NY, USA|Enhancing diverse human decision-making processes in an urban environment is a critical issue across various applications, including ride-sharing vehicle dispatching, public transportation management, and autonomous driving. Offline reinforcement learning (RL) is a promising approach to learn and optimize human urban strategies (or policies) from pre-collected human-generated spatial-temporal urban data. However, standard offline RL faces two significant challenges: (1) data scarcity and data heterogeneity, and (2) distributional shift. In this paper, we introduce MODA - a Multi-Task Offline Reinforcement Learning with Contrastive Data Sharing approach. MODA addresses the challenges of data scarcity and heterogeneity in a multi-task urban setting through Contrastive Data Sharing among tasks. This technique involves extracting latent representations of human behaviors by contrasting positive and negative data pairs. It then shares data presenting similar representations with the target task, facilitating data augmentation for each task. Moreover, MODA develops a novel model-based multi-task offline RL algorithm. This algorithm constructs a robust Markov Decision Process (MDP) by integrating a dynamics model with a Generative Adversarial Network (GAN). Once the robust MDP is established, any online RL or planning algorithm can be applied. Extensive experiments conducted in a real-world multi-task urban setting validate the effectiveness of MODA. The results demonstrate that MODA exhibits significant improvements compared to state-of-the-art baselines, showcasing its capability in advancing urban decision-making processes. We also made our code available to the research community.|在城市环境中增强多样化的人类决策过程是一个跨越多种应用的关键问题，包括拼车调度、公共交通管理和自主驾驶。离线强化学习是从预先收集的人类生成的时空城市数据中学习和优化人类城市策略(或政策)的一种很有前途的方法。然而，标准的离线 RL 面临两个重大挑战: (1)数据稀缺性和数据异构性，以及(2)分布式转移。这篇文章介绍了 MODA-一个基于对比数据共享的多任务脱机强化学习。MODA 通过任务之间的对比数据共享，解决了多任务城市环境中数据稀缺性和异质性的挑战。这种技术包括通过对比正负数据对来提取人类行为的潜在表征。然后，它与目标任务共享呈现类似表示的数据，这有助于为每个任务增强数据。此外，MODA 开发了一种新的基于模型的多任务离线 RL 算法。该算法通过将动力学模型与生成对抗网络(gAN)相结合来构建一个鲁棒的马可夫决策过程(mDP)。一旦稳健的 MDP 建立，任何在线 RL 或规划算法都可以应用。在现实世界多任务城市环境中进行的大量实验验证了 MODA 的有效性。结果表明，与最先进的基线相比，MODA 显示出显著的改进，显示了其推进城市决策进程的能力。我们还向研究团体提供了我们的代码。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Urban-Focused+Multi-Task+Offline+Reinforcement+Learning+with+Contrastive+Data+Sharing)|0| +|[Synthesizing Multimodal Electronic Health Records via Predictive Diffusion Models](https://doi.org/10.1145/3637528.3671836)|Yuan Zhong, Xiaochen Wang, Jiaqi Wang, Xiaokun Zhang, Yaqing Wang, Mengdi Huai, Cao Xiao, Fenglong Ma|The Penn State University, University Park, PA, USA; The Pennsylvania State University, University Park, PA, USA; Dalian University of Technology, Dalian, Liaoning, China; GE Healthcare, Seattle, WA, USA; Iowa State University, Ames, IA, USA; Purdue University, West Lafayette, IN, USA|Synthesizing electronic health records (EHR) data has become a preferredstrategy to address data scarcity, improve data quality, and model fairness inhealthcare. However, existing approaches for EHR data generation predominantlyrely on state-of-the-art generative techniques like generative adversarialnetworks, variational autoencoders, and language models. These methodstypically replicate input visits, resulting in inadequate modeling of temporaldependencies between visits and overlooking the generation of time information,a crucial element in EHR data. Moreover, their ability to learn visitrepresentations is limited due to simple linear mapping functions, thuscompromising generation quality. To address these limitations, we propose anovel EHR data generation model called EHRPD. It is a diffusion-based modeldesigned to predict the next visit based on the current one while alsoincorporating time interval estimation. To enhance generation quality anddiversity, we introduce a novel time-aware visit embedding module and apioneering predictive denoising diffusion probabilistic model (PDDPM).Additionally, we devise a predictive U-Net (PU-Net) to optimize P-DDPM.Weconduct experiments on two public datasets and evaluate EHRPD from fidelity,privacy, and utility perspectives. The experimental results demonstrate theefficacy and utility of the proposed EHRPD in addressing the aforementionedlimitations and advancing EHR data generation.|合成电子健康记录(EHR)数据已经成为解决数据稀缺性、提高数据质量和医疗保健公平性的首选策略。然而，现有的 EHR 数据生成方法主要依赖于最先进的生成技术，如生成对抗网络、变分自动编码器和语言模型。这些方法通常重复输入访问，导致访问之间的时间依赖性建模不足，并忽略了时间信息的生成，这是电子健康记录数据中的一个关键要素。此外，由于简单的线性映射函数，他们学习访问表示的能力受到限制，从而影响了生成质量。为了解决这些局限性，我们提出了一种新的 EHR 数据生成模型 EHRPD。这是一个基于扩散的模型，旨在预测下一次访问的基础上，当前的一个，同时也结合了时间间隔估计。为了提高产生的质量和多样性，我们引入了一种新的时间感知访问嵌入模块和先锋预测去噪扩散概率模型(PDDPM)。此外，我们设计了一个预测 U 网(PU-Net)来优化 P-DDPM。我们在两个公共数据集上进行实验，并从保真度、隐私和效用的角度评估 EHRPD。实验结果证明了提出的 EHRPD 在解决上述局限性和推进 EHR 数据生成方面的有效性和实用性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Synthesizing+Multimodal+Electronic+Health+Records+via+Predictive+Diffusion+Models)|0| |[Generative AI in E-Commerce: What Can We Expect?](https://doi.org/10.1145/3637528.3672503)|Haixun Wang|Instacart, San Francisco, CA, USA|The impact of generative AI on e-commerce is profound. It has significantly improved the understanding of user intent and serves as a comprehensive product knowledge graph. However, the most substantial disruptions are yet to come, partic- ularly through the rise of autonomous agents. In this talk, I will outline a tentative path toward a future where e-commerce not only offers an unparalleled customer experience but also thrives in a world dominated by generative AI and autonomous agents.|生成性人工智能对电子商务的影响是深远的。它显著提高了对用户意图的理解，并作为一个全面的产品知识图。然而，最重大的破坏还没有到来，特别是通过自主代理的兴起。在这次演讲中，我将概述一条通向未来的试探性道路，在这个未来，电子商务不仅能提供无与伦比的客户体验，而且还能在一个由生成性人工智能和自主代理主导的世界中蓬勃发展。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Generative+AI+in+E-Commerce:+What+Can+We+Expect?)|0| |[LiRank: Industrial Large Scale Ranking Models at LinkedIn](https://doi.org/10.1145/3637528.3671561)|Fedor Borisyuk, Mingzhou Zhou, Qingquan Song, Siyu Zhu, Birjodh Tiwana, Ganesh Parameswaran, Siddharth Dangi, Lars Hertel, Qiang Charles Xiao, Xiaochen Hou, Yunbo Ouyang, Aman Gupta, Sheallika Singh, Dan Liu, Hailing Cheng, Lei Le, Jonathan Hung, Sathiya Keerthi, Ruoyan Wang, Fengyu Zhang, Mohit Kothari, Chen Zhu, Daqi Sun, Yun Dai, Xun Luan, Sirou Zhu, Zhiwei Wang, Neil Daftary, Qianqi Shen, Chengming Jiang, Haichao Wei, Maneesh Varshney, Amol Ghoting, Souvik Ghosh|LinkedIn, Mountain View, CA, USA|We present LiRank, a large-scale ranking framework at LinkedIn that brings to production state-of-the-art modeling architectures and optimization methods. We unveil several modeling improvements, including Residual DCN, which adds attention and residual connections to the famous DCNv2 architecture. We share insights into combining and tuning SOTA architectures to create a unified model, including Dense Gating, Transformers and Residual DCN. We also propose novel techniques for calibration and describe how we productionalized deep learning based explore/exploit methods. To enable effective, production-grade serving of large ranking models, we detail how to train and compress models using quantization and vocabulary compression. We provide details about the deployment setup for large-scale use cases of Feed ranking, Jobs Recommendations, and Ads click-through rate (CTR) prediction. We summarize our learnings from various A/B tests by elucidating the most effective technical approaches. These ideas have contributed to relative metrics improvements across the board at LinkedIn: +0.5% member sessions in the Feed, +1.76% qualified job applications for Jobs search and recommendations, and +4.3% for Ads CTR. We hope this work can provide practical insights and solutions for practitioners interested in leveraging large-scale deep ranking systems.|我们介绍 LiRank，一个 LinkedIn 的大规模排名框架，它带来了最先进的建模架构和优化方法。我们揭示了几个建模改进，包括残余 DCN，它为著名的 DCNv2架构增加了注意力和残余连接。我们分享了结合和调整 SOTA 架构以创建统一模型的见解，包括致密门控、变压器和剩余 DCN。我们还提出了新的校准技术，并描述了如何生产基于深度学习的探索/开发方法。为了实现大型排名模型的高效、生产级服务，我们详细介绍了如何使用量化和词汇压缩对模型进行训练和压缩。我们提供了关于 Feed 排名、工作推荐和广告点进率(ctrl)预测等大规模用例部署设置的详细信息。我们通过阐明最有效的技术方法来总结我们从各种 A/B 测试中学到的东西。这些想法促进了 LinkedIn 的相关指标的全面改善: Feed 会员增加0.5% ，工作搜索和推荐的合格工作申请增加1.76% ，广告点击率增加4.3% 。我们希望这项工作可以提供实用的见解和解决方案的从业人员有兴趣利用大规模的深入排名系统。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=LiRank:+Industrial+Large+Scale+Ranking+Models+at+LinkedIn)|0| |[Enhancing Multi-field B2B Cloud Solution Matching via Contrastive Pre-training](https://doi.org/10.1145/3637528.3671513)|Haonan Chen, Zhicheng Dou, Xuetong Hao, Yunhao Tao, Shiren Song, Zhenli Sheng|Renmin University of China, Beijing, China; Huawei Cloud Computing, Hangzhou, China|Cloud solutions have gained significant popularity in the technology industry as they offer a combination of services and tools to tackle specific problems. However, despite their widespread use, the task of identifying appropriate company customers for a specific target solution to the sales team of a solution provider remains a complex business problem that existing matching systems have yet to adequately address. In this work, we study the B2B solution matching problem and identify two main challenges of this scenario: (1) the modeling of complex multi-field features and (2) the limited, incomplete, and sparse transaction data. To tackle these challenges, we propose a framework CAMA, which is built with a hierarchical multi-field matching structure as its backbone and supplemented by three data augmentation strategies and a contrastive pre-training objective to compensate for the imperfections in the available data. Through extensive experiments on a real-world dataset, we demonstrate that CAMA outperforms several strong baseline matching models significantly. Furthermore, we have deployed our matching framework on a system of Huawei Cloud. Our observations indicate an improvement of about 30% compared to the previous online model in terms of Conversion Rate (CVR), which demonstrates its great business value.|云解决方案已经在技术行业中大受欢迎，因为它们提供了解决特定问题的服务和工具的组合。然而，尽管它们被广泛使用，但是为解决方案供应商的销售团队确定合适的公司客户以提供特定目标解决方案的任务仍然是一个复杂的业务问题，现有的匹配系统尚未充分解决这个问题。在这项工作中，我们研究了 B2B 解决方案匹配问题，并确定了这个场景的两个主要挑战: (1)复杂的多领域特征的建模和(2)有限的，不完整的，稀疏的事务数据。为了应对这些挑战，我们提出了一个框架 CAMA，它以层次化的多领域匹配结构为骨干，并辅以三个数据增强策略和一个对比的预训练目标来弥补现有数据中的不完善。通过对一个真实世界数据集的大量实验，我们证明了 CAMA 显著优于几个强基线匹配模型。此外，我们已经在华为云系统上部署了我们的匹配框架。我们的观察表明，在转化率(CVR)方面，与以前的在线模型相比，大约有30% 的改进，这表明了其巨大的商业价值。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Enhancing+Multi-field+B2B+Cloud+Solution+Matching+via+Contrastive+Pre-training)|0| |[GRILLBot In Practice: Lessons and Tradeoffs Deploying Large Language Models for Adaptable Conversational Task Assistants](https://doi.org/10.1145/3637528.3671622)|Sophie Fischer, Carlos Gemmell, Niklas Tecklenburg, Iain Mackie, Federico Rossetto, Jeffrey Dalton|University of Glasgow, Glasgow, United Kingdom; University of Edinburgh, Edinburgh, United Kingdom|We tackle the challenge of building real-world multimodal assistants for complex real-world tasks. We describe the practicalities and challenges of developing and deploying GRILLBot, a leading (first and second prize winning in 2022 and 2023) system deployed in the Alexa Prize TaskBot Challenge. Building on our Open Assistant Toolkit (OAT) framework, we propose a hybrid architecture that leverages Large Language Models (LLMs) and specialised models tuned for specific subtasks requiring very low latency. OAT allows us to define when, how and which LLMs should be used in a structured and deployable manner. For knowledge-grounded question answering and live task adaptations, we show that LLM reasoning abilities over task context and world knowledge outweigh latency concerns. For dialogue state management, we implement a code generation approach and show that specialised smaller models have 84% effectiveness with 100x lower latency. Overall, we provide insights and discuss tradeoffs for deploying both traditional models and LLMs to users in complex real-world multimodal environments in the Alexa TaskBot challenge. These experiences will continue to evolve as LLMs become more capable and efficient -- fundamentally reshaping OAT and future assistant architectures.|我们应对为复杂的现实世界任务构建现实世界多式联运助手的挑战。我们描述了开发和部署 GRILLBot 的实用性和挑战性，这是一个在 Alexa Prize TaskBot 挑战赛中部署的领先(2022年和2023年获得一等奖和二等奖)系统。在我们的 Open Assistant Toolkit (OAT)框架的基础上，我们提出了一种混合体系结构，它利用大语言模型(LLM)和针对需要非常低延迟的特定子任务调优的专门模型。OAT 允许我们定义何时、如何以及以结构化和可部署的方式使用哪些 LLM。对于基于知识的问题回答和实时任务适应，我们表明 LLM 在任务上下文和世界知识上的推理能力大于对延迟的关注。对于对话状态管理，我们实现了一个代码生成方法，并显示专门的小型模型具有84% 的有效性，延迟减少了100倍。总的来说，我们在 Alexa TaskBot 挑战中为复杂的现实世界多通道环境中的用户部署传统模型和 LLM 提供了见解并讨论了折衷方案。随着 LLM 变得更加有能力和高效，这些经验将继续发展——从根本上重塑 OAT 和未来的助理架构。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=GRILLBot+In+Practice:+Lessons+and+Tradeoffs+Deploying+Large+Language+Models+for+Adaptable+Conversational+Task+Assistants)|0| -|[Enhancing E-commerce Spelling Correction with Fine-Tuned Transformer Models](https://doi.org/10.1145/3637528.3671625)|Arnab Dutta, Gleb Polushin, Xiaoshuang Zhang, Daniel Stein|eBay GmbH, Aachen, Germany; eBay GmbH, Dreilinden, Germany; eBay Inc., Shanghai, China|In the realm of e-commerce, the process of search stands as the primary point of interaction for users, wielding a profound influence on the platform's revenue generation. Notably, spelling correction assumes a pivotal role in shaping the user's search experience by rectifying erroneous query inputs, thus facilitating more accurate retrieval outcomes. Within the scope of this research paper, our aim is to enhance the existing state-of-the-art discriminative model performance with generative modelling strategies while concurrently addressing the engineering concerns associated with real-time online latency, inherent to models of this category. We endeavor to refine LSTM-based classification models for spelling correction through a generative fine-tuning approach hinged upon pre-trained language models. Our comprehensive offline assessments have yielded compelling results, showcasing that transformer-based architectures, such as BART (developed by Facebook) and T5 (a product of Google), have achieved a 4% enhancement in F1 score compared to baseline models for the English language sites. Furthermore, to mitigate the challenges posed by latency, we have incorporated model pruning techniques like no-teacher distillation. We have undertaken the deployment of our model (English only) as an A/B test candidate for real-time e-commerce traffic, encompassing customers from the US and the UK. The model attest to a 100% successful request service rate within real-time scenarios, with median, 90th percentile, and 99th percentile (p90/p99) latencies comfortably falling below production service level agreements. Notably, these achievements are further reinforced by positive customer engagement, transactional and search page metrics, including a significant reduction in instances of search results page with low or almost zero recall. Moreover, we have also extended our efforts into fine-tuning a multilingual model, which, notably, exhibits substantial accuracy enhancements, amounting to a minimum of 16%, across four distinct European languages and English.|在电子商务领域，搜索过程是用户交互的主要点，对平台的收入产生深远的影响。值得注意的是，拼写纠正通过纠正错误的查询输入，在塑造用户的搜索体验方面扮演着关键的角色，从而促进更准确的检索结果。在这篇研究论文的范围内，我们的目标是通过生成建模策略来提高现有的最先进的判别模型性能，同时解决与这类模型固有的实时在线延迟相关的工程问题。我们致力于完善基于 LSTM 的拼写校正分类模型，通过一种基于预训练语言模型的生成式微调方法。我们全面的离线评估已经产生了引人注目的结果，表明基于转换器的架构，如 BART (由 Facebook 开发)和 T5(谷歌的产品) ，与英语网站的基线模型相比，F1得分提高了4% 。此外，为了缓解延迟带来的挑战，我们已经采用了模型修剪技术，如非教师精馏。我们已经开始部署我们的模型(只有英语)作为一个 A/B 测试的实时电子商务流量的候选人，包括来自美国和英国的客户。该模型证明了在实时场景中100% 的成功请求服务率，中位数，第90百分位数和第99百分位数(p90/p99)延迟轻松地低于生产服务水平协议。值得注意的是，这些成就进一步得到了积极的客户参与度、交易和搜索页面指标的加强，包括搜索结果页面实例的显著减少，这些实例的召回率很低或几乎为零。此外，我们还将努力扩展到对多语言模型进行微调，特别是在四种不同的欧洲语言和英语之间，该模型显示出大幅度的准确性增强，最低达到16% 。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Enhancing+E-commerce+Spelling+Correction+with+Fine-Tuned+Transformer+Models)|0| -|[Personalised Drug Identifier for Cancer Treatment with Transformers using Auxiliary Information](https://doi.org/10.1145/3637528.3671652)|Aishwarya Jayagopal, Hansheng Xue, Ziyang He, Robert J. Walsh, Krishna Kumar Hariprasannan, David Shao Peng Tan, Tuan Zea Tan, Jason J. Pitt, Anand D. Jeyasekharan, Vaibhav Rajan|Cancer Science Institute of Singapore, Singapore, Singapore; National University Cancer Institute, Singapore, Singapore; National University of Singapore, Singapore, Singapore|Cancer remains a global challenge due to its growing clinical and economic burden. Its uniquely personal manifestation, which makes treatment difficult, has fuelled the quest for personalized treatment strategies. Thus, genomic profiling is increasingly becoming part of clinical diagnostic panels. Effective use of such panels requires accurate drug response prediction (DRP) models, which are challenging to build due to limited labelled patient data. Previous methods to address this problem have used various forms of transfer learning. However, they do not explicitly model the variable length sequential structure of the list of mutations in such diagnostic panels. Further, they do not utilize auxiliary information (like patient survival) for model training. We address these limitations through a novel transformer-based method, which surpasses the performance of state-of-the-art DRP models on benchmark data. Code for our method is available at https://github.com/CDAL-SOC/PREDICT-AI. We also present the design of a treatment recommendation system (TRS), which is currently deployed at the National University Hospital, Singapore and is being evaluated in a clinical trial. We discuss why the recommended drugs and their predicted scores alone, obtained from DRP models, are insufficient for treatment planning. Treatment planning for complex cancer cases, in the face of limited clinical validation, requires assessment of many other factors, including several indirect sources of evidence on drug efficacy. We discuss key lessons learnt on model validation and use of indirect supporting evidence to build clinicians' trust and aid their decision making.|癌症由于其日益增长的临床和经济负担，仍然是一个全球性的挑战。它独特的个人表现使得治疗变得困难，也促进了对个性化治疗策略的探索。因此，基因组图谱正日益成为临床诊断小组的一部分。有效使用这些小组需要准确的药物反应预测(DRP)模型，这是具有挑战性的建立由于有限的标记患者数据。以往解决这一问题的方法都采用了各种形式的迁移学习。然而，他们没有明确地模拟这些诊断小组中突变列表的可变长度顺序结构。此外，他们不利用辅助信息(如患者生存)的模型训练。我们通过一种新的基于变压器的方法来解决这些局限性，该方法在基准数据上的性能超过了最先进的 DRP 模型。我们方法的代码可在 https://github.com/cdal-soc/predict-ai 下载。我们还介绍了一个治疗推荐系统(TRS)的设计，该系统目前部署在新加坡国立大学医院，正在进行临床试验评估。我们讨论为什么推荐的药物和他们的预测评分单独从 DRP 模型获得，不足以进行治疗计划。复杂癌症病例的治疗计划，面对有限的临床验证，需要评估许多其他因素，包括药物疗效的几个间接证据来源。我们讨论在模型验证和使用间接支持证据建立临床医生的信任和帮助他们的决策的关键经验教训。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Personalised+Drug+Identifier+for+Cancer+Treatment+with+Transformers+using+Auxiliary+Information)|0| +|[Enhancing E-commerce Spelling Correction with Fine-Tuned Transformer Models](https://doi.org/10.1145/3637528.3671625)|Arnab Dutta, Gleb Polushin, Xiaoshuang Zhang, Daniel Stein|eBay Inc., Shanghai, China; eBay GmbH, Dreilinden, Germany; eBay GmbH, Aachen, Germany|In the realm of e-commerce, the process of search stands as the primary point of interaction for users, wielding a profound influence on the platform's revenue generation. Notably, spelling correction assumes a pivotal role in shaping the user's search experience by rectifying erroneous query inputs, thus facilitating more accurate retrieval outcomes. Within the scope of this research paper, our aim is to enhance the existing state-of-the-art discriminative model performance with generative modelling strategies while concurrently addressing the engineering concerns associated with real-time online latency, inherent to models of this category. We endeavor to refine LSTM-based classification models for spelling correction through a generative fine-tuning approach hinged upon pre-trained language models. Our comprehensive offline assessments have yielded compelling results, showcasing that transformer-based architectures, such as BART (developed by Facebook) and T5 (a product of Google), have achieved a 4% enhancement in F1 score compared to baseline models for the English language sites. Furthermore, to mitigate the challenges posed by latency, we have incorporated model pruning techniques like no-teacher distillation. We have undertaken the deployment of our model (English only) as an A/B test candidate for real-time e-commerce traffic, encompassing customers from the US and the UK. The model attest to a 100% successful request service rate within real-time scenarios, with median, 90th percentile, and 99th percentile (p90/p99) latencies comfortably falling below production service level agreements. Notably, these achievements are further reinforced by positive customer engagement, transactional and search page metrics, including a significant reduction in instances of search results page with low or almost zero recall. Moreover, we have also extended our efforts into fine-tuning a multilingual model, which, notably, exhibits substantial accuracy enhancements, amounting to a minimum of 16%, across four distinct European languages and English.|在电子商务领域，搜索过程是用户交互的主要点，对平台的收入产生深远的影响。值得注意的是，拼写纠正通过纠正错误的查询输入，在塑造用户的搜索体验方面扮演着关键的角色，从而促进更准确的检索结果。在这篇研究论文的范围内，我们的目标是通过生成建模策略来提高现有的最先进的判别模型性能，同时解决与这类模型固有的实时在线延迟相关的工程问题。我们致力于完善基于 LSTM 的拼写校正分类模型，通过一种基于预训练语言模型的生成式微调方法。我们全面的离线评估已经产生了引人注目的结果，表明基于转换器的架构，如 BART (由 Facebook 开发)和 T5(谷歌的产品) ，与英语网站的基线模型相比，F1得分提高了4% 。此外，为了缓解延迟带来的挑战，我们已经采用了模型修剪技术，如非教师精馏。我们已经开始部署我们的模型(只有英语)作为一个 A/B 测试的实时电子商务流量的候选人，包括来自美国和英国的客户。该模型证明了在实时场景中100% 的成功请求服务率，中位数，第90百分位数和第99百分位数(p90/p99)延迟轻松地低于生产服务水平协议。值得注意的是，这些成就进一步得到了积极的客户参与度、交易和搜索页面指标的加强，包括搜索结果页面实例的显著减少，这些实例的召回率很低或几乎为零。此外，我们还将努力扩展到对多语言模型进行微调，特别是在四种不同的欧洲语言和英语之间，该模型显示出大幅度的准确性增强，最低达到16% 。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Enhancing+E-commerce+Spelling+Correction+with+Fine-Tuned+Transformer+Models)|0| +|[Personalised Drug Identifier for Cancer Treatment with Transformers using Auxiliary Information](https://doi.org/10.1145/3637528.3671652)|Aishwarya Jayagopal, Hansheng Xue, Ziyang He, Robert J. Walsh, Krishna Kumar Hariprasannan, David Shao Peng Tan, Tuan Zea Tan, Jason J. Pitt, Anand D. Jeyasekharan, Vaibhav Rajan|National University of Singapore, Singapore, Singapore; Cancer Science Institute of Singapore, Singapore, Singapore; National University Cancer Institute, Singapore, Singapore|Cancer remains a global challenge due to its growing clinical and economic burden. Its uniquely personal manifestation, which makes treatment difficult, has fuelled the quest for personalized treatment strategies. Thus, genomic profiling is increasingly becoming part of clinical diagnostic panels. Effective use of such panels requires accurate drug response prediction (DRP) models, which are challenging to build due to limited labelled patient data. Previous methods to address this problem have used various forms of transfer learning. However, they do not explicitly model the variable length sequential structure of the list of mutations in such diagnostic panels. Further, they do not utilize auxiliary information (like patient survival) for model training. We address these limitations through a novel transformer-based method, which surpasses the performance of state-of-the-art DRP models on benchmark data. Code for our method is available at https://github.com/CDAL-SOC/PREDICT-AI. We also present the design of a treatment recommendation system (TRS), which is currently deployed at the National University Hospital, Singapore and is being evaluated in a clinical trial. We discuss why the recommended drugs and their predicted scores alone, obtained from DRP models, are insufficient for treatment planning. Treatment planning for complex cancer cases, in the face of limited clinical validation, requires assessment of many other factors, including several indirect sources of evidence on drug efficacy. We discuss key lessons learnt on model validation and use of indirect supporting evidence to build clinicians' trust and aid their decision making.|癌症由于其日益增长的临床和经济负担，仍然是一个全球性的挑战。它独特的个人表现使得治疗变得困难，也促进了对个性化治疗策略的探索。因此，基因组图谱正日益成为临床诊断小组的一部分。有效使用这些小组需要准确的药物反应预测(DRP)模型，这是具有挑战性的建立由于有限的标记患者数据。以往解决这一问题的方法都采用了各种形式的迁移学习。然而，他们没有明确地模拟这些诊断小组中突变列表的可变长度顺序结构。此外，他们不利用辅助信息(如患者生存)的模型训练。我们通过一种新的基于变压器的方法来解决这些局限性，该方法在基准数据上的性能超过了最先进的 DRP 模型。我们方法的代码可在 https://github.com/cdal-soc/predict-ai 下载。我们还介绍了一个治疗推荐系统(TRS)的设计，该系统目前部署在新加坡国立大学医院，正在进行临床试验评估。我们讨论为什么推荐的药物和他们的预测评分单独从 DRP 模型获得，不足以进行治疗计划。复杂癌症病例的治疗计划，面对有限的临床验证，需要评估许多其他因素，包括药物疗效的几个间接证据来源。我们讨论在模型验证和使用间接支持证据建立临床医生的信任和帮助他们的决策的关键经验教训。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Personalised+Drug+Identifier+for+Cancer+Treatment+with+Transformers+using+Auxiliary+Information)|0| |[ERASE: Benchmarking Feature Selection Methods for Deep Recommender Systems](https://doi.org/10.1145/3637528.3671571)|Pengyue Jia, Yejing Wang, Zhaocheng Du, Xiangyu Zhao, Yichao Wang, Bo Chen, Wanyu Wang, Huifeng Guo, Ruiming Tang|Huawei Noah's Ark Lab, Shenzhen, China; City University of Hong Kong, Hong Kong, China|Deep Recommender Systems (DRS) are increasingly dependent on a large number of feature fields for more precise recommendations. Effective feature selection methods are consequently becoming critical for further enhancing the accuracy and optimizing storage efficiencies to align with the deployment demands. This research area, particularly in the context of DRS, is nascent and faces three core challenges. Firstly, variant experimental setups across research papers often yield unfair comparisons, obscuring practical insights. Secondly, the existing literature's lack of detailed analysis on selection attributes, based on large-scale datasets and a thorough comparison among selection techniques and DRS backbones, restricts the generalizability of findings and impedes deployment on DRS. Lastly, research often focuses on comparing the peak performance achievable by feature selection methods. This approach is typically computationally infeasible for identifying the optimal hyperparameters and overlooks evaluating the robustness and stability of these methods. To bridge these gaps, this paper presents ERASE, a comprehensive bEnchmaRk for feAture SElection for DRS. ERASE comprises a thorough evaluation of eleven feature selection methods, covering both traditional and deep learning approaches, across four public datasets, private industrial datasets, and a real-world commercial platform, achieving significant enhancement. Our code is available online for ease of reproduction.|深度推荐系统(DRS)越来越依赖于大量的特征字段以获得更精确的推荐。因此，有效的特征选择方法对于进一步提高准确性和优化存储效率以满足部署需求变得至关重要。这一研究领域，特别是在 DRS 的背景下，还处于起步阶段，面临着三个核心挑战。首先，不同研究论文的不同实验设置往往产生不公平的比较，模糊了实践的洞察力。其次，现有文献缺乏对选择属性的详细分析，基于大规模的数据集，对选择技术和 DRS 骨干进行了深入的比较，限制了研究结果的普遍性，阻碍了 DRS 的部署。这种方法通常是计算不可行的，以确定最佳的超参数和忽略评估这些方法的稳健性和稳定性。为了弥补这些差距，本文提出了 ERASE，一个全面的 bEnchmaRk 特征选择为 DRS ERASE 包括十一个特征选择方法的彻底评估，涵盖传统和深度学习方法，四个公共数据集，私人工业数据集和一个真实世界的商业平台，实现了显着的增强。我们的代码可以在线复制。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=ERASE:+Benchmarking+Feature+Selection+Methods+for+Deep+Recommender+Systems)|0| |[Beyond Binary Preference: Leveraging Bayesian Approaches for Joint Optimization of Ranking and Calibration](https://doi.org/10.1145/3637528.3671577)|Chang Liu, Qiwei Wang, Wenqing Lin, Yue Ding, Hongtao Lu|Shanghai Jiao Tong University, Shanghai, China; Tencent, Shenzhen, China|Predicting click-through rate (CTR) is a critical task in recommendation systems, where the models are optimized with pointwise loss to infer the probability of items being clicked. In industrial practice, applications also require ranking items based on these probabilities. Existing solutions primarily combine the ranking-based loss, i.e., pairwise and listwise loss, with CTR prediction. However, they can hardly calibrate or generalize well in CTR scenarios where the clicks reflect the binary preference. This is because the binary click feedback leads to a large number of ties, which renders high data sparsity. In this paper, we propose an effective data augmentation strategy, named Beyond Binary Preference (BBP) training framework, to address this problem. Our key idea is to break the ties by leveraging Bayesian approaches, where the beta distribution models click behavior as probability distributions in the training data that naturally break ties. Therefore, we can obtain an auxiliary training label that generates more comparable pairs and improves the ranking performance. Besides, BBP formulates ranking and calibration as a multi-task framework to optimize both objectives simultaneously. Through extensive offline experiments and online tests on various datasets, we demonstrate that BBP significantly outperforms state-of-the-art methods in both ranking and calibration capabilities, showcasing its effectiveness in addressing the limitations of existing methods. Our code is available at https://github.com/AlvinIsonomia/BBP.|在推荐系统中，预测点进率(ctrl)是一项关键的任务，模型通过逐点丢失的方式进行优化，以推断项目被点击的概率。在工业实践中，应用程序还需要根据这些概率对项目进行排序。现有的解决方案主要结合了基于排名的损失，即成对损失和列表损失，以及 CTR 预测。然而，在点击反映二进制偏好的 CTR 场景中，它们很难校准或推广。这是因为二进制单击反馈会导致大量关联，从而导致高数据稀疏性。针对这一问题，本文提出了一种有效的数据增强策略——超越二进制偏好(Beyond Binary Preferences，BBP)训练框架。我们的主要想法是通过利用贝叶斯方法来打破这种联系，在贝叶斯方法中，Β分布模型将行为作为训练数据中的概率分布来点击，从而自然地打破联系。因此，我们可以得到一个辅助训练标签，生成更多的可比对，提高排序性能。此外，BBP 作为一个多任务框架制定排序和校准，以优化两个目标同时进行。通过大量的离线实验和对各种数据集的在线测试，我们证明了 BBP 在排序和校准能力方面显著优于最先进的方法，展示了它在解决现有方法的局限性方面的有效性。我们的代码可以在 https://github.com/alvinisonomia/bbp 找到。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Beyond+Binary+Preference:+Leveraging+Bayesian+Approaches+for+Joint+Optimization+of+Ranking+and+Calibration)|0| -|[Optimizing Novelty of Top-k Recommendations using Large Language Models and Reinforcement Learning](https://doi.org/10.1145/3637528.3671618)|Amit Sharma, Hua Li, Xue Li, Jian Jiao|Microsoft Bing Ads, Mountain View, USA; Microsoft Bing Ads, Redmond, USA; Microsoft Research, Bengaluru, India|Given an input query, a recommendation model is trained using user feedback data (e.g., click data) to output a ranked list of items. In real-world systems, besides accuracy, an important consideration for a new model is novelty of its top-k recommendations w.r.t. an existing deployed model. However, novelty of top-k items is a difficult goal to optimize a model for, since it involves a non-differentiable sorting operation on the model's predictions. Moreover, novel items, by definition, do not have any user feedback data. Given the semantic capabilities of large language models, we address these problems using a reinforcement learning (RL) formulation where large language models provide feedback for the novel items. However, given millions of candidate items, the sample complexity of a standard RL algorithm can be prohibitively high. To reduce sample complexity, we reduce the top-k list reward to a set of item-wise rewards and reformulate the state space to consist of tuples such that the action space is reduced to a binary decision; and show that this reformulation results in a significantly lower complexity when the number of items is large. We evaluate the proposed algorithm on improving novelty for a query-ad recommendation task on a large-scale search engine. Compared to supervised finetuning on recent pairs, the proposed RL-based algorithm leads to significant novelty gains with minimal loss in recall. We obtain similar results on the ORCAS query-webpage matching dataset and a product recommendation dataset based on Amazon reviews.|给定一个输入查询，使用用户反馈数据(例如，单击数据)来训练推荐模型，以输出一个排序的项目列表。在现实世界的系统中，除了准确性之外，新模型的一个重要考虑因素是它的 top-k 推荐 W.r.t. (现有的部署模型)的新颖性。然而，前 k 项的新颖性是一个很难优化模型的目标，因为它涉及到对模型预测的不可微排序操作。此外，根据定义，新项目没有任何用户反馈数据。考虑到大型语言模型的语义能力，我们使用强化学习(RL)公式来解决这些问题，其中大型语言模型为新项目提供反馈。然而，给定数百万个候选项，标准 RL 算法的样本复杂度可能高得令人望而却步。为了降低样本复杂度，我们将 top-k 列表奖励减少为一组项目奖励，并将状态空间重新表述为由元组组成的状态空间，从而将操作空间减少为二进制决策; 结果表明，当项目数量较大时，这种重新表述显著降低了复杂度。针对大规模搜索引擎中的查询广告推荐任务，评估了该算法的新颖性。与最近对的监督微调相比，本文提出的基于 RL 的算法在召回损失最小的情况下获得了显著的新颖性增益。在 ORCAS 查询-网页匹配数据集和基于 Amazon 评论的产品推荐数据集上，我们得到了类似的结果。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Optimizing+Novelty+of+Top-k+Recommendations+using+Large+Language+Models+and+Reinforcement+Learning)|0| +|[Optimizing Novelty of Top-k Recommendations using Large Language Models and Reinforcement Learning](https://doi.org/10.1145/3637528.3671618)|Amit Sharma, Hua Li, Xue Li, Jian Jiao|Microsoft Bing Ads, Redmond, USA; Microsoft Research, Bengaluru, India; Microsoft Bing Ads, Mountain View, USA|Given an input query, a recommendation model is trained using user feedback data (e.g., click data) to output a ranked list of items. In real-world systems, besides accuracy, an important consideration for a new model is novelty of its top-k recommendations w.r.t. an existing deployed model. However, novelty of top-k items is a difficult goal to optimize a model for, since it involves a non-differentiable sorting operation on the model's predictions. Moreover, novel items, by definition, do not have any user feedback data. Given the semantic capabilities of large language models, we address these problems using a reinforcement learning (RL) formulation where large language models provide feedback for the novel items. However, given millions of candidate items, the sample complexity of a standard RL algorithm can be prohibitively high. To reduce sample complexity, we reduce the top-k list reward to a set of item-wise rewards and reformulate the state space to consist of tuples such that the action space is reduced to a binary decision; and show that this reformulation results in a significantly lower complexity when the number of items is large. We evaluate the proposed algorithm on improving novelty for a query-ad recommendation task on a large-scale search engine. Compared to supervised finetuning on recent pairs, the proposed RL-based algorithm leads to significant novelty gains with minimal loss in recall. We obtain similar results on the ORCAS query-webpage matching dataset and a product recommendation dataset based on Amazon reviews.|给定一个输入查询，使用用户反馈数据(例如，单击数据)来训练推荐模型，以输出一个排序的项目列表。在现实世界的系统中，除了准确性之外，新模型的一个重要考虑因素是它的 top-k 推荐 W.r.t. (现有的部署模型)的新颖性。然而，前 k 项的新颖性是一个很难优化模型的目标，因为它涉及到对模型预测的不可微排序操作。此外，根据定义，新项目没有任何用户反馈数据。考虑到大型语言模型的语义能力，我们使用强化学习(RL)公式来解决这些问题，其中大型语言模型为新项目提供反馈。然而，给定数百万个候选项，标准 RL 算法的样本复杂度可能高得令人望而却步。为了降低样本复杂度，我们将 top-k 列表奖励减少为一组项目奖励，并将状态空间重新表述为由元组组成的状态空间，从而将操作空间减少为二进制决策; 结果表明，当项目数量较大时，这种重新表述显著降低了复杂度。针对大规模搜索引擎中的查询广告推荐任务，评估了该算法的新颖性。与最近对的监督微调相比，本文提出的基于 RL 的算法在召回损失最小的情况下获得了显著的新颖性增益。在 ORCAS 查询-网页匹配数据集和基于 Amazon 评论的产品推荐数据集上，我们得到了类似的结果。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Optimizing+Novelty+of+Top-k+Recommendations+using+Large+Language+Models+and+Reinforcement+Learning)|0| |[Measuring an LLM's Proficiency at using APIs: A Query Generation Strategy](https://doi.org/10.1145/3637528.3671592)|Ying Sheng, Sudeep Gandhe, Bhargav Kanagal, Nick Edmonds, Zachary Fisher, Sandeep Tata, Aarush Selvan|Google Research, Mountain View, CA, USA; Google, Mountain View, CA, USA|Connecting Large Language Models (LLMs) with the ability to leverage APIs (Web Search, Charting, Calculators, Calendar, Flight Search, Hotel Search, Data Lookup, etc. ) is likely to allow us to solve a variety of new hard problems. Several research efforts have made this observation and suggested recipes for LLMs to emit API calls, and proposed mechanisms by which they can generate additional text conditioned on the output for the API call. However, in practice, the focus has been on relatively simple slot-filling tasks that make an API call rather unlocking novel capabilities by combining different tools, reasoning over the response from a tool, making multiple invocations, or complex planning. In this paper, we pose the following question: what does it mean to say that an LLM is proficient at using a set of APIs? We answer this question in the context of structured APIs by defining seven capabilities for API-use. We provide an approach for generating synthetic tasks that exercise each of these capabilities given only the description of an API. We argue that this provides practitioners with a principled way to construct a dataset to evaluate an LLM's ability to use a given set of APIs. Through human evaluations, we show that our approach produces high-quality tasks for each of the seven capabilities. We also describe how we used this approach to on-board new API and create principled evaluation sets for multiple LLM-based products.|将大型语言模型(LLM)与能够利用 API (网络搜索、制图、计算器、日历、航班搜索、酒店搜索、数据查询等)联系起来，可能会让我们解决各种新的难题。一些研究工作已经做出了这样的观察，并提出了 LLM 发出 API 调用的方法，以及提出了一些机制，通过这些机制，LLM 可以根据 API 调用的输出生成额外的文本。然而，在实践中，重点一直放在相对简单的插槽填充任务上，这些任务通过组合不同的工具、对工具的响应进行推理、进行多次调用或复杂的计划，使 API 调用相当于解锁新功能。在本文中，我们提出以下问题: 说 LLM 精通使用一组 API 意味着什么？我们在结构化 API 的上下文中通过定义七种 API 使用功能来回答这个问题。我们提供了一种生成综合任务的方法，这些任务只需要对 API 进行描述，就可以实现这些功能中的每一个。我们认为，这为从业者提供了一种原则性的方法来构建数据集，以评估 LLM 使用给定 API 集的能力。通过人工评估，我们表明我们的方法为七种能力中的每一种都产生了高质量的任务。我们还描述了如何使用这种方法来开发新的 API，以及如何为多个基于 LLM 的产品创建原则性评估集。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Measuring+an+LLM's+Proficiency+at+using+APIs:+A+Query+Generation+Strategy)|0| |[PEMBOT: Pareto-Ensembled Multi-task Boosted Trees](https://doi.org/10.1145/3637528.3671619)|Gokul Swamy, Anoop Saladi, Arunita Das, Shobhit Niranjan|Amazon, Bengaluru, KA, India; Amazon, Seattle, WA, USA|Multi-task problems frequently arise in machine learning when there are multiple target variables, which share a common synergy while being sufficiently different that optimizing on any of the task does not necessarily imply an optimum for the others. In this work, we develop PEMBOT, a novel Pareto-based multi-task classification framework using a gradient boosted tree architecture. The proposed methodology involves a) generating multiple instances of Pareto optimal trees, b) diverse subset selection using a determinantal point process (DPP) model, and c) ensembling of diverse Pareto optimal trees to yield the final output. We tested our framework on a problem from an e-commerce domain wherein the task is to predict at order placement time the different adverse scenarios in the order shipment journey such as the package getting lost or damaged during shipment. This model enables us to take preemptive measures to prevent these scenarios from happening resulting in significant operational cost savings. Further, to show the generality of our approach, we demonstrate the performance of our algorithm on a publicly available wine quality prediction dataset and compare against state-of-the-art baselines.|多任务问题经常出现在机器学习时，有多个目标变量，共享一个共同的协同作用，同时是充分不同的，优化任何一个任务并不一定意味着最优的其他。在这项工作中，我们开发 PEMBOT，一个新颖的基于 Pareto 的多任务分类框架使用梯度增强树结构。提出的方法包括 a)生成多个帕累托最优树的实例，b)使用行列式点过程(DPP)模型进行多样化的子集选择，以及 c)将不同的帕累托最优树集合起来以产生最终的输出。我们测试了我们的框架从一个电子商务领域的问题，其中的任务是预测在订单放置时间不同的不利情况下的订单装运旅程，如包裹丢失或损坏在装运期间。该模型使我们能够采取先发制人的措施，防止这些情况发生，从而大大节省运营成本。此外，为了展示我们方法的通用性，我们在一个公开的葡萄酒质量预测数据集上演示了我们算法的性能，并与最先进的基线进行了比较。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=PEMBOT:+Pareto-Ensembled+Multi-task+Boosted+Trees)|0| -|[Enhancing Personalized Headline Generation via Offline Goal-conditioned Reinforcement Learning with Large Language Models](https://doi.org/10.1145/3637528.3671638)|Xiaoyu Tan, Leijun Cheng, Xihe Qiu, Shaojie Shi, Yuan Cheng, Wei Chu, Yinghui Xu, Yuan Qi|; INF Technology (Shanghai) Co., Ltd., Shanghai, China; AI3 Institute, Fudan University, Shanghai, China|Recently, significant advancements have been made in Large Language Models (LLMs) through the implementation of various alignment techniques. These techniques enable LLMs to generate highly tailored content in response to diverse user instructions. Consequently, LLMs have the potential to serve as robust, customizable recommendation systems in the field of content recommendation. However, using LLMs with user individual information and online exploration remains a challenge, which are important perspectives in developing personalized news headline generation algorithms. In this paper, we propose a novel framework to generate personalized news headlines using LLMs with extensive online exploration. The proposed approach involves initially training an offline goal-conditioned policy using supervised learning. Subsequently, online exploration is employed to collect new data for the next training iteration. Results from simulations, experiments, and real-word scenario demonstrate that our framework achieves outstanding performance on established benchmarks and can effectively generate personalized headlines under different reward settings. By treating the LLM as a goal-conditioned agent, the model can perform online exploration by modifying the goals without frequently retraining the model. To the best of our knowledge, this work represents the first investigation into the capability of LLMs to generate customized news headlines with goal-conditioned reinforcement learning via supervised learning within LLMs.|最近，通过实现各种对齐技术，大型语言模型(LLM)取得了重大进展。这些技术使 LLM 能够根据不同的用户指令生成高度定制的内容。因此，LLM 有可能成为内容推荐领域中健壮的、可定制的推荐系统。然而，利用具有用户个人信息和在线探索的 LLM 仍然是一个挑战，这是开发个性化新闻标题生成算法的重要视角。在本文中，我们提出了一个新的框架来生成个性化的新闻标题使用 LLM 广泛的在线探索。拟议中的方法包括最初使用监督式学习训练一种离线的以目标为条件的政策。随后，采用在线探索的方法为下一次训练迭代收集新的数据。模拟、实验和真实场景的结果表明，我们的框架在已建立的基准上取得了出色的性能，并且能够在不同的奖励设置下有效地生成个性化的标题。通过将 LLM 视为目标条件智能体，该模型可以通过修改目标进行在线探索，而无需频繁地对模型进行再训练。据我们所知，这项工作代表了首次调查的能力，生成定制的新闻标题与目标条件的强化学习通过监督式学习内部 LLM。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Enhancing+Personalized+Headline+Generation+via+Offline+Goal-conditioned+Reinforcement+Learning+with+Large+Language+Models)|0| +|[Enhancing Personalized Headline Generation via Offline Goal-conditioned Reinforcement Learning with Large Language Models](https://doi.org/10.1145/3637528.3671638)|Xiaoyu Tan, Leijun Cheng, Xihe Qiu, Shaojie Shi, Yuan Cheng, Wei Chu, Yinghui Xu, Yuan Qi|; AI3 Institute, Fudan University, Shanghai, China; INF Technology (Shanghai) Co., Ltd., Shanghai, China|Recently, significant advancements have been made in Large Language Models (LLMs) through the implementation of various alignment techniques. These techniques enable LLMs to generate highly tailored content in response to diverse user instructions. Consequently, LLMs have the potential to serve as robust, customizable recommendation systems in the field of content recommendation. However, using LLMs with user individual information and online exploration remains a challenge, which are important perspectives in developing personalized news headline generation algorithms. In this paper, we propose a novel framework to generate personalized news headlines using LLMs with extensive online exploration. The proposed approach involves initially training an offline goal-conditioned policy using supervised learning. Subsequently, online exploration is employed to collect new data for the next training iteration. Results from simulations, experiments, and real-word scenario demonstrate that our framework achieves outstanding performance on established benchmarks and can effectively generate personalized headlines under different reward settings. By treating the LLM as a goal-conditioned agent, the model can perform online exploration by modifying the goals without frequently retraining the model. To the best of our knowledge, this work represents the first investigation into the capability of LLMs to generate customized news headlines with goal-conditioned reinforcement learning via supervised learning within LLMs.|最近，通过实现各种对齐技术，大型语言模型(LLM)取得了重大进展。这些技术使 LLM 能够根据不同的用户指令生成高度定制的内容。因此，LLM 有可能成为内容推荐领域中健壮的、可定制的推荐系统。然而，利用具有用户个人信息和在线探索的 LLM 仍然是一个挑战，这是开发个性化新闻标题生成算法的重要视角。在本文中，我们提出了一个新的框架来生成个性化的新闻标题使用 LLM 广泛的在线探索。拟议中的方法包括最初使用监督式学习训练一种离线的以目标为条件的政策。随后，采用在线探索的方法为下一次训练迭代收集新的数据。模拟、实验和真实场景的结果表明，我们的框架在已建立的基准上取得了出色的性能，并且能够在不同的奖励设置下有效地生成个性化的标题。通过将 LLM 视为目标条件智能体，该模型可以通过修改目标进行在线探索，而无需频繁地对模型进行再训练。据我们所知，这项工作代表了首次调查的能力，生成定制的新闻标题与目标条件的强化学习通过监督式学习内部 LLM。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Enhancing+Personalized+Headline+Generation+via+Offline+Goal-conditioned+Reinforcement+Learning+with+Large+Language+Models)|0| |[Future Impact Decomposition in Request-level Recommendations](https://doi.org/10.1145/3637528.3671506)|Xiaobei Wang, Shuchang Liu, Xueliang Wang, Qingpeng Cai, Lantao Hu, Han Li, Peng Jiang, Kun Gai, Guangming Xie|Unaffiliated, Beijing, China; Peking University, Beijing, China; Kuaishou Technology, Beijing, China|In recommender systems, reinforcement learning solutions have shown promisingresults in optimizing the interaction sequence between users and the systemover the long-term performance. For practical reasons, the policy's actions aretypically designed as recommending a list of items to handle users' frequentand continuous browsing requests more efficiently. In this list-wiserecommendation scenario, the user state is updated upon every request in thecorresponding MDP formulation. However, this request-level formulation isessentially inconsistent with the user's item-level behavior. In this study, wedemonstrate that an item-level optimization approach can better utilize itemcharacteristics and optimize the policy's performance even under therequest-level MDP. We support this claim by comparing the performance ofstandard request-level methods with the proposed item-level actor-criticframework in both simulation and online experiments. Furthermore, we show thata reward-based future decomposition strategy can better express the item-wisefuture impact and improve the recommendation accuracy in the long term. Toachieve a more thorough understanding of the decomposition strategy, we proposea model-based re-weighting framework with adversarial learning that furtherboost the performance and investigate its correlation with the reward-basedstrategy.|在推荐系统中，强化学习解决方案在优化用户与系统之间的交互顺序方面取得了令人满意的效果。出于实际原因，该策略的行动通常被设计为推荐一个项目列表，以更有效地处理用户频繁和连续的浏览请求。在这个列表-智能推荐场景中，用户状态会根据相应 MDP 公式中的每个请求进行更新。但是，这个请求级别的公式与用户的项目级别行为本质上是不一致的。研究表明，即使在请求级 MDP 下，项目级优化方法也能更好地利用项目特征，优化策略性能。我们通过比较标准请求级方法和提出的项目级参与者批评框架在模拟和在线实验中的性能来支持这一说法。进一步，我们发现基于奖励的未来分解策略能够更好地表达项目对未来的影响，从长远来看能够提高推荐的准确性。为了更深入地理解分解策略，我们提出了基于模型的对抗性学习重新加权框架，进一步提高绩效，并研究其与奖励策略的相关性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Future+Impact+Decomposition+in+Request-level+Recommendations)|0| |[A Self-boosted Framework for Calibrated Ranking](https://doi.org/10.1145/3637528.3671570)|Shunyu Zhang, Hu Liu, Wentian Bao, Enyun Yu, Yang Song|Columbia University, Beijing, China; Northeasten University, Beijing, China; Kuaishou Technology, Beijing, China|Scale-calibrated ranking systems are ubiquitous in real-world applications nowadays, which pursue accurate ranking quality and calibrated probabilistic predictions simultaneously. For instance, in the advertising ranking system, the predicted click-through rate (CTR) is utilized for ranking and required to be calibrated for the downstream cost-per-click ads bidding. Recently, multi-objective based methods have been wildly adopted as a standard approach for Calibrated Ranking, which incorporates the combination of two loss functions: a pointwise loss that focuses on calibrated absolute values and a ranking loss that emphasizes relative orderings. However, when applied to industrial online applications, existing multi-objective CR approaches still suffer from two crucial limitations First, previous methods need to aggregate the full candidate list within a single mini-batch to compute the ranking loss. Such aggregation strategy violates extensive data shuffling which has long been proven beneficial for preventing overfitting, and thus degrades the training effectiveness. Second, existing multi-objective methods apply the two inherently conflicting loss functions on a single probabilistic prediction, which results in a sub-optimal trade-off between calibration and ranking. To tackle the two limitations, we propose a Self-Boosted framework for Calibrated Ranking (SBCR). In SBCR, the predicted ranking scores by the online deployed model are dumped into context features. With these additional context features, each single item can perceive the overall distribution of scores in the whole ranking list, so that the ranking loss can be constructed without the need for sample aggregation. As the deployed model is a few versions older than the training model, the dumped predictions reveal what was failed to learn and keep boosting the model to correct previously mis-predicted items. Moreover, a calibration module is introduced to decouple the point loss and ranking loss. The two losses are applied before and after the calibration module separately, which elegantly addresses the sub-optimal trade-off problem. We conduct comprehensive experiments on industrial scale datasets and online A/B tests, demonstrating that SBCR can achieve advanced performance on both calibration and ranking. Our method has been deployed on the video search system of Kuaishou, and results in significant performance improvements on CTR and the total amount of time users spend on Kuaishou.|标度校准的排序系统在现实世界的应用中是普遍存在的，它追求准确的排序质量和校准的概率预测同时进行。例如，在广告排名系统中，预测点进率(ctrl)被用于排名，并且需要根据下游按点击次数计费的广告投标进行校准。最近，基于多目标的方法已被广泛采用作为标准方法校准排名，其中包括两个损失函数的组合: 点态损失的重点是校准绝对值和排名损失的重点是相对排序。然而，现有的多目标 CR 方法在应用于工业在线应用时，仍然存在两个关键的局限性。首先，以前的方法需要在一个小批量内聚合完整的候选名单来计算排名损失。这种聚合策略违反了长期以来被证明有利于防止过拟合的广泛的数据重组策略，从而降低了训练效率。其次，现有的多目标方法将两个内在冲突的损失函数应用于单一的概率预测，导致校准和排序之间的次优平衡。为了解决这两个限制，我们提出了一个自我增强的校准排名(SBCR)框架。在 SBCR，通过在线部署模型预测的排名分数被转化为上下文特征。通过这些附加的上下文特征，每个单独的项目可以感知整个排名列表中分数的总体分布，从而不需要样本聚合就可以构造出排名损失。由于部署的模型比训练模型旧了几个版本，倾销预测揭示了未能学习的内容，并不断提升模型以纠正先前错误预测的项目。同时，引入了一个标定模块，实现了点损和等级损的解耦。这两种损耗分别应用于校准模块之前和之后，从而巧妙地解决了次优折衷问题。我们对工业规模的数据集和在线 A/B 测试进行了全面的实验，结果表明 SBCR 在校准和排序方面都具有较高的性能。我们的方法已经应用于 Kuaishou 的视频搜索系统，在点击率和用户在 Kuaishou 花费的总时间方面取得了显著的性能改善。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Self-boosted+Framework+for+Calibrated+Ranking)|0| -|[Bringing Multimodality to Amazon Visual Search System](https://doi.org/10.1145/3637528.3671640)|Xinliang Zhu, ShengWei Huang, Han Ding, Jinyu Yang, Kelvin Chen, Tao Zhou, Tal Neiman, Ouye Xie, Son Tran, Benjamin Z. Yao, Douglas Gray, Anuj Bindal, Arnab Dhua|Amazon.com, Santa Clara, CA, USA; Amazon.com, New York, New York, USA; Amazon.com, Palo Alto, CA, USA; Amazon.com, Seattle, WA, USA|Image to image matching has been well studied in the computer vision community. Previous studies mainly focus on training a deep metric learning model matching visual patterns between the query image and gallery images. In this study, we show that pure image-to- image matching suffers from false positives caused by matching to local visual patterns. To alleviate this issue, we propose to leverage recent advances in vision-language pretraining research. Specifically, we introduce additional image-text alignment losses into deep metric learning, which serve as constraints to the image-to-image matching loss. With additional alignments between the text (e.g., product title) and image pairs, the model can learn concepts from both modalities explicitly, which avoids matching low-level visual features. We progressively develop two variants, a 3-tower and a 4-tower model, where the latter takes one more short text query input. Through extensive experiments, we show that this change leads to a substantial improvement to the image to image matching problem. We further leveraged this model for multimodal search, which takes both image and reformulation text queries to improve search quality. Both offline and online experiments show strong improvements on the main metrics. Specifically, we see 4.95% relative improvement on image matching click through rate with the 3-tower model and 1.13% further improvement from the 4-tower model.|图像到图像的匹配已经在计算机视觉领域得到了很好的研究。以往的研究主要集中在训练一个匹配查询图像和画廊图像视觉模式的深度度量学习模型。在本研究中，我们发现纯粹的图像对图像的匹配会受到局部视觉模式匹配所引起的假阳性的影响。为了缓解这一问题，我们建议利用视觉语言预训研究的最新进展。具体地说，我们在深度度量学习中引入了额外的图像-文本对齐损失，作为图像-图像匹配损失的约束条件。通过在文本(例如，产品标题)和图像对之间进行额外的对齐，模型可以显式地从两种模式中学习概念，从而避免匹配低层次的视觉特征。我们逐步开发了两个变体，一个3塔模型和一个4塔模型，后者需要更多的短文本查询输入。通过大量的实验，我们发现这种改变使得图像到图像的匹配问题得到了实质性的改善。我们进一步利用该模型进行多模态搜索，该模型同时采用图像查询和重构文本查询来提高搜索质量。离线和在线实验都显示了主要指标的强大改进。具体来说，我们发现三塔模型的图像匹配点击率相对提高了4.95% ，而四塔模型的图像匹配点击率进一步提高了1.13% 。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Bringing+Multimodality+to+Amazon+Visual+Search+System)|0| +|[Bringing Multimodality to Amazon Visual Search System](https://doi.org/10.1145/3637528.3671640)|Xinliang Zhu, ShengWei Huang, Han Ding, Jinyu Yang, Kelvin Chen, Tao Zhou, Tal Neiman, Ouye Xie, Son Tran, Benjamin Z. Yao, Douglas Gray, Anuj Bindal, Arnab Dhua|Amazon.com, Palo Alto, CA, USA; Amazon.com, New York, New York, USA; Amazon.com, Santa Clara, CA, USA; Amazon.com, Seattle, WA, USA|Image to image matching has been well studied in the computer vision community. Previous studies mainly focus on training a deep metric learning model matching visual patterns between the query image and gallery images. In this study, we show that pure image-to- image matching suffers from false positives caused by matching to local visual patterns. To alleviate this issue, we propose to leverage recent advances in vision-language pretraining research. Specifically, we introduce additional image-text alignment losses into deep metric learning, which serve as constraints to the image-to-image matching loss. With additional alignments between the text (e.g., product title) and image pairs, the model can learn concepts from both modalities explicitly, which avoids matching low-level visual features. We progressively develop two variants, a 3-tower and a 4-tower model, where the latter takes one more short text query input. Through extensive experiments, we show that this change leads to a substantial improvement to the image to image matching problem. We further leveraged this model for multimodal search, which takes both image and reformulation text queries to improve search quality. Both offline and online experiments show strong improvements on the main metrics. Specifically, we see 4.95% relative improvement on image matching click through rate with the 3-tower model and 1.13% further improvement from the 4-tower model.|图像到图像的匹配已经在计算机视觉领域得到了很好的研究。以往的研究主要集中在训练一个匹配查询图像和画廊图像视觉模式的深度度量学习模型。在本研究中，我们发现纯粹的图像对图像的匹配会受到局部视觉模式匹配所引起的假阳性的影响。为了缓解这一问题，我们建议利用视觉语言预训研究的最新进展。具体地说，我们在深度度量学习中引入了额外的图像-文本对齐损失，作为图像-图像匹配损失的约束条件。通过在文本(例如，产品标题)和图像对之间进行额外的对齐，模型可以显式地从两种模式中学习概念，从而避免匹配低层次的视觉特征。我们逐步开发了两个变体，一个3塔模型和一个4塔模型，后者需要更多的短文本查询输入。通过大量的实验，我们发现这种改变使得图像到图像的匹配问题得到了实质性的改善。我们进一步利用该模型进行多模态搜索，该模型同时采用图像查询和重构文本查询来提高搜索质量。离线和在线实验都显示了主要指标的强大改进。具体来说，我们发现三塔模型的图像匹配点击率相对提高了4.95% ，而四塔模型的图像匹配点击率进一步提高了1.13% 。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Bringing+Multimodality+to+Amazon+Visual+Search+System)|0| |[A Survey on RAG Meeting LLMs: Towards Retrieval-Augmented Large Language Models](https://doi.org/10.1145/3637528.3671470)|Wenqi Fan, Yujuan Ding, Liangbo Ning, Shijie Wang, Hengyun Li, Dawei Yin, TatSeng Chua, Qing Li|Baidu Inc., Beijing, CN; National university of Singapore, Singapore, SG; The Hong Kong Polytechnic University, Hong Kong, China|As one of the most advanced techniques in AI, Retrieval-Augmented Generation (RAG) can offer reliable and up-to-date external knowledge, providing huge convenience for numerous tasks. Particularly in the era of AI-Generated Content (AIGC), the powerful capacity of retrieval in providing additional knowledge enables RAG to assist existing generative AI in producing high-quality outputs. Recently, Large Language Models (LLMs) have demonstrated revolutionary abilities in language understanding and generation, while still facing inherent limitations such as hallucinations and out-of-date internal knowledge. Given the powerful abilities of RAG in providing the latest and helpful auxiliary information, Retrieval-Augmented Large Language Models (RA-LLMs) have emerged to harness external and authoritative knowledge bases, rather than solely relying on the model's internal knowledge, to augment the quality of the generated content of LLMs. In this survey, we comprehensively review existing research studies in RA-LLMs, covering three primary technical perspectives: Furthermore, to deliver deeper insights, we discuss current limitations and several promising directions for future research. Updated information about this survey can be found at: https://advanced-recommender-systems.github.io/RAG-Meets-LLMs/|作为人工智能中最先进的技术之一，检索增强生成(RAG)技术可以提供可靠的、最新的外部知识，为大量的任务提供巨大的方便。特别是在人工智能生成内容(AIGC)的时代，强大的检索能力提供额外的知识使 RAG 能够协助现有的生成人工智能产生高质量的输出。近年来，大语言模型(LLM)在语言理解和语言生成方面显示出了革命性的能力，但仍然面临着诸如幻觉和过时的内部知识等固有的局限性。考虑到 RAG 在提供最新和有用的辅助信息方面的强大能力，检索增强大型语言模型(RA-LLM)已经出现，以利用外部和权威的知识库，而不是仅仅依赖于模型的内部知识，以增强 LLM 生成内容的质量。在这项调查中，我们全面回顾了 RA-LLM 的现有研究，包括三个主要的技术视角: 此外，为了提供更深入的见解，我们讨论了当前的局限性和未来研究的几个有希望的方向。有关这项调查的最新资料，可浏览以下 https://advanced-recommender-systems.github.io/rag-meets-llms/ :|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Survey+on+RAG+Meeting+LLMs:+Towards+Retrieval-Augmented+Large+Language+Models)|0| -|[Multimodal Pretraining, Adaptation, and Generation for Recommendation: A Survey](https://doi.org/10.1145/3637528.3671473)|Qijiong Liu, Jieming Zhu, Yanting Yang, Quanyu Dai, Zhaocheng Du, XiaoMing Wu, Zhou Zhao, Rui Zhang, Zhenhua Dong|The HK PolyU, Hong Kong, China; Huawei Noah's Ark Lab, Shenzhen, China; Huazhong University of Science and Technology & ruizhang.info, Shenzhen, China; Zhejiang University, Hangzhou, China; Huawei Noah Ark Lab, Shenzhen, China|Personalized recommendation serves as a ubiquitous channel for users to discover information tailored to their interests. However, traditional recommendation models primarily rely on unique IDs and categorical features for user-item matching, potentially overlooking the nuanced essence of raw item contents across multiple modalities such as text, image, audio, and video. This underutilization of multimodal data poses a limitation to recommender systems, especially in multimedia services like news, music, and short-video platforms. The recent advancements in large multimodal models offer new opportunities and challenges in developing content-aware recommender systems. This survey seeks to provide a comprehensive exploration of the latest advancements and future trajectories in multimodal pretraining, adaptation, and generation techniques, as well as their applications in enhancing recommender systems. Furthermore, we discuss current open challenges and opportunities for future research in this dynamic domain. We believe that this survey, alongside the curated resources, will provide valuable insights to inspire further advancements in this evolving landscape.|个性化推荐是用户发现适合自己兴趣的信息的一个无处不在的渠道。然而，传统的推荐模型主要依赖于用户项匹配的唯一 ID 和分类特性，这可能忽略了原始项目内容在多种模式(如文本、图像、音频和视频)中的细微差别。这种多模式数据的利用率不足对推荐系统造成了限制，特别是在新闻、音乐和短视频平台等多媒体服务中。大型多模式模型的最新进展为开发内容感知的推荐系统提供了新的机遇和挑战。本调查旨在全面探讨多模式预培训、适应和生成技术的最新进展和未来发展轨迹，以及它们在加强推荐系统方面的应用。此外，我们讨论了目前在这一动态领域的开放性挑战和未来研究的机遇。我们相信，这项调查，以及策划的资源，将提供宝贵的见解，以激励在这个不断变化的景观进一步发展。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Multimodal+Pretraining,+Adaptation,+and+Generation+for+Recommendation:+A+Survey)|0| -|[AI for Education (AI4EDU): Advancing Personalized Education with LLM and Adaptive Learning](https://doi.org/10.1145/3637528.3671498)|Qingsong Wen, Jing Liang, Carles Sierra, Rose Luckin, Richard Jiarui Tong, Zitao Liu, Peng Cui, Jiliang Tang|Michigan State University, East Lansing, USA; Jinan University, Guangzhou, China; Squirrel Ai Learning, Bellevue, USA; University College London, London, United Kingdom; Tsinghua University, Beijing, China; Squirrel Ai Learning, Shanghai, China; IIIA of the Spanish National Research Council, Barcelona, Spain|Recent advanced AI technologies, especially large language models (LLMs) like GPTs, have significantly advanced the field of data mining and led to the development of various LLM-based applications. AI for education (AI4EDU) is a vibrant multi-disciplinary field of data mining, machine learning, and education, with increasing importance and extraordinary potential. In this field, LLM and adaptive learning-based models can be utilized as interfaces in human-in-the-loop education systems, where the model serves as a mediator among the teacher, students, and machine capabilities, including its own. This perspective has several benefits, including the ability to personalize interactions, allow unprecedented flexibility and adaptivity for human-AI collaboration and improve the user experience. However, several challenges still exist, including the need for more robust and efficient algorithms, designing effective user interfaces, and ensuring ethical considerations are addressed. This workshop aims to bring together researchers and practitioners from academia and industry to explore cutting-edge AI technologies for personalized education, especially the potential of LLMs and adaptive learning technologies.|近年来，先进的人工智能技术，特别是大型语言模型(LLM) ，如 GPTs，极大地推动了数据挖掘领域的发展，并导致了各种基于 LLM 的应用程序的开发。人工智能教育(AI4EDU)是一个充满活力的多学科领域的数据挖掘，机器学习和教育，越来越重要和非凡的潜力。在这个领域，LLM 和基于自适应学习的模型可以用作人在环路教育系统的接口，在这个系统中，模型充当教师、学生和机器能力(包括它自己的能力)之间的中介。这种视角有几个好处，包括个性化交互的能力，允许前所未有的灵活性和人工智能协作的适应性，并改善用户体验。然而，仍然存在一些挑战，包括需要更加健壮和高效的算法，设计有效的用户界面，并确保道德考虑得到解决。这个工作坊旨在汇聚来自学术界和业界的研究人员和从业员，探讨尖端人工智能技术在个人化教育方面的应用，特别是 LLM 和在线机机器学习技术的潜力。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=AI+for+Education+(AI4EDU):+Advancing+Personalized+Education+with+LLM+and+Adaptive+Learning)|0| -|[Understanding Inter-Session Intentions via Complex Logical Reasoning](https://doi.org/10.1145/3637528.3671808)|Jiaxin Bai, Chen Luo, Zheng Li, Qingyu Yin, Yangqiu Song|Department of CSE, HKUST, Hong Kong, China; Amazon.com Inc, Palo Alto, USA|Understanding user intentions is essential for improving product recommendations, navigation suggestions, and query reformulations. However, user intentions can be intricate, involving multiple sessions and attribute requirements connected by logical operators such as And, Or, and Not. For instance, a user may search for Nike or Adidas running shoes across various sessions, with a preference for purple. In another example, a user may have purchased a mattress in a previous session and is now looking for a matching bed frame without intending to buy another mattress. Existing research on session understanding has not adequately addressed making product or attribute recommendations for such complex intentions. In this paper, we present the task of logical session complex query answering (LS-CQA), where sessions are treated as hyperedges of items, and we frame the problem of complex intention understanding as an LS-CQA task on an aggregated hypergraph of sessions, items, and attributes. This is a unique complex query answering task with sessions as ordered hyperedges. We also introduce a new model, the Logical Session Graph Transformer (LSGT), which captures interactions among items across different sessions and their logical connections using a transformer structure. We analyze the expressiveness of LSGT and prove the permutation invariance of the inputs for the logical operators. By evaluating LSGT on three datasets, we demonstrate that it achieves state-of-the-art results.|理解用户的意图对于改进产品推荐、导航建议和查询重新编排至关重要。但是，用户的意图可能很复杂，涉及多个会话和属性需求，这些需求由逻辑运算符连接，例如 And、 Or 和 Not。例如，用户可能在不同的会话中搜索耐克或阿迪达斯的跑鞋，偏爱紫色。在另一个例子中，用户可能已经在前一个阶段购买了一个床垫，现在正在寻找一个匹配的床架，而无意购买另一个床垫。关于会话理解的现有研究尚未充分解决为这种复杂意图提供产品或属性建议的问题。本文提出了逻辑会话复杂查询回答(LS-CQA)任务，其中会话被视为项目的超边缘，并将复杂意图理解问题框架为会话、项目和属性聚合超图上的 LS-CQA 任务。这是一个独特的复杂查询应答任务，会话是有序的超边缘。我们还介绍了一个新模型，逻辑会话图形转换器(Logical Session Graph former，LSGT) ，它使用转换器结构捕获跨不同会话的项之间的交互及其逻辑连接。分析了 LSGT 的表达性，证明了逻辑算子输入的置换不变性。通过对三个数据集上的 LSGT 进行评估，我们证明它达到了最先进的结果。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Understanding+Inter-Session+Intentions+via+Complex+Logical+Reasoning)|0| -|[Online Preference Weight Estimation Algorithm with Vanishing Regret for Car-Hailing in Road Network](https://doi.org/10.1145/3637528.3671664)|Yucen Gao, Zhehao Zhu, Mingqian Ma, Fei Gao, Hui Gao, Yangguang Shi, Xiaofeng Gao|Didi Global Inc., Beijing, China; Northwestern University, Evanston, IL, USA; Shandong University, Qingdao, China; Shanghai Jiao Tong University, Shanghai, China|Car-hailing services play an important role in the modern transportation system, and the utilities of the service providers highly depend on the efficiency of route planning algorithms. A widely adopted route planning framework is to assign weights to roads and compute the routes with the shortest path algorithms. Existing techniques of weight-assigning often focus on the traveling time and length of the roads, but cannot incorporate with the preferences of the passengers (users). In this paper, a set of preference weight estimation models is employed to capture the users' preferences over paths in car-hailing with their historical choices. Since the user preferences may vary dynamically over time, it is a challenging task to make real-time decisions over the models. The main technical contribution of this paper is to propose an online learning-based preference weight chasing (PWC) algorithm to solve this problem. The worst-case performance of PWC is analyzed with the metric regret, and it is proved that PWC has a vanishing regret, which means that the time-averaged loss concerning the fixed in-hindsight best model tends to zero. Experiments based on real-world datasets are conducted to verify the effectiveness and efficiency of our algorithm. The code is available at https://github.com/GaoYucen/PWC.|叫车服务在现代交通系统中占有举足轻重的地位，路径规划算法的有效性直接决定了叫车服务提供商的效用。一种广泛采用的路径规划框架是给道路赋权，并用最短路径算法计算路径。现有的权重分配技术往往侧重于道路的行驶时间和长度，但不能与乘客(用户)的偏好相结合。本文利用一组偏好权重估计模型，通过用户的历史选择来捕捉用户对叫车路径的偏好。由于用户偏好可能随时间动态变化，因此在模型上实时做出决策是一项具有挑战性的任务。本文的主要技术贡献是提出了一种基于在线学习的偏好权重追踪(PWC)算法来解决这一问题。利用度量遗憾对 PWC 的最坏情况性能进行了分析，证明了 PWC 具有消失遗憾，这意味着与固定的事后最佳模型相关的时间平均损失趋于零。基于实际数据集进行了实验，验证了算法的有效性和高效性。密码可在 https://github.com/gaoyucen/pwc 查阅。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Online+Preference+Weight+Estimation+Algorithm+with+Vanishing+Regret+for+Car-Hailing+in+Road+Network)|0| +|[Multimodal Pretraining, Adaptation, and Generation for Recommendation: A Survey](https://doi.org/10.1145/3637528.3671473)|Qijiong Liu, Jieming Zhu, Yanting Yang, Quanyu Dai, Zhaocheng Du, XiaoMing Wu, Zhou Zhao, Rui Zhang, Zhenhua Dong|Huawei Noah's Ark Lab, Shenzhen, China; The HK PolyU, Hong Kong, China; Zhejiang University, Hangzhou, China; Huawei Noah Ark Lab, Shenzhen, China; Huazhong University of Science and Technology & ruizhang.info, Shenzhen, China|Personalized recommendation serves as a ubiquitous channel for users to discover information tailored to their interests. However, traditional recommendation models primarily rely on unique IDs and categorical features for user-item matching, potentially overlooking the nuanced essence of raw item contents across multiple modalities such as text, image, audio, and video. This underutilization of multimodal data poses a limitation to recommender systems, especially in multimedia services like news, music, and short-video platforms. The recent advancements in large multimodal models offer new opportunities and challenges in developing content-aware recommender systems. This survey seeks to provide a comprehensive exploration of the latest advancements and future trajectories in multimodal pretraining, adaptation, and generation techniques, as well as their applications in enhancing recommender systems. Furthermore, we discuss current open challenges and opportunities for future research in this dynamic domain. We believe that this survey, alongside the curated resources, will provide valuable insights to inspire further advancements in this evolving landscape.|个性化推荐是用户发现适合自己兴趣的信息的一个无处不在的渠道。然而，传统的推荐模型主要依赖于用户项匹配的唯一 ID 和分类特性，这可能忽略了原始项目内容在多种模式(如文本、图像、音频和视频)中的细微差别。这种多模式数据的利用率不足对推荐系统造成了限制，特别是在新闻、音乐和短视频平台等多媒体服务中。大型多模式模型的最新进展为开发内容感知的推荐系统提供了新的机遇和挑战。本调查旨在全面探讨多模式预培训、适应和生成技术的最新进展和未来发展轨迹，以及它们在加强推荐系统方面的应用。此外，我们讨论了目前在这一动态领域的开放性挑战和未来研究的机遇。我们相信，这项调查，以及策划的资源，将提供宝贵的见解，以激励在这个不断变化的景观进一步发展。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Multimodal+Pretraining,+Adaptation,+and+Generation+for+Recommendation:+A+Survey)|0| +|[AI for Education (AI4EDU): Advancing Personalized Education with LLM and Adaptive Learning](https://doi.org/10.1145/3637528.3671498)|Qingsong Wen, Jing Liang, Carles Sierra, Rose Luckin, Richard Jiarui Tong, Zitao Liu, Peng Cui, Jiliang Tang|IIIA of the Spanish National Research Council, Barcelona, Spain; Squirrel Ai Learning, Shanghai, China; Tsinghua University, Beijing, China; Squirrel Ai Learning, Bellevue, USA; Michigan State University, East Lansing, USA; Jinan University, Guangzhou, China; University College London, London, United Kingdom|Recent advanced AI technologies, especially large language models (LLMs) like GPTs, have significantly advanced the field of data mining and led to the development of various LLM-based applications. AI for education (AI4EDU) is a vibrant multi-disciplinary field of data mining, machine learning, and education, with increasing importance and extraordinary potential. In this field, LLM and adaptive learning-based models can be utilized as interfaces in human-in-the-loop education systems, where the model serves as a mediator among the teacher, students, and machine capabilities, including its own. This perspective has several benefits, including the ability to personalize interactions, allow unprecedented flexibility and adaptivity for human-AI collaboration and improve the user experience. However, several challenges still exist, including the need for more robust and efficient algorithms, designing effective user interfaces, and ensuring ethical considerations are addressed. This workshop aims to bring together researchers and practitioners from academia and industry to explore cutting-edge AI technologies for personalized education, especially the potential of LLMs and adaptive learning technologies.|近年来，先进的人工智能技术，特别是大型语言模型(LLM) ，如 GPTs，极大地推动了数据挖掘领域的发展，并导致了各种基于 LLM 的应用程序的开发。人工智能教育(AI4EDU)是一个充满活力的多学科领域的数据挖掘，机器学习和教育，越来越重要和非凡的潜力。在这个领域，LLM 和基于自适应学习的模型可以用作人在环路教育系统的接口，在这个系统中，模型充当教师、学生和机器能力(包括它自己的能力)之间的中介。这种视角有几个好处，包括个性化交互的能力，允许前所未有的灵活性和人工智能协作的适应性，并改善用户体验。然而，仍然存在一些挑战，包括需要更加健壮和高效的算法，设计有效的用户界面，并确保道德考虑得到解决。这个工作坊旨在汇聚来自学术界和业界的研究人员和从业员，探讨尖端人工智能技术在个人化教育方面的应用，特别是 LLM 和在线机机器学习技术的潜力。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=AI+for+Education+(AI4EDU):+Advancing+Personalized+Education+with+LLM+and+Adaptive+Learning)|0| +|[Understanding Inter-Session Intentions via Complex Logical Reasoning](https://doi.org/10.1145/3637528.3671808)|Jiaxin Bai, Chen Luo, Zheng Li, Qingyu Yin, Yangqiu Song|Amazon.com Inc, Palo Alto, USA; Department of CSE, HKUST, Hong Kong, China|Understanding user intentions is essential for improving product recommendations, navigation suggestions, and query reformulations. However, user intentions can be intricate, involving multiple sessions and attribute requirements connected by logical operators such as And, Or, and Not. For instance, a user may search for Nike or Adidas running shoes across various sessions, with a preference for purple. In another example, a user may have purchased a mattress in a previous session and is now looking for a matching bed frame without intending to buy another mattress. Existing research on session understanding has not adequately addressed making product or attribute recommendations for such complex intentions. In this paper, we present the task of logical session complex query answering (LS-CQA), where sessions are treated as hyperedges of items, and we frame the problem of complex intention understanding as an LS-CQA task on an aggregated hypergraph of sessions, items, and attributes. This is a unique complex query answering task with sessions as ordered hyperedges. We also introduce a new model, the Logical Session Graph Transformer (LSGT), which captures interactions among items across different sessions and their logical connections using a transformer structure. We analyze the expressiveness of LSGT and prove the permutation invariance of the inputs for the logical operators. By evaluating LSGT on three datasets, we demonstrate that it achieves state-of-the-art results.|理解用户的意图对于改进产品推荐、导航建议和查询重新编排至关重要。但是，用户的意图可能很复杂，涉及多个会话和属性需求，这些需求由逻辑运算符连接，例如 And、 Or 和 Not。例如，用户可能在不同的会话中搜索耐克或阿迪达斯的跑鞋，偏爱紫色。在另一个例子中，用户可能已经在前一个阶段购买了一个床垫，现在正在寻找一个匹配的床架，而无意购买另一个床垫。关于会话理解的现有研究尚未充分解决为这种复杂意图提供产品或属性建议的问题。本文提出了逻辑会话复杂查询回答(LS-CQA)任务，其中会话被视为项目的超边缘，并将复杂意图理解问题框架为会话、项目和属性聚合超图上的 LS-CQA 任务。这是一个独特的复杂查询应答任务，会话是有序的超边缘。我们还介绍了一个新模型，逻辑会话图形转换器(Logical Session Graph former，LSGT) ，它使用转换器结构捕获跨不同会话的项之间的交互及其逻辑连接。分析了 LSGT 的表达性，证明了逻辑算子输入的置换不变性。通过对三个数据集上的 LSGT 进行评估，我们证明它达到了最先进的结果。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Understanding+Inter-Session+Intentions+via+Complex+Logical+Reasoning)|0| +|[Online Preference Weight Estimation Algorithm with Vanishing Regret for Car-Hailing in Road Network](https://doi.org/10.1145/3637528.3671664)|Yucen Gao, Zhehao Zhu, Mingqian Ma, Fei Gao, Hui Gao, Yangguang Shi, Xiaofeng Gao|Didi Global Inc., Beijing, China; Shanghai Jiao Tong University, Shanghai, China; Shandong University, Qingdao, China; Northwestern University, Evanston, IL, USA|Car-hailing services play an important role in the modern transportation system, and the utilities of the service providers highly depend on the efficiency of route planning algorithms. A widely adopted route planning framework is to assign weights to roads and compute the routes with the shortest path algorithms. Existing techniques of weight-assigning often focus on the traveling time and length of the roads, but cannot incorporate with the preferences of the passengers (users). In this paper, a set of preference weight estimation models is employed to capture the users' preferences over paths in car-hailing with their historical choices. Since the user preferences may vary dynamically over time, it is a challenging task to make real-time decisions over the models. The main technical contribution of this paper is to propose an online learning-based preference weight chasing (PWC) algorithm to solve this problem. The worst-case performance of PWC is analyzed with the metric regret, and it is proved that PWC has a vanishing regret, which means that the time-averaged loss concerning the fixed in-hindsight best model tends to zero. Experiments based on real-world datasets are conducted to verify the effectiveness and efficiency of our algorithm. The code is available at https://github.com/GaoYucen/PWC.|叫车服务在现代交通系统中占有举足轻重的地位，路径规划算法的有效性直接决定了叫车服务提供商的效用。一种广泛采用的路径规划框架是给道路赋权，并用最短路径算法计算路径。现有的权重分配技术往往侧重于道路的行驶时间和长度，但不能与乘客(用户)的偏好相结合。本文利用一组偏好权重估计模型，通过用户的历史选择来捕捉用户对叫车路径的偏好。由于用户偏好可能随时间动态变化，因此在模型上实时做出决策是一项具有挑战性的任务。本文的主要技术贡献是提出了一种基于在线学习的偏好权重追踪(PWC)算法来解决这一问题。利用度量遗憾对 PWC 的最坏情况性能进行了分析，证明了 PWC 具有消失遗憾，这意味着与固定的事后最佳模型相关的时间平均损失趋于零。基于实际数据集进行了实验，验证了算法的有效性和高效性。密码可在 https://github.com/gaoyucen/pwc 查阅。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Online+Preference+Weight+Estimation+Algorithm+with+Vanishing+Regret+for+Car-Hailing+in+Road+Network)|0| |[Robust Auto-Bidding Strategies for Online Advertising](https://doi.org/10.1145/3637528.3671729)|Qilong Lin, Zhenzhe Zheng, Fan Wu|Shanghai Jiao Tong University, Shanghai, China|In online advertising, existing auto-bidding strategies for bid shading mainly adopt the approach of first predicting the winning price distribution and then calculating the optimal bid. However, the winning price information available to the Demand Side Platforms (DSPs) is extremely limited, and the associated uncertainties make it challenging for DSPs to accurately estimate winning price distribution. To address this challenge, we conducted a comprehensive analysis of the process by which DSPs obtain winning price information, and abstracted two types of uncertainties from it: known uncertainty and unknown uncertainty. Based on these uncertainties, we proposed two levels of robust bidding strategies: Robust Bidding for Censorship (RBC) and Robust Bidding for Distribution Shift (RBDS), which offer guarantees for the surplus in the worst-case scenarios under uncertain conditions. Experimental results on public datasets demonstrate that our robust bidding strategies consistently enable DSPs to achieve superior surpluses, both on test sets and under worst-case conditions.|在网络广告中，现有的自动报价策略主要采用先预测中标价格分布，然后计算最优报价的方法。然而，需求侧平台(DSP)可获得的中标价格信息是极其有限的，并且相关的不确定性使得 DSP 准确估计中标价格分布具有挑战性。为了应对这一挑战，我们对 DSP 获取中标价格信息的过程进行了全面的分析，并从中提取出两类不确定性: 已知不确定性和未知不确定性。基于这些不确定性，我们提出了两个层次的鲁棒竞价策略: 鲁棒审查竞价(RBC)和鲁棒分配偏移竞价(RBDS) ，为不确定条件下最坏情况下的盈余提供保证。对公共数据集的实验结果表明，我们的稳健的投标策略始终使得 DSP 能够在测试集和最坏情况下实现优越的盈余。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Robust+Auto-Bidding+Strategies+for+Online+Advertising)|0| -|[QSketch: An Efficient Sketch for Weighted Cardinality Estimation in Streams](https://doi.org/10.1145/3637528.3671695)|Yiyan Qi, Rundong Li, Pinghui Wang, Yufang Sun, Rui Xing|MOE KLINNS Lab, Xi'an Jiaotong University, Xi'an, China; MOE KLINNS Lab, Shaanxi Normal University, Xi'an, China; International Digital Economy Academy (IDEA), Shenzhen, China|Estimating cardinality, i.e., the number of distinct elements, of a data stream is a fundamental problem in areas like databases, computer networks, and information retrieval. This study delves into a broader scenario where each element carries a positive weight. Unlike traditional cardinality estimation, limited research exists on weighted cardinality, with current methods requiring substantial memory and computational resources, challenging for devices with limited capabilities and real-time applications like anomaly detection. To address these issues, we propose QSketch, a memory-efficient sketch method for estimating weighted cardinality in streams. QSketch uses a quantization technique to condense continuous variables into a compact set of integer variables, with each variable requiring only 8 bits, making it 8 times smaller than previous methods. Furthermore, we leverage dynamic properties during QSketch generation to significantly enhance estimation accuracy and achieve a lower time complexity of O(1) for updating estimations upon encountering a new element. Experimental results on synthetic and real-world datasets show that QSketch is approximately 30% more accurate and two orders of magnitude faster than the state-of-the-art, using only 1/8 of the memory.|在数据库、计算机网络和信息检索等领域，估计数据流的基数(即不同元素的数量)是一个基本问题。这项研究深入到一个更广泛的情况下，其中每一个因素具有积极的权重。与传统的基数估计不同，对加权基数的研究有限，目前的方法需要大量的内存和计算资源，对于功能有限的设备和实时应用(如异常检测)具有挑战性。为了解决这些问题，我们提出了 QSketch，一种内存高效的草图方法，用于估计流中的加权基数。QSketch 使用量化技术将连续变量压缩为一组紧凑的整数变量，每个变量只需要8位，使其比以前的方法小8倍。此外，我们利用 QSketch 生成过程中的动态特性来显著提高估计精度，并且在遇到新元素时更新估计时降低了 O (1)的时间复杂度。在合成和真实数据集上的实验结果显示，QSketch 只使用了1/8的内存，比最先进的数量级提高了大约30% 的准确率和2% 的速度。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=QSketch:+An+Efficient+Sketch+for+Weighted+Cardinality+Estimation+in+Streams)|0| -|[Make Your Home Safe: Time-aware Unsupervised User Behavior Anomaly Detection in Smart Homes via Loss-guided Mask](https://doi.org/10.1145/3637528.3671708)|Jingyu Xiao, Zhiyao Xu, Qingsong Zou, Qing Li, Dan Zhao, Dong Fang, Ruoyu Li, Wenxin Tang, Kang Li, Xudong Zuo, Penghui Hu, Yong Jiang, Zixuan Weng, Michael R. Lyu|The Chinese University of Hong Kong, Hong Kong, China; Peng Cheng Laboratory & Tsinghua Shenzhen International Graduate School, Shenzhen, China; Xi'an University of Electronic Science and Technology, Xi'an, China; Beijing Jiaotong University, Beijing, China; Tencent, Shenzhen, China; Tsinghua Shenzhen International Graduate School, Shenzhen, China; Tsinghua University, Beijing, China; Peng Cheng Laboratory, Shenzhen, China; Peng Cheng Laborotary, Shenzhen, China; Tsinghua Shenzhen International Graduate School & Peng Cheng Laboratory, Shenzhen, China|Smart homes, powered by the Internet of Things, offer great convenience but also pose security concerns due to abnormal behaviors, such as improper operations of users and potential attacks from malicious attackers. Several behavior modeling methods have been proposed to identify abnormal behaviors and mitigate potential risks. However, their performance often falls short because they do not effectively learn less frequent behaviors, consider temporal context, or account for the impact of noise in human behaviors. In this paper, we propose SmartGuard, an autoencoder-based unsupervised user behavior anomaly detection framework. First, we design a Loss-guided Dynamic Mask Strategy (LDMS) to encourage the model to learn less frequent behaviors, which are often overlooked during learning. Second, we propose a Three-level Time-aware Position Embedding (TTPE) to incorporate temporal information into positional embedding to detect temporal context anomaly. Third, we propose a Noise-aware Weighted Reconstruction Loss (NWRL) that assigns different weights for routine behaviors and noise behaviors to mitigate the interference of noise behaviors during inference. Comprehensive experiments demonstrate that SmartGuard consistently outperforms state-of-the-art baselines and also offers highly interpretable results.|物联网驱动的智能家居不仅提供了极大的便利，而且由于用户的不正常操作和恶意攻击者的潜在攻击等不正常行为，也引起了安全方面的担忧。为了识别异常行为和降低潜在风险，人们提出了几种行为建模方法。然而，他们的表现往往不足，因为他们没有有效地学习较少的频繁行为，考虑时间背景，或说明噪音的影响，人类行为。在本文中，我们提出了 SmartGuard，一个基于自动编码器的无监督用户行为异常检测框架。首先，我们设计了一个损失引导的动态掩模策略(LDMS)来鼓励模型学习较少频繁的行为，这些行为在学习过程中经常被忽略。其次，提出了一种三级时间感知位置嵌入算法(TTPE) ，将时间信息融合到位置嵌入算法中，检测时间上下文异常。第三，提出了一种噪声感知加权重构损失(NWRL)算法，该算法对日常行为和噪声行为赋予不同的权重，以减轻推理过程中噪声行为的干扰。综合实验表明，SmartGuard 始终优于最先进的基线，并提供高度可解释的结果。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Make+Your+Home+Safe:+Time-aware+Unsupervised+User+Behavior+Anomaly+Detection+in+Smart+Homes+via+Loss-guided+Mask)|0| +|[QSketch: An Efficient Sketch for Weighted Cardinality Estimation in Streams](https://doi.org/10.1145/3637528.3671695)|Yiyan Qi, Rundong Li, Pinghui Wang, Yufang Sun, Rui Xing|MOE KLINNS Lab, Xi'an Jiaotong University, Xi'an, China; International Digital Economy Academy (IDEA), Shenzhen, China; MOE KLINNS Lab, Shaanxi Normal University, Xi'an, China|Estimating cardinality, i.e., the number of distinct elements, of a data stream is a fundamental problem in areas like databases, computer networks, and information retrieval. This study delves into a broader scenario where each element carries a positive weight. Unlike traditional cardinality estimation, limited research exists on weighted cardinality, with current methods requiring substantial memory and computational resources, challenging for devices with limited capabilities and real-time applications like anomaly detection. To address these issues, we propose QSketch, a memory-efficient sketch method for estimating weighted cardinality in streams. QSketch uses a quantization technique to condense continuous variables into a compact set of integer variables, with each variable requiring only 8 bits, making it 8 times smaller than previous methods. Furthermore, we leverage dynamic properties during QSketch generation to significantly enhance estimation accuracy and achieve a lower time complexity of O(1) for updating estimations upon encountering a new element. Experimental results on synthetic and real-world datasets show that QSketch is approximately 30% more accurate and two orders of magnitude faster than the state-of-the-art, using only 1/8 of the memory.|在数据库、计算机网络和信息检索等领域，估计数据流的基数(即不同元素的数量)是一个基本问题。这项研究深入到一个更广泛的情况下，其中每一个因素具有积极的权重。与传统的基数估计不同，对加权基数的研究有限，目前的方法需要大量的内存和计算资源，对于功能有限的设备和实时应用(如异常检测)具有挑战性。为了解决这些问题，我们提出了 QSketch，一种内存高效的草图方法，用于估计流中的加权基数。QSketch 使用量化技术将连续变量压缩为一组紧凑的整数变量，每个变量只需要8位，使其比以前的方法小8倍。此外，我们利用 QSketch 生成过程中的动态特性来显著提高估计精度，并且在遇到新元素时更新估计时降低了 O (1)的时间复杂度。在合成和真实数据集上的实验结果显示，QSketch 只使用了1/8的内存，比最先进的数量级提高了大约30% 的准确率和2% 的速度。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=QSketch:+An+Efficient+Sketch+for+Weighted+Cardinality+Estimation+in+Streams)|0| +|[Make Your Home Safe: Time-aware Unsupervised User Behavior Anomaly Detection in Smart Homes via Loss-guided Mask](https://doi.org/10.1145/3637528.3671708)|Jingyu Xiao, Zhiyao Xu, Qingsong Zou, Qing Li, Dan Zhao, Dong Fang, Ruoyu Li, Wenxin Tang, Kang Li, Xudong Zuo, Penghui Hu, Yong Jiang, Zixuan Weng, Michael R. Lyu|Beijing Jiaotong University, Beijing, China; Peng Cheng Laboratory & Tsinghua Shenzhen International Graduate School, Shenzhen, China; Peng Cheng Laboratory, Shenzhen, China; Tsinghua Shenzhen International Graduate School, Shenzhen, China; Tsinghua University, Beijing, China; Xi'an University of Electronic Science and Technology, Xi'an, China; The Chinese University of Hong Kong, Hong Kong, China; Tencent, Shenzhen, China; Peng Cheng Laborotary, Shenzhen, China; Tsinghua Shenzhen International Graduate School & Peng Cheng Laboratory, Shenzhen, China|Smart homes, powered by the Internet of Things, offer great convenience but also pose security concerns due to abnormal behaviors, such as improper operations of users and potential attacks from malicious attackers. Several behavior modeling methods have been proposed to identify abnormal behaviors and mitigate potential risks. However, their performance often falls short because they do not effectively learn less frequent behaviors, consider temporal context, or account for the impact of noise in human behaviors. In this paper, we propose SmartGuard, an autoencoder-based unsupervised user behavior anomaly detection framework. First, we design a Loss-guided Dynamic Mask Strategy (LDMS) to encourage the model to learn less frequent behaviors, which are often overlooked during learning. Second, we propose a Three-level Time-aware Position Embedding (TTPE) to incorporate temporal information into positional embedding to detect temporal context anomaly. Third, we propose a Noise-aware Weighted Reconstruction Loss (NWRL) that assigns different weights for routine behaviors and noise behaviors to mitigate the interference of noise behaviors during inference. Comprehensive experiments demonstrate that SmartGuard consistently outperforms state-of-the-art baselines and also offers highly interpretable results.|物联网驱动的智能家居不仅提供了极大的便利，而且由于用户的不正常操作和恶意攻击者的潜在攻击等不正常行为，也引起了安全方面的担忧。为了识别异常行为和降低潜在风险，人们提出了几种行为建模方法。然而，他们的表现往往不足，因为他们没有有效地学习较少的频繁行为，考虑时间背景，或说明噪音的影响，人类行为。在本文中，我们提出了 SmartGuard，一个基于自动编码器的无监督用户行为异常检测框架。首先，我们设计了一个损失引导的动态掩模策略(LDMS)来鼓励模型学习较少频繁的行为，这些行为在学习过程中经常被忽略。其次，提出了一种三级时间感知位置嵌入算法(TTPE) ，将时间信息融合到位置嵌入算法中，检测时间上下文异常。第三，提出了一种噪声感知加权重构损失(NWRL)算法，该算法对日常行为和噪声行为赋予不同的权重，以减轻推理过程中噪声行为的干扰。综合实验表明，SmartGuard 始终优于最先进的基线，并提供高度可解释的结果。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Make+Your+Home+Safe:+Time-aware+Unsupervised+User+Behavior+Anomaly+Detection+in+Smart+Homes+via+Loss-guided+Mask)|0| |[Top-Down Bayesian Posterior Sampling for Sum-Product Networks](https://doi.org/10.1145/3637528.3671876)|Soma Yokoi, Issei Sato|The University of Tokyo, Tokyo, Japan|Sum-product networks (SPNs) are probabilistic models characterized by exactand fast evaluation of fundamental probabilistic operations. Its superiorcomputational tractability has led to applications in many fields, such asmachine learning with time constraints or accuracy requirements and real-timesystems. The structural constraints of SPNs supporting fast inference, however,lead to increased learning-time complexity and can be an obstacle to buildinghighly expressive SPNs. This study aimed to develop a Bayesian learningapproach that can be efficiently implemented on large-scale SPNs. We derived anew full conditional probability of Gibbs sampling by marginalizing multiplerandom variables to expeditiously obtain the posterior distribution. Thecomplexity analysis revealed that our sampling algorithm works efficiently evenfor the largest possible SPN. Furthermore, we proposed a hyperparameter tuningmethod that balances the diversity of the prior distribution and optimizationefficiency in large-scale SPNs. Our method has improved learning-timecomplexity and demonstrated computational speed tens to more than one hundredtimes faster and superior predictive performance in numerical experiments onmore than 20 datasets.|和积网络(Sum-product network，SPN)是基本概率运算的精确和快速评估的概率模型拥有属性。其优越的计算易处理性在许多领域得到了应用，例如具有时间约束或精度要求的机器学习和实时系统。然而，支持快速推理的 SPN 的结构约束导致了学习时间复杂性的增加，并可能成为构建高表达性 SPN 的障碍。本研究旨在发展一个可以在大型 SPN 上有效执行的贝叶斯学习方法。我们通过边缘化多重随机变量得到了一个全新的吉布斯抽样条件概率，以便快速获得后验概率。复杂性分析表明，我们的抽样算法工作效率甚至最大的可能 SPN。在此基础上，提出了一种平衡先验分布多样性和优化效率的超参数调谐方法。在20多个数据集的数值实验中，该方法提高了学习时间的复杂度，并显示出数十倍至一百倍以上的计算速度和优越的预测性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Top-Down+Bayesian+Posterior+Sampling+for+Sum-Product+Networks)|0| -|[CompanyKG: A Large-Scale Heterogeneous Graph for Company Similarity Quantification](https://doi.org/10.1145/3637528.3671515)|Lele Cao, Vilhelm von Ehrenheim, Mark GranrothWilding, Richard Anselmo Stahl, Andrew McCornack, Armin Catovic, Dhiana Deva Cavalcanti Rocha|Motherbrain, EQT Group & QA.tech, Stockholm, Sweden; Motherbrain, EQT Group, Stockholm, Sweden; Motherbrain, EQT Group & Silo AI, Stockholm, Sweden|In the investment industry, it is often essential to carry out fine-grainedcompany similarity quantification for a range of purposes, including marketmapping, competitor analysis, and mergers and acquisitions. We propose andpublish a knowledge graph, named CompanyKG, to represent and learn diversecompany features and relations. Specifically, 1.17 million companies arerepresented as nodes enriched with company description embeddings; and 15different inter-company relations result in 51.06 million weighted edges. Toenable a comprehensive assessment of methods for company similarityquantification, we have devised and compiled three evaluation tasks withannotated test sets: similarity prediction, competitor retrieval and similarityranking. We present extensive benchmarking results for 11 reproduciblepredictive methods categorized into three groups: node-only, edge-only, andnode+edge. To the best of our knowledge, CompanyKG is the first large-scaleheterogeneous graph dataset originating from a real-world investment platform,tailored for quantifying inter-company similarity.|在投资行业，为了一系列目的(包括市场地图、竞争对手分析和併购)进行细粒度的公司相似性量化往往是必不可少的。我们提出并发布了一个知识图，命名为 CompanyKG，用于表示和学习公司的各种特征和关系。具体来说，117万家公司被表示为丰富了公司描述嵌入的节点; 15种不同的公司间关系产生了5106万个加权边。为了对企业相似性量化方法进行综合评价，我们设计并编制了三个评价任务: 相似性预测、竞争对手检索和相似性排名。我们提出了广泛的基准测试结果的11个可重复的预测方法分为三组: 节点只有，边缘，和节点 + 边缘。据我们所知，CompanyKG 是第一个来自现实世界投资平台的大规模异构图形数据集，专门用于量化公司间的相似性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=CompanyKG:+A+Large-Scale+Heterogeneous+Graph+for+Company+Similarity+Quantification)|0| +|[CompanyKG: A Large-Scale Heterogeneous Graph for Company Similarity Quantification](https://doi.org/10.1145/3637528.3671515)|Lele Cao, Vilhelm von Ehrenheim, Mark GranrothWilding, Richard Anselmo Stahl, Andrew McCornack, Armin Catovic, Dhiana Deva Cavalcanti Rocha|Motherbrain, EQT Group & QA.tech, Stockholm, Sweden; Motherbrain, EQT Group & Silo AI, Stockholm, Sweden; Motherbrain, EQT Group, Stockholm, Sweden|In the investment industry, it is often essential to carry out fine-grainedcompany similarity quantification for a range of purposes, including marketmapping, competitor analysis, and mergers and acquisitions. We propose andpublish a knowledge graph, named CompanyKG, to represent and learn diversecompany features and relations. Specifically, 1.17 million companies arerepresented as nodes enriched with company description embeddings; and 15different inter-company relations result in 51.06 million weighted edges. Toenable a comprehensive assessment of methods for company similarityquantification, we have devised and compiled three evaluation tasks withannotated test sets: similarity prediction, competitor retrieval and similarityranking. We present extensive benchmarking results for 11 reproduciblepredictive methods categorized into three groups: node-only, edge-only, andnode+edge. To the best of our knowledge, CompanyKG is the first large-scaleheterogeneous graph dataset originating from a real-world investment platform,tailored for quantifying inter-company similarity.|在投资行业，为了一系列目的(包括市场地图、竞争对手分析和併购)进行细粒度的公司相似性量化往往是必不可少的。我们提出并发布了一个知识图，命名为 CompanyKG，用于表示和学习公司的各种特征和关系。具体来说，117万家公司被表示为丰富了公司描述嵌入的节点; 15种不同的公司间关系产生了5106万个加权边。为了对企业相似性量化方法进行综合评价，我们设计并编制了三个评价任务: 相似性预测、竞争对手检索和相似性排名。我们提出了广泛的基准测试结果的11个可重复的预测方法分为三组: 节点只有，边缘，和节点 + 边缘。据我们所知，CompanyKG 是第一个来自现实世界投资平台的大规模异构图形数据集，专门用于量化公司间的相似性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=CompanyKG:+A+Large-Scale+Heterogeneous+Graph+for+Company+Similarity+Quantification)|0| |[CHILI: Chemically-Informed Large-scale Inorganic Nanomaterials Dataset for Advancing Graph Machine Learning](https://doi.org/10.1145/3637528.3671538)|Ulrik FriisJensen, Frederik L. Johansen, Andy S. Anker, Erik B. Dam, Kirsten M. Ø. Jensen, Raghavendra Selvan|; Department of Computer Science, University of Copenhagen, Copenhagen, Denmark; Department of Chemistry, University of Copenhagen, Copenhagen, Denmark|Advances in graph machine learning (ML) have been driven by applications in chemistry, as graphs have remained the most expressive representations of molecules. This has led to progress within both fields, as challenging chemical data has helped improve existing methods and to develop new ones. While early graph ML methods focused primarily on small organic molecules, more recently, the scope of graph ML has expanded to include inorganic materials. Modelling the periodicity and symmetry of inorganic crystalline materials poses unique challenges, which existing graph ML methods are unable to immediately address. Moving to inorganic nanomaterials further increases complexity as the scale of number of nodes within each graph can be broad (10 to 100k). In addition, the bulk of existing graph ML focuses on characterising molecules and materials by predicting target properties with graphs as input. The most exciting applications of graph ML will be in their generative capabilities, in order to explore the vast chemical space from a data-driven perspective. Currently, generative modelling of graphs is not at par with other domains such as images or text, as generating chemically valid molecules and materials of varying properties is not straightforward. In this work, we invite the graph ML community to address these open challenges by presenting two new chemically-informed large-scale inorganic (CHILI) nanomaterials datasets. These datasets contain nanomaterials of different scales and properties represented as graphs of varying sizes. The first dataset is a medium-scale dataset (with overall >6M nodes, >49M edges) of mono-metallic oxide nanomaterials generated from 12 selected crystal types (CHILI-3K). This dataset has a narrower chemical scope focused on an interesting part of chemical space with a lot of active research. The second is a large-scale dataset (with overall >183M nodes, >1.2B edges) of nanomaterials generated from experimentally determined crystal structures (CHILI-100K). The crystal structures used in CHILI-100K are obtained from a curated subset of the Crystallography Open Database (COD) and has a broader chemical scope covering database entries for 68 metals and 11 non-metals. We define 11 property prediction tasks covering node-, edge-, and graph- level tasks that span classification and regression. In addition we also define structure prediction tasks, which are of special interest for nanomaterial research. We benchmark the performance of a wide array of baseline methods starting with simple baselines to multiple off-the-shelf graph neural networks. Based on these benchmarking results, we highlight areas which need future work to achieve useful performance for applications in (nano) materials chemistry. To the best of our knowledge, CHILI-3K and CHILI-100K are the first open-source nanomaterial datasets of this scale - both on the individual graph level and of the dataset as a whole - and the only nanomaterials datasets with high structural and elemental diversity.|图形机器学习(ML)的发展受到化学应用的推动，因为图形仍然是分子最有表现力的表现形式。这导致了这两个领域的进展，因为具有挑战性的化学数据有助于改进现有方法和开发新的方法。虽然早期的图形 ML 方法主要集中在小有机分子，最近，图形 ML 的范围已经扩大到包括无机材料。模拟无机晶体材料的周期性和对称性提出了独特的挑战，现有的图 ML 方法无法立即解决。转向无机纳米材料进一步增加了复杂性，因为每个图中节点的数量范围可以很宽(10到100k)。此外，现有的大部分图 ML 侧重于通过以图形作为输入来预测目标特性来表征分子和材料。图形机器学习最令人兴奋的应用将在于它们的生成能力，以便从数据驱动的角度探索广阔的化学空间。目前，图形的生成建模与图像或文本等其他领域不同，因为生成具有不同性质的化学有效分子和材料并不简单。在这项工作中，我们邀请图形机器学习社区解决这些开放的挑战，提出两个新的化学信息大规模无机(CHILI)纳米材料数据集。这些数据集包含不同尺度和性质的纳米材料，表示为不同尺寸的图形。第一个数据集是由12种选定的晶体类型(CHILI-3K)产生的单金属氧化物纳米材料的中等规模数据集(总体 > 6M 节点，> 49M 边)。这个数据集有一个较窄的化学范围集中在一个有趣的部分的化学空间与许多活跃的研究。第二个是由实验确定的晶体结构(CHILI-100K)产生的纳米材料的大规模数据集(总体 > 183M 节点，> 1.2 B 边)。CHILI-100K 中使用的晶体结构是从晶体学开放数据库(COD)的一个精选子集中获得的，并且具有更广泛的化学范围，包括68种金属和11种非金属的数据库条目。我们定义了11个属性预测任务，涵盖了分类和回归的节点、边和图级任务。此外，我们还定义了纳米材料研究特别感兴趣的结构预测任务。从简单的基线到多个现成的图形神经网络，我们测试了大量基线方法的性能。在这些基准测试结果的基础上，我们强调了在(纳米)材料化学中实现有用性能需要进一步工作的领域。据我们所知，CHILI-3K 和 CHILI-100K 是第一个这种规模的开源纳米材料数据集-无论是在单个图形水平还是整个数据集-也是唯一具有高结构和元素多样性的纳米材料数据集。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=CHILI:+Chemically-Informed+Large-scale+Inorganic+Nanomaterials+Dataset+for+Advancing+Graph+Machine+Learning)|0| -|[Offline Reinforcement Learning for Optimizing Production Bidding Policies](https://doi.org/10.1145/3637528.3671555)|Dmytro Korenkevych, Frank Cheng, Artsiom Balakir, Alex Nikulkov, Lingnan Gao, Zhihao Cen, Zuobing Xu, Zheqing Zhu|AI at Meta, Menlo Park, USA; AI at Meta, Bellevue, USA; Meta Platform Inc., Menlo Park, USA; AI at Meta, Sunnyvale, USA|The online advertising market, with its thousands of auctions run per second, presents a daunting challenge for advertisers who wish to optimize their spend under a budget constraint. Thus, advertising platforms typically provide automated agents to their customers, which act on their behalf to bid for impression opportunities in real time at scale. Because these proxy agents are owned by the platform but use advertiser funds to operate, there is a strong practical need to balance reliability and explainability of the agent with optimizing power. We propose a generalizable approach to optimizing bidding policies in production environments by learning from real data using offline reinforcement learning. This approach can be used to optimize any differentiable base policy (practically, a heuristic policy based on principles which the advertiser can easily understand), and only requires data generated by the base policy itself. We use a hybrid agent architecture that combines arbitrary base policies with deep neural networks, where only the optimized base policy parameters are eventually deployed, and the neural network part is discarded after training. We demonstrate that such an architecture achieves statistically significant performance gains in both simulated and at-scale production bidding environments. Our approach does not incur additional infrastructure, safety, or explainability costs, as it directly optimizes parameters of existing production routines without replacing them with black box-style models like neural networks.|在线广告市场每秒钟有数千场拍卖，对于那些希望在预算线下优化支出的广告客户来说，这是一个艰巨的挑战。因此，广告平台通常向客户提供自动化代理，代表客户实时大规模地争取印象机会。由于这些代理商属于平台所有，但利用广告客户的资金进行运作，因此在代理商的可靠性和可解释性与优化权力之间存在着很强的现实需求。我们提出了一个通用的方法来优化投标政策在生产环境中通过学习使用离线强化学习的真实数据。这种方法可以用来优化任何可微的基本策略(实际上是一种基于广告商容易理解的原则的启发式策略) ，并且只需要基本策略本身生成的数据。我们采用混合智能体结构，将任意基本策略与深度神经网络相结合，最终只部署优化后的基本策略参数，训练后丢弃神经网络部分。我们证明了这样的体系结构在模拟和大规模生产投标环境中都能获得统计上显著的性能提高。我们的方法不会产生额外的基础设施、安全或可解释性成本，因为它直接优化了现有生产例程的参数，而没有用神经网络等黑箱模型来替代它们。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Offline+Reinforcement+Learning+for+Optimizing+Production+Bidding+Policies)|0| -|[Spending Programmed Bidding: Privacy-friendly Bid Optimization with ROI Constraint in Online Advertising](https://doi.org/10.1145/3637528.3671540)|Yumin Su, Min Xiang, Yifei Chen, Yanbiao Li, Tian Qin, Hongyi Zhang, Yasong Li, Xiaobing Liu|ByteDance Inc., Singapore; ByteDance Inc., San Jose, CA, USA; ByteDance Inc., Beijing, China|Privacy policies have disrupted the multi-billion dollar online advertising market by making real-time and precise user data untraceable, which poses significant challenges to the optimization of Return-On-Investment (ROI) constrained products in the online advertising industry. Privacy protection strategies, including event aggregation and reporting delays, hinder access to detailed and instantaneous feedback data, thus incapacitating traditional identity-revealing attribution techniques. In this paper, we introduces a novel Spending Programmed Bidding (SPB) framework to navigate these challenges. SPB is a two-stage framework that separates long horizon delivery spend planning (the macro stage) and short horizon bidding execution (the micro stage). The macro stage models the target ROI to achieve maximum utility and derives the expected spend, whereas the micro stage optimizes the bid price given the expected spend. We further extend our framework to the cross-channel scenario where the agent bids in both privacy-constrained and identity-revealing attribution channels. We find that when privacy-constrained channels are present, SPB is superior to state-of-the-art bidding methods in both offline datasets and online experiments on a large ad platform.|隐私政策扰乱了数十亿美元的在线广告市场，使实时和精确的用户数据无法追踪，这对在线广告业中投资回报率(ROI)受限产品的优化提出了重大挑战。隐私保护策略，包括事件聚合和报告延迟，阻碍访问详细的和即时的反馈数据，从而使传统的身份披露归因技术丧失能力。在本文中，我们介绍了一个新的支出计划投标(SPB)框架来应对这些挑战。SPB 是一个分为两个阶段的框架，分别为长期交付支出计划(宏观阶段)和短期投标执行(微观阶段)。宏观阶段建立目标投资回报率模型以实现效用最大化并导出预期支出，而微观阶段在给定预期支出的情况下优化投标价格。我们进一步将我们的框架扩展到跨通道场景，其中代理在隐私约束和身份披露的属性通道中竞标。我们发现，当隐私受限的渠道存在时，SPB 在离线数据集和大型广告平台上的在线实验中都优于最先进的投标方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Spending+Programmed+Bidding:+Privacy-friendly+Bid+Optimization+with+ROI+Constraint+in+Online+Advertising)|0| +|[Offline Reinforcement Learning for Optimizing Production Bidding Policies](https://doi.org/10.1145/3637528.3671555)|Dmytro Korenkevych, Frank Cheng, Artsiom Balakir, Alex Nikulkov, Lingnan Gao, Zhihao Cen, Zuobing Xu, Zheqing Zhu|Meta Platform Inc., Menlo Park, USA; AI at Meta, Sunnyvale, USA; AI at Meta, Bellevue, USA; AI at Meta, Menlo Park, USA|The online advertising market, with its thousands of auctions run per second, presents a daunting challenge for advertisers who wish to optimize their spend under a budget constraint. Thus, advertising platforms typically provide automated agents to their customers, which act on their behalf to bid for impression opportunities in real time at scale. Because these proxy agents are owned by the platform but use advertiser funds to operate, there is a strong practical need to balance reliability and explainability of the agent with optimizing power. We propose a generalizable approach to optimizing bidding policies in production environments by learning from real data using offline reinforcement learning. This approach can be used to optimize any differentiable base policy (practically, a heuristic policy based on principles which the advertiser can easily understand), and only requires data generated by the base policy itself. We use a hybrid agent architecture that combines arbitrary base policies with deep neural networks, where only the optimized base policy parameters are eventually deployed, and the neural network part is discarded after training. We demonstrate that such an architecture achieves statistically significant performance gains in both simulated and at-scale production bidding environments. Our approach does not incur additional infrastructure, safety, or explainability costs, as it directly optimizes parameters of existing production routines without replacing them with black box-style models like neural networks.|在线广告市场每秒钟有数千场拍卖，对于那些希望在预算线下优化支出的广告客户来说，这是一个艰巨的挑战。因此，广告平台通常向客户提供自动化代理，代表客户实时大规模地争取印象机会。由于这些代理商属于平台所有，但利用广告客户的资金进行运作，因此在代理商的可靠性和可解释性与优化权力之间存在着很强的现实需求。我们提出了一个通用的方法来优化投标政策在生产环境中通过学习使用离线强化学习的真实数据。这种方法可以用来优化任何可微的基本策略(实际上是一种基于广告商容易理解的原则的启发式策略) ，并且只需要基本策略本身生成的数据。我们采用混合智能体结构，将任意基本策略与深度神经网络相结合，最终只部署优化后的基本策略参数，训练后丢弃神经网络部分。我们证明了这样的体系结构在模拟和大规模生产投标环境中都能获得统计上显著的性能提高。我们的方法不会产生额外的基础设施、安全或可解释性成本，因为它直接优化了现有生产例程的参数，而没有用神经网络等黑箱模型来替代它们。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Offline+Reinforcement+Learning+for+Optimizing+Production+Bidding+Policies)|0| +|[Spending Programmed Bidding: Privacy-friendly Bid Optimization with ROI Constraint in Online Advertising](https://doi.org/10.1145/3637528.3671540)|Yumin Su, Min Xiang, Yifei Chen, Yanbiao Li, Tian Qin, Hongyi Zhang, Yasong Li, Xiaobing Liu|ByteDance Inc., Beijing, China; ByteDance Inc., Singapore; ByteDance Inc., San Jose, CA, USA|Privacy policies have disrupted the multi-billion dollar online advertising market by making real-time and precise user data untraceable, which poses significant challenges to the optimization of Return-On-Investment (ROI) constrained products in the online advertising industry. Privacy protection strategies, including event aggregation and reporting delays, hinder access to detailed and instantaneous feedback data, thus incapacitating traditional identity-revealing attribution techniques. In this paper, we introduces a novel Spending Programmed Bidding (SPB) framework to navigate these challenges. SPB is a two-stage framework that separates long horizon delivery spend planning (the macro stage) and short horizon bidding execution (the micro stage). The macro stage models the target ROI to achieve maximum utility and derives the expected spend, whereas the micro stage optimizes the bid price given the expected spend. We further extend our framework to the cross-channel scenario where the agent bids in both privacy-constrained and identity-revealing attribution channels. We find that when privacy-constrained channels are present, SPB is superior to state-of-the-art bidding methods in both offline datasets and online experiments on a large ad platform.|隐私政策扰乱了数十亿美元的在线广告市场，使实时和精确的用户数据无法追踪，这对在线广告业中投资回报率(ROI)受限产品的优化提出了重大挑战。隐私保护策略，包括事件聚合和报告延迟，阻碍访问详细的和即时的反馈数据，从而使传统的身份披露归因技术丧失能力。在本文中，我们介绍了一个新的支出计划投标(SPB)框架来应对这些挑战。SPB 是一个分为两个阶段的框架，分别为长期交付支出计划(宏观阶段)和短期投标执行(微观阶段)。宏观阶段建立目标投资回报率模型以实现效用最大化并导出预期支出，而微观阶段在给定预期支出的情况下优化投标价格。我们进一步将我们的框架扩展到跨通道场景，其中代理在隐私约束和身份披露的属性通道中竞标。我们发现，当隐私受限的渠道存在时，SPB 在离线数据集和大型广告平台上的在线实验中都优于最先进的投标方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Spending+Programmed+Bidding:+Privacy-friendly+Bid+Optimization+with+ROI+Constraint+in+Online+Advertising)|0| |[Know in AdVance: Linear-Complexity Forecasting of Ad Campaign Performance with Evolving User Interest](https://doi.org/10.1145/3637528.3671528)|Xiaoyu Wang, Yonghui Guo, Hui Sheng, Peili Lv, Chi Zhou, Wei Huang, Shiqin Ta, Dongbo Huang, Xiujin Yang, Lan Xu, Hao Zhou, Yusheng Ji|; Tencent Advertising, Shanghai, China; University of Science and Technology of China & National Institute of Informatics, Hefei, China|Real-time Bidding (RTB) advertisers wish to know in advance theexpected cost and yield of ad campaigns to avoid trial-and-error expenses.However, Campaign Performance Forecasting (CPF), a sequence modeling taskinvolving tens of thousands of ad auctions, poses challenges of evolving userinterest, auction representation, and long context, making coarse-grained andstatic-modeling methods sub-optimal. We propose AdVance, a time-awareframework that integrates local auction-level and global campaign-levelmodeling. User preference and fatigue are disentangled using a time-positionedsequence of clicked items and a concise vector of all displayed items.Cross-attention, conditioned on the fatigue vector, captures the dynamics ofuser interest toward each candidate ad. Bidders compete with each other,presenting a complete graph similar to the self-attention mechanism. Hence, weemploy a Transformer Encoder to compress each auction into embedding by solvingauxiliary tasks. These sequential embeddings are then summarized by aconditional state space model (SSM) to comprehend long-range dependencies whilemaintaining global linear complexity. Considering the irregular time intervalsbetween auctions, we make SSM's parameters dependent on the current auctionembedding and the time interval. We further condition SSM's global predictionson the accumulation of local results. Extensive evaluations and ablationstudies demonstrate its superiority over state-of-the-art methods. AdVance hasbeen deployed on the Tencent Advertising platform, and A/B tests show aremarkable 4.5% uplift in Average Revenue per User (ARPU).|实时竞价(RTB)广告商希望提前知道广告活动的预期成本和收益，以避免试错成本。然而，运动性能预测(CPF)是一个涉及数以万计的广告拍卖的序列建模任务，面临着用户兴趣、拍卖表示和长上下文的不断变化的挑战，使得粗粒度和静态建模方法不再是最优的。我们提出了一个时间感知框架，集成了本地拍卖级别和全球活动级别的建模。使用点击项目的时间定位序列和所有显示项目的简洁向量，用户偏好和疲劳被分离开来。交叉注意力，以疲劳向量为条件，捕捉用户对每个候选广告的兴趣动态。竞标者相互竞争，呈现出一个类似于自我注意机制的完整图表。因此，我们使用变压器编码器通过解决辅助任务将每个拍卖压缩成嵌入。然后用条件状态空间模型(SSM)对这些顺序嵌入进行总结，以便在保持全局线性复杂度的同时理解长程依赖关系。考虑到拍卖之间的时间间隔不规则，我们使得 SSM 的参数依赖于当前的拍卖嵌入和时间间隔。我们进一步将 SSM 的全局预测建立在局部结果累积的基础上。广泛的评估和消融研究表明其优于最先进的方法。在腾讯广告平台上已经部署了 Advanced，a/b 测试显示平均每用户收入(ARPU)显著提高了4.5% 。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Know+in+AdVance:++Linear-Complexity+Forecasting+of+Ad+Campaign+Performance+with+Evolving+User+Interest)|0| |[Trinity: Syncretizing Multi-/Long-Tail/Long-Term Interests All in One](https://doi.org/10.1145/3637528.3671651)|Jing Yan, Liu Jiang, Jianfei Cui, Zhichen Zhao, Xingyan Bin, Feng Zhang, Zuotao Liu|ByteDance Inc., Beijing, China; ByteDance Inc., Shanghai, China|Interest modeling in recommender system has been a constant topic for improving user experience, and typical interest modeling tasks (e.g. multi-interest, long-tail interest and long-term interest) have been investigated in many existing works. However, most of them only consider one interest in isolation, while neglecting their interrelationships. In this paper, we argue that these tasks suffer from a common "interest amnesia" problem, and a solution exists to mitigate it simultaneously. We propose a novel and unified framework in the retrieval stage, "Trinity", to solve interest amnesia problem and improve multiple interest modeling tasks. We construct a real-time clustering system that enables us to project items into enumerable clusters, and calculate statistical interest histograms over these clusters. Based on these histograms, Trinity recognizes underdelivered themes and remains stable when facing emerging hot topics. Its derived retrievers have been deployed on the recommender system of Douyin, significantly improving user experience and retention. We believe that such practical experience can be well generalized to other scenarios.|兴趣建模一直是提高用户体验的推荐系统，现有的许多研究工作都对典型的兴趣建模任务(如多重兴趣、长尾兴趣和长期兴趣)进行了研究。然而，他们中的大多数只考虑了孤立的一个利益，而忽视了他们之间的相互关系。在本文中，我们认为，这些任务遭受一个共同的“利益健忘症”问题，并存在一个解决方案，以减轻它同时。为了解决兴趣健忘问题和改进多兴趣建模任务，我们提出了一个新的统一的检索框架“三位一体”。我们构造了一个实时聚类系统，使我们能够将项目投射到可枚举的聚类中，并计算这些聚类上的统计兴趣直方图。基于这些直方图，三一认识到交付不足的主题，并保持稳定时，面对新兴的热门话题。它派生出来的检索器已经部署在 Douyin 的推荐系统上，大大提高了用户体验和保留率。我们相信，这种实践经验可以很好地推广到其他情况。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Trinity:+Syncretizing+Multi-/Long-Tail/Long-Term+Interests+All+in+One)|0| -|[Temporal Uplift Modeling for Online Marketing](https://doi.org/10.1145/3637528.3671560)|Xin Zhang, Kai Wang, Zengmao Wang, Bo Du, Shiwei Zhao, Runze Wu, Xudong Shen, Tangjie Lv, Changjie Fan|Wuhan University, Wuhan, China; Netease Fuxi AI Lab, Hangzhou, China; NetEase Fuxi AI Lab, Hangzhou, China|In recent years, uplift modeling, also known as individual treatment effect (ITE) estimation, has seen wide applications in online marketing, such as delivering one-time issuance of coupons or discounts to motivate users' purchases. However, complex yet more realistic scenarios involving multiple interventions over time on users are still rarely explored. The challenges include handling the bias from time-varying confounders, determining optimal treatment timing, and selecting among numerous treatments. In this paper, to tackle the aforementioned challenges, we present a temporal point process-based uplift model (TPPUM) that utilizes users' temporal event sequences to estimate treatment effects via counterfactual analysis and temporal point processes. In this model, marketing actions are considered as treatments, user purchases as outcome events, and how treatments alter the future conditional intensity function of generating outcome events as the uplift. Empirical evaluations demonstrate that our method outperforms existing baselines on both real-world and synthetic datasets. In the online experiment conducted in a discounted bundle recommendation scenario involving an average of 3 to 4 interventions per day and hundreds of treatment candidates, we demonstrate how our model outperforms current state-of-the-art methods in selecting the appropriate treatment and timing of treatment, resulting in a 3.6% increase in application-level revenue.|近年来，提升模型，也被称为个体治疗效果(ITE)评估，已经在网络营销中得到了广泛的应用，例如提供一次性发行优惠券或折扣，以激励用户的购买。然而，涉及随着时间的推移对用户进行多种干预的复杂但更现实的场景仍然很少被探索。这些挑战包括处理来自时变混杂因素的偏倚，确定最佳治疗时机，以及在众多治疗方案中进行选择。为了解决上述问题，本文提出了一种基于时间点过程的提升模型(TPPUM) ，该模型利用用户的时间事件序列，通过反事实分析和时间点过程来估计治疗效果。在这个模型中，营销行为被认为是治疗，用户购买作为结果事件，以及治疗如何改变未来的条件强度函数，产生结果事件作为提升。经验评估表明，我们的方法优于现有的基线上的真实世界和合成数据集。在折扣捆绑推荐情景下进行的在线实验中，涉及平均每天3至4次干预和数百个治疗候选人，我们证明了我们的模型在选择适当的治疗和治疗时机方面如何优于当前最先进的方法，导致应用级收入增加3.6% 。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Temporal+Uplift+Modeling+for+Online+Marketing)|0| -|[STATE: A Robust ATE Estimator of Heavy-Tailed Metrics for Variance Reduction in Online Controlled Experiments](https://doi.org/10.1145/3637528.3672352)|Hao Zhou, Kun Sun, Shaoming Li, Yangfeng Fan, Guibin Jiang, Jiaqi Zheng, Tao Li|State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China; State Key Laboratory for Novel Software Technology, Nanjing University & Meituan, Nanjing, China; Meituan, Beijing, China|Online controlled experiments play a crucial role in enabling data-driven decisions across a wide range of companies. Variance reduction is an effective technique to improve the sensitivity of experiments, achieving higher statistical power while using fewer samples and shorter experimental periods. However, typical variance reduction methods (e.g., regression-adjusted estimators) are built upon the intuitional assumption of Gaussian distributions and cannot properly characterize the real business metrics with heavy-tailed distributions. Furthermore, outliers diminish the correlation between pre-experiment covariates and outcome metrics, greatly limiting the effectiveness of variance reduction. In this paper, we develop a novel framework that integrates the Student's t-distribution with machine learning tools to fit heavy-tailed metrics and construct a robust average treatment effect estimator in online controlled experiments, which we call STATE. By adopting a variational EM method to optimize the loglikehood function, we can infer a robust solution that greatly eliminates the negative impact of outliers and achieves significant variance reduction. Moreover, we extend the STATE method from count metrics to ratio metrics by utilizing linear transformation that preserves unbiased estimation, whose variance reduction is more complex but less investigated in existing works. Finally, both simulations on synthetic data and long-term empirical results on Meituan experiment platform demonstrate the effectiveness of our method. Compared with the state-of-the-art estimators (CUPAC/MLRATE), STATE achieves over 50% variance reduction, indicating it can reach the same statistical power with only half of the observations, or half the experimental duration.|在线控制实验在许多公司实现数据驱动的决策方面发挥着至关重要的作用。方差约简是一种提高实验灵敏度的有效方法，可以在使用较少样本和较短实验周期的情况下获得较高的统计效率。然而，典型的方差减少方法(例如，回归调整估计)是建立在高斯分布的直观假设之上的，不能正确地描述实际业务指标的重尾分布。此外，异常值降低了实验前协变量与结果指标之间的相关性，极大地限制了方差减少的有效性。本文提出了一种新的学生 t 分布与机器学习工具相结合的框架，用于在线对照实验，构造了一个稳健的平均治疗效果估计器，我们称之为状态估计器。通过采用变分 EM 方法对对数似然函数进行优化，可以推导出一个鲁棒解，大大消除了异常值的负面影响，实现了显著的方差减少。此外，我们将 STATE 方法从计数指标扩展到比率指标，使用了保留无偏估计的线性映射，其方差减少更为复杂，但在现有工作中研究较少。最后，对合成数据的模拟和在美团实验平台上的长期实验结果都证明了该方法的有效性。与最先进的估计器(CUPAC/MLRATE)相比，STATE 实现了超过50% 的方差减少，表明它可以达到相同的统计功率只有一半的观察，或一半的实验持续时间。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=STATE:+A+Robust+ATE+Estimator+of+Heavy-Tailed+Metrics+for+Variance+Reduction+in+Online+Controlled+Experiments)|0| +|[Temporal Uplift Modeling for Online Marketing](https://doi.org/10.1145/3637528.3671560)|Xin Zhang, Kai Wang, Zengmao Wang, Bo Du, Shiwei Zhao, Runze Wu, Xudong Shen, Tangjie Lv, Changjie Fan|NetEase Fuxi AI Lab, Hangzhou, China; Wuhan University, Wuhan, China; Netease Fuxi AI Lab, Hangzhou, China|In recent years, uplift modeling, also known as individual treatment effect (ITE) estimation, has seen wide applications in online marketing, such as delivering one-time issuance of coupons or discounts to motivate users' purchases. However, complex yet more realistic scenarios involving multiple interventions over time on users are still rarely explored. The challenges include handling the bias from time-varying confounders, determining optimal treatment timing, and selecting among numerous treatments. In this paper, to tackle the aforementioned challenges, we present a temporal point process-based uplift model (TPPUM) that utilizes users' temporal event sequences to estimate treatment effects via counterfactual analysis and temporal point processes. In this model, marketing actions are considered as treatments, user purchases as outcome events, and how treatments alter the future conditional intensity function of generating outcome events as the uplift. Empirical evaluations demonstrate that our method outperforms existing baselines on both real-world and synthetic datasets. In the online experiment conducted in a discounted bundle recommendation scenario involving an average of 3 to 4 interventions per day and hundreds of treatment candidates, we demonstrate how our model outperforms current state-of-the-art methods in selecting the appropriate treatment and timing of treatment, resulting in a 3.6% increase in application-level revenue.|近年来，提升模型，也被称为个体治疗效果(ITE)评估，已经在网络营销中得到了广泛的应用，例如提供一次性发行优惠券或折扣，以激励用户的购买。然而，涉及随着时间的推移对用户进行多种干预的复杂但更现实的场景仍然很少被探索。这些挑战包括处理来自时变混杂因素的偏倚，确定最佳治疗时机，以及在众多治疗方案中进行选择。为了解决上述问题，本文提出了一种基于时间点过程的提升模型(TPPUM) ，该模型利用用户的时间事件序列，通过反事实分析和时间点过程来估计治疗效果。在这个模型中，营销行为被认为是治疗，用户购买作为结果事件，以及治疗如何改变未来的条件强度函数，产生结果事件作为提升。经验评估表明，我们的方法优于现有的基线上的真实世界和合成数据集。在折扣捆绑推荐情景下进行的在线实验中，涉及平均每天3至4次干预和数百个治疗候选人，我们证明了我们的模型在选择适当的治疗和治疗时机方面如何优于当前最先进的方法，导致应用级收入增加3.6% 。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Temporal+Uplift+Modeling+for+Online+Marketing)|0| +|[STATE: A Robust ATE Estimator of Heavy-Tailed Metrics for Variance Reduction in Online Controlled Experiments](https://doi.org/10.1145/3637528.3672352)|Hao Zhou, Kun Sun, Shaoming Li, Yangfeng Fan, Guibin Jiang, Jiaqi Zheng, Tao Li|State Key Laboratory for Novel Software Technology, Nanjing University & Meituan, Nanjing, China; Meituan, Beijing, China; State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China|Online controlled experiments play a crucial role in enabling data-driven decisions across a wide range of companies. Variance reduction is an effective technique to improve the sensitivity of experiments, achieving higher statistical power while using fewer samples and shorter experimental periods. However, typical variance reduction methods (e.g., regression-adjusted estimators) are built upon the intuitional assumption of Gaussian distributions and cannot properly characterize the real business metrics with heavy-tailed distributions. Furthermore, outliers diminish the correlation between pre-experiment covariates and outcome metrics, greatly limiting the effectiveness of variance reduction. In this paper, we develop a novel framework that integrates the Student's t-distribution with machine learning tools to fit heavy-tailed metrics and construct a robust average treatment effect estimator in online controlled experiments, which we call STATE. By adopting a variational EM method to optimize the loglikehood function, we can infer a robust solution that greatly eliminates the negative impact of outliers and achieves significant variance reduction. Moreover, we extend the STATE method from count metrics to ratio metrics by utilizing linear transformation that preserves unbiased estimation, whose variance reduction is more complex but less investigated in existing works. Finally, both simulations on synthetic data and long-term empirical results on Meituan experiment platform demonstrate the effectiveness of our method. Compared with the state-of-the-art estimators (CUPAC/MLRATE), STATE achieves over 50% variance reduction, indicating it can reach the same statistical power with only half of the observations, or half the experimental duration.|在线控制实验在许多公司实现数据驱动的决策方面发挥着至关重要的作用。方差约简是一种提高实验灵敏度的有效方法，可以在使用较少样本和较短实验周期的情况下获得较高的统计效率。然而，典型的方差减少方法(例如，回归调整估计)是建立在高斯分布的直观假设之上的，不能正确地描述实际业务指标的重尾分布。此外，异常值降低了实验前协变量与结果指标之间的相关性，极大地限制了方差减少的有效性。本文提出了一种新的学生 t 分布与机器学习工具相结合的框架，用于在线对照实验，构造了一个稳健的平均治疗效果估计器，我们称之为状态估计器。通过采用变分 EM 方法对对数似然函数进行优化，可以推导出一个鲁棒解，大大消除了异常值的负面影响，实现了显著的方差减少。此外，我们将 STATE 方法从计数指标扩展到比率指标，使用了保留无偏估计的线性映射，其方差减少更为复杂，但在现有工作中研究较少。最后，对合成数据的模拟和在美团实验平台上的长期实验结果都证明了该方法的有效性。与最先进的估计器(CUPAC/MLRATE)相比，STATE 实现了超过50% 的方差减少，表明它可以达到相同的统计功率只有一半的观察，或一半的实验持续时间。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=STATE:+A+Robust+ATE+Estimator+of+Heavy-Tailed+Metrics+for+Variance+Reduction+in+Online+Controlled+Experiments)|0| |[Practical Machine Learning for Streaming Data](https://doi.org/10.1145/3637528.3671442)|Heitor Murilo Gomes, Albert Bifet|; AI Institute, University of Waikato & LTCI, Télécom Paris, IP Paris, Waikato, New Zealand|Machine Learning for Data Streams has been an important area of research since the late 1990s, and its use in industry has grown significantly over the last few years. However, there is still a gap between the cutting-edge research and the tools that are readily available, which makes it challenging for practitioners, including experienced data scientists, to implement and evaluate these methods in this complex domain. Our tutorial aims to bridge this gap with a dual focus. We will discuss important research topics, such as partially delayed labeled streams, while providing practical demonstrations of their implementation and assessment using CapyMOA, an open-source library that provides efficient algorithm implementations through a high-level Python API. Source code is available in https://github.com/adaptive-machine-learning/CapyMOA while the accompanying tutorials and installation guide are available in https://capymoa.org/.|自20世纪90年代末以来，数据流机器学习一直是一个重要的研究领域，在过去几年中，它在工业中的应用显著增长。然而，在尖端研究和现有工具之间仍然存在差距，这使得从业人员，包括有经验的数据科学家，在这个复杂的领域实施和评估这些方法具有挑战性。我们的教程旨在通过双重关注来弥补这一差距。我们将讨论一些重要的研究主题，比如部分延迟的标记流，同时使用 CapyMOA 提供实际的实现和评估演示，CapyMOA 是一个开源库，通过高级 Python API 提供高效的算法实现。源代码可以在 https://github.com/adaptive-machine-learning/capymoa 中找到，相应的教程和安装指南也可以在 https://capymoa.org/中找到。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Practical+Machine+Learning+for+Streaming+Data)|0| |[Empower an End-to-end Scalable and Interpretable Data Science Ecosystem using Statistics, AI and Domain Science](https://doi.org/10.1145/3637528.3672194)|Xihong Lin|Harvard University, Boston, MA, USA|The data science ecosystem encompasses data fairness, statistical, ML and AI methods and tools, interpretable data analysis and results, and trustworthy decision-making. Rapid advancements in AI have revolutionized data utilization and enabled machines to learn from data more effectively. Statistics, as the science of learning from data while accounting for uncertainty, plays a pivotal role in addressing complex real-world problems and facilitating trustworthy decision-making. In this talk, I will discuss the challenges and opportunities involved in building an end-to-end scalable and interpretable data science ecosystem using the analysis of whole genome sequencing studies and biobanks that integrates statistics, ML/AI, and genomic and health science as an example. Biobanks collect whole genome data, electronic health records and epidemiological data. I will illustrate key points using the analysis of multi-ancestry whole genome sequencing studies and biobanks by discussing a few scalable and interpretable statistical and ML/AI methods, tools and data science resources. Specifically, first, data fairness and diversity is a critical pillar of a trustworthy data science ecosystem. About 85+% of genome wide association study samples in the last 15 years are European, resulting in disparity in genetic research. I will discuss the community effort on improving diversity in genetic studies in the last 10 years. I will present trans-ancestry polygenic risk scores (PRS) using millions of common genetic variants across the genome by leveraging large GWAS sample sizes of European and smaller sample sizes of under-represented populations for predicting disease risk using transfer learning and genetic association summary statistics. The performance of deep learning methods for PRS will also be discussed. Second, scalability in cloud platforms is critical for large scale affordable analysis for multi-ancestry biobanks and whole genome studies. I will discuss improving scalability in cloud-computing using interpretable sparsity via FastSparseGRM. To build an interpretable and powerful end-to-end ecosystem of rare variant analysis of large scale whole genome sequencing studies and biobanks, I will first introduce FAVOR, a multi-faceted variant functional annotation database and portal of all possible 9 billions of variants across the whole genome. I will discuss FAVOR-GPT, a LLM interface of the FAVOR functional annotation database to improve user experience for navigating FAVOR and performing variant functional annotation query and variant functional summary statistics calculations. I will also discuss FAVORannotator which can be used to functionally annotate any whole genome sequencing studies. I will also discuss STAAR and STAAR and STAARpipeline, the WGS rare variant analysis pipeline that boosts the power of WGS rare variant association analysis by dynamically incorporating multi-faceted variant functional annotations. Extension of incorporating single-cell data in WGS analysis will also be discussed. I will also discuss ensemble methods that improve the power of rare variant association tests. Cloud-deployment of these resources and tools in several ecosystems will be presented, such as RAP for the UK biobank, AnVIL for the NHGRI Genome Sequencing Program and All of Us, and BioData Catalyst for the NHLBI Trans-omics Precision Medine Program (TOPMed). This talk aims to ignite proactive and thought-provoking discussions, foster collaboration, and cultivate open-minded approaches to advance scientific discovery.|数据科学生态系统包括数据公平、统计学、机器学习和人工智能方法和工具、可解释的数据分析和结果以及可信赖的决策。人工智能的快速发展使数据利用发生了革命性的变化，使机器能够更有效地从数据中学习。统计学是一门从数据中学习同时考虑不确定性的科学，在解决复杂的现实世界问题和促进可信赖的决策方面发挥着关键作用。在这次演讲中，我将以整合统计学、机器学习/人工智能以及基因组和健康科学的全基因组测序研究和生物库分析为例，讨论建立一个端到端可扩展和可解释的数据科学生态系统所面临的挑战和机遇。生物库收集全基因组数据、电子健康记录和流行病学数据。我将通过讨论一些可扩展和可解释的统计学和机器学习/人工智能方法、工具和数据科学资源，用多血统全基因组测序研究和生物库的分析来说明要点。具体来说，首先，数据公平性和多样性是可信赖的数据科学生态系统的关键支柱。在过去的15年中，大约85% 以上的全基因组关联研究样本是欧洲的，这导致了遗传学研究的差异。我将讨论在过去10年中社区在提高基因研究多样性方面所做的努力。我将通过利用欧洲的大型 GWAS 样本量和代表性不足人群的较小样本量，使用转移学习和遗传关联总结统计来预测疾病风险，从而使用数百万个常见基因变体来呈现跨血统多基因风险评分(PRS)。本文还将讨论 PRS 深度学习方法的性能。其次，云平台的可扩展性对于多血统生物库和全基因组研究的大规模经济分析至关重要。我将讨论如何通过 FastSparseGRM 使用可解释的稀疏性来提高云计算的可伸缩性。为了建立一个可解释和强大的端到端生态系统的罕见变异分析的大规模全基因组测序研究和生物库，我将首先介绍 FAVOR，一个多方面的变异功能注释数据库和门户网站的所有可能的90亿个变异整个基因组。我将讨论 FAVOR-GPT，它是 FAVOR 功能注释数据库的 LLM 接口，用于改善用户在导航 FAVOR 和执行变体功能注释查询和变体功能摘要统计计算时的体验。我还将讨论 FAVORannotator，它可以用来对任何全基因组测序研究进行功能性注释。我还将讨论 STAAR 和 STAAR 以及 STAAR 流水线，这是 WGS 稀有变量分析流水线，它通过动态结合多方面的变量功能注释来提高 WGS 稀有变量关联分析的能力。还将讨论将单细胞数据纳入 WGS 分析的扩展。我还将讨论提高稀有变量关联测试能力的集成方法。将介绍这些资源和工具在几个生态系统中的云部署，例如英国生物库的 RAP，NHGRI 基因组测序计划和我们所有人的 AnVIL，以及 NHLBI 反式组学精确医学计划(TOPMed)的 BioData Catalyst。这次讲座的目的是激发积极主动和发人深省的讨论，促进合作，并培养开放的方法，以推动科学发现。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Empower+an+End-to-end+Scalable+and+Interpretable+Data+Science+Ecosystem+using+Statistics,+AI+and+Domain+Science)|0| |[Semi-Supervised Learning for Time Series Collected at a Low Sampling Rate](https://doi.org/10.1145/3637528.3672033)|Minyoung Bae, Yooju Shin, Youngeun Nam, Youngseop Lee, JaeGil Lee|KAIST, Daejeon, Republic of Korea; Samsung Electronics Co., Ltd., Suwon-si, Republic of Korea|Although time-series classification has many applications in healthcare and manufacturing, the high cost of data collection and labeling hinders its widespread use. To reduce data collection and labeling costs while maintaining high classification accuracy, we propose a novel problem setting, called semi-supervised learning with low-sampling-rate time series, in which the majority of time series are collected at a low sampling rate and are unlabeled whereas the minority of time series are collected at a high sampling rate and are labeled. For this novel problem scenario, we develop the SemiTSR framework equipped with the super-resolution module and the semi-supervised learning module. Here, low-sampling-rate time series are upsampled precisely, taking periodicity and trend at each timestamp into account, and both labeled and unlabeled high-sampling-rate time series are utilized for training. In particular, consistency regularization between artificially downsampled time series derived from an original high-sampling-rate time series is effective at overcoming limited sampling rates. We demonstrate that SemiTSR significantly outperforms conventional semi-supervised learning techniques by assuring high classification accuracy with low-sampling-rate time series.|尽管时间序列分类在医疗保健和制造业中有着广泛的应用，但是高昂的数据采集和标记成本阻碍了它的广泛应用。为了减少数据收集和标记成本，同时保持高分类准确性，我们提出了一个新的问题设置，称为低采样率时间序列的半监督学习，其中大多数时间序列是以低采样率收集和未标记，而少数时间序列是以高采样率收集和标记。对于这个新的问题场景，我们开发了配备了超分辨率模块和半监督学习模块的 SemitSR 框架。该方法对低采样率时间序列进行精确的上采样，同时考虑每个时间戳的周期性和趋势性，采用标记和未标记的高采样率时间序列进行训练。特别是，人工下采样时间序列之间的一致性正则化源于一个原始的高采样率时间序列是有效的克服有限的采样率。我们证明了 SemitSR 通过保证低采样率时间序列的高分类精度，明显优于传统的半监督学习分类技术。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Semi-Supervised+Learning+for+Time+Series+Collected+at+a+Low+Sampling+Rate)|0| |[Meta Clustering of Neural Bandits](https://doi.org/10.1145/3637528.3671691)|Yikun Ban, Yunzhe Qi, Tianxin Wei, Lihui Liu, Jingrui He|University of Illinois, Urbana Champaign, Champaign, IL, USA; University of Illinois at Urbana-Champaign, Champaign, IL, USA; University of Illinois, Urbana-Champaign, Champaign, IL, USA|The contextual bandit has been identified as a powerful framework to formulate the recommendation process as a sequential decision-making process, where each item is regarded as an arm and the objective is to minimize the regret of T rounds. In this paper, we study a new problem, Clustering of Neural Bandits, by extending previous work to the arbitrary reward function, to strike a balance between user heterogeneity and user correlations in the recommender system. To solve this problem, we propose a novel algorithm called M-CNB, which utilizes a meta-learner to represent and rapidly adapt to dynamic clusters, along with an informative Upper Confidence Bound (UCB)-based exploration strategy. We provide an instance-dependent performance guarantee for the proposed algorithm that withstands the adversarial context, and we further prove the guarantee is at least as good as state-of-the-art (SOTA) approaches under the same assumptions. In extensive experiments conducted in both recommendation and online classification scenarios, M-CNB outperforms SOTA baselines. This shows the effectiveness of the proposed approach in improving online recommendation and online classification performance.|环境强盗已被确定为一个强大的框架，以制定推荐过程作为一个顺序决策过程，其中每个项目被视为一个手臂，目标是尽量减少 T 轮的遗憾。在这篇文章中，我们研究了一个新的问题，神经盗贼聚类，通过扩展以前的工作到任意奖励函数，来平衡用户异质性和用户相关性的推荐系统。为了解决这一问题，我们提出了一种新的算法 M-CNB，该算法利用元学习器来表示和快速适应动态聚类，同时提出了一种基于信息上置信界(UCB)的探索策略。我们提供了一个实例相关的性能保证提出的算法，抵御对手的背景下，我们进一步证明的保证是至少一样好的国家-最先进的(SOTA)方法在相同的假设条件下。在推荐和在线分类场景中进行的大量实验中，M-CNB 的性能优于 SOTA 基线。这表明了该方法在改善在线推荐和在线分类性能方面的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Meta+Clustering+of+Neural+Bandits)|0| -|[Popularity-Aware Alignment and Contrast for Mitigating Popularity Bias](https://doi.org/10.1145/3637528.3671824)|Miaomiao Cai, Lei Chen, Yifan Wang, Haoyue Bai, Peijie Sun, Le Wu, Min Zhang, Meng Wang|Tsinghua University, Beijing, China; Hefei University of Technology, Hefei, China; DCST, Tsinghua University, Beijing, China; DCST, Tsinghua University & Quan Cheng Laboratory, Beijing, China|Collaborative Filtering (CF) typically suffers from the significant challengeof popularity bias due to the uneven distribution of items in real-worlddatasets. This bias leads to a significant accuracy gap between popular andunpopular items. It not only hinders accurate user preference understanding butalso exacerbates the Matthew effect in recommendation systems. To alleviatepopularity bias, existing efforts focus on emphasizing unpopular items orseparating the correlation between item representations and their popularity.Despite the effectiveness, existing works still face two persistent challenges:(1) how to extract common supervision signals from popular items to improve theunpopular item representations, and (2) how to alleviate the representationseparation caused by popularity bias. In this work, we conduct an empiricalanalysis of popularity bias and propose Popularity-Aware Alignment and Contrast(PAAC) to address two challenges. Specifically, we use the common supervisorysignals modeled in popular item representations and propose a novelpopularity-aware supervised alignment module to learn unpopular itemrepresentations. Additionally, we suggest re-weighting the contrastive learningloss to mitigate the representation separation from a popularity-centricperspective. Finally, we validate the effectiveness and rationale of PAAC inmitigating popularity bias through extensive experiments on three real-worlddatasets. Our code is available athttps://github.com/miaomiao-cai2/KDD2024-PAAC.|由于现实世界数据集中项目的分布不均匀，协同过滤(CF)通常会受到流行偏见的严重挑战。这种偏差导致流行和不流行的项目之间的准确性差距很大。它不仅阻碍了准确的用户偏好理解，而且加剧了推荐系统中的马太效应。为了减轻流行偏见，现有的研究集中在强调不受欢迎的项目或者区分项目表示和它们的流行度之间的关系。尽管有效，现有的研究仍然面临两个持续的挑战: (1)如何从流行项目中提取共同的监督信号来改善不流行项目的表征; (2)如何缓解流行偏差造成的表征分离。在这项工作中，我们进行了流行偏差的实证分析，并提出流行意识的对齐和对比(PAAC) ，以解决两个挑战。具体来说，我们使用流行项表示中的公共监督信号，并提出了一种新颖的流行感知监督对齐模块来学习不流行的项表示。此外，我们建议重新权衡对比学习损失，以减轻从流行为中心的观点表征分离。最后，我们通过在三个实际数据集上的大量实验，验证了 PAAC 消除流行偏差的有效性和合理性。我们的代码是可用的 https:// github.com/miaomiao-cai2/kdd2024-paac。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Popularity-Aware+Alignment+and+Contrast+for+Mitigating+Popularity+Bias)|0| +|[Popularity-Aware Alignment and Contrast for Mitigating Popularity Bias](https://doi.org/10.1145/3637528.3671824)|Miaomiao Cai, Lei Chen, Yifan Wang, Haoyue Bai, Peijie Sun, Le Wu, Min Zhang, Meng Wang|DCST, Tsinghua University & Quan Cheng Laboratory, Beijing, China; DCST, Tsinghua University, Beijing, China; Tsinghua University, Beijing, China; Hefei University of Technology, Hefei, China|Collaborative Filtering (CF) typically suffers from the significant challengeof popularity bias due to the uneven distribution of items in real-worlddatasets. This bias leads to a significant accuracy gap between popular andunpopular items. It not only hinders accurate user preference understanding butalso exacerbates the Matthew effect in recommendation systems. To alleviatepopularity bias, existing efforts focus on emphasizing unpopular items orseparating the correlation between item representations and their popularity.Despite the effectiveness, existing works still face two persistent challenges:(1) how to extract common supervision signals from popular items to improve theunpopular item representations, and (2) how to alleviate the representationseparation caused by popularity bias. In this work, we conduct an empiricalanalysis of popularity bias and propose Popularity-Aware Alignment and Contrast(PAAC) to address two challenges. Specifically, we use the common supervisorysignals modeled in popular item representations and propose a novelpopularity-aware supervised alignment module to learn unpopular itemrepresentations. Additionally, we suggest re-weighting the contrastive learningloss to mitigate the representation separation from a popularity-centricperspective. Finally, we validate the effectiveness and rationale of PAAC inmitigating popularity bias through extensive experiments on three real-worlddatasets. Our code is available athttps://github.com/miaomiao-cai2/KDD2024-PAAC.|由于现实世界数据集中项目的分布不均匀，协同过滤(CF)通常会受到流行偏见的严重挑战。这种偏差导致流行和不流行的项目之间的准确性差距很大。它不仅阻碍了准确的用户偏好理解，而且加剧了推荐系统中的马太效应。为了减轻流行偏见，现有的研究集中在强调不受欢迎的项目或者区分项目表示和它们的流行度之间的关系。尽管有效，现有的研究仍然面临两个持续的挑战: (1)如何从流行项目中提取共同的监督信号来改善不流行项目的表征; (2)如何缓解流行偏差造成的表征分离。在这项工作中，我们进行了流行偏差的实证分析，并提出流行意识的对齐和对比(PAAC) ，以解决两个挑战。具体来说，我们使用流行项表示中的公共监督信号，并提出了一种新颖的流行感知监督对齐模块来学习不流行的项表示。此外，我们建议重新权衡对比学习损失，以减轻从流行为中心的观点表征分离。最后，我们通过在三个实际数据集上的大量实验，验证了 PAAC 消除流行偏差的有效性和合理性。我们的代码是可用的 https:// github.com/miaomiao-cai2/kdd2024-paac。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Popularity-Aware+Alignment+and+Contrast+for+Mitigating+Popularity+Bias)|0| |[Enhancing Contrastive Learning on Graphs with Node Similarity](https://doi.org/10.1145/3637528.3671898)|Hongliang Chi, Yao Ma|Rensselaer Polytechnic Institute, Troy, NY, USA|Graph Neural Networks (GNNs) have achieved great success in learning graph representations and thus facilitating various graph-related tasks. However, most GNN methods adopt a supervised learning setting, which is not always feasible in real-world applications due to the difficulty to obtain labeled data. Hence, graph self-supervised learning has been attracting increasing attention. Graph contrastive learning (GCL) is a representative framework for self-supervised learning. In general, GCL learns node representations by contrasting semantically similar nodes (positive samples) and dissimilar nodes (negative samples) with anchor nodes. Without access to labels, positive samples are typically generated by data augmentation, and negative samples are uniformly sampled from the entire graph, which leads to a sub-optimal objective. Specifically, data augmentation naturally limits the number of positive samples that involve in the process (typically only one positive sample is adopted). On the other hand, the random sampling process would inevitably select false-negative samples (samples sharing the same semantics with the anchor). These issues limit the learning capability of GCL. In this work, we propose an enhanced objective that addresses the aforementioned issues. We first introduce an unachievable ideal objective that contains all positive samples and no false-negative samples. This ideal objective is then transformed into a probabilistic form based on the distributions for sampling positive and negative samples. We then model these distributions with node similarity and derive the enhanced objective. Comprehensive experiments on various datasets demonstrate the effectiveness of the proposed enhanced objective under different settings.|图形神经网络(GNN)在学习图形表示方面取得了巨大的成功，从而为各种图形相关的任务提供了便利。然而，大多数 GNN 方法采用监督式学习设置，这在现实应用中并不总是可行的，因为很难获得标记数据。因此，图的自监督学习越来越受到人们的关注。图形对比学习(GCL)是一种典型的自监督学习框架。通常，GCL 通过对比语义相似的节点(正样本)和不同的节点(负样本)与锚节点来学习节点表示。由于无法获得标签，正面样本通常通过数据增量生成，而负面样本则从整个图中均匀抽取，从而导致次优目标。具体来说，数据增强自然地限制了过程中涉及的阳性样本的数量(通常只采用一个阳性样本)。另一方面，随机抽样过程不可避免地会选择假阴性样本(样本与锚具有相同的语义)。这些问题限制了 GCL 的学习能力。在这项工作中，我们提出了一个解决上述问题的强化目标。我们首先介绍一个不可能实现的理想目标，它包含所有的正样本和没有假阴性样本。然后将这个理想目标转化为基于正负样本抽样分布的概率形式。然后利用节点相似性对这些分布进行建模，得到增强目标。在不同数据集上的综合实验表明了该增强目标在不同设置下的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Enhancing+Contrastive+Learning+on+Graphs+with+Node+Similarity)|0| |[Fairness in Streaming Submodular Maximization Subject to a Knapsack Constraint](https://doi.org/10.1145/3637528.3671778)|Shuang Cui, Kai Han, Shaojie Tang, Feng Li, Jun Luo|; The University of Texas at Dallas, Richardson, TX, USA; School of Computer Science and Technology, Soochow University, Suzhou, Jiangsu, China; Nanyang Technological University, Singapore, Singapore|Submodular optimization has been identified as a powerful tool for many data mining applications, where a representative subset of moderate size needs to be extracted from a large-scale dataset. In scenarios where data points possess sensitive attributes such as age, gender, or race, it becomes imperative to integrate fairness measures into submodular optimization to mitigate bias and discrimination. In this paper, we study the fundamental problem of fair submodular maximization subject to a knapsack constraint and propose the first streaming algorithm for it with provable performance guarantees for both monotone and non-monotone submodular functions. As a byproduct, we also propose a streaming algorithm for submodular maximization subject to a partition matroid and a knapsack constraint, significantly improving the performance bounds achieved by previous work. We conduct extensive experiments on real-world applications such as movie recommendation, image summarization, and maximum coverage in social networks. The experimental results strongly demonstrate the superiority of our proposed algorithms in terms of both fairness and utility.|子模块优化已被确定为许多数据挖掘应用程序的强大工具，其中需要从大规模数据集中提取中等规模的代表性子集。在数据点具有敏感属性(如年龄、性别或种族)的情况下，必须将公平措施纳入子模块优化，以减少偏见和歧视。本文研究了背包约束下的公平子模极大化问题，提出了第一种具有单调和非单调子模函数性能保证的流算法。作为一个副产品，我们还提出了一种子模块最大化的流算法，该算法受到分区拟阵和背包约束的影响，显著提高了以前的工作所取得的性能界限。我们在真实世界的应用上进行了广泛的实验，比如电影推荐、图片摘要和社交网络的最大覆盖率。实验结果有力地证明了我们提出的算法在公平性和实用性方面的优越性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Fairness+in+Streaming+Submodular+Maximization+Subject+to+a+Knapsack+Constraint)|0| -|[AGS-GNN: Attribute-guided Sampling for Graph Neural Networks](https://doi.org/10.1145/3637528.3671940)|Siddhartha Shankar Das, S. M. Ferdous, Mahantesh M. Halappanavar, Edoardo Serra, Alex Pothen|Boise State University, Boise, ID, USA; Purdue University, West Lafayette, IN, USA; Pacific Northwest National Lab., Richland, WA, USA|We propose AGS-GNN, a novel attribute-guided sampling algorithm for Graph Neural Networks (GNNs). AGS-GNN exploits the node features and the connectivity structure of a graph while simultaneously adapting for both homophily and heterophily in graphs. In homophilic graphs, vertices of the same class are more likely to be adjacent, but vertices of different classes tend to be adjacent in heterophilic graphs. GNNs have been successfully applied to homophilic graphs, but their utility to heterophilic graphs remains challenging. The state-of-the-art GNNs for heterophilic graphs use the full neighborhood of a node instead of sampling it, and hence do not scale to large graphs and are not inductive. We develop dual-channel sampling techniques based on feature-similarity and feature-diversity to select subsets of neighbors for a node that capture adaptive information from homophilic and heterophilic neighborhoods. Currently, AGS-GNN is the only algorithm that explicitly controls homophily in the sampled subgraph through similar and diverse neighborhood samples. For diverse neighborhood sampling, we employ submodularity, a novel contribution in this context. We pre-compute the sampling distribution in parallel, achieving the desired scalability. Using an extensive dataset consisting of 35 small (< 100K nodes) and large (- 100K nodes) homophilic and heterophilic graphs, we demonstrate the superiority of AGS-GNN compared to the state-of-the-art approaches. AGS-GNN achieves test accuracy comparable to the best-performing heterophilic GNNs, even outperforming methods that use the entire graph for node classification. AGS-GNN converges faster than methods that sample neighborhoods randomly, and can be incorporated into existing GNN models that employ node or graph sampling.|提出了一种新的图形神经网络属性导向采样算法 AGS-GNN。AGS-GNN 利用图的节点特征和连通结构，同时适应图的同质性和异质性。在同类图中，同类的顶点更容易相邻，但在异类图中，不同类的顶点更容易相邻。GNN 已经成功地应用于同亲图，但是它们在异亲图中的应用仍然具有挑战性。异质图的最新 GNN 使用节点的完全邻域代替抽样，因此不能伸缩到大图，也不能归纳。我们发展了基于特征相似性和特征多样性的双通道采样技术来选择一个节点的邻居子集，从同类和异类邻居中捕获自适应信息。目前，AGS-GNN 算法是唯一一种通过相似和不同的邻域样本显式控制采样子图同调性的算法。对于不同的邻域抽样，我们采用次模块化，这是在这种情况下的一个新的贡献。我们并行地预先计算了采样分布，达到了预期的可扩展性。使用由35个小(< 100K 节点)和大(- 100K 节点)同质和异质图组成的广泛数据集，我们证明了 AGS-GNN 与最先进的方法相比的优越性。AGS-GNN 获得了与性能最好的异质 GNN 相当的测试精度，甚至优于使用整个图进行节点分类的方法。AGS-GNN 比随机抽样邻域的方法收敛得更快，并且可以合并到采用节点或图抽样的现有 GNN 模型中。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=AGS-GNN:+Attribute-guided+Sampling+for+Graph+Neural+Networks)|0| +|[AGS-GNN: Attribute-guided Sampling for Graph Neural Networks](https://doi.org/10.1145/3637528.3671940)|Siddhartha Shankar Das, S. M. Ferdous, Mahantesh M. Halappanavar, Edoardo Serra, Alex Pothen|Purdue University, West Lafayette, IN, USA; Pacific Northwest National Lab., Richland, WA, USA; Boise State University, Boise, ID, USA|We propose AGS-GNN, a novel attribute-guided sampling algorithm for Graph Neural Networks (GNNs). AGS-GNN exploits the node features and the connectivity structure of a graph while simultaneously adapting for both homophily and heterophily in graphs. In homophilic graphs, vertices of the same class are more likely to be adjacent, but vertices of different classes tend to be adjacent in heterophilic graphs. GNNs have been successfully applied to homophilic graphs, but their utility to heterophilic graphs remains challenging. The state-of-the-art GNNs for heterophilic graphs use the full neighborhood of a node instead of sampling it, and hence do not scale to large graphs and are not inductive. We develop dual-channel sampling techniques based on feature-similarity and feature-diversity to select subsets of neighbors for a node that capture adaptive information from homophilic and heterophilic neighborhoods. Currently, AGS-GNN is the only algorithm that explicitly controls homophily in the sampled subgraph through similar and diverse neighborhood samples. For diverse neighborhood sampling, we employ submodularity, a novel contribution in this context. We pre-compute the sampling distribution in parallel, achieving the desired scalability. Using an extensive dataset consisting of 35 small (< 100K nodes) and large (- 100K nodes) homophilic and heterophilic graphs, we demonstrate the superiority of AGS-GNN compared to the state-of-the-art approaches. AGS-GNN achieves test accuracy comparable to the best-performing heterophilic GNNs, even outperforming methods that use the entire graph for node classification. AGS-GNN converges faster than methods that sample neighborhoods randomly, and can be incorporated into existing GNN models that employ node or graph sampling.|提出了一种新的图形神经网络属性导向采样算法 AGS-GNN。AGS-GNN 利用图的节点特征和连通结构，同时适应图的同质性和异质性。在同类图中，同类的顶点更容易相邻，但在异类图中，不同类的顶点更容易相邻。GNN 已经成功地应用于同亲图，但是它们在异亲图中的应用仍然具有挑战性。异质图的最新 GNN 使用节点的完全邻域代替抽样，因此不能伸缩到大图，也不能归纳。我们发展了基于特征相似性和特征多样性的双通道采样技术来选择一个节点的邻居子集，从同类和异类邻居中捕获自适应信息。目前，AGS-GNN 算法是唯一一种通过相似和不同的邻域样本显式控制采样子图同调性的算法。对于不同的邻域抽样，我们采用次模块化，这是在这种情况下的一个新的贡献。我们并行地预先计算了采样分布，达到了预期的可扩展性。使用由35个小(< 100K 节点)和大(- 100K 节点)同质和异质图组成的广泛数据集，我们证明了 AGS-GNN 与最先进的方法相比的优越性。AGS-GNN 获得了与性能最好的异质 GNN 相当的测试精度，甚至优于使用整个图进行节点分类的方法。AGS-GNN 比随机抽样邻域的方法收敛得更快，并且可以合并到采用节点或图抽样的现有 GNN 模型中。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=AGS-GNN:+Attribute-guided+Sampling+for+Graph+Neural+Networks)|0| |[Estimated Judge Reliabilities for Weighted Bradley-Terry-Luce Are Not Reliable](https://doi.org/10.1145/3637528.3671907)|Andrew F. Dreher, Etienne Vouga, Donald S. Fussell|The University of Texas at Austin, Austin, TX, USA|There are many applications for which we want to learn a latent scale for subjective properties, such as the excitement of a photo or the legibility of a font; however, obtaining human-labeled data is costly and time-consuming. One oft-used method for acquiring these labels, despite the cost being quadratic in the number of items, is the method of pairwise comparisons since this method minimizes the effect of biases and generally can be used effectively outside of a controlled environment. Crowdsourcing appears to be a panacea since online platforms provide affordable access to numerous people, but these participants, judges, vary in diligence and expertise. Several methods have been proposed to assign weights to judges based on their responses relative to everyone else, the goal being to reduce exposure to poor performers, hopefully upgrading the quality of the data. Our research focuses on two natural extensions to the Bradley-Terry-Luce formulation of scaling that jointly optimize for both scale value and judge weights. While both methods appear to perform at least as well as the unweighted formulation on average with well-behaved judges, we report a previously unknown flaw, revealing that the resultant judge weights should not be interpreted as reliabilities. Consequently, these values should not be leveraged for decisions about the judges, such as for active sampling or to validate the participant pool.|有许多应用程序，我们想要了解一个潜在的规模主观属性，如兴奋的照片或字体的可读性; 然而，获取人类标记的数据是昂贵的和耗时的。一个常用的获取这些标签的方法，尽管成本在项目数量上是二次的，是成对比较的方法，因为这种方法最大限度地减少了偏差的影响，通常可以在受控环境之外有效地使用。众包似乎是一种灵丹妙药，因为在线平台为许多人提供了负担得起的访问途径，但这些参与者、法官在勤奋程度和专业知识方面各不相同。已经提出了几种方法，根据法官相对于其他所有人的答复来确定其权重，目的是减少表现不佳的风险，希望能够提高数据的质量。我们的研究集中在两个自然扩展的布拉德利-特里-卢斯公式的尺度，共同优化的标度值和判断权重。虽然这两种方法在表现良好的法官中表现至少和未加权公式一样好，但我们报告了一个以前未知的缺陷，揭示了由此产生的法官权重不应该被解释为可靠性。因此，不应该将这些值用于有关法官的决策，例如用于主动抽样或验证参与者池。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Estimated+Judge+Reliabilities+for+Weighted+Bradley-Terry-Luce+Are+Not+Reliable)|0| -|[Influence Maximization via Graph Neural Bandits](https://doi.org/10.1145/3637528.3671983)|Yuting Feng, Vincent Y. F. Tan, Bogdan Cautis|; CNRS LISN, University of Paris-Saclay, Orsay, France; Department of Mathematics, Department of ECE, National University of Singapore, Singapore, Singapore|We consider a ubiquitous scenario in the study of Influence Maximization(IM), in which there is limited knowledge about the topology of the diffusionnetwork. We set the IM problem in a multi-round diffusion campaign, aiming tomaximize the number of distinct users that are influenced. Leveraging thecapability of bandit algorithms to effectively balance the objectives ofexploration and exploitation, as well as the expressivity of neural networks,our study explores the application of neural bandit algorithms to the IMproblem. We propose the framework IM-GNB (Influence Maximization with GraphNeural Bandits), where we provide an estimate of the users' probabilities ofbeing influenced by influencers (also known as diffusion seeds). This initialestimate forms the basis for constructing both an exploitation graph and anexploration one. Subsequently, IM-GNB handles the exploration-exploitationtradeoff, by selecting seed nodes in real-time using Graph ConvolutionalNetworks (GCN), in which the pre-estimated graphs are employed to refine theinfluencers' estimated rewards in each contextual setting. Through extensiveexperiments on two large real-world datasets, we demonstrate the effectivenessof IM-GNB compared with other baseline methods, significantly improving thespread outcome of such diffusion campaigns, when the underlying network isunknown.|在影响最大化(IM)的研究中，我们考虑了一个无处不在的场景，其中关于扩散网络的拓扑结构的知识是有限的。我们将 IM 问题设置为一个多轮扩散运动，目的是最大限度地增加受影响的不同用户的数量。利用强盗算法的能力来有效地平衡勘探和开发的目标，以及神经网络的表达能力，我们的研究探索了神经强盗算法在 IMN 问题中的应用。我们提出了 IM-GNB (影响最大化与图形神经绑定)的框架，其中我们提供了一个估计的用户的概率受影响者(也称为扩散种子)的影响。这个初始估计是构造开发图和开发图的基础。随后，IM-GNB 通过使用图卷积网络(Graph ConvolutionalNetworks，GCN)实时选择种子节点来处理探索-开发权衡，其中预估图被用于在每个上下文环境中细化影响者的估计奖励。通过在两个大型真实世界数据集上的广泛实验，我们证明了 IM-GNB 与其他基线方法相比的有效性，显着改善了这种扩散运动的传播结果，当底层网络是未知的。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Influence+Maximization+via+Graph+Neural+Bandits)|0| +|[Influence Maximization via Graph Neural Bandits](https://doi.org/10.1145/3637528.3671983)|Yuting Feng, Vincent Y. F. Tan, Bogdan Cautis|; Department of Mathematics, Department of ECE, National University of Singapore, Singapore, Singapore; CNRS LISN, University of Paris-Saclay, Orsay, France|We consider a ubiquitous scenario in the study of Influence Maximization(IM), in which there is limited knowledge about the topology of the diffusionnetwork. We set the IM problem in a multi-round diffusion campaign, aiming tomaximize the number of distinct users that are influenced. Leveraging thecapability of bandit algorithms to effectively balance the objectives ofexploration and exploitation, as well as the expressivity of neural networks,our study explores the application of neural bandit algorithms to the IMproblem. We propose the framework IM-GNB (Influence Maximization with GraphNeural Bandits), where we provide an estimate of the users' probabilities ofbeing influenced by influencers (also known as diffusion seeds). This initialestimate forms the basis for constructing both an exploitation graph and anexploration one. Subsequently, IM-GNB handles the exploration-exploitationtradeoff, by selecting seed nodes in real-time using Graph ConvolutionalNetworks (GCN), in which the pre-estimated graphs are employed to refine theinfluencers' estimated rewards in each contextual setting. Through extensiveexperiments on two large real-world datasets, we demonstrate the effectivenessof IM-GNB compared with other baseline methods, significantly improving thespread outcome of such diffusion campaigns, when the underlying network isunknown.|在影响最大化(IM)的研究中，我们考虑了一个无处不在的场景，其中关于扩散网络的拓扑结构的知识是有限的。我们将 IM 问题设置为一个多轮扩散运动，目的是最大限度地增加受影响的不同用户的数量。利用强盗算法的能力来有效地平衡勘探和开发的目标，以及神经网络的表达能力，我们的研究探索了神经强盗算法在 IMN 问题中的应用。我们提出了 IM-GNB (影响最大化与图形神经绑定)的框架，其中我们提供了一个估计的用户的概率受影响者(也称为扩散种子)的影响。这个初始估计是构造开发图和开发图的基础。随后，IM-GNB 通过使用图卷积网络(Graph ConvolutionalNetworks，GCN)实时选择种子节点来处理探索-开发权衡，其中预估图被用于在每个上下文环境中细化影响者的估计奖励。通过在两个大型真实世界数据集上的广泛实验，我们证明了 IM-GNB 与其他基线方法相比的有效性，显着改善了这种扩散运动的传播结果，当底层网络是未知的。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Influence+Maximization+via+Graph+Neural+Bandits)|0| |[A Unified Core Structure in Multiplex Networks: From Finding the Densest Subgraph to Modeling User Engagement](https://doi.org/10.1145/3637528.3672011)|Farnoosh Hashemi, Ali Behrouz|Cornell University, Ithaca, NY, USA|In many complex systems, the interactions between objects span multipleaspects. Multiplex networks are accurate paradigms to model such systems, whereeach edge is associated with a type. A key graph mining primitive is extractingdense subgraphs, and this has led to interesting notions such as K-cores, knownas building blocks of complex networks. Despite recent attempts to extend thenotion of core to multiplex networks, existing studies suffer from a subset ofthe following limitations: They 1) force all nodes to exhibit their high degreein the same set of relation types while in multiplex networks some connectiontypes can be noisy for some nodes, 2) either require high computational cost ormiss the complex information of multiplex networks, and 3) assume the sameimportance for all relation types. We introduce S-core, a novel and unifyingfamily of dense structures in multiplex networks that uses a function S(.) tosummarize the degree vector of each node. We then discuss how one can choose aproper S(.) from the data. To demonstrate the usefulness of S-cores, we focuson finding the densest subgraph as well as modeling user engagement inmultiplex networks. We present a new density measure in multiplex networks anddiscuss its advantages over existing density measures. We show that the problemof finding the densest subgraph in multiplex networks is NP-hard and design anefficient approximation algorithm based on S-cores. Finally, we present a newmathematical model of user engagement in the presence of different relationtypes. Our experiments shows the efficiency and effectiveness of our algorithmsand supports the proposed mathematical model of user engagement.|在许多复杂系统中，对象之间的交互跨越多个方面。多路网络是模拟这种系统的精确范例，其中每个边都与一个类型相关联。一个关键的图挖掘原语是提取稠密子图，这导致了一些有趣的概念，如 K 核，即复杂网络的构建块。尽管最近试图将核心概念扩展到多路网络，但现有的研究存在以下局限性的子集: 1)迫使所有节点在同一组关系类型中表现出高度的高度，而在多路网络中，一些连接类型对于某些节点可能是噪声的，2)要么需要高计算成本，要么忽略多路网络的复杂信息，3)对所有关系类型承担同样的重要性。我们介绍了 S- 核，一个新的和统一的密集结构家族在多路网络中使用一个函数 S (。)汇总每个节点的度向量。然后我们讨论如何选择一个合适的 S (。)从数据中。为了证明 S 核的有效性，我们着重于寻找密度最大的子图，以及在多路网络中建立用户参与模型。提出了一种新的多路网络密度度量方法，并讨论了它相对于现有密度度量方法的优越性。我们证明了在多路网络中寻找最密集子图的问题是 NP 难的，并且设计了一个基于 S 核的高效近似演算法。最后，我们提出了一个新的数学模型的用户参与在不同的关系类型的存在。实验结果表明了该算法的有效性，并支持所提出的用户参与的数学模型。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Unified+Core+Structure+in+Multiplex+Networks:+From+Finding+the+Densest+Subgraph+to+Modeling+User+Engagement)|0| |[Budgeted Multi-Armed Bandits with Asymmetric Confidence Intervals](https://doi.org/10.1145/3637528.3671833)|Marco Heyden, Vadim Arzamasov, Edouard Fouché, Klemens Böhm|Karlsruhe Institute of Technology, Karlsruhe, Germany|We study the stochastic Budgeted Multi-Armed Bandit (MAB) problem, where a player chooses from K arms with unknown expected rewards and costs. The goal is to maximize the total reward under a budget constraint. A player thus seeks to choose the arm with the highest reward-cost ratio as often as possible. Current approaches for this problem have several issues, which we illustrate. To overcome them, we propose a new upper confidence bound (UCB) sampling policy, ømega-UCB, that uses asymmetric confidence intervals. These intervals scale with the distance between the sample mean and the bounds of a random variable, yielding a more accurate and tight estimation of the reward-cost ratio compared to our competitors. We show that our approach has sublinear instance-dependent regret in general and logarithmic regret for parameter ρ ≥ 1, and that it outperforms existing policies consistently in synthetic and real settings.|我们研究随机预算多臂老虎机(MAB)问题，其中玩家从 k 武器选择未知的预期回报和成本。我们的目标是最大化预算线下的总回报。因此，玩家尽可能多地选择奖励成本比率最高的手臂。目前解决这个问题的方法有几个问题，我们将举例说明。为了克服这些问题，我们提出了一种新的上置信区间(UCB)抽样策略，使用非对称置信区间。这些区间随着样本平均值和随机变量界限之间的距离而变化，与我们的竞争对手相比，可以得到更准确和严格的报酬-成本比率估计。我们证明了我们的方法在一般情况下具有次线性实例依赖遗憾和参数 ρ ≥1的对数遗憾，并且它在合成和实际情况下一致地优于现有的策略。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Budgeted+Multi-Armed+Bandits+with+Asymmetric+Confidence+Intervals)|0| -|[Can Modifying Data Address Graph Domain Adaptation?](https://doi.org/10.1145/3637528.3672058)|Renhong Huang, Jiarong Xu, Xin Jiang, Ruichuan An, Yang Yang|Fudan University, Shanghai, China; Zhejiang University & Fudan University, Hangzhou, China; Zhejiang University, Hangzhou, China; Lehigh University, Bethlehem, PA, USA; Xi'an Jiaotong University, Xi'an, China|Graph neural networks (GNNs) have demonstrated remarkable success in numerous graph analytical tasks. Yet, their effectiveness is often compromised in real-world scenarios due to distribution shifts, limiting their capacity for knowledge transfer across changing environments or domains. Recently, Unsupervised Graph Domain Adaptation (UGDA) has been introduced to resolve this issue. UGDA aims to facilitate knowledge transfer from a labeled source graph to an unlabeled target graph. Current UGDA efforts primarily focus on model-centric methods, such as employing domain invariant learning strategies and designing model architectures. However, our critical examination reveals the limitations inherent to these model-centric methods, while a data-centric method allowed to modify the source graph provably demonstrates considerable potential. This insight motivates us to explore UGDA from a data-centric perspective. By revisiting the theoretical generalization bound for UGDA, we identify two data-centric principles for UGDA: alignment principle and rescaling principle. Guided by these principles, we propose GraphAlign, a novel UGDA method that generates a small yet transferable graph. By exclusively training a GNN on this new graph with classic Empirical Risk Minimization (ERM), GraphAlign attains exceptional performance on the target graph. Extensive experiments under various transfer scenarios demonstrate the GraphAlign outperforms the best baselines by an average of 2.16%, training on the generated graph as small as 0.25~1% of the original training graph.|图形神经网络(GNN)在许多图形分析任务中取得了显著的成功。然而，在现实世界中，由于分布变化，它们的有效性往往受到影响，限制了它们在不断变化的环境或领域中进行知识转移的能力。近年来，无监督图域自适应技术(UGDA)被引入解决这一问题。UGDA 的目的是促进知识从一个有标记的源图向一个无标记的目标图的转移。目前 UGDA 的工作主要集中在以模型为中心的方法上，如使用领域不变学习策略和设计模型体系结构。然而，我们的批判性研究揭示了这些以模型为中心的方法固有的局限性，而允许修改源图的以数据为中心的方法证明了相当大的潜力。这种洞察力促使我们从以数据为中心的角度探索 UGDA。通过回顾 UGDA 的理论推广界限，我们确定了 UGDA 的两个以数据为中心的原则: 对齐原则和重标度原则。在这些原则的指导下，我们提出了 GraphAlign，一种新颖的 UGDA 方法，它可以生成一个小而可转移的图。通过使用经典的经验风险最小化(ERM)在这个新图上专门训练 GNN，GraphAlign 在目标图上获得了出色的性能。在各种传输场景下进行的大量实验表明，GraphAlign 的性能平均比最佳基线高出2.16% ，在生成的图上进行训练，训练量仅为原始训练图的0.25 ~ 1% 。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Can+Modifying+Data+Address+Graph+Domain+Adaptation?)|0| +|[Can Modifying Data Address Graph Domain Adaptation?](https://doi.org/10.1145/3637528.3672058)|Renhong Huang, Jiarong Xu, Xin Jiang, Ruichuan An, Yang Yang|Zhejiang University & Fudan University, Hangzhou, China; Zhejiang University, Hangzhou, China; Lehigh University, Bethlehem, PA, USA; Fudan University, Shanghai, China; Xi'an Jiaotong University, Xi'an, China|Graph neural networks (GNNs) have demonstrated remarkable success in numerous graph analytical tasks. Yet, their effectiveness is often compromised in real-world scenarios due to distribution shifts, limiting their capacity for knowledge transfer across changing environments or domains. Recently, Unsupervised Graph Domain Adaptation (UGDA) has been introduced to resolve this issue. UGDA aims to facilitate knowledge transfer from a labeled source graph to an unlabeled target graph. Current UGDA efforts primarily focus on model-centric methods, such as employing domain invariant learning strategies and designing model architectures. However, our critical examination reveals the limitations inherent to these model-centric methods, while a data-centric method allowed to modify the source graph provably demonstrates considerable potential. This insight motivates us to explore UGDA from a data-centric perspective. By revisiting the theoretical generalization bound for UGDA, we identify two data-centric principles for UGDA: alignment principle and rescaling principle. Guided by these principles, we propose GraphAlign, a novel UGDA method that generates a small yet transferable graph. By exclusively training a GNN on this new graph with classic Empirical Risk Minimization (ERM), GraphAlign attains exceptional performance on the target graph. Extensive experiments under various transfer scenarios demonstrate the GraphAlign outperforms the best baselines by an average of 2.16%, training on the generated graph as small as 0.25~1% of the original training graph.|图形神经网络(GNN)在许多图形分析任务中取得了显著的成功。然而，在现实世界中，由于分布变化，它们的有效性往往受到影响，限制了它们在不断变化的环境或领域中进行知识转移的能力。近年来，无监督图域自适应技术(UGDA)被引入解决这一问题。UGDA 的目的是促进知识从一个有标记的源图向一个无标记的目标图的转移。目前 UGDA 的工作主要集中在以模型为中心的方法上，如使用领域不变学习策略和设计模型体系结构。然而，我们的批判性研究揭示了这些以模型为中心的方法固有的局限性，而允许修改源图的以数据为中心的方法证明了相当大的潜力。这种洞察力促使我们从以数据为中心的角度探索 UGDA。通过回顾 UGDA 的理论推广界限，我们确定了 UGDA 的两个以数据为中心的原则: 对齐原则和重标度原则。在这些原则的指导下，我们提出了 GraphAlign，一种新颖的 UGDA 方法，它可以生成一个小而可转移的图。通过使用经典的经验风险最小化(ERM)在这个新图上专门训练 GNN，GraphAlign 在目标图上获得了出色的性能。在各种传输场景下进行的大量实验表明，GraphAlign 的性能平均比最佳基线高出2.16% ，在生成的图上进行训练，训练量仅为原始训练图的0.25 ~ 1% 。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Can+Modifying+Data+Address+Graph+Domain+Adaptation?)|0| |[Uplift Modelling via Gradient Boosting](https://doi.org/10.1145/3637528.3672019)|Bulat Ibragimov, Anton Vakhrushev|; Sber AI Lab, Moscow, Russian Federation|The Gradient Boosting machine learning ensemble algorithm, well-known for its proficiency and superior performance in intricate machine learning tasks, has encountered limited success in the realm of uplift modeling. Uplift modeling is a challenging task that necessitates a known target for the precise computation of the training gradient. The prevailing two-model strategies, which separately model treatment and control outcomes, are encumbered with limitations as they fail to directly tackle the uplift problem. This paper presents an innovative approach to uplift modeling that employs Gradient Boosting. Unlike previous works, our algorithm utilizes multioutput boosting model and calculates the uplift gradient based on intermediate surrogate predictions and directly models the concealed target. This method circumvents the requirement for a known target and addresses the uplift problem more effectively than existing solutions. Moreover, we broaden the scope of this solution to encompass multitreatment settings, thereby enhancing its applicability. This novel approach not only overcomes the limitations of the traditional two-model strategies but also paves the way for more effective and efficient uplift modeling using Gradient Boosting.|梯度提升机器学习集成算法以其在复杂的机器学习任务中的熟练程度和卓越的性能而闻名，但在提升建模领域却只取得了有限的成功。提升建模是一项具有挑战性的任务，为了精确计算训练梯度，需要一个已知的目标。流行的两个模型的策略，其中单独的模型治疗和控制结果，受到限制，因为他们不能直接解决提升问题。本文提出了一个创新的方法来提升模型的使用梯度提升。与以往的算法不同，该算法采用多输出增强模型，基于中间代理预测计算上升梯度，直接对隐藏目标进行建模。该方法规避了对已知目标的要求，比现有方法更有效地解决了抬升问题。此外，我们扩大了这种解决方案的范围，以包括多种治疗设置，从而增强其适用性。这种新颖的方法不仅克服了传统的双模型策略的局限性，而且为利用梯度提升建立更有效和高效的抬升模型铺平了道路。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Uplift+Modelling+via+Gradient+Boosting)|0| -|[Mutual Distillation Extracting Spatial-temporal Knowledge for Lightweight Multi-channel Sleep Stage Classification](https://doi.org/10.1145/3637528.3671981)|Ziyu Jia, Haichao Wang, Yucheng Liu, Tianzi Jiang|Institute of Automation, Chinese Academy of Science, Beijing, China; Institute of Automation, Chinese Academy of Sciences, Beijing, China; University of Southern California, Los Angeles, USA; Tsinghua-Berkeley Shenzhen Institute, Tsinghua University, Shenzhen, China|Sleep stage classification has important clinical significance for the diagnosis of sleep-related diseases. To pursue more accurate sleep stage classification, multi-channel sleep signals are widely used due to the rich spatial-temporal information contained. However, it leads to a great increment in the size and computational costs, which constrain the application of multi-channel sleep models on hardware devices. Knowledge distillation is an effective way to compress models, yet existing knowledge distillation methods cannot fully extract and transfer the spatial-temporal knowledge in the multi-channel sleep signals. To solve the problem, we propose a general knowledge distillation framework for multi-channel sleep stage classification called spatial-temporal mutual distillation. Based on the spatial relationship of human body and the temporal transition rules of sleep signals, the spatial and temporal modules are designed to extract the spatial-temporal knowledge, thus help the lightweight student model learn the rich spatial-temporal knowledge from large-scale teacher model. The mutual distillation framework transfers the spatial-temporal knowledge mutually. Teacher model and student model can learn from each other, further improving the student model. The results on the ISRUC-III and MASS-SS3 datasets show that our proposed framework compresses the sleep models effectively with minimal performance loss and achieves the state-of-the-art performance compared to the baseline methods.|睡眠分期对睡眠相关疾病的诊断具有重要的临床意义。为了追求更准确的睡眠阶段分类，多通道睡眠信号由于包含了丰富的时空信息而被广泛应用。然而，这会导致系统规模和计算成本的大幅度增加，从而限制了多通道睡眠模型在硬件设备上的应用。知识提取是一种有效的模型压缩方法，但现有的知识提取方法不能完全提取和转移多通道睡眠信号中的时空知识。为了解决这个问题，我们提出了一个通用的多通道睡眠阶段分类知识提取框架，称为时空互提取。基于人体的空间关系和睡眠信号的时间转换规则，设计了空间和时间模块来提取时空知识，从而帮助轻量级学生模型从大规模教师模型中学习到丰富的时空知识。相互精馏框架相互传递时空知识。教师模式与学生模式可以相互借鉴，进一步完善学生模式。ISRUC-III 和 MASS-SS3数据集的结果表明，我们提出的框架有效地压缩了睡眠模型，性能损失最小，并达到了最先进的性能相比，基线方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Mutual+Distillation+Extracting+Spatial-temporal+Knowledge+for+Lightweight+Multi-channel+Sleep+Stage+Classification)|0| -|[Masked LoGoNet: Fast and Accurate 3D Image Analysis for Medical Domain](https://doi.org/10.1145/3637528.3672069)|Amin Karimi Monsefi, Payam Karisani, Mengxi Zhou, Stacey Choi, Nathan Doble, Heng Ji, Srinivasan Parthasarathy, Rajiv Ramnath|Department of Computer Science, University of Illinois Urbana-Champaign, Urbana, IL, USA; Department of Ophthalmology and Visual Science, The Ohio State University, Columbus, OH, USA; Department of Computer Science and Engineering, The Ohio State University, Columbus, OH, USA|Standard modern machine-learning-based imaging methods have faced challenges in medical applications due to the high cost of dataset construction and, thereby, the limited labeled training data available. Additionally, upon deployment, these methods are usually used to process a large volume of data on a daily basis, imposing a high maintenance cost on medical facilities. In this paper, we introduce a new neural network architecture, termed LoGoNet, with a tailored self-supervised learning (SSL) method to mitigate such challenges. LoGoNet integrates a novel feature extractor within a U-shaped architecture, leveraging Large Kernel Attention (LKA) and a dual encoding strategy to capture both long-range and short-range feature dependencies adeptly. This is in contrast to existing methods that rely on increasing network capacity to enhance feature extraction. This combination of novel techniques in our model is especially beneficial in medical image segmentation, given the difficulty of learning intricate and often irregular body organ shapes, such as the spleen. Complementary, we propose a novel SSL method tailored for 3D images to compensate for the lack of large labeled datasets. Our method combines masking and contrastive learning techniques within a multi-task learning framework and is compatible with both Vision Transformer (ViT) and CNN-based models. We demonstrate the efficacy of our methods in numerous tasks across two standard datasets (i.e., BTCV and MSD). Benchmark comparisons with eight state-of-the-art models highlight LoGoNet's superior performance in both inference time and accuracy. Code available at: https://github.com/aminK8/Masked-LoGoNet.|标准的现代机器学习成像方法在医学应用中面临的挑战，由于高成本的数据集建设，从而，有限的标记训练数据可用。此外，在部署时，这些方法通常用于每天处理大量数据，给医疗设施带来高昂的维护成本。在本文中，我们介绍了一种新的神经网络结构，称为 LoGoNet，它采用一种量身定制的自监督学习(SSL)方法来缓解这种挑战。LoGoNet 在 U 形架构中集成了一种新颖的特征提取器，利用大内核注意力(LKA)和双重编码策略来巧妙地捕获长期和短期特征依赖。这与现有的依靠增加网络容量来增强特征提取的方法形成了对比。在我们的模型中，这种新技术的结合在医学图像分割中尤其有益，因为学习复杂且通常不规则的身体器官形状(如脾脏)很困难。作为补充，我们提出了一种新的针对三维图像的 SSL 方法，以弥补缺少大型标记数据集的不足。该方法将掩蔽学习和对比学习技术结合在一个多任务学习框架中，兼容视觉变换器(ViT)和基于 CNN 的模型。我们证明了我们的方法在两个标准数据集(即 BTCV 和 MSD)的许多任务中的有效性。与八个最先进模型的基准比较突出了 LoGoNet 在推断时间和准确性方面的卓越性能。密码: https://github.com/amink8/masked-logonet。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Masked+LoGoNet:+Fast+and+Accurate+3D+Image+Analysis+for+Medical+Domain)|0| -|[Fast and Accurate Domain Adaptation for Irregular Tensor Decomposition](https://doi.org/10.1145/3637528.3671670)|Junghun Kim, Ka Hyun Park, JunGi Jang, U Kang|Seoul National University, Seoul, Republic of Korea; University of Illinois at Urbana-Champaign, Illinois, IL, USA|Given an irregular tensor from a newly emerging domain, how can we quickly and accurately capture its patterns utilizing existing irregular tensors in multiple domains? The problem is of great importance for various tasks such as finding patterns of a new disease using pre-existing diseases data. This is challenging as new target tensors have limited information due to their recent emergence. Thus, carefully utilizing the existing source tensors for analyzing the target tensor is helpful. PARAFAC2 decomposition is a strong tool for finding the patterns of irregular tensors, and the patterns are used in many applications such as missing value prediction and anomaly detection. However, previous PARAFAC2-based works cannot adaptably handle newly emerging target tensors utilizing the source tensors. In this work, we propose Meta-P2, a fast and accurate domain adaptation method for irregular tensor decomposition. Meta-P2 generates a meta factor matrix from the multiple source domains, by domain adaptation and meta-update steps. Meta-P2 quickly and accurately finds the patterns of the new irregular tensor utilizing the meta factor matrix. Extensive experiments on real-world datasets show that Meta-P2 achieves the best performance in various downstream tasks including missing value prediction and anomaly detection tasks.|假设一个不规则张量来自一个新兴的领域，我们如何能够快速准确地捕捉其模式利用现有的不规则张量在多个领域？这个问题对于各种任务都非常重要，例如利用已有的疾病数据找到新疾病的模式。这是具有挑战性的，因为新的目标张量由于最近的出现而信息有限。因此，仔细利用现有的源张量来分析目标张量是有帮助的。PARAFAC2分解是寻找不规则张量模式的一个强有力的工具，这些模式在许多应用中被使用，例如缺失值预测和异常检测。然而，以往基于 PARAFAC2的工作不能自适应地处理新出现的目标张量利用源张量。在这项工作中，我们提出了 Meta-P2，一个快速和准确的区域自适应方法的不规则张量分解。元 P2通过领域适应和元更新步骤从多个源域生成元因子矩阵。Meta-P2利用元因子矩阵快速准确地找到新的不规则张量的模式。对真实世界数据集的大量实验表明，Meta-p2在各种下游任务(包括缺失值预测和异常检测任务)中取得了最佳性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Fast+and+Accurate+Domain+Adaptation+for+Irregular+Tensor+Decomposition)|0| +|[Mutual Distillation Extracting Spatial-temporal Knowledge for Lightweight Multi-channel Sleep Stage Classification](https://doi.org/10.1145/3637528.3671981)|Ziyu Jia, Haichao Wang, Yucheng Liu, Tianzi Jiang|Institute of Automation, Chinese Academy of Science, Beijing, China; University of Southern California, Los Angeles, USA; Tsinghua-Berkeley Shenzhen Institute, Tsinghua University, Shenzhen, China; Institute of Automation, Chinese Academy of Sciences, Beijing, China|Sleep stage classification has important clinical significance for the diagnosis of sleep-related diseases. To pursue more accurate sleep stage classification, multi-channel sleep signals are widely used due to the rich spatial-temporal information contained. However, it leads to a great increment in the size and computational costs, which constrain the application of multi-channel sleep models on hardware devices. Knowledge distillation is an effective way to compress models, yet existing knowledge distillation methods cannot fully extract and transfer the spatial-temporal knowledge in the multi-channel sleep signals. To solve the problem, we propose a general knowledge distillation framework for multi-channel sleep stage classification called spatial-temporal mutual distillation. Based on the spatial relationship of human body and the temporal transition rules of sleep signals, the spatial and temporal modules are designed to extract the spatial-temporal knowledge, thus help the lightweight student model learn the rich spatial-temporal knowledge from large-scale teacher model. The mutual distillation framework transfers the spatial-temporal knowledge mutually. Teacher model and student model can learn from each other, further improving the student model. The results on the ISRUC-III and MASS-SS3 datasets show that our proposed framework compresses the sleep models effectively with minimal performance loss and achieves the state-of-the-art performance compared to the baseline methods.|睡眠分期对睡眠相关疾病的诊断具有重要的临床意义。为了追求更准确的睡眠阶段分类，多通道睡眠信号由于包含了丰富的时空信息而被广泛应用。然而，这会导致系统规模和计算成本的大幅度增加，从而限制了多通道睡眠模型在硬件设备上的应用。知识提取是一种有效的模型压缩方法，但现有的知识提取方法不能完全提取和转移多通道睡眠信号中的时空知识。为了解决这个问题，我们提出了一个通用的多通道睡眠阶段分类知识提取框架，称为时空互提取。基于人体的空间关系和睡眠信号的时间转换规则，设计了空间和时间模块来提取时空知识，从而帮助轻量级学生模型从大规模教师模型中学习到丰富的时空知识。相互精馏框架相互传递时空知识。教师模式与学生模式可以相互借鉴，进一步完善学生模式。ISRUC-III 和 MASS-SS3数据集的结果表明，我们提出的框架有效地压缩了睡眠模型，性能损失最小，并达到了最先进的性能相比，基线方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Mutual+Distillation+Extracting+Spatial-temporal+Knowledge+for+Lightweight+Multi-channel+Sleep+Stage+Classification)|0| +|[Masked LoGoNet: Fast and Accurate 3D Image Analysis for Medical Domain](https://doi.org/10.1145/3637528.3672069)|Amin Karimi Monsefi, Payam Karisani, Mengxi Zhou, Stacey Choi, Nathan Doble, Heng Ji, Srinivasan Parthasarathy, Rajiv Ramnath|Department of Computer Science and Engineering, The Ohio State University, Columbus, OH, USA; Department of Ophthalmology and Visual Science, The Ohio State University, Columbus, OH, USA; Department of Computer Science, University of Illinois Urbana-Champaign, Urbana, IL, USA|Standard modern machine-learning-based imaging methods have faced challenges in medical applications due to the high cost of dataset construction and, thereby, the limited labeled training data available. Additionally, upon deployment, these methods are usually used to process a large volume of data on a daily basis, imposing a high maintenance cost on medical facilities. In this paper, we introduce a new neural network architecture, termed LoGoNet, with a tailored self-supervised learning (SSL) method to mitigate such challenges. LoGoNet integrates a novel feature extractor within a U-shaped architecture, leveraging Large Kernel Attention (LKA) and a dual encoding strategy to capture both long-range and short-range feature dependencies adeptly. This is in contrast to existing methods that rely on increasing network capacity to enhance feature extraction. This combination of novel techniques in our model is especially beneficial in medical image segmentation, given the difficulty of learning intricate and often irregular body organ shapes, such as the spleen. Complementary, we propose a novel SSL method tailored for 3D images to compensate for the lack of large labeled datasets. Our method combines masking and contrastive learning techniques within a multi-task learning framework and is compatible with both Vision Transformer (ViT) and CNN-based models. We demonstrate the efficacy of our methods in numerous tasks across two standard datasets (i.e., BTCV and MSD). Benchmark comparisons with eight state-of-the-art models highlight LoGoNet's superior performance in both inference time and accuracy. Code available at: https://github.com/aminK8/Masked-LoGoNet.|标准的现代机器学习成像方法在医学应用中面临的挑战，由于高成本的数据集建设，从而，有限的标记训练数据可用。此外，在部署时，这些方法通常用于每天处理大量数据，给医疗设施带来高昂的维护成本。在本文中，我们介绍了一种新的神经网络结构，称为 LoGoNet，它采用一种量身定制的自监督学习(SSL)方法来缓解这种挑战。LoGoNet 在 U 形架构中集成了一种新颖的特征提取器，利用大内核注意力(LKA)和双重编码策略来巧妙地捕获长期和短期特征依赖。这与现有的依靠增加网络容量来增强特征提取的方法形成了对比。在我们的模型中，这种新技术的结合在医学图像分割中尤其有益，因为学习复杂且通常不规则的身体器官形状(如脾脏)很困难。作为补充，我们提出了一种新的针对三维图像的 SSL 方法，以弥补缺少大型标记数据集的不足。该方法将掩蔽学习和对比学习技术结合在一个多任务学习框架中，兼容视觉变换器(ViT)和基于 CNN 的模型。我们证明了我们的方法在两个标准数据集(即 BTCV 和 MSD)的许多任务中的有效性。与八个最先进模型的基准比较突出了 LoGoNet 在推断时间和准确性方面的卓越性能。密码: https://github.com/amink8/masked-logonet。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Masked+LoGoNet:+Fast+and+Accurate+3D+Image+Analysis+for+Medical+Domain)|0| +|[Fast and Accurate Domain Adaptation for Irregular Tensor Decomposition](https://doi.org/10.1145/3637528.3671670)|Junghun Kim, Ka Hyun Park, JunGi Jang, U Kang|University of Illinois at Urbana-Champaign, Illinois, IL, USA; Seoul National University, Seoul, Republic of Korea|Given an irregular tensor from a newly emerging domain, how can we quickly and accurately capture its patterns utilizing existing irregular tensors in multiple domains? The problem is of great importance for various tasks such as finding patterns of a new disease using pre-existing diseases data. This is challenging as new target tensors have limited information due to their recent emergence. Thus, carefully utilizing the existing source tensors for analyzing the target tensor is helpful. PARAFAC2 decomposition is a strong tool for finding the patterns of irregular tensors, and the patterns are used in many applications such as missing value prediction and anomaly detection. However, previous PARAFAC2-based works cannot adaptably handle newly emerging target tensors utilizing the source tensors. In this work, we propose Meta-P2, a fast and accurate domain adaptation method for irregular tensor decomposition. Meta-P2 generates a meta factor matrix from the multiple source domains, by domain adaptation and meta-update steps. Meta-P2 quickly and accurately finds the patterns of the new irregular tensor utilizing the meta factor matrix. Extensive experiments on real-world datasets show that Meta-P2 achieves the best performance in various downstream tasks including missing value prediction and anomaly detection tasks.|假设一个不规则张量来自一个新兴的领域，我们如何能够快速准确地捕捉其模式利用现有的不规则张量在多个领域？这个问题对于各种任务都非常重要，例如利用已有的疾病数据找到新疾病的模式。这是具有挑战性的，因为新的目标张量由于最近的出现而信息有限。因此，仔细利用现有的源张量来分析目标张量是有帮助的。PARAFAC2分解是寻找不规则张量模式的一个强有力的工具，这些模式在许多应用中被使用，例如缺失值预测和异常检测。然而，以往基于 PARAFAC2的工作不能自适应地处理新出现的目标张量利用源张量。在这项工作中，我们提出了 Meta-P2，一个快速和准确的区域自适应方法的不规则张量分解。元 P2通过领域适应和元更新步骤从多个源域生成元因子矩阵。Meta-P2利用元因子矩阵快速准确地找到新的不规则张量的模式。对真实世界数据集的大量实验表明，Meta-p2在各种下游任务(包括缺失值预测和异常检测任务)中取得了最佳性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Fast+and+Accurate+Domain+Adaptation+for+Irregular+Tensor+Decomposition)|0| |[SLADE: Detecting Dynamic Anomalies in Edge Streams without Labels via Self-Supervised Learning](https://doi.org/10.1145/3637528.3671845)|Jongha Lee, Sunwoo Kim, Kijung Shin|KAIST, Seoul, Republic of Korea|To detect anomalies in real-world graphs, such as social, email, and financial networks, various approaches have been developed. While they typically assume static input graphs, most real-world graphs grow over time, naturally represented as edge streams. In this context, we aim to achieve three goals: (a) instantly detecting anomalies as they occur, (b) adapting to dynamically changing states, and (c) handling the scarcity of dynamic anomaly labels. In this paper, we propose SLADE (Self-supervised Learning for Anomaly Detection in Edge Streams) for rapid detection of dynamic anomalies in edge streams, without relying on labels. SLADE detects the shifts of nodes into abnormal states by observing deviations in their interaction patterns over time. To this end, it trains a deep neural network to perform two self-supervised tasks: (a) minimizing drift in node representations and (b) generating long-term interaction patterns from short-term ones. Failure in these tasks for a node signals its deviation from the norm. Notably, the neural network and tasks are carefully designed so that all required operations can be performed in constant time (w.r.t. the graph size) in response to each new edge in the input stream. In dynamic anomaly detection across four real-world datasets, SLADE outperforms nine competing methods, even those leveraging label supervision. Our code and datasets are available at https://github.com/jhsk777/SLADE.|为了检测现实世界图表中的异常，如社交、电子邮件和金融网络，已经开发了各种方法。虽然它们通常假设静态输入图，但大多数真实世界的图随着时间的推移而增长，自然地表示为边流。在这种背景下，我们的目标是实现三个目标: (a)在异常发生时即时检测异常，(b)适应动态变化的状态，和(c)处理动态异常标签的稀缺性。在这篇文章中，我们提出了边缘流异常检测的自我监督学习(SLADE)来快速检测边缘流中的动态异常，而不依赖于标签。SLADE 通过观察节点交互模式随时间的变化来检测节点进入异常状态的变化。为此，它训练一个深层神经网络来执行两个自我监督的任务: (a)最小化节点表示的漂移和(b)从短期的产生长期的交互模式。节点在这些任务中的失败表明它偏离了规范。值得注意的是，神经网络和任务都经过了精心设计，以便所有需要的操作都可以在不变的时间内执行(图形大小) ，以响应输入流中的每个新边缘。在跨越四个现实世界数据集的动态异常检测中，SLADE 的表现优于9种竞争方法，甚至优于那些利用标签监督的方法。我们的代码和数据集 https://github.com/jhsk777/slade 可用。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=SLADE:+Detecting+Dynamic+Anomalies+in+Edge+Streams+without+Labels+via+Self-Supervised+Learning)|0| -|[Scalable Multitask Learning Using Gradient-based Estimation of Task Affinity](https://doi.org/10.1145/3637528.3671835)|Dongyue Li, Aneesh Sharma, Hongyang R. Zhang|Google, Mountain View, USA; Northeastern University, Boston, USA|Multitask learning is a widely used paradigm for training models on diverse tasks, with applications ranging from graph neural networks to language model fine-tuning. Since tasks may interfere with each other, a key notion for modeling their relationships is task affinity. This includes pairwise task affinity, computed among pairs of tasks, and higher-order affinity, computed among subsets of tasks. Naively computing either of them requires repeatedly training on data pooled from various task combinations, which is computationally intensive. We present a new algorithm Grad-TAG that can estimate task affinities without this repeated training. The key idea of Grad-TAG is to train a "base" model for all tasks and then use a linearization technique to estimate the loss of any other model with a specific task combination. The linearization works by computing a gradient-based first-order approximation of the loss, using low-dimensional projections of gradients as features in a logistic regression trained to predict labels for the specific task combination. We show theoretically that the linearized model can provably approximate the loss when the gradient-based approximation is accurate, and also empirically verify that on several large models. Then, given the estimated task affinity matrix, we design a semi-definite program for clustering to group similar tasks that maximize the average density of clusters. We evaluate Grad-TAG's performance across seven datasets, including multi-label classification on graphs, and instruction fine-tuning of language models. Our results show that our task affinity estimates are within 2.7% distance of the true affinities while needing only 3% of FLOPs compared to full training. On our largest graph with 21M edges and 500 labeling tasks, our algorithm delivers an estimate accurate to within 5% of the true affinities, while using only 112.3 GPU hours. Our results show that Grad-TAG achieves excellent performance and runtime tradeoffs compared to existing approaches.|多任务学习是一种广泛应用于不同任务的训练模型，应用范围从图形神经网络到语言模型微调。由于任务之间可能会相互干扰，因此对它们的关系进行建模的一个关键概念是任务相关性。这包括成对任务关联(在任务对之间计算)和高阶关联(在任务子集之间计算)。天真地计算其中任何一个都需要对从各种任务组合中汇集的数据进行反复训练，这是计算密集型的。我们提出了一种新的算法梯度 TAG，可以估计任务的亲和力，而无需这种重复的训练。梯度 TAG 的核心思想是为所有任务训练一个“基本”模型，然后使用线性化技术估计任何其他模型与特定任务组合的损失。线性化的工作原理是通过计算基于梯度的一阶近似损失，使用梯度的低维投影作为特征的 Logit模型训练来预测特定任务组合的标签。从理论上证明了当基于梯度的近似准确时，线性化模型可以有效地逼近损失，并在几个大型模型上进行了实验验证。然后，给出估计的任务亲和矩阵，设计一个半确定的聚类程序，将相似的任务分组，最大化聚类的平均密度。我们评估了 Grad-TAG 在七个数据集上的性能，包括图的多标签分类和语言模型的指令微调。我们的研究结果表明，我们的任务亲和力估计在2.7% 的距离真正的亲和力，而只需要3% 的 FLOP 相比，充分训练。在我们最大的21M 边和500个标记任务的图表中，我们的算法只使用了112.3 GPU 时间，但是估计的精确度在真实亲和力的5% 以内。我们的研究结果表明，与现有的方法相比，Grad-TAG 实现了出色的性能和运行时折衷。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Scalable+Multitask+Learning+Using+Gradient-based+Estimation+of+Task+Affinity)|0| -|[Truthful Bandit Mechanisms for Repeated Two-stage Ad Auctions](https://doi.org/10.1145/3637528.3671813)|Haoming Li, Yumou Liu, Zhenzhe Zheng, Zhilin Zhang, Jian Xu, Fan Wu|Alibaba Group, Beijing, China; The Chinese University of Hong Kong, Shenzhen, Shenzhen, China; Shanghai Jiao Tong University, Shanghai, China|Online advertising platforms leverage a two-stage auction architecture to deliver personalized ads to users with low latency. The first stage efficiently selects a small subset of promising candidates out of the complete pool of ads. In the second stage, an auction is conducted within the subset to determine the winning ad for display, using click-through-rate predictions from the second-stage machine learning model. In this work, we investigate the online learning process of the first-stage subset selection policy, while ensuring game-theoretic properties in repeated two-stage ad auctions. Specifically, we model the problem as designing a combinatorial bandit mechanism with a general reward function, as well as additional requirements of truthfulness and individual rationality (IR). We establish an O(T) regret lower bound for truthful bandit mechanisms, which demonstrates the challenge of simultaneously achieving allocation efficiency and truthfulness. To circumvent this impossibility result, we introduce truthful α-approximation oracles and evaluate the bandit mechanism through α-approximation regret. Two mechanisms are proposed, both of which are ex-post truthful and ex-post IR. The first mechanism is an explore-then-commit mechanism with regret O(T2/3 ), and the second mechanism achieves an improved O(log T /ΔΦ2) regret where ΔΦ is a distribution-dependent gap, but requires additional assumptions on the oracles and information about the strategic bidders.|在线广告平台利用两阶段拍卖架构向低延迟的用户提供个性化广告。第一阶段有效地从完整的广告库中选出一小部分有前途的候选人。在第二阶段，使用第二阶段机器学习模型的点击率预测，在子集内进行拍卖，以确定用于显示的获胜广告。本文在保证重复两阶段广告拍卖的博弈性质的前提下，研究了第一阶段子集选择策略的在线学习过程。具体地说，我们将这个问题建模为具有一般奖励函数的组合强盗机制的设计，以及对真实性和个体理性的附加要求。我们建立了真实性盗贼机制的 O (T)后悔下限，这表明了同时实现分配效率和真实性的挑战。为了避免这种不可能的结果，我们引入了真实的 α-近似神谕，并通过 α-近似悔恨来评估盗贼的机制。提出了两种机制，即事后真实机制和事后信息检索机制。第一种机制是带有遗憾 O (T2/3)的探索-然后提交机制，第二种机制实现了一个改进的 O (log T/ΔΦ2)遗憾，其中 ΔΦ 是一个分布依赖的缺口，但需要额外的先知假设和关于战略投标人的信息。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Truthful+Bandit+Mechanisms+for+Repeated+Two-stage+Ad+Auctions)|0| -|[Self-Distilled Disentangled Learning for Counterfactual Prediction](https://doi.org/10.1145/3637528.3671782)|Xinshu Li, Mingming Gong, Lina Yao|The University of New South Wales, Sydney, Australia; CSIRO's Data61 & The University of New South Wales, Sydney, Australia; The University of Melbourne & MBZUAI, Melbourne, Australia|The advancements in disentangled representation learning significantlyenhance the accuracy of counterfactual predictions by granting precise controlover instrumental variables, confounders, and adjustable variables. Anappealing method for achieving the independent separation of these factors ismutual information minimization, a task that presents challenges in numerousmachine learning scenarios, especially within high-dimensional spaces. Tocircumvent this challenge, we propose the Self-Distilled Disentanglementframework, referred to as SD^2. Grounded in information theory, it ensurestheoretically sound independent disentangled representations without intricatemutual information estimator designs for high-dimensional representations. Ourcomprehensive experiments, conducted on both synthetic and real-world datasets,confirms the effectiveness of our approach in facilitating counterfactualinference in the presence of both observed and unobserved confounders.|在分离表征学习的进步显着提高准确性的反事实预测授予精确控制工具变量，混杂因素和可调变量。实现这些因素独立分离的一个有吸引力的方法是相互信息最小化，这个任务在许多机器学习场景中提出了挑战，特别是在高维空间中。为了规避这个挑战，我们提出了自我提取的分离框架，称为 SD ^ 2。它以信息论为理论基础，保证了理论上独立的解纠缠表示，而不需要对高维表示进行复杂的互信息估计器设计。我们在合成和真实世界数据集上进行的综合实验证实了我们的方法在促进反事实推理方面的有效性，在观察到和未观察到的混杂因素存在的情况下。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Self-Distilled+Disentangled+Learning+for+Counterfactual+Prediction)|0| +|[Scalable Multitask Learning Using Gradient-based Estimation of Task Affinity](https://doi.org/10.1145/3637528.3671835)|Dongyue Li, Aneesh Sharma, Hongyang R. Zhang|Northeastern University, Boston, USA; Google, Mountain View, USA|Multitask learning is a widely used paradigm for training models on diverse tasks, with applications ranging from graph neural networks to language model fine-tuning. Since tasks may interfere with each other, a key notion for modeling their relationships is task affinity. This includes pairwise task affinity, computed among pairs of tasks, and higher-order affinity, computed among subsets of tasks. Naively computing either of them requires repeatedly training on data pooled from various task combinations, which is computationally intensive. We present a new algorithm Grad-TAG that can estimate task affinities without this repeated training. The key idea of Grad-TAG is to train a "base" model for all tasks and then use a linearization technique to estimate the loss of any other model with a specific task combination. The linearization works by computing a gradient-based first-order approximation of the loss, using low-dimensional projections of gradients as features in a logistic regression trained to predict labels for the specific task combination. We show theoretically that the linearized model can provably approximate the loss when the gradient-based approximation is accurate, and also empirically verify that on several large models. Then, given the estimated task affinity matrix, we design a semi-definite program for clustering to group similar tasks that maximize the average density of clusters. We evaluate Grad-TAG's performance across seven datasets, including multi-label classification on graphs, and instruction fine-tuning of language models. Our results show that our task affinity estimates are within 2.7% distance of the true affinities while needing only 3% of FLOPs compared to full training. On our largest graph with 21M edges and 500 labeling tasks, our algorithm delivers an estimate accurate to within 5% of the true affinities, while using only 112.3 GPU hours. Our results show that Grad-TAG achieves excellent performance and runtime tradeoffs compared to existing approaches.|多任务学习是一种广泛应用于不同任务的训练模型，应用范围从图形神经网络到语言模型微调。由于任务之间可能会相互干扰，因此对它们的关系进行建模的一个关键概念是任务相关性。这包括成对任务关联(在任务对之间计算)和高阶关联(在任务子集之间计算)。天真地计算其中任何一个都需要对从各种任务组合中汇集的数据进行反复训练，这是计算密集型的。我们提出了一种新的算法梯度 TAG，可以估计任务的亲和力，而无需这种重复的训练。梯度 TAG 的核心思想是为所有任务训练一个“基本”模型，然后使用线性化技术估计任何其他模型与特定任务组合的损失。线性化的工作原理是通过计算基于梯度的一阶近似损失，使用梯度的低维投影作为特征的 Logit模型训练来预测特定任务组合的标签。从理论上证明了当基于梯度的近似准确时，线性化模型可以有效地逼近损失，并在几个大型模型上进行了实验验证。然后，给出估计的任务亲和矩阵，设计一个半确定的聚类程序，将相似的任务分组，最大化聚类的平均密度。我们评估了 Grad-TAG 在七个数据集上的性能，包括图的多标签分类和语言模型的指令微调。我们的研究结果表明，我们的任务亲和力估计在2.7% 的距离真正的亲和力，而只需要3% 的 FLOP 相比，充分训练。在我们最大的21M 边和500个标记任务的图表中，我们的算法只使用了112.3 GPU 时间，但是估计的精确度在真实亲和力的5% 以内。我们的研究结果表明，与现有的方法相比，Grad-TAG 实现了出色的性能和运行时折衷。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Scalable+Multitask+Learning+Using+Gradient-based+Estimation+of+Task+Affinity)|0| +|[Truthful Bandit Mechanisms for Repeated Two-stage Ad Auctions](https://doi.org/10.1145/3637528.3671813)|Haoming Li, Yumou Liu, Zhenzhe Zheng, Zhilin Zhang, Jian Xu, Fan Wu|Alibaba Group, Beijing, China; Shanghai Jiao Tong University, Shanghai, China; The Chinese University of Hong Kong, Shenzhen, Shenzhen, China|Online advertising platforms leverage a two-stage auction architecture to deliver personalized ads to users with low latency. The first stage efficiently selects a small subset of promising candidates out of the complete pool of ads. In the second stage, an auction is conducted within the subset to determine the winning ad for display, using click-through-rate predictions from the second-stage machine learning model. In this work, we investigate the online learning process of the first-stage subset selection policy, while ensuring game-theoretic properties in repeated two-stage ad auctions. Specifically, we model the problem as designing a combinatorial bandit mechanism with a general reward function, as well as additional requirements of truthfulness and individual rationality (IR). We establish an O(T) regret lower bound for truthful bandit mechanisms, which demonstrates the challenge of simultaneously achieving allocation efficiency and truthfulness. To circumvent this impossibility result, we introduce truthful α-approximation oracles and evaluate the bandit mechanism through α-approximation regret. Two mechanisms are proposed, both of which are ex-post truthful and ex-post IR. The first mechanism is an explore-then-commit mechanism with regret O(T2/3 ), and the second mechanism achieves an improved O(log T /ΔΦ2) regret where ΔΦ is a distribution-dependent gap, but requires additional assumptions on the oracles and information about the strategic bidders.|在线广告平台利用两阶段拍卖架构向低延迟的用户提供个性化广告。第一阶段有效地从完整的广告库中选出一小部分有前途的候选人。在第二阶段，使用第二阶段机器学习模型的点击率预测，在子集内进行拍卖，以确定用于显示的获胜广告。本文在保证重复两阶段广告拍卖的博弈性质的前提下，研究了第一阶段子集选择策略的在线学习过程。具体地说，我们将这个问题建模为具有一般奖励函数的组合强盗机制的设计，以及对真实性和个体理性的附加要求。我们建立了真实性盗贼机制的 O (T)后悔下限，这表明了同时实现分配效率和真实性的挑战。为了避免这种不可能的结果，我们引入了真实的 α-近似神谕，并通过 α-近似悔恨来评估盗贼的机制。提出了两种机制，即事后真实机制和事后信息检索机制。第一种机制是带有遗憾 O (T2/3)的探索-然后提交机制，第二种机制实现了一个改进的 O (log T/ΔΦ2)遗憾，其中 ΔΦ 是一个分布依赖的缺口，但需要额外的先知假设和关于战略投标人的信息。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Truthful+Bandit+Mechanisms+for+Repeated+Two-stage+Ad+Auctions)|0| +|[Self-Distilled Disentangled Learning for Counterfactual Prediction](https://doi.org/10.1145/3637528.3671782)|Xinshu Li, Mingming Gong, Lina Yao|CSIRO's Data61 & The University of New South Wales, Sydney, Australia; The University of Melbourne & MBZUAI, Melbourne, Australia; The University of New South Wales, Sydney, Australia|The advancements in disentangled representation learning significantlyenhance the accuracy of counterfactual predictions by granting precise controlover instrumental variables, confounders, and adjustable variables. Anappealing method for achieving the independent separation of these factors ismutual information minimization, a task that presents challenges in numerousmachine learning scenarios, especially within high-dimensional spaces. Tocircumvent this challenge, we propose the Self-Distilled Disentanglementframework, referred to as SD^2. Grounded in information theory, it ensurestheoretically sound independent disentangled representations without intricatemutual information estimator designs for high-dimensional representations. Ourcomprehensive experiments, conducted on both synthetic and real-world datasets,confirms the effectiveness of our approach in facilitating counterfactualinference in the presence of both observed and unobserved confounders.|在分离表征学习的进步显着提高准确性的反事实预测授予精确控制工具变量，混杂因素和可调变量。实现这些因素独立分离的一个有吸引力的方法是相互信息最小化，这个任务在许多机器学习场景中提出了挑战，特别是在高维空间中。为了规避这个挑战，我们提出了自我提取的分离框架，称为 SD ^ 2。它以信息论为理论基础，保证了理论上独立的解纠缠表示，而不需要对高维表示进行复杂的互信息估计器设计。我们在合成和真实世界数据集上进行的综合实验证实了我们的方法在促进反事实推理方面的有效性，在观察到和未观察到的混杂因素存在的情况下。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Self-Distilled+Disentangled+Learning+for+Counterfactual+Prediction)|0| |[Predicting Long-term Dynamics of Complex Networks via Identifying Skeleton in Hyperbolic Space](https://doi.org/10.1145/3637528.3671968)|Ruikun Li, Huandong Wang, Jinghua Piao, Qingmin Liao, Yong Li|Department of Electronic Engineering BNRist, Tsinghua University, Beijing, China; Shenzhen International Graduate School, Tsinghua University, Shenzhen, China|Learning complex network dynamics is fundamental for understanding, modeling, and controlling real-world complex systems. Though great efforts have been made to predict the future states of nodes on networks, the capability of capturing long-term dynamics remains largely limited. This is because they overlook the fact that long-term dynamics in complex network are predominantly governed by their inherent low-dimensional manifolds, i.e., skeletons. Therefore, we propose the Dynamics-Invariant Skeleton Neural Network (DiskNet), which identifies skeletons of complex networks based on the renormalization group structure in hyperbolic space to preserve both topological and dynamics properties. Specifically, we first condense complex networks with various dynamics into simple skeletons through physics-informed hyperbolic embeddings. Further, we design graph neural ordinary differential equations to capture the condensed dynamics on the skeletons. Finally, we recover the skeleton networks and dynamics to the original ones using a degree-based super-resolution module. Extensive experiments across three representative dynamics as well as five real-world and two synthetic networks demonstrate the superior performances of the proposed DiskNet, which outperforms the state-of-the-art baselines by an average of 10.18% in terms of long-term prediction accuracy. Code for reproduction is available at: https://github.com/tsinghua-fib-lab/DiskNet.|学习复杂网络动力学是理解、建模和控制现实世界复杂系统的基础。尽管人们在预测网络节点的未来状态方面做出了巨大的努力，但是捕捉长期动态的能力仍然受到很大的限制。这是因为他们忽略了这样一个事实，即复杂网络中的长期动力学主要受其固有的低维流形支配，即骨架。因此，我们提出了动力学不变骨架神经网络(diskNet) ，它基于重整化群的双曲空间结构来识别复杂网络的骨架，以保持拓扑和动力学特性。具体来说，我们首先通过基于物理信息的双曲嵌入将具有各种动力学的复杂网络压缩成简单的骨架。进一步，我们设计图形神经元常微分方程来捕捉骨架上的凝聚动力学。最后，我们使用基于度的超分辨率模型恢复骨架网络和动力学模型。在三个有代表性的动态以及五个真实世界和两个合成网络上进行的广泛实验证明了所提议的 DiskNet 的优越性能，它在长期预测准确性方面比最先进的基线平均高出10.18% 。复制代码可在以下 https://github.com/tsinghua-fib-lab/disknet 找到:。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Predicting+Long-term+Dynamics+of+Complex+Networks+via+Identifying+Skeleton+in+Hyperbolic+Space)|0| |[Image Similarity Using an Ensemble of Context-Sensitive Models](https://doi.org/10.1145/3637528.3672004)|Zukang Liao, Min Chen|University of Oxford, Oxford, United Kingdom|Image similarity has been extensively studied in computer vision. In recent years, machine-learned models have shown their ability to encode more semantics than traditional multivariate metrics. However, in labelling semantic similarity, assigning a numerical score to a pair of images is impractical, making the improvement and comparisons on the task difficult. In this work, we present a more intuitive approach to build and compare image similarity models based on labelled data in the form of A:R vs B:R, i.e., determining if an image A is closer to a reference image R than another image B. We address the challenges of sparse sampling in the image space (R, A, B) and biases in the models trained with context-based data by using an ensemble model. Our testing results show that the ensemble model constructed performs ~5% better than the best individual context-sensitive models. They also performed better than the models that were directly fine-tuned using mixed imagery data as well as existing deep embeddings, e.g., CLIP [30] and DINO [3]. This work demonstrates that context-based labelling and model training can be effective when an appropriate ensemble approach is used to alleviate the limitation due to sparse sampling.|图像相似性在计算机视觉中得到了广泛的研究。近年来，机器学习模型已经显示出它们比传统的多元度量标准具有更强的语义编码能力。然而，在标注语义相似度时，为一对图像分配一个数值得分是不切实际的，这给任务的改进和比较带来了困难。在这项工作中，我们提出了一个更直观的方法来建立和比较图像相似性模型的基础上的标记数据的形式 A: R 对 B: R，即，确定是否一个图像 A 更接近一个参考图像 R 比另一个图像 B。针对图像空间(R，A，B)的稀疏采样和基于上下文数据训练的模型中的偏差问题，提出了一种集成模型。我们的测试结果表明，所构建的集成模型比最好的个体上下文敏感模型的性能要好约5% 。他们也比使用混合图像数据以及现有的深度嵌入(例如 CLIP [30]和 DINO [3])直接微调的模型表现得更好。这项工作表明，基于上下文的标记和模型训练可以有效地使用适当的集成方法，以减轻由于稀疏抽样的限制。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Image+Similarity+Using+an+Ensemble+of+Context-Sensitive+Models)|0| |[Neural Collapse Inspired Debiased Representation Learning for Min-max Fairness](https://doi.org/10.1145/3637528.3671902)|Shenyu Lu, Junyi Chai, Xiaoqian Wang|Purdue University, West Lafayette, IN, USA|Although machine learning algorithms demonstrate impressive performance, their trustworthiness remains a critical issue, particularly concerning fairness when implemented in real-world applications. Many notions of group fairness aim to minimize disparities in performance across protected groups. However, it can inadvertently reduce performance in certain groups, leading to sub-optimal outcomes. In contrast, Min-max group fairness notion prioritizes the improvement for the worst-performing group, thereby advocating a utility-promoting approach to fairness. However, it has been proven that existing efforts to achieve Min-max fairness exhibit limited effectiveness. In response to this challenge, we leverage the recently proposed "Neural Collapse'' framework to re-examine Empirical Risk Minimization (ERM) training, specifically investigating the root causes of poor performance in minority groups. The layer-peeled model is employed to decompose a network into two parts: an encoder to learn latent representation, and a subsequent classifier, with a systematic characterization of their training behaviors being conducted. Our analysis reveals that while classifiers achieve maximum separation, the separability of representations is insufficient, particularly for minority groups. This indicates the sub-optimal performance in minority groups stems from less separable representations, rather than classifiers. To tackle this issue, we introduce a novel strategy that incorporates a frozen classifier to directly enhance representation. Furthermore, we introduce two easily implemented loss functions to guide the learning process. The experimental assessments carried out on real-world benchmark datasets spanning the domains of Computer Vision, Natural Language Processing, and Tabular data demonstrate that our approach outperforms existing state-of-the-art methods in promoting the Min-max fairness notion.|尽管机器学习算法表现出了令人印象深刻的性能，但它们的可信性仍然是一个关键问题，特别是在实际应用中实现时的公平性。群体公平的许多概念旨在最大限度地缩小不同受保护群体之间的绩效差距。然而，它可能无意中降低某些群体的绩效，导致次优结果。相比之下，最小-最大群体公平理念优先考虑绩效最差群体的改善，从而提倡效用促进的公平方法。然而，已经证明现有的实现最小最大公平性的努力效果有限。为了应对这一挑战，我们利用最近提出的“神经崩溃”框架来重新审视经验风险最小化(ERM)训练，特别是调查少数群体表现不佳的根本原因。分层剥离模型被用来将网络分解为两部分: 一个编码器来学习潜在表征，一个后续的分类器，系统的角色塑造他们的训练行为被执行。我们的分析表明，虽然量词实现了最大的分离，但表征的可分性是不够的，特别是对于少数群体。这表明少数群体的次优性能源于较少的可分离表示，而不是分类器。为了解决这个问题，我们引入了一个新的策略，包括一个冻结的分类器，直接增强表示。此外，我们还引入了两个易于实现的损失函数来指导学习过程。对计算机视觉、自然语言处理和表格数据领域的现实世界基准数据集进行的实验评估表明，我们的方法在促进最小最大公平概念方面优于现有的最先进的方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Neural+Collapse+Inspired+Debiased+Representation+Learning+for+Min-max+Fairness)|0| -|[AdaGMLP: AdaBoosting GNN-to-MLP Knowledge Distillation](https://doi.org/10.1145/3637528.3671699)|Weigang Lu, Ziyu Guan, Wei Zhao, Yaming Yang|Xidian University, Xi'an, Shannxi, China; Xidian University, Xi'an, Shaanxi, China|Graph Neural Networks (GNNs) have revolutionized graph-based machinelearning, but their heavy computational demands pose challenges forlatency-sensitive edge devices in practical industrial applications. Inresponse, a new wave of methods, collectively known as GNN-to-MLP KnowledgeDistillation, has emerged. They aim to transfer GNN-learned knowledge to a moreefficient MLP student, which offers faster, resource-efficient inference whilemaintaining competitive performance compared to GNNs. However, these methodsface significant challenges in situations with insufficient training data andincomplete test data, limiting their applicability in real-world applications.To address these challenges, we propose AdaGMLP, an AdaBoosting GNN-to-MLPKnowledge Distillation framework. It leverages an ensemble of diverse MLPstudents trained on different subsets of labeled nodes, addressing the issue ofinsufficient training data. Additionally, it incorporates a Node Alignmenttechnique for robust predictions on test data with missing or incompletefeatures. Our experiments on seven benchmark datasets with different settingsdemonstrate that AdaGMLP outperforms existing G2M methods, making it suitablefor a wide range of latency-sensitive real-world applications. We havesubmitted our code to the GitHub repository(https://github.com/WeigangLu/AdaGMLP-KDD24).|图形神经网络(GNN)已经给基于图的机器学习带来了革命性的变化，但是它们繁重的计算需求给实际工业应用中的延迟敏感边缘设备带来了挑战。响应，一个新的方法的浪潮，统称为 GNN 到 MLP 知识蒸馏，已经出现。他们的目标是将 GNN 学到的知识传授给一个更有效率的 MLP 学生，MLP 学生能够提供更快速、资源效率更高的推理，同时保持与 GNN 学生相比的竞争力。然而，这些方法在训练数据不足和测试数据不完整的情况下面临着重大挑战，限制了它们在实际应用中的适用性。为了解决这些挑战，我们提出 AdaGMLP，一个 AdaBoosted GNN-to-MLP 知识提取框架。它利用不同的 MLP 学生在不同的标记节点子集上接受训练的集合，解决了训练数据不足的问题。此外，它还结合了节点对齐技术，用于对缺少或不完整特性的测试数据进行健壮的预测。我们在七个不同设置的基准数据集上的实验表明，AdaGMLP 优于现有的 G2M 方法，使其适用于各种对延迟敏感的现实世界应用。我们已经将代码提交给了 GitHub 存储库( https://GitHub.com/weiganglu/adagmlp-kdd24)。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=AdaGMLP:+AdaBoosting+GNN-to-MLP+Knowledge+Distillation)|0| +|[AdaGMLP: AdaBoosting GNN-to-MLP Knowledge Distillation](https://doi.org/10.1145/3637528.3671699)|Weigang Lu, Ziyu Guan, Wei Zhao, Yaming Yang|Xidian University, Xi'an, Shaanxi, China; Xidian University, Xi'an, Shannxi, China|Graph Neural Networks (GNNs) have revolutionized graph-based machinelearning, but their heavy computational demands pose challenges forlatency-sensitive edge devices in practical industrial applications. Inresponse, a new wave of methods, collectively known as GNN-to-MLP KnowledgeDistillation, has emerged. They aim to transfer GNN-learned knowledge to a moreefficient MLP student, which offers faster, resource-efficient inference whilemaintaining competitive performance compared to GNNs. However, these methodsface significant challenges in situations with insufficient training data andincomplete test data, limiting their applicability in real-world applications.To address these challenges, we propose AdaGMLP, an AdaBoosting GNN-to-MLPKnowledge Distillation framework. It leverages an ensemble of diverse MLPstudents trained on different subsets of labeled nodes, addressing the issue ofinsufficient training data. Additionally, it incorporates a Node Alignmenttechnique for robust predictions on test data with missing or incompletefeatures. Our experiments on seven benchmark datasets with different settingsdemonstrate that AdaGMLP outperforms existing G2M methods, making it suitablefor a wide range of latency-sensitive real-world applications. We havesubmitted our code to the GitHub repository(https://github.com/WeigangLu/AdaGMLP-KDD24).|图形神经网络(GNN)已经给基于图的机器学习带来了革命性的变化，但是它们繁重的计算需求给实际工业应用中的延迟敏感边缘设备带来了挑战。响应，一个新的方法的浪潮，统称为 GNN 到 MLP 知识蒸馏，已经出现。他们的目标是将 GNN 学到的知识传授给一个更有效率的 MLP 学生，MLP 学生能够提供更快速、资源效率更高的推理，同时保持与 GNN 学生相比的竞争力。然而，这些方法在训练数据不足和测试数据不完整的情况下面临着重大挑战，限制了它们在实际应用中的适用性。为了解决这些挑战，我们提出 AdaGMLP，一个 AdaBoosted GNN-to-MLP 知识提取框架。它利用不同的 MLP 学生在不同的标记节点子集上接受训练的集合，解决了训练数据不足的问题。此外，它还结合了节点对齐技术，用于对缺少或不完整特性的测试数据进行健壮的预测。我们在七个不同设置的基准数据集上的实验表明，AdaGMLP 优于现有的 G2M 方法，使其适用于各种对延迟敏感的现实世界应用。我们已经将代码提交给了 GitHub 存储库( https://GitHub.com/weiganglu/adagmlp-kdd24)。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=AdaGMLP:+AdaBoosting+GNN-to-MLP+Knowledge+Distillation)|0| |[Handling Varied Objectives by Online Decision Making](https://doi.org/10.1145/3637528.3671812)|Lanjihong Ma, ZhenYu Zhang, YaoXiang Ding, ZhiHua Zhou|; State Key Lab of CAD & CG, Zhejiang University, Hangzhou, China; Center for Advanced Intelligence Project, RIKEN, Tokyo, Japan|Conventional machine learning typically assume a fixed learning objective throughout the learning process. However, for real-world tasks in open and dynamic environments, objectives can change frequently. For example, in autonomous driving, a car has several default modes, but a user's concern for speed and fuel consumption varies depending on road conditions and personal needs. We formulate this problem as learning with varied objectives (LVO), where the goal is to optimize a dynamic weighted combination of multiple sub-objectives by sequentially selecting actions that incur different losses on these sub-objectives. We propose the VaRons algorithm, which estimates the action-wise performance on each sub-objective and adaptively selects decisions according to the dynamic requirements on different sub-objectives. Further, we extend our approach to cases involving contextual representations and propose the ConVaRons algorithm, assuming parameterized linear structure that links contextual features to the main objective. Both the VaRons and ConVaRons are provably minimax optimal with respect to the time horizon T, with ConVaRons showing better dependency with the number of sub-objectives K. Experiments on dynamic classifier and real-world cluster service allocation tasks validate the effectiveness of our methods and support our theoretical findings.|传统的机器学习通常在整个学习过程中假定一个固定的学习目标。然而，对于开放和动态环境中的实际任务，目标可能会频繁变化。例如，在自动驾驶中，汽车有几种默认模式，但是用户对于速度和油耗的关注取决于路况和个人需求。我们将这个问题表述为具有不同目标(LVO)的学习，其目标是通过依次选择在这些子目标上导致不同损失的行动来优化多个子目标的动态加权组合。我们提出了 VaRons 算法，该算法估计每个子目标的行动性能，并根据不同子目标的动态需求自适应地选择决策。进一步，我们将我们的方法扩展到涉及上下文表示的情况，并提出了 ConVaRons 算法，假设参数化的线性结构，连接上下文特征的主要目标。VaRons 和 ConVaRons 都可证明是最小最优的时间范围 T，与 ConVaRons 显示更好的依赖与子目标的数量 K 的实验动态分类器和现实世界的集群服务分配任务验证了我们的方法的有效性，并支持我们的理论研究结果。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Handling+Varied+Objectives+by+Online+Decision+Making)|0| |[Quantifying and Estimating the Predictability Upper Bound of Univariate Numeric Time Series](https://doi.org/10.1145/3637528.3671995)|Jamal Mohammed, Michael H. Böhlen, Sven Helmer|University of Zurich, Zurich, Switzerland|The intrinsic predictability of a given time series indicates how well an (ideal) algorithm could potentially predict it when trained on the time series data. Being able to compute the intrinsic predictability helps the developers of prediction algorithms immensely in deciding whether there is further optimization potential, as it tells them how close they are to what is (theoretically) achievable. We call the intrinsic predictability the predictability upper bound ¶imax and propose a novel method for quantifying and estimating it for univariate numeric time series. So far, this has only been done for symbolic time series, even though most real-world time series are numeric by nature. We base our technique on the close relationship between entropy and predictability, utilizing the entropy rate of a time series to compute ¶imax . Since existing entropy rate estimators, such as those based on the Lempel-Ziv compression algorithm, only work for symbolic data, we develop new estimators using tolerance thresholds for matching numeric values. We demonstrate that ¶imax is an effective upper bound that characterizes the intrinsic predictability of a time series. We give formal proofs and we validate our arguments experimentally by comparing ¶imax with the prediction accuracy of different state-of-the-art models on various real-world datasets from different domains.|给定时间序列的内在可预测性表明(理想的)算法在对时间序列数据进行训练时能够很好地预测它。能够计算内在的可预测性对预测算法的开发人员决定是否存在进一步的优化潜力有很大的帮助，因为它告诉他们离(理论上)可实现的目标有多近。本文将内在可预测性称为可预测性上界 imax，提出了一种新的单变量数值时间序列的可预测性上界的量化和估计方法。到目前为止，这只是针对符号时间序列，尽管大多数现实世界的时间序列本质上是数字的。我们的技术基于熵和可预测性之间的密切关系，利用时间序列的熵率来计算 imax。由于现有的熵速率估计器，例如基于 Lempel-Ziv 压缩算法的熵速率估计器，只对符号数据起作用，因此我们使用容差阈值来对数值进行匹配。我们证明了 imax 是表征时间序列内在可预测性的一个有效上界。我们给出了形式上的证明，并且通过比较 imax 与不同领域的不同现实世界数据集上不同最新模型的预测精度，实验验证了我们的论点。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Quantifying+and+Estimating+the+Predictability+Upper+Bound+of+Univariate+Numeric+Time+Series)|0| |[Scalable Rule Lists Learning with Sampling](https://doi.org/10.1145/3637528.3671989)|Leonardo Pellegrina, Fabio Vandin|Dept. of Information Engineering, University of Padova, Padova, Italy|Learning interpretable models has become a major focus of machine learningresearch, given the increasing prominence of machine learning in sociallyimportant decision-making. Among interpretable models, rule lists are among thebest-known and easily interpretable ones. However, finding optimal rule listsis computationally challenging, and current approaches are impractical forlarge datasets. We present a novel and scalable approach to learn nearly optimal rule listsfrom large datasets. Our algorithm uses sampling to efficiently obtain anapproximation of the optimal rule list with rigorous guarantees on the qualityof the approximation. In particular, our algorithm guarantees to find a rulelist with accuracy very close to the optimal rule list when a rule list withhigh accuracy exists. Our algorithm builds on the VC-dimension of rule lists,for which we prove novel upper and lower bounds. Our experimental evaluation onlarge datasets shows that our algorithm identifies nearly optimal rule listswith a speed-up up to two orders of magnitude over state-of-the-art exactapproaches. Moreover, our algorithm is as fast as, and sometimes faster than,recent heuristic approaches, while reporting higher quality rule lists. Inaddition, the rules reported by our algorithm are more similar to the rules inthe optimal rule list than the rules from heuristic approaches.|随着机器学习在社会决策中的重要性日益突出，学习可解释模型已经成为机器学习研究的一个主要焦点。在可解释的模型中，规则列表是最著名和易于解释的模型之一。然而，找到最佳的规则列表是计算上的挑战，并且当前的方法对于大型数据集是不切实际的。我们提出了一个新的和可扩展的方法来学习几乎最优的规则列表从大数据集。该算法采用抽样的方法，在严格保证近似质量的前提下，有效地获得了最优规则列表的近似。特别是当存在高精度规则列表时，我们的算法保证能够找到精度非常接近最优规则列表的规则列表。我们的算法建立在规则列表的 VC 维数的基础上，证明了新的上下界。我们在大型数据集上的实验评估表明，我们的算法识别几乎最优的规则列表的速度比最先进的精确方法快两数量级。此外，我们的算法与最近的启发式方法一样快，有时甚至更快，同时报告更高质量的规则列表。此外，我们的算法报告的规则更接近于最优规则列表中的规则，而不是启发式方法中的规则。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Scalable+Rule+Lists+Learning+with+Sampling)|0| |[Fredformer: Frequency Debiased Transformer for Time Series Forecasting](https://doi.org/10.1145/3637528.3671928)|Xihao Piao, Zheng Chen, Taichi Murayama, Yasuko Matsubara, Yasushi Sakurai|SANKEN, Osaka University, Osaka, Japan|The Transformer model has shown leading performance in time series forecasting. Nevertheless, in some complex scenarios, it tends to learn low-frequency features in the data and overlook high-frequency features, showing a frequency bias. This bias prevents the model from accurately capturing important high-frequency data features. In this paper, we undertake empirical analyses to understand this bias and discover that frequency bias results from the model disproportionately focusing on frequency features with higher energy. Based on our analysis, we formulate this bias and propose Fredformer, a Transformer-based framework designed to mitigate frequency bias by learning features equally across different frequency bands. This approach prevents the model from overlooking lower amplitude features important for accurate forecasting. Extensive experiments show the effectiveness of our proposed approach, which can outperform other baselines in different real-world time-series datasets. Furthermore, we introduce a lightweight variant of the Fredformer with an attention matrix approximation, which achieves comparable performance but with much fewer parameters and lower computation costs. The code is available at: https://github.com/chenzRG/Fredformer|变压器模型在时间序列预测中表现出领先的性能。然而，在一些复杂的场景中，它倾向于学习数据中的低频特征，而忽略高频特征，表现出频率偏差。这种偏差使模型无法准确地捕获重要的高频数据特征。本文通过实证分析了解这种偏差，发现模型的频率偏差过多地集中在高能量的频率特征上。基于我们的分析，我们制定了这种偏差，并提出了 Fredformer，一个基于变压器的框架，旨在减少频率偏差的学习功能在不同的频段相等。这种方法可以防止模型忽略对于精确预测非常重要的低振幅特征。大量的实验证明了该方法的有效性，在不同的实际时间序列数据集中，该方法的性能优于其他基线。此外，我们还引入了一个轻量级的 Fredform- 注意矩阵近似方法，该方法具有相当的性能，但是参数更少，计算量更小。密码可于以下 https://github.com/chenzrg/fredformer 索取:|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Fredformer:+Frequency+Debiased+Transformer+for+Time+Series+Forecasting)|0| |[ORCDF: An Oversmoothing-Resistant Cognitive Diagnosis Framework for Student Learning in Online Education Systems](https://doi.org/10.1145/3637528.3671988)|Hong Qian, Shuo Liu, Mingjia Li, Bingdong Li, Zhi Liu, Aimin Zhou|; School of Computer Science and Technology, East China Normal University, Shanghai, China|Cognitive diagnosis models (CDMs) are designed to learn students' mastery levels using their response logs. CDMs play a fundamental role in online education systems since they significantly influence downstream applications such as teachers' guidance and computerized adaptive testing. Despite the success achieved by existing CDMs, we find that they suffer from a thorny issue that the learned students' mastery levels are too similar. This issue, which we refer to as oversmoothing, could diminish the CDMs' effectiveness in downstream tasks. CDMs comprise two core parts: learning students' mastery levels and assessing mastery levels by fitting the response logs. This paper contends that the oversmoothing issue arises from that existing CDMs seldom utilize response signals on exercises in the learning part but only use them as labels in the assessing part. To this end, this paper proposes an oversmoothing-resistant cognitive diagnosis framework (ORCDF) to enhance existing CDMs by utilizing response signals in the learning part. Specifically, ORCDF introduces a novel response graph to inherently incorporate response signals as types of edges. Then, ORCDF designs a tailored response-aware graph convolution network (RGC) that effectively captures the crucial response signals within the response graph. Via ORCDF, existing CDMs are enhanced by replacing the input embeddings with the outcome of RGC, allowing for the consideration of response signals on exercises in the learning part. Extensive experiments on real-world datasets show that ORCDF not only helps existing CDMs alleviate the oversmoothing issue but also significantly enhances the models' prediction and interpretability performance. Moreover, the effectiveness of ORCDF is validated in the downstream task of computerized adaptive testing.|认知诊断模型(CDM)的目的是了解学生的掌握水平使用他们的反应日志。清洁发展机制在在线教育系统中发挥着重要作用，因为它们对教师指导和计算机化适应性测试等下游应用具有重要影响。尽管现有的清洁发展机制取得了成功，但我们发现它们存在一个棘手的问题，即学生的掌握水平过于相似。这个我们称之为过度平滑的问题，可能会削弱清洁发展机制在下游任务中的有效性。清洁发展机制包括两个核心部分: 学习学生的掌握水平和通过拟合响应日志评估掌握水平。本文认为，现有的清洁发展机制很少利用学习部分练习的反应信号，而只是在评估部分使用作为标记，从而产生了过度平滑的问题。为此，本文提出了一种抗过平滑认知诊断框架(ORCDF) ，利用学习部分的响应信号来增强现有的 CDM。具体来说，ORCDF 引入了一种新的响应图，将响应信号内在地合并为边的类型。然后，ORCDF 设计一个量身定制的响应感知图卷积网络(RGC) ，有效地捕获响应图中的关键响应信号。通过 ORCDF，现有的清洁发展机制得到了加强，将输入嵌入改为研资局的成果，从而允许在学习部分考虑练习的回应信号。在实际数据集上的大量实验表明，ORCDF 不仅有助于缓解现有 CDM 的过平滑问题，而且显著提高了模型的预测和可解释性能。在计算机自适应测试的下游任务中，验证了 ORCDF 算法的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=ORCDF:+An+Oversmoothing-Resistant+Cognitive+Diagnosis+Framework+for+Student+Learning+in+Online+Education+Systems)|0| |[LARP: Language Audio Relational Pre-training for Cold-Start Playlist Continuation](https://doi.org/10.1145/3637528.3671772)|Rebecca Salganik, Xiaohao Liu, Yunshan Ma, Jian Kang, TatSeng Chua|National University of Singapore, Singapore, Singapore; University of Rochester, Rochester, NY, USA|As online music consumption increasingly shifts towards playlist-based listening, the task of playlist continuation, in which an algorithm suggests songs to extend a playlist in a personalized and musically cohesive manner, has become vital to the success of music streaming services. Currently, many existing playlist continuation approaches rely on collaborative filtering methods to perform their recommendations. However, such methods will struggle to recommend songs that lack interaction data, an issue known as the cold-start problem. Current approaches to this challenge design complex mechanisms for extracting relational signals from sparse collaborative signals and integrating them into content representations. However, these approaches leave content representation learning out of scope and utilize frozen, pre-trained content models that may not be aligned with the distribution or format of a specific musical setting. Furthermore, even the musical state-of-the-art content modules are either (1) incompatible with the cold-start setting or (2) unable to effectively integrate cross-modal and relational signals. In this paper, we introduce LARP, a multi-modal cold-start playlist continuation model, to effectively overcome these limitations. LARP is a three-stage contrastive learning framework that integrates both multi-modal and relational signals into its learned representations. Our framework uses increasing stages of task-specific abstraction: within-track (language-audio) contrastive loss, track-track contrastive loss, and track-playlist contrastive loss. Experimental results on two publicly available datasets demonstrate the efficacy of LARP over uni-modal and multi-modal models for playlist continuation in a cold-start setting. Finally, this work pioneers the perspective of addressing cold-start recommendation via relational representation learning. Code and dataset are released at: https://github.com/Rsalganik1123/LARP/|随着在线音乐消费越来越多地转向基于播放列表的聆听，播放列表延续的任务已经成为音乐流媒体服务成功的关键。在播放列表延续中，算法建议歌曲以个性化和音乐内聚的方式扩展播放列表。目前，许多现有的播放列表延续方法依赖于协同过滤方法来执行他们的建议。然而，这种方法将很难推荐缺乏交互数据的歌曲，这个问题被称为冷启动问题。目前针对这一挑战的方法设计了复杂的机制，用于从稀疏的协作信号中提取关系信号，并将其集成到内容表示中。然而，这些方法使得内容表示学习脱离了范围，并且使用了冻结的、预先训练的内容模型，这些模型可能与特定音乐环境的分布或格式不一致。此外，即使音乐国家的最先进的内容模块要么(1)与冷启动设置不兼容，要么(2)不能有效地整合跨模态和关系信号。在本文中，我们引入了 LARP，一个多模态的冷启动播放列表延续模型，以有效地克服这些局限性。LARP 是一个三阶段对比学习框架，它将多模态信号和关系信号整合到学习表示中。我们的框架使用了不断增加的特定于任务的抽象阶段: 轨道内(语言-音频)对比度丢失、轨道-轨道对比度丢失和轨道-播放列表对比度丢失。在两个公开数据集上的实验结果证明了 LARP 在冷启动环境下对播放列表延续的单模态和多模态模型的有效性。最后，本文开创了通过关系表示学习解决冷启动推荐问题的先河。代码和数据集在以下 https://github.com/rsalganik1123/larp/发布|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=LARP:+Language+Audio+Relational+Pre-training+for+Cold-Start+Playlist+Continuation)|0| -|[CrossLight: Offline-to-Online Reinforcement Learning for Cross-City Traffic Signal Control](https://doi.org/10.1145/3637528.3671927)|Qian Sun, Rui Zha, Le Zhang, Jingbo Zhou, Yu Mei, Zhiling Li, Hui Xiong|; Department of Intelligent Transportation System, Baidu Inc., Beijing, China; School of Computer Science, University of Science and Technology of China, Hefei, China; Baidu Research, Baidu Inc., Beijing, China; Department of Intelligent Driving Group Business Management, Baidu Inc., Beijing, China|The recent advancements in Traffic Signal Control (TSC) have highlighted the potential of Reinforcement Learning (RL) as a promising solution to alleviate traffic congestion. Current research in this area primarily concentrates on either online or offline learning strategies, aiming to create optimized policies for specific cities. Nevertheless, the transferability of these policies to new cities is impeded by constraints such as the limited availability of high-quality data and the expensive and risky exploration process. To this end, in this paper, we present an innovative cross-city Traffic Signal Control (TSC) paradigm called CrossLight. Our approach involves meta training using offline data from source cities and adaptively fine-tuning in the target city. This novel methodology aims to address the challenges of transferring TSC policies across different cities effectively. In our proposed approach, we start by acquiring meta-decision pattern knowledge through trajectory dynamics reconstruction via pre-training in source cities. To address disparities in road network topologies between cities, we dynamically construct city topological structures based on the extracted meta-knowledge during the offline meta-training phase. These structures are then used to distill pattern-structure aware representations of decision trajectories from the source cities. To identify effective initial parameters for the learnable components, we employ the Model-Agnostic Meta-Learning (MAML) framework, a popular meta-learning approach. During adaptive fine-tuning in the target city, we introduce a replay buffer that is iteratively updated using online interactions with a rank and filter mechanism. This mechanism, along with a carefully designed exploration strategy, ensures a balance between exploitation and exploration, thereby fostering both the diversity and quality of the trajectories for fine-tuning. Finally, extensive experiments across four cities validate that CrossLight achieves comparable performance in new cities with minimal fine-tuning iterations, surpassing both existing online and offline methods. This success underscores that our CrossLight framework emerges as a groundbreaking and potent paradigm, offering a feasible and effective solution to the intelligent transportation community.|交通信号控制(TSC)的最新进展凸显了强化学习作为缓解交通交通堵塞的一个有前途的解决方案的潜力。目前该领域的研究主要集中在线上或线下学习策略，旨在为特定城市制定优化政策。然而，这些政策向新城市的转移受到诸如高质量数据有限以及昂贵和危险的勘探过程等制约因素的阻碍。为此，在本文中，我们提出了一个创新的跨城市交通信号控制(TSC)范例称为交叉灯。我们的方法包括使用来自源城市的离线数据进行元培训，并在目标城市进行自适应微调。这种新颖的方法旨在解决在不同城市之间有效转移 TSC 政策的挑战。在我们提出的方法中，我们从获取元决策模式的知识开始，通过轨迹动力学重建通过预训练在源城市。针对城市间道路网络拓扑结构的差异，在离线元训练阶段，基于提取的元知识动态构建城市拓扑结构。然后使用这些结构从源城市中提取决策轨迹的模式结构感知表示。为了确定可学习组件的有效初始参数，我们采用了模型不可知元学习(MAML)框架，这是一种流行的元学习方法。在目标城市的自适应微调过程中，我们引入了一个重放缓冲区，该缓冲区使用带有等级和过滤机制的在线交互进行迭代更新。这一机制连同精心设计的勘探战略，确保了开发和勘探之间的平衡，从而促进了微调轨迹的多样性和质量。最后，在四个城市进行的大量实验证实，CrossLight 在新城市中以最少的微调迭代实现了可比的性能，超过了现有的两种在线和离线方法。这一成功突出表明，我们的 CrossLight 框架是一个开创性的、有力的范例，为智能交通社区提供了一个可行的、有效的解决方案。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=CrossLight:+Offline-to-Online+Reinforcement+Learning+for+Cross-City+Traffic+Signal+Control)|0| +|[CrossLight: Offline-to-Online Reinforcement Learning for Cross-City Traffic Signal Control](https://doi.org/10.1145/3637528.3671927)|Qian Sun, Rui Zha, Le Zhang, Jingbo Zhou, Yu Mei, Zhiling Li, Hui Xiong|; Baidu Research, Baidu Inc., Beijing, China; Department of Intelligent Transportation System, Baidu Inc., Beijing, China; School of Computer Science, University of Science and Technology of China, Hefei, China; Department of Intelligent Driving Group Business Management, Baidu Inc., Beijing, China|The recent advancements in Traffic Signal Control (TSC) have highlighted the potential of Reinforcement Learning (RL) as a promising solution to alleviate traffic congestion. Current research in this area primarily concentrates on either online or offline learning strategies, aiming to create optimized policies for specific cities. Nevertheless, the transferability of these policies to new cities is impeded by constraints such as the limited availability of high-quality data and the expensive and risky exploration process. To this end, in this paper, we present an innovative cross-city Traffic Signal Control (TSC) paradigm called CrossLight. Our approach involves meta training using offline data from source cities and adaptively fine-tuning in the target city. This novel methodology aims to address the challenges of transferring TSC policies across different cities effectively. In our proposed approach, we start by acquiring meta-decision pattern knowledge through trajectory dynamics reconstruction via pre-training in source cities. To address disparities in road network topologies between cities, we dynamically construct city topological structures based on the extracted meta-knowledge during the offline meta-training phase. These structures are then used to distill pattern-structure aware representations of decision trajectories from the source cities. To identify effective initial parameters for the learnable components, we employ the Model-Agnostic Meta-Learning (MAML) framework, a popular meta-learning approach. During adaptive fine-tuning in the target city, we introduce a replay buffer that is iteratively updated using online interactions with a rank and filter mechanism. This mechanism, along with a carefully designed exploration strategy, ensures a balance between exploitation and exploration, thereby fostering both the diversity and quality of the trajectories for fine-tuning. Finally, extensive experiments across four cities validate that CrossLight achieves comparable performance in new cities with minimal fine-tuning iterations, surpassing both existing online and offline methods. This success underscores that our CrossLight framework emerges as a groundbreaking and potent paradigm, offering a feasible and effective solution to the intelligent transportation community.|交通信号控制(TSC)的最新进展凸显了强化学习作为缓解交通交通堵塞的一个有前途的解决方案的潜力。目前该领域的研究主要集中在线上或线下学习策略，旨在为特定城市制定优化政策。然而，这些政策向新城市的转移受到诸如高质量数据有限以及昂贵和危险的勘探过程等制约因素的阻碍。为此，在本文中，我们提出了一个创新的跨城市交通信号控制(TSC)范例称为交叉灯。我们的方法包括使用来自源城市的离线数据进行元培训，并在目标城市进行自适应微调。这种新颖的方法旨在解决在不同城市之间有效转移 TSC 政策的挑战。在我们提出的方法中，我们从获取元决策模式的知识开始，通过轨迹动力学重建通过预训练在源城市。针对城市间道路网络拓扑结构的差异，在离线元训练阶段，基于提取的元知识动态构建城市拓扑结构。然后使用这些结构从源城市中提取决策轨迹的模式结构感知表示。为了确定可学习组件的有效初始参数，我们采用了模型不可知元学习(MAML)框架，这是一种流行的元学习方法。在目标城市的自适应微调过程中，我们引入了一个重放缓冲区，该缓冲区使用带有等级和过滤机制的在线交互进行迭代更新。这一机制连同精心设计的勘探战略，确保了开发和勘探之间的平衡，从而促进了微调轨迹的多样性和质量。最后，在四个城市进行的大量实验证实，CrossLight 在新城市中以最少的微调迭代实现了可比的性能，超过了现有的两种在线和离线方法。这一成功突出表明，我们的 CrossLight 框架是一个开创性的、有力的范例，为智能交通社区提供了一个可行的、有效的解决方案。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=CrossLight:+Offline-to-Online+Reinforcement+Learning+for+Cross-City+Traffic+Signal+Control)|0| |[Going Where, by Whom, and at What Time: Next Location Prediction Considering User Preference and Temporal Regularity](https://doi.org/10.1145/3637528.3671916)|Tianao Sun, Ke Fu, Weiming Huang, Kai Zhao, Yongshun Gong, Meng Chen|School of Software, Shandong University, Jinan, China; Robinson College of Business, Georgia State University, Atlanta, GA, USA; School of Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore|Next location prediction is a crucial task in human mobility modeling, and is pivotal for many downstream applications like location-based recommendation and transportation planning. Although there has been a large body of research tackling this problem, the usefulness of user preference and temporal regularity remains underrepresented. Specifically, previous studies usually neglect the explicit user preference information entailed from human trajectories and fall short in utilizing the arrival time of next location, as a key determinant on next location. To address these limitations, we propose a Multi-Context aware Location Prediction model (MCLP) to predict next locations for individuals, where it explicitly models user preference and the next arrival time as context. First, we utilize a topic model to extract user preferences for different types of locations from historical human trajectories. Second, we develop an arrival time estimator to construct a robust arrival time embedding based on the multi-head attention mechanism. The two components provide pivotal contextual information for the subsequent prediction. Finally, we utilize the Transformer architecture to mine sequential patterns and integrate multiple contextual information to predict the next locations. Experimental results on two real-world mobility datasets show that our proposed MCLP outperforms baseline methods.|下一步的位置预测是人类流动性建模中的一个关键任务，对于基于位置的推荐和交通规划等许多下游应用来说都是至关重要的。虽然已有大量研究处理这一问题，但用户偏好和时间规律的有用性仍然没有得到充分体现。具体而言，以往的研究往往忽略了人类轨迹所带来的明确的用户偏好信息，而没有充分利用下一个位置的到达时间作为下一个位置的关键决定因素。为了解决这些局限性，我们提出了一个多上下文感知位置预测模型(MCLP)来预测个人的下一个位置，它显式地模拟用户偏好和下一个到达时间作为上下文。首先，我们利用主题模型从历史人类轨迹中提取不同类型位置的用户偏好。其次，提出了一种基于多目标注意机制的鲁棒到达时间估计器，构造了一种鲁棒到达时间嵌入算法。这两个组件为后续预测提供关键的上下文信息。最后，我们利用 former 体系结构来挖掘序列模式，并集成多个上下文信息来预测下一个位置。在两个实际移动数据集上的实验结果表明，我们提出的 MCLP 方法的性能优于基线方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Going+Where,+by+Whom,+and+at+What+Time:+Next+Location+Prediction+Considering+User+Preference+and+Temporal+Regularity)|0| -|[EcoVal: An Efficient Data Valuation Framework for Machine Learning](https://doi.org/10.1145/3637528.3672068)|Ayush K. Tarun, Vikram S. Chundawat, Murari Mandal, Hong Ming Tan, Bowei Chen, Mohan S. Kankanhalli|Adam Smith Business School, University of Glasgow, Glasgow, United Kingdom; Ola Krutrim, Bangalore, India; NUS Business School, National University of Singapore, Singapore, Singapore; National University of Singapore, Singapore, Singapore; RespAI Lab, Kalinga Institute of Industrial Technology, Bhubaneswar, India; RespAI Lab, Bhubaneswar, India|Quantifying the value of data within a machine learning workflow can play a pivotal role in making more strategic decisions in machine learning initiatives. The existing Shapley value based frameworks for data valuation in machine learning are computationally expensive as they require considerable amount of repeated training of the model to obtain the Shapley value. In this paper, we introduce an efficient data valuation framework EcoVal, to estimate the value of data for machine learning models in a fast and practical manner. Instead of directly working with individual data sample, we determine the value of a cluster of similar data points. This value is further propagated amongst all the member cluster points. We show that the overall value of the data can be determined by estimating the intrinsic and extrinsic value of each data. This is enabled by formulating the performance of a model as aproduction function, a concept which is popularly used to estimate the amount of output based on factors like labor and capital in a traditional free economic market. We provide a formal proof of our valuation technique and elucidate the principles and mechanisms that enable its accelerated performance. We demonstrate the real-world applicability of our method by showcasing its effectiveness for both in-distribution and out-of-sample data. This work addresses one of the core challenges of efficient data valuation at scale in machine learning models. The code is available at https://github.com/respai-lab/ecoval.|对机器学习工作流中的数据价值进行量化，可以在机器学习活动中作出更具战略性的决策方面发挥关键作用。现有的基于 Shapley 值的机器学习数据估值框架计算量很大，因为它们需要对模型进行大量的重复训练才能获得 Shapley 值。本文介绍了一种高效的数据价值评估框架 EcoVal，用于快速、实用地评估机器学习模型的数据价值。我们不直接处理单个数据样本，而是确定类似数据点集群的值。此值将在所有成员群集点之间进一步传播。我们表明，数据的总体价值可以通过估计每个数据的内在和外在价值来确定。这是通过将模型的性能表述为生产函数来实现的，在传统的自由经济市场中，生产函数这一概念被广泛用于根据劳动力和资本等因素来估计产出量。我们为我们的评估技术提供了一个正式的证明，并阐明了使其加速性能的原则和机制。我们通过展示其对分布内和样本外数据的有效性来证明我们的方法在现实世界中的适用性。这项工作解决了在机器学习模型的规模有效的数据估值的核心挑战之一。密码可在 https://github.com/respai-lab/ecoval 查阅。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=EcoVal:+An+Efficient+Data+Valuation+Framework+for+Machine+Learning)|0| +|[EcoVal: An Efficient Data Valuation Framework for Machine Learning](https://doi.org/10.1145/3637528.3672068)|Ayush K. Tarun, Vikram S. Chundawat, Murari Mandal, Hong Ming Tan, Bowei Chen, Mohan S. Kankanhalli|National University of Singapore, Singapore, Singapore; NUS Business School, National University of Singapore, Singapore, Singapore; RespAI Lab, Kalinga Institute of Industrial Technology, Bhubaneswar, India; RespAI Lab, Bhubaneswar, India; Adam Smith Business School, University of Glasgow, Glasgow, United Kingdom; Ola Krutrim, Bangalore, India|Quantifying the value of data within a machine learning workflow can play a pivotal role in making more strategic decisions in machine learning initiatives. The existing Shapley value based frameworks for data valuation in machine learning are computationally expensive as they require considerable amount of repeated training of the model to obtain the Shapley value. In this paper, we introduce an efficient data valuation framework EcoVal, to estimate the value of data for machine learning models in a fast and practical manner. Instead of directly working with individual data sample, we determine the value of a cluster of similar data points. This value is further propagated amongst all the member cluster points. We show that the overall value of the data can be determined by estimating the intrinsic and extrinsic value of each data. This is enabled by formulating the performance of a model as aproduction function, a concept which is popularly used to estimate the amount of output based on factors like labor and capital in a traditional free economic market. We provide a formal proof of our valuation technique and elucidate the principles and mechanisms that enable its accelerated performance. We demonstrate the real-world applicability of our method by showcasing its effectiveness for both in-distribution and out-of-sample data. This work addresses one of the core challenges of efficient data valuation at scale in machine learning models. The code is available at https://github.com/respai-lab/ecoval.|对机器学习工作流中的数据价值进行量化，可以在机器学习活动中作出更具战略性的决策方面发挥关键作用。现有的基于 Shapley 值的机器学习数据估值框架计算量很大，因为它们需要对模型进行大量的重复训练才能获得 Shapley 值。本文介绍了一种高效的数据价值评估框架 EcoVal，用于快速、实用地评估机器学习模型的数据价值。我们不直接处理单个数据样本，而是确定类似数据点集群的值。此值将在所有成员群集点之间进一步传播。我们表明，数据的总体价值可以通过估计每个数据的内在和外在价值来确定。这是通过将模型的性能表述为生产函数来实现的，在传统的自由经济市场中，生产函数这一概念被广泛用于根据劳动力和资本等因素来估计产出量。我们为我们的评估技术提供了一个正式的证明，并阐明了使其加速性能的原则和机制。我们通过展示其对分布内和样本外数据的有效性来证明我们的方法在现实世界中的适用性。这项工作解决了在机器学习模型的规模有效的数据估值的核心挑战之一。密码可在 https://github.com/respai-lab/ecoval 查阅。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=EcoVal:+An+Efficient+Data+Valuation+Framework+for+Machine+Learning)|0| |[Causal Estimation of Exposure Shifts with Neural Networks and an Application to Inform Air Quality Standards in the US](https://doi.org/10.1145/3637528.3671761)|Mauricio Tec, Kevin Josey, Oladimeji Mudele, Francesca Dominici|Harvard University, Cambridge, MA, USA; Colorado School of Public Health, Aurora, CO, USA|A fundamental task in causal inference is estimating the effect of a distribution shift in the treatment variable. We refer to this problem as shift-response function (SRF) estimation. Existing neural network methods for causal inference lack theoretical guarantees and practical implementations for SRF estimation. In this paper, we introduce Targeted Regularization for Exposure Shifts with Neural Networks (TRESNET), a method to estimate SRFs with robustness and efficiency guarantees. Our contributions are twofold. First, we propose a targeted regularization loss for neural networks with theoretical properties that ensure double robustness and asymptotic efficiency specific to SRF estimation. Second, we extend targeted regularization to support loss functions from the exponential family to accommodate non-continuous outcome distributions (e.g., discrete counts). We conduct benchmark experiments demonstrating TRESNET's broad applicability and competitiveness. We then apply our method to a key policy question in public health to estimate the causal effect of revising the US National Ambient Air Quality Standards (NAAQS) for PM 2.5 from 12 μg/m3 to 9 μg/m3. This change has been recently proposed by the US Environmental Protection Agency (EPA). Our goal is to estimate the reduction in deaths that would result from this anticipated revision using data consisting of 68 million individuals across the U.S.|因果推理的一个基本任务是估计治疗变量中分布偏移的影响。我们把这个问题称为移位响应函数(SRF)估计。现有的神经网络因果推理方法缺乏 SRF 估计的理论保证和实际应用。本文介绍了基于神经网络的曝光位移目标正则化方法(TRESNET) ，这是一种具有鲁棒性和有效性保证的 SRF 估计方法。我们的贡献是双重的。首先，我们提出了一个目标正则化损失的神经网络的理论性质，确保双鲁棒性和渐近效率特定的 SRF 估计。其次，我们扩展有针对性的正则化来支持来自指数族的损失函数，以适应非连续的结果分布(例如，离散计数)。我们进行的基准实验证明了 TRESNET 的广泛适用性和竞争力。然后，我们将我们的方法应用于公共卫生的一个关键政策问题，以估计将美国国家环境空气质量标准(NAAQS)的 PM2.5从12μg/m3修订为9μg/m3的因果效应。美国环境保护署(EPA)最近提出了这一改变。我们的目标是利用全美6800万个人的数据估计这一预期修正可能导致的死亡人数减少。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Causal+Estimation+of+Exposure+Shifts+with+Neural+Networks+and+an+Application+to+Inform+Air+Quality+Standards+in+the+US)|0| -|[Online Drift Detection with Maximum Concept Discrepancy](https://doi.org/10.1145/3637528.3672016)|Ke Wan, Yi Liang, Susik Yoon|Fudan University, Shanghai, China; Korea University, Seoul, Republic of Korea; University of Illinois at Urbana-Champaign, Urbana, IL, USA|Continuous learning from an immense volume of data streams becomes exceptionally critical in the internet era. However, data streams often do not conform to the same distribution over time, leading to a phenomenon called concept drift. Since a fixed static model is unreliable for inferring concept-drifted data streams, establishing an adaptive mechanism for detecting concept drift is crucial. Current methods for concept drift detection primarily assume that the labels or error rates of downstream models are given and/or underlying statistical properties exist in data streams. These approaches, however, struggle to address high-dimensional data streams with intricate irregular distribution shifts, which are more prevalent in real-world scenarios. In this paper, we propose MCD-DD, a novel concept drift detection method based on maximum concept discrepancy, inspired by the maximum mean discrepancy. Our method can adaptively identify varying forms of concept drift by contrastive learning of concept embeddings without relying on labels or statistical properties. With thorough experiments under synthetic and real-world scenarios, we demonstrate that the proposed method outperforms existing baselines in identifying concept drifts and enables qualitative analysis with high explainability.|在互联网时代，从海量数据流中不断学习变得尤为重要。然而，随着时间的推移，数据流往往不符合相同的分布，从而导致一种称为概念漂移的现象。由于固定的静态模型对于推断概念漂移数据流是不可靠的，因此建立一个自适应的概念漂移检测机制是至关重要的。目前的概念漂移检测方法主要假设下游模型的标签或错误率已经给出，并且/或者数据流中存在潜在的统计特性。然而，这些方法很难处理具有复杂的不规则分布变化的高维数据流，这种情况在现实世界中更为普遍。本文受最大均值偏差的启发，提出了一种新的基于最大均值偏差的概念漂移检测方法 MCD-DD。该方法通过概念嵌入的对比学习自适应地识别不同形式的概念漂移，而不依赖于标签或统计特性。通过在合成和真实场景下的全面实验，我们证明了该方法在识别概念漂移方面优于现有的基线，并且能够进行高解释性的定性分析。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Online+Drift+Detection+with+Maximum+Concept+Discrepancy)|0| -|[CE-RCFR: Robust Counterfactual Regression for Consensus-Enabled Treatment Effect Estimation](https://doi.org/10.1145/3637528.3672054)|Fan Wang, Chaochao Chen, Weiming Liu, Tianhao Fan, Xinting Liao, Yanchao Tan, Lianyong Qi, Xiaolin Zheng|College of Computer Science and Technology, Zhejiang University, Hangzhou, China; ; College of Computer and Data ScienceCollege of Software, Fuzhou University, Fuzhou, China|Estimating individual treatment effects (ITE) from observational data is challenging due to the absence of counterfactuals and the treatment selection bias. Prevalent ITE estimation methods tackle these challenges by aligning the treated and controlled distributions in the representational space. However, two critical issues have long been overlooked: (1)Mini-batch sampling sensitivity (MSS) issue, where representation distribution alignment at a mini-batch level is vulnerable to poor sampling cases, such as data imbalance and outliers; (2)Inconsistent representation learning (IRL) issue, where representation learning within a unified backbone network suffers from inconsistent gradient update directions due to the distribution skew between different treatment groups. To resolve these issues, we propose CE-RCFR, a Robust CounterFactual Regression framework for Consensus-Enabled causal effect estimation, including a relaxed distribution discrepancy regularizer (RDDR) module and a consensus-enabled aggregator (CEA) module. Specifically, for the robust representation alignment perspective, RDDR addresses the MSS issue by minimizing unbalanced optimal transport divergence between different treatment groups with a relaxed marginal constraint. For the accurate representation optimization perspective, CEA addresses the IRL issue by resolving the consistent gradient update directions on shared parameters within the backbone network. Extensive experiments demonstrate that CE-RCFR significantly outperforms the state-of-the-art methods in treatment effect estimations.|由于缺乏反事实因素和治疗选择偏倚，从观察数据估计个体治疗效果(ITE)是具有挑战性的。目前流行的 ITE 估计方法通过在表征空间中对齐处理过的和控制过的分布来应对这些挑战。然而，有两个关键问题长期以来一直被忽视: (1)小批量抽样敏感性(MSS)问题，其中表征分布在小批量水平上的比对容易受到不良抽样情况的影响，如数据不平衡和异常值; (2)不一致表征学习(IRL)问题，其中表征学习在一个统一的骨干网络中由于不同处理组之间的分布倾斜而遭受不一致的梯度更新方向。为了解决这些问题，我们提出了 CE-RCFR，一个用于共识支持的因果效应估计的鲁棒反事实回归框架，包括一个松弛的分布差异正则化(RDDR)模块和一个共识支持的聚合器(CEA)模块。具体而言，对于鲁棒表示比对的观点，RDDR 通过最小化不同治疗组之间不平衡的最佳运输分歧，并放松边际约束来解决 MSS 问题。从精确表示优化的角度出发，CEA 通过解决骨干网内共享参数的一致梯度更新方向来解决 IRL 问题。广泛的实验表明，CE-RCFR 在治疗效果评估方面显著优于最先进的方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=CE-RCFR:+Robust+Counterfactual+Regression+for+Consensus-Enabled+Treatment+Effect+Estimation)|0| -|[Learning from Emergence: A Study on Proactively Inhibiting the Monosemantic Neurons of Artificial Neural Networks](https://doi.org/10.1145/3637528.3671776)|Jiachuan Wang, Shimin Di, Lei Chen, Charles Wang Wai Ng|The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China; The Hong Kong University of Science and Technology, (Guangzhou), Guangzhou, China; The Hong Kong University of Science and Technology, Hong Kong SAR, China|Recently, emergence has received widespread attention from the research community along with the success of large-scale models. Different from the literature, we hypothesize a key factor that promotes the performance during the increase of scale: the reduction of monosemantic neurons that can only form one-to-one correlations with specific features. Monosemantic neurons tend to be sparser and have negative impacts on the performance in large models. Inspired by this insight, we propose an intuitive idea to identify monosemantic neurons and inhibit them. However, achieving this goal is a non-trivial task as there is no unified quantitative evaluation metric and simply banning monosemantic neurons does not promote polysemanticity in neural networks. Therefore, we first propose a new metric to measure the monosemanticity of neurons with the guarantee of efficiency for online computation, then introduce a theoretically supported method to suppress monosemantic neurons and proactively promote the ratios of polysemantic neurons in training neural networks. We validate our conjecture that monosemanticity brings about performance change at different model scales on a variety of neural networks and benchmark datasets in different areas, including language, image, and physics simulation tasks. Further experiments validate our analysis and theory regarding the inhibition of monosemanticity.|近年来，随着大规模模型的成功应用，涌现现象受到了研究界的广泛关注。与文献不同，我们假设一个关键因素，促进性能在规模的增加: 减少单语义神经元，只能形成一对一的相关性与特定的功能。在大型模型中，单义神经元往往比较稀疏，对性能有负面影响。受此启发，我们提出了一个直观的想法来识别和抑制单义神经元。然而，由于没有统一的定量评价指标，单义神经元的禁止并不能提高神经网络的多义性，因此实现这一目标是一项艰巨的任务。因此，我们首先提出了一种新的度量方法来衡量神经元的单语义性，保证了在线计算的有效性，然后介绍了一种理论支持的方法来抑制单语义神经元，并在训练神经网络中主动提高多语义神经元的比率。我们验证了我们的猜想，即单语义在不同的神经网络和不同领域的基准数据集上，包括语言、图像和物理模拟任务，在不同的模型尺度上带来性能变化。进一步的实验验证了我们关于单语义抑制的分析和理论。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Learning+from+Emergence:+A+Study+on+Proactively+Inhibiting+the+Monosemantic+Neurons+of+Artificial+Neural+Networks)|0| +|[Online Drift Detection with Maximum Concept Discrepancy](https://doi.org/10.1145/3637528.3672016)|Ke Wan, Yi Liang, Susik Yoon|Fudan University, Shanghai, China; University of Illinois at Urbana-Champaign, Urbana, IL, USA; Korea University, Seoul, Republic of Korea|Continuous learning from an immense volume of data streams becomes exceptionally critical in the internet era. However, data streams often do not conform to the same distribution over time, leading to a phenomenon called concept drift. Since a fixed static model is unreliable for inferring concept-drifted data streams, establishing an adaptive mechanism for detecting concept drift is crucial. Current methods for concept drift detection primarily assume that the labels or error rates of downstream models are given and/or underlying statistical properties exist in data streams. These approaches, however, struggle to address high-dimensional data streams with intricate irregular distribution shifts, which are more prevalent in real-world scenarios. In this paper, we propose MCD-DD, a novel concept drift detection method based on maximum concept discrepancy, inspired by the maximum mean discrepancy. Our method can adaptively identify varying forms of concept drift by contrastive learning of concept embeddings without relying on labels or statistical properties. With thorough experiments under synthetic and real-world scenarios, we demonstrate that the proposed method outperforms existing baselines in identifying concept drifts and enables qualitative analysis with high explainability.|在互联网时代，从海量数据流中不断学习变得尤为重要。然而，随着时间的推移，数据流往往不符合相同的分布，从而导致一种称为概念漂移的现象。由于固定的静态模型对于推断概念漂移数据流是不可靠的，因此建立一个自适应的概念漂移检测机制是至关重要的。目前的概念漂移检测方法主要假设下游模型的标签或错误率已经给出，并且/或者数据流中存在潜在的统计特性。然而，这些方法很难处理具有复杂的不规则分布变化的高维数据流，这种情况在现实世界中更为普遍。本文受最大均值偏差的启发，提出了一种新的基于最大均值偏差的概念漂移检测方法 MCD-DD。该方法通过概念嵌入的对比学习自适应地识别不同形式的概念漂移，而不依赖于标签或统计特性。通过在合成和真实场景下的全面实验，我们证明了该方法在识别概念漂移方面优于现有的基线，并且能够进行高解释性的定性分析。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Online+Drift+Detection+with+Maximum+Concept+Discrepancy)|0| +|[CE-RCFR: Robust Counterfactual Regression for Consensus-Enabled Treatment Effect Estimation](https://doi.org/10.1145/3637528.3672054)|Fan Wang, Chaochao Chen, Weiming Liu, Tianhao Fan, Xinting Liao, Yanchao Tan, Lianyong Qi, Xiaolin Zheng|; College of Computer Science and Technology, Zhejiang University, Hangzhou, China; College of Computer and Data ScienceCollege of Software, Fuzhou University, Fuzhou, China|Estimating individual treatment effects (ITE) from observational data is challenging due to the absence of counterfactuals and the treatment selection bias. Prevalent ITE estimation methods tackle these challenges by aligning the treated and controlled distributions in the representational space. However, two critical issues have long been overlooked: (1)Mini-batch sampling sensitivity (MSS) issue, where representation distribution alignment at a mini-batch level is vulnerable to poor sampling cases, such as data imbalance and outliers; (2)Inconsistent representation learning (IRL) issue, where representation learning within a unified backbone network suffers from inconsistent gradient update directions due to the distribution skew between different treatment groups. To resolve these issues, we propose CE-RCFR, a Robust CounterFactual Regression framework for Consensus-Enabled causal effect estimation, including a relaxed distribution discrepancy regularizer (RDDR) module and a consensus-enabled aggregator (CEA) module. Specifically, for the robust representation alignment perspective, RDDR addresses the MSS issue by minimizing unbalanced optimal transport divergence between different treatment groups with a relaxed marginal constraint. For the accurate representation optimization perspective, CEA addresses the IRL issue by resolving the consistent gradient update directions on shared parameters within the backbone network. Extensive experiments demonstrate that CE-RCFR significantly outperforms the state-of-the-art methods in treatment effect estimations.|由于缺乏反事实因素和治疗选择偏倚，从观察数据估计个体治疗效果(ITE)是具有挑战性的。目前流行的 ITE 估计方法通过在表征空间中对齐处理过的和控制过的分布来应对这些挑战。然而，有两个关键问题长期以来一直被忽视: (1)小批量抽样敏感性(MSS)问题，其中表征分布在小批量水平上的比对容易受到不良抽样情况的影响，如数据不平衡和异常值; (2)不一致表征学习(IRL)问题，其中表征学习在一个统一的骨干网络中由于不同处理组之间的分布倾斜而遭受不一致的梯度更新方向。为了解决这些问题，我们提出了 CE-RCFR，一个用于共识支持的因果效应估计的鲁棒反事实回归框架，包括一个松弛的分布差异正则化(RDDR)模块和一个共识支持的聚合器(CEA)模块。具体而言，对于鲁棒表示比对的观点，RDDR 通过最小化不同治疗组之间不平衡的最佳运输分歧，并放松边际约束来解决 MSS 问题。从精确表示优化的角度出发，CEA 通过解决骨干网内共享参数的一致梯度更新方向来解决 IRL 问题。广泛的实验表明，CE-RCFR 在治疗效果评估方面显著优于最先进的方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=CE-RCFR:+Robust+Counterfactual+Regression+for+Consensus-Enabled+Treatment+Effect+Estimation)|0| +|[Learning from Emergence: A Study on Proactively Inhibiting the Monosemantic Neurons of Artificial Neural Networks](https://doi.org/10.1145/3637528.3671776)|Jiachuan Wang, Shimin Di, Lei Chen, Charles Wang Wai Ng|The Hong Kong University of Science and Technology, Hong Kong SAR, China; The Hong Kong University of Science and Technology, (Guangzhou), Guangzhou, China; The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China|Recently, emergence has received widespread attention from the research community along with the success of large-scale models. Different from the literature, we hypothesize a key factor that promotes the performance during the increase of scale: the reduction of monosemantic neurons that can only form one-to-one correlations with specific features. Monosemantic neurons tend to be sparser and have negative impacts on the performance in large models. Inspired by this insight, we propose an intuitive idea to identify monosemantic neurons and inhibit them. However, achieving this goal is a non-trivial task as there is no unified quantitative evaluation metric and simply banning monosemantic neurons does not promote polysemanticity in neural networks. Therefore, we first propose a new metric to measure the monosemanticity of neurons with the guarantee of efficiency for online computation, then introduce a theoretically supported method to suppress monosemantic neurons and proactively promote the ratios of polysemantic neurons in training neural networks. We validate our conjecture that monosemanticity brings about performance change at different model scales on a variety of neural networks and benchmark datasets in different areas, including language, image, and physics simulation tasks. Further experiments validate our analysis and theory regarding the inhibition of monosemanticity.|近年来，随着大规模模型的成功应用，涌现现象受到了研究界的广泛关注。与文献不同，我们假设一个关键因素，促进性能在规模的增加: 减少单语义神经元，只能形成一对一的相关性与特定的功能。在大型模型中，单义神经元往往比较稀疏，对性能有负面影响。受此启发，我们提出了一个直观的想法来识别和抑制单义神经元。然而，由于没有统一的定量评价指标，单义神经元的禁止并不能提高神经网络的多义性，因此实现这一目标是一项艰巨的任务。因此，我们首先提出了一种新的度量方法来衡量神经元的单语义性，保证了在线计算的有效性，然后介绍了一种理论支持的方法来抑制单语义神经元，并在训练神经网络中主动提高多语义神经元的比率。我们验证了我们的猜想，即单语义在不同的神经网络和不同领域的基准数据集上，包括语言、图像和物理模拟任务，在不同的模型尺度上带来性能变化。进一步的实验验证了我们关于单语义抑制的分析和理论。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Learning+from+Emergence:+A+Study+on+Proactively+Inhibiting+the+Monosemantic+Neurons+of+Artificial+Neural+Networks)|0| |[POND: Multi-Source Time Series Domain Adaptation with Information-Aware Prompt Tuning](https://doi.org/10.1145/3637528.3671721)|Junxiang Wang, Guangji Bai, Wei Cheng, Zhengzhang Chen, Liang Zhao, Haifeng Chen|Emory University, Atlanta, GA, USA; NEC Labs America, Princeton, NJ, USA|Time series domain adaptation stands as a pivotal and intricate challengewith diverse applications, including but not limited to human activityrecognition, sleep stage classification, and machine fault diagnosis. Despitethe numerous domain adaptation techniques proposed to tackle this complexproblem, they primarily focus on domain adaptation from a single source domain.Yet, it is more crucial to investigate domain adaptation from multiple domainsdue to the potential for greater improvements. To address this, three importantchallenges need to be overcome: 1). The lack of exploration to utilizedomain-specific information for domain adaptation, 2). The difficulty to learndomain-specific information that changes over time, and 3). The difficulty toevaluate learned domain-specific information. In order to tackle thesechallenges simultaneously, in this paper, we introduce PrOmpt-based domaiNDiscrimination (POND), the first framework to utilize prompts for time seriesdomain adaptation. Specifically, to address Challenge 1, we extend the idea ofprompt tuning to time series analysis and learn prompts to capture common anddomain-specific information from all source domains. To handle Challenge 2, weintroduce a conditional module for each source domain to generate prompts fromtime series input data. For Challenge 3, we propose two criteria to select goodprompts, which are used to choose the most suitable source domain for domainadaptation. The efficacy and robustness of our proposed POND model areextensively validated through experiments across 50 scenarios encompassing fourdatasets. Experimental results demonstrate that our proposed POND modeloutperforms all state-of-the-art comparison methods by up to 66% on theF1-score.|时间序列领域适应是一个关键和复杂的挑战与多种应用，包括但不限于人类活动识别，睡眠阶段分类和机器故障诊断。尽管为了解决这个复杂的问题，提出了许多领域自适应技术，但它们主要集中在从单个源领域进行领域自适应。然而，由于存在更大的改进潜力，因此研究来自多个领域的领域适应性更为重要。为了解决这个问题，我们需要克服三个重要的挑战: 1)。缺乏利用特定领域信息进行领域适应的探索，2)。学习随时间变化的特定领域信息的困难，以及3)。评估学习领域特定信息的困难。为了同时解决这些问题，本文介绍了第一个利用提示进行时间序列域适应的框架——基于提示的域识别(POND)。具体来说，为了解决挑战1，我们将提示调优的思想扩展到时间序列分析，并学习提示从所有源域中捕获公共和特定领域的信息。为了处理挑战2，我们为每个源域引入一个条件模块，从时间序列输入数据生成提示。对于挑战3，我们提出了两个选择好提示的标准，用于选择最适合域适应的源域。我们提出的 POND 模型的有效性和鲁棒性通过包含四个数据集的50个场景的实验得到了广泛的验证。实验结果表明，我们提出的 POND 模型优于所有国家的最先进的比较方法，高达66% 的 F1得分。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=POND:+Multi-Source+Time+Series+Domain+Adaptation+with+Information-Aware+Prompt+Tuning)|0| |[DPSW-Sketch: A Differentially Private Sketch Framework for Frequency Estimation over Sliding Windows](https://doi.org/10.1145/3637528.3671694)|Yiping Wang, Yanhao Wang, Cen Chen|; East China Normal University, Shanghai, China|The sliding window model of computation captures scenarios in which data are continually arriving in the form of a stream, and only the most recent w items are used for analysis. In this setting, an algorithm needs to accurately track some desired statistics over the sliding window using a small space. When data streams contain sensitive information about individuals, the algorithm is also urgently needed to provide a provable guarantee of privacy. In this paper, we focus on the two fundamental problems of privately (1) estimating the frequency of an arbitrary item and (2) identifying the most frequent items (i.e., heavy hitters), in the sliding window model. We propose DPSW-Sketch, a sliding window framework based on the count-min sketch that not only satisfies differential privacy over the stream but also approximates the results for frequency and heavy-hitter queries within bounded errors in sublinear time and space w.r.t. w. Extensive experiments on five real-world and synthetic datasets show that DPSW-Sketch provides significantly better utility-privacy trade-offs than state-of-the-art methods.|计算的滑动窗口模型捕获数据以流的形式不断到达的场景，并且只使用最新的 w 项进行分析。在这种情况下，算法需要使用一个小空间在滑动窗口上精确地跟踪一些所需的统计信息。当数据流包含有关个人的敏感信息时，也迫切需要该算法提供可证明的隐私保证。本文主要研究滑动窗口模型中的两个基本问题: (1)估计任意项目的频率; (2)识别最频繁项目(即重点项目)。我们提出了 DPSW-Sketch，一个基于 count-min 草图的滑动窗口框架，它不仅满足流上的差分隐私，而且在次线性时间和空间的有界错误内，近似于频率和重点查询的结果。在五个真实世界和合成数据集上的大量实验表明，DPSW-Sketch 比最先进的方法提供了明显更好的效用-隐私权衡。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=DPSW-Sketch:+A+Differentially+Private+Sketch+Framework+for+Frequency+Estimation+over+Sliding+Windows)|0| |[DFGNN: Dual-frequency Graph Neural Network for Sign-aware Feedback](https://doi.org/10.1145/3637528.3671701)|Yiqing Wu, Ruobing Xie, Zhao Zhang, Xu Zhang, Fuzhen Zhuang, Leyu Lin, Zhanhui Kang, Yongjun Xu|; Tencent, Beijing, China; Institute of Artificial Intelligence, Beihang University & Zhongguancun Laboratory, Beijing, China; Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China|The graph-based recommendation has achieved great success in recent years.However, most existing graph-based recommendations focus on capturing userpreference based on positive edges/feedback, while ignoring negativeedges/feedback (e.g., dislike, low rating) that widely exist in real-worldrecommender systems. How to utilize negative feedback in graph-basedrecommendations still remains underexplored. In this study, we first conducteda comprehensive experimental analysis and found that (1) existing graph neuralnetworks are not well-suited for modeling negative feedback, which acts as ahigh-frequency signal in a user-item graph. (2) The graph-based recommendationsuffers from the representation degeneration problem. Based on the twoobservations, we propose a novel model that models positive and negativefeedback from a frequency filter perspective called Dual-frequency Graph NeuralNetwork for Sign-aware Recommendation (DFGNN). Specifically, in DFGNN, thedesigned dual-frequency graph filter (DGF) captures both low-frequency andhigh-frequency signals that contain positive and negative feedback.Furthermore, the proposed signed graph regularization is applied to maintainthe user/item embedding uniform in the embedding space to alleviate therepresentation degeneration problem. Additionally, we conduct extensiveexperiments on real-world datasets and demonstrate the effectiveness of theproposed model. Codes of our model will be released upon acceptance.|近年来，基于图形的推荐方法取得了巨大的成功。然而，大多数现有的基于图表的推荐关注于基于正面边缘/反馈来获取用户偏好，而忽略了现实中广泛存在的负面边缘/反馈(例如，不喜欢，低评价)。如何在基于图表的推荐中利用负面反馈仍然没有得到充分的探索。在本研究中，我们首先进行了全面的实验分析，发现(1)现有的图形神经网络并不适合建立负反馈模型，因为负反馈在用户项目图中扮演高频信号的角色。(2)基于图的推荐存在表示退化问题。基于这两个观察结果，我们提出了一种新的模型，从频率滤波器的角度模拟正反馈和负反馈，称为双频图神经网络(DFGNN)的符号感知推荐。特别是在 DFGNN，设计的双频图形滤波器(dGF)同时捕获包含正反馈和负反馈的低频和高频信号。在嵌入空间中采用符号图正则化方法保持用户/项目嵌入的一致性，以减少表示退化问题。此外，我们在真实世界的数据集上进行了广泛的实验，并证明了该模型的有效性。我们的型号代码将在验收后发布。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=DFGNN:+Dual-frequency+Graph+Neural+Network+for+Sign-aware+Feedback)|0| -|[Predicting Cascading Failures with a Hyperparametric Diffusion Model](https://doi.org/10.1145/3637528.3672048)|Bin Xiang, Bogdan Cautis, Xiaokui Xiao, Olga Mula, Dusit Niyato, Laks V. S. Lakshmanan|CNRSCREATE, Singapore, Singapore; National University of Singapore, Singapore, Singapore; University of Paris-Saclay, CNRS LISN, Saclay, France; University of British Columbia, Vancouver, Canada; Eindhoven University of Technology, Eindhoven, Netherlands; Nanyang Technological University, Singapore, Singapore|In this paper, we study cascading failures in power grids through the lens ofinformation diffusion models. Similar to the spread of rumors or influence inan online social network, it has been observed that failures (outages) in apower grid can spread contagiously, driven by viral spread mechanisms. Weemploy a stochastic diffusion model that is Markovian (memoryless) and local(the activation of one node, i.e., transmission line, can only be caused by itsneighbors). Our model integrates viral diffusion principles with physics-basedconcepts, by correlating the diffusion weights (contagion probabilities betweentransmission lines) with the hyperparametric Information Cascades (IC) model.We show that this diffusion model can be learned from traces of cascadingfailures, enabling accurate modeling and prediction of failure propagation.This approach facilitates actionable information through well-understood andefficient graph analysis methods and graph diffusion simulations. Furthermore,by leveraging the hyperparametric model, we can predict diffusion and mitigatethe risks of cascading failures even in unseen grid configurations, whereasexisting methods falter due to a lack of training data. Extensive experimentsbased on a benchmark power grid and simulations therein show that our approacheffectively captures the failure diffusion phenomena and guides decisions tostrengthen the grid, reducing the risk of large-scale cascading failures.Additionally, we characterize our model's sample complexity, improving upon theexisting bound.|本文通过信息扩散模型的透镜，研究了电网中的连锁故障。与在线社交网络中谣言或影响的传播类似，已经观察到电网故障(停电)可以通过病毒传播机制传染。我们采用马尔可夫(无记忆)和局部(一个节点的激活，即传输线，只能由它的邻居引起)的随机扩散模型。我们的模型通过将扩散权重(传输线之间的传染概率)与超参数信息级联(IC)模型相关联，将病毒扩散原理与基于物理的概念结合起来。我们表明，这种扩散模型可以从级联故障的痕迹中学习，从而能够准确建模和预测故障传播。这种方法通过充分理解和有效的图分析方法和图扩散模拟促进了可操作的信息。此外，通过利用超参数模型，我们可以预测扩散和减轻级联故障的风险，即使在看不见的网格配置，而现有的方法由于缺乏训练数据而步履蹒跚。基于基准电网的大量实验和仿真表明，我们的方法有效地捕获了故障扩散现象，并指导决策加强电网，减少大规模连锁故障的风险。此外，我们描述了我们的模型的样本复杂性，改进了现有的界限。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Predicting+Cascading+Failures+with+a+Hyperparametric+Diffusion+Model)|0| -|[FRNet: Frequency-based Rotation Network for Long-term Time Series Forecasting](https://doi.org/10.1145/3637528.3671713)|Xinyu Zhang, Shanshan Feng, Jianghong Ma, Huiwei Lin, Xutao Li, Yunming Ye, Fan Li, Yew Soon Ong|; Hong Kong Polytechnic University, Hong Kong, China; Centre for Frontier AI Research, ASTAR, Nanyang Technological University, Singapore, Singapore; Harbin Institute of Technology, Shenzhen, China|Long-term time series forecasting (LTSF) aims to predict future values for a long time based on historical data. The period term is an essential component of the time series, which is complex yet important for LTSF. Although existing studies have achieved promising results, they still have limitations in modeling dynamic complicated periods. Most studies only focus on static periods with fixed time steps, while very few studies attempt to capture dynamic periods in the time domain. In this paper, we dissect the original time series in time and frequency domains and empirically find that changes in periods are more easily captured and quantified in the frequency domain. Based on this observation, we propose to explore dynamic period features using rotation in the frequency domain. To this end, we develop the frequency-based rotation network (FRNet), a novel LTSF method to effectively capture the features of the dynamic complicated periods. FRNet decomposes the original time series into period and trend components. Based on the complex-valued linear networks, it leverages a period frequency rotation module to predict the period component and a patch frequency rotation module to predict the trend component, respectively. Extensive experiments on seven real-world datasets consistently demonstrate the superiority of FRNet over various state-of-the-art methods. The source code is available at https://github.com/SiriZhang45/FRNet.|长期时间序列预测(LTSF)的目的是根据历史数据预测未来很长一段时间内的价值。周期项是时间序列的一个重要组成部分，对于长期稳定因子来说，时间序列是复杂而重要的。虽然现有的研究已经取得了很好的成果，但是在动态复杂周期的建模方面仍然存在一定的局限性。大多数研究只关注固定时间步长的静态周期，很少有研究试图捕捉时间域中的动态周期。本文从时间和频率两个方面对原始时间序列进行了剖析，发现在频率域更容易捕捉和量化周期的变化。在此基础上，我们提出了在频域中利用旋转来探索动态周期特征的方法。为此，我们发展了基于频率的旋转网络(FRNet) ，一种新的 LTSF 方法来有效地捕捉动态复杂周期的特征。FRNet 将原始时间序列分解为周期分量和趋势分量。在复值线性网络的基础上，利用周期频率旋转模块预测周期分量，利用补丁频率旋转模块预测趋势分量。在七个真实世界数据集上的大量实验一致地证明了 FRNet 相对于各种最先进的方法的优越性。源代码可在 https://github.com/sirizhang45/frnet 下载。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=FRNet:+Frequency-based+Rotation+Network+for+Long-term+Time+Series+Forecasting)|0| -|[Hypformer: Exploring Efficient Transformer Fully in Hyperbolic Space](https://doi.org/10.1145/3637528.3672039)|Menglin Yang, Harshit Verma, Delvin Ce Zhang, Jiahong Liu, Irwin King, Rex Ying|Yale University, New Haven, CT, USA; The Chinese University of Hong Kong, Hong Kong, China; Birla Institute of Technology and Science, Hyderabad, India|Hyperbolic geometry have shown significant potential in modeling complex structured data, particularly those with underlying tree-like and hierarchical structures. Despite the impressive performance of various hyperbolic neural networks across numerous domains, research on adapting the Transformer to hyperbolic space remains limited. Previous attempts have mainly focused on modifying self-attention modules in the Transformer. However, these efforts have fallen short of developing a complete hyperbolic Transformer. This stems primarily from: (i) the absence of well-defined modules in hyperbolic space, including linear transformation layers, LayerNorm layers, activation functions, dropout operations, etc. (ii) the quadratic time complexity of the existing hyperbolic self-attention module w.r.t the number of input tokens, which hinders its scalability. To address these challenges, we propose, Hypformer, a novel hyperbolic Transformer based on the Lorentz model of hyperbolic geometry. In Hypformer, we introduce two foundational blocks that define the essential modules of the Transformer in hyperbolic space. Furthermore, we develop a linear self-attention mechanism in hyperbolic space, enabling hyperbolic Transformer to process billion-scale graph data and long-sequence inputs for the first time. Our experimental results confirm the effectiveness and efficiency of \method across various datasets, demonstrating its potential as an effective and scalable solution for large-scale data representation and large models.|双曲几何在建模复杂的结构化数据方面显示出巨大的潜力，特别是那些具有树状结构和层次结构的数据。尽管各种双曲神经网络在许多领域都有着令人印象深刻的性能，但是关于如何使变压器适应双曲空间的研究仍然很有限。以前的尝试主要集中在修改 Transformer 中的自我关注模块。然而，这些努力都没有开发出一个完整的双曲变压器。这主要是由于: (i)双曲空间中缺乏定义明确的模块，包括线性映射层、层规范层、激活函数、辍学操作等。(ii)现有双曲自我注意模块的二次时间复杂性。为了应对这些挑战，我们提出了一种基于洛伦兹双曲几何模型的新型双曲变压器。在 Hypformer，我们介绍了两个基本模块，它们定义了双曲空间变压器的基本模块。此外，我们还在双曲空间中开发了一种线性自我注意机制，使得双曲变压器第一次能够处理数十亿比例的图形数据和长序列输入。我们的实验结果证实了该方法在不同数据集之间的有效性和效率，证明了该方法作为大规模数据表示和大型模型的有效和可扩展的解决方案的潜力。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Hypformer:+Exploring+Efficient+Transformer+Fully+in+Hyperbolic+Space)|0| +|[Predicting Cascading Failures with a Hyperparametric Diffusion Model](https://doi.org/10.1145/3637528.3672048)|Bin Xiang, Bogdan Cautis, Xiaokui Xiao, Olga Mula, Dusit Niyato, Laks V. S. Lakshmanan|National University of Singapore, Singapore, Singapore; Eindhoven University of Technology, Eindhoven, Netherlands; CNRSCREATE, Singapore, Singapore; University of British Columbia, Vancouver, Canada; Nanyang Technological University, Singapore, Singapore; University of Paris-Saclay, CNRS LISN, Saclay, France|In this paper, we study cascading failures in power grids through the lens ofinformation diffusion models. Similar to the spread of rumors or influence inan online social network, it has been observed that failures (outages) in apower grid can spread contagiously, driven by viral spread mechanisms. Weemploy a stochastic diffusion model that is Markovian (memoryless) and local(the activation of one node, i.e., transmission line, can only be caused by itsneighbors). Our model integrates viral diffusion principles with physics-basedconcepts, by correlating the diffusion weights (contagion probabilities betweentransmission lines) with the hyperparametric Information Cascades (IC) model.We show that this diffusion model can be learned from traces of cascadingfailures, enabling accurate modeling and prediction of failure propagation.This approach facilitates actionable information through well-understood andefficient graph analysis methods and graph diffusion simulations. Furthermore,by leveraging the hyperparametric model, we can predict diffusion and mitigatethe risks of cascading failures even in unseen grid configurations, whereasexisting methods falter due to a lack of training data. Extensive experimentsbased on a benchmark power grid and simulations therein show that our approacheffectively captures the failure diffusion phenomena and guides decisions tostrengthen the grid, reducing the risk of large-scale cascading failures.Additionally, we characterize our model's sample complexity, improving upon theexisting bound.|本文通过信息扩散模型的透镜，研究了电网中的连锁故障。与在线社交网络中谣言或影响的传播类似，已经观察到电网故障(停电)可以通过病毒传播机制传染。我们采用马尔可夫(无记忆)和局部(一个节点的激活，即传输线，只能由它的邻居引起)的随机扩散模型。我们的模型通过将扩散权重(传输线之间的传染概率)与超参数信息级联(IC)模型相关联，将病毒扩散原理与基于物理的概念结合起来。我们表明，这种扩散模型可以从级联故障的痕迹中学习，从而能够准确建模和预测故障传播。这种方法通过充分理解和有效的图分析方法和图扩散模拟促进了可操作的信息。此外，通过利用超参数模型，我们可以预测扩散和减轻级联故障的风险，即使在看不见的网格配置，而现有的方法由于缺乏训练数据而步履蹒跚。基于基准电网的大量实验和仿真表明，我们的方法有效地捕获了故障扩散现象，并指导决策加强电网，减少大规模连锁故障的风险。此外，我们描述了我们的模型的样本复杂性，改进了现有的界限。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Predicting+Cascading+Failures+with+a+Hyperparametric+Diffusion+Model)|0| +|[FRNet: Frequency-based Rotation Network for Long-term Time Series Forecasting](https://doi.org/10.1145/3637528.3671713)|Xinyu Zhang, Shanshan Feng, Jianghong Ma, Huiwei Lin, Xutao Li, Yunming Ye, Fan Li, Yew Soon Ong|; Harbin Institute of Technology, Shenzhen, China; Hong Kong Polytechnic University, Hong Kong, China; Centre for Frontier AI Research, ASTAR, Nanyang Technological University, Singapore, Singapore|Long-term time series forecasting (LTSF) aims to predict future values for a long time based on historical data. The period term is an essential component of the time series, which is complex yet important for LTSF. Although existing studies have achieved promising results, they still have limitations in modeling dynamic complicated periods. Most studies only focus on static periods with fixed time steps, while very few studies attempt to capture dynamic periods in the time domain. In this paper, we dissect the original time series in time and frequency domains and empirically find that changes in periods are more easily captured and quantified in the frequency domain. Based on this observation, we propose to explore dynamic period features using rotation in the frequency domain. To this end, we develop the frequency-based rotation network (FRNet), a novel LTSF method to effectively capture the features of the dynamic complicated periods. FRNet decomposes the original time series into period and trend components. Based on the complex-valued linear networks, it leverages a period frequency rotation module to predict the period component and a patch frequency rotation module to predict the trend component, respectively. Extensive experiments on seven real-world datasets consistently demonstrate the superiority of FRNet over various state-of-the-art methods. The source code is available at https://github.com/SiriZhang45/FRNet.|长期时间序列预测(LTSF)的目的是根据历史数据预测未来很长一段时间内的价值。周期项是时间序列的一个重要组成部分，对于长期稳定因子来说，时间序列是复杂而重要的。虽然现有的研究已经取得了很好的成果，但是在动态复杂周期的建模方面仍然存在一定的局限性。大多数研究只关注固定时间步长的静态周期，很少有研究试图捕捉时间域中的动态周期。本文从时间和频率两个方面对原始时间序列进行了剖析，发现在频率域更容易捕捉和量化周期的变化。在此基础上，我们提出了在频域中利用旋转来探索动态周期特征的方法。为此，我们发展了基于频率的旋转网络(FRNet) ，一种新的 LTSF 方法来有效地捕捉动态复杂周期的特征。FRNet 将原始时间序列分解为周期分量和趋势分量。在复值线性网络的基础上，利用周期频率旋转模块预测周期分量，利用补丁频率旋转模块预测趋势分量。在七个真实世界数据集上的大量实验一致地证明了 FRNet 相对于各种最先进的方法的优越性。源代码可在 https://github.com/sirizhang45/frnet 下载。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=FRNet:+Frequency-based+Rotation+Network+for+Long-term+Time+Series+Forecasting)|0| +|[Hypformer: Exploring Efficient Transformer Fully in Hyperbolic Space](https://doi.org/10.1145/3637528.3672039)|Menglin Yang, Harshit Verma, Delvin Ce Zhang, Jiahong Liu, Irwin King, Rex Ying|The Chinese University of Hong Kong, Hong Kong, China; Yale University, New Haven, CT, USA; Birla Institute of Technology and Science, Hyderabad, India|Hyperbolic geometry have shown significant potential in modeling complex structured data, particularly those with underlying tree-like and hierarchical structures. Despite the impressive performance of various hyperbolic neural networks across numerous domains, research on adapting the Transformer to hyperbolic space remains limited. Previous attempts have mainly focused on modifying self-attention modules in the Transformer. However, these efforts have fallen short of developing a complete hyperbolic Transformer. This stems primarily from: (i) the absence of well-defined modules in hyperbolic space, including linear transformation layers, LayerNorm layers, activation functions, dropout operations, etc. (ii) the quadratic time complexity of the existing hyperbolic self-attention module w.r.t the number of input tokens, which hinders its scalability. To address these challenges, we propose, Hypformer, a novel hyperbolic Transformer based on the Lorentz model of hyperbolic geometry. In Hypformer, we introduce two foundational blocks that define the essential modules of the Transformer in hyperbolic space. Furthermore, we develop a linear self-attention mechanism in hyperbolic space, enabling hyperbolic Transformer to process billion-scale graph data and long-sequence inputs for the first time. Our experimental results confirm the effectiveness and efficiency of \method across various datasets, demonstrating its potential as an effective and scalable solution for large-scale data representation and large models.|双曲几何在建模复杂的结构化数据方面显示出巨大的潜力，特别是那些具有树状结构和层次结构的数据。尽管各种双曲神经网络在许多领域都有着令人印象深刻的性能，但是关于如何使变压器适应双曲空间的研究仍然很有限。以前的尝试主要集中在修改 Transformer 中的自我关注模块。然而，这些努力都没有开发出一个完整的双曲变压器。这主要是由于: (i)双曲空间中缺乏定义明确的模块，包括线性映射层、层规范层、激活函数、辍学操作等。(ii)现有双曲自我注意模块的二次时间复杂性。为了应对这些挑战，我们提出了一种基于洛伦兹双曲几何模型的新型双曲变压器。在 Hypformer，我们介绍了两个基本模块，它们定义了双曲空间变压器的基本模块。此外，我们还在双曲空间中开发了一种线性自我注意机制，使得双曲变压器第一次能够处理数十亿比例的图形数据和长序列输入。我们的实验结果证实了该方法在不同数据集之间的有效性和效率，证明了该方法作为大规模数据表示和大型模型的有效和可扩展的解决方案的潜力。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Hypformer:+Exploring+Efficient+Transformer+Fully+in+Hyperbolic+Space)|0| |[Practical Single Domain Generalization via Training-time and Test-time Learning](https://doi.org/10.1145/3637528.3671806)|Shuai Yang, Zhen Zhang, Lichuan Gu||Single domain generalization aims to learn a model that generalizes well to unseen target domains by using a related source domain. However, most existing methods only focus on improving the generalization performance of the model during training, making it difficult to achieve satisfactory performance when deployed in the target domain with large domain shifts. In this paper, we propose a Practical Single Domain Generalization (PSDG) method, which first leverages the knowledge in a source domain to establish a model with good generalization ability in the training phase, and subsequently updates the model to adapt to target domain data using knowledge in the unlabeled target domain during the testing phase. Specifically, during training, PSDG leverages a newly proposed style (e.g., background features) generator named StyIN to generate novel domain data. Moreover, PSDG introduces style-diversity regularization to constantly synthesize distinct styles to expand the coverage of training data, and introduces object-consistency regularization to capture consistency between the currently generated data and the original data, making the model filter style knowledge during training. During testing, PSDG uses a sample-aware and sharpness-aware minimization method to seek for a flat entropy minimum surface for further model optimization by using the knowledge in the unlabeled target domain. Using three real-world datasets the experiments have demonstrated the effectiveness of PSDG, in comparison with several state-of-the-art methods.|单域泛化的目的是学习一个模型，通过使用一个相关的源域很好地泛化到不可见的目标域。然而，现有的方法大多局限于提高模型在训练过程中的泛化性能，这使得在目标区域部署时，当目标区域移动较大时，很难获得令人满意的性能。本文提出了一种实用的单领域泛化(PSDG)方法，该方法首先利用源领域的知识在训练阶段建立一个具有良好泛化能力的模型，然后在测试阶段利用未标记目标领域的知识对模型进行更新以适应目标领域数据。具体来说，在培训期间，PSDG 利用一个新提出的样式(例如，背景特性)生成器 StyIN 来生成新的域数据。此外，PSDG 引入样式多样性正则化技术，不断综合不同的样式，扩大训练数据的覆盖范围; 引入对象一致性正则化技术，捕捉当前生成的数据与原始数据之间的一致性，使模型滤波样式知识在训练过程中得到充分利用。在测试过程中，PSDG 利用未标记目标域中的知识，采用样本感知和锐度感知的最小化方法寻找平坦熵最小曲面，以进一步优化模型。利用三个真实世界的数据集，实验证明了 PSDG 的有效性，并与几种最先进的方法进行了比较。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Practical+Single+Domain+Generalization+via+Training-time+and+Test-time+Learning)|0| -|[Rethinking Order Dispatching in Online Ride-Hailing Platforms](https://doi.org/10.1145/3637528.3672028)|Zhaoxing Yang, Haiming Jin, Guiyun Fan, Min Lu, Yiran Liu, Xinlang Yue, Hao Pan, Zhe Xu, Guobin Wu, Qun Li, Xiaotong Wang, Jiecheng Guo|Didi Chuxing, Beijing, China; Shanghai Jiao Tong University, Shanghai, China|Achieving optimal order dispatching has been a long-standing challenge for online ride-hailing platforms. Early methods would make shortsighted matchings as they only consider order prices alone as the edge weights in the driver-order bipartite graph, thus harming the platform's revenue. To address this problem, recent works evaluate the value of the order's destination region to be the long-term income a driver could obtain in average in such region and incorporate it into the order's edge weight to influence the matching results. However, they often result in insufficient driver supplies in many regions, as the values evaluated in different regions vary greatly, mainly because the impact of one region's value on the future number of drivers and revenue in other regions is overlooked. This paper models such impact within a cooperative Markov game, which involves each value's impact over the platform's revenue with the goal to find the optimal region values for revenue maximization. To solve this game, our work proposes a novelgoal-reaching collaboration (GRC) algorithm that realizes credit assignment from a novel goal-reaching perspective, addressing the difficulty for accurate credit assignment with large-scale agents of previous methods and resolving the conflict between credit assignment and offline reinforcement learning. Specifically, during training, GRC predicts the city's future state through an environment model and utilizes a scoring model to rate the predicted states to judge their levels of profitability, where high-scoring states are regarded as the goal states. Then, the policies in the game are updated to promote the city to stay in the goal states for as long as possible. To evaluate GRC, we deploy a baseline policy online in several cities for three weeks to collect real-world dataset. Training and testing results on the collected dataset indicate that our GRC consistently outperforms the baselines in different cities and peak periods.|实现最优订单调度一直是在线叫车平台面临的一个长期挑战。早期的方法只考虑订单价格作为驱动订单二部图中的边缘权重，会造成目光短浅的匹配，从而损害平台的收益。为了解决这个问题，最近的工作评估了订单的目的地区域的价值是驱动程序可以获得的长期收入在这些区域的平均值，并将其纳入订单的边缘权重，以影响匹配结果。然而，它们常常导致许多地区的驱动力供应不足，因为在不同地区评估的价值差异很大，主要是因为一个地区的价值对未来驱动力数量和其他地区的收入的影响被忽视。本文在合作马尔可夫博弈中建立了这种影响的模型，其中涉及到每个价值对平台收入的影响，目的是找到收入最大化的最优区域值。为了解决这个问题，我们的工作提出了一个新的目标达成协作(gc)算法，从一个新的目标达成的角度来实现信用分配，解决了以前方法的大规模代理人准确信用分配的困难，并解决了信用分配和离线强化学习之间的冲突。具体来说，在培训期间，GRC 通过一个环境模型预测城市的未来状态，并利用评分模型对预测状态进行评分，以判断其盈利水平，其中得分较高的状态被视为目标状态。然后，游戏中的政策被更新，以促使城市尽可能长时间地停留在目标状态。为了评估 GRC，我们在几个城市中部署了一个为期三周的在线基线策略来收集真实世界的数据集。对所收集数据集的训练和测试结果表明，我们的 GRC 在不同城市和高峰期的表现始终优于基线。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Rethinking+Order+Dispatching+in+Online+Ride-Hailing+Platforms)|0| +|[Rethinking Order Dispatching in Online Ride-Hailing Platforms](https://doi.org/10.1145/3637528.3672028)|Zhaoxing Yang, Haiming Jin, Guiyun Fan, Min Lu, Yiran Liu, Xinlang Yue, Hao Pan, Zhe Xu, Guobin Wu, Qun Li, Xiaotong Wang, Jiecheng Guo|Shanghai Jiao Tong University, Shanghai, China; Didi Chuxing, Beijing, China|Achieving optimal order dispatching has been a long-standing challenge for online ride-hailing platforms. Early methods would make shortsighted matchings as they only consider order prices alone as the edge weights in the driver-order bipartite graph, thus harming the platform's revenue. To address this problem, recent works evaluate the value of the order's destination region to be the long-term income a driver could obtain in average in such region and incorporate it into the order's edge weight to influence the matching results. However, they often result in insufficient driver supplies in many regions, as the values evaluated in different regions vary greatly, mainly because the impact of one region's value on the future number of drivers and revenue in other regions is overlooked. This paper models such impact within a cooperative Markov game, which involves each value's impact over the platform's revenue with the goal to find the optimal region values for revenue maximization. To solve this game, our work proposes a novelgoal-reaching collaboration (GRC) algorithm that realizes credit assignment from a novel goal-reaching perspective, addressing the difficulty for accurate credit assignment with large-scale agents of previous methods and resolving the conflict between credit assignment and offline reinforcement learning. Specifically, during training, GRC predicts the city's future state through an environment model and utilizes a scoring model to rate the predicted states to judge their levels of profitability, where high-scoring states are regarded as the goal states. Then, the policies in the game are updated to promote the city to stay in the goal states for as long as possible. To evaluate GRC, we deploy a baseline policy online in several cities for three weeks to collect real-world dataset. Training and testing results on the collected dataset indicate that our GRC consistently outperforms the baselines in different cities and peak periods.|实现最优订单调度一直是在线叫车平台面临的一个长期挑战。早期的方法只考虑订单价格作为驱动订单二部图中的边缘权重，会造成目光短浅的匹配，从而损害平台的收益。为了解决这个问题，最近的工作评估了订单的目的地区域的价值是驱动程序可以获得的长期收入在这些区域的平均值，并将其纳入订单的边缘权重，以影响匹配结果。然而，它们常常导致许多地区的驱动力供应不足，因为在不同地区评估的价值差异很大，主要是因为一个地区的价值对未来驱动力数量和其他地区的收入的影响被忽视。本文在合作马尔可夫博弈中建立了这种影响的模型，其中涉及到每个价值对平台收入的影响，目的是找到收入最大化的最优区域值。为了解决这个问题，我们的工作提出了一个新的目标达成协作(gc)算法，从一个新的目标达成的角度来实现信用分配，解决了以前方法的大规模代理人准确信用分配的困难，并解决了信用分配和离线强化学习之间的冲突。具体来说，在培训期间，GRC 通过一个环境模型预测城市的未来状态，并利用评分模型对预测状态进行评分，以判断其盈利水平，其中得分较高的状态被视为目标状态。然后，游戏中的政策被更新，以促使城市尽可能长时间地停留在目标状态。为了评估 GRC，我们在几个城市中部署了一个为期三周的在线基线策略来收集真实世界的数据集。对所收集数据集的训练和测试结果表明，我们的 GRC 在不同城市和高峰期的表现始终优于基线。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Rethinking+Order+Dispatching+in+Online+Ride-Hailing+Platforms)|0| |[BoKA: Bayesian Optimization based Knowledge Amalgamation for Multi-unknown-domain Text Classification](https://doi.org/10.1145/3637528.3671963)|Linzhu Yu, Huan Li, Ke Chen, Lidan Shou|The State Key Laboratory of Blockchain and Data Security, Zhejiang University, Hangzhou, China|With breakthroughs in pretrained language models, a large number of finetuned models specialized in distinct domains have surfaced online. Yet, when faced with a fresh dataset covering multiple (sub)domains, their performance might degrade. Reusing these available finetuned models to train a new model is a more feasible solution than the finetuning method that demands extensive manual labeling. Knowledge Amalgamation (KA) is such a model reusing technique, which derives a new model (termed student model) by amalgamating those trained models (termed teacher models) tailored for distinct domains, bypassing the need for manual labeling. However, when the domains of text samples are unknown, selecting a number of appropriate teacher models (simply called a combination) for reuse becomes complicated. To learn an accurate student model, the classical KA method resorts to manual selections, a process both tedious and inefficient. Our study pioneers the automation of this combination selection process for KA in the fundamental text classification task, an area previously unexplored. In this paper, we introduce BoKA : an automatic knowledge amalgamation framework for identifying a combination that can learn a superior student model without human labor. Through the lens of Bayesian optimization, BoKA iteratively samples a subset of possible combinations for amalgamation instead of manual selections. Furthermore, we introduce a novel KA method tailored for text classification, which guides the student model using both soft and pseudo-hard labels from the teacher models when their predictions are closely aligned; in cases of significant disagreement, it uses randomly generated labels. Experiments on two public multi-domain datasets show that BoKA achieves remarkable efficiency by sampling only up to 5.5% of all potential combinations. Moreover, BoKA is capable of matching or even surpassing leading zero-shot large language models, despite having dozens of times fewer parameters.|随着预先训练语言模型的突破，大量在不同领域专门的微调模型已经在网上浮出水面。然而，当面对覆盖多个(子)域的新数据集时，它们的性能可能会下降。与需要大量人工标记的微调方法相比，重用这些已有的微调模型来训练新模型是一种更可行的解决方案。知识合并(KA)就是这样一种模型重用技术，它通过合并那些为不同领域量身定制的训练模型(称为教师模型) ，绕过人工标记的需要，得到一个新的模型(称为学生模型)。然而，当文本示例的领域是未知的时候，为重用选择一些合适的教师模型(简称为组合)就变得复杂了。为了学习一个准确的学生模型，经典的 KA 方法采用手工选择，这是一个既繁琐又低效的过程。我们的研究开创了自动化的组合选择过程的 KA 在基本文本分类任务，一个领域以前未被探索。在本文中，我们介绍了 BoKA: 一个自动知识融合框架，以确定一个组合，可以学习一个优秀的学生模型没有人工劳动。通过贝叶斯优化的透镜，BoKA 迭代抽样一个子集的可能的组合合并，而不是手工选择。此外，我们还引入了一种适合文本分类的新的 KA 方法，当教师模型的预测紧密一致时，它使用软标签和伪硬标签来引导学生模型; 在出现重大分歧的情况下，它使用随机生成的标签。在两个公开的多领域数据集上进行的实验表明，BoKA 算法只对所有潜在组合的5.5% 进行抽样，就取得了显著的效果。此外，BoKA 能够匹配甚至超越领先的大型语言模型，尽管它的参数少了几十倍。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=BoKA:+Bayesian+Optimization+based+Knowledge+Amalgamation+for+Multi-unknown-domain+Text+Classification)|0| -|[Diverse Intra- and Inter-Domain Activity Style Fusion for Cross-Person Generalization in Activity Recognition](https://doi.org/10.1145/3637528.3671828)|Junru Zhang, Lang Feng, Zhidan Liu, Yuhan Wu, Yang He, Yabo Dong, Duanqing Xu|Zhejiang University, Hangzhou, Zhejiang, China; Shenzhen University, Shenzhen, China; Zhejiang University, Hangzhou, China|Existing domain generalization (DG) methods for cross-person generalization tasks often face challenges in capturing intra- and inter-domain style diversity, resulting in domain gaps with the target domain. In this study, we explore a novel perspective to tackle this problem, a process conceptualized as domain padding. This proposal aims to enrich the domain diversity by synthesizing intra- and inter-domain style data while maintaining robustness to class labels. We instantiate this concept using a conditional diffusion model and introduce a style-fused sampling strategy to enhance data generation diversity. In contrast to traditional condition-guided sampling, our style-fused sampling strategy allows for the flexible use of one or more random styles to guide data synthesis. This feature presents a notable advancement: it allows for the maximum utilization of possible permutations and combinations among existing styles to generate a broad spectrum of new style instances. Empirical evaluations on a broad range of datasets demonstrate that our generated data achieves remarkable diversity within the domain space. Both intra- and inter-domain generated data have proven to be significant and valuable, contributing to varying degrees of performance enhancements. Notably, our approach outperforms state-of-the-art DG methods in all human activity recognition tasks.|现有的跨人员泛化任务的域泛化方法在捕获域内和域间风格多样性时往往面临挑战，导致与目标域的域差异。在这项研究中，我们探索了一个新的视角来解决这个问题，一个过程概念化为域填充。该方案旨在通过合成域内和域间样式数据来丰富域多样性，同时保持对类别标签的鲁棒性。我们使用一个条件扩散模型来实例化这个概念，并引入一个样式融合抽样策略来增强数据生成的多样性。与传统的条件引导抽样不同，我们的样式融合抽样策略允许灵活地使用一种或多种随机样式来指导数据合成。这个特性提供了一个显著的进步: 它允许最大限度地利用现有样式之间可能的排列和组合，以生成广泛的新样式实例。对大范围数据集的实证评估表明，我们生成的数据在领域空间内实现了显著的多样性。域内和域间生成的数据都被证明是重要和有价值的，有助于不同程度的性能增强。值得注意的是，我们的方法在所有人类活动识别任务中都优于最先进的 DG 方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Diverse+Intra-+and+Inter-Domain+Activity+Style+Fusion+for+Cross-Person+Generalization+in+Activity+Recognition)|0| +|[Diverse Intra- and Inter-Domain Activity Style Fusion for Cross-Person Generalization in Activity Recognition](https://doi.org/10.1145/3637528.3671828)|Junru Zhang, Lang Feng, Zhidan Liu, Yuhan Wu, Yang He, Yabo Dong, Duanqing Xu|Shenzhen University, Shenzhen, China; Zhejiang University, Hangzhou, China; Zhejiang University, Hangzhou, Zhejiang, China|Existing domain generalization (DG) methods for cross-person generalization tasks often face challenges in capturing intra- and inter-domain style diversity, resulting in domain gaps with the target domain. In this study, we explore a novel perspective to tackle this problem, a process conceptualized as domain padding. This proposal aims to enrich the domain diversity by synthesizing intra- and inter-domain style data while maintaining robustness to class labels. We instantiate this concept using a conditional diffusion model and introduce a style-fused sampling strategy to enhance data generation diversity. In contrast to traditional condition-guided sampling, our style-fused sampling strategy allows for the flexible use of one or more random styles to guide data synthesis. This feature presents a notable advancement: it allows for the maximum utilization of possible permutations and combinations among existing styles to generate a broad spectrum of new style instances. Empirical evaluations on a broad range of datasets demonstrate that our generated data achieves remarkable diversity within the domain space. Both intra- and inter-domain generated data have proven to be significant and valuable, contributing to varying degrees of performance enhancements. Notably, our approach outperforms state-of-the-art DG methods in all human activity recognition tasks.|现有的跨人员泛化任务的域泛化方法在捕获域内和域间风格多样性时往往面临挑战，导致与目标域的域差异。在这项研究中，我们探索了一个新的视角来解决这个问题，一个过程概念化为域填充。该方案旨在通过合成域内和域间样式数据来丰富域多样性，同时保持对类别标签的鲁棒性。我们使用一个条件扩散模型来实例化这个概念，并引入一个样式融合抽样策略来增强数据生成的多样性。与传统的条件引导抽样不同，我们的样式融合抽样策略允许灵活地使用一种或多种随机样式来指导数据合成。这个特性提供了一个显著的进步: 它允许最大限度地利用现有样式之间可能的排列和组合，以生成广泛的新样式实例。对大范围数据集的实证评估表明，我们生成的数据在领域空间内实现了显著的多样性。域内和域间生成的数据都被证明是重要和有价值的，有助于不同程度的性能增强。值得注意的是，我们的方法在所有人类活动识别任务中都优于最先进的 DG 方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Diverse+Intra-+and+Inter-Domain+Activity+Style+Fusion+for+Cross-Person+Generalization+in+Activity+Recognition)|0| |[Knowledge Distillation with Perturbed Loss: From a Vanilla Teacher to a Proxy Teacher](https://doi.org/10.1145/3637528.3671851)|Rongzhi Zhang, Jiaming Shen, Tianqi Liu, Jialu Liu, Michael Bendersky, Marc Najork, Chao Zhang|Google, New York City, NY, USA; Georgia Institute of Technology, Atlanta, GA, USA|Knowledge distillation is a popular technique to transfer knowledge from a large teacher model to a small student model. Typically, the student learns to imitate the teacher by minimizing the KL divergence of its output distribution with the teacher's output distribution. In this work, we argue that such a learning objective is sub-optimal because there exists a discrepancy between the teacher's output distribution and the ground truth label distribution. Therefore, forcing the student to blindly imitate the unreliable teacher output distribution leads to inferior performance. To this end, we propose a novel knowledge distillation objective PTLoss by first representing the vanilla KL-based distillation loss function via a Maclaurin series and then perturbing the leading-order terms in this series. This perturbed loss implicitly transforms the original teacher into a proxy teacher with a distribution closer to the ground truth distribution. We establish the theoretical connection between this "distribution closeness'' and the student model generalizability, which enables us to select the PTLoss's perturbation coefficients in a principled way. Extensive experiments on six public benchmark datasets demonstrate the effectiveness of PTLoss with teachers of different scales.|知识提取是将知识从大型教师模型转化为小型学生模型的一种流行技术。通常情况下，学生学习模仿教师通过最小化其输出分布的 KL 散度与教师的输出分布。在本研究中，我们认为这样的学习目标是次优的，因为在教师的输出分布和地面真理标签分布之间存在差异。因此，迫使学生盲目模仿不可靠的教师产出分布，导致学习成绩不佳。为此，我们提出了一种新的知识蒸馏目标 PT损失，首先通过 Maclaurin 级数表示基于香草 KL 的蒸馏损失函数，然后扰动该级数的先导项。这种不安的损失隐含地将原来的教师转变为代理教师，代理教师的分布更接近于地面真理分布。我们建立了这种“分布接近度”与学生模型概化性之间的理论联系，从而使我们能够以一种原则性的方式选择 PT损失的扰动系数。在六个公共基准数据集上进行的大量实验证明了 PTloss 在不同尺度教师中的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Knowledge+Distillation+with+Perturbed+Loss:+From+a+Vanilla+Teacher+to+a+Proxy+Teacher)|0| |[Joint Auction in the Online Advertising Market](https://doi.org/10.1145/3637528.3671746)|Zhen Zhang, Weian Li, Yahui Lei, Bingzhe Wang, Zhicheng Zhang, Qi Qi, Qiang Liu, Xingxing Wang|Meituan Inc., Beijing, China; School of Software, Shandong University, Jinan, China; Gaoling School of Artificial Intelligence, Renmin University of China, Beijing, China|Online advertising is a primary source of income for e-commerce platforms. In the current advertising pattern, the oriented targets are the online store owners who are willing to pay extra fees to enhance the position of their stores. On the other hand, brand suppliers are also desirable to advertise their products in stores to boost brand sales. However, the currently used advertising mode cannot satisfy the demand of both stores and brand suppliers simultaneously. To address this, we innovatively propose a joint advertising model termed ''Joint Auction'', allowing brand suppliers and stores to collaboratively bid for advertising slots, catering to both their needs. However, conventional advertising auction mechanisms are not suitable for this novel scenario. In this paper, we propose JRegNet, a neural network architecture for the optimal joint auction design, to generate mechanisms that can achieve the optimal revenue and guarantee (near-)dominant strategy incentive compatibility and individual rationality. Finally, multiple experiments are conducted on synthetic and real data to demonstrate that our proposed joint auction significantly improves platform's revenue compared to the known baselines.|在线广告是电子商务平台的主要收入来源。在当前的广告模式下，网络商店的定位目标是那些愿意支付额外费用来提高自己店铺地位的网络商店老板。另一方面，品牌供应商也希望在商店里为他们的产品做广告，以促进品牌销售。然而，目前使用的广告模式并不能同时满足商店和品牌供应商的需求。为了解决这个问题，我们创新性地提出了一种名为“联合拍卖”的联合广告模式，允许品牌供应商和商店合作竞标广告位，以满足他们的需求。然而，传统的广告拍卖机制并不适合这种新颖的场景。在这篇文章中，我们提出了一个用于最优联合拍卖设计的神经网络结构—— JregNet，来生成能够实现最优收益和保证(近)主导策略激励相容和个人理性的机制。最后，通过对合成数据和实际数据的多重实验表明，与已知基线相比，本文提出的联合拍卖方案显著提高了平台的收益。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Joint+Auction+in+the+Online+Advertising+Market)|0| -|[Long-Term Vessel Trajectory Imputation with Physics-Guided Diffusion Probabilistic Model](https://doi.org/10.1145/3637528.3672086)|Zhiwen Zhang, Zipei Fan, Zewu Lv, Xuan Song, Ryosuke Shibasaki|; School of Artificial Intelligence, Jilin University, Changchun, China; Research & Development Department, LocationMind Inc., Tokyo, Japan|Maritime traffic management increasingly relies on vessel position information provided by terrestrial and satellite networks of the Automatic Identification System (AIS). Unfortunately, the problem of missing AIS data can lead to long-term gaps in vessel trajectory, raising corresponding security concerns regarding collision risks and illicit activities. Existing imputation approaches are often constrained by vehicle-based low-sampling trajectories, hindering their ability to address unique characteristics of maritime transportation systems and long-term missing scenarios. To tackle these challenges, we propose a novel generative framework for long-term vessel trajectory imputation. Our framework considers irregular tracks of vessels, which differ from those of cars due to the absence of a structured road network, and ensures the continuity of multi-point imputed trajectories. Specifically, we first utilize a pre-trained trajectory embedding block to capture patterns of vessel movements. Subsequently, we introduce a diffusion-based model for generating missing trajectories, where observed trajectory modeling with transformer encoding architecture and embeddings of both historical vessel trajectory and external factors serve as conditional information. In particular, we design a physics-guided discriminator in the training stage, which imposes kinematic constraints between locations and angles to improve the continuity of the imputed trajectories. Comprehensive experiments and analysis on a real-world AIS dataset confirm the effectiveness of our proposed approach.|海上交通管理越来越依赖自动识别系统地面和卫星网络提供的船只位置信息。遗憾的是，缺少自动识别系统数据的问题可能导致船舶航线的长期空白，从而引起对碰撞风险和非法活动的相应安全关切。现有的估算方法往往受到基于车辆的低采样轨迹的限制，妨碍了它们处理海上运输系统独特特征和长期缺失情景的能力。为了应对这些挑战，我们提出了一个新的长期船舶轨迹插补生成框架。我们的框架考虑了由于缺乏结构化道路网而不同于汽车的不规则船舶轨迹，并确保了多点估算轨迹的连续性。具体来说，我们首先利用一个预先训练的轨迹嵌入块来捕捉血管运动的模式。随后，我们介绍了一个基于扩散的缺失轨迹生成模型，其中观测轨迹建模与变压器编码结构和嵌入的历史船舶轨迹和外部因素作为条件信息。特别地，我们在训练阶段设计了一个物理导引的鉴别器，它在位置和角度之间施加运动学约束，以改善估计轨迹的连续性。通过对一个实际 AIS 数据集的全面实验和分析，证实了该方法的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Long-Term+Vessel+Trajectory+Imputation+with+Physics-Guided+Diffusion+Probabilistic+Model)|0| +|[Long-Term Vessel Trajectory Imputation with Physics-Guided Diffusion Probabilistic Model](https://doi.org/10.1145/3637528.3672086)|Zhiwen Zhang, Zipei Fan, Zewu Lv, Xuan Song, Ryosuke Shibasaki|; Research & Development Department, LocationMind Inc., Tokyo, Japan; School of Artificial Intelligence, Jilin University, Changchun, China|Maritime traffic management increasingly relies on vessel position information provided by terrestrial and satellite networks of the Automatic Identification System (AIS). Unfortunately, the problem of missing AIS data can lead to long-term gaps in vessel trajectory, raising corresponding security concerns regarding collision risks and illicit activities. Existing imputation approaches are often constrained by vehicle-based low-sampling trajectories, hindering their ability to address unique characteristics of maritime transportation systems and long-term missing scenarios. To tackle these challenges, we propose a novel generative framework for long-term vessel trajectory imputation. Our framework considers irregular tracks of vessels, which differ from those of cars due to the absence of a structured road network, and ensures the continuity of multi-point imputed trajectories. Specifically, we first utilize a pre-trained trajectory embedding block to capture patterns of vessel movements. Subsequently, we introduce a diffusion-based model for generating missing trajectories, where observed trajectory modeling with transformer encoding architecture and embeddings of both historical vessel trajectory and external factors serve as conditional information. In particular, we design a physics-guided discriminator in the training stage, which imposes kinematic constraints between locations and angles to improve the continuity of the imputed trajectories. Comprehensive experiments and analysis on a real-world AIS dataset confirm the effectiveness of our proposed approach.|海上交通管理越来越依赖自动识别系统地面和卫星网络提供的船只位置信息。遗憾的是，缺少自动识别系统数据的问题可能导致船舶航线的长期空白，从而引起对碰撞风险和非法活动的相应安全关切。现有的估算方法往往受到基于车辆的低采样轨迹的限制，妨碍了它们处理海上运输系统独特特征和长期缺失情景的能力。为了应对这些挑战，我们提出了一个新的长期船舶轨迹插补生成框架。我们的框架考虑了由于缺乏结构化道路网而不同于汽车的不规则船舶轨迹，并确保了多点估算轨迹的连续性。具体来说，我们首先利用一个预先训练的轨迹嵌入块来捕捉血管运动的模式。随后，我们介绍了一个基于扩散的缺失轨迹生成模型，其中观测轨迹建模与变压器编码结构和嵌入的历史船舶轨迹和外部因素作为条件信息。特别地，我们在训练阶段设计了一个物理导引的鉴别器，它在位置和角度之间施加运动学约束，以改善估计轨迹的连续性。通过对一个实际 AIS 数据集的全面实验和分析，证实了该方法的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Long-Term+Vessel+Trajectory+Imputation+with+Physics-Guided+Diffusion+Probabilistic+Model)|0| |[All in One and One for All: A Simple yet Effective Method towards Cross-domain Graph Pretraining](https://doi.org/10.1145/3637528.3671913)|Haihong Zhao, Aochuan Chen, Xiangguo Sun, Hong Cheng, Jia Li||Large Language Models (LLMs) have revolutionized the fields of computer vision (CV) and natural language processing (NLP). One of the most notable advancements of LLMs is that a single model is trained on vast and diverse datasets spanning multiple domains -- a paradigm we term `All in One'. This methodology empowers LLMs with super generalization capabilities, facilitating an encompassing comprehension of varied data distributions. Leveraging these capabilities, a single LLM demonstrates remarkable versatility across a variety of domains -- a paradigm we term `One for All'. However, applying this idea to the graph field remains a formidable challenge, with cross-domain pretraining often resulting in negative transfer. This issue is particularly important in few-shot learning scenarios, where the paucity of training data necessitates the incorporation of external knowledge sources. In response to this challenge, we propose a novel approach called Graph COordinators for PrEtraining (GCOPE), that harnesses the underlying commonalities across diverse graph datasets to enhance few-shot learning. Our novel methodology involves a unification framework that amalgamates disparate graph datasets during the pretraining phase to distill and transfer meaningful knowledge to target tasks. Extensive experiments across multiple graph datasets demonstrate the superior efficacy of our approach. By successfully leveraging the synergistic potential of multiple graph datasets for pretraining, our work stands as a pioneering contribution to the realm of graph foundational model.|大语言模型(LLM)彻底改变了计算机视觉(CV)和自然语言处理(NLP)领域。LLM 最显著的进步之一是，单个模型是在跨越多个领域的大量多样化数据集上进行训练的——我们称之为“一体化”模式。这种方法赋予 LLM 超级泛化能力，促进对各种数据分布的全面理解。利用这些能力，单个 LLM 在多个领域展示了非凡的多功能性——我们称之为“一个为所有人”的范例。然而，将这种思想应用到图论领域仍然是一个巨大的挑战，跨域预训练常常导致负迁移。这个问题在短期学习情况下尤其重要，因为培训数据不足，必须纳入外部知识来源。为了应对这一挑战，我们提出了一种新的方法，称为图形协调器的预训练(GCOPE) ，利用不同的图形数据集的基本共性，以增强少镜头学习。我们的新方法涉及一个统一的框架，在预训练阶段合并不同的图形数据集，以提取和转移有意义的知识到目标任务。跨多个图形数据集的广泛实验证明了我们的方法的优越功效。通过成功地利用多个图形数据集的协同潜力进行预训练，我们的工作成为对图形基础模型领域的开拓性贡献。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=All+in+One+and+One+for+All:+A+Simple+yet+Effective+Method+towards+Cross-domain+Graph+Pretraining)|0| |[Multi-source Unsupervised Domain Adaptation on Graphs with Transferability Modeling](https://doi.org/10.1145/3637528.3671829)|Tianxiang Zhao, Dongsheng Luo, Xiang Zhang, Suhang Wang|The Pennsylvania State University, State College, USA; Florida International University, Miami, USA|In this paper, we tackle a new problem ofmulti-source unsupervised domain adaptation (MSUDA) for graphs, where models trained on annotated source domains need to be transferred to the unsupervised target graph for node classification. Due to the discrepancy in distribution across domains, the key challenge is how to select good source instances and how to adapt the model. Diverse graph structures further complicate this problem, rendering previous MSUDA approaches less effective. In this work, we present the framework Selective Multi-source Adaptation for Graph (SelMAG ), with a graph-modeling-based domain selector, a sub-graph node selector, and a bi-level alignment objective for the adaptation. Concretely, to facilitate the identification of informative source data, the similarity across graphs is disentangled and measured with the transferability of a graph-modeling task set, and we use it as evidence for source domain selection. A node selector is further incorporated to capture the variation in transferability of nodes within the same source domain. To learn invariant features for adaptation, we align the target domain to selected source data both at the embedding space by minimizing the optimal transport distance and at the classification level by distilling the label function. Modules are explicitly learned to select informative source data and conduct the alignment in virtual training splits with a meta-learning strategy. Experimental results on five graph datasets show the effectiveness of the proposed method.|针对图的多源无监督域自适应(MSUDA)问题，在节点分类时，需要将经过注释源域训练的模型转移到无监督目标图上。由于不同领域之间的分布差异，关键的挑战是如何选择好的源实例以及如何适应模型。不同的图形结构使这个问题更加复杂，使得以前的 MSUDA 方法效率更低。本文提出了一种基于图模型的多源图选择自适应(SelMAG)框架，该框架包括一个基于图模型的域选择器、一个子图节点选择器和一个用于自适应的双层对齐目标。具体来说，为了方便信息源数据的识别，利用图建模任务集的可转移性对图间的相似性进行了解密和度量，并将其作为源域选择的依据。进一步合并节点选择器以捕获同一源域内节点可转移性的变化。为了学习自适应的不变特征，我们通过最小化最优传输距离在嵌入空间和通过提取标签函数在分类级别将目标域与选定的源数据对齐。模块被明确地学习来选择信息源数据，并使用元学习策略在虚拟训练中进行对齐。在五个图形数据集上的实验结果表明了该方法的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Multi-source+Unsupervised+Domain+Adaptation+on+Graphs+with+Transferability+Modeling)|0| |[Bridging and Compressing Feature and Semantic Spaces for Robust Graph Neural Networks: An Information Theory Perspective](https://doi.org/10.1145/3637528.3671870)|Luying Zhong, Renjie Lin, Jiayin Li, Shiping Wang, Zheyi Chen|Fujian Normal University, Fuzhou, China; Fuzhou University, Fuzhou, China|The emerging Graph Convolutional Networks (GCNs) have attracted widespread attention in graph learning, due to their good ability of aggregating the information between higher-order neighbors. However, real-world graph data contains high noise and redundancy, making it hard for GCNs to accurately depict the complete relationships between nodes, which seriously degrades the quality of graph representations. Moreover, existing studies commonly ignore the distribution difference between feature and semantic spaces in graphs, causing inferior model generalization. To address these challenges, we propose DIB-RGCN, a novel robust GCN framework, to explore the optimal graph representation with the guidance of the well-designed dual information bottleneck principle. First, we analyze the reasons for distribution differences and theoretically prove that minimal sufficient representations in specific spaces cannot promise optimal performance for downstream tasks. Next, we design new dual channels to regularize feature and semantic spaces, eliminating the sharing of task-irrelevant information between spaces. Different from existing denoising algorithms that adopt a random dropping manner, we innovatively replace potential noisy features and edges with local neighboring representations. This design lowers edge-specific coefficient assignment, alleviating the interference of original representations while retaining graph structures. Further, we maximize the sharing of task-relevant information between feature and semantic spaces to alleviate the difference between them. Using real-world datasets, extensive experiments demonstrate the robustness of the proposed DIB-RGCN, which outperforms state-of-the-art methods on classification tasks.|新兴的图卷积网络(GCNs)以其良好的高阶邻域信息聚合能力，在图学习中引起了广泛的关注。然而，真实世界的图形数据具有高噪声和冗余性，使得 GCNs 难以准确描述节点之间的完整关系，严重影响了图表示的质量。此外，现有的研究普遍忽视了图中特征空间和语义空间的分布差异，导致模型泛化效果不佳。为了应对这些挑战，我们提出了一种新的鲁棒 GCN 框架 DIB-RGCN，以设计良好的双信息瓶颈原则为指导，探索最优图表示。首先，我们分析了分布差异的原因，并从理论上证明了特定空间中的最小充分表示不能保证下游任务的最优性能。接下来，我们设计了新的双通道来规范特征和语义空间，消除了空间之间任务无关信息的共享。与现有的采用随机降噪方式的去噪算法不同，我们创新地用局部邻域表示来代替潜在的噪声特征和边缘。这种设计降低了边特定的系数分配，减少了原始表示的干扰，同时保留了图的结构。进一步，我们最大化特征空间和语义空间之间任务相关信息的共享，以减轻它们之间的差异。利用真实世界的数据集，大量的实验证明了所提出的 DIB-RGCN 算法的鲁棒性，它在分类任务中的性能优于最先进的方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Bridging+and+Compressing+Feature+and+Semantic+Spaces+for+Robust+Graph+Neural+Networks:+An+Information+Theory+Perspective)|0| -|[Dynamic Hotel Pricing at Online Travel Platforms: A Popularity and Competitiveness Aware Demand Learning Approach](https://doi.org/10.1145/3637528.3671921)|Fanwei Zhu, Wendong Xiao, Yao Yu, Zemin Liu, Zulong Chen, Weibin Cai|Alibaba Group, Hangzhou, China; Hangzhou City University, Hangzhou, China; Zhejiang University, Hangzhou, China; Syracuse University, Syracuse, USA|Dynamic pricing, which suggests the optimal prices based on the dynamic demands, has received considerable attention in academia and industry. On online hotel booking platforms, room demand fluctuates due to various factors, notably hotel popularity and competition. In this paper, we propose a dynamic pricing approach with popularity and competitiveness-aware demand learning. Specifically, we introduce a novel demand function that incorporates popularity and competitiveness coefficients to comprehensively model the price elasticity of demand. We develop a dynamic demand prediction network that focuses on learning these coefficients in the proposed demand function, enhancing the interpretability and accuracy of price suggestion. The model is trained in a multi-task framework that effectively leverages the correlations of demands among groups of similar hotels to alleviate data sparseness in room-level occupancy prediction. Comprehensive experiments conducted on real-world datasets validate the superiority of our method over state-of-the-art baselines in both demand prediction and dynamic pricing. Our model has been successfully deployed on a popular online travel platform, serving tens of millions of users and hoteliers.|动态定价是指基于动态需求的最优价格，已经受到学术界和工业界的广泛关注。在线酒店预订平台上，客房需求的波动受到多种因素的影响，特别是酒店的受欢迎程度和竞争程度。本文提出了一种基于知名度和竞争力的需求学习的动态定价方法。具体来说，我们引入了一个新的需求函数，将受欢迎程度和竞争力系数结合起来，以全面建立需求的价格弹性模型。我们开发了一个动态的需求预测网络，重点是在建议的需求函数中学习这些系数，提高价格建议的可解释性和准确性。该模型在多任务框架下进行训练，有效地利用了类似酒店群体之间需求的相关性，以缓解客房入住率预测中的数据稀缺性。在实际数据集上进行的综合实验验证了该方法在需求预测和动态定价方面优于最新的基线方法。我们的模式已经成功地部署在一个受欢迎的在线旅游平台上，为数千万用户和酒店管理者服务。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Dynamic+Hotel+Pricing+at+Online+Travel+Platforms:+A+Popularity+and+Competitiveness+Aware+Demand+Learning+Approach)|0| +|[Dynamic Hotel Pricing at Online Travel Platforms: A Popularity and Competitiveness Aware Demand Learning Approach](https://doi.org/10.1145/3637528.3671921)|Fanwei Zhu, Wendong Xiao, Yao Yu, Zemin Liu, Zulong Chen, Weibin Cai|Alibaba Group, Hangzhou, China; Zhejiang University, Hangzhou, China; Hangzhou City University, Hangzhou, China; Syracuse University, Syracuse, USA|Dynamic pricing, which suggests the optimal prices based on the dynamic demands, has received considerable attention in academia and industry. On online hotel booking platforms, room demand fluctuates due to various factors, notably hotel popularity and competition. In this paper, we propose a dynamic pricing approach with popularity and competitiveness-aware demand learning. Specifically, we introduce a novel demand function that incorporates popularity and competitiveness coefficients to comprehensively model the price elasticity of demand. We develop a dynamic demand prediction network that focuses on learning these coefficients in the proposed demand function, enhancing the interpretability and accuracy of price suggestion. The model is trained in a multi-task framework that effectively leverages the correlations of demands among groups of similar hotels to alleviate data sparseness in room-level occupancy prediction. Comprehensive experiments conducted on real-world datasets validate the superiority of our method over state-of-the-art baselines in both demand prediction and dynamic pricing. Our model has been successfully deployed on a popular online travel platform, serving tens of millions of users and hoteliers.|动态定价是指基于动态需求的最优价格，已经受到学术界和工业界的广泛关注。在线酒店预订平台上，客房需求的波动受到多种因素的影响，特别是酒店的受欢迎程度和竞争程度。本文提出了一种基于知名度和竞争力的需求学习的动态定价方法。具体来说，我们引入了一个新的需求函数，将受欢迎程度和竞争力系数结合起来，以全面建立需求的价格弹性模型。我们开发了一个动态的需求预测网络，重点是在建议的需求函数中学习这些系数，提高价格建议的可解释性和准确性。该模型在多任务框架下进行训练，有效地利用了类似酒店群体之间需求的相关性，以缓解客房入住率预测中的数据稀缺性。在实际数据集上进行的综合实验验证了该方法在需求预测和动态定价方面优于最新的基线方法。我们的模式已经成功地部署在一个受欢迎的在线旅游平台上，为数千万用户和酒店管理者服务。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Dynamic+Hotel+Pricing+at+Online+Travel+Platforms:+A+Popularity+and+Competitiveness+Aware+Demand+Learning+Approach)|0| |[Repeat-Aware Neighbor Sampling for Dynamic Graph Learning](https://doi.org/10.1145/3637528.3672001)|Tao Zou, Yuhao Mao, Junchen Ye, Bowen Du|; CCSE Lab, Beihang University, Beijing, China; School of Transportation Science and Engineering, Beihang University, Beijing, China|Dynamic graph learning equips the edges with time attributes and allowsmultiple links between two nodes, which is a crucial technology forunderstanding evolving data scenarios like traffic prediction andrecommendation systems. Existing works obtain the evolving patterns mainlydepending on the most recent neighbor sequences. However, we argue that whethertwo nodes will have interaction with each other in the future is highlycorrelated with the same interaction that happened in the past. Onlyconsidering the recent neighbors overlooks the phenomenon of repeat behaviorand fails to accurately capture the temporal evolution of interactions. To fillthis gap, this paper presents RepeatMixer, which considers evolving patterns offirst and high-order repeat behavior in the neighbor sampling strategy andtemporal information learning. Firstly, we define the first-order repeat-awarenodes of the source node as the destination nodes that have interactedhistorically and extend this concept to high orders as nodes in the destinationnode's high-order neighbors. Then, we extract neighbors of the source node thatinteracted before the appearance of repeat-aware nodes with a slide windowstrategy as its neighbor sequence. Next, we leverage both the first andhigh-order neighbor sequences of source and destination nodes to learn temporalpatterns of interactions via an MLP-based encoder. Furthermore, considering thevarying temporal patterns on different orders, we introduce a time-awareaggregation mechanism that adaptively aggregates the temporal representationsfrom different orders based on the significance of their interaction timesequences. Experimental results demonstrate the superiority of RepeatMixer overstate-of-the-art models in link prediction tasks, underscoring theeffectiveness of the proposed repeat-aware neighbor sampling strategy.|动态图学习使边具有时间属性，允许两个节点之间存在多个链接，这是理解流量预测和推荐系统等不断发展的数据场景的关键技术。现有的工作主要依靠最新的邻居序列获得演化模式。然而，我们认为两个节点在未来是否会有相互作用与过去发生的相同的相互作用高度相关。只考虑最近的邻居忽略了重复行为的现象，无法准确地捕捉相互作用的时间演化。为了填补这一空白，本文提出了一种在邻居采样策略和时间信息学习中首先考虑进化模式和高阶重复行为的迭代混合器。首先，我们将源节点的一阶重复感知节点定义为历史上相互作用过的目的节点，并将这一概念扩展为目的节点高阶邻居中的高阶节点。然后，我们以滑动窗口策略作为邻居序列，提取在重复感知节点出现之前相互作用的源节点的邻居。接下来，我们利用源节点和目标节点的第一个和高阶相邻序列，通过基于 MLP 的编码器学习交互的时间模式。在此基础上，针对不同时间序列的时间模式变化，提出了一种时间感知聚合机制，该机制根据不同时间序列的交互重要性，自适应地聚合不同时间序列的时间表示。实验结果表明，本文提出的重复感知邻居采样策略在链路预测任务中具有优越性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Repeat-Aware+Neighbor+Sampling+for+Dynamic+Graph+Learning)|0| |[Machine Learning for Clinical Management: From the Lab to the Hospital](https://doi.org/10.1145/3637528.3672497)|Ricard Gavaldà|Amalfi Analytics & Universitat Politècnica de Catalunya, BarcelonaTech (on leave), Barcelona, Spain|Population aging, increasing social demands, and rising costs of treatments are stressing healthcare systems to the point of risking the sustainability of universal and accessible healthcare. A hope in this dismal panorama is that there are large inefficiencies, and so opportunities for getting more from the same resources. To name a few, avoidable hospitalizations, unnecessary medication and tests, and lack of coordination among healthcare agents are estimated to cost several hundred billion euros per year in the EU. Technology can be useful for locating and reducing these inefficiencies, and within technology, the full exploitation of the data that the system collects to record its activity. In this talk, I will review the case for activity data analytics in healthcare, with two main considerations: 1) The need to include information about resources and costs in the models, in addition to clinical knowledge and patient outcomes, and 2) the need to use mostly data that healthcare organizations already collect and is not locked and distributed in silos. Fortunately, data collected for administrative and billing purposes, even though imperfect, partial, and low resolution, can be used to improve efficiency and safety, as well as fairness and equity. I will focus on the work carried out at Amalfi Analytics, a spin-off of my research group at UPC in Barcelona. On the one hand, we have addressed predictive management in hospitals, from influx to the emergency room to availability of surgical areas, beds, and staff. Anticipating activity, needs, and resource availability lets managers improve critical KPIs, e.g. waiting times, but also reduce staff stress, which leads to fewer medical errors and accidents. On the other hand, we have developed a patient cohort analyzer, based mostly on a recent clustering algorithm, that gives experts a fresh view of their patient population and lets them refine protocols and identify high-risk patient groups. This tool has also been used to support territorial planning and resource allocation. These problems have been extensively addressed in the past, but actual penetration of solutions in hospitals is smaller than one could expect. For example, one can find hundreds of papers on predicting influx to emergency rooms or bed demands, but many of them conclude after producing an AUC figure, and even fewer describe a working system that can be exported from the hospital where they were developed to others at an affordable cost. I will describe the approach taken at Amalfi so that hospitals can have such a solution up and running in a few days of work for their IT departments, in what I think is an interesting combination of software engineering and automatic Machine Learning.|人口老龄化、社会需求增加以及治疗成本上升，正在给医疗保健系统带来压力，以至于可能危及全民医疗保健的可持续性。在这个令人沮丧的全景中，一个希望是存在大量的低效率，因此有机会从同样的资源中获得更多。举几个例子，可避免的住院治疗、不必要的药物治疗和检测，以及医疗机构之间缺乏协调，估计每年在欧盟花费数千亿欧元。技术可以有助于查明和减少这些效率低下的情况，并在技术范围内充分利用系统为记录其活动而收集的数据。在这次演讲中，我将回顾医疗保健中活动数据分析的案例，主要考虑两点: 1)除了临床知识和患者结果之外，还需要在模型中包含有关资源和成本的信息; 2)需要使用医疗保健组织已经收集并且没有被锁定和分发的大部分数据。幸运的是，为管理和计费目的收集的数据，即使不完美、不完整和分辨率低，也可以用来提高效率和安全，以及公平和公正。我将专注于在阿马尔菲分析公司进行的工作，这是我在巴塞罗那的 UPC 研究小组的分支。一方面，我们已经解决了医院的预测性管理问题，从流入急诊室到手术区域、病床和工作人员的可用性。预测活动、需求和资源可用性可以让管理者改善关键的关键绩效指标，例如等待时间，但也可以减少员工压力，从而减少医疗差错和事故的发生。另一方面，我们已经开发了一个患者队列分析器，主要基于最近的聚类算法，使专家对他们的患者人口有一个新的看法，并让他们完善方案和确定高风险患者组。这个工具也被用来支持领土规划和资源分配。这些问题在过去已经得到了广泛的解决，但是解决方案在医院的实际渗透比人们预期的要小。例如，可以找到数百篇关于预测急诊室或床位需求的论文，但是其中许多都是在产生 AUC 数据之后得出的结论，更少的论文描述了可以从医院以负担得起的成本开发给其他人的工作系统。我将描述在阿马尔菲采取的方法，这样医院可以有这样一个解决方案，并在几天的工作为他们的信息技术部门运行，我认为这是一个有趣的组合软件工程和自动机器学习。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Machine+Learning+for+Clinical+Management:+From+the+Lab+to+the+Hospital)|0| -|[Metric Decomposition in A/B Tests](https://doi.org/10.1145/3637528.3671556)|Alex Deng, Luke Hagar, Nathaniel T. Stevens, Tatiana Xifara, Amit Gandhi|University of Waterloo, Waterloo, ON, Canada; Airbnb, Seattle, WA, USA; University of Pennsylvania, Philadelphia, PA, USA; Airbnb, San Francisco, CA, USA|More than a decade ago, CUPED (Controlled Experiments Utilizing Pre-Experiment Data) mainstreamed the idea of variance reduction leveraging pre-experiment covariates. Since its introduction, it has been implemented, extended, and modernized by major online experimentation platforms. Despite the wide adoption, it is known by practitioners that the variance reduction rate from CUPED utilizing pre-experimental data varies case by case and has a theoretical limit. In theory, CUPED can be extended to augment a treatment effect estimator utilizing in-experiment data, but practical guidance on how to construct such an augmentation is lacking. In this article, we fill this gap by proposing a new direction for sensitivity improvement via treatment effect augmentation whereby a target metric of interest is decomposed into components with high signal-to-noise disparity. Inference in the context of this decomposition is developed using both frequentist and Bayesian theory. We provide three real world applications demonstrating different flavors of metric decomposition; these applications illustrate the gain in agility metric decomposition yields relative to an un-decomposed analysis.|十多年前，CUPED (利用实验前数据的对照实验)将利用实验前协变量减少方差的想法主流化。自引入以来，它已经被主要的在线实验平台实现、扩展和现代化。尽管被广泛采用，但是从业人员都知道，利用实验前数据的 CUPED 方差减少率因情况而异，并且有一个理论上的限制。理论上，CUPED 可以扩展到利用实验数据来增强治疗效果估计器，但是对于如何构造这样的增强器缺乏实际指导。在本文中，我们填补了这一空白，提出了一个新的方向，通过治疗效果增强灵敏度改进，其中目标度量的兴趣是分解成具有高信噪比的组件。在这种分解的背景下的推理是发展使用频率论和贝叶斯理论。我们提供了三个现实世界中的应用程序，它们演示了不同风格的度量分解; 这些应用程序说明了相对于未分解的分析，敏捷度量分解产量的增长。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Metric+Decomposition+in+A/B+Tests)|0| -|[LASCA: A Large-Scale Stable Customer Segmentation Approach to Credit Risk Assessment](https://doi.org/10.1145/3637528.3671550)|Yongfeng Gu, Yupeng Wu, Huakang Lu, Xingyu Lu, Hong Qian, Jun Zhou, Aimin Zhou|Ant Group, Hangzhou, Zhejiang, China; School of Computer Science and Technology, East China Normal University, Shanghai, China|Customer segmentation plays a crucial role in credit risk assessment by dividing users into specific risk levels based on their credit scores. Previous methods fail to comprehensively consider the stability in the segmentation process, resulting in frequent changes and inconsistencies in users' risk levels over time. This increases potential risks to a company. To this end, this paper at first introduces and formalizes the concept of stability regret in the segmentation process. However, evaluating stability is challenging due to its black-box nature and the computational burden posed by vast user data sets. To address these challenges, this paper proposes a large-scale stable customer segmentation approach named LASCA. LASCA consists of two phases: high-quality dataset construction (HDC) and reliable data-driven optimization (RDO). Specifically, HDC utilizes an evolutionary algorithm to collect high-quality binning solutions. RDO subsequently builds a reliable surrogate model to search for the most stable binning solution based on the collected dataset. Extensive experiments conducted on real-world large-scale datasets (up to 0.8 billion) show that LASCA surpasses the state-of-the-art binning methods in finding the most stable binning solution. Notably, HDC greatly enhances data quality by 50%. RDO efficiently discovers more stable binning solutions with a 36% improvement in stability, accelerating the optimization process by 25 times via data-driven evaluation. Currently, LASCA has been successfully deployed in the large-scale credit risk assessment system of Alipay.|客户细分在信用风险评估中起着至关重要的作用，它根据用户的信用评分将用户划分为特定的风险级别。以往的方法未能全面考虑分割过程的稳定性，导致用户的风险水平随着时间的推移频繁变化和不一致。这增加了公司的潜在风险。为此，本文首先在分割过程中引入并形式化了稳定性遗憾的概念。然而，评估稳定性是具有挑战性的，因为它的黑盒性质和计算负担所造成的海量用户数据集。针对这些挑战，本文提出了一种大规模稳定的客户细分方法 LASCA。LASCA 包括两个阶段: 高质量数据集构建(HDC)和可靠的数据驱动优化(RDO)。具体来说，HDC 使用进化算法来收集高质量的分组解决方案。RDO 随后构建一个可靠的代理模型，以根据收集的数据集搜索最稳定的装箱解决方案。在现实世界的大规模数据集(多达8亿个)上进行的大量实验表明，LASCA 在寻找最稳定的装箱解决方案方面超过了最先进的装箱方法。值得注意的是，HDC 极大地提高了50% 的数据质量。RDO 有效地发现了更稳定的装箱解决方案，其稳定性提高了36% ，通过数据驱动的评估将优化过程加速了25倍。目前，LASCA 已成功应用于支付宝的大规模信用风险评估系统。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=LASCA:+A+Large-Scale+Stable+Customer+Segmentation+Approach+to+Credit+Risk+Assessment)|0| +|[Metric Decomposition in A/B Tests](https://doi.org/10.1145/3637528.3671556)|Alex Deng, Luke Hagar, Nathaniel T. Stevens, Tatiana Xifara, Amit Gandhi|University of Waterloo, Waterloo, ON, Canada; Airbnb, Seattle, WA, USA; Airbnb, San Francisco, CA, USA; University of Pennsylvania, Philadelphia, PA, USA|More than a decade ago, CUPED (Controlled Experiments Utilizing Pre-Experiment Data) mainstreamed the idea of variance reduction leveraging pre-experiment covariates. Since its introduction, it has been implemented, extended, and modernized by major online experimentation platforms. Despite the wide adoption, it is known by practitioners that the variance reduction rate from CUPED utilizing pre-experimental data varies case by case and has a theoretical limit. In theory, CUPED can be extended to augment a treatment effect estimator utilizing in-experiment data, but practical guidance on how to construct such an augmentation is lacking. In this article, we fill this gap by proposing a new direction for sensitivity improvement via treatment effect augmentation whereby a target metric of interest is decomposed into components with high signal-to-noise disparity. Inference in the context of this decomposition is developed using both frequentist and Bayesian theory. We provide three real world applications demonstrating different flavors of metric decomposition; these applications illustrate the gain in agility metric decomposition yields relative to an un-decomposed analysis.|十多年前，CUPED (利用实验前数据的对照实验)将利用实验前协变量减少方差的想法主流化。自引入以来，它已经被主要的在线实验平台实现、扩展和现代化。尽管被广泛采用，但是从业人员都知道，利用实验前数据的 CUPED 方差减少率因情况而异，并且有一个理论上的限制。理论上，CUPED 可以扩展到利用实验数据来增强治疗效果估计器，但是对于如何构造这样的增强器缺乏实际指导。在本文中，我们填补了这一空白，提出了一个新的方向，通过治疗效果增强灵敏度改进，其中目标度量的兴趣是分解成具有高信噪比的组件。在这种分解的背景下的推理是发展使用频率论和贝叶斯理论。我们提供了三个现实世界中的应用程序，它们演示了不同风格的度量分解; 这些应用程序说明了相对于未分解的分析，敏捷度量分解产量的增长。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Metric+Decomposition+in+A/B+Tests)|0| +|[LASCA: A Large-Scale Stable Customer Segmentation Approach to Credit Risk Assessment](https://doi.org/10.1145/3637528.3671550)|Yongfeng Gu, Yupeng Wu, Huakang Lu, Xingyu Lu, Hong Qian, Jun Zhou, Aimin Zhou|School of Computer Science and Technology, East China Normal University, Shanghai, China; Ant Group, Hangzhou, Zhejiang, China|Customer segmentation plays a crucial role in credit risk assessment by dividing users into specific risk levels based on their credit scores. Previous methods fail to comprehensively consider the stability in the segmentation process, resulting in frequent changes and inconsistencies in users' risk levels over time. This increases potential risks to a company. To this end, this paper at first introduces and formalizes the concept of stability regret in the segmentation process. However, evaluating stability is challenging due to its black-box nature and the computational burden posed by vast user data sets. To address these challenges, this paper proposes a large-scale stable customer segmentation approach named LASCA. LASCA consists of two phases: high-quality dataset construction (HDC) and reliable data-driven optimization (RDO). Specifically, HDC utilizes an evolutionary algorithm to collect high-quality binning solutions. RDO subsequently builds a reliable surrogate model to search for the most stable binning solution based on the collected dataset. Extensive experiments conducted on real-world large-scale datasets (up to 0.8 billion) show that LASCA surpasses the state-of-the-art binning methods in finding the most stable binning solution. Notably, HDC greatly enhances data quality by 50%. RDO efficiently discovers more stable binning solutions with a 36% improvement in stability, accelerating the optimization process by 25 times via data-driven evaluation. Currently, LASCA has been successfully deployed in the large-scale credit risk assessment system of Alipay.|客户细分在信用风险评估中起着至关重要的作用，它根据用户的信用评分将用户划分为特定的风险级别。以往的方法未能全面考虑分割过程的稳定性，导致用户的风险水平随着时间的推移频繁变化和不一致。这增加了公司的潜在风险。为此，本文首先在分割过程中引入并形式化了稳定性遗憾的概念。然而，评估稳定性是具有挑战性的，因为它的黑盒性质和计算负担所造成的海量用户数据集。针对这些挑战，本文提出了一种大规模稳定的客户细分方法 LASCA。LASCA 包括两个阶段: 高质量数据集构建(HDC)和可靠的数据驱动优化(RDO)。具体来说，HDC 使用进化算法来收集高质量的分组解决方案。RDO 随后构建一个可靠的代理模型，以根据收集的数据集搜索最稳定的装箱解决方案。在现实世界的大规模数据集(多达8亿个)上进行的大量实验表明，LASCA 在寻找最稳定的装箱解决方案方面超过了最先进的装箱方法。值得注意的是，HDC 极大地提高了50% 的数据质量。RDO 有效地发现了更稳定的装箱解决方案，其稳定性提高了36% ，通过数据驱动的评估将优化过程加速了25倍。目前，LASCA 已成功应用于支付宝的大规模信用风险评估系统。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=LASCA:+A+Large-Scale+Stable+Customer+Segmentation+Approach+to+Credit+Risk+Assessment)|0| |[Generative Auto-bidding via Conditional Diffusion Modeling](https://doi.org/10.1145/3637528.3671526)|Jiayan Guo, Yusen Huo, Zhilin Zhang, Tianyu Wang, Chuan Yu, Jian Xu, Bo Zheng, Yan Zhang|Alibaba Group, Beijing, China; Peking University & Alibaba Group, Beijing, China; Peking University, Beijing, China|Auto-bidding plays a crucial role in facilitating online advertising by automatically providing bids for advertisers. Reinforcement learning (RL) has gained popularity for auto-bidding. However, most current RL auto-bidding methods are modeled through the Markovian Decision Process (MDP), which assumes the Markovian state transition. This assumption restricts the ability to perform in long horizon scenarios and makes the model unstable when dealing with highly random online advertising environments. To tackle this issue, this paper introduces AI-Generated Bidding (AIGB), a novel paradigm for auto-bidding through generative modeling. In this paradigm, we propose DiffBid, a conditional diffusion modeling approach for bid generation. DiffBid directly models the correlation between the return and the entire trajectory, effectively avoiding error propagation across time steps in long horizons. Additionally, DiffBid offers a versatile approach for generating trajectories that maximize given targets while adhering to specific constraints. Extensive experiments conducted on the real-world dataset and online A/B test on Alibaba advertising platform demonstrate the effectiveness of DiffBid, achieving 2.81% increase in GMV and 3.36% increase in ROI.|自动竞价通过自动为广告商提供出价，在促进网络广告方面发挥着至关重要的作用。强化学习(RL)在自动竞投中越来越受欢迎。然而，大多数现有的 RL 自动竞价方法是通过马尔科夫决策过程(mDP)建模的，该过程假设 Markovian 政府的过渡。这种假设限制了在长期场景中的执行能力，并使模型在处理高度随机的在线广告环境时变得不稳定。为了解决这一问题，本文提出了一种基于生成模型的自动招标方法——人工智能生成招标(AIGB)。在这个范例中，我们提出了一种条件扩散建模的投标生成方法——迪夫出价。迪夫出价直接建模收益率和整个轨迹之间的相关性，有效地避免了错误传播跨时间步长的长期。此外，迪夫出价提供了一个多功能的方法来生成轨迹，最大限度地给定的目标，同时坚持特定的约束。在阿里巴巴广告平台上对现实世界的数据集和在线 A/B 测试进行了广泛的实验，结果证明了 DiffBid 的有效性，实现了2.81% 的 GMV 增长和3.36% 的投资回报率增长。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Generative+Auto-bidding+via+Conditional+Diffusion+Modeling)|0| -|[Learning Metrics that Maximise Power for Accelerated A/B-Tests](https://doi.org/10.1145/3637528.3671512)|Olivier Jeunen, Aleksei Ustimenko|ShareChat, London, United Kingdom; ShareChat, Edinburgh, United Kingdom|Online controlled experiments are a crucial tool to allow for confident decision-making in technology companies. A North Star metric is defined (such as long-term revenue or user retention), and system variants that statistically significantly improve on this metric in an A/B-test can be considered superior. North Star metrics are typically delayed and insensitive. As a result, the cost of experimentation is high: experiments need to run for a long time, and even then, type-II errors (i.e. false negatives) are prevalent. We propose to tackle this by learning metrics from short-term signals that directly maximise the statistical power they harness with respect to the North Star. We show that existing approaches are prone to overfitting, in that higher average metric sensitivity does not imply improved type-II errors, and propose to instead minimise the p-values a metric would have produced on a log of past experiments. We collect such datasets from two social media applications with over 160 million Monthly Active Users each, totalling over 153 A/B-pairs. Empirical results show that we are able to increase statistical power by up to 78% when using our learnt metrics stand-alone, and by up to 210% when used in tandem with the North Star. Alternatively, we can obtain constant statistical power at a sample size that is down to 12% of what the North Star requires, significantly reducing the cost of experimentation.|在线控制实验是科技公司做出自信决策的重要工具。定义了一个 North Star 指标(如长期收入或用户保留) ，在 A/B 测试中，在这个指标上有统计学显著改善的系统变体可以被认为是优越的。北极星指标通常是延迟和不敏感的。因此，实验的成本很高: 实验需要运行很长时间，即使这样，II 型错误(即假阴性)也很普遍。我们建议通过从短期信号中学习指标来解决这个问题，这些信号可以直接最大限度地利用它们对北极星的统计能力。我们表明，现有的方法容易过度拟合，因为较高的平均度量灵敏度并不意味着改善的 II 型误差，并建议相反，尽量减少在过去的实验日志中产生的度量的 p 值。我们从两个社会媒体应用程序中收集这样的数据集，每个应用程序的每月活跃用户超过1.6亿，总共超过153个 A/B 对。实证结果表明，我们能够增加高达78% 的统计权力时，使用我们的学习指标独立，并高达210% 时，与北极星使用的配合。或者，我们可以在样本大小下降到北极星所需要的12% 时获得恒定的统计功率，大大降低了实验的成本。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Learning+Metrics+that+Maximise+Power+for+Accelerated+A/B-Tests)|0| -|[Interpretable Cascading Mixture-of-Experts for Urban Traffic Congestion Prediction](https://doi.org/10.1145/3637528.3671507)|Wenzhao Jiang, Jindong Han, Hao Liu, Tao Tao, Naiqiang Tan, Hui Xiong|; The Hong Kong University of Science and Technology, Hong Kong, China; Didichuxing Co. Ltd, Beijing, China; The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, Guangdong, China|Rapid urbanization has significantly escalated traffic congestion,underscoring the need for advanced congestion prediction services to bolsterintelligent transportation systems. As one of the world's largest ride-hailingplatforms, DiDi places great emphasis on the accuracy of congestion predictionto enhance the effectiveness and reliability of their real-time services, suchas travel time estimation and route planning. Despite numerous efforts havebeen made on congestion prediction, most of them fall short in handlingheterogeneous and dynamic spatio-temporal dependencies (e.g., periodic andnon-periodic congestions), particularly in the presence of noisy and incompletetraffic data. In this paper, we introduce a Congestion PredictionMixture-of-Experts, CP-MoE, to address the above challenges. We first propose asparsely-gated Mixture of Adaptive Graph Learners (MAGLs) with congestion-awareinductive biases to improve the model capacity for efficiently capturingcomplex spatio-temporal dependencies in varying traffic scenarios. Then, wedevise two specialized experts to help identify stable trends and periodicpatterns within the traffic data, respectively. By cascading these experts withMAGLs, CP-MoE delivers congestion predictions in a more robust andinterpretable manner. Furthermore, an ordinal regression strategy is adopted tofacilitate effective collaboration among diverse experts. Extensive experimentson real-world datasets demonstrate the superiority of our proposed methodcompared with state-of-the-art spatio-temporal prediction models. Moreimportantly, CP-MoE has been deployed in DiDi to improve the accuracy andreliability of the travel time estimation system.|快速的城市化使交通堵塞显著升级，这突出表明需要先进的拥堵预测服务来支持智能交通系统。作为全球其中一个最大的网约车平台，滴滴非常重视交通挤塞预测的准确性，以提高其实时服务(例如行车时间估计和路线规划)的成效和可靠性。尽管在拥塞预测方面已经做了大量的工作，但是大多数工作在处理异构和动态的时空依赖性(例如，周期性和非周期性的拥塞)方面仍然存在不足，特别是在存在噪声和不完整的交通数据的情况下。在本文中，我们引入了一个拥塞预测混合专家，CP-MoE，以解决上述挑战。我们首先提出具有拥塞感知偏差的自适应图学习器(MAGL)的门限混合模型，以提高模型能力，有效地捕获不同交通场景中复杂的时空依赖关系。然后，我们分别召集两位专家来帮助识别交通数据中的稳定趋势和周期模式。通过将这些专家与 MAGL 级联，CP-MoE 以更强大和可解释的方式提供拥塞预测。此外，采用有序回归策略促进不同专家之间的有效协作。对实际数据集的大量实验表明，与最先进的时空预测模型相比，本文提出的方法具有优越性。更重要的是，滴滴已部署了运输部，以提高旅行时间估计系统的准确性和可靠性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Interpretable+Cascading+Mixture-of-Experts+for+Urban+Traffic+Congestion+Prediction)|0| +|[Learning Metrics that Maximise Power for Accelerated A/B-Tests](https://doi.org/10.1145/3637528.3671512)|Olivier Jeunen, Aleksei Ustimenko|ShareChat, Edinburgh, United Kingdom; ShareChat, London, United Kingdom|Online controlled experiments are a crucial tool to allow for confident decision-making in technology companies. A North Star metric is defined (such as long-term revenue or user retention), and system variants that statistically significantly improve on this metric in an A/B-test can be considered superior. North Star metrics are typically delayed and insensitive. As a result, the cost of experimentation is high: experiments need to run for a long time, and even then, type-II errors (i.e. false negatives) are prevalent. We propose to tackle this by learning metrics from short-term signals that directly maximise the statistical power they harness with respect to the North Star. We show that existing approaches are prone to overfitting, in that higher average metric sensitivity does not imply improved type-II errors, and propose to instead minimise the p-values a metric would have produced on a log of past experiments. We collect such datasets from two social media applications with over 160 million Monthly Active Users each, totalling over 153 A/B-pairs. Empirical results show that we are able to increase statistical power by up to 78% when using our learnt metrics stand-alone, and by up to 210% when used in tandem with the North Star. Alternatively, we can obtain constant statistical power at a sample size that is down to 12% of what the North Star requires, significantly reducing the cost of experimentation.|在线控制实验是科技公司做出自信决策的重要工具。定义了一个 North Star 指标(如长期收入或用户保留) ，在 A/B 测试中，在这个指标上有统计学显著改善的系统变体可以被认为是优越的。北极星指标通常是延迟和不敏感的。因此，实验的成本很高: 实验需要运行很长时间，即使这样，II 型错误(即假阴性)也很普遍。我们建议通过从短期信号中学习指标来解决这个问题，这些信号可以直接最大限度地利用它们对北极星的统计能力。我们表明，现有的方法容易过度拟合，因为较高的平均度量灵敏度并不意味着改善的 II 型误差，并建议相反，尽量减少在过去的实验日志中产生的度量的 p 值。我们从两个社会媒体应用程序中收集这样的数据集，每个应用程序的每月活跃用户超过1.6亿，总共超过153个 A/B 对。实证结果表明，我们能够增加高达78% 的统计权力时，使用我们的学习指标独立，并高达210% 时，与北极星使用的配合。或者，我们可以在样本大小下降到北极星所需要的12% 时获得恒定的统计功率，大大降低了实验的成本。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Learning+Metrics+that+Maximise+Power+for+Accelerated+A/B-Tests)|0| +|[Interpretable Cascading Mixture-of-Experts for Urban Traffic Congestion Prediction](https://doi.org/10.1145/3637528.3671507)|Wenzhao Jiang, Jindong Han, Hao Liu, Tao Tao, Naiqiang Tan, Hui Xiong|; Didichuxing Co. Ltd, Beijing, China; The Hong Kong University of Science and Technology, Hong Kong, China; The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, Guangdong, China|Rapid urbanization has significantly escalated traffic congestion,underscoring the need for advanced congestion prediction services to bolsterintelligent transportation systems. As one of the world's largest ride-hailingplatforms, DiDi places great emphasis on the accuracy of congestion predictionto enhance the effectiveness and reliability of their real-time services, suchas travel time estimation and route planning. Despite numerous efforts havebeen made on congestion prediction, most of them fall short in handlingheterogeneous and dynamic spatio-temporal dependencies (e.g., periodic andnon-periodic congestions), particularly in the presence of noisy and incompletetraffic data. In this paper, we introduce a Congestion PredictionMixture-of-Experts, CP-MoE, to address the above challenges. We first propose asparsely-gated Mixture of Adaptive Graph Learners (MAGLs) with congestion-awareinductive biases to improve the model capacity for efficiently capturingcomplex spatio-temporal dependencies in varying traffic scenarios. Then, wedevise two specialized experts to help identify stable trends and periodicpatterns within the traffic data, respectively. By cascading these experts withMAGLs, CP-MoE delivers congestion predictions in a more robust andinterpretable manner. Furthermore, an ordinal regression strategy is adopted tofacilitate effective collaboration among diverse experts. Extensive experimentson real-world datasets demonstrate the superiority of our proposed methodcompared with state-of-the-art spatio-temporal prediction models. Moreimportantly, CP-MoE has been deployed in DiDi to improve the accuracy andreliability of the travel time estimation system.|快速的城市化使交通堵塞显著升级，这突出表明需要先进的拥堵预测服务来支持智能交通系统。作为全球其中一个最大的网约车平台，滴滴非常重视交通挤塞预测的准确性，以提高其实时服务(例如行车时间估计和路线规划)的成效和可靠性。尽管在拥塞预测方面已经做了大量的工作，但是大多数工作在处理异构和动态的时空依赖性(例如，周期性和非周期性的拥塞)方面仍然存在不足，特别是在存在噪声和不完整的交通数据的情况下。在本文中，我们引入了一个拥塞预测混合专家，CP-MoE，以解决上述挑战。我们首先提出具有拥塞感知偏差的自适应图学习器(MAGL)的门限混合模型，以提高模型能力，有效地捕获不同交通场景中复杂的时空依赖关系。然后，我们分别召集两位专家来帮助识别交通数据中的稳定趋势和周期模式。通过将这些专家与 MAGL 级联，CP-MoE 以更强大和可解释的方式提供拥塞预测。此外，采用有序回归策略促进不同专家之间的有效协作。对实际数据集的大量实验表明，与最先进的时空预测模型相比，本文提出的方法具有优越性。更重要的是，滴滴已部署了运输部，以提高旅行时间估计系统的准确性和可靠性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Interpretable+Cascading+Mixture-of-Experts+for+Urban+Traffic+Congestion+Prediction)|0| |[False Positives in A/B Tests](https://doi.org/10.1145/3637528.3671631)|Ron Kohavi, Nanyu Chen|Expedia Group, San Francisco, CA, USA; Kohavi, Los Altos, CA, USA|A/B tests, or online controlled experiments, are used heavily in the software industry to evaluate implementations of ideas, as the paradigm is the gold standard in science for establishing causality: the changes introduced in the treatment caused the changes to the metrics of interest with high probability. What distinguishes software experiments, or A/B tests, from experiments in many other domains is the scale (e.g., over 100 experiment treatments may launch on a given workday in large companies) and the effect sizes that matter to the business are small (e.g., a 3% improvement to conversion rate from a single experiment is a cause for celebration). The humbling reality is that most experiments fail to improve key metrics, and success rates of only about 10-20% are most common. With low success rates, the industry standard alpha threshold of 0.05 implies a high probability of false positives. We begin with motivation about why false positives are expensive in many software domains. We then offer several approaches to estimate the true success rate of experiments, given the observed "win" rate (statistically significant positive improvements), and show examples from Expedia and Optimizely. We offer a modified procedure for experimentation, based in sequential group testing, that selectively extends experiments to reduce false positives, increase power, at a small increase to runtime. We conclude with a discussion of the difference between ideas and experiments in practice, terms that are often incorrectly used interchangeably.|A/B 测试，或在线控制实验，在软件行业被大量使用来评估思想的实施，因为范式是建立因果关系的科学黄金标准: 在治疗中引入的变化以高概率引起了感兴趣度量的变化。软件实验或者 A/B 测试与其他领域的实验的区别在于规模(例如，在大公司的某个工作日可能会有超过100个实验治疗方案)和对企业有影响的效应规模很小(例如，单个实验的转化率提高3% 是值得庆祝的)。令人难堪的现实是，大多数实验都无法改进关键指标，最常见的成功率只有10-20% 左右。由于成功率较低，行业标准 alpha 阈值为0.05意味着误报的可能性很高。我们从为什么假阳性在许多软件领域是昂贵的动机开始。然后，我们提供了几种方法来估计实验的真实成功率，给出观察到的“胜利”率(统计学上显著的积极改进) ，并展示了 Expedia 和 Optimizely 的例子。我们提供了一个修改过的实验过程，基于顺序组测试，有选择地扩展实验，以减少误报，增加功率，在运行时的小幅增加。最后，我们讨论了实践中思想和实验之间的区别，这些术语经常被错误地互换使用。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=False+Positives+in+A/B+Tests)|0| -|[Causal Machine Learning for Cost-Effective Allocation of Development Aid](https://doi.org/10.1145/3637528.3671551)|Milan Kuzmanovic, Dennis Frauen, Tobias Hatt, Stefan Feuerriegel|Munich Center for Machine Learning & LMU Munich, Munich, Germany; ETH Zurich, Zurich, Switzerland|The Sustainable Development Goals (SDGs) of the United Nations provide a blueprint of a better future by "leaving no one behind", and, to achieve the SDGs by 2030, poor countries require immense volumes of development aid. In this paper, we develop a causal machine learning framework for predicting heterogeneous treatment effects of aid disbursements to inform effective aid allocation. Specifically, our framework comprises three components: (i) a balancing autoencoder that uses representation learning to embed high-dimensional country characteristics while addressing treatment selection bias; (ii) a counterfactual generator to compute counterfactual outcomes for varying aid volumes to address small sample-size settings; and (iii) an inference model that is used to predict heterogeneous treatment-response curves. We demonstrate the effectiveness of our framework using data with official development aid earmarked to end HIV/AIDS in 105 countries, amounting to more than USD 5.2 billion. For this, we first show that our framework successfully computes heterogeneous treatment-response curves using semi-synthetic data. Then, we demonstrate our framework using real-world HIV data. Our framework points to large opportunities for a more effective aid allocation, suggesting that the total number of new HIV infections could be reduced by up to 3.3% (~50,000 cases) compared to the current allocation practice.|联合国的可持续发展目标(SDGs)提供了一个“不让任何人掉队”的美好未来蓝图。为了在2030年前实现这些目标，贫穷国家需要大量的发展援助。在本文中，我们建立了一个因果机器学习框架，用于预测援助支出的异质处理效果，从而为有效的援助分配提供信息。具体而言，我们的框架包括三个组成部分: (i)使用表示学习来嵌入高维国家特征的平衡自动编码器，同时解决治疗选择偏倚; (ii)反事实生成器计算不同援助量的反事实结果以解决小样本量设置; 和(iii)用于预测异质治疗-反应曲线的推理模型。我们利用官方发展援助的数据，在105个国家展示了我们框架的有效性，这些数据被指定用于终结艾滋病毒/艾滋病，总额超过52亿美元。为此，我们首先展示了我们的框架成功地使用半合成数据计算异质治疗-反应曲线。然后，我们使用真实世界的 HIV 数据来展示我们的框架。我们的框架指出了更有效的援助分配的巨大机会，表明与目前的分配做法相比，新的艾滋病毒感染总数可以减少3.3% (约5万例)。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Causal+Machine+Learning+for+Cost-Effective+Allocation+of+Development+Aid)|0| -|[Chromosomal Structural Abnormality Diagnosis by Homologous Similarity](https://doi.org/10.1145/3637528.3671642)|Juren Li, Fanzhe Fu, Ran Wei, Yifei Sun, Zeyu Lai, Ning Song, Xin Chen, Yang Yang|Zhejiang University, Hangzhou, China; Hangzhou Diagens Biotechnology Co., Ltd., Hangzhou, China|Pathogenic chromosome abnormalities are very common among the general population. While numerical chromosome abnormalities can be quickly and precisely detected, structural chromosome abnormalities are far more complex and typically require considerable efforts by human experts for identification. This paper focuses on investigating the modeling of chromosome features and the identification of chromosomes with structural abnormalities. Most existing data-driven methods concentrate on a single chromosome and consider each chromosome independently, overlooking the crucial aspect of homologous chromosomes. In normal cases, homologous chromosomes share identical structures, with the exception that one of them is abnormal. Therefore, we propose an adaptive method to align homologous chromosomes and diagnose structural abnormalities through homologous similarity. Inspired by the process of human expert diagnosis, we incorporate information from multiple pairs of homologous chromosomes simultaneously, aiming to reduce noise disturbance and improve prediction performance. Extensive experiments on real-world datasets validate the effectiveness of our model compared to baselines.|致病性染色体异常在一般人群中很常见。虽然数字染色体异常可以快速、准确地检测出来，但结构染色体异常要复杂得多，通常需要人类专家作出相当大的努力来鉴定。本文主要研究染色体特征的建模和结构异常染色体的识别。大多数现有的数据驱动方法集中在一个单一的染色体和考虑每个染色体独立，忽略了同源染色体的关键方面。在正常情况下，同源染色体共享相同的结构，除了其中一个是异常的。因此，我们提出了一种自适应的方法来排列同源染色体和诊断结构异常的同源相似性。受人类专家诊断过程的启发，我们同时整合多对同源染色体的信息，以减少噪声干扰，提高预测性能。在真实世界数据集上的大量实验验证了我们的模型与基线相比的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Chromosomal+Structural+Abnormality+Diagnosis+by+Homologous+Similarity)|0| +|[Causal Machine Learning for Cost-Effective Allocation of Development Aid](https://doi.org/10.1145/3637528.3671551)|Milan Kuzmanovic, Dennis Frauen, Tobias Hatt, Stefan Feuerriegel|ETH Zurich, Zurich, Switzerland; Munich Center for Machine Learning & LMU Munich, Munich, Germany|The Sustainable Development Goals (SDGs) of the United Nations provide a blueprint of a better future by "leaving no one behind", and, to achieve the SDGs by 2030, poor countries require immense volumes of development aid. In this paper, we develop a causal machine learning framework for predicting heterogeneous treatment effects of aid disbursements to inform effective aid allocation. Specifically, our framework comprises three components: (i) a balancing autoencoder that uses representation learning to embed high-dimensional country characteristics while addressing treatment selection bias; (ii) a counterfactual generator to compute counterfactual outcomes for varying aid volumes to address small sample-size settings; and (iii) an inference model that is used to predict heterogeneous treatment-response curves. We demonstrate the effectiveness of our framework using data with official development aid earmarked to end HIV/AIDS in 105 countries, amounting to more than USD 5.2 billion. For this, we first show that our framework successfully computes heterogeneous treatment-response curves using semi-synthetic data. Then, we demonstrate our framework using real-world HIV data. Our framework points to large opportunities for a more effective aid allocation, suggesting that the total number of new HIV infections could be reduced by up to 3.3% (~50,000 cases) compared to the current allocation practice.|联合国的可持续发展目标(SDGs)提供了一个“不让任何人掉队”的美好未来蓝图。为了在2030年前实现这些目标，贫穷国家需要大量的发展援助。在本文中，我们建立了一个因果机器学习框架，用于预测援助支出的异质处理效果，从而为有效的援助分配提供信息。具体而言，我们的框架包括三个组成部分: (i)使用表示学习来嵌入高维国家特征的平衡自动编码器，同时解决治疗选择偏倚; (ii)反事实生成器计算不同援助量的反事实结果以解决小样本量设置; 和(iii)用于预测异质治疗-反应曲线的推理模型。我们利用官方发展援助的数据，在105个国家展示了我们框架的有效性，这些数据被指定用于终结艾滋病毒/艾滋病，总额超过52亿美元。为此，我们首先展示了我们的框架成功地使用半合成数据计算异质治疗-反应曲线。然后，我们使用真实世界的 HIV 数据来展示我们的框架。我们的框架指出了更有效的援助分配的巨大机会，表明与目前的分配做法相比，新的艾滋病毒感染总数可以减少3.3% (约5万例)。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Causal+Machine+Learning+for+Cost-Effective+Allocation+of+Development+Aid)|0| +|[Chromosomal Structural Abnormality Diagnosis by Homologous Similarity](https://doi.org/10.1145/3637528.3671642)|Juren Li, Fanzhe Fu, Ran Wei, Yifei Sun, Zeyu Lai, Ning Song, Xin Chen, Yang Yang|Hangzhou Diagens Biotechnology Co., Ltd., Hangzhou, China; Zhejiang University, Hangzhou, China|Pathogenic chromosome abnormalities are very common among the general population. While numerical chromosome abnormalities can be quickly and precisely detected, structural chromosome abnormalities are far more complex and typically require considerable efforts by human experts for identification. This paper focuses on investigating the modeling of chromosome features and the identification of chromosomes with structural abnormalities. Most existing data-driven methods concentrate on a single chromosome and consider each chromosome independently, overlooking the crucial aspect of homologous chromosomes. In normal cases, homologous chromosomes share identical structures, with the exception that one of them is abnormal. Therefore, we propose an adaptive method to align homologous chromosomes and diagnose structural abnormalities through homologous similarity. Inspired by the process of human expert diagnosis, we incorporate information from multiple pairs of homologous chromosomes simultaneously, aiming to reduce noise disturbance and improve prediction performance. Extensive experiments on real-world datasets validate the effectiveness of our model compared to baselines.|致病性染色体异常在一般人群中很常见。虽然数字染色体异常可以快速、准确地检测出来，但结构染色体异常要复杂得多，通常需要人类专家作出相当大的努力来鉴定。本文主要研究染色体特征的建模和结构异常染色体的识别。大多数现有的数据驱动方法集中在一个单一的染色体和考虑每个染色体独立，忽略了同源染色体的关键方面。在正常情况下，同源染色体共享相同的结构，除了其中一个是异常的。因此，我们提出了一种自适应的方法来排列同源染色体和诊断结构异常的同源相似性。受人类专家诊断过程的启发，我们同时整合多对同源染色体的信息，以减少噪声干扰，提高预测性能。在真实世界数据集上的大量实验验证了我们的模型与基线相比的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Chromosomal+Structural+Abnormality+Diagnosis+by+Homologous+Similarity)|0| |[An Open and Large-Scale Dataset for Multi-Modal Climate Change-aware Crop Yield Predictions](https://doi.org/10.1145/3637528.3671536)|Fudong Lin, Kaleb Guillot, Summer Crawford, Yihe Zhang, Xu Yuan, NianFeng Tzeng|University of Louisiana at Lafeyette, Lafayette, LA, USA; University of Delaware, Newark, DE, USA|Precise crop yield predictions are of national importance for ensuring food security and sustainable agricultural practices. While AI-for-science approaches have exhibited promising achievements in solving many scientific problems such as drug discovery, precipitation nowcasting, etc., the development of deep learning models for predicting crop yields is constantly hindered by the lack of an open and large-scale deep learning-ready dataset with multiple modalities to accommodate sufficient information. To remedy this, we introduce the CropNet dataset, the first terabyte-sized, publicly available, and multi-modal dataset specifically targeting climate change-aware crop yield predictions for the contiguous United States (U.S.) continent at the county level. Our CropNet dataset is composed of three modalities of data, i.e., Sentinel-2 Imagery, WRF-HRRR Computed Dataset, and USDA Crop Dataset, for over 2200 U.S. counties spanning 6 years (2017-2022), expected to facilitate researchers in developing versatile deep learning models for timely and precisely predicting crop yields at the county-level, by accounting for the effects of both short-term growing season weather variations and long-term climate change on crop yields. Besides, we develop the CropNet package, offering three types of APIs, for facilitating researchers in downloading the CropNet data on the fly over the time and region of interest, and flexibly building their deep learning models for accurate crop yield predictions. Extensive experiments have been conducted on our CropNet dataset via employing various types of deep learning solutions, with the results validating the general applicability and the efficacy of the CropNet dataset in climate change-aware crop yield predictions. We have officially released our CropNet dataset on Hugging Face Datasets https://huggingface.co/datasets/CropNet/CropNet and our CropNet package on the Python Package Index (PyPI) https://pypi.org/project/cropnet. Code and tutorials are available at https://github.com/fudong03/CropNet.|准确的作物产量预测对于确保粮食安全和可持续农业做法具有国家重要性。虽然人工智能科学方法在解决诸如药物发现，降水临近预报等许多科学问题方面显示出有希望的成就，但是预测作物产量的深度学习模型的发展不断受到阻碍，因为缺乏具有多种方式以容纳足够信息的开放和大规模的深度学习准备数据集。为了解决这个问题，我们引入了 CropNet 数据集，这是第一个兆字节大小的公开可用的多模式数据集，专门针对县级美国本土(美国)大陆的气候变化意识作物产量预测。我们的 CropNet 数据集由三种模式的数据组成，即哨兵 -2图像，WRF-HRRR 计算数据集和美国农业部作物数据集，跨越6年(2017-2022)的2200多个美国县，预计将促进研究人员开发通用的深度学习模型，通过考虑短期生长季节气候变化和长期气候变化对作物产量的影响，及时和精确地预测县级作物产量。此外，我们还开发了 CropNet 软件包，提供三种类型的 API，以方便研究人员在感兴趣的时间和地区动态下载 CropNet 数据，并灵活地建立深度学习模型，以准确预测作物产量。通过使用各种类型的深度学习解决方案，在我们的 CropNet 数据集上进行了广泛的实验，结果验证了 CropNet 数据集在气候变化意识作物产量预测方面的普遍适用性和有效性。我们已经在 Hugging Face 数据集 https://huggingface.co/Datasets/CropNet/CropNet 上正式发布了我们的 CropNet 数据集，在 Python Package Index (PyPI) https://PyPI.org/project/CropNet 上正式发布了我们的 CropNet 包。代码和教程可在 https://github.com/fudong03/cropnet 下载。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=An+Open+and+Large-Scale+Dataset+for+Multi-Modal+Climate+Change-aware+Crop+Yield+Predictions)|0| -|[Modeling User Retention through Generative Flow Networks](https://doi.org/10.1145/3637528.3671531)|Ziru Liu, Shuchang Liu, Bin Yang, Zhenghai Xue, Qingpeng Cai, Xiangyu Zhao, Zijian Zhang, Lantao Hu, Han Li, Peng Jiang|Kuaishou Technology, Beijing, China; Nanyang Technological University, Singapore, Singapore; City University of Hong Kong, Hong Kong, China|Recommender systems aim to fulfill the user's daily demands. While mostexisting research focuses on maximizing the user's engagement with the system,it has recently been pointed out that how frequently the users come back forthe service also reflects the quality and stability of recommendations.However, optimizing this user retention behavior is non-trivial and posesseveral challenges including the intractable leave-and-return user activities,the sparse and delayed signal, and the uncertain relations between users'retention and their immediate feedback towards each item in the recommendationlist. In this work, we regard the retention signal as an overall estimation ofthe user's end-of-session satisfaction and propose to estimate this signalthrough a probabilistic flow. This flow-based modeling technique canback-propagate the retention reward towards each recommended item in the usersession, and we show that the flow combined with traditional learning-to-rankobjectives eventually optimizes a non-discounted cumulative reward for bothimmediate user feedback and user retention. We verify the effectiveness of ourmethod through both offline empirical studies on two public datasets and onlineA/B tests in an industrial platform.|推荐系统旨在满足用户的日常需求。虽然大多数现有的研究集中在最大限度地提高用户对系统的参与，但是最近有人指出，用户回访服务的频率也反映了推荐的质量和稳定性。然而，优化这种用户保留行为是非常重要的，并且存在一些挑战，包括棘手的离开和返回用户活动，稀疏和延迟信号，以及用户保留与他们对推荐列表中每个项目的即时反馈之间的不确定关系。在这项工作中，我们认为保留信号作为一个用户的会话结束满意度的总体估计，并建议通过一个概率流估计这个信号。这种基于流的建模技术可以反向传播用户会话中每个推荐项目的保留奖励，我们表明流与传统的学习到排名目标相结合，最终优化了用户即时反馈和用户保留的非折扣累积奖励。通过对两个公共数据集的离线实证研究和工业平台上的在线 A/B 检验，验证了本文方法的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Modeling+User+Retention+through+Generative+Flow+Networks)|0| -|[BacktrackSTL: Ultra-Fast Online Seasonal-Trend Decomposition with Backtrack Technique](https://doi.org/10.1145/3637528.3671510)|Haoyu Wang, Hongke Guo, Zhaoliang Zhu, You Zhang, Yu Zhou, Xudong Zheng|Alibaba Group, Beijing, China; Alibaba Group, Hangzhou, Zhejiang, China; Alibaba Group, Zhejiang, Hangzhou, China|Seasonal-trend decomposition (STD) is a crucial task in time series data analysis. Due to the challenges of scalability, there is a pressing need for an ultra-fast online algorithm. However, existing algorithms either fail to handle long-period time series (such as OnlineSTL), or need time-consuming iterative processes (such as OneShotSTL). Therefore, we propose BacktrackSTL, the first non-iterative online STD algorithm with period-independent O(1) update complexity. It is also robust to outlier, seasonality shift and trend jump because of the combination of outlier-resilient smoothing, non-local seasonal filtering and backtrack technique. Experimentally, BacktrackSTL decomposes a value within 1.6 μs, which is 15X faster than the state-of-the-art online algorithm OneShotSTL, while maintaining comparable accuracy to the best offline algorithm RobustSTL. We have also deployed BacktrackSTL on the top of Apache Flink to decompose monitoring metrics in Alibaba Cloud for over a year. Besides, we have open-sourced the artifact of this proposal on GitHub.|季节趋势分解(STD)是时间序列数据分析中的一项重要任务。由于可扩展性的挑战，迫切需要一种超快的在线算法。然而，现有的算法要么不能处理长周期时间序列(如 OnlineSTL) ，要么需要耗时的迭代过程(如 OneShotSTL)。因此，我们提出了 BacktrackSTL，这是第一个具有周期无关的 O (1)更新复杂度的非迭代在线 STD 算法。异常点弹性平滑、非局部季节性滤波和回溯技术相结合，使得该方法对异常点、季节性变化和趋势跳变具有较强的鲁棒性。实验上，BacktrackSTL 在1.6 μs 内分解一个值，这比最先进的在线算法 OneShotSTL 快15倍，同时保持与最好的离线算法 RobustSTL 相当的精度。我们还在 Apache Flink 顶部部署了 BacktrackSTL 来分解阿里巴巴云中的监控指标，时间已经超过一年。此外，我们已经在 GitHub 上开源了这个提议的工件。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=BacktrackSTL:+Ultra-Fast+Online+Seasonal-Trend+Decomposition+with+Backtrack+Technique)|0| +|[Modeling User Retention through Generative Flow Networks](https://doi.org/10.1145/3637528.3671531)|Ziru Liu, Shuchang Liu, Bin Yang, Zhenghai Xue, Qingpeng Cai, Xiangyu Zhao, Zijian Zhang, Lantao Hu, Han Li, Peng Jiang|Nanyang Technological University, Singapore, Singapore; City University of Hong Kong, Hong Kong, China; Kuaishou Technology, Beijing, China|Recommender systems aim to fulfill the user's daily demands. While mostexisting research focuses on maximizing the user's engagement with the system,it has recently been pointed out that how frequently the users come back forthe service also reflects the quality and stability of recommendations.However, optimizing this user retention behavior is non-trivial and posesseveral challenges including the intractable leave-and-return user activities,the sparse and delayed signal, and the uncertain relations between users'retention and their immediate feedback towards each item in the recommendationlist. In this work, we regard the retention signal as an overall estimation ofthe user's end-of-session satisfaction and propose to estimate this signalthrough a probabilistic flow. This flow-based modeling technique canback-propagate the retention reward towards each recommended item in the usersession, and we show that the flow combined with traditional learning-to-rankobjectives eventually optimizes a non-discounted cumulative reward for bothimmediate user feedback and user retention. We verify the effectiveness of ourmethod through both offline empirical studies on two public datasets and onlineA/B tests in an industrial platform.|推荐系统旨在满足用户的日常需求。虽然大多数现有的研究集中在最大限度地提高用户对系统的参与，但是最近有人指出，用户回访服务的频率也反映了推荐的质量和稳定性。然而，优化这种用户保留行为是非常重要的，并且存在一些挑战，包括棘手的离开和返回用户活动，稀疏和延迟信号，以及用户保留与他们对推荐列表中每个项目的即时反馈之间的不确定关系。在这项工作中，我们认为保留信号作为一个用户的会话结束满意度的总体估计，并建议通过一个概率流估计这个信号。这种基于流的建模技术可以反向传播用户会话中每个推荐项目的保留奖励，我们表明流与传统的学习到排名目标相结合，最终优化了用户即时反馈和用户保留的非折扣累积奖励。通过对两个公共数据集的离线实证研究和工业平台上的在线 A/B 检验，验证了本文方法的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Modeling+User+Retention+through+Generative+Flow+Networks)|0| +|[BacktrackSTL: Ultra-Fast Online Seasonal-Trend Decomposition with Backtrack Technique](https://doi.org/10.1145/3637528.3671510)|Haoyu Wang, Hongke Guo, Zhaoliang Zhu, You Zhang, Yu Zhou, Xudong Zheng|Alibaba Group, Beijing, China; Alibaba Group, Zhejiang, Hangzhou, China; Alibaba Group, Hangzhou, Zhejiang, China|Seasonal-trend decomposition (STD) is a crucial task in time series data analysis. Due to the challenges of scalability, there is a pressing need for an ultra-fast online algorithm. However, existing algorithms either fail to handle long-period time series (such as OnlineSTL), or need time-consuming iterative processes (such as OneShotSTL). Therefore, we propose BacktrackSTL, the first non-iterative online STD algorithm with period-independent O(1) update complexity. It is also robust to outlier, seasonality shift and trend jump because of the combination of outlier-resilient smoothing, non-local seasonal filtering and backtrack technique. Experimentally, BacktrackSTL decomposes a value within 1.6 μs, which is 15X faster than the state-of-the-art online algorithm OneShotSTL, while maintaining comparable accuracy to the best offline algorithm RobustSTL. We have also deployed BacktrackSTL on the top of Apache Flink to decompose monitoring metrics in Alibaba Cloud for over a year. Besides, we have open-sourced the artifact of this proposal on GitHub.|季节趋势分解(STD)是时间序列数据分析中的一项重要任务。由于可扩展性的挑战，迫切需要一种超快的在线算法。然而，现有的算法要么不能处理长周期时间序列(如 OnlineSTL) ，要么需要耗时的迭代过程(如 OneShotSTL)。因此，我们提出了 BacktrackSTL，这是第一个具有周期无关的 O (1)更新复杂度的非迭代在线 STD 算法。异常点弹性平滑、非局部季节性滤波和回溯技术相结合，使得该方法对异常点、季节性变化和趋势跳变具有较强的鲁棒性。实验上，BacktrackSTL 在1.6 μs 内分解一个值，这比最先进的在线算法 OneShotSTL 快15倍，同时保持与最好的离线算法 RobustSTL 相当的精度。我们还在 Apache Flink 顶部部署了 BacktrackSTL 来分解阿里巴巴云中的监控指标，时间已经超过一年。此外，我们已经在 GitHub 上开源了这个提议的工件。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=BacktrackSTL:+Ultra-Fast+Online+Seasonal-Trend+Decomposition+with+Backtrack+Technique)|0| |[Deep Ensemble Shape Calibration: Multi-Field Post-hoc Calibration in Online Advertising](https://doi.org/10.1145/3637528.3671529)|Shuai Yang, Hao Yang, Zhuang Zou, Linhe Xu, Shuo Yuan, Yifan Zeng|Shopee Discovery Ads, Beijing, China|In the e-commerce advertising scenario, estimating the true probabilities (known as a calibrated estimate) on Click-Through Rate (CTR) and Conversion Rate (CVR) is critical. Previous research has introduced numerous solutions for addressing the calibration problem. These methods typically involve the training of calibrators using a validation set and subsequently applying these calibrators to correct the original estimated values during online inference. However, what sets e-commerce advertising scenarios is the challenge of multi-field calibration. Multi-field calibration requires achieving calibration in each field. In order to achieve multi-field calibration, it is necessary to have a strong data utilization ability. Because the quantity of pCTR specified range for single field-value (such as user ID and item ID) sample is relatively small, which makes the calibrator more difficult to train. However, existing methods have difficulty effectively addressing these issues. To solve these problems, we propose a new method named Deep Ensemble Shape Calibration (DESC). In terms of business understanding and interpretability, we decompose multi-field calibration into value calibration and shape calibration. We introduce innovative basis calibration functions, which enhance both function expression capabilities and data utilization by combining these basis calibration functions. A significant advancement lies in the development of an allocator capable of allocating the most suitable calibrators to different estimation error distributions within diverse fields and values. We achieve significant improvements in both public and industrial datasets. In online experiments, we observe a +2.5% increase in CVR and +4.0% in GMV (Gross Merchandise Volume). Our code is now available at: https://github.com/HaoYang0123/DESC.|在电子商务广告场景中，估计点进率(CTR)和转化率(CVR)的真实概率(称为校准估计)是至关重要的。以往的研究已经提出了许多解决校准问题的方案。这些方法通常涉及使用验证集对校准器进行训练，然后应用这些校准器在在线推断期间校正原始估计值。然而，设置电子商务广告情景是多领域标定的挑战。多场校准要求实现每个场的校准。为了实现多场校准，必须具有较强的数据利用能力。由于单一场值(如用户 ID 和项目 ID)样品的 pCTR 指定范围的数量相对较小，使得校准器的训练更加困难。然而，现有的方法很难有效地解决这些问题。为了解决这些问题，我们提出了一种新的方法——深度集合形状标定(DESC)。从业务理解和可解释性的角度出发，将多场校准分解为数值校准和形状校准。我们引入了创新的基校正函数，通过将这些基校正函数结合起来，提高了函数表达能力和数据利用率。一个重要的进步在于分配器的发展，能够分配最合适的校准器，以不同的领域和价值不同的估计误差分布。我们在公共数据集和工业数据集方面都取得了显著的改进。在线实验中，我们观察到 CVR 增加了2.5% ，GMV (商品总量)增加了4.0% 。我们的代码现在可以在以下 https://github.com/haoyang0123/desc 找到。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Deep+Ensemble+Shape+Calibration:+Multi-Field+Post-hoc+Calibration+in+Online+Advertising)|0| -|[GraphStorm: All-in-one Graph Machine Learning Framework for Industry Applications](https://doi.org/10.1145/3637528.3671603)|Da Zheng, Xiang Song, Qi Zhu, Jian Zhang, Theodore Vasiloudis, Runjie Ma, Houyu Zhang, Zichen Wang, Soji Adeshina, Israt Nisa, Alejandro Mottini, Qingjun Cui, Huzefa Rangwala, Belinda Zeng, Christos Faloutsos, George Karypis|Amazon AWS AI, Seattle, WA, USA; Amazon AWS AI, Washington, D.C., USA; Amazon Search AI, Seattle, WA, USA; Amazon Search AI, Palo Alto, CA, USA; Amazon SP, Seattle, WA, USA; Amazon AWS AI, New York, NY, USA; Amazon AWS AI, Santa Clara, CA, USA|Graph machine learning (GML) is effective in many business applications.However, making GML easy to use and applicable to industry applications withmassive datasets remain challenging. We developed GraphStorm, which provides anend-to-end solution for scalable graph construction, graph model training andinference. GraphStorm has the following desirable properties: (a) Easy to use:it can perform graph construction and model training and inference with just asingle command; (b) Expert-friendly: GraphStorm contains many advanced GMLmodeling techniques to handle complex graph data and improve model performance;(c) Scalable: every component in GraphStorm can operate on graphs with billionsof nodes and can scale model training and inference to different hardwarewithout changing any code. GraphStorm has been used and deployed for over adozen billion-scale industry applications after its release in May 2023. It isopen-sourced in Github: https://github.com/awslabs/graphstorm.|图形机器学习(GML)在许多商业应用中非常有效。然而，使 GML 易于使用并适用于拥有大量数据集的工业应用程序仍然具有挑战性。我们开发了 GraphStorm，它为可伸缩图的构建、图模型的训练和推理提供了端到端的解决方案。GraphStorm 有以下令人满意的特性: (a)易于使用: 它只需一个命令就可以执行图形构造和模型训练和推理; (b)专家友好型: GraphStorm 包含许多先进的 GML 建模技术，可以处理复杂的图形数据和提高模型性能; (c)可扩展性: GraphStorm 中的每个组件都可以操作具有数十亿个节点的图形，并且可以在不改变任何代码的情况下对不同的硬件进行模型训练和推理。GraphStorm 自2023年5月发布以来，已经在超过10亿个规模的行业应用程序中使用和部署。它在 Github 中是开源的: https://Github.com/awslabs/graphstorm。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=GraphStorm:+All-in-one+Graph+Machine+Learning+Framework+for+Industry+Applications)|0| -|[A Tutorial on Multi-Armed Bandit Applications for Large Language Models](https://doi.org/10.1145/3637528.3671440)|Djallel Bouneffouf, Raphaël Féraud|Orange Orange Innovation, Lannion, France; IBM Research, New York, New York, USA|This tutorial offers a comprehensive guide on using multi-armed bandit (MAB) algorithms to improve Large Language Models (LLMs). As Natural Language Processing (NLP) tasks grow, efficient and adaptive language generation systems are increasingly needed. MAB algorithms, which balance exploration and exploitation under uncertainty, are promising for enhancing LLMs. The tutorial covers foundational MAB concepts, including the exploration-exploitation trade-off and strategies like epsilon-greedy, UCB (Upper Confidence Bound), and Thompson Sampling. It then explores integrating MAB with LLMs, focusing on designing architectures that treat text generation options as arms in a bandit problem. Practical aspects like reward design, exploration policies, and scalability are discussed. Real-world case studies demonstrate the benefits of MAB-augmented LLMs in content recommendation, dialogue generation, and personalized content creation, showing how these techniques improve relevance, diversity, and user engagement.|本教程提供了一个关于使用多臂老虎机(MAB)算法来改进大型语言模型(LLM)的全面指南。随着自然语言处理(NLP)任务的增加，对高效、自适应的语言生成系统的需求越来越大。MAB 算法能够在不确定条件下平衡勘探和开发，在提高 LLM 方面具有广阔的应用前景。本教程涵盖了基本的 MAB 概念，包括勘探-开发权衡和策略，如 epsilon- 贪婪、 UCB (上限置信区间)和 Thompson 抽样。然后，本文探讨了将 MAB 与 LLM 集成在一起的问题，重点是设计将文本生成选项视为土匪问题中的武器的体系结构。讨论了奖励设计、探索策略和可伸缩性等实际问题。现实世界的案例研究证明了 MAB 增强 LLM 在内容推荐、对话生成和个性化内容创建方面的好处，展示了这些技术如何提高相关性、多样性和用户参与度。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Tutorial+on+Multi-Armed+Bandit+Applications+for+Large+Language+Models)|0| -|[Domain-Driven LLM Development: Insights into RAG and Fine-Tuning Practices](https://doi.org/10.1145/3637528.3671445)|José Cassio dos Santos Junior, Rachel Hu, Richard Song, Yunfei Bai|Amazon Web Services, Seattle, Washington, USA; Epsilla, Jersey City, New Jersey, USA; CambioML Corp, San Jose, California, USA|To improve Large Language Model (LLM) performance on domain specific applications, ML developers often leverage Retrieval Augmented Generation (RAG) and LLM Fine-Tuning. RAG extends the capabilities of LLMs to specific domains or an organization's internal knowledge base, without the need to retrain the model. On the other hand, Fine-Tuning approach updates LLM weights with domain-specific data to improve performance on specific tasks. The fine-tuned model is particularly effective to systematically learn new comprehensive knowledge in a specific domain that is not covered by the LLM pre-training. This tutorial walks through the RAG and Fine-Tuning techniques, discusses the insights of their advantages and limitations, and provides best practices of adopting the methodologies for the LLM tasks and use cases. The hands-on labs demonstrate the advanced techniques to optimize the RAG and fine-tuned LLM architecture that handles domain specific LLM tasks. The labs in the tutorial are designed by using a set of open-source python libraries to implement the RAG and fine-tuned LLM architecture.|为了提高特定领域应用程序的大语言模型(LLM)性能，ML 开发人员经常利用检索增强生成(RAG)和 LLM 微调。RAG 将 LLM 的功能扩展到特定领域或组织的内部知识库，而不需要对模型进行再培训。另一方面，微调方法使用特定于领域的数据更新 LLM 权重，以提高特定任务的性能。经过微调的模型对于系统地学习某一特定领域的新的综合知识尤其有效，而这一领域不在 LLM 预训范围之内。本教程介绍了 RAG 和微调技术，讨论了它们的优点和局限性，并提供了为 LLM 任务和用例采用方法的最佳实践。实际操作的实验室演示了优化 RAG 的先进技术和处理领域特定 LLM 任务的微调 LLM 体系结构。本教程中的实验室是通过使用一组开放源码的 python 库来实现 RAG 和经过微调的 LLM 体系结构来设计的。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Domain-Driven+LLM+Development:+Insights+into+RAG+and+Fine-Tuning+Practices)|0| -|[Recent and Upcoming Developments in Randomized Numerical Linear Algebra for Machine Learning](https://doi.org/10.1145/3637528.3671461)|Michal Derezinski, Michael W. Mahoney|ICSI, LBNL, and University of California, Berkeley, USA; University of Michigan, Ann Arbor, USA|Large matrices arise in many machine learning and data analysis applications,including as representations of datasets, graphs, model weights, and first andsecond-order derivatives. Randomized Numerical Linear Algebra (RandNLA) is anarea which uses randomness to develop improved algorithms for ubiquitous matrixproblems. The area has reached a certain level of maturity; but recent hardwaretrends, efforts to incorporate RandNLA algorithms into core numericallibraries, and advances in machine learning, statistics, and random matrixtheory, have lead to new theoretical and practical challenges. This articleprovides a self-contained overview of RandNLA, in light of these developments.|大矩阵出现在许多机器学习和数据分析应用中，包括数据集、图形、模型权重以及一阶和二阶导数的表示。随机数值线性代数(RandNLA)是一个利用随机性开发改进算法的领域，用于解决无处不在的矩阵问题。这个领域已经达到了一定程度的成熟; 但是最近的硬件趋势，将 RandNLA 算法融入核心数字库的努力，以及机器学习、统计学和随机矩阵理论的进步，已经导致了新的理论和实践挑战。根据这些发展，本文提供了 RandNLA 的独立概述。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Recent+and+Upcoming+Developments+in+Randomized+Numerical+Linear+Algebra+for+Machine+Learning)|0| -|[Graph Machine Learning Meets Multi-Table Relational Data](https://doi.org/10.1145/3637528.3671471)|Quan Gan, Minjie Wang, David Wipf, Christos Faloutsos|CMU & Amazon, Pittsburgh, PA, USA; Amazon, Shanghai, China|While graph machine learning, and notably graph neural networks (GNNs), have gained immense traction in recent years, application is predicated on access to a known input graph upon which predictive models can be trained. And indeed, within the most widely-studied public evaluation benchmarks such graphs are provided, with performance comparisons conditioned on curated data explicitly adhering to this graph. However, in real-world industrial applications, the situation is often quite different. Instead of a known graph, data are originally collected and stored across multiple tables in a repository, at times with ambiguous or incomplete relational structure. As such, to leverage the latest GNN architectures it is then up to a skilled data scientist to first manually construct a graph using intuition and domain knowledge, a laborious process that may discourage adoption in the first place. To narrow this gap and broaden the applicability of graph ML, we survey existing tools and strategies that can be combined to address the more fundamental problem of predictive tabular modeling over data native to multiple tables, with no explicit relational structure assumed a priori. This involves tracing a comprehensive path through related table join discovery and fuzzy table joining, column alignment, automated relational database (RDB) construction, extracting graphs from RDBs, graph sampling, and finally, graph-centric trainable predictive architectures. Although efforts to build deployable systems that integrate all of these components while minimizing manual effort remain in their infancy, this survey will nonetheless reduce barriers to entry and help steer the graph ML community towards promising research directions and wider real-world impact.|虽然近年来图机学习，尤其是图神经网络(GNN)已经获得了巨大的推动力，但是应用的前提是获得一个可以训练预测模型的已知输入图。事实上，在研究最广泛的公共评价基准中，提供了这样的图表，其绩效比较的条件是明确遵守这一图表的精选数据。然而，在实际的工业应用中，情况往往大不相同。数据最初是通过存储库中的多个表收集和存储的，而不是已知的图形，有时关系结构不明确或不完整。因此，要利用最新的 GNN 架构，就需要一位技术娴熟的数据科学家首先利用直觉和领域知识手动构建一个图表，这是一个费力的过程，可能首先会阻碍采用。为了缩小这个差距和扩大图形 ML 的适用性，我们调查了现有的工具和策略，这些工具和策略可以结合起来解决更基本的问题，即对多个表本身的数据进行预测性表格建模，没有明确的关系结构假设先验。这包括通过相关的表连接发现和模糊表连接、列对齐、自动化关系数据库(RDB)构建、从关系数据库中提取图形、图形采样，以及以图形为中心的可训练预测架构来跟踪一个全面的路径。尽管建立集成所有这些组件同时尽量减少人工操作的可部署系统的努力仍然处于起步阶段，但这项调查将减少进入的障碍，并有助于引导机器学习图形社区朝着有前途的研究方向和更广泛的现实世界影响。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Graph+Machine+Learning+Meets+Multi-Table+Relational+Data)|0| -|[Systems for Scalable Graph Analytics and Machine Learning: Trends and Methods](https://doi.org/10.1145/3637528.3671472)|Da Yan, Lyuheng Yuan, Akhlaque Ahmad, Chenguang Zheng, Hongzhi Chen, James Cheng|; Kasma Pte. Ltd., Singapore, Singapore; Department of Computer Science, Indiana University Bloomington, Bloomington, Indiana, USA|Graph-theoretic algorithms and graph machine learning models are essential tools for addressing many real-life problems, such as social network analysis and bioinformatics. To support large-scale graph analytics, graph-parallel systems have been actively developed for over one decade, such as Google's Pregel and Spark's GraphX, which (i) promote a think-like-a-vertex computing model and target (ii) iterative algorithms and (iii) those problems that output a value for each vertex. However, this model is too restricted for supporting the rich set of heterogeneous operations for graph analytics and machine learning that many real applications demand. In recent years, two new trends emerge in graph-parallel systems research: (1) a novel think-like-a-task computing model that can efficiently support the various computationally expensive problems of subgraph search; and (2) scalable systems for learning graph neural networks. These systems effectively complement the diversity needs of graph-parallel tools that can flexibly work together in a comprehensive graph processing pipeline for real applications, with the capability of capturing structural features. This tutorial will provide an effective categorization of the recent systems in these two directions based on their computing models and adopted techniques, and will review the key design ideas of these systems.|图论算法和图机学习模型是解决社会网络分析和生物信息学等现实问题的重要工具。为了支持大规模的图形分析，图形并行系统已经积极开发了10多年，例如谷歌的 Pregel 和 Spark 的 GraphX，它们(i)推广像顶点一样思考的计算模型和目标(ii)迭代算法，以及(iii)那些为每个顶点输出一个值的问题。然而，这个模型对于支持图分析和机器学习所需的丰富的异构操作集是非常有限的，这是许多实际应用所需要的。近年来，图并行系统的研究出现了两个新的趋势: (1)一种新的类任务思维计算模型，能够有效地支持子图搜索的各种计算昂贵的问题; (2)学习图神经网络的可扩展系统。这些系统有效地补充了图形并行工具的多样性需求，能够在面向实际应用的综合图形处理流水线中灵活地协同工作，并具有捕获结构特征的能力。本教程将根据计算模型和采用的技术对这两个方向的最新系统进行有效的分类，并将回顾这些系统的关键设计思想。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Systems+for+Scalable+Graph+Analytics+and+Machine+Learning:+Trends+and+Methods)|0| -|[Machine Learning in Finance](https://doi.org/10.1145/3637528.3671488)|Leman Akoglu, Nitesh V. Chawla, Josep DomingoFerrer, Eren Kurshan, Senthil Kumar, Vidyut M. Naware, José A. RodríguezSerrano, Isha Chaturvedi, Saurabh Nagrecha, Mahashweta Das, Tanveer A. Faruquie|Rennes Sch Business, 2 Rue Robert Arbrissel, F-35065 Rennes, France; Rennes Sch Business, Dept Strategy & Innovat, Rennes, France; Rennes Sch Business, Dept Finance & Accounting, Rennes, France; Dublin City Univ, DCU Business Sch, Financial & Operat Performance Grp, Dublin, Ireland|We provide a first comprehensive structuring of the literature applying machine learning to finance. We use a probabilistic topic modeling approach to make sense of this diverse body of research spanning across the disciplines of finance, economics, computer sciences, and decision sciences. Through the topic modelling approach, a Latent Dirichlet Allocation technique, we are able to extract the 14 coherent research topics that are the focus of the 5,204 academic articles we analyze from the years 1990 to 2018. We first describe and structure these topics, and then further show how the topic focus has evolved over the last two decades. Our study thus provides a structured topography for finance researchers seeking to integrate machine learning research approaches in their exploration of finance phenomena. We also showcase the benefits to finance researchers of the method of probabilistic modeling of topics for deep comprehension of a body of literature, especially when that literature has diverse multi-disciplinary actors.|我们提供了第一个综合结构的文献应用机器学习金融。我们使用概率主题建模方法来理解这种跨越金融、经济、计算机科学和决策科学学科的多样化研究体系。通过主题建模(一种隐含狄利克雷分布技术) ，我们能够提取出14个连贯的研究主题，这些主题是我们从1990年到2018年分析的5204篇学术论文的重点。我们首先描述和构建这些主题，然后进一步说明主题焦点在过去二十年中是如何演变的。因此，我们的研究提供了一个结构化的地形金融研究人员寻求整合机器学习研究方法在他们的金融现象的探索。我们还展示了金融研究人员的好处，为深入理解文献主体的主题的概率建模方法，特别是当文献有不同的多学科行为者。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Machine+Learning+in+Finance)|0| +|[GraphStorm: All-in-one Graph Machine Learning Framework for Industry Applications](https://doi.org/10.1145/3637528.3671603)|Da Zheng, Xiang Song, Qi Zhu, Jian Zhang, Theodore Vasiloudis, Runjie Ma, Houyu Zhang, Zichen Wang, Soji Adeshina, Israt Nisa, Alejandro Mottini, Qingjun Cui, Huzefa Rangwala, Belinda Zeng, Christos Faloutsos, George Karypis|Amazon AWS AI, Washington, D.C., USA; Amazon AWS AI, Seattle, WA, USA; Amazon Search AI, Palo Alto, CA, USA; Amazon SP, Seattle, WA, USA; Amazon AWS AI, Santa Clara, CA, USA; Amazon AWS AI, New York, NY, USA; Amazon Search AI, Seattle, WA, USA|Graph machine learning (GML) is effective in many business applications.However, making GML easy to use and applicable to industry applications withmassive datasets remain challenging. We developed GraphStorm, which provides anend-to-end solution for scalable graph construction, graph model training andinference. GraphStorm has the following desirable properties: (a) Easy to use:it can perform graph construction and model training and inference with just asingle command; (b) Expert-friendly: GraphStorm contains many advanced GMLmodeling techniques to handle complex graph data and improve model performance;(c) Scalable: every component in GraphStorm can operate on graphs with billionsof nodes and can scale model training and inference to different hardwarewithout changing any code. GraphStorm has been used and deployed for over adozen billion-scale industry applications after its release in May 2023. It isopen-sourced in Github: https://github.com/awslabs/graphstorm.|图形机器学习(GML)在许多商业应用中非常有效。然而，使 GML 易于使用并适用于拥有大量数据集的工业应用程序仍然具有挑战性。我们开发了 GraphStorm，它为可伸缩图的构建、图模型的训练和推理提供了端到端的解决方案。GraphStorm 有以下令人满意的特性: (a)易于使用: 它只需一个命令就可以执行图形构造和模型训练和推理; (b)专家友好型: GraphStorm 包含许多先进的 GML 建模技术，可以处理复杂的图形数据和提高模型性能; (c)可扩展性: GraphStorm 中的每个组件都可以操作具有数十亿个节点的图形，并且可以在不改变任何代码的情况下对不同的硬件进行模型训练和推理。GraphStorm 自2023年5月发布以来，已经在超过10亿个规模的行业应用程序中使用和部署。它在 Github 中是开源的: https://Github.com/awslabs/graphstorm。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=GraphStorm:+All-in-one+Graph+Machine+Learning+Framework+for+Industry+Applications)|0| +|[A Tutorial on Multi-Armed Bandit Applications for Large Language Models](https://doi.org/10.1145/3637528.3671440)|Djallel Bouneffouf, Raphaël Féraud|IBM Research, New York, New York, USA; Orange Orange Innovation, Lannion, France|This tutorial offers a comprehensive guide on using multi-armed bandit (MAB) algorithms to improve Large Language Models (LLMs). As Natural Language Processing (NLP) tasks grow, efficient and adaptive language generation systems are increasingly needed. MAB algorithms, which balance exploration and exploitation under uncertainty, are promising for enhancing LLMs. The tutorial covers foundational MAB concepts, including the exploration-exploitation trade-off and strategies like epsilon-greedy, UCB (Upper Confidence Bound), and Thompson Sampling. It then explores integrating MAB with LLMs, focusing on designing architectures that treat text generation options as arms in a bandit problem. Practical aspects like reward design, exploration policies, and scalability are discussed. Real-world case studies demonstrate the benefits of MAB-augmented LLMs in content recommendation, dialogue generation, and personalized content creation, showing how these techniques improve relevance, diversity, and user engagement.|本教程提供了一个关于使用多臂老虎机(MAB)算法来改进大型语言模型(LLM)的全面指南。随着自然语言处理(NLP)任务的增加，对高效、自适应的语言生成系统的需求越来越大。MAB 算法能够在不确定条件下平衡勘探和开发，在提高 LLM 方面具有广阔的应用前景。本教程涵盖了基本的 MAB 概念，包括勘探-开发权衡和策略，如 epsilon- 贪婪、 UCB (上限置信区间)和 Thompson 抽样。然后，本文探讨了将 MAB 与 LLM 集成在一起的问题，重点是设计将文本生成选项视为土匪问题中的武器的体系结构。讨论了奖励设计、探索策略和可伸缩性等实际问题。现实世界的案例研究证明了 MAB 增强 LLM 在内容推荐、对话生成和个性化内容创建方面的好处，展示了这些技术如何提高相关性、多样性和用户参与度。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Tutorial+on+Multi-Armed+Bandit+Applications+for+Large+Language+Models)|0| +|[Domain-Driven LLM Development: Insights into RAG and Fine-Tuning Practices](https://doi.org/10.1145/3637528.3671445)|José Cassio dos Santos Junior, Rachel Hu, Richard Song, Yunfei Bai|Epsilla, Jersey City, New Jersey, USA; Amazon Web Services, Seattle, Washington, USA; CambioML Corp, San Jose, California, USA|To improve Large Language Model (LLM) performance on domain specific applications, ML developers often leverage Retrieval Augmented Generation (RAG) and LLM Fine-Tuning. RAG extends the capabilities of LLMs to specific domains or an organization's internal knowledge base, without the need to retrain the model. On the other hand, Fine-Tuning approach updates LLM weights with domain-specific data to improve performance on specific tasks. The fine-tuned model is particularly effective to systematically learn new comprehensive knowledge in a specific domain that is not covered by the LLM pre-training. This tutorial walks through the RAG and Fine-Tuning techniques, discusses the insights of their advantages and limitations, and provides best practices of adopting the methodologies for the LLM tasks and use cases. The hands-on labs demonstrate the advanced techniques to optimize the RAG and fine-tuned LLM architecture that handles domain specific LLM tasks. The labs in the tutorial are designed by using a set of open-source python libraries to implement the RAG and fine-tuned LLM architecture.|为了提高特定领域应用程序的大语言模型(LLM)性能，ML 开发人员经常利用检索增强生成(RAG)和 LLM 微调。RAG 将 LLM 的功能扩展到特定领域或组织的内部知识库，而不需要对模型进行再培训。另一方面，微调方法使用特定于领域的数据更新 LLM 权重，以提高特定任务的性能。经过微调的模型对于系统地学习某一特定领域的新的综合知识尤其有效，而这一领域不在 LLM 预训范围之内。本教程介绍了 RAG 和微调技术，讨论了它们的优点和局限性，并提供了为 LLM 任务和用例采用方法的最佳实践。实际操作的实验室演示了优化 RAG 的先进技术和处理领域特定 LLM 任务的微调 LLM 体系结构。本教程中的实验室是通过使用一组开放源码的 python 库来实现 RAG 和经过微调的 LLM 体系结构来设计的。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Domain-Driven+LLM+Development:+Insights+into+RAG+and+Fine-Tuning+Practices)|0| +|[Recent and Upcoming Developments in Randomized Numerical Linear Algebra for Machine Learning](https://doi.org/10.1145/3637528.3671461)|Michal Derezinski, Michael W. Mahoney|University of Michigan, Ann Arbor, USA; ICSI, LBNL, and University of California, Berkeley, USA|Large matrices arise in many machine learning and data analysis applications,including as representations of datasets, graphs, model weights, and first andsecond-order derivatives. Randomized Numerical Linear Algebra (RandNLA) is anarea which uses randomness to develop improved algorithms for ubiquitous matrixproblems. The area has reached a certain level of maturity; but recent hardwaretrends, efforts to incorporate RandNLA algorithms into core numericallibraries, and advances in machine learning, statistics, and random matrixtheory, have lead to new theoretical and practical challenges. This articleprovides a self-contained overview of RandNLA, in light of these developments.|大矩阵出现在许多机器学习和数据分析应用中，包括数据集、图形、模型权重以及一阶和二阶导数的表示。随机数值线性代数(RandNLA)是一个利用随机性开发改进算法的领域，用于解决无处不在的矩阵问题。这个领域已经达到了一定程度的成熟; 但是最近的硬件趋势，将 RandNLA 算法融入核心数字库的努力，以及机器学习、统计学和随机矩阵理论的进步，已经导致了新的理论和实践挑战。根据这些发展，本文提供了 RandNLA 的独立概述。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Recent+and+Upcoming+Developments+in+Randomized+Numerical+Linear+Algebra+for+Machine+Learning)|0| +|[Graph Machine Learning Meets Multi-Table Relational Data](https://doi.org/10.1145/3637528.3671471)|Quan Gan, Minjie Wang, David Wipf, Christos Faloutsos|Amazon, Shanghai, China; CMU & Amazon, Pittsburgh, PA, USA|While graph machine learning, and notably graph neural networks (GNNs), have gained immense traction in recent years, application is predicated on access to a known input graph upon which predictive models can be trained. And indeed, within the most widely-studied public evaluation benchmarks such graphs are provided, with performance comparisons conditioned on curated data explicitly adhering to this graph. However, in real-world industrial applications, the situation is often quite different. Instead of a known graph, data are originally collected and stored across multiple tables in a repository, at times with ambiguous or incomplete relational structure. As such, to leverage the latest GNN architectures it is then up to a skilled data scientist to first manually construct a graph using intuition and domain knowledge, a laborious process that may discourage adoption in the first place. To narrow this gap and broaden the applicability of graph ML, we survey existing tools and strategies that can be combined to address the more fundamental problem of predictive tabular modeling over data native to multiple tables, with no explicit relational structure assumed a priori. This involves tracing a comprehensive path through related table join discovery and fuzzy table joining, column alignment, automated relational database (RDB) construction, extracting graphs from RDBs, graph sampling, and finally, graph-centric trainable predictive architectures. Although efforts to build deployable systems that integrate all of these components while minimizing manual effort remain in their infancy, this survey will nonetheless reduce barriers to entry and help steer the graph ML community towards promising research directions and wider real-world impact.|虽然近年来图机学习，尤其是图神经网络(GNN)已经获得了巨大的推动力，但是应用的前提是获得一个可以训练预测模型的已知输入图。事实上，在研究最广泛的公共评价基准中，提供了这样的图表，其绩效比较的条件是明确遵守这一图表的精选数据。然而，在实际的工业应用中，情况往往大不相同。数据最初是通过存储库中的多个表收集和存储的，而不是已知的图形，有时关系结构不明确或不完整。因此，要利用最新的 GNN 架构，就需要一位技术娴熟的数据科学家首先利用直觉和领域知识手动构建一个图表，这是一个费力的过程，可能首先会阻碍采用。为了缩小这个差距和扩大图形 ML 的适用性，我们调查了现有的工具和策略，这些工具和策略可以结合起来解决更基本的问题，即对多个表本身的数据进行预测性表格建模，没有明确的关系结构假设先验。这包括通过相关的表连接发现和模糊表连接、列对齐、自动化关系数据库(RDB)构建、从关系数据库中提取图形、图形采样，以及以图形为中心的可训练预测架构来跟踪一个全面的路径。尽管建立集成所有这些组件同时尽量减少人工操作的可部署系统的努力仍然处于起步阶段，但这项调查将减少进入的障碍，并有助于引导机器学习图形社区朝着有前途的研究方向和更广泛的现实世界影响。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Graph+Machine+Learning+Meets+Multi-Table+Relational+Data)|0| +|[Systems for Scalable Graph Analytics and Machine Learning: Trends and Methods](https://doi.org/10.1145/3637528.3671472)|Da Yan, Lyuheng Yuan, Akhlaque Ahmad, Chenguang Zheng, Hongzhi Chen, James Cheng|; Department of Computer Science, Indiana University Bloomington, Bloomington, Indiana, USA; Kasma Pte. Ltd., Singapore, Singapore|Graph-theoretic algorithms and graph machine learning models are essential tools for addressing many real-life problems, such as social network analysis and bioinformatics. To support large-scale graph analytics, graph-parallel systems have been actively developed for over one decade, such as Google's Pregel and Spark's GraphX, which (i) promote a think-like-a-vertex computing model and target (ii) iterative algorithms and (iii) those problems that output a value for each vertex. However, this model is too restricted for supporting the rich set of heterogeneous operations for graph analytics and machine learning that many real applications demand. In recent years, two new trends emerge in graph-parallel systems research: (1) a novel think-like-a-task computing model that can efficiently support the various computationally expensive problems of subgraph search; and (2) scalable systems for learning graph neural networks. These systems effectively complement the diversity needs of graph-parallel tools that can flexibly work together in a comprehensive graph processing pipeline for real applications, with the capability of capturing structural features. This tutorial will provide an effective categorization of the recent systems in these two directions based on their computing models and adopted techniques, and will review the key design ideas of these systems.|图论算法和图机学习模型是解决社会网络分析和生物信息学等现实问题的重要工具。为了支持大规模的图形分析，图形并行系统已经积极开发了10多年，例如谷歌的 Pregel 和 Spark 的 GraphX，它们(i)推广像顶点一样思考的计算模型和目标(ii)迭代算法，以及(iii)那些为每个顶点输出一个值的问题。然而，这个模型对于支持图分析和机器学习所需的丰富的异构操作集是非常有限的，这是许多实际应用所需要的。近年来，图并行系统的研究出现了两个新的趋势: (1)一种新的类任务思维计算模型，能够有效地支持子图搜索的各种计算昂贵的问题; (2)学习图神经网络的可扩展系统。这些系统有效地补充了图形并行工具的多样性需求，能够在面向实际应用的综合图形处理流水线中灵活地协同工作，并具有捕获结构特征的能力。本教程将根据计算模型和采用的技术对这两个方向的最新系统进行有效的分类，并将回顾这些系统的关键设计思想。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Systems+for+Scalable+Graph+Analytics+and+Machine+Learning:+Trends+and+Methods)|0| +|[Machine Learning in Finance](https://doi.org/10.1145/3637528.3671488)|Leman Akoglu, Nitesh V. Chawla, Josep DomingoFerrer, Eren Kurshan, Senthil Kumar, Vidyut M. Naware, José A. RodríguezSerrano, Isha Chaturvedi, Saurabh Nagrecha, Mahashweta Das, Tanveer A. Faruquie|Dublin City Univ, DCU Business Sch, Financial & Operat Performance Grp, Dublin, Ireland; Rennes Sch Business, Dept Strategy & Innovat, Rennes, France; Rennes Sch Business, Dept Finance & Accounting, Rennes, France; Rennes Sch Business, 2 Rue Robert Arbrissel, F-35065 Rennes, France|We provide a first comprehensive structuring of the literature applying machine learning to finance. We use a probabilistic topic modeling approach to make sense of this diverse body of research spanning across the disciplines of finance, economics, computer sciences, and decision sciences. Through the topic modelling approach, a Latent Dirichlet Allocation technique, we are able to extract the 14 coherent research topics that are the focus of the 5,204 academic articles we analyze from the years 1990 to 2018. We first describe and structure these topics, and then further show how the topic focus has evolved over the last two decades. Our study thus provides a structured topography for finance researchers seeking to integrate machine learning research approaches in their exploration of finance phenomena. We also showcase the benefits to finance researchers of the method of probabilistic modeling of topics for deep comprehension of a body of literature, especially when that literature has diverse multi-disciplinary actors.|我们提供了第一个综合结构的文献应用机器学习金融。我们使用概率主题建模方法来理解这种跨越金融、经济、计算机科学和决策科学学科的多样化研究体系。通过主题建模(一种隐含狄利克雷分布技术) ，我们能够提取出14个连贯的研究主题，这些主题是我们从1990年到2018年分析的5204篇学术论文的重点。我们首先描述和构建这些主题，然后进一步说明主题焦点在过去二十年中是如何演变的。因此，我们的研究提供了一个结构化的地形金融研究人员寻求整合机器学习研究方法在他们的金融现象的探索。我们还展示了金融研究人员的好处，为深入理解文献主体的主题的概率建模方法，特别是当文献有不同的多学科行为者。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Machine+Learning+in+Finance)|0| |[From Word-prediction to Complex Skills: Compositional Thinking and Metacognition in LLMs](https://doi.org/10.1145/3637528.3672193)|Sanjeev Arora|Princeton University, Princeton, NJ, USA|The talk will present evidence that today's large language models (LLMs) display somewhat deeper "understanding'' than one would naively expect.1. When asked to solve a task by combining a set of k simpler skills ("test of compositional capability"), they are able to do so despite not having seen the same combination of skills during their training.2. They demonstrate ability to reason about of their own learning processes, which is analogous to "metacognitive knowledge"[Flavel'76] in humans. For instance, given examples of an evaluation task, they can produce a catalog of suitably named skills that are relevant for solving each example of that task. Furthermore, this catalog of skills is meaningful, in the sense that incorporating it into training pipelines improves performance (including of other unrelated LLMs) on that task.We discuss mechanisms by which such complex understanding could arise (including a theory by [Arora,Goyal'23] that tries to explain (a)) and also give examples of how to leverage LLM meta knowledge to improve LLM training pipelines as well as evaluations. 1. When asked to solve a task by combining a set of k simpler skills ("test of compositional capability"), they are able to do so despite not having seen the same combination of skills during their training. 2. They demonstrate ability to reason about of their own learning processes, which is analogous to "metacognitive knowledge"[Flavel'76] in humans. For instance, given examples of an evaluation task, they can produce a catalog of suitably named skills that are relevant for solving each example of that task. Furthermore, this catalog of skills is meaningful, in the sense that incorporating it into training pipelines improves performance (including of other unrelated LLMs) on that task.|这次演讲将提供证据，证明今天的大型语言模型(LLM)所表现出来的“理解”比人们天真地期望的要深刻得多。1。当被要求结合一系列简单的技能(“组合能力测试”)来解决一个任务时，他们能够做到这一点，尽管在他们的训练中没有看到相同的技能组合。2。他们展示了对自己的学习过程进行推理的能力，这类似于人类的“元认知知识”[ Flavel’76]。例如，给定一个评估任务的示例，他们可以生成一个适当命名的技能目录，这些技能与解决该任务的每个示例相关。此外，这个技能目录是有意义的，因为将其纳入培训管道可以提高该任务的表现(包括其他不相关的 LLM)。我们讨论了这种复杂的理解可能产生的机制(包括[ Arora，Goyal’23]试图解释(a)的理论) ，还举例说明如何利用 LLM 元知识来改善 LLM 培训管道以及评估。1.当被要求结合一系列简单的技能(“组合能力测试”)来解决一个任务时，他们能够做到这一点，尽管在他们的训练中没有看到相同的技能组合。2.他们展示了对自己的学习过程进行推理的能力，这类似于人类的“元认知知识”[ Flavel’76]。例如，给定一个评估任务的示例，他们可以生成一个适当命名的技能目录，这些技能与解决该任务的每个示例相关。此外，这种技能目录是有意义的，因为将其纳入培训管道可以提高该任务的性能(包括其他不相关的 LLM)。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=From+Word-prediction+to+Complex+Skills:+Compositional+Thinking+and+Metacognition+in+LLMs)|0| |[GEO: Generative Engine Optimization](https://doi.org/10.1145/3637528.3671900)|Pranjal Aggarwal, Vishvak Murahari, Tanmay Rajpurohit, Ashwin Kalyan, Karthik Narasimhan, Ameet Deshpande|Princeton University, Princeton, USA; Indian Institute of Technology Delhi, New Delhi, India; Independent, Seattle, USA|The advent of large language models (LLMs) has ushered in a new paradigm of search engines that use generative models to gather and summarize information to answer user queries. This emerging technology, which we formalize under the unified framework of generative engines (GEs), can generate accurate and personalized responses, rapidly replacing traditional search engines like Google and Bing. Generative Engines typically satisfy queries by synthesizing information from multiple sources and summarizing them using LLMs. While this shift significantly improvesuser utility and generative search engine traffic, it poses a huge challenge for the third stakeholder -- website and content creators. Given the black-box and fast-moving nature of generative engines, content creators have little to no control over when and how their content is displayed. With generative engines here to stay, we must ensure the creator economy is not disadvantaged. To address this, we introduce Generative Engine Optimization (GEO), the first novel paradigm to aid content creators in improving their content visibility in generative engine responses through a flexible black-box optimization framework for optimizing and defining visibility metrics. We facilitate systematic evaluation by introducing GEO-bench, a large-scale benchmark of diverse user queries across multiple domains, along with relevant web sources to answer these queries. Through rigorous evaluation, we demonstrate that GEO can boost visibility by up to 40% in generative engine responses. Moreover, we show the efficacy of these strategies varies across domains, underscoring the need for domain-specific optimization methods. Our work opens a new frontier in information discovery systems, with profound implications for both developers of generative engines and content creators.|大型语言模型(LLM)的出现开创了搜索引擎的新范式，这种搜索引擎使用生成模型来收集和总结信息，以回答用户的查询。这种新兴技术，我们在生成引擎(GEs)的统一框架下形式化，可以产生准确和个性化的响应，迅速取代传统的搜索引擎，如谷歌和必应。生成引擎通常通过合成来自多个源的信息并使用 LLM 对其进行汇总来满足查询。虽然这种转变显著地提高了用户效用和生成性搜索引擎流量，但是它对第三方利益相关者——网站和内容创建者——提出了巨大的挑战。考虑到生成引擎的黑盒子和快速移动的特性，内容创建者几乎不能控制何时以及如何显示他们的内容。随着生产引擎的存在，我们必须确保创造者经济不会处于不利地位。为了解决这个问题，我们引入了生成引擎优化(GEO) ，这是第一个通过灵活的黑盒优化框架来优化和定义可见性指标，从而帮助内容创建者在生成引擎响应中提高内容可见性的新范例。我们通过引入 GEO-bench 来促进系统评估，GEO-bench 是跨多个领域的不同用户查询的大规模基准，以及用于回答这些查询的相关网络资源。通过严格的评估，我们证明 GEO 可以提高可见性高达40% 的生成引擎响应。此外，我们显示这些策略的功效不同领域，强调需要领域特定的优化方法。我们的工作开辟了信息发现系统的新前沿，对生成引擎的开发人员和内容创建人员都具有深远的意义。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=GEO:+Generative+Engine+Optimization)|0| |[AI for Nature: From Science to Impact](https://doi.org/10.1145/3637528.3672192)|Tanya Y. BergerWolf|The Ohio State University, Columbus, OH, USA|Computation has fundamentally changed the way we study nature. New data collection technologies, such as GPS, high-definition cameras, autonomous vehicles under water, on the ground, and in the air, genotyping, acoustic sensors, and crowdsourcing, are generating data about life on the planet that are orders of magnitude richer than any previously collected. Yet, our ability to extract insight from this data lags substantially behind our ability to collect it. The need for understanding is more urgent than ever and the challenges are great. We are in the middle of the 6th extinction, losing the planet's biodiversity at an unprecedented rate and scale. In many cases, we do not even have the basic numbers of what species we are losing, which impacts our ability to understand biodiversity loss drivers, predict the impact on ecosystems, and implement policy. From the basic science perspective, the new data opens the possibility of understanding function of traits of organisms and ecosystems, which is critical for biologists to predict effects of environmental change or genetic manipulation and to understand the significance of patterns in the four-billion-year evolutionary history of life. The key to unlocking the potential of this data are machine learning (ML) and artificial intelligence (AI) methods, which are already beginning to have significant impacts on research across ecology and conservation. AI can turn data into high resolution information source about living organisms, enabling scientific inquiry, conservation, and policy decisions. The talk introduces a new field of science, imageomics, and presents a vision and examples of AI as a trustworthy partner both in science and biodiversity conservation, discussing opportunities and challenges.|计算从根本上改变了我们研究自然的方式。新的数据收集技术，如全球定位系统、高清摄像机、水下、地面和空中的自动驾驶汽车、基因分型、声学传感器和众包，正在生成有关地球上生命的数据，这些数据比以前收集到的任何数量级都要丰富。然而，我们从这些数据中提取洞察力的能力远远落后于我们收集数据的能力。对理解的需求比以往任何时候都更加迫切，挑战也是巨大的。我们正处于第六次物种灭绝的过程中，以前所未有的速度和规模丧失着地球上的生物多样性。在许多情况下，我们甚至不知道我们正在失去的物种的基本数量，这影响了我们了解生物多样性丧失驱动因素、预测对生态系统的影响以及执行政策的能力。从基础科学的角度来看，这些新数据为理解生物体和生态系统特征的功能开辟了可能性，这对于生物学家预测环境变化或基因操纵的影响以及理解40亿年生命进化史中模式的重要性至关重要。释放这些数据潜力的关键是机器学习(ML)和人工智能(AI)方法，它们已经开始对生态学和自然保护研究产生重大影响。人工智能可以将数据转化为有关生物体的高分辨率信息源，从而实现科学调查、保护和政策决策。演讲介绍了一个新的科学领域，图像组学，并提出了一个愿景和例子，人工智能作为一个值得信赖的合作伙伴，在科学和生物多样性保护，讨论机会和挑战。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=AI+for+Nature:+From+Science+to+Impact)|0| |[Statistical Models of Top-k Partial Orders](https://doi.org/10.1145/3637528.3672014)|Amel Awadelkarim, Johan Ugander|Stanford University, Stanford, CA, USA|In many contexts involving ranked preferences, agents submit partial orders over available alternatives. Statistical models often treat these as marginal in the space of total orders, but this approach overlooks information contained in the list length itself. In this work, we introduce and taxonomize approaches for jointly modeling distributions over top-k partial orders and list lengths k, considering two classes of approaches: composite models that view a partial order as a truncation of a total order, and augmented ranking models that model the construction of the list as a sequence of choice decisions, including the decision to stop. For composite models, we consider three dependency structures for joint modeling of order and truncation length. For augmented ranking models, we consider different assumptions on how the stop-token choice is modeled. Using data consisting of partial rankings from San Francisco school choice and San Francisco ranked choice elections, we evaluate how well the models predict observed data and generate realistic synthetic datasets. We find that composite models, explicitly modeling length as a categorical variable, produce synthetic datasets with accurate length distributions, and an augmented model with position-dependent item utilities jointly models length and preferences in the training data best, as measured by negative log loss. Methods from this work have significant implications on the simulation and evaluation of real-world social systems that solicit ranked preferences.|在许多涉及排序偏好的上下文中，代理提交的部分订单优于可用的替代方案。统计模型通常将这些信息视为总订单空间中的边际信息，但这种方法忽略了列表长度本身所包含的信息。在这项工作中，我们介绍和分类的方法联合建模分布在最高 k 偏序和列表长度 k，考虑到两类方法: 组合模型，视为一个总序的截断部分的偏序，和扩展排名模型，模型列表的结构作为一系列的选择决定，包括停止的决定。对于复合模型，我们考虑了三种依赖结构，用于顺序和截断长度的联合建模。对于扩展排序模型，我们考虑了关于如何建模停止令牌选择的不同假设。利用来自旧金山学校选择和旧金山排名选择的部分排名数据，我们评估模型预测观测数据和生成现实的合成数据集的效果。我们发现，明确地将长度建模为分类变量的复合模型产生具有精确长度分布的合成数据集，并且具有位置依赖性项目实用程序的增强模型联合模拟训练数据中的长度和偏好，如通过负对数损失测量的。从这项工作的方法有重大意义的模拟和评估现实世界的社会系统，征求排名的偏好。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Statistical+Models+of+Top-k+Partial+Orders)|0| -|[Resilient k-Clustering](https://doi.org/10.1145/3637528.3671888)|Sara Ahmadian, MohammadHossein Bateni, Hossein Esfandiari, Silvio Lattanzi, Morteza Monemizadeh, Ashkan NorouziFard|Google, Zurich, Switzerland; Department of Mathematics and Computer Science, TU Eindhoven, Eindhoven, Netherlands; Google, New york, USA; Google, Seattle, USA; Google, New York, USA; Google, Barcelona, USA|We study the problem of resilient clustering in the metric setting where one is interested in designing algorithms that return high quality solutions that preserve the clustering structure under perturbations of the input points. Our first contribution is to introduce a formal notion of algorithmic resiliency for clustering problems that, roughly speaking, requires an algorithm to have similar outputs on close inputs. Then, we notice that classic algorithms have weak resiliency guarantees and develop new algorithms for fundamental clustering problems such as k-center, k-median, and k-means. Finally, we complement our results with an experimental analysis showing the effectiveness of our techniques on real-world instances.|我们研究了度量设置中的弹性聚类问题，其中一个人感兴趣的是设计算法，返回高质量的解决方案，保留了在输入点扰动下的聚类结构。我们的第一个贡献是为聚类问题引入了算法弹性的形式化概念，粗略地说，这需要一个算法在相近的输入上具有相似的输出。然后，我们注意到经典算法具有较弱的弹性保证，并且针对基本聚类问题，如 k- 中心、 k- 中值和 k- 均值，提出了新的聚类算法。最后，我们用一个实验分析来补充我们的结果，该实验分析显示了我们的技术在真实世界实例中的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Resilient+k-Clustering)|0| +|[Resilient k-Clustering](https://doi.org/10.1145/3637528.3671888)|Sara Ahmadian, MohammadHossein Bateni, Hossein Esfandiari, Silvio Lattanzi, Morteza Monemizadeh, Ashkan NorouziFard|Department of Mathematics and Computer Science, TU Eindhoven, Eindhoven, Netherlands; Google, Barcelona, USA; Google, Seattle, USA; Google, New york, USA; Google, Zurich, Switzerland; Google, New York, USA|We study the problem of resilient clustering in the metric setting where one is interested in designing algorithms that return high quality solutions that preserve the clustering structure under perturbations of the input points. Our first contribution is to introduce a formal notion of algorithmic resiliency for clustering problems that, roughly speaking, requires an algorithm to have similar outputs on close inputs. Then, we notice that classic algorithms have weak resiliency guarantees and develop new algorithms for fundamental clustering problems such as k-center, k-median, and k-means. Finally, we complement our results with an experimental analysis showing the effectiveness of our techniques on real-world instances.|我们研究了度量设置中的弹性聚类问题，其中一个人感兴趣的是设计算法，返回高质量的解决方案，保留了在输入点扰动下的聚类结构。我们的第一个贡献是为聚类问题引入了算法弹性的形式化概念，粗略地说，这需要一个算法在相近的输入上具有相似的输出。然后，我们注意到经典算法具有较弱的弹性保证，并且针对基本聚类问题，如 k- 中心、 k- 中值和 k- 均值，提出了新的聚类算法。最后，我们用一个实验分析来补充我们的结果，该实验分析显示了我们的技术在真实世界实例中的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Resilient+k-Clustering)|0| |[A Learned Generalized Geodesic Distance Function-Based Approach for Node Feature Augmentation on Graphs](https://doi.org/10.1145/3637528.3671858)|Amitoz Azad, Yuan Fang|Singapore Management University, Singapore, Singapore|Geodesic distances on manifolds have numerous applications in image processing, computer graphics and computer vision. In this work, we introduce an approach called `LGGD' (Learned Generalized Geodesic Distances). This method involves generating node features by learning a generalized geodesic distance function through a training pipeline that incorporates training data, graph topology and the node content features. The strength of this method lies in the proven robustness of the generalized geodesic distances to noise and outliers. Our contributions encompass improved performance in node classification tasks, competitive results with state-of-the-art methods on real-world graph datasets, the demonstration of the learnability of parameters within the generalized geodesic equation on graph, and dynamic inclusion of new labels.|流形上的测地距离在图像处理、计算机图形学和计算机视觉等领域有着广泛的应用。在这项工作中，我们介绍了一种方法称为“ LGGD”(学习广义测地距离)。该方法通过训练流水线学习广义测地距离函数生成节点特征，该流水线包括训练数据、图拓扑结构和节点内容特征。该方法的优点在于证明了广义测地距离对噪声和离群点的鲁棒性。我们的贡献包括提高节点分类任务的性能，在真实世界图形数据集上使用最先进的方法获得竞争结果，在图形上展示广义测地方程中参数的可学性，以及动态包含新标签。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Learned+Generalized+Geodesic+Distance+Function-Based+Approach+for+Node+Feature+Augmentation+on+Graphs)|0| |[Improved Active Covering via Density-Based Space Transformation](https://doi.org/10.1145/3637528.3671794)|MohammadHossein Bateni, Hossein Esfandiari, Samira HosseinGhorban, Alipasha Montaseri|Google Research, New York City, New York, USA; Google Research, London, United Kingdom; School of Computer Science, Institute for Research in Fundamental Sciences, Tehran, Iran; Sharif University of Technology, Tehran, Iran|In this work, we study active covering, a variant of the active-learning problem that involves labeling (or identifying) all of the examples with a positive label. We propose a couple of algorithms, namely Density-Adjusted Non-Adaptive (DANA) learner and Density-Adjusted Adaptive (DAA) learner, that query the labels according to a distance function that is adjusted by the density function. Under mild assumptions, we prove that our algorithms discover all of the positive labels while querying only a sublinear number of examples from the support of negative labels for constant-dimensional spaces (see Theorems 5 and 6). Our experiments show that our champion algorithm DAA consistently improves over the prior work on some standard benchmark datasets, including those used by the previous work, as well as a couple of data sets on credit card fraud. For instance, when measuring performance using AUC, our algorithm is the best in 25 out of 27 experiments over 7 different datasets.|在这项工作中，我们研究主动覆盖，一个变种的主动学习问题，涉及标记(或识别)所有的例子与一个积极的标签。提出了密度调整非自适应(DANA)学习算法和密度调整自适应(DAA)学习算法，它们根据密度函数调整的距离函数对标签进行查询。在温和的假设下，我们证明了我们的算法发现所有的正标签，而查询只有一个次线性数量的例子从负标签的支持常量维空间(见定理5和6)。我们的实验表明，我们的冠军算法 DAA 在一些标准基准数据集(包括前面的工作所使用的数据集)以及一些信用卡欺诈数据集上的改进是一致的。例如，当使用 AUC 测量性能时，我们的算法在7个不同数据集的27个实验中的25个中是最好的。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Improved+Active+Covering+via+Density-Based+Space+Transformation)|0| -|[Towards Robust Information Extraction via Binomial Distribution Guided Counterpart Sequence](https://doi.org/10.1145/3637528.3672067)|Yinhao Bai, Yuhua Zhao, Zhixin Han, Hang Gao, Chao Xue, Mengting Hu|; College of Software, Nankai University, Tianjin, China; College of Artificial Intelligence, Tianjin University of Science and Technology, Tianjin, China; JD Explore Academy, Beijing, Chile|Information extraction (IE) aims to extract meaningful structured tuples from unstructured text. Existing studies usually utilize a pre-trained generative language model that rephrases the original sentence into a target sequence, which can be easily decoded as tuples. However, traditional evaluation metrics treat a slight error within the tuple as an entire prediction failure, which is unable to perceive the correctness extent of a tuple. For this reason, we first propose a novel IE evaluation metric called Matching Score to evaluate the correctness of the predicted tuples in more detail. Moreover, previous works have ignored the effects of semantic uncertainty when focusing on the generation of the target sequence. We argue that leveraging the built-in semantic uncertainty of language models is beneficial for improving its robustness. In this work, we propose Binomial distribution guided counterpart sequence (BCS) method, which is a model-agnostic approach. Specifically, we propose to quantify the built-in semantic uncertainty of the language model by bridging all local uncertainties with the whole sequence. Subsequently, with the semantic uncertainty and Matching Score, we formulate a unique binomial distribution for each local decoding step. By sampling from this distribution, a counterpart sequence is obtained, which can be regarded as a semantic complement to the target sequence. Finally, we employ the Kullback-Leibler divergence to align the semantics of the target sequence and its counterpart. Extensive experiments on 14 public datasets over 5 information extraction tasks demonstrate the effectiveness of our approach on various methods. Our code and dataset are available at https://github.com/byinhao/BCS.|信息抽取(IE)旨在从非结构化文本中提取有意义的结构化元组。现有的研究通常利用预先训练好的生成语言模型将原始句子重新组合成一个目标序列，这个目标序列可以很容易地被解码为元组。但是，传统的评估指标将元组中的一个轻微错误视为整个预测失败，无法察觉元组的正确程度。出于这个原因，我们首先提出了一种新的 IE 评估度量，称为匹配得分，以评估正确性的预测元组更详细。此外，以往的研究忽略了语义不确定性对目标语序列生成的影响。我们认为，利用语言模型内在的语义不确定性有利于提高其鲁棒性。在这项工作中，我们提出了二项分布引导的对应序列(BCS)方法，这是一种模型无关的方法。具体来说，我们提出通过将所有局部不确定性与整个序列连接起来，来量化语言模型内在的语义不确定性。随后，利用语义不确定性和匹配得分，我们为每个局部解码步骤制定一个独特的二项分布。从这个分布中抽样得到一个对应序列，可以看作是对目标序列的语义补充。最后，利用 Kullback-Leibler 散度对目标序列及其对应序列进行语义对齐。通过对14个公共数据集超过5个信息抽取任务的大量实验，证明了我们的方法在各种方法上的有效性。我们的代码和数据集可在 https://github.com/byinhao/bcs 下载。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Towards+Robust+Information+Extraction+via+Binomial+Distribution+Guided+Counterpart+Sequence)|0| +|[Towards Robust Information Extraction via Binomial Distribution Guided Counterpart Sequence](https://doi.org/10.1145/3637528.3672067)|Yinhao Bai, Yuhua Zhao, Zhixin Han, Hang Gao, Chao Xue, Mengting Hu|; JD Explore Academy, Beijing, Chile; College of Software, Nankai University, Tianjin, China; College of Artificial Intelligence, Tianjin University of Science and Technology, Tianjin, China|Information extraction (IE) aims to extract meaningful structured tuples from unstructured text. Existing studies usually utilize a pre-trained generative language model that rephrases the original sentence into a target sequence, which can be easily decoded as tuples. However, traditional evaluation metrics treat a slight error within the tuple as an entire prediction failure, which is unable to perceive the correctness extent of a tuple. For this reason, we first propose a novel IE evaluation metric called Matching Score to evaluate the correctness of the predicted tuples in more detail. Moreover, previous works have ignored the effects of semantic uncertainty when focusing on the generation of the target sequence. We argue that leveraging the built-in semantic uncertainty of language models is beneficial for improving its robustness. In this work, we propose Binomial distribution guided counterpart sequence (BCS) method, which is a model-agnostic approach. Specifically, we propose to quantify the built-in semantic uncertainty of the language model by bridging all local uncertainties with the whole sequence. Subsequently, with the semantic uncertainty and Matching Score, we formulate a unique binomial distribution for each local decoding step. By sampling from this distribution, a counterpart sequence is obtained, which can be regarded as a semantic complement to the target sequence. Finally, we employ the Kullback-Leibler divergence to align the semantics of the target sequence and its counterpart. Extensive experiments on 14 public datasets over 5 information extraction tasks demonstrate the effectiveness of our approach on various methods. Our code and dataset are available at https://github.com/byinhao/BCS.|信息抽取(IE)旨在从非结构化文本中提取有意义的结构化元组。现有的研究通常利用预先训练好的生成语言模型将原始句子重新组合成一个目标序列，这个目标序列可以很容易地被解码为元组。但是，传统的评估指标将元组中的一个轻微错误视为整个预测失败，无法察觉元组的正确程度。出于这个原因，我们首先提出了一种新的 IE 评估度量，称为匹配得分，以评估正确性的预测元组更详细。此外，以往的研究忽略了语义不确定性对目标语序列生成的影响。我们认为，利用语言模型内在的语义不确定性有利于提高其鲁棒性。在这项工作中，我们提出了二项分布引导的对应序列(BCS)方法，这是一种模型无关的方法。具体来说，我们提出通过将所有局部不确定性与整个序列连接起来，来量化语言模型内在的语义不确定性。随后，利用语义不确定性和匹配得分，我们为每个局部解码步骤制定一个独特的二项分布。从这个分布中抽样得到一个对应序列，可以看作是对目标序列的语义补充。最后，利用 Kullback-Leibler 散度对目标序列及其对应序列进行语义对齐。通过对14个公共数据集超过5个信息抽取任务的大量实验，证明了我们的方法在各种方法上的有效性。我们的代码和数据集可在 https://github.com/byinhao/bcs 下载。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Towards+Robust+Information+Extraction+via+Binomial+Distribution+Guided+Counterpart+Sequence)|0| |[Graph Mamba: Towards Learning on Graphs with State Space Models](https://doi.org/10.1145/3637528.3672044)|Ali Behrouz, Farnoosh Hashemi|Cornell University, Ithaca, NY, USA|Graph Neural Networks (GNNs) have shown promising potential in graph representation learning. The majority of GNNs define a local message-passing mechanism, propagating information over the graph by stacking multiple layers. These methods, however, are known to suffer from two major limitations: over-squashing and poor capturing of long-range dependencies. Recently, Graph Transformers (GTs) emerged as a powerful alternative to Message-Passing Neural Networks (MPNNs). GTs, however, have quadratic computational cost, lack inductive biases on graph structures, and rely on complex Positional Encodings (PE). In this paper, we show that while Transformers, complex message-passing, and PE are sufficient for good performance in practice, neither is necessary. Motivated by the recent success of State Space Models (SSMs), we present Graph Mamba Networks (GMNs), a framework for a new class of GNNs based on selective SSMs. We discuss the new challenges when adapting SSMs to graph-structured data, and present four required steps to design GMNs, where we choose (1) Neighborhood Tokenization, (2) Token Ordering, (3) Architecture of SSM Encoder, and (4) Local Encoding. We provide theoretical justification for the power of GMNs, and experimentally show that GMNs attain an outstanding performance in various benchmark datasets. The code is available in this link.|图形神经网络(GNN)在图形表示学习中显示出了巨大的潜力。大多数 GNN 定义了一种本地消息传递机制，通过叠加多个层在图上传播信息。然而，已知这些方法存在两个主要的局限性: 过度压缩和不能很好地捕获远程依赖关系。最近，图形变换器(GTs)作为消息传递神经网络(MPNN)的一个强有力的替代品出现了。然而，GT 具有二次计算开销，缺乏对图结构的归纳偏差，并且依赖于复杂的位置编码(PE)。在本文中，我们表明，虽然变压器，复杂的消息传递和 PE 足以在实践中取得良好的性能，但这两者都不是必要的。受最近国家空间模型(SSM)的成功的启发，我们提出了图曼巴网络(GMNs) ，一个基于选择性 SSM 的新类 GNNs 的框架。我们讨论了使 SSM 适应图形结构数据的新挑战，并提出了设计 GMN 所需的四个步骤，其中我们选择(1)邻域标记化，(2)标记排序，(3) SSM 编码器的体系结构，和(4)本地编码。我们从理论上证明了 GMN 的强大功能，并通过实验证明了 GMN 在各种基准数据集中都能获得优异的性能。该代码在此链接中可用。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Graph+Mamba:+Towards+Learning+on+Graphs+with+State+Space+Models)|0| -|[FaultInsight: Interpreting Hyperscale Data Center Host Faults](https://doi.org/10.1145/3637528.3672051)|Tingzhu Bi, Yang Zhang, Yicheng Pan, Yu Zhang, Meng Ma, Xinrui Jiang, Linlin Han, Feng Wang, Xian Liu, Ping Wang|Peking University, Beijing, China; Peking University & Shuanghu Laboratory, Beijing, China; ByteDance Inc., Beijing, China|Operating and maintaining hyperscale data centers involving millions of service hosts has been an extremely intricate task to tackle for top Internet companies. Incessant system failures cost operators countless hours of browsing through performance metrics to diagnose the underlying root cause to prevent the recurrence. Although many state-of-the-art (SOTA) methods have used time-series causal discovery to construct causal relationships among anomalous metrics, they only focus on homogeneous service-level performance metrics and fail to yield useful insights on heterogeneous host-level metrics. To address the challenge, this study presents FaultInsight, a highly interpretable deep causal host fault diagnosing framework that offers diagnostic insights from various perspectives to reduce human effort in troubleshooting. We evaluate FaultInsight using dozens of incidents collected from our production environment. FaultInsight provides markedly better root cause identification accuracy than SOTA baselines in our incident dataset. It also shows outstanding advantages in terms of deployability in real production systems. Our engineers are deeply impressed by FaultInsight's ability to interpret incidents from multiple perspectives, helping them quickly understand the mechanism behind the faults.|操作和维护涉及数百万服务主机的超大规模数据中心对于顶级互联网公司来说是一项极其复杂的任务。不断的系统故障使操作员花费无数小时浏览性能指标，以诊断潜在的根本原因，防止再次发生故障。虽然许多最先进的(SOTA)方法已经使用时间序列因果发现来构建异常指标之间的因果关系，但它们只关注同质服务水平的性能指标，并且不能产生关于异质主机水平指标的有用见解。为了应对这一挑战，本研究提出了 FaultInsight，一个高度可解释的深层因果主机故障诊断框架，从不同角度提供诊断见解，以减少人们在故障排除方面的努力。我们使用从生产环境中收集的几十个事件来评估 FaultInsight。在我们的事件数据集中，FaultInsight 比 SOTA 基线提供了明显更好的根本原因识别准确性。它还显示了在实际生产系统中可部署性方面的显著优势。我们的工程师对 FaultInsight 从多个角度解释事件的能力印象深刻，它帮助他们快速理解错误背后的机制。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=FaultInsight:+Interpreting+Hyperscale+Data+Center+Host+Faults)|0| -|[Making Temporal Betweenness Computation Faster and Restless](https://doi.org/10.1145/3637528.3671825)|Filippo Brunelli, Pierluigi Crescenzi, Laurent Viennot|European Commission -- JRC, Seville, Spain; Inria, DI ENS, Paris, France; Gran Sasso Science Institute, L'Aquila, Italy|Buss et al [KDD 2020] recently proved that the problem of computing the betweenness of all nodes of a temporal graph is computationally hard in the case of foremost and fastest paths, while it is solvable in time O(n3T2) in the case of shortest and shortest foremost paths, where n is the number of nodes and T is the number of distinct time steps. A new algorithm for temporal betweenness computation is introduced in this paper. In the case of shortest and shortest foremost paths, it requires O(n + M) space and runs in time O(nM)=O(n3T), where M is the number of temporal edges, thus significantly improving the algorithm of Buss et al in terms of time complexity (note that T is usually large). Experimental evidence is provided that our algorithm performs between twice and almost 250 times better than the algorithm of Buss et al. Moreover, we were able to compute the exact temporal betweenness values of several large temporal graphs with over a million of temporal edges. For such size, only approximate computation was possible by using the algorithm of Santoro and Sarpe [WWW 2022]. Maybe more importantly, our algorithm extends to the case of restless walks (that is, walks with waiting constraints in each node), thus providing a polynomial-time algorithm (with complexity O(nM)) for computing the temporal betweenness in the case of several different optimality criteria. Such restless computation was known only for the shortest criterion (Rymar et al [JGAA 2023]), with complexity O(n2MT2). We performed an extensive experimental validation by comparing different waiting constraints and different optimisation criteria. Moreover, as a case study, we investigate six public transit networks including Berlin, Rome, and Paris. Overall we find a general consistency between the different variants of betweenness centrality. However, we do measure a sensible influence of waiting constraints, and note some cases of low correlation for certain pairs of criteria in some networks.|Buss 等[ KDD 2020]最近证明，对于最短和最快路径，计算时间图中所有节点之间的间隔问题是计算困难的，而对于最短和最短的最优路径，n 是节点数，T 是不同时间步长的数目，则可以在时间 O (n3T2)内求解。本文介绍了一种新的时间间隔计算算法。在最短和最短前向路径的情况下，它需要 O (n + M)空间并且在时间 O (nM) = O (n3T)中运行，其中 M 是时间边的个数，从而在时间复杂度方面显著改进了 Buss 等人的算法(注意 T 通常很大)。实验结果表明，该算法的性能是 Buss 等算法的两倍至250倍。此外，我们能够精确地计算几个大型时间图的时间间隔值与超过一百万的时间边缘。对于这样的大小，只有近似计算是可能的使用 Santoro 和 Sarpe 的算法[ WWW 2022]。也许更重要的是，我们的算法扩展到不宁行走(即在每个节点中具有等待约束的行走)的情况，从而提供了一个多项式时间算法(具有复杂度 O (nM))来计算在几个不同的最优性准则的情况下的时间间隔。这种无休止的计算仅以最短的标准(Rymar 等[ JGAA 2023])而闻名，其复杂度为 O (n2MT2)。我们通过比较不同的等待约束和不同的优化标准进行了广泛的实验验证。此外，作为一个案例研究，我们调查了六个公共交通网络，包括柏林，罗马和巴黎。总的来说，我们发现了中间性中心性的不同变体之间的一般一致性。然而，我们确实测量了等待约束的明显影响，并注意到在一些网络中某些标准对的低相关性的情况。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Making+Temporal+Betweenness+Computation+Faster+and+Restless)|0| +|[FaultInsight: Interpreting Hyperscale Data Center Host Faults](https://doi.org/10.1145/3637528.3672051)|Tingzhu Bi, Yang Zhang, Yicheng Pan, Yu Zhang, Meng Ma, Xinrui Jiang, Linlin Han, Feng Wang, Xian Liu, Ping Wang|ByteDance Inc., Beijing, China; Peking University, Beijing, China; Peking University & Shuanghu Laboratory, Beijing, China|Operating and maintaining hyperscale data centers involving millions of service hosts has been an extremely intricate task to tackle for top Internet companies. Incessant system failures cost operators countless hours of browsing through performance metrics to diagnose the underlying root cause to prevent the recurrence. Although many state-of-the-art (SOTA) methods have used time-series causal discovery to construct causal relationships among anomalous metrics, they only focus on homogeneous service-level performance metrics and fail to yield useful insights on heterogeneous host-level metrics. To address the challenge, this study presents FaultInsight, a highly interpretable deep causal host fault diagnosing framework that offers diagnostic insights from various perspectives to reduce human effort in troubleshooting. We evaluate FaultInsight using dozens of incidents collected from our production environment. FaultInsight provides markedly better root cause identification accuracy than SOTA baselines in our incident dataset. It also shows outstanding advantages in terms of deployability in real production systems. Our engineers are deeply impressed by FaultInsight's ability to interpret incidents from multiple perspectives, helping them quickly understand the mechanism behind the faults.|操作和维护涉及数百万服务主机的超大规模数据中心对于顶级互联网公司来说是一项极其复杂的任务。不断的系统故障使操作员花费无数小时浏览性能指标，以诊断潜在的根本原因，防止再次发生故障。虽然许多最先进的(SOTA)方法已经使用时间序列因果发现来构建异常指标之间的因果关系，但它们只关注同质服务水平的性能指标，并且不能产生关于异质主机水平指标的有用见解。为了应对这一挑战，本研究提出了 FaultInsight，一个高度可解释的深层因果主机故障诊断框架，从不同角度提供诊断见解，以减少人们在故障排除方面的努力。我们使用从生产环境中收集的几十个事件来评估 FaultInsight。在我们的事件数据集中，FaultInsight 比 SOTA 基线提供了明显更好的根本原因识别准确性。它还显示了在实际生产系统中可部署性方面的显著优势。我们的工程师对 FaultInsight 从多个角度解释事件的能力印象深刻，它帮助他们快速理解错误背后的机制。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=FaultInsight:+Interpreting+Hyperscale+Data+Center+Host+Faults)|0| +|[Making Temporal Betweenness Computation Faster and Restless](https://doi.org/10.1145/3637528.3671825)|Filippo Brunelli, Pierluigi Crescenzi, Laurent Viennot|Gran Sasso Science Institute, L'Aquila, Italy; European Commission -- JRC, Seville, Spain; Inria, DI ENS, Paris, France|Buss et al [KDD 2020] recently proved that the problem of computing the betweenness of all nodes of a temporal graph is computationally hard in the case of foremost and fastest paths, while it is solvable in time O(n3T2) in the case of shortest and shortest foremost paths, where n is the number of nodes and T is the number of distinct time steps. A new algorithm for temporal betweenness computation is introduced in this paper. In the case of shortest and shortest foremost paths, it requires O(n + M) space and runs in time O(nM)=O(n3T), where M is the number of temporal edges, thus significantly improving the algorithm of Buss et al in terms of time complexity (note that T is usually large). Experimental evidence is provided that our algorithm performs between twice and almost 250 times better than the algorithm of Buss et al. Moreover, we were able to compute the exact temporal betweenness values of several large temporal graphs with over a million of temporal edges. For such size, only approximate computation was possible by using the algorithm of Santoro and Sarpe [WWW 2022]. Maybe more importantly, our algorithm extends to the case of restless walks (that is, walks with waiting constraints in each node), thus providing a polynomial-time algorithm (with complexity O(nM)) for computing the temporal betweenness in the case of several different optimality criteria. Such restless computation was known only for the shortest criterion (Rymar et al [JGAA 2023]), with complexity O(n2MT2). We performed an extensive experimental validation by comparing different waiting constraints and different optimisation criteria. Moreover, as a case study, we investigate six public transit networks including Berlin, Rome, and Paris. Overall we find a general consistency between the different variants of betweenness centrality. However, we do measure a sensible influence of waiting constraints, and note some cases of low correlation for certain pairs of criteria in some networks.|Buss 等[ KDD 2020]最近证明，对于最短和最快路径，计算时间图中所有节点之间的间隔问题是计算困难的，而对于最短和最短的最优路径，n 是节点数，T 是不同时间步长的数目，则可以在时间 O (n3T2)内求解。本文介绍了一种新的时间间隔计算算法。在最短和最短前向路径的情况下，它需要 O (n + M)空间并且在时间 O (nM) = O (n3T)中运行，其中 M 是时间边的个数，从而在时间复杂度方面显著改进了 Buss 等人的算法(注意 T 通常很大)。实验结果表明，该算法的性能是 Buss 等算法的两倍至250倍。此外，我们能够精确地计算几个大型时间图的时间间隔值与超过一百万的时间边缘。对于这样的大小，只有近似计算是可能的使用 Santoro 和 Sarpe 的算法[ WWW 2022]。也许更重要的是，我们的算法扩展到不宁行走(即在每个节点中具有等待约束的行走)的情况，从而提供了一个多项式时间算法(具有复杂度 O (nM))来计算在几个不同的最优性准则的情况下的时间间隔。这种无休止的计算仅以最短的标准(Rymar 等[ JGAA 2023])而闻名，其复杂度为 O (n2MT2)。我们通过比较不同的等待约束和不同的优化标准进行了广泛的实验验证。此外，作为一个案例研究，我们调查了六个公共交通网络，包括柏林，罗马和巴黎。总的来说，我们发现了中间性中心性的不同变体之间的一般一致性。然而，我们确实测量了等待约束的明显影响，并注意到在一些网络中某些标准对的低相关性的情况。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Making+Temporal+Betweenness+Computation+Faster+and+Restless)|0| |[Tackling Instance-Dependent Label Noise with Class Rebalance and Geometric Regularization](https://doi.org/10.1145/3637528.3671707)|Shuzhi Cao, Jianfei Ruan, Bo Dong, Bin Shi||In label-noise learning, accurately identifying the transition matrix is crucial for developing statistically consistent classifiers. This task is complicated by instance-dependent noise, which introduces identifiability challenges in the absence of stringent assumptions. Existing methods use neural networks to estimate the transition matrix by initially extracting confident clean instances. However, this extraction process is hindered by severe inter-class imbalance and a bias toward selecting unambiguous intra-class instances, leading to a distorted understanding of noise patterns. To tackle these challenges, our paper introduces a Class Rebalance and Geometric Regularization-based Framework (CRGR). CRGR employs a smoothed, noise-tolerant reweighting mechanism to equilibrate inter-class representation, thereby mitigating the risk of model overfitting to dominant classes. Additionally, recognizing that instances with similar characteristics often exhibit parallel noise patterns, we propose that the transition matrix should mirror the similarity of the feature space. This insight promotes the inclusion of ambiguous instances in training, serving as a form of geometric regularization. Such a strategy enhances the model's ability to navigate diverse noise patterns and strengthens its generalization capabilities. By addressing both inter-class and intra-class biases, CRGR offers a more balanced and robust classification model. Extensive experiments on both synthetic and real-world datasets demonstrate CRGR's superiority over existing state-of-the-art methods, significantly boosting classification accuracy and showcasing its effectiveness in handling instance-dependent noise.|在标签噪声学习中，准确识别转移矩阵对于开发统计一致的分类器至关重要。由于实例相关噪声的存在，这项任务变得更加复杂，在缺乏严格假设的情况下，噪声引入了可识别性的挑战。现有的方法使用神经网络通过最初提取可信的干净实例来估计转移矩阵。然而，这个提取过程受到严重的类间不平衡和偏向于选择明确的类内实例的阻碍，导致对噪声模式的理解扭曲。为了应对这些挑战，本文提出了一种基于类再平衡和几何正则化的框架(CRGR)。CRGR 采用了一种平滑的、容忍噪声的重新加权机制来平衡类间表示，从而减少了模型过度拟合到显性类的风险。此外，我们认识到具有相似特征的实例通常表现出平行的噪声模式，我们建议转移矩阵应该反映特征空间的相似性。这种洞察力促进了训练中模糊实例的包含，作为几何正则化的一种形式。这种策略增强了模型导航不同噪声模式的能力，并增强了其泛化能力。通过解决类间和类内偏差，CRGR 提供了一个更加平衡和健壮的分类模型。在合成和真实世界数据集上的大量实验证明了 CRGR 相对于现有最先进的方法的优越性，显著提高了分类的准确性，并展示了其在处理实例相关噪声方面的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Tackling+Instance-Dependent+Label+Noise+with+Class+Rebalance+and+Geometric+Regularization)|0| -|[DiffusionE: Reasoning on Knowledge Graphs via Diffusion-based Graph Neural Networks](https://doi.org/10.1145/3637528.3671997)|Zongsheng Cao, Jing Li, Zigan Wang, Jinliang Li|; University of Chinese Academy of Sciences, Beijing, China; School of Economics and Management, Tsinghua University, Haidian, Beijing, China; School of Economics and Management, Tsinghua University, Beijing, China|Graph Neural Networks (GNNs) have demonstrated powerful capabilities in reasoning within Knowledge Graphs (KGs), gathering increasing attention. Our idea stems from the observation that the prior work typically employs hand-designed or sample-designed paradigms in the process of message propagation, engaging a set of adjacent entities at each step of propagation. As a result, such methods struggle with the increasing number of entities involved as propagation steps extend. Moreover, they neglect the message interactions between adjacent entities and propagation relations in KG reasoning, leading to semantic inconsistency during the message aggregation phase. To address these issues, we introduce a novel knowledge graph embedding method through a diffusion process, termed DiffusionE. Specifically, we reformulate the message propagation in knowledge reasoning as a diffusion process, regarding the message semantics as the diffusion signal. In this sense, guided by semantic information, messages can be transmitted between nodes effectively and adaptively. Furthermore, the theoretical analysis suggests our method can leverage an optimal diffusivity for message propagation in the semantic interactions of KGs. It shows that DiffusionE effectively leverages message interactions between entities and propagation relations, ensuring semantic consistency in KG reasoning. Comprehensive experiments reveal that our method attains state-of-the-art performance compared to prior work on several well-established benchmarks.|图形神经网络(GNN)在知识图(KGs)中展示了强大的推理能力，引起了越来越多的关注。我们的想法来源于这样的观察: 在消息传播的过程中，先前的工作通常使用手工设计或样本设计的范例，在传播的每个步骤中使用一组相邻的实体。因此，随着传播步骤的扩展，此类方法会遇到实体数量不断增加的问题。此外，在 KG 推理中忽略了相邻实体之间的消息交互和传播关系，导致了在消息聚合阶段的语义不一致。为了解决这些问题，我们介绍了一种新的知识图嵌入方法，通过扩散过程，称为扩散 E。具体地说，我们将知识推理中的消息传播重新表述为一个扩散过程，将消息语义视为扩散信号。从这个意义上说，在语义信息的引导下，信息可以在节点之间有效和自适应地传输。此外，理论分析表明，我们的方法可以利用最佳扩散率的信息传播的语义交互的幼稚园。实验结果表明，在 KG 推理中，扩散 E 有效地利用了实体间的消息交互和传播关系，保证了语义的一致性。综合实验表明，我们的方法达到了最先进的性能相比，以前的工作在几个良好的基准。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=DiffusionE:+Reasoning+on+Knowledge+Graphs+via+Diffusion-based+Graph+Neural+Networks)|0| -|[Path-based Explanation for Knowledge Graph Completion](https://doi.org/10.1145/3637528.3671683)|Heng Chang, Jiangnan Ye, Alejo LopezAvila, Jinhua Du, Jia Li|Huawei Technologies Co., Ltd., London, United Kingdom; Hong Kong University of Science and Technology, Guangzhou, China; Huawei Technologies Co., Ltd., Beijing, China|Graph Neural Networks (GNNs) have achieved great success in Knowledge Graph Completion (KGC) by modelling how entities and relations interact in recent years. However, the explanation of the predicted facts has not caught the necessary attention. Proper explanations for the results of GNN-based KGC models increase model transparency and help researchers develop more reliable models. Existing practices for explaining KGC tasks rely on instance/subgraph-based approaches, while in some scenarios, paths can provide more user-friendly and interpretable explanations. Nonetheless, the methods for generating path-based explanations for KGs have not been well-explored. To address this gap, we propose Power-Link, the first path-based KGC explainer that explores GNN-based models. We design a novel simplified graph-powering technique, which enables the generation of path-based explanations with a fully parallelisable and memory-efficient training scheme. We further introduce three new metrics for quantitative evaluation of the explanations, together with a qualitative human evaluation. Extensive experiments demonstrate that Power-Link outperforms the SOTA baselines in interpretability, efficiency, and scalability. The code is available at https://github.com/OUTHIM/power-link|近年来，图神经网络(GNN)通过对实体和关系之间的相互作用进行建模，在知识图完成(KGC)方面取得了巨大的成功。然而，对预测事实的解释并没有引起必要的注意。对基于 GNN 的 KGC 模型结果的合理解释增加了模型的透明度，有助于研究人员开发更可靠的模型。解释 KGC 任务的现有实践依赖于基于实例/子图的方法，而在某些场景中，路径可以提供更加用户友好和可解释的解释。尽管如此，为幼儿园生成基于路径的解释的方法还没有得到很好的探索。为了解决这个差距，我们提出 Power-Link，第一个基于路径的 KGC 解释器，探索基于 GNN 的模型。我们设计了一种新颖的简化图形驱动技术，该技术能够生成基于路径的解释，并且具有完全并行和高效的记忆训练方案。我们进一步介绍了三个新的指标来定量评估的解释，以及定性的人类评价。大量的实验表明，Power-Link 在可解释性、效率和可扩展性方面优于 SOTA 基准。密码可在 https://github.com/outhim/power-link 查阅|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Path-based+Explanation+for+Knowledge+Graph+Completion)|0| -|[Cluster-Wide Task Slowdown Detection in Cloud System](https://doi.org/10.1145/3637528.3671936)|Feiyi Chen, Yingying Zhang, Lunting Fan, Yuxuan Liang, Guansong Pang, Qingsong Wen, Shuiguang Deng|Alibaba Group, Hangzhou, China; Squirrel AI, Bellevue, USA; Singapore Management University, Singapore, Singapore; Zhejiang University & Alibaba Group, Hangzhou, China; Zhejiang University, Hangzhou, China; The Hong Kong University of Science and Technology (Guangzhou), Hong Kong, China|Slow task detection is a critical problem in cloud operation and maintenance since it is highly related to user experience and can bring substantial liquidated damages. Most anomaly detection methods detect it from a single-task aspect. However, considering millions of concurrent tasks in large-scale cloud computing clusters, it becomes impractical and inefficient. Moreover, single-task slowdowns are very common and do not necessarily indicate a malfunction of a cluster due to its violent fluctuation nature in a virtual environment. Thus, we shift our attention to cluster-wide task slowdowns by utilizing the duration time distribution of tasks across a cluster, so that the computation complexity is not relevant to the number of tasks. The task duration time distribution often exhibits compound periodicity and local exceptional fluctuations over time. Though transformer-based methods are one of the most powerful methods to capture these time series normal variation patterns, we empirically find and theoretically explain the flaw of the standard attention mechanism in reconstructing subperiods with low amplitude when dealing with compound periodicity. To tackle these challenges, we propose SORN (i.e., Skimming Off subperiods in descending amplitude order and Reconstructing Non-slowing fluctuation), which consists of a Skimming Attention mechanism to reconstruct the compound periodicity and a Neural Optimal Transport module to distinguish cluster-wide slowdowns from other exceptional fluctuations. Furthermore, since anomalies in the training set are inevitable in a practical scenario, we propose a picky loss function, which adaptively assigns higher weights to reliable time slots in the training set. Extensive experiments demonstrate that SORN outperforms state-of-the-art methods on multiple real-world industrial datasets.|慢速任务检测是云操作和维护中的一个关键问题，因为它与用户体验密切相关，可以带来大量的 liquidated damage。大多数异常检测方法从单任务方面检测它。然而，考虑到大规模云计算集群中有数以百万计的并发任务，这就变得不切实际且效率低下。此外，单任务速度减慢非常普遍，并不一定表明由于集群在虚拟环境中的剧烈波动性而出现故障。因此，我们将注意力转移到集群范围的任务减速上，利用集群中任务的持续时间分布，因此计算复杂度与任务数量无关。任务持续时间分布通常表现为复合周期性和局部异常波动。虽然基于变压器的方法是获取这些时间序列正态变化模式的最有力的方法之一，但我们在处理复合周期时，从实证上发现并从理论上解释了标准注意机制在重构低振幅子周期时的缺陷。为了应对这些挑战，我们提出了 SORN (即，按降幅顺序略去子周期和重构非减速波动) ，它由略去注意机制重构复合周期和神经最优传输模块区分簇范围的减速和其他异常波动组成。此外，由于训练集中的异常在实际场景中是不可避免的，我们提出了一个挑剔的损失函数，它自适应地分配更高的权重给训练集中的可靠时隙。大量的实验表明，SORN 在多个真实世界的工业数据集上优于最先进的方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Cluster-Wide+Task+Slowdown+Detection+in+Cloud+System)|0| -|[Scalable Algorithm for Finding Balanced Subgraphs with Tolerance in Signed Networks](https://doi.org/10.1145/3637528.3671674)|Jingbang Chen, Qiuyang Mang, Hangrui Zhou, Richard Peng, Yu Gao, Chenhao Ma|Independent, Beijing, China; School of Data Science, The Chinese University of Hong Kong, Shenzhen, Shenzhen, Guangdong, China; Computer Science Department, Carnegie Mellon University, Pittsburgh, PA, USA; David R. Cheriton School of Computer Science, University of Waterloo, Waterloo, Ontario, Canada; Institute for Interdisciplinary Information Sciences (IIIS), Tsinghua University, Beijing, China|Signed networks, characterized by edges labeled as either positive or negative, offer nuanced insights into interaction dynamics beyond the capabilities of unsigned graphs. Central to this is the task of identifying the maximum balanced subgraph, crucial for applications like polarized community detection in social networks and portfolio analysis in finance. Traditional models, however, are limited by an assumption of perfect partitioning, which fails to mirror the complexities of real-world data. Addressing this gap, we introduce an innovative generalized balanced subgraph model that incorporates tolerance for imbalance. Our proposed region-based heuristic algorithm, tailored for this NP -hard problem, strikes a balance between low time complexity and high-quality outcomes. Comparative experiments validate its superior performance against leading solutions, delivering enhanced effectiveness (notably larger subgraph sizes) and efficiency (achieving up to 100× speedup) in both traditional and generalized contexts.|有符号的网络，拥有属性边缘标记为正面或负面，提供了对互动动力学的细致洞察，超越了无符号图形的能力。其中心任务是确定最大平衡子图，这对于社交网络中的极化社区检测和金融中的投资组合分析等应用至关重要。然而，传统模型受到完美分区假设的限制，无法反映真实世界数据的复杂性。针对这一差距，我们引入了一个创新的广义平衡子图模型，其中包含了对不平衡的容忍度。我们提出的基于区域的启发式算法，针对这个 NP 难题，取得了低时间复杂度和高质量的结果之间的平衡。比较实验验证了其对领先解决方案的优越性能，在传统和一般情况下都提供了增强的有效性(尤其是更大的子图大小)和效率(达到100倍加速)。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Scalable+Algorithm+for+Finding+Balanced+Subgraphs+with+Tolerance+in+Signed+Networks)|0| -|[QGRL: Quaternion Graph Representation Learning for Heterogeneous Feature Data Clustering](https://doi.org/10.1145/3637528.3671839)|Junyang Chen, Yuzhu Ji, Rong Zou, Yiqun Zhang, Yiuming Cheung|School of Computer Science and Technology, Guangdong University of Technology, Guangzhou, China; Department of Computer Science, Hong Kong Baptist University, Hong Kong SAR, China|Clustering is one of the most commonly used techniques for unsupervised data analysis. As real data sets are usually composed of numerical and categorical features that are heterogeneous in nature, the heterogeneity in the distance metric and feature coupling prevents deep representation learning from achieving satisfactory clustering accuracy. Currently, supervised Quaternion Representation Learning (QRL) has achieved remarkable success in efficiently learning informative representations of coupled features from multiple views derived endogenously from the original data. To inherit the advantages of QRL for unsupervised heterogeneous feature representation learning, we propose a deep QRL model that works in an encoder-decoder manner. To ensure that the implicit couplings of heterogeneous feature data can be well characterized by representation learning, a hierarchical coupling encoding strategy is designed to convert the data set into an attributed graph to be the input of QRL. We also integrate the clustering objective into the model training to facilitate a joint optimization of the representation and clustering. Extensive experimental evaluations illustrate the superiority of the proposed Quaternion Graph Representation Learning (QGRL) method in terms of clustering accuracy and robustness to various data sets composed of arbitrary combinations of numerical and categorical features. The source code is opened at https://github.com/Juny-Chen/QGRL.git.|聚类是无监督数据分析中最常用的技术之一。由于实际数据集通常由数值特征和分类特征组成，这些特征本质上是异构的，距离度量和特征耦合的异构性使得深度表示学习无法获得满意的聚类精度。目前，有监督的四元数表示学习(QRL)在有效地学习耦合特征的信息表示方面取得了显著的成功。为了继承 QRL 在非监督异构特征表示学习中的优势，本文提出了一种基于编译码方式的深层 QRL 模型。为了确保异构特征数据的隐式耦合能够很好地进行拥有属性表示学习，设计了一种分层耦合编码策略，将数据集转换为一个属性图作为 QRL 的输入。将聚类目标集成到模型训练中，实现了表示和聚类的联合优化。大量的实验结果表明，所提出的四元数图表示学习(QGRL)方法在聚类精度和对由任意数值和分类特征组合构成的各种数据集的鲁棒性方面具有优越性。源代码在 https://github.com/juny-chen/qgrl.git 打开。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=QGRL:+Quaternion+Graph+Representation+Learning+for+Heterogeneous+Feature+Data+Clustering)|0| -|[Can a Deep Learning Model be a Sure Bet for Tabular Prediction?](https://doi.org/10.1145/3637528.3671893)|Jintai Chen, Jiahuan Yan, Qiyuan Chen, Danny Z. Chen, Jian Wu, Jimeng Sun|Zhejiang University, Hangzhou, Zhejiang, China; University of Notre Dame, South Bend, IN, USA; Univ. of Illinois Urbana-Champaign, Urbana, IL, USA|Data organized in tabular format is ubiquitous in real-world applications, and users often craft tables with biased feature definitions and flexibly set prediction targets of their interests. Thus, a rapid development of a robust, effective, dataset-versatile, user-friendly tabular prediction approach is highly desired. While Gradient Boosting Decision Trees (GBDTs) and existing deep neural networks (DNNs) have been extensively utilized by professional users, they present several challenges for casual users, particularly: (i) the dilemma of model selection due to their different dataset preferences, and (ii) the need for heavy hyperparameter searching, failing which their performances are deemed inadequate. In this paper, we delve into this question: Can we develop a deep learning model that serves as a sure bet solution for a wide range of tabular prediction tasks, while also being user-friendly for casual users? We delve into three key drawbacks of deep tabular models, encompassing: (P1) lack of rotational variance property, (P2) large data demand, and (P3) over-smooth solution. We propose ExcelFormer, addressing these challenges through a semi-permeable attention module that effectively constrains the influence of less informative features to break the DNNs' rotational invariance property (for P1), data augmentation approaches tailored for tabular data (for P2), and attentive feedforward network to boost the model fitting capability (for P3). These designs collectively make ExcelFormer a sure bet solution for diverse tabular datasets. Extensive and stratified experiments conducted on real-world datasets demonstrate that our model outperforms previous approaches across diverse tabular data prediction tasks, and this framework can be friendly to casual users, offering ease of use without the heavy hyperparameter tuning. The codes are available at https://github.com/whatashot/excelformer.|以表格形式组织的数据在实际应用中无处不在，用户经常使用有偏差的特征定义和灵活设置他们感兴趣的预测目标来编制表格。因此，一个健壮的，有效的，数据集通用的，用户友好的表格预测方法的快速发展是迫切需要的。虽然梯度提升决策树(GBDTs)和现有的深度神经网络(DNN)已被专业用户广泛使用，但它们对普通用户提出了一些挑战，特别是: (i)由于他们不同的数据集偏好，模型选择的困境，以及(ii)需要大量的超参数搜索，否则他们的性能被认为是不够的。在本文中，我们深入研究这个问题: 我们能否开发一个深度学习模型，作为一个可靠的解决方案，为广泛的表格预测任务，同时也是用户友好的临时用户？我们深入研究了深表模型的三个主要缺点，包括: (P1)缺乏旋转方差性质，(P2)大数据需求，(P3)过于光滑的解决方案。我们建议使用 ExcelForm，通过一个半渗透注意模块来应对这些挑战，该模块有效地限制了信息量较小的特征对打破 DNN 的旋转不变性属性(对于 P1)的影响，为表格数据量身定制的数据增强方法(对于 P2) ，以及专注的前馈网络来提高模型拟合能力(对于 P3)。这些设计共同使得 ExcelForm 成为不同表格数据集的一个确定的解决方案。在真实世界的数据集上进行的广泛和分层的实验表明，我们的模型在不同的表格数据预测任务中优于以前的方法，并且该框架可以友好地对临时用户，提供易于使用而不需要大量的超参数调整。密码可以在 https://github.com/whatashot/excelformer 找到。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Can+a+Deep+Learning+Model+be+a+Sure+Bet+for+Tabular+Prediction?)|0| +|[DiffusionE: Reasoning on Knowledge Graphs via Diffusion-based Graph Neural Networks](https://doi.org/10.1145/3637528.3671997)|Zongsheng Cao, Jing Li, Zigan Wang, Jinliang Li|; School of Economics and Management, Tsinghua University, Haidian, Beijing, China; University of Chinese Academy of Sciences, Beijing, China; School of Economics and Management, Tsinghua University, Beijing, China|Graph Neural Networks (GNNs) have demonstrated powerful capabilities in reasoning within Knowledge Graphs (KGs), gathering increasing attention. Our idea stems from the observation that the prior work typically employs hand-designed or sample-designed paradigms in the process of message propagation, engaging a set of adjacent entities at each step of propagation. As a result, such methods struggle with the increasing number of entities involved as propagation steps extend. Moreover, they neglect the message interactions between adjacent entities and propagation relations in KG reasoning, leading to semantic inconsistency during the message aggregation phase. To address these issues, we introduce a novel knowledge graph embedding method through a diffusion process, termed DiffusionE. Specifically, we reformulate the message propagation in knowledge reasoning as a diffusion process, regarding the message semantics as the diffusion signal. In this sense, guided by semantic information, messages can be transmitted between nodes effectively and adaptively. Furthermore, the theoretical analysis suggests our method can leverage an optimal diffusivity for message propagation in the semantic interactions of KGs. It shows that DiffusionE effectively leverages message interactions between entities and propagation relations, ensuring semantic consistency in KG reasoning. Comprehensive experiments reveal that our method attains state-of-the-art performance compared to prior work on several well-established benchmarks.|图形神经网络(GNN)在知识图(KGs)中展示了强大的推理能力，引起了越来越多的关注。我们的想法来源于这样的观察: 在消息传播的过程中，先前的工作通常使用手工设计或样本设计的范例，在传播的每个步骤中使用一组相邻的实体。因此，随着传播步骤的扩展，此类方法会遇到实体数量不断增加的问题。此外，在 KG 推理中忽略了相邻实体之间的消息交互和传播关系，导致了在消息聚合阶段的语义不一致。为了解决这些问题，我们介绍了一种新的知识图嵌入方法，通过扩散过程，称为扩散 E。具体地说，我们将知识推理中的消息传播重新表述为一个扩散过程，将消息语义视为扩散信号。从这个意义上说，在语义信息的引导下，信息可以在节点之间有效和自适应地传输。此外，理论分析表明，我们的方法可以利用最佳扩散率的信息传播的语义交互的幼稚园。实验结果表明，在 KG 推理中，扩散 E 有效地利用了实体间的消息交互和传播关系，保证了语义的一致性。综合实验表明，我们的方法达到了最先进的性能相比，以前的工作在几个良好的基准。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=DiffusionE:+Reasoning+on+Knowledge+Graphs+via+Diffusion-based+Graph+Neural+Networks)|0| +|[Path-based Explanation for Knowledge Graph Completion](https://doi.org/10.1145/3637528.3671683)|Heng Chang, Jiangnan Ye, Alejo LopezAvila, Jinhua Du, Jia Li|Huawei Technologies Co., Ltd., London, United Kingdom; Huawei Technologies Co., Ltd., Beijing, China; Hong Kong University of Science and Technology, Guangzhou, China|Graph Neural Networks (GNNs) have achieved great success in Knowledge Graph Completion (KGC) by modelling how entities and relations interact in recent years. However, the explanation of the predicted facts has not caught the necessary attention. Proper explanations for the results of GNN-based KGC models increase model transparency and help researchers develop more reliable models. Existing practices for explaining KGC tasks rely on instance/subgraph-based approaches, while in some scenarios, paths can provide more user-friendly and interpretable explanations. Nonetheless, the methods for generating path-based explanations for KGs have not been well-explored. To address this gap, we propose Power-Link, the first path-based KGC explainer that explores GNN-based models. We design a novel simplified graph-powering technique, which enables the generation of path-based explanations with a fully parallelisable and memory-efficient training scheme. We further introduce three new metrics for quantitative evaluation of the explanations, together with a qualitative human evaluation. Extensive experiments demonstrate that Power-Link outperforms the SOTA baselines in interpretability, efficiency, and scalability. The code is available at https://github.com/OUTHIM/power-link|近年来，图神经网络(GNN)通过对实体和关系之间的相互作用进行建模，在知识图完成(KGC)方面取得了巨大的成功。然而，对预测事实的解释并没有引起必要的注意。对基于 GNN 的 KGC 模型结果的合理解释增加了模型的透明度，有助于研究人员开发更可靠的模型。解释 KGC 任务的现有实践依赖于基于实例/子图的方法，而在某些场景中，路径可以提供更加用户友好和可解释的解释。尽管如此，为幼儿园生成基于路径的解释的方法还没有得到很好的探索。为了解决这个差距，我们提出 Power-Link，第一个基于路径的 KGC 解释器，探索基于 GNN 的模型。我们设计了一种新颖的简化图形驱动技术，该技术能够生成基于路径的解释，并且具有完全并行和高效的记忆训练方案。我们进一步介绍了三个新的指标来定量评估的解释，以及定性的人类评价。大量的实验表明，Power-Link 在可解释性、效率和可扩展性方面优于 SOTA 基准。密码可在 https://github.com/outhim/power-link 查阅|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Path-based+Explanation+for+Knowledge+Graph+Completion)|0| +|[Cluster-Wide Task Slowdown Detection in Cloud System](https://doi.org/10.1145/3637528.3671936)|Feiyi Chen, Yingying Zhang, Lunting Fan, Yuxuan Liang, Guansong Pang, Qingsong Wen, Shuiguang Deng|The Hong Kong University of Science and Technology (Guangzhou), Hong Kong, China; Squirrel AI, Bellevue, USA; Singapore Management University, Singapore, Singapore; Zhejiang University, Hangzhou, China; Alibaba Group, Hangzhou, China; Zhejiang University & Alibaba Group, Hangzhou, China|Slow task detection is a critical problem in cloud operation and maintenance since it is highly related to user experience and can bring substantial liquidated damages. Most anomaly detection methods detect it from a single-task aspect. However, considering millions of concurrent tasks in large-scale cloud computing clusters, it becomes impractical and inefficient. Moreover, single-task slowdowns are very common and do not necessarily indicate a malfunction of a cluster due to its violent fluctuation nature in a virtual environment. Thus, we shift our attention to cluster-wide task slowdowns by utilizing the duration time distribution of tasks across a cluster, so that the computation complexity is not relevant to the number of tasks. The task duration time distribution often exhibits compound periodicity and local exceptional fluctuations over time. Though transformer-based methods are one of the most powerful methods to capture these time series normal variation patterns, we empirically find and theoretically explain the flaw of the standard attention mechanism in reconstructing subperiods with low amplitude when dealing with compound periodicity. To tackle these challenges, we propose SORN (i.e., Skimming Off subperiods in descending amplitude order and Reconstructing Non-slowing fluctuation), which consists of a Skimming Attention mechanism to reconstruct the compound periodicity and a Neural Optimal Transport module to distinguish cluster-wide slowdowns from other exceptional fluctuations. Furthermore, since anomalies in the training set are inevitable in a practical scenario, we propose a picky loss function, which adaptively assigns higher weights to reliable time slots in the training set. Extensive experiments demonstrate that SORN outperforms state-of-the-art methods on multiple real-world industrial datasets.|慢速任务检测是云操作和维护中的一个关键问题，因为它与用户体验密切相关，可以带来大量的 liquidated damage。大多数异常检测方法从单任务方面检测它。然而，考虑到大规模云计算集群中有数以百万计的并发任务，这就变得不切实际且效率低下。此外，单任务速度减慢非常普遍，并不一定表明由于集群在虚拟环境中的剧烈波动性而出现故障。因此，我们将注意力转移到集群范围的任务减速上，利用集群中任务的持续时间分布，因此计算复杂度与任务数量无关。任务持续时间分布通常表现为复合周期性和局部异常波动。虽然基于变压器的方法是获取这些时间序列正态变化模式的最有力的方法之一，但我们在处理复合周期时，从实证上发现并从理论上解释了标准注意机制在重构低振幅子周期时的缺陷。为了应对这些挑战，我们提出了 SORN (即，按降幅顺序略去子周期和重构非减速波动) ，它由略去注意机制重构复合周期和神经最优传输模块区分簇范围的减速和其他异常波动组成。此外，由于训练集中的异常在实际场景中是不可避免的，我们提出了一个挑剔的损失函数，它自适应地分配更高的权重给训练集中的可靠时隙。大量的实验表明，SORN 在多个真实世界的工业数据集上优于最先进的方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Cluster-Wide+Task+Slowdown+Detection+in+Cloud+System)|0| +|[Scalable Algorithm for Finding Balanced Subgraphs with Tolerance in Signed Networks](https://doi.org/10.1145/3637528.3671674)|Jingbang Chen, Qiuyang Mang, Hangrui Zhou, Richard Peng, Yu Gao, Chenhao Ma|Computer Science Department, Carnegie Mellon University, Pittsburgh, PA, USA; Independent, Beijing, China; Institute for Interdisciplinary Information Sciences (IIIS), Tsinghua University, Beijing, China; David R. Cheriton School of Computer Science, University of Waterloo, Waterloo, Ontario, Canada; School of Data Science, The Chinese University of Hong Kong, Shenzhen, Shenzhen, Guangdong, China|Signed networks, characterized by edges labeled as either positive or negative, offer nuanced insights into interaction dynamics beyond the capabilities of unsigned graphs. Central to this is the task of identifying the maximum balanced subgraph, crucial for applications like polarized community detection in social networks and portfolio analysis in finance. Traditional models, however, are limited by an assumption of perfect partitioning, which fails to mirror the complexities of real-world data. Addressing this gap, we introduce an innovative generalized balanced subgraph model that incorporates tolerance for imbalance. Our proposed region-based heuristic algorithm, tailored for this NP -hard problem, strikes a balance between low time complexity and high-quality outcomes. Comparative experiments validate its superior performance against leading solutions, delivering enhanced effectiveness (notably larger subgraph sizes) and efficiency (achieving up to 100× speedup) in both traditional and generalized contexts.|有符号的网络，拥有属性边缘标记为正面或负面，提供了对互动动力学的细致洞察，超越了无符号图形的能力。其中心任务是确定最大平衡子图，这对于社交网络中的极化社区检测和金融中的投资组合分析等应用至关重要。然而，传统模型受到完美分区假设的限制，无法反映真实世界数据的复杂性。针对这一差距，我们引入了一个创新的广义平衡子图模型，其中包含了对不平衡的容忍度。我们提出的基于区域的启发式算法，针对这个 NP 难题，取得了低时间复杂度和高质量的结果之间的平衡。比较实验验证了其对领先解决方案的优越性能，在传统和一般情况下都提供了增强的有效性(尤其是更大的子图大小)和效率(达到100倍加速)。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Scalable+Algorithm+for+Finding+Balanced+Subgraphs+with+Tolerance+in+Signed+Networks)|0| +|[QGRL: Quaternion Graph Representation Learning for Heterogeneous Feature Data Clustering](https://doi.org/10.1145/3637528.3671839)|Junyang Chen, Yuzhu Ji, Rong Zou, Yiqun Zhang, Yiuming Cheung|Department of Computer Science, Hong Kong Baptist University, Hong Kong SAR, China; School of Computer Science and Technology, Guangdong University of Technology, Guangzhou, China|Clustering is one of the most commonly used techniques for unsupervised data analysis. As real data sets are usually composed of numerical and categorical features that are heterogeneous in nature, the heterogeneity in the distance metric and feature coupling prevents deep representation learning from achieving satisfactory clustering accuracy. Currently, supervised Quaternion Representation Learning (QRL) has achieved remarkable success in efficiently learning informative representations of coupled features from multiple views derived endogenously from the original data. To inherit the advantages of QRL for unsupervised heterogeneous feature representation learning, we propose a deep QRL model that works in an encoder-decoder manner. To ensure that the implicit couplings of heterogeneous feature data can be well characterized by representation learning, a hierarchical coupling encoding strategy is designed to convert the data set into an attributed graph to be the input of QRL. We also integrate the clustering objective into the model training to facilitate a joint optimization of the representation and clustering. Extensive experimental evaluations illustrate the superiority of the proposed Quaternion Graph Representation Learning (QGRL) method in terms of clustering accuracy and robustness to various data sets composed of arbitrary combinations of numerical and categorical features. The source code is opened at https://github.com/Juny-Chen/QGRL.git.|聚类是无监督数据分析中最常用的技术之一。由于实际数据集通常由数值特征和分类特征组成，这些特征本质上是异构的，距离度量和特征耦合的异构性使得深度表示学习无法获得满意的聚类精度。目前，有监督的四元数表示学习(QRL)在有效地学习耦合特征的信息表示方面取得了显著的成功。为了继承 QRL 在非监督异构特征表示学习中的优势，本文提出了一种基于编译码方式的深层 QRL 模型。为了确保异构特征数据的隐式耦合能够很好地进行拥有属性表示学习，设计了一种分层耦合编码策略，将数据集转换为一个属性图作为 QRL 的输入。将聚类目标集成到模型训练中，实现了表示和聚类的联合优化。大量的实验结果表明，所提出的四元数图表示学习(QGRL)方法在聚类精度和对由任意数值和分类特征组合构成的各种数据集的鲁棒性方面具有优越性。源代码在 https://github.com/juny-chen/qgrl.git 打开。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=QGRL:+Quaternion+Graph+Representation+Learning+for+Heterogeneous+Feature+Data+Clustering)|0| +|[Can a Deep Learning Model be a Sure Bet for Tabular Prediction?](https://doi.org/10.1145/3637528.3671893)|Jintai Chen, Jiahuan Yan, Qiyuan Chen, Danny Z. Chen, Jian Wu, Jimeng Sun|Univ. of Illinois Urbana-Champaign, Urbana, IL, USA; Zhejiang University, Hangzhou, Zhejiang, China; University of Notre Dame, South Bend, IN, USA|Data organized in tabular format is ubiquitous in real-world applications, and users often craft tables with biased feature definitions and flexibly set prediction targets of their interests. Thus, a rapid development of a robust, effective, dataset-versatile, user-friendly tabular prediction approach is highly desired. While Gradient Boosting Decision Trees (GBDTs) and existing deep neural networks (DNNs) have been extensively utilized by professional users, they present several challenges for casual users, particularly: (i) the dilemma of model selection due to their different dataset preferences, and (ii) the need for heavy hyperparameter searching, failing which their performances are deemed inadequate. In this paper, we delve into this question: Can we develop a deep learning model that serves as a sure bet solution for a wide range of tabular prediction tasks, while also being user-friendly for casual users? We delve into three key drawbacks of deep tabular models, encompassing: (P1) lack of rotational variance property, (P2) large data demand, and (P3) over-smooth solution. We propose ExcelFormer, addressing these challenges through a semi-permeable attention module that effectively constrains the influence of less informative features to break the DNNs' rotational invariance property (for P1), data augmentation approaches tailored for tabular data (for P2), and attentive feedforward network to boost the model fitting capability (for P3). These designs collectively make ExcelFormer a sure bet solution for diverse tabular datasets. Extensive and stratified experiments conducted on real-world datasets demonstrate that our model outperforms previous approaches across diverse tabular data prediction tasks, and this framework can be friendly to casual users, offering ease of use without the heavy hyperparameter tuning. The codes are available at https://github.com/whatashot/excelformer.|以表格形式组织的数据在实际应用中无处不在，用户经常使用有偏差的特征定义和灵活设置他们感兴趣的预测目标来编制表格。因此，一个健壮的，有效的，数据集通用的，用户友好的表格预测方法的快速发展是迫切需要的。虽然梯度提升决策树(GBDTs)和现有的深度神经网络(DNN)已被专业用户广泛使用，但它们对普通用户提出了一些挑战，特别是: (i)由于他们不同的数据集偏好，模型选择的困境，以及(ii)需要大量的超参数搜索，否则他们的性能被认为是不够的。在本文中，我们深入研究这个问题: 我们能否开发一个深度学习模型，作为一个可靠的解决方案，为广泛的表格预测任务，同时也是用户友好的临时用户？我们深入研究了深表模型的三个主要缺点，包括: (P1)缺乏旋转方差性质，(P2)大数据需求，(P3)过于光滑的解决方案。我们建议使用 ExcelForm，通过一个半渗透注意模块来应对这些挑战，该模块有效地限制了信息量较小的特征对打破 DNN 的旋转不变性属性(对于 P1)的影响，为表格数据量身定制的数据增强方法(对于 P2) ，以及专注的前馈网络来提高模型拟合能力(对于 P3)。这些设计共同使得 ExcelForm 成为不同表格数据集的一个确定的解决方案。在真实世界的数据集上进行的广泛和分层的实验表明，我们的模型在不同的表格数据预测任务中优于以前的方法，并且该框架可以友好地对临时用户，提供易于使用而不需要大量的超参数调整。密码可以在 https://github.com/whatashot/excelformer 找到。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Can+a+Deep+Learning+Model+be+a+Sure+Bet+for+Tabular+Prediction?)|0| |[Profiling Urban Streets: A Semi-Supervised Prediction Model Based on Street View Imagery and Spatial Topology](https://doi.org/10.1145/3637528.3671918)|Meng Chen, Zechen Li, Weiming Huang, Yongshun Gong, Yilong Yin|School of Software, Shandong University, Jinan, Shandong, China; School of Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore|With the expansion and growth of cities, profiling urban areas with the advent of multi-modal urban datasets (e.g., points-of-interest and street view imagery) has become increasingly important in urban planing and management. Particularly, street view images have gained popularity for understanding the characteristics of urban areas due to its abundant visual information and inherent correlations with human activities. In this study, we define a street segment represented by multiple street view images as the minimum spatial unit for analysis and predict its functional and socioeconomic indicators, which presents several challenges in modeling spatial distributions of images on a street and the spatial topology (adjacency) of streets. Meanwhile, Large Language Models are capable of understanding imagery data based on its extraordinary knowledge base and unveil a remarkable opportunity for profiling streets with images. In view of the challenges and opportunity, we present a semi-supervised Urban Street Profiling Model (USPM) based on street view imagery and spatial adjacency of urban streets. Specifically, given a street with multiple images, we first employ a newly designed spatial context-based contrastive learning method to generate feature vectors of images and then apply the LSTM-based fusion method to encode multiple images on a street to yield the street visual representation; we then create the descriptions of street scenes for street view images based on the SPHINX (a large language model) and produce the street textual representation; finally, we build an urban street graph based on spatial topology (adjacency) and employ a semi-supervised graph learning algorithm to further encode the street representations for prediction. We conduct thorough experiments with real-world datasets to assess the proposed USPM. The experimental results demonstrate that USPM considerably outperforms baseline methods in two urban prediction tasks.|随着城市的扩张和发展，随着多模式城市数据集(如兴趣点和街景图像)的出现，对城市地区进行剖析已经成为城市规划和管理中越来越重要的内容。特别是街景图像，由于其丰富的视觉信息和与人类活动的内在联系，在理解城市地域特征方面得到了广泛的应用。在这项研究中，我们定义了一个由多个街景图像表示的街段作为分析和预测其功能和社会经济指标的最小空间单元，这在建模街道上的图像的空间分布和街道的空间拓扑(邻接)方面提出了几个挑战。与此同时，大语言模型能够理解图像数据的基础上，其非凡的知识库，揭示了一个显着的机会，以图像剖面街道。针对城市街道面临的挑战和机遇，提出了一种基于街景图像和城市街道空间邻接的半监督城市街道剖面模型(USPM)。具体来说，我们首先采用一种新设计的基于空间上下文的对比学习方法生成图像的特征向量，然后应用基于 LSTM 的融合方法对街道上的多幅图像进行编码，得到街道的视觉表示; 然后基于 SPHINX (一种大语言模型)生成街道图像的街道场景描述并生成街道文本表示; 最后，我们构建了一个基于空间拓扑(邻接)的城市街道图，并采用半监督图学习算法进一步编码街道表示进行预测。我们进行彻底的实验与现实世界的数据集，以评估提出的 USPM。实验结果表明，在两种城市预测任务中，USPM 方法的预测效果明显优于基线方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Profiling+Urban+Streets:+A+Semi-Supervised+Prediction+Model+Based+on+Street+View+Imagery+and+Spatial+Topology)|0| -|[Large Language Model-driven Meta-structure Discovery in Heterogeneous Information Network](https://doi.org/10.1145/3637528.3671965)|Lin Chen, Fengli Xu, Nian Li, Zhenyu Han, Meng Wang, Yong Li, Pan Hui|; Hong Kong University of Science and Technology, Hong Kong, China; Hefei University of Technology, Hefei, China; Shenzhen International Graduate School, Tsinghua University, Shenzhen, China; BNRist, Department of Electronic Engineering, Tsinghua University, Beijing, China|Heterogeneous information networks (HIN) have gained increasing popularity in recent years for capturing complex relations between diverse types of nodes. Meta-structures are proposed as a useful tool to identify the important patterns in HINs, but hand-crafted meta-structures pose significant challenges for scaling up, drawing wide research attention towards developing automatic search algorithms. Previous efforts primarily focused on searching for meta-structures with good empirical performance, overlooking the importance of human comprehensibility and generalizability. To address this challenge, we draw inspiration from the emergent reasoning abilities of large language models (LLMs). We propose ReStruct, a meta-structure search framework that integrates LLM reasoning into the evolutionary procedure. ReStruct uses a grammar translator to encode the meta-structures into natural language sentences, and leverages the reasoning power of LLMs to evaluate their semantic feasibility. Besides, ReStruct also employs performance-oriented evolutionary operations. These two competing forces allow ReStruct to jointly optimize the semantic explainability and empirical performance of meta-structures. Furthermore, ReStruct contains a differential LLM explainer to generate and refine natural language explanations for the discovered meta-structures by reasoning through the search history. Experiments on eight representative HIN datasets demonstrate that ReStruct achieves state-of-the-art performance in both recommendation and node classification tasks. Moreover, a survey study involving 73 graduate students shows that the discovered meta-structures and generated explanations by ReStruct are substantially more comprehensible. Our code and questionnaire are available at https://github.com/LinChen-65/ReStruct.|异构信息网络(HIN)近年来由于捕获不同类型节点之间的复杂关系而越来越受到人们的欢迎。元结构被认为是识别 HIN 中重要模式的有用工具，但手工制作的元结构对扩展提出了重大挑战，引起了研究人员对开发自动搜索算法的广泛关注。以往的研究主要集中在寻找具有良好经验性能的元结构，忽视了人类可理解性和普遍性的重要性。为了应对这一挑战，我们从大型语言模型(LLM)的突发推理能力中获得灵感。我们提出了一个元结构搜索框架 ReStruct，它将 LLM 推理集成到进化过程中。ReStruct 使用语法翻译器将元结构编码成自然语言句子，并利用 LLM 的推理能力来评估其语义可行性。此外，ReStruct 还采用了面向性能的进化操作。这两种相互竞争的力量使得重构能够共同优化元结构的语义可解释性和经验性能。此外，ReStruct 还包含一个差分 LLM 解释器，通过搜索历史推理为已发现的元结构生成和细化自然语言解释。在八个典型的 HIN 数据集上的实验表明，ReStruct 在推荐和节点分类任务上都取得了最好的性能。此外，一项涉及73名研究生的调查研究表明，ReStruct 发现的元结构和生成的解释更容易理解。我们的代码和问卷可在 https://github.com/linchen-65/restruct 下载。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Large+Language+Model-driven+Meta-structure+Discovery+in+Heterogeneous+Information+Network)|0| +|[Large Language Model-driven Meta-structure Discovery in Heterogeneous Information Network](https://doi.org/10.1145/3637528.3671965)|Lin Chen, Fengli Xu, Nian Li, Zhenyu Han, Meng Wang, Yong Li, Pan Hui|; Hefei University of Technology, Hefei, China; Hong Kong University of Science and Technology, Hong Kong, China; BNRist, Department of Electronic Engineering, Tsinghua University, Beijing, China; Shenzhen International Graduate School, Tsinghua University, Shenzhen, China|Heterogeneous information networks (HIN) have gained increasing popularity in recent years for capturing complex relations between diverse types of nodes. Meta-structures are proposed as a useful tool to identify the important patterns in HINs, but hand-crafted meta-structures pose significant challenges for scaling up, drawing wide research attention towards developing automatic search algorithms. Previous efforts primarily focused on searching for meta-structures with good empirical performance, overlooking the importance of human comprehensibility and generalizability. To address this challenge, we draw inspiration from the emergent reasoning abilities of large language models (LLMs). We propose ReStruct, a meta-structure search framework that integrates LLM reasoning into the evolutionary procedure. ReStruct uses a grammar translator to encode the meta-structures into natural language sentences, and leverages the reasoning power of LLMs to evaluate their semantic feasibility. Besides, ReStruct also employs performance-oriented evolutionary operations. These two competing forces allow ReStruct to jointly optimize the semantic explainability and empirical performance of meta-structures. Furthermore, ReStruct contains a differential LLM explainer to generate and refine natural language explanations for the discovered meta-structures by reasoning through the search history. Experiments on eight representative HIN datasets demonstrate that ReStruct achieves state-of-the-art performance in both recommendation and node classification tasks. Moreover, a survey study involving 73 graduate students shows that the discovered meta-structures and generated explanations by ReStruct are substantially more comprehensible. Our code and questionnaire are available at https://github.com/LinChen-65/ReStruct.|异构信息网络(HIN)近年来由于捕获不同类型节点之间的复杂关系而越来越受到人们的欢迎。元结构被认为是识别 HIN 中重要模式的有用工具，但手工制作的元结构对扩展提出了重大挑战，引起了研究人员对开发自动搜索算法的广泛关注。以往的研究主要集中在寻找具有良好经验性能的元结构，忽视了人类可理解性和普遍性的重要性。为了应对这一挑战，我们从大型语言模型(LLM)的突发推理能力中获得灵感。我们提出了一个元结构搜索框架 ReStruct，它将 LLM 推理集成到进化过程中。ReStruct 使用语法翻译器将元结构编码成自然语言句子，并利用 LLM 的推理能力来评估其语义可行性。此外，ReStruct 还采用了面向性能的进化操作。这两种相互竞争的力量使得重构能够共同优化元结构的语义可解释性和经验性能。此外，ReStruct 还包含一个差分 LLM 解释器，通过搜索历史推理为已发现的元结构生成和细化自然语言解释。在八个典型的 HIN 数据集上的实验表明，ReStruct 在推荐和节点分类任务上都取得了最好的性能。此外，一项涉及73名研究生的调查研究表明，ReStruct 发现的元结构和生成的解释更容易理解。我们的代码和问卷可在 https://github.com/linchen-65/restruct 下载。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Large+Language+Model-driven+Meta-structure+Discovery+in+Heterogeneous+Information+Network)|0| |[Hate Speech Detection with Generalizable Target-aware Fairness](https://doi.org/10.1145/3637528.3671821)|Tong Chen, Danny Wang, Xurong Liang, Marten Risius, Gianluca Demartini, Hongzhi Yin|The University of Queensland, Brisbane, Australia|To counter the side effect brought by the proliferation of social media platforms, hate speech detection (HSD) plays a vital role in halting the dissemination of toxic online posts at an early stage. However, given the ubiquitous topical communities on social media, a trained HSD classifier can easily become biased towards specific targeted groups (e.g.,female andblack people), where a high rate of either false positive or false negative results can significantly impair public trust in the fairness of content moderation mechanisms, and eventually harm the diversity of online society. Although existing fairness-aware HSD methods can smooth out some discrepancies across targeted groups, they are mostly specific to a narrow selection of targets that are assumed to be known and fixed. This inevitably prevents those methods from generalizing to real-world use cases where new targeted groups constantly emerge (e.g., new forums created on Reddit) over time. To tackle the defects of existing HSD practices, we propose Generalizable target-aware Fairness (GetFair), a new method for fairly classifying each post that contains diverse and even unseen targets during inference. To remove the HSD classifier's spurious dependence on target-related features, GetFair trains a series of filter functions in an adversarial pipeline, so as to deceive the discriminator that recovers the targeted group from filtered post embeddings. To maintain scalability and generalizability, we innovatively parameterize all filter functions via a hypernetwork. Taking a target's pretrained word embedding as input, the hypernetwork generates the weights used by each target-specific filter on-the-fly without storing dedicated filter parameters. In addition, a novel semantic gap alignment scheme is imposed on the generation process, such that the produced filter function for an unseen target is rectified by its semantic affinity with existing targets used for training. Finally, experiments are conducted on two benchmark HSD datasets, showing advantageous performance of GetFair on out-of-sample targets among baselines.|为了应对社交媒体平台扩散带来的副作用，仇恨言论检测(HSD)在及早阻止有毒网络帖子的传播方面发挥着至关重要的作用。然而，鉴于社交媒体上无处不在的话题社区，经过训练的 HSD 分类器很容易偏向特定的目标群体(例如，女性和黑人) ，其中高比率的假阳性或假阴性结果可以显着损害公众对内容调节机制的公平性的信任，并最终损害网络社会的多样性。虽然现有的具有公平意识的 HSD 方法可以消除目标群体之间的一些差异，但它们大多特定于假定已知和固定的狭窄目标选择。这不可避免地阻止了这些方法随着时间的推移将新的目标群体(例如，在 Reddit 上创建的新论坛)推广到现实世界中的用例。为了解决现有 HSD 实践的缺陷，我们提出了一种可概化的目标感知公平(Generalable target-aware Fairness，GetFair) ，这是一种在推理过程中对包含不同甚至不可见目标的每篇文章进行公平分类的新方法。为了消除 HSD 分类器对目标相关特征的虚假依赖，GetFair 在对抗流水线中训练一系列过滤函数，以欺骗从过滤后嵌入中恢复目标群的鉴别器。为了保持可扩展性和通用性，我们创新地通过一个超网络参数化所有的过滤函数。该超网络以目标预先训练好的嵌入词作为输入，在不存储专用滤波器参数的情况下，实时生成每个目标特定滤波器所使用的权值。此外，在生成过程中加入了一种新的语义间隙对齐方案，使得生成的未知目标的过滤函数通过其与用于训练的现有目标的语义亲和性进行校正。最后，在两个基准 HSD 数据集上进行了实验，结果表明 GetFair 对基准之间的样本外目标具有优越的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Hate+Speech+Detection+with+Generalizable+Target-aware+Fairness)|0| |[GraphWiz: An Instruction-Following Language Model for Graph Computational Problems](https://doi.org/10.1145/3637528.3672010)|Nuo Chen, Yuhan Li, Jianheng Tang, Jia Li||Large language models (LLMs) have achieved impressive success across various domains, but their capability in understanding and resolving complex graph problems is less explored. To bridge this gap, we introduce GraphInstruct, a novel instruction-tuning dataset aimed at enabling language models to tackle a broad spectrum of graph problems through explicit reasoning paths. Utilizing GraphInstruct, we build GraphWiz, an open-source language model capable of solving various graph computational problems while generating clear reasoning processes. To further enhance the model's performance and reliability, we integrate the Direct Preference Optimization (DPO) framework within the graph problem-solving context. The improved model, GraphWiz-DPO, achieves an average accuracy of 65% across nine tasks with different complexity levels, surpassing GPT-4 which has an average accuracy of 43.8%. Our study also investigates the relationship between training data volume and model performance, emphasizing the risk of overfitting as data volume increases. Additionally, we explore the transferability of the proposed model across different tasks and datasets, demonstrating its robust zero-shot generalization capability. GraphWiz offers a new blueprint and valuable insights for developing LLMs specialized in graph reasoning and problem-solving.|大型语言模型(LLM)已经在各个领域取得了令人印象深刻的成功，但是它们在理解和解决复杂图形问题方面的能力却很少被研究。为了弥补这一差距，我们引入了 GraphDirect，这是一个新颖的指令调优数据集，旨在使语言模型能够通过显式推理路径解决广泛的图形问题。我们利用图形指令构建了 GraphWiz，这是一个开源的语言模型，能够在生成清晰的推理过程的同时解决各种图形计算问题。为了进一步提高模型的性能和可靠性，我们将直接偏好优化(DPO)框架集成到图问题求解环境中。改进后的模型 GraphWiz-DPO 在9个不同复杂度级别的任务中达到了65% 的平均准确率，超过了平均准确率为43.8% 的 GPT-4。我们的研究还调查了训练数据量和模型性能之间的关系，强调了随着数据量的增加过度拟合的风险。此外，我们还研究了该模型在不同任务和数据集之间的可转移性，证明了其鲁棒的零镜头泛化能力。GraphWiz 为开发专门用于图形推理和问题解决的 LLM 提供了一个新的蓝图和有价值的见解。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=GraphWiz:+An+Instruction-Following+Language+Model+for+Graph+Computational+Problems)|0| -|[Calibration of Time-Series Forecasting: Detecting and Adapting Context-Driven Distribution Shift](https://doi.org/10.1145/3637528.3671926)|Mouxiang Chen, Lefei Shen, Han Fu, Zhuo Li, Jianling Sun, Chenghao Liu|State Street Technology (Zhejiang) Ltd., Hangzhou, China; Salesforce Research Asia, Singapore, Singapore; Zhejiang University, Hangzhou, China|Recent years have witnessed the success of introducing deep learning modelsto time series forecasting. From a data generation perspective, we illustratethat existing models are susceptible to distribution shifts driven by temporalcontexts, whether observed or unobserved. Such context-driven distributionshift (CDS) introduces biases in predictions within specific contexts and poseschallenges for conventional training paradigms. In this paper, we introduce auniversal calibration methodology for the detection and adaptation of CDS witha trained model. To this end, we propose a novel CDS detector, termed the"residual-based CDS detector" or "Reconditionor", which quantifies the model'svulnerability to CDS by evaluating the mutual information between predictionresiduals and their corresponding contexts. A high Reconditionor scoreindicates a severe susceptibility, thereby necessitating model adaptation. Inthis circumstance, we put forth a straightforward yet potent adapter frameworkfor model calibration, termed the "sample-level contextualized adapter" or"SOLID". This framework involves the curation of a contextually similar datasetto the provided test sample and the subsequent fine-tuning of the model'sprediction layer with a limited number of steps. Our theoretical analysisdemonstrates that this adaptation strategy can achieve an optimal bias-variancetrade-off. Notably, our proposed Reconditionor and SOLID are model-agnostic andreadily adaptable to a wide range of models. Extensive experiments show thatSOLID consistently enhances the performance of current forecasting models onreal-world datasets, especially on cases with substantial CDS detected by theproposed Reconditionor, thus validating the effectiveness of the calibrationapproach.|近年来，深度学习模型在时间序列预测中的应用取得了成功。从数据生成的角度，我们说明了现有的模型容易受到时间背景驱动的分布变化的影响，无论是观察到的还是未观察到的。这种上下文驱动的分布转移(CDS)在特定的上下文中引入了预测的偏差，并对传统的训练范式提出了挑战。本文介绍了一种基于训练模型的 CDS 检测与自适应通用标定方法。为此，我们提出了一种新的 CDS 检测器，称为“基于残差的 CDS 检测器”或“重构器”，它通过评估预测残差与其相应上下文之间的互信息来量化模型对 CDS 的脆弱性。一个高分表明一个严重的易感性，从而需要模型适应。在这种情况下，我们提出了一个简单而有效的适配器模型校准框架，称为“样本级上下文适配器”或“ SOLID”。这个框架涉及到与所提供的测试样本上下文相似的数据集的管理，以及随后用有限的步骤对模型的预测层进行微调。我们的理论分析表明，这种适应策略可以实现最佳的偏差-方差权衡。值得注意的是，我们提出的 Reconditionor 和 SOLID 是模型无关的，可以很容易地适应广泛的模型。大量的实验表明，SOLID 一致地提高了现有预测模型在真实世界数据集上的性能，特别是在具有大量 CDS 的情况下，由提出的重调器检测，从而验证了校准方法的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Calibration+of+Time-Series+Forecasting:+Detecting+and+Adapting+Context-Driven+Distribution+Shift)|0| +|[Calibration of Time-Series Forecasting: Detecting and Adapting Context-Driven Distribution Shift](https://doi.org/10.1145/3637528.3671926)|Mouxiang Chen, Lefei Shen, Han Fu, Zhuo Li, Jianling Sun, Chenghao Liu|Salesforce Research Asia, Singapore, Singapore; Zhejiang University, Hangzhou, China; State Street Technology (Zhejiang) Ltd., Hangzhou, China|Recent years have witnessed the success of introducing deep learning modelsto time series forecasting. From a data generation perspective, we illustratethat existing models are susceptible to distribution shifts driven by temporalcontexts, whether observed or unobserved. Such context-driven distributionshift (CDS) introduces biases in predictions within specific contexts and poseschallenges for conventional training paradigms. In this paper, we introduce auniversal calibration methodology for the detection and adaptation of CDS witha trained model. To this end, we propose a novel CDS detector, termed the"residual-based CDS detector" or "Reconditionor", which quantifies the model'svulnerability to CDS by evaluating the mutual information between predictionresiduals and their corresponding contexts. A high Reconditionor scoreindicates a severe susceptibility, thereby necessitating model adaptation. Inthis circumstance, we put forth a straightforward yet potent adapter frameworkfor model calibration, termed the "sample-level contextualized adapter" or"SOLID". This framework involves the curation of a contextually similar datasetto the provided test sample and the subsequent fine-tuning of the model'sprediction layer with a limited number of steps. Our theoretical analysisdemonstrates that this adaptation strategy can achieve an optimal bias-variancetrade-off. Notably, our proposed Reconditionor and SOLID are model-agnostic andreadily adaptable to a wide range of models. Extensive experiments show thatSOLID consistently enhances the performance of current forecasting models onreal-world datasets, especially on cases with substantial CDS detected by theproposed Reconditionor, thus validating the effectiveness of the calibrationapproach.|近年来，深度学习模型在时间序列预测中的应用取得了成功。从数据生成的角度，我们说明了现有的模型容易受到时间背景驱动的分布变化的影响，无论是观察到的还是未观察到的。这种上下文驱动的分布转移(CDS)在特定的上下文中引入了预测的偏差，并对传统的训练范式提出了挑战。本文介绍了一种基于训练模型的 CDS 检测与自适应通用标定方法。为此，我们提出了一种新的 CDS 检测器，称为“基于残差的 CDS 检测器”或“重构器”，它通过评估预测残差与其相应上下文之间的互信息来量化模型对 CDS 的脆弱性。一个高分表明一个严重的易感性，从而需要模型适应。在这种情况下，我们提出了一个简单而有效的适配器模型校准框架，称为“样本级上下文适配器”或“ SOLID”。这个框架涉及到与所提供的测试样本上下文相似的数据集的管理，以及随后用有限的步骤对模型的预测层进行微调。我们的理论分析表明，这种适应策略可以实现最佳的偏差-方差权衡。值得注意的是，我们提出的 Reconditionor 和 SOLID 是模型无关的，可以很容易地适应广泛的模型。大量的实验表明，SOLID 一致地提高了现有预测模型在真实世界数据集上的性能，特别是在具有大量 CDS 的情况下，由提出的重调器检测，从而验证了校准方法的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Calibration+of+Time-Series+Forecasting:+Detecting+and+Adapting+Context-Driven+Distribution+Shift)|0| |[Co-Neighbor Encoding Schema: A Light-cost Structure Encoding Method for Dynamic Link Prediction](https://doi.org/10.1145/3637528.3671770)|Ke Cheng, Linzhi Peng, Junchen Ye, Leilei Sun, Bowen Du|; CCSE Lab, Beihang University, Beijing, China; School of Transportation Science and Engineering, Beihang University, Beijing, China|Structure encoding has proven to be the key feature to distinguishing links in a graph. However, Structure encoding in the temporal graph keeps changing as the graph evolves, repeatedly computing such features can be time-consuming due to the high-order subgraph construction. We develop the Co-Neighbor Encoding Schema (CNES) to address this issue. Instead of recomputing the feature by the link, CNES stores information in the memory to avoid redundant calculations. Besides, unlike the existing memory-based dynamic graph learning method that stores node hidden states, we introduce a hashtable-based memory to compress the adjacency matrix for efficient structure feature construction and updating with vector computation in parallel. Furthermore, CNES introduces a Temporal-Diverse Memory to generate long-term and short-term structure encoding for neighbors with different structural information. A dynamic graph learning framework, Co-Neighbor Encoding Network (CNE-N), is proposed using the aforementioned techniques. Extensive experiments on thirteen public datasets verify the effectiveness and efficiency of the proposed method.|结构编码已被证明是区分图中链接的关键特征。然而，时态图中的结构编码随着图的演化而不断变化，由于子图结构的高阶性，重复计算这些特征可能会耗费大量的时间。为了解决这个问题，我们开发了协同邻居编码模式(CNES)。CNES 不通过链路重新计算特性，而是将信息存储在内存中以避免冗余计算。此外，不像现有的基于内存的动态图学习方法存储节点隐藏状态，我们引入了一个基于哈希表的内存来压缩邻接矩阵，以便有效地结构特征构建和并行矢量计算更新。此外，CNES 还引入了时间多样性存储器，为具有不同结构信息的邻居生成长期和短期的结构编码。利用上述技术，提出了一种动态图学习框架——协同邻域编码网络(CNE-N)。在十三个公共数据集上的大量实验验证了该方法的有效性和效率。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Co-Neighbor+Encoding+Schema:+A+Light-cost+Structure+Encoding+Method+for+Dynamic+Link+Prediction)|0| -|[Resurrecting Label Propagation for Graphs with Heterophily and Label Noise](https://doi.org/10.1145/3637528.3671774)|Yao Cheng, Caihua Shan, Yifei Shen, Xiang Li, Siqiang Luo, Dongsheng Li|Nanyang Technological University, Singapore, Singapore; Microsoft Research Asia, Shanghai, China; East China Normal University, Shanghai, China|Label noise is a common challenge in large datasets, as it can significantly degrade the generalization ability of deep neural networks. Most existing studies focus on noisy labels in computer vision; however, graph models encompass both node features and graph topology as input, and become more susceptible to label noise through message-passing mechanisms. Recently, only a few works have been proposed to tackle the label noise on graphs. One significant limitation is that they operate under the assumption that the graph exhibits homophily and that the labels are distributed smoothly. However, real-world graphs can exhibit varying degrees of heterophily, or even be dominated by heterophily, which results in the inadequacy of the current methods. In this paper, we study graph label noise in the context of arbitrary heterophily, with the aim of rectifying noisy labels and assigning labels to previously unlabeled nodes. We begin by conducting two empirical analyses to explore the impact of graph homophily on graph label noise. Following observations, we propose a efficient algorithm, denoted as R2LP. Specifically, R2LP is an iterative algorithm with three steps: (1) reconstruct the graph to recover the homophily property, (2) utilize label propagation to rectify the noisy labels, (3) select high-confidence labels to retain for the next iteration. By iterating these steps, we obtain a set of ''correct'' labels, ultimately achieving high accuracy in the node classification task. The theoretical analysis is also provided to demonstrate its remarkable denoising effect. Finally, we perform experiments on ten benchmark datasets with different levels of graph heterophily and various types of noise. In these experiments, we compare the performance of R2LP against ten typical baseline methods. Our results illustrate the superior performance of the proposed øurs. The code and data of this paper can be accessed at: https://github.com/cy623/R2LP.git.|在大型数据集中，标记噪声是一个常见的问题，因为它会显著降低深层神经网络的泛化能力。现有的研究大多集中在计算机视觉中的噪声标签，然而，图模型既包含节点特征又包含图拓扑作为输入，并且通过消息传递机制更容易受到标签噪声的影响。近年来，针对图的标签噪声问题的研究很少。一个重要的限制是，它们是在假设图表现出同质性和标签平滑分布的情况下运行的。然而，现实世界中的图可能表现出不同程度的异质性，甚至被异质性所支配，这导致了现有方法的不足。本文研究了任意异质性情况下的图标签噪声，目的是校正噪声标签，并将标签分配给以前未标记的节点。我们首先通过两个实证分析来探讨图同调性对图标噪声的影响。经过观察，我们提出了一种有效的算法，称为 R2LP。具体来说，R2LP 算法是一种迭代算法，它包括三个步骤: (1)重构图来恢复同质性; (2)利用标签传播来校正噪声标签; (3)选择高置信度标签来保留下一次迭代。通过迭代这些步骤，我们得到一组“正确”的标签，最终实现节点分类任务的高精度。理论分析也证明了其显著的去噪效果。最后，我们在十个基准数据集上进行了实验，这些数据集具有不同层次的图异质性和不同类型的噪声。在这些实验中，我们比较了 R2LP 和10种典型的基线方法的性能。我们的结果说明了提议的 øurs 的优越性能。本文件的编码及资料可浏览以下 https://github.com/cy623/r2lp.git。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Resurrecting+Label+Propagation+for+Graphs+with+Heterophily+and+Label+Noise)|0| -|[DyGKT: Dynamic Graph Learning for Knowledge Tracing](https://doi.org/10.1145/3637528.3671773)|Ke Cheng, Linzhi Peng, Pengyang Wang, Junchen Ye, Leilei Sun, Bowen Du|SKLSDE Lab, Beihang University, Beijing, China; School of Transportation Science and Engineering, Beihang University, Beijing, China; SKL-IOTSC, Department of Computer and Information Science, University of Macau, Macau, China|Knowledge Tracing aims to assess student learning states by predicting their performance in answering questions. Different from the existing research which utilizes fixed-length learning sequence to obtain the student states and regards KT as a static problem, this work is motivated by three dynamical characteristics: 1) The scales of students answering records are constantly growing; 2) The semantics of time intervals between the records vary; 3) The relationships between students, questions and concepts are evolving. The three dynamical characteristics above contain the great potential to revolutionize the existing knowledge tracing methods. Along this line, we propose a Dynamic Graph-based Knowledge Tracing model, namely DyGKT. In particular, a continuous-time dynamic question-answering graph for knowledge tracing is constructed to deal with the infinitely growing answering behaviors, and it is worth mentioning that it is the first time dynamic graph learning technology is used in this field. Then, a dual time encoder is proposed to capture long-term and short-term semantics among the different time intervals. Finally, a multiset indicator is utilized to model the evolving relationships between students, questions, and concepts via the graph structural feature. Numerous experiments are conducted on five real-world datasets, and the results demonstrate the superiority of our model. All the used resources are publicly available at https://github.com/PengLinzhi/DyGKT.|知识追踪旨在通过预测学生在回答问题时的表现来评估学生的学习状态。与现有的利用固定长度的学习序列获取学生状态并将 KT 视为静态问题的研究不同，本研究的动力来源于三个动态特征: 1)学生回答记录的规模在不断增长; 2)记录之间的时间间隔语义在不断变化; 3)学生、问题和概念之间的关系在不断演化。上述三个动态特征具有革新现有知识追踪方法的巨大潜力。在此基础上，我们提出了一个基于动态图的知识跟踪模型，即 DyGKT。特别是构造了一个用于知识跟踪的连续时间动态问答图来处理无限增长的应答行为，值得一提的是，这是动态图学习技术首次应用于该领域。然后，提出了一种双时间编码器来捕获不同时间间隔之间的长期和短期语义。最后，利用多集指标模型，通过图形结构特征对学生、问题和概念之间的演化关系进行建模。在五个实际数据集上进行了大量的实验，实验结果表明了该模型的优越性。所有使用过的资源都可以在 https://github.com/penglinzhi/dygkt 上公开获得。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=DyGKT:+Dynamic+Graph+Learning+for+Knowledge+Tracing)|0| -|[Conformal Counterfactual Inference under Hidden Confounding](https://doi.org/10.1145/3637528.3671976)|Zonghao Chen, Ruocheng Guo, JeanFrancois Ton, Yang Liu|Bytedance Research, San Jose, USA; University College London, London, United Kingdom; Bytedance Research, London, United Kingdom|Personalized decision making requires the knowledge of potential outcomes under different treatments, and confidence intervals about the potential outcomes further enrich this decision-making process and improve its reliability in high-stakes scenarios. Predicting potential outcomes along with its uncertainty in a counterfactual world poses the foundamental challenge in causal inference. Existing methods that construct confidence intervals for counterfactuals either rely on the assumption of strong ignorability that completely ignores hidden confounders, or need access to un-identifiable lower and upper bounds that characterize the difference between observational and interventional distributions. In this paper, to overcome these limitations, we first propose a novel approach wTCP-DR based on transductive weighted conformal prediction, which provides confidence intervals for counterfactual outcomes with marginal converage guarantees, even under hidden confounding. With less restrictive assumptions, our approach requires access to a fraction of interventional data (from randomized controlled trials) to account for the covariate shift from observational distributoin to interventional distribution. Theoretical results explicitly demonstrate the conditions under which our algorithm is strictly advantageous to the naive method that only uses interventional data. Since transductive conformal prediction is notoriously costly, we propose wSCP-DR, a two-stage variant of wTCP-DR, based on split conformal prediction with same marginal coverage guarantees but at a significantly lower computational cost. After ensuring valid intervals on counterfactuals, it is straightforward to construct intervals for individual treatment effects (ITEs). We demonstrate our method across synthetic and real-world data, including recommendation systems, to verify the superiority of our methods compared against state-of-the-art baselines in terms of both coverage and efficiency. Our code can be found at https://github.com/rguo12/KDD24-Conformal.|个性化决策需要了解不同治疗方案下的潜在结果，关于潜在结果的置信区间进一步丰富了这一决策过程，并提高了其在高风险情景下的可靠性。在一个反事实的世界中，预测潜在的结果及其不确定性构成了因果推理的基本挑战。为反事实构建置信区间的现有方法要么依赖于完全忽略隐藏混杂因素的强烈可忽略性的假设，要么需要访问表征观察和干预分布之间差异的不可识别的下限和上限。为了克服这些局限性，本文首先提出了一种基于传导加权保角预测的新方法 wTCP-DR，该方法为具有边际收敛保证的反事实结果提供置信区间，即使在隐藏混杂情况下也是如此。在限制性较少的假设下，我们的方法需要获得一小部分介入数据(来自随机对照试验) ，以解释从观察分布到介入分布的协变量转移。理论分析结果明确地说明了在什么条件下我们的算法比只使用介入数据的朴素方法具有严格的优势。由于传导共形预测的代价是众所周知的，我们提出了 wSCP-DR，一个两阶段的 wTCP-DR 的变体，基于分裂共形预测具有相同的边际覆盖保证，但计算成本显著降低。在确保反事实的有效间隔之后，就可以直接构建单个治疗效果(ITE)的间隔。我们通过合成和现实世界的数据(包括推荐系统)演示我们的方法，以验证我们的方法在覆盖面和效率方面与最先进的基线相比的优越性。我们的代码可以在 https://github.com/rguo12/kdd24-conformal 找到。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Conformal+Counterfactual+Inference+under+Hidden+Confounding)|0| +|[Resurrecting Label Propagation for Graphs with Heterophily and Label Noise](https://doi.org/10.1145/3637528.3671774)|Yao Cheng, Caihua Shan, Yifei Shen, Xiang Li, Siqiang Luo, Dongsheng Li|Nanyang Technological University, Singapore, Singapore; East China Normal University, Shanghai, China; Microsoft Research Asia, Shanghai, China|Label noise is a common challenge in large datasets, as it can significantly degrade the generalization ability of deep neural networks. Most existing studies focus on noisy labels in computer vision; however, graph models encompass both node features and graph topology as input, and become more susceptible to label noise through message-passing mechanisms. Recently, only a few works have been proposed to tackle the label noise on graphs. One significant limitation is that they operate under the assumption that the graph exhibits homophily and that the labels are distributed smoothly. However, real-world graphs can exhibit varying degrees of heterophily, or even be dominated by heterophily, which results in the inadequacy of the current methods. In this paper, we study graph label noise in the context of arbitrary heterophily, with the aim of rectifying noisy labels and assigning labels to previously unlabeled nodes. We begin by conducting two empirical analyses to explore the impact of graph homophily on graph label noise. Following observations, we propose a efficient algorithm, denoted as R2LP. Specifically, R2LP is an iterative algorithm with three steps: (1) reconstruct the graph to recover the homophily property, (2) utilize label propagation to rectify the noisy labels, (3) select high-confidence labels to retain for the next iteration. By iterating these steps, we obtain a set of ''correct'' labels, ultimately achieving high accuracy in the node classification task. The theoretical analysis is also provided to demonstrate its remarkable denoising effect. Finally, we perform experiments on ten benchmark datasets with different levels of graph heterophily and various types of noise. In these experiments, we compare the performance of R2LP against ten typical baseline methods. Our results illustrate the superior performance of the proposed øurs. The code and data of this paper can be accessed at: https://github.com/cy623/R2LP.git.|在大型数据集中，标记噪声是一个常见的问题，因为它会显著降低深层神经网络的泛化能力。现有的研究大多集中在计算机视觉中的噪声标签，然而，图模型既包含节点特征又包含图拓扑作为输入，并且通过消息传递机制更容易受到标签噪声的影响。近年来，针对图的标签噪声问题的研究很少。一个重要的限制是，它们是在假设图表现出同质性和标签平滑分布的情况下运行的。然而，现实世界中的图可能表现出不同程度的异质性，甚至被异质性所支配，这导致了现有方法的不足。本文研究了任意异质性情况下的图标签噪声，目的是校正噪声标签，并将标签分配给以前未标记的节点。我们首先通过两个实证分析来探讨图同调性对图标噪声的影响。经过观察，我们提出了一种有效的算法，称为 R2LP。具体来说，R2LP 算法是一种迭代算法，它包括三个步骤: (1)重构图来恢复同质性; (2)利用标签传播来校正噪声标签; (3)选择高置信度标签来保留下一次迭代。通过迭代这些步骤，我们得到一组“正确”的标签，最终实现节点分类任务的高精度。理论分析也证明了其显著的去噪效果。最后，我们在十个基准数据集上进行了实验，这些数据集具有不同层次的图异质性和不同类型的噪声。在这些实验中，我们比较了 R2LP 和10种典型的基线方法的性能。我们的结果说明了提议的 øurs 的优越性能。本文件的编码及资料可浏览以下 https://github.com/cy623/r2lp.git。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Resurrecting+Label+Propagation+for+Graphs+with+Heterophily+and+Label+Noise)|0| +|[DyGKT: Dynamic Graph Learning for Knowledge Tracing](https://doi.org/10.1145/3637528.3671773)|Ke Cheng, Linzhi Peng, Pengyang Wang, Junchen Ye, Leilei Sun, Bowen Du|SKLSDE Lab, Beihang University, Beijing, China; SKL-IOTSC, Department of Computer and Information Science, University of Macau, Macau, China; School of Transportation Science and Engineering, Beihang University, Beijing, China|Knowledge Tracing aims to assess student learning states by predicting their performance in answering questions. Different from the existing research which utilizes fixed-length learning sequence to obtain the student states and regards KT as a static problem, this work is motivated by three dynamical characteristics: 1) The scales of students answering records are constantly growing; 2) The semantics of time intervals between the records vary; 3) The relationships between students, questions and concepts are evolving. The three dynamical characteristics above contain the great potential to revolutionize the existing knowledge tracing methods. Along this line, we propose a Dynamic Graph-based Knowledge Tracing model, namely DyGKT. In particular, a continuous-time dynamic question-answering graph for knowledge tracing is constructed to deal with the infinitely growing answering behaviors, and it is worth mentioning that it is the first time dynamic graph learning technology is used in this field. Then, a dual time encoder is proposed to capture long-term and short-term semantics among the different time intervals. Finally, a multiset indicator is utilized to model the evolving relationships between students, questions, and concepts via the graph structural feature. Numerous experiments are conducted on five real-world datasets, and the results demonstrate the superiority of our model. All the used resources are publicly available at https://github.com/PengLinzhi/DyGKT.|知识追踪旨在通过预测学生在回答问题时的表现来评估学生的学习状态。与现有的利用固定长度的学习序列获取学生状态并将 KT 视为静态问题的研究不同，本研究的动力来源于三个动态特征: 1)学生回答记录的规模在不断增长; 2)记录之间的时间间隔语义在不断变化; 3)学生、问题和概念之间的关系在不断演化。上述三个动态特征具有革新现有知识追踪方法的巨大潜力。在此基础上，我们提出了一个基于动态图的知识跟踪模型，即 DyGKT。特别是构造了一个用于知识跟踪的连续时间动态问答图来处理无限增长的应答行为，值得一提的是，这是动态图学习技术首次应用于该领域。然后，提出了一种双时间编码器来捕获不同时间间隔之间的长期和短期语义。最后，利用多集指标模型，通过图形结构特征对学生、问题和概念之间的演化关系进行建模。在五个实际数据集上进行了大量的实验，实验结果表明了该模型的优越性。所有使用过的资源都可以在 https://github.com/penglinzhi/dygkt 上公开获得。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=DyGKT:+Dynamic+Graph+Learning+for+Knowledge+Tracing)|0| +|[Conformal Counterfactual Inference under Hidden Confounding](https://doi.org/10.1145/3637528.3671976)|Zonghao Chen, Ruocheng Guo, JeanFrancois Ton, Yang Liu|University College London, London, United Kingdom; Bytedance Research, London, United Kingdom; Bytedance Research, San Jose, USA|Personalized decision making requires the knowledge of potential outcomes under different treatments, and confidence intervals about the potential outcomes further enrich this decision-making process and improve its reliability in high-stakes scenarios. Predicting potential outcomes along with its uncertainty in a counterfactual world poses the foundamental challenge in causal inference. Existing methods that construct confidence intervals for counterfactuals either rely on the assumption of strong ignorability that completely ignores hidden confounders, or need access to un-identifiable lower and upper bounds that characterize the difference between observational and interventional distributions. In this paper, to overcome these limitations, we first propose a novel approach wTCP-DR based on transductive weighted conformal prediction, which provides confidence intervals for counterfactual outcomes with marginal converage guarantees, even under hidden confounding. With less restrictive assumptions, our approach requires access to a fraction of interventional data (from randomized controlled trials) to account for the covariate shift from observational distributoin to interventional distribution. Theoretical results explicitly demonstrate the conditions under which our algorithm is strictly advantageous to the naive method that only uses interventional data. Since transductive conformal prediction is notoriously costly, we propose wSCP-DR, a two-stage variant of wTCP-DR, based on split conformal prediction with same marginal coverage guarantees but at a significantly lower computational cost. After ensuring valid intervals on counterfactuals, it is straightforward to construct intervals for individual treatment effects (ITEs). We demonstrate our method across synthetic and real-world data, including recommendation systems, to verify the superiority of our methods compared against state-of-the-art baselines in terms of both coverage and efficiency. Our code can be found at https://github.com/rguo12/KDD24-Conformal.|个性化决策需要了解不同治疗方案下的潜在结果，关于潜在结果的置信区间进一步丰富了这一决策过程，并提高了其在高风险情景下的可靠性。在一个反事实的世界中，预测潜在的结果及其不确定性构成了因果推理的基本挑战。为反事实构建置信区间的现有方法要么依赖于完全忽略隐藏混杂因素的强烈可忽略性的假设，要么需要访问表征观察和干预分布之间差异的不可识别的下限和上限。为了克服这些局限性，本文首先提出了一种基于传导加权保角预测的新方法 wTCP-DR，该方法为具有边际收敛保证的反事实结果提供置信区间，即使在隐藏混杂情况下也是如此。在限制性较少的假设下，我们的方法需要获得一小部分介入数据(来自随机对照试验) ，以解释从观察分布到介入分布的协变量转移。理论分析结果明确地说明了在什么条件下我们的算法比只使用介入数据的朴素方法具有严格的优势。由于传导共形预测的代价是众所周知的，我们提出了 wSCP-DR，一个两阶段的 wTCP-DR 的变体，基于分裂共形预测具有相同的边际覆盖保证，但计算成本显著降低。在确保反事实的有效间隔之后，就可以直接构建单个治疗效果(ITE)的间隔。我们通过合成和现实世界的数据(包括推荐系统)演示我们的方法，以验证我们的方法在覆盖面和效率方面与最先进的基线相比的优越性。我们的代码可以在 https://github.com/rguo12/kdd24-conformal 找到。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Conformal+Counterfactual+Inference+under+Hidden+Confounding)|0| |[Leveraging Pedagogical Theories to Understand Student Learning Process with Graph-based Reasonable Knowledge Tracing](https://doi.org/10.1145/3637528.3671853)|Jiajun Cui, Hong Qian, Bo Jiang, Wei Zhang|East China Normal University, Shanghai, China|Knowledge tracing (KT) is a crucial task in intelligent education, focusingon predicting students' performance on given questions to trace their evolvingknowledge. The advancement of deep learning in this field has led todeep-learning knowledge tracing (DLKT) models that prioritize high predictiveaccuracy. However, many existing DLKT methods overlook the fundamental goal oftracking students' dynamical knowledge mastery. These models do not explicitlymodel knowledge mastery tracing processes or yield unreasonable results thateducators find difficulty to comprehend and apply in real teaching scenarios.In response, our research conducts a preliminary analysis of mainstream KTapproaches to highlight and explain such unreasonableness. We introduce GRKT, agraph-based reasonable knowledge tracing method to address these issues. Byleveraging graph neural networks, our approach delves into the mutualinfluences of knowledge concepts, offering a more accurate representation ofhow the knowledge mastery evolves throughout the learning process.Additionally, we propose a fine-grained and psychological three-stage modelingprocess as knowledge retrieval, memory strengthening, and knowledgelearning/forgetting, to conduct a more reasonable knowledge tracing process.Comprehensive experiments demonstrate that GRKT outperforms eleven baselinesacross three datasets, not only enhancing predictive accuracy but alsogenerating more reasonable knowledge tracing results. This makes our model apromising advancement for practical implementation in educational settings. Thesource code is available at https://github.com/JJCui96/GRKT.|知识追踪(KT)是智力教育中的一项重要任务，其重点是预测学生在给定问题上的表现，从而追踪他们的知识演化过程。深度学习在这个领域的进步导致了深度学习知识跟踪(DLKT)模型优先考虑高预测精度。然而，现有的 DLKT 方法忽视了跟踪学生动态知识掌握的基本目标。这些模型没有明确地模拟知识掌握追踪过程，也没有产生教育者难以理解和应用于真实教学情景的不合理结果。作为回应，我们的研究对主流 KT 方法进行了初步分析，以突出和解释这种不合理性。为了解决这些问题，我们引入了基于 GRKT、基于图的合理知识追踪方法。通过利用图形神经网络，我们的方法深入研究了知识概念的相互影响，提供了知识掌握如何在整个学习过程中发展的更准确的表示。此外，我们提出了一个细粒度的心理三阶段建模过程，即知识检索、记忆增强和知识学习/遗忘，以进行更合理的知识追踪过程。综合实验表明，GRKT 在三个数据集中优于十一个基线，不仅提高了预测的准确性，而且产生了更合理的知识跟踪结果。这使得我们的模型在教育环境中的实际应用有了很大的进步。源代码可在 https://github.com/jjcui96/grkt 下载。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Leveraging+Pedagogical+Theories+to+Understand+Student+Learning+Process+with+Graph-based+Reasonable+Knowledge+Tracing)|0| -|[Iterative Weak Learnability and Multiclass AdaBoost](https://doi.org/10.1145/3637528.3671842)|InKoo Cho, Jonathan A. Libgober, Cheng Ding|Emory University, ATLANTA, GA, USA; University of Southern California, Los Angeles, CA, USA; Emory University & Hanyang University, Atlanta, GA, USA|We propose an efficient boosting algorithm for multiclass classification, called AdaBoost.Iter, that extends SAMME and AdaBoost. The algorithm iteratively applies the weak learnability condition of SAMME to eliminate classes to find the correct classificiation. The iterative weak learnability is a sufficient and necessary condition for boostability, but it is also easier to validate than the EOR criterion of AdaBoost.MM \citeMukherjeeSchapire2013. We show that the training error of AdaBoost.Iter vanishes at the exponential rate, while the generalization error converges to zero at the same rate as AdaBoost. AdaBoost.Iter numerically outperforms SAMME and achieves performance comparable to AdaBoost.MM on benchmark datasets.|我们提出了一个有效的多元分类增强算法，叫做 AdaBoost。它扩展了 SAMME 和 AdaBoost。该算法迭代地利用 SAMME 的弱可学性条件来消除类，从而找到正确的分类。迭代的弱可学性是 Booability 的充分和必要条件，但是它也比 AdaBoost.MM citeMukherjeeSchapire2013的 EOR 标准更容易验证。我们展示了 AdaBoost 的训练错误。Iter 以指数级的速度消失，而泛化误差收敛到零的速度与 AdaBoost 相同。AdaBoost.Iter 在数值上优于 SAMME，并且在基准数据集上实现了与 AdaBoost.MM 相当的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Iterative+Weak+Learnability+and+Multiclass+AdaBoost)|0| -|[Divide and Denoise: Empowering Simple Models for Robust Semi-Supervised Node Classification against Label Noise](https://doi.org/10.1145/3637528.3671798)|Kaize Ding, Xiaoxiao Ma, Yixin Liu, Shirui Pan|Macquarie University, Sydney, Australia; Griffith University, Gold Coast, Australia; Monash University, Melbourne, Australia; Northwestern University, Evanston, USA|Graph neural networks (GNNs) based on message passing have achieved remarkable performance in graph machine learning. By combining it with the power of pseudo labeling, one can further push forward the performance on the task of semi-supervised node classification. However, most existing works assume that the training node labels are purely noise-free, while this strong assumption usually does not hold in practice. GNNs will overfit the noisy training labels and the adverse effects of mislabeled nodes can be exaggerated by being propagated to the remaining nodes through the graph structure, exacerbating the model failure. Worse still, the noisy pseudo labels could also largely undermine the model's reliability without special treatment. In this paper, we revisit the role of (1) message passing and (2) pseudo labels in the studied problem and try to address two denoising subproblems from the model architecture and algorithm perspective, respectively. Specifically, we first develop a label-noise robust GNN that discards the coupled message-passing scheme. Despite its simple architecture, this learning backbone prevents overfitting to noisy labels and also inherently avoids the noise propagation issue. Moreover, we propose a novel reliable graph pseudo labeling algorithm that can effectively leverage the knowledge of unlabeled nodes while mitigating the adverse effects of noisy pseudo labels. Based on those novel designs, we can attain exceptional effectiveness and efficiency in solving the studied problem. We conduct extensive experiments on benchmark datasets for semi-supervised node classification with different levels of label noise and show new state-of-the-art performance. The code is available at https://github.com/DND-NET/DND-NET.|基于消息传递的图神经网络在图机学习中取得了显著的效果。结合伪标记的能力，可以进一步提高半监督节点分类的性能。然而，现有的大多数工作假设训练节点标签是纯粹无噪声的，而这种强烈的假设通常不适用于实践。该算法通过图结构将误标节点传播到剩余的节点，从而加剧了模型的失效。更糟糕的是，嘈杂的伪标签也可能在很大程度上破坏模型的可靠性，而无需特殊处理。本文重新审视了(1)消息传递和(2)伪标签在所研究问题中的作用，并试图从模型结构和算法角度分别解决两个去噪子问题。具体来说，我们首先开发了一个标签噪声鲁棒 GNN，它丢弃了耦合消息传递方案。尽管它的结构简单，这种学习骨干防止过度拟合噪声标签，也内在地避免噪声传播的问题。此外，我们提出了一种新的可靠的图伪标记算法，可以有效地利用未标记节点的知识，同时减轻噪声伪标记的不利影响。基于这些新颖的设计，我们可以在解决所研究的问题中获得非常有效和高效的结果。针对不同标签噪声水平的半监督节点分类问题，我们对基准数据集进行了广泛的实验研究，并展示了新的性能。密码可在 https://github.com/dnd-net/dnd-net 查阅。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Divide+and+Denoise:+Empowering+Simple+Models+for+Robust+Semi-Supervised+Node+Classification+against+Label+Noise)|0| +|[Iterative Weak Learnability and Multiclass AdaBoost](https://doi.org/10.1145/3637528.3671842)|InKoo Cho, Jonathan A. Libgober, Cheng Ding|University of Southern California, Los Angeles, CA, USA; Emory University, ATLANTA, GA, USA; Emory University & Hanyang University, Atlanta, GA, USA|We propose an efficient boosting algorithm for multiclass classification, called AdaBoost.Iter, that extends SAMME and AdaBoost. The algorithm iteratively applies the weak learnability condition of SAMME to eliminate classes to find the correct classificiation. The iterative weak learnability is a sufficient and necessary condition for boostability, but it is also easier to validate than the EOR criterion of AdaBoost.MM \citeMukherjeeSchapire2013. We show that the training error of AdaBoost.Iter vanishes at the exponential rate, while the generalization error converges to zero at the same rate as AdaBoost. AdaBoost.Iter numerically outperforms SAMME and achieves performance comparable to AdaBoost.MM on benchmark datasets.|我们提出了一个有效的多元分类增强算法，叫做 AdaBoost。它扩展了 SAMME 和 AdaBoost。该算法迭代地利用 SAMME 的弱可学性条件来消除类，从而找到正确的分类。迭代的弱可学性是 Booability 的充分和必要条件，但是它也比 AdaBoost.MM citeMukherjeeSchapire2013的 EOR 标准更容易验证。我们展示了 AdaBoost 的训练错误。Iter 以指数级的速度消失，而泛化误差收敛到零的速度与 AdaBoost 相同。AdaBoost.Iter 在数值上优于 SAMME，并且在基准数据集上实现了与 AdaBoost.MM 相当的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Iterative+Weak+Learnability+and+Multiclass+AdaBoost)|0| +|[Divide and Denoise: Empowering Simple Models for Robust Semi-Supervised Node Classification against Label Noise](https://doi.org/10.1145/3637528.3671798)|Kaize Ding, Xiaoxiao Ma, Yixin Liu, Shirui Pan|Griffith University, Gold Coast, Australia; Northwestern University, Evanston, USA; Monash University, Melbourne, Australia; Macquarie University, Sydney, Australia|Graph neural networks (GNNs) based on message passing have achieved remarkable performance in graph machine learning. By combining it with the power of pseudo labeling, one can further push forward the performance on the task of semi-supervised node classification. However, most existing works assume that the training node labels are purely noise-free, while this strong assumption usually does not hold in practice. GNNs will overfit the noisy training labels and the adverse effects of mislabeled nodes can be exaggerated by being propagated to the remaining nodes through the graph structure, exacerbating the model failure. Worse still, the noisy pseudo labels could also largely undermine the model's reliability without special treatment. In this paper, we revisit the role of (1) message passing and (2) pseudo labels in the studied problem and try to address two denoising subproblems from the model architecture and algorithm perspective, respectively. Specifically, we first develop a label-noise robust GNN that discards the coupled message-passing scheme. Despite its simple architecture, this learning backbone prevents overfitting to noisy labels and also inherently avoids the noise propagation issue. Moreover, we propose a novel reliable graph pseudo labeling algorithm that can effectively leverage the knowledge of unlabeled nodes while mitigating the adverse effects of noisy pseudo labels. Based on those novel designs, we can attain exceptional effectiveness and efficiency in solving the studied problem. We conduct extensive experiments on benchmark datasets for semi-supervised node classification with different levels of label noise and show new state-of-the-art performance. The code is available at https://github.com/DND-NET/DND-NET.|基于消息传递的图神经网络在图机学习中取得了显著的效果。结合伪标记的能力，可以进一步提高半监督节点分类的性能。然而，现有的大多数工作假设训练节点标签是纯粹无噪声的，而这种强烈的假设通常不适用于实践。该算法通过图结构将误标节点传播到剩余的节点，从而加剧了模型的失效。更糟糕的是，嘈杂的伪标签也可能在很大程度上破坏模型的可靠性，而无需特殊处理。本文重新审视了(1)消息传递和(2)伪标签在所研究问题中的作用，并试图从模型结构和算法角度分别解决两个去噪子问题。具体来说，我们首先开发了一个标签噪声鲁棒 GNN，它丢弃了耦合消息传递方案。尽管它的结构简单，这种学习骨干防止过度拟合噪声标签，也内在地避免噪声传播的问题。此外，我们提出了一种新的可靠的图伪标记算法，可以有效地利用未标记节点的知识，同时减轻噪声伪标记的不利影响。基于这些新颖的设计，我们可以在解决所研究的问题中获得非常有效和高效的结果。针对不同标签噪声水平的半监督节点分类问题，我们对基准数据集进行了广泛的实验研究，并展示了新的性能。密码可在 https://github.com/dnd-net/dnd-net 查阅。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Divide+and+Denoise:+Empowering+Simple+Models+for+Robust+Semi-Supervised+Node+Classification+against+Label+Noise)|0| |[Unraveling Block Maxima Forecasting Models with Counterfactual Explanation](https://doi.org/10.1145/3637528.3671923)|Yue Deng, Asadullah Hill Galib, PangNing Tan, Lifeng Luo|Michigan State University, East Lansing, MI, USA|Disease surveillance, traffic management, and weather forecasting are some of the key applications that could benefit from block maxima forecasting of a time series as the extreme block maxima values often signify events of critical importance such as disease outbreaks, traffic gridlock, and severe weather conditions. As the use of deep neural network models for block maxima forecasting increases, so does the need for explainable AI methods that could unravel the inner workings of such black box models. To fill this need, this paper presents a novel counterfactual explanation framework for block maxima forecasting models. Unlike existing methods, our proposed framework, DiffusionCF, combines deep anomaly detection with a conditional diffusion model to identify unusual patterns in the time series that could help explain the forecasted extreme block maxima. Experimental results on several real-world datasets demonstrate the superiority of DiffusionCF over other baseline methods when evaluated according to various metrics, particularly their informativeness and closeness. Our data and codes are available at https://github.com/yue2023cs/DiffusionCF.|疾病监测、交通管理和天气预报是一些可以从时间序列的块极大值预测中受益的关键应用程序，因为块极大值通常意味着疾病爆发、交通堵塞和恶劣天气状况等至关重要的事件。随着深度神经网络模型在块极大值预测中的应用越来越多，解释人工智能方法的需求也越来越大，这些方法可以揭示这些黑箱模型的内部工作原理。为了满足这一需求，本文提出了一种新的块极大值预测模型的反事实解释框架。与现有方法不同的是，我们提出的扩散异常检测框架结合了深度扩散模型和条件扩散模型来识别时间序列中不寻常的模式，这有助于解释预测的极端块极大值。在几个实际数据集上的实验结果表明，当根据各种指标，特别是它们的信息量和接近度进行评估时，弥散 CF 优于其他基线方法。我们的数据和代码可以在 https://github.com/yue2023cs/diffusioncf 找到。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Unraveling+Block+Maxima+Forecasting+Models+with+Counterfactual+Explanation)|0| -|[Explanatory Model Monitoring to Understand the Effects of Feature Shifts on Performance](https://doi.org/10.1145/3637528.3671959)|Thomas Decker, Alexander Koebler, Michael Lebacher, Ingo Thon, Volker Tresp, Florian Buettner|Goethe University Frankfurt & Siemens AG, Frankfurt, Germany; Ludwig-Maximilians-Universität & Siemens AG, Munich, Germany; Ludwig-Maximilians-Universität & Munich Center for Machine Learning, Munich, Germany; Siemens AG, Munich, Germany|Monitoring and maintaining machine learning models are among the most critical challenges in translating recent advances in the field into real-world applications. However, current monitoring methods lack the capability of provide actionable insights answering the question of why the performance of a particular model really degraded. In this work, we propose a novel approach to explain the behavior of a black-box model under feature shifts by attributing an estimated performance change to interpretable input characteristics. We refer to our method that combines concepts from Optimal Transport and Shapley Values as Explanatory Performance Estimation (XPE). We analyze the underlying assumptions and demonstrate the superiority of our approach over several baselines on different data sets across various data modalities such as images, audio, and tabular data. We also indicate how the generated results can lead to valuable insights, enabling explanatory model monitoring by revealing potential root causes for model deterioration and guiding toward actionable countermeasures.|监测和维护机器学习模型是将该领域最新进展转化为现实应用的最关键挑战之一。然而，当前的监控方法缺乏提供可操作的洞察力来回答为什么特定模型的性能真正下降的问题。在这项工作中，我们提出了一个新颖的方法来解释黑盒模型的行为在特征移位，通过归因于一个估计的性能变化对可解释的输入特征。我们将结合最优运输和 Shapley 值的概念的方法称为解释性能估计(XPE)。我们分析了基本的假设，并证明了我们的方法在不同数据集的基线上的优越性，这些基线跨越了不同的数据模式，如图像、音频和表格数据。我们还指出，生成的结果如何能够产生有价值的见解，通过揭示模型恶化的潜在根源和指导可行的对策来实现解释性模型监测。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Explanatory+Model+Monitoring+to+Understand+the+Effects+of+Feature+Shifts+on+Performance)|0| -|[Fast Unsupervised Deep Outlier Model Selection with Hypernetworks](https://doi.org/10.1145/3637528.3672003)|Xueying Ding, Yue Zhao, Leman Akoglu|Carnegie Mellon University, Pittsburgh, PA, USA; University of Southern California, Los Angeles, CA, USA|Deep neural network based Outlier Detection (DOD) has seen a recent surge of attention thanks to the many advances in deep learning. In this paper, we consider a critical-yet-understudied challenge with unsupervised DOD, that is, effective hyperparameter (HP) tuning/model selection. While several prior work report the sensitivity of OD models to HP settings, the issue is ever so critical for the modern DOD models that exhibit a long list of HPs. We introduce HYPER for tuning DOD models, tackling two fundamental challenges: (1) validation without supervision (due to lack of labeled outliers), and (2) efficient search of the HP/model space (due to exponential growth in the number of HPs). A key idea is to design and train a novel hypernetwork (HN) that maps HPs onto optimal weights of the main DOD model. In turn, HYPER capitalizes on a single HN that can dynamically generate weights for many DOD models (corresponding to varying HPs), which offers significant speed-up. In addition, it employs meta-learning on historical OD tasks with labels to train a proxy validation function, likewise trained with our proposed HN efficiently. Extensive experiments on different OD tasks show that HYPER achieves competitive performance against 8 baselines with significant efficiency gains.|由于深度学习的许多进步，基于深度神经网络的异常检测(DOD)最近受到了广泛的关注。在本文中，我们考虑了一个非监督 DOD 的关键但未被充分研究的挑战，即有效的超参数(HP)调整/模型选择。虽然之前的一些工作报告了 OD 模型对 HP 设置的敏感性，但是这个问题对于现代 DOD 模型来说是非常关键的，因为它展示了一长串 HP 的列表。我们引入 HYPER 来调整国防部模型，解决两个基本的挑战: (1)没有监督的验证(由于缺乏标记的异常值)和(2)高效搜索惠普/模型空间(由于惠普数量的指数增长)。其中的一个关键思想是设计和训练一种新的超网络(HN) ，将 HP 映射到主要 DOD 模型的最优权值上。反过来，HYPER 利用一个单一的 HN，可以动态地为许多 DOD 模型生成权重(对应于不同的 HP) ，从而提供显著的加速。此外，它使用元学习的历史 OD 任务与标签训练的代理验证功能，同样训练了我们提出的 HN 有效。对不同 OD 任务的大量实验表明，HYPER 在8个基线上达到了具有竞争力的性能，并且有显著的效率提高。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Fast+Unsupervised+Deep+Outlier+Model+Selection+with+Hypernetworks)|0| -|[Enhancing On-Device LLM Inference with Historical Cloud-Based LLM Interactions](https://doi.org/10.1145/3637528.3671679)|Yucheng Ding, Chaoyue Niu, Fan Wu, Shaojie Tang, Chengfei Lyu, Guihai Chen|University of Texas at Dallas, Richardson, Texas, USA; Alibaba Group, Hangzhou, China; Shanghai Jiao Tong University, Shanghai, China|Many billion-scale large language models (LLMs) have been released for resource-constraint mobile devices to provide local LLM inference service when cloud-based powerful LLMs are not available. However, the capabilities of current on-device LLMs still lag behind those of cloud-based LLMs, and how to effectively and efficiently enhance on-device LLM inference becomes a practical requirement. We thus propose to collect the user's historical interactions with the cloud-based LLM and build an external datastore on the mobile device for enhancement using nearest neighbors search. Nevertheless, the full datastore improves the quality of token generation at the unacceptable expense of much slower generation speed. To balance performance and efficiency, we propose to select an optimal subset of the full datastore within the given size limit, the optimization objective of which is proven to be submodular. We further design an offline algorithm, which selects the subset after the construction of the full datastore, as well as an online algorithm, which performs selection over the stream and can be flexibly scheduled. We theoretically analyze the performance guarantee and the time complexity of the offline and the online designs to demonstrate effectiveness and scalability. We finally take three ChatGPT related dialogue datasets and four different on-device LLMs for evaluation. Evaluation results show that the proposed designs significantly enhance LLM performance in terms of perplexity while maintaining fast token generation speed. Practical overhead testing on the smartphone reveal the efficiency of on-device datastore subset selection from memory usage and computation overhead.|当基于云的强大的 LLM 不可用时，为资源受限的移动设备发布了许多数十亿级的大语言模型(LLM) ，以提供本地 LLM 推理服务。然而，当前设备上 LLM 的性能仍然落后于基于云的 LLM，如何有效地增强设备上 LLM 的推理成为一个实际的需求。因此，我们建议收集用户与基于云的 LLM 的历史交互，并在移动设备上建立一个外部数据存储，使用最近邻搜索进行增强。然而，完整的数据存储提高了令牌生成的质量，代价是生成速度大大降低，这是不可接受的。为了平衡性能和效率，我们提出在给定的大小限制内选择一个完整数据存储的最优子集，其优化目标被证明是子模块化的。我们进一步设计了一个离线算法，在构造完整的数据存储之后选择子集，以及一个在线算法，在流上执行选择，可以灵活调度。我们从理论上分析了离线和在线设计的性能保证和时间复杂度，以验证设计的有效性和可扩展性。最后，我们采用三个 ChatGPT 相关的对话数据集和四个不同的设备上 LLM 进行评估。评估结果表明，该设计在保持快速令牌生成速度的同时，显著提高了 LLM 的性能。智能手机上的实际开销测试揭示了从内存使用和计算开销中选择设备上数据存储子集的效率。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Enhancing+On-Device+LLM+Inference+with+Historical+Cloud-Based+LLM+Interactions)|0| +|[Explanatory Model Monitoring to Understand the Effects of Feature Shifts on Performance](https://doi.org/10.1145/3637528.3671959)|Thomas Decker, Alexander Koebler, Michael Lebacher, Ingo Thon, Volker Tresp, Florian Buettner|Ludwig-Maximilians-Universität & Munich Center for Machine Learning, Munich, Germany; Goethe University Frankfurt & Siemens AG, Frankfurt, Germany; Ludwig-Maximilians-Universität & Siemens AG, Munich, Germany; Siemens AG, Munich, Germany|Monitoring and maintaining machine learning models are among the most critical challenges in translating recent advances in the field into real-world applications. However, current monitoring methods lack the capability of provide actionable insights answering the question of why the performance of a particular model really degraded. In this work, we propose a novel approach to explain the behavior of a black-box model under feature shifts by attributing an estimated performance change to interpretable input characteristics. We refer to our method that combines concepts from Optimal Transport and Shapley Values as Explanatory Performance Estimation (XPE). We analyze the underlying assumptions and demonstrate the superiority of our approach over several baselines on different data sets across various data modalities such as images, audio, and tabular data. We also indicate how the generated results can lead to valuable insights, enabling explanatory model monitoring by revealing potential root causes for model deterioration and guiding toward actionable countermeasures.|监测和维护机器学习模型是将该领域最新进展转化为现实应用的最关键挑战之一。然而，当前的监控方法缺乏提供可操作的洞察力来回答为什么特定模型的性能真正下降的问题。在这项工作中，我们提出了一个新颖的方法来解释黑盒模型的行为在特征移位，通过归因于一个估计的性能变化对可解释的输入特征。我们将结合最优运输和 Shapley 值的概念的方法称为解释性能估计(XPE)。我们分析了基本的假设，并证明了我们的方法在不同数据集的基线上的优越性，这些基线跨越了不同的数据模式，如图像、音频和表格数据。我们还指出，生成的结果如何能够产生有价值的见解，通过揭示模型恶化的潜在根源和指导可行的对策来实现解释性模型监测。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Explanatory+Model+Monitoring+to+Understand+the+Effects+of+Feature+Shifts+on+Performance)|0| +|[Fast Unsupervised Deep Outlier Model Selection with Hypernetworks](https://doi.org/10.1145/3637528.3672003)|Xueying Ding, Yue Zhao, Leman Akoglu|University of Southern California, Los Angeles, CA, USA; Carnegie Mellon University, Pittsburgh, PA, USA|Deep neural network based Outlier Detection (DOD) has seen a recent surge of attention thanks to the many advances in deep learning. In this paper, we consider a critical-yet-understudied challenge with unsupervised DOD, that is, effective hyperparameter (HP) tuning/model selection. While several prior work report the sensitivity of OD models to HP settings, the issue is ever so critical for the modern DOD models that exhibit a long list of HPs. We introduce HYPER for tuning DOD models, tackling two fundamental challenges: (1) validation without supervision (due to lack of labeled outliers), and (2) efficient search of the HP/model space (due to exponential growth in the number of HPs). A key idea is to design and train a novel hypernetwork (HN) that maps HPs onto optimal weights of the main DOD model. In turn, HYPER capitalizes on a single HN that can dynamically generate weights for many DOD models (corresponding to varying HPs), which offers significant speed-up. In addition, it employs meta-learning on historical OD tasks with labels to train a proxy validation function, likewise trained with our proposed HN efficiently. Extensive experiments on different OD tasks show that HYPER achieves competitive performance against 8 baselines with significant efficiency gains.|由于深度学习的许多进步，基于深度神经网络的异常检测(DOD)最近受到了广泛的关注。在本文中，我们考虑了一个非监督 DOD 的关键但未被充分研究的挑战，即有效的超参数(HP)调整/模型选择。虽然之前的一些工作报告了 OD 模型对 HP 设置的敏感性，但是这个问题对于现代 DOD 模型来说是非常关键的，因为它展示了一长串 HP 的列表。我们引入 HYPER 来调整国防部模型，解决两个基本的挑战: (1)没有监督的验证(由于缺乏标记的异常值)和(2)高效搜索惠普/模型空间(由于惠普数量的指数增长)。其中的一个关键思想是设计和训练一种新的超网络(HN) ，将 HP 映射到主要 DOD 模型的最优权值上。反过来，HYPER 利用一个单一的 HN，可以动态地为许多 DOD 模型生成权重(对应于不同的 HP) ，从而提供显著的加速。此外，它使用元学习的历史 OD 任务与标签训练的代理验证功能，同样训练了我们提出的 HN 有效。对不同 OD 任务的大量实验表明，HYPER 在8个基线上达到了具有竞争力的性能，并且有显著的效率提高。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Fast+Unsupervised+Deep+Outlier+Model+Selection+with+Hypernetworks)|0| +|[Enhancing On-Device LLM Inference with Historical Cloud-Based LLM Interactions](https://doi.org/10.1145/3637528.3671679)|Yucheng Ding, Chaoyue Niu, Fan Wu, Shaojie Tang, Chengfei Lyu, Guihai Chen|Alibaba Group, Hangzhou, China; Shanghai Jiao Tong University, Shanghai, China; University of Texas at Dallas, Richardson, Texas, USA|Many billion-scale large language models (LLMs) have been released for resource-constraint mobile devices to provide local LLM inference service when cloud-based powerful LLMs are not available. However, the capabilities of current on-device LLMs still lag behind those of cloud-based LLMs, and how to effectively and efficiently enhance on-device LLM inference becomes a practical requirement. We thus propose to collect the user's historical interactions with the cloud-based LLM and build an external datastore on the mobile device for enhancement using nearest neighbors search. Nevertheless, the full datastore improves the quality of token generation at the unacceptable expense of much slower generation speed. To balance performance and efficiency, we propose to select an optimal subset of the full datastore within the given size limit, the optimization objective of which is proven to be submodular. We further design an offline algorithm, which selects the subset after the construction of the full datastore, as well as an online algorithm, which performs selection over the stream and can be flexibly scheduled. We theoretically analyze the performance guarantee and the time complexity of the offline and the online designs to demonstrate effectiveness and scalability. We finally take three ChatGPT related dialogue datasets and four different on-device LLMs for evaluation. Evaluation results show that the proposed designs significantly enhance LLM performance in terms of perplexity while maintaining fast token generation speed. Practical overhead testing on the smartphone reveal the efficiency of on-device datastore subset selection from memory usage and computation overhead.|当基于云的强大的 LLM 不可用时，为资源受限的移动设备发布了许多数十亿级的大语言模型(LLM) ，以提供本地 LLM 推理服务。然而，当前设备上 LLM 的性能仍然落后于基于云的 LLM，如何有效地增强设备上 LLM 的推理成为一个实际的需求。因此，我们建议收集用户与基于云的 LLM 的历史交互，并在移动设备上建立一个外部数据存储，使用最近邻搜索进行增强。然而，完整的数据存储提高了令牌生成的质量，代价是生成速度大大降低，这是不可接受的。为了平衡性能和效率，我们提出在给定的大小限制内选择一个完整数据存储的最优子集，其优化目标被证明是子模块化的。我们进一步设计了一个离线算法，在构造完整的数据存储之后选择子集，以及一个在线算法，在流上执行选择，可以灵活调度。我们从理论上分析了离线和在线设计的性能保证和时间复杂度，以验证设计的有效性和可扩展性。最后，我们采用三个 ChatGPT 相关的对话数据集和四个不同的设备上 LLM 进行评估。评估结果表明，该设计在保持快速令牌生成速度的同时，显著提高了 LLM 的性能。智能手机上的实际开销测试揭示了从内存使用和计算开销中选择设备上数据存储子集的效率。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Enhancing+On-Device+LLM+Inference+with+Historical+Cloud-Based+LLM+Interactions)|0| |[IDEA: A Flexible Framework of Certified Unlearning for Graph Neural Networks](https://doi.org/10.1145/3637528.3671744)|Yushun Dong, Binchi Zhang, Zhenyu Lei, Na Zou, Jundong Li|The University of Virginia, Charlottesville, VA, USA; The University of Houston, Houston, TX, USA; The University of Virginia, Charlottesville, USA|Graph Neural Networks (GNNs) have been increasingly deployed in a plethora of applications. However, the graph data used for training may contain sensitive personal information of the involved individuals. Once trained, GNNs typically encode such information in their learnable parameters. As a consequence, privacy leakage may happen when the trained GNNs are deployed and exposed to potential attackers. Facing such a threat, machine unlearning for GNNs has become an emerging technique that aims to remove certain personal information from a trained GNN. Among these techniques, certified unlearning stands out, as it provides a solid theoretical guarantee of the information removal effectiveness. Nevertheless, most of the existing certified unlearning methods for GNNs are only designed to handle node and edge unlearning requests. Meanwhile, these approaches are usually tailored for either a specific design of GNN or a specially designed training objective. These disadvantages significantly jeopardize their flexibility. In this paper, we propose a principled framework named IDEA to achieve flexible and certified unlearning for GNNs. Specifically, we first instantiate four types of unlearning requests on graphs, and then we propose an approximation approach to flexibly handle these unlearning requests over diverse GNNs. We further provide theoretical guarantee of the effectiveness for the proposed approach as a certification. Different from existing alternatives, IDEA is not designed for any specific GNNs or optimization objectives to perform certified unlearning, and thus can be easily generalized. Extensive experiments on real-world datasets demonstrate the superiority of IDEA in multiple key perspectives.|图形神经网络(GNN)已经越来越多地部署在过多的应用。然而，用于培训的图形数据可能包含涉及个人的敏感个人信息。一旦经过训练，GNN 通常会在其可学习的参数中对这些信息进行编码。因此，当经过训练的 GNN 被部署并暴露给潜在的攻击者时，隐私泄漏就可能发生。面对这样的威胁，GNN 的机器学习已经成为一种新兴的技术，旨在删除某些个人信息从训练的 GNN。在这些技术中，认证忘却技术脱颖而出，因为它为信息去除的有效性提供了坚实的理论保证。然而，大多数现有的认证的 GNN 去学习方法仅用于处理节点和边的去学习请求。同时，这些方法通常是为特定的 GNN 设计或特定的培训目标而量身定制的。这些缺点严重损害了它们的灵活性。在本文中，我们提出了一个原则性的框架 IDEA 来实现灵活和认证的 GNN 去学习。具体来说，我们首先在图上实例化了四种类型的忘记请求，然后我们提出了一种近似方法来灵活地处理不同 GNN 上的这些忘记请求。我们进一步提供理论保证的有效性，为提出的方法作为一种证明。与现有的替代方案不同，IDEA 不是为任何特定的 GNN 或优化目标设计的，以执行认证的无学习，因此可以很容易地推广。在实际数据集上的大量实验证明了 IDEA 在多个关键方面的优越性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=IDEA:+A+Flexible+Framework+of+Certified+Unlearning+for+Graph+Neural+Networks)|0| |[Unsupervised Alignment of Hypergraphs with Different Scales](https://doi.org/10.1145/3637528.3671955)|Manh Tuan Do, Kijung Shin|KAIST, Seoul, Republic of Korea|People usually interact in groups, and such groups may appear on different platforms. For instance, people often create various group chats on messaging apps (e.g., Facebook Messenger and WhatsApp) to communicate with families, friends, or colleagues. How do we identify the same people across the two platforms based on the information about the groups? This gives rise to the hypergraph alignment problem, whose objective is to find the correspondences between the sets of nodes of two hypergraphs. In a hypergraph, a node represents a person, and each hyperedge represents a group of several people. In addition, the two sets of hyperedges in the two hypergraphs can vary significantly in scales as people may use different apps at different time periods. In this work, we propose and tackle the problem of unsupervised hypergraph alignment. Given two hypergraphs with potentially different scales and without any side information or prior ground-truth correspondences, we develop ØurMethod, a learning framework, to find node correspondences across the two hypergraphs. ØurMethod directly addresses each challenge of the problem. In particular, it (a) extracts node features from the hypergraph topology, (b) employs contrastive learning, as a "supervised pseudo-alignment'' task to pre-train the learning model (c) applies topological augmentation to help a generative adversarial network to align the two embedding spaces from the two hypergraphs. The purpose of augmentation is to add virtual hyperedges from one hypergraph in order to the other to resolve the scale difference and share information across the two hypergraphs. Our extensive experiments on 12 real-world datasets demonstrate the significant and consistent superiority of ØurMethod over the baseline approaches.|人们通常以群体的形式互动，这样的群体可能出现在不同的平台上。例如，人们经常在消息应用程序(如 Facebook Messenger 和 WhatsApp)上创建各种群聊，以便与家人、朋友或同事交流。我们如何根据组的信息在两个平台上识别相同的人？这就产生了超图对齐问题，其目标是找到两个超图的节点集之间的对应关系。在超图中，一个节点代表一个人，每个超边代表一组几个人。此外，两个超图中的两组超边缘可能在尺度上有显著差异，因为人们可能在不同的时间段使用不同的应用程序。本文提出并解决了无监督超图对齐问题。给定两个具有潜在不同尺度的超图，在没有任何侧面信息或先验地面真相对应的情况下，我们开发了一个学习框架 ØurMethod，用于在两个超图之间寻找节点对应。ØurMethod 直接解决问题的每一个挑战。特别地，它(a)从超图拓扑中提取节点特征，(b)使用对比学习，作为一个“监督伪对齐”任务来预训练学习模型(c)应用拓扑增强来帮助一个生成的对抗网络来对齐两个超图的嵌入空间。增广的目的是从一个超图中添加虚拟超边，以便解决两个超图之间的尺度差异和共享信息。我们在12个真实世界数据集上的广泛实验证明了 ØurMethod 相对于基线方法的显著和一致的优越性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Unsupervised+Alignment+of+Hypergraphs+with+Different+Scales)|0| -|[Heterogeneity-Informed Meta-Parameter Learning for Spatiotemporal Time Series Forecasting](https://doi.org/10.1145/3637528.3671961)|Zheng Dong, Renhe Jiang, Haotian Gao, Hangchen Liu, Jinliang Deng, Qingsong Wen, Xuan Song|Jilin University & Southern University of Science and Technology, Changchun, China; Hong Kong University of Science and Technology, Hong Kong, China; The University of Tokyo, Tokyo, Japan; Squirrel AI, Seattle, USA; Southern University of Science and Technology, Shenzhen, China|Spatiotemporal time series forecasting plays a key role in a wide range of real-world applications. While significant progress has been made in this area, fully capturing and leveraging spatiotemporal heterogeneity remains a fundamental challenge. Therefore, we propose a novel Heterogeneity-Informed Meta-Parameter Learning scheme. Specifically, our approach implicitly captures spatiotemporal heterogeneity through learning spatial and temporal embeddings, which can be viewed as a clustering process. Then, a novel spatiotemporal meta-parameter learning paradigm is proposed to learn spatiotemporal-specific parameters from meta-parameter pools, which is informed by the captured heterogeneity. Based on these ideas, we develop a Heterogeneity-Informed Spatiotemporal Meta-Network (HimNet) for spatiotemporal time series forecasting. Extensive experiments on five widely-used benchmarks demonstrate our method achieves state-of-the-art performance while exhibiting superior interpretability. Our code is available at https://github.com/XDZhelheim/HimNet.|时空时间序列预测在广泛的实际应用中起着关键的作用。虽然在这一领域取得了重大进展，但充分捕捉和利用时空异质性仍然是一个根本性挑战。因此，我们提出了一种新的异质性信息元参数学习方案。具体来说，我们的方法通过学习空间和时间嵌入隐含地捕获时空异质性，这可以看作是一个聚类过程。然后，提出了一种新的时空元参数学习范式，用于从元参数池中学习时空特定的参数。基于这些思想，我们开发了一个异质性信息时空元网络(HimNet)用于时空时间序列预测。在五个广泛使用的基准测试上的大量实验表明，我们的方法实现了最先进的性能，同时显示出优越的可解释性。我们的代码可以在 https://github.com/xdzhelheim/himnet 找到。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Heterogeneity-Informed+Meta-Parameter+Learning+for+Spatiotemporal+Time+Series+Forecasting)|0| +|[Heterogeneity-Informed Meta-Parameter Learning for Spatiotemporal Time Series Forecasting](https://doi.org/10.1145/3637528.3671961)|Zheng Dong, Renhe Jiang, Haotian Gao, Hangchen Liu, Jinliang Deng, Qingsong Wen, Xuan Song|The University of Tokyo, Tokyo, Japan; Squirrel AI, Seattle, USA; Hong Kong University of Science and Technology, Hong Kong, China; Jilin University & Southern University of Science and Technology, Changchun, China; Southern University of Science and Technology, Shenzhen, China|Spatiotemporal time series forecasting plays a key role in a wide range of real-world applications. While significant progress has been made in this area, fully capturing and leveraging spatiotemporal heterogeneity remains a fundamental challenge. Therefore, we propose a novel Heterogeneity-Informed Meta-Parameter Learning scheme. Specifically, our approach implicitly captures spatiotemporal heterogeneity through learning spatial and temporal embeddings, which can be viewed as a clustering process. Then, a novel spatiotemporal meta-parameter learning paradigm is proposed to learn spatiotemporal-specific parameters from meta-parameter pools, which is informed by the captured heterogeneity. Based on these ideas, we develop a Heterogeneity-Informed Spatiotemporal Meta-Network (HimNet) for spatiotemporal time series forecasting. Extensive experiments on five widely-used benchmarks demonstrate our method achieves state-of-the-art performance while exhibiting superior interpretability. Our code is available at https://github.com/XDZhelheim/HimNet.|时空时间序列预测在广泛的实际应用中起着关键的作用。虽然在这一领域取得了重大进展，但充分捕捉和利用时空异质性仍然是一个根本性挑战。因此，我们提出了一种新的异质性信息元参数学习方案。具体来说，我们的方法通过学习空间和时间嵌入隐含地捕获时空异质性，这可以看作是一个聚类过程。然后，提出了一种新的时空元参数学习范式，用于从元参数池中学习时空特定的参数。基于这些思想，我们开发了一个异质性信息时空元网络(HimNet)用于时空时间序列预测。在五个广泛使用的基准测试上的大量实验表明，我们的方法实现了最先进的性能，同时显示出优越的可解释性。我们的代码可以在 https://github.com/xdzhelheim/himnet 找到。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Heterogeneity-Informed+Meta-Parameter+Learning+for+Spatiotemporal+Time+Series+Forecasting)|0| |[Representation Learning of Temporal Graphs with Structural Roles](https://doi.org/10.1145/3637528.3671854)|Huaming Du, Long Shi, Xingyan Chen, Yu Zhao, Hegui Zhang, Carl Yang, Fuzhen Zhuang, Gang Kou|; Department of Computer Science, Emory University, Atlanta, Georgia, USA|Temporal graph representation learning has drawn considerable attention in recent years. Most existing works mainly focus on modeling local structural dependencies of temporal graphs. However, underestimating the inherent global structural role information in many real-world temporal graphs inevitably leads to sub-optimal graph representations. To overcome this shortcoming, we propose a novel Role-based Temporal Graph Convolution Network (RTGCN) that fully leverages the global structural role information in temporal graphs. Specifically, RTGCN can effectively capture the static global structural roles by using hypergraph convolution neural networks. To capture the evolution of nodes' structural roles, we further design structural role-based gated recurrent units. Finally, we integrate structural role proximity in our objective function to preserve global structural similarity, further promoting temporal graph representation learning. Experimental results on multiple real-world datasets demonstrate that RTGCN consistently outperforms state-of-the-art temporal graph representation learning methods by significant margins in various temporal link prediction and node classification tasks. Specifically, RTGCN achieves AUC improvement of up to 5.1% for link prediction and F1 improvement of up to 6.2% for new link prediction. In addition, RTGCN achieves AUC improvement up to 4.6% for node classification and 2.7% for structural role classification.|近年来，时间图表示学习引起了人们的广泛关注。现有的工作主要集中在时态图的局部结构依赖建模上。然而，在许多现实世界的时间图中，低估固有的全局结构角色信息不可避免地导致图表示的次优化。为了克服这一缺点，我们提出了一种新的基于角色的时态图卷积网络(RTGCN) ，充分利用时态图中的全局结构角色信息。具体来说，RTGCN 可以利用超图卷积神经网络有效地捕获静态全局结构角色。为了捕捉节点结构角色的演化过程，我们进一步设计了基于结构角色的门控递归单元。最后，我们在目标函数中整合结构角色接近性，以保持全局结构相似性，进一步促进时间图表示学习。在多个实际数据集上的实验结果表明，在各种时间链路预测和节点分类任务中，RTGCN 的表现优于最新的时间图表示学习方法。具体来说，RTGCN 在链路预测方面实现了高达5.1% 的 AUC 改进，在新链路预测方面实现了高达6.2% 的 F1改进。此外，RTGCN 在节点分类和结构角色分类方面的 AUC 改进分别达到4.6% 和2.7% 。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Representation+Learning+of+Temporal+Graphs+with+Structural+Roles)|0| |[Reserving-Masking-Reconstruction Model for Self-Supervised Heterogeneous Graph Representation](https://doi.org/10.1145/3637528.3671719)|Haoran Duan, Cheng Xie, Linyu Li|Yunnan University, Kunming, China|Self-supervised Heterogeneous Graph Representation (SSHGRL) learning is widely used in data mining. The latest SSHGRL methods normally use metapaths to describe the heterogeneous information (multiple relations and node types) to learn the heterogeneous graph representation and achieve impressive results. However, establishing metapaths requires lofty computational costs that are too high for the medium and large graphs. To this end, this paper proposes a Reserving-Masking-Reconstruction (RMR) model that can fully consider heterogeneous information without relying on the metapaths. In detail, we propose a reserving method to reserve to-be-masked nodes' (target nodes) information before graph masking. Second, we split the reserved graph into relation subgraphs according to the type of relations that require much less computational overheads than metapath. Then, the target nodes in each relation subgraph are randomly masked with minimal topology information loss. After, a novel reconstruction method is proposed to reconstruct the masked nodes on different relation subgraphs to establish the self-supervised signal. The proposed method requires low computational complexity and can establish a self-supervised signal without deeply changing the graph topology. Experimental results show the proposed method achieves state-of-the-art records on medium and large-scale heterogeneous graphs and competitive records on small-scale heterogeneous graphs. The code is available at https://github.com/DuanhaoranCC/RMR.|自监督异构图表示(SSHGRL)学习在数据挖掘中有着广泛的应用。最新的 SSHGRL 方法通常使用元路径来描述异构信息(多个关系和节点类型) ，以学习异构图表示并取得令人印象深刻的结果。然而，建立元路径需要高昂的计算成本，这对于中型和大型图来说太高了。为此，本文提出了一种不依赖元路径而能够充分考虑异构信息的保留-掩蔽-重构(RMR)模型。详细地，我们提出了一种在图掩蔽之前保留待掩蔽节点(目标节点)信息的方法。其次，我们根据比元路径需要更少的计算开销的关系类型将保留图划分为关系子图。然后对每个关系子图中的目标节点进行最小拓扑信息丢失的随机掩蔽。然后，提出了一种新的重构方法来重构不同关系子图上的掩蔽节点，以建立自监督信号。该方法计算复杂度低，能够在不改变图形拓扑结构的情况下建立自监督信号。实验结果表明，该方法实现了中大规模异构图的最新记录和小规模异构图的竞争记录。密码可在 https://github.com/duanhaorancc/rmr 查阅。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Reserving-Masking-Reconstruction+Model+for+Self-Supervised+Heterogeneous+Graph+Representation)|0| -|[Pre-Training Identification of Graph Winning Tickets in Adaptive Spatial-Temporal Graph Neural Networks](https://doi.org/10.1145/3637528.3671912)|Wenying Duan, Tianxiang Fang, Hong Rao, Xiaoxi He|University of Macau; Nanchang University|In this paper, we present a novel method to significantly enhance the computational efficiency of Adaptive Spatial-Temporal Graph Neural Networks (ASTGNNs) by introducing the concept of the Graph Winning Ticket (GWT), derived from the Lottery Ticket Hypothesis (LTH). By adopting a pre-determined star topology as a GWT prior to training, we balance edge reduction with efficient information propagation, reducing computational demands while maintaining high model performance. Both the time and memory computational complexity of generating adaptive spatial-temporal graphs is significantly reduced from O(N2) to O(N). Our approach streamlines the ASTGNN deployment by eliminating the need for exhaustive training, pruning, and retraining cycles, and demonstrates empirically across various datasets that it is possible to achieve comparable performance to full models with substantially lower computational costs. Specifically, our approach enables training ASTGNNs on the largest scale spatial-temporal dataset using a single A6000 equipped with 48 GB of memory, overcoming the out-of-memory issue encountered during original training and even achieving state-of-the-art performance. Furthermore, we delve into the effectiveness of the GWT from the perspective of spectral graph theory, providing substantial theoretical support. This advancement not only proves the existence of efficient sub-networks within ASTGNNs but also broadens the applicability of the LTH in resource-constrained settings, marking a significant step forward in the field of graph neural networks. Code is available at https://anonymous.4open.science/r/paper-1430.|本文提出了一种新的方法来显著提高自适应时空图神经网络(ASTGNN)的计算效率，该方法引入了由彩票假说(LTH)衍生而来的图中奖票(GWT)概念。通过采用预先确定的星型拓扑作为训练前的 GWT，我们平衡了边缘约简和有效的信息传播，减少了计算需求，同时保持了高模型性能。生成自适应时空图的时间和内存计算复杂度从 O (N2)显著降低到 O (N)。我们的方法通过消除详尽的训练，修剪和再训练周期的需要来简化 ASTGNN 的部署，并且通过各种数据集经验证明，可以以大大降低的计算成本实现与全模型相当的性能。具体而言，我们的方法使得能够使用配备48GB 内存的单个 A6000在最大规模的空间-时间数据集上训练 ASTGNN，克服在原始训练期间遇到的内存不足问题，甚至实现最先进的性能。此外，本文还从谱图理论的角度对 GWT 的有效性进行了深入的研究，提供了实质性的理论支持。这一进展不仅证明了 ASTGNN 中存在有效的子网络，而且拓宽了 LTH 在资源受限环境中的适用性，标志着图神经网络领域向前迈进了一大步。密码可于 https://anonymous.4open.science/r/paper-1430索取。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Pre-Training+Identification+of+Graph+Winning+Tickets+in+Adaptive+Spatial-Temporal+Graph+Neural+Networks)|0| +|[Pre-Training Identification of Graph Winning Tickets in Adaptive Spatial-Temporal Graph Neural Networks](https://doi.org/10.1145/3637528.3671912)|Wenying Duan, Tianxiang Fang, Hong Rao, Xiaoxi He|Nanchang University; University of Macau|In this paper, we present a novel method to significantly enhance the computational efficiency of Adaptive Spatial-Temporal Graph Neural Networks (ASTGNNs) by introducing the concept of the Graph Winning Ticket (GWT), derived from the Lottery Ticket Hypothesis (LTH). By adopting a pre-determined star topology as a GWT prior to training, we balance edge reduction with efficient information propagation, reducing computational demands while maintaining high model performance. Both the time and memory computational complexity of generating adaptive spatial-temporal graphs is significantly reduced from O(N2) to O(N). Our approach streamlines the ASTGNN deployment by eliminating the need for exhaustive training, pruning, and retraining cycles, and demonstrates empirically across various datasets that it is possible to achieve comparable performance to full models with substantially lower computational costs. Specifically, our approach enables training ASTGNNs on the largest scale spatial-temporal dataset using a single A6000 equipped with 48 GB of memory, overcoming the out-of-memory issue encountered during original training and even achieving state-of-the-art performance. Furthermore, we delve into the effectiveness of the GWT from the perspective of spectral graph theory, providing substantial theoretical support. This advancement not only proves the existence of efficient sub-networks within ASTGNNs but also broadens the applicability of the LTH in resource-constrained settings, marking a significant step forward in the field of graph neural networks. Code is available at https://anonymous.4open.science/r/paper-1430.|本文提出了一种新的方法来显著提高自适应时空图神经网络(ASTGNN)的计算效率，该方法引入了由彩票假说(LTH)衍生而来的图中奖票(GWT)概念。通过采用预先确定的星型拓扑作为训练前的 GWT，我们平衡了边缘约简和有效的信息传播，减少了计算需求，同时保持了高模型性能。生成自适应时空图的时间和内存计算复杂度从 O (N2)显著降低到 O (N)。我们的方法通过消除详尽的训练，修剪和再训练周期的需要来简化 ASTGNN 的部署，并且通过各种数据集经验证明，可以以大大降低的计算成本实现与全模型相当的性能。具体而言，我们的方法使得能够使用配备48GB 内存的单个 A6000在最大规模的空间-时间数据集上训练 ASTGNN，克服在原始训练期间遇到的内存不足问题，甚至实现最先进的性能。此外，本文还从谱图理论的角度对 GWT 的有效性进行了深入的研究，提供了实质性的理论支持。这一进展不仅证明了 ASTGNN 中存在有效的子网络，而且拓宽了 LTH 在资源受限环境中的适用性，标志着图神经网络领域向前迈进了一大步。密码可于 https://anonymous.4open.science/r/paper-1430索取。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Pre-Training+Identification+of+Graph+Winning+Tickets+in+Adaptive+Spatial-Temporal+Graph+Neural+Networks)|0| |[Auctions with LLM Summaries](https://doi.org/10.1145/3637528.3672022)|Avinava Dubey, Zhe Feng, Rahul Kidambi, Aranyak Mehta, Di Wang|Google Research, Mountain View, CA, USA|We study an auction setting in which bidders bid for placement of theircontent within a summary generated by a large language model (LLM), e.g., an adauction in which the display is a summary paragraph of multiple ads. Thisgeneralizes the classic ad settings such as position auctions to an LLMgenerated setting, which allows us to handle general display formats. Wepropose a novel factorized framework in which an auction module and an LLMmodule work together via a prediction model to provide welfare maximizingsummary outputs in an incentive compatible manner. We provide a theoreticalanalysis of this framework and synthetic experiments to demonstrate thefeasibility and validity of the system together with welfare comparisons.|我们研究了一个拍卖设置，其中投标人在一个大型语言模型(LLM)生成的摘要中投标放置他们的内容，例如，一个广告拍卖，其中的显示是多个广告的摘要段落。这将经典的广告设置(如位置拍卖)推广到 LLM 生成设置，这使我们能够处理一般的显示格式。我们提出了一个新的因子分解框架，其中拍卖模块和 LLM 模块通过预测模型一起工作，以激励相容的方式提供福利最大化的总结输出。通过理论分析和综合实验验证了该系统的可行性和有效性，并进行了福利比较。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Auctions+with+LLM+Summaries)|0| |[GAugLLM: Improving Graph Contrastive Learning for Text-Attributed Graphs with Large Language Models](https://doi.org/10.1145/3637528.3672035)|Yi Fang, Dongzhe Fan, Daochen Zha, Qiaoyu Tan|SFSC of AI and DL, New York University (Shanghai), Shanghai, China; Department of Computer Science, Rice University, Huston, USA|This work studies self-supervised graph learning for text-attributed graphs (TAGs) where nodes are represented by textual attributes. Unlike traditional graph contrastive methods that perturb the numerical feature space and alter the graph's topological structure, we aim to improve view generation through language supervision. This is driven by the prevalence of textual attributes in real applications, which complement graph structures with rich semantic information. However, this presents challenges because of two major reasons. First, text attributes often vary in length and quality, making it difficulty to perturb raw text descriptions without altering their original semantic meanings. Second, although text attributes complement graph structures, they are not inherently well-aligned. To bridge the gap, we introduce GAugLLM, a novel framework for augmenting TAGs. It leverages advanced large language models like Mistral to enhance self-supervised graph learning. Specifically, we introduce a mixture-of-prompt-expert technique to generate augmented node features. This approach adaptively maps multiple prompt experts, each of which modifies raw text attributes using prompt engineering, into numerical feature space. Additionally, we devise a collaborative edge modifier to leverage structural and textual commonalities, enhancing edge augmentation by examining or building connections between nodes. Empirical results across five benchmark datasets spanning various domains underscore our framework's ability to enhance the performance of leading contrastive methods (e.g., BGRL, GraphCL, and GBT) as a plug-in tool. Notably, we observe that the augmented features and graph structure can also enhance the performance of standard generative methods (e.g., GraphMAE and S2GAE), as well as popular graph neural networks (e.g., GCN and GAT). The open-sourced implementation of our GAugLLM is available at https://github.com/NYUSHCS/GAugLLM.|研究了文本属性图(TAGs)的自监督图学习问题，其中节点由文本属性表示。与传统的图形对比方法不同，我们的目标是通过语言监督来改进视图的生成，从而扰乱图形的数值特征空间，改变图形的拓扑结构。这是由于在实际应用中文本属性的流行，这种补图结构具有丰富的语义信息。然而，由于两个主要原因，这带来了挑战。首先，文本属性通常在长度和质量上有所不同，因此很难在不改变原始语义含义的情况下扰乱原始文本描述。其次，尽管文本属性具有补图结构，但它们在本质上并不一致。为了弥补这一差距，我们引入了 GAugLLM，一种用于增强标签的新框架。它利用先进的大型语言模型，如 Mistro，以增强自监督图形学习。具体来说，我们引入了一种混合提示专家技术来生成增强的节点特征。该方法自适应地将多个提示专家映射到数值特征空间，每个提示专家使用提示工程修改原始文本属性。此外，我们还设计了一个协作式的边缘修饰符来利用结构和文本的共性，通过检查或建立节点之间的连接来增强边缘增强。跨越不同领域的五个基准数据集的实证结果强调了我们的框架作为插件工具提高领先对比方法(例如 BGRL、 GraphCL 和 GBT)性能的能力。值得注意的是，我们观察到增强的特征和图结构也可以提高标准生成方法(例如 GraphMAE 和 S2GAE)以及流行的图神经网络(例如 GCN 和 GAT)的性能。我们的 gaugLLM 的开源实现可以在 https://github.com/nyushcs/GAugLLM 找到。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=GAugLLM:+Improving+Graph+Contrastive+Learning+for+Text-Attributed+Graphs+with+Large+Language+Models)|0| -|[CAT: Interpretable Concept-based Taylor Additive Models](https://doi.org/10.1145/3637528.3672020)|Viet Duong, Qiong Wu, Zhengyi Zhou, Hongjue Zhao, Chenxiang Luo, Eric Zavesky, Huaxiu Yao, Huajie Shao|William & Mary, Williamsburg, VA, USA; AT&T Labs, Bedminster, NJ, USA; University of Illinois at Urbana-Champaign, Champaign, IL, USA; AT&T Labs, Austin, TX, USA; The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA|As an emerging interpretable technique, Generalized Additive Models (GAMs) adopt neural networks to individually learn non-linear functions for each feature, which are then combined through a linear model for final predictions. Although GAMs can explain deep neural networks (DNNs) at the feature level, they require large numbers of model parameters and are prone to overfitting, making them hard to train and scale. Additionally, in real-world datasets with many features, the interpretability of feature-based explanations diminishes for humans. To tackle these issues, recent research has shifted towards concept-based interpretable methods. These approaches try to integrate concept learning as an intermediate step before making predictions, explaining the predictions in terms of human-understandable concepts. However, these methods require domain experts to extensively label concepts with relevant names and their ground-truth values. In response, we propose CAT, a novel interpretable Concept-bAsed Taylor additive model to simplify this process. CAT does not require domain experts to annotate concepts and their ground-truth values. Instead, it only requires users to simply categorize input features into broad groups, which can be easily accomplished through a quick metadata review. Specifically, CAT first embeds each group of input features into one-dimensional high-level concept representation, and then feeds the concept representations into a new white-box Taylor Neural Network (TaylorNet). The TaylorNet aims to learn the non-linear relationship between the inputs and outputs using polynomials. Evaluation results across multiple benchmarks demonstrate that CAT can outperform or compete with the baselines while reducing the need of extensive model parameters. Importantly, it can effectively explain model predictions through high-level concepts. Source code is available at github.com/vduong143/CAT-KDD-2024.|作为一种新兴的可解释技术，广义可加模型(GAM)采用神经网络来单独学习每个特征的非线性函数，然后通过一个线性模型组合起来进行最终的预测。尽管 GAM 可以在特征层次上解释深度神经网络(DNN) ，但是它们需要大量的模型参数，并且容易过度拟合，使得它们难以训练和扩展。此外，在具有许多特征的真实世界数据集中，基于特征的解释对人类的可解释性降低。为了解决这些问题，最近的研究已经转向基于概念的可解释方法。这些方法试图将概念学习作为预测之前的一个中间步骤，用人类可理解的概念来解释预测。然而，这些方法需要领域专家用相关的名称和它们的地面真实值来广泛地标记概念。为了简化这一过程，我们提出了一种新的基于概念的可解释的泰勒可加模型 CAT。CAT 不要求领域专家对概念及其基本真理价值进行注释。相反，它只要求用户将输入特性简单地分为广泛的组，这可以通过快速的元数据审查轻松实现。具体来说，CAT 首先将每组输入特征嵌入到一维高层概念表示中，然后将概念表示输入到一个新的白盒泰勒神经网络(TaylorNet)中。泰勒网的目的是利用多项式学习输入和输出之间的非线性关系。跨多个基准的评价结果表明，计算机辅助测试的表现优于基准或与基准竞争，同时减少了对广泛模型参数的需求。重要的是，它可以通过高级概念有效地解释模型预测。源代码可在 github.com/vduong143/cat-kdd-2024下载。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=CAT:+Interpretable+Concept-based+Taylor+Additive+Models)|0| +|[CAT: Interpretable Concept-based Taylor Additive Models](https://doi.org/10.1145/3637528.3672020)|Viet Duong, Qiong Wu, Zhengyi Zhou, Hongjue Zhao, Chenxiang Luo, Eric Zavesky, Huaxiu Yao, Huajie Shao|AT&T Labs, Austin, TX, USA; University of Illinois at Urbana-Champaign, Champaign, IL, USA; The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; AT&T Labs, Bedminster, NJ, USA; William & Mary, Williamsburg, VA, USA|As an emerging interpretable technique, Generalized Additive Models (GAMs) adopt neural networks to individually learn non-linear functions for each feature, which are then combined through a linear model for final predictions. Although GAMs can explain deep neural networks (DNNs) at the feature level, they require large numbers of model parameters and are prone to overfitting, making them hard to train and scale. Additionally, in real-world datasets with many features, the interpretability of feature-based explanations diminishes for humans. To tackle these issues, recent research has shifted towards concept-based interpretable methods. These approaches try to integrate concept learning as an intermediate step before making predictions, explaining the predictions in terms of human-understandable concepts. However, these methods require domain experts to extensively label concepts with relevant names and their ground-truth values. In response, we propose CAT, a novel interpretable Concept-bAsed Taylor additive model to simplify this process. CAT does not require domain experts to annotate concepts and their ground-truth values. Instead, it only requires users to simply categorize input features into broad groups, which can be easily accomplished through a quick metadata review. Specifically, CAT first embeds each group of input features into one-dimensional high-level concept representation, and then feeds the concept representations into a new white-box Taylor Neural Network (TaylorNet). The TaylorNet aims to learn the non-linear relationship between the inputs and outputs using polynomials. Evaluation results across multiple benchmarks demonstrate that CAT can outperform or compete with the baselines while reducing the need of extensive model parameters. Importantly, it can effectively explain model predictions through high-level concepts. Source code is available at github.com/vduong143/CAT-KDD-2024.|作为一种新兴的可解释技术，广义可加模型(GAM)采用神经网络来单独学习每个特征的非线性函数，然后通过一个线性模型组合起来进行最终的预测。尽管 GAM 可以在特征层次上解释深度神经网络(DNN) ，但是它们需要大量的模型参数，并且容易过度拟合，使得它们难以训练和扩展。此外，在具有许多特征的真实世界数据集中，基于特征的解释对人类的可解释性降低。为了解决这些问题，最近的研究已经转向基于概念的可解释方法。这些方法试图将概念学习作为预测之前的一个中间步骤，用人类可理解的概念来解释预测。然而，这些方法需要领域专家用相关的名称和它们的地面真实值来广泛地标记概念。为了简化这一过程，我们提出了一种新的基于概念的可解释的泰勒可加模型 CAT。CAT 不要求领域专家对概念及其基本真理价值进行注释。相反，它只要求用户将输入特性简单地分为广泛的组，这可以通过快速的元数据审查轻松实现。具体来说，CAT 首先将每组输入特征嵌入到一维高层概念表示中，然后将概念表示输入到一个新的白盒泰勒神经网络(TaylorNet)中。泰勒网的目的是利用多项式学习输入和输出之间的非线性关系。跨多个基准的评价结果表明，计算机辅助测试的表现优于基准或与基准竞争，同时减少了对广泛模型参数的需求。重要的是，它可以通过高级概念有效地解释模型预测。源代码可在 github.com/vduong143/cat-kdd-2024下载。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=CAT:+Interpretable+Concept-based+Taylor+Additive+Models)|0| |[SensitiveHUE: Multivariate Time Series Anomaly Detection by Enhancing the Sensitivity to Normal Patterns](https://doi.org/10.1145/3637528.3671919)|Yuye Feng, Wei Zhang, Yao Fu, Weihao Jiang, Jiang Zhu, Wenqi Ren|Hikvision Research Institute, Hangzhou, China|Unsupervised anomaly detection in multivariate time series (MTS) has always been a challenging problem, and the modeling based on reconstruction has garnered significant attention. The insensitivity of these methods towards normal patterns poses challenges in distinguishing between normal and abnormal points. Firstly, the general reconstruction strategies may exhibit limited sensitivity to spatio-temporal dependencies, and their performance remains largely unaffected by such dependencies. Secondly, most methods fail to model the heteroscedastic uncertainty in MTS, hindering their abilities to derive a distinguishable criterion. For instance, normal data with high noise levels may lead to detection failure due to excessively high reconstruction errors. In this work, we emphasize the necessity of sensitivity to normal patterns, which could improve the discrimination between normal and abnormal points remarkably. To this end, we propose SensitiveHUE, a probabilistic network by implementing both reconstruction and heteroscedastic uncertainty estimation. Its core includes a statistical feature removal strategy to ensure the dependency sensitive property, and a novel MTS-NLL loss for modeling the normal patterns in important regions. Experimental results demonstrate that SensitiveHUE exhibits nontrivial sensitivity to normal patterns and outperforms the existing state-of-the-art alternatives by a large margin. Code is publicly available at this URL\footnotehttp://github.com/yuesuoqingqiu/SensitiveHUE.|多变量时间序列(mTS)中的无监督异常检测一直是一个具有挑战性的问题，基于重构的建模已经引起了人们的重视。这些方法对正常模式的不敏感性对区分正常点和异常点提出了挑战。首先，一般的重构策略对时空依赖的敏感性有限，其性能基本上不受这种依赖的影响。其次，大多数方法无法对多输入多输出系统的异方差不确定性进行建模，妨碍了它们推导判别准则的能力。例如，正常数据的高噪声水平可能导致检测失败，由于过高的重建误差。在这项工作中，我们强调对正常模式敏感的必要性，这可以显著提高正常和异常点之间的识别。为此，我们提出了灵敏度人工智能，一个概率网络通过实现重构和异方差不确定性估计。其核心包括统计特征去除策略以保证依赖敏感性，以及一种新的 MTS-NLL 损失模型在重要区域的正常模式。实验结果表明，该算法对正常模式具有非平凡的敏感性，并且性能优于现有的最先进的替代方案。代码可以在这个网址的脚注 http:// github.com/yuesuoqingqiu/sensitivehue 上公开获得。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=SensitiveHUE:+Multivariate+Time+Series+Anomaly+Detection+by+Enhancing+the+Sensitivity+to+Normal+Patterns)|0| -|[Communication-efficient Multi-service Mobile Traffic Prediction by Leveraging Cross-service Correlations](https://doi.org/10.1145/3637528.3671730)|Zhiying Feng, Qiong Wu, Xu Chen|School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China; |Mobile traffic prediction plays a crucial role in enabling efficient network management and service provisioning. Traditional prediction approaches treat different mobile application services (such as Uber, Facebook, Twitter, etc) as isolated entities, neglecting potential correlation among them. Moreover, such isolated prediction methods necessitate the uploading of historical traffic data from all regions to forecast city-wide traffic, resulting in consuming substantial bandwidth resources and risking prediction failure in the event of data loss in specific regions. To address these challenges, we propose a novel Cross-service Attention-based Spatial-Temporal Graph Convolutional Network (CsASTGCN) for precise and communication-efficient multi-service mobile traffic prediction. Our methodology allows each mobile service to transmit the traffic data of only a fraction of regions for city-wide traffic prediction of all mobile services, which reduces the resource consumption caused by data transmission. Specifically, the sparse traffic data are initially transmitted to the cloud server and the masked graph autoencoder is utilized to roughly reconstruct the traffic volume for regions with missing data. Subsequently, a cross-service attention-based predictor is designed to calculate the data correlation among different mobile services within the same region. Considering the constantly emerging mobile services, we incorporate a novel model-based adaptive transfer learning scheme to extract valuable knowledge from the existing models and expedite the training of a new model for a new service without training from scratch, thereby enhancing the scalability of our framework. Extensive experiments conducted on a large-scale real-world mobile traffic dataset demonstrate that our model greatly outperforms the existing schemes, enhancing both the communication-efficiency and robustness of large-scale multi-service traffic prediction.|移动流量预测在实现有效的网络管理和服务供应方面起着至关重要的作用。传统的预测方法将不同的移动应用服务(如 Uber、 Facebook、 Twitter 等)视为孤立的实体，而忽略了它们之间潜在的相关性。此外，这种孤立的预测方法需要上传来自所有区域的历史交通数据来预测全市的交通，这会消耗大量的带宽资源，并且在特定区域发生数据丢失时可能导致预测失败。为了应对这些挑战，我们提出了一种新颖的基于跨服务注意力的时空图卷积网络(CsASTGCN) ，用于精确高效的多服务移动流量预测。我们的方法允许每个移动服务只传输一小部分地区的交通数据，以便对所有移动服务进行全市范围的交通预测，从而减少数据传输造成的资源消耗。具体来说，稀疏的流量数据首先传输到云服务器，然后利用掩码图形自动编码器对缺少数据的区域进行粗略的流量重构。然后，设计了一个基于注意力的跨业务预测器，计算同一区域内不同移动业务之间的数据相关性。考虑到不断出现的移动服务，我们采用了一种新的基于模型的自适应转移学习方案，从现有模型中提取有价值的知识，并加快新模型的培训，而不需要从头开始培训新服务，从而提高了我们的框架的可扩展性。在大规模实际移动业务数据集上进行的大量实验表明，该模型的性能明显优于现有方案，提高了大规模多业务流量预测的通信效率和鲁棒性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Communication-efficient+Multi-service+Mobile+Traffic+Prediction+by+Leveraging+Cross-service+Correlations)|0| +|[Communication-efficient Multi-service Mobile Traffic Prediction by Leveraging Cross-service Correlations](https://doi.org/10.1145/3637528.3671730)|Zhiying Feng, Qiong Wu, Xu Chen|; School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China|Mobile traffic prediction plays a crucial role in enabling efficient network management and service provisioning. Traditional prediction approaches treat different mobile application services (such as Uber, Facebook, Twitter, etc) as isolated entities, neglecting potential correlation among them. Moreover, such isolated prediction methods necessitate the uploading of historical traffic data from all regions to forecast city-wide traffic, resulting in consuming substantial bandwidth resources and risking prediction failure in the event of data loss in specific regions. To address these challenges, we propose a novel Cross-service Attention-based Spatial-Temporal Graph Convolutional Network (CsASTGCN) for precise and communication-efficient multi-service mobile traffic prediction. Our methodology allows each mobile service to transmit the traffic data of only a fraction of regions for city-wide traffic prediction of all mobile services, which reduces the resource consumption caused by data transmission. Specifically, the sparse traffic data are initially transmitted to the cloud server and the masked graph autoencoder is utilized to roughly reconstruct the traffic volume for regions with missing data. Subsequently, a cross-service attention-based predictor is designed to calculate the data correlation among different mobile services within the same region. Considering the constantly emerging mobile services, we incorporate a novel model-based adaptive transfer learning scheme to extract valuable knowledge from the existing models and expedite the training of a new model for a new service without training from scratch, thereby enhancing the scalability of our framework. Extensive experiments conducted on a large-scale real-world mobile traffic dataset demonstrate that our model greatly outperforms the existing schemes, enhancing both the communication-efficiency and robustness of large-scale multi-service traffic prediction.|移动流量预测在实现有效的网络管理和服务供应方面起着至关重要的作用。传统的预测方法将不同的移动应用服务(如 Uber、 Facebook、 Twitter 等)视为孤立的实体，而忽略了它们之间潜在的相关性。此外，这种孤立的预测方法需要上传来自所有区域的历史交通数据来预测全市的交通，这会消耗大量的带宽资源，并且在特定区域发生数据丢失时可能导致预测失败。为了应对这些挑战，我们提出了一种新颖的基于跨服务注意力的时空图卷积网络(CsASTGCN) ，用于精确高效的多服务移动流量预测。我们的方法允许每个移动服务只传输一小部分地区的交通数据，以便对所有移动服务进行全市范围的交通预测，从而减少数据传输造成的资源消耗。具体来说，稀疏的流量数据首先传输到云服务器，然后利用掩码图形自动编码器对缺少数据的区域进行粗略的流量重构。然后，设计了一个基于注意力的跨业务预测器，计算同一区域内不同移动业务之间的数据相关性。考虑到不断出现的移动服务，我们采用了一种新的基于模型的自适应转移学习方案，从现有模型中提取有价值的知识，并加快新模型的培训，而不需要从头开始培训新服务，从而提高了我们的框架的可扩展性。在大规模实际移动业务数据集上进行的大量实验表明，该模型的性能明显优于现有方案，提高了大规模多业务流量预测的通信效率和鲁棒性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Communication-efficient+Multi-service+Mobile+Traffic+Prediction+by+Leveraging+Cross-service+Correlations)|0| |[Federated Graph Learning with Structure Proxy Alignment](https://doi.org/10.1145/3637528.3671717)|Xingbo Fu, Zihan Chen, Binchi Zhang, Chen Chen, Jundong Li|University of Virginia, Charlottesville, Virginia, USA|Federated Graph Learning (FGL) aims to learn graph learning models over graph data distributed in multiple data owners, which has been applied in various applications such as social recommendation and financial fraud detection. Inherited from generic Federated Learning (FL), FGL similarly has the data heterogeneity issue where the label distribution may vary significantly for distributed graph data across clients. For instance, a client can have the majority of nodes from a class, while another client may have only a few nodes from the same class. This issue results in divergent local objectives and impairs FGL convergence for node-level tasks, especially for node classification. Moreover, FGL also encounters a unique challenge for the node classification task: the nodes from a minority class in a client are more likely to have biased neighboring information, which prevents FGL from learning expressive node embeddings with Graph Neural Networks (GNNs). To grapple with the challenge, we propose FedSpray, a novel FGL framework that learns local class-wise structure proxies in the latent space and aligns them to obtain global structure proxies in the server. Our goal is to obtain the aligned structure proxies that can serve as reliable, unbiased neighboring information for node classification. To achieve this, FedSpray trains a global feature-structure encoder and generates unbiased soft targets with structure proxies to regularize local training of GNN models in a personalized way. We conduct extensive experiments over four datasets, and experiment results validate the superiority of FedSpray compared with other baselines. Our code is available at https://github.com/xbfu/FedSpray.|联邦图学习(Federated Graph Learning，FGL)旨在学习分布在多个数据所有者中的图形数据的图形学习模型，该模型已被广泛应用于社会推荐和金融欺诈检测等领域。FGL 继承自一般的联邦学习(Federated Learning，FL) ，同样也存在数据异构性问题，对于跨客户端的分布式图形数据，标签分布可能会有很大的不同。例如，一个客户端可以拥有一个类的大部分节点，而另一个客户端可能只拥有同一个类的几个节点。这个问题导致了局部目标的分歧，并且损害了节点级任务的 FGL 收敛性，特别是对于节点分类。此外，FGL 还面临着节点分类任务的独特挑战: 来自客户端少数类的节点更可能具有偏向的邻近信息，这阻碍了 FGL 利用图神经网络(GNN)学习表达式节点嵌入。为了应对这一挑战，我们提出了 FedSpray 框架，这是一种新的 FGL 框架，它在潜在空间中学习局部类结构代理，并将它们对齐以获得服务器中的全局结构代理。我们的目标是获得一致的结构代理，可以作为可靠的，无偏的相邻信息的节点分类。为了实现这一点，FedSpray 训练了一个全局特征结构编码器，并用结构代理生成无偏软目标，以个性化的方式规范 GNN 模型的局部训练。我们在四个数据集上进行了广泛的实验，实验结果验证了 FedSpray 相对于其他基线的优越性。我们的代码可以在 https://github.com/xbfu/fedspray 找到。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Federated+Graph+Learning+with+Structure+Proxy+Alignment)|0| |[Policy-Based Bayesian Active Causal Discovery with Deep Reinforcement Learning](https://doi.org/10.1145/3637528.3671705)|Heyang Gao, Zexu Sun, Hao Yang, Xu Chen|Gaoling School of Artificial Intelligence, Renmin University of China, Beijing, China|Causal discovery with observational and interventional data plays an important role in numerous fields. Due to the costly and potentially risky nature of intervention experiments, selecting informative interventions is critical in real-world situations. Several recent works introduce Bayesian active learning to select interventions that maximize the expected information gain about the underlying causal relationship at each optimization step. However, there are still some limitations within these methods: (1) Local optimality. With multiple intervention experiments, selecting optimal intervention myopically at each step may drop into the local optimal point. (2) Expensive time cost. Optimizing the most informative intervention at each step is time-consuming and not suitable for adaptive experiments with strict inference speed requirements. In this study, we propose a novel method called Reinforcement Learning-based Causal Bayesian Experimental Design (RL-CBED) to reduce the risk of local optimality and accelerate intervention selection inference. Specifically, we formulate the active causal discovery problem as a partially observable Markov decision process (POMDP). We design an information gain-based sparse reward function and then improve it to a dense reward function, providing fine-grained feedback to help the RL policy learn more quickly in complex environments. Moreover, we theoretically prove that the Q-function estimator can be learned using only trajectories sampled from the prior, which can significantly reduce the time cost of training process, enabling the real-world application of our method. Extensive experiments on both synthetic and real world-inspired semi-synthetic datasets demonstrate the effectiveness of our proposed method.|利用观测和介入数据进行因果发现在许多领域都发挥着重要作用。由于干预实验的成本高昂且具有潜在风险，因此在现实情况下选择信息丰富的干预措施至关重要。最近的一些工作介绍了贝叶斯主动学习，以选择干预措施，最大限度地获得关于每个优化步骤的潜在因果关系的预期信息。但是，这些方法仍然存在一些局限性: (1)局部最优性。通过多次干预实验，在每一步选择最佳干预措施可能会落入局部最佳点。(2)昂贵的时间成本。在每个步骤中优化信息量最大的干预是耗时的，不适合具有严格推理速度要求的自适应实验。在这项研究中，我们提出了一种新的方法，称为强化学习为基础的因果贝叶斯实验设计(RL-CBED) ，以降低局部最优的风险，加速干预选择推理。具体来说，我们将主动因果发现问题(active cause Discovery problem，简称 POMDP)表述为一个部分可观察马可夫决策过程。我们设计了一个基于信息增益的稀疏奖励函数，然后将其改进为密集奖励函数，提供细粒度的反馈，以帮助 RL 策略在复杂环境中更快地学习。此外，从理论上证明了 Q 函数估计器只需利用先验轨迹进行学习，可以显著降低训练过程的时间成本，使我们的方法能够在现实中应用。在合成和真实世界启发的半合成数据集上的大量实验证明了我们提出的方法的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Policy-Based+Bayesian+Active+Causal+Discovery+with+Deep+Reinforcement+Learning)|0| -|[Graph Condensation for Open-World Graph Learning](https://doi.org/10.1145/3637528.3671917)|Xinyi Gao, Tong Chen, Wentao Zhang, Yayong Li, Xiangguo Sun, Hongzhi Yin|Data 61, CSIRO, Brisbane, Australia; The University of Queensland, Brisbane, Australia; Peking University, Beijing, China; The Chinese University of Hong Kong, Hong Kong, China|The burgeoning volume of graph data presents significant computational challenges in training graph neural networks (GNNs), critically impeding their efficiency in various applications. To tackle this challenge, graph condensation (GC) has emerged as a promising acceleration solution, focusing on the synthesis of a compact yet representative graph for efficiently training GNNs while retaining performance. Despite the potential to promote scalable use of GNNs, existing GC methods are limited to aligning the condensed graph with merely the observed static graph distribution. This limitation significantly restricts the generalization capacity of condensed graphs, particularly in adapting to dynamic distribution changes. In real-world scenarios, however, graphs are dynamic and constantly evolving, with new nodes and edges being continually integrated. Consequently, due to the limited generalization capacity of condensed graphs, applications that employ GC for efficient GNN training end up with sub-optimal GNNs when confronted with evolving graph structures and distributions in dynamic real-world situations. To overcome this issue, we propose open-world graph condensation (OpenGC), a robust GC framework that integrates structure-aware distribution shift to simulate evolving graph patterns and exploit the temporal environments for invariance condensation. This approach is designed to extract temporal invariant patterns from the original graph, thereby enhancing the generalization capabilities of the condensed graph and, subsequently, the GNNs trained on it. Furthermore, to support the periodic re-condensation and expedite condensed graph updating in life-long graph learning, OpenGC reconstructs the sophisticated optimization scheme with kernel ridge regression and non-parametric graph convolution, significantly accelerating the condensation process while ensuring the exact solutions. Extensive experiments on both real-world and synthetic evolving graphs demonstrate that OpenGC outperforms state-of-the-art (SOTA) GC methods in adapting to dynamic changes in open-world graph environments.|图形数据量的迅速增长给训练图形神经网络(GNN)带来了巨大的计算挑战，严重影响了它们在各种应用中的效率。为了解决这个问题，图的压缩(GC)已经成为一个有前途的加速解决方案，集中在一个紧凑但有代表性的图的合成，有效地训练 GNN，同时保持性能。尽管有可能促进 GNN 的可扩展使用，但现有的 GC 方法仅限于将压缩图与观察到的静态图分布对齐。这种局限性极大地限制了压缩图的泛化能力，尤其是在适应动态分布变化方面。然而，在现实世界的场景中，图是动态的并且不断发展的，新的节点和边不断地被集成。因此，由于压缩图的泛化能力有限，使用 GC 进行高效 GNN 训练的应用程序在面对动态现实世界中不断演化的图结构和分布时，最终得到的 GNN 是次优的。为了克服这个问题，我们提出了开放世界图的压缩(OpenGC) ，一个健壮的 GC 框架，集成了结构感知的分布移位，以模拟演化的图模式，并利用不变压缩的时间环境。该方法从原始图中提取出时间不变的模式，从而提高了压缩图的泛化能力，进而提高了对压缩图进行训练的 GNN 的泛化能力。此外，为了支持终身图学习中的周期性重新压缩和加速压缩图的更新，OpenGC 利用核岭回归和非参数图的卷积重构了复杂的优化方案，在保证精确解的同时显著加快了压缩过程。在真实世界和合成演化图表上的大量实验表明，OpenGC 在适应开放世界图表环境中的动态变化方面优于最先进的(SOTA) GC 方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Graph+Condensation+for+Open-World+Graph+Learning)|0| +|[Graph Condensation for Open-World Graph Learning](https://doi.org/10.1145/3637528.3671917)|Xinyi Gao, Tong Chen, Wentao Zhang, Yayong Li, Xiangguo Sun, Hongzhi Yin|The Chinese University of Hong Kong, Hong Kong, China; The University of Queensland, Brisbane, Australia; Data 61, CSIRO, Brisbane, Australia; Peking University, Beijing, China|The burgeoning volume of graph data presents significant computational challenges in training graph neural networks (GNNs), critically impeding their efficiency in various applications. To tackle this challenge, graph condensation (GC) has emerged as a promising acceleration solution, focusing on the synthesis of a compact yet representative graph for efficiently training GNNs while retaining performance. Despite the potential to promote scalable use of GNNs, existing GC methods are limited to aligning the condensed graph with merely the observed static graph distribution. This limitation significantly restricts the generalization capacity of condensed graphs, particularly in adapting to dynamic distribution changes. In real-world scenarios, however, graphs are dynamic and constantly evolving, with new nodes and edges being continually integrated. Consequently, due to the limited generalization capacity of condensed graphs, applications that employ GC for efficient GNN training end up with sub-optimal GNNs when confronted with evolving graph structures and distributions in dynamic real-world situations. To overcome this issue, we propose open-world graph condensation (OpenGC), a robust GC framework that integrates structure-aware distribution shift to simulate evolving graph patterns and exploit the temporal environments for invariance condensation. This approach is designed to extract temporal invariant patterns from the original graph, thereby enhancing the generalization capabilities of the condensed graph and, subsequently, the GNNs trained on it. Furthermore, to support the periodic re-condensation and expedite condensed graph updating in life-long graph learning, OpenGC reconstructs the sophisticated optimization scheme with kernel ridge regression and non-parametric graph convolution, significantly accelerating the condensation process while ensuring the exact solutions. Extensive experiments on both real-world and synthetic evolving graphs demonstrate that OpenGC outperforms state-of-the-art (SOTA) GC methods in adapting to dynamic changes in open-world graph environments.|图形数据量的迅速增长给训练图形神经网络(GNN)带来了巨大的计算挑战，严重影响了它们在各种应用中的效率。为了解决这个问题，图的压缩(GC)已经成为一个有前途的加速解决方案，集中在一个紧凑但有代表性的图的合成，有效地训练 GNN，同时保持性能。尽管有可能促进 GNN 的可扩展使用，但现有的 GC 方法仅限于将压缩图与观察到的静态图分布对齐。这种局限性极大地限制了压缩图的泛化能力，尤其是在适应动态分布变化方面。然而，在现实世界的场景中，图是动态的并且不断发展的，新的节点和边不断地被集成。因此，由于压缩图的泛化能力有限，使用 GC 进行高效 GNN 训练的应用程序在面对动态现实世界中不断演化的图结构和分布时，最终得到的 GNN 是次优的。为了克服这个问题，我们提出了开放世界图的压缩(OpenGC) ，一个健壮的 GC 框架，集成了结构感知的分布移位，以模拟演化的图模式，并利用不变压缩的时间环境。该方法从原始图中提取出时间不变的模式，从而提高了压缩图的泛化能力，进而提高了对压缩图进行训练的 GNN 的泛化能力。此外，为了支持终身图学习中的周期性重新压缩和加速压缩图的更新，OpenGC 利用核岭回归和非参数图的卷积重构了复杂的优化方案，在保证精确解的同时显著加快了压缩过程。在真实世界和合成演化图表上的大量实验表明，OpenGC 在适应开放世界图表环境中的动态变化方面优于最先进的(SOTA) GC 方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Graph+Condensation+for+Open-World+Graph+Learning)|0| |[PATE: Proximity-Aware Time Series Anomaly Evaluation](https://doi.org/10.1145/3637528.3671971)|Ramin Ghorbani, Marcel J. T. Reinders, David M. J. Tax|Delft University of Technology, Delft, Netherlands|Evaluating anomaly detection algorithms in time series data is critical asinaccuracies can lead to flawed decision-making in various domains wherereal-time analytics and data-driven strategies are essential. Traditionalperformance metrics assume iid data and fail to capture the complex temporaldynamics and specific characteristics of time series anomalies, such as earlyand delayed detections. We introduce Proximity-Aware Time series anomalyEvaluation (PATE), a novel evaluation metric that incorporates the temporalrelationship between prediction and anomaly intervals. PATE usesproximity-based weighting considering buffer zones around anomaly intervals,enabling a more detailed and informed assessment of a detection. Using theseweights, PATE computes a weighted version of the area under the Precision andRecall curve. Our experiments with synthetic and real-world datasets show thesuperiority of PATE in providing more sensible and accurate evaluations thanother evaluation metrics. We also tested several state-of-the-art anomalydetectors across various benchmark datasets using the PATE evaluation scheme.The results show that a common metric like Point-Adjusted F1 Score fails tocharacterize the detection performances well, and that PATE is able to providea more fair model comparison. By introducing PATE, we redefine theunderstanding of model efficacy that steers future studies toward developingmore effective and accurate detection models.|对时间序列数据中的异常检测算法进行评估是至关重要的，因为不准确会导致不同领域的决策失误，而实时分析和数据驱动策略是必不可少的。传统的性能度量方法假定 id 数据，无法捕获复杂的时间动态和时间序列异常的具体特征，如早期和延迟检测。我们介绍了接近感知时间序列异常评估(PATE) ，一种新的评估度量，其中包含了预测和异常间隔之间的时间关系。PATE 使用基于接近度的加权方法，考虑到异常间隔周围的缓冲区，从而能够对检测进行更详细和知情的评估。使用这些权重，PATE 计算精度和召回曲线下面积的加权版本。我们对合成和真实世界数据集的实验显示了 PATE 在提供比其他评估指标更合理和准确的评估方面的优越性。我们还使用 PATE 评估方案在不同的基准数据集上测试了几个最先进的异常检测器。结果表明，点调整 F1得分这一常用指标不能很好地表征检测性能，PATE 能够提供更公平的模型比较。通过介绍 PATE，我们重新定义了模型功效的理解，引导未来的研究发展更有效和准确的检测模型。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=PATE:+Proximity-Aware+Time+Series+Anomaly+Evaluation)|0| |[Hierarchical Neural Constructive Solver for Real-world TSP Scenarios](https://doi.org/10.1145/3637528.3672053)|Yong Liang Goh, Zhiguang Cao, Yining Ma, Yanfei Dong, Mohammed Haroon Dupty, Wee Sun Lee|National University of Singapore, Singapore, Singapore; Singapore Management University, Singapore, Singapore; Grabtaxi Holdings Pte Ltd & National University of Singapore, Singapore, Singapore|Existing neural constructive solvers for routing problems have predominantly employed transformer architectures, conceptualizing the route construction as a set-to-sequence learning task. However, their efficacy has primarily been demonstrated on entirely random problem instances that inadequately capture real-world scenarios. In this paper, we introduce realistic Traveling Salesman Problem (TSP) scenarios relevant to industrial settings and derive the following insights: (1) The optimal next node (or city) to visit often lies within proximity to the current node, suggesting the potential benefits of biasing choices based on current locations. (2) Effectively solving the TSP requires robust tracking of unvisited nodes and warrants succinct grouping strategies. Building upon these insights, we propose integrating a learnable choice layer inspired by Hypernetworks to prioritize choices based on the current location, and a learnable approximate clustering algorithm inspired by the Expectation-Maximization algorithm to facilitate grouping the unvisited cities. Together, these two contributions form a hierarchical approach towards solving the realistic TSP by considering both immediate local neighbourhoods and learning an intermediate set of node representations. Our hierarchical approach yields superior performance compared to both classical and recent transformer models, showcasing the efficacy of the key designs.|现有的路由问题的神经构造解决方案主要采用变压器结构，将路由构造概念化为一个集合到序列的学习任务。然而，它们的有效性主要表现在完全随机的问题实例上，这些实例没有充分捕捉到真实世界的场景。在本文中，我们介绍了与工业环境相关的现实的旅行商问题(TSP)场景，并得出以下见解: (1)最佳的下一个节点(或城市)访问往往位于当前节点附近，提出了基于当前位置的偏差选择的潜在好处。(2)有效解决 TSP 问题需要对未访问节点进行鲁棒跟踪，并保证简洁的分组策略。基于这些见解，我们建议整合一个受超级网络启发的可学习的选择层，根据当前位置对选择进行优先排序，以及受期望最大化算法启发的可学习的近似聚类算法，以促进对未访问的城市进行分组。同时，这两个贡献形成了一个分层的方法来解决现实的 TSP 通过考虑直接的局部邻域和学习一个中间集的节点表示。我们的分层方法产生优越的性能相比，经典和最近的变压器模型，展示了关键设计的功效。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Hierarchical+Neural+Constructive+Solver+for+Real-world+TSP+Scenarios)|0| |[An Energy-centric Framework for Category-free Out-of-distribution Node Detection in Graphs](https://doi.org/10.1145/3637528.3671939)|Zheng Gong, Ying Sun||Graph neural networks have garnered notable attention for effectively processing graph-structured data. Prevalent models prioritize improving in-distribution (IND) data performance, frequently overlooking the risks from potential out-of-distribution (OOD) nodes during training and inference. In real-world graphs, the automated network construction can introduce noisy nodes from unknown distributions. Previous research into OOD node detection, typically referred to as entropy-based methods, calculates OOD measurements from the prediction entropy alongside category classification training. However, the nodes in the graph might not be pre-labeled with specific categories, rendering entropy-based OOD detectors inapplicable in such category-free situations. To tackle this issue, we propose an energy-centric density estimation framework for OOD node detection, referred to as EnergyDef. Within this framework, we introduce an energy-based GNN to compute node energies that act as indicators of node density and reveal the OOD uncertainty of nodes. Importantly, EnergyDef can efficiently identify OOD nodes with low-resource OOD node annotations, achieved by sampling hallucinated nodes via Langevin Dynamics and structure estimation, along with training through Contrastive Divergence. Our comprehensive experiments on real-world datasets substantiate that our framework markedly surpasses state-of-the-art methods in terms of detection quality, even under conditions of scarce or entirely absent OOD node annotations.|图形神经网络在有效处理图形结构数据方面受到了广泛的关注。流行的模型优先考虑改善内部分发(IND)数据性能，在训练和推断期间经常忽略潜在分发外(OOD)节点的风险。在实际图形中，自动化网络构造可以引入来自未知分布的噪声节点。以往对面向对象的节点检测的研究，通常被称为基于熵的方法，在分类训练的同时，根据预测熵计算面向对象的测量值。然而，图中的节点可能没有预先标记特定的类别，使得基于熵的 OOD 检测器不适用于这种无类别的情况。为了解决这个问题，我们提出了一个以能量为中心的 OOD 节点检测密度估计框架，称为 EnergyDef。在这个框架内，我们引入了一个基于能量的 GNN 来计算节点能量，它作为节点密度的指标，揭示了节点面向对象的不确定性。重要的是，EnergyDef 能够有效地识别具有低资源 OOD 节点注释的 OOD 节点，通过 Langevin Dynamics 和结构估计对幻觉节点进行采样，并通过对比发散进行训练。我们在真实世界数据集上的全面实验证实，我们的框架在检测质量方面明显超过了最先进的方法，即使在缺乏或完全没有 OOD 节点注释的情况下也是如此。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=An+Energy-centric+Framework+for+Category-free+Out-of-distribution+Node+Detection+in+Graphs)|0| -|[Investigating Out-of-Distribution Generalization of GNNs: An Architecture Perspective](https://doi.org/10.1145/3637528.3671792)|Kai Guo, Hongzhi Wen, Wei Jin, Yaming Guo, Jiliang Tang, Yi Chang|Department of Computer Science, Emory University, Atlanta, GA, USA; School of Artificial Intelligence, Jilin University, Changchun, Jilan, China; Department of Computer Science and Engineering, Michigan State University, East Lansing, MI, USA|Graph neural networks (GNNs) have exhibited remarkable performance under the assumption that test data comes from the same distribution of training data. However, in real-world scenarios, this assumption may not always be valid. Consequently, there is a growing focus on exploring the Out-of-Distribution (OOD) problem in the context of graphs. Most existing efforts have primarily concentrated on improving graph OOD generalization from two model-agnostic perspectives: data-driven methods and strategy-based learning. However, there has been limited attention dedicated to investigating the impact of well-known GNN model architectures on graph OOD generalization, which is orthogonal to existing research. In this work, we provide the first comprehensive investigation of OOD generalization on graphs from an architecture perspective, by examining the common building blocks of modern GNNs. Through extensive experiments, we reveal that both the graph self-attention mechanism and the decoupled architecture contribute positively to graph OOD generalization. In contrast, we observe that the linear classification layer tends to compromise graph OOD generalization capability. Furthermore, we provide in-depth theoretical insights and discussions to underpin these discoveries. These insights have empowered us to develop a novel GNN backbone model, DGat, designed to harness the robust properties of both graph self-attention mechanism and the decoupled architecture. Extensive experimental results demonstrate the effectiveness of our model under graph OOD, exhibiting substantial and consistent enhancements across various training strategies. Our codes are available at https://github.com/KaiGuo20/DGAT **REMOVE 2nd URL**://github.com/KaiGuo20/DGAT.|在假设测试数据来自同一分布的训练数据的前提下，图神经网络(GNN)表现出了显著的性能。然而，在现实世界的场景中，这个假设并不总是有效的。因此，在图的背景下探索分布外(OOD)问题越来越受到人们的关注。大多数现有的努力主要集中在从两个模型无关的角度改进图形 OOD 泛化: 数据驱动的方法和基于策略的学习。然而，目前研究 GNN 模型体系结构对图形 OOD 泛化的影响与现有研究是正交的，这方面的研究受到了一定的限制。在这项工作中，我们提供了第一个全面的研究面向对象设计概括图从体系结构的角度，通过检查现代 GNN 的共同构建模块。通过大量的实验，我们发现图的自注意机制和解耦结构都对图的面向对象的泛化起到了积极的作用。相比之下，我们观察到线性分类层往往会影响图的 OOD 泛化能力。此外，我们提供深入的理论见解和讨论，以支持这些发现。这些见解使我们有能力开发一个新的 GNN 骨干网模型，DGat，旨在利用图形自注意机制和解耦体系结构的鲁棒性。广泛的实验结果证明了我们的模型在面向对象的图形下的有效性，显示了实质性的和一致的增强跨各种训练策略。我们的代码可在 https://github.com/kaiguo20/dgat * * 删除第二个网址 * * :// github.com/kaiguo20/dgat。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Investigating+Out-of-Distribution+Generalization+of+GNNs:+An+Architecture+Perspective)|0| +|[Investigating Out-of-Distribution Generalization of GNNs: An Architecture Perspective](https://doi.org/10.1145/3637528.3671792)|Kai Guo, Hongzhi Wen, Wei Jin, Yaming Guo, Jiliang Tang, Yi Chang|School of Artificial Intelligence, Jilin University, Changchun, Jilan, China; Department of Computer Science and Engineering, Michigan State University, East Lansing, MI, USA; Department of Computer Science, Emory University, Atlanta, GA, USA|Graph neural networks (GNNs) have exhibited remarkable performance under the assumption that test data comes from the same distribution of training data. However, in real-world scenarios, this assumption may not always be valid. Consequently, there is a growing focus on exploring the Out-of-Distribution (OOD) problem in the context of graphs. Most existing efforts have primarily concentrated on improving graph OOD generalization from two model-agnostic perspectives: data-driven methods and strategy-based learning. However, there has been limited attention dedicated to investigating the impact of well-known GNN model architectures on graph OOD generalization, which is orthogonal to existing research. In this work, we provide the first comprehensive investigation of OOD generalization on graphs from an architecture perspective, by examining the common building blocks of modern GNNs. Through extensive experiments, we reveal that both the graph self-attention mechanism and the decoupled architecture contribute positively to graph OOD generalization. In contrast, we observe that the linear classification layer tends to compromise graph OOD generalization capability. Furthermore, we provide in-depth theoretical insights and discussions to underpin these discoveries. These insights have empowered us to develop a novel GNN backbone model, DGat, designed to harness the robust properties of both graph self-attention mechanism and the decoupled architecture. Extensive experimental results demonstrate the effectiveness of our model under graph OOD, exhibiting substantial and consistent enhancements across various training strategies. Our codes are available at https://github.com/KaiGuo20/DGAT **REMOVE 2nd URL**://github.com/KaiGuo20/DGAT.|在假设测试数据来自同一分布的训练数据的前提下，图神经网络(GNN)表现出了显著的性能。然而，在现实世界的场景中，这个假设并不总是有效的。因此，在图的背景下探索分布外(OOD)问题越来越受到人们的关注。大多数现有的努力主要集中在从两个模型无关的角度改进图形 OOD 泛化: 数据驱动的方法和基于策略的学习。然而，目前研究 GNN 模型体系结构对图形 OOD 泛化的影响与现有研究是正交的，这方面的研究受到了一定的限制。在这项工作中，我们提供了第一个全面的研究面向对象设计概括图从体系结构的角度，通过检查现代 GNN 的共同构建模块。通过大量的实验，我们发现图的自注意机制和解耦结构都对图的面向对象的泛化起到了积极的作用。相比之下，我们观察到线性分类层往往会影响图的 OOD 泛化能力。此外，我们提供深入的理论见解和讨论，以支持这些发现。这些见解使我们有能力开发一个新的 GNN 骨干网模型，DGat，旨在利用图形自注意机制和解耦体系结构的鲁棒性。广泛的实验结果证明了我们的模型在面向对象的图形下的有效性，显示了实质性的和一致的增强跨各种训练策略。我们的代码可在 https://github.com/kaiguo20/dgat * * 删除第二个网址 * * :// github.com/kaiguo20/dgat。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Investigating+Out-of-Distribution+Generalization+of+GNNs:+An+Architecture+Perspective)|0| |[HiFGL: A Hierarchical Framework for Cross-silo Cross-device Federated Graph Learning](https://doi.org/10.1145/3637528.3671660)|Zhuoning Guo, Duanyi Yao, Qiang Yang, Hao Liu|; The Hong Kong University of Science and Technology, Hong Kong, Hong Kong|Federated Graph Learning (FGL) has emerged as a promising way to learnhigh-quality representations from distributed graph data with privacypreservation. Despite considerable efforts have been made for FGL under eithercross-device or cross-silo paradigm, how to effectively capture graph knowledgein a more complicated cross-silo cross-device environment remains anunder-explored problem. However, this task is challenging because of theinherent hierarchy and heterogeneity of decentralized clients, diversifiedprivacy constraints in different clients, and the cross-client graph integrityrequirement. To this end, in this paper, we propose a Hierarchical FederatedGraph Learning (HiFGL) framework for cross-silo cross-device FGL. Specifically,we devise a unified hierarchical architecture to safeguard federated GNNtraining on heterogeneous clients while ensuring graph integrity. Moreover, wepropose a Secret Message Passing (SecMP) scheme to shield unauthorized accessto subgraph-level and node-level sensitive information simultaneously.Theoretical analysis proves that HiFGL achieves multi-level privacypreservation with complexity guarantees. Extensive experiments on real-worlddatasets validate the superiority of the proposed framework against severalbaselines. Furthermore, HiFGL's versatile nature allows for its application ineither solely cross-silo or cross-device settings, further broadening itsutility in real-world FGL applications.|联邦图学习(FGL)已经成为一种有前途的从分布式图形数据中学习高质量表示并保护隐私的方法。尽管在跨设备或跨竖井范式下已经为 FGL 做出了相当大的努力，但如何在更复杂的跨竖井跨设备环境中有效地获取图形知识仍然是一个尚未探索的问题。然而，由于分散客户端的内在层次性和异构性、不同客户端的多样化隐私约束以及跨客户端图形完整性要求，这项任务具有挑战性。为此，本文提出了一种基于层次联邦图学习(HiFGL)的跨筒仓跨设备 FGL 框架。具体来说，我们设计了一个统一的层次结构来保护异构客户端上的联邦 GNN 训练，同时保证图的完整性。此外，我们提出了一个秘密消息传递(SecMP)方案来同时屏蔽未经授权访问的子图级和节点级敏感信息。理论分析表明，HiFGL 在保证复杂度的前提下实现了多级隐私保护。在真实世界数据集上的大量实验验证了该框架对多个基线的优越性。此外，HiFGL 的多功能性质允许其应用在单独的跨筒仓或跨设备设置，进一步扩大其在现实世界的 FGL 应用的适用性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=HiFGL:+A+Hierarchical+Framework+for+Cross-silo+Cross-device+Federated+Graph+Learning)|0| |[AnyLoss: Transforming Classification Metrics into Loss Functions](https://doi.org/10.1145/3637528.3672017)|Do Heon Han, Nuno Moniz, Nitesh V. Chawla|; Lucy Family Institute for Data & Society, University of Notre Dame, Notre Dame, IN, USA|Many evaluation metrics can be used to assess the performance of models inbinary classification tasks. However, most of them are derived from a confusionmatrix in a non-differentiable form, making it very difficult to generate adifferentiable loss function that could directly optimize them. The lack ofsolutions to bridge this challenge not only hinders our ability to solvedifficult tasks, such as imbalanced learning, but also requires the deploymentof computationally expensive hyperparameter search processes in modelselection. In this paper, we propose a general-purpose approach that transformsany confusion matrix-based metric into a loss function, AnyLoss, thatis available in optimization processes. To this end, we use an approximationfunction to make a confusion matrix represented in a differentiable form, andthis approach enables any confusion matrix-based metric to be directly used asa loss function. The mechanism of the approximation function is provided toensure its operability and the differentiability of our loss functions isproved by suggesting their derivatives. We conduct extensive experiments underdiverse neural networks with many datasets, and we demonstrate their generalavailability to target any confusion matrix-based metrics. Our method,especially, shows outstanding achievements in dealing with imbalanced datasets,and its competitive learning speed, compared to multiple baseline models,underscores its efficiency.|许多评价指标可以用来评价模型在二进制分类任务中的性能。然而，它们中的大多数都是由不可微形式的混淆矩阵导出的，因此很难产生可微损失函数来直接优化它们。缺乏解决这一挑战的方案不仅阻碍了我们解决困难任务的能力，例如不平衡的学习，而且还需要在模型选择中部署计算昂贵的超参数搜索过程。在本文中，我们提出了一个通用的方法，转换任何混淆矩阵为基础的度量损失函数，AnyLoss，这是可用于优化过程。为了达到这个目的，我们使用一个近似函数来表示一个可微形式的混淆矩阵，这种方法使任何基于混淆矩阵的度量可以直接用作损失函数。给出了近似函数的机理，通过建立近似函数的导数，保证了近似函数的可操作性和损失函数的可微性。我们进行了广泛的实验在多样化的神经网络与许多数据集，并证明了它们的一般可用性的目标任何混淆矩阵为基础的度量。该方法在处理不平衡数据集方面取得了显著的成绩，与多个基线模型相比，该方法的学习速度具有竞争力，突出了该方法的效率。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=AnyLoss:+Transforming+Classification+Metrics+into+Loss+Functions)|0| -|[Expander Hierarchies for Normalized Cuts on Graphs](https://doi.org/10.1145/3637528.3671978)|Kathrin Hanauer, Monika Henzinger, Robin Münk, Harald Räcke, Maximilian Vötsch|; Institute of Science and Technology Austria (ISTA), Klosterneuburg, Austria; Faculty of Computer Science, University of Vienna, Vienna, Austria; Technical University of Munich, Munich, Germany|Expander decompositions of graphs have significantly advanced theunderstanding of many classical graph problems and led to numerous fundamentaltheoretical results. However, their adoption in practice has been hindered dueto their inherent intricacies and large hidden factors in their asymptoticrunning times. Here, we introduce the first practically efficient algorithm forcomputing expander decompositions and their hierarchies and demonstrate itseffectiveness and utility by incorporating it as the core component in a novelsolver for the normalized cut graph clustering objective. Our extensive experiments on a variety of large graphs show that ourexpander-based algorithm outperforms state-of-the-art solvers for normalizedcut with respect to solution quality by a large margin on a variety of graphclasses such as citation, e-mail, and social networks or web graphs whileremaining competitive in running time.|图的扩展分解极大地提高了对许多经典图问题的理解，并产生了许多基本的理论结果。然而，由于其固有的复杂性和大的隐藏因素在其渐近运行时间，它们在实践中的采用受到了阻碍。在这里，我们介绍了第一个实用有效的扩展器分解算法及其层次结构，并证明了其有效性和实用性，将其作为一个核心组件的规范化割图聚类目标规划求解器。我们在各种大型图表上的广泛实验表明，在各种图表类(如引用、电子邮件、社交网络或网络图表)上，我们基于 expander 的算法在规范化切割方面优于最先进的求解器，同时在运行时间上保持竞争力。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Expander+Hierarchies+for+Normalized+Cuts+on+Graphs)|0| -|[Model-Agnostic Random Weighting for Out-of-Distribution Generalization](https://doi.org/10.1145/3637528.3671762)|Yue He, Pengfei Tian, Renzhe Xu, Xinwei Shen, Xingxuan Zhang, Peng Cui|Tsinghua University, Beijing, China; ETH Zürich, Zürich, Switzerland|Despite the encouraging successes in numerous applications, machine learning methods grounded on the i.i.d. assumption often experience performance deterioration when confronted with the distribution shift between training and test data. This challenge has instigated recent research endeavors focusing on out-of-distribution (OOD) generalization. A particularly pervasive and intricate OOD problem is to enhance the model's generalization ability by training it on samples drawn from a single environment. In response to the problem, we propose a simple model-agnostic method tailored for a practical OOD scenario in this paper. Our approach centers on pursuing robust weighted empirical risks, utilizing randomly shifted training distributions derived through a specific sample-based weighting strategy. Furthermore, we theoretically establish that the expected risk of the shifted training distribution can bound the expected risk of the test distribution. This theoretical foundation ensures the improved prediction performance of our method when employed in uncertain test distributions. Extensive experiments conducted on diverse real-world datasets affirm the effectiveness of our method, highlighting its potential to address the distribution shifts in machine learning applications.|尽管在许多应用中取得了令人鼓舞的成功，但是当面对训练数据和测试数据之间的分布变化时，基于 ID 假设的机器学习方法往往会出现性能下降。这一挑战促使最近的研究致力于分布外(OOD)概括。一个特别普遍和复杂的面向对象设计(OOD)问题是通过对从单一环境中提取的样本进行训练来提高模型的泛化能力。针对这一问题，本文提出了一种适用于实际面向对象设计的简单模型无关方法。我们的方法集中在追求稳健的加权经验风险，利用随机移动的训练分布通过一个特定的样本为基础的加权策略。进一步，我们从理论上证明了偏移训练分布的期望风险可以约束测试分布的期望风险。这一理论基础保证了该方法在测试分布不确定时具有更好的预测性能。在不同的真实世界数据集上进行的大量实验证实了我们的方法的有效性，突出了其解决机器学习应用中的分布变化的潜力。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Model-Agnostic+Random+Weighting+for+Out-of-Distribution+Generalization)|0| -|[RoutePlacer: An End-to-End Routability-Aware Placer with Graph Neural Network](https://doi.org/10.1145/3637528.3671895)|Yunbo Hou, Haoran Ye, Yingxue Zhang, Siyuan Xu, Guojie Song|; School of Software and Microelectronics, Peking University, Beijing, China; Huawei Noah's Ark Lab, Markham, Canada; Huawei Noah's Ark Lab, Shenzhen, China|Placement is a critical and challenging step of modern chip design, with routability being an essential indicator of placement quality. Current routability-oriented placers typically apply an iterative two-stage approach, wherein the first stage generates a placement solution, and the second stage provides non-differentiable routing results to heuristically improve the solution quality. This method hinders jointly optimizing the routability aspect during placement. To address this problem, this work introduces RoutePlacer, an end-to-end routability-aware placement method. It trains RouteGNN, a customized graph neural network, to efficiently and accurately predict routability by capturing and fusing geometric and topological representations of placements. Well-trained RouteGNN then serves as a differentiable approximation of routability, enabling end-to-end gradient-based routability optimization. In addition, RouteGNN can improve two-stage placers as a plug-and-play alternative to external routers. Our experiments on DREAMPlace, an open-source AI4EDA platform, show that RoutePlacer can reduce Total Overflow by up to 16% while maintaining routed wirelength, compared to the state-of-the-art; integrating RouteGNN within two-stage placers leads to a 44% reduction in Total Overflow without compromising wirelength.|布局是现代芯片设计的一个关键和具有挑战性的步骤，可布局性是布局质量的重要指标。当前面向路由性的放置器通常应用迭代的两阶段方法，其中第一阶段生成放置解决方案，第二阶段提供不可微路由结果以启发性地提高解决方案的质量。这种方法阻碍了布局过程中路由性方面的联合优化。为了解决这个问题，本文介绍了 RoutePlacer，一种端到端的可路由性感知布局方法。它训练定制图形神经网络 RouteGNN，通过捕获和融合位置的几何和拓扑表示，有效和准确地预测路由性。训练有素的 RouteGNN 可以作为路由性的可微近似值，从而实现基于端到端梯度的路由性优化。此外，RouteGNN 可以改进两阶段占位器，作为外部路由器的即插即用替代方案。我们在开源 AI4EDA 平台 DREAMPlace 上的实验表明，与最先进的技术相比，RoutePlacer 可以在保持路由线长的同时减少总溢出达16% ; 在两阶段放置器中整合 RouteGNN 可以在不损害线长的情况下减少总溢出44% 。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=RoutePlacer:+An+End-to-End+Routability-Aware+Placer+with+Graph+Neural+Network)|0| -|[Is Aggregation the Only Choice? Federated Learning via Layer-wise Model Recombination](https://doi.org/10.1145/3637528.3671722)|Ming Hu, Zhihao Yue, Xiaofei Xie, Cheng Chen, Yihao Huang, Xian Wei, Xiang Lian, Yang Liu, Mingsong Chen|East China Normal University, Shanghai, China; Singapore Management University, Singapore, Singapore; Kent State University, Kent, OH, USA; Chinese Academy of Sciences, Shanghai, China; Nanyang Technological University, Singapore, Singapore|Although Federated Learning (FL) enables global model training across clients without compromising their raw data, due to the unevenly distributed data among clients, existing Federated Averaging (FedAvg)-based methods suffer from the problem of low inference performance. Specifically, different data distributions among clients lead to various optimization directions of local models. Aggregating local models usually results in a low-generalized global model, which performs worse on most of the clients. To address the above issue, inspired by the observation from a geometric perspective that a well-generalized solution is located in a flat area rather than a sharp area, we propose a novel and heuristic FL paradigm named FedMR (Federated Model Recombination). The goal of FedMR is to guide the recombined models to be trained towards a flat area. Unlike conventional FedAvg-based methods, in FedMR, the cloud server recombines collected local models by shuffling each layer of them to generate multiple recombined models for local training on clients rather than an aggregated global model. Since the area of the flat area is larger than the sharp area, when local models are located in different areas, recombined models have a higher probability of locating in a flat area. When all recombined models are located in the same flat area, they are optimized towards the same direction. We theoretically analyze the convergence of model recombination. Experimental results show that, compared with state-of-the-art FL methods, FedMR can significantly improve the inference accuracy without exposing the privacy of each client.|虽然联邦学习(FL)能够在不损害客户原始数据的情况下进行跨客户的全局模型训练，但由于客户之间数据分布不均匀，现有的基于联邦平均(FedAvg)的方法存在推理性能低的问题。具体来说，客户端之间的不同数据分布导致局部模型的不同优化方向。聚合局部模型通常会导致低通用性的全局模型，这种模型在大多数客户端上表现更差。为了解决上述问题，从几何角度观察，一个良好的广义解决方案位于一个平坦的区域，而不是一个尖锐的区域，我们提出了一个新颖的启发式 FL 范式称为 FedMR (联邦模型重组)。FedMR 的目标是引导被训练的重组模型朝向一个平坦的区域。与传统的基于 FedAvg 的方法不同，在 FedMR 中，云服务器重新组合收集的本地模型，通过重新组合每一层的模型，生成多个重新组合的模型，用于客户端的本地培训，而不是聚合的全局模型。由于平坦区域面积大于尖锐区域面积，当局部模型位于不同区域时，重组模型位于平坦区域的概率较高。当所有的重组模型位于相同的平面区域，他们朝着相同的方向优化。从理论上分析了模型重组的收敛性。实验结果表明，与现有的 FL 方法相比，FedMR 能够在不暴露每个客户端隐私的情况下显著提高推理精度。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Is+Aggregation+the+Only+Choice?+Federated+Learning+via+Layer-wise+Model+Recombination)|0| +|[Expander Hierarchies for Normalized Cuts on Graphs](https://doi.org/10.1145/3637528.3671978)|Kathrin Hanauer, Monika Henzinger, Robin Münk, Harald Räcke, Maximilian Vötsch|Institute of Science and Technology Austria (ISTA), Klosterneuburg, Austria; ; Technical University of Munich, Munich, Germany; Faculty of Computer Science, University of Vienna, Vienna, Austria|Expander decompositions of graphs have significantly advanced theunderstanding of many classical graph problems and led to numerous fundamentaltheoretical results. However, their adoption in practice has been hindered dueto their inherent intricacies and large hidden factors in their asymptoticrunning times. Here, we introduce the first practically efficient algorithm forcomputing expander decompositions and their hierarchies and demonstrate itseffectiveness and utility by incorporating it as the core component in a novelsolver for the normalized cut graph clustering objective. Our extensive experiments on a variety of large graphs show that ourexpander-based algorithm outperforms state-of-the-art solvers for normalizedcut with respect to solution quality by a large margin on a variety of graphclasses such as citation, e-mail, and social networks or web graphs whileremaining competitive in running time.|图的扩展分解极大地提高了对许多经典图问题的理解，并产生了许多基本的理论结果。然而，由于其固有的复杂性和大的隐藏因素在其渐近运行时间，它们在实践中的采用受到了阻碍。在这里，我们介绍了第一个实用有效的扩展器分解算法及其层次结构，并证明了其有效性和实用性，将其作为一个核心组件的规范化割图聚类目标规划求解器。我们在各种大型图表上的广泛实验表明，在各种图表类(如引用、电子邮件、社交网络或网络图表)上，我们基于 expander 的算法在规范化切割方面优于最先进的求解器，同时在运行时间上保持竞争力。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Expander+Hierarchies+for+Normalized+Cuts+on+Graphs)|0| +|[Model-Agnostic Random Weighting for Out-of-Distribution Generalization](https://doi.org/10.1145/3637528.3671762)|Yue He, Pengfei Tian, Renzhe Xu, Xinwei Shen, Xingxuan Zhang, Peng Cui|ETH Zürich, Zürich, Switzerland; Tsinghua University, Beijing, China|Despite the encouraging successes in numerous applications, machine learning methods grounded on the i.i.d. assumption often experience performance deterioration when confronted with the distribution shift between training and test data. This challenge has instigated recent research endeavors focusing on out-of-distribution (OOD) generalization. A particularly pervasive and intricate OOD problem is to enhance the model's generalization ability by training it on samples drawn from a single environment. In response to the problem, we propose a simple model-agnostic method tailored for a practical OOD scenario in this paper. Our approach centers on pursuing robust weighted empirical risks, utilizing randomly shifted training distributions derived through a specific sample-based weighting strategy. Furthermore, we theoretically establish that the expected risk of the shifted training distribution can bound the expected risk of the test distribution. This theoretical foundation ensures the improved prediction performance of our method when employed in uncertain test distributions. Extensive experiments conducted on diverse real-world datasets affirm the effectiveness of our method, highlighting its potential to address the distribution shifts in machine learning applications.|尽管在许多应用中取得了令人鼓舞的成功，但是当面对训练数据和测试数据之间的分布变化时，基于 ID 假设的机器学习方法往往会出现性能下降。这一挑战促使最近的研究致力于分布外(OOD)概括。一个特别普遍和复杂的面向对象设计(OOD)问题是通过对从单一环境中提取的样本进行训练来提高模型的泛化能力。针对这一问题，本文提出了一种适用于实际面向对象设计的简单模型无关方法。我们的方法集中在追求稳健的加权经验风险，利用随机移动的训练分布通过一个特定的样本为基础的加权策略。进一步，我们从理论上证明了偏移训练分布的期望风险可以约束测试分布的期望风险。这一理论基础保证了该方法在测试分布不确定时具有更好的预测性能。在不同的真实世界数据集上进行的大量实验证实了我们的方法的有效性，突出了其解决机器学习应用中的分布变化的潜力。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Model-Agnostic+Random+Weighting+for+Out-of-Distribution+Generalization)|0| +|[RoutePlacer: An End-to-End Routability-Aware Placer with Graph Neural Network](https://doi.org/10.1145/3637528.3671895)|Yunbo Hou, Haoran Ye, Yingxue Zhang, Siyuan Xu, Guojie Song|; Huawei Noah's Ark Lab, Shenzhen, China; School of Software and Microelectronics, Peking University, Beijing, China; Huawei Noah's Ark Lab, Markham, Canada|Placement is a critical and challenging step of modern chip design, with routability being an essential indicator of placement quality. Current routability-oriented placers typically apply an iterative two-stage approach, wherein the first stage generates a placement solution, and the second stage provides non-differentiable routing results to heuristically improve the solution quality. This method hinders jointly optimizing the routability aspect during placement. To address this problem, this work introduces RoutePlacer, an end-to-end routability-aware placement method. It trains RouteGNN, a customized graph neural network, to efficiently and accurately predict routability by capturing and fusing geometric and topological representations of placements. Well-trained RouteGNN then serves as a differentiable approximation of routability, enabling end-to-end gradient-based routability optimization. In addition, RouteGNN can improve two-stage placers as a plug-and-play alternative to external routers. Our experiments on DREAMPlace, an open-source AI4EDA platform, show that RoutePlacer can reduce Total Overflow by up to 16% while maintaining routed wirelength, compared to the state-of-the-art; integrating RouteGNN within two-stage placers leads to a 44% reduction in Total Overflow without compromising wirelength.|布局是现代芯片设计的一个关键和具有挑战性的步骤，可布局性是布局质量的重要指标。当前面向路由性的放置器通常应用迭代的两阶段方法，其中第一阶段生成放置解决方案，第二阶段提供不可微路由结果以启发性地提高解决方案的质量。这种方法阻碍了布局过程中路由性方面的联合优化。为了解决这个问题，本文介绍了 RoutePlacer，一种端到端的可路由性感知布局方法。它训练定制图形神经网络 RouteGNN，通过捕获和融合位置的几何和拓扑表示，有效和准确地预测路由性。训练有素的 RouteGNN 可以作为路由性的可微近似值，从而实现基于端到端梯度的路由性优化。此外，RouteGNN 可以改进两阶段占位器，作为外部路由器的即插即用替代方案。我们在开源 AI4EDA 平台 DREAMPlace 上的实验表明，与最先进的技术相比，RoutePlacer 可以在保持路由线长的同时减少总溢出达16% ; 在两阶段放置器中整合 RouteGNN 可以在不损害线长的情况下减少总溢出44% 。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=RoutePlacer:+An+End-to-End+Routability-Aware+Placer+with+Graph+Neural+Network)|0| +|[Is Aggregation the Only Choice? Federated Learning via Layer-wise Model Recombination](https://doi.org/10.1145/3637528.3671722)|Ming Hu, Zhihao Yue, Xiaofei Xie, Cheng Chen, Yihao Huang, Xian Wei, Xiang Lian, Yang Liu, Mingsong Chen|East China Normal University, Shanghai, China; Kent State University, Kent, OH, USA; Singapore Management University, Singapore, Singapore; Nanyang Technological University, Singapore, Singapore; Chinese Academy of Sciences, Shanghai, China|Although Federated Learning (FL) enables global model training across clients without compromising their raw data, due to the unevenly distributed data among clients, existing Federated Averaging (FedAvg)-based methods suffer from the problem of low inference performance. Specifically, different data distributions among clients lead to various optimization directions of local models. Aggregating local models usually results in a low-generalized global model, which performs worse on most of the clients. To address the above issue, inspired by the observation from a geometric perspective that a well-generalized solution is located in a flat area rather than a sharp area, we propose a novel and heuristic FL paradigm named FedMR (Federated Model Recombination). The goal of FedMR is to guide the recombined models to be trained towards a flat area. Unlike conventional FedAvg-based methods, in FedMR, the cloud server recombines collected local models by shuffling each layer of them to generate multiple recombined models for local training on clients rather than an aggregated global model. Since the area of the flat area is larger than the sharp area, when local models are located in different areas, recombined models have a higher probability of locating in a flat area. When all recombined models are located in the same flat area, they are optimized towards the same direction. We theoretically analyze the convergence of model recombination. Experimental results show that, compared with state-of-the-art FL methods, FedMR can significantly improve the inference accuracy without exposing the privacy of each client.|虽然联邦学习(FL)能够在不损害客户原始数据的情况下进行跨客户的全局模型训练，但由于客户之间数据分布不均匀，现有的基于联邦平均(FedAvg)的方法存在推理性能低的问题。具体来说，客户端之间的不同数据分布导致局部模型的不同优化方向。聚合局部模型通常会导致低通用性的全局模型，这种模型在大多数客户端上表现更差。为了解决上述问题，从几何角度观察，一个良好的广义解决方案位于一个平坦的区域，而不是一个尖锐的区域，我们提出了一个新颖的启发式 FL 范式称为 FedMR (联邦模型重组)。FedMR 的目标是引导被训练的重组模型朝向一个平坦的区域。与传统的基于 FedAvg 的方法不同，在 FedMR 中，云服务器重新组合收集的本地模型，通过重新组合每一层的模型，生成多个重新组合的模型，用于客户端的本地培训，而不是聚合的全局模型。由于平坦区域面积大于尖锐区域面积，当局部模型位于不同区域时，重组模型位于平坦区域的概率较高。当所有的重组模型位于相同的平面区域，他们朝着相同的方向优化。从理论上分析了模型重组的收敛性。实验结果表明，与现有的 FL 方法相比，FedMR 能够在不暴露每个客户端隐私的情况下显著提高推理精度。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Is+Aggregation+the+Only+Choice?+Federated+Learning+via+Layer-wise+Model+Recombination)|0| |[Privacy-Preserved Neural Graph Databases](https://doi.org/10.1145/3637528.3671678)|Qi Hu, Haoran Li, Jiaxin Bai, Zihao Wang, Yangqiu Song|Department of CSE, Hong Kong University of Science and Technology, Hong Kong, China|In the era of large language models (LLMs), efficient and accurate data retrieval has become increasingly crucial for the use of domain-specific or private data in the retrieval augmented generation (RAG). Neural graph databases (NGDBs) have emerged as a powerful paradigm that combines the strengths of graph databases (GDBs) and neural networks to enable efficient storage, retrieval, and analysis of graph-structured data which can be adaptively trained with LLMs. The usage of neural embedding storage and Complex neural logical Query Answering (CQA) provides NGDBs with generalization ability. When the graph is incomplete, by extracting latent patterns and representations, neural graph databases can fill gaps in the graph structure, revealing hidden relationships and enabling accurate query answering. Nevertheless, this capability comes with inherent trade-offs, as it introduces additional privacy risks to the domain-specific or private databases. Malicious attackers can infer more sensitive information in the database using well-designed queries such as from the answer sets of where Turing Award winners born before 1950 and after 1940 lived, the living places of Turing Award winner Hinton are probably exposed, although the living places may have been deleted in the training stage due to the privacy concerns. In this work, we propose a privacy-preserved neural graph database (P-NGDB) framework to alleviate the risks of privacy leakage in NGDBs. We introduce adversarial training techniques in the training stage to enforce the NGDBs to generate indistinguishable answers when queried with private information, enhancing the difficulty of inferring sensitive information through combinations of multiple innocuous queries. Extensive experimental results on three datasets show that our framework can effectively protect private information in the graph database while delivering high-quality public answers responses to queries. The code is available at https://github.com/HKUST-KnowComp/PrivateNGDB.|在大语言模型(LLM)时代，高效、准确的数据检索对于特定领域或私有数据在检索增强生成(RAG)中的应用越来越重要。神经图形数据库(NGDB)已经成为一种强大的范式，它结合了图形数据库(GDB)和神经网络的优势，能够有效地存储、检索和分析图形结构数据，这些数据可以用 LLM 自适应地训练。神经嵌入存储和复杂神经逻辑查询回答(CQA)的使用为 NGDB 提供了泛化能力。当图不完整时，神经图数据库通过提取潜在模式和表征，可以填补图结构中的空白，揭示隐藏的关系，实现准确的查询回答。然而，这种能力带来了内在的权衡，因为它给特定领域或私有数据库带来了额外的隐私风险。恶意攻击者可以通过精心设计的查询在数据库中推断出更敏感的信息，例如从图灵奖获得者1950年前和1940年后出生的地方的答案集中，图灵奖获得者 Hinton 的生活场所可能被暴露，尽管生活场所可能在训练阶段由于隐私问题而被删除。在这项工作中，我们提出了一个隐私保护的神经图数据库(P-NGDB)框架，以减少隐私泄露的风险在 NGDB。我们在训练阶段引入对抗性训练技术，强制 NGDB 在用私人信息查询时生成不可区分的答案，通过多个无害查询的组合增加推断敏感信息的难度。对三个数据集的大量实验结果表明，我们的框架能够有效地保护图形数据库中的私有信息，同时提供高质量的公开答复查询。密码可在 https://github.com/hkust-knowcomp/privatengdb 查阅。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Privacy-Preserved+Neural+Graph+Databases)|0| -|[EntropyStop: Unsupervised Deep Outlier Detection with Loss Entropy](https://doi.org/10.1145/3637528.3671943)|Yihong Huang, Yuang Zhang, Liping Wang, Fan Zhang, Xuemin Lin|Guangzhou University, Guangzhou, China; Shanghai Jiao Tong University, Shanghai, China; East China Normal University, Shanghai, China|Unsupervised Outlier Detection (UOD) is an important data mining task. With the advance of deep learning, deep Outlier Detection (OD) has received broad interest. Most deep UOD models are trained exclusively on clean datasets to learn the distribution of the normal data, which requires huge manual efforts to clean the real-world data if possible. Instead of relying on clean datasets, some approaches directly train and detect on unlabeled contaminated datasets, leading to the need for methods that are robust to such challenging conditions. Ensemble methods emerged as a superior solution to enhance model robustness against contaminated training sets. However, the training time is greatly increased by the ensemble mechanism. In this study, we investigate the impact of outliers on training, aiming to halt training on unlabeled contaminated datasets before performance degradation. Initially, we noted that blending normal and anomalous data causes AUC fluctuations-a label-dependent measure of detection accuracy. To circumvent the need for labels, we propose a zero-label entropy metric named Loss Entropy for loss distribution, enabling us to infer optimal stopping points for training without labels. Meanwhile, a negative correlation between entropy metric and the label-based AUC score is demonstrated by theoretical proofs. Based on this, an automated early-stopping algorithm called EntropyStop is designed to halt training when loss entropy suggests the maximum model detection capability. We conduct extensive experiments on ADBench (including 47 real datasets), and the overall results indicate that AutoEncoder (AE) enhanced by our approach not only achieves better performance than ensemble AEs but also requires under 2% of training time. Lastly, loss entropy and EntropyStop are evaluated on other deep OD models, exhibiting their broad potential applicability.|无监督异常检测(UOD)是一项重要的数据挖掘任务。随着深度学习的发展，深度异常检测已受到广泛关注。大多数深度 UOD 模型都是专门针对清洁数据集进行训练，以了解正常数据的分布情况，如果可能的话，这需要大量的人工努力来清洁现实世界中的数据。有些方法不依赖于干净的数据集，而是直接对未标记的污染数据集进行训练和检测，因此需要对这种具有挑战性的条件具有鲁棒性的方法。集成方法作为一种优越的解决方案出现，以增强模型对污染的训练集的鲁棒性。然而，集成机制大大增加了训练时间。在这项研究中，我们调查了异常值对训练的影响，目的是在性能下降之前停止对未标记污染数据集的训练。最初，我们注意到混合正常和异常数据会导致 AUC 波动-一个依赖于标签的检测准确性度量。为了规避标签的需要，我们提出了一个零标签熵度量，称为损失熵的损失分布，使我们能够推断最佳停止点的训练没有标签。同时，从理论上证明了熵度量与基于标签的 AUC 得分之间存在负相关关系。在此基础上，设计了一种自动提前停止算法熵停止训练时，损失熵建议的最大模型检测能力。我们在 ADBench (包括47个实际数据集)上进行了广泛的实验，总体结果表明，该方法增强的 AutoEncoder (AE)不仅比集成 AE 具有更好的性能，而且需要不到2% 的训练时间。最后，在其他深度 OD 模型上对损失熵和熵止进行了评价，展示了其广泛的适用性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=EntropyStop:+Unsupervised+Deep+Outlier+Detection+with+Loss+Entropy)|0| +|[EntropyStop: Unsupervised Deep Outlier Detection with Loss Entropy](https://doi.org/10.1145/3637528.3671943)|Yihong Huang, Yuang Zhang, Liping Wang, Fan Zhang, Xuemin Lin|Guangzhou University, Guangzhou, China; East China Normal University, Shanghai, China; Shanghai Jiao Tong University, Shanghai, China|Unsupervised Outlier Detection (UOD) is an important data mining task. With the advance of deep learning, deep Outlier Detection (OD) has received broad interest. Most deep UOD models are trained exclusively on clean datasets to learn the distribution of the normal data, which requires huge manual efforts to clean the real-world data if possible. Instead of relying on clean datasets, some approaches directly train and detect on unlabeled contaminated datasets, leading to the need for methods that are robust to such challenging conditions. Ensemble methods emerged as a superior solution to enhance model robustness against contaminated training sets. However, the training time is greatly increased by the ensemble mechanism. In this study, we investigate the impact of outliers on training, aiming to halt training on unlabeled contaminated datasets before performance degradation. Initially, we noted that blending normal and anomalous data causes AUC fluctuations-a label-dependent measure of detection accuracy. To circumvent the need for labels, we propose a zero-label entropy metric named Loss Entropy for loss distribution, enabling us to infer optimal stopping points for training without labels. Meanwhile, a negative correlation between entropy metric and the label-based AUC score is demonstrated by theoretical proofs. Based on this, an automated early-stopping algorithm called EntropyStop is designed to halt training when loss entropy suggests the maximum model detection capability. We conduct extensive experiments on ADBench (including 47 real datasets), and the overall results indicate that AutoEncoder (AE) enhanced by our approach not only achieves better performance than ensemble AEs but also requires under 2% of training time. Lastly, loss entropy and EntropyStop are evaluated on other deep OD models, exhibiting their broad potential applicability.|无监督异常检测(UOD)是一项重要的数据挖掘任务。随着深度学习的发展，深度异常检测已受到广泛关注。大多数深度 UOD 模型都是专门针对清洁数据集进行训练，以了解正常数据的分布情况，如果可能的话，这需要大量的人工努力来清洁现实世界中的数据。有些方法不依赖于干净的数据集，而是直接对未标记的污染数据集进行训练和检测，因此需要对这种具有挑战性的条件具有鲁棒性的方法。集成方法作为一种优越的解决方案出现，以增强模型对污染的训练集的鲁棒性。然而，集成机制大大增加了训练时间。在这项研究中，我们调查了异常值对训练的影响，目的是在性能下降之前停止对未标记污染数据集的训练。最初，我们注意到混合正常和异常数据会导致 AUC 波动-一个依赖于标签的检测准确性度量。为了规避标签的需要，我们提出了一个零标签熵度量，称为损失熵的损失分布，使我们能够推断最佳停止点的训练没有标签。同时，从理论上证明了熵度量与基于标签的 AUC 得分之间存在负相关关系。在此基础上，设计了一种自动提前停止算法熵停止训练时，损失熵建议的最大模型检测能力。我们在 ADBench (包括47个实际数据集)上进行了广泛的实验，总体结果表明，该方法增强的 AutoEncoder (AE)不仅比集成 AE 具有更好的性能，而且需要不到2% 的训练时间。最后，在其他深度 OD 模型上对损失熵和熵止进行了评价，展示了其广泛的适用性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=EntropyStop:+Unsupervised+Deep+Outlier+Detection+with+Loss+Entropy)|0| |[RC-Mixup: A Data Augmentation Strategy against Noisy Data for Regression Tasks](https://doi.org/10.1145/3637528.3671993)|Seonghyeon Hwang, Minsu Kim, Steven Euijong Whang|KAIST, Daejeon, Republic of Korea|We study the problem of robust data augmentation for regression tasks in the presence of noisy data. Data augmentation is essential for generalizing deep learning models, but most of the techniques like the popular Mixup are primarily designed for classification tasks on image data. Recently, there are also Mixup techniques that are specialized to regression tasks like C-Mixup. In comparison to Mixup, which takes linear interpolations of pairs of samples, C-Mixup is more selective in which samples to mix based on their label distances for better regression performance. However, C-Mixup does not distinguish noisy versus clean samples, which can be problematic when mixing and lead to suboptimal model performance. At the same time, robust training has been heavily studied where the goal is to train accurate models against noisy data through multiple rounds of model training. We thus propose our data augmentation strategy RC-Mixup, which tightly integrates C-Mixup with multi-round robust training methods for a synergistic effect. In particular, C-Mixup improves robust training in identifying clean data, while robust training provides cleaner data to C-Mixup for it to perform better. A key advantage of RC-Mixup is that it is data-centric where the robust model training algorithm itself does not need to be modified, but can simply benefit from data mixing. We show in our experiments that RC-Mixup significantly outperforms C-Mixup and robust training baselines on noisy data benchmarks and can be integrated with various robust training methods.|我们研究了回归任务在有噪声数据存在的情况下的鲁棒数据增强问题。数据增强对于推广深度学习模型至关重要，但是大多数技术，如流行的 Mixup，主要是为图像数据的分类任务而设计的。最近，还出现了专门用于回归任务(如 C-Mixup)的 Mixup 技术。与对样本进行线性插值的 Mixup 相比，C-Mixup 更有选择性地根据样本的标记距离进行混合，以获得更好的回归性能。然而，C-Mixup 不能区分噪声样本和干净样本，这在混合时可能会产生问题，并导致次优模型性能。与此同时，鲁棒训练也得到了广泛的研究，其目标是通过多轮模型训练对噪声数据进行精确的模型训练。因此，我们提出了我们的数据增强策略 RC-Mixup，它紧密集成了 C-Mixup 和多轮鲁棒训练方法的协同效应。特别是，C-Mixup 改进了识别干净数据的健壮训练，而健壮训练为 C-Mixup 提供了更干净的数据，使其表现得更好。RC-Mixup 的一个关键优点是它是以数据为中心的，不需要修改鲁棒模型训练算法本身，但是可以简单地从数据混合中受益。实验结果表明，RC-Mixup 算法在噪声数据基准上的性能明显优于 C-Mixup 算法和鲁棒训练基线，并且可以与各种鲁棒训练方法相结合。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=RC-Mixup:+A+Data+Augmentation+Strategy+against+Noisy+Data+for+Regression+Tasks)|0| |[Learn Together Stop Apart: An Inclusive Approach to Ensemble Pruning](https://doi.org/10.1145/3637528.3672018)|Bulat Ibragimov, Gleb Gusev|; Sber AI Lab, Moscow, Russian Federation|Gradient Boosting is a leading learning method that builds ensembles and adapts their sizes to particular tasks, consistently delivering top-tier results across various applications. However, determining the optimal number of models in the ensemble remains a critical yet underexplored aspect. Traditional approaches assume a universal ensemble size effective for all data points, which may not always hold true due to data heterogeneity. This paper introduces an adaptive approach to early stopping in Gradient Boosting, addressing data heterogeneity by assigning different stop moments to different data regions at inference time while still training a common ensemble on the entire dataset. We propose two methods: Direct Supervised Partition (DSP) and Indirect Supervised Partition (ISP). The DSP method uses a decision tree to partition the data based on learning curves, while ISP leverages the dataset's geometric and target distribution characteristics. An effective validation protocol is developed to determine the optimal number of early stopping regions or detect when the heterogeneity assumption does not hold. Experiments using state-of-the-art implementations of Gradient Boosting, LightGBM, and CatBoost, on standard benchmarks demonstrate that our methods enhance model precision by up to 2%, underscoring the significance of this research direction. This approach does not increase computational complexity and can be easily integrated into existing learning pipelines.|梯度提升是一种领先的学习方法，它可以构建集合，并根据特定任务调整其大小，从而在不同的应用程序中始终保持顶级结果。然而，确定最佳数量的模型在集合仍然是一个关键的，但未充分开发的方面。传统方法假设对所有数据点都有效的通用集合大小，但由于数据异构性，这种假设可能并不总是成立。本文介绍了一种自适应方法来提早停止在梯度提升，解决数据异质性分配不同的停止时刻到不同的数据区域在推断时间，同时仍然训练一个共同的集合在整个数据集。我们提出了两种方法: 直接监督分区(DSP)和间接监督分区(ISP)。DSP 方法利用决策树根据学习曲线对数据进行划分，ISP 则利用数据集的几何特征和目标分布特征。提出了一种有效的验证协议，以确定最佳数目的早期停止区域或检测时，异质性假设不成立。在标准基准上使用最先进的梯度提升、 LightGBM 和 CatBoost 实现的实验表明，我们的方法提高了模型精度2% ，强调了这一研究方向的重要性。这种方法不会增加计算的复杂性，并且可以很容易地集成到现有的学习管道中。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Learn+Together+Stop+Apart:+An+Inclusive+Approach+to+Ensemble+Pruning)|0| |[Efficient Discovery of Time Series Motifs under both Length Differences and Warping](https://doi.org/10.1145/3637528.3671726)|Makoto Imamura, Takaaki Nakamura|Mitsubishi Electric Corporation, Kamakura, Japan; Tokai University, Minato-ku, Tokyo, Japan|Over the past two decades, time series motif discovery has become a crucial subroutine for many time series data mining tasks; concurrently, it has been established that Dynamic Time Warping (DTW) outperforms other similarity measures like Euclidean Distance in most scenarios. Against this backdrop, a DTW motif discovery algorithm was recently developed; however, it is confined to working with fixed-length subsequences. In this work, we propose a novel approach that allows us to find motifs under both length differences and warping. Our algorithm exploits a promising time series representation called Spikelets and introduces the first lower bound for DTW in the Spikelet space. Extensive empirical studies demonstrate that our method scales effectively across various real-world datasets and efficiently identifies DTW motif pairs of different lengths.|在过去的二十年里，时间序列主题发现已经成为许多时间序列数据挖掘任务的一个重要子程序，同时，在大多数情况下，动态时间规整(dtW)的表现优于其他相似性度量，比如欧几里得度量。在这种背景下，一个 DTW 模式发现算法最近被开发出来，但是它仅限于处理固定长度的子序列。在这项工作中，我们提出了一个新颖的方法，让我们找到图案的长度差异和翘曲。我们的算法利用了一个很有前途的时间序列表示叫小穗，并引入了 DTW 在小穗空间的第一个下界。大量的实证研究表明，我们的方法可以有效地跨越各种真实世界的数据集，并有效地识别不同长度的 DTW 主题对。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Efficient+Discovery+of+Time+Series+Motifs+under+both+Length+Differences+and+Warping)|0| |[Promoting Fairness and Priority in Selecting k-Winners Using IRV](https://doi.org/10.1145/3637528.3671735)|Md Mouinul Islam, Soroush Vahidi, Baruch Schieber, Senjuti Basu Roy|CS, NJIT, Newark, NJ, USA|We investigate the problem of finding winner(s) given a large number of users' (voters') preferences casted as ballots, one from each of the m users, where each ballot is a ranked order of preference of up to ℓ out of n items (candidates). Given a group protected attribute with k different values and a priority that imposes a selection order among these groups, the goal is to satisfy the priority order and select a winner per group that is most representative. It is imperative that at times the original users' preferences may require further manipulation to meet these fairness and priority requirement. We consider manipulation by modifications and formalize the margin finding problem under modification problem. We study the suitability of Instant Run-off Voting (IRV) as a preference aggregation method and demonstrate its advantages over positional methods. We present a suite of technical results on the hardness of the problem, design algorithms with theoretical guarantees and further investigate efficiency opportunities. We present exhaustive experimental evaluations using multiple applications and large-scale datasets to demonstrate the effectiveness of IRV, and efficacy of our designed solutions qualitatively and scalability-wise.|我们研究的问题，找到获胜者给出了大量的用户的(选民的)偏好投下的选票，一个从每个 m 用户，其中每张选票是一个排名的优先顺序多达1个项目(候选人)。给定一个具有 k 不同值的群体保护属性和一个在这些群体之间强加选择顺序的优先级，目标是满足优先级顺序并为每个群体选择最具代表性的赢家。当务之急是，有时原始用户的首选项可能需要进一步的操作，以满足这些公平性和优先级要求。我们考虑修改操作，并将修改问题下的边界寻找问题形式化。研究了即时决选投票(IRV)作为偏好聚合方法的适用性，并论证了其相对于位置方法的优越性。我们提出了一套技术结果的问题的难度，设计算法与理论保证，并进一步研究效率的机会。我们使用多个应用程序和大规模数据集进行详尽的实验评估，以证明 IRV 的有效性，以及我们设计的解决方案在定性和可扩展性方面的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Promoting+Fairness+and+Priority+in+Selecting+k-Winners+Using+IRV)|0| |[FreQuant: A Reinforcement-Learning based Adaptive Portfolio Optimization with Multi-frequency Decomposition](https://doi.org/10.1145/3637528.3671668)|Jihyeong Jeon, Jiwon Park, Chanhee Park, U Kang|Seoul National University, Seoul, Republic of Korea; Seoul National University & DeepTrade Technologies Inc., Seoul, Republic of Korea|How can we leverage inherent frequency features of stock signals for effective portfolio optimization? Portfolio optimization in the domain of finance revolves around strategically allocating assets to maximize returns. Recent advancements highlight the efficacy of deep learning and reinforcement learning (RL) in capturing temporal asset patterns for portfolio optimization. However, previous methodologies focusing on time-domain often fail to detect sudden market shifts and abrupt events because their models are overly tailored to prevalent patterns, resulting in significant losses. In this paper, we propose FreQuant (Adaptive Portfolio Optimization via Multi-Frequency Quantitative Analysis), an effective deep RL framework for portfolio optimization that fully operates in the frequency domain, tackling the limitations of time domain-focused models. By bringing the analysis into the frequency domain with the Discrete Fourier Transform, our framework captures both prominent and subtle market frequencies, enhancing its adaptability and stability in response to market shifts. This approach allows FreQuant to adeptly identify primary asset patterns while also effectively responding to less common and abrupt market events, providing a more accurate and comprehensive asset representation. Empirical validation on diverse real-world trading datasets underscores the remarkable performance of FreQuant, showing its superiority in terms of profitability. Notably, FreQuant achieves up to 2.1x higher Annualized Rate of Return and 2.9x higher Portfolio Value than the best-performing competitors.|我们如何利用股票信号固有的频率特征进行有效的投资组合优化？金融领域的投资组合优化围绕着战略性地配置资产以实现收益最大化。最近的进展突出了深度学习和强化学习(RL)在捕捉投资组合优化的时间资产模式方面的功效。然而，以往侧重于时域的方法往往无法发现突发市场变化和突发事件，因为他们的模型过于适应流行的模式，导致重大损失。本文提出了一种基于频域的自适应投资组合优化框架 FreQuant，该框架能够有效地解决以时域为中心的投资组合优化模型的局限性。透过把分析结果引入频率范畴，我们的架构能捕捉到市场显著和微妙的频率离散傅里叶变换，加强其应变能力和稳定性，以应付市场转变。这种方法使 FreQuant 能够熟练地识别主要的资产模式，同时也能有效地应对不太常见和突然的市场事件，提供更准确和全面的资产表示。对多样化的实际交易数据集的实证验证强调了 FreQuant 的显著性能，显示了其在盈利能力方面的优势。值得注意的是，与业绩最好的竞争对手相比，FreQuant 的年收益率高出2.1倍，投资组合价值高出2.9倍。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=FreQuant:+A+Reinforcement-Learning+based+Adaptive+Portfolio+Optimization+with+Multi-frequency+Decomposition)|0| -|[Addressing Prediction Delays in Time Series Forecasting: A Continuous GRU Approach with Derivative Regularization](https://doi.org/10.1145/3637528.3671969)|Sheo Yon Jhin, Seojin Kim, Noseong Park|Yonsei University, Seoul, Seodaemun-gu, Republic of Korea; KAIST, Daejeon, Republic of Korea|Time series forecasting has been an essential field in many different application areas, including economic analysis, meteorology, and so forth. The majority of time series forecasting models are trained using the mean squared error (MSE). However, this training based on MSE causes a limitation known as prediction delay. The prediction delay, which implies the ground-truth precedes the prediction, can cause serious problems in a variety of fields, e.g., finance and weather forecasting --- as a matter of fact, predictions succeeding ground-truth observations are not practically meaningful although their MSEs can be low. This paper proposes a new perspective on traditional time series forecasting tasks and introduces a new solution to mitigate the prediction delay. We introduce a continuous-time gated recurrent unit (GRU) based on the neural ordinary differential equation (NODE) which can supervise explicit time-derivatives. We generalize the GRU architecture in a continuous-time manner and minimize the prediction delay through our time-derivative regularization. Our method outperforms in metrics such as MSE, Dynamic Time Warping (DTW) and Time Distortion Index (TDI). In addition, we demonstrate the low prediction delay of our method in a variety of datasets.|时间序列预测已成为经济分析、气象学等许多不同应用领域的重要研究内容。大多数时间序列预测模型都是使用均方差(MSE)进行训练的。然而，这种基于最小均方误差(MSE)的训练导致了一种称为预测延迟的限制。预测延迟意味着地面事实先于预测，可能会在各个领域引起严重问题，例如金融和天气预报——事实上，接着地面事实观测的预测实际上没有意义，尽管它们的微观经济指标可能较低。本文对传统的时间序列预测任务提出了一个新的视角，并介绍了一种减小预测延迟的新方法。我们引入了一个基于神经元常微分方程的连续时间门控递归单元(GRU) ，它可以监控显式的时间导数。我们以连续时间方式推广 GRU 结构，并通过时间导数正则化使预测延迟最小化。我们的方法在 MSE、动态时间规整(DTW)和时间扭曲指数(tDI)等度量指标上表现优异。此外，我们在各种数据集中展示了我们的方法的低预测延迟。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Addressing+Prediction+Delays+in+Time+Series+Forecasting:+A+Continuous+GRU+Approach+with+Derivative+Regularization)|0| +|[Addressing Prediction Delays in Time Series Forecasting: A Continuous GRU Approach with Derivative Regularization](https://doi.org/10.1145/3637528.3671969)|Sheo Yon Jhin, Seojin Kim, Noseong Park|KAIST, Daejeon, Republic of Korea; Yonsei University, Seoul, Seodaemun-gu, Republic of Korea|Time series forecasting has been an essential field in many different application areas, including economic analysis, meteorology, and so forth. The majority of time series forecasting models are trained using the mean squared error (MSE). However, this training based on MSE causes a limitation known as prediction delay. The prediction delay, which implies the ground-truth precedes the prediction, can cause serious problems in a variety of fields, e.g., finance and weather forecasting --- as a matter of fact, predictions succeeding ground-truth observations are not practically meaningful although their MSEs can be low. This paper proposes a new perspective on traditional time series forecasting tasks and introduces a new solution to mitigate the prediction delay. We introduce a continuous-time gated recurrent unit (GRU) based on the neural ordinary differential equation (NODE) which can supervise explicit time-derivatives. We generalize the GRU architecture in a continuous-time manner and minimize the prediction delay through our time-derivative regularization. Our method outperforms in metrics such as MSE, Dynamic Time Warping (DTW) and Time Distortion Index (TDI). In addition, we demonstrate the low prediction delay of our method in a variety of datasets.|时间序列预测已成为经济分析、气象学等许多不同应用领域的重要研究内容。大多数时间序列预测模型都是使用均方差(MSE)进行训练的。然而，这种基于最小均方误差(MSE)的训练导致了一种称为预测延迟的限制。预测延迟意味着地面事实先于预测，可能会在各个领域引起严重问题，例如金融和天气预报——事实上，接着地面事实观测的预测实际上没有意义，尽管它们的微观经济指标可能较低。本文对传统的时间序列预测任务提出了一个新的视角，并介绍了一种减小预测延迟的新方法。我们引入了一个基于神经元常微分方程的连续时间门控递归单元(GRU) ，它可以监控显式的时间导数。我们以连续时间方式推广 GRU 结构，并通过时间导数正则化使预测延迟最小化。我们的方法在 MSE、动态时间规整(DTW)和时间扭曲指数(tDI)等度量指标上表现优异。此外，我们在各种数据集中展示了我们的方法的低预测延迟。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Addressing+Prediction+Delays+in+Time+Series+Forecasting:+A+Continuous+GRU+Approach+with+Derivative+Regularization)|0| |[MemMap: An Adaptive and Latent Memory Structure for Dynamic Graph Learning](https://doi.org/10.1145/3637528.3672060)|Shuo Ji, Mingzhe Liu, Leilei Sun, Chuanren Liu, Tongyu Zhu|; The University of Tennessee, Knoxville, TN, USA; CCSE Lab, Beihang University, Beijing, China|Dynamic graph learning has attracted much attention in recent years due to the fact that most of the real-world graphs are dynamic and evolutionary. As a result, many dynamic learning methods have been proposed to cope with the changes of node states over time. Among these studies, a critical issue is how to update the representations of nodes when new temporal events are observed. In this paper, we provide a novel memory structure - Memory Map (MemMap) for this problem. MemMap is an adaptive and evolutionary latent memory space, where each cell corresponds to an evolving "topic" of the dynamic graph. Moreover, the representation of a node is generated from its semantically correlated memory cells, rather than linked neighbors of the node. We have conducted experiments on real-world datasets and compared our method with the SOTA ones. It can be concluded that: 1) By constructing an adaptive and evolving memory structure during the dynamic learning process, our method can capture the dynamic graph changes, and the learned MemMap is actually a compact evolving structure organized according to the latent "topics" of the graph nodes. 2) Our research suggests that it is a more effective and efficient way to generate node representations from a latent semantic space (like MemMap in our method) than from directly connected neighbors (like most of the previous graph learning methods). The reason is that the number of memory cells in latent space could be much smaller than the number of nodes in a real-world graph, and the representation learning process could well balance the global and local message passing by leveraging the semantic similarity of graph nodes via the correlated memory cells.|由于现实世界中的大多数图都是动态演化的，因此动态图学习近年来引起了人们的广泛关注。因此，人们提出了许多动态学习方法来应对节点状态随时间的变化。在这些研究中，一个关键问题是如何在观察到新的时间事件时更新节点的表示。本文针对这一问题提出了一种新的内存结构——内存映射(MemMap)。MemMap 是一个自适应和进化的潜在记忆空间，其中每个单元对应于动态图的一个进化的“主题”。此外，节点的表示是由其语义相关的存储单元生成的，而不是节点的链接邻居。在实际数据集上进行了实验，并与 SOTA 方法进行了比较。研究结果表明: 1)在动态学习过程中，通过构造一个自适应的演化记忆结构，我们的方法可以捕捉到动态图形的变化，而所学习的 MemMap 实际上是根据图形节点的潜在“主题”组织起来的一个紧凑的演化结构。2)我们的研究表明，从潜在的语义空间(如 MemMap 在我们的方法)生成节点表示比从直接相连的邻居(如大多数以前的图学习方法)更有效和有效。其原因是潜在空间中的记忆单元数量可能远小于现实世界图中的节点数量，表示学习过程通过相关记忆单元利用图中节点的语义相似性，可以很好地平衡全局和局部信息的传递。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=MemMap:+An+Adaptive+and+Latent+Memory+Structure+for+Dynamic+Graph+Learning)|0| |[Tensorized Unaligned Multi-view Clustering with Multi-scale Representation Learning](https://doi.org/10.1145/3637528.3671689)|Jintian Ji, Songhe Feng, Yidong Li||The Unaligned Multi-view Clustering (UMC) problem is currently receiving widespread attention, focusing on clustering unaligned multi-view data generated in real-world applications. Although some algorithms have emerged to address this issue, there still exist the following drawbacks: 1) The fully unknown correspondence of samples across views can significantly limit the exploration of consistent clustering structure. 2) The fixed representation space makes it difficult to mine the comprehensive information in the original data. 3) Unbiased tensor rank approximation is desired to capture the high-order correlation among different views. To address these issues, we proposed a novel UMC framework termed Tensorized Unaligned Multi-view Clustering with Multi-scale Representation Learning (TUMCR). Specifically, TUMCR designs a multi-scale representation learning and alignment framework, which constructs multi-scale representation spaces to comprehensively explore the unknown correspondence across views. Then, a tensorial multi-scale fusion module is proposed to fuse multi-scale representations and explore the high-order correlation hidden in different views, which utilizes the Enhanced Tensor Rank (ETR) to learn the low-rank structure. Furthermore, TUMCR is solved by an efficient algorithm with good convergence. Extensive experiments on different types of datasets demonstrate the effectiveness and superiority of our TUMCR compared with state-of-the-art methods. Our code is publicly available at: https://github.com/jijintian/TUMCR.|不对齐多视图聚类(UMC)问题目前受到广泛关注，主要集中在对现实应用程序中生成的不对齐多视图数据进行聚类。尽管已经出现了一些算法来解决这个问题，但是仍然存在以下缺陷: 1)视图间样本的完全未知对应性会严重限制对一致性聚类结构的探索。2)固定的表示空间使得对原始数据中的综合信息进行挖掘变得困难。3)无偏张量秩近似用于捕捉不同视图之间的高阶相关性。为了解决这些问题，我们提出了一个新的 UMC 框架，称为张量不对齐多视图聚类与多尺度表示学习(TUMCR)。具体来说，TUMCR 设计了一个多尺度表示学习和对齐框架，该框架构造了多尺度表示空间来全面探索视图之间的未知对应关系。然后，提出一种张量多尺度融合模块，利用增强张量秩(ETR)学习低阶结构，融合多尺度表示，探索隐藏在不同视图中的高阶相关性。此外，本文还提出了一种收敛性好的 TUMCR 算法。在不同类型的数据集上进行的大量实验表明，与最先进的方法相比，我们的 TUMCR 方法具有有效性和优越性。我们的代码可以在以下 https://github.com/jijintian/tumcr 公开获得。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Tensorized+Unaligned+Multi-view+Clustering+with+Multi-scale+Representation+Learning)|0| |[Killing Two Birds with One Stone: Cross-modal Reinforced Prompting for Graph and Language Tasks](https://doi.org/10.1145/3637528.3671742)|Wenyuan Jiang, Wenwei Wu, Le Zhang, Zixuan Yuan, Jian Xiang, Jingbo Zhou, Hui Xiong|; Baidu Research, Baidu Inc., Beijing, China|In recent years, Graph Neural Networks (GNNs) and Large Language Models (LLMs) have exhibited remarkable capability in addressing different graph learning and natural language tasks, respectively. Motivated by this, integrating LLMs with GNNs has been increasingly studied to acquire transferable knowledge across modalities, which leads to improved empirical performance in language and graph domains. However, existing studies mainly focused on a single-domain scenario by designing complicated integration techniques to manage multimodal data effectively. Therefore, a concise and generic learning framework for multi-domain tasks, i.e., graph and language domains, is highly desired yet remains under-exploited due to two major challenges. First, the language corpus of downstream tasks differs significantly from graph data, making it hard to bridge the knowledge gap between modalities. Second, not all knowledge demonstrates immediate benefits for downstream tasks, potentially introducing disruptive noise to context-sensitive models like LLMs. To tackle these challenges, we propose a novel plug-and-play framework for incorporating a lightweight cross-domain prompting method into both language and graph learning tasks. Specifically, we first convert the textual input into a domain-scalable prompt, which not only preserves the semantic and logical contents of the textual input, but also highlights related graph information as external knowledge for different domains. Then, we develop a reinforcement learning-based method to learn the optimal edge selection strategy for useful knowledge extraction, which profoundly sharpens the multi-domain model capabilities. In addition, we introduce a joint multi-view optimization module to regularize agent-level collaborative learning across two domains. Finally, extensive empirical justifications over 23 public and synthetic datasets demonstrate that our approach can be applied to diverse multi-domain tasks more accurately, robustly, and reasonably, and improve the performances of the state-of-the-art graph and language models in different learning paradigms.|近年来，图形神经网络(GNN)和大语言模型(LLM)分别在处理不同的图形学习和自然语言任务方面表现出了显著的能力。基于此，人们越来越多地研究将 LLM 与 GNN 相结合，以获得跨模式的可转移知识，从而提高语言和图形领域的经验性能。然而，现有的研究主要集中在单一领域的情况下，通过设计复杂的集成技术来有效地管理多模态数据。因此，一个简洁和通用的多领域任务学习框架，即图形和语言领域，是非常理想的，但仍然没有得到充分利用，由于两个主要的挑战。首先，下游任务的语言语料与图形数据存在显著差异，使得模式之间的知识差距难以弥合。其次，并非所有的知识都能为下游任务带来立竿见影的好处，这可能会给 LLM 等上下文敏感的模型带来干扰噪声。为了应对这些挑战，我们提出了一种新的即插即用框架，将一种轻量级的跨领域提示方法融入到语言和图形学习任务中。具体来说，我们首先将文本输入转换成一个域可伸缩的提示，它不仅保留了文本输入的语义和逻辑内容，而且突出了相关的图形信息作为不同领域的外部知识。然后，我们提出了一种基于强化学习的方法来学习有用知识提取的最优边缘选择策略，从而深刻地提高了多领域模型的能力。此外，我们还引入了一个联合的多视图优化模块来规范代理级别的合作学习。最后，通过对23个公共和合成数据集的大量实证分析表明，我们的方法可以更加准确、稳健和合理地应用于不同的多领域任务，并在不同的学习范式下提高最先进的图形和语言模型的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Killing+Two+Birds+with+One+Stone:+Cross-modal+Reinforced+Prompting+for+Graph+and+Language+Tasks)|0| |[Sketch-Based Replay Projection for Continual Learning](https://doi.org/10.1145/3637528.3671714)|Jack Julian, Yun Sing Koh, Albert Bifet|School of Computer Science, University of Auckland, Auckland, New Zealand; AI Institute, The University of Waikato & LTCI, Télécom Paris, IP Paris, Hamilton, New Zealand|Continual learning closely emulates human learning, which allows a model to learn from a stream of tasks sequentially without forgetting previously learned knowledge. Replay-based continual learning methods mitigate forgetting and improve performance by reintroducing data belonging to old tasks, however a replay method's performance may deteriorate when the reintroduced data does not effectively represent all experienced data. To address this concern, we propose the Sketch-based Replay Projection (SRP) method to capture and retain the original data stream's distribution within stored memory. SRP augments existing replay frameworks and introduces a two-fold approach. First, we develop a sketch-based sample selection technique to approximate feature distributions within distinct tasks, thereby capturing a wide distribution of examples for subsequent replay. Second, we propose a data compression method which projects examples into a reduced-dimensional space while preserving inter-example relationships and emphasizing inter-class disparities, encouraging diverse representations of each class while maintaining memory requirements similar to existing replay methodologies. Our experimental results demonstrate that SRP enhances replay diversity and improves the performance of existing replay models.|持续学习非常接近人类学习，它允许模型从一系列任务中顺序学习，而不会忘记先前学过的知识。基于重放的连续学习方法通过重新引入属于旧任务的数据来缓解遗忘和提高性能，但是当重新引入的数据不能有效地表示所有经验数据时，重放方法的性能可能会恶化。为了解决这个问题，我们提出了基于草图的重放投影(SRP)方法来捕获和保留原始数据流在存储器中的分布。SRP 扩展了现有的重播框架，并引入了双重方法。首先，我们开发了一个基于草图的样本选择技术，以近似特征分布在不同的任务，从而捕获广泛分布的例子，以便随后的重播。其次，我们提出了一种数据压缩方法，它将例子投射到一个简化的维度空间中，同时保持例子间的关系，强调类间的差异，鼓励每个类的不同表示，同时保持与现有重播方法类似的记忆需求。实验结果表明，SRP 增强了重放分集，提高了现有重放模型的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Sketch-Based+Replay+Projection+for+Continual+Learning)|0| -|[RCTD: Reputation-Constrained Truth Discovery in Sybil Attack Crowdsourcing Environment](https://doi.org/10.1145/3637528.3671803)|Xing Jin, Zhihai Gong, Jiuchuan Jiang, Chao Wang, Jian Zhang, Zhen Wang|; Research Center for Data Hub and Security, Zhejiang Lab, Hangzhou, Zhejiang, China; School of Cyberspace, Hangzhou Dianzi University, Hangzhou, Zhejiang, China|Sybil attacks are a prevalent concern within the realm of crowdsourcing, underscoring the significance of quality control in this domain. Truth discovery has been extensively studied to deduce the most trustworthy information from conflicting data based on the principle that reliable workers yield reliable answers. However, existing truth discovery approaches overlook the metric of workers' reputations, e.g., workers' historical approval rates on crowdsourcing platforms, despite being inflated and noisy, they offer a rough indication of workers' ability. In this paper, we first refine the approval rate using Wilson Lower Bound to enhance its confidence, and then mitigate its noise and inflation through a method based on ranking similarity. Specifically, we propose a method called RCTD (Reputation-Constrained Truth Discovery), which introduces a similarity metric between the rankings of workers' weights and the refined approval rates. This metric serves as a penalizing factor in the objective function of the truth discovery, restricting workers' weights to avoid excessively deviating from their historical reputation during the weight estimation process. We solve the objective function by introducing the block coordinate descent coupled with heuristics approach method. Experimental results on real-world datasets demonstrate that our approach achieves more accurate inference of true results in the Sybil attack environment compared to the state-of-the-art methods.|Sybil 攻击是众包领域的一个普遍关注的问题，强调了质量控制在这个领域的重要性。真相发现已被广泛研究，以从相互矛盾的数据中推断出最可信的信息，其基本原理是可靠的工作人员提供可靠的答案。然而，现有的真相发现方法忽视了员工声誉的度量，例如，员工在众包平台上的历史认可率，尽管夸大和喧闹，他们提供了员工能力的粗略指标。本文首先利用威尔逊下界细化批准率，以提高其置信度，然后通过一种基于排序相似度的方法来降低其噪声和通货膨胀。具体来说，我们提出了一种名为声誉约束真相发现(RCTD)的方法，该方法引入了工人权重排名和精确批准率之间的相似度量。这个度量标准在真相发现的客观功能中起着惩罚性因素的作用，它限制了工人的权重，以避免在权重估计过程中过度偏离他们的历史声誉。我们通过引入块坐标下降法和启发式方法来解决目标函数。在真实数据集上的实验结果表明，与现有的方法相比，该方法在 Sybil 攻击环境下能够更准确地推断出真实结果。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=RCTD:+Reputation-Constrained+Truth+Discovery+in+Sybil+Attack+Crowdsourcing+Environment)|0| +|[RCTD: Reputation-Constrained Truth Discovery in Sybil Attack Crowdsourcing Environment](https://doi.org/10.1145/3637528.3671803)|Xing Jin, Zhihai Gong, Jiuchuan Jiang, Chao Wang, Jian Zhang, Zhen Wang|; School of Cyberspace, Hangzhou Dianzi University, Hangzhou, Zhejiang, China; Research Center for Data Hub and Security, Zhejiang Lab, Hangzhou, Zhejiang, China|Sybil attacks are a prevalent concern within the realm of crowdsourcing, underscoring the significance of quality control in this domain. Truth discovery has been extensively studied to deduce the most trustworthy information from conflicting data based on the principle that reliable workers yield reliable answers. However, existing truth discovery approaches overlook the metric of workers' reputations, e.g., workers' historical approval rates on crowdsourcing platforms, despite being inflated and noisy, they offer a rough indication of workers' ability. In this paper, we first refine the approval rate using Wilson Lower Bound to enhance its confidence, and then mitigate its noise and inflation through a method based on ranking similarity. Specifically, we propose a method called RCTD (Reputation-Constrained Truth Discovery), which introduces a similarity metric between the rankings of workers' weights and the refined approval rates. This metric serves as a penalizing factor in the objective function of the truth discovery, restricting workers' weights to avoid excessively deviating from their historical reputation during the weight estimation process. We solve the objective function by introducing the block coordinate descent coupled with heuristics approach method. Experimental results on real-world datasets demonstrate that our approach achieves more accurate inference of true results in the Sybil attack environment compared to the state-of-the-art methods.|Sybil 攻击是众包领域的一个普遍关注的问题，强调了质量控制在这个领域的重要性。真相发现已被广泛研究，以从相互矛盾的数据中推断出最可信的信息，其基本原理是可靠的工作人员提供可靠的答案。然而，现有的真相发现方法忽视了员工声誉的度量，例如，员工在众包平台上的历史认可率，尽管夸大和喧闹，他们提供了员工能力的粗略指标。本文首先利用威尔逊下界细化批准率，以提高其置信度，然后通过一种基于排序相似度的方法来降低其噪声和通货膨胀。具体来说，我们提出了一种名为声誉约束真相发现(RCTD)的方法，该方法引入了工人权重排名和精确批准率之间的相似度量。这个度量标准在真相发现的客观功能中起着惩罚性因素的作用，它限制了工人的权重，以避免在权重估计过程中过度偏离他们的历史声誉。我们通过引入块坐标下降法和启发式方法来解决目标函数。在真实数据集上的实验结果表明，与现有的方法相比，该方法在 Sybil 攻击环境下能够更准确地推断出真实结果。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=RCTD:+Reputation-Constrained+Truth+Discovery+in+Sybil+Attack+Crowdsourcing+Environment)|0| |[Bivariate Decision Trees: Smaller, Interpretable, More Accurate](https://doi.org/10.1145/3637528.3671903)|Rasul Kairgeldin, Miguel Á. CarreiraPerpiñán|University of California, Merced, Merced, CA, USA|Univariate decision trees, commonly used since the 1950s, predict by asking questions about a single feature in each decision node. While they are interpretable, they often lack competitive predictive accuracy due to their inability to model feature correlations. Multivariate (oblique) trees use multiple features in each node, capturing high-dimensional correlations better, but sometimes they can be difficult to interpret. We advocate for a model that strikes a useful middle ground: bivariate decision trees, which use two features in each node. This typically produces trees that not only are more accurate than univariate trees, but much smaller, which offsets the small increase in node complexity and keeps them interpretable. They also help data mining by constructing new features that are useful for discrimination, and by providing a form of supervised, hierarchical 2D visualization that reveals patterns such as clusters or linear structure. We give two new algorithms to learn bivariate trees: a fast one based on CART; and a slower one based on alternating optimization with a feature regularization term, which produces the best trees while still scaling to large datasets.|单变量决策树，自20世纪50年代以来被广泛使用，通过询问每个决策节点中单个特征的问题来进行预测。虽然它们是可解释的，但由于它们无法建立特征相关性模型，它们往往缺乏具有竞争力的预测准确性。多变量(斜)树在每个节点中使用多个特征，可以更好地捕获高维相关性，但有时它们可能很难解释。我们提倡一个有用的中间地带的模型: 二元决策树，它在每个节点中使用两个特征。这通常产生的树不仅比单变量树更精确，而且要小得多，这抵消了节点复杂度的小幅增加，并保持了它们的可解释性。它们还有助于数据挖掘，因为它们构建了有助于区分的新特征，并提供了一种受监督的、分层的二维可视化形式，揭示了诸如聚类或线性结构之类的模式。本文提出了两种新的二元树学习算法: 基于 CART 的快速算法和基于特征正则项交替优化的慢速算法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Bivariate+Decision+Trees:+Smaller,+Interpretable,+More+Accurate)|0| |[CAFO: Feature-Centric Explanation on Time Series Classification](https://doi.org/10.1145/3637528.3671724)|Jaeho Kim, SeokJu Hahn, Yoontae Hwang, Junghye Lee, Seulki Lee||In multivariate time series (MTS) classification, finding the important features (e.g., sensors) for model performance is crucial yet challenging due to the complex, high-dimensional nature of MTS data, intricate temporal dynamics, and the necessity for domain-specific interpretations. Current explanation methods for MTS mostly focus on time-centric explanations, apt for pinpointing important time periods but less effective in identifying key features. This limitation underscores the pressing need for a feature-centric approach, a vital yet often overlooked perspective that complements time-centric analysis. To bridge this gap, our study introduces a novel feature-centric explanation and evaluation framework for MTS, named CAFO (Channel Attention and Feature Orthgonalization). CAFO employs a convolution-based approach with channel attention mechanisms, incorporating a depth-wise separable channel attention module (DepCA) and a QR decomposition-based loss for promoting feature-wise orthogonality. We demonstrate that this orthogonalization enhances the separability of attention distributions, thereby refining and stabilizing the ranking of feature importance. This improvement in feature-wise ranking enhances our understanding of feature explainability in MTS. Furthermore, we develop metrics to evaluate global and class-specific feature importance. Our framework's efficacy is validated through extensive empirical analyses on two major public benchmarks and real-world datasets, both synthetic and self-collected, specifically designed to highlight class-wise discriminative features. The results confirm CAFO's robustness and informative capacity in assessing feature importance in MTS classification tasks. This study not only advances the understanding of feature-centric explanations in MTS but also sets a foundation for future explorations in feature-centric explanations. The codes are available at https://github.com/eai-lab/CAFO.|在多变量时间序列(MTS)分类中，由于 MTS 数据的复杂，高维性质，复杂的时间动态以及特定领域解释的必要性，寻找模型性能的重要特征(例如传感器)是至关重要的，但具有挑战性。目前 MTS 的解释方法主要集中在以时间为中心的解释上，易于确定重要的时间段，但在识别关键特征方面效率较低。这种局限性强调了以功能为中心的方法的迫切需要，这是一个重要但经常被忽视的视角，补充了以时间为中心的分析。为了弥补这一差距，我们的研究引入了一个新的以特征为中心的 MTS 解释和评估框架，称为 CAFO (渠道注意力和特征正交化)。CAFO 采用基于卷积的方法和信道注意机制，结合基于深度的可分离信道注意模块(DepCA)和基于 QR 分解的损失来提高特征方向的正交性。我们证明这种正交化增强了注意力分布的可分性，从而改善和稳定了特征重要性的排名。这种特征方面排名的改进增强了我们对 MTS 中特征可解释性的理解。此外，我们开发度量来评估全局和类别特定的特征重要性。我们的框架的有效性是通过对两个主要的公共基准和真实世界的数据集进行广泛的实证分析来验证的，这些数据集包括合成的和自我收集的，专门设计用来突出类别的区分特征。结果证实了 CAFO 在 MTS 分类任务中评估特征重要性的鲁棒性和信息能力。本研究不仅提高了对 MTS 中以特征为中心的解释的理解，而且为今后进一步探索以特征为中心的解释奠定了基础。密码可以在 https://github.com/eai-lab/cafo 找到。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=CAFO:+Feature-Centric+Explanation+on+Time+Series+Classification)|0| -|[Gandalf: Learning Label-label Correlations in Extreme Multi-label Classification via Label Features](https://doi.org/10.1145/3637528.3672063)|Siddhant Kharbanda, Devaansh Gupta, Erik Schultheis, Atmadeep Banerjee, ChoJui Hsieh, Rohit Babbar|Aalto University, Espoo, Finland; University of California, Los Angeles, Los Angeles, USA; Aalto University & University of Bath, Espoo, Finland|Extreme Multi-label Text Classification (XMC) involves learning a classifier that can assign an input with a subset of most relevant labels from millions of label choices. Recent works in this domain have increasingly focused on a symmetric problem setting where both input instances and label features are short-text in nature. Short-text XMC with label features has found numerous applications in areas such as query-to-ad-phrase matching in search ads, title-based product recommendation, prediction of related searches. In this paper, we propose Gandalf, a novel approach which makes use of a label co-occurrence graph to leverage label features as additional data points to supplement the training distribution. By exploiting the characteristics of the short-text XMC problem, it leverages the label features to construct valid training instances, and uses the label graph for generating the corresponding soft-label targets, hence effectively capturing the label-label correlations. Surprisingly, models trained on these new training instances, although being less than half of the original dataset, can outperform models trained on the original dataset, particularly on the PSP@k metric for tail labels. With this insight, we aim to train existing XMC algorithms on both, the original and new training instances, leading to an average 5% relative improvements for 6 state-of-the-art algorithms across 4 benchmark datasets consisting of up to 1.3M labels. Gandalf can be applied in a plug-and-play manner to various methods and thus forwards the state-of-the-art in the domain, without incurring any additional computational overheads. Code has been open-sourced at www.github.com/xmc-aalto/InceptionXML.|极限多标签文本分类(XMC)涉及到学习一个分类器，该分类器可以从数百万个标签选择中分配一个输入，其中包含最相关标签的子集。最近在这个领域的工作越来越集中在一个对称的问题设置，其中输入实例和标签功能都是短文本性质。具有标签功能的短文 XMC 在搜索广告中的查询到广告短语匹配、基于标题的产品推荐、相关搜索预测等领域有着广泛的应用。在本文中，我们提出了一种新的方法，使用标签共现图利用标签特征作为额外的数据点，以补充训练分布。利用短文本 XMC 问题的特点，利用标签特征构造有效的训练实例，并利用标签图生成相应的软标签目标，从而有效地捕获标签-标签的相关性。令人惊讶的是，在这些新的训练实例上训练的模型，尽管不到原始数据集的一半，可以胜过在原始数据集上训练的模型，特别是在用于尾部标签的 PSP@k 度量上。有了这种洞察力，我们的目标是在原始和新的训练实例上对现有的 XMC 算法进行训练，从而使4个基准数据集(包括多达130万个标签)中的6个最先进的算法平均相对改进5% 。甘道夫可以以即插即用的方式应用于各种方法，从而在不引起任何额外计算开销的情况下，转发该领域的最新技术。代码已经在 www.github.com/xmc-aalto/inceptionxml 上开源。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Gandalf:+Learning+Label-label+Correlations+in+Extreme+Multi-label+Classification+via+Label+Features)|0| -|[OntoType: Ontology-Guided and Pre-Trained Language Model Assisted Fine-Grained Entity Typing](https://doi.org/10.1145/3637528.3671745)|Tanay Komarlu, Minhao Jiang, Xuan Wang, Jiawei Han|Virginia Tech, Blacksburg, VA, USA; University of Illinois Urbana-Champaign, Urbana, IL, USA|Fine-grained entity typing (FET), which assigns entities in text withcontext-sensitive, fine-grained semantic types, is a basic but important taskfor knowledge extraction from unstructured text. FET has been studiedextensively in natural language processing and typically relies onhuman-annotated corpora for training, which is costly and difficult to scale.Recent studies explore the utilization of pre-trained language models (PLMs) asa knowledge base to generate rich and context-aware weak supervision for FET.However, a PLM still requires direction and guidance to serve as a knowledgebase as they often generate a mixture of rough and fine-grained types, ortokens unsuitable for typing. In this study, we vision that an ontologyprovides a semantics-rich, hierarchical structure, which will help select thebest results generated by multiple PLM models and head words. Specifically, wepropose a novel annotation-free, ontology-guided FET method, OntoType, whichfollows a type ontological structure, from coarse to fine, ensembles multiplePLM prompting results to generate a set of type candidates, and refines itstype resolution, under the local context with a natural language inferencemodel. Our experiments on the Ontonotes, FIGER, and NYT datasets using theirassociated ontological structures demonstrate that our method outperforms thestate-of-the-art zero-shot fine-grained entity typing methods as well as atypical LLM method, ChatGPT. Our error analysis shows that refinement of theexisting ontology structures will further improve fine-grained entity typing.|细粒度实体分类(FET)是从非结构化文本中提取知识的一项基本而重要的任务，它为文本中的实体分配上下文敏感的细粒度语义类型。FET 在自然语言处理领域得到了广泛的研究，它通常依赖于人工注释的语料库进行训练，这种训练成本高昂，而且难以扩展。最近的研究探索利用预先训练的语言模型(PLM)作为知识库，为 FEM 产生丰富的和上下文感知的弱监督。然而，PLM 仍然需要方向和指导作为知识库，因为它们经常产生粗糙和细粒度类型的混合物，或者不适合打字的标记。在这项研究中，我们设想一个本体提供了一个语义丰富的层次结构，这将有助于选择由多个 PLM 模型和头文字产生的最佳结果。具体而言，我们提出了一种新的无注释，本体引导的 FET 方法 OntoType，它遵循从粗到细的类型本体结构，将 multiplePLM 集成在一起，提示结果以生成一组类型候选者，并使用自然语言推理模型在局部上下文中改进其类型分辨率。我们在 Ontonote，FIGER 和 NYT 数据集上使用其相关的本体结构的实验表明，我们的方法优于最先进的零拍细粒度实体分类方法以及非典型 LLM 方法 ChatGPT。我们的误差分析表明，细化现有的本体结构将进一步改善细粒度的实体分类。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=OntoType:+Ontology-Guided+and+Pre-Trained+Language+Model+Assisted+Fine-Grained+Entity+Typing)|0| +|[Gandalf: Learning Label-label Correlations in Extreme Multi-label Classification via Label Features](https://doi.org/10.1145/3637528.3672063)|Siddhant Kharbanda, Devaansh Gupta, Erik Schultheis, Atmadeep Banerjee, ChoJui Hsieh, Rohit Babbar|Aalto University & University of Bath, Espoo, Finland; Aalto University, Espoo, Finland; University of California, Los Angeles, Los Angeles, USA|Extreme Multi-label Text Classification (XMC) involves learning a classifier that can assign an input with a subset of most relevant labels from millions of label choices. Recent works in this domain have increasingly focused on a symmetric problem setting where both input instances and label features are short-text in nature. Short-text XMC with label features has found numerous applications in areas such as query-to-ad-phrase matching in search ads, title-based product recommendation, prediction of related searches. In this paper, we propose Gandalf, a novel approach which makes use of a label co-occurrence graph to leverage label features as additional data points to supplement the training distribution. By exploiting the characteristics of the short-text XMC problem, it leverages the label features to construct valid training instances, and uses the label graph for generating the corresponding soft-label targets, hence effectively capturing the label-label correlations. Surprisingly, models trained on these new training instances, although being less than half of the original dataset, can outperform models trained on the original dataset, particularly on the PSP@k metric for tail labels. With this insight, we aim to train existing XMC algorithms on both, the original and new training instances, leading to an average 5% relative improvements for 6 state-of-the-art algorithms across 4 benchmark datasets consisting of up to 1.3M labels. Gandalf can be applied in a plug-and-play manner to various methods and thus forwards the state-of-the-art in the domain, without incurring any additional computational overheads. Code has been open-sourced at www.github.com/xmc-aalto/InceptionXML.|极限多标签文本分类(XMC)涉及到学习一个分类器，该分类器可以从数百万个标签选择中分配一个输入，其中包含最相关标签的子集。最近在这个领域的工作越来越集中在一个对称的问题设置，其中输入实例和标签功能都是短文本性质。具有标签功能的短文 XMC 在搜索广告中的查询到广告短语匹配、基于标题的产品推荐、相关搜索预测等领域有着广泛的应用。在本文中，我们提出了一种新的方法，使用标签共现图利用标签特征作为额外的数据点，以补充训练分布。利用短文本 XMC 问题的特点，利用标签特征构造有效的训练实例，并利用标签图生成相应的软标签目标，从而有效地捕获标签-标签的相关性。令人惊讶的是，在这些新的训练实例上训练的模型，尽管不到原始数据集的一半，可以胜过在原始数据集上训练的模型，特别是在用于尾部标签的 PSP@k 度量上。有了这种洞察力，我们的目标是在原始和新的训练实例上对现有的 XMC 算法进行训练，从而使4个基准数据集(包括多达130万个标签)中的6个最先进的算法平均相对改进5% 。甘道夫可以以即插即用的方式应用于各种方法，从而在不引起任何额外计算开销的情况下，转发该领域的最新技术。代码已经在 www.github.com/xmc-aalto/inceptionxml 上开源。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Gandalf:+Learning+Label-label+Correlations+in+Extreme+Multi-label+Classification+via+Label+Features)|0| +|[OntoType: Ontology-Guided and Pre-Trained Language Model Assisted Fine-Grained Entity Typing](https://doi.org/10.1145/3637528.3671745)|Tanay Komarlu, Minhao Jiang, Xuan Wang, Jiawei Han|University of Illinois Urbana-Champaign, Urbana, IL, USA; Virginia Tech, Blacksburg, VA, USA|Fine-grained entity typing (FET), which assigns entities in text withcontext-sensitive, fine-grained semantic types, is a basic but important taskfor knowledge extraction from unstructured text. FET has been studiedextensively in natural language processing and typically relies onhuman-annotated corpora for training, which is costly and difficult to scale.Recent studies explore the utilization of pre-trained language models (PLMs) asa knowledge base to generate rich and context-aware weak supervision for FET.However, a PLM still requires direction and guidance to serve as a knowledgebase as they often generate a mixture of rough and fine-grained types, ortokens unsuitable for typing. In this study, we vision that an ontologyprovides a semantics-rich, hierarchical structure, which will help select thebest results generated by multiple PLM models and head words. Specifically, wepropose a novel annotation-free, ontology-guided FET method, OntoType, whichfollows a type ontological structure, from coarse to fine, ensembles multiplePLM prompting results to generate a set of type candidates, and refines itstype resolution, under the local context with a natural language inferencemodel. Our experiments on the Ontonotes, FIGER, and NYT datasets using theirassociated ontological structures demonstrate that our method outperforms thestate-of-the-art zero-shot fine-grained entity typing methods as well as atypical LLM method, ChatGPT. Our error analysis shows that refinement of theexisting ontology structures will further improve fine-grained entity typing.|细粒度实体分类(FET)是从非结构化文本中提取知识的一项基本而重要的任务，它为文本中的实体分配上下文敏感的细粒度语义类型。FET 在自然语言处理领域得到了广泛的研究，它通常依赖于人工注释的语料库进行训练，这种训练成本高昂，而且难以扩展。最近的研究探索利用预先训练的语言模型(PLM)作为知识库，为 FEM 产生丰富的和上下文感知的弱监督。然而，PLM 仍然需要方向和指导作为知识库，因为它们经常产生粗糙和细粒度类型的混合物，或者不适合打字的标记。在这项研究中，我们设想一个本体提供了一个语义丰富的层次结构，这将有助于选择由多个 PLM 模型和头文字产生的最佳结果。具体而言，我们提出了一种新的无注释，本体引导的 FET 方法 OntoType，它遵循从粗到细的类型本体结构，将 multiplePLM 集成在一起，提示结果以生成一组类型候选者，并使用自然语言推理模型在局部上下文中改进其类型分辨率。我们在 Ontonote，FIGER 和 NYT 数据集上使用其相关的本体结构的实验表明，我们的方法优于最先进的零拍细粒度实体分类方法以及非典型 LLM 方法 ChatGPT。我们的误差分析表明，细化现有的本体结构将进一步改善细粒度的实体分类。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=OntoType:+Ontology-Guided+and+Pre-Trained+Language+Model+Assisted+Fine-Grained+Entity+Typing)|0| |[LeMon: Automating Portrait Generation for Zero-Shot Story Visualization with Multi-Character Interactions](https://doi.org/10.1145/3637528.3671850)|Ziyi Kou, Shichao Pei, Xiangliang Zhang|Department of Computer Science, University of Massachusetts Boston, Boston, MA, USA; Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN, USA|Zero-Shot Story Visualization (ZSV) seeks to depict textual narratives through a sequence of images without relying on pre-existing text-image pairs for training. In this paper, we address the challenge of automated multi-character ZSV, aiming to create distinctive yet compatible character portraits for high-quality story visualization without the need of manual human interventions. Our study is motivated by the limitation of current ZSV approaches that necessitate inefficient manual collection of external images as initial character portraits and suffer from low-quality story visualization, especially with multi-character interactions, when the portraits are not well initiated. To overcome these issues, we develop LeMon, an LLM enhanced Multi-Character Zero-Shot Visualization framework that automates character portrait initialization and supports iterative portrait refinement by exploring the semantic content of the story. In particular, we design an LLM-based portrait generation strategy that matches the story characters with external movie characters, and leverage the matched resources as in-context learning (ICL) samples for LLMs to accurately initialize the character portraits. We then propose a graph-based Text2Image diffusion model that constructs a character interaction graph from the story to iteratively refine the character portraits by maximizing the distinctness of different characters while minimizing their incompatibility in the multi-character story visualization. Our evaluation results show that LeMon outperforms existing ZSV approaches in generating high-quality visualizations for stories across various types with multiple interacted characters. Our code is available at https://github.com/arxrean/LLM-LeMon.|零镜头故事可视化(Zero-Shot Story Visualization，ZSV)旨在通过一系列图像描述文本叙事，而不依赖于已有的文本-图像对进行训练。在本文中，我们解决的挑战，自动多字符 ZSV，旨在创建独特而兼容的人物肖像高质量的故事可视化不需要人工干预。我们的研究是受到当前 ZSV 方法的限制，这种方法需要低效的手工收集外部图像作为初始人物肖像，并且受到低质量故事可视化的影响，特别是在多人物交互的情况下，当肖像没有很好地启动时。为了克服这些问题，我们开发了 LeMon，一个 LLM 增强的多字符零拍摄可视化框架，它自动化人物肖像的初始化，并通过探索故事的语义内容支持迭代肖像细化。特别地，我们设计了一个基于 LLM 的肖像生成策略，将故事人物与外部电影人物进行匹配，并利用匹配的资源作为上下文学习(In-context learning，ICL)样本对 LLM 进行准确的人物肖像初始化。然后我们提出了一个基于图形的 Text2Image 扩散模型，该模型从故事中构建一个人物交互图，通过在多人物故事可视化中最大化不同人物的差异性，同时最小化他们的不兼容性，迭代地细化人物肖像。我们的评估结果表明，LeMon 优于现有的 ZSV 方法，可以为不同类型、多个交互角色的故事生成高质量的可视化效果。我们的代码可以在 https://github.com/arxrean/llm-lemon 找到。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=LeMon:+Automating+Portrait+Generation+for+Zero-Shot+Story+Visualization+with+Multi-Character+Interactions)|0| |[Attacking Graph Neural Networks with Bit Flips: Weisfeiler and Leman Go Indifferent](https://doi.org/10.1145/3637528.3671890)|Lorenz Kummer, Samir Moustafa, Sebastian Schrittwieser, Wilfried N. Gansterer, Nils M. Kriege|; Faculty of Computer Science, University of Vienna, Vienna, Austria|Prior attacks on graph neural networks have focused on graph poisoning and evasion, neglecting the network's weights and biases. For convolutional neural networks, however, the risk arising from bit flip attacks is well recognized. We show that the direct application of a traditional bit flip attack to graph neural networks is of limited effectivity. Hence, we discuss the Injectivity Bit Flip Attack, the first bit flip attack designed specifically for graph neural networks. Our attack targets the learnable neighborhood aggregation functions in quantized message passing neural networks, degrading their ability to distinguish graph structures and impairing the expressivity of the Weisfeiler-Leman test. We find that exploiting mathematical properties specific to certain graph neural networks significantly increases their vulnerability to bit flip attacks. The Injectivity Bit Flip Attack can degrade the maximal expressive Graph Isomorphism Networks trained on graph property prediction datasets to random output by flipping only a small fraction of the network's bits, demonstrating its higher destructive power compared to traditional bit flip attacks transferred from convolutional neural networks. Our attack is transparent, motivated by theoretical insights and confirmed by extensive empirical results.|先前对图神经网络的攻击主要集中在图的中毒和规避上，忽略了网络的权重和偏差。然而，对于卷积神经网络来说，由比特翻转攻击引起的风险是公认的。研究表明，将传统的比特翻转攻击直接应用于图形神经网络是有效的。因此，我们讨论的注入性位翻转攻击，第一位翻转攻击专门设计的图神经网络。我们的攻击目标是量化信息传递神经网络中可学习的邻域聚集函数，降低了它们区分图结构的能力，损害了 Weisfeiler-Leman 检验的表达能力。我们发现，利用特定图形神经网络的数学特性显著增加了它们对位翻转攻击的脆弱性。注入性比特翻转攻击通过只翻转网络比特的一小部分，就可以将基于图性质预测数据集的最大表达式图同构网络降级为随机输出，与传统的卷积神经网络比特翻转攻击相比，显示出更高的破坏力。我们的攻击是透明的，受到理论洞察力的激励，并得到广泛的实证结果的证实。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Attacking+Graph+Neural+Networks+with+Bit+Flips:+Weisfeiler+and+Leman+Go+Indifferent)|0| |[Max-Min Diversification with Asymmetric Distances](https://doi.org/10.1145/3637528.3671757)|Iiro Kumpulainen, Florian Adriaens, Nikolaj Tatti|University of Helsinki, Helsinki, Finland; University of Helsinki & HIIT, Helsinki, Finland|One of the most well-known and simplest models for diversity maximization is the Max-Min Diversification (MMD) model, which has been extensively studied in the data mining and database literature. In this paper, we initiate the study of the Asymmetric Max-Min Diversification (AMMD) problem. The input is a positive integer k and a complete digraph over n vertices, together with a nonnegative distance function over the edges obeying the directed triangle inequality. The objective is to select a set of k vertices, which maximizes the smallest pairwise distance between them. AMMD reduces to the well-studied MMD problem in case the distances are symmetric, and has natural applications to query result diversification, web search, and facility location problems. Although the MMD problem admits a simple 1/2-approximation by greedily selecting the next-furthest point, this strategy fails for AMMD and it remained unclear how to design good approximation algorithms for AMMD. We propose a combinatorial 1/(6k)-approximation algorithm for AMMD by leveraging connections with the Maximum Antichain problem. We discuss several ways of speeding up the algorithm and compare its performance against heuristic baselines on real-life and synthetic datasets.|最著名和最简单的多样性最大化模型之一是最大-最小多样化(MMD)模型，它在数据挖掘和数据库文献中得到了广泛的研究。本文首先研究了非对称最大-最小多样化(AMMD)问题。输入是一个正整数 k，n 个顶点上的完全有向图，以及服从有向三角不等式的边上的非负距离函数。目标是选择一组 k 顶点，使它们之间的最小成对距离最大化。在距离对称的情况下，AMMD 可以简化为已经深入研究过的 MMD 问题，并且具有查询结果多样化、网络搜索和设施位置问题的自然应用。虽然 MMD 问题通过贪婪地选择下一个最远点来承认一个简单的1/2近似，但这种策略对 AMMD 来说是失败的，而且目前还不清楚如何为 AMMD 设计好的近似算法。我们提出了一个组合1/(6k)-近似演算法的 AMMD 通过利用与最大反链问题的联系。我们讨论了几种提高算法速度的方法，并比较了在实际数据集和合成数据集上的启发式基线算法的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Max-Min+Diversification+with+Asymmetric+Distances)|0| |[Efficient Topology-aware Data Augmentation for High-Degree Graph Neural Networks](https://doi.org/10.1145/3637528.3671765)|Yurui Lai, Xiaoyang Lin, Renchi Yang, Hongtao Wang|Hong Kong Baptist University, Hong Kong, China|In recent years, graph neural networks (GNNs) have emerged as a potent tool for learning on graph-structured data and won fruitful successes in varied fields. The majority of GNNs follow the message-passing paradigm, where representations of each node are learned by recursively aggregating features of its neighbors. However, this mechanism brings severe over-smoothing and efficiency issues over high-degree graphs (HDGs), wherein most nodes have dozens (or even hundreds) of neighbors, such as social networks, transaction graphs, power grids, etc. Additionally, such graphs usually encompass rich and complex structure semantics, which are hard to capture merely by feature aggregations in GNNs. Motivated by the above limitations, we propose TADA, an efficient and effective front-mounted data augmentation framework for GNNs on HDGs. Under the hood, TADA includes two key modules: (i) feature expansion with structure embeddings, and (ii) topology- and attribute-aware graph sparsification. The former obtains augmented node features and enhanced model capacity by encoding the graph structure into high-quality structure embeddings with our highly-efficient sketching method. Further, by exploiting task-relevant features extracted from graph structures and attributes, the second module enables the accurate identification and reduction of numerous redundant/noisy edges from the input graph, thereby alleviating over-smoothing and facilitating faster feature aggregations over HDGs. Empirically, \algo considerably improves the predictive performance of mainstream GNN models on 8 real homophilic/heterophilic HDGs in terms of node classification, while achieving efficient training and inference processes.|近年来，图神经网络(GNN)已经成为学习图结构数据的有力工具，并在各个领域取得了丰硕的成果。大多数 GNN 遵循消息传递范式，通过递归聚合其邻居的特征来学习每个节点的表示。然而，这种机制给高度图(HDG)带来了严重的过度平滑和效率问题，其中大多数节点有几十个(甚至几百个)邻居，如社交网络、交易图、电网等。此外，这样的图通常包含丰富而复杂的结构语义，仅仅通过 GNN 中的特征聚合很难捕获这些语义。基于上述局限性，我们提出了 TADA，一个有效的面向 HDG 上 GNN 的前置数据增强框架。TADA 包括两个关键模块: (1)结构嵌入的特征扩展和(2)拓扑和属性感知的图稀疏化。前者采用高效的草图绘制方法，将图结构编码为高质量的结构嵌入，从而获得增强的节点特征和增强的模型容量。此外，通过利用从图结构和属性中提取的与任务相关的特征，第二个模块能够准确识别和减少来自输入图的许多冗余/噪声边缘，从而减轻过度平滑并促进 HDG 上更快的特征聚合。从经验上看，算法在节点分类方面显著提高了主流 GNN 模型对8个实际同亲/异亲 HDG 的预测性能，同时实现了有效的训练和推理过程。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Efficient+Topology-aware+Data+Augmentation+for+High-Degree+Graph+Neural+Networks)|0| |[ShapeFormer: Shapelet Transformer for Multivariate Time Series Classification](https://doi.org/10.1145/3637528.3671862)|XuanMay Thi Le, Ling Luo, Uwe Aickelin, MinhTuan Tran|Monash University, Melbourne, VIC, Australia; The University of Melbourne, Melbourne, VIC, Australia|Multivariate time series classification (MTSC) has attracted significantresearch attention due to its diverse real-world applications. Recently,exploiting transformers for MTSC has achieved state-of-the-art performance.However, existing methods focus on generic features, providing a comprehensiveunderstanding of data, but they ignore class-specific features crucial forlearning the representative characteristics of each class. This leads to poorperformance in the case of imbalanced datasets or datasets with similar overallpatterns but differing in minor class-specific details. In this paper, wepropose a novel Shapelet Transformer (ShapeFormer), which comprisesclass-specific and generic transformer modules to capture both of thesefeatures. In the class-specific module, we introduce the discovery method toextract the discriminative subsequences of each class (i.e. shapelets) from thetraining set. We then propose a Shapelet Filter to learn the differencefeatures between these shapelets and the input time series. We found that thedifference feature for each shapelet contains important class-specificfeatures, as it shows a significant distinction between its class and others.In the generic module, convolution filters are used to extract generic featuresthat contain information to distinguish among all classes. For each module, weemploy the transformer encoder to capture the correlation between theirfeatures. As a result, the combination of two transformer modules allows ourmodel to exploit the power of both types of features, thereby enhancing theclassification performance. Our experiments on 30 UEA MTSC datasets demonstratethat ShapeFormer has achieved the highest accuracy ranking compared tostate-of-the-art methods. The code is available athttps://github.com/xuanmay2701/shapeformer.|多变量时间序列分类(MTSC)因其在现实世界中的广泛应用而引起了人们的广泛关注。最近，开发变压器的 MTSC 已经取得了最先进的性能。然而，现有的方法侧重于通用特征，提供了对数据的全面理解，但它们忽略了对学习每个类的代表性特征至关重要的类特定特征。这导致在不平衡的数据集或具有相似总体模式但在次要类特定细节上不同的数据集的情况下性能较差。在本文中，我们提出了一个新颖的小波变换器(ShapeForm) ，它包括类特定的和通用的变换器模块来捕获这两个特征。在类特定模块中，我们引入发现方法从训练集中提取每个类的判别子序列(即形状)。然后，我们提出了一个小波滤波器来学习这些形状和输入时间序列之间的差异特征。我们发现每个小波的差异特征包含重要的类别特征，因为它显示了其类别和其他类别之间的显着区别。在通用模块中，卷积过滤器用于提取包含信息以区分所有类别的通用特征。对于每个模块，我们使用变压器编码器来捕获它们之间的相关性。因此，两个变压器模块的结合使我们的模型能够利用两种类型的功能，从而提高分类性能。我们在30个 UEA MTSC 数据集上的实验表明，与最先进的方法相比，ShapeFormer 获得了最高的准确度排名。该代码可以在 https:// github.com/xuanmay2701/shapeformer 上获得。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=ShapeFormer:+Shapelet+Transformer+for+Multivariate+Time+Series+Classification)|0| -|[ReCTSi: Resource-efficient Correlated Time Series Imputation via Decoupled Pattern Learning and Completeness-aware Attentions](https://doi.org/10.1145/3637528.3671816)|Zhichen Lai, Dalin Zhang, Huan Li, Dongxiang Zhang, Hua Lu, Christian S. Jensen|Department of Computer Science, Aalborg University, Aalborg, Denmark; The State Key Laboratory of Blockchain and Data Security, Zhejiang University, Hangzhou, China; Department of People and Technology, Roskilde University, Roskilde, Denmark|Imputation of Correlated Time Series (CTS) is essential in data preprocessing for many tasks, particularly when sensor data is often incomplete. Deep learning has enabled sophisticated models that improve CTS imputation by capturing temporal and spatial patterns. However, deep models often incur considerable consumption of computational resources and thus cannot be deployed in resource-limited settings. This paper presents ReCTSi (Resource-efficient CTS imputation), a method that adopts a new architecture for decoupled pattern learning in two phases: (1) the Persistent Pattern Extraction phase utilizes a multi-view learnable codebook mechanism to identify and archive persistent patterns common across different time series, enabling rapid pattern retrieval during inference. (2) the Transient Pattern Adaptation phase introduces completeness-aware attention modules that allocate attention to the complete and hence more reliable data segments. Extensive experimental results show that ReCTSi achieves state-of-the-art imputation accuracy while consuming much fewer computational resources than the leading existing model, consuming only 0.004% of the FLOPs for inference compared to its closest competitor. The blend of high accuracy and very low resource consumption makes ReCTSi the currently best method for resource-limited scenarios. The related code is available at https://github.com/ryanlaics/RECTSI.|相关时间序列(CTS)的插补是许多任务的数据预处理中必不可少的，特别是当传感器数据往往是不完整的。深度学习使得复杂的模型能够通过捕捉时间和空间模式来改进 CTS 估算。然而，深度模型通常会消耗大量的计算资源，因此无法在资源有限的环境中部署。本文提出了一种新的解耦模式学习方法 ReCTSi，该方法分两个阶段实现: (1)持久模式提取阶段利用多视图可学习的码本机制识别和归档不同时间序列中常见的持久模式，从而实现推理过程中的快速模式检索。(2)瞬态模式适应阶段引入完整性意识注意模块，将注意力分配给完整的、因此更可靠的数据段。大量的实验结果表明，ReCTSi 实现了最先进的插补精度，同时比现有的主流模型消耗更少的计算资源，与其最接近的竞争对手相比，只消耗 FLOP 的0.004% 。高精度和极低资源消耗的混合使 ReCTSi 成为当前资源有限场景的最佳方法。有关密码可于 https://github.com/ryanlaics/rectsi 索取。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=ReCTSi:+Resource-efficient+Correlated+Time+Series+Imputation+via+Decoupled+Pattern+Learning+and+Completeness-aware+Attentions)|0| +|[ReCTSi: Resource-efficient Correlated Time Series Imputation via Decoupled Pattern Learning and Completeness-aware Attentions](https://doi.org/10.1145/3637528.3671816)|Zhichen Lai, Dalin Zhang, Huan Li, Dongxiang Zhang, Hua Lu, Christian S. Jensen|Department of Computer Science, Aalborg University, Aalborg, Denmark; Department of People and Technology, Roskilde University, Roskilde, Denmark; The State Key Laboratory of Blockchain and Data Security, Zhejiang University, Hangzhou, China|Imputation of Correlated Time Series (CTS) is essential in data preprocessing for many tasks, particularly when sensor data is often incomplete. Deep learning has enabled sophisticated models that improve CTS imputation by capturing temporal and spatial patterns. However, deep models often incur considerable consumption of computational resources and thus cannot be deployed in resource-limited settings. This paper presents ReCTSi (Resource-efficient CTS imputation), a method that adopts a new architecture for decoupled pattern learning in two phases: (1) the Persistent Pattern Extraction phase utilizes a multi-view learnable codebook mechanism to identify and archive persistent patterns common across different time series, enabling rapid pattern retrieval during inference. (2) the Transient Pattern Adaptation phase introduces completeness-aware attention modules that allocate attention to the complete and hence more reliable data segments. Extensive experimental results show that ReCTSi achieves state-of-the-art imputation accuracy while consuming much fewer computational resources than the leading existing model, consuming only 0.004% of the FLOPs for inference compared to its closest competitor. The blend of high accuracy and very low resource consumption makes ReCTSi the currently best method for resource-limited scenarios. The related code is available at https://github.com/ryanlaics/RECTSI.|相关时间序列(CTS)的插补是许多任务的数据预处理中必不可少的，特别是当传感器数据往往是不完整的。深度学习使得复杂的模型能够通过捕捉时间和空间模式来改进 CTS 估算。然而，深度模型通常会消耗大量的计算资源，因此无法在资源有限的环境中部署。本文提出了一种新的解耦模式学习方法 ReCTSi，该方法分两个阶段实现: (1)持久模式提取阶段利用多视图可学习的码本机制识别和归档不同时间序列中常见的持久模式，从而实现推理过程中的快速模式检索。(2)瞬态模式适应阶段引入完整性意识注意模块，将注意力分配给完整的、因此更可靠的数据段。大量的实验结果表明，ReCTSi 实现了最先进的插补精度，同时比现有的主流模型消耗更少的计算资源，与其最接近的竞争对手相比，只消耗 FLOP 的0.004% 。高精度和极低资源消耗的混合使 ReCTSi 成为当前资源有限场景的最佳方法。有关密码可于 https://github.com/ryanlaics/rectsi 索取。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=ReCTSi:+Resource-efficient+Correlated+Time+Series+Imputation+via+Decoupled+Pattern+Learning+and+Completeness-aware+Attentions)|0| |[Layer-Wise Adaptive Gradient Norm Penalizing Method for Efficient and Accurate Deep Learning](https://doi.org/10.1145/3637528.3671728)|Sunwoo Lee|Department of Computer Engineering, Inha University, Incheon, Republic of Korea|Sharpness-aware minimization (SAM) is known to improve the generalization performance of neural networks. However, it is not widely used in real-world applications yet due to its expensive model perturbation cost. A few variants of SAM have been proposed to tackle such an issue, but they commonly do not alleviate the cost noticeably. In this paper, we propose a lightweight layer-wise gradient norm penalizing method that tackles the expensive computational cost of SAM while maintaining its superior generalization performance. Our study empirically proves that the gradient norm of the whole model can be effectively suppressed by penalizing the gradient norm of only a few critical layers. We also theoretically show that such a partial model perturbation does not harm the convergence rate of SAM, allowing them to be safely adapted in real-world applications. To demonstrate the efficacy of the proposed method, we perform extensive experiments comparing the proposed method to mini-batch SGD and the conventional SAM using representative computer vision and language modeling benchmarks.|锐度感知最小化(SAM)已知可以提高神经网络的泛化性能。然而，由于其昂贵的模型摄动成本，在实际应用中还没有得到广泛的应用。已经提出了 SAM 的一些变体来解决这个问题，但是它们通常不能显著地减少成本。本文提出了一种轻量级分层梯度范数惩罚方法，在保持 SAM 优越泛化性能的同时，解决了 SAM 计算量大的问题。我们的研究经验证明，整个模型的梯度范数可以有效地抑制惩罚只有少数临界层的梯度范数。理论上还证明了这种局部模型扰动不会影响 SAM 的收敛速度，使其能够安全地适应实际应用。为了验证该方法的有效性，我们利用具有代表性的计算机视觉和语言建模基准，对该方法与小批量 SGD 和常规 SAM 进行了广泛的实验比较。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Layer-Wise+Adaptive+Gradient+Norm+Penalizing+Method+for+Efficient+and+Accurate+Deep+Learning)|0| -|[Label Learning Method Based on Tensor Projection](https://doi.org/10.1145/3637528.3671671)|Jing Li, Quanxue Gao, Qianqian Wang, Cheng Deng, DeYan Xie|School of Science and Information Science, Qingdao Agricultural University, Qingdao, Shandong, China; School of Electronic Engineering, Xidian University, Xi'an, Shaanxi, China; School of Telecommunications Engineering, Xidian University, Xi'an, Shaanxi, China|Multi-view clustering method based on anchor graph has been widely concerneddue to its high efficiency and effectiveness. In order to avoidpost-processing, most of the existing anchor graph-based methods learnbipartite graphs with connected components. However, such methods have highrequirements on parameters, and in some cases it may not be possible to obtainbipartite graphs with clear connected components. To end this, we propose alabel learning method based on tensor projection (LLMTP). Specifically, weproject anchor graph into the label space through an orthogonal projectionmatrix to obtain cluster labels directly. Considering that the spatialstructure information of multi-view data may be ignored to a certain extentwhen projected in different views separately, we extend the matrix projectiontransformation to tensor projection, so that the spatial structure informationbetween views can be fully utilized. In addition, we introduce the tensorSchatten p-norm regularization to make the clustering label matrices ofdifferent views as consistent as possible. Extensive experiments have provedthe effectiveness of the proposed method.|基于锚图的多视图聚类方法因其高效性和有效性而受到广泛关注。为了避免后处理，现有的基于锚图的方法大多学习具有连通分量的二部图。然而，这些方法对参数有很高的要求，在某些情况下，可能不可能获得具有清晰连通分量的二部图。为此，我们提出了基于张量投影(LLMTP)的标记学习方法。具体地说，我们通过一个正交投影矩阵将锚图投影到标签空间中，直接得到聚类标签。考虑到多视图数据的空间结构信息在分别投影于不同视图时可能在一定程度上被忽略，将矩阵投影变换推广到张量投影，以充分利用视图间的空间结构信息。另外，我们引入了张量 Schatten p- 范数正则化，使得不同视图的聚类标记矩阵尽可能的一致。大量的实验证明了该方法的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Label+Learning+Method+Based+on+Tensor+Projection)|0| -|[Physics-informed Neural ODE for Post-disaster Mobility Recovery](https://doi.org/10.1145/3637528.3672027)|Jiahao Li, Huandong Wang, Xinlei Chen|Shenzhen International Graduate School, Tsinghua University & Pengcheng Laboratory, Shenzhen, China; Shenzhen International Graduate School, Tsinghua University, Shenzhen, China; Department of Electronic Engineering, Tsinghua University, Beijing, China|Urban mobility undergoes a profound decline in the aftermath of a disaster, subsequently exhibiting a complex recovery trajectory. Effectively capturing and predicting this dynamic recovery process holds paramount importance for devising more efficient post-disaster recovery strategies, such as resource allocation to areas with protracted recovery periods. Existing models for post-disaster mobility recovery predominantly employ basic mathematical methods, which are strongly based on simplifying assumptions, and their limited parameters restrict their capacity to fully capture the mobility recovery patterns. In response to this gap, we introduce the Coupled Dynamic Graph ODE Network (CDGON) to model the intricate dynamics of post-disaster mobility recovery. Our model seamlessly integrates existing physical knowledge pertaining to post-disaster mobility recovery and incorporates the nuanced interactions between intra-regional and inter-regional population flows. Extensive experimental results demonstrate the efficiency of our model in capturing the dynamic recovery patterns of urban population mobility in post-disaster scenarios, surpassing the capabilities of current dynamic graph prediction models.|灾后城市流动性大幅下降，随后呈现出复杂的恢复轨迹。有效捕捉和预测这一动态恢复过程对于制定更有效的灾后恢复战略至关重要，例如向恢复期较长的地区分配资源。现有的灾后机动性恢复模型主要采用基本的数学方法，这些方法强烈地基于简化的假设，其有限的参数限制了它们充分捕捉机动性恢复模式的能力。针对这一差距，我们引入耦合动态图 ODE 网络(CDON)来建立灾后移动性恢复的复杂动态模型。我们的模型无缝地整合了与灾后流动性恢复有关的现有物理知识，并纳入了区域内和区域间人口流动之间微妙的相互作用。大量的实验结果表明，我们的模型在捕捉灾后情景下城市人口流动动态恢复模式方面的效率超过了现有动态图预测模型的能力。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Physics-informed+Neural+ODE+for+Post-disaster+Mobility+Recovery)|0| -|[Causal Subgraph Learning for Generalizable Inductive Relation Prediction](https://doi.org/10.1145/3637528.3671972)|Mei Li, Xiaoguang Liu, Hua Ji, Shuangjia Zheng|; Department of Computer Science, Civil Aviation University of China, Tianjin, China; Global Institute of Future Technology, Shanghai Jiao Tong University, Shanghai, China; College of Computer Science, TMCC, SysNet, DISSec, GTIISC, Nankai University, Tianjin, China|Inductive relation reasoning in knowledge graphs aims at predicting missing triplets involving unseen entities and/or unseen relations. While subgraph-based methods that reason about the local structure surrounding a candidate triplet have shown promise, they often fall short in accurately modeling the causal dependence between a triplet's subgraph and its ground-truth label. This limitation typically results in a susceptibility to spurious correlations caused by confounders, adversely affecting generalization capabilities. Herein, we introduce a novel front-door adjustment-based approach designed to learn the causal relationship between subgraphs and their ground-truth labels, specifically for inductive relation prediction. We conceptualize the semantic information of subgraphs as a mediator and employ a graph data augmentation mechanism to create augmented subgraphs. Furthermore, we integrate a fusion module and a decoder within the front-door adjustment framework, enabling the estimation of the mediator's combination with augmented subgraphs. We also introduce the reparameterization trick in the fusion model to enhance model robustness. Extensive experiments on widely recognized benchmark datasets demonstrate the proposed method's superiority in inductive relation prediction, particularly for tasks involving unseen entities and unseen relations. Additionally, the subgraphs reconstructed by our decoder offer valuable insights into the model's decision-making process, enhancing transparency and interpretability.|知识图中归纳关系推理的目的是预测缺失的涉及看不见的实体和/或看不见的关系的三联体。虽然基于子图的方法，推理一个候选三元组周围的局部结构已经显示了希望，他们往往不能准确地建模一个三元组的子图和它的地面真理标签之间的因果关系。这种限制通常导致容易受到混杂因素引起的虚假相关性的影响，从而对泛化能力产生不利影响。在这里，我们介绍了一种新的前门调整为基础的方法，旨在了解因果关系之间的子图和他们的地面真理标签，特别是归纳关系预测。我们将子图的语义信息概念化为中介，并使用图形数据增强机制来创建增强子图。此外，我们在前门调整框架内集成了融合模块和解码器，使得能够估计中介者与增强子图的组合。为了增强模型的鲁棒性，我们还在融合模型中引入了重参数化技巧。通过对已被广泛认可的基准数据集的大量实验证明了该方法在归纳关系预测中的优越性，特别是对于涉及不可见实体和不可见关系的任务。此外，我们的解码器重建的子图提供了有价值的洞察力模型的决策过程，提高透明度和可解释性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Causal+Subgraph+Learning+for+Generalizable+Inductive+Relation+Prediction)|0| +|[Label Learning Method Based on Tensor Projection](https://doi.org/10.1145/3637528.3671671)|Jing Li, Quanxue Gao, Qianqian Wang, Cheng Deng, DeYan Xie|School of Science and Information Science, Qingdao Agricultural University, Qingdao, Shandong, China; School of Telecommunications Engineering, Xidian University, Xi'an, Shaanxi, China; School of Electronic Engineering, Xidian University, Xi'an, Shaanxi, China|Multi-view clustering method based on anchor graph has been widely concerneddue to its high efficiency and effectiveness. In order to avoidpost-processing, most of the existing anchor graph-based methods learnbipartite graphs with connected components. However, such methods have highrequirements on parameters, and in some cases it may not be possible to obtainbipartite graphs with clear connected components. To end this, we propose alabel learning method based on tensor projection (LLMTP). Specifically, weproject anchor graph into the label space through an orthogonal projectionmatrix to obtain cluster labels directly. Considering that the spatialstructure information of multi-view data may be ignored to a certain extentwhen projected in different views separately, we extend the matrix projectiontransformation to tensor projection, so that the spatial structure informationbetween views can be fully utilized. In addition, we introduce the tensorSchatten p-norm regularization to make the clustering label matrices ofdifferent views as consistent as possible. Extensive experiments have provedthe effectiveness of the proposed method.|基于锚图的多视图聚类方法因其高效性和有效性而受到广泛关注。为了避免后处理，现有的基于锚图的方法大多学习具有连通分量的二部图。然而，这些方法对参数有很高的要求，在某些情况下，可能不可能获得具有清晰连通分量的二部图。为此，我们提出了基于张量投影(LLMTP)的标记学习方法。具体地说，我们通过一个正交投影矩阵将锚图投影到标签空间中，直接得到聚类标签。考虑到多视图数据的空间结构信息在分别投影于不同视图时可能在一定程度上被忽略，将矩阵投影变换推广到张量投影，以充分利用视图间的空间结构信息。另外，我们引入了张量 Schatten p- 范数正则化，使得不同视图的聚类标记矩阵尽可能的一致。大量的实验证明了该方法的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Label+Learning+Method+Based+on+Tensor+Projection)|0| +|[Physics-informed Neural ODE for Post-disaster Mobility Recovery](https://doi.org/10.1145/3637528.3672027)|Jiahao Li, Huandong Wang, Xinlei Chen|Department of Electronic Engineering, Tsinghua University, Beijing, China; Shenzhen International Graduate School, Tsinghua University, Shenzhen, China; Shenzhen International Graduate School, Tsinghua University & Pengcheng Laboratory, Shenzhen, China|Urban mobility undergoes a profound decline in the aftermath of a disaster, subsequently exhibiting a complex recovery trajectory. Effectively capturing and predicting this dynamic recovery process holds paramount importance for devising more efficient post-disaster recovery strategies, such as resource allocation to areas with protracted recovery periods. Existing models for post-disaster mobility recovery predominantly employ basic mathematical methods, which are strongly based on simplifying assumptions, and their limited parameters restrict their capacity to fully capture the mobility recovery patterns. In response to this gap, we introduce the Coupled Dynamic Graph ODE Network (CDGON) to model the intricate dynamics of post-disaster mobility recovery. Our model seamlessly integrates existing physical knowledge pertaining to post-disaster mobility recovery and incorporates the nuanced interactions between intra-regional and inter-regional population flows. Extensive experimental results demonstrate the efficiency of our model in capturing the dynamic recovery patterns of urban population mobility in post-disaster scenarios, surpassing the capabilities of current dynamic graph prediction models.|灾后城市流动性大幅下降，随后呈现出复杂的恢复轨迹。有效捕捉和预测这一动态恢复过程对于制定更有效的灾后恢复战略至关重要，例如向恢复期较长的地区分配资源。现有的灾后机动性恢复模型主要采用基本的数学方法，这些方法强烈地基于简化的假设，其有限的参数限制了它们充分捕捉机动性恢复模式的能力。针对这一差距，我们引入耦合动态图 ODE 网络(CDON)来建立灾后移动性恢复的复杂动态模型。我们的模型无缝地整合了与灾后流动性恢复有关的现有物理知识，并纳入了区域内和区域间人口流动之间微妙的相互作用。大量的实验结果表明，我们的模型在捕捉灾后情景下城市人口流动动态恢复模式方面的效率超过了现有动态图预测模型的能力。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Physics-informed+Neural+ODE+for+Post-disaster+Mobility+Recovery)|0| +|[Causal Subgraph Learning for Generalizable Inductive Relation Prediction](https://doi.org/10.1145/3637528.3671972)|Mei Li, Xiaoguang Liu, Hua Ji, Shuangjia Zheng|; Global Institute of Future Technology, Shanghai Jiao Tong University, Shanghai, China; College of Computer Science, TMCC, SysNet, DISSec, GTIISC, Nankai University, Tianjin, China; Department of Computer Science, Civil Aviation University of China, Tianjin, China|Inductive relation reasoning in knowledge graphs aims at predicting missing triplets involving unseen entities and/or unseen relations. While subgraph-based methods that reason about the local structure surrounding a candidate triplet have shown promise, they often fall short in accurately modeling the causal dependence between a triplet's subgraph and its ground-truth label. This limitation typically results in a susceptibility to spurious correlations caused by confounders, adversely affecting generalization capabilities. Herein, we introduce a novel front-door adjustment-based approach designed to learn the causal relationship between subgraphs and their ground-truth labels, specifically for inductive relation prediction. We conceptualize the semantic information of subgraphs as a mediator and employ a graph data augmentation mechanism to create augmented subgraphs. Furthermore, we integrate a fusion module and a decoder within the front-door adjustment framework, enabling the estimation of the mediator's combination with augmented subgraphs. We also introduce the reparameterization trick in the fusion model to enhance model robustness. Extensive experiments on widely recognized benchmark datasets demonstrate the proposed method's superiority in inductive relation prediction, particularly for tasks involving unseen entities and unseen relations. Additionally, the subgraphs reconstructed by our decoder offer valuable insights into the model's decision-making process, enhancing transparency and interpretability.|知识图中归纳关系推理的目的是预测缺失的涉及看不见的实体和/或看不见的关系的三联体。虽然基于子图的方法，推理一个候选三元组周围的局部结构已经显示了希望，他们往往不能准确地建模一个三元组的子图和它的地面真理标签之间的因果关系。这种限制通常导致容易受到混杂因素引起的虚假相关性的影响，从而对泛化能力产生不利影响。在这里，我们介绍了一种新的前门调整为基础的方法，旨在了解因果关系之间的子图和他们的地面真理标签，特别是归纳关系预测。我们将子图的语义信息概念化为中介，并使用图形数据增强机制来创建增强子图。此外，我们在前门调整框架内集成了融合模块和解码器，使得能够估计中介者与增强子图的组合。为了增强模型的鲁棒性，我们还在融合模型中引入了重参数化技巧。通过对已被广泛认可的基准数据集的大量实验证明了该方法在归纳关系预测中的优越性，特别是对于涉及不可见实体和不可见关系的任务。此外，我们的解码器重建的子图提供了有价值的洞察力模型的决策过程，提高透明度和可解释性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Causal+Subgraph+Learning+for+Generalizable+Inductive+Relation+Prediction)|0| |[SimDiff: Simple Denoising Probabilistic Latent Diffusion Model for Data Augmentation on Multi-modal Knowledge Graph](https://doi.org/10.1145/3637528.3671769)|Ran Li, Shimin Di, Lei Chen, Xiaofang Zhou|HKUST(GZ) & HKUST, Guangzhou, China; HKUST, Hong Kong SAR, China|In this paper, we address the challenges of data augmentation in Multi-Modal Knowledge Graphs (MMKGs), a relatively under-explored area. We propose a novel diffusion-based generative model, the Simple Denoising Probabilistic Latent Diffusion Model (SimDiff). SimDiff is capable of handling different data modalities including the graph topology in a unified manner by the same diffusion model in the latent space. It enhances the utilization of multi-modal data and encourage the multi-modal fusion and reduces the dependency on limited training data. We validate our method in downstream Entity Alignment (EA) tasks in MMKGs, demonstrating that even when using only half of the seed entities in training, our methods can still achieve superior performance. This work contributes to the field by providing a new data generation or augmentation method for MMKGs, potentially paving the way for more effective use of MMKGs in various applications. Code is made available at https://github.com/ranlislz/SimDiff.|在本文中，我们讨论了多模态知识图(MMKGs)中数据增强的挑战，这是一个相对欠缺探索的领域。我们提出了一种新的基于扩散的生成模型，简单去噪概率潜在扩散模型(simDiff)。SimDiff 能够通过潜空间中相同的扩散模型统一处理包括图拓扑在内的不同数据模式。它提高了多模态数据的利用率，鼓励了多模态融合，减少了对有限训练数据的依赖。我们验证了我们的方法在 MMKG 的下游实体对齐(EA)任务，证明即使只使用一半的种子实体在训练，我们的方法仍然可以取得优越的性能。这项工作为 MMKG 提供了一种新的数据生成或增强方法，为 MMKG 在各种应用中更有效地使用铺平了道路，从而为该领域做出了贡献。代码可在 https://github.com/ranlislz/simdiff 查阅。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=SimDiff:+Simple+Denoising+Probabilistic+Latent+Diffusion+Model+for+Data+Augmentation+on+Multi-modal+Knowledge+Graph)|0| |[ITPNet: Towards Instantaneous Trajectory Prediction for Autonomous Driving](https://doi.org/10.1145/3637528.3671681)|Rongqing Li, Changsheng Li, Yuhang Li, Hanjie Li, Yi Chen, Ye Yuan, Guoren Wang|Beijing Institute of Technology, Beijing, China|Trajectory prediction of moving traffic agents is crucial for the safety of autonomous vehicles, whereas previous approaches usually rely on sufficiently long-observed trajectory (e.g., 2 seconds) to predict the future trajectory of the agents. However, in many real-world scenarios, it is not realistic to collect adequate observed locations for moving agents, leading to the collapse of most prediction models. For instance, when a moving car suddenly appears and is very close to an autonomous vehicle because of the obstruction, it is quite necessary for the autonomous vehicle to quickly and accurately predict the future trajectories of the car with limited observed trajectory locations. In light of this, we focus on investigating the task of instantaneous trajectory prediction, i.e., two observed locations are available during inference. To this end, we put forward a general and plug-and-play instantaneous trajectory prediction approach, called ITPNet. Specifically, we propose a backward forecasting mechanism to reversely predict the latent feature representations of unobserved historical trajectories of the agent based on its two observed locations and then leverage them as complementary information for future trajectory prediction. Meanwhile, due to the inevitable existence of noise and redundancy in the predicted latent feature representations, we further devise a Noise Redundancy Reduction Former (NRRFormer) module, which aims to filter out noise and redundancy from unobserved trajectories and integrate the filtered features and observed features into a compact query representation for future trajectory predictions. In essence, ITPNet can be naturally compatible with existing trajectory prediction models, enabling them to gracefully handle the case of instantaneous trajectory prediction. Extensive experiments on the Argoverse and nuScenes datasets demonstrate ITPNet outperforms the baselines by a large margin and shows its efficacy with different trajectory prediction models.|移动交通主体的轨迹预测对自主车辆的安全至关重要，而以前的方法通常依赖于足够长的观测轨迹(例如，2秒)来预测主体的未来轨迹。然而，在许多实际场景中，为移动代理收集足够的观察位置是不现实的，这会导致大多数预测模型的崩溃。例如，当一辆行驶中的汽车突然出现，并且因为障碍物而非常接近无人机时，无人机必须在有限的轨迹位置上快速而准确地预测汽车的未来轨迹。鉴于此，我们着重研究瞬时轨迹预测的任务，即在推断过程中有两个可观测的位置。为此，我们提出了一种通用的即插即用的瞬时轨迹预测方法 ITPNet。具体来说，我们提出了一种逆向预测机制，该机制根据 Agent 的两个观测位置反向预测未观测历史轨迹的潜在特征表示，然后利用它们作为未来轨迹预测的补充信息。同时，由于预测的潜在特征表示中不可避免地存在噪声和冗余，我们进一步设计了噪声冗余降低前(NRRForm)模块，旨在从未观测轨迹中过滤掉噪声和冗余，并将过滤后的特征和观测特征整合到一个紧凑的查询表示中，用于未来的轨迹预测。从本质上说，ITPNet 可以自然地与现有的轨道预测模型兼容，使它们能够优雅地处理瞬时轨道预测的情况。在 Argoverse 和 nuScenes 数据集上进行的大量实验表明，ITPNet 的性能大大优于基线，并且在不同的轨迹预测模型上显示了其有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=ITPNet:+Towards+Instantaneous+Trajectory+Prediction+for+Autonomous+Driving)|0| |[InLN: Knowledge-aware Incremental Leveling Network for Dynamic Advertising](https://doi.org/10.1145/3637528.3672032)|Xujia Li, Jingshu Peng, Lei Chen|; Hong Kong University of Science and Technology, Hong Kong SAR, China|In today's fast-paced world, advertisers are increasingly demanding real-time and accurate personalized ad delivery based on dynamic preference modeling, which emphasizes the temporality existing in both user preference and product characteristics. Meanwhile, with the development of graph neural networks (GNNs), E-commerce knowledge graphs (KG) with rich semantic relatedness are invoked to improve accuracy and provide appropriate explanations to encourage advertisers' willingness to invest in ad expenses. However, it is still challenging for existing methods to comprehensively consider both time-series interactions and graph-structured knowledge triples in a unified model, i.e., the case in knowledge-aware dynamic advertising. The interaction graph between users and products changes rapidly over time, while the knowledge in KG remains relatively stable. This results in an uneven distribution of temporal and semantic information, causing existing GNNs to fail in this scenario. In this work, we quantitatively define the above phenomenon as temporal unevenness and introduce the Incremental Leveling Network (InLN) with three novel techniques: the periodic-focusing window for node-level dynamic modeling, the biased temporal walk for subgraph-level dynamic modeling and the incremental leveling mechanism for KG updating. Verified by comprehensive and intensive experiments, InLN outperforms nine baseline models in three tasks by substantial margins, reaching up to a 9.9% improvement and averaging a 5.7% increase.|在当今快节奏的世界中，广告主对基于动态偏好建模的实时、准确的个性化广告投放提出了越来越高的要求。同时，随着图神经网络(GNN)的发展，电子商务知识图(KG)具有丰富的语义相关性，以提高准确性，并提供适当的解释，以鼓励广告商的意愿投资广告费用。然而，在统一的模型中，即知识感知的动态广告中，综合考虑时间序列交互作用和图形结构知识三元组仍然是现有方法面临的挑战。用户与产品之间的交互图随着时间的推移迅速变化，而 KG 中的知识保持相对稳定。这导致了时间和语义信息的不均衡分布，导致现有的 GNN 在这种情况下失败。本文将上述现象定量地定义为时间不均匀性，并引入了增量水准网络(InLN)的三种新技术: 用于节点级动态建模的周期聚焦窗口、用于子图级动态建模的有偏时间步行以及用于 KG 更新的增量水准机制。经过全面和深入的实验验证，InLN 在三个任务中的表现大大优于9个基线模型，提高了9.9% ，平均提高了5.7% 。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=InLN:+Knowledge-aware+Incremental+Leveling+Network+for+Dynamic+Advertising)|0| |[Bi-Objective Contract Allocation for Guaranteed Delivery Advertising](https://doi.org/10.1145/3637528.3671752)|Yan Li, Yundu Huang, Wuyang Mao, Furong Ye, Xiang He, Zhonglin Zu, Shaowei Cai|; Alibaba Group, Beijing, China; Alibaba Group, Hangzhou, China|Contemporary systems of Guaranteed Delivery (GD) advertising work with two different stages, namely, the offline selling stage and the online serving stage. The former deals with contract allocation, and the latter fulfills the impression allocation of signed contracts. Existing work usually handles these two stages separately. For example, contracts are formulated offline without concerning practical situations in the online serving stage. Therefore, we address in this paper a bi-objective contract allocation for GD advertising, which maximizes the impressions, i.e., Ad resource assignments, allocated for the new incoming advertising orders, and at the same time, controls the balance in the inventories. Since the proposed problem is high dimensional and heavily constrained, we design an efficient local search that focuses on the two objectives alternatively. The experimental results indicate that our algorithm outperforms multi-objective evolutionary algorithms and Gurobi, the former of which is commonly applied for multi-objective optimization and the latter of which is a well-known competitive commercial tool.|现代广告传播保证系统分为线下销售阶段和线上服务阶段。前者处理合同分配问题，后者实现已签订合同的印象分配。现有的工作通常分别处理这两个阶段。例如，合同是在线下制定的，与在线服务阶段的实际情况无关。因此，本文提出了一种双目标广告合同分配模型，即广告资源分配模型，该模型可以最大限度地提高广告客户对广告的印象，同时控制广告库存的平衡。由于提出的问题是高维和严重约束，我们设计了一个有效的局部搜索，重点是两个目标交替。实验结果表明，该算法的性能优于多目标进化算法和 Gurobi 算法，前者是多目标优化的常用算法，后者是众所周知的竞争性商业工具。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Bi-Objective+Contract+Allocation+for+Guaranteed+Delivery+Advertising)|0| -|[Improving Robustness of Hyperbolic Neural Networks by Lipschitz Analysis](https://doi.org/10.1145/3637528.3671875)|Yuekang Li, Yidan Mao, Yifei Yang, Dongmian Zou|Applied Mathematics and Computational Sciences, DNAS, Duke Kunshan University, Kunshan, China; Electronic Information School, Wuhan University, Wuhan, China; Zu Chongzhi Center and Data Science Research Center, DNAS, Duke Kunshan University, Kunshan, China|Hyperbolic neural networks (HNNs) are emerging as a promising tool for representing data embedded in non-Euclidean geometries, yet their adoption has been hindered by challenges related to stability and robustness. In this work, we conduct a rigorous Lipschitz analysis for HNNs and propose using Lipschitz regularization as a novel strategy to enhance their robustness. Our comprehensive investigation spans both the Poincaré ball model and the hyperboloid model, establishing Lipschitz bounds for HNN layers. Importantly, our analysis provides detailed insights into the behavior of the Lipschitz bounds as they relate to feature norms, particularly distinguishing between scenarios where features have unit norms and those with large norms. Further, we study regularization using the derived Lipschitz bounds. Our empirical validations demonstrate consistent improvements in HNN robustness against noisy perturbations.|双曲神经网络(HNN)正在成为表示非欧几里德几何中嵌入的数据的一种有前途的工具，然而它们的采用受到与稳定性和鲁棒性有关的挑战的阻碍。本文对 HNN 进行了严格的 Lipschitz 分析，提出了利用 Lipschitz 正则化作为一种新的增强鲁棒性的策略。我们的全面调查跨越了庞加莱圆盘模型和双曲面模型，确定了 HNN 层的 Lipschitz 界限。重要的是，我们的分析提供了关于 Lipschitz 界限与特征规范相关的行为的详细见解，特别是区分特征具有单位规范和具有大规范的情景。进一步，我们利用导出的 Lipschitz 界研究正则化。我们的经验验证表明，一致的改进 HNN 对噪声扰动的鲁棒性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Improving+Robustness+of+Hyperbolic+Neural+Networks+by+Lipschitz+Analysis)|0| -|[ZeroG: Investigating Cross-dataset Zero-shot Transferability in Graphs](https://doi.org/10.1145/3637528.3671982)|Yuhan Li, Peisong Wang, Zhixun Li, Jeffrey Xu Yu, Jia Li|HKUST (GZ), Guangzhou, China; THU, Shenzhen, China; CUHK, Hong Kong SAR, China|With the development of foundation models such as large language models, zero-shot transfer learning has become increasingly significant. This is highlighted by the generative capabilities of NLP models like GPT-4, and the retrieval-based approaches of CV models like CLIP, both of which effectively bridge the gap between seen and unseen data. In the realm of graph learning, the continuous emergence of new graphs and the challenges of human labeling also amplify the necessity for zero-shot transfer learning, driving the exploration of approaches that can generalize across diverse graph data without necessitating dataset-specific and label-specific fine-tuning. In this study, we extend such paradigms to Zero-shot transferability in Graphs by introducing ZeroG, a new framework tailored to enable cross-dataset generalization. Addressing the inherent challenges such as feature misalignment, mismatched label spaces, and negative transfer, we leverage a language model to encode both node attributes and class semantics, ensuring consistent feature dimensions across datasets. We also propose a prompt-based subgraph sampling module that enriches the semantic information and structure information of extracted subgraphs using prompting nodes and neighborhood aggregation, respectively. We further adopt a lightweight fine-tuning strategy that reduces the risk of overfitting and maintains the zero-shot learning efficacy of the language model. The results underscore the effectiveness of our model in achieving significant cross-dataset zero-shot transferability, opening pathways for the development of graph foundation models.|随着大型语言模型等基础模型的不断发展，零冲击迁移学习变得越来越重要。GPT-4等自然语言处理模型的生成能力和 CLIP 等 CV 模型的基于检索的方法都突出了这一点，这两种方法都有效地弥合了可见数据和不可见数据之间的差距。在图形学习领域，新图形的不断出现和人类标记的挑战也放大了零传递学习的必要性，推动了对可以在不同图形数据之间推广的方法的探索，而不需要数据集特定和标签特定的微调。在这项研究中，我们通过引入 ZeroG，将这些范例扩展到图中的零点可转移性，ZeroG 是一个新的框架，可以实现跨数据集的泛化。针对特征错位、标签空间不匹配和负迁移等内在挑战，我们利用语言模型对节点属性和类语义进行编码，确保跨数据集的特征维一致。我们还提出了一个基于提示的子图抽样模块，分别使用提示节点和邻域聚合来丰富提取子图的语义信息和结构信息。我们进一步采用了轻量级的微调策略，降低了过度拟合的风险，并保持了语言模型的零点学习效率。这些结果强调了我们的模型在实现显著的跨数据集零拍可转移性方面的有效性，为图形基础模型的开发打开了通路。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=ZeroG:+Investigating+Cross-dataset+Zero-shot+Transferability+in+Graphs)|0| -|[Rethinking Fair Graph Neural Networks from Re-balancing](https://doi.org/10.1145/3637528.3671826)|Zhixun Li, Yushun Dong, Qiang Liu, Jeffrey Xu Yu|University of Virginia, Charlottesville, USA; The Chinese University of Hong Kong, Hong Kong, Hong Kong; Institute of Automation, Chinese Academy of Sciences, Beijing, China|Driven by the powerful representation ability of Graph Neural Networks (GNNs), plentiful GNN models have been widely deployed in many real-world applications. Nevertheless, due to distribution disparities between different demographic groups, fairness in high-stake decision-making systems is receiving increasing attention. Although lots of recent works devoted to improving the fairness of GNNs and achieved considerable success, they all require significant architectural changes or additional loss functions requiring more hyper-parameter tuning. Surprisingly, we find that simple re-balancing methods can easily match or surpass existing fair GNN methods. We claim that the imbalance across different demographic groups is a significant source of unfairness, resulting in imbalanced contributions from each group to the parameters updating. However, these simple re-balancing methods have their own shortcomings during training. In this paper, we propose FairGB, Fair Graph Neural Network via re-Balancing, which mitigates the unfairness of GNNs by group balancing. Technically, FairGB consists of two modules: counterfactual node mixup and contribution alignment loss. Firstly, we select counterfactual pairs across inter-domain and inter-class, and interpolate the ego-networks to generate new samples. Guided by analysis, we can reveal the debiasing mechanism of our model by the causal view and prove that our strategy can make sensitive attributes statistically independent from target labels. Secondly, we reweigh the contribution of each group according to gradients. By combining these two modules, they can mutually promote each other. Experimental results on benchmark datasets show that our method can achieve state-of-the-art results concerning both utility and fairness metrics. Code is available at https://github.com/ZhixunLEE/FairGB.|由于图形神经网络(GNN)强大的表示能力，大量的 GNN 模型被广泛应用于实际应用中。然而，由于不同人口群体之间的分布差异，高风险决策系统的公平性越来越受到重视。尽管最近许多致力于提高 GNN 公平性的工作取得了相当大的成功，但它们都需要重大的体系结构改变或额外的损失函数，需要更多的超参数调整。令人惊讶的是，我们发现简单的重新平衡方法可以很容易地匹配或超过现有的公平 GNN 方法。我们认为，不同人口群体之间的不平衡是不公平的一个重要来源，导致每个群体对参数更新的贡献不平衡。然而，这些简单的再平衡方法在训练过程中也有自己的缺点。本文通过重新平衡提出了 FairGB，即公平图神经网络，通过组平衡来缓解 GNN 的不公平性。从技术上讲，FairGB 包括两个模块: 反事实节点混合和贡献对齐损失。首先，我们选择跨域和跨类的反事实对，并插值自我网络生成新的样本。在分析的指导下，我们可以通过因果观点揭示模型的去偏机制，并证明我们的策略可以使敏感属性在统计上独立于目标标签。其次，我们根据梯度重新衡量每组的贡献。通过这两个模块的结合，它们可以相互促进。在基准数据集上的实验结果表明，该方法在效用度量和公平度量方面都取得了较好的效果。密码可于 https://github.com/zhixunlee/fairgb 索取。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Rethinking+Fair+Graph+Neural+Networks+from+Re-balancing)|0| -|[MulSTE: A Multi-view Spatio-temporal Learning Framework with Heterogeneous Event Fusion for Demand-supply Prediction](https://doi.org/10.1145/3637528.3672030)|Li Lin, Zhiqiang Lu, Shuai Wang, Yunhuai Liu, Zhiqing Hong, Haotian Wang, Shuai Wang|Southeast University, Nanjing, China; Peking University, Beijing, China; Rutgers University, Piscataway, USA; Southeast University, Nanjing, Jiangsu, China; JD Logistics, Beijing, China|Recently, integrated warehouse and distribution logistics systems are widely used in E-commerce industries to adjust to constantly changing customer demands. It makes the prediction of purchase demand and delivery supply capacity a crucial problem to streamline operations and improve efficiency. The interaction between such demand and supply not only relies on their economic relationships but also on consumer psychology caused by daily events, such as epidemics, promotions, and festivals. Although existing studies have made great efforts in the joint prediction of demand and supply considering modeling the demand-supply interactions, they seldom refer to the impacts of diverse events. In this work, we propose MulSTE, a Multi-view Spatio-Temporal learning framework with heterogeneous Event fusion. Firstly, an Event Fusion Representation (EFR) module is designed to fuse the textual, numerical, and categorical heterogeneous information for emergent and periodic events. Secondly, a Multi-graph Adaptive Convolution Recurrent Network (MGACRN) is developed as the spatio-temporal encoder (ST-Encoder) to capture the evolutional features of demand, supply, and events. Thirdly, the Event Gated Demand-Supply Interaction Attention (EGIA) module is designed to model the demand-supply interactions during events. The evaluations are conducted on two real-world datasets collected from JD Logistics and public websites. The experimental results show that our method outperforms state-of-the-art baselines in various metrics.|近年来，仓储与配送一体化物流系统广泛应用于电子商务行业，以适应不断变化的客户需求。它使得采购需求和供应能力的预测成为精简业务和提高效率的关键问题。这种需求和供给之间的相互作用不仅依赖于它们之间的经济关系，而且依赖于日常事件(如流行病、促销和节日)引起的消费者心理。虽然现有的研究在考虑需求-供给相互作用模型的供需联合预测方面做出了很大的努力，但很少涉及到不同事件的影响。在这项工作中，我们提出了一个多视图的时空异质事件融合学习框架 MulSTE。首先，设计了事件融合表示(EFR)模块，对突发事件和周期性事件的文本、数字和分类异构信息进行融合。其次，提出了一种多图自适应卷积回归网络(MGACRN)作为时空编码器(ST-Encoder)来捕捉需求、供给和事件的演化特征。第三，设计了事件门限需求-供给交互注意(EGIA)模块，对事件期间的需求-供给交互进行建模。这些评估是在从 JD Logistics 和公共网站收集的两个真实世界数据集上进行的。实验结果表明，我们的方法在各种度量方面都优于最先进的基线。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=MulSTE:+A+Multi-view+Spatio-temporal+Learning+Framework+with+Heterogeneous+Event+Fusion+for+Demand-supply+Prediction)|0| -|[PSMC: Provable and Scalable Algorithms for Motif Conductance Based Graph Clustering](https://doi.org/10.1145/3637528.3671666)|Longlong Lin, Tao Jia, Zeli Wang, Jin Zhao, RongHua Li|; College of Computer and Information Science, Southwest University, Chongqing, China; Shenzhen Institute of Technology & Beijing Institute of Technology, Shenzhen, China; Chongqing University of Post and Telecommunications, Chongqing, China|Higher-order graph clustering aims to partition the graph using frequently occurring subgraphs (i.e., motifs), instead of the lower-order edges, as the atomic clustering unit, which has been recognized as the state-of-the-art solution in ground truth community detection and knowledge discovery. Motif conductance is one of the most promising higher-order graph clustering models due to its strong interpretability. However, existing motif conductance based graph clustering algorithms are mainly limited by a seminal two-stage reweighting computing framework, needing to enumerate all motif instances to obtain an edge-weighted graph for partitioning. However, such a framework has two-fold vital defects: (1) It can only provide a quadratic bound for the motif with three vertices, and whether there is provable clustering quality for other motifs is still an open question. (2) The enumeration procedure of motif instances incurs prohibitively high costs against large motifs or large dense graphs due to combinatorial explosions. Besides, expensive spectral clustering or local graph diffusion on the edge-weighted graph also makes existing methods unable to handle massive graphs with millions of nodes. To overcome these dilemmas, we propose a Provable and Scalable Motif Conductance algorithm PSMC, which has a fixed and motif-independent approximation ratio for any motif. Specifically, PSMC first defines a new vertex metric Motif Resident based on the given motif, which can be computed locally. Then, it iteratively deletes the vertex with the smallest motif resident value very efficiently using novel dynamic update technologies. Finally, it outputs the locally optimal result during the above iterative process. To further boost efficiency, we propose several effective bounds to estimate the motif resident value of each vertex, which can greatly reduce computational costs. Empirical results on real-life and synthetic demonstrate that our proposed algorithms achieve 3.2-32 times speedup and improve the quality by at least 12 times than the state-of-the art baselines.|高阶图聚类的目的是使用频繁出现的子图(即图案)来划分图，而不是使用低阶边作为原子聚类单元，这已经被认为是地面真相社区检测和知识发现的最先进的解决方案。基序电导是最有前途的高阶图聚类模型之一，因为它具有很强的可解释性。然而，现有的基于基序电导的图聚类算法主要受限于一个开创性的两阶段重权计算框架，需要枚举所有的基序实例才能得到用于划分的边加权图。然而，这种框架有两个致命的缺陷: (1)它只能为三个顶点的图案提供一个二次界，其他图案是否具有可证明的聚类质量仍然是一个悬而未决的问题。(2)由于组合爆炸的原因，对于大图案或大密度图，主题实例的计数过程会产生高得令人望而却步的代价。此外，边加权图上昂贵的 SVD 或局部图扩散也使得现有的方法无法处理具有数百万个节点的海量图。为了克服这些困难，我们提出了一个可证明的可扩展的基元电导算法 PSMC，它对任何基元都有一个固定的和基元无关的近似比率。具体来说，PSMC 首先定义一个新的基于给定主题的顶点度量主题驻留，它可以在局部计算。然后，利用新的动态更新技术，迭代地删除基元驻留值最小的顶点。最后，在上述迭代过程中输出局部最优结果。为了进一步提高效率，我们提出了几个有效的界来估计每个顶点的模驻留值，这可以大大降低计算量。实际和合成的实验结果表明，我们提出的算法比最先进的基线算法提高了3.2 -32倍的速度，至少提高了12倍的质量。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=PSMC:+Provable+and+Scalable+Algorithms+for+Motif+Conductance+Based+Graph+Clustering)|0| +|[Improving Robustness of Hyperbolic Neural Networks by Lipschitz Analysis](https://doi.org/10.1145/3637528.3671875)|Yuekang Li, Yidan Mao, Yifei Yang, Dongmian Zou|Zu Chongzhi Center and Data Science Research Center, DNAS, Duke Kunshan University, Kunshan, China; Electronic Information School, Wuhan University, Wuhan, China; Applied Mathematics and Computational Sciences, DNAS, Duke Kunshan University, Kunshan, China|Hyperbolic neural networks (HNNs) are emerging as a promising tool for representing data embedded in non-Euclidean geometries, yet their adoption has been hindered by challenges related to stability and robustness. In this work, we conduct a rigorous Lipschitz analysis for HNNs and propose using Lipschitz regularization as a novel strategy to enhance their robustness. Our comprehensive investigation spans both the Poincaré ball model and the hyperboloid model, establishing Lipschitz bounds for HNN layers. Importantly, our analysis provides detailed insights into the behavior of the Lipschitz bounds as they relate to feature norms, particularly distinguishing between scenarios where features have unit norms and those with large norms. Further, we study regularization using the derived Lipschitz bounds. Our empirical validations demonstrate consistent improvements in HNN robustness against noisy perturbations.|双曲神经网络(HNN)正在成为表示非欧几里德几何中嵌入的数据的一种有前途的工具，然而它们的采用受到与稳定性和鲁棒性有关的挑战的阻碍。本文对 HNN 进行了严格的 Lipschitz 分析，提出了利用 Lipschitz 正则化作为一种新的增强鲁棒性的策略。我们的全面调查跨越了庞加莱圆盘模型和双曲面模型，确定了 HNN 层的 Lipschitz 界限。重要的是，我们的分析提供了关于 Lipschitz 界限与特征规范相关的行为的详细见解，特别是区分特征具有单位规范和具有大规范的情景。进一步，我们利用导出的 Lipschitz 界研究正则化。我们的经验验证表明，一致的改进 HNN 对噪声扰动的鲁棒性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Improving+Robustness+of+Hyperbolic+Neural+Networks+by+Lipschitz+Analysis)|0| +|[ZeroG: Investigating Cross-dataset Zero-shot Transferability in Graphs](https://doi.org/10.1145/3637528.3671982)|Yuhan Li, Peisong Wang, Zhixun Li, Jeffrey Xu Yu, Jia Li|HKUST (GZ), Guangzhou, China; CUHK, Hong Kong SAR, China; THU, Shenzhen, China|With the development of foundation models such as large language models, zero-shot transfer learning has become increasingly significant. This is highlighted by the generative capabilities of NLP models like GPT-4, and the retrieval-based approaches of CV models like CLIP, both of which effectively bridge the gap between seen and unseen data. In the realm of graph learning, the continuous emergence of new graphs and the challenges of human labeling also amplify the necessity for zero-shot transfer learning, driving the exploration of approaches that can generalize across diverse graph data without necessitating dataset-specific and label-specific fine-tuning. In this study, we extend such paradigms to Zero-shot transferability in Graphs by introducing ZeroG, a new framework tailored to enable cross-dataset generalization. Addressing the inherent challenges such as feature misalignment, mismatched label spaces, and negative transfer, we leverage a language model to encode both node attributes and class semantics, ensuring consistent feature dimensions across datasets. We also propose a prompt-based subgraph sampling module that enriches the semantic information and structure information of extracted subgraphs using prompting nodes and neighborhood aggregation, respectively. We further adopt a lightweight fine-tuning strategy that reduces the risk of overfitting and maintains the zero-shot learning efficacy of the language model. The results underscore the effectiveness of our model in achieving significant cross-dataset zero-shot transferability, opening pathways for the development of graph foundation models.|随着大型语言模型等基础模型的不断发展，零冲击迁移学习变得越来越重要。GPT-4等自然语言处理模型的生成能力和 CLIP 等 CV 模型的基于检索的方法都突出了这一点，这两种方法都有效地弥合了可见数据和不可见数据之间的差距。在图形学习领域，新图形的不断出现和人类标记的挑战也放大了零传递学习的必要性，推动了对可以在不同图形数据之间推广的方法的探索，而不需要数据集特定和标签特定的微调。在这项研究中，我们通过引入 ZeroG，将这些范例扩展到图中的零点可转移性，ZeroG 是一个新的框架，可以实现跨数据集的泛化。针对特征错位、标签空间不匹配和负迁移等内在挑战，我们利用语言模型对节点属性和类语义进行编码，确保跨数据集的特征维一致。我们还提出了一个基于提示的子图抽样模块，分别使用提示节点和邻域聚合来丰富提取子图的语义信息和结构信息。我们进一步采用了轻量级的微调策略，降低了过度拟合的风险，并保持了语言模型的零点学习效率。这些结果强调了我们的模型在实现显著的跨数据集零拍可转移性方面的有效性，为图形基础模型的开发打开了通路。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=ZeroG:+Investigating+Cross-dataset+Zero-shot+Transferability+in+Graphs)|0| +|[Rethinking Fair Graph Neural Networks from Re-balancing](https://doi.org/10.1145/3637528.3671826)|Zhixun Li, Yushun Dong, Qiang Liu, Jeffrey Xu Yu|University of Virginia, Charlottesville, USA; Institute of Automation, Chinese Academy of Sciences, Beijing, China; The Chinese University of Hong Kong, Hong Kong, Hong Kong|Driven by the powerful representation ability of Graph Neural Networks (GNNs), plentiful GNN models have been widely deployed in many real-world applications. Nevertheless, due to distribution disparities between different demographic groups, fairness in high-stake decision-making systems is receiving increasing attention. Although lots of recent works devoted to improving the fairness of GNNs and achieved considerable success, they all require significant architectural changes or additional loss functions requiring more hyper-parameter tuning. Surprisingly, we find that simple re-balancing methods can easily match or surpass existing fair GNN methods. We claim that the imbalance across different demographic groups is a significant source of unfairness, resulting in imbalanced contributions from each group to the parameters updating. However, these simple re-balancing methods have their own shortcomings during training. In this paper, we propose FairGB, Fair Graph Neural Network via re-Balancing, which mitigates the unfairness of GNNs by group balancing. Technically, FairGB consists of two modules: counterfactual node mixup and contribution alignment loss. Firstly, we select counterfactual pairs across inter-domain and inter-class, and interpolate the ego-networks to generate new samples. Guided by analysis, we can reveal the debiasing mechanism of our model by the causal view and prove that our strategy can make sensitive attributes statistically independent from target labels. Secondly, we reweigh the contribution of each group according to gradients. By combining these two modules, they can mutually promote each other. Experimental results on benchmark datasets show that our method can achieve state-of-the-art results concerning both utility and fairness metrics. Code is available at https://github.com/ZhixunLEE/FairGB.|由于图形神经网络(GNN)强大的表示能力，大量的 GNN 模型被广泛应用于实际应用中。然而，由于不同人口群体之间的分布差异，高风险决策系统的公平性越来越受到重视。尽管最近许多致力于提高 GNN 公平性的工作取得了相当大的成功，但它们都需要重大的体系结构改变或额外的损失函数，需要更多的超参数调整。令人惊讶的是，我们发现简单的重新平衡方法可以很容易地匹配或超过现有的公平 GNN 方法。我们认为，不同人口群体之间的不平衡是不公平的一个重要来源，导致每个群体对参数更新的贡献不平衡。然而，这些简单的再平衡方法在训练过程中也有自己的缺点。本文通过重新平衡提出了 FairGB，即公平图神经网络，通过组平衡来缓解 GNN 的不公平性。从技术上讲，FairGB 包括两个模块: 反事实节点混合和贡献对齐损失。首先，我们选择跨域和跨类的反事实对，并插值自我网络生成新的样本。在分析的指导下，我们可以通过因果观点揭示模型的去偏机制，并证明我们的策略可以使敏感属性在统计上独立于目标标签。其次，我们根据梯度重新衡量每组的贡献。通过这两个模块的结合，它们可以相互促进。在基准数据集上的实验结果表明，该方法在效用度量和公平度量方面都取得了较好的效果。密码可于 https://github.com/zhixunlee/fairgb 索取。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Rethinking+Fair+Graph+Neural+Networks+from+Re-balancing)|0| +|[MulSTE: A Multi-view Spatio-temporal Learning Framework with Heterogeneous Event Fusion for Demand-supply Prediction](https://doi.org/10.1145/3637528.3672030)|Li Lin, Zhiqiang Lu, Shuai Wang, Yunhuai Liu, Zhiqing Hong, Haotian Wang, Shuai Wang|Southeast University, Nanjing, China; Peking University, Beijing, China; Rutgers University, Piscataway, USA; JD Logistics, Beijing, China; Southeast University, Nanjing, Jiangsu, China|Recently, integrated warehouse and distribution logistics systems are widely used in E-commerce industries to adjust to constantly changing customer demands. It makes the prediction of purchase demand and delivery supply capacity a crucial problem to streamline operations and improve efficiency. The interaction between such demand and supply not only relies on their economic relationships but also on consumer psychology caused by daily events, such as epidemics, promotions, and festivals. Although existing studies have made great efforts in the joint prediction of demand and supply considering modeling the demand-supply interactions, they seldom refer to the impacts of diverse events. In this work, we propose MulSTE, a Multi-view Spatio-Temporal learning framework with heterogeneous Event fusion. Firstly, an Event Fusion Representation (EFR) module is designed to fuse the textual, numerical, and categorical heterogeneous information for emergent and periodic events. Secondly, a Multi-graph Adaptive Convolution Recurrent Network (MGACRN) is developed as the spatio-temporal encoder (ST-Encoder) to capture the evolutional features of demand, supply, and events. Thirdly, the Event Gated Demand-Supply Interaction Attention (EGIA) module is designed to model the demand-supply interactions during events. The evaluations are conducted on two real-world datasets collected from JD Logistics and public websites. The experimental results show that our method outperforms state-of-the-art baselines in various metrics.|近年来，仓储与配送一体化物流系统广泛应用于电子商务行业，以适应不断变化的客户需求。它使得采购需求和供应能力的预测成为精简业务和提高效率的关键问题。这种需求和供给之间的相互作用不仅依赖于它们之间的经济关系，而且依赖于日常事件(如流行病、促销和节日)引起的消费者心理。虽然现有的研究在考虑需求-供给相互作用模型的供需联合预测方面做出了很大的努力，但很少涉及到不同事件的影响。在这项工作中，我们提出了一个多视图的时空异质事件融合学习框架 MulSTE。首先，设计了事件融合表示(EFR)模块，对突发事件和周期性事件的文本、数字和分类异构信息进行融合。其次，提出了一种多图自适应卷积回归网络(MGACRN)作为时空编码器(ST-Encoder)来捕捉需求、供给和事件的演化特征。第三，设计了事件门限需求-供给交互注意(EGIA)模块，对事件期间的需求-供给交互进行建模。这些评估是在从 JD Logistics 和公共网站收集的两个真实世界数据集上进行的。实验结果表明，我们的方法在各种度量方面都优于最先进的基线。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=MulSTE:+A+Multi-view+Spatio-temporal+Learning+Framework+with+Heterogeneous+Event+Fusion+for+Demand-supply+Prediction)|0| +|[PSMC: Provable and Scalable Algorithms for Motif Conductance Based Graph Clustering](https://doi.org/10.1145/3637528.3671666)|Longlong Lin, Tao Jia, Zeli Wang, Jin Zhao, RongHua Li|College of Computer and Information Science, Southwest University, Chongqing, China; ; Shenzhen Institute of Technology & Beijing Institute of Technology, Shenzhen, China; Chongqing University of Post and Telecommunications, Chongqing, China|Higher-order graph clustering aims to partition the graph using frequently occurring subgraphs (i.e., motifs), instead of the lower-order edges, as the atomic clustering unit, which has been recognized as the state-of-the-art solution in ground truth community detection and knowledge discovery. Motif conductance is one of the most promising higher-order graph clustering models due to its strong interpretability. However, existing motif conductance based graph clustering algorithms are mainly limited by a seminal two-stage reweighting computing framework, needing to enumerate all motif instances to obtain an edge-weighted graph for partitioning. However, such a framework has two-fold vital defects: (1) It can only provide a quadratic bound for the motif with three vertices, and whether there is provable clustering quality for other motifs is still an open question. (2) The enumeration procedure of motif instances incurs prohibitively high costs against large motifs or large dense graphs due to combinatorial explosions. Besides, expensive spectral clustering or local graph diffusion on the edge-weighted graph also makes existing methods unable to handle massive graphs with millions of nodes. To overcome these dilemmas, we propose a Provable and Scalable Motif Conductance algorithm PSMC, which has a fixed and motif-independent approximation ratio for any motif. Specifically, PSMC first defines a new vertex metric Motif Resident based on the given motif, which can be computed locally. Then, it iteratively deletes the vertex with the smallest motif resident value very efficiently using novel dynamic update technologies. Finally, it outputs the locally optimal result during the above iterative process. To further boost efficiency, we propose several effective bounds to estimate the motif resident value of each vertex, which can greatly reduce computational costs. Empirical results on real-life and synthetic demonstrate that our proposed algorithms achieve 3.2-32 times speedup and improve the quality by at least 12 times than the state-of-the art baselines.|高阶图聚类的目的是使用频繁出现的子图(即图案)来划分图，而不是使用低阶边作为原子聚类单元，这已经被认为是地面真相社区检测和知识发现的最先进的解决方案。基序电导是最有前途的高阶图聚类模型之一，因为它具有很强的可解释性。然而，现有的基于基序电导的图聚类算法主要受限于一个开创性的两阶段重权计算框架，需要枚举所有的基序实例才能得到用于划分的边加权图。然而，这种框架有两个致命的缺陷: (1)它只能为三个顶点的图案提供一个二次界，其他图案是否具有可证明的聚类质量仍然是一个悬而未决的问题。(2)由于组合爆炸的原因，对于大图案或大密度图，主题实例的计数过程会产生高得令人望而却步的代价。此外，边加权图上昂贵的 SVD 或局部图扩散也使得现有的方法无法处理具有数百万个节点的海量图。为了克服这些困难，我们提出了一个可证明的可扩展的基元电导算法 PSMC，它对任何基元都有一个固定的和基元无关的近似比率。具体来说，PSMC 首先定义一个新的基于给定主题的顶点度量主题驻留，它可以在局部计算。然后，利用新的动态更新技术，迭代地删除基元驻留值最小的顶点。最后，在上述迭代过程中输出局部最优结果。为了进一步提高效率，我们提出了几个有效的界来估计每个顶点的模驻留值，这可以大大降低计算量。实际和合成的实验结果表明，我们提出的算法比最先进的基线算法提高了3.2 -32倍的速度，至少提高了12倍的质量。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=PSMC:+Provable+and+Scalable+Algorithms+for+Motif+Conductance+Based+Graph+Clustering)|0| |[CONFIDE: Contextual Finite Difference Modelling of PDEs](https://doi.org/10.1145/3637528.3671676)|Ori Linial, Orly Avner, Dotan Di Castro||We introduce a method for inferring an explicit PDE from a data sample generated by previously unseen dynamics, based on a learned context. The training phase integrates knowledge of the form of the equation with a differential scheme, while the inference phase yields a PDE that fits the data sample and enables both signal prediction and data explanation. We include results of extensive experimentation, comparing our method to SOTA approaches, together with ablation studies that examine different flavors of our solution in terms of prediction error and explainability.|我们介绍了一种方法来推断一个明确的偏微分方程从数据样本生成以前看不见的动力学，基于学习上下文。训练阶段将方程形式的知识与微分格式结合起来，而推断阶段产生一个符合数据样本的偏微分方程，并且能够进行信号预测和数据解释。我们包括广泛的实验结果，比较我们的方法与 SOTA 方法，以及消融研究，检查我们的解决方案在预测误差和可解释性方面的不同口味。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=CONFIDE:+Contextual+Finite+Difference+Modelling+of+PDEs)|0| |[CASA: Clustered Federated Learning with Asynchronous Clients](https://doi.org/10.1145/3637528.3671979)|Boyi Liu, Yiming Ma, Zimu Zhou, Yexuan Shi, Shuyuan Li, Yongxin Tong|SKLCCSE Lab, Beihang University, Beijing, China; School of Data Science, City University of Hong Kong, Hong Kong, China|Clustered Federated Learning (CFL) is an emerging paradigm to extract insights from data on IoT devices. Through iterative client clustering and model aggregation, CFL adeptly manages data heterogeneity, ensures privacy, and delivers personalized models to heterogeneous devices. Traditional CFL approaches, which operate synchronously, suffer from prolonged latency for waiting slow devices during clustering and aggregation. This paper advocates a shift to asynchronous CFL, allowing the server to process client updates as they arrive. This shift enhances training efficiency yet introduces complexities to the iterative training cycle. To this end, we present CASA, a novel CFL scheme for Clustering-Aggregation Synergy under Asynchrony. Built upon a holistic theoretical understanding of asynchrony's impact on CFL, CASA adopts a bi-level asynchronous aggregation method and a buffer-aided dynamic clustering strategy to harmonize between clustering and aggregation. Extensive evaluations on standard benchmarks show that CASA outperforms representative baselines in model accuracy and achieves 2.28-6.49× higher convergence speed.|集群联邦学习(Clustered Federated Learning，CFL)是一种从物联网设备的数据中提取见解的新兴范式。通过迭代的客户端集群和模型聚合，CFL 能够很好地管理数据异构性，确保隐私，并向异构设备提供个性化的模型。传统的 CFL 方法是同步操作的，在集群和聚合过程中，由于等待速度较慢的设备而延迟较长。本文主张转向异步 CFL，允许服务器在客户端更新到达时进行处理。这种转变提高了训练效率，但同时也给迭代训练周期带来了复杂性。为此，我们提出了 CASA，这是一种新颖的非同步聚类-聚集协同 CFL 方案。基于对异步对 CFL 影响的全面理论认识，CASA 采用了双层异步聚合方法和缓冲区辅助的动态聚类策略来协调聚类和聚合。对标准基准的广泛评估表明，CASA 在模型精度方面优于典型基准，收敛速度达到2.28 -6.49倍。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=CASA:+Clustered+Federated+Learning+with+Asynchronous+Clients)|0| |[FAST: An Optimization Framework for Fast Additive Segmentation in Transparent ML](https://doi.org/10.1145/3637528.3671996)|Brian Liu, Rahul Mazumder|Massachusetts Institute of Technology, Cambridge, MA, USA|We present FAST, an optimization framework for fast additive segmentation. FAST segments piecewise constant shape functions for each feature in a dataset to produce transparent additive models. The framework leverages a novel optimization procedure to fit these models ~2 orders of magnitude faster than existing state-of-the-art methods, such as explainable boosting machines[20]. We also develop new feature selection algorithms in the FAST framework to fit parsimonious models that perform well. Through experiments and case studies, we show that FAST improves the computational efficiency and interpretability of additive models.|提出了一种快速加性分割的优化框架 FAST。FAST 分割数据集中每个特征的分段常数形状函数，以生成透明的可加模型。该框架利用一种新颖的优化程序来适应这些模型，比现有的最先进的方法(如可解释的增压机)数量级更快。我们还在 FAST 框架中开发了新的特征选择算法，以适应性能良好的简约模型。通过实验和案例研究表明，FAST 提高了可加模型的计算效率和可解释性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=FAST:+An+Optimization+Framework+for+Fast+Additive+Segmentation+in+Transparent+ML)|0| |[Asymmetric Beta Loss for Evidence-Based Safe Semi-Supervised Multi-Label Learning](https://doi.org/10.1145/3637528.3671756)|HaoZhe Liu, MingKun Xie, ChenChen Zong, ShengJun Huang|Nanjing University of Aeronautics and Astronautics, Nanjing, China|The goal of semi-supervised multi-label learning (SSMLL) is to improve model performance by leveraging the information of unlabeled data. Recent studies usually adopt the pseudo-labeling strategy to tackle unlabeled data based on the assumption that labeled and unlabeled data share the same distribution. However, in realistic scenarios, unlabeled examples are often collected through cost-effective methods, inevitably introducing out-of-distribution (OOD) data, leading to a significant decline in model performance. In this paper, we propose a safe semi-supervised multi-label learning framework based on the theory of evidential deep learning (EDL), with the goal of achieving robust and effective unlabeled data exploitation. On one hand, we propose the asymmetric beta loss to not only compensate for the lack of robustness in common MLL losses, but also to solve the inherent positive-negative imbalance problem faced by the EDL losses in MLL. On the other hand, to construct a robust SSMLL framework, we adopt a dual-head structure to generate class probabilities and instance uncertainties. The former are used to generate pseudo-labels, while the latter are utilized to filter OOD examples. To avoid the need for threshold estimation, we develop a dual-measurement weighted loss function to safely perform unlabeled training. Extensive experiments on multiple benchmark datasets verify the effectiveness of the proposed method in both OOD detection and SSMLL tasks.|半监督多标记学习(SSMLL)的目标是通过利用未标记数据的信息来提高模型的性能。最近的研究通常采用伪标记策略来处理未标记数据，这是基于标记数据和未标记数据分布相同的假设。然而，在现实的场景中，未标记的例子通常是通过具有成本效益的方法收集的，这不可避免地会引入分布外(OOD)数据，从而导致模型性能的显著下降。本文基于证据深度学习(EDL)理论，提出了一种安全的半监督多标记学习框架，目的是实现鲁棒有效的未标记数据开发。一方面，我们提出非对称贝塔损耗不仅可以弥补一般 MLL 损耗鲁棒性的不足，而且可以解决 MLL 中 EDL 损耗固有的正负不平衡问题。另一方面，为了构造一个健壮的 SSMLL 框架，我们采用双头结构来产生类概率和实例不确定性。前者用于生成伪标签，后者用于过滤 OOD 示例。为了避免阈值估计的需要，我们开发了一个双测量加权损失函数来安全地执行未标记训练。在多个基准数据集上的大量实验验证了该方法在面向对象检测和 SSMLL 任务中的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Asymmetric+Beta+Loss+for+Evidence-Based+Safe+Semi-Supervised+Multi-Label+Learning)|0| |[An Unsupervised Learning Framework Combined with Heuristics for the Maximum Minimal Cut Problem](https://doi.org/10.1145/3637528.3671704)|Huaiyuan Liu, Xianzhang Liu, Donghua Yang, Hongzhi Wang, Yingchi Long, Mengtong Ji, Dongjing Miao, Zhiyu Liang|Harbin Institute of Technology, Harbin, China|The Maximum Minimal Cut Problem (MMCP), a NP-hard combinatorial optimization (CO) problem, has not received much attention due to the demanding and challenging bi-connectivity constraint. Moreover, as a CO problem, it is also a daunting task for machine learning, especially without labeled instances. To deal with these problems, this work proposes an unsupervised learning framework combined with heuristics for MMCP that can provide valid and high-quality solutions. As far as we know, this is the first work that explores machine learning and heuristics to solve MMCP. The unsupervised solver is inspired by a relaxation-plus-rounding approach, the relaxed solution is parameterized by graph neural networks, and the cost and penalty of MMCP are explicitly written out, which can train the model end-to-end. A crucial observation is that each solution corresponds to at least one spanning tree. Based on this finding, a heuristic solver that implements tree transformations by adding vertices is utilized to repair and improve the solution quality of the unsupervised solver. Alternatively, the graph is simplified while guaranteeing solution consistency, which reduces the running time. We conduct extensive experiments to evaluate our framework and give a specific application. The results demonstrate the superiority of our method against two techniques designed.|最大最小切入问题(MMCP)是一个 NP 组合优化问题，由于双连通性约束的要求和挑战，它并没有得到很多关注。此外，作为一个 CO 问题，它也是一个令人畏缩的机器学习任务，特别是没有标记的实例。为了解决这些问题，这项工作提出了一个非监督式学习框架结合启发式的 MMCP，可以提供有效和高质量的解决方案。据我们所知，这是第一个研究机器学习和启发式算法来解决 MMCP 的工作。无监督求解器采用松弛-舍入法，松弛解用图形神经网络参数化，明确写出 MMCP 的代价和代价，可以对模型进行端到端的训练。一个重要的观察结果是，每个解对应至少一个生成树。在此基础上，提出了一种通过增加顶点实现树变换的启发式求解器，用于修复和提高无监督求解器的求解质量。或者，在保证解的一致性的同时简化图形，从而减少运行时间。我们进行了广泛的实验，以评估我们的框架，并给出了具体的应用。实验结果证明了该方法对两种设计技术的优越性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=An+Unsupervised+Learning+Framework+Combined+with+Heuristics+for+the+Maximum+Minimal+Cut+Problem)|0| |[ACER: Accelerating Complex Event Recognition via Two-Phase Filtering under Range Bitmap-Based Indexes](https://doi.org/10.1145/3637528.3671814)|Shizhe Liu, Haipeng Dai, Shaoxu Song, Meng Li, Jingsong Dai, Rong Gu, Guihai Chen|BNRist, Tsinghua University, Beijing, China; State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China|Complex event recognition (CER) refers to identifying specific patterns composed of several primitive events in event stores. Since full-scanning event stores to identify primitive events holding query constraint conditions will incur costly I/O overhead, a mainstream and practical approach is using index techniques to obtain these events. However, prior index-based approaches suffer from significant I/O and sorting overhead when dealing with high predicate selectivity or long query window (common in real-world applications), which leads to high query latency. To address this issue, we propose ACER, a Range Bitmap-based index, to accelerate CER. Firstly, ACER achieves a low index space overhead by grouping the events with the same type into a cluster and compressing the cluster data, alleviating the I/O overhead of reading indexes. Secondly, ACER builds Range Bitmaps in batch (block) for queried attributes and ensures that the events of each cluster in the index block are chronologically ordered. Then, ACER can always obtain ordered query results for a specific event type through merge operations, avoiding sorting overhead. Most importantly, ACER avoids unnecessary disk access in indexes and events via two-phase filtering based on the window condition, thus alleviating the I/O overhead further. Our experiments on six real-world and synthetic datasets demonstrate that ACER reduces the query latency by up to one order of magnitude compared with SOTA techniques.|复杂事件识别(CER)是指识别由事件存储中的几个基本事件组成的特定模式。由于全面扫描事件存储以识别持有查询约束条件的基本事件将产生高昂的 I/O 开销，因此主流和实用的方法是使用索引技术来获取这些事件。然而，以前的基于索引的方法在处理高谓词选择性或长查询窗口(在现实世界的应用程序中很常见)时会遇到严重的 I/O 和排序开销，从而导致高查询延迟。为了解决这个问题，我们提出 ACER，一个基于范围位图的索引，以加速 CER。首先，ACER 通过将相同类型的事件分组到一个集群中并压缩集群数据，降低了读取索引的 I/O 开销，从而实现了较低的索引空间开销。其次，ACER 以批处理(块)方式为查询属性构建 Range Bitmap，并确保索引块中每个集群的事件按时间顺序排序。然后，ACER 总是可以通过合并操作获得特定事件类型的有序查询结果，从而避免排序开销。最重要的是，ACER 通过基于窗口条件的两阶段过滤，避免了索引和事件中不必要的磁盘访问，从而进一步减少了 I/O 开销。我们在六个真实世界和合成数据集上的实验表明，与 SOTA 技术相比，ACER 可以减少多达一个数量级的查询延迟。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=ACER:+Accelerating+Complex+Event+Recognition+via+Two-Phase+Filtering+under+Range+Bitmap-Based+Indexes)|0| -|[Revisiting Modularity Maximization for Graph Clustering: A Contrastive Learning Perspective](https://doi.org/10.1145/3637528.3671967)|Yunfei Liu, Jintang Li, Yuehe Chen, Ruofan Wu, Ericbk Wang, Jing Zhou, Sheng Tian, Shuheng Shen, Xing Fu, Changhua Meng, Weiqiang Wang, Liang Chen|Ant Group, Hangzhou, China; Ant Group, Hangzhou, Zhejiang, China; Unaffiliated, Guangzhou, China|Graph clustering, a fundamental and challenging task in graph mining, aims to classify nodes in a graph into several disjoint clusters. In recent years, graph contrastive learning (GCL) has emerged as a dominant line of research in graph clustering and advances the new state-of-the-art. However, GCL-based methods heavily rely on graph augmentations and contrastive schemes, which may potentially introduce challenges such as semantic drift and scalability issues. Another promising line of research involves the adoption of modularity maximization, a popular and effective measure for community detection, as the guiding principle for clustering tasks. Despite the recent progress, the underlying mechanism of modularity maximization is still not well understood. In this work, we dig into the hidden success of modularity maximization for graph clustering. Our analysis reveals the strong connections between modularity maximization and graph contrastive learning, where positive and negative examples are naturally defined by modularity. In light of our results, we propose a community-aware graph clustering framework, coined øurs, which leverages modularity maximization as a contrastive pretext task to effectively uncover the underlying information of communities in graphs, while avoiding the problem of semantic drift. Extensive experiments on multiple graph datasets verify the effectiveness of øurs in terms of scalability and clustering performance compared to state-of-the-art graph clustering methods. Notably, øurs easily scales a sufficiently large graph with 100M nodes while outperforming strong baselines.|图聚类是图挖掘中的一项基础性和挑战性工作，其目的是将一个图中的节点划分为若干个不相交的聚类。近年来，图形对比学习(GCL)已经成为图形聚类的主流研究方向，并取得了新的进展。然而，基于 GCL 的方法在很大程度上依赖于图增强和对比方案，这可能会带来诸如语义漂移和可伸缩性问题等挑战。另一个很有前途的研究方向是采用模块化最大化作为聚类任务的指导原则。模块化最大化是一种流行的、有效的社区检测方法。尽管最近取得了一些进展，但是模块化最大化的潜在机制仍然没有得到很好的理解。在本文中，我们深入研究了图聚类中模块化最大化的隐藏成功。我们的分析揭示了模块化最大化和图的对比学习之间的紧密联系，其中积极和消极的例子是由模块化自然定义的。根据我们的研究结果，我们提出了一个社区感知的图聚类框架，称为 øurs，它利用模块化最大化作为一个对比的借口任务来有效地揭示图中社区的潜在信息，同时避免了语义漂移的问题。通过对多个图形数据集的大量实验，验证了 øurs 在可伸缩性和聚类性能方面的有效性，并与现有的图形聚类方法进行了比较。值得注意的是，øurs 可以轻松地扩展拥有100m 节点的足够大图，同时表现优于强基线。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Revisiting+Modularity+Maximization+for+Graph+Clustering:+A+Contrastive+Learning+Perspective)|0| +|[Revisiting Modularity Maximization for Graph Clustering: A Contrastive Learning Perspective](https://doi.org/10.1145/3637528.3671967)|Yunfei Liu, Jintang Li, Yuehe Chen, Ruofan Wu, Ericbk Wang, Jing Zhou, Sheng Tian, Shuheng Shen, Xing Fu, Changhua Meng, Weiqiang Wang, Liang Chen|Unaffiliated, Guangzhou, China; Ant Group, Hangzhou, China; Ant Group, Hangzhou, Zhejiang, China|Graph clustering, a fundamental and challenging task in graph mining, aims to classify nodes in a graph into several disjoint clusters. In recent years, graph contrastive learning (GCL) has emerged as a dominant line of research in graph clustering and advances the new state-of-the-art. However, GCL-based methods heavily rely on graph augmentations and contrastive schemes, which may potentially introduce challenges such as semantic drift and scalability issues. Another promising line of research involves the adoption of modularity maximization, a popular and effective measure for community detection, as the guiding principle for clustering tasks. Despite the recent progress, the underlying mechanism of modularity maximization is still not well understood. In this work, we dig into the hidden success of modularity maximization for graph clustering. Our analysis reveals the strong connections between modularity maximization and graph contrastive learning, where positive and negative examples are naturally defined by modularity. In light of our results, we propose a community-aware graph clustering framework, coined øurs, which leverages modularity maximization as a contrastive pretext task to effectively uncover the underlying information of communities in graphs, while avoiding the problem of semantic drift. Extensive experiments on multiple graph datasets verify the effectiveness of øurs in terms of scalability and clustering performance compared to state-of-the-art graph clustering methods. Notably, øurs easily scales a sufficiently large graph with 100M nodes while outperforming strong baselines.|图聚类是图挖掘中的一项基础性和挑战性工作，其目的是将一个图中的节点划分为若干个不相交的聚类。近年来，图形对比学习(GCL)已经成为图形聚类的主流研究方向，并取得了新的进展。然而，基于 GCL 的方法在很大程度上依赖于图增强和对比方案，这可能会带来诸如语义漂移和可伸缩性问题等挑战。另一个很有前途的研究方向是采用模块化最大化作为聚类任务的指导原则。模块化最大化是一种流行的、有效的社区检测方法。尽管最近取得了一些进展，但是模块化最大化的潜在机制仍然没有得到很好的理解。在本文中，我们深入研究了图聚类中模块化最大化的隐藏成功。我们的分析揭示了模块化最大化和图的对比学习之间的紧密联系，其中积极和消极的例子是由模块化自然定义的。根据我们的研究结果，我们提出了一个社区感知的图聚类框架，称为 øurs，它利用模块化最大化作为一个对比的借口任务来有效地揭示图中社区的潜在信息，同时避免了语义漂移的问题。通过对多个图形数据集的大量实验，验证了 øurs 在可伸缩性和聚类性能方面的有效性，并与现有的图形聚类方法进行了比较。值得注意的是，øurs 可以轻松地扩展拥有100m 节点的足够大图，同时表现优于强基线。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Revisiting+Modularity+Maximization+for+Graph+Clustering:+A+Contrastive+Learning+Perspective)|0| |[Graph Data Condensation via Self-expressive Graph Structure Reconstruction](https://doi.org/10.1145/3637528.3671710)|Zhanyu Liu, Chaolv Zeng, Guanjie Zheng|Shanghai Jiao Tong University, Shanghai, China|With the increasing demands of training graph neural networks (GNNs) on large-scale graphs, graph data condensation has emerged as a critical technique to relieve the storage and time costs during the training phase. It aims to condense the original large-scale graph to a much smaller synthetic graph while preserving the essential information necessary for efficiently training a downstream GNN. However, existing methods concentrate either on optimizing node features exclusively or endeavor to independently learn node features and the graph structure generator. They could not explicitly leverage the information of the original graph structure and failed to construct an interpretable graph structure for the synthetic dataset. To address these issues, we introduce a novel framework named Graph Data Condensation via Self-expressive Graph Structure Reconstruction (GCSR). Our method stands out by (1) explicitly incorporating the original graph structure into the condensing process and (2) capturing the nuanced interdependencies between the condensed nodes by reconstructing an interpretable self-expressive graph structure. Extensive experiments and comprehensive analysis validate the efficacy of the proposed method across diverse GNN models and datasets. Our code is available at https://github.com/zclzcl0223/GCSR.|随着训练图神经网络对大规模图形需求的不断增加，图形数据压缩已经成为降低训练阶段存储量和时间成本的关键技术。它的目的是将原始的大规模图压缩成一个小得多的合成图，同时保留有效训练下游 GNN 所需的必要信息。然而，现有的方法要么专门优化节点特征，要么致力于独立学习节点特征和图结构生成器。它们无法显式地利用原始图结构的信息，也无法为合成数据集构造可解释的图结构。为了解决这些问题，我们提出了一种基于自表达图结构重构(GCSR)的图数据压缩框架。该方法的突出之处在于: (1)将原始的图结构明确地融入到压缩过程中; (2)通过重构一个可解释的自我表达图结构来捕捉压缩节点之间微妙的相互依赖关系。大量的实验和综合分析验证了该方法在不同 GNN 模型和数据集上的有效性。我们的代码可以在 https://github.com/zclzcl0223/gcsr 找到。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Graph+Data+Condensation+via+Self-expressive+Graph+Structure+Reconstruction)|0| |[Generative Pretrained Hierarchical Transformer for Time Series Forecasting](https://doi.org/10.1145/3637528.3671855)|Zhiding Liu, Jiqian Yang, Mingyue Cheng, Yucong Luo, Zhi Li|; Shenzhen International Graduate School, Tsinghua University, Shenzhen, Guangdong, China|Recent efforts have been dedicated to enhancing time series forecasting accuracy by introducing advanced network architectures and self-supervised pretraining strategies. Nevertheless, existing approaches still exhibit two critical drawbacks. Firstly, these methods often rely on a single dataset for training, limiting the model's generalizability due to the restricted scale of the training data. Secondly, the one-step generation schema is widely followed, which necessitates a customized forecasting head and overlooks the temporal dependencies in the output series, and also leads to increased training costs under different horizon length settings. To address these issues, we propose a novel generative pretrained hierarchical transformer architecture for forecasting, named GPHT. There are two aspects of key designs in GPHT. On the one hand, we advocate for constructing a mixed dataset under the channel-independent assumption for pretraining our model, comprising various datasets from diverse data scenarios. This approach significantly expands the scale of training data, allowing our model to uncover commonalities in time series data and facilitating improved transfer to specific datasets. On the other hand, GPHT employs an auto-regressive forecasting approach, effectively modeling temporal dependencies in the output series. Importantly, no customized forecasting head is required, enablinga single model to forecast at arbitrary horizon settings. We conduct sufficient experiments on eight datasets with mainstream self-supervised pretraining models and supervised models. The results demonstrated that GPHT surpasses the baseline models across various fine-tuning and zero/few-shot learning settings in the traditional long-term forecasting task, providing support for verifying the feasibility of pretraining time series large models. We make our codes publicly available\footnotehttps://github.com/icantnamemyself/GPHT.|通过引入先进的网络结构和自我监督预训练策略，最近致力于提高时间序列预测的准确性。尽管如此，现有的方法仍然表现出两个关键的缺点。首先，这些方法往往依赖于一个单一的数据集进行训练，由于训练数据的规模有限，限制了模型的通用性。其次，一步发电模式得到了广泛的遵循，它需要一个定制的预测头，忽略了输出序列中的时间依赖性，并导致在不同的时域长度设置下培训成本的增加。为了解决这些问题，我们提出了一种新的预生成预训练分层变压器结构，称为 GPHT。GPHT 的关键设计包括两个方面。一方面，我们提倡在通道无关的假设下构建一个混合数据集来预训练我们的模型，包括来自不同数据场景的各种数据集。这种方法极大地扩展了训练数据的规模，使我们的模型能够发现时间序列数据中的共性，并促进改进对特定数据集的传输。另一方面，GPHT 采用自回归预测方法，有效地建立了输出序列中的时间依赖关系。重要的是，没有定制的预测头是必需的，使一个单一的模型能够预测在任意的水平设置。我们使用主流的自监督预训练模型和监督模型对八个数据集进行了充分的实验。结果表明，在传统的长期预测任务中，GPHT 超越了各种微调和零/少镜头学习环境下的基线模型，为验证预训练时间序列大模型的可行性提供了支持。我们使我们的代码公开脚注 https:// github.com/icantnamemyself/gpht。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Generative+Pretrained+Hierarchical+Transformer+for+Time+Series+Forecasting)|0| -|[AIM: Attributing, Interpreting, Mitigating Data Unfairness](https://doi.org/10.1145/3637528.3671797)|Zhining Liu, Ruizhong Qiu, Zhichen Zeng, Yada Zhu, Hendrik F. Hamann, Hanghang Tong|IBM Research, Yorktown Heights, NY, USA; University of Illinois Urbana-Champaign, Urbana, IL, USA|Data collected in the real world often encapsulates historical discriminationagainst disadvantaged groups and individuals. Existing fair machine learning(FairML) research has predominantly focused on mitigating discriminative biasin the model prediction, with far less effort dedicated towards exploring howto trace biases present in the data, despite its importance for thetransparency and interpretability of FairML. To fill this gap, we investigate anovel research problem: discovering samples that reflect biases/prejudices fromthe training data. Grounding on the existing fairness notions, we lay out asample bias criterion and propose practical algorithms for measuring andcountering sample bias. The derived bias score provides intuitive sample-levelattribution and explanation of historical bias in data. On this basis, wefurther design two FairML strategies via sample-bias-informed minimal dataediting. They can mitigate both group and individual unfairness at the cost ofminimal or zero predictive utility loss. Extensive experiments and analyses onmultiple real-world datasets demonstrate the effectiveness of our methods inexplaining and mitigating unfairness. Code is available athttps://github.com/ZhiningLiu1998/AIM.|在现实世界中收集的数据往往概括了历史上对弱势群体和个人的歧视。现有的公平机器学习(FairML)研究主要集中在减轻模型预测中的歧视性偏差，而很少致力于探索如何跟踪数据中存在的偏差，尽管它对于 FairML 的透明度和可解释性非常重要。为了填补这个空白，我们调查了一个新的研究问题: 发现样本反映训练数据的偏见/偏见。在现有公平性概念的基础上，给出了样本偏差的判定准则，并提出了测量和抵消样本偏差的实用算法。导出的偏差评分提供了直观的样本水平归因和历史偏差的数据解释。在此基础上，进一步设计了两种基于样本偏差信息的最小数据编辑的 FairML 策略。它们可以以最小或零预测效用损失为代价来缓解群体和个人的不公平。对多个真实世界数据集的大量实验和分析证明了我们的方法在解释和减少不公平方面的有效性。代码可以通过 https:// github.com/zhiningliu1998/aim 获得。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=AIM:+Attributing,+Interpreting,+Mitigating+Data+Unfairness)|0| -|[High-Dimensional Distributed Sparse Classification with Scalable Communication-Efficient Global Updates](https://doi.org/10.1145/3637528.3672038)|Fred Lu, Ryan R. Curtin, Edward Raff, Francis Ferraro, James Holt|Booz Allen Hamilton, McLean, USA; Laboratory for Physical Sciences, College Park, USA; University of Maryland, Baltimore County, Baltimore, USA; Booz Allen Hamilton & University of Maryland, Baltimore County, McLean, USA|As the size of datasets used in statistical learning continues to grow, distributed training of models has attracted increasing attention. These methods partition the data and exploit parallelism to reduce memory and runtime, but suffer increasingly from communication costs as the data size or the number of iterations grows. Recent work on linear models has shown that a surrogate likelihood can be optimized locally to iteratively improve on an initial solution in a communication-efficient manner. However, existing versions of these methods experience multiple shortcomings as the data size becomes massive, including diverging updates and efficiently handling sparsity. In this work we develop solutions to these problems which enable us to learn a communication-efficient distributed logistic regression model even beyond millions of features. In our experiments we demonstrate a large improvement in accuracy over distributed algorithms with only a few distributed update steps needed, and similar or faster runtimes. Our code is available at https://github.com/FutureComputing4AI/ProxCSL.|随着用于统计学习的数据集规模的不断扩大，模型的分布式训练越来越受到人们的关注。这些方法对数据进行分区并利用并行性来减少内存和运行时，但是随着数据大小或迭代次数的增加，通信成本越来越高。最近对线性模型的研究表明，代理似然可以局部优化，以便以通信有效的方式迭代地改进初始解。然而，这些方法的现有版本在数据量变得巨大时会遇到多重缺陷，包括不同的更新和有效地处理稀疏性。在这项工作中，我们开发这些问题的解决方案，使我们能够学习一个通信效率高的分布式 Logit模型模型，甚至超过数百万个功能。在我们的实验中，我们证明了分布式算法在精度上有了很大的提高，只需要少量的分布式更新步骤，以及类似或更快的运行时。我们的代码可以在 https://github.com/futurecomputing4ai/proxcsl 找到。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=High-Dimensional+Distributed+Sparse+Classification+with+Scalable+Communication-Efficient+Global+Updates)|0| -|[FUGNN: Harmonizing Fairness and Utility in Graph Neural Networks](https://doi.org/10.1145/3637528.3671834)|Renqiang Luo, Huafei Huang, Shuo Yu, Zhuoyang Han, Estrid He, Xiuzhen Zhang, Feng Xia|RMIT University, Melbourne, Victoria, Australia; Dalian University of Technology, Dalian, Liaoning, China|Fairness-aware Graph Neural Networks (GNNs) often face a challenging trade-off, where prioritizing fairness may require compromising utility. In this work, we re-examine fairness through the lens of spectral graph theory, aiming to reconcile fairness and utility within the framework of spectral graph learning. We explore the correlation between sensitive features and spectrum in GNNs, using theoretical analysis to delineate the similarity between original sensitive features and those after convolution under different spectra. Our analysis reveals a reduction in the impact of similarity when the eigenvectors associated with the largest magnitude eigenvalue exhibit directional similarity. Based on these theoretical insights, we propose FUGNN, a novel spectral graph learning approach that harmonizes the conflict between fairness and utility. FUGNN ensures algorithmic fairness and utility by truncating the spectrum and optimizing eigenvector distribution during the encoding process. The fairness-aware eigenvector selection reduces the impact of convolution on sensitive features while concurrently minimizing the sacrifice of utility. FUGNN further optimizes the distribution of eigenvectors through a transformer architecture. By incorporating the optimized spectrum into the graph convolution network, FUGNN effectively learns node representations. Experiments on six real-world datasets demonstrate the superiority of FUGNN over baseline methods. The codes are available at https://github.com/yushuowiki/FUGNN.|公平感知图形神经网络(GNN)经常面临一个具有挑战性的权衡，其中优先考虑公平可能需要妥协的效用。本文从光谱图理论的角度重新审视公平性问题，力求在光谱图学习的框架内协调公平性和效用性。通过理论分析，揭示了不同光谱下原始敏感特征与卷积后特征之间的相似性，探讨了不同光谱下原始敏感特征与卷积后特征之间的相似性。我们的分析表明，当与最大幅度特征值相关的特征向量表现出方向相似性时，相似性的影响减小。基于这些理论见解，我们提出了 FUGNN，一种新的光谱图学习方法，协调公平和效用之间的冲突。FUGNN 通过在编码过程中截断频谱和优化特征向量分布来保证算法的公平性和实用性。公平感知的特征向量选择降低了卷积对敏感特征的影响，同时最小化了效用的牺牲。FUGNN 通过变压器结构进一步优化了特征向量的分布。通过将优化后的频谱融入到图卷积网络中，FUGNN 有效地学习节点表示。在六个实际数据集上的实验表明了 FUGNN 方法优于基线方法。密码可以在 https://github.com/yushuowiki/fugnn 找到。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=FUGNN:+Harmonizing+Fairness+and+Utility+in+Graph+Neural+Networks)|0| -|[Learning Multi-view Molecular Representations with Structured and Unstructured Knowledge](https://doi.org/10.1145/3637528.3672043)|Yizhen Luo, Kai Yang, Massimo Hong, Xing Yi Liu, Zikun Nie, Hao Zhou, Zaiqing Nie|Institute for AI Industry Research (AIR), Tsinghua University & Pharmolix Inc., Beijing, China; Institute for AI Industry Research (AIR), Tsinghua University, Beijing, China|Capturing molecular knowledge with representation learning approaches holdssignificant potential in vast scientific fields such as chemistry and lifescience. An effective and generalizable molecular representation is expected tocapture the consensus and complementary molecular expertise from diverse viewsand perspectives. However, existing works fall short in learning multi-viewmolecular representations, due to challenges in explicitly incorporating viewinformation and handling molecular knowledge from heterogeneous sources. Toaddress these issues, we present MV-Mol, a molecular representation learningmodel that harvests multi-view molecular expertise from chemical structures,unstructured knowledge from biomedical texts, and structured knowledge fromknowledge graphs. We utilize text prompts to model view information and designa fusion architecture to extract view-based molecular representations. Wedevelop a two-stage pre-training procedure, exploiting heterogeneous data ofvarying quality and quantity. Through extensive experiments, we show thatMV-Mol provides improved representations that substantially benefit molecularproperty prediction. Additionally, MV-Mol exhibits state-of-the-art performancein multi-modal comprehension of molecular structures and texts. Code and dataare available at https://github.com/PharMolix/OpenBioMed.|利用表征学习方法获取分子知识在化学和生命科学等广阔的科学领域具有巨大的潜力。一个有效的和可推广的分子代表性预计将捕获共识和互补的分子专业知识从不同的观点和角度。然而，现有的工作在学习多视角分子表征方面存在不足，这是由于在明确合并视角信息和处理来自异质来源的分子知识方面存在挑战。为了解决这些问题，我们提出了 MV-Mol 分子表示学习模型，它从化学结构中收集多视角的分子专业知识，从生物医学文本中收集非结构化知识，从知识图表中收集结构化知识。利用文本提示对视图信息进行建模，设计融合结构提取基于视图的分子表示。我们开发了一个两阶段的预训练过程，利用不同质量和数量的异构数据。通过广泛的实验，我们表明，MV-Mol 提供了改进的表示，大大有利于分子性质预测。此外，MV-Mol 在分子结构和文本的多模态理解方面表现出最先进的性能。代码和数据可在 https://github.com/pharmolix/openbiomed 查阅。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Learning+Multi-view+Molecular+Representations+with+Structured+and+Unstructured+Knowledge)|0| -|[Cross-Context Backdoor Attacks against Graph Prompt Learning](https://doi.org/10.1145/3637528.3671956)|Xiaoting Lyu, Yufei Han, Wei Wang, Hangwei Qian, Ivor Tsang, Xiangliang Zhang|Inria, Univ. Rennes, IRISA, Rennes, France; School of Computer Science and Technology, Beijing Jiaotong University, Beijing, China; University of Notre Dame, Notre Dame, USA; CFAR, ASTAR, Singapore, Singapore; Beijing Jiaotong University & Xi'an Jiaotong University, Beijing, China|Graph Prompt Learning (GPL) bridges significant disparities betweenpretraining and downstream applications to alleviate the knowledge transferbottleneck in real-world graph learning. While GPL offers superioreffectiveness in graph knowledge transfer and computational efficiency, thesecurity risks posed by backdoor poisoning effects embedded in pretrainedmodels remain largely unexplored. Our study provides a comprehensive analysisof GPL's vulnerability to backdoor attacks. We introduce CrossBA, thefirst cross-context backdoor attack against GPL, which manipulates only thepretraining phase without requiring knowledge of downstream applications. Ourinvestigation reveals both theoretically and empirically that tuning triggergraphs, combined with prompt transformations, can seamlessly transfer thebackdoor threat from pretrained encoders to downstream applications. Throughextensive experiments involving 3 representative GPL methods across 5 distinctcross-context scenarios and 5 benchmark datasets of node and graphclassification tasks, we demonstrate that CrossBA consistentlyachieves high attack success rates while preserving the functionality ofdownstream applications over clean input. We also explore potentialcountermeasures against CrossBA and conclude that current defenses areinsufficient to mitigate CrossBA. Our study highlights the persistentbackdoor threats to GPL systems, raising trustworthiness concerns in thepractices of GPL techniques.|图形提示学习(Graph Prompt Learning，GPL)弥合了预训练和下游应用之间的显著差异，缓解了现实世界图形学习中的知识转移瓶颈。虽然 GPL 在图形知识转移和计算效率方面提供了优越的有效性，但是嵌入在预先训练的模型中的后门中毒效应所带来的安全风险仍然在很大程度上没有被探索。我们的研究提供了一个全面的分析 GPL 的脆弱性对后门攻击。我们引入了 CrossBA，第一个针对 GPL 的跨上下文后门攻击，它只操纵预训练阶段，而不需要了解下游应用程序。我们的研究从理论和经验上揭示了调优触发器，结合及时的转换，可以无缝地将后门威胁从预先训练的编码器转移到下游应用程序。通过涉及5个不同跨上下文场景和5个节点和图形分类任务的基准数据集的3种代表性 GPL 方法的广泛实验，我们证明 CrossBA 一致地实现高攻击成功率，同时保留下游应用程序的功能而不是干净的输入。我们还探讨了针对 CrossBA 的潜在对策，并得出结论: 目前的防御不足以减轻 CrossBA。我们的研究强调了对 GPL 系统持续的后门威胁，提高了 GPL 技术实践中的可信度问题。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Cross-Context+Backdoor+Attacks+against+Graph+Prompt+Learning)|0| +|[AIM: Attributing, Interpreting, Mitigating Data Unfairness](https://doi.org/10.1145/3637528.3671797)|Zhining Liu, Ruizhong Qiu, Zhichen Zeng, Yada Zhu, Hendrik F. Hamann, Hanghang Tong|University of Illinois Urbana-Champaign, Urbana, IL, USA; IBM Research, Yorktown Heights, NY, USA|Data collected in the real world often encapsulates historical discriminationagainst disadvantaged groups and individuals. Existing fair machine learning(FairML) research has predominantly focused on mitigating discriminative biasin the model prediction, with far less effort dedicated towards exploring howto trace biases present in the data, despite its importance for thetransparency and interpretability of FairML. To fill this gap, we investigate anovel research problem: discovering samples that reflect biases/prejudices fromthe training data. Grounding on the existing fairness notions, we lay out asample bias criterion and propose practical algorithms for measuring andcountering sample bias. The derived bias score provides intuitive sample-levelattribution and explanation of historical bias in data. On this basis, wefurther design two FairML strategies via sample-bias-informed minimal dataediting. They can mitigate both group and individual unfairness at the cost ofminimal or zero predictive utility loss. Extensive experiments and analyses onmultiple real-world datasets demonstrate the effectiveness of our methods inexplaining and mitigating unfairness. Code is available athttps://github.com/ZhiningLiu1998/AIM.|在现实世界中收集的数据往往概括了历史上对弱势群体和个人的歧视。现有的公平机器学习(FairML)研究主要集中在减轻模型预测中的歧视性偏差，而很少致力于探索如何跟踪数据中存在的偏差，尽管它对于 FairML 的透明度和可解释性非常重要。为了填补这个空白，我们调查了一个新的研究问题: 发现样本反映训练数据的偏见/偏见。在现有公平性概念的基础上，给出了样本偏差的判定准则，并提出了测量和抵消样本偏差的实用算法。导出的偏差评分提供了直观的样本水平归因和历史偏差的数据解释。在此基础上，进一步设计了两种基于样本偏差信息的最小数据编辑的 FairML 策略。它们可以以最小或零预测效用损失为代价来缓解群体和个人的不公平。对多个真实世界数据集的大量实验和分析证明了我们的方法在解释和减少不公平方面的有效性。代码可以通过 https:// github.com/zhiningliu1998/aim 获得。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=AIM:+Attributing,+Interpreting,+Mitigating+Data+Unfairness)|0| +|[High-Dimensional Distributed Sparse Classification with Scalable Communication-Efficient Global Updates](https://doi.org/10.1145/3637528.3672038)|Fred Lu, Ryan R. Curtin, Edward Raff, Francis Ferraro, James Holt|Booz Allen Hamilton, McLean, USA; University of Maryland, Baltimore County, Baltimore, USA; Laboratory for Physical Sciences, College Park, USA; Booz Allen Hamilton & University of Maryland, Baltimore County, McLean, USA|As the size of datasets used in statistical learning continues to grow, distributed training of models has attracted increasing attention. These methods partition the data and exploit parallelism to reduce memory and runtime, but suffer increasingly from communication costs as the data size or the number of iterations grows. Recent work on linear models has shown that a surrogate likelihood can be optimized locally to iteratively improve on an initial solution in a communication-efficient manner. However, existing versions of these methods experience multiple shortcomings as the data size becomes massive, including diverging updates and efficiently handling sparsity. In this work we develop solutions to these problems which enable us to learn a communication-efficient distributed logistic regression model even beyond millions of features. In our experiments we demonstrate a large improvement in accuracy over distributed algorithms with only a few distributed update steps needed, and similar or faster runtimes. Our code is available at https://github.com/FutureComputing4AI/ProxCSL.|随着用于统计学习的数据集规模的不断扩大，模型的分布式训练越来越受到人们的关注。这些方法对数据进行分区并利用并行性来减少内存和运行时，但是随着数据大小或迭代次数的增加，通信成本越来越高。最近对线性模型的研究表明，代理似然可以局部优化，以便以通信有效的方式迭代地改进初始解。然而，这些方法的现有版本在数据量变得巨大时会遇到多重缺陷，包括不同的更新和有效地处理稀疏性。在这项工作中，我们开发这些问题的解决方案，使我们能够学习一个通信效率高的分布式 Logit模型模型，甚至超过数百万个功能。在我们的实验中，我们证明了分布式算法在精度上有了很大的提高，只需要少量的分布式更新步骤，以及类似或更快的运行时。我们的代码可以在 https://github.com/futurecomputing4ai/proxcsl 找到。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=High-Dimensional+Distributed+Sparse+Classification+with+Scalable+Communication-Efficient+Global+Updates)|0| +|[FUGNN: Harmonizing Fairness and Utility in Graph Neural Networks](https://doi.org/10.1145/3637528.3671834)|Renqiang Luo, Huafei Huang, Shuo Yu, Zhuoyang Han, Estrid He, Xiuzhen Zhang, Feng Xia|Dalian University of Technology, Dalian, Liaoning, China; RMIT University, Melbourne, Victoria, Australia|Fairness-aware Graph Neural Networks (GNNs) often face a challenging trade-off, where prioritizing fairness may require compromising utility. In this work, we re-examine fairness through the lens of spectral graph theory, aiming to reconcile fairness and utility within the framework of spectral graph learning. We explore the correlation between sensitive features and spectrum in GNNs, using theoretical analysis to delineate the similarity between original sensitive features and those after convolution under different spectra. Our analysis reveals a reduction in the impact of similarity when the eigenvectors associated with the largest magnitude eigenvalue exhibit directional similarity. Based on these theoretical insights, we propose FUGNN, a novel spectral graph learning approach that harmonizes the conflict between fairness and utility. FUGNN ensures algorithmic fairness and utility by truncating the spectrum and optimizing eigenvector distribution during the encoding process. The fairness-aware eigenvector selection reduces the impact of convolution on sensitive features while concurrently minimizing the sacrifice of utility. FUGNN further optimizes the distribution of eigenvectors through a transformer architecture. By incorporating the optimized spectrum into the graph convolution network, FUGNN effectively learns node representations. Experiments on six real-world datasets demonstrate the superiority of FUGNN over baseline methods. The codes are available at https://github.com/yushuowiki/FUGNN.|公平感知图形神经网络(GNN)经常面临一个具有挑战性的权衡，其中优先考虑公平可能需要妥协的效用。本文从光谱图理论的角度重新审视公平性问题，力求在光谱图学习的框架内协调公平性和效用性。通过理论分析，揭示了不同光谱下原始敏感特征与卷积后特征之间的相似性，探讨了不同光谱下原始敏感特征与卷积后特征之间的相似性。我们的分析表明，当与最大幅度特征值相关的特征向量表现出方向相似性时，相似性的影响减小。基于这些理论见解，我们提出了 FUGNN，一种新的光谱图学习方法，协调公平和效用之间的冲突。FUGNN 通过在编码过程中截断频谱和优化特征向量分布来保证算法的公平性和实用性。公平感知的特征向量选择降低了卷积对敏感特征的影响，同时最小化了效用的牺牲。FUGNN 通过变压器结构进一步优化了特征向量的分布。通过将优化后的频谱融入到图卷积网络中，FUGNN 有效地学习节点表示。在六个实际数据集上的实验表明了 FUGNN 方法优于基线方法。密码可以在 https://github.com/yushuowiki/fugnn 找到。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=FUGNN:+Harmonizing+Fairness+and+Utility+in+Graph+Neural+Networks)|0| +|[Learning Multi-view Molecular Representations with Structured and Unstructured Knowledge](https://doi.org/10.1145/3637528.3672043)|Yizhen Luo, Kai Yang, Massimo Hong, Xing Yi Liu, Zikun Nie, Hao Zhou, Zaiqing Nie|Institute for AI Industry Research (AIR), Tsinghua University, Beijing, China; Institute for AI Industry Research (AIR), Tsinghua University & Pharmolix Inc., Beijing, China|Capturing molecular knowledge with representation learning approaches holdssignificant potential in vast scientific fields such as chemistry and lifescience. An effective and generalizable molecular representation is expected tocapture the consensus and complementary molecular expertise from diverse viewsand perspectives. However, existing works fall short in learning multi-viewmolecular representations, due to challenges in explicitly incorporating viewinformation and handling molecular knowledge from heterogeneous sources. Toaddress these issues, we present MV-Mol, a molecular representation learningmodel that harvests multi-view molecular expertise from chemical structures,unstructured knowledge from biomedical texts, and structured knowledge fromknowledge graphs. We utilize text prompts to model view information and designa fusion architecture to extract view-based molecular representations. Wedevelop a two-stage pre-training procedure, exploiting heterogeneous data ofvarying quality and quantity. Through extensive experiments, we show thatMV-Mol provides improved representations that substantially benefit molecularproperty prediction. Additionally, MV-Mol exhibits state-of-the-art performancein multi-modal comprehension of molecular structures and texts. Code and dataare available at https://github.com/PharMolix/OpenBioMed.|利用表征学习方法获取分子知识在化学和生命科学等广阔的科学领域具有巨大的潜力。一个有效的和可推广的分子代表性预计将捕获共识和互补的分子专业知识从不同的观点和角度。然而，现有的工作在学习多视角分子表征方面存在不足，这是由于在明确合并视角信息和处理来自异质来源的分子知识方面存在挑战。为了解决这些问题，我们提出了 MV-Mol 分子表示学习模型，它从化学结构中收集多视角的分子专业知识，从生物医学文本中收集非结构化知识，从知识图表中收集结构化知识。利用文本提示对视图信息进行建模，设计融合结构提取基于视图的分子表示。我们开发了一个两阶段的预训练过程，利用不同质量和数量的异构数据。通过广泛的实验，我们表明，MV-Mol 提供了改进的表示，大大有利于分子性质预测。此外，MV-Mol 在分子结构和文本的多模态理解方面表现出最先进的性能。代码和数据可在 https://github.com/pharmolix/openbiomed 查阅。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Learning+Multi-view+Molecular+Representations+with+Structured+and+Unstructured+Knowledge)|0| +|[Cross-Context Backdoor Attacks against Graph Prompt Learning](https://doi.org/10.1145/3637528.3671956)|Xiaoting Lyu, Yufei Han, Wei Wang, Hangwei Qian, Ivor Tsang, Xiangliang Zhang|University of Notre Dame, Notre Dame, USA; Inria, Univ. Rennes, IRISA, Rennes, France; School of Computer Science and Technology, Beijing Jiaotong University, Beijing, China; Beijing Jiaotong University & Xi'an Jiaotong University, Beijing, China; CFAR, ASTAR, Singapore, Singapore|Graph Prompt Learning (GPL) bridges significant disparities betweenpretraining and downstream applications to alleviate the knowledge transferbottleneck in real-world graph learning. While GPL offers superioreffectiveness in graph knowledge transfer and computational efficiency, thesecurity risks posed by backdoor poisoning effects embedded in pretrainedmodels remain largely unexplored. Our study provides a comprehensive analysisof GPL's vulnerability to backdoor attacks. We introduce CrossBA, thefirst cross-context backdoor attack against GPL, which manipulates only thepretraining phase without requiring knowledge of downstream applications. Ourinvestigation reveals both theoretically and empirically that tuning triggergraphs, combined with prompt transformations, can seamlessly transfer thebackdoor threat from pretrained encoders to downstream applications. Throughextensive experiments involving 3 representative GPL methods across 5 distinctcross-context scenarios and 5 benchmark datasets of node and graphclassification tasks, we demonstrate that CrossBA consistentlyachieves high attack success rates while preserving the functionality ofdownstream applications over clean input. We also explore potentialcountermeasures against CrossBA and conclude that current defenses areinsufficient to mitigate CrossBA. Our study highlights the persistentbackdoor threats to GPL systems, raising trustworthiness concerns in thepractices of GPL techniques.|图形提示学习(Graph Prompt Learning，GPL)弥合了预训练和下游应用之间的显著差异，缓解了现实世界图形学习中的知识转移瓶颈。虽然 GPL 在图形知识转移和计算效率方面提供了优越的有效性，但是嵌入在预先训练的模型中的后门中毒效应所带来的安全风险仍然在很大程度上没有被探索。我们的研究提供了一个全面的分析 GPL 的脆弱性对后门攻击。我们引入了 CrossBA，第一个针对 GPL 的跨上下文后门攻击，它只操纵预训练阶段，而不需要了解下游应用程序。我们的研究从理论和经验上揭示了调优触发器，结合及时的转换，可以无缝地将后门威胁从预先训练的编码器转移到下游应用程序。通过涉及5个不同跨上下文场景和5个节点和图形分类任务的基准数据集的3种代表性 GPL 方法的广泛实验，我们证明 CrossBA 一致地实现高攻击成功率，同时保留下游应用程序的功能而不是干净的输入。我们还探讨了针对 CrossBA 的潜在对策，并得出结论: 目前的防御不足以减轻 CrossBA。我们的研究强调了对 GPL 系统持续的后门威胁，提高了 GPL 技术实践中的可信度问题。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Cross-Context+Backdoor+Attacks+against+Graph+Prompt+Learning)|0| |[PolyFormer: Scalable Node-wise Filters via Polynomial Graph Transformer](https://doi.org/10.1145/3637528.3671849)|Jiahong Ma, Mingguo He, Zhewei Wei|Renmin University of China, Beijing, China|Spectral Graph Neural Networks have demonstrated superior performance in graph representation learning. However, many current methods focus on employing shared polynomial coefficients for all nodes, i.e., learning node-unified filters, which limits the filters' flexibility for node-level tasks. The recent DSF attempts to overcome this limitation by learning node-wise coefficients based on positional encoding. However, the initialization and updating process of the positional encoding are burdensome, hindering scalability on large-scale graphs. In this work, we propose a scalable node-wise filter, PolyAttn. Leveraging the attention mechanism, PolyAttn can directly learn node-wise filters in an efficient manner, offering powerful representation capabilities. Building on PolyAttn, we introduce the whole model, named PolyFormer. In the lens of Graph Transformer models, PolyFormer, which calculates attention scores within nodes, shows great scalability. Moreover, the model captures spectral information, enhancing expressiveness while maintaining efficiency. With these advantages, PolyFormer offers a desirable balance between scalability and expressiveness for node-level tasks. Extensive experiments demonstrate that our proposed methods excel at learning arbitrary node-wise filters, showing superior performance on both homophilic and heterophilic graphs, and handling graphs containing up to 100 million nodes. The code is available at https://github.com/air029/PolyFormer.|谱图神经网络在图表示学习中表现出了优越的性能。然而，目前的方法主要集中在对所有节点采用共享多项式系数，即学习节点统一过滤器，这限制了过滤器对节点级任务的灵活性。最近的 DSF 试图通过基于位置编码的节点系数学习来克服这一限制。然而，位置编码的初始化和更新过程非常繁琐，阻碍了大规模图的可扩展性。在这项工作中，我们提出了一个可伸缩的节点明智的过滤器，PolyAttn。利用注意力机制，PolyAttn 可以以一种有效的方式直接学习节点过滤器，提供强大的表示能力。在 PolyAttn 的基础上，我们引入了一个完整的模型，命名为 PolyForm。在图形变换模型的透镜中，计算节点内注意力分数的 PolyForm 具有很强的可扩展性。此外，该模型捕获光谱信息，提高了表现力，同时保持了效率。利用这些优势，PolyForm 为节点级任务提供了可伸缩性和表达性之间的理想平衡。广泛的实验表明，我们提出的方法擅长学习任意节点明确的过滤器，显示出优越的性能在同亲和异亲图，并处理图包含多达1亿个节点。密码可在 https://github.com/air029/polyformer 查阅。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=PolyFormer:+Scalable+Node-wise+Filters+via+Polynomial+Graph+Transformer)|0| -|[Scalable Differentiable Causal Discovery in the Presence of Latent Confounders with Skeleton Posterior](https://doi.org/10.1145/3637528.3672031)|Pingchuan Ma, Rui Ding, Qiang Fu, Jiaru Zhang, Shuai Wang, Shi Han, Dongmei Zhang|HKUST, Hong Kong, Hong Kong; Shanghai Jiao Tong University, Shanghai, China; Microsoft Research, Beijing, China|Differentiable causal discovery has made significant advancements in the learning of directed acyclic graphs. However, its application to real-world datasets remains restricted due to the ubiquity of latent confounders and the requirement to learn maximal ancestral graphs (MAGs). To date, existing differentiable MAG learning algorithms have been limited to small datasets and failed to scale to larger ones (e.g., with more than 50 variables). The key insight in this paper is that the causal skeleton, which is the undirected version of the causal graph, has potential for improving accuracy and reducing the search space of the optimization procedure, thereby enhancing the performance of differentiable causal discovery. Therefore, we seek to address a two-fold challenge to harness the potential of the causal skeleton for differentiable causal discovery in the presence of latent confounders: (1) scalable and accurate estimation of skeleton and (2) universal integration of skeleton estimation with differentiable causal discovery. To this end, we propose SPOT (Skeleton Posterior-guided OpTimization), a two-phase framework that harnesses skeleton posterior for differentiable causal discovery in the presence of latent confounders. On the contrary to a "point-estimation", SPOT seeks to estimate the posterior distribution of skeletons given the dataset. It first formulates the posterior inference as an instance of amortized inference problem and concretizes it with a supervised causal learning (SCL)-enabled solution to estimate the skeleton posterior. To incorporate the skeleton posterior with differentiable causal discovery, SPOT then features a skeleton posterior-guided stochastic optimization procedure to guide the optimization of MAGs. Extensive experiments on various datasets show that SPOT substantially outperforms SOTA methods for MAG learning. SPOT also demonstrates its effectiveness in the accuracy of skeleton posterior estimation in comparison with non-parametric bootstrap-based, or more recently, variational inference-based methods. Finally, we observe that the adoption of skeleton posterior exhibits strong promise in various causal discovery tasks.|可微因果发现在有向无环图的学习方面取得了显著的进展。然而，由于潜在混杂因素的普遍存在以及学习最大祖先图(MAG)的要求，其在现实世界数据集中的应用仍然受到限制。迄今为止，现有的可微 MAG 学习算法一直局限于小数据集，无法扩展到更大的数据集(例如，有超过50个变量)。本文的核心观点是因果骨架是因果图的无向版本，它有可能提高优化过程的精度，减少搜索空间，从而提高可微因果发现的性能。因此，我们试图解决在潜在混杂因素存在下利用因果骨架潜力进行可微因果发现的双重挑战: (1)骨架的可扩展和准确估计，以及(2)骨架估计与可微因果发现的普遍整合。为此，我们提出了 SPOT (骨架后验引导的优化) ，一个两阶段的框架，利用骨架后验在潜在的混杂因素存在的可微因果发现。与“点估计”相反，SPOT 试图估计给定数据集的骨架后验概率。该方法首先将后验推理作为摊销推理问题的一个实例进行描述，然后将其具体化为一个基于监督因果学习(SCL)的解决方案来估计骨架的后验。为了将骨架后验与可微因果发现结合起来，SPOT 提出了骨架后验引导的随机优化方法，用于指导 MAGs 的优化。在各种数据集上的大量实验表明，SPOT 方法在 MAG 学习方面优于 SOTA 方法。SPOT 算法在骨架后验估计的准确性方面也证明了其有效性，并与非参数自举方法或最近的变分推理方法进行了比较。最后，我们观察到骨架后验在各种因果发现任务中表现出很强的前景。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Scalable+Differentiable+Causal+Discovery+in+the+Presence+of+Latent+Confounders+with+Skeleton+Posterior)|0| -|[Graph Anomaly Detection with Few Labels: A Data-Centric Approach](https://doi.org/10.1145/3637528.3671929)|Xiaoxiao Ma, Ruikun Li, Fanzhen Liu, Kaize Ding, Jian Yang, Jia Wu|Business School, The University of Sydney, Sydney, New South Wales, Australia; School of Computing, Macquarie University, Sydney, New South Wales, Australia; Department of Statistics and Data Science, Northwestern University, Evanston, Illinois, USA|Anomalous node detection in a static graph faces significant challenges due to the rarity of anomalies and the substantial cost of labeling their deviant structure and attribute patterns. These challenges give rise to data-centric problems, including extremely imbalanced data distributions and intricate graph learning, which significantly impede machine learning and deep learning methods from discerning the patterns of graph anomalies with few labels. While these issues remain crucial, much of the current research focuses on addressing the induced technical challenges, treating the shortage of labeled data as a given. Distinct from previous efforts, this work focuses on tackling the data-centric problems by generating auxiliary training nodes that conform to the original graph topology and attribute distribution. We categorize this approach as data-centric, aiming to enhance existing anomaly detectors by training them on our synthetic data. However, the methods for generating nodes and the effectiveness of utilizing synthetic data for graph anomaly detection remain unexplored in the realm. To answer these questions, we thoroughly investigate the denoising diffusion model. Drawing from our observations on the diffusion process, we illuminate the shifts in graph energy distribution and establish two principles for designing denoising neural networks tailored to graph anomaly generation. From the insights, we propose a diffusion-based graph generation method to synthesize training nodes, which can be promptly integrated to work with existing anomaly detectors. The empirical results on eight widely-used datasets demonstrate our generated data can effectively enhance the nine state-of-the-art graph detectors' performance.|静态图中的异常节点检测由于异常的罕见性以及标记异常结构和属性模式的巨大成本而面临严峻的挑战。这些挑战引发了以数据为中心的问题，包括极不平衡的数据分布和复杂的图学习，这些问题严重阻碍了机器学习和深度学习方法识别少标签的图形异常模式。虽然这些问题仍然是至关重要的，目前的研究大多集中在解决诱发的技术挑战，治疗短缺的标记数据作为一个给定。与以往的工作不同，这项工作的重点是通过生成符合原始图拓扑和属性分布的辅助训练节点来解决以数据为中心的问题。我们把这种方法归类为以数据为中心，目的是通过在我们的合成数据上训练现有的异常检测器来增强它们。然而，生成节点的方法以及利用合成数据进行图形异常检测的有效性在该领域仍未得到探索。为了回答这些问题，我们深入研究了去噪扩散模型。根据我们对扩散过程的观察，我们阐明了图能量分布的变化，并建立了适合于图异常生成的去噪神经网络设计的两个原则。基于此，我们提出了一种基于扩散的图生成方法来合成训练节点，该方法可以及时地与现有的异常检测器集成。对八个广泛使用的数据集的实验结果表明，我们生成的数据可以有效地提高九个最先进的图形检测器的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Graph+Anomaly+Detection+with+Few+Labels:+A+Data-Centric+Approach)|0| +|[Scalable Differentiable Causal Discovery in the Presence of Latent Confounders with Skeleton Posterior](https://doi.org/10.1145/3637528.3672031)|Pingchuan Ma, Rui Ding, Qiang Fu, Jiaru Zhang, Shuai Wang, Shi Han, Dongmei Zhang|HKUST, Hong Kong, Hong Kong; Microsoft Research, Beijing, China; Shanghai Jiao Tong University, Shanghai, China|Differentiable causal discovery has made significant advancements in the learning of directed acyclic graphs. However, its application to real-world datasets remains restricted due to the ubiquity of latent confounders and the requirement to learn maximal ancestral graphs (MAGs). To date, existing differentiable MAG learning algorithms have been limited to small datasets and failed to scale to larger ones (e.g., with more than 50 variables). The key insight in this paper is that the causal skeleton, which is the undirected version of the causal graph, has potential for improving accuracy and reducing the search space of the optimization procedure, thereby enhancing the performance of differentiable causal discovery. Therefore, we seek to address a two-fold challenge to harness the potential of the causal skeleton for differentiable causal discovery in the presence of latent confounders: (1) scalable and accurate estimation of skeleton and (2) universal integration of skeleton estimation with differentiable causal discovery. To this end, we propose SPOT (Skeleton Posterior-guided OpTimization), a two-phase framework that harnesses skeleton posterior for differentiable causal discovery in the presence of latent confounders. On the contrary to a "point-estimation", SPOT seeks to estimate the posterior distribution of skeletons given the dataset. It first formulates the posterior inference as an instance of amortized inference problem and concretizes it with a supervised causal learning (SCL)-enabled solution to estimate the skeleton posterior. To incorporate the skeleton posterior with differentiable causal discovery, SPOT then features a skeleton posterior-guided stochastic optimization procedure to guide the optimization of MAGs. Extensive experiments on various datasets show that SPOT substantially outperforms SOTA methods for MAG learning. SPOT also demonstrates its effectiveness in the accuracy of skeleton posterior estimation in comparison with non-parametric bootstrap-based, or more recently, variational inference-based methods. Finally, we observe that the adoption of skeleton posterior exhibits strong promise in various causal discovery tasks.|可微因果发现在有向无环图的学习方面取得了显著的进展。然而，由于潜在混杂因素的普遍存在以及学习最大祖先图(MAG)的要求，其在现实世界数据集中的应用仍然受到限制。迄今为止，现有的可微 MAG 学习算法一直局限于小数据集，无法扩展到更大的数据集(例如，有超过50个变量)。本文的核心观点是因果骨架是因果图的无向版本，它有可能提高优化过程的精度，减少搜索空间，从而提高可微因果发现的性能。因此，我们试图解决在潜在混杂因素存在下利用因果骨架潜力进行可微因果发现的双重挑战: (1)骨架的可扩展和准确估计，以及(2)骨架估计与可微因果发现的普遍整合。为此，我们提出了 SPOT (骨架后验引导的优化) ，一个两阶段的框架，利用骨架后验在潜在的混杂因素存在的可微因果发现。与“点估计”相反，SPOT 试图估计给定数据集的骨架后验概率。该方法首先将后验推理作为摊销推理问题的一个实例进行描述，然后将其具体化为一个基于监督因果学习(SCL)的解决方案来估计骨架的后验。为了将骨架后验与可微因果发现结合起来，SPOT 提出了骨架后验引导的随机优化方法，用于指导 MAGs 的优化。在各种数据集上的大量实验表明，SPOT 方法在 MAG 学习方面优于 SOTA 方法。SPOT 算法在骨架后验估计的准确性方面也证明了其有效性，并与非参数自举方法或最近的变分推理方法进行了比较。最后，我们观察到骨架后验在各种因果发现任务中表现出很强的前景。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Scalable+Differentiable+Causal+Discovery+in+the+Presence+of+Latent+Confounders+with+Skeleton+Posterior)|0| +|[Graph Anomaly Detection with Few Labels: A Data-Centric Approach](https://doi.org/10.1145/3637528.3671929)|Xiaoxiao Ma, Ruikun Li, Fanzhen Liu, Kaize Ding, Jian Yang, Jia Wu|School of Computing, Macquarie University, Sydney, New South Wales, Australia; Department of Statistics and Data Science, Northwestern University, Evanston, Illinois, USA; Business School, The University of Sydney, Sydney, New South Wales, Australia|Anomalous node detection in a static graph faces significant challenges due to the rarity of anomalies and the substantial cost of labeling their deviant structure and attribute patterns. These challenges give rise to data-centric problems, including extremely imbalanced data distributions and intricate graph learning, which significantly impede machine learning and deep learning methods from discerning the patterns of graph anomalies with few labels. While these issues remain crucial, much of the current research focuses on addressing the induced technical challenges, treating the shortage of labeled data as a given. Distinct from previous efforts, this work focuses on tackling the data-centric problems by generating auxiliary training nodes that conform to the original graph topology and attribute distribution. We categorize this approach as data-centric, aiming to enhance existing anomaly detectors by training them on our synthetic data. However, the methods for generating nodes and the effectiveness of utilizing synthetic data for graph anomaly detection remain unexplored in the realm. To answer these questions, we thoroughly investigate the denoising diffusion model. Drawing from our observations on the diffusion process, we illuminate the shifts in graph energy distribution and establish two principles for designing denoising neural networks tailored to graph anomaly generation. From the insights, we propose a diffusion-based graph generation method to synthesize training nodes, which can be promptly integrated to work with existing anomaly detectors. The empirical results on eight widely-used datasets demonstrate our generated data can effectively enhance the nine state-of-the-art graph detectors' performance.|静态图中的异常节点检测由于异常的罕见性以及标记异常结构和属性模式的巨大成本而面临严峻的挑战。这些挑战引发了以数据为中心的问题，包括极不平衡的数据分布和复杂的图学习，这些问题严重阻碍了机器学习和深度学习方法识别少标签的图形异常模式。虽然这些问题仍然是至关重要的，目前的研究大多集中在解决诱发的技术挑战，治疗短缺的标记数据作为一个给定。与以往的工作不同，这项工作的重点是通过生成符合原始图拓扑和属性分布的辅助训练节点来解决以数据为中心的问题。我们把这种方法归类为以数据为中心，目的是通过在我们的合成数据上训练现有的异常检测器来增强它们。然而，生成节点的方法以及利用合成数据进行图形异常检测的有效性在该领域仍未得到探索。为了回答这些问题，我们深入研究了去噪扩散模型。根据我们对扩散过程的观察，我们阐明了图能量分布的变化，并建立了适合于图异常生成的去噪神经网络设计的两个原则。基于此，我们提出了一种基于扩散的图生成方法来合成训练节点，该方法可以及时地与现有的异常检测器集成。对八个广泛使用的数据集的实验结果表明，我们生成的数据可以有效地提高九个最先进的图形检测器的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Graph+Anomaly+Detection+with+Few+Labels:+A+Data-Centric+Approach)|0| |[A Uniformly Bounded Correlation Function for Spatial Point Patterns](https://doi.org/10.1145/3637528.3671891)|Evgenia Martynova, Johannes Textor|Radboud University, Nijmegen, Netherlands; Radboud University Medical Center, Nijmegen, Netherlands|A point pattern is a dataset of coordinates, typically in 2D or 3D space. Point patterns are ubiquitous in diverse applications including Geographic Information Systems, Astronomy, Ecology, Biology and Medicine. Among the statistics used to quantify point patterns, most are based on Ripley's K-function, which measures the deviation of the observed pattern from a completely random arrangement of points. This approach is useful for constructing null hypothesis tests, but Ripley's K and its variants are less suitable as quantitative effect sizes because their ranges and expected values generally depend on the scale or the size of the region in which the pattern is observed. To address this, we propose a new function that behaves like a correlation coefficient for point patterns: it is tightly bounded by -1 and 1, with a value of -1 corresponding to a maximally dispersed arrangement of points, 0 indicating complete spatial randomness, and 1 representing maximal clustering. These properties are independent of scale and observation window size assuming appropriate edge correction. Evaluating our function on simulated data, we show that it has comparable statistical calibration and power to K-based baselines. We hope that the ease of interpretation of our bounded function will facilitate the analysis of spatial data across multiple fields.|点模式是坐标的数据集，通常在2D 或3D 空间中。点模式在包括地理信息系统、天文学、生态学、生物学和医学在内的各种应用中无处不在。在用于量化点模式的统计数据中，大多数是基于 Ripley 的 K- 函数，它测量观察到的模式与完全随机的点排列的偏差。这种方法对于构建零假设检验是有用的，但是 Ripley’s K 及其变体不太适合作为定量效应大小，因为它们的范围和预期值通常取决于观察模式的区域的尺度或大小。为了解决这个问题，我们提出了一个新的函数，它的行为类似于点模式的相关系数: 它被 -1和1紧密约束，值为 -1对应于最大分散的点排列，0表示完全空间随机性，1表示最大聚类。假设适当的边缘校正，这些特性与尺度和观测窗口大小无关。在模拟数据上评估我们的函数，我们表明它具有可比较的统计校准和功能的 K 为基础的基线。我们希望我们有界函数的简易解释将有助于跨多个领域的空间数据分析。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Uniformly+Bounded+Correlation+Function+for+Spatial+Point+Patterns)|0| -|[Fair Column Subset Selection](https://doi.org/10.1145/3637528.3672005)|Antonis Matakos, Bruno Ordozgoiti, Suhas Thejaswi|Aalto University, Espoo, Finland; Unaffiliated, London, United Kingdom; Max Planck Institute for Software Systems, Kaiserslautern, Germany|We consider the problem of fair column subset selection. In particular, we assume that two groups are present in the data, and the chosen column subset must provide a good approximation for both, relative to their respective best rank-k approximations. We show that this fair setting introduces significant challenges: in order to extend known results, one cannot do better than the trivial solution of simply picking twice as many columns as the original methods. We adopt a known approach based on deterministic leverage-score sampling, and show that merely sampling a subset of appropriate size becomes NP-hard in the presence of two groups. Whereas finding a subset of two times the desired size is trivial, we provide an efficient algorithm that achieves the same guarantees with essentially 1.5 times that size. We validate our methods through an extensive set of experiments on real-world data.|我们考虑公平列子集选择问题。特别地，我们假设数据中存在两组，相对于它们各自的最佳秩-k 近似，所选择的列子集必须为两者提供一个很好的近似。我们展示了这种公平的设置带来了重大的挑战: 为了扩展已知的结果，最简单的解决方案就是简单地挑选两倍于原始方法的列。我们采用了一种基于确定性杠杆评分抽样的已知方法，并且证明了在两组情况下，仅仅抽样一个适当大小的子集就变得 NP 难。尽管寻找一个两倍于所需大小的子集是微不足道的，但是我们提供了一个有效的算法，它实现了1.5倍于所需大小的相同保证。我们通过对真实世界数据的大量实验来验证我们的方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Fair+Column+Subset+Selection)|0| -|[FLAIM: AIM-based Synthetic Data Generation in the Federated Setting](https://doi.org/10.1145/3637528.3671990)|Samuel Maddock, Graham Cormode, Carsten Maple|University of Warwick, Coventry, United Kingdom; Meta AI & University of Warwick, Coventry, United Kingdom|Preserving individual privacy while enabling collaborative data sharing is crucial for organizations. Synthetic data generation is one solution, producing artificial data that mirrors the statistical properties of private data. While numerous techniques have been devised under differential privacy, they predominantly assume data is centralized. However, data is often distributed across multiple clients in a federated manner. In this work, we initiate the study of federated synthetic tabular data generation. Building upon a SOTA central method known as AIM, we present DistAIM and FLAIM. We first show that it is straightforward to distribute AIM, extending a recent approach based on secure multi-party computation which necessitates additional overhead, making it less suited to federated scenarios. We then demonstrate that naively federating AIM can lead to substantial degradation in utility under the presence of heterogeneity. To mitigate both issues, we propose an augmented FLAIM approach that maintains a private proxy of heterogeneity. We simulate our methods across a range of benchmark datasets under different degrees of heterogeneity and show we can improve utility while reducing overhead.|在实现协作数据共享的同时保护个人隐私对组织来说至关重要。合成数据生成是一种解决方案，生成反映私有数据统计特性的人工数据。虽然在差分隐私下设计了许多技术，但它们主要假设数据是集中的。但是，数据通常以联邦的方式分布在多个客户机上。在这项工作中，我们开始了联邦综合表格数据生成的研究。在 SOTA 中心方法 AIM 的基础上，我们提出了 distAIM 和 FLAIM。我们首先展示了分发 AIM 的简单性，扩展了最近的一种基于安全多方计算的方法，这种方法需要额外的开销，因此不太适合联邦场景。然后我们证明，天真的联合 AIM 可以导致实用性在异质性的存在下显著降低。为了缓解这两个问题，我们提出了一个增强的 FLAIM 方法，维护一个私人代理的异构性。在不同程度的异构性下，我们在一系列基准数据集上模拟了我们的方法，并展示了我们可以在降低开销的同时提高效用。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=FLAIM:+AIM-based+Synthetic+Data+Generation+in+the+Federated+Setting)|0| -|[Interpretable Transformer Hawkes Processes: Unveiling Complex Interactions in Social Networks](https://doi.org/10.1145/3637528.3671720)|Zizhuo Meng, Ke Wan, Yadong Huang, Zhidong Li, Yang Wang, Feng Zhou|; The University of Technology Sydney, Sydney, Australia; University of Illinois at Urbana-Champaign, Urbana, IL, USA; Zhoushan Academy of Marine Data Science, Zhoushan, China|Social networks represent complex ecosystems where the interactions between users or groups play a pivotal role in information dissemination, opinion formation, and social interactions. Effectively harnessing event sequence data within social networks to unearth interactions among users or groups has persistently posed a challenging frontier within the realm of point processes. Current deep point process models face inherent limitations within the context of social networks, constraining both their interpretability and expressive power. These models encounter challenges in capturing interactions among users or groups and often rely on parameterized extrapolation methods when modeling intensity over non-event intervals, limiting their capacity to capture intricate intensity patterns, particularly beyond observed events. To address these challenges, this study proposes modifications to Transformer Hawkes processes (THP), leading to the development of interpretable Transformer Hawkes processes (ITHP). ITHP inherits the strengths of THP while aligning with statistical nonlinear Hawkes processes, thereby enhancing its interpretability and providing valuable insights into interactions between users or groups. Additionally, ITHP enhances the flexibility of the intensity function over non-event intervals, making it better suited to capture complex event propagation patterns in social networks. Experimental results, both on synthetic and real data, demonstrate the effectiveness of ITHP in overcoming the identified limitations. Moreover, they highlight ITHP's applicability in the context of exploring the complex impact of users or groups within social networks. Our code is available at https://github.com/waystogetthere/Interpretable-Transformer- Hawkes-Process.git.|社交网络代表了复杂的生态系统，其中用户或群体之间的互动在信息传播、舆论形成和社会互动中起着关键作用。有效地利用社交网络中的事件序列数据来挖掘用户或群体之间的互动一直是点过程领域的一个具有挑战性的前沿问题。当前的深点过程模型面临着社会网络背景下固有的局限性，限制了它们的可解释性和表达能力。这些模型在捕获用户或组之间的交互方面遇到挑战，并且在非事件间隔期间建模强度时常常依赖参数化外推方法，从而限制了它们捕获复杂强度模式的能力，特别是在观察到的事件之外。为了应对这些挑战，本研究提出了变压器霍克斯过程(THP)的修改，导致了可解释的变压器霍克斯过程(ITHP)的发展。ITHP 继承了 THP 的优势，同时与统计非线性霍克斯过程保持一致，从而提高了其可解释性，并为用户或群体之间的交互提供了有价值的见解。此外，ITHP 增强了强度函数在非事件间隔上的灵活性，使其更适合于捕获社交网络中复杂的事件传播模式。实验结果，无论是合成的和真实的数据，证明了 ITHP 的有效性，克服了识别的限制。此外，他们还强调了 ITHP 在探索社交网络中用户或群体的复杂影响方面的适用性。我们的代码可以在 Hawkes-Process https://github.com/waystogetthere/interpretable-transformer- 找到。饭桶。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Interpretable+Transformer+Hawkes+Processes:+Unveiling+Complex+Interactions+in+Social+Networks)|0| +|[Fair Column Subset Selection](https://doi.org/10.1145/3637528.3672005)|Antonis Matakos, Bruno Ordozgoiti, Suhas Thejaswi|Aalto University, Espoo, Finland; Max Planck Institute for Software Systems, Kaiserslautern, Germany; Unaffiliated, London, United Kingdom|We consider the problem of fair column subset selection. In particular, we assume that two groups are present in the data, and the chosen column subset must provide a good approximation for both, relative to their respective best rank-k approximations. We show that this fair setting introduces significant challenges: in order to extend known results, one cannot do better than the trivial solution of simply picking twice as many columns as the original methods. We adopt a known approach based on deterministic leverage-score sampling, and show that merely sampling a subset of appropriate size becomes NP-hard in the presence of two groups. Whereas finding a subset of two times the desired size is trivial, we provide an efficient algorithm that achieves the same guarantees with essentially 1.5 times that size. We validate our methods through an extensive set of experiments on real-world data.|我们考虑公平列子集选择问题。特别地，我们假设数据中存在两组，相对于它们各自的最佳秩-k 近似，所选择的列子集必须为两者提供一个很好的近似。我们展示了这种公平的设置带来了重大的挑战: 为了扩展已知的结果，最简单的解决方案就是简单地挑选两倍于原始方法的列。我们采用了一种基于确定性杠杆评分抽样的已知方法，并且证明了在两组情况下，仅仅抽样一个适当大小的子集就变得 NP 难。尽管寻找一个两倍于所需大小的子集是微不足道的，但是我们提供了一个有效的算法，它实现了1.5倍于所需大小的相同保证。我们通过对真实世界数据的大量实验来验证我们的方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Fair+Column+Subset+Selection)|0| +|[FLAIM: AIM-based Synthetic Data Generation in the Federated Setting](https://doi.org/10.1145/3637528.3671990)|Samuel Maddock, Graham Cormode, Carsten Maple|Meta AI & University of Warwick, Coventry, United Kingdom; University of Warwick, Coventry, United Kingdom|Preserving individual privacy while enabling collaborative data sharing is crucial for organizations. Synthetic data generation is one solution, producing artificial data that mirrors the statistical properties of private data. While numerous techniques have been devised under differential privacy, they predominantly assume data is centralized. However, data is often distributed across multiple clients in a federated manner. In this work, we initiate the study of federated synthetic tabular data generation. Building upon a SOTA central method known as AIM, we present DistAIM and FLAIM. We first show that it is straightforward to distribute AIM, extending a recent approach based on secure multi-party computation which necessitates additional overhead, making it less suited to federated scenarios. We then demonstrate that naively federating AIM can lead to substantial degradation in utility under the presence of heterogeneity. To mitigate both issues, we propose an augmented FLAIM approach that maintains a private proxy of heterogeneity. We simulate our methods across a range of benchmark datasets under different degrees of heterogeneity and show we can improve utility while reducing overhead.|在实现协作数据共享的同时保护个人隐私对组织来说至关重要。合成数据生成是一种解决方案，生成反映私有数据统计特性的人工数据。虽然在差分隐私下设计了许多技术，但它们主要假设数据是集中的。但是，数据通常以联邦的方式分布在多个客户机上。在这项工作中，我们开始了联邦综合表格数据生成的研究。在 SOTA 中心方法 AIM 的基础上，我们提出了 distAIM 和 FLAIM。我们首先展示了分发 AIM 的简单性，扩展了最近的一种基于安全多方计算的方法，这种方法需要额外的开销，因此不太适合联邦场景。然后我们证明，天真的联合 AIM 可以导致实用性在异质性的存在下显著降低。为了缓解这两个问题，我们提出了一个增强的 FLAIM 方法，维护一个私人代理的异构性。在不同程度的异构性下，我们在一系列基准数据集上模拟了我们的方法，并展示了我们可以在降低开销的同时提高效用。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=FLAIM:+AIM-based+Synthetic+Data+Generation+in+the+Federated+Setting)|0| +|[Interpretable Transformer Hawkes Processes: Unveiling Complex Interactions in Social Networks](https://doi.org/10.1145/3637528.3671720)|Zizhuo Meng, Ke Wan, Yadong Huang, Zhidong Li, Yang Wang, Feng Zhou|Zhoushan Academy of Marine Data Science, Zhoushan, China; ; University of Illinois at Urbana-Champaign, Urbana, IL, USA; The University of Technology Sydney, Sydney, Australia|Social networks represent complex ecosystems where the interactions between users or groups play a pivotal role in information dissemination, opinion formation, and social interactions. Effectively harnessing event sequence data within social networks to unearth interactions among users or groups has persistently posed a challenging frontier within the realm of point processes. Current deep point process models face inherent limitations within the context of social networks, constraining both their interpretability and expressive power. These models encounter challenges in capturing interactions among users or groups and often rely on parameterized extrapolation methods when modeling intensity over non-event intervals, limiting their capacity to capture intricate intensity patterns, particularly beyond observed events. To address these challenges, this study proposes modifications to Transformer Hawkes processes (THP), leading to the development of interpretable Transformer Hawkes processes (ITHP). ITHP inherits the strengths of THP while aligning with statistical nonlinear Hawkes processes, thereby enhancing its interpretability and providing valuable insights into interactions between users or groups. Additionally, ITHP enhances the flexibility of the intensity function over non-event intervals, making it better suited to capture complex event propagation patterns in social networks. Experimental results, both on synthetic and real data, demonstrate the effectiveness of ITHP in overcoming the identified limitations. Moreover, they highlight ITHP's applicability in the context of exploring the complex impact of users or groups within social networks. Our code is available at https://github.com/waystogetthere/Interpretable-Transformer- Hawkes-Process.git.|社交网络代表了复杂的生态系统，其中用户或群体之间的互动在信息传播、舆论形成和社会互动中起着关键作用。有效地利用社交网络中的事件序列数据来挖掘用户或群体之间的互动一直是点过程领域的一个具有挑战性的前沿问题。当前的深点过程模型面临着社会网络背景下固有的局限性，限制了它们的可解释性和表达能力。这些模型在捕获用户或组之间的交互方面遇到挑战，并且在非事件间隔期间建模强度时常常依赖参数化外推方法，从而限制了它们捕获复杂强度模式的能力，特别是在观察到的事件之外。为了应对这些挑战，本研究提出了变压器霍克斯过程(THP)的修改，导致了可解释的变压器霍克斯过程(ITHP)的发展。ITHP 继承了 THP 的优势，同时与统计非线性霍克斯过程保持一致，从而提高了其可解释性，并为用户或群体之间的交互提供了有价值的见解。此外，ITHP 增强了强度函数在非事件间隔上的灵活性，使其更适合于捕获社交网络中复杂的事件传播模式。实验结果，无论是合成的和真实的数据，证明了 ITHP 的有效性，克服了识别的限制。此外，他们还强调了 ITHP 在探索社交网络中用户或群体的复杂影响方面的适用性。我们的代码可以在 Hawkes-Process https://github.com/waystogetthere/interpretable-transformer- 找到。饭桶。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Interpretable+Transformer+Hawkes+Processes:+Unveiling+Complex+Interactions+in+Social+Networks)|0| |[Scaling Training Data with Lossy Image Compression](https://doi.org/10.1145/3637528.3671904)|Katherine L. Mentzer, Andrea Montanari|Granica, Mountain View, CA, USA|Empirically-determined scaling laws have been broadly successful in predicting the evolution of large machine learning models with training data and number of parameters. As a consequence, they have been useful for optimizing the allocation of limited resources, most notably compute time. In certain applications, storage space is an important constraint, and data format needs to be chosen carefully as a consequence. Computer vision is a prominent example: images are inherently analog, but are always stored in a digital format using a finite number of bits. Given a dataset of digital images, the number of bits L to store each of them can be further reduced using lossy data compression. This, however, can degrade the quality of the model trained on such images, since each example has lower resolution. In order to capture this trade-off and optimize storage of training data, we propose a `storage scaling law' that describes the joint evolution of test error with sample size and number of bits per image. We prove that this law holds within a stylized model for image compression, and verify it empirically on two computer vision tasks, extracting the relevant parameters. We then show that this law can be used to optimize the lossy compression level. At given storage, models trained on optimally compressed images present a significantly smaller test error with respect to models trained on the original data. Finally, we investigate the potential benefits of randomizing the compression level.|经验确定的标度律已广泛成功地预测演化的大型机器学习模型的训练数据和参数数量。因此，它们对于优化有限资源的分配非常有用，尤其是计算时间。在某些应用程序中，存储空间是一个重要的限制，因此需要仔细选择数据格式。计算机视觉是一个突出的例子: 图像本质上是模拟的，但总是以数字格式存储，使用有限的位数。给定一个数字图像数据集，使用有损数据压缩可以进一步减少存储每个图像的比特 L 的数量。然而，由于每个例子都具有较低的分辨率，这会降低在这些图像上训练的模型的质量。为了捕获这种权衡并优化训练数据的存储，我们提出了一种“存储缩放律”，描述了测试误差与每幅图像的样本量和比特数的联合演化。我们证明这一定律适用于图像压缩的程式化模型，并在两个计算机视觉任务中进行实验验证，提取相关参数。然后我们证明这个定律可以用来优化有损数据压缩水平。在给定的存储器中，对最佳压缩图像进行训练的模型与对原始数据进行训练的模型相比，测试误差要小得多。最后，我们研究了随机化压缩级别的潜在好处。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Scaling+Training+Data+with+Lossy+Image+Compression)|0| |[Learning Causal Networks from Episodic Data](https://doi.org/10.1145/3637528.3671999)|Osman Mian, Sarah Mameche, Jilles Vreeken|CISPA Helmholtz Center for Information Security, Saarbruecken, Germany|In numerous real-world domains, spanning from environmental monitoring to long-term medical studies, observations do not arrive in a single batch but rather over time in episodes. This challenges the traditional assumption in causal discovery of a single, observational dataset, not only because each episode may be a biased sample of the population but also because multiple episodes could differ in the causal interactions underlying the observed variables. We address these issues using notions of context switches and episodic selection bias, and introduce a framework for causal modeling of episodic data. We show under which conditions we can apply information-theoretic scoring criteria for causal discovery while preserving consistency. To in practice discover the causal model progressively over time, we propose the CONTINENT algorithm which, taking inspiration from continual learning, discovers the causal model in an online fashion without having to re-learn the model upon arrival of each new episode. Our experiments over a variety of settings including selection bias, unknown interventions, and network changes showcase that CONTINENT works well in practice and outperforms the baselines by a clear margin.|在许多现实世界的领域，从环境监测到长期的医学研究，观察到的不是一批，而是随着时间的推移而发生的事件。这挑战了单个观察性数据集因果发现的传统假设，不仅因为每个事件可能是人群的偏倚样本，而且因为多个事件可能在观察变量的因果相互作用中有所不同。我们使用上下文切换和情节选择偏差的概念来解决这些问题，并介绍了一个情节数据的因果建模框架。我们展示了在哪些条件下，我们可以应用信息论评分标准的因果发现，同时保持一致性。为了在实践中逐步发现因果模型，我们提出 CONTINENT 算法，它从持续学习中获得灵感，以在线方式发现因果模型，而不必在每个新片段到达时重新学习模型。我们在包括选择偏差、未知干预和网络变化在内的各种环境下进行的实验表明，CONTINENT 在实践中运作良好，并且明显优于基线。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Learning+Causal+Networks+from+Episodic+Data)|0| -|[Money Never Sleeps: Maximizing Liquidity Mining Yields in Decentralized Finance](https://doi.org/10.1145/3637528.3671942)|Wangze Ni, Yiwei Zhao, Weijie Sun, Lei Chen, Peng Cheng, Chen Jason Zhang, Xuemin Lin|Hong Kong Polytechnic University, Hong Kong, China; East China Normal University, Shanghai, China; Shanghai Jiao Tong University, Shanghai, China; Hong Kong University of Science and Technology, Hong Kong, China|The popularity of decentralized finance has drawn attention to liquidity mining (LM). In LM, a user deposits her cryptocurrencies into liquidity pools to provide liquidity for exchanges and earn yields. Different liquidity pools offer varying yields and require different pairs of cryptocurrencies. A user can exchange a cryptocurrency for another with some exchange costs. Thus, an LM solution consists of exchange transactions and deposit transactions, guaranteeing (1) each exchange transaction must exchange one cryptocurrency for another at a specific rate (i.e., the exchange constraint); (2) the amounts of cryptocurrencies deposited in a liquidity pool must exceed the required threshold (i.e., the minimum constraint); (3) each deposit transaction must deposit a specific pair of cryptocurrencies at a certain rate in a liquidity pool (i.e., the deposit constraint); and (4) the cryptocurrencies used in the solution do not exceed the cryptocurrencies that the user has (i.e., the budget constraint). Selecting the most profitable LM solution is challenging due to the vast number of candidate solutions. To address this challenge, we define the yield maximization liquidity mining (YMLM) problem. Given a set of liquidity pools, a set of the user's cryptocurrencies, a set of exchange rates, and an evaluation function, YMLM aims to find an LM solution with maximal yields, satisfying the minimum, exchange, deposit, and budget constraints. We prove that YMLM is NP-hard and cannot be solved by algorithms with constant approximation ratios. To tackle YMLM, we propose two algorithms, namely YMLM\_GD and YMLM\_SK, with parameterized approximation ratios. Extensive experiments on both real and synthetic datasets show that our approaches outperform the baselines in yields.|分散式金融的普及引起了人们对流动性挖掘(LM)的关注。在 LM 中，用户将自己的加密货币存入流动性池，为交易所提供流动性，并获得收益率。不同的流动性池提供不同的收益率，需要不同的加密货币对。用户可以用一种加密货币交换另一种加密货币，但需要付出一定的交换成本。因此，LM 解决方案由交易所交易和存款交易组成，保证(1)每笔交易必须以特定的汇率(即交易约束)将一种加密货币兑换成另一种加密货币; (2)存入流动性池的加密货币数量必须超过规定的阈值(即最低限度约束) ; (3)每笔存款交易必须以特定的汇率将一对加密货币存入流动性池(即存款约束) ; 以及(4)解决方案中使用的加密货币不超过用户拥有的加密货币(即预算线)。选择最有利可图的 LM 解决方案是具有挑战性的，因为有大量的候选解决方案。为了解决这个问题，我们定义了收益最大化流动性挖掘(YMLM)问题。给定一组流动性池、一组用户的加密货币、一组汇率和一个评估函数，YMLM 的目标是找到一个收益最大的 LM 解决方案，满足最小值、汇率、存款和预算约束。证明了 YMLM 是 NP 难的，不能用常数逼近比的算法求解。为了解决 YMLM 问题，我们提出了两种具有参数化逼近比的算法: YMLM _ GD 和 YMLM _ SK。对真实和合成数据集的大量实验表明，我们的方法在产量方面优于基线。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Money+Never+Sleeps:+Maximizing+Liquidity+Mining+Yields+in+Decentralized+Finance)|0| +|[Money Never Sleeps: Maximizing Liquidity Mining Yields in Decentralized Finance](https://doi.org/10.1145/3637528.3671942)|Wangze Ni, Yiwei Zhao, Weijie Sun, Lei Chen, Peng Cheng, Chen Jason Zhang, Xuemin Lin|Hong Kong Polytechnic University, Hong Kong, China; East China Normal University, Shanghai, China; Hong Kong University of Science and Technology, Hong Kong, China; Shanghai Jiao Tong University, Shanghai, China|The popularity of decentralized finance has drawn attention to liquidity mining (LM). In LM, a user deposits her cryptocurrencies into liquidity pools to provide liquidity for exchanges and earn yields. Different liquidity pools offer varying yields and require different pairs of cryptocurrencies. A user can exchange a cryptocurrency for another with some exchange costs. Thus, an LM solution consists of exchange transactions and deposit transactions, guaranteeing (1) each exchange transaction must exchange one cryptocurrency for another at a specific rate (i.e., the exchange constraint); (2) the amounts of cryptocurrencies deposited in a liquidity pool must exceed the required threshold (i.e., the minimum constraint); (3) each deposit transaction must deposit a specific pair of cryptocurrencies at a certain rate in a liquidity pool (i.e., the deposit constraint); and (4) the cryptocurrencies used in the solution do not exceed the cryptocurrencies that the user has (i.e., the budget constraint). Selecting the most profitable LM solution is challenging due to the vast number of candidate solutions. To address this challenge, we define the yield maximization liquidity mining (YMLM) problem. Given a set of liquidity pools, a set of the user's cryptocurrencies, a set of exchange rates, and an evaluation function, YMLM aims to find an LM solution with maximal yields, satisfying the minimum, exchange, deposit, and budget constraints. We prove that YMLM is NP-hard and cannot be solved by algorithms with constant approximation ratios. To tackle YMLM, we propose two algorithms, namely YMLM\_GD and YMLM\_SK, with parameterized approximation ratios. Extensive experiments on both real and synthetic datasets show that our approaches outperform the baselines in yields.|分散式金融的普及引起了人们对流动性挖掘(LM)的关注。在 LM 中，用户将自己的加密货币存入流动性池，为交易所提供流动性，并获得收益率。不同的流动性池提供不同的收益率，需要不同的加密货币对。用户可以用一种加密货币交换另一种加密货币，但需要付出一定的交换成本。因此，LM 解决方案由交易所交易和存款交易组成，保证(1)每笔交易必须以特定的汇率(即交易约束)将一种加密货币兑换成另一种加密货币; (2)存入流动性池的加密货币数量必须超过规定的阈值(即最低限度约束) ; (3)每笔存款交易必须以特定的汇率将一对加密货币存入流动性池(即存款约束) ; 以及(4)解决方案中使用的加密货币不超过用户拥有的加密货币(即预算线)。选择最有利可图的 LM 解决方案是具有挑战性的，因为有大量的候选解决方案。为了解决这个问题，我们定义了收益最大化流动性挖掘(YMLM)问题。给定一组流动性池、一组用户的加密货币、一组汇率和一个评估函数，YMLM 的目标是找到一个收益最大的 LM 解决方案，满足最小值、汇率、存款和预算约束。证明了 YMLM 是 NP 难的，不能用常数逼近比的算法求解。为了解决 YMLM 问题，我们提出了两种具有参数化逼近比的算法: YMLM _ GD 和 YMLM _ SK。对真实和合成数据集的大量实验表明，我们的方法在产量方面优于基线。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Money+Never+Sleeps:+Maximizing+Liquidity+Mining+Yields+in+Decentralized+Finance)|0| |[Mining of Switching Sparse Networks for Missing Value Imputation in Multivariate Time Series](https://doi.org/10.1145/3637528.3671760)|Kohei Obata, Koki Kawabata, Yasuko Matsubara, Yasushi Sakurai|SANKEN, Osaka University, Suita, Osaka, Japan|Multivariate time series data suffer from the problem of missing values, which hinders the application of many analytical methods. To achieve the accurate imputation of these missing values, exploiting inter-correlation by employing the relationships between sequences (i.e., a network) is as important as the use of temporal dependency, since a sequence normally correlates with other sequences. Moreover, exploiting an adequate network depending on time is also necessary since the network varies over time. However, in real-world scenarios, we normally know neither the network structure nor when the network changes beforehand. Here, we propose a missing value imputation method for multivariate time series, namely MissNet, that is designed to exploit temporal dependency with a state-space model and inter-correlation by switching sparse networks. The network encodes conditional independence between features, which helps us understand the important relationships for imputation visually. Our algorithm, which scales linearly with reference to the length of the data, alternatively infers networks and fills in missing values using the networks while discovering the switching of the networks. Extensive experiments demonstrate that MissNet outperforms the state-of-the-art algorithms for multivariate time series imputation and provides interpretable results.|多变量时间序列数据存在缺值问题，阻碍了许多分析方法的应用。为了实现这些缺失值的精确估算，利用序列之间的关系(即，网络)的相互关系与使用时间依赖性一样重要，因为序列通常与其他序列相关。此外，由于网络随着时间的变化而变化，因此根据时间利用一个足够的网络也是必要的。然而，在实际场景中，我们通常既不知道网络结构，也不知道网络什么时候事先发生了变化。在这里，我们提出了一种多变量时间序列的缺失值填补方法，即 MissNet，该方法利用状态空间模型的时间相关性和通过切换稀疏网络的互相关性。网络对特征之间的条件独立进行编码，这有助于我们在视觉上理解插补的重要关系。我们的算法可以根据数据的长度进行线性扩展，也可以推断网络并使用网络填充缺失值，同时发现网络的切换。大量实验表明，MissNet 在多变量时间序列插补方面优于最先进的算法，并提供了可解释的结果。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Mining+of+Switching+Sparse+Networks+for+Missing+Value+Imputation+in+Multivariate+Time+Series)|0| -|[Ontology Enrichment for Effective Fine-grained Entity Typing](https://doi.org/10.1145/3637528.3671857)|Siru Ouyang, Jiaxin Huang, Pranav Pillai, Yunyi Zhang, Yu Zhang, Jiawei Han|University of Illinois Urbana-Champaign, Urbana, USA; Washington University in Saint Louis, St. Louis, MO, USA; University of Illinois Urbana-Champaign, Urbana, IL, USA|Fine-grained entity typing (FET) is the task of identifying specific entity types at a fine-grained level for entity mentions based on their contextual information. Conventional methods for FET require extensive human annotation, which is time-consuming and costly given the massive scale of data. Recent studies have been developing weakly supervised or zero-shot approaches. We study the setting of zero-shot FET where only an ontology is provided. However, most existing ontology structures lack rich supporting information and even contain ambiguous relations, making them ineffective in guiding FET. Recently developed language models, though promising in various few-shot and zero-shot NLP tasks, may face challenges in zero-shot FET due to their lack of interaction with task-specific ontology. In this study, we propose øurs, where we (1) enrich each node in the ontology structure with two categories of extra information:instance information for training sample augmentation andtopic information to relate types with contexts, and (2) develop a coarse-to-fine typing algorithm that exploits the enriched information by training an entailment model with contrasting topics and instance-based augmented training samples. Our experiments show that øurs achieves high-quality fine-grained entity typing without human annotation, outperforming existing zero-shot methods by a large margin and rivaling supervised methods. øurs also enjoys strong transferability to unseen and finer-grained types. We will open source this work upon acceptance.|细粒度实体类型化(Fine-grainedtity type，FET)是在细粒度级别识别特定实体类型的任务，以便根据实体的上下文信息提及它们。传统的场效应管方法需要大量的人工注释，由于数据规模庞大，这种方法既费时又费钱。最近的研究一直在发展弱监督或零射击方法。本文研究了只提供本体的零激发场效应管的设置问题。然而，现有的大多数本体结构缺乏丰富的支持信息，甚至存在模糊关系，导致本体结构对 FET 的指导无效。最近开发的语言模型，虽然有希望在各种少镜头和零镜头自然语言处理任务，可能面临的挑战，在零镜头场效应管由于缺乏与任务特定的本体交互。在本研究中，我们提出了 øurs，其中我们(1)用两类额外信息来丰富本体结构中的每个节点: 用于训练样本扩充的实例信息和用于将类型与上下文关联的主题信息，以及(2)开发一个粗到精的类型算法，通过训练一个带有对比主题和基于实例的扩充训练样本的蕴涵模型来利用丰富的信息。我们的实验表明，øurs 在无需人工注释的情况下实现了高质量的细粒度实体分类，在很大程度上优于现有的零镜头方法，并且可以与监督方法相媲美。Øurs 还具有很强的可转移性，可以转移到看不见的和更细粒度的类型。我们将在接受后开源这项工作。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Ontology+Enrichment+for+Effective+Fine-grained+Entity+Typing)|0| +|[Ontology Enrichment for Effective Fine-grained Entity Typing](https://doi.org/10.1145/3637528.3671857)|Siru Ouyang, Jiaxin Huang, Pranav Pillai, Yunyi Zhang, Yu Zhang, Jiawei Han|University of Illinois Urbana-Champaign, Urbana, IL, USA; University of Illinois Urbana-Champaign, Urbana, USA; Washington University in Saint Louis, St. Louis, MO, USA|Fine-grained entity typing (FET) is the task of identifying specific entity types at a fine-grained level for entity mentions based on their contextual information. Conventional methods for FET require extensive human annotation, which is time-consuming and costly given the massive scale of data. Recent studies have been developing weakly supervised or zero-shot approaches. We study the setting of zero-shot FET where only an ontology is provided. However, most existing ontology structures lack rich supporting information and even contain ambiguous relations, making them ineffective in guiding FET. Recently developed language models, though promising in various few-shot and zero-shot NLP tasks, may face challenges in zero-shot FET due to their lack of interaction with task-specific ontology. In this study, we propose øurs, where we (1) enrich each node in the ontology structure with two categories of extra information:instance information for training sample augmentation andtopic information to relate types with contexts, and (2) develop a coarse-to-fine typing algorithm that exploits the enriched information by training an entailment model with contrasting topics and instance-based augmented training samples. Our experiments show that øurs achieves high-quality fine-grained entity typing without human annotation, outperforming existing zero-shot methods by a large margin and rivaling supervised methods. øurs also enjoys strong transferability to unseen and finer-grained types. We will open source this work upon acceptance.|细粒度实体类型化(Fine-grainedtity type，FET)是在细粒度级别识别特定实体类型的任务，以便根据实体的上下文信息提及它们。传统的场效应管方法需要大量的人工注释，由于数据规模庞大，这种方法既费时又费钱。最近的研究一直在发展弱监督或零射击方法。本文研究了只提供本体的零激发场效应管的设置问题。然而，现有的大多数本体结构缺乏丰富的支持信息，甚至存在模糊关系，导致本体结构对 FET 的指导无效。最近开发的语言模型，虽然有希望在各种少镜头和零镜头自然语言处理任务，可能面临的挑战，在零镜头场效应管由于缺乏与任务特定的本体交互。在本研究中，我们提出了 øurs，其中我们(1)用两类额外信息来丰富本体结构中的每个节点: 用于训练样本扩充的实例信息和用于将类型与上下文关联的主题信息，以及(2)开发一个粗到精的类型算法，通过训练一个带有对比主题和基于实例的扩充训练样本的蕴涵模型来利用丰富的信息。我们的实验表明，øurs 在无需人工注释的情况下实现了高质量的细粒度实体分类，在很大程度上优于现有的零镜头方法，并且可以与监督方法相媲美。Øurs 还具有很强的可转移性，可以转移到看不见的和更细粒度的类型。我们将在接受后开源这项工作。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Ontology+Enrichment+for+Effective+Fine-grained+Entity+Typing)|0| |[BTTackler: A Diagnosis-based Framework for Efficient Deep Learning Hyperparameter Optimization](https://doi.org/10.1145/3637528.3671933)|Zhongyi Pei, Zhiyao Cen, Yipeng Huang, Chen Wang, Lin Liu, Philip S. Yu, Mingsheng Long, Jianmin Wang|School of Software, Tsinghua University, Beijing, China; School of Software, BNRist, Tsinghua University, Beijing, China; School of Software, EIRI, Tsinghua University, Beijing, China|Hyperparameter optimization (HPO) is known to be costly in deep learning, especially when leveraging automated approaches. Most of the existing automated HPO methods are accuracy-based, i.e., accuracy metrics are used to guide the trials of different hyperparameter configurations amongst a specific search space. However, many trials may encounter severe training problems, such as vanishing gradients and insufficient convergence, which can hardly be reflected by accuracy metrics in the early stages of the training and often result in poor performance. This leads to an inefficient optimization trajectory because the bad trials occupy considerable computation resources and reduce the probability of finding excellent hyperparameter configurations within a time limitation. In this paper, we propose Bad Trial Tackler (BTTackler), a novel HPO framework that introduces training diagnosis to identify training problems automatically and hence tackles bad trials. BTTackler diagnoses each trial by calculating a set of carefully designed quantified indicators and triggers early termination if any training problems are detected. Evaluations are performed on representative HPO tasks consisting of three classical deep neural networks (DNN) and four widely used HPO methods. To better quantify the effectiveness of an automated HPO method, we propose two new measurements based on accuracy and time consumption. Results show the advantage of BTTackler on two-fold: (1) it reduces 40.33% of time consumption to achieve the same accuracy comparable to baseline methods on average and (2) it conducts 44.5% more top-10 trials than baseline methods on average within a given time budget. We also released an open-source Python library that allows users to easily apply BTTackler to automated HPO processes with minimal code changes\footnotehttps://github.com/thuml/BTTackler.|众所周知，超参数优化(HPO)在深度学习方面代价高昂，特别是在利用自动化方法时。大多数现有的自动化 HPO 方法是基于准确度的，即，准确度指标被用来指导特定搜索空间中不同超参数配置的试验。然而，许多试验可能会遇到严重的训练问题，如梯度消失和收敛不足，这很难反映在训练的早期阶段的准确性度量，往往导致表现不佳。这导致了一个低效的优化轨迹，因为糟糕的试验占用了大量的计算资源，并降低了在一定时间内找到优秀的超参数配置的概率。本文提出了一种新的 HPO 框架 BTTackler，它引入训练诊断，自动识别训练问题，从而处理不良试验。BTTackler 通过计算一组精心设计的量化指标来诊断每个试验，如果发现任何训练问题，就会提前终止试验。对三种经典的深层神经网络(DNN)和四种广泛使用的 HPO 方法组成的代表性 HPO 任务进行了评估。为了更好地量化自动 HPO 方法的有效性，我们提出了两种基于精度和时间消耗的新测量方法。结果表明，BTTackler 的优势有两个方面: (1)在给定的时间预算内，与基线方法平均相比，它减少了40.33% 的时间消耗，以达到相同的准确度; (2)它比基线方法平均多进行了44.5% 的前10项试验。我们还发布了一个开源的 Python 库，允许用户轻松地将 BTtackler 应用于自动化的 HPO 流程，并且代码更改脚注 https:// github.com/thuml/BTTackler。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=BTTackler:+A+Diagnosis-based+Framework+for+Efficient+Deep+Learning+Hyperparameter+Optimization)|0| |[Fast Multidimensional Partial Fourier Transform with Automatic Hyperparameter Selection](https://doi.org/10.1145/3637528.3671667)|Yongchan Park, Jongjin Kim, U Kang|Seoul National University, Seoul, Republic of Korea|Given a multidimensional array, how can we optimize the computation process for a part of Fourier coefficients? Discrete Fourier transform plays an overarching role in various data mining tasks. Recent interest has focused on efficiently calculating a small part of Fourier coefficients, exploiting the energy compaction property of real-world data. Current methods for partial Fourier transform frequently encounter efficiency issues, yet the adoption of pre-computation techniques within the PFT algorithm has shown promising performance. However, PFT still faces limitations in handling multidimensional data efficiently and requires manual hyperparameter tuning, leading to additional costs. In this paper, we propose Auto-MPFT (Automatic Multidimensional Partial Fourier Transform), which efficiently computes a subset of Fourier coefficients in multidimensional data without the need for manual hyperparameter search. Auto-MPFT leverages multivariate polynomial approximation for trigonometric functions, generalizing its domain to multidimensional Euclidean space. Moreover, we present a convex optimization-based algorithm for automatically selecting the optimal hyperparameter of Auto-MPFT. We provide a rigorous proof for the explicit reformulation of the original optimization problem of Auto-MPFT, demonstrating the process that converts it into a well-established unconstrained convex optimization problem. Extensive experiments show that Auto-MPFT surpasses existing partial Fourier transform methods and optimized FFT libraries, achieving up to 7.6x increase in speed without sacrificing accuracy. In addition, our optimization algorithm accurately finds the optimal hyperparameter for Auto-MPFT, significantly reducing the cost associated with hyperparameter search.|给定一个多维数组，我们如何优化一部分傅里叶系数的计算过程？离散傅里叶变换在各种数据挖掘任务中起着至关重要的作用。最近的兴趣集中在有效地计算一小部分傅里叶系数，利用真实世界数据的能量压缩性质。目前用于部分傅里叶变换的方法经常遇到效率问题，然而在 PFT 算法中采用预计算技术已经显示出良好的性能。然而，PFT 仍然面临着有效处理多维数据的局限性，需要手动调整超参数，从而导致额外的成本。在本文中，我们提出了自动多维部分傅里叶变换(auto-MPFT) ，它可以有效地计算多维数据中的傅里叶系数子集，而不需要手动进行超参数搜索。Auto-MPFT 利用多元多项式逼近三角函数，将其域推广到多维欧氏空间。此外，本文还提出了一种基于凸优化的自动选取 Auto-MPFT 最优超参数的算法。我们为 Auto-MPFT 的原始最佳化问题的明确重构提供了严格的证明，展示了将其转化为一个完善的无约束凸最佳化问题的过程。大量的实验表明，Auto-MPFT 超越了现有的部分傅里叶变换方法和优化的 FFT 库，在不牺牲精度的情况下，速度提高了7.6倍。此外，我们的优化算法准确地找到了自动 MPFT 的最优超参数，大大降低了与超参数搜索相关的成本。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Fast+Multidimensional+Partial+Fourier+Transform+with+Automatic+Hyperparameter+Selection)|0| |[CoMAL: Contrastive Active Learning for Multi-Label Text Classification](https://doi.org/10.1145/3637528.3671754)|Cheng Peng, Haobo Wang, Ke Chen, Lidan Shou, Chang Yao, Runze Wu, Gang Chen|Fuxi AI Lab, NetEase Corp., Hangzhou, China; School of Software Technology, Zhejiang University, Ningbo, China; State Key Lab of Blockchain and Data Security, Zhejiang University, Hangzhou, China|Multi-label text classification (MLTC) allows a given text to be associated with multiple labels, which well suits many real-world data mining scenarios. However, the annotation effort of MLTC is inevitably expensive and time-consuming. Although multi-label active learning provides a cost-effective solution, it still faces two major challenges: (i) constructing decent feature space to distinguish the confusing semantics of different labels; (ii) defining proper sampling criteria to measure a sample's joint effect over the entire label space. To bridge these gaps, we propose a Contrastive Multi-label Active Learning framework (CoMAL) that gives an effective data acquisition strategy. Specifically, a contrastive decoupling mechanism is introduced to fully release the semantic information of multiple labels into the latent space. Then, we devise a hybrid criterion that balances two data value measures: (i) similarity-enhanced label cardinality inconsistency reflects the uncertainty of data predictions. (ii) positive feature diversity evaluates the positive-propensity semantic diversity to handle the label sparsity. Extensive experiments demonstrate that our CoMAL outperforms the current state-of-the-art multi-label active learning approaches. Code for CoMAL is available at https://github.com/chengzju/CoMAL.|多标签文本分类(MLTC)允许将给定的文本与多个标签关联，这非常适合许多现实世界的数据挖掘场景。然而，MLTC 的注释工作不可避免地是昂贵的和耗时的。虽然多标签主动学习提供了一个成本效益的解决方案，它仍然面临两个主要的挑战: (i)构建体面的特征空间，以区分不同标签的混乱语义; (ii)定义适当的抽样标准，以衡量样本的联合效应，在整个标签空间。为了弥补这些差距，我们提出了一个对比多标签主动学习框架(CoMAL) ，提供了一个有效的数据采集策略。具体来说，我们引入了一个对比解耦机制，将多个标签的语义信息完全释放到潜在空间中。然后，我们设计了一个平衡两个数据值度量的混合准则: (i)相似性增强的标签基数不一致性反映了数据预测的不确定性。(ii)正特征多样性评价正倾向语义多样性来处理标签稀疏性。大量的实验表明，我们的 CoMAL 优于目前最先进的多标签主动学习方法。可于 https://github.com/chengzju/CoMAL 索取「协议编码」代码。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=CoMAL:+Contrastive+Active+Learning+for+Multi-Label+Text+Classification)|0| -|[TSC: A Simple Two-Sided Constraint against Over-Smoothing](https://doi.org/10.1145/3637528.3671954)|Furong Peng, Kang Liu, Xuan Lu, Yuhua Qian, HongRen Yan, Chao Ma|; College of Physics and Electronic Engineering, Shanxi University, Taiyuan, Shanxi, China; HOPERUN Infomation Technology, Nanjing, Jiangsu, China|Graph Convolutional Neural Network (GCN), a widely adopted method for analyzing relational data, enhances node discriminability through the aggregation of neighboring information. Usually, stacking multiple layers can improve the performance of GCN by leveraging information from high-order neighbors. However, the increase of the network depth will induce the over-smoothing problem, which can be attributed to the quality and quantity of neighbors changing: (a) neighbor quality, node's neighbors become overlapping in high order, leading to aggregated information becoming indistinguishable, (b) neighbor quantity, the exponentially growing aggregated neighbors submerges the node's initial feature by recursively aggregating operations. Current solutions mainly focus on one of the above causes and seldom consider both at once. Aiming at tackling both causes of over-smoothing in one shot, we introduce a simple Two-Sided Constraint (TSC) for GCNs, comprising two straightforward yet potent techniques: random masking and contrastive constraint. The random masking acts on the representation matrix's columns to regulate the degree of information aggregation from neighbors, thus preventing the convergence of node representations. Meanwhile, the contrastive constraint, applied to the representation matrix's rows, enhances the discriminability of the nodes. Designed as a plug-in module, TSC can be easily coupled with GCN or SGC architectures. Experimental analyses on diverse real-world graph datasets verify that our approach markedly reduces the convergence of node's representation and the performance degradation in deeper GCN.|图形卷积神经网络(GCN)是一种广泛采用的分析关系数据的方法，它通过聚合相邻的信息来提高节点的识别能力。通常，多层叠加可以通过利用高阶邻居的信息来提高 GCN 的性能。然而，随着网络深度的增加，过平滑问题会出现，这可以归因于邻居的质量和数量的变化: (a)邻居质量，节点的邻居高度重叠，导致聚合信息难以区分; (b)邻居数量，指数增长的聚合邻居通过递归聚合操作淹没节点的初始特征。目前的解决方案主要集中在上述原因之一，很少同时考虑两者。为了一次性解决导致过度平滑的两个原因，我们引入了一个简单的 GCNs 双边约束(TSC) ，包括两个简单而有效的技术: 随机掩蔽和对比约束。随机掩蔽作用在表示矩阵的列上，调节邻居信息的聚集程度，从而防止节点表示的收敛。同时，将对比约束应用于表示矩阵的行，增强了节点的可识别性。作为一个插件模块设计，TSC 可以很容易地与 GCN 或 SGC 体系结构耦合。通过对不同实际图形数据集的实验分析，证明该方法显著降低了深层 GCN 中节点表示的收敛性和性能下降。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=TSC:+A+Simple+Two-Sided+Constraint+against+Over-Smoothing)|0| -|[CASH via Optimal Diversity for Ensemble Learning](https://doi.org/10.1145/3637528.3671894)|Pranav Poduval, Sanjay Kumar Patnala, Gaurav Oberoi, Nitish Srivasatava, Siddhartha Asthana|MasterCard AI Garage, Gurgaon, India; Mastercard AI Garage, Gurgaon, India|The Combined Algorithm Selection and Hyperparameter Optimization (CASH) problem is pivotal in Automatic Machine Learning (AutoML). Most leading approaches combine Bayesian optimization with post-hoc ensemble building to create advanced AutoML systems. Bayesian optimization (BO) typically focuses on identifying a singular algorithm and its hyperparameters that outperform all other configurations. Recent developments have highlighted an oversight in prior CASH methods: the lack of consideration for diversity among the base learners of the ensemble. This oversight was overcome by explicitly injecting the search for diversity into the traditional CASH problem. However, despite recent developments, BO's limitation lies in its inability to directly optimize ensemble generalization error, offering no theoretical assurance that increased diversity correlates with enhanced ensemble performance. Our research addresses this gap by establishing a theoretical foundation that integrates diversity into the core of BO for direct ensemble learning. We explore a theoretically sound framework that describes the relationship between pair-wise diversity and ensemble performance, which allows our Bayesian optimization framework Optimal Diversity Bayesian Optimization (OptDivBO) to directly and efficiently minimize ensemble generalization error. OptDivBO guarantees an optimal balance between pairwise diversity and individual model performance, setting a new precedent in ensemble learning within CASH. Empirical results on 20 public datasets show that OptDivBO achieves the best average test ranks of 1.57 and 1.4 in classification and regression tasks.|组合算法选择和超参数优化(CASH)问题是自动机器学习(AutoML)中的关键问题。大多数领先的方法结合贝叶斯优化和事后集成建设，以创建先进的自动建模系统。贝叶斯优化(BO)通常侧重于识别一个奇异算法及其超参数，其性能优于所有其他配置。最近的事态发展突出表明，以前的 CASH 方法存在一个疏忽: 缺乏对集合基础学习者多样性的考虑。克服这一疏忽的办法是，明确地将寻求多样性的工作纳入传统的现金结算问题。然而，尽管有最近的发展，BO 的局限性在于它不能直接优化集合泛化误差，不能提供增加的多样性与增强的集合性能相关的理论保证。我们的研究通过建立一个理论基础，将多样性融入直接集成学习的 BO 核心，从而解决了这一差距。我们探索了一个理论上合理的框架来描述成对多样性和集合性能之间的关系，这使得我们的贝叶斯优化框架最佳多样性贝叶斯优化(optDivBO)能够直接有效地最小化集合泛化误差。OptDivBO 保证了成对多样性和个体模型性能之间的最佳平衡，在现金集成学习内开创了一个新的先例。对20个公共数据集的实证结果表明，OptDivBO 在分类和回归任务中取得了1.57和1.4的最佳平均测试排名。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=CASH+via+Optimal+Diversity+for+Ensemble+Learning)|0| +|[TSC: A Simple Two-Sided Constraint against Over-Smoothing](https://doi.org/10.1145/3637528.3671954)|Furong Peng, Kang Liu, Xuan Lu, Yuhua Qian, HongRen Yan, Chao Ma|; HOPERUN Infomation Technology, Nanjing, Jiangsu, China; College of Physics and Electronic Engineering, Shanxi University, Taiyuan, Shanxi, China|Graph Convolutional Neural Network (GCN), a widely adopted method for analyzing relational data, enhances node discriminability through the aggregation of neighboring information. Usually, stacking multiple layers can improve the performance of GCN by leveraging information from high-order neighbors. However, the increase of the network depth will induce the over-smoothing problem, which can be attributed to the quality and quantity of neighbors changing: (a) neighbor quality, node's neighbors become overlapping in high order, leading to aggregated information becoming indistinguishable, (b) neighbor quantity, the exponentially growing aggregated neighbors submerges the node's initial feature by recursively aggregating operations. Current solutions mainly focus on one of the above causes and seldom consider both at once. Aiming at tackling both causes of over-smoothing in one shot, we introduce a simple Two-Sided Constraint (TSC) for GCNs, comprising two straightforward yet potent techniques: random masking and contrastive constraint. The random masking acts on the representation matrix's columns to regulate the degree of information aggregation from neighbors, thus preventing the convergence of node representations. Meanwhile, the contrastive constraint, applied to the representation matrix's rows, enhances the discriminability of the nodes. Designed as a plug-in module, TSC can be easily coupled with GCN or SGC architectures. Experimental analyses on diverse real-world graph datasets verify that our approach markedly reduces the convergence of node's representation and the performance degradation in deeper GCN.|图形卷积神经网络(GCN)是一种广泛采用的分析关系数据的方法，它通过聚合相邻的信息来提高节点的识别能力。通常，多层叠加可以通过利用高阶邻居的信息来提高 GCN 的性能。然而，随着网络深度的增加，过平滑问题会出现，这可以归因于邻居的质量和数量的变化: (a)邻居质量，节点的邻居高度重叠，导致聚合信息难以区分; (b)邻居数量，指数增长的聚合邻居通过递归聚合操作淹没节点的初始特征。目前的解决方案主要集中在上述原因之一，很少同时考虑两者。为了一次性解决导致过度平滑的两个原因，我们引入了一个简单的 GCNs 双边约束(TSC) ，包括两个简单而有效的技术: 随机掩蔽和对比约束。随机掩蔽作用在表示矩阵的列上，调节邻居信息的聚集程度，从而防止节点表示的收敛。同时，将对比约束应用于表示矩阵的行，增强了节点的可识别性。作为一个插件模块设计，TSC 可以很容易地与 GCN 或 SGC 体系结构耦合。通过对不同实际图形数据集的实验分析，证明该方法显著降低了深层 GCN 中节点表示的收敛性和性能下降。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=TSC:+A+Simple+Two-Sided+Constraint+against+Over-Smoothing)|0| +|[CASH via Optimal Diversity for Ensemble Learning](https://doi.org/10.1145/3637528.3671894)|Pranav Poduval, Sanjay Kumar Patnala, Gaurav Oberoi, Nitish Srivasatava, Siddhartha Asthana|Mastercard AI Garage, Gurgaon, India; MasterCard AI Garage, Gurgaon, India|The Combined Algorithm Selection and Hyperparameter Optimization (CASH) problem is pivotal in Automatic Machine Learning (AutoML). Most leading approaches combine Bayesian optimization with post-hoc ensemble building to create advanced AutoML systems. Bayesian optimization (BO) typically focuses on identifying a singular algorithm and its hyperparameters that outperform all other configurations. Recent developments have highlighted an oversight in prior CASH methods: the lack of consideration for diversity among the base learners of the ensemble. This oversight was overcome by explicitly injecting the search for diversity into the traditional CASH problem. However, despite recent developments, BO's limitation lies in its inability to directly optimize ensemble generalization error, offering no theoretical assurance that increased diversity correlates with enhanced ensemble performance. Our research addresses this gap by establishing a theoretical foundation that integrates diversity into the core of BO for direct ensemble learning. We explore a theoretically sound framework that describes the relationship between pair-wise diversity and ensemble performance, which allows our Bayesian optimization framework Optimal Diversity Bayesian Optimization (OptDivBO) to directly and efficiently minimize ensemble generalization error. OptDivBO guarantees an optimal balance between pairwise diversity and individual model performance, setting a new precedent in ensemble learning within CASH. Empirical results on 20 public datasets show that OptDivBO achieves the best average test ranks of 1.57 and 1.4 in classification and regression tasks.|组合算法选择和超参数优化(CASH)问题是自动机器学习(AutoML)中的关键问题。大多数领先的方法结合贝叶斯优化和事后集成建设，以创建先进的自动建模系统。贝叶斯优化(BO)通常侧重于识别一个奇异算法及其超参数，其性能优于所有其他配置。最近的事态发展突出表明，以前的 CASH 方法存在一个疏忽: 缺乏对集合基础学习者多样性的考虑。克服这一疏忽的办法是，明确地将寻求多样性的工作纳入传统的现金结算问题。然而，尽管有最近的发展，BO 的局限性在于它不能直接优化集合泛化误差，不能提供增加的多样性与增强的集合性能相关的理论保证。我们的研究通过建立一个理论基础，将多样性融入直接集成学习的 BO 核心，从而解决了这一差距。我们探索了一个理论上合理的框架来描述成对多样性和集合性能之间的关系，这使得我们的贝叶斯优化框架最佳多样性贝叶斯优化(optDivBO)能够直接有效地最小化集合泛化误差。OptDivBO 保证了成对多样性和个体模型性能之间的最佳平衡，在现金集成学习内开创了一个新的先例。对20个公共数据集的实证结果表明，OptDivBO 在分类和回归任务中取得了1.57和1.4的最佳平均测试排名。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=CASH+via+Optimal+Diversity+for+Ensemble+Learning)|0| |[Unifying Evolution, Explanation, and Discernment: A Generative Approach for Dynamic Graph Counterfactuals](https://doi.org/10.1145/3637528.3671831)|Bardh Prenkaj, Mario VillaizánVallelado, Tobias Leemann, Gjergji Kasneci|University of Tübingen, Tübingen, Germany; University of Valladolid & Telefónica Research and Development, Valladolid, Spain; Technical University of Munich, Munich, Germany|We present GRACIE (Graph Recalibration and Adaptive Counterfactual Inspection and Explanation), a novel approach for generative classification and counterfactual explanations of dynamically changing graph data. We study graph classification problems through the lens of generative classifiers. We propose a dynamic, self-supervised latent variable model that updates by identifying plausible counterfactuals for input graphs and recalibrating decision boundaries through contrastive optimization. Unlike prior work, we do not rely on linear separability between the learned graph representations to find plausible counterfactuals. Moreover, GRACIE eliminates the need for stochastic sampling in latent spaces and graph-matching heuristics. Our work distills the implicit link between generative classification and loss functions in the latent space, a key insight to understanding recent successes with this architecture. We further observe the inherent trade-off between validity and pulling explainee instances towards the central region of the latent space, empirically demonstrating our theoretical findings. In extensive experiments on synthetic and real-world graph data, we attain considerable improvements, reaching ~99% validity when sampling sets of counterfactuals even in the challenging setting of dynamic data landscapes.|本文提出了一种动态变化的图形数据生成分类和反事实解释的新方法 GRACIE (图形重校正和自适应反事实检验与解释)。我们从生成分类器的角度研究图的分类问题。我们提出了一个动态的，自我监督的潜变量模型，通过识别合理的反事实输入图和重新校准决策边界通过对比优化更新。与之前的工作不同，我们不依赖于学习图表示之间的线性可分的来找到合理的反事实。此外，GRACIE 消除了在潜空间和图匹配启发式随机抽样的需要。我们的工作提取了潜在空间中生成分类和损失函数之间的隐含联系，这是理解该体系结构最近的成功的关键洞察力。我们进一步观察了有效性与将被解释实例拉向潜在空间中心区域之间的内在权衡，并通过实证证明了我们的理论发现。在对合成和真实世界图形数据的广泛实验中，我们获得了相当大的改进，即使在具有挑战性的动态数据景观环境中，当采样反事实集时，我们也达到了约99% 的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Unifying+Evolution,+Explanation,+and+Discernment:+A+Generative+Approach+for+Dynamic+Graph+Counterfactuals)|0| |[Reimagining Graph Classification from a Prototype View with Optimal Transport: Algorithm and Theorem](https://doi.org/10.1145/3637528.3671696)|Chen Qian, Huayi Tang, Hong Liang, Yong Liu|School of Electronic and Computer Engineering, Peking University, Shenzhen, China; Gaoling School of Artificial Intelligence, Renmin University of China, Beijing, China|Recently, Graph Neural Networks (GNNs) have achieved inspiring performances in graph classification tasks. However, the message passing mechanism in GNNs implicitly utilizes the topological information of the graph, which may lead to a potential loss of structural information. Furthermore, the graph classification decision process based on GNNs resembles a black box and lacks sufficient transparency. The non-linear classifier following the GNNs also defaults to the assumption that each class is represented by a single vector, thereby limiting the diversity of intra-class representations. To address these issues, we propose a novel prototype-based graph classification framework that introduces the Fused Gromov-Wasserstein (FGW) distance in Optimal Transport (OT) as the similarity measure. In this way, the model explicitly exploits the structural information on the graph through OT while leading to a more transparent and straightforward classification process. The introduction of prototypes also inherently addresses the issue of limited within-class representations. Besides, to alleviate the widely acknowledged computational complexity issue of FGW distance calculation, we devise a simple yet effective NN-based FGW distance approximator, which can enable full GPU training acceleration with a marginal performance loss. In theory, we analyze the generalization performance of the proposed method and derive an O (1 over N) generalization bound, where the proof techniques can be extended to a broader range of prototype-based classification frameworks. Experimental results show that the proposed framework achieves competitive and superior performance on several widely used graph classification benchmark datasets. The code is avaliable at https://github.com/ChnQ/PGOT.|近年来，图神经网络在图分类任务中取得了令人鼓舞的成绩。然而，GNN 中的消息传递机制隐含地利用了图的拓扑信息，这可能导致结构信息的丢失。此外，基于 GNN 的图分类决策过程类似于一个黑盒子，缺乏足够的透明度。GNN 后面的非线性分类器也默认每个类由一个向量表示，从而限制了类内表示的多样性。为了解决这些问题，我们提出了一种新的基于原型的图分类框架，该框架引入了最优运输(OT)中的融合 Gromov-Wasserstein (FGW)距离作为相似性度量。这样，该模型通过 OT 显式地利用图上的结构信息，同时导致一个更加透明和直接的分类过程。原型的引入本质上也解决了类内表示有限的问题。此外，为了解决普遍存在的 FGW 距离计算复杂性问题，我们设计了一个简单而有效的基于神经网络的 FGW 距离近似器，它可以在性能损失较小的情况下实现 GPU 的全训练加速。在理论上，我们分析了该方法的泛化性能，并得到了一个 O (1/N)泛化界，其中证明技术可以扩展到更广泛的基于原型的分类框架。实验结果表明，该框架在几个广泛使用的图分类基准数据集上取得了较好的性能。密码在 https://github.com/chnq/pgot 可用。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Reimagining+Graph+Classification+from+a+Prototype+View+with+Optimal+Transport:+Algorithm+and+Theorem)|0| -|[Pre-train and Refine: Towards Higher Efficiency in K-Agnostic Community Detection without Quality Degradation](https://doi.org/10.1145/3637528.3671686)|Meng Qin, Chaorui Zhang, Yu Gao, Weixi Zhang, DitYan Yeung|Theory Lab, Huawei, Hong Kong, Hong Kong; Department of CSE, HKUST, Hong Kong, Hong Kong; Theory Lab, Huawei, Beijing, China|Community detection (CD) is a classic graph inference task that partitionsnodes of a graph into densely connected groups. While many CD methods have beenproposed with either impressive quality or efficiency, balancing the twoaspects remains a challenge. This study explores the potential of deep graphlearning to achieve a better trade-off between the quality and efficiency ofK-agnostic CD, where the number of communities K is unknown. We propose PRoCD(Pre-training Refinement fOr Community Detection), a simple yet effectivemethod that reformulates K-agnostic CD as the binary node pair classification.PRoCD follows a pre-training refinement paradigm inspired by recent advancesin pre-training techniques. We first conduct the offline pre-training of PRoCDon small synthetic graphs covering various topology properties. Based on theinductive inference across graphs, we then generalize the pre-trained model(with frozen parameters) to large real graphs and use the derived CD results asthe initialization of an existing efficient CD method (e.g., InfoMap) tofurther refine the quality of CD results. In addition to benefiting from thetransfer ability regarding quality, the online generalization and refinementcan also help achieve high inference efficiency, since there is notime-consuming model optimization. Experiments on public datasets with variousscales demonstrate that PRoCD can ensure higher efficiency in K-agnostic CDwithout significant quality degradation.|社区检测(CD)是一个典型的图推理任务，它将图的节点划分为密集连通的群。虽然许多 CD 方法已经提出或令人印象深刻的质量或效率，平衡这两个方面仍然是一个挑战。这项研究探讨了深度图形学习的潜力，以实现更好的权衡之间的质量和效率的 K 不可知 CD，其中的社区 K 的数量是未知的。我们提出了 PRoCD (用于社区检测的预训练细化) ，一种简单而有效的方法，将 K 不可知 CD 重新制定为二进制节点对分类。 PRoCD 遵循受最近预训练技术进展启发的预训练细化范例。我们首先对覆盖各种拓扑性质的小合成图进行离线预训练。基于图之间的归纳推理，然后将预训练模型(具有冻结参数)推广到大实数图，并使用导出的 CD 结果作为现有有效 CD 方法(例如 InfoMap)的初始化，以进一步细化 CD 结果的质量。除了受益于关于质量的传递能力，在线泛化和细化也可以帮助实现高推理效率，因为没有耗时的模型优化。在不同尺度的公共数据集上进行的实验表明，PRoCD 能够在不显著降低质量的情况下保证 K 无关 CD 的高效率。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Pre-train+and+Refine:+Towards+Higher+Efficiency+in+K-Agnostic+Community+Detection+without+Quality+Degradation)|0| -|[RHiOTS: A Framework for Evaluating Hierarchical Time Series Forecasting Algorithms](https://doi.org/10.1145/3637528.3672062)|Luis Roque, Carlos Soares, Luís Torgo|LIACCFaculty of Engineering, University of Porto, Porto, Portugal; Dalhousie University, Halifax, Canada; LIACCFaculty of Engineering, University of Porto & Fraunhofer AICOS Portugal, Porto, Portugal|We introduce the Robustness of Hierarchically Organized Time Series (RHiOTS) framework, designed to assess the robustness of hierarchical time series forecasting models and algorithms on real-world datasets. Hierarchical time series, where lower-level forecasts must sum to upper-level ones, are prevalent in various contexts, such as retail sales across countries. Current empirical evaluations of forecasting methods are often limited to a small set of benchmark datasets, offering a narrow view of algorithm behavior. RHiOTS addresses this gap by systematically altering existing datasets and modifying the characteristics of individual series and their interrelations. It uses a set of parameterizable transformations to simulate those changes in the data distribution. Additionally, RHiOTS incorporates an innovative visualization component, turning complex, multidimensional robustness evaluation results into intuitive, easily interpretable visuals. This approach allows an in-depth analysis of algorithm and model behavior under diverse conditions. We illustrate the use of RHiOTS by analyzing the predictive performance of several algorithms. Our findings show that traditional statistical methods are more robust than state-of-the-art deep learning algorithms, except when the transformation effect is highly disruptive. Furthermore, we found no significant differences in the robustness of the algorithms when applying specific reconciliation methods, such as MinT. RHiOTS provides researchers with a comprehensive tool for understanding the nuanced behavior of forecasting algorithms, offering a more reliable basis for selecting the most appropriate method for a given problem.|本文介绍了层次组织时间序列(RHiOTS)框架，该框架旨在评估层次时间序列预测模型和算法对实际数据集的鲁棒性。分层时间序列，其中较低水平的预测必须总和较高水平的，普遍存在于各种情况下，如各国的零售销售。目前对预测方法的经验性评估通常局限于一小组基准数据集，从而对算法行为提供了一个狭窄的视角。RHiOTS 通过系统地改变现有数据集和修改单个序列的特征及其相互关系来弥补这一差距。它使用一组可参数化的转换来模拟数据分布中的这些更改。此外，RHiOTS 采用了创新的可视化组件，将复杂的、多维的鲁棒性评估结果转化为直观的、易于解释的视觉效果。这种方法允许在不同的条件下深入分析算法和模型行为。通过分析几种算法的预测性能，说明了 RHiOTS 算法的应用。我们的研究结果表明，传统的统计方法比最先进的深度学习算法更稳健，除非当转换效应是高度破坏性。此外，我们发现在应用特定的协调方法时，算法的鲁棒性没有显著差异，如 MinT。RHiOTS 为研究人员提供了一个全面的工具来理解预测算法的微妙行为，为选择最合适的方法来解决给定的问题提供了更可靠的基础。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=RHiOTS:+A+Framework+for+Evaluating+Hierarchical+Time+Series+Forecasting+Algorithms)|0| +|[Pre-train and Refine: Towards Higher Efficiency in K-Agnostic Community Detection without Quality Degradation](https://doi.org/10.1145/3637528.3671686)|Meng Qin, Chaorui Zhang, Yu Gao, Weixi Zhang, DitYan Yeung|Department of CSE, HKUST, Hong Kong, Hong Kong; Theory Lab, Huawei, Beijing, China; Theory Lab, Huawei, Hong Kong, Hong Kong|Community detection (CD) is a classic graph inference task that partitionsnodes of a graph into densely connected groups. While many CD methods have beenproposed with either impressive quality or efficiency, balancing the twoaspects remains a challenge. This study explores the potential of deep graphlearning to achieve a better trade-off between the quality and efficiency ofK-agnostic CD, where the number of communities K is unknown. We propose PRoCD(Pre-training Refinement fOr Community Detection), a simple yet effectivemethod that reformulates K-agnostic CD as the binary node pair classification.PRoCD follows a pre-training refinement paradigm inspired by recent advancesin pre-training techniques. We first conduct the offline pre-training of PRoCDon small synthetic graphs covering various topology properties. Based on theinductive inference across graphs, we then generalize the pre-trained model(with frozen parameters) to large real graphs and use the derived CD results asthe initialization of an existing efficient CD method (e.g., InfoMap) tofurther refine the quality of CD results. In addition to benefiting from thetransfer ability regarding quality, the online generalization and refinementcan also help achieve high inference efficiency, since there is notime-consuming model optimization. Experiments on public datasets with variousscales demonstrate that PRoCD can ensure higher efficiency in K-agnostic CDwithout significant quality degradation.|社区检测(CD)是一个典型的图推理任务，它将图的节点划分为密集连通的群。虽然许多 CD 方法已经提出或令人印象深刻的质量或效率，平衡这两个方面仍然是一个挑战。这项研究探讨了深度图形学习的潜力，以实现更好的权衡之间的质量和效率的 K 不可知 CD，其中的社区 K 的数量是未知的。我们提出了 PRoCD (用于社区检测的预训练细化) ，一种简单而有效的方法，将 K 不可知 CD 重新制定为二进制节点对分类。 PRoCD 遵循受最近预训练技术进展启发的预训练细化范例。我们首先对覆盖各种拓扑性质的小合成图进行离线预训练。基于图之间的归纳推理，然后将预训练模型(具有冻结参数)推广到大实数图，并使用导出的 CD 结果作为现有有效 CD 方法(例如 InfoMap)的初始化，以进一步细化 CD 结果的质量。除了受益于关于质量的传递能力，在线泛化和细化也可以帮助实现高推理效率，因为没有耗时的模型优化。在不同尺度的公共数据集上进行的实验表明，PRoCD 能够在不显著降低质量的情况下保证 K 无关 CD 的高效率。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Pre-train+and+Refine:+Towards+Higher+Efficiency+in+K-Agnostic+Community+Detection+without+Quality+Degradation)|0| +|[RHiOTS: A Framework for Evaluating Hierarchical Time Series Forecasting Algorithms](https://doi.org/10.1145/3637528.3672062)|Luis Roque, Carlos Soares, Luís Torgo|LIACCFaculty of Engineering, University of Porto & Fraunhofer AICOS Portugal, Porto, Portugal; LIACCFaculty of Engineering, University of Porto, Porto, Portugal; Dalhousie University, Halifax, Canada|We introduce the Robustness of Hierarchically Organized Time Series (RHiOTS) framework, designed to assess the robustness of hierarchical time series forecasting models and algorithms on real-world datasets. Hierarchical time series, where lower-level forecasts must sum to upper-level ones, are prevalent in various contexts, such as retail sales across countries. Current empirical evaluations of forecasting methods are often limited to a small set of benchmark datasets, offering a narrow view of algorithm behavior. RHiOTS addresses this gap by systematically altering existing datasets and modifying the characteristics of individual series and their interrelations. It uses a set of parameterizable transformations to simulate those changes in the data distribution. Additionally, RHiOTS incorporates an innovative visualization component, turning complex, multidimensional robustness evaluation results into intuitive, easily interpretable visuals. This approach allows an in-depth analysis of algorithm and model behavior under diverse conditions. We illustrate the use of RHiOTS by analyzing the predictive performance of several algorithms. Our findings show that traditional statistical methods are more robust than state-of-the-art deep learning algorithms, except when the transformation effect is highly disruptive. Furthermore, we found no significant differences in the robustness of the algorithms when applying specific reconciliation methods, such as MinT. RHiOTS provides researchers with a comprehensive tool for understanding the nuanced behavior of forecasting algorithms, offering a more reliable basis for selecting the most appropriate method for a given problem.|本文介绍了层次组织时间序列(RHiOTS)框架，该框架旨在评估层次时间序列预测模型和算法对实际数据集的鲁棒性。分层时间序列，其中较低水平的预测必须总和较高水平的，普遍存在于各种情况下，如各国的零售销售。目前对预测方法的经验性评估通常局限于一小组基准数据集，从而对算法行为提供了一个狭窄的视角。RHiOTS 通过系统地改变现有数据集和修改单个序列的特征及其相互关系来弥补这一差距。它使用一组可参数化的转换来模拟数据分布中的这些更改。此外，RHiOTS 采用了创新的可视化组件，将复杂的、多维的鲁棒性评估结果转化为直观的、易于解释的视觉效果。这种方法允许在不同的条件下深入分析算法和模型行为。通过分析几种算法的预测性能，说明了 RHiOTS 算法的应用。我们的研究结果表明，传统的统计方法比最先进的深度学习算法更稳健，除非当转换效应是高度破坏性。此外，我们发现在应用特定的协调方法时，算法的鲁棒性没有显著差异，如 MinT。RHiOTS 为研究人员提供了一个全面的工具来理解预测算法的微妙行为，为选择最合适的方法来解决给定的问题提供了更可靠的基础。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=RHiOTS:+A+Framework+for+Evaluating+Hierarchical+Time+Series+Forecasting+Algorithms)|0| |[A Fast Exact Algorithm to Enumerate Maximal Pseudo-cliques in Large Sparse Graphs](https://doi.org/10.1145/3637528.3672066)|Ahsanur Rahman, Kalyan Roy, Ramiza Maliha, Townim Faisal Chowdhury|Australian Institute for Machine Learning, University of Adelaide, Adelaide, Australia; North South University, Dhaka, Bangladesh|Pseudo-cliques (subgraphs with almost all possible edges) have many applications. But they do not satisfy the convertible antimonotone constraint (as we prove here). So, it is hard to reduce the search space of pseudo-cliques and list them efficiently. To our knowledge, only two exact algorithms, namely, ODES and PCE, were proposed for this purpose, but both have high execution times. Here, we present an exact algorithm named Fast Pseudo-Clique Enumerator (FPCE). It employs some pruning techniques we derived to reduce the search space. Our experiment on 15 real and 16 synthetic graphs shows that (i) on real graphs, FPCE is, on average, 38.6 and 6.5 times faster than ODES and PCE, respectively, whereas (ii) on synthetic graphs, FPCE is, on average, 39.7 and 3.1 times faster than ODES and PCE, respectively. We apply FPCE and a popular heuristic method on a PPI network to identify pseudo-cliques. FPCE outputs match with more known protein complexes, are more accurate, and are biologically more significant - suggesting that the exact computation of pseudo-cliques may give better insights. For its speed, FPCE is a suitable choice in such cases.|伪团(几乎具有所有可能边的子图)有许多应用。但是它们不满足可转换反单调约束(正如我们在这里证明的)。因此，很难减少伪链的搜索空间并有效地列出它们。据我们所知，只有两个精确的算法，即 ODES 和 PCE，提出了这一目的，但都有较高的执行时间。在这里，我们提出了一个精确的算法称为快速伪团枚举器(FPCE)。它使用了一些剪枝技术来减少搜索空间。对15个实图和16个合成图的实验结果表明: (1)在实图上，FPCE 的平均速度分别是 ODES 和 PCE 的38.6倍和6.5倍，而在合成图上，FPCE 的平均速度分别是 ODES 和 PCE 的39.7倍和3.1倍。我们应用 FPCE 和一种流行的启发式方法在 PPI 网络上识别伪团体。FPCE 输出与更多已知的蛋白质复合物相匹配，更准确，并且在生物学上更重要——这表明准确计算假团簇可能提供更好的见解。在这种情况下，FPCE 算法的速度是一个合适的选择。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Fast+Exact+Algorithm+to+Enumerate+Maximal+Pseudo-cliques+in+Large+Sparse+Graphs)|0| -|[CoSLight: Co-optimizing Collaborator Selection and Decision-making to Enhance Traffic Signal Control](https://doi.org/10.1145/3637528.3671998)|Jingqing Ruan, Ziyue Li, Hua Wei, Haoyuan Jiang, Jiaming Lu, Xuantang Xiong, Hangyu Mao, Rui Zhao|University of Cologne, EWI gGmbH, Cologne, Germany; Qing Yuan Research Institute, Shanghai Jiao Tong University, Shanghai, China; Baidu Inc., Shenzhen, China; Institute of Automation, Chinese Academy of Science, Chinese Academy of Sciences, Beijing, China; Peking University, Beijing, China; Fudan University, Shanghai, China; Arizona State University, Arizona, USA|Effective multi-intersection collaboration is pivotal for reinforcement-learning-based traffic signal control to alleviate congestion. Existing work mainly chooses neighboring intersections as collaborators. However, quite a lot of congestion, even some wide-range congestion, is caused by non-neighbors failing to collaborate. To address these issues, we propose to separate the collaborator selection as a second policy to be learned, concurrently being updated with the original signal-controlling policy. Specifically, the selection policy in real-time adaptively selects the best teammates according to phase- and intersection-level features. Empirical results on both synthetic and real-world datasets provide robust validation for the superiority of our approach, offering significant improvements over existing state-of-the-art methods. Code is available at https://github.com/bonaldli/CoSLight.|有效的多路口协同是基于强化学习的交通信号控制减缓交通拥堵的关键。现有的工作主要是选择相邻的交叉口作为协作者。然而，相当多的拥塞，甚至一些广泛的拥塞，是由于非邻居未能合作造成的。为了解决这些问题，我们建议将合作者选择作为第二个需要学习的策略，与原始的信号控制策略同时更新。具体来说，实时选择策略根据相位和交叉级特征自适应地选择最佳队友。对合成和真实世界数据集的实证结果为我们的方法的优越性提供了强有力的验证，提供了对现有最先进的方法的显著改进。密码可于 https://github.com/bonaldli/coslight 索取。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=CoSLight:+Co-optimizing+Collaborator+Selection+and+Decision-making+to+Enhance+Traffic+Signal+Control)|0| +|[CoSLight: Co-optimizing Collaborator Selection and Decision-making to Enhance Traffic Signal Control](https://doi.org/10.1145/3637528.3671998)|Jingqing Ruan, Ziyue Li, Hua Wei, Haoyuan Jiang, Jiaming Lu, Xuantang Xiong, Hangyu Mao, Rui Zhao|Baidu Inc., Shenzhen, China; Peking University, Beijing, China; Arizona State University, Arizona, USA; Institute of Automation, Chinese Academy of Science, Chinese Academy of Sciences, Beijing, China; Qing Yuan Research Institute, Shanghai Jiao Tong University, Shanghai, China; University of Cologne, EWI gGmbH, Cologne, Germany; Fudan University, Shanghai, China|Effective multi-intersection collaboration is pivotal for reinforcement-learning-based traffic signal control to alleviate congestion. Existing work mainly chooses neighboring intersections as collaborators. However, quite a lot of congestion, even some wide-range congestion, is caused by non-neighbors failing to collaborate. To address these issues, we propose to separate the collaborator selection as a second policy to be learned, concurrently being updated with the original signal-controlling policy. Specifically, the selection policy in real-time adaptively selects the best teammates according to phase- and intersection-level features. Empirical results on both synthetic and real-world datasets provide robust validation for the superiority of our approach, offering significant improvements over existing state-of-the-art methods. Code is available at https://github.com/bonaldli/CoSLight.|有效的多路口协同是基于强化学习的交通信号控制减缓交通拥堵的关键。现有的工作主要是选择相邻的交叉口作为协作者。然而，相当多的拥塞，甚至一些广泛的拥塞，是由于非邻居未能合作造成的。为了解决这些问题，我们建议将合作者选择作为第二个需要学习的策略，与原始的信号控制策略同时更新。具体来说，实时选择策略根据相位和交叉级特征自适应地选择最佳队友。对合成和真实世界数据集的实证结果为我们的方法的优越性提供了强有力的验证，提供了对现有最先进的方法的显著改进。密码可于 https://github.com/bonaldli/coslight 索取。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=CoSLight:+Co-optimizing+Collaborator+Selection+and+Decision-making+to+Enhance+Traffic+Signal+Control)|0| |[A Novel Feature Space Augmentation Method to Improve Classification Performance and Evaluation Reliability](https://doi.org/10.1145/3637528.3671736)|Sakhawat Hossain Saimon, Tanzira Najnin, Jianhua Ruan|Department of Computer Science, The University of Texas at San Antonio, San Antonio, TX, USA|Classification tasks in many real-world domains are exacerbated by class imbalance, relatively small sample sizes compared to high dimensionality, and measurement uncertainty. The problem of class imbalance has been extensively studied, and data augmentation methods based on interpolation of minority class instances have been proposed as a viable solution to mitigate imbalance. It remains to be seen whether augmentation can be applied to improve the overall performance while maintaining stability, especially with a limited number of samples. In this paper, we present a novel feature-space augmentation technique that can be applied to high-dimensional data for classification tasks and address these issues. Our method utilizes uniform random sampling and introduces synthetic instances by taking advantage of the local distributions of individual features in the observed instances. The core augmentation algorithm is class-invariant, which opens up an unexplored avenue of simultaneously improving and stabilizing performance by augmenting unlabeled instances. The proposed method is evaluated using a comprehensive performance analysis involving multiple classifiers and metrics. Comparative analysis with existing feature space augmentation methods strongly suggests that the proposed algorithm can result in improved classification performance while also increasing the overall reliability of the performance evaluation.|在现实世界的许多领域中，分类任务由于类别不平衡、与高维数相比较小的样本量以及测量不确定性而加剧。对类不平衡问题进行了广泛的研究，提出了基于少数类实例插值的数据增强方法，作为一种可行的解决方案。在保持稳定性的同时，特别是在样品数量有限的情况下，是否可以应用增强技术来改善整体性能，仍有待观察。本文提出了一种新的特征空间增强技术，可以应用于高维数据的分类任务，并解决了这些问题。该方法采用均匀随机抽样，利用观测实例中个体特征的局部分布，引入综合实例。核增强算法具有类不变性，为通过增强未标记实例来同时提高和稳定性能开辟了一条尚未探索的途径。提出的方法是评估使用综合性能分析涉及多个分类器和指标。通过与现有特征空间增强方法的对比分析，表明该算法在提高分类性能的同时，也提高了性能评估的整体可靠性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Novel+Feature+Space+Augmentation+Method+to+Improve+Classification+Performance+and+Evaluation+Reliability)|0| |[DPHGNN: A Dual Perspective Hypergraph Neural Networks](https://doi.org/10.1145/3637528.3672047)|Siddhant Saxena, Shounak Ghatak, Raghu Kolla, Debashis Mukherjee, Tanmoy Chakraborty|IIT Delhi, New Delhi, India; Meesho, Bangalore, India|Message passing on hypergraphs has been a standard framework for learning higher-order correlations between hypernodes. Recently-proposed hypergraph neural networks (HGNNs) can be categorized into spatial and spectral methods based on their design choices. In this work, we analyze the impact of change in hypergraph topology on the suboptimal performance of HGNNs and propose DPHGNN, a novel dual-perspective HGNN that introduces equivariant operator learning to capture lower-order semantics by inducing topology-aware spatial and spectral inductive biases. DPHGNN employs a unified framework to dynamically fuse lower-order explicit feature representations from the underlying graph into the super-imposed hypergraph structure. We benchmark DPHGNN over eight benchmark hypergraph datasets for the semi-supervised hypernode classification task and obtain superior performance compared to seven state-of-the-art baselines. We also provide a theoretical framework and a synthetic hypergraph isomorphism test to express the power of spatial HGNNs and quantify the expressivity of DPHGNN beyond the Generalized Weisfeiler Leman (1-GWL) test. Finally, DPHGNN was deployed by our partner e-commerce company, Meesho for the Return-to-Origin (RTO) prediction task, which shows ~7% higher macro F1-Score than the best baseline.|超图上的消息传递已经成为学习超节点之间高阶相关性的标准框架。最近提出的超图神经网络(HGNN)可以根据其设计选择分为空间方法和谱方法。本文分析了超图拓扑结构的变化对 HGNN 次优性能的影响，提出了一种新的双视角 HGNN，它引入等变算子学习，通过引入拓扑感知的空间和谱归纳偏差来捕获低阶语义。DPHGNN 采用统一的框架，动态地将底层图的低阶显式特征表示融合到叠加超图结构中。我们基准 DPHGNN 超过八个基准超图数据集的半监督超节点分类任务，并获得优于七个国家的最先进的基线性能。我们还提供了一个理论框架和一个综合的超图同构检验来表达空间 HGNN 的能力和量化 DPHGNN 的表达超越广义 Weisfeiler Leman (1-GWL)检验。最后，DPHGNN 由我们的合作伙伴电子商务公司 Meesho 部署，用于返回原产地(RTO)预测任务，其显示比最佳基线高约7% 的宏观 F1-得分。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=DPHGNN:+A+Dual+Perspective+Hypergraph+Neural+Networks)|0| -|[Self-Supervised Learning of Time Series Representation via Diffusion Process and Imputation-Interpolation-Forecasting Mask](https://doi.org/10.1145/3637528.3671673)|Zineb Senane, Lele Cao, Valentin Leonhard Buchner, Yusuke Tashiro, Lei You, Pawel Andrzej Herman, Mats Nordahl, Ruibo Tu, Vilhelm von Ehrenheim|KTH Royal Institute of Technology, Stockholm, Sweden; Motherbrain, EQT Group, Stockholm, Sweden; Motherbrain, EQT Group & QA.tech, Stockholm, Sweden; Technical University of Denmark, Ballerup, Denmark; Motherbrain, EQT Group & KTH Royal Institute of Technology, Stockholm, Sweden; Mitsubishi UFJ Trust Investment Technology Institute, Tokyo, Japan|Time Series Representation Learning (TSRL) focuses on generating informativerepresentations for various Time Series (TS) modeling tasks. TraditionalSelf-Supervised Learning (SSL) methods in TSRL fall into four main categories:reconstructive, adversarial, contrastive, and predictive, each with a commonchallenge of sensitivity to noise and intricate data nuances. Recently,diffusion-based methods have shown advanced generative capabilities. However,they primarily target specific application scenarios like imputation andforecasting, leaving a gap in leveraging diffusion models for generic TSRL. Ourwork, Time Series Diffusion Embedding (TSDE), bridges this gap as the firstdiffusion-based SSL TSRL approach. TSDE segments TS data into observed andmasked parts using an Imputation-Interpolation-Forecasting (IIF) mask. Itapplies a trainable embedding function, featuring dual-orthogonal Transformerencoders with a crossover mechanism, to the observed part. We train a reversediffusion process conditioned on the embeddings, designed to predict noiseadded to the masked part. Extensive experiments demonstrate TSDE's superiorityin imputation, interpolation, forecasting, anomaly detection, classification,and clustering. We also conduct an ablation study, present embeddingvisualizations, and compare inference speed, further substantiating TSDE'sefficiency and validity in learning representations of TS data.|时间序列表示学习(TSRL)专注于为各种时间序列(TS)建模任务生成信息表示。TSRL 中的传统自我监督学习(SSL)方法分为四大类: 重建，对抗性，对比性和预测性，每一类都具有对噪音和复杂数据细微差别的敏感性的共同挑战。最近，基于扩散的方法已经显示出先进的生成能力。然而，它们主要针对特定的应用场景，比如插补和预测，在利用通用 TSRL 的扩散模型方面留下了空白。我们的工作，时间序列扩散嵌入(TSDE) ，桥梁这一差距的第一扩散为基础的 SSL TSRL 方法。TSDE 使用插值-插值-预测(IIF)掩模将 TS 数据分割为观测部分和掩模部分。它应用了一个可训练的嵌入功能，具有双正交变换编码器与交叉机制，以观察的部分。我们训练了一个以嵌入为条件的反向扩散过程，用来预测加入到掩蔽部分的噪声。大量的实验证明了 TSDE 在插补、插值、预测、异常检测、分类和聚类方面的优势。我们还进行了消融研究，提出了嵌入可视化，并比较了推理速度，进一步证实了 TSDE 在 TS 数据学习表示中的效率和有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Self-Supervised+Learning+of+Time+Series+Representation+via+Diffusion+Process+and+Imputation-Interpolation-Forecasting+Mask)|0| +|[Self-Supervised Learning of Time Series Representation via Diffusion Process and Imputation-Interpolation-Forecasting Mask](https://doi.org/10.1145/3637528.3671673)|Zineb Senane, Lele Cao, Valentin Leonhard Buchner, Yusuke Tashiro, Lei You, Pawel Andrzej Herman, Mats Nordahl, Ruibo Tu, Vilhelm von Ehrenheim|Motherbrain, EQT Group & KTH Royal Institute of Technology, Stockholm, Sweden; Technical University of Denmark, Ballerup, Denmark; Motherbrain, EQT Group, Stockholm, Sweden; Mitsubishi UFJ Trust Investment Technology Institute, Tokyo, Japan; Motherbrain, EQT Group & QA.tech, Stockholm, Sweden; KTH Royal Institute of Technology, Stockholm, Sweden|Time Series Representation Learning (TSRL) focuses on generating informativerepresentations for various Time Series (TS) modeling tasks. TraditionalSelf-Supervised Learning (SSL) methods in TSRL fall into four main categories:reconstructive, adversarial, contrastive, and predictive, each with a commonchallenge of sensitivity to noise and intricate data nuances. Recently,diffusion-based methods have shown advanced generative capabilities. However,they primarily target specific application scenarios like imputation andforecasting, leaving a gap in leveraging diffusion models for generic TSRL. Ourwork, Time Series Diffusion Embedding (TSDE), bridges this gap as the firstdiffusion-based SSL TSRL approach. TSDE segments TS data into observed andmasked parts using an Imputation-Interpolation-Forecasting (IIF) mask. Itapplies a trainable embedding function, featuring dual-orthogonal Transformerencoders with a crossover mechanism, to the observed part. We train a reversediffusion process conditioned on the embeddings, designed to predict noiseadded to the masked part. Extensive experiments demonstrate TSDE's superiorityin imputation, interpolation, forecasting, anomaly detection, classification,and clustering. We also conduct an ablation study, present embeddingvisualizations, and compare inference speed, further substantiating TSDE'sefficiency and validity in learning representations of TS data.|时间序列表示学习(TSRL)专注于为各种时间序列(TS)建模任务生成信息表示。TSRL 中的传统自我监督学习(SSL)方法分为四大类: 重建，对抗性，对比性和预测性，每一类都具有对噪音和复杂数据细微差别的敏感性的共同挑战。最近，基于扩散的方法已经显示出先进的生成能力。然而，它们主要针对特定的应用场景，比如插补和预测，在利用通用 TSRL 的扩散模型方面留下了空白。我们的工作，时间序列扩散嵌入(TSDE) ，桥梁这一差距的第一扩散为基础的 SSL TSRL 方法。TSDE 使用插值-插值-预测(IIF)掩模将 TS 数据分割为观测部分和掩模部分。它应用了一个可训练的嵌入功能，具有双正交变换编码器与交叉机制，以观察的部分。我们训练了一个以嵌入为条件的反向扩散过程，用来预测加入到掩蔽部分的噪声。大量的实验证明了 TSDE 在插补、插值、预测、异常检测、分类和聚类方面的优势。我们还进行了消融研究，提出了嵌入可视化，并比较了推理速度，进一步证实了 TSDE 在 TS 数据学习表示中的效率和有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Self-Supervised+Learning+of+Time+Series+Representation+via+Diffusion+Process+and+Imputation-Interpolation-Forecasting+Mask)|0| |[Self-Explainable Temporal Graph Networks based on Graph Information Bottleneck](https://doi.org/10.1145/3637528.3671962)|Sangwoo Seo, Sungwon Kim, Jihyeong Jung, Yoonho Lee, Chanyoung Park|KAIST, Daejeon, Republic of Korea|Temporal Graph Neural Networks (TGNN) have the ability to capture both the graph topology and dynamic dependencies of interactions within a graph over time. There has been a growing need to explain the predictions of TGNN models due to the difficulty in identifying how past events influence their predictions. Since the explanation model for a static graph cannot be readily applied to temporal graphs due to its inability to capture temporal dependencies, recent studies proposed explanation models for temporal graphs. However, existing explanation models for temporal graphs rely on post-hoc explanations, requiring separate models for prediction and explanation, which is limited in two aspects: efficiency and accuracy of explanation. In this work, we propose a novel built-in explanation framework for temporal graphs, called Self-Explainable Temporal Graph Networks based on Graph Information Bottleneck (TGIB). TGIB provides explanations for event occurrences by introducing stochasticity in each temporal event based on the Information Bottleneck theory. Experimental results demonstrate the superiority of TGIB in terms of both the link prediction performance and explainability compared to state-of-the-art methods. This is the first work that simultaneously performs prediction and explanation for temporal graphs in an end-to-end manner. The source code of TGIB is available at https://github.com/sang-woo-seo/TGIB.|时态图神经网络(TGNN)具有捕获图的拓扑结构和图内交互的动态依赖关系的能力。由于难以确定过去事件如何影响 TGNN 模型的预测，越来越需要解释它们的预测。由于静态图的解释模型不能捕捉时态依赖，因此不能很容易地应用于时态图，最近的研究提出了时态图的解释模型。然而，现有的时间图解释模型依赖于事后解释，预测和解释需要独立的模型，这限制了解释的效率和准确性。在这项工作中，我们提出了一个新的内置的解释框架，时态图，称为自解释时态图网络的图信息瓶颈(TGIB)。TGIB 基于信息瓶颈理论，通过引入时间事件的随机性来解释事件的发生。实验结果表明，与现有的链路预测方法相比，TGIB 在链路预测性能和可解释性方面具有优越性。这是第一个以端到端的方式同时对时间图进行预测和解释的工作。资讯科技总监办公室的源代码可于 https://github.com/sang-woo-seo/TGIB 索取。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Self-Explainable+Temporal+Graph+Networks+based+on+Graph+Information+Bottleneck)|0| |[Offline Imitation Learning with Model-based Reverse Augmentation](https://doi.org/10.1145/3637528.3672059)|JieJing Shao, HaoSen Shi, LanZhe Guo, YuFeng Li|; National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China|In offline Imitation Learning (IL), one of the main challenges is the covariate shift between the expert observations and the actual distribution encountered by the agent, because it is difficult to determine what action an agent should take when outside the state distribution of the expert demonstrations. Recently, the model-free solutions introduced supplementary data and identified the latent expert-similar samples to augment the reliable samples during learning. Model-based solutions build forward dynamic models with conservatism quantification and then generate additional trajectories in the neighborhood of expert demonstrations. However, without reward supervision, these methods are often over-conservative in the out-of-expert-support regions, because only in states close to expert-observed states can there be a preferred action enabling policy optimization. To encourage more exploration on expert-unobserved states, we propose a novel model-based framework, called offline Imitation Learning with Self-paced Reverse Augmentation (SRA). Specifically, we build a reverse dynamic model from the offline demonstrations, which can efficiently generate trajectories leading to the expert-observed states in a self-paced style. Then, we use the subsequent reinforcement learning method to learn from the augmented trajectories and transit from expert-unobserved states to expert-observed states. This framework not only explores the expert-unobserved states but also guides maximizing long-term returns on these states, ultimately enabling generalization beyond the expert data. Empirical results show that our proposal could effectively mitigate the covariate shift and achieve the state-of-the-art performance on the offline imitation learning benchmarks. Project website: https://www.lamda.nju.edu.cn/shaojj/KDD24_SRA/.|在离线模仿学习(IL)中，一个主要的挑战是专家观察和代理遇到的实际分布之间的协变量转移，因为很难确定在专家演示的状态分布之外代理应该采取什么行动。最近，无模型解决方案引入了补充数据，并确定了潜在的专家相似样本，以增加学习过程中的可靠样本。基于模型的解决方案构建了具有保守量化的前向动态模型，然后在专家演示的邻近区域生成额外的轨迹。然而，在没有奖励监督的情况下，这些方法在专家支持范围之外的地区往往过于保守，因为只有在接近专家观察状态的状态下，才能有一个优先的行动来实现政策优化。为了鼓励对专家未观测状态的进一步研究，我们提出了一种新的基于模型的框架，称为自适应逆增强离线模仿学习(SRA)。具体来说，我们从离线演示中建立了一个反向动态模型，它可以有效地以自定步调的方式生成导致专家观察状态的轨迹。然后，我们使用后续的强化学习方法来学习增强轨迹，并从专家未观察状态过渡到专家观察状态。这个框架不仅探索了专家未观察到的状态，而且还指导了这些状态的长期回报最大化，最终实现了专家数据之外的泛化。实验结果表明，本文提出的方案能够有效地缓解协变量的偏移，并且在离线模仿学习基准上取得了最好的效果。项目网页: https://www.lamda.nju.edu.cn/shaojj/kdd24_sra/。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Offline+Imitation+Learning+with+Model-based+Reverse+Augmentation)|0| |[NeuroCut: A Neural Approach for Robust Graph Partitioning](https://doi.org/10.1145/3637528.3671815)|Rishi Shah, Krishnanshu Jain, Sahil Manchanda, Sourav Medya, Sayan Ranu||Graph partitioning aims to divide a graph into $k$ disjoint subsets while optimizing a specific partitioning objective. The majority of formulations related to graph partitioning exhibit NP-hardness due to their combinatorial nature. As a result, conventional approximation algorithms rely on heuristic methods, sometimes with approximation guarantees and sometimes without. Unfortunately, traditional approaches are tailored for specific partitioning objectives and do not generalize well across other known partitioning objectives from the literature. To overcome this limitation, and learn heuristics from the data directly, neural approaches have emerged, demonstrating promising outcomes. In this study, we extend this line of work through a novel framework, NeuroCut. NeuroCut introduces two key innovations over prevailing methodologies. First, it is inductive to both graph topology and the partition count, which is provided at query time. Second, by leveraging a reinforcement learning based framework over node representations derived from a graph neural network, NeuroCut can accommodate any optimization objective, even those encompassing non-differentiable functions. Through empirical evaluation, we demonstrate that NeuroCut excels in identifying high-quality partitions, showcases strong generalization across a wide spectrum of partitioning objectives, and exhibits resilience to topological modifications.|图划分的目的是将图划分为 $k $不相交子集，同时优化一个特定的划分目标。大多数与图划分有关的公式由于其组合性质而表现出 NP 难度。因此，传统的近似算法依赖于启发式方法，有时有近似保证，有时没有。遗憾的是，传统的方法是为特定的分区目标量身定制的，并且不能很好地推广到文献中其他已知的分区目标。为了克服这个限制，并从数据直接学习启发式，神经方法已经出现，显示了有希望的结果。在这项研究中，我们通过一个新的框架，NeuroCut 扩展了这一工作线。NeuroCut 在流行的方法论上引入了两个关键的创新。首先，它归纳了图的拓扑结构和查询时提供的分区计数。其次，通过利用一个基于强化学习的框架来处理来自图形神经网络的节点表示，NeuroCut 可以适应任何优化目标，即使是那些包含不可微函数的目标。通过实证评估，我们证明 NeuroCut 在识别高质量分区方面表现出色，在广泛的分区目标中表现出强大的泛化能力，并对拓扑修改表现出弹性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=NeuroCut:+A+Neural+Approach+for+Robust+Graph+Partitioning)|0| |[Capturing Homogeneous Influence among Students: Hypergraph Cognitive Diagnosis for Intelligent Education Systems](https://doi.org/10.1145/3637528.3672002)|Junhao Shen, Hong Qian, Shuo Liu, Wei Zhang, Bo Jiang, Aimin Zhou|; School of Computer Science and Technology, East China Normal University, Shanghai, China|Cognitive diagnosis is a vital upstream task in intelligent education systems. It models the student-exercise interaction, aiming to infer the students' proficiency levels on each knowledge concept. This paper observes that most existing methods can hardly effectively capture the homogeneous influence due to its inherent complexity. That is to say, although students exhibit similar performance on given exercises, their proficiency levels inferred by these methods vary significantly, resulting in shortcomings in interpretability and efficacy. Given the complexity of homogeneous influence, a hypergraph could be a choice due to its flexibility and capability of modeling high-order similarity which aligns with the nature of homogeneous influence. However, before incorporating hypergraph, one at first needs to address the challenges of distorted homogeneous influence, sparsity of response logs, and over-smoothing. To this end, this paper proposes a hypergraph cognitive diagnosis model (HyperCDM) to address these challenges and effectively capture the homogeneous influence. Specifically, to avoid distortion, HyperCDM employs a divide-and-conquer strategy to learn student, exercise and knowledge representations in their own hypergraphs respectively, and interconnects them via a feature-based interaction function. To construct hypergraphs based on sparse response logs, the auto-encoder is utilized to preprocess response logs and K-means is applied to cluster students. To mitigate over-smoothing, momentum hypergraph convolution networks are designed to partially keep previous representations during the message propagation. Extensive experiments on both offline and online real-world datasets show that HyperCDM achieves state-of-the-art performance in terms of interpretability and capturing homogeneous influence effectively, and is competitive in generalization. The ablation study verifies the efficacy of each component, and the case study explicitly showcases the homogeneous influence captured by HyperCDM.|认知诊断是智能教育系统的重要上游任务。它模拟学生与练习的互动，旨在推断学生对每个知识概念的熟练程度。本文发现，现有的方法由于其固有的复杂性，很难有效地捕捉同质影响。也就是说，尽管学生在给定的练习中表现出相似的表现，但是这些方法所推断出的水平差异很大，从而导致可解释性和有效性方面的缺陷。由于同质影响的复杂性，超图具有灵活性和建模高阶相似性的能力，符合同质影响的性质，可以作为一种选择。然而，在合并超图之前，首先需要解决同质影响扭曲、响应日志稀疏和过度平滑的挑战。为此，本文提出了一个超图认知诊断模型(HyperCDM) ，以解决这些挑战，并有效地捕捉同质的影响。为了避免失真，HyperCDM 采用了分而治之的策略，分别在各自的超图中学习学生、练习和知识表示，并通过一个基于特征的交互函数将它们互相连接起来。为了构造基于稀疏响应日志的超图，利用自动编码器对响应日志进行预处理，并对聚类学生应用 K 均值。为了减轻过度平滑，动量超图卷积网络被设计为在消息传播过程中部分保持先前的表示。在离线和在线真实世界数据集上的大量实验表明，HyperCDM 在可解释性和有效捕获同质影响方面达到了最先进的性能，并且在泛化方面具有竞争力。消融研究验证了每个组成部分的功效，并且案例研究明确地展示了 HyperCDM 捕获的同质影响。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Capturing+Homogeneous+Influence+among+Students:+Hypergraph+Cognitive+Diagnosis+for+Intelligent+Education+Systems)|0| -|[Optimizing OOD Detection in Molecular Graphs: A Novel Approach with Diffusion Models](https://doi.org/10.1145/3637528.3671785)|Xu Shen, Yili Wang, Kaixiong Zhou, Shirui Pan, Xin Wang|Griffith University, Goldcoast, Australia; Massachusetts Institute of Technology, Cambridge, MA, USA; Jilin University, Changchun, China|Despite the recent progress of molecular representation learning, its effectiveness is assumed on the close-world assumptions that training and testing graphs are from identical distribution. The open-world test dataset is often mixed with out-of-distribution (OOD) samples, where the deployed models will struggle to make accurate predictions. The misleading estimations of molecules' properties in drug screening or design can result in the tremendous waste of wet-lab resources and delay the discovery of novel therapies. Traditional detection methods need to trade off OOD detection and in-distribution (ID) classification performance since they share the same representation learning model. In this work, we propose to detect OOD molecules by adopting an auxiliary diffusion model-based framework, which compares similarities between input molecules and reconstructed graphs. Due to the generative bias towards reconstructing ID training samples, the similarity scores of OOD molecules will be much lower to facilitate detection. Although it is conceptually simple, extending this vanilla framework to practical detection applications is still limited by two significant challenges. First, the popular similarity metrics based on Euclidian distance fail to consider the complex graph structure. Second, the generative model involving iterative denoising steps is notoriously time-consuming especially when it runs on the enormous pool of drugs. To address these challenges, our research pioneers an approach of Prototypical Graph Reconstruction for Molecular OOd Detection, dubbed as PGR-MOOD. Specifically, PGR-MOOD hinges on three innovations: i) An effective metric to comprehensively quantify the matching degree of input and reconstructed molecules according to their discrete edges and continuous node features; ii) A creative graph generator to construct a list of prototypical graphs that are in line with ID distribution but away from OOD one; iii) An efficient and scalable OOD detector to compare the similarity between test samples and pre-constructed prototypical graphs and omit the generative process on every new molecule. Extensive experiments on ten benchmark datasets and six baselines are conducted to demonstrate our superiority: PGR-MOOD achieves more than 8% of average improvement in terms of detection AUC and AUPR accompanied by the reduced cost of testing time and memory consumption. The anonymous code is in: https://github.com/se7esx/PGR-MOOD.|尽管近年来分子表征学习取得了很大的进展，但它的有效性是建立在训练图和测试图来自同一分布的近似世界假设之上的。开放世界的测试数据集常常与超出分布(OOD)的样本混合在一起，在这种情况下，已部署的模型将难以做出准确的预测。在药物筛选或设计中对分子性质的误导性估计会导致实验室资源的巨大浪费，并延迟新疗法的发现。传统的检测方法由于具有相同的表示学习模型，需要在 OOD 检测和分布式(ID)分类性能之间进行权衡。在这项工作中，我们提出采用辅助扩散模型为基础的框架来检测面向对象分子，它比较了输入分子和重构图之间的相似性。由于生成性偏向于重建 ID 训练样本，OOD 分子的相似性得分将大大降低，以利于检测。尽管概念上很简单，但是将这个普通的框架扩展到实际的检测应用程序仍然受到两个重大挑战的限制。首先，目前流行的基于欧氏距离的相似性度量方法没有考虑到复杂的图形结构。其次，包括迭代去噪步骤的生成模型是出了名的耗时，尤其是当它运行在庞大的药物池上时。为了应对这些挑战，我们的研究开创了一种用于分子 OOD 检测的原型图重建方法，称为 PGR-MOOD。具体而言，PGR-MOOD 取决于三个创新: i)根据分子的离散边和连续节点特征全面量化输入和重构分子的匹配程度的有效度量; ii)创造性的图形生成器构建一个与 ID 分布一致但远离 OOD 的原型图列表; iii)高效和可扩展的 OOD 检测器，以比较测试样本和预先构建的原型图之间的相似性，并忽略每个新分子上的生成过程。在10个基准数据集和6个基线上进行了广泛的实验，证明了我们的优越性: PGR-MOOD 在检测 AUC 和 AUPR 方面达到了8% 以上的平均改善，同时降低了测试时间和内存消耗的成本。匿名代码在: https://github.com/se7esx/pgr-mood。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Optimizing+OOD+Detection+in+Molecular+Graphs:+A+Novel+Approach+with+Diffusion+Models)|0| +|[Optimizing OOD Detection in Molecular Graphs: A Novel Approach with Diffusion Models](https://doi.org/10.1145/3637528.3671785)|Xu Shen, Yili Wang, Kaixiong Zhou, Shirui Pan, Xin Wang|Massachusetts Institute of Technology, Cambridge, MA, USA; Jilin University, Changchun, China; Griffith University, Goldcoast, Australia|Despite the recent progress of molecular representation learning, its effectiveness is assumed on the close-world assumptions that training and testing graphs are from identical distribution. The open-world test dataset is often mixed with out-of-distribution (OOD) samples, where the deployed models will struggle to make accurate predictions. The misleading estimations of molecules' properties in drug screening or design can result in the tremendous waste of wet-lab resources and delay the discovery of novel therapies. Traditional detection methods need to trade off OOD detection and in-distribution (ID) classification performance since they share the same representation learning model. In this work, we propose to detect OOD molecules by adopting an auxiliary diffusion model-based framework, which compares similarities between input molecules and reconstructed graphs. Due to the generative bias towards reconstructing ID training samples, the similarity scores of OOD molecules will be much lower to facilitate detection. Although it is conceptually simple, extending this vanilla framework to practical detection applications is still limited by two significant challenges. First, the popular similarity metrics based on Euclidian distance fail to consider the complex graph structure. Second, the generative model involving iterative denoising steps is notoriously time-consuming especially when it runs on the enormous pool of drugs. To address these challenges, our research pioneers an approach of Prototypical Graph Reconstruction for Molecular OOd Detection, dubbed as PGR-MOOD. Specifically, PGR-MOOD hinges on three innovations: i) An effective metric to comprehensively quantify the matching degree of input and reconstructed molecules according to their discrete edges and continuous node features; ii) A creative graph generator to construct a list of prototypical graphs that are in line with ID distribution but away from OOD one; iii) An efficient and scalable OOD detector to compare the similarity between test samples and pre-constructed prototypical graphs and omit the generative process on every new molecule. Extensive experiments on ten benchmark datasets and six baselines are conducted to demonstrate our superiority: PGR-MOOD achieves more than 8% of average improvement in terms of detection AUC and AUPR accompanied by the reduced cost of testing time and memory consumption. The anonymous code is in: https://github.com/se7esx/PGR-MOOD.|尽管近年来分子表征学习取得了很大的进展，但它的有效性是建立在训练图和测试图来自同一分布的近似世界假设之上的。开放世界的测试数据集常常与超出分布(OOD)的样本混合在一起，在这种情况下，已部署的模型将难以做出准确的预测。在药物筛选或设计中对分子性质的误导性估计会导致实验室资源的巨大浪费，并延迟新疗法的发现。传统的检测方法由于具有相同的表示学习模型，需要在 OOD 检测和分布式(ID)分类性能之间进行权衡。在这项工作中，我们提出采用辅助扩散模型为基础的框架来检测面向对象分子，它比较了输入分子和重构图之间的相似性。由于生成性偏向于重建 ID 训练样本，OOD 分子的相似性得分将大大降低，以利于检测。尽管概念上很简单，但是将这个普通的框架扩展到实际的检测应用程序仍然受到两个重大挑战的限制。首先，目前流行的基于欧氏距离的相似性度量方法没有考虑到复杂的图形结构。其次，包括迭代去噪步骤的生成模型是出了名的耗时，尤其是当它运行在庞大的药物池上时。为了应对这些挑战，我们的研究开创了一种用于分子 OOD 检测的原型图重建方法，称为 PGR-MOOD。具体而言，PGR-MOOD 取决于三个创新: i)根据分子的离散边和连续节点特征全面量化输入和重构分子的匹配程度的有效度量; ii)创造性的图形生成器构建一个与 ID 分布一致但远离 OOD 的原型图列表; iii)高效和可扩展的 OOD 检测器，以比较测试样本和预先构建的原型图之间的相似性，并忽略每个新分子上的生成过程。在10个基准数据集和6个基线上进行了广泛的实验，证明了我们的优越性: PGR-MOOD 在检测 AUC 和 AUPR 方面达到了8% 以上的平均改善，同时降低了测试时间和内存消耗的成本。匿名代码在: https://github.com/se7esx/pgr-mood。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Optimizing+OOD+Detection+in+Molecular+Graphs:+A+Novel+Approach+with+Diffusion+Models)|0| |[Efficient and Long-Tailed Generalization for Pre-trained Vision-Language Model](https://doi.org/10.1145/3637528.3671945)|JiangXin Shi, Chi Zhang, Tong Wei, YuFeng Li|; National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China|Pre-trained vision-language models like CLIP have shown powerful zero-shotinference ability via image-text matching and prove to be strong few-shotlearners in various downstream tasks. However, in real-world scenarios,adapting CLIP to downstream tasks may encounter the following challenges: 1)data may exhibit long-tailed data distributions and might not have abundantsamples for all the classes; 2) There might be emerging tasks with new classesthat contain no samples at all. To overcome them, we propose a novel frameworkto achieve efficient and long-tailed generalization, which can be termed asCandle. During the training process, we propose compensating logit-adjustedloss to encourage large margins of prototypes and alleviate imbalance bothwithin the base classes and between the base and new classes. For efficientadaptation, we treat the CLIP model as a black box and leverage the extractedfeatures to obtain visual and textual prototypes for prediction. To make fulluse of multi-modal information, we also propose cross-modal attention to enrichthe features from both modalities. For effective generalization, we introducevirtual prototypes for new classes to make up for their lack of trainingimages. Candle achieves state-of-the-art performance over extensive experimentson 11 diverse datasets while substantially reducing the training time,demonstrating the superiority of our approach. The source code is available athttps://github.com/shijxcs/Candle.|像 CLIP 这样经过预先训练的视觉语言模型已经通过图像-文本匹配显示出强大的零拍摄能力，并且在各种下游任务中被证明是强大的少拍摄学习者。然而，在现实世界的场景中，使 CLIP 适应下游任务可能会遇到以下挑战: 1)数据可能显示长尾数据分布，并且可能没有所有类的大量样本; 2)可能会出现新的任务，其中包含根本不包含样本的新类。为了克服这些问题，我们提出了一个新的框架来实现高效和长尾泛化，这可以被称为蜡烛。在训练过程中，我们提出补偿 logit 调整损失，以鼓励原型的大幅度利润和缓解不平衡的基础类和基础之间的基础和新的类。为了有效地适应，我们将 CLIP 模型视为一个黑盒子，并利用提取的特征来获得用于预测的可视化和文本原型。为了充分利用多模态信息，我们还提出了交叉模态注意来丰富两种模态的特征。为了有效的推广，我们引入了新类的虚拟原型来弥补训练图像的不足。Candle 通过对11个不同数据集的大量实验，实现了最先进的性能，同时大大减少了训练时间，证明了我们方法的优越性。源代码可以在 https:// github.com/shijxcs/candle 找到。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Efficient+and+Long-Tailed+Generalization+for+Pre-trained+Vision-Language+Model)|0| |[MSPipe: Efficient Temporal GNN Training via Staleness-Aware Pipeline](https://doi.org/10.1145/3637528.3671844)|Guangming Sheng, Junwei Su, Chao Huang, Chuan Wu|The University of Hong Kong, Hong Kong, China|Memory-based Temporal Graph Neural Networks (MTGNNs) are a class of temporal graph neural networks that utilize a node memory module to capture and retain long-term temporal dependencies, leading to superior performance compared to memory-less counterparts. However, the iterative reading and updating process of the memory module in MTGNNs to obtain up-to-date information needs to follow the temporal dependencies. This introduces significant overhead and limits training throughput. Existing optimizations for static GNNs are not directly applicable to MTGNNs due to differences in training paradigm, model architecture, and the absence of a memory module. Moreover, these optimizations do not effectively address the challenges posed by temporal dependencies, making them ineffective for MTGNN training. In this paper, we propose MSPipe, a general and efficient framework for memory-based TGNNs that maximizes training throughput while maintaining model accuracy. Our design specifically addresses the unique challenges associated with fetching and updating node memory states in MTGNNs by integrating staleness into the memory module. However, simply introducing a predefined staleness bound in the memory module to break temporal dependencies may lead to suboptimal performance and lack of generalizability across different models and datasets. To overcome this, we introduce an online pipeline scheduling algorithm in MSPipe that strategically breaks temporal dependencies with minimal staleness and delays memory fetching to obtain fresher memory states. This is achieved without stalling the MTGNN training stage or causing resource contention. Additionally, we design a staleness mitigation mechanism to enhance training convergence and model accuracy. Furthermore, we provide convergence analysis and demonstrate that MSPipe maintains the same convergence rate as vanilla sampling-based GNN training. Experimental results show that MSPipe achieves up to 2.45× speed-up without sacrificing accuracy, making it a promising solution for efficient MTGNN training. The implementation of our paper can be found at the following link: https://github.com/PeterSH6/MSPipe.|基于记忆的时间图神经网络(MTGNN)是一类时间图神经网络，它利用节点记忆模块来捕获和保留长期的时间依赖关系，从而获得比无记忆对应物更好的性能。然而，MTGNN 中内存模块的迭代读取和更新过程需要遵循时间依赖关系来获取最新的信息。这引入了大量的开销并限制了培训的吞吐量。由于训练范式、模型结构和内存模块的不同，静态 GNN 的现有优化不能直接应用于 MTGNN。此外，这些优化不能有效地解决时间依赖性带来的挑战，使得它们对 MTGNN 训练无效。在本文中，我们提出了一个通用的和有效的框架，基于内存的 TGNN，最大限度地提高训练吞吐量，同时保持模型的准确性。我们的设计通过将过时性集成到内存模块中，特别解决了与获取和更新 MTGNN 中的节点内存状态相关的独特挑战。然而，简单地在内存模块中引入预定义的过时限制以打破时间依赖性可能会导致性能不够理想，并且在不同的模型和数据集之间缺乏通用性。为了克服这个问题，我们在 MSPipe 中引入了一个在线管道调度算法，该算法以最小的过时性和延迟内存获取策略性地打破了时间依赖，以获得更新的内存状态。这是在不拖延 MTGNN 培训阶段或引起资源争用的情况下实现的。此外，我们设计了一个时滞缓解机制，以提高训练收敛性和模型的准确性。此外，我们还提供了收敛性分析，并证明 MSPipe 保持了与基于香草抽样的 GNN 训练相同的收敛速度。实验结果表明，该算法在不牺牲精度的前提下，提高了2.45倍的速度，为 MTGNN 的有效训练提供了一种有前途的解决方案。有关本文件的执行情况，请浏览以下连结: https://github.com/petersh6/mspipe。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=MSPipe:+Efficient+Temporal+GNN+Training+via+Staleness-Aware+Pipeline)|0| -|[LPFormer: An Adaptive Graph Transformer for Link Prediction](https://doi.org/10.1145/3637528.3672025)|Harry Shomer, Yao Ma, Haitao Mao, Juanhui Li, Bo Wu, Jiliang Tang|Michigan State University, East Lansing, MI, USA; Colorado School of Mines, Golden, CO, USA; Rensselaer Polytechnic Institute, Troy, NY, USA|Link prediction is a common task on graph-structured data that has seen applications in a variety of domains. Classically, hand-crafted heuristics were used for this task. Heuristic measures are chosen such that they correlate well with the underlying factors related to link formation. In recent years, a new class of methods has emerged that combines the advantages of message-passing neural networks (MPNN) and heuristics methods. These methods perform predictions by using the output of an MPNN in conjunction with a "pairwise encoding" that captures the relationship between nodes in the candidate link. They have been shown to achieve strong performance on numerous datasets. However, current pairwise encodings often contain a strong inductive bias, using the same underlying factors to classify all links. This limits the ability of existing methods to learn how to properly classify a variety of different links that may form from different factors. To address this limitation, we propose a new method, LPFormer, which attempts to adaptively learn the pairwise encodings for each link. LPFormer models the link factors via an attention module that learns the pairwise encoding that exists between nodes by modeling multiple factors integral to link prediction. Extensive experiments demonstrate that LPFormer can achieve SOTA performance on numerous datasets while maintaining efficiency. The code is available at The code is available at https://github.com/HarryShomer/LPFormer.|链接预测是图结构化数据的一个常见任务，已经在各种领域得到应用。传统上，手工制作的启发式方法用于此任务。启发式措施的选择，使他们与相关的基本因素链接形成良好的相关性。近年来，出现了一类新的方法，结合了消息传递神经网络(MPNN)和启发式方法的优点。这些方法通过使用 MPNN 的输出结合捕获候选链接中节点之间关系的“成对编码”来执行预测。它们已经被证明可以在大量数据集上实现强大的性能。然而，目前的成对编码往往包含一个强烈的归纳偏差，使用相同的基本因素分类所有链接。这限制了现有方法学习如何正确分类可能由不同因素形成的各种不同链接的能力。为了解决这个问题，我们提出了一种新的方法 LPForm，它尝试自适应地学习每个链路的成对编码。LPForm 通过一个注意模块对链路因子进行建模，该注意模块通过对链路预测的多因子积分建模来学习节点之间存在的成对编码。大量的实验表明，LPForm 可以在保持效率的同时，在大量的数据集上实现 SOTA 性能。代码可在网上查阅代码可在 https://github.com/harryshomer/lpformer 查阅。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=LPFormer:+An+Adaptive+Graph+Transformer+for+Link+Prediction)|0| +|[LPFormer: An Adaptive Graph Transformer for Link Prediction](https://doi.org/10.1145/3637528.3672025)|Harry Shomer, Yao Ma, Haitao Mao, Juanhui Li, Bo Wu, Jiliang Tang|Rensselaer Polytechnic Institute, Troy, NY, USA; Michigan State University, East Lansing, MI, USA; Colorado School of Mines, Golden, CO, USA|Link prediction is a common task on graph-structured data that has seen applications in a variety of domains. Classically, hand-crafted heuristics were used for this task. Heuristic measures are chosen such that they correlate well with the underlying factors related to link formation. In recent years, a new class of methods has emerged that combines the advantages of message-passing neural networks (MPNN) and heuristics methods. These methods perform predictions by using the output of an MPNN in conjunction with a "pairwise encoding" that captures the relationship between nodes in the candidate link. They have been shown to achieve strong performance on numerous datasets. However, current pairwise encodings often contain a strong inductive bias, using the same underlying factors to classify all links. This limits the ability of existing methods to learn how to properly classify a variety of different links that may form from different factors. To address this limitation, we propose a new method, LPFormer, which attempts to adaptively learn the pairwise encodings for each link. LPFormer models the link factors via an attention module that learns the pairwise encoding that exists between nodes by modeling multiple factors integral to link prediction. Extensive experiments demonstrate that LPFormer can achieve SOTA performance on numerous datasets while maintaining efficiency. The code is available at The code is available at https://github.com/HarryShomer/LPFormer.|链接预测是图结构化数据的一个常见任务，已经在各种领域得到应用。传统上，手工制作的启发式方法用于此任务。启发式措施的选择，使他们与相关的基本因素链接形成良好的相关性。近年来，出现了一类新的方法，结合了消息传递神经网络(MPNN)和启发式方法的优点。这些方法通过使用 MPNN 的输出结合捕获候选链接中节点之间关系的“成对编码”来执行预测。它们已经被证明可以在大量数据集上实现强大的性能。然而，目前的成对编码往往包含一个强烈的归纳偏差，使用相同的基本因素分类所有链接。这限制了现有方法学习如何正确分类可能由不同因素形成的各种不同链接的能力。为了解决这个问题，我们提出了一种新的方法 LPForm，它尝试自适应地学习每个链路的成对编码。LPForm 通过一个注意模块对链路因子进行建模，该注意模块通过对链路预测的多因子积分建模来学习节点之间存在的成对编码。大量的实验表明，LPForm 可以在保持效率的同时，在大量的数据集上实现 SOTA 性能。代码可在网上查阅代码可在 https://github.com/harryshomer/lpformer 查阅。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=LPFormer:+An+Adaptive+Graph+Transformer+for+Link+Prediction)|0| |[Orthogonality Matters: Invariant Time Series Representation for Out-of-distribution Classification](https://doi.org/10.1145/3637528.3671768)|Ruize Shi, Hong Huang, Kehan Yin, Wei Zhou, Hai Jin|Huazhong University of Science and Technology, Wuhan, China|Previous works for time series classification tend to assume that both the training and testing sets originate from the same distribution. This oversimplification deviates from the complexity of reality and makes it challenging to generalize methods to out-of-distribution (OOD) time series data. Currently, there are limited works focusing on time series OOD generalization, and they typically disentangle time series into domain-agnostic and domain-specific features and design tasks to intensify the distinction between the two. However, previous models purportedly yielding domain-agnostic features continue to harbor domain-specific information, thereby diminishing their adaptability to OOD data. To address this gap, we introduce a novel model called Invariant Time Series Representation (ITSR). ITSR achieves a learnable orthogonal decomposition of time series using two sets of orthogonal axes. In detail, ITSR projects time series onto these two sets of axes separately and obtains mutually orthogonal invariant features and relevant features. ITSR theoretically ensures low similarity between these two features and further incorporates various tasks to optimize them. Furthermore, we explore the benefits of preserving orthogonality between invariant and relevant features for OOD time series classification in theory. The results on four real-world datasets underscore the superiority of ITSR over state-of-the-art methods and demonstrate the critical role of maintaining orthogonality between invariant and relevant features. Our code is available at https://github.com/CGCL-codes/ITSR.|前人对时间序列分类的研究往往假设训练集和测试集都来自同一个分布。这种过度简化背离了现实的复杂性，使得将方法推广到分布外(OOD)时间序列数据具有挑战性。目前，关于时间序列面向对象设计方法综合的研究有限，通常将时间序列分解为领域不可知特征和领域特定特征以及设计任务，以强化二者之间的区别。然而，以前的模型据称产生了领域不可知的特征，继续包含领域特定的信息，从而降低了它们对 OOD 数据的适应性。为了解决这个问题，我们引入了一个新的模型，称为不变时间序列表示(ITSR)。ITSR 利用两组正交轴对时间序列进行可学的正交分解。具体来说，ITSR 将时间序列分别投影到这两组轴上，得到相互正交的不变特征和相关特征。ITSR 在理论上确保了这两个特性之间的低相似性，并进一步整合了各种任务来优化它们。此外，我们还从理论上探讨了在面向对象时间序列分类中保持不变量与相关特征之间的正交性的好处。在四个真实世界数据集上的结果强调了 ITSR 相对于最先进的方法的优越性，并证明了保持不变特征和相关特征之间正交性的关键作用。我们的代码可以在 https://github.com/cgcl-codes/itsr 找到。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Orthogonality+Matters:+Invariant+Time+Series+Representation+for+Out-of-distribution+Classification)|0| |[CoLiDR: Concept Learning using Aggregated Disentangled Representations](https://doi.org/10.1145/3637528.3671938)|Sanchit Sinha, Guangzhi Xiong, Aidong Zhang|University of Virginia, Charlottesville, VA, USA|Interpretability of Deep Neural Networks using concept-based models offers a promising way to explain model behavior through human-understandable concepts. A parallel line of research focuses on disentangling the data distribution into its underlying generative factors, in turn explaining the data generation process. While both directions have received extensive attention, little work has been done on explaining concepts in terms of generative factors to unify mathematically disentangled representations and human-understandable concepts as an explanation for downstream tasks. In this paper, we propose a novel method CoLiDR - which utilizes a disentangled representation learning setup for learning mutually independent generative factors and subsequently learns to aggregate the said representations into human-understandable concepts using a novel aggregation/decomposition module. Experiments are conducted on datasets with both known and unknown latent generative factors. Our method successfully aggregates disentangled generative factors into concepts while maintaining parity with state-of-the-art concept-based approaches. Quantitative and visual analysis of the learned aggregation procedure demonstrates the advantages of our work compared to commonly used concept-based models over four challenging datasets. Lastly, our work is generalizable to an arbitrary number of concepts and generative factors - making it flexible enough to be suitable for various types of data.|基于概念模型的深度神经网络的可解释性为通过人类可理解的概念来解释模型行为提供了一种有前途的方法。一条平行的研究线集中于将数据分布分解为其潜在的生成因素，进而解释数据生成过程。虽然这两个方向都得到了广泛的关注，但是很少有人用生成因素来解释概念，以统一数学上的分离表征和人类可理解的概念，作为对下游任务的解释。在本文中，我们提出了一种新的 CoLiDR 方法-它利用一个离散表示学习设置来学习相互独立的生成因子，然后学习使用一个新的聚集/分解模块将所述表示聚合成人类可理解的概念。实验在具有已知和未知潜在生成因子的数据集上进行。我们的方法成功地将分离的生成因素集成到概念中，同时保持了与最先进的基于概念的方法的一致性。对学习聚合过程的定量和可视化分析表明，与四个具有挑战性的数据集中常用的基于概念的模型相比，我们的工作具有优势。最后，我们的工作可以推广到任意数量的概念和生成因素-使其足够灵活，以适合各种类型的数据。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=CoLiDR:+Concept+Learning+using+Aggregated+Disentangled+Representations)|0| -|[On Early Detection of Hallucinations in Factual Question Answering](https://doi.org/10.1145/3637528.3671796)|Ben Snyder, Marius Moisescu, Muhammad Bilal Zafar|; Amazon Web Services, Seattle, WA, USA; Amazon Web Services, Santa Clara, CA, USA|While large language models (LLMs) have taken great strides towards helping humans with a plethora of tasks, hallucinations remain a major impediment towards gaining user trust. The fluency and coherence of model generations even when hallucinating makes detection a difficult task. In this work, we explore if the artifacts associated with the model generations can provide hints that the generation will contain hallucinations. Specifically, we probe LLMs at 1) the inputs via Integrated Gradients based token attribution, 2) the outputs via the Softmax probabilities, and 3) the internal state via self-attention and fully-connected layer activations for signs of hallucinations on open-ended question answering tasks. Our results show that the distributions of these artifacts tend to differ between hallucinated and non-hallucinated generations. Building on this insight, we train binary classifiers that use these artifacts as input features to classify model generations into hallucinations and non-hallucinations. These hallucination classifiers achieve up to 0.80 AUROC. We also show that tokens preceding a hallucination can already predict the subsequent hallucination even before it occurs.|虽然大型语言模型(LLM)已经在帮助人类完成大量任务方面取得了长足的进步，但是幻觉仍然是获得用户信任的主要障碍。模型世代的流畅性和连贯性甚至在产生幻觉时也使得侦测成为一项困难的任务。在这项工作中，我们探索与模型世代相关的人工制品是否可以提供这一世代将包含幻觉的暗示。具体而言，我们探测 LLM: 1)通过基于集成梯度的令牌归属的输入，2)通过 Softmax 概率的输出，以及3)通过自我注意和完全连接层激活的内部状态，以获得开放式问题回答任务上的幻觉迹象。我们的研究结果表明，这些伪影的分布趋向于不同的幻觉和非幻觉世代。在此基础上，我们训练二进制分类器，使用这些工件作为输入特征，将模型代分为幻觉和非幻觉。这些幻觉分类器可以达到0.80 AUROC。我们还发现，幻觉之前的标记甚至在幻觉发生之前就已经能够预测后续的幻觉。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=On+Early+Detection+of+Hallucinations+in+Factual+Question+Answering)|0| +|[On Early Detection of Hallucinations in Factual Question Answering](https://doi.org/10.1145/3637528.3671796)|Ben Snyder, Marius Moisescu, Muhammad Bilal Zafar|; Amazon Web Services, Santa Clara, CA, USA; Amazon Web Services, Seattle, WA, USA|While large language models (LLMs) have taken great strides towards helping humans with a plethora of tasks, hallucinations remain a major impediment towards gaining user trust. The fluency and coherence of model generations even when hallucinating makes detection a difficult task. In this work, we explore if the artifacts associated with the model generations can provide hints that the generation will contain hallucinations. Specifically, we probe LLMs at 1) the inputs via Integrated Gradients based token attribution, 2) the outputs via the Softmax probabilities, and 3) the internal state via self-attention and fully-connected layer activations for signs of hallucinations on open-ended question answering tasks. Our results show that the distributions of these artifacts tend to differ between hallucinated and non-hallucinated generations. Building on this insight, we train binary classifiers that use these artifacts as input features to classify model generations into hallucinations and non-hallucinations. These hallucination classifiers achieve up to 0.80 AUROC. We also show that tokens preceding a hallucination can already predict the subsequent hallucination even before it occurs.|虽然大型语言模型(LLM)已经在帮助人类完成大量任务方面取得了长足的进步，但是幻觉仍然是获得用户信任的主要障碍。模型世代的流畅性和连贯性甚至在产生幻觉时也使得侦测成为一项困难的任务。在这项工作中，我们探索与模型世代相关的人工制品是否可以提供这一世代将包含幻觉的暗示。具体而言，我们探测 LLM: 1)通过基于集成梯度的令牌归属的输入，2)通过 Softmax 概率的输出，以及3)通过自我注意和完全连接层激活的内部状态，以获得开放式问题回答任务上的幻觉迹象。我们的研究结果表明，这些伪影的分布趋向于不同的幻觉和非幻觉世代。在此基础上，我们训练二进制分类器，使用这些工件作为输入特征，将模型代分为幻觉和非幻觉。这些幻觉分类器可以达到0.80 AUROC。我们还发现，幻觉之前的标记甚至在幻觉发生之前就已经能够预测后续的幻觉。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=On+Early+Detection+of+Hallucinations+in+Factual+Question+Answering)|0| |[MAML-en-LLM: Model Agnostic Meta-Training of LLMs for Improved In-Context Learning](https://doi.org/10.1145/3637528.3671905)|Sanchit Sinha, Yuguang Yue, Victor Soto, Mayank Kulkarni, Jianhua Lu, Aidong Zhang|University of Virginia, Charlottesville, VA, USA; Amazon AGI, New York, NY, USA; Amazon AGI, Cambridge, MA, USA|Adapting large language models (LLMs) to unseen tasks with incontext training samples without fine-tuning remains an important research problem. To learn a robust LLM that adapts well to unseen tasks, multiple meta-training approaches have been proposed such as MetaICL and MetaICT, which involve meta-training pre-trained LLMs on a wide variety of diverse tasks. These meta-training approaches essentially perform in-context multi-task fine-tuning and evaluate on a disjointed test set of tasks. Even though they achieve impressive performance, their goal is never to compute a truly general set of parameters. In this paper, we propose MAML-en-LLM, a novel method for meta-training LLMs, which can learn truly generalizable parameters that not only performs well on disjointed tasks but also adapts to unseen tasks. We see an average increase of 2% on unseen domains in the performance while a massive 4% improvement on adaptation performance. Furthermore, we demonstrate that MAML-en-LLM outperforms baselines in settings with limited amount of training data on both seen and unseen domains by an average of 2%. Finally, we discuss the effects of type of tasks, optimizers and task complexity, an avenue barely explored in metatraining literature. Exhaustive experiments across 7 task settings along with two data settings demonstrate that models trained with MAML-en-LLM outperform SOTA meta-training approaches.|使大语言模型(LLM)适应不需要微调的非上下文训练样本的未知任务仍然是一个重要的研究问题。为了学习能够很好地适应看不见的任务的强大的 LLM，已经提出了多种元培训方法，如 MetaICL 和 MetaICT，其中涉及对各种不同任务进行元培训预先培训的 LLM。这些元训练方法基本上执行上下文中的多任务微调，并对脱节的任务测试集进行评估。尽管它们取得了令人印象深刻的性能，但它们的目标从来不是计算一组真正通用的参数。本文提出了一种新的元训练 LLM 方法 MAML-en-LLM，它能够学习真正可推广的参数，这些参数不仅能够很好地处理不连续的任务，而且能够适应看不见的任务。我们看到在性能上看不见的领域平均增加了2% ，而在适应性能上则大幅提高了4% 。此外，我们证明，MAML-en-LLM 在有限数量的训练数据在可见和不可见领域中的平均表现优于基线2% 。最后，我们讨论了任务类型、优化器和任务复杂度的影响，这是元训练文献中几乎没有涉及到的一个途径。通过7个任务设置和两个数据设置的详尽实验表明，用 MAML-en-LLM 训练的模型优于 SOTA 元训练方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=MAML-en-LLM:+Model+Agnostic+Meta-Training+of+LLMs+for+Improved+In-Context+Learning)|0| |[Fast Computation for the Forest Matrix of an Evolving Graph](https://doi.org/10.1145/3637528.3671822)|Haoxin Sun, Xiaotian Zhou, Zhongzhi Zhang|Fudan University, Shanghai, China|The forest matrix plays a crucial role in network science, opinion dynamics, and machine learning, offering deep insights into the structure of and dynamics on networks. In this paper, we study the problem of querying entries of the forest matrix in evolving graphs, which more accurately represent the dynamic nature of real-world networks compared to static graphs. To address the unique challenges posed by evolving graphs, we first introduce two approximation algorithms, SFQ and SFQPlus, for static graphs. SFQ employs a probabilistic interpretation of the forest matrix, while SFQPlus incorporates a novel variance reduction technique and is theoretically proven to offer enhanced accuracy. Based on these two algorithms, we further devise two dynamic algorithms centered around efficiently maintaining a list of spanning converging forests. This approach ensures O(1) runtime complexity for updates, including edge additions and deletions, as well as for querying matrix elements, and provides an unbiased estimation of forest matrix entries. Finally, through extensive experiments on various real-world networks, we demonstrate the efficiency and effectiveness of our algorithms. Particularly, our algorithms are scalable to massive graphs with more than forty million nodes.|森林矩阵在网络科学、舆论动力学和机器学习中起着至关重要的作用，它提供了对网络结构和动力学的深刻见解。本文研究了进化图中森林矩阵条目的查询问题，与静态图相比，进化图更准确地表示了现实世界网络的动态特性。为了解决进化图所带来的独特挑战，我们首先介绍了静态图的两种近似算法 SFQ 和 SFQPlus。SFQ 采用了森林矩阵的概率解释，而 SFQPlus 采用了一种新的方差减少技术，并在理论上证明了它提供了更高的准确性。基于这两个算法，我们进一步设计了两个动态算法，围绕有效地维护跨越会聚森林的列表。这种方法确保了更新(包括边缘添加和删除)以及查询矩阵元素的 O (1)运行时复杂性，并提供了森林矩阵条目的无偏估计。最后，通过在各种实际网络上的大量实验，验证了算法的有效性。特别是，我们的算法可以扩展到具有超过四千万个节点的海量图形。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Fast+Computation+for+the+Forest+Matrix+of+an+Evolving+Graph)|0| |[Dual-Assessment Driven Pruning: Iterative Optimizing Layer-wise Sparsity for Large Language Model](https://doi.org/10.1145/3637528.3671780)|Qinghui Sun, Weilun Wang, Yanni Zhu, Shenghuan He, Hao Yi, Zehua Cai, Hong Liu|Alibaba Group, HangZhou, Zhejiang, China; Alibaba Group, Hangzhou, Zhejiang, China|Large Language Models (LLMs) have demonstrated efficacy in various domains, but deploying these models is economically challenging due to extensive parameter counts. Numerous efforts have been dedicated to reducing the parameter count of these models without compromising performance, employing a technique known as model pruning. Conventional pruning methods assess the significance of weights within individual layers and typically apply uniform sparsity levels across all layers, potentially neglecting the varying significance of each layer. To address this oversight, we first propose a dual-assessment driven pruning strategy that employs both intra-layer metric and global performance metric to comprehensively evaluate the impact of pruning. Then our method leverages an iterative optimization algorithm to find the optimal layer-wise sparsity distribution, thereby minimally impacting model performance. Extensive benchmark evaluations on state-of-the-art LLM architectures such as LLaMAv2 and OPT across a variety of NLP tasks demonstrate the effectiveness of our approach. When applied to the LLaMaV2-7B model with an overall pruning sparsity of 80%, our method achieves a 50% reduction in perplexity compared to the benchmark. The results indicate that our method significantly outperforms existing state-of-the-art methods in preserving performance after pruning.|大型语言模型(LLM)已经在不同的领域展示了其有效性，但是由于大量的参数计数，部署这些模型在经济上具有挑战性。许多努力致力于减少这些模型的参数计数而不损害性能，采用一种称为模型修剪的技术。传统的修剪方法评估单个层内权重的重要性，并且通常在所有层之间应用统一的稀疏水平，可能忽略了每一层的不同重要性。为了解决这个疏忽，我们首先提出了一个双重评估驱动的修剪策略，它同时使用层内度量和全球性能度量来全面评估修剪的影响。然后利用迭代优化算法寻找最优的分层稀疏分布，从而最小限度地影响模型的性能。对最先进的 LLM 架构(如 LLaMAv2和 OPT)进行跨多种 NLP 任务的广泛基准评估，证明了我们方法的有效性。当应用于 LLaMaV2-7B 模型时，整体修剪稀疏度为80% ，与基准相比，我们的方法实现了50% 的困惑减少。结果表明，我们的方法在修剪后保持性能方面明显优于现有的最先进的方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Dual-Assessment+Driven+Pruning:+Iterative+Optimizing+Layer-wise+Sparsity+for+Large+Language+Model)|0| @@ -411,55 +411,55 @@ |[URRL-IMVC: Unified and Robust Representation Learning for Incomplete Multi-View Clustering](https://doi.org/10.1145/3637528.3671887)|Ge Teng, Ting Mao, Chen Shen, Xiang Tian, Xuesong Liu, Yaowu Chen, Jieping Ye|; Zhejiang University, Hangzhou, China; Alibaba Cloud, Hangzhou, China|Incomplete multi-view clustering (IMVC) aims to cluster multi-view data that are only partially available. This poses two main challenges: effectively leveraging multi-view information and mitigating the impact of missing views. Prevailing solutions employ cross-view contrastive learning and missing view recovery techniques. However, they either neglect valuable complementary information by focusing only on consensus between views or provide unreliable recovered views due to the absence of supervision. To address these limitations, we propose a novel Unified and Robust Representation Learning for Incomplete Multi-View Clustering (URRL-IMVC). URRL-IMVC directly learns a unified embedding that is robust to view missing conditions by integrating information from multiple views and neighboring samples. Firstly, to overcome the limitations of cross-view contrastive learning, URRL-IMVC incorporates an attention-based auto-encoder framework to fuse multi-view information and generate unified embeddings. Secondly, URRL-IMVC directly enhances the robustness of the unified embedding against view-missing conditions through KNN imputation and data augmentation techniques, eliminating the need for explicit missing view recovery. Finally, incremental improvements are introduced to further enhance the overall performance, such as the Clustering Module and the customization of the Encoder. We extensively evaluate the proposed URRL-IMVC framework on various benchmark datasets, demonstrating its state-of-the-art performance. Furthermore, comprehensive ablation studies are performed to validate the effectiveness of our design.|不完全多视图聚类(IMVC)旨在对只有部分可用的多视图数据进行聚类。这带来了两个主要挑战: 有效地利用多视图信息和减轻缺少视图的影响。目前流行的解决方案采用横向视图对比学习和缺失视图恢复技术。但是，它们要么忽视了有价值的补充信息，只注重意见之间的共识，要么由于缺乏监督而提供不可靠的已恢复的意见。针对这些局限性，我们提出了一种新的不完全多视图聚类统一鲁棒表示学习方法(URL-IMVC)。IMVC 直接学习统一的嵌入，通过整合多个视图和相邻样本的信息，可以鲁棒地查看缺失条件。首先，为了克服跨视图对比学习的局限性，URRL-IMVC 采用基于注意力的自动编码框架融合多视图信息，生成统一的嵌入。其次，URRL-IMVC 通过 KNN 插值和数据增强技术直接增强了统一嵌入对视图缺失条件的鲁棒性，消除了显式缺失视图恢复的需要。最后，为了进一步提高系统的整体性能，引入了增量式改进，如集群模块和编码器的定制。我们在各种基准数据集上广泛评估了提议的 URRL-IMVC 框架，展示了其最先进的性能。此外，进行了全面的消融研究，以验证我们的设计的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=URRL-IMVC:+Unified+and+Robust+Representation+Learning+for+Incomplete+Multi-View+Clustering)|0| |[Rotative Factorization Machines](https://doi.org/10.1145/3637528.3671740)|Zhen Tian, Yuhong Shi, Xiangkun Wu, Wayne Xin Zhao, JiRong Wen|; Zhejiang University, Hangzhou, China|Feature interaction learning (FIL) focuses on capturing the complex relationships among multiple features for building predictive models, which is widely used in real-world tasks. Despite the research progress, existing FIL methods suffer from two major limitations. Firstly, they mainly model the feature interactions within a bounded order (e.g., small integer order) due to the exponential growth of the interaction terms. Secondly, the interaction order of each feature is often independently learned, which lacks the flexibility to capture the feature dependencies in varying contexts. To address these issues, we present Rotative Factorization Machines (RFM), based on the key idea that represents each feature as a polar angle in the complex plane. As such, the feature interactions are converted into a series of complex rotations, where the orders are cast into the rotation coefficients, thereby allowing for the learning of arbitrarily large order. Further, we propose a novel self-attentive rotation function that models the rotation coefficients through a rotation-based attention mechanism, which can adaptively learn the interaction orders under different interaction contexts. Moreover, it incorporates a modulus amplification network to learn the modulus of the complex features, which further enhances the expressive capacity. Our proposed approach provides a general FIL framework, and many existing models can be instantiated in this framework, e.g., factorization machines. In theory, it possesses more strong capacities to model complex feature relationships, and can learn arbitrary features from varied contexts. Extensive experiments conducted on five widely used datasets have demonstrated the effectiveness of our approach.|特征交互学习(FIL)关注于捕获多个特征之间的复杂关系，用于建立预测模型，在实际任务中得到了广泛的应用。尽管研究取得了进展，但现有的 FIL 方法仍然存在两大局限性。首先，由于交互项的指数增长，他们主要在一个有界顺序(例如小整数顺序)内对特征交互进行建模。其次，每个特征的交互顺序往往是独立学习的，缺乏在不同环境下捕获特征依赖关系的灵活性。为了解决这些问题，我们提出了旋转因子分解机(RFM)的基础上的关键思想，表示每个特征作为一个极角在复杂的平面。因此，特征交互被转换成一系列复杂的旋转，其中的顺序被转换成旋转系数，从而允许学习任意大的顺序。在此基础上，提出了一种新的自注意旋转函数，该函数通过基于旋转的注意机制对旋转系数进行建模，能够自适应地学习不同交互情境下的交互顺序。此外，它还结合了模放大网络来学习复杂特征的模，进一步提高了表达能力。我们提出的方法提供了一个通用的 FIL 框架，并且许多现有的模型可以在这个框架中实例化，例如，因子分解机。理论上，它具有较强的复杂特征关系建模能力，可以从不同的上下文中学习任意特征。在五个广泛使用的数据集上进行的大量实验已经证明了我们方法的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Rotative+Factorization+Machines)|0| |[Latent Diffusion-based Data Augmentation for Continuous-Time Dynamic Graph Model](https://doi.org/10.1145/3637528.3671863)|Yuxing Tian, Aiwen Jiang, Qi Huang, Jian Guo, Yiyan Qi|International Digital Economy Academy, IDEA Research, Shenzhen, China; Jiangxi Normal University, Nanchang, China|Continuous-Time Dynamic Graph (CTDG) precisely models evolving real-world relationships, drawing heightened interest in dynamic graph learning across academia and industry. However, existing CTDG models encounter challenges stemming from noise and limited historical data. Graph Data Augmentation (GDA) emerges as a critical solution, yet current approaches primarily focus on static graphs and struggle to effectively address the dynamics inherent in CTDGs. Moreover, these methods often demand substantial domain expertise for parameter tuning and lack theoretical guarantees for augmentation efficacy. To address these issues, we propose Conda, a novel latent diffusion-based GDA method tailored for CTDGs. Conda features a sandwich-like architecture, incorporating a Variational Auto-Encoder (VAE) and a conditional diffusion model, aimed at generating enhanced historical neighbor embeddings for target nodes. Unlike conventional diffusion models trained on entire graphs via pre-training, Conda requires historical neighbor sequence embeddings of target nodes for training, thus facilitating more targeted augmentation. We integrate Conda into the CTDG model and adopt an alternating training strategy to optimize performance. Extensive experimentation across six widely used real-world datasets showcases the consistent performance improvement of our approach, particularly in scenarios with limited historical data.|连续时间动态图(CTDG)精确地模拟演化的现实世界的关系，吸引了学术界和工业界对动态图学习的高度兴趣。然而，现有的 CTDG 模型遇到了来自噪音和有限的历史数据的挑战。图形数据增强(Graph Data Augging，GDA)是一个关键的解决方案，然而目前的方法主要集中于静态图形，并努力有效地解决 CTDGs 中固有的动态问题。此外，这些方法往往需要大量的领域专业知识的参数调整和缺乏增强效果的理论保证。为了解决这些问题，我们提出了一种新的基于潜在扩散的 GDA 方法 Conda。Conda 采用了一种类似三明治的体系结构，结合了变分自动编码器(VAE)和条件扩散模型，旨在为目标节点生成增强的历史邻居嵌入。与传统的扩散模型通过预训练对整个图进行训练不同，Conda 需要对目标节点进行历史邻居序列嵌入来进行训练，从而便于进行更有针对性的增强。我们将 Conda 集成到 CTDG 模型中，并采用交替培训策略来优化性能。对六个广泛使用的真实世界数据集进行的广泛实验显示了我们的方法的一致性能改进，特别是在历史数据有限的场景中。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Latent+Diffusion-based+Data+Augmentation+for+Continuous-Time+Dynamic+Graph+Model)|0| -|[Flexible Graph Neural Diffusion with Latent Class Representation Learning](https://doi.org/10.1145/3637528.3671860)|Liangtian Wan, Huijin Han, Lu Sun, Zixun Zhang, Zhaolong Ning, Xiaoran Yan, Feng Xia|; School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, Shenzhen, China; School of Computing Technologies, RMIT University, Melbourne, Australia; Research Center for Data Hub and Security, Zhejiang Lab, Hangzhou, China|In existing graph data, the connection relationships often exhibit uniform weights, leading to the model aggregating neighboring nodes with equal weights across various connection types. However, this uniform aggregation of diverse information diminishes the discriminability of node representations, contributing significantly to the over-smoothing issue in models. In this paper, we propose the Flexible Graph Neural Diffusion (FGND) model, incorporating latent class representation to address the misalignment between graph topology and node features. In particular, we combine latent class representation learning with the inherent graph topology to reconstruct the diffusion matrix during the graph diffusion process. We introduce the sim metric to quantify the degree of mismatch between graph topology and node features. By flexibly adjusting the dependency level on node features through the hyperparameter, we accommodate diverse adjacency relationships. The effective filtering of noise in the topology also allows the model to capture higher order information, significantly alleviating the over-smoothing problem. Meanwhile, we model the graphical diffusion process as a set of differential equations and employ advanced partial differential equation tools to obtain more accurate solutions. Empirical evaluations on five benchmarks reveal that our FGND model outperforms existing popular GNN methods in terms of both overall performance and stability under data perturbations. Meanwhile, our model exhibits superior performance in comparison to models tailored for heterogeneous graphs and those designed to address oversmoothing issues.|在现有的图形数据中，连接关系往往具有统一的权重，导致模型聚集相邻节点，在不同的连接类型中具有相同的权重。然而，这种不同信息的统一聚合降低了节点表示的可分辨性，极大地促进了模型中的过度平滑问题。本文提出了柔性图神经扩散(FGND)模型，结合潜类表示来解决图拓扑结构与节点特征之间的不匹配问题。特别地，我们将潜类表示学习与固有的图拓扑结合起来，在图扩散过程中重构扩散矩阵。引入相似度量来量化图的拓扑结构与节点特征之间的不匹配程度。通过超参数灵活调整节点特征的依赖关系，可以适应不同的邻接关系。在拓扑结构中对噪声进行有效的滤波，使得模型能够捕获更高阶的信息，显著地减轻了过平滑问题。同时，我们将图形扩散过程建模为一组微分方程，并使用先进的偏微分方程工具来获得更精确的解。对五个基准的实证评估表明，我们的 FGND 模型在整体性能和数据扰动下的稳定性方面都优于现有的流行 GNN 方法。与此同时，我们的模型表现出优越的性能相比，为异构图和那些旨在解决过度平滑问题的模型。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Flexible+Graph+Neural+Diffusion+with+Latent+Class+Representation+Learning)|0| -|[STONE: A Spatio-temporal OOD Learning Framework Kills Both Spatial and Temporal Shifts](https://doi.org/10.1145/3637528.3671680)|Binwu Wang, Jiaming Ma, Pengkun Wang, Xu Wang, Yudong Zhang, Zhengyang Zhou, Yang Wang|University of Science and Technology of China, Hefei, China; Suzhou Institute for Advanced Research, University of Science and Technology of China, Suzhou, China|Traffic prediction is a crucial task in the Intelligent Transportation System (ITS), receiving significant attention from both industry and academia. Numerous spatio-temporal graph convolutional networks have emerged for traffic prediction and achieved remarkable success. However, these models have limitations in terms of generalization and scalability when dealing with Out-of-Distribution (OOD) graph data with both structural and temporal shifts. To tackle the challenges of spatio-temporal shift, we propose a framework called STONE by learning invariable node dependencies, which achieve stable performance in variable environments. STONE initially employs gated-transformers to extract spatial and temporal semantic graphs. These two kinds of graphs represent spatial and temporal dependencies, respectively. Then we design three techniques to address spatio-temporal shifts. Firstly, we introduce a Fréchet embedding method that is insensitive to structural shifts, and this embedding space can integrate loose position dependencies of nodes within the graph. Secondly, we propose a graph intervention mechanism to generate multiple variant environments by perturbing two kinds of semantic graphs without any data augmentations, and STONE can explore invariant node representation from environments. Finally, we further introduce an explore-to-extrapolate risk objective to enhance the variety of generated environments. We conduct experiments on multiple traffic datasets, and the results demonstrate that our proposed model exhibits competitive performance in terms of generalization and scalability.|交通预测是智能交通系统(ITS)中的一项重要内容，受到了业界和学术界的广泛关注。许多时空图卷积网络已经应用于交通预测，并取得了显著的成功。然而，这些模型在处理具有结构和时间偏移的 OOD 图形数据时，在泛化和可扩展性方面存在局限性。为了解决时空转移的问题，我们提出了一种基于不变节点依赖性的 STONE 框架，该框架可以在多变环境下获得稳定的性能。STONE 最初使用门控转换器提取空间和时间语义图。这两种图分别表示空间和时间上的依赖关系。然后我们设计了三种技术来解决时空移位问题。首先，我们引入了一种对结构位移不敏感的 Fréchet 嵌入方法，该嵌入空间可以集成图中节点的松散位置依赖关系。其次，提出了一种图干预机制，通过对两种语义图进行干扰，在不增加任何数据的情况下生成多变量环境，并且 STONE 可以从环境中探索不变的节点表示。最后，我们进一步引入了一个探索性外推风险目标，以增强生成环境的多样性。我们在多个交通数据集上进行了实验，结果表明，我们提出的模型在泛化性和可扩展性方面具有竞争性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=STONE:+A+Spatio-temporal+OOD+Learning+Framework+Kills+Both+Spatial+and+Temporal+Shifts)|0| -|[Provable Adaptivity of Adam under Non-uniform Smoothness](https://doi.org/10.1145/3637528.3671718)|Bohan Wang, Yushun Zhang, Huishuai Zhang, Qi Meng, Ruoyu Sun, ZhiMing Ma, TieYan Liu, ZhiQuan Luo, Wei Chen|Peking University, Beijing, China; Microsoft, Beijing, China; Chinese Academy of Mathematics and Systems Science, Beijing, China; University of Science and Technology of China & Microsoft Research Asia, Beijing, Haidian, China; The Chinese University of Hong Kong, Shenzhen, Shenzhen, China; Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China; The Chinese University of Hong Kong, Shenzhen, Shenzhen, Guangdong, China|Adam is widely adopted in practical applications due to its fast convergence. However, its theoretical analysis is still far from satisfactory. Existing convergence analyses for Adam rely on the bounded smoothness assumption, referred to as the L-smooth condition. Unfortunately, this assumption does not hold for many deep learning tasks. Moreover, we believe that this assumption obscures the true benefit of Adam, as the algorithm can adapt its update magnitude according to local smoothness. This important feature of Adam becomes irrelevant when assuming globally bounded smoothness. This paper studies the convergence of randomly reshuffled Adam (RR Adam) with diminishing learning rate, which is the major version of Adam adopted in deep learning tasks. We present the first convergence analysis of RR Adam without the bounded smoothness assumption. We demonstrate that RR Adam can maintain its convergence properties when smoothness is linearly bounded by the gradient norm, referred to as the (L0, L1)-smooth condition. We further compare Adam to SGD when both methods use diminishing learning rate. We refine the existing lower bound of SGD and show that SGD can be slower than Adam. To our knowledge, this is the first time that Adam and SGD are rigorously compared in the same setting and the advantage of Adam is revealed.|亚当因其收敛速度快而在实际应用中得到了广泛的应用。然而，其理论分析还远远不能令人满意。现有的亚当收敛性分析依赖于有界光滑假设，称为 L- 光滑条件。不幸的是，这个假设并不适用于许多深度学习任务。此外，我们认为这个假设掩盖了亚当的真正好处，因为该算法可以根据局部平滑度调整其更新幅度。当假设全局有界平滑时，亚当的这个重要特征就变得无关紧要了。本文研究了随机重组亚当(RR 亚当)学习速率递减的收敛性，这是亚当在深度学习任务中采用的主要形式。我们提出了 RR 亚当的第一个收敛性分析，没有有界光滑假设。证明了当光滑度线性有界于梯度范数时(L0，L1)-光滑条件下，RR-Adam 可以保持其收敛性。当两种方法都使用递减学习率时，我们进一步将亚当比作 SGD。我们改进了现有的 SGD 下限，并表明新加坡元可以慢于亚当。据我们所知，这是第一次在同一背景下严格比较亚当和 SGD，并揭示了亚当的优势。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Provable+Adaptivity+of+Adam+under+Non-uniform+Smoothness)|0| -|[Multi-Scale Detection of Anomalous Spatio-Temporal Trajectories in Evolving Trajectory Datasets](https://doi.org/10.1145/3637528.3671874)|Chenhao Wang, Lisi Chen, Shuo Shang, Christian S. Jensen, Panos Kalnis|King Abdullah University of Science and Technology, Thuwal, Saudi Arabia; Aalborg University, Aalborg, Denmark; University of Electronic Science and Technology of China, Chengdu, China|A trajectory is a sequence of timestamped point locations that captures the movement of an object such as a vehicle. Such trajectories encode complex spatial and temporal patterns and provide rich information about object mobility and the underlying infrastructures, typically road networks, within which the movements occur. A trajectory dataset is evolving when new trajectories are included continuously. The ability to detect anomalous trajectories in online fashion in this setting is fundamental and challenging functionality that has many applications, e.g., location-based services. State-of-the-art solutions determine anomalies based on the shapes or routes of trajectories, ignoring potential anomalies caused by different sampling rates or time offsets. We propose a multi-scale model, termed MST-OATD, for anomalous streaming trajectory detection that considers both the spatial and temporal aspects of trajectories. The model's multi-scale capabilities aim to enable extraction of trajectory features at multiple scales. In addition, to improve model evolvability and to contend with changes in trajectory patterns, the model is equipped with a learned ranking model that updates the training set as new trajectories are included. Experiments on real datasets offer evidence that the model can outperform state-of-the-art solutions and is capable of real-time anomaly detection. Further, the learned ranking model achieves promising results when updating the training set with newly arrived trajectories.|轨迹是一系列有时间标记的点位置，用于捕捉物体(如车辆)的运动。这种轨迹编码复杂的空间和时间模式，并提供有关物体移动性和基础设施的丰富信息，通常是道路网络，其中发生的运动。当不断包含新的轨迹时，轨迹数据集就在演变。在这种情况下，以在线方式检测异常轨迹的能力是基本的和具有挑战性的功能，它有许多应用程序，例如，基于位置的服务。最先进的解决方案根据轨迹的形状或路线确定异常，忽略由不同采样率或时间偏移引起的潜在异常。我们提出了一个多尺度模型，称为 MST-OATD，异常流轨迹检测，考虑到空间和时间方面的轨迹。该模型的多尺度能力旨在提取多尺度的弹道特征。此外，为了提高模型的可演化性和应对轨迹模式的变化，该模型配备了一个学习排名模型，该模型在包括新的轨迹时更新训练集。在真实数据集上的实验证明，该模型的性能优于最先进的解决方案，并具有实时异常检测。此外，学习排序模型在用新到达的轨迹更新训练集时取得了令人满意的结果。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Multi-Scale+Detection+of+Anomalous+Spatio-Temporal+Trajectories+in+Evolving+Trajectory+Datasets)|0| -|[Global Human-guided Counterfactual Explanations for Molecular Properties via Reinforcement Learning](https://doi.org/10.1145/3637528.3672045)|Danqing Wang, Antonis Antoniades, KhaDinh Luong, Edwin Zhang, Mert Kosan, Jiachen Li, Ambuj Singh, William Yang Wang, Lei Li|Harvard University & Founding, Cambridge, MA, USA; University of California, Santa Barbara, Santa Barbara, CA, USA; Language Technologies Institute, Carnegie Mellon University, Pittsburgh, USA; University of California, Santa Barbara, Santa Barbara, USA; Language Technologies Institute, Carnegie Mellon University, Pittsburgh, PA, USA|Counterfactual explanations of Graph Neural Networks (GNNs) offer a powerful way to understand data that can naturally be represented by a graph structure. Furthermore, in many domains, it is highly desirable to derive data-driven global explanations or rules that can better explain the high-level properties of the models and data in question. However, evaluating global counterfactual explanations is hard in real-world datasets due to a lack of human-annotated ground truth, which limits their use in areas like molecular sciences. Additionally, the increasing scale of these datasets provides a challenge for random search-based methods. In this paper, we develop a novel global explanation model RLHEX for molecular property prediction. It aligns the counterfactual explanations with human-defined principles, making the explanations more interpretable and easy for experts to evaluate. RLHEX includes a VAE-based graph generator to generate global explanations and an adapter to adjust the latent representation space to human-defined principles. Optimized by Proximal Policy Optimization (PPO), the global explanations produced by RLHEX cover 4.12% more input graphs and reduce the distance between the counterfactual explanation set and the input set by 0.47% on average across three molecular datasets. RLHEX provides a flexible framework to incorporate different human-designed principles into the counterfactual explanation generation process, aligning these explanations with domain expertise. The code and data are released at https://github.com/dqwang122/RLHEX.|对图形神经网络(GNN)的反事实解释提供了一种强有力的方法来理解可以自然地用图形结构表示的数据。此外，在许多领域，非常需要推导出数据驱动的全球解释或规则，以便更好地解释有关模型和数据的高级特性。然而，在现实世界的数据集中评估全球反事实解释是困难的，因为缺乏人类注释的地面真相，这限制了它们在分子科学等领域的应用。此外，这些数据集的规模越来越大，对基于随机搜索的方法提出了挑战。本文提出了一种新的分子性质预测的全局解释模型 RLHEX。它将反事实解释与人为原则相结合，使解释更易于理解，更易于专家评价。RLHEX 包括一个基于 VAE 的图形生成器，用于生成全局解释，以及一个适配器，用于根据人定义的原则调整潜在表示空间。通过最近策略优化(PPO)优化，RLHEX 产生的全局解释覆盖了4.12% 以上的输入图，并在三个分子数据集中平均减少了反事实解释集和输入集之间的距离0.47% 。RLHEX 提供了一个灵活的框架，将不同的人类设计的原则纳入反事实解释的生成过程，使这些解释与领域专业知识相一致。代码和数据 https://github.com/dqwang122/rlhex 发布。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Global+Human-guided+Counterfactual+Explanations+for+Molecular+Properties+via+Reinforcement+Learning)|0| -|[Mastering Long-Tail Complexity on Graphs: Characterization, Learning, and Generalization](https://doi.org/10.1145/3637528.3671880)|Haohui Wang, Baoyu Jing, Kaize Ding, Yada Zhu, Wei Cheng, Si Zhang, Yonghui Fan, Liqing Zhang, Dawei Zhou|Amazon AGI, Sunnyvale, USA; Virginia Tech, Blacksburg, USA; Northwestern University, Evanston, USA; University of Illinois Urbana-Champaign, Urbana, USA; Meta, Sunnyvale, USA; IBM Research, Yorktown Heights, USA; NEC Labs America, Princeton, USA|In the context of long-tail classification on graphs, the vast majority of existing work primarily revolves around the development of model debiasing strategies, intending to mitigate class imbalances and enhance the overall performance. Despite the notable success, there is very limited literature that provides a theoretical tool for characterizing the behaviors of long-tail classes in graphs and gaining insight into generalization performance in real-world scenarios. To bridge this gap, we propose a generalization bound for long-tail classification on graphs by formulating the problem in the fashion of multi-task learning, i.e., each task corresponds to the prediction of one particular class. Our theoretical results show that the generalization performance of long-tail classification is dominated by the overall loss range and the task complexity. Building upon the theoretical findings, we propose a novel generic framework HierTail for long-tail classification on graphs. In particular, we start with a hierarchical task grouping module that allows us to assign related tasks into hypertasks and thus control the complexity of the task space; then, we further design a balanced contrastive learning module to adaptively balance the gradients of both head and tail classes to control the loss range across all tasks in a unified fashion. Extensive experiments demonstrate the effectiveness of HierTail in characterizing long-tail classes on real graphs, which achieves up to 12.9% improvement over the leading baseline method in balanced accuracy.|在图的长尾分类的背景下，绝大多数现有的工作主要围绕模型去偏策略的发展，旨在缓解类的不平衡和提高整体性能。尽管取得了显著的成功，但是提供描述图中长尾类行为的理论工具以及深入了解真实场景中的泛化性能的文献非常有限。为了弥补这一差距，我们提出了一个图的长尾分类的泛化界，即每个任务对应于一个特定类的预测。我们的理论结果表明，长尾分类的泛化性能主要取决于总损失范围和任务复杂度。在理论研究的基础上，我们提出了一个新的通用框架 HierTail 用于图的长尾分类。特别地，我们从一个分层的任务分组模块开始，它允许我们将相关的任务分配到超任务中，从而控制任务空间的复杂性; 然后，我们进一步设计了一个平衡的对比学习模块来自适应地平衡头部和尾部类的梯度，以统一的方式控制所有任务的丢失范围。大量的实验证明了 HierTail 方法在描述真实图上的长尾类时的有效性，在平衡精度方面比主基线方法提高了12.9% 。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Mastering+Long-Tail+Complexity+on+Graphs:+Characterization,+Learning,+and+Generalization)|0| -|[Unsupervised Heterogeneous Graph Rewriting Attack via Node Clustering](https://doi.org/10.1145/3637528.3671716)|Haosen Wang, Can Xu, Chenglong Shi, Pengfei Zheng, Shiming Zhang, Minhao Cheng, Hongyang Chen|Southeast University, Nanjing, China; Zhejiang Lab, Hangzhou, China; Pennsylvania State University, Philadelphia, PA, USA; University of Science and Technology of China, Hefei, China; Southeast University & Zhejiang Lab, Nanjing, China; East China Normal University, Shanghai, China|Self-supervised learning (SSL) has become one of the most popular learning paradigms and has achieved remarkable success in the graph field. Recently, a series of pre-training studies on heterogeneous graphs (HGs) using SSL have been proposed considering the heterogeneity of real-world graph data. However, verification of the robustness of heterogeneous graph pre-training is still a research gap. Most existing researches focus on supervised attacks on graphs, which are limited to a specific scenario and will not work when labels are not available. In this paper, we propose a novel unsupervised heterogeneous graph rewriting attack via node clustering (HGAC) that can effectively attack HG pre-training models without using labels. Specifically, a heterogeneous edge rewriting strategy is designed to ensure the rationality and concealment of the attacks. Then, a tailored heterogeneous graph contrastive learning (HGCL) is used as a surrogate model. Moreover, we leverage node clustering results of the clean HGs as the pseudo-labels to guide the optimization of structural attacks. Extensive experiments exhibit powerful attack performances of our HGAC on various downstream tasks (i.e., node classification, node clustering, metapath prediction, and visualization) under poisoning attack and evasion attack.|自监督学习(SSL)已经成为当前最流行的学习范式之一，并在图论领域取得了显著的成功。近年来，针对实际图形数据的异构性，提出了一系列基于 SSL 的异构图预训练方法。然而，对异构图预训练鲁棒性的验证仍然是一个研究空白。现有的研究大多集中于对图形的监督攻击，这种攻击局限于特定的场景，在没有标签的情况下不起作用。本文提出了一种基于节点聚类(HGAC)的无监督异构图重写攻击方法，该方法可以在不使用标签的情况下有效地攻击 HG 预训练模型。为了保证攻击的合理性和隐蔽性，设计了一种异构边缘重写策略。然后，采用定制的异构图对比学习(HGCL)作为代理模型。此外，我们利用干净 HG 的节点聚类结果作为伪标签来指导结构攻击的优化。广泛的实验表明，我们的 HGAC 在中毒攻击和规避攻击下对各种下游任务(即节点分类、节点聚类、元路径预测和可视化)具有强大的攻击性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Unsupervised+Heterogeneous+Graph+Rewriting+Attack+via+Node+Clustering)|0| +|[Flexible Graph Neural Diffusion with Latent Class Representation Learning](https://doi.org/10.1145/3637528.3671860)|Liangtian Wan, Huijin Han, Lu Sun, Zixun Zhang, Zhaolong Ning, Xiaoran Yan, Feng Xia|; School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, Shenzhen, China; Research Center for Data Hub and Security, Zhejiang Lab, Hangzhou, China; School of Computing Technologies, RMIT University, Melbourne, Australia|In existing graph data, the connection relationships often exhibit uniform weights, leading to the model aggregating neighboring nodes with equal weights across various connection types. However, this uniform aggregation of diverse information diminishes the discriminability of node representations, contributing significantly to the over-smoothing issue in models. In this paper, we propose the Flexible Graph Neural Diffusion (FGND) model, incorporating latent class representation to address the misalignment between graph topology and node features. In particular, we combine latent class representation learning with the inherent graph topology to reconstruct the diffusion matrix during the graph diffusion process. We introduce the sim metric to quantify the degree of mismatch between graph topology and node features. By flexibly adjusting the dependency level on node features through the hyperparameter, we accommodate diverse adjacency relationships. The effective filtering of noise in the topology also allows the model to capture higher order information, significantly alleviating the over-smoothing problem. Meanwhile, we model the graphical diffusion process as a set of differential equations and employ advanced partial differential equation tools to obtain more accurate solutions. Empirical evaluations on five benchmarks reveal that our FGND model outperforms existing popular GNN methods in terms of both overall performance and stability under data perturbations. Meanwhile, our model exhibits superior performance in comparison to models tailored for heterogeneous graphs and those designed to address oversmoothing issues.|在现有的图形数据中，连接关系往往具有统一的权重，导致模型聚集相邻节点，在不同的连接类型中具有相同的权重。然而，这种不同信息的统一聚合降低了节点表示的可分辨性，极大地促进了模型中的过度平滑问题。本文提出了柔性图神经扩散(FGND)模型，结合潜类表示来解决图拓扑结构与节点特征之间的不匹配问题。特别地，我们将潜类表示学习与固有的图拓扑结合起来，在图扩散过程中重构扩散矩阵。引入相似度量来量化图的拓扑结构与节点特征之间的不匹配程度。通过超参数灵活调整节点特征的依赖关系，可以适应不同的邻接关系。在拓扑结构中对噪声进行有效的滤波，使得模型能够捕获更高阶的信息，显著地减轻了过平滑问题。同时，我们将图形扩散过程建模为一组微分方程，并使用先进的偏微分方程工具来获得更精确的解。对五个基准的实证评估表明，我们的 FGND 模型在整体性能和数据扰动下的稳定性方面都优于现有的流行 GNN 方法。与此同时，我们的模型表现出优越的性能相比，为异构图和那些旨在解决过度平滑问题的模型。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Flexible+Graph+Neural+Diffusion+with+Latent+Class+Representation+Learning)|0| +|[STONE: A Spatio-temporal OOD Learning Framework Kills Both Spatial and Temporal Shifts](https://doi.org/10.1145/3637528.3671680)|Binwu Wang, Jiaming Ma, Pengkun Wang, Xu Wang, Yudong Zhang, Zhengyang Zhou, Yang Wang|Suzhou Institute for Advanced Research, University of Science and Technology of China, Suzhou, China; University of Science and Technology of China, Hefei, China|Traffic prediction is a crucial task in the Intelligent Transportation System (ITS), receiving significant attention from both industry and academia. Numerous spatio-temporal graph convolutional networks have emerged for traffic prediction and achieved remarkable success. However, these models have limitations in terms of generalization and scalability when dealing with Out-of-Distribution (OOD) graph data with both structural and temporal shifts. To tackle the challenges of spatio-temporal shift, we propose a framework called STONE by learning invariable node dependencies, which achieve stable performance in variable environments. STONE initially employs gated-transformers to extract spatial and temporal semantic graphs. These two kinds of graphs represent spatial and temporal dependencies, respectively. Then we design three techniques to address spatio-temporal shifts. Firstly, we introduce a Fréchet embedding method that is insensitive to structural shifts, and this embedding space can integrate loose position dependencies of nodes within the graph. Secondly, we propose a graph intervention mechanism to generate multiple variant environments by perturbing two kinds of semantic graphs without any data augmentations, and STONE can explore invariant node representation from environments. Finally, we further introduce an explore-to-extrapolate risk objective to enhance the variety of generated environments. We conduct experiments on multiple traffic datasets, and the results demonstrate that our proposed model exhibits competitive performance in terms of generalization and scalability.|交通预测是智能交通系统(ITS)中的一项重要内容，受到了业界和学术界的广泛关注。许多时空图卷积网络已经应用于交通预测，并取得了显著的成功。然而，这些模型在处理具有结构和时间偏移的 OOD 图形数据时，在泛化和可扩展性方面存在局限性。为了解决时空转移的问题，我们提出了一种基于不变节点依赖性的 STONE 框架，该框架可以在多变环境下获得稳定的性能。STONE 最初使用门控转换器提取空间和时间语义图。这两种图分别表示空间和时间上的依赖关系。然后我们设计了三种技术来解决时空移位问题。首先，我们引入了一种对结构位移不敏感的 Fréchet 嵌入方法，该嵌入空间可以集成图中节点的松散位置依赖关系。其次，提出了一种图干预机制，通过对两种语义图进行干扰，在不增加任何数据的情况下生成多变量环境，并且 STONE 可以从环境中探索不变的节点表示。最后，我们进一步引入了一个探索性外推风险目标，以增强生成环境的多样性。我们在多个交通数据集上进行了实验，结果表明，我们提出的模型在泛化性和可扩展性方面具有竞争性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=STONE:+A+Spatio-temporal+OOD+Learning+Framework+Kills+Both+Spatial+and+Temporal+Shifts)|0| +|[Provable Adaptivity of Adam under Non-uniform Smoothness](https://doi.org/10.1145/3637528.3671718)|Bohan Wang, Yushun Zhang, Huishuai Zhang, Qi Meng, Ruoyu Sun, ZhiMing Ma, TieYan Liu, ZhiQuan Luo, Wei Chen|Peking University, Beijing, China; The Chinese University of Hong Kong, Shenzhen, Shenzhen, Guangdong, China; Microsoft, Beijing, China; Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China; Chinese Academy of Mathematics and Systems Science, Beijing, China; The Chinese University of Hong Kong, Shenzhen, Shenzhen, China; University of Science and Technology of China & Microsoft Research Asia, Beijing, Haidian, China|Adam is widely adopted in practical applications due to its fast convergence. However, its theoretical analysis is still far from satisfactory. Existing convergence analyses for Adam rely on the bounded smoothness assumption, referred to as the L-smooth condition. Unfortunately, this assumption does not hold for many deep learning tasks. Moreover, we believe that this assumption obscures the true benefit of Adam, as the algorithm can adapt its update magnitude according to local smoothness. This important feature of Adam becomes irrelevant when assuming globally bounded smoothness. This paper studies the convergence of randomly reshuffled Adam (RR Adam) with diminishing learning rate, which is the major version of Adam adopted in deep learning tasks. We present the first convergence analysis of RR Adam without the bounded smoothness assumption. We demonstrate that RR Adam can maintain its convergence properties when smoothness is linearly bounded by the gradient norm, referred to as the (L0, L1)-smooth condition. We further compare Adam to SGD when both methods use diminishing learning rate. We refine the existing lower bound of SGD and show that SGD can be slower than Adam. To our knowledge, this is the first time that Adam and SGD are rigorously compared in the same setting and the advantage of Adam is revealed.|亚当因其收敛速度快而在实际应用中得到了广泛的应用。然而，其理论分析还远远不能令人满意。现有的亚当收敛性分析依赖于有界光滑假设，称为 L- 光滑条件。不幸的是，这个假设并不适用于许多深度学习任务。此外，我们认为这个假设掩盖了亚当的真正好处，因为该算法可以根据局部平滑度调整其更新幅度。当假设全局有界平滑时，亚当的这个重要特征就变得无关紧要了。本文研究了随机重组亚当(RR 亚当)学习速率递减的收敛性，这是亚当在深度学习任务中采用的主要形式。我们提出了 RR 亚当的第一个收敛性分析，没有有界光滑假设。证明了当光滑度线性有界于梯度范数时(L0，L1)-光滑条件下，RR-Adam 可以保持其收敛性。当两种方法都使用递减学习率时，我们进一步将亚当比作 SGD。我们改进了现有的 SGD 下限，并表明新加坡元可以慢于亚当。据我们所知，这是第一次在同一背景下严格比较亚当和 SGD，并揭示了亚当的优势。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Provable+Adaptivity+of+Adam+under+Non-uniform+Smoothness)|0| +|[Multi-Scale Detection of Anomalous Spatio-Temporal Trajectories in Evolving Trajectory Datasets](https://doi.org/10.1145/3637528.3671874)|Chenhao Wang, Lisi Chen, Shuo Shang, Christian S. Jensen, Panos Kalnis|Aalborg University, Aalborg, Denmark; University of Electronic Science and Technology of China, Chengdu, China; King Abdullah University of Science and Technology, Thuwal, Saudi Arabia|A trajectory is a sequence of timestamped point locations that captures the movement of an object such as a vehicle. Such trajectories encode complex spatial and temporal patterns and provide rich information about object mobility and the underlying infrastructures, typically road networks, within which the movements occur. A trajectory dataset is evolving when new trajectories are included continuously. The ability to detect anomalous trajectories in online fashion in this setting is fundamental and challenging functionality that has many applications, e.g., location-based services. State-of-the-art solutions determine anomalies based on the shapes or routes of trajectories, ignoring potential anomalies caused by different sampling rates or time offsets. We propose a multi-scale model, termed MST-OATD, for anomalous streaming trajectory detection that considers both the spatial and temporal aspects of trajectories. The model's multi-scale capabilities aim to enable extraction of trajectory features at multiple scales. In addition, to improve model evolvability and to contend with changes in trajectory patterns, the model is equipped with a learned ranking model that updates the training set as new trajectories are included. Experiments on real datasets offer evidence that the model can outperform state-of-the-art solutions and is capable of real-time anomaly detection. Further, the learned ranking model achieves promising results when updating the training set with newly arrived trajectories.|轨迹是一系列有时间标记的点位置，用于捕捉物体(如车辆)的运动。这种轨迹编码复杂的空间和时间模式，并提供有关物体移动性和基础设施的丰富信息，通常是道路网络，其中发生的运动。当不断包含新的轨迹时，轨迹数据集就在演变。在这种情况下，以在线方式检测异常轨迹的能力是基本的和具有挑战性的功能，它有许多应用程序，例如，基于位置的服务。最先进的解决方案根据轨迹的形状或路线确定异常，忽略由不同采样率或时间偏移引起的潜在异常。我们提出了一个多尺度模型，称为 MST-OATD，异常流轨迹检测，考虑到空间和时间方面的轨迹。该模型的多尺度能力旨在提取多尺度的弹道特征。此外，为了提高模型的可演化性和应对轨迹模式的变化，该模型配备了一个学习排名模型，该模型在包括新的轨迹时更新训练集。在真实数据集上的实验证明，该模型的性能优于最先进的解决方案，并具有实时异常检测。此外，学习排序模型在用新到达的轨迹更新训练集时取得了令人满意的结果。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Multi-Scale+Detection+of+Anomalous+Spatio-Temporal+Trajectories+in+Evolving+Trajectory+Datasets)|0| +|[Global Human-guided Counterfactual Explanations for Molecular Properties via Reinforcement Learning](https://doi.org/10.1145/3637528.3672045)|Danqing Wang, Antonis Antoniades, KhaDinh Luong, Edwin Zhang, Mert Kosan, Jiachen Li, Ambuj Singh, William Yang Wang, Lei Li|University of California, Santa Barbara, Santa Barbara, CA, USA; Language Technologies Institute, Carnegie Mellon University, Pittsburgh, PA, USA; Language Technologies Institute, Carnegie Mellon University, Pittsburgh, USA; Harvard University & Founding, Cambridge, MA, USA; University of California, Santa Barbara, Santa Barbara, USA|Counterfactual explanations of Graph Neural Networks (GNNs) offer a powerful way to understand data that can naturally be represented by a graph structure. Furthermore, in many domains, it is highly desirable to derive data-driven global explanations or rules that can better explain the high-level properties of the models and data in question. However, evaluating global counterfactual explanations is hard in real-world datasets due to a lack of human-annotated ground truth, which limits their use in areas like molecular sciences. Additionally, the increasing scale of these datasets provides a challenge for random search-based methods. In this paper, we develop a novel global explanation model RLHEX for molecular property prediction. It aligns the counterfactual explanations with human-defined principles, making the explanations more interpretable and easy for experts to evaluate. RLHEX includes a VAE-based graph generator to generate global explanations and an adapter to adjust the latent representation space to human-defined principles. Optimized by Proximal Policy Optimization (PPO), the global explanations produced by RLHEX cover 4.12% more input graphs and reduce the distance between the counterfactual explanation set and the input set by 0.47% on average across three molecular datasets. RLHEX provides a flexible framework to incorporate different human-designed principles into the counterfactual explanation generation process, aligning these explanations with domain expertise. The code and data are released at https://github.com/dqwang122/RLHEX.|对图形神经网络(GNN)的反事实解释提供了一种强有力的方法来理解可以自然地用图形结构表示的数据。此外，在许多领域，非常需要推导出数据驱动的全球解释或规则，以便更好地解释有关模型和数据的高级特性。然而，在现实世界的数据集中评估全球反事实解释是困难的，因为缺乏人类注释的地面真相，这限制了它们在分子科学等领域的应用。此外，这些数据集的规模越来越大，对基于随机搜索的方法提出了挑战。本文提出了一种新的分子性质预测的全局解释模型 RLHEX。它将反事实解释与人为原则相结合，使解释更易于理解，更易于专家评价。RLHEX 包括一个基于 VAE 的图形生成器，用于生成全局解释，以及一个适配器，用于根据人定义的原则调整潜在表示空间。通过最近策略优化(PPO)优化，RLHEX 产生的全局解释覆盖了4.12% 以上的输入图，并在三个分子数据集中平均减少了反事实解释集和输入集之间的距离0.47% 。RLHEX 提供了一个灵活的框架，将不同的人类设计的原则纳入反事实解释的生成过程，使这些解释与领域专业知识相一致。代码和数据 https://github.com/dqwang122/rlhex 发布。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Global+Human-guided+Counterfactual+Explanations+for+Molecular+Properties+via+Reinforcement+Learning)|0| +|[Mastering Long-Tail Complexity on Graphs: Characterization, Learning, and Generalization](https://doi.org/10.1145/3637528.3671880)|Haohui Wang, Baoyu Jing, Kaize Ding, Yada Zhu, Wei Cheng, Si Zhang, Yonghui Fan, Liqing Zhang, Dawei Zhou|Meta, Sunnyvale, USA; Amazon AGI, Sunnyvale, USA; NEC Labs America, Princeton, USA; Northwestern University, Evanston, USA; IBM Research, Yorktown Heights, USA; Virginia Tech, Blacksburg, USA; University of Illinois Urbana-Champaign, Urbana, USA|In the context of long-tail classification on graphs, the vast majority of existing work primarily revolves around the development of model debiasing strategies, intending to mitigate class imbalances and enhance the overall performance. Despite the notable success, there is very limited literature that provides a theoretical tool for characterizing the behaviors of long-tail classes in graphs and gaining insight into generalization performance in real-world scenarios. To bridge this gap, we propose a generalization bound for long-tail classification on graphs by formulating the problem in the fashion of multi-task learning, i.e., each task corresponds to the prediction of one particular class. Our theoretical results show that the generalization performance of long-tail classification is dominated by the overall loss range and the task complexity. Building upon the theoretical findings, we propose a novel generic framework HierTail for long-tail classification on graphs. In particular, we start with a hierarchical task grouping module that allows us to assign related tasks into hypertasks and thus control the complexity of the task space; then, we further design a balanced contrastive learning module to adaptively balance the gradients of both head and tail classes to control the loss range across all tasks in a unified fashion. Extensive experiments demonstrate the effectiveness of HierTail in characterizing long-tail classes on real graphs, which achieves up to 12.9% improvement over the leading baseline method in balanced accuracy.|在图的长尾分类的背景下，绝大多数现有的工作主要围绕模型去偏策略的发展，旨在缓解类的不平衡和提高整体性能。尽管取得了显著的成功，但是提供描述图中长尾类行为的理论工具以及深入了解真实场景中的泛化性能的文献非常有限。为了弥补这一差距，我们提出了一个图的长尾分类的泛化界，即每个任务对应于一个特定类的预测。我们的理论结果表明，长尾分类的泛化性能主要取决于总损失范围和任务复杂度。在理论研究的基础上，我们提出了一个新的通用框架 HierTail 用于图的长尾分类。特别地，我们从一个分层的任务分组模块开始，它允许我们将相关的任务分配到超任务中，从而控制任务空间的复杂性; 然后，我们进一步设计了一个平衡的对比学习模块来自适应地平衡头部和尾部类的梯度，以统一的方式控制所有任务的丢失范围。大量的实验证明了 HierTail 方法在描述真实图上的长尾类时的有效性，在平衡精度方面比主基线方法提高了12.9% 。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Mastering+Long-Tail+Complexity+on+Graphs:+Characterization,+Learning,+and+Generalization)|0| +|[Unsupervised Heterogeneous Graph Rewriting Attack via Node Clustering](https://doi.org/10.1145/3637528.3671716)|Haosen Wang, Can Xu, Chenglong Shi, Pengfei Zheng, Shiming Zhang, Minhao Cheng, Hongyang Chen|Southeast University, Nanjing, China; East China Normal University, Shanghai, China; Zhejiang Lab, Hangzhou, China; Southeast University & Zhejiang Lab, Nanjing, China; Pennsylvania State University, Philadelphia, PA, USA; University of Science and Technology of China, Hefei, China|Self-supervised learning (SSL) has become one of the most popular learning paradigms and has achieved remarkable success in the graph field. Recently, a series of pre-training studies on heterogeneous graphs (HGs) using SSL have been proposed considering the heterogeneity of real-world graph data. However, verification of the robustness of heterogeneous graph pre-training is still a research gap. Most existing researches focus on supervised attacks on graphs, which are limited to a specific scenario and will not work when labels are not available. In this paper, we propose a novel unsupervised heterogeneous graph rewriting attack via node clustering (HGAC) that can effectively attack HG pre-training models without using labels. Specifically, a heterogeneous edge rewriting strategy is designed to ensure the rationality and concealment of the attacks. Then, a tailored heterogeneous graph contrastive learning (HGCL) is used as a surrogate model. Moreover, we leverage node clustering results of the clean HGs as the pseudo-labels to guide the optimization of structural attacks. Extensive experiments exhibit powerful attack performances of our HGAC on various downstream tasks (i.e., node classification, node clustering, metapath prediction, and visualization) under poisoning attack and evasion attack.|自监督学习(SSL)已经成为当前最流行的学习范式之一，并在图论领域取得了显著的成功。近年来，针对实际图形数据的异构性，提出了一系列基于 SSL 的异构图预训练方法。然而，对异构图预训练鲁棒性的验证仍然是一个研究空白。现有的研究大多集中于对图形的监督攻击，这种攻击局限于特定的场景，在没有标签的情况下不起作用。本文提出了一种基于节点聚类(HGAC)的无监督异构图重写攻击方法，该方法可以在不使用标签的情况下有效地攻击 HG 预训练模型。为了保证攻击的合理性和隐蔽性，设计了一种异构边缘重写策略。然后，采用定制的异构图对比学习(HGCL)作为代理模型。此外，我们利用干净 HG 的节点聚类结果作为伪标签来指导结构攻击的优化。广泛的实验表明，我们的 HGAC 在中毒攻击和规避攻击下对各种下游任务(即节点分类、节点聚类、元路径预测和可视化)具有强大的攻击性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Unsupervised+Heterogeneous+Graph+Rewriting+Attack+via+Node+Clustering)|0| |[Effective Edge-wise Representation Learning in Edge-Attributed Bipartite Graphs](https://doi.org/10.1145/3637528.3671805)|Hewen Wang, Renchi Yang, Xiaokui Xiao|Hong Kong Baptist University, Hong Kong, China; National University of Singapore, Singapore, Singapore|Graph representation learning (GRL) is to encode graph elements into informative vector representations, which can be used in downstream tasks for analyzing graph-structured data and has seen extensive applications in various domains. However, the majority of extant studies on GRL are geared towards generating node representations, which cannot be readily employed to perform edge-based analytics tasks in edge-attributed bipartite graphs (EABGs) that pervade the real world, e.g., spam review detection in customer-product reviews and identifying fraudulent transactions in user-merchant networks. Compared to node-wise GRL, learning edge representations (ERL) on such graphs is challenging due to the need to incorporate the structure and attribute semantics from the perspective of edges while considering the separate influence of two heterogeneous node sets U and V in bipartite graphs. To our knowledge, despite its importance, limited research has been devoted to this frontier, and existing workarounds all suffer from sub-par results. Motivated by this, this paper designs EAGLE, an effective ERL method for EABGs. Building on an in-depth and rigorous theoretical analysis, we propose the factorized feature propagation (FFP) scheme for edge representations with adequate incorporation of long-range dependencies of edges/features without incurring tremendous computation overheads. We further ameliorate FFP as a dual-view FFP by taking into account the influences from nodes in U and V severally in ERL. Extensive experiments on 5 real datasets showcase the effectiveness of the proposed EAGLE models in semi-supervised edge classification tasks. In particular, EAGLE can attain a considerable gain of at most 38.11% in AP and 1.86% in AUC when compared to the best baselines.|图表示学习(GRL)是将图元编码成信息向量表示，用于分析图结构化数据的下游任务，在各个领域有着广泛的应用。然而，大多数现存的 GRL 研究都是面向生成节点表示的，这些节点表示不能很容易地被用来执行遍布现实世界的边缘属性二分图(EABG)中的基于边缘的分析任务，例如，在客户产品评论中检测垃圾邮件评论，以及在用户-商家网络中识别欺诈交易。与节点式 GRL 相比，二部图的学习边表示(ERL)具有挑战性，因为在考虑二部图中两个异质节点集 U 和 V 的分离影响时，需要从边的角度结合结构和属性语义。据我们所知，尽管它的重要性，有限的研究已经致力于这一前沿，现有的工作方法都遭受低于标准的结果。基于此，本文设计了一种有效的 EABG ERL 方法—— EAGLE。在深入和严格的理论分析的基础上，我们提出了一种边缘表示的分解特征传播(FFP)方案，该方案充分考虑了边缘/特征的长程依赖关系，并且不会产生巨大的计算开销。我们进一步改进了 FFP 作为一个双视图的 FFP，在 ERL 中分别考虑了 U 和 V 节点的影响。通过对5个实际数据集的大量实验，验证了 EAGLE 模型在半监督边缘分类任务中的有效性。特别是，与最佳基线相比，EAGLE 在 AP 和 AUC 中的最大增益分别为38.11% 和1.86% 。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Effective+Edge-wise+Representation+Learning+in+Edge-Attributed+Bipartite+Graphs)|0| |[FedNLR: Federated Learning with Neuron-wise Learning Rates](https://doi.org/10.1145/3637528.3672042)|Haozhao Wang, Peirong Zheng, Xingshuo Han, Wenchao Xu, Ruixuan Li, Tianwei Zhang|; Department of Computing, The Hong Kong Polytechnic University, Hong Kong, China; Nanyang Technological University, Singapore, Singapore|Federated Learning (FL) suffers from severe performance degradation due to the data heterogeneity among clients. Some existing work suggests that the fundamental reason is that data heterogeneity can cause local model drift, and therefore proposes to calibrate the direction of local updates to solve this problem. Though effective, existing methods generally take the model as a whole, which lacks a deep understanding of how the neurons within deep classification models evolve during local training to form model drift. In this paper, we bridge this gap by performing an intuitive and theoretical analysis of the activation changes of each neuron during local training. Our analysis shows that the high activation of some neurons on the samples of a certain class will be reduced during local training when these samples are not included in the client, which we call neuron drift, thus leading to the performance reduction of this class. Motivated by this, we propose a novel and simple algorithm called FedNLR, which utilizes Neuron-wise Learning Rates during the FL local training process. The principle behind this is to enhance the learning of neurons bound to local classes on local data knowledge while reducing the decay of non-local classes knowledge stored in neurons. Experimental results demonstrate that FedNLR achieves state-of-the-art performance on federated learning with popular deep neural networks.|由于客户端之间的数据异构性，联邦学习(FL)的性能严重下降。现有的一些工作表明，数据的异构性会引起局部模型漂移是造成这一问题的根本原因，因此提出校准局部更新的方向来解决这一问题。现有的方法虽然行之有效，但通常将模型作为一个整体，缺乏对深度分类模型中的神经元在局部训练形成模型漂移过程中如何演化的深入理解。在本文中，我们通过对局部训练过程中每个神经元的激活变化进行直观的理论分析来弥合这一差距。我们的分析表明，当这些样本不包括在客户端中时，在某个类别的样本上的一些神经元的高度激活将在局部训练期间减少，我们称之为神经元漂移，从而导致该类别的性能降低。基于此，我们提出了一种新的简单算法 FedNLR，该算法在 FL 局部训练过程中利用了神经元的学习速率。其背后的原理是增强神经元对局部数据知识的学习，同时减少存储在神经元中的非局部类知识的衰减。实验结果表明，FedNLR 利用流行的深层神经网络实现了最先进的联邦学习性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=FedNLR:+Federated+Learning+with+Neuron-wise+Learning+Rates)|0| -|[Robust Predictions with Ambiguous Time Delays: A Bootstrap Strategy](https://doi.org/10.1145/3637528.3671920)|Jiajie Wang, Zhiyuan Jerry Lin, Wen Chen|Changsha Research Institute of Mining and Metallurgy, Changsha, China; Meta, Menlo Park, USA|In contemporary data-driven environments, the generation and processing of multivariate time series data is an omnipresent challenge, often complicated by time delays between different time series. These delays, originating from a multitude of sources like varying data transmission dynamics, sensor interferences, and environmental changes, introduce significant complexities. Traditional Time Delay Estimation methods, which typically assume a fixed constant time delay, may not fully capture these variabilities, compromising the precision of predictive models in diverse settings. To address this issue, we introduce the Time Series Model Bootstrap (TSMB), a versatile framework designed to handle potentially varying or even nondeterministic time delays in time series modeling. Contrary to traditional approaches that hinge on the assumption of a single, consistent time delay, TSMB adopts a non-parametric stance, acknowledging and incorporating time delay uncertainties. TSMB significantly bolsters the performance of models that are trained and make predictions using this framework, making it highly suitable for a wide range of dynamic and interconnected data environments. Our comprehensive evaluations, conducted on real-world datasets with different types of time delays, confirm the adaptability and effectiveness of TSMB in multiple contexts. These include, but are not limited to, power and occupancy forecasting in intelligent infrastructures, air quality monitoring, and intricate processes like mineral processing. Further diagnostic analyses strengthen the case for the TSMB estimator's robustness, underlining its significance in scenarios where ambiguity in time delays can have a significant impact on the predictive task.|在当代数据驱动的环境中，多变量时间序列数据的生成和处理是一个无处不在的挑战，往往由于不同时间序列之间的时间延迟而变得复杂。这些延迟来源于各种各样的源头，比如不同的数据传输动力学、传感器干扰和环境变化，它们引入了显著的复杂性。传统的时间延迟估计方法通常假定时间延迟是固定不变的，可能无法完全捕捉到这些变量，从而影响了预测模型在不同环境下的精度。为了解决这个问题，我们引入了时间序列模型引导程序(Time Series Model Bootstrap，TSMB) ，这是一个多功能的框架，旨在处理时间序列建模中可能变化甚至不确定的时间延迟。相对于传统的假设一致的单一时延的方法，TSMB 采取了非参数的立场，承认和合并时延的不确定性。TSMB 显著提高了使用该框架训练和预测的模型的性能，使其非常适合于各种动态和互连的数据环境。我们对不同时延类型的实际数据集进行了全面的评估，证实了 TSMB 在多种情况下的适应性和有效性。这些技术包括但不限于智能基础设施的电力和占用率预测、空气质量监测以及选矿工程等复杂过程。进一步的诊断分析加强了 TSMB 估计器的稳健性，强调了其在时间延迟模糊性可能对预测任务产生显著影响的情况下的重要性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Robust+Predictions+with+Ambiguous+Time+Delays:+A+Bootstrap+Strategy)|0| -|[A Novel Prompt Tuning for Graph Transformers: Tailoring Prompts to Graph Topologies](https://doi.org/10.1145/3637528.3671804)|Jingchao Wang, Zhengnan Deng, Tongxu Lin, Wenyuan Li, Shaobin Ling|Guangdong University of Technology, Guangzhou, China; Beijing University of Posts and Telecommunications, Beijing, China; South China Normal University, Guangzhou, --- Select One ---, China; Guangdong University of Technology, Guangzhou, --- Select One ---, China|Deep graph prompt tuning (DeepGPT), which only tunes a set of continuous prompts for graph transformers, significantly decreases the storage usage during training. However, DeepGPT is limited by its uniform prompts to input graphs with various structures. This is because different graph structures dictate various feature interactions between nodes, while the uniform prompts are not dynamic to tailor the feature transformation for the graph topology. In this paper, we propose a Topo-specific Graph Prompt Tuning (TGPT ), which provides topo-specific prompts tailored to the topological structures of input graphs. Specifically, TGPT learns trainable embeddings for graphlets and frequencies, where graphlets are fundamental sub-graphs that describe the structure around specific nodes. Based on the statistic data about graphlets of input graph, topo-specific prompts are generated by graphlet embeddings and frequency embeddings. The topo-specific prompts include node-level topo-specific prompts for specified nodes, a graph-level topo-specific prompt for the entire graph, and a task-specific prompt to learn task-related information. They are all inserted into specific graph nodes to perform feature transformation, providing specified feature transformation for input graphs with different topological structures. Extensive experiments show that our method outperforms existing lightweight fine-tuning methods and DeepGPT in molecular graph classification and regression with comparable parameters.|深度图形提示调优(DeepGPT)只对图形转换器的一组连续提示进行调优，大大减少了培训期间的存储使用。然而，DeepGPT 受到其统一提示输入具有各种结构的图形的限制。这是因为不同的图结构决定了节点之间不同的特征交互，而统一的提示并不能动态地调整图拓扑的特征转换。本文提出了一种特定于拓扑结构的图提示优化算法(TGPT) ，该算法根据输入图的拓扑结构提供特定于拓扑结构的提示。具体来说，TGPT 学习了针对图形和频率的可训练嵌入，其中图形是描述特定节点周围结构的基本子图。基于输入图形的统计数据，通过图形嵌入和频率嵌入生成特定于拓扑的提示。特定于拓扑的提示包括指定节点的节点级特定于拓扑的提示、整个图的图级特定于拓扑的提示和学习任务相关信息的特定于任务的提示。它们都被插入到特定的图节点中进行特征变换，为具有不同拓扑结构的输入图提供特定的特征变换。大量实验表明，该方法在参数可比的分子图分类和回归方面优于现有的轻量级微调方法和 DeepGPT。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Novel+Prompt+Tuning+for+Graph+Transformers:+Tailoring+Prompts+to+Graph+Topologies)|0| +|[Robust Predictions with Ambiguous Time Delays: A Bootstrap Strategy](https://doi.org/10.1145/3637528.3671920)|Jiajie Wang, Zhiyuan Jerry Lin, Wen Chen|Meta, Menlo Park, USA; Changsha Research Institute of Mining and Metallurgy, Changsha, China|In contemporary data-driven environments, the generation and processing of multivariate time series data is an omnipresent challenge, often complicated by time delays between different time series. These delays, originating from a multitude of sources like varying data transmission dynamics, sensor interferences, and environmental changes, introduce significant complexities. Traditional Time Delay Estimation methods, which typically assume a fixed constant time delay, may not fully capture these variabilities, compromising the precision of predictive models in diverse settings. To address this issue, we introduce the Time Series Model Bootstrap (TSMB), a versatile framework designed to handle potentially varying or even nondeterministic time delays in time series modeling. Contrary to traditional approaches that hinge on the assumption of a single, consistent time delay, TSMB adopts a non-parametric stance, acknowledging and incorporating time delay uncertainties. TSMB significantly bolsters the performance of models that are trained and make predictions using this framework, making it highly suitable for a wide range of dynamic and interconnected data environments. Our comprehensive evaluations, conducted on real-world datasets with different types of time delays, confirm the adaptability and effectiveness of TSMB in multiple contexts. These include, but are not limited to, power and occupancy forecasting in intelligent infrastructures, air quality monitoring, and intricate processes like mineral processing. Further diagnostic analyses strengthen the case for the TSMB estimator's robustness, underlining its significance in scenarios where ambiguity in time delays can have a significant impact on the predictive task.|在当代数据驱动的环境中，多变量时间序列数据的生成和处理是一个无处不在的挑战，往往由于不同时间序列之间的时间延迟而变得复杂。这些延迟来源于各种各样的源头，比如不同的数据传输动力学、传感器干扰和环境变化，它们引入了显著的复杂性。传统的时间延迟估计方法通常假定时间延迟是固定不变的，可能无法完全捕捉到这些变量，从而影响了预测模型在不同环境下的精度。为了解决这个问题，我们引入了时间序列模型引导程序(Time Series Model Bootstrap，TSMB) ，这是一个多功能的框架，旨在处理时间序列建模中可能变化甚至不确定的时间延迟。相对于传统的假设一致的单一时延的方法，TSMB 采取了非参数的立场，承认和合并时延的不确定性。TSMB 显著提高了使用该框架训练和预测的模型的性能，使其非常适合于各种动态和互连的数据环境。我们对不同时延类型的实际数据集进行了全面的评估，证实了 TSMB 在多种情况下的适应性和有效性。这些技术包括但不限于智能基础设施的电力和占用率预测、空气质量监测以及选矿工程等复杂过程。进一步的诊断分析加强了 TSMB 估计器的稳健性，强调了其在时间延迟模糊性可能对预测任务产生显著影响的情况下的重要性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Robust+Predictions+with+Ambiguous+Time+Delays:+A+Bootstrap+Strategy)|0| +|[A Novel Prompt Tuning for Graph Transformers: Tailoring Prompts to Graph Topologies](https://doi.org/10.1145/3637528.3671804)|Jingchao Wang, Zhengnan Deng, Tongxu Lin, Wenyuan Li, Shaobin Ling|Beijing University of Posts and Telecommunications, Beijing, China; South China Normal University, Guangzhou, --- Select One ---, China; Guangdong University of Technology, Guangzhou, China; Guangdong University of Technology, Guangzhou, --- Select One ---, China|Deep graph prompt tuning (DeepGPT), which only tunes a set of continuous prompts for graph transformers, significantly decreases the storage usage during training. However, DeepGPT is limited by its uniform prompts to input graphs with various structures. This is because different graph structures dictate various feature interactions between nodes, while the uniform prompts are not dynamic to tailor the feature transformation for the graph topology. In this paper, we propose a Topo-specific Graph Prompt Tuning (TGPT ), which provides topo-specific prompts tailored to the topological structures of input graphs. Specifically, TGPT learns trainable embeddings for graphlets and frequencies, where graphlets are fundamental sub-graphs that describe the structure around specific nodes. Based on the statistic data about graphlets of input graph, topo-specific prompts are generated by graphlet embeddings and frequency embeddings. The topo-specific prompts include node-level topo-specific prompts for specified nodes, a graph-level topo-specific prompt for the entire graph, and a task-specific prompt to learn task-related information. They are all inserted into specific graph nodes to perform feature transformation, providing specified feature transformation for input graphs with different topological structures. Extensive experiments show that our method outperforms existing lightweight fine-tuning methods and DeepGPT in molecular graph classification and regression with comparable parameters.|深度图形提示调优(DeepGPT)只对图形转换器的一组连续提示进行调优，大大减少了培训期间的存储使用。然而，DeepGPT 受到其统一提示输入具有各种结构的图形的限制。这是因为不同的图结构决定了节点之间不同的特征交互，而统一的提示并不能动态地调整图拓扑的特征转换。本文提出了一种特定于拓扑结构的图提示优化算法(TGPT) ，该算法根据输入图的拓扑结构提供特定于拓扑结构的提示。具体来说，TGPT 学习了针对图形和频率的可训练嵌入，其中图形是描述特定节点周围结构的基本子图。基于输入图形的统计数据，通过图形嵌入和频率嵌入生成特定于拓扑的提示。特定于拓扑的提示包括指定节点的节点级特定于拓扑的提示、整个图的图级特定于拓扑的提示和学习任务相关信息的特定于任务的提示。它们都被插入到特定的图节点中进行特征变换，为具有不同拓扑结构的输入图提供特定的特征变换。大量实验表明，该方法在参数可比的分子图分类和回归方面优于现有的轻量级微调方法和 DeepGPT。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Novel+Prompt+Tuning+for+Graph+Transformers:+Tailoring+Prompts+to+Graph+Topologies)|0| |[DyPS: Dynamic Parameter Sharing in Multi-Agent Reinforcement Learning for Spatio-Temporal Resource Allocation](https://doi.org/10.1145/3637528.3672052)|Jingwei Wang, Qianyue Hao, Wenzhen Huang, Xiaochen Fan, Zhentao Tang, Bin Wang, Jianye Hao, Yong Li|Tianjin University & Huawei Noah's Ark Lab, Beijing, China; Huawei Noah's Ark Lab, Beijing, China; Department of EE, BNRist, Tsinghua University, Beijing, China|In large-scale metropolis, it is critical to efficiently allocate various resources such as electricity, medical care, and transportation to meet the living demands of citizens, according to the spatio-temporal distributions of resources and demands. Previous researchers have done plentiful work on such problems by leveraging Multi-Agent Reinforcement Learning (MARL) methods, where multiple agents cooperatively regulate and allocate the resources to meet the demands. However, facing the great number of agents in large cities, existing MARL methods lack efficient parameter sharing strategies among agents to reduce computational complexity. There remain two primary challenges in efficient parameter sharing: (1) during the RL training process, the behavior of agents changes significantly, limiting the performance of group parameter sharing based on fixed role division decided before training; (2) the behavior of agents forms complicated action trajectories, where their role characteristics are implicit, adding difficulty to dynamically adjusting agent role divisions during the training process. In this paper, we propose Dynamic Parameter Sharing (DyPS) to solve the above challenges. We design self-supervised learning tasks to extract the implicit behavioral characteristics from the action trajectories of agents. Based on the obtained behavioral characteristics, we propose a hierarchical MARL framework capable of dynamically revising the agent role divisions during the training process and thus shares parameters among agents with the same role, reducing computational complexity. In addition, our framework can be combined with various typical MARL algorithms, including IPPO, MAPPO, etc. We conduct 7 experiments in 4 representative resource allocation scenarios, where extensive results demonstrate our method's superior performance, outperforming the state-of-the-art baseline methods by up to 31%. Our source codes are available at https://github.com/tsinghua-fib-lab/DyPS.|在大城市中，根据资源和需求的时空分布，有效地配置电力、医疗、交通等各种资源，以满足市民的生活需求至关重要。先前的研究人员已经通过利用多代理强化学习(Multi-Agent)方法在这类问题上做了大量的工作，其中多个代理合作调节和分配资源以满足需求。然而，面对大城市中大量的智能体，现有的 MARL 方法缺乏有效的智能体间参数共享策略来降低计算复杂度。有效参数共享仍然面临两个主要挑战: (1)在 RL 训练过程中，Agent 的行为发生了显著的变化，限制了基于训练前确定的固定角色划分的群体参数共享性能; (2) Agent 的行为形成了复杂的行为轨迹，其角色特征是隐含的，增加了训练过程中动态调整 Agent 角色划分的难度。本文提出动态参数共享技术(DyPS)来解决上述问题。我们设计自我监督学习任务，从代理的行为轨迹中提取隐含的行为特征。基于所获得的行为特征，提出了一种分层 MARL 框架，该框架能够在训练过程中动态修正代理角色划分，从而在同一角色的代理之间共享参数，降低了计算复杂度。此外，我们的框架可以结合各种典型的 MARL 算法，包括 IPPO，MAPPO 等。我们在4个具有代表性的资源分配场景中进行了7个实验，广泛的结果证明了我们的方法的优越性能，比最先进的基线方法的性能高达31% 。我们的源代码可以在 https://github.com/tsinghua-fib-lab/dyps 找到。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=DyPS:+Dynamic+Parameter+Sharing+in+Multi-Agent+Reinforcement+Learning+for+Spatio-Temporal+Resource+Allocation)|0| -|[The Snowflake Hypothesis: Training and Powering GNN with One Node One Receptive Field](https://doi.org/10.1145/3637528.3671766)|Kun Wang, Guohao Li, Shilong Wang, Guibin Zhang, Kai Wang, Yang You, Junfeng Fang, Xiaojiang Peng, Yuxuan Liang, Yang Wang|Oxford University, London, United Kingdom; The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China; International Digital Economy Academy, Shanghai, China; University of Science and Technology of China (USTC), Hefei, China; National University of Singapore, Singapore, Singapore; Shenzhen Technology University, shenzhen, China; University of Science and Technology of China, Hefei, China|Despite Graph Neural Networks (GNNs) demonstrating considerable promise in graph representation learning tasks, GNNs predominantly face significant issues with overfitting and over-smoothing as they go deeper as models of computer vision (CV) realm. The success of artificial intelligence in computer vision and natural language processing largely stems from its ability to train deep models effectively. We have thus conducted a systematic study on deep GNN models. Our findings indicate that the current success of deep GNNs primarily stems from (I) the adoption of innovations from CNNs, such as residual/skip connections, or (II) the tailor-made aggregation algorithms like DropEdge. However, these algorithms often lack intrinsic interpretability and indiscriminately treat all nodes within a given layer in a similar manner, thereby failing to capture the nuanced differences among various nodes. In this paper, we introduce the Snowflake Hypothesis -- a novel paradigm underpinning the concept of "one node, one receptive field''. The hypothesis draws inspiration from the unique and individualistic patterns of each snowflake, proposing a corresponding uniqueness in the receptive fields of nodes in the GNNs. We employ the simplest gradient and node-level cosine distance as guiding principles to regulate the aggregation depth for each node, and conduct comprehensive experiments including: (1) different training scheme; (2) various shallow and deep GNN backbones; (3) various numbers of layers (8, 16, 32, 64) on multiple benchmarks; (4) compare with different aggregation strategies. The observational results demonstrate that our framework can serve as a universal operator for a range of tasks, and it displays tremendous potential on deep GNNs. Code is available at: https://github.com/CunWang520/Snowhypothe.|尽管图形神经网络(GNNs)在图形表示学习任务中表现出相当大的潜力，但是 GNNs 在计算机视觉(CV)领域中越来越深入，主要面临着过度拟合和过度平滑的重大问题。人工智能在计算机视觉和自然语言处理方面的成功很大程度上源于其有效地训练深度模型的能力。因此，我们对深层 GNN 模型进行了系统的研究。我们的研究结果表明，深度 GNN 目前的成功主要源于(I)采用来自 CNN 的创新，如残留/跳过连接，或(II)定制的聚合算法，如 DropEdge。然而，这些算法往往缺乏内在的可解释性，并且不加区分地以类似的方式处理给定层中的所有节点，从而无法捕捉各个节点之间的细微差异。本文介绍了“雪花假说”——一个支撑“一个节点，一个接受场”概念的新范式。该假设从每片雪花的独特和个性化模式中获得灵感，提出了 GNN 中节点接收域的相应独特性。我们采用最简单的梯度和节点级余弦距离作为指导原则来调节每个节点的聚集深度，并进行了全面的实验，包括: (1)不同的训练方案; (2)各种浅层和深层 GNN 骨干网; (3)多个基准上的不同层数(8,16,32,64) ; (4)比较不同的聚集策略。实验结果表明，我们的框架可以作为一个通用的操作员为一系列的任务，它显示了巨大的潜力深 GNN。密码可于以下 https://github.com/cunwang520/snowhypothe 索取:。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=The+Snowflake+Hypothesis:+Training+and+Powering+GNN+with+One+Node+One+Receptive+Field)|0| -|[The Heterophilic Snowflake Hypothesis: Training and Empowering GNNs for Heterophilic Graphs](https://doi.org/10.1145/3637528.3671791)|Kun Wang, Guibin Zhang, Xinnan Zhang, Junfeng Fang, Xun Wu, Guohao Li, Shirui Pan, Wei Huang, Yuxuan Liang|RIKEN AIP, Tokyo, Japan; The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China; Griffith University, Queensland, Australia; University of Science and Technology of China (USTC), Hefei, China; Tsinghua University, Beijing, China; Tongji University, Shanghai, China; University of Minnesota, Twin Cities, MN, USA; Oxford University, Oxford, United Kingdom|Graph Neural Networks (GNNs) have become pivotal tools for a range of graph-based learning tasks. Notably, most current GNN architectures operate under the assumption of homophily, whether explicitly or implicitly. While this underlying assumption is frequently adopted, it is not universally applicable, which can result in potential shortcomings in learning effectiveness. In this paper, or the first time, we transfer the prevailing concept of "one node one receptive field" to the heterophilic graph. By constructing a proxy label predictor, we enable each node to possess a latent prediction distribution, which assists connected nodes in determining whether they should aggregate their associated neighbors. Ultimately, every node can have its own unique aggregation hop and pattern, much like each snowflake is unique and possesses its own characteristics. Based on observations, we innovatively introduce the Heterophily Snowflake Hypothesis and provide an effective solution to guide and facilitate research on heterophilic graphs and beyond. We conduct comprehensive experiments including (1) main results on 10 graphs with varying heterophily ratios across 10 backbones; (2) scalability on various deep GNN backbones (SGC, JKNet, etc.) across various large number of layers (2,4,6,8,16,32 layers); (3) comparison with conventional snowflake hypothesis; (4) efficiency comparison with existing graph pruning algorithms. Our observations show that our framework acts as a versatile operator for diverse tasks. It can be integrated into various GNN frameworks, boosting performance in-depth and offering an explainable approach to choosing the optimal network depth. The source code is available at https://github.com/bingreeky/HeteroSnoH.|图形神经网络(GNN)已经成为一系列基于图形的学习任务的关键工具。值得注意的是，大多数当前的 GNN 体系结构都是在同质的假设下运行的，无论是显式的还是隐式的。虽然这一基本假设经常被采用，但并非普遍适用，这可能导致潜在的缺陷，在学习效果。本文首次将流行的“一个节点一个接受域”的概念转化为异质图。通过构造一个代理标签预测器，我们使每个节点具有一个潜在的预测分布，这有助于连接的节点决定是否应该聚合它们的相关邻居。最终，每个节点都可以有自己独特的聚合跳和模式，就像每片雪花都是独一无二的并且具有自己的特点。在观察的基础上，我们创新性地引入了异质性雪花假说，为指导和促进异质性图及其他图的研究提供了一个有效的解决方案。我们进行了全面的实验，包括(1)10个图的主要结果，在10个主干上具有不同的异质性比率; (2)各种深层 GNN 主干(SGC，JKNet 等)的可伸缩性，跨越各种大量的层(2,4,6,8,16,32层) ; (3)与传统雪花假说的比较; (4)与现有图剪枝算法的效率比较。我们的观察表明，我们的框架充当多种任务的多功能操作员。它可以集成到各种 GNN 框架中，提高性能的深度，并为选择最佳网络深度提供一种可解释的方法。源代码可在 https://github.com/bingreeky/heterosnoh 下载。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=The+Heterophilic+Snowflake+Hypothesis:+Training+and+Empowering+GNNs+for+Heterophilic+Graphs)|0| -|[CutAddPaste: Time Series Anomaly Detection by Exploiting Abnormal Knowledge](https://doi.org/10.1145/3637528.3671739)|Rui Wang, Xudong Mou, Renyu Yang, Kai Gao, Pin Liu, Chongwei Liu, Tianyu Wo, Xudong Liu|; School of Software, Beihang University, Beijing, China; School of Information Engineering, China University of Geosciences Beijing, Beijing, China; Institute of Future Cities, The Chinese University of Hong Kong, Hong Kong, China; School of Computer Science and Engineering, Beihang University, Beijing, China; School of Software, Beihang University & Zhongguancun Laboratory, Beijing, China; Kuaishou Inc., Beijing, China|Detecting time-series anomalies is extremely intricate due to the rarity of anomalies and imbalanced sample categories, which often result in costly and challenging anomaly labeling. Most of the existing approaches largely depend on assumptions of normality, overlooking labeled abnormal samples. While anomaly assumptions based methods can incorporate prior knowledge of anomalies for data augmentation in training classifiers, the adopted random or coarse-grained augmentation approaches solely focus on pointwise anomalies and lack cutting-edge domain knowledge, making them less likely to achieve better performance. This paper introduces CutAddPaste, a novel anomaly assumption-based approach for detecting time-series anomalies. It primarily employs a data augmentation strategy to generate pseudo anomalies, by exploiting prior knowledge of anomalies as much as possible. At the core of CutAddPaste is cutting patches from random positions in temporal subsequence samples, adding linear trend terms, and pasting them into other samples, so that it can well approximate a variety of anomalies, including point and pattern anomalies. Experiments on standard benchmark datasets demonstrate that our method outperforms the state-of-the-art approaches.|由于时间序列异常的罕见性和样本类别的不平衡性，检测时间序列异常极其复杂，往往导致昂贵和具有挑战性的异常标记。大多数现有的方法主要依赖于正常的假设，忽略了标记的异常样本。虽然基于异常假设的方法可以将异常的先验知识引入到训练分类器的数据增强中，但是所采用的随机或粗粒度增强方法只关注点态异常，缺乏尖端领域知识，使得它们不太可能获得更好的性能。本文介绍了一种新的基于异常假设的时间序列异常检测方法 CutAddPaste。它主要采用一种数据增强策略，通过尽可能多地利用异常的先验知识来产生伪异常。CutAddPaste 的核心是从时序子序列样本中的随机位置切割补丁，添加线性趋势项，并将其粘贴到其他样本中，以便能够很好地近似各种异常，包括点和模式异常。在标准基准数据集上的实验表明，我们的方法优于最先进的方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=CutAddPaste:+Time+Series+Anomaly+Detection+by+Exploiting+Abnormal+Knowledge)|0| -|[Advancing Molecule Invariant Representation via Privileged Substructure Identification](https://doi.org/10.1145/3637528.3671886)|Ruijia Wang, Haoran Dai, Cheng Yang, Le Song, Chuan Shi|; Beijing University of Post and Telecommunication, Beijing, China; Beijing University of Posts and Telecommunications, Beijing, China; BioMap Research & MBZUAI, Beijing, China|Graph neural networks (GNNs) have revolutionized molecule representation learning by modeling molecules as graphs, with atoms represented as nodes and chemical bonds as edges. Despite their progress, they struggle with out-of-distribution scenarios, such as changes in size or scaffold of molecules with identical properties. Some studies attempt to mitigate this issue through graph invariant learning, which penalizes prediction variance across environments to learn invariant representations. But in the realm of molecules, core functional groups forming privileged substructures dominate molecular properties and remain invariant across distribution shifts. This highlights the need for integrating this prior knowledge and ensuring the environment split compatible with molecule invariant learning. To bridge this gap, we propose a novel framework named MILI. Specifically, we first formalize molecule invariant learning based on privileged substructure identification and introduce substructure invariance constraint. Building on this foundation, we theoretically establish two criteria for environment splits conducive to molecule invariant learning. Inspired by these criteria, we develop a dual-head graph neural network. A shared identifier identifies privileged substructures, while environment and task heads generate predictions based on variant and privileged substructures. Through the interaction of two heads, the environments are split and optimized to meet our criteria. The unified MILI guarantees that molecule invariant learning and environment split achieve mutual enhancement from theoretical analysis and network design. Extensive experiments across eight benchmarks validate the effectiveness of MILI compared to state-of-the-art baselines.|图形神经网络(GNN)通过将分子建模为图形，将原子表示为节点，化学键表示为边，从而彻底改变了分子表示学习。尽管他们取得了进展，但是他们仍然在分布范围之外的场景中挣扎，比如具有相同性质的分子的大小或支架的变化。一些研究试图通过图不变学习来缓解这个问题，图不变学习通过惩罚环境间的预测方差来学习不变表示。但在分子领域，形成特权子结构的核心官能团支配着分子的性质，并且在分布变化中保持不变。这突出了整合这些先验知识和确保环境分裂与分子不变学习兼容的必要性。为了弥补这一差距，我们提出了一个新的框架 MILI。具体地说，我们首先将基于特权子结构识别的分子不变性学习形式化，并引入子结构不变性约束。在此基础上，我们从理论上建立了两个有利于分子不变学习的环境分裂准则。受这些准则的启发，我们开发了一个双头图神经网络。共享标识符识别特权子结构，而环境和任务负责人根据变体和特权子结构生成预测。通过两个头的相互作用，环境分裂和优化，以满足我们的标准。统一的 MILI 从理论分析和网络设计两个方面保证了分子不变学习和环境分裂的相互增强。通过八个基准的大量实验验证了 MILI 与最先进的基准相比的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Advancing+Molecule+Invariant+Representation+via+Privileged+Substructure+Identification)|0| -|[Optimizing Long-tailed Link Prediction in Graph Neural Networks through Structure Representation Enhancement](https://doi.org/10.1145/3637528.3671864)|Yakun Wang, Daixin Wang, Hongrui Liu, Binbin Hu, Yingcui Yan, Qiyang Zhang, Zhiqiang Zhang|Ant Group, Shanghai, China; Ant Group, Hangzhou, China; Ant Group, Beijing, China|Link prediction, as a fundamental task for graph neural networks (GNNs), has boasted significant progress in varied domains. Its success is typically influenced by the expressive power of node representation, but recent developments reveal the inferior performance of low-degree nodes owing to their sparse neighbor connections, known as the degree-based long-tailed problem. Will the degree-based long-tailed distribution similarly constrain the efficacy of GNNs on link prediction? Unexpectedly, our study reveals that only a mild correlation exists between node degree and predictive accuracy, and more importantly, the number of common neighbors between node pairs exhibits a strong correlation with accuracy. Considering node pairs with less common neighbors, i.e., tail node pairs, make up a substantial fraction of the dataset but achieve worse performance, we propose that link prediction also faces the long-tailed problem. Therefore, link prediction of GNNs is greatly hindered by the tail node pairs. After knowing the weakness of link prediction, a natural question is how can we eliminate the negative effects of the skewed long-tailed distribution on common neighbors so as to improve the performance of link prediction? Towards this end, we introduce our long-tailed framework (LTLP), which is designed to enhance the performance of tail node pairs on link prediction by increasing common neighbors. Two key modules in LTLP respectively supplement high-quality edges for tail node pairs and enforce representational alignment between head and tail node pairs within the same category, thereby improving the performance of tail node pairs. Empirical results across five datasets confirm that our approach not only achieves SOTA performance but also greatly reduces the performance bias between the head and tail. These findings underscore the efficacy and superiority of our framework in addressing the long-tailed problem in link prediction.|链接预测作为图形神经网络的基础性工作，在各个领域都取得了显著的进展。它的成功通常受到节点表示的表达能力的影响，但最近的发展揭示了低度节点由于其稀疏邻居连接性能较差，称为基于度的长尾问题。基于度的长尾分布同样会限制 GNN 链路预测的效率吗？出乎意料的是，我们的研究表明，节点程度与预测精度之间只存在轻微的相关性，更重要的是，节点对之间共同邻居的数量与预测精度有很强的相关性。考虑到具有较少共同邻居的节点对，即尾节点对，在数据集中占很大比例，但性能较差，我们认为链路预测也面临着长尾问题。因此，尾节点对极大地阻碍了 GNN 的链路预测。在认识到链路预测的弱点之后，如何消除偏斜长尾分布对公共邻居的负面影响，从而提高链路预测的性能，是一个很自然的问题？为此，我们引入了长尾结构(LTLP) ，该结构通过增加公共邻居来提高尾节点对的链路预测性能。LTLP 中的两个关键模块分别补充了尾节点对的高质量边，并在同一类别中加强了头尾节点对的表示对齐，从而提高了尾节点对的性能。实验结果表明，该方法不仅能够实现 SOTA 性能，而且能够大大降低 SOTA 的性能偏差。这些发现强调了我们的框架在解决链接预测中的长尾问题的有效性和优越性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Optimizing+Long-tailed+Link+Prediction+in+Graph+Neural+Networks+through+Structure+Representation+Enhancement)|0| +|[The Snowflake Hypothesis: Training and Powering GNN with One Node One Receptive Field](https://doi.org/10.1145/3637528.3671766)|Kun Wang, Guohao Li, Shilong Wang, Guibin Zhang, Kai Wang, Yang You, Junfeng Fang, Xiaojiang Peng, Yuxuan Liang, Yang Wang|Shenzhen Technology University, shenzhen, China; National University of Singapore, Singapore, Singapore; University of Science and Technology of China (USTC), Hefei, China; Oxford University, London, United Kingdom; International Digital Economy Academy, Shanghai, China; The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China; University of Science and Technology of China, Hefei, China|Despite Graph Neural Networks (GNNs) demonstrating considerable promise in graph representation learning tasks, GNNs predominantly face significant issues with overfitting and over-smoothing as they go deeper as models of computer vision (CV) realm. The success of artificial intelligence in computer vision and natural language processing largely stems from its ability to train deep models effectively. We have thus conducted a systematic study on deep GNN models. Our findings indicate that the current success of deep GNNs primarily stems from (I) the adoption of innovations from CNNs, such as residual/skip connections, or (II) the tailor-made aggregation algorithms like DropEdge. However, these algorithms often lack intrinsic interpretability and indiscriminately treat all nodes within a given layer in a similar manner, thereby failing to capture the nuanced differences among various nodes. In this paper, we introduce the Snowflake Hypothesis -- a novel paradigm underpinning the concept of "one node, one receptive field''. The hypothesis draws inspiration from the unique and individualistic patterns of each snowflake, proposing a corresponding uniqueness in the receptive fields of nodes in the GNNs. We employ the simplest gradient and node-level cosine distance as guiding principles to regulate the aggregation depth for each node, and conduct comprehensive experiments including: (1) different training scheme; (2) various shallow and deep GNN backbones; (3) various numbers of layers (8, 16, 32, 64) on multiple benchmarks; (4) compare with different aggregation strategies. The observational results demonstrate that our framework can serve as a universal operator for a range of tasks, and it displays tremendous potential on deep GNNs. Code is available at: https://github.com/CunWang520/Snowhypothe.|尽管图形神经网络(GNNs)在图形表示学习任务中表现出相当大的潜力，但是 GNNs 在计算机视觉(CV)领域中越来越深入，主要面临着过度拟合和过度平滑的重大问题。人工智能在计算机视觉和自然语言处理方面的成功很大程度上源于其有效地训练深度模型的能力。因此，我们对深层 GNN 模型进行了系统的研究。我们的研究结果表明，深度 GNN 目前的成功主要源于(I)采用来自 CNN 的创新，如残留/跳过连接，或(II)定制的聚合算法，如 DropEdge。然而，这些算法往往缺乏内在的可解释性，并且不加区分地以类似的方式处理给定层中的所有节点，从而无法捕捉各个节点之间的细微差异。本文介绍了“雪花假说”——一个支撑“一个节点，一个接受场”概念的新范式。该假设从每片雪花的独特和个性化模式中获得灵感，提出了 GNN 中节点接收域的相应独特性。我们采用最简单的梯度和节点级余弦距离作为指导原则来调节每个节点的聚集深度，并进行了全面的实验，包括: (1)不同的训练方案; (2)各种浅层和深层 GNN 骨干网; (3)多个基准上的不同层数(8,16,32,64) ; (4)比较不同的聚集策略。实验结果表明，我们的框架可以作为一个通用的操作员为一系列的任务，它显示了巨大的潜力深 GNN。密码可于以下 https://github.com/cunwang520/snowhypothe 索取:。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=The+Snowflake+Hypothesis:+Training+and+Powering+GNN+with+One+Node+One+Receptive+Field)|0| +|[The Heterophilic Snowflake Hypothesis: Training and Empowering GNNs for Heterophilic Graphs](https://doi.org/10.1145/3637528.3671791)|Kun Wang, Guibin Zhang, Xinnan Zhang, Junfeng Fang, Xun Wu, Guohao Li, Shirui Pan, Wei Huang, Yuxuan Liang|University of Science and Technology of China (USTC), Hefei, China; Tsinghua University, Beijing, China; RIKEN AIP, Tokyo, Japan; Tongji University, Shanghai, China; Griffith University, Queensland, Australia; The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China; University of Minnesota, Twin Cities, MN, USA; Oxford University, Oxford, United Kingdom|Graph Neural Networks (GNNs) have become pivotal tools for a range of graph-based learning tasks. Notably, most current GNN architectures operate under the assumption of homophily, whether explicitly or implicitly. While this underlying assumption is frequently adopted, it is not universally applicable, which can result in potential shortcomings in learning effectiveness. In this paper, or the first time, we transfer the prevailing concept of "one node one receptive field" to the heterophilic graph. By constructing a proxy label predictor, we enable each node to possess a latent prediction distribution, which assists connected nodes in determining whether they should aggregate their associated neighbors. Ultimately, every node can have its own unique aggregation hop and pattern, much like each snowflake is unique and possesses its own characteristics. Based on observations, we innovatively introduce the Heterophily Snowflake Hypothesis and provide an effective solution to guide and facilitate research on heterophilic graphs and beyond. We conduct comprehensive experiments including (1) main results on 10 graphs with varying heterophily ratios across 10 backbones; (2) scalability on various deep GNN backbones (SGC, JKNet, etc.) across various large number of layers (2,4,6,8,16,32 layers); (3) comparison with conventional snowflake hypothesis; (4) efficiency comparison with existing graph pruning algorithms. Our observations show that our framework acts as a versatile operator for diverse tasks. It can be integrated into various GNN frameworks, boosting performance in-depth and offering an explainable approach to choosing the optimal network depth. The source code is available at https://github.com/bingreeky/HeteroSnoH.|图形神经网络(GNN)已经成为一系列基于图形的学习任务的关键工具。值得注意的是，大多数当前的 GNN 体系结构都是在同质的假设下运行的，无论是显式的还是隐式的。虽然这一基本假设经常被采用，但并非普遍适用，这可能导致潜在的缺陷，在学习效果。本文首次将流行的“一个节点一个接受域”的概念转化为异质图。通过构造一个代理标签预测器，我们使每个节点具有一个潜在的预测分布，这有助于连接的节点决定是否应该聚合它们的相关邻居。最终，每个节点都可以有自己独特的聚合跳和模式，就像每片雪花都是独一无二的并且具有自己的特点。在观察的基础上，我们创新性地引入了异质性雪花假说，为指导和促进异质性图及其他图的研究提供了一个有效的解决方案。我们进行了全面的实验，包括(1)10个图的主要结果，在10个主干上具有不同的异质性比率; (2)各种深层 GNN 主干(SGC，JKNet 等)的可伸缩性，跨越各种大量的层(2,4,6,8,16,32层) ; (3)与传统雪花假说的比较; (4)与现有图剪枝算法的效率比较。我们的观察表明，我们的框架充当多种任务的多功能操作员。它可以集成到各种 GNN 框架中，提高性能的深度，并为选择最佳网络深度提供一种可解释的方法。源代码可在 https://github.com/bingreeky/heterosnoh 下载。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=The+Heterophilic+Snowflake+Hypothesis:+Training+and+Empowering+GNNs+for+Heterophilic+Graphs)|0| +|[CutAddPaste: Time Series Anomaly Detection by Exploiting Abnormal Knowledge](https://doi.org/10.1145/3637528.3671739)|Rui Wang, Xudong Mou, Renyu Yang, Kai Gao, Pin Liu, Chongwei Liu, Tianyu Wo, Xudong Liu|; School of Computer Science and Engineering, Beihang University, Beijing, China; Institute of Future Cities, The Chinese University of Hong Kong, Hong Kong, China; School of Information Engineering, China University of Geosciences Beijing, Beijing, China; Kuaishou Inc., Beijing, China; School of Software, Beihang University & Zhongguancun Laboratory, Beijing, China; School of Software, Beihang University, Beijing, China|Detecting time-series anomalies is extremely intricate due to the rarity of anomalies and imbalanced sample categories, which often result in costly and challenging anomaly labeling. Most of the existing approaches largely depend on assumptions of normality, overlooking labeled abnormal samples. While anomaly assumptions based methods can incorporate prior knowledge of anomalies for data augmentation in training classifiers, the adopted random or coarse-grained augmentation approaches solely focus on pointwise anomalies and lack cutting-edge domain knowledge, making them less likely to achieve better performance. This paper introduces CutAddPaste, a novel anomaly assumption-based approach for detecting time-series anomalies. It primarily employs a data augmentation strategy to generate pseudo anomalies, by exploiting prior knowledge of anomalies as much as possible. At the core of CutAddPaste is cutting patches from random positions in temporal subsequence samples, adding linear trend terms, and pasting them into other samples, so that it can well approximate a variety of anomalies, including point and pattern anomalies. Experiments on standard benchmark datasets demonstrate that our method outperforms the state-of-the-art approaches.|由于时间序列异常的罕见性和样本类别的不平衡性，检测时间序列异常极其复杂，往往导致昂贵和具有挑战性的异常标记。大多数现有的方法主要依赖于正常的假设，忽略了标记的异常样本。虽然基于异常假设的方法可以将异常的先验知识引入到训练分类器的数据增强中，但是所采用的随机或粗粒度增强方法只关注点态异常，缺乏尖端领域知识，使得它们不太可能获得更好的性能。本文介绍了一种新的基于异常假设的时间序列异常检测方法 CutAddPaste。它主要采用一种数据增强策略，通过尽可能多地利用异常的先验知识来产生伪异常。CutAddPaste 的核心是从时序子序列样本中的随机位置切割补丁，添加线性趋势项，并将其粘贴到其他样本中，以便能够很好地近似各种异常，包括点和模式异常。在标准基准数据集上的实验表明，我们的方法优于最先进的方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=CutAddPaste:+Time+Series+Anomaly+Detection+by+Exploiting+Abnormal+Knowledge)|0| +|[Advancing Molecule Invariant Representation via Privileged Substructure Identification](https://doi.org/10.1145/3637528.3671886)|Ruijia Wang, Haoran Dai, Cheng Yang, Le Song, Chuan Shi|; Beijing University of Posts and Telecommunications, Beijing, China; BioMap Research & MBZUAI, Beijing, China; Beijing University of Post and Telecommunication, Beijing, China|Graph neural networks (GNNs) have revolutionized molecule representation learning by modeling molecules as graphs, with atoms represented as nodes and chemical bonds as edges. Despite their progress, they struggle with out-of-distribution scenarios, such as changes in size or scaffold of molecules with identical properties. Some studies attempt to mitigate this issue through graph invariant learning, which penalizes prediction variance across environments to learn invariant representations. But in the realm of molecules, core functional groups forming privileged substructures dominate molecular properties and remain invariant across distribution shifts. This highlights the need for integrating this prior knowledge and ensuring the environment split compatible with molecule invariant learning. To bridge this gap, we propose a novel framework named MILI. Specifically, we first formalize molecule invariant learning based on privileged substructure identification and introduce substructure invariance constraint. Building on this foundation, we theoretically establish two criteria for environment splits conducive to molecule invariant learning. Inspired by these criteria, we develop a dual-head graph neural network. A shared identifier identifies privileged substructures, while environment and task heads generate predictions based on variant and privileged substructures. Through the interaction of two heads, the environments are split and optimized to meet our criteria. The unified MILI guarantees that molecule invariant learning and environment split achieve mutual enhancement from theoretical analysis and network design. Extensive experiments across eight benchmarks validate the effectiveness of MILI compared to state-of-the-art baselines.|图形神经网络(GNN)通过将分子建模为图形，将原子表示为节点，化学键表示为边，从而彻底改变了分子表示学习。尽管他们取得了进展，但是他们仍然在分布范围之外的场景中挣扎，比如具有相同性质的分子的大小或支架的变化。一些研究试图通过图不变学习来缓解这个问题，图不变学习通过惩罚环境间的预测方差来学习不变表示。但在分子领域，形成特权子结构的核心官能团支配着分子的性质，并且在分布变化中保持不变。这突出了整合这些先验知识和确保环境分裂与分子不变学习兼容的必要性。为了弥补这一差距，我们提出了一个新的框架 MILI。具体地说，我们首先将基于特权子结构识别的分子不变性学习形式化，并引入子结构不变性约束。在此基础上，我们从理论上建立了两个有利于分子不变学习的环境分裂准则。受这些准则的启发，我们开发了一个双头图神经网络。共享标识符识别特权子结构，而环境和任务负责人根据变体和特权子结构生成预测。通过两个头的相互作用，环境分裂和优化，以满足我们的标准。统一的 MILI 从理论分析和网络设计两个方面保证了分子不变学习和环境分裂的相互增强。通过八个基准的大量实验验证了 MILI 与最先进的基准相比的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Advancing+Molecule+Invariant+Representation+via+Privileged+Substructure+Identification)|0| +|[Optimizing Long-tailed Link Prediction in Graph Neural Networks through Structure Representation Enhancement](https://doi.org/10.1145/3637528.3671864)|Yakun Wang, Daixin Wang, Hongrui Liu, Binbin Hu, Yingcui Yan, Qiyang Zhang, Zhiqiang Zhang|Ant Group, Hangzhou, China; Ant Group, Beijing, China; Ant Group, Shanghai, China|Link prediction, as a fundamental task for graph neural networks (GNNs), has boasted significant progress in varied domains. Its success is typically influenced by the expressive power of node representation, but recent developments reveal the inferior performance of low-degree nodes owing to their sparse neighbor connections, known as the degree-based long-tailed problem. Will the degree-based long-tailed distribution similarly constrain the efficacy of GNNs on link prediction? Unexpectedly, our study reveals that only a mild correlation exists between node degree and predictive accuracy, and more importantly, the number of common neighbors between node pairs exhibits a strong correlation with accuracy. Considering node pairs with less common neighbors, i.e., tail node pairs, make up a substantial fraction of the dataset but achieve worse performance, we propose that link prediction also faces the long-tailed problem. Therefore, link prediction of GNNs is greatly hindered by the tail node pairs. After knowing the weakness of link prediction, a natural question is how can we eliminate the negative effects of the skewed long-tailed distribution on common neighbors so as to improve the performance of link prediction? Towards this end, we introduce our long-tailed framework (LTLP), which is designed to enhance the performance of tail node pairs on link prediction by increasing common neighbors. Two key modules in LTLP respectively supplement high-quality edges for tail node pairs and enforce representational alignment between head and tail node pairs within the same category, thereby improving the performance of tail node pairs. Empirical results across five datasets confirm that our approach not only achieves SOTA performance but also greatly reduces the performance bias between the head and tail. These findings underscore the efficacy and superiority of our framework in addressing the long-tailed problem in link prediction.|链接预测作为图形神经网络的基础性工作，在各个领域都取得了显著的进展。它的成功通常受到节点表示的表达能力的影响，但最近的发展揭示了低度节点由于其稀疏邻居连接性能较差，称为基于度的长尾问题。基于度的长尾分布同样会限制 GNN 链路预测的效率吗？出乎意料的是，我们的研究表明，节点程度与预测精度之间只存在轻微的相关性，更重要的是，节点对之间共同邻居的数量与预测精度有很强的相关性。考虑到具有较少共同邻居的节点对，即尾节点对，在数据集中占很大比例，但性能较差，我们认为链路预测也面临着长尾问题。因此，尾节点对极大地阻碍了 GNN 的链路预测。在认识到链路预测的弱点之后，如何消除偏斜长尾分布对公共邻居的负面影响，从而提高链路预测的性能，是一个很自然的问题？为此，我们引入了长尾结构(LTLP) ，该结构通过增加公共邻居来提高尾节点对的链路预测性能。LTLP 中的两个关键模块分别补充了尾节点对的高质量边，并在同一类别中加强了头尾节点对的表示对齐，从而提高了尾节点对的性能。实验结果表明，该方法不仅能够实现 SOTA 性能，而且能够大大降低 SOTA 的性能偏差。这些发现强调了我们的框架在解决链接预测中的长尾问题的有效性和优越性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Optimizing+Long-tailed+Link+Prediction+in+Graph+Neural+Networks+through+Structure+Representation+Enhancement)|0| |[DiffCrime: A Multimodal Conditional Diffusion Model for Crime Risk Map Inference](https://doi.org/10.1145/3637528.3671843)|Shuliang Wang, Xinyu Pan, Sijie Ruan, Haoyu Han, Ziyu Wang, Hanning Yuan, Jiabao Zhu, Qi Li|Beijing Institute of Technology, Beijing, China|Crime risk map plays a crucial role in urban planning and public security management. Traditionally, it is obtained solely from historical crime incidents or inferred from limited environmental factors, which are not sufficient to accurately model the occurrences of crimes over the geographical space well. Motivated by the impressive and realistic conditional generating power of diffusion models, in this paper, we propose a multimodal conditional diffusion method, namely, DiffCrime, to infer the crime risk map based on datasets in various domains, i.e., historical crime incidents, satellite imagery, and map imagery. It is equipped with a history-gated multimodal denoising network, i.e., HamNet, dedicated to the crime risk map inference. HamNet emphasizes the importance of historical crime data via a Gated-based History Fusion (GHF) module and adaptively controls multimodal conditions to be fused across different diffusion time steps via a Time step-Aware Modality Fusion (TAMF) module. Extensive experiments on two real-world datasets demonstrate the effectiveness of DiffCrime, which outperforms baselines by at least 43% and 31% in terms of RMSE, respectively.|犯罪风险图在城市规划和治安管理中具有重要作用。传统上，它仅仅是从历史犯罪事件中获得的，或者是从有限的环境因素中推断出来的，这些因素不足以很好地准确地模拟地理空间上的犯罪事件。本文基于扩散模型令人印象深刻的现实条件生成能力，提出了一种多模态条件扩散方法，即“区分犯罪”，该方法基于不同领域的数据集，即历史犯罪事件、卫星地图和地图图像，推断犯罪风险图。它配备了一个历史门控的多模式去噪网络，即 HamNet，专门用于犯罪风险图推断。HamNet 通过一个基于门的历史融合(GHF)模块强调历史犯罪数据的重要性，并通过一个时间步感知模态融合(TAMF)模块自适应地控制跨不同扩散时间步骤进行融合的多模态条件。在两个真实世界数据集上的大量实验证明了区分犯罪的有效性，根据 RMSE，区分犯罪的性能至少比基线高出43% 和31% 。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=DiffCrime:+A+Multimodal+Conditional+Diffusion+Model+for+Crime+Risk+Map+Inference)|0| |[AsyncET: Asynchronous Representation Learning for Knowledge Graph Entity Typing](https://doi.org/10.1145/3637528.3671832)|YunCheng Wang, Xiou Ge, Bin Wang, C.C. Jay Kuo||||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=AsyncET:+Asynchronous+Representation+Learning+for+Knowledge+Graph+Entity+Typing)|0| |[Unveiling Global Interactive Patterns across Graphs: Towards Interpretable Graph Neural Networks](https://doi.org/10.1145/3637528.3671838)|Yuwen Wang, Shunyu Liu, Tongya Zheng, Kaixuan Chen, Mingli Song||Graph Neural Networks (GNNs) have emerged as a prominent framework for graph mining, leading to significant advances across various domains. Stemmed from the node-wise representations of GNNs, existing explanation studies have embraced the subgraph-specific viewpoint that attributes the decision results to the salient features and local structures of nodes. However, graph-level tasks necessitate long-range dependencies and global interactions for advanced GNNs, deviating significantly from subgraph-specific explanations. To bridge this gap, this paper proposes a novel intrinsically interpretable scheme for graph classification, termed as Global Interactive Pattern (GIP) learning, which introduces learnable global interactive patterns to explicitly interpret decisions. GIP first tackles the complexity of interpretation by clustering numerous nodes using a constrained graph clustering module. Then, it matches the coarsened global interactive instance with a batch of self-interpretable graph prototypes, thereby facilitating a transparent graph-level reasoning process. Extensive experiments conducted on both synthetic and real-world benchmarks demonstrate that the proposed GIP yields significantly superior interpretability and competitive performance to the state-of-the-art counterparts. Our code will be made publicly available¹.|图神经网络(GNN)已经成为图挖掘的一个重要框架，在各个领域都取得了显著的进展。从 GNN 的节点表示出发，现有的解释研究采用子图特定的观点，将决策结果归因于节点的显著特征和局部结构。然而，图级任务需要高级 GNN 的远程依赖和全局交互，这与特定于子图的解释大相径庭。为了弥补这一差距，本文提出了一种新的图分类方法——全局交互模式(GIP)学习方法，该方法引入了可学习的全局交互模式来显式地解释决策。GIP 首先使用一个约束图聚类模块对大量节点进行聚类，从而解决解释的复杂性问题。然后，将粗化的全局交互实例与一批可自解释的图原型进行匹配，从而实现透明的图级推理过程。在合成和现实世界基准上进行的大量实验表明，所提议的 GIP 产生的可解释性和竞争性能明显优于最先进的对应方。我们的代码将公开发布1。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Unveiling+Global+Interactive+Patterns+across+Graphs:+Towards+Interpretable+Graph+Neural+Networks)|0| -|[Self-Supervised Learning for Graph Dataset Condensation](https://doi.org/10.1145/3637528.3671682)|Yuxiang Wang, Xiao Yan, Shiyu Jin, Hao Huang, Quanqing Xu, Qingchen Zhang, Bo Du, Jiawei Jiang|School of Computer Science, Wuhan University, Wuhan, China; School of Computer Science and Technology, Hainan University, Haikou, China; OceanBase, Ant Group, Hangzhou, China; Centre for Perceptual and Interactive Intelligence (CPII), Hong Kong, China|Graph dataset condensation (GDC) reduces a dataset with many graphs into a smaller dataset with fewer graphs while maintaining model training accuracy. GDC saves the storage cost and hence accelerates training. Although several GDC methods have been proposed, they are all supervised and require massive labels for the graphs, while graph labels can be scarce in many practical scenarios. To fill this gap, we propose a self-supervised graph dataset condensation method called SGDC, which does not require label information. Our initial design starts with the classical bilevel optimization paradigm for dataset condensation and incorporates contrastive learning techniques. But such a solution yields poor accuracy due to the biased gradient estimation caused by data augmentation. To solve this problem, we introduce representation matching, which conducts training by aligning the representations produced by the condensed graphs with the target representations generated by a pre-trained SSL model. This design eliminates the need for data augmentation and avoids biased gradient. We further propose a graph attention kernel, which not only improves accuracy but also reduces running time when combined with self-supervised kernel ridge regression (KRR). To simplify SGDC and make it more robust, we adopt a adjacency matrix reusing approach, which reuses the topology of the original graphs for the condensed graphs instead of repeatedly learning topology during training. Our evaluations on seven graph datasets find that SGDC improves model accuracy by up to 9.7% compared with 5 state-of-the-art baselines, even if they use label information. Moreover, SGDC is significantly more efficient than the baselines.|图形数据集压缩(GDC)在保持模型训练精度的同时，将多个图形的数据集压缩成较小的图形数据集。GDC 节省了存储成本，因此加速了培训。虽然已经提出了几种 GDC 方法，但它们都是有监督的，需要对图进行大量的标记，而在许多实际场景中，图标记是稀缺的。为了填补这一空白，我们提出了一种称为 SGDC 的自监督图形数据集压缩方法，该方法不需要标签信息。我们最初的设计开始于经典的数据集压缩的双层优化范例，并采用了对比学习技术。但是由于数据增强引起的梯度估计偏差，使得这种解的精度较差。为了解决这个问题，我们引入了表示匹配技术，该技术通过将压缩图生成的表示与预先训练好的 SSL 模型生成的目标表示对齐来进行训练。这种设计消除了数据增强的需要，避免了有偏的梯度。进一步提出了一种图注意核，它与自监督核岭回归(KRR)相结合，不仅提高了精度，而且减少了运行时间。为了简化 SGDC 并使其更加健壮，我们采用了一种邻接矩阵重用的方法，在训练过程中重用原始图的拓扑结构来代替重复学习拓扑结构。我们对七个图形数据集的评估发现，与5个最先进的基线相比，即使使用标签信息，SGDC 也提高了模型的准确性达9.7% 。此外，SGDC 明显比基线更有效。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Self-Supervised+Learning+for+Graph+Dataset+Condensation)|0| +|[Self-Supervised Learning for Graph Dataset Condensation](https://doi.org/10.1145/3637528.3671682)|Yuxiang Wang, Xiao Yan, Shiyu Jin, Hao Huang, Quanqing Xu, Qingchen Zhang, Bo Du, Jiawei Jiang|OceanBase, Ant Group, Hangzhou, China; Centre for Perceptual and Interactive Intelligence (CPII), Hong Kong, China; School of Computer Science, Wuhan University, Wuhan, China; School of Computer Science and Technology, Hainan University, Haikou, China|Graph dataset condensation (GDC) reduces a dataset with many graphs into a smaller dataset with fewer graphs while maintaining model training accuracy. GDC saves the storage cost and hence accelerates training. Although several GDC methods have been proposed, they are all supervised and require massive labels for the graphs, while graph labels can be scarce in many practical scenarios. To fill this gap, we propose a self-supervised graph dataset condensation method called SGDC, which does not require label information. Our initial design starts with the classical bilevel optimization paradigm for dataset condensation and incorporates contrastive learning techniques. But such a solution yields poor accuracy due to the biased gradient estimation caused by data augmentation. To solve this problem, we introduce representation matching, which conducts training by aligning the representations produced by the condensed graphs with the target representations generated by a pre-trained SSL model. This design eliminates the need for data augmentation and avoids biased gradient. We further propose a graph attention kernel, which not only improves accuracy but also reduces running time when combined with self-supervised kernel ridge regression (KRR). To simplify SGDC and make it more robust, we adopt a adjacency matrix reusing approach, which reuses the topology of the original graphs for the condensed graphs instead of repeatedly learning topology during training. Our evaluations on seven graph datasets find that SGDC improves model accuracy by up to 9.7% compared with 5 state-of-the-art baselines, even if they use label information. Moreover, SGDC is significantly more efficient than the baselines.|图形数据集压缩(GDC)在保持模型训练精度的同时，将多个图形的数据集压缩成较小的图形数据集。GDC 节省了存储成本，因此加速了培训。虽然已经提出了几种 GDC 方法，但它们都是有监督的，需要对图进行大量的标记，而在许多实际场景中，图标记是稀缺的。为了填补这一空白，我们提出了一种称为 SGDC 的自监督图形数据集压缩方法，该方法不需要标签信息。我们最初的设计开始于经典的数据集压缩的双层优化范例，并采用了对比学习技术。但是由于数据增强引起的梯度估计偏差，使得这种解的精度较差。为了解决这个问题，我们引入了表示匹配技术，该技术通过将压缩图生成的表示与预先训练好的 SSL 模型生成的目标表示对齐来进行训练。这种设计消除了数据增强的需要，避免了有偏的梯度。进一步提出了一种图注意核，它与自监督核岭回归(KRR)相结合，不仅提高了精度，而且减少了运行时间。为了简化 SGDC 并使其更加健壮，我们采用了一种邻接矩阵重用的方法，在训练过程中重用原始图的拓扑结构来代替重复学习拓扑结构。我们对七个图形数据集的评估发现，与5个最先进的基线相比，即使使用标签信息，SGDC 也提高了模型的准确性达9.7% 。此外，SGDC 明显比基线更有效。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Self-Supervised+Learning+for+Graph+Dataset+Condensation)|0| |[From Supervised to Generative: A Novel Paradigm for Tabular Deep Learning with Large Language Models](https://doi.org/10.1145/3637528.3671975)|Xumeng Wen, Han Zhang, Shun Zheng, Wei Xu, Jiang Bian|Tsinghua University, Beijing, China; Microsoft Research Asia, Beijing, China|Tabular data is foundational to predictive modeling in various crucial industries, including healthcare, finance, retail, sustainability, etc. Despite the progress made in specialized models, there is an increasing demand for universal models that can transfer knowledge, generalize from limited data, and follow human instructions. These are challenges that current tabular deep learning approaches have not fully tackled. Here we introduce Generative Tabular Learning (GTL), a novel framework that integrates the advanced functionalities of large language models (LLMs)-such as prompt-based zero-shot generalization and in-context learning-into tabular deep learning. GTL capitalizes on the pre-training of LLMs on diverse tabular data, enhancing their understanding of domain-specific knowledge, numerical sequences, and statistical dependencies critical for accurate predictions. Our empirical study spans 384 public datasets, rigorously analyzing GTL's convergence and scaling behaviors and assessing the impact of varied data templates. The GTL-enhanced LLaMA-2 model demonstrates superior zero-shot and in-context learning capabilities across numerous classification and regression tasks. Notably, it achieves this without fine-tuning, outperforming traditional methods and rivaling state-of-the-art models like GPT-4 in certain cases. Through GTL, we not only foster a deeper integration of LLMs' sophisticated abilities into tabular data comprehension and application but also offer a new training resource and a test bed for LLMs to enhance their ability to comprehend tabular data. To facilitate reproducible research, we release our code, data, and model checkpoints at https://github.com/microsoft/Industrial-Foundation-Models.|表格数据是各种关键行业预测建模的基础，包括医疗、金融、零售、可持续发展等。尽管在专业模型方面取得了进展，但对能够传递知识、从有限的数据中归纳出结论并遵循人类指令的通用模型的需求日益增长。这些是目前表格式深度学习方法尚未完全解决的挑战。在这里，我们介绍了生成表学习(GTL) ，一个新的框架，集成了大型语言模型(LLM)的先进功能-如基于提示的零拍泛化和在上下文学习-到表深度学习。GTL 利用 LLM 对不同表格数据的预训练，增强他们对领域特定知识、数字序列和对准确预测至关重要的统计依赖性的理解。我们的实证研究涵盖了384个公共数据集，严格分析了 GTL 的收敛和缩放行为，并评估了不同数据模板的影响。GTL 增强的 LLaMA-2模型展示了在众多分类和回归任务中优越的零击和上下文学习能力。值得注意的是，它实现这一点没有微调，超过传统的方法和竞争国家的最先进的模型，如 GPT-4在某些情况下。通过 GTL，我们不仅促进了 LLM 复杂能力在表格数据理解和应用中的深入整合，而且为 LLM 提供了一种新的训练资源和测试平台，以提高 LLM 理解表格数据的能力。为了便于重复研究，我们在 https://github.com/microsoft/industrial-foundation-models 发布代码、数据和模型检查点。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=From+Supervised+to+Generative:+A+Novel+Paradigm+for+Tabular+Deep+Learning+with+Large+Language+Models)|0| -|[Dense Subgraph Discovery Meets Strong Triadic Closure](https://doi.org/10.1145/3637528.3671697)|Chamalee Wickrama Arachchi, Iiro Kumpulainen, Nikolaj Tatti|HIIT, University of Helsinki, Helsinki, Finland; University of Helsinki, Helsinki, Finland|Finding dense subgraphs is a core problem with numerous graph mining applications such as community detection in social networks and anomaly detection. However, in many real-world networks connections are not equal. One way to label edges as either strong or weak is to use strong triadic closure~(STC). Here, if one node connects strongly with two other nodes, then those two nodes should be connected at least with a weak edge. STC-labelings are not unique and finding the maximum number of strong edges is NP-hard. In this paper, we apply STC to dense subgraph discovery. More formally, our score for a given subgraph is the ratio between the sum of the number of strong edges and weak edges, weighted by a user parameter λ, and the number of nodes of the subgraph. Our goal is to find a subgraph and an STC-labeling maximizing the score. We show that for λ = 1, our problem is equivalent to finding the densest subgraph, while for λ = 0, our problem is equivalent to finding the largest clique, making our problem NP-hard. We propose an exact algorithm based on integer linear programming and four practical polynomial-time heuristics. We present an extensive experimental study that shows that our algorithms can find the ground truth in synthetic datasets and run efficiently in real-world datasets.|寻找稠密子图是许多图挖掘应用程序的核心问题，比如社交网络和异常检测中的社区检测。然而，在现实世界中，许多网络连接是不相等的。将边标记为强或弱的一种方法是使用强三元闭包 ~ (STC)。在这里，如果一个节点与另外两个节点强烈连接，那么这两个节点至少应该用弱边连接。STC 标记不是唯一的，寻找强边的最大个数是 NP 难的。本文将 STC 应用于稠密子图的发现。更正式地说，我们给定子图的得分是由用户参数 λ 加权的强边数和弱边数之和与子图的节点数之比。我们的目标是找到一个子图和一个 STC 标记最大化得分。证明了对于 λ = 1，我们的问题等价于求最密子图，而对于 λ = 0，我们的问题等价于求最大团，使我们的问题 NP 难。我们提出了一个基于整数线性规划和四个实际的多项式时间启发式的精确算法。我们提出了一个广泛的实验研究表明，我们的算法可以找到地面真理合成数据集和运行在真实世界的数据集有效。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Dense+Subgraph+Discovery+Meets+Strong+Triadic+Closure)|0| -|[FedBiOT: LLM Local Fine-tuning in Federated Learning without Full Model](https://doi.org/10.1145/3637528.3671897)|Feijie Wu, Zitao Li, Yaliang Li, Bolin Ding, Jing Gao|Purdue University, West Lafayette, USA; Alibaba Group, Bellevue, USA|Large language models (LLMs) show amazing performance on many domain-specific tasks after fine-tuning with some appropriate data. However, many domain-specific data are privately distributed across multiple owners. Thus, this dilemma raises the interest in how to perform LLM fine-tuning in federated learning (FL). However, confronted with limited computation and communication capacities, FL clients struggle to fine-tune an LLM effectively. To this end, we introduce FedBiOT, a resource-efficient LLM fine-tuning approach to FL. Specifically, our method involves the server generating a compressed LLM and aligning its performance with the full model. Subsequently, the clients fine-tune a lightweight yet important part of the compressed model, referred to as an adapter. Notice that as the server has no access to the private data owned by the clients, the data used for alignment by the server has a different distribution from the one used for fine-tuning by clients. We formulate the problem into a bi-level optimization problem to minimize the negative effect of data discrepancy and derive the updating rules for the server and clients. We conduct extensive experiments on LLaMA-2, empirically showing that the adapter has exceptional performance when reintegrated into the global LLM. The results also indicate that the proposed FedBiOT significantly reduces resource consumption compared to existing benchmarks, all while achieving comparable performance levels.|在使用适当的数据进行微调之后，大型语言模型(LLM)在许多特定于领域的任务上显示出惊人的性能。但是，许多特定于域的数据是私有地分布在多个所有者之间的。因此，这种困境引起了人们对如何在联邦学习(FL)中执行 LLM 微调的兴趣。然而，面对有限的计算和通信能力，FL 客户端很难对 LLM 进行有效的微调。为此，我们介绍了 FedBiOT，一种资源有效的 LLM 微调方法。具体来说，我们的方法涉及到服务器生成一个压缩的 LLM 并使其性能与完整模型保持一致。随后，客户机对压缩模型的一个轻量级但重要的部分(称为适配器)进行微调。请注意，由于服务器不能访问客户机拥有的私有数据，因此用于服务器对齐的数据与用于客户机微调的数据具有不同的分布。我们把问题分解成两个最佳化问题，以尽量减少数据差异的负面影响，并推导出服务器和客户端的更新规则。我们在 LLaMA-2上进行了广泛的实验，经验表明，当重新集成到全局 LLM 时，该适配器具有优异的性能。结果还表明，与现有基准相比，提出的 FedBiOT 显著降低了资源消耗，同时达到了可比的性能水平。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=FedBiOT:+LLM+Local+Fine-tuning+in+Federated+Learning+without+Full+Model)|0| +|[Dense Subgraph Discovery Meets Strong Triadic Closure](https://doi.org/10.1145/3637528.3671697)|Chamalee Wickrama Arachchi, Iiro Kumpulainen, Nikolaj Tatti|University of Helsinki, Helsinki, Finland; HIIT, University of Helsinki, Helsinki, Finland|Finding dense subgraphs is a core problem with numerous graph mining applications such as community detection in social networks and anomaly detection. However, in many real-world networks connections are not equal. One way to label edges as either strong or weak is to use strong triadic closure~(STC). Here, if one node connects strongly with two other nodes, then those two nodes should be connected at least with a weak edge. STC-labelings are not unique and finding the maximum number of strong edges is NP-hard. In this paper, we apply STC to dense subgraph discovery. More formally, our score for a given subgraph is the ratio between the sum of the number of strong edges and weak edges, weighted by a user parameter λ, and the number of nodes of the subgraph. Our goal is to find a subgraph and an STC-labeling maximizing the score. We show that for λ = 1, our problem is equivalent to finding the densest subgraph, while for λ = 0, our problem is equivalent to finding the largest clique, making our problem NP-hard. We propose an exact algorithm based on integer linear programming and four practical polynomial-time heuristics. We present an extensive experimental study that shows that our algorithms can find the ground truth in synthetic datasets and run efficiently in real-world datasets.|寻找稠密子图是许多图挖掘应用程序的核心问题，比如社交网络和异常检测中的社区检测。然而，在现实世界中，许多网络连接是不相等的。将边标记为强或弱的一种方法是使用强三元闭包 ~ (STC)。在这里，如果一个节点与另外两个节点强烈连接，那么这两个节点至少应该用弱边连接。STC 标记不是唯一的，寻找强边的最大个数是 NP 难的。本文将 STC 应用于稠密子图的发现。更正式地说，我们给定子图的得分是由用户参数 λ 加权的强边数和弱边数之和与子图的节点数之比。我们的目标是找到一个子图和一个 STC 标记最大化得分。证明了对于 λ = 1，我们的问题等价于求最密子图，而对于 λ = 0，我们的问题等价于求最大团，使我们的问题 NP 难。我们提出了一个基于整数线性规划和四个实际的多项式时间启发式的精确算法。我们提出了一个广泛的实验研究表明，我们的算法可以找到地面真理合成数据集和运行在真实世界的数据集有效。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Dense+Subgraph+Discovery+Meets+Strong+Triadic+Closure)|0| +|[FedBiOT: LLM Local Fine-tuning in Federated Learning without Full Model](https://doi.org/10.1145/3637528.3671897)|Feijie Wu, Zitao Li, Yaliang Li, Bolin Ding, Jing Gao|Alibaba Group, Bellevue, USA; Purdue University, West Lafayette, USA|Large language models (LLMs) show amazing performance on many domain-specific tasks after fine-tuning with some appropriate data. However, many domain-specific data are privately distributed across multiple owners. Thus, this dilemma raises the interest in how to perform LLM fine-tuning in federated learning (FL). However, confronted with limited computation and communication capacities, FL clients struggle to fine-tune an LLM effectively. To this end, we introduce FedBiOT, a resource-efficient LLM fine-tuning approach to FL. Specifically, our method involves the server generating a compressed LLM and aligning its performance with the full model. Subsequently, the clients fine-tune a lightweight yet important part of the compressed model, referred to as an adapter. Notice that as the server has no access to the private data owned by the clients, the data used for alignment by the server has a different distribution from the one used for fine-tuning by clients. We formulate the problem into a bi-level optimization problem to minimize the negative effect of data discrepancy and derive the updating rules for the server and clients. We conduct extensive experiments on LLaMA-2, empirically showing that the adapter has exceptional performance when reintegrated into the global LLM. The results also indicate that the proposed FedBiOT significantly reduces resource consumption compared to existing benchmarks, all while achieving comparable performance levels.|在使用适当的数据进行微调之后，大型语言模型(LLM)在许多特定于领域的任务上显示出惊人的性能。但是，许多特定于域的数据是私有地分布在多个所有者之间的。因此，这种困境引起了人们对如何在联邦学习(FL)中执行 LLM 微调的兴趣。然而，面对有限的计算和通信能力，FL 客户端很难对 LLM 进行有效的微调。为此，我们介绍了 FedBiOT，一种资源有效的 LLM 微调方法。具体来说，我们的方法涉及到服务器生成一个压缩的 LLM 并使其性能与完整模型保持一致。随后，客户机对压缩模型的一个轻量级但重要的部分(称为适配器)进行微调。请注意，由于服务器不能访问客户机拥有的私有数据，因此用于服务器对齐的数据与用于客户机微调的数据具有不同的分布。我们把问题分解成两个最佳化问题，以尽量减少数据差异的负面影响，并推导出服务器和客户端的更新规则。我们在 LLaMA-2上进行了广泛的实验，经验表明，当重新集成到全局 LLM 时，该适配器具有优异的性能。结果还表明，与现有基准相比，提出的 FedBiOT 显著降低了资源消耗，同时达到了可比的性能水平。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=FedBiOT:+LLM+Local+Fine-tuning+in+Federated+Learning+without+Full+Model)|0| |[Neural Manifold Operators for Learning the Evolution of Physical Dynamics](https://doi.org/10.1145/3637528.3671779)|Hao Wu, Kangyu Weng, Shuyi Zhou, Xiaomeng Huang, Wei Xiong|; Xingjian College, Tsinghua University, Beijing, China; Tencent TEG, Beijing, China|Modeling the evolution of physical dynamics is a foundational problem in science and engineering, and it is regarded as the modeling of an operator mapping between infinite-dimensional functional spaces. Operator learning methods, learning the underlying infinite-dimensional operator in a high-dimensional latent space, have shown significant potential in modeling physical dynamics. However, there remains insufficient research on how to approximate an infinite-dimensional operator using a finite-dimensional parameter space. Inappropriate dimensionality representation of the underlying operator leads to convergence difficulties, decreasing generalization capability, and violating the physical consistency. To address the problem, we present Neural Manifold Operator (NMO) to learn the invariant subspace with the intrinsic dimension to parameterize infinite-dimensional underlying operators. NMO achieves state-of-the-art performance in statistical and physical metrics and gains 23.35% average improvement on three real-world scenarios and four equation-governed scenarios across a wide range of multi-disciplinary fields. Our paradigm has demonstrated universal effectiveness across various model structure implementations, including Multi-Layer Perceptron, Convolutional Neural Networks, and Transformers. Experimentally, we prove that the intrinsic dimension calculated by our paradigm is the optimal dimensional representation of the underlying operators. We release our code at https://github.com/AI4EarthLab/Neural-Manifold-Operators.|物理动力学演化建模是科学和工程领域的一个基础性问题，它被认为是无限维函数空间之间算子映射的建模。算子学习方法，即在高维潜空间中学习潜在的无穷维算子，在物理动力学建模中显示出巨大的潜力。然而，对于如何利用有限维参数空间逼近无穷维算子，目前还缺乏足够的研究。底层算子的维数表示不当会导致收敛困难、泛化能力下降和物理一致性的破坏。为了解决这个问题，我们提出神经流形算子(NMO)来学习不变子空间的本征维度，以参数化无限维的底层算子。NMO 在统计和物理指标方面实现了最先进的性能，在三个现实世界场景和四个多学科领域的方程式控制场景方面平均提高了23.35% 。我们的范式已经证明了在各种模型结构实现的通用有效性，包括多层感知器，卷积神经网络和变压器。在实验上，我们证明了由我们的范式计算出的本征维度是潜在运算符的最佳维度表示。我们在 https://github.com/ai4earthlab/neural-manifold-operators 发布我们的代码。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Neural+Manifold+Operators+for+Learning+the+Evolution+of+Physical+Dynamics)|0| |[Distributional Network of Networks for Modeling Data Heterogeneity](https://doi.org/10.1145/3637528.3671994)|Jun Wu, Jingrui He, Hanghang Tong|University of Illinois at Urbana-Champaign, Champaign, IL, USA|Heterogeneous data widely exists in various high-impact applications. Domain adaptation and out-of-distribution generalization paradigms have been formulated to handle the data heterogeneity across domains. However, most existing domain adaptation and out-of-distribution generalization algorithms do not explicitly explain how the label information can be adaptively propagated from the source domains to the target domain. Furthermore, little effort has been devoted to theoretically understanding the convergence of existing algorithms based on neural networks. To address these problems, in this paper, we propose a generic distributional network of networks (TENON) framework, where each node of the main network represents an individual domain associated with a domain-specific network. In this case, the edges within the main network indicate the domain similarity, and the edges within each network indicate the sample similarity. The crucial idea of TENON is to characterize the within-domain label smoothness and cross-domain parameter smoothness in a unified framework. The convergence and optimality of TENON are theoretically analyzed. Furthermore, we show that based on the TENON framework, domain adaptation and out-of-distribution generalization can be naturally formulated as transductive and inductive distribution learning problems, respectively. This motivates us to develop two instantiated algorithms (TENON-DA and TENON-OOD) of the proposed TENON framework for domain adaptation and out-of-distribution generalization. The effectiveness and efficiency of TENON-DA and TENON-OOD are verified both theoretically and empirically.|异构数据广泛存在于各种高影响的应用程序中。领域适应和分布外泛化范例已经被制定来处理跨领域的数据异构性。然而，大多数现有的域自适应和分布外泛化算法并没有明确地解释如何将标签信息从源域自适应地传播到目标域。此外，在理论上对现有的基于神经网络的算法的收敛性研究很少。为了解决这些问题，本文提出了一个通用分布式网络(TENON)框架，其中主网络的每个节点代表一个与特定领域网络相关联的独立领域。在这种情况下，主网络中的边表示领域相似性，每个网络中的边表示样本相似性。TENON 的核心思想是在一个统一的框架内描述域内标签平滑性和跨域参数平滑性。从理论上分析了 TENON 的收敛性和最优性。此外，基于 TENON 框架，领域适应和分布外泛化可以分别自然地表述为导性分布学习问题和归纳分布学习问题。这促使我们开发两个实例化算法(TENON-DA 和 TENON-OOD)的提议的 TENON 框架领域适应和分布外泛化。从理论和实验两方面验证了 TENON-DA 和 TENON-OOD 方法的有效性和效率。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Distributional+Network+of+Networks+for+Modeling+Data+Heterogeneity)|0| |[Fake News in Sheep's Clothing: Robust Fake News Detection Against LLM-Empowered Style Attacks](https://doi.org/10.1145/3637528.3671977)|Jiaying Wu, Jiafeng Guo, Bryan Hooi|University of Chinese Academy of Sciences & Institute of Computing Technology, CAS, Beijing, China; National University of Singapore, Singapore, Singapore|It is commonly perceived that fake news and real news exhibit distinct writing styles, such as the use of sensationalist versus objective language. However, we emphasize that style-related features can also be exploited for style-based attacks. Notably, the advent of powerful Large Language Models (LLMs) has empowered malicious actors to mimic the style of trustworthy news sources, doing so swiftly, cost-effectively, and at scale. Our analysis reveals that LLM-camouflaged fake news content significantly undermines the effectiveness of state-of-the-art text-based detectors (up to 38% decrease in F1 Score), implying a severe vulnerability to stylistic variations. To address this, we introduce SheepDog, a style-robust fake news detector that prioritizes content over style in determining news veracity. SheepDog achieves this resilience through (1) LLM-empowered news reframings that inject style diversity into the training process by customizing articles to match different styles; (2) a style-agnostic training scheme that ensures consistent veracity predictions across style-diverse reframings; and (3) content-focused veracity attributions that distill content-centric guidelines from LLMs for debunking fake news, offering supplementary cues and potential intepretability that assist veracity prediction. Extensive experiments on three real-world benchmarks demonstrate SheepDog's style robustness and adaptability to various backbones.|人们通常认为，假新闻和真新闻表现出不同的写作风格，如使用耸人听闻和客观的语言。然而，我们强调与样式相关的特性也可以用于基于样式的攻击。值得注意的是，强大的大型语言模型(LLM)的出现使恶意的参与者能够模仿可信赖的新闻来源的风格，这样做迅速、具有成本效益并且具有规模效应。我们的分析表明，LLM 伪装的假新闻内容显着破坏了最先进的基于文本的检测器的有效性(F1分数下降了38%) ，这意味着对风格变化的严重脆弱性。为了解决这个问题，我们引入了 SheepDog，一个风格健壮的假新闻检测器，它在确定新闻真实性时优先考虑内容而不是风格。SheepDog 通过(1) LLM 授权的新闻重构，通过定制文章以匹配不同的风格，将风格多样性注入到培训过程中; (2)风格不可知的培训方案，确保在风格多样性的重构过程中保持一致的准确性预测; (3)内容为中心的准确性属性，从 LLM 中提取内容为中心的指导方针，以揭穿假新闻，提供补充线索和潜在的可理解性，协助准确性预测。在三个真实世界基准上的大量实验证明了 SheepDog 的风格健壮性和对各种主干的适应性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Fake+News+in+Sheep's+Clothing:+Robust+Fake+News+Detection+Against+LLM-Empowered+Style+Attacks)|0| -|[Counterfactual Generative Models for Time-Varying Treatments](https://doi.org/10.1145/3637528.3671950)|Shenghao Wu, Wenbin Zhou, Minshuo Chen, Shixiang Zhu|Carnegie Mellon University, Pittsburgh, PA, USA; Princeton University, Princeton, NJ, USA|Estimating the counterfactual outcome of treatment is essential for decision-making in public health and clinical science, among others. Often, treatments are administered in a sequential, time-varying manner, leading to an exponentially increased number of possible counterfactual outcomes. Furthermore, in modern applications, the outcomes are high-dimensional and conventional average treatment effect estimation fails to capture disparities in individuals. To tackle these challenges, we propose a novel conditional generative framework capable of producing counterfactual samples under time-varying treatment, without the need for explicit density estimation. Our method carefully addresses the distribution mismatch between the observed and counterfactual distributions via a loss function based on inverse probability re-weighting, and supports integration with state-of-the-art conditional generative models such as the guided diffusion and conditional variational autoencoder. We present a thorough evaluation of our method using both synthetic and real-world data. Our results demonstrate that our method is capable of generating high-quality counterfactual samples and outperforms the state-of-the-art baselines.|估计治疗的反事实结果对于公共卫生和临床科学等领域的决策至关重要。通常，治疗是以连续的、时间变化的方式进行的，导致可能的反事实结果的数量呈指数级增长。此外，在现代应用，结果是高维和传统的平均治疗效果估计不能捕捉个人的差异。为了应对这些挑战，我们提出了一种新的条件生成框架，它能够在时变处理下产生反事实样本，而不需要显式的密度估计。我们的方法通过基于逆概率重新加权的损失函数仔细地处理观测分布和反事实分布之间的分布不匹配，并支持与最先进的条件生成模型(如引导扩散和条件变分自动编码器)的集成。我们提出了一个彻底的评估我们的方法使用合成和真实世界的数据。我们的结果表明，我们的方法能够生成高质量的反事实样本，并优于国家的最先进的基线。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Counterfactual+Generative+Models+for+Time-Varying+Treatments)|0| -|[ProCom: A Few-shot Targeted Community Detection Algorithm](https://doi.org/10.1145/3637528.3671749)|Xixi Wu, Kaiyu Xiong, Yun Xiong, Xiaoxin He, Yao Zhang, Yizhu Jiao, Jiawei Zhang|; University of Illinois at Urbana-Champaign, Champaign, Illinois, USA; IFM Lab, Department of Computer Science, University of California, Davis, Davis, California, USA; National University of Singapore, Singapore, Singapore|Targeted community detection aims to distinguish a particular type of community in the network. This is an important task with a lot of real-world applications, e.g., identifying fraud groups in transaction networks. Traditional community detection methods fail to capture the specific features of the targeted community and detect all types of communities indiscriminately. Semi-supervised community detection algorithms, emerged as a feasible alternative, are inherently constrained by their limited adaptability and substantial reliance on a large amount of labeled data, which demands extensive domain knowledge and manual effort. In this paper, we address the aforementioned weaknesses in targeted community detection by focusing on few-shot scenarios. We propose ProCom, a novel framework that extends the "pre-train, prompt'' paradigm, offering a low-resource, high-efficiency, and transferable solution. Within the framework, we devise a dual-level context-aware pre-training method that fosters a deep understanding of latent communities in the network, establishing a rich knowledge foundation for downstream tasks. In the prompt learning stage, we reformulate the targeted community detection task into pre-training objectives, allowing the extraction of specific knowledge relevant to the targeted community to facilitate effective and efficient inference. By leveraging both the general community knowledge acquired during pre-training and the specific insights gained from the prompt communities, ProCom exhibits remarkable adaptability across different datasets. We conduct extensive experiments on five benchmarks to evaluate the ProCom framework, demonstrating its SOTA performance under few-shot scenarios, strong efficiency, and transferability across diverse datasets.|有针对性的社区检测旨在区分网络中特定类型的社区。对于许多实际应用程序来说，这是一项重要的任务，例如，识别交易网络中的欺诈团伙。传统的社区检测方法不能捕捉目标社区的具体特征，不加区分地检测所有类型的社区。半监督社区检测算法作为一种可行的替代算法，由于其适应性有限和对大量标记数据的严重依赖而受到固有的限制，需要广泛的领域知识和人工操作。在本文中，我们针对上述的弱点，在目标社区检测的重点是少数拍摄场景。我们提出 ProCom，一个新的框架，它扩展了“预训练，及时”范式，提供了一个低资源，高效率，可转移的解决方案。在这个框架内，我们设计了一个双层次上下文感知的预训练方法，促进了对网络中潜在群体的深入理解，为下游任务建立了丰富的知识基础。在即时学习阶段，我们会把目标社群侦测工作重新制订为训练前的目标，以便提取与目标社群有关的特定知识，从而促进有效和高效率的推论。通过利用在培训前获得的一般社区知识和从迅速的社区获得的具体见解，ProCom 在不同的数据集中表现出显著的适应性。我们在五个基准上进行了广泛的实验来评估 ProCom 框架，展示了它在短镜头场景下的 SOTA 性能，强大的效率和跨不同数据集的可转移性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=ProCom:+A+Few-shot+Targeted+Community+Detection+Algorithm)|0| -|[Cost-Efficient Fraud Risk Optimization with Submodularity in Insurance Claim](https://doi.org/10.1145/3637528.3672012)|Yupeng Wu, Zhibo Zhu, Chaoyi Ma, Hong Qian, Xingyu Lu, Yangwenhui Zhang, Xiaobo Qin, Binjie Fei, Jun Zhou, Aimin Zhou|AntGroup, Hangzhou, China; Ant Group, Hangzhou, China; School of Computer Science and Technology, East China Normal University, Shanghai, China|The fraudulent insurance claim is critical for the insurance industry. Insurance companies or agency platforms aim to confidently estimate the fraud risk of claims by gathering data from various sources. Although more data sources can improve the estimation accuracy, they inevitably lead to increased costs. Therefore, a great challenge of fraud risk verification lies in well balancing these two aspects. To this end, this paper proposes a framework named cost-efficient fraud risk optimization with submodularity (CEROS) to optimize the process of fraud risk verification. CEROS efficiently allocates investigation resources across multiple information sources, balancing the trade-off between accuracy and cost. CEROS consists of two parts that we propose: a submodular set-wise classification model called SSCM to estimate the submodular objective function, and a primal-dual algorithm with segmentation point called PDA-SP to solve the objective function. Specifically, SSCM models the fraud probability associated with multiple information sources and ensures the properties of submodularity of fraud risk without making independence assumption. The submodularity in SSCM enables PDA-SP to significantly speed up dual optimization. Theoretically, we disclose that when PDA-SP optimizes this dual optimization problem, the process is monotonicity. Finally, the trade-off coefficients output by PDA-SP that balance accuracy and cost in fraud risk verification are applied to online insurance claim decision-making. We conduct experiments on offline trials and online A/B tests in two business areas at Alipay: healthcare insurance recommendation and claim verification. The extensive results indicate that, compared with other methods, CEROS achieves acceleration of 66.9% in convergence speed and meanwhile 18.8% in cost reduction. Currently, CEROS has been successfully deployed in Alipay.|欺诈性保险索赔对保险业至关重要。保险公司或代理平台的目的是通过从各种来源收集数据，有信心地估计索赔的欺诈风险。尽管更多的数据源可以提高估计的准确性，但它们不可避免地会导致成本增加。因此，欺诈风险验证的一个巨大挑战就是如何很好地平衡这两个方面。为此，本文提出了一种基于次模块化的具有成本效益的欺诈风险优化(CEROS)框架来优化欺诈风险验证过程。CEROS 在多个信息来源之间有效地分配调查资源，在准确性和成本之间取得平衡。CEROS 由两部分组成: 子模块集合分类模型 SSCM 估计子模块目标函数，原始-对偶算法 PDA-SP 求解目标函数。具体来说，SSCM 建立了与多个信息源相关的欺诈概率模型，保证了欺诈风险的子模块性，而没有做出独立的假设。SSCM 的次模块性使得 PDA-SP 可以大大加快双重优化的速度。理论上，我们揭示了当 PDA-SP 优化这个双最佳化问题时，过程是单调的。最后，将 PDA-SP 输出的权衡系数应用于在线保险索赔决策中，平衡了欺诈风险验证的准确性和成本。我们在支付宝的两个业务领域进行离线试验和在线 A/B 测试: 医疗保险推荐和理赔验证。广泛的结果表明，与其他方法相比，CEROS 算法在收敛速度上提高了66.9% ，同时降低了成本18.8% 。目前，CEROS 已经成功地部署在支付宝上。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Cost-Efficient+Fraud+Risk+Optimization+with+Submodularity+in+Insurance+Claim)|0| -|[A Deep Prediction Framework for Multi-Source Information via Heterogeneous GNN](https://doi.org/10.1145/3637528.3671966)|Zhen Wu, Jingya Zhou, Jinghui Zhang, Ling Liu, Chizhou Huang|; School of Computer Science and Technology, Soochow University, Suzhou, China; School of Computer Science, Georgia Institute of Technology, Atlanta, USA; School of Computer Science and Engineering, Southeast University, Nanjing, China|Predicting information diffusion is a fundamental task in online social networks (OSNs). Recent studies mainly focus on the popularity prediction of specific content but ignore the correlation between multiple pieces of information. The topic is often used to correlate such information and can correspond to multi-source information. The popularity of a topic relies not only on information diffusion time but also on users' followership. Current solutions concentrate on hard time partition, lacking versatility. Meanwhile, the hop-based sampling adopted in state-of-the-art (SOTA) methods encounters redundant user followership. Moreover, many SOTA methods are not designed with good modularity and lack evaluation for each functional module and enlightening discussion. This paper presents a novel extensible framework, coined as HIF, for effective popularity prediction in OSNs with four original contributions. First, HIF adopts a soft partition of users and time intervals to better learn users' behavioral preferences over time. Second, HIF utilizes weighted sampling to optimize the construction of heterogeneous graphs and reduce redundancy. Furthermore, HIF supports multi-task collaborative optimization to improve its learning capability. Finally, as an extensible framework, HIF provides generic module slots to combine different submodules (e.g., RNNs, Transformer encoders). Experiments show that HIF significantly improves performance and interpretability compared to SOTAs.|预测信息扩散是在线社交网络(OSNs)的一项基本任务。目前的研究主要集中在对特定内容的流行程度进行预测，而忽略了多个信息片段之间的相关性。该主题通常用于关联此类信息，并且可以对应于多源信息。一个话题的受欢迎程度不仅取决于信息的传播时间，还取决于用户的追随者。目前的解决方案集中在硬时间划分，缺乏通用性。同时，最先进的 SOTA 方法中采用的基于跳数的抽样方法遇到了冗余的用户跟随问题。此外，许多 SOTA 方法的设计缺乏良好的模块性，缺乏对各功能模块的评价和有启发性的讨论。本文提出了一个新的可扩展框架，称为 HIF，用于有效预测开源网络的流行程度。首先，HIF 采用用户的软分区和时间间隔，以更好地了解用户的行为偏好随着时间的推移。其次，HIF 利用加权抽样优化异构图的构造，减少冗余;。此外，HIF 还支持多任务协同优化，以提高其学习能力。最后，作为一个可扩展的框架，HIF 提供了通用的模块插槽来组合不同的子模块(例如，RNN、 Transformer 编码器)。实验表明，与 SOTA 相比，HIF 显著提高了性能和可解释性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Deep+Prediction+Framework+for+Multi-Source+Information+via+Heterogeneous+GNN)|0| +|[Counterfactual Generative Models for Time-Varying Treatments](https://doi.org/10.1145/3637528.3671950)|Shenghao Wu, Wenbin Zhou, Minshuo Chen, Shixiang Zhu|Princeton University, Princeton, NJ, USA; Carnegie Mellon University, Pittsburgh, PA, USA|Estimating the counterfactual outcome of treatment is essential for decision-making in public health and clinical science, among others. Often, treatments are administered in a sequential, time-varying manner, leading to an exponentially increased number of possible counterfactual outcomes. Furthermore, in modern applications, the outcomes are high-dimensional and conventional average treatment effect estimation fails to capture disparities in individuals. To tackle these challenges, we propose a novel conditional generative framework capable of producing counterfactual samples under time-varying treatment, without the need for explicit density estimation. Our method carefully addresses the distribution mismatch between the observed and counterfactual distributions via a loss function based on inverse probability re-weighting, and supports integration with state-of-the-art conditional generative models such as the guided diffusion and conditional variational autoencoder. We present a thorough evaluation of our method using both synthetic and real-world data. Our results demonstrate that our method is capable of generating high-quality counterfactual samples and outperforms the state-of-the-art baselines.|估计治疗的反事实结果对于公共卫生和临床科学等领域的决策至关重要。通常，治疗是以连续的、时间变化的方式进行的，导致可能的反事实结果的数量呈指数级增长。此外，在现代应用，结果是高维和传统的平均治疗效果估计不能捕捉个人的差异。为了应对这些挑战，我们提出了一种新的条件生成框架，它能够在时变处理下产生反事实样本，而不需要显式的密度估计。我们的方法通过基于逆概率重新加权的损失函数仔细地处理观测分布和反事实分布之间的分布不匹配，并支持与最先进的条件生成模型(如引导扩散和条件变分自动编码器)的集成。我们提出了一个彻底的评估我们的方法使用合成和真实世界的数据。我们的结果表明，我们的方法能够生成高质量的反事实样本，并优于国家的最先进的基线。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Counterfactual+Generative+Models+for+Time-Varying+Treatments)|0| +|[ProCom: A Few-shot Targeted Community Detection Algorithm](https://doi.org/10.1145/3637528.3671749)|Xixi Wu, Kaiyu Xiong, Yun Xiong, Xiaoxin He, Yao Zhang, Yizhu Jiao, Jiawei Zhang|; University of Illinois at Urbana-Champaign, Champaign, Illinois, USA; National University of Singapore, Singapore, Singapore; IFM Lab, Department of Computer Science, University of California, Davis, Davis, California, USA|Targeted community detection aims to distinguish a particular type of community in the network. This is an important task with a lot of real-world applications, e.g., identifying fraud groups in transaction networks. Traditional community detection methods fail to capture the specific features of the targeted community and detect all types of communities indiscriminately. Semi-supervised community detection algorithms, emerged as a feasible alternative, are inherently constrained by their limited adaptability and substantial reliance on a large amount of labeled data, which demands extensive domain knowledge and manual effort. In this paper, we address the aforementioned weaknesses in targeted community detection by focusing on few-shot scenarios. We propose ProCom, a novel framework that extends the "pre-train, prompt'' paradigm, offering a low-resource, high-efficiency, and transferable solution. Within the framework, we devise a dual-level context-aware pre-training method that fosters a deep understanding of latent communities in the network, establishing a rich knowledge foundation for downstream tasks. In the prompt learning stage, we reformulate the targeted community detection task into pre-training objectives, allowing the extraction of specific knowledge relevant to the targeted community to facilitate effective and efficient inference. By leveraging both the general community knowledge acquired during pre-training and the specific insights gained from the prompt communities, ProCom exhibits remarkable adaptability across different datasets. We conduct extensive experiments on five benchmarks to evaluate the ProCom framework, demonstrating its SOTA performance under few-shot scenarios, strong efficiency, and transferability across diverse datasets.|有针对性的社区检测旨在区分网络中特定类型的社区。对于许多实际应用程序来说，这是一项重要的任务，例如，识别交易网络中的欺诈团伙。传统的社区检测方法不能捕捉目标社区的具体特征，不加区分地检测所有类型的社区。半监督社区检测算法作为一种可行的替代算法，由于其适应性有限和对大量标记数据的严重依赖而受到固有的限制，需要广泛的领域知识和人工操作。在本文中，我们针对上述的弱点，在目标社区检测的重点是少数拍摄场景。我们提出 ProCom，一个新的框架，它扩展了“预训练，及时”范式，提供了一个低资源，高效率，可转移的解决方案。在这个框架内，我们设计了一个双层次上下文感知的预训练方法，促进了对网络中潜在群体的深入理解，为下游任务建立了丰富的知识基础。在即时学习阶段，我们会把目标社群侦测工作重新制订为训练前的目标，以便提取与目标社群有关的特定知识，从而促进有效和高效率的推论。通过利用在培训前获得的一般社区知识和从迅速的社区获得的具体见解，ProCom 在不同的数据集中表现出显著的适应性。我们在五个基准上进行了广泛的实验来评估 ProCom 框架，展示了它在短镜头场景下的 SOTA 性能，强大的效率和跨不同数据集的可转移性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=ProCom:+A+Few-shot+Targeted+Community+Detection+Algorithm)|0| +|[Cost-Efficient Fraud Risk Optimization with Submodularity in Insurance Claim](https://doi.org/10.1145/3637528.3672012)|Yupeng Wu, Zhibo Zhu, Chaoyi Ma, Hong Qian, Xingyu Lu, Yangwenhui Zhang, Xiaobo Qin, Binjie Fei, Jun Zhou, Aimin Zhou|Ant Group, Hangzhou, China; AntGroup, Hangzhou, China; School of Computer Science and Technology, East China Normal University, Shanghai, China|The fraudulent insurance claim is critical for the insurance industry. Insurance companies or agency platforms aim to confidently estimate the fraud risk of claims by gathering data from various sources. Although more data sources can improve the estimation accuracy, they inevitably lead to increased costs. Therefore, a great challenge of fraud risk verification lies in well balancing these two aspects. To this end, this paper proposes a framework named cost-efficient fraud risk optimization with submodularity (CEROS) to optimize the process of fraud risk verification. CEROS efficiently allocates investigation resources across multiple information sources, balancing the trade-off between accuracy and cost. CEROS consists of two parts that we propose: a submodular set-wise classification model called SSCM to estimate the submodular objective function, and a primal-dual algorithm with segmentation point called PDA-SP to solve the objective function. Specifically, SSCM models the fraud probability associated with multiple information sources and ensures the properties of submodularity of fraud risk without making independence assumption. The submodularity in SSCM enables PDA-SP to significantly speed up dual optimization. Theoretically, we disclose that when PDA-SP optimizes this dual optimization problem, the process is monotonicity. Finally, the trade-off coefficients output by PDA-SP that balance accuracy and cost in fraud risk verification are applied to online insurance claim decision-making. We conduct experiments on offline trials and online A/B tests in two business areas at Alipay: healthcare insurance recommendation and claim verification. The extensive results indicate that, compared with other methods, CEROS achieves acceleration of 66.9% in convergence speed and meanwhile 18.8% in cost reduction. Currently, CEROS has been successfully deployed in Alipay.|欺诈性保险索赔对保险业至关重要。保险公司或代理平台的目的是通过从各种来源收集数据，有信心地估计索赔的欺诈风险。尽管更多的数据源可以提高估计的准确性，但它们不可避免地会导致成本增加。因此，欺诈风险验证的一个巨大挑战就是如何很好地平衡这两个方面。为此，本文提出了一种基于次模块化的具有成本效益的欺诈风险优化(CEROS)框架来优化欺诈风险验证过程。CEROS 在多个信息来源之间有效地分配调查资源，在准确性和成本之间取得平衡。CEROS 由两部分组成: 子模块集合分类模型 SSCM 估计子模块目标函数，原始-对偶算法 PDA-SP 求解目标函数。具体来说，SSCM 建立了与多个信息源相关的欺诈概率模型，保证了欺诈风险的子模块性，而没有做出独立的假设。SSCM 的次模块性使得 PDA-SP 可以大大加快双重优化的速度。理论上，我们揭示了当 PDA-SP 优化这个双最佳化问题时，过程是单调的。最后，将 PDA-SP 输出的权衡系数应用于在线保险索赔决策中，平衡了欺诈风险验证的准确性和成本。我们在支付宝的两个业务领域进行离线试验和在线 A/B 测试: 医疗保险推荐和理赔验证。广泛的结果表明，与其他方法相比，CEROS 算法在收敛速度上提高了66.9% ，同时降低了成本18.8% 。目前，CEROS 已经成功地部署在支付宝上。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Cost-Efficient+Fraud+Risk+Optimization+with+Submodularity+in+Insurance+Claim)|0| +|[A Deep Prediction Framework for Multi-Source Information via Heterogeneous GNN](https://doi.org/10.1145/3637528.3671966)|Zhen Wu, Jingya Zhou, Jinghui Zhang, Ling Liu, Chizhou Huang|; School of Computer Science, Georgia Institute of Technology, Atlanta, USA; School of Computer Science and Engineering, Southeast University, Nanjing, China; School of Computer Science and Technology, Soochow University, Suzhou, China|Predicting information diffusion is a fundamental task in online social networks (OSNs). Recent studies mainly focus on the popularity prediction of specific content but ignore the correlation between multiple pieces of information. The topic is often used to correlate such information and can correspond to multi-source information. The popularity of a topic relies not only on information diffusion time but also on users' followership. Current solutions concentrate on hard time partition, lacking versatility. Meanwhile, the hop-based sampling adopted in state-of-the-art (SOTA) methods encounters redundant user followership. Moreover, many SOTA methods are not designed with good modularity and lack evaluation for each functional module and enlightening discussion. This paper presents a novel extensible framework, coined as HIF, for effective popularity prediction in OSNs with four original contributions. First, HIF adopts a soft partition of users and time intervals to better learn users' behavioral preferences over time. Second, HIF utilizes weighted sampling to optimize the construction of heterogeneous graphs and reduce redundancy. Furthermore, HIF supports multi-task collaborative optimization to improve its learning capability. Finally, as an extensible framework, HIF provides generic module slots to combine different submodules (e.g., RNNs, Transformer encoders). Experiments show that HIF significantly improves performance and interpretability compared to SOTAs.|预测信息扩散是在线社交网络(OSNs)的一项基本任务。目前的研究主要集中在对特定内容的流行程度进行预测，而忽略了多个信息片段之间的相关性。该主题通常用于关联此类信息，并且可以对应于多源信息。一个话题的受欢迎程度不仅取决于信息的传播时间，还取决于用户的追随者。目前的解决方案集中在硬时间划分，缺乏通用性。同时，最先进的 SOTA 方法中采用的基于跳数的抽样方法遇到了冗余的用户跟随问题。此外，许多 SOTA 方法的设计缺乏良好的模块性，缺乏对各功能模块的评价和有启发性的讨论。本文提出了一个新的可扩展框架，称为 HIF，用于有效预测开源网络的流行程度。首先，HIF 采用用户的软分区和时间间隔，以更好地了解用户的行为偏好随着时间的推移。其次，HIF 利用加权抽样优化异构图的构造，减少冗余;。此外，HIF 还支持多任务协同优化，以提高其学习能力。最后，作为一个可扩展的框架，HIF 提供了通用的模块插槽来组合不同的子模块(例如，RNN、 Transformer 编码器)。实验表明，与 SOTA 相比，HIF 显著提高了性能和可解释性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Deep+Prediction+Framework+for+Multi-Source+Information+via+Heterogeneous+GNN)|0| |[Fast Computation of Kemeny's Constant for Directed Graphs](https://doi.org/10.1145/3637528.3671859)|Haisong Xia, Zhongzhi Zhang|Fudan University, Shanghai, China|Kemeny's constant for random walks on a graph is defined as the mean hitting time from one node to another selected randomly according to the stationary distribution. It has found numerous applications and attracted considerable research interest. However, exact computation of Kemeny's constant requires matrix inversion, which scales poorly for large networks with millions of nodes. Existing approximation algorithms either leverage properties exclusive to undirected graphs or involve inefficient simulation, leaving room for further optimization. To address these limitations for directed graphs, we propose two novel approximation algorithms for estimating Kemeny's constant on directed graphs with theoretical error guarantees. Extensive numerical experiments on real-world networks validate the superiority of our algorithms over baseline methods in terms of efficiency and accuracy.|图上随机游动的 Kemeny 常数定义为根据平稳分布随机选择的从一个节点到另一个节点的平均到达时间。它已经发现了许多应用，并引起了相当大的研究兴趣。然而，精确计算 Kemeny 常数需要矩阵求逆，这对于有数百万个节点的大型网络来说是很困难的。现有的近似算法要么利用无向图的特性，要么涉及低效的仿真，为进一步的优化留下了空间。针对有向图的这些局限性，我们提出了两种新的近似算法来估计有向图上的 Kemeny 常数，并给出了理论误差保证。在实际网络上的大量数值实验验证了我们的算法在效率和准确性方面优于基线方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Fast+Computation+of+Kemeny's+Constant+for+Directed+Graphs)|0| -|[FLea: Addressing Data Scarcity and Label Skew in Federated Learning via Privacy-preserving Feature Augmentation](https://doi.org/10.1145/3637528.3671899)|Tong Xia, Abhirup Ghosh, Xinchi Qiu, Cecilia Mascolo|University of Birmingham & University of Cambridge, Birmingham, United Kingdom; University of Cambridge, Cambridge, United Kingdom|Federated Learning (FL) enables model development by leveraging data distributed across numerous edge devices without transferring local data to a central server. However, existing FL methods still face challenges when dealing with scarce and label-skewed data across devices, resulting in local model overfitting and drift, consequently hindering the performance of the global model. In response to these challenges, we propose a pioneering framework called FLea, incorporating the following key components: i) A global feature buffer that stores activation-target pairs shared from multiple clients to support local training. This design mitigates local model drift caused by the absence of certain classes; ii) A feature augmentation approach based on local and global activation mix-ups for local training. This strategy enlarges the training samples, thereby reducing the risk of local overfitting; iii) An obfuscation method to minimize the correlation between intermediate activations and the source data, enhancing the privacy of shared features. To verify the superiority of FLea, we conduct extensive experiments using a wide range of data modalities, simulating different levels of local data scarcity and label skew. The results demonstrate that FLea consistently outperforms state-of-the-art FL counterparts (among 13 of the experimented 18 settings, the improvement is over 5%) while concurrently mitigating the privacy vulnerabilities associated with shared features. Code is available at https://github.com/XTxiatong/FLea.git|联邦学习(Federated Learning，FL)通过利用分布在众多边缘设备上的数据实现模型开发，而无需将本地数据传输到中央服务器。然而，现有的 FL 方法在跨设备处理稀缺和标签倾斜的数据时仍然面临挑战，导致局部模型过拟合和漂移，从而阻碍了全局模型的性能。为了应对这些挑战，我们提出了一个名为 FLea 的开创性框架，其中包含以下关键组件: i)一个全局功能缓冲区，存储多个客户共享的激活-目标对，以支持本地培训。该设计缓解了由于缺少特定类而引起的局部模型漂移问题; ii)一种基于局部和全局激活混合的局部训练特征增强方法。该策略扩大了训练样本，从而降低了局部过拟合的风险; 三)模糊化方法，最小化中间激活和源数据之间的相关性，增强了共享特征的隐私性。为了验证 FLea 的优越性，我们使用广泛的数据模式进行了广泛的实验，模拟了不同程度的局部数据稀缺性和标签倾斜。结果表明，FLea 始终优于最先进的 FL 同类产品(在试验的18种设置中，13种设置的改进超过5%) ，同时缓解了与共享功能相关的隐私漏洞。密码可于 https://github.com/xtxiatong/flea.git 索取|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=FLea:+Addressing+Data+Scarcity+and+Label+Skew+in+Federated+Learning+via+Privacy-preserving+Feature+Augmentation)|0| -|[Motif-Consistent Counterfactuals with Adversarial Refinement for Graph-level Anomaly Detection](https://doi.org/10.1145/3637528.3672050)|Chunjing Xiao, Shikang Pang, Wenxin Tai, Yanlong Huang, Goce Trajcevski, Fan Zhou|University of Electronic Science and Technology of China, Chengdu, China; Henan University, Kaifeng, China; Iowa State University, Ames, IA, USA|Graph-level anomaly detection is significant in diverse domains. To improve detection performance, counterfactual graphs have been exploited to benefit the generalization capacity by learning causal relations. Most existing studies directly introduce perturbations (e.g., flipping edges) to generate counterfactual graphs, which are prone to alter the semantics of generated examples and make them off the data manifold, resulting in sub-optimal performance. To address these issues, we propose a novel approach, Motif-consistent Counterfactuals with Adversarial Refinement (MotifCAR), for graph-level anomaly detection. The model combines the motif of one graph, the core subgraph containing the identification (category) information, and the contextual subgraph (non-motif) of another graph to produce a raw counterfactual graph. However, the produced raw graph might be distorted and cannot satisfy the important counterfactual properties: Realism, Validity, Proximity and Sparsity. Towards that, we present a Generative Adversarial Network (GAN)-based graph optimizer to refine the raw counterfactual graphs. It adopts the discriminator to guide the generator to generate graphs close to realistic data, i.e., meet the property Realism. Further, we design the motif consistency to force the motif of the generated graphs to be consistent with the realistic graphs, meeting the property Validity. Also, we devise the contextual loss and connection loss to control the contextual subgraph and the newly added links to meet the properties Proximity and Sparsity. As a result, the model can generate high-quality counterfactual graphs. Experiments demonstrate the superiority of MotifCAR.|图形级别的异常检测在不同的领域有着重要的意义。为了提高检测性能，通过学习因果关系，利用反事实图来提高泛化能力。大多数现有的研究直接引入扰动(例如，翻转边缘)来生成反事实图，这容易改变生成的例子的语义，使它们脱离数据流形，导致次优性能。为了解决这些问题，我们提出了一种新的方法——基于对抗精炼的主题一致反事实异常检测(motifCAR)。该模型将一个图的主题、包含识别(类别)信息的核心子图和另一个图的上下文子图(非主题)结合起来，生成一个原始的反事实图。然而，生成的原始图可能会失真，不能满足重要的反事实性质: 现实性、有效性、接近性和稀疏性。为此，我们提出了一个基于生成对抗网络(GAN)的图优化器来细化原始的反事实图。它采用鉴别器引导生成器生成接近真实数据的图形，即满足真实感的性质。进一步，我们设计了基元一致性来强制生成的图的基元与现实图的基元一致，满足性质的有效性。此外，我们设计了上下文丢失和连接丢失来控制上下文子图和新增加的链接，以满足接近性和稀疏性的性质。因此，该模型可以生成高质量的反事实图。实验证明了 MotifCAR 的优越性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Motif-Consistent+Counterfactuals+with+Adversarial+Refinement+for+Graph-level+Anomaly+Detection)|0| -|[ReFound: Crafting a Foundation Model for Urban Region Understanding upon Language and Visual Foundations](https://doi.org/10.1145/3637528.3671992)|Congxi Xiao, Jingbo Zhou, Yixiong Xiao, Jizhou Huang, Hui Xiong|; Baidu Inc., Beijing, China; Business Intelligence Lab, Baidu Research, Beijing, China|Understanding urban regional characteristics is pivotal in driving critical insights for urban planning and management. We have witnessed the successful application of pre-trained Foundation Models (FMs) in generating universal representations for various downstream tasks. However, applying this principle to the geospatial domain remains challenging, primarily due to the difficulty of gathering extensive data for developing a dedicated urban foundation model. Though there have been some attempts to empower the existing FMs with urban data, most of them focus on single-modality FMs without considering the multi-modality nature of urban region understanding tasks. To address this gap, we introduce ReFound - a novel framework for Re-training a Foundation model for urban region understanding, harnessing the strengths of both language and visual FMs. In this framework, we first invent a Mixture-of-Geospatial-Expert (MoGE) Transformer, to effectively integrate the embedding of multi-source geospatial data. Building on this, ReFound is enhanced by jointly distilling knowledge from language, visual, and visual-language FMs respectively, thus augmenting its generalization capabilities. Meanwhile, we design a masked geospatial data modeling approach alongside a cross-modal spatial alignment mechanism, to enhance the spatial knowledge of ReFound derived from geospatial data. Extensive experiments conducted on six real-world datasets over three urban region understanding tasks demonstrate the superior performance of our framework.|理解城市区域特征对于推动城市规划和管理的关键见解至关重要。我们已经见证了预先训练的基础模型(FM)在为各种下游任务生成通用表示方面的成功应用。然而，将这一原则应用于地理空间领域仍然具有挑战性，主要是因为难以收集广泛的数据来开发一个专门的城市地基模型。虽然已经有一些尝试将城市数据赋予现有的建筑模型，但大多数侧重于单一模式的建筑模型，而没有考虑到城市区域理解任务的多模式性质。为了解决这一差距，我们引入了 ReFound-一个新的框架，用于再培训城市地区理解的基础模型，同时利用语言和视觉模型的优势。在这个框架中，我们首先发明了一个混合地理空间专家(MoGE)转换器，以有效地整合多源地理空间数据的嵌入。在此基础上，通过分别从语言、可视和可视语言调用表中联合提取知识，增强了 ReFound 的泛化能力。同时，我们设计了一个隐蔽的地理空间数据建模方法和一个跨模态的空间对齐机制，以增强从地理空间数据中获取的 ReFind 的空间知识。在三个城市区域理解任务的六个真实世界数据集上进行的大量实验证明了我们的框架的优越性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=ReFound:+Crafting+a+Foundation+Model+for+Urban+Region+Understanding+upon+Language+and+Visual+Foundations)|0| -|[How to Avoid Jumping to Conclusions: Measuring the Robustness of Outstanding Facts in Knowledge Graphs](https://doi.org/10.1145/3637528.3671763)|Hanhua Xiao, Yuchen Li, Yanhao Wang, Panagiotis Karras, Kyriakos Mouratidis, Natalia Rozalia Avlona|Singapore Management University, Singapore, Singapore; University of Copenhagen, Copenhagen, Denmark; East China Normal University, Shanghai, China|An outstanding fact (OF) is a striking claim by which some entities stand out from their peers on some attribute. OFs serve data journalism, fact checking, and recommendation. However, one could jump to conclusions by selecting truthful OFs while intentionally or inadvertently ignoring lateral contexts and data that render them less striking. This jumping conclusion bias from unstable OFs may disorient the public, including voters and consumers, raising concerns about fairness and transparency in political and business competition. It is thus ethically imperative for several stakeholders to measure the robustness of OFs with respect to lateral contexts and data. Unfortunately, a capacity for such inspection of OFs mined from knowledge graphs (KGs) is missing. In this paper, we propose a methodology that inspects the robustness of OFs in KGs by perturbation analysis. We define (1) entity perturbation, which detects outlying contexts by perturbing context entities in the OF; and (2) data perturbation, which considers plausible data that render an OF less striking. We compute the expected strikingness scores of OFs over perturbation relevance distributions and assess an OF as robust if its measured strikingness does not deviate significantly from the expected. We devise a suite of exact and sampling algorithms for perturbation analysis on large KGs. Extensive experiments reveal that our methodology accurately and efficiently detects frail OFs generated by existing mining approaches on KGs. We also show the effectiveness of our approaches through case and user studies.|一个突出的事实(OF)是一个引人注目的主张，其中一些实体脱颖而出，从他们的同行在某些属性。开放式数据库服务于数据新闻、事实核查和推荐。然而，人们可以通过选择真实的 OFs，而有意或无意地忽略使它们不那么引人注目的横向背景和数据，从而直接得出结论。来自不稳定开放式基金的这种跳跃性结论偏见，可能会让包括选民和消费者在内的公众感到迷惑，从而引发人们对政治和商业竞争中的公平性和透明度的担忧。因此，从伦理上讲，若干利益攸关方必须衡量开放式框架在横向背景和数据方面的稳健性。不幸的是，从知识图(KGs)中挖掘的 OFs 缺乏这种检查能力。在本文中，我们提出了一种方法，检查的稳健性开关在 KG 的摄动分析。我们定义了(1)实体扰动，它通过扰动 OF 中的上下文实体来检测外围上下文; (2)数据扰动，它考虑使 OF 不那么引人注目的似是而非的数据。我们计算了扰动相关分布上的运算符的预期显著性分数，并且如果它的测量显著性没有显著偏离预期，那么它就是稳健的。我们设计了一套精确的采样算法用于大型 KG 的摄动分析。广泛的实验表明，我们的方法准确和有效地检测脆弱的开放的现有采矿方法产生的幼稚园。我们还通过案例和用户研究展示了我们的方法的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=How+to+Avoid+Jumping+to+Conclusions:+Measuring+the+Robustness+of+Outstanding+Facts+in+Knowledge+Graphs)|0| +|[FLea: Addressing Data Scarcity and Label Skew in Federated Learning via Privacy-preserving Feature Augmentation](https://doi.org/10.1145/3637528.3671899)|Tong Xia, Abhirup Ghosh, Xinchi Qiu, Cecilia Mascolo|University of Cambridge, Cambridge, United Kingdom; University of Birmingham & University of Cambridge, Birmingham, United Kingdom|Federated Learning (FL) enables model development by leveraging data distributed across numerous edge devices without transferring local data to a central server. However, existing FL methods still face challenges when dealing with scarce and label-skewed data across devices, resulting in local model overfitting and drift, consequently hindering the performance of the global model. In response to these challenges, we propose a pioneering framework called FLea, incorporating the following key components: i) A global feature buffer that stores activation-target pairs shared from multiple clients to support local training. This design mitigates local model drift caused by the absence of certain classes; ii) A feature augmentation approach based on local and global activation mix-ups for local training. This strategy enlarges the training samples, thereby reducing the risk of local overfitting; iii) An obfuscation method to minimize the correlation between intermediate activations and the source data, enhancing the privacy of shared features. To verify the superiority of FLea, we conduct extensive experiments using a wide range of data modalities, simulating different levels of local data scarcity and label skew. The results demonstrate that FLea consistently outperforms state-of-the-art FL counterparts (among 13 of the experimented 18 settings, the improvement is over 5%) while concurrently mitigating the privacy vulnerabilities associated with shared features. Code is available at https://github.com/XTxiatong/FLea.git|联邦学习(Federated Learning，FL)通过利用分布在众多边缘设备上的数据实现模型开发，而无需将本地数据传输到中央服务器。然而，现有的 FL 方法在跨设备处理稀缺和标签倾斜的数据时仍然面临挑战，导致局部模型过拟合和漂移，从而阻碍了全局模型的性能。为了应对这些挑战，我们提出了一个名为 FLea 的开创性框架，其中包含以下关键组件: i)一个全局功能缓冲区，存储多个客户共享的激活-目标对，以支持本地培训。该设计缓解了由于缺少特定类而引起的局部模型漂移问题; ii)一种基于局部和全局激活混合的局部训练特征增强方法。该策略扩大了训练样本，从而降低了局部过拟合的风险; 三)模糊化方法，最小化中间激活和源数据之间的相关性，增强了共享特征的隐私性。为了验证 FLea 的优越性，我们使用广泛的数据模式进行了广泛的实验，模拟了不同程度的局部数据稀缺性和标签倾斜。结果表明，FLea 始终优于最先进的 FL 同类产品(在试验的18种设置中，13种设置的改进超过5%) ，同时缓解了与共享功能相关的隐私漏洞。密码可于 https://github.com/xtxiatong/flea.git 索取|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=FLea:+Addressing+Data+Scarcity+and+Label+Skew+in+Federated+Learning+via+Privacy-preserving+Feature+Augmentation)|0| +|[Motif-Consistent Counterfactuals with Adversarial Refinement for Graph-level Anomaly Detection](https://doi.org/10.1145/3637528.3672050)|Chunjing Xiao, Shikang Pang, Wenxin Tai, Yanlong Huang, Goce Trajcevski, Fan Zhou|Iowa State University, Ames, IA, USA; University of Electronic Science and Technology of China, Chengdu, China; Henan University, Kaifeng, China|Graph-level anomaly detection is significant in diverse domains. To improve detection performance, counterfactual graphs have been exploited to benefit the generalization capacity by learning causal relations. Most existing studies directly introduce perturbations (e.g., flipping edges) to generate counterfactual graphs, which are prone to alter the semantics of generated examples and make them off the data manifold, resulting in sub-optimal performance. To address these issues, we propose a novel approach, Motif-consistent Counterfactuals with Adversarial Refinement (MotifCAR), for graph-level anomaly detection. The model combines the motif of one graph, the core subgraph containing the identification (category) information, and the contextual subgraph (non-motif) of another graph to produce a raw counterfactual graph. However, the produced raw graph might be distorted and cannot satisfy the important counterfactual properties: Realism, Validity, Proximity and Sparsity. Towards that, we present a Generative Adversarial Network (GAN)-based graph optimizer to refine the raw counterfactual graphs. It adopts the discriminator to guide the generator to generate graphs close to realistic data, i.e., meet the property Realism. Further, we design the motif consistency to force the motif of the generated graphs to be consistent with the realistic graphs, meeting the property Validity. Also, we devise the contextual loss and connection loss to control the contextual subgraph and the newly added links to meet the properties Proximity and Sparsity. As a result, the model can generate high-quality counterfactual graphs. Experiments demonstrate the superiority of MotifCAR.|图形级别的异常检测在不同的领域有着重要的意义。为了提高检测性能，通过学习因果关系，利用反事实图来提高泛化能力。大多数现有的研究直接引入扰动(例如，翻转边缘)来生成反事实图，这容易改变生成的例子的语义，使它们脱离数据流形，导致次优性能。为了解决这些问题，我们提出了一种新的方法——基于对抗精炼的主题一致反事实异常检测(motifCAR)。该模型将一个图的主题、包含识别(类别)信息的核心子图和另一个图的上下文子图(非主题)结合起来，生成一个原始的反事实图。然而，生成的原始图可能会失真，不能满足重要的反事实性质: 现实性、有效性、接近性和稀疏性。为此，我们提出了一个基于生成对抗网络(GAN)的图优化器来细化原始的反事实图。它采用鉴别器引导生成器生成接近真实数据的图形，即满足真实感的性质。进一步，我们设计了基元一致性来强制生成的图的基元与现实图的基元一致，满足性质的有效性。此外，我们设计了上下文丢失和连接丢失来控制上下文子图和新增加的链接，以满足接近性和稀疏性的性质。因此，该模型可以生成高质量的反事实图。实验证明了 MotifCAR 的优越性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Motif-Consistent+Counterfactuals+with+Adversarial+Refinement+for+Graph-level+Anomaly+Detection)|0| +|[ReFound: Crafting a Foundation Model for Urban Region Understanding upon Language and Visual Foundations](https://doi.org/10.1145/3637528.3671992)|Congxi Xiao, Jingbo Zhou, Yixiong Xiao, Jizhou Huang, Hui Xiong|; Business Intelligence Lab, Baidu Research, Beijing, China; Baidu Inc., Beijing, China|Understanding urban regional characteristics is pivotal in driving critical insights for urban planning and management. We have witnessed the successful application of pre-trained Foundation Models (FMs) in generating universal representations for various downstream tasks. However, applying this principle to the geospatial domain remains challenging, primarily due to the difficulty of gathering extensive data for developing a dedicated urban foundation model. Though there have been some attempts to empower the existing FMs with urban data, most of them focus on single-modality FMs without considering the multi-modality nature of urban region understanding tasks. To address this gap, we introduce ReFound - a novel framework for Re-training a Foundation model for urban region understanding, harnessing the strengths of both language and visual FMs. In this framework, we first invent a Mixture-of-Geospatial-Expert (MoGE) Transformer, to effectively integrate the embedding of multi-source geospatial data. Building on this, ReFound is enhanced by jointly distilling knowledge from language, visual, and visual-language FMs respectively, thus augmenting its generalization capabilities. Meanwhile, we design a masked geospatial data modeling approach alongside a cross-modal spatial alignment mechanism, to enhance the spatial knowledge of ReFound derived from geospatial data. Extensive experiments conducted on six real-world datasets over three urban region understanding tasks demonstrate the superior performance of our framework.|理解城市区域特征对于推动城市规划和管理的关键见解至关重要。我们已经见证了预先训练的基础模型(FM)在为各种下游任务生成通用表示方面的成功应用。然而，将这一原则应用于地理空间领域仍然具有挑战性，主要是因为难以收集广泛的数据来开发一个专门的城市地基模型。虽然已经有一些尝试将城市数据赋予现有的建筑模型，但大多数侧重于单一模式的建筑模型，而没有考虑到城市区域理解任务的多模式性质。为了解决这一差距，我们引入了 ReFound-一个新的框架，用于再培训城市地区理解的基础模型，同时利用语言和视觉模型的优势。在这个框架中，我们首先发明了一个混合地理空间专家(MoGE)转换器，以有效地整合多源地理空间数据的嵌入。在此基础上，通过分别从语言、可视和可视语言调用表中联合提取知识，增强了 ReFound 的泛化能力。同时，我们设计了一个隐蔽的地理空间数据建模方法和一个跨模态的空间对齐机制，以增强从地理空间数据中获取的 ReFind 的空间知识。在三个城市区域理解任务的六个真实世界数据集上进行的大量实验证明了我们的框架的优越性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=ReFound:+Crafting+a+Foundation+Model+for+Urban+Region+Understanding+upon+Language+and+Visual+Foundations)|0| +|[How to Avoid Jumping to Conclusions: Measuring the Robustness of Outstanding Facts in Knowledge Graphs](https://doi.org/10.1145/3637528.3671763)|Hanhua Xiao, Yuchen Li, Yanhao Wang, Panagiotis Karras, Kyriakos Mouratidis, Natalia Rozalia Avlona|East China Normal University, Shanghai, China; Singapore Management University, Singapore, Singapore; University of Copenhagen, Copenhagen, Denmark|An outstanding fact (OF) is a striking claim by which some entities stand out from their peers on some attribute. OFs serve data journalism, fact checking, and recommendation. However, one could jump to conclusions by selecting truthful OFs while intentionally or inadvertently ignoring lateral contexts and data that render them less striking. This jumping conclusion bias from unstable OFs may disorient the public, including voters and consumers, raising concerns about fairness and transparency in political and business competition. It is thus ethically imperative for several stakeholders to measure the robustness of OFs with respect to lateral contexts and data. Unfortunately, a capacity for such inspection of OFs mined from knowledge graphs (KGs) is missing. In this paper, we propose a methodology that inspects the robustness of OFs in KGs by perturbation analysis. We define (1) entity perturbation, which detects outlying contexts by perturbing context entities in the OF; and (2) data perturbation, which considers plausible data that render an OF less striking. We compute the expected strikingness scores of OFs over perturbation relevance distributions and assess an OF as robust if its measured strikingness does not deviate significantly from the expected. We devise a suite of exact and sampling algorithms for perturbation analysis on large KGs. Extensive experiments reveal that our methodology accurately and efficiently detects frail OFs generated by existing mining approaches on KGs. We also show the effectiveness of our approaches through case and user studies.|一个突出的事实(OF)是一个引人注目的主张，其中一些实体脱颖而出，从他们的同行在某些属性。开放式数据库服务于数据新闻、事实核查和推荐。然而，人们可以通过选择真实的 OFs，而有意或无意地忽略使它们不那么引人注目的横向背景和数据，从而直接得出结论。来自不稳定开放式基金的这种跳跃性结论偏见，可能会让包括选民和消费者在内的公众感到迷惑，从而引发人们对政治和商业竞争中的公平性和透明度的担忧。因此，从伦理上讲，若干利益攸关方必须衡量开放式框架在横向背景和数据方面的稳健性。不幸的是，从知识图(KGs)中挖掘的 OFs 缺乏这种检查能力。在本文中，我们提出了一种方法，检查的稳健性开关在 KG 的摄动分析。我们定义了(1)实体扰动，它通过扰动 OF 中的上下文实体来检测外围上下文; (2)数据扰动，它考虑使 OF 不那么引人注目的似是而非的数据。我们计算了扰动相关分布上的运算符的预期显著性分数，并且如果它的测量显著性没有显著偏离预期，那么它就是稳健的。我们设计了一套精确的采样算法用于大型 KG 的摄动分析。广泛的实验表明，我们的方法准确和有效地检测脆弱的开放的现有采矿方法产生的幼稚园。我们还通过案例和用户研究展示了我们的方法的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=How+to+Avoid+Jumping+to+Conclusions:+Measuring+the+Robustness+of+Outstanding+Facts+in+Knowledge+Graphs)|0| |[Temporal Prototype-Aware Learning for Active Voltage Control on Power Distribution Networks](https://doi.org/10.1145/3637528.3671790)|Feiyang Xu, Shunyu Liu, Yunpeng Qing, Yihe Zhou, Yuwen Wang, Mingli Song||Active Voltage Control (AVC) on the Power Distribution Networks (PDNs) aims to stabilize the voltage levels to ensure efficient and reliable operation of power systems. With the increasing integration of distributed energy resources, recent efforts have explored employing multi-agent reinforcement learning (MARL) techniques to realize effective AVC. Existing methods mainly focus on the acquisition of short-term AVC strategies, i.e., only learning AVC within the short-term training trajectories of a singular diurnal cycle. However, due to the dynamic nature of load demands and renewable energy, the operation states of real-world PDNs may exhibit significant distribution shifts across varying timescales (e.g., daily and seasonal changes). This can render those short-term strategies suboptimal or even obsolete when performing continuous AVC over extended periods. In this paper, we propose a novel temporal prototype-aware learning method, abbreviated as TPA, to learn time-adaptive AVC under short-term training trajectories. At the heart of TPA are two complementary components, namely multi-scale dynamic encoder and temporal prototype-aware policy, that can be readily incorporated into various MARL methods. The former component integrates a stacked transformer network to learn underlying temporal dependencies at different timescales of the PDNs, while the latter implements a learnable prototype matching mechanism to construct a dedicated AVC policy that can dynamically adapt to the evolving operation states. Experimental results on the AVC benchmark with different PDN sizes demonstrate that the proposed TPA surpasses the state-of-the-art counterparts not only in terms of control performance but also by offering model transferability. Our code is available at https://github.com/Canyizl/TPA-for-AVC.|配电网有源电压控制(AVC)的目的是稳定电压水平，以确保电力系统的高效和可靠运行。随着分布式能源资源的日益整合，最近人们开始探索使用多代理强化学习技术来实现有效的 AVC。现有的方法主要集中在短期 AVC 策略的获取上，即只学习周期为单日周期的短期 AVC 训练轨迹。然而，由于负荷需求和可再生能源的动态特性，实际 PDN 的运行状态可能会在不同的时间尺度上表现出显著的分布变化(例如，日变化和季节变化)。这可以使这些短期策略次优，甚至过时时，执行连续 AVC 超过延长的时间。本文提出了一种新的时间原型感知学习方法，简称 TPA，以学习短期训练轨迹下的时间自适应 AVC。TPA 的核心是两个互补的组成部分，即多尺度动态编码器和时间原型感知策略，可以很容易地并入各种 MARL 方法。前者集成了一个叠加的变压器网络来学习 PDN 在不同时间尺度上的时间依赖关系，而后者实现了一个可学习的原型匹配机制来构造一个专用的 AVC 策略，该策略能够动态地适应不断变化的运行状态。在不同 PDN 尺寸的 AVC 基准上的实验结果表明，所提出的 TPA 不仅在控制性能方面优于最先进的 AVC 基准，而且提供了模型的可转移性。我们的代码可以在 https://github.com/canyizl/tpa-for-avc 找到。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Temporal+Prototype-Aware+Learning+for+Active+Voltage+Control+on+Power+Distribution+Networks)|0| |[FlexCare: Leveraging Cross-Task Synergy for Flexible Multimodal Healthcare Prediction](https://doi.org/10.1145/3637528.3671974)|Muhao Xu, Zhenfeng Zhu, Youru Li, Shuai Zheng, Yawei Zhao, Kunlun He, Yao Zhao|; Medical Big Data Research Center, Chinese PLA General Hospital, Beijing, China|Multimodal electronic health record (EHR) data can offer a holistic assessment of a patient's health status, supporting various predictive healthcare tasks. Recently, several studies have embraced the multitask learning approach in the healthcare domain, exploiting the inherent correlations among clinical tasks to predict multiple outcomes simultaneously. However, existing methods necessitate samples to possess complete labels for all tasks, which places heavy demands on the data and restricts the flexibility of the model. Meanwhile, within a multitask framework with multimodal inputs, how to comprehensively consider the information disparity among modalities and among tasks still remains a challenging problem. To tackle these issues, a unified healthcare prediction model, also named by FlexCare, is proposed to flexibly accommodate incomplete multimodal inputs, promoting the adaption to multiple healthcare tasks. The proposed model breaks the conventional paradigm of parallel multitask prediction by decomposing it into a series of asynchronous single-task prediction. Specifically, a task-agnostic multimodal information extraction module is presented to capture decorrelated representations of diverse intra- and inter-modality patterns. Taking full account of the information disparities between different modalities and different tasks, we present a task-guided hierarchical multimodal fusion module that integrates the refined modality-level representations into an individual patient-level representation. Experimental results on multiple tasks from MIMIC-IV/MIMIC-CXR/MIMIC-NOTE datasets demonstrate the effectiveness of the proposed method. Additionally, further analysis underscores the feasibility and potential of employing such a multitask strategy in the healthcare domain. The source code is available at https://github.com/mhxu1998/FlexCare **REMOVE 2nd URL**://github.com/mhxu1998/FlexCare.|多模式电子健康记录(EHR)数据可以提供对患者健康状况的全面评估，支持各种预测性医疗任务。最近，一些研究已经在医疗保健领域采用了多任务学习方法，利用临床任务之间固有的相关性来同时预测多个结果。然而，现有的方法要求样本对所有任务都具有完整的标签，这对数据的要求很高，限制了模型的灵活性。同时，在具有多模式输入的多任务框架内，如何全面考虑模式间和任务间的信息差异仍然是一个具有挑战性的问题。为了解决这些问题，提出了一个统一的医疗保健预测模型，也被称为 FlexCare，以灵活地适应不完整的多模式输入，促进适应多种医疗保健任务。该模型打破了传统的并行多任务预测模型，将其分解为一系列异步单任务预测模型。具体来说，提出了一个任务无关的多模态信息抽取模块，以捕获不同的内部和跨模态模式的去相关表示。充分考虑到不同模式和不同任务之间的信息差异，我们提出了一个任务指导的分层多模式融合模块，该模块将精细的模式级表示集成到个体患者级表示中。对 MIMIC-IV/MIMIC-CXR/MIMIC-NOTE 数据集的多任务实验结果表明了该方法的有效性。此外，进一步的分析强调了在医疗领域采用这种多任务策略的可行性和潜力。源代码可以在 https://github.com/mhxu1998/flexcare * * 删除第二个网址 * * :// github.com/mhxu1998/flexcare。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=FlexCare:+Leveraging+Cross-Task+Synergy+for+Flexible+Multimodal+Healthcare+Prediction)|0| |[PeFAD: A Parameter-Efficient Federated Framework for Time Series Anomaly Detection](https://doi.org/10.1145/3637528.3671753)|Ronghui Xu, Hao Miao, Senzhang Wang, Philip S. Yu, Jianxin Wang|Aalborg University, Aalborg, Denmark; Central South University, Changsha, China; University of Illinois at Chicago, Chicago, USA|With the proliferation of mobile sensing techniques, huge amounts of timeseries data are generated and accumulated in various domains, fueling plenty ofreal-world applications. In this setting, time series anomaly detection ispractically important. It endeavors to identify deviant samples from the normalsample distribution in time series. Existing approaches generally assume thatall the time series is available at a central location. However, we arewitnessing the decentralized collection of time series due to the deployment ofvarious edge devices. To bridge the gap between the decentralized time seriesdata and the centralized anomaly detection algorithms, we propose aParameter-efficient Federated Anomaly Detection framework named PeFAD with theincreasing privacy concerns. PeFAD for the first time employs the pre-trainedlanguage model (PLM) as the body of the client's local model, which can benefitfrom its cross-modality knowledge transfer capability. To reduce thecommunication overhead and local model adaptation cost, we propose aparameter-efficient federated training module such that clients only need tofine-tune small-scale parameters and transmit them to the server for update.PeFAD utilizes a novel anomaly-driven mask selection strategy to mitigate theimpact of neglected anomalies during training. A knowledge distillationoperation on a synthetic privacy-preserving dataset that is shared by all theclients is also proposed to address the data heterogeneity issue acrossclients. We conduct extensive evaluations on four real datasets, where PeFADoutperforms existing state-of-the-art baselines by up to 28.74%.|随着移动传感技术的迅速发展，大量的时间序列数据在各个领域中产生和积累，推动了大量的现实应用。在这种情况下，时间序列异常检测实际上非常重要。该方法从时间序列的正态样本分布中识别偏差样本。现有的方法通常假定所有的时间序列都可以在一个中心位置获得。然而，由于各种边缘设备的部署，我们正在目睹时间序列的分散收集。为了弥补分散式时间序列数据和集中式异常检测算法之间的差距，我们提出了一个参数高效的联邦异常检测框架，命名为 PeFAD，并考虑到隐私问题。PeFAD 首次采用预训练语言模型(PLM)作为客户端本地模型的主体，利用其跨模态知识转移能力。为了降低通信开销和本地模型自适应成本，我们提出了参数有效的联邦训练模块，客户端只需微调小规模的参数并将其传输到服务器进行更新。 PeFAD 采用了一种新的异常驱动的掩码选择策略，以减轻训练过程中被忽视的异常的影响。针对跨客户端的数据异构问题，提出了一种基于全客户端共享的综合隐私保护数据集的知识提取方法。我们对四个实际数据集进行了广泛的评估，其中 PEFAD 的性能比现有的最先进的基线高出28.74% 。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=PeFAD:+A+Parameter-Efficient+Federated+Framework+for+Time+Series+Anomaly+Detection)|0| |[ProtoMix: Augmenting Health Status Representation Learning via Prototype-based Mixup](https://doi.org/10.1145/3637528.3671937)|Yongxin Xu, Xinke Jiang, Xu Chu, Yuzhen Xiao, Chaohe Zhang, Hongxin Ding, Junfeng Zhao, Yasha Wang, Bing Xie||With the widespread adoption of electronic health records (EHR) data, deep learning techniques have been broadly utilized for various health prediction tasks. Nevertheless, the labeled data scarcity issue restricts the prediction power of these deep models. To enhance the generalization capability of deep learning models when faced with such situations, a common trend is to train generative adversarial networks (GANs) or diffusion models for data augmentation. However, due to limitations in sample size and potential label imbalance issues, these methods are prone to mode collapse problems. This results in the generation of new samples that fail to preserve the subtype structure within EHR data, thereby limiting their practicality in health prediction tasks that generally require detailed patient phenotyping. Aiming at the above problems, we propose a Prototype-based Mixup method, dubbed ProtoMix, which combines prior knowledge of intrinsic data features from subtype centroids (i.e., prototypes) to guide the synthesis of new samples. Specifically, ProtoMix employs a prototype-guided mixup training task to shift the decision boundary away from the subtypes. Then, ProtoMix optimizes the sampling weights in different areas of the data manifold via a prototype-guided mixup sampling strategy. Throughout the training process, ProtoMix dynamically expands the training distribution using an adaptive mixing coefficient computation method. Experimental evaluations on three real-world datasets demonstrate the efficacy of ProtoMix.|随着电子健康记录(EHR)数据的广泛应用，深度学习技术被广泛应用于各种健康预测任务。然而，标记数据稀缺性问题限制了这些深度模型的预测能力。为了提高深度学习模型在这种情况下的泛化能力，一个共同的趋势是训练生成对抗网络(GAN)或扩散模型的数据增强。然而，由于样本量的限制和潜在的标签不平衡问题，这些方法很容易出现模式崩溃问题。这导致新样本的产生，不能保留 EHR 数据中的亚型结构，从而限制了它们在通常需要详细的患者表型的健康预测任务中的实用性。针对上述问题，本文提出了一种基于原型的混合方法，称之为 ProtoMix，该方法结合子类质心(即原型)内在数据特征的先验知识来指导新样本的合成。具体来说，ProtoMix 采用了一个原型引导的混合训练任务，将决策边界从子类型中转移出来。然后，ProtoMix 通过原型引导的混合采样策略对数据流形中不同区域的采样权重进行优化。在整个训练过程中，ProtoMix 采用自适应混合系数计算方法动态扩展训练分布。通过对三个实际数据集的实验评估，验证了 ProtoMix 的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=ProtoMix:+Augmenting+Health+Status+Representation+Learning+via+Prototype-based+Mixup)|0| -|[FedRoLA: Robust Federated Learning Against Model Poisoning via Layer-based Aggregation](https://doi.org/10.1145/3637528.3671906)|Gang Yan, Hao Wang, Xu Yuan, Jian Li|Stevens Institute of Technology, Hoboken, NJ, USA; University of Delaware, Newark, DE, USA; Stony Brook University, Stony Brook, NY, USA; Binghamton University, Binghamton, NY, USA|Federated Learning (FL) is increasingly vulnerable to model poisoning attacks, where malicious clients degrade the global model's accuracy with manipulated updates. Unfortunately, most existing defenses struggle to handle the scenarios when multiple adversaries exist, and often rely on historical or validation data, rendering them ill-suited for the dynamic and diverse nature of real-world FL environments. Exacerbating these limitations is the fact that most existing defenses also fail to account for the distinctive contributions of Deep Neural Network (DNN) layers in detecting malicious activity, leading to the unnecessary rejection of benign updates. To bridge these gaps, we introduce FedRoLa, a cutting-edge similarity-based defense method optimized for FL. Specifically, FedRoLa leverages global model parameters and client updates independently, moving away from reliance on historical or validation data. It features a unique layer-based aggregation with dynamic layer selection, enhancing threat detection, and includes a dynamic probability method for balanced security and model performance. Through comprehensive evaluations using different DNN models and real-world datasets, FedRoLa demonstrates substantial improvements over the status quo approaches in global model accuracy, achieving up to 4% enhancement in terms of accuracy, reducing false positives to 6.4%, and securing an 92.8% true positive rate.|联邦学习(Federated Learning，FL)越来越容易受到模型中毒攻击的影响，在这种攻击中，恶意客户端通过操纵更新降低全局模型的准确性。不幸的是，当存在多个对手时，大多数现有的防御系统难以处理场景，并且常常依赖于历史数据或验证数据，这使得它们不适合真实世界 FL 环境的动态和多样性。加剧这些局限性的事实是，大多数现有的防御也未能解释深度神经网络(DNN)层在检测恶意活动方面的独特贡献，导致对良性更新的不必要拒绝。为了弥补这些差距，我们引入了 FedRoLa，一种针对 FL 优化的基于前沿相似性的防御方法。具体来说，FedRoLa 独立利用全局模型参数和客户端更新，摆脱了对历史数据或验证数据的依赖。它具有独特的基于层次的聚合特性，采用动态层次选择，增强了威胁检测能力，并提出了一种平衡安全性和模型性能的动态概率方法。通过使用不同的 DNN 模型和真实世界数据集的综合评估，FedRoLa 显示了全球模型准确性方面的现状方法的实质性改进，在准确性方面提高了4% ，将假阳性降低到6.4% ，并确保了92.8% 的真阳性率。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=FedRoLA:+Robust+Federated+Learning+Against+Model+Poisoning+via+Layer-based+Aggregation)|0| +|[FedRoLA: Robust Federated Learning Against Model Poisoning via Layer-based Aggregation](https://doi.org/10.1145/3637528.3671906)|Gang Yan, Hao Wang, Xu Yuan, Jian Li|Stevens Institute of Technology, Hoboken, NJ, USA; Stony Brook University, Stony Brook, NY, USA; University of Delaware, Newark, DE, USA; Binghamton University, Binghamton, NY, USA|Federated Learning (FL) is increasingly vulnerable to model poisoning attacks, where malicious clients degrade the global model's accuracy with manipulated updates. Unfortunately, most existing defenses struggle to handle the scenarios when multiple adversaries exist, and often rely on historical or validation data, rendering them ill-suited for the dynamic and diverse nature of real-world FL environments. Exacerbating these limitations is the fact that most existing defenses also fail to account for the distinctive contributions of Deep Neural Network (DNN) layers in detecting malicious activity, leading to the unnecessary rejection of benign updates. To bridge these gaps, we introduce FedRoLa, a cutting-edge similarity-based defense method optimized for FL. Specifically, FedRoLa leverages global model parameters and client updates independently, moving away from reliance on historical or validation data. It features a unique layer-based aggregation with dynamic layer selection, enhancing threat detection, and includes a dynamic probability method for balanced security and model performance. Through comprehensive evaluations using different DNN models and real-world datasets, FedRoLa demonstrates substantial improvements over the status quo approaches in global model accuracy, achieving up to 4% enhancement in terms of accuracy, reducing false positives to 6.4%, and securing an 92.8% true positive rate.|联邦学习(Federated Learning，FL)越来越容易受到模型中毒攻击的影响，在这种攻击中，恶意客户端通过操纵更新降低全局模型的准确性。不幸的是，当存在多个对手时，大多数现有的防御系统难以处理场景，并且常常依赖于历史数据或验证数据，这使得它们不适合真实世界 FL 环境的动态和多样性。加剧这些局限性的事实是，大多数现有的防御也未能解释深度神经网络(DNN)层在检测恶意活动方面的独特贡献，导致对良性更新的不必要拒绝。为了弥补这些差距，我们引入了 FedRoLa，一种针对 FL 优化的基于前沿相似性的防御方法。具体来说，FedRoLa 独立利用全局模型参数和客户端更新，摆脱了对历史数据或验证数据的依赖。它具有独特的基于层次的聚合特性，采用动态层次选择，增强了威胁检测能力，并提出了一种平衡安全性和模型性能的动态概率方法。通过使用不同的 DNN 模型和真实世界数据集的综合评估，FedRoLa 显示了全球模型准确性方面的现状方法的实质性改进，在准确性方面提高了4% ，将假阳性降低到6.4% ，并确保了92.8% 的真阳性率。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=FedRoLA:+Robust+Federated+Learning+Against+Model+Poisoning+via+Layer-based+Aggregation)|0| |[Team up GBDTs and DNNs: Advancing Efficient and Effective Tabular Prediction with Tree-hybrid MLPs](https://doi.org/10.1145/3637528.3671964)|Jiahuan Yan, Jintai Chen, Qianxing Wang, Danny Z. Chen, Jian Wu|University of Notre Dame, Notre Dame, IN, USA; Zhejiang University, Hangzhou, China; University of Illinois at Urbana-Champaign, Urbana, IL, USA|Tabular datasets play a crucial role in various applications. Thus, developing efficient, effective, and widely compatible prediction algorithms for tabular data is important. Currently, two prominent model types, Gradient Boosted Decision Trees (GBDTs) and Deep Neural Networks (DNNs), have demonstrated performance advantages on distinct tabular prediction tasks. However, selecting an effective model for a specific tabular dataset is challenging, often demanding time-consuming hyperparameter tuning. To address this model selection dilemma, this paper proposes a new framework that amalgamates the advantages of both GBDTs and DNNs, resulting in a DNN algorithm that is as efficient as GBDTs and is competitively effective regardless of dataset preferences for GBDTs or DNNs. Our idea is rooted in an observation that deep learning (DL) offers a larger parameter space that can represent a well-performing GBDT model, yet the current back-propagation optimizer struggles to efficiently discover such optimal functionality. On the other hand, during GBDT development, hard tree pruning, entropy-driven feature gate, and model ensemble have proved to be more adaptable to tabular data. By combining these key components, we present a Tree-hybrid simple MLP (T-MLP). In our framework, a tensorized, rapidly trained GBDT feature gate, a DNN architecture pruning approach, as well as a vanilla back-propagation optimizer collaboratively train a randomly initialized MLP model. Comprehensive experiments show that T-MLP is competitive with extensively tuned DNNs and GBDTs in their dominating tabular benchmarks (88 datasets) respectively, all achieved with compact model storage and significantly reduced training duration. The codes and full experiment results are available at https://github.com/jyansir/tmlp.|表格数据集在各种应用程序中起着至关重要的作用。因此，开发高效、有效和广泛兼容的表格数据预测算法非常重要。目前，两种突出的模型类型，梯度增强决策树(GBDTs)和深度神经网络(DNN) ，已经证明了在不同的表格预测任务的性能优势。然而，为特定的表格数据集选择有效的模型是具有挑战性的，通常需要耗费时间的超参数调优。为了解决这个模型选择困境，本文提出了一个新的框架，融合了 GBDTs 和 DNN 的优势，产生了一个 DNN 算法，它与 GBDTs 一样有效，并且无论 GBDTs 或 DNN 的数据集偏好如何，都具有竞争性。我们的想法植根于一个观察，即深度学习(DL)提供了一个更大的参数空间，可以表示一个性能良好的 GBDT 模型，然而当前的反向传播优化器努力有效地发现这样的最佳功能。另一方面，在 GBDT 开发过程中，硬树剪枝、熵驱动特征门和模型集成等技术对表格数据的适应性更强。通过组合这些关键部分，我们提出了一个树杂交简单 MLP (T-MLP)。在我们的框架中，一个张量化的，快速训练的 GBDT 特征门，一个 DNN 体系结构修剪方法，以及一个普通的反向传播优化器协作训练一个随机初始化的 MLP 模型。综合实验表明，T-MLP 与广泛调整的 DNN 和 GBDTs 分别在其主要的表格基准(88个数据集)上具有竞争力，所有这些都是通过紧凑的模型存储和显著减少训练持续时间来实现的。代码和完整的实验结果可在 https://github.com/jyansir/tmlp 下载。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Team+up+GBDTs+and+DNNs:+Advancing+Efficient+and+Effective+Tabular+Prediction+with+Tree-hybrid+MLPs)|0| |[Efficient Mixture of Experts based on Large Language Models for Low-Resource Data Preprocessing](https://doi.org/10.1145/3637528.3671873)|Mengyi Yan, Yaoshu Wang, Kehan Pang, Min Xie, Jianxin Li|Shenzhen Institute of Computing Sciences, Shenzhen, China; Beihang University, Beijing, China|Data preprocessing (DP) that transforms erroneous and raw data to a clean version is a cornerstone of the data mining pipeline. Due to the diverse requirements of downstream tasks, data scientists and domain experts have to handcraft domain-specific rules or train ML models with annotated examples, which is costly/time-consuming. In this paper, we present MELD (Mixture of Experts on Large Language Models for Data Preprocessing), a universal solver for low-resource DP. MELD adopts a Mixture-of-Experts (MoE) architecture that enables the amalgamation and enhancement of domain-specific experts trained on limited annotated examples. To fine-tune MELD, we develop a suite of expert-tuning and MoE-tuning techniques, including a retrieval augmented generation (RAG) system, meta-path search for data augmentation, expert refinement and router network training based on information bottleneck. To further verify the effectiveness of MELD, we theoretically prove that MoE in MELD is superior than a single expert and the router network is able to dispatch data to the right experts. Finally, we conducted extensive experiments on 19 datasets over 10 DP tasks to show that MELD outperforms the state-of-the-art methods in both effectiveness and efficiency. More importantly, MELD is able to be fine-tuned in a low-resource environment, e.g. a local, single and low-priced 3090 GPU.|数据预处理(DP)将错误和原始数据转换为干净的版本，这是数据挖掘流水线的基石。由于下游任务的不同需求，数据科学家和领域专家不得不手工制定特定领域的规则或用注释示例训练机器学习模型，这是昂贵的/耗时的。在本文中，我们提出了 MELD (大型语言模型数据预处理混合专家) ，一个低资源 DP 的通用解决方案。MELD 采用了一种专家混合体系结构，这种体系结构能够合并和增强受过有限注释示例培训的特定领域专家。为了对 MELD 进行微调，我们开发了一套专家调优和 MoE 调优技术，包括基于信息瓶颈的检索增强生成(RAG)系统、用于数据增强的元路径搜索、专家调优和路由器网络训练。为了进一步验证 MELD 的有效性，我们从理论上证明了 MELD 中的 MoE 优于单个专家，并且路由器网络能够向合适的专家发送数据。最后，我们在10个 DP 任务的19个数据集上进行了广泛的实验，结果表明 MELD 在有效性和效率方面都优于最先进的方法。更重要的是，MELD 能够在低资源环境中进行微调，例如本地、单一和低价格的3090 GPU。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Efficient+Mixture+of+Experts+based+on+Large+Language+Models+for+Low-Resource+Data+Preprocessing)|0| -|[An Efficient Subgraph GNN with Provable Substructure Counting Power](https://doi.org/10.1145/3637528.3671731)|Zuoyu Yan, Junru Zhou, Liangcai Gao, Zhi Tang, Muhan Zhang|Wangxuan Institute of Computer Technology, Peking University, Beijing, China; Beijing Institute for General Artificial Intelligence, Peking University, Beijing, China; Institute for Artificial Intelligence, Peking University, Beijing, China|We investigate the enhancement of graph neural networks' (GNNs)representation power through their ability in substructure counting. Recentadvances have seen the adoption of subgraph GNNs, which partition an inputgraph into numerous subgraphs, subsequently applying GNNs to each to augmentthe graph's overall representation. Despite their ability to identify varioussubstructures, subgraph GNNs are hindered by significant computational andmemory costs. In this paper, we tackle a critical question: Is it possible forGNNs to count substructures both efficiently and provably?Our approach begins with a theoretical demonstration that the distance torooted nodes in subgraphs is key to boosting the counting power of subgraphGNNs. To avoid the need for repetitively applying GNN across all subgraphs, weintroduce precomputed structural embeddings that encapsulate this crucialdistance information. Experiments validate that our proposed model retains thecounting power of subgraph GNNs while achieving significantly fasterperformance.|我们研究了通过图神经网络子结构计数的能力来增强其表示能力的问题。最近的进步已经看到了子图 GNN 的采用，它将输入图划分为许多子图，随后将 GNN 应用于每个子图以增强图的整体表示。尽管子图 GNN 具有识别各种子结构的能力，但是它们受到计算和存储成本的限制。在本文中，我们处理了一个关键问题: GNN 是否有可能同时有效和可证明地计算子结构？我们的方法首先从理论上证明了子图中的距离根节点是提高子图 GNN 计数能力的关键。为了避免在所有子图中重复应用 GNN，我们引入了预计算结构嵌入，它封装了这个关键的距离信息。实验验证了该模型在保持子图 GNN 计数能力的同时，显著提高了性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=An+Efficient+Subgraph+GNN+with+Provable+Substructure+Counting+Power)|0| +|[An Efficient Subgraph GNN with Provable Substructure Counting Power](https://doi.org/10.1145/3637528.3671731)|Zuoyu Yan, Junru Zhou, Liangcai Gao, Zhi Tang, Muhan Zhang|Institute for Artificial Intelligence, Peking University, Beijing, China; Beijing Institute for General Artificial Intelligence, Peking University, Beijing, China; Wangxuan Institute of Computer Technology, Peking University, Beijing, China|We investigate the enhancement of graph neural networks' (GNNs)representation power through their ability in substructure counting. Recentadvances have seen the adoption of subgraph GNNs, which partition an inputgraph into numerous subgraphs, subsequently applying GNNs to each to augmentthe graph's overall representation. Despite their ability to identify varioussubstructures, subgraph GNNs are hindered by significant computational andmemory costs. In this paper, we tackle a critical question: Is it possible forGNNs to count substructures both efficiently and provably?Our approach begins with a theoretical demonstration that the distance torooted nodes in subgraphs is key to boosting the counting power of subgraphGNNs. To avoid the need for repetitively applying GNN across all subgraphs, weintroduce precomputed structural embeddings that encapsulate this crucialdistance information. Experiments validate that our proposed model retains thecounting power of subgraph GNNs while achieving significantly fasterperformance.|我们研究了通过图神经网络子结构计数的能力来增强其表示能力的问题。最近的进步已经看到了子图 GNN 的采用，它将输入图划分为许多子图，随后将 GNN 应用于每个子图以增强图的整体表示。尽管子图 GNN 具有识别各种子结构的能力，但是它们受到计算和存储成本的限制。在本文中，我们处理了一个关键问题: GNN 是否有可能同时有效和可证明地计算子结构？我们的方法首先从理论上证明了子图中的距离根节点是提高子图 GNN 计数能力的关键。为了避免在所有子图中重复应用 GNN，我们引入了预计算结构嵌入，它封装了这个关键的距离信息。实验验证了该模型在保持子图 GNN 计数能力的同时，显著提高了性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=An+Efficient+Subgraph+GNN+with+Provable+Substructure+Counting+Power)|0| |[Towards Test Time Adaptation via Calibrated Entropy Minimization](https://doi.org/10.1145/3637528.3671672)|Hao Yang, Min Wang, Jinshen Jiang, Yun Zhou|National University of Defense Technology, ChangSha, China|Robust models must demonstrate strong generalizability, even amid environmental changes. However, the complex variability and noise in real-world data often lead to a pronounced performance gap between the training and testing phases. Researchers have recently introduced test-time-domain adaptation (TTA) to address this challenge. TTA methods primarily adapt source-pretrained models to a target domain using only unlabeled test data. This study found that existing TTA methods consider only the largest logit as a pseudo-label and aim to minimize the entropy of test time predictions. This maximizes the predictive confidence of the model. However, this corresponds to the model being overconfident in the local test scenarios. In response, we introduce a novel confidence-calibration loss function called Calibrated Entropy Test-Time Adaptation (CETA), which considers the model's largest logit and the next-highest-ranked one, aiming to strike a balance between overconfidence and underconfidence. This was achieved by incorporating a sample-wise regularization term. We also provide a theoretical foundation for the proposed loss function. Experimentally, our method outperformed existing strategies on benchmark corruption datasets across multiple models, underscoring the efficacy of our approach.|即使在环境变化的情况下，健壮的模型也必须表现出强大的概括性。然而，真实世界数据中复杂的可变性和噪声常常导致训练和测试阶段之间的性能差距。研究人员最近引入了测试时域适应(TTA)来应对这一挑战。TTA 方法主要使用未标记的测试数据将源预先训练的模型适应目标域。本研究发现现有的 TTA 方法只考虑最大 logit 作为伪标签，目的是使测试时间预测的熵最小化。这使模型的预测置信度最大化。然而，这对应于模型在本地测试场景中过度自信。作为回应，我们引入了一种新的置信度校准损失函数，称为校准熵测试时间适应(CETA) ，它考虑了模型的最大 logit 和排名次高的 logit，旨在在过度自信和不自信之间取得平衡。这是通过合并一个样本明智的正则化术语来实现的。我们还为所提出的损失函数提供了理论基础。在实验上，我们的方法在多个模型的基准腐败数据集上优于现有的策略，突出了我们方法的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Towards+Test+Time+Adaptation+via+Calibrated+Entropy+Minimization)|0| -|[Noisy Label Removal for Partial Multi-Label Learning](https://doi.org/10.1145/3637528.3671677)|Fuchao Yang, Yuheng Jia, Hui Liu, Yongqiang Dong, Junhui Hou|School of Computing & Information Sciences, Saint Francis University, Hong Kong, China; College of Software Engineering, Southeast University, Nanjing, China; Department of Computer Science, City University of Hong Kong, Hong Kong, China; School of Computer Science and Engineering, Southeast University, Nanjing, China|This paper addresses the problem of partial multi-label learning (PML), a challenging weakly supervised learning framework, where each sample is associated with a candidate label set comprising both ground-true labels and noisy labels. We theoretically reveal that an increased number of noisy labels in the candidate label set leads to an enlarged generalization error bound, consequently degrading the classification performance. Accordingly, the key to solving PML lies in accurately removing the noisy labels within the candidate label set. To achieve this objective, we leverage prior knowledge about the noisy labels in PML, which suggests that they only exist within the candidate label set and possess binary values. Specifically, we propose a constrained regression model to learn a PML classifier and select the noisy labels. The constraints of the model strictly enforce the location and value of the noisy labels. Simultaneously, the supervision information provided by the candidate label set is unreliable due to the presence of noisy labels. In contrast, the non-candidate labels of a sample precisely indicate the classes to which the sample does not belong. To aid in the selection of noisy labels, we construct a competitive classifier based on the non-candidate labels. The PML classifier and the competitive classifier form a competitive relationship, encouraging mutual learning. We formulate the proposed model as a discrete optimization problem to effectively remove the noisy labels, and we solve it using an alternative algorithm. Extensive experiments conducted on 6 real-world partial multi-label data sets and 7 synthetic data sets, employing various evaluation metrics, demonstrate that our method significantly outperforms state-of-the-art PML methods. The code implementation is publicly available at https://github.com/Yangfc-ML/NLR.|本文讨论了部分多标签学习(pML)问题，这是一个具有挑战性的弱监督式学习框架，其中每个样本都与一个包含地面真实标签和噪声标签的候选标签集相关联。理论上，我们发现候选标签集中的噪声标签数目增加会导致泛化误差界扩大，从而降低分类性能。因此，解决 PML 的关键在于准确地去除候选标签集中的噪声标签。为了实现这一目标，我们利用了 PML 中关于噪声标签的先验知识，这表明它们只存在于候选标签集中，并且具有二进制值。具体来说，我们提出了一个约束回归模型来学习 PML 分类器和选择有噪声的标签。模型的约束条件严格限制了噪声标签的位置和值。同时，由于标签噪声的存在，候选标签集提供的监督信息是不可靠的。相反，示例的非候选标签精确地指示示例不属于的类。为了帮助噪声标签的选择，我们构造了一个基于非候选标签的竞争分类器。PML 分类器与竞争分类器形成竞争关系，促进相互学习。我们将建议的模型作为一个离散优化问题来制定，以有效地去除噪音标签，并使用另一种算法来解决这个问题。在6个现实世界的部分多标签数据集和7个综合数据集上进行了广泛的实验，采用了各种评估指标，表明我们的方法明显优于最先进的 PML 方法。代码实现可在 https://github.com/yangfc-ml/nlr 公开获得。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Noisy+Label+Removal+for+Partial+Multi-Label+Learning)|0| +|[Noisy Label Removal for Partial Multi-Label Learning](https://doi.org/10.1145/3637528.3671677)|Fuchao Yang, Yuheng Jia, Hui Liu, Yongqiang Dong, Junhui Hou|Department of Computer Science, City University of Hong Kong, Hong Kong, China; School of Computing & Information Sciences, Saint Francis University, Hong Kong, China; School of Computer Science and Engineering, Southeast University, Nanjing, China; College of Software Engineering, Southeast University, Nanjing, China|This paper addresses the problem of partial multi-label learning (PML), a challenging weakly supervised learning framework, where each sample is associated with a candidate label set comprising both ground-true labels and noisy labels. We theoretically reveal that an increased number of noisy labels in the candidate label set leads to an enlarged generalization error bound, consequently degrading the classification performance. Accordingly, the key to solving PML lies in accurately removing the noisy labels within the candidate label set. To achieve this objective, we leverage prior knowledge about the noisy labels in PML, which suggests that they only exist within the candidate label set and possess binary values. Specifically, we propose a constrained regression model to learn a PML classifier and select the noisy labels. The constraints of the model strictly enforce the location and value of the noisy labels. Simultaneously, the supervision information provided by the candidate label set is unreliable due to the presence of noisy labels. In contrast, the non-candidate labels of a sample precisely indicate the classes to which the sample does not belong. To aid in the selection of noisy labels, we construct a competitive classifier based on the non-candidate labels. The PML classifier and the competitive classifier form a competitive relationship, encouraging mutual learning. We formulate the proposed model as a discrete optimization problem to effectively remove the noisy labels, and we solve it using an alternative algorithm. Extensive experiments conducted on 6 real-world partial multi-label data sets and 7 synthetic data sets, employing various evaluation metrics, demonstrate that our method significantly outperforms state-of-the-art PML methods. The code implementation is publicly available at https://github.com/Yangfc-ML/NLR.|本文讨论了部分多标签学习(pML)问题，这是一个具有挑战性的弱监督式学习框架，其中每个样本都与一个包含地面真实标签和噪声标签的候选标签集相关联。理论上，我们发现候选标签集中的噪声标签数目增加会导致泛化误差界扩大，从而降低分类性能。因此，解决 PML 的关键在于准确地去除候选标签集中的噪声标签。为了实现这一目标，我们利用了 PML 中关于噪声标签的先验知识，这表明它们只存在于候选标签集中，并且具有二进制值。具体来说，我们提出了一个约束回归模型来学习 PML 分类器和选择有噪声的标签。模型的约束条件严格限制了噪声标签的位置和值。同时，由于标签噪声的存在，候选标签集提供的监督信息是不可靠的。相反，示例的非候选标签精确地指示示例不属于的类。为了帮助噪声标签的选择，我们构造了一个基于非候选标签的竞争分类器。PML 分类器与竞争分类器形成竞争关系，促进相互学习。我们将建议的模型作为一个离散优化问题来制定，以有效地去除噪音标签，并使用另一种算法来解决这个问题。在6个现实世界的部分多标签数据集和7个综合数据集上进行了广泛的实验，采用了各种评估指标，表明我们的方法明显优于最先进的 PML 方法。代码实现可在 https://github.com/yangfc-ml/nlr 公开获得。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Noisy+Label+Removal+for+Partial+Multi-Label+Learning)|0| |[Balanced Confidence Calibration for Graph Neural Networks](https://doi.org/10.1145/3637528.3671741)|Hao Yang, Min Wang, Qi Wang, Mingrui Lao, Yun Zhou|National University of Defense Technology, ChangSha, China|This paper delves into the confidence calibration in prediction when using Graph Neural Networks (GNNs), which has emerged as a notable challenge in the field. Despite their remarkable capabilities in processing graph-structured data, GNNs are prone to exhibit lower confidence in their predictions than what the actual accuracy warrants. Recent advances attempt to address this by minimizing prediction entropy to enhance confidence levels. However, this method inadvertently risks leading to over-confidence in model predictions. Our investigation in this work reveals that most existing GNN calibration methods predominantly focus on the highest logit, thereby neglecting the entire spectrum of prediction probabilities. To alleviate this limitation, we introduce a novel framework called Balanced Calibrated Graph Neural Network (BCGNN), specifically designed to establish a balanced calibration between over-confidence and under-confidence in GNNs' prediction. To theoretically support our proposed method, we further demonstrate the mechanism of the BCGNN framework in effective confidence calibration and significant trustworthiness improvement in prediction. We conduct extensive experiments to examine the developed framework. The empirical results show our method's superior performance in predictive confidence and trustworthiness, affirming its practical applicability and effectiveness in real-world scenarios.|本文研究了图形神经网络(GNN)在预测中的置信度校正问题。尽管 GNN 在处理图形结构化数据方面具有显著的能力，但它们在预测方面的可信度往往低于实际的准确度。最近的进展试图通过最小化预测熵来提高置信水平来解决这个问题。然而，这种方法无意中有可能导致对模型预测的过度自信。我们在这项工作中的研究表明，大多数现有的 GNN 校准方法主要集中在最高对数，从而忽略了整个谱的预测概率。为了缓解这一局限性，我们引入了一个新的框架，称为平衡校准图神经网络(BCGNN) ，专门设计来建立一个平衡校准之间的过度置信和不置信的 GNN 的预测。为了从理论上支持我们提出的方法，我们进一步论证了 BCGNN 框架在有效的置信度校正和显著的预测可信度改善方面的机制。我们进行了广泛的实验来检验开发的框架。实证结果表明，该方法具有较好的预测置信度和可信度，验证了该方法在实际场景中的实用性和有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Balanced+Confidence+Calibration+for+Graph+Neural+Networks)|0| |[Efficient Decision Rule List Learning via Unified Sequence Submodular Optimization](https://doi.org/10.1145/3637528.3671827)|Linxiao Yang, Jingbang Yang, Liang Sun|DAMO Academy, Alibaba Group, Hangzhou, Zhejiang, China|Interpretable models are crucial in many high-stakes decision-making applications. In this paper, we focus on learning a decision rule list for binary and multi-class classification. Different from rule set learning problems, learning an optimal rule list involves not only learning a set of rules, but also their orders. In addition, many existing algorithms rely on rule pre-mining to handle large-scale high-dimensional data, which leads to suboptimal rule list model and degrades its generalization accuracy and interpretablity. In this paper, we learn a rule list from the sequence submodular perspective. We consider the rule list as a sequence and define the cover set for each rule. Then we formulate a sequence function which combines both model complexity and classification accuracy. Based on its appealing sequence submodular property, we propose a general distorted greedy insert algorithm under Minorization-Maximization (MM) framework, which gradually inserts rules with highest inserting gain to the rule list. The rule generation process is treated as a subproblem, allowing our method to learn the rule list through a unified framework which avoids rule pre-mining. We further provide a theoretical lower bound of our greedy insert algorithm in rule list learning. Experimental results show that our algorithm achieves better accuracy and interpretability than the state-of-the-art rule learning methods, and in particular it scales well on large-scale datasets, especially on high-dimensional data.|可解释的模型在许多高风险的决策应用中至关重要。本文主要研究二进制和多类分类决策规则表的学习问题。与规则集学习问题不同，学习最优规则表不仅需要学习一组规则，还需要学习它们的顺序。此外，现有的许多算法都依赖于规则预挖掘来处理大规模的高维数据，这导致了规则列表模型的次优化，降低了其泛化精度和可解释性。在本文中，我们从序列子模的角度学习了一个规则列表。我们将规则列表视为一个序列，并为每个规则定义覆盖集。然后构造了一个模型复杂度和分类精度相结合的序列函数。基于其吸引序列的子模特性，提出了一种通用的少数化-最大化(MM)框架下的扭曲贪婪插入算法，该算法在规则列表中逐步插入插入增益最大的规则。将规则生成过程作为一个子问题来处理，使得我们的方法能够通过一个统一的框架来学习规则列表，避免了规则的预挖掘。我们进一步给出了规则表学习中贪婪插入算法的理论下界。实验结果表明，该算法比现有的规则学习方法具有更好的准确性和可解释性，特别是在大规模数据集上，尤其是在高维数据上具有良好的扩展性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Efficient+Decision+Rule+List+Learning+via+Unified+Sequence+Submodular+Optimization)|0| -|[Effective Clustering on Large Attributed Bipartite Graphs](https://doi.org/10.1145/3637528.3671764)|Renchi Yang, Yidu Wu, Xiaoyang Lin, Qichen Wang, Tsz Nam Chan, Jieming Shi|Hong Kong Polytechnic University, Hong Kong, China; Chinese University of Hong Kong, Hong Kong, China; Hong Kong Baptist University, Hong Kong, China; Shenzhen University, Shenzhen, China|Attributed bipartite graphs (ABGs) are an expressive data model fordescribing the interactions between two sets of heterogeneous nodes that areassociated with rich attributes, such as customer-product purchase networks andauthor-paper authorship graphs. Partitioning the target node set in such graphsinto k disjoint clusters (referred to as k-ABGC) finds widespread use invarious domains, including social network analysis, recommendation systems,information retrieval, and bioinformatics. However, the majority of existingsolutions towards k-ABGC either overlook attribute information or fail tocapture bipartite graph structures accurately, engendering severely compromisedresult quality. The severity of these issues is accentuated in real ABGs, whichoften encompass millions of nodes and a sheer volume of attribute data,rendering effective k-ABGC over such graphs highly challenging. In this paper, we propose TPO, an effective and efficient approach to k-ABGCthat achieves superb clustering performance on multiple real datasets. TPOobtains high clustering quality through two major contributions: (i) a novelformulation and transformation of the k-ABGC problem based on multi-scaleattribute affinity specialized for capturing attribute affinities between nodeswith the consideration of their multi-hop connections in ABGs, and (ii) ahighly efficient solver that includes a suite of carefully-craftedoptimizations for sidestepping explicit affinity matrix construction andfacilitating faster convergence. Extensive experiments, comparing TPO against19 baselines over 5 real ABGs, showcase the superior clustering quality of TPOmeasured against ground-truth labels. Moreover, compared to the state of thearts, TPO is often more than 40x faster over both small and large ABGs.|属性二部图(ABG)是描述两组异构节点之间相互作用的表达式数据模型，这两组异构节点具有丰富的属性，如客户-产品购买网络和作者-论文作者关系图。在这样的图表中，将目标节点集划分为 k 个不相交的集群(称为 k-ABGC) ，在不同的领域得到了广泛的应用，包括社交网络分析、推荐系统、信息检索和生物信息学。然而，现有的大多数 k-ABGC 解决方案要么忽略了属性信息，要么未能准确地捕获二部图结构，从而严重影响了结果质量。这些问题的严重性在真实的 ABG 中得到了强调，这些 ABG 通常包含数百万个节点和大量的属性数据，使得在这样的图上有效的 k-ABGC 非常具有挑战性。在本文中，我们提出了 TPO，一种有效的 k-ABGA 方法，可以在多个真实数据集上实现卓越的聚类性能。TPOO 通过两个主要贡献获得高聚类质量: (i)基于多尺度属性亲和力的 k-ABGC 问题的新公式和转换，专门用于捕获节点之间的属性亲和力，同时考虑它们在 ABG 中的多跳连接; (ii)包括一套精心设计的优化解决方案，以避免显式亲和力矩阵构造和促进更快的收敛。广泛的实验，比较 TPO 与19个基线超过5个真实的 ABG，展示了对地面真相标签测量的 TPO 的优越聚类质量。此外，与心脏的状态相比，无论是小型还是大型动脉血气分析仪，TPO 的速度通常都要快40倍以上。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Effective+Clustering+on+Large+Attributed+Bipartite+Graphs)|0| +|[Effective Clustering on Large Attributed Bipartite Graphs](https://doi.org/10.1145/3637528.3671764)|Renchi Yang, Yidu Wu, Xiaoyang Lin, Qichen Wang, Tsz Nam Chan, Jieming Shi|Shenzhen University, Shenzhen, China; Hong Kong Baptist University, Hong Kong, China; Hong Kong Polytechnic University, Hong Kong, China; Chinese University of Hong Kong, Hong Kong, China|Attributed bipartite graphs (ABGs) are an expressive data model fordescribing the interactions between two sets of heterogeneous nodes that areassociated with rich attributes, such as customer-product purchase networks andauthor-paper authorship graphs. Partitioning the target node set in such graphsinto k disjoint clusters (referred to as k-ABGC) finds widespread use invarious domains, including social network analysis, recommendation systems,information retrieval, and bioinformatics. However, the majority of existingsolutions towards k-ABGC either overlook attribute information or fail tocapture bipartite graph structures accurately, engendering severely compromisedresult quality. The severity of these issues is accentuated in real ABGs, whichoften encompass millions of nodes and a sheer volume of attribute data,rendering effective k-ABGC over such graphs highly challenging. In this paper, we propose TPO, an effective and efficient approach to k-ABGCthat achieves superb clustering performance on multiple real datasets. TPOobtains high clustering quality through two major contributions: (i) a novelformulation and transformation of the k-ABGC problem based on multi-scaleattribute affinity specialized for capturing attribute affinities between nodeswith the consideration of their multi-hop connections in ABGs, and (ii) ahighly efficient solver that includes a suite of carefully-craftedoptimizations for sidestepping explicit affinity matrix construction andfacilitating faster convergence. Extensive experiments, comparing TPO against19 baselines over 5 real ABGs, showcase the superior clustering quality of TPOmeasured against ground-truth labels. Moreover, compared to the state of thearts, TPO is often more than 40x faster over both small and large ABGs.|属性二部图(ABG)是描述两组异构节点之间相互作用的表达式数据模型，这两组异构节点具有丰富的属性，如客户-产品购买网络和作者-论文作者关系图。在这样的图表中，将目标节点集划分为 k 个不相交的集群(称为 k-ABGC) ，在不同的领域得到了广泛的应用，包括社交网络分析、推荐系统、信息检索和生物信息学。然而，现有的大多数 k-ABGC 解决方案要么忽略了属性信息，要么未能准确地捕获二部图结构，从而严重影响了结果质量。这些问题的严重性在真实的 ABG 中得到了强调，这些 ABG 通常包含数百万个节点和大量的属性数据，使得在这样的图上有效的 k-ABGC 非常具有挑战性。在本文中，我们提出了 TPO，一种有效的 k-ABGA 方法，可以在多个真实数据集上实现卓越的聚类性能。TPOO 通过两个主要贡献获得高聚类质量: (i)基于多尺度属性亲和力的 k-ABGC 问题的新公式和转换，专门用于捕获节点之间的属性亲和力，同时考虑它们在 ABG 中的多跳连接; (ii)包括一套精心设计的优化解决方案，以避免显式亲和力矩阵构造和促进更快的收敛。广泛的实验，比较 TPO 与19个基线超过5个真实的 ABG，展示了对地面真相标签测量的 TPO 的优越聚类质量。此外，与心脏的状态相比，无论是小型还是大型动脉血气分析仪，TPO 的速度通常都要快40倍以上。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Effective+Clustering+on+Large+Attributed+Bipartite+Graphs)|0| |[ReCDA: Concept Drift Adaptation with Representation Enhancement for Network Intrusion Detection](https://doi.org/10.1145/3637528.3672007)|Shuo Yang, Xinran Zheng, Jinze Li, Jinfeng Xu, Xingjun Wang, Edith C. H. Ngai|Tsinghua University, Beijing, China; The University of Hong Kong, Hong Kong SAR, China|The deployment of learning-based models to detect malicious activities in network traffic flows is significantly challenged by concept drift. With evolving attack technology and dynamic attack behaviors, the underlying data distribution of recently arrived traffic flows deviates from historical empirical distributions over time. Existing approaches depend on a significant amount of labeled drifting samples to facilitate the deep model to handle concept drift, which faces labor-intensive manual labeling and the risk of label noise. In this paper, we propose ReCDA, a Concept Drift Adaptation method with Representation enhancement, which consists of a self-supervised representation enhancement stage and a weakly-supervised classifier tuning stage. Specifically, in the initial stage, ReCDA introduces drift-aware perturbation and representation alignment to facilitate the model in acquiring robust representations from drift-aware and drift-invariant perspectives. Moreover, in the subsequent stage, a meticulously crafted instructive sampling strategy and a robust representation constraint encourage the model to learn discriminative knowledge about benign and malicious activities during fine-tuning, thereby enhancing performance further. We conduct comprehensive evaluations on several benchmark datasets under varying degrees of concept drift. The experiment results demonstrate the superior adaptability and robustness of the proposed method.|概念漂移对基于学习的网络流量恶意行为检测模型的部署提出了严峻的挑战。随着攻击技术的不断发展和攻击行为的动态变化，最近到达的业务流的底层数据分布随时间的推移而偏离历史经验分布。现有的方法依赖于大量的标记漂移样本，以方便深度模型处理概念漂移，面临着劳动密集型人工标记和标记噪声的风险。本文提出了一种具有表示增强的概念漂移自适应方法 ReCDA，该方法由自监督表示增强阶段和弱监督分类器调整阶段组成。具体来说，在初始阶段，ReCDA 引入了漂移感知扰动和表示对齐，以方便模型从漂移感知和漂移不变的角度获得稳健的表示。此外，在随后的阶段，精心制定的指导性抽样策略和强有力的表示约束鼓励模型在微调期间学习关于良性和恶意活动的区分性知识，从而进一步提高性能。我们对不同概念漂移程度下的几个基准数据集进行了综合评价。实验结果表明，该方法具有较好的适应性和鲁棒性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=ReCDA:+Concept+Drift+Adaptation+with+Representation+Enhancement+for+Network+Intrusion+Detection)|0| |[Your Neighbor Matters: Towards Fair Decisions Under Networked Interference](https://doi.org/10.1145/3637528.3671960)|Wenjing Yang, Haotian Wang, Haoxuan Li, Hao Zou, Ruochun Jin, Kun Kuang, Peng Cui|; Institute of Artificial Intelligence, Zhejiang University, Hangzhou, China; Peking University, Beijing, China; Tsinghua University, Beijing, China; ZGC laboratory, Beijing, China|In the era of big data, decision-making in social networks may introduce bias due to interconnected individuals. For instance, in peer-to-peer loan platforms on the Web, considering an individual's attributes along with those of their interconnected neighbors, including sensitive attributes, is vital for loan approval or rejection downstream. Unfortunately, conventional fairness approaches often assume independent individuals, overlooking the impact of one person's sensitive attribute on others' decisions. To fill this gap, we introduce "Interference-aware Fairness" (IAF) by defining two forms of discrimination as Self-Fairness (SF) and Peer-Fairness (PF), leveraging advances in interference analysis within causal inference. Specifically, SF and PF causally capture and distinguish discrimination stemming from an individual's sensitive attributes (with fixed neighbors' sensitive attributes) and from neighbors' sensitive attributes (with fixed self's sensitive attributes), separately. Hence, a network-informed decision model is fair only when SF and PF are satisfied simultaneously, as interventions in individuals' sensitive attributes or those of their peers both yield equivalent outcomes. To achieve IAF, we develop a deep doubly robust framework to estimate and regularize SF and PF metrics for decision models. Extensive experiments on synthetic and real-world datasets validate our proposed concepts and methods.|在大数据时代，社交网络中的决策可能会因为相互关联的个体而带来偏见。例如，在互联网上的 P2P 贷款平台中，考虑个人的属性以及他们相互关联的邻居的属性，包括敏感属性，对于下游的贷款批准或拒绝是至关重要的。不幸的是，传统的公平方法往往假设独立的个体，忽视了一个人的敏感属性对其他人的决定的影响。为了填补这一空白，我们引入了“干扰意识公平”(IAF) ，将两种形式的歧视定义为自我公平(SF)和同伴公平(PF) ，利用因果推理中干扰分析的进展。具体来说，SF 和 PF 分别捕获和区分个体敏感属性(固定邻居敏感属性)和邻居敏感属性(固定自我敏感属性)所产生的歧视。因此，只有当 SF 和 PF 同时满足时，网络知情决策模型才是公平的，因为对个体敏感属性或同伴敏感属性的干预都会产生等效的结果。为了实现 IAF，我们开发了一个深入的双健壮性框架来估计和规范决策模型的 SF 和 PF 度量。在合成和真实世界数据集上的大量实验验证了我们提出的概念和方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Your+Neighbor+Matters:+Towards+Fair+Decisions+Under+Networked+Interference)|0| |[SEBot: Structural Entropy Guided Multi-View Contrastive learning for Social Bot Detection](https://doi.org/10.1145/3637528.3671871)|Yingguang Yang, Qi Wu, Buyun He, Hao Peng, Renyu Yang, Zhifeng Hao, Yong Liao|Shantou University, Shantou, China; University of Science and Technology of China, Hefei, China; Beihang University, Beijing, China|Recent advancements in social bot detection have been driven by the adoption of Graph Neural Networks. The social graph, constructed from social network interactions, contains benign and bot accounts that influence each other. However, previous graph-based detection methods that follow the transductive message-passing paradigm may not fully utilize hidden graph information and are vulnerable to adversarial bot behavior. The indiscriminate message passing between nodes from different categories and communities results in excessively homogeneous node representations, ultimately reducing the effectiveness of social bot detectors. In this paper, we propose \SEBot, a novel multi-view graph-based contrastive learning-enabled social bot detector. In particular, we use structural entropy as an uncertainty metric to optimize the entire graph's structure and subgraph-level granularity, revealing the implicitly existing hierarchical community structure. And we design an encoder to enable message passing beyond the homophily assumption, enhancing robustness to adversarial behaviors of social bots. Finally, we employ multi-view contrastive learning to maximize mutual information between different views and enhance the detection performance through multi-task learning. Experimental results demonstrate that our approach significantly improves the performance of social bot detection compared with SOTA methods.|图形神经网络的应用推动了社交机器人检测技术的发展。由社交网络互动构建的社交图包含了互相影响的良性账户和机器人账户。然而，以往的基于图的检测方法遵循传导性消息传递的范式，可能不能充分利用隐藏的图信息，并易受敌对机器人行为。来自不同类别和社区的节点之间不加区分的消息传递导致了过度同质的节点表示，最终降低了社会机器人检测器的有效性。在本文中，我们提出了一种新的基于多视图图形的对比学习社会机器人检测器 SEBot。特别地，我们使用结构熵作为一个不确定性度量来优化整个图的结构和子图级粒度，揭示隐含存在的层次化社区结构。我们设计了一个编码器，使信息传递超越了同质假设，增强了对社会机器人敌对行为的鲁棒性。最后，采用多视图对比学习，最大化不同视图之间的相互信息，通过多任务学习提高检测性能。实验结果表明，与 SOTA 方法相比，该方法显著提高了社会机器人检测的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=SEBot:+Structural+Entropy+Guided+Multi-View+Contrastive+learning+for+Social+Bot+Detection)|0| @@ -469,156 +469,156 @@ |[Self-consistent Deep Geometric Learning for Heterogeneous Multi-source Spatial Point Data Prediction](https://doi.org/10.1145/3637528.3671737)|Dazhou Yu, Xiaoyun Gong, Yun Li, Meikang Qiu, Liang Zhao|Emory University, Atlanta, GA, USA; Augusta University, Augusta, GA, USA|Multi-source spatial point data prediction is crucial in fields like environmental monitoring and natural resource management, where integrating data from various sensors is the key to achieving a holistic environmental understanding. Existing models in this area often fall short due to their domain-specific nature and lack a strategy for integrating information from various sources in the absence of ground truth labels. Key challenges include evaluating the quality of different data sources and modeling spatial relationships among them effectively. Addressing these issues, we introduce an innovative multi-source spatial point data prediction framework that adeptly aligns information from varied sources without relying on ground truth labels. A unique aspect of our method is the 'fidelity score,' a quantitative measure for evaluating the reliability of each data source. Furthermore, we develop a geo-location-aware graph neural network tailored to accurately depict spatial relationships between data points. Our framework has been rigorously tested on two real-world datasets and one synthetic dataset. The results consistently demonstrate its superior performance over existing state-of-the-art methods.|多源空间点数据预测在环境监测和自然资源管理等领域至关重要，在这些领域，整合来自各种传感器的数据是实现全面环境认识的关键。这一领域的现有模型往往由于其特定领域的性质而不足，并且缺乏在没有地面真相标签的情况下整合来自各种来源的信息的战略。主要挑战包括评估不同数据源的质量和有效地建立它们之间的空间关系。针对这些问题，我们引入了一个创新的多源空间点数据预测框架，它能够在不依赖地面真相标签的情况下，灵活地对齐来自不同来源的信息。我们的方法的一个独特方面是“保真度评分”，一种用于评估每个数据源的可靠性的定量度量。此外，我们开发了一个地理位置感知图神经网络，以准确描述数据点之间的空间关系。我们的框架已经在两个真实数据集和一个合成数据集上进行了严格的测试。结果一致表明，其优越的性能超过现有的国家的最先进的方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Self-consistent+Deep+Geometric+Learning+for+Heterogeneous+Multi-source+Spatial+Point+Data+Prediction)|0| |[PolygonGNN: Representation Learning for Polygonal Geometries with Heterogeneous Visibility Graph](https://doi.org/10.1145/3637528.3671738)|Dazhou Yu, Yuntong Hu, Yun Li, Liang Zhao|Emory University, Atlanta, GA, USA|Polygon representation learning is essential for diverse applications, encompassing tasks such as shape coding, building pattern classification, and geographic question answering. While recent years have seen considerable advancements in this field, much of the focus has been on single polygons, overlooking the intricate inner- and inter-polygonal relationships inherent in multipolygons. To address this gap, our study introduces a comprehensive framework specifically designed for learning representations of polygonal geometries, particularly multipolygons. Central to our approach is the incorporation of a heterogeneous visibility graph, which seamlessly integrates both inner- and inter-polygonal relationships. To enhance computational efficiency and minimize graph redundancy, we implement a heterogeneous spanning tree sampling method. Additionally, we devise a rotation-translation invariant geometric representation, ensuring broader applicability across diverse scenarios. Finally, we introduce Multipolygon-GNN, a novel model tailored to leverage the spatial and semantic heterogeneity inherent in the visibility graph. Experiments on five real-world and synthetic datasets demonstrate its ability to capture informative representations for polygonal geometries.|多边形表示学习对于不同的应用是必不可少的，包括任务，如形状编码，建筑模式分类和地理问题的回答。虽然近年来在这一领域取得了相当大的进展，但大部分的焦点都集中在单个多边形上，忽略了多边形固有的错综复杂的内多边形和内多边形之间的关系。为了解决这一差距，我们的研究引入了一个全面的框架，专门为学习表示多边形几何，特别是多边形。我们的方法的核心是引入一个异构的可见性图，它无缝地集成了内部和内部多边形关系。为了提高计算效率和减少图冗余，我们实现了一种异构生成树抽样方法。此外，我们设计了一个旋转平移不变的几何表示，确保更广泛的适用性跨不同的场景。最后，我们介绍了多边形 GNN，一个新的模型，以利用空间和语义的异质性固有的可见性图。在五个真实世界和合成数据集上的实验证明了它捕获多边形几何信息表示的能力。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=PolygonGNN:+Representation+Learning+for+Polygonal+Geometries+with+Heterogeneous+Visibility+Graph)|0| |[GinAR: An End-To-End Multivariate Time Series Forecasting Model Suitable for Variable Missing](https://doi.org/10.1145/3637528.3672055)|Chengqing Yu, Fei Wang, Zezhi Shao, Tangwen Qian, Zhao Zhang, Wei Wei, Yongjun Xu|; Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China|Multivariate time series forecasting (MTSF) is crucial for decision-making to precisely forecast the future values/trends, based on the complex relationships identified from historical observations of multiple sequences. Recently, Spatial-Temporal Graph Neural Networks (STGNNs) have gradually become the theme of MTSF model as their powerful capability in mining spatial-temporal dependencies, but almost of them heavily rely on the assumption of historical data integrity. In reality, due to factors such as data collector failures and time-consuming repairment, it is extremely challenging to collect the whole historical observations without missing any variable. In this case, STGNNs can only utilize a subset of normal variables and easily suffer from the incorrect spatial-temporal dependency modeling issue, resulting in the degradation of their forecasting performance. To address the problem, in this paper, we propose a novel Graph Interpolation Attention Recursive Network (named GinAR) to precisely model the spatial-temporal dependencies over the limited collected data for forecasting. In GinAR, it consists of two key components, that is, interpolation attention and adaptive graph convolution to take place of the fully connected layer of simple recursive units, and thus are capable of recovering all missing variables and reconstructing the correct spatial-temporal dependencies for recursively modeling of multivariate time series data, respectively. Extensive experiments conducted on five real-world datasets demonstrate that GinAR outperforms 11 SOTA baselines, and even when 90% of variables are missing, it can still accurately predict the future values of all variables.|多变量时间序列预测(MTSF)是决策的关键，以精确预测未来的价值/趋势，基于从多个序列的历史观察所确定的复杂关系。近年来，时空图形神经网络(STGNN)以其强大的挖掘时空依赖性的能力逐渐成为 MTSF 模型的主题，但是它们大多依赖于历史数据完整性的假设。实际上，由于诸如数据收集器故障和耗时的维修等因素，在不遗漏任何变量的情况下收集整个历史观测数据是极具挑战性的。在这种情况下，STGNN 只能利用正态变量的一个子集，很容易受到不正确的时空依赖性建模问题，导致其预测性能的下降。为了解决这一问题，本文提出了一种新的图插值注意力递归网络(GinAR)来精确模拟有限采集数据的时空依赖关系。在 GinAR 中，它由插值注意和自适应图卷积两个关键部分组成，分别代替简单递归单元的完全连通层，从而能够恢复所有缺失的变量，并为多变量时间序列数据的递归建模重建正确的时空依赖关系。在五个实际数据集上进行的大量实验表明，GinAR 优于11个 SOTA 基线，即使缺少90% 的变量，它仍然可以准确地预测所有变量的未来值。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=GinAR:+An+End-To-End+Multivariate+Time+Series+Forecasting+Model+Suitable+for+Variable+Missing)|0| -|[RIGL: A Unified Reciprocal Approach for Tracing the Independent and Group Learning Processes](https://doi.org/10.1145/3637528.3671711)|Xiaoshan Yu, Chuan Qin, Dazhong Shen, Shangshang Yang, Haiping Ma, Hengshu Zhu, Xingyi Zhang|; Career Science Lab, BOSS Zhipin, Beijing, China; School of Computer Science and Technology, Anhui University, Hefei, Anhui, China; Career Science Lab, BOSS Zhipin & PBC School of Finance, Tsinghua University, Beijing, China; School of Artificial Intelligence, Anhui University, Hefei, Anhui, China; Shanghai Artificial Intelligence Laboratory, Shanghai, China|In the realm of education, both independent learning and group learning are esteemed as the most classic paradigms. The former allows learners to self-direct their studies, while the latter is typically characterized by teacher-directed scenarios. Recent studies in the field of intelligent education have leveraged deep temporal models to trace the learning process, capturing the dynamics of students' knowledge states, and have achieved remarkable performance. However, existing approaches have primarily focused on modeling the independent learning process, with the group learning paradigm receiving less attention. Moreover, the reciprocal effect between the two learning processes, especially their combined potential to foster holistic student development, remains inadequately explored. To this end, in this paper, we propose RIGL, a unified Reciprocal model to trace knowledge states at both the individual and group levels, drawing from the Independent and Group Learning processes. Specifically, we first introduce a time frame-aware reciprocal embedding module to concurrently model both student and group response interactions across various time frames. Subsequently, we employ reciprocal enhanced learning modeling to fully exploit the comprehensive and complementary information between the two behaviors. Furthermore, we design a relation-guided temporal attentive network, comprised of dynamic graph modeling coupled with a temporal self-attention mechanism. It is used to delve into the dynamic influence of individual and group interactions throughout the learning processes, which is crafted to explore the dynamic intricacies of both individual and group interactions during the learning sequences. Conclusively, we introduce a bias-aware contrastive learning module to bolster the stability of the model's training. Extensive experiments on four real-world educational datasets clearly demonstrate the effectiveness of the proposed RIGL model. Our codes are available at https://github.com/LabyrinthineLeo/RIGL.|在教育领域，自主学习和小组学习被认为是最经典的范式。前者允许学习者自我指导他们的学习，而后者则是典型的拥有属性教师指导的情景。近年来，智能教育领域的研究利用深度时间模型来跟踪学习过程，捕捉学生知识状态的动态变化，取得了显著的效果。然而，现有的研究方法主要集中在对自主学习过程进行建模，而对小组学习范式的关注较少。此外，这两个学习过程之间的相互作用，特别是它们促进学生整体发展的综合潜力，仍然没有得到充分的探索。为此，在本文中，我们提出了 RIGL，一个统一的互惠模型，以追踪知识状态在个人和团体水平，借鉴独立和团体学习过程。具体来说，我们首先引入一个时间框架感知的互惠嵌入模块来并发模拟跨不同时间框架的学生和团体响应交互。然后，我们采用互惠增强的学习模型来充分利用这两种行为之间的综合信息和互补信息。在此基础上，设计了一个关系引导的时间注意网络，该网络由动态图模型和时间自我注意机制组成。它被用来研究个人和群体互动在整个学习过程中的动态影响，它被精心设计来探索在学习过程中个人和群体互动的动态错综复杂性。最后，我们引入了一个偏差感知的对比学习模块，以增强模型训练的稳定性。在四个真实世界的教育数据集上的大量实验清楚地证明了所提出的 RIGL 模型的有效性。我们的密码可以在 https://github.com/labyrinthineleo/rigl 找到。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=RIGL:+A+Unified+Reciprocal+Approach+for+Tracing+the+Independent+and+Group+Learning+Processes)|0| -|[Unveiling Privacy Vulnerabilities: Investigating the Role of Structure in Graph Data](https://doi.org/10.1145/3637528.3672013)|Hanyang Yuan, Jiarong Xu, Cong Wang, Ziqi Yang, Chunping Wang, Keting Yin, Yang Yang|Peking University, Beijing, China; Fudan University, Shanghai, China; Zhejiang University & Fudan University, Hangzhou, China; Zhejiang University, Hangzhou, China; Finvolution Group, Shanghai, China|The public sharing of user information opens the door for adversaries to infer private data, leading to privacy breaches and facilitating malicious activities. While numerous studies have concentrated on privacy leakage via public user attributes, the threats associated with the exposure of user relationships, particularly through network structure, are often neglected. This study aims to fill this critical gap by advancing the understanding and protection against privacy risks emanating from network structure, moving beyond direct connections with neighbors to include the broader implications of indirect network structural patterns. To achieve this, we first investigate the problem of Graph Privacy Leakage via Structure (GPS), and introduce a novel measure, the Generalized Homophily Ratio, to quantify the various mechanisms contributing to privacy breach risks in GPS. Based on this insight, we develop a novel graph private attribute inference attack, which acts as a pivotal tool for evaluating the potential for privacy leakage through network structures under worst-case scenarios. To protect users' private data from such vulnerabilities, we propose a graph data publishing method incorporating a learnable graph sampling technique, effectively transforming the original graph into a privacy-preserving version. Extensive experiments demonstrate that our attack model poses a significant threat to user privacy, and our graph data publishing method successfully achieves the optimal privacy-utility trade-off compared to baselines.|公开分享用户信息为对手推断私人数据打开了大门，导致侵犯隐私和助长恶意活动。虽然大量的研究集中在通过公共用户属性的隐私泄露，与用户关系的暴露相关的威胁，特别是通过网络结构，往往被忽视。本研究旨在通过提高对网络结构引起的隐私风险的认识和保护，超越与邻居的直接联系，将间接网络结构模式的更广泛影响纳入其中，从而填补这一重要空白。为了实现这一目标，我们首先研究了基于结构(GPS)的图形隐私泄漏问题，并引入了一种新的度量方法——广义同伦比，来量化导致 GPS 隐私泄漏风险的各种机制。基于这种认识，我们开发了一种新的图私有属性推理攻击，它可以作为评估最坏情况下通过网络结构发生隐私泄漏的潜在可能性的关键工具。为了保护用户的私有数据不受此类漏洞的影响，提出了一种图形数据发布方法，该方法结合了可学习的图形采样技术，有效地将原始图形转换为保护用户隐私的版本。大量的实验表明，我们的攻击模型对用户隐私构成了严重的威胁，我们的图形数据发布方法成功地实现了与基线相比的最优隐私-效用权衡。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Unveiling+Privacy+Vulnerabilities:+Investigating+the+Role+of+Structure+in+Graph+Data)|0| -|[Graph Cross Supervised Learning via Generalized Knowledge](https://doi.org/10.1145/3637528.3671830)|Xiangchi Yuan, Yijun Tian, Chunhui Zhang, Yanfang Ye, Nitesh V. Chawla, Chuxu Zhang|Brandeis University & Georgia Institute of Technology, Waltham, MA, USA; Brandeis University, Waltham, MA, USA; University of Notre Dame, South Bend, IN, USA; Dartmouth College, Hanover, NH, USA|The success of GNNs highly relies on the accurate labeling of data. Existing methods of ensuring accurate labels, such as weakly-supervised learning, mainly focus on the existing nodes in the graphs. However, in reality, new nodes always continuously emerge on dynamic graphs, with different categories and even label noises. To this end, we formulate a new problem, Graph Cross-Supervised Learning, or Graph Weak-Shot Learning, that describes the challenges of modeling new nodes with novel classes and potential label noises. To solve this problem, we propose Lipshitz-regularized Mixture-of-Experts similarity network (LIME), a novel framework to encode new nodes and handle label noises. Specifically, we first design a node similarity network to capture the knowledge from the original classes, aiming to obtain insights for the emerging novel classes. Then, to enhance the similarity network's generalization to new nodes that could have a distribution shift, we employ the Mixture-of-Experts technique to increase the generalization of knowledge learned by the similarity network. To further avoid losing generalization ability during training, we introduce the Lipschitz bound to stabilize model output and alleviate the distribution shift issue. Empirical experiments validate LIME's effectiveness: we observe a substantial enhancement of up to 11.34% in node classification accuracy compared to the backbone model when subjected to the challenges of label noise on novel classes across five benchmark datasets. The code can be accessed through https://github.com/xiangchi-yuan/Graph-Cross-Supervised-Learning.|GNN 的成功在很大程度上依赖于数据的准确标记。现有的确保标签准确性的方法，如弱监督学习，主要集中在图中存在的节点。然而，在现实生活中，动态图上总是不断出现新的节点，它们具有不同的类别，甚至带有标签噪声。为此，我们提出了一个新的问题，图交叉监督学习，或图弱镜头学习，描述了建模新的节点与新的类和潜在的标签噪声的挑战。为了解决这一问题，我们提出了一种基于 Lipshitz 正则化的专家混合相似网络(LIME) ，这是一种新的节点编码和标签噪声处理框架。具体来说，我们首先设计一个节点相似性网络来获取来自原始类的知识，旨在获得对新兴新类的洞察力。然后，为了增强相似网络对可能发生分布偏移的新节点的泛化能力，采用专家混合技术增强相似网络所学知识的泛化能力。为了进一步避免在训练过程中失去泛化能力，我们引入 Lipschitz 界来稳定模型输出，减轻分布移位问题。实验验证了 LIME 算法的有效性: 当受到5个基准数据集上新类别的标签噪声的挑战时，我们观察到与骨干模型相比，LIME 算法在节点分类准确率方面有了大幅度的提高，提高了11.34% 。代码可以通过 https://github.com/xiangchi-yuan/graph-cross-supervised-learning 访问。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Graph+Cross+Supervised+Learning+via+Generalized+Knowledge)|0| -|[Effective Generation of Feasible Solutions for Integer Programming via Guided Diffusion](https://doi.org/10.1145/3637528.3671783)|Hao Zeng, Jiaqi Wang, Avirup Das, Junying He, Kunpeng Han, Haoyuan Hu, Mingfei Sun|University of Manchester, Manchester, United Kingdom; Cainiao Network, Hangzhou, China|Feasible solutions are crucial for Integer Programming (IP) since they can substantially speed up the solving process. In many applications, similar IP instances often exhibit similar structures and shared solution distributions, which can be potentially modeled by deep learning methods. Unfortunately, existing deep-learning-based algorithms, such as Neural Diving [21] and Predict-and-search framework [8], are limited to generating only partial feasible solutions, and they must rely on solvers like SCIP and Gurobi to complete the solutions for a given IP problem. In this paper, we propose a novel framework that generates complete feasible solutions end-to-end. Our framework leverages contrastive learning to characterize the relationship between IP instances and solutions, and learns latent embeddings for both IP instances and their solutions. Further, the framework employs diffusion models to learn the distribution of solution embeddings conditioned on IP representations, with a dedicated guided sampling strategy that accounts for both constraints and objectives. We empirically evaluate our framework on four typical datasets of IP problems, and show that it effectively generates complete feasible solutions with a high probability (> 89.7 %) without the reliance of Solvers and the quality of solutions is comparable to the best heuristic solutions from Gurobi. Furthermore, by integrating our method's sampled partial solutions with the CompleteSol heuristic from SCIP [19], the resulting feasible solutions outperform those from state-of-the-art methods across all datasets, exhibiting a 3.7 to 33.7% improvement in the gap to optimal values, and maintaining a feasible ratio of over 99.7% for all datasets.|可行的解决方案对于整数规划来说至关重要，因为它们可以大大加快解决过程。在许多应用中，相似的 IP 实例往往表现出相似的结构和共享的解决方案分布，这可能是由深度学习方法建模。不幸的是，现有的基于深度学习的算法，如神经潜水和预测与搜索框架，仅限于产生部分可行的解决方案，他们必须依靠解决方案，如 SCIP 和 Gurobi，以完成一个给定的知识产权问题的解决方案。在本文中，我们提出了一个新的框架，生成完全可行的解决方案端到端。我们的框架利用对比学习来描述 IP 实例和解决方案之间的关系，并学习 IP 实例及其解决方案的潜在嵌入。此外，该框架使用扩散模型来学习以 IP 表示为条件的解嵌入的分布，并使用一个专门的引导抽样策略来解释约束和目标。我们根据四个典型的知识产权问题数据集对我们的框架进行了实证评估，结果表明，在不依赖 Solvers 的情况下，它有效地产生了高概率(> 89.7%)的完全可行的解决方案，而且解决方案的质量堪比 Gurobi 的最佳启发式解决方案。此外，通过将我们的方法的采样部分解决方案与 SCIP [19]的 CompleteSol 启发式相结合，所得到的可行解决方案在所有数据集中都优于最先进的方法，显示出与最佳值的差距提高了3.7% 至33.7% ，并保持所有数据集的可行比率超过99.7% 。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Effective+Generation+of+Feasible+Solutions+for+Integer+Programming+via+Guided+Diffusion)|0| +|[RIGL: A Unified Reciprocal Approach for Tracing the Independent and Group Learning Processes](https://doi.org/10.1145/3637528.3671711)|Xiaoshan Yu, Chuan Qin, Dazhong Shen, Shangshang Yang, Haiping Ma, Hengshu Zhu, Xingyi Zhang|Shanghai Artificial Intelligence Laboratory, Shanghai, China; ; School of Computer Science and Technology, Anhui University, Hefei, Anhui, China; School of Artificial Intelligence, Anhui University, Hefei, Anhui, China; Career Science Lab, BOSS Zhipin, Beijing, China; Career Science Lab, BOSS Zhipin & PBC School of Finance, Tsinghua University, Beijing, China|In the realm of education, both independent learning and group learning are esteemed as the most classic paradigms. The former allows learners to self-direct their studies, while the latter is typically characterized by teacher-directed scenarios. Recent studies in the field of intelligent education have leveraged deep temporal models to trace the learning process, capturing the dynamics of students' knowledge states, and have achieved remarkable performance. However, existing approaches have primarily focused on modeling the independent learning process, with the group learning paradigm receiving less attention. Moreover, the reciprocal effect between the two learning processes, especially their combined potential to foster holistic student development, remains inadequately explored. To this end, in this paper, we propose RIGL, a unified Reciprocal model to trace knowledge states at both the individual and group levels, drawing from the Independent and Group Learning processes. Specifically, we first introduce a time frame-aware reciprocal embedding module to concurrently model both student and group response interactions across various time frames. Subsequently, we employ reciprocal enhanced learning modeling to fully exploit the comprehensive and complementary information between the two behaviors. Furthermore, we design a relation-guided temporal attentive network, comprised of dynamic graph modeling coupled with a temporal self-attention mechanism. It is used to delve into the dynamic influence of individual and group interactions throughout the learning processes, which is crafted to explore the dynamic intricacies of both individual and group interactions during the learning sequences. Conclusively, we introduce a bias-aware contrastive learning module to bolster the stability of the model's training. Extensive experiments on four real-world educational datasets clearly demonstrate the effectiveness of the proposed RIGL model. Our codes are available at https://github.com/LabyrinthineLeo/RIGL.|在教育领域，自主学习和小组学习被认为是最经典的范式。前者允许学习者自我指导他们的学习，而后者则是典型的拥有属性教师指导的情景。近年来，智能教育领域的研究利用深度时间模型来跟踪学习过程，捕捉学生知识状态的动态变化，取得了显著的效果。然而，现有的研究方法主要集中在对自主学习过程进行建模，而对小组学习范式的关注较少。此外，这两个学习过程之间的相互作用，特别是它们促进学生整体发展的综合潜力，仍然没有得到充分的探索。为此，在本文中，我们提出了 RIGL，一个统一的互惠模型，以追踪知识状态在个人和团体水平，借鉴独立和团体学习过程。具体来说，我们首先引入一个时间框架感知的互惠嵌入模块来并发模拟跨不同时间框架的学生和团体响应交互。然后，我们采用互惠增强的学习模型来充分利用这两种行为之间的综合信息和互补信息。在此基础上，设计了一个关系引导的时间注意网络，该网络由动态图模型和时间自我注意机制组成。它被用来研究个人和群体互动在整个学习过程中的动态影响，它被精心设计来探索在学习过程中个人和群体互动的动态错综复杂性。最后，我们引入了一个偏差感知的对比学习模块，以增强模型训练的稳定性。在四个真实世界的教育数据集上的大量实验清楚地证明了所提出的 RIGL 模型的有效性。我们的密码可以在 https://github.com/labyrinthineleo/rigl 找到。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=RIGL:+A+Unified+Reciprocal+Approach+for+Tracing+the+Independent+and+Group+Learning+Processes)|0| +|[Unveiling Privacy Vulnerabilities: Investigating the Role of Structure in Graph Data](https://doi.org/10.1145/3637528.3672013)|Hanyang Yuan, Jiarong Xu, Cong Wang, Ziqi Yang, Chunping Wang, Keting Yin, Yang Yang|Zhejiang University & Fudan University, Hangzhou, China; Peking University, Beijing, China; Finvolution Group, Shanghai, China; Zhejiang University, Hangzhou, China; Fudan University, Shanghai, China|The public sharing of user information opens the door for adversaries to infer private data, leading to privacy breaches and facilitating malicious activities. While numerous studies have concentrated on privacy leakage via public user attributes, the threats associated with the exposure of user relationships, particularly through network structure, are often neglected. This study aims to fill this critical gap by advancing the understanding and protection against privacy risks emanating from network structure, moving beyond direct connections with neighbors to include the broader implications of indirect network structural patterns. To achieve this, we first investigate the problem of Graph Privacy Leakage via Structure (GPS), and introduce a novel measure, the Generalized Homophily Ratio, to quantify the various mechanisms contributing to privacy breach risks in GPS. Based on this insight, we develop a novel graph private attribute inference attack, which acts as a pivotal tool for evaluating the potential for privacy leakage through network structures under worst-case scenarios. To protect users' private data from such vulnerabilities, we propose a graph data publishing method incorporating a learnable graph sampling technique, effectively transforming the original graph into a privacy-preserving version. Extensive experiments demonstrate that our attack model poses a significant threat to user privacy, and our graph data publishing method successfully achieves the optimal privacy-utility trade-off compared to baselines.|公开分享用户信息为对手推断私人数据打开了大门，导致侵犯隐私和助长恶意活动。虽然大量的研究集中在通过公共用户属性的隐私泄露，与用户关系的暴露相关的威胁，特别是通过网络结构，往往被忽视。本研究旨在通过提高对网络结构引起的隐私风险的认识和保护，超越与邻居的直接联系，将间接网络结构模式的更广泛影响纳入其中，从而填补这一重要空白。为了实现这一目标，我们首先研究了基于结构(GPS)的图形隐私泄漏问题，并引入了一种新的度量方法——广义同伦比，来量化导致 GPS 隐私泄漏风险的各种机制。基于这种认识，我们开发了一种新的图私有属性推理攻击，它可以作为评估最坏情况下通过网络结构发生隐私泄漏的潜在可能性的关键工具。为了保护用户的私有数据不受此类漏洞的影响，提出了一种图形数据发布方法，该方法结合了可学习的图形采样技术，有效地将原始图形转换为保护用户隐私的版本。大量的实验表明，我们的攻击模型对用户隐私构成了严重的威胁，我们的图形数据发布方法成功地实现了与基线相比的最优隐私-效用权衡。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Unveiling+Privacy+Vulnerabilities:+Investigating+the+Role+of+Structure+in+Graph+Data)|0| +|[Graph Cross Supervised Learning via Generalized Knowledge](https://doi.org/10.1145/3637528.3671830)|Xiangchi Yuan, Yijun Tian, Chunhui Zhang, Yanfang Ye, Nitesh V. Chawla, Chuxu Zhang|Brandeis University & Georgia Institute of Technology, Waltham, MA, USA; Brandeis University, Waltham, MA, USA; Dartmouth College, Hanover, NH, USA; University of Notre Dame, South Bend, IN, USA|The success of GNNs highly relies on the accurate labeling of data. Existing methods of ensuring accurate labels, such as weakly-supervised learning, mainly focus on the existing nodes in the graphs. However, in reality, new nodes always continuously emerge on dynamic graphs, with different categories and even label noises. To this end, we formulate a new problem, Graph Cross-Supervised Learning, or Graph Weak-Shot Learning, that describes the challenges of modeling new nodes with novel classes and potential label noises. To solve this problem, we propose Lipshitz-regularized Mixture-of-Experts similarity network (LIME), a novel framework to encode new nodes and handle label noises. Specifically, we first design a node similarity network to capture the knowledge from the original classes, aiming to obtain insights for the emerging novel classes. Then, to enhance the similarity network's generalization to new nodes that could have a distribution shift, we employ the Mixture-of-Experts technique to increase the generalization of knowledge learned by the similarity network. To further avoid losing generalization ability during training, we introduce the Lipschitz bound to stabilize model output and alleviate the distribution shift issue. Empirical experiments validate LIME's effectiveness: we observe a substantial enhancement of up to 11.34% in node classification accuracy compared to the backbone model when subjected to the challenges of label noise on novel classes across five benchmark datasets. The code can be accessed through https://github.com/xiangchi-yuan/Graph-Cross-Supervised-Learning.|GNN 的成功在很大程度上依赖于数据的准确标记。现有的确保标签准确性的方法，如弱监督学习，主要集中在图中存在的节点。然而，在现实生活中，动态图上总是不断出现新的节点，它们具有不同的类别，甚至带有标签噪声。为此，我们提出了一个新的问题，图交叉监督学习，或图弱镜头学习，描述了建模新的节点与新的类和潜在的标签噪声的挑战。为了解决这一问题，我们提出了一种基于 Lipshitz 正则化的专家混合相似网络(LIME) ，这是一种新的节点编码和标签噪声处理框架。具体来说，我们首先设计一个节点相似性网络来获取来自原始类的知识，旨在获得对新兴新类的洞察力。然后，为了增强相似网络对可能发生分布偏移的新节点的泛化能力，采用专家混合技术增强相似网络所学知识的泛化能力。为了进一步避免在训练过程中失去泛化能力，我们引入 Lipschitz 界来稳定模型输出，减轻分布移位问题。实验验证了 LIME 算法的有效性: 当受到5个基准数据集上新类别的标签噪声的挑战时，我们观察到与骨干模型相比，LIME 算法在节点分类准确率方面有了大幅度的提高，提高了11.34% 。代码可以通过 https://github.com/xiangchi-yuan/graph-cross-supervised-learning 访问。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Graph+Cross+Supervised+Learning+via+Generalized+Knowledge)|0| +|[Effective Generation of Feasible Solutions for Integer Programming via Guided Diffusion](https://doi.org/10.1145/3637528.3671783)|Hao Zeng, Jiaqi Wang, Avirup Das, Junying He, Kunpeng Han, Haoyuan Hu, Mingfei Sun|Cainiao Network, Hangzhou, China; University of Manchester, Manchester, United Kingdom|Feasible solutions are crucial for Integer Programming (IP) since they can substantially speed up the solving process. In many applications, similar IP instances often exhibit similar structures and shared solution distributions, which can be potentially modeled by deep learning methods. Unfortunately, existing deep-learning-based algorithms, such as Neural Diving [21] and Predict-and-search framework [8], are limited to generating only partial feasible solutions, and they must rely on solvers like SCIP and Gurobi to complete the solutions for a given IP problem. In this paper, we propose a novel framework that generates complete feasible solutions end-to-end. Our framework leverages contrastive learning to characterize the relationship between IP instances and solutions, and learns latent embeddings for both IP instances and their solutions. Further, the framework employs diffusion models to learn the distribution of solution embeddings conditioned on IP representations, with a dedicated guided sampling strategy that accounts for both constraints and objectives. We empirically evaluate our framework on four typical datasets of IP problems, and show that it effectively generates complete feasible solutions with a high probability (> 89.7 %) without the reliance of Solvers and the quality of solutions is comparable to the best heuristic solutions from Gurobi. Furthermore, by integrating our method's sampled partial solutions with the CompleteSol heuristic from SCIP [19], the resulting feasible solutions outperform those from state-of-the-art methods across all datasets, exhibiting a 3.7 to 33.7% improvement in the gap to optimal values, and maintaining a feasible ratio of over 99.7% for all datasets.|可行的解决方案对于整数规划来说至关重要，因为它们可以大大加快解决过程。在许多应用中，相似的 IP 实例往往表现出相似的结构和共享的解决方案分布，这可能是由深度学习方法建模。不幸的是，现有的基于深度学习的算法，如神经潜水和预测与搜索框架，仅限于产生部分可行的解决方案，他们必须依靠解决方案，如 SCIP 和 Gurobi，以完成一个给定的知识产权问题的解决方案。在本文中，我们提出了一个新的框架，生成完全可行的解决方案端到端。我们的框架利用对比学习来描述 IP 实例和解决方案之间的关系，并学习 IP 实例及其解决方案的潜在嵌入。此外，该框架使用扩散模型来学习以 IP 表示为条件的解嵌入的分布，并使用一个专门的引导抽样策略来解释约束和目标。我们根据四个典型的知识产权问题数据集对我们的框架进行了实证评估，结果表明，在不依赖 Solvers 的情况下，它有效地产生了高概率(> 89.7%)的完全可行的解决方案，而且解决方案的质量堪比 Gurobi 的最佳启发式解决方案。此外，通过将我们的方法的采样部分解决方案与 SCIP [19]的 CompleteSol 启发式相结合，所得到的可行解决方案在所有数据集中都优于最先进的方法，显示出与最佳值的差距提高了3.7% 至33.7% ，并保持所有数据集的可行比率超过99.7% 。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Effective+Generation+of+Feasible+Solutions+for+Integer+Programming+via+Guided+Diffusion)|0| |[Path-Specific Causal Reasoning for Fairness-aware Cognitive Diagnosis](https://doi.org/10.1145/3637528.3672049)|Dacao Zhang, Kun Zhang, Le Wu, Mi Tian, Richang Hong, Meng Wang||Cognitive Diagnosis~(CD), which leverages students and exercise data to predict students' proficiency levels on different knowledge concepts, is one of fundamental components in Intelligent Education. Due to the scarcity of student-exercise interaction data, most existing methods focus on making the best use of available data, such as exercise content and student information~(e.g., educational context). Despite the great progress, the abuse of student sensitive information has not been paid enough attention. Due to the important position of CD in Intelligent Education, employing sensitive information when making diagnosis predictions will cause serious social issues. Moreover, data-driven neural networks are easily misled by the shortcut between input data and output prediction, exacerbating this problem. Therefore, it is crucial to eliminate the negative impact of sensitive information in CD models. In response, we argue that sensitive attributes of students can also provide useful information, and only the shortcuts directly related to the sensitive information should be eliminated from the diagnosis process. Thus, we employ causal reasoning and design a novel Path-Specific Causal Reasoning Framework (PSCRF) to achieve this goal. Specifically, we first leverage an encoder to extract features and generate embeddings for general information and sensitive information of students. Then, we design a novel attribute-oriented predictor to decouple the sensitive attributes, in which fairness-related sensitive features will be eliminated and other useful information will be retained. Finally, we designed a multi-factor constraint to ensure the performance of fairness and diagnosis performance simultaneously. Extensive experiments over real-world datasets (e.g., PISA dataset) demonstrate the effectiveness of our proposed PSCRF.|认知诊断是智能教育的一个基本组成部分，它利用学生和练习数据来预测学生对不同知识概念的熟练程度。由于学生-练习交互数据的缺乏，现有的研究方法大多侧重于充分利用现有的数据，如练习内容和学生信息 ~ (例如，教育背景)。尽管取得了很大的进步，但对学生敏感信息的滥用问题却没有引起足够的重视。由于光盘在智能教育中的重要地位，利用敏感信息进行诊断预测会引起严重的社会问题。此外，数据驱动的神经网络很容易被输入数据和输出预测之间的捷径误导，加剧了这个问题。因此，消除光盘模型中敏感信息的负面影响至关重要。作为回应，我们认为学生的敏感属性也可以提供有用的信息，只有与敏感信息直接相关的快捷方式应该被排除在诊断过程之外。因此，我们采用因果推理并设计了一个新的路径特定因果推理框架(pSCRF)来实现这个目标。具体来说，我们首先利用一个编码器来提取特征，并为学生的一般信息和敏感信息生成嵌入。然后，我们设计了一个新的面向属性的预测器来解耦敏感属性，其中与公平性相关的敏感特征将被消除，其他有用的信息将被保留。最后，我们设计了一个多因素约束来同时保证公平性和诊断性能。对现实世界数据集(例如 PISA 数据集)的大量实验证明了我们提出的 PSCRF 的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Path-Specific+Causal+Reasoning+for+Fairness-aware+Cognitive+Diagnosis)|0| |[Brant-X: A Unified Physiological Signal Alignment Framework](https://doi.org/10.1145/3637528.3671953)|Daoze Zhang, Zhizhang Yuan, Junru Chen, Kerui Chen, Yang Yang|Zhejiang University, Hangzhou, China|Physiological signals serve as indispensable clues for understanding various physiological states of human bodies. Most existing works have focused on a single type of physiological signals for a range of application scenarios. However, as the body is a holistic biological system, the inherent interconnection among various physiological data should not be neglected. In particular, given the brain's role as the control center for vital activities, electroencephalogram (EEG) exhibits significant correlations with other physiological signals. Therefore, the correlation between EEG and other physiological signals holds potential to improve performance in various scenarios. Nevertheless, achieving this goal is still constrained by several challenges: the scarcity of simultaneously collected physiological data, the differences in correlations between various signals, and the correlation differences between various tasks. To address these issues, we propose a unified physiological signal alignment framework, Brant-X, to model the correlation between EEG and other signals. Our approach (1) employs the EEG foundation model to data-efficiently transfer the rich knowledge in EEG to other physiological signals, and (2) introduces the two-level alignment to fully align the semantics of EEG and other signals from different semantic scales. In the experiments, Brant-X achieves state-of-the-art performance compared with task-agnostic and task-specific baselines on various downstream tasks in diverse scenarios, including sleep stage classification, emotion recognition, freezing of gaits detection, and eye movement communication. Moreover, the analysis on the arrhythmia detection task and the visualization in case study further illustrate the effectiveness of Brant-X in the knowledge transfer from EEG to other physiological signals. The model's homepage is at https://github.com/zjunet/Brant-X/.|生理信号是理解人体各种生理状态不可缺少的线索。大多数现有的工作集中在一个单一类型的生理信号的一系列应用场景。然而，身体是一个整体的生物系统，各种生理数据之间的内在联系不容忽视。特别是，考虑到大脑作为重要活动的控制中心的作用，脑电图(EEG)与其他生理信号显示出显著的相关性。因此，脑电信号与其他生理信号之间的相关性具有改善各种情况下性能的潜力。然而，实现这一目标仍然受到几个挑战的限制: 同时收集的生理数据的稀缺性，不同信号之间相关性的差异，以及不同任务之间的相关性差异。为了解决这些问题，我们提出了一个统一的生理信号对齐框架 Brant-X，来模拟脑电信号与其他信号之间的相关性。该方法(1)利用脑电基础模型将脑电信号中丰富的知识有效地传递给其他生理信号，(2)引入两级对齐，从不同的语义尺度完全对齐脑电信号和其他信号的语义。在实验中，Brant-X 在不同情景下的各种下游任务中，与任务无关和任务特定的基线相比，取得了最好的表现，包括睡眠阶段分类、情绪识别、步态检测冻结和眼球运动交流。此外，对心律失常检测任务的分析和案例研究的可视化进一步说明了 Brant-X 在脑电信号向其他生理信号传递知识方面的有效性。模特的主页已经上了 https://github.com/zjunet/brant-x/。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Brant-X:+A+Unified+Physiological+Signal+Alignment+Framework)|0| -|[Subspace Selection based Prompt Tuning with Nonconvex Nonsmooth Black-Box Optimization](https://doi.org/10.1145/3637528.3671986)|Haozhen Zhang, Hualin Zhang, Bin Gu, Yi Chang|; School of Artificial Intelligence, Jilin University, Changchun, Jilin, China; Mohamed bin Zayed University of Artificial Intelligence, Masdar, United Arab Emirates|In this paper, we introduce a novel framework for black-box prompt tuning with a subspace learning and selection strategy, leveraging derivative-free optimization algorithms. This approach is crucial for scenarios where user interaction with language models is restricted to API usage, without direct access to their internal structures or gradients, a situation typical in Language-Model-as-a-Service (LMaaS). Our framework focuses on exploring the low-dimensional subspace of continuous prompts. Previous work on black-box prompt tuning necessitates a substantial number of API calls due to the random choice of the subspace. To tackle this problem, we propose to use a simple zeroth-order optimization algorithm to tackle nonconvex optimization challenges with nonsmooth nonconvex regularizers: the Zeroth-Order Mini-Batch Stochastic Proximal Gradient method (ZO-MB-SPG). A key innovation is the incorporation of nonsmooth nonconvex regularizers, including the indicator function of the l0 constraint, which enhances our ability to select optimal subspaces for prompt optimization. The experimental results show that our proposed black-box prompt tuning method on a few labeled samples can attain similar performance to the methods applicable to LMaaS with much fewer API calls.|本文利用无导数优化算法，提出了一种基于子空间学习和选择策略的黑盒提示调优框架。这种方法对于那些用户与语言模型的交互仅限于 API 使用，而不能直接访问其内部结构或梯度的场景来说是至关重要的，这种情况在 Language-Model-as-a-Service 很典型。我们的框架侧重于探索连续提示的低维子空间。由于子空间的随机选择，以前关于黑盒提示符调优的工作需要大量的 API 调用。为了解决这个问题，我们提出了一个简单的零阶优化算法来解决非光滑非凸正则化的非凸优化问题: 零阶小批量随机近似梯度法(ZO-MB-SPG)。一个关键的创新是引入了非光滑非凸正则化子，包括 l0约束的指示函数，这增强了我们为及时优化选择最优子空间的能力。实验结果表明，我们提出的针对少量标记样本的黑盒提示调优方法能够以更少的 API 调用获得与 LMaaS 相似的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Subspace+Selection+based+Prompt+Tuning+with+Nonconvex+Nonsmooth+Black-Box+Optimization)|0| +|[Subspace Selection based Prompt Tuning with Nonconvex Nonsmooth Black-Box Optimization](https://doi.org/10.1145/3637528.3671986)|Haozhen Zhang, Hualin Zhang, Bin Gu, Yi Chang|; Mohamed bin Zayed University of Artificial Intelligence, Masdar, United Arab Emirates; School of Artificial Intelligence, Jilin University, Changchun, Jilin, China|In this paper, we introduce a novel framework for black-box prompt tuning with a subspace learning and selection strategy, leveraging derivative-free optimization algorithms. This approach is crucial for scenarios where user interaction with language models is restricted to API usage, without direct access to their internal structures or gradients, a situation typical in Language-Model-as-a-Service (LMaaS). Our framework focuses on exploring the low-dimensional subspace of continuous prompts. Previous work on black-box prompt tuning necessitates a substantial number of API calls due to the random choice of the subspace. To tackle this problem, we propose to use a simple zeroth-order optimization algorithm to tackle nonconvex optimization challenges with nonsmooth nonconvex regularizers: the Zeroth-Order Mini-Batch Stochastic Proximal Gradient method (ZO-MB-SPG). A key innovation is the incorporation of nonsmooth nonconvex regularizers, including the indicator function of the l0 constraint, which enhances our ability to select optimal subspaces for prompt optimization. The experimental results show that our proposed black-box prompt tuning method on a few labeled samples can attain similar performance to the methods applicable to LMaaS with much fewer API calls.|本文利用无导数优化算法，提出了一种基于子空间学习和选择策略的黑盒提示调优框架。这种方法对于那些用户与语言模型的交互仅限于 API 使用，而不能直接访问其内部结构或梯度的场景来说是至关重要的，这种情况在 Language-Model-as-a-Service 很典型。我们的框架侧重于探索连续提示的低维子空间。由于子空间的随机选择，以前关于黑盒提示符调优的工作需要大量的 API 调用。为了解决这个问题，我们提出了一个简单的零阶优化算法来解决非光滑非凸正则化的非凸优化问题: 零阶小批量随机近似梯度法(ZO-MB-SPG)。一个关键的创新是引入了非光滑非凸正则化子，包括 l0约束的指示函数，这增强了我们为及时优化选择最优子空间的能力。实验结果表明，我们提出的针对少量标记样本的黑盒提示调优方法能够以更少的 API 调用获得与 LMaaS 相似的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Subspace+Selection+based+Prompt+Tuning+with+Nonconvex+Nonsmooth+Black-Box+Optimization)|0| |[Heuristic Learning with Graph Neural Networks: A Unified Framework for Link Prediction](https://doi.org/10.1145/3637528.3671946)|Juzheng Zhang, Lanning Wei, Zhen Xu, Quanming Yao|Department of Electronic Engineering, Tsinghua University, Beijing, China|Link prediction is a fundamental task in graph learning, inherently shaped bythe topology of the graph. While traditional heuristics are grounded in graphtopology, they encounter challenges in generalizing across diverse graphs.Recent research efforts have aimed to leverage the potential of heuristics, yeta unified formulation accommodating both local and global heuristics remainsundiscovered. Drawing insights from the fact that both local and globalheuristics can be represented by adjacency matrix multiplications, we propose aunified matrix formulation to accommodate and generalize various heuristics. Wefurther propose the Heuristic Learning Graph Neural Network (HL-GNN) toefficiently implement the formulation. HL-GNN adopts intra-layer propagationand inter-layer connections, allowing it to reach a depth of around 20 layerswith lower time complexity than GCN. Extensive experiments on the Planetoid,Amazon, and OGB datasets underscore the effectiveness and efficiency of HL-GNN.It outperforms existing methods by a large margin in prediction performance.Additionally, HL-GNN is several orders of magnitude faster thanheuristic-inspired methods while requiring only a few trainable parameters. Thecase study further demonstrates that the generalized heuristics and learnedweights are highly interpretable.|链路预测是图形学习中的一个基本任务，其本质是由图的拓扑结构决定的。虽然传统的启发式算法以图形拓扑为基础，但它们在跨不同图形进行泛化时遇到了挑战。最近的研究工作旨在利用启发式的潜力，Yeta 统一的公式适应本地和全球启发式仍然未被发现。基于局部和全局启发式都可以用邻接矩阵乘法表示的事实，我们提出了一个统一的矩阵公式来适应和推广各种启发式。进一步提出了启发式学习图神经网络(HL-GNN)来有效地实现这一公式。HL-GNN 采用层内传播和层间连接，使其能够以比 GCN 更低的时间复杂度达到20层左右的深度。在行星、亚马逊和 OGB 数据集上的广泛实验强调了 HL-GNN 的有效性和效率。它在预测性能上大大优于现有的方法。此外，HL-GNN 比启发式方法快了几个数量级，同时只需要一些可训练的参数。案例研究进一步表明，广义启发式和学习权重是高度可解释的。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Heuristic+Learning+with+Graph+Neural+Networks:+A+Unified+Framework+for+Link+Prediction)|0| |[Asynchronous Vertical Federated Learning for Kernelized AUC Maximization](https://doi.org/10.1145/3637528.3671930)|Ke Zhang, Ganyu Wang, Han Li, Yulong Wang, Hong Chen, Bin Gu|; College of Informatics, Huazhong Agricultural University, Wuhan, Hubei, China; Department of Computer Science, Western University, London, Ontario, Canada|Vertical Federated Learning (VFL) has garnered significant attention due to its applicability in multi-party collaborative learning and the increasing demand for privacy-preserving measures. Most existing VFL algorithms primarily focus on accuracy as the training model metric. However, the data we access is often imbalanced in the real world, making it difficult for models based on accuracy to correctly classify minority samples. The Area Under the Curve (AUC) serves as an effective metric to evaluate the performance of a model on imbalanced data. Therefore, optimizing AUC can enhance the model's ability to handle imbalanced data. Besides, computational resources within VFL systems are also imbalanced, which makes synchronous VFL algorithms are difficult to apply in the real world. To address the double imbalance issue, we propose Asynchronous Vertical Federated Kernelized AUC Maximization (AVFKAM). Specifically, AVFKAM asynchronously updates a kernel model based on triply stochastic gradients with respect to (w.r.t.) the pairwise loss and random feature approximation. To facilitate theoretical analysis, we transfer the asynchrony of model coefficients to the functional gradient through a dual relationship between coefficients and objective function. Furthermore, we demonstrate that AVFKAM converges to the optimal solution at a rate of O(1/t), where t represents the global iteration number, and discuss the security of the model. If t is denoted as the global iteration number, we provide that it converges to the optimal solution with the rate of O(1/t). Finally, experimental results on various benchmark datasets demonstrate that AVFKAM maintains high AUC performance and efficiency.|垂直联邦学习(VFL)由于其在多方合作学习中的适用性以及对保护隐私措施的需求日益增长而引起了人们的广泛关注。大多数现有的 VFL 算法主要集中在精度作为训练模型度量。然而，我们访问的数据往往在现实世界中是不平衡的，使得基于准确性的模型难以正确分类少数样本。曲线下面积(AUC)是评价不平衡数据模型性能的有效指标。因此，优化 AUC 可以提高模型处理不平衡数据的能力。此外，VFL 系统中的计算资源也是不平衡的，这使得同步 VFL 算法难以在现实世界中应用。为了解决双不平衡问题，我们提出异步垂直联邦核化 AUC 最大化(AVFKAM)。具体来说，AVFKAM 异步更新基于三重随机梯度的核模型相对于成对损失和随机特征近似。为了便于理论分析，我们通过系数与目标函数之间的对偶关系将模型系数的异步性转化为函数梯度。此外，我们证明了 AVFKAM 以 O (1/t)的速率收敛到最优解，其中 t 表示全局迭代次数，并讨论了模型的安全性。如果 t 表示为全局迭代次数，则证明它以 O (1/t)的速率收敛到最优解。最后，在各种基准数据集上的实验结果表明，AVFKAM 保持了较高的 AUC 性能和效率。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Asynchronous+Vertical+Federated+Learning+for+Kernelized+AUC+Maximization)|0| |[Multivariate Log-based Anomaly Detection for Distributed Database](https://doi.org/10.1145/3637528.3671725)|Lingzhe Zhang, Tong Jia, Mengxi Jia, Ying Li, Yong Yang, Zhonghai Wu|Peking University, Beijing, China|Distributed databases are fundamental infrastructures of today's large-scalesoftware systems such as cloud systems. Detecting anomalies in distributeddatabases is essential for maintaining software availability. Existingapproaches, predominantly developed using Loghub-a comprehensive collection oflog datasets from various systems-lack datasets specifically tailored todistributed databases, which exhibit unique anomalies. Additionally, there's anotable absence of datasets encompassing multi-anomaly, multi-node logs.Consequently, models built upon these datasets, primarily designed forstandalone systems, are inadequate for distributed databases, and the prevalentmethod of deeming an entire cluster anomalous based on irregularities in asingle node leads to a high false-positive rate. This paper addresses theunique anomalies and multivariate nature of logs in distributed databases. Weexpose the first open-sourced, comprehensive dataset with multivariate logsfrom distributed databases. Utilizing this dataset, we conduct an extensivestudy to identify multiple database anomalies and to assess the effectivenessof state-of-the-art anomaly detection using multivariate log data. Our findingsreveal that relying solely on logs from a single node is insufficient foraccurate anomaly detection on distributed database. Leveraging these insights,we propose MultiLog, an innovative multivariate log-based anomaly detectionapproach tailored for distributed databases. Our experiments, based on thisnovel dataset, demonstrate MultiLog's superiority, outperforming existingstate-of-the-art methods by approximately 12|分布式数据库是当今大规模软件系统(如云系统)的基础设施。检测分布式数据库中的异常对于维护软件可用性至关重要。现有的方法，主要是使用 Loghub 开发的——一个来自不同系统的日志数据集的综合集合——缺乏专门针对分布式数据库的数据集，这些数据集表现出独特的异常。此外，还缺少包含多异常、多节点日志的数据集。因此，建立在这些数据集上的模型，主要是为独立系统设计的，对于分布式数据库来说是不够的，基于单个节点的不规则性来判断整个簇异常的流行方法导致了很高的假阳性率。本文讨论了分布式数据库中日志的独特异常和多变量特性。我们使用分布式数据库中的多变量日志来公开第一个开源的、全面的数据集。利用这个数据集，我们进行了一个广泛的研究，以确定多个数据库的异常，并评估使用多变量日志数据的最新异常检测的有效性。我们的研究结果表明，仅仅依靠单一节点的日志是不足以对异常检测分布式数据库进行精确计算的。利用这些见解，我们提出 MultiLog，一种创新的基于多元日志的异常检测方法，专为分布式数据库而设计。我们的实验，基于这个新颖的数据集，证明了 MultiLog 的优越性，比现有的最先进的方法高出大约12个|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Multivariate+Log-based+Anomaly+Detection+for+Distributed+Database)|0| -|[Logical Reasoning with Relation Network for Inductive Knowledge Graph Completion](https://doi.org/10.1145/3637528.3671911)|Qinggang Zhang, Keyu Duan, Junnan Dong, Pai Zheng, Xiao Huang|The Hong Kong Polytechnic University, Kowloon, Hong Kong; The Hong Kong Polytechnic University, Hung Hom, Hong Kong; National University of Singapore, Singapore, Singapore|Inductive knowledge graph completion (KGC) aims to infer the missing relationfor a set of newly-coming entities that never appeared in the training set.Such a setting is more in line with reality, as real-world KGs are constantlyevolving and introducing new knowledge. Recent studies have shown promisingresults using message passing over subgraphs to embed newly-coming entities forinductive KGC. However, the inductive capability of these methods is usuallylimited by two key issues. (i) KGC always suffers from data sparsity, and thesituation is even exacerbated in inductive KGC where new entities often havefew or no connections to the original KG. (ii) Cold-start problem. It is overcoarse-grained for accurate KG reasoning to generate representations for newentities by gathering the local information from few neighbors. To this end, wepropose a novel iNfOmax RelAtion Network, namely NORAN, for inductive KGcompletion. It aims to mine latent relation patterns for inductive KGcompletion. Specifically, by centering on relations, NORAN provides a hyperview towards KG modeling, where the correlations between relations can benaturally captured as entity-independent logical evidence to conduct inductiveKGC. Extensive experiment results on five benchmarks show that our frameworksubstantially outperforms the state-of-the-art KGC methods.|归纳知识图完成(KGC)的目的是推断一组新生实体在训练集中从未出现过的缺失关系。这样的设置更符合现实，因为现实世界的幼儿园不断发展和引入新的知识。最近的研究表明，使用信息传递子图嵌入新的实体归纳 KGC 的结果是有希望的。然而，这些方法的归纳能力通常受到两个关键问题的限制。(i) KGC 总是受到数据稀疏的影响，在归纳 KGC 中，这种情况甚至更加恶化，因为新的实体通常与原始 KG 没有或几乎没有连接。(ii)冷启动问题。对于精确的 KG 推理来说，通过收集少数邻居的局部信息来生成新实体的表示是过于粗粒度的。为此，我们提出了一个新颖的 iNfOmax 关系网络，即 NORAN，用于归纳学习。目的在于挖掘归纳 KG 完成的潜在关系模式。具体来说，以关系为中心，NORAN 提供了一个 KG 建模的全景，其中关系之间的相关性可以自然地被捕获为实体无关的逻辑证据来进行归纳 KGC。对五个基准测试的大量实验结果表明，我们的框架大大优于最先进的 KGC 方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Logical+Reasoning+with+Relation+Network+for+Inductive+Knowledge+Graph+Completion)|0| -|[Towards Adaptive Neighborhood for Advancing Temporal Interaction Graph Modeling](https://doi.org/10.1145/3637528.3671877)|Siwei Zhang, Xi Chen, Yun Xiong, Xixi Wu, Yao Zhang, Yongrui Fu, Yinglong Zhao, Jiawei Zhang|; Ant Group, Shanghai, China; IFM Lab, Department of Computer Science, University of California, Davis, Davis, CA, USA|Temporal Graph Networks (TGNs) have demonstrated their remarkable performance in modeling temporal interaction graphs. These works can generate temporal node representations by encoding the surrounding neighborhoods for the target node. However, an inherent limitation of existing TGNs is their reliance onfixed, hand-crafted rules for neighborhood encoding, overlooking the necessity for an adaptive and learnable neighborhood that can accommodate both personalization and temporal evolution across different timestamps. In this paper, we aim to enhance existing TGNs by introducing anadaptive neighborhood encoding mechanism. We present SEAN (Selective Encoding for Adaptive Neighborhood), a flexible plug-and-play model that can be seamlessly integrated with existing TGNs, effectively boosting their performance. To achieve this, we decompose the adaptive neighborhood encoding process into two phases: (i) representative neighbor selection, and (ii) temporal-aware neighborhood information aggregation. Specifically, we propose the Representative Neighbor Selector component, which automatically pinpoints the most important neighbors for the target node. It offers a tailored understanding of each node's unique surrounding context, facilitating personalization. Subsequently, we propose a Temporal-aware Aggregator, which synthesizes neighborhood aggregation by selectively determining the utilization of aggregation routes and decaying the outdated information, allowing our model to adaptively leverage both the contextually significant and current information during aggregation. We conduct extensive experiments by integrating SEAN into three representative TGNs, evaluating their performance on four public datasets and one financial benchmark dataset introduced in this paper. The results demonstrate that SEAN consistently leads to performance improvements across all models, achieving SOTA performance and exceptional robustness.|时态图网络(TGNs)在时态交互图建模方面表现出了显著的性能。这些工作可以通过编码目标节点的周围邻域来产生时间节点表示。然而，现有 TGN 的一个固有局限性是它们依赖于固定的手工制作的邻域编码规则，忽视了自适应和可学习的邻域的必要性，这种邻域可以容纳跨不同时间戳的个性化和时间演变。本文通过引入一种自适应邻域编码机制来增强现有的 TGNs。我们提出了 SEAN (自适应邻域选择性编码) ，一个灵活的即插即用模型，可以无缝集成到现有的 TGN，有效地提高他们的性能。为了实现这一目标，我们将自适应邻域编码过程分解为两个阶段: (i)代表性邻域选择阶段和(ii)时间感知邻域信息聚合阶段。具体来说，我们提出了代表性邻居选择器组件，它自动精确定位目标节点的最重要的邻居。它提供了对每个节点独特的周围环境的定制理解，促进了个性化。随后，我们提出了一个时间感知聚合器，它通过选择性地确定聚合路径的利用率和衰减过时的信息来综合邻域聚合，允许我们的模型在聚合过程中适应性地利用上下文重要性和当前信息。本文通过将 SEAN 集成到三个具有代表性的 TGNs 中，在四个公共数据集和一个金融基准数据集上进行了广泛的实验，评价了它们的性能。结果表明，SEAN 在所有模型中始终如一地导致性能改进，实现了 SOTA 性能和出色的鲁棒性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Towards+Adaptive+Neighborhood+for+Advancing+Temporal+Interaction+Graph+Modeling)|0| -|[Irregular Traffic Time Series Forecasting Based on Asynchronous Spatio-Temporal Graph Convolutional Networks](https://doi.org/10.1145/3637528.3671665)|Weijia Zhang, Le Zhang, Jindong Han, Hao Liu, Yanjie Fu, Jingbo Zhou, Yu Mei, Hui Xiong|HKUST(GZ), Guangzhou, China; HKUST(GZ) & HKUST, Guangzhou, China; Baidu Inc., Beijing, China; Baidu Research, Beijing, China; HKUST, Hong Kong, China; Arizona State University, Phoenix, USA|Accurate traffic forecasting is crucial for the development of Intelligent Transportation Systems (ITS), playing a pivotal role in modern urban traffic management. Traditional forecasting methods, however, struggle with the irregular traffic time series resulting from adaptive traffic signal controls, presenting challenges in asynchronous spatial dependency, irregular temporal dependency, and predicting variable-length sequences. To this end, we propose an Asynchronous Spatio-tEmporal graph convolutional nEtwoRk (ASeer) tailored for irregular traffic time series forecasting. Specifically, we first propose an Asynchronous Graph Diffusion Network to capture the spatial dependency between asynchronously measured traffic states regulated by adaptive traffic signals. After that, to capture the temporal dependency within irregular traffic state sequences, a personalized time encoding is devised to embed the continuous time signals. Then, we propose a Transformable Time-aware Convolution Network, which adapts meta-filters for time-aware convolution on the sequences with inconsistent temporal flow. Additionally, a Semi-Autoregressive Prediction Network, comprising a state evolution unit and a semiautoregressive predictor, is designed to predict variable-length traffic sequences effectively and efficiently. Extensive experiments on a newly established benchmark demonstrate the superiority of ASeer compared with twelve competitive baselines across six metrics.|准确的交通量预测是智能交通系统(ITS)发展的关键，在现代城市交通管理中起着举足轻重的作用。然而，传统的交通流预测方法很难克服自适应交通信号控制产生的不规则交通流时间序列，在异步空间依赖性、不规则时间依赖性以及预测变长序列等方面都面临着挑战。为此，我们提出了一种适用于不规则交通时间序列预测的异步时空图卷积网络(ASeer)。具体来说，我们首先提出一个异步图扩散网络来捕获由自适应交通信号调节的异步测量交通状态之间的空间依赖关系。然后，为了捕获不规则交通状态序列中的时间相关性，设计了一种个性化的时间编码方法来嵌入连续的时间信号。然后，提出了一种可变时间感知卷积网络，该网络对时间流不一致的序列采用元滤波器进行时间感知卷积。此外，设计了一个由状态演化单元和半自回归预测器组成的半自回归预测网络，以有效地预测变长业务序列。在一个新建立的基准上进行的大量实验表明，与横跨6个指标的十二个具有竞争力的基准相比，ASeer 具有优越性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Irregular+Traffic+Time+Series+Forecasting+Based+on+Asynchronous+Spatio-Temporal+Graph+Convolutional+Networks)|0| -|[A Multimodal Foundation Agent for Financial Trading: Tool-Augmented, Diversified, and Generalist](https://doi.org/10.1145/3637528.3671801)|Wentao Zhang, Lingxuan Zhao, Haochong Xia, Shuo Sun, Jiaze Sun, Molei Qin, Xinyi Li, Yuqing Zhao, Yilei Zhao, Xinyu Cai, Longtao Zheng, Xinrun Wang, Bo An|National Technological University, Singapore, Singapore; National University of Singapore, Singapore, Singapore; Nanyang Technological University & Skywork AI, Singapore, Singapore; Zhejiang University, Hangzhou, China; Nanyang Technological University, Singapore, Singapore|Financial trading is a crucial component of the markets, informed by a multimodal information landscape encompassing news, prices, and Kline charts, and encompasses diverse tasks such as quantitative trading and high-frequency trading with various assets. While advanced AI techniques like deep learning and reinforcement learning are extensively utilized in finance, their application in financial trading tasks often faces challenges due to inadequate handling of multimodal data and limited generalizability across various tasks. To address these challenges, we present FinAgent, a multimodal foundational agent with tool augmentation for financial trading. FinAgent's market intelligence module processes a diverse range of data-numerical, textual, and visual-to accurately analyze the financial market. Its unique dual-level reflection module not only enables rapid adaptation to market dynamics but also incorporates a diversified memory retrieval system, enhancing the agent's ability to learn from historical data and improve decision-making processes. The agent's emphasis on reasoning for actions fosters trust in its financial decisions. Moreover, FinAgent integrates established trading strategies and expert insights, ensuring that its trading approaches are both data-driven and rooted in sound financial principles. With comprehensive experiments on 6 financial datasets, including stocks and Crypto, FinAgent significantly outperforms 12 state-of-the-art baselines in terms of 6 financial metrics with over 36% average improvement on profit. Specifically, a 92.27% return (a 84.39% relative improvement) is achieved on one dataset. Notably, FinAgent is the first advanced multimodal foundation agent designed for financial trading tasks.|金融交易是市场的一个重要组成部分，由包括新闻、价格和克莱恩图表在内的多模式信息环境提供信息，并包括各种各样的任务，如定量交易和各种资产的高频交易。尽管深度学习和强化学习等先进的人工智能技术在金融领域得到广泛应用，但由于多模式数据处理不足以及各种任务的普遍性有限，它们在金融交易任务中的应用往往面临挑战。为了应对这些挑战，我们介绍了 FinAgent，一个多模式的基础代理，为金融交易提供了工具增强。FinAgent 的市场情报模块处理各种各样的数据——数字、文本和可视化——以准确地分析金融市场。其独特的双层反射模块不仅能够快速适应市场动态，而且还包括一个多样化的记忆检索系统，增强代理人从历史数据中学习和改进决策过程的能力。代理人强调对行为的推理，从而培养了对其财务决策的信任。此外，FinAgent 整合了既定的交易战略和专家见解，确保其交易方法既是数据驱动的，又植根于健全的财务原则。通过对包括股票和 Crypto 在内的6个财务数据集的全面实验，FinAgent 在6个财务指标方面显著优于12个最先进的基线，平均利润提高超过36% 。具体来说，在一个数据集上实现了92.27% 的回报率(相对提高了84.39%)。值得注意的是，FinAgent 是第一个为金融交易任务设计的高级多模式基础代理。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Multimodal+Foundation+Agent+for+Financial+Trading:+Tool-Augmented,+Diversified,+and+Generalist)|0| -|[Geometric View of Soft Decorrelation in Self-Supervised Learning](https://doi.org/10.1145/3637528.3671914)|Yifei Zhang, Hao Zhu, Zixing Song, Yankai Chen, Xinyu Fu, Ziqiao Meng, Piotr Koniusz, Irwin King|; CSIRO, Sydney, Australia; The Chinese University of Hong Kong, Hong Kong, China; Data61, CSIRO, Canberra, Australia|Contrastive learning, a form of Self-Supervised Learning (SSL), typically consists of an alignment term and a regularization term. The alignment term minimizes the distance between the embeddings of a positive pair, while the regularization term prevents trivial solutions and expresses prior beliefs about the embeddings. As a widely used regularization technique, soft decorrelation has been employed by several non-contrastive SSL methods to avoid trivial solutions. While the decorrelation term is designed to address the issue of dimensional collapse, we find that it fails to achieve this goal theoretically and experimentally. Based on such a finding, we extend the soft decorrelation regularization to minimize the distance between the covariance matrix and an identity matrix. We provide a new perspective on the geometric distance between positive definite matrices to investigate why the soft decorrelation cannot efficiently solve the dimensional collapse. Furthermore, we construct a family of loss functions utilizing the Bregman Matrix Divergence (BMD), with the soft decorrelation representing a specific instance within this family. We prove that a loss function (LogDet) in this family can solve the issue of dimensional collapse. Our novel loss functions based on BMD exhibit superior performance compared to the soft decorrelation and other baseline techniques, as demonstrated by experimental results on graph and image datasets.|对比学习是自我监督学习(SSL)的一种形式，通常由一个校准项和一个正则项组成。对齐项使正对的嵌入之间的距离最小，而正则项防止平凡的解和表达关于嵌入的先验信念。软解相关作为一种广泛应用的正则化技术，已被多种非对比 SSL 方法所采用，以避免平凡的解。去相关项用于解决量纲坍缩问题，但在理论和实验上均未能达到这一目的。基于这一发现，我们扩展了软去相关正则化，以最小化协方差矩阵与恒等矩阵之间的距离。本文从正定矩阵之间的几何距离出发，研究了软去相关不能有效解决维数塌陷问题的原因。此外，我们利用 Bregman 矩阵散度(BMD)构造了一个损失函数族，其中软去相关表示该族中的一个特定实例。证明了这一族中的一个损失函数(LogDet)可以解决维数崩溃问题。我们的新型基于骨密度的损失函数表现出优于软去相关和其他基线技术的性能，如图形和图像数据集的实验结果所证明的。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Geometric+View+of+Soft+Decorrelation+in+Self-Supervised+Learning)|0| +|[Logical Reasoning with Relation Network for Inductive Knowledge Graph Completion](https://doi.org/10.1145/3637528.3671911)|Qinggang Zhang, Keyu Duan, Junnan Dong, Pai Zheng, Xiao Huang|The Hong Kong Polytechnic University, Hung Hom, Hong Kong; National University of Singapore, Singapore, Singapore; The Hong Kong Polytechnic University, Kowloon, Hong Kong|Inductive knowledge graph completion (KGC) aims to infer the missing relationfor a set of newly-coming entities that never appeared in the training set.Such a setting is more in line with reality, as real-world KGs are constantlyevolving and introducing new knowledge. Recent studies have shown promisingresults using message passing over subgraphs to embed newly-coming entities forinductive KGC. However, the inductive capability of these methods is usuallylimited by two key issues. (i) KGC always suffers from data sparsity, and thesituation is even exacerbated in inductive KGC where new entities often havefew or no connections to the original KG. (ii) Cold-start problem. It is overcoarse-grained for accurate KG reasoning to generate representations for newentities by gathering the local information from few neighbors. To this end, wepropose a novel iNfOmax RelAtion Network, namely NORAN, for inductive KGcompletion. It aims to mine latent relation patterns for inductive KGcompletion. Specifically, by centering on relations, NORAN provides a hyperview towards KG modeling, where the correlations between relations can benaturally captured as entity-independent logical evidence to conduct inductiveKGC. Extensive experiment results on five benchmarks show that our frameworksubstantially outperforms the state-of-the-art KGC methods.|归纳知识图完成(KGC)的目的是推断一组新生实体在训练集中从未出现过的缺失关系。这样的设置更符合现实，因为现实世界的幼儿园不断发展和引入新的知识。最近的研究表明，使用信息传递子图嵌入新的实体归纳 KGC 的结果是有希望的。然而，这些方法的归纳能力通常受到两个关键问题的限制。(i) KGC 总是受到数据稀疏的影响，在归纳 KGC 中，这种情况甚至更加恶化，因为新的实体通常与原始 KG 没有或几乎没有连接。(ii)冷启动问题。对于精确的 KG 推理来说，通过收集少数邻居的局部信息来生成新实体的表示是过于粗粒度的。为此，我们提出了一个新颖的 iNfOmax 关系网络，即 NORAN，用于归纳学习。目的在于挖掘归纳 KG 完成的潜在关系模式。具体来说，以关系为中心，NORAN 提供了一个 KG 建模的全景，其中关系之间的相关性可以自然地被捕获为实体无关的逻辑证据来进行归纳 KGC。对五个基准测试的大量实验结果表明，我们的框架大大优于最先进的 KGC 方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Logical+Reasoning+with+Relation+Network+for+Inductive+Knowledge+Graph+Completion)|0| +|[Towards Adaptive Neighborhood for Advancing Temporal Interaction Graph Modeling](https://doi.org/10.1145/3637528.3671877)|Siwei Zhang, Xi Chen, Yun Xiong, Xixi Wu, Yao Zhang, Yongrui Fu, Yinglong Zhao, Jiawei Zhang|; IFM Lab, Department of Computer Science, University of California, Davis, Davis, CA, USA; Ant Group, Shanghai, China|Temporal Graph Networks (TGNs) have demonstrated their remarkable performance in modeling temporal interaction graphs. These works can generate temporal node representations by encoding the surrounding neighborhoods for the target node. However, an inherent limitation of existing TGNs is their reliance onfixed, hand-crafted rules for neighborhood encoding, overlooking the necessity for an adaptive and learnable neighborhood that can accommodate both personalization and temporal evolution across different timestamps. In this paper, we aim to enhance existing TGNs by introducing anadaptive neighborhood encoding mechanism. We present SEAN (Selective Encoding for Adaptive Neighborhood), a flexible plug-and-play model that can be seamlessly integrated with existing TGNs, effectively boosting their performance. To achieve this, we decompose the adaptive neighborhood encoding process into two phases: (i) representative neighbor selection, and (ii) temporal-aware neighborhood information aggregation. Specifically, we propose the Representative Neighbor Selector component, which automatically pinpoints the most important neighbors for the target node. It offers a tailored understanding of each node's unique surrounding context, facilitating personalization. Subsequently, we propose a Temporal-aware Aggregator, which synthesizes neighborhood aggregation by selectively determining the utilization of aggregation routes and decaying the outdated information, allowing our model to adaptively leverage both the contextually significant and current information during aggregation. We conduct extensive experiments by integrating SEAN into three representative TGNs, evaluating their performance on four public datasets and one financial benchmark dataset introduced in this paper. The results demonstrate that SEAN consistently leads to performance improvements across all models, achieving SOTA performance and exceptional robustness.|时态图网络(TGNs)在时态交互图建模方面表现出了显著的性能。这些工作可以通过编码目标节点的周围邻域来产生时间节点表示。然而，现有 TGN 的一个固有局限性是它们依赖于固定的手工制作的邻域编码规则，忽视了自适应和可学习的邻域的必要性，这种邻域可以容纳跨不同时间戳的个性化和时间演变。本文通过引入一种自适应邻域编码机制来增强现有的 TGNs。我们提出了 SEAN (自适应邻域选择性编码) ，一个灵活的即插即用模型，可以无缝集成到现有的 TGN，有效地提高他们的性能。为了实现这一目标，我们将自适应邻域编码过程分解为两个阶段: (i)代表性邻域选择阶段和(ii)时间感知邻域信息聚合阶段。具体来说，我们提出了代表性邻居选择器组件，它自动精确定位目标节点的最重要的邻居。它提供了对每个节点独特的周围环境的定制理解，促进了个性化。随后，我们提出了一个时间感知聚合器，它通过选择性地确定聚合路径的利用率和衰减过时的信息来综合邻域聚合，允许我们的模型在聚合过程中适应性地利用上下文重要性和当前信息。本文通过将 SEAN 集成到三个具有代表性的 TGNs 中，在四个公共数据集和一个金融基准数据集上进行了广泛的实验，评价了它们的性能。结果表明，SEAN 在所有模型中始终如一地导致性能改进，实现了 SOTA 性能和出色的鲁棒性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Towards+Adaptive+Neighborhood+for+Advancing+Temporal+Interaction+Graph+Modeling)|0| +|[Irregular Traffic Time Series Forecasting Based on Asynchronous Spatio-Temporal Graph Convolutional Networks](https://doi.org/10.1145/3637528.3671665)|Weijia Zhang, Le Zhang, Jindong Han, Hao Liu, Yanjie Fu, Jingbo Zhou, Yu Mei, Hui Xiong|HKUST, Hong Kong, China; HKUST(GZ), Guangzhou, China; HKUST(GZ) & HKUST, Guangzhou, China; Baidu Research, Beijing, China; Arizona State University, Phoenix, USA; Baidu Inc., Beijing, China|Accurate traffic forecasting is crucial for the development of Intelligent Transportation Systems (ITS), playing a pivotal role in modern urban traffic management. Traditional forecasting methods, however, struggle with the irregular traffic time series resulting from adaptive traffic signal controls, presenting challenges in asynchronous spatial dependency, irregular temporal dependency, and predicting variable-length sequences. To this end, we propose an Asynchronous Spatio-tEmporal graph convolutional nEtwoRk (ASeer) tailored for irregular traffic time series forecasting. Specifically, we first propose an Asynchronous Graph Diffusion Network to capture the spatial dependency between asynchronously measured traffic states regulated by adaptive traffic signals. After that, to capture the temporal dependency within irregular traffic state sequences, a personalized time encoding is devised to embed the continuous time signals. Then, we propose a Transformable Time-aware Convolution Network, which adapts meta-filters for time-aware convolution on the sequences with inconsistent temporal flow. Additionally, a Semi-Autoregressive Prediction Network, comprising a state evolution unit and a semiautoregressive predictor, is designed to predict variable-length traffic sequences effectively and efficiently. Extensive experiments on a newly established benchmark demonstrate the superiority of ASeer compared with twelve competitive baselines across six metrics.|准确的交通量预测是智能交通系统(ITS)发展的关键，在现代城市交通管理中起着举足轻重的作用。然而，传统的交通流预测方法很难克服自适应交通信号控制产生的不规则交通流时间序列，在异步空间依赖性、不规则时间依赖性以及预测变长序列等方面都面临着挑战。为此，我们提出了一种适用于不规则交通时间序列预测的异步时空图卷积网络(ASeer)。具体来说，我们首先提出一个异步图扩散网络来捕获由自适应交通信号调节的异步测量交通状态之间的空间依赖关系。然后，为了捕获不规则交通状态序列中的时间相关性，设计了一种个性化的时间编码方法来嵌入连续的时间信号。然后，提出了一种可变时间感知卷积网络，该网络对时间流不一致的序列采用元滤波器进行时间感知卷积。此外，设计了一个由状态演化单元和半自回归预测器组成的半自回归预测网络，以有效地预测变长业务序列。在一个新建立的基准上进行的大量实验表明，与横跨6个指标的十二个具有竞争力的基准相比，ASeer 具有优越性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Irregular+Traffic+Time+Series+Forecasting+Based+on+Asynchronous+Spatio-Temporal+Graph+Convolutional+Networks)|0| +|[A Multimodal Foundation Agent for Financial Trading: Tool-Augmented, Diversified, and Generalist](https://doi.org/10.1145/3637528.3671801)|Wentao Zhang, Lingxuan Zhao, Haochong Xia, Shuo Sun, Jiaze Sun, Molei Qin, Xinyi Li, Yuqing Zhao, Yilei Zhao, Xinyu Cai, Longtao Zheng, Xinrun Wang, Bo An|National University of Singapore, Singapore, Singapore; National Technological University, Singapore, Singapore; Zhejiang University, Hangzhou, China; Nanyang Technological University & Skywork AI, Singapore, Singapore; Nanyang Technological University, Singapore, Singapore|Financial trading is a crucial component of the markets, informed by a multimodal information landscape encompassing news, prices, and Kline charts, and encompasses diverse tasks such as quantitative trading and high-frequency trading with various assets. While advanced AI techniques like deep learning and reinforcement learning are extensively utilized in finance, their application in financial trading tasks often faces challenges due to inadequate handling of multimodal data and limited generalizability across various tasks. To address these challenges, we present FinAgent, a multimodal foundational agent with tool augmentation for financial trading. FinAgent's market intelligence module processes a diverse range of data-numerical, textual, and visual-to accurately analyze the financial market. Its unique dual-level reflection module not only enables rapid adaptation to market dynamics but also incorporates a diversified memory retrieval system, enhancing the agent's ability to learn from historical data and improve decision-making processes. The agent's emphasis on reasoning for actions fosters trust in its financial decisions. Moreover, FinAgent integrates established trading strategies and expert insights, ensuring that its trading approaches are both data-driven and rooted in sound financial principles. With comprehensive experiments on 6 financial datasets, including stocks and Crypto, FinAgent significantly outperforms 12 state-of-the-art baselines in terms of 6 financial metrics with over 36% average improvement on profit. Specifically, a 92.27% return (a 84.39% relative improvement) is achieved on one dataset. Notably, FinAgent is the first advanced multimodal foundation agent designed for financial trading tasks.|金融交易是市场的一个重要组成部分，由包括新闻、价格和克莱恩图表在内的多模式信息环境提供信息，并包括各种各样的任务，如定量交易和各种资产的高频交易。尽管深度学习和强化学习等先进的人工智能技术在金融领域得到广泛应用，但由于多模式数据处理不足以及各种任务的普遍性有限，它们在金融交易任务中的应用往往面临挑战。为了应对这些挑战，我们介绍了 FinAgent，一个多模式的基础代理，为金融交易提供了工具增强。FinAgent 的市场情报模块处理各种各样的数据——数字、文本和可视化——以准确地分析金融市场。其独特的双层反射模块不仅能够快速适应市场动态，而且还包括一个多样化的记忆检索系统，增强代理人从历史数据中学习和改进决策过程的能力。代理人强调对行为的推理，从而培养了对其财务决策的信任。此外，FinAgent 整合了既定的交易战略和专家见解，确保其交易方法既是数据驱动的，又植根于健全的财务原则。通过对包括股票和 Crypto 在内的6个财务数据集的全面实验，FinAgent 在6个财务指标方面显著优于12个最先进的基线，平均利润提高超过36% 。具体来说，在一个数据集上实现了92.27% 的回报率(相对提高了84.39%)。值得注意的是，FinAgent 是第一个为金融交易任务设计的高级多模式基础代理。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Multimodal+Foundation+Agent+for+Financial+Trading:+Tool-Augmented,+Diversified,+and+Generalist)|0| +|[Geometric View of Soft Decorrelation in Self-Supervised Learning](https://doi.org/10.1145/3637528.3671914)|Yifei Zhang, Hao Zhu, Zixing Song, Yankai Chen, Xinyu Fu, Ziqiao Meng, Piotr Koniusz, Irwin King|The Chinese University of Hong Kong, Hong Kong, China; ; CSIRO, Sydney, Australia; Data61, CSIRO, Canberra, Australia|Contrastive learning, a form of Self-Supervised Learning (SSL), typically consists of an alignment term and a regularization term. The alignment term minimizes the distance between the embeddings of a positive pair, while the regularization term prevents trivial solutions and expresses prior beliefs about the embeddings. As a widely used regularization technique, soft decorrelation has been employed by several non-contrastive SSL methods to avoid trivial solutions. While the decorrelation term is designed to address the issue of dimensional collapse, we find that it fails to achieve this goal theoretically and experimentally. Based on such a finding, we extend the soft decorrelation regularization to minimize the distance between the covariance matrix and an identity matrix. We provide a new perspective on the geometric distance between positive definite matrices to investigate why the soft decorrelation cannot efficiently solve the dimensional collapse. Furthermore, we construct a family of loss functions utilizing the Bregman Matrix Divergence (BMD), with the soft decorrelation representing a specific instance within this family. We prove that a loss function (LogDet) in this family can solve the issue of dimensional collapse. Our novel loss functions based on BMD exhibit superior performance compared to the soft decorrelation and other baseline techniques, as demonstrated by experimental results on graph and image datasets.|对比学习是自我监督学习(SSL)的一种形式，通常由一个校准项和一个正则项组成。对齐项使正对的嵌入之间的距离最小，而正则项防止平凡的解和表达关于嵌入的先验信念。软解相关作为一种广泛应用的正则化技术，已被多种非对比 SSL 方法所采用，以避免平凡的解。去相关项用于解决量纲坍缩问题，但在理论和实验上均未能达到这一目的。基于这一发现，我们扩展了软去相关正则化，以最小化协方差矩阵与恒等矩阵之间的距离。本文从正定矩阵之间的几何距离出发，研究了软去相关不能有效解决维数塌陷问题的原因。此外，我们利用 Bregman 矩阵散度(BMD)构造了一个损失函数族，其中软去相关表示该族中的一个特定实例。证明了这一族中的一个损失函数(LogDet)可以解决维数崩溃问题。我们的新型基于骨密度的损失函数表现出优于软去相关和其他基线技术的性能，如图形和图像数据集的实验结果所证明的。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Geometric+View+of+Soft+Decorrelation+in+Self-Supervised+Learning)|0| |[Representation Learning of Geometric Trees](https://doi.org/10.1145/3637528.3671688)|Zheng Zhang, Allen Zhang, Ruth Nelson, Giorgio Ascoli, Liang Zhao|Yale University, New Haven, CT, USA; Emory University, Atlanta, GA, USA; George Mason University, Fairfax, VA, USA; Georgia Institute of Technology, Atlanta, GA, USA|Geometric trees are characterized by their tree-structured layout and spatially constrained nodes and edges, which significantly impacts their topological attributes. This inherent hierarchical structure plays a crucial role in domains such as neuron morphology and river geomorphology, but traditional graph representation methods often overlook these specific characteristics of tree structures. To address this, we introduce a new representation learning framework tailored for geometric trees. It first features a unique message passing neural network, which is both provably geometrical structure-recoverable and rotation-translation invariant. To address the data label scarcity issue, our approach also includes two innovative training targets that reflect the hierarchical ordering and geometric structure of these geometric trees. This enables fully self-supervised learning without explicit labels. We validate our method's effectiveness on eight real-world datasets, demonstrating its capability to represent geometric trees.|几何树木的拥有属性是树状结构，节点和边缘在空间上受到限制，这对它们的拓扑属性有很大的影响。这种固有的层次结构在神经元形态学和河流地貌学等领域起着至关重要的作用，但传统的图表示方法往往忽视了树状结构的这些特殊性。为了解决这一问题，我们提出了一种适合于几何树的表示学习框架。它首先具有唯一的消息传递神经网络，既是可证明的几何结构可恢复的，又是旋转平移不变的。为了解决数据标签稀缺性问题，我们的方法还包括两个创新的训练目标，反映这些几何树的层次排序和几何结构。这使得完全自我监督的学习没有明确的标签。我们验证了该方法在八个实际数据集上的有效性，证明了其表示几何树的能力。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Representation+Learning+of+Geometric+Trees)|0| |[Rethinking Graph Backdoor Attacks: A Distribution-Preserving Perspective](https://doi.org/10.1145/3637528.3671910)|Zhiwei Zhang, Minhua Lin, Enyan Dai, Suhang Wang|The Pennsylvania State University, State College, PA, USA|Graph Neural Networks (GNNs) have shown remarkable performance in varioustasks. However, recent works reveal that GNNs are vulnerable to backdoorattacks. Generally, backdoor attack poisons the graph by attaching backdoortriggers and the target class label to a set of nodes in the training graph. AGNN trained on the poisoned graph will then be misled to predict test nodesattached with trigger to the target class. Despite their effectiveness, ourempirical analysis shows that triggers generated by existing methods tend to beout-of-distribution (OOD), which significantly differ from the clean data.Hence, these injected triggers can be easily detected and pruned with widelyused outlier detection methods in real-world applications. Therefore, in thispaper, we study a novel problem of unnoticeable graph backdoor attacks within-distribution (ID) triggers. To generate ID triggers, we introduce an OODdetector in conjunction with an adversarial learning strategy to generate theattributes of the triggers within distribution. To ensure a high attack successrate with ID triggers, we introduce novel modules designed to enhance triggermemorization by the victim model trained on poisoned graph. Extensiveexperiments on real-world datasets demonstrate the effectiveness of theproposed method in generating in distribution triggers that can by-pass variousdefense strategies while maintaining a high attack success rate.|图形神经网络(GNN)在各种任务中表现出显著的性能。然而，最近的工作表明，GNN 是脆弱的后门攻击。一般来说，后门攻击通过将后门触发器和目标类标签附加到训练图中的一组节点上来破坏图。对中毒图进行训练的 AGNN 将被误导以预测连接到目标类的触发器的测试节点。尽管有效，但实证分析表明，现有方法产生的触发器倾向于超出分布(OOD) ，这与清洁数据有显著差异。因此，这些注入触发器可以很容易地被检测到，并在现实应用中被广泛使用的异常检测方法进行修剪。因此，本文研究了一个新的不易被察觉的分布式(ID)触发器图后门攻击问题。为了生成 ID 触发器，我们引入了一个面向对象的检测器和一个对抗性学习策略来生成分布中触发器的属性。为了保证 ID 触发器具有较高的攻击成功率，我们引入了新的模块，通过在中毒图上训练的受害者模型来增强触发记忆。在实际数据集上的大量实验证明了该方法在生成分布式触发器方面的有效性，该触发器可以绕过各种防御策略，同时保持较高的攻击成功率。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Rethinking+Graph+Backdoor+Attacks:+A+Distribution-Preserving+Perspective)|0| |[Learning Flexible Time-windowed Granger Causality Integrating Heterogeneous Interventional Time Series Data](https://doi.org/10.1145/3637528.3672023)|Ziyi Zhang, Shaogang Ren, Xiaoning Qian, Nick Duffield|Texas A&M University, College Station, Texas, USA; Texas A&M University & Brookhaven National Laboratory, College Station, Texas, USA|Granger causality, commonly used for inferring causal structures from timeseries data, has been adopted in widespread applications across various fieldsdue to its intuitive explainability and high compatibility with emerging deepneural network prediction models. To alleviate challenges in better decipheringcausal structures unambiguously from time series, the use of interventionaldata has become a practical approach. However, existing methods have yet to beexplored in the context of imperfect interventions with unknown targets, whichare more common and often more beneficial in a wide range of real-worldapplications. Additionally, the identifiability issues of Granger causalitywith unknown interventional targets in complex network models remain unsolved.Our work presents a theoretically-grounded method that infers Granger causalstructure and identifies unknown targets by leveraging heterogeneousinterventional time series data. We further illustrate that learning Grangercausal structure and recovering interventional targets can mutually promoteeach other. Comparative experiments demonstrate that our method outperformsseveral robust baseline methods in learning Granger causal structure frominterventional time series data.|格兰杰因果关系，通常用于从时间序列数据推断因果结构，由于其直观的解释性和与新兴的深层神经网络预测模型的高度兼容性，已被广泛应用于各个领域。为了减轻从时间序列中更好地解释因果结构的挑战，使用干预数据已经成为一种实用的方法。然而，现有的方法尚未在不完善的干预措施和未知目标的背景下进行探索，这些干预措施在现实世界的广泛应用中更为常见，而且往往更为有益。此外，在复杂网络模型中，未知干预目标的 Granger 因果关系的可识别性问题仍然没有得到解决。我们的工作提出了一个理论基础的方法，推断格兰杰因果结构和识别未知目标利用异质干预时间序列数据。我们进一步说明了学习格兰杰因果结构和恢复干预目标可以相互促进。对比实验表明，该方法在学习常规时间序列数据的格兰杰因果结构方面优于几种稳健的基线方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Learning+Flexible+Time-windowed+Granger+Causality+Integrating+Heterogeneous+Interventional+Time+Series+Data)|0| -|[Algorithmic Fairness Generalization under Covariate and Dependence Shifts Simultaneously](https://doi.org/10.1145/3637528.3671909)|Chen Zhao, Kai Jiang, Xintao Wu, Haoliang Wang, Latifur Khan, Christan Grant, Feng Chen|Baylor University, Waco, TX, USA; University of Florida, Gainesville, FL, USA; The University of Texas, Dallas, Richardson, TX, USA; The University of Texas at Dallas, Richardson, TX, USA; University of Arkansas, Fayetteville, AR, USA|The endeavor to preserve the generalization of a fair and invariantclassifier across domains, especially in the presence of distribution shifts,becomes a significant and intricate challenge in machine learning. In responseto this challenge, numerous effective algorithms have been developed with afocus on addressing the problem of fairness-aware domain generalization. Thesealgorithms are designed to navigate various types of distribution shifts, witha particular emphasis on covariate and dependence shifts. In this context,covariate shift pertains to changes in the marginal distribution of inputfeatures, while dependence shift involves alterations in the joint distributionof the label variable and sensitive attributes. In this paper, we introduce asimple but effective approach that aims to learn a fair and invariantclassifier by simultaneously addressing both covariate and dependence shiftsacross domains. We assert the existence of an underlying transformation modelcan transform data from one domain to another, while preserving the semanticsrelated to non-sensitive attributes and classes. By augmenting varioussynthetic data domains through the model, we learn a fair and invariantclassifier in source domains. This classifier can then be generalized tounknown target domains, maintaining both model prediction and fairnessconcerns. Extensive empirical studies on four benchmark datasets demonstratethat our approach surpasses state-of-the-art methods.|在机器学习中，如何保持分类器的公平性和不变性，特别是在分布偏移的情况下，成为一个重要而复杂的挑战。为了应对这一挑战，许多有效的算法已经开发出来，重点是解决公平意识的领域泛化问题。这些算法被设计用于导航各种类型的分布移位，特别强调协变量和依赖性移位。在这种情况下，协变量变化涉及输入特征边缘分布的变化，而依赖性变化涉及标签变量和敏感属性的联合分布的变化。本文介绍了一种简单而有效的方法，通过同时处理协变量和依赖域间的移位来学习一个公平且不变的分类器。我们断言底层转换模型的存在可以将数据从一个域转换到另一个域，同时保留与非敏感属性和类相关的语义。通过该模型对多个合成数据域进行扩充，在源域中学习了一个公平的、不变的分类器。该分类器可以广义化未知目标域，同时保持模型预测和公平性。对四个基准数据集的大量实证研究表明，我们的方法超越了最先进的方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Algorithmic+Fairness+Generalization+under+Covariate+and+Dependence+Shifts+Simultaneously)|0| -|[VertiMRF: Differentially Private Vertical Federated Data Synthesis](https://doi.org/10.1145/3637528.3671771)|Fangyuan Zhao, Zitao Li, Xuebin Ren, Bolin Ding, Shusen Yang, Yaliang Li|Alibaba Group, Bellevue, WA, USA; Xi'an Jiaotong University, Xi'an, China|Data synthesis is a promising solution to share data for various downstream analytic tasks without exposing raw data. However, without a theoretical privacy guarantee, a synthetic dataset would still leak some sensitive information in raw data. As a countermeasure, differential privacy is widely adopted to safeguard data synthesis by strictly limiting the released information. This technique is advantageous yet presents significant challenges in the vertical federated setting, where data attributes are distributed among different data parties. The main challenge lies in maintaining privacy while efficiently and precisely reconstructing the correlation between attributes. In this paper, we propose a novel algorithm called VertiMRF, designed explicitly for generating synthetic data in the vertical setting and providing differential privacy protection for all information shared from data parties. We introduce techniques based on the Flajolet-Martin (FM) sketch for encoding local data satisfying differential privacy and estimating cross-party marginals. We provide theoretical privacy and utility proof for encoding in this multi-attribute data. Collecting the locally generated private Markov Random Field (MRF) and the sketches, a central server can reconstruct a global MRF, maintaining the most useful information. Two critical techniques introduced in our VertiMRF are dimension reduction and consistency enforcement, preventing the noise of FM sketch from overwhelming the information of attributes with large domain sizes when building the global MRF. These two techniques allow flexible and inconsistent binning strategies of local private MRF and the data sketching module, which can preserve information to the greatest extent. We conduct extensive experiments on four real-world datasets to evaluate the effectiveness of VertiMRF. End-to-end comparisons demonstrate the superiority of VertiMRF.|数据合成是一个很有前途的解决方案，可以在不暴露原始数据的情况下共享各种下游分析任务的数据。但是，如果没有理论上的隐私保证，合成数据集仍然会泄露原始数据中的一些敏感信息。作为一种对策，差分隐私被广泛采用，通过严格限制公布的信息来保护数据合成。这种技术很有优势，但是在垂直联邦设置中面临重大挑战，在垂直联邦设置中，数据属性分布在不同的数据方之间。主要的挑战在于维护隐私，同时有效和准确地重建属性之间的关联。在本文中，我们提出了一种名为 VertiMRF 的新算法，该算法明确地设计用于在垂直环境下生成合成数据，并为数据方共享的所有信息提供差分隐私保护。我们引入了基于 Flajolet-Martin (FM)草图的技术，用于编码满足差分隐私和估计跨党派边际的本地数据。我们为这种多属性数据的编码提供了理论上的隐私性和实用性证明。收集本地生成的专用马尔可夫网络(MRF)和草图，中央服务器可以重建全局 MRF，保持最有用的信息。在我们的 VertiMRF 中引入的两个关键技术是维度减化和一致性强制，防止 FM 草图的噪音淹没了大域大小的属性信息，当构建全局 MRF 时。这两种技术允许本地私有 MRF 和数据草图模块采用灵活且不一致的分组策略，最大限度地保留信息。我们在四个真实世界的数据集上进行了广泛的实验来评估 VertiMRF 的有效性。端到端的比较证明了 VertiMRF 的优越性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=VertiMRF:+Differentially+Private+Vertical+Federated+Data+Synthesis)|0| -|[Pre-Training and Prompting for Few-Shot Node Classification on Text-Attributed Graphs](https://doi.org/10.1145/3637528.3671952)|Huanjing Zhao, Beining Yang, Yukuo Cen, Junyu Ren, Chenhui Zhang, Yuxiao Dong, Evgeny Kharlamov, Shu Zhao, Jie Tang|Software Engineering, Tsinghua University, Beijing, China; Bosch Center for Artifcial Intelligence, Renningen, Germany; University of Edinburgh, Edinburgh, United Kingdom; Tsinghua University, Beijing, China; Anhui University, Hefei, Anhui, China; Zhipu AI, Beijing, China|The text-attributed graph (TAG) is one kind of important real-world graph-structured data with each node associated with raw texts. For TAGs, traditional few-shot node classification methods directly conduct training on the pre-processed node features and do not consider the raw texts. The performance is highly dependent on the choice of the feature pre-processing method. In this paper, we propose P2TAG, a framework designed for few-shot node classification on TAGs with graph pre-training and prompting. P2TAG first pre-trains the language model (LM) and graph neural network (GNN) on TAGs with self-supervised loss. To fully utilize the ability of language models, we adapt the masked language modeling objective for our framework. The pre-trained model is then used for the few-shot node classification with a mixed prompt method, which simultaneously considers both text and graph information. We conduct experiments on six real-world TAGs, including paper citation networks and product co-purchasing networks. Experimental results demonstrate that our proposed framework outperforms existing graph few-shot learning methods on these datasets with +18.98% ~ +32.14% improvements.|文本属性图(TAG)是一种重要的现实图形结构数据，其中每个节点都与原始文本相关联。对于标签，传统的少镜头节点分类方法直接对预处理的节点特征进行训练，而不考虑原始文本。特征预处理方法的选择对系统的性能有很大的影响。在本文中，我们提出了 P2TAG，一个基于图预训练和提示的少镜头节点分类框架。P2TAG 首先在带有自监督损失的 TAG 上预训练语言模型(LM)和图神经网络(GNN)。为了充分利用语言模型的能力，我们将掩蔽语言建模目标应用到我们的框架中。然后利用预训练模型，采用混合提示方法对少镜头节点进行分类，该方法同时考虑了文本和图形信息。我们在六个真实世界的标签上进行实验，包括论文引用网络和产品联合采购网络。实验结果表明，我们提出的框架在这些数据集上的性能优于现有的图形少镜头学习方法，提高了 + 18.98% ~ + 32.14% 。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Pre-Training+and+Prompting+for+Few-Shot+Node+Classification+on+Text-Attributed+Graphs)|0| -|[Conformalized Link Prediction on Graph Neural Networks](https://doi.org/10.1145/3637528.3672061)|Tianyi Zhao, Jian Kang, Lu Cheng|University of Illinois Chicago, Chicago, IL, USA; University of Rochester, Rochester, NY, USA; University of Southern California, Los Angeles, CA, USA|Graph Neural Networks (GNNs) excel in diverse tasks, yet their applications in high-stakes domains are often hampered by unreliable predictions. Although numerous uncertainty quantification methods have been proposed to address this limitation, they often lackrigorous uncertainty estimates. This work makes the first attempt to introduce a distribution-free and model-agnostic uncertainty quantification approach to construct a predictive interval with a statistical guarantee for GNN-based link prediction. We term it asconformalized link prediction. Our approach builds upon conformal prediction (CP), a framework that promises to construct statistically robust prediction sets or intervals. There are two primary challenges: first, given dependent data like graphs, it is unclear whether the critical assumption in CP --- exchangeability --- still holds when applied to link prediction. Second, even if the exchangeability assumption is valid for conformalized link prediction, we need to ensure high efficiency, i.e., the resulting prediction set or the interval length is small enough to provide useful information. To tackle these challenges, we first theoretically and empirically establish a permutation invariance condition for the application of CP in link prediction tasks, along with an exact test-time coverage. Leveraging the important structural information in graphs, we then identify a novel and crucial connection between a graph's adherence to the power law distribution and the efficiency of CP. This insight leads to the development of a simple yet effective sampling-based method to align the graph structure with a power law distribution prior to the standard CP procedure. Extensive experiments demonstrate that for conformalized link prediction, our approach achieves the desired marginal coverage while significantly improving the efficiency of CP compared to baseline methods.|图形神经网络(GNN)在不同的任务中表现出色，但它们在高风险领域的应用往往受到不可靠预测的阻碍。尽管已经提出了许多不确定性量化方法来解决这一局限性，但它们往往缺乏严格的不确定性估计。本文首次尝试引入一种无分布和模型无关的不确定性量化方法来构造一个具有统计保证的基于 GNN 的链路预测预测区间。我们称之为共形链路预测。我们的方法建立在保形预测(CP)的基础上，保形预测是一个承诺构建统计稳健预测集或区间的框架。这里有两个主要的挑战: 首先，考虑到依赖数据如图表，目前还不清楚 CP 中的关键假设——可交换性——在应用于链接预测时是否仍然成立。其次，即使可交换性假设对于共形链路预测是有效的，我们也需要确保高效率，即所得到的预测集或区间长度足够小以提供有用的信息。为了解决这些问题，我们首先从理论和经验上建立了 CP 在链路预测任务中应用的置换不变性条件，以及精确的测试时间覆盖率。然后，利用图中的重要结构信息，我们确定了图对幂律分布的依从性和 CP 的效率之间的一个新颖而关键的联系。这种见解导致了一种简单而有效的基于抽样的方法的发展，以便在标准 CP 程序之前将图结构与幂律分布对齐。大量的实验表明，对于共形链路预测，我们的方法达到了预期的边缘覆盖，同时显著提高了 CP 的效率相比，基线方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Conformalized+Link+Prediction+on+Graph+Neural+Networks)|0| +|[Algorithmic Fairness Generalization under Covariate and Dependence Shifts Simultaneously](https://doi.org/10.1145/3637528.3671909)|Chen Zhao, Kai Jiang, Xintao Wu, Haoliang Wang, Latifur Khan, Christan Grant, Feng Chen|The University of Texas, Dallas, Richardson, TX, USA; University of Arkansas, Fayetteville, AR, USA; Baylor University, Waco, TX, USA; The University of Texas at Dallas, Richardson, TX, USA; University of Florida, Gainesville, FL, USA|The endeavor to preserve the generalization of a fair and invariantclassifier across domains, especially in the presence of distribution shifts,becomes a significant and intricate challenge in machine learning. In responseto this challenge, numerous effective algorithms have been developed with afocus on addressing the problem of fairness-aware domain generalization. Thesealgorithms are designed to navigate various types of distribution shifts, witha particular emphasis on covariate and dependence shifts. In this context,covariate shift pertains to changes in the marginal distribution of inputfeatures, while dependence shift involves alterations in the joint distributionof the label variable and sensitive attributes. In this paper, we introduce asimple but effective approach that aims to learn a fair and invariantclassifier by simultaneously addressing both covariate and dependence shiftsacross domains. We assert the existence of an underlying transformation modelcan transform data from one domain to another, while preserving the semanticsrelated to non-sensitive attributes and classes. By augmenting varioussynthetic data domains through the model, we learn a fair and invariantclassifier in source domains. This classifier can then be generalized tounknown target domains, maintaining both model prediction and fairnessconcerns. Extensive empirical studies on four benchmark datasets demonstratethat our approach surpasses state-of-the-art methods.|在机器学习中，如何保持分类器的公平性和不变性，特别是在分布偏移的情况下，成为一个重要而复杂的挑战。为了应对这一挑战，许多有效的算法已经开发出来，重点是解决公平意识的领域泛化问题。这些算法被设计用于导航各种类型的分布移位，特别强调协变量和依赖性移位。在这种情况下，协变量变化涉及输入特征边缘分布的变化，而依赖性变化涉及标签变量和敏感属性的联合分布的变化。本文介绍了一种简单而有效的方法，通过同时处理协变量和依赖域间的移位来学习一个公平且不变的分类器。我们断言底层转换模型的存在可以将数据从一个域转换到另一个域，同时保留与非敏感属性和类相关的语义。通过该模型对多个合成数据域进行扩充，在源域中学习了一个公平的、不变的分类器。该分类器可以广义化未知目标域，同时保持模型预测和公平性。对四个基准数据集的大量实证研究表明，我们的方法超越了最先进的方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Algorithmic+Fairness+Generalization+under+Covariate+and+Dependence+Shifts+Simultaneously)|0| +|[VertiMRF: Differentially Private Vertical Federated Data Synthesis](https://doi.org/10.1145/3637528.3671771)|Fangyuan Zhao, Zitao Li, Xuebin Ren, Bolin Ding, Shusen Yang, Yaliang Li|Xi'an Jiaotong University, Xi'an, China; Alibaba Group, Bellevue, WA, USA|Data synthesis is a promising solution to share data for various downstream analytic tasks without exposing raw data. However, without a theoretical privacy guarantee, a synthetic dataset would still leak some sensitive information in raw data. As a countermeasure, differential privacy is widely adopted to safeguard data synthesis by strictly limiting the released information. This technique is advantageous yet presents significant challenges in the vertical federated setting, where data attributes are distributed among different data parties. The main challenge lies in maintaining privacy while efficiently and precisely reconstructing the correlation between attributes. In this paper, we propose a novel algorithm called VertiMRF, designed explicitly for generating synthetic data in the vertical setting and providing differential privacy protection for all information shared from data parties. We introduce techniques based on the Flajolet-Martin (FM) sketch for encoding local data satisfying differential privacy and estimating cross-party marginals. We provide theoretical privacy and utility proof for encoding in this multi-attribute data. Collecting the locally generated private Markov Random Field (MRF) and the sketches, a central server can reconstruct a global MRF, maintaining the most useful information. Two critical techniques introduced in our VertiMRF are dimension reduction and consistency enforcement, preventing the noise of FM sketch from overwhelming the information of attributes with large domain sizes when building the global MRF. These two techniques allow flexible and inconsistent binning strategies of local private MRF and the data sketching module, which can preserve information to the greatest extent. We conduct extensive experiments on four real-world datasets to evaluate the effectiveness of VertiMRF. End-to-end comparisons demonstrate the superiority of VertiMRF.|数据合成是一个很有前途的解决方案，可以在不暴露原始数据的情况下共享各种下游分析任务的数据。但是，如果没有理论上的隐私保证，合成数据集仍然会泄露原始数据中的一些敏感信息。作为一种对策，差分隐私被广泛采用，通过严格限制公布的信息来保护数据合成。这种技术很有优势，但是在垂直联邦设置中面临重大挑战，在垂直联邦设置中，数据属性分布在不同的数据方之间。主要的挑战在于维护隐私，同时有效和准确地重建属性之间的关联。在本文中，我们提出了一种名为 VertiMRF 的新算法，该算法明确地设计用于在垂直环境下生成合成数据，并为数据方共享的所有信息提供差分隐私保护。我们引入了基于 Flajolet-Martin (FM)草图的技术，用于编码满足差分隐私和估计跨党派边际的本地数据。我们为这种多属性数据的编码提供了理论上的隐私性和实用性证明。收集本地生成的专用马尔可夫网络(MRF)和草图，中央服务器可以重建全局 MRF，保持最有用的信息。在我们的 VertiMRF 中引入的两个关键技术是维度减化和一致性强制，防止 FM 草图的噪音淹没了大域大小的属性信息，当构建全局 MRF 时。这两种技术允许本地私有 MRF 和数据草图模块采用灵活且不一致的分组策略，最大限度地保留信息。我们在四个真实世界的数据集上进行了广泛的实验来评估 VertiMRF 的有效性。端到端的比较证明了 VertiMRF 的优越性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=VertiMRF:+Differentially+Private+Vertical+Federated+Data+Synthesis)|0| +|[Pre-Training and Prompting for Few-Shot Node Classification on Text-Attributed Graphs](https://doi.org/10.1145/3637528.3671952)|Huanjing Zhao, Beining Yang, Yukuo Cen, Junyu Ren, Chenhui Zhang, Yuxiao Dong, Evgeny Kharlamov, Shu Zhao, Jie Tang|University of Edinburgh, Edinburgh, United Kingdom; Tsinghua University, Beijing, China; Software Engineering, Tsinghua University, Beijing, China; Anhui University, Hefei, Anhui, China; Bosch Center for Artifcial Intelligence, Renningen, Germany; Zhipu AI, Beijing, China|The text-attributed graph (TAG) is one kind of important real-world graph-structured data with each node associated with raw texts. For TAGs, traditional few-shot node classification methods directly conduct training on the pre-processed node features and do not consider the raw texts. The performance is highly dependent on the choice of the feature pre-processing method. In this paper, we propose P2TAG, a framework designed for few-shot node classification on TAGs with graph pre-training and prompting. P2TAG first pre-trains the language model (LM) and graph neural network (GNN) on TAGs with self-supervised loss. To fully utilize the ability of language models, we adapt the masked language modeling objective for our framework. The pre-trained model is then used for the few-shot node classification with a mixed prompt method, which simultaneously considers both text and graph information. We conduct experiments on six real-world TAGs, including paper citation networks and product co-purchasing networks. Experimental results demonstrate that our proposed framework outperforms existing graph few-shot learning methods on these datasets with +18.98% ~ +32.14% improvements.|文本属性图(TAG)是一种重要的现实图形结构数据，其中每个节点都与原始文本相关联。对于标签，传统的少镜头节点分类方法直接对预处理的节点特征进行训练，而不考虑原始文本。特征预处理方法的选择对系统的性能有很大的影响。在本文中，我们提出了 P2TAG，一个基于图预训练和提示的少镜头节点分类框架。P2TAG 首先在带有自监督损失的 TAG 上预训练语言模型(LM)和图神经网络(GNN)。为了充分利用语言模型的能力，我们将掩蔽语言建模目标应用到我们的框架中。然后利用预训练模型，采用混合提示方法对少镜头节点进行分类，该方法同时考虑了文本和图形信息。我们在六个真实世界的标签上进行实验，包括论文引用网络和产品联合采购网络。实验结果表明，我们提出的框架在这些数据集上的性能优于现有的图形少镜头学习方法，提高了 + 18.98% ~ + 32.14% 。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Pre-Training+and+Prompting+for+Few-Shot+Node+Classification+on+Text-Attributed+Graphs)|0| +|[Conformalized Link Prediction on Graph Neural Networks](https://doi.org/10.1145/3637528.3672061)|Tianyi Zhao, Jian Kang, Lu Cheng|University of Southern California, Los Angeles, CA, USA; University of Rochester, Rochester, NY, USA; University of Illinois Chicago, Chicago, IL, USA|Graph Neural Networks (GNNs) excel in diverse tasks, yet their applications in high-stakes domains are often hampered by unreliable predictions. Although numerous uncertainty quantification methods have been proposed to address this limitation, they often lackrigorous uncertainty estimates. This work makes the first attempt to introduce a distribution-free and model-agnostic uncertainty quantification approach to construct a predictive interval with a statistical guarantee for GNN-based link prediction. We term it asconformalized link prediction. Our approach builds upon conformal prediction (CP), a framework that promises to construct statistically robust prediction sets or intervals. There are two primary challenges: first, given dependent data like graphs, it is unclear whether the critical assumption in CP --- exchangeability --- still holds when applied to link prediction. Second, even if the exchangeability assumption is valid for conformalized link prediction, we need to ensure high efficiency, i.e., the resulting prediction set or the interval length is small enough to provide useful information. To tackle these challenges, we first theoretically and empirically establish a permutation invariance condition for the application of CP in link prediction tasks, along with an exact test-time coverage. Leveraging the important structural information in graphs, we then identify a novel and crucial connection between a graph's adherence to the power law distribution and the efficiency of CP. This insight leads to the development of a simple yet effective sampling-based method to align the graph structure with a power law distribution prior to the standard CP procedure. Extensive experiments demonstrate that for conformalized link prediction, our approach achieves the desired marginal coverage while significantly improving the efficiency of CP compared to baseline methods.|图形神经网络(GNN)在不同的任务中表现出色，但它们在高风险领域的应用往往受到不可靠预测的阻碍。尽管已经提出了许多不确定性量化方法来解决这一局限性，但它们往往缺乏严格的不确定性估计。本文首次尝试引入一种无分布和模型无关的不确定性量化方法来构造一个具有统计保证的基于 GNN 的链路预测预测区间。我们称之为共形链路预测。我们的方法建立在保形预测(CP)的基础上，保形预测是一个承诺构建统计稳健预测集或区间的框架。这里有两个主要的挑战: 首先，考虑到依赖数据如图表，目前还不清楚 CP 中的关键假设——可交换性——在应用于链接预测时是否仍然成立。其次，即使可交换性假设对于共形链路预测是有效的，我们也需要确保高效率，即所得到的预测集或区间长度足够小以提供有用的信息。为了解决这些问题，我们首先从理论和经验上建立了 CP 在链路预测任务中应用的置换不变性条件，以及精确的测试时间覆盖率。然后，利用图中的重要结构信息，我们确定了图对幂律分布的依从性和 CP 的效率之间的一个新颖而关键的联系。这种见解导致了一种简单而有效的基于抽样的方法的发展，以便在标准 CP 程序之前将图结构与幂律分布对齐。大量的实验表明，对于共形链路预测，我们的方法达到了预期的边缘覆盖，同时显著提高了 CP 的效率相比，基线方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Conformalized+Link+Prediction+on+Graph+Neural+Networks)|0| |[GeoMix: Towards Geometry-Aware Data Augmentation](https://doi.org/10.1145/3637528.3671700)|Wentao Zhao, Qitian Wu, Chenxiao Yang, Junchi Yan||Mixup has shown considerable success in mitigating the challenges posed by limited labeled data in image classification. By synthesizing samples through the interpolation of features and labels, Mixup effectively addresses the issue of data scarcity. However, it has rarely been explored in graph learning tasks due to the irregularity and connectivity of graph data. Specifically, in node classification tasks, Mixup presents a challenge in creating connections for synthetic data. In this paper, we propose Geometric Mixup (GeoMix), a simple and interpretable Mixup approach leveraging in-place graph editing. It effectively utilizes geometry information to interpolate features and labels with those from the nearby neighborhood, generating synthetic nodes and establishing connections for them. We conduct theoretical analysis to elucidate the rationale behind employing geometry information for node Mixup, emphasizing the significance of locality enhancement-a critical aspect of our method's design. Extensive experiments demonstrate that our lightweight Geometric Mixup achieves state-of-the-art results on a wide variety of standard datasets with limited labeled data. Furthermore, it significantly improves the generalization capability of underlying GNNs across various challenging out-of-distribution generalization tasks. Our code is available at https://github.com/WtaoZhao/geomix.|在缓解图像分类中有限的标记数据所带来的挑战方面，Mixup 已经取得了相当大的成功。通过特征和标签的插值合成样本，混合有效地解决了数据稀缺的问题。然而，由于图形数据的不规则性和连通性，它在图形学习任务中很少被探讨。具体来说，在节点分类任务中，Mixup 在为合成数据创建连接方面提出了挑战。在本文中，我们提出了几何混合(GeoMix) ，一个简单的和可解释的混合方法，利用就地图编辑。它有效地利用几何信息来插值特征和标签来自附近的邻居，生成合成节点和建立连接。我们进行理论分析，以阐明背后的理论基础使用几何信息的节点混合，强调的意义，局部增强-一个关键的方面，我们的方法的设计。大量的实验表明，我们的轻量级几何混合实现了国家的最先进的结果在各种各样的标准数据集与有限的标签数据。此外，它显著提高了底层 GNN 在各种具有挑战性的分布外泛化任务中的泛化能力。我们的代码可以在 https://github.com/wtaozhao/geomix 找到。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=GeoMix:+Towards+Geometry-Aware+Data+Augmentation)|0| |[Spuriousness-Aware Meta-Learning for Learning Robust Classifiers](https://doi.org/10.1145/3637528.3672006)|Guangtao Zheng, Wenqian Ye, Aidong Zhang|University of Virginia, Charlottesville, VA, USA|Spurious correlations are brittle associations between certain attributes of inputs and target variables, such as the correlation between an image background and an object class. Deep image classifiers often leverage them for predictions, leading to poor generalization on the data where the correlations do not hold. Mitigating the impact of spurious correlations is crucial towards robust model generalization, but it often requires annotations of the spurious correlations in data -- a strong assumption in practice. In this paper, we propose a novel learning framework based on meta-learning, termed SPUME -- SPUriousness-aware MEta-learning, to train an image classifier to be robust to spurious correlations. We design the framework to iteratively detect and mitigate the spurious correlations that the classifier excessively relies on for predictions. To achieve this, we first propose to utilize a pre-trained vision-language model to extract text-format attributes from images. These attributes enable us to curate data with various class-attribute correlations, and we formulate a novel metric to measure the degree of these correlations' spuriousness. Then, to mitigate the reliance on spurious correlations, we propose a meta-learning strategy in which the support (training) sets and query (test) sets in tasks are curated with different spurious correlations that have high degrees of spuriousness. By meta-training the classifier on these spuriousness-aware meta-learning tasks, our classifier can learn to be invariant to the spurious correlations. We demonstrate that our method is robust to spurious correlations without knowing them a priori and achieves the best on five benchmark datasets with different robustness measures. Our code is available at https://github.com/gtzheng/SPUME.|伪相关是指输入的某些属性与目标变量之间的脆弱关联，例如图像背景与目标类之间的相关性。深度图像分类器经常利用它们进行预测，导致在相关性不存在的情况下，对数据的概括性较差。减轻虚假相关性的影响对于健壮的模型泛化至关重要，但是它通常需要对数据中的虚假相关性进行注释——这在实践中是一个强有力的假设。本文提出了一种新的基于元学习的图像分类器学习框架 SPUME —— SPUriness 感知元学习，以训练图像分类器对伪相关的鲁棒性。我们设计这个框架来迭代地检测和减轻分类器过度依赖于预测的伪相关性。为了实现这一目标，我们首先提出利用一个预先训练好的视觉语言模型来从图像中提取文本格式属性。这些属性使我们能够用各种类属性相关性来管理数据，并且我们制定了一个新的度量来衡量这些相关性的虚假程度。然后，为了减轻对伪相关性的依赖，我们提出了一种元学习策略，其中任务中的支持(训练)集和查询(测试)集被策划为具有高度伪相关性的不同伪相关性。通过对分类器进行元训练，我们的分类器可以学会对虚假关联保持不变。我们证明了我们的方法是鲁棒的虚假相关性不知道他们的先验，并取得了最佳的五个基准数据集与不同的鲁棒性措施。我们的代码可以在 https://github.com/gtzheng/spume 找到。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Spuriousness-Aware+Meta-Learning+for+Learning+Robust+Classifiers)|0| -|[SiGeo: Sub-One-Shot NAS via Geometry of Loss Landscape](https://doi.org/10.1145/3637528.3671712)|Hua Zheng, KuangHung Liu, Igor Fedorov, Xin Zhang, WenYen Chen, Wei Wen|Meta, Menlo Park, CA, USA; Northeastern University, Boston, MA, USA|Neural Architecture Search (NAS) has become a widely used tool for automating neural network design. While one-shot NAS methods have successfully reduced computational requirements, they often require extensive training. On the other hand, zero-shot NAS utilizes training-free proxies to evaluate a candidate architecture's test performance but has two limitations: (1) inability to use the information gained as a network improves with training and (2) unreliable performance, particularly in complex domains like RecSys, due to the multi-modal data inputs and complex architecture configurations. To synthesize the benefits of both methods, we introduce a "sub-one-shot" paradigm that serves as a bridge between zero-shot and one-shot NAS. In sub-one-shot NAS, the supernet is trained using only a small subset of the training data, a phase we refer to as "warm-up." Within this framework, we present SiGeo, a proxy founded on a novel theoretical framework that connects the supernet warm-up with the efficacy of the proxy. Extensive experiments have consistently shown that SiGeo, when properly warmed up, surpasses state-of-the-art NAS proxies in many established NAS benchmarks in the computer vision domain. Furthermore, when tested on recommendation system benchmarks, SiGeo demonstrates its ability to match the performance of state-of-the-art weight-sharing one-shot NAS methods while significantly reducing computational costs by approximately 60%.|神经网络结构搜索(NAS)已经成为神经网络自动化设计的一个广泛应用的工具。虽然一次性 NAS 方法成功地减少了计算需求，但是它们通常需要大量的训练。另一方面，零拍 NAS 利用无训练代理来评估候选架构的测试性能，但有两个限制: (1)无法使用随着训练而改善的网络获得的信息; (2)由于多模态数据输入和复杂的架构配置，特别是在诸如 RecSys 这样的复杂领域，性能不可靠。为了综合这两种方法的优点，我们引入了一个“子一击”范例，作为零击和一击 NAS 之间的桥梁。在一次性 NAS 中，超网只使用训练数据的一小部分进行训练，这个阶段我们称之为“预热”在这个框架内，我们提出了 SiGeo，一个建立在一个新的理论框架上的代理，该框架将超级网络的热身与代理的功效联系起来。大量的实验一致表明，SiGeo 在适当预热时，在计算机视觉领域的许多已建立的 NAS 基准中超越了最先进的 NAS 代理。此外，当在推荐系统基准上进行测试时，SiGeo 证明了其能够匹配最先进的权重共享一次性 NAS 方法的性能，同时显著降低大约60% 的计算成本。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=SiGeo:+Sub-One-Shot+NAS+via+Geometry+of+Loss+Landscape)|0| -|[Relaxing Continuous Constraints of Equivariant Graph Neural Networks for Broad Physical Dynamics Learning](https://doi.org/10.1145/3637528.3671957)|Zinan Zheng, Yang Liu, Jia Li, Jianhua Yao, Yu Rong|; Tencent AI Lab, Shenzhen, China; Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China|Incorporating Euclidean symmetries (e.g. rotation equivariance) as inductive biases into graph neural networks has improved their generalization ability and data efficiency in unbounded physical dynamics modeling. However, in various scientific and engineering applications, the symmetries of dynamics are frequently discrete due to the boundary conditions. Thus, existing GNNs either overlook necessary symmetry, resulting in suboptimal representation ability, or impose excessive equivariance, which fails to generalize to unobserved symmetric dynamics. In this work, we propose a general Discrete Equivariant Graph Neural Network (DEGNN) that guarantees equivariance to a given discrete point group. Specifically, we show that such discrete equivariant message passing could be constructed by transforming geometric features into permutation-invariant embeddings. Through relaxing continuous equivariant constraints, DEGNN can employ more geometric feature combinations to approximate unobserved physical object interaction functions. Two implementation approaches of DEGNN are proposed based on ranking or pooling permutation-invariant functions. We apply DEGNN to various physical dynamics, ranging from particle, molecular, crowd to vehicle dynamics. In twenty scenarios, DEGNN significantly outperforms existing state-of-the-art approaches. Moreover, we show that DEGNN is data efficient, learning with less data, and can generalize across scenarios such as unobserved orientation.|将欧氏对称(如旋转等方差)作为归纳偏差引入到图形神经网络中，提高了无界物理动力学建模的泛化能力和数据效率。然而，在各种科学和工程应用中，由于边界条件的限制，动力学的对称性往往是离散的。因此，现有的 GNN 要么忽视必要的对称性，导致次优表示能力，要么施加过多的等方差，这不能推广到未观察到的对称动力学。在这项工作中，我们提出了一个广义的离散等变图神经网络(DEGNN) ，保证等方差给定的离散点群。具体地说，我们证明了这样的离散等变消息传递可以通过将几何特征转换为置换不变嵌入来构造。通过放松连续等变约束，DEGNN 可以采用更多的几何特征组合来逼近未观测到的物理对象相互作用函数。提出了两种基于排序或池置换不变函数的 DEGNN 实现方法。我们将 DEGNN 应用于各种物理动力学，从粒子、分子、群体到车辆动力学。在二十种情况下，DEGNN 显著优于现有的最先进的方法。此外，我们表明，DEGNN 是数据高效率，学习与较少的数据，并可以推广的情况下，如未观察到的方向。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Relaxing+Continuous+Constraints+of+Equivariant+Graph+Neural+Networks+for+Broad+Physical+Dynamics+Learning)|0| -|[LogParser-LLM: Advancing Efficient Log Parsing with Large Language Models](https://doi.org/10.1145/3637528.3671810)|Aoxiao Zhong, Dengyao Mo, Guiyang Liu, Jinbu Liu, Qingda Lu, Qi Zhou, Jiesheng Wu, Quanzheng Li, Qingsong Wen|Alibaba Group, Bellevue, WA, USA; Harvard University & Alibaba Group, Cambridge, MA, USA; Alibaba Group, Hangzhou, China; CAMCA, Harvard Medical School, Massachusetts General Hospital, Boston, MA, USA|Logs are ubiquitous digital footprints, playing an indispensable role in system diagnostics, security analysis, and performance optimization. The extraction of actionable insights from logs is critically dependent on the log parsing process, which converts raw logs into structured formats for downstream analysis. Yet, the complexities of contemporary systems and the dynamic nature of logs pose significant challenges to existing automatic parsing techniques. The emergence of Large Language Models (LLM) offers new horizons. With their expansive knowledge and contextual prowess, LLMs have been transformative across diverse applications. Building on this, we introduce LogParser-LLM, a novel log parser integrated with LLM capabilities. This union seamlessly blends semantic insights with statistical nuances, obviating the need for hyper-parameter tuning and labeled training data, while ensuring rapid adaptability through online parsing. Further deepening our exploration, we address the intricate challenge of parsing granularity, proposing a new metric and integrating human interactions to allow users to calibrate granularity to their specific needs. Our method's efficacy is empirically demonstrated through evaluations on the Loghub-2k and the large-scale LogPub benchmark. In evaluations on the LogPub benchmark, involving an average of 3.6 million logs per dataset across 14 datasets, our LogParser-LLM requires only 272.5 LLM invocations on average, achieving a 90.6% F1 score for grouping accuracy and an 81.1% for parsing accuracy. These results demonstrate the method's high efficiency and accuracy, outperforming current state-of-the-art log parsers, including pattern-based, neural network-based, and existing LLM-enhanced approaches.|日志是无处不在的数字足迹，在系统诊断、安全分析和性能优化中起着不可或缺的作用。从日志中提取可操作的见解严重依赖于日志解析过程，该过程将原始日志转换为结构化格式以进行下游分析。然而，当代系统的复杂性和日志的动态特性对现有的自动解析技术提出了重大挑战。大型语言模型(LLM)的出现提供了新的视野。LLM 凭借其广博的知识和上下文能力，在各种应用中发挥了变革性的作用。在此基础上，我们引入 LogParser-LLM，这是一种集成了 LLM 功能的新型日志解析器。这种结合无缝地融合了语义洞察力和统计细微差别，避免了超参数调整和标记训练数据的需要，同时通过在线解析确保了快速适应性。进一步深化了我们的探索，我们解决了解析粒度的复杂挑战，提出了一个新的度量标准并集成了人类交互，以允许用户根据他们的具体需求校准粒度。通过对 Loghub-2k 和大规模 LogPub 基准的评估，我们的方法的有效性得到了经验证明。在对 LogPub 基准的评估中，涉及14个数据集中每个数据集平均360万个日志，我们的 LogParser-LLM 平均只需要272.5个 LLM 调用，分组准确率达到90.6% 的 F1分数，解析准确率达到81.1% 。这些结果证明了该方法的高效率和准确性，优于目前最先进的日志解析器，包括基于模式、基于神经网络和现有的 LLM 增强方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=LogParser-LLM:+Advancing+Efficient+Log+Parsing+with+Large+Language+Models)|0| +|[SiGeo: Sub-One-Shot NAS via Geometry of Loss Landscape](https://doi.org/10.1145/3637528.3671712)|Hua Zheng, KuangHung Liu, Igor Fedorov, Xin Zhang, WenYen Chen, Wei Wen|Northeastern University, Boston, MA, USA; Meta, Menlo Park, CA, USA|Neural Architecture Search (NAS) has become a widely used tool for automating neural network design. While one-shot NAS methods have successfully reduced computational requirements, they often require extensive training. On the other hand, zero-shot NAS utilizes training-free proxies to evaluate a candidate architecture's test performance but has two limitations: (1) inability to use the information gained as a network improves with training and (2) unreliable performance, particularly in complex domains like RecSys, due to the multi-modal data inputs and complex architecture configurations. To synthesize the benefits of both methods, we introduce a "sub-one-shot" paradigm that serves as a bridge between zero-shot and one-shot NAS. In sub-one-shot NAS, the supernet is trained using only a small subset of the training data, a phase we refer to as "warm-up." Within this framework, we present SiGeo, a proxy founded on a novel theoretical framework that connects the supernet warm-up with the efficacy of the proxy. Extensive experiments have consistently shown that SiGeo, when properly warmed up, surpasses state-of-the-art NAS proxies in many established NAS benchmarks in the computer vision domain. Furthermore, when tested on recommendation system benchmarks, SiGeo demonstrates its ability to match the performance of state-of-the-art weight-sharing one-shot NAS methods while significantly reducing computational costs by approximately 60%.|神经网络结构搜索(NAS)已经成为神经网络自动化设计的一个广泛应用的工具。虽然一次性 NAS 方法成功地减少了计算需求，但是它们通常需要大量的训练。另一方面，零拍 NAS 利用无训练代理来评估候选架构的测试性能，但有两个限制: (1)无法使用随着训练而改善的网络获得的信息; (2)由于多模态数据输入和复杂的架构配置，特别是在诸如 RecSys 这样的复杂领域，性能不可靠。为了综合这两种方法的优点，我们引入了一个“子一击”范例，作为零击和一击 NAS 之间的桥梁。在一次性 NAS 中，超网只使用训练数据的一小部分进行训练，这个阶段我们称之为“预热”在这个框架内，我们提出了 SiGeo，一个建立在一个新的理论框架上的代理，该框架将超级网络的热身与代理的功效联系起来。大量的实验一致表明，SiGeo 在适当预热时，在计算机视觉领域的许多已建立的 NAS 基准中超越了最先进的 NAS 代理。此外，当在推荐系统基准上进行测试时，SiGeo 证明了其能够匹配最先进的权重共享一次性 NAS 方法的性能，同时显著降低大约60% 的计算成本。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=SiGeo:+Sub-One-Shot+NAS+via+Geometry+of+Loss+Landscape)|0| +|[Relaxing Continuous Constraints of Equivariant Graph Neural Networks for Broad Physical Dynamics Learning](https://doi.org/10.1145/3637528.3671957)|Zinan Zheng, Yang Liu, Jia Li, Jianhua Yao, Yu Rong|; Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China; Tencent AI Lab, Shenzhen, China|Incorporating Euclidean symmetries (e.g. rotation equivariance) as inductive biases into graph neural networks has improved their generalization ability and data efficiency in unbounded physical dynamics modeling. However, in various scientific and engineering applications, the symmetries of dynamics are frequently discrete due to the boundary conditions. Thus, existing GNNs either overlook necessary symmetry, resulting in suboptimal representation ability, or impose excessive equivariance, which fails to generalize to unobserved symmetric dynamics. In this work, we propose a general Discrete Equivariant Graph Neural Network (DEGNN) that guarantees equivariance to a given discrete point group. Specifically, we show that such discrete equivariant message passing could be constructed by transforming geometric features into permutation-invariant embeddings. Through relaxing continuous equivariant constraints, DEGNN can employ more geometric feature combinations to approximate unobserved physical object interaction functions. Two implementation approaches of DEGNN are proposed based on ranking or pooling permutation-invariant functions. We apply DEGNN to various physical dynamics, ranging from particle, molecular, crowd to vehicle dynamics. In twenty scenarios, DEGNN significantly outperforms existing state-of-the-art approaches. Moreover, we show that DEGNN is data efficient, learning with less data, and can generalize across scenarios such as unobserved orientation.|将欧氏对称(如旋转等方差)作为归纳偏差引入到图形神经网络中，提高了无界物理动力学建模的泛化能力和数据效率。然而，在各种科学和工程应用中，由于边界条件的限制，动力学的对称性往往是离散的。因此，现有的 GNN 要么忽视必要的对称性，导致次优表示能力，要么施加过多的等方差，这不能推广到未观察到的对称动力学。在这项工作中，我们提出了一个广义的离散等变图神经网络(DEGNN) ，保证等方差给定的离散点群。具体地说，我们证明了这样的离散等变消息传递可以通过将几何特征转换为置换不变嵌入来构造。通过放松连续等变约束，DEGNN 可以采用更多的几何特征组合来逼近未观测到的物理对象相互作用函数。提出了两种基于排序或池置换不变函数的 DEGNN 实现方法。我们将 DEGNN 应用于各种物理动力学，从粒子、分子、群体到车辆动力学。在二十种情况下，DEGNN 显著优于现有的最先进的方法。此外，我们表明，DEGNN 是数据高效率，学习与较少的数据，并可以推广的情况下，如未观察到的方向。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Relaxing+Continuous+Constraints+of+Equivariant+Graph+Neural+Networks+for+Broad+Physical+Dynamics+Learning)|0| +|[LogParser-LLM: Advancing Efficient Log Parsing with Large Language Models](https://doi.org/10.1145/3637528.3671810)|Aoxiao Zhong, Dengyao Mo, Guiyang Liu, Jinbu Liu, Qingda Lu, Qi Zhou, Jiesheng Wu, Quanzheng Li, Qingsong Wen|CAMCA, Harvard Medical School, Massachusetts General Hospital, Boston, MA, USA; Alibaba Group, Hangzhou, China; Harvard University & Alibaba Group, Cambridge, MA, USA; Alibaba Group, Bellevue, WA, USA|Logs are ubiquitous digital footprints, playing an indispensable role in system diagnostics, security analysis, and performance optimization. The extraction of actionable insights from logs is critically dependent on the log parsing process, which converts raw logs into structured formats for downstream analysis. Yet, the complexities of contemporary systems and the dynamic nature of logs pose significant challenges to existing automatic parsing techniques. The emergence of Large Language Models (LLM) offers new horizons. With their expansive knowledge and contextual prowess, LLMs have been transformative across diverse applications. Building on this, we introduce LogParser-LLM, a novel log parser integrated with LLM capabilities. This union seamlessly blends semantic insights with statistical nuances, obviating the need for hyper-parameter tuning and labeled training data, while ensuring rapid adaptability through online parsing. Further deepening our exploration, we address the intricate challenge of parsing granularity, proposing a new metric and integrating human interactions to allow users to calibrate granularity to their specific needs. Our method's efficacy is empirically demonstrated through evaluations on the Loghub-2k and the large-scale LogPub benchmark. In evaluations on the LogPub benchmark, involving an average of 3.6 million logs per dataset across 14 datasets, our LogParser-LLM requires only 272.5 LLM invocations on average, achieving a 90.6% F1 score for grouping accuracy and an 81.1% for parsing accuracy. These results demonstrate the method's high efficiency and accuracy, outperforming current state-of-the-art log parsers, including pattern-based, neural network-based, and existing LLM-enhanced approaches.|日志是无处不在的数字足迹，在系统诊断、安全分析和性能优化中起着不可或缺的作用。从日志中提取可操作的见解严重依赖于日志解析过程，该过程将原始日志转换为结构化格式以进行下游分析。然而，当代系统的复杂性和日志的动态特性对现有的自动解析技术提出了重大挑战。大型语言模型(LLM)的出现提供了新的视野。LLM 凭借其广博的知识和上下文能力，在各种应用中发挥了变革性的作用。在此基础上，我们引入 LogParser-LLM，这是一种集成了 LLM 功能的新型日志解析器。这种结合无缝地融合了语义洞察力和统计细微差别，避免了超参数调整和标记训练数据的需要，同时通过在线解析确保了快速适应性。进一步深化了我们的探索，我们解决了解析粒度的复杂挑战，提出了一个新的度量标准并集成了人类交互，以允许用户根据他们的具体需求校准粒度。通过对 Loghub-2k 和大规模 LogPub 基准的评估，我们的方法的有效性得到了经验证明。在对 LogPub 基准的评估中，涉及14个数据集中每个数据集平均360万个日志，我们的 LogParser-LLM 平均只需要272.5个 LLM 调用，分组准确率达到90.6% 的 F1分数，解析准确率达到81.1% 。这些结果证明了该方法的高效率和准确性，优于目前最先进的日志解析器，包括基于模式、基于神经网络和现有的 LLM 增强方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=LogParser-LLM:+Advancing+Efficient+Log+Parsing+with+Large+Language+Models)|0| |[BitLINK: Temporal Linkage of Address Clusters in Bitcoin Blockchain](https://doi.org/10.1145/3637528.3672037)|Sheng Zhong, Abdullah Mueen|The University of New Mexico, Albuquerque, NM, USA|In the Bitcoin blockchain, an entity (e.g., a gambling service) may control multiple distinct address clusters. Links (i.e., trust relationships) between these disjoint address clusters can be established when one cluster is abandoned, and a new one is formed shortly thereafter. To link the clusters across time, we have developed a deep neural network model that exploits these synchronous actions derived from unlabeled data in a self-supervised manner. This model assesses whether two clusters exhibit synchronous temporal signatures indicative of a shared entity ownership. We validated our model on 26 real-world entities identified by WalletExplorer [36]. In addition to the existing knowledge, our analysis revealed more transaction history by linking address clusters for three major services: HelixMixer, Primedice, and Bitcoin Fog, as well as 60 other services. This enables us to address questions related to the revenue and expenditures of these services and create informative aggregate statistics. Readers can find code and data on our support website: http://www.bitlinkwallet.com.|在比特币区块链中，一个实体(例如，赌博服务)可能控制多个不同的地址集群。这些不相交的地址集群之间的链接(即信任关系)可以在一个集群被放弃时建立起来，不久之后就会形成一个新的集群。为了跨时间链接集群，我们开发了一个深度神经网络模型，利用这些来自未标记数据的同步行动，以自我监督的方式。这个模型评估两个集群是否表现出表明共享实体所有权的同步时间签名。我们在 WalletExplorer 标识的26个实际实体上验证了我们的模型[36]。除了现有的知识，我们的分析揭示了更多的交易历史，通过链接地址集群的三个主要服务: HelixMixer，Primedice，比特币雾，以及其他60个服务。这使我们能够处理与这些服务的收入和支出有关的问题，并创建信息丰富的总体统计数据。读者可以在我们的支援网站找到代码及资料: http://www.bitlinkwallet.com。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=BitLINK:+Temporal+Linkage+of+Address+Clusters+in+Bitcoin+Blockchain)|0| |[Efficient and Effective Implicit Dynamic Graph Neural Network](https://doi.org/10.1145/3637528.3672026)|Yongjian Zhong, Hieu Vu, Tianbao Yang, Bijaya Adhikari|Department of Computer Science and Engineering, Texas A&M University, College Station, TX, USA; Department of Computer Science, University of Iowa, Iowa City, IA, USA|Implicit graph neural networks have gained popularity in recent years as they capture long-range dependencies while improving predictive performance in static graphs. Despite the tussle between performance degradation due to the oversmoothing of learned embeddings and long-range dependency being more pronounced in dynamic graphs, as features are aggregated both across neighborhood and time, no prior work has proposed an implicit graph neural model in a dynamic setting. In this paper, we present Implicit Dynamic Graph Neural Network (IDGNN) a novel implicit neural network for dynamic graphs which is the first of its kind. A key characteristic of IDGNN is that it demonstrably is well-posed, i.e., it is theoretically guaranteed to have a fixed-point representation. We then demonstrate that the standard iterative algorithm often used to train implicit models is computationally expensive in our dynamic setting as it involves computing gradients, which themselves have to be estimated in an iterative manner. To overcome this, we pose an equivalent bilevel optimization problem and propose an efficient single-loop training algorithm that avoids iterative computation by maintaining moving averages of key components of the gradients. We conduct extensive experiments on real-world datasets on both classification and regression tasks to demonstrate the superiority of our approach over state-of-the-art baselines. We also demonstrate that our bi-level optimization framework maintains the performance of the expensive iterative algorithm while obtaining up to 1600x speed-up.|近年来，隐式图神经网络由于在提高静态图预测性能的同时捕获长期依赖关系而得到了广泛的应用。尽管由于学习嵌入的过度平滑导致的性能下降和长程依赖在动态图中更加明显，因为特征是跨邻域和时间聚合的，但是没有先前的工作在动态环境中提出隐式图神经模型。本文提出了一种新的用于动态图的隐式神经网络——隐式动态图神经网络(IDGNN)。IDGNN 的一个关键特征是它明显是适定的，也就是说，它在理论上保证具有一个不动点表示。然后，我们证明了常用于训练隐式模型的标准迭代算法在我们的动态设置中是计算昂贵的，因为它涉及到计算梯度，它们本身必须以迭代方式估计。为了克服这个问题，我们提出了一个等效的双层最佳化问题，并提出了一个有效的单循环训练算法，通过保持梯度关键部分的移动平均值来避免迭代计算。我们在分类和回归任务上对真实世界的数据集进行了广泛的实验，以证明我们的方法优于最先进的基线。我们还证明，我们的双层优化框架保持了昂贵的迭代算法的性能，同时获得1600倍的加速。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Efficient+and+Effective+Implicit+Dynamic+Graph+Neural+Network)|0| |[CURLS: Causal Rule Learning for Subgroups with Significant Treatment Effect](https://doi.org/10.1145/3637528.3671951)|Jiehui Zhou, Linxiao Yang, Xingyu Liu, Xinyue Gu, Liang Sun, Wei Chen|; State Key Lab of CAD&CG, Zhejiang University, Hangzhou, Zhejiang, China; DAMO Academy, Alibaba Group, Hangzhou, Zhejiang, China|In causal inference, estimating heterogeneous treatment effects (HTE) is critical for identifying how different subgroups respond to interventions, with broad applications in fields such as precision medicine and personalized advertising. Although HTE estimation methods aim to improve accuracy, how to provide explicit subgroup descriptions remains unclear, hindering data interpretation and strategic intervention management. In this paper, we propose CURLS, a novel rule learning method leveraging HTE, which can effectively describe subgroups with significant treatment effects. Specifically, we frame causal rule learning as a discrete optimization problem, finely balancing treatment effect with variance and considering the rule interpretability. We design an iterative procedure based on the minorize-maximization algorithm and solve a submodular lower bound as an approximation for the original. Quantitative experiments and qualitative case studies verify that compared with state-of-the-art methods, CURLS can find subgroups where the estimated and true effects are 16.1% and 13.8% higher and the variance is 12.0% smaller, while maintaining similar or better estimation accuracy and rule interpretability. Code is available at https://osf.io/zwp2k/.|在因果推断中，估计异质治疗效果(HTE)对于确定不同亚组对干预的反应至关重要，在精准医学和个性化广告等领域有着广泛的应用。虽然 HTE 评估方法的目的是提高准确性，但如何提供明确的子群描述仍然不清楚，阻碍了数据解释和战略干预管理。在本文中，我们提出了一种新的规则学习方法 CURLS，利用 HTE，它可以有效地描述具有显著治疗效果的子群。具体来说，我们将因果规则学习视为一个离散优化问题，在处理效果与方差之间进行微妙的平衡，并考虑规则的可解释性。我们设计了一个基于最小化-最大化算法的迭代过程，并且求解了一个次模下界作为原算法的近似。定量实验和定性案例研究证实，与最先进的方法相比，CURLS 可以找到估计效应和真实效应分别高出16.1% 和13.8% ，方差小于12.0% 的亚组，同时保持相似或更好的估计准确性和规则可解释性。密码可于 https://osf.io/zwp2k/索取。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=CURLS:+Causal+Rule+Learning+for+Subgroups+with+Significant+Treatment+Effect)|0| |[Neural Collapse Anchored Prompt Tuning for Generalizable Vision-Language Models](https://doi.org/10.1145/3637528.3671690)|Didi Zhu, Zexi Li, Min Zhang, Junkun Yuan, Jiashuo Liu, Kun Kuang, Chao Wu|Tsinghua University, Beijing, China; Zhejiang University, Hangzhou, China|Large-scale vision-language (V-L) models have demonstrated remarkable generalization capabilities for downstream tasks through prompt tuning. However, the mechanisms behind the learned text representations are unknown, limiting further generalization gains, and the limitations are more severe when faced with the prevalent class imbalances seen in web-sourced datasets. Recent advances in the neural collapse (NC) phenomenon of vision-only models suggest that the optimal representation structure is the simplex ETF, which paves the way to study representations in V-L models. In this paper, we make the first attempt to use NC for examining the representations in V-L models via prompt tuning. It is found that NC optimality of text-to-image representations shows a positive correlation with downstream generalizability, which is more severe under class imbalance settings. To improve the representations, we propose Neural-collapse-anchored Prompt Tuning (NPT), a novel method that learns prompts with text and image representations that satisfy the same simplex Equiangular Tight Frame (ETF). NPT incorporates two regularization terms: language-modality collapse and multi-modality isomorphism; and it is compatible with other prompt tuning methods. Extensive experiments show that NPT can consistently help to improve existing prompt tuning techniques across 11 datasets for both balanced and imbalanced settings.|大规模可视化语言(V-L)模型通过及时调优显示了对下游任务的显著泛化能力。然而，学习文本表示背后的机制是未知的，限制了进一步的一般化收益，并且当面对在网络来源数据集中看到的普遍的类不平衡时，局限性更加严重。纯视觉模型的神经崩溃(NC)现象的最新进展表明，最优表示结构是单纯形 ETF，这为研究 V-L 模型中的表示铺平了道路。在本文中，我们首次尝试使用数控技术通过提示调整来检查 V-L 模型中的表示。研究发现，文本-图像表示的 NC 优化与下游泛化能力呈正相关，在类别不平衡设置下，下游泛化能力更强。为了改善表示，我们提出了神经崩溃锚定提示调整(NPT) ，一种新的方法，学习与文本和图像表示提示，满足相同的单纯等角紧框架(ETF)。NPT 包含两个正则化项: 语言-模态崩溃和多模态同构，与其他快速调谐方法兼容。广泛的实验表明，NPT 可以持续地帮助改进现有的11个数据集的平衡和不平衡设置的快速调优技术。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Neural+Collapse+Anchored+Prompt+Tuning+for+Generalizable+Vision-Language+Models)|0| |[Distributed Thresholded Counting with Limited Interaction](https://doi.org/10.1145/3637528.3671868)|Xiaoyi Zhu, Yuxiang Tian, Zengfeng Huang|School of Data Science, Fudan University, Shanghai, China|Problems in the area of distributed computing have been extensively studied. In this paper, we focus on the Distributed Thresholded Counting problem in the coordinator model. In this problem, we have k sites holding their input and communicating with a central coordinator. The coordinator's task is to determine whether the sum of inputs is larger than a threshold. While the communication complexity of this basic problem has been studied for decades, it is still not well understood. Our work considers the worst-case communication cost for an algorithm that uses limited interaction - i.e. a bounded number of rounds of communication. Algorithms in previous research usually need O(łogłog N) or O(k) rounds. In comparison, in the deterministic case, our algorithm achieves optimal communication complexity in only α(k) rounds, where α(k) denotes the inverse Ackermann function and is nearly constant. We also give a randomized algorithm that balances communication, rounds, and error probability.|分布式计算方面的问题已被广泛研究。本文主要研究协调器模型中的分布式阈值计数问题。在这个问题中，我们有 k 个站点保存它们的输入并与一个中心协调器通信。协调器的任务是确定输入的总和是否大于阈值。虽然这个基本问题的通信复杂性已经研究了几十年，但仍然没有得到很好的理解。我们的工作考虑了使用有限交互的算法的最坏情况下的通信成本——也就是有限的几轮通信。在以前的研究中，算法通常需要 O (og og N)或 O (k)轮。相比之下，在确定性情况下，我们的算法只需要 α (k)轮即可达到最佳通信复杂度，其中 α (k)表示反阿克曼函数并且几乎是常数。我们还给出了一个平衡通信、回合和错误概率的随机化算法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Distributed+Thresholded+Counting+with+Limited+Interaction)|0| |[Propagation Structure-Aware Graph Transformer for Robust and Interpretable Fake News Detection](https://doi.org/10.1145/3637528.3672024)|Junyou Zhu, Chao Gao, Ze Yin, Xianghua Li, Jürgen Kurths|; College of Computer Science and Electronic Engineering, Hunan University, Changsha, China|The rise of social media has intensified fake news risks, prompting a growing focus on leveraging graph learning methods such as graph neural networks (GNNs) to understand post-spread patterns of news. However, existing methods often produce less robust and interpretable results as they assume that all information within the propagation graph is relevant to the news item, without adequately eliminating noise from engaged users. Furthermore, they inadequately capture intricate patterns inherent in long-sequence dependencies of news propagation due to their use of shallow GNNs aimed at avoiding the over-smoothing issue, consequently diminishing their overall accuracy. In this paper, we address these issues by proposing the Propagation Structure-aware Graph Transformer (PSGT). Specifically, to filter out noise from users within propagation graphs, PSGT first designs a noise-reduction self-attention mechanism based on the information bottleneck principle, aiming to minimize or completely remove the noise attention links among task-irrelevant users. Moreover, to capture multi-scale propagation structures while considering long-sequence features, we present a novel relational propagation graph as a position encoding for the graph Transformer, enabling the model to capture both propagation depth and distance relationships of users. Extensive experiments demonstrate the effectiveness, interpretability, and robustness of our PSGT.|社交媒体的兴起加剧了虚假新闻的风险，促使人们越来越关注利用图形学习方法，如图形神经网络(GNN)来理解新闻传播后的模式。然而，现有的方法往往产生不那么健壮和可解释的结果，因为它们假定传播图中的所有信息都与新闻条目相关，而没有充分消除来自参与用户的噪声。此外，它们没有充分捕捉到新闻传播的长序列依赖性所固有的复杂模式，因为它们使用浅层 GNN，目的是避免过于平滑的问题，从而降低了它们的总体准确性。本文通过提出传播结构感知图形变换(PSGT)来解决这些问题。为了从传播图中过滤出用户的噪声，PSGT 首先设计了一种基于信息瓶颈原理的降噪自注意机制，旨在最小化或完全消除任务无关用户之间的噪声注意链。此外，为了在考虑长序列特征的情况下捕获多尺度传播结构，我们提出了一种新的关系传播图作为图形变压器的位置编码，使模型能够同时捕获用户的传播深度和距离关系。大量的实验证明了我们的 PSGT 的有效性、可解释性和鲁棒性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Propagation+Structure-Aware+Graph+Transformer+for+Robust+and+Interpretable+Fake+News+Detection)|0| -|[ControlTraj: Controllable Trajectory Generation with Topology-Constrained Diffusion Model](https://doi.org/10.1145/3637528.3671866)|Yuanshao Zhu, James Jian Qiao Yu, Xiangyu Zhao, Qidong Liu, Yongchao Ye, Wei Chen, Zijian Zhang, Xuetao Wei, Yuxuan Liang|Jilin University & City University of Hong Kong, Jilin, China; Xi'an Jiao Tong University & City University of Hong Kong, Xi'an, China; The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China; Southern University of Science and Technology, Shenzhen, China; The Hong Kong University of Science and Technology (Guangzhou), Guanzhou, China; Southern University of Science and Technology & City University of Hong Kong, Shenzhen, China; City University of Hong Kong, Hong Kong, China; University of York, York, United Kingdom|Generating trajectory data is among promising solutions to addressing privacyconcerns, collection costs, and proprietary restrictions usually associatedwith human mobility analyses. However, existing trajectory generation methodsare still in their infancy due to the inherent diversity and unpredictabilityof human activities, grappling with issues such as fidelity, flexibility, andgeneralizability. To overcome these obstacles, we propose ControlTraj, aControllable Trajectory generation framework with the topology-constraineddiffusion model. Distinct from prior approaches, ControlTraj utilizes adiffusion model to generate high-fidelity trajectories while integrating thestructural constraints of road network topology to guide the geographicaloutcomes. Specifically, we develop a novel road segment autoencoder to extractfine-grained road segment embedding. The encoded features, along with tripattributes, are subsequently merged into the proposed geographic denoising UNetarchitecture, named GeoUNet, to synthesize geographic trajectories from whitenoise. Through experimentation across three real-world data settings,ControlTraj demonstrates its ability to produce human-directed, high-fidelitytrajectory generation with adaptability to unexplored geographical contexts.|生成轨迹数据是解决隐私问题、收集成本和通常与人员流动性分析相关的专利限制的有希望的解决方案之一。然而，由于人类活动固有的多样性和不可预测性，现有的轨迹生成方法仍处于起步阶段，需要解决诸如忠实性、灵活性和普遍性等问题。为了克服这些障碍，我们提出了基于拓扑约束扩散模型的 ControlTraj 可控轨迹生成框架。与以前的方法不同，ControlTraj 利用扩散模型来生成高保真轨迹，同时整合道路网络拓扑的结构约束来指导地理结果。具体来说，我们开发了一种新的路段自动编码器来提取细粒度的路段嵌入。编码后的特征和贡献值随后被合并到提出的地理去噪 UNetArchitecture 中，命名为 GeoUNet，以便从白噪声中合成地理轨迹。通过对三个真实世界数据设置的实验，ControlTraj 展示了其生成人为导向的高保真轨迹生成的能力，并能适应未探索的地理环境。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=ControlTraj:+Controllable+Trajectory+Generation+with+Topology-Constrained+Diffusion+Model)|0| +|[ControlTraj: Controllable Trajectory Generation with Topology-Constrained Diffusion Model](https://doi.org/10.1145/3637528.3671866)|Yuanshao Zhu, James Jian Qiao Yu, Xiangyu Zhao, Qidong Liu, Yongchao Ye, Wei Chen, Zijian Zhang, Xuetao Wei, Yuxuan Liang|University of York, York, United Kingdom; Jilin University & City University of Hong Kong, Jilin, China; Xi'an Jiao Tong University & City University of Hong Kong, Xi'an, China; City University of Hong Kong, Hong Kong, China; Southern University of Science and Technology & City University of Hong Kong, Shenzhen, China; The Hong Kong University of Science and Technology (Guangzhou), Guanzhou, China; The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China; Southern University of Science and Technology, Shenzhen, China|Generating trajectory data is among promising solutions to addressing privacyconcerns, collection costs, and proprietary restrictions usually associatedwith human mobility analyses. However, existing trajectory generation methodsare still in their infancy due to the inherent diversity and unpredictabilityof human activities, grappling with issues such as fidelity, flexibility, andgeneralizability. To overcome these obstacles, we propose ControlTraj, aControllable Trajectory generation framework with the topology-constraineddiffusion model. Distinct from prior approaches, ControlTraj utilizes adiffusion model to generate high-fidelity trajectories while integrating thestructural constraints of road network topology to guide the geographicaloutcomes. Specifically, we develop a novel road segment autoencoder to extractfine-grained road segment embedding. The encoded features, along with tripattributes, are subsequently merged into the proposed geographic denoising UNetarchitecture, named GeoUNet, to synthesize geographic trajectories from whitenoise. Through experimentation across three real-world data settings,ControlTraj demonstrates its ability to produce human-directed, high-fidelitytrajectory generation with adaptability to unexplored geographical contexts.|生成轨迹数据是解决隐私问题、收集成本和通常与人员流动性分析相关的专利限制的有希望的解决方案之一。然而，由于人类活动固有的多样性和不可预测性，现有的轨迹生成方法仍处于起步阶段，需要解决诸如忠实性、灵活性和普遍性等问题。为了克服这些障碍，我们提出了基于拓扑约束扩散模型的 ControlTraj 可控轨迹生成框架。与以前的方法不同，ControlTraj 利用扩散模型来生成高保真轨迹，同时整合道路网络拓扑的结构约束来指导地理结果。具体来说，我们开发了一种新的路段自动编码器来提取细粒度的路段嵌入。编码后的特征和贡献值随后被合并到提出的地理去噪 UNetArchitecture 中，命名为 GeoUNet，以便从白噪声中合成地理轨迹。通过对三个真实世界数据设置的实验，ControlTraj 展示了其生成人为导向的高保真轨迹生成的能力，并能适应未探索的地理环境。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=ControlTraj:+Controllable+Trajectory+Generation+with+Topology-Constrained+Diffusion+Model)|0| |[One Fits All: Learning Fair Graph Neural Networks for Various Sensitive Attributes](https://doi.org/10.1145/3637528.3672029)|Yuchang Zhu, Jintang Li, Yatao Bian, Zibin Zheng, Liang Chen|Sun Yat-sen University, Guangzhou, China; Tencent AI Lab, Shenzhen, China|Recent studies have highlighted fairness issues in Graph Neural Networks(GNNs), where they produce discriminatory predictions against specificprotected groups categorized by sensitive attributes such as race and age.While various efforts to enhance GNN fairness have made significant progress,these approaches are often tailored to specific sensitive attributes.Consequently, they necessitate retraining the model from scratch to accommodatechanges in the sensitive attribute requirement, resulting in high computationalcosts. To gain deeper insights into this issue, we approach the graph fairnessproblem from a causal modeling perspective, where we identify the confoundingeffect induced by the sensitive attribute as the underlying reason. Motivatedby this observation, we formulate the fairness problem in graphs from aninvariant learning perspective, which aims to learn invariant representationsacross environments. Accordingly, we propose a graph fairness framework basedon invariant learning, namely FairINV, which enables the training of fair GNNsto accommodate various sensitive attributes within a single training session.Specifically, FairINV incorporates sensitive attribute partition and trainsfair GNNs by eliminating spurious correlations between the label and varioussensitive attributes. Experimental results on several real-world datasetsdemonstrate that FairINV significantly outperforms state-of-the-art fairnessapproaches, underscoring its effectiveness. Our code is available via:https://github.com/ZzoomD/FairINV/.|最近的研究强调了图形神经网络(GNNs)中的公平性问题，在这些问题中，它们对按照种族和年龄等敏感属性分类的特定受保护群体产生歧视性预测。尽管增强 GNN 公平性的各种努力取得了重大进展，但这些方法往往是针对特定的敏感属性而量身定制的。因此，他们需要从头开始重新训练模型，以适应敏感属性需求的变化，从而导致高计算成本。为了深入了解这一问题，我们从因果建模的角度探讨了图的公平性问题，其中我们确定了由敏感属性引起的混杂效应作为潜在的原因。基于这一观察，我们从不变学习的角度提出了图的公平性问题，目的是学习环境中的不变表示。因此，我们提出了一个基于不变学习的图公平性框架 FairINV，该框架使公平 GNN 的训练能够在一个训练会话中容纳各种敏感属性。特别地，FairINV 通过消除标签和各种敏感属性之间的伪相关性，整合了敏感属性分区和 train sfair GNN。在几个真实世界数据集上的实验结果表明，FairINV 显著优于最先进的公平性方法，强调了其有效性。我们的代码可以通过以下 https://github.com/zzoomd/fairinv/获得:。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=One+Fits+All:+Learning+Fair+Graph+Neural+Networks+for+Various+Sensitive+Attributes)|0| |[Topology-monitorable Contrastive Learning on Dynamic Graphs](https://doi.org/10.1145/3637528.3671777)|Zulun Zhu, Kai Wang, Haoyu Liu, Jintang Li, Siqiang Luo|Sun Yat-Sen University, Guangzhou, China; Nanyang Technological University, Singapore, Singapore|Graph contrastive learning is a representative self-supervised graph learning that has demonstrated excellent performance in learning node representations. Despite the extensive studies on graph contrastive learning models, most existing models are tailored to static graphs, hindering their application to real-world graphs which are often dynamically evolving. Directly applying these models to dynamic graphs brings in severe efficiency issues in repetitively updating the learned embeddings. To address this challenge, we propose IDOL, a novel contrastive learning framework for dynamic graph representation learning. IDOL conducts the graph propagation process based on a specially designed Personalized PageRank algorithm which can capture the topological changes incrementally. This effectively eliminates heavy recomputation while maintaining high learning quality. Our another main design is a topology-monitorable sampling strategy which lays the foundation of graph contrastive learning. We further show that the design in IDOL achieves a desired performance guarantee. Our experimental results on multiple dynamic graphs show that IDOL outperforms the strongest baselines on node classification tasks in various performance metrics.|图形对比学习是一种有代表性的自监督图形学习，在学习节点表示方面表现出了优异的性能。尽管对图形对比学习模型进行了广泛的研究，但大多数现有的模型都是针对静态图形的，这阻碍了它们在动态演化的现实世界图形中的应用。将这些模型直接应用于动态图中，会带来重复更新学习嵌入的严重效率问题。为了解决这一问题，我们提出了一种新的动态图表示学习对比学习框架 IDOL。IDOL 基于专门设计的个性化 PageRank 算法进行图传播过程，该算法可以逐步捕获拓扑变化。这有效地消除了重复计算，同时保持了高学习质量。我们的另一个主要设计是一个拓扑可监控的抽样策略，它为图的对比学习奠定了基础。我们进一步表明，IDOL 的设计达到了预期的性能保证。我们在多个动态图表上的实验结果表明，IDOL 在各种性能指标上都优于节点分类任务的最强基线。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Topology-monitorable+Contrastive+Learning+on+Dynamic+Graphs)|0| -|[MacroHFT: Memory Augmented Context-aware Reinforcement Learning On High Frequency Trading](https://doi.org/10.1145/3637528.3672064)|Chuqiao Zong, Chaojie Wang, Molei Qin, Lei Feng, Xinrun Wang, Bo An|Singapore University of Technology and Design, Singapore, Singapore; Skywork AI, Singapore, Singapore; Nanyang Technological University, Singapore, Singapore|High-frequency trading (HFT) that executes algorithmic trading in short time scales, has recently occupied the majority of cryptocurrency market. Besides traditional quantitative trading methods, reinforcement learning (RL) has become another appealing approach for HFT due to its terrific ability of handling high-dimensional financial data and solving sophisticated sequential decision-making problems, e.g., hierarchical reinforcement learning (HRL) has shown its promising performance on second-level HFT by training a router to select only one sub-agent from the agent pool to execute the current transaction. However, existing RL methods for HFT still have some defects: 1) standard RL-based trading agents suffer from the overfitting issue, preventing them from making effective policy adjustments based on financial context; 2) due to the rapid changes in market conditions, investment decisions made by an individual agent are usually one-sided and highly biased, which might lead to significant loss in extreme markets. To tackle these problems, we propose a novel Memory Augmented Context-aware Reinforcement learning method On HFT, a.k.a. MacroHFT, which consists of two training phases: 1) we first train multiple types of sub-agents with the market data decomposed according to various financial indicators, specifically market trend and volatility, where each agent owns a conditional adapter to adjust its trading policy according to market conditions; 2) then we train a hyper-agent to mix the decisions from these sub-agents and output a consistently profitable meta-policy to handle rapid market fluctuations, equipped with a memory mechanism to enhance the capability of decision-making. Extensive experiments on various cryptocurrency markets demonstrate that MacroHFT can achieve state-of-the-art performance on minute-level trading tasks. Code has been released in https://github.com/ZONG0004/MacroHFT.|在短时间内执行算法交易的高频交易(hFT)最近占据了加密货币市场的大部分份额。除了传统的定量交易方法外，强化学习交易(HRL)由于其处理高维金融数据和解决复杂的顺序决策问题的卓越能力，已成为高频交易的另一个有吸引力的方法，例如，分层强化学习(HRL)已经显示出其在二级高频交易中的良好表现，通过训练路由器只从代理池中选择一个子代理来执行当前的交易。然而，现有的高频交易 RL 方法仍然存在一些缺陷: 1)标准的基于 RL 的交易代理受到过度拟合问题的困扰，无法根据金融环境进行有效的政策调整; 2)由于市场环境的迅速变化，单个代理人的投资决策往往是片面的、高度偏颇的，这可能导致极端市场中的重大损失。为了解决这些问题，我们提出了一种新的记忆增强上下文感知强化学习方法在高频交易中，即 MacroHFT，它由两个训练阶段组成: 1)我们首先训练多种类型的子代理，根据不同的财务指标，特别是市场趋势和波动性分解市场数据，其中每个代理拥有一个条件适配器来根据市场条件调整其交易政策; 2)然后我们训练一个超代理混合这些子代理的决定，输出一个持续盈利的元政策来处理快速市场波动，配备一个记忆机制来提高决策。在各种加密货币市场上的大量实验表明，MacroHFT 可以在分钟级交易任务上实现最先进的性能。代码已经在 https://github.com/zong0004/macrohft 中发布。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=MacroHFT:+Memory+Augmented+Context-aware+Reinforcement+Learning+On+High+Frequency+Trading)|0| +|[MacroHFT: Memory Augmented Context-aware Reinforcement Learning On High Frequency Trading](https://doi.org/10.1145/3637528.3672064)|Chuqiao Zong, Chaojie Wang, Molei Qin, Lei Feng, Xinrun Wang, Bo An|Skywork AI, Singapore, Singapore; Singapore University of Technology and Design, Singapore, Singapore; Nanyang Technological University, Singapore, Singapore|High-frequency trading (HFT) that executes algorithmic trading in short time scales, has recently occupied the majority of cryptocurrency market. Besides traditional quantitative trading methods, reinforcement learning (RL) has become another appealing approach for HFT due to its terrific ability of handling high-dimensional financial data and solving sophisticated sequential decision-making problems, e.g., hierarchical reinforcement learning (HRL) has shown its promising performance on second-level HFT by training a router to select only one sub-agent from the agent pool to execute the current transaction. However, existing RL methods for HFT still have some defects: 1) standard RL-based trading agents suffer from the overfitting issue, preventing them from making effective policy adjustments based on financial context; 2) due to the rapid changes in market conditions, investment decisions made by an individual agent are usually one-sided and highly biased, which might lead to significant loss in extreme markets. To tackle these problems, we propose a novel Memory Augmented Context-aware Reinforcement learning method On HFT, a.k.a. MacroHFT, which consists of two training phases: 1) we first train multiple types of sub-agents with the market data decomposed according to various financial indicators, specifically market trend and volatility, where each agent owns a conditional adapter to adjust its trading policy according to market conditions; 2) then we train a hyper-agent to mix the decisions from these sub-agents and output a consistently profitable meta-policy to handle rapid market fluctuations, equipped with a memory mechanism to enhance the capability of decision-making. Extensive experiments on various cryptocurrency markets demonstrate that MacroHFT can achieve state-of-the-art performance on minute-level trading tasks. Code has been released in https://github.com/ZONG0004/MacroHFT.|在短时间内执行算法交易的高频交易(hFT)最近占据了加密货币市场的大部分份额。除了传统的定量交易方法外，强化学习交易(HRL)由于其处理高维金融数据和解决复杂的顺序决策问题的卓越能力，已成为高频交易的另一个有吸引力的方法，例如，分层强化学习(HRL)已经显示出其在二级高频交易中的良好表现，通过训练路由器只从代理池中选择一个子代理来执行当前的交易。然而，现有的高频交易 RL 方法仍然存在一些缺陷: 1)标准的基于 RL 的交易代理受到过度拟合问题的困扰，无法根据金融环境进行有效的政策调整; 2)由于市场环境的迅速变化，单个代理人的投资决策往往是片面的、高度偏颇的，这可能导致极端市场中的重大损失。为了解决这些问题，我们提出了一种新的记忆增强上下文感知强化学习方法在高频交易中，即 MacroHFT，它由两个训练阶段组成: 1)我们首先训练多种类型的子代理，根据不同的财务指标，特别是市场趋势和波动性分解市场数据，其中每个代理拥有一个条件适配器来根据市场条件调整其交易政策; 2)然后我们训练一个超代理混合这些子代理的决定，输出一个持续盈利的元政策来处理快速市场波动，配备一个记忆机制来提高决策。在各种加密货币市场上的大量实验表明，MacroHFT 可以在分钟级交易任务上实现最先进的性能。代码已经在 https://github.com/zong0004/macrohft 中发布。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=MacroHFT:+Memory+Augmented+Context-aware+Reinforcement+Learning+On+High+Frequency+Trading)|0| |[Lessons Learned while Running ML Models in Harsh Environments](https://doi.org/10.1145/3637528.3672499)|Pedro Bizarro|Research, Feedzai, Lisboa, Portugal|Once a very large payment processor client told us: 'if we are down for 5 minutes, we open the evening news - so don't screw up'. Processing billions of dollars per day, many financial institutions, need to continuously fight organized crime in the form of transaction fraud, stolen cards, anti-money laundering, account opening fraud, impersonations scams, phishing, and many other exotic and ever changing attacks from organized crime groups worldwide. In fact, it is estimated that in 2023 the global losses in fraud scams and bank fraud reached 485.6 billion. However, in addition to having very good detection rates and very low false positive rates, financial institutions also need to maintain very high availability rates, very low latencies, very high throughputs, automatic fault tolerance, auto scale up and down, and more. In this talk we cover some lessons related to running ML models in harsh, mission critical environments. We describe data issues, scale issues, ethical issues, system issues, security issues, compliance issues, business and regulation issues, and some architectural tradeoffs and architectural evolutions.|有一次，一个非常大的支付处理器客户告诉我们: “如果我们停机5分钟，我们就打开晚间新闻——所以不要搞砸了。”。每天处理数十亿美元的资金，许多金融机构，需要不断地打击有组织犯罪，包括交易欺诈、被盗卡、反洗钱、开户欺诈、模仿诈骗、网络钓鱼，以及来自世界各地有组织犯罪集团的许多其他奇异的、不断变化的攻击。事实上，据估计，2023年全球诈骗和银行诈骗造成的损失高达4856亿美元。然而，除了拥有非常好的检测率和非常低的假阳性率之外，金融机构还需要维持非常低的高可用性、非常低的延迟、非常高的吞吐量、自动容错、自动放大和放小等等。在这个演讲中，我们将介绍一些关于在严酷的任务关键环境中运行机器学习模型的经验教训。我们描述了数据问题、规模问题、道德问题、系统问题、安全问题、法规遵循问题、业务和法规问题，以及一些架构折衷和架构演进。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Lessons+Learned+while+Running+ML+Models+in+Harsh+Environments)|0| |[Next-generation Intelligent Assistants for Wearable Devices](https://doi.org/10.1145/3637528.3672500)|Xin Luna Dong|Northwestern Polytech Univ, Sch Chem & Chem Engn, Shaanxi Key Lab Macromol Sci & Technol, Xian 710072, Shaanxi, Peoples R China; Shaanxi Univ Sci & Technol, Coll Chem & Chem Engn, Key Lab Auxiliary Chem & Technol Chem Ind, Minist Educ,Shaanxi Key Lab Chem Addit Ind, Xian 710021, Shaanxi, Peoples R China|Multifunctional wearable electronic devices based on natural materials are highly desirable for versatile applications of energy conversion, electronic skin and artificial intelligence. Herein, multifunctional wearable silver nanowire decorated leather (AgNW/leather) nanocomposites with hierarchical structures for integrated visual Joule heating, electromagnetic interference (EMI) shielding and piezoresistive sensing are fabricated via the facile vacuum-assisted filtration process. The AgNWs penetrate the micro-nanoporous structures in the corium side of leather constructing highly-efficient conductive networks. The resultant flexible and mechanically strong AgNW/leather nanocomposites exhibit extremely low sheet resistance of 0.8 omega/sq, superior visual Joule heating temperatures up to 108 degrees C at low supplied voltage of 2.0 V due to efficient energy conversion, excellent EMI shielding effectiveness (EMI SE) of approximate to 55 dB and outstanding piezoresistive sensing ability in human motion detection. This work demonstrates the fabrication of multifunctional AgNW/leather nanocomposites for next-generation wearable electronic devices in energy conversion, electronic skin and artificial intelligence, etc.|以天然材料为基础的多功能可穿戴电子设备在能量转换、电子皮肤和人工智能的多种应用中都是非常理想的。在这里，多功能可穿戴银纳米线装饰皮革(AgNW/皮革)纳米复合材料具有分层结构的集成视觉焦耳加热，电磁干扰(EMI)屏蔽和压阻传感通过简单的真空辅助过滤过程。AgNWs 穿透皮革真皮侧的微纳米多孔结构，构成高效的导电网络。由此产生的具有柔韧性和机械强度的 AgNW/皮革纳米复合材料表现出极低的片状电阻，为0.8欧米伽/平方米，优越的视觉焦耳加热温度高达108摄氏度，在2.0 V 的低供电电压下，由于有效的能量转换，优异的电磁干扰屏蔽效果(EMI SE)约为55分贝，并在人类动作感应中具有突出的压阻传感能力。这项工作展示了多功能 AgNW/皮革纳米复合材料的制造，用于下一代可穿戴电子设备在能量转换，电子皮肤和人工智能等。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Next-generation+Intelligent+Assistants+for+Wearable+Devices)|0| |[Scalable Graph Learning for your Enterprise](https://doi.org/10.1145/3637528.3672501)|Hema Raghavan|Kumo.AI., Inc., Mountain View, CA, USA|Much of the world's most valued data is stored in relational databases and data warehouses, where the data is organized into many tables connected by primary-foreign key relations. However, building machine learning models using this data is both challenging and time consuming. The core problem is that no machine learning method is capable of learning on multiple tables interconnected by primary-foreign key relations. Current methods can only learn from a single table, so the data must first be manually joined and aggregated into a single training table, the process known as feature engineering. Feature engineering is slow, error prone and leads to suboptimal models. At Kumo.ai we have worked with researchers worldwide to develop an end-to-end deep representation learning approach to directly learn on data laid out across multiple tables [1]. We name our approach Relational Deep Learning (RDL). The core idea is to view relational databases as a temporal, heterogeneous graph, with a node for each row in each table, and edges specified by primary-foreign key links. Message Passing Graph Neural Networks can then automatically learn across the graph to extract representations that leverage all input data, without any manual feature engineering. Our relational deep learning method to encode graph structure into low-dimensional embeddings brings several benefits: (1) automatic learning from the entire data spread across multiple relational tables (2) no manual feature engineering as the system learns optimal embeddings for a target problem; (3) state-of-the-art predictive performance.|世界上大部分最有价值的数据都存储在关系数据库和数据仓库中，在这些数据中，数据被组织成许多由主键-外键关系连接的表。然而，利用这些数据建立机器学习模型既具有挑战性又耗费时间。其核心问题是没有一种机器学习方法能够在由主-外键关系相互连接的多个表上进行学习。当前的方法只能从单个表中学习，因此必须首先手动将数据合并并聚合到单个训练表中，这个过程称为特征工程。特征工程是缓慢的，容易出错，并导致次优模型。在 Kumo.ai，我们与世界各地的研究人员合作，开发了一种端到端的深度表示学习方法，可以直接学习多个表格中的数据[1]。我们将这种方法命名为关系深度学习(RDL)。其核心思想是将关系数据库视为一个时态的、异构的图形，每个表中的每一行都有一个节点，边由主-外键链接指定。然后，消息传递图形神经网络可以自动学习整个图形，以提取利用所有输入数据的表示，而不需要任何人工特征工程。我们的关系深度学习方法将图形结构编码为低维嵌入带来了几个好处: (1)从跨多个关系表的整个数据中自动学习(2)不需要手动特征工程，因为系统学习了目标问题的最佳嵌入; (3)最先进的预测性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Scalable+Graph+Learning+for+your+Enterprise)|0| |[Dynamic Pricing for Multi-Retailer Delivery Platforms with Additive Deep Learning and Evolutionary Optimization](https://doi.org/10.1145/3637528.3671634)|Ahmed Abdulaal, Ali Polat, Hari Narayan, Wenrong Zeng, Yimin Yi|Walmart Global Tech, Sunnyvale, California, USA; Shipt, San Francisco, California, USA|Dynamic Pricing for online retail has been discussed extensively in literature. However, past solutions fell short of addressing the unique challenges of independent multi-retailer platforms for grocery delivery. From limited visibility of retailers' inventories to diverse demand-side dynamics across retail brands and locations, the highly decentralized nature of multi-retailer platforms deviates from the classical framework of modeling price elasticity and cross-elasticity of demand. In this paper, we present a novel scheme to scalable and practical price adjustment in the highly dynamic multi-retailer context. First, we present a deep learning framework to distinctly model complex cross-elasticity relationships via additive neural networks augmented with adversarial data. Second, we present evolutionary optimization agents for adjusting itemized prices in a location-decentralized manner, while adhering to custom business constraints and objectives. The optimization utilizes the genetic algorithm structure, where we introduce a potential mechanism, inspired by bandit algorithms, in order to improve convergence speed by managing exploitation and exploration trade-offs. Our solution is deployed at Shipt and is extendable to other types of multi-retailer platforms, such as restaurant delivery. Finally, we empirically demonstrate performance using public and industry datasets of hundreds and thousands of diverse products across tens of stores, offering an optimization targets coverage scale in the tens of thousands, far larger than experimental setups in past research.|网上零售的动态定价在文献中得到了广泛的讨论。然而，过去的解决方案未能解决独立的多零售商平台的杂货配送的独特挑战。从零售商库存的有限可见性到零售品牌和零售地点之间多样化的需求侧动态，多零售商平台的高度分散性背离了建模价格弹性和需求交叉弹性的经典框架。本文在高度动态的多零售商环境下，提出了一种新的可扩展的、实用的价格调整方案。首先，我们提出了一个深度学习框架，通过加性神经网络增强敌对数据来清楚地模拟复杂的交叉弹性关系。其次，我们提出的进化优化代理调整逐项价格在位置分散的方式，同时坚持自定义业务的约束和目标。优化采用遗传算法结构，其中引入了一种潜在的机制，灵感来自于匪徒算法，以提高收敛速度，管理开发和勘探的权衡。我们的解决方案部署在 Shipt，并可扩展到其他类型的多零售商平台，如餐厅配送。最后，我们通过使用数十家商店的成百上千种不同产品的公共和行业数据集来实证性地演示性能，提供了数万个优化目标覆盖范围，远远大于过去研究中的实验设置。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Dynamic+Pricing+for+Multi-Retailer+Delivery+Platforms+with+Additive+Deep+Learning+and+Evolutionary+Optimization)|0| -|[Television Discourse Decoded: Comprehensive Multimodal Analytics at Scale](https://doi.org/10.1145/3637528.3671532)|Anmol Agarwal, Pratyush Priyadarshi, Shiven Sinha, Shrey Gupta, Hitkul Jangra, Ponnurangam Kumaraguru, Kiran Garimella|Indraprastha Institute of Information Technology, Delhi, India; International Institute of Information Technology, Hyderabad, India; Rutgers University, New Brunswick, USA|In this paper, we tackle the complex task of analyzing televised debates, with a focus on a prime time news debate show from India. Previous methods, which often relied solely on text, fall short in capturing the multimodal essence of these debates [27]. To address this gap, we introduce a comprehensive automated toolkit that employs advanced computer vision and speech-to-text techniques for large-scale multimedia analysis. Utilizing state-of-the-art computer vision algorithms and speech-to-text methods, we transcribe, diarize, and analyze thousands of YouTube videos of a prime-time television debate show in India. These debates are a central part of Indian media but have been criticized for compromised journalistic integrity and excessive dramatization [18]. Our toolkit provides concrete metrics to assess bias and incivility, capturing a comprehensive multimedia perspective that includes text, audio utterances, and video frames. Our findings reveal significant biases in topic selection and panelist representation, along with alarming levels of incivility. This work offers a scalable, automated approach for future research in multimedia analysis, with profound implications for the quality of public discourse and democratic debate. To catalyze further research in this area, we also release the code, dataset collected and supplemental pdf.1|在本文中，我们将分析电视辩论这一复杂的任务，重点放在来自印度的黄金时段新闻辩论节目上。以前的方法往往只依赖于文本，不能捕捉这些辩论的多模式本质[27]。为了弥补这一差距，我们引入了一个全面的自动化工具包，它采用了先进的计算机视觉和语音到文本的技术进行大规模的多媒体分析。利用最先进的计算机视觉算法和语音文本转换方法，我们转录、日记和分析了印度黄金时段电视辩论节目的数千个 YouTube 视频。这些辩论是印度媒体的一个核心部分，但却因为损害新闻诚信和过度戏剧化而受到批评[18]。我们的工具包提供了评估偏见和不礼貌的具体指标，捕获了一个全面的多媒体视角，包括文本，音频话语和视频帧。我们的研究结果显示，在选题和小组成员的代表性方面存在明显的偏差，同时还存在令人担忧的不礼貌行为。这项工作为未来的多媒体分析研究提供了一种可扩展的、自动化的方法，对公共话语和民主辩论的质量有着深远的影响。为了促进该领域的进一步研究，我们还发布了代码、收集的数据集和补充 pdf|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Television+Discourse+Decoded:+Comprehensive+Multimodal+Analytics+at+Scale)|0| -|[Large Scale Generative AI Text Applied to Sports and Music](https://doi.org/10.1145/3637528.3671542)|Aaron K. Baughman, Eduardo Morales, Rahul Agarwal, Gozde Akay, Rogério Feris, Tony Johnson, Stephen Hammer, Leonid Karlinsky|IBM, RTP, NC, USA; IBM, Fredericton, NB, Canada; IBM, New York, NY, USA; IBM, Atlanta, GA, USA; IBM, Boston, MA, USA|We address the problem of scaling up the production of media content,including commentary and personalized news stories, for large-scale sports andmusic events worldwide. Our approach relies on generative AI models totransform a large volume of multimodal data (e.g., videos, articles, real-timescoring feeds, statistics, and fact sheets) into coherent and fluent text.Based on this approach, we introduce, for the first time, an AI commentarysystem, which was deployed to produce automated narrations for highlightpackages at the 2023 US Open, Wimbledon, and Masters tournaments. In the samevein, our solution was extended to create personalized content for ESPN FantasyFootball and stories about music artists for the Grammy awards. Theseapplications were built using a common software architecture achieved a 15xspeed improvement with an average Rouge-L of 82.00 and perplexity of 6.6. Ourwork was successfully deployed at the aforementioned events, supporting 90million fans around the world with 8 billion page views, continuously pushingthe bounds on what is possible at the intersection of sports, entertainment,and AI.|我们致力于解决为全球大型体育和音乐活动扩大媒体内容生产的问题，包括评论和个性化新闻报道。我们的方法依赖于生成式人工智能模型将大量的多模态数据(例如，视频、文章、实时提要、统计数据和概况介绍)转换为连贯流畅的文本。基于这种方法，我们首次引入了一个人工智能评论系统，该系统被部署用于为2023年美国公开赛、温布尔登和大师赛的亮点包生成自动解说。同时，我们的解决方案被扩展到为 ESPN FantasyFootball 创建个性化内容，以及为格莱美奖创建音乐艺术家的故事。这些应用程序使用一个通用的软件架构构建，实现了15倍的速度改进，平均 Rouge-L 为82.00，困惑度为6.6。我们的工作成功地部署在上述事件，支持世界各地的9000万球迷与80亿页面浏览量，不断推进的界限，在体育，娱乐和人工智能的交叉点可能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Large+Scale+Generative+AI+Text+Applied+to+Sports+and+Music)|0| +|[Television Discourse Decoded: Comprehensive Multimodal Analytics at Scale](https://doi.org/10.1145/3637528.3671532)|Anmol Agarwal, Pratyush Priyadarshi, Shiven Sinha, Shrey Gupta, Hitkul Jangra, Ponnurangam Kumaraguru, Kiran Garimella|Rutgers University, New Brunswick, USA; Indraprastha Institute of Information Technology, Delhi, India; International Institute of Information Technology, Hyderabad, India|In this paper, we tackle the complex task of analyzing televised debates, with a focus on a prime time news debate show from India. Previous methods, which often relied solely on text, fall short in capturing the multimodal essence of these debates [27]. To address this gap, we introduce a comprehensive automated toolkit that employs advanced computer vision and speech-to-text techniques for large-scale multimedia analysis. Utilizing state-of-the-art computer vision algorithms and speech-to-text methods, we transcribe, diarize, and analyze thousands of YouTube videos of a prime-time television debate show in India. These debates are a central part of Indian media but have been criticized for compromised journalistic integrity and excessive dramatization [18]. Our toolkit provides concrete metrics to assess bias and incivility, capturing a comprehensive multimedia perspective that includes text, audio utterances, and video frames. Our findings reveal significant biases in topic selection and panelist representation, along with alarming levels of incivility. This work offers a scalable, automated approach for future research in multimedia analysis, with profound implications for the quality of public discourse and democratic debate. To catalyze further research in this area, we also release the code, dataset collected and supplemental pdf.1|在本文中，我们将分析电视辩论这一复杂的任务，重点放在来自印度的黄金时段新闻辩论节目上。以前的方法往往只依赖于文本，不能捕捉这些辩论的多模式本质[27]。为了弥补这一差距，我们引入了一个全面的自动化工具包，它采用了先进的计算机视觉和语音到文本的技术进行大规模的多媒体分析。利用最先进的计算机视觉算法和语音文本转换方法，我们转录、日记和分析了印度黄金时段电视辩论节目的数千个 YouTube 视频。这些辩论是印度媒体的一个核心部分，但却因为损害新闻诚信和过度戏剧化而受到批评[18]。我们的工具包提供了评估偏见和不礼貌的具体指标，捕获了一个全面的多媒体视角，包括文本，音频话语和视频帧。我们的研究结果显示，在选题和小组成员的代表性方面存在明显的偏差，同时还存在令人担忧的不礼貌行为。这项工作为未来的多媒体分析研究提供了一种可扩展的、自动化的方法，对公共话语和民主辩论的质量有着深远的影响。为了促进该领域的进一步研究，我们还发布了代码、收集的数据集和补充 pdf|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Television+Discourse+Decoded:+Comprehensive+Multimodal+Analytics+at+Scale)|0| +|[Large Scale Generative AI Text Applied to Sports and Music](https://doi.org/10.1145/3637528.3671542)|Aaron K. Baughman, Eduardo Morales, Rahul Agarwal, Gozde Akay, Rogério Feris, Tony Johnson, Stephen Hammer, Leonid Karlinsky|IBM, Atlanta, GA, USA; IBM, New York, NY, USA; IBM, RTP, NC, USA; IBM, Boston, MA, USA; IBM, Fredericton, NB, Canada|We address the problem of scaling up the production of media content,including commentary and personalized news stories, for large-scale sports andmusic events worldwide. Our approach relies on generative AI models totransform a large volume of multimodal data (e.g., videos, articles, real-timescoring feeds, statistics, and fact sheets) into coherent and fluent text.Based on this approach, we introduce, for the first time, an AI commentarysystem, which was deployed to produce automated narrations for highlightpackages at the 2023 US Open, Wimbledon, and Masters tournaments. In the samevein, our solution was extended to create personalized content for ESPN FantasyFootball and stories about music artists for the Grammy awards. Theseapplications were built using a common software architecture achieved a 15xspeed improvement with an average Rouge-L of 82.00 and perplexity of 6.6. Ourwork was successfully deployed at the aforementioned events, supporting 90million fans around the world with 8 billion page views, continuously pushingthe bounds on what is possible at the intersection of sports, entertainment,and AI.|我们致力于解决为全球大型体育和音乐活动扩大媒体内容生产的问题，包括评论和个性化新闻报道。我们的方法依赖于生成式人工智能模型将大量的多模态数据(例如，视频、文章、实时提要、统计数据和概况介绍)转换为连贯流畅的文本。基于这种方法，我们首次引入了一个人工智能评论系统，该系统被部署用于为2023年美国公开赛、温布尔登和大师赛的亮点包生成自动解说。同时，我们的解决方案被扩展到为 ESPN FantasyFootball 创建个性化内容，以及为格莱美奖创建音乐艺术家的故事。这些应用程序使用一个通用的软件架构构建，实现了15倍的速度改进，平均 Rouge-L 为82.00，困惑度为6.6。我们的工作成功地部署在上述事件，支持世界各地的9000万球迷与80亿页面浏览量，不断推进的界限，在体育，娱乐和人工智能的交叉点可能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Large+Scale+Generative+AI+Text+Applied+to+Sports+and+Music)|0| |[LiGNN: Graph Neural Networks at LinkedIn](https://doi.org/10.1145/3637528.3671566)|Fedor Borisyuk, Shihai He, Yunbo Ouyang, Morteza Ramezani, Peng Du, Xiaochen Hou, Chengming Jiang, Nitin Pasumarthy, Priya Bannur, Birjodh Tiwana, Ping Liu, Siddharth Dangi, Daqi Sun, Zhoutao Pei, Xiao Shi, Sirou Zhu, Qianqi Shen, KuangHsuan Lee, David Stein, Baolei Li, Haichao Wei, Amol Ghoting, Souvik Ghosh|LinkedIn, Mountain View, CA, USA|In this paper, we present LiGNN, a deployed large-scale Graph Neural Networks (GNNs) Framework. We share our insight on developing and deployment of GNNs at large scale at LinkedIn. We present a set of algorithmic improvements to the quality of GNN representation learning including temporal graph architectures with long term losses, effective cold start solutions via graph densification, ID embeddings and multi-hop neighbor sampling. We explain how we built and sped up by 7x our large-scale training on LinkedIn graphs with adaptive sampling of neighbors, grouping and slicing of training data batches, specialized shared-memory queue and local gradient optimization. We summarize our deployment lessons and learnings gathered from A/B test experiments. The techniques presented in this work have contributed to an approximate relative improvements of 1% of Job application hearing back rate, 2% Ads CTR lift, 0.5% of Feed engaged daily active users, 0.2% session lift and 0.1% weekly active user lift from people recommendation. We believe that this work can provide practical solutions and insights for engineers who are interested in applying Graph neural networks at large scale.|本文提出了一种已部署的大规模图形神经网络(GNN)框架 LiGNN。我们在 LinkedIn 上分享了我们对大规模开发和部署 GNN 的见解。本文提出了一套改进 GNN 表示学习质量的算法，包括具有长期损失的时间图结构、通过图的密集化、 ID 嵌入和多跳邻居采样的有效冷启动解决方案。我们解释了我们是如何通过对 LinkedIn 图的7倍大规模训练来建立和加快训练速度的，这些训练包括邻居的自适应抽样、训练数据批的分组和切片、专门的共享内存队列和局部梯度优化。我们总结了从 A/B 测试实验中获得的部署经验和教训。这项工作中提出的技术有助于近似相对改善1% 的求职申请听力回复率，2% 的广告点击率提升，0.5% 的订阅每日活跃用户，0.2% 的会话提升和0.1% 的每周活跃用户提升从人们的推荐。我们相信，这项工作可以提供实际的解决方案和见解的工程师谁有兴趣应用图形神经网络的大规模。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=LiGNN:+Graph+Neural+Networks+at+LinkedIn)|0| -|[Diffusion Model-based Mobile Traffic Generation with Open Data for Network Planning and Optimization](https://doi.org/10.1145/3637528.3671544)|Haoye Chai, Tao Jiang, Li Yu|; Department of Electronic Engineering, BNRist, Tsinghua University, Beijing, China; Chinamobile Research Institute, Beijing, China|With the rapid development of the Fifth Generation Mobile Communication Technology (5G) networks, network planning and optimization have become increasingly crucial. Generating high-fidelity network traffic data can preemptively estimate the network demands of mobile users, which holds potential for network operators to improve network performance. However, the data required by existing generation methods is predominantly inaccessible to the public, resulting in a lack of reproducibility for the models and high deployment costs in practice. In this article, we propose an Open data-based Diffusion model for mobile traffic generation (OpenDiff), where a multi-positive contrastive learning algorithm is designed to construct conditional information for the diffusion model using entirely publicly available satellite remote sensing images, Point of Interest (POI), and population data. The conditional information contains relevant human activities in geographical areas, which can effectively guide the generation of network traffic data. We further design an attention-based fusion mechanism to capture the implicit correlations between network traffic and human activity features, enhancing the model's controllable generation capability. We conduct evaluations on three different cities with varying scales, where experimental results verify that our proposed model outperforms existing methods by 14.36% and 13.05% in terms of generation fidelity and controllability. To further validate the effectiveness of the model, we leverage the generated traffic data to assist the operators with network planning on a real-world network optimization platform of China Mobile Communications Corporation. The source code is available online:https://github.com/impchai/OpenDiff-diffusion-model-with-open-data.|随着第五代移动通信技术(5G)网络的迅速发展，网络规划和优化已经变得越来越重要。生成高保真的网络流量数据可以预先估计移动用户的网络需求，为网络运营商提高网络性能提供了可能。然而，现有生成方法所需的数据大多不为公众所获取，导致模型缺乏可重复性，实际部署成本高昂。提出了一种基于开放数据的移动通信扩散模型 OpenDiff，该模型利用完全公开的卫星遥感图像、兴趣点(POI)和人口数据构造扩散模型的条件信息。条件信息包含了地理区域内相关的人类活动，可以有效地指导网络流量数据的生成。进一步设计了基于注意的融合机制，捕捉网络流量与人类活动特征之间的隐式关联，提高了模型的可控生成能力。我们对三个不同规模的城市进行了评估，实验结果表明，我们提出的模型在生成保真度和可控性方面比现有方法分别提高了14.36% 和13.05% 。为了进一步验证该模型的有效性，我们利用生成的流量数据，在中国移动通信公司的一个实际网络优化平台上协助运营商进行网络规划。源代码可在网上获得: https://github.com/impchai/opendiff-diffusion-model-with-open-data。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Diffusion+Model-based+Mobile+Traffic+Generation+with+Open+Data+for+Network+Planning+and+Optimization)|0| +|[Diffusion Model-based Mobile Traffic Generation with Open Data for Network Planning and Optimization](https://doi.org/10.1145/3637528.3671544)|Haoye Chai, Tao Jiang, Li Yu|; Chinamobile Research Institute, Beijing, China; Department of Electronic Engineering, BNRist, Tsinghua University, Beijing, China|With the rapid development of the Fifth Generation Mobile Communication Technology (5G) networks, network planning and optimization have become increasingly crucial. Generating high-fidelity network traffic data can preemptively estimate the network demands of mobile users, which holds potential for network operators to improve network performance. However, the data required by existing generation methods is predominantly inaccessible to the public, resulting in a lack of reproducibility for the models and high deployment costs in practice. In this article, we propose an Open data-based Diffusion model for mobile traffic generation (OpenDiff), where a multi-positive contrastive learning algorithm is designed to construct conditional information for the diffusion model using entirely publicly available satellite remote sensing images, Point of Interest (POI), and population data. The conditional information contains relevant human activities in geographical areas, which can effectively guide the generation of network traffic data. We further design an attention-based fusion mechanism to capture the implicit correlations between network traffic and human activity features, enhancing the model's controllable generation capability. We conduct evaluations on three different cities with varying scales, where experimental results verify that our proposed model outperforms existing methods by 14.36% and 13.05% in terms of generation fidelity and controllability. To further validate the effectiveness of the model, we leverage the generated traffic data to assist the operators with network planning on a real-world network optimization platform of China Mobile Communications Corporation. The source code is available online:https://github.com/impchai/OpenDiff-diffusion-model-with-open-data.|随着第五代移动通信技术(5G)网络的迅速发展，网络规划和优化已经变得越来越重要。生成高保真的网络流量数据可以预先估计移动用户的网络需求，为网络运营商提高网络性能提供了可能。然而，现有生成方法所需的数据大多不为公众所获取，导致模型缺乏可重复性，实际部署成本高昂。提出了一种基于开放数据的移动通信扩散模型 OpenDiff，该模型利用完全公开的卫星遥感图像、兴趣点(POI)和人口数据构造扩散模型的条件信息。条件信息包含了地理区域内相关的人类活动，可以有效地指导网络流量数据的生成。进一步设计了基于注意的融合机制，捕捉网络流量与人类活动特征之间的隐式关联，提高了模型的可控生成能力。我们对三个不同规模的城市进行了评估，实验结果表明，我们提出的模型在生成保真度和可控性方面比现有方法分别提高了14.36% 和13.05% 。为了进一步验证该模型的有效性，我们利用生成的流量数据，在中国移动通信公司的一个实际网络优化平台上协助运营商进行网络规划。源代码可在网上获得: https://github.com/impchai/opendiff-diffusion-model-with-open-data。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Diffusion+Model-based+Mobile+Traffic+Generation+with+Open+Data+for+Network+Planning+and+Optimization)|0| |[RareBench: Can LLMs Serve as Rare Diseases Specialists?](https://doi.org/10.1145/3637528.3671576)|Xuanzhong Chen, Xiaohao Mao, Qihan Guo, Lun Wang, Shuyang Zhang, Ting Chen|Tsinghua University, Beijing, China; Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China|Generalist Large Language Models (LLMs), such as GPT-4, have shownconsiderable promise in various domains, including medical diagnosis. Rarediseases, affecting approximately 300 million people worldwide, often haveunsatisfactory clinical diagnosis rates primarily due to a lack of experiencedphysicians and the complexity of differentiating among many rare diseases. Inthis context, recent news such as "ChatGPT correctly diagnosed a 4-year-old'srare disease after 17 doctors failed" underscore LLMs' potential, yetunderexplored, role in clinically diagnosing rare diseases. To bridge thisresearch gap, we introduce RareBench, a pioneering benchmark designed tosystematically evaluate the capabilities of LLMs on 4 critical dimensionswithin the realm of rare diseases. Meanwhile, we have compiled the largestopen-source dataset on rare disease patients, establishing a benchmark forfuture studies in this domain. To facilitate differential diagnosis of rarediseases, we develop a dynamic few-shot prompt methodology, leveraging acomprehensive rare disease knowledge graph synthesized from multiple knowledgebases, significantly enhancing LLMs' diagnostic performance. Moreover, wepresent an exhaustive comparative study of GPT-4's diagnostic capabilitiesagainst those of specialist physicians. Our experimental findings underscorethe promising potential of integrating LLMs into the clinical diagnosticprocess for rare diseases. This paves the way for exciting possibilities infuture advancements in this field.|通用型大语言模型(LLM) ，如 GPT-4，在各个领域，包括医学诊断方面都显示出相当大的前景。影响全世界大约3亿人的罕见疾病的临床诊断率往往不能令人满意，这主要是由于缺乏经验丰富的医生和区分许多罕见疾病的复杂性。在这种情况下，最近的新闻，如“ ChatGPT 在17名医生失败后正确诊断了一个4岁的‘罕见疾病’”强调了 LLM 的潜力，但尚未充分探讨，在临床诊断罕见疾病中的作用。为了弥补这一研究差距，我们引入了 RareBench，这是一个开创性的基准，旨在系统地评估 LLM 在罕见疾病领域4个关键方面的能力。与此同时，我们已经汇编了最大的开源数据集关于罕见病患者，建立了一个基准在这个领域的未来研究。为了促进稀有疾病的鑑别诊断，我们开发了动态的少数快速方法，利用从多种知识库综合而成的全面的稀有疾病知识图，显著提高 LLM 的诊断性能。此外，我们提出了一个详尽的比较研究的 GPT-4的诊断能力与那些专科医生。我们的实验结果强调了将 LLM 整合到罕见疾病的临床诊断过程中的潜力。这为该领域未来的进步铺平了道路。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=RareBench:+Can+LLMs+Serve+as+Rare+Diseases+Specialists?)|0| |[MARLP: Time-series Forecasting Control for Agricultural Managed Aquifer Recharge](https://doi.org/10.1145/3637528.3671533)|Yuning Chen, Kang Yang, Zhiyu An, Brady Holder, Luke Paloutzian, Khaled M. Bali, Wan Du|University of California, Agriculture and Natural Resources, Parlier, CA, USA; University of California, Merced, Merced, CA, USA|The rapid decline in groundwater around the world poses a significant challenge to sustainable agriculture. To address this issue, agricultural managed aquifer recharge (Ag-MAR) is proposed to recharge the aquifer by artificially flooding agricultural lands using surface water. Ag-MAR requires a carefully selected flooding schedule to avoid affecting the oxygen absorption of crop roots. However, current Ag-MAR scheduling does not take into account complex environmental factors such as weather and soil oxygen, resulting in crop damage and insufficient recharging amounts. This paper proposes MARLP, the first end-to-end data-driven control system for Ag-MAR. We first formulate Ag-MAR as an optimization problem. To that end, we analyze four-year in-field datasets, which reveal the multi-periodicity feature of the soil oxygen level trends and the opportunity to use external weather forecasts and flooding proposals as exogenous clues for soil oxygen prediction. Then, we design a two-stage forecasting framework. In the first stage, it extracts both the cross-variate dependency and the periodic patterns from historical data to conduct preliminary forecasting. In the second stage, it uses weather-soil and flooding-soil causality to facilitate an accurate prediction of soil oxygen levels. Finally, we conduct model predictive control (MPC) for Ag-MAR flooding. To address the challenge of large action spaces, we devise a heuristic planning module to reduce the number of flooding proposals to enable the search for optimal solutions. Real-world experiments show that MARLP reduces the oxygen deficit ratio by 86.8% while improving the recharging amount in unit time by 35.8%, compared with the previous four years.|世界各地地下水的迅速减少对可持续农业构成了重大挑战。为了解决这一问题，农业管理含水层补给(Ag-MAR)被提议通过利用地表水人为地淹没农田来补给含水层。Ag-MAR 需要一个仔细选择的淹水时间表，以避免影响作物根系的氧吸收。然而，目前的 Ag-MAR 调度没有考虑到复杂的环境因素，如天气和土壤氧气，导致作物损害和补给量不足。本文提出了第一个基于数据驱动的 Ag-MAR 端到端控制系统 MARLP。我们首先把银-最佳化问题系数作为一个参数。为此，我们分析了四年的实地数据集，这些数据集揭示了土壤氧含量趋势的多周期性特征，以及利用外部天气预报和洪水预报作为土壤氧含量预测的外部线索的机会。然后，我们设计了一个两阶段的预测框架。第一阶段从历史数据中提取相关性和周期模式进行初步预测;。在第二阶段，它利用气候-土壤和洪水-土壤的因果关系来促进对土壤氧含量的准确预测。最后，我们对 Ag-MAR 洪水进行了模型预估计控制分析。为了解决大行动空间的挑战，我们设计了一个启发式规划模块，以减少洪泛建议的数量，使寻找最佳解决方案。实际试验结果表明，与前四年相比，MARLP 降低了氧缺乏率86.8% ，同时提高了单位时间内的充电量35.8% 。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=MARLP:+Time-series+Forecasting+Control+for+Agricultural+Managed+Aquifer+Recharge)|0| -|[Time-Aware Attention-Based Transformer (TAAT) for Cloud Computing System Failure Prediction](https://doi.org/10.1145/3637528.3671547)|Lingfei Deng, Yunong Wang, Haoran Wang, Xuhua Ma, Xiaoming Du, Xudong Zheng, Dongrui Wu|Huazhong University of Science and Technology, Wuhan, China; Alibaba Cloud, Alibaba Group, Hangzhou, China|Log-based failure prediction helps identify and mitigate system failures ahead of time, increasing the reliability of cloud elastic computing systems. However, most existing log-based failure prediction approaches only focus on semantic information, and do not make full use of the information contained in the timestamps of log messages. This paper proposes time-aware attention-based transformer (TAAT), a failure prediction approach that extracts semantic and temporal information simultaneously from log messages and their timestamps. TAAT first tokenizes raw log messages into specific exceptions, and then performs: 1) exception sequence embedding that reorganizes the exceptions of each node as an ordered sequence and converts them to vectors; 2) time relation estimation that computes time relation matrices from the timestamps; and, 3) time-aware attention that computes semantic correlation matrices from the exception sequences and then combines them with time relation matrices. Experiments on Alibaba Cloud demonstrated that TAAT achieves an approximately 10% performance improvement compared with the state-of-the-art approaches. TAAT is now used in the daily operation of Alibaba Cloud. Moreover, this paper also releases the real-world cloud computing failure prediction dataset used in our study, which consists of about 2.7 billion syslogs from about 300,000 node controllers during a 4-month period. To our knowledge, this is the largest dataset of its kind, and is expected to be very useful to the community.|基于日志的故障预测有助于提前识别和减轻系统故障，提高云弹性计算系统的可靠性。然而，大多数现有的基于日志的故障预测方法只关注语义信息，并没有充分利用日志消息时间戳中包含的信息。提出了一种基于时间感知的注意力转换器(TAAT)的故障预测方法，该方法同时从日志消息及其时间戳中提取语义和时间信息。TAAT 首先将原始日志消息标记为特定的异常，然后执行: 1)异常序列嵌入，将每个节点的异常重组为有序序列，并将其转换为向量; 2)时间关系估计，从时间戳计算时间关系矩阵; 3)时间感知注意，从异常序列计算语义相关矩阵，然后将它们与时间关系矩阵组合。阿里巴巴云的实验表明，与最先进的方法相比，TAAT 的性能提高了大约10% 。TAAT 现在用于阿里巴巴云的日常运作。此外，本文还发布了我们研究中使用的实际云计算失败预测数据集，该数据集包括来自约30万个节点控制器的约27亿个 syslog，时间为4个月。据我们所知，这是同类中最大的数据集，预计将对社区非常有用。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Time-Aware+Attention-Based+Transformer+(TAAT)+for+Cloud+Computing+System+Failure+Prediction)|0| -|[FNSPID: A Comprehensive Financial News Dataset in Time Series](https://doi.org/10.1145/3637528.3671629)|Zihan Dong, Xinyu Fan, Zhiyuan Peng|North Carolina State University, Raleigh, NC, USA; SiChuan University, Chengdu, Sichuan Province, China|Financial market predictions utilize historical data to anticipate future stock prices and market trends. Traditionally, these predictions have focused on the statistical analysis of quantitative factors, such as stock prices, trading volumes, inflation rates, and changes in industrial production. Recent advancements in large language models motivate the integrated financial analysis of both sentiment data, particularly market news, and numerical factors. Nonetheless, this methodology frequently encounters constraints due to the paucity of extensive datasets that amalgamate both quantitative and qualitative sentiment analyses. To address this challenge, we introduce a large-scale financial dataset, namely, Financial News and Stock Price Integration Dataset (FNSPID). It comprises 29.7 million stock prices and 15.7 million time-aligned financial news records for 4,775 S&P500 companies, covering the period from 1999 to 2023, sourced from 4 stock market news websites. We demonstrate that FNSPID excels existing stock market datasets in scale and diversity while uniquely incorporating sentiment information. Through financial analysis experiments on FNSPID, we propose: (1) the dataset's size and quality significantly boost market prediction accuracy; (2) adding sentiment scores modestly enhances performance on the transformer-based model; (3) a reproducible procedure that can update the dataset. Completed work, code, documentation, and examples are available at this http URL. FNSPID offers unprecedented opportunities for the financial research community to advance predictive modeling and analysis.|金融市场预测利用历史数据来预测未来的股票价格和市场趋势。传统上，这些预测侧重于对数量因素的统计分析，如股票价格、交易量、通货膨胀率和工业生产的变化。大型语言模型的最新进展推动了情绪数据(尤其是市场新闻)和数字因素的综合财务分析。尽管如此，由于缺乏将定量和定性情绪分析结合起来的大量数据集，这种方法经常遇到限制。为了应对这一挑战，我们引入了一个大规模的金融数据集，即财经新闻和股票价格整合数据集(FNSPID)。该报告包括来自4个股市新闻网站的4775家标准普尔500指数成分股公司1999年至2023年期间的2970万股股价和1570万条与时间一致的财务新闻记录。我们证明，FNSPID 在规模和多样性方面优于现有的股票市场数据集，同时独特地结合了情绪信息。通过在 FNSPID 上的财务分析实验，我们提出: (1)数据集的规模和质量显著提高了市场预测的准确性; (2)增加情绪得分适度提高了基于变压器的模型的性能; (3)一个可重复的过程，可以更新数据集。已完成的工作、代码、文档和示例可在此 http URL 获得。FNSPID 为金融研究界提供了前所未有的机会来推进预测建模和分析。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=FNSPID:+A+Comprehensive+Financial+News+Dataset+in+Time+Series)|0| -|[Transportation Marketplace Rate Forecast Using Signature Transform](https://doi.org/10.1145/3637528.3671637)|Haotian Gu, Xin Guo, Timothy L. Jacobs, Philip M. Kaminsky, Xinyu Li|Worldwide Operations Research Science, Amazon.com Inc., Bellevue, WA, USA; Middle Mile Marketplace Science; University of California, Berkeley, Berkeley, CA, USA; University of California Department of Industrial Engineering & Operations Research|Freight transportation marketplace rates are typically challenging to forecast accurately. In this work, we have developed a novel statistical technique based on signature transforms and have built a predictive and adaptive model to forecast these marketplace rates. Our technique is based on two key elements of the signature transform: one being its universal nonlinearity property, which linearizes the feature space and hence translates the forecasting problem into linear regression, and the other being the signature kernel, which allows for comparing computationally efficiently similarities between time series data. Combined, it allows for efficient feature generation and precise identification of seasonality and regime switching in the forecasting process. An algorithm based on our technique has been deployed by Amazon trucking operations, with far superior forecast accuracy and better interpretability versus commercially available industry models, even during the COVID-19 pandemic and the Ukraine conflict. Furthermore, our technique is able to capture the influence of business cycles and the heterogeneity of the marketplace, improving prediction accuracy by more than fivefold, with an estimated annualized saving of \50 million.|货运市场利率通常难以准确预测。在这项工作中，我们开发了一种新的基于签名变换的统计技术，并建立了一个预测和自适应模型来预测这些市场价格。我们的技术基于签名变换的两个关键要素: 一个是它的通用非线性特性，它线性化了特征空间，从而将预测问题转化为线性回归; 另一个是签名核，它允许在计算上有效地比较时间序列数据之间的相似性。结合，它允许有效的特征生成和精确识别季节性和制度转换在预测过程中。基于我们的技术的算法已经在亚马逊的卡车运输业务中使用，即使在2019冠状病毒疾病大流行和乌克兰冲突期间，与商业化的行业模型相比，它也具有更高的预测准确性和更好的可解释性。此外，我们的技术能够捕捉商业周期和市场异质性的影响，提高预测的准确性五倍以上，估计每年节省5000万。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Transportation+Marketplace+Rate+Forecast+Using+Signature+Transform)|0| -|[Intelligent Agents with LLM-based Process Automation](https://doi.org/10.1145/3637528.3671646)|Yanchu Guan, Dong Wang, Zhixuan Chu, Shiyu Wang, Feiyue Ni, Ruihua Song, Chenyi Zhuang|Renmin University of China, Beijing, China; Ant Group, Hangzhou, China; Zhejiang University, Hangzhou, China|While intelligent virtual assistants like Siri, Alexa, and Google Assistant have become ubiquitous in modern life, they still face limitations in their ability to follow multi-step instructions and accomplish complex goals articulated in natural language. However, recent breakthroughs in large language models (LLMs) show promise for overcoming existing barriers by enhancing natural language processing and reasoning capabilities. Though promising, applying LLMs to create more advanced virtual assistants still faces challenges like ensuring robust performance and handling variability in real-world user commands. This paper proposes a novel LLM-based virtual assistant that can automatically perform multi-step operations within mobile apps based on high-level user requests. The system represents an advance in assistants by providing an end-to-end solution for parsing instructions, reasoning about goals, and executing actions. LLM-based Process Automation (LLMPA) has modules for decomposing instructions, generating descriptions, detecting interface elements, predicting next actions, and error checking. Experiments demonstrate the system completing complex mobile operation tasks in Alipay based on natural language instructions. This showcases how large language models can enable automated assistants to accomplish real-world tasks. The main contributions are the novel LLMPA architecture optimized for app process automation, the methodology for applying LLMs to mobile apps, and demonstrations of multi-step task completion in a real-world environment. Notably, this work represents the first real-world deployment and extensive evaluation of a large language model-based virtual assistant in a widely used mobile application with an enormous user base numbering in the hundreds of millions.|尽管像 Siri、 Alexa 和 Google Assistant 这样的智能虚拟助手在现代生活中已经无处不在，但它们在遵循多步指令和完成用自然语言表达的复杂目标方面仍然面临局限。然而，最近在大型语言模型(LLM)方面的突破表明，通过增强自然语言处理和推理能力，有望克服现有的障碍。虽然 LLM 很有前途，但是应用 LLM 来创建更高级的虚拟助理仍然面临诸如确保健壮的性能和处理现实世界中用户命令的可变性等挑战。提出了一种基于 LLM 的虚拟助手，该虚拟助手可以根据用户的高级请求在移动应用中自动执行多步操作。该系统通过提供解析指令、推理目标和执行操作的端到端解决方案来代表助手的进步。基于 LLM 的过程自动化(LLMPA)具有分解指令、生成描述、检测接口元素、预测下一步操作和错误检查的模块。实验表明，该系统基于自然语言指令在支付宝中完成了复杂的移动操作任务。这展示了大型语言模型如何使自动化助手能够完成真实世界的任务。主要贡献包括为应用程序过程自动化而优化的新型 LLMPA 架构，将 LLM 应用于移动应用程序的方法，以及在现实环境中多步骤任务完成的演示。值得注意的是，这项工作代表了第一个真实世界的部署和广泛的评估大型语言模型为基础的虚拟助手在一个广泛使用的移动应用程序与庞大的用户数以亿计。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Intelligent+Agents+with+LLM-based+Process+Automation)|0| +|[Time-Aware Attention-Based Transformer (TAAT) for Cloud Computing System Failure Prediction](https://doi.org/10.1145/3637528.3671547)|Lingfei Deng, Yunong Wang, Haoran Wang, Xuhua Ma, Xiaoming Du, Xudong Zheng, Dongrui Wu|Alibaba Cloud, Alibaba Group, Hangzhou, China; Huazhong University of Science and Technology, Wuhan, China|Log-based failure prediction helps identify and mitigate system failures ahead of time, increasing the reliability of cloud elastic computing systems. However, most existing log-based failure prediction approaches only focus on semantic information, and do not make full use of the information contained in the timestamps of log messages. This paper proposes time-aware attention-based transformer (TAAT), a failure prediction approach that extracts semantic and temporal information simultaneously from log messages and their timestamps. TAAT first tokenizes raw log messages into specific exceptions, and then performs: 1) exception sequence embedding that reorganizes the exceptions of each node as an ordered sequence and converts them to vectors; 2) time relation estimation that computes time relation matrices from the timestamps; and, 3) time-aware attention that computes semantic correlation matrices from the exception sequences and then combines them with time relation matrices. Experiments on Alibaba Cloud demonstrated that TAAT achieves an approximately 10% performance improvement compared with the state-of-the-art approaches. TAAT is now used in the daily operation of Alibaba Cloud. Moreover, this paper also releases the real-world cloud computing failure prediction dataset used in our study, which consists of about 2.7 billion syslogs from about 300,000 node controllers during a 4-month period. To our knowledge, this is the largest dataset of its kind, and is expected to be very useful to the community.|基于日志的故障预测有助于提前识别和减轻系统故障，提高云弹性计算系统的可靠性。然而，大多数现有的基于日志的故障预测方法只关注语义信息，并没有充分利用日志消息时间戳中包含的信息。提出了一种基于时间感知的注意力转换器(TAAT)的故障预测方法，该方法同时从日志消息及其时间戳中提取语义和时间信息。TAAT 首先将原始日志消息标记为特定的异常，然后执行: 1)异常序列嵌入，将每个节点的异常重组为有序序列，并将其转换为向量; 2)时间关系估计，从时间戳计算时间关系矩阵; 3)时间感知注意，从异常序列计算语义相关矩阵，然后将它们与时间关系矩阵组合。阿里巴巴云的实验表明，与最先进的方法相比，TAAT 的性能提高了大约10% 。TAAT 现在用于阿里巴巴云的日常运作。此外，本文还发布了我们研究中使用的实际云计算失败预测数据集，该数据集包括来自约30万个节点控制器的约27亿个 syslog，时间为4个月。据我们所知，这是同类中最大的数据集，预计将对社区非常有用。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Time-Aware+Attention-Based+Transformer+(TAAT)+for+Cloud+Computing+System+Failure+Prediction)|0| +|[FNSPID: A Comprehensive Financial News Dataset in Time Series](https://doi.org/10.1145/3637528.3671629)|Zihan Dong, Xinyu Fan, Zhiyuan Peng|SiChuan University, Chengdu, Sichuan Province, China; North Carolina State University, Raleigh, NC, USA|Financial market predictions utilize historical data to anticipate future stock prices and market trends. Traditionally, these predictions have focused on the statistical analysis of quantitative factors, such as stock prices, trading volumes, inflation rates, and changes in industrial production. Recent advancements in large language models motivate the integrated financial analysis of both sentiment data, particularly market news, and numerical factors. Nonetheless, this methodology frequently encounters constraints due to the paucity of extensive datasets that amalgamate both quantitative and qualitative sentiment analyses. To address this challenge, we introduce a large-scale financial dataset, namely, Financial News and Stock Price Integration Dataset (FNSPID). It comprises 29.7 million stock prices and 15.7 million time-aligned financial news records for 4,775 S&P500 companies, covering the period from 1999 to 2023, sourced from 4 stock market news websites. We demonstrate that FNSPID excels existing stock market datasets in scale and diversity while uniquely incorporating sentiment information. Through financial analysis experiments on FNSPID, we propose: (1) the dataset's size and quality significantly boost market prediction accuracy; (2) adding sentiment scores modestly enhances performance on the transformer-based model; (3) a reproducible procedure that can update the dataset. Completed work, code, documentation, and examples are available at this http URL. FNSPID offers unprecedented opportunities for the financial research community to advance predictive modeling and analysis.|金融市场预测利用历史数据来预测未来的股票价格和市场趋势。传统上，这些预测侧重于对数量因素的统计分析，如股票价格、交易量、通货膨胀率和工业生产的变化。大型语言模型的最新进展推动了情绪数据(尤其是市场新闻)和数字因素的综合财务分析。尽管如此，由于缺乏将定量和定性情绪分析结合起来的大量数据集，这种方法经常遇到限制。为了应对这一挑战，我们引入了一个大规模的金融数据集，即财经新闻和股票价格整合数据集(FNSPID)。该报告包括来自4个股市新闻网站的4775家标准普尔500指数成分股公司1999年至2023年期间的2970万股股价和1570万条与时间一致的财务新闻记录。我们证明，FNSPID 在规模和多样性方面优于现有的股票市场数据集，同时独特地结合了情绪信息。通过在 FNSPID 上的财务分析实验，我们提出: (1)数据集的规模和质量显著提高了市场预测的准确性; (2)增加情绪得分适度提高了基于变压器的模型的性能; (3)一个可重复的过程，可以更新数据集。已完成的工作、代码、文档和示例可在此 http URL 获得。FNSPID 为金融研究界提供了前所未有的机会来推进预测建模和分析。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=FNSPID:+A+Comprehensive+Financial+News+Dataset+in+Time+Series)|0| +|[Transportation Marketplace Rate Forecast Using Signature Transform](https://doi.org/10.1145/3637528.3671637)|Haotian Gu, Xin Guo, Timothy L. Jacobs, Philip M. Kaminsky, Xinyu Li|Middle Mile Marketplace Science; University of California, Berkeley, Berkeley, CA, USA; University of California Department of Industrial Engineering & Operations Research; Worldwide Operations Research Science, Amazon.com Inc., Bellevue, WA, USA|Freight transportation marketplace rates are typically challenging to forecast accurately. In this work, we have developed a novel statistical technique based on signature transforms and have built a predictive and adaptive model to forecast these marketplace rates. Our technique is based on two key elements of the signature transform: one being its universal nonlinearity property, which linearizes the feature space and hence translates the forecasting problem into linear regression, and the other being the signature kernel, which allows for comparing computationally efficiently similarities between time series data. Combined, it allows for efficient feature generation and precise identification of seasonality and regime switching in the forecasting process. An algorithm based on our technique has been deployed by Amazon trucking operations, with far superior forecast accuracy and better interpretability versus commercially available industry models, even during the COVID-19 pandemic and the Ukraine conflict. Furthermore, our technique is able to capture the influence of business cycles and the heterogeneity of the marketplace, improving prediction accuracy by more than fivefold, with an estimated annualized saving of \50 million.|货运市场利率通常难以准确预测。在这项工作中，我们开发了一种新的基于签名变换的统计技术，并建立了一个预测和自适应模型来预测这些市场价格。我们的技术基于签名变换的两个关键要素: 一个是它的通用非线性特性，它线性化了特征空间，从而将预测问题转化为线性回归; 另一个是签名核，它允许在计算上有效地比较时间序列数据之间的相似性。结合，它允许有效的特征生成和精确识别季节性和制度转换在预测过程中。基于我们的技术的算法已经在亚马逊的卡车运输业务中使用，即使在2019冠状病毒疾病大流行和乌克兰冲突期间，与商业化的行业模型相比，它也具有更高的预测准确性和更好的可解释性。此外，我们的技术能够捕捉商业周期和市场异质性的影响，提高预测的准确性五倍以上，估计每年节省5000万。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Transportation+Marketplace+Rate+Forecast+Using+Signature+Transform)|0| +|[Intelligent Agents with LLM-based Process Automation](https://doi.org/10.1145/3637528.3671646)|Yanchu Guan, Dong Wang, Zhixuan Chu, Shiyu Wang, Feiyue Ni, Ruihua Song, Chenyi Zhuang|Ant Group, Hangzhou, China; Renmin University of China, Beijing, China; Zhejiang University, Hangzhou, China|While intelligent virtual assistants like Siri, Alexa, and Google Assistant have become ubiquitous in modern life, they still face limitations in their ability to follow multi-step instructions and accomplish complex goals articulated in natural language. However, recent breakthroughs in large language models (LLMs) show promise for overcoming existing barriers by enhancing natural language processing and reasoning capabilities. Though promising, applying LLMs to create more advanced virtual assistants still faces challenges like ensuring robust performance and handling variability in real-world user commands. This paper proposes a novel LLM-based virtual assistant that can automatically perform multi-step operations within mobile apps based on high-level user requests. The system represents an advance in assistants by providing an end-to-end solution for parsing instructions, reasoning about goals, and executing actions. LLM-based Process Automation (LLMPA) has modules for decomposing instructions, generating descriptions, detecting interface elements, predicting next actions, and error checking. Experiments demonstrate the system completing complex mobile operation tasks in Alipay based on natural language instructions. This showcases how large language models can enable automated assistants to accomplish real-world tasks. The main contributions are the novel LLMPA architecture optimized for app process automation, the methodology for applying LLMs to mobile apps, and demonstrations of multi-step task completion in a real-world environment. Notably, this work represents the first real-world deployment and extensive evaluation of a large language model-based virtual assistant in a widely used mobile application with an enormous user base numbering in the hundreds of millions.|尽管像 Siri、 Alexa 和 Google Assistant 这样的智能虚拟助手在现代生活中已经无处不在，但它们在遵循多步指令和完成用自然语言表达的复杂目标方面仍然面临局限。然而，最近在大型语言模型(LLM)方面的突破表明，通过增强自然语言处理和推理能力，有望克服现有的障碍。虽然 LLM 很有前途，但是应用 LLM 来创建更高级的虚拟助理仍然面临诸如确保健壮的性能和处理现实世界中用户命令的可变性等挑战。提出了一种基于 LLM 的虚拟助手，该虚拟助手可以根据用户的高级请求在移动应用中自动执行多步操作。该系统通过提供解析指令、推理目标和执行操作的端到端解决方案来代表助手的进步。基于 LLM 的过程自动化(LLMPA)具有分解指令、生成描述、检测接口元素、预测下一步操作和错误检查的模块。实验表明，该系统基于自然语言指令在支付宝中完成了复杂的移动操作任务。这展示了大型语言模型如何使自动化助手能够完成真实世界的任务。主要贡献包括为应用程序过程自动化而优化的新型 LLMPA 架构，将 LLM 应用于移动应用程序的方法，以及在现实环境中多步骤任务完成的演示。值得注意的是，这项工作代表了第一个真实世界的部署和广泛的评估大型语言模型为基础的虚拟助手在一个广泛使用的移动应用程序与庞大的用户数以亿计。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Intelligent+Agents+with+LLM-based+Process+Automation)|0| |[SentHYMNent: An Interpretable and Sentiment-Driven Model for Algorithmic Melody Harmonization](https://doi.org/10.1145/3637528.3671626)|Stephen Hahn, Jerry Yin, Rico Zhu, Weihan Xu, Yue Jiang, Simon Mak, Cynthia Rudin|Duke University, Durham, NC, USA|Music composition and analysis is an inherently creative task, involving a combination of heart and mind. However, the vast majority of algorithmic music models completely ignore the "heart" component of music, resulting in output that often lacks the rich emotional direction found in human-composed music. Models that try to incorporate musical sentiment rely on a "valence-arousal" model, which insufficiently characterizes emotion in two dimensions. Furthermore, existing methods typically adopt a black-box, music agnostic approach, treating music-theoretical and sentimental understanding as a by-product that can be inferred given sufficient data. In this study, we introduce two major novel elements: a nuanced mixture-based representation for musical sentiment, including a web tool to gather data, as well as a sentiment- and theory-driven harmonization model, SentHYMNent. SentHYMNent employs a novel Hidden Markov Model based on both key and chord transitions, as well as sentiment mixtures, to provide a probabilistic framework for learning key modulations and chordal progressions from a given melodic line and sentiment. Furthermore, our approach leverages compositional principles, resulting in a simpler model that significantly reduces computational burden and enhances interpretability compared to current state-of-the-art algorithmic harmonization methods. Importantly, as shown in our experiments, these improvements do not come at the expense of harmonization quality. We also provide a web app where users can upload their own melodies for SentHYMNent to harmonize.|音乐创作和分析是一项内在的创造性任务，涉及心灵和思想的结合。然而，绝大多数的算法音乐模型完全忽略了音乐的“心脏”部分，导致输出往往缺乏丰富的情感方向发现人类作曲的音乐。试图融合音乐情感的模型依赖于“价觉-唤醒”模型，该模型没有充分描述情感的两个维度。此外，现有的方法通常采用黑箱，音乐不可知论的方法，把音乐理论和情感的理解作为一个副产品，可以推断给予足够的数据。在这项研究中，我们介绍了两个主要的小说元素: 一个微妙的混合为基础的音乐情感表示，包括一个网络工具收集数据，以及情感和理论驱动的协调模型，SentHYMNent。SentHYMNent 采用了一种新颖的隐马尔可夫模型，基于键和和弦的过渡，以及情绪的混合，提供了一个概率框架来学习关键调制和和弦进展从一个给定的旋律线和情绪。此外，我们的方法利用组合原则，导致一个更简单的模型，大大减少计算负担和提高可解释性相比，目前的国家最先进的算法协调方法。重要的是，正如我们的实验所显示的那样，这些改进并不以牺牲协调质量为代价。我们还提供了一个网络应用程序，用户可以上传自己的旋律来协调 SentHYMNent。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=SentHYMNent:+An+Interpretable+and+Sentiment-Driven+Model+for+Algorithmic+Melody+Harmonization)|0| -|[FedSecurity: A Benchmark for Attacks and Defenses in Federated Learning and Federated LLMs](https://doi.org/10.1145/3637528.3671545)|Shanshan Han, Baturalp Buyukates, Zijian Hu, Han Jin, Weizhao Jin, Lichao Sun, Xiaoyang Wang, Wenxuan Wu, Chulin Xie, Yuhang Yao, Kai Zhang, Qifan Zhang, Yuhui Zhang, Carlee JoeWong, Salman Avestimehr, Chaoyang He|University of Southern California, Los Angeles, CA, USA; Carnegie Mellon University, Pittsburgh, PA, USA; TensorOpera Inc., Palo Alto, CA, USA; Texas A&M University, College Station, TX, USA; University of California, Irvine, Irvine, CA, USA; Zhejiang University, Hangzhou, China; Lehigh University, Bethlehem, PA, USA; UIUC, Urbana, IL, USA|This paper introduces FedSecurity, an end-to-end benchmark that serves as asupplementary component of the FedML library for simulating adversarial attacksand corresponding defense mechanisms in Federated Learning (FL). FedSecurityeliminates the need for implementing the fundamental FL procedures, e.g., FLtraining and data loading, from scratch, thus enables users to focus ondeveloping their own attack and defense strategies. It contains two keycomponents, including FedAttacker that conducts a variety of attacks during FLtraining, and FedDefender that implements defensive mechanisms to counteractthese attacks. FedSecurity has the following features: i) It offers extensivecustomization options to accommodate a broad range of machine learning models(e.g., Logistic Regression, ResNet, and GAN) and FL optimizers (e.g., FedAVG,FedOPT, and FedNOVA); ii) it enables exploring the effectiveness of attacks anddefenses across different datasets and models; and iii) it supports flexibleconfiguration and customization through a configuration file and some APIs. Wefurther demonstrate FedSecurity's utility and adaptability through federatedtraining of Large Language Models (LLMs) to showcase its potential on a widerange of complex applications.|本文介绍了 FedSecurity，这是一个端到端的基准，它作为 FedML 库的补充组件，用于模拟 FL 中的敌对攻击和相应的防御机制。FedSecurity 消除了从头开始实现基本的 FL 过程的需要，例如，FLtraining 和数据加载，从而使用户能够专注于开发自己的攻击和防御策略。它包含两个关键组件，包括在 FLtraining 中进行各种攻击的 FedAtacker 和实现防御机制来抵抗这些攻击的 FedDefender。FedSecurity 有以下特点: i)它提供广泛的定制选项，以适应广泛的机器学习模型(例如，Logit模型、 ResNet 和 GAN)和 FL 优化器(例如，FedAVG、 FedOPT 和 FedNOVA) ; ii)它能够探索不同数据集和模型的攻击和防御的有效性; iii)它通过配置文件和一些 API 支持灵活的配置和定制。通过对大型语言模型(LLM)的联合培训，进一步展示了 FedSecurity 的实用性和适应性，以展示其在广泛的复杂应用程序中的潜力。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=FedSecurity:+A+Benchmark+for+Attacks+and+Defenses+in+Federated+Learning+and+Federated+LLMs)|0| -|[Paths2Pair: Meta-path Based Link Prediction in Billion-Scale Commercial Heterogeneous Graphs](https://doi.org/10.1145/3637528.3671563)|Jinquan Hang, Zhiqing Hong, Xinyue Feng, Guang Wang, Guang Yang, Feng Li, Xining Song, Desheng Zhang|Florida State University, Tallahassee, FL, USA; JD Logistics & Rutgers University, Beijing, China; JD Logistics, Beijing, China; Rutgers University, Piscataway, NJ, USA|Link prediction, determining if a relation exists between two entities, is an essential task in the analysis of heterogeneous graphs with diverse entities and relations. Despite extensive research in link prediction, most existing works focus on predicting the relation type between given pairs of entities. However, it is almost impractical to check every entity pair when trying to find most hidden relations in a billion-scale heterogeneous graph due to the billion squared number of possible pairs. Meanwhile, most methods aggregate information at the node level, potentially leading to the loss of direct connection information between the two nodes. In this paper, we introduce Paths2Pair, a novel framework to address these limitations for link prediction in billion-scale commercial heterogeneous graphs. (i) First, it selects a subset of reliable entity pairs for prediction based on relevant meta-paths. (ii) Then, it utilizes various types of content information from the meta-paths between each selected entity pair to predict whether a target relation exists. We first evaluate our Paths2Pair based on a large-scale dataset, and results show Paths2Pair outperforms state-of-the-art baselines significantly. We then deploy our Paths2Pair on JD Logistics, one of the largest logistics companies in the world, for business expansion. The uncovered relations by Paths2Pair have helped JD Logistics identify 108,709 contacts to attract new company customers, resulting in an 84% increase in the success rate compared to the state-of-the-practice solution, demonstrating the practical value of our framework. We have released the code of our framework at https://github.com/JQHang/Paths2Pair.|链接预测是确定两个实体之间是否存在关系的一个基本问题，是分析具有不同实体和关系的异构图的一个基本问题。尽管在链接预测方面进行了广泛的研究，但现有的工作大多集中于预测给定对实体之间的关系类型。然而，在十亿尺度的异构图中，由于每个实体对的平方数是十亿个，因此在寻找最隐藏的关系时，检查每个实体对几乎是不切实际的。同时，大多数方法在节点级聚合信息，这可能导致两个节点之间直接连接信息的丢失。在本文中，我们介绍 Paths2Pair，一个新的框架，以解决这些限制，链接预测在十亿尺度的商业异构图。(i)首先根据相关元路径选择可靠实体对的子集进行预测。然后，利用每个选定实体对之间的元路径中的各种类型的内容信息来预测目标关系是否存在。我们首先基于大规模数据集评估 Paths2Pair，结果显示 Paths2Pair 的性能明显优于最先进的基线。然后，我们将 Paths2Pair 部署到 JD 物流公司，这是世界上最大的物流公司之一，用于业务扩展。Paths2Pair 发现的关系已经帮助 JD 物流确定了108,709个联系人来吸引新的公司客户，结果与实践中的解决方案相比，成功率提高了84% ，证明了我们框架的实用价值。我们已经在 https://github.com/jqhang/paths2pair 发布了框架代码。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Paths2Pair:+Meta-path+Based+Link+Prediction+in+Billion-Scale+Commercial+Heterogeneous+Graphs)|0| -|[Distributed Harmonization: Federated Clustered Batch Effect Adjustment and Generalization](https://doi.org/10.1145/3637528.3671590)|Bao Hoang, Yijiang Pang, Siqi Liang, Liang Zhan, Paul M. Thompson, Jiayu Zhou|University of Pittsburgh, Pittsburgh, Pennsylvania, USA; Michigan State University, East Lansing, Michigan, USA; University of Southern California, Los Angeles, California, USA|Independent and identically distributed (i.i.d.) data is essential to many data analysis and modeling techniques. In the medical domain, collecting data from multiple sites or institutions is a common strategy that guarantees sufficient clinical diversity, determined by the decentralized nature of medical data. However, data from various sites are easily biased by the local environment or facilities, thereby violating the i.i.d. rule. A common strategy is to harmonize the site bias while retaining important biological information. The ComBat is among the most popular harmonization approaches and has recently been extended to handle distributed sites. However, when faced with situations involving newly joined sites in training or evaluating data from unknown/unseen sites, ComBat lacks compatibility and requires retraining with data from all the sites. The retraining leads to significant computational and logistic overhead that is usually prohibitive. In this work, we develop a novel Cluster ComBat harmonization algorithm, which leverages cluster patterns of the data in different sites and greatly advances the usability of ComBat harmonization. We use extensive simulation and real medical imaging data from ADNI to demonstrate the superiority of the proposed approach. Our codes are provided in https://github.com/illidanlab/distributed-cluster-harmonization.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Distributed+Harmonization:+Federated+Clustered+Batch+Effect+Adjustment+and+Generalization)|0| +|[FedSecurity: A Benchmark for Attacks and Defenses in Federated Learning and Federated LLMs](https://doi.org/10.1145/3637528.3671545)|Shanshan Han, Baturalp Buyukates, Zijian Hu, Han Jin, Weizhao Jin, Lichao Sun, Xiaoyang Wang, Wenxuan Wu, Chulin Xie, Yuhang Yao, Kai Zhang, Qifan Zhang, Yuhui Zhang, Carlee JoeWong, Salman Avestimehr, Chaoyang He|University of Southern California, Los Angeles, CA, USA; University of California, Irvine, Irvine, CA, USA; TensorOpera Inc., Palo Alto, CA, USA; Zhejiang University, Hangzhou, China; Carnegie Mellon University, Pittsburgh, PA, USA; UIUC, Urbana, IL, USA; Lehigh University, Bethlehem, PA, USA; Texas A&M University, College Station, TX, USA|This paper introduces FedSecurity, an end-to-end benchmark that serves as asupplementary component of the FedML library for simulating adversarial attacksand corresponding defense mechanisms in Federated Learning (FL). FedSecurityeliminates the need for implementing the fundamental FL procedures, e.g., FLtraining and data loading, from scratch, thus enables users to focus ondeveloping their own attack and defense strategies. It contains two keycomponents, including FedAttacker that conducts a variety of attacks during FLtraining, and FedDefender that implements defensive mechanisms to counteractthese attacks. FedSecurity has the following features: i) It offers extensivecustomization options to accommodate a broad range of machine learning models(e.g., Logistic Regression, ResNet, and GAN) and FL optimizers (e.g., FedAVG,FedOPT, and FedNOVA); ii) it enables exploring the effectiveness of attacks anddefenses across different datasets and models; and iii) it supports flexibleconfiguration and customization through a configuration file and some APIs. Wefurther demonstrate FedSecurity's utility and adaptability through federatedtraining of Large Language Models (LLMs) to showcase its potential on a widerange of complex applications.|本文介绍了 FedSecurity，这是一个端到端的基准，它作为 FedML 库的补充组件，用于模拟 FL 中的敌对攻击和相应的防御机制。FedSecurity 消除了从头开始实现基本的 FL 过程的需要，例如，FLtraining 和数据加载，从而使用户能够专注于开发自己的攻击和防御策略。它包含两个关键组件，包括在 FLtraining 中进行各种攻击的 FedAtacker 和实现防御机制来抵抗这些攻击的 FedDefender。FedSecurity 有以下特点: i)它提供广泛的定制选项，以适应广泛的机器学习模型(例如，Logit模型、 ResNet 和 GAN)和 FL 优化器(例如，FedAVG、 FedOPT 和 FedNOVA) ; ii)它能够探索不同数据集和模型的攻击和防御的有效性; iii)它通过配置文件和一些 API 支持灵活的配置和定制。通过对大型语言模型(LLM)的联合培训，进一步展示了 FedSecurity 的实用性和适应性，以展示其在广泛的复杂应用程序中的潜力。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=FedSecurity:+A+Benchmark+for+Attacks+and+Defenses+in+Federated+Learning+and+Federated+LLMs)|0| +|[Paths2Pair: Meta-path Based Link Prediction in Billion-Scale Commercial Heterogeneous Graphs](https://doi.org/10.1145/3637528.3671563)|Jinquan Hang, Zhiqing Hong, Xinyue Feng, Guang Wang, Guang Yang, Feng Li, Xining Song, Desheng Zhang|JD Logistics, Beijing, China; Florida State University, Tallahassee, FL, USA; Rutgers University, Piscataway, NJ, USA; JD Logistics & Rutgers University, Beijing, China|Link prediction, determining if a relation exists between two entities, is an essential task in the analysis of heterogeneous graphs with diverse entities and relations. Despite extensive research in link prediction, most existing works focus on predicting the relation type between given pairs of entities. However, it is almost impractical to check every entity pair when trying to find most hidden relations in a billion-scale heterogeneous graph due to the billion squared number of possible pairs. Meanwhile, most methods aggregate information at the node level, potentially leading to the loss of direct connection information between the two nodes. In this paper, we introduce Paths2Pair, a novel framework to address these limitations for link prediction in billion-scale commercial heterogeneous graphs. (i) First, it selects a subset of reliable entity pairs for prediction based on relevant meta-paths. (ii) Then, it utilizes various types of content information from the meta-paths between each selected entity pair to predict whether a target relation exists. We first evaluate our Paths2Pair based on a large-scale dataset, and results show Paths2Pair outperforms state-of-the-art baselines significantly. We then deploy our Paths2Pair on JD Logistics, one of the largest logistics companies in the world, for business expansion. The uncovered relations by Paths2Pair have helped JD Logistics identify 108,709 contacts to attract new company customers, resulting in an 84% increase in the success rate compared to the state-of-the-practice solution, demonstrating the practical value of our framework. We have released the code of our framework at https://github.com/JQHang/Paths2Pair.|链接预测是确定两个实体之间是否存在关系的一个基本问题，是分析具有不同实体和关系的异构图的一个基本问题。尽管在链接预测方面进行了广泛的研究，但现有的工作大多集中于预测给定对实体之间的关系类型。然而，在十亿尺度的异构图中，由于每个实体对的平方数是十亿个，因此在寻找最隐藏的关系时，检查每个实体对几乎是不切实际的。同时，大多数方法在节点级聚合信息，这可能导致两个节点之间直接连接信息的丢失。在本文中，我们介绍 Paths2Pair，一个新的框架，以解决这些限制，链接预测在十亿尺度的商业异构图。(i)首先根据相关元路径选择可靠实体对的子集进行预测。然后，利用每个选定实体对之间的元路径中的各种类型的内容信息来预测目标关系是否存在。我们首先基于大规模数据集评估 Paths2Pair，结果显示 Paths2Pair 的性能明显优于最先进的基线。然后，我们将 Paths2Pair 部署到 JD 物流公司，这是世界上最大的物流公司之一，用于业务扩展。Paths2Pair 发现的关系已经帮助 JD 物流确定了108,709个联系人来吸引新的公司客户，结果与实践中的解决方案相比，成功率提高了84% ，证明了我们框架的实用价值。我们已经在 https://github.com/jqhang/paths2pair 发布了框架代码。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Paths2Pair:+Meta-path+Based+Link+Prediction+in+Billion-Scale+Commercial+Heterogeneous+Graphs)|0| +|[Distributed Harmonization: Federated Clustered Batch Effect Adjustment and Generalization](https://doi.org/10.1145/3637528.3671590)|Bao Hoang, Yijiang Pang, Siqi Liang, Liang Zhan, Paul M. Thompson, Jiayu Zhou|Michigan State University, East Lansing, Michigan, USA; University of Southern California, Los Angeles, California, USA; University of Pittsburgh, Pittsburgh, Pennsylvania, USA|Independent and identically distributed (i.i.d.) data is essential to many data analysis and modeling techniques. In the medical domain, collecting data from multiple sites or institutions is a common strategy that guarantees sufficient clinical diversity, determined by the decentralized nature of medical data. However, data from various sites are easily biased by the local environment or facilities, thereby violating the i.i.d. rule. A common strategy is to harmonize the site bias while retaining important biological information. The ComBat is among the most popular harmonization approaches and has recently been extended to handle distributed sites. However, when faced with situations involving newly joined sites in training or evaluating data from unknown/unseen sites, ComBat lacks compatibility and requires retraining with data from all the sites. The retraining leads to significant computational and logistic overhead that is usually prohibitive. In this work, we develop a novel Cluster ComBat harmonization algorithm, which leverages cluster patterns of the data in different sites and greatly advances the usability of ComBat harmonization. We use extensive simulation and real medical imaging data from ADNI to demonstrate the superiority of the proposed approach. Our codes are provided in https://github.com/illidanlab/distributed-cluster-harmonization.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Distributed+Harmonization:+Federated+Clustered+Batch+Effect+Adjustment+and+Generalization)|0| |[Explainable and Interpretable Forecasts on Non-Smooth Multivariate Time Series for Responsible Gameplay](https://doi.org/10.1145/3637528.3671657)|Hussain Jagirdar, Rukma Talwadker, Aditya Pareek, Pulkit Agrawal, Tridib Mukherjee|Games24x7, Bengaluru, India|Multi-variate Time Series (MTS) forecasting has made large strides (with very negligible errors) through recent advancements in neural networks, e.g., Transformers. However, in critical situations like predicting gaming overindulgence that affects one's mental well-being; an accurate forecast without a contributing evidence (explanation) is irrelevant. Hence, it becomes important that the forecasts are Interpretable - intermediate representation of the forecasted trajectory is comprehensible; as well as Explainable - attentive input features and events are accessible for a personalized and timely intervention of players at risk. While the contributing state of the art research on interpretability primarily focuses on temporally-smooth single-process driven time series data, our online multi-player gameplay data demonstrates intractable temporal randomness due to intrinsic orthogonality between player's game outcome and their intent to engage further. We introduce a novel deep Actionable Forecasting Network (AFN), which addresses the inter-dependent challenges associated with three exclusive objectives - 1) forecasting accuracy; 2) smooth comprehensible trajectory and 3) explanations via multi-dimensional input features while tackling the challenges introduced by our non-smooth temporal data, together in one single solution. AFN establishes a new benchmark via: (i) achieving 25% improvement on the MSE of the forecasts on player data in comparison to the SOM-VAE based SOTA networks; (ii) attributing unfavourable progression of a player's time series to a specific future time step(s), with the premise of eliminating near-future overindulgent player volume by over 18% with player specific actionable inputs feature(s) and (iii) proactively detecting over 23% (100% jump from SOTA) of the to-be overindulgent, players on an average, 4 weeks in advance.|多变量时间序列(MTS)预测通过神经网络(如变压器)的最新进展已经取得了长足的进步(误差非常小)。然而，在一些关键情况下，比如预测游戏过度会影响一个人的心理健康，没有证据(解释)的准确预测是无关紧要的。因此，重要的是，预测是可解释的——预测轨迹的中间表示是可以理解的; 以及可解释的——注意输入特征和事件是可以获得的，以便风险行为者个性化和及时干预。虽然可解释性的最新研究主要集中在时间平稳的单进程驱动的时间序列数据上，但我们的在线多玩家游戏数据表明，由于玩家的游戏结果与其进一步参与的意图之间的内在正交性，难以处理的时间随机性。我们引入了一个新的深度可操作预测网络(AFN) ，它解决了与三个独有的目标相关的相互依赖的挑战: 1)预测精度; 2)平滑可理解的轨迹和3)通过多维输入特征的解释，同时解决我们的非平滑时间数据引入的挑战，在一个单一的解决方案。AFN 通过以下方式建立了一个新的基准: (i)与基于 SOM-VAE 的 SOTA 网络相比，玩家数据预测的 MSE 提高了25% ; (ii)将玩家时间序列的不利进展归因于特定的未来时间步骤，前提是消除近期过度放纵的玩家数量超过18% ，具有玩家特定的可操作输入特征; (iii)主动检测超过23% (从 SOTA 跳跃100%)的未来过度放纵的玩家，平均提前4周。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Explainable+and+Interpretable+Forecasts+on+Non-Smooth+Multivariate+Time+Series+for+Responsible+Gameplay)|0| -|[Decomposed Attention Segment Recurrent Neural Network for Orbit Prediction](https://doi.org/10.1145/3637528.3671546)|Seungwon Jeong, Soyeon Woo, Daewon Chung, Simon S. Woo, Youjin Shin|Computer Science & Engineering Department, Sungkyunkwan University, Suwon, Republic of Korea; The Catholic University of Korea, Bucheon, Republic of Korea; National Satellite Operation Center Korea Aerospace Research Institute, Daejeon, Republic of Korea; Sejong University, Seoul, Republic of Korea|As the focus of space exploration shifts from national agencies to private companies, the interest in space industry has been steadily increasing. With the increasing number of satellites, the risk of collisions between satellites and space debris has escalated, potentially leading to significant property and human losses. Therefore, accurately modeling the orbit is critical for satellite operations. In this work, we propose the Decomposed Attention Segment Recurrent Neural Network (DASR) model, adding two key components, Multi-Head Attention and Tensor Train Decomposition, to SegRNN for orbit prediction. The DASR model applies Multi-Head Attention before segmenting at input data and before the input of the GRU layers. In addition, Tensor Train (TT) Decomposition is applied to the weight matrices of the Multi-Head Attention in both the encoder and decoder. For evaluation, we use three real-world satellite datasets from the Korea Aerospace Research Institute (KARI), which are currently operating: KOMPSAT-3, KOMPSAT-3A, and KOMPSAT-5 satellites. Our proposed model demonstrates superior performance compared to other SOTA baseline models. We demonstrate that our approach has 94.13% higher predictive performance than the second-best model in the KOMPSAT-3 dataset, 89.79% higher in the KOMPSAT-3A dataset, and 76.71% higher in the KOMPSAT-5 dataset.|随着空间探索的重点从国家机构转移到私营公司，人们对空间工业的兴趣一直在稳步增加。随着卫星数量的增加，卫星与空间碎片碰撞的风险已经升级，可能导致重大财产和人员损失。因此，精确的轨道建模对卫星运行至关重要。在这项工作中，我们提出了分解注意分段递归神经网络(DASR)模型，在 SegRNN 中加入两个关键组件: 多头注意和张量列分解，用于轨道预测。DASR 模型在分割输入数据和 GRU 层输入数据之前应用多头注意。此外，本文还将张量训练分解应用于编码器和解码器中的多头注意权矩阵。为了进行评估，我们使用了来自韩国航空宇宙研究院(kARI)的三个现实世界卫星数据集，它们目前正在运行: KOMPSAT-3、 KOMPSAT-3a 和 KOMPSAT-5卫星。与其他 SOTA 基线模型相比，我们提出的模型具有更好的性能。我们证明，我们的方法比 KOMPSAT-3数据集中的第二最佳模型具有94.13% 的预测性能，KOMPSAT-3A 数据集中的预测性能高出89.79% ，KOMPSAT-5数据集中的预测性能高出76.71% 。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Decomposed+Attention+Segment+Recurrent+Neural+Network+for+Orbit+Prediction)|0| +|[Decomposed Attention Segment Recurrent Neural Network for Orbit Prediction](https://doi.org/10.1145/3637528.3671546)|Seungwon Jeong, Soyeon Woo, Daewon Chung, Simon S. Woo, Youjin Shin|Sejong University, Seoul, Republic of Korea; The Catholic University of Korea, Bucheon, Republic of Korea; National Satellite Operation Center Korea Aerospace Research Institute, Daejeon, Republic of Korea; Computer Science & Engineering Department, Sungkyunkwan University, Suwon, Republic of Korea|As the focus of space exploration shifts from national agencies to private companies, the interest in space industry has been steadily increasing. With the increasing number of satellites, the risk of collisions between satellites and space debris has escalated, potentially leading to significant property and human losses. Therefore, accurately modeling the orbit is critical for satellite operations. In this work, we propose the Decomposed Attention Segment Recurrent Neural Network (DASR) model, adding two key components, Multi-Head Attention and Tensor Train Decomposition, to SegRNN for orbit prediction. The DASR model applies Multi-Head Attention before segmenting at input data and before the input of the GRU layers. In addition, Tensor Train (TT) Decomposition is applied to the weight matrices of the Multi-Head Attention in both the encoder and decoder. For evaluation, we use three real-world satellite datasets from the Korea Aerospace Research Institute (KARI), which are currently operating: KOMPSAT-3, KOMPSAT-3A, and KOMPSAT-5 satellites. Our proposed model demonstrates superior performance compared to other SOTA baseline models. We demonstrate that our approach has 94.13% higher predictive performance than the second-best model in the KOMPSAT-3 dataset, 89.79% higher in the KOMPSAT-3A dataset, and 76.71% higher in the KOMPSAT-5 dataset.|随着空间探索的重点从国家机构转移到私营公司，人们对空间工业的兴趣一直在稳步增加。随着卫星数量的增加，卫星与空间碎片碰撞的风险已经升级，可能导致重大财产和人员损失。因此，精确的轨道建模对卫星运行至关重要。在这项工作中，我们提出了分解注意分段递归神经网络(DASR)模型，在 SegRNN 中加入两个关键组件: 多头注意和张量列分解，用于轨道预测。DASR 模型在分割输入数据和 GRU 层输入数据之前应用多头注意。此外，本文还将张量训练分解应用于编码器和解码器中的多头注意权矩阵。为了进行评估，我们使用了来自韩国航空宇宙研究院(kARI)的三个现实世界卫星数据集，它们目前正在运行: KOMPSAT-3、 KOMPSAT-3a 和 KOMPSAT-5卫星。与其他 SOTA 基线模型相比，我们提出的模型具有更好的性能。我们证明，我们的方法比 KOMPSAT-3数据集中的第二最佳模型具有94.13% 的预测性能，KOMPSAT-3A 数据集中的预测性能高出89.79% ，KOMPSAT-5数据集中的预测性能高出76.71% 。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Decomposed+Attention+Segment+Recurrent+Neural+Network+for+Orbit+Prediction)|0| |[RJUA-MedDQA: A Multimodal Benchmark for Medical Document Question Answering and Clinical Reasoning](https://doi.org/10.1145/3637528.3671644)|Congyun Jin, Ming Zhang, Weixiao Ma, Yujiao Li, Yingbo Wang, Yabo Jia, Yuliang Du, Tao Sun, Haowen Wang, Cong Fan, Jinjie Gu, Chenfei Chi, Xiangguo Lv, Fangzhou Li, Wei Xue, Yiran Huang|; Ant Group, Shanghai, China; Ant Group, Hangzhou, China; Shanghai Jiao Tong University School of Medicine Affiliated Renji Hospital, Qingdao, Shandong, China; Shanghai Jiao Tong University School of Medicine Affiliated Renji Hospital, Shanghai, China|Recent advancements in Large Language Models (LLMs) and Large Multi-modal Models (LMMs) have shown potential in various medical applications, such as Intelligent Medical Diagnosis. Although impressive results have been achieved, we find that existing benchmarks do not reflect the complexity of real medical reports and specialized in-depth reasoning capabilities. In this work, we establish a comprehensive benchmark in the field of medical specialization and introduced RJUA-MedDQA, which contains 2000 real-world Chinese medical report images poses several challenges: comprehensively interpreting imgage content across a wide variety of challenging layouts, possessing the numerical reasoning ability to identify abnormal indicators and demonstrating robust clinical reasoning ability to provide the statement of disease diagnosis, status and advice based on a collection of medical contexts. We carefully design the data generation pipeline and proposed the Efficient Structural Restoration Annotation (ESRA) Method, aimed at restoring textual and tabular content in medical report images. This method substantially enhances annotation efficiency, doubling the productivity of each annotator, and yields a 26.8% improvement in accuracy. We conduct extensive evaluations, including few-shot assessments of 5 LMMs which are capable of solving Chinese medical QA tasks. To further investigate the limitations and potential of current LMMs, we conduct comparative experiments on a set of strong LLMs by using image-text generated by ESRA method. We report the performance of baselines and offer several observations: (1) The overall performance of existing LMMs is still limited; however LMMs more robust to low-quality and diverse-structured images compared to LLMs. (3) Reasoning across context and image content present significant challenges. We hope this benchmark helps the community make progress on these challenging tasks in multi-modal medical document understanding and facilitate its application in healthcare. Our dataset will be publicly available for noncommercial use at https://github.com/Alipay-Med/medDQA_benchmark.git|大型语言模型(LLM)和大型多模态模型(LMM)的最新进展已经显示出在各种医疗应用中的潜力，例如智能医疗诊断。虽然取得了令人印象深刻的成果，但我们发现，现有的基准并不能反映真实医疗报告的复杂性和专门的深入推理能力。在这项工作中，我们在医学专业领域建立了一个全面的基准，并介绍了 RJUA-MedDQA，其中包含2000个真实世界的中国医疗报告图像提出了几个挑战: 全面解释图像内容在各种具有挑战性的布局，具有数字推理能力来识别异常指标，并表现出强大的临床推理能力，提供疾病诊断，状态和建议的陈述基于医疗背景的收集。我们精心设计了数据生成流水线，提出了一种有效的结构恢复注释(ESRA)方法，旨在恢复医学报告图像中的文本和表格内容。这种方法大大提高了注释效率，使每个注释器的生产率提高了一倍，并使准确率提高了26.8% 。我们进行了广泛的评估，包括对能够解决中医质量保证任务的5个 LMM 进行少量评估。为了进一步研究现有 LMM 的局限性和潜力，我们利用 ESRA 方法生成的图像文本对一组强 LMM 进行了对比实验。我们报告了基线的性能，并提供了几个观察结果: (1)现有的 LMM 的整体性能仍然有限，但是 LMM 对低质量和多样化结构的图像比 LLM 更强大。(3)跨语境和图像内容的推理是一个重大的挑战。我们希望这个基准能够帮助社区在多模式医疗文档理解方面取得进展，并促进其在医疗保健中的应用。我们的数据集将在 https://github.com/alipay-med/meddqa_benchmark.git 公开供非商业使用|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=RJUA-MedDQA:+A+Multimodal+Benchmark+for+Medical+Document+Question+Answering+and+Clinical+Reasoning)|0| -|[Large Scale Hierarchical Industrial Demand Time-Series Forecasting incorporating Sparsity](https://doi.org/10.1145/3637528.3671632)|Harshavardhan Kamarthi, Aditya B. Sasanur, Xinjie Tong, Xingyu Zhou, James Peters, Joe Czyzyk, B. Aditya Prakash|The Dow Chemical Company, Houston, TX, USA; The Dow Chemical Company, Midland, MI, USA; Georgia Institute of Technology, Atlanta, GA, USA|Hierarchical time-series forecasting (HTSF) is an important problem for many real-world business applications where the goal is to simultaneously forecast multiple time-series that are related to each other via a hierarchical relation. Recent works, however, do not address two important challenges that are typically observed in many demand forecasting applications at large companies. First, many time-series at lower levels of the hierarchy have high sparsity i.e., they have a significant number of zeros. Most HTSF methods do not address this varying sparsity across the hierarchy. Further, they do not scale well to the large size of the real-world hierarchy typically unseen in benchmarks used in literature. We resolve both these challenges by proposing HAILS, a novel probabilistic hierarchical model that enables accurate and calibrated probabilistic forecasts across the hierarchy by adaptively modeling sparse and dense time-series with different distributional assumptions and reconciling them to adhere to hierarchical constraints. We show the scalability and effectiveness of our methods by evaluating them against real-world demand forecasting datasets. We deploy HAILS at a large chemical manufacturing company for a product demand forecasting application with over ten thousand products and observe a significant 8.5% improvement in forecast accuracy and 23% better improvement for sparse time-series. The enhanced accuracy and scalability make HAILS a valuable tool for improved business planning and customer experience.|分层时间序列预测(HTSF)是许多实际业务应用中的一个重要问题，其目标是通过层次关系同时预测相互关联的多个时间序列。然而，最近的工作并没有解决两个重要的挑战，这两个挑战通常在大公司的许多需求预测应用程序中观察到。首先，层次结构较低层次的许多时间序列具有较高的稀疏性，也就是说，它们有大量的零。大多数 HTSF 方法并不解决这种层次结构间的可变稀疏性。此外，它们不能很好地扩展到文献中使用的基准中通常看不到的现实世界等级的巨大规模。我们通过提出 HAILS 来解决这两个挑战，HAILS 是一种新型的概率层次模型，通过自适应地用不同的分布假设对稀疏和密集的时间序列建模，并协调它们以坚持层次约束，从而实现跨层次的准确和校准的概率预测。我们通过对比真实世界的需求预测数据集来评估我们的方法的可扩展性和有效性。我们在一家大型化工制造公司部署了 HAILS，用于产品需求预测应用程序，产品超过一万种，预测准确率显著提高了8.5% ，稀疏时间序列的预测准确率提高了23% 。增强的准确性和可扩展性使 HAILS 成为改善业务规划和客户体验的宝贵工具。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Large+Scale+Hierarchical+Industrial+Demand+Time-Series+Forecasting+incorporating+Sparsity)|0| +|[Large Scale Hierarchical Industrial Demand Time-Series Forecasting incorporating Sparsity](https://doi.org/10.1145/3637528.3671632)|Harshavardhan Kamarthi, Aditya B. Sasanur, Xinjie Tong, Xingyu Zhou, James Peters, Joe Czyzyk, B. Aditya Prakash|The Dow Chemical Company, Midland, MI, USA; The Dow Chemical Company, Houston, TX, USA; Georgia Institute of Technology, Atlanta, GA, USA|Hierarchical time-series forecasting (HTSF) is an important problem for many real-world business applications where the goal is to simultaneously forecast multiple time-series that are related to each other via a hierarchical relation. Recent works, however, do not address two important challenges that are typically observed in many demand forecasting applications at large companies. First, many time-series at lower levels of the hierarchy have high sparsity i.e., they have a significant number of zeros. Most HTSF methods do not address this varying sparsity across the hierarchy. Further, they do not scale well to the large size of the real-world hierarchy typically unseen in benchmarks used in literature. We resolve both these challenges by proposing HAILS, a novel probabilistic hierarchical model that enables accurate and calibrated probabilistic forecasts across the hierarchy by adaptively modeling sparse and dense time-series with different distributional assumptions and reconciling them to adhere to hierarchical constraints. We show the scalability and effectiveness of our methods by evaluating them against real-world demand forecasting datasets. We deploy HAILS at a large chemical manufacturing company for a product demand forecasting application with over ten thousand products and observe a significant 8.5% improvement in forecast accuracy and 23% better improvement for sparse time-series. The enhanced accuracy and scalability make HAILS a valuable tool for improved business planning and customer experience.|分层时间序列预测(HTSF)是许多实际业务应用中的一个重要问题，其目标是通过层次关系同时预测相互关联的多个时间序列。然而，最近的工作并没有解决两个重要的挑战，这两个挑战通常在大公司的许多需求预测应用程序中观察到。首先，层次结构较低层次的许多时间序列具有较高的稀疏性，也就是说，它们有大量的零。大多数 HTSF 方法并不解决这种层次结构间的可变稀疏性。此外，它们不能很好地扩展到文献中使用的基准中通常看不到的现实世界等级的巨大规模。我们通过提出 HAILS 来解决这两个挑战，HAILS 是一种新型的概率层次模型，通过自适应地用不同的分布假设对稀疏和密集的时间序列建模，并协调它们以坚持层次约束，从而实现跨层次的准确和校准的概率预测。我们通过对比真实世界的需求预测数据集来评估我们的方法的可扩展性和有效性。我们在一家大型化工制造公司部署了 HAILS，用于产品需求预测应用程序，产品超过一万种，预测准确率显著提高了8.5% ，稀疏时间序列的预测准确率提高了23% 。增强的准确性和可扩展性使 HAILS 成为改善业务规划和客户体验的宝贵工具。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Large+Scale+Hierarchical+Industrial+Demand+Time-Series+Forecasting+incorporating+Sparsity)|0| |[Know, Grow, and Protect Net Worth: Using ML for Asset Protection by Preventing Overdraft Fees](https://doi.org/10.1145/3637528.3671628)|Avishek Kumar, Tyson Silver|Lightcast, Moscow, ID, USA; Intuit CreditKarma, Oakland, CA, USA|When a customer overdraws their bank account and their balance is negative they are assessed an overdraft fee. Americans pay approximately $15 billion in unnecessary overdraft fees a year, often in $35 increments; users of the Mint personal finance app pay approximately $250 million in fees a year in particular. These overdraft fees are an excessive financial burden and lead to cascading overdraft fees trapping customers in financial hardship. To address this problem, we have created an ML-driven overdraft early warning system (ODEWS) that assesses a customer's risk of overdrafting within the next week using their banking and transaction data in the Mint app. At-risk customers are sent an alert so they can take steps to avoid the fee, ultimately changing their behavior and financial habits. The system deployed resulted in a $3 million savings in overdraft fees for Mint customers compared to a control group. Moreover, the methodology outlined here is part of a greater effort to provide ML-driven personalized financial advice to help our members know, grow, and protect their net worth, ultimately, achieving their financial goals.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Know,+Grow,+and+Protect+Net+Worth:+Using+ML+for+Asset+Protection+by+Preventing+Overdraft+Fees)|0| |[AutoWebGLM: A Large Language Model-based Web Navigating Agent](https://doi.org/10.1145/3637528.3671620)|Hanyu Lai, Xiao Liu, Iat Long Iong, Shuntian Yao, Yuxuan Chen, Pengbo Shen, Hao Yu, Hanchen Zhang, Xiaohan Zhang, Yuxiao Dong, Jie Tang|University of Chinese Academy of Sciences, Beijing, China; Tsinghua University, Beijing, China; Beijing University of Posts and Telecommunications, Beijing, China; Tsinghua University & Zhipu AI, Beijing, China; Zhipu AI, Beijing, China|Large language models (LLMs) have fueled many intelligent web agents, but most existing ones perform far from satisfying in real-world web navigation tasks due to three factors: (1) the complexity of HTML text data (2) versatility of actions on webpages, and (3) task difficulty due to the open-domain nature of the web. In light of these challenges, we develop the open AutoWebGLM based on ChatGLM3-6B. AutoWebGLM can serve as a powerful automated web navigation agent that outperform GPT-4. Inspired by human browsing patterns, we first design an HTML simplification algorithm to represent webpages with vital information preserved succinctly. We then employ a hybrid human-AI method to build web browsing data for curriculum training. Finally, we bootstrap the model by reinforcement learning and rejection sampling to further facilitate webpage comprehension, browser operations, and efficient task decomposition by itself. For comprehensive evaluation, we establish a bilingual benchmark---AutoWebBench---for real-world web navigation tasks. We evaluate AutoWebGLM across diverse web navigation benchmarks, demonstrating its potential to tackle challenging tasks in real environments. Related code, model, and data are released at https://github.com/THUDM/AutoWebGLM.|大型语言模型(LLM)为许多智能网络代理提供了动力，但大多数现有的 LLM 模型在现实世界的网络导航任务中表现得远远不能令人满意，原因有三: (1) HTML 文本数据的复杂性(2)网页操作的多样性(3)由于网络的开放领域特性，任务难度。针对这些挑战，我们开发了基于 ChatGLM3-6B 的开放式 AutoWebGLM。AutoWebGLM 可以作为一个强大的自动化 Web 导航代理，其性能优于 GPT-4。受到人类浏览模式的启发，我们首先设计了一个 HTML 简化算法来表示保存了重要信息的网页。然后采用人工智能和人工智能相结合的方法建立网页浏览数据，用于课程培训。最后，我们通过强化学习和拒绝抽样来引导模型，以进一步促进网页理解、浏览器操作和有效的任务分解。为了进行全面的评估，我们建立了一个双语基准—— AutoWebBench ——用于真实世界的网页导航任务。我们评估 AutoWebGLM 跨不同的网页导航基准，展示其潜力，以解决具有挑战性的任务在真实的环境。相关的代码、模型和数据在 https://github.com/thudm/autowebglm 发布。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=AutoWebGLM:+A+Large+Language+Model-based+Web+Navigating+Agent)|0| |[SEFraud: Graph-based Self-Explainable Fraud Detection via Interpretative Mask Learning](https://doi.org/10.1145/3637528.3671534)|Kaidi Li, Tianmeng Yang, Min Zhou, Jiahao Meng, Shendi Wang, Yihui Wu, Boshuai Tan, Hu Song, Lujia Pan, Fan Yu, Zhenli Sheng, Yunhai Tong|Huawei Inc, Shenzhen, China; Peking University, Beijing, China; ICBC Limited, Shanghai, China|Graph-based fraud detection has widespread application in modern industry scenarios, such as spam review and malicious account detection. While considerable efforts have been devoted to designing adequate fraud detectors, the interpretability of their results has often been overlooked. Previous works have attempted to generate explanations for specific instances using post-hoc explaining methods such as a GNNExplainer. However, post-hoc explanations can not facilitate the model predictions and the computational cost of these methods cannot meet practical requirements, thus limiting their application in real-world scenarios. To address these issues, we propose SEFraud, a novel graph-based self-explainable fraud detection framework that simultaneously tackles fraud detection and result in interpretability. Concretely, SEFraud first leverages customized heterogeneous graph transformer networks with learnable feature masks and edge masks to learn expressive representations from the informative heterogeneously typed transactions. A new triplet loss is further designed to enhance the performance of mask learning. Empirical results on various datasets demonstrate the effectiveness of SEFraud as it shows considerable advantages in both the fraud detection performance and interpretability of prediction results. Specifically, SEFraud achieves the most significant improvement with 8.6% on AUC and 8.5% on Recall over the second best on fraud detection, as well as an average of 10x speed-up regarding the inference time. Last but not least, SEFraud has been deployed and offers explainable fraud detection service for the largest bank in China, Industrial and Commercial Bank of China Limited (ICBC). Results collected from the production environment of ICBC show that SEFraud can provide accurate detection results and comprehensive explanations that align with the expert business understanding, confirming its efficiency and applicability in large-scale online services.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=SEFraud:+Graph-based+Self-Explainable+Fraud+Detection+via+Interpretative+Mask+Learning)|0| |[Harvesting Efficient On-Demand Order Pooling from Skilled Couriers: Enhancing Graph Representation Learning for Refining Real-time Many-to-One Assignments](https://doi.org/10.1145/3637528.3671643)|Yile Liang, Jiuxia Zhao, Donghui Li, Jie Feng, Chen Zhang, Xuetao Ding, Jinghua Hao, Renqing He|Tsinghua University, Beijing, China; Meituan, Beijing, China|The recent past has witnessed a notable surge in on-demand food delivery (OFD) services, offering delivery fulfillment within dozens of minutes after an order is placed. In OFD, pooling multiple orders for simultaneous delivery in real-time order assignment is a pivotal efficiency source, which may in turn extend delivery time. Constructing high-quality order pooling to harmonize platform efficiency with the experiences of consumers and couriers, is crucial to OFD platforms. However, the complexity and real-time nature of order assignment, making extensive calculations impractical, significantly limit the potential for order consolidation. Moreover, offline environment is frequently riddled with unknown factors, posing challenges for the platform's perceptibility and pooling decisions. Nevertheless, delivery behaviors of skilled couriers (SCs) who know the environment well, can improve system awareness and effectively inform decisions. Hence a SC delivery network (SCDN) is constructed, based on an enhanced attributed heterogeneous network embedding approach tailored for OFD. It aims to extract features from rich temporal and spatial information, and uncover the latent potential for order combinations embedded within SC trajectories. Accordingly, the vast search space of order assignment can be effectively pruned through scalable similarity calculations of low-dimensional vectors, making comprehensive and high-quality pooling outcomes more easily identified in real time. In addition, the acquired embedding outcomes highlight promising subspaces embedded within this space, i.e., scale-effect hotspot areas, which can offer significant potential for elevating courier efficiency. SCDN has now been deployed in Meituan dispatch system. Online tests reveal that with SCDN, the pooling quality and extent have been greatly improved. And our system can boost couriers' efficiency by 45-55% during noon peak hours, while upholding the timely delivery commitment.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Harvesting+Efficient+On-Demand+Order+Pooling+from+Skilled+Couriers:+Enhancing+Graph+Representation+Learning+for+Refining+Real-time+Many-to-One+Assignments)|0| |[Hyper-Local Deformable Transformers for Text Spotting on Historical Maps](https://doi.org/10.1145/3637528.3671589)|Yijun Lin, YaoYi Chiang|University of Minnesota, Twin Cities, Minneapolis, MN, USA|Text on historical maps contains valuable information providing georeferenced historical, political, and cultural contexts. However, text extraction from historical maps has been challenging due to the lack of (1) effective methods and (2) training data. Previous approaches use ad-hoc steps tailored to only specific map styles. Recent machine learning-based text spotters (e.g., for scene images) have the potential to solve these challenges because of their flexibility in supporting various types of text instances. However, these methods remain challenges in extracting precise image features for predicting every sub-component (boundary points and characters) in a text instance. This is critical because map text can be lengthy and highly rotated with complex backgrounds, posing difficulties in detecting relevant image features from a rough text region. This paper proposes PALETTE, an end-to-end text spotter for scanned historical maps of a wide variety. PALETTE introduces a novel hyper-local sampling module to explicitly learn localized image features around the target boundary points and characters of a text instance for detection and recognition. PALETTE also enables hyper-local positional embeddings to learn spatial interactions between boundary points and characters within and across text instances. In addition, this paper presents a novel approach to automatically generate synthetic map images, SYNTHMAP+, for training text spotters for historical maps. The experiment shows that PALETTE with SYNTHMAP+ outperforms SOTA text spotters on two new benchmark datasets of historical maps, particularly for long and angled text. We have deployed PALETTE with SYNTHMAP+ to process over 60,000 maps in the David Rumsey Historical Map collection and generated over 100 million text labels to support map searching.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Hyper-Local+Deformable+Transformers+for+Text+Spotting+on+Historical+Maps)|0| -|[Source Localization for Cross Network Information Diffusion](https://doi.org/10.1145/3637528.3671624)|Chen Ling, Tanmoy Chowdhury, Jie Ji, Sirui Li, Andreas Züfle, Liang Zhao|Emory University, Atlanta, GA, USA; Emory University, Atlanta, VA, USA|Source localization aims to locate information diffusion sources only given the diffusion observation, which has attracted extensive attention in the past few years. Existing methods are mostly tailored for single networks and may not be generalized to handle more complex networks like cross-networks. Cross-network is defined as two interconnected networks, where one network's functionality depends on the other. Source localization on cross-networks entails locating diffusion sources on the source network by only giving the diffused observation in the target network. The task is challenging due to challenges including: 1) diffusion sources distribution modeling; 2) jointly considering both static and dynamic node features; and 3) heterogeneous diffusion patterns learning. In this work, we propose a novel method, namely CNSL, to handle the three primary challenges. Specifically, we propose to learn the distribution of diffusion sources through Bayesian inference and leverage disentangled encoders to learn static and dynamic node features separately. The learning objective is coupled with the cross-network information propagation estimation model to make the inference of diffusion sources considering the overall diffusion process. Additionally, we also provide two novel cross-network datasets collected by ourselves. Extensive experiments are conducted on both datasets to demonstrate the effectiveness of CNSL in handling the source localization on cross-networks.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Source+Localization+for+Cross+Network+Information+Diffusion)|0| +|[Source Localization for Cross Network Information Diffusion](https://doi.org/10.1145/3637528.3671624)|Chen Ling, Tanmoy Chowdhury, Jie Ji, Sirui Li, Andreas Züfle, Liang Zhao|Emory University, Atlanta, VA, USA; Emory University, Atlanta, GA, USA|Source localization aims to locate information diffusion sources only given the diffusion observation, which has attracted extensive attention in the past few years. Existing methods are mostly tailored for single networks and may not be generalized to handle more complex networks like cross-networks. Cross-network is defined as two interconnected networks, where one network's functionality depends on the other. Source localization on cross-networks entails locating diffusion sources on the source network by only giving the diffused observation in the target network. The task is challenging due to challenges including: 1) diffusion sources distribution modeling; 2) jointly considering both static and dynamic node features; and 3) heterogeneous diffusion patterns learning. In this work, we propose a novel method, namely CNSL, to handle the three primary challenges. Specifically, we propose to learn the distribution of diffusion sources through Bayesian inference and leverage disentangled encoders to learn static and dynamic node features separately. The learning objective is coupled with the cross-network information propagation estimation model to make the inference of diffusion sources considering the overall diffusion process. Additionally, we also provide two novel cross-network datasets collected by ourselves. Extensive experiments are conducted on both datasets to demonstrate the effectiveness of CNSL in handling the source localization on cross-networks.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Source+Localization+for+Cross+Network+Information+Diffusion)|0| |[MFTCoder: Boosting Code LLMs with Multitask Fine-Tuning](https://doi.org/10.1145/3637528.3671609)|Bingchang Liu, Chaoyu Chen, Zi Gong, Cong Liao, Huan Wang, Zhichao Lei, Ming Liang, Dajun Chen, Min Shen, Hailian Zhou, Wei Jiang, Hang Yu, Jianguo Li||Code LLMs have emerged as a specialized research field, with remarkable studies dedicated to enhancing model's coding capabilities through fine-tuning on pre-trained models. Previous fine-tuning approaches were typically tailored to specific downstream tasks or scenarios, which meant separate fine-tuning for each task, requiring extensive training resources and posing challenges in terms of deployment and maintenance. Furthermore, these approaches failed to leverage the inherent interconnectedness among different code-related tasks. To overcome these limitations, we present a multi-task fine-tuning framework, MFTcoder, that enables simultaneous and parallel fine-tuning on multiple tasks. By incorporating various loss functions, we effectively address common challenges in multi-task learning, such as data imbalance, varying difficulty levels, and inconsistent convergence speeds. Extensive experiments have conclusively demonstrated that our multi-task fine-tuning approach outperforms both individual fine-tuning on single tasks and fine-tuning on a mixed ensemble of tasks. Moreover, MFTcoder offers efficient training capabilities, including efficient data tokenization modes and PEFT fine-tuning, resulting in significantly improved speed compared to traditional fine-tuning methods. MFTcoder seamlessly integrates with several mainstream open-source LLMs, such as CodeLLama and Qwen. Leveraging the CodeLLama foundation, our MFTcoder fine-tuned model, \textsc{CodeFuse-CodeLLama-34B}, achieves an impressive pass@1 score of 74.4\% on the HumaneEval benchmark, surpassing GPT-4 performance (67\%, zero-shot). MFTCoder is open-sourced at \url{https://github.com/codefuse-ai/MFTCOder}||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=MFTCoder:+Boosting+Code+LLMs+with+Multitask+Fine-Tuning)|0| -|[Towards Automatic Evaluation for LLMs' Clinical Capabilities: Metric, Data, and Algorithm](https://doi.org/10.1145/3637528.3671575)|Lei Liu, Xiaoyan Yang, Fangzhou Li, Chenfei Chi, Yue Shen, Shiwei Lyu, Ming Zhang, Xiaowei Ma, Xiangguo Lv, Liya Ma, Zhiqiang Zhang, Wei Xue, Yiran Huang, Jinjie Gu|Ant Group, Shanghai, China; The Chinese University of Hong Kong, Shenzhen, Shenzhen, China; Ant Group, Hangzhou, China; Renji Hospital, Shanghai, China|Large language models (LLMs) are gaining increasing interests to improve clinical efficiency, owing to their unprecedented performance in modelling natural language. Ensuring the reliable clinical applications, the evaluation of LLMs indeed becomes critical for better mitigating the potential risks, e.g., hallucinations. However, current evaluation methods heavily rely on labor-intensive human participation to achieve human-preferred judgements. To overcome this challenge, we propose an automatic evaluation paradigm tailored to assess the LLMs' capabilities in delivering clinical services, e.g., disease diagnosis and treatment. The evaluation paradigm contains three basic elements: metric, data, and algorithm. Specifically, inspired by professional clinical practice pathways, we formulate a LLM-specific clinical pathway (LCP) to define the clinical capabilities that a doctor agent should possess. Then, Standardized Patients (SPs) from the medical education are introduced as the guideline for collecting medical data for evaluation, which can well ensure the completeness of the evaluation procedure. Leveraging these steps, we develop a multi-agent framework to simulate the interactive environment between SPs and a doctor agent, which is equipped with a Retrieval-Augmented Evaluation (RAE) to determine whether the behaviors of a doctor agent are in accordance with LCP. The above paradigm can be extended to any similar clinical scenarios to automatically evaluate the LLMs' medical capabilities. Applying such paradigm, we construct an evaluation benchmark in the field of urology, including a LCP, a SPs dataset, and an automated RAE. Extensive experiments are conducted to demonstrate the effectiveness of the proposed approach, providing more insights for LLMs' safe and reliable deployments in clinical practice.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Towards+Automatic+Evaluation+for+LLMs'+Clinical+Capabilities:+Metric,+Data,+and+Algorithm)|0| +|[Towards Automatic Evaluation for LLMs' Clinical Capabilities: Metric, Data, and Algorithm](https://doi.org/10.1145/3637528.3671575)|Lei Liu, Xiaoyan Yang, Fangzhou Li, Chenfei Chi, Yue Shen, Shiwei Lyu, Ming Zhang, Xiaowei Ma, Xiangguo Lv, Liya Ma, Zhiqiang Zhang, Wei Xue, Yiran Huang, Jinjie Gu|Ant Group, Hangzhou, China; Renji Hospital, Shanghai, China; Ant Group, Shanghai, China; The Chinese University of Hong Kong, Shenzhen, Shenzhen, China|Large language models (LLMs) are gaining increasing interests to improve clinical efficiency, owing to their unprecedented performance in modelling natural language. Ensuring the reliable clinical applications, the evaluation of LLMs indeed becomes critical for better mitigating the potential risks, e.g., hallucinations. However, current evaluation methods heavily rely on labor-intensive human participation to achieve human-preferred judgements. To overcome this challenge, we propose an automatic evaluation paradigm tailored to assess the LLMs' capabilities in delivering clinical services, e.g., disease diagnosis and treatment. The evaluation paradigm contains three basic elements: metric, data, and algorithm. Specifically, inspired by professional clinical practice pathways, we formulate a LLM-specific clinical pathway (LCP) to define the clinical capabilities that a doctor agent should possess. Then, Standardized Patients (SPs) from the medical education are introduced as the guideline for collecting medical data for evaluation, which can well ensure the completeness of the evaluation procedure. Leveraging these steps, we develop a multi-agent framework to simulate the interactive environment between SPs and a doctor agent, which is equipped with a Retrieval-Augmented Evaluation (RAE) to determine whether the behaviors of a doctor agent are in accordance with LCP. The above paradigm can be extended to any similar clinical scenarios to automatically evaluate the LLMs' medical capabilities. Applying such paradigm, we construct an evaluation benchmark in the field of urology, including a LCP, a SPs dataset, and an automated RAE. Extensive experiments are conducted to demonstrate the effectiveness of the proposed approach, providing more insights for LLMs' safe and reliable deployments in clinical practice.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Towards+Automatic+Evaluation+for+LLMs'+Clinical+Capabilities:+Metric,+Data,+and+Algorithm)|0| |[DAG: Deep Adaptive and Generative K-Free Community Detection on Attributed Graphs](https://doi.org/10.1145/3637528.3671615)|Chang Liu, Yuwen Yang, Yue Ding, Hongtao Lu, Wenqing Lin, Ziming Wu, Wendong Bi|Shanghai Jiao Tong University, Shanghai, China; Tencent, Shenzhen, China|Community detection on attributed graphs with rich semantic and topological information offers great potential for real-world network analysis, especially user matching in online games. Graph Neural Networks (GNNs) have recently enabled Deep Graph Clustering (DGC) methods to learn cluster assignments from semantic and topological information. However, their success depends on the prior knowledge related to the number of communities K, which is unrealistic due to the high costs and privacy issues of acquisition. In this paper, we investigate the community detection problem without prior K, referred to as K-Free Community Detection problem. To address this problem, we propose a novel Deep Adaptive and Generative model~(DAG) for community detection without specifying the prior K. DAG consists of three key components, i.e., a node representation learning module with masked attribute reconstruction, a community affiliation readout module, and a community number search module with group sparsity. These components enable DAG to convert the process of non-differentiable grid search for the community number, i.e., a discrete hyperparameter in existing DGC methods, into a differentiable learning process. In such a way, DAG can simultaneously perform community detection and community number search end-to-end. To alleviate the cost of acquiring community labels in real-world applications, we design a new metric, EDGE, to evaluate community detection methods even when the labels are not feasible. Extensive offline experiments on five public datasets and a real-world online mobile game dataset demonstrate the superiority of our DAG over the existing state-of-the-art (SOTA) methods. DAG has a relative increase of 7.35% in teams in a Tencent online game compared with the best competitor.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=DAG:+Deep+Adaptive+and+Generative+K-Free+Community+Detection+on+Attributed+Graphs)|0| -|[EmoLLMs: A Series of Emotional Large Language Models and Annotation Tools for Comprehensive Affective Analysis](https://doi.org/10.1145/3637528.3671552)|Zhiwei Liu, Kailai Yang, Qianqian Xie, Tianlin Zhang, Sophia Ananiadou|The University of Manchester & Artificial Intelligence Research Center, Manchester, United Kingdom; The University of Manchester, Manchester, United Kingdom|Sentiment analysis and emotion detection are important research topics in natural language processing (NLP) and benefit many downstream tasks. With the widespread application of large language models (LLMs), researchers have started exploring the application of LLMs based on instruction-tuning in the field of sentiment analysis. However, these models only focus on single aspects of affective classification tasks (e.g. sentimental polarity or categorical emotions), and overlook the regression tasks (e.g. sentiment strength or emotion intensity), which leads to poor performance in downstream tasks. The main reason is the lack of comprehensive affective instruction tuning datasets and evaluation benchmarks, which cover various affective classification and regression tasks. Moreover, although emotional information is useful for downstream tasks, existing downstream datasets lack high-quality and comprehensive affective annotations. In this paper, we propose EmoLLMs, the first series of open-sourced instruction-following LLMs for comprehensive affective analysis based on fine-tuning various LLMs with instruction data, the first multi-task affective analysis instruction dataset (AAID) with 234K data samples based on 3 classification tasks and 2 regression tasks to support LLM instruction tuning, and a comprehensive affective evaluation benchmark (AEB) with 8 regression tasks and 6 classification tasks from various sources and domains to test the generalization ability of LLMs. We propose a series of EmoLLMs by fine-tuning LLMs with AAID to solve various affective instruction tasks. We compare our models with a variety of LLMs and sentiment analysis tools on AEB, where our models outperform all other open-sourced LLMs and sentiment analysis tools, and surpass ChatGPT and GPT-4 in most tasks, which shows that the series of EmoLLMs achieve the ChatGPT-level and GPT-4-level generalization capabilities on affective analysis tasks, and demonstrates our models can be used as affective annotation tools. This project is available at https://github.com/lzw108/EmoLLMs/.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=EmoLLMs:+A+Series+of+Emotional+Large+Language+Models+and+Annotation+Tools+for+Comprehensive+Affective+Analysis)|0| -|[MISP: A Multimodal-based Intelligent Server Failure Prediction Model for Cloud Computing Systems](https://doi.org/10.1145/3637528.3671568)|Xianting Lu, Yunong Wang, Yu Fu, Qi Sun, Xuhua Ma, Xudong Zheng, Cheng Zhuo|Lanzhou University & Zhejiang University, Lanzhou, China; Alibaba Cloud, Alibaba Group, Hangzhou, China; Zhejiang University, Hangzhou, China|Traditional server failure prediction methods predominantly rely on single-modality data such as system logs or system status curves. This reliance may lead to an incomplete understanding of system health and impending issues, proving inadequate for the complex and dynamic landscape of contemporary cloud computing environments. The potential of multimodal data to provide comprehensive insights is widely acknowledged, yet the lack of a holistic dataset and the challenges inherent in integrating features from both structured and unstructured data have impeded the exploration of multimodal-based server failure prediction. Addressing these challenges, this paper presents an industrial-scale, comprehensive dataset for server failure prediction, comprising nearly 80 types of structured and unstructured data sourced from real-world industrial cloud systems 1. Building on this resource, we introduce MISP, a model that leverages multimodal fusion techniques for server failure prediction. MISP transforms multimodal data into multi-dimensional sequences, extracts and encodes features both within and across the modalities, and ultimately computes the failure probability from the synthesized features. Experiments demonstrate that MISP significantly outperforms existing methods, enhancing prediction accuracy by approximately 25% over previous state-of-the-art approaches.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=MISP:+A+Multimodal-based+Intelligent+Server+Failure+Prediction+Model+for+Cloud+Computing+Systems)|0| +|[EmoLLMs: A Series of Emotional Large Language Models and Annotation Tools for Comprehensive Affective Analysis](https://doi.org/10.1145/3637528.3671552)|Zhiwei Liu, Kailai Yang, Qianqian Xie, Tianlin Zhang, Sophia Ananiadou|The University of Manchester, Manchester, United Kingdom; The University of Manchester & Artificial Intelligence Research Center, Manchester, United Kingdom|Sentiment analysis and emotion detection are important research topics in natural language processing (NLP) and benefit many downstream tasks. With the widespread application of large language models (LLMs), researchers have started exploring the application of LLMs based on instruction-tuning in the field of sentiment analysis. However, these models only focus on single aspects of affective classification tasks (e.g. sentimental polarity or categorical emotions), and overlook the regression tasks (e.g. sentiment strength or emotion intensity), which leads to poor performance in downstream tasks. The main reason is the lack of comprehensive affective instruction tuning datasets and evaluation benchmarks, which cover various affective classification and regression tasks. Moreover, although emotional information is useful for downstream tasks, existing downstream datasets lack high-quality and comprehensive affective annotations. In this paper, we propose EmoLLMs, the first series of open-sourced instruction-following LLMs for comprehensive affective analysis based on fine-tuning various LLMs with instruction data, the first multi-task affective analysis instruction dataset (AAID) with 234K data samples based on 3 classification tasks and 2 regression tasks to support LLM instruction tuning, and a comprehensive affective evaluation benchmark (AEB) with 8 regression tasks and 6 classification tasks from various sources and domains to test the generalization ability of LLMs. We propose a series of EmoLLMs by fine-tuning LLMs with AAID to solve various affective instruction tasks. We compare our models with a variety of LLMs and sentiment analysis tools on AEB, where our models outperform all other open-sourced LLMs and sentiment analysis tools, and surpass ChatGPT and GPT-4 in most tasks, which shows that the series of EmoLLMs achieve the ChatGPT-level and GPT-4-level generalization capabilities on affective analysis tasks, and demonstrates our models can be used as affective annotation tools. This project is available at https://github.com/lzw108/EmoLLMs/.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=EmoLLMs:+A+Series+of+Emotional+Large+Language+Models+and+Annotation+Tools+for+Comprehensive+Affective+Analysis)|0| +|[MISP: A Multimodal-based Intelligent Server Failure Prediction Model for Cloud Computing Systems](https://doi.org/10.1145/3637528.3671568)|Xianting Lu, Yunong Wang, Yu Fu, Qi Sun, Xuhua Ma, Xudong Zheng, Cheng Zhuo|Alibaba Cloud, Alibaba Group, Hangzhou, China; Lanzhou University & Zhejiang University, Lanzhou, China; Zhejiang University, Hangzhou, China|Traditional server failure prediction methods predominantly rely on single-modality data such as system logs or system status curves. This reliance may lead to an incomplete understanding of system health and impending issues, proving inadequate for the complex and dynamic landscape of contemporary cloud computing environments. The potential of multimodal data to provide comprehensive insights is widely acknowledged, yet the lack of a holistic dataset and the challenges inherent in integrating features from both structured and unstructured data have impeded the exploration of multimodal-based server failure prediction. Addressing these challenges, this paper presents an industrial-scale, comprehensive dataset for server failure prediction, comprising nearly 80 types of structured and unstructured data sourced from real-world industrial cloud systems 1. Building on this resource, we introduce MISP, a model that leverages multimodal fusion techniques for server failure prediction. MISP transforms multimodal data into multi-dimensional sequences, extracts and encodes features both within and across the modalities, and ultimately computes the failure probability from the synthesized features. Experiments demonstrate that MISP significantly outperforms existing methods, enhancing prediction accuracy by approximately 25% over previous state-of-the-art approaches.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=MISP:+A+Multimodal-based+Intelligent+Server+Failure+Prediction+Model+for+Cloud+Computing+Systems)|0| |[Integrating System State into Spatio Temporal Graph Neural Network for Microservice Workload Prediction](https://doi.org/10.1145/3637528.3671508)|Yang Luo, Mohan Gao, Zhemeng Yu, Haoyuan Ge, Xiaofeng Gao, Tengwei Cai, Guihai Chen|Ant Group, Hangzhou, China; Shanghai Jiao Tong University, Shanghai, China|Microservice architecture has become a driving force in enhancing the modularity and scalability of web applications, as evidenced by the Alipay platform's operational success. However, a prevalent issue within such infrastructures is the suboptimal utilization of CPU resources due to inflexible resource allocation policies. This inefficiency necessitates the development of dynamic, accurate workload prediction methods to improve resource allocation. In response to this challenge, we present STAMP, a Spatio Temporal Graph Network for Microservice Workload Prediction. STAMP is designed to comprehensively address the multifaceted interdependencies between microservices, the temporal variability of workloads, and the critical role of system state in resource utilization. Through a graph-based representation, STAMP effectively maps the intricate network of microservice interactions. It employs time series analysis to capture the dynamic nature of workload changes and integrates system state insights to enhance prediction accuracy. Our empirical analysis, using three distinct real-world datasets, establishes that STAMP exceeds baselines by achieving an average boost of 5.72% in prediction precision, as measured by RMSE. Upon deployment in Alipay's microservice environment, STAMP achieves a 33.10% reduction in resource consumption, significantly outperforming existing online methods. This research solidifies STAMP as a validated framework, offering meaningful contributions to the field of resource management in microservice architecture-based applications.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Integrating+System+State+into+Spatio+Temporal+Graph+Neural+Network+for+Microservice+Workload+Prediction)|0| |[FusionSF: Fuse Heterogeneous Modalities in a Vector Quantized Framework for Robust Solar Power Forecasting](https://doi.org/10.1145/3637528.3671509)|Ziqing Ma, Wenwei Wang, Tian Zhou, Chao Chen, Bingqing Peng, Liang Sun, Rong Jin||Accurate solar power forecasting is crucial to integrate photovoltaic plants into the electric grid, schedule and secure the power grid safety. This problem becomes more demanding for those newly installed solar plants which lack sufficient data. Current research predominantly relies on historical solar power data or numerical weather prediction in a single-modality format, ignoring the complementary information provided in different modalities. In this paper, we propose a multi-modality fusion framework to integrate historical power data, numerical weather prediction, and satellite images, significantly improving forecast performance. We introduce a vector quantized framework that aligns modalities with varying information densities, striking a balance between integrating sufficient information and averting model overfitting. Our framework demonstrates strong zero-shot forecasting capability, which is especially useful for those newly installed plants. Moreover, we collect and release a multi-modal solar power (MMSP) dataset from real-world plants to further promote the research of multi-modal solar forecasting algorithms. Our extensive experiments show that our model not only operates with robustness but also boosts accuracy in both zero-shot forecasting and scenarios rich with training data, surpassing leading models. We have incorporated it into our eForecaster platform and deployed it for more than 300 solar plants with a capacity of over 15GW.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=FusionSF:+Fuse+Heterogeneous+Modalities+in+a+Vector+Quantized+Framework+for+Robust+Solar+Power+Forecasting)|0| -|[Valuing an Engagement Surface using a Large Scale Dynamic Causal Model](https://doi.org/10.1145/3637528.3671604)|Abhimanyu Mukerji, Sushant More, Ashwin Viswanathan Kannan, Lakshmi Ravi, Hua Chen, Naman Kohli, Chris Khawand, Dinesh Mandalapu|Amazon, Vancouver, BC, Canada; Amazon, Sunnyvale, CA, USA; Amazon, Seattle, WA, USA; Amazon, Sunnyvale, WA, USA|With recent rapid growth in online shopping, AI-powered Engagement Surfaces (ES) have become ubiquitous across retail services. These engagement surfaces perform an increasing range of functions, including recommending new products for purchase, reminding customers of their orders and providing delivery notifications. Understanding the causal effect of engagement surfaces on value driven for customers and businesses remains an open scientific question. In this paper, we develop a dynamic causal model at scale to disentangle value attributable to an ES, and to assess its effectiveness. We demonstrate the application of this model to inform business decision-making by understanding returns on investment in the ES, and identifying product lines and features where the ES adds the most value.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Valuing+an+Engagement+Surface+using+a+Large+Scale+Dynamic+Causal+Model)|0| -|[EEG2Rep: Enhancing Self-supervised EEG Representation Through Informative Masked Inputs](https://doi.org/10.1145/3637528.3671600)|Navid Mohammadi Foumani, Geoffrey Mackellar, Soheila Ghane, Saad Irtza, Nam Nguyen, Mahsa Salehi|Monash University, Melbourne, Australia; Emotiv Research, Melbourne, Australia; Emotiv Research, Sydney, Australia|Self-supervised approaches for electroencephalography (EEG) representationlearning face three specific challenges inherent to EEG data: (1) The lowsignal-to-noise ratio which challenges the quality of the representationlearned, (2) The wide range of amplitudes from very small to relatively largedue to factors such as the inter-subject variability, risks the models to bedominated by higher amplitude ranges, and (3) The absence of explicitsegmentation in the continuous-valued sequences which can result in lessinformative representations. To address these challenges, we introduce EEG2Rep,a self-prediction approach for self-supervised representation learning fromEEG. Two core novel components of EEG2Rep are as follows: 1) Instead oflearning to predict the masked input from raw EEG, EEG2Rep learns to predictmasked input in latent representation space, and 2) Instead of conventionalmasking methods, EEG2Rep uses a new semantic subsequence preserving (SSP)method which provides informative masked inputs to guide EEG2Rep to generaterich semantic representations. In experiments on 6 diverse EEG tasks withsubject variability, EEG2Rep significantly outperforms state-of-the-artmethods. We show that our semantic subsequence preserving improves the existingmasking methods in self-prediction literature and find that preserving 50% ofEEG recordings will result in the most accurate results on all 6 tasks onaverage. Finally, we show that EEG2Rep is robust to noise addressing asignificant challenge that exists in EEG data. Models and code are availableat: https://github.com/Navidfoumani/EEG2Rep||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=EEG2Rep:+Enhancing+Self-supervised+EEG+Representation+Through+Informative+Masked+Inputs)|0| +|[Valuing an Engagement Surface using a Large Scale Dynamic Causal Model](https://doi.org/10.1145/3637528.3671604)|Abhimanyu Mukerji, Sushant More, Ashwin Viswanathan Kannan, Lakshmi Ravi, Hua Chen, Naman Kohli, Chris Khawand, Dinesh Mandalapu|Amazon, Sunnyvale, WA, USA; Amazon, Vancouver, BC, Canada; Amazon, Sunnyvale, CA, USA; Amazon, Seattle, WA, USA|With recent rapid growth in online shopping, AI-powered Engagement Surfaces (ES) have become ubiquitous across retail services. These engagement surfaces perform an increasing range of functions, including recommending new products for purchase, reminding customers of their orders and providing delivery notifications. Understanding the causal effect of engagement surfaces on value driven for customers and businesses remains an open scientific question. In this paper, we develop a dynamic causal model at scale to disentangle value attributable to an ES, and to assess its effectiveness. We demonstrate the application of this model to inform business decision-making by understanding returns on investment in the ES, and identifying product lines and features where the ES adds the most value.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Valuing+an+Engagement+Surface+using+a+Large+Scale+Dynamic+Causal+Model)|0| +|[EEG2Rep: Enhancing Self-supervised EEG Representation Through Informative Masked Inputs](https://doi.org/10.1145/3637528.3671600)|Navid Mohammadi Foumani, Geoffrey Mackellar, Soheila Ghane, Saad Irtza, Nam Nguyen, Mahsa Salehi|Emotiv Research, Melbourne, Australia; Monash University, Melbourne, Australia; Emotiv Research, Sydney, Australia|Self-supervised approaches for electroencephalography (EEG) representationlearning face three specific challenges inherent to EEG data: (1) The lowsignal-to-noise ratio which challenges the quality of the representationlearned, (2) The wide range of amplitudes from very small to relatively largedue to factors such as the inter-subject variability, risks the models to bedominated by higher amplitude ranges, and (3) The absence of explicitsegmentation in the continuous-valued sequences which can result in lessinformative representations. To address these challenges, we introduce EEG2Rep,a self-prediction approach for self-supervised representation learning fromEEG. Two core novel components of EEG2Rep are as follows: 1) Instead oflearning to predict the masked input from raw EEG, EEG2Rep learns to predictmasked input in latent representation space, and 2) Instead of conventionalmasking methods, EEG2Rep uses a new semantic subsequence preserving (SSP)method which provides informative masked inputs to guide EEG2Rep to generaterich semantic representations. In experiments on 6 diverse EEG tasks withsubject variability, EEG2Rep significantly outperforms state-of-the-artmethods. We show that our semantic subsequence preserving improves the existingmasking methods in self-prediction literature and find that preserving 50% ofEEG recordings will result in the most accurate results on all 6 tasks onaverage. Finally, we show that EEG2Rep is robust to noise addressing asignificant challenge that exists in EEG data. Models and code are availableat: https://github.com/Navidfoumani/EEG2Rep||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=EEG2Rep:+Enhancing+Self-supervised+EEG+Representation+Through+Informative+Masked+Inputs)|0| |[Detecting Abnormal Operations in Concentrated Solar Power Plants from Irregular Sequences of Thermal Images](https://doi.org/10.1145/3637528.3671623)|Sukanya Patra, Nicolas Sournac, Souhaib Ben Taieb|University of Mons, Mons, Belgium|Concentrated Solar Power (CSP) plants store energy by heating a storage medium with an array of mirrors that focus sunlight onto solar receivers atop a central tower. Operating at high temperatures these receivers face risks such as freezing, deformation, and corrosion, leading to operational failures, downtime, or costly equipment damage. We study the problem of anomaly detection (AD) in sequences of thermal images collected over a year from an operational CSP plant. These images are captured at irregular intervals ranging from one to five minutes throughout the day by infrared cameras mounted on solar receivers. Our goal is to develop a method to extract useful representations from high-dimensional thermal images for AD. It should be able to handle temporal features of the data, which include irregularity, temporal dependency between images and non-stationarity due to a strong daily seasonal pattern. The co-occurrence of low-temperature anomalies that resemble normal images from the start and the end of the operational cycle with high-temperature anomalies poses an additional challenge. We first evaluate state-of-the-art deep image-based AD methods, which have been shown to be effective in deriving meaningful image representations for the detection of anomalies. Then, we introduce a forecasting-based AD method that predicts future thermal images from past sequences and timestamps via a deep sequence model. This method effectively captures specific temporal data features and distinguishes between difficult-to-detect temperature-based anomalies. Our experiments demonstrate the effectiveness of our approach compared to multiple SOTA baselines across multiple evaluation metrics. We have also successfully deployed our solution on five months of unseen data, providing critical insights to our industry partner for the maintenance of the CSP plant. Our code is publicly accessible from https://github.com/sukanyapatra1997/ForecastAD. Additionally, as our dataset is confidential, we release a simulated dataset at https://tinyurl.com/kdd2024Dataset.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Detecting+Abnormal+Operations+in+Concentrated+Solar+Power+Plants+from+Irregular+Sequences+of+Thermal+Images)|0| -|[Spatio-Temporal Consistency Enhanced Differential Network for Interpretable Indoor Temperature Prediction](https://doi.org/10.1145/3637528.3671608)|Dekang Qi, Xiuwen Yi, Chengjie Guo, Yanyong Huang, Junbo Zhang, Tianrui Li, Yu Zheng|Southwestern University of Finance and Economics, Chengdu, China; JD iCity, JD Technology & JD Intelligent Cities Research, Beijing, China; Xidian University, Xi'an, China; Southwest Jiaotong University, Chengdu, China; Southwest Jiaotong University & JD iCity, JD Technology, Chengdu, China|Indoor temperature prediction is crucial for decision-making in central heating systems. Beyond accuracy, predictions shall be interpretable, i.e. conform to the laws of physics; otherwise, it may lead to system failures or unsafe conditions. However, deep learning models often face criticism regarding interpretability, which limits their application in such settings. To this end, we propose a Spatio-Temporal Consistency enhanced Differential Network (CONST) for interpretable indoor temperature prediction. Our approach mainly consists of a differential predictive module and a spatio-temporal consistency module. Modeling the influential factors, the first module solves the issue of multicollinearity through the differential operation. Considering the heterogeneity of global and local data distributions, the second module characterizes the temporal and spatial consistency to mine the universal pattern by multi-task learning, thereby improving the prediction interpretability. Besides, we propose a set of interpretability metrics to overcome the drawbacks of partial dependence plot metric, which are more practical, zero-centered, flexible, and numerical. We conclude experiments on a real-world dataset with four heating stations. The results demonstrate the advantages of our approach over various baselines, where the interpretability can be improved by more than 8 times on cRPD while maintaining high accuracy. We developed CONST on the SmartHeat system, providing hourly indoor temperature forecasts for 13 heating stations in northern China.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Spatio-Temporal+Consistency+Enhanced+Differential+Network+for+Interpretable+Indoor+Temperature+Prediction)|0| -|[Addressing Shortcomings in Fair Graph Learning Datasets: Towards a New Benchmark](https://doi.org/10.1145/3637528.3671616)|Xiaowei Qian, Zhimeng Guo, Jialiang Li, Haitao Mao, Bingheng Li, Suhang Wang, Yao Ma|New Jersey Institute of Technology, Newark, NJ, USA; Michigan State University, East Lansing, MI, USA; The Pennsylvania State University, University Park, PA, USA; Rensselaer Polytechnic Institute, Troy, NY, USA|Fair graph learning plays a pivotal role in numerous practical applications.Recently, many fair graph learning methods have been proposed; however, theirevaluation often relies on poorly constructed semi-synthetic datasets orsubstandard real-world datasets. In such cases, even a basic MultilayerPerceptron (MLP) can outperform Graph Neural Networks (GNNs) in both utilityand fairness. In this work, we illustrate that many datasets fail to providemeaningful information in the edges, which may challenge the necessity of usinggraph structures in these problems. To address these issues, we develop andintroduce a collection of synthetic, semi-synthetic, and real-world datasetsthat fulfill a broad spectrum of requirements. These datasets are thoughtfullydesigned to include relevant graph structures and bias information crucial forthe fair evaluation of models. The proposed synthetic and semi-syntheticdatasets offer the flexibility to create data with controllable biasparameters, thereby enabling the generation of desired datasets withuser-defined bias values with ease. Moreover, we conduct systematic evaluationsof these proposed datasets and establish a unified evaluation approach for fairgraph learning models. Our extensive experimental results with fair graphlearning methods across our datasets demonstrate their effectiveness inbenchmarking the performance of these methods. Our datasets and the code forreproducing our experiments are available athttps://github.com/XweiQ/Benchmark-GraphFairness.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Addressing+Shortcomings+in+Fair+Graph+Learning+Datasets:+Towards+a+New+Benchmark)|0| -|[Class-incremental Learning for Time Series: Benchmark and Evaluation](https://doi.org/10.1145/3637528.3671581)|Zhongzheng Qiao, Quang Pham, Zhen Cao, Hoang H. Le, Ponnuthurai N. Suganthan, Xudong Jiang, Savitha Ramasamy|School of Electrical and Electronic Engineering, NTU, Singapore, Singapore; I2R, ASTAR, Singapore, Singapore; Qatar University, Dohar, Qatar; Ho Chi Minh University of Science, Vietnam National University, Ho Chi Minh City, Vietnam; I2R, ASTAR & CNRSCREATE, Singapore, Singapore; IGP-ERIN, NTU & I2R, ASTAR, Singapore, Singapore|Real-world environments are inherently non-stationary, frequently introducingnew classes over time. This is especially common in time series classification,such as the emergence of new disease classification in healthcare or theaddition of new activities in human activity recognition. In such cases, alearning system is required to assimilate novel classes effectively whileavoiding catastrophic forgetting of the old ones, which gives rise to theClass-incremental Learning (CIL) problem. However, despite the encouragingprogress in the image and language domains, CIL for time series data remainsrelatively understudied. Existing studies suffer from inconsistent experimentaldesigns, necessitating a comprehensive evaluation and benchmarking of methodsacross a wide range of datasets. To this end, we first present an overview ofthe Time Series Class-incremental Learning (TSCIL) problem, highlight itsunique challenges, and cover the advanced methodologies. Further, based onstandardized settings, we develop a unified experimental framework thatsupports the rapid development of new algorithms, easy integration of newdatasets, and standardization of the evaluation process. Using this framework,we conduct a comprehensive evaluation of various generic andtime-series-specific CIL methods in both standard and privacy-sensitivescenarios. Our extensive experiments not only provide a standard baseline tosupport future research but also shed light on the impact of various designfactors such as normalization layers or memory budget thresholds. Codes areavailable at https://github.com/zqiao11/TSCIL.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Class-incremental+Learning+for+Time+Series:+Benchmark+and+Evaluation)|0| +|[Spatio-Temporal Consistency Enhanced Differential Network for Interpretable Indoor Temperature Prediction](https://doi.org/10.1145/3637528.3671608)|Dekang Qi, Xiuwen Yi, Chengjie Guo, Yanyong Huang, Junbo Zhang, Tianrui Li, Yu Zheng|Southwest Jiaotong University, Chengdu, China; Southwestern University of Finance and Economics, Chengdu, China; Xidian University, Xi'an, China; Southwest Jiaotong University & JD iCity, JD Technology, Chengdu, China; JD iCity, JD Technology & JD Intelligent Cities Research, Beijing, China|Indoor temperature prediction is crucial for decision-making in central heating systems. Beyond accuracy, predictions shall be interpretable, i.e. conform to the laws of physics; otherwise, it may lead to system failures or unsafe conditions. However, deep learning models often face criticism regarding interpretability, which limits their application in such settings. To this end, we propose a Spatio-Temporal Consistency enhanced Differential Network (CONST) for interpretable indoor temperature prediction. Our approach mainly consists of a differential predictive module and a spatio-temporal consistency module. Modeling the influential factors, the first module solves the issue of multicollinearity through the differential operation. Considering the heterogeneity of global and local data distributions, the second module characterizes the temporal and spatial consistency to mine the universal pattern by multi-task learning, thereby improving the prediction interpretability. Besides, we propose a set of interpretability metrics to overcome the drawbacks of partial dependence plot metric, which are more practical, zero-centered, flexible, and numerical. We conclude experiments on a real-world dataset with four heating stations. The results demonstrate the advantages of our approach over various baselines, where the interpretability can be improved by more than 8 times on cRPD while maintaining high accuracy. We developed CONST on the SmartHeat system, providing hourly indoor temperature forecasts for 13 heating stations in northern China.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Spatio-Temporal+Consistency+Enhanced+Differential+Network+for+Interpretable+Indoor+Temperature+Prediction)|0| +|[Addressing Shortcomings in Fair Graph Learning Datasets: Towards a New Benchmark](https://doi.org/10.1145/3637528.3671616)|Xiaowei Qian, Zhimeng Guo, Jialiang Li, Haitao Mao, Bingheng Li, Suhang Wang, Yao Ma|The Pennsylvania State University, University Park, PA, USA; New Jersey Institute of Technology, Newark, NJ, USA; Rensselaer Polytechnic Institute, Troy, NY, USA; Michigan State University, East Lansing, MI, USA|Fair graph learning plays a pivotal role in numerous practical applications.Recently, many fair graph learning methods have been proposed; however, theirevaluation often relies on poorly constructed semi-synthetic datasets orsubstandard real-world datasets. In such cases, even a basic MultilayerPerceptron (MLP) can outperform Graph Neural Networks (GNNs) in both utilityand fairness. In this work, we illustrate that many datasets fail to providemeaningful information in the edges, which may challenge the necessity of usinggraph structures in these problems. To address these issues, we develop andintroduce a collection of synthetic, semi-synthetic, and real-world datasetsthat fulfill a broad spectrum of requirements. These datasets are thoughtfullydesigned to include relevant graph structures and bias information crucial forthe fair evaluation of models. The proposed synthetic and semi-syntheticdatasets offer the flexibility to create data with controllable biasparameters, thereby enabling the generation of desired datasets withuser-defined bias values with ease. Moreover, we conduct systematic evaluationsof these proposed datasets and establish a unified evaluation approach for fairgraph learning models. Our extensive experimental results with fair graphlearning methods across our datasets demonstrate their effectiveness inbenchmarking the performance of these methods. Our datasets and the code forreproducing our experiments are available athttps://github.com/XweiQ/Benchmark-GraphFairness.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Addressing+Shortcomings+in+Fair+Graph+Learning+Datasets:+Towards+a+New+Benchmark)|0| +|[Class-incremental Learning for Time Series: Benchmark and Evaluation](https://doi.org/10.1145/3637528.3671581)|Zhongzheng Qiao, Quang Pham, Zhen Cao, Hoang H. Le, Ponnuthurai N. Suganthan, Xudong Jiang, Savitha Ramasamy|I2R, ASTAR, Singapore, Singapore; Qatar University, Dohar, Qatar; Ho Chi Minh University of Science, Vietnam National University, Ho Chi Minh City, Vietnam; IGP-ERIN, NTU & I2R, ASTAR, Singapore, Singapore; I2R, ASTAR & CNRSCREATE, Singapore, Singapore; School of Electrical and Electronic Engineering, NTU, Singapore, Singapore|Real-world environments are inherently non-stationary, frequently introducingnew classes over time. This is especially common in time series classification,such as the emergence of new disease classification in healthcare or theaddition of new activities in human activity recognition. In such cases, alearning system is required to assimilate novel classes effectively whileavoiding catastrophic forgetting of the old ones, which gives rise to theClass-incremental Learning (CIL) problem. However, despite the encouragingprogress in the image and language domains, CIL for time series data remainsrelatively understudied. Existing studies suffer from inconsistent experimentaldesigns, necessitating a comprehensive evaluation and benchmarking of methodsacross a wide range of datasets. To this end, we first present an overview ofthe Time Series Class-incremental Learning (TSCIL) problem, highlight itsunique challenges, and cover the advanced methodologies. Further, based onstandardized settings, we develop a unified experimental framework thatsupports the rapid development of new algorithms, easy integration of newdatasets, and standardization of the evaluation process. Using this framework,we conduct a comprehensive evaluation of various generic andtime-series-specific CIL methods in both standard and privacy-sensitivescenarios. Our extensive experiments not only provide a standard baseline tosupport future research but also shed light on the impact of various designfactors such as normalization layers or memory budget thresholds. Codes areavailable at https://github.com/zqiao11/TSCIL.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Class-incremental+Learning+for+Time+Series:+Benchmark+and+Evaluation)|0| |[Leveraging Exposure Networks for Detecting Fake News Sources](https://doi.org/10.1145/3637528.3671539)|Maor Reuben, Lisa Friedland, Rami Puzis, Nir Grinberg|Ben-Gurion University of the Negev Software and Information Systems Engineering, Beer Sheva, Israel; Independent researcher, Boston, MA, USA|The scale and dynamic nature of the Web makes real-time detection of misinformation an extremely difficult task. Prior research mostly focused on offline (retrospective) detection of stories or claims using linguistic features of the content, flagging by users, and crowdsourced labels. Here, we develop a novel machine-learning methodology for detecting fake news sources using active learning, and examine the contribution of network, audience, and text features to the model accuracy. Importantly, we evaluate performance in both offline and online settings, mimicking the strategic choices fact-checkers have to make in practice as news sources emerge over time. We find that exposure networks provide information on considerably more sources than sharing networks (+49.6%), and that the inclusion of exposure features greatly improves classification PR-AUC in both offline (+33%) and online (+69.2%) settings. Textual features perform best in offline settings, but their performance deteriorates by 12.0-18.7% in online settings. Finally, the results show that a few iterations of active learning are sufficient for our model to attain predictive performance to comparable exhaustive labeling while incurring only 24.7% of the labeling costs. These results stress the importance of exposure networks as a source of valuable information for the investigation of information dissemination in social networks and question the robustness of textual features.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Leveraging+Exposure+Networks+for+Detecting+Fake+News+Sources)|0| |[Tackling Concept Shift in Text Classification using Entailment-style Modeling](https://doi.org/10.1145/3637528.3671541)|Sumegh Roychowdhury, Karan Gupta, Siva Rajesh Kasa, Prasanna Srinivasa Murthy|Amazon, Bengaluru, India; Amazon, Bangalore, India|Pre-trained language models (PLMs) have seen tremendous success in text classification (TC) problems in the context of Natural Language Processing (NLP). In many real-world text classification tasks, the class definitions being learned do not remain constant but rather change with time - this is known as concept shift. Most techniques for handling concept shift rely on retraining the old classifiers with the newly labelled data. However, given the amount of training data required to fine-tune large DL models for the new concepts, the associated labelling costs can be prohibitively expensive and time consuming. In this work, we propose a reformulation, converting vanilla classification into an entailment-style problem that requires significantly less data to re-train the text classifier to adapt to new concepts. We demonstrate the effectiveness of our proposed method on both real world & synthetic datasets achieving absolute F1 gains upto ~6% and ~30% respectively in few-shot settings. Further, upon deployment, our solution also helped save 75% direct labeling costs and 40% downstream labeling costs overall in a span of 3 months.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Tackling+Concept+Shift+in+Text+Classification+using+Entailment-style+Modeling)|0| -|[Hierarchical Knowledge Guided Fault Intensity Diagnosis of Complex Industrial Systems](https://doi.org/10.1145/3637528.3671610)|Yu Sha, Shuiping Gou, Bo Liu, Johannes Faber, Ningtao Liu, Stefan Schramm, Horst Stoecker, Thomas Steckenreiter, Domagoj Vnucec, Nadine Wetzstein, Andreas Widl, Kai Zhou|Xidian University, Xian, Shaanxi, China; FIAS, Frankfurt, Hessian, Germany; CUHK-SZ and FIAS, Shenzhen, Guangdong, China; FIAS, Goethe Universität and GSI, Frankfurt, Hessian, Germany; Xidian University, FIAS and XF-IJRC, Xian, Shaanxi, China; SAMSON AG, Feankfurt, Hessian, Germany; SAMSON AG, Frankfurt, Hessian, Germany|Fault intensity diagnosis (FID) plays a pivotal role in monitoring and maintaining mechanical devices within complex industrial systems. As current FID methods are based on chain of thought without considering dependencies among target classes. To capture and explore dependencies, we propose a hierarchical knowledge guided fault intensity diagnosis framework (HKG) inspired by the tree of thought, which is amenable to any representation learning methods. The HKG uses graph convolutional networks to map the hierarchical topological graph of class representations into a set of interdependent global hierarchical classifiers, where each node is denoted by word embeddings of a class. These global hierarchical classifiers are applied to learned deep features extracted by representation learning, allowing the entire model to be end-to-end learnable. In addition, we develop a re-weighted hierarchical knowledge correlation matrix (Re-HKCM) scheme by embedding inter-class hierarchical knowledge into a data-driven statistical correlation matrix (SCM) which effectively guides the information sharing of nodes in graphical convolutional neural networks and avoids over-smoothing issues. The Re-HKCM is derived from the SCM through a series of mathematical transformations. Extensive experiments are performed on four real-world datasets from different industrial domains (three cavitation datasets from SAMSON AG and one existing publicly) for FID, all showing superior results and outperform recent state-of-the-art FID methods.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Hierarchical+Knowledge+Guided+Fault+Intensity+Diagnosis+of+Complex+Industrial+Systems)|0| -|[Lumos: Empowering Multimodal LLMs with Scene Text Recognition](https://doi.org/10.1145/3637528.3671633)|Ashish Shenoy, Yichao Lu, Srihari Jayakumar, Debojeet Chatterjee, Mohsen Moslehpour, Pierce Chuang, Abhay Harpale, Vikas Bhardwaj, Di Xu, Shicong Zhao, Longfang Zhao, Ankit Ramchandani, Xin Luna Dong, Anuj Kumar|Meta, Menlo Park, CA, USA; Meta Reality Labs, Menlo Park, CA, USA; Reality Labs, Meta, Redmond, WA, USA; Meta Reality Labs, Redmond, WA, USA|We introduce Lumos, the first end-to-end multimodal question-answering systemwith text understanding capabilities. At the core of Lumos is a Scene TextRecognition (STR) component that extracts text from first person point-of-viewimages, the output of which is used to augment input to a Multimodal LargeLanguage Model (MM-LLM). While building Lumos, we encountered numerouschallenges related to STR quality, overall latency, and model inference. Inthis paper, we delve into those challenges, and discuss the systemarchitecture, design choices, and modeling techniques employed to overcomethese obstacles. We also provide a comprehensive evaluation for each component,showcasing high quality and efficiency.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Lumos:+Empowering+Multimodal+LLMs+with+Scene+Text+Recognition)|0| -|[From Variability to Stability: Advancing RecSys Benchmarking Practices](https://doi.org/10.1145/3637528.3671655)|Valeriy Shevchenko, Nikita Belousov, Alexey Vasilev, Vladimir Zholobov, Artyom Sosedka, Natalia Semenova, Anna Volodkevich, Andrey Savchenko, Alexey Zaytsev|Skoltech, Moscow, Russian Federation; Skoltech & MIPT, Moscow, Russian Federation; AIRI & Sber AI Lab, Moscow, Russian Federation; Sber AI Lab, Moscow, Russian Federation; Skoltech & BIMSA, Moscow, Russian Federation|In the rapidly evolving domain of Recommender Systems (RecSys), newalgorithms frequently claim state-of-the-art performance based on evaluationsover a limited set of arbitrarily selected datasets. However, this approach mayfail to holistically reflect their effectiveness due to the significant impactof dataset characteristics on algorithm performance. Addressing thisdeficiency, this paper introduces a novel benchmarking methodology tofacilitate a fair and robust comparison of RecSys algorithms, thereby advancingevaluation practices. By utilizing a diverse set of 30 open datasets,including two introduced in this work, and evaluating 11 collaborativefiltering algorithms across 9 metrics, we critically examine the influence ofdataset characteristics on algorithm performance. We further investigate thefeasibility of aggregating outcomes from multiple datasets into a unifiedranking. Through rigorous experimental analysis, we validate the reliability ofour methodology under the variability of datasets, offering a benchmarkingstrategy that balances quality and computational demands. This methodologyenables a fair yet effective means of evaluating RecSys algorithms, providingvaluable guidance for future research endeavors.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=From+Variability+to+Stability:+Advancing+RecSys+Benchmarking+Practices)|0| +|[Hierarchical Knowledge Guided Fault Intensity Diagnosis of Complex Industrial Systems](https://doi.org/10.1145/3637528.3671610)|Yu Sha, Shuiping Gou, Bo Liu, Johannes Faber, Ningtao Liu, Stefan Schramm, Horst Stoecker, Thomas Steckenreiter, Domagoj Vnucec, Nadine Wetzstein, Andreas Widl, Kai Zhou|Xidian University, FIAS and XF-IJRC, Xian, Shaanxi, China; FIAS, Frankfurt, Hessian, Germany; SAMSON AG, Frankfurt, Hessian, Germany; FIAS, Goethe Universität and GSI, Frankfurt, Hessian, Germany; CUHK-SZ and FIAS, Shenzhen, Guangdong, China; Xidian University, Xian, Shaanxi, China; SAMSON AG, Feankfurt, Hessian, Germany|Fault intensity diagnosis (FID) plays a pivotal role in monitoring and maintaining mechanical devices within complex industrial systems. As current FID methods are based on chain of thought without considering dependencies among target classes. To capture and explore dependencies, we propose a hierarchical knowledge guided fault intensity diagnosis framework (HKG) inspired by the tree of thought, which is amenable to any representation learning methods. The HKG uses graph convolutional networks to map the hierarchical topological graph of class representations into a set of interdependent global hierarchical classifiers, where each node is denoted by word embeddings of a class. These global hierarchical classifiers are applied to learned deep features extracted by representation learning, allowing the entire model to be end-to-end learnable. In addition, we develop a re-weighted hierarchical knowledge correlation matrix (Re-HKCM) scheme by embedding inter-class hierarchical knowledge into a data-driven statistical correlation matrix (SCM) which effectively guides the information sharing of nodes in graphical convolutional neural networks and avoids over-smoothing issues. The Re-HKCM is derived from the SCM through a series of mathematical transformations. Extensive experiments are performed on four real-world datasets from different industrial domains (three cavitation datasets from SAMSON AG and one existing publicly) for FID, all showing superior results and outperform recent state-of-the-art FID methods.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Hierarchical+Knowledge+Guided+Fault+Intensity+Diagnosis+of+Complex+Industrial+Systems)|0| +|[Lumos: Empowering Multimodal LLMs with Scene Text Recognition](https://doi.org/10.1145/3637528.3671633)|Ashish Shenoy, Yichao Lu, Srihari Jayakumar, Debojeet Chatterjee, Mohsen Moslehpour, Pierce Chuang, Abhay Harpale, Vikas Bhardwaj, Di Xu, Shicong Zhao, Longfang Zhao, Ankit Ramchandani, Xin Luna Dong, Anuj Kumar|Reality Labs, Meta, Redmond, WA, USA; Meta, Menlo Park, CA, USA; Meta Reality Labs, Menlo Park, CA, USA; Meta Reality Labs, Redmond, WA, USA|We introduce Lumos, the first end-to-end multimodal question-answering systemwith text understanding capabilities. At the core of Lumos is a Scene TextRecognition (STR) component that extracts text from first person point-of-viewimages, the output of which is used to augment input to a Multimodal LargeLanguage Model (MM-LLM). While building Lumos, we encountered numerouschallenges related to STR quality, overall latency, and model inference. Inthis paper, we delve into those challenges, and discuss the systemarchitecture, design choices, and modeling techniques employed to overcomethese obstacles. We also provide a comprehensive evaluation for each component,showcasing high quality and efficiency.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Lumos:+Empowering+Multimodal+LLMs+with+Scene+Text+Recognition)|0| +|[From Variability to Stability: Advancing RecSys Benchmarking Practices](https://doi.org/10.1145/3637528.3671655)|Valeriy Shevchenko, Nikita Belousov, Alexey Vasilev, Vladimir Zholobov, Artyom Sosedka, Natalia Semenova, Anna Volodkevich, Andrey Savchenko, Alexey Zaytsev|Skoltech, Moscow, Russian Federation; Skoltech & MIPT, Moscow, Russian Federation; Sber AI Lab, Moscow, Russian Federation; Skoltech & BIMSA, Moscow, Russian Federation; AIRI & Sber AI Lab, Moscow, Russian Federation|In the rapidly evolving domain of Recommender Systems (RecSys), newalgorithms frequently claim state-of-the-art performance based on evaluationsover a limited set of arbitrarily selected datasets. However, this approach mayfail to holistically reflect their effectiveness due to the significant impactof dataset characteristics on algorithm performance. Addressing thisdeficiency, this paper introduces a novel benchmarking methodology tofacilitate a fair and robust comparison of RecSys algorithms, thereby advancingevaluation practices. By utilizing a diverse set of 30 open datasets,including two introduced in this work, and evaluating 11 collaborativefiltering algorithms across 9 metrics, we critically examine the influence ofdataset characteristics on algorithm performance. We further investigate thefeasibility of aggregating outcomes from multiple datasets into a unifiedranking. Through rigorous experimental analysis, we validate the reliability ofour methodology under the variability of datasets, offering a benchmarkingstrategy that balances quality and computational demands. This methodologyenables a fair yet effective means of evaluating RecSys algorithms, providingvaluable guidance for future research endeavors.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=From+Variability+to+Stability:+Advancing+RecSys+Benchmarking+Practices)|0| |[Improving Ego-Cluster for Network Effect Measurement](https://doi.org/10.1145/3637528.3671557)|Wentao Su, Weitao Duan|LinkedIn Corporation, Sunnyvale, CA, USA|Network effect is common in social network platforms. Many new features in social networks are designed to specifically create network effect to improve user engagement. For example, content creators tend to produce more when their articles and posts receive more positive feedback from followers. This paper discusses a new cluster-level experimentation methodology to measure the creator-side metrics in the context of A/B experiment. The methodology is designed to address the cases when the experiment randomization unit and the metric measurement unit are not the same, and it is a part of the overall strategy at LinkedIn to promote a robust creator community and ecosystem. The method is developed based on the widely-cited research at LinkedIn, but significantly improves the clustering algorithm efficiency and flexibility, leading to a stronger capability of the creator-side metrics measurement and increasing velocity for creator-related experiments.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Improving+Ego-Cluster+for+Network+Effect+Measurement)|0| -|[Beimingwu: A Learnware Dock System](https://doi.org/10.1145/3637528.3671617)|ZhiHao Tan, JianDong Liu, XiaoDong Bi, Peng Tan, QinCheng Zheng, HaiTian Liu, Yi Xie, XiaoChuan Zou, Yang Yu, ZhiHua Zhou|; Nanjing University; Nanjing University School of Artificial Intelligence|The learnware paradigm proposed by Zhou (2016) aims to enable users to leverage numerous existing high-performing models instead of building machine learning models from scratch. This paradigm envisions that: Any developer worldwide can submit their well-trained models spontaneously into a learnware dock system (formerly known as learnware market). The system uniformly generates a specification for each model to form a learnware and accommodates it. As the key component, a specification should represent the capabilities of the model while preserving developer's original data. Based on the specifications, the learnware dock system can identify and assemble existing learnwares for users to solve new machine learning tasks. Recently, based on reduced kernel mean embedding (RKME) specification, a series of studies have shown the effectiveness of the learnware paradigm theoretically and empirically. However, the realization of a learnware dock system is still missing and remains a big challenge. This paper proposes Beimingwu, the first open-source learnware dock system, providing foundational support for future research. The system provides implementations and extensibility for the entire process of learnware paradigm, including the submitting, usability testing, organization, identification, deployment, and reuse of learnwares. Utilizing Beimingwu, the model development for new user tasks can be significantly streamlined, thanks to integrated architecture and engine design, specifying unified learnware structure and scalable APIs, and the integration of various algorithms for learnware identification and reuse. Notably, this is possible even for users with limited data and minimal expertise in machine learning, without compromising the raw data's security. The system facilitates the future research implementations in learnware-related algorithms and systems, and lays the ground for hosting a vast array of learnwares and establishing a learnware ecosystem. The system is fully open-source and we expect the research community to benefit from the system. The system and research toolkit have been released on GitLink and GitHub.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Beimingwu:+A+Learnware+Dock+System)|0| +|[Beimingwu: A Learnware Dock System](https://doi.org/10.1145/3637528.3671617)|ZhiHao Tan, JianDong Liu, XiaoDong Bi, Peng Tan, QinCheng Zheng, HaiTian Liu, Yi Xie, XiaoChuan Zou, Yang Yu, ZhiHua Zhou|; Nanjing University School of Artificial Intelligence; Nanjing University|The learnware paradigm proposed by Zhou (2016) aims to enable users to leverage numerous existing high-performing models instead of building machine learning models from scratch. This paradigm envisions that: Any developer worldwide can submit their well-trained models spontaneously into a learnware dock system (formerly known as learnware market). The system uniformly generates a specification for each model to form a learnware and accommodates it. As the key component, a specification should represent the capabilities of the model while preserving developer's original data. Based on the specifications, the learnware dock system can identify and assemble existing learnwares for users to solve new machine learning tasks. Recently, based on reduced kernel mean embedding (RKME) specification, a series of studies have shown the effectiveness of the learnware paradigm theoretically and empirically. However, the realization of a learnware dock system is still missing and remains a big challenge. This paper proposes Beimingwu, the first open-source learnware dock system, providing foundational support for future research. The system provides implementations and extensibility for the entire process of learnware paradigm, including the submitting, usability testing, organization, identification, deployment, and reuse of learnwares. Utilizing Beimingwu, the model development for new user tasks can be significantly streamlined, thanks to integrated architecture and engine design, specifying unified learnware structure and scalable APIs, and the integration of various algorithms for learnware identification and reuse. Notably, this is possible even for users with limited data and minimal expertise in machine learning, without compromising the raw data's security. The system facilitates the future research implementations in learnware-related algorithms and systems, and lays the ground for hosting a vast array of learnwares and establishing a learnware ecosystem. The system is fully open-source and we expect the research community to benefit from the system. The system and research toolkit have been released on GitLink and GitHub.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Beimingwu:+A+Learnware+Dock+System)|0| |[Business Policy Experiments using Fractional Factorial Designs: Consumer Retention on DoorDash](https://doi.org/10.1145/3637528.3671574)|Yixin Tang, Yicong Lin, Navdeep S. Sahni|Stanford GSB, Stanford, CA, USA; DoorDash, Inc., San Francisco, CA, USA|This paper investigates an approach to both speed up business decision-making and lower the cost of learning through experimentation by factorizing business policies and employing fractional factorial experimental designs for their evaluation. We illustrate how this method integrates with advances in the estimation of heterogeneous treatment effects, elaborating on its advantages and foundational assumptions. We empirically demonstrate the implementation and benefits of our approach and assess its validity in evaluating consumer promotion policies at DoorDash, which is one of the largest delivery platforms in the US. Our approach discovers a policy with 5% incremental profit at 67% lower implementation cost.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Business+Policy+Experiments+using+Fractional+Factorial+Designs:+Consumer+Retention+on+DoorDash)|0| -|[TnT-LLM: Text Mining at Scale with Large Language Models](https://doi.org/10.1145/3637528.3671647)|Mengting Wan, Tara Safavi, Sujay Kumar Jauhar, Yujin Kim, Scott Counts, Jennifer Neville, Siddharth Suri, Chirag Shah, Ryen W. White, Longqi Yang, Reid Andersen, Georg Buscher, Dhruv Joshi, Nagu Rangan|University of Washington, Seattle, WA, USA; Microsoft Corporation, Redmond, WA, USA|Transforming unstructured text into structured and meaningful forms, organized by useful category labels, is a fundamental step in text mining for downstream analysis and application. However, most existing methods for producing label taxonomies and building text-based label classifiers still rely heavily on domain expertise and manual curation, making the process expensive and time-consuming. This is particularly challenging when the label space is under-specified and large-scale data annotations are unavailable. In this paper, we address these challenges with Large Language Models (LLMs), whose prompt-based interface facilitates the induction and use of large-scale pseudo labels. We propose TnT-LLM, a two-phase framework that employs LLMs to automate the process of end-to-end label generation and assignment with minimal human effort for any given use-case. In the first phase, we introduce a zero-shot, multi-stage reasoning approach which enables LLMs to produce and refine a label taxonomy iteratively. In the second phase, LLMs are used as data labelers that yield training samples so that lightweight supervised classifiers can be reliably built, deployed, and served at scale. We apply TnT-LLM to the analysis of user intent and conversational domain for Bing Copilot (formerly Bing Chat), an open-domain chat-based search engine. Extensive experiments using both human and automatic evaluation metrics demonstrate that TnT-LLM generates more accurate and relevant label taxonomies when compared against state-of-the-art baselines, and achieves a favorable balance between accuracy and efficiency for classification at scale.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=TnT-LLM:+Text+Mining+at+Scale+with+Large+Language+Models)|0| -|[Know Your Needs Better: Towards Structured Understanding of Marketer Demands with Analogical Reasoning Augmented LLMs](https://doi.org/10.1145/3637528.3671583)|Junjie Wang, Dan Yang, Binbin Hu, Yue Shen, Wen Zhang, Jinjie Gu|Zhejiang University, Hangzhou, China; Ant Group, Hangzhou, China; Zhejiang University & Ant Group, Hangzhou, China|In this paper, we explore a new way for user targeting, where non-expert marketers could select their target users solely given demands in natural language form. The key to this issue is how to transform natural languages into practical structured logical languages, i.e., the structured understanding of marketer demands. In practical scenarios, the demands of non-expert marketers are often abstract and diverse. Considering the impressive natural language processing ability of large language models (LLMs), we try to leverage LLMs to solve this issue. To stimulate the LLMs' reasoning ability, the chain-of-thought (CoT) prompting method is widely used, but existing methods still have some limitations in our scenario: (1) Previous methods either use simple "Let's think step by step" spells or provide fixed examples in demonstrations without considering compatibility between prompts and concrete questions, making LLMs ineffective when the marketers' demands are abstract and diverse. (2) Previous methods are often implemented in closed-source models or excessively large models, which is not suitable in industrial practical scenarios. Based on these, we propose ARALLM (i.e., Analogical Reasoning Augmented Large Language Models) consisting of two modules: Analogical Reasoning based Prompting and Reasoning-Augmented Multi-Task Model Distillation. Then, we adopt a retrieval-based method to conduct analogical reasoning with the help of the reasoning library. The experimental results show that this prompting strategy achieves better performance than the ordinary prompting method. Beyond that, we distill knowledge from super LLMs (GPT-3.5) to fine-tune smaller student LLMs in a multi-task training paradigm, enabling the models to be easily deployed in practical environments. Part of our data and code can be found at https://github.com/alipay/Analogic-Reasoning-Augmented-Large-Language-Model.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Know+Your+Needs+Better:+Towards+Structured+Understanding+of+Marketer+Demands+with+Analogical+Reasoning+Augmented+LLMs)|0| -|[COMET: NFT Price Prediction with Wallet Profiling](https://doi.org/10.1145/3637528.3671621)|Tianfu Wang, Liwei Deng, Chao Wang, Jianxun Lian, Yue Yan, Nicholas Jing Yuan, Qi Zhang, Hui Xiong|; Microsoft Inc., Suzhou, China; Microsoft Inc., Beijing, China; Microsoft Research Asia, Beijing, China|As the non-fungible token (NFT) market flourishes, price prediction emergesas a pivotal direction for investors gaining valuable insight to maximizereturns. However, existing works suffer from a lack of practical definitionsand standardized evaluations, limiting their practical application. Moreover,the influence of users' multi-behaviour transactions that are publiclyaccessible on NFT price is still not explored and exhibits challenges. In thispaper, we address these gaps by presenting a practical and hierarchical problemdefinition. This approach unifies both collection-level and token-level taskand evaluation methods, which cater to varied practical requirements ofinvestors. To further understand the impact of user behaviours on the variationof NFT price, we propose a general wallet profiling framework and develop aCOmmunity enhanced Multi-bEhavior Transaction graph model, named COMET. COMETprofiles wallets with a comprehensive view and considers the impact of diverserelations and interactions within the NFT ecosystem on NFT price variations,thereby improving prediction performance. Extensive experiments conducted inour deployed system demonstrate the superiority of COMET, underscoring itspotential in the insight toolkit for NFT investors.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=COMET:+NFT+Price+Prediction+with+Wallet+Profiling)|0| -|[Neural Optimization with Adaptive Heuristics for Intelligent Marketing System](https://doi.org/10.1145/3637528.3671591)|Changshuai Wei, Benjamin Zelditch, Joyce Chen, Andre Assuncao Silva T. Ribeiro, Jingyi Kenneth Tay, Borja Ocejo Elizondo, Sathiya Keerthi Selvaraj, Aman Gupta, Licurgo Benemann De Almeida|LinkedIn Corporation, Sunnyvale, USA; LinkedIn Corporation, New York, USA; LinkedIn Corporation, New York, NY, USA; LinkedIn Corporation, Seattle, USA|Computational marketing has become increasingly important in today's digitalworld, facing challenges such as massive heterogeneous data, multi-channelcustomer journeys, and limited marketing budgets. In this paper, we propose ageneral framework for marketing AI systems, the Neural Optimization withAdaptive Heuristics (NOAH) framework. NOAH is the first general framework formarketing optimization that considers both to-business (2B) and to-consumer(2C) products, as well as both owned and paid channels. We describe key modulesof the NOAH framework, including prediction, optimization, and adaptiveheuristics, providing examples for bidding and content optimization. We thendetail the successful application of NOAH to LinkedIn's email marketing system,showcasing significant wins over the legacy ranking system. Additionally, weshare details and insights that are broadly useful, particularly on: (i)addressing delayed feedback with lifetime value, (ii) performing large-scalelinear programming with randomization, (iii) improving retrieval with audienceexpansion, (iv) reducing signal dilution in targeting tests, and (v) handlingzero-inflated heavy-tail metrics in statistical testing.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Neural+Optimization+with+Adaptive+Heuristics+for+Intelligent+Marketing+System)|0| +|[TnT-LLM: Text Mining at Scale with Large Language Models](https://doi.org/10.1145/3637528.3671647)|Mengting Wan, Tara Safavi, Sujay Kumar Jauhar, Yujin Kim, Scott Counts, Jennifer Neville, Siddharth Suri, Chirag Shah, Ryen W. White, Longqi Yang, Reid Andersen, Georg Buscher, Dhruv Joshi, Nagu Rangan|Microsoft Corporation, Redmond, WA, USA; University of Washington, Seattle, WA, USA|Transforming unstructured text into structured and meaningful forms, organized by useful category labels, is a fundamental step in text mining for downstream analysis and application. However, most existing methods for producing label taxonomies and building text-based label classifiers still rely heavily on domain expertise and manual curation, making the process expensive and time-consuming. This is particularly challenging when the label space is under-specified and large-scale data annotations are unavailable. In this paper, we address these challenges with Large Language Models (LLMs), whose prompt-based interface facilitates the induction and use of large-scale pseudo labels. We propose TnT-LLM, a two-phase framework that employs LLMs to automate the process of end-to-end label generation and assignment with minimal human effort for any given use-case. In the first phase, we introduce a zero-shot, multi-stage reasoning approach which enables LLMs to produce and refine a label taxonomy iteratively. In the second phase, LLMs are used as data labelers that yield training samples so that lightweight supervised classifiers can be reliably built, deployed, and served at scale. We apply TnT-LLM to the analysis of user intent and conversational domain for Bing Copilot (formerly Bing Chat), an open-domain chat-based search engine. Extensive experiments using both human and automatic evaluation metrics demonstrate that TnT-LLM generates more accurate and relevant label taxonomies when compared against state-of-the-art baselines, and achieves a favorable balance between accuracy and efficiency for classification at scale.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=TnT-LLM:+Text+Mining+at+Scale+with+Large+Language+Models)|0| +|[Know Your Needs Better: Towards Structured Understanding of Marketer Demands with Analogical Reasoning Augmented LLMs](https://doi.org/10.1145/3637528.3671583)|Junjie Wang, Dan Yang, Binbin Hu, Yue Shen, Wen Zhang, Jinjie Gu|Zhejiang University, Hangzhou, China; Zhejiang University & Ant Group, Hangzhou, China; Ant Group, Hangzhou, China|In this paper, we explore a new way for user targeting, where non-expert marketers could select their target users solely given demands in natural language form. The key to this issue is how to transform natural languages into practical structured logical languages, i.e., the structured understanding of marketer demands. In practical scenarios, the demands of non-expert marketers are often abstract and diverse. Considering the impressive natural language processing ability of large language models (LLMs), we try to leverage LLMs to solve this issue. To stimulate the LLMs' reasoning ability, the chain-of-thought (CoT) prompting method is widely used, but existing methods still have some limitations in our scenario: (1) Previous methods either use simple "Let's think step by step" spells or provide fixed examples in demonstrations without considering compatibility between prompts and concrete questions, making LLMs ineffective when the marketers' demands are abstract and diverse. (2) Previous methods are often implemented in closed-source models or excessively large models, which is not suitable in industrial practical scenarios. Based on these, we propose ARALLM (i.e., Analogical Reasoning Augmented Large Language Models) consisting of two modules: Analogical Reasoning based Prompting and Reasoning-Augmented Multi-Task Model Distillation. Then, we adopt a retrieval-based method to conduct analogical reasoning with the help of the reasoning library. The experimental results show that this prompting strategy achieves better performance than the ordinary prompting method. Beyond that, we distill knowledge from super LLMs (GPT-3.5) to fine-tune smaller student LLMs in a multi-task training paradigm, enabling the models to be easily deployed in practical environments. Part of our data and code can be found at https://github.com/alipay/Analogic-Reasoning-Augmented-Large-Language-Model.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Know+Your+Needs+Better:+Towards+Structured+Understanding+of+Marketer+Demands+with+Analogical+Reasoning+Augmented+LLMs)|0| +|[COMET: NFT Price Prediction with Wallet Profiling](https://doi.org/10.1145/3637528.3671621)|Tianfu Wang, Liwei Deng, Chao Wang, Jianxun Lian, Yue Yan, Nicholas Jing Yuan, Qi Zhang, Hui Xiong|; Microsoft Inc., Suzhou, China; Microsoft Research Asia, Beijing, China; Microsoft Inc., Beijing, China|As the non-fungible token (NFT) market flourishes, price prediction emergesas a pivotal direction for investors gaining valuable insight to maximizereturns. However, existing works suffer from a lack of practical definitionsand standardized evaluations, limiting their practical application. Moreover,the influence of users' multi-behaviour transactions that are publiclyaccessible on NFT price is still not explored and exhibits challenges. In thispaper, we address these gaps by presenting a practical and hierarchical problemdefinition. This approach unifies both collection-level and token-level taskand evaluation methods, which cater to varied practical requirements ofinvestors. To further understand the impact of user behaviours on the variationof NFT price, we propose a general wallet profiling framework and develop aCOmmunity enhanced Multi-bEhavior Transaction graph model, named COMET. COMETprofiles wallets with a comprehensive view and considers the impact of diverserelations and interactions within the NFT ecosystem on NFT price variations,thereby improving prediction performance. Extensive experiments conducted inour deployed system demonstrate the superiority of COMET, underscoring itspotential in the insight toolkit for NFT investors.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=COMET:+NFT+Price+Prediction+with+Wallet+Profiling)|0| +|[Neural Optimization with Adaptive Heuristics for Intelligent Marketing System](https://doi.org/10.1145/3637528.3671591)|Changshuai Wei, Benjamin Zelditch, Joyce Chen, Andre Assuncao Silva T. Ribeiro, Jingyi Kenneth Tay, Borja Ocejo Elizondo, Sathiya Keerthi Selvaraj, Aman Gupta, Licurgo Benemann De Almeida|LinkedIn Corporation, Sunnyvale, USA; LinkedIn Corporation, Seattle, USA; LinkedIn Corporation, New York, USA; LinkedIn Corporation, New York, NY, USA|Computational marketing has become increasingly important in today's digitalworld, facing challenges such as massive heterogeneous data, multi-channelcustomer journeys, and limited marketing budgets. In this paper, we propose ageneral framework for marketing AI systems, the Neural Optimization withAdaptive Heuristics (NOAH) framework. NOAH is the first general framework formarketing optimization that considers both to-business (2B) and to-consumer(2C) products, as well as both owned and paid channels. We describe key modulesof the NOAH framework, including prediction, optimization, and adaptiveheuristics, providing examples for bidding and content optimization. We thendetail the successful application of NOAH to LinkedIn's email marketing system,showcasing significant wins over the legacy ranking system. Additionally, weshare details and insights that are broadly useful, particularly on: (i)addressing delayed feedback with lifetime value, (ii) performing large-scalelinear programming with randomization, (iii) improving retrieval with audienceexpansion, (iv) reducing signal dilution in targeting tests, and (v) handlingzero-inflated heavy-tail metrics in statistical testing.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Neural+Optimization+with+Adaptive+Heuristics+for+Intelligent+Marketing+System)|0| |[On Finding Bi-objective Pareto-optimal Fraud Prevention Rule Sets for Fintech Applications](https://doi.org/10.1145/3637528.3671521)|Chengyao Wen, Yin Lou||Rules are widely used in Fintech institutions to make fraud prevention decisions, since rules are highly interpretable thanks to their intuitive if-then structure. In practice, a two-stage framework of fraud prevention decision rule set mining is usually employed in large Fintech institutions. This paper is concerned with finding high-quality rule subsets in a bi-objective space (such as precision and recall) from an initial pool of rules. To this end, we adopt the concept of Pareto optimality and aim to find a set of non-dominated rule subsets, which constitutes a Pareto front. We propose a heuristic-based framework called PORS and we identify that the core of PORS is the problem of solution selection on the front (SSF). We provide a systematic categorization of the SSF problem and a thorough empirical evaluation of various SSF methods on both public and proprietary datasets. We also introduce a novel variant of sequential covering algorithm called SpectralRules to encourage the diversity of the initial rule set and we empirically find that SpectralRules further improves the quality of the found Pareto front. On two real application scenarios within Alipay, we demonstrate the advantages of our proposed methodology compared to existing work.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=On+Finding+Bi-objective+Pareto-optimal+Fraud+Prevention+Rule+Sets+for+Fintech+Applications)|0| |[Nested Fusion: A Method for Learning High Resolution Latent Structure of Multi-Scale Measurement Data on Mars](https://doi.org/10.1145/3637528.3671596)|Austin P. Wright, Scott Davidoff, Duen Horng Chau|California Institute of Technology, Jet Propulsion Laboratory, Pasadena, CA, USA; Georgia Tech, Atlanta, GA, USA|The Mars Perseverance Rover represents a generational change in the scale of measurements that can be taken on Mars, however this increased resolution introduces new challenges for techniques in exploratory data analysis. The multiple different instruments on the rover each measures specific properties of interest to scientists, so analyzing how underlying phenomena affect multiple different instruments together is important to understand the full picture. However each instrument has a unique resolution, making the mapping between overlapping layers of data non-trivial. In this work, we introduce Nested Fusion, a method to combine arbitrarily layered datasets of different resolutions and produce a latent distribution at the highest possible resolution, encoding complex interrelationships between different measurements and scales. Our method is efficient for large datasets, can perform inference even on unseen data, and outperforms existing methods of dimensionality reduction and latent analysis on real-world Mars rover data. We have deployed our method Nested Fusion within a Mars science team at NASA Jet Propulsion Laboratory (JPL) and through multiple rounds of participatory design enabled greatly enhanced exploratory analysis workflows for real scientists. To ensure the reproducibility of our work we have open sourced our code on GitHub at https://github.com/pixlise/NestedFusion.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Nested+Fusion:+A+Method+for+Learning+High+Resolution+Latent+Structure+of+Multi-Scale+Measurement+Data+on+Mars)|0| -|[TrajRecovery: An Efficient Vehicle Trajectory Recovery Framework based on Urban-Scale Traffic Camera Records](https://doi.org/10.1145/3637528.3671558)|Dongen Wu, Ziquan Fang, Qichen Sun, Lu Chen, Haiyang Hu, Fei Wang, Yunjun Gao|Huawei Cloud Computing Technologies Co., Ltd, Hangzhou, China; Zhejiang University, Hangzhou, China|Accurate vehicle trajectory recovery enables providing indispensable data foundations in intelligent urban transportation. However, existing methods face two challenges: i) the inability to process city-wide vehicle trajectories, and ii) the dependence on a substantial amount of accurate GPS trajectories for model training, leading to poor generalization ability. To address these issues, we propose a novel trajectory recovery system based on vehicle snapshots captured by traffic cameras, named TrajRecovery. TrajRecovery consists of three main components: i) Preprocessor processes traffic cameras and vehicle snapshots to provide necessary data for trajectory recovery; ii) Spatial Transfer Probabilistic Model (STPM) integrates road conditions and driver behavior to compute turning probability at intersections; iii) Trajectory Generator utilizes the output probabilities from STPM to recover a continuous and most likely complete trajectory. We evaluate TrajRecovery on two real datasets from a city in China, demonstrating substantial performance gains compared to state-of-the-art methods. Furthermore, our system is deployed in practical applications at Huawei Company, achieving extraordinary profits in business scenarios.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=TrajRecovery:+An+Efficient+Vehicle+Trajectory+Recovery+Framework+based+on+Urban-Scale+Traffic+Camera+Records)|0| -|[LaDe: The First Comprehensive Last-mile Express Dataset from Industry](https://doi.org/10.1145/3637528.3671548)|Lixia Wu, Haomin Wen, Haoyuan Hu, Xiaowei Mao, Yutong Xia, Ergang Shan, Jianbin Zheng, Junhong Lou, Yuxuan Liang, Liuqing Yang, Roger Zimmermann, Youfang Lin, Huaiyu Wan|; Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China; Cainiao Network, Hangzhou, China; Jiaotong University; National University of Singapore, Singapore, Singapore; School of Computer and Information Technology, Beijing Jiaotong University, Beijing, China|Real-world last-mile express datasets are crucial for research in logistics, supply chain management, and spatio-temporal data mining. Despite a plethora of algorithms developed to date, no widely accepted, publicly available last-mile express dataset exists to support research in this field. In this paper, we introduce LaDe, the first publicly available last-mile express dataset with millions of packages from the industry. LaDe has three unique characteristics: (1)Large-scale. It involves 10,677k packages of 21k couriers over 6 months of real-world operation. (2)Comprehensive information. It offers original package information, task-event information, as well as couriers' detailed trajecotries and road networks. (3)Diversity. The dataset includes data from various scenarios, including package pick-up and delivery, and from multiple cities, each with its unique spatio-temporal patterns due to their distinct characteristics such as populations. We verify LaDe on three tasks by running several classical baseline models per task. We believe that the large-scale, comprehensive, diverse feature of LaDe can offer unparalleled opportunities to researchers in the supply chain community, data mining community, and beyond. The dataset and code is publicly available at https://huggingface.co/datasets/Cainiao-AI/LaDe.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=LaDe:+The+First+Comprehensive+Last-mile+Express+Dataset+from+Industry)|0| -|[Xinyu: An Efficient LLM-based System for Commentary Generation](https://doi.org/10.1145/3637528.3671537)|Yiquan Wu, Bo Tang, Chenyang Xi, Yu Yu, Pengyu Wang, Yifei Liu, Kun Kuang, Haiying Deng, Zhiyu Li, Feiyu Xiong, Jie Hu, Peng Cheng, Zhonghao Wang, Yi Wang, Yi Luo, Mingchuan Yang|Institute for Advanced Algorithms Research, Shanghai, China; Northeastern University, Shenyang, China; State Key Laboratory of Media Convergence Production Technology and Systems, Beijing, China; Research Institute of China Telecom, Beijing, China; Zhejiang University, Hangzhou, China|Commentary provides readers with a deep understanding of events by presenting diverse arguments and evidence. However, creating commentary is a time-consuming task, even for skilled commentators. Large language models (LLMs) have simplified the process of natural language generation, but their direct application in commentary creation still faces challenges due to unique task requirements. These requirements can be categorized into two levels: 1) fundamental requirements, which include creating well-structured and logically consistent narratives, and 2) advanced requirements, which involve generating quality arguments and providing convincing evidence. In this paper, we introduce Xinyu, an efficient LLM-based system designed to assist commentators in generating Chinese commentaries. To meet the fundamental requirements, we deconstruct the generation process into sequential steps, proposing targeted strategies and supervised fine-tuning (SFT) for each step. To address the advanced requirements, we present an argument ranking model for arguments and establish a comprehensive evidence database that includes up-to-date events and classic books, thereby strengthening the substantiation of the evidence with retrieval augmented generation (RAG) technology. To evaluate the generated commentaries more fairly, corresponding to the two-level requirements, we introduce a comprehensive evaluation metric that considers five distinct perspectives in commentary generation. Our experiments confirm the effectiveness of our proposed system. We also observe a significant increase in the efficiency of commentators in real-world scenarios, with the average time spent on creating a commentary dropping from 4 hours to 20 minutes. Importantly, such an increase in efficiency does not compromise the quality of the commentaries.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Xinyu:+An+Efficient+LLM-based+System+for+Commentary+Generation)|0| -|[DuMapNet: An End-to-End Vectorization System for City-Scale Lane-Level Map Generation](https://doi.org/10.1145/3637528.3671579)|Deguo Xia, Weiming Zhang, Xiyan Liu, Wei Zhang, Chenting Gong, Jizhou Huang, Mengmeng Yang, Diange Yang|Tsinghua University, Beijing, China; Baidu Inc., Beijing, China; Tsinghua University & Baidu Inc., Beijing, China|Generating city-scale lane-level maps faces significant challenges due to the intricate urban environments, such as blurred or absent lane markings. Additionally, a standard lane-level map requires a comprehensive organization of lane groupings, encompassing lane direction, style, boundary, and topology, yet has not been thoroughly examined in prior research. These obstacles result in labor-intensive human annotation and high maintenance costs. This paper overcomes these limitations and presents an industrial-grade solution named DuMapNet that outputs standardized, vectorized map elements and their topology in an end-to-end paradigm. To this end, we propose a group-wise lane prediction (GLP) system that outputs vectorized results of lane groups by meticulously tailoring a transformer-based network. Meanwhile, to enhance generalization in challenging scenarios, such as road wear and occlusions, as well as to improve global consistency, a contextual prompts encoder (CPE) module is proposed, which leverages the predicted results of spatial neighborhoods as contextual information. Extensive experiments conducted on large-scale real-world datasets demonstrate the superiority and effectiveness of DuMapNet. Additionally, DuMapNet has already been deployed in production at Baidu Maps since June 2023, supporting lane-level map generation tasks for over 360 cities while bringing a 95% reduction in costs. This demonstrates that DuMapNet serves as a practical and cost-effective industrial solution for city-scale lane-level map generation.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=DuMapNet:+An+End-to-End+Vectorization+System+for+City-Scale+Lane-Level+Map+Generation)|0| -|[VecAug: Unveiling Camouflaged Frauds with Cohort Augmentation for Enhanced Detection](https://doi.org/10.1145/3637528.3671527)|Fei Xiao, Shaofeng Cai, Gang Chen, H. V. Jagadish, Beng Chin Ooi, Meihui Zhang|National University of Singapore & Shopee Singapore, Singapore, Singapore; National University of Singapore, Singapore, Singapore; University of Michigan, Ann Arbor, USA; Zhejiang University, Hangzhou, China; Beijing Institute of Technology, Beijing, China|Fraud detection presents a challenging task characterized by ever-evolving fraud patterns and scarce labeled data. Existing methods predominantly rely on graph-based or sequence-based approaches. While graph-based approaches connect users through shared entities to capture structural information, they remain vulnerable to fraudsters who can disrupt or manipulate these connections. In contrast, sequence-based approaches analyze users' behavioral patterns, offering robustness against tampering but overlooking the interactions between similar users. Inspired by cohort analysis in retention and healthcare, this paper introduces VecAug, a novel cohort-augmented learning framework that addresses these challenges by enhancing the representation learning of target users with personalized cohort information. To this end, we first propose a vector burn-in technique for automatic cohort identification, which retrieves a task-specific cohort for each target user. Then, to fully exploit the cohort information, we introduce an attentive cohort aggregation technique for augmenting target user representations. To improve the robustness of such cohort augmentation, we also propose a novel label-aware cohort neighbor separation mechanism to distance negative cohort neighbors and calibrate the aggregated cohort information. By integrating this cohort information with target user representations, VecAug enhances the modeling capacity and generalization capabilities of the model to be augmented. Our framework is flexible and can be seamlessly integrated with existing fraud detection models. We deploy our framework on e-commerce platforms and evaluate it on three fraud detection datasets, and results show that VecAug improves the detection performance of base models by up to 2.48% in AUC and 22.5% in R@P_0.9, outperforming state-of-the-art methods significantly.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=VecAug:+Unveiling+Camouflaged+Frauds+with+Cohort+Augmentation+for+Enhanced+Detection)|0| -|[Weather Knows What Will Occur: Urban Public Nuisance Events Prediction and Control with Meteorological Assistance](https://doi.org/10.1145/3637528.3671639)|Yi Xie, Tianyu Qiu, Yun Xiong, Xiuqi Huang, Xiaofeng Gao, Chao Chen, Qiang Wang, Haihong Li|MoE Key Lab of Artificial Intelligence, Shanghai Jiao Tong University, Shanghai, China; Shanghai Key Lab of Data Science, School of Computer Science, Fudan University, Shanghai, China; Meteorological Disaster Prevention Centre, Shanghai Meteorological Bureau, Shanghai, China; College of Computer Science, Chongqing University, Chongqing, China|Urban public nuisance events, like garbage exposure, illegal parking, facilities damage, and etc., impair the quality of life for city residents. Predicting and controlling these nuisances is crucial but complicated due to their ties to subjective and psychological factors. In this study, we reveal a significant correlation between such nuisances and meteorological indicators, influenced by the impact of climate on people's psychological states. We employ meteorology predictions that are integrated in Hawkes processes to enhance the accuracy of predicting the category and timing of these nuisances. To this end, we propose Spatial-Temporal Two-Tower Transformer (ST-T3), which simultaneously considers spatial data and further improves the prediction accuracy. Evaluated by about three-year data from both downtown and suburban Shanghai, our method outperforms both traditional and advanced prediction systems. We share a portion of the de-identified dataset for open research.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Weather+Knows+What+Will+Occur:+Urban+Public+Nuisance+Events+Prediction+and+Control+with+Meteorological+Assistance)|0| -|[Microservice Root Cause Analysis With Limited Observability Through Intervention Recognition in the Latent Space](https://doi.org/10.1145/3637528.3671530)|Zhe Xie, Shenglin Zhang, Yitong Geng, Yao Zhang, Minghua Ma, Xiaohui Nie, Zhenhe Yao, Longlong Xu, Yongqian Sun, Wentao Li, Dan Pei|Microsoft, Redmond, USA; eBay Inc., Shanghai, China; BNRist, Tsinghua University, Beijing, China; Nankai University, Tianjin, China; Computer Network Information Center, Chinese Academy of Sciences, Beijing, China|Many failure root cause analysis (RCA) algorithms for microservices have been proposed with the widespread adoption of microservices systems. Existing algorithms generally focus on RCA with ranking single-level (e.g. metric-level or service-level) root cause candidates (RCCs) with comprehensive monitoring metrics. However, many heterogeneous RCCs exist with limited observability in real-world microservices systems. Further, we find that the limited observability may result in inaccurate RCA through real-world failures in eBay. In this paper, for the first time, we propose to "model RCCs as latent variables". The core idea is to infer the status of RCCs as latent variables with related monitoring metrics instead of directly extracting features from only the observable metrics. Based on this, we propose LatentScope, an unsupervised RCA framework with heterogeneous RCCs under limited observability. A dual-space graph is proposed to model both observable and unobservable variables, with many-to-many relationships between spaces. To achieve fast inference of latent variables and RCA, we propose the LatentRegressor algorithm, which includes Regression-based Latent-space Intervention Recognition (RLIR) to achieve intervention recognition-based RCA in latent space. LatentScope has been deployed in eBay's production environment and evaluated on both eBay's real-world failures and a testbed dataset. The evaluation results show that, compared with baseline algorithms, our model significantly improves the Top-1 recall by 9.7%-57.9%. The source code of LatentScope and the dataset are available at https://github.com/NetManAIOps/LatentScope.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Microservice+Root+Cause+Analysis+With+Limited+Observability+Through+Intervention+Recognition+in+the+Latent+Space)|0| -|[Understanding the Weakness of Large Language Model Agents within a Complex Android Environment](https://doi.org/10.1145/3637528.3671650)|Mingzhe Xing, Rongkai Zhang, Hui Xue, Qi Chen, Fan Yang, Zhen Xiao|Peking University, Beijing, China; Microsoft Research, Beijing, China|Large language models (LLMs) have empowered intelligent agents to execute intricate tasks within domain-specific software such as browsers and games. However, when applied to general-purpose software systems like operating systems, LLM agents face three primary challenges. Firstly, the action space is vast and dynamic, posing difficulties for LLM agents to maintain an up-to-date understanding and deliver accurate responses. Secondly, real-world tasks often require inter-application cooperation, demanding farsighted planning from LLM agents. Thirdly, agents need to identify optimal solutions aligning with user constraints, such as security concerns and preferences. These challenges motivate AndroidArena, an environment and benchmark designed to evaluate LLM agents on a modern operating system. To address high-cost of manpower, we design a scalable and semi-automated method to construct the benchmark. In the task evaluation, AndroidArena incorporates accurate and adaptive metrics to address the issue of non-unique solutions. Our findings reveal that even state-of-the-art LLM agents struggle in cross-APP scenarios and adhering to specific constraints. Additionally, we identify a lack of four key capabilities, i.e. understanding, reasoning, exploration, and reflection, as primary reasons for the failure of LLM agents. Furthermore, we provide empirical analysis on the failure of reflection, and improve the success rate by 27% with our proposed exploration strategy. This work is the first to present valuable insights in understanding fine-grained weakness of LLM agents, and offers a path forward for future research in this area. Environment, benchmark, prompt, and evaluation code for AndroidArena are released at https://github.com/AndroidArenaAgent/AndroidArena.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Understanding+the+Weakness+of+Large+Language+Model+Agents+within+a+Complex+Android+Environment)|0| -|[XRL-Bench: A Benchmark for Evaluating and Comparing Explainable Reinforcement Learning Techniques](https://doi.org/10.1145/3637528.3671595)|Yu Xiong, Zhipeng Hu, Ye Huang, Runze Wu, Kai Guan, Xingchen Fang, Ji Jiang, Tianze Zhou, Yujing Hu, Haoyu Liu, Tangjie Lyu, Changjie Fan|Fuxi AI Lab, NetEase Inc., Hangzhou, Zhejiang, China; Fuxi AI Lab, NetEase Inc., Hangzhou, China|Reinforcement Learning (RL) has demonstrated substantial potential acrossdiverse fields, yet understanding its decision-making process, especially inreal-world scenarios where rationality and safety are paramount, is an ongoingchallenge. This paper delves in to Explainable RL (XRL), a subfield ofExplainable AI (XAI) aimed at unravelling the complexities of RL models. Ourfocus rests on state-explaining techniques, a crucial subset within XRLmethods, as they reveal the underlying factors influencing an agent's actionsat any given time. Despite their significant role, the lack of a unifiedevaluation framework hinders assessment of their accuracy and effectiveness. Toaddress this, we introduce XRL-Bench, a unified standardized benchmark tailoredfor the evaluation and comparison of XRL methods, encompassing three mainmodules: standard RL environments, explainers based on state importance, andstandard evaluators. XRL-Bench supports both tabular and image data for stateexplanation. We also propose TabularSHAP, an innovative and competitive XRLmethod. We demonstrate the practical utility of TabularSHAP in real-worldonline gaming services and offer an open-source benchmark platform for thestraightforward implementation and evaluation of XRL methods. Our contributionsfacilitate the continued progression of XRL technology.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=XRL-Bench:+A+Benchmark+for+Evaluating+and+Comparing+Explainable+Reinforcement+Learning+Techniques)|0| -|[FedGTP: Exploiting Inter-Client Spatial Dependency in Federated Graph-based Traffic Prediction](https://doi.org/10.1145/3637528.3671613)|Linghua Yang, Wantong Chen, Xiaoxi He, Shuyue Wei, Yi Xu, Zimu Zhou, Yongxin Tong|Faculty of Science and Technology, University of Macau, Macau, China; SKLCCSE Lab, Beihang University, Beijing, China; SKLCCSE Lab, Institute of Artificial Intelligence, Beihang University, Beijing, China; School of Data Science, City University of Hong Kong, Hong Kong, China|Graph-based methods have witnessed tremendous success in traffic prediction, largely attributed to their superior ability in capturing and modeling spatial dependencies. However, urban-scale traffic data are usually distributed among various owners, limited in sharing due to privacy restrictions. This fragmentation of data severely hinders interaction across clients, impeding the utilization of inter-client spatial dependencies. Existing studies have yet to address this non-trivial issue, thereby leading to sub-optimal performance. To fill this gap, we propose FedGTP, a new federated graph-based traffic prediction framework that promotes adaptive exploitation of inter-client spatial dependencies to recover close-to-optimal performance complying with privacy regulations like GDPR. We validate FedGTP via large-scale application-driven experiments on real-world datasets. Extensive baseline comparison, ablation study and case study demonstrate that FedGTP indeed surpasses existing methods through fully recovering inter-client spatial dependencies, achieving 21.08%, 13.48%, 19.90% decrease on RMSE, MAE and MAPE, respectively. Our code is available at https://github.com/LarryHawkingYoung/KDD2024_FedGTP||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=FedGTP:+Exploiting+Inter-Client+Spatial+Dependency+in+Federated+Graph-based+Traffic+Prediction)|0| -|[OpenFedLLM: Training Large Language Models on Decentralized Private Data via Federated Learning](https://doi.org/10.1145/3637528.3671582)|Rui Ye, Wenhao Wang, Jingyi Chai, Dihan Li, Zexi Li, Yinda Xu, Yaxin Du, Yanfeng Wang, Siheng Chen|Shanghai Jiao Tong University & Shanghai AI Laboratory, Shanghai, China; University of Southern California, Los Angeles, USA; Zhejiang University, Zhejiang, China; Shanghai Jiao Tong University, Shanghai, China|Trained on massive publicly available data, large language models (LLMs) have demonstrated tremendous success across various fields. While more data contributes to better performance, a disconcerting reality is that high-quality public data will be exhausted in a few years. In this paper, we offer a potential next step for contemporary LLMs: collaborative and privacy-preserving LLM training on the underutilized distributed private data via federated learning (FL), where multiple data owners collaboratively train a shared model without transmitting raw data. To achieve this, we build a concise, integrated, and research-friendly framework/codebase, named OpenFedLLM. It covers federated instruction tuning for enhancing instruction-following capability, federated value alignment for aligning with human values, and 7 representative FL algorithms. Besides, OpenFedLLM supports training on diverse domains, where we cover 8 training datasets; and provides comprehensive evaluations, where we cover 30+ evaluation metrics. Through extensive experiments, we observe that all FL algorithms outperform local training on training LLMs, demonstrating a clear performance improvement across a variety of settings. Notably, in a financial benchmark, Llama2-7B fine-tuned by applying any FL algorithm can outperform GPT-4 by a significant margin, while the model obtained through individual training cannot, demonstrating strong motivation for clients to participate in FL. The code is available at https://github.com/rui-ye/OpenFedLLM. The full version of our paper is available at https://arxiv.org/pdf/2402.06954.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=OpenFedLLM:+Training+Large+Language+Models+on+Decentralized+Private+Data+via+Federated+Learning)|0| +|[TrajRecovery: An Efficient Vehicle Trajectory Recovery Framework based on Urban-Scale Traffic Camera Records](https://doi.org/10.1145/3637528.3671558)|Dongen Wu, Ziquan Fang, Qichen Sun, Lu Chen, Haiyang Hu, Fei Wang, Yunjun Gao|Zhejiang University, Hangzhou, China; Huawei Cloud Computing Technologies Co., Ltd, Hangzhou, China|Accurate vehicle trajectory recovery enables providing indispensable data foundations in intelligent urban transportation. However, existing methods face two challenges: i) the inability to process city-wide vehicle trajectories, and ii) the dependence on a substantial amount of accurate GPS trajectories for model training, leading to poor generalization ability. To address these issues, we propose a novel trajectory recovery system based on vehicle snapshots captured by traffic cameras, named TrajRecovery. TrajRecovery consists of three main components: i) Preprocessor processes traffic cameras and vehicle snapshots to provide necessary data for trajectory recovery; ii) Spatial Transfer Probabilistic Model (STPM) integrates road conditions and driver behavior to compute turning probability at intersections; iii) Trajectory Generator utilizes the output probabilities from STPM to recover a continuous and most likely complete trajectory. We evaluate TrajRecovery on two real datasets from a city in China, demonstrating substantial performance gains compared to state-of-the-art methods. Furthermore, our system is deployed in practical applications at Huawei Company, achieving extraordinary profits in business scenarios.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=TrajRecovery:+An+Efficient+Vehicle+Trajectory+Recovery+Framework+based+on+Urban-Scale+Traffic+Camera+Records)|0| +|[LaDe: The First Comprehensive Last-mile Express Dataset from Industry](https://doi.org/10.1145/3637528.3671548)|Lixia Wu, Haomin Wen, Haoyuan Hu, Xiaowei Mao, Yutong Xia, Ergang Shan, Jianbin Zheng, Junhong Lou, Yuxuan Liang, Liuqing Yang, Roger Zimmermann, Youfang Lin, Huaiyu Wan|; Jiaotong University; National University of Singapore, Singapore, Singapore; Cainiao Network, Hangzhou, China; Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China; School of Computer and Information Technology, Beijing Jiaotong University, Beijing, China|Real-world last-mile express datasets are crucial for research in logistics, supply chain management, and spatio-temporal data mining. Despite a plethora of algorithms developed to date, no widely accepted, publicly available last-mile express dataset exists to support research in this field. In this paper, we introduce LaDe, the first publicly available last-mile express dataset with millions of packages from the industry. LaDe has three unique characteristics: (1)Large-scale. It involves 10,677k packages of 21k couriers over 6 months of real-world operation. (2)Comprehensive information. It offers original package information, task-event information, as well as couriers' detailed trajecotries and road networks. (3)Diversity. The dataset includes data from various scenarios, including package pick-up and delivery, and from multiple cities, each with its unique spatio-temporal patterns due to their distinct characteristics such as populations. We verify LaDe on three tasks by running several classical baseline models per task. We believe that the large-scale, comprehensive, diverse feature of LaDe can offer unparalleled opportunities to researchers in the supply chain community, data mining community, and beyond. The dataset and code is publicly available at https://huggingface.co/datasets/Cainiao-AI/LaDe.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=LaDe:+The+First+Comprehensive+Last-mile+Express+Dataset+from+Industry)|0| +|[Xinyu: An Efficient LLM-based System for Commentary Generation](https://doi.org/10.1145/3637528.3671537)|Yiquan Wu, Bo Tang, Chenyang Xi, Yu Yu, Pengyu Wang, Yifei Liu, Kun Kuang, Haiying Deng, Zhiyu Li, Feiyu Xiong, Jie Hu, Peng Cheng, Zhonghao Wang, Yi Wang, Yi Luo, Mingchuan Yang|Northeastern University, Shenyang, China; Zhejiang University, Hangzhou, China; Institute for Advanced Algorithms Research, Shanghai, China; State Key Laboratory of Media Convergence Production Technology and Systems, Beijing, China; Research Institute of China Telecom, Beijing, China|Commentary provides readers with a deep understanding of events by presenting diverse arguments and evidence. However, creating commentary is a time-consuming task, even for skilled commentators. Large language models (LLMs) have simplified the process of natural language generation, but their direct application in commentary creation still faces challenges due to unique task requirements. These requirements can be categorized into two levels: 1) fundamental requirements, which include creating well-structured and logically consistent narratives, and 2) advanced requirements, which involve generating quality arguments and providing convincing evidence. In this paper, we introduce Xinyu, an efficient LLM-based system designed to assist commentators in generating Chinese commentaries. To meet the fundamental requirements, we deconstruct the generation process into sequential steps, proposing targeted strategies and supervised fine-tuning (SFT) for each step. To address the advanced requirements, we present an argument ranking model for arguments and establish a comprehensive evidence database that includes up-to-date events and classic books, thereby strengthening the substantiation of the evidence with retrieval augmented generation (RAG) technology. To evaluate the generated commentaries more fairly, corresponding to the two-level requirements, we introduce a comprehensive evaluation metric that considers five distinct perspectives in commentary generation. Our experiments confirm the effectiveness of our proposed system. We also observe a significant increase in the efficiency of commentators in real-world scenarios, with the average time spent on creating a commentary dropping from 4 hours to 20 minutes. Importantly, such an increase in efficiency does not compromise the quality of the commentaries.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Xinyu:+An+Efficient+LLM-based+System+for+Commentary+Generation)|0| +|[DuMapNet: An End-to-End Vectorization System for City-Scale Lane-Level Map Generation](https://doi.org/10.1145/3637528.3671579)|Deguo Xia, Weiming Zhang, Xiyan Liu, Wei Zhang, Chenting Gong, Jizhou Huang, Mengmeng Yang, Diange Yang|Tsinghua University & Baidu Inc., Beijing, China; Tsinghua University, Beijing, China; Baidu Inc., Beijing, China|Generating city-scale lane-level maps faces significant challenges due to the intricate urban environments, such as blurred or absent lane markings. Additionally, a standard lane-level map requires a comprehensive organization of lane groupings, encompassing lane direction, style, boundary, and topology, yet has not been thoroughly examined in prior research. These obstacles result in labor-intensive human annotation and high maintenance costs. This paper overcomes these limitations and presents an industrial-grade solution named DuMapNet that outputs standardized, vectorized map elements and their topology in an end-to-end paradigm. To this end, we propose a group-wise lane prediction (GLP) system that outputs vectorized results of lane groups by meticulously tailoring a transformer-based network. Meanwhile, to enhance generalization in challenging scenarios, such as road wear and occlusions, as well as to improve global consistency, a contextual prompts encoder (CPE) module is proposed, which leverages the predicted results of spatial neighborhoods as contextual information. Extensive experiments conducted on large-scale real-world datasets demonstrate the superiority and effectiveness of DuMapNet. Additionally, DuMapNet has already been deployed in production at Baidu Maps since June 2023, supporting lane-level map generation tasks for over 360 cities while bringing a 95% reduction in costs. This demonstrates that DuMapNet serves as a practical and cost-effective industrial solution for city-scale lane-level map generation.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=DuMapNet:+An+End-to-End+Vectorization+System+for+City-Scale+Lane-Level+Map+Generation)|0| +|[VecAug: Unveiling Camouflaged Frauds with Cohort Augmentation for Enhanced Detection](https://doi.org/10.1145/3637528.3671527)|Fei Xiao, Shaofeng Cai, Gang Chen, H. V. Jagadish, Beng Chin Ooi, Meihui Zhang|University of Michigan, Ann Arbor, USA; National University of Singapore, Singapore, Singapore; National University of Singapore & Shopee Singapore, Singapore, Singapore; Zhejiang University, Hangzhou, China; Beijing Institute of Technology, Beijing, China|Fraud detection presents a challenging task characterized by ever-evolving fraud patterns and scarce labeled data. Existing methods predominantly rely on graph-based or sequence-based approaches. While graph-based approaches connect users through shared entities to capture structural information, they remain vulnerable to fraudsters who can disrupt or manipulate these connections. In contrast, sequence-based approaches analyze users' behavioral patterns, offering robustness against tampering but overlooking the interactions between similar users. Inspired by cohort analysis in retention and healthcare, this paper introduces VecAug, a novel cohort-augmented learning framework that addresses these challenges by enhancing the representation learning of target users with personalized cohort information. To this end, we first propose a vector burn-in technique for automatic cohort identification, which retrieves a task-specific cohort for each target user. Then, to fully exploit the cohort information, we introduce an attentive cohort aggregation technique for augmenting target user representations. To improve the robustness of such cohort augmentation, we also propose a novel label-aware cohort neighbor separation mechanism to distance negative cohort neighbors and calibrate the aggregated cohort information. By integrating this cohort information with target user representations, VecAug enhances the modeling capacity and generalization capabilities of the model to be augmented. Our framework is flexible and can be seamlessly integrated with existing fraud detection models. We deploy our framework on e-commerce platforms and evaluate it on three fraud detection datasets, and results show that VecAug improves the detection performance of base models by up to 2.48% in AUC and 22.5% in R@P_0.9, outperforming state-of-the-art methods significantly.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=VecAug:+Unveiling+Camouflaged+Frauds+with+Cohort+Augmentation+for+Enhanced+Detection)|0| +|[Weather Knows What Will Occur: Urban Public Nuisance Events Prediction and Control with Meteorological Assistance](https://doi.org/10.1145/3637528.3671639)|Yi Xie, Tianyu Qiu, Yun Xiong, Xiuqi Huang, Xiaofeng Gao, Chao Chen, Qiang Wang, Haihong Li|Shanghai Key Lab of Data Science, School of Computer Science, Fudan University, Shanghai, China; College of Computer Science, Chongqing University, Chongqing, China; MoE Key Lab of Artificial Intelligence, Shanghai Jiao Tong University, Shanghai, China; Meteorological Disaster Prevention Centre, Shanghai Meteorological Bureau, Shanghai, China|Urban public nuisance events, like garbage exposure, illegal parking, facilities damage, and etc., impair the quality of life for city residents. Predicting and controlling these nuisances is crucial but complicated due to their ties to subjective and psychological factors. In this study, we reveal a significant correlation between such nuisances and meteorological indicators, influenced by the impact of climate on people's psychological states. We employ meteorology predictions that are integrated in Hawkes processes to enhance the accuracy of predicting the category and timing of these nuisances. To this end, we propose Spatial-Temporal Two-Tower Transformer (ST-T3), which simultaneously considers spatial data and further improves the prediction accuracy. Evaluated by about three-year data from both downtown and suburban Shanghai, our method outperforms both traditional and advanced prediction systems. We share a portion of the de-identified dataset for open research.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Weather+Knows+What+Will+Occur:+Urban+Public+Nuisance+Events+Prediction+and+Control+with+Meteorological+Assistance)|0| +|[Microservice Root Cause Analysis With Limited Observability Through Intervention Recognition in the Latent Space](https://doi.org/10.1145/3637528.3671530)|Zhe Xie, Shenglin Zhang, Yitong Geng, Yao Zhang, Minghua Ma, Xiaohui Nie, Zhenhe Yao, Longlong Xu, Yongqian Sun, Wentao Li, Dan Pei|BNRist, Tsinghua University, Beijing, China; Computer Network Information Center, Chinese Academy of Sciences, Beijing, China; Microsoft, Redmond, USA; eBay Inc., Shanghai, China; Nankai University, Tianjin, China|Many failure root cause analysis (RCA) algorithms for microservices have been proposed with the widespread adoption of microservices systems. Existing algorithms generally focus on RCA with ranking single-level (e.g. metric-level or service-level) root cause candidates (RCCs) with comprehensive monitoring metrics. However, many heterogeneous RCCs exist with limited observability in real-world microservices systems. Further, we find that the limited observability may result in inaccurate RCA through real-world failures in eBay. In this paper, for the first time, we propose to "model RCCs as latent variables". The core idea is to infer the status of RCCs as latent variables with related monitoring metrics instead of directly extracting features from only the observable metrics. Based on this, we propose LatentScope, an unsupervised RCA framework with heterogeneous RCCs under limited observability. A dual-space graph is proposed to model both observable and unobservable variables, with many-to-many relationships between spaces. To achieve fast inference of latent variables and RCA, we propose the LatentRegressor algorithm, which includes Regression-based Latent-space Intervention Recognition (RLIR) to achieve intervention recognition-based RCA in latent space. LatentScope has been deployed in eBay's production environment and evaluated on both eBay's real-world failures and a testbed dataset. The evaluation results show that, compared with baseline algorithms, our model significantly improves the Top-1 recall by 9.7%-57.9%. The source code of LatentScope and the dataset are available at https://github.com/NetManAIOps/LatentScope.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Microservice+Root+Cause+Analysis+With+Limited+Observability+Through+Intervention+Recognition+in+the+Latent+Space)|0| +|[Understanding the Weakness of Large Language Model Agents within a Complex Android Environment](https://doi.org/10.1145/3637528.3671650)|Mingzhe Xing, Rongkai Zhang, Hui Xue, Qi Chen, Fan Yang, Zhen Xiao|Microsoft Research, Beijing, China; Peking University, Beijing, China|Large language models (LLMs) have empowered intelligent agents to execute intricate tasks within domain-specific software such as browsers and games. However, when applied to general-purpose software systems like operating systems, LLM agents face three primary challenges. Firstly, the action space is vast and dynamic, posing difficulties for LLM agents to maintain an up-to-date understanding and deliver accurate responses. Secondly, real-world tasks often require inter-application cooperation, demanding farsighted planning from LLM agents. Thirdly, agents need to identify optimal solutions aligning with user constraints, such as security concerns and preferences. These challenges motivate AndroidArena, an environment and benchmark designed to evaluate LLM agents on a modern operating system. To address high-cost of manpower, we design a scalable and semi-automated method to construct the benchmark. In the task evaluation, AndroidArena incorporates accurate and adaptive metrics to address the issue of non-unique solutions. Our findings reveal that even state-of-the-art LLM agents struggle in cross-APP scenarios and adhering to specific constraints. Additionally, we identify a lack of four key capabilities, i.e. understanding, reasoning, exploration, and reflection, as primary reasons for the failure of LLM agents. Furthermore, we provide empirical analysis on the failure of reflection, and improve the success rate by 27% with our proposed exploration strategy. This work is the first to present valuable insights in understanding fine-grained weakness of LLM agents, and offers a path forward for future research in this area. Environment, benchmark, prompt, and evaluation code for AndroidArena are released at https://github.com/AndroidArenaAgent/AndroidArena.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Understanding+the+Weakness+of+Large+Language+Model+Agents+within+a+Complex+Android+Environment)|0| +|[XRL-Bench: A Benchmark for Evaluating and Comparing Explainable Reinforcement Learning Techniques](https://doi.org/10.1145/3637528.3671595)|Yu Xiong, Zhipeng Hu, Ye Huang, Runze Wu, Kai Guan, Xingchen Fang, Ji Jiang, Tianze Zhou, Yujing Hu, Haoyu Liu, Tangjie Lyu, Changjie Fan|Fuxi AI Lab, NetEase Inc., Hangzhou, China; Fuxi AI Lab, NetEase Inc., Hangzhou, Zhejiang, China|Reinforcement Learning (RL) has demonstrated substantial potential acrossdiverse fields, yet understanding its decision-making process, especially inreal-world scenarios where rationality and safety are paramount, is an ongoingchallenge. This paper delves in to Explainable RL (XRL), a subfield ofExplainable AI (XAI) aimed at unravelling the complexities of RL models. Ourfocus rests on state-explaining techniques, a crucial subset within XRLmethods, as they reveal the underlying factors influencing an agent's actionsat any given time. Despite their significant role, the lack of a unifiedevaluation framework hinders assessment of their accuracy and effectiveness. Toaddress this, we introduce XRL-Bench, a unified standardized benchmark tailoredfor the evaluation and comparison of XRL methods, encompassing three mainmodules: standard RL environments, explainers based on state importance, andstandard evaluators. XRL-Bench supports both tabular and image data for stateexplanation. We also propose TabularSHAP, an innovative and competitive XRLmethod. We demonstrate the practical utility of TabularSHAP in real-worldonline gaming services and offer an open-source benchmark platform for thestraightforward implementation and evaluation of XRL methods. Our contributionsfacilitate the continued progression of XRL technology.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=XRL-Bench:+A+Benchmark+for+Evaluating+and+Comparing+Explainable+Reinforcement+Learning+Techniques)|0| +|[FedGTP: Exploiting Inter-Client Spatial Dependency in Federated Graph-based Traffic Prediction](https://doi.org/10.1145/3637528.3671613)|Linghua Yang, Wantong Chen, Xiaoxi He, Shuyue Wei, Yi Xu, Zimu Zhou, Yongxin Tong|Faculty of Science and Technology, University of Macau, Macau, China; SKLCCSE Lab, Beihang University, Beijing, China; School of Data Science, City University of Hong Kong, Hong Kong, China; SKLCCSE Lab, Institute of Artificial Intelligence, Beihang University, Beijing, China|Graph-based methods have witnessed tremendous success in traffic prediction, largely attributed to their superior ability in capturing and modeling spatial dependencies. However, urban-scale traffic data are usually distributed among various owners, limited in sharing due to privacy restrictions. This fragmentation of data severely hinders interaction across clients, impeding the utilization of inter-client spatial dependencies. Existing studies have yet to address this non-trivial issue, thereby leading to sub-optimal performance. To fill this gap, we propose FedGTP, a new federated graph-based traffic prediction framework that promotes adaptive exploitation of inter-client spatial dependencies to recover close-to-optimal performance complying with privacy regulations like GDPR. We validate FedGTP via large-scale application-driven experiments on real-world datasets. Extensive baseline comparison, ablation study and case study demonstrate that FedGTP indeed surpasses existing methods through fully recovering inter-client spatial dependencies, achieving 21.08%, 13.48%, 19.90% decrease on RMSE, MAE and MAPE, respectively. Our code is available at https://github.com/LarryHawkingYoung/KDD2024_FedGTP||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=FedGTP:+Exploiting+Inter-Client+Spatial+Dependency+in+Federated+Graph-based+Traffic+Prediction)|0| +|[OpenFedLLM: Training Large Language Models on Decentralized Private Data via Federated Learning](https://doi.org/10.1145/3637528.3671582)|Rui Ye, Wenhao Wang, Jingyi Chai, Dihan Li, Zexi Li, Yinda Xu, Yaxin Du, Yanfeng Wang, Siheng Chen|Shanghai Jiao Tong University & Shanghai AI Laboratory, Shanghai, China; University of Southern California, Los Angeles, USA; Shanghai Jiao Tong University, Shanghai, China; Zhejiang University, Zhejiang, China|Trained on massive publicly available data, large language models (LLMs) have demonstrated tremendous success across various fields. While more data contributes to better performance, a disconcerting reality is that high-quality public data will be exhausted in a few years. In this paper, we offer a potential next step for contemporary LLMs: collaborative and privacy-preserving LLM training on the underutilized distributed private data via federated learning (FL), where multiple data owners collaboratively train a shared model without transmitting raw data. To achieve this, we build a concise, integrated, and research-friendly framework/codebase, named OpenFedLLM. It covers federated instruction tuning for enhancing instruction-following capability, federated value alignment for aligning with human values, and 7 representative FL algorithms. Besides, OpenFedLLM supports training on diverse domains, where we cover 8 training datasets; and provides comprehensive evaluations, where we cover 30+ evaluation metrics. Through extensive experiments, we observe that all FL algorithms outperform local training on training LLMs, demonstrating a clear performance improvement across a variety of settings. Notably, in a financial benchmark, Llama2-7B fine-tuned by applying any FL algorithm can outperform GPT-4 by a significant margin, while the model obtained through individual training cannot, demonstrating strong motivation for clients to participate in FL. The code is available at https://github.com/rui-ye/OpenFedLLM. The full version of our paper is available at https://arxiv.org/pdf/2402.06954.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=OpenFedLLM:+Training+Large+Language+Models+on+Decentralized+Private+Data+via+Federated+Learning)|0| |[PAIL: Performance based Adversarial Imitation Learning Engine for Carbon Neutral Optimization](https://doi.org/10.1145/3637528.3671611)|Yuyang Ye, LuAn Tang, Haoyu Wang, Runlong Yu, Wenchao Yu, Erhu He, Haifeng Chen, Hui Xiong|; Department of Computer Science, University of Pittsburgh, Pittsburgh, PA, USA; Department of Data Science and System Security, NEC Laboratories, Princeton, NJ, USA|Achieving carbon neutrality within industrial operations has become increasingly imperative for sustainable development. It is both a significant challenge and a key opportunity for operational optimization in industry 4.0. In recent years, Deep Reinforcement Learning (DRL) based methods offer promising enhancements for sequential optimization processes and can be used for reducing carbon emissions. However, existing DRL methods need a pre-defined reward function to assess the impact of each action on the final sustainable development goals (SDG). In many real applications, such a reward function cannot be given in advance. To address the problem, this study proposes a Performance based Adversarial Imitation Learning (PAIL) engine. It is a novel method to acquire optimal operational policies for carbon neutrality without any pre-defined action rewards. Specifically, PAIL employs a Transformer-based policy generator to encode historical information and predict following actions within a multi-dimensional space. The entire action sequence will be iteratively updated by an environmental simulator. Then PAIL uses a discriminator to minimize the discrepancy between generated sequences and real-world samples of high SDG. In parallel, a Q-learning framework based performance estimator is designed to estimate the impact of each action on SDG. Based on these estimations, PAIL refines generated policies with the rewards from both discriminator and performance estimator. PAIL is evaluated on multiple real-world application cases and datasets. The experiment results demonstrate the effectiveness of PAIL comparing to other state-of-the-art baselines. In addition, PAIL offers meaningful interpretability for the optimization in carbon neutrality.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=PAIL:+Performance+based+Adversarial+Imitation+Learning+Engine+for+Carbon+Neutral+Optimization)|0| -|[SepsisLab: Early Sepsis Prediction with Uncertainty Quantification and Active Sensing](https://doi.org/10.1145/3637528.3671586)|Changchang Yin, PinYu Chen, Bingsheng Yao, Dakuo Wang, Jeffrey M. Caterino, Ping Zhang|Northeastern University, Boston, MA, USA; IBM Research, Yorktown Heights, NY, USA; The Ohio State University, Columbus, OH, USA; The Ohio State University Wexner Medical Center, Columbus, OH, USA|Sepsis is the leading cause of in-hospital mortality in the USA. Early sepsis onset prediction and diagnosis could significantly improve the survival of sepsis patients. Existing predictive models are usually trained on high-quality data with few missing information, while missing values widely exist in real-world clinical scenarios (especially in the first hours of admissions to the hospital), which causes a significant decrease in accuracy and an increase in uncertainty for the predictive models. The common method to handle missing values is imputation, which replaces the unavailable variables with estimates from the observed data. The uncertainty of imputation results can be propagated to the sepsis prediction outputs, which have not been studied in existing works on either sepsis prediction or uncertainty quantification. In this study, we first define such propagated uncertainty as the variance of prediction output and then introduce uncertainty propagation methods to quantify the propagated uncertainty. Moreover, for the potential high-risk patients with low confidence due to limited observations, we propose a robust active sensing algorithm to increase confidence by actively recommending clinicians to observe the most informative variables. We validate the proposed models in both publicly available data (i.e., MIMIC-III and AmsterdamUMCdb) and proprietary data in The Ohio State University Wexner Medical Center (OSUWMC). The experimental results show that the propagated uncertainty is dominant at the beginning of admissions to hospitals and the proposed algorithm outperforms state-of-the-art active sensing methods. Finally, we implement a SepsisLab system for early sepsis prediction and active sensing based on our pre-trained models. Clinicians and potential sepsis patients can benefit from the system in early prediction and diagnosis of sepsis.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=SepsisLab:+Early+Sepsis+Prediction+with+Uncertainty+Quantification+and+Active+Sensing)|0| -|[Pre-trained KPI Anomaly Detection Model Through Disentangled Transformer](https://doi.org/10.1145/3637528.3671522)|Zhaoyang Yu, Changhua Pei, Xin Wang, Minghua Ma, Chetan Bansal, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang, Xidao Wen, Jianhui Li, Gaogang Xie, Dan Pei|BizSeer Technology, Beijing, China; Microsoft, Redmond, USA; Microsoft, Beijing, China; Stony Brook University, New York, USA; Tsinghua University & BNRist, Beijing, China; Computer Network Information Center, Chinese Academy of Sciences, Beijing, China|In large-scale online service systems, numerous Key Performance Indicators (KPIs), such as service response time and error rate, are gathered in a time-series format. KPI Anomaly Detection (KAD) is a critical data mining problem due to its widespread applications in real-world scenarios. However, KAD faces the challenges of dealing with KPI heterogeneity and noisy data. We propose KAD-Disformer, a KPI Anomaly Detection approach through Disentangled Transformer. KAD-Disformer pre-trains a model on existing accessible KPIs, and the pre-trained model can be effectively "fine-tuned" to unseen KPI using only a handful of samples from the unseen KPI. We propose a series of innovative designs, including disentangled projection for transformer, unsupervised few-shot fine-tuning (uTune), and denoising modules, each of which significantly contributes to the overall performance. Our extensive experiments demonstrate that KAD-Disformer surpasses the state-of-the-art universal anomaly detection model by 13% in F1-score and achieves comparable performance using only 1/8 of the finetuning samples saving about 25 hours. KAD-Disformer has been successfully deployed in the real-world cloud system serving millions of users, attesting to its feasibility and robustness. Our code is available at https://github.com/NetManAIOps/KAD-Disformer.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Pre-trained+KPI+Anomaly+Detection+Model+Through+Disentangled+Transformer)|0| +|[SepsisLab: Early Sepsis Prediction with Uncertainty Quantification and Active Sensing](https://doi.org/10.1145/3637528.3671586)|Changchang Yin, PinYu Chen, Bingsheng Yao, Dakuo Wang, Jeffrey M. Caterino, Ping Zhang|Northeastern University, Boston, MA, USA; The Ohio State University, Columbus, OH, USA; The Ohio State University Wexner Medical Center, Columbus, OH, USA; IBM Research, Yorktown Heights, NY, USA|Sepsis is the leading cause of in-hospital mortality in the USA. Early sepsis onset prediction and diagnosis could significantly improve the survival of sepsis patients. Existing predictive models are usually trained on high-quality data with few missing information, while missing values widely exist in real-world clinical scenarios (especially in the first hours of admissions to the hospital), which causes a significant decrease in accuracy and an increase in uncertainty for the predictive models. The common method to handle missing values is imputation, which replaces the unavailable variables with estimates from the observed data. The uncertainty of imputation results can be propagated to the sepsis prediction outputs, which have not been studied in existing works on either sepsis prediction or uncertainty quantification. In this study, we first define such propagated uncertainty as the variance of prediction output and then introduce uncertainty propagation methods to quantify the propagated uncertainty. Moreover, for the potential high-risk patients with low confidence due to limited observations, we propose a robust active sensing algorithm to increase confidence by actively recommending clinicians to observe the most informative variables. We validate the proposed models in both publicly available data (i.e., MIMIC-III and AmsterdamUMCdb) and proprietary data in The Ohio State University Wexner Medical Center (OSUWMC). The experimental results show that the propagated uncertainty is dominant at the beginning of admissions to hospitals and the proposed algorithm outperforms state-of-the-art active sensing methods. Finally, we implement a SepsisLab system for early sepsis prediction and active sensing based on our pre-trained models. Clinicians and potential sepsis patients can benefit from the system in early prediction and diagnosis of sepsis.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=SepsisLab:+Early+Sepsis+Prediction+with+Uncertainty+Quantification+and+Active+Sensing)|0| +|[Pre-trained KPI Anomaly Detection Model Through Disentangled Transformer](https://doi.org/10.1145/3637528.3671522)|Zhaoyang Yu, Changhua Pei, Xin Wang, Minghua Ma, Chetan Bansal, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang, Xidao Wen, Jianhui Li, Gaogang Xie, Dan Pei|Computer Network Information Center, Chinese Academy of Sciences, Beijing, China; Stony Brook University, New York, USA; Microsoft, Beijing, China; BizSeer Technology, Beijing, China; Microsoft, Redmond, USA; Tsinghua University & BNRist, Beijing, China|In large-scale online service systems, numerous Key Performance Indicators (KPIs), such as service response time and error rate, are gathered in a time-series format. KPI Anomaly Detection (KAD) is a critical data mining problem due to its widespread applications in real-world scenarios. However, KAD faces the challenges of dealing with KPI heterogeneity and noisy data. We propose KAD-Disformer, a KPI Anomaly Detection approach through Disentangled Transformer. KAD-Disformer pre-trains a model on existing accessible KPIs, and the pre-trained model can be effectively "fine-tuned" to unseen KPI using only a handful of samples from the unseen KPI. We propose a series of innovative designs, including disentangled projection for transformer, unsupervised few-shot fine-tuning (uTune), and denoising modules, each of which significantly contributes to the overall performance. Our extensive experiments demonstrate that KAD-Disformer surpasses the state-of-the-art universal anomaly detection model by 13% in F1-score and achieves comparable performance using only 1/8 of the finetuning samples saving about 25 hours. KAD-Disformer has been successfully deployed in the real-world cloud system serving millions of users, attesting to its feasibility and robustness. Our code is available at https://github.com/NetManAIOps/KAD-Disformer.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Pre-trained+KPI+Anomaly+Detection+Model+Through+Disentangled+Transformer)|0| |[An Offline Meta Black-box Optimization Framework for Adaptive Design of Urban Traffic Light Management Systems](https://doi.org/10.1145/3637528.3671606)|Taeyoung Yun, Kanghoon Lee, Sujin Yun, Ilmyung Kim, WonWoo Jung, MinCheol Kwon, Kyujin Choi, Yoohyeon Lee, Jinkyoo Park|KAIST, Daejeon, Republic of Korea; Korea Telecom, Seoul, Republic of Korea|Complex urban road networks with high vehicle occupancy frequently face severe traffic congestion. Designing an effective strategy for managing multiple traffic lights plays a crucial role in managing congestion. However, most current traffic light management systems rely on human-crafted decisions, which may not adapt well to diverse traffic patterns. In this paper, we delve into two pivotal design components of the traffic light management system that can be dynamically adjusted to various traffic conditions: phase combination and phase time allocation. While numerous studies have sought an efficient strategy for managing traffic lights, most of these approaches consider a fixed traffic pattern and are limited to relatively small road networks. To overcome these limitations, we introduce a novel and practical framework to formulate the optimization of such design components using an offline meta black-box optimization. We then present a simple yet effective method to efficiently find a solution for the aforementioned problem. In our framework, we first collect an offline meta dataset consisting of pairs of design choices and corresponding congestion measures from various traffic patterns. After collecting the dataset, we employ the Attentive Neural Process (ANP) to predict the impact of the proposed design on congestion across various traffic patterns with well-calibrated uncertainty. Finally, Bayesian optimization, with ANP as a surrogate model, is utilized to find an optimal design for unseen traffic patterns through limited online simulations. Our experiment results show that our method outperforms state-of-the-art baselines on complex road networks in terms of the number of waiting vehicles. Surprisingly, the deployment of our method into a real-world traffic system was able to improve traffic throughput by 4.80% compared to the original strategy.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=An+Offline+Meta+Black-box+Optimization+Framework+for+Adaptive+Design+of+Urban+Traffic+Light+Management+Systems)|0| -|[OAG-Bench: A Human-Curated Benchmark for Academic Graph Mining](https://doi.org/10.1145/3637528.3672354)|Fanjin Zhang, Shijie Shi, Yifan Zhu, Bo Chen, Yukuo Cen, Jifan Yu, Yelin Chen, Lulu Wang, Qingfei Zhao, Yuqing Cheng, Tianyi Han, Yuwei An, Dan Zhang, Weng Lam Tam, Kun Cao, Yunhe Pang, Xinyu Guan, Huihui Yuan, Jian Song, Xiaoyan Li, Yuxiao Dong, Jie Tang|Tsinghua University, Beijing, China; Beijing University of Posts and Telecommunications, Beijing, China; Biendata, Beijing, China; Zhipu AI, Beijing, China|With the rapid proliferation of scientific literature, versatile academicknowledge services increasingly rely on comprehensive academic graph mining.Despite the availability of public academic graphs, benchmarks, and datasets,these resources often fall short in multi-aspect and fine-grained annotations,are constrained to specific task types and domains, or lack underlying realacademic graphs. In this paper, we present OAG-Bench, a comprehensive,multi-aspect, and fine-grained human-curated benchmark based on the OpenAcademic Graph (OAG). OAG-Bench covers 10 tasks, 20 datasets, 70+ baselines,and 120+ experimental results to date. We propose new data annotationstrategies for certain tasks and offer a suite of data pre-processing codes,algorithm implementations, and standardized evaluation protocols to facilitateacademic graph mining. Extensive experiments reveal that even advancedalgorithms like large language models (LLMs) encounter difficulties inaddressing key challenges in certain tasks, such as paper source tracing andscholar profiling. We also introduce the Open Academic Graph Challenge(OAG-Challenge) to encourage community input and sharing. We envisage thatOAG-Bench can serve as a common ground for the community to evaluate andcompare algorithms in academic graph mining, thereby accelerating algorithmdevelopment and advancement in this field. OAG-Bench is accessible athttps://www.aminer.cn/data/.|随着科学文献的快速增长，多功能学术知识服务越来越依赖于全面的学术图形挖掘。尽管有公开的学术图表、基准和数据集，但这些资源往往缺乏多方面和细粒度的注释，受限于特定的任务类型和领域，或缺乏基础的真实学术图表。本文介绍了 OAG-Bench，它是一个基于开放学术图的全面的、多方面的、细粒度的人类管理基准。OAG-Bench 涵盖了10个任务、20个数据集、70 + 基线和迄今为止的120 + 实验结果。我们针对某些任务提出了新的数据注释/策略，并提供了一套数据预处理代码、算法实现和标准化评估协议，以促进学术图形挖掘。大量的实验表明，即使是像大型语言模型(LLM)这样的高级算法，在处理某些任务中的关键挑战时也会遇到困难，例如文件来源跟踪和学者剖析。我们还推出了开放学术图形挑战(OAG-Challenge) ，以鼓励社区投入和分享。我们设想 OAG-Bench 可以作为社区评估和比较学术图形挖掘算法的共同基础，从而加速算法的发展和进步。OAG-bench 可访问 https:// www.aminer.cn/data/。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=OAG-Bench:+A+Human-Curated+Benchmark+for+Academic+Graph+Mining)|0| -|[Dólares or Dollars? Unraveling the Bilingual Prowess of Financial LLMs Between Spanish and English](https://doi.org/10.1145/3637528.3671554)|Xiao Zhang, Ruoyu Xiang, Chenhan Yuan, Duanyu Feng, Weiguang Han, Alejandro LopezLira, XiaoYang Liu, Meikang Qiu, Sophia Ananiadou, Min Peng, Jimin Huang, Qianqian Xie|; The Fin AI, Singapore, Singapore; University of Florida, Gainesville, USA; Wuhan University, Wuhan, Hubei, China; Augusta University, Augusta, USA; The University of Manchester, Manchester, United Kingdom; Columbia University, New York, NY, USA; Sichuan University, Chengdu, Sichuan, China|Despite Spanish's pivotal role in the global finance industry, a pronouncedgap exists in Spanish financial natural language processing (NLP) andapplication studies compared to English, especially in the era of largelanguage models (LLMs). To bridge this gap, we unveil Toisón de Oro, thefirst bilingual framework that establishes instruction datasets, finetunedLLMs, and evaluation benchmark for financial LLMs in Spanish joint withEnglish. We construct a rigorously curated bilingual instruction datasetincluding over 144K Spanish and English samples from 15 datasets covering 7tasks. Harnessing this, we introduce FinMA-ES, an LLM designed for bilingualfinancial applications. We evaluate our model and existing LLMs using FLARE-ES,the first comprehensive bilingual evaluation benchmark with 21 datasetscovering 9 tasks. The FLARE-ES benchmark results reveal a significantmultilingual performance gap and bias in existing LLMs. FinMA-ES models surpassSOTA LLMs such as GPT-4 in Spanish financial tasks, due to strategicinstruction tuning and leveraging data from diverse linguistic resources,highlighting the positive impact of cross-linguistic transfer. All ourdatasets, models, and benchmarks have been released.|尽管西班牙语在全球金融业中扮演着举足轻重的角色，但与英语相比，尤其是在大语言模型时代，西班牙语的金融自然语言处理(NLP)和应用研究方面存在着明显的差距。为了弥补这一差距，我们推出了 Toisón de Oro，这是第一个建立教学数据集，finetunedLLM，以及西班牙语和英语联合金融 LLM 评估基准的双语框架。我们建立了一个严格管理的双语教学数据集，包括超过144K 的西班牙语和英语样本从15个数据集涵盖7个任务。利用这一点，我们介绍了 FinMA-ES，一个为双语金融应用而设计的 LLM。我们使用 FLARE-ES 评估我们的模型和现有的 LLM。 FLARE-ES 是第一个全面的双语评估基准，共有21个数据集，涵盖9个任务。FLARE-ES 基准测试结果显示现有 LLM 存在显著的多语言性能差距和偏差。FinMA-ES 模型在西班牙金融任务方面超过了 GPT-4等 SOTA LLM 模型，这是由于战略指令调整和利用来自不同语言资源的数据，突出了跨语言转换的积极影响。我们所有的数据集、模型和基准已经发布。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Dólares+or+Dollars?+Unraveling+the+Bilingual+Prowess+of+Financial+LLMs+Between+Spanish+and+English)|0| -|[Large Language Model with Curriculum Reasoning for Visual Concept Recognition](https://doi.org/10.1145/3637528.3671653)|Yipeng Zhang, Xin Wang, Hong Chen, Jiapei Fan, Weigao Wen, Hui Xue, Hong Mei, Wenwu Zhu|DCST, BNRist, Tsinghua University, Beijing, China; Alibaba Group, Hangzhou, China; DCST, Tsinghua University, Beijing, China; MoE Lab, Peking University, Beijing, China|Visual concept recognition aims to capture the basic attributes of an image and reason about the relationships among them to determine whether the image satisfies a certain concept, and has been widely used in various tasks such as human action recognition and image risk warning. Most existing works adopt deep neural networks for visual concept recognition, which are black-box and incomprehensible to humans, thus making them unacceptable for sensitive domains such as prohibited event detection and risk early warning etc. To address this issue, we propose to combine large language model (LLM) with explainable symbolic reasoning via curriculum reweighting to increase the interpretability and accuracy of visual concept recognition in this paper. However, realizing this goal is challenging given that i) the performance of symbolic representations are limited by the lack of annotated reasoning symbols and rules for most tasks, and ii) the LLMs may suffer from knowlege hallucination and dynamic open environment. To address these issues, in this paper, we propose CurLLM-Reasoner, a curriculum reasoning method based on symbolic reasoning and large language model for visual concept recognition. Specifically, we propose a novel rule enhancement module with a tool library, which fully leverage the reasoning capability of large language models and can generate human-understandable rules without any annotation. We further propose a curriculum data resampling methodology to help the large language model accurately extract from easy to complex rules at different reasoning stages. Extensive experiments on various datasets demonstrate that CurLLM-Reasoner can achieve the state-of-the-art visual concept recognition results with explainable rules while free of human annotations.|视觉概念识别的目的是获取图像的基本属性以及它们之间关系的原因，以确定图像是否满足一定的概念，已广泛应用于人类行为识别和图像风险预警等各种任务中。现有作品大多采用深层神经网络进行视觉概念识别，这是一个人类难以理解的黑盒子，因此不适用于敏感领域，如违禁事件检测和风险预警等。针对这一问题，本文提出通过课程重新加权，将大语言模型(LLM)与可解释的符号推理相结合，提高视觉概念识别的可解释性和准确性。然而，实现这一目标是具有挑战性的，因为 i)符号表示的性能受到大多数任务缺乏注释推理符号和规则的限制，以及 ii) LLM 可能遭受知识幻觉和动态开放环境。针对这些问题，本文提出了一种基于符号推理和大语言模型的课程推理方法 CurLLM-Requoner，用于视觉概念识别。具体地说，我们提出了一种新的规则增强模块，该模块使用了工具库，充分利用了大型语言模型的推理能力，可以在不需要任何注释的情况下生成人们可以理解的规则。进一步提出了一种课程数据重采样方法，以帮助大语言模型在不同的推理阶段准确地提取易于复杂的规则。在各种数据集上的大量实验表明，CurLLM-Requoner 能够在不需要人工注释的情况下，通过可解释的规则实现最先进的视觉概念识别。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Large+Language+Model+with+Curriculum+Reasoning+for+Visual+Concept+Recognition)|0| -|[GraSS: Combining Graph Neural Networks with Expert Knowledge for SAT Solver Selection](https://doi.org/10.1145/3637528.3671627)|Zhanguang Zhang, Didier Chételat, Joseph Cotnareanu, Amur Ghose, Wenyi Xiao, HuiLing Zhen, Yingxue Zhang, Jianye Hao, Mark Coates, Mingxuan Yuan|Huawei Noah's Ark Lab, Beijing, China; Huawei Noah's Ark Lab, Montreal, Canada; McGill University, Montreal, Canada; Huawei Noah's Ark Lab, Hong Kong, China|Boolean satisfiability (SAT) problems are routinely solved by SAT solvers inreal-life applications, yet solving time can vary drastically between solversfor the same instance. This has motivated research into machine learning modelsthat can predict, for a given SAT instance, which solver to select amongseveral options. Existing SAT solver selection methods all rely on somehand-picked instance features, which are costly to compute and ignore thestructural information in SAT graphs. In this paper we present GraSS, a novelapproach for automatic SAT solver selection based on tripartite graphrepresentations of instances and a heterogeneous graph neural network (GNN)model. While GNNs have been previously adopted in other SAT-related tasks, theydo not incorporate any domain-specific knowledge and ignore the runtimevariation introduced by different clause orders. We enrich the graphrepresentation with domain-specific decisions, such as novel node featuredesign, positional encodings for clauses in the graph, a GNN architecturetailored to our tripartite graphs and a runtime-sensitive loss function.Through extensive experiments, we demonstrate that this combination of rawrepresentations and domain-specific choices leads to improvements in runtimefor a pool of seven state-of-the-art solvers on both an industrial circuitdesign benchmark, and on instances from the 20-year Anniversary Track of the2022 SAT Competition.|布尔可满足性(SAT)问题通常由 SAT 求解器在实际应用中解决，但在同一实例中，求解时间可能会因求解器的不同而有很大差异。这激发了对机器学习模型的研究，该模型可以预测给定的 SAT 实例中哪个求解器可以从多个选项中进行选择。现有的 SAT 求解器选择方法都依赖于精心挑选的实例特征，计算和忽略 SAT 图中的结构信息代价高昂。本文提出了一种基于实例三部图表示和异构图神经网络(GNN)模型的自动 SAT 求解器选择新方法—— GraSS。虽然 GNN 以前已经在其他 SAT 相关任务中采用，但它们没有包含任何特定领域的知识，并且忽略了不同子句顺序引入的运行时变化。我们用特定领域的决策来丰富图表示，例如新的节点特征设计，图中子句的位置编码，根据我们的三部图定制的 GNN 体系结构和运行时敏感的损失函数。通过大量的实验，我们证明了这种原始表示和领域特定选择的结合导致了在工业电路设计基准和2022年 SAT 竞赛20周年纪念赛道上的七个最先进的解决方案池的运行时间的改善。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=GraSS:+Combining+Graph+Neural+Networks+with+Expert+Knowledge+for+SAT+Solver+Selection)|0| -|[Diet-ODIN: A Novel Framework for Opioid Misuse Detection with Interpretable Dietary Patterns](https://doi.org/10.1145/3637528.3671587)|Zheyuan Zhang, Zehong Wang, Shifu Hou, Evan Hall, Landon Bachman, Jasmine White, Vincent Galassi, Nitesh V. Chawla, Chuxu Zha, Yanfang Ye|Brandeis University, Waltham, MA, USA; University of Notre Dame, Notre Dame, IN, USA; Purdue University, West Lafayette, IN, USA|The opioid crisis has been one of the most critical society concerns in the United States. Although the medication assisted treatment (MAT) is recognized as the most effective treatment for opioid misuse and addiction, the various side effects can trigger opioid relapse. In addition to MAT, the dietary nutrition intervention has been demonstrated its importance in opioid misuse prevention and recovery. However, research on the alarming connections between dietary patterns and opioid misuse remain under-explored. In response to this gap, in this paper, we first establish a large-scale multifaceted dietary benchmark dataset related to opioid users at the first attempt and then develop a novel framework - i.e., namely Opioid Misuse Detection with INterpretable Dietary Patterns (Diet-ODIN) - to bridge heterogeneous graph (HG) and large language model (LLM) for the identification of users with opioid misuse and the interpretation of their associated dietary patterns. Specifically, in Diet-ODIN, we first construct an HG to comprehensively incorporate both dietary and health-related information, and then we devise a holistic graph learning framework with noise reduction to fully capitalize both users' individual dietary habits and shared dietary patterns for the detection of users with opioid misuse. To further delve into the intricate correlations between dietary patterns and opioid misuse, we exploit an LLM by utilizing the knowledge obtained from the graph learning model for interpretation. The extensive experimental results based on our established benchmark with quantitative and qualitative measures demonstrate the outstanding performance of Diet-ODIN on exploring the complex interplay between opioid misuse and dietary patterns, by comparison with state-of-the-art baseline methods. Our code, built benchmark and system demo are available at https://github.com/JasonZhangzy1757/Diet-ODIN.|阿片类药物危机一直是美国社会最关注的问题之一。虽然药物辅助治疗(MAT)被认为是最有效的阿片类药物滥用和成瘾的治疗方法，但各种副作用可能引发阿片类药物复发。除 MAT 外，膳食营养干预在阿片类药物滥用预防和康复中的重要性也得到了证实。然而，关于饮食模式与阿片类药物滥用之间令人担忧的联系的研究仍然不足。针对这一差距，在本文中，我们首先建立了一个与阿片类药物使用者相关的大规模多方面饮食基准数据集，然后开发了一个新的框架，即具有可解释饮食模式(Diet-ODIN)的阿片类误用检测，以桥接异质图(HG)和大语言模型(LLM) ，用于识别滥用阿片类药物的使用者并解释其相关的饮食模式。具体而言，在 Diet-ODIN 中，我们首先构建一个 HG 来全面纳入饮食和健康相关信息，然后我们设计一个具有降噪的整体图形学习框架，以充分利用用户的个人饮食习惯和共享饮食模式来检测阿片类药物滥用的用户。为了进一步研究饮食模式和阿片类药物滥用之间错综复杂的相关性，我们利用从图形学习模型中获得的知识来解释 LLM。基于我们建立的定量和定性测量基准的广泛实验结果表明，与最先进的基线方法相比，Diet-ODIN 在探索阿片类药物滥用和饮食模式之间复杂的相互作用方面表现出色。我们的代码，建立的基准和系统演示可在 https://github.com/jasonzhangzy1757/diet-odin 下载。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Diet-ODIN:+A+Novel+Framework+for+Opioid+Misuse+Detection+with+Interpretable+Dietary+Patterns)|0| +|[OAG-Bench: A Human-Curated Benchmark for Academic Graph Mining](https://doi.org/10.1145/3637528.3672354)|Fanjin Zhang, Shijie Shi, Yifan Zhu, Bo Chen, Yukuo Cen, Jifan Yu, Yelin Chen, Lulu Wang, Qingfei Zhao, Yuqing Cheng, Tianyi Han, Yuwei An, Dan Zhang, Weng Lam Tam, Kun Cao, Yunhe Pang, Xinyu Guan, Huihui Yuan, Jian Song, Xiaoyan Li, Yuxiao Dong, Jie Tang|Beijing University of Posts and Telecommunications, Beijing, China; Tsinghua University, Beijing, China; Zhipu AI, Beijing, China; Biendata, Beijing, China|With the rapid proliferation of scientific literature, versatile academicknowledge services increasingly rely on comprehensive academic graph mining.Despite the availability of public academic graphs, benchmarks, and datasets,these resources often fall short in multi-aspect and fine-grained annotations,are constrained to specific task types and domains, or lack underlying realacademic graphs. In this paper, we present OAG-Bench, a comprehensive,multi-aspect, and fine-grained human-curated benchmark based on the OpenAcademic Graph (OAG). OAG-Bench covers 10 tasks, 20 datasets, 70+ baselines,and 120+ experimental results to date. We propose new data annotationstrategies for certain tasks and offer a suite of data pre-processing codes,algorithm implementations, and standardized evaluation protocols to facilitateacademic graph mining. Extensive experiments reveal that even advancedalgorithms like large language models (LLMs) encounter difficulties inaddressing key challenges in certain tasks, such as paper source tracing andscholar profiling. We also introduce the Open Academic Graph Challenge(OAG-Challenge) to encourage community input and sharing. We envisage thatOAG-Bench can serve as a common ground for the community to evaluate andcompare algorithms in academic graph mining, thereby accelerating algorithmdevelopment and advancement in this field. OAG-Bench is accessible athttps://www.aminer.cn/data/.|随着科学文献的快速增长，多功能学术知识服务越来越依赖于全面的学术图形挖掘。尽管有公开的学术图表、基准和数据集，但这些资源往往缺乏多方面和细粒度的注释，受限于特定的任务类型和领域，或缺乏基础的真实学术图表。本文介绍了 OAG-Bench，它是一个基于开放学术图的全面的、多方面的、细粒度的人类管理基准。OAG-Bench 涵盖了10个任务、20个数据集、70 + 基线和迄今为止的120 + 实验结果。我们针对某些任务提出了新的数据注释/策略，并提供了一套数据预处理代码、算法实现和标准化评估协议，以促进学术图形挖掘。大量的实验表明，即使是像大型语言模型(LLM)这样的高级算法，在处理某些任务中的关键挑战时也会遇到困难，例如文件来源跟踪和学者剖析。我们还推出了开放学术图形挑战(OAG-Challenge) ，以鼓励社区投入和分享。我们设想 OAG-Bench 可以作为社区评估和比较学术图形挖掘算法的共同基础，从而加速算法的发展和进步。OAG-bench 可访问 https:// www.aminer.cn/data/。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=OAG-Bench:+A+Human-Curated+Benchmark+for+Academic+Graph+Mining)|0| +|[Dólares or Dollars? Unraveling the Bilingual Prowess of Financial LLMs Between Spanish and English](https://doi.org/10.1145/3637528.3671554)|Xiao Zhang, Ruoyu Xiang, Chenhan Yuan, Duanyu Feng, Weiguang Han, Alejandro LopezLira, XiaoYang Liu, Meikang Qiu, Sophia Ananiadou, Min Peng, Jimin Huang, Qianqian Xie|; Columbia University, New York, NY, USA; The Fin AI, Singapore, Singapore; The University of Manchester, Manchester, United Kingdom; University of Florida, Gainesville, USA; Sichuan University, Chengdu, Sichuan, China; Augusta University, Augusta, USA; Wuhan University, Wuhan, Hubei, China|Despite Spanish's pivotal role in the global finance industry, a pronouncedgap exists in Spanish financial natural language processing (NLP) andapplication studies compared to English, especially in the era of largelanguage models (LLMs). To bridge this gap, we unveil Toisón de Oro, thefirst bilingual framework that establishes instruction datasets, finetunedLLMs, and evaluation benchmark for financial LLMs in Spanish joint withEnglish. We construct a rigorously curated bilingual instruction datasetincluding over 144K Spanish and English samples from 15 datasets covering 7tasks. Harnessing this, we introduce FinMA-ES, an LLM designed for bilingualfinancial applications. We evaluate our model and existing LLMs using FLARE-ES,the first comprehensive bilingual evaluation benchmark with 21 datasetscovering 9 tasks. The FLARE-ES benchmark results reveal a significantmultilingual performance gap and bias in existing LLMs. FinMA-ES models surpassSOTA LLMs such as GPT-4 in Spanish financial tasks, due to strategicinstruction tuning and leveraging data from diverse linguistic resources,highlighting the positive impact of cross-linguistic transfer. All ourdatasets, models, and benchmarks have been released.|尽管西班牙语在全球金融业中扮演着举足轻重的角色，但与英语相比，尤其是在大语言模型时代，西班牙语的金融自然语言处理(NLP)和应用研究方面存在着明显的差距。为了弥补这一差距，我们推出了 Toisón de Oro，这是第一个建立教学数据集，finetunedLLM，以及西班牙语和英语联合金融 LLM 评估基准的双语框架。我们建立了一个严格管理的双语教学数据集，包括超过144K 的西班牙语和英语样本从15个数据集涵盖7个任务。利用这一点，我们介绍了 FinMA-ES，一个为双语金融应用而设计的 LLM。我们使用 FLARE-ES 评估我们的模型和现有的 LLM。 FLARE-ES 是第一个全面的双语评估基准，共有21个数据集，涵盖9个任务。FLARE-ES 基准测试结果显示现有 LLM 存在显著的多语言性能差距和偏差。FinMA-ES 模型在西班牙金融任务方面超过了 GPT-4等 SOTA LLM 模型，这是由于战略指令调整和利用来自不同语言资源的数据，突出了跨语言转换的积极影响。我们所有的数据集、模型和基准已经发布。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Dólares+or+Dollars?+Unraveling+the+Bilingual+Prowess+of+Financial+LLMs+Between+Spanish+and+English)|0| +|[Large Language Model with Curriculum Reasoning for Visual Concept Recognition](https://doi.org/10.1145/3637528.3671653)|Yipeng Zhang, Xin Wang, Hong Chen, Jiapei Fan, Weigao Wen, Hui Xue, Hong Mei, Wenwu Zhu|DCST, Tsinghua University, Beijing, China; Alibaba Group, Hangzhou, China; MoE Lab, Peking University, Beijing, China; DCST, BNRist, Tsinghua University, Beijing, China|Visual concept recognition aims to capture the basic attributes of an image and reason about the relationships among them to determine whether the image satisfies a certain concept, and has been widely used in various tasks such as human action recognition and image risk warning. Most existing works adopt deep neural networks for visual concept recognition, which are black-box and incomprehensible to humans, thus making them unacceptable for sensitive domains such as prohibited event detection and risk early warning etc. To address this issue, we propose to combine large language model (LLM) with explainable symbolic reasoning via curriculum reweighting to increase the interpretability and accuracy of visual concept recognition in this paper. However, realizing this goal is challenging given that i) the performance of symbolic representations are limited by the lack of annotated reasoning symbols and rules for most tasks, and ii) the LLMs may suffer from knowlege hallucination and dynamic open environment. To address these issues, in this paper, we propose CurLLM-Reasoner, a curriculum reasoning method based on symbolic reasoning and large language model for visual concept recognition. Specifically, we propose a novel rule enhancement module with a tool library, which fully leverage the reasoning capability of large language models and can generate human-understandable rules without any annotation. We further propose a curriculum data resampling methodology to help the large language model accurately extract from easy to complex rules at different reasoning stages. Extensive experiments on various datasets demonstrate that CurLLM-Reasoner can achieve the state-of-the-art visual concept recognition results with explainable rules while free of human annotations.|视觉概念识别的目的是获取图像的基本属性以及它们之间关系的原因，以确定图像是否满足一定的概念，已广泛应用于人类行为识别和图像风险预警等各种任务中。现有作品大多采用深层神经网络进行视觉概念识别，这是一个人类难以理解的黑盒子，因此不适用于敏感领域，如违禁事件检测和风险预警等。针对这一问题，本文提出通过课程重新加权，将大语言模型(LLM)与可解释的符号推理相结合，提高视觉概念识别的可解释性和准确性。然而，实现这一目标是具有挑战性的，因为 i)符号表示的性能受到大多数任务缺乏注释推理符号和规则的限制，以及 ii) LLM 可能遭受知识幻觉和动态开放环境。针对这些问题，本文提出了一种基于符号推理和大语言模型的课程推理方法 CurLLM-Requoner，用于视觉概念识别。具体地说，我们提出了一种新的规则增强模块，该模块使用了工具库，充分利用了大型语言模型的推理能力，可以在不需要任何注释的情况下生成人们可以理解的规则。进一步提出了一种课程数据重采样方法，以帮助大语言模型在不同的推理阶段准确地提取易于复杂的规则。在各种数据集上的大量实验表明，CurLLM-Requoner 能够在不需要人工注释的情况下，通过可解释的规则实现最先进的视觉概念识别。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Large+Language+Model+with+Curriculum+Reasoning+for+Visual+Concept+Recognition)|0| +|[GraSS: Combining Graph Neural Networks with Expert Knowledge for SAT Solver Selection](https://doi.org/10.1145/3637528.3671627)|Zhanguang Zhang, Didier Chételat, Joseph Cotnareanu, Amur Ghose, Wenyi Xiao, HuiLing Zhen, Yingxue Zhang, Jianye Hao, Mark Coates, Mingxuan Yuan|Huawei Noah's Ark Lab, Hong Kong, China; McGill University, Montreal, Canada; Huawei Noah's Ark Lab, Beijing, China; Huawei Noah's Ark Lab, Montreal, Canada|Boolean satisfiability (SAT) problems are routinely solved by SAT solvers inreal-life applications, yet solving time can vary drastically between solversfor the same instance. This has motivated research into machine learning modelsthat can predict, for a given SAT instance, which solver to select amongseveral options. Existing SAT solver selection methods all rely on somehand-picked instance features, which are costly to compute and ignore thestructural information in SAT graphs. In this paper we present GraSS, a novelapproach for automatic SAT solver selection based on tripartite graphrepresentations of instances and a heterogeneous graph neural network (GNN)model. While GNNs have been previously adopted in other SAT-related tasks, theydo not incorporate any domain-specific knowledge and ignore the runtimevariation introduced by different clause orders. We enrich the graphrepresentation with domain-specific decisions, such as novel node featuredesign, positional encodings for clauses in the graph, a GNN architecturetailored to our tripartite graphs and a runtime-sensitive loss function.Through extensive experiments, we demonstrate that this combination of rawrepresentations and domain-specific choices leads to improvements in runtimefor a pool of seven state-of-the-art solvers on both an industrial circuitdesign benchmark, and on instances from the 20-year Anniversary Track of the2022 SAT Competition.|布尔可满足性(SAT)问题通常由 SAT 求解器在实际应用中解决，但在同一实例中，求解时间可能会因求解器的不同而有很大差异。这激发了对机器学习模型的研究，该模型可以预测给定的 SAT 实例中哪个求解器可以从多个选项中进行选择。现有的 SAT 求解器选择方法都依赖于精心挑选的实例特征，计算和忽略 SAT 图中的结构信息代价高昂。本文提出了一种基于实例三部图表示和异构图神经网络(GNN)模型的自动 SAT 求解器选择新方法—— GraSS。虽然 GNN 以前已经在其他 SAT 相关任务中采用，但它们没有包含任何特定领域的知识，并且忽略了不同子句顺序引入的运行时变化。我们用特定领域的决策来丰富图表示，例如新的节点特征设计，图中子句的位置编码，根据我们的三部图定制的 GNN 体系结构和运行时敏感的损失函数。通过大量的实验，我们证明了这种原始表示和领域特定选择的结合导致了在工业电路设计基准和2022年 SAT 竞赛20周年纪念赛道上的七个最先进的解决方案池的运行时间的改善。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=GraSS:+Combining+Graph+Neural+Networks+with+Expert+Knowledge+for+SAT+Solver+Selection)|0| +|[Diet-ODIN: A Novel Framework for Opioid Misuse Detection with Interpretable Dietary Patterns](https://doi.org/10.1145/3637528.3671587)|Zheyuan Zhang, Zehong Wang, Shifu Hou, Evan Hall, Landon Bachman, Jasmine White, Vincent Galassi, Nitesh V. Chawla, Chuxu Zha, Yanfang Ye|University of Notre Dame, Notre Dame, IN, USA; Purdue University, West Lafayette, IN, USA; Brandeis University, Waltham, MA, USA|The opioid crisis has been one of the most critical society concerns in the United States. Although the medication assisted treatment (MAT) is recognized as the most effective treatment for opioid misuse and addiction, the various side effects can trigger opioid relapse. In addition to MAT, the dietary nutrition intervention has been demonstrated its importance in opioid misuse prevention and recovery. However, research on the alarming connections between dietary patterns and opioid misuse remain under-explored. In response to this gap, in this paper, we first establish a large-scale multifaceted dietary benchmark dataset related to opioid users at the first attempt and then develop a novel framework - i.e., namely Opioid Misuse Detection with INterpretable Dietary Patterns (Diet-ODIN) - to bridge heterogeneous graph (HG) and large language model (LLM) for the identification of users with opioid misuse and the interpretation of their associated dietary patterns. Specifically, in Diet-ODIN, we first construct an HG to comprehensively incorporate both dietary and health-related information, and then we devise a holistic graph learning framework with noise reduction to fully capitalize both users' individual dietary habits and shared dietary patterns for the detection of users with opioid misuse. To further delve into the intricate correlations between dietary patterns and opioid misuse, we exploit an LLM by utilizing the knowledge obtained from the graph learning model for interpretation. The extensive experimental results based on our established benchmark with quantitative and qualitative measures demonstrate the outstanding performance of Diet-ODIN on exploring the complex interplay between opioid misuse and dietary patterns, by comparison with state-of-the-art baseline methods. Our code, built benchmark and system demo are available at https://github.com/JasonZhangzy1757/Diet-ODIN.|阿片类药物危机一直是美国社会最关注的问题之一。虽然药物辅助治疗(MAT)被认为是最有效的阿片类药物滥用和成瘾的治疗方法，但各种副作用可能引发阿片类药物复发。除 MAT 外，膳食营养干预在阿片类药物滥用预防和康复中的重要性也得到了证实。然而，关于饮食模式与阿片类药物滥用之间令人担忧的联系的研究仍然不足。针对这一差距，在本文中，我们首先建立了一个与阿片类药物使用者相关的大规模多方面饮食基准数据集，然后开发了一个新的框架，即具有可解释饮食模式(Diet-ODIN)的阿片类误用检测，以桥接异质图(HG)和大语言模型(LLM) ，用于识别滥用阿片类药物的使用者并解释其相关的饮食模式。具体而言，在 Diet-ODIN 中，我们首先构建一个 HG 来全面纳入饮食和健康相关信息，然后我们设计一个具有降噪的整体图形学习框架，以充分利用用户的个人饮食习惯和共享饮食模式来检测阿片类药物滥用的用户。为了进一步研究饮食模式和阿片类药物滥用之间错综复杂的相关性，我们利用从图形学习模型中获得的知识来解释 LLM。基于我们建立的定量和定性测量基准的广泛实验结果表明，与最先进的基线方法相比，Diet-ODIN 在探索阿片类药物滥用和饮食模式之间复杂的相互作用方面表现出色。我们的代码，建立的基准和系统演示可在 https://github.com/jasonzhangzy1757/diet-odin 下载。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Diet-ODIN:+A+Novel+Framework+for+Opioid+Misuse+Detection+with+Interpretable+Dietary+Patterns)|0| |[TACCO: Task-guided Co-clustering of Clinical Concepts and Patient Visits for Disease Subtyping based on EHR Data](https://doi.org/10.1145/3637528.3671594)|Ziyang Zhang, Hejie Cui, Ran Xu, Yuzhang Xie, Joyce C. Ho, Carl Yang|Emory University, Atlanta, GA, USA|The growing availability of well-organized Electronic Health Records (EHR) data has enabled the development of various machine learning models towards disease risk prediction. However, existing risk prediction methods overlook the heterogeneity of complex diseases, failing to model the potential disease subtypes regarding their corresponding patient visits and clinical concept subgroups. In this work, we introduce TACCO, a novel framework that jointly discovers clusters of clinical concepts and patient visits based on a hypergraph modeling of EHR data. Specifically, we develop a novel self-supervised co-clustering framework that can be guided by the risk prediction task of specific diseases. Furthermore, we enhance the hypergraph model of EHR data with textual embeddings and enforce the alignment between the clusters of clinical concepts and patient visits through a contrastive objective. Comprehensive experiments conducted on the public MIMIC-III dataset and Emory internal CRADLE dataset over the downstream clinical tasks of phenotype classification and cardiovascular risk prediction demonstrate an average 31.25% performance improvement compared to traditional ML baselines and a 5.26% improvement on top of the vanilla hypergraph model without our co-clustering mechanism. In-depth model analysis, clustering results analysis, and clinical case studies further validate the improved utilities and insightful interpretations delivered by TACCO. Code is available at https://github.com/PericlesHat/TACCO.|组织良好的电子健康记录(EHR)数据的不断增加使得各种机器学习模型朝着疾病风险预测的方向发展。然而，现有的风险预测方法忽视了复杂疾病的异质性，未能根据相应的患者就诊和临床概念亚组对潜在的疾病亚型进行建模。在这项工作中，我们介绍了 TACCO，一个新的框架，共同发现临床概念和病人访问的超图建模的基础上的 EHR 数据簇。具体来说，我们开发了一个新的自我监督协聚类框架，可以指导特定疾病的风险预测任务。此外，我们通过文本嵌入增强 EHR 数据的超图模型，并通过对比目标强化临床概念簇和患者访问之间的对齐。在公共 MIMIC-III 数据集和 Emory 内部 CRADLE 数据集上对表型分类和心血管风险预测的下游临床任务进行的综合实验表明，与传统 ML 基线相比，平均性能提高了31.25% ，在香草超图模型的基础上提高了5.26% ，而没有我们的共聚类机制。深入的模型分析、聚类结果分析和临床病例研究进一步验证了 TACCO 改进的实用性和深刻的解释。密码可于 https://github.com/pericleshat/tacco 索取。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=TACCO:+Task-guided+Co-clustering+of+Clinical+Concepts+and+Patient+Visits+for+Disease+Subtyping+based+on+EHR+Data)|0| -|[DUE: Dynamic Uncertainty-Aware Explanation Supervision via 3D Imputation](https://doi.org/10.1145/3637528.3671641)|Qilong Zhao, Yifei Zhang, Mengdan Zhu, Siyi Gu, Yuyang Gao, Xiaofeng Yang, Liang Zhao|Emory University, Atlanta, GA, USA; The Home Depot, Atlanta, GA, USA; Stanford University, Palo Alto, CA, USA|Explanation supervision aims to enhance deep learning models by integrating additional signals to guide the generation of model explanations, showcasing notable improvements in both the predictability and explainability of the model. However, the application of explanation supervision to higher-dimensional data, such as 3D medical images, remains an under-explored domain. Challenges associated with supervising visual explanations in the presence of an additional dimension include: 1) spatial correlation changed, 2) lack of direct 3D annotations, and 3) uncertainty varies across different parts of the explanation. To address these challenges, we propose a Dynamic Uncertainty-aware Explanation supervision (DUE\footnoteCode available at: https://github.com/AlexQilong/DUE.) framework for 3D explanation supervision that ensures uncertainty-aware explanation guidance when dealing with sparsely annotated 3D data with diffusion-based 3D interpolation. Our proposed framework is validated through comprehensive experiments on diverse real-world medical imaging datasets. The results demonstrate the effectiveness of our framework in enhancing the predictability and explainability of deep learning models in the context of medical imaging diagnosis applications.|解释监督旨在通过整合额外的信号来指导模型解释的生成，从而增强深度学习模型，显示模型在可预测性和可解释性方面的显著改善。然而，解释监督应用于高维数据，如三维医学图像，仍然是一个探索不足的领域。在存在额外维度的情况下，与监督视觉解释相关的挑战包括: 1)空间相关性改变，2)缺乏直接的3D 注释，3)解释的不同部分存在不同的不确定性。为了应对这些挑战，我们提出了一个动态不确定性解释监督(DUE footescode，可在以下 https://github.com/alexqilong/DUE 获得)三维解释监督框架，确保在处理基于扩散的三维插值的稀疏注释的三维数据时，不确定性意识的解释指导。我们提出的框架是通过对不同的现实世界医学影像数据集的综合实验验证。结果表明，我们的框架在提高医学影像诊断应用背景下的深度学习模型的可预测性和可解释性的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=DUE:+Dynamic+Uncertainty-Aware+Explanation+Supervision+via+3D+Imputation)|0| +|[DUE: Dynamic Uncertainty-Aware Explanation Supervision via 3D Imputation](https://doi.org/10.1145/3637528.3671641)|Qilong Zhao, Yifei Zhang, Mengdan Zhu, Siyi Gu, Yuyang Gao, Xiaofeng Yang, Liang Zhao|The Home Depot, Atlanta, GA, USA; Emory University, Atlanta, GA, USA; Stanford University, Palo Alto, CA, USA|Explanation supervision aims to enhance deep learning models by integrating additional signals to guide the generation of model explanations, showcasing notable improvements in both the predictability and explainability of the model. However, the application of explanation supervision to higher-dimensional data, such as 3D medical images, remains an under-explored domain. Challenges associated with supervising visual explanations in the presence of an additional dimension include: 1) spatial correlation changed, 2) lack of direct 3D annotations, and 3) uncertainty varies across different parts of the explanation. To address these challenges, we propose a Dynamic Uncertainty-aware Explanation supervision (DUE\footnoteCode available at: https://github.com/AlexQilong/DUE.) framework for 3D explanation supervision that ensures uncertainty-aware explanation guidance when dealing with sparsely annotated 3D data with diffusion-based 3D interpolation. Our proposed framework is validated through comprehensive experiments on diverse real-world medical imaging datasets. The results demonstrate the effectiveness of our framework in enhancing the predictability and explainability of deep learning models in the context of medical imaging diagnosis applications.|解释监督旨在通过整合额外的信号来指导模型解释的生成，从而增强深度学习模型，显示模型在可预测性和可解释性方面的显著改善。然而，解释监督应用于高维数据，如三维医学图像，仍然是一个探索不足的领域。在存在额外维度的情况下，与监督视觉解释相关的挑战包括: 1)空间相关性改变，2)缺乏直接的3D 注释，3)解释的不同部分存在不同的不确定性。为了应对这些挑战，我们提出了一个动态不确定性解释监督(DUE footescode，可在以下 https://github.com/alexqilong/DUE 获得)三维解释监督框架，确保在处理基于扩散的三维插值的稀疏注释的三维数据时，不确定性意识的解释指导。我们提出的框架是通过对不同的现实世界医学影像数据集的综合实验验证。结果表明，我们的框架在提高医学影像诊断应用背景下的深度学习模型的可预测性和可解释性的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=DUE:+Dynamic+Uncertainty-Aware+Explanation+Supervision+via+3D+Imputation)|0| |[Lookahead: An Inference Acceleration Framework for Large Language Model with Lossless Generation Accuracy](https://doi.org/10.1145/3637528.3671614)|Yao Zhao, Zhitian Xie, Chen Liang, Chenyi Zhuang, Jinjie Gu|Ant Group, Hangzhou, China|As Large Language Models (LLMs) have made significant advancements across various tasks, such as question answering, translation, text summarization, and dialogue systems, the need for accuracy in information becomes crucial, especially for serious financial products serving billions of users like Alipay. However, for a real-world product serving millions of users, the inference speed of LLMs becomes a critical factor compared to a mere experimental model. Hence, this paper presents a generic framework for accelerating the inference process, resulting in a substantial increase in speed and cost reduction for our LLM-based scenarios, with lossless generation accuracy. In the traditional inference process, each token is generated sequentially by the LLM, leading to a time consumption proportional to the number of generated tokens. To enhance this process, our framework, named lookahead, introduces a multi-branch strategy. Instead of generating a single token at a time, we propose a Trie-based retrieval and verification mechanism to be able to accept several tokens at a forward step. Our strategy offers two distinct advantages: (1) it guarantees absolute correctness of the output, avoiding any approximation algorithms, and (2) the worst-case performance of our approach could be comparable with the performance of the conventional process. We conduct extensive experiments to demonstrate the significant improvements achieved by applying our inference acceleration framework. Our framework has been widely deployed in Alipay since April 2023, and obtained remarkable 2.66x to 6.26x speedup. Our code is available at https://github.com/alipay/PainlessInferenceAcceleration.|随着大型语言模型(LLM)在诸如问答、翻译、文本摘要和对话系统等各种任务中取得重大进展，对信息准确性的需求变得至关重要，尤其是对于像支付宝这样为数十亿用户服务的重要金融产品。然而，对于一个服务于数百万用户的实际产品来说，LLM 的推理速度成为一个关键因素，而不仅仅是一个实验模型。因此，本文提出了一个加速推理过程的通用框架，从而大大提高了基于 LLM 的场景的速度和降低了成本，并且具有无损生成精度。在传统的推理过程中，每个令牌都是由 LLM 顺序生成的，从而导致与生成的令牌数量成比例的时间消耗。为了增强这个过程，我们的框架(名为 lookahead)引入了一个多分支策略。我们提出了一种基于 Trie 的检索和验证机制，以便能够在一个转发步骤中接受多个令牌，而不是一次生成一个令牌。我们的策略提供了两个明显的优势: (1)它保证了输出的绝对正确性，避免了任何近似算法，和(2)我们的方法的最坏情况下的性能可以与传统过程的性能相媲美。我们进行了广泛的实验来证明通过应用我们的推理加速框架所取得的重大改进。我们的框架从2023年4月开始在支付宝上广泛应用，并获得了2.66 ~ 6.26倍的显著加速。我们的代码可以在 https://github.com/alipay/painlessinferenceacceleration 找到。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Lookahead:+An+Inference+Acceleration+Framework+for+Large+Language+Model+with+Lossless+Generation+Accuracy)|0| -|[Decision Focused Causal Learning for Direct Counterfactual Marketing Optimization](https://doi.org/10.1145/3637528.3672353)|Hao Zhou, Rongxiao Huang, Shaoming Li, Guibin Jiang, Jiaqi Zheng, Bing Cheng, Wei Lin|State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China; State Key Laboratory for Novel Software Technology, Nanjing University & Meituan, Nanjing, China; Meituan, Beijing, China|Marketing optimization plays an important role to enhance user engagement in online Internet platforms. Existing studies usually formulate this problem as a budget allocation problem and solve it by utilizing two fully decoupled stages, i.e., machine learning (ML) and operation research (OR). However, the learning objective in ML does not take account of the downstream optimization task in OR, which causes that the prediction accuracy in ML may be not positively related to the decision quality. Decision Focused Learning (DFL) integrates ML and OR into an end-to-end framework, which takes the objective of the downstream task as the decision loss function and guarantees the consistency of the optimization direction between ML and OR. However, deploying DFL in marketing is non-trivial due to multiple technological challenges. Firstly, the budget allocation problem in marketing is a 0-1 integer stochastic programming problem and the budget is uncertain and fluctuates a lot in real-world settings, which is beyond the general problem background in DFL. Secondly, the counterfactual in marketing causes that the decision loss cannot be directly computed and the optimal solution can never be obtained, both of which disable the common gradient-estimation approaches in DFL. Thirdly, the OR solver is called frequently to compute the decision loss during model training in DFL, which produces huge computational cost and cannot support large-scale training data. In this paper, we propose a decision focused causal learning framework (DFCL) for direct counterfactual marketing optimization, which overcomes the above technological challenges. Both offline experiments and online A/B testing demonstrate the effectiveness of DFCL over the state-of-the-art methods. Currently, DFCL has been deployed in several marketing scenarios in Meituan, one of the largest online food delivery platform in the world.|营销优化对于提高用户在互联网平台上的参与度起着重要作用。现有的研究通常将这个问题表述为一个预算分配问题，并利用机器学习(ML)和运筹学(OR)两个完全解耦的阶段来解决这个问题。然而，机器学习中的学习目标没有考虑或者问题中的下游优化任务，导致机器学习中的预测精度可能与决策质量没有正相关关系。决策聚焦学习(DFL)将机器学习和运算符集成到一个端到端的框架中，以下游任务的目标作为决策损失函数，保证机器学习和运算符优化方向的一致性。然而，部署 DFL 在市场营销是不平凡的，由于多种技术挑战。首先，市场营销中的预算分配问题是一个0-1整数随机规划问题，在现实环境中预算具有不确定性且波动较大，超出了 DFL 中的一般问题背景。其次，市场营销中的反事实导致决策损失不能直接计算，最优解永远不能得到，这两个问题都使 DFL 中常用的梯度估计方法失效。第三，在 DFL 模型训练过程中频繁调用 OR 求解器来计算决策损失，计算量大，不能支持大规模的训练数据。本文提出了一个基于决策的因果学习框架(DFCL) ，用于直接反事实营销优化，克服了上述技术难题。离线实验和在线 A/B 测试都证明了 DFCL 相对于最先进的方法的有效性。目前，DfCL 已在世界上最大的在线食品配送平台之一的美团内部署了多个营销场景。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Decision+Focused+Causal+Learning+for+Direct+Counterfactual+Marketing+Optimization)|0| -|[A Hands-on Introduction to Time Series Classification and Regression](https://doi.org/10.1145/3637528.3671443)|Anthony J. Bagnall, Matthew Middlehurst, Germain Forestier, Ali IsmailFawaz, Antoine Guillaume, David GuijoRubio, Chang Wei Tan, Angus Dempster, Geoffrey I. Webb|IRIMAS, Université de Haute-Alsace, Mulhouse, France; Monash University, Melbourne, Australia; Universidad de Córdoba, Córdoba, Spain; University of Southampton, Southampton, United Kingdom; Novahe & Constellation, Saint-Cloud, France|Time series classification and regression are rapidly evolving fields that find areas of application in all domains of machine learning and data science. This hands on tutorial will provide an accessible overview of the recent research in these fields, using code examples to introduce the process of implementing and evaluating an estimator. We will show how to easily reproduce published results and how to compare a new algorithm to state-of-the-art. Finally, we will work through real world examples from the field of Electroencephalogram (EEG) classification and regression. EEG machine learning tasks arise in medicine, brain-computer interface research and psychology. We use these problems to how to compare algorithms on problems from a single domain and how to deal with data with different characteristics, such as missing values, unequal length and high dimensionality. The latest advances in the fields of time series classification and regression are all available through the aeon toolkit, an open source, scikit-learn compatible framework for time series machine learning which we use to provide our code examples.|时间序列分类和回归是一个迅速发展的领域，在机器学习和数据科学的所有领域都有广泛的应用。本教程将提供这些领域的最新研究的一个可访问的概述，使用代码示例来介绍实现和评估估算器的过程。我们将展示如何轻松地复制已发表的结果，以及如何将新算法与最先进的算法进行比较。最后，我们将从脑电图的分类和回归的角度来研究真实世界的例子。脑电图机器学习任务出现在医学、脑机接口研究和心理学领域。我们利用这些问题来比较单一领域问题的算法，以及如何处理具有不同特征的数据，如缺失值、不等长度和高维数等。时间序列分类和回归领域的最新进展都可以通过 aeon 工具包获得，aeon 工具包是一个开源的、与 scikit 学习兼容的时间序列机器学习框架，我们用它来提供我们的代码示例。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Hands-on+Introduction+to+Time+Series+Classification+and+Regression)|0| -|[Multi-modal Data Processing for Foundation Models: Practical Guidances and Use Cases](https://doi.org/10.1145/3637528.3671441)|Daoyuan Chen, Yaliang Li, Bolin Ding|Alibaba Group, Hangzhou, China; Alibaba Group, Bellevue, USA|In the foundation models era, efficiently processing multi-modal data is crucial. This tutorial covers key techniques for multi-modal data processing and introduces the open-source Data-Juicer system, designed to tackle the complexities of data variety, quality, and scale. Participants will learn how to use Data-Juicer's operators and tools for formatting, mapping, filtering, deduplicating, and selecting multi-modal data efficiently and effectively. They will also be familiar with the Data-Juicer Sandbox Lab, where users can easily experiment with diverse data recipes that represent methodical sequences of operators and streamline the creation of scalable data processing pipelines. This experience solidifies the concepts discussed, as well as provides a space for innovation and exploration, highlighting how data recipes can be optimized and deployed in high-performance distributed environments. By the end of this tutorial, attendees will be equipped with the practical knowledge and skills to navigate the multi-modal data processing for foundation models. They will leave with actionable knowledge with an industrial open-source system and an enriched perspective on the importance of high-quality data in AI, poised to implement sustainable and scalable solutions in their projects. The system and related materials are available at https://github.com/modelscope/data-juicer.|在基础模型时代，有效处理多模态数据至关重要。本教程涵盖了多模态数据处理的关键技术，并介绍了开源 Data-Juicer 系统，该系统旨在解决数据多样性、质量和规模的复杂性。参加者将学习如何使用 Data-Juicer 的操作员和工具来有效地格式化、映射、过滤、去重复和选择多模态数据。他们还将熟悉 Data-Juicer Sandbox 实验室，在那里，用户可以轻松地试验各种数据配方，这些配方代表有条不紊的操作员序列，并简化可伸缩数据处理管道的创建。这种体验巩固了所讨论的概念，并为创新和探索提供了空间，突出了如何在高性能分布式环境中优化和部署数据菜谱。在本教程结束时，与会者将获得实际知识和技能，以导航的基础模型的多模态数据处理。他们将带着可操作的知识离开工业开源系统和对人工智能中高质量数据重要性的丰富视角，准备在他们的项目中实施可持续和可扩展的解决方案。有关系统及相关资料可于 https://github.com/modelscope/data-juicer 索取。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Multi-modal+Data+Processing+for+Foundation+Models:+Practical+Guidances+and+Use+Cases)|0| -|[DARE to Diversify: DAta Driven and Diverse LLM REd Teaming](https://doi.org/10.1145/3637528.3671444)|Manish Nagireddy, Bernat Guillen Pegueroles, Ioana Baldini|Google, Zurich, CH; IBM Research, Cambridge, Massachusetts, USA; IBM Research, Yorktown Heights, New York, USA|Large language models (LLMs) have been rapidly adopted, as showcased by ChatGPT's overnight popularity, and are integrated in products used by millions of people every day, such as search engines and productivity suites. Yet the societal impact of LLMs, encompassing both benefits and harms, is not well understood. Inspired by cybersecurity practices, red-teaming is emerging as a technique to uncover model vulnerabilities. Despite increasing attention from industry, academia, and government centered around red-teaming LLMs, such efforts are still limited in the diversity of the red-teaming focus, approaches and participants. Importantly, given that LLMs are becoming ubiquitous, it is imperative that red-teaming efforts are scaled out to include large segments of the research, practitioners and the people whom are directly affected by the deployment of these systems. The goal of this tutorial is two fold. First, we introduce the topic of LLM red-teaming by reviewing the state of the art for red-teaming practices, from participatory events to automatic AI-focused approaches, exposing the gaps in both the techniques and coverage of the targeted harms. Second, we plan to engage the audience in a hands-on and interactive exercise in LLM red-teaming to showcase the ease (or difficulty) of exposing model vulnerabilities, contingent on both the targeted harm and model capabilities. We believe that the KDD community of researchers and practitioners are in a unique position to address the existing gaps in red-teaming approaches, given their longstanding research and practice of extracting knowledge from data.|大语言模型(LLM)已经被迅速采用，正如 ChatGPT 一夜成名所展示的那样，并被集成到每天数百万人使用的产品中，例如搜索引擎和生产力套件。然而，LLM 的社会影响，包括利益和危害，还没有得到很好的理解。受网络安全实践的启发，红队正在成为一种发现模型漏洞的技术。尽管业界、学术界和政府越来越多地关注红色团队 LLM，但这种努力在红色团队关注点、方法和参与者的多样性方面仍然有限。重要的是，鉴于 LLM 正变得无处不在，红色团队的努力必须扩大到包括大部分研究、从业人员和直接受到这些系统部署影响的人。本教程的目标是双重的。首先，我们通过回顾红色团队实践的现状，从参与性事件到自动 AI 重点方法，介绍 LLM 红色团队的主题，揭示目标危害的技术和覆盖面的差距。其次，我们计划让受众参与 LLM 红色团队的实践和互动练习，以展示暴露模型漏洞的容易程度(或难度) ，这取决于目标伤害和模型能力。我们认为，KDD 研究人员和从业人员群体具有独特的地位，可以弥补红队方法中现有的差距，因为他们长期从数据中提取知识的研究和实践。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=DARE+to+Diversify:+DAta+Driven+and+Diverse+LLM+REd+Teaming)|0| +|[Decision Focused Causal Learning for Direct Counterfactual Marketing Optimization](https://doi.org/10.1145/3637528.3672353)|Hao Zhou, Rongxiao Huang, Shaoming Li, Guibin Jiang, Jiaqi Zheng, Bing Cheng, Wei Lin|State Key Laboratory for Novel Software Technology, Nanjing University & Meituan, Nanjing, China; Meituan, Beijing, China; State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China|Marketing optimization plays an important role to enhance user engagement in online Internet platforms. Existing studies usually formulate this problem as a budget allocation problem and solve it by utilizing two fully decoupled stages, i.e., machine learning (ML) and operation research (OR). However, the learning objective in ML does not take account of the downstream optimization task in OR, which causes that the prediction accuracy in ML may be not positively related to the decision quality. Decision Focused Learning (DFL) integrates ML and OR into an end-to-end framework, which takes the objective of the downstream task as the decision loss function and guarantees the consistency of the optimization direction between ML and OR. However, deploying DFL in marketing is non-trivial due to multiple technological challenges. Firstly, the budget allocation problem in marketing is a 0-1 integer stochastic programming problem and the budget is uncertain and fluctuates a lot in real-world settings, which is beyond the general problem background in DFL. Secondly, the counterfactual in marketing causes that the decision loss cannot be directly computed and the optimal solution can never be obtained, both of which disable the common gradient-estimation approaches in DFL. Thirdly, the OR solver is called frequently to compute the decision loss during model training in DFL, which produces huge computational cost and cannot support large-scale training data. In this paper, we propose a decision focused causal learning framework (DFCL) for direct counterfactual marketing optimization, which overcomes the above technological challenges. Both offline experiments and online A/B testing demonstrate the effectiveness of DFCL over the state-of-the-art methods. Currently, DFCL has been deployed in several marketing scenarios in Meituan, one of the largest online food delivery platform in the world.|营销优化对于提高用户在互联网平台上的参与度起着重要作用。现有的研究通常将这个问题表述为一个预算分配问题，并利用机器学习(ML)和运筹学(OR)两个完全解耦的阶段来解决这个问题。然而，机器学习中的学习目标没有考虑或者问题中的下游优化任务，导致机器学习中的预测精度可能与决策质量没有正相关关系。决策聚焦学习(DFL)将机器学习和运算符集成到一个端到端的框架中，以下游任务的目标作为决策损失函数，保证机器学习和运算符优化方向的一致性。然而，部署 DFL 在市场营销是不平凡的，由于多种技术挑战。首先，市场营销中的预算分配问题是一个0-1整数随机规划问题，在现实环境中预算具有不确定性且波动较大，超出了 DFL 中的一般问题背景。其次，市场营销中的反事实导致决策损失不能直接计算，最优解永远不能得到，这两个问题都使 DFL 中常用的梯度估计方法失效。第三，在 DFL 模型训练过程中频繁调用 OR 求解器来计算决策损失，计算量大，不能支持大规模的训练数据。本文提出了一个基于决策的因果学习框架(DFCL) ，用于直接反事实营销优化，克服了上述技术难题。离线实验和在线 A/B 测试都证明了 DFCL 相对于最先进的方法的有效性。目前，DfCL 已在世界上最大的在线食品配送平台之一的美团内部署了多个营销场景。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Decision+Focused+Causal+Learning+for+Direct+Counterfactual+Marketing+Optimization)|0| +|[A Hands-on Introduction to Time Series Classification and Regression](https://doi.org/10.1145/3637528.3671443)|Anthony J. Bagnall, Matthew Middlehurst, Germain Forestier, Ali IsmailFawaz, Antoine Guillaume, David GuijoRubio, Chang Wei Tan, Angus Dempster, Geoffrey I. Webb|IRIMAS, Université de Haute-Alsace, Mulhouse, France; Monash University, Melbourne, Australia; Universidad de Córdoba, Córdoba, Spain; Novahe & Constellation, Saint-Cloud, France; University of Southampton, Southampton, United Kingdom|Time series classification and regression are rapidly evolving fields that find areas of application in all domains of machine learning and data science. This hands on tutorial will provide an accessible overview of the recent research in these fields, using code examples to introduce the process of implementing and evaluating an estimator. We will show how to easily reproduce published results and how to compare a new algorithm to state-of-the-art. Finally, we will work through real world examples from the field of Electroencephalogram (EEG) classification and regression. EEG machine learning tasks arise in medicine, brain-computer interface research and psychology. We use these problems to how to compare algorithms on problems from a single domain and how to deal with data with different characteristics, such as missing values, unequal length and high dimensionality. The latest advances in the fields of time series classification and regression are all available through the aeon toolkit, an open source, scikit-learn compatible framework for time series machine learning which we use to provide our code examples.|时间序列分类和回归是一个迅速发展的领域，在机器学习和数据科学的所有领域都有广泛的应用。本教程将提供这些领域的最新研究的一个可访问的概述，使用代码示例来介绍实现和评估估算器的过程。我们将展示如何轻松地复制已发表的结果，以及如何将新算法与最先进的算法进行比较。最后，我们将从脑电图的分类和回归的角度来研究真实世界的例子。脑电图机器学习任务出现在医学、脑机接口研究和心理学领域。我们利用这些问题来比较单一领域问题的算法，以及如何处理具有不同特征的数据，如缺失值、不等长度和高维数等。时间序列分类和回归领域的最新进展都可以通过 aeon 工具包获得，aeon 工具包是一个开源的、与 scikit 学习兼容的时间序列机器学习框架，我们用它来提供我们的代码示例。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Hands-on+Introduction+to+Time+Series+Classification+and+Regression)|0| +|[Multi-modal Data Processing for Foundation Models: Practical Guidances and Use Cases](https://doi.org/10.1145/3637528.3671441)|Daoyuan Chen, Yaliang Li, Bolin Ding|Alibaba Group, Bellevue, USA; Alibaba Group, Hangzhou, China|In the foundation models era, efficiently processing multi-modal data is crucial. This tutorial covers key techniques for multi-modal data processing and introduces the open-source Data-Juicer system, designed to tackle the complexities of data variety, quality, and scale. Participants will learn how to use Data-Juicer's operators and tools for formatting, mapping, filtering, deduplicating, and selecting multi-modal data efficiently and effectively. They will also be familiar with the Data-Juicer Sandbox Lab, where users can easily experiment with diverse data recipes that represent methodical sequences of operators and streamline the creation of scalable data processing pipelines. This experience solidifies the concepts discussed, as well as provides a space for innovation and exploration, highlighting how data recipes can be optimized and deployed in high-performance distributed environments. By the end of this tutorial, attendees will be equipped with the practical knowledge and skills to navigate the multi-modal data processing for foundation models. They will leave with actionable knowledge with an industrial open-source system and an enriched perspective on the importance of high-quality data in AI, poised to implement sustainable and scalable solutions in their projects. The system and related materials are available at https://github.com/modelscope/data-juicer.|在基础模型时代，有效处理多模态数据至关重要。本教程涵盖了多模态数据处理的关键技术，并介绍了开源 Data-Juicer 系统，该系统旨在解决数据多样性、质量和规模的复杂性。参加者将学习如何使用 Data-Juicer 的操作员和工具来有效地格式化、映射、过滤、去重复和选择多模态数据。他们还将熟悉 Data-Juicer Sandbox 实验室，在那里，用户可以轻松地试验各种数据配方，这些配方代表有条不紊的操作员序列，并简化可伸缩数据处理管道的创建。这种体验巩固了所讨论的概念，并为创新和探索提供了空间，突出了如何在高性能分布式环境中优化和部署数据菜谱。在本教程结束时，与会者将获得实际知识和技能，以导航的基础模型的多模态数据处理。他们将带着可操作的知识离开工业开源系统和对人工智能中高质量数据重要性的丰富视角，准备在他们的项目中实施可持续和可扩展的解决方案。有关系统及相关资料可于 https://github.com/modelscope/data-juicer 索取。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Multi-modal+Data+Processing+for+Foundation+Models:+Practical+Guidances+and+Use+Cases)|0| +|[DARE to Diversify: DAta Driven and Diverse LLM REd Teaming](https://doi.org/10.1145/3637528.3671444)|Manish Nagireddy, Bernat Guillen Pegueroles, Ioana Baldini|IBM Research, Cambridge, Massachusetts, USA; Google, Zurich, CH; IBM Research, Yorktown Heights, New York, USA|Large language models (LLMs) have been rapidly adopted, as showcased by ChatGPT's overnight popularity, and are integrated in products used by millions of people every day, such as search engines and productivity suites. Yet the societal impact of LLMs, encompassing both benefits and harms, is not well understood. Inspired by cybersecurity practices, red-teaming is emerging as a technique to uncover model vulnerabilities. Despite increasing attention from industry, academia, and government centered around red-teaming LLMs, such efforts are still limited in the diversity of the red-teaming focus, approaches and participants. Importantly, given that LLMs are becoming ubiquitous, it is imperative that red-teaming efforts are scaled out to include large segments of the research, practitioners and the people whom are directly affected by the deployment of these systems. The goal of this tutorial is two fold. First, we introduce the topic of LLM red-teaming by reviewing the state of the art for red-teaming practices, from participatory events to automatic AI-focused approaches, exposing the gaps in both the techniques and coverage of the targeted harms. Second, we plan to engage the audience in a hands-on and interactive exercise in LLM red-teaming to showcase the ease (or difficulty) of exposing model vulnerabilities, contingent on both the targeted harm and model capabilities. We believe that the KDD community of researchers and practitioners are in a unique position to address the existing gaps in red-teaming approaches, given their longstanding research and practice of extracting knowledge from data.|大语言模型(LLM)已经被迅速采用，正如 ChatGPT 一夜成名所展示的那样，并被集成到每天数百万人使用的产品中，例如搜索引擎和生产力套件。然而，LLM 的社会影响，包括利益和危害，还没有得到很好的理解。受网络安全实践的启发，红队正在成为一种发现模型漏洞的技术。尽管业界、学术界和政府越来越多地关注红色团队 LLM，但这种努力在红色团队关注点、方法和参与者的多样性方面仍然有限。重要的是，鉴于 LLM 正变得无处不在，红色团队的努力必须扩大到包括大部分研究、从业人员和直接受到这些系统部署影响的人。本教程的目标是双重的。首先，我们通过回顾红色团队实践的现状，从参与性事件到自动 AI 重点方法，介绍 LLM 红色团队的主题，揭示目标危害的技术和覆盖面的差距。其次，我们计划让受众参与 LLM 红色团队的实践和互动练习，以展示暴露模型漏洞的容易程度(或难度) ，这取决于目标伤害和模型能力。我们认为，KDD 研究人员和从业人员群体具有独特的地位，可以弥补红队方法中现有的差距，因为他们长期从数据中提取知识的研究和实践。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=DARE+to+Diversify:+DAta+Driven+and+Diverse+LLM+REd+Teaming)|0| |[Privacy-Preserving Federated Learning using Flower Framework](https://doi.org/10.1145/3637528.3671447)|Mohammad Naseri, Javier FernándezMarqués, Yan Gao, Heng Pan|Flower Labs, Cambridge, UK|AI projects often face the challenge of limited access to meaningful amounts of training data. In traditional approaches, collecting data in a central location can be problematic, especially in industry settings with sensitive and distributed data. However, there is a solution -"moving the computation to the data" through Federated Learning. Federated Learning, a distributed machine learning approach, offers a promising solution by enabling model training across devices. It is a data minimization approach where direct access to data is not required. Furthermore, federated learning can be combined with techniques like differential privacy, secure aggregation, homomorphic encryption, and others, to further enhance privacy protection. In this hands-on tutorial, we delve into the realm of privacy-preserving machine learning using federated learning, leveraging the Flower framework which is specifically designed to simplify the process of building federated learning systems, as our primary tool. Moreover, we present the foundations of federated learning, explore how different techniques can enhance its privacy aspects, how it is being used in real-world settings today and a series of practical, hands-on code examples that showcase how you can federate any AI project with Flower, an open-source framework for all-this federated.|人工智能项目往往面临获得大量培训数据的机会有限的挑战。在传统方法中，在中心位置收集数据可能会有问题，特别是在拥有敏感和分布式数据的行业环境中。然而，有一个解决方案——通过联邦学习(Federated Learning)“将计算转移到数据”。联邦学习(Federated Learning)是一种分布式机器学习方法，通过支持跨设备的模型培训，提供了一种有前途的解决方案。这是一种不需要直接访问数据的数据最小化方法。此外，联合学习还可以与差分隐私、安全聚合、同态加密等技术相结合，进一步加强隐私保护。在这个实践教程中，我们深入研究了使用联邦学习保护隐私的机器学习领域，利用 Flower 框架，它是专门设计来简化构建联邦学习系统的过程的，作为我们的主要工具。此外，我们还介绍了联合学习的基础，探索了不同的技术如何增强其隐私方面，如何在现实世界的环境中使用它，以及一系列实用的、动手操作的代码示例，展示了如何将任何人工智能项目与 Flower 联合起来，这是一个面向所有人的开源框架。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Privacy-Preserving+Federated+Learning+using+Flower+Framework)|0| -|[Graph Reasoning with LLMs (GReaL)](https://doi.org/10.1145/3637528.3671448)|Anton Tsitsulin, Bryan Perozzi, Bahare Fatemi, Jonathan J. Halcrow|Google Research, Montreal, QC, Canada; Google Research, Atlanta, GA, USA; Google Research, New York, NY, USA|Graphs are a powerful tool for representing and analyzing complex relationships in real-world applications. Large Language Models (LLMs) have demonstrated impressive capabilities by advancing state-of-the-art on many language-based benchmarks. Their ability to process and understand natural language open exciting possibilities in various domains. Despite the remarkable progress in automated reasoning with natural text, reasoning on graphs with LLMs remains an understudied problem that has recently gained more attention. This tutorial builds upon recent advances in expressing reasoning problems through the lens of tasks on graph data. The first part of the tutorial will provide an in-depth discussion of techniques for representing graphs as inputs to LLMs. The second, hands-on, portion will demonstrate these techniques in a practical setting. As a learning outcome of participating in the tutorial, participants will be able to analyze graphs either on free-tier Colab or their local machines with the help of LLMs.|图是表示和分析真实世界应用程序中复杂关系的强大工具。大型语言模型(LLM)通过在许多基于语言的基准测试上推进最先进的技术，展示了令人印象深刻的能力。他们处理和理解自然语言的能力在各个领域开辟了令人兴奋的可能性。尽管在自然文本自动推理方面取得了显著的进步，但用 LLM 进行图形推理仍然是一个被忽视的问题，最近得到了更多的关注。本教程建立在通过图形数据上的任务透镜来表达推理问题的最新进展之上。本教程的第一部分将深入讨论将图表示为 LLM 输入的技术。第二部分是实践部分，将在实际环境中演示这些技术。作为参与本教程的学习成果，参与者将能够在自由层 Colab 上或者在 LLM 的帮助下分析本地机器上的图形。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Graph+Reasoning+with+LLMs+(GReaL))|0| -|[Breaking Barriers: A Hands-On Tutorial on AI-Enabled Accessibility to Social Media Content](https://doi.org/10.1145/3637528.3671446)|Julio Villena, Rosa Català, Janine García, Concepción Polo, Yessika Labrador, Francisco delValle, Bhargav Ayyagari|Reddit, Inc., Madrid, Spain; Reddit, Inc., Toronto, Canada; Reddit, Inc., San Francisco, USA|Reddit's mission is to bring community, belonging, and empowerment to everyone in the world. This hands-on tutorial explores the immense potential of Artificial Intelligence (AI) to improve accessibility to social media content for individuals with different disabilities, including hearing, visual, and cognitive impairments. We will design and implement a variety of AI-based approaches based on multimodal open-source Large Language Models (LLMs) to bridge the gap between research and real-world applications.|Reddit 的使命是为世界上的每一个人带来社区、归属感和权力。本实践教程探索了人工智能(AI)的巨大潜力，以改善不同残疾人士(包括听力、视力和认知障碍)对社交媒体内容的无障碍访问。我们将设计和实现基于多模态开源大语言模型(LLM)的各种基于人工智能的方法，以弥合研究和现实应用之间的差距。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Breaking+Barriers:+A+Hands-On+Tutorial+on+AI-Enabled+Accessibility+to+Social+Media+Content)|0| +|[Graph Reasoning with LLMs (GReaL)](https://doi.org/10.1145/3637528.3671448)|Anton Tsitsulin, Bryan Perozzi, Bahare Fatemi, Jonathan J. Halcrow|Google Research, Atlanta, GA, USA; Google Research, New York, NY, USA; Google Research, Montreal, QC, Canada|Graphs are a powerful tool for representing and analyzing complex relationships in real-world applications. Large Language Models (LLMs) have demonstrated impressive capabilities by advancing state-of-the-art on many language-based benchmarks. Their ability to process and understand natural language open exciting possibilities in various domains. Despite the remarkable progress in automated reasoning with natural text, reasoning on graphs with LLMs remains an understudied problem that has recently gained more attention. This tutorial builds upon recent advances in expressing reasoning problems through the lens of tasks on graph data. The first part of the tutorial will provide an in-depth discussion of techniques for representing graphs as inputs to LLMs. The second, hands-on, portion will demonstrate these techniques in a practical setting. As a learning outcome of participating in the tutorial, participants will be able to analyze graphs either on free-tier Colab or their local machines with the help of LLMs.|图是表示和分析真实世界应用程序中复杂关系的强大工具。大型语言模型(LLM)通过在许多基于语言的基准测试上推进最先进的技术，展示了令人印象深刻的能力。他们处理和理解自然语言的能力在各个领域开辟了令人兴奋的可能性。尽管在自然文本自动推理方面取得了显著的进步，但用 LLM 进行图形推理仍然是一个被忽视的问题，最近得到了更多的关注。本教程建立在通过图形数据上的任务透镜来表达推理问题的最新进展之上。本教程的第一部分将深入讨论将图表示为 LLM 输入的技术。第二部分是实践部分，将在实际环境中演示这些技术。作为参与本教程的学习成果，参与者将能够在自由层 Colab 上或者在 LLM 的帮助下分析本地机器上的图形。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Graph+Reasoning+with+LLMs+(GReaL))|0| +|[Breaking Barriers: A Hands-On Tutorial on AI-Enabled Accessibility to Social Media Content](https://doi.org/10.1145/3637528.3671446)|Julio Villena, Rosa Català, Janine García, Concepción Polo, Yessika Labrador, Francisco delValle, Bhargav Ayyagari|Reddit, Inc., Toronto, Canada; Reddit, Inc., San Francisco, USA; Reddit, Inc., Madrid, Spain|Reddit's mission is to bring community, belonging, and empowerment to everyone in the world. This hands-on tutorial explores the immense potential of Artificial Intelligence (AI) to improve accessibility to social media content for individuals with different disabilities, including hearing, visual, and cognitive impairments. We will design and implement a variety of AI-based approaches based on multimodal open-source Large Language Models (LLMs) to bridge the gap between research and real-world applications.|Reddit 的使命是为世界上的每一个人带来社区、归属感和权力。本实践教程探索了人工智能(AI)的巨大潜力，以改善不同残疾人士(包括听力、视力和认知障碍)对社交媒体内容的无障碍访问。我们将设计和实现基于多模态开源大语言模型(LLM)的各种基于人工智能的方法，以弥合研究和现实应用之间的差距。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Breaking+Barriers:+A+Hands-On+Tutorial+on+AI-Enabled+Accessibility+to+Social+Media+Content)|0| |[Decoding the AI Pen: Techniques and Challenges in Detecting AI-Generated Text](https://doi.org/10.1145/3637528.3671463)|Sara Abdali, Richard Anarfi, C. J. Barberan, Jia He|Microsoft, Redmond, WA, USA; Microsoft, Cambridge, MA, USA|Large Language Models (LLMs) have revolutionized the field of NaturalLanguage Generation (NLG) by demonstrating an impressive ability to generatehuman-like text. However, their widespread usage introduces challenges thatnecessitate thoughtful examination, ethical scrutiny, and responsiblepractices. In this study, we delve into these challenges, explore existingstrategies for mitigating them, with a particular emphasis on identifyingAI-generated text as the ultimate solution. Additionally, we assess thefeasibility of detection from a theoretical perspective and propose novelresearch directions to address the current limitations in this domain.|大语言模型(LLM)通过展示生成类似人类文本的令人印象深刻的能力，彻底改变了自然语言生成(NLG)领域。然而，它们的广泛使用带来了挑战，需要深思熟虑的审查，道德审查和负责任的做法。在这项研究中，我们深入研究这些挑战，探索减轻这些挑战的现有策略，特别强调识别人工智能生成的文本作为最终解决方案。此外，我们从理论角度评估检测的可行性，并提出新的研究方向，以解决目前在这一领域的局限性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Decoding+the+AI+Pen:+Techniques+and+Challenges+in+Detecting+AI-Generated+Text)|0| -|[Advances in Human Event Modeling: From Graph Neural Networks to Language Models](https://doi.org/10.1145/3637528.3671466)|Songgaojun Deng, Maarten de Rijke, Yue Ning|University of Amsterdam, Amsterdam, The Netherlands; Stevens Institute of Technology, Hoboken, New Jersey, USA|Human events such as hospital visits, protests, and epidemic outbreaks directly affect individuals, communities, and societies. These events are often influenced by factors such as economics, politics, and public policies of our society. The abundance of online data sources such as social networks, official news articles, and personal blogs chronicle societal events, facilitating the development of AI models for social science, public health care, and decision making. Human event modeling generally comprises both the forecasting stage, which estimates future events based on historical data, and interpretation, which seeks to identify influential factors of such events to understand their causative attributes. Recent achievements, fueled by deep learning and the availability of public data, have significantly advanced the field of human event modeling. This survey offers a systematic overview of deep learning technologies for forecasting and interpreting human events, with a primary focus on political events. We first introduce the existing challenges and background in this domain. We then present the problem formulation of event forecasting and interpretation. We investigate recent achievements in graph neural networks, owing to the prevalence of relational data and the efficacy of graph learning models. We also discuss the latest studies that utilize large language models for event reasoning. Lastly, we provide summaries of data resources, open challenges, and future research directions in the study of human event modeling.|人类活动，如医院访问、抗议和流行病爆发直接影响个人、社区和社会。这些事件往往受到经济、政治和社会公共政策等因素的影响。丰富的在线数据源，如社交网络、官方新闻文章和个人博客记录了社会事件，促进了人工智能模型在社会科学、公共卫生保健和决策方面的发展。人类事件建模一般包括预测阶段(基于历史数据估计未来事件)和解释阶段(寻求识别此类事件的影响因素以了解其致因属性)。在深度学习和公共数据可用性的推动下，最近的成就显著地推进了人类事件建模领域。这项调查提供了一个系统的深度学习技术预测和解释人类事件的概述，主要侧重于政治事件。我们首先介绍了该领域存在的挑战和背景。然后，我们提出了事件预测和解释的问题表述。由于关系数据的普遍性和图学习模型的有效性，我们研究了图神经网络的最新成果。我们还讨论了利用大型语言模型进行事件推理的最新研究。最后，对人类事件建模的数据资源、开放性挑战以及未来的研究方向进行了总结。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Advances+in+Human+Event+Modeling:+From+Graph+Neural+Networks+to+Language+Models)|0| -|[Reasoning and Planning with Large Language Models in Code Development](https://doi.org/10.1145/3637528.3671452)|Hao Ding, Ziwei Fan, Ingo Gühring, Gaurav Gupta, Wooseok Ha, Jun Huan, Linbo Liu, Behrooz OmidvarTehrani, Shiqi Wang, Hao Zhou|AWS AI Labs, Santa Clara, CA, USA; AWS AI Labs, Berlin, Germany; AWS AI labs, Santa Clara, CA, USA; AWS AI Labs, New York, NY, USA|Large Language Models (LLMs) are revolutionizing the field of code development by leveraging their deep understanding of code patterns, syntax, and semantics to assist developers in various tasks, from code generation and testing to code understanding and documentation. In this survey, accompanying our proposed lecture-style tutorial for KDD 2024, we explore the multifaceted impact of LLMs on the code development, delving into techniques for generating a high-quality code, creating comprehensive test cases, automatically generating documentation, and engaging in an interactive code reasoning. Throughout the survey, we highlight some crucial components surrounding LLMs, including pre-training, fine-tuning, prompt engineering, iterative refinement, agent planning, and hallucination mitigation. We put forward that such ingredients are essential to harness the full potential of these powerful AI models in revolutionizing software engineering and paving the way for a more efficient, effective, and innovative future in code development.|大型语言模型(LLM)通过利用它们对代码模式、语法和语义的深刻理解来帮助开发人员完成各种任务，从代码生成和测试到代码理解和文档编写，从而彻底改变了代码开发领域。在这个调查中，伴随着我们提出的 KDD 2024讲座式教程，我们探索 LLM 对代码开发的多方面影响，深入研究生成高质量代码的技术，创建全面的测试用例，自动生成文档，并参与交互式代码推理。在整个调查中，我们强调了关于 LLM 的一些重要组成部分，包括预训练、微调、快速工程、迭代求精、代理计划和幻觉缓解。我们提出，这些要素对于充分利用这些强大的人工智能模型的潜力是必不可少的，它们将彻底改革软件工程，并为代码开发中更有效、更有效和更具创新性的未来铺平道路。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Reasoning+and+Planning+with+Large+Language+Models+in+Code+Development)|0| +|[Advances in Human Event Modeling: From Graph Neural Networks to Language Models](https://doi.org/10.1145/3637528.3671466)|Songgaojun Deng, Maarten de Rijke, Yue Ning|Stevens Institute of Technology, Hoboken, New Jersey, USA; University of Amsterdam, Amsterdam, The Netherlands|Human events such as hospital visits, protests, and epidemic outbreaks directly affect individuals, communities, and societies. These events are often influenced by factors such as economics, politics, and public policies of our society. The abundance of online data sources such as social networks, official news articles, and personal blogs chronicle societal events, facilitating the development of AI models for social science, public health care, and decision making. Human event modeling generally comprises both the forecasting stage, which estimates future events based on historical data, and interpretation, which seeks to identify influential factors of such events to understand their causative attributes. Recent achievements, fueled by deep learning and the availability of public data, have significantly advanced the field of human event modeling. This survey offers a systematic overview of deep learning technologies for forecasting and interpreting human events, with a primary focus on political events. We first introduce the existing challenges and background in this domain. We then present the problem formulation of event forecasting and interpretation. We investigate recent achievements in graph neural networks, owing to the prevalence of relational data and the efficacy of graph learning models. We also discuss the latest studies that utilize large language models for event reasoning. Lastly, we provide summaries of data resources, open challenges, and future research directions in the study of human event modeling.|人类活动，如医院访问、抗议和流行病爆发直接影响个人、社区和社会。这些事件往往受到经济、政治和社会公共政策等因素的影响。丰富的在线数据源，如社交网络、官方新闻文章和个人博客记录了社会事件，促进了人工智能模型在社会科学、公共卫生保健和决策方面的发展。人类事件建模一般包括预测阶段(基于历史数据估计未来事件)和解释阶段(寻求识别此类事件的影响因素以了解其致因属性)。在深度学习和公共数据可用性的推动下，最近的成就显著地推进了人类事件建模领域。这项调查提供了一个系统的深度学习技术预测和解释人类事件的概述，主要侧重于政治事件。我们首先介绍了该领域存在的挑战和背景。然后，我们提出了事件预测和解释的问题表述。由于关系数据的普遍性和图学习模型的有效性，我们研究了图神经网络的最新成果。我们还讨论了利用大型语言模型进行事件推理的最新研究。最后，对人类事件建模的数据资源、开放性挑战以及未来的研究方向进行了总结。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Advances+in+Human+Event+Modeling:+From+Graph+Neural+Networks+to+Language+Models)|0| +|[Reasoning and Planning with Large Language Models in Code Development](https://doi.org/10.1145/3637528.3671452)|Hao Ding, Ziwei Fan, Ingo Gühring, Gaurav Gupta, Wooseok Ha, Jun Huan, Linbo Liu, Behrooz OmidvarTehrani, Shiqi Wang, Hao Zhou|AWS AI labs, Santa Clara, CA, USA; AWS AI Labs, Santa Clara, CA, USA; AWS AI Labs, New York, NY, USA; AWS AI Labs, Berlin, Germany|Large Language Models (LLMs) are revolutionizing the field of code development by leveraging their deep understanding of code patterns, syntax, and semantics to assist developers in various tasks, from code generation and testing to code understanding and documentation. In this survey, accompanying our proposed lecture-style tutorial for KDD 2024, we explore the multifaceted impact of LLMs on the code development, delving into techniques for generating a high-quality code, creating comprehensive test cases, automatically generating documentation, and engaging in an interactive code reasoning. Throughout the survey, we highlight some crucial components surrounding LLMs, including pre-training, fine-tuning, prompt engineering, iterative refinement, agent planning, and hallucination mitigation. We put forward that such ingredients are essential to harness the full potential of these powerful AI models in revolutionizing software engineering and paving the way for a more efficient, effective, and innovative future in code development.|大型语言模型(LLM)通过利用它们对代码模式、语法和语义的深刻理解来帮助开发人员完成各种任务，从代码生成和测试到代码理解和文档编写，从而彻底改变了代码开发领域。在这个调查中，伴随着我们提出的 KDD 2024讲座式教程，我们探索 LLM 对代码开发的多方面影响，深入研究生成高质量代码的技术，创建全面的测试用例，自动生成文档，并参与交互式代码推理。在整个调查中，我们强调了关于 LLM 的一些重要组成部分，包括预训练、微调、快速工程、迭代求精、代理计划和幻觉缓解。我们提出，这些要素对于充分利用这些强大的人工智能模型的潜力是必不可少的，它们将彻底改革软件工程，并为代码开发中更有效、更有效和更具创新性的未来铺平道路。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Reasoning+and+Planning+with+Large+Language+Models+in+Code+Development)|0| |[Sharing is Caring: A Practical Guide to FAIR(ER) Open Data Release](https://doi.org/10.1145/3637528.3671468)|Amelia Henriksen, Miranda Mundt|Sandia National Laboratories, Albuquerque, New Mexico, USA|Findable. Accessible. Interoperable. Reusable. Since their introduction in 2016, the FAIR data principles have defined the standards by which scientific researchers share data. However, modern research in data editing and management consistently shows that while the FAIR data principles are widely accepted in theory, they can be much more difficult to understand and implement in practice. In this tutorial, we explore some of the simple, realistic steps scientists can take to FAIRly release open data. We also explore areas where the current FAIR guidelines fall short and offer practical suggestions for making open data FAIR(ER): more Equitable and Realistic. This first involves ways to make datasets themselves more equitably accessible for researchers with disabilities. While equitably accessible data design has some research overlap with paper, presentation, and website design, we suggest several unique distinctions specific to datasets. The "Realistic'' aspect of FAIR(ER) data facilitates a path to translate open data (and research on that data) back to true applications. Driven by national security applications pipelines, we call out important considerations for balancing data editing against data realism.|找得到。无障碍。可互操作。可重复使用。自2016年引入以来，FAIR 数据原则已经确定了科学研究人员共享数据的标准。然而，现代数据编辑和管理研究一致表明，虽然 FAIR 数据原则在理论上被广泛接受，但在实践中可能更难理解和实施。在本教程中，我们将探索一些科学家可以采取的简单、现实的步骤，以公平地发布开放数据。我们还探讨了当前公平竞争指南的不足之处，并提供了实用的建议，使开放数据公平竞争(ER) : 更加公平和现实。这首先涉及到如何让残疾研究人员更公平地获取数据集。虽然公平可访问的数据设计与论文、演示文稿和网站设计有一些研究重叠，但我们提出了一些特定于数据集的独特区别。FAIR (ER)数据的“现实”方面促进了将开放数据(以及对该数据的研究)转换回真正应用程序的途径。在国家安全应用管道的驱动下，我们呼吁在数据编辑和数据现实主义之间进行平衡的重要考虑。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Sharing+is+Caring:+A+Practical+Guide+to+FAIR(ER)+Open+Data+Release)|0| |[Grounding and Evaluation for Large Language Models: Practical Challenges and Lessons Learned (Survey)](https://doi.org/10.1145/3637528.3671467)|Krishnaram Kenthapadi, Mehrnoosh Sameki, Ankur Taly|Oracle Health AI, Redwood City, CA, USA; Google Cloud AI, Sunnyvale, CA, USA; Microsoft Azure AI, Boston, MA, USA|With the ongoing rapid adoption of Artificial Intelligence (AI)-based systems in high-stakes domains, ensuring the trustworthiness, safety, and observability of these systems has become crucial. It is essential to evaluate and monitor AI systems not only for accuracy and quality-related metrics but also for robustness, bias, security, interpretability, and other responsible AI dimensions. We focus on large language models (LLMs) and other generative AI models, which present additional challenges such as hallucinations, harmful and manipulative content, and copyright infringement. In this survey article accompanying our tutorial, we highlight a wide range of harms associated with generative AI systems, and survey state of the art approaches (along with open challenges) to address these harms.|随着基于人工智能(AI)的系统在高风险领域的快速应用，确保这些系统的可靠性、安全性和可观测性已变得至关重要。评估和监控人工智能系统不仅是为了准确性和质量相关指标，而且也是为了鲁棒性、偏差、安全性、可解释性和其他负责任的人工智能维度。我们专注于大型语言模型(LLMs)和其他生成性人工智能模型，这些模型提出了额外的挑战，比如幻觉、有害和操纵性内容以及盗版。在本教程附带的这篇调查文章中，我们强调了与生成性 AI 系统相关的一系列危害，并调查了解决这些危害的最新方法(以及开放式挑战)。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Grounding+and+Evaluation+for+Large+Language+Models:+Practical+Challenges+and+Lessons+Learned+(Survey))|0| -|[A Survey on Hypergraph Neural Networks: An In-Depth and Step-By-Step Guide](https://doi.org/10.1145/3637528.3671457)|Sunwoo Kim, Soo Yong Lee, Yue Gao, Alessia Antelmi, Mirko Polato, Kijung Shin|KAIST, Seoul, Republic of Korea; University of Turin, Turin, Italy; Tsinghua University, Beijing, China|Higher-order interactions (HOIs) are ubiquitous in real-world complex systems and applications. Investigation of deep learning for HOIs, thus, has become a valuable agenda for the data mining and machine learning communities. As networks of HOIs are expressed mathematically as hypergraphs, hypergraph neural networks (HNNs) have emerged as a powerful tool for representation learning on hypergraphs. Given the emerging trend, we present the first survey dedicated to HNNs, with an in-depth and step-by-step guide. Broadly, the present survey overviews HNN architectures, training strategies, and applications. First, we break existing HNNs down into four design components: (i) input features, (ii) input structures, (iii) message-passing schemes, and (iv) training strategies. Second, we examine how HNNs address and learn HOIs with each of their components. Third, we overview the recent applications of HNNs in recommendation, bioinformatics and medical science, time series analysis, and computer vision. Lastly, we conclude with a discussion on limitations and future directions.|高阶交互(HOI)在现实世界的复杂系统和应用中无处不在。因此，研究 HOI 的深度学习已经成为数据挖掘和机器学习领域的一个有价值的议题。由于 HOI 网络被数学上表示为超图，超图神经网络(HNN)已经成为超图表示学习的有力工具。鉴于新兴的趋势，我们提出了第一个调查专门针对 HNN，与一个深入和逐步指南。概括地说，本调查综述了 HNN 的体系结构、培训策略和应用。首先，我们将现有的 HNN 分解为四个设计组件: (i)输入特性，(ii)输入结构，(iii)消息传递方案和(iv)训练策略。其次，我们研究了 HNN 如何通过它们的每个组件来寻址和学习 HOI。第三，我们概述了 HNN 在推荐、生物信息学和医学科学、时间序列分析和计算机视觉等方面的最新应用。最后，我们讨论了本文的局限性和未来的发展方向。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Survey+on+Hypergraph+Neural+Networks:+An+In-Depth+and+Step-By-Step+Guide)|0| +|[A Survey on Hypergraph Neural Networks: An In-Depth and Step-By-Step Guide](https://doi.org/10.1145/3637528.3671457)|Sunwoo Kim, Soo Yong Lee, Yue Gao, Alessia Antelmi, Mirko Polato, Kijung Shin|Tsinghua University, Beijing, China; KAIST, Seoul, Republic of Korea; University of Turin, Turin, Italy|Higher-order interactions (HOIs) are ubiquitous in real-world complex systems and applications. Investigation of deep learning for HOIs, thus, has become a valuable agenda for the data mining and machine learning communities. As networks of HOIs are expressed mathematically as hypergraphs, hypergraph neural networks (HNNs) have emerged as a powerful tool for representation learning on hypergraphs. Given the emerging trend, we present the first survey dedicated to HNNs, with an in-depth and step-by-step guide. Broadly, the present survey overviews HNN architectures, training strategies, and applications. First, we break existing HNNs down into four design components: (i) input features, (ii) input structures, (iii) message-passing schemes, and (iv) training strategies. Second, we examine how HNNs address and learn HOIs with each of their components. Third, we overview the recent applications of HNNs in recommendation, bioinformatics and medical science, time series analysis, and computer vision. Lastly, we conclude with a discussion on limitations and future directions.|高阶交互(HOI)在现实世界的复杂系统和应用中无处不在。因此，研究 HOI 的深度学习已经成为数据挖掘和机器学习领域的一个有价值的议题。由于 HOI 网络被数学上表示为超图，超图神经网络(HNN)已经成为超图表示学习的有力工具。鉴于新兴的趋势，我们提出了第一个调查专门针对 HNN，与一个深入和逐步指南。概括地说，本调查综述了 HNN 的体系结构、培训策略和应用。首先，我们将现有的 HNN 分解为四个设计组件: (i)输入特性，(ii)输入结构，(iii)消息传递方案和(iv)训练策略。其次，我们研究了 HNN 如何通过它们的每个组件来寻址和学习 HOI。第三，我们概述了 HNN 在推荐、生物信息学和医学科学、时间序列分析和计算机视觉等方面的最新应用。最后，我们讨论了本文的局限性和未来的发展方向。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Survey+on+Hypergraph+Neural+Networks:+An+In-Depth+and+Step-By-Step+Guide)|0| |[Graph Intelligence with Large Language Models and Prompt Learning](https://doi.org/10.1145/3637528.3671456)|Jia Li, Xiangguo Sun, Yuhan Li, Zhixun Li, Hong Cheng, Jeffrey Xu Yu|The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China; The Chinese University of Hong Kong, HongKong, China|Graph plays a significant role in representing and analyzing complex relationships in real-world applications such as citation networks, social networks, and biological data. Graph intelligence is rapidly becoming a crucial aspect of understanding and exploiting the intricate interconnections within graph data. Recently, large language models (LLMs) and prompt learning techniques have pushed graph intelligence forward, outperforming traditional Graph Neural Network (GNN) pre-training methods and setting new benchmarks for performance. In this tutorial, we begin by offering a comprehensive review and analysis of existing methods that integrate LLMs with graphs. We introduce existing works based on a novel taxonomy that classifies them into three distinct categories according to the roles of LLMs in graph tasks: as enhancers, predictors, or alignment components. Secondly, we introduce a new learning method that utilizes prompting on graphs, offering substantial potential to enhance graph transfer capabilities across diverse tasks and domains. We discuss existing works on graph prompting within a unified framework and introduce our developed tool for executing a variety of graph prompting tasks. Additionally, we discuss the applications of combining Graphs, LLMs, and prompt learning across various tasks, such as urban computing, recommendation systems, and anomaly detection. This lecture-style tutorial is an extension of our original work published in IJCAI 2024[44] and arXiv[77] with the invitation of KDD24.|在引用网络、社会网络和生物数据等现实应用中，图表在表示和分析复杂关系方面发挥着重要作用。图形智能正在迅速成为理解和利用图形数据中错综复杂的相互关系的一个关键方面。近年来，大语言模型(LLM)和快速学习技术推动了图形智能的发展，其性能优于传统的图形神经网络(GNN)预训练方法，并为性能设定了新的基准。在本教程中，我们首先全面回顾和分析集成 LLM 和图形的现有方法。我们介绍现有的工作基于一个新的分类法，根据 LLM 在图形任务中的作用将它们分为三个不同的类别: 作为增强子、预测器或对齐组件。其次，我们介绍了一种新的学习方法，利用图上的提示，提供了大量的潜力，以提高图的传输能力跨不同的任务和领域。在统一的框架下讨论了现有的图形提示工作，并介绍了我们开发的用于执行各种图形提示任务的工具。此外，我们还讨论了结合图形、 LLM 和跨不同任务的快速学习的应用，例如城市计算、推荐系统和异常检测。这个演讲风格的教程是我们的原始工作的延伸，应 KDD24的邀请发表在 IJCAI 2024[44]和 arXiv [77]。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Graph+Intelligence+with+Large+Language+Models+and+Prompt+Learning)|0| -|[Foundation Models for Time Series Analysis: A Tutorial and Survey](https://doi.org/10.1145/3637528.3671451)|Yuxuan Liang, Haomin Wen, Yuqi Nie, Yushan Jiang, Ming Jin, Dongjin Song, Shirui Pan, Qingsong Wen|Princeton University, Princeton, NJ, USA; Griffith University, Brisbane, Australia; University of Connecticut, Storrs, CT, USA; The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China; The Hong Kong University of Science and Technology (Guangzhou; Squirrel AI, Seattle, WA, USA; Monash University, Melbourne, Australia|Time series analysis stands as a focal point within the data mining community, serving as a cornerstone for extracting valuable insights crucial to a myriad of real-world applications. Recent advances in Foundation Models (FMs) have fundamentally reshaped the paradigm of model design for time series analysis, boosting various downstream tasks in practice. These innovative approaches often leverage pre-trained or fine-tuned FMs to harness generalized knowledge tailored for time series analysis. This survey aims to furnish a comprehensive and up-to-date overview of FMs for time series analysis. While prior surveys have predominantly focused on either application or pipeline aspects of FMs in time series analysis, they have often lacked an in-depth understanding of the underlying mechanisms that elucidate why and how FMs benefit time series analysis. To address this gap, our survey adopts a methodology-centric classification, delineating various pivotal elements of time-series FMs, including model architectures, pre-training techniques, adaptation methods, and data modalities. Overall, this survey serves to consolidate the latest advancements in FMs pertinent to time series analysis, accentuating their theoretical underpinnings, recent strides in development, and avenues for future exploration.|时间序列分析是数据挖掘领域的焦点，是提取对于大量现实应用程序至关重要的宝贵见解的基石。基础模型的最新进展从根本上重塑了时间序列分析的模型设计范式，推动了实践中各种下游任务的完成。这些创新的方法往往利用预先训练或微调的模拟器来利用为时间序列分析量身定制的通用知识。本调查旨在为时间序列分析提供全面和最新的调查表概述。虽然以前的调查主要集中在时间序列分析中的建模方法的应用或流水线方面，但它们往往缺乏对阐明建模方法有利于时间序列分析的原因和方式的基本机制的深入理解。为了弥补这一差距，我们的调查采用了以方法论为中心的分类，描述了时间序列模型的各种关键要素，包括模型架构、预训练技术、适应方法和数据模式。总体而言，本调查旨在巩固与时间序列分析相关的 FM 的最新进展，强调它们的理论基础、最近的发展进展以及未来探索的途径。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Foundation+Models+for+Time+Series+Analysis:+A+Tutorial+and+Survey)|0| +|[Foundation Models for Time Series Analysis: A Tutorial and Survey](https://doi.org/10.1145/3637528.3671451)|Yuxuan Liang, Haomin Wen, Yuqi Nie, Yushan Jiang, Ming Jin, Dongjin Song, Shirui Pan, Qingsong Wen|Griffith University, Brisbane, Australia; The Hong Kong University of Science and Technology (Guangzhou; Princeton University, Princeton, NJ, USA; Monash University, Melbourne, Australia; Squirrel AI, Seattle, WA, USA; The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China; University of Connecticut, Storrs, CT, USA|Time series analysis stands as a focal point within the data mining community, serving as a cornerstone for extracting valuable insights crucial to a myriad of real-world applications. Recent advances in Foundation Models (FMs) have fundamentally reshaped the paradigm of model design for time series analysis, boosting various downstream tasks in practice. These innovative approaches often leverage pre-trained or fine-tuned FMs to harness generalized knowledge tailored for time series analysis. This survey aims to furnish a comprehensive and up-to-date overview of FMs for time series analysis. While prior surveys have predominantly focused on either application or pipeline aspects of FMs in time series analysis, they have often lacked an in-depth understanding of the underlying mechanisms that elucidate why and how FMs benefit time series analysis. To address this gap, our survey adopts a methodology-centric classification, delineating various pivotal elements of time-series FMs, including model architectures, pre-training techniques, adaptation methods, and data modalities. Overall, this survey serves to consolidate the latest advancements in FMs pertinent to time series analysis, accentuating their theoretical underpinnings, recent strides in development, and avenues for future exploration.|时间序列分析是数据挖掘领域的焦点，是提取对于大量现实应用程序至关重要的宝贵见解的基石。基础模型的最新进展从根本上重塑了时间序列分析的模型设计范式，推动了实践中各种下游任务的完成。这些创新的方法往往利用预先训练或微调的模拟器来利用为时间序列分析量身定制的通用知识。本调查旨在为时间序列分析提供全面和最新的调查表概述。虽然以前的调查主要集中在时间序列分析中的建模方法的应用或流水线方面，但它们往往缺乏对阐明建模方法有利于时间序列分析的原因和方式的基本机制的深入理解。为了弥补这一差距，我们的调查采用了以方法论为中心的分类，描述了时间序列模型的各种关键要素，包括模型架构、预训练技术、适应方法和数据模式。总体而言，本调查旨在巩固与时间序列分析相关的 FM 的最新进展，强调它们的理论基础、最近的发展进展以及未来探索的途径。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Foundation+Models+for+Time+Series+Analysis:+A+Tutorial+and+Survey)|0| |[Symbolic Regression: A Pathway to Interpretability Towards Automated Scientific Discovery](https://doi.org/10.1145/3637528.3671464)|Nour Makke, Sanjay Chawla|Qatar Airways (Qatar)|Symbolic regression is a machine learning technique employed for learning mathematical equations directly from data. Mathematical equations capture both functional and causal relationships in the data. In addition, they are simple, compact, generalizable, and interpretable models, making them the best candidates for i) learning inherently transparent models and ii) boosting scientific discovery. Symbolic regression has received a growing interest since the last decade and is tackled using different approaches in supervised and unsupervised deep learning, thanks to the enormous progress achieved in deep learning in the last twenty years. Symbolic regression remains underestimated in conference coverage as a primary form of interpretable AI and a potential candidate for automating scientific discovery. This tutorial overviews symbolic regression: problem definition, approaches, and key limitations, discusses why physical sciences are beneficial to symbolic regression, and explores possible future directions in this research area.|符号回归是一种直接从数据中学习数学方程的机器学习技术。数学方程捕捉数据中的函数关系和因果关系。此外，它们是简单、紧凑、可推广和可解释的模型，使它们成为 i)学习固有的透明模型和 ii)促进科学发现的最佳候选者。自过去十年以来，符号回归得到了越来越多的关注，并且由于过去二十年在深度学习方面取得的巨大进展，在有监督和无监督的深度学习中使用了不同的方法来处理符号回归。象征性回归作为可解释人工智能的主要形式和自动化科学发现的潜在候选者，在会议覆盖面方面仍然被低估。本教程概述了符号回归: 问题的定义，方法和关键的局限性，讨论了为什么物理科学有利于符号回归，并探讨了在这个研究领域可能的未来方向。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Symbolic+Regression:+A+Pathway+to+Interpretability+Towards+Automated+Scientific+Discovery)|0| -|[A Survey of Large Language Models for Graphs](https://doi.org/10.1145/3637528.3671460)|Xubin Ren, Jiabin Tang, Dawei Yin, Nitesh V. Chawla, Chao Huang|Baidu, Beijing, China; University of Hong Kong, Hong Kong, China; University of Notre Dame, Indiana, USA|Graphs are an essential data structure utilized to represent relationships in real-world scenarios. Prior research has established that Graph Neural Networks (GNNs) deliver impressive outcomes in graph-centric tasks, such as link prediction and node classification. Despite these advancements, challenges like data sparsity and limited generalization capabilities continue to persist. Recently, Large Language Models (LLMs) have gained attention in natural language processing. They excel in language comprehension and summarization. Integrating LLMs with graph learning techniques has attracted interest as a way to enhance performance in graph learning tasks. In this survey, we conduct an in-depth review of the latest state-of-the-art LLMs applied in graph learning and introduce a novel taxonomy to categorize existing methods based on their framework design. We detail four unique designs: i) GNNs as Prefix, ii) LLMs as Prefix, iii) LLMs-Graphs Integration, and iv) LLMs-Only, highlighting key methodologies within each category. We explore the strengths and limitations of each framework, and emphasize potential avenues for future research, including overcoming current integration challenges between LLMs and graph learning techniques, and venturing into new application areas. This survey aims to serve as a valuable resource for researchers and practitioners eager to leverage large language models in graph learning, and to inspire continued progress in this dynamic field. We consistently maintain the related open-source materials at https://github.com/HKUDS/Awesome-LLM4Graph-Papers.|图是在现实场景中用于表示关系的基本数据结构。先前的研究已经证实，图形神经网络(GNN)在以图形为中心的任务(如链路预测和节点分类)中能够产生令人印象深刻的结果。尽管取得了这些进步，但数据稀少和泛化能力有限等挑战仍然存在。近年来，大语言模型(LLM)在自然语言处理领域引起了广泛的关注。他们擅长语言理解和总结。将 LLM 与图形学习技术相结合作为一种提高图形学习任务性能的方法引起了人们的兴趣。在这篇综述中，我们对最新的应用于图形学习的最小二乘法进行了深入的回顾，并引入了一种新的分类方法来根据它们的框架设计对现有的方法进行分类。我们详细介绍了四种独特的设计: i) GNN 作为前缀，ii) LLM 作为前缀，iii) LLM-Graphs 集成，以及 iv) LLM-Only，突出每个类别中的关键方法。我们探讨了每个框架的优势和局限性，并强调了未来研究的潜在途径，包括克服当前 LLM 和图形学习技术之间的集成挑战，以及进入新的应用领域。这项调查的目的是作为一个宝贵的资源，为研究人员和从业人员渴望利用大型语言模型在图形学习，并激励在这个动态领域的持续进展。我们一直在 https://github.com/hkuds/awesome-llm4graph-papers 保存相关的开源材料。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Survey+of+Large+Language+Models+for+Graphs)|0| +|[A Survey of Large Language Models for Graphs](https://doi.org/10.1145/3637528.3671460)|Xubin Ren, Jiabin Tang, Dawei Yin, Nitesh V. Chawla, Chao Huang|University of Notre Dame, Indiana, USA; Baidu, Beijing, China; University of Hong Kong, Hong Kong, China|Graphs are an essential data structure utilized to represent relationships in real-world scenarios. Prior research has established that Graph Neural Networks (GNNs) deliver impressive outcomes in graph-centric tasks, such as link prediction and node classification. Despite these advancements, challenges like data sparsity and limited generalization capabilities continue to persist. Recently, Large Language Models (LLMs) have gained attention in natural language processing. They excel in language comprehension and summarization. Integrating LLMs with graph learning techniques has attracted interest as a way to enhance performance in graph learning tasks. In this survey, we conduct an in-depth review of the latest state-of-the-art LLMs applied in graph learning and introduce a novel taxonomy to categorize existing methods based on their framework design. We detail four unique designs: i) GNNs as Prefix, ii) LLMs as Prefix, iii) LLMs-Graphs Integration, and iv) LLMs-Only, highlighting key methodologies within each category. We explore the strengths and limitations of each framework, and emphasize potential avenues for future research, including overcoming current integration challenges between LLMs and graph learning techniques, and venturing into new application areas. This survey aims to serve as a valuable resource for researchers and practitioners eager to leverage large language models in graph learning, and to inspire continued progress in this dynamic field. We consistently maintain the related open-source materials at https://github.com/HKUDS/Awesome-LLM4Graph-Papers.|图是在现实场景中用于表示关系的基本数据结构。先前的研究已经证实，图形神经网络(GNN)在以图形为中心的任务(如链路预测和节点分类)中能够产生令人印象深刻的结果。尽管取得了这些进步，但数据稀少和泛化能力有限等挑战仍然存在。近年来，大语言模型(LLM)在自然语言处理领域引起了广泛的关注。他们擅长语言理解和总结。将 LLM 与图形学习技术相结合作为一种提高图形学习任务性能的方法引起了人们的兴趣。在这篇综述中，我们对最新的应用于图形学习的最小二乘法进行了深入的回顾，并引入了一种新的分类方法来根据它们的框架设计对现有的方法进行分类。我们详细介绍了四种独特的设计: i) GNN 作为前缀，ii) LLM 作为前缀，iii) LLM-Graphs 集成，以及 iv) LLM-Only，突出每个类别中的关键方法。我们探讨了每个框架的优势和局限性，并强调了未来研究的潜在途径，包括克服当前 LLM 和图形学习技术之间的集成挑战，以及进入新的应用领域。这项调查的目的是作为一个宝贵的资源，为研究人员和从业人员渴望利用大型语言模型在图形学习，并激励在这个动态领域的持续进展。我们一直在 https://github.com/hkuds/awesome-llm4graph-papers 保存相关的开源材料。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Survey+of+Large+Language+Models+for+Graphs)|0| |[Explainable Artificial Intelligence on Biosignals for Clinical Decision Support](https://doi.org/10.1145/3637528.3671459)|Miriam Cindy Maurer, Jacqueline Michelle Metsch, Philip Hempel, Theresa Bender, Nicolai Spicher, AnneChristin Hauschild|; Department of Medical Informatics, University Medical Center, Göttingen, Germany|Deep learning has proven effective in several areas, including computer vision, natural language processing, and disease prediction, which can support clinicians in making decisions along the clinical pathway. However, in order to successfully integrate these algorithms into clinical practice, it is important that their decision-making processes are transparent, explainable, and interpretable. Firstly, this tutorial will introduce targeted eXplainable Artificial Intelligence (XAI) methods to address the urgent need for explainability of deep learning in healthcare applications. In particular, it focuses on algorithms for raw biosignals without prior feature extraction that enable medical diagnoses, specifically electrocardiograms (ECG) -- stemming from the heart -- and electroencephalograms (EEG) representing the electrical activity of the brain. Secondly, participants are provided with a comprehensive workflow that includes both data processing and an introduction to relevant network architectures. Subsequently, various XAI methods are described and it is shown, how the resulting relevance attributions can be visualized on biosignals. Finally, two compelling real-world use cases are presented that demonstrate the effectiveness of XAI in analyzing ECG and EEG signals for disease prediction and sleep classification, respectively. In summary, the tutorial will provide the skills required for gaining insight into the decision process of deep neural networks processing authentic clinical biosignal data.|深度学习已被证明在多个领域有效，包括计算机视觉、自然语言处理和疾病预测，这可以支持临床医生根据临床路径做出决策。然而，为了成功地将这些算法集成到临床实践中，它们的决策过程是透明的、可解释的和可解释的是非常重要的。首先，本教程将介绍目标可解释的人工智能(XAI)方法，以满足医疗应用程序中对深度学习可解释性的迫切需求。特别是，它专注于原始生物信号的算法，而不需要事先提取特征来进行医学诊断，特别是心电图(ECG)——源自心脏——和脑电图(EEG)代表大脑的电活动。其次，向参与者提供一个全面的工作流程，包括数据处理和相关网络架构的介绍。随后，描述了各种 XAI 方法，并展示了如何在生物信号上可视化得到的相关属性。最后，给出了两个令人信服的现实应用案例，分别证明了 XAI 在分析心电信号和脑电信号用于疾病预测和睡眠分类方面的有效性。总之，本教程将提供深入了解处理真实临床生物信号数据的深层神经网络的决策过程所需的技能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Explainable+Artificial+Intelligence+on+Biosignals+for+Clinical+Decision+Support)|0| |[Urban Foundation Models: A Survey](https://doi.org/10.1145/3637528.3671453)|Weijia Zhang, Jindong Han, Zhao Xu, Hang Ni, Hao Liu, Hui Xiong|HKUST(GZ) & HKUST, Guangzhou, China; HKUST, Hong Kong, China; HKUST(GZ), Guangzhou, China|Machine learning techniques are now integral to the advancement of intelligent urban services, playing a crucial role in elevating the efficiency, sustainability, and livability of urban environments. The recent emergence of foundation models such as ChatGPT marks a revolutionary shift in the fields of machine learning and artificial intelligence. Their unparalleled capabilities in contextual understanding, problem solving, and adaptability across a wide range of tasks suggest that integrating these models into urban domains could have a transformative impact on the development of smart cities. Despite growing interest in Urban Foundation Models (UFMs), this burgeoning field faces challenges such as a lack of clear definitions and systematic reviews. To this end, this paper first introduces the concept of UFMs and discusses the unique challenges involved in building them. We then propose a data-centric taxonomy that categorizes and clarifies current UFM-related works, based on urban data modalities and types. Furthermore, we explore the application landscape of UFMs, detailing their potential impact in various urban contexts. Relevant papers and open-source resources have been collated and are continuously updated at: https://github.com/usail-hkust/Awesome-Urban-Foundation-Models.|机器学习技术现在已经成为智能城市服务的一个组成部分，在提高城市环境的效率、可持续性和宜居性方面发挥着至关重要的作用。ChatGPT 等基础模型的出现标志着机器学习和人工智能领域的革命性转变。它们在背景理解、解决问题和适应各种任务方面的无与伦比的能力表明，将这些模型纳入城市领域可以对智慧城市的发展产生变革性的影响。尽管人们对城市基础模型(UFM)的兴趣日益增长，但这一新兴领域仍面临诸如缺乏清晰定义和系统评价等挑战。为此，本文首先介绍了 UFM 的概念，并讨论了构建它们所涉及的独特挑战。然后，我们提出了一个以数据为中心的分类法，根据城市数据模式和类型对当前 UFM 相关的工作进行分类和澄清。此外，我们探讨了 UFM 的应用景观，详细说明了它们在各种城市背景下的潜在影响。相关文件和开放源码资源已经整理并不断更新， https://github.com/usail-hkust/awesome-urban-foundation-models 如下:。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Urban+Foundation+Models:+A+Survey)|0| -|[Inference Optimization of Foundation Models on AI Accelerators](https://doi.org/10.1145/3637528.3671465)|Youngsuk Park, Kailash Budhathoki, Liangfu Chen, Jonas M. Kübler, Jiaji Huang, Matthäus Kleindessner, Jun Huan, Volkan Cevher, Yida Wang, George Karypis|AWS AI & EPFL, Tübingen, Germany; AWS AI, Santa Clara, CA, USA; AWS AI, Tübingen, Germany|Powerful foundation models, including large language models (LLMs), with Transformer architectures have ushered in a new era of Generative AI across various industries. Industry and research community have witnessed a large number of new applications, based on those foundation models. Such applications include question and answer, customer services, image and video generation, and code completions, among others. However, as the number of model parameters reaches to hundreds of billions, their deployment incurs prohibitive inference costs and high latency in real-world scenarios. As a result, the demand for cost-effective and fast inference using AI accelerators is ever more higher. To this end, our tutorial offers a comprehensive discussion on complementary inference optimization techniques using AI accelerators. Beginning with an overview of basic Transformer architectures and deep learning system frameworks, we deep dive into system optimization techniques for fast and memory-efficient attention computations and discuss how they can be implemented efficiently on AI accelerators. Next, we describe architectural elements that are key for fast transformer inference. Finally, we examine various model compression and fast decoding strategies in the same context.|包括大型语言模型(LLM)在内的强大的基础模型，以及变压器架构，已经在各个行业开创了生成式人工智能的新时代。在这些基础模型的基础上，工业界和研究界目睹了大量新的应用。此类应用程序包括问答、客户服务、图像和视频生成以及代码完成等。然而，当模型参数的数量达到数千亿时，它们的部署在实际场景中会产生令人望而却步的推理成本和高延迟。因此，对使用人工智能加速器的高性价比和快速推理的需求越来越高。为此，我们的教程提供了关于使用 AI 加速器的互补推理优化技术的全面讨论。从变压器基本结构和深度学习系统框架的概述开始，我们深入研究了快速和高效的注意力计算系统优化技术，并讨论了如何在 AI 加速器上有效地实现这些技术。接下来，我们将描述对于快速转换器推理非常关键的体系结构元素。最后，我们研究了在相同环境下的各种模型压缩和快速译码策略。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Inference+Optimization+of+Foundation+Models+on+AI+Accelerators)|0| +|[Inference Optimization of Foundation Models on AI Accelerators](https://doi.org/10.1145/3637528.3671465)|Youngsuk Park, Kailash Budhathoki, Liangfu Chen, Jonas M. Kübler, Jiaji Huang, Matthäus Kleindessner, Jun Huan, Volkan Cevher, Yida Wang, George Karypis|AWS AI, Tübingen, Germany; AWS AI, Santa Clara, CA, USA; AWS AI & EPFL, Tübingen, Germany|Powerful foundation models, including large language models (LLMs), with Transformer architectures have ushered in a new era of Generative AI across various industries. Industry and research community have witnessed a large number of new applications, based on those foundation models. Such applications include question and answer, customer services, image and video generation, and code completions, among others. However, as the number of model parameters reaches to hundreds of billions, their deployment incurs prohibitive inference costs and high latency in real-world scenarios. As a result, the demand for cost-effective and fast inference using AI accelerators is ever more higher. To this end, our tutorial offers a comprehensive discussion on complementary inference optimization techniques using AI accelerators. Beginning with an overview of basic Transformer architectures and deep learning system frameworks, we deep dive into system optimization techniques for fast and memory-efficient attention computations and discuss how they can be implemented efficiently on AI accelerators. Next, we describe architectural elements that are key for fast transformer inference. Finally, we examine various model compression and fast decoding strategies in the same context.|包括大型语言模型(LLM)在内的强大的基础模型，以及变压器架构，已经在各个行业开创了生成式人工智能的新时代。在这些基础模型的基础上，工业界和研究界目睹了大量新的应用。此类应用程序包括问答、客户服务、图像和视频生成以及代码完成等。然而，当模型参数的数量达到数千亿时，它们的部署在实际场景中会产生令人望而却步的推理成本和高延迟。因此，对使用人工智能加速器的高性价比和快速推理的需求越来越高。为此，我们的教程提供了关于使用 AI 加速器的互补推理优化技术的全面讨论。从变压器基本结构和深度学习系统框架的概述开始，我们深入研究了快速和高效的注意力计算系统优化技术，并讨论了如何在 AI 加速器上有效地实现这些技术。接下来，我们将描述对于快速转换器推理非常关键的体系结构元素。最后，我们研究了在相同环境下的各种模型压缩和快速译码策略。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Inference+Optimization+of+Foundation+Models+on+AI+Accelerators)|0| |[Automated Mining of Structured Knowledge from Text in the Era of Large Language Models](https://doi.org/10.1145/3637528.3671469)|Yunyi Zhang, Ming Zhong, Siru Ouyang, Yizhu Jiao, Sizhe Zhou, Linyi Ding, Jiawei Han|University of Illinois Urbana-Champaign, Urbana, IL, USA|Massive amount of unstructured text data are generated daily, ranging from news articles to scientific papers. How to mine structured knowledge from the text data remains a crucial research question. Recently, large language models (LLMs) have shed light on the text mining field with their superior text understanding and instruction-following ability. There are typically two ways of utilizing LLMs: fine-tune the LLMs with human-annotated training data, which is labor intensive and hard to scale; prompt the LLMs in a zero-shot or few-shot way, which cannot take advantage of the useful information in the massive text data. Therefore, it remains a challenge on automated mining of structured knowledge from massive text data in the era of large language models. In this tutorial, we cover the recent advancements in mining structured knowledge using language models with very weak supervision. We will introduce the following topics in this tutorial: (1) introduction to large language models, which serves as the foundation for recent text mining tasks, (2) ontology construction, which automatically enriches an ontology from a massive corpus, (3) weakly-supervised text classification in flat and hierarchical label space, (4) weakly-supervised information extraction, which extracts entity and relation structures.|从新闻文章到科学论文，每天都会生成大量的非结构化文本数据。如何从文本数据中挖掘出结构化知识仍然是一个关键的研究问题。近年来，大语言模型(LLM)以其优越的文本理解和指令跟踪能力为文本挖掘领域带来了新的发展。通常有两种利用 LLM 的方法: 使用人工注释的训练数据对 LLM 进行微调，这是一种劳动密集型且难以扩展的方法; 以零拍摄或少拍摄的方式提示 LLM，这种方法不能利用海量文本数据中的有用信息。因此，在大语言模型时代，从海量文本数据中自动挖掘结构化知识仍然是一个挑战。在本教程中，我们将介绍在使用监督非常薄弱的语言模型挖掘结构化知识方面的最新进展。在本教程中，我们将介绍以下主题: (1)大型语言模型的介绍，它是最近文本挖掘任务的基础; (2)本体构建，它可以从大量的语料库中自动丰富本体; (3)平面和分层标签空间中的弱监督文本分类; (4)弱监督信息抽取，它可以提取实体和关系结构。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Automated+Mining+of+Structured+Knowledge+from+Text+in+the+Era+of+Large+Language+Models)|0| |[Causal Inference with Latent Variables: Recent Advances and Future Prospectives](https://doi.org/10.1145/3637528.3671450)|Yaochen Zhu, Yinhan He, Jing Ma, Mengxuan Hu, Sheng Li, Jundong Li|University of Virginia, Charlottesville, VA, USA; Case Western Reserve University, Charlottesville, VA, USA|Causality lays the foundation for the trajectory of our world. Causalinference (CI), which aims to infer intrinsic causal relations among variablesof interest, has emerged as a crucial research topic. Nevertheless, the lack ofobservation of important variables (e.g., confounders, mediators, exogenousvariables, etc.) severely compromises the reliability of CI methods. The issuemay arise from the inherent difficulty in measuring the variables.Additionally, in observational studies where variables are passively recorded,certain covariates might be inadvertently omitted by the experimenter.Depending on the type of unobserved variables and the specific CI task, variousconsequences can be incurred if these latent variables are carelessly handled,such as biased estimation of causal effects, incomplete understanding of causalmechanisms, lack of individual-level causal consideration, etc. In this survey,we provide a comprehensive review of recent developments in CI with latentvariables. We start by discussing traditional CI techniques when variables ofinterest are assumed to be fully observed. Afterward, under the taxonomy ofcircumvention and inference-based methods, we provide an in-depth discussion ofvarious CI strategies to handle latent variables, covering the tasks of causaleffect estimation, mediation analysis, counterfactual reasoning, and causaldiscovery. Furthermore, we generalize the discussion to graph data whereinterference among units may exist. Finally, we offer fresh aspects for furtheradvancement of CI with latent variables, especially new opportunities in theera of large language models (LLMs).|因果关系奠定了我们世界轨迹的基础。因果推理(CI)是一个重要的研究课题，其目的是推断感兴趣的变量之间的内在因果关系。然而，缺乏对重要变量(如混杂因素、中介因素、外生变量等)的观察严重影响了 CI 方法的可靠性。这个问题可能源于测量变量的固有困难。此外，在被动记录变量的观察性研究中，某些协变量可能会被实验者无意中忽略。根据未观察到的变量的类型和具体的 CI 任务，如果这些潜在变量处理不当，可能会产生各种后果，例如因果效应的偏倚估计，对因果机制的不完全理解，缺乏个体层面的因果考虑等。在这项调查中，我们提供了一个潜在变量的近期发展综述 CI。我们从讨论传统的 CI 技术开始，当感兴趣的变量被假定为被充分观察时。然后，在规避和推理方法的分类下，我们深入讨论了处理潜在变量的各种 CI 策略，包括因果效应估计，中介分析，反事实推理和因果发现的任务。此外，我们将讨论推广到可能存在单位间干扰的图形数据。最后，我们为潜变量 CI 的进一步发展提供了新的方向，特别是在大语言模型时代提供了新的机遇。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Causal+Inference+with+Latent+Variables:+Recent+Advances+and+Future+Prospectives)|0| -|[A Survey on Safe Multi-Modal Learning Systems](https://doi.org/10.1145/3637528.3671462)|Tianyi Zhao, Liangliang Zhang, Yao Ma, Lu Cheng|Rensselaer Polytechnic Institute, Troy, USA; University of Southern California, Los Angeles, USA; University of Illinois Chicago, Chicago, USA|In the rapidly evolving landscape of artificial intelligence, multimodal learning systems (MMLS) have gained traction for their ability to process and integrate information from diverse modality inputs. Their expanding use in vital sectors such as healthcare has made safety assurance a critical concern. However, the absence of systematic research into their safety is a significant barrier to progress in this field. To bridge the gap, we present the first taxonomy that systematically categorizes and assesses MMLS safety. This taxonomy is structured around four fundamental pillars that are critical to ensuring the safety of MMLS: robustness, alignment, monitoring, and controllability. Leveraging this taxonomy, we review existing methodologies, benchmarks, and the current state of research, while also pinpointing the principal limitations and gaps in knowledge. Finally, we discuss unique challenges in MMLS safety. In illuminating these challenges, we aim to pave the way for future research, proposing potential directions that could lead to significant advancements in the safety protocols of MMLS.|在快速发展的人工智能领域，多模态学习系统(MMLS)因其处理和整合来自不同模态输入的信息的能力而受到关注。它们在医疗等关键领域的使用不断扩大，使得安全保障成为一个关键问题。然而，缺乏对其安全性的系统研究是这一领域取得进展的一个重大障碍。为了弥补这一差距，我们提出了第一个系统分类和评估 MMLS 安全性的分类法。这个分类法是围绕四个基本支柱构建的，这四个支柱对于确保 MMLS 的安全性至关重要: 健壮性、对齐性、监视性和可控性。利用这种分类法，我们回顾了现有的方法、基准和当前的研究状况，同时也指出了知识中的主要局限性和差距。最后，我们讨论了 MMLS 安全性的独特挑战。在阐明这些挑战时，我们的目标是为未来的研究铺平道路，提出可能导致 MMLS 安全协议显著进步的潜在方向。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Survey+on+Safe+Multi-Modal+Learning+Systems)|0| +|[A Survey on Safe Multi-Modal Learning Systems](https://doi.org/10.1145/3637528.3671462)|Tianyi Zhao, Liangliang Zhang, Yao Ma, Lu Cheng|University of Illinois Chicago, Chicago, USA; University of Southern California, Los Angeles, USA; Rensselaer Polytechnic Institute, Troy, USA|In the rapidly evolving landscape of artificial intelligence, multimodal learning systems (MMLS) have gained traction for their ability to process and integrate information from diverse modality inputs. Their expanding use in vital sectors such as healthcare has made safety assurance a critical concern. However, the absence of systematic research into their safety is a significant barrier to progress in this field. To bridge the gap, we present the first taxonomy that systematically categorizes and assesses MMLS safety. This taxonomy is structured around four fundamental pillars that are critical to ensuring the safety of MMLS: robustness, alignment, monitoring, and controllability. Leveraging this taxonomy, we review existing methodologies, benchmarks, and the current state of research, while also pinpointing the principal limitations and gaps in knowledge. Finally, we discuss unique challenges in MMLS safety. In illuminating these challenges, we aim to pave the way for future research, proposing potential directions that could lead to significant advancements in the safety protocols of MMLS.|在快速发展的人工智能领域，多模态学习系统(MMLS)因其处理和整合来自不同模态输入的信息的能力而受到关注。它们在医疗等关键领域的使用不断扩大，使得安全保障成为一个关键问题。然而，缺乏对其安全性的系统研究是这一领域取得进展的一个重大障碍。为了弥补这一差距，我们提出了第一个系统分类和评估 MMLS 安全性的分类法。这个分类法是围绕四个基本支柱构建的，这四个支柱对于确保 MMLS 的安全性至关重要: 健壮性、对齐性、监视性和可控性。利用这种分类法，我们回顾了现有的方法、基准和当前的研究状况，同时也指出了知识中的主要局限性和差距。最后，我们讨论了 MMLS 安全性的独特挑战。在阐明这些挑战时，我们的目标是为未来的研究铺平道路，提出可能导致 MMLS 安全协议显著进步的潜在方向。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Survey+on+Safe+Multi-Modal+Learning+Systems)|0| |[Responsible AI Day](https://doi.org/10.1145/3637528.3673867)|Ricardo BaezaYates, Nataly Buslón||We summarize the goals of the Responsible AI day, giving a glimpse on the program as well as a short biography of the organizers.|我们总结了负责任的人工智能日的目标，让一个程序的一瞥，以及一个简短的传记的组织者。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Responsible+AI+Day)|0| |[Heterogeneous Contrastive Learning for Foundation Models and Beyond](https://doi.org/10.1145/3637528.3671454)|Lecheng Zheng, Baoyu Jing, Zihao Li, Hanghang Tong, Jingrui He|University of Illinois at Urbana-Champaign, Champaign, USA|In the era of big data and Artificial Intelligence, an emerging paradigm isto utilize contrastive self-supervised learning to model large-scaleheterogeneous data. Many existing foundation models benefit from thegeneralization capability of contrastive self-supervised learning by learningcompact and high-quality representations without relying on any labelinformation. Amidst the explosive advancements in foundation models acrossmultiple domains, including natural language processing and computer vision, athorough survey on heterogeneous contrastive learning for the foundation modelis urgently needed. In response, this survey critically evaluates the currentlandscape of heterogeneous contrastive learning for foundation models,highlighting the open challenges and future trends of contrastive learning. Inparticular, we first present how the recent advanced contrastive learning-basedmethods deal with view heterogeneity and how contrastive learning is applied totrain and fine-tune the multi-view foundation models. Then, we move tocontrastive learning methods for task heterogeneity, including pretrainingtasks and downstream tasks, and show how different tasks are combined withcontrastive learning loss for different purposes. Finally, we conclude thissurvey by discussing the open challenges and shedding light on the futuredirections of contrastive learning.|在大数据和人工智能时代，利用对比自监督学习对大规模异构数据进行建模是一种新兴的模式。许多现有的基础模型都受益于对比自监督学习的泛化能力，它不依赖任何标签信息，而是通过学习紧凑而高质量的表示。随着基础模型在自然语言处理和计算机视觉等多个领域的突飞猛进，基础模型的异构对比学习研究迫在眉睫。作为回应，本调查批判性地评价了当前异质对比学习的基础模型，强调了开放的挑战和对比学习的未来趋势。特别是，我们首先介绍了当前先进的基于对比学习的方法是如何处理视图异质性的，以及如何应用对比学习对多视图基础模型进行训练和微调。然后，我们针对任务异质性提出了对比学习方法，包括预训练任务和下游任务，并展示了不同任务如何为不同目的组合对比学习损失。最后，我们通过讨论开放性挑战和对比学习未来发展方向的启示，得出本调查的结论。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Heterogeneous+Contrastive+Learning+for+Foundation+Models+and+Beyond)|0| -|[Equity, Diversity & Inclusion (EDI): Special Day at ACM KDD 2024](https://doi.org/10.1145/3637528.3673870)|Tania Cerquitelli, Amin Mantrach|Amazon, Luxembourg, Luxembourg; Politecnico di Torino, Turin, Piedmont, Italy|The Equity, Diversity & Inclusion event is a special day organized in conjunction with KDD '24, the 30 ^th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, which will take place from Sunday, August 25 to Thursday, August 29, 2024 at the Center de Convencions Internacional de Barcelona in Barcelona, Spain. This special day, scheduled for August 28, 2024, promotes equity, diversity, and inclusion (EDI) in data science, artificial intelligence, and beyond. It will bring together academics, researchers, practitioners, and human resources professionals (i) to present algorithms, techniques, methodologies, and projects in data science that enable responsible data processing and modeling; (ii) to discuss policies, best practices, and guidelines to promote an inclusive work environment and effective collaboration; (iii) to share personal stories to encourage young researchers, including those from groups unrepresented in the research community, to develop strong careers in data science; and (iv) to collaboratively develop and discuss an EDI Manifesto to promote an inclusive workplace environment and guiding principles in the development of research activities.|公平、多样性和包容性活动是与第30届 ACM SIGKDD 知识发现和数据挖掘会议 KDD’24共同组织的一个特殊日子，该会议将于2024年8月25日星期日至8月29日星期四在西班牙巴塞罗那国际会议中心举行。这个特殊的日子定于2024年8月28日，旨在促进数据科学、人工智能等领域的公平、多样性和包容性(EDI)。它将汇集学者、研究人员、从业人员和人力资源专业人员(i)提出数据科学中的算法、技术、方法和项目，使负责任的数据处理和建模成为可能; (ii)讨论促进包容性工作环境和有效合作的政策、最佳实践和指导方针; (iii)分享个人故事，以鼓励年轻研究人员，包括那些来自研究界无代表团体的研究人员，在数据科学领域发展强大的职业生涯; (iv)合作制定和讨论电子数据交换宣言，以促进包容性工。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Equity,+Diversity+&+Inclusion+(EDI):+Special+Day+at+ACM+KDD+2024)|0| +|[Equity, Diversity & Inclusion (EDI): Special Day at ACM KDD 2024](https://doi.org/10.1145/3637528.3673870)|Tania Cerquitelli, Amin Mantrach|Politecnico di Torino, Turin, Piedmont, Italy; Amazon, Luxembourg, Luxembourg|The Equity, Diversity & Inclusion event is a special day organized in conjunction with KDD '24, the 30 ^th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, which will take place from Sunday, August 25 to Thursday, August 29, 2024 at the Center de Convencions Internacional de Barcelona in Barcelona, Spain. This special day, scheduled for August 28, 2024, promotes equity, diversity, and inclusion (EDI) in data science, artificial intelligence, and beyond. It will bring together academics, researchers, practitioners, and human resources professionals (i) to present algorithms, techniques, methodologies, and projects in data science that enable responsible data processing and modeling; (ii) to discuss policies, best practices, and guidelines to promote an inclusive work environment and effective collaboration; (iii) to share personal stories to encourage young researchers, including those from groups unrepresented in the research community, to develop strong careers in data science; and (iv) to collaboratively develop and discuss an EDI Manifesto to promote an inclusive workplace environment and guiding principles in the development of research activities.|公平、多样性和包容性活动是与第30届 ACM SIGKDD 知识发现和数据挖掘会议 KDD’24共同组织的一个特殊日子，该会议将于2024年8月25日星期日至8月29日星期四在西班牙巴塞罗那国际会议中心举行。这个特殊的日子定于2024年8月28日，旨在促进数据科学、人工智能等领域的公平、多样性和包容性(EDI)。它将汇集学者、研究人员、从业人员和人力资源专业人员(i)提出数据科学中的算法、技术、方法和项目，使负责任的数据处理和建模成为可能; (ii)讨论促进包容性工作环境和有效合作的政策、最佳实践和指导方针; (iii)分享个人故事，以鼓励年轻研究人员，包括那些来自研究界无代表团体的研究人员，在数据科学领域发展强大的职业生涯; (iv)合作制定和讨论电子数据交换宣言，以促进包容性工。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Equity,+Diversity+&+Inclusion+(EDI):+Special+Day+at+ACM+KDD+2024)|0| |[Health Day: Building Health AI Ecosystem: From Data Harmonization to Knowledge Discovery](https://doi.org/10.1145/3637528.3673866)|Jake Chen, Peipei Ping|; School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA|The ACM KDD 2024 Health Day theme, "Building Health AI Ecosystem: From Data Harmonization to Knowledge Discovery," highlights the transformative potential of AI-driven ecosystems in healthcare, translational biomedical research, and basic biological research. This extended abstract discusses recent advancements, challenges, and future directions, focusing on integrating AI-ready data sets, interdisciplinary collaborations, and ethical AI practices. It aims to catalyze discussions on the potential of AI ecosystems in revolutionizing healthcare and related fields.|ACM kDD 2024年健康日的主题是“建立健康的人工智能生态系统: 从数据协调到知识发现”，强调了人工智能驱动的生态系统在医疗、转化生物医学研究和基础生物学研究方面的变革潜力。这个扩展的摘要讨论了最近的进步，挑战和未来的方向，重点集成人工智能准备的数据集，跨学科的合作，和道德的人工智能实践。它旨在促进关于人工智能生态系统在医疗保健和相关领域革命化的潜力的讨论。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Health+Day:+Building+Health+AI+Ecosystem:+From+Data+Harmonization+to+Knowledge+Discovery)|0| |[Overview of ACM SIGKDD 2024 AI4Science4AI Special Day](https://doi.org/10.1145/3637528.3673871)|Wei Ding, Gustau CampsValls|; Image Processing Laboratory (IPL), Universitat de València, Paterna, Spain|This paper provides an overview of the ACM SIGKDD 2024 AI4Science4AI special day. It includes information about the organizers, invited speakers, keynote speakers, the event agenda, and insights from related workshops. The AI4Science4AI special day aims to bring together experts in artificial intelligence (AI) and science to discuss the latest developments, challenges, and future directions.|本文提供了 ACM SIGKDD 2024 AI4Science4AI 特别日的概述。其中包括有关组织者、特邀演讲者、主旨演讲者、活动议程以及相关研讨会的见解的信息。AI4Science4AI 特别日旨在汇集人工智能(AI)和科学方面的专家，讨论最新的发展、挑战和未来的方向。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Overview+of+ACM+SIGKDD+2024+AI4Science4AI+Special+Day)|0| |[KDD 2024 Special Day - AI for Environment](https://doi.org/10.1145/3637528.3673869)|Karina Gibert, Wee Hyong Tok, Miquel SànchezMarrè|; Microsoft, Redmond, Washington, USA|Environmental problems such as air pollution monitoring and prevention, flood detection and prevention, land use, forest management, river water quality, wastewater treatment supervision, etc. are more complex than typical real-world problems usually AI faces to. This added complexity rises from several aspects, such as the randomness shown by most of environmental processes involved, the 2D/3D nature of involved problems, the temporal aspects, the spatial aspects, the inexactness of the information, etc. In fact, environmental problems belong to the most difficult problems with a lot of inexactness and uncertainty, and possibly conflicting objectives to be solved according to several classifications such as the one by Funtowicz & Ravetz (Funtowicz & Ravetz, 1999), which states that there are 3 kinds of problems. Also, they are non-structured problems in the classification proposed by H. Simon (Simon, 1966). All this complexity means that to effectively solve those problems a lot of knowledge is needed. This knowledge can be theoretical knowledge expressed in mechanistic models, such as the Gravidity Newton's Theory, or it can be empirical knowledge that can be expressed by means of empirical models, originated by some data and observations (data-driven knowledge) or by the expertise gathered by people when coping with such problems (model-driven knowledge, particularly expert-based knowledge). The KDD 2024 Special Day for AI for environment brings together researchers and practitioners to present their perspective on this very timely topic on how AI can be used for good, and improving the environment where we all live in.|大气污染监测与防治、洪水探测与防治、土地利用、森林管理、河流水质、污水处理监督等环境问题比人工智能面临的典型现实问题更为复杂。这种增加的复杂性来自几个方面，例如所涉及的大多数环境过程的随机性、所涉及问题的2D/3D 性质、时间方面、空间方面、信息的不准确性等。事实上，环境问题属于最困难的问题，具有很多不精确性和不确定性，可能存在冲突的目标，需要根据几个分类来解决，如 Funtowicz & Ravetz (Funtowicz & Ravetz，1999)的分类。此外，它们是 H。 Simon (Simon，1966)提出的分类中的非结构化问题。所有这些复杂性意味着，要有效地解决这些问题，需要大量的知识。这种知识可以是在机械模型中表达的理论知识，例如引力牛顿理论，也可以是经验知识，可以通过经验模型表达，起源于一些数据和观察(数据驱动的知识)或由人们在处理这些问题时收集的专业知识(模型驱动的知识，特别是基于专家的知识)。KDD 2024人工智能环境特别日汇集了研究人员和从业人员，就这个非常及时的主题提出他们的观点，如何利用人工智能做好事，并改善我们所有人生活的环境。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=KDD+2024+Special+Day+-+AI+for+Environment)|0| |[European Data Science Day: KDD-2024 Special Day](https://doi.org/10.1145/3637528.3673868)|Dunja Mladenic, Dumitru Roman|Jozef Stefan Institute, Ljubljana, Slovenia; Oslo Metropolitan University, SINTEF AS, Oslo, Norway|The European Data Science Day offers a full day focused exclusively on innovative KDD-relevant research and development projects from national and regional funding programs, as well as corporate, start-up, and nonprofit channels. The idea is to bring together a diverse community of researchers in Data Science, Machine Learning, Language Technologies, and Knowledge Discovery, as well as partnerships in the social and physical sciences/arts, to showcase the state-of-the-art in research and applications.|欧洲数据科学日是一个专注于从国家及地区资助项目、企业、初创公司及非营利渠道中获取的与KDD相关的创新研究与开发项目的全天活动。其核心理念是将数据科学、机器学习、语言技术和知识发现领域的多样化研究社群，以及社会与自然科学/艺术领域的合作伙伴汇聚一堂，展示当前最前沿的研究成果与应用。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=European+Data+Science+Day:+KDD-2024+Special+Day)|0| |[Generative AI Day](https://doi.org/10.1145/3637528.3673872)|Jie Tang, Yuxiao Dong, Michalis Vazirgiannis|Tsinghua University, Beijing, China; Ecole Polytechnique & Mohamed bin Zayed University of Artificial Intelligence, Paris, France|The Generative AI (AIGC) Day at KDD'24 is a dedicated full-day event for generative AI at KDD. This is an opportunity to bring together researchers, practitioners, and startups to share the insights about the cutting-edge advancements and to discuss the potential societal impacts of LLMs and AIGC. It is exciting that this year, we have invited speakers from both industry (e.g., Amazon, Zhipu AI) and academia (e.g., USC, UCLA). The topics cover various perspectives of generative AI including foundation models, streaming LLMs, LLM training and inference. As demonstrated, data plays a crucial role in developing cutting-edge generative AI models. For example, the Gemini Team has found that "data quality is an important factor for highly-performing models...''. To date, there is still significant room to define design principles and develop methods for improved data collection, selection, and synthetic data generation for the pre-training and alignment of language, vision, and multi-modal models. Therefore, the Day will invite the speakers and KDD audience to discuss the challenges and opportunities for data mining researchers in the era of generative AI.|KDD'24上的生成式AI（AIGC）日是一个专门为KDD举办的生成式AI全天活动。这是一个汇聚研究人员、从业者和初创企业，分享关于生成式AI前沿进展的见解，并讨论大型语言模型（LLMs）和AIGC潜在社会影响的机会。今年，我们邀请了来自工业界（如亚马逊、智谱AI）和学术界（如南加州大学、加州大学洛杉矶分校）的演讲者，令人振奋。话题涵盖了生成式AI的多个视角，包括基础模型、流式LLMs、LLM训练与推理。如所展示的，数据在开发尖端生成式AI模型中起着至关重要的作用。例如，Gemini团队发现“数据质量是高性能模型的重要因素……”。迄今为止，仍有很大的空间来定义设计原则，并开发改进数据收集、选择和合成数据生成的方法，以用于语言、视觉和多模态模型的预训练和对齐。因此，本次活动将邀请演讲者和KDD的观众一起讨论生成式AI时代数据挖掘研究者的挑战与机遇。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Generative+AI+Day)|0| |[KDD 2024 Finance Day](https://doi.org/10.1145/3637528.3673865)|Guiling Wang, Daniel Borrajo|New Jersey Institute of Technology, Newark, NJ, USA; Universidad Carlos III de Madrid, Madrid, Spain|The Finance Day at KDD 2024 will take place on August 26th in Barcelona, Spain. Following the success of the inaugural event last year, the second edition highlights the significant role of AI in transforming the financial industry. This special day serves as a forum for discussion of innovations at the intersection of AI and finance. An exciting lineup of 12 influential speakers from nine different countries will be featured, representing a mix of government organizations, leading banks, innovative hedge funds, and top academic institutions. These experts will delve into a range of topics, from cutting-edge FinTech innovations to ethical considerations in machine learning, providing a comprehensive overview of the finance and AI. The distinguished speakers include Avanidhar Subrahmanyam from UCLA, Henrike Mueller from the Financial Conduct Authority, Claudia Perlich from Two Sigma, Eyke Hüllermeier from Ludwig-Maximilians-Universität München, Senthil Kumar from Capital One, Stefan Zohren from the University of Oxford, Dumitru Roman from SINTEF ICT, Kubilay Atasu from TU Delft, Xiao-Ming Wu from Hong Kong Polytechnic University, Yongjae Lee from UNIST, Jundong Li from the University of Virginia, and Milos Blagojevic from BlackRock.|2024年KDD金融日将于8月26日在西班牙巴塞罗那举行。继去年首届活动取得成功后，第二届活动强调了人工智能在转变金融行业中的重要作用。这一特别日活动为探讨人工智能与金融交叉领域的创新提供了一个论坛。届时将有一系列由来自九个不同国家的12位有影响力的演讲者组成的精彩阵容，他们代表了政府机构、领先银行、创新对冲基金和顶级学术机构。这些专家将深入探讨从尖端金融科技创新到机器学习中的伦理考量等一系列话题，全面概述金融与人工智能的现状。知名演讲者包括来自加州大学洛杉矶分校的Avanidhar Subrahmanyam、金融行为监管局的Henrike Mueller、Two Sigma的Claudia Perlich、慕尼黑路德维希-马克西米利安大学的Eyke Hüllermeier、Capital One的Senthil Kumar、牛津大学的Stefan Zohren、SINTEF ICT的Dumitru Roman、代尔夫特理工大学的Kubilay Atasu、香港理工大学的Xiao-Ming Wu、UNIST的Yongjae Lee、弗吉尼亚大学的Jundong Li以及BlackRock的Milos Blagojevic。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=KDD+2024+Finance+Day)|0| -|[AdKDD 2024](https://doi.org/10.1145/3637528.3671476)|Abraham Bagherjeiran, Nemanja Djuric, KuangChih Lee, Linsey Pang, Vladan Radosavljevic, Suju Rajan|Salesforce, San Francisco, CA, USA; Amazon, Palo Alto, CA, USA; Spotify, New York, NY, USA; eBay, Inc., San Jose, CA, USA; Walmart, Sunnyvale, CA, United States; Aurora Innovation, Inc., Pittsburgh, PA, USA|The digital advertising field has always had challenging ML problems, learning from petabytes of data that is highly imbalanced, reactivity times in the milliseconds, and more recently compounded with the complex user's path to purchase across devices, across platforms, and even online/real-world behavior. The AdKDD workshop continues to be a forum for researchers in advertising, during and after KDD. Our website which hosts slides and abstracts receives approximately 2,000 monthly visits and 1,800 active users during the KDD 2021. In surveys during AdKDD 2019 and 2020, over 60% agreed that AdKDD is the reason they attended KDD, and over 90% indicated they would attend next year. The 2024 edition is particularly timely because of the increasing application of Graph-based NN and Generative AI models in advertising. Coupled with privacy-preserving initiatives enforced by GDPR, CCPA the future of computational advertising is at an interesting crossroads. For this edition, we plan to solicit papers that span the spectrum of deep user understanding while remaining privacy-preserving. In addition, we will seek papers that discuss fairness in the context of advertising, to what extent does hyper-personalization work, and whether the ad industry as a whole needs to think through more effective business models such as incrementality. We have hosted several academic and industry luminaries as keynote speakers and have found our invited speaker series hosting expert practitioners to be an audience favorite. We will continue fielding a diverse set of keynote speakers and invited talks for this edition as well. As with past editions, we hope to motivate researchers in this space to think not only about the ML aspects but also to spark conversations about the societal impact of online advertising.|数字广告领域一直面临着具有挑战性的机器学习问题，这些问题源自于处理海量且高度不平衡的数据，响应时间以毫秒计，并且近年来由于用户在不同设备、平台间甚至线上线下行为的复杂购买路径而变得更加复杂。AdKDD研讨会继续作为广告领域研究者在KDD期间及之后的交流平台。我们的网站托管了幻灯片和摘要，每月大约有2000次访问，在KDD 2021期间有1800名活跃用户。在AdKDD 2019和2020的调查中，超过60%的人同意AdKDD是他们参加KDD的原因，超过90%的人表示明年还会参加。2024年的研讨会尤为及时，因为基于图的神经网络和生成式AI模型在广告中的应用日益增多。结合GDPR和CCPA实施的隐私保护举措，计算广告的未来正处于一个有趣的十字路口。对于这一期，我们计划征集涵盖深度用户理解同时保持隐私保护的论文。此外，我们还将寻求探讨广告公平性、超个性化在多大程度上有效以及广告行业是否需要思考更有效的商业模式（如增量性）的论文。我们曾邀请过几位学术界和行业的杰出人士作为主讲嘉宾，并发现邀请专家实践者的系列演讲深受观众喜爱。我们也将继续为这一期邀请多样化的主讲嘉宾和特邀演讲。与往届一样，我们希望激励该领域的研究者不仅思考机器学习方面，还能引发关于在线广告社会影响的讨论。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=AdKDD+2024)|0| -|[Fragile Earth: Generative and Foundational Models for Sustainable Development](https://doi.org/10.1145/3637528.3671493)|Emre Eftelioglu, Bistra Dilkina, Naoki Abe, Ramakrishnan Kannan, Yuzhou Chen, Yulia R. Gel, Kathleen Buckingham, Auroop R. Ganguly, James Hodson, Jiafu Mao|Temple University, Philadelphia, PA, USA; Oak Ridge National Laboratory, Oak Ridge, TN, USA; University of Southern California, Los Angeles, CA, USA; Northeastern University, Boston, MA, USA; Amazon, Bellevue, WA, USA; veritree, Vancouver, BC, Canada; AIForGood, San Francisco, CA, USA; The University of Texas at Dallas, Richardson, TX, USA; IBM Research, Yorktown Heights, New York, USA|The Fragile Earth Workshop is a recurring event in ACM's KDD Conference on research in knowledge discovery and data mining that gathers the research community to find and explore how data science can measure and progress climate and social issues, following the United Nations Sustainable Development Goals (SDGs) framework.|“脆弱地球”研讨会是ACM知识发现与数据挖掘（KDD）会议的定期活动，旨在召集研究社区，探讨如何利用数据科学来衡量并推动气候和社会问题的发展，这一探讨遵循联合国可持续发展目标（SDGs）框架。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Fragile+Earth:+Generative+and+Foundational+Models+for+Sustainable+Development)|0| -|[Artificial Intelligence and Data Science for Healthcare: Bridging Data-Centric AI and People-Centric Healthcare](https://doi.org/10.1145/3637528.3671497)|Shenda Hong, Daoxin Yin, Gongzheng Tang, Tianfan Fu, Liantao Ma, Junyi Gao, Mengling Feng, Mai Wang, Yu Yang, Fei Wang, Hongfang Liu, Luxia Zhang|National Institute of Health Data Science, Peking University, Beijing, China; National Engineering Research Center for Software, Peking University, Beijing, China; Computer Science Department, Rensselaer Polytechnic Institute, New York, USA; Department of Population Health Sciences, Cornell University, New York, NY, USA; Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore; Health Data Research UK, University of Edinburgh, Edinburgh, United Kingdom; Department of Health Data Science and Artificial Intelligence, UTHealth Houston, Houston, TX, USA|KDD AIDSH 2024 aims to foster discussions and developments that push the boundaries of Artificial Intelligence (AI) and Data Science (DS) in healthcare, enhance diagnostic accuracy and promote human-centric approaches to healthcare, thus stimulating future interdisciplinary collaborations. This year's symposium will focus on expanding the application of AI/DS in healthcare/medicine and bridging existing gaps. The workshop invites submissions of full papers as well as work-in-progress on the application of AI/DS in healthcare. The workshop will feature three invited talks from eminent speakers, spanning academia, industry, and clinical researchers. In addition, selected papers will be invited to publish in Health Data Science, a Science Partner Journal. This summary provides a brief description of the half-day workshop to be held on August 26th, 2024. The webpage for the workshop can be found at https://aimel.ai/kdd2024aidsh.|KDD AIDSH 2024旨在推动人工智能（AI）和数据科学（DS）在医疗领域的讨论与发展，提升诊断准确性，并促进以人为本的医疗方法，从而激发未来的跨学科合作。本次研讨会将聚焦于扩大AI/DS在医疗/医学中的应用，并弥合现有差距。工作坊欢迎提交完整论文以及正在进行的研究工作，内容涉及AI/DS在医疗领域的应用。工作坊将邀请三位知名专家进行主题演讲，涵盖学术界、工业界和临床研究领域。此外，精选论文将被邀请发表在《健康数据科学》（Health Data Science）期刊上，该期刊是Science的合作期刊。本摘要简要介绍了将于2024年8月26日举行的半天工作坊。工作坊的网页地址为https://aimel.ai/kdd2024aidsh。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Artificial+Intelligence+and+Data+Science+for+Healthcare:+Bridging+Data-Centric+AI+and+People-Centric+Healthcare)|0| -|[TSMO 2024: Two-sided Marketplace Optimization](https://doi.org/10.1145/3637528.3671484)|Mihajlo Grbovic, Vladan Radosavljevic, Minmin Chen, Katerina IliakopoulouZanos, Thanasis Noulas, Amit Goyal, Fabrizio Silvestri|Meta, New York City, NY, USA; Sapienza University of Rome, Rome, Italy; Amazon, San Francisco, CA, USA; Airbnb, Inc., San Francisco, CA, USA; Google Deepmind, Mountain View, CA, USA; Spotify, New York City, NY, USA; Bitvavo, Thessaloniki, Greece|In recent years, two-sided marketplaces have emerged as viable business models in many real-world applications. In particular, we have moved from the social network paradigm to a network with two distinct types of participants representing the supply and demand of a specific good. Examples of industries include but are not limited to accommodation (Airbnb, Booking.com), video content (YouTube, Instagram, TikTok), ridesharing (Uber, Lyft), online shops (Etsy, Ebay, Facebook Marketplace), music (Spotify, Amazon), app stores (Apple App Store, Google App Store) or job sites (LinkedIn). The traditional research in most of these industries focused on satisfying the demand. OTAs would sell hotel accommodation, TV networks would broadcast their own content, or taxi companies would own their own vehicle fleet. In modern examples like Airbnb, YouTube, Instagram, or Uber, the platforms operate by outsourcing the service they provide to their users, whether they are hosts, content creators or drivers, and have to develop their models considering their needs and goals.|近年来，双边市场已成为许多实际应用中可行的商业模式。特别是，我们从社交网络模式转变为一个包含两种不同类型参与者的网络，这些参与者分别代表特定商品的供需双方。此类行业的例子包括但不限于住宿服务（如Airbnb、Booking.com）、视频内容（如YouTube、Instagram、TikTok）、拼车服务（如Uber、Lyft）、在线商店（如Etsy、Ebay、Facebook Marketplace）、音乐平台（如Spotify、Amazon）、应用商店（如Apple App Store、Google App Store）或求职网站（如LinkedIn）。在大多数这些行业中，传统研究主要集中在满足需求上。例如，在线旅行社（OTAs）会销售酒店住宿，电视网络会播放自有内容，出租车公司会拥有自己的车队。而在现代平台如Airbnb、YouTube、Instagram或Uber中，这些平台通过将其提供的服务外包给用户（无论是房东、内容创作者还是司机）来运作，并需要在其模型开发中考虑到这些用户的需求和目标。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=TSMO+2024:+Two-sided+Marketplace+Optimization)|0| -|[KDD workshop on Evaluation and Trustworthiness of Generative AI Models](https://doi.org/10.1145/3637528.3671481)|Yuan Ling, Shujing Dong, Yarong Feng, Zongyi Joe Liu, George Karypis, Chandan K. Reddy|Univ. of Minnesota Amazon, Santa Clara, CA, USA; Virginia Tech Amazon, Arlington, VA, USA; Amazon, Irvine, WA, USA; Amazon, Seattle, WA, USA|The KDD workshop on Evaluation and Trustworthiness of Generative AI Models aims to address the critical need for reliable generative AI technologies by exploring comprehensive evaluation strategies. This workshop will delve into various aspects of assessing generative AI models, including Large Language Models (LLMs) and diffusion models, focusing on trustworthiness, safety, bias, fairness, and ethical considerations. With an emphasis on interdisciplinary collaboration, the workshop will feature invited talks, peer-reviewed paper presentations, and panel discussions to advance the state of the art in generative AI evaluation.|KDD生成式AI模型评估与可信度研讨会旨在通过探索全面的评估策略，解决生成式AI技术可靠性这一关键需求。本次研讨会将深入探讨评估生成式AI模型的各个方面，包括大型语言模型（LLMs）和扩散模型，重点关注可信度、安全性、偏见、公平性及伦理考量。研讨会强调跨学科合作，将通过邀请演讲、同行评审论文展示及小组讨论等形式，推动生成式AI评估领域的技术进步。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=KDD+workshop+on+Evaluation+and+Trustworthiness+of+Generative+AI+Models)|0| -|[NL2Code-Reasoning and Planning with LLMs for Code Development](https://doi.org/10.1145/3637528.3671505)|Ye Xing, Jun Huan, Wee Hyong Tok, Cong Shen, Johannes Gehrke, Katherine Lin, Arjun Guha, Omer Tripp, Murali Krishna Ramanathan|Microsoft, Richmond, USA; Microsoft, Redmond, USA; Amazon, Seattle, USA; Northeastern University, Boston, USA; Microsoft, Boston, USA; University of Virginia, Charlottesville, USA|There is huge value in making software development more productive with AI. An important component of this vision is the capability to translate natural language to a programming language ("NL2Code") and thus to significantly accelerate the speed at which code is written. This workshop gathers researchers, practitioners, and users from industry and academia that are working on NL2Code, specifically on the problem of using large language models to convert statements posed in a human language to a formal programming language.|利用人工智能提升软件开发效率具有巨大的价值。这一愿景的重要组成部分是实现将自然语言转化为编程语言（即“NL2Code”）的能力，从而显著加快代码编写速度。本次研讨会汇聚了来自学术界和工业界的研究人员、从业者及用户，他们专注于NL2Code领域，特别是利用大型语言模型将人类语言表述的语句转化为正式编程语言的问题。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=NL2Code-Reasoning+and+Planning+with+LLMs+for+Code+Development)|0| +|[AdKDD 2024](https://doi.org/10.1145/3637528.3671476)|Abraham Bagherjeiran, Nemanja Djuric, KuangChih Lee, Linsey Pang, Vladan Radosavljevic, Suju Rajan|eBay, Inc., San Jose, CA, USA; Walmart, Sunnyvale, CA, United States; Aurora Innovation, Inc., Pittsburgh, PA, USA; Salesforce, San Francisco, CA, USA; Spotify, New York, NY, USA; Amazon, Palo Alto, CA, USA|The digital advertising field has always had challenging ML problems, learning from petabytes of data that is highly imbalanced, reactivity times in the milliseconds, and more recently compounded with the complex user's path to purchase across devices, across platforms, and even online/real-world behavior. The AdKDD workshop continues to be a forum for researchers in advertising, during and after KDD. Our website which hosts slides and abstracts receives approximately 2,000 monthly visits and 1,800 active users during the KDD 2021. In surveys during AdKDD 2019 and 2020, over 60% agreed that AdKDD is the reason they attended KDD, and over 90% indicated they would attend next year. The 2024 edition is particularly timely because of the increasing application of Graph-based NN and Generative AI models in advertising. Coupled with privacy-preserving initiatives enforced by GDPR, CCPA the future of computational advertising is at an interesting crossroads. For this edition, we plan to solicit papers that span the spectrum of deep user understanding while remaining privacy-preserving. In addition, we will seek papers that discuss fairness in the context of advertising, to what extent does hyper-personalization work, and whether the ad industry as a whole needs to think through more effective business models such as incrementality. We have hosted several academic and industry luminaries as keynote speakers and have found our invited speaker series hosting expert practitioners to be an audience favorite. We will continue fielding a diverse set of keynote speakers and invited talks for this edition as well. As with past editions, we hope to motivate researchers in this space to think not only about the ML aspects but also to spark conversations about the societal impact of online advertising.|数字广告领域一直面临着具有挑战性的机器学习问题，这些问题源自于处理海量且高度不平衡的数据，响应时间以毫秒计，并且近年来由于用户在不同设备、平台间甚至线上线下行为的复杂购买路径而变得更加复杂。AdKDD研讨会继续作为广告领域研究者在KDD期间及之后的交流平台。我们的网站托管了幻灯片和摘要，每月大约有2000次访问，在KDD 2021期间有1800名活跃用户。在AdKDD 2019和2020的调查中，超过60%的人同意AdKDD是他们参加KDD的原因，超过90%的人表示明年还会参加。2024年的研讨会尤为及时，因为基于图的神经网络和生成式AI模型在广告中的应用日益增多。结合GDPR和CCPA实施的隐私保护举措，计算广告的未来正处于一个有趣的十字路口。对于这一期，我们计划征集涵盖深度用户理解同时保持隐私保护的论文。此外，我们还将寻求探讨广告公平性、超个性化在多大程度上有效以及广告行业是否需要思考更有效的商业模式（如增量性）的论文。我们曾邀请过几位学术界和行业的杰出人士作为主讲嘉宾，并发现邀请专家实践者的系列演讲深受观众喜爱。我们也将继续为这一期邀请多样化的主讲嘉宾和特邀演讲。与往届一样，我们希望激励该领域的研究者不仅思考机器学习方面，还能引发关于在线广告社会影响的讨论。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=AdKDD+2024)|0| +|[Fragile Earth: Generative and Foundational Models for Sustainable Development](https://doi.org/10.1145/3637528.3671493)|Emre Eftelioglu, Bistra Dilkina, Naoki Abe, Ramakrishnan Kannan, Yuzhou Chen, Yulia R. Gel, Kathleen Buckingham, Auroop R. Ganguly, James Hodson, Jiafu Mao|Northeastern University, Boston, MA, USA; University of Southern California, Los Angeles, CA, USA; Amazon, Bellevue, WA, USA; Temple University, Philadelphia, PA, USA; The University of Texas at Dallas, Richardson, TX, USA; IBM Research, Yorktown Heights, New York, USA; AIForGood, San Francisco, CA, USA; Oak Ridge National Laboratory, Oak Ridge, TN, USA; veritree, Vancouver, BC, Canada|The Fragile Earth Workshop is a recurring event in ACM's KDD Conference on research in knowledge discovery and data mining that gathers the research community to find and explore how data science can measure and progress climate and social issues, following the United Nations Sustainable Development Goals (SDGs) framework.|“脆弱地球”研讨会是ACM知识发现与数据挖掘（KDD）会议的定期活动，旨在召集研究社区，探讨如何利用数据科学来衡量并推动气候和社会问题的发展，这一探讨遵循联合国可持续发展目标（SDGs）框架。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Fragile+Earth:+Generative+and+Foundational+Models+for+Sustainable+Development)|0| +|[Artificial Intelligence and Data Science for Healthcare: Bridging Data-Centric AI and People-Centric Healthcare](https://doi.org/10.1145/3637528.3671497)|Shenda Hong, Daoxin Yin, Gongzheng Tang, Tianfan Fu, Liantao Ma, Junyi Gao, Mengling Feng, Mai Wang, Yu Yang, Fei Wang, Hongfang Liu, Luxia Zhang|National Engineering Research Center for Software, Peking University, Beijing, China; Computer Science Department, Rensselaer Polytechnic Institute, New York, USA; Health Data Research UK, University of Edinburgh, Edinburgh, United Kingdom; National Institute of Health Data Science, Peking University, Beijing, China; Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore; Department of Health Data Science and Artificial Intelligence, UTHealth Houston, Houston, TX, USA; Department of Population Health Sciences, Cornell University, New York, NY, USA|KDD AIDSH 2024 aims to foster discussions and developments that push the boundaries of Artificial Intelligence (AI) and Data Science (DS) in healthcare, enhance diagnostic accuracy and promote human-centric approaches to healthcare, thus stimulating future interdisciplinary collaborations. This year's symposium will focus on expanding the application of AI/DS in healthcare/medicine and bridging existing gaps. The workshop invites submissions of full papers as well as work-in-progress on the application of AI/DS in healthcare. The workshop will feature three invited talks from eminent speakers, spanning academia, industry, and clinical researchers. In addition, selected papers will be invited to publish in Health Data Science, a Science Partner Journal. This summary provides a brief description of the half-day workshop to be held on August 26th, 2024. The webpage for the workshop can be found at https://aimel.ai/kdd2024aidsh.|KDD AIDSH 2024旨在推动人工智能（AI）和数据科学（DS）在医疗领域的讨论与发展，提升诊断准确性，并促进以人为本的医疗方法，从而激发未来的跨学科合作。本次研讨会将聚焦于扩大AI/DS在医疗/医学中的应用，并弥合现有差距。工作坊欢迎提交完整论文以及正在进行的研究工作，内容涉及AI/DS在医疗领域的应用。工作坊将邀请三位知名专家进行主题演讲，涵盖学术界、工业界和临床研究领域。此外，精选论文将被邀请发表在《健康数据科学》（Health Data Science）期刊上，该期刊是Science的合作期刊。本摘要简要介绍了将于2024年8月26日举行的半天工作坊。工作坊的网页地址为https://aimel.ai/kdd2024aidsh。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Artificial+Intelligence+and+Data+Science+for+Healthcare:+Bridging+Data-Centric+AI+and+People-Centric+Healthcare)|0| +|[TSMO 2024: Two-sided Marketplace Optimization](https://doi.org/10.1145/3637528.3671484)|Mihajlo Grbovic, Vladan Radosavljevic, Minmin Chen, Katerina IliakopoulouZanos, Thanasis Noulas, Amit Goyal, Fabrizio Silvestri|Spotify, New York City, NY, USA; Amazon, San Francisco, CA, USA; Airbnb, Inc., San Francisco, CA, USA; Meta, New York City, NY, USA; Google Deepmind, Mountain View, CA, USA; Sapienza University of Rome, Rome, Italy; Bitvavo, Thessaloniki, Greece|In recent years, two-sided marketplaces have emerged as viable business models in many real-world applications. In particular, we have moved from the social network paradigm to a network with two distinct types of participants representing the supply and demand of a specific good. Examples of industries include but are not limited to accommodation (Airbnb, Booking.com), video content (YouTube, Instagram, TikTok), ridesharing (Uber, Lyft), online shops (Etsy, Ebay, Facebook Marketplace), music (Spotify, Amazon), app stores (Apple App Store, Google App Store) or job sites (LinkedIn). The traditional research in most of these industries focused on satisfying the demand. OTAs would sell hotel accommodation, TV networks would broadcast their own content, or taxi companies would own their own vehicle fleet. In modern examples like Airbnb, YouTube, Instagram, or Uber, the platforms operate by outsourcing the service they provide to their users, whether they are hosts, content creators or drivers, and have to develop their models considering their needs and goals.|近年来，双边市场已成为许多实际应用中可行的商业模式。特别是，我们从社交网络模式转变为一个包含两种不同类型参与者的网络，这些参与者分别代表特定商品的供需双方。此类行业的例子包括但不限于住宿服务（如Airbnb、Booking.com）、视频内容（如YouTube、Instagram、TikTok）、拼车服务（如Uber、Lyft）、在线商店（如Etsy、Ebay、Facebook Marketplace）、音乐平台（如Spotify、Amazon）、应用商店（如Apple App Store、Google App Store）或求职网站（如LinkedIn）。在大多数这些行业中，传统研究主要集中在满足需求上。例如，在线旅行社（OTAs）会销售酒店住宿，电视网络会播放自有内容，出租车公司会拥有自己的车队。而在现代平台如Airbnb、YouTube、Instagram或Uber中，这些平台通过将其提供的服务外包给用户（无论是房东、内容创作者还是司机）来运作，并需要在其模型开发中考虑到这些用户的需求和目标。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=TSMO+2024:+Two-sided+Marketplace+Optimization)|0| +|[KDD workshop on Evaluation and Trustworthiness of Generative AI Models](https://doi.org/10.1145/3637528.3671481)|Yuan Ling, Shujing Dong, Yarong Feng, Zongyi Joe Liu, George Karypis, Chandan K. Reddy|Univ. of Minnesota Amazon, Santa Clara, CA, USA; Amazon, Irvine, WA, USA; Virginia Tech Amazon, Arlington, VA, USA; Amazon, Seattle, WA, USA|The KDD workshop on Evaluation and Trustworthiness of Generative AI Models aims to address the critical need for reliable generative AI technologies by exploring comprehensive evaluation strategies. This workshop will delve into various aspects of assessing generative AI models, including Large Language Models (LLMs) and diffusion models, focusing on trustworthiness, safety, bias, fairness, and ethical considerations. With an emphasis on interdisciplinary collaboration, the workshop will feature invited talks, peer-reviewed paper presentations, and panel discussions to advance the state of the art in generative AI evaluation.|KDD生成式AI模型评估与可信度研讨会旨在通过探索全面的评估策略，解决生成式AI技术可靠性这一关键需求。本次研讨会将深入探讨评估生成式AI模型的各个方面，包括大型语言模型（LLMs）和扩散模型，重点关注可信度、安全性、偏见、公平性及伦理考量。研讨会强调跨学科合作，将通过邀请演讲、同行评审论文展示及小组讨论等形式，推动生成式AI评估领域的技术进步。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=KDD+workshop+on+Evaluation+and+Trustworthiness+of+Generative+AI+Models)|0| +|[NL2Code-Reasoning and Planning with LLMs for Code Development](https://doi.org/10.1145/3637528.3671505)|Ye Xing, Jun Huan, Wee Hyong Tok, Cong Shen, Johannes Gehrke, Katherine Lin, Arjun Guha, Omer Tripp, Murali Krishna Ramanathan|University of Virginia, Charlottesville, USA; Amazon, Seattle, USA; Northeastern University, Boston, USA; Microsoft, Redmond, USA; Microsoft, Richmond, USA; Microsoft, Boston, USA|There is huge value in making software development more productive with AI. An important component of this vision is the capability to translate natural language to a programming language ("NL2Code") and thus to significantly accelerate the speed at which code is written. This workshop gathers researchers, practitioners, and users from industry and academia that are working on NL2Code, specifically on the problem of using large language models to convert statements posed in a human language to a formal programming language.|利用人工智能提升软件开发效率具有巨大的价值。这一愿景的重要组成部分是实现将自然语言转化为编程语言（即“NL2Code”）的能力，从而显著加快代码编写速度。本次研讨会汇聚了来自学术界和工业界的研究人员、从业者及用户，他们专注于NL2Code领域，特别是利用大型语言模型将人类语言表述的语句转化为正式编程语言的问题。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=NL2Code-Reasoning+and+Planning+with+LLMs+for+Code+Development)|0| diff --git a/papers/recsys/recsys2023.md b/papers/recsys/recsys2023.md index ded6f6b6..bb1ae9ac 100644 --- a/papers/recsys/recsys2023.md +++ b/papers/recsys/recsys2023.md @@ -9,42 +9,42 @@ |[AdaptEx: A Self-Service Contextual Bandit Platform](https://doi.org/10.1145/3604915.3608870)|William Black, Ercument Ilhan, Andrea Marchini, Vilda Markeviciute||||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=AdaptEx:+A+Self-Service+Contextual+Bandit+Platform)|1| |[Learning from Negative User Feedback and Measuring Responsiveness for Sequential Recommenders](https://doi.org/10.1145/3604915.3610244)|Yueqi Wang, Yoni Halpern, Shuo Chang, Jingchen Feng, Elaine Ya Le, Longfei Li, Xujian Liang, MinCheng Huang, Shane Li, Alex Beutel, Yaping Zhang, Shuchao Bi|Google, Mountain View, CA 94043 USA|Sequential recommenders have been widely used in industry due to their strength in modeling user preferences. While these models excel at learning a user's positive interests, less attention has been paid to learning from negative user feedback. Negative user feedback is an important lever of user control, and comes with an expectation that recommenders should respond quickly and reduce similar recommendations to the user. However, negative feedback signals are often ignored in the training objective of sequential retrieval models, which primarily aim at predicting positive user interactions. In this work, we incorporate explicit and implicit negative user feedback into the training objective of sequential recommenders in the retrieval stage using a "not-to-recommend" loss function that optimizes for the log-likelihood of not recommending items with negative feedback. We demonstrate the effectiveness of this approach using live experiments on a large-scale industrial recommender system. Furthermore, we address a challenge in measuring recommender responsiveness to negative feedback by developing a counterfactual simulation framework to compare recommender responses between different user actions, showing improved responsiveness from the modeling change.|由于序列推荐器在建模用户偏好方面的优势，它在工业中得到了广泛的应用。虽然这些模型擅长于学习用户的积极兴趣，但很少有人注意从消极的用户反馈中学习。负面的用户反馈是用户控制的一个重要杠杆，并伴随着一个期望，即推荐者应该快速响应并减少对用户的类似推荐。然而，在序贯检索模型的训练目标中，负反馈信号往往被忽略，而序贯检索模型的训练目标主要是预测正向用户交互。在这项工作中，我们将显性和隐性的负面用户反馈纳入顺序推荐者在检索阶段的训练目标中，使用一个“不推荐”的损失函数，该函数优化了不推荐负面反馈项目的对数可能性。我们通过在大规模工业推荐系统上的实验证明了这种方法的有效性。此外，我们通过开发一个反事实模拟框架来比较不同用户行为之间的推荐响应，从而解决了测量推荐响应负面反馈的挑战，显示了来自建模更改的更好响应。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Learning+from+Negative+User+Feedback+and+Measuring+Responsiveness+for+Sequential+Recommenders)|0| |[gSASRec: Reducing Overconfidence in Sequential Recommendation Trained with Negative Sampling](https://doi.org/10.1145/3604915.3608783)|Aleksandr Vladimirovich Petrov, Craig MacDonald|University of Glasgow, United Kingdom; School of Computing Science, University of Glasgow, United Kingdom|A large catalogue size is one of the central challenges in training recommendation models: a large number of items makes them memory and computationally inefficient to compute scores for all items during training, forcing these models to deploy negative sampling. However, negative sampling increases the proportion of positive interactions in the training data, and therefore models trained with negative sampling tend to overestimate the probabilities of positive interactions a phenomenon we call overconfidence. While the absolute values of the predicted scores or probabilities are not important for the ranking of retrieved recommendations, overconfident models may fail to estimate nuanced differences in the top-ranked items, resulting in degraded performance. In this paper, we show that overconfidence explains why the popular SASRec model underperforms when compared to BERT4Rec. This is contrary to the BERT4Rec authors explanation that the difference in performance is due to the bi-directional attention mechanism. To mitigate overconfidence, we propose a novel Generalised Binary Cross-Entropy Loss function (gBCE) and theoretically prove that it can mitigate overconfidence. We further propose the gSASRec model, an improvement over SASRec that deploys an increased number of negatives and the gBCE loss. We show through detailed experiments on three datasets that gSASRec does not exhibit the overconfidence problem. As a result, gSASRec can outperform BERT4Rec (e.g. +9.47% NDCG on the MovieLens-1M dataset), while requiring less training time (e.g. -73% training time on MovieLens-1M). Moreover, in contrast to BERT4Rec, gSASRec is suitable for large datasets that contain more than 1 million items.|大目录规模是培训推荐模型的核心挑战之一: 大量的项目使得它们在计算培训期间所有项目的分数时内存和计算效率低下，迫使这些模型部署负抽样。然而，负抽样增加了训练数据中正相互作用的比例，因此用负抽样训练的模型倾向于高估正相互作用的概率，我们称之为过度自信现象。虽然预测分数或概率的绝对值对检索推荐的排名并不重要，但过度自信的模型可能无法估计排名最高的项目的细微差异，导致性能下降。在本文中，我们表明，过度自信解释了为什么流行的 SASRec 模型表现不如 BERT4Rec。这与 BERT4Rec 的作者解释的性能差异是由于双向注意机制相反。为了减轻过度自信，我们提出了一种新的广义二元交叉熵损失函数(gBCE) ，并从理论上证明了它可以减轻过度自信。我们进一步提出了 gSASRec 模型，这是对 SASRec 模型的一个改进，它部署了更多的负片和 gBCE 损失。通过对三个数据集的详细实验，我们发现 gSASRec 不存在过度自信问题。因此，gSASRec 的性能优于 BERT4Rec (例如，在 MovieLens-1M 数据集上 + 9.47% NDCG) ，同时需要较少的训练时间(例如，在 MovieLens-1M 上 -73% 的训练时间)。此外，与 BERT4Rec 不同，gSASRec 适用于包含超过100万个项目的大型数据集。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=gSASRec:+Reducing+Overconfidence+in+Sequential+Recommendation+Trained+with+Negative+Sampling)|0| -|[Rethinking Multi-Interest Learning for Candidate Matching in Recommender Systems](https://doi.org/10.1145/3604915.3608766)|Yueqi Xie, Jingqi Gao, Peilin Zhou, Qichen Ye, Yining Hua, Jae Boum Kim, Fangzhao Wu, Sunghun Kim|HKUST gz, Hong Kong, Peoples R China; MIT, Cambridge, MA 02139 USA; Upstage, Hong Kong, Peoples R China; HKUST, Hong Kong, Peoples R China; MSRA, Beijing, Peoples R China; Peking Univ, Beijing, Peoples R China|Existing research efforts for multi-interest candidate matching in recommender systems mainly focus on improving model architecture or incorporating additional information, neglecting the importance of training schemes. This work revisits the training framework and uncovers two major problems hindering the expressiveness of learned multi-interest representations. First, the current training objective (i.e., uniformly sampled softmax) fails to effectively train discriminative representations in a multi-interest learning scenario due to the severe increase in easy negative samples. Second, a routing collapse problem is observed where each learned interest may collapse to express information only from a single item, resulting in information loss. To address these issues, we propose the REMI framework, consisting of an Interest-aware Hard Negative mining strategy (IHN) and a Routing Regularization (RR) method. IHN emphasizes interest-aware hard negatives by proposing an ideal sampling distribution and developing a Monte-Carlo strategy for efficient approximation. RR prevents routing collapse by introducing a novel regularization term on the item-to-interest routing matrices. These two components enhance the learned multi-interest representations from both the optimization objective and the composition information. REMI is a general framework that can be readily applied to various existing multi-interest candidate matching methods. Experiments on three real-world datasets show our method can significantly improve state-of-the-art methods with easy implementation and negligible computational overhead. The source code is available at https://github.com/Tokkiu/REMI.|推荐系统中多兴趣候选人匹配的研究主要集中在改进模型结构或引入额外信息上，忽视了培训方案的重要性。这项工作重新审视了训练框架，发现了两个主要问题，阻碍了学习的多重兴趣表征的表达。首先，当前的训练目标(即均匀采样的软最大值)不能有效地训练多兴趣学习场景中的区分性表示，因为容易出现负样本的严重增加。其次，观察到一个路由折叠问题，其中每个学习兴趣可能会折叠成只表达单个项目的信息，从而导致信息损失。为了解决这些问题，我们提出了 REMI 框架，包括一个感兴趣的硬负面挖掘策略(IHN)和一个路由正则化(RR)方法。IHN 强调感兴趣的硬负面提出了一个理想的抽样分布和发展蒙特卡罗策略的有效逼近。RR 通过在项目感兴趣的路由矩阵上引入一个新的正则化项来防止路由崩溃。这两个部分从优化目标和组合信息两个方面增强了学习到的多兴趣表示。REMI 是一个通用框架，可以很容易地应用于各种现有的多兴趣候选匹配方法。在三个实际数据集上的实验表明，该方法可以显著改善最先进的方法，并且易于实现，计算开销可以忽略。源代码可在 https://github.com/tokkiu/remi 下载。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Rethinking+Multi-Interest+Learning+for+Candidate+Matching+in+Recommender+Systems)|0| -|[Understanding and Modeling Passive-Negative Feedback for Short-video Sequential Recommendation](https://doi.org/10.1145/3604915.3608814)|Yunzhu Pan, Chen Gao, Jianxin Chang, Yanan Niu, Yang Song, Kun Gai, Depeng Jin, Yong Li|Univ Elect Sci & Technol China, Chengdu, Peoples R China; Tsinghua Univ, Beijing Natl Res Ctr Informat Sci & Technol, Dept Elect Engn, Beijing, Peoples R China; Beijing Kuaishou Technol Co Ltd, Beijing, Peoples R China; Unaffiliated, Beijing, Peoples R China|Sequential recommendation is one of the most important tasks in recommender systems, which aims to recommend the next interacted item with historical behaviors as input. Traditional sequential recommendation always mainly considers the collected positive feedback such as click, purchase, etc. However, in short-video platforms such as TikTok, video viewing behavior may not always represent positive feedback. Specifically, the videos are played automatically, and users passively receive the recommended videos. In this new scenario, users passively express negative feedback by skipping over videos they do not like, which provides valuable information about their preferences. Different from the negative feedback studied in traditional recommender systems, this passive-negative feedback can reflect users' interests and serve as an important supervision signal in extracting users' preferences. Therefore, it is essential to carefully design and utilize it in this novel recommendation scenario. In this work, we first conduct analyses based on a large-scale real-world short-video behavior dataset and illustrate the significance of leveraging passive feedback. We then propose a novel method that deploys the sub-interest encoder, which incorporates positive feedback and passive-negative feedback as supervision signals to learn the user's current active sub-interest. Moreover, we introduce an adaptive fusion layer to integrate various sub-interests effectively. To enhance the robustness of our model, we then introduce a multi-task learning module to simultaneously optimize two kinds of feedback - passive-negative feedback and traditional randomly-sampled negative feedback. The experiments on two large-scale datasets verify that the proposed method can significantly outperform state-of-the-art approaches. The code is released at https:// github.com/ tsinghua-fib-lab/ RecSys2023-SINE to benefit the community.|序贯推荐是推荐系统中最重要的任务之一，其目的是推荐下一个以历史行为为输入的交互项。传统的顺序推荐主要考虑收集到的积极反馈，如点击、购买等。然而，在像 TikTok 这样的短视频平台中，视频观看行为可能并不总是代表正反馈。具体来说，视频是自动播放的，用户被动地接收推荐的视频。在这个新的场景中，用户通过跳过他们不喜欢的视频被动地表达负面反馈，这提供了关于他们偏好的有价值的信息。与传统推荐系统研究的负反馈不同，这种被动负反馈能够反映用户的兴趣，是提取用户偏好的重要监督信号。因此，在这个新颖的推荐场景中仔细设计和使用它是非常重要的。在这项工作中，我们首先进行分析的基础上，大规模的现实世界的短视频行为数据集，并说明了利用被动反馈的重要性。然后我们提出了一种新的方法，部署子兴趣编码器，其中结合正反馈和被动负反馈作为监督信号，以了解用户当前的主动子兴趣。此外，我们还引入了一个自适应融合层来有效地整合各种子利益。为了提高模型的鲁棒性，我们引入了一个多任务学习模块来同时优化两种反馈-被动-负反馈和传统的随机抽样负反馈。在两个大规模数据集上的实验表明，该方法的性能明显优于目前最先进的方法。该代码在 https://github.com/tsinghua-fib-lab/RecSys2023-SINE 发布，以造福社区。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Understanding+and+Modeling+Passive-Negative+Feedback+for+Short-video+Sequential+Recommendation)|0| +|[Rethinking Multi-Interest Learning for Candidate Matching in Recommender Systems](https://doi.org/10.1145/3604915.3608766)|Yueqi Xie, Jingqi Gao, Peilin Zhou, Qichen Ye, Yining Hua, Jae Boum Kim, Fangzhao Wu, Sunghun Kim|Upstage, Hong Kong, Peoples R China; MIT, Cambridge, MA 02139 USA; MSRA, Beijing, Peoples R China; HKUST, Hong Kong, Peoples R China; Peking Univ, Beijing, Peoples R China; HKUST gz, Hong Kong, Peoples R China|Existing research efforts for multi-interest candidate matching in recommender systems mainly focus on improving model architecture or incorporating additional information, neglecting the importance of training schemes. This work revisits the training framework and uncovers two major problems hindering the expressiveness of learned multi-interest representations. First, the current training objective (i.e., uniformly sampled softmax) fails to effectively train discriminative representations in a multi-interest learning scenario due to the severe increase in easy negative samples. Second, a routing collapse problem is observed where each learned interest may collapse to express information only from a single item, resulting in information loss. To address these issues, we propose the REMI framework, consisting of an Interest-aware Hard Negative mining strategy (IHN) and a Routing Regularization (RR) method. IHN emphasizes interest-aware hard negatives by proposing an ideal sampling distribution and developing a Monte-Carlo strategy for efficient approximation. RR prevents routing collapse by introducing a novel regularization term on the item-to-interest routing matrices. These two components enhance the learned multi-interest representations from both the optimization objective and the composition information. REMI is a general framework that can be readily applied to various existing multi-interest candidate matching methods. Experiments on three real-world datasets show our method can significantly improve state-of-the-art methods with easy implementation and negligible computational overhead. The source code is available at https://github.com/Tokkiu/REMI.|推荐系统中多兴趣候选人匹配的研究主要集中在改进模型结构或引入额外信息上，忽视了培训方案的重要性。这项工作重新审视了训练框架，发现了两个主要问题，阻碍了学习的多重兴趣表征的表达。首先，当前的训练目标(即均匀采样的软最大值)不能有效地训练多兴趣学习场景中的区分性表示，因为容易出现负样本的严重增加。其次，观察到一个路由折叠问题，其中每个学习兴趣可能会折叠成只表达单个项目的信息，从而导致信息损失。为了解决这些问题，我们提出了 REMI 框架，包括一个感兴趣的硬负面挖掘策略(IHN)和一个路由正则化(RR)方法。IHN 强调感兴趣的硬负面提出了一个理想的抽样分布和发展蒙特卡罗策略的有效逼近。RR 通过在项目感兴趣的路由矩阵上引入一个新的正则化项来防止路由崩溃。这两个部分从优化目标和组合信息两个方面增强了学习到的多兴趣表示。REMI 是一个通用框架，可以很容易地应用于各种现有的多兴趣候选匹配方法。在三个实际数据集上的实验表明，该方法可以显著改善最先进的方法，并且易于实现，计算开销可以忽略。源代码可在 https://github.com/tokkiu/remi 下载。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Rethinking+Multi-Interest+Learning+for+Candidate+Matching+in+Recommender+Systems)|0| +|[Understanding and Modeling Passive-Negative Feedback for Short-video Sequential Recommendation](https://doi.org/10.1145/3604915.3608814)|Yunzhu Pan, Chen Gao, Jianxin Chang, Yanan Niu, Yang Song, Kun Gai, Depeng Jin, Yong Li|Univ Elect Sci & Technol China, Chengdu, Peoples R China; Tsinghua Univ, Beijing Natl Res Ctr Informat Sci & Technol, Dept Elect Engn, Beijing, Peoples R China; Unaffiliated, Beijing, Peoples R China; Beijing Kuaishou Technol Co Ltd, Beijing, Peoples R China|Sequential recommendation is one of the most important tasks in recommender systems, which aims to recommend the next interacted item with historical behaviors as input. Traditional sequential recommendation always mainly considers the collected positive feedback such as click, purchase, etc. However, in short-video platforms such as TikTok, video viewing behavior may not always represent positive feedback. Specifically, the videos are played automatically, and users passively receive the recommended videos. In this new scenario, users passively express negative feedback by skipping over videos they do not like, which provides valuable information about their preferences. Different from the negative feedback studied in traditional recommender systems, this passive-negative feedback can reflect users' interests and serve as an important supervision signal in extracting users' preferences. Therefore, it is essential to carefully design and utilize it in this novel recommendation scenario. In this work, we first conduct analyses based on a large-scale real-world short-video behavior dataset and illustrate the significance of leveraging passive feedback. We then propose a novel method that deploys the sub-interest encoder, which incorporates positive feedback and passive-negative feedback as supervision signals to learn the user's current active sub-interest. Moreover, we introduce an adaptive fusion layer to integrate various sub-interests effectively. To enhance the robustness of our model, we then introduce a multi-task learning module to simultaneously optimize two kinds of feedback - passive-negative feedback and traditional randomly-sampled negative feedback. The experiments on two large-scale datasets verify that the proposed method can significantly outperform state-of-the-art approaches. The code is released at https:// github.com/ tsinghua-fib-lab/ RecSys2023-SINE to benefit the community.|序贯推荐是推荐系统中最重要的任务之一，其目的是推荐下一个以历史行为为输入的交互项。传统的顺序推荐主要考虑收集到的积极反馈，如点击、购买等。然而，在像 TikTok 这样的短视频平台中，视频观看行为可能并不总是代表正反馈。具体来说，视频是自动播放的，用户被动地接收推荐的视频。在这个新的场景中，用户通过跳过他们不喜欢的视频被动地表达负面反馈，这提供了关于他们偏好的有价值的信息。与传统推荐系统研究的负反馈不同，这种被动负反馈能够反映用户的兴趣，是提取用户偏好的重要监督信号。因此，在这个新颖的推荐场景中仔细设计和使用它是非常重要的。在这项工作中，我们首先进行分析的基础上，大规模的现实世界的短视频行为数据集，并说明了利用被动反馈的重要性。然后我们提出了一种新的方法，部署子兴趣编码器，其中结合正反馈和被动负反馈作为监督信号，以了解用户当前的主动子兴趣。此外，我们还引入了一个自适应融合层来有效地整合各种子利益。为了提高模型的鲁棒性，我们引入了一个多任务学习模块来同时优化两种反馈-被动-负反馈和传统的随机抽样负反馈。在两个大规模数据集上的实验表明，该方法的性能明显优于目前最先进的方法。该代码在 https://github.com/tsinghua-fib-lab/RecSys2023-SINE 发布，以造福社区。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Understanding+and+Modeling+Passive-Negative+Feedback+for+Short-video+Sequential+Recommendation)|0| |[Adaptive Collaborative Filtering with Personalized Time Decay Functions for Financial Product Recommendation](https://doi.org/10.1145/3604915.3608832)|Ashraf Ghiye, Baptiste Barreau, Laurent Carlier, Michalis Vazirgiannis|BNP Paribas Corp & Inst Banking, Global Markets Data & AI Lab, Paris, France; Ecole Polytechn, Comp Sci Lab, LIX, Palaiseau, France|Classical recommender systems often assume that historical data are stationary and fail to account for the dynamic nature of user preferences, limiting their ability to provide reliable recommendations in time-sensitive settings. This assumption is particularly problematic in finance, where financial products exhibit continuous changes in valuations, leading to frequent shifts in client interests. These evolving interests, summarized in the past client-product interactions, see their utility fade over time with a degree that might differ from one client to another. To address this challenge, we propose a time-dependent collaborative filtering algorithm that can adaptively discount distant client-product interactions using personalized decay functions. Our approach is designed to handle the non-stationarity of financial data and produce reliable recommendations by modeling the dynamic collaborative signals between clients and products. We evaluate our method using a proprietary dataset from BNP Paribas and demonstrate significant improvements over state-of-the-art benchmarks from relevant literature. Our findings emphasize the importance of incorporating time explicitly in the model to enhance the accuracy of financial product recommendation.|传统的推荐系统往往假设历史数据是静态的，不能解释用户偏好的动态特性，从而限制了它们在时间敏感设置中提供可靠推荐的能力。这种假设在金融领域尤其成问题，因为金融产品的估值会不断变化，导致客户利益频繁变化。这些不断发展的兴趣，总结在过去的客户-产品交互中，看到它们的效用随着时间的推移而消失，程度可能因客户而异。为了解决这个问题，我们提出了一个时间相关的协同过滤算法，可以使用个性化的衰减函数自适应地折现远距离的客户-产品相互作用。我们的方法旨在处理财务数据的非平稳性，并通过建模客户和产品之间的动态协作信号产生可靠的建议。我们使用来自法国巴黎银行的专有数据集来评估我们的方法，并证明了相对于相关文献中的最先进基准的显著改进。我们的研究结果强调了将时间明确纳入模型的重要性，以提高金融产品推荐的准确性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Adaptive+Collaborative+Filtering+with+Personalized+Time+Decay+Functions+for+Financial+Product+Recommendation)|0| -|[Integrating Item Relevance in Training Loss for Sequential Recommender Systems](https://doi.org/10.1145/3604915.3610643)|Andrea Bacciu, Federico Siciliano, Nicola Tonellotto, Fabrizio Silvestri|Sapienza Univ Rome, Rome, Italy; Univ Pisa, Pisa, Italy|Sequential Recommender Systems (SRSs) are a popular type of recommender system that leverages user history to predict the next item of interest. However, the presence of noise in user interactions, stemming from account sharing, inconsistent preferences, or accidental clicks, can significantly impact the robustness and performance of SRSs, particularly when the entire item set to be predicted is noisy. This situation is more prevalent when only one item is used to train and evaluate the SRSs. To tackle this challenge, we propose a novel approach that addresses the issue of noise in SRSs. First, we propose a sequential multi-relevant future items training objective, leveraging a loss function aware of item relevance, thereby enhancing their robustness against noise in the training data. Additionally, to mitigate the impact of noise at evaluation time, we propose multi-relevant future items evaluation (MRFI-evaluation), aiming to improve overall performance. Our relevance-aware models obtain an improvement of 1.58% of NDCG@10 and 0.96% in terms of HR@10 in the traditional evaluation protocol, the one which utilizes one relevant future item. In the MRFI-evaluation protocol, using multiple future items, the improvement is 2.82% of NDCG@10 and 0.64% of HR@10 w.r.t the best baseline model.|顺序推荐系统(SRSs)是一种流行的推荐系统，它利用用户历史来预测下一个感兴趣的项目。然而，在用户交互中存在的噪声，来自帐户共享、不一致的偏好或偶然的点击，可以显著影响 SRS 的健壮性和性能，特别是当整个项目集被预测是噪声的时候。当只有一个项目被用来训练和评估战略参考系时，这种情况更为普遍。为了应对这一挑战，我们提出了一种新的方法，解决噪音问题的 SRS。首先，我们提出了一个连续的多相关未来项目的训练目标，利用损失函数意识到项目的相关性，从而增强了他们对训练数据中的噪声的鲁棒性。此外，为了减轻噪声对评价时间的影响，我们提出了多相关的未来项目评价(MRFI- 评价) ，旨在提高整体性能。我们的相关意识模型在传统的评估方案中获得了1.58% 的 NDCG@10和0.96% 的 HR@10的改善，其中利用了一个相关的未来项目。在 MRFI 评估方案中，使用多个未来项目，改善率为 NDCG 的2.82% (10%)和 HR 的0.64% (10%)是最佳基线模型。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Integrating+Item+Relevance+in+Training+Loss+for+Sequential+Recommender+Systems)|0| -|[Integrating the ACT-R Framework with Collaborative Filtering for Explainable Sequential Music Recommendation](https://doi.org/10.1145/3604915.3608838)|Marta Moscati, Christian Wallmann, Markus ReiterHaas, Dominik Kowald, Elisabeth Lex, Markus Schedl|Graz Univ Technol, Graz, Austria; Johannes Kepler Univ Linz, Inst Computat Percept, Linz, Austria; Welser Profile GmbH, Gresten, Austria|Music listening sessions often consist of sequences including repeating tracks. Modeling such relistening behavior with models of human memory has been proven effective in predicting the next track of a session. However, these models intrinsically lack the capability of recommending novel tracks that the target user has not listened to in the past. Collaborative filtering strategies, on the contrary, provide novel recommendations by leveraging past collective behaviors but are often limited in their ability to provide explanations. To narrow this gap, we propose four hybrid algorithms that integrate collaborative filtering with the cognitive architecture ACT-R. We compare their performance in terms of accuracy, novelty, diversity, and popularity bias, to baselines of different types, including pure ACT-R, kNN-based, and neural-networks-based approaches. We show that the proposed algorithms are able to achieve the best performances in terms of novelty and diversity, and simultaneously achieve a higher accuracy of recommendation with respect to pure ACT-R models. Furthermore, we illustrate how the proposed models can provide explainable recommendations.|音乐聆听课程通常包括一系列重复的曲目。利用人类记忆模型对这种重听行为进行建模已被证明对预测会话的下一个轨迹是有效的。然而，这些模型本质上缺乏推荐目标用户过去没有听过的新曲目的能力。相反，协同过滤策略通过利用过去的集体行为提供新颖的建议，但它们提供解释的能力往往有限。为了缩小这个差距，我们提出了四种混合算法，将协同过滤与认知结构 ACT-R 整合在一起。我们比较了它们在准确性、新颖性、多样性和受欢迎程度方面的表现，以及不同类型的基线，包括纯 ACT-R、基于 kNN 和基于神经网络的方法。结果表明，该算法能够在新颖性和多样性方面达到最佳性能，同时对于纯 ACT-R 模型也能够达到较高的推荐精度。此外，我们说明了所提出的模型如何能够提供可解释的建议。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Integrating+the+ACT-R+Framework+with+Collaborative+Filtering+for+Explainable+Sequential+Music+Recommendation)|0| +|[Integrating Item Relevance in Training Loss for Sequential Recommender Systems](https://doi.org/10.1145/3604915.3610643)|Andrea Bacciu, Federico Siciliano, Nicola Tonellotto, Fabrizio Silvestri|Univ Pisa, Pisa, Italy; Sapienza Univ Rome, Rome, Italy|Sequential Recommender Systems (SRSs) are a popular type of recommender system that leverages user history to predict the next item of interest. However, the presence of noise in user interactions, stemming from account sharing, inconsistent preferences, or accidental clicks, can significantly impact the robustness and performance of SRSs, particularly when the entire item set to be predicted is noisy. This situation is more prevalent when only one item is used to train and evaluate the SRSs. To tackle this challenge, we propose a novel approach that addresses the issue of noise in SRSs. First, we propose a sequential multi-relevant future items training objective, leveraging a loss function aware of item relevance, thereby enhancing their robustness against noise in the training data. Additionally, to mitigate the impact of noise at evaluation time, we propose multi-relevant future items evaluation (MRFI-evaluation), aiming to improve overall performance. Our relevance-aware models obtain an improvement of 1.58% of NDCG@10 and 0.96% in terms of HR@10 in the traditional evaluation protocol, the one which utilizes one relevant future item. In the MRFI-evaluation protocol, using multiple future items, the improvement is 2.82% of NDCG@10 and 0.64% of HR@10 w.r.t the best baseline model.|顺序推荐系统(SRSs)是一种流行的推荐系统，它利用用户历史来预测下一个感兴趣的项目。然而，在用户交互中存在的噪声，来自帐户共享、不一致的偏好或偶然的点击，可以显著影响 SRS 的健壮性和性能，特别是当整个项目集被预测是噪声的时候。当只有一个项目被用来训练和评估战略参考系时，这种情况更为普遍。为了应对这一挑战，我们提出了一种新的方法，解决噪音问题的 SRS。首先，我们提出了一个连续的多相关未来项目的训练目标，利用损失函数意识到项目的相关性，从而增强了他们对训练数据中的噪声的鲁棒性。此外，为了减轻噪声对评价时间的影响，我们提出了多相关的未来项目评价(MRFI- 评价) ，旨在提高整体性能。我们的相关意识模型在传统的评估方案中获得了1.58% 的 NDCG@10和0.96% 的 HR@10的改善，其中利用了一个相关的未来项目。在 MRFI 评估方案中，使用多个未来项目，改善率为 NDCG 的2.82% (10%)和 HR 的0.64% (10%)是最佳基线模型。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Integrating+Item+Relevance+in+Training+Loss+for+Sequential+Recommender+Systems)|0| +|[Integrating the ACT-R Framework with Collaborative Filtering for Explainable Sequential Music Recommendation](https://doi.org/10.1145/3604915.3608838)|Marta Moscati, Christian Wallmann, Markus ReiterHaas, Dominik Kowald, Elisabeth Lex, Markus Schedl|Welser Profile GmbH, Gresten, Austria; Johannes Kepler Univ Linz, Inst Computat Percept, Linz, Austria; Graz Univ Technol, Graz, Austria|Music listening sessions often consist of sequences including repeating tracks. Modeling such relistening behavior with models of human memory has been proven effective in predicting the next track of a session. However, these models intrinsically lack the capability of recommending novel tracks that the target user has not listened to in the past. Collaborative filtering strategies, on the contrary, provide novel recommendations by leveraging past collective behaviors but are often limited in their ability to provide explanations. To narrow this gap, we propose four hybrid algorithms that integrate collaborative filtering with the cognitive architecture ACT-R. We compare their performance in terms of accuracy, novelty, diversity, and popularity bias, to baselines of different types, including pure ACT-R, kNN-based, and neural-networks-based approaches. We show that the proposed algorithms are able to achieve the best performances in terms of novelty and diversity, and simultaneously achieve a higher accuracy of recommendation with respect to pure ACT-R models. Furthermore, we illustrate how the proposed models can provide explainable recommendations.|音乐聆听课程通常包括一系列重复的曲目。利用人类记忆模型对这种重听行为进行建模已被证明对预测会话的下一个轨迹是有效的。然而，这些模型本质上缺乏推荐目标用户过去没有听过的新曲目的能力。相反，协同过滤策略通过利用过去的集体行为提供新颖的建议，但它们提供解释的能力往往有限。为了缩小这个差距，我们提出了四种混合算法，将协同过滤与认知结构 ACT-R 整合在一起。我们比较了它们在准确性、新颖性、多样性和受欢迎程度方面的表现，以及不同类型的基线，包括纯 ACT-R、基于 kNN 和基于神经网络的方法。结果表明，该算法能够在新颖性和多样性方面达到最佳性能，同时对于纯 ACT-R 模型也能够达到较高的推荐精度。此外，我们说明了所提出的模型如何能够提供可解释的建议。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Integrating+the+ACT-R+Framework+with+Collaborative+Filtering+for+Explainable+Sequential+Music+Recommendation)|0| |[An Industrial Framework for Personalized Serendipitous Recommendation in E-commerce](https://doi.org/10.1145/3604915.3610234)|Zongyi Wang, Yanyan Zou, Anyu Dai, Linfang Hou, Nan Qiao, Luobao Zou, Mian Ma, Zhuoye Ding, Sulong Xu|JD com, Beijing, Peoples R China|Classical recommendation methods typically face the filter bubble problem where users likely receive recommendations of their familiar items, making them bored and dissatisfied. To alleviate such an issue, this applied paper introduces a novel framework for personalized serendipitous recommendation in an e-commerce platform (i.e., JD.com), which allows to present user unexpected and satisfying items deviating from user's prior behaviors, considering both accuracy and novelty. To achieve such a goal, it is crucial yet challenging to recognize when a user is willing to receive serendipitous items and how many novel items are expected. To address above two challenges, a two-stage framework is designed. Firstly, a DNN-based scorer is deployed to quantify the novelty degree of a product category based on user behavior history. Then, we resort to a potential outcome framework to decide the optimal timing to recommend a user serendipitous items and the novelty degree of the recommendation. Online A/B test on the e-commerce recommender platform in JD.com demonstrates that our model achieves significant gains on various metrics, 0.54% relative increase of impressive depth, 0.8% of average user click count, 3.23% and 1.38% of number of novel impressive and clicked items individually.|传统的推荐方法通常面临过滤器泡沫问题，用户可能会收到他们熟悉的项目的推荐，使他们感到厌烦和不满。为了解决这一问题，本文在电子商务平台(如京东)上引入了一个新的个性化意外推荐框架，该框架可以在考虑准确性和新颖性的情况下，提供与用户之前的行为不同的用户意想不到的、令人满意的推荐信息。要实现这样一个目标，识别用户何时愿意接收意外收获的项目以及期望接收多少新项目是至关重要的，但也是具有挑战性的。为了解决上述两个挑战，设计了一个两阶段框架。首先采用基于 DNN 的记分器，根据用户行为历史对产品类别的新颖度进行量化。然后，利用一个潜在的结果框架来决定推荐用户偶然项目的最佳时机和推荐的新颖程度。在京东的电子商务推荐平台上进行的在线 A/B 测试表明，该模型在各个指标上都取得了显著的进步，令人印象深刻的深度相对增加了0.54% ，平均用户点击次数增加了0.8% ，新颖的令人印象深刻的项目和单独点击项目的数量分别增加了3.23% 和1.38% 。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=An+Industrial+Framework+for+Personalized+Serendipitous+Recommendation+in+E-commerce)|0| |[Full Index Deep Retrieval: End-to-End User and Item Structures for Cold-start and Long-tail Item Recommendation](https://doi.org/10.1145/3604915.3608773)|Zhen Gong, Xin Wu, Lei Chen, Zhenzhe Zheng, Shengjie Wang, Anran Xu, Chong Wang, Fan Wu|Shanghai Jiao Tong Univ, Shanghai, Peoples R China; Bytedance Inc, Mountain View, CA USA|End-to-end retrieval models, such as Tree-based Models (TDM) and Deep Retrieval (DR), have attracted a lot of attention, but they cannot handle cold-start and long-tail item recommendation scenarios well. Specifically, DR learns a compact indexing structure, enabling efficient and accurate retrieval for large recommendation systems. However, it is discovered that DR largely fails on retrieving coldstart and long-tail items. This is because DR only utilizes user-item interaction data, which is rare and often noisy for cold-start and long-tail items. Besides, end-to-end retrieval models are unable to make use of the rich item content features. To address this issue while maintaining the efficiency of DR indexing structure, we propose Full Index Deep Retrieval (FIDR) that learns indices for the full corpus items, including cold-start and long-tail items. In addition to the original structure in DR (called User Structure in FIDR) that learns with user-item interaction data (e.g., clicks), we add an Item Structure to embed items directly based on item content features (e.g., categories). With joint efforts of User Structure and Item Structure, FIDR makes cold-start items retrievable and also improves the recommendation quality of long-tail items. To our best knowledge, FIDR is the first to solve the cold-start and longtail recommendation problem for the end-to-end retrieval models. Through extensive experiments on three real-world datasets, we demonstrate that FIDR can effectively recommend cold-start as well as long-tail items, and largely promote overall recommendation performance without sacrificing inference efficiency. According to the experiments, the recall of FIDR is improved by 8.8%similar to 11.9%, while the inference of FIDR is as efficient as DR.|端到端的检索模型，如基于树的模型(TDM)和深度检索(DR) ，已经引起了人们的广泛关注，但它们不能很好地处理冷启动和长尾项目推荐场景。具体来说，DR 学习了一种紧凑的索引结构，从而能够为大型推荐系统提供高效和准确的检索。然而，发现 DR 在检索冷启动项和长尾项时大多失败。这是因为 DR 仅利用用户项交互数据，这对于冷启动和长尾项目来说是罕见的，而且通常很吵。此外，端到端的检索模型不能利用丰富的项目内容特征。为了解决这个问题，同时保持 DR 索引结构的效率，我们提出了全索引深度检索(FIDR) ，学习完整语料库项目的索引，包括冷启动和长尾项目。除了 DR 中的原始结构(FIDR 中称为用户结构)通过用户项目交互数据(例如，点击)学习之外，我们还添加了一个项目结构来直接基于项目内容特征(例如，类别)嵌入项目。在用户结构和项目结构的共同努力下，FIDR 使冷启动项目可检索，提高了长尾项目的推荐质量。据我们所知，FIDR 首先解决了端到端检索模型的冷启动和长尾推荐问题。通过对三个实际数据集的大量实验，我们证明了 FIDR 可以有效地推荐冷启动和长尾项目，并在不牺牲推理效率的情况下大大提高整体推荐性能。实验表明，FIDR 的召回率提高了8.8% ，相当于11.9% ，而 FIDR 的推理效率与 DR 相当。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Full+Index+Deep+Retrieval:+End-to-End+User+and+Item+Structures+for+Cold-start+and+Long-tail+Item+Recommendation)|0| -|[Online Matching: A Real-time Bandit System for Large-scale Recommendations](https://doi.org/10.1145/3604915.3608792)|Xinyang Yi, ShaoChuan Wang, Ruining He, Hariharan Chandrasekaran, Charles Wu, Lukasz Heldt, Lichan Hong, Minmin Chen, Ed H. Chi|Google Deepmind, Mountain View, CA 94043 USA; Google Inc, Mountain View, CA USA|The last decade has witnessed many successes of deep learning-based models for industry-scale recommender systems. These models are typically trained offline in a batch manner. While being effective in capturing users' past interactions with recommendation platforms, batch learning suffers from long model-update latency and is vulnerable to system biases, making it hard to adapt to distribution shift and explore new items or user interests. Although online learning-based approaches (e.g., multi-armed bandits) have demonstrated promising theoretical results in tackling these challenges, their practical real-time implementation in large-scale recommender systems remains limited. First, the scalability of online approaches in servicing a massive online traffic while ensuring timely updates of bandit parameters poses a significant challenge. Additionally, exploring uncertainty in recommender systems can easily result in unfavorable user experience, highlighting the need for devising intricate strategies that effectively balance the trade-off between exploitation and exploration. In this paper, we introduce Online Matching: a scalable closed-loop bandit system learning from users' direct feedback on items in real time. We present a hybrid offline + online approach for constructing this system, accompanied by a comprehensive exposition of the end-to-end system architecture. We propose Diag-LinUCB - a novel extension of the LinUCB algorithm - to enable distributed updates of bandits parameter in a scalable and timely manner. We conduct live experiments in YouTube and show that Online Matching is able to enhance the capabilities of fresh content discovery and item exploration in the present platform.|过去十年见证了行业级推荐系统中基于深度学习的模型的许多成功。这些模型通常以批处理的方式离线训练。批量学习能够有效地捕捉用户过去与推荐平台的交互，但由于模型更新延迟较长，容易受到系统偏差的影响，难以适应分布变化，难以探索新的项目或用户兴趣。尽管基于在线学习的方法(例如，多武装匪徒)在应对这些挑战方面已经证明了有希望的理论成果，但它们在大规模推荐系统中的实际实时实施仍然有限。首先，在为大量在线流量提供服务的同时确保及时更新盗贼参数的在线方法的可扩展性构成了一个重大挑战。此外，在推荐系统中探索不确定性很容易导致不利的用户体验，突出需要设计复杂的策略，有效地平衡开发和勘探之间的权衡。本文介绍了在线匹配: 一个可扩展的、利用用户对项目的直接反馈进行实时学习的闭环盗窃系统。我们提出了一种混合的离线 + 在线的方法来构建这个系统，同时对端到端的系统架构进行了全面的阐述。我们提出了 Diag-LinUCB 算法—— LinUCB 算法的一个新的扩展——以便能够以可扩展和及时的方式分布式更新土匪参数。我们在 YouTube 上进行了实验，结果表明在线匹配可以增强现有平台上新内容发现和项目探索的能力。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Online+Matching:+A+Real-time+Bandit+System+for+Large-scale+Recommendations)|0| -|[Exploring False Hard Negative Sample in Cross-Domain Recommendation](https://doi.org/10.1145/3604915.3608791)|Haokai Ma, Ruobing Xie, Lei Meng, Xin Chen, Xu Zhang, Leyu Lin, Jie Zhou|Shandong Univ, Sch Software, Jinan, Peoples R China; Tencent, WeChat, Beijing, Peoples R China|Negative Sampling in recommendation aims to capture informative negative instances for the sparse user-item interactions to improve the performance. Conventional negative sampling methods tend to select informative hard negative samples (HNS) besides the default random samples. However, these hard negative sampling methods usually struggle with false hard negative samples (FHNS), which happens when a user-item interaction has not been observed yet and is picked as a negative sample, while the user will actually interact with this item once exposed to it. Such FHNS issues may seriously confuse the model training, while most conventional hard negative sampling methods do not systematically explore and distinguish FHNS from HNS. To address this issue, we propose a novel model-agnostic Real Hard Negative Sampling (RealHNS) framework specially for cross-domain recommendation (CDR), which aims to discover the false and refine the real from all HNS via both general and cross-domain real hard negative sample selectors. For the general part, we conduct the coarse- and fine-grained real HNS selectors sequentially, armed with a dynamic item-based FHNS filter to find high-quality HNS. For the cross-domain part, we further design a new cross-domain HNS for alleviating negative transfer in CDR and discover its corresponding FHNS via a dynamic user-based FHNS filter to keep its power. We conduct experiments on four datasets based on three representative hard negative sampling methods, along with extensive model analyses, ablation studies, and universality analyses. The consistent improvements indicate the effectiveness, robustness, and universality of RealHNS, which is also easy-to-deploy in real-world systems as a plug-and-play strategy. The source code is avaliable in https://github.com/hulkima/RealHNS.|推荐中的负抽样旨在为稀疏的用户项交互捕获信息丰富的负实例，以提高性能。传统的阴性抽样方法除了选择默认随机样本外，还倾向于选择信息量大的硬阴性样本(HNS)。然而，这些硬阴性抽样方法通常与假硬阴性样本(FHNS)作斗争，这种情况发生在用户与项目的交互尚未被观察到并被选为负面样本时，而用户一旦接触到这个项目，实际上将与其交互。这样的 FHNS 问题可能会严重混淆模型训练，而大多数传统的硬阴性抽样方法没有系统地探索和区分 FHNS 和 HNS。针对这一问题，本文提出了一种针对跨域推荐(CDR)的模型无关真实硬负采样(RealHNS)框架，该框架旨在通过通用和跨域真实硬负采样选择器，发现虚假信息，并从所有 HNS 中提取真实信息。对于一般部分，我们依次进行粗粒度和细粒度的实际 HNS 选择器，并配备一个基于动态项目的 FHNS 滤波器来寻找高质量的 HNS。在跨域部分，我们进一步设计了一个新的跨域 HNS 来减轻 CDR 中的负转移，并通过一个基于用户的动态 FHNS 滤波器来发现相应的 FHNS 以保持其功率。基于三种典型的硬负取样方法，我们对四个数据集进行了实验，同时进行了广泛的模型分析、烧蚀研究和通用性分析。一致的改进表明了 RealHNS 的有效性、健壮性和通用性，作为一种即插即用策略，它也很容易在现实世界的系统中部署。源代码有 https://github.com/hulkima/realhns。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Exploring+False+Hard+Negative+Sample+in+Cross-Domain+Recommendation)|0| +|[Online Matching: A Real-time Bandit System for Large-scale Recommendations](https://doi.org/10.1145/3604915.3608792)|Xinyang Yi, ShaoChuan Wang, Ruining He, Hariharan Chandrasekaran, Charles Wu, Lukasz Heldt, Lichan Hong, Minmin Chen, Ed H. Chi|Google Inc, Mountain View, CA USA; Google Deepmind, Mountain View, CA 94043 USA|The last decade has witnessed many successes of deep learning-based models for industry-scale recommender systems. These models are typically trained offline in a batch manner. While being effective in capturing users' past interactions with recommendation platforms, batch learning suffers from long model-update latency and is vulnerable to system biases, making it hard to adapt to distribution shift and explore new items or user interests. Although online learning-based approaches (e.g., multi-armed bandits) have demonstrated promising theoretical results in tackling these challenges, their practical real-time implementation in large-scale recommender systems remains limited. First, the scalability of online approaches in servicing a massive online traffic while ensuring timely updates of bandit parameters poses a significant challenge. Additionally, exploring uncertainty in recommender systems can easily result in unfavorable user experience, highlighting the need for devising intricate strategies that effectively balance the trade-off between exploitation and exploration. In this paper, we introduce Online Matching: a scalable closed-loop bandit system learning from users' direct feedback on items in real time. We present a hybrid offline + online approach for constructing this system, accompanied by a comprehensive exposition of the end-to-end system architecture. We propose Diag-LinUCB - a novel extension of the LinUCB algorithm - to enable distributed updates of bandits parameter in a scalable and timely manner. We conduct live experiments in YouTube and show that Online Matching is able to enhance the capabilities of fresh content discovery and item exploration in the present platform.|过去十年见证了行业级推荐系统中基于深度学习的模型的许多成功。这些模型通常以批处理的方式离线训练。批量学习能够有效地捕捉用户过去与推荐平台的交互，但由于模型更新延迟较长，容易受到系统偏差的影响，难以适应分布变化，难以探索新的项目或用户兴趣。尽管基于在线学习的方法(例如，多武装匪徒)在应对这些挑战方面已经证明了有希望的理论成果，但它们在大规模推荐系统中的实际实时实施仍然有限。首先，在为大量在线流量提供服务的同时确保及时更新盗贼参数的在线方法的可扩展性构成了一个重大挑战。此外，在推荐系统中探索不确定性很容易导致不利的用户体验，突出需要设计复杂的策略，有效地平衡开发和勘探之间的权衡。本文介绍了在线匹配: 一个可扩展的、利用用户对项目的直接反馈进行实时学习的闭环盗窃系统。我们提出了一种混合的离线 + 在线的方法来构建这个系统，同时对端到端的系统架构进行了全面的阐述。我们提出了 Diag-LinUCB 算法—— LinUCB 算法的一个新的扩展——以便能够以可扩展和及时的方式分布式更新土匪参数。我们在 YouTube 上进行了实验，结果表明在线匹配可以增强现有平台上新内容发现和项目探索的能力。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Online+Matching:+A+Real-time+Bandit+System+for+Large-scale+Recommendations)|0| +|[Exploring False Hard Negative Sample in Cross-Domain Recommendation](https://doi.org/10.1145/3604915.3608791)|Haokai Ma, Ruobing Xie, Lei Meng, Xin Chen, Xu Zhang, Leyu Lin, Jie Zhou|Tencent, WeChat, Beijing, Peoples R China; Shandong Univ, Sch Software, Jinan, Peoples R China|Negative Sampling in recommendation aims to capture informative negative instances for the sparse user-item interactions to improve the performance. Conventional negative sampling methods tend to select informative hard negative samples (HNS) besides the default random samples. However, these hard negative sampling methods usually struggle with false hard negative samples (FHNS), which happens when a user-item interaction has not been observed yet and is picked as a negative sample, while the user will actually interact with this item once exposed to it. Such FHNS issues may seriously confuse the model training, while most conventional hard negative sampling methods do not systematically explore and distinguish FHNS from HNS. To address this issue, we propose a novel model-agnostic Real Hard Negative Sampling (RealHNS) framework specially for cross-domain recommendation (CDR), which aims to discover the false and refine the real from all HNS via both general and cross-domain real hard negative sample selectors. For the general part, we conduct the coarse- and fine-grained real HNS selectors sequentially, armed with a dynamic item-based FHNS filter to find high-quality HNS. For the cross-domain part, we further design a new cross-domain HNS for alleviating negative transfer in CDR and discover its corresponding FHNS via a dynamic user-based FHNS filter to keep its power. We conduct experiments on four datasets based on three representative hard negative sampling methods, along with extensive model analyses, ablation studies, and universality analyses. The consistent improvements indicate the effectiveness, robustness, and universality of RealHNS, which is also easy-to-deploy in real-world systems as a plug-and-play strategy. The source code is avaliable in https://github.com/hulkima/RealHNS.|推荐中的负抽样旨在为稀疏的用户项交互捕获信息丰富的负实例，以提高性能。传统的阴性抽样方法除了选择默认随机样本外，还倾向于选择信息量大的硬阴性样本(HNS)。然而，这些硬阴性抽样方法通常与假硬阴性样本(FHNS)作斗争，这种情况发生在用户与项目的交互尚未被观察到并被选为负面样本时，而用户一旦接触到这个项目，实际上将与其交互。这样的 FHNS 问题可能会严重混淆模型训练，而大多数传统的硬阴性抽样方法没有系统地探索和区分 FHNS 和 HNS。针对这一问题，本文提出了一种针对跨域推荐(CDR)的模型无关真实硬负采样(RealHNS)框架，该框架旨在通过通用和跨域真实硬负采样选择器，发现虚假信息，并从所有 HNS 中提取真实信息。对于一般部分，我们依次进行粗粒度和细粒度的实际 HNS 选择器，并配备一个基于动态项目的 FHNS 滤波器来寻找高质量的 HNS。在跨域部分，我们进一步设计了一个新的跨域 HNS 来减轻 CDR 中的负转移，并通过一个基于用户的动态 FHNS 滤波器来发现相应的 FHNS 以保持其功率。基于三种典型的硬负取样方法，我们对四个数据集进行了实验，同时进行了广泛的模型分析、烧蚀研究和通用性分析。一致的改进表明了 RealHNS 的有效性、健壮性和通用性，作为一种即插即用策略，它也很容易在现实世界的系统中部署。源代码有 https://github.com/hulkima/realhns。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Exploring+False+Hard+Negative+Sample+in+Cross-Domain+Recommendation)|0| |[Contrastive Learning with Frequency-Domain Interest Trends for Sequential Recommendation](https://doi.org/10.1145/3604915.3608790)|Yichi Zhang, Guisheng Yin, Yuxin Dong|Harbin Engn Univ, Harbin, Heilongjiang, Peoples R China|Recently, contrastive learning for sequential recommendation has demonstrated its powerful ability to learn high-quality user representations. However, constructing augmented samples in the time domain poses challenges due to various reasons, such as fast-evolving trends, interest shifts, and system factors. Furthermore, the F-principle indicates that deep learning preferentially fits the low-frequency part, resulting in poor performance on high-frequency tasks. The complexity of time series and the low-frequency preference limit the utility of sequence encoders. To address these challenges, we need to construct augmented samples from the frequency domain, thus improving the ability to accommodate events of different frequency sizes. To this end, we propose a novel Contrastive Learning with Frequency-Domain Interest Trends for Sequential Recommendation (CFIT4SRec). We treat the embedding representations of historical interactions as "images" and introduce the secondorder Fourier transform to construct augmented samples. The components of different frequency sizes reflect the interest trends between attributes and their surroundings in the hidden space. We introduce three data augmentation operations to accommodate events of different frequency sizes: low-pass augmentation, high-pass augmentation, and band-stop augmentation. Extensive experiments on four public benchmark datasets demonstrate the superiority of CFIT4SRec over the state-of-the-art baselines. The implementation code is available at https://github.com/zhangyichi1Z/CFIT4SRec.|近年来，序贯推荐的对比学习已经证明了其学习高质量用户表示的强大能力。然而，由于各种原因，如快速发展的趋势、兴趣转移和系统因素等，在时间域构造增广样本提出了挑战。此外，F- 原理表明，深度学习优先适用于低频部分，导致高频任务的性能较差。时间序列的复杂性和低频偏好限制了序列编码器的实用性。为了应对这些挑战，我们需要从频率域构造增强样本，从而提高容纳不同频率大小事件的能力。为此，我们提出了一种新的对比学习与频域兴趣趋势顺序推荐(CFIT4SRec)。我们把历史相互作用的嵌入表示当作“图像”，并引入二阶傅里叶变换来构造增广样本。不同频率大小的分量反映了隐藏空间中属性与环境之间的兴趣趋势。我们引入三种数据增强操作来适应不同频率大小的事件: 低通增强、高通增强和带阻增强。在四个公共基准数据集上的大量实验证明了 CFIT4SRec 相对于最先进的基准线的优越性。实施守则可于 https://github.com/zhangyichi1z/cfit4srec 索取。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Contrastive+Learning+with+Frequency-Domain+Interest+Trends+for+Sequential+Recommendation)|0| -|[Multi-task Item-attribute Graph Pre-training for Strict Cold-start Item Recommendation](https://doi.org/10.1145/3604915.3608806)|Yuwei Cao, Liangwei Yang, Chen Wang, Zhiwei Liu, Hao Peng, Chenyu You, Philip S. Yu|Yale Univ, New Haven, CT USA; Beihang Univ, Beijing, Peoples R China; Univ Illinois, Chicago, IL USA; Salesforce AI, Washington, DC USA|Recommendation systems suffer in the strict cold-start (SCS) scenario, where the user-item interactions are entirely unavailable. The well-established, dominating identity (ID)-based approaches completely fail to work. Cold-start recommenders, on the other hand, leverage item contents ( brand, title, descriptions, etc.) to map the new items to the existing ones. However, the existing SCS recommenders explore item contents in coarse-grained manners that introduce noise or information loss. Moreover, informative data sources other than item contents, such as users' purchase sequences and review texts, are largely ignored. In this work, we explore the role of the fine-grained item attributes in bridging the gaps between the existing and the SCS items and pre-train a knowledgeable item-attribute graph for SCS item recommendation. Our proposed framework, ColdGPT, models item-attribute correlations into an item-attribute graph by extracting fine-grained attributes from item contents. ColdGPT then transfers knowledge into the item-attribute graph from various available data sources, i.e., item contents, historical purchase sequences, and review texts of the existing items, via multi-task learning. To facilitate the positive transfer, ColdGPT designs specific submodules according to the natural forms of the data sources and proposes to coordinate the multiple pre-training tasks via unified alignment-and-uniformity losses. Our pre-trained item-attribute graph acts as an implicit, extendable item embedding matrix, which enables the SCS item embeddings to be easily acquired by inserting these items into the item-attribute graph and propagating their attributes' embeddings. We carefully process three public datasets, i.e., Yelp, Amazon-home, and Amazon-sports, to guarantee the SCS setting for evaluation. Extensive experiments show that ColdGPT consistently outperforms the existing SCS recommenders by large margins and even surpasses models that are pre-trained on 75 - 224 times more, cross-domain data on two out of four datasets. Our code and pre-processed datasets for SCS evaluations are publicly available to help future SCS studies.|推荐系统在严格的冷启动(SCS)场景中受到影响，其中用户-项交互是完全不可用的。基于身份(ID)的成熟的、占主导地位的方法完全不起作用。另一方面，冷启动推荐器利用项目内容(品牌、标题、描述等)将新项目映射到现有项目。然而，现有的 SCS 推荐标准以粗粒度的方式探索项目内容，导致噪声或信息丢失。此外，除了项目内容之外的信息性数据源，如用户的购买顺序和评论文本，在很大程度上被忽略。在本研究中，我们探讨细粒度项目属性在弥补现有项目与 SCS 项目之间的差距方面所起的作用，并预先训练出一个知识化的项目属性图来进行 SCS 项目推荐。我们提出的框架 ColdGPT 通过从项目内容中提取细粒度属性，将项目-属性关系建模成项目-属性图。ColdGPT 然后通过多任务学习，将来自各种可用数据源的知识转移到项目属性图中，即项目内容、历史购买顺序和审查现有项目的文本。为了便于正向传输，ColdGPT 根据数据源的自然形式设计了具体的子模块，并提出通过统一的对齐和一致性损失来协调多个预训练任务。我们预先训练的项目属性图作为一个隐式的、可扩展的项目嵌入矩阵，通过将这些项目插入到项目属性图中并传播它们的属性嵌入，可以方便地获得 SCS 项目嵌入。我们仔细处理三个公共数据集，即 Yelp、 Amazon-home 和 Amazon-sports，以保证 SCS 设置用于评估。大量的实验表明，ColdGPT 始终优于现有的 SCS 推荐器，甚至超过预先训练75-224倍以上的模型，跨域数据在四个数据集中的两个。我们的代码和 SCS 评估的预处理数据集是公开的，以帮助未来的 SCS 研究。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Multi-task+Item-attribute+Graph+Pre-training+for+Strict+Cold-start+Item+Recommendation)|0| +|[Multi-task Item-attribute Graph Pre-training for Strict Cold-start Item Recommendation](https://doi.org/10.1145/3604915.3608806)|Yuwei Cao, Liangwei Yang, Chen Wang, Zhiwei Liu, Hao Peng, Chenyu You, Philip S. Yu|Yale Univ, New Haven, CT USA; Univ Illinois, Chicago, IL USA; Beihang Univ, Beijing, Peoples R China; Salesforce AI, Washington, DC USA|Recommendation systems suffer in the strict cold-start (SCS) scenario, where the user-item interactions are entirely unavailable. The well-established, dominating identity (ID)-based approaches completely fail to work. Cold-start recommenders, on the other hand, leverage item contents ( brand, title, descriptions, etc.) to map the new items to the existing ones. However, the existing SCS recommenders explore item contents in coarse-grained manners that introduce noise or information loss. Moreover, informative data sources other than item contents, such as users' purchase sequences and review texts, are largely ignored. In this work, we explore the role of the fine-grained item attributes in bridging the gaps between the existing and the SCS items and pre-train a knowledgeable item-attribute graph for SCS item recommendation. Our proposed framework, ColdGPT, models item-attribute correlations into an item-attribute graph by extracting fine-grained attributes from item contents. ColdGPT then transfers knowledge into the item-attribute graph from various available data sources, i.e., item contents, historical purchase sequences, and review texts of the existing items, via multi-task learning. To facilitate the positive transfer, ColdGPT designs specific submodules according to the natural forms of the data sources and proposes to coordinate the multiple pre-training tasks via unified alignment-and-uniformity losses. Our pre-trained item-attribute graph acts as an implicit, extendable item embedding matrix, which enables the SCS item embeddings to be easily acquired by inserting these items into the item-attribute graph and propagating their attributes' embeddings. We carefully process three public datasets, i.e., Yelp, Amazon-home, and Amazon-sports, to guarantee the SCS setting for evaluation. Extensive experiments show that ColdGPT consistently outperforms the existing SCS recommenders by large margins and even surpasses models that are pre-trained on 75 - 224 times more, cross-domain data on two out of four datasets. Our code and pre-processed datasets for SCS evaluations are publicly available to help future SCS studies.|推荐系统在严格的冷启动(SCS)场景中受到影响，其中用户-项交互是完全不可用的。基于身份(ID)的成熟的、占主导地位的方法完全不起作用。另一方面，冷启动推荐器利用项目内容(品牌、标题、描述等)将新项目映射到现有项目。然而，现有的 SCS 推荐标准以粗粒度的方式探索项目内容，导致噪声或信息丢失。此外，除了项目内容之外的信息性数据源，如用户的购买顺序和评论文本，在很大程度上被忽略。在本研究中，我们探讨细粒度项目属性在弥补现有项目与 SCS 项目之间的差距方面所起的作用，并预先训练出一个知识化的项目属性图来进行 SCS 项目推荐。我们提出的框架 ColdGPT 通过从项目内容中提取细粒度属性，将项目-属性关系建模成项目-属性图。ColdGPT 然后通过多任务学习，将来自各种可用数据源的知识转移到项目属性图中，即项目内容、历史购买顺序和审查现有项目的文本。为了便于正向传输，ColdGPT 根据数据源的自然形式设计了具体的子模块，并提出通过统一的对齐和一致性损失来协调多个预训练任务。我们预先训练的项目属性图作为一个隐式的、可扩展的项目嵌入矩阵，通过将这些项目插入到项目属性图中并传播它们的属性嵌入，可以方便地获得 SCS 项目嵌入。我们仔细处理三个公共数据集，即 Yelp、 Amazon-home 和 Amazon-sports，以保证 SCS 设置用于评估。大量的实验表明，ColdGPT 始终优于现有的 SCS 推荐器，甚至超过预先训练75-224倍以上的模型，跨域数据在四个数据集中的两个。我们的代码和 SCS 评估的预处理数据集是公开的，以帮助未来的 SCS 研究。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Multi-task+Item-attribute+Graph+Pre-training+for+Strict+Cold-start+Item+Recommendation)|0| |[BVAE: Behavior-aware Variational Autoencoder for Multi-Behavior Multi-Task Recommendation](https://doi.org/10.1145/3604915.3608781)|Qianzhen Rao, Yang Liu, Weike Pan, Zhong Ming|Shenzhen Univ, Coll Comp Sci & Software Engn, Shenzhen, Peoples R China|A practical recommender system should be able to handle heterogeneous behavioral feedback as inputs and has multi-task outputs ability. Although the heterogeneous one-class collaborative filtering (HOCCF) and multi-task learning (MTL) methods has been well studied, there is still a lack of targeted manner in their combined fields, i.e., Multi-behavior Multi-task Recommendation (MMR). To fill the gap, we propose a novel recommendation framework called Behavior-aware Variational AutoEncoder (BVAE), which meliorates the parameter sharing and loss minimization method with the VAE structure to address the MMR problem. Specifically, our BVAE includes behavior-aware semi-encoders and decoders, and a target feature fusion network with a global feature filtering network, while using standard deviation to weigh loss. These modules generate the behavior-aware recommended item list via constructing better semantic feature vectors for users, i.e., from dual perspectives of behavioral preference and global interaction. In addition, we optimize our BVAE in terms of adaptability and robustness, i.e., it is concise and flexible to consume any amount of behaviors with different distributions. Extensive empirical studies on two real and widely used datasets confirm the validity of our design and show that our BVAE can outperform the state-of-the-art related baseline methods under multiple evaluation metrics. The processed datasets, source code, and scripts necessary to reproduce the results can be available at https://github.com/WitnessForest/BVAE.|一个实际的推荐系统应该能够处理异质的行为反馈作为输入，并具有多任务输出的能力。虽然单一类别协同过滤(HOCCF)和多任务学习(MTL)方法已经得到了很好的研究，但是在它们的组合领域，即多行为多任务推荐(mMR) ，仍然缺乏有针对性的方式。为了填补这一空白，我们提出了一种新的推荐框架，称为行为感知变量自动编码器(BVAE) ，它改进了参数共享和损失最小化方法与 VAE 结构，以解决 MMR 问题。具体来说，我们的 BVAE 包括行为感知半编码器和解码器，目标特征融合网络与全球特征过滤网络，同时使用标准差来衡量损失。这些模块通过为用户构造更好的语义特征向量，即从行为偏好和全局交互的双重视角生成行为感知的推荐项目列表。此外，我们在适应性和健壮性方面对 BVAE 进行了优化，也就是说，使用不同分布的任何数量的行为都是简洁和灵活的。对两个实际和广泛使用的数据集的大量实证研究证实了我们的设计的有效性，并表明我们的 BVAE 能够在多个评估指标下超越最先进的相关基线方法。处理过的数据集、源代码和重现结果所需的脚本可以在 https://github.com/witnessforest/bvae 上找到。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=BVAE:+Behavior-aware+Variational+Autoencoder+for+Multi-Behavior+Multi-Task+Recommendation)|0| |[Looks Can Be Deceiving: Linking User-Item Interactions and User's Propensity Towards Multi-Objective Recommendations](https://doi.org/10.1145/3604915.3608848)|Patrik Dokoupil, Ladislav Peska, Ludovico Boratto|Charles Univ Prague, Fac Math & Phys, Prague, Czech Republic; Univ Cagliari, Cagliari, Italy|Multi-objective recommender systems (MORS) provide suggestions to users according to multiple (and possibly conflicting) goals. When a system optimizes its results at the individual-user level, it tailors them on a user's propensity towards the different objectives. Hence, the capability to understand users' fine-grained needs towards each goal is crucial. In this paper, we present the results of a user study in which we monitored the way users interacted with recommended items, as well as their self-proclaimed propensities towards relevance, novelty, and diversity objectives. The study was divided into several sessions, where users evaluated recommendation lists originating from a relevance-only single-objective baseline as well as MORS. We show that, despite MORS-based recommendations attracting fewer selections, their presence in the early sessions are crucial for users' satisfaction in the later stages. Surprisingly, the self-proclaimed willingness of users to interact with novel and diverse items is not always reflected in the recommendations they accept. Post-study questionnaires provide insights on how to deal with this matter, suggesting that MORS-based results should be accompanied by elements that allow users to understand the recommendations, so as to facilitate the choice of whether a recommendation should be accepted or not. Detailed study results are available at https://bit.ly/looks-can-be-deceiving-repo.|多目标推荐系统(MORS)根据多个(可能存在冲突的)目标向用户提供建议。当一个系统在个人用户层面优化其结果时，它会根据用户对不同目标的倾向来调整结果。因此，理解用户对每个目标的细粒度需求的能力是至关重要的。在本文中，我们介绍了一项用户研究的结果，其中我们监测用户与推荐项目的互动方式，以及他们自称的相关性，新颖性和多样性的目标的倾向。这项研究分为几个阶段，用户评估源自相关性单一目标基线的推荐名单以及 MORS。我们表明，尽管基于 MORS 的推荐吸引了较少的选择，但是它们在早期会话中的出现对于用户在后期阶段的满意度是至关重要的。令人惊讶的是，用户自称愿意与新颖和多样化的项目进行互动，但并不总是反映在他们接受的建议中。研究后调查问卷提供了关于如何处理这一问题的见解，表明基于 MORS 的结果应该伴随着使用者能够理解建议的要素，以便于选择是否接受建议。详细研究结果载于 https://bit.ly/looks-can-be-deceiving-repo。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Looks+Can+Be+Deceiving:+Linking+User-Item+Interactions+and+User's+Propensity+Towards+Multi-Objective+Recommendations)|0| |[Scaling Session-Based Transformer Recommendations using Optimized Negative Sampling and Loss Functions](https://doi.org/10.1145/3604915.3610236)|Timo Wilm, Philipp Normann, Sophie Baumeister, PaulVincent Kobow|OTTO GmbH & Co KG, Hamburg, Germany|This work introduces TRON, a scalable session-based Transformer Recommender using Optimized Negative-sampling. Motivated by the scalability and performance limitations of prevailing models such as SASRec and GRU4Rec(+), TRON integrates top-k negative sampling and listwise loss functions to enhance its recommendation accuracy. Evaluations on relevant large-scale e-commerce datasets show that TRON improves upon the recommendation quality of current methods while maintaining training speeds similar to SAS-Rec. A live A/B test yielded an 18.14% increase in click-through rate over SASRec, highlighting the potential of TRON in practical settings. For further research, we provide access to our source code(1) and an anonymized dataset(2).|本文介绍了 TRON，一个可扩展的基于会话的变压器优化负采样推荐器。由于 SASRec 和 GRU4Rec (+)等主流模型的可扩展性和性能限制，TRON 集成了 top-k 负采样和列表损失功能，以提高其推荐的准确性。对相关大规模电子商务数据集的评估表明，TRON 在保持与 SAS-Rec 类似的训练速度的同时，提高了现有方法的推荐质量。现场 A/B 测试的点进率比 SASrec 增加了18.14% ，突出了 TRON 在实际环境中的潜力。为了进一步研究，我们提供了对源代码(1)和匿名数据集(2)的访问。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Scaling+Session-Based+Transformer+Recommendations+using+Optimized+Negative+Sampling+and+Loss+Functions)|0| -|[Pairwise Intent Graph Embedding Learning for Context-Aware Recommendation](https://doi.org/10.1145/3604915.3608815)|Dugang Liu, Yuhao Wu, Weixin Li, Xiaolian Zhang, Hao Wang, Qinjuan Yang, Zhong Ming|Huawei 2012 Lab, Shenzhen, Peoples R China; Shenzhen Univ, Guangdong Lab Artificial Intelligence & Digital E, Shenzhen, Peoples R China; Shenzhen Univ, Coll Comp Sci & Software Engn, Shenzhen, Peoples R China|Although knowledge graph has shown their effectiveness in mitigating data sparsity in many recommendation tasks, they remain underutilized in context-aware recommender systems (CARS) with the specific sparsity challenges associated with the contextual features, i.e., feature sparsity and interaction sparsity. To bridge this gap, in this paper, we propose a novel pairwise intent graph embedding learning (PING) framework to efficiently integrate knowledge graphs into CARS. Specifically, our PING contains three modules: 1) a graph construction module is used to obtain a pairwise intent graph (PIG) containing nodes for users, items, entities, and enhanced intent, where enhanced intent nodes are generated by applying user intent fusion (UIF) on relational intent and contextual intent, and two sub-intents are derived from the semantic information and contextual information, respectively; 2) a pairwise intent joint graph convolution module is used to obtain the refined embeddings of all the features by executing a customized convolution strategy on PIG, where each enhanced intent node acts as a hub to efficiently propagate information among different features and between all the features and knowledge graph; 3) a recommendation module with the refined embeddings is used to replace the randomly initialized embeddings of downstream recommendation models to improve model performance. Finally, we conduct extensive experiments on three public datasets to verify the effectiveness and compatibility of our PING.|尽管知识图表显示了它们在许多推荐任务中缓解数据稀疏的有效性，但是它们在上下文感知的推荐系统(CARS)中仍然没有得到充分利用，与上下文特征相关的特定稀疏性挑战，即特征稀疏和交互稀疏。为了弥补这一差距，本文提出了一种新的成对意图嵌入学习(PING)框架，有效地将知识图集成到 CARS 中。具体来说，我们的 pING 包含三个模块: 1)一个图形构造模块用于获得包含用户、项目、实体和增强意图节点的成对意图图(pIG) ，其中增强意图节点是通过在关系意图和上下文意图上应用用户意图融合(UIF)而生成的，并且两个子意图分别来自语义信息和上下文信息;2)通过在 PIG 上执行定制的卷积策略，使用成对意图联合图卷积模块来获得所有特征的精细嵌入，其中每个增强意图节点作为一个中心，在不同特征之间以及所有特征和知识图之间有效地传播信息; 3)使用具有精细嵌入的推荐模块来代替下游推荐模型的随机初始化嵌入，以提高模型性能。最后，我们在三个公共数据集上进行了广泛的实验，以验证我们的 PING 的有效性和兼容性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Pairwise+Intent+Graph+Embedding+Learning+for+Context-Aware+Recommendation)|0| +|[Pairwise Intent Graph Embedding Learning for Context-Aware Recommendation](https://doi.org/10.1145/3604915.3608815)|Dugang Liu, Yuhao Wu, Weixin Li, Xiaolian Zhang, Hao Wang, Qinjuan Yang, Zhong Ming|Shenzhen Univ, Coll Comp Sci & Software Engn, Shenzhen, Peoples R China; Huawei 2012 Lab, Shenzhen, Peoples R China; Shenzhen Univ, Guangdong Lab Artificial Intelligence & Digital E, Shenzhen, Peoples R China|Although knowledge graph has shown their effectiveness in mitigating data sparsity in many recommendation tasks, they remain underutilized in context-aware recommender systems (CARS) with the specific sparsity challenges associated with the contextual features, i.e., feature sparsity and interaction sparsity. To bridge this gap, in this paper, we propose a novel pairwise intent graph embedding learning (PING) framework to efficiently integrate knowledge graphs into CARS. Specifically, our PING contains three modules: 1) a graph construction module is used to obtain a pairwise intent graph (PIG) containing nodes for users, items, entities, and enhanced intent, where enhanced intent nodes are generated by applying user intent fusion (UIF) on relational intent and contextual intent, and two sub-intents are derived from the semantic information and contextual information, respectively; 2) a pairwise intent joint graph convolution module is used to obtain the refined embeddings of all the features by executing a customized convolution strategy on PIG, where each enhanced intent node acts as a hub to efficiently propagate information among different features and between all the features and knowledge graph; 3) a recommendation module with the refined embeddings is used to replace the randomly initialized embeddings of downstream recommendation models to improve model performance. Finally, we conduct extensive experiments on three public datasets to verify the effectiveness and compatibility of our PING.|尽管知识图表显示了它们在许多推荐任务中缓解数据稀疏的有效性，但是它们在上下文感知的推荐系统(CARS)中仍然没有得到充分利用，与上下文特征相关的特定稀疏性挑战，即特征稀疏和交互稀疏。为了弥补这一差距，本文提出了一种新的成对意图嵌入学习(PING)框架，有效地将知识图集成到 CARS 中。具体来说，我们的 pING 包含三个模块: 1)一个图形构造模块用于获得包含用户、项目、实体和增强意图节点的成对意图图(pIG) ，其中增强意图节点是通过在关系意图和上下文意图上应用用户意图融合(UIF)而生成的，并且两个子意图分别来自语义信息和上下文信息;2)通过在 PIG 上执行定制的卷积策略，使用成对意图联合图卷积模块来获得所有特征的精细嵌入，其中每个增强意图节点作为一个中心，在不同特征之间以及所有特征和知识图之间有效地传播信息; 3)使用具有精细嵌入的推荐模块来代替下游推荐模型的随机初始化嵌入，以提高模型性能。最后，我们在三个公共数据集上进行了广泛的实验，以验证我们的 PING 的有效性和兼容性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Pairwise+Intent+Graph+Embedding+Learning+for+Context-Aware+Recommendation)|0| |[A Model-Agnostic Framework for Recommendation via Interest-aware Item Embeddings](https://doi.org/10.1145/3604915.3610660)|Amit Kumar Jaiswal, Yu Xiong||Item representation holds significant importance in recommendation systems, which encompasses domains such as news, retail, and videos. Retrieval and ranking models utilise item representation to capture the user-item relationship based on user behaviours. While existing representation learning methods primarily focus on optimising item-based mechanisms, such as attention and sequential modelling. However, these methods lack a modelling mechanism to directly reflect user interests within the learned item representations. Consequently, these methods may be less effective in capturing user interests indirectly. To address this challenge, we propose a novel Interest-aware Capsule network (IaCN) recommendation model, a model-agnostic framework that directly learns interest-oriented item representations. IaCN serves as an auxiliary task, enabling the joint learning of both item-based and interest-based representations. This framework adopts existing recommendation models without requiring substantial redesign. We evaluate the proposed approach on benchmark datasets, exploring various scenarios involving different deep neural networks, behaviour sequence lengths, and joint learning ratios of interest-oriented item representations. Experimental results demonstrate significant performance enhancements across diverse recommendation models, validating the effectiveness of our approach.|项目表示在推荐系统中具有重要意义，推荐系统包括新闻、零售和视频等领域。检索和排序模型利用项目表示来捕获基于用户行为的用户-项目关系。而现有的表征学习方法主要集中在优化项目为基础的机制，如注意力和顺序建模。然而，这些方法缺乏直接反映用户兴趣的建模机制。因此，这些方法在间接捕获用户兴趣方面可能不太有效。为了应对这一挑战，我们提出了一种新的兴趣感知胶囊网络(IaCN)推荐模型，这是一个直接学习兴趣导向的项目表示的模型无关框架。IaCN 作为辅助任务，支持基于项目和基于兴趣的表示的联合学习。该框架采用现有的推荐模型，无需重新设计。我们评估了所提出的基准数据集方法，探索了涉及不同深度神经网络、行为序列长度和兴趣导向项目表示的联合学习比率的各种场景。实验结果显示了不同推荐模型的性能显著提高，验证了我们方法的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Model-Agnostic+Framework+for+Recommendation+via+Interest-aware+Item+Embeddings)|0| -|[Gradient Matching for Categorical Data Distillation in CTR Prediction](https://doi.org/10.1145/3604915.3608769)|Cheng Wang, Jiacheng Sun, Zhenhua Dong, Ruixuan Li, Rui Zhang|Huawei Noahs Ark Lab, Shenzhen, Peoples R China; Ruizhang Info, Shenzhen, Peoples R China; Huazhong Univ Sci & Technol, Wuhan, Peoples R China|The cost of hardware and energy consumption on training a click-through rate (CTR) model is highly prohibitive. A recent promising direction for reducing such costs is data distillation with gradient matching, which aims to synthesize a small distilled dataset to guide the model to a similar parameter space as those trained on real data. However, there are two main challenges to implementing such a method in the recommendation field: (1) The categorical recommended data are high dimensional and sparse one- or multi-hot data which will block the gradient flow, causing backpropagation-based data distillation invalid. (2) The data distillation process with gradient matching is computationally expensive due to the bi-level optimization. To this end, we investigate efficient data distillation tailored for recommendation data with plenty of side information where we formulate the discrete data to the dense and continuous data format. Then, we further introduce a one-step gradient matching scheme, which performs gradient matching for only a single step to overcome the inefficient training process. The overall proposed method is called Categorical data distillation with Gradient Matching (CGM), which is capable of distilling a large dataset into a small of informative synthetic data for training CTR models from scratch. Experimental results show that our proposed method not only outperforms the state-of-the-art coreset selection and data distillation methods but also has remarkable cross-architecture performance. Moreover, we explore the application of CGM on model retraining and mitigate the effect of different random seeds on the training results.|培训点进率模型的硬件和能源消耗成本高得令人望而却步。最近一个有希望降低这种成本的方向是使用梯度匹配的数据提取，其目的是合成一个小的提取的数据集，以引导模型到一个类似的参数空间，因为这些训练的实际数据。然而，这种方法在推荐领域的实现面临两个主要挑战: (1)分类推荐数据是高维稀疏的一个或多个热点数据，会阻塞梯度流，导致基于反向传播的数据精馏失效。(2)采用梯度匹配的数据精馏过程，由于采用了双层优化算法，计算量较大。为此，我们研究了针对推荐数据的有效数据精馏，这些推荐数据具有丰富的侧信息，我们将离散数据表述为密集和连续的数据格式。然后，我们进一步引入了一个一步梯度匹配方案，该方案仅对一个步骤进行梯度匹配，以克服训练过程的低效性。提出了一种基于梯度匹配(CGM)的分类数据提取方法，该方法能够将大量的数据集提取为一小部分信息量大的综合数据，用于从头开始训练 CTR 模型。实验结果表明，该方法不仅优于现有的复位选择和数据提取方法，而且具有显著的交叉结构性能。此外，我们还探讨了 CGM 在模型再训练中的应用，以减轻不同随机种子对训练结果的影响。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Gradient+Matching+for+Categorical+Data+Distillation+in+CTR+Prediction)|0| -|[Augmented Negative Sampling for Collaborative Filtering](https://doi.org/10.1145/3604915.3608811)|Yuhan Zhao, Rui Chen, Riwei Lai, Qilong Han, Hongtao Song, Li Chen|Hong Kong Baptist Univ, Hong Kong, Peoples R China; Harbin Engn Univ, Harbin, Peoples R China|Negative sampling is essential for implicit-feedback-based collaborative filtering, which is used to constitute negative signals from massive unlabeled data to guide supervised learning. The state-of-the-art idea is to utilize hard negative samples that carry more useful information to form a better decision boundary. To balance efficiency and effectiveness, the vast majority of existing methods follow the two-pass approach, in which the first pass samples a fixed number of unobserved items by a simple static distribution and then the second pass selects the final negative items using a more sophisticated negative sampling strategy. However, selecting negative samples from the original items in a dataset is inherently restricted due to the limited available choices, and thus may not be able to contrast positive samples well. In this paper, we confirm this observation via carefully designed experiments and introduce two major limitations of existing solutions: ambiguous trap and information discrimination. Our response to such limitations is to introduce "augmented" negative samples that may not exist in the original dataset. This direction renders a substantial technical challenge because constructing unconstrained negative samples may introduce excessive noise that eventually distorts the decision boundary. To this end, we introduce a novel generic augmented negative sampling (ANS) paradigm and provide a concrete instantiation. First, we disentangle hard and easy factors of negative items. Next, we generate new candidate negative samples by augmenting only the easy factors in a regulated manner: the direction and magnitude of the augmentation are carefully calibrated. Finally, we design an advanced negative sampling strategy to identify the final augmented negative samples, which considers not only the score function used in existing methods but also a new metric called augmentation gain. Extensive experiments on real-world datasets demonstrate that our method significantly outperforms state-of-the-art baselines. Our code is publicly available at https://github.com/Asa9aoTK/ANS-Recbole.|对于基于内隐反馈的协同过滤来说，负采样是必不可少的，它用来从大量未标记的数据中构成负信号来引导监督式学习。最先进的想法是利用带有更多有用信息的硬阴性样本来形成更好的决策边界。为了平衡效率和有效性，绝大多数现有的方法遵循双通道方法，其中第一通道通过一个简单的静态分布采样一个固定数量的未观测项目，然后第二通道选择最终的负项目使用一个更复杂的负面采样策略。然而，从数据集中的原始项目中选择阴性样本本质上受到限制，因为可用的选择有限，因此可能无法很好地对比阳性样本。在本文中，我们通过精心设计的实验证实了这一观察，并介绍了现有解决方案的两个主要局限性: 模糊陷阱和信息辨别。我们对这些限制的反应是引入“增强”的负样本，这些样本可能不存在于原始数据集中。这种方向带来了巨大的技术挑战，因为构建无约束的负样本可能会引入过多的噪音，最终导致决策边界失真。为此，我们引入了一个新的通用增广负抽样(ANS)范式，并提供了一个具体的实例。首先，我们对消极项目中的难易因素进行了解析。接下来，我们通过以一种有规律的方式增加简单因子来产生新的候选阴性样本: 增加的方向和幅度被仔细校准。最后，我们设计了一个先进的负抽样策略来识别最终的增广负样本，它不仅考虑了现有方法中使用的得分函数，而且还考虑了一个新的度量称为增广增益。对真实世界数据集的大量实验表明，我们的方法明显优于最先进的基线。我们的代码可以在 https://github.com/asa9aotk/ans-recbole 上公开获取。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Augmented+Negative+Sampling+for+Collaborative+Filtering)|0| +|[Gradient Matching for Categorical Data Distillation in CTR Prediction](https://doi.org/10.1145/3604915.3608769)|Cheng Wang, Jiacheng Sun, Zhenhua Dong, Ruixuan Li, Rui Zhang|Ruizhang Info, Shenzhen, Peoples R China; Huazhong Univ Sci & Technol, Wuhan, Peoples R China; Huawei Noahs Ark Lab, Shenzhen, Peoples R China|The cost of hardware and energy consumption on training a click-through rate (CTR) model is highly prohibitive. A recent promising direction for reducing such costs is data distillation with gradient matching, which aims to synthesize a small distilled dataset to guide the model to a similar parameter space as those trained on real data. However, there are two main challenges to implementing such a method in the recommendation field: (1) The categorical recommended data are high dimensional and sparse one- or multi-hot data which will block the gradient flow, causing backpropagation-based data distillation invalid. (2) The data distillation process with gradient matching is computationally expensive due to the bi-level optimization. To this end, we investigate efficient data distillation tailored for recommendation data with plenty of side information where we formulate the discrete data to the dense and continuous data format. Then, we further introduce a one-step gradient matching scheme, which performs gradient matching for only a single step to overcome the inefficient training process. The overall proposed method is called Categorical data distillation with Gradient Matching (CGM), which is capable of distilling a large dataset into a small of informative synthetic data for training CTR models from scratch. Experimental results show that our proposed method not only outperforms the state-of-the-art coreset selection and data distillation methods but also has remarkable cross-architecture performance. Moreover, we explore the application of CGM on model retraining and mitigate the effect of different random seeds on the training results.|培训点进率模型的硬件和能源消耗成本高得令人望而却步。最近一个有希望降低这种成本的方向是使用梯度匹配的数据提取，其目的是合成一个小的提取的数据集，以引导模型到一个类似的参数空间，因为这些训练的实际数据。然而，这种方法在推荐领域的实现面临两个主要挑战: (1)分类推荐数据是高维稀疏的一个或多个热点数据，会阻塞梯度流，导致基于反向传播的数据精馏失效。(2)采用梯度匹配的数据精馏过程，由于采用了双层优化算法，计算量较大。为此，我们研究了针对推荐数据的有效数据精馏，这些推荐数据具有丰富的侧信息，我们将离散数据表述为密集和连续的数据格式。然后，我们进一步引入了一个一步梯度匹配方案，该方案仅对一个步骤进行梯度匹配，以克服训练过程的低效性。提出了一种基于梯度匹配(CGM)的分类数据提取方法，该方法能够将大量的数据集提取为一小部分信息量大的综合数据，用于从头开始训练 CTR 模型。实验结果表明，该方法不仅优于现有的复位选择和数据提取方法，而且具有显著的交叉结构性能。此外，我们还探讨了 CGM 在模型再训练中的应用，以减轻不同随机种子对训练结果的影响。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Gradient+Matching+for+Categorical+Data+Distillation+in+CTR+Prediction)|0| +|[Augmented Negative Sampling for Collaborative Filtering](https://doi.org/10.1145/3604915.3608811)|Yuhan Zhao, Rui Chen, Riwei Lai, Qilong Han, Hongtao Song, Li Chen|Harbin Engn Univ, Harbin, Peoples R China; Hong Kong Baptist Univ, Hong Kong, Peoples R China|Negative sampling is essential for implicit-feedback-based collaborative filtering, which is used to constitute negative signals from massive unlabeled data to guide supervised learning. The state-of-the-art idea is to utilize hard negative samples that carry more useful information to form a better decision boundary. To balance efficiency and effectiveness, the vast majority of existing methods follow the two-pass approach, in which the first pass samples a fixed number of unobserved items by a simple static distribution and then the second pass selects the final negative items using a more sophisticated negative sampling strategy. However, selecting negative samples from the original items in a dataset is inherently restricted due to the limited available choices, and thus may not be able to contrast positive samples well. In this paper, we confirm this observation via carefully designed experiments and introduce two major limitations of existing solutions: ambiguous trap and information discrimination. Our response to such limitations is to introduce "augmented" negative samples that may not exist in the original dataset. This direction renders a substantial technical challenge because constructing unconstrained negative samples may introduce excessive noise that eventually distorts the decision boundary. To this end, we introduce a novel generic augmented negative sampling (ANS) paradigm and provide a concrete instantiation. First, we disentangle hard and easy factors of negative items. Next, we generate new candidate negative samples by augmenting only the easy factors in a regulated manner: the direction and magnitude of the augmentation are carefully calibrated. Finally, we design an advanced negative sampling strategy to identify the final augmented negative samples, which considers not only the score function used in existing methods but also a new metric called augmentation gain. Extensive experiments on real-world datasets demonstrate that our method significantly outperforms state-of-the-art baselines. Our code is publicly available at https://github.com/Asa9aoTK/ANS-Recbole.|对于基于内隐反馈的协同过滤来说，负采样是必不可少的，它用来从大量未标记的数据中构成负信号来引导监督式学习。最先进的想法是利用带有更多有用信息的硬阴性样本来形成更好的决策边界。为了平衡效率和有效性，绝大多数现有的方法遵循双通道方法，其中第一通道通过一个简单的静态分布采样一个固定数量的未观测项目，然后第二通道选择最终的负项目使用一个更复杂的负面采样策略。然而，从数据集中的原始项目中选择阴性样本本质上受到限制，因为可用的选择有限，因此可能无法很好地对比阳性样本。在本文中，我们通过精心设计的实验证实了这一观察，并介绍了现有解决方案的两个主要局限性: 模糊陷阱和信息辨别。我们对这些限制的反应是引入“增强”的负样本，这些样本可能不存在于原始数据集中。这种方向带来了巨大的技术挑战，因为构建无约束的负样本可能会引入过多的噪音，最终导致决策边界失真。为此，我们引入了一个新的通用增广负抽样(ANS)范式，并提供了一个具体的实例。首先，我们对消极项目中的难易因素进行了解析。接下来，我们通过以一种有规律的方式增加简单因子来产生新的候选阴性样本: 增加的方向和幅度被仔细校准。最后，我们设计了一个先进的负抽样策略来识别最终的增广负样本，它不仅考虑了现有方法中使用的得分函数，而且还考虑了一个新的度量称为增广增益。对真实世界数据集的大量实验表明，我们的方法明显优于最先进的基线。我们的代码可以在 https://github.com/asa9aotk/ans-recbole 上公开获取。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Augmented+Negative+Sampling+for+Collaborative+Filtering)|0| |[LightSAGE: Graph Neural Networks for Large Scale Item Retrieval in Shopee's Advertisement Recommendation](https://doi.org/10.1145/3604915.3608863)|Dang Minh Nguyen, Chenfei Wang, Yan Shen, Yifan Zeng|SEA Grp, Shopee, Beijing, Peoples R China; SEA Grp, Shopee, Singapore, Singapore|Graph Neural Network (GNN) is the trending solution for item retrieval in recommendation problems. Most recent reports, however, focus heavily on new model architectures. This may bring some gaps when applying GNN in the industrial setup, where, besides the model, constructing the graph and handling data sparsity also play critical roles in the overall success of the project. In this work, we report how GNN is applied for large-scale e-commerce item retrieval at Shopee. We introduce our simple yet novel and impactful techniques in graph construction, modeling, and handling data skewness. Specifically, we construct high-quality item graphs by combining strong-signal user behaviors with high-precision collaborative filtering (CF) algorithm. We then develop a new GNN architecture named LightSAGE to produce high-quality items' embeddings for vector search. Finally, we design multiple strategies to handle cold-start and long-tail items, which are critical in an advertisement (ads) system. Our models bring improvement in offline evaluations, online A/B tests, and are deployed to the main traffic of Shopee's Recommendation Advertisement system.|图形神经网络(GNN)是推荐问题中项目检索的趋势解决方案。然而，最近的大多数报告主要关注于新的模型架构。这可能会带来一些差距时，GNN 在工业设置，其中，除了模型，构造图和处理数据稀疏也发挥关键作用的项目的整体成功。在这项工作中，我们报告了 GNN 是如何应用于 Shopee 的大规模电子商务项目检索。我们介绍了我们在图形构造、建模和处理数据偏态方面的简单而新颖且有影响力的技术。具体来说，我们通过结合强信号用户行为和高精度协同过滤(CF)算法来构建高质量的项目图。然后，我们开发了一个名为 LightSAGE 的新 GNN 体系结构，以生成用于向量搜索的高质量条目嵌入。最后，我们设计了多种策略来处理冷启动和长尾项目，这是一个广告系统的关键。我们的模型带来了线下评估和在线 A/B 测试的改进，并被部署到 Shopee 推荐广告系统的主要流量中。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=LightSAGE:+Graph+Neural+Networks+for+Large+Scale+Item+Retrieval+in+Shopee's+Advertisement+Recommendation)|0| |[Goal-Oriented Multi-Modal Interactive Recommendation with Verbal and Non-Verbal Relevance Feedback](https://doi.org/10.1145/3604915.3608775)|Yaxiong Wu, Craig Macdonald, Iadh Ounis|Univ Glasgow, Glasgow, Lanark, Scotland|Interactive recommendation enables users to provide verbal and non-verbal relevance feedback (such as natural-language critiques and likes/dislikes) when viewing a ranked list of recommendations (such as images of fashion products), in order to guide the recommender system towards their desired items (i.e. goals) across multiple interaction turns. Such a multi-modal interactive recommendation (MMIR) task has been successfully formulated with deep reinforcement learning (DRL) algorithms by simulating the interactions between an environment (i.e. a user) and an agent (i.e. a recommender system). However, it is typically challenging and unstable to optimise the agent to improve the recommendation quality associated with implicit learning of multi-modal representations in an end-to-end fashion in DRL. This is known as the coupling of policy optimisation and representation learning. To address this coupling issue, we propose a novel goal-oriented multi-modal interactive recommendation model (GOMMIR) that uses both verbal and non-verbal relevance feedback to effectively incorporate the users' preferences over time. Specifically, our GOMMIR model employs a multi-task learning approach to explicitly learn the multi-modal representations using a multi-modal composition network when optimising the recommendation agent. Moreover, we formulate the MMIR task using goal-oriented reinforcement learning and enhance the optimisation objective by leveraging non-verbal relevance feedback for hard negative sampling and providing extra goal-oriented rewards to effectively optimise the recommendation agent. Following previous work, we train and evaluate our GOMMIR model by using user simulators that can generate natural-language feedback about the recommendations as a surrogate for real human users. Experiments conducted on four well-known fashion datasets demonstrate that our proposed GOMMIR model yields significant improvements in comparison to the existing state-of-the-art baseline models.|交互式推荐可以让用户在浏览排名推荐列表(如时尚产品图片)时提供语言和非语言关联反馈(如自然语言评论和喜欢/不喜欢) ，以便引导推荐系统在多个互动回合中朝着他们想要的项目(即目标)前进。这样一个多模态交互推荐(MMIR)任务已经成功地通过深度强化学习(DRL)算法模拟了环境(比如用户)和代理(比如推荐系统)之间的交互。然而，在 DRL 中以端到端的方式优化代理以提高与多模态表示的隐式学习相关的推荐质量通常具有挑战性和不稳定性。这就是所谓的政策优化和表示学习的耦合。为了解决这个耦合问题，我们提出了一个新的目标导向的多模式交互式推荐模型(GOMMIR) ，它使用语言和非语言的关联反馈来有效地整合用户的喜好随着时间的推移。我们的 GOMMIR 模型采用多任务学习方法，在优化推荐代理时使用多模态组合网络显式学习多模态表示。此外，我们利用目标导向的强化学习制定 MMIR 任务，并利用非语言关联反馈进行硬性负面抽样，以及提供额外的目标导向奖励，以提高优化目标，从而有效地优化推荐代理。在以前的工作之后，我们训练和评估我们的 GOMMIR 模型，使用用户模拟器，可以生成自然语言反馈的建议作为一个替代真正的人类用户。在四个著名的时尚数据集上进行的实验表明，我们提出的 GOMMIR 模型与现有的最先进的基线模型相比，产生了显著的改进。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Goal-Oriented+Multi-Modal+Interactive+Recommendation+with+Verbal+and+Non-Verbal+Relevance+Feedback)|0| |[DREAM: Decoupled Representation via Extraction Attention Module and Supervised Contrastive Learning for Cross-Domain Sequential Recommender](https://doi.org/10.1145/3604915.3608780)|Xiaoxin Ye, Yun Li, Lina Yao|UNSW, Sch Comp Sci & Engn, Sydney, NSW, Australia; CSIROs Data61, Sydney, NSW, Australia|Cross-Domain Sequential Recommendation(CDSR) aims to generate accurate predictions for future interactions by leveraging users' cross-domain historical interactions. One major challenge of CDSR is howto jointly learn the single- and cross-domain user preferences efficiently. To enhance the target domain's performance, most existing solutions start by learning the single-domain user preferences within each domain and then transferring the acquired knowledge from the rich domain to the target domain. However, this approach ignores the inter-sequence item relationship and also limits the opportunities for target domain knowledge to enhance the rich domain performance. Moreover, it also ignores the information within the cross-domain sequence. Despite cross-domain sequences being generally noisy and hard to learn directly, they contain valuable user behavior patterns with great potential to enhance performance. Another key challenge of CDSR is data sparsity, which also exists in other recommendation system problems. In the real world, the data distribution of the recommendation system is highly skewed to the popular products, especially on the large-scale dataset with millions of users and items. One more challenge is the class imbalance problem, inherited by the sequential recommendation problem. Generally, each sample only has one positive and thousands of negative samples. To address the above problems together, an innovative Decoupled Representation via Extraction Attention Module (DREAM) is proposed for CDSR to simultaneously learn singleand cross-domain user preference via decoupled representations. A novel Supervised Contrastive Learning framework is introduced to model the inter-sequence relationship as well as address the data sparsity via data augmentations. DREAM also leverages Focal Loss to put more weight on misclassified samples to address the class-imbalance problem, with another uplift on the overall model performance. Extensive experiments had been conducted on two cross-domain recommendation datasets, demonstrating DREAM outperforms various SOTA cross-domain recommendation algorithms achieving up to a 75% uplift in Movie-Book Scenarios.|跨域序列推荐(CDSR)旨在通过利用用户的跨域历史交互产生对未来交互的准确预测。CDSR 的一个主要挑战是如何有效地联合学习单域和跨域用户偏好。为了提高目标领域的性能，大多数现有的解决方案都是从学习每个领域内的单领域用户偏好开始，然后将获得的知识从富领域转移到目标领域。然而，这种方法忽略了序列间的项目关系，同时也限制了目标领域知识提高丰富领域性能的机会。此外，它还忽略了跨域序列中的信息。尽管跨域序列通常有噪声且难以直接学习，但它们包含有价值的用户行为模式，具有提高性能的巨大潜力。CDSR 的另一个关键挑战是数据稀疏性，这也存在于其他推荐系统问题中。在现实世界中，推荐系统的数据分布高度偏向于流行产品，特别是在拥有数百万用户和项目的大规模数据集上。另一个挑战是由顺序推荐问题继承的类不平衡问题。一般来说，每个样本只有一个阳性和数千个阴性样本。针对上述问题，提出了一种新的基于抽取注意模块的解耦表示方法(DREAM) ，使 CDSR 能够通过解耦表示同时学习单域和跨域用户偏好。提出了一种新的有监督对比学习框架，通过数据增强对序列间的关系进行建模，并解决了数据稀疏问题。梦想也利用焦损更加重视错误分类的样本，以解决类不平衡的问题，与另一个提升整体模型的性能。在两个跨域推荐数据集上进行了广泛的实验，证明了 DREAM 优于各种 SOTA 跨域推荐算法，在电影图书场景中实现了高达75% 的提升。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=DREAM:+Decoupled+Representation+via+Extraction+Attention+Module+and+Supervised+Contrastive+Learning+for+Cross-Domain+Sequential+Recommender)|0| |[A Multi-view Graph Contrastive Learning Framework for Cross-Domain Sequential Recommendation](https://doi.org/10.1145/3604915.3608785)|Zitao Xu, Weike Pan, Zhong Ming|Shenzhen Univ, Coll Comp Sci & Software Engn, Shenzhen, Peoples R China|Sequential recommendation methods play an irreplaceable role in recommender systems which can capture the users' dynamic preferences from the behavior sequences. Despite their success, these works usually suffer from the sparsity problem commonly existed in real applications. Cross-domain sequential recommendation aims to alleviate this problem by introducing relatively richer source-domain data. However, most existing methods capture the users' preferences independently of each domain, which may neglect the item transition patterns across sequences from different domains, i.e., a user's interaction in one domain may influence his/her next interaction in other domains. Moreover, the data sparsity problem still exists since some items in the target and source domains are interacted with only a limited number of times. To address these issues, in this paper we propose a generic framework named multi-view graph contrastive learning (MGCL). Specifically, we adopt the contrastive mechanism in an intra-domain item representation view and an inter-domain user preference view. The former is to jointly learn the dynamic sequential information in the user sequence graph and the static collaborative information in the cross-domain global graph, while the latter is to capture the complementary information of the user's preferences from different domains. Extensive empirical studies on three real-world datasets demonstrate that our MGCL significantly outperforms the state-of-the-art methods.|序列推荐方法在推荐系统中起着不可替代的作用，它可以从用户的行为序列中获取用户的动态偏好。尽管这些作品取得了成功，但它们在实际应用中普遍存在着稀疏性问题。跨域顺序推荐旨在通过引入相对丰富的源域数据来缓解这一问题。然而，大多数现有的方法捕获用户的偏好独立于每个领域，这可能会忽略来自不同领域的序列之间的项目转换模式，也就是说，一个用户在一个领域的交互可能会影响他/她在其他领域的下一个交互。此外，由于目标域和源域中的某些项目只能进行有限次数的交互，因此数据稀疏问题仍然存在。为了解决这些问题，本文提出了一种通用的多视图图形对比学习(MGCL)框架。具体来说，我们在域内项表示视图和域间用户首选项视图中采用了对比机制。前者是联合学习用户序列图中的动态序列信息和跨域全局图中的静态协作信息，后者是从不同领域获取用户偏好的互补信息。对三个真实世界数据集的大量实证研究表明，我们的 MGCL 明显优于最先进的方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Multi-view+Graph+Contrastive+Learning+Framework+for+Cross-Domain+Sequential+Recommendation)|0| -|[STAN: Stage-Adaptive Network for Multi-Task Recommendation by Learning User Lifecycle-Based Representation](https://doi.org/10.1145/3604915.3608796)|Wanda Li, Wenhao Zheng, Xuanji Xiao, Suhang Wang|Penn State Univ, University Pk, PA 16802 USA; Shopee Co, Beijing, Peoples R China; Tsinghua Univ, Beijing, Peoples R China|Recommendation systems play a vital role in many online platforms, with their primary objective being to satisfy and retain users. As directly optimizing user retention is challenging, multiple evaluation metrics are often employed. Current methods often use multi-task learning to optimize these measures. However, they usually miss that users have personal preferences for different tasks, which can change over time. Identifying and tracking the evolution of user preferences can lead to better user retention. To address this issue, we introduce the concept of "user lifecycle," consisting of multiple stages characterized by users' varying preferences for different tasks. We propose a novel Stage-Adaptive Network (STAN) framework for modeling user lifecycle stages. STAN first identifies latent user lifecycle stages based on learned user preferences and then employs the stage representation to enhance multi-task learning performance. Our experimental results using both public and industrial datasets demonstrate that the proposed model significantly improves multi-task prediction performance compared to state-of-the-art methods, highlighting the importance of considering user lifecycle stages in recommendation systems. Online A/B testing reveals that our model outperforms the existing model, achieving a significant improvement of 3.05% in staytime per user and 0.88% in CVR. We have deployed STAN on all Shopee live-streaming recommendation services.|推荐系统在许多在线平台中发挥着至关重要的作用，其主要目标是满足和留住用户。由于直接优化用户保留是具有挑战性的，因此经常采用多种评估指标。目前的方法往往采用多任务学习来优化这些措施。然而，他们通常忽略了用户对不同任务的个人偏好，这种偏好可能随着时间的推移而改变。识别和跟踪用户偏好的演变可以更好地保留用户。为了解决这个问题，我们引入了“用户生命周期”的概念，包括多个阶段，拥有属性用户对不同任务的不同偏好。我们提出了一个新的阶段自适应网络(STAN)框架，用于建模用户生命周期阶段。STAN 首先根据学习用户的偏好识别潜在的用户生命周期阶段，然后利用阶段表示来提高多任务学习性能。我们使用公共数据集和工业数据集的实验结果表明，与最先进的方法相比，该模型显著提高了多任务预测性能，突出了在推荐系统中考虑用户生命周期阶段的重要性。在线 A/B 测试表明，我们的模型优于现有的模型，在每个用户的停留时间和 CVR 分别达到了3.05% 和0.88% 的显著改善。我们已经在所有 Shopee 直播推荐服务上部署了 STAN。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=STAN:+Stage-Adaptive+Network+for+Multi-Task+Recommendation+by+Learning+User+Lifecycle-Based+Representation)|0| +|[STAN: Stage-Adaptive Network for Multi-Task Recommendation by Learning User Lifecycle-Based Representation](https://doi.org/10.1145/3604915.3608796)|Wanda Li, Wenhao Zheng, Xuanji Xiao, Suhang Wang|Shopee Co, Beijing, Peoples R China; Penn State Univ, University Pk, PA 16802 USA; Tsinghua Univ, Beijing, Peoples R China|Recommendation systems play a vital role in many online platforms, with their primary objective being to satisfy and retain users. As directly optimizing user retention is challenging, multiple evaluation metrics are often employed. Current methods often use multi-task learning to optimize these measures. However, they usually miss that users have personal preferences for different tasks, which can change over time. Identifying and tracking the evolution of user preferences can lead to better user retention. To address this issue, we introduce the concept of "user lifecycle," consisting of multiple stages characterized by users' varying preferences for different tasks. We propose a novel Stage-Adaptive Network (STAN) framework for modeling user lifecycle stages. STAN first identifies latent user lifecycle stages based on learned user preferences and then employs the stage representation to enhance multi-task learning performance. Our experimental results using both public and industrial datasets demonstrate that the proposed model significantly improves multi-task prediction performance compared to state-of-the-art methods, highlighting the importance of considering user lifecycle stages in recommendation systems. Online A/B testing reveals that our model outperforms the existing model, achieving a significant improvement of 3.05% in staytime per user and 0.88% in CVR. We have deployed STAN on all Shopee live-streaming recommendation services.|推荐系统在许多在线平台中发挥着至关重要的作用，其主要目标是满足和留住用户。由于直接优化用户保留是具有挑战性的，因此经常采用多种评估指标。目前的方法往往采用多任务学习来优化这些措施。然而，他们通常忽略了用户对不同任务的个人偏好，这种偏好可能随着时间的推移而改变。识别和跟踪用户偏好的演变可以更好地保留用户。为了解决这个问题，我们引入了“用户生命周期”的概念，包括多个阶段，拥有属性用户对不同任务的不同偏好。我们提出了一个新的阶段自适应网络(STAN)框架，用于建模用户生命周期阶段。STAN 首先根据学习用户的偏好识别潜在的用户生命周期阶段，然后利用阶段表示来提高多任务学习性能。我们使用公共数据集和工业数据集的实验结果表明，与最先进的方法相比，该模型显著提高了多任务预测性能，突出了在推荐系统中考虑用户生命周期阶段的重要性。在线 A/B 测试表明，我们的模型优于现有的模型，在每个用户的停留时间和 CVR 分别达到了3.05% 和0.88% 的显著改善。我们已经在所有 Shopee 直播推荐服务上部署了 STAN。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=STAN:+Stage-Adaptive+Network+for+Multi-Task+Recommendation+by+Learning+User+Lifecycle-Based+Representation)|0| |[Bootstrapped Personalized Popularity for Cold Start Recommender Systems](https://doi.org/10.1145/3604915.3608820)|Iason Chaimalas, Duncan Martin Walker, Edoardo Gruppi, Benjamin Richard Clark, Laura Toni|British Broadcasting Corp, London, England; UCL, London, England|Recommender Systems are severely hampered by the well-known Cold Start problem, identified by the lack of information on new items and users. This has led to research efforts focused on data imputation and augmentation models as predominantly data preprocessing strategies, yet their improvement of cold-user performance is largely indirect and often comes at the price of a reduction in accuracy for warmer users. To address these limitations, we propose Bootstrapped Personalized Popularity (B2P), a novel framework that improves performance for cold users (directly) and cold items (implicitly) via popularity models personalized with item metadata. B2P is scalable to very large datasets and directly addresses the Cold Start problem, so it can complement existing Cold Start strategies. Experiments on a real-world dataset from the BBC iPlayer and a public dataset demonstrate that B2P (1) significantly improves cold-user performance, (2) boosts warm-user performance for bootstrapped models by lowering their training sparsity, and (3) improves total recommendation accuracy at a competitive diversity level relative to existing high-performing Collaborative Filtering models. We demonstrate that B2P is a powerful and scalable framework for strongly cold datasets.|推荐系统受到众所周知的冷启动问题的严重阻碍，因为缺乏关于新项目和用户的信息。这导致研究工作将重点放在数据估算和增强模型上，将其作为主要的数据预处理战略，但它们对冷用户性能的改善在很大程度上是间接的，而且往往是以降低较温暖用户的准确性为代价的。为了解决这些局限性，我们提出了一种新的引导式个性化流行(Bootstrap Personalization Popular，B2P)框架，该框架通过使用项目元数据个性化的流行模型来提高冷用户(直接)和冷项目(隐式)的性能。B2P 可以扩展到非常大的数据集，并且可以直接解决冷启动问题，因此它可以补充现有的冷启动策略。在来自 BBC iPlayer 的现实数据集和一个公共数据集上的实验表明，B2P (1)显著提高了冷用户的性能，(2)通过降低训练稀疏性提高了自举模型的热用户性能，(3)相对于现有的高性能协同过滤模型，在竞争多样性水平上提高了总体推荐的准确性。我们证明了 B2P 对于强冷数据集是一个强大的、可扩展的框架。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Bootstrapped+Personalized+Popularity+for+Cold+Start+Recommender+Systems)|0| |[Co-occurrence Embedding Enhancement for Long-tail Problem in Multi-Interest Recommendation](https://doi.org/10.1145/3604915.3608835)|Yaokun Liu, Xiaowang Zhang, Minghui Zou, Zhiyong Feng|Tianjin Univ, Tianjin, Peoples R China|Multi-interest recommendation methods extract multiple interest vectors to represent the user comprehensively. Despite their success in the matching stage, previous works overlook the long-tail problem. This results in the model excelling at suggesting head items, while the performance for tail items, which make up more than 70% of all items, remains suboptimal. Hence, enhancing the tail item recommendation capability holds great potential for improving the performance of the multi-interest model. Through experimental analysis, we reveal that the insufficient context for embedding learning is the reason behind the under-performance of tail items. Meanwhile, we face two challenges in addressing this issue: the absence of supplementary item features and the need to maintain head item performance. To tackle these challenges, we propose a CoLT module (Co-occurrence embedding enhancement for Long-Tail problem) that replaces the embedding layer of existing multi-interest frameworks. By linking co-occurring items to establish "assistance relationships", CoLT aggregates information from relevant head items into tail item embeddings and enables joint gradient updates. Experiments on three datasets show our method outperforms SOTA models by 21.86% Recall@50 and improves the Recall@50 of tail items by 14.62% on average.|多兴趣推荐方法提取多个兴趣向量来全面表示用户。尽管他们的成功在匹配阶段，以往的作品忽视了长尾问题。这导致模型在建议首项方面表现出色，而尾项(占所有项目的70% 以上)的表现仍然不理想。因此，提高尾部项目推荐能力对于提高多兴趣模型的性能具有很大的潜力。通过实验分析，我们发现嵌入式学习环境的不足是尾项表现不佳的原因。与此同时，我们在解决这一问题时面临两个挑战: 缺乏补充项目特征和需要保持首项目的性能。为了应对这些挑战，我们提出了一个 CoLT 模块(针对 Long-Tail 问题的共现嵌入增强) ，以取代现有多重兴趣框架的嵌入层。CoLT 通过链接共现项目来建立“协助关系”，将相关头项目的信息聚合成尾项目嵌入，并实现联合梯度更新。在三个数据集上的实验表明，该方法比 SOTA 模型提高了21.86% 的召回率@50，平均提高了14.62% 的尾项召回率@50。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Co-occurrence+Embedding+Enhancement+for+Long-tail+Problem+in+Multi-Interest+Recommendation)|0| -|[On the Consistency of Average Embeddings for Item Recommendation](https://doi.org/10.1145/3604915.3608837)|Walid Bendada, Guillaume SalhaGalvan, Romain Hennequin, Thomas Bouabça, Tristan Cazenave|Univ Paris 09, Deezer, Paris, Dauphine, France; Univ Paris 09, LAMSADE, PSL, Paris, Dauphine, France; Deezer, Paris, France|A prevalent practice in recommender systems consists of averaging item embeddings to represent users or higher-level concepts in the same embedding space. This paper investigates the relevance of such a practice. For this purpose, we propose an expected precision score, designed to measure the consistency of an average embedding relative to the items used for its construction. We subsequently analyze the mathematical expression of this score in a theoretical setting with specific assumptions, as well as its empirical behavior on real-world data from music streaming services. Our results emphasize that real-world averages are less consistent for recommendation, which paves the way for future research to better align real-world embeddings with assumptions from our theoretical setting.|在推荐系统中，一个流行的实践是在相同的嵌入空间中平均条目嵌入来表示用户或者更高层次的概念。本文探讨了这种实践的相关性。为此，我们提出了一个期望精度得分，设计用来衡量平均嵌入的一致性相对于其构造所使用的项目。随后，我们分析了这个乐谱的数学表达式在一个理论设置与具体的假设，以及它的经验行为对现实世界的数据从音乐流媒体服务。我们的研究结果强调，现实世界的平均值与推荐的一致性较差，这为以后的研究更好地将现实世界的嵌入与我们理论设置的假设结合起来铺平了道路。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=On+the+Consistency+of+Average+Embeddings+for+Item+Recommendation)|0| +|[On the Consistency of Average Embeddings for Item Recommendation](https://doi.org/10.1145/3604915.3608837)|Walid Bendada, Guillaume SalhaGalvan, Romain Hennequin, Thomas Bouabça, Tristan Cazenave|Deezer, Paris, France; Univ Paris 09, Deezer, Paris, Dauphine, France; Univ Paris 09, LAMSADE, PSL, Paris, Dauphine, France|A prevalent practice in recommender systems consists of averaging item embeddings to represent users or higher-level concepts in the same embedding space. This paper investigates the relevance of such a practice. For this purpose, we propose an expected precision score, designed to measure the consistency of an average embedding relative to the items used for its construction. We subsequently analyze the mathematical expression of this score in a theoretical setting with specific assumptions, as well as its empirical behavior on real-world data from music streaming services. Our results emphasize that real-world averages are less consistent for recommendation, which paves the way for future research to better align real-world embeddings with assumptions from our theoretical setting.|在推荐系统中，一个流行的实践是在相同的嵌入空间中平均条目嵌入来表示用户或者更高层次的概念。本文探讨了这种实践的相关性。为此，我们提出了一个期望精度得分，设计用来衡量平均嵌入的一致性相对于其构造所使用的项目。随后，我们分析了这个乐谱的数学表达式在一个理论设置与具体的假设，以及它的经验行为对现实世界的数据从音乐流媒体服务。我们的研究结果强调，现实世界的平均值与推荐的一致性较差，这为以后的研究更好地将现实世界的嵌入与我们理论设置的假设结合起来铺平了道路。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=On+the+Consistency+of+Average+Embeddings+for+Item+Recommendation)|0| |[Progressive Horizon Learning: Adaptive Long Term Optimization for Personalized Recommendation](https://doi.org/10.1145/3604915.3608852)|Congrui Yi, David Zumwalt, Zijian Ni, Shreya Chakrabarti|Amazon, Seattle, WA 98109 USA|As E-commerce and subscription services scale, personalized recommender systems are often needed to further drive long term business growth in acquisition, engagement, and retention of customers. However, long-term metrics associated with these goals can require several months to mature. Additionally, deep personalization also demands a large volume of training data that take a long time to collect. These factors incur substantial lead time for training a model to optimize a long-term metric. Before such model is deployed, a recommender system has to rely on a simple policy (e.g. random) to collect customer feedback data for training, inflicting high opportunity cost and delaying optimization of the target metric. Besides, as customer preferences can shift over time, a large temporal gap between inputs and outcome poses a high risk of data staleness and suboptimal learning. Existing approaches involve various compromises. For instance, contextual bandits often optimize short-term surrogate metrics with simple model structure, which can be suboptimal in the long run, while Reinforcement Learning approaches rely on an abundance of historical data for offline training, which essentially means long lead time before deployment. To address these problems, we propose Progressive Horizon Learning Recommender (PHLRec), a personalized model that can progressively learn metric patterns and adaptively evolve from short- to long-term optimization over time. Through simulations and real data experiments, we demonstrated that PHLRec outperforms competing methods, achieving optimality in both deployment speed and long-term metric performances.|随着电子商务和订阅服务规模的扩大，个性化推荐系统往往需要进一步推动长期业务增长的收购，参与和保留客户。然而，与这些目标相关的长期指标可能需要几个月才能成熟。此外，深度个性化还需要大量的培训数据，这些数据需要很长时间才能收集到。这些因素导致培训模型以优化长期度量的大量提前时间。在采用这种模式之前，推荐系统必须依靠一个简单的策略(例如随机)来收集客户反馈数据，以便进行培训，造成较高的机会成本，并延迟目标指标的优化。此外，由于客户偏好可以随时间变化，输入和输出之间的时间差距很大，造成数据过时和次优学习的高风险。现有的方法涉及各种折衷方案。例如，上下文强盗经常使用简单的模型结构优化短期替代指标，从长远来看这可能是次优的，而强化学习方法依赖于大量的离线培训历史数据，这基本上意味着部署前的长时间准备时间。为了解决这些问题，我们提出了渐进式视野学习推荐器(PHLRec) ，这是一个个性化的模型，它可以逐步学习度量模式，并随着时间的推移自适应地从短期优化演变为长期优化。通过仿真和实际数据实验，我们证明了 PHLRec 优于竞争方法，在部署速度和长期指标性能方面都达到了最优。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Progressive+Horizon+Learning:+Adaptive+Long+Term+Optimization+for+Personalized+Recommendation)|0| |[From Research to Production: Towards Scalable and Sustainable Neural Recommendation Models on Commodity CPU Hardware](https://doi.org/10.1145/3604915.3610249)|Anshumali Shrivastava, Vihan Lakshman, Tharun Medini, Nicholas Meisburger, Joshua Engels, David Torres Ramos, Benito Geordie, Pratik Pranav, Shubh Gupta, Yashwanth Adunukota, Siddharth Jain|ThirdAI Corp, Houston, TX 77027 USA|In the last decade, large-scale deep learning has fundamentally transformed industrial recommendation systems. However, this revolutionary technology remains prohibitively expensive due to the need for costly and scarce specialized hardware, such as Graphics Processing Units (GPUs), to train and serve models. In this talk, we share our multi-year journey at ThirdAI in developing efficient neural recommendation models that can be trained and deployed on commodity CPU machines without the need for costly accelerators like GPUs. In particular, we discuss the limitations of the current GPU-based ecosystem in machine learning, why recommendation systems are amenable to the strengths of CPU devices, and present results from our efforts to translate years of academic research into a deployable system that fundamentally shifts the economics of training and operating large-scale machine learning models.|在过去的十年里，大规模的深度学习从根本上改变了行业推荐系统。然而，这种革命性的技术仍然昂贵，由于需要昂贵和稀缺的专业硬件，如图形处理单元(GPU) ，培训和服务模型。在这个演讲中，我们分享了我们在 ThirdAI 多年的发展有效的神经推荐模型的旅程，这些模型可以在普通的 CPU 机器上训练和部署，而不需要像 GPU 这样昂贵的加速器。特别是，我们讨论了当前基于 GPU 的机器学习生态系统的局限性，为什么推荐系统适合 CPU 设备的优势，并介绍了我们将多年的学术研究转化为可部署系统的努力的结果，从根本上改变了培训和操作大规模机器学习模型的经济学。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=From+Research+to+Production:+Towards+Scalable+and+Sustainable+Neural+Recommendation+Models+on+Commodity+CPU+Hardware)|0| |[User-Centric Conversational Recommendation: Adapting the Need of User with Large Language Models](https://doi.org/10.1145/3604915.3608885)|Gangyi Zhang|Univ Sci & Technol China, Hefei, Peoples R China|Conversational recommender systems (CRS) promise to provide a more natural user experience for exploring and discovering items of interest through ongoing conversation. However, effectively modeling and adapting to users' complex and changing preferences remains challenging. This research develops user-centric methods that focus on understanding and adapting to users throughout conversations to provide the most helpful recommendations. First, a graph-based Conversational Path Reasoning (CPR) framework is proposed that represents dialogs as interactive reasoning over a knowledge graph to capture nuanced user interests and explain recommendations. To further enhance relationship modeling, graph neural networks are incorporated for improved representation learning. Next, to address uncertainty in user needs, the Vague Preference Multi-round Conversational Recommendation (VPMCR) scenario and matching Adaptive Vague Preference Policy Learning (AVPPL) solution are presented using reinforcement learning to tailor recommendations to evolving preferences. Finally, opportunities to leverage large language models are discussed to further advance user experiences via advanced user modeling, policy learning, and response generation. Overall, this research focuses on designing conversational recommender systems that continuously understand and adapt to users' ambiguous, complex and changing needs during natural conversations.|会话推荐系统(CRS)承诺通过正在进行的会话为探索和发现感兴趣的项目提供更自然的用户体验。然而，有效地建模和适应用户复杂和不断变化的偏好仍然具有挑战性。这项研究开发了以用户为中心的方法，重点是在整个对话过程中理解和适应用户，以提供最有用的建议。首先，提出了一个基于图的会话路径推理(CPR)框架，该框架将对话表示为知识图上的交互式推理，以获取细微差别的用户兴趣并解释推荐。为了进一步加强关系建模，引入了图神经网络来改进表示学习。接下来，为了解决用户需求中的不确定性，我们提出了 Vague 偏好多轮对话推荐(vPMCR)场景和匹配的自适应 Vague 偏好政策学习(AVPPL)解决方案，使用强化学习来调整推荐以适应不断变化的偏好。最后，讨论了利用大型语言模型的机会，通过高级用户建模、策略学习和响应生成来进一步提升用户体验。总的来说，本研究的重点是设计会话推荐系统，不断理解和适应用户在自然会话过程中模糊、复杂和不断变化的需求。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=User-Centric+Conversational+Recommendation:+Adapting+the+Need+of+User+with+Large+Language+Models)|0| |[Integrating Offline Reinforcement Learning with Transformers for Sequential Recommendation](https://doi.org/10.1145/3604915.3610641)|Xumei Xi, Yuke Zhao, Quan Liu, Liwen Ouyang, Yang Wu||We consider the problem of sequential recommendation, where the current recommendation is made based on past interactions. This recommendation task requires efficient processing of the sequential data and aims to provide recommendations that maximize the long-term reward. To this end, we train a farsighted recommender by using an offline RL algorithm with the policy network in our model architecture that has been initialized from a pre-trained transformer model. The pre-trained model leverages the superb ability of the transformer to process sequential information. Compared to prior works that rely on online interaction via simulation, we focus on implementing a fully offline RL framework that is able to converge in a fast and stable way. Through extensive experiments on public datasets, we show that our method is robust across various recommendation regimes, including e-commerce and movie suggestions. Compared to state-of-the-art supervised learning algorithms, our algorithm yields recommendations of higher quality, demonstrating the clear advantage of combining RL and transformers.|我们考虑顺序推荐的问题，其中当前的推荐是基于过去的交互作用。这项推荐任务需要有效处理顺序数据，目的是提供建议，最大限度地实现长期回报。为此，我们在模型结构中使用策略网络的离线 RL 算法来训练一个有远见的推荐器，该模型已经从一个预先训练好的变压器模型初始化。预先训练的模型利用变压器处理顺序信息的卓越能力。与以往依赖于通过仿真进行在线交互的工作相比，我们侧重于实现一个完全离线的 RL 框架，该框架能够快速、稳定地收敛。通过在公共数据集上的大量实验，我们发现我们的方法在不同的推荐机制下都是稳健的，包括电子商务和电影推荐。与最先进的监督式学习算法相比，我们的算法产生了更高质量的推荐，展示了结合 RL 和变压器的明显优势。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Integrating+Offline+Reinforcement+Learning+with+Transformers+for+Sequential+Recommendation)|0| |[Fast and Examination-agnostic Reciprocal Recommendation in Matching Markets](https://doi.org/10.1145/3604915.3608774)|Yoji Tomita, Riku Togashi, Yuriko Hashizume, Naoto Ohsaka|CyberAgent Inc, Tokyo, Japan|In matching markets such as job posting and online dating platforms, the recommender system plays a critical role in the success of the platform. Unlike standard recommender systems that suggest items to users, reciprocal recommender systems (RRSs) that suggest other users must take into account the mutual interests of users. In addition, ensuring that recommendation opportunities do not disproportionately favor popular users is essential for the total number of matches and for fairness among users. Existing recommendation methods in matching markets, however, face computational challenges on real-world scale platforms and depend on specific examination functions in the position-based model (PBM). In this paper, we introduce the reciprocal recommendation method based on the matching with transferable utility (TU matching) model in the context of ranking recommendations in matching markets, and propose a faster and examination-agnostic algorithm. Furthermore, we evaluate our approach on experiments with synthetic data and real-world data from an online dating platform in Japan. Our method performs better than or as well as existing methods in terms of the total number of matches and works well even in relatively large datasets for which one existing method does not work.|在招聘和在线约会平台等匹配市场方面，推荐系统对平台的成功起着关键作用。不像标准的推荐系统，建议项目给用户，互惠推荐系统(RRS) ，建议其他用户必须考虑到用户的共同利益。此外，确保推荐机会不会不成比例地偏袒流行用户，对于匹配的总数和用户之间的公平性至关重要。然而，现有的匹配市场推荐方法在实际规模的平台上面临着计算上的挑战，并且依赖于基于位置模型(PBM)中的特定检验函数。本文在匹配市场推荐排序的背景下，介绍了基于匹配可转移效用(TU 匹配)模型的互惠推荐方法，并提出了一种更快、考试无关的算法。此外，我们评估了我们的实验方法与合成数据和真实世界的数据从一个在线约会平台在日本。就匹配总数而言，我们的方法比现有方法执行得更好，甚至在一个现有方法不适用的相对较大的数据集中也能很好地工作。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Fast+and+Examination-agnostic+Reciprocal+Recommendation+in+Matching+Markets)|0| |[✨ Going Beyond Local: Global Graph-Enhanced Personalized News Recommendations](https://doi.org/10.1145/3604915.3608801)|Boming Yang, Dairui Liu, Toyotaro Suzumura, Ruihai Dong, Irene Li||||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=✨+Going+Beyond+Local:+Global+Graph-Enhanced+Personalized+News+Recommendations)|0| -|[Distribution-based Learnable Filters with Side Information for Sequential Recommendation](https://doi.org/10.1145/3604915.3608782)|Haibo Liu, Zhixiang Deng, Liang Wang, Jinjia Peng, Shi Feng|Northeastern Univ, Sch Comp Sci & Engn, Shenyang, Peoples R China; HeBei Univ, Sch Cyber Secur & Comp, Baoding, Peoples R China|Sequential Recommendation aims to predict the next item by mining out the dynamic preference from user previous interactions. However, most methods represent each item as a single fixed vector, which is incapable of capturing the uncertainty of item-item transitions that result from time-dependent and multifarious interests of users. Besides, they struggle to effectively exploit side information that helps to better express user preferences. Finally, the noise in user's access sequence, which is due to accidental clicks, can interfere with the next item prediction and lead to lower recommendation performance. To deal with these issues, we propose DLFS-Rec, a simple and novel model that combines Distribution-based Learnable Filters with Side information for sequential Recommendation. Specifically, items and their side information are represented by stochastic Gaussian distribution, which is described by mean and covariance embeddings, and then the corresponding embeddings are fused to generate a final representation for each item. To attenuate noise, stacked learnable filter layers are applied to smooth the fused embeddings. Extensive experiments on four public real-world datasets demonstrate the superiority of the proposed model over state-of-the-art baselines, especially on cold start users and items. Codes are available at https://github.com/zxiang30/DLFS-Rec.|序贯推荐的目的是通过挖掘用户之前交互中的动态偏好来预测下一个项目。然而，大多数方法将每个项目表示为一个单一的固定向量，不能捕捉由于时间依赖性和用户兴趣的多样性而产生的项目-项目转换的不确定性。此外，他们努力有效地利用有助于更好地表达用户偏好的副信息。最后，用户访问序列中由于偶然点击而产生的噪声会干扰下一个项目的预测，从而降低推荐性能。为了解决这些问题，我们提出了 DLFS-Rec 模型，这是一个简单而新颖的模型，它将基于分布的可学习过滤器和侧信息结合起来用于顺序推荐。具体来说，项目和它们的侧面信息用随机正态分布表示，这种表示用均值和协方差嵌入来描述，然后对相应的嵌入进行融合，为每个项目生成最终的表示。为了抑制噪声，采用叠加的可学习滤波层来平滑融合嵌入。在四个公共真实世界数据集上的大量实验表明，该模型优于最先进的基线，特别是在冷启动用户和项目上。密码可在 https://github.com/zxiang30/dlfs-rec 索取。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Distribution-based+Learnable+Filters+with+Side+Information+for+Sequential+Recommendation)|0| +|[Distribution-based Learnable Filters with Side Information for Sequential Recommendation](https://doi.org/10.1145/3604915.3608782)|Haibo Liu, Zhixiang Deng, Liang Wang, Jinjia Peng, Shi Feng|HeBei Univ, Sch Cyber Secur & Comp, Baoding, Peoples R China; Northeastern Univ, Sch Comp Sci & Engn, Shenyang, Peoples R China|Sequential Recommendation aims to predict the next item by mining out the dynamic preference from user previous interactions. However, most methods represent each item as a single fixed vector, which is incapable of capturing the uncertainty of item-item transitions that result from time-dependent and multifarious interests of users. Besides, they struggle to effectively exploit side information that helps to better express user preferences. Finally, the noise in user's access sequence, which is due to accidental clicks, can interfere with the next item prediction and lead to lower recommendation performance. To deal with these issues, we propose DLFS-Rec, a simple and novel model that combines Distribution-based Learnable Filters with Side information for sequential Recommendation. Specifically, items and their side information are represented by stochastic Gaussian distribution, which is described by mean and covariance embeddings, and then the corresponding embeddings are fused to generate a final representation for each item. To attenuate noise, stacked learnable filter layers are applied to smooth the fused embeddings. Extensive experiments on four public real-world datasets demonstrate the superiority of the proposed model over state-of-the-art baselines, especially on cold start users and items. Codes are available at https://github.com/zxiang30/DLFS-Rec.|序贯推荐的目的是通过挖掘用户之前交互中的动态偏好来预测下一个项目。然而，大多数方法将每个项目表示为一个单一的固定向量，不能捕捉由于时间依赖性和用户兴趣的多样性而产生的项目-项目转换的不确定性。此外，他们努力有效地利用有助于更好地表达用户偏好的副信息。最后，用户访问序列中由于偶然点击而产生的噪声会干扰下一个项目的预测，从而降低推荐性能。为了解决这些问题，我们提出了 DLFS-Rec 模型，这是一个简单而新颖的模型，它将基于分布的可学习过滤器和侧信息结合起来用于顺序推荐。具体来说，项目和它们的侧面信息用随机正态分布表示，这种表示用均值和协方差嵌入来描述，然后对相应的嵌入进行融合，为每个项目生成最终的表示。为了抑制噪声，采用叠加的可学习滤波层来平滑融合嵌入。在四个公共真实世界数据集上的大量实验表明，该模型优于最先进的基线，特别是在冷启动用户和项目上。密码可在 https://github.com/zxiang30/dlfs-rec 索取。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Distribution-based+Learnable+Filters+with+Side+Information+for+Sequential+Recommendation)|0| |[Reciprocal Sequential Recommendation](https://doi.org/10.1145/3604915.3608798)|Bowen Zheng, Yupeng Hou, Wayne Xin Zhao, Yang Song, Hengshu Zhu|BOSS Zhipin, Beijing, Peoples R China; Renmin Univ China, Gaoling Sch Artificial Intelligence, Beijing, Peoples R China|Reciprocal recommender system (RRS), considering a two-way matching between two parties, has been widely applied in online platforms like online dating and recruitment. Existing RRS models mainly capture static user preferences, which have neglected the evolving user tastes and the dynamic matching relation between the two parties. Although dynamic user modeling has been well-studied in sequential recommender systems, existing solutions are developed in a user-oriented manner. Therefore, it is non-trivial to adapt sequential recommendation algorithms to reciprocal recommendation. In this paper, we formulate RRS as a distinctive sequence matching task, and further propose a new approach ReSeq for RRS, which is short for Reciprocal Sequential recommendation. To capture dual-perspective matching, we propose to learn fine-grained sequence similarities by co-attention mechanism across different time steps. Further, to improve the inference efficiency, we introduce the self-distillation technique to distill knowledge from the fine-grained matching module into the more efficient student module. In the deployment stage, only the efficient student module is used, greatly speeding up the similarity computation. Extensive experiments on five real-world datasets from two scenarios demonstrate the effectiveness and efficiency of the proposed method. Our code is available at https://github.com/RUCAIBox/ReSeq/.|考虑双方双向匹配的互惠推荐系统已被广泛应用于在线约会和招聘等在线平台。现有的 RRS 模型主要捕捉静态用户偏好，忽略了用户偏好的变化和双方之间的动态匹配关系。尽管动态用户建模已经在顺序推荐系统中得到了很好的研究，但是现有的解决方案都是以面向用户的方式开发的。因此，将顺序推荐算法应用到互惠推荐中具有重要意义。本文将 RRS 作为一个独特的序列匹配任务，并进一步提出了一种新的 RRS 方法 ReSeq，即相互序列推荐的简称。为了捕获双视角匹配，我们提出了通过跨不同时间步长的共注意机制来学习细粒度序列相似性。进一步，为了提高推理效率，我们引入了自蒸馏技术，从细粒度匹配模块中提取知识到更高效的学生模块中。在部署阶段，只使用了有效的学生模块，大大加快了相似度计算的速度。通过对来自两个场景的五个真实世界数据集的大量实验，证明了该方法的有效性和高效性。我们的代码可以在 https://github.com/rucaibox/reseq/找到。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Reciprocal+Sequential+Recommendation)|0| -|[STRec: Sparse Transformer for Sequential Recommendations](https://doi.org/10.1145/3604915.3608779)|Chengxi Li, Yejing Wang, Qidong Liu, Xiangyu Zhao, Wanyu Wang, Yiqi Wang, Lixin Zou, Wenqi Fan, Qing Li|Michigan State Univ, E Lansing, MI 48824 USA; City Univ Hong Kong, Hong Kong, Peoples R China; Wuhan Univ, Wuhan, Peoples R China; Hong Kong Polytech Univ, Hong Kong, Peoples R China|With the rapid evolution of transformer architectures, researchers are exploring their application in sequential recommender systems (SRSs) and presenting promising performance on SRS tasks compared with former SRS models. However, most existing transformer-based SRS frameworks retain the vanilla attention mechanism, which calculates the attention scores between all item-item pairs. With this setting, redundant item interactions can harm the model performance and consume much computation time and memory. In this paper, we identify the sparse attention phenomenon in transformer-based SRS models and propose Sparse Transformer for sequential Recommendation tasks (STRec) to achieve the efficient computation and improved performance. Specifically, we replace self-attention with cross-attention, making the model concentrate on the most relevant item interactions. To determine these necessary interactions, we design a novel sampling strategy to detect relevant items based on temporal information. Extensive experimental results validate the effectiveness of STRec, which achieves the state-of-the-art accuracy while reducing 54% inference time and 70% memory cost. We also provide massive extended experiments to further investigate the property of our framework.|随着变压器结构的快速发展，研究人员正在探索其在顺序推荐系统(SRS)中的应用，并与以往的 SRS 模型相比，在 SRS 任务中表现出了良好的性能。然而，大多数现有的基于转换器的 SRS 框架保留了普通的注意机制，它计算所有项目-项目对之间的注意得分。通过这种设置，冗余的项目交互会损害模型的性能，并消耗大量的计算时间和内存。针对基于变压器的 SRS 模型中存在的稀疏注意现象，提出了针对序贯推荐任务的稀疏变压器算法(STRec) ，以提高计算效率和性能。具体来说，我们用交叉注意代替自我注意，使模型集中于最相关的项目交互。为了确定这些必要的交互作用，我们设计了一种新的基于时间信息的抽样策略来检测相关项目。大量的实验结果验证了 STRec 算法的有效性，该算法在减少54% 的推理时间和70% 的内存开销的同时，达到了最先进的精度。我们还提供了大量的扩展实验，以进一步研究我们的框架的性质。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=STRec:+Sparse+Transformer+for+Sequential+Recommendations)|0| -|[Deep Situation-Aware Interaction Network for Click-Through Rate Prediction](https://doi.org/10.1145/3604915.3608793)|Yimin Lv, Shuli Wang, Beihong Jin, Yisong Yu, Yapeng Zhang, Jian Dong, Yongkang Wang, Xingxing Wang, Dong Wang|Univ Chinese Acad Sci, Chinese Acad Sci, Inst Software, Beijing, Peoples R China; Meituan, Beijing, Peoples R China|User behavior sequence modeling plays a significant role in Click-Through Rate (CTR) prediction on e-commerce platforms. Except for the interacted items, user behaviors contain rich interaction information, such as the behavior type, time, location, etc. However, so far, the information related to user behaviors has not yet been fully exploited. In the paper, we propose the concept of a situation and situational features for distinguishing interaction behaviors and then design a CTR model named Deep Situation-Aware Interaction Network (DSAIN). DSAIN first adopts the reparameterization trick to reduce noise in the original user behavior sequences. Then it learns the embeddings of situational features by feature embedding parameterization and tri-directional correlation fusion. Finally, it obtains the embedding of behavior sequence via heterogeneous situation aggregation. We conduct extensive offline experiments on three real-world datasets. Experimental results demonstrate the superiority of the proposed DSAIN model. More importantly, DSAIN has increased the CTR by 2.70%, the CPM by 2.62%, and the GMV by 2.16% in the online A/B test. Now, DSAIN has been deployed on the Meituan food delivery platform and serves the main traffic of the Meituan takeout app. Our source code is available at https://github.com/W-void/DSAIN.|用户行为序列建模在电子商务平台的点进率预测中扮演着重要的角色。除了交互项，用户行为还包含丰富的交互信息，如行为类型、时间、地点等。然而，到目前为止，与用户行为相关的信息还没有被充分利用。提出了区分交互行为的情境和情境特征的概念，并设计了一个名为深度情境感知交互网络(DSAIN)的 CTR 模型。DSAIN 首先采用重新参数化技巧来降低原始用户行为序列中的噪声。然后通过特征嵌入参量化和三向相关融合学习情景特征的嵌入。最后，通过异构情景聚合得到行为序列的嵌入。我们在三个真实世界的数据集上进行了大量的离线实验。实验结果表明了所提出的 DSAIN 模型的优越性。更重要的是，在线 A/B 测试中，DSAIN 使 CTR 提高了2.70% ，CPM 提高了2.62% ，GMV 提高了2.16% 。现在，DSAIN 已经部署在美团外卖平台上，为美团外卖应用程序的主要流量提供服务。我们的源代码可以在 https://github.com/w-void/dsain 找到。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Deep+Situation-Aware+Interaction+Network+for+Click-Through+Rate+Prediction)|0| +|[STRec: Sparse Transformer for Sequential Recommendations](https://doi.org/10.1145/3604915.3608779)|Chengxi Li, Yejing Wang, Qidong Liu, Xiangyu Zhao, Wanyu Wang, Yiqi Wang, Lixin Zou, Wenqi Fan, Qing Li|Hong Kong Polytech Univ, Hong Kong, Peoples R China; City Univ Hong Kong, Hong Kong, Peoples R China; Wuhan Univ, Wuhan, Peoples R China; Michigan State Univ, E Lansing, MI 48824 USA|With the rapid evolution of transformer architectures, researchers are exploring their application in sequential recommender systems (SRSs) and presenting promising performance on SRS tasks compared with former SRS models. However, most existing transformer-based SRS frameworks retain the vanilla attention mechanism, which calculates the attention scores between all item-item pairs. With this setting, redundant item interactions can harm the model performance and consume much computation time and memory. In this paper, we identify the sparse attention phenomenon in transformer-based SRS models and propose Sparse Transformer for sequential Recommendation tasks (STRec) to achieve the efficient computation and improved performance. Specifically, we replace self-attention with cross-attention, making the model concentrate on the most relevant item interactions. To determine these necessary interactions, we design a novel sampling strategy to detect relevant items based on temporal information. Extensive experimental results validate the effectiveness of STRec, which achieves the state-of-the-art accuracy while reducing 54% inference time and 70% memory cost. We also provide massive extended experiments to further investigate the property of our framework.|随着变压器结构的快速发展，研究人员正在探索其在顺序推荐系统(SRS)中的应用，并与以往的 SRS 模型相比，在 SRS 任务中表现出了良好的性能。然而，大多数现有的基于转换器的 SRS 框架保留了普通的注意机制，它计算所有项目-项目对之间的注意得分。通过这种设置，冗余的项目交互会损害模型的性能，并消耗大量的计算时间和内存。针对基于变压器的 SRS 模型中存在的稀疏注意现象，提出了针对序贯推荐任务的稀疏变压器算法(STRec) ，以提高计算效率和性能。具体来说，我们用交叉注意代替自我注意，使模型集中于最相关的项目交互。为了确定这些必要的交互作用，我们设计了一种新的基于时间信息的抽样策略来检测相关项目。大量的实验结果验证了 STRec 算法的有效性，该算法在减少54% 的推理时间和70% 的内存开销的同时，达到了最先进的精度。我们还提供了大量的扩展实验，以进一步研究我们的框架的性质。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=STRec:+Sparse+Transformer+for+Sequential+Recommendations)|0| +|[Deep Situation-Aware Interaction Network for Click-Through Rate Prediction](https://doi.org/10.1145/3604915.3608793)|Yimin Lv, Shuli Wang, Beihong Jin, Yisong Yu, Yapeng Zhang, Jian Dong, Yongkang Wang, Xingxing Wang, Dong Wang|Meituan, Beijing, Peoples R China; Univ Chinese Acad Sci, Chinese Acad Sci, Inst Software, Beijing, Peoples R China|User behavior sequence modeling plays a significant role in Click-Through Rate (CTR) prediction on e-commerce platforms. Except for the interacted items, user behaviors contain rich interaction information, such as the behavior type, time, location, etc. However, so far, the information related to user behaviors has not yet been fully exploited. In the paper, we propose the concept of a situation and situational features for distinguishing interaction behaviors and then design a CTR model named Deep Situation-Aware Interaction Network (DSAIN). DSAIN first adopts the reparameterization trick to reduce noise in the original user behavior sequences. Then it learns the embeddings of situational features by feature embedding parameterization and tri-directional correlation fusion. Finally, it obtains the embedding of behavior sequence via heterogeneous situation aggregation. We conduct extensive offline experiments on three real-world datasets. Experimental results demonstrate the superiority of the proposed DSAIN model. More importantly, DSAIN has increased the CTR by 2.70%, the CPM by 2.62%, and the GMV by 2.16% in the online A/B test. Now, DSAIN has been deployed on the Meituan food delivery platform and serves the main traffic of the Meituan takeout app. Our source code is available at https://github.com/W-void/DSAIN.|用户行为序列建模在电子商务平台的点进率预测中扮演着重要的角色。除了交互项，用户行为还包含丰富的交互信息，如行为类型、时间、地点等。然而，到目前为止，与用户行为相关的信息还没有被充分利用。提出了区分交互行为的情境和情境特征的概念，并设计了一个名为深度情境感知交互网络(DSAIN)的 CTR 模型。DSAIN 首先采用重新参数化技巧来降低原始用户行为序列中的噪声。然后通过特征嵌入参量化和三向相关融合学习情景特征的嵌入。最后，通过异构情景聚合得到行为序列的嵌入。我们在三个真实世界的数据集上进行了大量的离线实验。实验结果表明了所提出的 DSAIN 模型的优越性。更重要的是，在线 A/B 测试中，DSAIN 使 CTR 提高了2.70% ，CPM 提高了2.62% ，GMV 提高了2.16% 。现在，DSAIN 已经部署在美团外卖平台上，为美团外卖应用程序的主要流量提供服务。我们的源代码可以在 https://github.com/w-void/dsain 找到。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Deep+Situation-Aware+Interaction+Network+for+Click-Through+Rate+Prediction)|0| |[Equivariant Contrastive Learning for Sequential Recommendation](https://doi.org/10.1145/3604915.3608786)|Peilin Zhou, Jingqi Gao, Yueqi Xie, Qichen Ye, Yining Hua, Jaeboum Kim, Shoujin Wang, Sunghun Kim||||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Equivariant+Contrastive+Learning+for+Sequential+Recommendation)|0| |[Task Aware Feature Extraction Framework for Sequential Dependence Multi-Task Learning](https://doi.org/10.1145/3604915.3608772)|Xuewen Tao, Mingming Ha, Qiongxu Ma, Hongwei Cheng, Wenfang Lin, Xiaobo Guo, Linxun Cheng, Bing Han||||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Task+Aware+Feature+Extraction+Framework+for+Sequential+Dependence+Multi-Task+Learning)|0| |[AutoOpt: Automatic Hyperparameter Scheduling and Optimization for Deep Click-through Rate Prediction](https://doi.org/10.1145/3604915.3608800)|Yujun Li, Xing Tang, Bo Chen, Yimin Huang, Ruiming Tang, Zhenguo Li||||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=AutoOpt:+Automatic+Hyperparameter+Scheduling+and+Optimization+for+Deep+Click-through+Rate+Prediction)|0| diff --git a/papers/sigir/sigir2022.md b/papers/sigir/sigir2022.md index 3e31035a..99dc71aa 100644 --- a/papers/sigir/sigir2022.md +++ b/papers/sigir/sigir2022.md @@ -2,34 +2,34 @@ |论文|作者|组织|摘要|翻译|代码|引用数| |---|---|---|---|---|---|---| -|[Hypergraph Contrastive Collaborative Filtering](https://doi.org/10.1145/3477495.3532058)|Lianghao Xia, Chao Huang, Yong Xu, Jiashu Zhao, Dawei Yin, Jimmy X. Huang|Baidu, Beijing, China; University of Hong Kong, Hong Kong, Hong Kong; Wilfrid Laurier University, Waterloo, Canada; York University, Toronto, Canada; South China University Of Technology, Guangzhou, China|Collaborative Filtering (CF) has emerged as fundamental paradigms for parameterizing users and items into latent representation space, with their correlative patterns from interaction data. Among various CF techniques, the development of GNN-based recommender systems, e.g., PinSage and LightGCN, has offered the state-of-the-art performance. However, two key challenges have not been well explored in existing solutions: i) The over-smoothing effect with deeper graph-based CF architecture, may cause the indistinguishable user representations and degradation of recommendation results. ii) The supervision signals (i.e., user-item interactions) are usually scarce and skewed distributed in reality, which limits the representation power of CF paradigms. To tackle these challenges, we propose a new self-supervised recommendation framework Hypergraph Contrastive Collaborative Filtering (HCCF) to jointly capture local and global collaborative relations with a hypergraph-enhanced cross-view contrastive learning architecture. In particular, the designed hypergraph structure learning enhances the discrimination ability of GNN-based CF paradigm, in comprehensively capturing the complex high-order dependencies among users. Additionally, our HCCF model effectively integrates the hypergraph structure encoding with self-supervised learning to reinforce the representation quality of recommender systems, based on the hypergraph self-discrimination. Extensive experiments on three benchmark datasets demonstrate the superiority of our model over various state-of-the-art recommendation methods, and the robustness against sparse user interaction data. The implementation codes are available at https://github.com/akaxlh/HCCF.|协同过滤(CF)已经成为将用户和项目参数化为潜在表征空间的基本范例，其相关模式来自交互数据。在各种 CF 技术中，基于 GNN 的推荐系统(如 PinSage 和 LightGCN)的开发提供了最先进的性能。然而，在现有的解决方案中，有两个关键的挑战还没有得到很好的探索: i)基于更深层次的基于图的 CF 架构的过度平滑效应，可能导致难以区分的用户表示和推荐结果的退化。监督信号(即用户-项目交互)在现实生活中往往是稀缺的、偏态分布的，这限制了 CF 范式的表示能力。为了应对这些挑战，我们提出了一个新的自我监督推荐框架 Hypergraph 对比度协同过滤(hCCF) ，通过一个超图增强的跨视图对比度学习架构，联合捕捉本地和全球的协作关系。特别是，所设计的超图结构学习提高了基于 GNN 的 CF 范式的识别能力，能够全面捕获用户之间复杂的高阶依赖关系。此外，我们的 HCCF 模型有效地将超图结构编码与自监督学习相结合，以增强基于超图自辨识的推荐系统的表示质量。在三个基准数据集上的大量实验表明，该模型优于各种最新的推荐方法，并且对稀疏用户交互数据具有鲁棒性。实施守则可于 https://github.com/akaxlh/hccf 索取。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Hypergraph+Contrastive+Collaborative+Filtering)|22| -|[Are Graph Augmentations Necessary?: Simple Graph Contrastive Learning for Recommendation](https://doi.org/10.1145/3477495.3531937)|Junliang Yu, Hongzhi Yin, Xin Xia, Tong Chen, Lizhen Cui, Quoc Viet Hung Nguyen|Shandong University, Jinan, China; The University of Queensland, Brisbane, QLD, Australia; Griffith University, Gold Coast, Australia|Contrastive learning (CL) recently has spurred a fruitful line of research in the field of recommendation, since its ability to extract self-supervised signals from the raw data is well-aligned with recommender systems' needs for tackling the data sparsity issue. A typical pipeline of CL-based recommendation models is first augmenting the user-item bipartite graph with structure perturbations, and then maximizing the node representation consistency between different graph augmentations. Although this paradigm turns out to be effective, what underlies the performance gains is still a mystery. In this paper, we first experimentally disclose that, in CL-based recommendation models, CL operates by learning more uniform user/item representations that can implicitly mitigate the popularity bias. Meanwhile, we reveal that the graph augmentations, which used to be considered necessary, just play a trivial role. Based on this finding, we propose a simple CL method which discards the graph augmentations and instead adds uniform noises to the embedding space for creating contrastive views. A comprehensive experimental study on three benchmark datasets demonstrates that, though it appears strikingly simple, the proposed method can smoothly adjust the uniformity of learned representations and has distinct advantages over its graph augmentation-based counterparts in terms of recommendation accuracy and training efficiency. The code is released at https://github.com/Coder-Yu/QRec.|对比学习(CL)最近在推荐领域激发了一系列富有成效的研究，因为它从原始数据中提取自我监督信号的能力与推荐系统解决数据稀疏问题的需求非常一致。基于 CL 的推荐模型的一个典型流水线是首先用结构扰动对用户项二分图进行扩展，然后使不同图扩展之间的节点表示一致性最大化。尽管这个范例被证明是有效的，但是性能提升的基础是什么仍然是一个谜。在本文中，我们首先通过实验揭示了在基于 CL 的推荐模型中，CL 是通过学习更加统一的用户/项目表示来实现的，这种表示可以隐含地减少流行偏差。同时，我们发现，图的增广，过去被认为是必要的，只是起到一个微不足道的作用。基于这一发现，我们提出了一种简单的 CL 方法，该方法抛弃了图的增广，而是在嵌入空间中加入均匀的噪声来创建对比视图。通过对三个基准数据集的全面实验研究表明，该方法虽然看似简单，但能够平滑地调整学习表示的一致性，在推荐精度和训练效率方面明显优于基于图增强的推荐方法。密码在 https://github.com/coder-yu/qrec 发布。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Are+Graph+Augmentations+Necessary?:+Simple+Graph+Contrastive+Learning+for+Recommendation)|20| -|[From Distillation to Hard Negative Sampling: Making Sparse Neural IR Models More Effective](https://doi.org/10.1145/3477495.3531857)|Thibault Formal, Carlos Lassance, Benjamin Piwowarski, Stéphane Clinchant|Naver Labs Europe / Sorbonne Université, ISIR, Meylan, France; Naver Labs Europe, Meylan, France; Sorbonne Université, ISIR / CNRS, Paris, France|Neural retrievers based on dense representations combined with Approximate Nearest Neighbors search have recently received a lot of attention, owing their success to distillation and/or better sampling of examples for training -- while still relying on the same backbone architecture. In the meantime, sparse representation learning fueled by traditional inverted indexing techniques has seen a growing interest, inheriting from desirable IR priors such as explicit lexical matching. While some architectural variants have been proposed, a lesser effort has been put in the training of such models. In this work, we build on SPLADE -- a sparse expansion-based retriever -- and show to which extent it is able to benefit from the same training improvements as dense models, by studying the effect of distillation, hard-negative mining as well as the Pre-trained Language Model initialization. We furthermore study the link between effectiveness and efficiency, on in-domain and zero-shot settings, leading to state-of-the-art results in both scenarios for sufficiently expressive models.|基于密集表示结合近似最近邻搜索的神经检索器最近受到了很多关注，因为它们的成功归功于提取和/或更好的训练样本采样——同时仍然依赖于相同的骨干架构。与此同时，传统的反向索引技术所激发的稀疏表示学习受到了越来越多的关注，它继承了外显词汇匹配等可取的信息检索先验。虽然已经提出了一些体系结构变体，但在这类模型的培训方面投入的努力较少。在这项工作中，我们建立在 SPLADE ——一个基于稀疏扩展的检索器——的基础上，通过研究蒸馏、硬负面挖掘以及预训练语言模型初始化的效果，展示了它在多大程度上能够从与密集模型相同的训练改进中受益。我们进一步研究了效率和效果之间的联系，在领域内和零射击设置，导致国家的最先进的结果，在两种情况下充分表达模型。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=From+Distillation+to+Hard+Negative+Sampling:+Making+Sparse+Neural+IR+Models+More+Effective)|10| -|[Multi-level Cross-view Contrastive Learning for Knowledge-aware Recommender System](https://doi.org/10.1145/3477495.3532025)|Ding Zou, Wei Wei, XianLing Mao, Ziyang Wang, Minghui Qiu, Feida Zhu, Xin Cao|Alibaba Group, Hangzhou, China; The University of New South Wales, Sydney, NSW, Australia; Huazhong University of Science and Technology, Wuhan, China; Singapore Management University, Singapore, Singapore; Beijing Institute of Technology, Beijing, China|Knowledge graph (KG) plays an increasingly important role in recommender systems. Recently, graph neural networks (GNNs) based model has gradually become the theme of knowledge-aware recommendation (KGR). However, there is a natural deficiency for GNN-based KGR models, that is, the sparse supervised signal problem, which may make their actual performance drop to some extent. Inspired by the recent success of contrastive learning in mining supervised signals from data itself, in this paper, we focus on exploring the contrastive learning in KG-aware recommendation and propose a novel multi-level cross-view contrastive learning mechanism, named MCCLK. Different from traditional contrastive learning methods which generate two graph views by uniform data augmentation schemes such as corruption or dropping, we comprehensively consider three different graph views for KG-aware recommendation, including global-level structural view, local-level collaborative and semantic views. Specifically, we consider the user-item graph as a collaborative view, the item-entity graph as a semantic view, and the user-item-entity graph as a structural view. MCCLK hence performs contrastive learning across three views on both local and global levels, mining comprehensive graph feature and structure information in a self-supervised manner. Besides, in semantic view, a k-Nearest-Neighbor (k NN) item-item semantic graph construction module is proposed, to capture the important item-item semantic relation which is usually ignored by previous work. Extensive experiments conducted on three benchmark datasets show the superior performance of our proposed method over the state-of-the-arts. The implementations are available at: https://github.com/CCIIPLab/MCCLK.|知识图在推荐系统中起着越来越重要的作用。近年来，基于图神经网络(GNN)的知识推荐模型逐渐成为知识感知推荐(KGR)的主题。然而，基于 GNN 的 KGR 模型存在一个自然的缺陷，即稀疏监督信号问题，这可能使其实际性能有所下降。受近年来对比学习在数据挖掘中的成功启发，本文重点研究了 KG 推荐中的对比学习，并提出了一种新的多层次跨视图对比学习机制 MCCLK。与传统的对比学习方法不同，传统的对比学习方法通过统一的数据增强方案(如腐败或丢弃)生成两个图形视图，我们综合考虑了三种不同的 KG 感知推荐图形视图，包括全局层面的结构视图、局部层面的协作视图和语义视图。具体来说，我们将用户项目图视为协作视图，将项目实体图视为语义视图，将用户项目实体图视为结构视图。因此，MCCLK 在局部和全局层面上对三种视图进行对比学习，以自监督的方式挖掘综合图形特征和结构信息。此外，在语义视图中，提出了一个 k 近邻(k NN)项目-项目语义图的构造模块，以捕获以往工作中经常被忽略的重要项目-项目语义关系。在三个基准数据集上进行的大量实验表明，我们提出的方法的性能优于目前的技术水平。有关实施方案可参阅以下 https://github.com/cciiplab/mcclk :。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Multi-level+Cross-view+Contrastive+Learning+for+Knowledge-aware+Recommender+System)|10| +|[Hypergraph Contrastive Collaborative Filtering](https://doi.org/10.1145/3477495.3532058)|Lianghao Xia, Chao Huang, Yong Xu, Jiashu Zhao, Dawei Yin, Jimmy X. Huang|Wilfrid Laurier University, Waterloo, Canada; University of Hong Kong, Hong Kong, Hong Kong; York University, Toronto, Canada; South China University Of Technology, Guangzhou, China; Baidu, Beijing, China|Collaborative Filtering (CF) has emerged as fundamental paradigms for parameterizing users and items into latent representation space, with their correlative patterns from interaction data. Among various CF techniques, the development of GNN-based recommender systems, e.g., PinSage and LightGCN, has offered the state-of-the-art performance. However, two key challenges have not been well explored in existing solutions: i) The over-smoothing effect with deeper graph-based CF architecture, may cause the indistinguishable user representations and degradation of recommendation results. ii) The supervision signals (i.e., user-item interactions) are usually scarce and skewed distributed in reality, which limits the representation power of CF paradigms. To tackle these challenges, we propose a new self-supervised recommendation framework Hypergraph Contrastive Collaborative Filtering (HCCF) to jointly capture local and global collaborative relations with a hypergraph-enhanced cross-view contrastive learning architecture. In particular, the designed hypergraph structure learning enhances the discrimination ability of GNN-based CF paradigm, in comprehensively capturing the complex high-order dependencies among users. Additionally, our HCCF model effectively integrates the hypergraph structure encoding with self-supervised learning to reinforce the representation quality of recommender systems, based on the hypergraph self-discrimination. Extensive experiments on three benchmark datasets demonstrate the superiority of our model over various state-of-the-art recommendation methods, and the robustness against sparse user interaction data. The implementation codes are available at https://github.com/akaxlh/HCCF.|协同过滤(CF)已经成为将用户和项目参数化为潜在表征空间的基本范例，其相关模式来自交互数据。在各种 CF 技术中，基于 GNN 的推荐系统(如 PinSage 和 LightGCN)的开发提供了最先进的性能。然而，在现有的解决方案中，有两个关键的挑战还没有得到很好的探索: i)基于更深层次的基于图的 CF 架构的过度平滑效应，可能导致难以区分的用户表示和推荐结果的退化。监督信号(即用户-项目交互)在现实生活中往往是稀缺的、偏态分布的，这限制了 CF 范式的表示能力。为了应对这些挑战，我们提出了一个新的自我监督推荐框架 Hypergraph 对比度协同过滤(hCCF) ，通过一个超图增强的跨视图对比度学习架构，联合捕捉本地和全球的协作关系。特别是，所设计的超图结构学习提高了基于 GNN 的 CF 范式的识别能力，能够全面捕获用户之间复杂的高阶依赖关系。此外，我们的 HCCF 模型有效地将超图结构编码与自监督学习相结合，以增强基于超图自辨识的推荐系统的表示质量。在三个基准数据集上的大量实验表明，该模型优于各种最新的推荐方法，并且对稀疏用户交互数据具有鲁棒性。实施守则可于 https://github.com/akaxlh/hccf 索取。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Hypergraph+Contrastive+Collaborative+Filtering)|22| +|[Are Graph Augmentations Necessary?: Simple Graph Contrastive Learning for Recommendation](https://doi.org/10.1145/3477495.3531937)|Junliang Yu, Hongzhi Yin, Xin Xia, Tong Chen, Lizhen Cui, Quoc Viet Hung Nguyen|The University of Queensland, Brisbane, QLD, Australia; Griffith University, Gold Coast, Australia; Shandong University, Jinan, China|Contrastive learning (CL) recently has spurred a fruitful line of research in the field of recommendation, since its ability to extract self-supervised signals from the raw data is well-aligned with recommender systems' needs for tackling the data sparsity issue. A typical pipeline of CL-based recommendation models is first augmenting the user-item bipartite graph with structure perturbations, and then maximizing the node representation consistency between different graph augmentations. Although this paradigm turns out to be effective, what underlies the performance gains is still a mystery. In this paper, we first experimentally disclose that, in CL-based recommendation models, CL operates by learning more uniform user/item representations that can implicitly mitigate the popularity bias. Meanwhile, we reveal that the graph augmentations, which used to be considered necessary, just play a trivial role. Based on this finding, we propose a simple CL method which discards the graph augmentations and instead adds uniform noises to the embedding space for creating contrastive views. A comprehensive experimental study on three benchmark datasets demonstrates that, though it appears strikingly simple, the proposed method can smoothly adjust the uniformity of learned representations and has distinct advantages over its graph augmentation-based counterparts in terms of recommendation accuracy and training efficiency. The code is released at https://github.com/Coder-Yu/QRec.|对比学习(CL)最近在推荐领域激发了一系列富有成效的研究，因为它从原始数据中提取自我监督信号的能力与推荐系统解决数据稀疏问题的需求非常一致。基于 CL 的推荐模型的一个典型流水线是首先用结构扰动对用户项二分图进行扩展，然后使不同图扩展之间的节点表示一致性最大化。尽管这个范例被证明是有效的，但是性能提升的基础是什么仍然是一个谜。在本文中，我们首先通过实验揭示了在基于 CL 的推荐模型中，CL 是通过学习更加统一的用户/项目表示来实现的，这种表示可以隐含地减少流行偏差。同时，我们发现，图的增广，过去被认为是必要的，只是起到一个微不足道的作用。基于这一发现，我们提出了一种简单的 CL 方法，该方法抛弃了图的增广，而是在嵌入空间中加入均匀的噪声来创建对比视图。通过对三个基准数据集的全面实验研究表明，该方法虽然看似简单，但能够平滑地调整学习表示的一致性，在推荐精度和训练效率方面明显优于基于图增强的推荐方法。密码在 https://github.com/coder-yu/qrec 发布。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Are+Graph+Augmentations+Necessary?:+Simple+Graph+Contrastive+Learning+for+Recommendation)|20| +|[From Distillation to Hard Negative Sampling: Making Sparse Neural IR Models More Effective](https://doi.org/10.1145/3477495.3531857)|Thibault Formal, Carlos Lassance, Benjamin Piwowarski, Stéphane Clinchant|Sorbonne Université, ISIR / CNRS, Paris, France; Naver Labs Europe / Sorbonne Université, ISIR, Meylan, France; Naver Labs Europe, Meylan, France|Neural retrievers based on dense representations combined with Approximate Nearest Neighbors search have recently received a lot of attention, owing their success to distillation and/or better sampling of examples for training -- while still relying on the same backbone architecture. In the meantime, sparse representation learning fueled by traditional inverted indexing techniques has seen a growing interest, inheriting from desirable IR priors such as explicit lexical matching. While some architectural variants have been proposed, a lesser effort has been put in the training of such models. In this work, we build on SPLADE -- a sparse expansion-based retriever -- and show to which extent it is able to benefit from the same training improvements as dense models, by studying the effect of distillation, hard-negative mining as well as the Pre-trained Language Model initialization. We furthermore study the link between effectiveness and efficiency, on in-domain and zero-shot settings, leading to state-of-the-art results in both scenarios for sufficiently expressive models.|基于密集表示结合近似最近邻搜索的神经检索器最近受到了很多关注，因为它们的成功归功于提取和/或更好的训练样本采样——同时仍然依赖于相同的骨干架构。与此同时，传统的反向索引技术所激发的稀疏表示学习受到了越来越多的关注，它继承了外显词汇匹配等可取的信息检索先验。虽然已经提出了一些体系结构变体，但在这类模型的培训方面投入的努力较少。在这项工作中，我们建立在 SPLADE ——一个基于稀疏扩展的检索器——的基础上，通过研究蒸馏、硬负面挖掘以及预训练语言模型初始化的效果，展示了它在多大程度上能够从与密集模型相同的训练改进中受益。我们进一步研究了效率和效果之间的联系，在领域内和零射击设置，导致国家的最先进的结果，在两种情况下充分表达模型。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=From+Distillation+to+Hard+Negative+Sampling:+Making+Sparse+Neural+IR+Models+More+Effective)|10| +|[Multi-level Cross-view Contrastive Learning for Knowledge-aware Recommender System](https://doi.org/10.1145/3477495.3532025)|Ding Zou, Wei Wei, XianLing Mao, Ziyang Wang, Minghui Qiu, Feida Zhu, Xin Cao|Huazhong University of Science and Technology, Wuhan, China; Singapore Management University, Singapore, Singapore; The University of New South Wales, Sydney, NSW, Australia; Beijing Institute of Technology, Beijing, China; Alibaba Group, Hangzhou, China|Knowledge graph (KG) plays an increasingly important role in recommender systems. Recently, graph neural networks (GNNs) based model has gradually become the theme of knowledge-aware recommendation (KGR). However, there is a natural deficiency for GNN-based KGR models, that is, the sparse supervised signal problem, which may make their actual performance drop to some extent. Inspired by the recent success of contrastive learning in mining supervised signals from data itself, in this paper, we focus on exploring the contrastive learning in KG-aware recommendation and propose a novel multi-level cross-view contrastive learning mechanism, named MCCLK. Different from traditional contrastive learning methods which generate two graph views by uniform data augmentation schemes such as corruption or dropping, we comprehensively consider three different graph views for KG-aware recommendation, including global-level structural view, local-level collaborative and semantic views. Specifically, we consider the user-item graph as a collaborative view, the item-entity graph as a semantic view, and the user-item-entity graph as a structural view. MCCLK hence performs contrastive learning across three views on both local and global levels, mining comprehensive graph feature and structure information in a self-supervised manner. Besides, in semantic view, a k-Nearest-Neighbor (k NN) item-item semantic graph construction module is proposed, to capture the important item-item semantic relation which is usually ignored by previous work. Extensive experiments conducted on three benchmark datasets show the superior performance of our proposed method over the state-of-the-arts. The implementations are available at: https://github.com/CCIIPLab/MCCLK.|知识图在推荐系统中起着越来越重要的作用。近年来，基于图神经网络(GNN)的知识推荐模型逐渐成为知识感知推荐(KGR)的主题。然而，基于 GNN 的 KGR 模型存在一个自然的缺陷，即稀疏监督信号问题，这可能使其实际性能有所下降。受近年来对比学习在数据挖掘中的成功启发，本文重点研究了 KG 推荐中的对比学习，并提出了一种新的多层次跨视图对比学习机制 MCCLK。与传统的对比学习方法不同，传统的对比学习方法通过统一的数据增强方案(如腐败或丢弃)生成两个图形视图，我们综合考虑了三种不同的 KG 感知推荐图形视图，包括全局层面的结构视图、局部层面的协作视图和语义视图。具体来说，我们将用户项目图视为协作视图，将项目实体图视为语义视图，将用户项目实体图视为结构视图。因此，MCCLK 在局部和全局层面上对三种视图进行对比学习，以自监督的方式挖掘综合图形特征和结构信息。此外，在语义视图中，提出了一个 k 近邻(k NN)项目-项目语义图的构造模块，以捕获以往工作中经常被忽略的重要项目-项目语义关系。在三个基准数据集上进行的大量实验表明，我们提出的方法的性能优于目前的技术水平。有关实施方案可参阅以下 https://github.com/cciiplab/mcclk :。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Multi-level+Cross-view+Contrastive+Learning+for+Knowledge-aware+Recommender+System)|10| |[Knowledge Graph Contrastive Learning for Recommendation](https://doi.org/10.1145/3477495.3532009)|Yuhao Yang, Chao Huang, Lianghao Xia, Chenliang Li|University of Hong Kong, Hong Kong, China; Wuhan University, Wuhan, China; University of Hong Kong, Hong Kong, Hong Kong|Knowledge Graphs (KGs) have been utilized as useful side information to improve recommendation quality. In those recommender systems, knowledge graph information often contains fruitful facts and inherent semantic relatedness among items. However, the success of such methods relies on the high quality knowledge graphs, and may not learn quality representations with two challenges: i) The long-tail distribution of entities results in sparse supervision signals for KG-enhanced item representation; ii) Real-world knowledge graphs are often noisy and contain topic-irrelevant connections between items and entities. Such KG sparsity and noise make the item-entity dependent relations deviate from reflecting their true characteristics, which significantly amplifies the noise effect and hinders the accurate representation of user's preference. To fill this research gap, we design a general Knowledge Graph Contrastive Learning framework (KGCL) that alleviates the information noise for knowledge graph-enhanced recommender systems. Specifically, we propose a knowledge graph augmentation schema to suppress KG noise in information aggregation, and derive more robust knowledge-aware representations for items. In addition, we exploit additional supervision signals from the KG augmentation process to guide a cross-view contrastive learning paradigm, giving a greater role to unbiased user-item interactions in gradient descent and further suppressing the noise. Extensive experiments on three public datasets demonstrate the consistent superiority of our KGCL over state-of-the-art techniques. KGCL also achieves strong performance in recommendation scenarios with sparse user-item interactions, long-tail and noisy KG entities. Our implementation codes are available at https://github.com/yuh-yang/KGCL-SIGIR22.|知识图(KG)已经被用作提高推荐质量的有用的辅助信息。在这些推荐系统中，知识图信息往往包含丰富的事实和项目之间固有的语义关系。然而，这种方法的成功依赖于高质量的知识图，并且可能不会学习质量表示法，这有两个挑战: i)实体的长尾分布导致 KG 增强的项目表示的稀疏监督信号; ii)真实世界的知识图通常是噪音的，并且包含项目和实体之间的主题不相关的连接。这种 KG 稀疏性和噪声使得项目-实体依赖关系偏离了真实特征，显著放大了噪声效应，阻碍了用户偏好的准确表达。为了填补这一研究空白，我们设计了一个通用的知识图对比学习框架(KGCL) ，以减轻知识图增强的推荐系统的信息噪声。具体地说，我们提出了一个知识图增强模式来抑制信息聚合中的 KG 噪声，并且得到了更加健壮的知识感知项表示。此外，我们利用来自 KG 增强过程的额外监督信号来指导横向对比学习范式，让无偏见的用户项目交互在梯度下降法中发挥更大的作用，并进一步抑制噪音。在三个公共数据集上的大量实验表明，我们的 KGCL 相对于最先进的技术具有一致的优越性。KGCL 还可以在用户-项目交互稀疏、 KG 实体长尾和噪声较大的推荐场景中获得强大的性能。我们的执行守则可在 https://github.com/yuh-yang/kgcl-sigir22索取。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Knowledge+Graph+Contrastive+Learning+for+Recommendation)|10| -|[Meta-Knowledge Transfer for Inductive Knowledge Graph Embedding](https://doi.org/10.1145/3477495.3531757)|Mingyang Chen, Wen Zhang, Yushan Zhu, Hongting Zhou, Zonggang Yuan, Changliang Xu, Huajun Chen|State Key Laboratory of Media Convergence Production Technology and Systems, Beijing, China; Huawei Technologies Co., Ltd., Nanjing, China; Zhejiang University, Hangzhou, China; Zhejiang University & Alibaba-Zhejiang University Joint Institute of Frontier Technologies, Hangzhou, China|Knowledge graphs (KGs) consisting of a large number of triples have become widespread recently, and many knowledge graph embedding (KGE) methods are proposed to embed entities and relations of a KG into continuous vector spaces. Such embedding methods simplify the operations of conducting various in-KG tasks (e.g., link prediction) and out-of-KG tasks (e.g., question answering). They can be viewed as general solutions for representing KGs. However, existing KGE methods are not applicable to inductive settings, where a model trained on source KGs will be tested on target KGs with entities unseen during model training. Existing works focusing on KGs in inductive settings can only solve the inductive relation prediction task. They can not handle other out-of-KG tasks as general as KGE methods since they don't produce embeddings for entities. In this paper, to achieve inductive knowledge graph embedding, we propose a model MorsE, which does not learn embeddings for entities but learns transferable meta-knowledge that can be used to produce entity embeddings. Such meta-knowledge is modeled by entity-independent modules and learned by meta-learning. Experimental results show that our model significantly outperforms corresponding baselines for in-KG and out-of-KG tasks in inductive settings.|由大量三元组构成的知识图(KG)近年来得到了广泛的应用，为了将 KG 的实体和关系嵌入到连续向量空间中，提出了许多知识图嵌入(KGE)方法。这种嵌入方法简化了进行各种内 KG 任务(例如，链接预测)和外 KG 任务(例如，问题回答)的操作。他们可以被视为代表幼稚园的一般解决方案。然而，现有的 KGE 方法不适用于归纳环境，在这种情况下，以源幼儿园为培训对象的模型将在模型培训过程中看不到实体的目标幼儿园中进行测试。现有的针对归纳环境下幼儿园的工作只能解决归纳关系预测任务。它们不能像 KGE 方法那样处理其他超出 KG 的任务，因为它们不为实体生成嵌入。为了实现归纳知识图的嵌入，本文提出了一种 MorsE 模型，该模型不学习实体的嵌入，而是学习可转移的元知识，从而生成实体的嵌入。这种元知识由独立于实体的模块建模，并通过元学习来学习。实验结果表明，在感应环境下，我们的模型对于在 KG 内和在 KG 外的任务的性能明显优于相应的基线。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Meta-Knowledge+Transfer+for+Inductive+Knowledge+Graph+Embedding)|8| -|[CPFair: Personalized Consumer and Producer Fairness Re-ranking for Recommender Systems](https://doi.org/10.1145/3477495.3531959)|Mohammadmehdi Naghiaei, Hossein A. Rahmani, Yashar Deldjoo|Polytechnic University of Bari, Bari, Italy; University College London, London, United Kingdom; University of Southern California, California, CA, USA|Recently, there has been a rising awareness that when machine learning (ML) algorithms are used to automate choices, they may treat/affect individuals unfairly, with legal, ethical, or economic consequences. Recommender systems are prominent examples of such ML systems that assist users in making high-stakes judgments. A common trend in the previous literature research on fairness in recommender systems is that the majority of works treat user and item fairness concerns separately, ignoring the fact that recommender systems operate in a two-sided marketplace. In this work, we present an optimization-based re-ranking approach that seamlessly integrates fairness constraints from both the consumer and producer-side in a joint objective framework. We demonstrate through large-scale experiments on 8 datasets that our proposed method is capable of improving both consumer and producer fairness without reducing overall recommendation quality, demonstrating the role algorithms may play in minimizing data biases.|最近，人们越来越意识到，当机器学习(ML)算法被用于自动选择时，它们可能会不公平地对待/影响个人，产生法律、道德或经济后果。推荐系统是这种机器学习系统的突出例子，它可以帮助用户做出高风险的判断。在以往关于推荐系统公平性的文献研究中，一个普遍的趋势是，大多数研究将用户和项目的公平性问题分开处理，忽略了推荐系统在双边市场中运行的事实。在这项工作中，我们提出了一个基于优化的重新排序方法，在一个联合目标框架中无缝地整合来自消费者和生产者方面的公平约束。我们通过在8个数据集上的大规模实验证明了我们提出的方法能够在不降低整体推荐质量的情况下提高消费者和生产者的公平性，并且证明了算法在最小化数据偏差方面可能发挥的作用。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=CPFair:+Personalized+Consumer+and+Producer+Fairness+Re-ranking+for+Recommender+Systems)|7| +|[Meta-Knowledge Transfer for Inductive Knowledge Graph Embedding](https://doi.org/10.1145/3477495.3531757)|Mingyang Chen, Wen Zhang, Yushan Zhu, Hongting Zhou, Zonggang Yuan, Changliang Xu, Huajun Chen|Huawei Technologies Co., Ltd., Nanjing, China; State Key Laboratory of Media Convergence Production Technology and Systems, Beijing, China; Zhejiang University, Hangzhou, China; Zhejiang University & Alibaba-Zhejiang University Joint Institute of Frontier Technologies, Hangzhou, China|Knowledge graphs (KGs) consisting of a large number of triples have become widespread recently, and many knowledge graph embedding (KGE) methods are proposed to embed entities and relations of a KG into continuous vector spaces. Such embedding methods simplify the operations of conducting various in-KG tasks (e.g., link prediction) and out-of-KG tasks (e.g., question answering). They can be viewed as general solutions for representing KGs. However, existing KGE methods are not applicable to inductive settings, where a model trained on source KGs will be tested on target KGs with entities unseen during model training. Existing works focusing on KGs in inductive settings can only solve the inductive relation prediction task. They can not handle other out-of-KG tasks as general as KGE methods since they don't produce embeddings for entities. In this paper, to achieve inductive knowledge graph embedding, we propose a model MorsE, which does not learn embeddings for entities but learns transferable meta-knowledge that can be used to produce entity embeddings. Such meta-knowledge is modeled by entity-independent modules and learned by meta-learning. Experimental results show that our model significantly outperforms corresponding baselines for in-KG and out-of-KG tasks in inductive settings.|由大量三元组构成的知识图(KG)近年来得到了广泛的应用，为了将 KG 的实体和关系嵌入到连续向量空间中，提出了许多知识图嵌入(KGE)方法。这种嵌入方法简化了进行各种内 KG 任务(例如，链接预测)和外 KG 任务(例如，问题回答)的操作。他们可以被视为代表幼稚园的一般解决方案。然而，现有的 KGE 方法不适用于归纳环境，在这种情况下，以源幼儿园为培训对象的模型将在模型培训过程中看不到实体的目标幼儿园中进行测试。现有的针对归纳环境下幼儿园的工作只能解决归纳关系预测任务。它们不能像 KGE 方法那样处理其他超出 KG 的任务，因为它们不为实体生成嵌入。为了实现归纳知识图的嵌入，本文提出了一种 MorsE 模型，该模型不学习实体的嵌入，而是学习可转移的元知识，从而生成实体的嵌入。这种元知识由独立于实体的模块建模，并通过元学习来学习。实验结果表明，在感应环境下，我们的模型对于在 KG 内和在 KG 外的任务的性能明显优于相应的基线。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Meta-Knowledge+Transfer+for+Inductive+Knowledge+Graph+Embedding)|8| +|[CPFair: Personalized Consumer and Producer Fairness Re-ranking for Recommender Systems](https://doi.org/10.1145/3477495.3531959)|Mohammadmehdi Naghiaei, Hossein A. Rahmani, Yashar Deldjoo|University College London, London, United Kingdom; University of Southern California, California, CA, USA; Polytechnic University of Bari, Bari, Italy|Recently, there has been a rising awareness that when machine learning (ML) algorithms are used to automate choices, they may treat/affect individuals unfairly, with legal, ethical, or economic consequences. Recommender systems are prominent examples of such ML systems that assist users in making high-stakes judgments. A common trend in the previous literature research on fairness in recommender systems is that the majority of works treat user and item fairness concerns separately, ignoring the fact that recommender systems operate in a two-sided marketplace. In this work, we present an optimization-based re-ranking approach that seamlessly integrates fairness constraints from both the consumer and producer-side in a joint objective framework. We demonstrate through large-scale experiments on 8 datasets that our proposed method is capable of improving both consumer and producer fairness without reducing overall recommendation quality, demonstrating the role algorithms may play in minimizing data biases.|最近，人们越来越意识到，当机器学习(ML)算法被用于自动选择时，它们可能会不公平地对待/影响个人，产生法律、道德或经济后果。推荐系统是这种机器学习系统的突出例子，它可以帮助用户做出高风险的判断。在以往关于推荐系统公平性的文献研究中，一个普遍的趋势是，大多数研究将用户和项目的公平性问题分开处理，忽略了推荐系统在双边市场中运行的事实。在这项工作中，我们提出了一个基于优化的重新排序方法，在一个联合目标框架中无缝地整合来自消费者和生产者方面的公平约束。我们通过在8个数据集上的大规模实验证明了我们提出的方法能够在不降低整体推荐质量的情况下提高消费者和生产者的公平性，并且证明了算法在最小化数据偏差方面可能发挥的作用。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=CPFair:+Personalized+Consumer+and+Producer+Fairness+Re-ranking+for+Recommender+Systems)|7| |[Curriculum Learning for Dense Retrieval Distillation](https://doi.org/10.1145/3477495.3531791)|Hansi Zeng, Hamed Zamani, Vishwa Vinay|University of Massachusetts Amherst, Amherst, MA, USA; Adobe Research, Bangalore, AA, India|Recent work has shown that more effective dense retrieval models can be obtained by distilling ranking knowledge from an existing base re-ranking model. In this paper, we propose a generic curriculum learning based optimization framework called CL-DRD that controls the difficulty level of training data produced by the re-ranking (teacher) model. CL-DRD iteratively optimizes the dense retrieval (student) model by increasing the difficulty of the knowledge distillation data made available to it. In more detail, we initially provide the student model coarse-grained preference pairs between documents in the teacher's ranking, and progressively move towards finer-grained pairwise document ordering requirements. In our experiments, we apply a simple implementation of the CL-DRD framework to enhance two state-of-the-art dense retrieval models. Experiments on three public passage retrieval datasets demonstrate the effectiveness of our proposed framework.|最近的研究表明，从现有的基础重排序模型中提取排序知识可以获得更有效的稠密检索模型。本文提出了一个基于课程学习的通用优化框架 CL-DRD，该框架控制重新排序(教师)模型产生的训练数据的难易程度。CL-DRD 通过增加提供给它的知识提取数据的难度，迭代优化了密集检索(学生)模型。更详细地说，我们首先在教师排名中提供文档之间的学生模型粗粒度偏好对，然后逐步向更细粒度的成对文档排序需求转移。在我们的实验中，我们应用了一个简单的 CL-DRD 框架实现来增强两个最先进的稠密检索模型。在三个公共通道检索数据集上的实验证明了该框架的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Curriculum+Learning+for+Dense+Retrieval+Distillation)|7| |[Constructing Better Evaluation Metrics by Incorporating the Anchoring Effect into the User Model](https://doi.org/10.1145/3477495.3531953)|Nuo Chen, Fan Zhang, Tetsuya Sakai|Wuhan University, Wuhan, China; Waseda University, Tokyo, Japan|Models of existing evaluation metrics assume that users are rational decision-makers trying to pursue maximised utility. However, studies in behavioural economics show that people are not always rational when making decisions. Previous studies showed that the anchoring effect can influence the relevance judgement of a document. In this paper, we challenge the rational user assumption and introduce the anchoring effect into user models. We first propose a framework for query-level evaluation metrics by incorporating the anchoring effect into the user model. In the framework, the magnitude of the anchoring effect is related to the quality of the previous document. We then apply our framework to several query-level evaluation metrics and compare them with their vanilla version as the baseline in terms of user satisfaction on a publicly available search dataset. As a result, our Anchoring-aware Metrics (AMs) outperformed their baselines in term of correlation with user satisfaction. The result suggests that we can better predict user query satisfaction feedbacks by incorporating the anchoring effect into user models of existing evaluating metrics. As far as we know, we are the first to introduce the anchoring effect into information retrieval evaluation metrics. Our findings provide a perspective from behavioural economics to better understand user behaviour and satisfaction in search interaction.|现有的评估指标模型假设用户是理性的决策者，试图追求效用最大化。然而，行为经济学的研究表明，人们在做决定时并不总是理性的。先前的研究表明，锚定效应可以影响文档的相关性判断。在本文中，我们挑战了理性的用户假设，并将锚定效应引入到用户模型中。我们首先通过将锚定效应整合到用户模型中，提出了一个查询级评估度量的框架。在框架中，锚定效应的大小与前一份文件的质量有关。然后，我们将框架应用于几个查询级评估指标，并将它们与普通版本进行比较，作为公开可用搜索数据集上用户满意度的基线。因此，我们的锚定感知度量(AMs)在与用户满意度的相关性方面表现优于他们的基线。结果表明，我们可以更好地预测用户查询满意度反馈，将锚定效应纳入用户模型的现有评估指标。据我们所知，我们是第一个将锚定效应引入信息检索评估指标的公司。我们的研究结果提供了一个从行为经济学的角度来更好地理解用户行为和搜索互动的满意度。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Constructing+Better+Evaluation+Metrics+by+Incorporating+the+Anchoring+Effect+into+the+User+Model)|7| |[Graph Trend Filtering Networks for Recommendation](https://doi.org/10.1145/3477495.3531985)|Wenqi Fan, Xiaorui Liu, Wei Jin, Xiangyu Zhao, Jiliang Tang, Qing Li||||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Graph+Trend+Filtering+Networks+for+Recommendation)|6| |[An Efficiency Study for SPLADE Models](https://doi.org/10.1145/3477495.3531833)|Carlos Lassance, Stéphane Clinchant|Naver Labs Europe, Meylan, France|Latency and efficiency issues are often overlooked when evaluating IR models based on Pretrained Language Models (PLMs) in reason of multiple hardware and software testing scenarios. Nevertheless, efficiency is an important part of such systems and should not be overlooked. In this paper, we focus on improving the efficiency of the SPLADE model since it has achieved state-of-the-art zero-shot performance and competitive results on TREC collections. SPLADE efficiency can be controlled via a regularization factor, but solely controlling this regularization has been shown to not be efficient enough. In order to reduce the latency gap between SPLADE and traditional retrieval systems, we propose several techniques including L1 regularization for queries, a separation of document/query encoders, a FLOPS-regularized middle-training, and the use of faster query encoders. Our benchmark demonstrates that we can drastically improve the efficiency of these models while increasing the performance metrics on in-domain data. To our knowledge, we propose the first neural models that, under the same computing constraints, achieve similar latency (less than 4ms difference) as traditional BM25, while having similar performance (less than 10% [email protected] reduction) as the state-of-the-art single-stage neural rankers on in-domain data.|在评估基于预训练语言模型(PLM)的 IR 模型时，由于存在多种硬件和软件测试场景，延迟和效率问题常常被忽视。然而，效率是这些系统的一个重要组成部分，不应该被忽视。在本文中，我们关注于提高 SPLADE 模型的效率，因为它已经实现了最先进的零拍性能和 TREC 集合的竞争结果。SPLADE 的效率可以通过一个正则化因子来控制，但仅仅控制这个正则化因子是不够有效的。为了减少 SPLADE 与传统检索系统之间的延迟差距，我们提出了几种技术，包括查询的 L1正则化、文档/查询编码器的分离、 FLOPS 正则化的中间训练以及使用更快的查询编码器。我们的基准测试表明，我们可以大大提高这些模型的效率，同时提高域内数据的性能指标。据我们所知，我们提出了第一个神经模型，在相同的计算约束下，实现与传统 BM25相似的延迟(小于4ms 的差异) ，同时具有与领域内数据的最先进的单阶段神经排序相似的性能(少于10% [电子邮件保护]减少)。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=An+Efficiency+Study+for+SPLADE+Models)|6| -|[Improving Conversational Recommender Systems via Transformer-based Sequential Modelling](https://doi.org/10.1145/3477495.3531852)|Jie Zou, Evangelos Kanoulas, Pengjie Ren, Zhaochun Ren, Aixin Sun, Cheng Long|Shandong University, Qingdao, China; [email protected]; University of Amsterdam, Amsterdam, Netherlands|In Conversational Recommender Systems (CRSs), conversations usually involve a set of related items and entities e.g., attributes of items. These items and entities are mentioned in order following the development of a dialogue. In other words, potential sequential dependencies exist in conversations. However, most of the existing CRSs neglect these potential sequential dependencies. In this paper, we propose a Transformer-based sequential conversational recommendation method, named TSCR, which models the sequential dependencies in the conversations to improve CRS. We represent conversations by items and entities, and construct user sequences to discover user preferences by considering both mentioned items and entities. Based on the constructed sequences, we deploy a Cloze task to predict the recommended items along a sequence. Experimental results demonstrate that our TSCR model significantly outperforms state-of-the-art baselines.|在会话推荐系统(CRS)中，会话通常涉及一组相关的项目和实体，例如，项目的属性。这些项目和实体将在对话发展之后按顺序提及。换句话说，潜在的顺序依赖性存在于会话中。然而，大多数现有的 CRS 忽略了这些潜在的顺序依赖关系。本文提出了一种基于变压器的顺序会话推荐方法 TSCR，该方法通过建立会话中的顺序依赖关系来改进 CRS。我们通过条目和实体表示会话，并通过考虑上述条目和实体构造用户序列来发现用户偏好。基于构造的序列，我们部署完形填空任务来预测一个序列中推荐的项目。实验结果表明，我们的 TSCR 模型明显优于最先进的基线。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Improving+Conversational+Recommender+Systems+via+Transformer-based+Sequential+Modelling)|5| +|[Improving Conversational Recommender Systems via Transformer-based Sequential Modelling](https://doi.org/10.1145/3477495.3531852)|Jie Zou, Evangelos Kanoulas, Pengjie Ren, Zhaochun Ren, Aixin Sun, Cheng Long|[email protected]; Shandong University, Qingdao, China; University of Amsterdam, Amsterdam, Netherlands|In Conversational Recommender Systems (CRSs), conversations usually involve a set of related items and entities e.g., attributes of items. These items and entities are mentioned in order following the development of a dialogue. In other words, potential sequential dependencies exist in conversations. However, most of the existing CRSs neglect these potential sequential dependencies. In this paper, we propose a Transformer-based sequential conversational recommendation method, named TSCR, which models the sequential dependencies in the conversations to improve CRS. We represent conversations by items and entities, and construct user sequences to discover user preferences by considering both mentioned items and entities. Based on the constructed sequences, we deploy a Cloze task to predict the recommended items along a sequence. Experimental results demonstrate that our TSCR model significantly outperforms state-of-the-art baselines.|在会话推荐系统(CRS)中，会话通常涉及一组相关的项目和实体，例如，项目的属性。这些项目和实体将在对话发展之后按顺序提及。换句话说，潜在的顺序依赖性存在于会话中。然而，大多数现有的 CRS 忽略了这些潜在的顺序依赖关系。本文提出了一种基于变压器的顺序会话推荐方法 TSCR，该方法通过建立会话中的顺序依赖关系来改进 CRS。我们通过条目和实体表示会话，并通过考虑上述条目和实体构造用户序列来发现用户偏好。基于构造的序列，我们部署完形填空任务来预测一个序列中推荐的项目。实验结果表明，我们的 TSCR 模型明显优于最先进的基线。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Improving+Conversational+Recommender+Systems+via+Transformer-based+Sequential+Modelling)|5| |[BERT-ER: Query-specific BERT Entity Representations for Entity Ranking](https://doi.org/10.1145/3477495.3531944)|Shubham Chatterjee, Laura Dietz|University of New Hampshire, Durham, NH, USA|Entity-oriented search systems often learn vector representations of entities via the introductory paragraph from the Wikipedia page of the entity. As such representations are the same for every query, our hypothesis is that the representations are not ideal for IR tasks. In this work, we present BERT Entity Representations (BERT-ER) which are query-specific vector representations of entities obtained from text that describes how an entity is relevant for a query. Using BERT-ER in a downstream entity ranking system, we achieve a performance improvement of 13-42% (Mean Average Precision) over a system that uses the BERT embedding of the introductory paragraph from Wikipedia on two large-scale test collections. Our approach also outperforms entity ranking systems using entity embeddings from Wikipedia2Vec, ERNIE, and E-BERT. We show that our entity ranking system using BERT-ER can increase precision at the top of the ranking by promoting relevant entities to the top. With this work, we release our BERT models and query-specific entity embeddings fine-tuned for the entity ranking task.|面向实体的搜索系统通常通过实体的 Wikipedia 页面中的介绍性段落学习实体的向量表示。由于这种表示对于每个查询都是相同的，我们的假设是，这种表示对于 IR 任务来说并不理想。在这项工作中，我们提出了 BERT 实体表示(BERT-ER) ，它是从文本中获得的实体的特定于查询的向量表示，描述了一个实体如何与一个查询相关。在下游实体排名系统中使用 BERT-ER，我们比在两个大规模测试集合中使用 BERT 嵌入的介绍性段落的系统获得了13-42% 的性能提高(均值平均精度)。我们的方法也优于使用 Wikipedia2Vec、 ERNIE 和 E-BERT 的实体嵌入的实体排序系统。结果表明，使用 BERT-ER 的实体排名系统可以通过将相关实体提升到顶端来提高排名的精度。通过这项工作，我们发布了我们的 BERT 模型和针对实体排序任务的查询特定实体嵌入。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=BERT-ER:+Query-specific+BERT+Entity+Representations+for+Entity+Ranking)|5| |[Reduce, Reuse, Recycle: Green Information Retrieval Research](https://doi.org/10.1145/3477495.3531766)|Harrisen Scells, Shengyao Zhuang, Guido Zuccon|The University of Queensland, Brisbane, QLD, Australia|Recent advances in Information Retrieval utilise energy-intensive hardware to produce state-of-the-art results. In areas of research highly related to Information Retrieval, such as Natural Language Processing and Machine Learning, there have been efforts to quantify and reduce the power and emissions produced by methods that depend on such hardware. Research that is conscious of the environmental impacts of its experimentation and takes steps to mitigate some of these impacts is considered 'Green'. Given the continuous demand for more data and power-hungry techniques, Green research is likely to become more important within the broader research community. Therefore, within the Information Retrieval community, the consequences of non-Green (in other words, Red) research should at least be considered and acknowledged. As such, the aims of this perspective paper are fourfold: (1) to review the Green literature not only for Information Retrieval but also for related domains in order to identify transferable Green techniques; (2) to provide measures for quantifying the power usage and emissions of Information Retrieval research; (3) to report the power usage and emission impacts for various current IR methods; and (4) to provide a framework to guide Green Information Retrieval research, taking inspiration from 'reduce, reuse, recycle' waste management campaigns, including salient examples from the literature that implement these concepts.|最近在信息检索方面的进展利用能源密集型硬件来产生最先进的结果。在与信息检索高度相关的研究领域，如自然语言处理和机器学习，一直在努力量化和减少依赖这些硬件的方法所产生的功率和排放。意识到实验对环境的影响并采取措施减轻其中一些影响的研究被认为是“绿色”的。鉴于对更多数据和耗电技术的持续需求，绿色研究可能在更广泛的研究领域变得更加重要。因此，在信息检索社区内，非绿色(换句话说，红色)研究的后果至少应该被考虑和承认。因此，本文件的目的有四个: (1)不仅为信息检索而且为相关领域查阅绿色文献，以便确定可转用的绿色技术; (2)提供量化信息检索研究的用电量和排放量的措施; (3)报告各种现行红外线方法的用电量和排放影响; (4)提供一个框架，以指导绿色信息检索研究，从“减少、再使用、回收”废物管理运动中获得启发，包括实施这些概念的文献中的突出例子。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Reduce,+Reuse,+Recycle:+Green+Information+Retrieval+Research)|5| -|[On-Device Next-Item Recommendation with Self-Supervised Knowledge Distillation](https://doi.org/10.1145/3477495.3531775)|Xin Xia, Hongzhi Yin, Junliang Yu, Qinyong Wang, Guandong Xu, Quoc Viet Hung Nguyen|The University of Queensland, Brisbane, QLD, Australia; Baidu Inc., Beijing, China; University of Technology Sydney, Sydney, NSW, Australia; Griffith University, Gold Coast, Australia|Session-based recommender systems (SBR) are becoming increasingly popular because they can predict user interests without relying on long-term user profile and support login-free recommendation. Modern recommender systems operate in a fully server-based fashion. To cater to millions of users, the frequent model maintaining and the high-speed processing for concurrent user requests are required, which comes at the cost of a huge carbon footprint. Meanwhile, users need to upload their behavior data even including the immediate environmental context to the server, raising the public concern about privacy. On-device recommender systems circumvent these two issues with cost-conscious settings and local inference. However, due to the limited memory and computing resources, on-device recommender systems are confronted with two fundamental challenges: (1) how to reduce the size of regular models to fit edge devices? (2) how to retain the original capacity? Previous research mostly adopts tensor decomposition techniques to compress regular recommendation models with low compression rates so as to avoid drastic performance degradation. In this paper, we explore ultra-compact models for next-item recommendation, by loosing the constraint of dimensionality consistency in tensor decomposition. To compensate for the capacity loss caused by compression, we develop a self-supervised knowledge distillation framework which enables the compressed model (student) to distill the essential information lying in the raw data, and improves the long-tail item recommendation through an embedding-recombination strategy with the original model (teacher). The extensive experiments on two benchmarks demonstrate that, with 30x size reduction, the compressed model almost comes with no accuracy loss, and even outperforms its uncompressed counterpart. The code is released at https://github.com/xiaxin1998/OD-Rec.|基于会话的推荐系统正变得越来越流行，因为它们可以预测用户的兴趣而不依赖于长期的用户配置文件和支持免登录的推荐。现代推荐系统以完全基于服务器的方式运行。为了满足数百万用户的需求，需要频繁的模型维护和对并发用户请求的高速处理，这需要付出巨大的碳足印。与此同时，用户需要上传他们的行为数据，甚至包括即时的环境背景到服务器，引起公众对隐私的关注。设备上的推荐系统通过成本意识设置和本地推断来规避这两个问题。然而，由于有限的内存和计算资源，设备上的推荐系统面临着两个基本的挑战: (1)如何减少常规模型的大小，以适应边缘设备？(2)如何保留原有能力？以往的研究大多采用张量分解技术对低压缩率的常规推荐模型进行压缩，以避免性能的急剧下降。本文通过放松张量分解中维度一致性的约束，探索了下一项推荐的超紧模型。为了弥补压缩带来的容量损失，提出了一种自监督知识提取框架，使压缩模型(学生)能够提取原始数据中的关键信息，并通过与原始模型(教师)的嵌入-重组策略改进长尾项目推荐。在两个基准上的大量实验表明，在缩小了30倍尺寸的情况下，压缩模型几乎没有精度损失，甚至优于未压缩模型。密码在 https://github.com/xiaxin1998/od-rec 发布。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=On-Device+Next-Item+Recommendation+with+Self-Supervised+Knowledge+Distillation)|5| +|[On-Device Next-Item Recommendation with Self-Supervised Knowledge Distillation](https://doi.org/10.1145/3477495.3531775)|Xin Xia, Hongzhi Yin, Junliang Yu, Qinyong Wang, Guandong Xu, Quoc Viet Hung Nguyen|The University of Queensland, Brisbane, QLD, Australia; Griffith University, Gold Coast, Australia; University of Technology Sydney, Sydney, NSW, Australia; Baidu Inc., Beijing, China|Session-based recommender systems (SBR) are becoming increasingly popular because they can predict user interests without relying on long-term user profile and support login-free recommendation. Modern recommender systems operate in a fully server-based fashion. To cater to millions of users, the frequent model maintaining and the high-speed processing for concurrent user requests are required, which comes at the cost of a huge carbon footprint. Meanwhile, users need to upload their behavior data even including the immediate environmental context to the server, raising the public concern about privacy. On-device recommender systems circumvent these two issues with cost-conscious settings and local inference. However, due to the limited memory and computing resources, on-device recommender systems are confronted with two fundamental challenges: (1) how to reduce the size of regular models to fit edge devices? (2) how to retain the original capacity? Previous research mostly adopts tensor decomposition techniques to compress regular recommendation models with low compression rates so as to avoid drastic performance degradation. In this paper, we explore ultra-compact models for next-item recommendation, by loosing the constraint of dimensionality consistency in tensor decomposition. To compensate for the capacity loss caused by compression, we develop a self-supervised knowledge distillation framework which enables the compressed model (student) to distill the essential information lying in the raw data, and improves the long-tail item recommendation through an embedding-recombination strategy with the original model (teacher). The extensive experiments on two benchmarks demonstrate that, with 30x size reduction, the compressed model almost comes with no accuracy loss, and even outperforms its uncompressed counterpart. The code is released at https://github.com/xiaxin1998/OD-Rec.|基于会话的推荐系统正变得越来越流行，因为它们可以预测用户的兴趣而不依赖于长期的用户配置文件和支持免登录的推荐。现代推荐系统以完全基于服务器的方式运行。为了满足数百万用户的需求，需要频繁的模型维护和对并发用户请求的高速处理，这需要付出巨大的碳足印。与此同时，用户需要上传他们的行为数据，甚至包括即时的环境背景到服务器，引起公众对隐私的关注。设备上的推荐系统通过成本意识设置和本地推断来规避这两个问题。然而，由于有限的内存和计算资源，设备上的推荐系统面临着两个基本的挑战: (1)如何减少常规模型的大小，以适应边缘设备？(2)如何保留原有能力？以往的研究大多采用张量分解技术对低压缩率的常规推荐模型进行压缩，以避免性能的急剧下降。本文通过放松张量分解中维度一致性的约束，探索了下一项推荐的超紧模型。为了弥补压缩带来的容量损失，提出了一种自监督知识提取框架，使压缩模型(学生)能够提取原始数据中的关键信息，并通过与原始模型(教师)的嵌入-重组策略改进长尾项目推荐。在两个基准上的大量实验表明，在缩小了30倍尺寸的情况下，压缩模型几乎没有精度损失，甚至优于未压缩模型。密码在 https://github.com/xiaxin1998/od-rec 发布。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=On-Device+Next-Item+Recommendation+with+Self-Supervised+Knowledge+Distillation)|5| |[DisenCDR: Learning Disentangled Representations for Cross-Domain Recommendation](https://doi.org/10.1145/3477495.3531967)|Jiangxia Cao, Xixun Lin, Xin Cong, Jing Ya, Tingwen Liu, Bin Wang|Xiaomi Inc., Beijing, China; Institute of Information Engineering, Chinese Academy of Sciences & University of Chinese Academy of Sciences, Beijing, China|Data sparsity is a long-standing problem in recommender systems. To alleviate it, Cross-Domain Recommendation (CDR) has attracted a surge of interests, which utilizes the rich user-item interaction information from the related source domain to improve the performance on the sparse target domain. Recent CDR approaches pay attention to aggregating the source domain information to generate better user representations for the target domain. However, they focus on designing more powerful interaction encoders to learn both domains simultaneously, but fail to model different user preferences of different domains. Particularly, domain-specific preferences of the source domain usually provide useless information to enhance the performance in the target domain, and directly aggregating the domain-shared and domain-specific information together maybe hurts target domain performance. This work considers a key challenge of CDR: How do we transfer shared information across domains? Grounded in the information theory, we propose DisenCDR, a novel model to disentangle the domain-shared and domain-specific information. To reach our goal, we propose two mutual-information-based disentanglement regularizers. Specifically, an exclusive regularizer aims to enforce the user domain-shared representations and domain-specific representations encoding exclusive information. An information regularizer is to encourage the user domain-shared representations encoding predictive information for both domains. Based on them, we further derive a tractable bound of our disentanglement objective to learn desirable disentangled representations. Extensive experiments show that DisenCDR achieves significant improvements over state-of-the-art baselines on four real-world datasets.|数据稀疏是推荐系统中一个长期存在的问题。为了解决这一问题，跨域推荐技术(CDR)引起了人们的极大兴趣，它利用来自相关源域的丰富的用户项交互信息来提高稀疏目标域的性能。最近的 CDR 方法注重聚合源域信息，以便为目标域生成更好的用户表示。然而，他们专注于设计更强大的交互编码器来同时学习这两个域，但未能建模不同域的不同用户偏好。特别地，源域的特定领域偏好通常提供无用的信息来提高目标域的性能，直接将共享的和特定领域的信息聚合在一起可能会损害目标域的性能。这项工作认为 CDR 的一个关键挑战是: 我们如何跨域传输共享信息？在信息论的基础上，提出了一种新的区分领域共享信息和特定领域信息的模型 DisenCDR。为了达到这个目标，我们提出了两个基于互信息的解纠缠正则化器。具体地说，独占正则化程序旨在强制用户域共享表示和编码独占信息的特定于域的表示。信息正则化器是鼓励用户域共享表示，对两个域的预测信息进行编码。在此基础上，我们进一步推导出我们的解纠缠目标的一个易处理的界，以学习理想的解纠缠表示。大量的实验表明，DisenCDR 在四个真实世界的数据集上比最先进的基线获得了显著的改进。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=DisenCDR:+Learning+Disentangled+Representations+for+Cross-Domain+Recommendation)|5| -|[Personalized Fashion Compatibility Modeling via Metapath-guided Heterogeneous Graph Learning](https://doi.org/10.1145/3477495.3532038)|Weili Guan, Fangkai Jiao, Xuemeng Song, Haokun Wen, ChungHsing Yeh, Xiaojun Chang|University of Technology Sydney, Sydney, NSW, Australia; Monash University, Melbourne, VIC, Australia; Shandong University, Qingdao, China|Fashion Compatibility Modeling (FCM) is a new yet challenging task, which aims to automatically access the matching degree among a set of complementary items. Most of existing methods evaluate the fashion compatibility from the common perspective, but overlook the user's personal preference. Inspired by this, a few pioneers study the Personalized Fashion Compatibility Modeling (PFCM). Despite their significance, these PFCM methods mainly concentrate on the user and item entities, as well as their interactions, but ignore the attribute entities, which contain rich semantics. To address this problem, we propose to fully explore the related entities and their relations involved in PFCM to boost the PFCM performance. This is, however, non-trivial due to the heterogeneous contents of different entities, embeddings for new users, and various high-order relations. Towards these ends, we present a novel metapath-guided personalized fashion compatibility modeling, dubbed as MG-PFCM. In particular, we creatively build a heterogeneous graph to unify the three types of entities (i.e., users, items, and attributes) and their relations (i.e., user-item interactions, item-item matching relations, and item-attribute association relations). Thereafter, we design a multi-modal content-oriented user embedding module to learn user representations by inheriting the contents of their interacted items. Meanwhile, we define the user-oriented and item-oriented metapaths, and perform the metapath-guided heterogeneous graph learning to enhance the user and item embeddings. In addition, we introduce the contrastive regularization to improve the model performance. We conduct extensive experiments on the real-world benchmark dataset, which verifies the superiority of our proposed scheme over several cutting-edge baselines. As a byproduct, we have released our source codes to benefit other researchers.|时尚相容性建模(FCM)是一项新兴的具有挑战性的任务，其目标是自动获取一组互补项之间的匹配度。大多数现有的方法从一般的角度评估时尚兼容性，但是忽略了用户的个人偏好。受此启发，一些先驱者开始研究个性化时尚兼容性建模(PFCM)。尽管其重要性，这些 PFCM 方法主要集中在用户和项目实体，以及它们的交互，但是忽略了属性实体，其中包含丰富的语义。为了解决这一问题，我们建议充分探讨 PFCM 中涉及的相关实体及其关系，以提高 PFCM 的性能。然而，由于不同实体的内容异构、新用户的嵌入以及各种高阶关系，这是非常重要的。为此，我们提出了一种新的元路径引导的个性化时尚兼容性模型，称为 MG-PFCM。特别是，我们创造性地构建了一个异构图来统一三种类型的实体(即用户、项目和属性)及其关系(即用户-项目交互、项目-项目匹配关系和项目-属性关系)。然后，我们设计了一个面向多模态内容的用户嵌入模块，通过继承用户交互项的内容来学习用户表示。同时，定义了面向用户和面向项目的元路径，并通过元路径引导的异构图学习来增强用户和项目的嵌入。此外，为了提高模型的性能，我们引入了对比正则化方法。我们在现实世界的基准数据集上进行了广泛的实验，验证了我们提出的方案相对于几个前沿基线的优越性。作为一个副产品，我们已经发布了我们的源代码，以利于其他研究人员。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Personalized+Fashion+Compatibility+Modeling+via+Metapath-guided+Heterogeneous+Graph+Learning)|5| -|[Explainable Fairness in Recommendation](https://doi.org/10.1145/3477495.3531973)|Yingqiang Ge, Juntao Tan, Yan Zhu, Yinglong Xia, Jiebo Luo, Shuchang Liu, Zuohui Fu, Shijie Geng, Zelong Li, Yongfeng Zhang|Meta Platforms, Inc., palo alto, CA, USA; University of Rochester, Rochester, NY, USA; Rutgers University, New Brunswick, NJ, USA|Existing research on fairness-aware recommendation has mainly focused on the quantification of fairness and the development of fair recommendation models, neither of which studies a more substantial problem--identifying the underlying reason of model disparity in recommendation. This information is critical for recommender system designers to understand the intrinsic recommendation mechanism and provides insights on how to improve model fairness to decision makers. Fortunately, with the rapid development of Explainable AI, we can use model explainability to gain insights into model (un)fairness. In this paper, we study the problem ofexplainable fairness, which helps to gain insights about why a system is fair or unfair, and guides the design of fair recommender systems with a more informed and unified methodology. Particularly, we focus on a common setting with feature-aware recommendation and exposure unfairness, but the proposed explainable fairness framework is general and can be applied to other recommendation settings and fairness definitions. We propose a Counterfactual Explainable Fairness framework, called CEF, which generates explanations about model fairness that can improve the fairness without significantly hurting the performance. The CEF framework formulates an optimization problem to learn the "minimal'' change of the input features that changes the recommendation results to a certain level of fairness. Based on the counterfactual recommendation result of each feature, we calculate an explainability score in terms of the fairness-utility trade-off to rank all the feature-based explanations, and select the top ones as fairness explanations. Experimental results on several real-world datasets validate that our method is able to effectively provide explanations to the model disparities and these explanations can achieve better fairness-utility trade-off when using them for recommendation than all the baselines.|现有关于公平意识推荐的研究主要集中在公平性的量化和公平推荐模型的发展两个方面，这两个方面都没有研究更实质性的问题——确定推荐模型差异的根本原因。这些信息对于推荐系统设计者理解内在的推荐机制至关重要，并为如何提高模型对决策者的公平性提供了见解。幸运的是，随着可解释人工智能的快速发展，我们可以利用模型可解释性来深入了解模型(非)公平性。本文通过对解释公平问题的研究，有助于深入了解一个系统为什么是公平或不公平的，并以一种更为知情和统一的方法论指导公平推荐系统的设计。特别地，我们关注的是一个具有特征感知推荐和暴露不公平性的公共设置，但是提出的可解释公平性框架是通用的，可以应用于其他推荐设置和公平性定义。我们提出了一个反事实可解释的公平性框架，称为 CEF，它生成了对模型公平性的解释，可以在不严重损害绩效的情况下提高公平性。基金框架制定了一个最佳化问题，让学生了解输入功能的「最小」改变，会令推荐结果有一定程度的公平性。基于每个特征的反事实推荐结果，以公平-效用权衡的方式计算可解释性得分，对所有基于特征的解释进行排序，并选择最高的解释作为公平解释。在实际数据集上的实验结果验证了该方法能够有效地解释模型间的差异，并且这些解释在使用它们进行推荐时能够比所有基线更好地实现公平-效用的权衡。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Explainable+Fairness+in+Recommendation)|5| +|[Personalized Fashion Compatibility Modeling via Metapath-guided Heterogeneous Graph Learning](https://doi.org/10.1145/3477495.3532038)|Weili Guan, Fangkai Jiao, Xuemeng Song, Haokun Wen, ChungHsing Yeh, Xiaojun Chang|University of Technology Sydney, Sydney, NSW, Australia; Shandong University, Qingdao, China; Monash University, Melbourne, VIC, Australia|Fashion Compatibility Modeling (FCM) is a new yet challenging task, which aims to automatically access the matching degree among a set of complementary items. Most of existing methods evaluate the fashion compatibility from the common perspective, but overlook the user's personal preference. Inspired by this, a few pioneers study the Personalized Fashion Compatibility Modeling (PFCM). Despite their significance, these PFCM methods mainly concentrate on the user and item entities, as well as their interactions, but ignore the attribute entities, which contain rich semantics. To address this problem, we propose to fully explore the related entities and their relations involved in PFCM to boost the PFCM performance. This is, however, non-trivial due to the heterogeneous contents of different entities, embeddings for new users, and various high-order relations. Towards these ends, we present a novel metapath-guided personalized fashion compatibility modeling, dubbed as MG-PFCM. In particular, we creatively build a heterogeneous graph to unify the three types of entities (i.e., users, items, and attributes) and their relations (i.e., user-item interactions, item-item matching relations, and item-attribute association relations). Thereafter, we design a multi-modal content-oriented user embedding module to learn user representations by inheriting the contents of their interacted items. Meanwhile, we define the user-oriented and item-oriented metapaths, and perform the metapath-guided heterogeneous graph learning to enhance the user and item embeddings. In addition, we introduce the contrastive regularization to improve the model performance. We conduct extensive experiments on the real-world benchmark dataset, which verifies the superiority of our proposed scheme over several cutting-edge baselines. As a byproduct, we have released our source codes to benefit other researchers.|时尚相容性建模(FCM)是一项新兴的具有挑战性的任务，其目标是自动获取一组互补项之间的匹配度。大多数现有的方法从一般的角度评估时尚兼容性，但是忽略了用户的个人偏好。受此启发，一些先驱者开始研究个性化时尚兼容性建模(PFCM)。尽管其重要性，这些 PFCM 方法主要集中在用户和项目实体，以及它们的交互，但是忽略了属性实体，其中包含丰富的语义。为了解决这一问题，我们建议充分探讨 PFCM 中涉及的相关实体及其关系，以提高 PFCM 的性能。然而，由于不同实体的内容异构、新用户的嵌入以及各种高阶关系，这是非常重要的。为此，我们提出了一种新的元路径引导的个性化时尚兼容性模型，称为 MG-PFCM。特别是，我们创造性地构建了一个异构图来统一三种类型的实体(即用户、项目和属性)及其关系(即用户-项目交互、项目-项目匹配关系和项目-属性关系)。然后，我们设计了一个面向多模态内容的用户嵌入模块，通过继承用户交互项的内容来学习用户表示。同时，定义了面向用户和面向项目的元路径，并通过元路径引导的异构图学习来增强用户和项目的嵌入。此外，为了提高模型的性能，我们引入了对比正则化方法。我们在现实世界的基准数据集上进行了广泛的实验，验证了我们提出的方案相对于几个前沿基线的优越性。作为一个副产品，我们已经发布了我们的源代码，以利于其他研究人员。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Personalized+Fashion+Compatibility+Modeling+via+Metapath-guided+Heterogeneous+Graph+Learning)|5| +|[Explainable Fairness in Recommendation](https://doi.org/10.1145/3477495.3531973)|Yingqiang Ge, Juntao Tan, Yan Zhu, Yinglong Xia, Jiebo Luo, Shuchang Liu, Zuohui Fu, Shijie Geng, Zelong Li, Yongfeng Zhang|University of Rochester, Rochester, NY, USA; Meta Platforms, Inc., palo alto, CA, USA; Rutgers University, New Brunswick, NJ, USA|Existing research on fairness-aware recommendation has mainly focused on the quantification of fairness and the development of fair recommendation models, neither of which studies a more substantial problem--identifying the underlying reason of model disparity in recommendation. This information is critical for recommender system designers to understand the intrinsic recommendation mechanism and provides insights on how to improve model fairness to decision makers. Fortunately, with the rapid development of Explainable AI, we can use model explainability to gain insights into model (un)fairness. In this paper, we study the problem ofexplainable fairness, which helps to gain insights about why a system is fair or unfair, and guides the design of fair recommender systems with a more informed and unified methodology. Particularly, we focus on a common setting with feature-aware recommendation and exposure unfairness, but the proposed explainable fairness framework is general and can be applied to other recommendation settings and fairness definitions. We propose a Counterfactual Explainable Fairness framework, called CEF, which generates explanations about model fairness that can improve the fairness without significantly hurting the performance. The CEF framework formulates an optimization problem to learn the "minimal'' change of the input features that changes the recommendation results to a certain level of fairness. Based on the counterfactual recommendation result of each feature, we calculate an explainability score in terms of the fairness-utility trade-off to rank all the feature-based explanations, and select the top ones as fairness explanations. Experimental results on several real-world datasets validate that our method is able to effectively provide explanations to the model disparities and these explanations can achieve better fairness-utility trade-off when using them for recommendation than all the baselines.|现有关于公平意识推荐的研究主要集中在公平性的量化和公平推荐模型的发展两个方面，这两个方面都没有研究更实质性的问题——确定推荐模型差异的根本原因。这些信息对于推荐系统设计者理解内在的推荐机制至关重要，并为如何提高模型对决策者的公平性提供了见解。幸运的是，随着可解释人工智能的快速发展，我们可以利用模型可解释性来深入了解模型(非)公平性。本文通过对解释公平问题的研究，有助于深入了解一个系统为什么是公平或不公平的，并以一种更为知情和统一的方法论指导公平推荐系统的设计。特别地，我们关注的是一个具有特征感知推荐和暴露不公平性的公共设置，但是提出的可解释公平性框架是通用的，可以应用于其他推荐设置和公平性定义。我们提出了一个反事实可解释的公平性框架，称为 CEF，它生成了对模型公平性的解释，可以在不严重损害绩效的情况下提高公平性。基金框架制定了一个最佳化问题，让学生了解输入功能的「最小」改变，会令推荐结果有一定程度的公平性。基于每个特征的反事实推荐结果，以公平-效用权衡的方式计算可解释性得分，对所有基于特征的解释进行排序，并选择最高的解释作为公平解释。在实际数据集上的实验结果验证了该方法能够有效地解释模型间的差异，并且这些解释在使用它们进行推荐时能够比所有基线更好地实现公平-效用的权衡。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Explainable+Fairness+in+Recommendation)|5| |[Measuring Fairness in Ranked Results: An Analytical and Empirical Comparison](https://doi.org/10.1145/3477495.3532018)|Amifa Raj, Michael D. Ekstrand|Boise State University, Boise, ID, USA|Information access systems, such as search and recommender systems, often use ranked lists to present results believed to be relevant to the user's information need. Evaluating these lists for their fairness along with other traditional metrics provides a more complete understanding of an information access system's behavior beyond accuracy or utility constructs. To measure the (un)fairness of rankings, particularly with respect to the protected group(s) of producers or providers, several metrics have been proposed in the last several years. However, an empirical and comparative analyses of these metrics showing the applicability to specific scenario or real data, conceptual similarities, and differences is still lacking. We aim to bridge the gap between theoretical and practical ap-plication of these metrics. In this paper we describe several fair ranking metrics from the existing literature in a common notation, enabling direct comparison of their approaches and assumptions, and empirically compare them on the same experimental setup and data sets in the context of three information access tasks. We also provide a sensitivity analysis to assess the impact of the design choices and parameter settings that go in to these metrics and point to additional work needed to improve fairness measurement.|信息访问系统，例如搜索和推荐系统，经常使用排名列表来显示被认为与用户信息需求相关的结果。评估这些列表的公平性以及其他传统指标，可以更全面地了解信息访问系统的行为，而不仅仅是准确性或效用结构。为了衡量排名的(不)公平性，特别是对于受保护的生产者或提供者群体，在过去几年中已经提出了几个指标。然而，对这些指标的实证和比较分析表明适用于具体情景或实际数据，概念上的相似性和差异仍然缺乏。我们的目标是弥合这些指标的理论和实际应用之间的差距。在本文中，我们描述了几个公平排序度量从现有的文献在一个共同的符号，使直接比较他们的方法和假设，并经验比较他们在相同的实验设置和数据集在三个信息访问任务的背景下。我们还提供了一个敏感度分析来评估设计选择和参数设置对这些指标的影响，并指出改善公平性度量所需的额外工作。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Measuring+Fairness+in+Ranked+Results:+An+Analytical+and+Empirical+Comparison)|5| -|[Webformer: Pre-training with Web Pages for Information Retrieval](https://doi.org/10.1145/3477495.3532086)|Yu Guo, Zhengyi Ma, Jiaxin Mao, Hongjin Qian, Xinyu Zhang, Hao Jiang, Zhao Cao, Zhicheng Dou|Distributed and Parallel Software Lab, Huawei, Beijing, China; Gaoling School of Artificial Intelligence, Renmin University of China & Beijing Key Laboratory of Big Data Management and Analysis Methods, Beijing, China; Gaoling School of Artificial Intelligence, Renmin University of China, Beijing, China|Pre-trained language models (PLMs) have achieved great success in the area of Information Retrieval. Studies show that applying these models to ad-hoc document ranking can achieve better retrieval effectiveness. However, on the Web, most information is organized in the form of HTML web pages. In addition to the pure text content, the structure of the content organized by HTML tags is also an important part of the information delivered on a web page. Currently, such structured information is totally ignored by pre-trained models which are trained solely based on text content. In this paper, we propose to leverage large-scale web pages and their DOM (Document Object Model) tree structures to pre-train models for information retrieval. We argue that using the hierarchical structure contained in web pages, we can get richer contextual information for training better language models. To exploit this kind of information, we devise four pre-training objectives based on the structure of web pages, then pre-train a Transformer model towards these tasks jointly with traditional masked language model objective. Experimental results on two authoritative ad-hoc retrieval datasets prove that our model can significantly improve ranking performance compared to existing pre-trained models.|经过培训的语言模型(PLM)在信息检索领域取得了巨大的成功。研究表明，将这些模型应用于自组织文档排序可以获得更好的检索效果。然而，在 Web 上，大多数信息是以 HTML 网页的形式组织的。除了纯文本内容之外，HTML 标签组织的内容结构也是网页信息传递的重要组成部分。目前，这种结构化信息完全被预先训练的模型所忽视，而这些模型仅仅基于文本内容进行训练。在本文中，我们建议利用大规模网页及其 DOM (文档对象模型)树结构来预训练信息检索模型。我们认为，利用网页所包含的层次结构，我们可以获得更丰富的上下文信息来训练更好的语言模型。为了充分利用这种信息，我们根据网页的结构设计了四个预训练目标，然后结合传统的掩蔽语言模型目标，针对这些目标预训练了一个变换器模型。在两个权威的自组织检索数据集上的实验结果表明，与已有的预训练模型相比，该模型可以显著提高排序性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Webformer:+Pre-training+with+Web+Pages+for+Information+Retrieval)|5| -|[BARS: Towards Open Benchmarking for Recommender Systems](https://doi.org/10.1145/3477495.3531723)|Jieming Zhu, Quanyu Dai, Liangcai Su, Rong Ma, Jinyang Liu, Guohao Cai, Xi Xiao, Rui Zhang|The Chinese University of Hong Kong, Hong Kong, China; Huawei Noah's Ark Lab, Shenzhen, China; ruizhang.info, Shenzhen, China; Tsinghua University, Beijing, China; Tsinghua University, Shenzhen, China|The past two decades have witnessed the rapid development of personalized recommendation techniques. Despite the significant progress made in both research and practice of recommender systems, to date, there is a lack of a widely-recognized benchmarking standard in this field. Many of the existing studies perform model evaluations and comparisons in an ad-hoc manner, for example, by employing their own private data splits or using a different experimental setting. However, such conventions not only increase the difficulty in reproducing existing studies, but also lead to inconsistent experimental results among them. This largely limits the credibility and practical value of research results in this field. To tackle these issues, we present an initiative project aimed for open benchmarking for recommender systems. In contrast to some earlier attempts towards this goal, we take one further step by setting up a standardized benchmarking pipeline for reproducible research, which integrates all the details about datasets, source code, hyper-parameter settings, running logs, and evaluation results. The benchmark is designed with comprehensiveness and sustainability in mind. It spans both matching and ranking tasks, and also allows anyone to easily follow and contribute. We believe that our benchmark could not only reduce the redundant efforts of researchers to re-implement or re-run existing baselines, but also drive more solid and reproducible research on recommender systems.|近二十年来，个性化推荐技术发展迅速。尽管在推荐系统的研究和实践方面取得了重大进展，但迄今为止，在这一领域还缺乏一个得到广泛认可的基准标准。许多现有的研究以临时方式进行模型评估和比较，例如，通过使用自己的私人数据分割或使用不同的实验设置。然而，这些惯例不仅增加了复制现有研究的难度，而且导致它们之间的实验结果不一致。这在很大程度上限制了该领域研究成果的可信度和实用价值。为了解决这些问题，我们提出了一个倡议项目，旨在为推荐系统开放基准测试。与之前的一些尝试相比，我们更进一步，为可重复性研究建立了一个标准化的基准管道，其中集成了关于数据集、源代码、超参数设置、运行日志和评估结果的所有细节。基准的设计考虑了全面性和可持续性。它跨越了匹配和排序任务，并且允许任何人轻松地跟随和贡献。我们相信，我们的基准不仅可以减少研究人员重新实现或重新运行现有基准的冗余工作，而且还可以推动对推荐系统进行更可靠和可重复的研究。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=BARS:+Towards+Open+Benchmarking+for+Recommender+Systems)|5| +|[Webformer: Pre-training with Web Pages for Information Retrieval](https://doi.org/10.1145/3477495.3532086)|Yu Guo, Zhengyi Ma, Jiaxin Mao, Hongjin Qian, Xinyu Zhang, Hao Jiang, Zhao Cao, Zhicheng Dou|Gaoling School of Artificial Intelligence, Renmin University of China & Beijing Key Laboratory of Big Data Management and Analysis Methods, Beijing, China; Distributed and Parallel Software Lab, Huawei, Beijing, China; Gaoling School of Artificial Intelligence, Renmin University of China, Beijing, China|Pre-trained language models (PLMs) have achieved great success in the area of Information Retrieval. Studies show that applying these models to ad-hoc document ranking can achieve better retrieval effectiveness. However, on the Web, most information is organized in the form of HTML web pages. In addition to the pure text content, the structure of the content organized by HTML tags is also an important part of the information delivered on a web page. Currently, such structured information is totally ignored by pre-trained models which are trained solely based on text content. In this paper, we propose to leverage large-scale web pages and their DOM (Document Object Model) tree structures to pre-train models for information retrieval. We argue that using the hierarchical structure contained in web pages, we can get richer contextual information for training better language models. To exploit this kind of information, we devise four pre-training objectives based on the structure of web pages, then pre-train a Transformer model towards these tasks jointly with traditional masked language model objective. Experimental results on two authoritative ad-hoc retrieval datasets prove that our model can significantly improve ranking performance compared to existing pre-trained models.|经过培训的语言模型(PLM)在信息检索领域取得了巨大的成功。研究表明，将这些模型应用于自组织文档排序可以获得更好的检索效果。然而，在 Web 上，大多数信息是以 HTML 网页的形式组织的。除了纯文本内容之外，HTML 标签组织的内容结构也是网页信息传递的重要组成部分。目前，这种结构化信息完全被预先训练的模型所忽视，而这些模型仅仅基于文本内容进行训练。在本文中，我们建议利用大规模网页及其 DOM (文档对象模型)树结构来预训练信息检索模型。我们认为，利用网页所包含的层次结构，我们可以获得更丰富的上下文信息来训练更好的语言模型。为了充分利用这种信息，我们根据网页的结构设计了四个预训练目标，然后结合传统的掩蔽语言模型目标，针对这些目标预训练了一个变换器模型。在两个权威的自组织检索数据集上的实验结果表明，与已有的预训练模型相比，该模型可以显著提高排序性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Webformer:+Pre-training+with+Web+Pages+for+Information+Retrieval)|5| +|[BARS: Towards Open Benchmarking for Recommender Systems](https://doi.org/10.1145/3477495.3531723)|Jieming Zhu, Quanyu Dai, Liangcai Su, Rong Ma, Jinyang Liu, Guohao Cai, Xi Xiao, Rui Zhang|Huawei Noah's Ark Lab, Shenzhen, China; ruizhang.info, Shenzhen, China; Tsinghua University, Beijing, China; The Chinese University of Hong Kong, Hong Kong, China; Tsinghua University, Shenzhen, China|The past two decades have witnessed the rapid development of personalized recommendation techniques. Despite the significant progress made in both research and practice of recommender systems, to date, there is a lack of a widely-recognized benchmarking standard in this field. Many of the existing studies perform model evaluations and comparisons in an ad-hoc manner, for example, by employing their own private data splits or using a different experimental setting. However, such conventions not only increase the difficulty in reproducing existing studies, but also lead to inconsistent experimental results among them. This largely limits the credibility and practical value of research results in this field. To tackle these issues, we present an initiative project aimed for open benchmarking for recommender systems. In contrast to some earlier attempts towards this goal, we take one further step by setting up a standardized benchmarking pipeline for reproducible research, which integrates all the details about datasets, source code, hyper-parameter settings, running logs, and evaluation results. The benchmark is designed with comprehensiveness and sustainability in mind. It spans both matching and ranking tasks, and also allows anyone to easily follow and contribute. We believe that our benchmark could not only reduce the redundant efforts of researchers to re-implement or re-run existing baselines, but also drive more solid and reproducible research on recommender systems.|近二十年来，个性化推荐技术发展迅速。尽管在推荐系统的研究和实践方面取得了重大进展，但迄今为止，在这一领域还缺乏一个得到广泛认可的基准标准。许多现有的研究以临时方式进行模型评估和比较，例如，通过使用自己的私人数据分割或使用不同的实验设置。然而，这些惯例不仅增加了复制现有研究的难度，而且导致它们之间的实验结果不一致。这在很大程度上限制了该领域研究成果的可信度和实用价值。为了解决这些问题，我们提出了一个倡议项目，旨在为推荐系统开放基准测试。与之前的一些尝试相比，我们更进一步，为可重复性研究建立了一个标准化的基准管道，其中集成了关于数据集、源代码、超参数设置、运行日志和评估结果的所有细节。基准的设计考虑了全面性和可持续性。它跨越了匹配和排序任务，并且允许任何人轻松地跟随和贡献。我们相信，我们的基准不仅可以减少研究人员重新实现或重新运行现有基准的冗余工作，而且还可以推动对推荐系统进行更可靠和可重复的研究。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=BARS:+Towards+Open+Benchmarking+for+Recommender+Systems)|5| |[V2P: Vision-to-Prompt based Multi-Modal Product Summary Generation](https://doi.org/10.1145/3477495.3532076)|Xuemeng Song, Liqiang Jing, Dengtian Lin, Zhongzhou Zhao, Haiqing Chen, Liqiang Nie|Alibaba Group, Hangzhou, China; Shandong University, Qingdao, China|Multi-modal Product Summary Generation is a new yet challenging task, which aims to generate a concise and readable summary for a product given its multi-modal content, e.g., its long text description and image. Although existing methods have achieved great success, they still suffer from three key limitations: 1) overlook the benefit of pre-training, 2) lack the representation-level supervision, and 3) ignore the diversity of the seller-generated data. To address these limitations, in this work, we propose a Vision-to-Prompt based multi-modal product summary generation framework, dubbed as V2P, where a Generative Pre-trained Language Model (GPLM) is adopted as the backbone. In particular, to maintain the original text capability of the GPLM and fully utilize the high-level concepts contained in the product image, we design V2P with two key components: vision-based prominent attribute prediction, and attribute prompt-guided summary generation. The first component works on obtaining the vital semantic attributes of the product from its image by the Swin Transformer, while the second component aims to generate the summary based on the product's long text description and the attribute prompts yielded by the first component with a GPLM. Towards comprehensive supervision over the second component, apart from the conventional output-level supervision, we introduce the representation-level regularization. Meanwhile, we design the data augmentation-based robustness regularization to handle the diverse inputs and improve the robustness of the second component. Extensive experiments on a large-scale Chinese dataset verify the superiority of our model over cutting-edge methods.|多模态产品摘要生成是一项新的具有挑战性的任务，其目标是根据产品的多模态内容(如长文本描述和图像)为其生成一个简明易读的摘要。虽然现有的方法已经取得了巨大的成功，但仍然存在三个关键的局限性: 1)忽视了预训练的好处，2)缺乏表征级别的监督，3)忽视了销售者生成的数据的多样性。为了解决这些局限性，在本文中，我们提出了一个基于视觉到提示的多模态产品摘要生成框架，称为 V2P，其中采用了生成预训练语言模型(GPLM)作为骨干。特别是，为了保持 GPLM 的原始文本能力，充分利用产品图像中包含的高层次概念，我们设计了 V2P 的两个关键组件: 基于视觉的突出属性预测和属性提示引导的摘要生成。第一个组件的工作是通过 Swin former 从产品图像中获取重要的语义属性，而第二个组件的目标是基于产品的长文本描述和由第一个组件通过 GPLM 产生的属性提示生成摘要。对于第二部分的综合监管，除了传统的产出层面的监管外，我们还引入了代表层面的规范化。同时，我们设计了基于数据增强的鲁棒正则化方法来处理不同的输入，提高了第二分量的鲁棒性。通过在大规模中文数据集上的大量实验，验证了该模型相对于前沿方法的优越性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=V2P:+Vision-to-Prompt+based+Multi-Modal+Product+Summary+Generation)|5| -|[A Weakly Supervised Propagation Model for Rumor Verification and Stance Detection with Multiple Instance Learning](https://doi.org/10.1145/3477495.3531930)|Ruichao Yang, Jing Ma, Hongzhan Lin, Wei Gao|Singapore Management University, Singapore, Singapore; Hong Kong Baptist University, Hong Kong SAR, Hong Kong; Beijing University of Posts and Telecommunications, Hong Kong Baptist University, Beijing, Hong Kong SAR, China|The diffusion of rumors on social media generally follows a propagation tree structure, which provides valuable clues on how an original message is transmitted and responded by users over time. Recent studies reveal that rumor verification and stance detection are two relevant tasks that can jointly enhance each other despite their differences. For example, rumors can be debunked by cross-checking the stances conveyed by their relevant posts, and stances are also conditioned on the nature of the rumor. However, stance detection typically requires a large training set of labeled stances at post level, which are rare and costly to annotate. Enlightened by Multiple Instance Learning (MIL) scheme, we propose a novel weakly supervised joint learning framework for rumor verification and stance detection which only requires bag-level class labels concerning the rumor's veracity. Specifically, based on the propagation trees of source posts, we convert the two multi-class problems into multiple MIL-based binary classification problems where each binary model is focused on differentiating a target class (of rumor or stance) from the remaining classes. Then, we propose a hierarchical attention mechanism to aggregate the binary predictions, including (1) a bottom-up/top-down tree attention layer to aggregate binary stances into binary veracity; and (2) a discriminative attention layer to aggregate the binary class into finer-grained classes. Extensive experiments conducted on three Twitter-based datasets demonstrate promising performance of our model on both claim-level rumor detection and post-level stance classification compared with state-of-the-art methods.|谣言在社交媒体上的传播通常遵循传播树结构，这种结构提供了有价值的线索，说明原始信息是如何随着时间的推移被用户传播和回应的。最近的研究表明，谣言验证和姿态识别是两个相互关联的任务，尽管它们之间存在差异，但可以相互促进。例如，谣言可以通过反复核对其相关帖子所传达的立场来揭穿，而立场也取决于谣言的性质。然而，姿态检测通常需要在后期水平上进行大量标记姿态的训练，这种训练很少，而且注释成本很高。在多实例学习(MIL)方案的启发下，我们提出了一种新的弱监督联合学习框架，用于谣言验证和姿态检测，该框架只需要袋级类标签就可以验证谣言的真实性。具体来说，基于源文章的传播树，我们将这两个多类问题转换为多个基于 MIL 的二进制分类问题，其中每个二进制模型的重点是区分目标类(谣言或立场)与其余类。然后，我们提出了一个分层的注意机制来聚集二元预测，包括(1)一个自下而上/自上而下的注意树层来聚集二元立场成为二元准确性; (2)一个区分性的注意层来聚集二元类成为更细粒度的类。在三个基于 Twitter 的数据集上进行的大量实验表明，与最先进的方法相比，我们的模型在索赔级谣言检测和后级立场分类方面具有良好的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Weakly+Supervised+Propagation+Model+for+Rumor+Verification+and+Stance+Detection+with+Multiple+Instance+Learning)|5| +|[A Weakly Supervised Propagation Model for Rumor Verification and Stance Detection with Multiple Instance Learning](https://doi.org/10.1145/3477495.3531930)|Ruichao Yang, Jing Ma, Hongzhan Lin, Wei Gao|Beijing University of Posts and Telecommunications, Hong Kong Baptist University, Beijing, Hong Kong SAR, China; Singapore Management University, Singapore, Singapore; Hong Kong Baptist University, Hong Kong SAR, Hong Kong|The diffusion of rumors on social media generally follows a propagation tree structure, which provides valuable clues on how an original message is transmitted and responded by users over time. Recent studies reveal that rumor verification and stance detection are two relevant tasks that can jointly enhance each other despite their differences. For example, rumors can be debunked by cross-checking the stances conveyed by their relevant posts, and stances are also conditioned on the nature of the rumor. However, stance detection typically requires a large training set of labeled stances at post level, which are rare and costly to annotate. Enlightened by Multiple Instance Learning (MIL) scheme, we propose a novel weakly supervised joint learning framework for rumor verification and stance detection which only requires bag-level class labels concerning the rumor's veracity. Specifically, based on the propagation trees of source posts, we convert the two multi-class problems into multiple MIL-based binary classification problems where each binary model is focused on differentiating a target class (of rumor or stance) from the remaining classes. Then, we propose a hierarchical attention mechanism to aggregate the binary predictions, including (1) a bottom-up/top-down tree attention layer to aggregate binary stances into binary veracity; and (2) a discriminative attention layer to aggregate the binary class into finer-grained classes. Extensive experiments conducted on three Twitter-based datasets demonstrate promising performance of our model on both claim-level rumor detection and post-level stance classification compared with state-of-the-art methods.|谣言在社交媒体上的传播通常遵循传播树结构，这种结构提供了有价值的线索，说明原始信息是如何随着时间的推移被用户传播和回应的。最近的研究表明，谣言验证和姿态识别是两个相互关联的任务，尽管它们之间存在差异，但可以相互促进。例如，谣言可以通过反复核对其相关帖子所传达的立场来揭穿，而立场也取决于谣言的性质。然而，姿态检测通常需要在后期水平上进行大量标记姿态的训练，这种训练很少，而且注释成本很高。在多实例学习(MIL)方案的启发下，我们提出了一种新的弱监督联合学习框架，用于谣言验证和姿态检测，该框架只需要袋级类标签就可以验证谣言的真实性。具体来说，基于源文章的传播树，我们将这两个多类问题转换为多个基于 MIL 的二进制分类问题，其中每个二进制模型的重点是区分目标类(谣言或立场)与其余类。然后，我们提出了一个分层的注意机制来聚集二元预测，包括(1)一个自下而上/自上而下的注意树层来聚集二元立场成为二元准确性; (2)一个区分性的注意层来聚集二元类成为更细粒度的类。在三个基于 Twitter 的数据集上进行的大量实验表明，与最先进的方法相比，我们的模型在索赔级谣言检测和后级立场分类方面具有良好的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Weakly+Supervised+Propagation+Model+for+Rumor+Verification+and+Stance+Detection+with+Multiple+Instance+Learning)|5| |[Positive, Negative and Neutral: Modeling Implicit Feedback in Session-based News Recommendation](https://doi.org/10.1145/3477495.3532040)|Shansan Gong, Kenny Q. Zhu|Shanghai Jiao Tong University, Shanghai, China|News recommendation for anonymous readers is a useful but challenging task for many news portals, where interactions between readers and articles are limited within a temporary login session. Previous works tend to formulate session-based recommendation as a next item prediction task, while they neglect the implicit feedback from user behaviors, which indicates what users really like or dislike. Hence, we propose a comprehensive framework to model user behaviors through positive feedback (i.e., the articles they spend more time on) and negative feedback (i.e., the articles they choose to skip without clicking in). Moreover, the framework implicitly models the user using their session start time, and the article using its initial publishing time, in what we call neutral feedback. Empirical evaluation on three real-world news datasets shows the framework's promising performance of more accurate, diverse and even unexpectedness recommendations than other state-of-the-art session-based recommendation approaches.|对于许多新闻门户网站来说，为匿名读者推荐新闻是一项有用但具有挑战性的任务，因为在临时登录会话中，读者和文章之间的交互受到限制。以往的研究倾向于将基于会话的推荐作为下一个项目的预测任务，而忽略了来自用户行为的隐性反馈，这表明用户真正喜欢或不喜欢什么。因此，我们提出了一个全面的框架，通过积极反馈(即，他们花费更多时间的文章)和消极反馈(即，他们选择不点击跳过的文章)来模拟用户行为。此外，框架使用用户的会话开始时间隐式地对用户进行建模，并使用文章的初始发布时间隐式地对文章进行建模，我们称之为中性反馈。对三个真实世界新闻数据集的实证评估表明，与其他基于会话的最新推荐方法相比，该框架具有更准确、更多样、甚至更出人意料的推荐性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Positive,+Negative+and+Neutral:+Modeling+Implicit+Feedback+in+Session-based+News+Recommendation)|4| |[Multi-Behavior Sequential Transformer Recommender](https://doi.org/10.1145/3477495.3532023)|Enming Yuan, Wei Guo, Zhicheng He, Huifeng Guo, Chengkai Liu, Ruiming Tang|Tsinghua University, Beijing, China; Noah's Ark Lab, Huawei, Shenzhen, China; Shanghai Jiao Tong University, Shanghai, China|In most real-world recommender systems, users interact with items in a sequential and multi-behavioral manner. Exploring the fine-grained relationship of items behind the users' multi-behavior interactions is critical in improving the performance of recommender systems. Despite the great successes, existing methods seem to have limitations on modelling heterogeneous item-level multi-behavior dependencies, capturing diverse multi-behavior sequential dynamics, or alleviating data sparsity problems. In this paper, we show it is possible to derive a framework to address all the above three limitations. The proposed framework MB-STR, a Multi-Behavior Sequential Transformer Recommender, is equipped with the multi-behavior transformer layer (MB-Trans), the multi-behavior sequential pattern generator (MB-SPG) and the behavior-aware prediction module (BA-Pred). Compared with a typical transformer, we design MB-Trans to capture multi-behavior heterogeneous dependencies as well as behavior-specific semantics, propose MB-SPG to encode the diverse sequential patterns among multiple behaviors, and incorporate BA-Pred to better leverage multi-behavior supervision. Comprehensive experiments on three real-world datasets show the effectiveness of MB-STR by significantly boosting the recommendation performance compared with various competitive baselines. Further ablation studies demonstrate the superiority of different modules of MB-STR.|在大多数现实世界的推荐系统中，用户以一种顺序和多行为的方式与项目交互。探索用户多行为交互背后的细粒度关系对于提高推荐系统的性能至关重要。尽管取得了巨大的成功，但现有的方法似乎在建模异构项目级多行为依赖、捕获多种多行为顺序动态或缓解数据稀疏问题方面存在局限性。在本文中，我们展示了推导一个框架来解决上述三个限制是可能的。提出的多行为顺序变压器推荐系统框架 MB-STR 具有多行为顺序变压器层(MB-Trans)、多行为顺序模式发生器(MB-SPG)和行为感知预测模块(BA-Pred)。与典型的变压器相比，我们设计 MB-Trans 来捕获多行为异构依赖和行为特定的语义，提出 MB-SPG 来编码多行为之间的不同序列模式，并结合 BA-Pred 来更好地利用多行为监督。在三个实际数据集上的综合实验表明，与不同的竞争基线相比，MB-STR 能够显著提高推荐性能。进一步的消融研究证明了 MB-STR 不同模块的优越性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Multi-Behavior+Sequential+Transformer+Recommender)|4| |[News Recommendation with Candidate-aware User Modeling](https://doi.org/10.1145/3477495.3531778)|Tao Qi, Fangzhao Wu, Chuhan Wu, Yongfeng Huang|Tsinghua University, Beijing, China; Microsoft Research Asia, Beijing, China|News recommendation aims to match news with personalized user interest. Existing methods for news recommendation usually model user interest from historical clicked news without the consideration of candidate news. However, each user usually has multiple interests, and it is difficult for these methods to accurately match a candidate news with a specific user interest. In this paper, we present a candidate-aware user modeling method for personalized news recommendation, which can incorporate candidate news into user modeling for better matching between candidate news and user interest. We propose a candidate-aware self-attention network that uses candidate news as clue to model candidate-aware global user interest. In addition, we propose a candidate-aware CNN network to incorporate candidate news into local behavior context modeling and learn candidate-aware short-term user interest. Besides, we use a candidate-aware attention network to aggregate previously clicked news weighted by their relevance with candidate news to build candidate-aware user representation. Experiments on real-world datasets show the effectiveness of our method in improving news recommendation performance.|新闻推荐旨在使新闻与个性化的用户兴趣相匹配。现有的新闻推荐方法通常根据历史点击新闻建立用户兴趣模型，而不考虑候选新闻。然而，每个用户通常有多个兴趣，这些方法很难准确地匹配一个候选新闻和一个特定的用户兴趣。提出了一种基于候选者感知的个性化新闻推荐用户建模方法，该方法将候选新闻融入到用户建模中，以更好地匹配候选新闻和用户兴趣。我们提出了一个以候选新闻为线索的候选感知自我注意网络，该网络对全球候选感知用户兴趣进行建模。此外，我们提出了一个候选人感知的 CNN 网络，将候选人新闻纳入本地行为上下文建模，并学习候选人感知的短期用户兴趣。此外，我们利用一个候选人感知的注意网络来聚合先前点击过的新闻，并根据它们与候选人新闻的相关性来加权，从而建立一个候选人感知的用户表示。在实际数据集上的实验结果表明了该方法在提高新闻推荐性能方面的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=News+Recommendation+with+Candidate-aware+User+Modeling)|4| -|[Price DOES Matter!: Modeling Price and Interest Preferences in Session-based Recommendation](https://doi.org/10.1145/3477495.3532043)|Xiaokun Zhang, Bo Xu, Liang Yang, Chenliang Li, Fenglong Ma, Haifeng Liu, Hongfei Lin|Dalian University of Technology, Dalian, China; Wuhan University, Wuhan, China; Pennsylvania State University, Pennsylvania, PA, USA|Session-based recommendation aims to predict items that an anonymous user would like to purchase based on her short behavior sequence. The current approaches towards session-based recommendation only focus on modeling users' interest preferences, while they all ignore a key attribute of an item, i.e., the price. Many marketing studies have shown that the price factor significantly influences users' behaviors and the purchase decisions of users are determined by both price and interest preferences simultaneously. However, it is nontrivial to incorporate price preferences for session-based recommendation. Firstly, it is hard to handle heterogeneous information from various features of items to capture users' price preferences. Secondly, it is difficult to model the complex relations between price and interest preferences in determining user choices. To address the above challenges, we propose a novel method Co-guided Heterogeneous Hypergraph Network (CoHHN) for session-based recommendation. Towards the first challenge, we devise a heterogeneous hypergraph to represent heterogeneous information and rich relations among them. A dual-channel aggregating mechanism is then designed to aggregate various information in the heterogeneous hypergraph. After that, we extract users' price preferences and interest preferences via attention layers. As to the second challenge, a co-guided learning scheme is designed to model the relations between price and interest preferences and enhance the learning of each other. Finally, we predict user actions based on item features and users' price and interest preferences. Extensive experiments on three real-world datasets demonstrate the effectiveness of the proposed CoHHN. Further analysis reveals the significance of price for session-based recommendation.|基于会话的推荐旨在根据匿名用户的短行为序列预测其想要购买的商品。当前基于会话的推荐方法只关注于建模用户的兴趣偏好，而他们都忽略了一个项目的关键属性，即价格。许多市场研究表明，价格因素对用户行为有显著影响，用户的购买决策同时由价格和利益偏好决定。然而，为基于会话的推荐引入价格偏好并非易事。首先，很难处理来自商品不同特征的异构信息来捕捉用户的价格偏好;。其次，在决定用户选择时，很难建立价格和利益偏好之间的复杂关系模型。针对上述挑战，我们提出了一种基于会话的协同引导异构超图网络(CoHHN)的推荐方法。针对第一个挑战，我们设计了一个异构超图来表示异构信息和它们之间的丰富关系。然后设计了一种双通道聚集机制来聚集异构超图中的各种信息。然后，通过注意层提取用户的价格偏好和兴趣偏好。针对第二个挑战，设计了一个共同导向学习方案，以建立价格和兴趣偏好之间的关系模型，并加强相互之间的学习。最后，根据商品特征以及用户的价格和兴趣偏好对用户行为进行预测。在三个真实世界数据集上的大量实验证明了所提出的 CoHHN 算法的有效性。进一步的分析揭示了基于会话的推荐价格的重要性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Price+DOES+Matter!:+Modeling+Price+and+Interest+Preferences+in+Session-based+Recommendation)|4| -|[CORE: Simple and Effective Session-based Recommendation within Consistent Representation Space](https://doi.org/10.1145/3477495.3531955)|Yupeng Hou, Binbin Hu, Zhiqiang Zhang, Wayne Xin Zhao|Renmin University of China, Beijing, China; Ant Group, Hangzhou, China; Renmin University of China & Beijing Academy of Artificial Intelligence, Beijing, China|Session-based Recommendation (SBR) refers to the task of predicting the next item based on short-term user behaviors within an anonymous session. However, session embedding learned by a non-linear encoder is usually not in the same representation space as item embeddings, resulting in the inconsistent prediction issue while recommending items. To address this issue, we propose a simple and effective framework named CORE, which can unify the representation space for both the encoding and decoding processes. Firstly, we design a representation-consistent encoder that takes the linear combination of input item embeddings as session embedding, guaranteeing that sessions and items are in the same representation space. Besides, we propose a robust distance measuring method to prevent overfitting of embeddings in the consistent representation space. Extensive experiments conducted on five public real-world datasets demonstrate the effectiveness and efficiency of the proposed method. The code is available at: https://github.com/RUCAIBox/CORE.|基于会话的推荐(Session-based 汪汪)是指基于匿名会话中的短期用户行为来预测下一个项目的任务。然而，非线性编码器学习的会话嵌入通常与项目嵌入不在同一表示空间，在推荐项目时会产生不一致的预测问题。为了解决这个问题，我们提出了一个简单有效的框架 CORE，它可以统一编码和解码过程的表示空间。首先，我们设计了一个表示一致的编码器，将输入项嵌入的线性组合作为会话嵌入，保证会话和项处于相同的表示空间。此外，我们提出了一种鲁棒的距离测量方法，以防止过拟合嵌入在一致的表示空间。在五个公共实际数据集上进行的大量实验表明了该方法的有效性和高效性。密码可于以下 https://github.com/rucaibox/core 索取:。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=CORE:+Simple+and+Effective+Session-based+Recommendation+within+Consistent+Representation+Space)|4| +|[Price DOES Matter!: Modeling Price and Interest Preferences in Session-based Recommendation](https://doi.org/10.1145/3477495.3532043)|Xiaokun Zhang, Bo Xu, Liang Yang, Chenliang Li, Fenglong Ma, Haifeng Liu, Hongfei Lin|Dalian University of Technology, Dalian, China; Pennsylvania State University, Pennsylvania, PA, USA; Wuhan University, Wuhan, China|Session-based recommendation aims to predict items that an anonymous user would like to purchase based on her short behavior sequence. The current approaches towards session-based recommendation only focus on modeling users' interest preferences, while they all ignore a key attribute of an item, i.e., the price. Many marketing studies have shown that the price factor significantly influences users' behaviors and the purchase decisions of users are determined by both price and interest preferences simultaneously. However, it is nontrivial to incorporate price preferences for session-based recommendation. Firstly, it is hard to handle heterogeneous information from various features of items to capture users' price preferences. Secondly, it is difficult to model the complex relations between price and interest preferences in determining user choices. To address the above challenges, we propose a novel method Co-guided Heterogeneous Hypergraph Network (CoHHN) for session-based recommendation. Towards the first challenge, we devise a heterogeneous hypergraph to represent heterogeneous information and rich relations among them. A dual-channel aggregating mechanism is then designed to aggregate various information in the heterogeneous hypergraph. After that, we extract users' price preferences and interest preferences via attention layers. As to the second challenge, a co-guided learning scheme is designed to model the relations between price and interest preferences and enhance the learning of each other. Finally, we predict user actions based on item features and users' price and interest preferences. Extensive experiments on three real-world datasets demonstrate the effectiveness of the proposed CoHHN. Further analysis reveals the significance of price for session-based recommendation.|基于会话的推荐旨在根据匿名用户的短行为序列预测其想要购买的商品。当前基于会话的推荐方法只关注于建模用户的兴趣偏好，而他们都忽略了一个项目的关键属性，即价格。许多市场研究表明，价格因素对用户行为有显著影响，用户的购买决策同时由价格和利益偏好决定。然而，为基于会话的推荐引入价格偏好并非易事。首先，很难处理来自商品不同特征的异构信息来捕捉用户的价格偏好;。其次，在决定用户选择时，很难建立价格和利益偏好之间的复杂关系模型。针对上述挑战，我们提出了一种基于会话的协同引导异构超图网络(CoHHN)的推荐方法。针对第一个挑战，我们设计了一个异构超图来表示异构信息和它们之间的丰富关系。然后设计了一种双通道聚集机制来聚集异构超图中的各种信息。然后，通过注意层提取用户的价格偏好和兴趣偏好。针对第二个挑战，设计了一个共同导向学习方案，以建立价格和兴趣偏好之间的关系模型，并加强相互之间的学习。最后，根据商品特征以及用户的价格和兴趣偏好对用户行为进行预测。在三个真实世界数据集上的大量实验证明了所提出的 CoHHN 算法的有效性。进一步的分析揭示了基于会话的推荐价格的重要性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Price+DOES+Matter!:+Modeling+Price+and+Interest+Preferences+in+Session-based+Recommendation)|4| +|[CORE: Simple and Effective Session-based Recommendation within Consistent Representation Space](https://doi.org/10.1145/3477495.3531955)|Yupeng Hou, Binbin Hu, Zhiqiang Zhang, Wayne Xin Zhao|Renmin University of China & Beijing Academy of Artificial Intelligence, Beijing, China; Ant Group, Hangzhou, China; Renmin University of China, Beijing, China|Session-based Recommendation (SBR) refers to the task of predicting the next item based on short-term user behaviors within an anonymous session. However, session embedding learned by a non-linear encoder is usually not in the same representation space as item embeddings, resulting in the inconsistent prediction issue while recommending items. To address this issue, we propose a simple and effective framework named CORE, which can unify the representation space for both the encoding and decoding processes. Firstly, we design a representation-consistent encoder that takes the linear combination of input item embeddings as session embedding, guaranteeing that sessions and items are in the same representation space. Besides, we propose a robust distance measuring method to prevent overfitting of embeddings in the consistent representation space. Extensive experiments conducted on five public real-world datasets demonstrate the effectiveness and efficiency of the proposed method. The code is available at: https://github.com/RUCAIBox/CORE.|基于会话的推荐(Session-based 汪汪)是指基于匿名会话中的短期用户行为来预测下一个项目的任务。然而，非线性编码器学习的会话嵌入通常与项目嵌入不在同一表示空间，在推荐项目时会产生不一致的预测问题。为了解决这个问题，我们提出了一个简单有效的框架 CORE，它可以统一编码和解码过程的表示空间。首先，我们设计了一个表示一致的编码器，将输入项嵌入的线性组合作为会话嵌入，保证会话和项处于相同的表示空间。此外，我们提出了一种鲁棒的距离测量方法，以防止过拟合嵌入在一致的表示空间。在五个公共实际数据集上进行的大量实验表明了该方法的有效性和高效性。密码可于以下 https://github.com/rucaibox/core 索取:。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=CORE:+Simple+and+Effective+Session-based+Recommendation+within+Consistent+Representation+Space)|4| |[Unlearning Protected User Attributes in Recommendations with Adversarial Training](https://doi.org/10.1145/3477495.3531820)|Christian Ganhör, David Penz, Navid Rekabsaz, Oleg Lesota, Markus Schedl|Johannes Kepler University Linz & Linz Institute of Technology, Linz, Austria; Johannes Kepler University Linz & TU Wien, Linz and Vienna, Austria; Johannes Kepler University Linz, Linz, Austria|Collaborative filtering algorithms capture underlying consumption patterns, including the ones specific to particular demographics or protected information of users, e.g., gender, race, and location. These encoded biases can influence the decision of a recommendation system (RS) towards further separation of the contents provided to various demographic subgroups, and raise privacy concerns regarding the disclosure of users' protected attributes. In this work, we investigate the possibility and challenges of removing specific protected information of users from the learned interaction representations of a RS algorithm, while maintaining its effectiveness. Specifically, we incorporate adversarial training into the state-of-the-art MultVAE architecture, resulting in a novel model, Adversarial Variational Auto-Encoder with Multinomial Likelihood (Adv-MultVAE), which aims at removing the implicit information of protected attributes while preserving recommendation performance. We conduct experiments on the MovieLens-1M and LFM-2b-DemoBias datasets, and evaluate the effectiveness of the bias mitigation method based on the inability of external attackers in revealing the users' gender information from the model. Comparing with baseline MultVAE, the results show that Adv-MultVAE, with marginal deterioration in performance (w.r.t. NDCG and recall), largely mitigates inherent biases in the model on both datasets.|协同过滤算法捕捉潜在的消费模式，包括特定人群或受保护的用户信息，如性别、种族和地点。这些编码偏差可以影响推荐系统(RS)对进一步分离提供给不同人口亚群的内容的决策，并引起关于用户受保护属性披露的隐私问题。在这项工作中，我们研究的可能性和挑战，删除具体的保护信息的用户从学习交互表示的 RS 算法，同时保持其有效性。具体而言，我们将对抗性训练纳入最先进的 MultVAE 体系结构，产生了一种新的模型，具有多项式似然的对抗性变分自动编码器(Adv-MultVAE) ，其目的是消除受保护属性的隐含信息，同时保持推荐性能。我们在 MovieLens-1M 和 LFM-2b-DemoBias 数据集上进行了实验，评估了基于外部攻击者无法从模型中揭示用户性别信息的偏差缓解方法的有效性。与基线 MultVAE 相比，结果显示 Adv-MultVAE 的性能边际恶化(w.r.t.NDCG 和召回)在很大程度上减轻了两个数据集上模型的固有偏差。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Unlearning+Protected+User+Attributes+in+Recommendations+with+Adversarial+Training)|4| |[Deep Multi-Representational Item Network for CTR Prediction](https://doi.org/10.1145/3477495.3531845)|Jihai Zhang, Fangquan Lin, Cheng Yang, Wei Wang|Alibaba Group, Hangzhou, China|Click-through rate (CTR) prediction is essential in the modelling of a recommender system. Previous studies mainly focus on user behavior modelling, while few of them consider candidate item representations. This makes the models strongly dependent on user representations, and less effective when user behavior is sparse. Furthermore, most existing works regard the candidate item as one fixed embedding and ignore the multi-representational characteristics of the item. To handle the above issues, we propose a Deep multi-Representational Item NetworK (DRINK) for CTR prediction. Specifically, to tackle the sparse user behavior problem, we construct a sequence of interacting users and timestamps to represent the candidate item; to dynamically capture the characteristics of the item, we propose a transformer-based multi-representational item network consisting of a multi-CLS representation submodule and contextualized global item representation submodule. In addition, we propose to decouple the time information and item behavior to avoid information overwhelming. Outputs of the above components are concatenated and fed into a MLP layer to fit the CTR. We conduct extensive experiments on real-world datasets of Amazon and the results demonstrate the effectiveness of the proposed model.|点进率(CTR)预测是建立推荐系统模型的关键。以往的研究主要集中在用户行为建模，而很少考虑候选项表示。这使得模型强烈地依赖于用户表示，并且在用户行为稀疏时效率较低。此外，现有的大多数作品都将候选项作为一个固定的嵌入，而忽略了项目的多重表征特征。针对上述问题，本文提出了一种基于深度多表征项目网络(DRINK)的点击率预测方法。为了解决稀疏用户行为问题，我们构造了一个交互用户序列和时间戳来表示候选项目; 为了动态捕获候选项目的特征，我们提出了一个基于变换器的多表示项目网络，该网络由一个多 CLS 表示子模块和上下文化的全局项目表示子模块组成。此外，我们建议解耦的时间信息和项目行为，以避免信息铺天盖地。上述组件的输出被连接并输入 MLP 层以适应 CTR。我们在亚马逊的实际数据集上进行了广泛的实验，实验结果证明了该模型的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Deep+Multi-Representational+Item+Network+for+CTR+Prediction)|4| |[GETNext: Trajectory Flow Map Enhanced Transformer for Next POI Recommendation](https://doi.org/10.1145/3477495.3531983)|Song Yang, Jiamou Liu, Kaiqi Zhao|Univ Auckland, Auckland, New Zealand|Next POI recommendation intends to forecast users' immediate future movements given their current status and historical information, yielding great values for both users and service providers. However, this problem is perceptibly complex because various data trends need to be considered together. This includes the spatial locations, temporal contexts, user's preferences, etc. Most existing studies view the next POI recommendation as a sequence prediction problem while omitting the collaborative signals from other users. Instead, we propose a user-agnostic global trajectory flow map and a novel Graph Enhanced Transformer model (GETNext) to better exploit the extensive collaborative signals for a more accurate next POI prediction, and alleviate the cold start problem in the meantime. GETNext incorporates the global transition patterns, user's general preference, spatio-temporal context, and time-aware category embeddings together into a transformer model to make the prediction of user's future moves. With this design, our model outperforms the state-of-the-art methods with a large margin and also sheds light on the cold start challenges within the spatio-temporal involved recommendation problems.|下一个 POI 建议旨在根据用户当前的状态和历史信息预测用户近期的动向，从而为用户和服务提供商带来巨大的价值。然而，这个问题显然很复杂，因为各种数据趋势需要一起考虑。这包括空间位置、时间上下文、用户偏好等。大多数现有的研究认为下一个 POI 建议是一个序列预测问题，而忽略了来自其他用户的协作信号。相反，我们提出了一个用户无关的全局轨迹流图和一个新的图形增强变压器模型(GETNext) ，以更好地利用广泛的协作信号来更准确地预测下一个 POI，同时缓解冷启动问题。GETNext 将全局转换模式、用户的一般偏好、时空上下文和时间感知类别嵌入到一个转换器模型中，对用户的未来动向进行预测。通过这种设计，我们的模型在很大程度上优于最先进的方法，同时也揭示了涉及时空的推荐问题中的冷启动挑战。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=GETNext:+Trajectory+Flow+Map+Enhanced+Transformer+for+Next+POI+Recommendation)|4| @@ -39,9 +39,9 @@ |[GERE: Generative Evidence Retrieval for Fact Verification](https://doi.org/10.1145/3477495.3531827)|Jiangui Chen, Ruqing Zhang, Jiafeng Guo, Yixing Fan, Xueqi Cheng|ICT, CAS & University of Chinese Academy of Sciences, Beijing, China|Fact verification (FV) is a challenging task which aims to verify a claim using multiple evidential sentences from trustworthy corpora, e.g., Wikipedia. Most existing approaches follow a three-step pipeline framework, including document retrieval, sentence retrieval and claim verification. High-quality evidences provided by the first two steps are the foundation of the effective reasoning in the last step. Despite being important, high-quality evidences are rarely studied by existing works for FV, which often adopt the off-the-shelf models to retrieve relevant documents and sentences in an "index-retrieve-then-rank'' fashion. This classical approach has clear drawbacks as follows: i) a large document index as well as a complicated search process is required, leading to considerable memory and computational overhead; ii) independent scoring paradigms fail to capture the interactions among documents and sentences in ranking; iii) a fixed number of sentences are selected to form the final evidence set. In this work, we proposeGERE, the first system that retrieves evidences in a generative fashion, i.e., generating the document titles as well as evidence sentence identifiers. This enables us to mitigate the aforementioned technical issues since: i) the memory and computational cost is greatly reduced because the document index is eliminated and the heavy ranking process is replaced by a light generative process; ii) the dependency between documents and that between sentences could be captured via sequential generation process; iii) the generative formulation allows us to dynamically select a precise set of relevant evidences for each claim. The experimental results on the FEVER dataset show that GERE achieves significant improvements over the state-of-the-art baselines, with both time-efficiency and memory-efficiency.|事实验证(FV)是一项具有挑战性的任务，其目的是利用可信语料库中的多个证据句来验证一个声明，例如维基百科。大多数现有的方法遵循三步流水线框架，包括文献检索、句子检索和索赔验证。前两步提供的高质量证据是最后一步有效推理的基础。尽管高质量的证据具有重要意义，但现有的 FV 研究很少对其进行研究，往往采用现成的模型，以“先索引后排名”的方式检索相关文档和句子。这种经典的方法有以下明显的缺点: i)需要大量的文档索引以及复杂的搜索过程，导致相当大的内存和计算开销; ii)独立的评分范例不能捕获排名中文档和句子之间的相互作用; iii)选择固定数量的句子来形成最终的证据集。在这项工作中，我们提出了 Gere，第一个以生成方式检索证据的系统，即生成文档标题以及证据句标识符。这使我们能够减轻上述技术问题，因为: i)内存和计算成本大大降低，因为文档索引被消除，重排序过程被轻生成过程取代; ii)文档之间的依赖性和句子之间的依赖性可以通过顺序生成过程捕获; iii)生成公式允许我们动态选择每个索赔的相关证据的精确集合。在 FEVER 数据集上的实验结果表明，在时间效率和内存效率方面，GARE 比最先进的基线有了显著的提高。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=GERE:+Generative+Evidence+Retrieval+for+Fact+Verification)|4| |[Towards Feature Selection for Ranking and Classification Exploiting Quantum Annealers](https://doi.org/10.1145/3477495.3531755)|Maurizio Ferrari Dacrema, Fabio Moroni, Riccardo Nembrini, Nicola Ferro, Guglielmo Faggioli, Paolo Cremonesi|Politecnico Di Milano, Milano, Italy; Università degli Studi di Padova, Padova, Italy; Politecnico di Milano, ContentWise, Milano, Italy|Feature selection is a common step in many ranking, classification, or prediction tasks and serves many purposes. By removing redundant or noisy features, the accuracy of ranking or classification can be improved and the computational cost of the subsequent learning steps can be reduced. However, feature selection can be itself a computationally expensive process. While for decades confined to theoretical algorithmic papers, quantum computing is now becoming a viable tool to tackle realistic problems, in particular special-purpose solvers based on the Quantum Annealing paradigm. This paper aims to explore the feasibility of using currently available quantum computing architectures to solve some quadratic feature selection algorithms for both ranking and classification. The experimental analysis includes 15 state-of-the-art datasets. The effectiveness obtained with quantum computing hardware is comparable to that of classical solvers, indicating that quantum computers are now reliable enough to tackle interesting problems. In terms of scalability, current generation quantum computers are able to provide a limited speedup over certain classical algorithms and hybrid quantum-classical strategies show lower computational cost for problems of more than a thousand features.|在许多排序、分类或预测任务中，特征选择是一个常见的步骤，有许多用途。通过去除冗余或噪声特征，可以提高排序或分类的准确性，降低后续学习步骤的计算成本。然而，特征选择本身就是一个计算开销很大的过程。数十年来，量子计算一直局限于理论算法论文，但现在已经成为解决现实问题的可行工具，特别是基于量子退火范式的特殊用途解决方案。本文旨在探讨利用现有的量子计算结构来解决一些二次特征选择算法的排序和分类的可行性。实验分析包括15个最先进的数据集。量子计算硬件获得的有效性与经典的解决方案相当，这表明量子计算机现在足够可靠，可以处理有趣的问题。在可扩展性方面，当前的量子计算机能够比某些经典算法提供有限的加速，混合量子-经典策略显示对于超过1000个特征的问题计算成本较低。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Towards+Feature+Selection+for+Ranking+and+Classification+Exploiting+Quantum+Annealers)|4| |[Continual Learning Dialogue Systems - Learning during Conversation](https://doi.org/10.1145/3477495.3532677)|Sahisnu Mazumder, Bing Liu|University of Illinois at Chicago, Chicago, IL, USA; Intel Labs, Santa Clara, CA, USA|Dialogue systems, commonly known as Chatbots, have gained escalating popularity in recent years due to their wide-spread applications in carrying out chit-chat conversations with users and accomplishing various tasks as personal assistants. However, they still have some major weaknesses. One key weakness is that they are typically trained from pre-collected and manually-labeled data and/or written with handcrafted rules. Their knowledge bases (KBs) are also fixed and pre-compiled by human experts. Due to the huge amount of manual effort involved, they are difficult to scale and also tend to produce many errors ought to their limited ability to understand natural language and the limited knowledge in their KBs. Thus, when these systems are deployed, the level of user satisfactory is often low. In this tutorial, we introduce and discuss methods to give chatbots the ability to continuously and interactively learn new knowledge during conversation, i.e. "on-the-job" by themselves so that as the systems chat more and more with users, they become more and more knowledgeable and improve their performance over time. The first half of the tutorial focuses on introducing the paradigm of lifelong and continual learning and discuss various related problems and challenges in conversational AI applications. In the second half, we present recent advancements on the topic, with a focus on continuous lexical and factual knowledge learning in dialogues, open-domain dialogue learning after deployment and learning of new language expressions via user interactions for language grounding applications (e.g. natural language interfaces). Finally, we conclude with a discussion on the scopes for continual conversational skill learning and present some open challenges for future research.|通常被称为聊天机器人的对话系统，由于其广泛应用于与用户进行聊天对话以及作为个人助理完成各种任务，近年来越来越受欢迎。然而，他们仍然有一些主要的弱点。一个关键的弱点是，它们通常是从预先收集和手动标记的数据和/或用手工制定的规则编写而来的。它们的知识库(KB)也是由人类专家固定和预编译的。由于涉及大量的人工操作，它们难以扩展，并且由于它们理解自然语言的能力有限和知识库中的知识有限，往往会产生许多错误。因此，在部署这些系统时，用户满意度通常很低。在本教程中，我们将介绍和讨论一些方法，让聊天机器人能够在对话过程中不断地、交互式地学习新知识，比如“在工作中”自己学习，这样随着系统与用户聊天越来越多，它们就会变得越来越有知识，并随着时间的推移提高性能。本教程的前半部分重点介绍了终身学习和持续学习的范例，并讨论了会话 AI 应用中的各种相关问题和挑战。在下半部分，我们介绍了这一主题的最新进展，重点是在对话中持续的词汇和事实知识学习，部署后的开放领域对话学习，以及通过语言基础应用程序(如自然语言界面)的用户交互学习新的语言表达式。最后，我们讨论了继续会话技能学习的范围，并对未来的研究提出了一些开放性的挑战。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Continual+Learning+Dialogue+Systems+-+Learning+during+Conversation)|4| -|[Conversational Information Seeking: Theory and Application](https://doi.org/10.1145/3477495.3532678)|Jeffrey Dalton, Sophie Fischer, Paul Owoicho, Filip Radlinski, Federico Rossetto, Johanne R. Trippas, Hamed Zamani|University of Glasgow, Glasgow, United Kingdom; Google, London, United Kingdom; University of Massachusetts Amherst, Amherst, MA, USA; RMIT University, Melbourne, Australia|Conversational information seeking (CIS) involves interaction sequences between one or more users and an information system. Interactions in CIS are primarily based on natural language dialogue, while they may include other types of interactions, such as click, touch, and body gestures. CIS recently attracted significant attention and advancements continue to be made. This tutorial follows the content of the recent Conversational Information Seeking book authored by several of the tutorial presenters. The tutorial aims to be an introduction to CIS for newcomers to CIS in addition to the recent advanced topics and state-of-the-art approaches for students and researchers with moderate knowledge of the topic. A significant part of the tutorial is dedicated to hands-on experiences based on toolkits developed by the presenters for conversational passage retrieval and multi-modal task-oriented dialogues. The outcomes of this tutorial include theoretical and practical knowledge, including a forum to meet researchers interested in CIS.|会话信息搜索(CIS)涉及一个或多个用户与信息系统之间的交互序列。CIS 中的交互主要基于自然语言对话，而它们可能包括其他类型的交互，如点击、触摸和肢体动作。独联体最近引起了重视，并继续取得进展。本教程遵循几位教程主持人最近撰写的《会话信息搜索》一书的内容。本教程的目的是除了为学生和研究人员提供最新的高级主题和最先进的方法之外，为初到 CIS 的人介绍 CIS。本教程的一个重要部分是专门介绍基于演讲者开发的工具包的实践经验，这些工具包用于会话文章检索和面向多模态任务的对话。本教程的成果包括理论和实践知识，包括一个论坛，以满足对 CIS 感兴趣的研究人员。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Conversational+Information+Seeking:+Theory+and+Application)|4| +|[Conversational Information Seeking: Theory and Application](https://doi.org/10.1145/3477495.3532678)|Jeffrey Dalton, Sophie Fischer, Paul Owoicho, Filip Radlinski, Federico Rossetto, Johanne R. Trippas, Hamed Zamani|University of Massachusetts Amherst, Amherst, MA, USA; RMIT University, Melbourne, Australia; University of Glasgow, Glasgow, United Kingdom; Google, London, United Kingdom|Conversational information seeking (CIS) involves interaction sequences between one or more users and an information system. Interactions in CIS are primarily based on natural language dialogue, while they may include other types of interactions, such as click, touch, and body gestures. CIS recently attracted significant attention and advancements continue to be made. This tutorial follows the content of the recent Conversational Information Seeking book authored by several of the tutorial presenters. The tutorial aims to be an introduction to CIS for newcomers to CIS in addition to the recent advanced topics and state-of-the-art approaches for students and researchers with moderate knowledge of the topic. A significant part of the tutorial is dedicated to hands-on experiences based on toolkits developed by the presenters for conversational passage retrieval and multi-modal task-oriented dialogues. The outcomes of this tutorial include theoretical and practical knowledge, including a forum to meet researchers interested in CIS.|会话信息搜索(CIS)涉及一个或多个用户与信息系统之间的交互序列。CIS 中的交互主要基于自然语言对话，而它们可能包括其他类型的交互，如点击、触摸和肢体动作。独联体最近引起了重视，并继续取得进展。本教程遵循几位教程主持人最近撰写的《会话信息搜索》一书的内容。本教程的目的是除了为学生和研究人员提供最新的高级主题和最先进的方法之外，为初到 CIS 的人介绍 CIS。本教程的一个重要部分是专门介绍基于演讲者开发的工具包的实践经验，这些工具包用于会话文章检索和面向多模态任务的对话。本教程的成果包括理论和实践知识，包括一个论坛，以满足对 CIS 感兴趣的研究人员。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Conversational+Information+Seeking:+Theory+and+Application)|4| |[MuMiN: A Large-Scale Multilingual Multimodal Fact-Checked Misinformation Social Network Dataset](https://doi.org/10.1145/3477495.3531744)|Dan Saattrup Nielsen, Ryan McConville|University of Bristol, Bristol, United Kingdom|Misinformation is becoming increasingly prevalent on social media and in news articles. It has become so widespread that we require algorithmic assistance utilising machine learning to detect such content. Training these machine learning models require datasets of sufficient scale, diversity and quality. However, datasets in the field of automatic misinformation detection are predominantly monolingual, include a limited amount of modalities and are not of sufficient scale and quality. Addressing this, we develop a data collection and linking system (MuMiN-trawl), to build a public misinformation graph dataset (MuMiN), containing rich social media data (tweets, replies, users, images, articles, hashtags) spanning 21 million tweets belonging to 26 thousand Twitter threads, each of which have been semantically linked to 13 thousand fact-checked claims across dozens of topics, events and domains, in 41 different languages, spanning more than a decade. The dataset is made available as a heterogeneous graph via a Python package (mumin). We provide baseline results for two node classification tasks related to the veracity of a claim involving social media, and demonstrate that these are challenging tasks, with the highest macro-average F1-score being 62.55% and 61.45% for the two tasks, respectively. The MuMiN ecosystem is available at https://mumin-dataset.github.io/, including the data, documentation, tutorials and leaderboards.|虚假信息在社交媒体和新闻文章中越来越普遍。它已经变得如此广泛，以至于我们需要利用机器学习来检测这些内容的算法辅助。训练这些机器学习模型需要有足够规模、多样性和质量的数据集。然而，自动错误信息检测领域的数据集主要是单语种的，包括数量有限的模式，规模和质量都不够。为了解决这个问题，我们开发了一个数据收集和链接系统(MuMiN-trawl) ，来建立一个公共错误信息图表数据集(MuMiN) ，其中包含丰富的社交媒体数据(tweet、回复、用户、图片、文章、标签) ，跨越2100万条 tweet，属于26000条 Twitter 线程，每条线程在语义上都与13000条事实核查声明相关联，涉及几十个主题、事件和域名，使用41种不同的语言，跨越10多年。数据集可以通过 Python 包(umin)作为异构图形提供。我们提供了两个节点分类任务的基线结果相关的准确性声明涉及社会媒体，并证明这些是具有挑战性的任务，最高的宏观平均 F1得分分别为62.55% 和61.45% 的两个任务。MumiN 的生态系统可以在 https://MuMiN-dataset.github.io/上使用，包括数据、文档、教程和排行榜。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=MuMiN:+A+Large-Scale+Multilingual+Multimodal+Fact-Checked+Misinformation+Social+Network+Dataset)|4| -|[A Non-Factoid Question-Answering Taxonomy](https://doi.org/10.1145/3477495.3531926)|Valeria Bolotova, Vladislav Blinov, Falk Scholer, W. Bruce Croft, Mark Sanderson|Ural Federal University, Melbourne, VIC, Australia; RMIT University, Melbourne, VIC, Australia; University of Massachusetts Amherst, Amherst, MA, USA|Non-factoid question answering (NFQA) is a challenging and under-researched task that requires constructing long-form answers, such as explanations or opinions, to open-ended non-factoid questions - NFQs. There is still little understanding of the categories of NFQs that people tend to ask, what form of answers they expect to see in return, and what the key research challenges of each category are. This work presents the first comprehensive taxonomy of NFQ categories and the expected structure of answers. The taxonomy was constructed with a transparent methodology and extensively evaluated via crowdsourcing. The most challenging categories were identified through an editorial user study. We also release a dataset of categorised NFQs and a question category classifier. Finally, we conduct a quantitative analysis of the distribution of question categories using major NFQA datasets, showing that the NFQ categories that are the most challenging for current NFQA systems are poorly represented in these datasets. This imbalance may lead to insufficient system performance for challenging categories. The new taxonomy, along with the category classifier, will aid research in the area, helping to create more balanced benchmarks and to focus models on addressing specific categories.|非事实性问题回答(NFQA)是一个具有挑战性和研究不足的任务，需要构建长形式的答案，如解释或意见，开放式非事实性问题-NFQs。对于人们倾向于询问的 NFQ 类别，他们期望看到的回答形式，以及每个类别的关键研究挑战是什么，人们仍然知之甚少。这项工作提出了第一个全面的分类 NFQ 类别和预期的答案结构。分类法采用透明的方法，并通过众包进行广泛评估。最具挑战性的类别是通过编辑用户研究确定的。我们还发布了一个分类的 NFQ 数据集和一个问题类别分类器。最后，我们使用主要的 NFQA 数据集对问题类别的分布进行了定量分析，表明对当前 NFQA 系统最具挑战性的 NFQ 类别在这些数据集中表现不佳。这种不平衡可能导致具有挑战性的类别的系统性能不足。新的分类法，连同类别分类器，将有助于该领域的研究，有助于创建更平衡的基准，并将重点放在解决特定类别的模型。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Non-Factoid+Question-Answering+Taxonomy)|4| +|[A Non-Factoid Question-Answering Taxonomy](https://doi.org/10.1145/3477495.3531926)|Valeria Bolotova, Vladislav Blinov, Falk Scholer, W. Bruce Croft, Mark Sanderson|University of Massachusetts Amherst, Amherst, MA, USA; Ural Federal University, Melbourne, VIC, Australia; RMIT University, Melbourne, VIC, Australia|Non-factoid question answering (NFQA) is a challenging and under-researched task that requires constructing long-form answers, such as explanations or opinions, to open-ended non-factoid questions - NFQs. There is still little understanding of the categories of NFQs that people tend to ask, what form of answers they expect to see in return, and what the key research challenges of each category are. This work presents the first comprehensive taxonomy of NFQ categories and the expected structure of answers. The taxonomy was constructed with a transparent methodology and extensively evaluated via crowdsourcing. The most challenging categories were identified through an editorial user study. We also release a dataset of categorised NFQs and a question category classifier. Finally, we conduct a quantitative analysis of the distribution of question categories using major NFQA datasets, showing that the NFQ categories that are the most challenging for current NFQA systems are poorly represented in these datasets. This imbalance may lead to insufficient system performance for challenging categories. The new taxonomy, along with the category classifier, will aid research in the area, helping to create more balanced benchmarks and to focus models on addressing specific categories.|非事实性问题回答(NFQA)是一个具有挑战性和研究不足的任务，需要构建长形式的答案，如解释或意见，开放式非事实性问题-NFQs。对于人们倾向于询问的 NFQ 类别，他们期望看到的回答形式，以及每个类别的关键研究挑战是什么，人们仍然知之甚少。这项工作提出了第一个全面的分类 NFQ 类别和预期的答案结构。分类法采用透明的方法，并通过众包进行广泛评估。最具挑战性的类别是通过编辑用户研究确定的。我们还发布了一个分类的 NFQ 数据集和一个问题类别分类器。最后，我们使用主要的 NFQA 数据集对问题类别的分布进行了定量分析，表明对当前 NFQA 系统最具挑战性的 NFQ 类别在这些数据集中表现不佳。这种不平衡可能导致具有挑战性的类别的系统性能不足。新的分类法，连同类别分类器，将有助于该领域的研究，有助于创建更平衡的基准，并将重点放在解决特定类别的模型。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Non-Factoid+Question-Answering+Taxonomy)|4| |[A Multitask Framework for Sentiment, Emotion and Sarcasm aware Cyberbullying Detection from Multi-modal Code-Mixed Memes](https://doi.org/10.1145/3477495.3531925)|Krishanu Maity, Prince Jha, Sriparna Saha, Pushpak Bhattacharyya|Indian Institute of Technology Bombay, Bombay, India; Indian Institute of Technology Patna, Patna, India|Detecting cyberbullying from memes is highly challenging, because of the presence of the implicit affective content which is also often sarcastic, and multi-modality (image + text). The current work is the first attempt, to the best of our knowledge, in investigating the role of sentiment, emotion and sarcasm in identifying cyberbullying from multi-modal memes in a code-mixed language setting. As a contribution, we have created a benchmark multi-modal meme dataset called MultiBully annotated with bully, sentiment, emotion and sarcasm labels collected from open-source Twitter and Reddit platforms. Moreover, the severity of the cyberbullying posts is also investigated by adding a harmfulness score to each of the memes. The created dataset consists of two modalities, text and image. Most of the texts in our dataset are in code-mixed form, which captures the seamless transitions between languages for multilingual users. Two different multimodal multitask frameworks (BERT+ResNET-Feedback and CLIP-CentralNet) have been proposed for cyberbullying detection (CD), the three auxiliary tasks being sentiment analysis (SA), emotion recognition (ER) and sarcasm detection (SAR). Experimental results indicate that compared to uni-modal and single-task variants, the proposed frameworks improve the performance of the main task, i.e., CD, by 3.18% and 3.10% in terms of accuracy and F1 score, respectively.|网络欺凌的模因检测具有很大的挑战性，因为网络欺凌的内隐情感内容往往是讽刺性的，而且是多模态的(图片 + 文本)。目前的工作是第一次尝试，就我们所知，在调查的作用，情感，情绪和讽刺识别网络欺凌从多模态模因在编码混合的语言环境。作为一个贡献，我们创建了一个基准的多模态 Meme 数据集，称为 MultiBully，注释了从开源 Twitter 和 Reddit 平台收集的恶霸、情绪、情感和讽刺标签。此外，网络欺凌帖子的严重性也通过添加一个危害性评分到每个模因进行调查。创建的数据集由文本和图像两种模式组成。我们数据集中的大多数文本都是代码混合形式的，它捕获了多语言用户语言之间的无缝转换。针对网络欺凌检测(CD) ，提出了两种不同的多模态多任务框架(BERT + R esNET-Feeback 和 CLIP-CentralNet) ，即情绪分析(SA)、情绪识别(ER)和讽刺检测(SAR)三种辅助任务。实验结果表明，与单模态和单任务变体相比，提出的框架在准确性和 F1评分方面分别提高了主要任务 CD 的执行效率3.18% 和3.10% 。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Multitask+Framework+for+Sentiment,+Emotion+and+Sarcasm+aware+Cyberbullying+Detection+from+Multi-modal+Code-Mixed+Memes)|4| |[MetaCVR: Conversion Rate Prediction via Meta Learning in Small-Scale Recommendation Scenarios](https://doi.org/10.1145/3477495.3531733)|Xiaofeng Pan, Ming Li, Jing Zhang, Keren Yu, Hong Wen, Luping Wang, Chengjun Mao, Bo Cao|Alibaba Group, Hangzhou, China; The University of Sydney, Sydney, NSW, China|Different from large-scale platforms such as Taobao and Amazon, CVR modeling in small-scale recommendation scenarios is more challenging due to the severe Data Distribution Fluctuation (DDF) issue. DDF prevents existing CVR models from being effective since 1) several months of data are needed to train CVR models sufficiently in small scenarios, leading to considerable distribution discrepancy between training and online serving; and 2) e-commerce promotions have significant impacts on small scenarios, leading to distribution uncertainty of the upcoming time period. In this work, we propose a novel CVR method named MetaCVR from a perspective of meta learning to address the DDF issue. Firstly, a base CVR model which consists of a Feature Representation Network (FRN) and output layers is designed and trained sufficiently with samples across months. Then we treat time periods with different data distributions as different occasions and obtain positive and negative prototypes for each occasion using the corresponding samples and the pre-trained FRN. Subsequently, a Distance Metric Network (DMN) is devised to calculate the distance metrics between each sample and all prototypes to facilitate mitigating the distribution uncertainty. At last, we develop an Ensemble Prediction Network (EPN) which incorporates the output of FRN and DMN to make the final CVR prediction. In this stage, we freeze the FRN and train the DMN and EPN with samples from recent time period, therefore effectively easing the distribution discrepancy. To the best of our knowledge, this is the first study of CVR prediction targeting the DDF issue in small-scale recommendation scenarios. Experimental results on real-world datasets validate the superiority of our MetaCVR and online A/B test also shows our model achieves impressive gains of 11.92% on PCVR and 8.64% on GMV.|与淘宝和亚马逊等大型平台不同，由于数据分布波动(DDF)问题严重，小规模推荐场景下的 CVR 建模更具挑战性。DDF 阻止了现有的 CVR 模型的有效性，因为1)需要几个月的数据来充分训练 CVR 模型在小场景中，导致训练和在线服务之间的相当大的分布差异; 2)电子商务促销对小场景有重大影响，导致即将到来的时间段的分布不确定性。本文从元学习的角度出发，提出了一种新的 CVR 方法 MetaCVR 来解决 DDF 问题。首先，设计了一个由特征表示网络(FRN)和输出层组成的基本 CVR 模型，并对该模型进行了数月的样本训练。然后将不同时间段的数据分布视为不同的场合，利用相应的样本和预先训练好的 FRN，得到每个场合的正负原型。随后，设计了一个距离度量网络(DMN)来计算每个样本和所有原型之间的距离度量，以减少分布的不确定性。最后，我们开发了一个集成预测网络(EPN) ，将 FRN 和 DMN 的输出结合起来进行 CVR 的最终预测。在这一阶段，我们冻结 FRN，并用最近时间段的样本训练 DMN 和 EPN，从而有效地缓解了分布差异。据我们所知，这是第一个针对小规模推荐场景中 DDF 问题的 CVR 预测研究。在实际数据集上的实验结果验证了我们的 MetaCVR 模型的优越性，在线 A/B 测试也表明我们的模型在 PCVR 和 GMV 上分别取得了令人印象深刻的11.92% 和8.64% 的增益。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=MetaCVR:+Conversion+Rate+Prediction+via+Meta+Learning+in+Small-Scale+Recommendation+Scenarios)|3| |[Can Clicks Be Both Labels and Features?: Unbiased Behavior Feature Collection and Uncertainty-aware Learning to Rank](https://doi.org/10.1145/3477495.3531948)|Tao Yang, Chen Luo, Hanqing Lu, Parth Gupta, Bing Yin, Qingyao Ai|University of Utah, Salt Lake City, UT, USA; Amazon Search, Palo Alto, CA, USA|Using implicit feedback collected from user clicks as training labels for learning-to-rank algorithms is a well-developed paradigm that has been extensively studied and used in modern IR systems. Using user clicks as ranking features, on the other hand, has not been fully explored in existing literature. Despite its potential in improving short-term system performance, whether the incorporation of user clicks as ranking features is beneficial for learning-to-rank systems in the long term is still questionable. Two of the most important problems are (1) the explicit bias introduced by noisy user behavior, and (2) the implicit bias, which we refer to as the exploitation bias, introduced by the dynamic training and serving of learning-to-rank systems with behavior features. In this paper, we explore the possibility of incorporating user clicks as both training labels and ranking features for learning to rank. We formally investigate the problems in feature collection and model training, and propose a counterfactual feature projection function and a novel uncertainty-aware learning to rank framework. Experiments on public datasets show that ranking models learned with the proposed framework can significantly outperform models built with raw click features and algorithms that rank items without considering model uncertainty.|使用从用户点击收集的隐式反馈作为学习到排序算法的训练标签是一个发展良好的范例，已经在现代 IR 系统中得到了广泛的研究和应用。使用用户点击作为排名功能，另一方面，还没有充分探讨现有的文献。尽管它在提高短期系统性能方面具有潜力，但是从长远来看，将用户点击作为排名功能的结合是否有利于学习排名系统仍然值得怀疑。其中最重要的两个问题是: (1)噪声用户行为引入的显性偏差和(2)具有行为特征的学习排序系统的动态训练和服务引入的隐性偏差，我们称之为剥削偏差。在这篇文章中，我们探讨了将用户点击作为训练标签和排名特征来学习排名的可能性。我们正式研究了特征收集和模型训练中的问题，提出了一种反事实特征投影函数和一种新的不确定性学习排序框架。对公共数据集的实验表明，使用所提出的框架学习的排序模型可以显著优于使用原始点击特征和算法建立的模型，这些模型不考虑模型的不确定性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Can+Clicks+Be+Both+Labels+and+Features?:+Unbiased+Behavior+Feature+Collection+and+Uncertainty-aware+Learning+to+Rank)|3| @@ -53,99 +53,99 @@ |[Mitigating Bias in Search Results Through Contextual Document Reranking and Neutrality Regularization](https://doi.org/10.1145/3477495.3531891)|George Zerveas, Navid Rekabsaz, Daniel Cohen, Carsten Eickhoff|Johannes Kepler University Linz & Linz Institute of Technology, Linz, Austria; Brown University, Providence, RI, USA|Societal biases can influence Information Retrieval system results, and conversely, search results can potentially reinforce existing societal biases. Recent research has therefore focused on developing methods for quantifying and mitigating bias in search results and applied them to contemporary retrieval systems that leverage transformer-based language models. In the present work, we expand this direction of research by considering bias mitigation within a framework for contextual document embedding reranking. In this framework, the transformer-based query encoder is optimized for relevance ranking through a list-wise objective, by jointly scoring for the same query a large set of candidate document embeddings in the context of one another, instead of in isolation. At the same time, we impose a regularization loss which penalizes highly scoring documents that deviate from neutrality with respect to a protected attribute (e.g., gender). Our approach for bias mitigation is end-to-end differentiable and efficient. Compared to the existing alternatives for deep neural retrieval architectures, which are based on adversarial training, we demonstrate that it can attain much stronger bias mitigation/fairness. At the same time, for the same amount of bias mitigation, it offers significantly better relevance performance (utility). Crucially, our method allows for a more finely controllable and predictable intensity of bias mitigation, which is essential for practical deployment in production systems.|社会偏见可以影响信息检索系统的结果，相反，搜索结果可能会加强现有的社会偏见。因此，最近的研究侧重于开发用于量化和减轻搜索结果偏差的方法，并将其应用于利用基于转换器的语言模型的当代检索系统。在本文的工作中，我们扩展了这个研究方向，在上下文文档嵌入重排序的框架内考虑了偏差缓解。在这个框架中，基于转换器的查询编码器通过列表目标优化相关性排序，为同一个查询联合评分一大组候选文档嵌入在另一个上下文中，而不是孤立。与此同时，我们强制规范化的损失，惩罚高得分的文件偏离中立方面的保护属性(例如，性别)。我们的减少偏差的方法是端到端可微和有效的。与已有的基于对抗训练的深度神经检索结构相比，我们证明了该结构可以获得更强的偏差缓解/公平性。同时，对于相同数量的偏差缓解，它提供了明显更好的相关性能(效用)。至关重要的是，我们的方法允许更精细地控制和可预测的偏差缓解强度，这对于生产系统中的实际部署是必不可少的。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Mitigating+Bias+in+Search+Results+Through+Contextual+Document+Reranking+and+Neutrality+Regularization)|3| |[Curriculum Contrastive Context Denoising for Few-shot Conversational Dense Retrieval](https://doi.org/10.1145/3477495.3531961)|Kelong Mao, Zhicheng Dou, Hongjin Qian|Renmin University of China, Beijing, China; Renmin University of China & Beijing Key Laboratory of Big Data Management and Analysis Methods, Beijing, China|Conversational search is a crucial and promising branch in information retrieval. In this paper, we reveal that not all historical conversational turns are necessary for understanding the intent of the current query. The redundant noisy turns in the context largely hinder the improvement of search performance. However, enhancing the context denoising ability for conversational search is quite challenging due to data scarcity and the steep difficulty for simultaneously learning conversational query encoding and context denoising. To address these issues, in this paper, we present a novel Curriculum cOntrastive conTExt Denoising framework, COTED, towards few-shot conversational dense retrieval. Under a curriculum training order, we progressively endow the model with the capability of context denoising via contrastive learning between noised samples and denoised samples generated by a new conversation data augmentation strategy. Three curriculums tailored to conversational search are exploited in our framework. Extensive experiments on two few-shot conversational search datasets, i.e., CAsT-19 and CAsT-20, validate the effectiveness and superiority of our method compared with the state-of-the-art baselines.|会话搜索是信息检索中一个重要而有前途的分支。本文揭示了并非所有的历史会话转折都是理解当前查询意图的必要条件。上下文中的冗余噪声转折很大程度上阻碍了搜索性能的提高。然而，由于数据的稀缺性和同时学习会话查询编码和上下文去噪的困难性，提高会话搜索的上下文去噪能力是一个相当具有挑战性的问题。为了解决这些问题，本文提出了一种新的课程对比语境去噪框架 COTED，该框架旨在实现少镜头的会话密集检索。在课程训练顺序下，我们逐步赋予该模型通过对比学习去除噪声样本和通过一种新的会话数据增强策略产生的去噪样本的上下文去噪能力。在我们的框架中开发了三个适合会话搜索的课程。在两个少量会话搜索数据集，即 CAsT-19和 CAsT-20上的广泛实验验证了我们的方法与最先进的基线相比的有效性和优越性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Curriculum+Contrastive+Context+Denoising+for+Few-shot+Conversational+Dense+Retrieval)|3| |[Decoupled Side Information Fusion for Sequential Recommendation](https://doi.org/10.1145/3477495.3531963)|Yueqi Xie, Peilin Zhou, Sunghun Kim|HKUST, Hong Kong, Hong Kong; Upstage, Hong Kong, Hong Kong|Side information fusion for sequential recommendation (SR) aims to effectively leverage various side information to enhance the performance of next-item prediction. Most state-of-the-art methods build on self-attention networks and focus on exploring various solutions to integrate the item embedding and side information embeddings before the attention layer. However, our analysis shows that the early integration of various types of embeddings limits the expressiveness of attention matrices due to a rank bottleneck and constrains the flexibility of gradients. Also, it involves mixed correlations among the different heterogeneous information resources, which brings extra disturbance to attention calculation. Motivated by this, we propose Decoupled Side Information Fusion for Sequential Recommendation (DIF-SR), which moves the side information from the input to the attention layer and decouples the attention calculation of various side information and item representation. We theoretically and empirically show that the proposed solution allows higher-rank attention matrices and flexible gradients to enhance the modeling capacity of side information fusion. Also, auxiliary attribute predictors are proposed to further activate the beneficial interaction between side information and item representation learning. Extensive experiments on four real-world datasets demonstrate that our proposed solution stably outperforms state-of-the-art SR models. Further studies show that our proposed solution can be readily incorporated into current attention-based SR models and significantly boost performance. Our source code is available at https://github.com/AIM-SE/DIF-SR.|序贯推荐侧信息融合是为了有效地利用各种侧信息来提高下一个项目的预测性能。大多数最新的方法都是建立在自我注意网络的基础上，着重于探索各种解决方案，以整合注意层之前的项目嵌入和侧信息嵌入。然而，我们的分析表明，由于等级瓶颈的存在，各种嵌入类型的早期集成限制了注意矩阵的表达能力，同时也限制了梯度的灵活性。此外，它还涉及到不同异质信息资源之间的混合相关，给注意计算带来额外的干扰。在此基础上，提出了基于解耦的序贯推荐侧信息融合方法(DIF-SR) ，该方法将侧信息从输入层移动到注意层，并对各侧信息的注意计算和项目表示进行解耦。理论和实验结果表明，该方法允许高阶注意矩阵和灵活的梯度，提高了侧向信息融合的建模能力。同时，提出了辅助属性预测器，以进一步激活侧信息与项目表征学习之间的有益交互作用。在四个真实世界数据集上的大量实验表明，我们提出的解决方案稳定地优于最先进的 SR 模型。进一步的研究表明，我们提出的解决方案可以很容易地纳入目前的注意为基础的 SR 模型，并显着提高性能。我们的源代码可以在 https://github.com/aim-se/dif-sr 找到。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Decoupled+Side+Information+Fusion+for+Sequential+Recommendation)|3| -|[Is News Recommendation a Sequential Recommendation Task?](https://doi.org/10.1145/3477495.3531862)|Chuhan Wu, Fangzhao Wu, Tao Qi, Chenliang Li, Yongfeng Huang|Renmin University of China, Beijing, China; Beijing University of Posts and Telecommunications, Beijing, China; York University, Toronto, Canada|For sequential recommendation, it is essential to capture and predict future or long-term user preference for generating accurate recommendation over time. To improve the predictive capacity, we adopt reinforcement learning (RL) for developing effective sequential recommenders. However, user-item interaction data is likely to be sparse, complicated and time-varying. It is not easy to directly apply RL techniques to improve the performance of sequential recommendation. Inspired by the availability of knowledge graph (KG), we propose a novel Knowledge-guidEd Reinforcement Learning model (KERL for short) for fusing KG information into a RL framework for sequential recommendation. Specifically, we formalize the sequential recommendation task as a Markov Decision Process (MDP), and make three major technical extensions in this framework, including state representation, reward function and learning algorithm. First, we propose to enhance the state representations with KG information considering both exploitation and exploration. Second, we carefully design a composite reward function that is able to compute both sequence- and knowledge-level rewards. Third, we propose a new algorithm for more effectively learning the proposed model. To our knowledge, it is the first time that knowledge information has been explicitly discussed and utilized in RL-based sequential recommenders, especially for the exploration process. Extensive experiment results on both next-item and next-session recommendation tasks show that our model can significantly outperform the baselines on four real-world datasets.|对于连续推荐，必须捕获和预测未来或长期的用户偏好，以便随着时间的推移产生准确的推荐。为了提高预测能力，我们采用强化学习(RL)来开发有效的顺序推荐系统。然而，用户项交互数据可能是稀疏的、复杂的和时变的。直接应用 RL 技术来提高顺序推荐的性能并不容易。受到知识图表(kG)的启发，我们提出了一种新的知识引导强化学习模型(简称 KERL) ，用于将 kG 信息融合到一个 RL 框架中，用于连续推荐。具体来说，我们将顺序推荐任务形式化为一个马可夫决策过程(mDP) ，并在这个框架中进行了三个主要的技术扩展，包括状态表示、奖励函数和学习算法。首先，我们提出了利用 KG 信息同时考虑开发和探索的方法来增强状态表示。其次，我们仔细设计了一个复合奖励函数，它能够计算序列和知识水平的奖励。第三，我们提出了一个新的算法，以更有效地学习所提出的模型。据我们所知，知识信息首次被明确地讨论和利用在基于 RL 的顺序推荐系统中，特别是在探索过程中。对下一个项目和下一个会话推荐任务的大量实验结果表明，我们的模型可以显著优于四个真实世界数据集的基线。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Is+News+Recommendation+a+Sequential+Recommendation+Task?)|3| +|[Is News Recommendation a Sequential Recommendation Task?](https://doi.org/10.1145/3477495.3531862)|Chuhan Wu, Fangzhao Wu, Tao Qi, Chenliang Li, Yongfeng Huang|York University, Toronto, Canada; Beijing University of Posts and Telecommunications, Beijing, China; Renmin University of China, Beijing, China|For sequential recommendation, it is essential to capture and predict future or long-term user preference for generating accurate recommendation over time. To improve the predictive capacity, we adopt reinforcement learning (RL) for developing effective sequential recommenders. However, user-item interaction data is likely to be sparse, complicated and time-varying. It is not easy to directly apply RL techniques to improve the performance of sequential recommendation. Inspired by the availability of knowledge graph (KG), we propose a novel Knowledge-guidEd Reinforcement Learning model (KERL for short) for fusing KG information into a RL framework for sequential recommendation. Specifically, we formalize the sequential recommendation task as a Markov Decision Process (MDP), and make three major technical extensions in this framework, including state representation, reward function and learning algorithm. First, we propose to enhance the state representations with KG information considering both exploitation and exploration. Second, we carefully design a composite reward function that is able to compute both sequence- and knowledge-level rewards. Third, we propose a new algorithm for more effectively learning the proposed model. To our knowledge, it is the first time that knowledge information has been explicitly discussed and utilized in RL-based sequential recommenders, especially for the exploration process. Extensive experiment results on both next-item and next-session recommendation tasks show that our model can significantly outperform the baselines on four real-world datasets.|对于连续推荐，必须捕获和预测未来或长期的用户偏好，以便随着时间的推移产生准确的推荐。为了提高预测能力，我们采用强化学习(RL)来开发有效的顺序推荐系统。然而，用户项交互数据可能是稀疏的、复杂的和时变的。直接应用 RL 技术来提高顺序推荐的性能并不容易。受到知识图表(kG)的启发，我们提出了一种新的知识引导强化学习模型(简称 KERL) ，用于将 kG 信息融合到一个 RL 框架中，用于连续推荐。具体来说，我们将顺序推荐任务形式化为一个马可夫决策过程(mDP) ，并在这个框架中进行了三个主要的技术扩展，包括状态表示、奖励函数和学习算法。首先，我们提出了利用 KG 信息同时考虑开发和探索的方法来增强状态表示。其次，我们仔细设计了一个复合奖励函数，它能够计算序列和知识水平的奖励。第三，我们提出了一个新的算法，以更有效地学习所提出的模型。据我们所知，知识信息首次被明确地讨论和利用在基于 RL 的顺序推荐系统中，特别是在探索过程中。对下一个项目和下一个会话推荐任务的大量实验结果表明，我们的模型可以显著优于四个真实世界数据集的基线。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Is+News+Recommendation+a+Sequential+Recommendation+Task?)|3| |[Dual Contrastive Network for Sequential Recommendation](https://doi.org/10.1145/3477495.3531918)|Guanyu Lin, Chen Gao, Yinfeng Li, Yu Zheng, Zhiheng Li, Depeng Jin, Yong Li|Tsinghua University, Beijing, China|Widely applied in today's recommender systems, sequential recommendation predicts the next interacted item for a given user via his/her historical item sequence. However, sequential recommendation suffers data sparsity issue like most recommenders. To extract auxiliary signals from the data, some recent works exploit self-supervised learning to generate augmented data via dropout strategy, which, however, leads to sparser sequential data and obscure signals. In this paper, we propose D ual C ontrastive N etwork (DCN) to boost sequential recommendation, from a new perspective of integrating auxiliary user-sequence for items. Specifically, we propose two kinds of contrastive learning. The first one is the dual representation contrastive learning that minimizes the distances between embeddings and sequence-representations of users/items. The second one is the dual interest contrastive learning which aims to self-supervise the static interest with the dynamic interest of next item prediction via auxiliary training. We also incorporate the auxiliary task of predicting next user for a given item's historical user sequence, which can capture the trends of items preferred by certain types of users. Experiments on benchmark datasets verify the effectiveness of our proposed method. Further ablation study also illustrates the boosting effect of the proposed components upon different sequential models.|顺序推荐在当今的推荐系统中被广泛应用，它通过用户的历史项目顺序来预测给定用户的下一个交互项目。然而，与大多数推荐程序一样，顺序推荐也存在数据稀疏的问题。为了从数据中提取辅助信号，最近的一些工作利用自监督学习通过丢失策略生成增强数据，但是这样会导致序列数据更稀疏，信号更模糊。本文从集成项目辅助用户序列的新角度出发，提出了 D-C 对比 N 网络(DCN)来增强序列推荐。具体来说，我们提出了两种对比学习。第一种是双重表示对比学习，它最小化了嵌入和用户/项目序列表示之间的距离。第二种是双兴趣对比学习，目的是通过辅助训练对静态兴趣和下一项预测的动态兴趣进行自我监督。我们还结合了辅助任务，即预测给定项目的历史用户序列的下一个用户，这可以捕获某些类型的用户喜欢的项目的趋势。在基准数据集上的实验验证了该方法的有效性。进一步的消融研究也说明了所提出的组件对不同序列模型的增强效应。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Dual+Contrastive+Network+for+Sequential+Recommendation)|3| |[PKG: A Personal Knowledge Graph for Recommendation](https://doi.org/10.1145/3477495.3531671)|Yu Yang, Jiangxu Lin, Xiaolian Zhang, Meng Wang|Southeast University, Nanjing, China; Huawei Technologies Co. Ltd., Shenzhen, China|Mobile internet users generate personal data on the devices all the time in this era. In this paper, we demonstrate a novel system for integrating the data of a user from different sources into a Personal Knowledge Graph, i.e., PKG. We show how a user's intention can be detected and how the personal data can be aligned and connected by the user behaviors. The constructed PKG allows the system makes reasonable and accurate recommendations for users by a "neural + symbolic'' approach across different services. Our system is shown in https://youtu.be/hWuo8KCDrto.|在这个时代，移动互联网用户一直在设备上生成个人数据。在本文中，我们展示了一个新的系统来整合来自不同来源的用户的数据到一个个人知识图，即 PKG。我们展示了如何检测用户的意图，以及如何通过用户行为来校准和连接个人数据。构建的 PKG 允许系统通过“神经 + 符号”的方法跨越不同的服务为用户提供合理和准确的建议。我们的系统以 https://youtu.be/hwuo8kcdrto 显示。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=PKG:+A+Personal+Knowledge+Graph+for+Recommendation)|3| |[Co-training Disentangled Domain Adaptation Network for Leveraging Popularity Bias in Recommenders](https://doi.org/10.1145/3477495.3531952)|Zhihong Chen, Jiawei Wu, Chenliang Li, Jingxu Chen, Rong Xiao, Binqiang Zhao|Alibaba Group, Hangzhou, China; Wuhan University, Wuhan, China|Recommender system usually faces popularity bias. From the popularity distribution shift perspective, the normal paradigm trained on exposed items (most are hot items) identifies that recommending popular items more frequently can achieve lower loss, thus injecting popularity information into item property embedding, e.g., id embedding. From the long-tail distribution shift perspective, the sparse interactions of long-tail items lead to insufficient learning of them. The resultant distribution discrepancy between hot and long-tail items would not only inherit the bias, but also amplify the bias. Existing work addresses this issue with inverse propensity scoring (IPS) or causal embeddings. However, we argue that not all popularity biases mean bad effects, i.e., some items show higher popularity due to better quality or conform to current trends, which deserve more recommendations. Blindly seeking unbiased learning may inhibit high-quality or fashionable items. To make better use of the popularity bias, we propose a co-training disentangled domain adaptation network (CD$^2$AN), which can co-train both biased and unbiased models. Specifically, for popularity distribution shift, CD$^2$AN disentangles item property representation and popularity representation from item property embedding. For long-tail distribution shift, we introduce additional unexposed items (most are long-tail items) to align the distribution of hot and long-tail item property representations. Further, from the instances perspective, we carefully design the item similarity regularization to learn comprehensive item representation, which encourages item pairs with more effective co-occurrences patterns to have more similar item property representations. Based on offline evaluations and online A/B tests, we show that CD$^2$AN outperforms the existing debiased solutions. Currently, CD$^2$AN has been successfully deployed at Mobile Taobao App and handling major online traffic.|推荐系统通常面临受欢迎程度的偏见。从受欢迎度分布转移的角度来看，通常对曝光项目(大多数是热门项目)进行训练的范式认为，更频繁地推荐受欢迎项目可以获得更低的损失，从而将受欢迎信息注入项目属性嵌入，例如，ID 嵌入。从长尾分布偏移的角度来看，长尾项目之间稀疏的交互作用导致对它们的学习不足。由此产生的热点项目与长尾项目之间的分布差异不仅会继承偏差，而且会放大偏差。现有的工作解决这个问题的逆倾向评分(IPS)或因果嵌入。然而，我们认为并非所有的流行偏见都意味着负面影响，例如，一些项目由于质量更好或符合当前趋势而显示出更高的流行度，这值得更多的推荐。盲目追求无偏见的学习可能会抑制高质量或时尚的项目。为了更好地利用流行度偏差，我们提出了一种协同训练的去纠缠域自适应网络(CD $^ 2 $AN) ，它可以同时训练有偏和无偏模型。具体地说，对于流行度分布转移，CD $^ 2 $AN 将项目属性表示和流行度表示从项目属性嵌入中分离出来。对于长尾分布转移，我们引入了额外的未公开项(大多数是长尾项)来对齐热点和长尾项属性表示的分布。此外，从实例的角度，我们仔细设计了项目相似性正则化，以学习综合项目表示，鼓励具有更有效的共现模式的项目对具有更多相似的项目属性表示。基于离线评估和在线 A/B 测试，我们表明 CD $^ 2 $AN 优于现有的去偏解决方案。目前，CD $^ 2 $AN 已经成功部署在移动淘宝应用程序上，并处理主要的在线流量。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Co-training+Disentangled+Domain+Adaptation+Network+for+Leveraging+Popularity+Bias+in+Recommenders)|3| -|[Multi-Level Interaction Reranking with User Behavior History](https://doi.org/10.1145/3477495.3532026)|Yunjia Xi, Weiwen Liu, Jieming Zhu, Xilong Zhao, Xinyi Dai, Ruiming Tang, Weinan Zhang, Rui Zhang, Yong Yu|ruizhang.info, Shenzhen, China; Huawei Noah's Ark Lab, Shenzhen, China; Shanghai Jiao Tong University, Shanghai, China|As the final stage of the multi-stage recommender system (MRS), reranking directly affects users' experience and satisfaction, thus playing a critical role in MRS. Despite the improvement achieved in the existing work, three issues are yet to be solved. First, users' historical behaviors contain rich preference information, such as users' long and short-term interests, but are not fully exploited in reranking. Previous work typically treats items in history equally important, neglecting the dynamic interaction between the history and candidate items. Second, existing reranking models focus on learning interactions at the item level while ignoring the fine-grained feature-level interactions. Lastly, estimating the reranking score on the ordered initial list before reranking may lead to the early scoring problem, thereby yielding suboptimal reranking performance. To address the above issues, we propose a framework named Multi-level Interaction Reranking (MIR). MIR combines low-level cross-item interaction and high-level set-to-list interaction, where we view the candidate items to be reranked as a set and the users' behavior history in chronological order as a list. We design a novel SLAttention structure for modeling the set-to-list interactions with personalized long-short term interests. Moreover, feature-level interactions are incorporated to capture the fine-grained influence among items. We design MIR in such a way that any permutation of the input items would not change the output ranking, and we theoretically prove it. Extensive experiments on three public and proprietary datasets show that MIR significantly outperforms the state-of-the-art models using various ranking and utility metrics.|作为多阶段推荐系统(MRS)的最后阶段，重新排名直接影响用户的体验和满意度，因此在 MRS 中发挥着关键作用。尽管现有工作已有所改善，但仍有三个问题有待解决。首先，用户的历史行为包含了丰富的偏好信息，如用户的长期和短期兴趣，但在重新排序时没有得到充分的利用。以往的研究通常认为历史条目同等重要，而忽视了历史条目与候选条目之间的动态互动。其次，现有的重新排序模型侧重于项目层面的学习交互，而忽略了细粒度的特征层面的交互。最后，在重新排序之前估计排序初始列表上的重新排序得分可能会导致早期得分问题，从而产生次优的重新排序性能。为了解决上述问题，我们提出了一个名为多级交互重排(MIR)的框架。MIR 结合了低层次的跨项目交互和高层次的集合-列表交互，我们将待重新排序的候选项作为一个集合，将用户的行为历史按照时间顺序作为一个列表。我们设计了一个新颖的空间注意结构，用于建模具有个性化长期短期兴趣的集合列表交互。此外，特征层次的交互作用被合并来捕获项目之间的细粒度影响。我们设计 MIR 的方法使得输入项的任何排列都不会改变输出的排名，并且我们从理论上证明了这一点。对三个公共和专有数据集的大量实验表明，使用各种排名和效用指标，MIR 显著优于最先进的模型。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Multi-Level+Interaction+Reranking+with+User+Behavior+History)|3| -|[RankFlow: Joint Optimization of Multi-Stage Cascade Ranking Systems as Flows](https://doi.org/10.1145/3477495.3532050)|Jiarui Qin, Jiachen Zhu, Bo Chen, Zhirong Liu, Weiwen Liu, Ruiming Tang, Rui Zhang, Yong Yu, Weinan Zhang|ruizhang.info, Shenzhen, China; Huawei Noah's Ark Lab, Shenzhen, China; Shanghai Jiao Tong University, Shanghai, China|Building a multi-stage cascade ranking system is a commonly used solution to balance the efficiency and effectiveness in modern information retrieval (IR) applications, such as recommendation and web search. Despite the popularity in practice, the literature specific on multi-stage cascade ranking systems is relatively scarce. The common practice is to train rankers of each stage independently using the same user feedback data (a.k.a., impression data), disregarding the data flow and the possible interactions between stages. This straightforward solution could lead to a sub-optimal system because of the sample selection bias (SSB) issue, which is especially damaging for cascade rankers due to the negative effect accumulated in the multiple stages. Worse still, the interactions between the rankers of each stage are not fully exploited. This paper provides an elaborate analysis of this commonly used solution to reveal its limitations. By studying the essence of cascade ranking, we propose a joint training framework named RankFlow to alleviate the SSB issue and exploit the interactions between the cascade rankers, which is the first systematic solution for this topic. We propose a paradigm of training cascade rankers that emphasizes the importance of fitting rankers on stage-specific data distributions instead of the unified user feedback distribution. We design the RankFlow framework based on this paradigm: The training data of each stage is generated by its preceding stages while the guidance signals not only come from the logs but its successors. Extensive experiments are conducted on various IR scenarios, including recommendation, web search and advertisement. The results verify the efficacy and superiority of RankFlow.|建立一个多阶段的级联排名系统是现代信息检索应用(如推荐和网络搜索)中一个常用的平衡效率和有效性的解决方案。尽管在实践中很流行，但是关于多级级联排序系统的文献相对较少。通常的做法是使用相同的用户反馈数据(也就是印象数据)独立训练每个阶段的排名，忽略数据流和阶段之间可能的交互作用。由于样本选择偏差(SSB)问题，这种直接的解决方案可能会导致系统的次优化，而由于多阶段累积的负面效应，SSB 问题对级联排序尤其有害。更糟糕的是，每个阶段的排名之间的相互作用没有得到充分利用。本文对这种常用的解决方案进行了详细的分析，以揭示其局限性。通过研究级联排序的本质，提出了一种联合训练框架 RankFlow 来缓解 SSB 问题，并利用级联排序器之间的相互作用，这是本课题的第一个系统解决方案。我们提出了一个训练级联排名的范式，强调拟合排名的重要性阶段特定的数据分布，而不是统一的用户反馈分布。在此基础上，我们设计了 RankFlow 框架: 每个阶段的训练数据由前一阶段生成，而制导信号不仅来自日志，还来自后一阶段。在不同的信息检索场景中进行了广泛的实验，包括推荐、网络搜索和广告。结果验证了 RankFlow 的有效性和优越性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=RankFlow:+Joint+Optimization+of+Multi-Stage+Cascade+Ranking+Systems+as+Flows)|3| -|[Towards Suicide Ideation Detection Through Online Conversational Context](https://doi.org/10.1145/3477495.3532068)|Ramit Sawhney, Shivam Agarwal, Atula Tejaswi Neerkaje, Nikolaos Aletras, Preslav Nakov, Lucie Flek|University of Illinois at Urbana-Champaign & Conversational AI and Social Analytics (CAISA) Lab, University of Marburg, Urbana-Champaign, IL, USA; University of Sheffield, Sheffield, United Kingdom; Georgia Institute of Technology & Conversational AI and Social Analytics (CAISA) Lab, University of Marburg, Atlanta, GA, USA; Qatar Computing Research Institute, HBKU, Doha, Qatar; Conversational AI and Social Analytics (CAISA) Lab, University of Marburg, Marburg, Germany|Social media enable users to share their feelings and emotional struggles. They also offer an opportunity to provide community support to suicidal users. Recent studies on suicide risk assessment have explored the user's historic timeline and information from their social network to analyze their emotional state. However, such methods often require a large amount of user-centric data. A less intrusive alternative is to only use conversation trees arising from online community responses. Modeling such online conversations between the community and a person in distress is an important context for understanding that person's mental state. However, it is not trivial to model the vast number of conversation trees on social media, since each comment has a diverse influence on a user in distress. Typically, a handful of comments/posts receive a significantly high number of replies, which results in scale-free dynamics in the conversation tree. Moreover, psychological studies suggested that it is important to capture the fine-grained temporal irregularities in the release of vast volumes of comments, since suicidal users react quickly to online community support. Building on these limitations and psychological studies, we propose HCN, a Hyperbolic Conversation Network, which is a less user-intrusive method for suicide ideation detection. HCN leverages the hyperbolic space to represent the scale-free dynamics of online conversations. Through extensive quantitative, qualitative, and ablative experiments on real-world Twitter data, we find that HCN outperforms state-of-the art methods, while using 98% less user-specific data, and while maintaining a 74% lower carbon footprint and a 94% smaller model size. We also find that the comments within the first half an hour are most important to identify at-risk users.|社交媒体使用户能够分享他们的感受和情感挣扎。他们还提供了一个机会，为自杀使用者提供社区支持。最近有关自杀风险评估的研究探讨了使用者的历史时间线和来自他们的社交网络的信息，以分析他们的情绪状态。然而，这样的方法通常需要大量以用户为中心的数据。一个较少干扰的替代方法是只使用在线社区响应中产生的对话树。在社区和一个处于困境中的人之间建立这样的在线对话模型是理解这个人的精神状态的重要环境。然而，在社交媒体上建立大量的对话树并非易事，因为每条评论都会对处于困境中的用户产生不同的影响。通常，少量的评论/帖子会收到大量的回复，这导致了会话树中的无标度动态。此外，心理学研究表明，重要的是捕捉细粒度的时间不规则的发布大量的评论，因为自杀用户反应迅速在线社区支持。在这些局限性和心理学研究的基础上，我们提出了双曲对话网络 HCN，这是一种用户侵入性较低的自杀意念检测方法。HCN 利用双曲空间来表现在线对话的无标度动态。通过对真实世界的 Twitter 数据进行广泛的定量、定性和消融实验，我们发现 HCN 的表现优于最先进的方法，同时使用的用户特定数据减少了98% ，同时保持了74% 的低碳足印和94% 的小模型尺寸。我们还发现，在前半个小时内的评论对于识别高危用户是最重要的。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Towards+Suicide+Ideation+Detection+Through+Online+Conversational+Context)|3| +|[Multi-Level Interaction Reranking with User Behavior History](https://doi.org/10.1145/3477495.3532026)|Yunjia Xi, Weiwen Liu, Jieming Zhu, Xilong Zhao, Xinyi Dai, Ruiming Tang, Weinan Zhang, Rui Zhang, Yong Yu|Huawei Noah's Ark Lab, Shenzhen, China; Shanghai Jiao Tong University, Shanghai, China; ruizhang.info, Shenzhen, China|As the final stage of the multi-stage recommender system (MRS), reranking directly affects users' experience and satisfaction, thus playing a critical role in MRS. Despite the improvement achieved in the existing work, three issues are yet to be solved. First, users' historical behaviors contain rich preference information, such as users' long and short-term interests, but are not fully exploited in reranking. Previous work typically treats items in history equally important, neglecting the dynamic interaction between the history and candidate items. Second, existing reranking models focus on learning interactions at the item level while ignoring the fine-grained feature-level interactions. Lastly, estimating the reranking score on the ordered initial list before reranking may lead to the early scoring problem, thereby yielding suboptimal reranking performance. To address the above issues, we propose a framework named Multi-level Interaction Reranking (MIR). MIR combines low-level cross-item interaction and high-level set-to-list interaction, where we view the candidate items to be reranked as a set and the users' behavior history in chronological order as a list. We design a novel SLAttention structure for modeling the set-to-list interactions with personalized long-short term interests. Moreover, feature-level interactions are incorporated to capture the fine-grained influence among items. We design MIR in such a way that any permutation of the input items would not change the output ranking, and we theoretically prove it. Extensive experiments on three public and proprietary datasets show that MIR significantly outperforms the state-of-the-art models using various ranking and utility metrics.|作为多阶段推荐系统(MRS)的最后阶段，重新排名直接影响用户的体验和满意度，因此在 MRS 中发挥着关键作用。尽管现有工作已有所改善，但仍有三个问题有待解决。首先，用户的历史行为包含了丰富的偏好信息，如用户的长期和短期兴趣，但在重新排序时没有得到充分的利用。以往的研究通常认为历史条目同等重要，而忽视了历史条目与候选条目之间的动态互动。其次，现有的重新排序模型侧重于项目层面的学习交互，而忽略了细粒度的特征层面的交互。最后，在重新排序之前估计排序初始列表上的重新排序得分可能会导致早期得分问题，从而产生次优的重新排序性能。为了解决上述问题，我们提出了一个名为多级交互重排(MIR)的框架。MIR 结合了低层次的跨项目交互和高层次的集合-列表交互，我们将待重新排序的候选项作为一个集合，将用户的行为历史按照时间顺序作为一个列表。我们设计了一个新颖的空间注意结构，用于建模具有个性化长期短期兴趣的集合列表交互。此外，特征层次的交互作用被合并来捕获项目之间的细粒度影响。我们设计 MIR 的方法使得输入项的任何排列都不会改变输出的排名，并且我们从理论上证明了这一点。对三个公共和专有数据集的大量实验表明，使用各种排名和效用指标，MIR 显著优于最先进的模型。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Multi-Level+Interaction+Reranking+with+User+Behavior+History)|3| +|[RankFlow: Joint Optimization of Multi-Stage Cascade Ranking Systems as Flows](https://doi.org/10.1145/3477495.3532050)|Jiarui Qin, Jiachen Zhu, Bo Chen, Zhirong Liu, Weiwen Liu, Ruiming Tang, Rui Zhang, Yong Yu, Weinan Zhang|Huawei Noah's Ark Lab, Shenzhen, China; Shanghai Jiao Tong University, Shanghai, China; ruizhang.info, Shenzhen, China|Building a multi-stage cascade ranking system is a commonly used solution to balance the efficiency and effectiveness in modern information retrieval (IR) applications, such as recommendation and web search. Despite the popularity in practice, the literature specific on multi-stage cascade ranking systems is relatively scarce. The common practice is to train rankers of each stage independently using the same user feedback data (a.k.a., impression data), disregarding the data flow and the possible interactions between stages. This straightforward solution could lead to a sub-optimal system because of the sample selection bias (SSB) issue, which is especially damaging for cascade rankers due to the negative effect accumulated in the multiple stages. Worse still, the interactions between the rankers of each stage are not fully exploited. This paper provides an elaborate analysis of this commonly used solution to reveal its limitations. By studying the essence of cascade ranking, we propose a joint training framework named RankFlow to alleviate the SSB issue and exploit the interactions between the cascade rankers, which is the first systematic solution for this topic. We propose a paradigm of training cascade rankers that emphasizes the importance of fitting rankers on stage-specific data distributions instead of the unified user feedback distribution. We design the RankFlow framework based on this paradigm: The training data of each stage is generated by its preceding stages while the guidance signals not only come from the logs but its successors. Extensive experiments are conducted on various IR scenarios, including recommendation, web search and advertisement. The results verify the efficacy and superiority of RankFlow.|建立一个多阶段的级联排名系统是现代信息检索应用(如推荐和网络搜索)中一个常用的平衡效率和有效性的解决方案。尽管在实践中很流行，但是关于多级级联排序系统的文献相对较少。通常的做法是使用相同的用户反馈数据(也就是印象数据)独立训练每个阶段的排名，忽略数据流和阶段之间可能的交互作用。由于样本选择偏差(SSB)问题，这种直接的解决方案可能会导致系统的次优化，而由于多阶段累积的负面效应，SSB 问题对级联排序尤其有害。更糟糕的是，每个阶段的排名之间的相互作用没有得到充分利用。本文对这种常用的解决方案进行了详细的分析，以揭示其局限性。通过研究级联排序的本质，提出了一种联合训练框架 RankFlow 来缓解 SSB 问题，并利用级联排序器之间的相互作用，这是本课题的第一个系统解决方案。我们提出了一个训练级联排名的范式，强调拟合排名的重要性阶段特定的数据分布，而不是统一的用户反馈分布。在此基础上，我们设计了 RankFlow 框架: 每个阶段的训练数据由前一阶段生成，而制导信号不仅来自日志，还来自后一阶段。在不同的信息检索场景中进行了广泛的实验，包括推荐、网络搜索和广告。结果验证了 RankFlow 的有效性和优越性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=RankFlow:+Joint+Optimization+of+Multi-Stage+Cascade+Ranking+Systems+as+Flows)|3| +|[Towards Suicide Ideation Detection Through Online Conversational Context](https://doi.org/10.1145/3477495.3532068)|Ramit Sawhney, Shivam Agarwal, Atula Tejaswi Neerkaje, Nikolaos Aletras, Preslav Nakov, Lucie Flek|Conversational AI and Social Analytics (CAISA) Lab, University of Marburg, Marburg, Germany; Georgia Institute of Technology & Conversational AI and Social Analytics (CAISA) Lab, University of Marburg, Atlanta, GA, USA; Qatar Computing Research Institute, HBKU, Doha, Qatar; University of Illinois at Urbana-Champaign & Conversational AI and Social Analytics (CAISA) Lab, University of Marburg, Urbana-Champaign, IL, USA; University of Sheffield, Sheffield, United Kingdom|Social media enable users to share their feelings and emotional struggles. They also offer an opportunity to provide community support to suicidal users. Recent studies on suicide risk assessment have explored the user's historic timeline and information from their social network to analyze their emotional state. However, such methods often require a large amount of user-centric data. A less intrusive alternative is to only use conversation trees arising from online community responses. Modeling such online conversations between the community and a person in distress is an important context for understanding that person's mental state. However, it is not trivial to model the vast number of conversation trees on social media, since each comment has a diverse influence on a user in distress. Typically, a handful of comments/posts receive a significantly high number of replies, which results in scale-free dynamics in the conversation tree. Moreover, psychological studies suggested that it is important to capture the fine-grained temporal irregularities in the release of vast volumes of comments, since suicidal users react quickly to online community support. Building on these limitations and psychological studies, we propose HCN, a Hyperbolic Conversation Network, which is a less user-intrusive method for suicide ideation detection. HCN leverages the hyperbolic space to represent the scale-free dynamics of online conversations. Through extensive quantitative, qualitative, and ablative experiments on real-world Twitter data, we find that HCN outperforms state-of-the art methods, while using 98% less user-specific data, and while maintaining a 74% lower carbon footprint and a 94% smaller model size. We also find that the comments within the first half an hour are most important to identify at-risk users.|社交媒体使用户能够分享他们的感受和情感挣扎。他们还提供了一个机会，为自杀使用者提供社区支持。最近有关自杀风险评估的研究探讨了使用者的历史时间线和来自他们的社交网络的信息，以分析他们的情绪状态。然而，这样的方法通常需要大量以用户为中心的数据。一个较少干扰的替代方法是只使用在线社区响应中产生的对话树。在社区和一个处于困境中的人之间建立这样的在线对话模型是理解这个人的精神状态的重要环境。然而，在社交媒体上建立大量的对话树并非易事，因为每条评论都会对处于困境中的用户产生不同的影响。通常，少量的评论/帖子会收到大量的回复，这导致了会话树中的无标度动态。此外，心理学研究表明，重要的是捕捉细粒度的时间不规则的发布大量的评论，因为自杀用户反应迅速在线社区支持。在这些局限性和心理学研究的基础上，我们提出了双曲对话网络 HCN，这是一种用户侵入性较低的自杀意念检测方法。HCN 利用双曲空间来表现在线对话的无标度动态。通过对真实世界的 Twitter 数据进行广泛的定量、定性和消融实验，我们发现 HCN 的表现优于最先进的方法，同时使用的用户特定数据减少了98% ，同时保持了74% 的低碳足印和94% 的小模型尺寸。我们还发现，在前半个小时内的评论对于识别高危用户是最重要的。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Towards+Suicide+Ideation+Detection+Through+Online+Conversational+Context)|3| |[Socially-aware Dual Contrastive Learning for Cold-Start Recommendation](https://doi.org/10.1145/3477495.3531780)|Jing Du, Zesheng Ye, Lina Yao, Bin Guo, Zhiwen Yu|The University of New South Wales, Sydney, NSW, Australia; Northwestern Polytechnical University, Xi'an, Shaanxi, China|Social recommendation with Graph Neural Networks(GNNs) learns to represent cold users by fusing user-user social relations with user-item interactions, thereby alleviating the cold-start problem associated with recommender systems. Despite being well adapted to social relations and user-item interactions, these supervised models are still susceptible to popularity bias. Contrastive learning helps resolve this dilemma by identifying the properties that distinguish positive from negative samples. In its previous combinations with recommender systems, social relationships and cold-start cases in this context are not considered. Also, they primarily focus on collaborative features between users and items, leaving the similarity between items under-utilized. In this work, we propose socially-aware dual contrastive learning for cold-start recommendation, where cold users can be modeled in the same way as warm users. To take full advantage of social relations, we create dynamic node embeddings for each user by aggregating information from different neighbors according to each different query item, in the form of user-item pairs. We further design a dual-branch self-supervised contrastive objective to account for user-item collaborative features and item-item mutual information, respectively. On one hand, our framework eliminates popularity bias with proper negative sampling in contrastive learning, without extra ground-truth supervision. On the other hand, we extend previous contrastive learning methods to provide a solution to cold-start problem with social relations included. Extensive experiments on two real-world social recommendation datasets demonstrate its effectiveness.|图形神经网络的社会推荐学习通过融合用户-用户社会关系和用户-项目交互来表示冷用户，从而缓解推荐系统的冷启动问题。尽管这些被监督的模型很好地适应了社会关系和用户项目交互，但是仍然容易受到流行偏见的影响。对比学习有助于解决这一困境，识别的属性，区分积极的样本和消极的样本。在其以往与推荐系统的结合中，社会关系和这方面的冷启动案例没有得到考虑。此外，他们主要关注用户和项目之间的协作特性，而没有充分利用项目之间的相似性。在这项工作中，我们提出了具有社会意识的双重对比学习的冷启动推荐，其中冷用户可以按照与暖用户相同的方式建模。为了充分利用社会关系，我们根据不同的查询项目，以用户-项目对的形式聚合来自不同邻居的信息，为每个用户创建动态节点嵌入。进一步设计了一个双分支自监督对比目标，分别考虑了用户项目的协同特征和项目项目间的相互信息。一方面，我们的框架消除了流行偏见与适当的负面抽样在对比学习，没有额外的地面真相监督。另一方面，我们扩展了以往的对比学习方法，提供了一个解决冷启动问题，其中包括社会关系。在两个真实世界的社交推荐数据集上进行的大量实验证明了该方法的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Socially-aware+Dual+Contrastive+Learning+for+Cold-Start+Recommendation)|3| |[A Multi-Task Based Neural Model to Simulate Users in Goal Oriented Dialogue Systems](https://doi.org/10.1145/3477495.3531814)|To Eun Kim, Aldo Lipani|University College London, London, United Kingdom|A human-like user simulator that anticipates users' satisfaction scores, actions, and utterances can help goal-oriented dialogue systems in evaluating the conversation and refining their dialogue strategies. However, little work has experimented with user simulators which can generate users' utterances. In this paper, we propose a deep learning-based user simulator that predicts users' satisfaction scores and actions while also jointly generating users' utterances in a multi-task manner. In particular, we show that 1) the proposed deep text-to-text multi-task neural model achieves state-of-the-art performance in the users' satisfaction scores and actions prediction tasks, and 2) in an ablation analysis, user satisfaction score prediction, action prediction, and utterance generation tasks can boost the performance with each other via positive transfers across the tasks. The source code and model checkpoints used for the experiments run in this paper are available at the following weblink: \urlhttps://github.com/kimdanny/user-simulation-t5.|一个类人的用户模拟器，可以预测用户的满意度分数、行为和话语，可以帮助目标导向的对话系统评估对话和完善他们的对话策略。然而，很少有工作已经试验用户模拟器，可以产生用户的话语。在本文中，我们提出了一个基于深度学习的用户模拟器，预测用户的满意度分数和行为，同时也联合生成用户的话语在多任务的方式。实验结果表明: (1)本文提出的深度文本-文本多任务神经模型在用户满意度分数和行为预测任务方面达到了最高水平; (2)在消融分析方面，用户满意度分数预测、行为预测和话语生成任务可以通过任务之间的正向传递相互提高性能。本文中用于实验的源代码和模型检查点可以在以下网站找到: urlhttps:// github.com/kimdanny/user-simulation-t5。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Multi-Task+Based+Neural+Model+to+Simulate+Users+in+Goal+Oriented+Dialogue+Systems)|3| -|[Investigating Accuracy-Novelty Performance for Graph-based Collaborative Filtering](https://doi.org/10.1145/3477495.3532005)|Minghao Zhao, Le Wu, Yile Liang, Lei Chen, Jian Zhang, Qilin Deng, Kai Wang, Xudong Shen, Tangjie Lv, Runze Wu|Zhejiang University of Technology, Hangzhou, China; Fuxi AI Lab, NetEase Games, Hangzhou, China; Hefei University of Technology, Hefei, China|Recent years have witnessed the great accuracy performance of graph-based Collaborative Filtering (CF) models for recommender systems. By taking the user-item interaction behavior as a graph, these graph-based CF models borrow the success of Graph Neural Networks (GNN), and iteratively perform neighborhood aggregation to propagate the collaborative signals. While conventional CF models are known for facing the challenges of the popularity bias that favors popular items, one may wonder "Whether the existing graph-based CF models alleviate or exacerbate the popularity bias of recommender systems?" To answer this question, we first investigate the two-fold performances w.r.t. accuracy and novelty for existing graph-based CF methods. The empirical results show that symmetric neighborhood aggregation adopted by most existing graph-based CF models exacerbates the popularity bias and this phenomenon becomes more serious as the depth of graph propagation increases. Further, we theoretically analyze the cause of popularity bias for graph-based CF. Then, we propose a simple yet effective plugin, namely r-AdjNorm, to achieve an accuracy-novelty trade-off by controlling the normalization strength in the neighborhood aggregation process. Meanwhile, r-AdjNorm can be smoothly applied to the existing graph-based CF backbones without additional computation. Finally, experimental results on three benchmark datasets show that our proposed method can improve novelty without sacrificing accuracy under various graph-based CF backbones.|近年来，基于图形的协同过滤(CF)模型在推荐系统中的准确性表现非常出色。这些基于图的协同过程模型借鉴了图神经网络(GNN)的成功之处，以用户项交互行为为图形，迭代地进行邻域聚合来传播协同信号。虽然传统的 CF 模型面临着流行偏好的挑战，有人可能会问: “现有的基于图表的 CF 模型是否减轻或加剧了推荐系统的流行偏好?”为了回答这个问题，我们首先研究了现有的基于图的 CF 方法的双重性能。实验结果表明，现有的基于图的 CF 模型所采用的对称邻域聚集加剧了流行偏差，并且随着图的传播深度的增加，这种现象变得更加严重。进一步从理论上分析了基于图的流行度偏差产生的原因，提出了一种简单有效的插件 r-AdjNorm，通过控制邻域聚合过程中的归一化强度来实现精度-新颖性的权衡。同时，r-AdjNorm 可以顺利地应用于现有的基于图的 CF 骨干，而不需要额外的计算。最后，在三个基准数据集上的实验结果表明，在不同的基于图的 CF 骨架下，本文提出的方法可以在不牺牲精度的前提下提高新颖性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Investigating+Accuracy-Novelty+Performance+for+Graph-based+Collaborative+Filtering)|3| -|[Learning to Denoise Unreliable Interactions for Graph Collaborative Filtering](https://doi.org/10.1145/3477495.3531889)|Changxin Tian, Yuexiang Xie, Yaliang Li, Nan Yang, Wayne Xin Zhao|Renmin University of China, Beijing, China; Alibaba Group, Hangzhou, China; Alibaba Group, Bellevue, WA, USA|Recently, graph neural networks (GNN) have been successfully applied to recommender systems as an effective collaborative filtering (CF) approach. However, existing GNN-based CF models suffer from noisy user-item interaction data, which seriously affects the effectiveness and robustness in real-world applications. Although there have been several studies on data denoising in recommender systems, they either neglect direct intervention of noisy interaction in the message-propagation of GNN, or fail to preserve the diversity of recommendation when denoising. To tackle the above issues, this paper presents a novel GNN-based CF model, named Robust Graph Collaborative Filtering (RGCF), to denoise unreliable interactions for recommendation. Specifically, RGCF consists of a graph denoising module and a diversity preserving module. The graph denoising module is designed for reducing the impact of noisy interactions on the representation learning of GNN, by adopting both a hard denoising strategy (i.e., discarding interactions that are confidently estimated as noise) and a soft denoising strategy (i.e., assigning reliability weights for each remaining interaction). In the diversity preserving module, we build up a diversity augmented graph and propose an auxiliary self-supervised task based on mutual information maximization (MIM) for enhancing the denoised representation and preserving the diversity of recommendation. These two modules are integrated in a multi-task learning manner that jointly improves the recommendation performance. We conduct extensive experiments on three real-world datasets and three synthesized datasets. Experiment results show that RGCF is more robust against noisy interactions and achieves significant improvement compared with baseline models.|最近，图形神经网络(GNN)已成功应用于推荐系统，作为一种有效的协同过滤(CF)方法。然而，现有的基于 GNN 的 CF 模型存在着用户项交互数据的噪声，严重影响了实际应用的有效性和鲁棒性。对于推荐系统中的数据去噪问题，目前已经有了一些研究，但是这些研究要么忽略了噪声交互对 GNN 信息传播的直接干预，要么在去噪时未能保持推荐信息的多样性。为了解决上述问题，本文提出了一种新的基于 GNN 的 CF 模型，称为鲁棒图协同过滤(rgCF) ，用于去除不可靠的推荐交互。具体来说，RGCF 由图的去噪模块和多样性保持模块组成。图形去噪模块是为了减少噪声交互对 GNN 表示学习的影响而设计的，它采用了硬去噪策略(即放弃可信地估计为噪声的交互)和软去噪策略(即为每个剩余交互指定可靠性权重)。在多样性保持模块中，我们建立了一个多样性增强图，并提出了一个基于互信息最大化(MIM)的辅助自监督任务，以提高去噪表示和保持推荐的多样性。这两个模块以多任务学习方式集成，共同提高了推荐性能。我们在三个真实数据集和三个合成数据集上进行了广泛的实验。实验结果表明，与基线模型相比，RGCF 对噪声干扰具有更强的鲁棒性，并取得了显著的改善。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Learning+to+Denoise+Unreliable+Interactions+for+Graph+Collaborative+Filtering)|3| -|[DAWAR: Diversity-aware Web APIs Recommendation for Mashup Creation based on Correlation Graph](https://doi.org/10.1145/3477495.3531962)|Wenwen Gong, Xuyun Zhang, Yifei Chen, Qiang He, Amin Beheshti, Xiaolong Xu, Chao Yan, Lianyong Qi|Nanjing University of Information Science and Technology, Nanjing, China; Macquarie University, Sydney, NSW, Australia; Qufu Normal University, Rizhao, China; China Agricultural University, Beijing, China; Swinburne University of Technology, Melbourne, VIC, Australia; Qufu Normal University & Nanjing University, Rizhao, China|With the ever-increasing popularity of microservice architecture, a considerable number of enterprises or organizations have encapsulated their complex business services into various lightweight functions as published them accessible APIs (Application Programming Interfaces). Through keyword search, a software developer could select a set of APIs from a massive number of candidates to implement the functions of a complex mashup, which reduces the development cost significantly. However, traditional keyword search methods for APIs often suffer from several critical issues such as functional compatibility and limited diversity in search results, which may lead to mashup creation failures and lower development productivity. To deal with these challenges, this paper designs DAWAR, a diversity-aware Web APIs recommendation approach that finds diversified and compatible APIs for mashup creation. Specifically, the APIs recommendation problem for mashup creating is modelled as a graph search problem that aims to find the minimal group Steiner trees in a correlation graph of APIs. DAWAR innovatively employs the determinantal point processes to diversify the recommended results. Empirical evaluation is performed on commonly-used real-world datasets, and the statistic results show that DAWAR is able to achieve significant improvements in terms of recommendation diversity, accuracy, and compatibility.|随着微服务体系结构的日益普及，相当数量的企业或组织已经将其复杂的业务服务封装成各种轻量级功能，并将其发布为可访问的 API (应用程序编程接口)。通过关键字搜索，软件开发人员可以从大量候选 API 中选择一组 API 来实现复杂 mashup 的功能，这大大降低了开发成本。然而，用于 API 的传统关键字搜索方法经常遇到一些关键问题，如功能兼容性和搜索结果的多样性有限，这可能导致 mashup 创建失败和开发效率降低。为了应对这些挑战，本文设计了 DAWAR，这是一种多样性感知的 Web API 推荐方法，它为 mashup 创建寻找多样性和兼容的 API。具体来说，用于 mashup 创建的 API 推荐问题被建模为一个图搜索问题，其目的是在 API 的相关图中找到最小组 Steiner 树。DAWAR 创新地使用决定点过程来使推荐的结果多样化。对现实世界中常用的数据集进行了实证评估，统计结果表明，DAWAR 在推荐多样性、准确性和兼容性方面都有显著的提高。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=DAWAR:+Diversity-aware+Web+APIs+Recommendation+for+Mashup+Creation+based+on+Correlation+Graph)|3| +|[Investigating Accuracy-Novelty Performance for Graph-based Collaborative Filtering](https://doi.org/10.1145/3477495.3532005)|Minghao Zhao, Le Wu, Yile Liang, Lei Chen, Jian Zhang, Qilin Deng, Kai Wang, Xudong Shen, Tangjie Lv, Runze Wu|Zhejiang University of Technology, Hangzhou, China; Hefei University of Technology, Hefei, China; Fuxi AI Lab, NetEase Games, Hangzhou, China|Recent years have witnessed the great accuracy performance of graph-based Collaborative Filtering (CF) models for recommender systems. By taking the user-item interaction behavior as a graph, these graph-based CF models borrow the success of Graph Neural Networks (GNN), and iteratively perform neighborhood aggregation to propagate the collaborative signals. While conventional CF models are known for facing the challenges of the popularity bias that favors popular items, one may wonder "Whether the existing graph-based CF models alleviate or exacerbate the popularity bias of recommender systems?" To answer this question, we first investigate the two-fold performances w.r.t. accuracy and novelty for existing graph-based CF methods. The empirical results show that symmetric neighborhood aggregation adopted by most existing graph-based CF models exacerbates the popularity bias and this phenomenon becomes more serious as the depth of graph propagation increases. Further, we theoretically analyze the cause of popularity bias for graph-based CF. Then, we propose a simple yet effective plugin, namely r-AdjNorm, to achieve an accuracy-novelty trade-off by controlling the normalization strength in the neighborhood aggregation process. Meanwhile, r-AdjNorm can be smoothly applied to the existing graph-based CF backbones without additional computation. Finally, experimental results on three benchmark datasets show that our proposed method can improve novelty without sacrificing accuracy under various graph-based CF backbones.|近年来，基于图形的协同过滤(CF)模型在推荐系统中的准确性表现非常出色。这些基于图的协同过程模型借鉴了图神经网络(GNN)的成功之处，以用户项交互行为为图形，迭代地进行邻域聚合来传播协同信号。虽然传统的 CF 模型面临着流行偏好的挑战，有人可能会问: “现有的基于图表的 CF 模型是否减轻或加剧了推荐系统的流行偏好?”为了回答这个问题，我们首先研究了现有的基于图的 CF 方法的双重性能。实验结果表明，现有的基于图的 CF 模型所采用的对称邻域聚集加剧了流行偏差，并且随着图的传播深度的增加，这种现象变得更加严重。进一步从理论上分析了基于图的流行度偏差产生的原因，提出了一种简单有效的插件 r-AdjNorm，通过控制邻域聚合过程中的归一化强度来实现精度-新颖性的权衡。同时，r-AdjNorm 可以顺利地应用于现有的基于图的 CF 骨干，而不需要额外的计算。最后，在三个基准数据集上的实验结果表明，在不同的基于图的 CF 骨架下，本文提出的方法可以在不牺牲精度的前提下提高新颖性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Investigating+Accuracy-Novelty+Performance+for+Graph-based+Collaborative+Filtering)|3| +|[Learning to Denoise Unreliable Interactions for Graph Collaborative Filtering](https://doi.org/10.1145/3477495.3531889)|Changxin Tian, Yuexiang Xie, Yaliang Li, Nan Yang, Wayne Xin Zhao|Alibaba Group, Hangzhou, China; Renmin University of China, Beijing, China; Alibaba Group, Bellevue, WA, USA|Recently, graph neural networks (GNN) have been successfully applied to recommender systems as an effective collaborative filtering (CF) approach. However, existing GNN-based CF models suffer from noisy user-item interaction data, which seriously affects the effectiveness and robustness in real-world applications. Although there have been several studies on data denoising in recommender systems, they either neglect direct intervention of noisy interaction in the message-propagation of GNN, or fail to preserve the diversity of recommendation when denoising. To tackle the above issues, this paper presents a novel GNN-based CF model, named Robust Graph Collaborative Filtering (RGCF), to denoise unreliable interactions for recommendation. Specifically, RGCF consists of a graph denoising module and a diversity preserving module. The graph denoising module is designed for reducing the impact of noisy interactions on the representation learning of GNN, by adopting both a hard denoising strategy (i.e., discarding interactions that are confidently estimated as noise) and a soft denoising strategy (i.e., assigning reliability weights for each remaining interaction). In the diversity preserving module, we build up a diversity augmented graph and propose an auxiliary self-supervised task based on mutual information maximization (MIM) for enhancing the denoised representation and preserving the diversity of recommendation. These two modules are integrated in a multi-task learning manner that jointly improves the recommendation performance. We conduct extensive experiments on three real-world datasets and three synthesized datasets. Experiment results show that RGCF is more robust against noisy interactions and achieves significant improvement compared with baseline models.|最近，图形神经网络(GNN)已成功应用于推荐系统，作为一种有效的协同过滤(CF)方法。然而，现有的基于 GNN 的 CF 模型存在着用户项交互数据的噪声，严重影响了实际应用的有效性和鲁棒性。对于推荐系统中的数据去噪问题，目前已经有了一些研究，但是这些研究要么忽略了噪声交互对 GNN 信息传播的直接干预，要么在去噪时未能保持推荐信息的多样性。为了解决上述问题，本文提出了一种新的基于 GNN 的 CF 模型，称为鲁棒图协同过滤(rgCF) ，用于去除不可靠的推荐交互。具体来说，RGCF 由图的去噪模块和多样性保持模块组成。图形去噪模块是为了减少噪声交互对 GNN 表示学习的影响而设计的，它采用了硬去噪策略(即放弃可信地估计为噪声的交互)和软去噪策略(即为每个剩余交互指定可靠性权重)。在多样性保持模块中，我们建立了一个多样性增强图，并提出了一个基于互信息最大化(MIM)的辅助自监督任务，以提高去噪表示和保持推荐的多样性。这两个模块以多任务学习方式集成，共同提高了推荐性能。我们在三个真实数据集和三个合成数据集上进行了广泛的实验。实验结果表明，与基线模型相比，RGCF 对噪声干扰具有更强的鲁棒性，并取得了显著的改善。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Learning+to+Denoise+Unreliable+Interactions+for+Graph+Collaborative+Filtering)|3| +|[DAWAR: Diversity-aware Web APIs Recommendation for Mashup Creation based on Correlation Graph](https://doi.org/10.1145/3477495.3531962)|Wenwen Gong, Xuyun Zhang, Yifei Chen, Qiang He, Amin Beheshti, Xiaolong Xu, Chao Yan, Lianyong Qi|Nanjing University of Information Science and Technology, Nanjing, China; Qufu Normal University & Nanjing University, Rizhao, China; Macquarie University, Sydney, NSW, Australia; Qufu Normal University, Rizhao, China; China Agricultural University, Beijing, China; Swinburne University of Technology, Melbourne, VIC, Australia|With the ever-increasing popularity of microservice architecture, a considerable number of enterprises or organizations have encapsulated their complex business services into various lightweight functions as published them accessible APIs (Application Programming Interfaces). Through keyword search, a software developer could select a set of APIs from a massive number of candidates to implement the functions of a complex mashup, which reduces the development cost significantly. However, traditional keyword search methods for APIs often suffer from several critical issues such as functional compatibility and limited diversity in search results, which may lead to mashup creation failures and lower development productivity. To deal with these challenges, this paper designs DAWAR, a diversity-aware Web APIs recommendation approach that finds diversified and compatible APIs for mashup creation. Specifically, the APIs recommendation problem for mashup creating is modelled as a graph search problem that aims to find the minimal group Steiner trees in a correlation graph of APIs. DAWAR innovatively employs the determinantal point processes to diversify the recommended results. Empirical evaluation is performed on commonly-used real-world datasets, and the statistic results show that DAWAR is able to achieve significant improvements in terms of recommendation diversity, accuracy, and compatibility.|随着微服务体系结构的日益普及，相当数量的企业或组织已经将其复杂的业务服务封装成各种轻量级功能，并将其发布为可访问的 API (应用程序编程接口)。通过关键字搜索，软件开发人员可以从大量候选 API 中选择一组 API 来实现复杂 mashup 的功能，这大大降低了开发成本。然而，用于 API 的传统关键字搜索方法经常遇到一些关键问题，如功能兼容性和搜索结果的多样性有限，这可能导致 mashup 创建失败和开发效率降低。为了应对这些挑战，本文设计了 DAWAR，这是一种多样性感知的 Web API 推荐方法，它为 mashup 创建寻找多样性和兼容的 API。具体来说，用于 mashup 创建的 API 推荐问题被建模为一个图搜索问题，其目的是在 API 的相关图中找到最小组 Steiner 树。DAWAR 创新地使用决定点过程来使推荐的结果多样化。对现实世界中常用的数据集进行了实证评估，统计结果表明，DAWAR 在推荐多样性、准确性和兼容性方面都有显著的提高。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=DAWAR:+Diversity-aware+Web+APIs+Recommendation+for+Mashup+Creation+based+on+Correlation+Graph)|3| |[Post Processing Recommender Systems with Knowledge Graphs for Recency, Popularity, and Diversity of Explanations](https://doi.org/10.1145/3477495.3532041)|Giacomo Balloccu, Ludovico Boratto, Gianni Fenu, Mirko Marras|University of Cagliari, Cagliari, Italy|Existing explainable recommender systems have mainly modeled relationships between recommended and already experienced products, and shaped explanation types accordingly (e.g., movie "x" starred by actress "y" recommended to a user because that user watched other movies with "y" as an actress). However, none of these systems has investigated the extent to which properties of a single explanation (e.g., the recency of interaction with that actress) and of a group of explanations for a recommended list (e.g., the diversity of the explanation types) can influence the perceived explaination quality. In this paper, we conceptualized three novel properties that model the quality of the explanations (linking interaction recency, shared entity popularity, and explanation type diversity) and proposed re-ranking approaches able to optimize for these properties. Experiments on two public data sets showed that our approaches can increase explanation quality according to the proposed properties, fairly across demographic groups, while preserving recommendation utility. The source code and data are available at https://github.com/giacoballoccu/explanation-quality-recsys.|现有的可解释推荐系统主要模拟推荐产品和已经有经验的产品之间的关系，并相应地形成解释类型(例如，由女演员“ y”主演的电影“ x”被推荐给用户，因为该用户观看了其他以“ y”为女演员的电影)。然而，这些系统都没有研究单一解释(例如，与女演员互动的近期性)和推荐列表的一组解释(例如，解释类型的多样性)的特性在多大程度上影响感知的解释质量。在本文中，我们概念化了三个新的性质来模拟解释的质量(连接互动的新近性，共享实体的流行性和解释类型的多样性) ，并提出了重新排序的方法，能够优化这些性质。对两个公共数据集的实验表明，我们的方法可以提高解释质量根据建议的性质，相当跨人口组，同时保留推荐效用。源代码和数据可在 https://github.com/giacoballoccu/explanation-quality-recsys 查阅。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Post+Processing+Recommender+Systems+with+Knowledge+Graphs+for+Recency,+Popularity,+and+Diversity+of+Explanations)|3| |[Learning Graph-based Disentangled Representations for Next POI Recommendation](https://doi.org/10.1145/3477495.3532012)|Zhaobo Wang, Yanmin Zhu, Haobing Liu, Chunyang Wang|Shanghai Jiao Tong University, Shanghai, China|Next Point-of-Interest (POI) recommendation plays a critical role in many location-based applications as it provides personalized suggestions on attractive destinations for users. Since users' next movement is highly related to the historical visits, sequential methods such as recurrent neural networks are widely used in this task for modeling check-in behaviors. However, existing methods mainly focus on modeling the sequential regularity of check-in sequences but pay little attention to the intrinsic characteristics of POIs, neglecting the entanglement of the diverse influence stemming from different aspects of POIs. In this paper, we propose a novel Disentangled Representation-enhanced Attention Network (DRAN) for next POI recommendation, which leverages the disentangled representations to explicitly model different aspects and corresponding influence for representing a POI more precisely. Specifically, we first design a propagation rule to learn graph-based disentangled representations by refining two types of POI relation graphs, making full use of the distance-based and transition-based influence for representation learning. Then, we extend the attention architecture to aggregate personalized spatio-temporal information for modeling dynamic user preferences on the next timestamp, while maintaining the different components of disentangled representations independent. Extensive experiments on two real-world datasets demonstrate the superior performance of our model to state-of-the-art approaches. Further studies confirm the effectiveness of DRAN in representation disentanglement.|下一个兴趣点(POI)推荐在许多基于位置的应用程序中起着至关重要的作用，因为它为用户提供有吸引力的目的地的个性化建议。由于用户的下一步行动与历史访问量密切相关，因此在这项任务中广泛采用了循环神经网络等顺序方法来建立签入行为模型。然而，现有的方法主要侧重于对检入序列的顺序规律性进行建模，很少关注检入序列的内在特性，忽视了检入序列不同方面所产生的不同影响的纠缠。本文提出了一种新的面向下一个 POI 推荐的分离表示增强注意网络(DRAN) ，该网络利用分离表示对不同方面和相应影响进行显式建模，以更精确地表示一个 POI。具体来说，我们首先通过对两类 POI 关系图的细化，充分利用基于距离和基于转移的影响来学习基于图的分离表示，设计了一种基于图的传播规则来学习基于图的分离表示。然后，我们将注意结构扩展到聚合个性化的时空信息，以便在下一个时间戳上建立动态用户偏好模型，同时保持分离表征的不同组件之间的独立性。在两个真实世界数据集上的大量实验表明，我们的模型的性能优于最先进的方法。进一步的研究证实了 DRAN 在表征解纠缠中的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Learning+Graph-based+Disentangled+Representations+for+Next+POI+Recommendation)|3| |[AutoLossGen: Automatic Loss Function Generation for Recommender Systems](https://doi.org/10.1145/3477495.3531941)|Zelong Li, Jianchao Ji, Yingqiang Ge, Yongfeng Zhang|Rutgers University, New Brunswick, NJ, USA|In recommendation systems, the choice of loss function is critical since a good loss may significantly improve the model performance. However, manually designing a good loss is a big challenge due to the complexity of the problem. A large fraction of previous work focuses on handcrafted loss functions, which needs significant expertise and human effort. In this paper, inspired by the recent development of automated machine learning, we propose an automatic loss function generation framework, AutoLossGen, which is able to generate loss functions directly constructed from basic mathematical operators without prior knowledge on loss structure. More specifically, we develop a controller model driven by reinforcement learning to generate loss functions, and develop iterative and alternating optimization schedule to update the parameters of both the controller model and the recommender model. One challenge for automatic loss generation in recommender systems is the extreme sparsity of recommendation datasets, which leads to the sparse reward problem for loss generation and search. To solve the problem, we further develop a reward filtering mechanism for efficient and effective loss generation. Experimental results show that our framework manages to create tailored loss functions for different recommendation models and datasets, and the generated loss gives better recommendation performance than commonly used baseline losses. Besides, most of the generated losses are transferable, i.e., the loss generated based on one model and dataset also works well for another model or dataset. Source code of the work is available at https://github.com/rutgerswiselab/AutoLossGen.|在推荐系统中，损失函数的选择至关重要，因为一个好的损失可以显著提高模型的性能。然而，由于问题的复杂性，手工设计一个好的损失是一个很大的挑战。以前的大部分工作集中在手工制作的损失功能，这需要大量的专业知识和人力。本文受机器学习的启发，提出了一种自动损失函数生成框架 AutoLossGen，它可以直接由基本的数学算子构造损失函数，而不需要知道损失结构。更具体地说，我们开发了一个由强化学习驱动的控制器模型来产生损失函数，并开发了迭代和交替优化时间表来更新控制器模型和推荐模型的参数。推荐系统中自动丢失生成的一个挑战是推荐数据集的极端稀疏性，这导致了丢失生成和搜索的稀疏奖励问题。为了解决这个问题，我们进一步开发了一个有效的奖励过滤机制，有效地产生损失。实验结果表明，我们的框架能够为不同的推荐模型和数据集创建量身定制的损失函数，并且产生的损失比常用的基准损失提供了更好的推荐性能。此外，产生的损失大部分是可转移的，即基于一个模型和数据集产生的损失也适用于另一个模型或数据集。作品的源代码可于 https://github.com/rutgerswiselab/autolossgen 下载。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=AutoLossGen:+Automatic+Loss+Function+Generation+for+Recommender+Systems)|3| -|[MGPolicy: Meta Graph Enhanced Off-policy Learning for Recommendations](https://doi.org/10.1145/3477495.3532021)|Xiangmeng Wang, Qian Li, Dianer Yu, Zhichao Wang, Hongxu Chen, Guandong Xu|Curtin University, Perth, WA, Australia; University of Technology Sydney, Sydney, NSW, Australia; University of New South Wales, Sydney, NSW, Australia|Off-policy learning has drawn huge attention in recommender systems (RS), which provides an opportunity for reinforcement learning to abandon the expensive online training. However, off-policy learning from logged data suffers biases caused by the policy shift between the target policy and the logging policy. Consequently, most off-policy learning resorts to inverse propensity scoring (IPS) which however tends to be over-fitted over exposed (or recommended) items and thus fails to explore unexposed items. In this paper, we propose meta graph enhanced off-policy learning (MGPolicy), which is the first recommendation model for correcting the off-policy bias via contextual information. In particular, we explicitly leverage rich semantics in meta graphs for user state representation, and then train the candidate generation model to promote an efficient search in the action space. lMoreover, our MGpolicy is designed with counterfactual risk minimization, which can correct poicy learning bias and ultimately yield an effective target policy to maximize the long-run rewards for the recommendation. We extensively evaluate our method through a series of simulations and large-scale real-world datasets, achieving favorable results compared with state-of-the-art methods. Our code is currently available online.|非政策性学习在推荐系统(RS)中引起了巨大的关注，它为强化学习提供了一个放弃昂贵的在线培训的机会。然而，由于目标策略和日志策略之间的策略转移，从日志数据中进行的非策略学习会产生偏差。因此，大多数非政策学习诉诸于逆倾向评分(IPS) ，然而倾向于过度拟合暴露(或推荐)项目，因此未能探索未暴露项目。本文提出了元图增强的非策略学习(MGPolicy)模型，这是第一个通过上下文信息来纠正非策略偏差的推荐模型。特别地，我们显式地利用元图中丰富的语义来表示用户的状态，然后训练候选生成模型来促进行动空间中的有效搜索。此外，我们的 MG 策略是设计与反事实风险最小化，这可以纠正策略学习偏差，并最终产生一个有效的目标策略，以最大限度地长期回报的建议。我们通过一系列的模拟和大规模的真实世界数据集对我们的方法进行了广泛的评估，与最先进的方法相比取得了良好的结果。我们的代码目前可在线使用。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=MGPolicy:+Meta+Graph+Enhanced+Off-policy+Learning+for+Recommendations)|3| -|[Privacy-Preserving Synthetic Data Generation for Recommendation Systems](https://doi.org/10.1145/3477495.3532044)|Fan Liu, Zhiyong Cheng, Huilin Chen, Yinwei Wei, Liqiang Nie, Mohan S. Kankanhalli|Qilu University of Technology (Shandong Artificial Intelligence Institute), Jinan, China; Shandong University, Jinan, China; Tianjin University of Technology, Tianjin, China; National University of Singapore, Singapore, Singapore|Recommendation systems make predictions chiefly based on users' historical interaction data (e.g., items previously clicked or purchased). There is a risk of privacy leakage when collecting the users' behavior data for building the recommendation model. However, existing privacy-preserving solutions are designed for tackling the privacy issue only during the model training [32] and results collection [40] phases. The problem of privacy leakage still exists when directly sharing the private user interaction data with organizations or releasing them to the public. To address this problem, in this paper, we present a User Privacy Controllable Synthetic Data Generation model (short for UPC-SDG), which generates synthetic interaction data for users based on their privacy preferences. The generation model aims to provide certain privacy guarantees while maximizing the utility of the generated synthetic data at both data level and item level. Specifically, at the data level, we design a selection module that selects those items that contribute less to a user's preferences from the user's interaction data. At the item level, a synthetic data generation module is proposed to generate a synthetic item corresponding to the selected item based on the user's preferences. Furthermore, we also present a privacy-utility trade-off strategy to balance the privacy and utility of the synthetic data. Extensive experiments and ablation studies have been conducted on three publicly accessible datasets to justify our method, demonstrating its effectiveness in generating synthetic data under users' privacy preferences.|推荐系统主要根据用户的历史交互数据(例如，以前点击或购买的项目)进行预测。在收集用户行为数据以构建推荐模型时，存在隐私泄漏风险。然而，现有的保护隐私的解决方案只是为了在模型培训[32]和结果收集[40]阶段解决隐私问题而设计的。在与组织直接共享或向公众发布用户交互数据时，仍然存在隐私泄露问题。针对这一问题，本文提出了一种用户隐私可控合成数据生成模型(UPC-SDG) ，该模型根据用户的隐私偏好为用户生成合成交互数据。该生成模型旨在提供一定的隐私保护，同时最大限度地利用生成的合成数据在数据级和项级。具体来说，在数据级别，我们设计一个选择模块，从用户的交互数据中选择那些对用户偏好贡献较小的项目。在项目级，提出了一个综合数据生成模块，该模块根据用户的偏好生成与所选项目相对应的综合项目。此外，我们还提出了一个隐私-效用权衡策略来平衡合成数据的隐私和效用。在三个公开的数据集上进行了广泛的实验和消融研究，证明了我们的方法在用户隐私偏好下生成合成数据的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Privacy-Preserving+Synthetic+Data+Generation+for+Recommendation+Systems)|3| +|[MGPolicy: Meta Graph Enhanced Off-policy Learning for Recommendations](https://doi.org/10.1145/3477495.3532021)|Xiangmeng Wang, Qian Li, Dianer Yu, Zhichao Wang, Hongxu Chen, Guandong Xu|University of Technology Sydney, Sydney, NSW, Australia; University of New South Wales, Sydney, NSW, Australia; Curtin University, Perth, WA, Australia|Off-policy learning has drawn huge attention in recommender systems (RS), which provides an opportunity for reinforcement learning to abandon the expensive online training. However, off-policy learning from logged data suffers biases caused by the policy shift between the target policy and the logging policy. Consequently, most off-policy learning resorts to inverse propensity scoring (IPS) which however tends to be over-fitted over exposed (or recommended) items and thus fails to explore unexposed items. In this paper, we propose meta graph enhanced off-policy learning (MGPolicy), which is the first recommendation model for correcting the off-policy bias via contextual information. In particular, we explicitly leverage rich semantics in meta graphs for user state representation, and then train the candidate generation model to promote an efficient search in the action space. lMoreover, our MGpolicy is designed with counterfactual risk minimization, which can correct poicy learning bias and ultimately yield an effective target policy to maximize the long-run rewards for the recommendation. We extensively evaluate our method through a series of simulations and large-scale real-world datasets, achieving favorable results compared with state-of-the-art methods. Our code is currently available online.|非政策性学习在推荐系统(RS)中引起了巨大的关注，它为强化学习提供了一个放弃昂贵的在线培训的机会。然而，由于目标策略和日志策略之间的策略转移，从日志数据中进行的非策略学习会产生偏差。因此，大多数非政策学习诉诸于逆倾向评分(IPS) ，然而倾向于过度拟合暴露(或推荐)项目，因此未能探索未暴露项目。本文提出了元图增强的非策略学习(MGPolicy)模型，这是第一个通过上下文信息来纠正非策略偏差的推荐模型。特别地，我们显式地利用元图中丰富的语义来表示用户的状态，然后训练候选生成模型来促进行动空间中的有效搜索。此外，我们的 MG 策略是设计与反事实风险最小化，这可以纠正策略学习偏差，并最终产生一个有效的目标策略，以最大限度地长期回报的建议。我们通过一系列的模拟和大规模的真实世界数据集对我们的方法进行了广泛的评估，与最先进的方法相比取得了良好的结果。我们的代码目前可在线使用。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=MGPolicy:+Meta+Graph+Enhanced+Off-policy+Learning+for+Recommendations)|3| +|[Privacy-Preserving Synthetic Data Generation for Recommendation Systems](https://doi.org/10.1145/3477495.3532044)|Fan Liu, Zhiyong Cheng, Huilin Chen, Yinwei Wei, Liqiang Nie, Mohan S. Kankanhalli|National University of Singapore, Singapore, Singapore; Qilu University of Technology (Shandong Artificial Intelligence Institute), Jinan, China; Shandong University, Jinan, China; Tianjin University of Technology, Tianjin, China|Recommendation systems make predictions chiefly based on users' historical interaction data (e.g., items previously clicked or purchased). There is a risk of privacy leakage when collecting the users' behavior data for building the recommendation model. However, existing privacy-preserving solutions are designed for tackling the privacy issue only during the model training [32] and results collection [40] phases. The problem of privacy leakage still exists when directly sharing the private user interaction data with organizations or releasing them to the public. To address this problem, in this paper, we present a User Privacy Controllable Synthetic Data Generation model (short for UPC-SDG), which generates synthetic interaction data for users based on their privacy preferences. The generation model aims to provide certain privacy guarantees while maximizing the utility of the generated synthetic data at both data level and item level. Specifically, at the data level, we design a selection module that selects those items that contribute less to a user's preferences from the user's interaction data. At the item level, a synthetic data generation module is proposed to generate a synthetic item corresponding to the selected item based on the user's preferences. Furthermore, we also present a privacy-utility trade-off strategy to balance the privacy and utility of the synthetic data. Extensive experiments and ablation studies have been conducted on three publicly accessible datasets to justify our method, demonstrating its effectiveness in generating synthetic data under users' privacy preferences.|推荐系统主要根据用户的历史交互数据(例如，以前点击或购买的项目)进行预测。在收集用户行为数据以构建推荐模型时，存在隐私泄漏风险。然而，现有的保护隐私的解决方案只是为了在模型培训[32]和结果收集[40]阶段解决隐私问题而设计的。在与组织直接共享或向公众发布用户交互数据时，仍然存在隐私泄露问题。针对这一问题，本文提出了一种用户隐私可控合成数据生成模型(UPC-SDG) ，该模型根据用户的隐私偏好为用户生成合成交互数据。该生成模型旨在提供一定的隐私保护，同时最大限度地利用生成的合成数据在数据级和项级。具体来说，在数据级别，我们设计一个选择模块，从用户的交互数据中选择那些对用户偏好贡献较小的项目。在项目级，提出了一个综合数据生成模块，该模块根据用户的偏好生成与所选项目相对应的综合项目。此外，我们还提出了一个隐私-效用权衡策略来平衡合成数据的隐私和效用。在三个公开的数据集上进行了广泛的实验和消融研究，证明了我们的方法在用户隐私偏好下生成合成数据的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Privacy-Preserving+Synthetic+Data+Generation+for+Recommendation+Systems)|3| |[Self-Guided Learning to Denoise for Robust Recommendation](https://doi.org/10.1145/3477495.3532059)|Yunjun Gao, Yuntao Du, Yujia Hu, Lu Chen, Xinjun Zhu, Ziquan Fang, Baihua Zheng|Singapore Management University, Singapore, Singapore; Zhejiang University, Hangzhou, China|The ubiquity of implicit feedback makes them the default choice to build modern recommender systems. Generally speaking, observed interactions are considered as positive samples, while unobserved interactions are considered as negative ones. However, implicit feedback is inherently noisy because of the ubiquitous presence of noisy-positive and noisy-negative interactions. Recently, some studies have noticed the importance of denoising implicit feedback for recommendations, and enhanced the robustness of recommendation models to some extent. Nonetheless, they typically fail to (1) capture the hard yet clean interactions for learning comprehensive user preference, and (2) provide a universal denoising solution that can be applied to various kinds of recommendation models. In this paper, we thoroughly investigate the memorization effect of recommendation models, and propose a new denoising paradigm, i.e., Self-Guided Denoising Learning (SGDL), which is able to collect memorized interactions at the early stage of the training (i.e., ''noise-resistant'' period), and leverage those data as denoising signals to guide the following training (i.e., ''noise-sensitive'' period) of the model in a meta-learning manner. Besides, our method can automatically switch its learning phase at the memorization point from memorization to self-guided learning, and select clean and informative memorized data via a novel adaptive denoising scheduler to improve the robustness. We incorporate SGDL with four representative recommendation models (i.e., NeuMF, CDAE, NGCF and LightGCN) and different loss functions (i.e., binary cross-entropy and BPR loss). The experimental results on three benchmark datasets demonstrate the effectiveness of SGDL over the state-of-the-art denoising methods like T-CE, IR, DeCA, and even state-of-the-art robust graph-based methods like SGCN and SGL.|无处不在的隐式反馈使它们成为构建现代推荐系统的默认选择。一般来说，观察到的相互作用被认为是积极的样本，而未观察到的相互作用被认为是消极的。然而，由于噪声-正向和噪声-负向相互作用的普遍存在，隐式反馈本质上是有噪声的。近年来，一些研究已经注意到去除推荐内隐反馈的重要性，并在一定程度上增强了推荐模型的鲁棒性。尽管如此，它们通常无法(1)捕捉到用于学习全面的用户偏好的硬而干净的交互，(2)提供一个可以应用于各种推荐模型的通用去噪解决方案。本文对推荐模型的记忆效应进行了深入研究，提出了一种新的去噪范式，即自我引导去噪学习(SGDL) ，它能够在训练的早期阶段(即“噪声抵抗期”)收集记忆交互信息，并利用这些数据作为去噪信号，以元学习的方式指导模型的后续训练(即“噪声敏感期”)。此外，该方法可以在记忆点自动切换学习阶段，由记忆阶段转变为自我引导学习阶段，并通过一种新的自适应去噪调度器选择清晰、信息丰富的记忆数据，提高了算法的鲁棒性。我们将 SGDL 与四个代表性的推荐模型(即 NeuMF，CDAE，NGCF 和 LightGCN)和不同的损失函数(即二进制交叉熵和 BPR 损失)结合起来。在三个基准数据集上的实验结果证明了 SGDL 相对于最先进的去噪方法如 T-CE、 IR、 DeCA，甚至是最先进的基于图的鲁棒性方法如 SGCN 和 SGL 的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Self-Guided+Learning+to+Denoise+for+Robust+Recommendation)|3| |[Faster Learned Sparse Retrieval with Guided Traversal](https://doi.org/10.1145/3477495.3531774)|Antonio Mallia, Joel Mackenzie, Torsten Suel, Nicola Tonellotto|The University of Queensland, Brisbane, Australia; University of Pisa, Pisa, Italy; New York University, Brooklyn, NY, USA|Neural information retrieval architectures based on transformers such as BERT are able to significantly improve system effectiveness over traditional sparse models such as BM25. Though highly effective, these neural approaches are very expensive to run, making them difficult to deploy under strict latency constraints. To address this limitation, recent studies have proposed new families of learned sparse models that try to match the effectiveness of learned dense models, while leveraging the traditional inverted index data structure for efficiency. Current learned sparse models learn the weights of terms in documents and, sometimes, queries; however, they exploit different vocabulary structures, document expansion techniques, and query expansion strategies, which can make them slower than traditional sparse models such as BM25. In this work, we propose a novel indexing and query processing technique that exploits a traditional sparse model's "guidance" to efficiently traverse the index, allowing the more effective learned model to execute fewer scoring operations. Our experiments show that our guided processing heuristic is able to boost the efficiency of the underlying learned sparse model by a factor of four without any measurable loss of effectiveness.|基于诸如 BERT 等变压器的神经信息检索架构能够比传统的稀疏模型如 BM25显著提高系统效率。尽管这些神经方法非常有效，但是运行成本非常高，因此在严格的延迟约束下很难部署它们。为了解决这一局限性，最近的研究提出了新的学习稀疏模型系列，试图匹配学习密集模型的有效性，同时利用传统的倒排索引数据结构来提高效率。目前学习的稀疏模型学习文档中的术语权重，有时也学习查询; 然而，它们利用不同的词汇结构、文档扩展技术和查询扩展策略，这使得它们比传统的稀疏模型如 BM25更慢。在这项工作中，我们提出了一个新的索引和查询处理技术，利用传统的稀疏模型的“指导”，有效地遍历索引，使更有效的学习模型执行更少的评分操作。我们的实验表明，我们的引导处理启发式能够提高效率的基础学习稀疏模型的四倍，没有任何可测量的效率损失。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Faster+Learned+Sparse+Retrieval+with+Guided+Traversal)|3| |[Analysing the Robustness of Dual Encoders for Dense Retrieval Against Misspellings](https://doi.org/10.1145/3477495.3531818)|Georgios Sidiropoulos, Evangelos Kanoulas|University of Amsterdam, Amsterdam, Netherlands|Dense retrieval is becoming one of the standard approaches for document and passage ranking. The dual-encoder architecture is widely adopted for scoring question-passage pairs due to its efficiency and high performance. Typically, dense retrieval models are evaluated on clean and curated datasets. However, when deployed in real-life applications, these models encounter noisy user-generated text. That said, the performance of state-of-the-art dense retrievers can substantially deteriorate when exposed to noisy text. In this work, we study the robustness of dense retrievers against typos in the user question. We observe a significant drop in the performance of the dual-encoder model when encountering typos and explore ways to improve its robustness by combining data augmentation with contrastive learning. Our experiments on two large-scale passage ranking and open-domain question answering datasets show that our proposed approach outperforms competing approaches. Additionally, we perform a thorough analysis on robustness. Finally, we provide insights on how different typos affect the robustness of embeddings differently and how our method alleviates the effect of some typos but not of others.|密集检索正在成为文献和段落排序的标准方法之一。双编码器结构以其高效、高性能的特点被广泛应用于问答题-段落对的评分。通常，密集检索模型是在干净和精选的数据集上进行评估的。然而，当部署到实际应用程序中时，这些模型会遇到用户生成的嘈杂文本。也就是说，当暴露在嘈杂的文本中时，最先进的稠密检索器的性能会大大恶化。在这项工作中，我们研究了密集检索器对用户问题中的输入错误的鲁棒性。我们观察到当遇到拼写错误时，双编码器模型的性能显著下降，并探索通过数据增强和对比学习相结合来提高其鲁棒性的方法。我们在两个大规模段落排序和开放领域问答数据集上的实验表明，我们提出的方法优于竞争方法。此外，我们还对鲁棒性进行了全面的分析。最后，我们提供了关于不同类型错误如何不同地影响嵌入的稳健性的见解，以及我们的方法如何减轻一些类型错误的影响，而不是其他的。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Analysing+the+Robustness+of+Dual+Encoders+for+Dense+Retrieval+Against+Misspellings)|3| |[Cross-Probe BERT for Fast Cross-Modal Search](https://doi.org/10.1145/3477495.3531826)|Tan Yu, Hongliang Fei, Ping Li|Baidu Research, Bellevue, WA, USA|Owing to the effectiveness of cross-modal attentions, text-vision BERT models have achieved excellent performance in text-image retrieval. Nevertheless, cross-modal attentions in text-vision BERT models require expensive computation cost when tackling text-vision retrieval due to their pairwise input. Therefore, normally, it is impractical for deploying them for large-scale cross-modal retrieval in real applications. To address the inefficiency issue in exiting text-vision BERT models, in this work, we develop a novel architecture, cross-probe BERT. It devises a small number of text and vision probes, and the cross-modal attentions are efficiency achieved through the interactions between text and vision probes. It takes lightweight computation cost, and meanwhile effectively exploits cross-modal attention. Systematic experiments on public benchmarks demonstrate the excellent effectiveness and efficiency of our cross-probe BERT.|由于跨模态注意的有效性，文本视觉 BERT 模型在文本图像检索中取得了良好的效果。然而，文本视觉 BERT 模型中的跨模式注意力由于其成对输入而在处理文本视觉检索时需要昂贵的计算成本。因此，在实际应用中部署它们进行大规模的跨模态检索通常是不切实际的。为了解决现有文本视觉 BERT 模型效率低下的问题，本文提出了一种新的体系结构——交叉探测 BERT。它设计了少量的文本和视觉探测器，通过文本和视觉探测器之间的交互实现了跨模态注意的有效性。它具有计算量小的特点，同时有效地利用了交叉模态注意力。通过对公共基准测试的系统实验，验证了交叉探测误码率测试的有效性和高效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Cross-Probe+BERT+for+Fast+Cross-Modal+Search)|3| -|[DH-HGCN: Dual Homogeneity Hypergraph Convolutional Network for Multiple Social Recommendations](https://doi.org/10.1145/3477495.3531828)|Jiadi Han, Qian Tao, Yufei Tang, Yuhan Xia|Florida Atlantic University, Boca Raton, FL, USA; Northeastern University, Shenyang, China; South China University of Technology, Guangzhou, China|Social relations are often used as auxiliary information to improve recommendations. In the real-world, social relations among users are complex and diverse. However, most existing recommendation methods assume only single social relation (i.e., exploit pairwise relations to mine user preferences), ignoring the impact of multifaceted social relations on user preferences (i.e., high order complexity of user relations). Moreover, an observing fact is that similar items always have similar attractiveness when exposed to users, indicating a potential connection among the static attributes of items. Here, we advocate modeling the dual homogeneity from social relations and item connections by hypergraph convolution networks, named DH-HGCN, to obtain high-order correlations among users and items. Specifically, we use sentiment analysis to extract comment relation and use the k-means clustering to construct item-item correlations, and we then optimize those heterogeneous graphs in a unified framework. Extensive experiments on two real-world datasets demonstrate the effectiveness of our model.|社会关系往往被用作辅助信息，以改善建议。在现实世界中，用户之间的社会关系是复杂多样的。然而，大多数现有的推荐方法只假设单一的社会关系(即，利用成对关系挖掘用户偏好) ，忽略了多方面的社会关系对用户偏好的影响(即，用户关系的高度复杂性)。此外，一个观察事实是，相似的项目总是具有相似的吸引力时，暴露给用户，表明项目的静态属性之间的潜在联系。本文主张利用超图卷积网络(DH-HGCN)从社会关系和项目连接两方面建立双同质性模型，以获得用户和项目之间的高阶相关性。具体来说，我们使用情绪分析来提取评论关系，并使用 K平均算法来构建项目-项目关联，然后在一个统一的框架内优化这些异构图。在两个实际数据集上的大量实验证明了该模型的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=DH-HGCN:+Dual+Homogeneity+Hypergraph+Convolutional+Network+for+Multiple+Social+Recommendations)|3| +|[DH-HGCN: Dual Homogeneity Hypergraph Convolutional Network for Multiple Social Recommendations](https://doi.org/10.1145/3477495.3531828)|Jiadi Han, Qian Tao, Yufei Tang, Yuhan Xia|South China University of Technology, Guangzhou, China; Northeastern University, Shenyang, China; Florida Atlantic University, Boca Raton, FL, USA|Social relations are often used as auxiliary information to improve recommendations. In the real-world, social relations among users are complex and diverse. However, most existing recommendation methods assume only single social relation (i.e., exploit pairwise relations to mine user preferences), ignoring the impact of multifaceted social relations on user preferences (i.e., high order complexity of user relations). Moreover, an observing fact is that similar items always have similar attractiveness when exposed to users, indicating a potential connection among the static attributes of items. Here, we advocate modeling the dual homogeneity from social relations and item connections by hypergraph convolution networks, named DH-HGCN, to obtain high-order correlations among users and items. Specifically, we use sentiment analysis to extract comment relation and use the k-means clustering to construct item-item correlations, and we then optimize those heterogeneous graphs in a unified framework. Extensive experiments on two real-world datasets demonstrate the effectiveness of our model.|社会关系往往被用作辅助信息，以改善建议。在现实世界中，用户之间的社会关系是复杂多样的。然而，大多数现有的推荐方法只假设单一的社会关系(即，利用成对关系挖掘用户偏好) ，忽略了多方面的社会关系对用户偏好的影响(即，用户关系的高度复杂性)。此外，一个观察事实是，相似的项目总是具有相似的吸引力时，暴露给用户，表明项目的静态属性之间的潜在联系。本文主张利用超图卷积网络(DH-HGCN)从社会关系和项目连接两方面建立双同质性模型，以获得用户和项目之间的高阶相关性。具体来说，我们使用情绪分析来提取评论关系，并使用 K平均算法来构建项目-项目关联，然后在一个统一的框架内优化这些异构图。在两个实际数据集上的大量实验证明了该模型的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=DH-HGCN:+Dual+Homogeneity+Hypergraph+Convolutional+Network+for+Multiple+Social+Recommendations)|3| |[To Interpolate or not to Interpolate: PRF, Dense and Sparse Retrievers](https://doi.org/10.1145/3477495.3531884)|Hang Li, Shuai Wang, Shengyao Zhuang, Ahmed Mourad, Xueguang Ma, Jimmy Lin, Guido Zuccon|University of Waterloo, Waterloo, ON, Canada; The University of Queensland, Brisbane, QLD, Australia; The University of Queensland, Brisbane, VIC, Australia|Current pre-trained language model approaches to information retrieval can be broadly divided into two categories: sparse retrievers (to which belong also non-neural approaches such as bag-of-words methods, e.g., BM25) and dense retrievers. Each of these categories appears to capture different characteristics of relevance. Previous work has investigated how relevance signals from sparse retrievers could be combined with those from dense retrievers via interpolation. Such interpolation would generally lead to higher retrieval effectiveness. In this paper we consider the problem of combining the relevance signals from sparse and dense retrievers in the context of Pseudo Relevance Feedback (PRF). This context poses two key challenges: (1) When should interpolation occur: before, after, or both before and after the PRF process? (2) Which sparse representation should be considered: a zero-shot bag-of-words model (BM25), or a learned sparse representation? To answer these questions we perform a thorough empirical evaluation considering an effective and scalable neural PRF approach (Vector-PRF), three effective dense retrievers (ANCE, TCTv2, DistillBERT), and one state-of-the-art learned sparse retriever (uniCOIL). The empirical findings from our experiments suggest that, regardless of sparse representation and dense retriever, interpolation both before and after PRF achieves the highest effectiveness across most datasets and metrics.|目前预先训练的信息检索语言模型方法可以大致分为两类: 稀疏检索器(也属于非神经方法，如单词袋法，例如，BM25)和稠密检索器。这些类别中的每一个似乎都捕获了不同的相关性特征。先前的工作已经研究了如何通过插值将稀疏检索器的相关信号与密集检索器的相关信号相结合。这样的插值通常会导致更高的检索效率。在本文中，我们考虑了在伪关联反馈(PRF)的情况下合并稀疏和密集检索器的相关信号的问题。这种情况提出了两个关键的挑战: (1)什么时候应该插值发生: 之前，之后，或两者之前和之后的 PRF 过程？(2)应该考虑哪种稀疏表示法: 零词袋模型表示法(BM25) ，还是学习型稀疏表示法？为了回答这些问题，我们考虑到有效和可扩展的神经 PRF 方法(Vector-PRF) ，三个有效的稠密检索器(ANCE，TCTv2，DistillBERT)和一个最先进的稀疏检索器(uniCOIL)进行了彻底的经验评估。我们的实验结果表明，不管稀疏表示和密集检索，在 PRF 之前和之后的插值在大多数数据集和度量中都达到了最高的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=To+Interpolate+or+not+to+Interpolate:+PRF,+Dense+and+Sparse+Retrievers)|3| -|[Selective Fairness in Recommendation via Prompts](https://doi.org/10.1145/3477495.3531913)|Yiqing Wu, Ruobing Xie, Yongchun Zhu, Fuzhen Zhuang, Xiang Ao, Xu Zhang, Leyu Lin, Qing He|Institute of Computing Technology, Chinese Academy of Sciences & University of Chinese Academy of Sciences, Beijing, China; Tencent, Beijing, China; Institute of Computing Technology, Chinese Academy of Sciences, University of Chinese Academy of Sciences, & Tencent, Beijing, China; Beihang University, Beijing, China|Recommendation fairness has attracted great attention recently. In real-world systems, users usually have multiple sensitive attributes (e.g. age, gender, and occupation), and users may not want their recommendation results influenced by those attributes. Moreover, which of and when these user attributes should be considered in fairness-aware modeling should depend on users' specific demands. In this work, we define the selective fairness task, where users can flexibly choose which sensitive attributes should the recommendation model be bias-free. We propose a novel parameter-efficient prompt-based fairness-aware recommendation (PFRec) framework, which relies on attribute-specific prompt-based bias eliminators with adversarial training, enabling selective fairness with different attribute combinations on sequential recommendation. Both task-specific and user-specific prompts are considered. We conduct extensive evaluations to verify PFRec's superiority in selective fairness. The source codes are released in \urlhttps://github.com/wyqing20/PFRec.|推荐公平性近年来引起了人们的广泛关注。在现实世界的系统中，用户通常有多个敏感属性(例如年龄、性别和职业) ，用户可能不希望他们的推荐结果受到这些属性的影响。此外，在公平感知的建模中，应该考虑哪些用户属性以及何时考虑这些用户属性，应该取决于用户的具体需求。在本文中，我们定义了选择性公平任务，用户可以灵活地选择推荐模型无偏差的敏感属性。我们提出了一种新的参数高效的基于提示的公平感知推荐(PFRec)框架，该框架依赖于具有对抗性训练的特定属性的基于提示的偏差消除器，使得在顺序推荐上具有不同属性组合的选择性公平性成为可能。同时考虑特定于任务和特定于用户的提示。我们进行了广泛的评估，以验证 PFRec 在选择公平性方面的优越性。源代码以 urlhttps:// github.com/wyqing20/pfrec 发布。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Selective+Fairness+in+Recommendation+via+Prompts)|3| -|[ReLoop: A Self-Correction Continual Learning Loop for Recommender Systems](https://doi.org/10.1145/3477495.3531922)|Guohao Cai, Jieming Zhu, Quanyu Dai, Zhenhua Dong, Xiuqiang He, Ruiming Tang, Rui Zhang|Shenzhen, Huawei Noah's Ark Lab, China; www.ruizhang.info, Shenzhen, China; Huawei Noah's Ark Lab, Shenzhen, China|Deep learning-based recommendation has become a widely adopted technique in various online applications. Typically, a deployed model undergoes frequent re-training to capture users' dynamic behaviors from newly collected interaction logs. However, the current model training process only acquires users' feedbacks as labels, but fails to take into account the errors made in previous recommendations. Inspired by the intuition that humans usually reflect and learn from mistakes, in this paper, we attempt to build a self-correction continual learning loop (dubbed ReLoop) for recommender systems. In particular, a new customized loss is employed to encourage every new model version to reduce prediction errors over the previous model version during training. Our ReLoop learning framework enables a continual self-correction process in the long run and thus is expected to obtain better performance over existing training strategies. Both offline experiments and an online A/B test have been conducted to validate the effectiveness of ReLoop.|基于深度学习的推荐已经成为各种在线应用中广泛采用的技术。通常，已部署的模型要经过频繁的重新训练，以从新收集的交互日志中捕获用户的动态行为。然而，目前的模型培训过程只是获取用户的反馈作为标签，而没有考虑到以前的建议中的错误。受到人类常常反思和从错误中学习的直觉的启发，本文尝试为推荐系统构建一个自我修正的连续学习循环(称为 ReLoop)。特别是，一个新的定制损失被用来鼓励每一个新的模型版本，以减少训练期间的预测错误超过以前的模型版本。我们的 ReLoop 学习框架能够在长期内实现持续的自我修正过程，因此预计将获得比现有培训策略更好的性能。离线实验和在线 A/B 测试都验证了 ReLoop 的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=ReLoop:+A+Self-Correction+Continual+Learning+Loop+for+Recommender+Systems)|3| -|[SoChainDB: A Database for Storing and Retrieving Blockchain-Powered Social Network Data](https://doi.org/10.1145/3477495.3531735)|Hoang H. Nguyen, Dmytro Bozhkov, Zahra Ahmadi, NhatMinh Nguyen, ThanhNam Doan|Sunshine Tech Ho Chi Minh, Ho Chi Minh City, Vietnam; L3S Research Center, Leibniz Universität Hannover, Hannover, Germany; Independent Researcher, Atlanta, GA, USA|Social networks have become an inseparable part of human activities. Most existing social networks follow a centralized system model, which despite storing valuable information of users, arise many critical concerns such as content ownership and over-commercialization. Recently, decentralized social networks, built primarily on blockchain technology, have been proposed as a substitution to eliminate these concerns. Since decentralized architectures are mature enough to be on par with the centralized ones, decentralized social networks are becoming more and more popular. Decentralized social networks can offer both common options like writing posts and comments and more advanced options such as reward systems and voting mechanisms. They provide rich eco-systems for the influencers to interact with their followers and other users via staking systems based on cryptocurrency tokens. The vast and valuable data of the decentralized social networks open several new directions for the research community to extend human behavior knowledge. However, accessing and collecting data from these social networks is not easy because it requires strong blockchain knowledge, which is not the main focus of computer science and social science researchers. Hence, our work proposes the SoChainDB framework that facilitates obtaining data from these new social networks. To show the capacity and strength of SoChainDB, we crawl and publish Hive data - one of the largest blockchain-based social networks. We conduct extensive analyses to understand the insight of Hive data and discuss some interesting applications, e.g., game, non-fungible tokens market built upon Hive. It is worth mentioning that our framework is well-adaptable to other blockchain social networks with minimal modification. SoChainDB is publicly accessible at http://sochaindb.com and the dataset is available under the CC BY-SA 4.0 license.|社交网络已经成为人类活动不可分割的一部分。大多数现有的社交网络遵循一种集中的系统模型，尽管它存储了用户的有价值的信息，但是也引起了许多关键的问题，如内容所有权和过度商业化。最近，分散的社会网络，主要建立在区块链技术，已提出作为一种替代，以消除这些关注。由于分散式体系结构已经足够成熟，可以与集中式体系结构相媲美，分散式社交网络正变得越来越流行。分散的社交网络既可以提供一般的选择，比如写帖子和评论，也可以提供更高级的选择，比如奖励制度和投票机制。它们为影响者提供了丰富的生态系统，通过基于加密货币令牌的标注系统与其追随者和其他用户进行交互。去中心化的社会网络的大量有价值的数据为研究团体拓展人类行为知识开辟了几个新的方向。然而，从这些社交网络访问和收集数据并不容易，因为它需要强大的区块链知识，这并不是计算机科学和社会科学研究人员的主要重点。因此，我们的工作提出了 SoChainDB 框架，该框架有助于从这些新的社交网络获取数据。为了展示 SoChainDB 的能力和强度，我们抓取并发布了 Hive 数据——最大的基于区块链的社交网络之一。我们进行了广泛的分析，以了解蜂巢数据的洞察力，并讨论了一些有趣的应用程序，例如，游戏，不可替代的令牌市场建立在蜂巢。值得一提的是，我们的框架可以很好地适应其他区块链社交网络，只需要最小限度的修改。SochainDB 可以在 http://SoChainDB.com 上公开访问，并且数据集可以在 CC BY-SA 4.0许可下获得。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=SoChainDB:+A+Database+for+Storing+and+Retrieving+Blockchain-Powered+Social+Network+Data)|3| -|[From Little Things Big Things Grow: A Collection with Seed Studies for Medical Systematic Review Literature Search](https://doi.org/10.1145/3477495.3531748)|Shuai Wang, Harrisen Scells, Justin Clark, Bevan Koopman, Guido Zuccon|Bond Institute for Evidence-Based Healthcare, Gold Coast, Australia; The University of Queensland, St Lucia, QLD, Australia; CSIRO, Herston, Australia|Medical systematic review query formulation is a highly complex task done by trained information specialists. Complexity comes from the reliance on lengthy Boolean queries, which express a detailed research question. To aid query formulation, information specialists use a set of exemplar documents, called 'seed studies', prior to query formulation. Seed studies help verify the effectiveness of a query prior to the full assessment of retrieved studies. Beyond this use of seeds, specific IR methods can exploit seed studies for guiding both automatic query formulation and new retrieval models. One major limitation of work to date is that these methods exploit 'pseudo seed studies' through retrospective use of included studies (i.e., relevance assessments). However, we show pseudo seed studies are not representative of real seed studies used by information specialists. Hence, we provide a test collection with real world seed studies used to assist with the formulation of queries. To support our collection, we provide an analysis, previously not possible, on how seed studies impact retrieval and perform several experiments using seed study based methods to compare the effectiveness of using seed studies versus pseudo seed studies. We make our test collection and the results of all of our experiments and analysis available at http://github.com/ielab/sysrev-seed-collection.|医疗系统综述查询制定是一项高度复杂的任务，由训练有素的信息专家完成。复杂性来自于对冗长的布尔查询的依赖，布尔查询表达了一个详细的研究问题。为了帮助查询公式化，信息专家在查询公式化之前使用一组示例文档，称为“种子研究”。种子研究有助于在对检索到的研究进行全面评估之前验证查询的有效性。除了使用种子之外，特定的 IR 方法还可以利用种子研究来指导自动查询表达和新的检索模型。迄今为止工作的一个主要局限性是，这些方法通过回顾性使用纳入研究(即相关性评估)来利用“伪种子研究”。然而，我们表明，伪种子研究并不代表真正的种子研究所使用的信息专家。因此，我们提供了一个具有真实世界种子研究的测试集合，这些种子研究用于帮助制定查询。为了支持我们的收集，我们提供了一个以前不可能的关于种子研究如何影响检索的分析，并使用基于种子研究的方法进行了几个实验，以比较使用种子研究和伪种子研究的有效性。我们把我们的测试收集和所有实验和分析的结果在 http://github.com/ielab/sysrev-seed-collection 上公布。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=From+Little+Things+Big+Things+Grow:+A+Collection+with+Seed+Studies+for+Medical+Systematic+Review+Literature+Search)|3| -|[Assessing Student's Dynamic Knowledge State by Exploring the Question Difficulty Effect](https://doi.org/10.1145/3477495.3531939)|Shuanghong Shen, Zhenya Huang, Qi Liu, Yu Su, Shijin Wang, Enhong Chen|Anhui Province Key Laboratory of Big Data Analysis and Application, School of Computer Science and Technology, University of Science and Technology of China & State Key Laboratory of Cognitive Intelligence, Hefei, China; Anhui Province Key Laboratory of Big Data Analysis and Application, School of Data Science, University of Science and Technology of China & State Key Laboratory of Cognitive Intelligence, Hefei, China; State Key Laboratory of Cognitive Intelligence & iFLYTEK AI Research (Central China), iFLYTEK Co., Ltd, Hefei, China; School of Computer Science and Technology, Hefei Normal University & Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, China|Knowledge Tracing (KT), which aims to assess students' dynamic knowledge states when practicing on various questions, is a fundamental research task for offering intelligent services in online learning systems. Researchers have devoted significant efforts to developing KT models with impressive performance. However, in existing KT methods, the related question difficulty level, which directly affects students' knowledge state in learning, has not been effectively explored and employed. In this paper, we focus on exploring the question difficulty effect on learning to improve student's knowledge state assessment and propose the DIfficulty Matching Knowledge Tracing (DIMKT) model. Specifically, we first explicitly incorporate the difficulty level into the question representation. Then, to establish the relation between students' knowledge state and the question difficulty level during the practice process, we accordingly design an adaptive sequential neural network in three stages: (1) measuring students' subjective feelings of the question difficulty before practice; (2) estimating students' personalized knowledge acquisition while answering questions of different difficulty levels; (3) updating students' knowledge state in varying degrees to match the question difficulty level after practice. Finally, we conduct extensive experiments on real-world datasets, and the results demonstrate that DIMKT outperforms state-of-the-art KT models. Moreover, DIMKT shows superior interpretability by exploring the question difficulty effect when making predictions. Our codes are available at https://github.com/shshen-closer/DIMKT.|知识追踪(KT)是在线学习系统中提供智能服务的基础性研究课题，其目的是评估学生在各种问题上的动态知识状态。研究人员已经投入了大量的努力来开发具有令人印象深刻的性能的 KT 模型。然而，在现有的 KT 教学方法中，直接影响学生学习知识状态的相关问题难度水平还没有得到有效的探索和应用。本文着重探讨了问题难度对学习的影响，以提高学生的知识状态评价，并提出了难度匹配知识跟踪模型。具体来说，我们首先明确地将难度水平纳入问题表征。然后，为了建立学生在练习过程中的知识状态与问题难度水平之间的关系，我们设计了一个自适应序贯神经网络: (1)在练习前测量学生对问题难度的主观感受; (2)在回答不同难度水平的问题时估计学生的个性化知识获得; (3)在练习后不同程度地更新学生的知识状态以匹配问题难度水平。最后，我们在实际数据集上进行了广泛的实验，结果表明 DIMKT 模型的性能优于最先进的 KT 模型。此外，DIMKT 在预测问题时通过探索问题难度效应显示出更好的可解释性。我们的代码可以在 https://github.com/shshen-closer/dimkt 找到。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Assessing+Student's+Dynamic+Knowledge+State+by+Exploring+the+Question+Difficulty+Effect)|3| -|[MetaCare++: Meta-Learning with Hierarchical Subtyping for Cold-Start Diagnosis Prediction in Healthcare Data](https://doi.org/10.1145/3477495.3532020)|Yanchao Tan, Carl Yang, Xiangyu Wei, Chaochao Chen, Weiming Liu, Longfei Li, Jun Zhou, Xiaolin Zheng|Emory University, Atlanta, GA, USA; Ant Group, Hangzhou, China; Zhejiang University, Hangzhou, China|Cold-start diagnosis prediction is a challenging task for AI in healthcare, where often only a few visits per patient and a few observations per disease can be exploited. Although meta-learning is widely adopted to address the data sparsity problem in general domains, directly applying it to healthcare data is less effective, since it is unclear how to capture both the temporal relations in clinical visits and the complicated relations among syndromic diseases for precise personalized diagnosis. To this end, we first propose a novel Meta-learning framework for cold-start diagnosis prediction in healthCare data (MetaCare). By explicitly encoding the effects of disease progress over time as a generalization prior, MetaCare dynamically predicts future diagnosis and timestamp for infrequent patients. Then, to model complicated relations among rare diseases, we propose to utilize domain knowledge of hierarchical relations among diseases, and further perform diagnosis subtyping to mine the latent syndromic relations among diseases. Finally, to tailor the generic meta-learning framework with personalized parameters, we design a hierarchical patient subtyping mechanism and bridge the modeling of both infrequent patients and rare diseases. We term the joint model as MetaCare++. Extensive experiments on two real-world benchmark datasets show significant performance gains brought by MetaCare++, yielding average improvements of 7.71% for diagnosis prediction and 13.94% for diagnosis time prediction over the state-of-the-art baselines.|冷启动诊断预测是医疗保健中 AI 的一项具有挑战性的任务，通常每个病人只有几次就诊，每个疾病只有几次观察。尽管元学习被广泛用于解决一般领域的数据稀疏问题，但直接应用于医疗保健数据的效果较差，因为目前还不清楚如何捕获临床访视中的时间关系和综合征疾病之间的复杂关系以进行精确的个性化诊断。为此，我们首先提出了一个新的元学习框架，用于医疗保健数据(MetaCare)中的冷启动诊断预测。通过明确编码疾病进展随时间推移的影响作为一个普遍的先例，MetaCare 动态预测未来的诊断和时间戳的罕见病人。然后，利用疾病之间等级关系的领域知识，进一步进行诊断分型，挖掘疾病之间潜在的综合征关系，建立罕见病之间复杂关系的模型。最后，为了使通用的元学习框架具有个性化的参数，我们设计了一个分层的患者亚型机制，并在罕见患者和罕见疾病的建模之间架起了桥梁。我们将联合模型称为 MetaCare + + 。在两个真实世界基准数据集上的广泛实验显示 MetaCare + + 带来了显着的性能提高，在最先进的基线上诊断预测平均提高了7.71% ，诊断时间预测平均提高了13.94% 。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=MetaCare++:+Meta-Learning+with+Hierarchical+Subtyping+for+Cold-Start+Diagnosis+Prediction+in+Healthcare+Data)|3| +|[Selective Fairness in Recommendation via Prompts](https://doi.org/10.1145/3477495.3531913)|Yiqing Wu, Ruobing Xie, Yongchun Zhu, Fuzhen Zhuang, Xiang Ao, Xu Zhang, Leyu Lin, Qing He|Tencent, Beijing, China; Institute of Computing Technology, Chinese Academy of Sciences, University of Chinese Academy of Sciences, & Tencent, Beijing, China; Institute of Computing Technology, Chinese Academy of Sciences & University of Chinese Academy of Sciences, Beijing, China; Beihang University, Beijing, China|Recommendation fairness has attracted great attention recently. In real-world systems, users usually have multiple sensitive attributes (e.g. age, gender, and occupation), and users may not want their recommendation results influenced by those attributes. Moreover, which of and when these user attributes should be considered in fairness-aware modeling should depend on users' specific demands. In this work, we define the selective fairness task, where users can flexibly choose which sensitive attributes should the recommendation model be bias-free. We propose a novel parameter-efficient prompt-based fairness-aware recommendation (PFRec) framework, which relies on attribute-specific prompt-based bias eliminators with adversarial training, enabling selective fairness with different attribute combinations on sequential recommendation. Both task-specific and user-specific prompts are considered. We conduct extensive evaluations to verify PFRec's superiority in selective fairness. The source codes are released in \urlhttps://github.com/wyqing20/PFRec.|推荐公平性近年来引起了人们的广泛关注。在现实世界的系统中，用户通常有多个敏感属性(例如年龄、性别和职业) ，用户可能不希望他们的推荐结果受到这些属性的影响。此外，在公平感知的建模中，应该考虑哪些用户属性以及何时考虑这些用户属性，应该取决于用户的具体需求。在本文中，我们定义了选择性公平任务，用户可以灵活地选择推荐模型无偏差的敏感属性。我们提出了一种新的参数高效的基于提示的公平感知推荐(PFRec)框架，该框架依赖于具有对抗性训练的特定属性的基于提示的偏差消除器，使得在顺序推荐上具有不同属性组合的选择性公平性成为可能。同时考虑特定于任务和特定于用户的提示。我们进行了广泛的评估，以验证 PFRec 在选择公平性方面的优越性。源代码以 urlhttps:// github.com/wyqing20/pfrec 发布。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Selective+Fairness+in+Recommendation+via+Prompts)|3| +|[ReLoop: A Self-Correction Continual Learning Loop for Recommender Systems](https://doi.org/10.1145/3477495.3531922)|Guohao Cai, Jieming Zhu, Quanyu Dai, Zhenhua Dong, Xiuqiang He, Ruiming Tang, Rui Zhang|Huawei Noah's Ark Lab, Shenzhen, China; www.ruizhang.info, Shenzhen, China; Shenzhen, Huawei Noah's Ark Lab, China|Deep learning-based recommendation has become a widely adopted technique in various online applications. Typically, a deployed model undergoes frequent re-training to capture users' dynamic behaviors from newly collected interaction logs. However, the current model training process only acquires users' feedbacks as labels, but fails to take into account the errors made in previous recommendations. Inspired by the intuition that humans usually reflect and learn from mistakes, in this paper, we attempt to build a self-correction continual learning loop (dubbed ReLoop) for recommender systems. In particular, a new customized loss is employed to encourage every new model version to reduce prediction errors over the previous model version during training. Our ReLoop learning framework enables a continual self-correction process in the long run and thus is expected to obtain better performance over existing training strategies. Both offline experiments and an online A/B test have been conducted to validate the effectiveness of ReLoop.|基于深度学习的推荐已经成为各种在线应用中广泛采用的技术。通常，已部署的模型要经过频繁的重新训练，以从新收集的交互日志中捕获用户的动态行为。然而，目前的模型培训过程只是获取用户的反馈作为标签，而没有考虑到以前的建议中的错误。受到人类常常反思和从错误中学习的直觉的启发，本文尝试为推荐系统构建一个自我修正的连续学习循环(称为 ReLoop)。特别是，一个新的定制损失被用来鼓励每一个新的模型版本，以减少训练期间的预测错误超过以前的模型版本。我们的 ReLoop 学习框架能够在长期内实现持续的自我修正过程，因此预计将获得比现有培训策略更好的性能。离线实验和在线 A/B 测试都验证了 ReLoop 的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=ReLoop:+A+Self-Correction+Continual+Learning+Loop+for+Recommender+Systems)|3| +|[SoChainDB: A Database for Storing and Retrieving Blockchain-Powered Social Network Data](https://doi.org/10.1145/3477495.3531735)|Hoang H. Nguyen, Dmytro Bozhkov, Zahra Ahmadi, NhatMinh Nguyen, ThanhNam Doan|L3S Research Center, Leibniz Universität Hannover, Hannover, Germany; Sunshine Tech Ho Chi Minh, Ho Chi Minh City, Vietnam; Independent Researcher, Atlanta, GA, USA|Social networks have become an inseparable part of human activities. Most existing social networks follow a centralized system model, which despite storing valuable information of users, arise many critical concerns such as content ownership and over-commercialization. Recently, decentralized social networks, built primarily on blockchain technology, have been proposed as a substitution to eliminate these concerns. Since decentralized architectures are mature enough to be on par with the centralized ones, decentralized social networks are becoming more and more popular. Decentralized social networks can offer both common options like writing posts and comments and more advanced options such as reward systems and voting mechanisms. They provide rich eco-systems for the influencers to interact with their followers and other users via staking systems based on cryptocurrency tokens. The vast and valuable data of the decentralized social networks open several new directions for the research community to extend human behavior knowledge. However, accessing and collecting data from these social networks is not easy because it requires strong blockchain knowledge, which is not the main focus of computer science and social science researchers. Hence, our work proposes the SoChainDB framework that facilitates obtaining data from these new social networks. To show the capacity and strength of SoChainDB, we crawl and publish Hive data - one of the largest blockchain-based social networks. We conduct extensive analyses to understand the insight of Hive data and discuss some interesting applications, e.g., game, non-fungible tokens market built upon Hive. It is worth mentioning that our framework is well-adaptable to other blockchain social networks with minimal modification. SoChainDB is publicly accessible at http://sochaindb.com and the dataset is available under the CC BY-SA 4.0 license.|社交网络已经成为人类活动不可分割的一部分。大多数现有的社交网络遵循一种集中的系统模型，尽管它存储了用户的有价值的信息，但是也引起了许多关键的问题，如内容所有权和过度商业化。最近，分散的社会网络，主要建立在区块链技术，已提出作为一种替代，以消除这些关注。由于分散式体系结构已经足够成熟，可以与集中式体系结构相媲美，分散式社交网络正变得越来越流行。分散的社交网络既可以提供一般的选择，比如写帖子和评论，也可以提供更高级的选择，比如奖励制度和投票机制。它们为影响者提供了丰富的生态系统，通过基于加密货币令牌的标注系统与其追随者和其他用户进行交互。去中心化的社会网络的大量有价值的数据为研究团体拓展人类行为知识开辟了几个新的方向。然而，从这些社交网络访问和收集数据并不容易，因为它需要强大的区块链知识，这并不是计算机科学和社会科学研究人员的主要重点。因此，我们的工作提出了 SoChainDB 框架，该框架有助于从这些新的社交网络获取数据。为了展示 SoChainDB 的能力和强度，我们抓取并发布了 Hive 数据——最大的基于区块链的社交网络之一。我们进行了广泛的分析，以了解蜂巢数据的洞察力，并讨论了一些有趣的应用程序，例如，游戏，不可替代的令牌市场建立在蜂巢。值得一提的是，我们的框架可以很好地适应其他区块链社交网络，只需要最小限度的修改。SochainDB 可以在 http://SoChainDB.com 上公开访问，并且数据集可以在 CC BY-SA 4.0许可下获得。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=SoChainDB:+A+Database+for+Storing+and+Retrieving+Blockchain-Powered+Social+Network+Data)|3| +|[From Little Things Big Things Grow: A Collection with Seed Studies for Medical Systematic Review Literature Search](https://doi.org/10.1145/3477495.3531748)|Shuai Wang, Harrisen Scells, Justin Clark, Bevan Koopman, Guido Zuccon|The University of Queensland, St Lucia, QLD, Australia; Bond Institute for Evidence-Based Healthcare, Gold Coast, Australia; CSIRO, Herston, Australia|Medical systematic review query formulation is a highly complex task done by trained information specialists. Complexity comes from the reliance on lengthy Boolean queries, which express a detailed research question. To aid query formulation, information specialists use a set of exemplar documents, called 'seed studies', prior to query formulation. Seed studies help verify the effectiveness of a query prior to the full assessment of retrieved studies. Beyond this use of seeds, specific IR methods can exploit seed studies for guiding both automatic query formulation and new retrieval models. One major limitation of work to date is that these methods exploit 'pseudo seed studies' through retrospective use of included studies (i.e., relevance assessments). However, we show pseudo seed studies are not representative of real seed studies used by information specialists. Hence, we provide a test collection with real world seed studies used to assist with the formulation of queries. To support our collection, we provide an analysis, previously not possible, on how seed studies impact retrieval and perform several experiments using seed study based methods to compare the effectiveness of using seed studies versus pseudo seed studies. We make our test collection and the results of all of our experiments and analysis available at http://github.com/ielab/sysrev-seed-collection.|医疗系统综述查询制定是一项高度复杂的任务，由训练有素的信息专家完成。复杂性来自于对冗长的布尔查询的依赖，布尔查询表达了一个详细的研究问题。为了帮助查询公式化，信息专家在查询公式化之前使用一组示例文档，称为“种子研究”。种子研究有助于在对检索到的研究进行全面评估之前验证查询的有效性。除了使用种子之外，特定的 IR 方法还可以利用种子研究来指导自动查询表达和新的检索模型。迄今为止工作的一个主要局限性是，这些方法通过回顾性使用纳入研究(即相关性评估)来利用“伪种子研究”。然而，我们表明，伪种子研究并不代表真正的种子研究所使用的信息专家。因此，我们提供了一个具有真实世界种子研究的测试集合，这些种子研究用于帮助制定查询。为了支持我们的收集，我们提供了一个以前不可能的关于种子研究如何影响检索的分析，并使用基于种子研究的方法进行了几个实验，以比较使用种子研究和伪种子研究的有效性。我们把我们的测试收集和所有实验和分析的结果在 http://github.com/ielab/sysrev-seed-collection 上公布。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=From+Little+Things+Big+Things+Grow:+A+Collection+with+Seed+Studies+for+Medical+Systematic+Review+Literature+Search)|3| +|[Assessing Student's Dynamic Knowledge State by Exploring the Question Difficulty Effect](https://doi.org/10.1145/3477495.3531939)|Shuanghong Shen, Zhenya Huang, Qi Liu, Yu Su, Shijin Wang, Enhong Chen|Anhui Province Key Laboratory of Big Data Analysis and Application, School of Data Science, University of Science and Technology of China & State Key Laboratory of Cognitive Intelligence, Hefei, China; School of Computer Science and Technology, Hefei Normal University & Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, China; State Key Laboratory of Cognitive Intelligence & iFLYTEK AI Research (Central China), iFLYTEK Co., Ltd, Hefei, China; Anhui Province Key Laboratory of Big Data Analysis and Application, School of Computer Science and Technology, University of Science and Technology of China & State Key Laboratory of Cognitive Intelligence, Hefei, China|Knowledge Tracing (KT), which aims to assess students' dynamic knowledge states when practicing on various questions, is a fundamental research task for offering intelligent services in online learning systems. Researchers have devoted significant efforts to developing KT models with impressive performance. However, in existing KT methods, the related question difficulty level, which directly affects students' knowledge state in learning, has not been effectively explored and employed. In this paper, we focus on exploring the question difficulty effect on learning to improve student's knowledge state assessment and propose the DIfficulty Matching Knowledge Tracing (DIMKT) model. Specifically, we first explicitly incorporate the difficulty level into the question representation. Then, to establish the relation between students' knowledge state and the question difficulty level during the practice process, we accordingly design an adaptive sequential neural network in three stages: (1) measuring students' subjective feelings of the question difficulty before practice; (2) estimating students' personalized knowledge acquisition while answering questions of different difficulty levels; (3) updating students' knowledge state in varying degrees to match the question difficulty level after practice. Finally, we conduct extensive experiments on real-world datasets, and the results demonstrate that DIMKT outperforms state-of-the-art KT models. Moreover, DIMKT shows superior interpretability by exploring the question difficulty effect when making predictions. Our codes are available at https://github.com/shshen-closer/DIMKT.|知识追踪(KT)是在线学习系统中提供智能服务的基础性研究课题，其目的是评估学生在各种问题上的动态知识状态。研究人员已经投入了大量的努力来开发具有令人印象深刻的性能的 KT 模型。然而，在现有的 KT 教学方法中，直接影响学生学习知识状态的相关问题难度水平还没有得到有效的探索和应用。本文着重探讨了问题难度对学习的影响，以提高学生的知识状态评价，并提出了难度匹配知识跟踪模型。具体来说，我们首先明确地将难度水平纳入问题表征。然后，为了建立学生在练习过程中的知识状态与问题难度水平之间的关系，我们设计了一个自适应序贯神经网络: (1)在练习前测量学生对问题难度的主观感受; (2)在回答不同难度水平的问题时估计学生的个性化知识获得; (3)在练习后不同程度地更新学生的知识状态以匹配问题难度水平。最后，我们在实际数据集上进行了广泛的实验，结果表明 DIMKT 模型的性能优于最先进的 KT 模型。此外，DIMKT 在预测问题时通过探索问题难度效应显示出更好的可解释性。我们的代码可以在 https://github.com/shshen-closer/dimkt 找到。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Assessing+Student's+Dynamic+Knowledge+State+by+Exploring+the+Question+Difficulty+Effect)|3| +|[MetaCare++: Meta-Learning with Hierarchical Subtyping for Cold-Start Diagnosis Prediction in Healthcare Data](https://doi.org/10.1145/3477495.3532020)|Yanchao Tan, Carl Yang, Xiangyu Wei, Chaochao Chen, Weiming Liu, Longfei Li, Jun Zhou, Xiaolin Zheng|Ant Group, Hangzhou, China; Emory University, Atlanta, GA, USA; Zhejiang University, Hangzhou, China|Cold-start diagnosis prediction is a challenging task for AI in healthcare, where often only a few visits per patient and a few observations per disease can be exploited. Although meta-learning is widely adopted to address the data sparsity problem in general domains, directly applying it to healthcare data is less effective, since it is unclear how to capture both the temporal relations in clinical visits and the complicated relations among syndromic diseases for precise personalized diagnosis. To this end, we first propose a novel Meta-learning framework for cold-start diagnosis prediction in healthCare data (MetaCare). By explicitly encoding the effects of disease progress over time as a generalization prior, MetaCare dynamically predicts future diagnosis and timestamp for infrequent patients. Then, to model complicated relations among rare diseases, we propose to utilize domain knowledge of hierarchical relations among diseases, and further perform diagnosis subtyping to mine the latent syndromic relations among diseases. Finally, to tailor the generic meta-learning framework with personalized parameters, we design a hierarchical patient subtyping mechanism and bridge the modeling of both infrequent patients and rare diseases. We term the joint model as MetaCare++. Extensive experiments on two real-world benchmark datasets show significant performance gains brought by MetaCare++, yielding average improvements of 7.71% for diagnosis prediction and 13.94% for diagnosis time prediction over the state-of-the-art baselines.|冷启动诊断预测是医疗保健中 AI 的一项具有挑战性的任务，通常每个病人只有几次就诊，每个疾病只有几次观察。尽管元学习被广泛用于解决一般领域的数据稀疏问题，但直接应用于医疗保健数据的效果较差，因为目前还不清楚如何捕获临床访视中的时间关系和综合征疾病之间的复杂关系以进行精确的个性化诊断。为此，我们首先提出了一个新的元学习框架，用于医疗保健数据(MetaCare)中的冷启动诊断预测。通过明确编码疾病进展随时间推移的影响作为一个普遍的先例，MetaCare 动态预测未来的诊断和时间戳的罕见病人。然后，利用疾病之间等级关系的领域知识，进一步进行诊断分型，挖掘疾病之间潜在的综合征关系，建立罕见病之间复杂关系的模型。最后，为了使通用的元学习框架具有个性化的参数，我们设计了一个分层的患者亚型机制，并在罕见患者和罕见疾病的建模之间架起了桥梁。我们将联合模型称为 MetaCare + + 。在两个真实世界基准数据集上的广泛实验显示 MetaCare + + 带来了显着的性能提高，在最先进的基线上诊断预测平均提高了7.71% ，诊断时间预测平均提高了13.94% 。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=MetaCare++:+Meta-Learning+with+Hierarchical+Subtyping+for+Cold-Start+Diagnosis+Prediction+in+Healthcare+Data)|3| |[Generalizing to the Future: Mitigating Entity Bias in Fake News Detection](https://doi.org/10.1145/3477495.3531816)|Yongchun Zhu, Qiang Sheng, Juan Cao, Shuokai Li, Danding Wang, Fuzhen Zhuang|Institute of Computing Technology, Chinese Academy of Sciences & University of Chinese Academy of Sciences, Beijing, China; Beihang University, Beijing, China|The wide dissemination of fake news is increasingly threatening both individuals and society. Fake news detection aims to train a model on the past news and detect fake news of the future. Though great efforts have been made, existing fake news detection methods overlooked the unintended entity bias in the real-world data, which seriously influences models' generalization ability to future data. For example, 97% of news pieces in 2010-2017 containing the entity 'Donald Trump' are real in our data, but the percentage falls down to merely 33% in 2018. This would lead the model trained on the former set to hardly generalize to the latter, as it tends to predict news pieces about 'Donald Trump' as real for lower training loss. In this paper, we propose an entity debiasing framework (ENDEF) which generalizes fake news detection models to the future data by mitigating entity bias from a cause-effect perspective. Based on the causal graph among entities, news contents, and news veracity, we separately model the contribution of each cause (entities and contents) during training. In the inference stage, we remove the direct effect of the entities to mitigate entity bias. Extensive offline experiments on the English and Chinese datasets demonstrate that the proposed framework can largely improve the performance of base fake news detectors, and online tests verify its superiority in practice. To the best of our knowledge, this is the first work to explicitly improve the generalization ability of fake news detection models to the future data. The code has been released at https://github.com/ICTMCG/ENDEF-SIGIR2022.|假新闻的广泛传播日益威胁着个人和社会。假新闻检测的目的是训练一个模型，以过去的新闻和检测假新闻的未来。现有的假新闻检测方法忽视了现实数据中存在的无意识实体偏差，严重影响了模型对未来数据的泛化能力。例如，在我们的数据中，2010-2017年包含实体“唐纳德 · 特朗普”的新闻报道有97% 是真实的，但这一比例在2018年下降到仅有33% 。这将导致在前者上训练的模型很难推广到后者，因为它往往预测关于“唐纳德 · 特朗普”的新闻报道是真实的，以降低训练损失。本文提出了一个实体去偏框架(ENDEF) ，从因果关系的角度将虚假新闻检测模型推广到未来数据，减轻实体偏差。基于实体、新闻内容和新闻准确性之间的因果图，我们分别建立了训练过程中各个因素(实体和内容)的贡献模型。在推理阶段，我们消除了实体的直接影响，以减轻实体偏差。在中英文数据集上进行的大量离线实验表明，该框架能够显著提高基本假新闻检测器的性能，在线测试验证了该框架在实际应用中的优越性。据我们所知，这是第一项明确提高假新闻检测模型对未来数据的泛化能力的工作。密码已经在 https://github.com/ictmcg/endef-sigir2022公布了。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Generalizing+to+the+Future:+Mitigating+Entity+Bias+in+Fake+News+Detection)|3| -|[ORCAS-I: Queries Annotated with Intent using Weak Supervision](https://doi.org/10.1145/3477495.3531737)|Daria Alexander, Wojciech Kusa, Arjen P. de Vries|TU Wien, Vienna, Austria; Radboud University, Nijmegen, Netherlands; Radboud University & Spinque, Nijmegen, Netherlands|User intent classification is an important task in information retrieval. In this work, we introduce a revised taxonomy of user intent. We take the widely used differentiation between navigational, transactional and informational queries as a starting point, and identify three different sub-classes for the informational queries: instrumental, factual and abstain. The resulting classification of user queries is more fine-grained, reaches a high level of consistency between annotators, and can serve as the basis for an effective automatic classification process. The newly introduced categories help distinguish between types of queries that a retrieval system could act upon, for example by prioritizing different types of results in the ranking. We have used a weak supervision approach based on Snorkel to annotate the ORCAS dataset according to our new user intent taxonomy, utilising established heuristics and keywords to construct rules for the prediction of the intent category. We then present a series of experiments with a variety of machine learning models, using the labels from the weak supervision stage as training data, but find that the results produced by Snorkel are not outperformed by these competing approaches and can be considered state-of-the-art. The advantage of a rule-based approach like Snorkel's is its efficient deployment in an actual system, where intent classification would be executed for every query issued. The resource released with this paper is the ORCAS-I dataset: a labelled version of the ORCAS click-based dataset of Web queries, which provides 18 million connections to 10 million distinct queries. We anticipate the usage of this resource in a scenario where the retrieval system would change its internal workings and search user interface to match the type of information request. For example, a navigational query could trigger just a short result list; and, for instrumental intent the system could rank tutorials and instructions higher than for other types of queries.|用户意图分类是信息检索的一项重要工作。在这项工作中，我们介绍了修订后的用户意图分类法。我们以广泛使用的导航查询、事务性查询和信息性查询之间的区别为出发点，确定了信息性查询的三个不同的子类: 工具性查询、事实性查询和弃权查询。由此产生的用户查询分类更加细粒度，达到了注释者之间的高度一致性，可以作为一个有效的自动分类过程的基础。新引入的类别有助于区分检索系统可以处理的查询类型，例如，通过在排名中对不同类型的结果进行优先排序。我们使用了基于 Snorkel 的弱监督方法来根据我们新的用户意图分类法对 ORCAS 数据集进行注释，使用已建立的启发式和关键字来构建用于预测意图类别的规则。然后，我们用各种机器学习模型进行了一系列的实验，使用来自弱监督阶段的标签作为训练数据，但是发现 Snorkel 产生的结果并没有被这些相互竞争的方法所超越，并且可以被认为是最先进的。像 Snorkel 这样的基于规则的方法的优势在于它在实际系统中的高效部署，在实际系统中，将对发出的每个查询执行意图分类。与本文一起发布的资源是 ORCAS-I 数据集: 基于 ORCAS 单击的 Web 查询数据集的标记版本，它提供了1800万个对1000万个不同查询的连接。我们预期在检索系统将改变其内部工作方式并搜索用户界面以匹配信息请求类型的场景中使用此资源。例如，导航查询只能触发一个简短的结果列表; 而且，对于工具意图，系统可以将教程和指令排序得比其他类型的查询更高。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=ORCAS-I:+Queries+Annotated+with+Intent+using+Weak+Supervision)|3| +|[ORCAS-I: Queries Annotated with Intent using Weak Supervision](https://doi.org/10.1145/3477495.3531737)|Daria Alexander, Wojciech Kusa, Arjen P. de Vries|Radboud University, Nijmegen, Netherlands; TU Wien, Vienna, Austria; Radboud University & Spinque, Nijmegen, Netherlands|User intent classification is an important task in information retrieval. In this work, we introduce a revised taxonomy of user intent. We take the widely used differentiation between navigational, transactional and informational queries as a starting point, and identify three different sub-classes for the informational queries: instrumental, factual and abstain. The resulting classification of user queries is more fine-grained, reaches a high level of consistency between annotators, and can serve as the basis for an effective automatic classification process. The newly introduced categories help distinguish between types of queries that a retrieval system could act upon, for example by prioritizing different types of results in the ranking. We have used a weak supervision approach based on Snorkel to annotate the ORCAS dataset according to our new user intent taxonomy, utilising established heuristics and keywords to construct rules for the prediction of the intent category. We then present a series of experiments with a variety of machine learning models, using the labels from the weak supervision stage as training data, but find that the results produced by Snorkel are not outperformed by these competing approaches and can be considered state-of-the-art. The advantage of a rule-based approach like Snorkel's is its efficient deployment in an actual system, where intent classification would be executed for every query issued. The resource released with this paper is the ORCAS-I dataset: a labelled version of the ORCAS click-based dataset of Web queries, which provides 18 million connections to 10 million distinct queries. We anticipate the usage of this resource in a scenario where the retrieval system would change its internal workings and search user interface to match the type of information request. For example, a navigational query could trigger just a short result list; and, for instrumental intent the system could rank tutorials and instructions higher than for other types of queries.|用户意图分类是信息检索的一项重要工作。在这项工作中，我们介绍了修订后的用户意图分类法。我们以广泛使用的导航查询、事务性查询和信息性查询之间的区别为出发点，确定了信息性查询的三个不同的子类: 工具性查询、事实性查询和弃权查询。由此产生的用户查询分类更加细粒度，达到了注释者之间的高度一致性，可以作为一个有效的自动分类过程的基础。新引入的类别有助于区分检索系统可以处理的查询类型，例如，通过在排名中对不同类型的结果进行优先排序。我们使用了基于 Snorkel 的弱监督方法来根据我们新的用户意图分类法对 ORCAS 数据集进行注释，使用已建立的启发式和关键字来构建用于预测意图类别的规则。然后，我们用各种机器学习模型进行了一系列的实验，使用来自弱监督阶段的标签作为训练数据，但是发现 Snorkel 产生的结果并没有被这些相互竞争的方法所超越，并且可以被认为是最先进的。像 Snorkel 这样的基于规则的方法的优势在于它在实际系统中的高效部署，在实际系统中，将对发出的每个查询执行意图分类。与本文一起发布的资源是 ORCAS-I 数据集: 基于 ORCAS 单击的 Web 查询数据集的标记版本，它提供了1800万个对1000万个不同查询的连接。我们预期在检索系统将改变其内部工作方式并搜索用户界面以匹配信息请求类型的场景中使用此资源。例如，导航查询只能触发一个简短的结果列表; 而且，对于工具意图，系统可以将教程和指令排序得比其他类型的查询更高。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=ORCAS-I:+Queries+Annotated+with+Intent+using+Weak+Supervision)|3| |[Unified Dialog Model Pre-training for Task-Oriented Dialog Understanding and Generation](https://doi.org/10.1145/3477495.3532069)|Wanwei He, Yinpei Dai, Min Yang, Jian Sun, Fei Huang, Luo Si, Yongbin Li|Alibaba Group, Beijing, China; Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China|Recently, pre-training methods have shown remarkable success in task-oriented dialog (TOD) systems. However, most existing pre-trained models for TOD focus on either dialog understanding or dialog generation, but not both. In this paper, we propose SPACE, a novel unified pre-trained dialog model learning from large-scale dialog corpora with limited annotations, which can be effectively fine-tuned on a wide range of downstream dialog tasks. Specifically, SPACE consists of four successive components in a single transformer to maintain a task-flow in TOD systems: (i) a dialog encoding module to encode dialog history, (ii) a dialog understanding module to extract semantic vectors from either user queries or system responses, (iii) a dialog policy module to generate a policy vector that contains high-level semantics of the response, and (iv) a dialog generation module to produce appropriate responses. We design a dedicated pre-training objective for each component. Concretely, we pre-train the dialog encoding module with span mask language modeling to learn contextualized dialog information. To capture the structured dialog semantics, we pre-train the dialog understanding module via a novel tree-induced semi-supervised contrastive learning objective with the help of extra dialog annotations. In addition, we pre-train the dialog policy module by minimizing the ℒ2 distance between its output policy vector and the semantic vector of the response for policy optimization. Finally, the dialog generation model is pre-trained by language modeling. Results show that SPACE achieves state-of-the-art performance on eight downstream dialog benchmarks, including intent prediction, dialog state tracking, and end-to-end dialog modeling. We also show that SPACE has a stronger few-shot ability than existing models under the low-resource setting.|近年来，在任务导向对话(TOD)系统中，预训练方法取得了显著的成功。然而，大多数现有的预先训练的 TOD 模型只关注对话理解或对话生成，而不是两者兼顾。在本文中，我们提出了一种新的统一的预训练对话模型 SPACE，该模型基于有限注释的大规模对话语料库，可以在大范围的下游对话任务中进行有效的微调。具体而言，SPACE 由单个转换器中的四个连续组件组成，以维持 TOD 系统中的任务流: (i)用于编码对话历史的对话编码模块，(ii)用于从用户查询或系统响应中提取语义向量的对话理解模块，(iii)用于生成包含响应的高级语义的策略向量的对话策略模块，以及(iv)用于产生适当响应的对话生成模块。我们为每个部分设计一个专门的培训前目标。具体来说，我们使用跨度掩码语言模型对对话框编码模块进行了预训练，以学习上下文化的对话框信息。为了捕获结构化对话语义，我们利用额外的对话注释，通过一个新的树引导的半监督对比学习目标对对话理解模块进行预训练。此外，我们预先训练对话策略模块，使其输出策略向量与策略优化响应的语义向量之间的 L2距离最小。最后，通过语言建模对对话生成模型进行预训练。结果表明，SPACE 在8个下游对话框基准测试中取得了最佳性能，包括意图预测、对话框状态跟踪和端到端对话框建模。我们还表明，在低资源设置下，SPACE 比现有的模型具有更强的少拍摄能力。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Unified+Dialog+Model+Pre-training+for+Task-Oriented+Dialog+Understanding+and+Generation)|3| -|[Introducing Problem Schema with Hierarchical Exercise Graph for Knowledge Tracing](https://doi.org/10.1145/3477495.3532004)|Hanshuang Tong, Zhen Wang, Yun Zhou, Shiwei Tong, Wenyuan Han, Qi Liu|School of Computer Science and Technology, University of Science and Technology of China, Beijing, China; Microsoft Corporation, Beijing, China, Beijing, China; AIXUEXI Education Group Ltd, Beijing, China, Beijing, China; School of Computer Science and Technology, University of Science and Technology of China, Hefei, China|Knowledge tracing (KT) which aims at predicting learner's knowledge mastery plays an important role in the computer-aided educational system. The goal of KT is to provide personalized learning paths for learners by diagnosing the mastery of each knowledge, thus improving the learning efficiency. In recent years, many deep learning models have been applied to tackle the KT task, which has shown promising results. However, most existing methods simplify the exercising records as knowledge sequences, which fail to explore the rich information that existed in exercises. Besides, the existing diagnosis results of knowledge tracing are not convincing enough since they neglect hierarchical relations between exercises. To solve the above problems, we propose a hierarchical graph knowledge tracing model called HGKT to explore the latent complex relations between exercises. Specifically, we introduce the concept of problem schema to construct a hierarchical exercise graph that could model the exercise learning dependencies. Moreover, we employ two attention mechanisms to highlight important historical states of learners. In the testing stage, we present a knowledge&schema diagnosis matrix that could trace the transition of mastery of knowledge and problem schema, which can be more easily applied to different applications. Extensive experiments show the effectiveness and interpretability of our proposed model.|知识追踪是计算机辅助教育系统中的一个重要组成部分，其目的是预测学习者的知识掌握情况。KT 的目标是通过诊断学习者对各种知识的掌握情况，为学习者提供个性化的学习途径，从而提高学习效率。近年来，许多深度学习模型被应用于解决 KT 问题，并取得了良好的效果。然而，现有的方法大多将习题记录简化为知识序列，未能探索习题中存在的丰富信息。此外，现有的知识追踪诊断结果由于忽视了习题之间的层次关系而不够令人信服。为了解决上述问题，本文提出了一种层次化的图形知识跟踪模型 HGKT，用于探索习题之间潜在的复杂关系。具体地说，我们引入了问题模式的概念，构造了一个层次化的练习图，可以对练习学习的依赖关系进行建模。此外，我们采用了两种注意机制来突出学习者的重要历史状态。在测试阶段，我们提出了一个知识和图式诊断矩阵，它可以跟踪知识掌握和问题图式的转换，可以更容易地应用到不同的应用程序中。大量的实验表明了我们提出的模型的有效性和可解释性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Introducing+Problem+Schema+with+Hierarchical+Exercise+Graph+for+Knowledge+Tracing)|3| -|[A Flexible Framework for Offline Effectiveness Metrics](https://doi.org/10.1145/3477495.3531924)|Alistair Moffat, Joel Mackenzie, Paul Thomas, Leif Azzopardi|Microsoft, Canberra, Australia; The University of Queensland, Brisbane, QLD, Australia; The University of Melbourne, Melbourne, VIC, Australia; University of Strathclyde, Glasgow, United Kingdom|The use of offline effectiveness metrics is one of the cornerstones of evaluation in information retrieval. Static resources that include test collections and sets of topics, the corresponding relevance judgments connecting them, and metrics that map document rankings from a retrieval system to numeric scores have been used for multiple decades as an important way of comparing systems. The basis behind this experimental structure is that the metric score for a system can serve as a surrogate measurement for user satisfaction. Here we introduce a user behavior framework that extends the C/W/L family. The essence of the new framework - which we call C/W/L/A - is that the user actions that are undertaken while reading the ranking can be considered separately from the benefit that each user will have derived as they exit the ranking. This split structure allows the great majority of current effectiveness metrics to be systematically categorized, and thus their relative properties and relationships to be better understood; and at the same time permits a wide range of novel combinations to be considered. We then carry out experiments using relevance judgments, document rankings, and user satisfaction data from two distinct sources, comparing the patterns of metric scores generated, and showing that those metrics vary quite markedly in terms of their ability to predict user satisfaction.|离线效能指标的使用是信息检索评估的基石之一。静态资源，包括测试集合和主题集合，相应的相关性判断连接它们，以及将文档排名从检索系统映射到数字分数的指标，已经作为比较系统的一个重要方法使用了几十年。这个实验结构的基础是系统的度量分数可以作为用户满意度的替代指标。这里我们介绍一个扩展 C/W/L 家族的用户行为框架。我们称之为 C/W/L/A 的新框架的本质是，用户在阅读排名时所采取的行动可以与每个用户在退出排名时获得的好处分开考虑。这种分离结构允许对大多数当前有效性指标进行系统分类，从而更好地理解它们的相对属性和关系; 同时允许考虑范围广泛的新颖组合。然后，我们使用来自两个不同来源的相关性判断、文档排名和用户满意度数据进行实验，比较产生的度量得分模式，并显示这些度量在预测用户满意度方面的能力差异非常显著。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Flexible+Framework+for+Offline+Effectiveness+Metrics)|3| -|[Incorporating Context Graph with Logical Reasoning for Inductive Relation Prediction](https://doi.org/10.1145/3477495.3531996)|Qika Lin, Jun Liu, Fangzhi Xu, Yudai Pan, Yifan Zhu, Lingling Zhang, Tianzhe Zhao|Tsinghua University, Beijing, China; Xi'an Jiaotong University, Xi'an, China; National Engineering Lab for Big Data Analytics, Xi'an, China|Relation prediction on knowledge graphs (KGs) aims to infer missing valid triples from observed ones. Although this task has been deeply studied, most previous studies are limited to the transductive setting and cannot handle emerging entities. Actually, the inductive setting is closer to real-life scenarios because it allows entities in the testing phase to be unseen during training. However, it is challenging to precisely conduct inductive relation prediction as there exists requirements of entity-independent relation modeling and discrete logical reasoning for interoperability. To this end, we propose a novel model ConGLR to incorporate context graph with logical reasoning. Firstly, the enclosing subgraph w.r.t. target head and tail entities are extracted and initialized by the double radius labeling. And then the context graph involving relational paths, relations and entities is introduced. Secondly, two graph convolutional networks (GCNs) with the information interaction of entities and relations are carried out to process the subgraph and context graph respectively. Considering the influence of different edges and target relations, we introduce edge-aware and relation-aware attention mechanisms for the subgraph GCN. Finally, by treating the relational path as rule body and target relation as rule head, we integrate neural calculating and logical reasoning to obtain inductive scores. And to focus on the specific modeling goals of each module, the stop-gradient is utilized in the information interaction between context graph and subgraph GCNs in the training process. In this way, ConGLR satisfies two inductive requirements at the same time. Extensive experiments demonstrate that ConGLR obtains outstanding performance against state-of-the-art baselines on twelve inductive dataset versions of three common KGs.|知识图关系预测的目的是从观察到的三元组中推断出缺失的有效三元组。虽然这项任务已经深入研究，大多数以前的研究仅限于传导性的设置，不能处理新兴的实体。实际上，归纳设置更接近于实际场景，因为它允许测试阶段的实体在训练期间不被看到。然而，精确地进行归纳关系预测是具有挑战性的，因为存在实体无关的关系建模和互操作性的离散逻辑推理的要求。为此，我们提出了一个新的模型 ConGLR，将上下文图和逻辑推理结合起来。首先，通过双半径标记提取封闭子图的目标头部和尾部实体，并对其进行初始化;。然后介绍了涉及关系路径、关系和实体的上下文图。其次，分别对子图和上下文图进行处理，构造了两个具有实体和关系信息交互的图卷积网络。考虑到不同边和目标关系的影响，我们在子图 GCN 中引入了边感知和关系感知的注意机制。最后，我们把关系路径当作规则体，把目标关系当作规则头，结合神经计算和逻辑推理来获得归纳分数。针对每个模块的特定建模目标，在训练过程中，利用停止梯度进行上下文图和子图 GCNs 之间的信息交互。这样，ConGLR 可以同时满足两个感应要求。大量的实验表明，ConGLR 在三个常见 KG 的12个归纳数据集版本上对最先进的基线获得了出色的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Incorporating+Context+Graph+with+Logical+Reasoning+for+Inductive+Relation+Prediction)|3| +|[Introducing Problem Schema with Hierarchical Exercise Graph for Knowledge Tracing](https://doi.org/10.1145/3477495.3532004)|Hanshuang Tong, Zhen Wang, Yun Zhou, Shiwei Tong, Wenyuan Han, Qi Liu|Microsoft Corporation, Beijing, China, Beijing, China; AIXUEXI Education Group Ltd, Beijing, China, Beijing, China; School of Computer Science and Technology, University of Science and Technology of China, Hefei, China; School of Computer Science and Technology, University of Science and Technology of China, Beijing, China|Knowledge tracing (KT) which aims at predicting learner's knowledge mastery plays an important role in the computer-aided educational system. The goal of KT is to provide personalized learning paths for learners by diagnosing the mastery of each knowledge, thus improving the learning efficiency. In recent years, many deep learning models have been applied to tackle the KT task, which has shown promising results. However, most existing methods simplify the exercising records as knowledge sequences, which fail to explore the rich information that existed in exercises. Besides, the existing diagnosis results of knowledge tracing are not convincing enough since they neglect hierarchical relations between exercises. To solve the above problems, we propose a hierarchical graph knowledge tracing model called HGKT to explore the latent complex relations between exercises. Specifically, we introduce the concept of problem schema to construct a hierarchical exercise graph that could model the exercise learning dependencies. Moreover, we employ two attention mechanisms to highlight important historical states of learners. In the testing stage, we present a knowledge&schema diagnosis matrix that could trace the transition of mastery of knowledge and problem schema, which can be more easily applied to different applications. Extensive experiments show the effectiveness and interpretability of our proposed model.|知识追踪是计算机辅助教育系统中的一个重要组成部分，其目的是预测学习者的知识掌握情况。KT 的目标是通过诊断学习者对各种知识的掌握情况，为学习者提供个性化的学习途径，从而提高学习效率。近年来，许多深度学习模型被应用于解决 KT 问题，并取得了良好的效果。然而，现有的方法大多将习题记录简化为知识序列，未能探索习题中存在的丰富信息。此外，现有的知识追踪诊断结果由于忽视了习题之间的层次关系而不够令人信服。为了解决上述问题，本文提出了一种层次化的图形知识跟踪模型 HGKT，用于探索习题之间潜在的复杂关系。具体地说，我们引入了问题模式的概念，构造了一个层次化的练习图，可以对练习学习的依赖关系进行建模。此外，我们采用了两种注意机制来突出学习者的重要历史状态。在测试阶段，我们提出了一个知识和图式诊断矩阵，它可以跟踪知识掌握和问题图式的转换，可以更容易地应用到不同的应用程序中。大量的实验表明了我们提出的模型的有效性和可解释性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Introducing+Problem+Schema+with+Hierarchical+Exercise+Graph+for+Knowledge+Tracing)|3| +|[A Flexible Framework for Offline Effectiveness Metrics](https://doi.org/10.1145/3477495.3531924)|Alistair Moffat, Joel Mackenzie, Paul Thomas, Leif Azzopardi|The University of Queensland, Brisbane, QLD, Australia; University of Strathclyde, Glasgow, United Kingdom; Microsoft, Canberra, Australia; The University of Melbourne, Melbourne, VIC, Australia|The use of offline effectiveness metrics is one of the cornerstones of evaluation in information retrieval. Static resources that include test collections and sets of topics, the corresponding relevance judgments connecting them, and metrics that map document rankings from a retrieval system to numeric scores have been used for multiple decades as an important way of comparing systems. The basis behind this experimental structure is that the metric score for a system can serve as a surrogate measurement for user satisfaction. Here we introduce a user behavior framework that extends the C/W/L family. The essence of the new framework - which we call C/W/L/A - is that the user actions that are undertaken while reading the ranking can be considered separately from the benefit that each user will have derived as they exit the ranking. This split structure allows the great majority of current effectiveness metrics to be systematically categorized, and thus their relative properties and relationships to be better understood; and at the same time permits a wide range of novel combinations to be considered. We then carry out experiments using relevance judgments, document rankings, and user satisfaction data from two distinct sources, comparing the patterns of metric scores generated, and showing that those metrics vary quite markedly in terms of their ability to predict user satisfaction.|离线效能指标的使用是信息检索评估的基石之一。静态资源，包括测试集合和主题集合，相应的相关性判断连接它们，以及将文档排名从检索系统映射到数字分数的指标，已经作为比较系统的一个重要方法使用了几十年。这个实验结构的基础是系统的度量分数可以作为用户满意度的替代指标。这里我们介绍一个扩展 C/W/L 家族的用户行为框架。我们称之为 C/W/L/A 的新框架的本质是，用户在阅读排名时所采取的行动可以与每个用户在退出排名时获得的好处分开考虑。这种分离结构允许对大多数当前有效性指标进行系统分类，从而更好地理解它们的相对属性和关系; 同时允许考虑范围广泛的新颖组合。然后，我们使用来自两个不同来源的相关性判断、文档排名和用户满意度数据进行实验，比较产生的度量得分模式，并显示这些度量在预测用户满意度方面的能力差异非常显著。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Flexible+Framework+for+Offline+Effectiveness+Metrics)|3| +|[Incorporating Context Graph with Logical Reasoning for Inductive Relation Prediction](https://doi.org/10.1145/3477495.3531996)|Qika Lin, Jun Liu, Fangzhi Xu, Yudai Pan, Yifan Zhu, Lingling Zhang, Tianzhe Zhao|Tsinghua University, Beijing, China; National Engineering Lab for Big Data Analytics, Xi'an, China; Xi'an Jiaotong University, Xi'an, China|Relation prediction on knowledge graphs (KGs) aims to infer missing valid triples from observed ones. Although this task has been deeply studied, most previous studies are limited to the transductive setting and cannot handle emerging entities. Actually, the inductive setting is closer to real-life scenarios because it allows entities in the testing phase to be unseen during training. However, it is challenging to precisely conduct inductive relation prediction as there exists requirements of entity-independent relation modeling and discrete logical reasoning for interoperability. To this end, we propose a novel model ConGLR to incorporate context graph with logical reasoning. Firstly, the enclosing subgraph w.r.t. target head and tail entities are extracted and initialized by the double radius labeling. And then the context graph involving relational paths, relations and entities is introduced. Secondly, two graph convolutional networks (GCNs) with the information interaction of entities and relations are carried out to process the subgraph and context graph respectively. Considering the influence of different edges and target relations, we introduce edge-aware and relation-aware attention mechanisms for the subgraph GCN. Finally, by treating the relational path as rule body and target relation as rule head, we integrate neural calculating and logical reasoning to obtain inductive scores. And to focus on the specific modeling goals of each module, the stop-gradient is utilized in the information interaction between context graph and subgraph GCNs in the training process. In this way, ConGLR satisfies two inductive requirements at the same time. Extensive experiments demonstrate that ConGLR obtains outstanding performance against state-of-the-art baselines on twelve inductive dataset versions of three common KGs.|知识图关系预测的目的是从观察到的三元组中推断出缺失的有效三元组。虽然这项任务已经深入研究，大多数以前的研究仅限于传导性的设置，不能处理新兴的实体。实际上，归纳设置更接近于实际场景，因为它允许测试阶段的实体在训练期间不被看到。然而，精确地进行归纳关系预测是具有挑战性的，因为存在实体无关的关系建模和互操作性的离散逻辑推理的要求。为此，我们提出了一个新的模型 ConGLR，将上下文图和逻辑推理结合起来。首先，通过双半径标记提取封闭子图的目标头部和尾部实体，并对其进行初始化;。然后介绍了涉及关系路径、关系和实体的上下文图。其次，分别对子图和上下文图进行处理，构造了两个具有实体和关系信息交互的图卷积网络。考虑到不同边和目标关系的影响，我们在子图 GCN 中引入了边感知和关系感知的注意机制。最后，我们把关系路径当作规则体，把目标关系当作规则头，结合神经计算和逻辑推理来获得归纳分数。针对每个模块的特定建模目标，在训练过程中，利用停止梯度进行上下文图和子图 GCNs 之间的信息交互。这样，ConGLR 可以同时满足两个感应要求。大量的实验表明，ConGLR 在三个常见 KG 的12个归纳数据集版本上对最先进的基线获得了出色的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Incorporating+Context+Graph+with+Logical+Reasoning+for+Inductive+Relation+Prediction)|3| |[Hybrid Transformer with Multi-level Fusion for Multimodal Knowledge Graph Completion](https://doi.org/10.1145/3477495.3531992)|Xiang Chen, Ningyu Zhang, Lei Li, Shumin Deng, Chuanqi Tan, Changliang Xu, Fei Huang, Luo Si, Huajun Chen|Alibaba Group, Hangzhou, China; State Key Laboratory of Media Convergence Production Technology and Systems, Beijing, China; Zhejiang University, Hangzhou, China|Multimodal Knowledge Graphs (MKGs), which organize visual-text factual knowledge, have recently been successfully applied to tasks such as information retrieval, question answering, and recommendation system. Since most MKGs are far from complete, extensive knowledge graph completion studies have been proposed focusing on the multimodal entity, relation extraction and link prediction. However, different tasks and modalities require changes to the model architecture, and not all images/objects are relevant to text input, which hinders the applicability to diverse real-world scenarios. In this paper, we propose a hybrid transformer with multi-level fusion to address those issues. Specifically, we leverage a hybrid transformer architecture with unified input-output for diverse multimodal knowledge graph completion tasks. Moreover, we propose multi-level fusion, which integrates visual and text representation via coarse-grained prefix-guided interaction and fine-grained correlation-aware fusion modules. We conduct extensive experiments to validate that our MKGformer can obtain SOTA performance on four datasets of multimodal link prediction, multimodal RE, and multimodal NER1. https://github.com/zjunlp/MKGformer.|多模态知识图(multimodalKnowledge Graphs，MKGs)组织视觉文本事实知识，最近已成功应用于信息检索、问答和推荐系统等任务。由于大多数 MKG 还不完备，因此人们提出了广泛的知识图完备性研究，主要集中在多模态实体、关系提取和链路预测方面。然而，不同的任务和模式需要对模型架构进行更改，并非所有的图像/对象都与文本输入相关，这阻碍了对不同的现实世界场景的适用性。本文提出了一种多电平融合的混合变压器来解决这些问题。具体来说，我们利用一个具有统一输入输出的混合变压器架构来完成不同的多模态知识图完成任务。此外，我们还提出了多级融合，通过粗粒度前缀引导交互和细粒度相关感知融合模块，实现了视觉和文本表示的集成。我们进行了广泛的实验，以验证我们的 MKGformer 可以获得 SOTA 性能的四个数据集的多模态链接预测，多模态 RE 和多模态 NER1。Https://github.com/zjunlp/mkgformer.|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Hybrid+Transformer+with+Multi-level+Fusion+for+Multimodal+Knowledge+Graph+Completion)|3| |[HTKG: Deep Keyphrase Generation with Neural Hierarchical Topic Guidance](https://doi.org/10.1145/3477495.3531990)|Yuxiang Zhang, Tao Jiang, Tianyu Yang, Xiaoli Li, Suge Wang|Shanxi University, Taiyuan, China; Civil Aviation University of China, Tianjin, China; A*STAR, Singapore, Singapore, Singapore|Keyphrases can concisely describe the high-level topics discussed in a document that usually possesses hierarchical topic structures. Thus, it is crucial to understand the hierarchical topic structures and employ it to guide the keyphrase identification. However, integrating the hierarchical topic information into a deep keyphrase generation model is unexplored. In this paper, we focus on how to effectively exploit the hierarchical topic to improve the keyphrase generation performance (HTKG). Specifically, we propose a novel hierarchical topic-guided variational neural sequence generation method for keyphrase generation, which consists of two major modules: a neural hierarchical topic model that learns the latent topic tree across the whole corpus of documents, and a variational neural keyphrase generation model to generate keyphrases under hierarchical topic guidance. Finally, these two modules are jointly trained to help them learn complementary information from each other. To the best of our knowledge, this is the first attempt to leverage the neural hierarchical topic to guide keyphrase generation. The experimental results demonstrate that our method significantly outperforms the existing state-of-the-art methods across five benchmark datasets.|关键词可以简洁地描述文档中讨论的高级主题，该文档通常具有分层主题结构。因此，理解分层主题结构并利用它来指导关键词的识别是至关重要的。然而，将层次化的主题信息集成到一个深层次的关键词生成模型中还没有被探索。本文主要研究如何有效地利用分层主题来提高关键词生成性能。提出了一种基于层次主题引导的变分神经序列生成方法，该方法包括两个主要模块: 学习整个文档语料库中潜在主题树的神经层次主题模型和层次主题引导下的变分神经关键词生成模型。最后，对这两个模块进行联合培训，以帮助它们相互学习互补的信息。据我们所知，这是第一次尝试利用神经分层主题来指导关键词生成。实验结果表明，该方法在五个基准数据集上的性能明显优于现有的最先进的方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=HTKG:+Deep+Keyphrase+Generation+with+Neural+Hierarchical+Topic+Guidance)|3| -|[Logiformer: A Two-Branch Graph Transformer Network for Interpretable Logical Reasoning](https://doi.org/10.1145/3477495.3532016)|Fangzhi Xu, Jun Liu, Qika Lin, Yudai Pan, Lingling Zhang|Xi'an Jiaotong University, Xi'an, China; National Engineering Lab for Big Data Analytics, Xi'an, China|Machine reading comprehension has aroused wide concerns, since it explores the potential of model for text understanding. To further equip the machine with the reasoning capability, the challenging task of logical reasoning is proposed. Previous works on logical reasoning have proposed some strategies to extract the logical units from different aspects. However, there still remains a challenge to model the long distance dependency among the logical units. Also, it is demanding to uncover the logical structures of the text and further fuse the discrete logic to the continuous text embedding. To tackle the above issues, we propose an end-to-end model Logiformer which utilizes a two-branch graph transformer network for logical reasoning of text. Firstly, we introduce different extraction strategies to split the text into two sets of logical units, and construct the logical graph and the syntax graph respectively. The logical graph models the causal relations for the logical branch while the syntax graph captures the co-occurrence relations for the syntax branch. Secondly, to model the long distance dependency, the node sequence from each graph is fed into the fully connected graph transformer structures. The two adjacent matrices are viewed as the attention biases for the graph transformer layers, which map the discrete logical structures to the continuous text embedding space. Thirdly, a dynamic gate mechanism and a question-aware self-attention module are introduced before the answer prediction to update the features. The reasoning process provides the interpretability by employing the logical units, which are consistent with human cognition. The experimental results show the superiority of our model, which outperforms the state-of-the-art single model on two logical reasoning benchmarks.|机器阅读理解已经引起了广泛的关注，因为它探索了文本理解模型的潜力。为了进一步提高机器的推理能力，提出了具有挑战性的逻辑推理任务。以往关于逻辑推理的研究提出了一些策略来从不同方面提取逻辑单元。然而，在逻辑单元之间建立长距离依赖关系仍然是一个挑战。此外，还需要揭示文本的逻辑结构，进一步将离散逻辑与连续文本嵌入相融合。为了解决上述问题，我们提出了一个端到端模型 Logigformer，它利用一个两分支的图形转换网络进行文本逻辑推理。首先，引入不同的抽取策略，将文本分割成两组逻辑单元，分别构造逻辑图和句法图。逻辑图模拟逻辑分支的因果关系，而语法图捕获语法分支的共现关系。其次，为了建立长距离依赖关系模型，将每个图的节点序列输入到完全连通的图变换结构中。将两个相邻矩阵视为图形转换层的注意偏差，将离散逻辑结构映射到连续文本嵌入空间。第三，在答案预测之前引入动态门机制和问题感知自我注意模块来更新特征。推理过程通过使用与人类认知相一致的逻辑单元来提供可解释性。实验结果显示了我们的模型的优越性，它在两个逻辑推理基准上优于最先进的单一模型。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Logiformer:+A+Two-Branch+Graph+Transformer+Network+for+Interpretable+Logical+Reasoning)|3| +|[Logiformer: A Two-Branch Graph Transformer Network for Interpretable Logical Reasoning](https://doi.org/10.1145/3477495.3532016)|Fangzhi Xu, Jun Liu, Qika Lin, Yudai Pan, Lingling Zhang|National Engineering Lab for Big Data Analytics, Xi'an, China; Xi'an Jiaotong University, Xi'an, China|Machine reading comprehension has aroused wide concerns, since it explores the potential of model for text understanding. To further equip the machine with the reasoning capability, the challenging task of logical reasoning is proposed. Previous works on logical reasoning have proposed some strategies to extract the logical units from different aspects. However, there still remains a challenge to model the long distance dependency among the logical units. Also, it is demanding to uncover the logical structures of the text and further fuse the discrete logic to the continuous text embedding. To tackle the above issues, we propose an end-to-end model Logiformer which utilizes a two-branch graph transformer network for logical reasoning of text. Firstly, we introduce different extraction strategies to split the text into two sets of logical units, and construct the logical graph and the syntax graph respectively. The logical graph models the causal relations for the logical branch while the syntax graph captures the co-occurrence relations for the syntax branch. Secondly, to model the long distance dependency, the node sequence from each graph is fed into the fully connected graph transformer structures. The two adjacent matrices are viewed as the attention biases for the graph transformer layers, which map the discrete logical structures to the continuous text embedding space. Thirdly, a dynamic gate mechanism and a question-aware self-attention module are introduced before the answer prediction to update the features. The reasoning process provides the interpretability by employing the logical units, which are consistent with human cognition. The experimental results show the superiority of our model, which outperforms the state-of-the-art single model on two logical reasoning benchmarks.|机器阅读理解已经引起了广泛的关注，因为它探索了文本理解模型的潜力。为了进一步提高机器的推理能力，提出了具有挑战性的逻辑推理任务。以往关于逻辑推理的研究提出了一些策略来从不同方面提取逻辑单元。然而，在逻辑单元之间建立长距离依赖关系仍然是一个挑战。此外，还需要揭示文本的逻辑结构，进一步将离散逻辑与连续文本嵌入相融合。为了解决上述问题，我们提出了一个端到端模型 Logigformer，它利用一个两分支的图形转换网络进行文本逻辑推理。首先，引入不同的抽取策略，将文本分割成两组逻辑单元，分别构造逻辑图和句法图。逻辑图模拟逻辑分支的因果关系，而语法图捕获语法分支的共现关系。其次，为了建立长距离依赖关系模型，将每个图的节点序列输入到完全连通的图变换结构中。将两个相邻矩阵视为图形转换层的注意偏差，将离散逻辑结构映射到连续文本嵌入空间。第三，在答案预测之前引入动态门机制和问题感知自我注意模块来更新特征。推理过程通过使用与人类认知相一致的逻辑单元来提供可解释性。实验结果显示了我们的模型的优越性，它在两个逻辑推理基准上优于最先进的单一模型。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Logiformer:+A+Two-Branch+Graph+Transformer+Network+for+Interpretable+Logical+Reasoning)|3| |[Constrained Sequence-to-Tree Generation for Hierarchical Text Classification](https://doi.org/10.1145/3477495.3531765)|Chao Yu, Yi Shen, Yue Mao||Hierarchical Text Classification (HTC) is a challenging task where a document can be assigned to multiple hierarchically structured categories within a taxonomy. The majority of prior studies consider HTC as a flat multi-label classification problem, which inevitably leads to ''label inconsistency'' problem. In this paper, we formulate HTC as a sequence generation task and introduce a sequence-to-tree framework (Seq2Tree) for modeling the hierarchical label structure. Moreover, we design a constrained decoding strategy with dynamic vocabulary to secure the label consistency of the results. Compared with previous works, the proposed approach achieves significant and consistent improvements on three benchmark datasets.|层次化文本分类(HTC)是一个具有挑战性的任务，其中一个文档可以分配到一个分类法中的多个层次结构化类别。以往的大多数研究认为 HTC 是一个扁平的多标签分类问题，这不可避免地导致了“标签不一致”问题。本文将 HTC 描述为一个序列生成任务，并引入了一个序列到树的框架(Seq2Tree)来建模分层标签结构。此外，我们设计了一个具有动态词汇的约束解码策略，以保证解码结果的标签一致性。与以往的工作相比，该方法在三个基准数据集上取得了显著和一致的改进。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Constrained+Sequence-to-Tree+Generation+for+Hierarchical+Text+Classification)|3| -|[Answering Count Queries with Explanatory Evidence](https://doi.org/10.1145/3477495.3531870)|Shrestha Ghosh, Simon Razniewski, Gerhard Weikum|Max Planck Institute for Informatics, Saarbruecken, Germany; Max Planck Institute for Informatics & Saarland University, Saarbruecken, Germany|A challenging case in web search and question answering are count queries, such as"number of songs by John Lennon''. Prior methods merely answer these with a single, and sometimes puzzling number or return a ranked list of text snippets with different numbers. This paper proposes a methodology for answering count queries with inference, contextualization and explanatory evidence. Unlike previous systems, our method infers final answers from multiple observations, supports semantic qualifiers for the counts, and provides evidence by enumerating representative instances. Experiments with a wide variety of queries show the benefits of our method. To promote further research on this underexplored topic, we release an annotated dataset of 5k queries with 200k relevant text spans.|在网络搜索和问题回答中一个具有挑战性的例子是计数查询，例如“约翰 · 列侬的歌曲数量”。先前的方法只用一个单独的、有时令人费解的数字来回答这些问题，或者返回一个具有不同数字的文本片段的排序列表。本文提出了一种用推理、上下文化和解释性证据来回答计数查询的方法。与以前的系统不同，我们的方法从多个观察结果中推断出最终答案，支持计数的语义限定符，并通过枚举代表性实例提供证据。对各种查询的实验显示了我们的方法的优点。为了促进对这个未被充分探索的主题的进一步研究，我们发布了一个有注释的5k 查询数据集，其相关文本跨度为200k。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Answering+Count+Queries+with+Explanatory+Evidence)|3| -|[Space4HGNN: A Novel, Modularized and Reproducible Platform to Evaluate Heterogeneous Graph Neural Network](https://doi.org/10.1145/3477495.3531720)|Tianyu Zhao, Cheng Yang, Yibo Li, Quan Gan, Zhenyi Wang, Fengqi Liang, Huan Zhao, Yingxia Shao, Xiao Wang, Chuan Shi|4Paradigm Inc., Beijing, China; Beijing University of Posts and Telecommunications, Beijing, China; AWS Shanghai AI Lab, Shanghai, China; Beijing University of Posts and Telecommunications & Peng Cheng Laboratory, Beijing, China|Heterogeneous Graph Neural Network (HGNN) has been successfully employed in various tasks, but we cannot accurately know the importance of different design dimensions of HGNNs due to diverse architectures and applied scenarios. Besides, in the research community of HGNNs, implementing and evaluating various tasks still need much human effort. To mitigate these issues, we first propose a unified framework covering most HGNNs, consisting of three components: heterogeneous linear transformation, heterogeneous graph transformation, and heterogeneous message passing layer. Then we build a platform Space4HGNN by defining a design space for HGNNs based on the unified framework, which offers modularized components, reproducible implementations, and standardized evaluation for HGNNs. Finally, we conduct experiments to analyze the effect of different designs. With the insights found, we distill a condensed design space and verify its effectiveness.|异构图形神经网络(HGNN)已经成功地应用于各种任务中，但由于其结构和应用场景的多样性，我们无法准确地了解 HGNN 不同设计维数的重要性。此外，在 HGNN 的研究领域中，实施和评估各种任务仍然需要大量的人力。为了缓解这些问题，我们首先提出了一个涵盖大多数 HGNN 的统一框架，该框架由三个组件组成: 异构线性映射、异构图转换和异构消息传递层。然后基于统一框架定义了 HGNN 的设计空间，提供了 HGNN 的模块化组件、可重复实现和标准化评估，构建了一个平台 Space4HGNN。最后，通过实验分析了不同设计方案的效果。根据所发现的见解，我们提炼出一个浓缩的设计空间并验证其有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Space4HGNN:+A+Novel,+Modularized+and+Reproducible+Platform+to+Evaluate+Heterogeneous+Graph+Neural+Network)|3| +|[Answering Count Queries with Explanatory Evidence](https://doi.org/10.1145/3477495.3531870)|Shrestha Ghosh, Simon Razniewski, Gerhard Weikum|Max Planck Institute for Informatics & Saarland University, Saarbruecken, Germany; Max Planck Institute for Informatics, Saarbruecken, Germany|A challenging case in web search and question answering are count queries, such as"number of songs by John Lennon''. Prior methods merely answer these with a single, and sometimes puzzling number or return a ranked list of text snippets with different numbers. This paper proposes a methodology for answering count queries with inference, contextualization and explanatory evidence. Unlike previous systems, our method infers final answers from multiple observations, supports semantic qualifiers for the counts, and provides evidence by enumerating representative instances. Experiments with a wide variety of queries show the benefits of our method. To promote further research on this underexplored topic, we release an annotated dataset of 5k queries with 200k relevant text spans.|在网络搜索和问题回答中一个具有挑战性的例子是计数查询，例如“约翰 · 列侬的歌曲数量”。先前的方法只用一个单独的、有时令人费解的数字来回答这些问题，或者返回一个具有不同数字的文本片段的排序列表。本文提出了一种用推理、上下文化和解释性证据来回答计数查询的方法。与以前的系统不同，我们的方法从多个观察结果中推断出最终答案，支持计数的语义限定符，并通过枚举代表性实例提供证据。对各种查询的实验显示了我们的方法的优点。为了促进对这个未被充分探索的主题的进一步研究，我们发布了一个有注释的5k 查询数据集，其相关文本跨度为200k。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Answering+Count+Queries+with+Explanatory+Evidence)|3| +|[Space4HGNN: A Novel, Modularized and Reproducible Platform to Evaluate Heterogeneous Graph Neural Network](https://doi.org/10.1145/3477495.3531720)|Tianyu Zhao, Cheng Yang, Yibo Li, Quan Gan, Zhenyi Wang, Fengqi Liang, Huan Zhao, Yingxia Shao, Xiao Wang, Chuan Shi|AWS Shanghai AI Lab, Shanghai, China; Beijing University of Posts and Telecommunications, Beijing, China; 4Paradigm Inc., Beijing, China; Beijing University of Posts and Telecommunications & Peng Cheng Laboratory, Beijing, China|Heterogeneous Graph Neural Network (HGNN) has been successfully employed in various tasks, but we cannot accurately know the importance of different design dimensions of HGNNs due to diverse architectures and applied scenarios. Besides, in the research community of HGNNs, implementing and evaluating various tasks still need much human effort. To mitigate these issues, we first propose a unified framework covering most HGNNs, consisting of three components: heterogeneous linear transformation, heterogeneous graph transformation, and heterogeneous message passing layer. Then we build a platform Space4HGNN by defining a design space for HGNNs based on the unified framework, which offers modularized components, reproducible implementations, and standardized evaluation for HGNNs. Finally, we conduct experiments to analyze the effect of different designs. With the insights found, we distill a condensed design space and verify its effectiveness.|异构图形神经网络(HGNN)已经成功地应用于各种任务中，但由于其结构和应用场景的多样性，我们无法准确地了解 HGNN 不同设计维数的重要性。此外，在 HGNN 的研究领域中，实施和评估各种任务仍然需要大量的人力。为了缓解这些问题，我们首先提出了一个涵盖大多数 HGNN 的统一框架，该框架由三个组件组成: 异构线性映射、异构图转换和异构消息传递层。然后基于统一框架定义了 HGNN 的设计空间，提供了 HGNN 的模块化组件、可重复实现和标准化评估，构建了一个平台 Space4HGNN。最后，通过实验分析了不同设计方案的效果。根据所发现的见解，我们提炼出一个浓缩的设计空间并验证其有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Space4HGNN:+A+Novel,+Modularized+and+Reproducible+Platform+to+Evaluate+Heterogeneous+Graph+Neural+Network)|3| |[NeuralKG: An Open Source Library for Diverse Representation Learning of Knowledge Graphs](https://doi.org/10.1145/3477495.3531669)|Wen Zhang, Xiangnan Chen, Zhen Yao, Mingyang Chen, Yushan Zhu, Hongtao Yu, Yufeng Huang, Yajing Xu, Ningyu Zhang, Zezhong Xu, Zonggang Yuan, Feiyu Xiong, Huajun Chen||||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=NeuralKG:+An+Open+Source+Library+for+Diverse+Representation+Learning+of+Knowledge+Graphs)|3| |[Deep Knowledge Graph Representation Learning for Completion, Alignment, and Question Answering](https://doi.org/10.1145/3477495.3532679)|Soumen Chakrabarti||||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Deep+Knowledge+Graph+Representation+Learning+for+Completion,+Alignment,+and+Question+Answering)|3| -|[HIEN: Hierarchical Intention Embedding Network for Click-Through Rate Prediction](https://doi.org/10.1145/3477495.3531988)|Zuowu Zheng, Changwang Zhang, Xiaofeng Gao, Guihai Chen|Tencent Inc., Shenzhen, China; Shanghai Jiao Tong University, Shanghai, China|Click-through rate (CTR) prediction plays an important role in online advertising and recommendation systems, which aims at estimating the probability of a user clicking on a specific item. Feature interaction modeling and user interest modeling methods are two popular domains in CTR prediction, and they have been studied extensively in recent years. However, these methods still suffer from two limitations. First, traditional methods regard item attributes as ID features, while neglecting structure information and relation dependencies among attributes. Second, when mining user interests from user-item interactions, current models ignore user intents and item intents for different attributes, which lacks interpretability. Based on this observation, in this paper, we propose a novel approach Hierarchical Intention Embedding Network (HIEN), which considers dependencies of attributes based on bottom-up tree aggregation in the constructed attribute graph. HIEN also captures user intents for different item attributes as well as item intents based on our proposed hierarchical attention mechanism. Extensive experiments on both public and production datasets show that the proposed model significantly outperforms the state-of-the-art methods. In addition, HIEN can be applied as an input module to state-of-the-art CTR prediction methods, bringing further performance lift for these existing models that might already be intensively used in real systems.|在在线广告和推荐系统中，点进率预测(ctrl)扮演着重要的角色，其目的是估计用户点击特定项目的概率。特征交互建模和用户兴趣建模是 CTR 预测的两个热门领域，近年来得到了广泛的研究。然而，这些方法仍然存在两个局限性。首先，传统的方法把项目属性看作 ID 特征，而忽略了结构信息和属性之间的关系依赖。其次，当从用户-项目交互中挖掘用户兴趣时，目前的模型忽略了不同属性的用户意图和项目意图，缺乏可解释性。在此基础上，本文提出了一种新的层次意图嵌入网络(HIEN)方法，该方法在构造的属性图中考虑基于自底向上树聚集的属性依赖关系。HIEN 还根据我们提出的分层注意机制捕获不同项目属性的用户意图以及项目意图。在公共数据集和生产数据集上的大量实验表明，所提出的模型明显优于最先进的方法。此外，HIEN 还可以作为最先进的 CTR 预测方法的输入模块，为这些可能已经在实际系统中广泛使用的现有模型带来进一步的性能提升。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=HIEN:+Hierarchical+Intention+Embedding+Network+for+Click-Through+Rate+Prediction)|2| +|[HIEN: Hierarchical Intention Embedding Network for Click-Through Rate Prediction](https://doi.org/10.1145/3477495.3531988)|Zuowu Zheng, Changwang Zhang, Xiaofeng Gao, Guihai Chen|Shanghai Jiao Tong University, Shanghai, China; Tencent Inc., Shenzhen, China|Click-through rate (CTR) prediction plays an important role in online advertising and recommendation systems, which aims at estimating the probability of a user clicking on a specific item. Feature interaction modeling and user interest modeling methods are two popular domains in CTR prediction, and they have been studied extensively in recent years. However, these methods still suffer from two limitations. First, traditional methods regard item attributes as ID features, while neglecting structure information and relation dependencies among attributes. Second, when mining user interests from user-item interactions, current models ignore user intents and item intents for different attributes, which lacks interpretability. Based on this observation, in this paper, we propose a novel approach Hierarchical Intention Embedding Network (HIEN), which considers dependencies of attributes based on bottom-up tree aggregation in the constructed attribute graph. HIEN also captures user intents for different item attributes as well as item intents based on our proposed hierarchical attention mechanism. Extensive experiments on both public and production datasets show that the proposed model significantly outperforms the state-of-the-art methods. In addition, HIEN can be applied as an input module to state-of-the-art CTR prediction methods, bringing further performance lift for these existing models that might already be intensively used in real systems.|在在线广告和推荐系统中，点进率预测(ctrl)扮演着重要的角色，其目的是估计用户点击特定项目的概率。特征交互建模和用户兴趣建模是 CTR 预测的两个热门领域，近年来得到了广泛的研究。然而，这些方法仍然存在两个局限性。首先，传统的方法把项目属性看作 ID 特征，而忽略了结构信息和属性之间的关系依赖。其次，当从用户-项目交互中挖掘用户兴趣时，目前的模型忽略了不同属性的用户意图和项目意图，缺乏可解释性。在此基础上，本文提出了一种新的层次意图嵌入网络(HIEN)方法，该方法在构造的属性图中考虑基于自底向上树聚集的属性依赖关系。HIEN 还根据我们提出的分层注意机制捕获不同项目属性的用户意图以及项目意图。在公共数据集和生产数据集上的大量实验表明，所提出的模型明显优于最先进的方法。此外，HIEN 还可以作为最先进的 CTR 预测方法的输入模块，为这些可能已经在实际系统中广泛使用的现有模型带来进一步的性能提升。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=HIEN:+Hierarchical+Intention+Embedding+Network+for+Click-Through+Rate+Prediction)|2| |[Neural Query Synthesis and Domain-Specific Ranking Templates for Multi-Stage Clinical Trial Matching](https://doi.org/10.1145/3477495.3531853)|Ronak Pradeep, Yilin Li, Yuetong Wang, Jimmy Lin|University of Waterloo, Waterloo, ON, Canada; University of Waterloo, Waterloo, Canada|In this work, we propose an effective multi-stage neural ranking system for the clinical trial matching problem. First, we introduce NQS, a neural query synthesis method that leverages a zero-shot document expansion model to generate multiple sentence-long queries from lengthy patient descriptions. These queries are independently issued to a search engine and the results are fused. We find that on the TREC 2021 Clinical Trials Track, this method outperforms strong traditional baselines like BM25 and BM25 + RM3 by about 12 points in [email protected] , a relative improvement of 34%. This simple method is so effective that even a state-of-the-art neural relevance ranking method trained on the medical subset of MS MARCO passage, when reranking the results of NQS, fails to improve on the ranked list. Second, we introduce a two-stage neural reranking pipeline trained on clinical trial matching data using tailored ranking templates. In this setting, we can train a pointwise reranker using just 1.1k positive examples and obtain effectiveness improvements over NQS by 24 points. This end-to-end multi-stage system demonstrates a 20% relative effectiveness gain compared to the second-best submission at TREC 2021, making it an important step towards better automated clinical trial matching.|在这项工作中，我们提出了一个有效的多阶段神经排序系统的临床试验匹配问题。首先，我们介绍了 NQS，这是一种神经查询合成方法，它利用一个零拍文档扩展模型，从冗长的患者描述中生成多个句子长的查询。这些查询被独立地发送给搜索引擎，并且结果被融合。我们发现，在 TREC 2021临床试验跟踪中，这种方法比强大的传统基线如 BM25和 BM25 + RM3在[ email protected ]中高出约12分，相对提高了34% 。这种简单的方法是如此有效，以至于即使是在 MS MARCO 通道的医学子集上训练的最先进的神经相关性排序方法，在对 NQS 的结果重新排序时，也不能改进排序列表。其次，我们介绍了一个两阶段的神经重新排序管道训练临床试验匹配数据使用定制的排序模板。在这种情况下，我们可以训练一个点态的重新排序使用只有1.1 k 正的例子，并获得24点的有效性改进超过 NQS。这种端到端的多阶段系统与 TREC 2021年第二好的提交相比，显示出20% 的相对有效性增益，这使得它朝着更好的自动化临床试验匹配迈出了重要的一步。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Neural+Query+Synthesis+and+Domain-Specific+Ranking+Templates+for+Multi-Stage+Clinical+Trial+Matching)|2| |[Improving Contrastive Learning of Sentence Embeddings with Case-Augmented Positives and Retrieved Negatives](https://doi.org/10.1145/3477495.3531823)|Wei Wang, Liangzhu Ge, Jingqiao Zhang, Cheng Yang|Alibaba Group, Hangzhou, China|Following SimCSE, contrastive learning based methods have achieved the state-of-the-art (SOTA) performance in learning sentence embeddings. However, the unsupervised contrastive learning methods still lag far behind the supervised counterparts. We attribute this to the quality of positive and negative samples, and aim to improve both. Specifically, for positive samples, we propose switch-case augmentation to flip the case of the first letter of randomly selected words in a sentence. This is to counteract the intrinsic bias of pre-trained token embeddings to frequency, word cases and subwords. For negative samples, we sample hard negatives from the whole dataset based on a pre-trained language model. Combining the above two methods with SimCSE, our proposed Contrastive learning with Augmented and Retrieved Data for Sentence embedding (CARDS) method significantly surpasses the current SOTA on STS benchmarks in the unsupervised setting.|继 SimCSE 之后，基于对比学习的方法在学习句子嵌入方面取得了先进的性能。然而，无监督对比学习方法仍然远远落后于有监督对比学习方法。我们将此归因于阳性和阴性样本的质量，并致力于改善这两种情况。特别地，对于正样本，我们提出开关格增强来翻转句子中随机选择的单词的第一个字母的情况。这是为了抵消预先训练的标记嵌入对频率、词例和子词的内在偏差。对于阴性样本，我们基于预训练语言模型从整个数据集中抽取硬阴性样本。将上述两种方法与 SimCSE 相结合，我们提出的对比学习与增强和检索数据的句子嵌入(CARDS)方法显着超过目前的 SOTA 的 STS 基准在无监督的设置。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Improving+Contrastive+Learning+of+Sentence+Embeddings+with+Case-Augmented+Positives+and+Retrieved+Negatives)|2| |[Diversity Vs Relevance: A Practical Multi-objective Study in Luxury Fashion Recommendations](https://doi.org/10.1145/3477495.3531866)|João Sá, Vanessa Queiroz Marinho, Ana Rita Magalhães, Tiago Lacerda, Diogo Gonçalves|Farfetch, London, United Kingdom|Personalized algorithms focusing uniquely on accuracy might provide highly relevant recommendations, but the recommended items could be too similar to current users' preferences. Therefore, recommenders might prevent users from exploring new products and brands (filter bubbles). This is especially critical for luxury fashion recommendations because luxury shoppers expect to discover exclusive and rare items. Thus, recommender systems for fashion need to consider diversity and elevate the shopping experience by recommending new brands and products from the catalog. In this work, we explored a handful of diversification strategies to rerank the output of a relevance-focused recommender system. Subsequently, we conducted a multi-objective offline experiment optimizing for relevance and diversity simultaneously. We measured diversity with commonly used metrics such as coverage, serendipity, and neighborhood distance, whereas, for relevance, we selected ranking metrics such as recall. The best diversification strategy offline improved user engagement by 2% in click-through rate and presented an uplift of 46% in distinct brands recommended when AB tested against real users. These results reinforced the importance of considering accuracy and diversity metrics when developing a recommender system.|专注于准确性的个性化算法可能会提供高度相关的推荐，但推荐的项目可能与当前用户的偏好过于相似。因此，推荐者可能会阻止用户探索新的产品和品牌(过滤气泡)。这对于奢侈品时尚推荐来说尤其重要，因为奢侈品消费者希望发现独一无二的稀有物品。因此，时尚推荐系统需要考虑多样性，并通过推荐目录中的新品牌和产品来提升购物体验。在这项工作中，我们探索了一些多样化策略，以重新排列以相关性为中心的推荐系统的产出。随后，我们进行了多目标离线实验，同时对相关性和多样性进行了优化。我们使用常用的指标(如覆盖率、意外发现和邻近距离)来衡量多样性，而对于相关性，我们选择了排名指标(如回忆)。线下最佳多样化策略提高了2% 的用户参与点进率，当 AB 公司对真实用户进行测试时，推荐的不同品牌的用户参与度提高了46% 。这些结果强调了在开发推荐系统时考虑准确性和多样性指标的重要性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Diversity+Vs+Relevance:+A+Practical+Multi-objective+Study+in+Luxury+Fashion+Recommendations)|2| |[Exploiting Variational Domain-Invariant User Embedding for Partially Overlapped Cross Domain Recommendation](https://doi.org/10.1145/3477495.3531975)|Weiming Liu, Xiaolin Zheng, Jiajie Su, Mengling Hu, Yanchao Tan, Chaochao Chen|Zhejiang University, Hangzhou, China|Cross-Domain Recommendation (CDR) has been popularly studied to utilize different domain knowledge to solve the cold-start problem in recommender systems. Most of the existing CDR models assume that both the source and target domains share the same overlapped user set for knowledge transfer. However, only few proportion of users simultaneously activate on both the source and target domains in practical CDR tasks. In this paper, we focus on the Partially Overlapped Cross-Domain Recommendation (POCDR) problem, that is, how to leverage the information of both the overlapped and non-overlapped users to improve recommendation performance. Existing approaches cannot fully utilize the useful knowledge behind the non-overlapped users across domains, which limits the model performance when the majority of users turn out to be non-overlapped. To address this issue, we propose an end-to-end Dual-autoencoder with Variational Domain-invariant Embedding Alignment (VDEA) model, a cross-domain recommendation framework for the POCDR problem, which utilizes dual variational autoencoders with both local and global embedding alignment for exploiting domain-invariant user embedding. VDEA first adopts variational inference to capture collaborative user preferences, and then utilizes Gromov-Wasserstein distribution co-clustering optimal transport to cluster the users with similar rating interaction behaviors. Our empirical studies on Douban and Amazon datasets demonstrate that VDEA significantly outperforms the state-of-the-art models, especially under the POCDR setting.|跨域推荐(CDR)是一种利用不同领域知识解决推荐系统冷启动问题的方法。现有的 CDR 模型大多假设源域和目标域共享相同的重叠用户集进行知识转移。然而，在实际的 CDR 任务中，只有少数用户在源域和目标域同时激活。本文主要研究部分重叠跨域推荐(POCDR)问题，即如何利用重叠用户和非重叠用户的信息来提高推荐性能。现有的方法不能充分利用跨领域非重叠用户背后的有用知识，这限制了大多数用户不重叠时的模型性能。针对这一问题，本文提出了一种基于变分域不变嵌入对齐(VDEA)模型的端到端双变分自动编码器，该模型利用具有局部和全局嵌入对齐的双变分自动编码器进行域不变用户嵌入。VDEA 首先采用变分推理获取协同用户偏好，然后利用 Gromov-Wasserstein 分布协聚类最优传输对具有相似评分交互行为的用户进行聚类。我们对 Douban 和亚马逊数据集的实证研究表明，VDEA 显著优于最先进的模型，特别是在 POCDR 设置下。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Exploiting+Variational+Domain-Invariant+User+Embedding+for+Partially+Overlapped+Cross+Domain+Recommendation)|2| -|[Variational Reasoning about User Preferences for Conversational Recommendation](https://doi.org/10.1145/3477495.3532077)|Zhaochun Ren, Zhi Tian, Dongdong Li, Pengjie Ren, Liu Yang, Xin Xin, Huasheng Liang, Maarten de Rijke, Zhumin Chen|Shandong University, Qingdao, China; University of Amsterdam, Amsterdam, Netherlands; WeChat, Tencent, Shenzhen, China|Conversational recommender systems (CRSs) provide recommendations through interactive conversations. CRSs typically provide recommendations through relatively straightforward interactions, where the system continuously inquires about a user's explicit attribute-aware preferences and then decides which items to recommend. In addition, topic tracking is often used to provide naturally sounding responses. However, merely tracking topics is not enough to recognize a user's real preferences in a dialogue. In this paper, we address the problem of accurately recognizing and maintaining user preferences in CRSs. Three challenges come with this problem: (1) An ongoing dialogue only provides the user's short-term feedback; (2) Annotations of user preferences are not available; and (3) There may be complex semantic correlations among items that feature in a dialogue. We tackle these challenges by proposing an end-to-end variational reasoning approach to the task of conversational recommendation. We model both long-term preferences and short-term preferences as latent variables with topical priors for explicit long-term and short-term preference exploration, respectively. We use an efficient stochastic gradient variational Bayesian (SGVB) estimator for optimizing the derived evidence lower bound. A policy network is then used to predict topics for a clarification utterance or items for a recommendation response. The use of explicit sequences of preferences with multi-hop reasoning in a heterogeneous knowledge graph helps to provide more accurate conversational recommendation results. Extensive experiments conducted on two benchmark datasets show that our proposed method outperforms state-of-the-art baselines in terms of both objective and subjective evaluation metric|会话推荐系统(CRS)通过交互式对话提供推荐。CRS 通常通过相对简单的交互提供推荐，系统不断地查询用户的明确的属性感知偏好，然后决定推荐哪些项目。此外，主题跟踪通常用于提供听起来很自然的回答。但是，仅仅跟踪主题不足以识别用户在对话中的真实偏好。在本文中，我们解决了准确识别和维护用户偏好的问题。这个问题带来了三个挑战: (1)持续的对话只提供用户的短期反馈; (2)用户偏好的注释不可用; (3)对话中的项目之间可能存在复杂的语义关联。我们通过提出一种端到端的变分推理方法来解决这些挑战。我们将长期偏好和短期偏好分别建模为具有主题先验的潜在变量，用于显性长期和短期偏好探索。我们使用一个有效的随机梯度变分贝叶斯(SGVB)估计器来优化导出的证据下界。然后使用策略网络来预测澄清话语的主题或推荐响应的项目。在异构知识图中使用多跳推理的显式偏好序列有助于提供更准确的会话推荐结果。在两个基准数据集上进行的大量实验表明，我们提出的方法在客观和主观评价指标方面都优于最先进的基准|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Variational+Reasoning+about+User+Preferences+for+Conversational+Recommendation)|2| -|[Analyzing and Simulating User Utterance Reformulation in Conversational Recommender Systems](https://doi.org/10.1145/3477495.3531936)|Shuo Zhang, MuChun Wang, Krisztian Balog|University of Stavanger, Stavanger, Norway; University of Science and Technology of China, Hefei, China; Bloomberg, London, United Kingdom|User simulation has been a cost-effective technique for evaluating conversational recommender systems. However, building a human-like simulator is still an open challenge. In this work, we focus on how users reformulate their utterances when a conversational agent fails to understand them. First, we perform a user study, involving five conversational agents across different domains, to identify common reformulation types and their transition relationships. A common pattern that emerges is that persistent users would first try to rephrase, then simplify, before giving up. Next, to incorporate the observed reformulation behavior in a user simulator, we introduce the task of reformulation sequence generation: to generate a sequence of reformulated utterances with a given intent (rephrase or simplify). We develop methods by extending transformer models guided by the reformulation type and perform further filtering based on estimated reading difficulty. We demonstrate the effectiveness of our approach using both automatic and human evaluation.|用户仿真已经成为评估会话推荐系统的一种经济有效的技术。然而，建立一个类似人的模拟器仍然是一个公开的挑战。在这项工作中，我们的重点是如何用户重新组织他们的话语时，一个会话代理无法理解他们。首先，我们进行了一个用户研究，涉及五个不同领域的会话代理，以确定常见的重构类型及其转换关系。出现的一种常见模式是，持久用户在放弃之前会首先尝试重新措辞，然后进行简化。接下来，为了在用户模拟器中整合观察到的重新表述行为，我们引入了重新表述序列生成的任务: 生成具有给定意图(重新表述或简化)的重新表述话语序列。我们发展的方法，扩展变压器模型指导下的重新公式类型和进一步滤波的基础上估计读取困难。我们使用自动评估和人工评估证明了我们的方法的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Analyzing+and+Simulating+User+Utterance+Reformulation+in+Conversational+Recommender+Systems)|2| +|[Variational Reasoning about User Preferences for Conversational Recommendation](https://doi.org/10.1145/3477495.3532077)|Zhaochun Ren, Zhi Tian, Dongdong Li, Pengjie Ren, Liu Yang, Xin Xin, Huasheng Liang, Maarten de Rijke, Zhumin Chen|University of Amsterdam, Amsterdam, Netherlands; Shandong University, Qingdao, China; WeChat, Tencent, Shenzhen, China|Conversational recommender systems (CRSs) provide recommendations through interactive conversations. CRSs typically provide recommendations through relatively straightforward interactions, where the system continuously inquires about a user's explicit attribute-aware preferences and then decides which items to recommend. In addition, topic tracking is often used to provide naturally sounding responses. However, merely tracking topics is not enough to recognize a user's real preferences in a dialogue. In this paper, we address the problem of accurately recognizing and maintaining user preferences in CRSs. Three challenges come with this problem: (1) An ongoing dialogue only provides the user's short-term feedback; (2) Annotations of user preferences are not available; and (3) There may be complex semantic correlations among items that feature in a dialogue. We tackle these challenges by proposing an end-to-end variational reasoning approach to the task of conversational recommendation. We model both long-term preferences and short-term preferences as latent variables with topical priors for explicit long-term and short-term preference exploration, respectively. We use an efficient stochastic gradient variational Bayesian (SGVB) estimator for optimizing the derived evidence lower bound. A policy network is then used to predict topics for a clarification utterance or items for a recommendation response. The use of explicit sequences of preferences with multi-hop reasoning in a heterogeneous knowledge graph helps to provide more accurate conversational recommendation results. Extensive experiments conducted on two benchmark datasets show that our proposed method outperforms state-of-the-art baselines in terms of both objective and subjective evaluation metric|会话推荐系统(CRS)通过交互式对话提供推荐。CRS 通常通过相对简单的交互提供推荐，系统不断地查询用户的明确的属性感知偏好，然后决定推荐哪些项目。此外，主题跟踪通常用于提供听起来很自然的回答。但是，仅仅跟踪主题不足以识别用户在对话中的真实偏好。在本文中，我们解决了准确识别和维护用户偏好的问题。这个问题带来了三个挑战: (1)持续的对话只提供用户的短期反馈; (2)用户偏好的注释不可用; (3)对话中的项目之间可能存在复杂的语义关联。我们通过提出一种端到端的变分推理方法来解决这些挑战。我们将长期偏好和短期偏好分别建模为具有主题先验的潜在变量，用于显性长期和短期偏好探索。我们使用一个有效的随机梯度变分贝叶斯(SGVB)估计器来优化导出的证据下界。然后使用策略网络来预测澄清话语的主题或推荐响应的项目。在异构知识图中使用多跳推理的显式偏好序列有助于提供更准确的会话推荐结果。在两个基准数据集上进行的大量实验表明，我们提出的方法在客观和主观评价指标方面都优于最先进的基准|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Variational+Reasoning+about+User+Preferences+for+Conversational+Recommendation)|2| +|[Analyzing and Simulating User Utterance Reformulation in Conversational Recommender Systems](https://doi.org/10.1145/3477495.3531936)|Shuo Zhang, MuChun Wang, Krisztian Balog|University of Stavanger, Stavanger, Norway; Bloomberg, London, United Kingdom; University of Science and Technology of China, Hefei, China|User simulation has been a cost-effective technique for evaluating conversational recommender systems. However, building a human-like simulator is still an open challenge. In this work, we focus on how users reformulate their utterances when a conversational agent fails to understand them. First, we perform a user study, involving five conversational agents across different domains, to identify common reformulation types and their transition relationships. A common pattern that emerges is that persistent users would first try to rephrase, then simplify, before giving up. Next, to incorporate the observed reformulation behavior in a user simulator, we introduce the task of reformulation sequence generation: to generate a sequence of reformulated utterances with a given intent (rephrase or simplify). We develop methods by extending transformer models guided by the reformulation type and perform further filtering based on estimated reading difficulty. We demonstrate the effectiveness of our approach using both automatic and human evaluation.|用户仿真已经成为评估会话推荐系统的一种经济有效的技术。然而，建立一个类似人的模拟器仍然是一个公开的挑战。在这项工作中，我们的重点是如何用户重新组织他们的话语时，一个会话代理无法理解他们。首先，我们进行了一个用户研究，涉及五个不同领域的会话代理，以确定常见的重构类型及其转换关系。出现的一种常见模式是，持久用户在放弃之前会首先尝试重新措辞，然后进行简化。接下来，为了在用户模拟器中整合观察到的重新表述行为，我们引入了重新表述序列生成的任务: 生成具有给定意图(重新表述或简化)的重新表述话语序列。我们发展的方法，扩展变压器模型指导下的重新公式类型和进一步滤波的基础上估计读取困难。我们使用自动评估和人工评估证明了我们的方法的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Analyzing+and+Simulating+User+Utterance+Reformulation+in+Conversational+Recommender+Systems)|2| |[Learning to Infer User Implicit Preference in Conversational Recommendation](https://doi.org/10.1145/3477495.3531844)|Chenhao Hu, Shuhua Huang, Yansen Zhang, Yubao Liu|Sun Yat-Sen University, Guangzhou, China; Sun Yat-Sen University & Guangdong Key Laboratory of Big Data Analysis and Processing, Guangzhou, China|Conversational recommender systems (CRS) enable traditional recommender systems to interact with users by asking questions about attributes and recommending items. The attribute-level and item-level feedback of users can be utilized to estimate users' preferences. However, existing works do not fully exploit the advantage of explicit item feedback --- they only use the item feedback in rather implicit ways such as updating the latent user and item representation. Since CRS has multiple chances to interact with users, leveraging the context in the conversation may help infer users' implicit feedback (e.g., some specific attributes) when recommendations get rejected. To address the limitations of existing methods, we propose a new CRS framework called Conversational Recommender with Implicit Feedback (CRIF). CRIF formulates the conversational recommendation scheme as a four-phase process consisting of offline representation learning, tracking, decision, and inference. In the inference module, by fully utilizing the relation between users' attribute-level and item-level feedback, our method can explicitly deduce users' implicit preferences. Therefore, CRIF is able to achieve more accurate user preference estimation. Besides, in the decision module, to better utilize the attribute-level and item-level feedback, we adopt inverse reinforcement learning to learn a flexible decision strategy that selects the suitable action at each conversation turn. Through extensive experiments on four benchmark CRS datasets, we validate the effectiveness of our approach, which significantly outperforms the state-of-the-art CRS methods.|会话推荐系统(CRS)使得传统的推荐系统能够通过询问关于属性的问题和推荐项目与用户进行交互。用户的属性级反馈和项目级反馈可以用来估计用户的偏好。然而，现有的作品并没有充分利用显式项目反馈的优势——他们只是以相当隐式的方式使用项目反馈，比如更新潜在用户和项目表示。由于 CRS 有多个与用户交互的机会，当推荐被拒绝时，利用会话中的上下文可能有助于推断用户的隐式反馈(例如，一些特定的属性)。为了解决现有方法的局限性，我们提出了一个新的 CRS 框架，称为隐式反馈会话推荐(CRIF)。CRIF 将会话推荐方案设计为离线表征学习、跟踪、决策和推理四个阶段。在推理模块中，通过充分利用用户属性级反馈和项目级反馈之间的关系，可以明确推断出用户的隐性偏好。因此，CRIF 能够实现更准确的用户偏好估计。此外，在决策模块中，为了更好地利用属性级别和项目级别的反馈，我们采用逆向强化学习学习灵活的决策策略，在每次谈话转折点选择合适的行动。通过对四个基准 CRS 数据集的大量实验，我们验证了该方法的有效性，其性能明显优于目前最先进的 CRS 方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Learning+to+Infer+User+Implicit+Preference+in+Conversational+Recommendation)|2| -|[Recognizing Medical Search Query Intent by Few-shot Learning](https://doi.org/10.1145/3477495.3531789)|Yaqing Wang, Song Wang, Yanyan Li, Dejing Dou|Baidu Inc., Beijing, China; Baidu Inc. & University of Virginia, Beijing, China|Online healthcare services can provide unlimited and in-time medical information to users, which promotes social goods and breaks the barriers of locations. However, understanding the user intents behind the medical related queries is a challenging problem. Medical search queries are usually short and noisy, lack strict syntactic structure, and also require professional background to understand the medical terms. The medical intents are fine-grained, making them hard to recognize. In addition, many intents only have a few labeled data. To handle these problems, we propose a few-shot learning method for medical search query intent recognition called MEDIC. We extract co-click queries from user search logs as weak supervision to compensate for the lack of labeled data. We also design a new query encoder which learns to represent queries as a combination of semantic knowledge recorded in an external medical knowledge graph, syntactic knowledge which marks the grammatical role of each word in the query, and generic knowledge which is captured by language models pretrained from large-scale text corpus. Experimental results on a real medical search query intent recognition dataset validate the effectiveness of MEDIC.|在线医疗服务可以向用户提供无限制和及时的医疗信息，促进社会商品，打破地理位置的障碍。然而，理解医疗相关查询背后的用户意图是一个具有挑战性的问题。医学检索查询通常短小而嘈杂，缺乏严格的句法结构，而且需要专业背景才能理解医学术语。医学意图是细粒度的，很难识别。此外，许多意图只有少量带标签的数据。针对这些问题，本文提出了一种基于少镜头学习的医学搜索查询意图识别方法 MEDIC。我们从用户搜索日志中提取共同点击查询作为薄弱的监督，以弥补标记数据的缺乏。我们还设计了一种新的查询编码器，该编码器学习将外部医学知识图中记录的语义知识、标记查询中每个词语法角色的句法知识以及从大规模文本语料库中预先训练的语言模型获取的通用知识组合起来来表示查询。在一个真实的医学搜索查询意图识别数据集上的实验结果验证了 MEDIC 算法的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Recognizing+Medical+Search+Query+Intent+by+Few-shot+Learning)|2| +|[Recognizing Medical Search Query Intent by Few-shot Learning](https://doi.org/10.1145/3477495.3531789)|Yaqing Wang, Song Wang, Yanyan Li, Dejing Dou|Baidu Inc. & University of Virginia, Beijing, China; Baidu Inc., Beijing, China|Online healthcare services can provide unlimited and in-time medical information to users, which promotes social goods and breaks the barriers of locations. However, understanding the user intents behind the medical related queries is a challenging problem. Medical search queries are usually short and noisy, lack strict syntactic structure, and also require professional background to understand the medical terms. The medical intents are fine-grained, making them hard to recognize. In addition, many intents only have a few labeled data. To handle these problems, we propose a few-shot learning method for medical search query intent recognition called MEDIC. We extract co-click queries from user search logs as weak supervision to compensate for the lack of labeled data. We also design a new query encoder which learns to represent queries as a combination of semantic knowledge recorded in an external medical knowledge graph, syntactic knowledge which marks the grammatical role of each word in the query, and generic knowledge which is captured by language models pretrained from large-scale text corpus. Experimental results on a real medical search query intent recognition dataset validate the effectiveness of MEDIC.|在线医疗服务可以向用户提供无限制和及时的医疗信息，促进社会商品，打破地理位置的障碍。然而，理解医疗相关查询背后的用户意图是一个具有挑战性的问题。医学检索查询通常短小而嘈杂，缺乏严格的句法结构，而且需要专业背景才能理解医学术语。医学意图是细粒度的，很难识别。此外，许多意图只有少量带标签的数据。针对这些问题，本文提出了一种基于少镜头学习的医学搜索查询意图识别方法 MEDIC。我们从用户搜索日志中提取共同点击查询作为薄弱的监督，以弥补标记数据的缺乏。我们还设计了一种新的查询编码器，该编码器学习将外部医学知识图中记录的语义知识、标记查询中每个词语法角色的句法知识以及从大规模文本语料库中预先训练的语言模型获取的通用知识组合起来来表示查询。在一个真实的医学搜索查询意图识别数据集上的实验结果验证了 MEDIC 算法的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Recognizing+Medical+Search+Query+Intent+by+Few-shot+Learning)|2| |[PERD: Personalized Emoji Recommendation with Dynamic User Preference](https://doi.org/10.1145/3477495.3531779)|Xuanzhi Zheng, Guoshuai Zhao, Li Zhu, Xueming Qian|Xi'an Jiaotong University, Xi'an, China|Emoji recommendation is an important task to help users find appropriate emojis from thousands of candidates based on a short tweet text. Traditional emoji recommendation methods lack personalized recommendation and ignore user historical information in selecting emojis. In this paper, we propose a personalized emoji recommendation with dynamic user preference (PERD) which contains a text encoder and a personalized attention mechanism. In text encoder, a BERT model is contained to learn dense and low-dimensional representations of tweets. In personalized attention, user dynamic preferences are learned according to semantic and sentimental similarity between historical tweets and the tweet which is waiting for emoji recommendation. Informative historical tweets are selected and highlighted. Experiments are carried out on two real-world datasets from Sina Weibo and Twitter. Experimental results validate the superiority of our approach on personalized emoji recommendation.|表情符号推荐是一项重要的任务，它可以帮助用户根据一条短短的推文从数千名候选人中找到合适的表情符号。传统的表情符号推荐方法缺乏个性化推荐，在选择表情符号时忽略了用户的历史信息。本文提出了一种基于动态用户偏好的个性化表情推荐(PERD) ，它包含一个文本编码器和一个个性化的注意机制。在文本编码器中，BERT 模型用于学习 tweet 的稠密和低维表示。在个性化关注中，根据历史推文和等待表情推荐的推文之间的语义和情感相似性来学习用户的动态偏好。选择并突出显示信息丰富的历史 tweet。实验是在来自新浪微博和推特的两个真实世界的数据集上进行的。实验结果验证了该方法在个性化表情符号推荐中的优越性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=PERD:+Personalized+Emoji+Recommendation+with+Dynamic+User+Preference)|2| -|[Sequential/Session-based Recommendations: Challenges, Approaches, Applications and Opportunities](https://doi.org/10.1145/3477495.3532685)|Shoujin Wang, Qi Zhang, Liang Hu, Xiuzhen Zhang, Yan Wang, Charu Aggarwal|RMIT University & Macquarie University, Melbourne , Australia; DeepBlue Academy of Sciences, Shanghai, China; Tongji University, Shanghai , China; Macquarie University, Sydney , Australia; RMIT University, Melbourne, Australia; IBM T. J. Watson Research Center, Yorktown Heights, NY, USA|In recent years, sequential recommender systems (SRSs) and session-based recommender systems (SBRSs) have emerged as a new paradigm of RSs to capture users' short-term but dynamic preferences for enabling more timely and accurate recommendations. Although SRSs and SBRSs have been extensively studied, there are many inconsistencies in this area caused by the diverse descriptions, settings, assumptions and application domains. There is no work to provide a unified framework and problem statement to remove the commonly existing and various inconsistencies in the area of SR/SBR. There is a lack of work to provide a comprehensive and systematic demonstration of the data characteristics, key challenges, most representative and state-of-the-art approaches, typical real- world applications and important future research directions in the area. This work aims to fill in these gaps so as to facilitate further research in this exciting and vibrant area.|近年来，顺序推荐系统(SRS)和基于会话的推荐系统(SBRS)已经成为一种新的 RSS 模式，它们可以捕获用户短期的动态偏好，从而实现更及时、更准确的推荐。尽管 SRS 和 SBRS 已经得到了广泛的研究，但是由于描述、设置、假设和应用领域的不同，在这个领域还存在着许多不一致之处。目前还没有提供一个统一的框架和问题说明来消除 SR/SBR 领域中普遍存在的各种不一致之处。目前缺乏对数据特征、主要挑战、最具代表性和最先进的方法、典型的现实世界应用和该领域未来重要研究方向进行全面和系统的论证的工作。这项工作旨在填补这些空白，以促进在这个令人兴奋和充满活力的领域的进一步研究。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Sequential/Session-based+Recommendations:+Challenges,+Approaches,+Applications+and+Opportunities)|2| +|[Sequential/Session-based Recommendations: Challenges, Approaches, Applications and Opportunities](https://doi.org/10.1145/3477495.3532685)|Shoujin Wang, Qi Zhang, Liang Hu, Xiuzhen Zhang, Yan Wang, Charu Aggarwal|IBM T. J. Watson Research Center, Yorktown Heights, NY, USA; Tongji University, Shanghai , China; DeepBlue Academy of Sciences, Shanghai, China; Macquarie University, Sydney , Australia; RMIT University, Melbourne, Australia; RMIT University & Macquarie University, Melbourne , Australia|In recent years, sequential recommender systems (SRSs) and session-based recommender systems (SBRSs) have emerged as a new paradigm of RSs to capture users' short-term but dynamic preferences for enabling more timely and accurate recommendations. Although SRSs and SBRSs have been extensively studied, there are many inconsistencies in this area caused by the diverse descriptions, settings, assumptions and application domains. There is no work to provide a unified framework and problem statement to remove the commonly existing and various inconsistencies in the area of SR/SBR. There is a lack of work to provide a comprehensive and systematic demonstration of the data characteristics, key challenges, most representative and state-of-the-art approaches, typical real- world applications and important future research directions in the area. This work aims to fill in these gaps so as to facilitate further research in this exciting and vibrant area.|近年来，顺序推荐系统(SRS)和基于会话的推荐系统(SBRS)已经成为一种新的 RSS 模式，它们可以捕获用户短期的动态偏好，从而实现更及时、更准确的推荐。尽管 SRS 和 SBRS 已经得到了广泛的研究，但是由于描述、设置、假设和应用领域的不同，在这个领域还存在着许多不一致之处。目前还没有提供一个统一的框架和问题说明来消除 SR/SBR 领域中普遍存在的各种不一致之处。目前缺乏对数据特征、主要挑战、最具代表性和最先进的方法、典型的现实世界应用和该领域未来重要研究方向进行全面和系统的论证的工作。这项工作旨在填补这些空白，以促进在这个令人兴奋和充满活力的领域的进一步研究。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Sequential/Session-based+Recommendations:+Challenges,+Approaches,+Applications+and+Opportunities)|2| |[Probabilistic Permutation Graph Search: Black-Box Optimization for Fairness in Ranking](https://doi.org/10.1145/3477495.3532045)|Ali Vardasbi, Fatemeh Sarvi, Maarten de Rijke|University of Amsterdam, Amsterdam, Netherlands|There are several measures for fairness in ranking, based on different underlying assumptions and perspectives. \acPL optimization with the REINFORCE algorithm can be used for optimizing black-box objective functions over permutations. In particular, it can be used for optimizing fairness measures. However, though effective for queries with a moderate number of repeating sessions, \acPL optimization has room for improvement for queries with a small number of repeating sessions. In this paper, we present a novel way of representing permutation distributions, based on the notion of permutation graphs. Similar to~\acPL, our distribution representation, called~\acPPG, can be used for black-box optimization of fairness. Different from~\acPL, where pointwise logits are used as the distribution parameters, in~\acPPG pairwise inversion probabilities together with a reference permutation construct the distribution. As such, the reference permutation can be set to the best sampled permutation regarding the objective function, making~\acPPG suitable for both deterministic and stochastic rankings. Our experiments show that~\acPPG, while comparable to~\acPL for larger session repetitions (i.e., stochastic ranking), improves over~\acPL for optimizing fairness metrics for queries with one session (i.e., deterministic ranking). Additionally, when accurate utility estimations are available, e.g., in tabular models, the performance of \acPPG in fairness optimization is significantly boosted compared to lower quality utility estimations from a learning to rank model, leading to a large performance gap with PL. Finally, the pairwise probabilities make it possible to impose pairwise constraints such as "item $d_1$ should always be ranked higher than item $d_2$.'' Such constraints can be used to simultaneously optimize the fairness metric and control another objective such as ranking performance.|根据不同的基本假设和观点，有几种衡量排名公平性的方法。使用 REINFORCE 算法的 acPL 优化可以用于优化黑盒目标函数。特别是可以用来优化公平性措施。然而，尽管对于具有中等数量重复会话的查询有效，但是对于具有少量重复会话的查询，acPL 优化还有改进的空间。本文基于置换图的概念，提出了一种表示置换分布的新方法。与 ~ acPL 类似，我们的分布表示(称为 ~ acPPG)可以用于公平性的黑盒优化。与以逐点对数作为分布参数的 ~ acPPG 不同，在 ~ acPPG 中成对反演概率与参考置换构成分布。因此，对于目标函数，可以将参考排序设置为最佳采样排序，使 ~ acPPG 既适用于确定性排序，也适用于随机排序。我们的实验表明 ~ acPPG 在较大的会话重复(即随机排名)方面与 ~ acPL 相当，但在优化一个会话查询(即确定性排名)的公平性指标方面优于 ~ acPL。此外，当准确的效用估计可用时，例如在表格模型中，acPPG 在公平优化中的表现显着提高，与来自学习到等级模型的较低质量效用估计相比，导致与 PL 的巨大性能差距。最后，成对概率使得可以施加成对约束，例如“项 $d _ 1 $应该总是排在项 $d _ 2 $之前”这种约束可以用来同时优化公平性度量和控制另一个目标，如排名性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Probabilistic+Permutation+Graph+Search:+Black-Box+Optimization+for+Fairness+in+Ranking)|2| -|[Less is More: Reweighting Important Spectral Graph Features for Recommendation](https://doi.org/10.1145/3477495.3532014)|Shaowen Peng, Kazunari Sugiyama, Tsunenori Mine|Kyoto University, Kyoto, Japan; Kyushu University, Fukuoka, Japan|As much as Graph Convolutional Networks (GCNs) have shown tremendous success in recommender systems and collaborative filtering (CF), the mechanism of how they, especially the core components (\textiti.e., neighborhood aggregation) contribute to recommendation has not been well studied. To unveil the effectiveness of GCNs for recommendation, we first analyze them in a spectral perspective and discover two important findings: (1) only a small portion of spectral graph features that emphasize the neighborhood smoothness and difference contribute to the recommendation accuracy, whereas most graph information can be considered as noise that even reduces the performance, and (2) repetition of the neighborhood aggregation emphasizes smoothed features and filters out noise information in an ineffective way. Based on the two findings above, we propose a new GCN learning scheme for recommendation by replacing neihgborhood aggregation with a simple yet effective Graph Denoising Encoder (GDE), which acts as a band pass filter to capture important graph features. We show that our proposed method alleviates the over-smoothing and is comparable to an indefinite-layer GCN that can take any-hop neighborhood into consideration. Finally, we dynamically adjust the gradients over the negative samples to expedite model training without introducing additional complexity. Extensive experiments on five real-world datasets show that our proposed method not only outperforms state-of-the-arts but also achieves 12x speedup over LightGCN.|尽管图形卷积网络在推荐系统和协同过滤(CF)方面取得了巨大的成功，但其核心组件(textititi.e. 邻域聚合)对推荐的贡献机制还没有得到很好的研究。为了揭示 GCNs 在推荐中的有效性，我们首先从谱的角度分析它们，发现两个重要的结果: (1)只有一小部分强调邻域平滑和差异的谱图特征有助于推荐精度，而大多数图形信息可以被视为噪声，甚至降低性能; (2)重复的邻域聚合强调平滑的特征，并以无效的方式过滤掉噪声信息。基于以上两个发现，我们提出了一种新的 GCN 推荐学习方案，用一种简单而有效的图形去噪编码器(GDE)代替邻域聚合，GDE 作为带通滤波器来捕捉重要的图形特征。结果表明，我们提出的方法减轻了过度平滑，并可比拟一个不确定层 GCN，可以考虑任何跳邻居。最后，我们动态调整负样本上的梯度，以加快模型训练而不引入额外的复杂性。在五个真实世界数据集上的大量实验表明，我们提出的方法不仅优于最新技术，而且比 LightGCN 提高了12倍的速度。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Less+is+More:+Reweighting+Important+Spectral+Graph+Features+for+Recommendation)|2| +|[Less is More: Reweighting Important Spectral Graph Features for Recommendation](https://doi.org/10.1145/3477495.3532014)|Shaowen Peng, Kazunari Sugiyama, Tsunenori Mine|Kyushu University, Fukuoka, Japan; Kyoto University, Kyoto, Japan|As much as Graph Convolutional Networks (GCNs) have shown tremendous success in recommender systems and collaborative filtering (CF), the mechanism of how they, especially the core components (\textiti.e., neighborhood aggregation) contribute to recommendation has not been well studied. To unveil the effectiveness of GCNs for recommendation, we first analyze them in a spectral perspective and discover two important findings: (1) only a small portion of spectral graph features that emphasize the neighborhood smoothness and difference contribute to the recommendation accuracy, whereas most graph information can be considered as noise that even reduces the performance, and (2) repetition of the neighborhood aggregation emphasizes smoothed features and filters out noise information in an ineffective way. Based on the two findings above, we propose a new GCN learning scheme for recommendation by replacing neihgborhood aggregation with a simple yet effective Graph Denoising Encoder (GDE), which acts as a band pass filter to capture important graph features. We show that our proposed method alleviates the over-smoothing and is comparable to an indefinite-layer GCN that can take any-hop neighborhood into consideration. Finally, we dynamically adjust the gradients over the negative samples to expedite model training without introducing additional complexity. Extensive experiments on five real-world datasets show that our proposed method not only outperforms state-of-the-arts but also achieves 12x speedup over LightGCN.|尽管图形卷积网络在推荐系统和协同过滤(CF)方面取得了巨大的成功，但其核心组件(textititi.e. 邻域聚合)对推荐的贡献机制还没有得到很好的研究。为了揭示 GCNs 在推荐中的有效性，我们首先从谱的角度分析它们，发现两个重要的结果: (1)只有一小部分强调邻域平滑和差异的谱图特征有助于推荐精度，而大多数图形信息可以被视为噪声，甚至降低性能; (2)重复的邻域聚合强调平滑的特征，并以无效的方式过滤掉噪声信息。基于以上两个发现，我们提出了一种新的 GCN 推荐学习方案，用一种简单而有效的图形去噪编码器(GDE)代替邻域聚合，GDE 作为带通滤波器来捕捉重要的图形特征。结果表明，我们提出的方法减轻了过度平滑，并可比拟一个不确定层 GCN，可以考虑任何跳邻居。最后，我们动态调整负样本上的梯度，以加快模型训练而不引入额外的复杂性。在五个真实世界数据集上的大量实验表明，我们提出的方法不仅优于最新技术，而且比 LightGCN 提高了12倍的速度。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Less+is+More:+Reweighting+Important+Spectral+Graph+Features+for+Recommendation)|2| |[Interpolative Distillation for Unifying Biased and Debiased Recommendation](https://doi.org/10.1145/3477495.3532002)|Sihao Ding, Fuli Feng, Xiangnan He, Jinqiu Jin, Wenjie Wang, Yong Liao, Yongdong Zhang|National University of Singapore, Singapore, China; University of Science and Technology of China & CCCD Key Lab of MCT, Hefei, China; University of Science and Technology of China, Hefei, China|Most recommender systems evaluate model performance offline through either: 1) normal biased test on factual interactions; or 2) debiased test with records from the randomized controlled trial. In fact, both tests only reflect part of the whole picture: factual interactions are collected from the recommendation policy, fitting them better implies benefiting the platform with higher click or conversion rate; in contrast, debiased test eliminates system-induced biases and thus is more reflective of user true preference. Nevertheless, we find that existing models exhibit trade-off on the two tests, and there lacks methods that perform well on both tests. In this work, we aim to develop a win-win recommendation method that is strong on both tests. It is non-trivial, since it requires to learn a model that can make accurate prediction in both factual environment (ie normal biased test) and counterfactual environment (ie debiased test). Towards the goal, we perform environment-aware recommendation modeling by considering both environments. In particular, we propose an Interpolative Distillation (InterD) framework, which interpolates the biased and debiased models at user-item pair level by distilling a student model. We conduct experiments on three real-world datasets with both tests. Empirical results justify the rationality and effectiveness of InterD, which stands out on both tests especially demonstrates remarkable gains on less popular items.|大多数推荐系统通过以下两种方式评估模型的离线性能: 1)对实际交互进行正常偏向测试; 或者2)利用随机对照试验记录进行偏向测试。事实上，这两个测试只反映了整体情况的一部分: 实际的交互是从推荐策略中收集的，更好地适应它们意味着更高的点击率或转化率有利于平台; 相反，去偏向测试消除了系统引起的偏见，因此更能反映用户的真实偏好。尽管如此，我们发现现有的模型在两个测试中表现出了折衷，并且缺乏在两个测试中都表现良好的方法。在这项工作中，我们的目标是开发一个双赢的推荐方法，在两个测试中都很强。它是非平凡的，因为它需要学习一个模型，可以作出准确的预测既在事实环境(即正常偏向测试)和反事实环境(即去偏向测试)。为了实现这个目标，我们通过考虑两种环境来执行环境感知的推荐建模。特别地，我们提出了一个插值蒸馏(InterD)框架，它通过提取学生模型在用户项目对水平上插值有偏和无偏模型。我们用这两个测试在三个真实世界的数据集上进行实验。实证结果证明了 InterD 的合理性和有效性，它在两个测试中都表现突出，尤其是在不太受欢迎的项目上表现出显著的收益。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Interpolative+Distillation+for+Unifying+Biased+and+Debiased+Recommendation)|2| -|[Enhancing Hypergraph Neural Networks with Intent Disentanglement for Session-based Recommendation](https://doi.org/10.1145/3477495.3531794)|Yinfeng Li, Chen Gao, Hengliang Luo, Depeng Jin, Yong Li|Tsinghua University, Beijing, China; Meituan Inc., Beijing, China|Session-based recommendation (SBR) aims at the next-item prediction with a short behavior session. Existing solutions fail to address two main challenges: 1) user interests are shown as dynamically coupled intents, and 2) sessions always contain noisy signals. To address them, in this paper, we propose a hypergraph-based solution, HIDE. Specifically, HIDE first constructs a hypergraph for each session to model the possible interest transitions from distinct perspectives. HIDE then disentangles the intents under each item click in micro and macro manners. In the micro-disentanglement, we perform intent-aware embedding propagation on session hypergraph to adaptively activate disentangled intents from noisy data. In the macro-disentanglement, we introduce an auxiliary intent-classification task to encourage the independence of different intents. Finally, we generate the intent-specific representations for the given session to make the final recommendation. Benchmark evaluations demonstrate the significant performance gain of our HIDE over the state-of-the-art methods.|基于会话的推荐(SBR)针对具有短行为会话的下一项预测。现有的解决方案未能解决两个主要挑战: 1)用户兴趣以动态耦合意图的形式显示，2)会话总是包含噪声信号。为了解决这些问题，本文提出了一种基于超图的解决方案—— HIDE。具体来说，HIDE 首先为每个会话构造一个超图，从不同的角度对可能的兴趣转换进行建模。然后隐藏解开每个项目下的微观和宏观方式点击的意图。在微分离中，我们在会话超图上进行意图感知的嵌入传播，以自适应地激活噪声数据中的意图。在宏观分离中，我们引入了一个辅助的意图分类任务，以鼓励不同意图的独立性。最后，我们为给定的会话生成特定于意图的表示以提出最终的建议。基准评估显示了我们的 HIDE 相对于最先进的方法的显著性能增益。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Enhancing+Hypergraph+Neural+Networks+with+Intent+Disentanglement+for+Session-based+Recommendation)|2| -|[Generative Adversarial Framework for Cold-Start Item Recommendation](https://doi.org/10.1145/3477495.3531897)|Hao Chen, Zefan Wang, Feiran Huang, Xiao Huang, Yue Xu, Yishi Lin, Peng He, Zhoujun Li|Alibaba Group, Hangzhou, China; Jinan University, Guangzhou, China; The Hong Kong Polytechnic University, Hong Kong, Hong Kong; Beihang University, Beijing, China; Tencent Inc., Shenzhen, China|The cold-start problem has been a long-standing issue in recommendation. Embedding-based recommendation models provide recommendations by learning embeddings for each user and item from historical interactions. Therefore, such embedding-based models perform badly for cold items which haven't emerged in the training set. The most common solutions are to generate the cold embedding for the cold item from its content features. However, the cold embeddings generated from contents have different distribution as the warm embeddings are learned from historical interactions. In this case, current cold-start methods are facing an interesting seesaw phenomenon, which improves the recommendation of either the cold items or the warm items but hurts the opposite ones. To this end, we propose a general framework named Generative Adversarial Recommendation (GAR). By training the generator and the recommender adversarially, the generated cold item embeddings can have similar distribution as the warm embeddings that can even fool the recommender. Simultaneously, the recommender is fine-tuned to correctly rank the "fake'' warm embeddings and the real warm embeddings. Consequently, the recommendation of the warms and the colds will not influence each other, thus avoiding the seesaw phenomenon. Additionally, GAR could be applied to any off-the-shelf recommendation model. Experiments on two datasets present that GAR has strong overall recommendation performance in cold-starting both the CF-based model (improved by over 30.18%) and the GNN-based model (improved by over 17.78%).|冷启动问题一直是建议中的一个长期存在的问题。基于嵌入的推荐模型通过从历史交互中学习每个用户和项目的嵌入来提供推荐。因此，这种基于嵌入的模型对于训练集中没有出现的冷项目表现很差。最常见的解决方案是根据冷藏项目的内容特征生成冷藏嵌入。然而，由内容所产生的冷嵌体在历史互动中学习到的热嵌体，其分布是不同的。在这种情况下，目前的冷启动方法正面临着一个有趣的跷跷板现象，这改善了推荐的冷项目或温暖的项目，但伤害了相反的。为此，我们提出了一个名为生成对抗性建议(GAR)的通用框架。通过对生成器和推荐器进行对抗性训练，生成的冷嵌入项可以像暖嵌入项一样具有相似的分布，甚至可以欺骗推荐器。同时，对推荐进行微调，以正确排列“假的”暖嵌入和真正的暖嵌入。因此，建议的温暖和寒冷将不会相互影响，从而避免跷跷板现象。此外，GAR 可以应用于任何现成的推荐模型。在两个数据集上的实验表明，GAR 在冷启动基于 CF 的模型(改进超过30.18%)和基于 GNN 的模型(改进超过17.78%)方面具有很强的总体推荐性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Generative+Adversarial+Framework+for+Cold-Start+Item+Recommendation)|2| -|[Dynamics-Aware Adaptation for Reinforcement Learning Based Cross-Domain Interactive Recommendation](https://doi.org/10.1145/3477495.3531969)|Junda Wu, Zhihui Xie, Tong Yu, Handong Zhao, Ruiyi Zhang, Shuai Li|Adobe Research, San Jose, CA, USA; Shanghai Jiao Tong University, Shanghai, China; New York University, New York, NY, USA|Interactive recommender systems (IRS) have received wide attention in recent years. To capture users' dynamic preferences and maximize their long-term engagement, IRS are usually formulated as reinforcement learning (RL) problems. Despite the promise to solve complex decision-making problems, RL-based methods generally require a large amount of online interaction, restricting their applications due to economic considerations. One possible direction to alleviate this issue is cross-domain recommendation that aims to leverage abundant logged interaction data from a source domain (e.g., adventure genre in movie recommendation) to improve the recommendation quality in the target domain (e.g., crime genre). Nevertheless, prior studies mostly focus on adapting the static representations of users/items. Few have explored how the temporally dynamic user-item interaction patterns transform across domains. Motivated by the above consideration, we propose DACIR, a novel Doubly-Adaptive deep RL-based framework for Cross-domain Interactive Recommendation. We first pinpoint how users behave differently in two domains and highlight the potential to leverage the shared user dynamics to boost IRS. To transfer static user preferences across domains, DACIR enforces consistency of item representation by aligning embeddings into a shared latent space. In addition, given the user dynamics in IRS, DACIR calibrates the dynamic interaction patterns in two domains via reward correlation. Once the double adaptation narrows the cross-domain gap, we are able to learn a transferable policy for the target recommender by leveraging logged data. Experiments on real-world datasets validate the superiority of our approach, which consistently achieves significant improvements over the baselines.|交互式推荐系统(IRS)近年来受到了广泛的关注。为了捕捉用户的动态偏好并最大化他们的长期参与，IRS 通常被定义为强化学习(RL)问题。尽管有望解决复杂的决策问题，但基于 RL 的方法通常需要大量的在线交互，出于经济考虑，限制了它们的应用。缓解这个问题的一个可能的方向是跨域推荐，旨在利用来自源域的大量日志交互数据(例如，电影推荐中的冒险类型)来提高目标域的推荐质量(例如，犯罪类型)。尽管如此，以往的研究主要集中在调整用户/项目的静态表征。很少有人研究过时态动态的用户项交互模式如何跨域转换。基于上述考虑，我们提出了一种新的基于双重自适应深度 RL 的跨域交互推荐框架 DACIR。我们首先精确地指出用户在两个领域中的不同行为，并强调利用共享用户动态来提高 IRS 的潜力。为了跨域传输静态用户首选项，DACIR 通过将嵌入对齐到共享的潜在空间来实现项表示的一致性。此外，给定 IRS 中的用户动态，DACIR 通过奖励相关校正两个域中的动态交互模式。一旦双重适应缩小了跨域间的差距，我们就能够通过利用已记录的数据为目标推荐程序学习一种可转移的策略。在实际数据集上的实验验证了该方法的优越性，在基线上取得了明显的改进。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Dynamics-Aware+Adaptation+for+Reinforcement+Learning+Based+Cross-Domain+Interactive+Recommendation)|2| +|[Enhancing Hypergraph Neural Networks with Intent Disentanglement for Session-based Recommendation](https://doi.org/10.1145/3477495.3531794)|Yinfeng Li, Chen Gao, Hengliang Luo, Depeng Jin, Yong Li|Meituan Inc., Beijing, China; Tsinghua University, Beijing, China|Session-based recommendation (SBR) aims at the next-item prediction with a short behavior session. Existing solutions fail to address two main challenges: 1) user interests are shown as dynamically coupled intents, and 2) sessions always contain noisy signals. To address them, in this paper, we propose a hypergraph-based solution, HIDE. Specifically, HIDE first constructs a hypergraph for each session to model the possible interest transitions from distinct perspectives. HIDE then disentangles the intents under each item click in micro and macro manners. In the micro-disentanglement, we perform intent-aware embedding propagation on session hypergraph to adaptively activate disentangled intents from noisy data. In the macro-disentanglement, we introduce an auxiliary intent-classification task to encourage the independence of different intents. Finally, we generate the intent-specific representations for the given session to make the final recommendation. Benchmark evaluations demonstrate the significant performance gain of our HIDE over the state-of-the-art methods.|基于会话的推荐(SBR)针对具有短行为会话的下一项预测。现有的解决方案未能解决两个主要挑战: 1)用户兴趣以动态耦合意图的形式显示，2)会话总是包含噪声信号。为了解决这些问题，本文提出了一种基于超图的解决方案—— HIDE。具体来说，HIDE 首先为每个会话构造一个超图，从不同的角度对可能的兴趣转换进行建模。然后隐藏解开每个项目下的微观和宏观方式点击的意图。在微分离中，我们在会话超图上进行意图感知的嵌入传播，以自适应地激活噪声数据中的意图。在宏观分离中，我们引入了一个辅助的意图分类任务，以鼓励不同意图的独立性。最后，我们为给定的会话生成特定于意图的表示以提出最终的建议。基准评估显示了我们的 HIDE 相对于最先进的方法的显著性能增益。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Enhancing+Hypergraph+Neural+Networks+with+Intent+Disentanglement+for+Session-based+Recommendation)|2| +|[Generative Adversarial Framework for Cold-Start Item Recommendation](https://doi.org/10.1145/3477495.3531897)|Hao Chen, Zefan Wang, Feiran Huang, Xiao Huang, Yue Xu, Yishi Lin, Peng He, Zhoujun Li|The Hong Kong Polytechnic University, Hong Kong, Hong Kong; Jinan University, Guangzhou, China; Beihang University, Beijing, China; Alibaba Group, Hangzhou, China; Tencent Inc., Shenzhen, China|The cold-start problem has been a long-standing issue in recommendation. Embedding-based recommendation models provide recommendations by learning embeddings for each user and item from historical interactions. Therefore, such embedding-based models perform badly for cold items which haven't emerged in the training set. The most common solutions are to generate the cold embedding for the cold item from its content features. However, the cold embeddings generated from contents have different distribution as the warm embeddings are learned from historical interactions. In this case, current cold-start methods are facing an interesting seesaw phenomenon, which improves the recommendation of either the cold items or the warm items but hurts the opposite ones. To this end, we propose a general framework named Generative Adversarial Recommendation (GAR). By training the generator and the recommender adversarially, the generated cold item embeddings can have similar distribution as the warm embeddings that can even fool the recommender. Simultaneously, the recommender is fine-tuned to correctly rank the "fake'' warm embeddings and the real warm embeddings. Consequently, the recommendation of the warms and the colds will not influence each other, thus avoiding the seesaw phenomenon. Additionally, GAR could be applied to any off-the-shelf recommendation model. Experiments on two datasets present that GAR has strong overall recommendation performance in cold-starting both the CF-based model (improved by over 30.18%) and the GNN-based model (improved by over 17.78%).|冷启动问题一直是建议中的一个长期存在的问题。基于嵌入的推荐模型通过从历史交互中学习每个用户和项目的嵌入来提供推荐。因此，这种基于嵌入的模型对于训练集中没有出现的冷项目表现很差。最常见的解决方案是根据冷藏项目的内容特征生成冷藏嵌入。然而，由内容所产生的冷嵌体在历史互动中学习到的热嵌体，其分布是不同的。在这种情况下，目前的冷启动方法正面临着一个有趣的跷跷板现象，这改善了推荐的冷项目或温暖的项目，但伤害了相反的。为此，我们提出了一个名为生成对抗性建议(GAR)的通用框架。通过对生成器和推荐器进行对抗性训练，生成的冷嵌入项可以像暖嵌入项一样具有相似的分布，甚至可以欺骗推荐器。同时，对推荐进行微调，以正确排列“假的”暖嵌入和真正的暖嵌入。因此，建议的温暖和寒冷将不会相互影响，从而避免跷跷板现象。此外，GAR 可以应用于任何现成的推荐模型。在两个数据集上的实验表明，GAR 在冷启动基于 CF 的模型(改进超过30.18%)和基于 GNN 的模型(改进超过17.78%)方面具有很强的总体推荐性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Generative+Adversarial+Framework+for+Cold-Start+Item+Recommendation)|2| +|[Dynamics-Aware Adaptation for Reinforcement Learning Based Cross-Domain Interactive Recommendation](https://doi.org/10.1145/3477495.3531969)|Junda Wu, Zhihui Xie, Tong Yu, Handong Zhao, Ruiyi Zhang, Shuai Li|New York University, New York, NY, USA; Shanghai Jiao Tong University, Shanghai, China; Adobe Research, San Jose, CA, USA|Interactive recommender systems (IRS) have received wide attention in recent years. To capture users' dynamic preferences and maximize their long-term engagement, IRS are usually formulated as reinforcement learning (RL) problems. Despite the promise to solve complex decision-making problems, RL-based methods generally require a large amount of online interaction, restricting their applications due to economic considerations. One possible direction to alleviate this issue is cross-domain recommendation that aims to leverage abundant logged interaction data from a source domain (e.g., adventure genre in movie recommendation) to improve the recommendation quality in the target domain (e.g., crime genre). Nevertheless, prior studies mostly focus on adapting the static representations of users/items. Few have explored how the temporally dynamic user-item interaction patterns transform across domains. Motivated by the above consideration, we propose DACIR, a novel Doubly-Adaptive deep RL-based framework for Cross-domain Interactive Recommendation. We first pinpoint how users behave differently in two domains and highlight the potential to leverage the shared user dynamics to boost IRS. To transfer static user preferences across domains, DACIR enforces consistency of item representation by aligning embeddings into a shared latent space. In addition, given the user dynamics in IRS, DACIR calibrates the dynamic interaction patterns in two domains via reward correlation. Once the double adaptation narrows the cross-domain gap, we are able to learn a transferable policy for the target recommender by leveraging logged data. Experiments on real-world datasets validate the superiority of our approach, which consistently achieves significant improvements over the baselines.|交互式推荐系统(IRS)近年来受到了广泛的关注。为了捕捉用户的动态偏好并最大化他们的长期参与，IRS 通常被定义为强化学习(RL)问题。尽管有望解决复杂的决策问题，但基于 RL 的方法通常需要大量的在线交互，出于经济考虑，限制了它们的应用。缓解这个问题的一个可能的方向是跨域推荐，旨在利用来自源域的大量日志交互数据(例如，电影推荐中的冒险类型)来提高目标域的推荐质量(例如，犯罪类型)。尽管如此，以往的研究主要集中在调整用户/项目的静态表征。很少有人研究过时态动态的用户项交互模式如何跨域转换。基于上述考虑，我们提出了一种新的基于双重自适应深度 RL 的跨域交互推荐框架 DACIR。我们首先精确地指出用户在两个领域中的不同行为，并强调利用共享用户动态来提高 IRS 的潜力。为了跨域传输静态用户首选项，DACIR 通过将嵌入对齐到共享的潜在空间来实现项表示的一致性。此外，给定 IRS 中的用户动态，DACIR 通过奖励相关校正两个域中的动态交互模式。一旦双重适应缩小了跨域间的差距，我们就能够通过利用已记录的数据为目标推荐程序学习一种可转移的策略。在实际数据集上的实验验证了该方法的优越性，在基线上取得了明显的改进。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Dynamics-Aware+Adaptation+for+Reinforcement+Learning+Based+Cross-Domain+Interactive+Recommendation)|2| |[A Study of Cross-Session Cross-Device Search Within an Academic Digital Library](https://doi.org/10.1145/3477495.3531929)|Sebastian Gomes, Miriam Boon, Orland Hoeber|University of Regina, Regina, SK, Canada|Information seeking in an academic digital library is complex in nature, often spanning multiple search sessions. Resuming academic search tasks requires significant cognitive effort as searchers must re-acquaint themselves with previous search session activities and previously discovered documents before resuming their search. Further, some academic searchers may find it convenient to initiate such searches on their mobile devices during short gaps in time (e.g., between classes), and resume them later in a desktop environment when they can use the extra screen space and more convenient document storage capabilities of their computers. To support such searching, we have developed an academic digital library search interface that assists searchers in managing cross-session search tasks even when moving between mobile and desktop environments. Using a controlled laboratory study we compared our approach (Dilex) to a standard academic digital library search interface. We found increased user engagement in both the initial (mobile) and resumed (desktop) search activities, and that participants spent more time on the search results pages and had an increased degree of interaction with information and personalization features during the resumed tasks. These results provide evidence that the participants were able to make effective use of the visualization features in Dilex, which enabled them to readily resume their search tasks and stay engaged in the search activities. This work represents an example of how semi-automatic search task/session management and visualization features can support cross-session search, and how designing for both mobile and desktop use can support cross-device search.|学术数字图书馆的信息搜索本质上是复杂的，通常跨越多个搜索环节。恢复学术搜索任务需要大量的认知努力，因为搜索者必须在恢复搜索之前重新熟悉以前的搜索会话活动和以前发现的文档。此外，部分学术搜寻人士可能会觉得在短时间内(例如课间)在流动装置上进行这类搜寻比较方便，稍后当他们可以使用电脑额外的屏幕空间和更方便的文件储存功能时，便可在桌面环境中继续进行这类搜寻。为了支持这种搜索，我们开发了一个学术数字图书馆搜索界面，协助搜索人员管理跨会话搜索任务，即使在移动和桌面环境之间移动。使用一个受控的实验室研究，我们比较了我们的方法(Dilex)与标准的学术数字图书馆搜索界面。我们发现，用户在初始(移动)和恢复(桌面)搜索活动中的参与度都有所提高，参与者在搜索结果页面上花费的时间更多，在恢复任务期间与信息和个性化功能的交互程度也有所提高。这些结果证明参与者能够有效地利用 Dilex 的可视化功能，使他们能够随时恢复搜索任务并继续参与搜索活动。这项工作代表了一个半自动搜索任务/会话管理和可视化特性如何支持跨会话搜索的例子，以及如何设计为移动和桌面使用都可以支持跨设备搜索。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Study+of+Cross-Session+Cross-Device+Search+Within+an+Academic+Digital+Library)|2| -|[Locality-Sensitive State-Guided Experience Replay Optimization for Sparse Rewards in Online Recommendation](https://doi.org/10.1145/3477495.3532015)|Xiaocong Chen, Lina Yao, Julian J. McAuley, Weili Guan, Xiaojun Chang, Xianzhi Wang|University of California, San Diego, San Diego, CA, USA; Monash University, Melbourne, VIC, Australia; University of Technology Sydney, Sydney, NSW, Australia; University of New South Wales, Sydney, NSW, Australia|Online recommendation requires handling rapidly changing user preferences. Deep reinforcement learning (DRL) is an effective means of capturing users' dynamic interest during interactions with recommender systems. Generally, it is challenging to train a DRL agent in online recommender systems because of the sparse rewards caused by the large action space (e.g., candidate item space) and comparatively fewer user interactions. Leveraging experience replay (ER) has been extensively studied to conquer the issue of sparse rewards. However, they adapt poorly to the complex environment of online recommender systems and are inefficient in learning an optimal strategy from past experience. As a step to filling this gap, we propose a novel state-aware experience replay model, in which the agent selectively discovers the most relevant and salient experiences and is guided to find the optimal policy for online recommendations. In particular, a locality-sensitive hashing method is proposed to selectively retain the most meaningful experience at scale and a prioritized reward-driven strategy is designed to replay more valuable experiences with higher chance. We formally show that the proposed method guarantees the upper and lower bound on experience replay and optimizes the space complexity, as well as empirically demonstrate our model's superiority to several existing experience replay methods over three benchmark simulation platforms.|在线推荐需要处理快速变化的用户首选项。深度强化学习(DRL)是在与推荐系统交互时捕捉用户动态兴趣的有效手段。一般来说，在在线推荐系统中训练 DRL 代理是一个挑战，因为大的动作空间(例如候选项空间)和相对较少的用户交互造成了稀疏的奖励。杠杆经验重放(ER)已被广泛研究，以克服稀疏奖励的问题。然而，它们不能很好地适应在线推荐系统的复杂环境，并且在从过去的经验中学习最佳策略方面效率低下。作为填补这一空白的一个步骤，我们提出了一个新的状态感知经验重放模型，其中代理人选择性地发现最相关和突出的经验，并指导寻找最佳策略的在线推荐。特别是，一个局部性敏感哈希的方法被提出来选择性地保留大规模的最有意义的经验，一个优先奖励驱动的策略被设计来回放更有价值的经验和更高的机会。在三个基准仿真平台上，我们正式证明了该方法保证了经验重放的上下界，优化了空间复杂度，并且实证证明了该模型相对于现有的几种经验重放方法的优越性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Locality-Sensitive+State-Guided+Experience+Replay+Optimization+for+Sparse+Rewards+in+Online+Recommendation)|2| -|[An Attribute-Driven Mirror Graph Network for Session-based Recommendation](https://doi.org/10.1145/3477495.3531935)|Siqi Lai, Erli Meng, Fan Zhang, Chenliang Li, Bin Wang, Aixin Sun|Xiaomi.com, Beijing, China; Nanyang Technological University, Singapore, Singapore, Singapore; Wuhan University, Wuhan, China|Session-based recommendation (SBR) aims to predict a user's next clicked item based on an anonymous yet short interaction sequence. Previous SBR models, which rely only on the limited short-term transition information without utilizing extra valuable knowledge, have suffered a lot from the problem of data sparsity. This paper proposes a novel mirror graph enhanced neural model for session-based recommendation (MGS), to exploit item attribute information over item embeddings for more accurate preference estimation. Specifically, MGS utilizes two kinds of graphs to learn item representations. One is a session graph generated from the user interaction sequence describing users' preference based on transition patterns. Another is a mirror graph built by an attribute-aware module that selects the most attribute-representative information for each session item by integrating items' attribute information. We applied an iterative dual refinement mechanism to propagate information between the session and mirror graphs. To further guide the training process of the attribute-aware module, we also introduce a contrastive learning strategy that compares two mirror graphs generated for the same session by randomly sampling the attribute-same neighbors. Experiments on three real-world datasets exhibit that the performance of MGS surpasses many state-of-the-art models.|基于会话(Session-based)的推荐(SBR)旨在基于一个匿名但很短的交互序列来预测用户的下一个点击项目。以往的 SBR 模型仅仅依赖于有限的短期转换信息，而没有利用额外的有价值的知识，已经遭受了很多数据稀疏的问题。提出了一种新的基于会话推荐(MGS)的镜像图增强神经网络模型，利用项目属性信息对项目嵌入进行更精确的偏好估计。具体来说，MGS 利用两种图来学习项目表示。一个是由用户交互序列生成的会话图，描述基于转换模式的用户偏好。另一个是由属性感知模块构建的镜像图，该模块通过集成会话项的属性信息来为每个会话项选择最具代表性的属性信息。我们应用一个迭代对偶精化机制来传播会话和镜像图之间的信息。为了进一步指导属性感知模块的训练过程，我们还引入了一种对比学习策略，通过对属性相同的邻居进行随机抽样，比较同一会话中生成的两个镜像图。在三个实际数据集上的实验表明，MGS 的性能优于许多最先进的模型。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=An+Attribute-Driven+Mirror+Graph+Network+for+Session-based+Recommendation)|2| +|[Locality-Sensitive State-Guided Experience Replay Optimization for Sparse Rewards in Online Recommendation](https://doi.org/10.1145/3477495.3532015)|Xiaocong Chen, Lina Yao, Julian J. McAuley, Weili Guan, Xiaojun Chang, Xianzhi Wang|University of Technology Sydney, Sydney, NSW, Australia; University of New South Wales, Sydney, NSW, Australia; University of California, San Diego, San Diego, CA, USA; Monash University, Melbourne, VIC, Australia|Online recommendation requires handling rapidly changing user preferences. Deep reinforcement learning (DRL) is an effective means of capturing users' dynamic interest during interactions with recommender systems. Generally, it is challenging to train a DRL agent in online recommender systems because of the sparse rewards caused by the large action space (e.g., candidate item space) and comparatively fewer user interactions. Leveraging experience replay (ER) has been extensively studied to conquer the issue of sparse rewards. However, they adapt poorly to the complex environment of online recommender systems and are inefficient in learning an optimal strategy from past experience. As a step to filling this gap, we propose a novel state-aware experience replay model, in which the agent selectively discovers the most relevant and salient experiences and is guided to find the optimal policy for online recommendations. In particular, a locality-sensitive hashing method is proposed to selectively retain the most meaningful experience at scale and a prioritized reward-driven strategy is designed to replay more valuable experiences with higher chance. We formally show that the proposed method guarantees the upper and lower bound on experience replay and optimizes the space complexity, as well as empirically demonstrate our model's superiority to several existing experience replay methods over three benchmark simulation platforms.|在线推荐需要处理快速变化的用户首选项。深度强化学习(DRL)是在与推荐系统交互时捕捉用户动态兴趣的有效手段。一般来说，在在线推荐系统中训练 DRL 代理是一个挑战，因为大的动作空间(例如候选项空间)和相对较少的用户交互造成了稀疏的奖励。杠杆经验重放(ER)已被广泛研究，以克服稀疏奖励的问题。然而，它们不能很好地适应在线推荐系统的复杂环境，并且在从过去的经验中学习最佳策略方面效率低下。作为填补这一空白的一个步骤，我们提出了一个新的状态感知经验重放模型，其中代理人选择性地发现最相关和突出的经验，并指导寻找最佳策略的在线推荐。特别是，一个局部性敏感哈希的方法被提出来选择性地保留大规模的最有意义的经验，一个优先奖励驱动的策略被设计来回放更有价值的经验和更高的机会。在三个基准仿真平台上，我们正式证明了该方法保证了经验重放的上下界，优化了空间复杂度，并且实证证明了该模型相对于现有的几种经验重放方法的优越性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Locality-Sensitive+State-Guided+Experience+Replay+Optimization+for+Sparse+Rewards+in+Online+Recommendation)|2| +|[An Attribute-Driven Mirror Graph Network for Session-based Recommendation](https://doi.org/10.1145/3477495.3531935)|Siqi Lai, Erli Meng, Fan Zhang, Chenliang Li, Bin Wang, Aixin Sun|Xiaomi.com, Beijing, China; Wuhan University, Wuhan, China; Nanyang Technological University, Singapore, Singapore, Singapore|Session-based recommendation (SBR) aims to predict a user's next clicked item based on an anonymous yet short interaction sequence. Previous SBR models, which rely only on the limited short-term transition information without utilizing extra valuable knowledge, have suffered a lot from the problem of data sparsity. This paper proposes a novel mirror graph enhanced neural model for session-based recommendation (MGS), to exploit item attribute information over item embeddings for more accurate preference estimation. Specifically, MGS utilizes two kinds of graphs to learn item representations. One is a session graph generated from the user interaction sequence describing users' preference based on transition patterns. Another is a mirror graph built by an attribute-aware module that selects the most attribute-representative information for each session item by integrating items' attribute information. We applied an iterative dual refinement mechanism to propagate information between the session and mirror graphs. To further guide the training process of the attribute-aware module, we also introduce a contrastive learning strategy that compares two mirror graphs generated for the same session by randomly sampling the attribute-same neighbors. Experiments on three real-world datasets exhibit that the performance of MGS surpasses many state-of-the-art models.|基于会话(Session-based)的推荐(SBR)旨在基于一个匿名但很短的交互序列来预测用户的下一个点击项目。以往的 SBR 模型仅仅依赖于有限的短期转换信息，而没有利用额外的有价值的知识，已经遭受了很多数据稀疏的问题。提出了一种新的基于会话推荐(MGS)的镜像图增强神经网络模型，利用项目属性信息对项目嵌入进行更精确的偏好估计。具体来说，MGS 利用两种图来学习项目表示。一个是由用户交互序列生成的会话图，描述基于转换模式的用户偏好。另一个是由属性感知模块构建的镜像图，该模块通过集成会话项的属性信息来为每个会话项选择最具代表性的属性信息。我们应用一个迭代对偶精化机制来传播会话和镜像图之间的信息。为了进一步指导属性感知模块的训练过程，我们还引入了一种对比学习策略，通过对属性相同的邻居进行随机抽样，比较同一会话中生成的两个镜像图。在三个实际数据集上的实验表明，MGS 的性能优于许多最先进的模型。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=An+Attribute-Driven+Mirror+Graph+Network+for+Session-based+Recommendation)|2| |[FUM: Fine-grained and Fast User Modeling for News Recommendation](https://doi.org/10.1145/3477495.3531790)|Tao Qi, Fangzhao Wu, Chuhan Wu, Yongfeng Huang|Tsinghua University, Beijing, China; Tsinghua Unvisersity, Beijing, China; Microsoft Research Asia, Beijing, China|User modeling is important for news recommendation. Existing methods usually first encode user's clicked news into news embeddings independently and then aggregate them into user embedding. However, the word-level interactions across different clicked news from the same user, which contain rich detailed clues to infer user interest, are ignored by these methods. In this paper, we propose a fine-grained and fast user modeling framework (FUM) to model user interest from fine-grained behavior interactions for news recommendation. The core idea of FUM is to concatenate the clicked news into a long document and transform user modeling into a document modeling task with both intra-news and inter-news word-level interactions. Since vanilla transformer cannot efficiently handle long document, we apply an efficient transformer named Fastformer to model fine-grained behavior interactions. Extensive experiments on two real-world datasets verify that FUM can effectively and efficiently model user interest for news recommendation.|用户建模对新闻推荐非常重要。现有方法通常首先将用户点击新闻独立编码为新闻嵌入，然后将其聚合为用户嵌入。但是，这些方法忽略了来自同一用户的不同点击新闻之间的词级交互，这些新闻包含丰富的细节线索来推断用户的兴趣。在本文中，我们提出了一个细粒度和快速的用户建模框架(FUM)来模型用户的兴趣从细粒度的行为交互的新闻推荐。FUM 的核心思想是将点击的新闻连接到一个长文档中，并将用户建模转换为具有内部新闻和内部新闻字级交互的文档建模任务。由于普通的转换器不能有效地处理长文档，因此我们应用一个名为 Fastformer 的高效转换器来对细粒度的行为交互进行建模。在两个实际数据集上的大量实验证明，FUM 可以有效地为新闻推荐建立用户兴趣模型。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=FUM:+Fine-grained+and+Fast+User+Modeling+for+News+Recommendation)|2| -|[Experiments on Generalizability of User-Oriented Fairness in Recommender Systems](https://doi.org/10.1145/3477495.3531718)|Hossein A. Rahmani, Mohammadmehdi Naghiaei, Mahdi Dehghan, Mohammad Aliannejadi|Shahid Beheshti University, Tehran, Iran; University College London, London, United Kingdom; University of Amsterdam, Amsterdam, Netherlands; University of Southern California, California, CA, USA|Recent work in recommender systems mainly focuses on fairness in recommendations as an important aspect of measuring recommendations quality. A fairness-aware recommender system aims to treat different user groups similarly. Relevant work on user-oriented fairness highlights the discriminant behavior of fairness-unaware recommendation algorithms towards a certain user group, defined based on users' activity level. Typical solutions include proposing a user-centered fairness re-ranking framework applied on top of a base ranking model to mitigate its unfair behavior towards a certain user group i.e., disadvantaged group. In this paper, we re-produce a user-oriented fairness study and provide extensive experiments to analyze the dependency of their proposed method on various fairness and recommendation aspects, including the recommendation domain, nature of the base ranking model, and user grouping method. Moreover, we evaluate the final recommendations provided by the re-ranking framework from both user- (e.g., NDCG, user-fairness) and item-side (e.g., novelty, item-fairness) metrics. We discover interesting trends and trade-offs between the model's performance in terms of different evaluation metrics. For instance, we see that the definition of the advantaged/disadvantaged user groups plays a crucial role in the effectiveness of the fairness algorithm and how it improves the performance of specific base ranking models. Finally, we highlight some important open challenges and future directions in this field. We release the data, evaluation pipeline, and the trained models publicly on https://github.com/rahmanidashti/FairRecSys.|推荐系统最近的工作主要侧重于推荐的公平性，这是衡量推荐质量的一个重要方面。公平意识推荐系统的目标是对不同的用户群体采取类似的对待方式。面向用户的公平性的相关工作强调了公平性无意识推荐算法对特定用户群的区分行为，这种区分行为是根据用户的活动水平来定义的。典型的解决方案包括提出一种以用户为中心的公平性重新排序框架，该框架应用于基本排序模型之上，以减轻其对特定用户群体(即弱势群体)的不公平行为。本文重现了一个面向用户的公平性研究，并通过大量实验分析了他们提出的方法对各种公平性和推荐方面的依赖性，包括推荐域、基本排名模型的性质和用户分组方法。此外，我们从用户(如 NDCG，用户公平性)和项目方(如新颖性，项目公平性)指标评估重新排名框架提供的最终建议。我们发现了一些有趣的趋势，以及模型在不同评估指标方面的表现之间的权衡。例如，我们发现优势/劣势用户群的定义对于公平算法的有效性以及如何提高特定基本排序模型的性能起着至关重要的作用。最后，我们强调一些重要的公开挑战和未来的方向在这一领域。我们在 https://github.com/rahmanidashti/fairrecsys 上公布数据、评估流程和训练有素的模型。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Experiments+on+Generalizability+of+User-Oriented+Fairness+in+Recommender+Systems)|2| +|[Experiments on Generalizability of User-Oriented Fairness in Recommender Systems](https://doi.org/10.1145/3477495.3531718)|Hossein A. Rahmani, Mohammadmehdi Naghiaei, Mahdi Dehghan, Mohammad Aliannejadi|University College London, London, United Kingdom; University of Southern California, California, CA, USA; Shahid Beheshti University, Tehran, Iran; University of Amsterdam, Amsterdam, Netherlands|Recent work in recommender systems mainly focuses on fairness in recommendations as an important aspect of measuring recommendations quality. A fairness-aware recommender system aims to treat different user groups similarly. Relevant work on user-oriented fairness highlights the discriminant behavior of fairness-unaware recommendation algorithms towards a certain user group, defined based on users' activity level. Typical solutions include proposing a user-centered fairness re-ranking framework applied on top of a base ranking model to mitigate its unfair behavior towards a certain user group i.e., disadvantaged group. In this paper, we re-produce a user-oriented fairness study and provide extensive experiments to analyze the dependency of their proposed method on various fairness and recommendation aspects, including the recommendation domain, nature of the base ranking model, and user grouping method. Moreover, we evaluate the final recommendations provided by the re-ranking framework from both user- (e.g., NDCG, user-fairness) and item-side (e.g., novelty, item-fairness) metrics. We discover interesting trends and trade-offs between the model's performance in terms of different evaluation metrics. For instance, we see that the definition of the advantaged/disadvantaged user groups plays a crucial role in the effectiveness of the fairness algorithm and how it improves the performance of specific base ranking models. Finally, we highlight some important open challenges and future directions in this field. We release the data, evaluation pipeline, and the trained models publicly on https://github.com/rahmanidashti/FairRecSys.|推荐系统最近的工作主要侧重于推荐的公平性，这是衡量推荐质量的一个重要方面。公平意识推荐系统的目标是对不同的用户群体采取类似的对待方式。面向用户的公平性的相关工作强调了公平性无意识推荐算法对特定用户群的区分行为，这种区分行为是根据用户的活动水平来定义的。典型的解决方案包括提出一种以用户为中心的公平性重新排序框架，该框架应用于基本排序模型之上，以减轻其对特定用户群体(即弱势群体)的不公平行为。本文重现了一个面向用户的公平性研究，并通过大量实验分析了他们提出的方法对各种公平性和推荐方面的依赖性，包括推荐域、基本排名模型的性质和用户分组方法。此外，我们从用户(如 NDCG，用户公平性)和项目方(如新颖性，项目公平性)指标评估重新排名框架提供的最终建议。我们发现了一些有趣的趋势，以及模型在不同评估指标方面的表现之间的权衡。例如，我们发现优势/劣势用户群的定义对于公平算法的有效性以及如何提高特定基本排序模型的性能起着至关重要的作用。最后，我们强调一些重要的公开挑战和未来的方向在这一领域。我们在 https://github.com/rahmanidashti/fairrecsys 上公布数据、评估流程和训练有素的模型。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Experiments+on+Generalizability+of+User-Oriented+Fairness+in+Recommender+Systems)|2| |[Geometric Disentangled Collaborative Filtering](https://doi.org/10.1145/3477495.3531982)|Yiding Zhang, Chaozhuo Li, Xing Xie, Xiao Wang, Chuan Shi, Yuming Liu, Hao Sun, Liangjie Zhang, Weiwei Deng, Qi Zhang|Beijing University of Posts and Telecommunications, Beijing, China; Microsoft Research Asia, Beijing, China; Microsoft, Beijing, China|Learning informative representations of users and items from the historical interactions is crucial to collaborative filtering (CF). Existing CF approaches usually model interactions solely within the Euclidean space. However, the sophisticated user-item interactions inherently present highly non-Euclidean anatomy with various types of geometric patterns (i.e., tree-likeness and cyclic structures). The Euclidean-based models may be inadequate to fully uncover the intent factors beneath such hybrid-geometry interactions. To remedy this deficiency, in this paper, we study the novel problem of Geometric Disentangled Collaborative Filtering (GDCF), which aims to reveal and disentangle the latent intent factors across multiple geometric spaces. A novel generative GDCF model is proposed to learn geometric disentangled representations by inferring the high-level concepts associated with user intentions and various geometries. Empirically, our proposal is extensively evaluated over five real-world datasets, and the experimental results demonstrate the superiority of GDCF.|从历史交互中学习用户和项目的信息表示对于协同过滤(CF)至关重要。现有的 CF 方法通常仅在欧几里得空间内模拟交互作用。然而，复杂的用户-项目交互本质上呈现出高度非欧几里德解剖学和各种几何模式(例如，树状结构和循环结构)。基于欧几里得的模型可能不足以完全揭示这种混合几何相互作用下的意图因素。为了弥补这一不足，本文研究了几何解缠协同过滤(gdCF)这一新问题，旨在揭示和解缠跨多个几何空间的潜在意图因素。提出了一种新的生成性 GDCF 模型，通过推导与用户意图和各种几何形状相关的高层概念来学习几何分离表示。实验结果证明了 GDCF 算法的优越性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Geometric+Disentangled+Collaborative+Filtering)|2| |[Generating Clarifying Questions with Web Search Results](https://doi.org/10.1145/3477495.3531981)|Ziliang Zhao, Zhicheng Dou, Jiaxin Mao, JiRong Wen|Renmin University of China, Beijing, China; Beijing Key Laboratory of Big Data Management and Analysis Methods & Key Laboratory of Data Engineering and Knowledge Engineering, MOE, Beijing, China|Asking clarifying questions is an interactive way to effectively clarify user intent. When a user submits a query, the search engine will return a clarifying question with several clickable items of sub-intents for clarification. According to the existing definition, the key to asking high-quality questions is to generate good descriptions for submitted queries and provided items. However, existing methods mainly based on static knowledge bases are difficult to find descriptions for many queries because of the lack of entities within these queries and their corresponding items. For such a query, it is unable to generate an informative question. To alleviate this problem, we propose leveraging top search results of the query to help generate better descriptions because we deem that the top retrieved documents contain rich and relevant contexts of the query. Specifically, we first design a rule-based algorithm to extract description candidates from search results and rank them by various human-designed features. Then, we apply an learning-to-rank model and another generative model for generalization and further improve the quality of clarifying questions. Experimental results show that our proposed methods can generate more readable and informative questions compared with existing methods. The results prove that search results can be utilized to improve users' search experience for search clarification in conversational search systems.|提出澄清问题是一种有效澄清用户意图的互动方式。当用户提交查询时，搜索引擎将返回一个澄清问题，其中包含若干可单击的子意图项以便澄清。根据现有的定义，提出高质量问题的关键是为提交的查询和提供的项目生成良好的描述。然而，现有的基于静态知识库的查询方法由于缺乏查询实体及其对应的项目，很难为许多查询找到描述。对于这样的查询，它无法生成提供信息的问题。为了缓解这个问题，我们建议利用查询的顶部搜索结果来帮助生成更好的描述，因为我们认为顶部检索的文档包含查询的丰富和相关的上下文。具体来说，我们首先设计了一个基于规则的算法，从搜索结果中提取描述候选者，并根据各种人工设计的特征对它们进行排序。然后，我们应用一个学习排序模型和另一个生成模型进行归纳，进一步提高澄清问题的质量。实验结果表明，与现有方法相比，本文提出的方法能够产生更具可读性和信息性的问题。实验结果表明，在会话搜索系统中，利用搜索结果可以提高用户的搜索体验。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Generating+Clarifying+Questions+with+Web+Search+Results)|2| |[Enhancing CTR Prediction with Context-Aware Feature Representation Learning](https://doi.org/10.1145/3477495.3531970)|Fangye Wang, Yingxu Wang, Dongsheng Li, Hansu Gu, Tun Lu, Peng Zhang, Ning Gu|Fudan University, Shanghai, China; Microsoft Research Asia, Shanghai, China; Independent, Seattle, WA, USA|CTR prediction has been widely used in the real world. Many methods model feature interaction to improve their performance. However, most methods only learn a fixed representation for each feature without considering the varying importance of each feature under different contexts, resulting in inferior performance. Recently, several methods tried to learn vector-level weights for feature representations to address the fixed representation issue. However, they only produce linear transformations to refine the fixed feature representations, which are still not flexible enough to capture the varying importance of each feature under different contexts. In this paper, we propose a novel module named Feature Refinement Network (FRNet), which learns context-aware feature representations at bit-level for each feature in different contexts. FRNet consists of two key components: 1) Information Extraction Unit (IEU), which captures contextual information and cross-feature relationships to guide context-aware feature refinement; and 2) Complementary Selection Gate (CSGate), which adaptively integrates the original and complementary feature representations learned in IEU with bit-level weights. Notably, FRNet is orthogonal to existing CTR methods and thus can be applied in many existing methods to boost their performance. Comprehensive experiments are conducted to verify the effectiveness, efficiency, and compatibility of FRNet.|CTR 预测在现实世界中得到了广泛的应用。许多方法对特征交互进行建模以提高其性能。然而，大多数方法只学习每个特征的固定表示，而不考虑每个特征在不同环境下的不同重要性，导致性能较差。最近，一些方法尝试学习特征表示的矢量级权重，以解决固定表示问题。然而，它们仅仅产生线性变换来细化固定特征表示，这些线性变换仍然不够灵活，不足以捕获不同上下文环境下每个特征的不同重要性。本文提出了一种新颖的特征精化网络(FRNet)模型，该模型能够在不同的上下文环境下对每个特征进行位级的上下文感知特征表示。FRNet 由两个关键组成部分组成: 1)信息抽取单元(IEU) ，它捕获上下文信息和跨特征关系，以指导上下文感知特征细化; 2)补充选择门(CSGate) ，它自适应地将在 IEU 中学到的原始和补充特征表示与位级权重结合起来。值得注意的是，FRNet 与现有的 CTR 方法是正交的，因此可以应用在许多现有的方法中来提高它们的性能。通过综合实验验证了 FRNet 的有效性、高效性和兼容性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Enhancing+CTR+Prediction+with+Context-Aware+Feature+Representation+Learning)|2| -|[Joint Multisided Exposure Fairness for Recommendation](https://doi.org/10.1145/3477495.3532007)|Haolun Wu, Bhaskar Mitra, Chen Ma, Fernando Diaz, Xue Liu|Microsoft, Montréal, PQ, Canada; McGill University, Montréal, PQ, Canada; City University of Hong Kong, Hong Kong SAR, Hong Kong; Canadian CIFAR AI Chair & Google, Montréal, PQ, Canada|Prior research on exposure fairness in the context of recommender systems has focused mostly on disparities in the exposure of individual or groups of items to individual users of the system. The problem of how individual or groups of items may be systemically under or over exposed to groups of users, or even all users, has received relatively less attention. However, such systemic disparities in information exposure can result in observable social harms, such as withholding economic opportunities from historically marginalized groups (allocative harm) or amplifying gendered and racialized stereotypes (representational harm). Previously, Diaz et al. developed the expected exposure metric---that incorporates existing user browsing models that have previously been developed for information retrieval---to study fairness of content exposure to individual users. We extend their proposed framework to formalize a family of exposure fairness metrics that model the problem jointly from the perspective of both the consumers and producers. Specifically, we consider group attributes for both types of stakeholders to identify and mitigate fairness concerns that go beyond individual users and items towards more systemic biases in recommendation. Furthermore, we study and discuss the relationships between the different exposure fairness dimensions proposed in this paper, as well as demonstrate how stochastic ranking policies can be optimized towards said fairness goals.|以往关于推荐系统中曝光公平性的研究主要侧重于系统个别用户对个别或组别项目的曝光方面的差异。个人或项目组如何可能系统地暴露于一组用户甚至所有用户之下或过度暴露于这些用户之下的问题，受到的关注相对较少。然而，这种信息暴露的系统性差异可能导致可观察到的社会危害，如阻止历史上被边缘化的群体获得经济机会(分配伤害)或放大性别和种族化的刻板印象(代表性伤害)。此前，迪亚兹等人开发了预期的曝光度量标准——该标准结合了以前为信息检索开发的现有用户浏览模型——以研究内容曝光对个人用户的公平性。我们扩展了他们提出的框架，从消费者和生产者的角度建立了一系列的暴露公平度量模型。具体来说，我们考虑两种类型的利益相关者的群体属性，以确定和减轻超越个人用户和项目的建议中更系统的偏见的公平性问题。此外，我们还研究和讨论了本文提出的不同暴露公平维度之间的关系，并论证了随机排序策略是如何朝着公平目标进行优化的。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Joint+Multisided+Exposure+Fairness+for+Recommendation)|2| +|[Joint Multisided Exposure Fairness for Recommendation](https://doi.org/10.1145/3477495.3532007)|Haolun Wu, Bhaskar Mitra, Chen Ma, Fernando Diaz, Xue Liu|McGill University, Montréal, PQ, Canada; Microsoft, Montréal, PQ, Canada; City University of Hong Kong, Hong Kong SAR, Hong Kong; Canadian CIFAR AI Chair & Google, Montréal, PQ, Canada|Prior research on exposure fairness in the context of recommender systems has focused mostly on disparities in the exposure of individual or groups of items to individual users of the system. The problem of how individual or groups of items may be systemically under or over exposed to groups of users, or even all users, has received relatively less attention. However, such systemic disparities in information exposure can result in observable social harms, such as withholding economic opportunities from historically marginalized groups (allocative harm) or amplifying gendered and racialized stereotypes (representational harm). Previously, Diaz et al. developed the expected exposure metric---that incorporates existing user browsing models that have previously been developed for information retrieval---to study fairness of content exposure to individual users. We extend their proposed framework to formalize a family of exposure fairness metrics that model the problem jointly from the perspective of both the consumers and producers. Specifically, we consider group attributes for both types of stakeholders to identify and mitigate fairness concerns that go beyond individual users and items towards more systemic biases in recommendation. Furthermore, we study and discuss the relationships between the different exposure fairness dimensions proposed in this paper, as well as demonstrate how stochastic ranking policies can be optimized towards said fairness goals.|以往关于推荐系统中曝光公平性的研究主要侧重于系统个别用户对个别或组别项目的曝光方面的差异。个人或项目组如何可能系统地暴露于一组用户甚至所有用户之下或过度暴露于这些用户之下的问题，受到的关注相对较少。然而，这种信息暴露的系统性差异可能导致可观察到的社会危害，如阻止历史上被边缘化的群体获得经济机会(分配伤害)或放大性别和种族化的刻板印象(代表性伤害)。此前，迪亚兹等人开发了预期的曝光度量标准——该标准结合了以前为信息检索开发的现有用户浏览模型——以研究内容曝光对个人用户的公平性。我们扩展了他们提出的框架，从消费者和生产者的角度建立了一系列的暴露公平度量模型。具体来说，我们考虑两种类型的利益相关者的群体属性，以确定和减轻超越个人用户和项目的建议中更系统的偏见的公平性问题。此外，我们还研究和讨论了本文提出的不同暴露公平维度之间的关系，并论证了随机排序策略是如何朝着公平目标进行优化的。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Joint+Multisided+Exposure+Fairness+for+Recommendation)|2| |[Pre-train a Discriminative Text Encoder for Dense Retrieval via Contrastive Span Prediction](https://doi.org/10.1145/3477495.3531772)|Xinyu Ma, Jiafeng Guo, Ruqing Zhang, Yixing Fan, Xueqi Cheng|ICT, CAS & University of Chinese Academy of Sciences, Beijing, China|Dense retrieval has shown promising results in many information retrieval (IR) related tasks, whose foundation is high-quality text representation learning for effective search. Some recent studies have shown that autoencoder-based language models are able to boost the dense retrieval performance using a weak decoder. However, we argue that 1) it is not discriminative to decode all the input texts and, 2) even a weak decoder has the bypass effect on the encoder. Therefore, in this work, we introduce a novel contrastive span prediction task to pre-train the encoder alone, but still retain the bottleneck ability of the autoencoder. In this way, we can 1) learn discriminative text representations efficiently with the group-wise contrastive learning over spans and, 2) avoid the bypass effect of the decoder thoroughly. Comprehensive experiments over publicly available retrieval benchmark datasets show that our approach can outperform existing pre-training methods for dense retrieval significantly.|密集检索在许多与信息检索(IR)相关的任务中显示出有希望的结果，其基础是高质量的文本表示学习，以便有效地搜索。最近的一些研究表明，基于自动编码器的语言模型能够通过弱解码器提高密集检索性能。然而，我们认为: 1)解码所有的输入文本是没有区别的，2)即使是弱解码器对编码器也有旁路效应。因此，本文提出了一种新的对比跨度预测任务来单独对编码器进行预训练，同时保留了自动编码器的瓶颈能力。这样，我们可以1)利用跨区域的分组对比学习有效地学习歧视性文本表示，2)彻底避免解码器的旁路效应。对公开检索基准数据集的综合实验表明，该方法在密集检索方面的性能明显优于现有的预训练方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Pre-train+a+Discriminative+Text+Encoder+for+Dense+Retrieval+via+Contrastive+Span+Prediction)|2| |[CenterCLIP: Token Clustering for Efficient Text-Video Retrieval](https://doi.org/10.1145/3477495.3531950)|Shuai Zhao, Linchao Zhu, Xiaohan Wang, Yi Yang|University of Technology Sydney, Sydney, NSW, Australia; Zhejiang University, Hangzhou, China|Recently, large-scale pre-training methods like CLIP have made great progress in multi-modal research such as text-video retrieval. In CLIP, transformers are vital for modeling complex multi-modal relations. However, in the vision transformer of CLIP, the essential visual tokenization process, which produces discrete visual token sequences, generates many homogeneous tokens due to the redundancy nature of consecutive and similar frames in videos. This significantly increases computation costs and hinders the deployment of video retrieval models in web applications. In this paper, to reduce the number of redundant video tokens, we design a multi-segment token clustering algorithm to find the most representative tokens and drop the non-essential ones. As the frame redundancy occurs mostly in consecutive frames, we divide videos into multiple segments and conduct segment-level clustering. Center tokens from each segment are later concatenated into a new sequence, while their original spatial-temporal relations are well maintained. We instantiate two clustering algorithms to efficiently find deterministic medoids and iteratively partition groups in high dimensional space. Through this token clustering and center selection procedure, we successfully reduce computation costs by removing redundant visual tokens. This method further enhances segment-level semantic alignment between video and text representations, enforcing the spatio-temporal interactions of tokens from within-segment frames. Our method, coined as CenterCLIP, surpasses existing state-of-the-art by a large margin on typical text-video benchmarks, while reducing the training memory cost by 35% and accelerating the inference speed by 14% at the best case. The code is available at https://github.com/mzhaoshuai/CenterCLIP https://github.com/mzhaoshuai/CenterCLIP.|近年来，CLIP 等大规模预训练方法在文本-视频检索等多模态研究方面取得了很大进展。在 CLIP 中，变压器对于建立复杂的多模态关系至关重要。然而，在 CLIP 的视觉转换器中，由于视频中连续帧和相似帧的冗余性，产生离散视觉标记序列的基本视觉标记化过程会产生许多同质标记。这大大增加了计算成本，并阻碍了在 Web 应用程序中部署视频检索模型。为了减少冗余视频令牌的数量，本文设计了一种多段令牌聚类算法来寻找最有代表性的令牌并去除非必需的令牌。由于帧冗余主要发生在连续的帧中，我们将视频分割成多个片段，进行片段级聚类。来自每个片段的中心标记后来被连接成一个新的序列，同时它们原来的时空关系得到很好的保持。我们实例化了两个聚类算法，以有效地发现确定性中介体和迭代划分群在高维空间。通过这种令牌聚类和中心选择过程，我们成功地消除了冗余的可视令牌，降低了计算成本。该方法进一步增强了视频和文本表示之间的分段级语义对齐，增强了分段帧内标记的时空交互作用。我们的方法被称为 CenterCLIP，它在典型的文本视频基准测试上大大超越了现有的最新技术，同时降低了35% 的训练记忆成本，并且在最好的情况下加快了14% 的推理速度。密码可在 https://github.com/mzhaoshuai/centerclip https://github.com/mzhaoshuai/centerclip 查阅。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=CenterCLIP:+Token+Clustering+for+Efficient+Text-Video+Retrieval)|2| -|[ProFairRec: Provider Fairness-aware News Recommendation](https://doi.org/10.1145/3477495.3532046)|Tao Qi, Fangzhao Wu, Chuhan Wu, Peijie Sun, Le Wu, Xiting Wang, Yongfeng Huang, Xing Xie|Tsinghua University, Beijing, China; Hefei University of Technology, Hefei, China; Microsoft Research Asia, Beijing, China|News recommendation aims to help online news platform users find their preferred news articles. Existing news recommendation methods usually learn models from historical user behaviors on news. However, these behaviors are usually biased on news providers. Models trained on biased user data may capture and even amplify the biases on news providers, and are unfair for some minority news providers. In this paper, we propose a provider fairness-aware news recommendation framework (named ProFairRec), which can learn news recommendation models fair for different news providers from biased user data. The core idea of ProFairRec is to learn provider-fair news representations and provider-fair user representations to achieve provider fairness. To learn provider-fair representations from biased data, we employ provider-biased representations to inherit provider bias from data. Provider-fair and -biased news representations are learned from news content and provider IDs respectively, which are further aggregated to build fair and biased user representations based on user click history. All of these representations are used in model training while only fair representations are used for user-news matching to achieve fair news recommendation. Besides, we propose an adversarial learning task on news provider discrimination to prevent provider-fair news representation from encoding provider bias. We also propose an orthogonal regularization on provider-fair and -biased representations to better reduce provider bias in provider-fair representations. Moreover, ProFairRec is a general framework and can be applied to different news recommendation methods. Extensive experiments on a public dataset verify that our ProFairRec approach can effectively improve the provider fairness of many existing methods and meanwhile maintain their recommendation accuracy.|新闻推荐旨在帮助在线新闻平台用户找到他们喜欢的新闻文章。现有的新闻推荐方法通常借鉴新闻历史用户行为的模型。然而，这些行为通常对新闻提供者有偏见。对有偏见的用户数据进行培训的模式可能捕捉甚至放大对新闻提供者的偏见，这对一些少数群体的新闻提供者是不公平的。本文提出了一个提供者公平感知的新闻推荐框架(ProFairRec) ，该框架可以从有偏差的用户数据中学习不同新闻提供者的新闻推荐模型。ProFairRec 的核心思想是通过学习提供者-公平新闻表示和提供者-公平用户表示来实现提供者公平。为了从有偏差的数据中学习提供者公平表示，我们使用提供者偏好表示从数据中继承提供者偏好。分别从新闻内容和提供者 ID 中学习提供者公平和有偏见的新闻表示，并进一步聚合它们，构建基于用户点击历史的公平和有偏见的用户表示。所有这些表示都用于模型训练，只有公平表示用于用户新闻匹配以实现公平新闻推荐。此外，我们提出了一个关于新闻提供者歧视的对抗性学习任务，以防止提供者-公平的新闻表征对提供者偏见进行编码。我们还提出了一个正交正则化的提供者公平和偏见的表示，以更好地减少提供者偏见提供者公平的表示。此外，ProFairRec 是一个通用框架，可以应用于不同的新闻推荐方法。在一个公共数据集上的大量实验证明，我们的 ProFairRec 方法能够有效地提高许多现有方法的提供者公平性，同时保持它们的推荐准确性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=ProFairRec:+Provider+Fairness-aware+News+Recommendation)|2| -|[Rethinking Reinforcement Learning for Recommendation: A Prompt Perspective](https://doi.org/10.1145/3477495.3531714)|Xin Xin, Tiago Pimentel, Alexandros Karatzoglou, Pengjie Ren, Konstantina Christakopoulou, Zhaochun Ren|University of Cambridge, Cambridge, United Kingdom; Google, Mountain View, CA, USA; Google Research, London, United Kingdom; Shandong University, Qingdao City, China|Modern recommender systems aim to improve user experience. As reinforcement learning (RL) naturally fits this objective---maximizing an user's reward per session---it has become an emerging topic in recommender systems. Developing RL-based recommendation methods, however, is not trivial due to the offline training challenge. Specifically, the keystone of traditional RL is to train an agent with large amounts of online exploration making lots of 'errors' in the process. In the recommendation setting, though, we cannot afford the price of making 'errors' online. As a result, the agent needs to be trained through offline historical implicit feedback, collected under different recommendation policies; traditional RL algorithms may lead to sub-optimal policies under these offline training settings. Here we propose a new learning paradigm---namely Prompt-Based Reinforcement Learning (PRL)---for the offline training of RL-based recommendation agents. While traditional RL algorithms attempt to map state-action input pairs to their expected rewards (e.g., Q-values), PRL directly infers actions (i.e., recommended items) from state-reward inputs. In short, the agents are trained to predict a recommended item given the prior interactions and an observed reward value---with simple supervised learning. At deployment time, this historical (training) data acts as a knowledge base, while the state-reward pairs are used as a prompt. The agents are thus used to answer the question: Which item should be recommended given the prior interactions & the prompted reward value? We implement PRL with four notable recommendation models and conduct experiments on two real-world e-commerce datasets. Experimental results demonstrate the superior performance of our proposed methods.|现代推荐系统旨在改善用户体验。由于强化学习(rL)自然符合这一目标——最大化用户每次会话的回报——它已经成为推荐系统中一个新兴的话题。然而，由于离线培训的挑战，开发基于 RL 的推荐方法并非易事。具体来说，传统 RL 的核心是训练一个具有大量在线探索并在过程中犯下大量“错误”的代理。然而，在推荐设置中，我们承担不起在网上犯“错误”的代价。因此，代理需要通过在不同推荐策略下收集的离线历史隐式反馈进行训练; 传统的 RL 算法在这些离线训练设置下可能导致策略次优。在这里，我们提出了一个新的学习范式——即基于提示的强化学习(PRL)——用于基于提示的推荐代理的离线培训。传统的 RL 算法试图将状态-动作输入对映射到它们的预期奖励(例如，Q 值) ，而 PRL 直接从状态-奖励输入推断动作(例如，推荐项)。简而言之，经纪人接受训练，根据之前的互动和观察到的奖励价值，预测一个推荐的项目——用简单的监督式学习。在部署时，这个历史(培训)数据充当知识库，而状态-奖励对用作提示符。因此，代理人被用来回答这个问题: 鉴于之前的互动和提示的奖励价值，应该推荐哪个项目？我们使用四个值得注意的推荐模型来实现 PRL，并在两个真实的电子商务数据集上进行了实验。实验结果表明，该方法具有良好的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Rethinking+Reinforcement+Learning+for+Recommendation:+A+Prompt+Perspective)|2| -|[Alleviating Spurious Correlations in Knowledge-aware Recommendations through Counterfactual Generator](https://doi.org/10.1145/3477495.3531934)|Shanlei Mu, Yaliang Li, Wayne Xin Zhao, Jingyuan Wang, Bolin Ding, JiRong Wen|Alibaba Group, Bellevue, WA, USA; Peng Cheng Laboratory, Shenzhen, Shenzhen, China; Renmin University of China & Beijing Key Laboratory of Big Data Management and Analysis Methods, Beijing, China|Limited by the statistical-based machine learning framework, a spurious correlation is likely to appear in existing knowledge-aware recommendation methods. It refers to a knowledge fact that appears causal to the user behaviors (inferred by the recommender) but is not in fact. For tackling this issue, we present a novel approach to discovering and alleviating the potential spurious correlations from a counterfactual perspective. To be specific, our approach consists of two counterfactual generators and a recommender. The counterfactual generators are designed to generate counterfactual interactions via reinforcement learning, while the recommender is implemented with two different graph neural networks to aggregate the information from KG and user-item interactions respectively. The counterfactual generators and recommender are integrated in a mutually collaborative way. With this approach, the recommender helps the counterfactual generators better identify potential spurious correlations and generate high-quality counterfactual interactions, while the counterfactual generators help the recommender weaken the influence of the potential spurious correlations simultaneously. Extensive experiments on three real-world datasets have shown the effectiveness of the proposed approach by comparing it with a number of competitive baselines. Our implementation code is available at: https://github.com/RUCAIBox/CGKR.|由于受到基于统计的机器学习框架的限制，现有的知识推荐方法可能会出现伪相关。它指的是一个知识事实，似乎是因果关系的用户行为(推荐) ，但不是事实。为了解决这个问题，我们提出了一个新颖的方法来发现和减轻潜在的虚假相关性从一个反事实的角度。具体来说，我们的方法由两个反事实生成器和一个推荐器组成。反事实生成器的设计目的是通过强化学习生成反事实交互，而推荐器是通过两种不同的图形神经网络实现的，分别聚合来自 KG 和用户项目交互的信息。反事实生成器和推荐器以一种相互协作的方式集成在一起。通过这种方法，推荐器可以帮助反事实生成器更好地识别潜在的虚假关联并产生高质量的反事实交互，而反事实生成器可以帮助推荐器同时削弱潜在虚假关联的影响。在三个真实世界数据集上的大量实验表明，通过与一些竞争性基线进行比较，该方法是有效的。我们的实施守则可于以下 https://github.com/rucaibox/cgkr 索取:。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Alleviating+Spurious+Correlations+in+Knowledge-aware+Recommendations+through+Counterfactual+Generator)|2| -|[HAKG: Hierarchy-Aware Knowledge Gated Network for Recommendation](https://doi.org/10.1145/3477495.3531987)|Yuntao Du, Xinjun Zhu, Lu Chen, Baihua Zheng, Yunjun Gao|Singapore Management University, Singapore, Singapore; Zhejiang University, Ningbo, China; Zhejiang University, Hangzhou, China|Knowledge graph (KG) plays an increasingly important role to improve the recommendation performance and interpretability. A recent technical trend is to design end-to-end models based on the information propagation schemes. However, existing propagation-based methods fail to (1) model the underlying hierarchical structures and relations, and (2) capture the high-order collaborative signals of items for learning high-quality user and item representations. In this paper, we propose a new model, called Hierarchy-Aware Knowledge Gated Network (HAKG), to tackle the aforementioned problems. Technically, we model users and items (that are captured by a user-item graph), as well as entities and relations (that are captured in a KG) in hyperbolic space, and design a new hyperbolic aggregation scheme to gather relational contexts over KG. Meanwhile, we introduce a novel angle constraint to preserve characteristics of items in the embedding space. Furthermore, we propose the dual item embeddings design to represent and propagate collaborative signals and knowledge associations separately, and leverage the gated aggregation to distill discriminative information for better capturing user behavior patterns. Experimental results on three benchmark datasets show that, HAKG achieves significant improvement over the state-of-the-art methods like CKAN, Hyper-Know, and KGIN. Further analyses on the learned hyperbolic embeddings confirm that HAKG can offer meaningful insights into the hierarchies of data.|知识图(KG)在提高推荐性能和可解释性方面发挥着越来越重要的作用。基于信息传播方案的端到端模型设计是近年来的一个技术趋势。然而，现有的基于传播的方法无法(1)对底层的层次结构和关系进行建模，(2)捕获项目的高阶协作信号来学习高质量的用户和项目表示。本文提出了一种新的层次感知知识门限网络(HAKG)模型来解决上述问题。从技术上讲，我们对用户和项目(通过用户项目图捕获)以及实体和关系(通过 KG 捕获)进行双曲空间建模，并设计一个新的双曲线聚合方案，通过 KG 收集关系上下文。同时，我们引入了一种新的角度约束来保持嵌入空间中项目的特征。此外，我们提出了双项嵌入设计，分别表示和传播协作信号和知识关联，并利用门限聚合提取区分信息，以更好地捕捉用户行为模式。在三个基准数据集上的实验结果表明，HAKG 比最先进的 CKAN、 Hyper-Know 和 KGIN 方法有明显的改进。进一步分析学习双曲嵌入，证实 HAKG 可以提供有意义的洞察数据层次。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=HAKG:+Hierarchy-Aware+Knowledge+Gated+Network+for+Recommendation)|2| +|[ProFairRec: Provider Fairness-aware News Recommendation](https://doi.org/10.1145/3477495.3532046)|Tao Qi, Fangzhao Wu, Chuhan Wu, Peijie Sun, Le Wu, Xiting Wang, Yongfeng Huang, Xing Xie|Tsinghua University, Beijing, China; Microsoft Research Asia, Beijing, China; Hefei University of Technology, Hefei, China|News recommendation aims to help online news platform users find their preferred news articles. Existing news recommendation methods usually learn models from historical user behaviors on news. However, these behaviors are usually biased on news providers. Models trained on biased user data may capture and even amplify the biases on news providers, and are unfair for some minority news providers. In this paper, we propose a provider fairness-aware news recommendation framework (named ProFairRec), which can learn news recommendation models fair for different news providers from biased user data. The core idea of ProFairRec is to learn provider-fair news representations and provider-fair user representations to achieve provider fairness. To learn provider-fair representations from biased data, we employ provider-biased representations to inherit provider bias from data. Provider-fair and -biased news representations are learned from news content and provider IDs respectively, which are further aggregated to build fair and biased user representations based on user click history. All of these representations are used in model training while only fair representations are used for user-news matching to achieve fair news recommendation. Besides, we propose an adversarial learning task on news provider discrimination to prevent provider-fair news representation from encoding provider bias. We also propose an orthogonal regularization on provider-fair and -biased representations to better reduce provider bias in provider-fair representations. Moreover, ProFairRec is a general framework and can be applied to different news recommendation methods. Extensive experiments on a public dataset verify that our ProFairRec approach can effectively improve the provider fairness of many existing methods and meanwhile maintain their recommendation accuracy.|新闻推荐旨在帮助在线新闻平台用户找到他们喜欢的新闻文章。现有的新闻推荐方法通常借鉴新闻历史用户行为的模型。然而，这些行为通常对新闻提供者有偏见。对有偏见的用户数据进行培训的模式可能捕捉甚至放大对新闻提供者的偏见，这对一些少数群体的新闻提供者是不公平的。本文提出了一个提供者公平感知的新闻推荐框架(ProFairRec) ，该框架可以从有偏差的用户数据中学习不同新闻提供者的新闻推荐模型。ProFairRec 的核心思想是通过学习提供者-公平新闻表示和提供者-公平用户表示来实现提供者公平。为了从有偏差的数据中学习提供者公平表示，我们使用提供者偏好表示从数据中继承提供者偏好。分别从新闻内容和提供者 ID 中学习提供者公平和有偏见的新闻表示，并进一步聚合它们，构建基于用户点击历史的公平和有偏见的用户表示。所有这些表示都用于模型训练，只有公平表示用于用户新闻匹配以实现公平新闻推荐。此外，我们提出了一个关于新闻提供者歧视的对抗性学习任务，以防止提供者-公平的新闻表征对提供者偏见进行编码。我们还提出了一个正交正则化的提供者公平和偏见的表示，以更好地减少提供者偏见提供者公平的表示。此外，ProFairRec 是一个通用框架，可以应用于不同的新闻推荐方法。在一个公共数据集上的大量实验证明，我们的 ProFairRec 方法能够有效地提高许多现有方法的提供者公平性，同时保持它们的推荐准确性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=ProFairRec:+Provider+Fairness-aware+News+Recommendation)|2| +|[Rethinking Reinforcement Learning for Recommendation: A Prompt Perspective](https://doi.org/10.1145/3477495.3531714)|Xin Xin, Tiago Pimentel, Alexandros Karatzoglou, Pengjie Ren, Konstantina Christakopoulou, Zhaochun Ren|University of Cambridge, Cambridge, United Kingdom; Shandong University, Qingdao City, China; Google Research, London, United Kingdom; Google, Mountain View, CA, USA|Modern recommender systems aim to improve user experience. As reinforcement learning (RL) naturally fits this objective---maximizing an user's reward per session---it has become an emerging topic in recommender systems. Developing RL-based recommendation methods, however, is not trivial due to the offline training challenge. Specifically, the keystone of traditional RL is to train an agent with large amounts of online exploration making lots of 'errors' in the process. In the recommendation setting, though, we cannot afford the price of making 'errors' online. As a result, the agent needs to be trained through offline historical implicit feedback, collected under different recommendation policies; traditional RL algorithms may lead to sub-optimal policies under these offline training settings. Here we propose a new learning paradigm---namely Prompt-Based Reinforcement Learning (PRL)---for the offline training of RL-based recommendation agents. While traditional RL algorithms attempt to map state-action input pairs to their expected rewards (e.g., Q-values), PRL directly infers actions (i.e., recommended items) from state-reward inputs. In short, the agents are trained to predict a recommended item given the prior interactions and an observed reward value---with simple supervised learning. At deployment time, this historical (training) data acts as a knowledge base, while the state-reward pairs are used as a prompt. The agents are thus used to answer the question: Which item should be recommended given the prior interactions & the prompted reward value? We implement PRL with four notable recommendation models and conduct experiments on two real-world e-commerce datasets. Experimental results demonstrate the superior performance of our proposed methods.|现代推荐系统旨在改善用户体验。由于强化学习(rL)自然符合这一目标——最大化用户每次会话的回报——它已经成为推荐系统中一个新兴的话题。然而，由于离线培训的挑战，开发基于 RL 的推荐方法并非易事。具体来说，传统 RL 的核心是训练一个具有大量在线探索并在过程中犯下大量“错误”的代理。然而，在推荐设置中，我们承担不起在网上犯“错误”的代价。因此，代理需要通过在不同推荐策略下收集的离线历史隐式反馈进行训练; 传统的 RL 算法在这些离线训练设置下可能导致策略次优。在这里，我们提出了一个新的学习范式——即基于提示的强化学习(PRL)——用于基于提示的推荐代理的离线培训。传统的 RL 算法试图将状态-动作输入对映射到它们的预期奖励(例如，Q 值) ，而 PRL 直接从状态-奖励输入推断动作(例如，推荐项)。简而言之，经纪人接受训练，根据之前的互动和观察到的奖励价值，预测一个推荐的项目——用简单的监督式学习。在部署时，这个历史(培训)数据充当知识库，而状态-奖励对用作提示符。因此，代理人被用来回答这个问题: 鉴于之前的互动和提示的奖励价值，应该推荐哪个项目？我们使用四个值得注意的推荐模型来实现 PRL，并在两个真实的电子商务数据集上进行了实验。实验结果表明，该方法具有良好的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Rethinking+Reinforcement+Learning+for+Recommendation:+A+Prompt+Perspective)|2| +|[Alleviating Spurious Correlations in Knowledge-aware Recommendations through Counterfactual Generator](https://doi.org/10.1145/3477495.3531934)|Shanlei Mu, Yaliang Li, Wayne Xin Zhao, Jingyuan Wang, Bolin Ding, JiRong Wen|Peng Cheng Laboratory, Shenzhen, Shenzhen, China; Renmin University of China & Beijing Key Laboratory of Big Data Management and Analysis Methods, Beijing, China; Alibaba Group, Bellevue, WA, USA|Limited by the statistical-based machine learning framework, a spurious correlation is likely to appear in existing knowledge-aware recommendation methods. It refers to a knowledge fact that appears causal to the user behaviors (inferred by the recommender) but is not in fact. For tackling this issue, we present a novel approach to discovering and alleviating the potential spurious correlations from a counterfactual perspective. To be specific, our approach consists of two counterfactual generators and a recommender. The counterfactual generators are designed to generate counterfactual interactions via reinforcement learning, while the recommender is implemented with two different graph neural networks to aggregate the information from KG and user-item interactions respectively. The counterfactual generators and recommender are integrated in a mutually collaborative way. With this approach, the recommender helps the counterfactual generators better identify potential spurious correlations and generate high-quality counterfactual interactions, while the counterfactual generators help the recommender weaken the influence of the potential spurious correlations simultaneously. Extensive experiments on three real-world datasets have shown the effectiveness of the proposed approach by comparing it with a number of competitive baselines. Our implementation code is available at: https://github.com/RUCAIBox/CGKR.|由于受到基于统计的机器学习框架的限制，现有的知识推荐方法可能会出现伪相关。它指的是一个知识事实，似乎是因果关系的用户行为(推荐) ，但不是事实。为了解决这个问题，我们提出了一个新颖的方法来发现和减轻潜在的虚假相关性从一个反事实的角度。具体来说，我们的方法由两个反事实生成器和一个推荐器组成。反事实生成器的设计目的是通过强化学习生成反事实交互，而推荐器是通过两种不同的图形神经网络实现的，分别聚合来自 KG 和用户项目交互的信息。反事实生成器和推荐器以一种相互协作的方式集成在一起。通过这种方法，推荐器可以帮助反事实生成器更好地识别潜在的虚假关联并产生高质量的反事实交互，而反事实生成器可以帮助推荐器同时削弱潜在虚假关联的影响。在三个真实世界数据集上的大量实验表明，通过与一些竞争性基线进行比较，该方法是有效的。我们的实施守则可于以下 https://github.com/rucaibox/cgkr 索取:。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Alleviating+Spurious+Correlations+in+Knowledge-aware+Recommendations+through+Counterfactual+Generator)|2| +|[HAKG: Hierarchy-Aware Knowledge Gated Network for Recommendation](https://doi.org/10.1145/3477495.3531987)|Yuntao Du, Xinjun Zhu, Lu Chen, Baihua Zheng, Yunjun Gao|Singapore Management University, Singapore, Singapore; Zhejiang University, Hangzhou, China; Zhejiang University, Ningbo, China|Knowledge graph (KG) plays an increasingly important role to improve the recommendation performance and interpretability. A recent technical trend is to design end-to-end models based on the information propagation schemes. However, existing propagation-based methods fail to (1) model the underlying hierarchical structures and relations, and (2) capture the high-order collaborative signals of items for learning high-quality user and item representations. In this paper, we propose a new model, called Hierarchy-Aware Knowledge Gated Network (HAKG), to tackle the aforementioned problems. Technically, we model users and items (that are captured by a user-item graph), as well as entities and relations (that are captured in a KG) in hyperbolic space, and design a new hyperbolic aggregation scheme to gather relational contexts over KG. Meanwhile, we introduce a novel angle constraint to preserve characteristics of items in the embedding space. Furthermore, we propose the dual item embeddings design to represent and propagate collaborative signals and knowledge associations separately, and leverage the gated aggregation to distill discriminative information for better capturing user behavior patterns. Experimental results on three benchmark datasets show that, HAKG achieves significant improvement over the state-of-the-art methods like CKAN, Hyper-Know, and KGIN. Further analyses on the learned hyperbolic embeddings confirm that HAKG can offer meaningful insights into the hierarchies of data.|知识图(KG)在提高推荐性能和可解释性方面发挥着越来越重要的作用。基于信息传播方案的端到端模型设计是近年来的一个技术趋势。然而，现有的基于传播的方法无法(1)对底层的层次结构和关系进行建模，(2)捕获项目的高阶协作信号来学习高质量的用户和项目表示。本文提出了一种新的层次感知知识门限网络(HAKG)模型来解决上述问题。从技术上讲，我们对用户和项目(通过用户项目图捕获)以及实体和关系(通过 KG 捕获)进行双曲空间建模，并设计一个新的双曲线聚合方案，通过 KG 收集关系上下文。同时，我们引入了一种新的角度约束来保持嵌入空间中项目的特征。此外，我们提出了双项嵌入设计，分别表示和传播协作信号和知识关联，并利用门限聚合提取区分信息，以更好地捕捉用户行为模式。在三个基准数据集上的实验结果表明，HAKG 比最先进的 CKAN、 Hyper-Know 和 KGIN 方法有明显的改进。进一步分析学习双曲嵌入，证实 HAKG 可以提供有意义的洞察数据层次。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=HAKG:+Hierarchy-Aware+Knowledge+Gated+Network+for+Recommendation)|2| |[Entity-aware Transformers for Entity Search](https://doi.org/10.1145/3477495.3531971)|Emma J. Gerritse, Faegheh Hasibi, Arjen P. de Vries|Radboud University, Nijmegen, Netherlands|Pre-trained language models such as BERT have been a key ingredient to achieve state-of-the-art results on a variety of tasks in natural language processing and, more recently, also in information retrieval. Recent research even claims that BERT is able to capture factual knowledge about entity relations and properties, the information that is commonly obtained from knowledge graphs. This paper investigates the following question: Do BERT-based entity retrieval models benefit from additional entity information stored in knowledge graphs? To address this research question, we map entity embeddings into the same input space as a pre-trained BERT model and inject these entity embeddings into the BERT model. This entity-enriched language model is then employed on the entity retrieval task. We show that the entity-enriched BERT model improves effectiveness on entity-oriented queries over a regular BERT model, establishing a new state-of-the-art result for the entity retrieval task, with substantial improvements for complex natural language queries and queries requesting a list of entities with a certain property. Additionally, we show that the entity information provided by our entity-enriched model particularly helps queries related to less popular entities. Last, we observe empirically that the entity-enriched BERT models enable fine-tuning on limited training data, which otherwise would not be feasible due to the known instabilities of BERT in few-sample fine-tuning, thereby contributing to data-efficient training of BERT for entity search.|在自然语言处理以及最近的信息检索中，像 BERT 这样经过预先训练的语言模型已经成为在各种任务中取得最先进结果的关键因素。最近的研究甚至声称 BERT 能够捕获关于实体关系和属性的实际知识，这些信息通常是从知识图表中获得的。本文研究了以下问题: 基于 BERT 的实体检索模型是否受益于存储在知识图中的附加实体信息？为了解决这个问题，我们将实体嵌入映射到与预先训练的 BERT 模型相同的输入空间中，并将这些实体嵌入注入到 BERT 模型中。然后将这种实体丰富的语言模型应用于实体检索任务。实验结果表明，与常规的 BERT 模型相比，实体增强的 BERT 模型提高了面向实体查询的效率，为实体检索任务建立了一个新的最先进的结果，对于复杂的自然语言查询和请求具有特定属性的实体列表的查询有了实质性的改进。此外，我们还展示了实体丰富模型提供的实体信息特别有助于与不太流行的实体相关的查询。最后，我们实验观察到实体增强的 BERT 模型能够对有限的训练数据进行微调，否则由于已知的 BERT 在少样本微调中的不稳定性，这是不可行的，从而有助于为实体搜索进行数据有效的 BERT 训练。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Entity-aware+Transformers+for+Entity+Search)|2| |[CharacterBERT and Self-Teaching for Improving the Robustness of Dense Retrievers on Queries with Typos](https://doi.org/10.1145/3477495.3531951)|Shengyao Zhuang, Guido Zuccon|The University of Queensland, Brisbane, QLD, Australia|Current dense retrievers are not robust to out-of-domain and outlier queries, i.e. their effectiveness on these queries is much poorer than what one would expect. In this paper, we consider a specific instance of such queries: queries that contain typos. We show that a small character level perturbation in queries (as caused by typos) highly impacts the effectiveness of dense retrievers. We then demonstrate that the root cause of this resides in the input tokenization strategy employed by BERT. In BERT, tokenization is performed using the BERT's WordPiece tokenizer and we show that a token with a typo will significantly change the token distributions obtained after tokenization. This distribution change translates to changes in the input embeddings passed to the BERT-based query encoder of dense retrievers. We then turn our attention to devising dense retriever methods that are robust to such queries with typos, while still being as performant as previous methods on queries without typos. For this, we use CharacterBERT as the backbone encoder and an efficient yet effective training method, called Self-Teaching (ST), that distills knowledge from queries without typos into the queries with typos. Experimental results show that CharacterBERT in combination with ST achieves significantly higher effectiveness on queries with typos compared to previous methods. Along with these results and the open-sourced implementation of the methods, we also provide a new passage retrieval dataset consisting of real-world queries with typos and associated relevance assessments on the MS MARCO corpus, thus supporting the research community in the investigation of effective and robust dense retrievers. Code, experimental results and dataset are made available at https://github.com/ielab/CharacterBERT-DR.|当前的密集检索器对域外查询和离群查询不够健壮，即它们对这些查询的有效性比人们预期的要差得多。在本文中，我们考虑这种查询的一个特定实例: 包含输入错误的查询。我们表明，查询中的小字符级别扰动(由输入错误引起)会严重影响稠密检索器的有效性。然后，我们证明了造成这种情况的根本原因在于 BERT 采用的输入标记化策略。在 BERT 中，标记化是使用 BERT 的 WordPiece 标记化程序执行的，我们展示了带有输入错误的标记将显著改变标记化后获得的标记分发。这种分布更改转化为传递给稠密检索器的基于 BERT 的查询编码器的输入嵌入的更改。然后，我们将注意力转向设计密集的检索方法，这些方法对于这种带有输入错误的查询是健壮的，同时在没有输入错误的查询上仍然和以前的方法一样性能良好。为此，我们使用 CHARTERBERT 作为骨干编码器和一种有效的训练方法，称为自学(ST) ，它将知识从没有拼写错误的查询中提取到有拼写错误的查询中。实验结果表明，与以前的方法相比，采用与 ST 相结合的方法对拼写错误的查询具有更高的查询效率。随着这些结果和方法的开源实现，我们还提供了一个新的通道检索数据集，包括在 MS MARCO 语料库上输入错误的真实世界查询和相关的相关性评估，从而支持研究社区调查有效和健壮的密集检索器。代码、实验结果和数据集可在 https://github.com/ielab/characterbert-dr 查阅。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=CharacterBERT+and+Self-Teaching+for+Improving+the+Robustness+of+Dense+Retrievers+on+Queries+with+Typos)|2| -|[Thinking inside The Box: Learning Hypercube Representations for Group Recommendation](https://doi.org/10.1145/3477495.3532066)|Tong Chen, Hongzhi Yin, Jing Long, Quoc Viet Hung Nguyen, Yang Wang, Meng Wang|The University of Queensland, Brisbane, QLD, Australia; Hefei University of Technology, Hefei, China; Griffith University, Gold Coast, Australia|As a step beyond traditional personalized recommendation, group recommendation is the task of suggesting items that can satisfy a group of users. In group recommendation, the core is to design preference aggregation functions to obtain a quality summary of all group members' preferences. Such user and group preferences are commonly represented as points in the vector space (i.e., embeddings), where multiple user embeddings are compressed into one to facilitate ranking for group-item pairs. However, the resulted group representations, as points, lack adequate flexibility and capacity to account for the multi-faceted user preferences. Also, the point embedding-based preference aggregation is a less faithful reflection of a group's decision-making process, where all users have to agree on a certain value in each embedding dimension instead of a negotiable interval. In this paper, we propose a novel representation of groups via the notion of hypercubes, which are subspaces containing innumerable points in the vector space. Specifically, we design the hypercube recommender (CubeRec) to adaptively learn group hypercubes from user embeddings with minimal information loss during preference aggregation, and to leverage a revamped distance metric to measure the affinity between group hypercubes and item points. Moreover, to counteract the long-standing issue of data sparsity in group recommendation, we make full use of the geometric expressiveness of hypercubes and innovatively incorporate self-supervision by intersecting two groups. Experiments on four real-world datasets have validated the superiority of CubeRec over state-of-the-art baselines.|作为超越传统个性化推荐的一个步骤，群体推荐的任务是推荐能够满足一组用户的项目。在群组推荐中，核心是设计偏好聚合函数，以获得群组成员偏好的高质量汇总。这样的用户和组首选项通常表示为向量空间中的点(即嵌入) ，其中多个用户嵌入被压缩为一个，以便于对组-项目对进行排序。然而，由此产生的群体表示，作为点，缺乏足够的灵活性和能力来说明多方面的用户偏好。另外，基于点嵌入的偏好聚合是群体决策过程的一种不那么忠实的反映，即所有用户都必须在每个嵌入维度上同意一个确定的值，而不是一个可协商的区间。本文利用超立方体的概念，提出了一种新的群表示方法，超立方体是向量空间中包含无数个点的子空间。具体来说，我们设计了超立方体推荐器(CubeRec)来自适应地从用户嵌入中学习组超立方体，在偏好聚合过程中以最小的信息损失，并利用改进的距离度量来度量组超立方体和项目点之间的亲和力。此外，为了解决群体推荐中长期存在的数据稀疏问题，我们充分利用了超立方体的几何表达能力，创新性地将自我监督融入到群体推荐中。在四个实际数据集上的实验验证了 CubeRec 相对于最先进的基线的优越性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Thinking+inside+The+Box:+Learning+Hypercube+Representations+for+Group+Recommendation)|2| +|[Thinking inside The Box: Learning Hypercube Representations for Group Recommendation](https://doi.org/10.1145/3477495.3532066)|Tong Chen, Hongzhi Yin, Jing Long, Quoc Viet Hung Nguyen, Yang Wang, Meng Wang|The University of Queensland, Brisbane, QLD, Australia; Griffith University, Gold Coast, Australia; Hefei University of Technology, Hefei, China|As a step beyond traditional personalized recommendation, group recommendation is the task of suggesting items that can satisfy a group of users. In group recommendation, the core is to design preference aggregation functions to obtain a quality summary of all group members' preferences. Such user and group preferences are commonly represented as points in the vector space (i.e., embeddings), where multiple user embeddings are compressed into one to facilitate ranking for group-item pairs. However, the resulted group representations, as points, lack adequate flexibility and capacity to account for the multi-faceted user preferences. Also, the point embedding-based preference aggregation is a less faithful reflection of a group's decision-making process, where all users have to agree on a certain value in each embedding dimension instead of a negotiable interval. In this paper, we propose a novel representation of groups via the notion of hypercubes, which are subspaces containing innumerable points in the vector space. Specifically, we design the hypercube recommender (CubeRec) to adaptively learn group hypercubes from user embeddings with minimal information loss during preference aggregation, and to leverage a revamped distance metric to measure the affinity between group hypercubes and item points. Moreover, to counteract the long-standing issue of data sparsity in group recommendation, we make full use of the geometric expressiveness of hypercubes and innovatively incorporate self-supervision by intersecting two groups. Experiments on four real-world datasets have validated the superiority of CubeRec over state-of-the-art baselines.|作为超越传统个性化推荐的一个步骤，群体推荐的任务是推荐能够满足一组用户的项目。在群组推荐中，核心是设计偏好聚合函数，以获得群组成员偏好的高质量汇总。这样的用户和组首选项通常表示为向量空间中的点(即嵌入) ，其中多个用户嵌入被压缩为一个，以便于对组-项目对进行排序。然而，由此产生的群体表示，作为点，缺乏足够的灵活性和能力来说明多方面的用户偏好。另外，基于点嵌入的偏好聚合是群体决策过程的一种不那么忠实的反映，即所有用户都必须在每个嵌入维度上同意一个确定的值，而不是一个可协商的区间。本文利用超立方体的概念，提出了一种新的群表示方法，超立方体是向量空间中包含无数个点的子空间。具体来说，我们设计了超立方体推荐器(CubeRec)来自适应地从用户嵌入中学习组超立方体，在偏好聚合过程中以最小的信息损失，并利用改进的距离度量来度量组超立方体和项目点之间的亲和力。此外，为了解决群体推荐中长期存在的数据稀疏问题，我们充分利用了超立方体的几何表达能力，创新性地将自我监督融入到群体推荐中。在四个实际数据集上的实验验证了 CubeRec 相对于最先进的基线的优越性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Thinking+inside+The+Box:+Learning+Hypercube+Representations+for+Group+Recommendation)|2| |[Multi-modal Graph Contrastive Learning for Micro-video Recommendation](https://doi.org/10.1145/3477495.3532027)|Zixuan Yi, Xi Wang, Iadh Ounis, Craig MacDonald|University of Glasgow, Glasgow, United Kingdom|Recently micro-videos have become more popular in social media platforms such as TikTok and Instagram. Engagements in these platforms are facilitated by multi-modal recommendation systems. Indeed, such multimedia content can involve diverse modalities, often represented as visual, acoustic, and textual features to the recommender model. Existing works in micro-video recommendation tend to unify the multi-modal channels, thereby treating each modality with equal importance. However, we argue that these approaches are not sufficient to encode item representations with multiple modalities, since the used methods cannot fully disentangle the users' tastes on different modalities. To tackle this problem, we propose a novel learning method named Multi-Modal Graph Contrastive Learning (MMGCL), which aims to explicitly enhance multi-modal representation learning in a self-supervised learning manner. In particular, we devise two augmentation techniques to generate the multiple views of a user/item: modality edge dropout and modality masking. Furthermore, we introduce a novel negative sampling technique that allows to learn the correlation between modalities and ensures the effective contribution of each modality. Extensive experiments conducted on two micro-video datasets demonstrate the superiority of our proposed MMGCL method over existing state-of-the-art approaches in terms of both recommendation performance and training convergence speed.|最近，微视频在 TikTok 和 Instagram 等社交媒体平台上变得越来越流行。多模式推荐系统促进了这些平台的参与。事实上，这样的多媒体内容可以涉及不同的形式，通常表示为视觉，声学和文本特征的推荐模型。现有的微视频推荐作品倾向于统一多通道，从而对各种通道给予同等重视。然而，我们认为这些方法不足以用多种模式来编码项目表示，因为所使用的方法不能完全区分使用者对不同模式的喜好。为了解决这一问题，我们提出了一种新的学习方法——多模态图形对比学习(MMGCL) ，该方法旨在以自监督学习的方式明确地增强多模态表示学习。特别地，我们设计了两种增强技术来产生一个用户/项目的多视图: 模态边缘丢失和模态掩蔽。此外，我们还引入了一种新颖的负抽样技术，它可以学习模式之间的相关性，并确保每种模式的有效贡献。在两个微视频数据集上进行的大量实验表明，我们提出的 MMGCL 方法在推荐性能和训练收敛速度方面都优于现有的最新方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Multi-modal+Graph+Contrastive+Learning+for+Micro-video+Recommendation)|2| -|[InPars: Unsupervised Dataset Generation for Information Retrieval](https://doi.org/10.1145/3477495.3531863)|Luiz Henrique Bonifacio, Hugo Abonizio, Marzieh Fadaee, Rodrigo Frassetto Nogueira|Zeta Alpha & NeuralMind, Amsterdam, Netherlands; Zeta Alpha, Amsterdam, Netherlands; Zeta Alpha, NeuralMind, University of Campinas, & University of Waterloo, Amsterdam, Netherlands; Zeta Alpha, NeuralMind, & University of Campinas, Amsterdam, Netherlands|The Information Retrieval (IR) community has recently witnessed a revolution due to large pretrained transformer models. Another key ingredient for this revolution was the MS MARCO dataset, whose scale and diversity has enabled zero-shot transfer learning to various tasks. However, not all IR tasks and domains can benefit from one single dataset equally. Extensive research in various NLP tasks has shown that using domain-specific training data, as opposed to a general-purpose one, improves the performance of neural models. In this work, we harness the few-shot capabilities of large pretrained language models as synthetic data generators for IR tasks. We show that models finetuned solely on our synthetic datasets outperform strong baselines such as BM25 as well as recently proposed self-supervised dense retrieval methods. Code, models, and data are available at https://github.com/zetaalphavector/inpars.|由于大型预先训练的变压器模型，信息检索(IR)社区最近经历了一场革命。这场革命的另一个关键因素是微软 MARCO 数据集，它的规模和多样性使得零镜头转移学习能够应用于各种任务。然而，并不是所有的 IR 任务和领域都能从一个单一的数据集中获益。对各种自然语言处理任务的广泛研究表明，与通用训练数据相比，使用领域特定的训练数据可以提高神经模型的性能。在这项工作中，我们利用大型预先训练的语言模型作为 IR 任务的合成数据生成器的少数镜头功能。我们表明，模型微调仅在我们的合成数据集优于强基线，如 BM25以及最近提出的自我监督密集检索方法。代码、模型和数据可在 https://github.com/zetaalphavector/inpars 获得。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=InPars:+Unsupervised+Dataset+Generation+for+Information+Retrieval)|2| -|[Addressing Gender-related Performance Disparities in Neural Rankers](https://doi.org/10.1145/3477495.3531882)|Shirin Seyedsalehi, Amin Bigdeli, Negar Arabzadeh, Morteza Zihayat, Ebrahim Bagheri|Ryerson University, Toronto, ON, Canada; University of Waterloo, Waterloo, Canada; Ryerson University, Toronto, Canada|While neural rankers continue to show notable performance improvements over a wide variety of information retrieval tasks, there have been recent studies that show such rankers may intensify certain stereotypical biases. In this paper, we investigate whether neural rankers introduce retrieval effectiveness (performance) disparities over queries related to different genders. We specifically study whether there are significant performance differences between male and female queries when retrieved by neural rankers. Through our empirical study over the MS MARCO collection, we find that such performance disparities are notable and that the performance disparities may be due to the difference between how queries and their relevant judgements are collected and distributed for different gendered queries. More specifically, we observe that male queries are more closely associated with their relevant documents compared to female queries and hence neural rankers are able to more easily learn associations between male queries and their relevant documents. We show that it is possible to systematically balance relevance judgment collections in order to reduce performance disparity between different gendered queries without negatively compromising overall model performance.|虽然神经系统排名在各种信息检索任务中表现显著，但最近的研究表明，这种排名可能会加剧某些刻板的偏见。在本文中，我们研究了神经排序器是否引入检索效率(性能)差异相关的查询不同性别。我们专门研究是否有显着性能差异的男性和女性查询时，检索的神经排序。通过对 MS MARCO 集合的实证研究，我们发现这种性能差异是显著的，性能差异可能是由于不同性别的查询如何收集和分配查询及其相关判断之间的差异造成的。更具体地说，我们观察到，与女性查询相比，男性查询与其相关文档关联更密切，因此神经排序器能够更容易地学习男性查询与其相关文档之间的关联。我们表明，系统地平衡相关性判断集合是可能的，以减少不同性别查询之间的性能差异，而不会对整体模型性能造成负面影响。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Addressing+Gender-related+Performance+Disparities+in+Neural+Rankers)|2| +|[InPars: Unsupervised Dataset Generation for Information Retrieval](https://doi.org/10.1145/3477495.3531863)|Luiz Henrique Bonifacio, Hugo Abonizio, Marzieh Fadaee, Rodrigo Frassetto Nogueira|Zeta Alpha & NeuralMind, Amsterdam, Netherlands; Zeta Alpha, NeuralMind, University of Campinas, & University of Waterloo, Amsterdam, Netherlands; Zeta Alpha, NeuralMind, & University of Campinas, Amsterdam, Netherlands; Zeta Alpha, Amsterdam, Netherlands|The Information Retrieval (IR) community has recently witnessed a revolution due to large pretrained transformer models. Another key ingredient for this revolution was the MS MARCO dataset, whose scale and diversity has enabled zero-shot transfer learning to various tasks. However, not all IR tasks and domains can benefit from one single dataset equally. Extensive research in various NLP tasks has shown that using domain-specific training data, as opposed to a general-purpose one, improves the performance of neural models. In this work, we harness the few-shot capabilities of large pretrained language models as synthetic data generators for IR tasks. We show that models finetuned solely on our synthetic datasets outperform strong baselines such as BM25 as well as recently proposed self-supervised dense retrieval methods. Code, models, and data are available at https://github.com/zetaalphavector/inpars.|由于大型预先训练的变压器模型，信息检索(IR)社区最近经历了一场革命。这场革命的另一个关键因素是微软 MARCO 数据集，它的规模和多样性使得零镜头转移学习能够应用于各种任务。然而，并不是所有的 IR 任务和领域都能从一个单一的数据集中获益。对各种自然语言处理任务的广泛研究表明，与通用训练数据相比，使用领域特定的训练数据可以提高神经模型的性能。在这项工作中，我们利用大型预先训练的语言模型作为 IR 任务的合成数据生成器的少数镜头功能。我们表明，模型微调仅在我们的合成数据集优于强基线，如 BM25以及最近提出的自我监督密集检索方法。代码、模型和数据可在 https://github.com/zetaalphavector/inpars 获得。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=InPars:+Unsupervised+Dataset+Generation+for+Information+Retrieval)|2| +|[Addressing Gender-related Performance Disparities in Neural Rankers](https://doi.org/10.1145/3477495.3531882)|Shirin Seyedsalehi, Amin Bigdeli, Negar Arabzadeh, Morteza Zihayat, Ebrahim Bagheri|Ryerson University, Toronto, ON, Canada; Ryerson University, Toronto, Canada; University of Waterloo, Waterloo, Canada|While neural rankers continue to show notable performance improvements over a wide variety of information retrieval tasks, there have been recent studies that show such rankers may intensify certain stereotypical biases. In this paper, we investigate whether neural rankers introduce retrieval effectiveness (performance) disparities over queries related to different genders. We specifically study whether there are significant performance differences between male and female queries when retrieved by neural rankers. Through our empirical study over the MS MARCO collection, we find that such performance disparities are notable and that the performance disparities may be due to the difference between how queries and their relevant judgements are collected and distributed for different gendered queries. More specifically, we observe that male queries are more closely associated with their relevant documents compared to female queries and hence neural rankers are able to more easily learn associations between male queries and their relevant documents. We show that it is possible to systematically balance relevance judgment collections in order to reduce performance disparity between different gendered queries without negatively compromising overall model performance.|虽然神经系统排名在各种信息检索任务中表现显著，但最近的研究表明，这种排名可能会加剧某些刻板的偏见。在本文中，我们研究了神经排序器是否引入检索效率(性能)差异相关的查询不同性别。我们专门研究是否有显着性能差异的男性和女性查询时，检索的神经排序。通过对 MS MARCO 集合的实证研究，我们发现这种性能差异是显著的，性能差异可能是由于不同性别的查询如何收集和分配查询及其相关判断之间的差异造成的。更具体地说，我们观察到，与女性查询相比，男性查询与其相关文档关联更密切，因此神经排序器能够更容易地学习男性查询与其相关文档之间的关联。我们表明，系统地平衡相关性判断集合是可能的，以减少不同性别查询之间的性能差异，而不会对整体模型性能造成负面影响。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Addressing+Gender-related+Performance+Disparities+in+Neural+Rankers)|2| |[State Encoders in Reinforcement Learning for Recommendation: A Reproducibility Study](https://doi.org/10.1145/3477495.3531716)|Jin Huang, Harrie Oosterhuis, Bunyamin Cetinkaya, Thijs Rood, Maarten de Rijke|Radboud University, Nijmegen, Netherlands; University of Amsterdam, Amsterdam, Netherlands|Methods for reinforcement learning for recommendation are increasingly receiving attention as they can quickly adapt to user feedback. A typical RL4Rec framework consists of (1) a state encoder to encode the state that stores the users' historical interactions, and (2) an RL method to take actions and observe rewards. Prior work compared four state encoders in an environment where user feedback is simulated based on real-world logged user data. An attention-based state encoder was found to be the optimal choice as it reached the highest performance. However, this finding is limited to the actor-critic method, four state encoders, and evaluation-simulators that do not debias logged user data. In response to these shortcomings, we reproduce and expand on the existing comparison of attention-based state encoders (1) in the publicly available debiased RL4Rec SOFA simulator with (2) a different RL method, (3) more state encoders, and (4) a different dataset. Importantly, our experimental results indicate that existing findings do not generalize to the debiased SOFA simulator generated from a different dataset and a DQN-based method when compared with more state encoders.|强化学习推荐方法越来越受到关注，因为它们能够迅速适应用户的反馈。典型的 RL4Rec 框架包括(1)一个状态编码器来编码存储用户历史交互的状态，(2)一个 RL 方法来采取行动和观察奖励。先前的工作比较了四个状态编码器在一个环境中，其中用户反馈是基于真实世界的日志用户数据模拟的。结果表明，基于注意的状态编码器性能最好。然而，这一发现仅限于参与者批评方法、四个状态编码器和评估模拟器，它们不会减少记录的用户数据。针对这些缺点，我们复制和扩展了公开可用的无偏 RL4Rec SOFA 模拟器中基于注意力的状态编码器(1)与(2)不同的 RL 方法，(3)更多的状态编码器和(4)不同的数据集的现有比较。重要的是，我们的实验结果表明，与更多的状态编码器相比，现有的发现不能推广到由不同数据集和基于 DQN 的方法产生的去偏 SOFA 模拟器。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=State+Encoders+in+Reinforcement+Learning+for+Recommendation:+A+Reproducibility+Study)|2| |[Wikimarks: Harvesting Relevance Benchmarks from Wikipedia](https://doi.org/10.1145/3477495.3531731)|Laura Dietz, Shubham Chatterjee, Connor Lennox, Sumanta Kashyapi, Pooja Oza, Ben Gamari|University of New Hampshire, Durham, NH, USA; Well-Typed LLP, London, United Kingdom|We provide a resource for automatically harvesting relevance benchmarks from Wikipedia -- which we refer to as "Wikimarks" to differentiate them from manually created benchmarks. Unlike simulated benchmarks, they are based on manual annotations of Wikipedia authors. Studies on the TREC Complex Answer Retrieval track demonstrated that leaderboards under Wikimarks and manually annotated benchmarks are very similar. Because of their availability, Wikimarks can fill an important need for Information Retrieval research. We provide a meta-resource to harvest Wikimarks for several information retrieval tasks across different languages: paragraph retrieval, entity ranking, query-specific clustering, outline prediction, and relevant entity linking and many more. In addition, we provide example Wikimarks for English, Simple English, and Japanese derived from the 01/01/2022 Wikipedia dump. Resource available: https://trema-unh.github.io/wikimarks/|我们提供了一个从 Wikipedia 自动获取相关性基准的资源——我们称之为“ Wikimark”，以区分它们与手工创建的基准。与模拟基准测试不同，它们基于 Wikipedia 作者的手动注释。TREC 复杂答案检索跟踪的研究表明，Wikimark 下的排行榜和手动注释的基准测试非常相似。由于其可用性，wikimark 可以满足信息检索研究的重要需求。我们提供了一个元资源来收集维基信息检索，用于不同语言的多项任务: 段落检索、实体排名、特定于查询的聚类、大纲预测、相关实体链接等等。此外，我们还提供了源自01/01/2022 Wikipedia 转储的英语、简单英语和日语的维基标记示例。可供使用的资源: https://trema-unh.github.io/wikimarks/|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Wikimarks:+Harvesting+Relevance+Benchmarks+from+Wikipedia)|2| |[Gender Fairness in Information Retrieval Systems](https://doi.org/10.1145/3477495.3532680)|Amin Bigdeli, Negar Arabzadeh, Shirin Seyedsalehi, Morteza Zihayat, Ebrahim Bagheri|University of Waterloo, Waterloo, Canada; Ryerson University, Toronto, Canada|Recent studies have shown that it is possible for stereotypical gender biases to find their way into representational and algorithmic aspects of retrieval methods; hence, exhibit themselves in retrieval outcomes. In this tutorial, we inform the audience of various studies that have systematically reported the presence of stereotypical gender biases in Information Retrieval (IR) systems. We further classify existing work on gender biases in IR systems as being related to (1) relevance judgement datasets, (2) structure of retrieval methods, and (3) representations learnt for queries and documents. We present how each of these components can be impacted by or cause intensified biases during retrieval. Based on these identified issues, we then present a collection of approaches from the literature that have discussed how such biases can be measured, controlled, or mitigated. Additionally, we introduce publicly available datasets that are often used for investigating gender biases in IR systems as well as evaluation methodology adopted for determining the utility of gender bias mitigation strategies.|最近的研究表明，陈规定型的性别偏见有可能进入检索方法的表征和算法方面; 因此，在检索结果中表现出来。在这个教程中，我们告诉观众的各种研究，已经系统地报告了存在的陈规定型性别偏见的信息检索(IR)系统。我们进一步将关于 IR 系统中性别偏见的现有工作分类为: (1)相关性判断数据集，(2)检索方法的结构，(3)查询和文档所学到的表征。我们介绍了这些组件中的每一个在检索过程中是如何受到偏差的影响或引起偏差加剧的。基于这些已确定的问题，我们然后从文献中提出了一系列方法，这些方法讨论了如何可以测量、控制或减轻这些偏差。此外，我们还介绍了公开可用的数据集，这些数据集通常用于调查 IR 系统中的性别偏见，以及用于确定性别偏见缓解策略效用的评估方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Gender+Fairness+in+Information+Retrieval+Systems)|2| |[Fairness of Exposure in Light of Incomplete Exposure Estimation](https://doi.org/10.1145/3477495.3531977)|Maria Heuss, Fatemeh Sarvi, Maarten de Rijke|AIRLab & University of Amsterdam, Amsterdam , Netherlands; University of Amsterdam, Amsterdam, Netherlands|Fairness of exposure is a commonly used notion of fairness for ranking systems. It is based on the idea that all items or item groups should get exposure proportional to the merit of the item or the collective merit of the items in the group. Often, stochastic ranking policies are used to ensure fairness of exposure. Previous work unrealistically assumes that we can reliably estimate the expected exposure for all items in each ranking produced by the stochastic policy. In this work, we discuss how to approach fairness of exposure in cases where the policy contains rankings of which, due to inter-item dependencies, we cannot reliably estimate the exposure distribution. In such cases, we cannot determine whether the policy can be considered fair. % Our contributions in this paper are twofold. First, we define a method called \method for finding stochastic policies that avoid showing rankings with unknown exposure distribution to the user without having to compromise user utility or item fairness. Second, we extend the study of fairness of exposure to the top-k setting and also assess \method in this setting. We find that \method can significantly reduce the number of rankings with unknown exposure distribution without a drop in user utility or fairness compared to existing fair ranking methods, both for full-length and top-k rankings. This is an important first step in developing fair ranking methods for cases where we have incomplete knowledge about the user's behaviour.|曝光公平是排名系统中常用的公平概念。它是基于这样的想法，即所有项目或项目组应获得与项目的价值或集体价值的项目组成比例的曝光。通常，随机排序策略用于确保公平的曝光。以往的工作不切实际地假设我们可以可靠地估计所有项目的预期风险在每个排名所产生的随机政策。在这项工作中，我们讨论了如何接近公平的情况下，政策包含排名，由于项目间的相关性，我们不能可靠地估计曝光分布。在这种情况下，我们无法确定这项政策是否公平。% 我们在这篇论文中的贡献是双重的。首先，我们定义了一种方法，叫做随机策略发现方法，避免显示排名与未知的暴露分布给用户，而不必妥协用户效用或项目的公平性。其次，我们将公平性的研究扩展到 Top-k 环境，并在此环境下进行了评价方法的探讨。我们发现，与现有的公平排名方法相比，该方法可以显著减少未知曝光分布的排名数量，而不会降低用户效用或公平性，无论是全长排名还是前 K 排名。对于我们不完全了解用户行为的情况，这是开发公平排名方法的重要第一步。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Fairness+of+Exposure+in+Light+of+Incomplete+Exposure+Estimation)|2| -|[Few-Shot Stance Detection via Target-Aware Prompt Distillation](https://doi.org/10.1145/3477495.3531979)|Yan Jiang, Jinhua Gao, Huawei Shen, Xueqi Cheng|CAS Key Lab of Network Data Science and Technology, Institute of Computing Technology, Chinese Academy of Sciences & University of Chinese Academy of Sciences, Beijing, China; Data Intelligence System Research Center, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China; Data Intelligence System Research Center, Institute of Computing Technology, Chinese Academy of Sciences & University of Chinese Academy of Sciences, Beijing, China|Stance detection aims to identify whether the author of a text is in favor of, against, or neutral to a given target. The main challenge of this task comes two-fold: few-shot learning resulting from the varying targets and the lack of contextual information of the targets. Existing works mainly focus on solving the second issue by designing attention-based models or introducing noisy external knowledge, while the first issue remains under-explored. In this paper, inspired by the potential capability of pre-trained language models (PLMs) serving as knowledge bases and few-shot learners, we propose to introduce prompt-based fine-tuning for stance detection. PLMs can provide essential contextual information for the targets and enable few-shot learning via prompts. Considering the crucial role of the target in stance detection task, we design target-aware prompts and propose a novel verbalizer. Instead of mapping each label to a concrete word, our verbalizer maps each label to a vector and picks the label that best captures the correlation between the stance and the target. Moreover, to alleviate the possible defect of dealing with varying targets with a single hand-crafted prompt, we propose to distill the information learned from multiple prompts. Experimental results show the superior performance of our proposed model in both full-data and few-shot scenarios.|立场检测的目的是确定文本的作者是否赞成、反对或中立给定的目标。这项任务的主要挑战来自两个方面: 由于目标的不同和缺乏目标的上下文信息导致的少量学习。现有的研究主要集中在通过设计基于注意的模型或引入噪声外部知识来解决第二个问题，而第一个问题仍然没有得到充分的研究。本文受预训练语言模型(PLM)作为知识库和少镜头学习者的潜在能力的启发，提出了一种基于提示的姿态检测微调方法。PLM 可以为目标提供必要的上下文信息，并通过提示实现少量学习。考虑到目标在姿态检测任务中的重要作用，本文设计了目标感知提示，并提出了一种新的语言表达方法。代替将每个标签映射到一个具体的单词，我们的语言表达器将每个标签映射到一个向量，并选择最好地捕获立场和目标之间相关性的标签。此外，为了减少单一手工提示处理不同目标的可能缺陷，我们建议提取从多个提示中学到的信息。实验结果表明，该模型在全数据场景和少镜头场景下均具有良好的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Few-Shot+Stance+Detection+via+Target-Aware+Prompt+Distillation)|2| +|[Few-Shot Stance Detection via Target-Aware Prompt Distillation](https://doi.org/10.1145/3477495.3531979)|Yan Jiang, Jinhua Gao, Huawei Shen, Xueqi Cheng|Data Intelligence System Research Center, Institute of Computing Technology, Chinese Academy of Sciences & University of Chinese Academy of Sciences, Beijing, China; Data Intelligence System Research Center, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China; CAS Key Lab of Network Data Science and Technology, Institute of Computing Technology, Chinese Academy of Sciences & University of Chinese Academy of Sciences, Beijing, China|Stance detection aims to identify whether the author of a text is in favor of, against, or neutral to a given target. The main challenge of this task comes two-fold: few-shot learning resulting from the varying targets and the lack of contextual information of the targets. Existing works mainly focus on solving the second issue by designing attention-based models or introducing noisy external knowledge, while the first issue remains under-explored. In this paper, inspired by the potential capability of pre-trained language models (PLMs) serving as knowledge bases and few-shot learners, we propose to introduce prompt-based fine-tuning for stance detection. PLMs can provide essential contextual information for the targets and enable few-shot learning via prompts. Considering the crucial role of the target in stance detection task, we design target-aware prompts and propose a novel verbalizer. Instead of mapping each label to a concrete word, our verbalizer maps each label to a vector and picks the label that best captures the correlation between the stance and the target. Moreover, to alleviate the possible defect of dealing with varying targets with a single hand-crafted prompt, we propose to distill the information learned from multiple prompts. Experimental results show the superior performance of our proposed model in both full-data and few-shot scenarios.|立场检测的目的是确定文本的作者是否赞成、反对或中立给定的目标。这项任务的主要挑战来自两个方面: 由于目标的不同和缺乏目标的上下文信息导致的少量学习。现有的研究主要集中在通过设计基于注意的模型或引入噪声外部知识来解决第二个问题，而第一个问题仍然没有得到充分的研究。本文受预训练语言模型(PLM)作为知识库和少镜头学习者的潜在能力的启发，提出了一种基于提示的姿态检测微调方法。PLM 可以为目标提供必要的上下文信息，并通过提示实现少量学习。考虑到目标在姿态检测任务中的重要作用，本文设计了目标感知提示，并提出了一种新的语言表达方法。代替将每个标签映射到一个具体的单词，我们的语言表达器将每个标签映射到一个向量，并选择最好地捕获立场和目标之间相关性的标签。此外，为了减少单一手工提示处理不同目标的可能缺陷，我们建议提取从多个提示中学到的信息。实验结果表明，该模型在全数据场景和少镜头场景下均具有良好的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Few-Shot+Stance+Detection+via+Target-Aware+Prompt+Distillation)|2| |[Unsupervised Belief Representation Learning with Information-Theoretic Variational Graph Auto-Encoders](https://doi.org/10.1145/3477495.3532072)|Jinning Li, Huajie Shao, Dachun Sun, Ruijie Wang, Yuchen Yan, Jinyang Li, Shengzhong Liu, Hanghang Tong, Tarek F. Abdelzaher||This paper develops a novel unsupervised algorithm for belief representation learning in polarized networks that (i) uncovers the latent dimensions of the underlying belief space and (ii) jointly embeds users and content items (that they interact with) into that space in a manner that facilitates a number of downstream tasks, such as stance detection, stance prediction, and ideology mapping. Inspired by total correlation in information theory, we propose the Information-Theoretic Variational Graph Auto-Encoder (InfoVGAE) that learns to project both users and content items (e.g., posts that represent user views) into an appropriate disentangled latent space. To better disentangle latent variables in that space, we develop a total correlation regularization module, a Proportional-Integral (PI) control module, and adopt rectified Gaussian distribution to ensure the orthogonality. The latent representation of users and content can then be used to quantify their ideological leaning and detect/predict their stances on issues. We evaluate the performance of the proposed InfoVGAE on three real-world datasets, of which two are collected from Twitter and one from U.S. Congress voting records. The evaluation results show that our model outperforms state-of-the-art unsupervised models by reducing 10.5% user clustering errors and achieving 12.1% higher F1 scores for stance separation of content items. In addition, InfoVGAE produces a comparable result with supervised models. We also discuss its performance on stance prediction and user ranking within ideological groups.|本文提出了一种新的极化网络信念表示学习的无监督算法，该算法(i)揭示潜在信念空间的潜在维度，(ii)联合嵌入用户和内容项(他们交互的)到该空间中，以促进一些下游任务，如姿态检测，姿态预测和意识形态映射。受到信息论中的完全相关性的启发，我们提出了信息论变分图自动编码器(InfoVGAE) ，它学会将用户和内容项(例如，代表用户视图的帖子)投射到适当的分离潜在空间中。为了更好地分离该空间中的潜变量，我们开发了一个全相关正则化模块，一个比例积分(PI)控制模块，并采用校正正态分布来确保正交性。用户和内容的潜在表征可以用来量化他们的意识形态倾向，并发现/预测他们对问题的立场。我们在三个真实世界的数据集上评估提议的 InfoVGAE 的性能，其中两个数据集是从 Twitter 收集的，一个来自美国国会的投票记录。评估结果表明，我们的模型优于国家的最先进的无监督模型，减少了10.5% 的用户聚类错误，实现了12.1% 更高的 F1得分的立场分离的内容项目。此外，InfoVGAE 产生与监督模型相当的结果。并讨论了它在思想群体中的姿态预测和用户排名方面的表现。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Unsupervised+Belief+Representation+Learning+with+Information-Theoretic+Variational+Graph+Auto-Encoders)|2| |[Towards Motivational and Empathetic Response Generation in Online Mental Health Support](https://doi.org/10.1145/3477495.3531912)|Tulika Saha, Vaibhav Gakhreja, Anindya Sundar Das, Souhitya Chakraborty, Sriparna Saha|Indian Institute of Technology Patna, Patna, India|The scarcity of Mental Health Professionals (MHPs) available to assist patients underlines the need for developing automated systems to help MHPs combat the grievous mental illness called Major Depressive Disorder. In this paper, we develop a Virtual Assistant (VA) that serves as a first point of contact for users who are depressed or disheartened. In support based conversations, two primary components have been identified to produce positive outcomes,empathy andmotivation. While empathy necessitates acknowledging the feelings of the users with a desire to help, imparting hope and motivation uplifts the spirit of support seekers in distress. A combination of these aspects will ensure generalized positive outcome and beneficial alliance in mental health support. The VA, thus, should be capable of generating empathetic and motivational responses, continuously demonstrating positive sentiment by the VA. The end-to-end system employs two mechanisms in a pipe-lined manner : (i)Motivational Response Generator (MRG) : a sentiment driven Reinforcement Learning (RL) based motivational response generator; and (ii)Empathetic Rewriting Framework (ERF) : a transformer based model that rewrites the response from MRG to induce empathy. Experimental results indicate that our proposed VA outperforms several of its counterparts. To the best of our knowledge, this is the first work that seeks to incorporate these aspects together in an end-to-end system.|可用于帮助患者的精神卫生专业人员(MHPs)的稀缺性突出表明，需要开发自动化系统来帮助 MHPs 对抗称为重性抑郁障碍的严重精神疾病。在本文中，我们开发了一个虚拟助手(VA) ，作为用户的第一联系点谁是沮丧或心灰意冷。在以支持为基础的对话中，两个主要的组成部分已经被确定来产生积极的结果，移情和动机。虽然移情需要承认用户的感受与愿望帮助，传递希望和动机提升精神的支持寻求者在困境中。这些方面的结合将确保普遍积极的结果和有益的联盟在心理健康支持。因此，退伍军人事务部应该能够产生同理心和激励性的反应，不断表现出 VA. 的积极情绪。端到端系统以管道式的方式使用两种机制: (i)动机反应生成器(MRG) : 基于情感驱动的强化学习(RL)的动机反应生成器; (ii)移情重写框架(ERF) : 基于转换器的模型，重写来自 MRG 的反应以诱导移情。实验结果表明，我们提出的 VA 性能优于其他几个竞争对手。据我们所知，这是第一个试图将这些方面整合到一个端到端系统中的工作。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Towards+Motivational+and+Empathetic+Response+Generation+in+Online+Mental+Health+Support)|2| |[Multimodal Entity Linking with Gated Hierarchical Fusion and Contrastive Training](https://doi.org/10.1145/3477495.3531867)|Peng Wang, Jiangheng Wu, Xiaohang Chen|Southeast University, Nanjing, China|Previous entity linking methods in knowledge graphs (KGs) mostly link the textual mentions to corresponding entities. However, they have deficiencies in processing numerous multimodal data, when the text is too short to provide enough context. Consequently, we conceive the idea of introducing valuable information of other modalities, and propose a novel multimodal entity linking method with gated hierarchical multimodal fusion and contrastive training (GHMFC). Firstly, in order to discover the fine-grained inter-modal correlations, GHMFC extracts the hierarchical features of text and visual co-attention through the multi-modal co-attention mechanism: textual-guided visual attention and visual-guided textual attention. The former attention obtains weighted visual features under the guidance of textual information. In contrast, the latter attention produces weighted textual features under the guidance of visual information. Afterwards, gated fusion is used to evaluate the importance of hierarchical features of different modalities and integrate them into the final multimodal representations of mentions. Subsequently, contrastive training with two types of contrastive losses is designed to learn more generic multimodal features and reduce noise. Finally, the linking entities are selected by calculating the cosine similarity between representations of mentions and entities in KGs. To evaluate the proposed method, this paper releases two new open multimodal entity linking datasets: WikiMEL and Richpedia-MEL. Experimental results demonstrate that GHMFC can learn meaningful multimodal representation and significantly outperforms most of the baseline methods.|以往知识图中的实体链接方法大多将文本提及链接到相应的实体。然而，当文本太短而无法提供足够的上下文时，它们在处理大量多模式数据方面存在缺陷。因此，我们提出了引入其他模式的有价值信息的思想，并提出了一种新的多模式实体连接方法与门限层次多模式融合和对比训练(GHMFC)。首先，为了发现细粒度的多模态关联，GHMFC 通过文本引导的视觉注意和视觉引导的文本注意这两种多模态共注意机制提取文本和视觉共注意的层次特征。前者在文本信息的引导下获得加权视觉特征。相反，后者在视觉信息的引导下产生加权的文本特征。然后，门限融合被用来评估不同模式的等级特征的重要性，并将它们整合到提及的最终多模式表示中。随后，设计了两种对比损失的对比训练，以学习更一般的多模态特征和降低噪声。最后，通过计算幼儿园中提及和实体的表示之间的余弦距离来选择链接实体。为了评估提出的方法，本文发布了两个新的开放的多模式实体链接数据集: WikiMEL 和 Richpedia-MEL。实验结果表明，GHMFC 能够学习有意义的多模态表示，其性能明显优于大多数基线方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Multimodal+Entity+Linking+with+Gated+Hierarchical+Fusion+and+Contrastive+Training)|2| -|[Tag-assisted Multimodal Sentiment Analysis under Uncertain Missing Modalities](https://doi.org/10.1145/3477495.3532064)|Jiandian Zeng, Tianyi Liu, Jiantao Zhou|University of Macau, Macau, China; Shanghai Jiao Tong University, Shanghai, China|Multimodal sentiment analysis has been studied under the assumption that all modalities are available. However, such a strong assumption does not always hold in practice, and most of multimodal fusion models may fail when partial modalities are missing. Several works have addressed the missing modality problem; but most of them only considered the single modality missing case, and ignored the practically more general cases of multiple modalities missing. To this end, in this paper, we propose a Tag-Assisted Transformer Encoder (TATE) network to handle the problem of missing uncertain modalities. Specifically, we design a tag encoding module to cover both the single modality and multiple modalities missing cases, so as to guide the network's attention to those missing modalities. Besides, we adopt a new space projection pattern to align common vectors. Then, a Transformer encoder-decoder network is utilized to learn the missing modality features. At last, the outputs of the Transformer encoder are used for the final sentiment classification. Extensive experiments are conducted on CMU-MOSI and IEMOCAP datasets, showing that our method can achieve significant improvements compared with several baselines.|多模态情绪分析已研究的假设下，所有的模式是可用的。然而，这样一个强大的假设并不总是适用于实践，并且大多数多模态融合模型可能会失败时，部分模式缺失。有几部著作论述了缺失模式问题; 但大多数著作只考虑了单一模式缺失的情况，而忽略了实际上更为普遍的多模式缺失的情况。为此，本文提出了一种标签辅助变压器编码器(TATE)网络来处理丢失不确定模式的问题。具体来说，我们设计了一个标签编码模块来同时涵盖单个模态和多个模态的缺失情况，从而引导网络对这些缺失模态的注意。此外，我们还采用了一种新的空间投影模式来对齐公共向量。然后，利用变压器编译码网络来学习缺失的模态特征。最后，利用变压器编码器的输出进行最终的情感分类。在 CMU-MOSI 和 IEMOCAP 数据集上进行了大量的实验，结果表明，与几个基线相比，本文提出的方法可以取得显著的改进。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Tag-assisted+Multimodal+Sentiment+Analysis+under+Uncertain+Missing+Modalities)|2| -|[Enhancing Zero-Shot Stance Detection via Targeted Background Knowledge](https://doi.org/10.1145/3477495.3531807)|Qinglin Zhu, Bin Liang, Jingyi Sun, Jiachen Du, Lanjun Zhou, Ruifeng Xu|Harbin Institute of Technology, Shenzhen, Shenzhen, China; None, Shenzhen, China; Harbin Institute of Technology, Shenzhen & Peng Cheng Laboratory, Shenzhen, China|Stance detection aims to identify the stance of the text towards a target. Different from conventional stance detection, Zero-Shot Stance Detection (ZSSD) needs to predict the stances of the unseen targets during the inference stage. For human beings, we generally tend to reason the stance of a new target by linking it with the related knowledge learned from the known ones. Therefore, in this paper, to better generalize the target-related stance features learned from the known targets to the unseen ones, we incorporate the targeted background knowledge from Wikipedia into the model. The background knowledge can be considered as a bridge for connecting the meanings between known targets and the unseen ones, which enables the generalization and reasoning ability of the model to be improved in dealing with ZSSD. Extensive experimental results demonstrate that our model outperforms the state-of-the-art methods on the ZSSD task.|姿态检测的目的是识别文本对目标的姿态。与传统的姿态检测不同，零拍姿态检测(ZSSD)需要在推理阶段预测未知目标的姿态。对于人类来说，我们通常倾向于将新目标的立场与从已知目标中学到的相关知识联系起来进行推理。因此，为了更好地将从已知目标中学到的与目标相关的姿态特征推广到未知目标中，本文将维基百科中的目标背景知识引入到模型中。背景知识可以作为连接已知目标和未知目标意义的桥梁，提高模型在处理 ZSSD 时的推理能力。大量的实验结果表明，我们的模型在 ZSSD 任务上优于最先进的方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Enhancing+Zero-Shot+Stance+Detection+via+Targeted+Background+Knowledge)|2| +|[Tag-assisted Multimodal Sentiment Analysis under Uncertain Missing Modalities](https://doi.org/10.1145/3477495.3532064)|Jiandian Zeng, Tianyi Liu, Jiantao Zhou|Shanghai Jiao Tong University, Shanghai, China; University of Macau, Macau, China|Multimodal sentiment analysis has been studied under the assumption that all modalities are available. However, such a strong assumption does not always hold in practice, and most of multimodal fusion models may fail when partial modalities are missing. Several works have addressed the missing modality problem; but most of them only considered the single modality missing case, and ignored the practically more general cases of multiple modalities missing. To this end, in this paper, we propose a Tag-Assisted Transformer Encoder (TATE) network to handle the problem of missing uncertain modalities. Specifically, we design a tag encoding module to cover both the single modality and multiple modalities missing cases, so as to guide the network's attention to those missing modalities. Besides, we adopt a new space projection pattern to align common vectors. Then, a Transformer encoder-decoder network is utilized to learn the missing modality features. At last, the outputs of the Transformer encoder are used for the final sentiment classification. Extensive experiments are conducted on CMU-MOSI and IEMOCAP datasets, showing that our method can achieve significant improvements compared with several baselines.|多模态情绪分析已研究的假设下，所有的模式是可用的。然而，这样一个强大的假设并不总是适用于实践，并且大多数多模态融合模型可能会失败时，部分模式缺失。有几部著作论述了缺失模式问题; 但大多数著作只考虑了单一模式缺失的情况，而忽略了实际上更为普遍的多模式缺失的情况。为此，本文提出了一种标签辅助变压器编码器(TATE)网络来处理丢失不确定模式的问题。具体来说，我们设计了一个标签编码模块来同时涵盖单个模态和多个模态的缺失情况，从而引导网络对这些缺失模态的注意。此外，我们还采用了一种新的空间投影模式来对齐公共向量。然后，利用变压器编译码网络来学习缺失的模态特征。最后，利用变压器编码器的输出进行最终的情感分类。在 CMU-MOSI 和 IEMOCAP 数据集上进行了大量的实验，结果表明，与几个基线相比，本文提出的方法可以取得显著的改进。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Tag-assisted+Multimodal+Sentiment+Analysis+under+Uncertain+Missing+Modalities)|2| +|[Enhancing Zero-Shot Stance Detection via Targeted Background Knowledge](https://doi.org/10.1145/3477495.3531807)|Qinglin Zhu, Bin Liang, Jingyi Sun, Jiachen Du, Lanjun Zhou, Ruifeng Xu|None, Shenzhen, China; Harbin Institute of Technology, Shenzhen & Peng Cheng Laboratory, Shenzhen, China; Harbin Institute of Technology, Shenzhen, Shenzhen, China|Stance detection aims to identify the stance of the text towards a target. Different from conventional stance detection, Zero-Shot Stance Detection (ZSSD) needs to predict the stances of the unseen targets during the inference stage. For human beings, we generally tend to reason the stance of a new target by linking it with the related knowledge learned from the known ones. Therefore, in this paper, to better generalize the target-related stance features learned from the known targets to the unseen ones, we incorporate the targeted background knowledge from Wikipedia into the model. The background knowledge can be considered as a bridge for connecting the meanings between known targets and the unseen ones, which enables the generalization and reasoning ability of the model to be improved in dealing with ZSSD. Extensive experimental results demonstrate that our model outperforms the state-of-the-art methods on the ZSSD task.|姿态检测的目的是识别文本对目标的姿态。与传统的姿态检测不同，零拍姿态检测(ZSSD)需要在推理阶段预测未知目标的姿态。对于人类来说，我们通常倾向于将新目标的立场与从已知目标中学到的相关知识联系起来进行推理。因此，为了更好地将从已知目标中学到的与目标相关的姿态特征推广到未知目标中，本文将维基百科中的目标背景知识引入到模型中。背景知识可以作为连接已知目标和未知目标意义的桥梁，提高模型在处理 ZSSD 时的推理能力。大量的实验结果表明，我们的模型在 ZSSD 任务上优于最先进的方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Enhancing+Zero-Shot+Stance+Detection+via+Targeted+Background+Knowledge)|2| |[Relation-Guided Few-Shot Relational Triple Extraction](https://doi.org/10.1145/3477495.3531831)|Xin Cong, Jiawei Sheng, Shiyao Cui, Bowen Yu, Tingwen Liu, Bin Wang|Xiaomi Inc., Beijing, China; Institute of Information Engineering, Chinese Academy of Sciences & University of Chinese Academy of Sciences, Beijing, China|In few-shot relational triple extraction (FS-RTE), one seeks to extract relational triples from plain texts by utilizing only few annotated samples. Recent work first extracts all entities and then classifies their relations. Such an entity-then-relation paradigm ignores the entity discrepancy between relations. To address it, we propose a novel task decomposition strategy, Relation-then-Entity, for FS-RTE. It first detects relations occurred in a sentence and then extracts the corresponding head/tail entities of the detected relations. To instantiate this strategy, we further propose a model, RelATE, which builds a dual-level attention to aggregate relation-relevant information to detect the relation occurrence and utilizes the annotated samples of the detected relations to extract the corresponding head/tail entities. Experimental results show that our model outperforms previous work by an absolute gain (18.98%, 28.85% in F1 in two few-shot settings).|在少镜头关系三元组提取(few-shot Relations Triples，FS-RTE)中，人们试图通过只使用少量带注释的样本从纯文本中提取关系三元组。最近的工作首先提取所有的实体，然后分类它们的关系。这种先实体后关系的范式忽略了关系之间的实体差异。为了解决这个问题，我们提出了一个新的任务分解策略，即关系然后实体，用于 FS-RTE。它首先检测句子中出现的关系，然后提取被检测关系的相应头尾实体。为了实例化这一策略，我们进一步提出了一个名为 RelATE 的模型，该模型构建了对聚合关系相关信息的双层关注来检测关系的发生，并利用检测到的关系的注释样本来提取相应的头/尾实体。实验结果表明，我们的模型优于以前的工作的绝对增益(18.98% ，在 F1在两个少拍设置28.85%)。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Relation-Guided+Few-Shot+Relational+Triple+Extraction)|2| -|[Summarizing Legal Regulatory Documents using Transformers](https://doi.org/10.1145/3477495.3531872)|Svea Klaus, Ria Van Hecke, Kaweh Djafari Naini, Ismail Sengor Altingovde, Juan BernabéMoreno, Enrique HerreraViedma|University of Granada, Granada, Spain; Middle East Technical University, Ankara, Turkey; E.ON Digital Technology GmbH, Hannover, Germany; University of Granada & E.ON Digital Technology GmbH, Granada, Spain|Companies invest a substantial amount of time and resources in ensuring the compliance to the existing regulations or in the form of fines when compliance cannot be proven in auditing procedures. The topic is not only relevant, but also highly complex, given the frequency of changes and amendments, the complexity of the cases and the difficulty of the juristic language. This paper aims at applying advanced extractive summarization to democratize the understanding of regulations, so that non-jurists can decide which regulations deserve further follow-up. To achieve that, we first create a corpus named EUR-LexSum EUR-LexSum containing 4595 curated European regulatory documents and their corresponding summaries. We then fine-tune transformer-based models which, applied to this corpus, yield a superior performance (in terms of ROUGE metrics) compared to a traditional extractive summarization baseline. Our experiments reveal that even with limited amounts of data such transformer-based models are effective in the field of legal document summarization.|公司投入大量时间和资源，确保遵守现行条例，或在审计程序无法证明遵守情况时以罚款的形式进行。鉴于变更和修正的频率、案件的复杂性和法律语言的困难，这一专题不仅相关，而且非常复杂。本文旨在运用先进的抽象概括方法，使法规的理解民主化，使非法学家能够决定哪些法规值得进一步跟进。为了实现这一目标，我们首先创建了一个名为 EUR-LexSum 的语料库，其中包含4595份精选的欧洲监管文件及其相应的摘要。然后，我们对基于转换器的模型进行微调，这些模型应用于这个语料库，与传统的提取摘要基线相比，产生了更好的性能(在 ROUGE 指标方面)。我们的实验表明，即使在数据量有限的情况下，这种基于转换器的模型在法律文档摘要领域仍然是有效的。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Summarizing+Legal+Regulatory+Documents+using+Transformers)|2| +|[Summarizing Legal Regulatory Documents using Transformers](https://doi.org/10.1145/3477495.3531872)|Svea Klaus, Ria Van Hecke, Kaweh Djafari Naini, Ismail Sengor Altingovde, Juan BernabéMoreno, Enrique HerreraViedma|University of Granada, Granada, Spain; Middle East Technical University, Ankara, Turkey; University of Granada & E.ON Digital Technology GmbH, Granada, Spain; E.ON Digital Technology GmbH, Hannover, Germany|Companies invest a substantial amount of time and resources in ensuring the compliance to the existing regulations or in the form of fines when compliance cannot be proven in auditing procedures. The topic is not only relevant, but also highly complex, given the frequency of changes and amendments, the complexity of the cases and the difficulty of the juristic language. This paper aims at applying advanced extractive summarization to democratize the understanding of regulations, so that non-jurists can decide which regulations deserve further follow-up. To achieve that, we first create a corpus named EUR-LexSum EUR-LexSum containing 4595 curated European regulatory documents and their corresponding summaries. We then fine-tune transformer-based models which, applied to this corpus, yield a superior performance (in terms of ROUGE metrics) compared to a traditional extractive summarization baseline. Our experiments reveal that even with limited amounts of data such transformer-based models are effective in the field of legal document summarization.|公司投入大量时间和资源，确保遵守现行条例，或在审计程序无法证明遵守情况时以罚款的形式进行。鉴于变更和修正的频率、案件的复杂性和法律语言的困难，这一专题不仅相关，而且非常复杂。本文旨在运用先进的抽象概括方法，使法规的理解民主化，使非法学家能够决定哪些法规值得进一步跟进。为了实现这一目标，我们首先创建了一个名为 EUR-LexSum 的语料库，其中包含4595份精选的欧洲监管文件及其相应的摘要。然后，我们对基于转换器的模型进行微调，这些模型应用于这个语料库，与传统的提取摘要基线相比，产生了更好的性能(在 ROUGE 指标方面)。我们的实验表明，即使在数据量有限的情况下，这种基于转换器的模型在法律文档摘要领域仍然是有效的。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Summarizing+Legal+Regulatory+Documents+using+Transformers)|2| |[An Inspection of the Reproducibility and Replicability of TCT-ColBERT](https://doi.org/10.1145/3477495.3531721)|Xiao Wang, Sean MacAvaney, Craig Macdonald, Iadh Ounis|University of Glasgow, Glasgow, United Kingdom|Dense retrieval approaches are of increasing interest because they can better capture contextualised similarity compared to sparse retrieval models such as BM25. Among the most prominent of these approaches is TCT-ColBERT, which trains a light-weight "student'' model from a more expensive "teacher'' model. In this work, we take a closer look into TCT-ColBERT concerning its reproducibility and replicability. To structure our study, we propose a three-stage perspective on reproducing the training, inference, and evaluation of model-focused papers, each using artefacts produced from different stages in the pipeline. We find that --- perhaps as expected --- precise reproduction is more challenging when the complete training process is conducted, rather than just inference from a released trained model. Each stage provides the opportunity to perform replication and ablation experiments. We are able to replicate (i.e., produce an effective independent implementation) for model inference and dense indexing/retrieval, but are unable to replicate the training process. We conduct several ablations to cover gaps in the original paper, and make the following observations: (1) the model can function as an inexpensive re-ranker, establishing a new Pareto-optimal result; (2) the index size can be reduced by using lower-precision floating point values, but only if ties in scores are handled appropriately; (3) training needs to be conducted for the entire suggested duration to achieve optimal performance; and (4) student initialisation from the teacher is not necessary.|密集检索方法越来越受到人们的关注，因为与稀疏检索模型(如 BM25)相比，密集检索方法能够更好地捕获上下文相似性。其中最突出的方法是 TCT-ColBERT，它从更昂贵的“教师”模型中培训轻量级的“学生”模型。在这项工作中，我们对 TCT-ColBERT 的重复性和可复制性进行了更深入的研究。为了构建我们的研究，我们提出了一个三阶段的视角来重现模型关注论文的训练、推理和评估，每一个阶段都使用流水线中不同阶段产生的人工制品。我们发现——也许正如预期的那样——当完整的训练过程进行时，精确的复制比仅仅从已发布的训练模型中推断更具挑战性。每个阶段都提供了进行复制和消融实验的机会。我们能够复制(即，生成一个有效的独立实现)模型推理和密集索引/检索，但无法复制培训过程。我们进行了几次消融，以弥补原始论文中的空白，并做出以下观察: (1)该模型可以作为一个廉价的重新排序，建立一个新的帕累托最优结果; (2)指数大小可以通过使用较低精度的浮点数值减少，但只有当分数的关系处理得当时; (3)培训需要进行整个建议的持续时间，以达到最佳的表现; (4)学生初始化是不必要的教师。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=An+Inspection+of+the+Reproducibility+and+Replicability+of+TCT-ColBERT)|2| |[MET-Meme: A Multimodal Meme Dataset Rich in Metaphors](https://doi.org/10.1145/3477495.3532019)|Bo Xu, Tingting Li, Junzhe Zheng, Mehdi Naseriparsa, Zhehuan Zhao, Hongfei Lin, Feng Xia|Dalian University of Technology, Dalian, China; Federation University Australia, Ballarat, VIC, Australia|Memes have become the popular means of communication for Internet users worldwide. Understanding the Internet meme is one of the most tricky challenges in natural language processing (NLP) tasks due to its convenient non-standard writing and network vocabulary. Recently, many linguists suggested that memes contain rich metaphorical information. However, the existing researches ignore this key feature. Therefore, to incorporate informative metaphors into the meme analysis, we introduce a novel multimodal meme dataset called MET-Meme, which is rich in metaphorical features. It contains 10045 text-image pairs, with manual annotations of the metaphor occurrence, sentiment categories, intentions, and offensiveness degree. Moreover, we propose a range of strong baselines to demonstrate the importance of combining metaphorical features for meme sentiment analysis and semantic understanding tasks, respectively. MET-Meme, and its code are released publicly for research in \urlhttps://github.com/liaolianfoka/MET-Meme-A-Multi-modal-Meme-Dataset-Rich-in-Metaphors.|文化基因已经成为世界范围内互联网用户流行的交流方式。自然语言处理(NLP)任务中最棘手的挑战之一就是理解网络文化基因，因为它具有方便的非标准写作和网络词汇。近年来，许多语言学家认为模因包含丰富的隐喻信息。然而，现有的研究忽视了这一关键特征。因此，为了将信息隐喻引入到模因分析中，我们引入了一个新的多模式模因数据集 MET-Meme，它具有丰富的隐喻特征。它包含10045个文本-图像对，手动注释的隐喻出现，情感类别，意图，和冒犯程度。此外，我们提出了一系列强基线来证明隐喻特征相结合对于模因情感分析和语义理解任务的重要性。MET-meme 及其代码在 urlhttps:// github.com/liaolianfoka/MET-Meme-a-multi-modal-meme-dataset-rich-in-metaphors 上公开发布以供研究。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=MET-Meme:+A+Multimodal+Meme+Dataset+Rich+in+Metaphors)|2| |[Fostering Coopetition While Plugging Leaks: The Design and Implementation of the MS MARCO Leaderboards](https://doi.org/10.1145/3477495.3531725)|Jimmy Lin, Daniel Campos, Nick Craswell, Bhaskar Mitra, Emine Yilmaz||||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Fostering+Coopetition+While+Plugging+Leaks:+The+Design+and+Implementation+of+the+MS+MARCO+Leaderboards)|2| @@ -154,101 +154,101 @@ |[ClueWeb22: 10 Billion Web Documents with Rich Information](https://doi.org/10.1145/3477495.3536321)|Arnold Overwijk, Chenyan Xiong, Jamie Callan||||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=ClueWeb22:+10+Billion+Web+Documents+with+Rich+Information)|2| |[User-Aware Multi-Interest Learning for Candidate Matching in Recommenders](https://doi.org/10.1145/3477495.3532073)|Zheng Chai, Zhihong Chen, Chenliang Li, Rong Xiao, Houyi Li, Jiawei Wu, Jingxu Chen, Haihong Tang|Alibaba Group, Hangzhou, China; Zhejiang University, Hangzhou, China; Wuhan University, Wuhan, China|Recommender systems have become a fundamental service in most E-Commerce platforms, in which the matching stage aims to retrieve potentially relevant candidate items to users for further ranking. Recently, some efforts on extracting multi-interests from user's historical behaviors have demonstrated superior performance. However, the historical behaviors are not noise-free due to the possible misclicks or disturbances. Existing works mainly overlook the fact that the interests of a user are not only reflected by the historical behaviors, but also inherently regulated by the profile information. Hence, we are interested in exploiting the benefit of user profile in multi-interest learning to enhance candidate matching performance. To this end, a user-aware multi-interest learning framework (named UMI) is proposed in this paper to exploit both user profile and behavior information for candidate matching. Specifically, UMI consists of two main components: dual-attention routing and interest refinement. In the dual-attention routing, we firstly introduce a user-guided attention network to identify the important historical items with respect to the user profile. Then, the resultant importance weights are leveraged via the dual-attentive capsule network to extract the user's multi-interests. Afterwards, the extracted interests are utilized to highlight the corresponding user profile features for interest refinement, such that different user profiles can be incorporated into interest learning for diverse user preference understanding. Besides, to improve the model's discriminative capacity, we further devise a harder-negatives strategy to support model optimization. Extensive experiments show that UMI significantly outperforms state-of-the-art multi-interest modeling alternatives. Currently, UMI has been successfully deployed at Taobao App in Alibaba, serving hundreds of millions of users.|推荐系统已经成为大多数电子商务平台的基本服务，其中匹配阶段的目的是检索潜在的相关候选项目，以便用户进一步排名。最近，一些从用户历史行为中提取多重利益的尝试已经显示出了卓越的性能。然而，由于可能的错误或干扰，历史行为并不是无噪声的。现有的研究工作主要忽视了用户的利益不仅反映在历史行为上，而且内在地受到个人资料信息的调节。因此，我们有兴趣在多兴趣学习中利用用户资料的好处来提高候选人匹配性能。为此，本文提出了一种基于用户感知的多兴趣学习框架(UMI) ，该框架利用用户信息和行为信息进行候选人匹配。具体来说，UMI 包括两个主要组成部分: 双注意路由和兴趣细化。在双注意路由中，我们首先引入一个用户引导的注意网络来识别与用户资料相关的重要历史项目。然后，通过双注意胶囊网络利用得到的重要性权重来提取用户的多重兴趣。然后利用提取出的兴趣特征突出相应的用户兴趣特征进行兴趣细化，从而将不同的用户兴趣特征融入到兴趣学习中以获得不同的用户偏好理解。此外，为了提高模型的判别能力，我们进一步设计了一个较难否定的策略来支持模型优化。大量的实验表明，UMI 明显优于最先进的多重兴趣建模方案。目前，用户界面已经在阿里巴巴的淘宝应用上成功部署，为数亿用户提供服务。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=User-Aware+Multi-Interest+Learning+for+Candidate+Matching+in+Recommenders)|1| |[ESCM2: Entire Space Counterfactual Multi-Task Model for Post-Click Conversion Rate Estimation](https://doi.org/10.1145/3477495.3531972)|Hao Wang, TaiWei Chang, Tianqiao Liu, Jianmin Huang, Zhichao Chen, Chao Yu, Ruopeng Li, Wei Chu|Ant Group, Hangzhou, China|Accurate estimation of post-click conversion rate is critical for building recommender systems, which has long been confronted with sample selection bias and data sparsity issues. Methods in the Entire Space Multi-task Model (ESMM) family leverage the sequential pattern of user actions, \ie $impression\rightarrow click \rightarrow conversion$ to address data sparsity issue. However, they still fail to ensure the unbiasedness of CVR estimates. In this paper, we theoretically demonstrate that ESMM suffers from the following two problems: (1) Inherent Estimation Bias (IEB) for CVR estimation, where the CVR estimate is inherently higher than the ground truth; (2) Potential Independence Priority (PIP) for CTCVR estimation, where ESMM might overlook the causality from click to conversion. To this end, we devise a principled approach named Entire Space Counterfactual Multi-task Modelling (ESCM$^2$), which employs a counterfactual risk miminizer as a regularizer in ESMM to address both IEB and PIP issues simultaneously. Extensive experiments on offline datasets and online environments demonstrate that our proposed ESCM$^2$ can largely mitigate the inherent IEB and PIP issues and achieve better performance than baseline models.|准确估计点击后的转换率是建立推荐系统的关键，长期以来推荐系统一直面临样本选择偏差和数据稀疏问题。整个空间多任务模型(ESMM)家族中的方法利用了用户操作的顺序模式，例如: $pressionright-tarrow 单击 right-tarrow 转换 $来解决数据稀疏问题。然而，他们仍然不能确保 CVR 估计的公正性。在本文中，我们从理论上证明了 ESMM 存在以下两个问题: (1) CVR 估计的内在估计偏差(IEB) ，其中 CVR 估计固有地高于地面真值; (2) CTCVR 估计的潜在独立优先级(PIP) ，其中 ESMM 可能忽略从点击到转换的因果关系。为此，我们设计了一种名为“整个空间反事实多任务建模”(ESCM $^ 2 $)的原则性方法，该方法使用反事实风险模拟器作为 ESMM 中的规则化器，同时解决 IEB 和 PIP 问题。在离线数据集和在线环境上的大量实验表明，我们提出的 ESCM $^ 2 $可以在很大程度上缓解内在的 IEB 和 PIP 问题，并取得比基线模型更好的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=ESCM2:+Entire+Space+Counterfactual+Multi-Task+Model+for+Post-Click+Conversion+Rate+Estimation)|1| -|[Single-shot Embedding Dimension Search in Recommender System](https://doi.org/10.1145/3477495.3532060)|Liang Qu, Yonghong Ye, Ningzhi Tang, Lixin Zhang, Yuhui Shi, Hongzhi Yin|The University of Queensland, Brisbane, QLD, Australia; WeChat, Tencent, Shenzhen, China; Southern University of Science and Technology, Shenzhen, China|As a crucial component of most modern deep recommender systems, feature embedding maps high-dimensional sparse user/item features into low-dimensional dense embeddings. However, these embeddings are usually assigned a unified dimension, which suffers from the following issues: (1) high memory usage and computation cost. (2) sub-optimal performance due to inferior dimension assignments. In order to alleviate the above issues, some works focus on automated embedding dimension search by formulating it as hyper-parameter optimization or embedding pruning problems. However, they either require well-designed search space for hyperparameters or need time-consuming optimization procedures. In this paper, we propose a Single-Shot Embedding Dimension Search method, called SSEDS, which can efficiently assign dimensions for each feature field via a single-shot embedding pruning operation while maintaining the recommendation accuracy of the model. Specifically, it introduces a criterion for identifying the importance of each embedding dimension for each feature field. As a result, SSEDS could automatically obtain mixed-dimensional embeddings by explicitly reducing redundant embedding dimensions based on the corresponding dimension importance ranking and the predefined parameter budget. Furthermore, the proposed SSEDS is model-agnostic, meaning that it could be integrated into different base recommendation models. The extensive offline experiments are conducted on two widely used public datasets for CTR (Click Through Rate) prediction task, and the results demonstrate that SSEDS can still achieve strong recommendation performance even if it has reduced 90% parameters. Moreover, SSEDS has also been deployed on the WeChat Subscription platform for practical recommendation services. The 7-day online A/B test results show that SSEDS can significantly improve the performance of the online recommendation model while reducing resource consumption.|作为现代深度推荐系统的重要组成部分，特征嵌入将高维稀疏用户/项目特征映射为低维密集嵌入。然而，这些嵌入通常被分配一个统一的维度，这受到以下问题: (1)高内存使用和计算成本。(2)由于尺寸分配不合理而导致性能次优。为了解决上述问题，一些工作将嵌入维搜索问题转化为超参数优化问题或嵌入剪枝问题。然而，它们要么需要设计良好的超参数搜索空间，要么需要耗时的优化过程。本文提出了一种单镜头嵌入维度搜索方法 SSEDS，该方法通过单镜头嵌入剪枝操作，可以有效地为每个特征域分配维度，同时保持模型的推荐精度。具体地说，它引入了一个标准来识别每个特征字段的每个嵌入维的重要性。因此，SSEDS 可以根据相应的维重要性排序和预定义的参数预算，通过显式地减少冗余嵌入维数来自动获得混合维嵌入。此外，提出的 SSEDS 是模型无关的，这意味着它可以集成到不同的基本推荐模型中。在两个广泛使用的公共数据集上进行了广泛的离线实验，结果表明，即使 SSEDS 减少了90% 的参数，仍然可以获得很好的推荐性能。此外，SSEDS 还被部署在微信订阅平台上，提供实用的推荐服务。为期7天的在线 A/B 测试结果表明，SSEDS 在降低资源消耗的同时，可以显著提高在线推荐模型的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Single-shot+Embedding+Dimension+Search+in+Recommender+System)|1| +|[Single-shot Embedding Dimension Search in Recommender System](https://doi.org/10.1145/3477495.3532060)|Liang Qu, Yonghong Ye, Ningzhi Tang, Lixin Zhang, Yuhui Shi, Hongzhi Yin|The University of Queensland, Brisbane, QLD, Australia; Southern University of Science and Technology, Shenzhen, China; WeChat, Tencent, Shenzhen, China|As a crucial component of most modern deep recommender systems, feature embedding maps high-dimensional sparse user/item features into low-dimensional dense embeddings. However, these embeddings are usually assigned a unified dimension, which suffers from the following issues: (1) high memory usage and computation cost. (2) sub-optimal performance due to inferior dimension assignments. In order to alleviate the above issues, some works focus on automated embedding dimension search by formulating it as hyper-parameter optimization or embedding pruning problems. However, they either require well-designed search space for hyperparameters or need time-consuming optimization procedures. In this paper, we propose a Single-Shot Embedding Dimension Search method, called SSEDS, which can efficiently assign dimensions for each feature field via a single-shot embedding pruning operation while maintaining the recommendation accuracy of the model. Specifically, it introduces a criterion for identifying the importance of each embedding dimension for each feature field. As a result, SSEDS could automatically obtain mixed-dimensional embeddings by explicitly reducing redundant embedding dimensions based on the corresponding dimension importance ranking and the predefined parameter budget. Furthermore, the proposed SSEDS is model-agnostic, meaning that it could be integrated into different base recommendation models. The extensive offline experiments are conducted on two widely used public datasets for CTR (Click Through Rate) prediction task, and the results demonstrate that SSEDS can still achieve strong recommendation performance even if it has reduced 90% parameters. Moreover, SSEDS has also been deployed on the WeChat Subscription platform for practical recommendation services. The 7-day online A/B test results show that SSEDS can significantly improve the performance of the online recommendation model while reducing resource consumption.|作为现代深度推荐系统的重要组成部分，特征嵌入将高维稀疏用户/项目特征映射为低维密集嵌入。然而，这些嵌入通常被分配一个统一的维度，这受到以下问题: (1)高内存使用和计算成本。(2)由于尺寸分配不合理而导致性能次优。为了解决上述问题，一些工作将嵌入维搜索问题转化为超参数优化问题或嵌入剪枝问题。然而，它们要么需要设计良好的超参数搜索空间，要么需要耗时的优化过程。本文提出了一种单镜头嵌入维度搜索方法 SSEDS，该方法通过单镜头嵌入剪枝操作，可以有效地为每个特征域分配维度，同时保持模型的推荐精度。具体地说，它引入了一个标准来识别每个特征字段的每个嵌入维的重要性。因此，SSEDS 可以根据相应的维重要性排序和预定义的参数预算，通过显式地减少冗余嵌入维数来自动获得混合维嵌入。此外，提出的 SSEDS 是模型无关的，这意味着它可以集成到不同的基本推荐模型中。在两个广泛使用的公共数据集上进行了广泛的离线实验，结果表明，即使 SSEDS 减少了90% 的参数，仍然可以获得很好的推荐性能。此外，SSEDS 还被部署在微信订阅平台上，提供实用的推荐服务。为期7天的在线 A/B 测试结果表明，SSEDS 在降低资源消耗的同时，可以显著提高在线推荐模型的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Single-shot+Embedding+Dimension+Search+in+Recommender+System)|1| |[Zero-shot Query Contextualization for Conversational Search](https://doi.org/10.1145/3477495.3531769)|Antonios Minas Krasakis, Andrew Yates, Evangelos Kanoulas|University of Amsterdam, Amsterdam, Netherlands|Current conversational passage retrieval systems cast conversational search into ad-hoc search by using an intermediate query resolution step that places the user's question in context of the conversation. While the proposed methods have proven effective, they still assume the availability of large-scale question resolution and conversational search datasets. To waive the dependency on the availability of such data, we adapt a pre-trained token-level dense retriever on ad-hoc search data to perform conversational search with no additional fine-tuning. The proposed method allows to contextualize the user question within the conversation history, but restrict the matching only between question and potential answer. Our experiments demonstrate the effectiveness of the proposed approach. We also perform an analysis that provides insights of how contextualization works in the latent space, in essence introducing a bias towards salient terms from the conversation.|当前的会话文本检索系统通过使用一个中间查询解析步骤，将用户的问题置于会话上下文中，从而将会话搜索转换为特定搜索。虽然提出的方法已被证明是有效的，但它们仍然假设大规模的问题解决和会话搜索数据集的可用性。为了摆脱对这些数据可用性的依赖，我们在自组织搜索数据上采用了一个预先训练的令牌级密集检索器来执行会话搜索，而不需要进行额外的微调。该方法允许在会话历史中上下文化用户问题，但只限制问题和潜在答案之间的匹配。实验证明了该方法的有效性。我们还进行了一个分析，提供了如何在潜在空间的情境化工作的见解，在本质上引入了一个从会话突出术语的偏见。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Zero-shot+Query+Contextualization+for+Conversational+Search)|1| -|[DisenCTR: Dynamic Graph-based Disentangled Representation for Click-Through Rate Prediction](https://doi.org/10.1145/3477495.3531851)|Yifan Wang, Yifang Qin, Fang Sun, Bo Zhang, Xuyang Hou, Ke Hu, Jia Cheng, Jun Lei, Ming Zhang|Peking University, Beijing, China; Meituan, Beijing, China|Click-through rate (CTR) prediction plays a critical role in recommender systems and other applications. Recently, modeling user behavior sequences attracts much attention and brings great improvements in the CTR field. Many existing works utilize attention mechanism or recurrent neural networks to exploit user interest from the sequence, but fail to recognize the simple truth that a user's real-time interests are inherently diverse and fluid. In this paper, we propose DisenCTR, a novel dynamic graph-based disentangled representation framework for CTR prediction. The key novelty of our method compared with existing approaches is to model evolving diverse interests of users. Specifically, we construct a time-evolving user-item interaction graph induced by historical interactions. And based on the rich dynamics supplied by the graph, we propose a disentangled graph representation module to extract diverse user interests. We further exploit the fluidity of user interests and model the temporal effect of historical behaviors using Mixture of Hawkes Process. Extensive experiments on three real-world datasets demonstrate the superior performance of our method comparing to state-of-the-art approaches.|在推荐系统和其他应用程序中，点进率(ctrl)预测起着至关重要的作用。近年来，用户行为序列建模引起了人们的广泛关注，并在 CTR 领域得到了很大的发展。现有的许多作品利用注意机制或反复神经网络从序列中挖掘用户兴趣，但未能认识到用户的实时兴趣具有内在的多样性和流动性这一简单事实。本文提出了一种新的基于动态图的分离表示框架 DisenCTR，用于 CTR 预测。与现有方法相比，我们的方法的关键新颖之处在于对用户的不同兴趣进行建模。具体来说，我们构造了一个由历史交互作用引起的时间演化的用户-项目交互图。基于图所提供的丰富的动态性，我们提出了一个分离的图表示模块来提取不同的用户兴趣。进一步利用用户兴趣的流动性，利用霍克斯过程混合模型对历史行为的时间效应进行建模。在三个真实世界数据集上的大量实验表明，与最先进的方法相比，我们的方法具有更好的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=DisenCTR:+Dynamic+Graph-based+Disentangled+Representation+for+Click-Through+Rate+Prediction)|1| +|[DisenCTR: Dynamic Graph-based Disentangled Representation for Click-Through Rate Prediction](https://doi.org/10.1145/3477495.3531851)|Yifan Wang, Yifang Qin, Fang Sun, Bo Zhang, Xuyang Hou, Ke Hu, Jia Cheng, Jun Lei, Ming Zhang|Meituan, Beijing, China; Peking University, Beijing, China|Click-through rate (CTR) prediction plays a critical role in recommender systems and other applications. Recently, modeling user behavior sequences attracts much attention and brings great improvements in the CTR field. Many existing works utilize attention mechanism or recurrent neural networks to exploit user interest from the sequence, but fail to recognize the simple truth that a user's real-time interests are inherently diverse and fluid. In this paper, we propose DisenCTR, a novel dynamic graph-based disentangled representation framework for CTR prediction. The key novelty of our method compared with existing approaches is to model evolving diverse interests of users. Specifically, we construct a time-evolving user-item interaction graph induced by historical interactions. And based on the rich dynamics supplied by the graph, we propose a disentangled graph representation module to extract diverse user interests. We further exploit the fluidity of user interests and model the temporal effect of historical behaviors using Mixture of Hawkes Process. Extensive experiments on three real-world datasets demonstrate the superior performance of our method comparing to state-of-the-art approaches.|在推荐系统和其他应用程序中，点进率(ctrl)预测起着至关重要的作用。近年来，用户行为序列建模引起了人们的广泛关注，并在 CTR 领域得到了很大的发展。现有的许多作品利用注意机制或反复神经网络从序列中挖掘用户兴趣，但未能认识到用户的实时兴趣具有内在的多样性和流动性这一简单事实。本文提出了一种新的基于动态图的分离表示框架 DisenCTR，用于 CTR 预测。与现有方法相比，我们的方法的关键新颖之处在于对用户的不同兴趣进行建模。具体来说，我们构造了一个由历史交互作用引起的时间演化的用户-项目交互图。基于图所提供的丰富的动态性，我们提出了一个分离的图表示模块来提取不同的用户兴趣。进一步利用用户兴趣的流动性，利用霍克斯过程混合模型对历史行为的时间效应进行建模。在三个真实世界数据集上的大量实验表明，与最先进的方法相比，我们的方法具有更好的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=DisenCTR:+Dynamic+Graph-based+Disentangled+Representation+for+Click-Through+Rate+Prediction)|1| |[BERT-based Dense Intra-ranking and Contextualized Late Interaction via Multi-task Learning for Long Document Retrieval](https://doi.org/10.1145/3477495.3531856)|Minghan Li, Éric Gaussier|Univ. Grenoble Alpes, CNRS, LIG, Grenoble, France|Combining query tokens and document tokens and inputting them to pre-trained transformer models like BERT, an approach known as interaction-based, has shown state-of-the-art effectiveness for information retrieval. However, the computational complexity of this approach is high due to the online self-attention computation. In contrast, dense retrieval methods in representation-based approaches are known to be efficient, however less effective. A tradeoff between the two is reached with late interaction methods like ColBERT, which attempt to benefit from both approaches: contextualized token embeddings can be pre-calculated over BERT for fine-grained effective interaction while preserving efficiency. However, despite its success in passage retrieval, it's not straightforward to use this approach for long document retrieval. In this paper, we propose a cascaded late interaction approach using a single model for long document retrieval. Fast intra-ranking by dot product is used to select relevant passages, then fine-grained interaction of pre-stored token embeddings is used to generate passage scores which are aggregated to the final document score. Multi-task learning is used to train a BERT model to optimize both a dot product and a fine-grained interaction loss functions. Our experiments reveal that the proposed approach obtains near state-of-the-art level effectiveness while being efficient on such collections as TREC 2019.|将查询令牌和文档令牌结合起来，并将它们输入到像 BERT 这样的经过预先训练的转换器模型中，这种方法被称为基于交互的方法，已经显示出对于信息检索的最先进的有效性。然而，由于在线自注意计算，这种方法的计算复杂度很高。相比之下，基于表示的方法中的密集检索方法已知是有效的，但是效率较低。这两种方法之间的折衷是通过 ColBERT 这样的后期交互方法实现的，它们试图从两种方法中受益: 上下文化的令牌嵌入可以在 BERT 上预先计算出细粒度的有效交互，同时保持效率。然而，尽管这种方法在文章检索方面取得了成功，但是要长时间地使用这种方法并不是一件简单的文献检索。在这篇文章中，我们提出了一个级联的晚期交互方法，使用单一模型的长期文献检索。首先利用点乘快速内排序来选择相关段落，然后利用预存储令牌嵌入的细粒度交互来生成段落分数，并将这些分数聚合为最终的文档分数。多任务学习用于训练 BERT 模型，以优化网点积和细粒度交互损失函数。我们的实验表明，所提出的方法获得接近最先进水平的效率，同时对 TREC 2019这样的集合是有效的。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=BERT-based+Dense+Intra-ranking+and+Contextualized+Late+Interaction+via+Multi-task+Learning+for+Long+Document+Retrieval)|1| |[RESETBERT4Rec: A Pre-training Model Integrating Time And User Historical Behavior for Sequential Recommendation](https://doi.org/10.1145/3477495.3532054)|Qihang Zhao|University of Science and Technology of China & JD AI Research, Hefei & Shanghai, China|Sequential recommendation methods are very important in modern recommender systems because they can well capture users' dynamic interests from their interaction history, and make accurate recommendations for users, thereby helping enterprises succeed in business. However, despite the great success of existing sequential recommendation-based methods, they focus too much on item-level modeling of users' click history and lack information about the user's entire click history (such as click order, click time, etc.). To tackle this problem, inspired by recent advances in pre-training techniques in the field of natural language processing, we build a new pre-training task based on the original BERT pre-training framework and incorporate temporal information. Specifically, we propose a new model called the RE arrange S equence prE -training and T ime embedding model via BERT for sequential R ecommendation (RESETBERT4Rec ) \footnoteThis work was completed during JD internship., it further captures the information of the user's whole click history by adding a rearrange sequence prediction task to the original BERT pre-training framework, while it integrates different views of time information. Comprehensive experiments on two public datasets as well as one e-commerce dataset demonstrate that RESETBERT4Rec achieves state-of-the-art performance over existing baselines.|序贯推荐方法在现代推荐系统中具有重要意义，因为它能够很好地从用户的交互历史中捕捉用户的动态兴趣，为用户提供准确的推荐，从而帮助企业获得成功。然而，尽管现有的基于顺序推荐的方法取得了巨大的成功，但它们过于关注用户点击历史的项目级建模，缺乏关于用户整个点击历史的信息(如点击顺序、点击时间等)。为了解决这一问题，受自然语言处理领域预训练技术的最新进展的启发，我们在原有的 BERT 预训练框架的基础上，结合时间信息构建了一个新的预训练任务。具体来说，我们提出了一个新的模型，称为 RE 安排 S 序列预训练和 T 时间嵌入模型，通过 BERT 进行顺序 R 推荐(RESETBERT4Rec)注释这项工作是在 JD 实习期间完成的，它通过在原有的 BERT 预训练框架中增加一个重排序列预测任务，进一步获取用户的整个点击历史信息，同时整合不同的时间信息视图。对两个公共数据集和一个电子商务数据集的综合实验表明，RESETBERT4Rec 在现有的基线上取得了最先进的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=RESETBERT4Rec:+A+Pre-training+Model+Integrating+Time+And+User+Historical+Behavior+for+Sequential+Recommendation)|1| -|[Clustering based Behavior Sampling with Long Sequential Data for CTR Prediction](https://doi.org/10.1145/3477495.3531829)|Yuren Zhang, Enhong Chen, Binbin Jin, Hao Wang, Min Hou, Wei Huang, Runlong Yu|Huawei Cloud Computing Technologies Co., Ltd., Hangzhou, Zhejiang, China; University of Science and Technology of China & State Key Laboratory of Cognitive Intelligence, Hefei, Anhui, China|Click-through rate (CTR) prediction is fundamental in many industrial applications, such as online advertising and recommender systems. With the development of the online platforms, the sequential user behaviors grow rapidly, bringing us great opportunity to better understand user preferences.However, it is extremely challenging for existing sequential models to effectively utilize the entire behavior history of each user. First, there is a lot of noise in such long histories, which can seriously hurt the prediction performance. Second, feeding the long behavior sequence directly results in infeasible inference time and storage cost. In order to tackle these challenges, in this paper we propose a novel framework, which we name as User Behavior Clustering Sampling (UBCS). In UBCS, short sub-sequences will be obtained from the whole user history sequence with two cascaded modules: (i) Behavior Sampling module samples short sequences related to candidate items using a novel sampling method which takes relevance and temporal information into consideration; (ii) Item Clustering module clusters items into a small number of cluster centroids, mitigating the impact of noise and improving efficiency. Then, the sampled short sub-sequences will be fed into the CTR prediction module for efficient prediction. Moreover, we conduct a self-supervised consistency pre-training task to extract user persona preference and optimize the sampling module effectively. Experiments on real-world datasets demonstrate the superiority and efficiency of our proposed framework.|在许多工业应用中，如在线广告和推荐系统中，点进率(ctrl)预测是基础。随着在线平台的发展，连续用户行为迅速增长，为我们更好地理解用户偏好带来了巨大的机遇。然而，对于现有的顺序模型来说，有效地利用每个用户的整个行为历史是极具挑战性的。首先，在如此长的历史中存在大量的噪声，这会严重影响预测性能。其次，长行为序列直接导致不可行的推理时间和存储成本。为了应对这些挑战，本文提出了一个新的框架，我们称之为用户行为聚类抽样(UBCS)。在 UBCS 中，通过两个级联模块从整个用户历史序列中获取短子序列: (1)行为采样模块采用一种新的考虑相关性和时间信息的采样方法对与候选项相关的短子序列进行采样; (2)项目聚类模块将项目聚类为少量的聚类质心，减少噪声的影响，提高效率。然后，将采样的短子序列输入 CTR 预测模块进行有效预测。此外，我们进行了自我监督的一致性预训练任务，以提取用户的人物偏好，并有效地优化抽样模块。在实际数据集上的实验表明了该框架的优越性和有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Clustering+based+Behavior+Sampling+with+Long+Sequential+Data+for+CTR+Prediction)|1| -|[Matching Search Result Diversity with User Diversity Acceptance in Web Search Sessions](https://doi.org/10.1145/3477495.3531880)|Jiqun Liu, Fangyuan Han|The University of Oklahoma, Norman, OK, USA; Xiamen University, Xiamen, China|Promoting diversity in ranking while maintaining the relevance of ranked results is critical for enhancing human-centered search systems. While existing ranking algorithm and diversity IR metrics provide a solid basis for evaluating and improving search result diversification in offline experiments, it misses out possible divergences and temporal changes of users' levels of Diversity Acceptance, which in this work refers to the extent to which users actually prefer to interact with topically diversified search results. To address this gap between offline evaluations and users' expectations, we proposed an intuitive diversity acceptance measure and ran experiments for diversity acceptance prediction and diversity-aware re-ranking based on datasets from both controlled lab and naturalistic settings. Our results demonstrate that: 1) user diversity acceptance change across different query segments and session contexts, and can be predicted from search interaction signals; 2) our diversity-aware re-ranking algorithm utilizing predicted diversity acceptance and estimated relevance labels can effectively minimize the gap between diversity acceptance and result diversity, while maintaining SERP relevance levels. Our research presents an initial attempt on balancing user needs, result diversity, and SERP relevance in sessions and highlights the importance of studying diversity acceptance in promoting effective result diversification.|促进排名的多样性，同时保持排名结果的相关性，对于加强以人为中心的搜索系统至关重要。虽然现有的排名算法和多样性 IR 指标为评估和改善离线实验中的搜索结果多样性提供了坚实的基础，但它忽略了用户多样性接受水平的可能的分歧和时间变化，这在本文中指的是用户实际上更喜欢与主题多样化的搜索结果交互的程度。为了解决离线评估和用户期望之间的差距，我们提出了一个直观的多样性接受度量，并进行了基于受控实验室和自然环境数据集的多样性接受预测和多样性感知重新排序的实验。研究结果表明: 1)用户多样性接受度在不同查询段和会话上下文之间的变化，可以通过搜索交互信号进行预测; 2)我们的多样性感知重排算法利用预测的多样性接受度和估计的相关标签，可以有效地最小化多样性接受度和结果多样性之间的差距，同时保持 SERP 相关水平。我们的研究提出了在会议中平衡用户需求、结果多样性和 SERP 相关性的初步尝试，并强调了研究多样性接受在促进有效结果多样化中的重要性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Matching+Search+Result+Diversity+with+User+Diversity+Acceptance+in+Web+Search+Sessions)|1| +|[Clustering based Behavior Sampling with Long Sequential Data for CTR Prediction](https://doi.org/10.1145/3477495.3531829)|Yuren Zhang, Enhong Chen, Binbin Jin, Hao Wang, Min Hou, Wei Huang, Runlong Yu|University of Science and Technology of China & State Key Laboratory of Cognitive Intelligence, Hefei, Anhui, China; Huawei Cloud Computing Technologies Co., Ltd., Hangzhou, Zhejiang, China|Click-through rate (CTR) prediction is fundamental in many industrial applications, such as online advertising and recommender systems. With the development of the online platforms, the sequential user behaviors grow rapidly, bringing us great opportunity to better understand user preferences.However, it is extremely challenging for existing sequential models to effectively utilize the entire behavior history of each user. First, there is a lot of noise in such long histories, which can seriously hurt the prediction performance. Second, feeding the long behavior sequence directly results in infeasible inference time and storage cost. In order to tackle these challenges, in this paper we propose a novel framework, which we name as User Behavior Clustering Sampling (UBCS). In UBCS, short sub-sequences will be obtained from the whole user history sequence with two cascaded modules: (i) Behavior Sampling module samples short sequences related to candidate items using a novel sampling method which takes relevance and temporal information into consideration; (ii) Item Clustering module clusters items into a small number of cluster centroids, mitigating the impact of noise and improving efficiency. Then, the sampled short sub-sequences will be fed into the CTR prediction module for efficient prediction. Moreover, we conduct a self-supervised consistency pre-training task to extract user persona preference and optimize the sampling module effectively. Experiments on real-world datasets demonstrate the superiority and efficiency of our proposed framework.|在许多工业应用中，如在线广告和推荐系统中，点进率(ctrl)预测是基础。随着在线平台的发展，连续用户行为迅速增长，为我们更好地理解用户偏好带来了巨大的机遇。然而，对于现有的顺序模型来说，有效地利用每个用户的整个行为历史是极具挑战性的。首先，在如此长的历史中存在大量的噪声，这会严重影响预测性能。其次，长行为序列直接导致不可行的推理时间和存储成本。为了应对这些挑战，本文提出了一个新的框架，我们称之为用户行为聚类抽样(UBCS)。在 UBCS 中，通过两个级联模块从整个用户历史序列中获取短子序列: (1)行为采样模块采用一种新的考虑相关性和时间信息的采样方法对与候选项相关的短子序列进行采样; (2)项目聚类模块将项目聚类为少量的聚类质心，减少噪声的影响，提高效率。然后，将采样的短子序列输入 CTR 预测模块进行有效预测。此外，我们进行了自我监督的一致性预训练任务，以提取用户的人物偏好，并有效地优化抽样模块。在实际数据集上的实验表明了该框架的优越性和有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Clustering+based+Behavior+Sampling+with+Long+Sequential+Data+for+CTR+Prediction)|1| +|[Matching Search Result Diversity with User Diversity Acceptance in Web Search Sessions](https://doi.org/10.1145/3477495.3531880)|Jiqun Liu, Fangyuan Han|Xiamen University, Xiamen, China; The University of Oklahoma, Norman, OK, USA|Promoting diversity in ranking while maintaining the relevance of ranked results is critical for enhancing human-centered search systems. While existing ranking algorithm and diversity IR metrics provide a solid basis for evaluating and improving search result diversification in offline experiments, it misses out possible divergences and temporal changes of users' levels of Diversity Acceptance, which in this work refers to the extent to which users actually prefer to interact with topically diversified search results. To address this gap between offline evaluations and users' expectations, we proposed an intuitive diversity acceptance measure and ran experiments for diversity acceptance prediction and diversity-aware re-ranking based on datasets from both controlled lab and naturalistic settings. Our results demonstrate that: 1) user diversity acceptance change across different query segments and session contexts, and can be predicted from search interaction signals; 2) our diversity-aware re-ranking algorithm utilizing predicted diversity acceptance and estimated relevance labels can effectively minimize the gap between diversity acceptance and result diversity, while maintaining SERP relevance levels. Our research presents an initial attempt on balancing user needs, result diversity, and SERP relevance in sessions and highlights the importance of studying diversity acceptance in promoting effective result diversification.|促进排名的多样性，同时保持排名结果的相关性，对于加强以人为中心的搜索系统至关重要。虽然现有的排名算法和多样性 IR 指标为评估和改善离线实验中的搜索结果多样性提供了坚实的基础，但它忽略了用户多样性接受水平的可能的分歧和时间变化，这在本文中指的是用户实际上更喜欢与主题多样化的搜索结果交互的程度。为了解决离线评估和用户期望之间的差距，我们提出了一个直观的多样性接受度量，并进行了基于受控实验室和自然环境数据集的多样性接受预测和多样性感知重新排序的实验。研究结果表明: 1)用户多样性接受度在不同查询段和会话上下文之间的变化，可以通过搜索交互信号进行预测; 2)我们的多样性感知重排算法利用预测的多样性接受度和估计的相关标签，可以有效地最小化多样性接受度和结果多样性之间的差距，同时保持 SERP 相关水平。我们的研究提出了在会议中平衡用户需求、结果多样性和 SERP 相关性的初步尝试，并强调了研究多样性接受在促进有效结果多样化中的重要性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Matching+Search+Result+Diversity+with+User+Diversity+Acceptance+in+Web+Search+Sessions)|1| |[Distill-VQ: Learning Retrieval Oriented Vector Quantization By Distilling Knowledge from Dense Embeddings](https://doi.org/10.1145/3477495.3531799)|Shitao Xiao, Zheng Liu, Weihao Han, Jianjin Zhang, Defu Lian, Yeyun Gong, Qi Chen, Fan Yang, Hao Sun, Yingxia Shao, Xing Xie||Vector quantization (VQ) based ANN indexes, such as Inverted File System (IVF) and Product Quantization (PQ), have been widely applied to embedding based document retrieval thanks to the competitive time and memory efficiency. Originally, VQ is learned to minimize the reconstruction loss, i.e., the distortions between the original dense embeddings and the reconstructed embeddings after quantization. Unfortunately, such an objective is inconsistent with the goal of selecting ground-truth documents for the input query, which may cause severe loss of retrieval quality. Recent works identify such a defect, and propose to minimize the retrieval loss through contrastive learning. However, these methods intensively rely on queries with ground-truth documents, whose performance is limited by the insufficiency of labeled data. In this paper, we propose Distill-VQ, which unifies the learning of IVF and PQ within a knowledge distillation framework. In Distill-VQ, the dense embeddings are leveraged as "teachers'', which predict the query's relevance to the sampled documents. The VQ modules are treated as the "students'', which are learned to reproduce the predicted relevance, such that the reconstructed embeddings may fully preserve the retrieval result of the dense embeddings. By doing so, Distill-VQ is able to derive substantial training signals from the massive unlabeled data, which significantly contributes to the retrieval quality. We perform comprehensive explorations for the optimal conduct of knowledge distillation, which may provide useful insights for the learning of VQ based ANN index. We also experimentally show that the labeled data is no longer a necessity for high-quality vector quantization, which indicates Distill-VQ's strong applicability in practice. The evaluations are performed on MS MARCO and Natural Questions benchmarks, where Distill-VQ notably outperforms the SOTA VQ methods in Recall and MRR. Our code is avaliable at https://github.com/staoxiao/LibVQ.|基于向量量化(vQ)的人工神经网络索引，例如倒置文件系统(IVF)和产品量化(PQ) ，由于具有竞争性的时间和存储效率，已被广泛应用于基于嵌入的文献检索。最初，学习 VQ 来最小化重构损失，即原始密集嵌入和量化后重构嵌入之间的失真。遗憾的是，这样的目标不符合为输入查询选择地面真实文档的目标，这可能导致检索质量的严重损失。最近的工作发现了这一缺陷，并提出通过对比学习来最小化检索损失。然而，这些方法主要依赖于对地面真相文档的查询，其性能受到标记数据不足的限制。在本文中，我们提出了提取 VQ，它在一个知识提取框架内将 IVF 和 PQ 的学习结合起来。在蒸馏 VQ 中，密集嵌入被用作“教师”，用于预测查询与采样文档的相关性。将 VQ 模块视为“学生”，学习再现预测的相关性，使得重构嵌入能够完全保留密集嵌入的检索结果。通过这种方法，DistilVQ 能够从海量的未标记数据中提取出大量的训练信号，从而大大提高了检索质量。本文对知识提取的最优化进行了全面的探索，为基于矢量量化的神经网络指标的学习提供了有益的启示。我们还通过实验表明，标记数据不再是高质量向量量化的必要条件，这表明蒸馏 VQ 在实践中的强大适用性。评估是在微软 MARCO 和自然问题基准上进行的，其中蒸馏 VQ 明显优于召回和 MRR 中的 SOTA VQ 方法。我们的代码 https://github.com/staoxiao/libvq 可用。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Distill-VQ:+Learning+Retrieval+Oriented+Vector+Quantization+By+Distilling+Knowledge+from+Dense+Embeddings)|1| |[Neural Pseudo-Relevance Feedback Models for Sparse and Dense Retrieval](https://doi.org/10.1145/3477495.3531685)|Xiao Wang|University of Glasgow, Glasgow, Scotland, United Kingdom|Pseudo-relevance feedback mechanisms have long served as an effective technique to improve the retrieval effectiveness in information retrieval. Recently, large pre-trained language models, such as T5 and BERT, have shown a strong capacity to capture the latent traits of texts. Given the success of these models, we seek to study the capacity of these models for query reformulation. In addition, the BERT models have demonstrated further promise for dense retrieval, where the query and documents are encoded into the contextualised embeddings and relevant documents are retrieved by conducting the semantic matching operation. Although the success of pseudo-relevance feedback for sparse retrieval is well documented, effective pseudo-relevance feedback approaches for dense retrieval paradigm are still in their infancy. Thus, we are concerned with excavating the potential of the pseudo-relevance feedback information combined with the large pre-trained models to conduct effective query reformulation operating on both sparse retrieval and dense retrieval.|长期以来，伪相关反馈机制一直是提高信息检索检索效率的有效方法。近年来，大型的预训练语言模型，如 T5和 BERT，已经显示出很强的捕捉文本潜在特征的能力。鉴于这些模型的成功，我们寻求研究这些模型的查询重构能力。此外，BERT 模型还进一步展示了密集检索的前景，其中查询和文档被编码到上下文嵌入中，相关文档通过进行语义匹配操作进行检索。虽然伪相关反馈在稀疏检索方面的成功已有文献记载，但有效的伪相关反馈方法在密集检索范式中仍处于起步阶段。因此，我们致力于挖掘伪相关反馈信息与大型预训练模型相结合的潜力，对稀疏检索和密集检索进行有效的查询重构。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Neural+Pseudo-Relevance+Feedback+Models+for+Sparse+and+Dense+Retrieval)|1| -|[Neighbour Interaction based Click-Through Rate Prediction via Graph-masked Transformer](https://doi.org/10.1145/3477495.3532031)|Erxue Min, Yu Rong, Tingyang Xu, Yatao Bian, Da Luo, Kangyi Lin, Junzhou Huang, Sophia Ananiadou, Peilin Zhao|The University of Manchester, Manchester, UNK, United Kingdom; Tencent AI Lab, Shenzhen, UNK, China; Tencent AI lab, Shenzhen, UNK, China; University of Manchester, Manchester, UNK, United Kingdom; Weixin Open Platform, Tencent, Guangzhou, UNK, China; University of Texas at Arlington, Arlington, UNK, USA|Click-Through Rate (CTR) prediction, which aims to estimate the probability that a user will click an item, is an essential component of online advertising. Existing methods mainly attempt to mine user interests from users' historical behaviours, which contain users' directly interacted items. Although these methods have made great progress, they are often limited by the recommender system's direct exposure and inactive interactions, and thus fail to mine all potential user interests. To tackle these problems, we propose Neighbor-Interaction based CTR prediction (NI-CTR), which considers this task under a Heterogeneous Information Network (HIN) setting. In short, Neighbor-Interaction based CTR prediction involves the local neighborhood of the target user-item pair in the HIN to predict their linkage. In order to guide the representation learning of the local neighbourhood, we further consider different kinds of interactions among the local neighborhood nodes from both explicit and implicit perspective, and propose a novel Graph-Masked Transformer (GMT) to effectively incorporates these kinds of interactions to produce highly representative embeddings for the target user-item pair. Moreover, in order to improve model robustness against neighbour sampling, we enforce a consistency regularization loss over the neighbourhood embedding. We conduct extensive experiments on two real-world datasets with millions of instances and the experimental results show that our proposed method outperforms state-of-the-art CTR models significantly. Meanwhile, the comprehensive ablation studies verify the effectiveness of every component of our model. Furthermore, we have deployed this framework on the WeChat Official Account Platform with billions of users. The online A/B tests demonstrate an average CTR improvement of 21.9% against all online baselines.|点进率预测是在线广告的一个重要组成部分，其目的是估计用户点击某个项目的概率。现有的方法主要是从用户的历史行为中挖掘用户兴趣，这些历史行为包含了用户直接交互的项目。尽管这些方法已经取得了很大的进步，但它们往往受到推荐系统直接暴露和非活动交互的限制，因此无法挖掘所有潜在的用户兴趣。为了解决这些问题，我们提出了基于邻居交互的点击率预测(NI-CTR) ，它考虑了异构信息网络(HIN)环境下的任务。简而言之，基于邻域交互的 CTR 预测涉及到 HIN 中目标用户-项目对的局部邻域来预测它们之间的联系。为了指导局部邻域的表示学习，我们进一步从显式和隐式两个角度考虑局部邻域节点之间的不同交互，并提出了一种新的图掩盖变换器(GMT) ，以有效地整合这些交互，为目标用户项对产生高度代表性的嵌入。此外，为了提高模型对邻域采样的鲁棒性，我们对邻域嵌入增加了一个一致性正则化损失。我们在两个实际数据集上进行了大量的实验，实验结果表明，我们提出的方法明显优于最先进的 CTR 模型。同时，综合烧蚀研究验证了模型各组成部分的有效性。此外，我们已经在微信官方账号平台上部署了这个框架，拥有数十亿用户。在线 A/B 测试显示，与所有在线基线相比，点击率平均提高了21.9% 。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Neighbour+Interaction+based+Click-Through+Rate+Prediction+via+Graph-masked+Transformer)|1| +|[Neighbour Interaction based Click-Through Rate Prediction via Graph-masked Transformer](https://doi.org/10.1145/3477495.3532031)|Erxue Min, Yu Rong, Tingyang Xu, Yatao Bian, Da Luo, Kangyi Lin, Junzhou Huang, Sophia Ananiadou, Peilin Zhao|University of Texas at Arlington, Arlington, UNK, USA; Tencent AI lab, Shenzhen, UNK, China; Tencent AI Lab, Shenzhen, UNK, China; The University of Manchester, Manchester, UNK, United Kingdom; University of Manchester, Manchester, UNK, United Kingdom; Weixin Open Platform, Tencent, Guangzhou, UNK, China|Click-Through Rate (CTR) prediction, which aims to estimate the probability that a user will click an item, is an essential component of online advertising. Existing methods mainly attempt to mine user interests from users' historical behaviours, which contain users' directly interacted items. Although these methods have made great progress, they are often limited by the recommender system's direct exposure and inactive interactions, and thus fail to mine all potential user interests. To tackle these problems, we propose Neighbor-Interaction based CTR prediction (NI-CTR), which considers this task under a Heterogeneous Information Network (HIN) setting. In short, Neighbor-Interaction based CTR prediction involves the local neighborhood of the target user-item pair in the HIN to predict their linkage. In order to guide the representation learning of the local neighbourhood, we further consider different kinds of interactions among the local neighborhood nodes from both explicit and implicit perspective, and propose a novel Graph-Masked Transformer (GMT) to effectively incorporates these kinds of interactions to produce highly representative embeddings for the target user-item pair. Moreover, in order to improve model robustness against neighbour sampling, we enforce a consistency regularization loss over the neighbourhood embedding. We conduct extensive experiments on two real-world datasets with millions of instances and the experimental results show that our proposed method outperforms state-of-the-art CTR models significantly. Meanwhile, the comprehensive ablation studies verify the effectiveness of every component of our model. Furthermore, we have deployed this framework on the WeChat Official Account Platform with billions of users. The online A/B tests demonstrate an average CTR improvement of 21.9% against all online baselines.|点进率预测是在线广告的一个重要组成部分，其目的是估计用户点击某个项目的概率。现有的方法主要是从用户的历史行为中挖掘用户兴趣，这些历史行为包含了用户直接交互的项目。尽管这些方法已经取得了很大的进步，但它们往往受到推荐系统直接暴露和非活动交互的限制，因此无法挖掘所有潜在的用户兴趣。为了解决这些问题，我们提出了基于邻居交互的点击率预测(NI-CTR) ，它考虑了异构信息网络(HIN)环境下的任务。简而言之，基于邻域交互的 CTR 预测涉及到 HIN 中目标用户-项目对的局部邻域来预测它们之间的联系。为了指导局部邻域的表示学习，我们进一步从显式和隐式两个角度考虑局部邻域节点之间的不同交互，并提出了一种新的图掩盖变换器(GMT) ，以有效地整合这些交互，为目标用户项对产生高度代表性的嵌入。此外，为了提高模型对邻域采样的鲁棒性，我们对邻域嵌入增加了一个一致性正则化损失。我们在两个实际数据集上进行了大量的实验，实验结果表明，我们提出的方法明显优于最先进的 CTR 模型。同时，综合烧蚀研究验证了模型各组成部分的有效性。此外，我们已经在微信官方账号平台上部署了这个框架，拥有数十亿用户。在线 A/B 测试显示，与所有在线基线相比，点击率平均提高了21.9% 。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Neighbour+Interaction+based+Click-Through+Rate+Prediction+via+Graph-masked+Transformer)|1| |[Why Don't You Click: Understanding Non-Click Results in Web Search with Brain Signals](https://doi.org/10.1145/3477495.3532082)|Ziyi Ye, Xiaohui Xie, Yiqun Liu, Zhihong Wang, Xuancheng Li, Jiaji Li, Xuesong Chen, Min Zhang, Shaoping Ma|Tsinghua University, Beijing, China; The Chinese University of Hong Kong, Shenzhen, Shenzhen, China|Web search heavily relies on click-through behavior as an essential feedback signal for performance evaluation and improvement. Traditionally, click is usually treated as a positive implicit feedback signal of relevance or usefulness, while non-click is regarded as a signal of irrelevance or uselessness. However, there are many cases where users satisfy their information need with the contents shown on the Search Engine Result Page (SERP). This raises the problem of measuring the usefulness of non-click results and modeling user satisfaction in such circumstances. For a long period, understanding non-click results is challenging owing to the lack of user interactions. In recent years, the rapid development of neuroimaging technologies constitutes a paradigm shift in various industries, e.g., search, entertainment, and education. Therefore, we benefit from these technologies and apply them to bridge the gap between the human mind and the external search system in non-click situations. To this end, we analyze the differences in brain signals between the examination of non-click search results in different usefulness levels. Inspired by these findings, we conduct supervised learning tasks to estimate the usefulness of non-click results with brain signals and conventional information (i.e., content and context factors). Furthermore, we devise two re-ranking methods, i.e., a Personalized Method (PM) and a Generalized Intent modeling Method (GIM), for search result re-ranking with the estimated usefulness. Results show that it is feasible to utilize brain signals to improve usefulness estimation performance and enhance human-computer interactions by search result re-ranking.|网络搜索在很大程度上依赖于点击行为作为性能评估和改进的重要反馈信号。传统上，点击通常被视为相关性或有用性的积极隐性反馈信号，而非点击则被视为无关性或无用性的信号。但是，在许多情况下，用户使用搜索引擎结果页(SERP)上显示的内容来满足他们的信息需求。这就提出了测量非点击结果的有用性以及在这种情况下建立用户满意度模型的问题。长期以来，由于缺乏用户交互，理解非点击结果是一项挑战。近年来，神经影像技术的快速发展构成了搜索、娱乐和教育等多个行业的范式转变。因此，我们从这些技术中受益，并应用它们在非点击情况下架起人类思维和外部搜索系统之间的桥梁。为此，我们分析了不同有用性水平的非点击检索结果在大脑信号方面的差异。受这些发现的启发，我们进行了一些监督式学习的任务，用大脑信号和传统信息(即内容和上下文因素)来评估非点击结果的有用性。此外，我们设计了两个重新排序的方法，即个性化方法(PM)和广义意图建模方法(GIM) ，用于搜索结果的重新排序与估计的有用性。实验结果表明，利用大脑信号进行搜索结果重排可以提高有用性估计性能，增强人机交互。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Why+Don't+You+Click:+Understanding+Non-Click+Results+in+Web+Search+with+Brain+Signals)|1| -|[Hierarchical Multi-Task Graph Recurrent Network for Next POI Recommendation](https://doi.org/10.1145/3477495.3531989)|Nicholas Lim, Bryan Hooi, SeeKiong Ng, Yong Liang Goh, Renrong Weng, Rui Tan|National University of Singapore, Singapore, Singapore; GrabTaxi Holdings, Singapore, Singapore|Learning which Point-of-Interest (POI) a user will visit next is a challenging task for personalized recommender systems due to the large search space of possible POIs in the region. A recurring problem among existing works that makes it difficult to learn and perform well is the sparsity of the User-POI matrix. In this paper, we propose our Hierarchical Multi-Task Graph Recurrent Network (HMT-GRN) approach, which alleviates the data sparsity problem by learning different User-Region matrices of lower sparsities in a multi-task setting. We then perform a Hierarchical Beam Search (HBS) on the different region and POI distributions to hierarchically reduce the search space with increasing spatial granularity and predict the next POI. Our HBS provides efficiency gains by reducing the search space, resulting in speedups of 5 to 7 times over an exhaustive approach. In addition, we also propose a novel selectivity layer to predict if the next POI has been visited before by the user to balance between personalization and exploration. Experimental results on two real-world Location-Based Social Network (LBSN) datasets show that our model significantly outperforms baseline and the state-of-the-art methods.|由于该地区可能存在的 POI 搜索空间很大，因此了解用户下一步将访问哪个 POI 对于个性化推荐系统来说是一项具有挑战性的任务。现有作品中反复出现的一个难以学习和执行的问题是 User-POI 矩阵的稀疏性。本文提出了一种分层多任务图回归网络(HMT-GRN)方法，通过在多任务环境下学习不同稀疏度较低的用户区域矩阵来解决数据稀疏问题。然后对不同区域和 POI 分布进行分层束搜索(HBS) ，随着空间粒度的增加逐步减少搜索空间，并预测下一个 POI。我们的 HBS 通过减少搜索空间提供了效率增益，在一个详尽的方法中导致5到7倍的加速。此外，我们还提出了一个新的选择层来预测下一个 POI 是否已经被用户访问过，以便在个性化和探索之间取得平衡。在两个实际的基于位置的社会网络(LBSN)数据集上的实验结果表明，我们的模型明显优于基线和最先进的方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Hierarchical+Multi-Task+Graph+Recurrent+Network+for+Next+POI+Recommendation)|1| -|[Progressive Self-Attention Network with Unsymmetrical Positional Encoding for Sequential Recommendation](https://doi.org/10.1145/3477495.3531800)|Yuehua Zhu, Bo Huang, Shaohua Jiang, Muli Yang, Yanhua Yang, Wenliang Zhong|Xidian University, Xian, China; AntGroup, Hangzhou, China|In real-world recommendation systems, the preferences of users are often affected by long-term constant interests and short-term temporal needs. The recently proposed Transformer-based models have proved superior in the sequential recommendation, modeling temporal dynamics globally via the remarkable self-attention mechanism. However, all equivalent item-item interactions in original self-attention are cumbersome, failing to capture the drifting of users' local preferences, which contain abundant short-term patterns. In this paper, we propose a novel interpretable convolutional self-attention, which efficiently captures both short- and long-term patterns with a progressive attention distribution. Specifically, a down-sampling convolution module is proposed to segment the overall long behavior sequence into a series of local subsequences. Accordingly, the segments are interacted with each item in the self-attention layer to produce locality-aware contextual representations, during which the quadratic complexity in original self-attention is reduced to nearly linear complexity. Moreover, to further enhance the robust feature learning in the context of Transformers, an unsymmetrical positional encoding strategy is carefully designed. Extensive experiments are carried out on real-world datasets, \eg ML-1M, Amazon Books, and Yelp, indicating that the proposed method outperforms the state-of-the-art methods w.r.t. both effectiveness and efficiency.|在实际的推荐系统中，用户的偏好往往受到长期不变的利益和短期的时间需求的影响。最近提出的变压器为基础的模型已被证明优越的顺序推荐，建模全球时间动态通过显着的自我注意机制。然而，原始自我注意中的所有等效项目-项目交互都是繁琐的，未能捕捉到用户本地偏好的漂移，其中包含了丰富的短期模式。在本文中，我们提出了一种新的可解释的卷积自我注意，有效地捕捉短期和长期的模式与逐步注意分布。特别地，提出了一种下采样卷积模块，将整个长行为序列分割成一系列局部子序列。相应地，这些片段与自我注意层中的每个项目相互作用，产生具有局部感知的上下文表征，在此过程中，原始自我注意的二次复杂度降低到接近线性复杂度。此外，为了进一步提高变压器环境下的鲁棒性特征学习，设计了一种非对称的位置编码策略。在现实世界的数据集上进行了大量的实验，例如 ML-1M，Amazon Books 和 Yelp，表明所提出的方法在效率和效果上都优于最先进的方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Progressive+Self-Attention+Network+with+Unsymmetrical+Positional+Encoding+for+Sequential+Recommendation)|1| +|[Hierarchical Multi-Task Graph Recurrent Network for Next POI Recommendation](https://doi.org/10.1145/3477495.3531989)|Nicholas Lim, Bryan Hooi, SeeKiong Ng, Yong Liang Goh, Renrong Weng, Rui Tan|GrabTaxi Holdings, Singapore, Singapore; National University of Singapore, Singapore, Singapore|Learning which Point-of-Interest (POI) a user will visit next is a challenging task for personalized recommender systems due to the large search space of possible POIs in the region. A recurring problem among existing works that makes it difficult to learn and perform well is the sparsity of the User-POI matrix. In this paper, we propose our Hierarchical Multi-Task Graph Recurrent Network (HMT-GRN) approach, which alleviates the data sparsity problem by learning different User-Region matrices of lower sparsities in a multi-task setting. We then perform a Hierarchical Beam Search (HBS) on the different region and POI distributions to hierarchically reduce the search space with increasing spatial granularity and predict the next POI. Our HBS provides efficiency gains by reducing the search space, resulting in speedups of 5 to 7 times over an exhaustive approach. In addition, we also propose a novel selectivity layer to predict if the next POI has been visited before by the user to balance between personalization and exploration. Experimental results on two real-world Location-Based Social Network (LBSN) datasets show that our model significantly outperforms baseline and the state-of-the-art methods.|由于该地区可能存在的 POI 搜索空间很大，因此了解用户下一步将访问哪个 POI 对于个性化推荐系统来说是一项具有挑战性的任务。现有作品中反复出现的一个难以学习和执行的问题是 User-POI 矩阵的稀疏性。本文提出了一种分层多任务图回归网络(HMT-GRN)方法，通过在多任务环境下学习不同稀疏度较低的用户区域矩阵来解决数据稀疏问题。然后对不同区域和 POI 分布进行分层束搜索(HBS) ，随着空间粒度的增加逐步减少搜索空间，并预测下一个 POI。我们的 HBS 通过减少搜索空间提供了效率增益，在一个详尽的方法中导致5到7倍的加速。此外，我们还提出了一个新的选择层来预测下一个 POI 是否已经被用户访问过，以便在个性化和探索之间取得平衡。在两个实际的基于位置的社会网络(LBSN)数据集上的实验结果表明，我们的模型明显优于基线和最先进的方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Hierarchical+Multi-Task+Graph+Recurrent+Network+for+Next+POI+Recommendation)|1| +|[Progressive Self-Attention Network with Unsymmetrical Positional Encoding for Sequential Recommendation](https://doi.org/10.1145/3477495.3531800)|Yuehua Zhu, Bo Huang, Shaohua Jiang, Muli Yang, Yanhua Yang, Wenliang Zhong|AntGroup, Hangzhou, China; Xidian University, Xian, China|In real-world recommendation systems, the preferences of users are often affected by long-term constant interests and short-term temporal needs. The recently proposed Transformer-based models have proved superior in the sequential recommendation, modeling temporal dynamics globally via the remarkable self-attention mechanism. However, all equivalent item-item interactions in original self-attention are cumbersome, failing to capture the drifting of users' local preferences, which contain abundant short-term patterns. In this paper, we propose a novel interpretable convolutional self-attention, which efficiently captures both short- and long-term patterns with a progressive attention distribution. Specifically, a down-sampling convolution module is proposed to segment the overall long behavior sequence into a series of local subsequences. Accordingly, the segments are interacted with each item in the self-attention layer to produce locality-aware contextual representations, during which the quadratic complexity in original self-attention is reduced to nearly linear complexity. Moreover, to further enhance the robust feature learning in the context of Transformers, an unsymmetrical positional encoding strategy is carefully designed. Extensive experiments are carried out on real-world datasets, \eg ML-1M, Amazon Books, and Yelp, indicating that the proposed method outperforms the state-of-the-art methods w.r.t. both effectiveness and efficiency.|在实际的推荐系统中，用户的偏好往往受到长期不变的利益和短期的时间需求的影响。最近提出的变压器为基础的模型已被证明优越的顺序推荐，建模全球时间动态通过显着的自我注意机制。然而，原始自我注意中的所有等效项目-项目交互都是繁琐的，未能捕捉到用户本地偏好的漂移，其中包含了丰富的短期模式。在本文中，我们提出了一种新的可解释的卷积自我注意，有效地捕捉短期和长期的模式与逐步注意分布。特别地，提出了一种下采样卷积模块，将整个长行为序列分割成一系列局部子序列。相应地，这些片段与自我注意层中的每个项目相互作用，产生具有局部感知的上下文表征，在此过程中，原始自我注意的二次复杂度降低到接近线性复杂度。此外，为了进一步提高变压器环境下的鲁棒性特征学习，设计了一种非对称的位置编码策略。在现实世界的数据集上进行了大量的实验，例如 ML-1M，Amazon Books 和 Yelp，表明所提出的方法在效率和效果上都优于最先进的方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Progressive+Self-Attention+Network+with+Unsymmetrical+Positional+Encoding+for+Sequential+Recommendation)|1| |[A New Sequential Prediction Framework with Spatial-temporal Embedding](https://doi.org/10.1145/3477495.3531846)|Jihai Zhang, Fangquan Lin, Cheng Yang, Wei Jiang|Alibaba Group, Hangzhou, China|Sequential prediction is one of the key components in recommendation. In online e-commerce recommendation system, user behavior consists of the sequential visiting logs and item behavior contains the interacted user list in order. Most of the existing state-of-the-art sequential prediction methods only consider the user behavior while ignoring the item behavior. In addition, we find that user behavior varies greatly at different time, and most existing models fail to characterize the rich temporal information. To address the above problems, we propose a transformer-based spatial-temporal recommendation framework (STEM). In the STEM framework, we first utilize attention mechanisms to model user behavior and item behavior, and then exploit spatial and temporal information through a transformer-based model. The STEM framework, as a plug-in, is able to be incorporated into many neural network-based sequential recommendation methods to improve performance. We conduct extensive experiments on three real-world Amazon datasets. The results demonstrate the effectiveness of our proposed framework.|序贯预测是推荐系统的关键组成部分之一。在在线电子商务推荐系统中，用户行为由顺序访问日志组成，项目行为按顺序包含交互式用户列表。现有的大多数最先进的顺序预测方法只考虑用户行为，而忽略项目行为。此外，我们发现用户行为在不同的时间变化很大，现有的模型不能刻画丰富的时间信息。为了解决上述问题，我们提出了一个基于变压器的时空推荐框架(STEM)。在 STEM 框架中，我们首先利用注意机制对用户行为和项目行为进行建模，然后通过一个基于转换器的模型来利用空间和时间信息。STEM 框架作为一个插件，能够被整合到许多基于神经网络的顺序推荐方法中，以提高性能。我们在三个真实的亚马逊数据集上进行了广泛的实验。仿真结果表明了该框架的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+New+Sequential+Prediction+Framework+with+Spatial-temporal+Embedding)|1| -|[Mitigating the Filter Bubble While Maintaining Relevance: Targeted Diversification with VAE-based Recommender Systems](https://doi.org/10.1145/3477495.3531890)|Zhaolin Gao, Tianshu Shen, Zheda Mai, Mohamed Reda Bouadjenek, Isaac Waller, Ashton Anderson, Ron Bodkin, Scott Sanner|Optimy AI, Toronto, ON, Canada; Vector Institute for Artificial Intelligence, Toronto, ON, Canada; University of Toronto, Toronto, ON, Canada; Deakin University, Geelong, Australia|Online recommendation systems are prone to create filter bubbles, whereby users are only recommended content narrowly aligned with their historical interests. In the case of media recommendation, this can reinforce political polarization by recommending topical content (e.g., on the economy) at one extreme end of the political spectrum even though this topic has broad coverage from multiple political viewpoints that would provide a more balanced and informed perspective for the user. Historically, Maximal Marginal Relevance (MMR) has been used to diversify result lists and even mitigate filter bubbles, but suffers from three key drawbacks: (1)~MMR directly sacrifices relevance for diversity, (2)~MMR typically diversifies across all content and not just targeted dimensions (e.g., political polarization), and (3)~MMR is inefficient in practice due to the need to compute pairwise similarities between recommended items. To simultaneously address these limitations, we propose a novel methodology that trains Concept Activation Vectors (CAVs) for targeted topical dimensions (e.g., political polarization). We then modulate the latent embeddings of user preferences in a state-of-the-art VAE-based recommender system to diversify along the targeted dimension while preserving topical relevance across orthogonal dimensions. Our experiments show that our Targeted Diversification VAE-based Collaborative Filtering (TD-VAE-CF) methodology better preserves relevance of content to user preferences across a range of diversification levels in comparison to both untargeted and targeted variations of Maximum Marginal Relevance (MMR); TD-VAE-CF is also much more computationally efficient than the post-hoc re-ranking approach of MMR.|在线推荐系统容易产生过滤气泡，用户只能被推荐与他们的历史兴趣狭窄地一致的内容。就媒体推荐而言，这可能会加剧政治两极分化，推荐政治光谱一端的主题内容(例如经济) ，尽管这个主题有多种政治观点的广泛覆盖，可以为用户提供一个更加平衡和知情的视角。从历史上看，最大边际相关(MMR)一直被用来使结果列表多样化，甚至减轻过滤器泡沫，但遭受三个关键的缺点: (1) ~ MMR 直接牺牲相关性的多样性，(2) ~ MMR 通常多样化跨所有内容，而不仅仅是有针对性的维度(例如，政治极化) ，和(3) ~ MMR 在实践中是低效的，因为需要计算推荐项目之间的成对相似性。为了同时解决这些局限性，我们提出了一种新的方法，训练概念激活向量(CAV)的目标主题维度(例如，政治极化)。然后，我们调整潜在的嵌入用户偏好在一个国家的最先进的 VAE 为基础的推荐系统，以多样化沿着目标的维度，同时保持跨正交维度的主题相关性。我们的实验表明，我们的基于目标多样化 VAE 的协同过滤(TD-VAE-CF)方法更好地保留了多样化水平范围内的用户偏好的内容相关性，与非目标和有针对性的最大边际相关性(MMR)变化相比，TD-VAE-CF 也比 MMR 的事后重新排序方法计算效率高得多。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Mitigating+the+Filter+Bubble+While+Maintaining+Relevance:+Targeted+Diversification+with+VAE-based+Recommender+Systems)|1| +|[Mitigating the Filter Bubble While Maintaining Relevance: Targeted Diversification with VAE-based Recommender Systems](https://doi.org/10.1145/3477495.3531890)|Zhaolin Gao, Tianshu Shen, Zheda Mai, Mohamed Reda Bouadjenek, Isaac Waller, Ashton Anderson, Ron Bodkin, Scott Sanner|Optimy AI, Toronto, ON, Canada; University of Toronto, Toronto, ON, Canada; Vector Institute for Artificial Intelligence, Toronto, ON, Canada; Deakin University, Geelong, Australia|Online recommendation systems are prone to create filter bubbles, whereby users are only recommended content narrowly aligned with their historical interests. In the case of media recommendation, this can reinforce political polarization by recommending topical content (e.g., on the economy) at one extreme end of the political spectrum even though this topic has broad coverage from multiple political viewpoints that would provide a more balanced and informed perspective for the user. Historically, Maximal Marginal Relevance (MMR) has been used to diversify result lists and even mitigate filter bubbles, but suffers from three key drawbacks: (1)~MMR directly sacrifices relevance for diversity, (2)~MMR typically diversifies across all content and not just targeted dimensions (e.g., political polarization), and (3)~MMR is inefficient in practice due to the need to compute pairwise similarities between recommended items. To simultaneously address these limitations, we propose a novel methodology that trains Concept Activation Vectors (CAVs) for targeted topical dimensions (e.g., political polarization). We then modulate the latent embeddings of user preferences in a state-of-the-art VAE-based recommender system to diversify along the targeted dimension while preserving topical relevance across orthogonal dimensions. Our experiments show that our Targeted Diversification VAE-based Collaborative Filtering (TD-VAE-CF) methodology better preserves relevance of content to user preferences across a range of diversification levels in comparison to both untargeted and targeted variations of Maximum Marginal Relevance (MMR); TD-VAE-CF is also much more computationally efficient than the post-hoc re-ranking approach of MMR.|在线推荐系统容易产生过滤气泡，用户只能被推荐与他们的历史兴趣狭窄地一致的内容。就媒体推荐而言，这可能会加剧政治两极分化，推荐政治光谱一端的主题内容(例如经济) ，尽管这个主题有多种政治观点的广泛覆盖，可以为用户提供一个更加平衡和知情的视角。从历史上看，最大边际相关(MMR)一直被用来使结果列表多样化，甚至减轻过滤器泡沫，但遭受三个关键的缺点: (1) ~ MMR 直接牺牲相关性的多样性，(2) ~ MMR 通常多样化跨所有内容，而不仅仅是有针对性的维度(例如，政治极化) ，和(3) ~ MMR 在实践中是低效的，因为需要计算推荐项目之间的成对相似性。为了同时解决这些局限性，我们提出了一种新的方法，训练概念激活向量(CAV)的目标主题维度(例如，政治极化)。然后，我们调整潜在的嵌入用户偏好在一个国家的最先进的 VAE 为基础的推荐系统，以多样化沿着目标的维度，同时保持跨正交维度的主题相关性。我们的实验表明，我们的基于目标多样化 VAE 的协同过滤(TD-VAE-CF)方法更好地保留了多样化水平范围内的用户偏好的内容相关性，与非目标和有针对性的最大边际相关性(MMR)变化相比，TD-VAE-CF 也比 MMR 的事后重新排序方法计算效率高得多。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Mitigating+the+Filter+Bubble+While+Maintaining+Relevance:+Targeted+Diversification+with+VAE-based+Recommender+Systems)|1| |[Modality-Balanced Embedding for Video Retrieval](https://doi.org/10.1145/3477495.3531899)|Xun Wang, Bingqing Ke, Xuanping Li, Fangyu Liu, Mingyu Zhang, Xiao Liang, Qiushi Xiao||Video search has become the main routine for users to discover videos relevant to a text query on large short-video sharing platforms. During training a query-video bi-encoder model using online search logs,\textit we identify a modality bias phenomenon that the video encoder almost entirely relies on text matching, neglecting other modalities of the videos such as vision, audio, \etc This modality imbalance results from a) modality gap: the relevance between a query and a video text is much easier to learn as the query is also a piece of text, with the same modality as the video text; b) data bias: most training samples can be solved solely by text matching. Here we share our practices to improve the first retrieval stage including our solution for the modality imbalance issue. We propose \modelname (short for Modality Balanced Video Retrieval) with two key components: manually generated modality-shuffled (MS) samples and a dynamic margin (DM) based on visual relevance. They can encourage the video encoder to pay balanced attentions to each modality. Through extensive experiments on a real world dataset, we show empirically that our method is both effective and efficient in solving modality bias problem. We have also deployed our ~\modelname~ in a large video platform and observed statistically significant boost over a highly optimized baseline in an A/B test and manual GSB evaluations.|视频搜索已成为用户在大型短视频分享平台上发现与文本查询相关的视频的主要程序。在使用在线搜索日志(texttit)对查询-视频双编码器模型进行训练时，我们发现了一种模态偏差现象，即视频编码器几乎完全依赖于文本匹配，而忽略了视频的其他模态，如视觉、音频等。这种模态偏差源于: a)模态差异: 查询和视频文本之间的相关性更容易学习，因为查询也是一段文本，具有与视频文本相同的模态; b)数据偏差: 大多数训练样本可以单独通过文本匹配来解决。在这里，我们分享我们的做法，以改善第一个检索阶段，包括我们的解决方案的形式不平衡的问题。提出了一种基于视觉相关性的动态边界检索模型，该模型由两个关键部分组成: 手动生成的模态混合样本(MS)和动态边界(DM)。它们可以鼓励视频编码器对每种模式给予均衡的关注。通过对实际数据集的大量实验，证明了该方法在解决模态偏差问题上的有效性。我们还在一个大型视频平台上部署了我们的“模型名”，并在 A/B 测试和手动 GSB 评估中观察到统计学上显著提高了高度优化的基线。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Modality-Balanced+Embedding+for+Video+Retrieval)|1| |[Expanded Lattice Embeddings for Spoken Document Retrieval on Informal Meetings](https://doi.org/10.1145/3477495.3531921)|Esaú VillatoroTello, Srikanth R. Madikeri, Petr Motlícek, Aravind Ganapathiraju, Alexei V. Ivanov|Idiap Research Institute, Martigny, Switzerland; Uniphore Software Systems Inc., Palo Alto, CA, USA|In this paper, we evaluate different alternatives to process richer forms of Automatic Speech Recognition (ASR) output based on lattice expansion algorithms for Spoken Document Retrieval (SDR). Typically, SDR systems employ ASR transcripts to index and retrieve relevant documents. However, ASR errors negatively affect the retrieval performance. Multiple alternative hypotheses can also be used to augment the input to document retrieval to compensate for the erroneous one-best hypothesis. In Weighted Finite State Transducer-based ASR systems, using the n-best output (i.e. the top "n'' scoring hypotheses) for the retrieval task is common, since they can easily be fed to a traditional Information Retrieval (IR) pipeline. However, the n-best hypotheses are terribly redundant, and do not sufficiently encapsulate the richness of the ASR output, which is represented as an acyclic directed graph called the lattice. In particular, we utilize the lattice's constrained minimum path cover to generate a minimum set of hypotheses that serve as input to the reranking phase of IR. The novelty of our proposed approach is the incorporation of the lattice as an input for neural reranking by considering a set of hypotheses that represents every arc in the lattice. The obtained hypotheses are encoded through sentence embeddings using BERT-based models, namely SBERT and RoBERTa, and the final ranking of the retrieved segments is obtained with a max-pooling operation over the computed scores among the input query and the hypotheses set. We present our evaluation on the publicly available AMI meeting corpus. Our results indicate that the proposed use of hypotheses from the expanded lattice improves the SDR performance significantly over the n-best ASR output.|在这篇文章中，我们评估了处理更丰富形式的自动语音识别(ASR)输出的不同方案，这些方案是基于文献检索的格展开算法。通常，SDR 系统使用 ASR 记录来索引和检索相关文档。但是，ASR 错误会对检索性能产生负面影响。多重替代假设也可以用来增加对文献检索的输入，以弥补错误的最佳假设。在基于加权有限状态转换器的 ASR 系统中，对于检索任务使用 n 个最佳输出(即顶部的“ n”评分假设)是很常见的，因为它们可以很容易地提供给传统的信息检索(IR)流水线。然而，n 个最佳假设是非常多余的，并且没有充分封装 ASR 输出的丰富性，ASR 输出被表示为一个称为格的非循环有向图。特别地，我们利用格子的约束最小路覆盖生成一组最小假设，作为 IR 重新排序阶段的输入。我们提出的方法的新颖之处在于，通过考虑一组代表格子中每个弧的假设，将格子作为神经重新排序的输入。所得到的假设通过使用基于 BERT 的模型(即 SBERT 和 RoBERTa)的句子嵌入进行编码，并且通过对输入查询和假设集之间计算得分的最大池操作获得检索段的最终排序。我们提出了我们的评价公开可用的 AMI 会议语料库。我们的结果表明，提出的假设使用从扩展格提高软件无线电性能显着超过 n 最佳的 ASR 输出。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Expanded+Lattice+Embeddings+for+Spoken+Document+Retrieval+on+Informal+Meetings)|1| |[Improving Item Cold-start Recommendation via Model-agnostic Conditional Variational Autoencoder](https://doi.org/10.1145/3477495.3531902)|Xu Zhao, Yi Ren, Ying Du, Shenzheng Zhang, Nian Wang|Tencent News, Beijing, China|Embedding & MLP has become a paradigm for modern large-scale recommendation system. However, this paradigm suffers from the cold-start problem which will seriously compromise the ecological health of recommendation systems. This paper attempts to tackle the item cold-start problem by generating enhanced warmed-up ID embeddings for cold items with historical data and limited interaction records. From the aspect of industrial practice, we mainly focus on the following three points of item cold-start: 1) How to conduct cold-start without additional data requirements and make strategy easy to be deployed in online recommendation scenarios. 2) How to leverage both historical records and constantly emerging interaction data of new items. 3) How to model the relationship between item ID and side information stably from interaction data. To address these problems, we propose a model-agnostic Conditional Variational Autoencoder based Recommendation(CVAR) framework with some advantages including compatibility on various backbones, no extra requirements for data, utilization of both historical data and recent emerging interactions. CVAR uses latent variables to learn a distribution over item side information and generates desirable item ID embeddings using a conditional decoder. The proposed method is evaluated by extensive offline experiments on public datasets and online A/B tests on Tencent News recommendation platform, which further illustrate the advantages and robustness of CVAR.|嵌入式 MLP 已经成为现代大规模推荐系统的典范。然而，这种模式受到冷启动问题的影响，这将严重损害推荐系统的生态健康。本文试图利用历史数据和有限的交互记录为冷藏物品生成增强的预热 ID 嵌入，从而解决物品冷启动问题。从工业实践的角度出发，重点研究了项目冷启动的三个方面: 1)如何在不增加额外数据需求的情况下进行冷启动，使策略易于在在线推荐场景中部署。2)如何利用历史记录和新项目不断涌现的交互数据。3)如何从交互数据中稳定地建立项目 ID 与侧信息之间的关系模型。为了解决这些问题，我们提出了一个基于模型无关的条件变分自动编码器推荐(CVAR)框架，该框架具有一些优点，包括在不同骨干上的兼容性，对数据没有额外的要求，利用历史数据和最近出现的交互。CVAR 使用潜变量学习项目边信息的分布，并使用条件解码器生成所需的项目 ID 嵌入。该方法通过在公共数据集上的大量离线实验和在腾讯新闻推荐平台上的在线 A/B 测试进行了评估，进一步说明了 CVAR 的优势和稳健性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Improving+Item+Cold-start+Recommendation+via+Model-agnostic+Conditional+Variational+Autoencoder)|1| |[Multi-Faceted Global Item Relation Learning for Session-Based Recommendation](https://doi.org/10.1145/3477495.3532024)|Qilong Han, Chi Zhang, Rui Chen, Riwei Lai, Hongtao Song, Li Li|Harbin Engineering University, Harbin, China; University of Delaware, Newark, DE, USA|As an emerging paradigm, session-based recommendation is aimed at recommending the next item based on a set of anonymous sessions. Effectively representing a session that is normally a short interaction sequence renders a major technical challenge. In view of the limitations of pioneering studies that explore collaborative information from other sessions, in this paper we propose a new direction to enhance session representations by learning multi-faceted session-independent global item relations. In particular, we identify three types of advantageous global item relations, including negative relations that have not been studied before, and propose different graph construction methods to capture such relations. We then devise a novel multi-faceted global item relation (MGIR) model to encode different relations using different aggregation layers and generate enhanced session representations by fusing positive and negative relations. Our solution is flexible to accommodate new item relations and can easily integrate existing session representation learning methods to generate better representations from global relation enhanced session information. Extensive experiments on three benchmark datasets demonstrate the superiority of our model over a large number of state-of-the-art methods. Specifically, we show that learning negative relations is critical for session-based recommendation.|作为一种新兴的模式，基于会议的建议旨在根据一组匿名会议推荐下一个项目。有效地表示一个通常是短交互序列的会话会带来重大的技术挑战。针对开拓性研究在探索其他会议协作信息方面的局限性，本文提出了一个通过学习多方面会议独立的全局项目关系来增强会议表征的新方向。特别地，我们确定了三种有利的全局项目关系，包括以前没有研究过的负关系，并提出了不同的图构造方法来捕获这些关系。然后设计了一个新的多方面全局项目关系(MGIR)模型，利用不同的聚合层对不同的关系进行编码，并通过融合正负关系生成增强的会话表示。我们的解决方案是灵活的，以适应新的项目关系，可以很容易地集成现有的会话表示学习方法，从全局关系增强会话信息生成更好的表示。在三个基准数据集上的大量实验表明，我们的模型优于大量的最先进的方法。具体来说，我们表明，学习负关系是至关重要的会话为基础的推荐。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Multi-Faceted+Global+Item+Relation+Learning+for+Session-Based+Recommendation)|1| -|[Item Similarity Mining for Multi-Market Recommendation](https://doi.org/10.1145/3477495.3531839)|Jiangxia Cao, Xin Cong, Tingwen Liu, Bin Wang|Xiaomi AI Lab, Xiaomi Inc., Beijing, China; Institute of Information Engineering, CAS & UCAS, Beijing, China|Real-world web applications such as Amazon and Netflix often provide services in multiple countries and regions (i.e., markets) around the world. Generally, different markets share similar item sets while containing different amounts of interaction data. Some markets are data-scarce and others are data-rich and leveraging those data from similar and data-rich auxiliary markets could enhance the data-scarce markets. In this paper, we explore multi-market recommendation (MMR), and propose a novel model called M$^3$Rec to improve all markets recommendation simultaneously. Since items play the role to bridge different markets, we argue that mining the similarities among items is the key point of MMR. Our M^3Rec preprocess two global item similarities: intra- and inter- market similarities. Specifically, we first learn the second-order intra-market similarity by adopting linear models with closed-form solutions, and then capture the high-order inter-market similarity by the random walk. Afterward, we incorporate the global item similarities for each local market. We conduct extensive experiments on five public available markets and compare with several state-of-the-art methods. Detailed experimental results demonstrate the effectiveness of our proposed method.|现实世界的网络应用程序，如亚马逊和 Netflix，通常在世界各地的多个国家和地区(即市场)提供服务。通常，不同的市场共享相似的项目集，同时包含不同数量的交互数据。一些市场数据稀缺，另一些市场数据丰富，利用类似和数据丰富的辅助市场的数据可以加强数据稀缺市场。在本文中，我们探讨了多市场推荐(MMR) ，并提出了一种新的模型 M $^ 3 $Rec 来同时改进所有市场的推荐。由于产品在不同市场之间起着桥梁作用，我们认为挖掘产品之间的相似性是 MMR 的关键所在。我们的 M ^ 3Rec 预处理两个全球项目的相似性: 市场内部和市场间的相似性。具体来说，我们首先通过采用具有封闭解的线性模型来学习二阶市场内部相似性，然后通过随机游走来获取高阶市场内部相似性。然后，我们为每个当地市场整合全球产品的相似性。我们在五个公开市场上进行了广泛的实验，并与几种最先进的方法进行了比较。详细的实验结果证明了该方法的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Item+Similarity+Mining+for+Multi-Market+Recommendation)|1| +|[Item Similarity Mining for Multi-Market Recommendation](https://doi.org/10.1145/3477495.3531839)|Jiangxia Cao, Xin Cong, Tingwen Liu, Bin Wang|Institute of Information Engineering, CAS & UCAS, Beijing, China; Xiaomi AI Lab, Xiaomi Inc., Beijing, China|Real-world web applications such as Amazon and Netflix often provide services in multiple countries and regions (i.e., markets) around the world. Generally, different markets share similar item sets while containing different amounts of interaction data. Some markets are data-scarce and others are data-rich and leveraging those data from similar and data-rich auxiliary markets could enhance the data-scarce markets. In this paper, we explore multi-market recommendation (MMR), and propose a novel model called M$^3$Rec to improve all markets recommendation simultaneously. Since items play the role to bridge different markets, we argue that mining the similarities among items is the key point of MMR. Our M^3Rec preprocess two global item similarities: intra- and inter- market similarities. Specifically, we first learn the second-order intra-market similarity by adopting linear models with closed-form solutions, and then capture the high-order inter-market similarity by the random walk. Afterward, we incorporate the global item similarities for each local market. We conduct extensive experiments on five public available markets and compare with several state-of-the-art methods. Detailed experimental results demonstrate the effectiveness of our proposed method.|现实世界的网络应用程序，如亚马逊和 Netflix，通常在世界各地的多个国家和地区(即市场)提供服务。通常，不同的市场共享相似的项目集，同时包含不同数量的交互数据。一些市场数据稀缺，另一些市场数据丰富，利用类似和数据丰富的辅助市场的数据可以加强数据稀缺市场。在本文中，我们探讨了多市场推荐(MMR) ，并提出了一种新的模型 M $^ 3 $Rec 来同时改进所有市场的推荐。由于产品在不同市场之间起着桥梁作用，我们认为挖掘产品之间的相似性是 MMR 的关键所在。我们的 M ^ 3Rec 预处理两个全球项目的相似性: 市场内部和市场间的相似性。具体来说，我们首先通过采用具有封闭解的线性模型来学习二阶市场内部相似性，然后通过随机游走来获取高阶市场内部相似性。然后，我们为每个当地市场整合全球产品的相似性。我们在五个公开市场上进行了广泛的实验，并与几种最先进的方法进行了比较。详细的实验结果证明了该方法的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Item+Similarity+Mining+for+Multi-Market+Recommendation)|1| |[Interpreting Patient Descriptions using Distantly Supervised Similar Case Retrieval](https://doi.org/10.1145/3477495.3532003)|Israa Alghanmi, Luis Espinosa Anke, Steven Schockaert|Cardiff University, Cardiff, United Kingdom|Biomedical natural language processing often involves the interpretation of patient descriptions, for instance for diagnosis or for recommending treatments. Current methods, based on biomedical language models, have been found to struggle with such tasks. Moreover, retrieval augmented strategies have only had limited success, as it is rare to find sentences which express the exact type of knowledge that is needed for interpreting a given patient description. For this reason, rather than attempting to retrieve explicit medical knowledge, we instead propose to rely on a nearest neighbour strategy. First, we retrieve text passages that are similar to the given patient description, and are thus likely to describe patients in similar situations, while also mentioning some hypothesis (e.g.\ a possible diagnosis of the patient). We then judge the likelihood of the hypothesis based on the similarity of the retrieved passages. Identifying similar cases is challenging, however, as descriptions of similar patients may superficially look rather different, among others because they often contain an abundance of irrelevant details. To address this challenge, we propose a strategy that relies on a distantly supervised cross-encoder. Despite its conceptual simplicity, we find this strategy to be effective in practice.|生物医学自然语言处理通常涉及对患者描述的解释，例如用于诊断或推荐治疗。目前的方法，基于生物医学语言模型，已被发现与这样的任务斗争。此外，提取增强策略只取得了有限的成功，因为很少能找到表达解释给定患者描述所需的确切知识类型的句子。基于这个原因，我们不尝试检索显性医学知识，而是建议依赖于最近邻策略。首先，我们检索与给定患者描述相似的文本段落，因此可能描述处于相似情况的患者，同时也提到一些假设(例如患者的可能诊断)。然后，我们根据检索到的段落的相似性来判断假设的可能性。然而，鉴别相似病例是具有挑战性的，因为对相似病例的描述可能在表面上看起来相当不同，因为它们往往包含大量不相关的细节。为了应对这一挑战，我们提出了一种依赖于远程监督交叉编码器的策略。尽管其概念简单，我们发现这种策略在实践中是有效的。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Interpreting+Patient+Descriptions+using+Distantly+Supervised+Similar+Case+Retrieval)|1| |[Towards Explainable Search Results: A Listwise Explanation Generator](https://doi.org/10.1145/3477495.3532067)|Puxuan Yu, Razieh Rahimi, James Allan|University of Massachusetts Amherst, Amherst, MA, USA|It has been shown that the interpretability of search results is enhanced when query aspects covered by documents are explicitly provided. However, existing work on aspect-oriented explanation of search results explains each document independently. These explanations thus cannot describe the differences between documents. This issue is also true for existing models on query aspect generation. Furthermore, these models provide a single query aspect for each document, even though documents often cover multiple query aspects. To overcome these limitations, we propose LiEGe, an approach that jointly explains all documents in a search result list. LiEGe provides semantic representations at two levels of granularity -- documents and their tokens -- using different interaction signals including cross-document interactions. These allow listwise modeling of a search result list as well as the generation of coherent explanations for documents. To appropriately explain documents that cover multiple query aspects, we introduce two settings for search result explanation: comprehensive and novelty explanation generation. LiEGe is trained and evaluated for both settings. We evaluate LiEGe on datasets built from Wikipedia and real query logs of the Bing search engine. Our experimental results demonstrate that LiEGe outperforms all baselines, with improvements that are substantial and statistically significant.|研究表明，如果明确提供文件所涉查询方面，搜索结果的可解释性就会得到提高。但是，现有的面向方面的搜索结果解释工作独立地解释每个文档。因此，这些解释无法描述文档之间的差异。对于生成查询方面的现有模型，也存在这个问题。此外，这些模型为每个文档提供单个查询方面，即使文档通常包含多个查询方面。为了克服这些限制，我们提出了 LiEGe，一种联合解释搜索结果列表中所有文档的方法。LiEGe 使用不同的交互信号(包括跨文档交互)在两个粒度级别(文档及其标记)提供语义表示。它们允许对搜索结果列表进行列表建模，以及为文档生成连贯的解释。为了恰当地解释涵盖多个查询方面的文档，我们引入了两种用于搜索结果解释的设置: 全面解释生成和新颖解释生成。对 LiEGe 进行了两种设置的培训和评估。我们根据维基百科建立的数据集和 Bing 搜索引擎的实际查询日志来评估 LiEGe。我们的实验结果表明，LiEGe 的性能优于所有基线，具有实质性的改进和统计学意义。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Towards+Explainable+Search+Results:+A+Listwise+Explanation+Generator)|1| -|[Bit-aware Semantic Transformer Hashing for Multi-modal Retrieval](https://doi.org/10.1145/3477495.3531947)|Wentao Tan, Lei Zhu, Weili Guan, Jingjing Li, Zhiyong Cheng|Monash University, Clayton, Australia; Shandong Artificial Intelligence Institute, Jinan, China; University of Electronic Science and Technology of China, Chengdu, China; Shandong Normal University, Jinan, China|Multi-modal hashing learns binary hash codes with extremely low storage cost and high retrieval speed. It can support efficient multi-modal retrieval well. However, most existing methods still suffer from three important problems: 1) Limited semantic representation capability with shallow learning. 2) Mandatory feature-level multi-modal fusion ignores heterogeneous multi-modal semantic gaps. 3) Direct coarse pairwise semantic preserving cannot effectively capture the fine-grained semantic correlations. For solving these problems, in this paper, we propose a Bit-aware Semantic Transformer Hashing (BSTH) framework to excavate bit-wise semantic concepts and simultaneously align the heterogeneous modalities for multi-modal hash learning on the concept-level. Specifically, the bit-wise implicit semantic concepts are learned with the transformer in a self-attention manner, which can achieve implicit semantic alignment on the fine-grained concept-level and reduce the heterogeneous modality gaps. Then, the concept-level multi-modal fusion is performed to enhance the semantic representation capability of each implicit concept and the fused concept representations are further encoded to the corresponding hash bits via bit-wise hash functions. Further, to supervise the bit-aware transformer module, a label prototype learning module is developed to learn prototype embeddings for all categories that capture the explicit semantic correlations on the category-level by considering the co-occurrence priors. Experiments on three widely tested multi-modal retrieval datasets demonstrate the superiority of the proposed method from various aspects.|多模态哈希学习二进制哈希码具有极低的存储成本和极高的检索速度。它能很好地支持有效的多模态检索。然而，现有的大多数方法仍然存在三个重要问题: 1)语义表示能力有限，浅层学习。2)强制特征级多模态融合忽略异质多模态语义缺口。3)直接粗对语义保持不能有效地捕获细粒度的语义关联。为了解决这些问题，本文提出了一种比特感知的语义变换哈希(BSTH)框架来挖掘比特感知的语义概念，同时在概念层次上对多模态哈希学习的异构模式进行校准。具体来说，通过变换器以自我注意的方式学习位隐含语义概念，可以在细粒度的概念水平上实现隐含语义对齐，减少异质情态差异。然后进行概念级多模态融合以提高每个隐式概念的语义表示能力，并通过逐位哈希函数将融合后的概念表示进一步编码到相应的哈希位。此外，为了监督位感知转换器模块，开发了一个标签原型学习模块来学习所有类别的原型嵌入，这些类别通过考虑共现先验在类别层面上捕获显式的语义相关性。在三个被广泛测试的多模态检索数据集上的实验从各个方面证明了该方法的优越性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Bit-aware+Semantic+Transformer+Hashing+for+Multi-modal+Retrieval)|1| -|[Multimodal Disentanglement Variational AutoEncoders for Zero-Shot Cross-Modal Retrieval](https://doi.org/10.1145/3477495.3532028)|Jialin Tian, Kai Wang, Xing Xu, Zuo Cao, Fumin Shen, Heng Tao Shen|Meituan, Shanghai, China; University of Electronic Science and Technology of China, Chengdu, China|Zero-Shot Cross-Modal Retrieval (ZS-CMR) has recently drawn increasing attention as it focuses on a practical retrieval scenario, i.e., the multimodal test set consists of unseen classes that are disjoint with seen classes in the training set. The recently proposed methods typically adopt the generative model as the main framework to learn a joint latent embedding space to alleviate the modality gap. Generally, these methods largely rely on auxiliary semantic embeddings for knowledge transfer across classes and unconsciously neglect the effect of the data reconstruction manner in the adopted generative model. To address this issue, we propose a novel ZS-CMR model termed Multimodal Disentanglement Variational AutoEncoders (MDVAE), which consists of two coupled disentanglement variational autoencoders (DVAEs) and a fusion-exchange VAE (FVAE). Specifically, DVAE is developed to disentangle the original representations of each modality into modality-invariant and modality-specific features. FVAE is designed to fuse and exchange information of multimodal data by the reconstruction and alignment process without pre-extracted semantic embeddings. Moreover, an advanced counter-intuitive cross-reconstruction scheme is further proposed to enhance the informativeness and generalizability of the modality-invariant features for more effective knowledge transfer. The comprehensive experiments on four image-text retrieval and two image-sketch retrieval datasets consistently demonstrate that our method establishes the new state-of-the-art performance.|零拍交叉模态检索(Zero-Shot Cross-Modal Retrieval，ZS-CMR)近年来受到越来越多的关注，因为它集中在一个实际的检索场景，即多模态测试集包含不相交的看不见的类和训练集中的看得见的类。最近提出的方法通常采用生成模型作为主要框架，学习一个联合潜在嵌入空间，以缓解模态差距。一般而言，这些方法主要依赖于辅助语义嵌入来跨类别传递知识，而无意识地忽略了所采用的生成模型中数据重构方式的影响。为了解决这个问题，我们提出了一种新的 ZS-CMR 模型，称为多模态解缠变分自动编码器(MDVAE) ，它由两个耦合的解缠变分自动编码器(DVAE)和一个融合交换 VAE (FVAE)组成。具体来说，DVAE 的开发是为了将每种模态的原始表示分解为模态不变和模态特定的特征。FVAE 通过重构和对齐过程实现多模态数据信息的融合和交换，不需要预先提取语义嵌入。进一步提出了一种改进的反直观交叉重构方案，以提高模态不变特征的信息量和泛化能力，实现更有效的知识转移。在四个图像文本检索和两个图像素描检索数据集上的综合实验表明，该方法建立了新的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Multimodal+Disentanglement+Variational+AutoEncoders+for+Zero-Shot+Cross-Modal+Retrieval)|1| -|[User-controllable Recommendation Against Filter Bubbles](https://doi.org/10.1145/3477495.3532075)|Wenjie Wang, Fuli Feng, Liqiang Nie, TatSeng Chua|Shandong University, Qingdao, China; University of Science and Technology of China, Hefei, China; National University of Singapore, Singapore, Singapore|Recommender systems usually face the issue of filter bubbles: over-recommending homogeneous items based on user features and historical interactions. Filter bubbles will grow along the feedback loop and inadvertently narrow user interests. Existing work usually mitigates filter bubbles by incorporating objectives apart from accuracy such as diversity and fairness. However, they typically sacrifice accuracy, hurting model fidelity and user experience. Worse still, users have to passively accept the recommendation strategy and influence the system in an inefficient manner with high latency, e.g., keeping providing feedback (e.g., like and dislike) until the system recognizes the user intention. This work proposes a new recommender prototype called User-Controllable Recommender System (UCRS), which enables users to actively control the mitigation of filter bubbles. Functionally, 1) UCRS can alert users if they are deeply stuck in filter bubbles. 2) UCRS supports four kinds of control commands for users to mitigate the bubbles at different granularities. 3) UCRS can respond to the controls and adjust the recommendations on the fly. The key to adjusting lies in blocking the effect of out-of-date user representations on recommendations, which contains historical information inconsistent with the control commands. As such, we develop a causality-enhanced User-Controllable Inference (UCI) framework, which can quickly revise the recommendations based on user controls in the inference stage and utilize counterfactual inference to mitigate the effect of out-of-date user representations. Experiments on three datasets validate that the UCI framework can effectively recommend more desired items based on user controls, showing promising performance w.r.t. both accuracy and diversity.|推荐系统通常面临过滤气泡的问题: 过度推荐基于用户特征和历史交互的同类项目。过滤气泡会沿着反馈回路增长，不经意间会缩小用户的兴趣。现有的工作通常通过将目标与准确性(如多样性和公平性)相结合来减少过滤泡沫。然而，他们通常牺牲准确性，损害模型的保真度和用户体验。更糟糕的是，用户必须被动地接受推荐策略，并以高延迟的低效方式影响系统，例如，不断提供反馈(例如，喜欢和不喜欢) ，直到系统认识到用户的意图。这项工作提出了一个新的推荐原型，称为用户可控推荐系统(UCRS) ，它使用户能够积极控制过滤气泡的缓解。在功能上，1) UCRS 可以提醒用户，如果他们深深陷入过滤泡。2) UCRS 支持四种控制命令，用户可以根据不同的粒度缓解气泡。3) UCRS 可以对控制措施做出响应，并在运行中调整建议。调整的关键在于阻止过时的用户表示对建议的影响，其中包含与控制命令不一致的历史信息。因此，我们开发了一个因果关系增强的用户可控推理(UCI)框架，它可以在推理阶段快速修改基于用户控制的推荐，并利用反事实推理来减轻过时用户表示的影响。在三个数据集上的实验验证了 UCI 框架基于用户控件能够有效地推荐更多期望的项目，在准确性和多样性方面都显示出良好的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=User-controllable+Recommendation+Against+Filter+Bubbles)|1| +|[Bit-aware Semantic Transformer Hashing for Multi-modal Retrieval](https://doi.org/10.1145/3477495.3531947)|Wentao Tan, Lei Zhu, Weili Guan, Jingjing Li, Zhiyong Cheng|Monash University, Clayton, Australia; University of Electronic Science and Technology of China, Chengdu, China; Shandong Artificial Intelligence Institute, Jinan, China; Shandong Normal University, Jinan, China|Multi-modal hashing learns binary hash codes with extremely low storage cost and high retrieval speed. It can support efficient multi-modal retrieval well. However, most existing methods still suffer from three important problems: 1) Limited semantic representation capability with shallow learning. 2) Mandatory feature-level multi-modal fusion ignores heterogeneous multi-modal semantic gaps. 3) Direct coarse pairwise semantic preserving cannot effectively capture the fine-grained semantic correlations. For solving these problems, in this paper, we propose a Bit-aware Semantic Transformer Hashing (BSTH) framework to excavate bit-wise semantic concepts and simultaneously align the heterogeneous modalities for multi-modal hash learning on the concept-level. Specifically, the bit-wise implicit semantic concepts are learned with the transformer in a self-attention manner, which can achieve implicit semantic alignment on the fine-grained concept-level and reduce the heterogeneous modality gaps. Then, the concept-level multi-modal fusion is performed to enhance the semantic representation capability of each implicit concept and the fused concept representations are further encoded to the corresponding hash bits via bit-wise hash functions. Further, to supervise the bit-aware transformer module, a label prototype learning module is developed to learn prototype embeddings for all categories that capture the explicit semantic correlations on the category-level by considering the co-occurrence priors. Experiments on three widely tested multi-modal retrieval datasets demonstrate the superiority of the proposed method from various aspects.|多模态哈希学习二进制哈希码具有极低的存储成本和极高的检索速度。它能很好地支持有效的多模态检索。然而，现有的大多数方法仍然存在三个重要问题: 1)语义表示能力有限，浅层学习。2)强制特征级多模态融合忽略异质多模态语义缺口。3)直接粗对语义保持不能有效地捕获细粒度的语义关联。为了解决这些问题，本文提出了一种比特感知的语义变换哈希(BSTH)框架来挖掘比特感知的语义概念，同时在概念层次上对多模态哈希学习的异构模式进行校准。具体来说，通过变换器以自我注意的方式学习位隐含语义概念，可以在细粒度的概念水平上实现隐含语义对齐，减少异质情态差异。然后进行概念级多模态融合以提高每个隐式概念的语义表示能力，并通过逐位哈希函数将融合后的概念表示进一步编码到相应的哈希位。此外，为了监督位感知转换器模块，开发了一个标签原型学习模块来学习所有类别的原型嵌入，这些类别通过考虑共现先验在类别层面上捕获显式的语义相关性。在三个被广泛测试的多模态检索数据集上的实验从各个方面证明了该方法的优越性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Bit-aware+Semantic+Transformer+Hashing+for+Multi-modal+Retrieval)|1| +|[Multimodal Disentanglement Variational AutoEncoders for Zero-Shot Cross-Modal Retrieval](https://doi.org/10.1145/3477495.3532028)|Jialin Tian, Kai Wang, Xing Xu, Zuo Cao, Fumin Shen, Heng Tao Shen|University of Electronic Science and Technology of China, Chengdu, China; Meituan, Shanghai, China|Zero-Shot Cross-Modal Retrieval (ZS-CMR) has recently drawn increasing attention as it focuses on a practical retrieval scenario, i.e., the multimodal test set consists of unseen classes that are disjoint with seen classes in the training set. The recently proposed methods typically adopt the generative model as the main framework to learn a joint latent embedding space to alleviate the modality gap. Generally, these methods largely rely on auxiliary semantic embeddings for knowledge transfer across classes and unconsciously neglect the effect of the data reconstruction manner in the adopted generative model. To address this issue, we propose a novel ZS-CMR model termed Multimodal Disentanglement Variational AutoEncoders (MDVAE), which consists of two coupled disentanglement variational autoencoders (DVAEs) and a fusion-exchange VAE (FVAE). Specifically, DVAE is developed to disentangle the original representations of each modality into modality-invariant and modality-specific features. FVAE is designed to fuse and exchange information of multimodal data by the reconstruction and alignment process without pre-extracted semantic embeddings. Moreover, an advanced counter-intuitive cross-reconstruction scheme is further proposed to enhance the informativeness and generalizability of the modality-invariant features for more effective knowledge transfer. The comprehensive experiments on four image-text retrieval and two image-sketch retrieval datasets consistently demonstrate that our method establishes the new state-of-the-art performance.|零拍交叉模态检索(Zero-Shot Cross-Modal Retrieval，ZS-CMR)近年来受到越来越多的关注，因为它集中在一个实际的检索场景，即多模态测试集包含不相交的看不见的类和训练集中的看得见的类。最近提出的方法通常采用生成模型作为主要框架，学习一个联合潜在嵌入空间，以缓解模态差距。一般而言，这些方法主要依赖于辅助语义嵌入来跨类别传递知识，而无意识地忽略了所采用的生成模型中数据重构方式的影响。为了解决这个问题，我们提出了一种新的 ZS-CMR 模型，称为多模态解缠变分自动编码器(MDVAE) ，它由两个耦合的解缠变分自动编码器(DVAE)和一个融合交换 VAE (FVAE)组成。具体来说，DVAE 的开发是为了将每种模态的原始表示分解为模态不变和模态特定的特征。FVAE 通过重构和对齐过程实现多模态数据信息的融合和交换，不需要预先提取语义嵌入。进一步提出了一种改进的反直观交叉重构方案，以提高模态不变特征的信息量和泛化能力，实现更有效的知识转移。在四个图像文本检索和两个图像素描检索数据集上的综合实验表明，该方法建立了新的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Multimodal+Disentanglement+Variational+AutoEncoders+for+Zero-Shot+Cross-Modal+Retrieval)|1| +|[User-controllable Recommendation Against Filter Bubbles](https://doi.org/10.1145/3477495.3532075)|Wenjie Wang, Fuli Feng, Liqiang Nie, TatSeng Chua|National University of Singapore, Singapore, Singapore; University of Science and Technology of China, Hefei, China; Shandong University, Qingdao, China|Recommender systems usually face the issue of filter bubbles: over-recommending homogeneous items based on user features and historical interactions. Filter bubbles will grow along the feedback loop and inadvertently narrow user interests. Existing work usually mitigates filter bubbles by incorporating objectives apart from accuracy such as diversity and fairness. However, they typically sacrifice accuracy, hurting model fidelity and user experience. Worse still, users have to passively accept the recommendation strategy and influence the system in an inefficient manner with high latency, e.g., keeping providing feedback (e.g., like and dislike) until the system recognizes the user intention. This work proposes a new recommender prototype called User-Controllable Recommender System (UCRS), which enables users to actively control the mitigation of filter bubbles. Functionally, 1) UCRS can alert users if they are deeply stuck in filter bubbles. 2) UCRS supports four kinds of control commands for users to mitigate the bubbles at different granularities. 3) UCRS can respond to the controls and adjust the recommendations on the fly. The key to adjusting lies in blocking the effect of out-of-date user representations on recommendations, which contains historical information inconsistent with the control commands. As such, we develop a causality-enhanced User-Controllable Inference (UCI) framework, which can quickly revise the recommendations based on user controls in the inference stage and utilize counterfactual inference to mitigate the effect of out-of-date user representations. Experiments on three datasets validate that the UCI framework can effectively recommend more desired items based on user controls, showing promising performance w.r.t. both accuracy and diversity.|推荐系统通常面临过滤气泡的问题: 过度推荐基于用户特征和历史交互的同类项目。过滤气泡会沿着反馈回路增长，不经意间会缩小用户的兴趣。现有的工作通常通过将目标与准确性(如多样性和公平性)相结合来减少过滤泡沫。然而，他们通常牺牲准确性，损害模型的保真度和用户体验。更糟糕的是，用户必须被动地接受推荐策略，并以高延迟的低效方式影响系统，例如，不断提供反馈(例如，喜欢和不喜欢) ，直到系统认识到用户的意图。这项工作提出了一个新的推荐原型，称为用户可控推荐系统(UCRS) ，它使用户能够积极控制过滤气泡的缓解。在功能上，1) UCRS 可以提醒用户，如果他们深深陷入过滤泡。2) UCRS 支持四种控制命令，用户可以根据不同的粒度缓解气泡。3) UCRS 可以对控制措施做出响应，并在运行中调整建议。调整的关键在于阻止过时的用户表示对建议的影响，其中包含与控制命令不一致的历史信息。因此，我们开发了一个因果关系增强的用户可控推理(UCI)框架，它可以在推理阶段快速修改基于用户控制的推荐，并利用反事实推理来减轻过时用户表示的影响。在三个数据集上的实验验证了 UCI 框架基于用户控件能够有效地推荐更多期望的项目，在准确性和多样性方面都显示出良好的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=User-controllable+Recommendation+Against+Filter+Bubbles)|1| |[Evaluation of Herd Behavior Caused by Population-scale Concept Drift in Collaborative Filtering](https://doi.org/10.1145/3477495.3531792)|Chenglong Ma, Yongli Ren, Pablo Castells, Mark Sanderson|RMIT University, Melbourne, VIC, Australia; Universidad Autónoma de Madrid, Madrid , Spain|Concept drift in stream data has been well studied in machine learning applications. In the field of recommender systems, this issue is also widely observed, as known as temporal dynamics in user behavior. Furthermore, in the context of COVID-19 pandemic related contingencies, people shift their behavior patterns extremely and tend to imitate others' opinions. The changes in user behavior may not be always rational. Thus, irrational behavior may impair the knowledge learned by the algorithm. It can cause herd effects and aggravate the popularity bias in recommender systems due to the irrational behavior of users. However, related research usually pays attention to the concept drift of individuals and overlooks the synergistic effect among users in the same social group. We conduct a study on user behavior to detect the collaborative concept drifts among users. Also, we empirically study the increase of experience of individuals can weaken herding effects. Our results suggest the CF models are highly impacted by the herd behavior and our findings could provide useful implications for the design of future recommender algorithms.|流数据中的概念漂移已经在机器学习应用中得到了很好的研究。在推荐系统领域，这个问题也被广泛观察到，被称为用户行为的时间动态。此外，在与2019冠状病毒疾病相关的突发事件中，人们的行为模式会发生极大的变化，并倾向于模仿他人的观点。用户行为的变化可能并不总是理性的。因此，非理性行为可能会损害算法所学到的知识。在推荐系统中，由于用户的非理性行为，会引起羊群效应，加剧推荐系统的受欢迎程度偏差。然而，相关研究往往关注个体的概念漂移，忽视了同一社会群体中用户之间的协同效应。本文以用户行为为研究对象，检测用户之间的协作概念漂移。同时，我们实证研究了个体经验的增加会削弱羊群效应。我们的研究结果表明 CF 模型受到群体行为的高度影响，我们的研究结果可以为未来推荐算法的设计提供有用的启示。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Evaluation+of+Herd+Behavior+Caused+by+Population-scale+Concept+Drift+in+Collaborative+Filtering)|1| -|[A 'Pointwise-Query, Listwise-Document' based Query Performance Prediction Approach](https://doi.org/10.1145/3477495.3531821)|Suchana Datta, Sean MacAvaney, Debasis Ganguly, Derek Greene|Univ Glasgow, Glasgow, Lanark, Scotland; Univ Coll Dublin, Dublin, Ireland|The task of Query Performance Prediction (QPP) in Information Retrieval (IR) involves predicting the relative effectiveness of a search system for a given input query. Supervised approaches for QPP, such as NeuralQPP [24] are often trained on pairs of queries to capture their relative retrieval performance. However, point-wise approaches, such as the recently proposed BERT-QPP [1], are generally preferable for efficiency reasons. In this paper, we propose a novel end-to-end neural cross-encoder-based approach that is trained pointwise on individual queries, but listwise over the top ranked documents (split into chunks). In contrast to prior work, the network is then trained to predict the number of relevant documents in each chunk for a given query. Our method is thus a split-n-merge technique that instead of predicting the likely number of relevant documents in the top-k [1], rather predicts the number of relevant documents for each fixed chunk size p (p < k) and then aggregates them for QPP on top-k. Experiments demonstrate that our method is significantly more effective than other supervised and unsupervised QPP approaches yielding improvements of up to 30% on the TREC-DL'20 dataset and by nearly 9% for the MS MARCO Dev set.|查询性能预测的任务(QPP)在信息检索(IR)包括预测一个给定输入查询的搜索系统的相对有效性。QPP 的有监督的方法，如 NeuralQPP [24] ，经常在查询对上进行训练，以捕获它们的相对检索性能。然而，出于效率的考虑，最近提出的 BERT-QPP [1]等逐点方法通常是可取的。在本文中，我们提出了一种新的端到端神经交叉编码器为基础的方法，是点式训练的个别查询，但列表上的排名最高的文档(分成块)。与先前的工作相反，网络然后被训练来预测给定查询的每个块中相关文档的数量。因此，我们的方法是一种拆分-n-merge 技术，它不是预测 top-k [1]中相关文档的可能数量，而是预测每个固定块大小 p (p < k)的相关文档的数量，然后将它们聚合为 top-k 上的 QPP。实验表明，我们的方法比其他有监督和无监督的 QPP 方法显着更有效，在 TREC-DL’20数据集上提高了30% ，在 MS MARCO Dev 集上提高了近9% 。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+'Pointwise-Query,+Listwise-Document'+based+Query+Performance+Prediction+Approach)|1| -|[AHP: Learning to Negative Sample for Hyperedge Prediction](https://doi.org/10.1145/3477495.3531836)|Hyunjin Hwang, Seungwoo Lee, Chanyoung Park, Kijung Shin|KAIST, Seoul, Republic of Korea; KAIST, Daejeon, Republic of Korea|Hypergraphs (i.e., sets of hyperedges) naturally represent group relations (e.g., researchers co-authoring a paper and ingredients used together in a recipe), each of which corresponds to a hyperedge (i.e., a subset of nodes). Predicting future or missing hyperedges bears significant implications for many applications (e.g., collaboration and recipe recommendation). What makes hyperedge prediction particularly challenging is the vast number of non-hyperedge subsets, which grows exponentially with the number of nodes. Since it is prohibitive to use all of them as negative examples for model training, it is inevitable to sample a very small portion of them, and to this end, heuristic sampling schemes have been employed. However, trained models suffer from poor generalization capability for examples of different natures. In this paper, we propose AHP, an adversarial training-based hyperedge-prediction method. It learns to sample negative examples without relying on any heuristic schemes. Using six real hypergraphs, we show that AHP generalizes better to negative examples of various natures. It yields up to 28.2% higher AUROC than the best existing methods and often even outperforms its variants with sampling schemes tailored to test sets.|超图(即超边集合)自然地表示群体关系(例如，研究人员共同撰写一篇论文和一个食谱中的成分) ，每一个都对应于一个超边(即节点的子集)。预测未来或缺失的超边界对于许多应用程序(例如，协作和菜谱推荐)具有重要意义。使得超边缘预测特别具有挑战性的是大量的非超边缘子集，它们随着节点的数量呈指数增长。由于在模型训练中禁止使用所有的负例子，因此不可避免地需要对其中的一小部分进行抽样，为此，采用了启发式抽样方案。然而，经过训练的模型对于不同性质的例子的泛化能力较差。本文提出了一种基于对抗训练的 AHP 超边缘预测方法。它学习不依赖任何启发式方案来抽样否定的例子。利用六个实超图，我们证明了层次分析法能够更好地推广各种性质的负例子。与现有的最佳方法相比，它的 AUROC 提高了28.2% ，而且通常在根据测试集量身定制的抽样方案上甚至优于其变体。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=AHP:+Learning+to+Negative+Sample+for+Hyperedge+Prediction)|1| +|[A 'Pointwise-Query, Listwise-Document' based Query Performance Prediction Approach](https://doi.org/10.1145/3477495.3531821)|Suchana Datta, Sean MacAvaney, Debasis Ganguly, Derek Greene|Univ Coll Dublin, Dublin, Ireland; Univ Glasgow, Glasgow, Lanark, Scotland|The task of Query Performance Prediction (QPP) in Information Retrieval (IR) involves predicting the relative effectiveness of a search system for a given input query. Supervised approaches for QPP, such as NeuralQPP [24] are often trained on pairs of queries to capture their relative retrieval performance. However, point-wise approaches, such as the recently proposed BERT-QPP [1], are generally preferable for efficiency reasons. In this paper, we propose a novel end-to-end neural cross-encoder-based approach that is trained pointwise on individual queries, but listwise over the top ranked documents (split into chunks). In contrast to prior work, the network is then trained to predict the number of relevant documents in each chunk for a given query. Our method is thus a split-n-merge technique that instead of predicting the likely number of relevant documents in the top-k [1], rather predicts the number of relevant documents for each fixed chunk size p (p < k) and then aggregates them for QPP on top-k. Experiments demonstrate that our method is significantly more effective than other supervised and unsupervised QPP approaches yielding improvements of up to 30% on the TREC-DL'20 dataset and by nearly 9% for the MS MARCO Dev set.|查询性能预测的任务(QPP)在信息检索(IR)包括预测一个给定输入查询的搜索系统的相对有效性。QPP 的有监督的方法，如 NeuralQPP [24] ，经常在查询对上进行训练，以捕获它们的相对检索性能。然而，出于效率的考虑，最近提出的 BERT-QPP [1]等逐点方法通常是可取的。在本文中，我们提出了一种新的端到端神经交叉编码器为基础的方法，是点式训练的个别查询，但列表上的排名最高的文档(分成块)。与先前的工作相反，网络然后被训练来预测给定查询的每个块中相关文档的数量。因此，我们的方法是一种拆分-n-merge 技术，它不是预测 top-k [1]中相关文档的可能数量，而是预测每个固定块大小 p (p < k)的相关文档的数量，然后将它们聚合为 top-k 上的 QPP。实验表明，我们的方法比其他有监督和无监督的 QPP 方法显着更有效，在 TREC-DL’20数据集上提高了30% ，在 MS MARCO Dev 集上提高了近9% 。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+'Pointwise-Query,+Listwise-Document'+based+Query+Performance+Prediction+Approach)|1| +|[AHP: Learning to Negative Sample for Hyperedge Prediction](https://doi.org/10.1145/3477495.3531836)|Hyunjin Hwang, Seungwoo Lee, Chanyoung Park, Kijung Shin|KAIST, Daejeon, Republic of Korea; KAIST, Seoul, Republic of Korea|Hypergraphs (i.e., sets of hyperedges) naturally represent group relations (e.g., researchers co-authoring a paper and ingredients used together in a recipe), each of which corresponds to a hyperedge (i.e., a subset of nodes). Predicting future or missing hyperedges bears significant implications for many applications (e.g., collaboration and recipe recommendation). What makes hyperedge prediction particularly challenging is the vast number of non-hyperedge subsets, which grows exponentially with the number of nodes. Since it is prohibitive to use all of them as negative examples for model training, it is inevitable to sample a very small portion of them, and to this end, heuristic sampling schemes have been employed. However, trained models suffer from poor generalization capability for examples of different natures. In this paper, we propose AHP, an adversarial training-based hyperedge-prediction method. It learns to sample negative examples without relying on any heuristic schemes. Using six real hypergraphs, we show that AHP generalizes better to negative examples of various natures. It yields up to 28.2% higher AUROC than the best existing methods and often even outperforms its variants with sampling schemes tailored to test sets.|超图(即超边集合)自然地表示群体关系(例如，研究人员共同撰写一篇论文和一个食谱中的成分) ，每一个都对应于一个超边(即节点的子集)。预测未来或缺失的超边界对于许多应用程序(例如，协作和菜谱推荐)具有重要意义。使得超边缘预测特别具有挑战性的是大量的非超边缘子集，它们随着节点的数量呈指数增长。由于在模型训练中禁止使用所有的负例子，因此不可避免地需要对其中的一小部分进行抽样，为此，采用了启发式抽样方案。然而，经过训练的模型对于不同性质的例子的泛化能力较差。本文提出了一种基于对抗训练的 AHP 超边缘预测方法。它学习不依赖任何启发式方案来抽样否定的例子。利用六个实超图，我们证明了层次分析法能够更好地推广各种性质的负例子。与现有的最佳方法相比，它的 AUROC 提高了28.2% ，而且通常在根据测试集量身定制的抽样方案上甚至优于其变体。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=AHP:+Learning+to+Negative+Sample+for+Hyperedge+Prediction)|1| |[Interactive Query Clarification and Refinement via User Simulation](https://doi.org/10.1145/3477495.3531871)|Pierre Erbacher, Ludovic Denoyer, Laure Soulier|Sorbonne Université, Paris, France|When users initiate search sessions, their query are often ambiguous or might lack of context; this resulting in non-efficient document ranking. Multiple approaches have been proposed by the Information Retrieval community to add context and retrieve documents aligned with users' intents. While some work focus on query disambiguation using users' browsing history, a recent line of work proposes to interact with users by asking clarification questions or/and proposing clarification panels. However, these approaches count either a limited number (i.e., 1) of interactions with user or log-based interactions. In this paper, we propose and evaluate a fully simulated query clarification framework allowing multi-turn interactions between IR systems and user agents.|当用户启动搜索会话时，他们的查询通常是模棱两可的，或者可能缺乏上下文; 这导致文档排序效率低下。信息检索社区提出了多种方法来添加上下文和检索符合用户意图的文档。虽然一些工作侧重于利用用户的浏览历史消除查询歧义，但最近的一项工作建议通过提出澄清问题或/和提出澄清面板与用户进行交互。但是，这些方法只计算与用户或基于日志的交互的有限数量(即1)。本文提出并评估了一个完全模拟的查询澄清框架，该框架允许信息检索系统和用户代理之间进行多次交互。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Interactive+Query+Clarification+and+Refinement+via+User+Simulation)|1| |[Explainable Session-based Recommendation with Meta-path Guided Instances and Self-Attention Mechanism](https://doi.org/10.1145/3477495.3531895)|Jiayin Zheng, Juanyun Mai, Yanlong Wen|Nankai University, Tianjin, China|Session-based recommendation (SR) gains increasing popularity because it helps greatly maintain users' privacy. Aside from its efficacy, explainability is also critical for developing a successful SR model, since it can improve the persuasiveness of the results, the users' satisfaction, and the debugging efficiency. However, the majority of current SR models are unexplainable and even those that claim to be interpretable cannot provide clear and convincing explanations of users' intentions and how they influence the models' decisions. To solve this problem, in this research, we propose a meta-path guided model which uses path instances to capture item dependencies, explicitly reveal the underlying motives, and illustrate the entire reasoning process. To begin with, our model explores meta-path guided instances and leverages the multi-head self-attention mechanism to disclose the hidden motivations beneath these path instances. To comprehensively model the user interest and interest shifting, we search paths in both adjacent and non-adjacent items. Then, we update item representations by incorporating the user-item interactions and meta-path-based context sequentially. Compared with recent strong baselines, our method is competent to the SOTA performance on three datasets and meanwhile provides sound and clear explanations.|基于会话的推荐(SR)越来越受欢迎，因为它极大地保护了用户的隐私。除了功效之外，可解释性对于建立一个成功的 SR 模型也是至关重要的，因为它可以提高结果的说服力、用户的满意度和调试效率。然而，目前大多数 SR 模型是无法解释的，甚至那些声称可以解释的模型也不能提供清晰和令人信服的解释，说明用户的意图以及他们如何影响模型的决策。为了解决这一问题，本研究提出了一个元路径引导模型，该模型利用路径实例来捕获项目依赖，明确揭示项目依赖背后的动机，并说明整个推理过程。首先，我们的模型探索元路径引导实例，并利用多头自我关注机制来揭示这些路径实例下隐藏的动机。为了对用户兴趣和兴趣转移进行综合建模，我们在相邻和非相邻项目中搜索路径。然后，通过合并用户-项目交互和基于元路径的上下文顺序更新项目表示。与最近的强基线相比，我们的方法能够胜任三个数据集的 SOTA 性能，同时提供了合理而清晰的解释。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Explainable+Session-based+Recommendation+with+Meta-path+Guided+Instances+and+Self-Attention+Mechanism)|1| |[QSG Transformer: Transformer with Query-Attentive Semantic Graph for Query-Focused Summarization](https://doi.org/10.1145/3477495.3531901)|Choongwon Park, Youngjoong Ko|Sungkyunkwan University, Suwon-si, Gyeonggi-do, Republic of Korea|Query-Focused Summarization (QFS) is a task that aims to extract essential information from a long document and organize it into a summary that can answer a query. Recently, Transformer-based summarization models have been widely used in QFS. However, the simple Transformer architecture cannot utilize the relationships between distant words and information from a query directly. In this study, we propose the QSG Transformer, a novel QFS model that leverages structure information on Query-attentive Semantic Graph (QSG) to address these issues. Specifically, in the QSG Transformer, QSG node representation is improved by a proposed query-attentive graph attention network, which spreads the information of the query node into QSG using Personalized PageRank, and it is used to generate a summary that better reflects the information from the relationships of a query and document. The proposed method is evaluated on two QFS datasets, and it achieves superior performances over the state-of-the-art models.|查询聚焦摘要(Query-Focus Summarization，QFS)是一项任务，旨在从长文档中提取重要信息，并将其组织成可以回答查询的摘要。近年来，基于变压器的汇总模型在 QFS 中得到了广泛的应用。但是，简单的 Transformer 体系结构不能直接利用远程单词和来自查询的信息之间的关系。在这项研究中，我们提出了 QSG 转换器，一个新的 QFS 模型，利用查询注意语义图(QSG)的结构信息来解决这些问题。提出了一种基于查询-注意图注意网络的 QSG 节点表示方法，该方法利用个性化 PageRank 将查询节点的信息扩展到 QSG 中，生成能够更好地反映查询和文档关系信息的摘要。在两个 QFS 数据集上对该方法进行了评估，结果表明该方法的性能优于现有的模型。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=QSG+Transformer:+Transformer+with+Query-Attentive+Semantic+Graph+for+Query-Focused+Summarization)|1| -|[Next Point-of-Interest Recommendation with Auto-Correlation Enhanced Multi-Modal Transformer Network](https://doi.org/10.1145/3477495.3531905)|Yanjun Qin, Yuchen Fang, Haiyong Luo, Fang Zhao, Chenxing Wang|Institute of Computing Technology Chinese Academy Of Sciences, Beijing, China; Beijing University of Posts and Telecommunications, Beijing, China|Next Point-of-Interest (POI) recommendation is a pivotal issue for researchers in the field of location-based social networks. While many recent efforts show the effectiveness of recurrent neural network-based next POI recommendation algorithms, several important challenges have not been well addressed yet: (i) The majority of previous models only consider the dependence of consecutive visits, while ignoring the intricate dependencies of POIs in traces; (ii) The nature of hierarchical and the matching of sub-sequence in POI sequences are hardly model in prior methods; (iii) Most of the existing solutions neglect the interactions between two modals of POI and the density category. To tackle the above challenges, we propose an auto-correlation enhanced multi-modal Transformer network (AutoMTN) for the next POI recommendation. Particularly, AutoMTN uses the Transformer network to explicitly exploits connections of all the POIs along the trace. Besides, to discover the dependencies at the sub-sequence level and attend to cross-modal interactions between POI and category sequences, we replace self-attention in Transformer with the auto-correlation mechanism and design a multi-modal network. Experiments results on two real-world datasets demonstrate the ascendancy of AutoMTN contra state-of-the-art methods in the next POI recommendation.|下一个兴趣点(POI)推荐是基于位置的社交网络研究领域的一个关键问题。尽管最近的许多努力显示了基于循环神经网络的下一个 POI 推荐算法的有效性，但是一些重要的挑战还没有得到很好的解决: (i)以前的大多数模型只考虑连续访问的依赖性，而忽略了跟踪中 POI 的复杂依赖性; (ii) POI 序列中分层的性质和子序列的匹配在以前的方法中几乎不是模型; (iii)大多数现有的解决方案忽略了 POI 的两个模态和密度类别之间的相互作用。为了应对上述挑战，我们提出了一个自相关增强型多模态变压器网络(AutoMTN)作为下一个 POI 建议。特别是，AutoMTN 使用 Transformer 网络显式地利用跟踪过程中所有 POI 的连接。此外，为了发现子序列层次上的依赖关系，并处理 POI 与类别序列之间的跨模态交互作用，我们用自相关机制取代了主变压器中的自注意机制，并设计了一个多模态网络。在两个实际数据集上的实验结果表明，AutoMTN 方法在下一个 POI 推荐中占优势。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Next+Point-of-Interest+Recommendation+with+Auto-Correlation+Enhanced+Multi-Modal+Transformer+Network)|1| -|[Neutralizing Popularity Bias in Recommendation Models](https://doi.org/10.1145/3477495.3531907)|Guipeng Xv, Chen Lin, Hui Li, Jinsong Su, Weiyao Ye, Yewang Chen|Huaqiao University, Xiamen, China; Xiamen University, Xiamen, China|Most existing recommendation models learn vectorized representations for items, i.e., item embeddings to make predictions. Item embeddings inherit popularity bias from the data, which leads to biased recommendations. We use this observation to design two simple and effective strategies, which can be flexibly plugged into different backbone recommendation models, to learn popularity neutral item representations. One strategy isolates popularity bias in one embedding direction and neutralizes the popularity direction post-training. The other strategy encourages all embedding directions to be disentangled and popularity neutral. We demonstrate that the proposed strategies outperform state-of-the-art debiasing methods on various real-world datasets, and improve recommendation quality of shallow and deep backbone models.|大多数现有的推荐模型学习项目的向量化表示，即项目嵌入来进行预测。项目嵌入从数据中继承了受欢迎程度的偏差，从而导致偏差推荐。利用这一观察结果，我们设计了两个简单有效的策略，可以灵活地插入到不同的骨干推荐模型中，来学习流行度中性的项目表示。一种策略是在一个嵌入方向上隔离流行偏差，在训练后中和流行方向。另一种策略鼓励所有的嵌入方向都是分离的和流行中性的。实验结果表明，所提出的策略优于现有的各种实际数据集的去偏方法，提高了浅层和深层骨架模型的推荐质量。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Neutralizing+Popularity+Bias+in+Recommendation+Models)|1| +|[Next Point-of-Interest Recommendation with Auto-Correlation Enhanced Multi-Modal Transformer Network](https://doi.org/10.1145/3477495.3531905)|Yanjun Qin, Yuchen Fang, Haiyong Luo, Fang Zhao, Chenxing Wang|Beijing University of Posts and Telecommunications, Beijing, China; Institute of Computing Technology Chinese Academy Of Sciences, Beijing, China|Next Point-of-Interest (POI) recommendation is a pivotal issue for researchers in the field of location-based social networks. While many recent efforts show the effectiveness of recurrent neural network-based next POI recommendation algorithms, several important challenges have not been well addressed yet: (i) The majority of previous models only consider the dependence of consecutive visits, while ignoring the intricate dependencies of POIs in traces; (ii) The nature of hierarchical and the matching of sub-sequence in POI sequences are hardly model in prior methods; (iii) Most of the existing solutions neglect the interactions between two modals of POI and the density category. To tackle the above challenges, we propose an auto-correlation enhanced multi-modal Transformer network (AutoMTN) for the next POI recommendation. Particularly, AutoMTN uses the Transformer network to explicitly exploits connections of all the POIs along the trace. Besides, to discover the dependencies at the sub-sequence level and attend to cross-modal interactions between POI and category sequences, we replace self-attention in Transformer with the auto-correlation mechanism and design a multi-modal network. Experiments results on two real-world datasets demonstrate the ascendancy of AutoMTN contra state-of-the-art methods in the next POI recommendation.|下一个兴趣点(POI)推荐是基于位置的社交网络研究领域的一个关键问题。尽管最近的许多努力显示了基于循环神经网络的下一个 POI 推荐算法的有效性，但是一些重要的挑战还没有得到很好的解决: (i)以前的大多数模型只考虑连续访问的依赖性，而忽略了跟踪中 POI 的复杂依赖性; (ii) POI 序列中分层的性质和子序列的匹配在以前的方法中几乎不是模型; (iii)大多数现有的解决方案忽略了 POI 的两个模态和密度类别之间的相互作用。为了应对上述挑战，我们提出了一个自相关增强型多模态变压器网络(AutoMTN)作为下一个 POI 建议。特别是，AutoMTN 使用 Transformer 网络显式地利用跟踪过程中所有 POI 的连接。此外，为了发现子序列层次上的依赖关系，并处理 POI 与类别序列之间的跨模态交互作用，我们用自相关机制取代了主变压器中的自注意机制，并设计了一个多模态网络。在两个实际数据集上的实验结果表明，AutoMTN 方法在下一个 POI 推荐中占优势。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Next+Point-of-Interest+Recommendation+with+Auto-Correlation+Enhanced+Multi-Modal+Transformer+Network)|1| +|[Neutralizing Popularity Bias in Recommendation Models](https://doi.org/10.1145/3477495.3531907)|Guipeng Xv, Chen Lin, Hui Li, Jinsong Su, Weiyao Ye, Yewang Chen|Xiamen University, Xiamen, China; Huaqiao University, Xiamen, China|Most existing recommendation models learn vectorized representations for items, i.e., item embeddings to make predictions. Item embeddings inherit popularity bias from the data, which leads to biased recommendations. We use this observation to design two simple and effective strategies, which can be flexibly plugged into different backbone recommendation models, to learn popularity neutral item representations. One strategy isolates popularity bias in one embedding direction and neutralizes the popularity direction post-training. The other strategy encourages all embedding directions to be disentangled and popularity neutral. We demonstrate that the proposed strategies outperform state-of-the-art debiasing methods on various real-world datasets, and improve recommendation quality of shallow and deep backbone models.|大多数现有的推荐模型学习项目的向量化表示，即项目嵌入来进行预测。项目嵌入从数据中继承了受欢迎程度的偏差，从而导致偏差推荐。利用这一观察结果，我们设计了两个简单有效的策略，可以灵活地插入到不同的骨干推荐模型中，来学习流行度中性的项目表示。一种策略是在一个嵌入方向上隔离流行偏差，在训练后中和流行方向。另一种策略鼓励所有的嵌入方向都是分离的和流行中性的。实验结果表明，所提出的策略优于现有的各种实际数据集的去偏方法，提高了浅层和深层骨架模型的推荐质量。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Neutralizing+Popularity+Bias+in+Recommendation+Models)|1| |[ROGUE: A System for Exploratory Search of GANs](https://doi.org/10.1145/3477495.3531675)|Yang Liu, Alan Medlar, Dorota Glowacka|University of Helsinki, Helsinki, Finland|Image retrieval from generative adversarial networks (GANs) is challenging for several reasons. First, there are no clear mappings between the GAN's latent space and useful semantic features, making it difficult for users to navigate. Second, the number of unique images that can be generated is exceptionally high, taxing the scaling properties of existing search algorithms. In this article, we present ROGUE, a system to support exploratory search of images generated from GANs. We demonstrate how to implement features that are commonly found in exploratory search interfaces, such as faceted search and relevance feedback, in the context of GAN search. We additionally use reinforcement learning to help users navigate the image space [8], trading off exploration (showing diverse images) and exploitation (showing images predicted to receive positive relevance feedback). Finally, we present a usability study where participants were situated in the role of a casting director who needs to explore actors' headshots for an upcoming movie. The system obtained an average SUS score of 72.8 and all participants reported being either satisfied or very satisfied with the images they identified with the system. The system is shown in this accompanying video: https://vimeo.com/680036160.|基于生成对抗网络(GAN)的图像检索是一个具有挑战性的问题。首先，在 GAN 的潜在空间和有用的语义特性之间没有清晰的映射，这使得用户很难导航。其次，可以生成的独特图像的数量异常之高，这对现有搜索算法的缩放特性造成了压力。在本文中，我们提出了 ROGUE，一个系统，以支持探索性搜索的图像生成的 GAN。我们将演示如何在广域网搜索环境中实现探索性搜索界面中常见的特性，例如分面搜索和关联反馈搜索。此外，我们还使用强化学习来帮助用户浏览图片空间，在探索(显示不同的图片)和利用(显示预计会收到正面关联反馈的图片)之间进行权衡。最后，我们提出了一个可用性研究，其中的参与者位于一个角色的选角导演谁需要探索演员的头像为即将到来的电影。该系统获得的 SUS 平均分为72.8分，所有参与者报告说，他们对系统识别的图像要么感到满意，要么非常满意。该系统在下面的视频中显示: https://vimeo.com/680036160。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=ROGUE:+A+System+for+Exploratory+Search+of+GANs)|1| |[Searching for a New and Better Future of Work](https://doi.org/10.1145/3477495.3532088)|Jaime Teevan|Microsoft, Redmond, WA, USA|Search engines were one of the first intelligent cloud-based applications that people used to get things done, and they have since become an extremely important productivity tool. This is in part because much of what a person is doing when they search is thinking. Search engines do not merely support that thinking, however, but can also actually shape it. For example, some search results are more likely to spur learning than others and influence a person's future queries [1]. This means that as information retrieval researchers the new approaches that we develop can actively shape the future of work. The world is now in the middle of the most significant change to work practices in a generation, and it is one that will make search technology even more central to work in the years to come. For the past several millennia, space was the primary technology that people used to get things done. The coming Hybrid Work Era, however, will be shaped by digital technology. The recent rapid shift to remote work significantly accelerated the digital transformation already underway at many companies, and new types of work-related data are now being generated at an unprecedented rate. For example, the use of meeting recordings in Microsoft Stream has more than doubled from March 2020 to February 2022 [2]. Knowledge exists in this newly captured data, but figuring out how to make sense of it is overwhelming. The information retrieval community knows how to process large amounts of data, build usable intelligent systems, and learn from behavioral data, and we have a unique opportunity right now to apply this expertise to new corpora and in new scenarios in a meaningful way. In this talk I will give an overview of what research tells us about emerging work practices, and explore how the SIGIR community can build on these findings to help create a new - and better - future of work.|搜索引擎是人们用来完成工作的第一批基于云的智能应用程序之一，自那以后，它们已经成为极其重要的生产力工具。这在一定程度上是因为一个人在搜索时所做的大部分事情都是在思考。然而，搜索引擎不仅仅支持这种想法，而且实际上还可以塑造这种想法。例如，一些搜索结果比其他结果更有可能刺激学习，并影响一个人未来的查询[1]。这意味着，作为信息检索研究人员，我们开发的新方法可以积极地塑造未来的工作。这个世界现在正处于一代人以来最重大的工作实践变革之中，这将使搜索技术在未来几年的工作中变得更加重要。在过去的几千年里，太空是人们用来完成任务的主要技术。然而，即将到来的混合工作时代将由数字技术塑造。最近向远程工作的迅速转变极大地加速了许多公司已经在进行的数字化转变，现在正以前所未有的速度产生新类型的与工作有关的数据。例如，从2020年3月到2022年2月，微软 Stream 会议录音的使用量增加了一倍多[2]。知识存在于这些新捕获的数据中，但是弄清楚如何理解这些数据是非常困难的。信息检索社区知道如何处理大量的数据，构建可用的智能系统，并从行为数据中学习，我们现在有一个独特的机会，可以将这些专业知识以一种有意义的方式应用到新的语料库和新的场景中。在这个演讲中，我将概述研究告诉我们的新兴工作实践，并探讨 SIGIR 社区如何能够建立在这些发现的基础上，帮助创造一个新的、更好的工作未来。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Searching+for+a+New+and+Better+Future+of+Work)|1| |[INMO: A Model-Agnostic and Scalable Module for Inductive Collaborative Filtering](https://doi.org/10.1145/3477495.3532000)|Yunfan Wu, Qi Cao, Huawei Shen, Shuchang Tao, Xueqi Cheng|Institute of Computing Technology, Chinese Academy of Sciences & University of Chinese Academy of Sciences, Beijing, China; Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China|Collaborative filtering is one of the most common scenarios and popular research topics in recommender systems. Among existing methods, latent factor models, i.e., learning a specific embedding for each user/item by reconstructing the observed interaction matrix, have shown excellent performances. However, such user-specific and item-specific embeddings are intrinsically transductive, making it difficult for them to deal with new users and new items unseen during training. Besides, the number of model parameters heavily depends on the number of all users and items, restricting their scalability to real-world applications. To solve the above challenges, in this paper, we propose a novel model-agnostic and scalable Inductive Embedding Module for collaborative filtering, namely INMO. INMO generates the inductive embeddings for users (items) by characterizing their interactions with some template items (template users), instead of employing an embedding lookup table. Under the theoretical analysis, we further propose an effective indicator for the selection of template users and template items. Our proposed INMO can be attached to existing latent factor models as a pre-module, inheriting the expressiveness of backbone models, while bringing the inductive ability and reducing model parameters. We validate the generality of INMO by attaching it to Matrix Factorization (MF) and LightGCN, which are two representative latent factor models for collaborative filtering. Extensive experiments on three public benchmarks demonstrate the effectiveness and efficiency of INMO in both transductive and inductive recommendation scenarios.|协同过滤是推荐系统中最常见的场景和流行的研究主题之一。在已有的方法中，潜因子模型(即通过重构观察到的交互矩阵学习每个用户/项目的特定嵌入)表现出了良好的性能。然而，这种特定于用户和特定于项目的嵌入在本质上具有传导性，使他们难以处理培训期间看不到的新用户和新项目。此外，模型参数的数量很大程度上取决于所有用户和项目的数量，限制了它们对现实应用程序的可伸缩性。为了解决上述挑战，本文提出了一种新颖的模型无关性和可扩展性的感应嵌入模块，即 INMO 协同过滤。INMO 通过描述用户与某些模板项(模板用户)的交互来为用户(项)生成归纳嵌入，而不是使用嵌入查找表。在理论分析的基础上，进一步提出了模板用户和模板项选择的有效指标。我们提出的 INMO 可以作为一个预模块附加到现有的潜因子模型，继承骨干模型的表达能力，同时带来归纳能力和减少模型参数。我们通过将 INMO 附加到矩阵分解(MF)和 LightGCN (这两个是协同过滤的两个代表性潜在因素模型)来验证 INMO 的普遍性。对三个公共基准进行的广泛试验表明，INMO 在推导和归纳推荐两种情况下都具有有效性和效率。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=INMO:+A+Model-Agnostic+and+Scalable+Module+for+Inductive+Collaborative+Filtering)|1| |[Conversational Question Answering on Heterogeneous Sources](https://doi.org/10.1145/3477495.3531815)|Philipp Christmann, Rishiraj Saha Roy, Gerhard Weikum|Max Planck Institute for Informatics, Saarbrücken, Germany|Conversational question answering (ConvQA) tackles sequential information needs where contexts in follow-up questions are left implicit. Current ConvQA systems operate over homogeneous sources of information: either a knowledge base (KB), or a text corpus, or a collection of tables. This paper addresses the novel issue of jointly tapping into all of these together, this way boosting answer coverage and confidence. We present CONVINSE, an end-to-end pipeline for ConvQA over heterogeneous sources, operating in three stages: i) learning an explicit structured representation of an incoming question and its conversational context, ii) harnessing this frame-like representation to uniformly capture relevant evidences from KB, text, and tables, and iii) running a fusion-in-decoder model to generate the answer. We construct and release the first benchmark, ConvMix, for ConvQA over heterogeneous sources, comprising 3000 real-user conversations with 16000 questions, along with entity annotations, completed question utterances, and question paraphrases. Experiments demonstrate the viability and advantages of our method, compared to state-of-the-art baselines.|会话问题回答(ConvQA)处理后续问题中的上下文隐含的连续信息需求。当前的 ConvQA 系统是在同一信息源上运行的: 知识库(KB)、文本语料库或表集合。本文提出了一个新颖的问题，即共同利用所有这些问题，这样可以提高答案的覆盖面和信心。我们提出 CONVINSE，一种针对异构来源的 ConvQA 端到端管道，分三个阶段进行操作: i)学习传入问题及其会话上下文的显式结构化表示，ii)利用这种框架样表示来统一地从知识库，文本和表格中捕获相关证据，以及 iii)运行融合解码模型来生成答案。我们构建并发布了第一个基准，ConvMix，用于异构来源的 ConvQA，包括3000个实际用户对话和16000个问题，以及实体注释，完成的问题语句和问题转述。与最先进的基线相比，实验证明了我们方法的可行性和优势。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Conversational+Question+Answering+on+Heterogeneous+Sources)|1| -|[COSPLAY: Concept Set Guided Personalized Dialogue Generation Across Both Party Personas](https://doi.org/10.1145/3477495.3531957)|Chen Xu, Piji Li, Wei Wang, Haoran Yang, Siyun Wang, Chuangbai Xiao|The Chinese University of Hong Kong, Hong Kong, AA, China; Beijing University of Technology, Beijing, AA, China; Tencent AI Lab, Shenzhen, AA, China; University of Southern California, Los Angeles, AA, USA; Tsinghua University, Beijing, AA, China|Maintaining a consistent persona is essential for building a human-like conversational model. However, the lack of attention to the partner makes the model more egocentric: they tend to show their persona by all means such as twisting the topic stiffly, pulling the conversation to their own interests regardless, and rambling their persona with little curiosity to the partner. In this work, we propose COSPLAY(COncept Set guided PersonaLized dialogue generation Across both partY personas) that considers both parties as a "team": expressing self-persona while keeping curiosity toward the partner, leading responses around mutual personas, and finding the common ground. Specifically, we first represent self-persona, partner persona and mutual dialogue all in the concept sets. Then, we propose the Concept Set framework with a suite of knowledge-enhanced operations to process them such as set algebras, set expansion, and set distance. Based on these operations as medium, we train the model by utilizing 1) concepts of both party personas, 2) concept relationship between them, and 3) their relationship to the future dialogue. Extensive experiments on a large public dataset, Persona-Chat, demonstrate that our model outperforms state-of-the-art baselines for generating less egocentric, more human-like, and higher quality responses in both automatic and human evaluations.|保持一致的人物角色对于构建类似人类的会话模型至关重要。然而，缺乏对伴侣的关注使得这种模式更加自我中心: 他们倾向于通过各种方式来展示自己的角色，比如僵硬地扭曲话题，不顾一切地把谈话拉向自己的兴趣，对伴侣毫无好奇心地胡扯自己的角色。在这项工作中，我们提出 COSPLAY (概念集引导的个性化对话生成跨越双方角色) ，认为双方是一个“团队”: 表达自我角色，同时保持对伴侣的好奇心，引导对相互角色的反应，并找到共同点。具体来说，我们首先在概念集中表现自我人格、伴侣人格和相互对话。然后，我们提出了概念集框架和一套知识增强操作来处理它们，例如集代数、集展开和集距离。基于这些操作作为媒介，我们利用1)双方角色的概念，2)他们之间的概念关系，3)他们与未来对话的关系来训练模型。在一个大型公共数据集“人物聊天”上进行的大量实验表明，我们的模型在自动和人工评估中产生更少的自我中心，更多的人性化和更高质量的响应方面优于最先进的基线。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=COSPLAY:+Concept+Set+Guided+Personalized+Dialogue+Generation+Across+Both+Party+Personas)|1| +|[COSPLAY: Concept Set Guided Personalized Dialogue Generation Across Both Party Personas](https://doi.org/10.1145/3477495.3531957)|Chen Xu, Piji Li, Wei Wang, Haoran Yang, Siyun Wang, Chuangbai Xiao|University of Southern California, Los Angeles, AA, USA; The Chinese University of Hong Kong, Hong Kong, AA, China; Tencent AI Lab, Shenzhen, AA, China; Tsinghua University, Beijing, AA, China; Beijing University of Technology, Beijing, AA, China|Maintaining a consistent persona is essential for building a human-like conversational model. However, the lack of attention to the partner makes the model more egocentric: they tend to show their persona by all means such as twisting the topic stiffly, pulling the conversation to their own interests regardless, and rambling their persona with little curiosity to the partner. In this work, we propose COSPLAY(COncept Set guided PersonaLized dialogue generation Across both partY personas) that considers both parties as a "team": expressing self-persona while keeping curiosity toward the partner, leading responses around mutual personas, and finding the common ground. Specifically, we first represent self-persona, partner persona and mutual dialogue all in the concept sets. Then, we propose the Concept Set framework with a suite of knowledge-enhanced operations to process them such as set algebras, set expansion, and set distance. Based on these operations as medium, we train the model by utilizing 1) concepts of both party personas, 2) concept relationship between them, and 3) their relationship to the future dialogue. Extensive experiments on a large public dataset, Persona-Chat, demonstrate that our model outperforms state-of-the-art baselines for generating less egocentric, more human-like, and higher quality responses in both automatic and human evaluations.|保持一致的人物角色对于构建类似人类的会话模型至关重要。然而，缺乏对伴侣的关注使得这种模式更加自我中心: 他们倾向于通过各种方式来展示自己的角色，比如僵硬地扭曲话题，不顾一切地把谈话拉向自己的兴趣，对伴侣毫无好奇心地胡扯自己的角色。在这项工作中，我们提出 COSPLAY (概念集引导的个性化对话生成跨越双方角色) ，认为双方是一个“团队”: 表达自我角色，同时保持对伴侣的好奇心，引导对相互角色的反应，并找到共同点。具体来说，我们首先在概念集中表现自我人格、伴侣人格和相互对话。然后，我们提出了概念集框架和一套知识增强操作来处理它们，例如集代数、集展开和集距离。基于这些操作作为媒介，我们利用1)双方角色的概念，2)他们之间的概念关系，3)他们与未来对话的关系来训练模型。在一个大型公共数据集“人物聊天”上进行的大量实验表明，我们的模型在自动和人工评估中产生更少的自我中心，更多的人性化和更高质量的响应方面优于最先进的基线。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=COSPLAY:+Concept+Set+Guided+Personalized+Dialogue+Generation+Across+Both+Party+Personas)|1| |[KETCH: Knowledge Graph Enhanced Thread Recommendation in Healthcare Forums](https://doi.org/10.1145/3477495.3532008)|Limeng Cui, Dongwon Lee|The Pennsylvania State University, University Park, PA, USA|Health thread recommendation methods aim to suggest the most relevant existing threads for a user. Most of the existing methods tend to rely on modeling the post contents to retrieve relevant answers. However, some posts written by users with different clinical conditions can be lexically similar, as unrelated diseases (e.g., Angina and Osteoporosis) may have the same symptoms (e.g., back pain), yet irrelevant threads to a user. Therefore, it is critical to not only consider the connections between users and threads, but also the descriptions of users' symptoms and clinical conditions. In this paper, towards this problem of thread recommendation in online healthcare forums, we propose a knowledge graph enhanced Threads Recommendation (KETCH) model, which leverages graph neural networks to model the interactions among users and threads, and learn their representations. In our model, the users, threads and posts are three types of nodes in a graph, linked through their associations. KETCH uses the message passing strategy by aggregating information along with the network. In addition, we introduce a knowledge-enhanced attention mechanism to capture the latent conditions and symptoms. We also apply the method to the task of predicting the side effects of drugs, to show that KETCH has the potential to complement the medical knowledge graph. Comparing with the best results of seven competing methods, in terms of MRR, KETCH outperforms all methods by at least 0.125 on the MedHelp dataset, 0.048 on the Patient dataset and 0.092 on HealthBoards dataset, respectively. We release the source code of KETCH at: https://github.com/cuilimeng/KETCH.|健康线程推荐方法旨在为用户推荐最相关的现有线程。现有的大多数方法倾向于依赖于对文章内容进行建模来检索相关的答案。然而，不同临床状况的用户写的一些帖子在词汇上可能是相似的，因为不相关的疾病(如心绞痛和骨质疏松症)可能有相同的症状(如背痛) ，但对用户来说是不相关的。因此，不仅要考虑用户和线程之间的连接，还要考虑用户症状和临床状况的描述。针对在线医疗论坛中的线程推荐问题，提出了一种知识图增强型线程推荐(KETCH)模型，该模型利用图神经网络对用户和线程之间的交互进行建模，并学习用户和线程之间的表示。在我们的模型中，用户、线程和帖子是图中的三种类型的节点，通过它们的关联进行链接。KETCH 通过将信息与网络一起聚合来使用消息传递策略。此外，我们还引入了一种知识增强的注意机制来捕捉潜在的条件和症状。我们还将该方法应用于预测药物副作用的任务，表明 KETCH 具有补充医学知识图谱的潜力。与七种竞争方法的最佳结果相比，就 MRR 而言，KETCH 在 MedHelp 数据集上至少优于所有方法0.125，在患者数据集上优于0.048，在 HealthBoards 数据集上优于0.092。我们在 https://github.com/cuilimeng/KETCH 发布 KETCH 的源代码。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=KETCH:+Knowledge+Graph+Enhanced+Thread+Recommendation+in+Healthcare+Forums)|1| -|[Explainable Legal Case Matching via Inverse Optimal Transport-based Rationale Extraction](https://doi.org/10.1145/3477495.3531974)|Weijie Yu, Zhongxiang Sun, Jun Xu, Zhenhua Dong, Xu Chen, Hongteng Xu, JiRong Wen|Renmin University of China, Beijing, China; Huawei Noah's Ark Lab, Shenzhen, China|As an essential operation of legal retrieval, legal case matching plays a central role in intelligent legal systems. This task has a high demand on the explainability of matching results because of its critical impacts on downstream applications --- the matched legal cases may provide supportive evidence for the judgments of target cases and thus influence the fairness and justice of legal decisions. Focusing on this challenging task, we propose a novel and explainable method, namely IOT-Match, with the help of computational optimal transport, which formulates the legal case matching problem as an inverse optimal transport (IOT) problem. Different from most existing methods, which merely focus on the sentence-level semantic similarity between legal cases, our IOT-Match learns to extract rationales from paired legal cases based on both semantics and legal characteristics of their sentences. The extracted rationales are further applied to generate faithful explanations and conduct matching. Moreover, the proposed IOT-Match is robust to the alignment label insufficiency issue commonly in practical legal case matching tasks, which is suitable for both supervised and semi-supervised learning paradigms. To demonstrate the superiority of our IOT-Match method and construct a benchmark of explainable legal case matching task, we not only extend the well-known Challenge of AI in Law (CAIL) dataset but also build a new Explainable Legal cAse Matching (ELAM) dataset, which contains lots of legal cases with detailed and explainable annotations. Experiments on these two datasets show that our IOT-Match outperforms state-of-the-art methods consistently on matching prediction, rationale extraction, and explanation generation.|法律案件匹配作为法律检索的一项基本操作，在智能法律系统中起着核心作用。这项任务对匹配结果的解释性要求很高，因为它对下游应用产生了重要影响——匹配的法律案件可以为目标案件的判决提供支持性证据，从而影响法律判决的公正性和正义性。针对这一具有挑战性的任务，我们提出了一种新颖的解释方法，即物联网匹配(IOT-Match) ，借助于计算最优传输，将法律案例匹配问题表述为一个逆最优传输(IOT)问题。不同于现有的方法，我们的 IOT-Match 仅仅着眼于法律案件之间的句子层面的语义相似性，而是学习从成对的法律案件中根据其句子的语义和法律特征提取理据。提取的理论基础进一步应用于产生忠实的解释和进行匹配。此外，建议的物联网匹配对于实际案例匹配任务中常见的校准标签不足问题具有稳健性，适用于监督范式和半监督学习范式。为了证明我们的物联网匹配方法的优越性，构建一个可解释的法律案例匹配任务的基准，我们不仅扩展了著名的 CAIL 数据集，而且建立了一个新的可解释的法律案例匹配(ELAM)数据集，其中包含了大量的法律案例和详细的可解释的注释。在这两个数据集上的实验表明，我们的 IOT-Match 方法在匹配预测、理论提取和解释生成方面都优于最先进的方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Explainable+Legal+Case+Matching+via+Inverse+Optimal+Transport-based+Rationale+Extraction)|1| +|[Explainable Legal Case Matching via Inverse Optimal Transport-based Rationale Extraction](https://doi.org/10.1145/3477495.3531974)|Weijie Yu, Zhongxiang Sun, Jun Xu, Zhenhua Dong, Xu Chen, Hongteng Xu, JiRong Wen|Huawei Noah's Ark Lab, Shenzhen, China; Renmin University of China, Beijing, China|As an essential operation of legal retrieval, legal case matching plays a central role in intelligent legal systems. This task has a high demand on the explainability of matching results because of its critical impacts on downstream applications --- the matched legal cases may provide supportive evidence for the judgments of target cases and thus influence the fairness and justice of legal decisions. Focusing on this challenging task, we propose a novel and explainable method, namely IOT-Match, with the help of computational optimal transport, which formulates the legal case matching problem as an inverse optimal transport (IOT) problem. Different from most existing methods, which merely focus on the sentence-level semantic similarity between legal cases, our IOT-Match learns to extract rationales from paired legal cases based on both semantics and legal characteristics of their sentences. The extracted rationales are further applied to generate faithful explanations and conduct matching. Moreover, the proposed IOT-Match is robust to the alignment label insufficiency issue commonly in practical legal case matching tasks, which is suitable for both supervised and semi-supervised learning paradigms. To demonstrate the superiority of our IOT-Match method and construct a benchmark of explainable legal case matching task, we not only extend the well-known Challenge of AI in Law (CAIL) dataset but also build a new Explainable Legal cAse Matching (ELAM) dataset, which contains lots of legal cases with detailed and explainable annotations. Experiments on these two datasets show that our IOT-Match outperforms state-of-the-art methods consistently on matching prediction, rationale extraction, and explanation generation.|法律案件匹配作为法律检索的一项基本操作，在智能法律系统中起着核心作用。这项任务对匹配结果的解释性要求很高，因为它对下游应用产生了重要影响——匹配的法律案件可以为目标案件的判决提供支持性证据，从而影响法律判决的公正性和正义性。针对这一具有挑战性的任务，我们提出了一种新颖的解释方法，即物联网匹配(IOT-Match) ，借助于计算最优传输，将法律案例匹配问题表述为一个逆最优传输(IOT)问题。不同于现有的方法，我们的 IOT-Match 仅仅着眼于法律案件之间的句子层面的语义相似性，而是学习从成对的法律案件中根据其句子的语义和法律特征提取理据。提取的理论基础进一步应用于产生忠实的解释和进行匹配。此外，建议的物联网匹配对于实际案例匹配任务中常见的校准标签不足问题具有稳健性，适用于监督范式和半监督学习范式。为了证明我们的物联网匹配方法的优越性，构建一个可解释的法律案例匹配任务的基准，我们不仅扩展了著名的 CAIL 数据集，而且建立了一个新的可解释的法律案例匹配(ELAM)数据集，其中包含了大量的法律案例和详细的可解释的注释。在这两个数据集上的实验表明，我们的 IOT-Match 方法在匹配预测、理论提取和解释生成方面都优于最先进的方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Explainable+Legal+Case+Matching+via+Inverse+Optimal+Transport-based+Rationale+Extraction)|1| |[Optimizing Generalized Gini Indices for Fairness in Rankings](https://doi.org/10.1145/3477495.3532035)|Virginie Do, Nicolas Usunier|Meta AI Research & Université Paris Dauphine-PSL, Paris, France; Meta AI Research, Paris, France|There is growing interest in designing recommender systems that aim at being fair towards item producers or their least satisfied users. Inspired by the domain of inequality measurement in economics, this paper explores the use of generalized Gini welfare functions (GGFs) as a means to specify the normative criterion that recommender systems should optimize for. GGFs weight individuals depending on their ranks in the population, giving more weight to worse-off individuals to promote equality. Depending on these weights, GGFs minimize the Gini index of item exposure to promote equality between items, or focus on the performance on specific quantiles of least satisfied users. GGFs for ranking are challenging to optimize because they are non-differentiable. We resolve this challenge by leveraging tools from non-smooth optimization and projection operators used in differentiable sorting. We present experiments using real datasets with up to 15k users and items, which show that our approach obtains better trade-offs than the baselines on a variety of recommendation tasks and fairness criteria.|人们对设计推荐系统越来越感兴趣，这种系统旨在公平对待项目生产者或其最不满意的用户。受经济学中不平等测度领域的启发，本文探讨了广义基尼福利函数(GGFs)作为指定推荐系统优化标准的一种方法。GGFs 根据个体在人口中的等级来衡量个体的重量，将更多的重量赋予经济状况较差的个体，以促进平等。根据这些权重，GGFs 最小化项目曝光的基尼指数，以促进项目之间的平等，或者关注最不满意用户的特定分位数的性能。用于排名的 GGF 很难优化，因为它们是不可微的。我们通过利用非光滑优化工具和可微排序中使用的投影运算符来解决这一挑战。我们提出的实验使用真实的数据集多达15000个用户和项目，这表明我们的方法获得更好的权衡比基线的各种推荐任务和公平性标准。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Optimizing+Generalized+Gini+Indices+for+Fairness+in+Rankings)|1| -|[Video Moment Retrieval from Text Queries via Single Frame Annotation](https://doi.org/10.1145/3477495.3532078)|Ran Cui, Tianwen Qian, Pai Peng, Elena Daskalaki, Jingjing Chen, Xiaowei Guo, Huyang Sun, YuGang Jiang|Fudan University, Shanghai, China; The Australian National University, Canberra, Australia; bilibili, Shanghai, China|Video moment retrieval aims at finding the start and end timestamps of a moment (part of a video) described by a given natural language query. Fully supervised methods need complete temporal boundary annotations to achieve promising results, which is costly since the annotator needs to watch the whole moment. Weakly supervised methods only rely on the paired video and query, but the performance is relatively poor. In this paper, we look closer into the annotation process and propose a new paradigm called "glance annotation". This paradigm requires the timestamp of only one single random frame, which we refer to as a "glance", within the temporal boundary of the fully supervised counterpart. We argue this is beneficial because comparing to weak supervision, trivial cost is added yet more potential in performance is provided. Under the glance annotation setting, we propose a method named as Video moment retrieval via Glance Annotation (ViGA) based on contrastive learning. ViGA cuts the input video into clips and contrasts between clips and queries, in which glance guided Gaussian distributed weights are assigned to all clips. Our extensive experiments indicate that ViGA achieves better results than the state-of-the-art weakly supervised methods by a large margin, even comparable to fully supervised methods in some cases.|视频矩检索的目的是查找给定的自然语言查询所描述的矩(视频的一部分)的开始和结束时间戳。完全监督的方法需要完整的时间边界注释来获得有希望的结果，这是昂贵的，因为注释者需要观察整个时刻。弱监督方法只依赖于配对视频和查询，但性能相对较差。在本文中，我们仔细研究了注释过程，并提出了一个新的范式称为“一瞥注释”。这个范例只需要一个随机帧的时间戳，我们称之为“一瞥”，在完全监督的对应物的时间边界内。我们认为这是有益的，因为相对于薄弱的监督，微不足道的成本增加了更多的性能潜力提供。在目视注释设置下，提出了一种基于对比学习的目视注释视频矩检索方法。ViGA 将输入的视频剪切成片段，并在片段和查询之间进行对比，给所有片段分配目视导引的高斯分布权值。我们的大量实验表明，ViGA 比最先进的弱监督方法获得了更好的结果，甚至在某些情况下可以与全监督方法相媲美。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Video+Moment+Retrieval+from+Text+Queries+via+Single+Frame+Annotation)|1| -|[You Need to Read Again: Multi-granularity Perception Network for Moment Retrieval in Videos](https://doi.org/10.1145/3477495.3532083)|Xin Sun, Xuan Wang, Jialin Gao, Qiong Liu, Xi Zhou|Peking University, Beijing, China; CloudWalk Technology Co., Ltd, Shanghai, China; Shanghai Jiaotong University, Shanghai, China|Moment retrieval in videos is a challenging task that aims to retrieve the most relevant video moment in an untrimmed video given a sentence description. Previous methods tend to perform self-modal learning and cross-modal interaction in a coarse manner, which neglect fine-grained clues contained in video content, query context, and their alignment. To this end, we propose a novel Multi-Granularity Perception Network (MGPN) that perceives intra-modality and inter-modality information at a multi-granularity level. Specifically, we formulate moment retrieval as a multi-choice reading comprehension task and integrate human reading strategies into our framework. A coarse-grained feature encoder and a co-attention mechanism are utilized to obtain a preliminary perception of intra-modality and inter-modality information. Then a fine-grained feature encoder and a conditioned interaction module are introduced to enhance the initial perception inspired by how humans address reading comprehension problems. Moreover, to alleviate the huge computation burden of some existing methods, we further design an efficient choice comparison module and reduce the hidden size with imperceptible quality loss. Extensive experiments on Charades-STA, TACoS, and ActivityNet Captions datasets demonstrate that our solution outperforms existing state-of-the-art methods.|视频中的瞬间检索是一项具有挑战性的任务，其目的是在给定句子描述的未修剪视频中检索最相关的视频瞬间。以往的方法倾向于以粗糙的方式进行自模态学习和跨模态交互，忽略了视频内容、查询上下文及其对齐中包含的细粒度线索。为此，我们提出了一种新的多粒度感知网络(MGPN) ，它在多粒度级别上感知模式内和模式间的信息。具体来说，我们把瞬间提取作为一项多项选择的阅读理解任务，并将人类阅读策略整合到我们的框架中。利用粗粒度特征编码器和共注意机制获得初步的模式内和模式间信息感知。然后引入细粒度特征编码器和条件交互模块，以增强受人类处理阅读理解问题启发而产生的初始感知。此外，为了减轻现有方法的巨大计算负担，我们进一步设计了一个有效的选择比较模块，减少了隐藏的大小和不易察觉的质量损失。对 Charades-STA、 TACoS 和 ActivityNet Captions 数据集的大量实验表明，我们的解决方案优于现有的最先进的方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=You+Need+to+Read+Again:+Multi-granularity+Perception+Network+for+Moment+Retrieval+in+Videos)|1| -|[Personalized Abstractive Opinion Tagging](https://doi.org/10.1145/3477495.3532037)|Mengxue Zhao, Yang Yang, Miao Li, Jingang Wang, Wei Wu, Pengjie Ren, Maarten de Rijke, Zhaochun Ren|The University of Melbourne, Melbourne, Australia; Shandong University, Qingdao, China; Meituan, Beijing, China; University of Amsterdam, Amsterdam, Netherlands|An opinion tag is a sequence of words on a specific aspect of a product or service. Opinion tags reflect key characteristics of product reviews and help users quickly understand their content in e-commerce portals. The task of abstractive opinion tagging has previously been proposed to automatically generate a ranked list of opinion tags for a given review. However, current models for opinion tagging are not personalized, even though personalization is an essential ingredient of engaging user interactions, especially in e-commerce. In this paper, we focus on the task of personalized abstractive opinion tagging. There are two main challenges when developing models for the end-to-end generation of personalized opinion tags: sparseness of reviews and difficulty to integrate multi-type signals, i.e., explicit review signals and implicit behavioral signals. To address these challenges, we propose an end-to-end model, named POT, that consists of three main components: (1) a review-based explicit preference tracker component based on a hierarchical heterogeneous review graph to track user preferences from reviews; (2)a behavior-based implicit preference tracker component using a heterogeneous behavior graph to track the user preferences from implicit behaviors; and (3) a personalized rank-aware tagging component to generate a ranked sequence of personalized opinion tags. In our experiments, we evaluate POT on a real-world dataset collected from e-commerce platforms and the results demonstrate that it significantly outperforms strong baselines.|意见标签是关于产品或服务特定方面的一系列单词。意见标签反映了产品评论的关键特征，帮助用户快速理解电子商务门户中的内容。抽象意见标签的任务以前曾被提议为给定的评论自动生成一个意见标签的排序列表。然而，目前的意见标签模型并不个性化，即使个性化是参与用户交互的一个重要组成部分，特别是在电子商务中。本文主要研究个性化抽象意见标注的任务。在开发端到端的个性化评价标签生成模型时，存在两个主要的挑战: 评价的稀疏性和整合多类型信号的困难，即显性评价信号和隐性行为信号。为了应对这些挑战，我们提出了一个端到端模型，命名为 POT，该模型由三个主要组成部分组成: (1)一个基于评论的显性偏好跟踪组件，该组件基于分层异构评论图来跟踪来自评论的用户偏好; (2)一个基于行为的隐性偏好跟踪组件，该组件使用一个异构行为图来跟踪来自隐性行为的用户偏好; (3)一个个性化的等级感知标签组件来生成一个个性。在我们的实验中，我们评估了从电子商务平台收集的真实世界数据集上的 POT，结果表明，它明显优于强基线。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Personalized+Abstractive+Opinion+Tagging)|1| -|[Contrastive Learning with Hard Negative Entities for Entity Set Expansion](https://doi.org/10.1145/3477495.3531954)|Yinghui Li, Yangning Li, Yuxin He, Tianyu Yu, Ying Shen, HaiTao Zheng|Tsinghua University, Shenzhen, Guangdong, China; Sun-Yat Sen University, Shenzhen, Guangdong, China; Harbin Institute of Technology, Shenzhen, Guangdong, China|Entity Set Expansion (ESE) is a promising task which aims to expand entities of the target semantic class described by a small seed entity set. Various NLP and IR applications will benefit from ESE due to its ability to discover knowledge. Although previous ESE methods have achieved great progress, most of them still lack the ability to handle hard negative entities (i.e., entities that are difficult to distinguish from the target entities), since two entities may or may not belong to the same semantic class based on different granularity levels we analyze on. To address this challenge, we devise an entity-level masked language model with contrastive learning to refine the representation of entities. In addition, we propose the ProbExpan, a novel probabilistic ESE framework utilizing the entity representation obtained by the aforementioned language model to expand entities. Extensive experiments and detailed analyses on three datasets show that our method outperforms previous state-of-the-art methods.|实体集扩展(ESE)是一个很有前途的任务，其目的是扩展由一个小的种子实体集描述的目标语义类的实体。由于 ESE 具有发现知识的能力，各种自然语言处理和红外应用程序将从中受益。尽管以前的 ESE 方法已经取得了很大的进步，但是它们中的大多数仍然缺乏处理硬负实体的能力(例如，难以区分目标实体的实体) ，因为两个实体可能属于或不属于同一个语义类，基于我们分析的不同粒度级别。为了解决这个问题，我们设计了一个带有对比学习的实体级掩蔽语言模型来改进实体的表示。此外，我们还提出了一种新的概率 ESE 框架，该框架利用上述语言模型获得的实体表示来扩展实体。对三个数据集的大量实验和详细分析表明，我们的方法优于以前的最先进的方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Contrastive+Learning+with+Hard+Negative+Entities+for+Entity+Set+Expansion)|1| +|[Video Moment Retrieval from Text Queries via Single Frame Annotation](https://doi.org/10.1145/3477495.3532078)|Ran Cui, Tianwen Qian, Pai Peng, Elena Daskalaki, Jingjing Chen, Xiaowei Guo, Huyang Sun, YuGang Jiang|Fudan University, Shanghai, China; bilibili, Shanghai, China; The Australian National University, Canberra, Australia|Video moment retrieval aims at finding the start and end timestamps of a moment (part of a video) described by a given natural language query. Fully supervised methods need complete temporal boundary annotations to achieve promising results, which is costly since the annotator needs to watch the whole moment. Weakly supervised methods only rely on the paired video and query, but the performance is relatively poor. In this paper, we look closer into the annotation process and propose a new paradigm called "glance annotation". This paradigm requires the timestamp of only one single random frame, which we refer to as a "glance", within the temporal boundary of the fully supervised counterpart. We argue this is beneficial because comparing to weak supervision, trivial cost is added yet more potential in performance is provided. Under the glance annotation setting, we propose a method named as Video moment retrieval via Glance Annotation (ViGA) based on contrastive learning. ViGA cuts the input video into clips and contrasts between clips and queries, in which glance guided Gaussian distributed weights are assigned to all clips. Our extensive experiments indicate that ViGA achieves better results than the state-of-the-art weakly supervised methods by a large margin, even comparable to fully supervised methods in some cases.|视频矩检索的目的是查找给定的自然语言查询所描述的矩(视频的一部分)的开始和结束时间戳。完全监督的方法需要完整的时间边界注释来获得有希望的结果，这是昂贵的，因为注释者需要观察整个时刻。弱监督方法只依赖于配对视频和查询，但性能相对较差。在本文中，我们仔细研究了注释过程，并提出了一个新的范式称为“一瞥注释”。这个范例只需要一个随机帧的时间戳，我们称之为“一瞥”，在完全监督的对应物的时间边界内。我们认为这是有益的，因为相对于薄弱的监督，微不足道的成本增加了更多的性能潜力提供。在目视注释设置下，提出了一种基于对比学习的目视注释视频矩检索方法。ViGA 将输入的视频剪切成片段，并在片段和查询之间进行对比，给所有片段分配目视导引的高斯分布权值。我们的大量实验表明，ViGA 比最先进的弱监督方法获得了更好的结果，甚至在某些情况下可以与全监督方法相媲美。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Video+Moment+Retrieval+from+Text+Queries+via+Single+Frame+Annotation)|1| +|[You Need to Read Again: Multi-granularity Perception Network for Moment Retrieval in Videos](https://doi.org/10.1145/3477495.3532083)|Xin Sun, Xuan Wang, Jialin Gao, Qiong Liu, Xi Zhou|CloudWalk Technology Co., Ltd, Shanghai, China; Peking University, Beijing, China; Shanghai Jiaotong University, Shanghai, China|Moment retrieval in videos is a challenging task that aims to retrieve the most relevant video moment in an untrimmed video given a sentence description. Previous methods tend to perform self-modal learning and cross-modal interaction in a coarse manner, which neglect fine-grained clues contained in video content, query context, and their alignment. To this end, we propose a novel Multi-Granularity Perception Network (MGPN) that perceives intra-modality and inter-modality information at a multi-granularity level. Specifically, we formulate moment retrieval as a multi-choice reading comprehension task and integrate human reading strategies into our framework. A coarse-grained feature encoder and a co-attention mechanism are utilized to obtain a preliminary perception of intra-modality and inter-modality information. Then a fine-grained feature encoder and a conditioned interaction module are introduced to enhance the initial perception inspired by how humans address reading comprehension problems. Moreover, to alleviate the huge computation burden of some existing methods, we further design an efficient choice comparison module and reduce the hidden size with imperceptible quality loss. Extensive experiments on Charades-STA, TACoS, and ActivityNet Captions datasets demonstrate that our solution outperforms existing state-of-the-art methods.|视频中的瞬间检索是一项具有挑战性的任务，其目的是在给定句子描述的未修剪视频中检索最相关的视频瞬间。以往的方法倾向于以粗糙的方式进行自模态学习和跨模态交互，忽略了视频内容、查询上下文及其对齐中包含的细粒度线索。为此，我们提出了一种新的多粒度感知网络(MGPN) ，它在多粒度级别上感知模式内和模式间的信息。具体来说，我们把瞬间提取作为一项多项选择的阅读理解任务，并将人类阅读策略整合到我们的框架中。利用粗粒度特征编码器和共注意机制获得初步的模式内和模式间信息感知。然后引入细粒度特征编码器和条件交互模块，以增强受人类处理阅读理解问题启发而产生的初始感知。此外，为了减轻现有方法的巨大计算负担，我们进一步设计了一个有效的选择比较模块，减少了隐藏的大小和不易察觉的质量损失。对 Charades-STA、 TACoS 和 ActivityNet Captions 数据集的大量实验表明，我们的解决方案优于现有的最先进的方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=You+Need+to+Read+Again:+Multi-granularity+Perception+Network+for+Moment+Retrieval+in+Videos)|1| +|[Personalized Abstractive Opinion Tagging](https://doi.org/10.1145/3477495.3532037)|Mengxue Zhao, Yang Yang, Miao Li, Jingang Wang, Wei Wu, Pengjie Ren, Maarten de Rijke, Zhaochun Ren|The University of Melbourne, Melbourne, Australia; Meituan, Beijing, China; Shandong University, Qingdao, China; University of Amsterdam, Amsterdam, Netherlands|An opinion tag is a sequence of words on a specific aspect of a product or service. Opinion tags reflect key characteristics of product reviews and help users quickly understand their content in e-commerce portals. The task of abstractive opinion tagging has previously been proposed to automatically generate a ranked list of opinion tags for a given review. However, current models for opinion tagging are not personalized, even though personalization is an essential ingredient of engaging user interactions, especially in e-commerce. In this paper, we focus on the task of personalized abstractive opinion tagging. There are two main challenges when developing models for the end-to-end generation of personalized opinion tags: sparseness of reviews and difficulty to integrate multi-type signals, i.e., explicit review signals and implicit behavioral signals. To address these challenges, we propose an end-to-end model, named POT, that consists of three main components: (1) a review-based explicit preference tracker component based on a hierarchical heterogeneous review graph to track user preferences from reviews; (2)a behavior-based implicit preference tracker component using a heterogeneous behavior graph to track the user preferences from implicit behaviors; and (3) a personalized rank-aware tagging component to generate a ranked sequence of personalized opinion tags. In our experiments, we evaluate POT on a real-world dataset collected from e-commerce platforms and the results demonstrate that it significantly outperforms strong baselines.|意见标签是关于产品或服务特定方面的一系列单词。意见标签反映了产品评论的关键特征，帮助用户快速理解电子商务门户中的内容。抽象意见标签的任务以前曾被提议为给定的评论自动生成一个意见标签的排序列表。然而，目前的意见标签模型并不个性化，即使个性化是参与用户交互的一个重要组成部分，特别是在电子商务中。本文主要研究个性化抽象意见标注的任务。在开发端到端的个性化评价标签生成模型时，存在两个主要的挑战: 评价的稀疏性和整合多类型信号的困难，即显性评价信号和隐性行为信号。为了应对这些挑战，我们提出了一个端到端模型，命名为 POT，该模型由三个主要组成部分组成: (1)一个基于评论的显性偏好跟踪组件，该组件基于分层异构评论图来跟踪来自评论的用户偏好; (2)一个基于行为的隐性偏好跟踪组件，该组件使用一个异构行为图来跟踪来自隐性行为的用户偏好; (3)一个个性化的等级感知标签组件来生成一个个性。在我们的实验中，我们评估了从电子商务平台收集的真实世界数据集上的 POT，结果表明，它明显优于强基线。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Personalized+Abstractive+Opinion+Tagging)|1| +|[Contrastive Learning with Hard Negative Entities for Entity Set Expansion](https://doi.org/10.1145/3477495.3531954)|Yinghui Li, Yangning Li, Yuxin He, Tianyu Yu, Ying Shen, HaiTao Zheng|Sun-Yat Sen University, Shenzhen, Guangdong, China; Harbin Institute of Technology, Shenzhen, Guangdong, China; Tsinghua University, Shenzhen, Guangdong, China|Entity Set Expansion (ESE) is a promising task which aims to expand entities of the target semantic class described by a small seed entity set. Various NLP and IR applications will benefit from ESE due to its ability to discover knowledge. Although previous ESE methods have achieved great progress, most of them still lack the ability to handle hard negative entities (i.e., entities that are difficult to distinguish from the target entities), since two entities may or may not belong to the same semantic class based on different granularity levels we analyze on. To address this challenge, we devise an entity-level masked language model with contrastive learning to refine the representation of entities. In addition, we propose the ProbExpan, a novel probabilistic ESE framework utilizing the entity representation obtained by the aforementioned language model to expand entities. Extensive experiments and detailed analyses on three datasets show that our method outperforms previous state-of-the-art methods.|实体集扩展(ESE)是一个很有前途的任务，其目的是扩展由一个小的种子实体集描述的目标语义类的实体。由于 ESE 具有发现知识的能力，各种自然语言处理和红外应用程序将从中受益。尽管以前的 ESE 方法已经取得了很大的进步，但是它们中的大多数仍然缺乏处理硬负实体的能力(例如，难以区分目标实体的实体) ，因为两个实体可能属于或不属于同一个语义类，基于我们分析的不同粒度级别。为了解决这个问题，我们设计了一个带有对比学习的实体级掩蔽语言模型来改进实体的表示。此外，我们还提出了一种新的概率 ESE 框架，该框架利用上述语言模型获得的实体表示来扩展实体。对三个数据集的大量实验和详细分析表明，我们的方法优于以前的最先进的方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Contrastive+Learning+with+Hard+Negative+Entities+for+Entity+Set+Expansion)|1| |[EFLEC: Efficient Feature-LEakage Correction in GNN based Recommendation Systems](https://doi.org/10.1145/3477495.3531770)|Ishaan Kumar, Yaochen Hu, Yingxue Zhang|Huawei Technologies Canada, Montreal, PQ, Canada|Graph Convolutional Neural Networks (GNN) based recommender systems are state-of-the-art since they can capture the high order collaborative signals between users and items. However, they suffer from the feature leakage problem since label information determined by edges can be leaked into node embeddings through the GNN aggregation procedure guided by the same set of edges, leading to poor generalization. We propose the accurate removal algorithm to generate the final embedding. For each edge, the embeddings of the two end nodes are evaluated on a graph with that edge removed. We devise an algebraic trick to efficiently compute this procedure without explicitly constructing separate graphs for the LightGCN model. Experiments on four datasets demonstrate that our algorithm can perform better on datasets with sparse interactions, while the training time is significantly reduced.|基于图形卷积神经网络(GNN)的推荐系统能够捕获用户和商品之间的高阶协作信号，因而是目前最先进的推荐系统。然而，由于由边确定的标签信息可以通过由同一组边引导的 GNN 聚合过程泄漏到节点嵌入中，从而导致特征泄漏问题，从而导致特征泛化能力差。我们提出了精确去除算法来生成最终的嵌入。对于每个边，两端节点的嵌入在一个去除了该边的图上进行计算。我们设计了一个代数技巧来有效地计算这个过程，而不显式地为 LightGCN 模型构造单独的图。在四个数据集上的实验结果表明，该算法能够较好地处理交互稀疏的数据集，同时显著地减少了训练时间。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=EFLEC:+Efficient+Feature-LEakage+Correction+in+GNN+based+Recommendation+Systems)|1| -|[P3 Ranker: Mitigating the Gaps between Pre-training and Ranking Fine-tuning with Prompt-based Learning and Pre-finetuning](https://doi.org/10.1145/3477495.3531786)|Xiaomeng Hu, Shi Yu, Chenyan Xiong, Zhenghao Liu, Zhiyuan Liu, Ge Yu|Tsinghua University, Beijing, China; Northeastern University, Shenyang, China; Microsoft, Redmond, WA, USA|Compared to other language tasks, applying pre-trained language models (PLMs) for search ranking often requires more nuances and training signals. In this paper, we identify and study the two mismatches between pre-training and ranking fine-tuning: the training schema gap regarding the differences in training objectives and model architectures, and the task knowledge gap considering the discrepancy between the knowledge needed in ranking and that learned during pre-training. To mitigate these gaps, we propose Pre-trained, Prompt-learned and Pre-finetuned Neural Ranker (P3 Ranker). P3 Ranker leverages prompt-based learning to convert the ranking task into a pre-training like schema and uses pre-finetuning to initialize the model on intermediate supervised tasks. Experiments on MS MARCO and Robust04 show the superior performances of P3 Ranker in few-shot ranking. Analyses reveal that P3 Ranker is able to better accustom to the ranking task through prompt-based learning and retrieve necessary ranking-oriented knowledge gleaned in pre-finetuning, resulting in data-efficient PLM adaptation. Our code is available at https://github.com/NEUIR/P3Ranker.|与其他语言任务相比，应用预训练语言模型(PLM)进行搜索排序往往需要更多的细微差别和训练信号。本文通过分析训练前和排序微调之间的两种不匹配现象: 训练目标和模型结构差异的训练模式差异和排序需要的知识与训练前学习的知识差异的任务知识差异。为了弥补这些差距，我们提出预训练，即时学习和预微调神经排序(P3排序)。P3 Ranker 利用基于提示的学习将排序任务转换为类似于预训练的模式，并使用预微调来初始化中间监督任务的模型。在 MSMARCO 和 Robust04上的实验表明，P3 Ranker 在少镜头排序方面具有优越的性能。分析表明，P3 Ranker 能够更好地适应排序任务，通过提示式学习和检索必要的排序导向的知识收集预微调，导致数据高效的 PLM 适应。我们的代码可以在 https://github.com/neuir/p3ranker 找到。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=P3+Ranker:+Mitigating+the+Gaps+between+Pre-training+and+Ranking+Fine-tuning+with+Prompt-based+Learning+and+Pre-finetuning)|1| +|[P3 Ranker: Mitigating the Gaps between Pre-training and Ranking Fine-tuning with Prompt-based Learning and Pre-finetuning](https://doi.org/10.1145/3477495.3531786)|Xiaomeng Hu, Shi Yu, Chenyan Xiong, Zhenghao Liu, Zhiyuan Liu, Ge Yu|Microsoft, Redmond, WA, USA; Tsinghua University, Beijing, China; Northeastern University, Shenyang, China|Compared to other language tasks, applying pre-trained language models (PLMs) for search ranking often requires more nuances and training signals. In this paper, we identify and study the two mismatches between pre-training and ranking fine-tuning: the training schema gap regarding the differences in training objectives and model architectures, and the task knowledge gap considering the discrepancy between the knowledge needed in ranking and that learned during pre-training. To mitigate these gaps, we propose Pre-trained, Prompt-learned and Pre-finetuned Neural Ranker (P3 Ranker). P3 Ranker leverages prompt-based learning to convert the ranking task into a pre-training like schema and uses pre-finetuning to initialize the model on intermediate supervised tasks. Experiments on MS MARCO and Robust04 show the superior performances of P3 Ranker in few-shot ranking. Analyses reveal that P3 Ranker is able to better accustom to the ranking task through prompt-based learning and retrieve necessary ranking-oriented knowledge gleaned in pre-finetuning, resulting in data-efficient PLM adaptation. Our code is available at https://github.com/NEUIR/P3Ranker.|与其他语言任务相比，应用预训练语言模型(PLM)进行搜索排序往往需要更多的细微差别和训练信号。本文通过分析训练前和排序微调之间的两种不匹配现象: 训练目标和模型结构差异的训练模式差异和排序需要的知识与训练前学习的知识差异的任务知识差异。为了弥补这些差距，我们提出预训练，即时学习和预微调神经排序(P3排序)。P3 Ranker 利用基于提示的学习将排序任务转换为类似于预训练的模式，并使用预微调来初始化中间监督任务的模型。在 MSMARCO 和 Robust04上的实验表明，P3 Ranker 在少镜头排序方面具有优越的性能。分析表明，P3 Ranker 能够更好地适应排序任务，通过提示式学习和检索必要的排序导向的知识收集预微调，导致数据高效的 PLM 适应。我们的代码可以在 https://github.com/neuir/p3ranker 找到。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=P3+Ranker:+Mitigating+the+Gaps+between+Pre-training+and+Ranking+Fine-tuning+with+Prompt-based+Learning+and+Pre-finetuning)|1| |[Empowering Next POI Recommendation with Multi-Relational Modeling](https://doi.org/10.1145/3477495.3531801)|Zheng Huang, Jing Ma, Yushun Dong, Natasha Zhang Foutz, Jundong Li|University of Virginia, Charlottesville, VA, USA|With the wide adoption of mobile devices and web applications, location-based social networks (LBSNs) offer large-scale individual-level location-related activities and experiences. Next point-of-interest (POI) recommendation is one of the most important tasks in LBSNs, aiming to make personalized recommendations of next suitable locations to users by discovering preferences from users' historical activities. Noticeably, LBSNs have offered unparalleled access to abundant heterogeneous relational information about users and POIs (including user-user social relations, such as families or colleagues; and user-POI visiting relations). Such relational information holds great potential to facilitate the next POI recommendation. However, most existing methods either focus on merely the user-POI visits, or handle different relations based on over-simplified assumptions while neglecting relational heterogeneities. To fill these critical voids, we propose a novel framework, MEMO, which effectively utilizes the heterogeneous relations with a multi-network representation learning module, and explicitly incorporates the inter-temporal user-POI mutual influence with the coupled recurrent neural networks. Extensive experiments on real-world LBSN data validate the superiority of our framework over the state-of-the-art next POI recommendation methods.|随着移动设备和网络应用的广泛应用，基于位置的社交网络(LBSNs)提供了大规模的个人层面的位置相关活动和体验。下一个兴趣点(POI)推荐是 LBSNs 中最重要的任务之一，目的是通过发现用户历史活动的偏好，为用户提供下一个合适位置的个性化推荐。值得注意的是，LBSNs 提供了对关于用户和 POI (包括用户-用户社会关系，如家庭或同事; 以及用户-POI 访问关系)的大量异构关系信息的无与伦比的访问。这样的关系信息对于促进下一个 POI 建议具有很大的潜力。然而，大多数现有的方法要么仅仅关注用户 POI 访问，要么在过于简化的假设基础上处理不同的关系，而忽略了关系异构性。为了填补这些关键空白，我们提出了一种新的框架 MEMO，它有效地利用了多网络表示学习模块的异构关系，并将用户间 POI 的相互影响明确地与耦合的递归神经网络结合起来。对实际 LBSN 数据的大量实验验证了该框架相对于下一代 POI 推荐方法的优越性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Empowering+Next+POI+Recommendation+with+Multi-Relational+Modeling)|1| |[Learning Trustworthy Web Sources to Derive Correct Answers and Reduce Health Misinformation in Search](https://doi.org/10.1145/3477495.3531812)|Dake Zhang, Amir Vakili Tahami, Mustafa Abualsaud, Mark D. Smucker|University of Waterloo, Waterloo, ON, Canada; Thomson Reuters Labs, Toronto, ON, Canada|When searching the web for answers to health questions, people can make incorrect decisions that have a negative effect on their lives if the search results contain misinformation. To reduce health misinformation in search results, we need to be able to detect documents with correct answers and promote them over documents containing misinformation. Determining the correct answer has been a difficult hurdle to overcome for participants in the TREC Health Misinformation Track. In the 2021 track, automatic runs were not allowed to use the known answer to a topic's health question, and as a result, the top automatic run had a compatibility-difference score of 0.043 while the top manual run, which used the known answer, had a score of 0.259. The compatibility-difference measures the ability of methods to rank correct and credible documents before incorrect and non-credible documents. By using an existing set of health questions and their known answers, we show it is possible to learn which web hosts are trustworthy, from which we can predict the correct answers to the 2021 health questions with an accuracy of 76%. Using our predicted answers, we can promote documents that we predict contain this answer and achieve a compatibility-difference score of 0.129, which is a three-fold increase in performance over the best previous automatic method.|在网上搜索健康问题的答案时，如果搜索结果包含错误信息，人们可能会做出对他们的生活有负面影响的错误决定。为了减少搜索结果中的健康错误信息，我们需要能够发现带有正确答案的文档，并将其推广到含有错误信息的文档之上。对于 TREC 健康错误信息跟踪的参与者来说，确定正确的答案是一个难以克服的障碍。在2021年的赛道上，自动跑步不允许使用已知的答案来回答某个话题的健康问题，结果，自动跑步得分最高的相容性差异得分为0.043，而使用已知答案的手动跑步得分最高的相容性差异得分为0.259。兼容性差异衡量的是将正确和可信的文件排在不正确和不可信的文件之前的方法的能力。通过使用现有的一组健康问题及其已知答案，我们可以了解哪些网站主机是值得信赖的，从中我们可以预测2021年健康问题的正确答案，准确率为76% 。使用我们预测的答案，我们可以推广我们预测包含这个答案的文档，并获得0.129的兼容性差异评分，这比以前最好的自动化方法的性能提高了三倍。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Learning+Trustworthy+Web+Sources+to+Derive+Correct+Answers+and+Reduce+Health+Misinformation+in+Search)|1| |[On Optimizing Top-K Metrics for Neural Ranking Models](https://doi.org/10.1145/3477495.3531849)|Rolf Jagerman, Zhen Qin, Xuanhui Wang, Michael Bendersky, Marc Najork|Google Research, Mountain View, CA, USA|Top-K metrics such as [email protected] are frequently used to evaluate ranking performance. The traditional tree-based models such as LambdaMART, which are based on Gradient Boosted Decision Trees (GBDT), are designed to optimize [email protected] using the LambdaRank losses. Recently, there is a good amount of research interest on neural ranking models for learning-to-rank tasks. These models are fundamentally different from the decision tree models and behave differently with respect to different loss functions. For example, the most popular ranking losses used in neural models are the Softmax loss and the GumbelApproxNDCG loss. These losses do not connect to top-K metrics such as [email protected] naturally. It remains a question on how to effectively optimize [email protected] for neural ranking models. In this paper, we follow the LambdaLoss framework and design novel and theoretically sound losses for [email protected] metrics, while the original LambdaLoss paper can only do so using an unsound heuristic. We study the new losses on the LETOR benchmark datasets and show that the new losses work better than other losses for neural ranking models.|诸如[ email protected ]之类的 Top-K 指标经常被用来评估排名表现。传统的基于树的模型，如 LambdaMART，基于梯度增强决策树(GBDT) ，旨在利用 LambdaRank 损失优化[ email protected ]。近年来，针对学习排序任务的神经排序模型的研究引起了人们的广泛兴趣。这些模型与决策树模型有着根本的不同，对于不同的损失函数表现也不同。例如，在神经模型中最常用的排名损失是 Softmax 损失和 GumbelApprovxNDCG 损失。这些损失不会自然而然地与诸如[ email protected ]之类的 top-K 指标联系起来。这仍然是一个问题，如何有效地优化[电子邮件保护]的神经排序模型。在本文中，我们遵循 LambdaLoss 框架，为[电子邮件保护]指标设计新颖且理论上可靠的损失，而原始的 LambdaLoss 论文只能使用不可靠的启发式方法来做到这一点。我们研究了 LETOR 基准数据集上的新损失，发现新的损失比其他神经排序模型的损失要好。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=On+Optimizing+Top-K+Metrics+for+Neural+Ranking+Models)|1| -|[Identifying Argumentative Questions in Web Search Logs](https://doi.org/10.1145/3477495.3531864)|Yamen Ajjour, Pavel Braslavski, Alexander Bondarenko, Benno Stein|Bauhaus-Universität Weimar, Weimar, Germany; Martin Luther Universität Halle-Wittenberg, Halle, Germany; Leipzig University & Bauhaus-Universität Weimar, Leipzig, Germany; Ural Federal University & HSE University, Yekaterinburg, Russian Fed.|We present an approach to identify argumentative questions among web search queries. Argumentative questions ask for reasons to support a certain stance on a controversial topic, such as ''Should marijuana be legalized?'' Controversial topics entail opposing stances, and hence can be supported or opposed by various arguments. Argumentative questions pose a challenge for search engines since they should be answered with both pro and con arguments in order to not bias a user toward a certain stance. To further analyze the problem, we sampled questions about 19 controversial topics from a large Yandex search log and let human annotators label them as one of factual, method, or argumentative. The result is a collection of 39,340 labeled questions, 28% of which are argumentative, demonstrating the need to develop dedicated systems for this type of questions. A comparative analysis of the three question types shows that asking for reasons and predictions are among the most important features of argumentative questions. To demonstrate the feasibility of the classification task, we developed a BERT-based classifier to map questions to the question types, reaching a promising macro-averaged F>sub>1-score of 0.78.|我们提出了一种识别网络搜索查询中争议性问题的方法。有争议的问题会询问支持某种立场的理由，比如“大麻是否应该合法化?”有争议的话题包含相反的立场，因此可以支持或反对的各种论点。争议性问题对搜索引擎来说是一个挑战，因为它们应该同时回答正反两方面的争议，以避免用户偏向某一立场。为了进一步分析这个问题，我们从一个大型 Yandex 搜索日志中抽取了19个有争议话题的问题，并让人工注释者将它们标记为事实、方法或论证性的问题。结果是收集了39,340个带标签的问题，其中28% 是有争议的，表明需要为这类问题开发专门的系统。对三种问题类型的比较分析表明，提问原因和预测是议论题的最重要特征之一。为了验证分类任务的可行性，我们开发了一个基于 BERT 的分类器来将问题映射到问题类型，得到了一个很有前途的宏观平均 F > 小于1的得分为0.78。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Identifying+Argumentative+Questions+in+Web+Search+Logs)|1| +|[Identifying Argumentative Questions in Web Search Logs](https://doi.org/10.1145/3477495.3531864)|Yamen Ajjour, Pavel Braslavski, Alexander Bondarenko, Benno Stein|Leipzig University & Bauhaus-Universität Weimar, Leipzig, Germany; Bauhaus-Universität Weimar, Weimar, Germany; Ural Federal University & HSE University, Yekaterinburg, Russian Fed.; Martin Luther Universität Halle-Wittenberg, Halle, Germany|We present an approach to identify argumentative questions among web search queries. Argumentative questions ask for reasons to support a certain stance on a controversial topic, such as ''Should marijuana be legalized?'' Controversial topics entail opposing stances, and hence can be supported or opposed by various arguments. Argumentative questions pose a challenge for search engines since they should be answered with both pro and con arguments in order to not bias a user toward a certain stance. To further analyze the problem, we sampled questions about 19 controversial topics from a large Yandex search log and let human annotators label them as one of factual, method, or argumentative. The result is a collection of 39,340 labeled questions, 28% of which are argumentative, demonstrating the need to develop dedicated systems for this type of questions. A comparative analysis of the three question types shows that asking for reasons and predictions are among the most important features of argumentative questions. To demonstrate the feasibility of the classification task, we developed a BERT-based classifier to map questions to the question types, reaching a promising macro-averaged F>sub>1-score of 0.78.|我们提出了一种识别网络搜索查询中争议性问题的方法。有争议的问题会询问支持某种立场的理由，比如“大麻是否应该合法化?”有争议的话题包含相反的立场，因此可以支持或反对的各种论点。争议性问题对搜索引擎来说是一个挑战，因为它们应该同时回答正反两方面的争议，以避免用户偏向某一立场。为了进一步分析这个问题，我们从一个大型 Yandex 搜索日志中抽取了19个有争议话题的问题，并让人工注释者将它们标记为事实、方法或论证性的问题。结果是收集了39,340个带标签的问题，其中28% 是有争议的，表明需要为这类问题开发专门的系统。对三种问题类型的比较分析表明，提问原因和预测是议论题的最重要特征之一。为了验证分类任务的可行性，我们开发了一个基于 BERT 的分类器来将问题映射到问题类型，得到了一个很有前途的宏观平均 F > 小于1的得分为0.78。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Identifying+Argumentative+Questions+in+Web+Search+Logs)|1| |[An MLP-based Algorithm for Efficient Contrastive Graph Recommendations](https://doi.org/10.1145/3477495.3531874)|Siwei Liu, Iadh Ounis, Craig Macdonald|University of Glasgow, Glasgow, United Kingdom|Graph-based recommender systems (GBRSs) have achieved promising performance by incorporating the user-item bipartite graph using the Graph Neural Network (GNN). Among GBRSs, the information from each user and item's multi-hop neighbours is effectively conveyed between nodes through neighbourhood aggregation and message passing. Although effective, existing neighbourhood information aggregation and passing functions are usually computationally expensive. Motivated by the emerging contrastive learning technique, we design a simple neighbourhood construction method in conjunction with the contrastive objective function to simulate the neighbourhood information processing of GNN. In addition, we propose a simple algorithm based on Multilayer Perceptron (MLP) for learning users and items' representations with extra non-linearity while lowering computational burden compared with multi-layers GNNs. Our extensive empirical experiments on three public datasets demonstrate that our proposed model, i.e. MLP-CGRec, can reduce the GPU memory consumption and training time by up to 24.0% and 33.1%, respectively, without significantly degenerating the recommendation accuracy in comparison with competitive baselines.|基于图形的推荐系统(GBRS)通过使用图形神经网络(GNN)结合用户项二分图已经取得了良好的性能。在 GBRS 中，每个用户和项目的多跳邻居信息通过邻居聚合和消息传递在节点之间有效地传递。虽然有效，但现有的邻里信息聚合和传递函数通常计算开销很大。受新兴的对比学习技术的启发，我们设计了一种简单的邻域构造方法，结合对比目标函数来模拟 GNN 的邻域信息处理。此外，我们提出了一个简单的基于多层感知机(MLP)的学习算法，用于学习用户和项目的额外非线性表示，同时与多层 GNN 相比，降低了计算负担。我们在三个公共数据集上的广泛实验表明，我们提出的模型，即 MLP-CGRec，可以分别减少 GPU 内存消耗和训练时间高达24.0% 和33.1% ，与竞争性基线相比，不会显着降低推荐准确性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=An+MLP-based+Algorithm+for+Efficient+Contrastive+Graph+Recommendations)|1| -|[Entity-Conditioned Question Generation for Robust Attention Distribution in Neural Information Retrieval](https://doi.org/10.1145/3477495.3531878)|Revanth Gangi Reddy, Md. Arafat Sultan, Martin Franz, Avirup Sil, Heng Ji|UIUC, Champaign, IL, USA; IBM Research AI, Yorktown Heights, NY, USA|We show that supervised neural information retrieval (IR) models are prone to learning sparse attention patterns over passage tokens, which can result in key phrases including named entities receiving low attention weights, eventually leading to model under-performance. Using a novel targeted synthetic data generation method that identifies poorly attended entities and conditions the generation episodes on those, we teach neural IR to attend more uniformly and robustly to all entities in a given passage. On two public IR benchmarks, we empirically show that the proposed method helps improve both the model's attention patterns and retrieval performance, including in zero-shot settings.|我们发现监督神经信息检索(IR)模型更倾向于学习通道标记上的稀疏注意模式，这可能导致包括命名实体在内的关键短语注意力权重较低，最终导致模型表现不佳。使用一种新的有针对性的合成数据生成方法，识别参与程度较低的实体，并对这些实体上的生成事件进行条件化处理，我们教导神经 IR 在给定的段落中更加统一和稳健地参与所有实体。实验结果表明，该方法有助于提高模型的注意模式和检索性能，包括在零镜头设置下的检索性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Entity-Conditioned+Question+Generation+for+Robust+Attention+Distribution+in+Neural+Information+Retrieval)|1| -|[C3: Continued Pretraining with Contrastive Weak Supervision for Cross Language Ad-Hoc Retrieval](https://doi.org/10.1145/3477495.3531886)|Eugene Yang, Suraj Nair, Ramraj Chandradevan, Rebecca IglesiasFlores, Douglas W. Oard|University of Maryland, College Park, College Park, MD, USA; Emory University, Atlanta, GA, USA; University of Pennsylvania, Philadelphia, PA, USA; Johns Hopkins University, Baltimore, MD, USA|Pretrained language models have improved effectiveness on numerous tasks, including ad-hoc retrieval. Recent work has shown that continuing to pretrain a language model with auxiliary objectives before fine-tuning on the retrieval task can further improve retrieval effectiveness. Unlike monolingual retrieval, designing an appropriate auxiliary task for cross-language mappings is challenging. To address this challenge, we use comparable Wikipedia articles in different languages to further pretrain off-the-shelf multilingual pretrained models before fine-tuning on the retrieval task. We show that our approach yields improvements in retrieval effectiveness.|预先训练的语言模型可以提高包括即席检索在内的许多任务的有效性。最近的研究表明，在对检索任务进行微调之前，继续预训练带有辅助目标的语言模型可以进一步提高检索效率。与单语检索不同，为跨语言映射设计合适的辅助任务具有挑战性。为了解决这个问题，我们使用不同语言的维基百科文章来进一步预训练现成的多语言预训模型，然后再对检索任务进行微调。我们展示了我们的方法在检索效率方面的改进。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=C3:+Continued+Pretraining+with+Contrastive+Weak+Supervision+for+Cross+Language+Ad-Hoc+Retrieval)|1| +|[Entity-Conditioned Question Generation for Robust Attention Distribution in Neural Information Retrieval](https://doi.org/10.1145/3477495.3531878)|Revanth Gangi Reddy, Md. Arafat Sultan, Martin Franz, Avirup Sil, Heng Ji|IBM Research AI, Yorktown Heights, NY, USA; UIUC, Champaign, IL, USA|We show that supervised neural information retrieval (IR) models are prone to learning sparse attention patterns over passage tokens, which can result in key phrases including named entities receiving low attention weights, eventually leading to model under-performance. Using a novel targeted synthetic data generation method that identifies poorly attended entities and conditions the generation episodes on those, we teach neural IR to attend more uniformly and robustly to all entities in a given passage. On two public IR benchmarks, we empirically show that the proposed method helps improve both the model's attention patterns and retrieval performance, including in zero-shot settings.|我们发现监督神经信息检索(IR)模型更倾向于学习通道标记上的稀疏注意模式，这可能导致包括命名实体在内的关键短语注意力权重较低，最终导致模型表现不佳。使用一种新的有针对性的合成数据生成方法，识别参与程度较低的实体，并对这些实体上的生成事件进行条件化处理，我们教导神经 IR 在给定的段落中更加统一和稳健地参与所有实体。实验结果表明，该方法有助于提高模型的注意模式和检索性能，包括在零镜头设置下的检索性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Entity-Conditioned+Question+Generation+for+Robust+Attention+Distribution+in+Neural+Information+Retrieval)|1| +|[C3: Continued Pretraining with Contrastive Weak Supervision for Cross Language Ad-Hoc Retrieval](https://doi.org/10.1145/3477495.3531886)|Eugene Yang, Suraj Nair, Ramraj Chandradevan, Rebecca IglesiasFlores, Douglas W. Oard|Johns Hopkins University, Baltimore, MD, USA; Emory University, Atlanta, GA, USA; University of Maryland, College Park, College Park, MD, USA; University of Pennsylvania, Philadelphia, PA, USA|Pretrained language models have improved effectiveness on numerous tasks, including ad-hoc retrieval. Recent work has shown that continuing to pretrain a language model with auxiliary objectives before fine-tuning on the retrieval task can further improve retrieval effectiveness. Unlike monolingual retrieval, designing an appropriate auxiliary task for cross-language mappings is challenging. To address this challenge, we use comparable Wikipedia articles in different languages to further pretrain off-the-shelf multilingual pretrained models before fine-tuning on the retrieval task. We show that our approach yields improvements in retrieval effectiveness.|预先训练的语言模型可以提高包括即席检索在内的许多任务的有效性。最近的研究表明，在对检索任务进行微调之前，继续预训练带有辅助目标的语言模型可以进一步提高检索效率。与单语检索不同，为跨语言映射设计合适的辅助任务具有挑战性。为了解决这个问题，我们使用不同语言的维基百科文章来进一步预训练现成的多语言预训模型，然后再对检索任务进行微调。我们展示了我们的方法在检索效率方面的改进。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=C3:+Continued+Pretraining+with+Contrastive+Weak+Supervision+for+Cross+Language+Ad-Hoc+Retrieval)|1| |[A Meta-learning Approach to Fair Ranking](https://doi.org/10.1145/3477495.3531892)|Yuan Wang, Zhiqiang Tao, Yi Fang|Santa Clara University, Santa Clara, CA, USA|In recent years, the fairness in information retrieval (IR) system has received increasing research attention. While the data-driven ranking models achieve significant improvements over traditional methods, the dataset used to train such models is usually biased, which causes unfairness in the ranking models. For example, the collected imbalance dataset on the subject of the expert search usually leads to systematic discrimination on the specific demographic groups such as race, gender, etc, which further reduces the exposure for the minority group. To solve this problem, we propose a Meta-learning based Fair Ranking (MFR) model that could alleviate the data bias for protected groups through an automatically-weighted loss. Specifically, we adopt a meta-learning framework to explicitly train a meta-learner from an unbiased sampled dataset (meta-dataset), and simultaneously, train a listwise learning-to-rank (LTR) model on the whole (biased) dataset governed by "fair" loss weights. The meta-learner serves as a weighting function to make the ranking loss attend more on the minority group. To update the parameters of the weighting function and the ranking model, we formulate the proposed MFR as a bilevel optimization problem and solve it using the gradients through gradients. Experimental results on several real-world datasets demonstrate that the proposed method achieves a comparable ranking performance and significantly improves the fairness metric compared with state-of-the-art methods.|近年来，信息检索制度的公平性越来越受到研究人员的关注。虽然数据驱动的排序模型比传统的方法有了显著的改进，但是用于训练这些模型的数据集往往存在偏差，从而导致排序模型的不公平性。例如，所收集的关于专家搜索主题的不平衡数据集通常导致对特定人口群体如种族、性别等的系统性歧视，从而进一步减少少数群体的暴露程度。为了解决这一问题，我们提出了一种基于元学习的公平排序(MFR)模型，该模型通过自动加权损失来减轻受保护群体的数据偏差。具体而言，我们采用元学习框架来显式地从无偏的采样数据集(元数据集)中训练元学习者，同时在受“公平”损失权重管理的整个(有偏的)数据集上训练列表学习到排名(LTR)模型。元学习者作为一个权重函数，使排名的损失更多地出现在少数群体中。为了更新权重函数和排名模型的参数，我们将建议的最小生成最佳化问题(mFR)表示为一个双层次模型，并使用梯度来解决这个问题。在实际数据集上的实验结果表明，与现有方法相比，该方法具有可比较的排序性能，并显著提高了公平性度量。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Meta-learning+Approach+to+Fair+Ranking)|1| |[Where Does the Performance Improvement Come From?: - A Reproducibility Concern about Image-Text Retrieval](https://doi.org/10.1145/3477495.3531715)|Jun Rao, Fei Wang, Liang Ding, Shuhan Qi, Yibing Zhan, Weifeng Liu, Dacheng Tao|JD Explore Academy, beijing, China; Harbin Institute of Technology, Shenzhen, shenzhen, China; China University of Petroleum (East China), qingdao, China|This article aims to provide the information retrieval community with some reflections on recent advances in retrieval learning by analyzing the reproducibility of image-text retrieval models. Due to the increase of multimodal data over the last decade, image-text retrieval has steadily become a major research direction in the field of information retrieval. Numerous researchers train and evaluate image-text retrieval algorithms using benchmark datasets such as MS-COCO and Flickr30k. Research in the past has mostly focused on performance, with multiple state-of-the-art methodologies being suggested in a variety of ways. According to their assertions, these techniques provide improved modality interactions and hence more precise multimodal representations. In contrast to previous works, we focus on the reproducibility of the approaches and the examination of the elements that lead to improved performance by pretrained and nonpretrained models in retrieving images and text. To be more specific, we first examine the related reproducibility concerns and explain why our focus is on image-text retrieval tasks. Second, we systematically summarize the current paradigm of image-text retrieval models and the stated contributions of those approaches. Third, we analyze various aspects of the reproduction of pretrained and nonpretrained retrieval models. To complete this, we conducted ablation experiments and obtained some influencing factors that affect retrieval recall more than the improvement claimed in the original paper. Finally, we present some reflections and challenges that the retrieval community should consider in the future. Our source code is publicly available at https://github.com/WangFei-2019/Image-text-Retrieval.|本文旨在通过分析图像-文本检索模型的可重复性，为信息检索提供有关检索学习最新进展的一些反思。由于过去十年来多模态数据的增加，图像-文本检索逐渐成为信息检索领域的一个主要研究方向。许多研究人员使用基准数据集(如 MS-COCO 和 Flickr30k)训练和评估图像-文本检索算法。过去的研究主要集中在性能方面，以各种方式提出了多种最先进的方法。根据他们的断言，这些技术提供了改进的模态交互，因此更精确的多模态表示。与以往的工作相比，我们关注的方法的可重复性和检查的元素，导致改善性能的预训练和未经预训练的模型检索图像和文本。更具体地说，我们首先考察相关的重复性问题，并解释为什么我们的重点是图像文本检索任务。其次，系统地总结了当前图像文本检索模型的研究范式以及这些方法的贡献。第三，我们分析了预训练和非预训练检索模型复制的各个方面。为此，我们进行了消融实验，得到了一些影响检索回忆的因素，这些因素对检索回忆的影响大于原文中提出的改进。最后，我们提出了一些反思和挑战，检索社区应该考虑在未来。我们的源代码可以在 https://github.com/wangfei-2019/image-text-retrieval 上公开。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Where+Does+the+Performance+Improvement+Come+From?:+-+A+Reproducibility+Concern+about+Image-Text+Retrieval)|1| -|[Competitive Search](https://doi.org/10.1145/3477495.3532771)|Oren Kurland, Moshe Tennenholtz|Univ Chicago, Booth Sch Business, Chicago, IL 60637 USA; Zhejiang Univ, Hangzhou, Peoples R China; European Univ Inst, Fiesole, Italy; Univ New South Wales, Business Sch, Sydney, NSW, Australia|This essay surveys the literature on directed search and competitive search equilibrium, covering theory and a variety of applications. These models share features with traditional search theory, but also differ in important ways. They share features with general equilibrium theory, but with explicit frictions. Equilibria are often efficient, mainly because markets price goods plus the time required to get them. The approach is tractable and arguably realistic. Results are presented for finite and continuum economies. Private information and sorting with heterogeneity are analyzed. While emphasizing issues and applications, we also provide several hard-to-find technical results.|本文综述了有关定向搜索和竞争搜索均衡、覆盖理论和各种应用的文献。这些模型具有与传统搜索理论相同的特点，但在一些重要方面又有所不同。它们与一般均衡理论有共同的特点，但存在明显的摩擦。均衡往往是有效的，主要是因为市场对商品定价，加上购买商品所需的时间。这种方法容易处理，而且可以说是现实的。给出了有限经济体和连续经济体的结果。分析了私有信息和异构排序问题。在强调问题和应用程序的同时，我们还提供了一些难以找到的技术结果。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Competitive+Search)|1| +|[Competitive Search](https://doi.org/10.1145/3477495.3532771)|Oren Kurland, Moshe Tennenholtz|Zhejiang Univ, Hangzhou, Peoples R China; Univ Chicago, Booth Sch Business, Chicago, IL 60637 USA; European Univ Inst, Fiesole, Italy; Univ New South Wales, Business Sch, Sydney, NSW, Australia|This essay surveys the literature on directed search and competitive search equilibrium, covering theory and a variety of applications. These models share features with traditional search theory, but also differ in important ways. They share features with general equilibrium theory, but with explicit frictions. Equilibria are often efficient, mainly because markets price goods plus the time required to get them. The approach is tractable and arguably realistic. Results are presented for finite and continuum economies. Private information and sorting with heterogeneity are analyzed. While emphasizing issues and applications, we also provide several hard-to-find technical results.|本文综述了有关定向搜索和竞争搜索均衡、覆盖理论和各种应用的文献。这些模型具有与传统搜索理论相同的特点，但在一些重要方面又有所不同。它们与一般均衡理论有共同的特点，但存在明显的摩擦。均衡往往是有效的，主要是因为市场对商品定价，加上购买商品所需的时间。这种方法容易处理，而且可以说是现实的。给出了有限经济体和连续经济体的结果。分析了私有信息和异构排序问题。在强调问题和应用程序的同时，我们还提供了一些难以找到的技术结果。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Competitive+Search)|1| |[A Dataset for Sentence Retrieval for Open-Ended Dialogues](https://doi.org/10.1145/3477495.3531727)|Itay Harel, Hagai Taitelbaum, Idan Szpektor, Oren Kurland|Technion - Israel Institute of Technology, Haifa, Israel; Google Research, Tel Aviv, Israel; TSG IT Advanced Systems Ltd., Tel Aviv, Israel|We address the task of sentence retrieval for open-ended dialogues. The goal is to retrieve sentences from a document corpus that contain information useful for generating the next turn in a given dialogue. Prior work on dialogue-based retrieval focused on specific types of dialogues: either conversational QA or conversational search. To address a broader scope of this task where any type of dialogue can be used, we constructed a dataset that includes open-ended dialogues from Reddit, candidate sentences from Wikipedia for each dialogue and human annotations for the sentences. We report the performance of several retrieval baselines, including neural retrieval models, over the dataset. To adapt neural models to the types of dialogues in the dataset, we explored an approach to induce a large-scale weakly supervised training data from Reddit. Using this training set significantly improved the performance over training on the MS MARCO dataset.|我们讨论开放式对话的句子检索任务。其目的是从文档语料库中检索句子，这些句子包含有用的信息，用于在给定的对话中生成下一个回合。先前的基于对话的检索工作集中在特定类型的对话: 会话 QA 或会话搜索。为了扩大任何类型的对话都可以使用的范围，我们构建了一个数据集，其中包括来自 Reddit 的开放式对话、来自 Wikipedia 的每个对话的候选句子以及句子的人工注释。我们报告了在数据集上的几个检索基线的性能，包括神经检索模型。为了使神经模型适应数据集中的对话类型，我们探索了一种从 Reddit 引入大规模弱监督训练数据的方法。使用这个训练集显著提高了在 MS MARCO 数据集上的训练性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Dataset+for+Sentence+Retrieval+for+Open-Ended+Dialogues)|1| -|[iRec: An Interactive Recommendation Framework](https://doi.org/10.1145/3477495.3531754)|Thiago Silva, Nícollas Silva, Heitor Werneck, Carlos Mito, Adriano C. M. Pereira, Leonardo Rocha|Universidade Federal de São João Del Rei, São João Del Rei, Brazil; Universidade Federal de Minas Gerais, Belo Horizonte, Brazil|Nowadays, most e-commerce and entertainment services have adopted interactive Recommender Systems (RS) to guide the entire journey of users into the system. This task has been addressed as a Multi-Armed Bandit problem where systems must continuously learn and recommend at each iteration. However, despite the recent advances, there is still a lack of consensus on the best practices to evaluate such bandit solutions. Several variables might affect the evaluation process, but most of the works have only been concerned about the accuracy of each method. Thus, this work proposes an interactive RS framework named iRec. It covers the whole experimentation process by following the main RS guidelines. The iRec provides three modules to prepare the dataset, create new recommendation agents, and simulate the interactive scenario. Moreover, it also contains several state-of-the-art algorithms, a hyperparameter tuning module, distinct evaluation metrics, different ways of visualizing the results, and statistical validation.|目前，大多数电子商务和娱乐服务都采用交互式推荐系统(RS)来引导用户进入系统的整个过程。这个任务已经被解决为一个多臂老虎机问题，系统必须在每次迭代中不断学习和推荐。然而，尽管最近取得了一些进展，但是在评估这种土匪解决方案的最佳实践方面仍然缺乏共识。有几个变量可能会影响评价过程，但大多数工作只关心每种方法的准确性。因此，本文提出了一个交互式 RS 框架 iRec。它涵盖了遵循 RS 主要准则的整个实验过程。IRec 提供了三个模块来准备数据集、创建新的推荐代理和模拟交互场景。此外，它还包含一些最先进的算法、一个超参数调整模块、不同的评估指标、不同的结果可视化方法以及统计验证。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=iRec:+An+Interactive+Recommendation+Framework)|1| -|[RecDelta: An Interactive Dashboard on Top-k Recommendation for Cross-model Evaluation](https://doi.org/10.1145/3477495.3531674)|YiShyuan Chiang, YuZe Liu, ChenFeng Tsai, JingKai Lou, MingFeng Tsai, ChuanJu Wang|National Chengchi University, Taipei, Taiwan Roc; Academia Sinica, Taipei, Taiwan Roc; KKStream Technologies, Taipei, Taiwan Roc|In this demonstration, we present RecDelta, an interactive tool for the cross-model evaluation of top-k recommendation. RecDelta is a web-based information system where people visually compare the performance of various recommendation algorithms and their recommended items. In the proposed system, we visualize the distribution of the δ scores between algorithms--a distance metric measuring the intersection between recommendation lists. Such visualization allows for rapid identification of users for whom the items recommended by different algorithms diverge or vice versa; then, one can further select the desired user to present the relationship between recommended items and his/her historical behavior. RecDelta benefits both academics and practitioners by enhancing model explainability as they develop recommendation algorithms with their newly gained insights. Note that while the system is now online at https://cfda.csie.org/recdelta, we also provide a video recording at https://tinyurl.com/RecDelta to introduce the concept and the usage of our system.|在这个演示中，我们展示了 RecDelta，一个用于 top-k 推荐的跨模型评估的交互式工具。RecDelta 是一个基于网络的信息系统，人们可以在这个系统中直观地比较各种推荐算法及其推荐项目的性能。在所提出的系统中，我们可视化算法之间 δ 分数的分布——一个测量推荐列表之间交集的距离度量。这种可视化允许快速识别由不同算法推荐的条目有差异的用户，反之亦然; 然后，人们可以进一步选择所需的用户来表示推荐条目和他/她的历史行为之间的关系。RecDelta 通过增强模型的可解释性使学者和从业者受益，因为他们利用新获得的见解开发推荐算法。请注意，虽然该系统现已上网，但我们亦提供 https://cfda.csie.org/recdelta 录像 https://tinyurl.com/recdelta ，介绍该系统的概念及用途。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=RecDelta:+An+Interactive+Dashboard+on+Top-k+Recommendation+for+Cross-model+Evaluation)|1| +|[iRec: An Interactive Recommendation Framework](https://doi.org/10.1145/3477495.3531754)|Thiago Silva, Nícollas Silva, Heitor Werneck, Carlos Mito, Adriano C. M. Pereira, Leonardo Rocha|Universidade Federal de Minas Gerais, Belo Horizonte, Brazil; Universidade Federal de São João Del Rei, São João Del Rei, Brazil|Nowadays, most e-commerce and entertainment services have adopted interactive Recommender Systems (RS) to guide the entire journey of users into the system. This task has been addressed as a Multi-Armed Bandit problem where systems must continuously learn and recommend at each iteration. However, despite the recent advances, there is still a lack of consensus on the best practices to evaluate such bandit solutions. Several variables might affect the evaluation process, but most of the works have only been concerned about the accuracy of each method. Thus, this work proposes an interactive RS framework named iRec. It covers the whole experimentation process by following the main RS guidelines. The iRec provides three modules to prepare the dataset, create new recommendation agents, and simulate the interactive scenario. Moreover, it also contains several state-of-the-art algorithms, a hyperparameter tuning module, distinct evaluation metrics, different ways of visualizing the results, and statistical validation.|目前，大多数电子商务和娱乐服务都采用交互式推荐系统(RS)来引导用户进入系统的整个过程。这个任务已经被解决为一个多臂老虎机问题，系统必须在每次迭代中不断学习和推荐。然而，尽管最近取得了一些进展，但是在评估这种土匪解决方案的最佳实践方面仍然缺乏共识。有几个变量可能会影响评价过程，但大多数工作只关心每种方法的准确性。因此，本文提出了一个交互式 RS 框架 iRec。它涵盖了遵循 RS 主要准则的整个实验过程。IRec 提供了三个模块来准备数据集、创建新的推荐代理和模拟交互场景。此外，它还包含一些最先进的算法、一个超参数调整模块、不同的评估指标、不同的结果可视化方法以及统计验证。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=iRec:+An+Interactive+Recommendation+Framework)|1| +|[RecDelta: An Interactive Dashboard on Top-k Recommendation for Cross-model Evaluation](https://doi.org/10.1145/3477495.3531674)|YiShyuan Chiang, YuZe Liu, ChenFeng Tsai, JingKai Lou, MingFeng Tsai, ChuanJu Wang|National Chengchi University, Taipei, Taiwan Roc; KKStream Technologies, Taipei, Taiwan Roc; Academia Sinica, Taipei, Taiwan Roc|In this demonstration, we present RecDelta, an interactive tool for the cross-model evaluation of top-k recommendation. RecDelta is a web-based information system where people visually compare the performance of various recommendation algorithms and their recommended items. In the proposed system, we visualize the distribution of the δ scores between algorithms--a distance metric measuring the intersection between recommendation lists. Such visualization allows for rapid identification of users for whom the items recommended by different algorithms diverge or vice versa; then, one can further select the desired user to present the relationship between recommended items and his/her historical behavior. RecDelta benefits both academics and practitioners by enhancing model explainability as they develop recommendation algorithms with their newly gained insights. Note that while the system is now online at https://cfda.csie.org/recdelta, we also provide a video recording at https://tinyurl.com/RecDelta to introduce the concept and the usage of our system.|在这个演示中，我们展示了 RecDelta，一个用于 top-k 推荐的跨模型评估的交互式工具。RecDelta 是一个基于网络的信息系统，人们可以在这个系统中直观地比较各种推荐算法及其推荐项目的性能。在所提出的系统中，我们可视化算法之间 δ 分数的分布——一个测量推荐列表之间交集的距离度量。这种可视化允许快速识别由不同算法推荐的条目有差异的用户，反之亦然; 然后，人们可以进一步选择所需的用户来表示推荐条目和他/她的历史行为之间的关系。RecDelta 通过增强模型的可解释性使学者和从业者受益，因为他们利用新获得的见解开发推荐算法。请注意，虽然该系统现已上网，但我们亦提供 https://cfda.csie.org/recdelta 录像 https://tinyurl.com/recdelta ，介绍该系统的概念及用途。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=RecDelta:+An+Interactive+Dashboard+on+Top-k+Recommendation+for+Cross-model+Evaluation)|1| |[Asyncval: A Toolkit for Asynchronously Validating Dense Retriever Checkpoints During Training](https://doi.org/10.1145/3477495.3531658)|Shengyao Zhuang, Guido Zuccon|The University of Queensland, Brisbane, QLD, Australia|The process of model checkpoint validation refers to the evaluation of the performance of a model checkpoint executed on a held-out portion of the training data while learning the hyperparameters of the model. This model checkpoint validation process is used to avoid over-fitting and determine when the model has converged so as to stop training. A simple and efficient strategy to validate deep learning checkpoints is the addition of validation loops to execute during training. However, the validation of dense retrievers (DR) checkpoints is not as trivial -- and the addition of validation loops is not efficient. This is because, in order to accurately evaluate the performance of a DR checkpoint, the whole document corpus needs to be encoded into vectors using the current checkpoint before any actual retrieval operation for checkpoint validation can be performed. This corpus encoding process can be very time-consuming if the document corpus contains millions of documents (e.g., 8.8M for MS MARCO v1 and 21M for Natural Questions). Thus, a naïve use of validation loops during training will significantly increase training time. To address this issue, we propose Asyncval: a Python-based toolkit for efficiently validating DR checkpoints during training. Instead of pausing the training loop for validating DR checkpoints, Asyncval decouples the validation loop from the training loop, uses another GPU to automatically validate new DR checkpoints and thus permits to perform validation asynchronously from training. Asyncval also implements a range of different corpus subset sampling strategies for validating DR checkpoints; these strategies allow to further speed up the validation process. We provide an investigation of these methods in terms of their impact on validation time and validation fidelity. Asyncval is made available as an open-source project at https://github.com/ielab/asyncval.|模型检查点验证过程是指在学习模型超参数的同时，对在训练数据的保持部分上执行的模型检查点的性能进行评估。该模型检查点验证过程用于避免过拟合，判断模型何时收敛，从而停止训练。验证深度学习检查点的一个简单而有效的策略是在训练期间添加验证循环来执行。然而，密集检索器(DR)检查点的验证并不那么简单——并且添加验证循环的效率也不高。这是因为，为了准确地评估 DR 检查点的性能，在执行检查点验证的任何实际检索操作之前，需要使用当前检查点将整个文档语料库编码到向量中。如果文档语料库包含数百万个文档(例如，MS MARCO v1为8.8 M，自然问题为21M) ，那么这个语料库编码过程可能非常耗时。因此，在训练期间天真地使用验证循环将显著增加训练时间。为了解决这个问题，我们提出了 Asyncval: 一个基于 Python 的工具包，用于在培训期间有效地验证 DR 检查点。Asyncval 没有暂停用于验证 DR 检查点的训练循环，而是将验证循环与训练循环解耦，使用另一个 GPU 自动验证新的 DR 检查点，从而允许从训练中异步执行验证。Asyncval 还实现了一系列不同的语料库子集采样策略来验证 DR 检查点; 这些策略允许进一步加快验证过程。我们根据这些方法对验证时间和验证保真度的影响对它们进行了研究。Asyncval 作为一个开源项目在 https://github.com/ielab/Asyncval 上可以使用。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Asyncval:+A+Toolkit+for+Asynchronously+Validating+Dense+Retriever+Checkpoints+During+Training)|1| -|[TaskMAD: A Platform for Multimodal Task-Centric Knowledge-Grounded Conversational Experimentation](https://doi.org/10.1145/3477495.3531679)|Alessandro Speggiorin, Jeffrey Dalton, Anton Leuski|University of Glasgow, Glasgow, United Kingdom; University of Southern California, Los Angeles, CA, USA|The role of conversational assistants continues to evolve, beyond simple voice commands to ones that support rich and complex tasks in the home, car, and even virtual reality. Going beyond simple voice command and control requires agents and datasets blending structured dialogue, information seeking, grounded reasoning, and contextual question-answering in a multimodal environment with rich image and video content. In this demo, we introduce Task-oriented Multimodal Agent Dialogue (TaskMAD), a new platform that supports the creation of interactive multimodal and task-centric datasets in a Wizard-of-Oz experimental setup. TaskMAD includes support for text and voice, federated retrieval from text and knowledge bases, and structured logging of interactions for offline labeling. Its architecture supports a spectrum of tasks that span open-domain exploratory search to traditional frame-based dialogue tasks. It's open-source and offers rich capability as a platform used to collect data for the Amazon Alexa Prize Taskbot challenge, TREC Conversational Assistance track, undergraduate student research, and others. TaskMAD is distributed under the MIT license.|会话助理的角色不断演变，从简单的语音命令到支持家庭、汽车甚至虚拟现实中丰富而复杂的任务的语音命令。超越简单的语音命令和控制需要代理和数据集在一个具有丰富图像和视频内容的多通道环境中，将结构化对话、信息搜索、基础推理和上下文问答相结合。在这个演示中，我们介绍了面向任务的多通道 Agent 对话(TaskMAD) ，这是一个新的平台，支持在 Wizard-of-Oz 实验设置中创建交互式多通道和以任务为中心的数据集。TaskMAD 包括对文本和语音的支持、对文本和知识库的联合检索，以及用于离线标记的交互的结构化日志记录。它的体系结构支持一系列任务，从开放领域的探索性搜索到传统的基于框架的对话任务。它是开源的，提供了丰富的能力，作为一个平台，用于收集数据的亚马逊 Alexa 奖任务机器人挑战，TREC 对话援助轨道，本科生研究，和其他。TaskMAD 是在 MIT 许可下发布的。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=TaskMAD:+A+Platform+for+Multimodal+Task-Centric+Knowledge-Grounded+Conversational+Experimentation)|1| +|[TaskMAD: A Platform for Multimodal Task-Centric Knowledge-Grounded Conversational Experimentation](https://doi.org/10.1145/3477495.3531679)|Alessandro Speggiorin, Jeffrey Dalton, Anton Leuski|University of Southern California, Los Angeles, CA, USA; University of Glasgow, Glasgow, United Kingdom|The role of conversational assistants continues to evolve, beyond simple voice commands to ones that support rich and complex tasks in the home, car, and even virtual reality. Going beyond simple voice command and control requires agents and datasets blending structured dialogue, information seeking, grounded reasoning, and contextual question-answering in a multimodal environment with rich image and video content. In this demo, we introduce Task-oriented Multimodal Agent Dialogue (TaskMAD), a new platform that supports the creation of interactive multimodal and task-centric datasets in a Wizard-of-Oz experimental setup. TaskMAD includes support for text and voice, federated retrieval from text and knowledge bases, and structured logging of interactions for offline labeling. Its architecture supports a spectrum of tasks that span open-domain exploratory search to traditional frame-based dialogue tasks. It's open-source and offers rich capability as a platform used to collect data for the Amazon Alexa Prize Taskbot challenge, TREC Conversational Assistance track, undergraduate student research, and others. TaskMAD is distributed under the MIT license.|会话助理的角色不断演变，从简单的语音命令到支持家庭、汽车甚至虚拟现实中丰富而复杂的任务的语音命令。超越简单的语音命令和控制需要代理和数据集在一个具有丰富图像和视频内容的多通道环境中，将结构化对话、信息搜索、基础推理和上下文问答相结合。在这个演示中，我们介绍了面向任务的多通道 Agent 对话(TaskMAD) ，这是一个新的平台，支持在 Wizard-of-Oz 实验设置中创建交互式多通道和以任务为中心的数据集。TaskMAD 包括对文本和语音的支持、对文本和知识库的联合检索，以及用于离线标记的交互的结构化日志记录。它的体系结构支持一系列任务，从开放领域的探索性搜索到传统的基于框架的对话任务。它是开源的，提供了丰富的能力，作为一个平台，用于收集数据的亚马逊 Alexa 奖任务机器人挑战，TREC 对话援助轨道，本科生研究，和其他。TaskMAD 是在 MIT 许可下发布的。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=TaskMAD:+A+Platform+for+Multimodal+Task-Centric+Knowledge-Grounded+Conversational+Experimentation)|1| |[IRVILAB: Gamified Searching on Multilingual Wikipedia](https://doi.org/10.1145/3477495.3531662)|Paavo Arvola, Tuulikki Alamettälä|Tampere University, Tampere, Finland|Information retrieval (IR) evaluation can be considered as a form of competition in matching documents and queries. This paper introduces a learning environment based on gamification of query construction for document retrieval, called IRVILAB (Information Retrieval Virtual Lab). The lab has modules for creating standard evaluation settings, one for topic creation including relevance assessments and another for performance evaluation of user queries. In addition, multilingual Wikipedia online collection enables a module, where relevance assessments are translated to other languages. The underlying game utilizes IR performance metrics to measure and give feedback on participants' information retrieval performance. It aims to improve participants' search skills, subject knowledge and contributes to science education by introducing an experimental method. Distinctive features of the system include algorithmic relevance assessments and automatic recall base translation.|信息检索评估可被视为一种在配对文件和查询方面的竞争形式。本文介绍了一个基于文献检索查询结构游戏化的学习环境 IRVILAB (信息检索虚拟实验室)。该实验室有用于创建标准评估设置的模块，一个用于主题创建，包括相关性评估，另一个用于用户查询的性能评估。此外，多语言维基百科在线收集支持一个模块，其中相关性评估被翻译成其他语言。基础游戏利用红外表现指标来衡量和反馈参与者的信息检索表现。它旨在提高参与者的搜索技能，学科知识和有助于科学教育的实验方法。该系统的显著特点包括算法相关性评估和自动回忆库翻译。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=IRVILAB:+Gamified+Searching+on+Multilingual+Wikipedia)|1| |[Improving Efficiency and Robustness of Transformer-based Information Retrieval Systems](https://doi.org/10.1145/3477495.3532681)|Edmon Begoli, Sudarshan Srinivasan, Maria Mahbub|Oak Ridge National Laboratory (ORNL), Oak Ridge, TN, USA|This tutorial focuses on both theoretical and practical aspects of improving the efficiency and robustness of transformer-based approaches, so that these can be effectively used in practical, high-scale, and high-volume information retrieval (IR) scenarios. The tutorial is inspired and informed by our work and experience while working with massive narrative datasets (8.5 billion medical notes), and by our basic research and academic experience with transformer-based IR tasks. Additionally, the tutorial focuses on techniques for making transformer-based IR robust against adversarial (AI) exploitation. This is a recent concern in the IR domain that we needed to take into concern, and we want to want to share some of the lessons learned and applicable principles with our audience. Finally, an important, if not critical, element of this tutorial is its focus on didacticism -- delivering tutorial content in a clear, intuitive, plain-speak fashion. Transformers are a challenging subject, and, through our teaching experience, we observed a great value and a great need to explain all relevant aspects of this architecture and related principles in the most straightforward, precise, and intuitive manner. That is the defining style of our proposed tutorial.|本教程重点介绍提高基于变压器的方法的效率和稳健性的理论和实践方面，以便这些方法能够有效地用于实际的、大规模的和大容量的信息检索(IR)场景。本教程的灵感来自于我们在处理大量叙述性数据集(85亿医学笔记)时的工作和经验，以及我们在基于变压器的 IR 任务方面的基础研究和学术经验。此外，本教程还重点介绍了使基于变压器的 IR 针对对手(AI)开发具有鲁棒性的技术。这是国际关系领域最近关注的一个问题，我们需要加以关注，我们希望与我们的听众分享一些经验教训和适用的原则。最后，本教程的一个重要的(如果不是关键的)元素是它对教学法的关注——以一种清晰、直观、直白的方式提供教程内容。变压器是一个具有挑战性的课题，并且，通过我们的教学经验，我们观察到一个巨大的价值和一个巨大的需要，以最直接，精确和直观的方式解释这个架构和相关原则的所有相关方面。这就是我们提议的教程的定义风格。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Improving+Efficiency+and+Robustness+of+Transformer-based+Information+Retrieval+Systems)|1| |[Self-Supervised Learning for Recommender System](https://doi.org/10.1145/3477495.3532684)|Chao Huang, Xiang Wang, Xiangnan He, Dawei Yin|Baidu Inc., Beijing, China; University of Science and Technology of China, Hefei, China; University of Hong Kong, Hong Kong, Hong Kong|Recommender systems have become key components for a wide spectrum of web applications (e.g., E-commerce sites, video sharing platforms, lifestyle applications, etc), so as to alleviate the information overload and suggest items for users. However, most existing recommendation models follow a supervised learning manner, which notably limits their representation ability with the ubiquitous sparse and noisy data in practical applications. Recently, self-supervised learning (SSL) has become a promising learning paradigm to distill informative knowledge from unlabeled data, without the heavy reliance on sufficient supervision signals. Inspired by the effectiveness of self-supervised learning, recent efforts bring SSL's superiority into various recommendation representation learning scenarios with augmented auxiliary learning tasks. In this tutorial, we aim to provide a systemic review of existing self-supervised learning frameworks and analyze the corresponding challenges for various recommendation scenarios, such as general collaborative filtering paradigm, social recommendation, sequential recommendation, and multi-behavior recommendation. We then raise discussions and future directions of this area. With the introduction of this emerging and promising topic, we expect the audience to have a deep understanding of this domain. We also seek to promote more ideas and discussions, which facilitates the development of self-supervised learning recommendation techniques.|推荐系统已成为一系列网上应用程式(例如电子商贸网站、影片分享平台、生活方式应用程式等)的重要组成部分，以纾缓信息超载及为使用者提供建议。然而，现有的大多数推荐模型都遵循监督式学习的方式，这明显地限制了它们在实际应用中对普遍存在的稀疏和嘈杂数据的表示能力。近年来，自监督学习(SSL)已成为从未标记数据中提取信息知识的一种很有前途的学习方法，而不需要过多地依赖于足够的监督信号。受自我监督学习效果的启发，最近的研究将 SSL 的优越性应用到各种推荐表示学习场景中，并增加了辅助学习任务。在本教程中，我们的目标是提供一个现有的自我监督学习框架的系统回顾，并分析各种推荐场景的相应挑战，如一般协同过滤范式，社会推荐，顺序推荐和多行为推荐。然后，我们提出这一领域的讨论和未来方向。随着这个新兴的和有前途的主题的介绍，我们希望听众对这个领域有一个深刻的理解。我们也寻求促进更多的想法和讨论，以促进自我监督学习推荐技术的发展。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Self-Supervised+Learning+for+Recommender+System)|1| -|[ReNeuIR: Reaching Efficiency in Neural Information Retrieval](https://doi.org/10.1145/3477495.3531704)|Sebastian Bruch, Claudio Lucchese, Franco Maria Nardini|Pinecone, New York, NY, USA; ISTI-CNR, Pisa, Italy; Ca' Foscary University of Venice, Venice, Italy|Perhaps the applied nature of information retrieval research goes some way to explain the community's rich history of evaluating machine learning models holistically, understanding that efficacy matters but so does the computational cost incurred to achieve it. This is evidenced, for example, by more than a decade of research on efficient training and inference of large decision forest models in learning-to-rank. As the community adopts even more complex, neural network-based models in a wide range of applications, questions on efficiency have once again become relevant. We propose this workshop as a forum for a critical discussion of efficiency in the era of neural information retrieval, to encourage debate on the current state and future directions of research in this space, and to promote more sustainable research by identifying best practices in the development and evaluation of neural models for information retrieval.|也许信息检索研究的应用性质在某种程度上可以解释社区整体评估机器学习模型的丰富历史，理解功效很重要，但实现它所需的计算成本也很重要。例如，十多年来对大型决策森林模型在学习排序中的有效训练和推理的研究就证明了这一点。随着社会采用更为复杂的、基于神经网络的模型在广泛的应用中，有关效率的问题再次变得相关。我们建议这个研讨会作为一个论坛，就神经信息检索时代的效率进行批判性讨论，鼓励就该领域的研究现状和未来方向展开辩论，并通过确定开发和评估神经信息检索模型的最佳做法，促进更可持续的研究。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=ReNeuIR:+Reaching+Efficiency+in+Neural+Information+Retrieval)|1| +|[ReNeuIR: Reaching Efficiency in Neural Information Retrieval](https://doi.org/10.1145/3477495.3531704)|Sebastian Bruch, Claudio Lucchese, Franco Maria Nardini|Ca' Foscary University of Venice, Venice, Italy; ISTI-CNR, Pisa, Italy; Pinecone, New York, NY, USA|Perhaps the applied nature of information retrieval research goes some way to explain the community's rich history of evaluating machine learning models holistically, understanding that efficacy matters but so does the computational cost incurred to achieve it. This is evidenced, for example, by more than a decade of research on efficient training and inference of large decision forest models in learning-to-rank. As the community adopts even more complex, neural network-based models in a wide range of applications, questions on efficiency have once again become relevant. We propose this workshop as a forum for a critical discussion of efficiency in the era of neural information retrieval, to encourage debate on the current state and future directions of research in this space, and to promote more sustainable research by identifying best practices in the development and evaluation of neural models for information retrieval.|也许信息检索研究的应用性质在某种程度上可以解释社区整体评估机器学习模型的丰富历史，理解功效很重要，但实现它所需的计算成本也很重要。例如，十多年来对大型决策森林模型在学习排序中的有效训练和推理的研究就证明了这一点。随着社会采用更为复杂的、基于神经网络的模型在广泛的应用中，有关效率的问题再次变得相关。我们建议这个研讨会作为一个论坛，就神经信息检索时代的效率进行批判性讨论，鼓励就该领域的研究现状和未来方向展开辩论，并通过确定开发和评估神经信息检索模型的最佳做法，促进更可持续的研究。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=ReNeuIR:+Reaching+Efficiency+in+Neural+Information+Retrieval)|1| |[Generating Knowledge-based Explanation for Recommendation from Review](https://doi.org/10.1145/3477495.3531683)|Zuoxi Yang|South China University of Technology, Tianhe, Guangzhou, China|Reasonable explanation is helpful to increase the trust and satisfaction of user to the recommender system. Among many previous studies, there is growing concern about generating explanation based on review text. Collaborative filtering is one of the most successful approaches to predict user's preference. However, most of them suffer from data sparsity problem. Researcher often utilizes auxiliary data to address this problem, such as review, knowledge graph (KG), image and so on. Some researchers have proven that recommendation accuracy can be improved via incorporating rating and review data. Besides, neural network is also applied to learn more powerful representations for user and item from the review data. For example, convolution neural network (CNN) is used to extract representation from review text by using convolutional filters. Recurrent neural network (RNN) is another widely used model, which can encode the sequential behaviours as hidden states. However, most of them lack the ability to generate explanation. In order to generate explanation, there are two main approaches are used, i.e., template-based approach and generation-based approach. It is usually necessary for the templated-based approach to define serval templates. Then, these templates will be further filled with different personalized features/words. Although they can offer readable explanations, they rely heavily on pre-defined templates. It causes large manual efforts, limiting their explanation expression. Due to the strong generation ability of natural language model, the generation-based approach is capable to generate explanation without templates, which can largely enhance the expression of the generated sentence. Although they can generate more free and flexible explanation, the explanation might tend to be uninformative. To tackle these challenges of the above-mentioned work, we propose a Generating Knowldge-based Explanation for Recommendation from Review (GKER) to provide informative explanation. Unlike the traditional generation-based approach with a multi-task framework, we design a single-task framework to simultaneously model user's preference and explanation generation. The multi-task training usually needs more manual effort and time overhead. In this unitary framework, we inject the user's sentiment preference into the explanation generation, aiming at capturing the user's interest while producing high-quality explanation. Specifically, we build three graphs, including a bipartite graph, a KG and a co-occur graph. All of them are integrated to form a unitary graph, thus bringing the semantic among user-item interaction, KG and review. Based on this integrated graph, it is possible to learn more effective representations for user and item. To make better use of the integrated KG, a graph convolution network (GCN) is utilized to obtain improved embeddings due to its superior representation learning ability. We argue that these embeddings can contain more semantic interaction signals with the help of the integrated KG and GCN. After obtaining these extensive embeddings, a multilayer perceptron (MLP) layer is further employed to capture non-linear interaction signals between user and item, aiming at predicting user's rating accurately. The predicted rating would be regarded as a sentiment indicator to explore why the user likes or dislikes the target item. To investigate the association between sentiment indicator and the related review data, a transformer-enhanced encoder-decoder architecture is designed to produce informative and topic-relevant explanation. Besides, the aspect semantic is added in this architecture through an attention mechanism. In this framework, the transformer is utilized as a "teacher" model to supervise the generation of the encoder-decoder process. Finally, experiments conducted on three datasets have shown the state-of-the-art performance of GKER. There are some research issues for discussion: 1) although KG is a useful tool for recommendation accuracy and explainability, it is always incomplete in the real world. Hence, it is worth completing it for the recommendation. 2) Besides, as for explainable, it still needs more metrics to evaluate the quality of its explanation.|合理的解释有助于提高用户对推荐系统的信任和满意度。在以往的许多研究中，人们越来越关注基于评论文本生成解释。协同过滤是预测用户偏好最成功的方法之一。然而，它们中的大多数都存在数据稀疏问题。研究人员经常利用辅助数据来解决这个问题，如复习、知识图(KG)、图像等。一些研究人员已经证明，通过整合评分和评论数据，可以提高推荐的准确性。此外，还应用神经网络从复习数据中学习更强大的用户和项目表示。例如，卷积神经网络(CNN)通过使用卷积滤波器从复习文本中提取表示。递归神经网络(RNN)是另一种广泛使用的模型，它可以将序列行为编码为隐藏状态。然而，他们中的大多数缺乏产生解释的能力。为了产生解释，主要使用了两种方法，即基于模板的方法和基于生成的方法。基于模板的方法通常需要定义几个模板。然后，这些模板将进一步填充不同的个性化特征/词语。尽管它们可以提供可读的解释，但它们严重依赖于预定义的模板。它导致大量的人工操作，限制了它们的解释表达。由于自然语言模型具有很强的生成能力，基于生成的方法能够在没有模板的情况下生成解释，从而大大提高了生成句子的表达能力。虽然他们可以产生更自由和灵活的解释，解释可能往往是无益的。为了应对上述工作的这些挑战，我们提出了一个基于知识生成的评审推荐解释(GKER)来提供信息解释。与传统的基于生成的多任务框架方法不同，我们设计了一个单任务框架来同时模拟用户的偏好和解释生成。多任务训练通常需要更多的人工努力和时间开销。在这个统一的框架中，我们将用户的情感偏好引入到解释生成中，目的是在获取用户兴趣的同时生成高质量的解释。具体来说，我们构造了三个图，包括一个二部图、一个 KG 图和一个共现图。它们集成在一起形成一个统一的图形，从而实现了用户-项目交互、 KG 和评论之间的语义关系。基于这个集成图表，可以学习更有效的表示用户和项目。为了更好地利用集成 KG，利用图卷积网络(GCN)优越的表示学习能力来获得改进的嵌入。我们认为，这些嵌入可以包含更多的语义交互信号的帮助下，整合 KG 和 GCN。在获得这些广泛的嵌入之后，一个多层感知机(MLP)层被进一步用来捕获用户和项目之间的非线性交互信号，旨在准确预测用户的评分。预测的评分将被视为一个情绪指标，以探索为什么用户喜欢或不喜欢的目标项目。为了研究情绪指标与相关评论数据之间的关联，设计了一个变压器增强型编解码器结构，以产生信息丰富和与主题相关的解释。此外，通过注意机制在体系结构中添加了方面语义。在这个框架中，变压器被用作“教师”模型来监督编码器-解码器过程的生成。最后，在三个数据集上进行的实验显示了 GKER 的最新性能。有一些研究问题值得讨论: 1)虽然 KG 是一个有效的推荐准确性和可解释性的工具，但在现实世界中它总是不完整的。因此，为了获得推荐，完成它是值得的。2)对于可解释性，还需要更多的指标来评价其解释的质量。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Generating+Knowledge-based+Explanation+for+Recommendation+from+Review)|1| |[Improving Fairness and Transparency for Artists in Music Recommender Systems](https://doi.org/10.1145/3477495.3531681)|Karlijn Dinnissen|Utrecht University, Utrecht, Netherlands|Streaming services have become one of today's main sources of music consumption, with music recommender systems (MRS) as important components. The MRS' choices strongly influence what users consume, and vice versa. Therefore, there is a growing interest in ensuring the fairness of these choices for all stakeholders involved. Firstly, for users, unfairness might result in some users receiving lower-quality recommendations in terms of accuracy and coverage. Secondly, item provider (i.e. artist) unfairness might result in some artists receiving less exposure, and therefore less revenue. However, it is challenging to improve fairness without a decrease in, for instance, overall recommendation quality or user satisfaction. Additional complications arise when balancing possibly domain-specific objectives for multiple stakeholders at once. While fairness research exists from both the user and artist perspective in the music domain, there is a lack of research directly consulting artists---with Ferraro et al. (2021) as an exception. When interacting with recommendation systems and evaluating their fairness, the many factors influencing recommendation system decisions can cause another difficulty: lack of transparency. Artists indicate they would appreciate more transparency in MRS---both towards the user and themselves. While e.g. Millecamp et al. (2019) use explanations to increase transparency for MRS users, to the best of our knowledge, no research has addressed improving transparency for artists this way.|流媒体服务已经成为当今音乐消费的主要来源之一，音乐推荐系统(MRS)是其中的重要组成部分。MRS 的选择强烈地影响用户的消费，反之亦然。因此，确保这些选择对所有相关利益攸关方的公平性越来越受到关注。首先，对于用户来说，不公平可能会导致一些用户在准确性和覆盖率方面得到低质量的推荐。其次，项目提供者(即艺术家)的不公平可能导致一些艺术家获得较少的曝光率，从而减少收入。然而，在不降低例如整体推荐质量或用户满意度的情况下提高公平性是具有挑战性的。当同时为多个涉众平衡可能的领域特定目标时，会出现额外的复杂性。虽然公平性研究存在于音乐领域的用户和艺术家两个角度，但缺乏直接咨询艺术家的研究——费拉罗等人(2021)是一个例外。当与推荐系统进行交互并评估其公平性时，影响推荐系统决策的诸多因素会导致另一个困难: 缺乏透明度。艺术家们表示，他们希望 MRS 更加透明——无论是对用户还是对他们自己。虽然 Millecamp 等人(2019)使用解释来增加 MRS 用户的透明度，据我们所知，还没有研究以这种方式提高艺术家的透明度。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Improving+Fairness+and+Transparency+for+Artists+in+Music+Recommender+Systems)|1| |[Exploring Modular Task Decomposition in Cross-domain Named Entity Recognition](https://doi.org/10.1145/3477495.3531976)|Xinghua Zhang, Bowen Yu, Yubin Wang, Tingwen Liu, Taoyu Su, Hongbo Xu|Institute of Information Engineering, Chinese Academy of Sciences & University of Chinese Academy of Sciences, Beijing, China|Cross-domain Named Entity Recognition (NER) aims to transfer knowledge from the source domain to the target, alleviating expensive labeling costs in the target domain. Most prior studies acquire domain-invariant features under the end-to-end sequence-labeling framework where each token is assigned a compositional label (e.g., B-LOC). However, the complexity of cross-domain transfer may be increased over this complicated labeling scheme, which leads to sub-optimal results, especially when there are significantly distinct entity categories across domains. In this paper, we aim to explore the task decomposition in cross-domain NER. Concretely, we suggest a modular learning approach in which two sub-tasks (entity span detection and type classification) are learned by separate functional modules to perform respective cross-domain transfer with corresponding strategies. Compared with the compositional labeling scheme, the label spaces are smaller and closer across domains especially in entity span detection, leading to easier transfer in each sub-task. And then we combine two sub-tasks to achieve the final result with modular interaction mechanism, and deploy the adversarial regularization for generalized and robust learning in low-resource target domains. Extensive experiments over 10 diverse domain pairs demonstrate that the proposed method is superior to state-of-the-art cross-domain NER methods in an end-to-end fashion (about average 6.4% absolute F1 score increase). Further analyses show the effectiveness of modular task decomposition and its great potential in cross-domain NER.|跨域命名实体识别(NER)旨在将知识从源域传递到目标域，减少目标域中昂贵的标记代价。大多数先前的研究在端到端序列标记框架下获得域不变特征，其中每个标记被分配一个组合标签(例如，B-LOC)。然而，这种复杂的标记方案可能会增加跨域传输的复杂性，从而导致次优结果，特别是当跨域存在明显不同的实体类别时。本文旨在研究跨域 NER 中的任务分解问题。具体地说，我们提出了一种模块化学习方法，其中两个子任务(实体跨度检测和类型分类)由不同的功能模块学习，以执行各自的跨域传输和相应的策略。与组合标记方案相比，标记空间更小，跨域更紧密，特别是在实体跨度检测中，使得每个子任务之间的传递更加容易。然后将两个子任务结合起来，采用模块化交互机制实现最终结果，并在低资源目标域上部署广义鲁棒学习的对抗正则化。超过10个不同域对的广泛实验表明，所提出的方法以端到端的方式优于最先进的跨域 NER 方法(平均绝对 F1评分增加约6.4%)。进一步的分析表明模块化任务分解的有效性及其在跨域 NER 中的巨大潜力。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Exploring+Modular+Task+Decomposition+in+Cross-domain+Named+Entity+Recognition)|1| -|[Graph Adaptive Semantic Transfer for Cross-domain Sentiment Classification](https://doi.org/10.1145/3477495.3531984)|Kai Zhang, Qi Liu, Zhenya Huang, Mingyue Cheng, Kun Zhang, Mengdi Zhang, Wei Wu, Enhong Chen|Anhui Province Key Lab. of Big Data Analysis and Application, University of S&T of China, Hefei, China; Anhui Province Key Lab. of Big Data Analysis and Application, University of S&T of China & State Key Laboratory of Cognitive Intelligence, Hefei, China; Meituan, Beijing, China; Hefei University of Technology, Hefei, China|Cross-domain sentiment classification (CDSC) aims to use the transferable semantics learned from the source domain to predict the sentiment of reviews in the unlabeled target domain. Existing studies in this task attach more attention to the sequence modeling of sentences while largely ignoring the rich domain-invariant semantics embedded in graph structures (i.e., the part-of-speech tags and dependency relations). As an important aspect of exploring characteristics of language comprehension, adaptive graph representations have played an essential role in recent years. To this end, in the paper, we aim to explore the possibility of learning invariant semantic features from graph-like structures in CDSC. Specifically, we present Graph Adaptive Semantic Transfer (GAST) model, an adaptive syntactic graph embedding method that is able to learn domain-invariant semantics from both word sequences and syntactic graphs. More specifically, we first raise a POS-Transformer module to extract sequential semantic features from the word sequences as well as the part-of-speech tags. Then, we design a Hybrid Graph Attention (HGAT) module to generate syntax-based semantic features by considering the transferable dependency relations. Finally, we devise an Integrated aDaptive Strategy (IDS) to guide the joint learning process of both modules. Extensive experiments on four public datasets indicate that GAST achieves comparable effectiveness to a range of state-of-the-art models.|跨域情感分类(CDSC)的目的是利用从源域学到的可转移语义来预测未标记目标域中的评论情感。现有的研究更多地关注句子的序列建模，而忽略了图结构中的丰富的领域不变语义(即词性标签和依赖关系)。自适应图表示作为探索语言理解特征的一个重要方面，近年来发挥了重要作用。为此，本文旨在探索在 CDSC 中从类图结构中学习不变语义特征的可能性。具体来说，我们提出了一种自适应语义转移(GAST)模型，这是一种自适应语法图嵌入方法，能够从词序列和语法图中学习领域不变语义。更具体地说，我们首先提出一个 POS 转换器模块来从词序列和词性标签中提取序列语义特征。然后，我们设计了一个混合图注意(HGAT)模块，通过考虑可转移的依赖关系来生成基于语法的语义特征。最后，我们设计了一个综合自适应策略(IDS)来指导两个模块的联合学习过程。对四个公共数据集的大量实验表明，GAST 达到了与一系列最先进的模型相当的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Graph+Adaptive+Semantic+Transfer+for+Cross-domain+Sentiment+Classification)|1| +|[Graph Adaptive Semantic Transfer for Cross-domain Sentiment Classification](https://doi.org/10.1145/3477495.3531984)|Kai Zhang, Qi Liu, Zhenya Huang, Mingyue Cheng, Kun Zhang, Mengdi Zhang, Wei Wu, Enhong Chen|Meituan, Beijing, China; Anhui Province Key Lab. of Big Data Analysis and Application, University of S&T of China, Hefei, China; Hefei University of Technology, Hefei, China; Anhui Province Key Lab. of Big Data Analysis and Application, University of S&T of China & State Key Laboratory of Cognitive Intelligence, Hefei, China|Cross-domain sentiment classification (CDSC) aims to use the transferable semantics learned from the source domain to predict the sentiment of reviews in the unlabeled target domain. Existing studies in this task attach more attention to the sequence modeling of sentences while largely ignoring the rich domain-invariant semantics embedded in graph structures (i.e., the part-of-speech tags and dependency relations). As an important aspect of exploring characteristics of language comprehension, adaptive graph representations have played an essential role in recent years. To this end, in the paper, we aim to explore the possibility of learning invariant semantic features from graph-like structures in CDSC. Specifically, we present Graph Adaptive Semantic Transfer (GAST) model, an adaptive syntactic graph embedding method that is able to learn domain-invariant semantics from both word sequences and syntactic graphs. More specifically, we first raise a POS-Transformer module to extract sequential semantic features from the word sequences as well as the part-of-speech tags. Then, we design a Hybrid Graph Attention (HGAT) module to generate syntax-based semantic features by considering the transferable dependency relations. Finally, we devise an Integrated aDaptive Strategy (IDS) to guide the joint learning process of both modules. Extensive experiments on four public datasets indicate that GAST achieves comparable effectiveness to a range of state-of-the-art models.|跨域情感分类(CDSC)的目的是利用从源域学到的可转移语义来预测未标记目标域中的评论情感。现有的研究更多地关注句子的序列建模，而忽略了图结构中的丰富的领域不变语义(即词性标签和依赖关系)。自适应图表示作为探索语言理解特征的一个重要方面，近年来发挥了重要作用。为此，本文旨在探索在 CDSC 中从类图结构中学习不变语义特征的可能性。具体来说，我们提出了一种自适应语义转移(GAST)模型，这是一种自适应语法图嵌入方法，能够从词序列和语法图中学习领域不变语义。更具体地说，我们首先提出一个 POS 转换器模块来从词序列和词性标签中提取序列语义特征。然后，我们设计了一个混合图注意(HGAT)模块，通过考虑可转移的依赖关系来生成基于语法的语义特征。最后，我们设计了一个综合自适应策略(IDS)来指导两个模块的联合学习过程。对四个公共数据集的大量实验表明，GAST 达到了与一系列最先进的模型相当的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Graph+Adaptive+Semantic+Transfer+for+Cross-domain+Sentiment+Classification)|1| |[Hybrid CNN Based Attention with Category Prior for User Image Behavior Modeling](https://doi.org/10.1145/3477495.3531854)|Xin Chen, Qingtao Tang, Ke Hu, Yue Xu, Shihang Qiu, Jia Cheng, Jun Lei|Meituan, Shanghai, China|User historical behaviors are proved useful for Click Through Rate (CTR) prediction in online advertising system. In Meituan, one of the largest e-commerce platform in China, an item is typically displayed with its image and whether a user clicks the item or not is usually influenced by its image, which implies that user's image behaviors are helpful for understanding user's visual preference and improving the accuracy of CTR prediction. Existing user image behavior models typically use a two-stage architecture, which extracts visual embeddings of images through off-the-shelf Convolutional Neural Networks (CNNs) in the first stage, and then jointly trains a CTR model with those visual embeddings and non-visual features. We find that the two-stage architecture is sub-optimal for CTR prediction. Meanwhile, precisely labeled categories in online ad systems contain abundant visual prior information, which can enhance the modeling of user image behaviors. However, off-the-shelf CNNs without category prior may extract category unrelated features, limiting CNN's expression ability. To address the two issues, we propose a hybrid CNN based attention module, unifying user's image behaviors and category prior, for CTR prediction. Our approach achieves significant improvements in both online and offline experiments on a billion scale real serving dataset.|在线广告系统中，用户的历史行为对点击率(CTR)的预测非常有用。美团是中国最大的电子商务平台之一，一个商品通常与其图像一起显示，用户是否点击该商品通常受其图像的影响，这意味着用户的图像行为有助于理解用户的视觉偏好，提高点击率预测的准确性。现有的用户图像行为模型通常采用两阶段结构，在第一阶段通过现成的卷积神经网络(CNN)提取图像的视觉嵌入，然后将这些视觉嵌入和非视觉特征联合训练 CTR 模型。我们发现两阶段结构对于 CTR 预测是次优的。同时，在线广告系统中精确标注的类别含有丰富的视觉先验信息，可以增强用户图像行为的建模能力。然而，没有类别先验的现成 CNN 可能会提取类别不相关的特征，限制了 CNN 的表达能力。针对这两个问题，提出了一种基于混合细胞神经网络的注意模块，统一用户的图像行为和类别先验，用于 CTR 预测。我们的方法在10亿规模的实际服务数据集的两个在线和离线实验中都取得了显著的改进。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Hybrid+CNN+Based+Attention+with+Category+Prior+for+User+Image+Behavior+Modeling)|1| |[When Online Meets Offline: Exploring Periodicity for Travel Destination Prediction](https://doi.org/10.1145/3477495.3531859)|Wanjie Tao, Liangyue Li, Chen Chen, Zulong Chen, Hong Wen|University of Virginia, Petersburg, UNK, USA; Alibaba Group, Hangzhou, UNK, China|Online travel platforms (OTPs), e.g., booking.com and Ctrip.com, deliver travel experiences to online users by providing travel-related products. One key problem facing OTPs is to predict users' future travel destination, which has many important applications, e.g., proactively recommending users flight tickets or hotels in the destination city. Although much progress has been made for the next POI recommendation, they are largely sub-optimal for travel destination prediction on OTPs, due to the unique characteristics exhibited from users' travel behaviors such as offline spatial-temporal periodicity and online multi-interest exploration. In this paper, we propose an online-offline periodicity-aware information gain network, OOPIN, for travel destination prediction on OTPs. The key components of the model are (1) an offline mobility pattern extractor, which extracts spatial-temporal periodicity along with the sequential dependencies from the visited city sequence; and (2) an online multi-interests exploration module that discovers destinations that the user might be interested in but not yet visited from their online interaction data.Comprehensive experiments on real-world OTP demonstrate the superior performance of the proposed model for travel destination prediction compared with state-of-the-art methods.|在线旅游平台(OTP) ，例如 booking.com 和 Trip.com，通过提供与旅游相关的产品，为在线用户提供旅游体验。OTP 面临的一个关键问题是预测用户未来的旅游目的地，这有许多重要的应用程序，例如，主动向用户推荐目的地城市的机票或酒店。尽管下一个 POI 推荐已经取得了很大的进展，但由于用户的出行行为表现出离线时空周期性和在线多兴趣探索等独特特征，它们在 OTP 上对旅游目的地的预测大多是次优的。在本文中，我们提出了一个在线-离线周期感知信息增益网络，OOPIN，用于 OTP 上的旅游目的地预测。该模型的关键组成部分是: (1)离线移动模式提取器，它从访问的城市序列中提取出时空周期以及顺序依赖关系; (2)在线多兴趣探索模块，它从用户的在线交互数据中发现用户可能感兴趣但尚未访问的目的地。在现实生活中的 OTP 实验表明，与现有的预测方法相比，本文提出的旅游目的地预测模型具有更好的预测性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=When+Online+Meets+Offline:+Exploring+Periodicity+for+Travel+Destination+Prediction)|1| |[Modeling User Behavior With Interaction Networks for Spam Detection](https://doi.org/10.1145/3477495.3531875)|Prabhat Agarwal, Manisha Srivastava, Vishwakarma Singh, Charles Rosenberg|Pinterest, San Francisco, CA, USA|Spam is a serious problem plaguing web-scale digital platforms which facilitate user content creation and distribution. It compromises platform's integrity, performance of services like recommendation and search, and overall business. Spammers engage in a variety of abusive and evasive behavior which are distinct from non-spammers. Users' complex behavior can be well represented by a heterogeneous graph rich with node and edge attributes. Learning to identify spammers in such a graph for a web-scale platform is challenging because of its structural complexity and size. In this paper, we propose SEINE (Spam DEtection using Interaction NEtworks), a spam detection model over a novel graph framework. Our graph simultaneously captures rich users' details and behavior and enables learning on a billion-scale graph. Our model considers neighborhood along with edge types and attributes, allowing it to capture a wide range of spammers. SEINE, trained on a real dataset of tens of millions of nodes and billions of edges, achieves a high performance of 80% recall with 1% false positive rate. SEINE achieves comparable performance to the state-of-the-art techniques on a public dataset while being pragmatic to be used in a large-scale production system.|垃圾邮件是困扰网络规模的数字平台的一个严重问题，这些平台促进了用户内容的创建和分发。它损害了平台的完整性、推荐和搜索等服务的性能以及整体业务。垃圾邮件发送者与非垃圾邮件发送者有所不同，他们从事各种辱骂和回避行为。用户的复杂行为可以很好地用一个具有丰富节点和边属性的异构图来表示。由于其结构的复杂性和规模，学习在这样一个网络规模平台的图表中识别垃圾邮件发送者是具有挑战性的。在本文中，我们提出了 SEINE (使用交互网络的垃圾邮件检测) ，一个新的图形框架下的垃圾邮件检测模型。我们的图形同时捕捉丰富用户的细节和行为，并支持在十亿级图形上学习。我们的模型考虑了邻域以及边缘类型和属性，允许它捕获范围广泛的垃圾邮件发送者。SEINE 在数千万个节点和数十亿条边的真实数据集上进行训练，实现了80% 的高性能召回率和1% 的假阳性率。SEINE 在公共数据集上实现了与最先进技术相当的性能，同时在大规模生产系统中使用也很实用。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Modeling+User+Behavior+With+Interaction+Networks+for+Spam+Detection)|1| -|[ArchivalQA: A Large-scale Benchmark Dataset for Open-Domain Question Answering over Historical News Collections](https://doi.org/10.1145/3477495.3531734)|Jiexin Wang, Adam Jatowt, Masatoshi Yoshikawa|University of Innsbruck, Innsbruck, Austria; Kyoto University, Kyoto, Japan|In the last few years, open-domain question answering (ODQA) has advanced rapidly due to the development of deep learning techniques and the availability of large-scale QA datasets. However, the current datasets are essentially designed for synchronic document collections (e.g., Wikipedia). Temporal news collections such as long-term news archives spanning decades are rarely used in training the models despite they are quite valuable for our society. To foster the research in the field of ODQA on such historical collections, we present ArchivalQA, a large question answering dataset consisting of 532,444 question-answer pairs which is designed for temporal news QA. We divide our dataset into four subparts based on the question difficulty levels and the containment of temporal expressions, which we believe are useful for training and testing ODQA systems characterized by different strengths and abilities. The novel QA dataset-constructing framework that we introduce can be also applied to generate high-quality, non-ambiguous questions over other types of temporal document collections.|近年来，随着深度学习技术的发展和大规模问答数据集的出现，开放域问答技术得到了迅速的发展。然而，当前的数据集基本上是为同步文档集合而设计的(例如，Wikipedia)。时态新闻集合，如跨越数十年的长期新闻档案，很少用于训练模型，尽管它们对我们的社会有相当大的价值。为了促进 ODQA 领域对这些历史文献的研究，我们提出了 ArchivalQA，一个由532,444个问答对组成的大型问答数据集，它是为时事新闻 QA 而设计的。我们根据问题的难度水平和时间表达式的限制将数据集分为四个子部分，我们相信这对于训练和测试具有不同拥有属性和能力的 ODQA 系统是有用的。我们介绍的新的 QA 数据集构建框架也可以应用于生成高质量的、非歧义的问题，而不是其他类型的时态文档集合。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=ArchivalQA:+A+Large-scale+Benchmark+Dataset+for+Open-Domain+Question+Answering+over+Historical+News+Collections)|1| -|[Structure and Semantics Preserving Document Representations](https://doi.org/10.1145/3477495.3532062)|Natraj Raman, Sameena Shah, Manuela Veloso|J.P. Morgan AI Research, New York, NY, USA; J.P. Morgan AI Research, London, United Kingdom|Retrieving relevant documents from a corpus is typically based on the semantic similarity between the document content and query text. The inclusion of structural relationship between documents can benefit the retrieval mechanism by addressing semantic gaps. However, incorporating these relationships requires tractable mechanisms that balance structure with semantics and take advantage of the prevalent pre-train/fine-tune paradigm. We propose here a holistic approach to learning document representations by integrating intra-document content with inter-document relations. Our deep metric learning solution analyzes the complex neighborhood structure in the relationship network to efficiently sample similar/dissimilar document pairs and defines a novel quintuplet loss function that simultaneously encourages document pairs that are semantically relevant to be closer and structurally unrelated to be far apart in the representation space. Furthermore, the separation margins between the documents are varied flexibly to encode the heterogeneity in relationship strengths. The model is fully fine-tunable and natively supports query projection during inference. We demonstrate that it outperforms competing methods on multiple datasets for document retrieval tasks.|从语料库中检索相关文档通常是基于文档内容和查询文本之间的语义相似性。文档之间结构化关系的引入有利于解决语义空白，提高检索机制的效率。然而，合并这些关系需要易处理的机制，平衡结构与语义，并利用流行的预训练/微调范式的优势。在这里，我们提出了一种通过整合文档内容和文档间关系来学习文档表示的整体方法。我们的深度度量学习解决方案分析了关系网络中复杂的邻域结构，以有效地采样相似/不相似的文档对，并定义了一种新的五元组丢失函数，同时鼓励语义相关的文档对在表示空间中更加紧密，结构上不相关。此外，文档之间的分离边界可以灵活变化，以编码关系强度的异质性。该模型是完全可调的，并且在推理过程中本身支持查询投影。我们展示了它在多个数据集的文献检索任务中优于竞争方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Structure+and+Semantics+Preserving+Document+Representations)|1| +|[ArchivalQA: A Large-scale Benchmark Dataset for Open-Domain Question Answering over Historical News Collections](https://doi.org/10.1145/3477495.3531734)|Jiexin Wang, Adam Jatowt, Masatoshi Yoshikawa|Kyoto University, Kyoto, Japan; University of Innsbruck, Innsbruck, Austria|In the last few years, open-domain question answering (ODQA) has advanced rapidly due to the development of deep learning techniques and the availability of large-scale QA datasets. However, the current datasets are essentially designed for synchronic document collections (e.g., Wikipedia). Temporal news collections such as long-term news archives spanning decades are rarely used in training the models despite they are quite valuable for our society. To foster the research in the field of ODQA on such historical collections, we present ArchivalQA, a large question answering dataset consisting of 532,444 question-answer pairs which is designed for temporal news QA. We divide our dataset into four subparts based on the question difficulty levels and the containment of temporal expressions, which we believe are useful for training and testing ODQA systems characterized by different strengths and abilities. The novel QA dataset-constructing framework that we introduce can be also applied to generate high-quality, non-ambiguous questions over other types of temporal document collections.|近年来，随着深度学习技术的发展和大规模问答数据集的出现，开放域问答技术得到了迅速的发展。然而，当前的数据集基本上是为同步文档集合而设计的(例如，Wikipedia)。时态新闻集合，如跨越数十年的长期新闻档案，很少用于训练模型，尽管它们对我们的社会有相当大的价值。为了促进 ODQA 领域对这些历史文献的研究，我们提出了 ArchivalQA，一个由532,444个问答对组成的大型问答数据集，它是为时事新闻 QA 而设计的。我们根据问题的难度水平和时间表达式的限制将数据集分为四个子部分，我们相信这对于训练和测试具有不同拥有属性和能力的 ODQA 系统是有用的。我们介绍的新的 QA 数据集构建框架也可以应用于生成高质量的、非歧义的问题，而不是其他类型的时态文档集合。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=ArchivalQA:+A+Large-scale+Benchmark+Dataset+for+Open-Domain+Question+Answering+over+Historical+News+Collections)|1| +|[Structure and Semantics Preserving Document Representations](https://doi.org/10.1145/3477495.3532062)|Natraj Raman, Sameena Shah, Manuela Veloso|J.P. Morgan AI Research, London, United Kingdom; J.P. Morgan AI Research, New York, NY, USA|Retrieving relevant documents from a corpus is typically based on the semantic similarity between the document content and query text. The inclusion of structural relationship between documents can benefit the retrieval mechanism by addressing semantic gaps. However, incorporating these relationships requires tractable mechanisms that balance structure with semantics and take advantage of the prevalent pre-train/fine-tune paradigm. We propose here a holistic approach to learning document representations by integrating intra-document content with inter-document relations. Our deep metric learning solution analyzes the complex neighborhood structure in the relationship network to efficiently sample similar/dissimilar document pairs and defines a novel quintuplet loss function that simultaneously encourages document pairs that are semantically relevant to be closer and structurally unrelated to be far apart in the representation space. Furthermore, the separation margins between the documents are varied flexibly to encode the heterogeneity in relationship strengths. The model is fully fine-tunable and natively supports query projection during inference. We demonstrate that it outperforms competing methods on multiple datasets for document retrieval tasks.|从语料库中检索相关文档通常是基于文档内容和查询文本之间的语义相似性。文档之间结构化关系的引入有利于解决语义空白，提高检索机制的效率。然而，合并这些关系需要易处理的机制，平衡结构与语义，并利用流行的预训练/微调范式的优势。在这里，我们提出了一种通过整合文档内容和文档间关系来学习文档表示的整体方法。我们的深度度量学习解决方案分析了关系网络中复杂的邻域结构，以有效地采样相似/不相似的文档对，并定义了一种新的五元组丢失函数，同时鼓励语义相关的文档对在表示空间中更加紧密，结构上不相关。此外，文档之间的分离边界可以灵活变化，以编码关系强度的异质性。该模型是完全可调的，并且在推理过程中本身支持查询投影。我们展示了它在多个数据集的文献检索任务中优于竞争方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Structure+and+Semantics+Preserving+Document+Representations)|1| |[Aspect Feature Distillation and Enhancement Network for Aspect-based Sentiment Analysis](https://doi.org/10.1145/3477495.3531938)|Rui Liu, Jiahao Cao, Nannan Sun, Lei Jiang|Institute of Information Engineering, Chinese Academy of Sciences & University of Chinese Academy of Sciences, Beijing, China|Aspect-based sentiment analysis (ABSA) is a fine-grained sentiment analysis task designed to identify the polarity of a target aspect. Some works introduce various attention mechanisms to fully mine the relevant context words of different aspects, and use the traditional cross-entropy loss to fine-tune the models for the ABSA task. However, the attention mechanism paying partial attention to aspect-unrelated words inevitably introduces irrelevant noise. Moreover, the cross-entropy loss lacks discriminative learning of features, which makes it difficult to exploit the implicit information of intra-class compactness and inter-class separability. To overcome these challenges, we propose an Aspect Feature Distillation and Enhancement Network (AFDEN) for the ABSA task. We first propose a dual-feature extraction module to extract aspect-related and aspect-unrelated features through the attention mechanisms and graph convolutional networks. Then, to eliminate the interference of aspect-unrelated words, we design a novel aspect-feature distillation module containing a gradient reverse layer that learns aspect-unrelated contextual features through adversarial training, and an aspect-specific orthogonal projection layer to further project aspect-related features into the orthogonal space of aspect-unrelated features. Finally, we propose an aspect-feature enhancement module that leverages supervised contrastive learning to capture the implicit information between the same sentiment labels and between different sentiment labels. Experimental results on three public datasets demonstrate that our AFDEN model achieves state-of-the-art performance and verify the effectiveness and robustness of our model.|基于方面的情绪分析(ABSA)是一个细粒度的情绪分析任务，旨在识别目标方面的极性。一些工作引入了各种注意机制来充分挖掘不同方面的相关上下文词，并利用传统的交叉熵损失对 ABSA 任务的模型进行了微调。然而，部分关注体无关词的注意机制不可避免地会引入不相关的噪声。此外，交叉熵损失缺乏对特征的判别学习，这使得利用类内紧性和类间可分性的隐含信息变得困难。为了克服这些挑战，我们提出了一个面向特征提取和增强网络(AFDEN)的 ABSA 任务。首先提出一个双特征提取模块，通过注意机制和图卷积网络提取与方面相关和与方面无关的特征。然后，为了消除方面无关词的干扰，设计了一个新的方面特征提取模块，该模块包含一个梯度反向层，通过对抗性训练学习方面无关的上下文特征，以及一个方面特定的正交投影层，进一步将方面相关特征投影到方面无关特征的正交空间中。最后，我们提出了一个侧面特征增强模块，该模块利用监督对比学习来捕获相同情感标签之间和不同情感标签之间的隐含信息。在三个公共数据集上的实验结果表明，我们的 AFDEN 模型达到了最先进的性能，验证了模型的有效性和鲁棒性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Aspect+Feature+Distillation+and+Enhancement+Network+for+Aspect-based+Sentiment+Analysis)|1| |[Detecting Frozen Phrases in Open-Domain Question Answering](https://doi.org/10.1145/3477495.3531793)|Mostafa Yadegari, Ehsan Kamalloo, Davood Rafiei|University of Alberta, Edmonton, AB, Canada|There is essential information in the underlying structure of words and phrases in natural language questions, and this structure has been extensively studied. In this paper, we study one particular structure, referred to as frozen phrases, that is highly expected to transfer as a whole from questions to answer passages. Frozen phrases, if detected, can be helpful in open-domain Question Answering (QA) where identifying the localized context of a given input question is crucial. An interesting question is if frozen phrases can be accurately detected. We cast the problem as a sequence-labeling task and create synthetic data from existing QA datasets to train a model. We further plug this model into a sparse retriever that is made aware of the detected phrases. Our experiments reveal that detecting frozen phrases whose presence in answer documents are highly plausible yields significant improvements in retrievals as well as in the end-to-end accuracy of open-domain QA models.|自然语言问题中的词和短语的基本结构蕴含着重要的信息，这种结构已经得到了广泛的研究。在本文中，我们研究了一个特殊的结构，称为冻结短语，这是高度期望转移作为一个整体从问题，以回答段落。如果检测到冻结的短语，在开放域问答(QA)中可能会有所帮助，因为识别给定输入问题的本地化上下文是至关重要的。一个有趣的问题是，是否可以准确地检测到冻结的短语。我们将问题转换为序列标记任务，并从现有的 QA 数据集创建合成数据来训练模型。我们进一步将这个模型插入到一个稀疏的检索器中，这个检索器可以识别检测到的短语。我们的实验表明，检测在应答文档中出现的高度合理的冻结短语可以显著提高检索效率以及开放域 QA 模型的端到端准确性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Detecting+Frozen+Phrases+in+Open-Domain+Question+Answering)|1| |[Understanding User Satisfaction with Task-oriented Dialogue Systems](https://doi.org/10.1145/3477495.3531798)|Clemencia Siro, Mohammad Aliannejadi, Maarten de Rijke|University of Amsterdam, Amsterdam, Netherlands|\beginabstract \AcpDS are evaluated depending on their type and purpose. Two categories are often distinguished: \beginenumerate* \item \acpTDS, which are typically evaluated on utility, i.e., their ability to complete a specified task, and \item open-domain chat-bots, which are evaluated on the user experience, i.e., based on their ability to engage a person. \endenumerate* What is the influence of user experience on the user satisfaction rating of \acpTDS as opposed to, or in addition to, utility ? We collect data by providing an additional annotation layer for dialogues sampled from the ReDial dataset, a widely used conversational recommendation dataset. Unlike prior work, we annotate the sampled dialogues at both the turn and dialogue level on six dialogue aspects: relevance, interestingness, understanding, task completion, efficiency, and interest arousal. The annotations allow us to study how different dialogue aspects influence user satisfaction. We introduce a comprehensive set of user experience aspects derived from the annotators' open comments that can influence users' overall impression. We find that the concept of satisfaction varies across annotators and dialogues, and show that a relevant turn is significant for some annotators, while for others, an interesting turn is all they need. Our analysis indicates that the proposed user experience aspects provide a fine-grained analysis of user satisfaction that is not captured by a monolithic overall human rating. \endabstract|根据其类型和用途对 AcpDS 进行评估。通常区分为两类: 初始列举 * 项目 acpTDS，通常根据实用性进行评估，例如，它们完成指定任务的能力; 以及项目开放域聊天机器人，根据用户体验进行评估，例如，根据它们吸引人的能力进行评估。相对于效用，用户体验对 acpTDS 的用户满意度有什么影响？我们通过为从 ReDial 数据集(一个广泛使用的会话推荐数据集)采样的对话提供额外的注释层来收集数据。与之前的工作不同，我们在转向和对话两个层面对样本对话进行了注释: 相关性、趣味性、理解性、任务完成、效率和兴趣激发。注释允许我们研究不同的对话方面如何影响用户满意度。我们介绍了一套全面的用户体验方面来自注释者的开放评论，可以影响用户的整体印象。我们发现满意度的概念在不同的注释者和对话中是不同的，并且表明相关的转向对于一些注释者是重要的，而对于其他人，一个有趣的转向是他们所需要的。我们的分析表明，建议的用户体验方面提供了一个细粒度的用户满意度分析，而不是由单一的整体人类评分。结束摘要|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Understanding+User+Satisfaction+with+Task-oriented+Dialogue+Systems)|1| |[On Survivorship Bias in MS MARCO](https://doi.org/10.1145/3477495.3531832)|Prashansa Gupta, Sean MacAvaney|University of Glasgow, Glasgow, United Kingdom|Survivorship bias is the tendency to concentrate on the positive outcomes of a selection process and overlook the results that generate negative outcomes. We observe that this bias could be present in the popular MS MARCO dataset, given that annotators could not find answers to 38--45% of the queries, leading to these queries being discarded in training and evaluation processes. Although we find that some discarded queries in MS MARCO are ill-defined or otherwise unanswerable, many are valid questions that could be answered had the collection been annotated more completely (around two thirds using modern ranking techniques). This survivability problem distorts the MS MARCO collection in several ways. We find that it affects the natural distribution of queries in terms of the type of information needed. When used for evaluation, we find that the bias likely yields a significant distortion of the absolute performance scores observed. Finally, given that MS MARCO is frequently used for model training, we train models based on subsets of MS MARCO that simulates more survivorship bias. We find that models trained in this setting are up to 9.9% worse when evaluated on versions of the dataset with more complete annotations, and up to 3.5% worse at zero-shot transfer. Our findings are complementary to other recent suggestions for further annotation of MS MARCO, but with a focus on discarded queries.|倖存者偏差就是倾向于专注于选择过程的积极结果，而忽视产生消极结果的结果。我们观察到，这种偏见可能存在于流行的 MS MARCO 数据集中，因为注释者无法找到38-45% 的查询的答案，导致这些查询在培训和评估过程中被丢弃。尽管我们发现 MS MARCO 中的一些丢弃的查询是不明确的或者无法回答的，但是许多是有效的问题，如果集合被更完整地注释(使用现代排序技术约占三分之二) ，则可以回答这些问题。这个生存性问题在几个方面扭曲了 MS MARCO 集合。我们发现，根据所需信息的类型，它会影响查询的自然分布。当用于评估时，我们发现这种偏差很可能导致观察到的绝对绩效得分的显著扭曲。最后，鉴于微软 MARCO 经常被用于模型训练，我们基于微软 MARCO 的子集来训练模型，以模拟更多的倖存者偏差。我们发现，在这种情况下训练的模型在使用更完整注释的数据集版本进行评估时差异高达9.9% ，而在零镜头传输时差异高达3.5% 。我们的发现是对其他最近的建议进一步注释微软 MARCO 的补充，但重点放在丢弃的查询。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=On+Survivorship+Bias+in+MS+MARCO)|1| |[Bias Mitigation for Evidence-aware Fake News Detection by Causal Intervention](https://doi.org/10.1145/3477495.3531850)|Junfei Wu, Qiang Liu, Weizhi Xu, Shu Wu|Institute of Automation, Chinese Academy of Sciences & University of Chinese Academy of Sciences, Beijing, China|Evidence-based fake news detection is to judge the veracity of news against relevant evidences. However, models tend to memorize the dataset biases within spurious correlations between news patterns and veracity labels as shortcuts, rather than learning how to integrate the information behind them to reason. As a consequence, models may suffer from a serious failure when facing real-life conditions where most news has different patterns. Inspired by the success of causal inference, we propose a novel framework for debiasing evidence-based fake news detection\footnoteCode available at https://github.com/CRIPAC-DIG/CF-FEND by causal intervention. Under this framework, the model is first trained on the original biased dataset like ordinary work, then it makes conventional predictions and counterfactual predictions simultaneously in the testing stage, where counterfactual predictions are based on the intervened evidence. Relatively unbiased predictions are obtained by subtracting intervened outputs from the conventional ones. Extensive experiments conducted on several datasets demonstrate our method's effectiveness and generality on debiased datasets.|基于证据的假新闻检测就是根据相关证据来判断新闻的真实性。然而，模型倾向于记住新闻模式和准确性标签之间的虚假相关性中的数据集偏差作为快捷方式，而不是学习如何整合它们背后的信息进行推理。因此，模型可能会遭受严重的失败，当面对现实生活的情况下，大多数新闻有不同的模式。受到因果推理成功的启发，我们提出了一个新的框架，用于消除基于证据的假新闻检测脚注通过因果干预可以获得的 https://github.com/cripac-dig/cf-fend 代码。在此框架下，该模型首先像普通工作一样对原始偏差数据集进行训练，然后在测试阶段同时进行常规预测和反事实预测，反事实预测是基于介入的证据。相对无偏的预测是通过减去传统的干预输出得到的。在多个数据集上进行的大量实验证明了该方法在去偏数据集上的有效性和通用性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Bias+Mitigation+for+Evidence-aware+Fake+News+Detection+by+Causal+Intervention)|1| -|[Preference Enhanced Social Influence Modeling for Network-Aware Cascade Prediction](https://doi.org/10.1145/3477495.3532042)|Likang Wu, Hao Wang, Enhong Chen, Zhi Li, Hongke Zhao, Jianhui Ma|Tianjin University, Hefei, China; University of Science and Technology of China & State Key Laboratory of Cognitive Intelligence, Hefei, China|Network-aware cascade size prediction aims to predict the final reposted number of user-generated information via modeling the propagation process in social networks. Estimating the user's reposting probability by social influence, namely state activation plays an important role in the information diffusion process. Therefore, Graph Neural Networks (GNN), which can simulate the information interaction between nodes, has been proved as an effective scheme to handle this prediction task. However, existing studies including GNN-based models usually neglect a vital factor of user's preference which influences the state activation deeply. To that end, we propose a novel framework to promote cascade size prediction by enhancing the user preference modeling according to three stages, i.e., preference topics generation, preference shift modeling, and social influence activation. Our end-to-end method makes the user activating process of information diffusion more adaptive and accurate. Extensive experiments on two large-scale real-world datasets have clearly demonstrated the effectiveness of our proposed model compared to state-of-the-art baselines.|网络感知级联规模预测的目的是通过建立社交网络中的传播过程模型来预测用户生成信息的最终转发数量。通过社会影响即状态激活来估计用户的转发概率在信息传播过程中起着重要作用。因此，能够模拟节点间信息交互的图神经网络(GNN)已被证明是处理这一预测任务的有效方案。然而，现有的研究，包括基于 GNN 的模型，往往忽略了用户偏好的一个重要因素，这个因素对状态激活有着深刻的影响。为此，我们提出了一个新的框架来促进级联规模预测，通过增强用户偏好建模的三个阶段，即偏好主题的生成，偏好转移模型和社会影响激活。我们的端到端方法使得信息传播的用户激活过程更具适应性和准确性。在两个大规模真实世界数据集上的大量实验已经清楚地证明了我们提出的模型相对于最先进的基线的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Preference+Enhanced+Social+Influence+Modeling+for+Network-Aware+Cascade+Prediction)|1| +|[Preference Enhanced Social Influence Modeling for Network-Aware Cascade Prediction](https://doi.org/10.1145/3477495.3532042)|Likang Wu, Hao Wang, Enhong Chen, Zhi Li, Hongke Zhao, Jianhui Ma|University of Science and Technology of China & State Key Laboratory of Cognitive Intelligence, Hefei, China; Tianjin University, Hefei, China|Network-aware cascade size prediction aims to predict the final reposted number of user-generated information via modeling the propagation process in social networks. Estimating the user's reposting probability by social influence, namely state activation plays an important role in the information diffusion process. Therefore, Graph Neural Networks (GNN), which can simulate the information interaction between nodes, has been proved as an effective scheme to handle this prediction task. However, existing studies including GNN-based models usually neglect a vital factor of user's preference which influences the state activation deeply. To that end, we propose a novel framework to promote cascade size prediction by enhancing the user preference modeling according to three stages, i.e., preference topics generation, preference shift modeling, and social influence activation. Our end-to-end method makes the user activating process of information diffusion more adaptive and accurate. Extensive experiments on two large-scale real-world datasets have clearly demonstrated the effectiveness of our proposed model compared to state-of-the-art baselines.|网络感知级联规模预测的目的是通过建立社交网络中的传播过程模型来预测用户生成信息的最终转发数量。通过社会影响即状态激活来估计用户的转发概率在信息传播过程中起着重要作用。因此，能够模拟节点间信息交互的图神经网络(GNN)已被证明是处理这一预测任务的有效方案。然而，现有的研究，包括基于 GNN 的模型，往往忽略了用户偏好的一个重要因素，这个因素对状态激活有着深刻的影响。为此，我们提出了一个新的框架来促进级联规模预测，通过增强用户偏好建模的三个阶段，即偏好主题的生成，偏好转移模型和社会影响激活。我们的端到端方法使得信息传播的用户激活过程更具适应性和准确性。在两个大规模真实世界数据集上的大量实验已经清楚地证明了我们提出的模型相对于最先进的基线的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Preference+Enhanced+Social+Influence+Modeling+for+Network-Aware+Cascade+Prediction)|1| |[Users and Contemporary SERPs: A (Re-)Investigation](https://doi.org/10.1145/3477495.3531719)|Nirmal Roy, David Maxwell, Claudia Hauff|Delft University of Technology, Delft, Netherlands|TheSearch Engine Results Page (SERP) has evolved significantly over the last two decades, moving away from the simple ten blue links paradigm to considerably more complex presentations that contain results from multiple verticals and granularities of textual information. Prior works have investigated how user interactions on the SERP are influenced by the presence or absence of heterogeneous content (e.g., images, videos, or news content), the layout of the SERP (\emphlist vs. grid layout), and task complexity. In this paper, we reproduce the user studies conducted in prior works---specifically those of~\citetarguello2012task and~\citetsiu2014first ---to explore to what extent the findings from research conducted five to ten years ago still hold today as the average web user has become accustomed to SERPs with ever-increasing presentational complexity. To this end, we designed and ran a user study with four different SERP interfaces:(i) ~\empha heterogeneous grid ;(ii) ~\empha heterogeneous list ;(iii) ~\empha simple grid ; and(iv) ~\empha simple list. We collected the interactions of $41$ study participants over $12$ search tasks for our analyses. We observed that SERP types and task complexity affect user interactions with search results. We also find evidence to support most (6 out of 8) observations from~\citearguello2012task,siu2014first indicating that user interactions with different interfaces and to solve tasks of different complexity have remained mostly similar over time.|搜索引擎结果页面(SERP)在过去的二十年中发生了巨大的变化，从简单的十个蓝色链接范式转变为包含多个垂直结果和文本信息粒度的更复杂的表示。先前的工作已经研究了用户在 SERP 上的交互是如何受到异构内容(如图像、视频或新闻内容)、 SERP 布局(强调列表与网格布局)和任务复杂性的影响的。在这篇论文中，我们重现了在以前的研究中进行的用户研究——特别是那些 ~ citetguello2012task 和 ~ citetsiu2014first 的研究——来探索五到十年前的研究结果在多大程度上仍然适用于今天，因为普通的网络用户已经习惯了不断增加的表现复杂度的 SERP。为此，我们设计并运行了一个使用四种不同的 SERP 接口的用户研究: (i) ~ empa 异构网格; (ii) ~ empa 异构列表; (iii) ~ empa 简单网格; 和(iv) ~ empa 简单列表。我们收集了 $41 $研究参与者在 $12 $搜索任务中的交互作用，用于我们的分析。我们观察到 SERP 类型和任务复杂性影响用户与搜索结果的交互。我们还发现支持 ~ citearguello2012task，siu2014的大多数(8个中的6个)观察的证据首先表明，用户与不同界面的交互以及解决不同复杂度的任务随着时间的推移大多保持相似。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Users+and+Contemporary+SERPs:+A+(Re-)Investigation)|1| -|[ReMeDi: Resources for Multi-domain, Multi-service, Medical Dialogues](https://doi.org/10.1145/3477495.3531809)|Guojun Yan, Jiahuan Pei, Pengjie Ren, Zhaochun Ren, Xin Xin, Huasheng Liang, Maarten de Rijke, Zhumin Chen|Shandong University, Qingdao, China; WeChat Tencent, Qingdao, China; University of Amsterdam, Amsterdam, Netherlands|\AcpMDS aim to assist doctors and patients with a range of professional medical services, i.e., diagnosis, treatment and consultation. The development of \acpMDS is hindered because of a lack of resources. In particular. \beginenumerate* [label=(\arabic*) ] \item there is no dataset with large-scale medical dialogues that covers multiple medical services and contains fine-grained medical labels (i.e., intents, actions, slots, values), and \item there is no set of established benchmarks for \acpMDS for multi-domain, multi-service medical dialogues. \endenumerate* In this paper, we present \acsReMeDi, a set of \aclReMeDi \acusedReMeDi. ØurResources consists of two parts, the ØurResources dataset and the ØurResources benchmarks. The ØurResources dataset contains 96,965 conversations between doctors and patients, including 1,557 conversations with fine-gained labels. It covers 843 types of diseases, 5,228 medical entities, and 3 specialties of medical services across 40 domains. To the best of our knowledge, the ØurResources dataset is the only medical dialogue dataset that covers multiple domains and services, and has fine-grained medical labels. The second part of the ØurResources resources consists of a set of state-of-the-art models for (medical) dialogue generation. The ØurResources benchmark has the following methods: \beginenumerate* \item pretrained models (i.e., BERT-WWM, BERT-MED, GPT2, and MT5) trained, validated, and tested on the ØurResources dataset, and \item a \acfSCL method to expand the ØurResources dataset and enhance the training of the state-of-the-art pretrained models. \endenumerate* We describe the creation of the ØurResources dataset, the ØurResources benchmarking methods, and establish experimental results using the ØurResources benchmarking methods on the ØurResources dataset for future research to compare against. With this paper, we share the dataset, implementations of the benchmarks, and evaluation scripts.|该计划旨在为医生和病人提供一系列的专业医疗服务，包括诊断、治疗和咨询。由于缺乏资源，阻碍了 ACpMDS 的发展。尤其是。开始列举 * [ label = (阿拉伯语 *)]项目没有涵盖多种医疗服务并包含细粒度医疗标签(即意图，行动，插槽，价值)的大规模医疗对话的数据集，并且没有一套针对多领域，多服务医疗对话的 acpMDS 的既定基准。在本文中，我们介绍 acsReMeDi，一组 aclReMeDi acusedReMeDi。ØurResources 由两部分组成，ØurResources 数据集和 ØurResources 基准。ØurResources 的数据集包含96,965次医生与病人之间的对话，其中包括1557次带有罚款标签的对话。它涵盖了40个领域的843种疾病，5228个医疗实体和3个医疗服务专业。就我们所知，ØurResources 数据集是唯一一个涵盖多个领域和服务的医疗对话数据集，并具有细粒度的医疗标签。ØurResources 资源的第二部分包含一组用于(医疗)对话生成的最先进模型。ØurResources 基准有以下方法: 在 ØurResources 数据集上训练、验证和测试的 * 项目预训模型(即 BERT-WMM、 BERT-MED、 GPT2和 MT5) ，以及扩展 ØurResources 数据集和加强最先进的预训模型的训练的 acfSCL 方法。* 我们描述了 ØurResources 数据集的创建、 ØurResources 基准测试方法，并使用 ØurResources 基准测试方法建立了实验结果，以便将来的研究与之进行比较。在本文中，我们共享数据集、基准测试的实现和评估脚本。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=ReMeDi:+Resources+for+Multi-domain,+Multi-service,+Medical+Dialogues)|1| +|[ReMeDi: Resources for Multi-domain, Multi-service, Medical Dialogues](https://doi.org/10.1145/3477495.3531809)|Guojun Yan, Jiahuan Pei, Pengjie Ren, Zhaochun Ren, Xin Xin, Huasheng Liang, Maarten de Rijke, Zhumin Chen|WeChat Tencent, Qingdao, China; Shandong University, Qingdao, China; University of Amsterdam, Amsterdam, Netherlands|\AcpMDS aim to assist doctors and patients with a range of professional medical services, i.e., diagnosis, treatment and consultation. The development of \acpMDS is hindered because of a lack of resources. In particular. \beginenumerate* [label=(\arabic*) ] \item there is no dataset with large-scale medical dialogues that covers multiple medical services and contains fine-grained medical labels (i.e., intents, actions, slots, values), and \item there is no set of established benchmarks for \acpMDS for multi-domain, multi-service medical dialogues. \endenumerate* In this paper, we present \acsReMeDi, a set of \aclReMeDi \acusedReMeDi. ØurResources consists of two parts, the ØurResources dataset and the ØurResources benchmarks. The ØurResources dataset contains 96,965 conversations between doctors and patients, including 1,557 conversations with fine-gained labels. It covers 843 types of diseases, 5,228 medical entities, and 3 specialties of medical services across 40 domains. To the best of our knowledge, the ØurResources dataset is the only medical dialogue dataset that covers multiple domains and services, and has fine-grained medical labels. The second part of the ØurResources resources consists of a set of state-of-the-art models for (medical) dialogue generation. The ØurResources benchmark has the following methods: \beginenumerate* \item pretrained models (i.e., BERT-WWM, BERT-MED, GPT2, and MT5) trained, validated, and tested on the ØurResources dataset, and \item a \acfSCL method to expand the ØurResources dataset and enhance the training of the state-of-the-art pretrained models. \endenumerate* We describe the creation of the ØurResources dataset, the ØurResources benchmarking methods, and establish experimental results using the ØurResources benchmarking methods on the ØurResources dataset for future research to compare against. With this paper, we share the dataset, implementations of the benchmarks, and evaluation scripts.|该计划旨在为医生和病人提供一系列的专业医疗服务，包括诊断、治疗和咨询。由于缺乏资源，阻碍了 ACpMDS 的发展。尤其是。开始列举 * [ label = (阿拉伯语 *)]项目没有涵盖多种医疗服务并包含细粒度医疗标签(即意图，行动，插槽，价值)的大规模医疗对话的数据集，并且没有一套针对多领域，多服务医疗对话的 acpMDS 的既定基准。在本文中，我们介绍 acsReMeDi，一组 aclReMeDi acusedReMeDi。ØurResources 由两部分组成，ØurResources 数据集和 ØurResources 基准。ØurResources 的数据集包含96,965次医生与病人之间的对话，其中包括1557次带有罚款标签的对话。它涵盖了40个领域的843种疾病，5228个医疗实体和3个医疗服务专业。就我们所知，ØurResources 数据集是唯一一个涵盖多个领域和服务的医疗对话数据集，并具有细粒度的医疗标签。ØurResources 资源的第二部分包含一组用于(医疗)对话生成的最先进模型。ØurResources 基准有以下方法: 在 ØurResources 数据集上训练、验证和测试的 * 项目预训模型(即 BERT-WMM、 BERT-MED、 GPT2和 MT5) ，以及扩展 ØurResources 数据集和加强最先进的预训模型的训练的 acfSCL 方法。* 我们描述了 ØurResources 数据集的创建、 ØurResources 基准测试方法，并使用 ØurResources 基准测试方法建立了实验结果，以便将来的研究与之进行比较。在本文中，我们共享数据集、基准测试的实现和评估脚本。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=ReMeDi:+Resources+for+Multi-domain,+Multi-service,+Medical+Dialogues)|1| |[Online DATEing: A Web Interface for Temporal Annotations](https://doi.org/10.1145/3477495.3531670)|Dennis Aumiller, Satya Almasian, David Pohl, Michael Gertz|Heidelberg University, Heidelberg, Germany|Despite more than two decades of research on temporal tagging and temporal relation extraction, usable tools for annotating text remain very basic and hard to set up from an average end-user perspective, limiting the applicability of developments to a selected group of invested researchers. In this work, we aim to increase the accessibility of temporal tagging systems by presenting an intuitive web interface, called "Online DATEing", which simplifies the interaction with existing temporal annotation frameworks. Our system integrates several approaches in a single interface and streamlines the process of importing (and tagging) groups of documents, as well as making it accessible through a programmatic API. It further enables users to interactively investigate and visualize tagged texts, and is designed with an extensible API for the inclusion of new models or data formats. A web demonstration of our tool is available at https://onlinedating.ifi.uni-heidelberg.de and public code accessible at https://github.com/satya77/Temporal_Tagger_Service.|尽管在时间标签和时间关系提取方面进行了二十多年的研究，但用于文本注释的可用工具仍然非常基础，从一般最终用户的角度来看很难建立起来，这限制了开发的适用性，使其只能适用于一组经过投资的研究人员。在这项工作中，我们的目标是通过提出一个直观的网络界面，称为“在线 DATEing”，简化与现有的时态标注框架的交互，提高时态标注系统的可访问性。我们的系统在单个接口中集成了多种方法，并简化了导入(和标记)文档组的过程，同时还通过编程 API 使其可访问。它进一步使用户能够交互式地调查和可视化标记的文本，并且设计了一个可扩展的 API，用于包含新的模型或数据格式。我们的工具的网页演示可在 https://onlinedating.ifi.uni-heidelberg.de 下载，公众代码亦可在 https://github.com/satya77/temporal_tagger_service 下载。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Online+DATEing:+A+Web+Interface+for+Temporal+Annotations)|1| |[Few-shot Node Classification on Attributed Networks with Graph Meta-learning](https://doi.org/10.1145/3477495.3531978)|Yonghao Liu, Mengyu Li, Ximing Li, Fausto Giunchiglia, Xiaoyue Feng, Renchu Guan|University of Trento, Trento, Italy; Jilin University, Changchun, China|Attributed networks, as a manifestation of data in non-Euclidean domains, have a wide range of applications in the real world, such as molecular property prediction, social network analysis and anomaly detection. Node classification, as a fundamental research problem in attributed networks, has attracted increasing attention among research communities. However, most existing models cannot be directly applied to the data with limited labeled instances (\textiti.e., the few-shot scenario). Few-shot node classification on attributed networks is gradually becoming a research hotspot. Although several methods aim to integrate meta-learning with graph neural networks to address this problem, some limitations remain. First, they all assume node representation learning using graph neural networks in homophilic graphs. %Hence, suboptimal performance is obtained when these models are applied to heterophilic graphs. Second, existing models based on meta-learning entirely depend on instance-based statistics. %which in few-shot settings are unavoidably degraded by data noise or outliers. Third, most previous models treat all sampled tasks equally and fail to adapt their uniqueness. %which has a significant impact on the overall performance of the model. To solve the above three limitations, we propose a novel graph Meta -learning framework called G raph learning based on P rototype and S caling & shifting transformation (Meta-GPS ). More specifically, we introduce an efficient method for learning expressive node representations even on heterophilic graphs and propose utilizing a prototype-based approach to initialize parameters in meta-learning. Moreover, we also leverage S$^2$ (scaling & shifting) transformation to learn effective transferable knowledge from diverse tasks. Extensive experimental results on six real-world datasets demonstrate the superiority of our proposed framework, which outperforms other state-of-the-art baselines by up to 13% absolute improvement in terms of related metrics.|属性网络作为非欧几里德领域数据的表现形式，在现实世界中有着广泛的应用，如分子特性预测、社会网络分析和异常检测分析。节点分类作为属性网络的一个基础研究问题，越来越受到研究界的重视。然而，大多数现有的模型不能直接应用于带有有限标签的实例的数据(textti.e.a few-shot 场景)。基于属性网络的少镜头节点分类正逐渐成为研究热点。虽然有几种方法旨在将元学习与图神经网络相结合来解决这个问题，但仍然存在一些局限性。首先，它们都假定在同态图中使用图神经网络进行节点表示学习。因此，当这些模型应用于异质图时，得到了次优的性能。其次，现有的基于元学习的模型完全依赖于基于实例的统计。在少镜头设置中，不可避免地会受到数据噪声或异常值的影响。第三，大多数以前的模型对所有抽样任务一视同仁，不能适应它们的唯一性。% ，这对模型的整体性能有重大影响。为了解决这三个问题，提出了一种新的图元学习框架——基于 P 原型和 S 标定与移位变换的 G 图学习(Meta-GPS)。更具体地说，我们介绍了一种有效的学习表达式节点表示的方法，甚至在异质图上，并提出了利用基于原型的方法来初始化元学习中的参数。此外，我们还利用 S $^ 2 $(缩放和转移)转换来学习不同任务中有效的可转移知识。在六个真实世界数据集上的大量实验结果证明了我们提出的框架的优越性，它在相关指标方面比其他最先进的基线表现出高达13% 的绝对改进。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Few-shot+Node+Classification+on+Attributed+Networks+with+Graph+Meta-learning)|1| |[Co-clustering Interactions via Attentive Hypergraph Neural Network](https://doi.org/10.1145/3477495.3531868)|Tianchi Yang, Cheng Yang, Luhao Zhang, Chuan Shi, Maodi Hu, Huaijun Liu, Tao Li, Dong Wang|Beijing University of Posts and Telecommunications, Beijing, China; Meituan, Beijing, China|With the rapid growth of interaction data, many clustering methods have been proposed to discover interaction patterns as prior knowledge beneficial to downstream tasks. Considering that an interaction can be seen as an action occurring among multiple objects, most existing methods model the objects and their pair-wise relations as nodes and links in graphs. However, they only model and leverage part of the information in real entire interactions, i.e., either decompose the entire interaction into several pair-wise sub-interactions for simplification, or only focus on clustering some specific types of objects, which limits the performance and explainability of clustering. To tackle this issue, we propose to Co-cluster the Interactions via Attentive Hypergraph neural network (CIAH). Particularly, with more comprehensive modeling of interactions by hypergraph, we propose an attentive hypergraph neural network to encode the entire interactions, where an attention mechanism is utilized to select important attributes for explanations. Then, we introduce a salient method to guide the attention to be more consistent with real importance of attributes, namely saliency-based consistency. Moreover, we propose a novel co-clustering method to perform a joint clustering for the representations of interactions and the corresponding distributions of attribute selection, namely cluster-based consistency. Extensive experiments demonstrate that our CIAH significantly outperforms state-of-the-art clustering methods on both public datasets and real industrial datasets.|随着交互数据的快速增长，人们提出了许多聚类方法来发现交互模式作为有利于下游任务的先验知识。考虑到一个交互可以看作是多个对象之间的一个动作，现有的方法大多将对象及其成对关系建模为图中的节点和链接。然而，他们只是在真实的整个交互中对部分信息进行建模和利用，也就是说，要么将整个交互分解为几个成对的子交互以进行简化，要么只关注某些特定类型的对象的聚类，这限制了聚类的性能和可解释性。为了解决这一问题，我们提出了通过注意超图神经网络(CIAH)对相互作用进行共聚类。特别是，随着超图对交互作用建模的不断深入，我们提出了一种注意超图神经网络对整个交互作用进行编码，利用注意机制选择重要属性进行解释。然后，我们介绍了一种引导注意与属性的真实重要性更加一致的显著方法，即基于显著性的一致性。此外，我们提出了一种新的共聚类方法，即基于聚类的一致性，对交互作用的表示和相应的属性选择分布进行联合聚类。大量的实验表明，我们的 CIAH 在公共数据集和实际工业数据集上都显著优于最先进的聚类方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Co-clustering+Interactions+via+Attentive+Hypergraph+Neural+Network)|1| |[Mutual Disentanglement Learning for Joint Fine-Grained Sentiment Classification and Controllable Text Generation](https://doi.org/10.1145/3477495.3532029)|Hao Fei, Chenliang Li, Donghong Ji, Fei Li|Wuhan University, Wuhan, China|Fine-grained sentiment classification (FGSC) task and fine-grained controllable text generation (FGSG) task are two representative applications of sentiment analysis, two of which together can actually form an inverse task prediction, i.e., the former aims to infer the fine-grained sentiment polarities given a text piece, while the latter generates text content that describes the input fine-grained opinions. Most of the existing work solves the FGSC and the FGSG tasks in isolation, while ignoring the complementary benefits in between. This paper combines FGSC and FGSG as a joint dual learning system, encouraging them to learn the advantages from each other. Based on the dual learning framework, we further propose decoupling the feature representations in two tasks into fine-grained aspect-oriented opinion variables and content variables respectively, by performing mutual disentanglement learning upon them. We also propose to transform the difficult "data-to-text'' generation fashion widely used in FGSG into an easier text-to-text generation fashion by creating surrogate natural language text as the model inputs. Experimental results on 7 sentiment analysis benchmarks including both the document-level and sentence-level datasets show that our method significantly outperforms the current strong-performing baselines on both the FGSC and FGSG tasks. Automatic and human evaluations demonstrate that our FGSG model successfully generates fluent, diverse and rich content conditioned on fine-grained sentiments.|细粒度情绪分类任务(FGSC)和细粒度可控文本生成任务(FGSG)是情绪分析的两个具有代表性的应用，两者结合起来实际上可以形成一个反向的任务预测，即前者旨在推断给定文本片段的细粒度情绪极性，而后者生成描述输入细粒度意见的文本内容。现有的大多数工作是孤立地解决 FGSC 和 FGSG 任务，而忽略了它们之间的互补性。本文将 FGSC 和 FGSG 作为一个联合的双学习系统结合起来，鼓励它们相互学习对方的优势。在对偶学习框架的基础上，进一步提出将两个任务的特征表示分别解耦为细粒度面向方面的观点变量和内容变量，并对它们进行相互解缠学习。我们还建议通过创建替代自然语言文本作为模型输入，将 FGSG 中广泛使用的困难的“数据到文本”生成方式转换为更容易的文本到文本生成方式。对包括文档级和句子级数据集在内的7个情绪分析基准的实验结果表明，我们的方法在 FGSC 和 FGSG 任务上都显著优于目前表现强劲的基准。自动和人工评估表明，我们的 FGSG 模型成功地产生流畅，多样和丰富的内容条件下细粒度的情感。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Mutual+Disentanglement+Learning+for+Joint+Fine-Grained+Sentiment+Classification+and+Controllable+Text+Generation)|1| |[Learning Disentangled Representations for Counterfactual Regression via Mutual Information Minimization](https://doi.org/10.1145/3477495.3532011)|Mingyuan Cheng, Xinru Liao, Quan Liu, Bin Ma, Jian Xu, Bo Zheng|Alibaba Group, Beijing, China; Alibaba Group, Hangzhou, China|Learning individual-level treatment effect is a fundamental problem in causal inference and has received increasing attention in many areas, especially in the user growth area which concerns many internet companies. Recently, disentangled representation learning methods that decompose covariates into three latent factors, including instrumental, confounding and adjustment factors, have witnessed great success in treatment effect estimation. However, it remains an open problem how to learn the underlying disentangled factors precisely. Specifically, previous methods fail to obtain independent disentangled factors, which is a necessary condition for identifying treatment effect. In this paper, we propose Disentangled Representations for Counterfactual Regression via Mutual Information Minimization (MIM-DRCFR), which uses a multi-task learning framework to share information when learning the latent factors and incorporates MI minimization learning criteria to ensure the independence of these factors. Extensive experiments including public benchmarks and real-world industrial user growth datasets demonstrate that our method performs much better than state-of-the-art methods.|个体水平治疗效应的学习是因果推理中的一个基本问题，在许多领域，特别是在涉及到许多互联网公司的用户增长领域受到越来越多的关注。近年来，将协变量分解为工具因素、混杂因素和调整因素的分离表征学习方法在评估治疗效果方面取得了很大的成功。然而，如何准确地认识这些潜在的分离因素仍然是一个悬而未决的问题。具体来说，以往的方法不能获得独立的解缠因子，这是确定治疗效果的必要条件。本文提出了基于互信息最小化的反事实回归分离表示方法(MIM-DRCFR) ，该方法采用多任务学习框架，在学习潜在因素时共享信息，并结合 MI 最小化学习准则，保证了这些因素的独立性。包括公共基准和真实世界工业用户增长数据集在内的大量实验表明，我们的方法比最先进的方法表现得更好。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Learning+Disentangled+Representations+for+Counterfactual+Regression+via+Mutual+Information+Minimization)|1| -|[L3E-HD: A Framework Enabling Efficient Ensemble in High-Dimensional Space for Language Tasks](https://doi.org/10.1145/3477495.3531761)|Fangxin Liu, Haomin Li, Xiaokang Yang, Li Jiang|Shanghai Jiao Tong University, Shanghai , China; Tianjin University, Tianjin, China; Shanghai Jiao Tong University, Shanghai, China|Brain-inspired hyperdimensional computing (HDC) has been introduced as an alternative computing paradigm to achieve efficient and robust learning. HDC simulates cognitive tasks by mapping all data points to patterns of neural activity in the high-dimensional space, which has demonstrated promising performances in a wide range of applications such as robotics, biomedical signal processing, and genome sequencing. Language tasks, generally solved using machine learning methods, are widely deployed on low-power embedded devices. However, existing HDC solutions suffer from major challenges that impede the deployment of low-power embedded devices: the storage and computation overhead of HDC models grows dramatically with (i) the number of dimensions and (ii) the complex similarity metric during the inference. In this paper, we proposed a novel ensemble framework for the language task, termed L3E-HD, which enables efficient HDC on low-power edge devices. L3E-HD accelerates the inference by mapping data points to a high-dimensional binary space to simplify similarity search, which dominates costly and frequent operation in HDC. Through marrying HDC with the ensemble technique, L3E-HD also addresses the severe accuracy degradation induced by the compression of the dimension and precision of the model. Our experiments show that the ensemble technique is naturally a perfect fit to boost HDCs. We find that our L3E-HD, which is faster, more efficient, and more accurate than conventional machine learning methods, can even surpass the accuracy of the full-precision model at a smaller model size. Code is released at: https://github.com/MXHX7199/SIGIR22-EnsembleHDC.|大脑启发的高维计算(HDC)已被引入作为一种替代计算范式，以实现有效和健壮的学习。HDC 通过将所有数据点映射到高维空间中的神经活动模式来模拟认知任务，这已经在机器人、生医信号处理和基因组测序等广泛的应用中展示了有前途的性能。语言任务通常采用机器学习方法来解决，在低功耗嵌入式设备上得到了广泛的应用。然而，现有的 HDC 解决方案面临着阻碍低功耗嵌入式设备部署的主要挑战: HDC 模型的存储和计算开销随着(i)维数和(ii)推断期间复杂的相似度量急剧增长。在本文中，我们提出了一个新的语言任务集成框架，称为 L3E-HD，它可以在低功耗边缘设备上实现有效的 HDC。通过将数据点映射到一个高维的二进制空间来简化最近邻搜索，L3E-HD 加快了推理速度。通过将 HDC 与集成技术相结合，L3E-HD 还解决了由于模型尺寸和精度的压缩而引起的精度严重下降的问题。我们的实验表明，集成技术自然是一个完美的适合增强 HDC。我们发现我们的 L3E-HD 比传统的机器学习方法更快、更有效、更精确，甚至可以在更小的模型尺寸下超过全精度模型的精度。密码发布于: https://github.com/mxhx7199/sigir22-ensemblehdc。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=L3E-HD:+A+Framework+Enabling+Efficient+Ensemble+in+High-Dimensional+Space+for+Language+Tasks)|1| +|[L3E-HD: A Framework Enabling Efficient Ensemble in High-Dimensional Space for Language Tasks](https://doi.org/10.1145/3477495.3531761)|Fangxin Liu, Haomin Li, Xiaokang Yang, Li Jiang|Tianjin University, Tianjin, China; Shanghai Jiao Tong University, Shanghai, China; Shanghai Jiao Tong University, Shanghai , China|Brain-inspired hyperdimensional computing (HDC) has been introduced as an alternative computing paradigm to achieve efficient and robust learning. HDC simulates cognitive tasks by mapping all data points to patterns of neural activity in the high-dimensional space, which has demonstrated promising performances in a wide range of applications such as robotics, biomedical signal processing, and genome sequencing. Language tasks, generally solved using machine learning methods, are widely deployed on low-power embedded devices. However, existing HDC solutions suffer from major challenges that impede the deployment of low-power embedded devices: the storage and computation overhead of HDC models grows dramatically with (i) the number of dimensions and (ii) the complex similarity metric during the inference. In this paper, we proposed a novel ensemble framework for the language task, termed L3E-HD, which enables efficient HDC on low-power edge devices. L3E-HD accelerates the inference by mapping data points to a high-dimensional binary space to simplify similarity search, which dominates costly and frequent operation in HDC. Through marrying HDC with the ensemble technique, L3E-HD also addresses the severe accuracy degradation induced by the compression of the dimension and precision of the model. Our experiments show that the ensemble technique is naturally a perfect fit to boost HDCs. We find that our L3E-HD, which is faster, more efficient, and more accurate than conventional machine learning methods, can even surpass the accuracy of the full-precision model at a smaller model size. Code is released at: https://github.com/MXHX7199/SIGIR22-EnsembleHDC.|大脑启发的高维计算(HDC)已被引入作为一种替代计算范式，以实现有效和健壮的学习。HDC 通过将所有数据点映射到高维空间中的神经活动模式来模拟认知任务，这已经在机器人、生医信号处理和基因组测序等广泛的应用中展示了有前途的性能。语言任务通常采用机器学习方法来解决，在低功耗嵌入式设备上得到了广泛的应用。然而，现有的 HDC 解决方案面临着阻碍低功耗嵌入式设备部署的主要挑战: HDC 模型的存储和计算开销随着(i)维数和(ii)推断期间复杂的相似度量急剧增长。在本文中，我们提出了一个新的语言任务集成框架，称为 L3E-HD，它可以在低功耗边缘设备上实现有效的 HDC。通过将数据点映射到一个高维的二进制空间来简化最近邻搜索，L3E-HD 加快了推理速度。通过将 HDC 与集成技术相结合，L3E-HD 还解决了由于模型尺寸和精度的压缩而引起的精度严重下降的问题。我们的实验表明，集成技术自然是一个完美的适合增强 HDC。我们发现我们的 L3E-HD 比传统的机器学习方法更快、更有效、更精确，甚至可以在更小的模型尺寸下超过全精度模型的精度。密码发布于: https://github.com/mxhx7199/sigir22-ensemblehdc。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=L3E-HD:+A+Framework+Enabling+Efficient+Ensemble+in+High-Dimensional+Space+for+Language+Tasks)|1| |[Graph Capsule Network with a Dual Adaptive Mechanism](https://doi.org/10.1145/3477495.3531764)|Xiangping Zheng, Xun Liang, Bo Wu, Yuhui Guo, Xuan Zhang|Renmin University of China, Beijing, China|While Graph Convolutional Networks (GCNs) have been extended to various fields of artificial intelligence with their powerful representation capabilities, recent studies have revealed that their ability to capture the part-whole structure of the graph is limited. Furthermore, though many GCNs variants have been proposed and obtained state-of-the-art results, they face the situation that much early information may be lost during the graph convolution step. To this end, we innovatively present an Graph Capsule Network with a Dual Adaptive Mechanism (DA-GCN) to tackle the above challenges. Specifically, this powerful mechanism is a dual-adaptive mechanism to capture the part-whole structure of the graph. One is an adaptive node interaction module to explore the potential relationship between interactive nodes. The other is an adaptive attention-based graph dynamic routing to select appropriate graph capsules, so that only favorable graph capsules are gathered and redundant graph capsules are restrained for better capturing the whole structure between graphs. Experiments demonstrate that our proposed algorithm has achieved the most advanced or competitive results on all datasets.|虽然图卷积网络(GCNs)以其强大的表示能力已经扩展到人工智能的各个领域，但最近的研究表明，它们捕获图的部分-整体结构的能力是有限的。此外，虽然已经提出了许多 GCNs 变体，并获得了最先进的结果，但是它们面临的情况是，在图卷积步骤中，许多早期信息可能丢失。为此，我们创新性地提出了一个具有双重适应机制的图胶囊网络(DA-GCN)来应对上述挑战。具体来说，这种强大的机制是一种双自适应机制，用于捕获图的部分-整体结构。一种是自适应节点交互模块，用于探索交互节点之间的潜在关系。另一种是基于自适应注意的图动态路由选择合适的图胶囊，从而只收集有利的图胶囊，抑制冗余的图胶囊，以便更好地捕捉图间的整体结构。实验结果表明，本文提出的算法在所有数据集上都取得了最先进或最有竞争力的结果。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Graph+Capsule+Network+with+a+Dual+Adaptive+Mechanism)|1| |[Training Entire-Space Models for Target-oriented Opinion Words Extraction](https://doi.org/10.1145/3477495.3531768)|Yuncong Li, Fang Wang, ShengHua Zhong|Shenzhen University, Shenzhen, China; Tencent Inc, Shenzhen, China|Target-oriented opinion words extraction (TOWE) is a subtask of aspect-based sentiment analysis (ABSA). Given a sentence and an aspect term occurring in the sentence, TOWE extracts the corresponding opinion words for the aspect term. TOWE has two types of instance. In the first type, aspect terms are associated with at least one opinion word, while in the second type, aspect terms do not have corresponding opinion words. However, previous researches trained and evaluated their models with only the first type of instance, resulting in a sample selection bias problem. Specifically, TOWE models were trained with only the first type of instance, while these models would be utilized to make inference on the entire space with both the first type of instance and the second type of instance. Thus, the generalization performance will be hurt. Moreover, the performance of these models on the first type of instance cannot reflect their performance on entire space. To validate the sample selection bias problem, four popular TOWE datasets containing only aspect terms associated with at least one opinion word are extended and additionally include aspect terms without corresponding opinion words. Experimental results on these datasets show that training TOWE models on entire space will significantly improve model performance and evaluating TOWE models only on the first type of instance will overestimate model performance.|面向目标的意见词提取(TOWE)是基于方面的情感分析(ABSA)的一个子任务。给定一个句子和一个体项出现在句子中，TOWE 提取相应的体项意见词。TOWE 有两种类型的实例。在第一种类型中，体词至少与一个意见词相关联，而在第二种类型中，体词没有相应的意见词。然而，以往的研究仅用第一类实例对模型进行训练和评价，导致样本选择偏差问题。具体来说，TOWE 模型只用第一种类型的实例进行训练，而这些模型将被用来对整个空间进行第一种类型的实例和第二种类型的实例的推断。因此，泛化性能将受到影响。此外，这些模型在第一类实例上的性能不能反映它们在整个空间上的性能。为了验证样本选择偏差问题，扩展了四个常用的 TOWE 数据集，其中只包含与至少一个意见词相关的方面词，并且还包含了没有相应意见词的方面词。在这些数据集上的实验结果表明，在整个空间上训练 TOWE 模型将显著提高模型的性能，只有在第一类实例上评估 TOWE 模型才会高估模型的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Training+Entire-Space+Models+for+Target-oriented+Opinion+Words+Extraction)|1| |[Point Prompt Tuning for Temporally Language Grounding](https://doi.org/10.1145/3477495.3531795)|Yawen Zeng|Tencent Inc., Shenzhen, China|The task of temporally language grounding (TLG) aims to locate a video moment from an untrimmed video that match a given textual query, which has attracted considerable research attention. In recent years, typical retrieval-based TLG methods are inefficient due to pre-segmented candidate moments, while localization-based TLG solutions adopt reinforcement learning resulting in unstable convergence. Therefore, how to perform TLG task efficiently and stably is a non-trivial work. Toward this end, we innovatively contribute a solution, Point Prompt Tuning (PPT), which formulates this task as a prompt-based multi-modal problem and integrates multiple sub-tasks to tuning performance. Specifically, a flexible prompt strategy is contributed to rewrite the query firstly, which contains both query, start point and end point. Thereafter, a multi-modal Transformer is adopted to fully learn the multi-modal context. Meanwhile, we design various sub-tasks to constrain the novel framework, namely matching task and localization task. Finally, the start and end points of matched video moment are straightforward predicted, simply yet stably. Extensive experiments on two real-world datasets have well verified the effectiveness of our proposed solution.|时间语言接地任务(TLG)是从未经修剪的视频中定位匹配给定文本查询的视频片段，已经引起了相当多的研究关注。近年来，典型的基于检索的 TLG 方法由于预分割候选矩而效率低下，而基于定位的 TLG 解决方案则采用强化学习，导致收敛不稳定。因此，如何高效、稳定地完成 TLG 任务是一项非常重要的工作。为此，我们创新性地提供了一个解决方案，即 Point Prompt Tuning (PPT) ，它将此任务描述为一个基于提示的多模态问题，并将多个子任务集成到性能调优中。提出了一种灵活的提示策略，首先对查询进行重写，包括查询、起始点和终结点。此后，采用多模态变压器，以充分了解多模态背景。同时，我们设计了各种子任务来约束新的框架，即匹配任务和定位任务。最后，对匹配视频时刻的起始点和终止点进行了简单而稳定的预测。在两个实际数据集上的大量实验已经很好地验证了我们提出的解决方案的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Point+Prompt+Tuning+for+Temporally+Language+Grounding)|1| |[What Makes a Good Podcast Summary?](https://doi.org/10.1145/3477495.3531802)|Rezvaneh Rezapour, Sravana Reddy, Rosie Jones, Ian Soboroff|Spotify, Boston, MA, USA; ASAPP, New York, NY, USA; Drexel University, Philadelphia, PA, USA; NIST, Gaithersburg, MD, USA|Abstractive summarization of podcasts is motivated by the growing popularity of podcasts and the needs of their listeners. Podcasting is a markedly different domain from news and other media that are commonly studied in the context of automatic summarization. As such, the qualities of a good podcast summary are yet unknown. Using a collection of podcast summaries produced by different algorithms alongside human judgments of summary quality obtained from the TREC 2020 Podcasts Track, we study the correlations between various automatic evaluation metrics and human judgments, as well as the linguistic aspects of summaries that result in strong evaluations.|播客的抽象摘要是受到日益流行的播客和听众需求的推动。播客是一个明显不同的领域，从新闻和其他媒体，通常研究的背景下的自动汇总。因此，一个好的播客总结的质量还是未知数。使用由不同算法产生的播客摘要集合以及从 TREC 2020播客跟踪获得的总结质量的人类判断，我们研究各种自动评估指标和人类判断之间的相关性，以及导致强烈评估的总结的语言方面。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=What+Makes+a+Good+Podcast+Summary?)|1| -|[BSAL: A Framework of Bi-component Structure and Attribute Learning for Link Prediction](https://doi.org/10.1145/3477495.3531804)|Bisheng Li, Min Zhou, Shengzhong Zhang, Menglin Yang, Defu Lian, Zengfeng Huang|Fudan University, Shanghai, China; The Chinese University of Hong Kong, Hong Kong, China; University of Science and Technology of China, Hefei, China; Huawei Noah's Ark Lab, Shenzhen, China|Given the ubiquitous existence of graph-structured data, learning the representations of nodes for the downstream tasks ranging from node classification, link prediction to graph classification is of crucial importance. Regarding missing link inference of diverse networks, we revisit the link prediction techniques and identify the importance of both the structural and attribute information. However, the available techniques either heavily count on the network topology which is spurious in practice, or cannot integrate graph topology and features properly. To bridge the gap, we propose a bicomponent structural and attribute learning framework (BSAL) that is designed to adaptively leverage information from topology and feature spaces. Specifically, BSAL constructs a semantic topology via the node attributes and then gets the embeddings regarding the semantic view, which provides a flexible and easy-to-implement solution to adaptively incorporate the information carried by the node attributes. Then the semantic embedding together with topology embedding are fused together using attention mechanism for the final prediction. Extensive experiments show the superior performance of our proposal and it significantly outperforms baselines on diverse research benchmarks.|由于图结构数据的普遍存在，学习下游任务的节点表示，从节点分类、链路预测到图分类，都是至关重要的。关于不同网络的缺失链接推断，我们重新审视了链接预测技术，并确定了结构和属性信息的重要性。然而，现有的技术要么严重依赖于在实践中虚假的网络拓扑，要么不能恰当地整合图形拓扑和特性。为了弥补这一差距，我们提出了一个双组件结构和属性学习框架(BSAL) ，该框架旨在自适应地利用拓扑和特征空间中的信息。具体来说，BSAL 通过节点属性构造一个语义拓扑，然后得到关于语义视图的嵌入，这为自适应地合并节点属性所携带的信息提供了一个灵活且易于实现的解决方案。然后利用注意机制将语义嵌入和拓扑嵌入融合在一起进行最终预测。广泛的实验表明，我们的建议的优越性能，它明显优于基线的不同研究基准。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=BSAL:+A+Framework+of+Bi-component+Structure+and+Attribute+Learning+for+Link+Prediction)|1| -|[Tensor-based Graph Modularity for Text Data Clustering](https://doi.org/10.1145/3477495.3531834)|Rafika Boutalbi, Mira Ait Saada, Anastasiia Iurshina, Steffen Staab, Mohamed Nadif|University of Stuttgart, Stuttgart, Germany; Université de Paris, Paris, France|Graphs are used in several applications to represent similarities between instances. For text data, we can represent texts by different features such as bag-of-words, static embeddings (Word2vec, GloVe, etc.), and contextual embeddings (BERT, RoBERTa, etc.), leading to multiple similarities (or graphs) based on each representation. The proposal posits that incorporating the local invariance within every graph and the consistency across different graphs leads to a consensus clustering that improves the document clustering. This problem is complex and challenged with the sparsity and the noisy data included in each graph. To this end, we rely on the modularity metric, which effectively evaluates graph clustering in such circumstances. Therefore, we present a novel approach for text clustering based on both a sparse tensor representation and graph modularity. This leads to cluster texts (nodes) while capturing information arising from the different graphs. We iteratively maximize a Tensor-based Graph Modularity criterion. Extensive experiments on benchmark text clustering datasets are performed, showing that the proposed algorithm referred to as Tensor Graph Modularity -TGM- outperforms other baseline methods in terms of clustering task. The source code is available at https://github.com/TGMclustering/TGMclustering.|在几个应用程序中使用图表来表示实例之间的相似性。对于文本数据，我们可以通过不同的特性来表示文本，例如词包、静态嵌入(Word2vec、 GloVe 等)和上下文嵌入(BERT、 RoBERTa 等) ，从而基于每种表示形式产生多种相似性(或图形)。该方案假定，在每个图中引入局部不变性和不同图之间的一致性，可以形成共识聚类，从而改善文档聚类。这个问题是复杂的和挑战的稀疏性和噪声数据包含在每个图。为此，我们依赖于模块化度量，它可以有效地评估在这种情况下的图聚类。因此，我们提出了一种基于稀疏张量表示和图模块化的文本聚类方法。这将导致集群文本(节点) ，同时捕获来自不同图表的信息。我们迭代地最大化一个基于张量的图模块化准则。对基准文本聚类数据集进行了广泛的实验，结果表明该算法在聚类任务方面优于其他基准方法。源代码可在 https://github.com/tgmclustering/tgmclustering 下载。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Tensor-based+Graph+Modularity+for+Text+Data+Clustering)|1| +|[BSAL: A Framework of Bi-component Structure and Attribute Learning for Link Prediction](https://doi.org/10.1145/3477495.3531804)|Bisheng Li, Min Zhou, Shengzhong Zhang, Menglin Yang, Defu Lian, Zengfeng Huang|Huawei Noah's Ark Lab, Shenzhen, China; Fudan University, Shanghai, China; University of Science and Technology of China, Hefei, China; The Chinese University of Hong Kong, Hong Kong, China|Given the ubiquitous existence of graph-structured data, learning the representations of nodes for the downstream tasks ranging from node classification, link prediction to graph classification is of crucial importance. Regarding missing link inference of diverse networks, we revisit the link prediction techniques and identify the importance of both the structural and attribute information. However, the available techniques either heavily count on the network topology which is spurious in practice, or cannot integrate graph topology and features properly. To bridge the gap, we propose a bicomponent structural and attribute learning framework (BSAL) that is designed to adaptively leverage information from topology and feature spaces. Specifically, BSAL constructs a semantic topology via the node attributes and then gets the embeddings regarding the semantic view, which provides a flexible and easy-to-implement solution to adaptively incorporate the information carried by the node attributes. Then the semantic embedding together with topology embedding are fused together using attention mechanism for the final prediction. Extensive experiments show the superior performance of our proposal and it significantly outperforms baselines on diverse research benchmarks.|由于图结构数据的普遍存在，学习下游任务的节点表示，从节点分类、链路预测到图分类，都是至关重要的。关于不同网络的缺失链接推断，我们重新审视了链接预测技术，并确定了结构和属性信息的重要性。然而，现有的技术要么严重依赖于在实践中虚假的网络拓扑，要么不能恰当地整合图形拓扑和特性。为了弥补这一差距，我们提出了一个双组件结构和属性学习框架(BSAL) ，该框架旨在自适应地利用拓扑和特征空间中的信息。具体来说，BSAL 通过节点属性构造一个语义拓扑，然后得到关于语义视图的嵌入，这为自适应地合并节点属性所携带的信息提供了一个灵活且易于实现的解决方案。然后利用注意机制将语义嵌入和拓扑嵌入融合在一起进行最终预测。广泛的实验表明，我们的建议的优越性能，它明显优于基线的不同研究基准。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=BSAL:+A+Framework+of+Bi-component+Structure+and+Attribute+Learning+for+Link+Prediction)|1| +|[Tensor-based Graph Modularity for Text Data Clustering](https://doi.org/10.1145/3477495.3531834)|Rafika Boutalbi, Mira Ait Saada, Anastasiia Iurshina, Steffen Staab, Mohamed Nadif|Université de Paris, Paris, France; University of Stuttgart, Stuttgart, Germany|Graphs are used in several applications to represent similarities between instances. For text data, we can represent texts by different features such as bag-of-words, static embeddings (Word2vec, GloVe, etc.), and contextual embeddings (BERT, RoBERTa, etc.), leading to multiple similarities (or graphs) based on each representation. The proposal posits that incorporating the local invariance within every graph and the consistency across different graphs leads to a consensus clustering that improves the document clustering. This problem is complex and challenged with the sparsity and the noisy data included in each graph. To this end, we rely on the modularity metric, which effectively evaluates graph clustering in such circumstances. Therefore, we present a novel approach for text clustering based on both a sparse tensor representation and graph modularity. This leads to cluster texts (nodes) while capturing information arising from the different graphs. We iteratively maximize a Tensor-based Graph Modularity criterion. Extensive experiments on benchmark text clustering datasets are performed, showing that the proposed algorithm referred to as Tensor Graph Modularity -TGM- outperforms other baseline methods in terms of clustering task. The source code is available at https://github.com/TGMclustering/TGMclustering.|在几个应用程序中使用图表来表示实例之间的相似性。对于文本数据，我们可以通过不同的特性来表示文本，例如词包、静态嵌入(Word2vec、 GloVe 等)和上下文嵌入(BERT、 RoBERTa 等) ，从而基于每种表示形式产生多种相似性(或图形)。该方案假定，在每个图中引入局部不变性和不同图之间的一致性，可以形成共识聚类，从而改善文档聚类。这个问题是复杂的和挑战的稀疏性和噪声数据包含在每个图。为此，我们依赖于模块化度量，它可以有效地评估在这种情况下的图聚类。因此，我们提出了一种基于稀疏张量表示和图模块化的文本聚类方法。这将导致集群文本(节点) ，同时捕获来自不同图表的信息。我们迭代地最大化一个基于张量的图模块化准则。对基准文本聚类数据集进行了广泛的实验，结果表明该算法在聚类任务方面优于其他基准方法。源代码可在 https://github.com/tgmclustering/tgmclustering 下载。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Tensor-based+Graph+Modularity+for+Text+Data+Clustering)|1| |[GraphAD: A Graph Neural Network for Entity-Wise Multivariate Time-Series Anomaly Detection](https://doi.org/10.1145/3477495.3531848)|Xu Chen, Qiu Qiu, Changshan Li, Kunqing Xie|Peking University, Beijing, China; Alibaba DAMO Academy, Hangzhou, China|In recent years, the emergence and development of third-party platforms have greatly facilitated the growth of the Online to Offline (O2O) business. However, the large amount of transaction data raises new challenges for retailers, especially anomaly detection in operating conditions. Thus, platforms begin to develop intelligent business assistants with embedded anomaly detection methods to reduce the management burden on retailers. Traditional time-series anomaly detection methods capture underlying patterns from the perspectives of time and attributes, ignoring the difference between retailers in this scenario. Besides, similar transaction patterns extracted by the platforms can also provide guidance to individual retailers and enrich their available information without privacy issues. In this paper, we pose an entity-wise multivariate time-series anomaly detection problem that considers the time-series of each unique entity. To address this challenge, we propose GraphAD, a novel multivariate time-series anomaly detection model based on the graph neural network. GraphAD decomposes the Key Performance Indicator (KPI) into stable and volatility components and extracts their patterns in terms of attributes, entities and temporal perspectives via graph neural networks. We also construct a real-world entity-wise multivariate time-series dataset from the business data of Ele.me. The experimental results on this dataset show that GraphAD significantly outperforms existing anomaly detection methods.|近年来，第三方平台的出现和发展极大地促进了 Online To Offline线上到线下业务的发展。然而，大量的交易数据给零售商带来了新的挑战，尤其是在经营异常检测方面。因此，平台开始开发具有嵌入式异常检测方法的智能商务助理，以减轻零售商的管理负担。传统的时间序列异常检测方法从时间和属性的角度捕捉潜在的模式，忽略了在这种情况下零售商之间的差异。此外，平台所提取的类似交易模式亦可为个别零售商提供指引，并在不涉及私隐问题的情况下丰富他们的资料。在本文中，我们提出了一个实体多变量时间序列异常检测问题，考虑了每个独特的实体的时间序列。为了应对这一挑战，我们提出了一种新的基于图形神经网络的多变量时间序列异常检测模型 GraphAD。GraphAD 将关键绩效指标分解成稳定和波动的分量，并通过图形神经网络从属性、实体和时间角度提取它们的模式。我们还利用 Ele.me 的业务数据构造了一个真实世界的实体多变量时间序列数据集。在这个数据集上的实验结果显示，GraphAD 显著优于现有的异常检测方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=GraphAD:+A+Graph+Neural+Network+for+Entity-Wise+Multivariate+Time-Series+Anomaly+Detection)|1| |[Lightweight Meta-Learning for Low-Resource Abstractive Summarization](https://doi.org/10.1145/3477495.3531908)|Taehun Huh, Youngjoong Ko|Sungkyunkwan University, Suwon-si, Gyeonggi-do, Republic of Korea|Recently, supervised abstractive summarization using high-resource datasets, such as CNN/DailyMail and Xsum, has achieved significant performance improvements. However, most of the existing high-resource dataset is biased towards a specific domain like news, and annotating document-summary pairs for low-resource datasets is too expensive. Furthermore, the need for low-resource abstractive summarization task is emerging but existing methods for the task such as transfer learning still have domain shifting and overfitting problems. To address these problems, we propose a new framework for low-resource abstractive summarization using a meta-learning algorithm that can quickly adapt to a new domain using small data. For adaptive meta-learning, we introduce a lightweight module inserted into the attention mechanism of a pre-trained language model; the module is first meta-learned with high-resource task-related datasets and then is fine-tuned with the low-resource target dataset. We evaluate our model on 11 different datasets. Experimental results show that the proposed method achieves the state-of-the-art on 9 datasets in low-resource abstractive summarization.|最近，使用高资源数据集(如 CNN/DailyMail 和 Xsum)的监督抽象摘要已经取得了显著的性能改进。然而，大多数现有的高资源数据集偏向于新闻这样的特定领域，并且为低资源数据集注释文档-摘要对过于昂贵。此外，对低资源抽象摘要任务的需求正在出现，但现有的任务转移学习等方法仍然存在领域移位和过拟合问题。为了解决这些问题，我们提出了一个新的框架，低资源抽象摘要使用元学习算法，可以快速适应新的领域使用小数据。对于自适应元学习，我们在预训练语言模型的注意机制中引入了一个轻量级模块，该模块首先对高资源任务相关数据集进行元学习，然后对低资源目标数据集进行微调。我们在11个不同的数据集上评估我们的模型。实验结果表明，该方法在低资源抽象摘要的9个数据集上达到了最高水平。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Lightweight+Meta-Learning+for+Low-Resource+Abstractive+Summarization)|1| |[Task-Oriented Dialogue System as Natural Language Generation](https://doi.org/10.1145/3477495.3531920)|Weizhi Wang, Zhirui Zhang, Junliang Guo, Yinpei Dai, Boxing Chen, Weihua Luo||In this paper, we propose to formulate the task-oriented dialogue system as the purely natural language generation task, so as to fully leverage the large-scale pre-trained models like GPT-2 and simplify complicated delexicalization prepossessing. However, directly applying this method heavily suffers from the dialogue entity inconsistency caused by the removal of delexicalized tokens, as well as the catastrophic forgetting problem of the pre-trained model during fine-tuning, leading to unsatisfactory performance. To alleviate these problems, we design a novel GPT-Adapter-CopyNet network, which incorporates the lightweight adapter and CopyNet modules into GPT-2 to achieve better performance on transfer learning and dialogue entity generation. Experimental results conducted on the DSTC8 Track 1 benchmark and MultiWOZ dataset demonstrate that our proposed approach significantly outperforms baseline models with a remarkable performance on automatic and human evaluations.|本文提出将任务导向的对话系统设计为纯自然语言生成任务，以充分利用 GPT-2等大规模预训练模型，简化复杂的去词化过程。然而，直接应用这种方法，由于去词化标记引起的对话实体不一致，以及预训练模型在微调过程中的灾难性遗忘问题，导致性能不理想。为了解决这些问题，我们设计了一种新型的 GPT-Adapter-CopyNet 网络，它将轻量级适配器和 CopyNet 模块集成到 GPT-2中，以实现更好的传递学习和对话实体生成。在 DSTC8 Track1基准和 MultiWOZ 数据集上进行的实验结果表明，我们提出的方法显著优于基线模型，在自动和人工评估方面具有显著的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Task-Oriented+Dialogue+System+as+Natural+Language+Generation)|1| @@ -258,63 +258,63 @@ |[ViQuAE, a Dataset for Knowledge-based Visual Question Answering about Named Entities](https://doi.org/10.1145/3477495.3531753)|Paul Lerner, Olivier Ferret, Camille Guinaudeau, Hervé Le Borgne, Romaric Besançon, José G. Moreno, Jesús LovónMelgarejo||||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=ViQuAE,+a+Dataset+for+Knowledge-based+Visual+Question+Answering+about+Named+Entities)|1| |[DIANES: A DEI Audit Toolkit for News Sources](https://doi.org/10.1145/3477495.3531660)|Xiaoxiao Shang, Zhiyuan Peng, Qiming Yuan, Sabiq Khan, Lauren Xie, Yi Fang, Subramaniam Vincent||||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=DIANES:+A+DEI+Audit+Toolkit+for+News+Sources)|1| |[NAS-CTR: Efficient Neural Architecture Search for Click-Through Rate Prediction](https://doi.org/10.1145/3477495.3532030)|Guanghui Zhu, Feng Cheng, Defu Lian, Chunfeng Yuan, Yihua Huang|University of Science and Technology of China, Hefei, China; Nanjing University, Nanjing, China|Click-Through Rate (CTR) prediction has been widely used in many machine learning tasks such as online advertising and personalization recommendation. Unfortunately, given a domain-specific dataset, searching effective feature interaction operations and combinations from a huge candidate space requires significant expert experience and computational costs. Recently, Neural Architecture Search (NAS) has achieved great success in discovering high-quality network architectures automatically. However, due to the diversity of feature interaction operations and combinations, the existing NAS-based work that treats the architecture search as a black-box optimization problem over a discrete search space suffers from low efficiency. Therefore, it is essential to explore a more efficient architecture search method. To achieve this goal, we propose NAS-CTR, a differentiable neural architecture search approach for CTR prediction. First, we design a novel and expressive architecture search space and a continuous relaxation scheme to make the search space differentiable. Second, we formulate the architecture search for CTR prediction as a joint optimization problem with discrete constraints on architectures and leverage proximal iteration to solve the constrained optimization problem. Additionally, a straightforward yet effective method is proposed to eliminate the aggregation of skip connections. Extensive experimental results reveal that NAS-CTR can outperform the SOTA human-crafted architectures and other NAS-based methods in both test accuracy and search efficiency.|点进率预测已经广泛应用于许多机器学习任务，例如在线广告和个性化推荐。不幸的是，给定一个特定领域的数据集，从一个巨大的候选空间中搜索有效的特征交互操作和组合需要大量的专家经验和计算成本。近年来，神经网络体系结构搜索(NAS)在自动发现高质量的网络体系结构方面取得了巨大的成功。然而，由于功能交互操作和组合的多样性，现有的基于 NAS 的工作将体系结构搜索视为离散搜索空间上的黑盒子最佳化问题，效率低下。因此，有必要探索一种更有效的体系结构搜索方法。为了实现这一目标，我们提出了 NAS-CTR，一种用于 CTR 预测的可微分神经结构搜索方法。首先，我们设计了一个新颖的、具有表现力的体系结构搜索空间和一个连续松弛方案，使搜索空间具有可微性。其次，我们将 CTR 预测的体系结构搜索描述为一个联合最佳化问题，对体系结构进行离散约束，并利用近端迭代来解决约束最佳化问题。此外，提出了一种简单而有效的方法来消除跳跃连接的聚集。大量的实验结果表明，NAS-CTR 在测试精度和搜索效率方面都优于 SOTA 人工架构和其他基于 NAS 的方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=NAS-CTR:+Efficient+Neural+Architecture+Search+for+Click-Through+Rate+Prediction)|0| -|[Learning to Enrich Query Representation with Pseudo-Relevance Feedback for Cross-lingual Retrieval](https://doi.org/10.1145/3477495.3532013)|Ramraj Chandradevan, Eugene Yang, Mahsa Yarmohammadi, Eugene Agichtein|Emory University, Atlanta, GA, USA; Johns Hopkins University, Baltimore, MD, USA|Cross-lingual information retrieval (CLIR) aims to provide access to information across languages. Recent pre-trained multilingual language models brought large improvements to the natural language tasks, including cross-lingual adhoc retrieval. However, pseudo-relevance feedback (PRF), a family of techniques for improving ranking using the contents of top initially retrieved items, has not been explored with neural CLIR retrieval models. Two of the challenges are incorporating feedback from long documents, and cross-language knowledge transfer. To address these challenges, we propose a novel neural CLIR architecture, NCLPRF, capable of incorporating PRF feedback from multiple potentially long documents, which enables improvements to query representation in the shared semantic space between query and document languages. The additional information that the feedback documents provide in a target language, can enrich the query representation, bringing it closer to relevant documents in the embedding space. The proposed model performance across three CLIR test collections in Chinese, Russian, and Persian languages, exhibits significant improvements over traditional and SOTA neural CLIR baselines across all three collections.|跨语言信息检索(CLIR)旨在提供跨语言的信息获取途径。最近预先训练的多语言模型给自然语言任务带来了很大的改进，包括跨语言的即席检索。然而，伪相关反馈(PRF)作为一种利用最初检索项目的内容来提高排名的技术，尚未在神经元 CLIR 检索模型中得到应用。其中两个挑战是整合来自长文档的反馈，以及跨语言的知识转移。为了应对这些挑战，我们提出了一个新的神经 CLIR 架构，NCLPRF，能够合并来自多个潜在的长文档的 PRF 反馈，这使得查询语言和文档语言之间的共享语义空间中的查询表示得到改进。反馈文档以目标语言提供的附加信息可以丰富查询表示，使其更接近嵌入空间中的相关文档。在中文，俄文和波斯语的三个 CLIR 测试集合中，所提出的模型性能比所有三个集合中的传统和 SOTA 神经 CLIR 基线都有显著的改进。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Learning+to+Enrich+Query+Representation+with+Pseudo-Relevance+Feedback+for+Cross-lingual+Retrieval)|0| +|[Learning to Enrich Query Representation with Pseudo-Relevance Feedback for Cross-lingual Retrieval](https://doi.org/10.1145/3477495.3532013)|Ramraj Chandradevan, Eugene Yang, Mahsa Yarmohammadi, Eugene Agichtein|Johns Hopkins University, Baltimore, MD, USA; Emory University, Atlanta, GA, USA|Cross-lingual information retrieval (CLIR) aims to provide access to information across languages. Recent pre-trained multilingual language models brought large improvements to the natural language tasks, including cross-lingual adhoc retrieval. However, pseudo-relevance feedback (PRF), a family of techniques for improving ranking using the contents of top initially retrieved items, has not been explored with neural CLIR retrieval models. Two of the challenges are incorporating feedback from long documents, and cross-language knowledge transfer. To address these challenges, we propose a novel neural CLIR architecture, NCLPRF, capable of incorporating PRF feedback from multiple potentially long documents, which enables improvements to query representation in the shared semantic space between query and document languages. The additional information that the feedback documents provide in a target language, can enrich the query representation, bringing it closer to relevant documents in the embedding space. The proposed model performance across three CLIR test collections in Chinese, Russian, and Persian languages, exhibits significant improvements over traditional and SOTA neural CLIR baselines across all three collections.|跨语言信息检索(CLIR)旨在提供跨语言的信息获取途径。最近预先训练的多语言模型给自然语言任务带来了很大的改进，包括跨语言的即席检索。然而，伪相关反馈(PRF)作为一种利用最初检索项目的内容来提高排名的技术，尚未在神经元 CLIR 检索模型中得到应用。其中两个挑战是整合来自长文档的反馈，以及跨语言的知识转移。为了应对这些挑战，我们提出了一个新的神经 CLIR 架构，NCLPRF，能够合并来自多个潜在的长文档的 PRF 反馈，这使得查询语言和文档语言之间的共享语义空间中的查询表示得到改进。反馈文档以目标语言提供的附加信息可以丰富查询表示，使其更接近嵌入空间中的相关文档。在中文，俄文和波斯语的三个 CLIR 测试集合中，所提出的模型性能比所有三个集合中的传统和 SOTA 神经 CLIR 基线都有显著的改进。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Learning+to+Enrich+Query+Representation+with+Pseudo-Relevance+Feedback+for+Cross-lingual+Retrieval)|0| |[Incorporating Retrieval Information into the Truncation of Ranking Lists for Better Legal Search](https://doi.org/10.1145/3477495.3531998)|Yixiao Ma, Qingyao Ai, Yueyue Wu, Yunqiu Shao, Yiqun Liu, Min Zhang, Shaoping Ma|Tsinghua University, Beijing, China; University of Utah, Salt Lake City, UT, USA|The truncation of ranking lists predicted by retrieval models is vital to ensure users' search experience. Particularly, in specific vertical domains where documents are usually complicated and extensive (e.g., legal cases), the cost of browsing results is much higher than traditional IR tasks (e.g., Web search) and setting a reasonable cut-off position is quite necessary. While it is straightforward to apply existing result list truncation approaches to legal case retrieval, the effectiveness of these methods is limited because they only focus on simple document statistics and usually fail to capture the context information of documents in the ranking list. These existing efforts also treat result list truncation as an isolated task instead of a component in the entire ranking process, limiting the usage of truncation in practical systems. To tackle these limitations, we propose LeCut, a ranking list truncation model for legal case retrieval. LeCut utilizes contextual features of the retrieval task to capture the semantic-level similarity between documents and decides the best cut-off position with attention mechanisms. We further propose a Joint Optimization of Truncation and Reranking (JOTR) framework based on LeCut to improve the performance of truncation and retrieval tasks simultaneously. Comparison against competitive baselines on public benchmark datasets demonstrates the effectiveness of LeCut and JOTR. A case study is conducted to visualize the cut-off positions of LeCut and the process of how JOTR improves both retrieval and truncation tasks.|检索模型预测的排名列表的截断对于保证用户的搜索体验至关重要。特别是，在特定的垂直领域，文档通常是复杂和广泛的(例如，法律案件) ，浏览结果的成本远远高于传统的 IR 任务(例如，网络搜索) ，设置一个合理的截止位置是非常必要的。虽然将现有的结果清单截断方法应用于法律案件检索很简单，但这些方法的有效性有限，因为它们只侧重于简单的文件统计，通常无法捕捉排名清单中文件的上下文信息。这些现有的工作还将结果列表截断视为一个孤立的任务，而不是整个排序过程中的一个组件，从而限制了截断在实际系统中的使用。为了解决这些局限性，我们提出了 LeCut，一种用于法律案例检索的排序列表截断模型。LeCut 利用检索任务的上下文特征来捕获文档之间的语义级相似性，并通过注意机制确定最佳截止位置。进一步提出了一种基于 LeCut 的联合优化截断与重排(JOTR)框架，以同时提高截断与检索任务的性能。与公共基准数据集的竞争基线进行比较，可以证明 LeCut 和 JOTR 的有效性。通过案例研究，可视化 LeCut 的截止位置以及 JOTR 如何改进检索和截断任务的过程。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Incorporating+Retrieval+Information+into+the+Truncation+of+Ranking+Lists+for+Better+Legal+Search)|0| |[Ada-Ranker: A Data Distribution Adaptive Ranking Paradigm for Sequential Recommendation](https://doi.org/10.1145/3477495.3531931)|Xinyan Fan, Jianxun Lian, Wayne Xin Zhao, Zheng Liu, Chaozhuo Li, Xing Xie|Renmin University of China, Beijing, China; Microsoft Research Asia, Beijing, China|A large-scale recommender system usually consists of recall and ranking modules. The goal of ranking modules (aka rankers) is to elaborately discriminate users' preference on item candidates proposed by recall modules. With the success of deep learning techniques in various domains, we have witnessed the mainstream rankers evolve from traditional models to deep neural models. However, the way that we design and use rankers remains unchanged: offline training the model, freezing the parameters, and deploying it for online serving. Actually, the candidate items are determined by specific user requests, in which underlying distributions (e.g., the proportion of items for different categories, the proportion of popular or new items) are highly different from one another in a production environment. The classical parameter-frozen inference manner cannot adapt to dynamic serving circumstances, making rankers' performance compromised. In this paper, we propose a new training and inference paradigm, termed as Ada-Ranker, to address the challenges of dynamic online serving. Instead of using parameter-frozen models for universal serving, Ada-Ranker can adaptively modulate parameters of a ranker according to the data distribution of the current group of item candidates. We first extract distribution patterns from the item candidates. Then, we modulate the ranker by the patterns to make the ranker adapt to the current data distribution. Finally, we use the revised ranker to score the candidate list. In this way, we empower the ranker with the capacity of adapting from a global model to a local model which better handles the current task. As a first study, we examine our Ada-Ranker paradigm in the sequential recommendation scenario. Experiments on three datasets demonstrate that Ada-Ranker can effectively enhance various base sequential models and also outperform a comprehensive set of competitive baselines.|一个大规模的推荐系统通常包括召回和排名模块。排序模块(又称排序器)的目标是精心区分用户对召回模块提出的候选项的偏好。随着深度学习技术在各个领域的成功，我们目睹了主流的排名从传统模型演变为深度神经模型。然而，我们设计和使用排名的方式保持不变: 离线训练模型，冻结参数，并部署它在线服务。实际上，候选项是由特定的用户请求决定的，在这种情况下，底层分布(例如，不同类别的项目比例，流行项目或新项目的比例)在生产环境中彼此之间差异很大。传统的参数冻结推理方式不能适应动态服务环境，使得排序器的性能受到影响。在本文中，我们提出了一个新的训练和推理范式，称为 Ada-Ranker，以解决动态在线服务的挑战。Ada-Ranker 可以根据当前项目候选者组的数据分布自适应地调整排序器的参数，而不必使用通用服务的参数冻结模型。我们首先从候选项中提取分布模式。然后，根据模式对排序器进行调整，使排序器适应当前的数据分布。最后，我们使用修改后的排名对候选人列表进行评分。通过这种方式，我们赋予排名者从全球模型到更好地处理当前任务的局部模型的适应能力。作为第一个研究，我们在顺序推荐场景中检查我们的 Ada-Ranker 范式。在三个数据集上的实验表明，Ada-Ranker 能够有效地增强各种基本序列模型，并且表现优于一组综合的竞争基线。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Ada-Ranker:+A+Data+Distribution+Adaptive+Ranking+Paradigm+for+Sequential+Recommendation)|0| -|[Retrieval and Recommendation Systems at the Crossroads of Artificial Intelligence, Ethics, and Regulation](https://doi.org/10.1145/3477495.3532683)|Markus Schedl, Emilia Gómez, Elisabeth Lex|Graz University of Technology, Graz, Austria; European Commission, Joint Research Centre and Universitat Pompeu Fabra, Seville/Barcelona, Spain; Johannes Kepler University Linz & Linz Institue of Technology, Linz, Austria|This tutorial aims at providing its audience an interdisciplinary overview about the topics of fairness and non-discrimination, diversity, and transparency of AI systems, tailored to the research fields of information retrieval and recommender systems. By means of this tutorial, we would like to equip the mostly technical audience of SIGIR with the necessary understanding of the ethical implications of their research and development on the one hand, and of recent political and legal regulations that address the aforementioned challenges on the other hand.|本教程旨在为读者提供一个关于人工智能系统的公平性和非歧视性、多样性和透明度等主题的跨学科概述，适用于信息检索和推荐系统的研究领域。通过本教程，我们希望让 SIGIR 的大多数技术读者一方面了解他们的研究和发展的道德影响，另一方面了解解决上述挑战的最新政治和法律法规。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Retrieval+and+Recommendation+Systems+at+the+Crossroads+of+Artificial+Intelligence,+Ethics,+and+Regulation)|0| -|[Adversarial Filtering Modeling on Long-term User Behavior Sequences for Click-Through Rate Prediction](https://doi.org/10.1145/3477495.3531788)|Xiaochen Li, Jian Liang, Xialong Liu, Yu Zhang|Alibaba Group, Beijing, China; Lazada Group, Beijing, China|Rich user behavior information is of great importance for capturing and understanding user interest in click-through rate (CTR) prediction. To improve the richness, collecting long-term behaviors becomes a typical approach in academy and industry but at the cost of increasing online storage and latency. Recently, researchers have proposed several approaches to shorten long-term behavior sequence and then model user interests. These approaches reduce online cost efficiently but do not well handle the noisy information in long-term user behavior, which may deteriorate the performance of CTR prediction significantly. To obtain better cost/performance trade-off, we propose a novel Adversarial Filtering Model (ADFM) to model long-term user behavior. ADFM uses a hierarchical aggregation representation to compress raw behavior sequence and then learns to remove useless behavior information with an adversarial filtering mechanism. The selected user behaviors are fed into interest extraction module for CTR prediction. Experimental results on public datasets and industrial dataset demonstrate that our method achieves significant improvements over state-of-the-art models.|丰富的用户行为信息对于捕捉和理解用户对点进率预测的兴趣非常重要。为了提高丰富性，收集长期行为成为学术界和工业界的一种典型方法，但代价是增加在线存储和延迟。最近，研究人员提出了几种方法来缩短长期行为序列，然后模型用户的兴趣。这些方法有效地降低了在线成本，但不能很好地处理长期用户行为中的噪声信息，这可能会严重影响 CTR 预测的性能。为了获得更好的性价比，我们提出了一种新的对抗过滤模型(ADFM)来模拟长期用户行为。ADFM 使用分层聚合表示来压缩原始行为序列，然后学习使用对抗性过滤机制去除无用的行为信息。将选定的用户行为反馈到兴趣提取模块中进行点击率预测。在公共数据集和工业数据集上的实验结果表明，该方法比现有的模型有明显的改进。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Adversarial+Filtering+Modeling+on+Long-term+User+Behavior+Sequences+for+Click-Through+Rate+Prediction)|0| -|[LoL: A Comparative Regularization Loss over Query Reformulation Losses for Pseudo-Relevance Feedback](https://doi.org/10.1145/3477495.3532017)|Yunchang Zhu, Liang Pang, Yanyan Lan, Huawei Shen, Xueqi Cheng|Data Intelligence System Research Center, Institute of Computing Technology, CAS, Beijing, China; Institute of Computing Technology, CAS & University of Chinese Academy of Sciences, Beijing, China; Data Intelligence System Research Center, Institute of Computing Technology, CAS & University of Chinese Academy of Sciences, Beijing, China; Tsinghua University, Beijing, China|Pseudo-relevance feedback (PRF) has proven to be an effective query reformulation technique to improve retrieval accuracy. It aims to alleviate the mismatch of linguistic expressions between a query and its potential relevant documents. Existing PRF methods independently treat revised queries originating from the same query but using different numbers of feedback documents, resulting in severe query drift. Without comparing the effects of two different revisions from the same query, a PRF model may incorrectly focus on the additional irrelevant information increased in the more feedback, and thus reformulate a query that is less effective than the revision using the less feedback. Ideally, if a PRF model can distinguish between irrelevant and relevant information in the feedback, the more feedback documents there are, the better the revised query will be. To bridge this gap, we propose the Loss-over-Loss (LoL) framework to compare the reformulation losses between different revisions of the same query during training. Concretely, we revise an original query multiple times in parallel using different amounts of feedback and compute their reformulation losses. Then, we introduce an additional regularization loss on these reformulation losses to penalize revisions that use more feedback but gain larger losses. With such comparative regularization, the PRF model is expected to learn to suppress the extra increased irrelevant information by comparing the effects of different revised queries. Further, we present a differentiable query reformulation method to implement this framework. This method revises queries in the vector space and directly optimizes the retrieval performance of query vectors, applicable for both sparse and dense retrieval models. Empirical evaluation demonstrates the effectiveness and robustness of our method for two typical sparse and dense retrieval models.|伪相关反馈(PRF)已被证明是一种有效的查询重构技术，以提高检索的准确性。它旨在缓解查询与潜在相关文档之间的语言表达不匹配问题。现有的 PRF 方法独立处理来自同一查询但使用不同数量的反馈文档的修改查询，导致严重的查询漂移。如果不比较来自同一查询的两个不同修订的效果，PRF 模型可能会错误地关注更多反馈中增加的附加不相关信息，从而使用更少的反馈重新表述比修订更低效的查询。理想情况下，如果 PRF 模型能够区分反馈中的不相关信息和相关信息，那么反馈文档越多，修改后的查询就越好。为了弥补这一差距，我们提出了损失超过损失(LoL)框架来比较同一查询在培训期间不同修订版本之间的重构损失。具体来说，我们使用不同数量的反馈并行多次修改原始查询，并计算它们的重新表述损失。然后，我们引入一个额外的正则化损失对这些重制损失，以惩罚修订使用更多的反馈，但获得更大的损失。通过这种比较正则化，PRF 模型可以通过比较不同修订查询的效果来抑制额外增加的不相关信息。进一步，我们提出了一个可微查询重构方法来实现这个框架。该方法对向量空间中的查询进行修正，直接优化查询向量的检索性能，适用于稀疏和密集检索模型。实验结果表明，该方法对两种典型的稀疏和密集检索模型具有较好的鲁棒性和有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=LoL:+A+Comparative+Regularization+Loss+over+Query+Reformulation+Losses+for+Pseudo-Relevance+Feedback)|0| -|[Determinantal Point Process Likelihoods for Sequential Recommendation](https://doi.org/10.1145/3477495.3531965)|Yuli Liu, Christian J. Walder, Lexing Xie|Data61, CSIRO & Australian National University, Canberra, Australia; Australian National University & Data61, CSIRO, Canberra, Australia|Sequential recommendation is a popular task in academic research and close to real-world application scenarios, where the goal is to predict the next action(s) of the user based on his/her previous sequence of actions. In the training process of recommender systems, the loss function plays an essential role in guiding the optimization of recommendation models to generate accurate suggestions for users. However, most existing sequential recommendation tech- niques focus on designing algorithms or neural network architectures, and few efforts have been made to tailor loss functions that fit naturally into the practical application scenario of sequential recommender systems. Ranking-based losses, such as cross-entropy and Bayesian Personalized Ranking (BPR) are widely used in the sequential recommendation area. We argue that such objective functions suffer from two inherent drawbacks: i) the dependencies among elements of a sequence are overlooked in these loss formulations; ii) instead of balancing accuracy (quality) and diversity, only generating accurate results has been over emphasized. We therefore propose two new loss functions based on the Determinantal Point Process (DPP) likelihood, that can be adaptively applied to estimate the subsequent item or items. The DPP-distributed item set captures natural dependencies among temporal actions, and a quality vs. diversity decomposition of the DPP kernel pushes us to go beyond accuracy-oriented loss functions. Experimental results using the proposed loss functions on three real-world datasets show marked improvements over state-of-the-art sequential recommendation methods in both quality and diversity metrics.|顺序推荐是学术研究中的一个热门任务，它接近于真实的应用场景，其目标是根据用户以前的操作顺序预测他/她的下一个操作。在推荐系统的培训过程中，损失函数对于指导推荐模型的优化，为用户提供准确的建议起着至关重要的作用。然而，现有的顺序推荐技术大多侧重于设计算法或神经网络体系结构，很少有人努力去调整自然适合顺序推荐系统实际应用场景的损失函数。基于排序的损失，如交叉熵和贝叶斯个性化排序(BPR)被广泛应用于序列推荐领域。我们认为这样的目标函数有两个固有的缺点: i)在这些损失公式中忽略了序列元素之间的依赖关系; ii)没有平衡准确性(质量)和多样性，只有产生准确的结果被过分强调。因此，我们提出两个新的基于行列式点过程(DPP)可能性的损失函数，可以自适应地应用于估计随后的项目。DPP 分布式项目集捕获时间操作之间的自然依赖关系，DPP 内核的质量与多样性分解促使我们超越面向准确性的损失函数。在三个实际数据集上使用提出的损失函数的实验结果显示，在质量和多样性度量方面，该方法比最先进的顺序推荐方法有明显的改进。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Determinantal+Point+Process+Likelihoods+for+Sequential+Recommendation)|0| -|[Re-weighting Negative Samples for Model-Agnostic Matching](https://doi.org/10.1145/3477495.3532053)|Jiazhen Lou, Hong Wen, Fuyu Lv, Jing Zhang, Tengfei Yuan, Zhao Li|The University of Sydney, Darlington, NSW, Australia; Alibaba Group, Hangzhou, China; Zhejiang University, Hangzhou, China|Recommender Systems (RS), as an efficient tool to discover users' interested items from a very large corpus, has attracted more and more attention from academia and industry. As the initial stage of RS, large-scale matching is fundamental yet challenging. A typical recipe is to learn user and item representations with a two-tower architecture and then calculate the similarity score between both representation vectors, which however still struggles in how to properly deal with negative samples. In this paper, we find that the common practice that randomly sampling negative samples from the entire space and treating them equally is not an optimal choice, since the negative samples from different sub-spaces at different stages have different importance to a matching model. To address this issue, we propose a novel method named Unbiased Model-Agnostic Matching Approach (UMA2). It consists of two basic modules including 1) General Matching Model (GMM), which is model-agnostic and can be implemented as any embedding-based two-tower models; and 2) Negative Samples Debias Network (NSDN), which discriminates negative samples by borrowing the idea of Inverse Propensity Weighting (IPW) and re-weighs the loss in GMM. UMA$^2$ seamlessly integrates these two modules in an end-to-end multi-task learning framework. Extensive experiments on both real-world offline dataset and online A/B test demonstrate its superiority over state-of-the-art methods.|推荐系统(RS)作为一种从庞大的语料库中发现用户感兴趣的项目的有效工具，越来越受到学术界和业界的关注。作为遥感的初始阶段，大规模匹配是一个基础性的挑战。一个典型的方法是使用双塔体系结构学习用户和项目表示，然后计算两个表示向量之间的相似度得分，但是如何正确处理负样本仍然是一个难题。在本文中，我们发现从整个空间中随机抽取负样本并平等对待它们的常见做法并不是最优选择，因为不同阶段不同子空间中的负样本对匹配模型的重要性不同。为了解决这一问题，我们提出了一种新的方法——无偏模型-不可知匹配方法(UMA2)。它包括两个基本模块: 1)通用匹配模型(GMM) ，该模型与模型无关，可以作为任何嵌入式双塔模型实现; 2)负样本偏差网络(NSDN) ，该网络借助逆倾向加权(IPW)的思想对负样本进行判别，并在 GMM 中重新权衡损失。UMA $^ 2 $在端到端多任务学习框架中无缝地集成了这两个模块。通过对现实世界离线数据集和在线 A/B 测试的大量实验，证明了该方法优于最先进的方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Re-weighting+Negative+Samples+for+Model-Agnostic+Matching)|0| -|[Item-Provider Co-learning for Sequential Recommendation](https://doi.org/10.1145/3477495.3531756)|Lei Chen, Jingtao Ding, Min Yang, Chengming Li, Chonggang Song, Lingling Yi|Tencent Inc., Shenzhen, China; Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China; Sun Yat-sen University, Shenzhen, China|Sequential recommender systems (SRSs) have become a research hotspot recently due to its powerful ability in capturing users' dynamic preferences. The key idea behind SRSs is to model the sequential dependencies over the user-item interactions. However, we argue that users' preferences are not only determined by their view or purchase items but also affected by the item-providers with which users have interacted. For instance, in a short-video scenario, a user may click on a video because he/she is attracted to either the video content or simply the video-providers as the vloggers are his/her idols. Motivated by the above observations, in this paper, we propose IPSRec, a novel Item-Provider co-learning framework for Sequential Recommendation. Specifically, we propose two representation learning methods (single-steam and cross-stream) to learn comprehensive item and user representations based on the user's historical item sequence and provider sequence. Then, contrastive learning is employed to further enhance the user embeddings in a self-supervised manner, which treats the representations of a specific user learned from the item side as well as the item-provider side as the positive pair and treats the representations of different users in the batch as the negative samples. Extensive experiments on three real-world SRS datasets demonstrate that IPSRec achieves substantially better results than the strong competitors. For reproducibility, our code and data are available at https://github.com/siat-nlp/IPSRec.|顺序推荐系统(SRS)由于具有捕获用户动态偏好的强大功能，近年来成为研究的热点。SRS 背后的关键思想是在用户-项目交互之间建立顺序依赖关系模型。然而，我们认为用户的偏好不仅取决于他们的观点或购买项目，而且还受到项目供应商的用户已经互动。例如，在一个短视频场景中，用户可能会点击一个视频，因为他/她要么被视频内容吸引，要么被视频提供商吸引，因为视频博客是他/她的偶像。基于上述观察，本文提出了一种新的项目提供者协同学习的序贯推荐框架 IPSRec。具体来说，我们提出了两种表示学习方法(单蒸汽和跨流)来学习综合项目和用户表示基于用户的历史项目序列和提供者序列。然后，采用对比学习的方法，以自监督的方式进一步增强用户嵌入，将从项目侧和项目提供者侧学习到的特定用户的表征视为正对，将批处理中不同用户的表征视为负样本。对三个实际 SRS 数据集的大量实验表明，IPSRec 比强大的竞争对手获得了更好的结果。为确保重复性，我们的代码和数据可在 https://github.com/siat-nlp/ipsrec 查阅。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Item-Provider+Co-learning+for+Sequential+Recommendation)|0| -|[Inconsistent Ranking Assumptions in Medical Search and Their Downstream Consequences](https://doi.org/10.1145/3477495.3531898)|Daniel Cohen, Kevin Du, Bhaskar Mitra, Laura Mercurio, Navid Rekabsaz, Carsten Eickhoff|Brown Univ, Alpert Med Sch, Providence, RI 02912 USA; Brown Univ, Providence, RI 02912 USA; Microsoft, Montreal, PQ, Canada; Johannes Kepler Univ Linz, Linz, Austria|Given a query, neural retrieval models predict point estimates of relevance for each document; however, a significant drawback of relying solely on point estimates is that they contain no indication of the model's confidence in its predictions. Despite this lack of information, downstream methods such as reranking, cutoff prediction, and none-of-the-above classification are still able to learn effective functions to accomplish their respective tasks. Unfortunately, these downstream methods can suffer poor performance when the initial ranking model loses confidence in its score predictions. This becomes increasingly important in high-stakes settings, such as medical searches that can influence health decision making. Recent work has resolved this lack of information by introducing Bayesian uncertainty to capture the possible distribution of a document score. This paper presents the use of this uncertainty information as an indicator of how well downstream methods will function over a ranklist. We highlight a significant bias against certain disease-related queries within the posterior distribution of a neural model, and show that this bias in a model's predictive distribution propagates to downstream methods. Finally, we introduce a multi-distribution uncertainty metric, confidence decay, as a valid way of partially identifying these failure cases in an offline setting without the need of any user feedback.|给定一个查询，神经检索模型预测每个文档的相关性点估计; 然而，仅仅依赖点估计的一个显著缺点是，它们不包含模型对其预测的置信度的指示。尽管缺乏这种信息，下游方法，如重新排序，截止预测，以及没有上述分类仍然能够学习有效的功能，以完成各自的任务。不幸的是，当初始排名模型对其分数预测失去信心时，这些下游方法的性能可能会很差。这在高风险环境中变得越来越重要，例如可以影响健康决策的医学搜索。最近的工作通过引入贝叶斯不确定性来捕获文档分数的可能分布，解决了这种信息缺乏的问题。本文介绍了使用这种不确定性信息作为一个指标，以及下游方法将如何在一个排名表的功能。我们强调，在神经模型的后验概率中，某些与疾病相关的查询存在显著的偏差，并表明模型预测分布的这种偏差会传播到下游方法。最后，我们介绍了一个多分布不确定性度量，置信度衰减，作为一个有效的方法，部分识别这些失败案例在脱机设置，而不需要任何用户反馈。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Inconsistent+Ranking+Assumptions+in+Medical+Search+and+Their+Downstream+Consequences)|0| +|[Retrieval and Recommendation Systems at the Crossroads of Artificial Intelligence, Ethics, and Regulation](https://doi.org/10.1145/3477495.3532683)|Markus Schedl, Emilia Gómez, Elisabeth Lex|Graz University of Technology, Graz, Austria; Johannes Kepler University Linz & Linz Institue of Technology, Linz, Austria; European Commission, Joint Research Centre and Universitat Pompeu Fabra, Seville/Barcelona, Spain|This tutorial aims at providing its audience an interdisciplinary overview about the topics of fairness and non-discrimination, diversity, and transparency of AI systems, tailored to the research fields of information retrieval and recommender systems. By means of this tutorial, we would like to equip the mostly technical audience of SIGIR with the necessary understanding of the ethical implications of their research and development on the one hand, and of recent political and legal regulations that address the aforementioned challenges on the other hand.|本教程旨在为读者提供一个关于人工智能系统的公平性和非歧视性、多样性和透明度等主题的跨学科概述，适用于信息检索和推荐系统的研究领域。通过本教程，我们希望让 SIGIR 的大多数技术读者一方面了解他们的研究和发展的道德影响，另一方面了解解决上述挑战的最新政治和法律法规。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Retrieval+and+Recommendation+Systems+at+the+Crossroads+of+Artificial+Intelligence,+Ethics,+and+Regulation)|0| +|[Adversarial Filtering Modeling on Long-term User Behavior Sequences for Click-Through Rate Prediction](https://doi.org/10.1145/3477495.3531788)|Xiaochen Li, Jian Liang, Xialong Liu, Yu Zhang|Lazada Group, Beijing, China; Alibaba Group, Beijing, China|Rich user behavior information is of great importance for capturing and understanding user interest in click-through rate (CTR) prediction. To improve the richness, collecting long-term behaviors becomes a typical approach in academy and industry but at the cost of increasing online storage and latency. Recently, researchers have proposed several approaches to shorten long-term behavior sequence and then model user interests. These approaches reduce online cost efficiently but do not well handle the noisy information in long-term user behavior, which may deteriorate the performance of CTR prediction significantly. To obtain better cost/performance trade-off, we propose a novel Adversarial Filtering Model (ADFM) to model long-term user behavior. ADFM uses a hierarchical aggregation representation to compress raw behavior sequence and then learns to remove useless behavior information with an adversarial filtering mechanism. The selected user behaviors are fed into interest extraction module for CTR prediction. Experimental results on public datasets and industrial dataset demonstrate that our method achieves significant improvements over state-of-the-art models.|丰富的用户行为信息对于捕捉和理解用户对点进率预测的兴趣非常重要。为了提高丰富性，收集长期行为成为学术界和工业界的一种典型方法，但代价是增加在线存储和延迟。最近，研究人员提出了几种方法来缩短长期行为序列，然后模型用户的兴趣。这些方法有效地降低了在线成本，但不能很好地处理长期用户行为中的噪声信息，这可能会严重影响 CTR 预测的性能。为了获得更好的性价比，我们提出了一种新的对抗过滤模型(ADFM)来模拟长期用户行为。ADFM 使用分层聚合表示来压缩原始行为序列，然后学习使用对抗性过滤机制去除无用的行为信息。将选定的用户行为反馈到兴趣提取模块中进行点击率预测。在公共数据集和工业数据集上的实验结果表明，该方法比现有的模型有明显的改进。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Adversarial+Filtering+Modeling+on+Long-term+User+Behavior+Sequences+for+Click-Through+Rate+Prediction)|0| +|[LoL: A Comparative Regularization Loss over Query Reformulation Losses for Pseudo-Relevance Feedback](https://doi.org/10.1145/3477495.3532017)|Yunchang Zhu, Liang Pang, Yanyan Lan, Huawei Shen, Xueqi Cheng|Data Intelligence System Research Center, Institute of Computing Technology, CAS & University of Chinese Academy of Sciences, Beijing, China; Tsinghua University, Beijing, China; Institute of Computing Technology, CAS & University of Chinese Academy of Sciences, Beijing, China; Data Intelligence System Research Center, Institute of Computing Technology, CAS, Beijing, China|Pseudo-relevance feedback (PRF) has proven to be an effective query reformulation technique to improve retrieval accuracy. It aims to alleviate the mismatch of linguistic expressions between a query and its potential relevant documents. Existing PRF methods independently treat revised queries originating from the same query but using different numbers of feedback documents, resulting in severe query drift. Without comparing the effects of two different revisions from the same query, a PRF model may incorrectly focus on the additional irrelevant information increased in the more feedback, and thus reformulate a query that is less effective than the revision using the less feedback. Ideally, if a PRF model can distinguish between irrelevant and relevant information in the feedback, the more feedback documents there are, the better the revised query will be. To bridge this gap, we propose the Loss-over-Loss (LoL) framework to compare the reformulation losses between different revisions of the same query during training. Concretely, we revise an original query multiple times in parallel using different amounts of feedback and compute their reformulation losses. Then, we introduce an additional regularization loss on these reformulation losses to penalize revisions that use more feedback but gain larger losses. With such comparative regularization, the PRF model is expected to learn to suppress the extra increased irrelevant information by comparing the effects of different revised queries. Further, we present a differentiable query reformulation method to implement this framework. This method revises queries in the vector space and directly optimizes the retrieval performance of query vectors, applicable for both sparse and dense retrieval models. Empirical evaluation demonstrates the effectiveness and robustness of our method for two typical sparse and dense retrieval models.|伪相关反馈(PRF)已被证明是一种有效的查询重构技术，以提高检索的准确性。它旨在缓解查询与潜在相关文档之间的语言表达不匹配问题。现有的 PRF 方法独立处理来自同一查询但使用不同数量的反馈文档的修改查询，导致严重的查询漂移。如果不比较来自同一查询的两个不同修订的效果，PRF 模型可能会错误地关注更多反馈中增加的附加不相关信息，从而使用更少的反馈重新表述比修订更低效的查询。理想情况下，如果 PRF 模型能够区分反馈中的不相关信息和相关信息，那么反馈文档越多，修改后的查询就越好。为了弥补这一差距，我们提出了损失超过损失(LoL)框架来比较同一查询在培训期间不同修订版本之间的重构损失。具体来说，我们使用不同数量的反馈并行多次修改原始查询，并计算它们的重新表述损失。然后，我们引入一个额外的正则化损失对这些重制损失，以惩罚修订使用更多的反馈，但获得更大的损失。通过这种比较正则化，PRF 模型可以通过比较不同修订查询的效果来抑制额外增加的不相关信息。进一步，我们提出了一个可微查询重构方法来实现这个框架。该方法对向量空间中的查询进行修正，直接优化查询向量的检索性能，适用于稀疏和密集检索模型。实验结果表明，该方法对两种典型的稀疏和密集检索模型具有较好的鲁棒性和有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=LoL:+A+Comparative+Regularization+Loss+over+Query+Reformulation+Losses+for+Pseudo-Relevance+Feedback)|0| +|[Determinantal Point Process Likelihoods for Sequential Recommendation](https://doi.org/10.1145/3477495.3531965)|Yuli Liu, Christian J. Walder, Lexing Xie|Australian National University & Data61, CSIRO, Canberra, Australia; Data61, CSIRO & Australian National University, Canberra, Australia|Sequential recommendation is a popular task in academic research and close to real-world application scenarios, where the goal is to predict the next action(s) of the user based on his/her previous sequence of actions. In the training process of recommender systems, the loss function plays an essential role in guiding the optimization of recommendation models to generate accurate suggestions for users. However, most existing sequential recommendation tech- niques focus on designing algorithms or neural network architectures, and few efforts have been made to tailor loss functions that fit naturally into the practical application scenario of sequential recommender systems. Ranking-based losses, such as cross-entropy and Bayesian Personalized Ranking (BPR) are widely used in the sequential recommendation area. We argue that such objective functions suffer from two inherent drawbacks: i) the dependencies among elements of a sequence are overlooked in these loss formulations; ii) instead of balancing accuracy (quality) and diversity, only generating accurate results has been over emphasized. We therefore propose two new loss functions based on the Determinantal Point Process (DPP) likelihood, that can be adaptively applied to estimate the subsequent item or items. The DPP-distributed item set captures natural dependencies among temporal actions, and a quality vs. diversity decomposition of the DPP kernel pushes us to go beyond accuracy-oriented loss functions. Experimental results using the proposed loss functions on three real-world datasets show marked improvements over state-of-the-art sequential recommendation methods in both quality and diversity metrics.|顺序推荐是学术研究中的一个热门任务，它接近于真实的应用场景，其目标是根据用户以前的操作顺序预测他/她的下一个操作。在推荐系统的培训过程中，损失函数对于指导推荐模型的优化，为用户提供准确的建议起着至关重要的作用。然而，现有的顺序推荐技术大多侧重于设计算法或神经网络体系结构，很少有人努力去调整自然适合顺序推荐系统实际应用场景的损失函数。基于排序的损失，如交叉熵和贝叶斯个性化排序(BPR)被广泛应用于序列推荐领域。我们认为这样的目标函数有两个固有的缺点: i)在这些损失公式中忽略了序列元素之间的依赖关系; ii)没有平衡准确性(质量)和多样性，只有产生准确的结果被过分强调。因此，我们提出两个新的基于行列式点过程(DPP)可能性的损失函数，可以自适应地应用于估计随后的项目。DPP 分布式项目集捕获时间操作之间的自然依赖关系，DPP 内核的质量与多样性分解促使我们超越面向准确性的损失函数。在三个实际数据集上使用提出的损失函数的实验结果显示，在质量和多样性度量方面，该方法比最先进的顺序推荐方法有明显的改进。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Determinantal+Point+Process+Likelihoods+for+Sequential+Recommendation)|0| +|[Re-weighting Negative Samples for Model-Agnostic Matching](https://doi.org/10.1145/3477495.3532053)|Jiazhen Lou, Hong Wen, Fuyu Lv, Jing Zhang, Tengfei Yuan, Zhao Li|Alibaba Group, Hangzhou, China; The University of Sydney, Darlington, NSW, Australia; Zhejiang University, Hangzhou, China|Recommender Systems (RS), as an efficient tool to discover users' interested items from a very large corpus, has attracted more and more attention from academia and industry. As the initial stage of RS, large-scale matching is fundamental yet challenging. A typical recipe is to learn user and item representations with a two-tower architecture and then calculate the similarity score between both representation vectors, which however still struggles in how to properly deal with negative samples. In this paper, we find that the common practice that randomly sampling negative samples from the entire space and treating them equally is not an optimal choice, since the negative samples from different sub-spaces at different stages have different importance to a matching model. To address this issue, we propose a novel method named Unbiased Model-Agnostic Matching Approach (UMA2). It consists of two basic modules including 1) General Matching Model (GMM), which is model-agnostic and can be implemented as any embedding-based two-tower models; and 2) Negative Samples Debias Network (NSDN), which discriminates negative samples by borrowing the idea of Inverse Propensity Weighting (IPW) and re-weighs the loss in GMM. UMA$^2$ seamlessly integrates these two modules in an end-to-end multi-task learning framework. Extensive experiments on both real-world offline dataset and online A/B test demonstrate its superiority over state-of-the-art methods.|推荐系统(RS)作为一种从庞大的语料库中发现用户感兴趣的项目的有效工具，越来越受到学术界和业界的关注。作为遥感的初始阶段，大规模匹配是一个基础性的挑战。一个典型的方法是使用双塔体系结构学习用户和项目表示，然后计算两个表示向量之间的相似度得分，但是如何正确处理负样本仍然是一个难题。在本文中，我们发现从整个空间中随机抽取负样本并平等对待它们的常见做法并不是最优选择，因为不同阶段不同子空间中的负样本对匹配模型的重要性不同。为了解决这一问题，我们提出了一种新的方法——无偏模型-不可知匹配方法(UMA2)。它包括两个基本模块: 1)通用匹配模型(GMM) ，该模型与模型无关，可以作为任何嵌入式双塔模型实现; 2)负样本偏差网络(NSDN) ，该网络借助逆倾向加权(IPW)的思想对负样本进行判别，并在 GMM 中重新权衡损失。UMA $^ 2 $在端到端多任务学习框架中无缝地集成了这两个模块。通过对现实世界离线数据集和在线 A/B 测试的大量实验，证明了该方法优于最先进的方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Re-weighting+Negative+Samples+for+Model-Agnostic+Matching)|0| +|[Item-Provider Co-learning for Sequential Recommendation](https://doi.org/10.1145/3477495.3531756)|Lei Chen, Jingtao Ding, Min Yang, Chengming Li, Chonggang Song, Lingling Yi|Sun Yat-sen University, Shenzhen, China; Tencent Inc., Shenzhen, China; Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China|Sequential recommender systems (SRSs) have become a research hotspot recently due to its powerful ability in capturing users' dynamic preferences. The key idea behind SRSs is to model the sequential dependencies over the user-item interactions. However, we argue that users' preferences are not only determined by their view or purchase items but also affected by the item-providers with which users have interacted. For instance, in a short-video scenario, a user may click on a video because he/she is attracted to either the video content or simply the video-providers as the vloggers are his/her idols. Motivated by the above observations, in this paper, we propose IPSRec, a novel Item-Provider co-learning framework for Sequential Recommendation. Specifically, we propose two representation learning methods (single-steam and cross-stream) to learn comprehensive item and user representations based on the user's historical item sequence and provider sequence. Then, contrastive learning is employed to further enhance the user embeddings in a self-supervised manner, which treats the representations of a specific user learned from the item side as well as the item-provider side as the positive pair and treats the representations of different users in the batch as the negative samples. Extensive experiments on three real-world SRS datasets demonstrate that IPSRec achieves substantially better results than the strong competitors. For reproducibility, our code and data are available at https://github.com/siat-nlp/IPSRec.|顺序推荐系统(SRS)由于具有捕获用户动态偏好的强大功能，近年来成为研究的热点。SRS 背后的关键思想是在用户-项目交互之间建立顺序依赖关系模型。然而，我们认为用户的偏好不仅取决于他们的观点或购买项目，而且还受到项目供应商的用户已经互动。例如，在一个短视频场景中，用户可能会点击一个视频，因为他/她要么被视频内容吸引，要么被视频提供商吸引，因为视频博客是他/她的偶像。基于上述观察，本文提出了一种新的项目提供者协同学习的序贯推荐框架 IPSRec。具体来说，我们提出了两种表示学习方法(单蒸汽和跨流)来学习综合项目和用户表示基于用户的历史项目序列和提供者序列。然后，采用对比学习的方法，以自监督的方式进一步增强用户嵌入，将从项目侧和项目提供者侧学习到的特定用户的表征视为正对，将批处理中不同用户的表征视为负样本。对三个实际 SRS 数据集的大量实验表明，IPSRec 比强大的竞争对手获得了更好的结果。为确保重复性，我们的代码和数据可在 https://github.com/siat-nlp/ipsrec 查阅。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Item-Provider+Co-learning+for+Sequential+Recommendation)|0| +|[Inconsistent Ranking Assumptions in Medical Search and Their Downstream Consequences](https://doi.org/10.1145/3477495.3531898)|Daniel Cohen, Kevin Du, Bhaskar Mitra, Laura Mercurio, Navid Rekabsaz, Carsten Eickhoff|Microsoft, Montreal, PQ, Canada; Brown Univ, Providence, RI 02912 USA; Brown Univ, Alpert Med Sch, Providence, RI 02912 USA; Johannes Kepler Univ Linz, Linz, Austria|Given a query, neural retrieval models predict point estimates of relevance for each document; however, a significant drawback of relying solely on point estimates is that they contain no indication of the model's confidence in its predictions. Despite this lack of information, downstream methods such as reranking, cutoff prediction, and none-of-the-above classification are still able to learn effective functions to accomplish their respective tasks. Unfortunately, these downstream methods can suffer poor performance when the initial ranking model loses confidence in its score predictions. This becomes increasingly important in high-stakes settings, such as medical searches that can influence health decision making. Recent work has resolved this lack of information by introducing Bayesian uncertainty to capture the possible distribution of a document score. This paper presents the use of this uncertainty information as an indicator of how well downstream methods will function over a ranklist. We highlight a significant bias against certain disease-related queries within the posterior distribution of a neural model, and show that this bias in a model's predictive distribution propagates to downstream methods. Finally, we introduce a multi-distribution uncertainty metric, confidence decay, as a valid way of partially identifying these failure cases in an offline setting without the need of any user feedback.|给定一个查询，神经检索模型预测每个文档的相关性点估计; 然而，仅仅依赖点估计的一个显著缺点是，它们不包含模型对其预测的置信度的指示。尽管缺乏这种信息，下游方法，如重新排序，截止预测，以及没有上述分类仍然能够学习有效的功能，以完成各自的任务。不幸的是，当初始排名模型对其分数预测失去信心时，这些下游方法的性能可能会很差。这在高风险环境中变得越来越重要，例如可以影响健康决策的医学搜索。最近的工作通过引入贝叶斯不确定性来捕获文档分数的可能分布，解决了这种信息缺乏的问题。本文介绍了使用这种不确定性信息作为一个指标，以及下游方法将如何在一个排名表的功能。我们强调，在神经模型的后验概率中，某些与疾病相关的查询存在显著的偏差，并表明模型预测分布的这种偏差会传播到下游方法。最后，我们介绍了一个多分布不确定性度量，置信度衰减，作为一个有效的方法，部分识别这些失败案例在脱机设置，而不需要任何用户反馈。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Inconsistent+Ranking+Assumptions+in+Medical+Search+and+Their+Downstream+Consequences)|0| |[Exploiting Session Information in BERT-based Session-aware Sequential Recommendation](https://doi.org/10.1145/3477495.3531910)|Jinseok Jamie Seol, Youngrok Ko, Sanggoo Lee|Seoul National University, Seoul, Republic of Korea|In recommendation systems, utilizing the user interaction history as sequential information has resulted in great performance improvement. However, in many online services, user interactions are commonly grouped by sessions that presumably share preferences, which requires a different approach from ordinary sequence representation techniques. To this end, sequence representation models with a hierarchical structure or various viewpoints have been developed but with a rather complex network structure. In this paper, we propose three methods to improve recommendation performance by exploiting session information while minimizing additional parameters in a BERT-based sequential recommendation model: using session tokens, adding session segment embeddings, and a time-aware self-attention. We demonstrate the feasibility of the proposed methods through experiments on widely used recommendation datasets.|在推荐系统中，利用用户交互历史作为序列信息，可以大大提高推荐系统的性能。然而，在许多在线服务中，用户交互通常按照可能共享首选项的会话进行分组，这需要一种不同于普通序列表示技术的方法。为此，开发了具有层次结构或不同视点的序列表示模型，但其网络结构相当复杂。在基于 BERT 的顺序推荐模型中，我们提出了利用会话信息同时最小化附加参数来提高推荐性能的三种方法: 使用会话令牌、增加会话段嵌入和有时间意识的自我注意。通过在广泛使用的推荐数据集上的实验，验证了该方法的可行性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Exploiting+Session+Information+in+BERT-based+Session-aware+Sequential+Recommendation)|0| -|[Towards Reproducible Machine Learning Research in Information Retrieval](https://doi.org/10.1145/3477495.3532686)|Ana Lucic, Maurits J. R. Bleeker, Maarten de Rijke, Koustuv Sinha, Sami Jullien, Robert Stojnic|McGill University, Montreal, Canada; Facebook AI Research, London, United Kingdom; University of Amsterdam, Amsterdam, Netherlands|While recent progress in the field of machine learning (ML) and information retrieval (IR) has been significant, the reproducibility of these cutting-edge results is often lacking, with many submissions failing to provide the necessary information in order to ensure subsequent reproducibility. Despite the introduction of self-check mechanisms before submission (such as the Reproducibility Checklist, criteria for evaluating reproducibility during reviewing at several major conferences, artifact review and badging framework, and dedicated reproducibility tracks and challenges at major IR conferences, the motivation for executing reproducible research is lacking in the broader information community. We propose this tutorial as a gentle introduction to help ensure reproducible research in IR, with a specific emphasis on ML aspects of IR research.|虽然机器学习(ML)和信息检索学习(IR)领域的最新进展显著，但这些尖端结果的可重复性往往缺乏，许多提交的文件未能提供必要的信息，以确保随后的可重复性。尽管在提交之前引入了自我检查机制(例如重现性检查表，在几个主要会议上评估重现性的标准，工件审查和徽章框架，以及在主要 IR 会议上专门的重现性轨道和挑战，在更广泛的信息社区中缺乏执行可重现性研究的动机。我们建议本教程作为一个温和的介绍，以帮助确保在 IR 的重复性研究，并特别强调机器学习方面的 IR 研究。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Towards+Reproducible+Machine+Learning+Research+in+Information+Retrieval)|0| -|[Structured and Natural Responses Co-generation for Conversational Search](https://doi.org/10.1145/3477495.3532063)|Chenchen Ye, Lizi Liao, Fuli Feng, Wei Ji, TatSeng Chua|University of Science and Technology of China, Heifei, China; Singapore Management University, Singapore, Singapore; Sea-NExT Joint Lab, National University of Singapore, Singapore, Singapore; National University of Singapore, Singapore, Singapore|Generating fluent and informative natural responses while main- taining representative internal states for search optimization is critical for conversational search systems. Existing approaches ei- ther 1) predict structured dialog acts first and then generate natural response; or 2) map conversation context to natural responses di- rectly in an end-to-end manner. Both kinds of approaches have shortcomings. The former suffers from error accumulation while the semantic associations between structured acts and natural re- sponses are confined in single direction. The latter emphasizes generating natural responses but fails to predict structured acts. Therefore, we propose a neural co-generation model that gener- ates the two concurrently. The key lies in a shared latent space shaped by two informed priors. Specifically, we design structured dialog acts and natural response auto-encoding as two auxiliary tasks in an interconnected network architecture. It allows for the concurrent generation and bidirectional semantic associations. The shared latent space also enables asynchronous reinforcement learn- ing for further joint optimization. Experiments show that our model achieves significant performance improvements.|生成流畅和信息丰富的自然反应，同时为搜索引擎优化保留有代表性的内部状态，这对会话搜索系统至关重要。现有的方法包括: 1)预测结构化对话首先发生，然后产生自然反应; 或者2)以端到端的方式将对话上下文直接映射到自然反应。这两种方法都有缺点。结构化行为和自然反应之间的语义联系是单向的，而结构化行为和自然反应之间的语义联系是单向的。后者强调产生自然反应，但无法预测结构化行为。因此，我们提出了一个神经元协同生成模型，并生成两个。关键在于一个共享的潜在空间，由两个知情的前任塑造。具体来说，我们设计了结构化对话行为和自然响应自动编码作为互联网络体系结构中的两个辅助任务。它允许并发生成和双向语义关联。共享潜在空间还支持异步强化学习，以进一步优化联合。实验结果表明，该模型取得了显著的性能改善。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Structured+and+Natural+Responses+Co-generation+for+Conversational+Search)|0| +|[Towards Reproducible Machine Learning Research in Information Retrieval](https://doi.org/10.1145/3477495.3532686)|Ana Lucic, Maurits J. R. Bleeker, Maarten de Rijke, Koustuv Sinha, Sami Jullien, Robert Stojnic|Facebook AI Research, London, United Kingdom; McGill University, Montreal, Canada; University of Amsterdam, Amsterdam, Netherlands|While recent progress in the field of machine learning (ML) and information retrieval (IR) has been significant, the reproducibility of these cutting-edge results is often lacking, with many submissions failing to provide the necessary information in order to ensure subsequent reproducibility. Despite the introduction of self-check mechanisms before submission (such as the Reproducibility Checklist, criteria for evaluating reproducibility during reviewing at several major conferences, artifact review and badging framework, and dedicated reproducibility tracks and challenges at major IR conferences, the motivation for executing reproducible research is lacking in the broader information community. We propose this tutorial as a gentle introduction to help ensure reproducible research in IR, with a specific emphasis on ML aspects of IR research.|虽然机器学习(ML)和信息检索学习(IR)领域的最新进展显著，但这些尖端结果的可重复性往往缺乏，许多提交的文件未能提供必要的信息，以确保随后的可重复性。尽管在提交之前引入了自我检查机制(例如重现性检查表，在几个主要会议上评估重现性的标准，工件审查和徽章框架，以及在主要 IR 会议上专门的重现性轨道和挑战，在更广泛的信息社区中缺乏执行可重现性研究的动机。我们建议本教程作为一个温和的介绍，以帮助确保在 IR 的重复性研究，并特别强调机器学习方面的 IR 研究。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Towards+Reproducible+Machine+Learning+Research+in+Information+Retrieval)|0| +|[Structured and Natural Responses Co-generation for Conversational Search](https://doi.org/10.1145/3477495.3532063)|Chenchen Ye, Lizi Liao, Fuli Feng, Wei Ji, TatSeng Chua|University of Science and Technology of China, Heifei, China; Sea-NExT Joint Lab, National University of Singapore, Singapore, Singapore; National University of Singapore, Singapore, Singapore; Singapore Management University, Singapore, Singapore|Generating fluent and informative natural responses while main- taining representative internal states for search optimization is critical for conversational search systems. Existing approaches ei- ther 1) predict structured dialog acts first and then generate natural response; or 2) map conversation context to natural responses di- rectly in an end-to-end manner. Both kinds of approaches have shortcomings. The former suffers from error accumulation while the semantic associations between structured acts and natural re- sponses are confined in single direction. The latter emphasizes generating natural responses but fails to predict structured acts. Therefore, we propose a neural co-generation model that gener- ates the two concurrently. The key lies in a shared latent space shaped by two informed priors. Specifically, we design structured dialog acts and natural response auto-encoding as two auxiliary tasks in an interconnected network architecture. It allows for the concurrent generation and bidirectional semantic associations. The shared latent space also enables asynchronous reinforcement learn- ing for further joint optimization. Experiments show that our model achieves significant performance improvements.|生成流畅和信息丰富的自然反应，同时为搜索引擎优化保留有代表性的内部状态，这对会话搜索系统至关重要。现有的方法包括: 1)预测结构化对话首先发生，然后产生自然反应; 或者2)以端到端的方式将对话上下文直接映射到自然反应。这两种方法都有缺点。结构化行为和自然反应之间的语义联系是单向的，而结构化行为和自然反应之间的语义联系是单向的。后者强调产生自然反应，但无法预测结构化行为。因此，我们提出了一个神经元协同生成模型，并生成两个。关键在于一个共享的潜在空间，由两个知情的前任塑造。具体来说，我们设计了结构化对话行为和自然响应自动编码作为互联网络体系结构中的两个辅助任务。它允许并发生成和双向语义关联。共享潜在空间还支持异步强化学习，以进一步优化联合。实验结果表明，该模型取得了显著的性能改善。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Structured+and+Natural+Responses+Co-generation+for+Conversational+Search)|0| |[PEVAE: A Hierarchical VAE for Personalized Explainable Recommendation](https://doi.org/10.1145/3477495.3532039)|Zefeng Cai, Zerui Cai|East China Normal University, Shanghai, China|Variational autoencoders (VAEs) have been widely applied in recommendations. One reason is that their amortized inferences are beneficial for overcoming the data sparsity. However, in explainable recommendation that generates natural language explanations, they are still rarely explored. Thus, we aim to extend VAE to explainable recommendation. In this task, we find that VAE can generate acceptable explanations for users with few relevant training samples, however, it tends to generate less personalized explanations for users with relatively sufficient samples than autoencoders (AEs). We conjecture that information shared by different users in VAE disturbs the information for a specific user. To deal with this problem, we present PErsonalized VAE (PEVAE) that generates personalized natural language explanations for explainable recommendation. Moreover, we propose two novel mechanisms to aid our model in generating more personalized explanations, including 1) Self-Adaption Fusion (SAF) manipulates the latent space in a self-adaption manner for controlling the influence of shared information. In this way, our model can enjoy the advantage of overcoming the sparsity of data while generating more personalized explanations for a user with relatively sufficient training samples. 2) DEpendence Maximization (DEM) strengthens dependence between recommendations and explanations by maximizing the mutual information. It makes the explanation more specific to the input user-item pair and thus improves the personalization of the generated explanations. Extensive experiments show PEVAE can generate more personalized explanations and further analyses demonstrate the practical effect of our proposed methods.|变分自动编码器(VAE)已被广泛应用于建议。一个原因是，他们的摊销推断有利于克服数据稀疏。然而，在生成自然语言解释的可解释推荐中，它们仍然很少被探索。因此，我们的目标是将 VAE 扩展到可解释的推荐。在这个任务中，我们发现 VAE 可以在相关训练样本较少的情况下为用户生成可接受的解释，但是，与自动编码器(AE)相比，它往往在样本相对充足的情况下为用户生成较少的个性化解释。我们推测 VAE 中不同用户共享的信息会干扰特定用户的信息。为了解决这个问题，我们提出了个性化的 VAE (PEVAE) ，它可以为解释性推荐生成个性化的自然语言解释。此外，我们提出了两种新的机制来帮助我们的模型产生更多的个性化解释，包括1)自适应融合(SAF)以自适应的方式操纵潜在空间来控制共享信息的影响。通过这种方式，我们的模型可以在克服数据稀疏性的同时，通过相对充足的训练样本为用户生成更加个性化的解释。2)依赖最大化(DEM)通过最大化相互信息来增强推荐与解释之间的依赖性。它使解释更加具体到输入用户项对，从而改进了生成的解释的个性化。大量的实验表明，PEVAE 可以产生更加个性化的解释，进一步的分析表明，我们提出的方法的实际效果。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=PEVAE:+A+Hierarchical+VAE+for+Personalized+Explainable+Recommendation)|0| -|[Counterfactual Learning To Rank for Utility-Maximizing Query Autocompletion](https://doi.org/10.1145/3477495.3531958)|Adam Block, Rahul Kidambi, Daniel N. Hill, Thorsten Joachims, Inderjit S. Dhillon|University of Texas at Austin, Austin, TX, USA; Massachusetts Institute of Technology, Cambridge, MA, USA; Amazon Music, San Fransisco, CA, USA; Amazon Search, Berkeley, CA, USA|Conventional methods for query autocompletion aim to predict which completed query a user will select from a list. A shortcoming of this approach is that users often do not know which query will provide the best retrieval performance on the current information retrieval system, meaning that any query autocompletion methods trained to mimic user behavior can lead to suboptimal query suggestions. To overcome this limitation, we propose a new approach that explicitly optimizes the query suggestions for downstream retrieval performance. We formulate this as a problem of ranking a set of rankings, where each query suggestion is represented by the downstream item ranking it produces. We then present a learning method that ranks query suggestions by the quality of their item rankings. The algorithm is based on a counterfactual learning approach that is able to leverage feedback on the items (e.g., clicks, purchases) to evaluate query suggestions through an unbiased estimator, thus avoiding the assumption that users write or select optimal queries. We establish theoretical support for the proposed approach and provide learning-theoretic guarantees. We also present empirical results on publicly available datasets, and demonstrate real-world applicability using data from an online shopping store.|查询自动完成的传统方法旨在预测用户将从列表中选择哪个已完成的查询。这种方法的一个缺点是，用户通常不知道哪个查询将在当前的信息检索系统上提供最佳的检索性能，这意味着任何经过训练的模拟用户行为的查询自动完成方法都可能导致次优的查询建议。为了克服这一限制，我们提出了一种新的方法，显式优化查询建议的下游检索性能。我们将这个问题表述为对一组排名进行排序的问题，其中每个查询建议由它产生的下游项目排名表示。然后，我们提出了一种学习方法，根据项目排名的质量对查询建议进行排序。该算法基于一种反事实学习方法，能够利用对项目(如点击、购买)的反馈，通过一个无偏估计器来评估查询建议，从而避免了用户编写或选择最佳查询的假设。我们为提出的方法建立了理论支持，并提供了学习理论保证。我们还提出了公开可用数据集的实证结果，并证明了真实世界的适用性使用数据从网上购物商店。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Counterfactual+Learning+To+Rank+for+Utility-Maximizing+Query+Autocompletion)|0| -|[Automatic Expert Selection for Multi-Scenario and Multi-Task Search](https://doi.org/10.1145/3477495.3531942)|Xinyu Zou, Zhi Hu, Yiming Zhao, Xuchu Ding, Zhongyi Liu, Chenliang Li, Aixin Sun|Ant Group, Hangzhou, China; Wuhan University, Wuhan, China; Nanyang Technological University, Singapore, Singapore|Multi-scenario learning (MSL) enables a service provider to cater for users' fine-grained demands by separating services for different user sectors, e.g., by user's geographical region. Under each scenario there is a need to optimize multiple task-specific targets e.g., click through rate and conversion rate, known as multi-task learning (MTL). Recent solutions for MSL and MTL are mostly based on the multi-gate mixture-of-experts (MMoE) architecture. MMoE structure is typically static and its design requires domain-specific knowledge, making it less effective in handling both MSL and MTL. In this paper, we propose a novel Automatic Expert Selection framework for Multi-scenario and Multi-task search, named AESM2. AESM2 integrates both MSL and MTL into a unified framework with an automatic structure learning. Specifically, AESM2 stacks multi-task layers over multi-scenario layers. This hierarchical design enables us to flexibly establish intrinsic connections between different scenarios, and at the same time also supports high-level feature extraction for different tasks. At each multi-scenario/multi-task layer, a novel expert selection algorithm is proposed to automatically identify scenario-/task-specific and shared experts for each input. Experiments over two real-world large-scale datasets demonstrate the effectiveness of AESM2 over a battery of strong baselines. Online A/B test also shows substantial performance gain on multiple metrics. Currently, AESM2 has been deployed online for serving major traffic.|多场景学习可让服务供应商因应用户的细粒度需求，将不同用户界别的服务(例如按用户地理地区)分开。在每种情况下，都需要优化多个特定任务的目标，例如点击率和转换率，称为多任务学习(MTL)。最近的 MSL 和 MTL 解决方案大多基于多门混合专家(MMoE)体系结构。MMoE 结构通常是静态的，其设计需要特定于领域的知识，因此在处理 MSL 和 MTL 时效率较低。本文提出了一种面向多场景多任务搜索的自动专家选择框架 AESM2。AESM2将 MSL 和 MTL 集成到一个具有自动结构学习的统一框架中。具体来说，AESM2在多场景层上堆叠多任务层。这种分层设计使我们能够灵活地建立不同场景之间的内在联系，同时也支持不同任务的高级特征提取。在每个多场景/多任务层，提出了一种新的专家选择算法来自动识别每个输入的场景/任务特定的和共享的专家。通过两个真实世界的大规模数据集的实验证明了 AESM2在一组强基线上的有效性。在线 A/B 测试还显示了在多个指标上的大量性能增益。目前，AESM2已经部署在线服务主要流量。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Automatic+Expert+Selection+for+Multi-Scenario+and+Multi-Task+Search)|0| -|[Multi-Agent RL-based Information Selection Model for Sequential Recommendation](https://doi.org/10.1145/3477495.3532022)|Kaiyuan Li, Pengfei Wang, Chenliang Li|Wuhan University, Beijing, China; Beijing University of Posts and Telecommunications, Beijing, China|For sequential recommender, the coarse-grained yet sparse sequential signals mined from massive user-item interactions have become the bottleneck to further improve the recommendation performance. To alleviate the spareness problem, exploiting auxiliary semantic features (\eg textual descriptions, visual images and knowledge graph) to enrich contextual information then turns into a mainstream methodology. Though effective, we argue that these different heterogeneous features certainly include much noise which may overwhelm the valuable sequential signals, and therefore easily reach the phenomenon of negative collaboration (ie 1 + 1 > 2). How to design a flexible strategy to select proper auxiliary information and alleviate the negative collaboration towards a better recommendation is still an interesting and open question. Unfortunately, few works have addressed this challenge in sequential recommendation. In this paper, we introduce a Multi-Agent RL-based Information S election Model (named MARIS) to explore an effective collaboration between different kinds of auxiliary information and sequential signals in an automatic way. Specifically, MARIS formalizes the auxiliary feature selection as a cooperative Multi-agent Markov Decision Process. For each auxiliary feature type, MARIS resorts to using an agent to determine whether a specific kind of auxiliary feature should be imported to achieve a positive collaboration. In between, a QMIX network is utilized to cooperate their joint selection actions and produce an episode corresponding an effective combination of different auxiliary features for the whole historical sequence. Considering the lack of supervised selection signals, we further devise a novel reward-guided sampling strategy to leverage exploitation and exploration scheme for episode sampling. By preserving them in a replay buffer, MARIS learns the action-value function and the reward alternatively for optimization. Extensive experiments on four real-world datasets demonstrate that our model obtains significant performance improvement over up-to-date state-of-the-art recommendation models.|对于序列推荐系统来说，从大量用户交互中挖掘出的粗粒度稀疏序列信号已经成为进一步提高推荐性能的瓶颈。为了解决这一问题，利用辅助语义特征(如文本描述、视觉图像和知识图形)丰富上下文信息成为主流方法论。虽然有效，我们认为这些不同的异质性特征肯定包括大量的噪声，这可能压倒有价值的序列信号，因此很容易达到负协作现象(即1 + 1 > 2)。如何设计一种灵活的策略，选择合适的辅助信息，减轻负面协作，以获得更好的推荐，仍然是一个有趣而开放的问题。不幸的是，很少有作品在连续推荐中解决了这个问题。本文提出了一种基于多 Agent RL 的信息 S 选择模型(MARIS) ，用于探索不同辅助信息与序列信号之间的自动有效协作。具体来说，MARIS 将辅助特征选择形式化为一个合作的多代理马可夫决策过程。对于每一种辅助特征类型，MARIS 都使用一个代理来确定是否需要导入一种特定的辅助特征来实现积极的协作。其间，利用 QMIX 网络协同它们的联合选择行动，产生对应于整个历史序列的不同辅助特征的有效组合的情节。考虑到缺乏监督选择信号，我们进一步设计了一种新的奖励引导抽样策略，以利用开发和探索方案的情节抽样。通过将它们保存在一个重播缓冲区中，MARIS 学习动作-价值函数和优化的报酬。在四个真实世界数据集上的大量实验表明，我们的模型比最新的最先进的推荐模型获得了显著的性能改进。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Multi-Agent+RL-based+Information+Selection+Model+for+Sequential+Recommendation)|0| +|[Counterfactual Learning To Rank for Utility-Maximizing Query Autocompletion](https://doi.org/10.1145/3477495.3531958)|Adam Block, Rahul Kidambi, Daniel N. Hill, Thorsten Joachims, Inderjit S. Dhillon|Amazon Search, Berkeley, CA, USA; Massachusetts Institute of Technology, Cambridge, MA, USA; Amazon Music, San Fransisco, CA, USA; University of Texas at Austin, Austin, TX, USA|Conventional methods for query autocompletion aim to predict which completed query a user will select from a list. A shortcoming of this approach is that users often do not know which query will provide the best retrieval performance on the current information retrieval system, meaning that any query autocompletion methods trained to mimic user behavior can lead to suboptimal query suggestions. To overcome this limitation, we propose a new approach that explicitly optimizes the query suggestions for downstream retrieval performance. We formulate this as a problem of ranking a set of rankings, where each query suggestion is represented by the downstream item ranking it produces. We then present a learning method that ranks query suggestions by the quality of their item rankings. The algorithm is based on a counterfactual learning approach that is able to leverage feedback on the items (e.g., clicks, purchases) to evaluate query suggestions through an unbiased estimator, thus avoiding the assumption that users write or select optimal queries. We establish theoretical support for the proposed approach and provide learning-theoretic guarantees. We also present empirical results on publicly available datasets, and demonstrate real-world applicability using data from an online shopping store.|查询自动完成的传统方法旨在预测用户将从列表中选择哪个已完成的查询。这种方法的一个缺点是，用户通常不知道哪个查询将在当前的信息检索系统上提供最佳的检索性能，这意味着任何经过训练的模拟用户行为的查询自动完成方法都可能导致次优的查询建议。为了克服这一限制，我们提出了一种新的方法，显式优化查询建议的下游检索性能。我们将这个问题表述为对一组排名进行排序的问题，其中每个查询建议由它产生的下游项目排名表示。然后，我们提出了一种学习方法，根据项目排名的质量对查询建议进行排序。该算法基于一种反事实学习方法，能够利用对项目(如点击、购买)的反馈，通过一个无偏估计器来评估查询建议，从而避免了用户编写或选择最佳查询的假设。我们为提出的方法建立了理论支持，并提供了学习理论保证。我们还提出了公开可用数据集的实证结果，并证明了真实世界的适用性使用数据从网上购物商店。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Counterfactual+Learning+To+Rank+for+Utility-Maximizing+Query+Autocompletion)|0| +|[Automatic Expert Selection for Multi-Scenario and Multi-Task Search](https://doi.org/10.1145/3477495.3531942)|Xinyu Zou, Zhi Hu, Yiming Zhao, Xuchu Ding, Zhongyi Liu, Chenliang Li, Aixin Sun|Ant Group, Hangzhou, China; Nanyang Technological University, Singapore, Singapore; Wuhan University, Wuhan, China|Multi-scenario learning (MSL) enables a service provider to cater for users' fine-grained demands by separating services for different user sectors, e.g., by user's geographical region. Under each scenario there is a need to optimize multiple task-specific targets e.g., click through rate and conversion rate, known as multi-task learning (MTL). Recent solutions for MSL and MTL are mostly based on the multi-gate mixture-of-experts (MMoE) architecture. MMoE structure is typically static and its design requires domain-specific knowledge, making it less effective in handling both MSL and MTL. In this paper, we propose a novel Automatic Expert Selection framework for Multi-scenario and Multi-task search, named AESM2. AESM2 integrates both MSL and MTL into a unified framework with an automatic structure learning. Specifically, AESM2 stacks multi-task layers over multi-scenario layers. This hierarchical design enables us to flexibly establish intrinsic connections between different scenarios, and at the same time also supports high-level feature extraction for different tasks. At each multi-scenario/multi-task layer, a novel expert selection algorithm is proposed to automatically identify scenario-/task-specific and shared experts for each input. Experiments over two real-world large-scale datasets demonstrate the effectiveness of AESM2 over a battery of strong baselines. Online A/B test also shows substantial performance gain on multiple metrics. Currently, AESM2 has been deployed online for serving major traffic.|多场景学习可让服务供应商因应用户的细粒度需求，将不同用户界别的服务(例如按用户地理地区)分开。在每种情况下，都需要优化多个特定任务的目标，例如点击率和转换率，称为多任务学习(MTL)。最近的 MSL 和 MTL 解决方案大多基于多门混合专家(MMoE)体系结构。MMoE 结构通常是静态的，其设计需要特定于领域的知识，因此在处理 MSL 和 MTL 时效率较低。本文提出了一种面向多场景多任务搜索的自动专家选择框架 AESM2。AESM2将 MSL 和 MTL 集成到一个具有自动结构学习的统一框架中。具体来说，AESM2在多场景层上堆叠多任务层。这种分层设计使我们能够灵活地建立不同场景之间的内在联系，同时也支持不同任务的高级特征提取。在每个多场景/多任务层，提出了一种新的专家选择算法来自动识别每个输入的场景/任务特定的和共享的专家。通过两个真实世界的大规模数据集的实验证明了 AESM2在一组强基线上的有效性。在线 A/B 测试还显示了在多个指标上的大量性能增益。目前，AESM2已经部署在线服务主要流量。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Automatic+Expert+Selection+for+Multi-Scenario+and+Multi-Task+Search)|0| +|[Multi-Agent RL-based Information Selection Model for Sequential Recommendation](https://doi.org/10.1145/3477495.3532022)|Kaiyuan Li, Pengfei Wang, Chenliang Li|Beijing University of Posts and Telecommunications, Beijing, China; Wuhan University, Beijing, China|For sequential recommender, the coarse-grained yet sparse sequential signals mined from massive user-item interactions have become the bottleneck to further improve the recommendation performance. To alleviate the spareness problem, exploiting auxiliary semantic features (\eg textual descriptions, visual images and knowledge graph) to enrich contextual information then turns into a mainstream methodology. Though effective, we argue that these different heterogeneous features certainly include much noise which may overwhelm the valuable sequential signals, and therefore easily reach the phenomenon of negative collaboration (ie 1 + 1 > 2). How to design a flexible strategy to select proper auxiliary information and alleviate the negative collaboration towards a better recommendation is still an interesting and open question. Unfortunately, few works have addressed this challenge in sequential recommendation. In this paper, we introduce a Multi-Agent RL-based Information S election Model (named MARIS) to explore an effective collaboration between different kinds of auxiliary information and sequential signals in an automatic way. Specifically, MARIS formalizes the auxiliary feature selection as a cooperative Multi-agent Markov Decision Process. For each auxiliary feature type, MARIS resorts to using an agent to determine whether a specific kind of auxiliary feature should be imported to achieve a positive collaboration. In between, a QMIX network is utilized to cooperate their joint selection actions and produce an episode corresponding an effective combination of different auxiliary features for the whole historical sequence. Considering the lack of supervised selection signals, we further devise a novel reward-guided sampling strategy to leverage exploitation and exploration scheme for episode sampling. By preserving them in a replay buffer, MARIS learns the action-value function and the reward alternatively for optimization. Extensive experiments on four real-world datasets demonstrate that our model obtains significant performance improvement over up-to-date state-of-the-art recommendation models.|对于序列推荐系统来说，从大量用户交互中挖掘出的粗粒度稀疏序列信号已经成为进一步提高推荐性能的瓶颈。为了解决这一问题，利用辅助语义特征(如文本描述、视觉图像和知识图形)丰富上下文信息成为主流方法论。虽然有效，我们认为这些不同的异质性特征肯定包括大量的噪声，这可能压倒有价值的序列信号，因此很容易达到负协作现象(即1 + 1 > 2)。如何设计一种灵活的策略，选择合适的辅助信息，减轻负面协作，以获得更好的推荐，仍然是一个有趣而开放的问题。不幸的是，很少有作品在连续推荐中解决了这个问题。本文提出了一种基于多 Agent RL 的信息 S 选择模型(MARIS) ，用于探索不同辅助信息与序列信号之间的自动有效协作。具体来说，MARIS 将辅助特征选择形式化为一个合作的多代理马可夫决策过程。对于每一种辅助特征类型，MARIS 都使用一个代理来确定是否需要导入一种特定的辅助特征来实现积极的协作。其间，利用 QMIX 网络协同它们的联合选择行动，产生对应于整个历史序列的不同辅助特征的有效组合的情节。考虑到缺乏监督选择信号，我们进一步设计了一种新的奖励引导抽样策略，以利用开发和探索方案的情节抽样。通过将它们保存在一个重播缓冲区中，MARIS 学习动作-价值函数和优化的报酬。在四个真实世界数据集上的大量实验表明，我们的模型比最新的最先进的推荐模型获得了显著的性能改进。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Multi-Agent+RL-based+Information+Selection+Model+for+Sequential+Recommendation)|0| |[Neural Statistics for Click-Through Rate Prediction](https://doi.org/10.1145/3477495.3531762)|Yanhua Huang, Hangyu Wang, Yiyun Miao, Ruiwen Xu, Lei Zhang, Weinan Zhang|Xiaohongshu Inc., Shanghai, China; Shanghai Jiao Tong University, Shanghai, China|With the success of deep learning, click-through rate (CTR) predictions are transitioning from shallow approaches to deep architectures. Current deep CTR prediction usually follows the Embedding & MLP paradigm, where the model embeds categorical features into latent semantic space. This paper introduces a novel embedding technique called neural statistics that instead learns explicit semantics of categorical features by incorporating feature engineering as an innate prior into the deep architecture in an end-to-end manner. Besides, since the statistical information changes over time, we study how to adapt to the distribution shift in the MLP module efficiently. Offline experiments on two public datasets validate the effectiveness of neural statistics against state-of-the-art models. We also apply it to a large-scale recommender system via online A/B tests, where the user's satisfaction is significantly improved.|随着深度学习的成功，点进率预测(ctrl)正从浅层方法向深层架构过渡。目前的深度 CTR 预测通常遵循嵌入与 MLP 范式，该模型将范畴特征嵌入到潜在语义空间中。本文介绍了一种新的嵌入技术，称为神经统计学，通过将特征工程作为一种先天优势以端到端的方式结合到深层体系结构中，来学习范畴特征的显性语义。此外，由于统计信息随时间变化，我们研究了如何有效地适应 MLP 模块中的分布变化。在两个公共数据集上的离线实验验证了针对最先进模型的神经统计的有效性。我们还通过在线 A/B 测试将其应用于大规模的推荐系统测试，用户的满意度显著提高。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Neural+Statistics+for+Click-Through+Rate+Prediction)|0| |[Towards Results-level Proportionality for Multi-objective Recommender Systems](https://doi.org/10.1145/3477495.3531787)|Ladislav Peska, Patrik Dokoupil|Charles University, Prague, Czech Rep|The main focus of our work is the problem of multiple objectives optimization (MOO) while providing a final list of recommendations to the user. Currently, system designers can tune MOO by setting importance of individual objectives, usually in some kind of weighted average setting. However, this does not have to translate into the presence of such objectives in the final results. In contrast, in our work we would like to allow system designers or end-users to directly quantify the required relative ratios of individual objectives in the resulting recommendations, e.g., the final results should have 60% relevance, 30% diversity and 10% novelty. If individual objectives are transformed to represent quality on the same scale, these result conditioning expressions may greatly contribute towards recommendations tuneability and explainability as well as user's control over recommendations. To achieve this task, we propose an iterative algorithm inspired by the mandates allocation problem in public elections. The algorithm is applicable as long as per-item marginal gains of individual objectives can be calculated. Effectiveness of the algorithm is evaluated on several settings of relevance-novelty-diversity optimization problem. Furthermore, we also outline several options to scale individual objectives to represent similar value for the user.|我们工作的主要重点是多目标优化(MOO)问题，同时向用户提供最终的建议列表。目前，系统设计者可以通过设定个人目标的重要性来调整 MOO，通常是在某种加权平均数设置中。然而，这并不意味着在最终结果中存在这样的目标。相比之下，在我们的工作中，我们希望允许系统设计者或最终用户直接量化结果建议中各个目标所需的相对比例，例如，最终结果应该有60% 的相关性，30% 的多样性和10% 的新颖性。如果将单个目标转换为在同一尺度上表示质量，那么这些结果条件表达式可能极大地有助于建议的可调整性和可解释性，以及用户对建议的控制。为了实现这一任务，我们提出了一个迭代算法的启发任务分配问题在公共选举。只要能够计算出单个目标的单项边际收益，该算法是可行的。该算法的有效性是根据相关性-新颖性-多样性最佳化问题进行评估的。此外，我们还概述了几个选项，以缩放单个目标，表示用户的类似价值。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Towards+Results-level+Proportionality+for+Multi-objective+Recommender+Systems)|0| |[Transform Cold-Start Users into Warm via Fused Behaviors in Large-Scale Recommendation](https://doi.org/10.1145/3477495.3531797)|Pengyang Li, Rong Chen, Quan Liu, Jian Xu, Bo Zheng|Alibaba Group, Hangzhou, China; Zhejiang University, Hangzhou, China|Recommendation for cold-start users who have very limited data is a canonical challenge in recommender systems. Existing deep recommender systems utilize user content features and behaviors to produce personalized recommendations, yet often face significant performance degradation on cold-start users compared to existing ones due to the following challenges: (1) Cold-start users may have a quite different distribution of features from existing users. (2) The few behaviors of cold-start users are hard to be exploited. In this paper, we propose a recommender system called Cold-Transformer to alleviate these problems. Specifically, we design context-based Embedding Adaption to offset the differences in feature distribution. It transforms the embedding of cold-start users into a warm state that is more like existing ones to represent corresponding user preferences. Furthermore, to exploit the few behaviors of cold-start users and characterize the user context, we propose Label Encoding that models Fused Behaviors of positive and negative feedback simultaneously, which are relatively more sufficient. Last, to perform large-scale industrial recommendations, we keep the two-tower architecture that de-couples user and target item. Extensive experiments on public and industrial datasets show that Cold-Transformer significantly outperforms state-of-the-art methods, including those that are deep coupled and less scalable.|对于数据非常有限的冷启动用户的推荐在推荐系统中是一个典型的挑战。现有的深度推荐系统利用用户内容特征和行为来产生个性化的推荐，但是由于以下挑战，冷启动用户的性能往往比现有的推荐系统有显著的下降: (1)冷启动用户可能具有与现有用户完全不同的特征分布。(2)冷启动用户的少数行为很难被利用。在这篇文章中，我们提出了一个叫做冷变压器的推荐系统来缓解这些问题。具体来说，我们设计了基于上下文的嵌入适应，以抵消特征分布的差异。它将冷启动用户的嵌入转换为更像现有用户的暖状态，以表示相应的用户偏好。此外，为了充分利用冷启动用户的少数行为并刻画用户上下文特征，我们提出了标签编码方法，该方法同时对正反馈和负反馈的融合行为进行建模，相对来说比较充分。最后，为了执行大规模的工业建议，我们保留了解耦用户和目标项目的双塔架构。在公共和工业数据集上进行的大量实验表明，冷变压器的性能明显优于最先进的方法，包括那些深度耦合和可伸缩性较差的方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Transform+Cold-Start+Users+into+Warm+via+Fused+Behaviors+in+Large-Scale+Recommendation)|0| -|[Coarse-to-Fine Sparse Sequential Recommendation](https://doi.org/10.1145/3477495.3531732)|Jiacheng Li, Tong Zhao, Jin Li, Jim Chan, Christos Faloutsos, George Karypis, SooMin Pantel, Julian J. McAuley|Carnegie Mellon University, Pittsburgh, PA, USA; Amazon, Seattle, WA, USA; University of California, San Diego, La Jolla, CA, USA|Sequential recommendation aims to model dynamic user behavior from historical interactions. Self-attentive methods have proven effective at capturing short-term dynamics and long-term preferences. Despite their success, these approaches still struggle to model sparse data, on which they struggle to learn high-quality item representations. We propose to model user dynamics from shopping intents and interacted items simultaneously. The learned intents are coarse-grained and work as prior knowledge for item recommendation. To this end, we present a coarse-to-fine self-attention framework, namely CaFe, which explicitly learns coarse-grained and fine-grained sequential dynamics. Specifically, CaFe first learns intents from coarse-grained sequences which are dense and hence provide high-quality user intent representations. Then, CaFe fuses intent representations into item encoder outputs to obtain improved item representations. Finally, we infer recommended items based on representations of items and corresponding intents. Experiments on sparse datasets show that CaFe outperforms state-of-the-art self-attentive recommenders by 44.03% [email protected] on average.|顺序推荐旨在从历史交互中建立动态用户行为模型。事实证明，自我关注的方法在捕捉短期动态和长期偏好方面是有效的。尽管这些方法取得了成功，但它们仍然难以建立稀疏数据的模型，难以在稀疏数据上学习高质量的项目表示。我们建议同时从购物意图和交互项目建立用户动态模型。学习意图是粗粒度的，作为项目推荐的先验知识。为此，我们提出了一个由粗到细的自我注意框架，即 CaFe，它显式地学习粗粒度和细粒度的序列动力学。具体来说，CaFe 首先从密集的粗粒度序列中学习意图，因此提供高质量的用户意图表示。然后，CaFe 将意图表示融合到项编码器输出中，以获得改进的项表示。最后，根据项目的表示和相应的意图推断推荐项目。在稀疏数据集上的实验表明，CaFe 的性能平均比最先进的自我关注推荐系统高出44.03% 。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Coarse-to-Fine+Sparse+Sequential+Recommendation)|0| +|[Coarse-to-Fine Sparse Sequential Recommendation](https://doi.org/10.1145/3477495.3531732)|Jiacheng Li, Tong Zhao, Jin Li, Jim Chan, Christos Faloutsos, George Karypis, SooMin Pantel, Julian J. McAuley|Carnegie Mellon University, Pittsburgh, PA, USA; University of California, San Diego, La Jolla, CA, USA; Amazon, Seattle, WA, USA|Sequential recommendation aims to model dynamic user behavior from historical interactions. Self-attentive methods have proven effective at capturing short-term dynamics and long-term preferences. Despite their success, these approaches still struggle to model sparse data, on which they struggle to learn high-quality item representations. We propose to model user dynamics from shopping intents and interacted items simultaneously. The learned intents are coarse-grained and work as prior knowledge for item recommendation. To this end, we present a coarse-to-fine self-attention framework, namely CaFe, which explicitly learns coarse-grained and fine-grained sequential dynamics. Specifically, CaFe first learns intents from coarse-grained sequences which are dense and hence provide high-quality user intent representations. Then, CaFe fuses intent representations into item encoder outputs to obtain improved item representations. Finally, we infer recommended items based on representations of items and corresponding intents. Experiments on sparse datasets show that CaFe outperforms state-of-the-art self-attentive recommenders by 44.03% [email protected] on average.|顺序推荐旨在从历史交互中建立动态用户行为模型。事实证明，自我关注的方法在捕捉短期动态和长期偏好方面是有效的。尽管这些方法取得了成功，但它们仍然难以建立稀疏数据的模型，难以在稀疏数据上学习高质量的项目表示。我们建议同时从购物意图和交互项目建立用户动态模型。学习意图是粗粒度的，作为项目推荐的先验知识。为此，我们提出了一个由粗到细的自我注意框架，即 CaFe，它显式地学习粗粒度和细粒度的序列动力学。具体来说，CaFe 首先从密集的粗粒度序列中学习意图，因此提供高质量的用户意图表示。然后，CaFe 将意图表示融合到项编码器输出中，以获得改进的项表示。最后，根据项目的表示和相应的意图推断推荐项目。在稀疏数据集上的实验表明，CaFe 的性能平均比最先进的自我关注推荐系统高出44.03% 。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Coarse-to-Fine+Sparse+Sequential+Recommendation)|0| |[Conversational Recommendation via Hierarchical Information Modeling](https://doi.org/10.1145/3477495.3531830)|Quan Tu, Shen Gao, Yanran Li, Jianwei Cui, Bin Wang, Rui Yan|Renmin University of China, Beijing, China; Peking University, Beijing, China; Xiaomi AI Lab, Beijing, China|Conversational recommendation system aims to recommend appropriate items to user by directly asking preference on attributes or recommending item list. However, most of existing methods only employ the flat item and attribute relationship, and ignore the hierarchical relationship connected by the similar user which can provide more comprehensive information. And these methods usually use the user accepted attributes to represent the conversational history and ignore the hierarchical information of sequential transition in the historical turns. In this paper, we propose Hierarchical Information-aware Conversational Recommender (HICR) to model the two types of hierarchical information to boost the performance of CRS. Experiments conducted on four benchmark datasets verify the effectiveness of our proposed model.|会话推荐系统旨在通过直接询问用户对属性的偏好或推荐项目列表来向用户推荐合适的项目。然而，现有的方法大多只使用平面项目和属性关系，而忽略了相似用户之间的层次关系，这样可以提供更全面的信息。这些方法通常使用用户接受的属性来表示会话历史，而忽略了历史转折中顺序转换的层次信息。本文提出了基于层次信息感知的会话推荐系统(HICR) ，对两种层次信息进行建模，以提高会话推荐系统的性能。在四个基准数据集上进行的实验验证了该模型的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Conversational+Recommendation+via+Hierarchical+Information+Modeling)|0| -|[CTnoCVR: A Novelty Auxiliary Task Making the Lower-CTR-Higher-CVR Upper](https://doi.org/10.1145/3477495.3531843)|Dandan Zhang, Haotian Wu, Guanqi Zeng, Yao Yang, Weijiang Qiu, Yujie Chen, Haoyuan Hu|Zhejiang Lab, Hangzhou, China; China Electric Power Research Institute, Beijing, China; Cainiao Network, Hangzhou, China; Beijing Jiaotong University, Beijing, China|In recent years, multi-task learning models based on deep learning in recommender systems have attracted increasing attention from researchers in industry and academia. Accurately estimating post-click conversion rate (CVR) is often considered as the primary task of multi-task learning in recommender systems. However, some advertisers may try to get higher click-through rates (CTR) by over-decorating their ads, which may result in excessive exposure to samples with lower CVR. For example, some only eye-catching clickbait have higher CTR, but actually, CVR is very low. As a result, the overall performance of the recommender system will be hurt. In this paper, we introduce a novelty auxiliary task called CTnoCVR, which aims to predict the probability of events with click but no-conversion, in various state-of-the-art multi-task models of recommender systems to promote samples with high CVR but low CTR. Plentiful Experiments on a large-scale dataset gathered from traffic logs of Taobao's recommender system demonstrate that the introduction of CTnoCVR task significantly improves the prediction effect of CVR under various multi-task frameworks. In addition, we conduct the online test and evaluate the effectiveness of our proposed method to make those samples with high CVR and low CTR rank higher.|近年来，推荐系统中基于深度学习的多任务学习模型越来越受到业界和学术界的关注。在推荐系统中，准确估计点击后转换率(CVR)常常被认为是多任务学习的首要任务。然而，一些广告商可能试图通过过度装饰他们的广告来获得更高的点击率(CTR) ，这可能导致过度暴露于低 CVR 的样品。例如，一些只有吸引眼球的点击诱饵有较高的点击率，但实际上，CVR 是非常低的。因此，推荐系统的整体表现将受到影响。本文介绍了一种新颖的辅助任务 CTnoCVR，该任务在推荐系统的多任务模型中预测点击不转换的事件发生概率，以提升高 CVR 低 CTR 的样本。大量的实验表明，在多任务框架下，引入 CTnoCVR 任务可以显著提高 CVR 的预测效果。这些实验都是从淘宝推荐系统的流量日志中收集的大规模数据集中得到的。此外，我们进行了在线测试，并评估了我们提出的方法的有效性，使高 CVR 和低 CTR 排名的样本。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=CTnoCVR:+A+Novelty+Auxiliary+Task+Making+the+Lower-CTR-Higher-CVR+Upper)|0| +|[CTnoCVR: A Novelty Auxiliary Task Making the Lower-CTR-Higher-CVR Upper](https://doi.org/10.1145/3477495.3531843)|Dandan Zhang, Haotian Wu, Guanqi Zeng, Yao Yang, Weijiang Qiu, Yujie Chen, Haoyuan Hu|Beijing Jiaotong University, Beijing, China; Cainiao Network, Hangzhou, China; Zhejiang Lab, Hangzhou, China; China Electric Power Research Institute, Beijing, China|In recent years, multi-task learning models based on deep learning in recommender systems have attracted increasing attention from researchers in industry and academia. Accurately estimating post-click conversion rate (CVR) is often considered as the primary task of multi-task learning in recommender systems. However, some advertisers may try to get higher click-through rates (CTR) by over-decorating their ads, which may result in excessive exposure to samples with lower CVR. For example, some only eye-catching clickbait have higher CTR, but actually, CVR is very low. As a result, the overall performance of the recommender system will be hurt. In this paper, we introduce a novelty auxiliary task called CTnoCVR, which aims to predict the probability of events with click but no-conversion, in various state-of-the-art multi-task models of recommender systems to promote samples with high CVR but low CTR. Plentiful Experiments on a large-scale dataset gathered from traffic logs of Taobao's recommender system demonstrate that the introduction of CTnoCVR task significantly improves the prediction effect of CVR under various multi-task frameworks. In addition, we conduct the online test and evaluate the effectiveness of our proposed method to make those samples with high CVR and low CTR rank higher.|近年来，推荐系统中基于深度学习的多任务学习模型越来越受到业界和学术界的关注。在推荐系统中，准确估计点击后转换率(CVR)常常被认为是多任务学习的首要任务。然而，一些广告商可能试图通过过度装饰他们的广告来获得更高的点击率(CTR) ，这可能导致过度暴露于低 CVR 的样品。例如，一些只有吸引眼球的点击诱饵有较高的点击率，但实际上，CVR 是非常低的。因此，推荐系统的整体表现将受到影响。本文介绍了一种新颖的辅助任务 CTnoCVR，该任务在推荐系统的多任务模型中预测点击不转换的事件发生概率，以提升高 CVR 低 CTR 的样本。大量的实验表明，在多任务框架下，引入 CTnoCVR 任务可以显著提高 CVR 的预测效果。这些实验都是从淘宝推荐系统的流量日志中收集的大规模数据集中得到的。此外，我们进行了在线测试，并评估了我们提出的方法的有效性，使高 CVR 和低 CTR 排名的样本。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=CTnoCVR:+A+Novelty+Auxiliary+Task+Making+the+Lower-CTR-Higher-CVR+Upper)|0| |[Smooth-AUC: Smoothing the Path Towards Rank-based CTR Prediction](https://doi.org/10.1145/3477495.3531865)|Shuang Tang, Fangyuan Luo, Jun Wu|Beijing Jiaotong University, Beijing, China|Deep neural networks (DNNs) have been a key technique for click-through rate (CTR) estimation, yet existing DNNs-based CTR models neglect the inconsistency between their optimization objectives (e.g., Binary Cross Entropy, BCE) and CTR ranking metrics (e.g., Area Under the ROC Curve, AUC). It is noteworthy that directly optimizing AUC by gradient-descent methods is difficult due to the non-differentiable Heaviside function built-in AUC. To this end, we propose a smooth approximation of AUC, called smooth-AUC (SAUC), towards the rank-based CTR prediction. Specifically, SAUC relaxes the Heaviside function via sigmoid with a temperature coefficient (aiming at controlling the function sharpness) in order to facilitate the gradient-based optimization. Furthermore, SAUC is a plug-and-play objective that can be used in any DNNs-based CTR model. Experimental results on two real-world datasets demonstrate that SAUC consistently improves the recommendation accuracy of current DNNs-based CTR models.|深度神经网络(DNN)一直是点进率评估的关键技术，然而现有的基于 DNN 的 CTR 模型忽略了它们的优化目标(例如，二进制交叉熵，BCE)和 CTR 排名指标(例如，ROC Curve 下面积，AUC)之间的不一致性。值得注意的是，由于 AUC 内置的不可微单位阶跃函数，用梯度下降法直接优化 AUC 是困难的。为此，我们提出了一种平滑近似的 AUC，称为平滑 AUC (SAUC) ，用于基于秩的 CTR 预测。具体来说，SAUC 通过 sigmoid 放松单位阶跃函数(目的是控制函数的清晰度) ，以便于基于梯度的优化温度系数。此外，SAUC 是一个即插即用的目标，可以在任何基于 DNN 的 CTR 模型中使用。在两个实际数据集上的实验结果表明，SAUC 一致地提高了当前基于 DNN 的 CTR 模型的推荐精度。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Smooth-AUC:+Smoothing+the+Path+Towards+Rank-based+CTR+Prediction)|0| |[Alignment Rationale for Query-Document Relevance](https://doi.org/10.1145/3477495.3531883)|Youngwoo Kim, Razieh Rahimi, James Allan|University of Massachusetts Amherst, Amherst, MA, USA|Deep neural networks are widely used for text pair classification tasks such as as adhoc information retrieval. These deep neural networks are not inherently interpretable and require additional efforts to get rationale behind their decisions. Existing explanation models are not yet capable of inducing alignments between the query terms and the document terms -- which part of the document rationales are responsible for which part of the query? In this paper, we study how the input perturbations can be used to infer or evaluate alignments between the query and document spans, which best explain the black-box ranker's relevance prediction. We use different perturbation strategies and accordingly propose a set of metrics to evaluate the faithfulness of alignment rationales to the model. Our experiments show that the defined metrics based on substitution-based perturbation are more successful in preferring higher-quality alignments, compared to the deletion-based metrics.|深度神经网络广泛用于文本对分类任务，如自组织信息检索。这些深层神经网络本质上是不可解释的，需要额外的努力来获得其决策背后的理由。现有的解释模型还不能在查询术语和文档术语之间引入对齐——文档基本原理的哪一部分负责查询的哪一部分？在本文中，我们研究了如何利用输入扰动来推断或评估查询和文档跨度之间的对齐，这最好地解释了黑盒排名的相关性预测。我们使用不同的摄动策略，并相应地提出了一套度量来评估对齐基本原理的忠实性模型。我们的实验表明，与基于删除的度量相比，基于替换扰动的度量更容易获得高质量的比对。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Alignment+Rationale+for+Query-Document+Relevance)|0| |[Learning to Rank Knowledge Subgraph Nodes for Entity Retrieval](https://doi.org/10.1145/3477495.3531888)|Parastoo Jafarzadeh, Zahra Amirmahani, Faezeh Ensan|Ferdowsi University of Mashhad, Mashhad, Iran; Ryerson University, Toronto, ON, Canada|The importance of entity retrieval, the task of retrieving a ranked list of related entities from big knowledge bases given a textual query, has been widely acknowledged in the literature. In this paper, we propose a novel entity retrieval method that addresses the important challenge that revolves around the need to effectively represent and model context in which entities relate to each other. Based on our proposed method, a model is firstly trained to retrieve and prune a subgraph of a textual knowledge graph that represents contextual relationships between entities. Secondly, a deep model is introduced to reason over the textual content of nodes, edges, and the given question and score and rank entities in the subgraph. We show experimentally that our approach outperforms state-of-the-art methods on a number of benchmarks for entity retrieval.|实体检索的重要性在文献中得到了广泛的认可。在本文中，我们提出了一种新的实体检索方法，以解决围绕着需要有效地表示和模型实体相互关联的上下文的重要挑战。基于该方法，首先训练一个模型来检索和剪枝表示实体间上下文关系的文本知识图的子图。其次，引入一个深度模型来推理子图中节点、边和给定问题的文本内容以及子图中的得分和排序实体。我们的实验表明，我们的方法在实体检索的许多基准上优于最先进的方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Learning+to+Rank+Knowledge+Subgraph+Nodes+for+Entity+Retrieval)|0| |[ELECRec: Training Sequential Recommenders as Discriminators](https://doi.org/10.1145/3477495.3531894)|Yongjun Chen, Jia Li, Caiming Xiong|Salesforce Research, Palo Alto, CA, USA|Sequential recommendation is often considered as a generative task, i.e., training a sequential encoder to generate the next item of a user's interests based on her historical interacted items. Despite their prevalence, these methods usually require training with more meaningful samples to be effective, which otherwise will lead to a poorly trained model. In this work, we propose to train the sequential recommenders as discriminators rather than generators. Instead of predicting the next item, our method trains a discriminator to distinguish if a sampled item is a 'real' target item or not. A generator, as an auxiliary model, is trained jointly with the discriminator to sample plausible alternative next items and will be thrown out after training. The trained discriminator is considered as the final SR model and denoted as \modelname. Experiments conducted on four datasets demonstrate the effectiveness and efficiency of the proposed approach.|顺序推荐通常被认为是一个生成任务，例如，训练一个顺序编码器根据用户的历史交互项目生成下一个用户感兴趣的项目。尽管这些方法普遍存在，但通常需要训练更有意义的样本才能有效，否则将导致训练不足的模型。在这项工作中，我们建议训练顺序推荐器作为鉴别器，而不是生成器。我们的方法不是预测下一个项目，而是训练一个鉴别器来区分一个采样的项目是否是“真正的”目标项目。发电机作为辅助模型，与鉴别器联合训练，以抽样合理的替代下一个项目，并将在训练后抛出。训练后的鉴别器被认为是最终的 SR 模型，并表示为模型名。在四个数据集上进行的实验表明了该方法的有效性和高效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=ELECRec:+Training+Sequential+Recommenders+as+Discriminators)|0| |[A2A-API: A Prototype for Biomedical Information Retrieval Research and Benchmarking](https://doi.org/10.1145/3477495.3531667)|Maciej Rybinski, Liam Watts, Sarvnaz Karimi|CSIRO Data61, Sydney, NSW, Australia|Finding relevant literature is crucial for biomedical research and in the practice of evidence-based medicine, making biomedical search an important application area within the field of information retrieval. This is recognised by the broader IR community, and in particular by the organisers of Text Retrieval Conference (TREC) as early as 2003. While TREC provides crucial evaluation resources, to get started in biomedical IR one needs to tackle an important software engineering hurdle of parsing, indexing, and deploying several large document collections. Moreover, many newcomers to the field often face a steep learning curve, where theoretical concepts are tangled up with technical aspects. Finally, many of the existing baselines and systems are difficult to reproduce. We aim to alleviate all three of these bottlenecks with the launch of A2A-API. It is a RESTful API which serves as an easy-to-use and programming-language-independent interface to existing biomedical TREC collections. It builds upon A2A, our system for biomedical information retrieval benchmarking, and extends it with additional functionalities. Apart from providing programmatic access to the features of the original A2A system - focused principally on benchmarking - A2A-API supports biomedical IR researchers in development of systems featuring reranking and query reformulation components. In this demonstration, we illustrate the capabilities of A2A-API with comprehensive use cases.|寻找相关文献对于生物医学研究和循证医学的实践至关重要，这使得生物医学搜索成为信息检索领域的一个重要应用领域。早在2003年，更广泛的信息检索社区，特别是文本检索会议(TREC)的组织者就认识到了这一点。虽然 TREC 提供了关键的评估资源，但要开始学习生物医学 IR，需要解决一个重要的软件工程障碍，即解析、索引和部署几个大型文档集。此外，该领域的许多新手往往面临一个陡峭的学习曲线，其中理论概念与技术方面纠缠在一起。最后，许多现有的基线和系统很难再现。我们的目标是通过推出 A2A-API 来缓解所有这三个瓶颈。它是一个 RESTful API，作为一个易于使用和独立于编程语言的接口，用于现有的生物医学 TREC 集合。它建立在我们的生物医学信息检索基准测试系统 A2A 的基础上，并扩展了其他功能。除了提供对原始 A2A 系统特性的程序访问(主要侧重于基准测试)外，A2A-API 还支持生物医学红外研究人员开发具有重新排序和查询重新制定组件的系统。在本演示中，我们通过全面的用例说明了 A2A-API 的功能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A2A-API:+A+Prototype+for+Biomedical+Information+Retrieval+Research+and+Benchmarking)|0| -|[Learning to Rank Instant Search Results with Multiple Indices: A Case Study in Search Aggregation for Entertainment](https://doi.org/10.1145/3477495.3536334)|Scott Rome, Sardar Hamidian, Richard Walsh, Kevin Foley, Ferhan Ture|Comcast, Sunnyvale, CA, USA; Comcast, Washington, DC, USA; Comcast, Philadelphia, PA, USA|At Xfinity, an instant search system provides a variety of results for a given query from different sources. For each keystroke, new results are rendered on screen to the user, which could contain movies, television series, sporting events, music videos, news clips, person pages, and other result types. Users are also able to use the Xfinity Voice Remote to submit longer queries, some of which are more open-ended. Examples of queries include incomplete words which match multiple results through lexical matching (i.e., "ali"), topical searches ("vampire movies"), and more specific longer searches ("Movies with Adam Sandler"). Since results can be based on lexical matches, semantic matches, item-to-item similarity matches, or a variety of business logic driven sources, a key challenge is how to combine results into a single list. To accomplish this, we propose merging the lists via a Learning to Rank (LTR) neural model which takes into account the search query. This combined list can be personalized via a second LTR neural model with knowledge of the user's search history and metadata of the programs. Because instant search is under-represented in the literature, we present our learnings from research to aid other practitioners.|在 Xfinity，即时搜索系统为来自不同来源的特定查询提供多种结果。对于每次按键，新的结果都会在屏幕上呈现给用户，其中可能包含电影、电视剧、体育赛事、音乐视频、新闻剪辑、人物页面和其他结果类型。用户还可以使用 Xfinity Voice Remote 提交更长的查询，其中一些查询更为开放。查询的例子包括通过词汇匹配(例如“ ali”)匹配多个结果的不完整单词、主题搜索(“吸血鬼电影”)和更具体的长搜索(“与 Adam Sandler 的电影”)。由于结果可以基于词汇匹配、语义匹配、项目间相似性匹配或各种业务逻辑驱动源，因此一个关键的挑战是如何将结果组合成一个单独的列表。为了实现这一点，我们建议通过一个学习排序(LTR)神经模型，考虑到搜索查询合并列表。这个组合列表可以通过第二个具有用户搜索历史和程序元数据知识的 LTR 神经模型进行个性化。因为即时搜索在文献中的代表性不足，我们提出我们从研究中学到的东西来帮助其他从业者。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Learning+to+Rank+Instant+Search+Results+with+Multiple+Indices:+A+Case+Study+in+Search+Aggregation+for+Entertainment)|0| +|[Learning to Rank Instant Search Results with Multiple Indices: A Case Study in Search Aggregation for Entertainment](https://doi.org/10.1145/3477495.3536334)|Scott Rome, Sardar Hamidian, Richard Walsh, Kevin Foley, Ferhan Ture|Comcast, Washington, DC, USA; Comcast, Sunnyvale, CA, USA; Comcast, Philadelphia, PA, USA|At Xfinity, an instant search system provides a variety of results for a given query from different sources. For each keystroke, new results are rendered on screen to the user, which could contain movies, television series, sporting events, music videos, news clips, person pages, and other result types. Users are also able to use the Xfinity Voice Remote to submit longer queries, some of which are more open-ended. Examples of queries include incomplete words which match multiple results through lexical matching (i.e., "ali"), topical searches ("vampire movies"), and more specific longer searches ("Movies with Adam Sandler"). Since results can be based on lexical matches, semantic matches, item-to-item similarity matches, or a variety of business logic driven sources, a key challenge is how to combine results into a single list. To accomplish this, we propose merging the lists via a Learning to Rank (LTR) neural model which takes into account the search query. This combined list can be personalized via a second LTR neural model with knowledge of the user's search history and metadata of the programs. Because instant search is under-represented in the literature, we present our learnings from research to aid other practitioners.|在 Xfinity，即时搜索系统为来自不同来源的特定查询提供多种结果。对于每次按键，新的结果都会在屏幕上呈现给用户，其中可能包含电影、电视剧、体育赛事、音乐视频、新闻剪辑、人物页面和其他结果类型。用户还可以使用 Xfinity Voice Remote 提交更长的查询，其中一些查询更为开放。查询的例子包括通过词汇匹配(例如“ ali”)匹配多个结果的不完整单词、主题搜索(“吸血鬼电影”)和更具体的长搜索(“与 Adam Sandler 的电影”)。由于结果可以基于词汇匹配、语义匹配、项目间相似性匹配或各种业务逻辑驱动源，因此一个关键的挑战是如何将结果组合成一个单独的列表。为了实现这一点，我们建议通过一个学习排序(LTR)神经模型，考虑到搜索查询合并列表。这个组合列表可以通过第二个具有用户搜索历史和程序元数据知识的 LTR 神经模型进行个性化。因为即时搜索在文献中的代表性不足，我们提出我们从研究中学到的东西来帮助其他从业者。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Learning+to+Rank+Instant+Search+Results+with+Multiple+Indices:+A+Case+Study+in+Search+Aggregation+for+Entertainment)|0| |[Scalable Exploration for Neural Online Learning to Rank with Perturbed Feedback](https://doi.org/10.1145/3477495.3532057)|Yiling Jia, Hongning Wang|University of Virginia, Charlottesville, VA, USA|Deep neural networks (DNNs) demonstrates significant advantages in improving ranking performance in retrieval tasks. Driven by the recent developments in optimization and generalization of DNNs, learning a neural ranking model online from its interactions with users becomes possible. However, the required exploration for model learning has to be performed in the entire neural network parameter space, which is prohibitively expensive and limits the application of such online solutions in practice. In this work, we propose an efficient exploration strategy for online interactive neural ranker learning based on bootstrapping. Our solution is based on an ensemble of ranking models trained with perturbed user click feedback. The proposed method eliminates explicit confidence set construction and the associated computational overhead, which enables the online neural rankers training to be efficiently executed in practice with theoretical guarantees. Extensive comparisons with an array of state-of-the-art OL2R algorithms on two public learning to rank benchmark datasets demonstrate the effectiveness and computational efficiency of our proposed neural OL2R solution.|深层神经网络(DNN)在提高检索任务的排序性能方面具有显著的优势。在 DNN 优化和泛化的最新发展的驱动下，从与用户的交互中学习在线神经排序模型成为可能。然而，模型学习所需要的探索必须在整个神经网络参数空间中进行，这是非常昂贵的，并且限制了这种在线解决方案在实际中的应用。本文提出了一种基于自举的在线交互式神经排序学习的有效探索策略。我们的解决方案是基于一个排名模型的集合训练与不安的用户点击反馈。该方法消除了显式置信集结构和相关的计算开销，使在线神经排序训练能够在理论保证的情况下在实际应用中有效地执行。通过与一系列最先进的 OL2R 算法在两个公共学习基准数据集上的广泛比较，证明了我们提出的神经 OL2R 解决方案的有效性和计算效率。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Scalable+Exploration+for+Neural+Online+Learning+to+Rank+with+Perturbed+Feedback)|0| |[Towards Validating Long-Term User Feedbacks in Interactive Recommendation Systems](https://doi.org/10.1145/3477495.3531869)|Hojoon Lee, Dongyoon Hwang, Kyushik Min, Jaegul Choo|KAIST, SeongNam, Republic of Korea; KAKAO Enterprise, SeongNam, Republic of Korea|Interactive Recommender Systems (IRSs) have attracted a lot of attention, due to their ability to model interactive processes between users and recommender systems. Numerous approaches have adopted Reinforcement Learning (RL) algorithms, as these can directly maximize users' cumulative rewards. In IRS, researchers commonly utilize publicly available review datasets to compare and evaluate algorithms. However, user feedback provided in public datasets merely includes instant responses (e.g., a rating), with no inclusion of delayed responses (e.g., the dwell time and the lifetime value). Thus, the question remains whether these review datasets are an appropriate choice to evaluate the long-term effects in IRS. In this work, we revisited experiments on IRS with review datasets and compared RL-based models with a simple reward model that greedily recommends the item with the highest one-step reward. Following extensive analysis, we can reveal three main findings: First, a simple greedy reward model consistently outperforms RL-based models in maximizing cumulative rewards. Second, applying higher weighting to long-term rewards leads to degradation of recommendation performance. Third, user feedbacks have mere long-term effects in the benchmark datasets. Based on our findings, we conclude that a dataset has to be carefully verified and that a simple greedy baseline should be included for a proper evaluation of RL-based IRS approaches. Our code and dataset are available at https://github.com/dojeon-ai/irs_validation.|交互式推荐系统(IRS)由于能够对用户和推荐系统之间的交互过程进行建模而引起了人们的广泛关注。许多方法都采用了强化学习算法，因为这些算法可以直接最大化用户的累积回报。在 IRS 中，研究人员通常利用公开的评论数据集来比较和评估算法。然而，在公共数据集中提供的用户反馈只包括即时响应(例如，评级) ，没有包括延迟响应(例如，停留时间和生命周期值)。因此，问题仍然是这些审查数据集是否是评估 IRS 长期影响的合适选择。在这项工作中，我们重新回顾了 IRS 的实验与评论数据集，并比较了基于 RL 的模型与一个简单的奖励模型，贪婪地推荐项目具有最高的一步奖励。经过广泛的分析，我们可以揭示三个主要的发现: 第一，一个简单的贪婪报酬模型在最大化累积报酬方面始终优于基于 RL 的模型。其次，对长期奖励加权会导致推荐绩效的下降。第三，用户反馈在基准数据集中只有长期效果。基于我们的研究结果，我们得出结论，一个数据集必须被仔细验证，并且一个简单的贪婪基线应该被包括在一个基于 RL 的 IRS 方法的正确评估中。我们的代码和数据集可在 https://github.com/dojeon-ai/irs_validation 下载。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Towards+Validating+Long-Term+User+Feedbacks+in+Interactive+Recommendation+Systems)|0| -|[Structure-Aware Semantic-Aligned Network for Universal Cross-Domain Retrieval](https://doi.org/10.1145/3477495.3532061)|Jialin Tian, Xing Xu, Kai Wang, Zuo Cao, Xunliang Cai, Heng Tao Shen|Meituan, Shanghai, China; University of Electronic Science and Technology of China & Peng Cheng Laboratory, Chengdu, China; University of Electronic Science and Technology of China, Chengdu, China|The goal of cross-domain retrieval (CDR) is to search for instances of the same category in one domain by using a query from another domain. Existing CDR approaches mainly consider the standard scenario that the cross-domain data for both training and testing come from the same categories and underlying distributions. However, these methods cannot be well extended to the newly emerging task of universal cross-domain retrieval (UCDR), where the testing data belong to the domain and categories not present during training. Compared to CDR, the UCDR task is more challenging due to (1) visually diverse data from multi-source domains, (2) the domain shift between seen and unseen domains, and (3) the semantic shift across seen and unseen categories. To tackle these problems, we propose a novel model termed Structure-Aware Semantic-Aligned Network (SASA) to align the heterogeneous representations of multi-source domains without loss of generalizability for the UCDR task. Specifically, we leverage the advanced Vision Transformer (ViT) as the backbone and devise a distillation-alignment ViT (DAViT) with a novel token-based strategy, which incorporates two complementary distillation and alignment tokens into the ViT architecture. In addition, the distillation token is devised to improve the generalizability of our model by structure information preservation and the alignment token is used to improve discriminativeness with trainable categorical prototypes. Extensive experiments on three large-scale benchmarks, i.e., Sketchy, TU-Berlin, and DomainNet, demonstrate the superiority of our SASA method over the state-of-the-art UCDR and ZS-SBIR methods.|跨域检索(CDR)的目标是通过使用来自另一个域的查询在一个域中搜索相同类别的实例。现有的 CDR 方法主要考虑这样的标准场景: 用于培训和测试的跨域数据来自相同的类别和底层分布。然而，这些方法不能很好地推广到新出现的通用跨域检索(UCDR)任务，其中的测试数据属于领域和类别不存在的训练过程中。与 CDR 相比，UCDR 任务更具挑战性，因为(1)来自多源域的视觉多样化数据，(2)可见和不可见域之间的域转移，以及(3)跨可见和不可见类别的语义转移。为了解决这些问题，我们提出了一种称为结构感知语义对齐网络(SASA)的新模型，该模型可以在不损失 UCDR 任务通用性的前提下对多源域的异构表示进行对齐。具体而言，我们利用先进的视觉变压器(ViT)作为骨干，并设计了一种蒸馏对准 ViT (DAViT) ，其具有基于令牌的新策略，其将两个互补的蒸馏和对准令牌合并到 ViT 体系结构中。此外，通过结构信息的保留，设计了精馏令牌来提高模型的泛化能力，并利用对齐令牌来提高可训练范畴原型的区分能力。在 Sketchy、 TU-Berlin 和 DomainNet 这三个大型基准测试上的大量实验证明了我们的 SASA 方法优于最先进的 UCDR 和 ZS-SBIR 方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Structure-Aware+Semantic-Aligned+Network+for+Universal+Cross-Domain+Retrieval)|0| -|[Enhancing Top-N Item Recommendations by Peer Collaboration](https://doi.org/10.1145/3477495.3531773)|Yang Sun, Fajie Yuan, Min Yang, Alexandros Karatzoglou, Li Shen, Xiaoyan Zhao|Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China; Google Research, London, United Kingdom; Westlake University, Hangzhou, China; Harbin Institute of Technology, Shenzhen, Shenzhen, China; JD Explore Academy, Beijing, China|Deep neural networks (DNN) based recommender models often require numerous parameters to achieve remarkable performance. However, this inevitably brings redundant neurons, a phenomenon referred to as over-parameterization. In this paper, we plan to exploit such redundancy phenomena for recommender systems (RS), and propose a top-N item recommendation framework called PCRec that leverages collaborative training of two recommender models of the same network structure, termed peer collaboration. We first introduce two criteria to identify the importance of parameters of a given recommender model. Then, we rejuvenate the unimportant parameters by copying parameters from its peer network. After such an operation and retraining, the original recommender model is endowed with more representation capacity by possessing more functional model parameters. To show its generality, we instantiate PCRec by using three well-known recommender models. We conduct extensive experiments on two real-world datasets, and show that PCRec yields significantly better performance than its counterpart with the same model (parameter) size.|基于深度神经网络(DNN)的推荐模型往往需要大量的参数才能达到显著的性能。然而，这不可避免地带来了多余的神经元，这种现象被称为过度参数化。在本文中，我们计划在推荐系统中利用这种冗余现象，并提出了一个名为 PCRec 的前 N 项推荐框架，该框架利用了两个相同网络结构的推荐模型的协同训练，称为对等协作。我们首先引入两个标准来确定一个给定的推荐模型参数的重要性。然后，我们通过从其对等网络中复制参数来恢复不重要的参数。经过这样的操作和再训练，原有的推荐模型具有更多的功能模型参数，从而具有更强的表示能力。为了显示其通用性，我们使用三个著名的推荐模型来实例化 PCRec。我们在两个真实世界的数据集上进行了广泛的实验，结果表明，与相同模型(参数)大小的同类数据集相比，PCRec 产生了明显更好的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Enhancing+Top-N+Item+Recommendations+by+Peer+Collaboration)|0| +|[Structure-Aware Semantic-Aligned Network for Universal Cross-Domain Retrieval](https://doi.org/10.1145/3477495.3532061)|Jialin Tian, Xing Xu, Kai Wang, Zuo Cao, Xunliang Cai, Heng Tao Shen|University of Electronic Science and Technology of China, Chengdu, China; University of Electronic Science and Technology of China & Peng Cheng Laboratory, Chengdu, China; Meituan, Shanghai, China|The goal of cross-domain retrieval (CDR) is to search for instances of the same category in one domain by using a query from another domain. Existing CDR approaches mainly consider the standard scenario that the cross-domain data for both training and testing come from the same categories and underlying distributions. However, these methods cannot be well extended to the newly emerging task of universal cross-domain retrieval (UCDR), where the testing data belong to the domain and categories not present during training. Compared to CDR, the UCDR task is more challenging due to (1) visually diverse data from multi-source domains, (2) the domain shift between seen and unseen domains, and (3) the semantic shift across seen and unseen categories. To tackle these problems, we propose a novel model termed Structure-Aware Semantic-Aligned Network (SASA) to align the heterogeneous representations of multi-source domains without loss of generalizability for the UCDR task. Specifically, we leverage the advanced Vision Transformer (ViT) as the backbone and devise a distillation-alignment ViT (DAViT) with a novel token-based strategy, which incorporates two complementary distillation and alignment tokens into the ViT architecture. In addition, the distillation token is devised to improve the generalizability of our model by structure information preservation and the alignment token is used to improve discriminativeness with trainable categorical prototypes. Extensive experiments on three large-scale benchmarks, i.e., Sketchy, TU-Berlin, and DomainNet, demonstrate the superiority of our SASA method over the state-of-the-art UCDR and ZS-SBIR methods.|跨域检索(CDR)的目标是通过使用来自另一个域的查询在一个域中搜索相同类别的实例。现有的 CDR 方法主要考虑这样的标准场景: 用于培训和测试的跨域数据来自相同的类别和底层分布。然而，这些方法不能很好地推广到新出现的通用跨域检索(UCDR)任务，其中的测试数据属于领域和类别不存在的训练过程中。与 CDR 相比，UCDR 任务更具挑战性，因为(1)来自多源域的视觉多样化数据，(2)可见和不可见域之间的域转移，以及(3)跨可见和不可见类别的语义转移。为了解决这些问题，我们提出了一种称为结构感知语义对齐网络(SASA)的新模型，该模型可以在不损失 UCDR 任务通用性的前提下对多源域的异构表示进行对齐。具体而言，我们利用先进的视觉变压器(ViT)作为骨干，并设计了一种蒸馏对准 ViT (DAViT) ，其具有基于令牌的新策略，其将两个互补的蒸馏和对准令牌合并到 ViT 体系结构中。此外，通过结构信息的保留，设计了精馏令牌来提高模型的泛化能力，并利用对齐令牌来提高可训练范畴原型的区分能力。在 Sketchy、 TU-Berlin 和 DomainNet 这三个大型基准测试上的大量实验证明了我们的 SASA 方法优于最先进的 UCDR 和 ZS-SBIR 方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Structure-Aware+Semantic-Aligned+Network+for+Universal+Cross-Domain+Retrieval)|0| +|[Enhancing Top-N Item Recommendations by Peer Collaboration](https://doi.org/10.1145/3477495.3531773)|Yang Sun, Fajie Yuan, Min Yang, Alexandros Karatzoglou, Li Shen, Xiaoyan Zhao|Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China; Westlake University, Hangzhou, China; Harbin Institute of Technology, Shenzhen, Shenzhen, China; JD Explore Academy, Beijing, China; Google Research, London, United Kingdom|Deep neural networks (DNN) based recommender models often require numerous parameters to achieve remarkable performance. However, this inevitably brings redundant neurons, a phenomenon referred to as over-parameterization. In this paper, we plan to exploit such redundancy phenomena for recommender systems (RS), and propose a top-N item recommendation framework called PCRec that leverages collaborative training of two recommender models of the same network structure, termed peer collaboration. We first introduce two criteria to identify the importance of parameters of a given recommender model. Then, we rejuvenate the unimportant parameters by copying parameters from its peer network. After such an operation and retraining, the original recommender model is endowed with more representation capacity by possessing more functional model parameters. To show its generality, we instantiate PCRec by using three well-known recommender models. We conduct extensive experiments on two real-world datasets, and show that PCRec yields significantly better performance than its counterpart with the same model (parameter) size.|基于深度神经网络(DNN)的推荐模型往往需要大量的参数才能达到显著的性能。然而，这不可避免地带来了多余的神经元，这种现象被称为过度参数化。在本文中，我们计划在推荐系统中利用这种冗余现象，并提出了一个名为 PCRec 的前 N 项推荐框架，该框架利用了两个相同网络结构的推荐模型的协同训练，称为对等协作。我们首先引入两个标准来确定一个给定的推荐模型参数的重要性。然后，我们通过从其对等网络中复制参数来恢复不重要的参数。经过这样的操作和再训练，原有的推荐模型具有更多的功能模型参数，从而具有更强的表示能力。为了显示其通用性，我们使用三个著名的推荐模型来实例化 PCRec。我们在两个真实世界的数据集上进行了广泛的实验，结果表明，与相同模型(参数)大小的同类数据集相比，PCRec 产生了明显更好的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Enhancing+Top-N+Item+Recommendations+by+Peer+Collaboration)|0| |[Learning-to-Rank at the Speed of Sampling: Plackett-Luce Gradient Estimation with Minimal Computational Complexity](https://doi.org/10.1145/3477495.3531842)|Harrie Oosterhuis|Radboud University, Nijmegen, Netherlands|Plackett-Luce gradient estimation enables the optimization of stochastic ranking models within feasible time constraints through sampling techniques. Unfortunately, the computational complexity of existing methods does not scale well with the length of the rankings, i.e. the ranking cutoff, nor with the item collection size. In this paper, we introduce the novel PL-Rank-3 algorithm that performs unbiased gradient estimation with a computational complexity comparable to the best sorting algorithms. As a result, our novel learning-to-rank method is applicable in any scenario where standard sorting is feasible in reasonable time. Our experimental results indicate large gains in the time required for optimization, without any loss in performance. For the field, our contribution could potentially allow state-of-the-art learning-to-rank methods to be applied to much larger scales than previously feasible.|Plackett-Luce 梯度估计可以通过抽样技术在可行的时间约束下优化随机排序模型。遗憾的是，现有方法的计算复杂度并不能很好地与排名的长度(即排名截止值)和项目集合的大小相适应。在本文中，我们介绍了一种新的 PL-Rank-3算法，该算法执行无偏梯度估计，其计算复杂度与最佳排序算法相当。因此，我们的新学习排序方法适用于任何情况下，标准排序是可行的在合理的时间。我们的实验结果表明，优化所需的时间大大增加，性能没有任何损失。对于这个领域，我们的贡献可能使最先进的学习排名方法应用于比以前可行的更大的范围。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Learning-to-Rank+at+the+Speed+of+Sampling:+Plackett-Luce+Gradient+Estimation+with+Minimal+Computational+Complexity)|0| |[Rethinking Correlation-based Item-Item Similarities for Recommender Systems](https://doi.org/10.1145/3477495.3532055)|Katsuhiko Hayashi|Hokkaido University, Sapporo, Japan|This paper studies correlation-based item-item similarity measures for recommendation systems. While current research on recommender systems is directed toward deep learning-based approaches, nearest neighbor methods have been still used extensively in commercial recommender systems due to their simplicity. A crucial step in item-based nearest neighbor methods is to compute similarities between items, which are generally estimated through correlation measures like Pearson. The purpose of this paper is to re-investigate the effectiveness of correlation-based nearest neighbor methods on several benchmark datasets that have been used for recommendation evaluation in recent years. This paper also provides a more effective estimation method for correlation measures than the classical Pearson correlation coefficient and shows that this leads to significant improvements in recommendation performance.|本文研究了基于相关性的推荐系统项目相似性度量。虽然目前对推荐系统的研究主要集中在基于深度学习的方法上，但是最近邻方法由于其简单性在商业推荐系统中仍然得到了广泛的应用。基于项目的最近邻方法的一个关键步骤是计算项目之间的相似性，这通常通过相关度量(如 Pearson)来估计。本文旨在重新研究基于相关性的最近邻方法在近年来用于推荐评价的几个基准数据集上的有效性。本文还提供了一种比经典的皮尔逊相关系数更有效的相关度量估计方法，结果表明这种方法可以显著提高推荐性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Rethinking+Correlation-based+Item-Item+Similarities+for+Recommender+Systems)|0| -|[DeSCoVeR: Debiased Semantic Context Prior for Venue Recommendation](https://doi.org/10.1145/3477495.3531877)|Sailaja Rajanala, Arghya Pal, Manish Singh, Raphael C.W. Phan, KokSheik Wong|Indian Institute of Technology Hyderabad, Hyderabad, India; Harvard Medical School, Boston, MA, USA; Monash University Malaysia, Bandar Sunway, Malaysia|We present a novel semantic context prior-based venue recommendation system that uses only the title and the abstract of a paper. Based on the intuition that the text in the title and abstract have both semantic and syntactic components, we demonstrate that a joint training of a semantic feature extractor and syntactic feature extractor collaboratively leverages meaningful information that helps to provide venues for papers. The proposed methodology that we call DeSCoVeR at first elicits these semantic and syntactic features using a Neural Topic Model and text classifier respectively. The model then executes a transfer learning optimization procedure to perform a contextual transfer between the feature distributions of the Neural Topic Model and the text classifier during the training phase. DeSCoVeR also mitigates the document-level label bias using a Causal back-door path criterion and a sentence-level keyword bias removal technique. Experiments on the DBLP dataset show that DeSCoVeR outperforms the state-of-the-art methods.|我们提出了一个新的基于语义上下文先验的场地推荐系统，它只使用文章的标题和摘要。基于标题和摘要中的文本同时具有语义和句法成分的直觉，我们证明了语义特征提取器和句法特征提取器的联合训练协同利用有意义的信息，有助于为论文提供场所。我们提出的方法，我们称为 DeSCoVeR 首先引出这些语义和句法特征使用神经主题模型和文本分类器分别。然后，该模型执行一个迁移学习优化过程，在训练阶段在神经主题模型的特征分布和文本分类器之间进行上下文迁移。DeSCoVeR 还使用因果后门路径标准和句子级关键字偏差消除技术来减轻文档级标签偏差。在 DBLP 数据集上的实验表明，DeSCoVeR 方法的性能优于最先进的方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=DeSCoVeR:+Debiased+Semantic+Context+Prior+for+Venue+Recommendation)|0| -|[Revisiting Bundle Recommendation: Datasets, Tasks, Challenges and Opportunities for Intent-aware Product Bundling](https://doi.org/10.1145/3477495.3531904)|Zhu Sun, Jie Yang, Kaidong Feng, Hui Fang, Xinghua Qu, Yew Soon Ong|Bytedance AI Lab, Singapore, Singapore; Yanshan University, Qinhuangdao, China; Shanghai University of Finance and Economics, Shanghai, China; Institute of High Performance Computing and Centre for Frontier AI Research, A*STAR, Singapore, Singapore; A*STAR Centre for Frontier AI Research and Nanyang Technological University, Singapore, Singapore; Delft University of Technology, Delft, Netherlands|Product bundling is a commonly-used marketing strategy in both offline retailers and online e-commerce systems. Current research on bundle recommendation is limited by: (1) noisy datasets, where bundles are defined by heuristics, e.g., products co-purchased in the same session; and (2) specific tasks, holding unrealistic assumptions, e.g., the availability of bundles for recommendation directly. In this paper, we propose to take a step back and consider the process of bundle recommendation from a holistic user experience perspective. We first construct high-quality bundle datasets with rich meta information, particularly bundle intents, through a carefully designed crowd-sourcing task. We then define a series of tasks that together, support all key steps in a typical bundle recommendation process, from bundle detection, completion, ranking, to explanation and auto-naming. Finally, we conduct extensive experiments and in-depth analysis that demonstrate the challenges of bundle recommendation, arising from the need for capturing complex relations among users, products, and bundles, as well as the research opportunities, especially in graph-based neural methods. To sum up, our study delivers new data sources, opens up new research directions, and provides useful guidance for product bundling in real e-commerce platforms. Our datasets are available at GitHub (\urlhttps://github.com/BundleRec/bundle_recommendation ).|绑售是线下零售商和在线电子商务系统中常用的营销策略。目前对捆绑推荐的研究受到以下因素的限制: (1)有噪音的数据集，其中捆绑包是由启发式定义的，例如，在同一会话中共同购买的产品; (2)具体的任务，持有不切实际的假设，例如，捆绑包的可用性直接推荐。在本文中，我们建议退一步，从整体用户体验的角度来考虑捆绑推荐的过程。我们首先通过一个精心设计的众包任务，构建包含丰富元信息的高质量捆绑数据集，特别是捆绑意图。然后，我们定义一系列任务，这些任务一起支持典型的包推荐过程中的所有关键步骤，从包检测、完成、排名到解释和自动命名。最后，我们进行了广泛的实验和深入的分析，展示了捆绑推荐的挑战，由于需要捕获用户、产品和捆绑之间的复杂关系，以及研究机会，特别是在基于图的神经方法。总之，我们的研究提供了新的数据来源，开辟了新的研究方向，并为绑售在真正的电子商务平台上提供了有用的指导。我们的数据集可以在 GitHub 上获得(urlhttps:// GitHub.com/bundlerec/bundle_recommendation )。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Revisiting+Bundle+Recommendation:+Datasets,+Tasks,+Challenges+and+Opportunities+for+Intent-aware+Product+Bundling)|0| +|[DeSCoVeR: Debiased Semantic Context Prior for Venue Recommendation](https://doi.org/10.1145/3477495.3531877)|Sailaja Rajanala, Arghya Pal, Manish Singh, Raphael C.W. Phan, KokSheik Wong|Monash University Malaysia, Bandar Sunway, Malaysia; Indian Institute of Technology Hyderabad, Hyderabad, India; Harvard Medical School, Boston, MA, USA|We present a novel semantic context prior-based venue recommendation system that uses only the title and the abstract of a paper. Based on the intuition that the text in the title and abstract have both semantic and syntactic components, we demonstrate that a joint training of a semantic feature extractor and syntactic feature extractor collaboratively leverages meaningful information that helps to provide venues for papers. The proposed methodology that we call DeSCoVeR at first elicits these semantic and syntactic features using a Neural Topic Model and text classifier respectively. The model then executes a transfer learning optimization procedure to perform a contextual transfer between the feature distributions of the Neural Topic Model and the text classifier during the training phase. DeSCoVeR also mitigates the document-level label bias using a Causal back-door path criterion and a sentence-level keyword bias removal technique. Experiments on the DBLP dataset show that DeSCoVeR outperforms the state-of-the-art methods.|我们提出了一个新的基于语义上下文先验的场地推荐系统，它只使用文章的标题和摘要。基于标题和摘要中的文本同时具有语义和句法成分的直觉，我们证明了语义特征提取器和句法特征提取器的联合训练协同利用有意义的信息，有助于为论文提供场所。我们提出的方法，我们称为 DeSCoVeR 首先引出这些语义和句法特征使用神经主题模型和文本分类器分别。然后，该模型执行一个迁移学习优化过程，在训练阶段在神经主题模型的特征分布和文本分类器之间进行上下文迁移。DeSCoVeR 还使用因果后门路径标准和句子级关键字偏差消除技术来减轻文档级标签偏差。在 DBLP 数据集上的实验表明，DeSCoVeR 方法的性能优于最先进的方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=DeSCoVeR:+Debiased+Semantic+Context+Prior+for+Venue+Recommendation)|0| +|[Revisiting Bundle Recommendation: Datasets, Tasks, Challenges and Opportunities for Intent-aware Product Bundling](https://doi.org/10.1145/3477495.3531904)|Zhu Sun, Jie Yang, Kaidong Feng, Hui Fang, Xinghua Qu, Yew Soon Ong|Bytedance AI Lab, Singapore, Singapore; Shanghai University of Finance and Economics, Shanghai, China; Institute of High Performance Computing and Centre for Frontier AI Research, A*STAR, Singapore, Singapore; A*STAR Centre for Frontier AI Research and Nanyang Technological University, Singapore, Singapore; Delft University of Technology, Delft, Netherlands; Yanshan University, Qinhuangdao, China|Product bundling is a commonly-used marketing strategy in both offline retailers and online e-commerce systems. Current research on bundle recommendation is limited by: (1) noisy datasets, where bundles are defined by heuristics, e.g., products co-purchased in the same session; and (2) specific tasks, holding unrealistic assumptions, e.g., the availability of bundles for recommendation directly. In this paper, we propose to take a step back and consider the process of bundle recommendation from a holistic user experience perspective. We first construct high-quality bundle datasets with rich meta information, particularly bundle intents, through a carefully designed crowd-sourcing task. We then define a series of tasks that together, support all key steps in a typical bundle recommendation process, from bundle detection, completion, ranking, to explanation and auto-naming. Finally, we conduct extensive experiments and in-depth analysis that demonstrate the challenges of bundle recommendation, arising from the need for capturing complex relations among users, products, and bundles, as well as the research opportunities, especially in graph-based neural methods. To sum up, our study delivers new data sources, opens up new research directions, and provides useful guidance for product bundling in real e-commerce platforms. Our datasets are available at GitHub (\urlhttps://github.com/BundleRec/bundle_recommendation ).|绑售是线下零售商和在线电子商务系统中常用的营销策略。目前对捆绑推荐的研究受到以下因素的限制: (1)有噪音的数据集，其中捆绑包是由启发式定义的，例如，在同一会话中共同购买的产品; (2)具体的任务，持有不切实际的假设，例如，捆绑包的可用性直接推荐。在本文中，我们建议退一步，从整体用户体验的角度来考虑捆绑推荐的过程。我们首先通过一个精心设计的众包任务，构建包含丰富元信息的高质量捆绑数据集，特别是捆绑意图。然后，我们定义一系列任务，这些任务一起支持典型的包推荐过程中的所有关键步骤，从包检测、完成、排名到解释和自动命名。最后，我们进行了广泛的实验和深入的分析，展示了捆绑推荐的挑战，由于需要捕获用户、产品和捆绑之间的复杂关系，以及研究机会，特别是在基于图的神经方法。总之，我们的研究提供了新的数据来源，开辟了新的研究方向，并为绑售在真正的电子商务平台上提供了有用的指导。我们的数据集可以在 GitHub 上获得(urlhttps:// GitHub.com/bundlerec/bundle_recommendation )。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Revisiting+Bundle+Recommendation:+Datasets,+Tasks,+Challenges+and+Opportunities+for+Intent-aware+Product+Bundling)|0| |[Query Facet Mapping and its Applications in Streaming Services: The Netflix Case Study](https://doi.org/10.1145/3477495.3536330)|Sudeep Das, Ivan Provalov, Vickie Zhang, Weidong Zhang|Netflix Inc., Los Gatos, CA, USA|In an instant search setting such as Netflix Search where results are returned in response to every keystroke, determining how a partial query maps onto broad classes of relevant entities orfacets --- such as videos, talent, and genres --- can facilitate a better understanding of the underlying objective of that query. Such a query-to-facet mapping system has a multitude of applications. It can help improve the quality of search results, drive meaningful result organization, and can be leveraged to establish trust by being transparent with Netflix members when they search for an entity that is not available on the service. By anticipating the relevant facets with each keystroke entry, the system can also better guide the experience within a search session. When aggregated across queries, the facets can reveal interesting patterns of member interest. A key challenge for building such a system is to judiciously balance lexical similarity with behavioral relevance. In this paper, we present a high level overview of a Query Facet Mapping system that we have developed at Netflix, describe its main components, provide evaluation results with real-world data, and outline several potential applications.|在像 Netflix Search 这样的即时搜索设置中，每次按键都会返回结果，确定一个部分查询如何映射到相关实体或方面的广泛类别——比如视频、人才和类型——可以促进对该查询的潜在目标的更好理解。这种查询到面的映射系统有大量的应用程序。它可以帮助提高搜索结果的质量，推动有意义的结果组织，并且可以通过在 Netflix 成员搜索服务中不可用的实体时对其保持透明来建立信任。通过预测每个按键输入的相关方面，系统还可以更好地指导搜索会话中的体验。当跨查询聚合时，方面可以显示成员感兴趣的有趣模式。建立这样一个系统的关键挑战是明智地平衡词汇相似性和行为相关性。本文对我们在 Netflix 上开发的 Query Facet Mapping 系统进行了高层次的概述，描述了它的主要组件，提供了实际数据的评估结果，并概述了几个潜在的应用。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Query+Facet+Mapping+and+its+Applications+in+Streaming+Services:+The+Netflix+Case+Study)|0| |[Implicit Feedback for Dense Passage Retrieval: A Counterfactual Approach](https://doi.org/10.1145/3477495.3531994)|Shengyao Zhuang, Hang Li, Guido Zuccon|The University of Queensland, Brisbane, QLD, Australia|In this paper we study how to effectively exploit implicit feedback in Dense Retrievers (DRs). We consider the specific case in which click data from a historic click log is available as implicit feedback. We then exploit such historic implicit interactions to improve the effectiveness of a DR. A key challenge that we study is the effect that biases in the click signal, such as position bias, have on the DRs. To overcome the problems associated with the presence of such bias, we propose the Counterfactual Rocchio (CoRocchio) algorithm for exploiting implicit feedback in Dense Retrievers. We demonstrate both theoretically and empirically that dense query representations learnt with CoRocchio are unbiased with respect to position bias and lead to higher retrieval effectiveness. We make available the implementations of the proposed methods and the experimental framework, along with all results at https://github.com/ielab/Counterfactual-DR.|本文研究了密集检索器(DRs)中如何有效地利用内隐反馈。我们考虑这样一个特定的情况，在这种情况下，来自历史点击日志的点击数据可以作为隐式反馈使用。然后，我们利用这种历史性的隐性相互作用来提高 DR 的有效性。我们研究的一个关键挑战是点击信号中的偏差(如位置偏差)对 DR 的影响。为了克服与存在这种偏差相关的问题，我们提出反事实 Rocchio (CoRocchio)算法用于利用致密检索器中的隐性反馈。我们从理论和实验两方面证明了 CoRocchio 学习的密集查询表示对位置偏差是无偏的，从而提高了检索效率。我们提供了建议方法和实验框架的实施，以及所有 https://github.com/ielab/counterfactual-dr 的结果。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Implicit+Feedback+for+Dense+Passage+Retrieval:+A+Counterfactual+Approach)|0| -|[Offline Evaluation of Ranked Lists using Parametric Estimation of Propensities](https://doi.org/10.1145/3477495.3532032)|Vishwa Vinay, Manoj Kilaru, David Arbour|University of California, San Diego, CA, USA; Adobe Research, Bangalore, India; Adobe Research, San Jose, CA, USA|Search engines and recommendation systems attempt to continually improve the quality of the experience they afford to their users. Refining the ranker that produces the lists displayed in response to user requests is an important component of this process. A common practice is for the service providers to make changes (e.g. new ranking features, different ranking models) and A/B test them on a fraction of their users to establish the value of the change. An alternative approach estimates the effectiveness of the proposed changes offline, utilising previously collected clickthrough data on the old ranker to posit what the user behaviour on ranked lists produced by the new ranker would have been. A majority of offline evaluation approaches invoke the well studied inverse propensity weighting to adjust for biases inherent in logged data. In this paper, we propose the use of parametric estimates for these propensities. Specifically, by leveraging well known learning-to-rank methods as subroutines, we show how accurate offline evaluation can be achieved when the new rankings to be evaluated differ from the logged ones.|搜索引擎和推荐系统试图不断提高它们为用户提供的体验的质量。优化生成响应用户请求的列表的排名是这个过程的一个重要组成部分。一个常见的做法是，服务提供商进行更改(例如，新的排名功能，不同的排名模型)和 A/B 测试他们的一小部分用户，以建立变化的价值。另一种方法是利用先前收集到的老排名者的点击数据，来估计新排名者生成的排名表上的用户行为的有效性。大多数离线评估方法都会调用经过充分研究的倾向性反向加权来调整测井数据中固有的偏差。在本文中，我们提出了这些倾向的参数估计的使用。具体来说，通过利用众所周知的学习排名方法作为子程序，我们展示了当评估的新排名与记录的排名不同时，如何实现准确的离线评估。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Offline+Evaluation+of+Ranked+Lists+using+Parametric+Estimation+of+Propensities)|0| -|[CAPTOR: A Crowd-Aware Pre-Travel Recommender System for Out-of-Town Users](https://doi.org/10.1145/3477495.3531949)|Haoran Xin, Xinjiang Lu, Nengjun Zhu, Tong Xu, Dejing Dou, Hui Xiong|The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China; Baidu Research, Beijing, China; University of Science and Technology of China, Hefei, China; Shanghai University, Shanghai, China|Pre-travel out-of-town recommendation aims to recommend Point-of-Interests (POIs) to the users who plan to travel out of their hometown in the near future yet have not decided where to go, i.e., their destination regions and POIs both remain unknown. It is a non-trivial task since the searching space is vast, which may lead to distinct travel experiences in different out-of-town regions and eventually confuse decision-making. Besides, users' out-of-town travel behaviors are affected not only by their personalized preferences but heavily by others' travel behaviors. To this end, we propose a Crowd-Aware Pre-Travel Out-of-town Recommendation framework (CAPTOR) consisting of two major modules: spatial-affined conditional random field (SA-CRF) and crowd behavior memory network (CBMN). Specifically, SA-CRF captures the spatial affinity among POIs while preserving the inherent information of POIs. Then, CBMN is proposed to maintain the crowd travel behaviors w.r.t. each region through three affiliated blocks reading and writing the memory adaptively. We devise the elaborated metric space with a dynamic mapping mechanism, where the users and POIs are distinguishable both inherently and geographically. Extensive experiments on two real-world nationwide datasets validate the effectiveness of CAPTOR against the pre-travel out-of-town recommendation task.|旅行前出城推荐的目的是向那些计划在不久的将来离开家乡但还没有决定去哪里旅行的用户推荐他们的兴趣点，也就是说，他们的目的地和兴趣点都是未知的。由于搜索空间巨大，这是一个非常重要的任务，可能会导致在不同的城外地区有不同的旅行体验，并最终混淆决策。此外，用户的出城旅游行为不仅受到个人偏好的影响，还受到他人旅游行为的影响。为此，我们提出了一个基于人群感知的预先出城推荐框架(CAPTOR) ，该框架由两个主要模块组成: 空间仿真条件随机域(SA-CRF)和人群行为记忆网络(cBMN)。特别地，SA-CRF 捕获 POI 之间的空间亲和性，同时保留 POI 的固有信息。然后，提出了通过三个附属块自适应地读写记忆来维持每个区域的人群出行行为。我们使用动态映射机制设计了详细的度量空间，其中用户和 POI 在本质上和地理上都是可以区分的。在两个真实世界的全国性数据集上进行了大量的实验，验证了 CAPTOR 对于出城前的推荐任务的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=CAPTOR:+A+Crowd-Aware+Pre-Travel+Recommender+System+for+Out-of-Town+Users)|0| +|[Offline Evaluation of Ranked Lists using Parametric Estimation of Propensities](https://doi.org/10.1145/3477495.3532032)|Vishwa Vinay, Manoj Kilaru, David Arbour|Adobe Research, Bangalore, India; Adobe Research, San Jose, CA, USA; University of California, San Diego, CA, USA|Search engines and recommendation systems attempt to continually improve the quality of the experience they afford to their users. Refining the ranker that produces the lists displayed in response to user requests is an important component of this process. A common practice is for the service providers to make changes (e.g. new ranking features, different ranking models) and A/B test them on a fraction of their users to establish the value of the change. An alternative approach estimates the effectiveness of the proposed changes offline, utilising previously collected clickthrough data on the old ranker to posit what the user behaviour on ranked lists produced by the new ranker would have been. A majority of offline evaluation approaches invoke the well studied inverse propensity weighting to adjust for biases inherent in logged data. In this paper, we propose the use of parametric estimates for these propensities. Specifically, by leveraging well known learning-to-rank methods as subroutines, we show how accurate offline evaluation can be achieved when the new rankings to be evaluated differ from the logged ones.|搜索引擎和推荐系统试图不断提高它们为用户提供的体验的质量。优化生成响应用户请求的列表的排名是这个过程的一个重要组成部分。一个常见的做法是，服务提供商进行更改(例如，新的排名功能，不同的排名模型)和 A/B 测试他们的一小部分用户，以建立变化的价值。另一种方法是利用先前收集到的老排名者的点击数据，来估计新排名者生成的排名表上的用户行为的有效性。大多数离线评估方法都会调用经过充分研究的倾向性反向加权来调整测井数据中固有的偏差。在本文中，我们提出了这些倾向的参数估计的使用。具体来说，通过利用众所周知的学习排名方法作为子程序，我们展示了当评估的新排名与记录的排名不同时，如何实现准确的离线评估。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Offline+Evaluation+of+Ranked+Lists+using+Parametric+Estimation+of+Propensities)|0| +|[CAPTOR: A Crowd-Aware Pre-Travel Recommender System for Out-of-Town Users](https://doi.org/10.1145/3477495.3531949)|Haoran Xin, Xinjiang Lu, Nengjun Zhu, Tong Xu, Dejing Dou, Hui Xiong|Baidu Research, Beijing, China; Shanghai University, Shanghai, China; The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China; University of Science and Technology of China, Hefei, China|Pre-travel out-of-town recommendation aims to recommend Point-of-Interests (POIs) to the users who plan to travel out of their hometown in the near future yet have not decided where to go, i.e., their destination regions and POIs both remain unknown. It is a non-trivial task since the searching space is vast, which may lead to distinct travel experiences in different out-of-town regions and eventually confuse decision-making. Besides, users' out-of-town travel behaviors are affected not only by their personalized preferences but heavily by others' travel behaviors. To this end, we propose a Crowd-Aware Pre-Travel Out-of-town Recommendation framework (CAPTOR) consisting of two major modules: spatial-affined conditional random field (SA-CRF) and crowd behavior memory network (CBMN). Specifically, SA-CRF captures the spatial affinity among POIs while preserving the inherent information of POIs. Then, CBMN is proposed to maintain the crowd travel behaviors w.r.t. each region through three affiliated blocks reading and writing the memory adaptively. We devise the elaborated metric space with a dynamic mapping mechanism, where the users and POIs are distinguishable both inherently and geographically. Extensive experiments on two real-world nationwide datasets validate the effectiveness of CAPTOR against the pre-travel out-of-town recommendation task.|旅行前出城推荐的目的是向那些计划在不久的将来离开家乡但还没有决定去哪里旅行的用户推荐他们的兴趣点，也就是说，他们的目的地和兴趣点都是未知的。由于搜索空间巨大，这是一个非常重要的任务，可能会导致在不同的城外地区有不同的旅行体验，并最终混淆决策。此外，用户的出城旅游行为不仅受到个人偏好的影响，还受到他人旅游行为的影响。为此，我们提出了一个基于人群感知的预先出城推荐框架(CAPTOR) ，该框架由两个主要模块组成: 空间仿真条件随机域(SA-CRF)和人群行为记忆网络(cBMN)。特别地，SA-CRF 捕获 POI 之间的空间亲和性，同时保留 POI 的固有信息。然后，提出了通过三个附属块自适应地读写记忆来维持每个区域的人群出行行为。我们使用动态映射机制设计了详细的度量空间，其中用户和 POI 在本质上和地理上都是可以区分的。在两个真实世界的全国性数据集上进行了大量的实验，验证了 CAPTOR 对于出城前的推荐任务的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=CAPTOR:+A+Crowd-Aware+Pre-Travel+Recommender+System+for+Out-of-Town+Users)|0| |[Unify Local and Global Information for Top-N Recommendation](https://doi.org/10.1145/3477495.3532070)|Xiaoming Liu, Shaocong Wu, Zhaohan Zhang, Chao Shen||Knowledge graph (KG), integrating complex information and containing rich semantics, is widely considered as side information to enhance the recommendation systems. However, most of the existing KG-based methods concentrate on encoding the structural information in the graph, without utilizing the collaborative signals in user-item interaction data, which are important for understanding user preferences. Therefore, the representations learned by these models are insufficient for representing semantic information of users and items in the recommendation environment. The combination of both kinds of data provides a good chance to solve this problem, but it faces the following challenges: i) the inner correlations in user-item interaction data are difficult to capture from one side of the user or item; ii) capturing the knowledge associations on the whole KG would introduce noises and variously influence the recommendation results; iii) the semantic gap between both kinds of data is hard to alleviate. To tackle this research gap, we propose a novel duet representation learning framework named KADM to fuse local information (user-item interaction data) and global information (external knowledge graph) for the top-N recommendation, which is composed of two separate sub-models. One learns the local representations by discovering the inner correlations in local information with a knowledge-aware co-attention mechanism, and another learns the global representations by encoding the knowledge associations in global information with a relation-aware attention network. The two sub-models are jointly trained as part of the semantic fusion network to compute the user preferences, which discriminates the contribution of the two sub-models under the special context. We conduct experiments on two real-world datasets, and the evaluations show that KADM significantly outperforms state-of-art methods. Further ablation studies confirm that the duet architecture performs significantly better than either sub-model on the recommendation tasks.|知识图集成了复杂的信息，包含丰富的语义，被广泛认为是增强推荐系统的边信息。然而，现有的基于 KG 的方法大多集中于对图中的结构信息进行编码，而没有利用用户交互数据中的协作信号，这对于理解用户偏好非常重要。因此，这些模型所学到的表示方法不足以表示推荐环境中用户和项目的语义信息。这两种数据的结合为解决这一问题提供了很好的机会，但它面临着以下挑战: 1)用户项目交互数据的内部相关性难以从用户或项目的一侧获取; 2)捕捉整个 KG 的知识关联会引入噪声并对推荐结果产生各种影响; 3)两种数据之间的语义差异难以缓解。为了解决这一问题，本文提出了一种新的二元表示学习框架 KADM，它融合了顶层 N 推荐的局部信息(用户项目交互数据)和全局信息(外部知识图) ，该框架由两个独立的子模型组成。一种是通过知识感知共注意机制发现局部信息的内在相关性来学习局部表征，另一种是通过关系感知注意网络对全局信息中的知识关联进行编码来学习全局表征。将这两个子模型作为语义融合网络的一部分进行联合训练，以计算用户偏好，从而区分两个子模型在特定语境下的贡献。我们在两个真实世界的数据集上进行了实验，结果表明 KADM 的性能明显优于最先进的方法。进一步的消融研究证实，二重奏架构在推荐任务上的表现明显优于任何一个子模型。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Unify+Local+and+Global+Information+for+Top-N+Recommendation)|0| |[Deployable and Continuable Meta-learning-Based Recommender System with Fast User-Incremental Updates](https://doi.org/10.1145/3477495.3531964)|Renchu Guan, Haoyu Pang, Fausto Giunchiglia, Ximing Li, Xuefeng Yang, Xiaoyue Feng|University of Trento, Trento, Italy; Jilin University, Changchun, China; Tencent, Shenzhen, China|User cold-start is a major challenge in building personalized recommender systems. Due to the lack of sufficient interactions, it is difficult to effectively model new users. One of the main solutions is to obtain an initial model through meta-learning (mainly gradient-based methods) and adapt it to new users with a few steps of gradient descent. Although these methods have achieved remarkable performance, they are still far from being usable in real-world applications due to their high-demand data processing, heavy computational burden, and inability to perform effective user-incremental update. In this paper, we propose a d eployable and c ontinuable m eta-learning-based r ecommendation (DCMR) approach, which can achieve fast user-incremental updating with task replay and first-order gradient descent. Specifically, we introduce a dual-constrained task sampler, distillation-based loss functions, and an adaptive controller in this framework to balance the trade-off between stability and plasticity in updating. In summary, DCMR can be updated while serving new users; in other words, it learns continuously and rapidly from a sequential user stream and is able to make recommendations at any time. The extensive experiments conducted on three benchmark datasets illustrate the superiority of our model.|用户冷启动是构建个性化推荐系统的主要挑战。由于缺乏足够的交互，很难对新用户进行有效的建模。其中一个主要的解决方案是通过元学习(主要是基于梯度的方法)获得一个初始模型，并通过几个步骤使其适应新用户的梯度下降法。虽然这些方法已经取得了显著的性能，但是由于其高需求的数据处理、沉重的计算负担以及不能执行有效的用户增量更新，它们在实际应用中仍然远远不能使用。在本文中，我们提出了一种可部署和可持续的基于元学习的 r 推荐(dCMR)方法，它可以通过任务重播和一阶梯度下降法实现快速的用户增量更新。具体来说，我们引入了一个双约束任务采样器，基于蒸馏的损失函数，以及在这个框架中的一个自适应控制器，以平衡稳定性和可塑性之间的权衡在更新。总之，DCMR 可以在为新用户提供服务的同时进行更新; 换句话说，它可以从连续的用户流中不断快速地学习，并且能够在任何时候提出建议。在三个基准数据集上进行的大量实验表明了该模型的优越性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Deployable+and+Continuable+Meta-learning-Based+Recommender+System+with+Fast+User-Incremental+Updates)|0| -|[Bias Mitigation for Toxicity Detection via Sequential Decisions](https://doi.org/10.1145/3477495.3531945)|Lu Cheng, Ahmadreza Mosallanezhad, Yasin N. Silva, Deborah L. Hall, Huan Liu|Arizona State University, Tempe, AZ, USA; Loyola University Chicago, Chicago, IL, USA; Arizona State University, Glendale, AZ, USA|Increased social media use has contributed to the greater prevalence of abusive, rude, and offensive textual comments. Machine learning models have been developed to detect toxic comments online, yet these models tend to show biases against users with marginalized or minority identities (e.g., females and African Americans). Established research in debiasing toxicity classifiers often (1) takes a static or batch approach, assuming that all information is available and then making a one-time decision; and (2) uses a generic strategy to mitigate different biases (e.g., gender and racial biases) that assumes the biases are independent of one another. However, in real scenarios, the input typically arrives as a sequence of comments/words over time instead of all at once. Thus, decisions based on partial information must be made while additional input is arriving. Moreover, social bias is complex by nature. Each type of bias is defined within its unique context, which, consistent with intersectionality theory within the social sciences, might be correlated with the contexts of other forms of bias. In this work, we consider debiasing toxicity detection as a sequential decision-making process where different biases can be interdependent. In particular, we study debiasing toxicity detection with two aims: (1) to examine whether different biases tend to correlate with each other; and (2) to investigate how to jointly mitigate these correlated biases in an interactive manner to minimize the total amount of bias. At the core of our approach is a framework built upon theories of sequential Markov Decision Processes that seeks to maximize the prediction accuracy and minimize the bias measures tailored to individual biases. Evaluations on two benchmark datasets empirically validate the hypothesis that biases tend to be correlated and corroborate the effectiveness of the proposed sequential debiasing strategy.|越来越多的社交媒体使用导致了辱骂、粗鲁和冒犯性的文字评论更加普遍。机器学习模型已经被开发用来检测网上的有毒评论，然而这些模型往往显示出对边缘化或少数族裔身份的用户(例如，女性和非裔美国人)的偏见。已建立的减少毒性分类器的研究通常(1)采用静态或批量方法，假设所有信息都可用，然后做出一次性决策; (2)使用通用策略来减轻假定偏见彼此独立的不同偏见(例如性别和种族偏见)。然而，在真实的场景中，输入通常是以注释/单词序列的形式随着时间的推移而到达，而不是一次性全部到达。因此，当额外的输入到达时，必须根据部分信息做出决策。此外，社会偏见本质上是复杂的。每种类型的偏见都是在其独特的背景下定义的，这与社会科学中的交叉性理论一致，可能与其他形式的偏见的背景相关。在这项工作中，我们认为去偏毒性检测是一个连续的决策过程中，不同的偏见可以相互依赖。具体而言，我们研究去偏毒性检测有两个目的: (1)检查不同的偏倚是否倾向于相互关联; (2)研究如何以交互方式共同减轻这些相关偏倚，以最小化偏倚总量。我们的方法的核心是一个建立在序贯马尔可夫决策过程理论基础上的框架，该框架寻求最大限度地提高预测的准确性，最小化针对个别偏差的偏差测量。对两个基准数据集的评估经验验证了偏差倾向于相关的假设，并证实了所提出的序贯去偏策略的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Bias+Mitigation+for+Toxicity+Detection+via+Sequential+Decisions)|0| +|[Bias Mitigation for Toxicity Detection via Sequential Decisions](https://doi.org/10.1145/3477495.3531945)|Lu Cheng, Ahmadreza Mosallanezhad, Yasin N. Silva, Deborah L. Hall, Huan Liu|Arizona State University, Tempe, AZ, USA; Arizona State University, Glendale, AZ, USA; Loyola University Chicago, Chicago, IL, USA|Increased social media use has contributed to the greater prevalence of abusive, rude, and offensive textual comments. Machine learning models have been developed to detect toxic comments online, yet these models tend to show biases against users with marginalized or minority identities (e.g., females and African Americans). Established research in debiasing toxicity classifiers often (1) takes a static or batch approach, assuming that all information is available and then making a one-time decision; and (2) uses a generic strategy to mitigate different biases (e.g., gender and racial biases) that assumes the biases are independent of one another. However, in real scenarios, the input typically arrives as a sequence of comments/words over time instead of all at once. Thus, decisions based on partial information must be made while additional input is arriving. Moreover, social bias is complex by nature. Each type of bias is defined within its unique context, which, consistent with intersectionality theory within the social sciences, might be correlated with the contexts of other forms of bias. In this work, we consider debiasing toxicity detection as a sequential decision-making process where different biases can be interdependent. In particular, we study debiasing toxicity detection with two aims: (1) to examine whether different biases tend to correlate with each other; and (2) to investigate how to jointly mitigate these correlated biases in an interactive manner to minimize the total amount of bias. At the core of our approach is a framework built upon theories of sequential Markov Decision Processes that seeks to maximize the prediction accuracy and minimize the bias measures tailored to individual biases. Evaluations on two benchmark datasets empirically validate the hypothesis that biases tend to be correlated and corroborate the effectiveness of the proposed sequential debiasing strategy.|越来越多的社交媒体使用导致了辱骂、粗鲁和冒犯性的文字评论更加普遍。机器学习模型已经被开发用来检测网上的有毒评论，然而这些模型往往显示出对边缘化或少数族裔身份的用户(例如，女性和非裔美国人)的偏见。已建立的减少毒性分类器的研究通常(1)采用静态或批量方法，假设所有信息都可用，然后做出一次性决策; (2)使用通用策略来减轻假定偏见彼此独立的不同偏见(例如性别和种族偏见)。然而，在真实的场景中，输入通常是以注释/单词序列的形式随着时间的推移而到达，而不是一次性全部到达。因此，当额外的输入到达时，必须根据部分信息做出决策。此外，社会偏见本质上是复杂的。每种类型的偏见都是在其独特的背景下定义的，这与社会科学中的交叉性理论一致，可能与其他形式的偏见的背景相关。在这项工作中，我们认为去偏毒性检测是一个连续的决策过程中，不同的偏见可以相互依赖。具体而言，我们研究去偏毒性检测有两个目的: (1)检查不同的偏倚是否倾向于相互关联; (2)研究如何以交互方式共同减轻这些相关偏倚，以最小化偏倚总量。我们的方法的核心是一个建立在序贯马尔可夫决策过程理论基础上的框架，该框架寻求最大限度地提高预测的准确性，最小化针对个别偏差的偏差测量。对两个基准数据集的评估经验验证了偏差倾向于相关的假设，并证实了所提出的序贯去偏策略的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Bias+Mitigation+for+Toxicity+Detection+via+Sequential+Decisions)|0| |[Regulating Group Exposure for Item Providers in Recommendation](https://doi.org/10.1145/3477495.3531760)|Mirko Marras, Ludovico Boratto, Guilherme Ramos, Gianni Fenu|University of Cagliari, Cagliari, Italy; University of Lisbon, Lisbon, Portugal|Engaging all content providers, including newcomers or minority demographic groups, is crucial for online platforms to keep growing and working. Hence, while building recommendation services, the interests of those providers should be valued. In this paper, we consider providers as grouped based on a common characteristic in settings in which certain provider groups have low representation of items in the catalog and, thus, in the user interactions. Then, we envision a scenario wherein platform owners seek to control the degree of exposure to such groups in the recommendation process. To support this scenario, we rely on disparate exposure measures that characterize the gap between the share of recommendations given to groups and the target level of exposure pursued by the platform owners. We then propose a re-ranking procedure that ensures desired levels of exposure are met. Experiments show that, while supporting certain groups of providers by rendering them with the target exposure, beyond-accuracy objectives experience significant gains with negligible impact in recommendation utility.|吸引所有内容提供商，包括新来者或少数族裔群体，对于在线平台保持增长和运作至关重要。因此，在构建推荐服务时，应该重视这些提供者的利益。在本文中，我们认为提供程序是基于一个共同特征进行分组的，在这种情况下，某些提供程序组在目录中的项表示较低，因此在用户交互中也是如此。然后，我们设想一个场景，其中平台所有者寻求控制在推荐过程中暴露于这些群体的程度。为了支持这一设想，我们依靠不同的曝光度量标准，这些标准体现了给予群体的建议份额与平台所有者追求的曝光度目标水平之间的差距。然后，我们提出了一个重新排序的程序，以确保所需的暴露水平得到满足。实验表明，虽然支持某些群体的供应商，使他们的目标暴露，超过准确性的目标经历了显着的收益，对推荐效用的影响可以忽略不计。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Regulating+Group+Exposure+for+Item+Providers+in+Recommendation)|0| -|[IPR: Interaction-level Preference Ranking for Explicit feedback](https://doi.org/10.1145/3477495.3531777)|ShihYang Liu, HsienHao Chen, ChihMing Chen, MingFeng Tsai, ChuanJu Wang|Academia Sinica, Taipei, Taiwan Roc; National Chengchi University, Academia Sinica, Taipei, Taiwan Roc; National Chengchi University, Taipei, Taiwan Roc|Explicit feedback---user input regarding their interest in an item---is the most helpful information for recommendation as it comes directly from the user and shows their direct interest in the item. Most approaches either treat the recommendation given such feedback as a typical regression problem or regard such data as implicit and then directly adopt approaches for implicit feedback; both methods, however,tend to yield unsatisfactory performance in top-k recommendation. In this paper, we propose interaction-level preference ranking(IPR), a novel pairwise ranking embedding learning approach to better utilize explicit feedback for recommendation. Experiments conducted on three real-world datasets show that IPR yields the best results compared to six strong baselines.|明确的反馈——用户关于他们对某个项目感兴趣的输入——是对推荐最有帮助的信息，因为它直接来自用户，并显示了他们对该项目的直接兴趣。大多数方法要么将给出的这种反馈视为典型的回归问题，要么将这种数据视为隐式的，然后直接采用隐式反馈的方法; 然而，这两种方法在 top-k 推荐中的表现往往都不令人满意。在本文中，我们提出了交互层次偏好排序(IPR) ，这是一种新的嵌入学习的成对排序方法，以更好地利用显式反馈进行推荐。在三个实际数据集上进行的实验表明，与六个强基线相比，IPR 产生的结果最好。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=IPR:+Interaction-level+Preference+Ranking+for+Explicit+feedback)|0| -|[MP2: A Momentum Contrast Approach for Recommendation with Pointwise and Pairwise Learning](https://doi.org/10.1145/3477495.3531813)|Menghan Wang, Yuchen Guo, Zhenqi Zhao, Guangzheng Hu, Yuming Shen, Mingming Gong, Philip H. S. Torr|University of Oxford, London, United Kingdom; eBay Inc., Shanghai, China; The University of Melbourne, Melbourne, VIC, Australia; Tencent Inc., Shanghai, China|Binary pointwise labels (aka implicit feedback) are heavily leveraged by deep learning based recommendation algorithms nowadays. In this paper we discuss the limited expressiveness of these labels may fail to accommodate varying degrees of user preference, and thus lead to conflicts during model training, which we call annotation bias. To solve this issue, we find the soft-labeling property of pairwise labels could be utilized to alleviate the bias of pointwise labels. To this end, we propose a momentum contrast framework (\method ) that combines pointwise and pairwise learning for recommendation. \method has a three-tower network structure: one user network and two item networks. The two item networks are used for computing pointwise and pairwise loss respectively. To alleviate the influence of the annotation bias, we perform a momentum update to ensure a consistent item representation. Extensive experiments on real-world datasets demonstrate the superiority of our method against state-of-the-art recommendation algorithms.|二进制点态标签(即隐式反馈)是当今基于深度学习的推荐算法的重要组成部分。在本文中，我们讨论了这些标签的有限表达可能无法适应不同程度的用户偏好，从而导致模型训练过程中的冲突，我们称之为注释偏差。为了解决这个问题，我们发现可以利用成对标签的软标签特性来缓解点态标签的偏差。为此，我们提出了一个动量对比框架(方法) ，结合点态和成对学习的推荐。方法具有三塔网络结构: 一个用户网络和两个项目网络。两项网络分别用于计算逐点损失和成对损失。为了减轻注释偏差的影响，我们进行动量更新以确保项目表示的一致性。在真实世界数据集上的大量实验证明了我们的方法对最先进的推荐算法的优越性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=MP2:+A+Momentum+Contrast+Approach+for+Recommendation+with+Pointwise+and+Pairwise+Learning)|0| +|[IPR: Interaction-level Preference Ranking for Explicit feedback](https://doi.org/10.1145/3477495.3531777)|ShihYang Liu, HsienHao Chen, ChihMing Chen, MingFeng Tsai, ChuanJu Wang|National Chengchi University, Taipei, Taiwan Roc; National Chengchi University, Academia Sinica, Taipei, Taiwan Roc; Academia Sinica, Taipei, Taiwan Roc|Explicit feedback---user input regarding their interest in an item---is the most helpful information for recommendation as it comes directly from the user and shows their direct interest in the item. Most approaches either treat the recommendation given such feedback as a typical regression problem or regard such data as implicit and then directly adopt approaches for implicit feedback; both methods, however,tend to yield unsatisfactory performance in top-k recommendation. In this paper, we propose interaction-level preference ranking(IPR), a novel pairwise ranking embedding learning approach to better utilize explicit feedback for recommendation. Experiments conducted on three real-world datasets show that IPR yields the best results compared to six strong baselines.|明确的反馈——用户关于他们对某个项目感兴趣的输入——是对推荐最有帮助的信息，因为它直接来自用户，并显示了他们对该项目的直接兴趣。大多数方法要么将给出的这种反馈视为典型的回归问题，要么将这种数据视为隐式的，然后直接采用隐式反馈的方法; 然而，这两种方法在 top-k 推荐中的表现往往都不令人满意。在本文中，我们提出了交互层次偏好排序(IPR) ，这是一种新的嵌入学习的成对排序方法，以更好地利用显式反馈进行推荐。在三个实际数据集上进行的实验表明，与六个强基线相比，IPR 产生的结果最好。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=IPR:+Interaction-level+Preference+Ranking+for+Explicit+feedback)|0| +|[MP2: A Momentum Contrast Approach for Recommendation with Pointwise and Pairwise Learning](https://doi.org/10.1145/3477495.3531813)|Menghan Wang, Yuchen Guo, Zhenqi Zhao, Guangzheng Hu, Yuming Shen, Mingming Gong, Philip H. S. Torr|eBay Inc., Shanghai, China; University of Oxford, London, United Kingdom; Tencent Inc., Shanghai, China; The University of Melbourne, Melbourne, VIC, Australia|Binary pointwise labels (aka implicit feedback) are heavily leveraged by deep learning based recommendation algorithms nowadays. In this paper we discuss the limited expressiveness of these labels may fail to accommodate varying degrees of user preference, and thus lead to conflicts during model training, which we call annotation bias. To solve this issue, we find the soft-labeling property of pairwise labels could be utilized to alleviate the bias of pointwise labels. To this end, we propose a momentum contrast framework (\method ) that combines pointwise and pairwise learning for recommendation. \method has a three-tower network structure: one user network and two item networks. The two item networks are used for computing pointwise and pairwise loss respectively. To alleviate the influence of the annotation bias, we perform a momentum update to ensure a consistent item representation. Extensive experiments on real-world datasets demonstrate the superiority of our method against state-of-the-art recommendation algorithms.|二进制点态标签(即隐式反馈)是当今基于深度学习的推荐算法的重要组成部分。在本文中，我们讨论了这些标签的有限表达可能无法适应不同程度的用户偏好，从而导致模型训练过程中的冲突，我们称之为注释偏差。为了解决这个问题，我们发现可以利用成对标签的软标签特性来缓解点态标签的偏差。为此，我们提出了一个动量对比框架(方法) ，结合点态和成对学习的推荐。方法具有三塔网络结构: 一个用户网络和两个项目网络。两项网络分别用于计算逐点损失和成对损失。为了减轻注释偏差的影响，我们进行动量更新以确保项目表示的一致性。在真实世界数据集上的大量实验证明了我们的方法对最先进的推荐算法的优越性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=MP2:+A+Momentum+Contrast+Approach+for+Recommendation+with+Pointwise+and+Pairwise+Learning)|0| |[Deep Page-Level Interest Network in Reinforcement Learning for Ads Allocation](https://doi.org/10.1145/3477495.3531847)|Guogang Liao, Xiaowen Shi, Ze Wang, Xiaoxu Wu, Chuheng Zhang, Yongkang Wang, Xingxing Wang, Dong Wang|Tsinghua University, Beijing, China; Meituan, Beijing, China|A mixed list of ads and organic items is usually displayed in feed and how to allocate the limited slots to maximize the overall revenue is a key problem. Meanwhile, user behavior modeling is essential in recommendation and advertising (e.g., CTR prediction and ads allocation). Most previous works only model point-level positive feedback (i.e., click), which neglect the page-level information of feedback and other types of feedback. To this end, we propose Deep Page-level Interest Network (DPIN) to model the page-level user preference and exploit multiple types of feedback. Specifically, we introduce four different types of page-level feedback, and capture user preference for item arrangement under different receptive fields through the multi-channel interaction module. Through extensive offline and online experiments on Meituan food delivery platform, we demonstrate that DPIN can effectively model the page-level user preference and increase the revenue.|一个广告和有机项目的混合列表通常显示在饲料和如何分配有限的插槽，以最大限度地提高总收入是一个关键问题。同时，用户行为建模对于推荐和广告(例如，点击率预测和广告分配)至关重要。大多数以前的作品只是模拟点级别的正反馈(例如，点击) ，而忽略了反馈和其他类型的反馈的页级信息。为此，我们提出了深层页面级兴趣网络(DPIN)来建模页面级用户偏好，并利用多种类型的反馈。具体来说，我们引入了四种不同类型的页面级反馈，并通过多通道交互模块捕捉用户对不同接收域下项目排列的偏好。通过在美团外卖平台上进行的大量线下和线上实验，我们证明了 DPIN 可以有效地模拟页面级别的用户偏好，并增加收入。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Deep+Page-Level+Interest+Network+in+Reinforcement+Learning+for+Ads+Allocation)|0| |[Improving Micro-video Recommendation via Contrastive Multiple Interests](https://doi.org/10.1145/3477495.3531861)|Beibei Li, Beihong Jin, Jiageng Song, Yisong Yu, Yiyuan Zheng, Wei Zhou|MX Media Co., Ltd., Singapore, Singapore; Institute of Software, Chinese Academy of Sciences & University of Chinese Academy of Sciences, Beijing, China|With the rapid increase of micro-video creators and viewers, how to make personalized recommendations from a large number of candidates to viewers begins to attract more and more attention. However, existing micro-video recommendation models rely on expensive multi-modal information and learn an overall interest embedding that cannot reflect the user's multiple interests in micro-videos. Recently, contrastive learning provides a new opportunity for refining the existing recommendation techniques. Therefore, in this paper, we propose to extract contrastive multi-interests and devise a micro-video recommendation model CMI. Specifically, CMI learns multiple interest embeddings for each user from his/her historical interaction sequence, in which the implicit orthogonal micro-video categories are used to decouple multiple user interests. Moreover, it establishes the contrastive multi-interest loss to improve the robustness of interest embeddings and the performance of recommendations. The results of experiments on two micro-video datasets demonstrate that CMI achieves state-of-the-art performance over existing baselines.|随着微视频制作者和观众的迅速增多，如何从大量的候选人中向观众提供个性化的推荐，开始引起越来越多的关注。然而，现有的微视频推荐模型依赖于昂贵的多模态信息，学习的总体兴趣嵌入不能反映用户在微视频中的多重兴趣。近年来，对比学习为完善现有的推荐技术提供了一个新的机会。因此，本文提出提取对比多兴趣并设计一个微视频推荐模型 CMI。具体来说，CMI 从每个用户的历史交互序列中学习多个兴趣嵌入，其中使用隐式正交微视频类别来解耦多个用户兴趣。此外，本文还建立了对比的多利益损失模型，以提高利益嵌入的鲁棒性和建议的执行效率。在两个微视频数据集上的实验结果表明，CMI 在现有的基线上取得了最好的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Improving+Micro-video+Recommendation+via+Contrastive+Multiple+Interests)|0| |[Can Users Predict Relative Query Effectiveness?](https://doi.org/10.1145/3477495.3531893)|Oleg Zendel, Melika P. Ebrahim, J. Shane Culpepper, Alistair Moffat, Falk Scholer|RMIT University, Melbourne, VIC, Australia; The University of Melbourne, Melbourne, VIC, Australia|Any given information need can be expressed via a wide range of possible queries. Recent work with such query variations has demonstrated that different queries can fetch notably divergent sets of documents, even when the queries have identical intents and superficial similarity. That is, different users might receive SERPs of quite different effectiveness for the same information need. That observation then raises an interesting question: do users have a sense of how useful any given query will be? Can they anticipate the effectiveness of alternative queries for the same retrieval need? To explore that question we designed and carried out a crowd-sourced user study in which we asked subjects to consider an information need statement expressed as a backstory, and then provide their opinions as to the relative usefulness of a set of queries ostensibly addressing that objective. We solicited opinions using two different interfaces: one that collected absolute ratings of queries, and one that required that the subjects place a set of queries into "order". We found that crowd workers are reasonably consistent in their estimates of how effective queries are likely to be, and also that their estimates correlate positively with actual system performance.|任何给定的信息需求都可以通过各种可能的查询来表示。最近对这种查询变体的研究表明，不同的查询可以获取明显不同的文档集，即使查询具有相同的意图和表面上的相似性。也就是说，对于相同的信息需求，不同的用户可能会收到效果完全不同的 SERP。这种观察提出了一个有趣的问题: 用户是否知道任何给定的查询有多大用处？他们能够预测相同检索需求的替代查询的有效性吗？为了探索这个问题，我们设计并进行了一个众包用户研究，在这个研究中，我们要求受试者考虑一个表达为背景故事的信息需求陈述，然后提供他们对一组表面上针对该目标的查询的相对有用性的意见。我们使用两种不同的界面来征求意见: 一种是收集查询的绝对评分，另一种是要求被试将一组查询按顺序排列。我们发现，人群工作者对查询可能的有效性的估计是相当一致的，而且他们的估计与实际系统性能正相关。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Can+Users+Predict+Relative+Query+Effectiveness?)|0| |[Is Non-IID Data a Threat in Federated Online Learning to Rank?](https://doi.org/10.1145/3477495.3531709)|Shuyi Wang, Guido Zuccon|The University of Queensland, Brisbane, QLD, Australia|In this perspective paper we study the effect of non independent and identically distributed (non-IID) data on federated online learning to rank (FOLTR) and chart directions for future work in this new and largely unexplored research area of Information Retrieval. In the FOLTR process, clients participate in a federation to jointly create an effective ranker from the implicit click signal originating in each client, without the need to share data (documents, queries, clicks). A well-known factor that affects the performance of federated learning systems, and that poses serious challenges to these approaches, is that there may be some type of bias in the way data is distributed across clients. While FOLTR systems are on their own rights a type of federated learning system, the presence and effect of non-IID data in FOLTR has not been studied. To this aim, we first enumerate possible data distribution settings that may showcase data bias across clients and thus give rise to the non-IID problem. Then, we study the impact of each setting on the performance of the current state-of-the-art FOLTR approach, the Federated Pairwise Differentiable Gradient Descent (FPDGD), and we highlight which data distributions may pose a problem for FOLTR methods. We also explore how common approaches proposed in the federated learning literature address non-IID issues in FOLTR. This allows us to unveil new research gaps that, we argue, future research in FOLTR should consider.|在这篇前瞻性的论文中，我们研究了非独立和同分布(非 IID)数据对联邦在线学习排名(FOLTR)的影响，以及未来工作的图表方向，这是一个新的、很大程度上尚未探索的信息检索研究领域。在 FOLTR 过程中，客户端参与到一个联合中，从每个客户端发出的隐式点击信号中共同创建一个有效的排名，而不需要共享数据(文档、查询、点击)。影响联邦学习系统性能的一个众所周知的因素是，数据在客户端之间的分布方式可能存在某种偏差，这对这些方法提出了严峻的挑战。虽然 FOLTR 系统本身就是一种联邦学习系统，但是对于 FOLTR 中非 IID 数据的存在和影响还没有进行研究。为此，我们首先列举可能的数据分布设置，这些设置可能显示客户端之间的数据偏差，从而引起非 IID 问题。然后，我们研究了每种设置对当前最先进的 FOLTR 方法——联邦成对可微分梯度下降法(fPDGD)——性能的影响，并强调了哪些数据分布可能会给 FOLTR 方法带来问题。我们还探讨了联合学习文献中提出的常用方法如何解决 FOLTR 中的非 IID 问题。这使我们能够揭示新的研究差距，我们认为，在 FOLTR 的未来研究应该考虑。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Is+Non-IID+Data+a+Threat+in+Federated+Online+Learning+to+Rank?)|0| -|[On Natural Language User Profiles for Transparent and Scrutable Recommendation](https://doi.org/10.1145/3477495.3531873)|Filip Radlinski, Krisztian Balog, Fernando Diaz, Lucas Dixon, Ben Wedin|Google, Stavanger, Norway; Google, Cambridge, MA, USA; Google, Montreal, Canada; Google, London, United Kingdom; Google, Paris, France|Natural interaction with recommendation and personalized search systems has received tremendous attention in recent years. We focus on the challenge of supporting people's understanding and control of these systems and explore a fundamentally new way of thinking about representation of knowledge in recommendation and personalization systems. Specifically, we argue that it may be both desirable and possible for algorithms that use natural language representations of users' preferences to be developed. We make the case that this could provide significantly greater transparency, as well as affordances for practical actionable interrogation of, and control over, recommendations. Moreover, we argue that such an approach, if successfully applied, may enable a major step towards systems that rely less on noisy implicit observations while increasing portability of knowledge of one's interests.|近年来，与推荐系统和个性化检索系统的自然交互受到了极大的关注。我们重点关注支持人们理解和控制这些系统的挑战，并探索一种在推荐和个性化系统中表示知识的全新思维方式。具体来说，我们认为开发使用用户偏好的自然语言表示的算法是可取的，也是可能的。我们认为，这可以提供更大的透明度，以及提供实际可行的审讯和控制，建议。此外，我们认为，这种方法，如果成功地应用，可能使一个重大的步骤，系统的依赖噪音较少的隐含观察，同时增加了一个人的兴趣知识的可移植性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=On+Natural+Language+User+Profiles+for+Transparent+and+Scrutable+Recommendation)|0| -|[Retrieval-Enhanced Machine Learning](https://doi.org/10.1145/3477495.3531722)|Hamed Zamani, Fernando Diaz, Mostafa Dehghani, Donald Metzler, Michael Bendersky|University of Massachusetts Amherst, Amherst, MA, USA; Google Research, Mountain View, CA, USA; Google Research, Amsterdam, Netherlands; Google Research, Montréal, PQ, Canada|Although information access systems have long supportedpeople in accomplishing a wide range of tasks, we propose broadening the scope of users of information access systems to include task-driven machines, such as machine learning models. In this way, the core principles of indexing, representation, retrieval, and ranking can be applied and extended to substantially improve model generalization, scalability, robustness, and interpretability. We describe a generic retrieval-enhanced machine learning (REML) framework, which includes a number of existing models as special cases. REML challenges information retrieval conventions, presenting opportunities for novel advances in core areas, including optimization. The REML research agenda lays a foundation for a new style of information access research and paves a path towards advancing machine learning and artificial intelligence.|尽管信息访问系统长期以来一直支持人们完成各种各样的任务，但我们建议扩大信息访问系统的用户范围，以包括任务驱动的机器，如机器学习模型。通过这种方式，可以应用和扩展索引、表示、检索和排序的核心原则，从而大大提高模型泛化、可伸缩性、健壮性和可解释性。我们描述了一个通用的检索增强机器学习(REML)框架，其中包括一些现有的模型作为特殊情况。REML 挑战了信息检索惯例，为核心领域的新进展提供了机会，包括优化。REML 研究议程为新型的信息获取研究奠定了基础，为机器学习和人工智能的发展铺平了道路。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Retrieval-Enhanced+Machine+Learning)|0| +|[On Natural Language User Profiles for Transparent and Scrutable Recommendation](https://doi.org/10.1145/3477495.3531873)|Filip Radlinski, Krisztian Balog, Fernando Diaz, Lucas Dixon, Ben Wedin|Google, Stavanger, Norway; Google, Paris, France; Google, Montreal, Canada; Google, London, United Kingdom; Google, Cambridge, MA, USA|Natural interaction with recommendation and personalized search systems has received tremendous attention in recent years. We focus on the challenge of supporting people's understanding and control of these systems and explore a fundamentally new way of thinking about representation of knowledge in recommendation and personalization systems. Specifically, we argue that it may be both desirable and possible for algorithms that use natural language representations of users' preferences to be developed. We make the case that this could provide significantly greater transparency, as well as affordances for practical actionable interrogation of, and control over, recommendations. Moreover, we argue that such an approach, if successfully applied, may enable a major step towards systems that rely less on noisy implicit observations while increasing portability of knowledge of one's interests.|近年来，与推荐系统和个性化检索系统的自然交互受到了极大的关注。我们重点关注支持人们理解和控制这些系统的挑战，并探索一种在推荐和个性化系统中表示知识的全新思维方式。具体来说，我们认为开发使用用户偏好的自然语言表示的算法是可取的，也是可能的。我们认为，这可以提供更大的透明度，以及提供实际可行的审讯和控制，建议。此外，我们认为，这种方法，如果成功地应用，可能使一个重大的步骤，系统的依赖噪音较少的隐含观察，同时增加了一个人的兴趣知识的可移植性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=On+Natural+Language+User+Profiles+for+Transparent+and+Scrutable+Recommendation)|0| +|[Retrieval-Enhanced Machine Learning](https://doi.org/10.1145/3477495.3531722)|Hamed Zamani, Fernando Diaz, Mostafa Dehghani, Donald Metzler, Michael Bendersky|University of Massachusetts Amherst, Amherst, MA, USA; Google Research, Amsterdam, Netherlands; Google Research, Mountain View, CA, USA; Google Research, Montréal, PQ, Canada|Although information access systems have long supportedpeople in accomplishing a wide range of tasks, we propose broadening the scope of users of information access systems to include task-driven machines, such as machine learning models. In this way, the core principles of indexing, representation, retrieval, and ranking can be applied and extended to substantially improve model generalization, scalability, robustness, and interpretability. We describe a generic retrieval-enhanced machine learning (REML) framework, which includes a number of existing models as special cases. REML challenges information retrieval conventions, presenting opportunities for novel advances in core areas, including optimization. The REML research agenda lays a foundation for a new style of information access research and paves a path towards advancing machine learning and artificial intelligence.|尽管信息访问系统长期以来一直支持人们完成各种各样的任务，但我们建议扩大信息访问系统的用户范围，以包括任务驱动的机器，如机器学习模型。通过这种方式，可以应用和扩展索引、表示、检索和排序的核心原则，从而大大提高模型泛化、可伸缩性、健壮性和可解释性。我们描述了一个通用的检索增强机器学习(REML)框架，其中包括一些现有的模型作为特殊情况。REML 挑战了信息检索惯例，为核心领域的新进展提供了机会，包括优化。REML 研究议程为新型的信息获取研究奠定了基础，为机器学习和人工智能的发展铺平了道路。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Retrieval-Enhanced+Machine+Learning)|0| |[Multi-CPR: A Multi Domain Chinese Dataset for Passage Retrieval](https://doi.org/10.1145/3477495.3531736)|Dingkun Long, Qiong Gao, Kuan Zou, Guangwei Xu, Pengjun Xie, Ruijie Guo, Jian Xu, Guanjun Jiang, Luxi Xing, Ping Yang|Alibaba Group, Hangzhou, China|Passage retrieval is a fundamental task in information retrieval (IR) research, which has drawn much attention recently. In the English field, the availability of large-scale annotated dataset (e.g, MS MARCO) and the emergence of deep pre-trained language models (e.g, BERT) has resulted in a substantial improvement of existing passage retrieval systems. However, in the Chinese field, especially for specific domains, passage retrieval systems are still immature due to quality-annotated dataset being limited by scale. Therefore, in this paper, we present a novel multi-domain Chinese dataset for passage retrieval (Multi-CPR). The dataset is collected from three different domains, including E-commerce, Entertainment video and Medical. Each dataset contains millions of passages and a certain amount of human annotated query-passage related pairs. We implement various representative passage retrieval methods as baselines. We find that the performance of retrieval models trained on dataset from general domain will inevitably decrease on specific domain. Nevertheless, a passage retrieval system built on in-domain annotated dataset can achieve significant improvement, which indeed demonstrates the necessity of domain labeled data for further optimization. We hope the release of the Multi-CPR dataset could benchmark Chinese passage retrieval task in specific domain and also make advances for future studies.|短文检索是信息检索研究中的一项基础性工作，近年来备受关注。在英语领域，大规模注释数据集(例如 MS MARCO)的可用性和深度预训练语言模型(例如 BERT)的出现使现有的文章检索系统得到了实质性的改进。然而，在中文领域，特别是在特定领域，由于质量注释数据集受到规模的限制，文章检索系统还不成熟。因此，本文提出了一种新的多领域中文文本检索数据集(Multi-CPR)。该数据集收集自三个不同的领域，包括电子商务，娱乐视频和医疗。每个数据集包含数百万个段落和一定数量的人工注释的查询-段落相关对。我们实现了各种具有代表性的文章检索方法作为基线。研究发现，对一般领域数据集训练的检索模型在特定领域的性能不可避免地会下降。然而，建立在域内注释数据集上的文章检索系统可以取得显著的改进，这确实说明了域标记数据进一步优化的必要性。我们希望通过多 CPR 数据集的发布，能够为特定领域的中文文章检索任务提供基准，并为今后的研究提供参考。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Multi-CPR:+A+Multi+Domain+Chinese+Dataset+for+Passage+Retrieval)|0| -|[MIMICS-Duo: Offline & Online Evaluation of Search Clarification](https://doi.org/10.1145/3477495.3531750)|Leila Tavakoli, Johanne R. Trippas, Hamed Zamani, Falk Scholer, Mark Sanderson|University of Massachusetts Amherst, Amherst, MA, USA; University of Melbourne, Melbourne, VIC, Australia; RMIT University, Melbourne, VIC, Australia|Asking clarification questions is an active area of research; however, resources for training and evaluating search clarification methods are not sufficient. To address this issue, we describe MIMICS-Duo, a new freely available dataset of 306 search queries with multiple clarifications (a total of 1,034 query-clarification pairs). MIMICS-Duo contains fine-grained annotations on clarification questions and their candidate answers and enhances the existing MIMICS datasets by enabling multi-dimensional evaluation of search clarification methods, including online and offline evaluation. We conduct extensive analysis to demonstrate the relationship between offline and online search clarification datasets and outline several research directions enabled by MIMICS-Duo. We believe that this resource will help researchers better understand clarification in search.|提出澄清问题是一个活跃的研究领域，然而，培训和评估搜索澄清方法的资源是不够的。为了解决这个问题，我们描述了 MIMICS-Duo，这是一个新的免费数据集，包含306个具有多重澄清的搜索查询(总共1,034个查询-澄清对)。MIMICS-Duo 包含关于澄清问题及其候选答案的细粒度注释，并通过支持搜索澄清方法的多维评估(包括在线和离线评估)来增强现有的 MIMICS 数据集。我们进行了广泛的分析，以证明离线和在线搜索澄清数据集之间的关系，并概述了由 MIMICS-Duo 实现的几个研究方向。我们相信，这一资源将有助于研究人员更好地理解在搜索澄清。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=MIMICS-Duo:+Offline+&+Online+Evaluation+of+Search+Clarification)|0| -|[A Common Framework for Exploring Document-at-a-Time and Score-at-a-Time Retrieval Methods](https://doi.org/10.1145/3477495.3531657)|Andrew Trotman, Joel Mackenzie, Pradeesh Parameswaran, Jimmy Lin|The University of Queensland, Brisbane, QLD, Australia; University of Otago, Dunedin, New Zealand; University of Waterloo, Waterloo, Canada|Document-at-a-time (DaaT) and score-at-a-time (SaaT) query evaluation techniques are different approaches to top-k retrieval with inverted indexes. While modern systems are dominated by DaaT, the academic literature has seen decades of debate about the merits of each. Recently, there has been renewed interest in SaaT methods for learned sparse lexical models, where studies have shown that transformers generate "wacky weights" that appear to reduce opportunities for optimizations in DaaT methods. However, researchers currently lack an easy-to-use SaaT system to support further exploration. This is the gap that our work fills. Starting with a modern SaaT system (JASS), we built Python bindings in order to integrate into the DaaT Pyserini IR toolkit (Lucene). The result is a common frontend to both a DaaT and a SaaT system. We demonstrate how recent experiments with a wide range of learned sparse lexical models can be easily reproduced. Our contribution is a framework that enables future research comparing DaaT and SaaT methods in the context of modern neural retrieval models.|一次文档(DaaT)和一次得分(SaaT)查询评估技术是两种不同的方法，用于带有倒排索引的 top-k 检索。虽然现代系统是由 DaaT 主导的，但学术文献已经对每个系统的优点进行了数十年的争论。最近，人们对学习稀疏词汇模型的 SaaT 方法重新产生了兴趣，研究表明，变压器产生的“古怪的权重”似乎减少了 DaaT 方法优化的机会。然而，研究人员目前缺乏一个易于使用的 SaaT 系统来支持进一步的探索。这是我们的工作填补的空白。从一个现代 SaaT 系统(JASS)开始，我们构建了 Python 绑定，以便集成到 DaaT Pyserini IR 工具包(Lucene)中。其结果是 DaaT 和 SaaT 系统的共同前端。我们证明了最近的实验与广泛的学习稀疏词汇模型可以很容易地再现。我们的贡献是一个框架，使未来的研究比较 DaaT 和 SaaT 方法在现代神经检索模型的背景下。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Common+Framework+for+Exploring+Document-at-a-Time+and+Score-at-a-Time+Retrieval+Methods)|0| -|[BiTe-REx: An Explainable Bilingual Text Retrieval System in the Automotive Domain](https://doi.org/10.1145/3477495.3531665)|Viju Sudhi, Sabine Wehnert, Norbert Michael Homner, Sebastian Ernst, Mark Gonter, Andreas Krug, Ernesto William De Luca|Audi AG, Ingolstadt, Germany; Otto von Guericke University, Magdeburg, Germany|To satiate the comprehensive information need of users, retrieval systems surpassing the boundaries of language are inevitable in the present digital space in the wake of an ever-rising multilingualism. This work presents the first-of-its-kind Bilingual Text Retrieval Explanations (BiTe-REx) aimed at users performing competitor or wage analysis in the automotive domain. BiTe-REx supports users to gather a more comprehensive picture of their query by retrieving results regardless of the query language and enables them to make a more informed decision by exposing how the underlying model judges the relevance of documents. With a user study, we demonstrate statistically significant results on the understandability and helpfulness of the explanations provided by the system.|为了满足用户的综合信息需求，随着多种语言的日益普及，超越语言边界的检索系统在当今数字化空间中是不可避免的。这项工作提出了第一种双语文本检索解释(BiTe-REx) ，旨在用户执行竞争对手或工资分析在汽车领域。BiTe-REx 支持用户通过检索结果(不管查询语言如何)收集更全面的查询信息，并通过揭示底层模型如何判断文档的相关性，使用户能够做出更明智的决策。通过用户研究，我们证明了系统提供的解释的可理解性和有用性的统计学显著结果。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=BiTe-REx:+An+Explainable+Bilingual+Text+Retrieval+System+in+the+Automotive+Domain)|0| +|[MIMICS-Duo: Offline & Online Evaluation of Search Clarification](https://doi.org/10.1145/3477495.3531750)|Leila Tavakoli, Johanne R. Trippas, Hamed Zamani, Falk Scholer, Mark Sanderson|University of Melbourne, Melbourne, VIC, Australia; University of Massachusetts Amherst, Amherst, MA, USA; RMIT University, Melbourne, VIC, Australia|Asking clarification questions is an active area of research; however, resources for training and evaluating search clarification methods are not sufficient. To address this issue, we describe MIMICS-Duo, a new freely available dataset of 306 search queries with multiple clarifications (a total of 1,034 query-clarification pairs). MIMICS-Duo contains fine-grained annotations on clarification questions and their candidate answers and enhances the existing MIMICS datasets by enabling multi-dimensional evaluation of search clarification methods, including online and offline evaluation. We conduct extensive analysis to demonstrate the relationship between offline and online search clarification datasets and outline several research directions enabled by MIMICS-Duo. We believe that this resource will help researchers better understand clarification in search.|提出澄清问题是一个活跃的研究领域，然而，培训和评估搜索澄清方法的资源是不够的。为了解决这个问题，我们描述了 MIMICS-Duo，这是一个新的免费数据集，包含306个具有多重澄清的搜索查询(总共1,034个查询-澄清对)。MIMICS-Duo 包含关于澄清问题及其候选答案的细粒度注释，并通过支持搜索澄清方法的多维评估(包括在线和离线评估)来增强现有的 MIMICS 数据集。我们进行了广泛的分析，以证明离线和在线搜索澄清数据集之间的关系，并概述了由 MIMICS-Duo 实现的几个研究方向。我们相信，这一资源将有助于研究人员更好地理解在搜索澄清。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=MIMICS-Duo:+Offline+&+Online+Evaluation+of+Search+Clarification)|0| +|[A Common Framework for Exploring Document-at-a-Time and Score-at-a-Time Retrieval Methods](https://doi.org/10.1145/3477495.3531657)|Andrew Trotman, Joel Mackenzie, Pradeesh Parameswaran, Jimmy Lin|The University of Queensland, Brisbane, QLD, Australia; University of Waterloo, Waterloo, Canada; University of Otago, Dunedin, New Zealand|Document-at-a-time (DaaT) and score-at-a-time (SaaT) query evaluation techniques are different approaches to top-k retrieval with inverted indexes. While modern systems are dominated by DaaT, the academic literature has seen decades of debate about the merits of each. Recently, there has been renewed interest in SaaT methods for learned sparse lexical models, where studies have shown that transformers generate "wacky weights" that appear to reduce opportunities for optimizations in DaaT methods. However, researchers currently lack an easy-to-use SaaT system to support further exploration. This is the gap that our work fills. Starting with a modern SaaT system (JASS), we built Python bindings in order to integrate into the DaaT Pyserini IR toolkit (Lucene). The result is a common frontend to both a DaaT and a SaaT system. We demonstrate how recent experiments with a wide range of learned sparse lexical models can be easily reproduced. Our contribution is a framework that enables future research comparing DaaT and SaaT methods in the context of modern neural retrieval models.|一次文档(DaaT)和一次得分(SaaT)查询评估技术是两种不同的方法，用于带有倒排索引的 top-k 检索。虽然现代系统是由 DaaT 主导的，但学术文献已经对每个系统的优点进行了数十年的争论。最近，人们对学习稀疏词汇模型的 SaaT 方法重新产生了兴趣，研究表明，变压器产生的“古怪的权重”似乎减少了 DaaT 方法优化的机会。然而，研究人员目前缺乏一个易于使用的 SaaT 系统来支持进一步的探索。这是我们的工作填补的空白。从一个现代 SaaT 系统(JASS)开始，我们构建了 Python 绑定，以便集成到 DaaT Pyserini IR 工具包(Lucene)中。其结果是 DaaT 和 SaaT 系统的共同前端。我们证明了最近的实验与广泛的学习稀疏词汇模型可以很容易地再现。我们的贡献是一个框架，使未来的研究比较 DaaT 和 SaaT 方法在现代神经检索模型的背景下。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Common+Framework+for+Exploring+Document-at-a-Time+and+Score-at-a-Time+Retrieval+Methods)|0| +|[BiTe-REx: An Explainable Bilingual Text Retrieval System in the Automotive Domain](https://doi.org/10.1145/3477495.3531665)|Viju Sudhi, Sabine Wehnert, Norbert Michael Homner, Sebastian Ernst, Mark Gonter, Andreas Krug, Ernesto William De Luca|Otto von Guericke University, Magdeburg, Germany; Audi AG, Ingolstadt, Germany|To satiate the comprehensive information need of users, retrieval systems surpassing the boundaries of language are inevitable in the present digital space in the wake of an ever-rising multilingualism. This work presents the first-of-its-kind Bilingual Text Retrieval Explanations (BiTe-REx) aimed at users performing competitor or wage analysis in the automotive domain. BiTe-REx supports users to gather a more comprehensive picture of their query by retrieving results regardless of the query language and enables them to make a more informed decision by exposing how the underlying model judges the relevance of documents. With a user study, we demonstrate statistically significant results on the understandability and helpfulness of the explanations provided by the system.|为了满足用户的综合信息需求，随着多种语言的日益普及，超越语言边界的检索系统在当今数字化空间中是不可避免的。这项工作提出了第一种双语文本检索解释(BiTe-REx) ，旨在用户执行竞争对手或工资分析在汽车领域。BiTe-REx 支持用户通过检索结果(不管查询语言如何)收集更全面的查询信息，并通过揭示底层模型如何判断文档的相关性，使用户能够做出更明智的决策。通过用户研究，我们证明了系统提供的解释的可理解性和有用性的统计学显著结果。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=BiTe-REx:+An+Explainable+Bilingual+Text+Retrieval+System+in+the+Automotive+Domain)|0| |[Are Taylor's Posts Risky? Evaluating Cumulative Revelations in Online Personal Data: A persona-based tool for evaluating awareness of online risks and harms](https://doi.org/10.1145/3477495.3531659)|Leif Azzopardi, Jo Briggs, Melissa Duheric, Callum Nash, Emma Nicol, Wendy Moncur, Burkhard Schafer||||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Are+Taylor's+Posts+Risky?+Evaluating+Cumulative+Revelations+in+Online+Personal+Data:+A+persona-based+tool+for+evaluating+awareness+of+online+risks+and+harms)|0| |[DDEN: A Heterogeneous Learning-to-Rank Approach with Deep Debiasing Experts Network](https://doi.org/10.1145/3477495.3536320)|Wenchao Xiu, Yiran Wang, Taofeng Xue, Kai Zhang, Qin Zhang, Zhonghuo Wu, Yifan Yang, Gong Zhang|Meituan, Shanghai, China|Learning-to-Rank(LTR) is widely used in many Information Retrieval(IR) scenarios, including web search and Location Based Services(LBS) search. However, most existing LTR techniques mainly focus on homogeneous ranking. Taking QAC in Dianping search as an example, heterogeneous documents including suggested queries (SQ) and Point-of-Interests(POI) need to be ranked and presented to enhance user experience. New challenges are faced when conducting heterogeneous ranking, including inconsistent feature space and more serious position bias caused by distinct representation spaces. Therefore, we propose Deep Debiasing Experts Network (DDEN), a novel heterogeneous LTR approach based on Mixture-of-Experts architecture and gating network, to deal with the inconsistent feature space of documents in ranking system. Furthermore, DDEN mitigates the position bias by adopting adversarial-debiasing framework embedded with heterogeneous LTR techniques. We conduct reproducible experiments on industrial datasets from Dianping, one of the largest local life platforms, and deploy DDEN in online application. Results show that DDEN substantially improves ranking performance in offline evaluation and boost the overall click-through rate in online A/B test by 2.1%.|学习到排名(learning-to-Rank，LTR)广泛应用于许多信息检索场景，包括网络搜索和基于位置的服务(Location Based Services，LBS)搜索。然而，大多数现有的 LTR 技术主要集中在同质排序。以点评搜索中的质量控制(QAC)为例，需要对包括建议查询(SQ)和兴趣点(POI)在内的异构文档进行排序和呈现，以提高用户体验。异构排序面临的新挑战包括不一致的特征空间和不同表示空间引起的更严重的位置偏差。为此，本文提出了一种基于专家混合体系结构和门网络的异构 LTR 方法——深度去偏专家网络(Deep Debioning Expert Network，DDEN) ，用于处理排序系统中文档的不一致特征空间。此外，DDEN 通过采用嵌入异构 LTR 技术的对抗性消偏框架来缓解位置偏差。我们在本地最大的生活平台之一 Dianping 的工业数据集上进行可重复的实验，并在在线应用中部署 DDEN。结果显示，DDEN 大大提高了离线评估的排名表现，并使在线 A/B 测试的整体点进率提高了2.1% 。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=DDEN:+A+Heterogeneous+Learning-to-Rank+Approach+with+Deep+Debiasing+Experts+Network)|0| |[An Intelligent Advertisement Short Video Production System via Multi-Modal Retrieval](https://doi.org/10.1145/3477495.3536323)|Yanheng Wei, Lianghua Huang, Yanhao Zhang, Yun Zheng, Pan Pan|Alibaba Group, Beijing, China|In its most basic form, advertising video production communicates a message about a product or service to the public. In the age of digital marketing, where the most popular way to connect with audiences is through advertising videos. However, advertising video production is a costly and complicated process from creation, material shooting, editing to the final commercial video. Therefore, producing qualified advertising videos is a capital and talent-intensive task, which poses a huge challenge for start-ups or inexperienced ad creators. paper proposes an intelligent advertising video production system driven by multi-modal retrieval, which only requires the input of descriptive copy. This system can automatically generate scripts, then extract key queries, retrieve related short video materials in the video library, and finally synthesize short advertising videos. The whole process minimizes human input, greatly reduces the threshold for advertising video production and greatly improves output and efficiency. It has a modular design to encourage the study of new multi-modal algorithms, which can be evaluated in batch mode. It can also integrate with a user interface, which allows user studies and data collection in an interactive mode, where the back end can be fully algorithmic or a wizard of oz setup. The proposed system has been fully verified and has broad prospects in the production of short videos for commodity advertisements within Alibaba.|在最基本的形式中，广告视频制作向公众传达了关于产品或服务的信息。在数字营销时代，最流行的与观众联系的方式是通过广告视频。然而，广告视频制作是一个昂贵而复杂的过程，从创作、素材拍摄、编辑到最终的商业视频。因此，制作合格的广告视频是一项资本和人才密集型的任务，这对初创企业或缺乏经验的广告创作者来说是一个巨大的挑战。提出了一种基于多模态检索的智能广告视频制作系统，该系统只需要输入描述性文本。该系统可以自动生成脚本，然后提取关键查询，检索视频库中相关的短视频资料，最后合成广告短视频。整个过程最大限度地减少了人工投入，大大降低了广告视频制作的门槛，大大提高了产量和效率。它采用模块化设计，以鼓励对新的多模态算法的研究，这些算法可以在批处理模式下进行评估。它还可以集成一个用户界面，允许用户研究和数据收集在一个交互模式，其中后端可以完全算法或绿野仙踪设置向导。建议的系统已经全面验证，在阿里巴巴制作商品广告短片方面具有广阔前景。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=An+Intelligent+Advertisement+Short+Video+Production+System+via+Multi-Modal+Retrieval)|0| @@ -322,52 +322,52 @@ |[What the Actual...Examining User Behaviour in Information Retrieval](https://doi.org/10.1145/3477495.3532687)|George Buchanan, Dana McKay|University of Melbourne, Melbourne, VIC, Australia; RMIT University, Melbourne, VIC, Australia|Conducting studies involving actual users is a recurring challenge in information retrieval. In this tutorial we will address the main strategic and tactical choices for engaging with, designing and executing user studies, considering both evaluation and formative investigation. The tension between reproducibility and ensuring natural user behaviour will be a recurring focus, seeking to help individual researchers make an intentional and well-argued choice for their research. The presenters have over fifty years of combined experience working in interactive information retrieval, and information interaction in general.|进行涉及实际使用者的研究是信息检索的一个反复出现的挑战。在本教程中，我们将讨论参与、设计和执行用户研究的主要战略和战术选择，同时考虑评估和形成性调查。可重复性和确保自然使用者行为之间的紧张关系将是一个反复出现的焦点，目的是帮助个别研究人员为其研究做出有意识和有充分理由的选择。主持人在互动信息检索和一般的信息互动方面有超过五十年的工作经验。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=What+the+Actual...Examining+User+Behaviour+in+Information+Retrieval)|0| |[User-centered Non-factoid Answer Retrieval](https://doi.org/10.1145/3477495.3531689)|Marwah Alaofi|RMIT University, Melbourne, VIC, Australia|In this research, we aim to examine the assumptions made about users when searching for non-factoid answers using search engines. That is, the way they approach non-factoid question-answering tasks, the language they use to express their questions, the variability in their queries and their behavior towards the provided answers. The investigation will also examine the extent to which these neglected factors affect retrieval performance and potentially highlight the importance of building more realistic methodologies and test collections that capture the real nature of this task. Through our preliminary work, we have begun to explore the characteristics of non-factoid question-answering queries and investigate query variability and their impact on modern retrieval models. Our preliminary results demonstrate notable differences between non-factoid questions sampled from a large query log and those used in QA datasets. In addition, our results demonstrate a profound effect of query variability on retrieval consistency, indicating a potential impact on retrieval performance that is worth studying. We highlight the importance of understanding user behaviour while searching for non-factoid answers, specifically the way they behave in response to receiving an answer. This should advance our understanding of the support users require across different types of non-factoid questions and inform the design of interaction models that support learning and encourage exploring.|本研究旨在探讨使用搜寻引擎搜寻非事实性答案时，对使用者所作的假设。也就是说，他们处理非事实性问答任务的方式，他们用来表达他们的问题的语言，他们的查询的可变性和他们对提供的答案的行为。调查还将审查这些被忽视的因素在多大程度上影响检索性能，并可能强调建立更现实的方法和测试收集的重要性，以捕捉这一任务的真实性质。通过我们的初步工作，我们已经开始探索非事实问答查询的特点，并调查查询的可变性及其对现代检索模型的影响。我们的初步结果表明，从大型查询日志中抽样的非事实性问题与 QA 数据集中使用的问题之间存在显著差异。此外，我们的研究结果显示了查询变异性对检索一致性的深刻影响，表明了对检索性能的潜在影响，值得研究。我们强调了理解用户行为的重要性，同时寻找非事实性的答案，特别是他们的行为方式，以回应收到的答案。这将提高我们对用户在不同类型的非事实性问题中需要的支持的理解，并为支持学习和鼓励探索的交互模型的设计提供信息。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=User-centered+Non-factoid+Answer+Retrieval)|0| |[Intelligent Conversational Agents for Ambient Computing](https://doi.org/10.1145/3477495.3532087)|Ruhi Sarikaya|Amazon, Seattle, WA, USA|We are in the midst of an AI revolution. Three primary disruptive changes set off this revolution: 1) increase in compute power, mobile internet, and advances in deep learning. The next decade is expected to be about the proliferation of Internet-of-Things (IoT) devices and sensors, which will generate exponentially larger amounts of data to reason over and pave the way for ambient computing. This will also give rise to new forms of interaction patterns with these systems. Users will have to interact with these systems under increasingly richer context and in real-time. Conversational AI has a critical role to play in this revolution, but only if it delivers on its promise of enabling natural, frictionless, and personalized interactions in any context the user is in, while hiding the complexity of these systems through ambient intelligence. However, current commercial conversational AI systems are trained primarily with a supervised learning paradigm, which is difficult, if not impossible, to scale by manually annotating data for increasingly complex sets of contextual conditions. Inherent ambiguity in natural language further complicates the problem. We need to devise new forms of learning paradigms and frameworks that will scale to this complexity. In this talk, we present some early steps we are taking with Alexa, Amazon's Conversational AI system, to move from supervised learning to self-learning methods, where the AI relies on customer interactions for supervision in our journey to ambient intelligence.|我们正处于人工智能革命的中期。三个主要的颠覆性变化引发了这场革命: 1)计算能力的提高，移动互联网的发展，以及深度学习的进步。下一个十年预计将是物联网设备和传感器的激增，它们将产生指数级数量的数据来进行推理，并为环境计算铺平道路。这也将产生与这些系统交互模式的新形式。用户将不得不在日益丰富的上下文环境下与这些系统进行实时交互。对话式人工智能在这场革命中扮演着关键的角色，但前提是它能够在用户所处的任何环境中实现自然、无摩擦和个性化的交互，同时通过环境智能隐藏这些系统的复杂性。然而，目前的商业会话人工智能系统主要使用监督式学习范式进行训练，这种范式很难(如果不是不可能的话)通过手动为日益复杂的上下文条件集注释数据来扩展。自然语言中固有的歧义使问题进一步复杂化。我们需要设计新的学习范式和框架，以适应这种复杂性。在本次演讲中，我们将介绍亚马逊的对话式人工智能系统 Alexa 的一些早期步骤，该系统将从监督式学习转向自学习方法，在我们的环境智能过程中，人工智能依赖客户互动进行监督。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Intelligent+Conversational+Agents+for+Ambient+Computing)|0| -|[A Robust Computerized Adaptive Testing Approach in Educational Question Retrieval](https://doi.org/10.1145/3477495.3531928)|Yan Zhuang, Qi Liu, Zhenya Huang, Zhi Li, Binbin Jin, Haoyang Bi, Enhong Chen, Shijin Wang|Huawei Cloud Computing Technologies Co., Ltd, Hangzhou, China; State Key Laboratory of Cognitive Intelligence & iFLYTEK AI Research (Central China), iFLYTEK Co., Ltd, Hefei, China; Anhui Province Key Laboratory of Big Data Analysis and Application, University of Science and Technology of China & State Key Laboratory of Cognitive Intelligence, Hefei, China; Anhui Province Key Laboratory of Big Data Analysis and Application, University of Science and Technology of China & Institute of Artificial Intelligence, Hefei Comprehensive National Science Center & State Key Laboratory of Cognitive Intelligence, Hefei, China|Computerized Adaptive Testing (CAT) is a promising testing mode in personalized online education (e.g., GRE), which aims at measuring student's proficiency accurately and reducing test length. The "adaptive" is reflected in its selection algorithm that can retrieve best-suited questions for student based on his/her estimated proficiency at each test step. Although there are many sophisticated selection algorithms for improving CAT's effectiveness, they are restricted and perturbed by the accuracy of current proficiency estimate, thus lacking robustness. To this end, we investigate a general method to enhance the robustness of existing algorithms by leveraging student's "multi-facet" nature during tests. Specifically, we present a generic optimization criterion Robust Adaptive Testing (RAT) for proficiency estimation via fusing multiple estimates at each step, which maintains a multi-facet description of student's potential proficiency. We further provide theoretical analyses of such estimator's desirable statistical properties: asymptotic unbiasedness, efficiency, and consistency. Extensive experiments on perturbed synthetic data and three real-world datasets show that selection algorithms in our RAT framework are robust and yield substantial improvements.|计算机自适应测试(CAT)是个性化网络教育(如 GRE)中一种很有前途的测试模式，其目的是准确测量学生的水平，减少测试时间。“适应性”反映在其选择算法中，该算法可以根据学生在每个测试步骤中的估计熟练程度为学生检索最适合的问题。虽然有许多复杂的选择算法来提高 CAT 的有效性，但它们都受到当前水平估计精度的限制和干扰，因此缺乏鲁棒性。为此，我们研究了一种通用的方法，以增强现有的算法的健壮性，利用学生的“多方面”的性质在测试。具体来说，我们提出了一个通用的优化标准鲁棒自适应测试(RAT)的水平估计融合多个估计在每一个步骤，它保持了一个学生的潜在水平的多方面的描述。进一步从理论上分析了这类估计量的理想统计性质: 渐近无偏性、有效性和一致性。在扰动合成数据和三个实际数据集上的大量实验表明，我们的 RAT 框架中的选择算法是健壮的，并且产生了实质性的改进。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Robust+Computerized+Adaptive+Testing+Approach+in+Educational+Question+Retrieval)|0| +|[A Robust Computerized Adaptive Testing Approach in Educational Question Retrieval](https://doi.org/10.1145/3477495.3531928)|Yan Zhuang, Qi Liu, Zhenya Huang, Zhi Li, Binbin Jin, Haoyang Bi, Enhong Chen, Shijin Wang|Anhui Province Key Laboratory of Big Data Analysis and Application, University of Science and Technology of China & Institute of Artificial Intelligence, Hefei Comprehensive National Science Center & State Key Laboratory of Cognitive Intelligence, Hefei, China; State Key Laboratory of Cognitive Intelligence & iFLYTEK AI Research (Central China), iFLYTEK Co., Ltd, Hefei, China; Huawei Cloud Computing Technologies Co., Ltd, Hangzhou, China; Anhui Province Key Laboratory of Big Data Analysis and Application, University of Science and Technology of China & State Key Laboratory of Cognitive Intelligence, Hefei, China|Computerized Adaptive Testing (CAT) is a promising testing mode in personalized online education (e.g., GRE), which aims at measuring student's proficiency accurately and reducing test length. The "adaptive" is reflected in its selection algorithm that can retrieve best-suited questions for student based on his/her estimated proficiency at each test step. Although there are many sophisticated selection algorithms for improving CAT's effectiveness, they are restricted and perturbed by the accuracy of current proficiency estimate, thus lacking robustness. To this end, we investigate a general method to enhance the robustness of existing algorithms by leveraging student's "multi-facet" nature during tests. Specifically, we present a generic optimization criterion Robust Adaptive Testing (RAT) for proficiency estimation via fusing multiple estimates at each step, which maintains a multi-facet description of student's potential proficiency. We further provide theoretical analyses of such estimator's desirable statistical properties: asymptotic unbiasedness, efficiency, and consistency. Extensive experiments on perturbed synthetic data and three real-world datasets show that selection algorithms in our RAT framework are robust and yield substantial improvements.|计算机自适应测试(CAT)是个性化网络教育(如 GRE)中一种很有前途的测试模式，其目的是准确测量学生的水平，减少测试时间。“适应性”反映在其选择算法中，该算法可以根据学生在每个测试步骤中的估计熟练程度为学生检索最适合的问题。虽然有许多复杂的选择算法来提高 CAT 的有效性，但它们都受到当前水平估计精度的限制和干扰，因此缺乏鲁棒性。为此，我们研究了一种通用的方法，以增强现有的算法的健壮性，利用学生的“多方面”的性质在测试。具体来说，我们提出了一个通用的优化标准鲁棒自适应测试(RAT)的水平估计融合多个估计在每一个步骤，它保持了一个学生的潜在水平的多方面的描述。进一步从理论上分析了这类估计量的理想统计性质: 渐近无偏性、有效性和一致性。在扰动合成数据和三个实际数据集上的大量实验表明，我们的 RAT 框架中的选择算法是健壮的，并且产生了实质性的改进。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Robust+Computerized+Adaptive+Testing+Approach+in+Educational+Question+Retrieval)|0| |[Forest-based Deep Recommender](https://doi.org/10.1145/3477495.3531980)|Chao Feng, Defu Lian, Zheng Liu, Xing Xie, Le Wu, Enhong Chen|Hefei university of Technology, Hefei, China; University of Science and Technology of China, Hefei, China; Microsoft Research Asia, Beijing, China|With the development of deep learning techniques, deep recommendation models also achieve remarkable improvements in terms of recommendation accuracy. However, due to the large number of candidate items in practice and the high cost of preference computation, these methods also suffer from low efficiency of recommendation. The recently proposed tree-based deep recommendation models alleviate the problem by directly learning tree structure and representations under the guidance of recommendation objectives. However, such models have two shortcomings. First, the max-heap assumption in the hierarchical tree, in which the preference for a parent node should be the maximum between the preferences for its children, is difficult to satisfy in their binary classification objectives. Second, the learned index only includes a single tree, which is different from the widely-used multiple trees index, providing an opportunity to improve the accuracy of recommendation. To this end, we propose a Deep Forest-based Recommender (DeFoRec for short) for an efficient recommendation. In DeFoRec, all the trees generated during training process are retained to form the forest. When learning node representation of each tree, we have to satisfy the max-heap assumption as much as possible and mimic beam search behavior over the tree in the training stage. This is achieved by DeFoRec to regard the training task as multi-classification over tree nodes at the same level. However, the number of tree nodes grows exponentially with levels, making us to train the preference model by the guidance of sampled-softmax technique. The experiments are conducted on real-world datasets, validating the effectiveness of the proposed preference model learning method and tree learning method.|随着深度学习技术的发展，深度推荐模型在推荐精度方面也取得了显著的提高。然而，由于实际中候选项数量大，偏好计算成本高，这些方法也存在推荐效率低的问题。最近提出的基于树的深度推荐模型通过在推荐目标的指导下直接学习树的结构和表示来解决这个问题。然而，这种模式有两个缺点。首先，层次树中的最大堆假设(父节点的首选项应该是其子节点的首选项之间的最大值)难以满足其二进制分类目标。其次，学习索引只包括一棵树，这与广泛使用的多棵树索引不同，为提高推荐的准确性提供了机会。为此，我们提出了一个基于深度森林的推荐器(简称 DeFoRec)来实现有效的推荐。在 DeFoRec 中，所有在训练过程中生成的树被保留以形成森林。在学习每棵树的节点表示时，必须尽可能满足最大堆假设，并在训练阶段模拟树上的束搜索行为。DeFoRec 将训练任务视为同一层次上的树节点上的多分类，从而实现了这一目标。然而，树节点的数量随着层次的增加呈指数增长，这使得我们在采样-软极大技术的指导下对偏好模型进行训练。在实际数据集上进行了实验，验证了所提出的偏好模型学习方法和树学习方法的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Forest-based+Deep+Recommender)|0| -|[Ranking Interruptus: When Truncated Rankings Are Better and How to Measure That](https://doi.org/10.1145/3477495.3532051)|Enrique Amigó, Stefano Mizzaro, Damiano Spina|University of Udine, Udine, Italy; UNED NLP & IR Group, Madrid, Spain; RMIT University, Melbourne, VIC, Australia|Most of information retrieval effectiveness evaluation metrics assume that systems appending irrelevant documents at the bottom of the ranking are as effective as (or not worse than) systems that have a stopping criteria to 'truncate' the ranking at the right position to avoid retrieving those irrelevant documents at the end. It can be argued, however, that such truncated rankings are more useful to the end user. It is thus important to understand how to measure retrieval effectiveness in this scenario. In this paper we provide both theoretical and experimental contributions. We first define formal properties to analyze how effectiveness metrics behave when evaluating truncated rankings. Our theoretical analysis shows that de-facto standard metrics do not satisfy desirable properties to evaluate truncated rankings: only Observational Information Effectiveness (OIE) -- a metric based on Shannon's information theory -- satisfies them all. We then perform experiments to compare several metrics on nine TREC datasets. According to our experimental results, the most appropriate metrics for truncated rankings are OIE and a novel extension of Rank-Biased Precision that adds a user effort factor penalizing the retrieval of irrelevant documents.|大多数信息检索有效性评估指标都假定，在排名底部附加不相关文档的系统与那些有停止标准的系统一样有效(或者不比那些有停止标准的系统差) ，后者会在正确的位置“截断”排名，以避免在最后检索到那些不相关的文档。然而，可以说，这种截断的排名对最终用户更有用。因此，了解如何在此场景中度量检索效率非常重要。在本文中，我们提供了理论和实验的贡献。我们首先定义形式属性来分析效率指标在评估截断排名时的表现。我们的理论分析表明，事实上的标准指标不能满足评估截断排名的理想属性: 只有观测信息有效性(OIE)——一个基于香农信息理论的指标——能够满足所有这些指标。然后，我们进行实验来比较九个 TREC 数据集上的几个指标。根据我们的实验结果，最适合截断排名的指标是 OIE 和一个新的扩展排名偏差精度，增加了用户的努力因素惩罚检索不相关的文档。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Ranking+Interruptus:+When+Truncated+Rankings+Are+Better+and+How+to+Measure+That)|0| +|[Ranking Interruptus: When Truncated Rankings Are Better and How to Measure That](https://doi.org/10.1145/3477495.3532051)|Enrique Amigó, Stefano Mizzaro, Damiano Spina|UNED NLP & IR Group, Madrid, Spain; University of Udine, Udine, Italy; RMIT University, Melbourne, VIC, Australia|Most of information retrieval effectiveness evaluation metrics assume that systems appending irrelevant documents at the bottom of the ranking are as effective as (or not worse than) systems that have a stopping criteria to 'truncate' the ranking at the right position to avoid retrieving those irrelevant documents at the end. It can be argued, however, that such truncated rankings are more useful to the end user. It is thus important to understand how to measure retrieval effectiveness in this scenario. In this paper we provide both theoretical and experimental contributions. We first define formal properties to analyze how effectiveness metrics behave when evaluating truncated rankings. Our theoretical analysis shows that de-facto standard metrics do not satisfy desirable properties to evaluate truncated rankings: only Observational Information Effectiveness (OIE) -- a metric based on Shannon's information theory -- satisfies them all. We then perform experiments to compare several metrics on nine TREC datasets. According to our experimental results, the most appropriate metrics for truncated rankings are OIE and a novel extension of Rank-Biased Precision that adds a user effort factor penalizing the retrieval of irrelevant documents.|大多数信息检索有效性评估指标都假定，在排名底部附加不相关文档的系统与那些有停止标准的系统一样有效(或者不比那些有停止标准的系统差) ，后者会在正确的位置“截断”排名，以避免在最后检索到那些不相关的文档。然而，可以说，这种截断的排名对最终用户更有用。因此，了解如何在此场景中度量检索效率非常重要。在本文中，我们提供了理论和实验的贡献。我们首先定义形式属性来分析效率指标在评估截断排名时的表现。我们的理论分析表明，事实上的标准指标不能满足评估截断排名的理想属性: 只有观测信息有效性(OIE)——一个基于香农信息理论的指标——能够满足所有这些指标。然后，我们进行实验来比较九个 TREC 数据集上的几个指标。根据我们的实验结果，最适合截断排名的指标是 OIE 和一个新的扩展排名偏差精度，增加了用户的努力因素惩罚检索不相关的文档。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Ranking+Interruptus:+When+Truncated+Rankings+Are+Better+and+How+to+Measure+That)|0| |[Offline Retrieval Evaluation Without Evaluation Metrics](https://doi.org/10.1145/3477495.3532033)|Fernando Diaz, Andres Ferraro|Mila - Quebec Artificial Intelligence Institute, Montréal, PQ, Canada; Google, Montréal, PQ, Canada|Offline evaluation of information retrieval and recommendation has traditionally focused on distilling the quality of a ranking into a scalar metric such as average precision or normalized discounted cumulative gain. We can use this metric to compare the performance of multiple systems for the same request. Although evaluation metrics provide a convenient summary of system performance, they also collapse subtle differences across users into a single number and can carry assumptions about user behavior and utility not supported across retrieval scenarios. We propose recall-paired preference (RPP), a metric-free evaluation method based on directly computing a preference between ranked lists. RPP simulates multiple user subpopulations per query and compares systems across these pseudo-populations. Our results across multiple search and recommendation tasks demonstrate that RPP substantially improves discriminative power while correlating well with existing metrics and being equally robust to incomplete data.|对信息检索和推荐的离线评估传统上侧重于将排名的质量提炼为一个标量指标，如平均精度或标准化折现累计增益。我们可以使用这个度量来比较同一个请求的多个系统的性能。虽然评估指标提供了一个方便的系统性能总结，但是它们也将用户之间的细微差异折叠成一个数字，并且可以对不支持检索场景的用户行为和实用程序进行假设。我们提出了一种基于直接计算排名表之间偏好的无度量评价方法——召回配对偏好(RPP)。RPP 模拟每个查询的多个用户子种群，并比较这些伪种群中的系统。我们在多个搜索和推荐任务中的结果表明，RPP 大大提高了识别能力，同时与现有指标关联良好，对不完整数据具有同样的鲁棒性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Offline+Retrieval+Evaluation+Without+Evaluation+Metrics)|0| -|[Pareto-Optimal Fairness-Utility Amortizations in Rankings with a DBN Exposure Model](https://doi.org/10.1145/3477495.3532036)|Till Kletti, JeanMichel Renders, Patrick Loiseau|Naver Labs Europe, Meylan, France; Univ. Grenoble Alpes, Inria, CNRS, Grenoble INP, LIG, Grenoble, France|In recent years, it has become clear that rankings delivered in many areas need not only be useful to the users but also respect fairness of exposure for the item producers. We consider the problem of finding ranking policies that achieve a Pareto-optimal tradeoff between these two aspects. Several methods were proposed to solve it; for instance a popular one is to use linear programming with a Birkhoff-von Neumann decomposition. These methods, however, are based on a classical Position Based exposure Model (PBM), which assumes independence between the items (hence the exposure only depends on the rank). In many applications, this assumption is unrealistic and the community increasingly moves towards considering other models that include dependences, such as the Dynamic Bayesian Network (DBN) exposure model. For such models, computing (exact) optimal fair ranking policies remains an open question. In this paper, we answer this question by leveraging a new geometrical method based on the so-called expohedron proposed recently for the PBM (Kletti et al., WSDM'22). We lay out the structure of a new geometrical object (the DBN-expohedron), and propose for it a Carathéodory decomposition algorithm of complexity $O(n^3)$, where n is the number of documents to rank. Such an algorithm enables expressing any feasible expected exposure vector as a distribution over at most n rankings; furthermore we show that we can compute the whole set of Pareto-optimal expected exposure vectors with the same complexity $O(n^3)$. Our work constitutes the first exact algorithm able to efficiently find a Pareto-optimal distribution of rankings. It is applicable to a broad range of fairness notions, including classical notions of meritocratic and demographic fairness. We empirically evaluate our method on the TREC2020 and MSLR datasets and compare it to several baselines in terms of Pareto-optimality and speed.|近年来，很明显，在许多领域提供的排名不仅需要对用户有用，而且还要尊重项目制作者的公平曝光。我们考虑的问题，找到排序的政策，实现了帕累托最优权衡这两个方面。人们提出了几种方法来解决这个问题，例如，一种流行的方法是使用伯克霍夫-冯诺依曼分解的线性规划。然而，这些方法是基于经典的基于位置的曝光模型(PBM) ，该模型假设项目之间的独立性(因此曝光只取决于排名)。在许多应用程序中，这种假设是不现实的，社区越来越倾向于考虑包含依赖关系的其他模型，例如动态贝氏网路暴露模型。对于这样的模型，计算(精确的)最优公平排序策略仍然是一个悬而未决的问题。在本文中，我们回答这个问题，利用一个新的几何方法的基础上，所谓的外三面体最近提出的 PBM (Kletti 等，WSDM’22)。我们给出了一个新的几何对象(DBN-expohedron)的结构，并提出了一个复杂度为 $O (n ^ 3) $的 Carathéodory 分解算法，其中 n 是要排序的文档数。这种算法能够表示任何可行的期望暴露矢量作为一个分布在最多 n 个排名; 此外，我们表明，我们可以计算整个集合的帕累托最优期望暴露矢量具有相同的复杂度 $O (n ^ 3) $。我们的工作构成了第一个精确的算法，能够有效地找到排名的帕累托最优分布。它适用于广泛的公平概念，包括精英统治和人口统计公平的经典概念。我们在 TREC2020和 MSLR 数据集上经验性地评估了我们的方法，并将其与几个基线在帕累托最优性和速度方面进行了比较。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Pareto-Optimal+Fairness-Utility+Amortizations+in+Rankings+with+a+DBN+Exposure+Model)|0| -|[Risk-Sensitive Deep Neural Learning to Rank](https://doi.org/10.1145/3477495.3532056)|Pedro Henrique Silva Rodrigues, Daniel Xavier de Sousa, Thierson Couto Rosa, Marcos André Gonçalves|Federal University of Goiás - UFG, Goiânia, Brazil; Federal University of Minas Gerais - UFMG, Belo Horizonte, Brazil; Federal Institute of Goiás - IFG, Anápolis, Brazil|Learning to Rank (L2R) is the core task of many Information Retrieval systems. Recently, a great effort has been put on exploring Deep Neural Networks (DNNs) for L2R, with significant results. However, risk-sensitiveness, an important and recent advance in the L2R arena, that reduces variability and increases trust, has not been incorporated into Deep Neural L2R yet. Risk-sensitive measures are important to assess the risk of an IR system to perform worse than a set of baseline IR systems for several queries. However, the risk-sensitive measures described in the literature have a non-smooth behavior, making them difficult, if not impossible, to be optimized by DNNs. In this work we solve this difficult problem by proposing a family of new loss functions -- \riskloss\ -- that support a smooth risk-sensitive optimization. \riskloss\ introduces two important contributions: (i) the substitution of the traditional NDCG or MAP metrics in risk-sensitive measures with smooth loss functions that evaluate the correlation between the predicted and the true relevance order of documents for a given query and (ii) the use of distinct versions of the same DNN architecture as baselines by means of a multi-dropout technique during the smooth risk-sensitive optimization, avoiding the inconvenience of assessing multiple IR systems as part of DNN training. We empirically demonstrate significant achievements of the proposed \riskloss\ functions when used with recent DNN methods in the context of well-known web-search datasets such as WEB10K, YAHOO, and MQ2007. Our solutions reach improvements of 8% in effectiveness (NDCG) while improving in around 5% the risk-sensitiveness (\grisk\ measure) when applied together with a state-of-the-art Self-Attention DNN-L2R architecture. Furthermore, \riskloss\ is capable of reducing by 28% the losses over the best evaluated baselines and significantly improving over the risk-sensitive state-of-the-art non-DNN method (by up to 13.3%) while keeping (or even increasing) overall effectiveness. All these results ultimately establish a new level for the state-of-the-art on risk-sensitiveness and DNN-L2R research.|学习排名(L2R)是许多信息检索系统的核心任务。近年来，针对 L2R 的深层神经网络(DNN)的研究取得了显著的成果。然而，风险敏感性，一个重要的和最近在 L2R 领域的进展，减少变异性和增加信任，尚未被纳入深层神经 L2R。风险敏感度量对于评估一个 IR 系统在几个查询中的性能低于一组基准 IR 系统的风险非常重要。然而，文献中描述的风险敏感性措施有一个不平滑的行为，使他们难以，如果不是不可能，被 DNN 优化。在这项工作中，我们通过提出一系列新的损失函数——风险损失——来解决这个难题，这些函数支持平稳的风险敏感优化。风险损失引入了两个重要贡献: (i)用平滑损失函数替换风险敏感度量中的传统 NDCG 或 MAP 指标，评估给定查询的文档的预测和真实相关顺序之间的相关性; (ii)通过平滑风险敏感性优化期间的多退出技术使用相同 DNN 架构的不同版本作为基线，避免了评估多个 IR 系统作为 DNN 训练的一部分的不便。当与最近的 DNN 方法在诸如 WEB10K，YAHOO 和 MQ2007等著名的网络搜索数据集的背景下使用时，我们经验性地证明了所提出的风险损失函数的显着成就。我们的解决方案在与最先进的自我注意 DNN-L2R 架构一起应用时，有效性(NDCG)提高了8% ，而风险敏感性(风险测量)提高了约5% 。此外，风险损失能够比最佳评估基线减少28% 的损失，并且比风险敏感的最先进的非 DNN 方法(高达13.3%)显着改善，同时保持(甚至增加)总体有效性。所有这些结果最终为风险敏感性和 DNN-L2R 研究的最新水平奠定了一个新的基础。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Risk-Sensitive+Deep+Neural+Learning+to+Rank)|0| +|[Pareto-Optimal Fairness-Utility Amortizations in Rankings with a DBN Exposure Model](https://doi.org/10.1145/3477495.3532036)|Till Kletti, JeanMichel Renders, Patrick Loiseau|Univ. Grenoble Alpes, Inria, CNRS, Grenoble INP, LIG, Grenoble, France; Naver Labs Europe, Meylan, France|In recent years, it has become clear that rankings delivered in many areas need not only be useful to the users but also respect fairness of exposure for the item producers. We consider the problem of finding ranking policies that achieve a Pareto-optimal tradeoff between these two aspects. Several methods were proposed to solve it; for instance a popular one is to use linear programming with a Birkhoff-von Neumann decomposition. These methods, however, are based on a classical Position Based exposure Model (PBM), which assumes independence between the items (hence the exposure only depends on the rank). In many applications, this assumption is unrealistic and the community increasingly moves towards considering other models that include dependences, such as the Dynamic Bayesian Network (DBN) exposure model. For such models, computing (exact) optimal fair ranking policies remains an open question. In this paper, we answer this question by leveraging a new geometrical method based on the so-called expohedron proposed recently for the PBM (Kletti et al., WSDM'22). We lay out the structure of a new geometrical object (the DBN-expohedron), and propose for it a Carathéodory decomposition algorithm of complexity $O(n^3)$, where n is the number of documents to rank. Such an algorithm enables expressing any feasible expected exposure vector as a distribution over at most n rankings; furthermore we show that we can compute the whole set of Pareto-optimal expected exposure vectors with the same complexity $O(n^3)$. Our work constitutes the first exact algorithm able to efficiently find a Pareto-optimal distribution of rankings. It is applicable to a broad range of fairness notions, including classical notions of meritocratic and demographic fairness. We empirically evaluate our method on the TREC2020 and MSLR datasets and compare it to several baselines in terms of Pareto-optimality and speed.|近年来，很明显，在许多领域提供的排名不仅需要对用户有用，而且还要尊重项目制作者的公平曝光。我们考虑的问题，找到排序的政策，实现了帕累托最优权衡这两个方面。人们提出了几种方法来解决这个问题，例如，一种流行的方法是使用伯克霍夫-冯诺依曼分解的线性规划。然而，这些方法是基于经典的基于位置的曝光模型(PBM) ，该模型假设项目之间的独立性(因此曝光只取决于排名)。在许多应用程序中，这种假设是不现实的，社区越来越倾向于考虑包含依赖关系的其他模型，例如动态贝氏网路暴露模型。对于这样的模型，计算(精确的)最优公平排序策略仍然是一个悬而未决的问题。在本文中，我们回答这个问题，利用一个新的几何方法的基础上，所谓的外三面体最近提出的 PBM (Kletti 等，WSDM’22)。我们给出了一个新的几何对象(DBN-expohedron)的结构，并提出了一个复杂度为 $O (n ^ 3) $的 Carathéodory 分解算法，其中 n 是要排序的文档数。这种算法能够表示任何可行的期望暴露矢量作为一个分布在最多 n 个排名; 此外，我们表明，我们可以计算整个集合的帕累托最优期望暴露矢量具有相同的复杂度 $O (n ^ 3) $。我们的工作构成了第一个精确的算法，能够有效地找到排名的帕累托最优分布。它适用于广泛的公平概念，包括精英统治和人口统计公平的经典概念。我们在 TREC2020和 MSLR 数据集上经验性地评估了我们的方法，并将其与几个基线在帕累托最优性和速度方面进行了比较。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Pareto-Optimal+Fairness-Utility+Amortizations+in+Rankings+with+a+DBN+Exposure+Model)|0| +|[Risk-Sensitive Deep Neural Learning to Rank](https://doi.org/10.1145/3477495.3532056)|Pedro Henrique Silva Rodrigues, Daniel Xavier de Sousa, Thierson Couto Rosa, Marcos André Gonçalves|Federal University of Minas Gerais - UFMG, Belo Horizonte, Brazil; Federal Institute of Goiás - IFG, Anápolis, Brazil; Federal University of Goiás - UFG, Goiânia, Brazil|Learning to Rank (L2R) is the core task of many Information Retrieval systems. Recently, a great effort has been put on exploring Deep Neural Networks (DNNs) for L2R, with significant results. However, risk-sensitiveness, an important and recent advance in the L2R arena, that reduces variability and increases trust, has not been incorporated into Deep Neural L2R yet. Risk-sensitive measures are important to assess the risk of an IR system to perform worse than a set of baseline IR systems for several queries. However, the risk-sensitive measures described in the literature have a non-smooth behavior, making them difficult, if not impossible, to be optimized by DNNs. In this work we solve this difficult problem by proposing a family of new loss functions -- \riskloss\ -- that support a smooth risk-sensitive optimization. \riskloss\ introduces two important contributions: (i) the substitution of the traditional NDCG or MAP metrics in risk-sensitive measures with smooth loss functions that evaluate the correlation between the predicted and the true relevance order of documents for a given query and (ii) the use of distinct versions of the same DNN architecture as baselines by means of a multi-dropout technique during the smooth risk-sensitive optimization, avoiding the inconvenience of assessing multiple IR systems as part of DNN training. We empirically demonstrate significant achievements of the proposed \riskloss\ functions when used with recent DNN methods in the context of well-known web-search datasets such as WEB10K, YAHOO, and MQ2007. Our solutions reach improvements of 8% in effectiveness (NDCG) while improving in around 5% the risk-sensitiveness (\grisk\ measure) when applied together with a state-of-the-art Self-Attention DNN-L2R architecture. Furthermore, \riskloss\ is capable of reducing by 28% the losses over the best evaluated baselines and significantly improving over the risk-sensitive state-of-the-art non-DNN method (by up to 13.3%) while keeping (or even increasing) overall effectiveness. All these results ultimately establish a new level for the state-of-the-art on risk-sensitiveness and DNN-L2R research.|学习排名(L2R)是许多信息检索系统的核心任务。近年来，针对 L2R 的深层神经网络(DNN)的研究取得了显著的成果。然而，风险敏感性，一个重要的和最近在 L2R 领域的进展，减少变异性和增加信任，尚未被纳入深层神经 L2R。风险敏感度量对于评估一个 IR 系统在几个查询中的性能低于一组基准 IR 系统的风险非常重要。然而，文献中描述的风险敏感性措施有一个不平滑的行为，使他们难以，如果不是不可能，被 DNN 优化。在这项工作中，我们通过提出一系列新的损失函数——风险损失——来解决这个难题，这些函数支持平稳的风险敏感优化。风险损失引入了两个重要贡献: (i)用平滑损失函数替换风险敏感度量中的传统 NDCG 或 MAP 指标，评估给定查询的文档的预测和真实相关顺序之间的相关性; (ii)通过平滑风险敏感性优化期间的多退出技术使用相同 DNN 架构的不同版本作为基线，避免了评估多个 IR 系统作为 DNN 训练的一部分的不便。当与最近的 DNN 方法在诸如 WEB10K，YAHOO 和 MQ2007等著名的网络搜索数据集的背景下使用时，我们经验性地证明了所提出的风险损失函数的显着成就。我们的解决方案在与最先进的自我注意 DNN-L2R 架构一起应用时，有效性(NDCG)提高了8% ，而风险敏感性(风险测量)提高了约5% 。此外，风险损失能够比最佳评估基线减少28% 的损失，并且比风险敏感的最先进的非 DNN 方法(高达13.3%)显着改善，同时保持(甚至增加)总体有效性。所有这些结果最终为风险敏感性和 DNN-L2R 研究的最新水平奠定了一个新的基础。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Risk-Sensitive+Deep+Neural+Learning+to+Rank)|0| |[Adaptable Text Matching via Meta-Weight Regulator](https://doi.org/10.1145/3477495.3531932)|Bo Zhang, Chen Zhang, Fang Ma, Dawei Song|Beijing Institute of Technology, Beijing, China|Neural text matching models have been used in a range of applications such as question answering and natural language inference, and have yielded a good performance. However, these neural models are of a limited adaptability, resulting in a decline in performance when encountering test examples from a different dataset or even a different task. The adaptability is particularly important in the few-shot setting: in many cases, there is only a limited amount of labeled data available for a target dataset or task, while we may have access to a richly labeled source dataset or task. However, adapting a model trained on the abundant source data to a few-shot target dataset or task is challenging. To tackle this challenge, we propose a Meta-Weight Regulator (MWR), which is a meta-learning approach that learns to assign weights to the source examples based on their relevance to the target loss. Specifically, MWR first trains the model on the uniformly weighted source examples, and measures the efficacy of the model on the target examples via a loss function. By iteratively performing a (meta) gradient descent, high-order gradients are propagated to the source examples. These gradients are then used to update the weights of source examples, in a way that is relevant to the target performance. As MWR is model-agnostic, it can be applied to any backbone neural model. Extensive experiments are conducted with various backbone text matching models, on four widely used datasets and two tasks. The results demonstrate that our proposed approach significantly outperforms a number of existing adaptation methods and effectively improves the cross-dataset and cross-task adaptability of the neural text matching models in the few-shot setting.|神经文本匹配模型已经在问答、自然语言推理等领域得到了广泛的应用，并取得了良好的效果。然而，这些神经模型的适应性有限，当遇到来自不同数据集甚至不同任务的测试例子时，会导致性能下降。适应性在少镜头设置中尤其重要: 在许多情况下，目标数据集或任务只有有限数量的标记数据可用，而我们可以访问标记丰富的源数据集或任务。然而，将一个基于大量源数据训练的模型应用于少量目标数据集或任务是具有挑战性的。为了应对这一挑战，我们提出了一种元权重调节器(MWR) ，它是一种元学习方法，学习根据源示例与目标损失的相关性为其分配权重。具体来说，MWR 首先在均匀加权的源例子上训练模型，然后通过损失函数来度量模型对目标例子的有效性。通过迭代执行一个(元)梯度下降法，高阶梯度被传播到源示例。然后使用这些渐变来更新源示例的权重，其方式与目标性能相关。由于 MWR 是模型无关的，因此它可以应用于任何骨干神经网络模型。在四个广泛使用的数据集和两个任务上，使用各种骨干文本匹配模型进行了广泛的实验。结果表明，本文提出的方法明显优于现有的一些自适应方法，有效地提高了神经元文本匹配模型在少镜头情况下的跨数据集和跨任务适应性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Adaptable+Text+Matching+via+Meta-Weight+Regulator)|0| -|[Re-thinking Knowledge Graph Completion Evaluation from an Information Retrieval Perspective](https://doi.org/10.1145/3477495.3532052)|Ying Zhou, Xuanang Chen, Ben He, Zheng Ye, Le Sun|Institute of Software, Chinese Academy of Sciences, Beijing, China; South-Central University for Nationalities, Wuhan, China; University of Chinese Academy of Sciences & Institute of Software, Chinese Academy of Sciences, Beijing, China|Knowledge graph completion (KGC) aims to infer missing knowledge triples based on known facts in a knowledge graph. Current KGC research mostly follows an entity ranking protocol, wherein the effectiveness is measured by the predicted rank of a masked entity in a test triple. The overall performance is then given by a micro(-average) metric over all individual answer entities. Due to the incomplete nature of the large-scale knowledge bases, such an entity ranking setting is likely affected by unlabelled top-ranked positive examples, raising questions on whether the current evaluation protocol is sufficient to guarantee a fair comparison of KGC systems. To this end, this paper presents a systematic study on whether and how the label sparsity affects the current KGC evaluation with the popular micro metrics. Specifically, inspired by the TREC paradigm for large-scale information retrieval (IR) experimentation, we create a relatively "complete" judgment set based on a sample from the popular FB15k-237 dataset following the TREC pooling method. According to our analysis, it comes as a surprise that switching from the original labels to our "complete" labels results in a drastic change of system ranking of a variety of 13 popular KGC models in terms of micro metrics. Further investigation indicates that the IR-like macro(-average) metrics are more stable and discriminative under different settings, meanwhile, less affected by label sparsity. Thus, for KGC evaluation, we recommend conducting TREC-style pooling to balance between human efforts and label completeness, and reporting also the IR-like macro metrics to reflect the ranking nature of the KGC task.|知识图完成(KGC)是基于知识图中已知事实推断出缺失的知识三元组。目前的 KGC 研究大多遵循一个实体排名协议，其中的有效性是衡量一个被掩盖的实体在一个测试三元组的预测排名。然后，通过对所有单个答案实体的微观(平均)度量给出总体表现。由于大规模知识库的不完整性，这种实体排名设置可能会受到没有标记的排名最高的积极实例的影响，从而引起目前的评价议定书是否足以保证公平比较 KGC 系统的问题。为此，本文利用当前流行的微观指标，对标签稀疏性是否以及如何影响当前 KGC 评价进行了系统的研究。具体来说，受到 TREC 大规模信息检索(IR)实验范例的启发，我们创建了一个相对“完整”的判断集，该判断集基于流行的 FB15k-237数据集的样本，采用 TREC 汇集方法。根据我们的分析，令人惊讶的是，从原始标签切换到我们的“完整”标签导致系统排名的急剧变化的各种13个流行的 KGC 模型在微观指标方面。进一步的研究表明，类 IR 宏(平均)指标在不同的设置下更加稳定和具有区分性，同时受标签稀疏性的影响较小。因此，对于 KGC 评估，我们建议进行 TREC 风格的池来平衡人工努力和标签完整性，并报告类似 IR 的宏指标来反映 KGC 任务的排名性质。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Re-thinking+Knowledge+Graph+Completion+Evaluation+from+an+Information+Retrieval+Perspective)|0| +|[Re-thinking Knowledge Graph Completion Evaluation from an Information Retrieval Perspective](https://doi.org/10.1145/3477495.3532052)|Ying Zhou, Xuanang Chen, Ben He, Zheng Ye, Le Sun|South-Central University for Nationalities, Wuhan, China; University of Chinese Academy of Sciences & Institute of Software, Chinese Academy of Sciences, Beijing, China; Institute of Software, Chinese Academy of Sciences, Beijing, China|Knowledge graph completion (KGC) aims to infer missing knowledge triples based on known facts in a knowledge graph. Current KGC research mostly follows an entity ranking protocol, wherein the effectiveness is measured by the predicted rank of a masked entity in a test triple. The overall performance is then given by a micro(-average) metric over all individual answer entities. Due to the incomplete nature of the large-scale knowledge bases, such an entity ranking setting is likely affected by unlabelled top-ranked positive examples, raising questions on whether the current evaluation protocol is sufficient to guarantee a fair comparison of KGC systems. To this end, this paper presents a systematic study on whether and how the label sparsity affects the current KGC evaluation with the popular micro metrics. Specifically, inspired by the TREC paradigm for large-scale information retrieval (IR) experimentation, we create a relatively "complete" judgment set based on a sample from the popular FB15k-237 dataset following the TREC pooling method. According to our analysis, it comes as a surprise that switching from the original labels to our "complete" labels results in a drastic change of system ranking of a variety of 13 popular KGC models in terms of micro metrics. Further investigation indicates that the IR-like macro(-average) metrics are more stable and discriminative under different settings, meanwhile, less affected by label sparsity. Thus, for KGC evaluation, we recommend conducting TREC-style pooling to balance between human efforts and label completeness, and reporting also the IR-like macro metrics to reflect the ranking nature of the KGC task.|知识图完成(KGC)是基于知识图中已知事实推断出缺失的知识三元组。目前的 KGC 研究大多遵循一个实体排名协议，其中的有效性是衡量一个被掩盖的实体在一个测试三元组的预测排名。然后，通过对所有单个答案实体的微观(平均)度量给出总体表现。由于大规模知识库的不完整性，这种实体排名设置可能会受到没有标记的排名最高的积极实例的影响，从而引起目前的评价议定书是否足以保证公平比较 KGC 系统的问题。为此，本文利用当前流行的微观指标，对标签稀疏性是否以及如何影响当前 KGC 评价进行了系统的研究。具体来说，受到 TREC 大规模信息检索(IR)实验范例的启发，我们创建了一个相对“完整”的判断集，该判断集基于流行的 FB15k-237数据集的样本，采用 TREC 汇集方法。根据我们的分析，令人惊讶的是，从原始标签切换到我们的“完整”标签导致系统排名的急剧变化的各种13个流行的 KGC 模型在微观指标方面。进一步的研究表明，类 IR 宏(平均)指标在不同的设置下更加稳定和具有区分性，同时受标签稀疏性的影响较小。因此，对于 KGC 评估，我们建议进行 TREC 风格的池来平衡人工努力和标签完整性，并报告类似 IR 的宏指标来反映 KGC 任务的排名性质。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Re-thinking+Knowledge+Graph+Completion+Evaluation+from+an+Information+Retrieval+Perspective)|0| |[CRET: Cross-Modal Retrieval Transformer for Efficient Text-Video Retrieval](https://doi.org/10.1145/3477495.3531960)|Kaixiang Ji, Jiajia Liu, Weixiang Hong, Liheng Zhong, Jian Wang, Jingdong Chen, Wei Chu|Ant Group, Hangzhou, China|Given a text query, the text-to-video retrieval task aims to find the relevant videos in the database. Recently, model-based (MDB) methods have demonstrated superior accuracy than embedding-based (EDB) methods due to their excellent capacity of modeling local video/text correspondences, especially when equipped with large-scale pre-training schemes like ClipBERT. Generally speaking, MDB methods take a text-video pair as input and harness deep models to predict the mutual similarity, while EDB methods first utilize modality-specific encoders to extract embeddings for text and video, then evaluate the distance based on the extracted embeddings. Notably, MDB methods cannot produce explicit representations for text and video, instead, they have to exhaustively pair the query with every database item to predict their mutual similarities in the inference stage, which results in significant inefficiency in practical applications. In this work, we propose a novel EDB method CRET (Cross-modal REtrieval Transformer), which not only demonstrates promising efficiency in retrieval tasks, but also achieves better accuracy than existing MDB methods. The credits are mainly attributed to our proposed Cross-modal Correspondence Modeling (CCM) module and Gaussian Estimation of Embedding Space (GEES) loss. Specifically, the CCM module is composed by transformer decoders and a set of decoder centers. With the help of the learned decoder centers, the text/video embeddings can be efficiently aligned, without suffering from pairwise model-based inference. Moreover, to balance the information loss and computational overhead when sampling frames from a given video, we present a novel GEES loss, which implicitly conducts dense sampling in the video embedding space, without suffering from heavy computational cost. Extensive experiments show that without pre-training on extra datasets, our proposed CRET outperforms the state-of-the-art MDB methods that were pre-trained on additional datasets, meanwhile still shows promising efficiency in retrieval tasks.|给定一个文本查询，文本到视频检索任务的目的是在数据库中找到相关的视频。近年来，基于模型(MDB)的方法由于其优异的局部视频/文本对应建模能力而显示出优于基于嵌入的方法的准确性，特别是当配备了大规模的预训练方案，如 ClipBERT。一般来说，MDB 方法以文本-视频对为输入，利用深度模型来预测相似度，而 EDB 方法首先利用特定于模态的编码器来提取文本和视频的嵌入，然后根据提取的嵌入来评估距离。值得注意的是，MDB 方法不能生成文本和视频的显式表示，相反，它们必须将查询与每个数据库项穷举地配对，以预测它们在推理阶段的相似性，这导致了实际应用中的显著效率低下。本文提出了一种新的多模态检索转换器(CRET)方法，该方法不仅在检索任务中表现出良好的效率，而且比现有的 MDB 方法具有更高的准确率。这主要归功于我们提出的交叉模态对应建模(CCM)模块和嵌入空间的高斯估计(GEES)损失。具体来说，CCM 模块由变压器解码器和一组解码中心组成。借助于所学习的解码中心，文本/视频嵌入可以有效地对齐，而不会受到基于成对模型的推理的影响。此外，为了平衡从给定视频帧采样时的信息损失和计算开销，我们提出了一种新的 GEES 损失算法，该算法在视频嵌入空间中隐式地进行密集采样，不需要承担大量的计算开销。大量的实验表明，在不对额外数据集进行预训练的情况下，我们提出的 CRET 方法优于对额外数据集进行预训练的最先进的 MDB 方法，同时在检索任务中仍然显示出有希望的效率。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=CRET:+Cross-Modal+Retrieval+Transformer+for+Efficient+Text-Video+Retrieval)|0| |[Learn from Unlabeled Videos for Near-duplicate Video Retrieval](https://doi.org/10.1145/3477495.3532010)|Xiangteng He, Yulin Pan, Mingqian Tang, Yiliang Lv, Yuxin Peng|Alibaba Group, Hangzhou, China; Peking University, Beijing, China|Near-duplicate video retrieval (NDVR) aims to find the copies or transformations of the query video from a massive video database. It plays an important role in many video related applications, including copyright protection, tracing, filtering and etc. Video representation and similarity search are crucial to any video retrieval system. To derive effective video representation, most video retrieval systems require a large amount of manually annotated data for training, making it costly inefficient. In addition, most retrieval systems are based on frame-level features for video similarity searching, making it expensive both storage wise and search wise. To address the above issues, we propose a video representation learning (VRL) approach to effectively address the above shortcomings. It first effectively learns video representation from unlabeled videos via contrastive learning to avoid the expensive cost of manual annotation. Then, it exploits transformer structure to aggregate frame-level features into clip-level to reduce both storage space and search complexity. It can learn the complementary and discriminative information from the interactions among clip frames, as well as acquire the frame permutation and missing invariant ability to support more flexible retrieval manners. Comprehensive experiments on two challenging near-duplicate video retrieval datasets, namely FIVR-200K and SVD, verify the effectiveness of our proposed VRL approach, which achieves the best performance of video retrieval on accuracy and efficiency.|近重复视频检索(NDVR)的目标是从海量视频数据库中查找查询视频的副本或变换。它在许多视频相关应用中起着重要作用，包括版权保护、跟踪、过滤等。视频表示和最近邻搜索对于任何视频检索系统都至关重要。为了获得有效的视频表示，大多数视频检索系统需要大量的人工注释数据进行训练，这使得系统效率低下。此外，大多数检索系统基于帧级特征进行视频相似性搜索，这使得存储和搜索成本都很高。针对上述问题，本文提出了一种视频表示学习(VRL)方法，有效地解决了上述问题。它首先通过对比学习有效地从未标记的视频中学习视频表示，从而避免了人工标注的昂贵成本。然后，利用变压器结构将帧级特征聚合为剪辑级特征，降低存储空间和搜索复杂度。它可以从剪辑帧之间的交互中学习互补信息和鉴别信息，获得帧排列和缺失不变量能力，支持更灵活的检索方式。通过对 FIVR-200K 和 SVD 两个具有挑战性的近重复视频检索数据集的综合实验，验证了本文提出的 VRL 方法的有效性，在准确性和效率方面达到了最佳的视频检索性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Learn+from+Unlabeled+Videos+for+Near-duplicate+Video+Retrieval)|0| |[Progressive Learning for Image Retrieval with Hybrid-Modality Queries](https://doi.org/10.1145/3477495.3532047)|Yida Zhao, Yuqing Song, Qin Jin|Renmin University of China, Beijing, China|Image retrieval with hybrid-modality queries, also known as composing text and image for image retrieval (CTI-IR), is a retrieval task where the search intention is expressed in a more complex query format, involving both vision and text modalities. For example, a target product image is searched using a reference product image along with text about changing certain attributes of the reference image as the query. It is a more challenging image retrieval task that requires both semantic space learning and cross-modal fusion. Previous approaches that attempt to deal with both aspects achieve unsatisfactory performance. In this paper, we decompose the CTI-IR task into a three-stage learning problem to progressively learn the complex knowledge for image retrieval with hybrid-modality queries. We first leverage the semantic embedding space for open-domain image-text retrieval, and then transfer the learned knowledge to the fashion-domain with fashion-related pre-training tasks. Finally, we enhance the pre-trained model from single-query to hybrid-modality query for the CTI-IR task. Furthermore, as the contribution of individual modality in the hybrid-modality query varies for different retrieval scenarios, we propose a self-supervised adaptive weighting strategy to dynamically determine the importance of image and text in the hybrid-modality query for better retrieval. Extensive experiments show that our proposed model significantly outperforms state-of-the-art methods in the mean of [email protected] by 24.9% and 9.5% on the Fashion-IQ and Shoes benchmark datasets respectively.|基于混合模态查询的图像检索，即组合文本和图像进行图像检索(CTI-IR) ，是一种以更复杂的查询格式表达搜索意图的检索任务，涉及视觉和文本模态。例如，使用参考产品图像以及关于将参考图像的某些属性更改为查询的文本搜索目标产品图像。语义空间学习和跨模态融合是图像检索中一个更具挑战性的任务。以前试图同时处理这两个方面的方法的性能都不令人满意。本文将 CTI-IR 任务分解为三阶段学习问题，逐步学习用于图像检索的复杂知识。我们首先利用语义嵌入空间进行开放领域的图文检索，然后利用与时尚相关的预训练任务将所学知识转移到时尚领域。最后，对 CTI-IR 任务的预训练模型进行了改进，从单查询模型改进为混合模态查询模型。此外，由于个体模态在混合模态查询中的贡献因检索场景的不同而不同，我们提出了一种自监督自适应加权策略，动态确定图像和文本在混合模态查询中的重要性，以便更好地检索。大量的实验表明，在 Fashion-IQ 和 Shoes 基准数据集上，我们提出的模型在平均值(电子邮件受保护)方面明显优于最先进的方法，分别为24.9% 和9.5% 。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Progressive+Learning+for+Image+Retrieval+with+Hybrid-Modality+Queries)|0| |[Incorporating Explicit Knowledge in Pre-trained Language Models for Passage Re-ranking](https://doi.org/10.1145/3477495.3531997)|Qian Dong, Yiding Liu, Suqi Cheng, Shuaiqiang Wang, Zhicong Cheng, Shuzi Niu, Dawei Yin|Institute of Software, Chinese Academy of Sciences, Beijing, China; Baidu Inc., Beijing, China|Passage re-ranking is to obtain a permutation over the candidate passage set from retrieval stage. Re-rankers have been boomed by Pre-trained Language Models (PLMs) due to their overwhelming advantages in natural language understanding. However, existing PLM based re-rankers may easily suffer from vocabulary mismatch and lack of domain specific knowledge. To alleviate these problems, explicit knowledge contained in knowledge graph is carefully introduced in our work. Specifically, we employ the existing knowledge graph which is incomplete and noisy, and first apply it in passage re-ranking task. To leverage a reliable knowledge, we propose a novel knowledge graph distillation method and obtain a knowledge meta graph as the bridge between query and passage. To align both kinds of embedding in the latent space, we employ PLM as text encoder and graph neural network over knowledge meta graph as knowledge encoder. Besides, a novel knowledge injector is designed for the dynamic interaction between text and knowledge encoder. Experimental results demonstrate the effectiveness of our method especially in queries requiring in-depth domain knowledge.|文章重新排序是从检索阶段对候选文章集合进行排列。由于预训练语言模型在自然语言理解方面具有压倒性的优势，重新排名的语言模型得到了蓬勃发展。然而，现有的基于 PLM 的重新排名可能很容易受到词汇不匹配和缺乏领域特定知识的影响。为了解决这些问题，我们在工作中仔细介绍了知识图表中的外显知识。具体地说，我们利用现有的不完备且有噪声的知识图，首先将其应用于段落重排任务。为了利用可靠的知识，我们提出了一种新的知识图提取方法，并得到一个知识元图作为查询和文章之间的桥梁。为了在潜空间中对齐这两种嵌入，我们采用 PLM 作为文本编码器，知识元图上的图形神经网络作为知识编码器。此外，还设计了一种新颖的知识注入器，用于文本和知识编码器之间的动态交互。实验结果表明了该方法的有效性，特别是在需要深度领域知识的查询中。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Incorporating+Explicit+Knowledge+in+Pre-trained+Language+Models+for+Passage+Re-ranking)|0| -|[Axiomatically Regularized Pre-training for Ad hoc Search](https://doi.org/10.1145/3477495.3531943)|Jia Chen, Yiqun Liu, Yan Fang, Jiaxin Mao, Hui Fang, Shenghao Yang, Xiaohui Xie, Min Zhang, Shaoping Ma|Tsinghua University, Beijing, China; Renmin University of China, Beijing, China; University of Delaware, Newark, DE, USA|Recently, pre-training methods tailored for IR tasks have achieved great success. However, as the mechanisms behind the performance improvement remain under-investigated, the interpretability and robustness of these pre-trained models still need to be improved. Axiomatic IR aims to identify a set of desirable properties expressed mathematically as formal constraints to guide the design of ranking models. Existing studies have already shown that considering certain axioms may help improve the effectiveness and interpretability of IR models. However, there still lack efforts of incorporating these IR axioms into pre-training methodologies. To shed light on this research question, we propose a novel pre-training method with \underlineA xiomatic \underlineRe gularization for ad hoc \underlineS earch (ARES). In the ARES framework, a number of existing IR axioms are re-organized to generate training samples to be fitted in the pre-training process. These training samples then guide neural rankers to learn the desirable ranking properties. Compared to existing pre-training approaches, ARES is more intuitive and explainable. Experimental results on multiple publicly available benchmark datasets have shown the effectiveness of ARES in both full-resource and low-resource (e.g., zero-shot and few-shot) settings. An intuitive case study also indicates that ARES has learned useful knowledge that existing pre-trained models (e.g., BERT and PROP) fail to possess. This work provides insights into improving the interpretability of pre-trained models and the guidance of incorporating IR axioms or human heuristics into pre-training methods.|近年来，针对 IR 任务的预训练方法取得了很大的成功。然而，由于性能改进背后的机制仍然没有得到充分的研究，这些预先训练的模型的可解释性和鲁棒性仍然需要改进。公理 IR 旨在识别一组用数学方法表示为形式约束的理想属性，以指导排序模型的设计。现有的研究已经表明，考虑某些公理可能有助于提高红外模型的有效性和可解释性。然而，仍然缺乏将这些 IR 公理纳入预训练方法的努力。针对这一问题，本文提出了一种基于下划线公理化下划线正则化的自组织下划线搜索(ARES)预训练方法。在 ARES 框架中，对一些现有的信息检索公理进行了重新组织，以生成培训样本，用于培训前进程。这些训练样本然后指导神经排序学习理想的排序属性。与现有的预训练方法相比，ARES 更加直观和易于解释。在多个公开可用的基准数据集上的实验结果显示了 ARES 在全资源和低资源(例如，零拍摄和少拍摄)环境下的有效性。一个直观的案例研究还表明，ARES 已经学到了有用的知识，现有的预训练模型(例如，BERT 和 PROP)不能拥有。这项工作为提高预训练模型的可解释性提供了见解，并指导将 IR 公理或人类启发式融入预训练方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Axiomatically+Regularized+Pre-training+for+Ad+hoc+Search)|0| -|[On the Role of Relevance in Natural Language Processing Tasks](https://doi.org/10.1145/3477495.3532034)|Artsiom Sauchuk, James Thorne, Alon Y. Halevy, Nicola Tonellotto, Fabrizio Silvestri|University of Pisa, Pisa, Italy; Sapienza University of Rome, Roma, Italy; Cambridge University, London, United Kingdom; Meta AI, Menlo Park, CA, USA|Many recent Natural Language Processing (NLP) task formulations, such as question answering and fact verification, are implemented as a two-stage cascading architecture. In the first stage an IR system retrieves "relevant'' documents containing the knowledge, and in the second stage an NLP system performs reasoning to solve the task. Optimizing the IR system for retrieving relevant documents ensures that the NLP system has sufficient information to operate over. These recent NLP task formulations raise interesting and exciting challenges for IR, where the end-user of an IR system is not a human with an information need, but another system exploiting the documents retrieved by the IR system to perform reasoning and address the user information need. Among these challenges, as we will show, is that noise from the IR system, such as retrieving spurious or irrelevant documents, can negatively impact the accuracy of the downstream reasoning module. Hence, there is the need to balance maximizing relevance while minimizing noise in the IR system. This paper presents experimental results on two NLP tasks implemented as a two-stage cascading architecture. We show how spurious or irrelevant retrieved results from the first stage can induce errors in the second stage. We use these results to ground our discussion of the research challenges that the IR community should address in the context of these knowledge-intensive NLP tasks.|近年来，自然语言处理(NLP)的许多任务公式，如问题回答和事实验证，都是作为一个两阶段级联结构实现的。在第一阶段，IR 系统检索包含知识的“相关”文档，在第二阶段，NLP 系统执行推理来解决任务。优化检索相关文件的红外系统，确保自然语言处理系统有足够的信息进行操作。这些最新的 NLP 任务公式为 IR 提出了有趣和令人兴奋的挑战，其中 IR 系统的最终用户不是一个有信息需求的人，而是另一个利用 IR 系统检索到的文档进行推理并满足用户信息需求的系统。在这些挑战中，正如我们将要展示的，来自 IR 系统的噪音，例如检索虚假或不相关的文档，可能会对下游推理模块的准确性产生负面影响。因此，需要在最大相关性和最小噪声之间取得平衡。本文给出了两个实现为两级级联结构的自然语言处理任务的实验结果。我们展示了如何伪造或不相关的检索结果从第一阶段可以导致错误的第二阶段。我们使用这些结果来基础我们的研究挑战的讨论，IR 社区应该在这些知识密集型的自然语言处理任务的背景下解决。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=On+the+Role+of+Relevance+in+Natural+Language+Processing+Tasks)|0| +|[Axiomatically Regularized Pre-training for Ad hoc Search](https://doi.org/10.1145/3477495.3531943)|Jia Chen, Yiqun Liu, Yan Fang, Jiaxin Mao, Hui Fang, Shenghao Yang, Xiaohui Xie, Min Zhang, Shaoping Ma|Renmin University of China, Beijing, China; Tsinghua University, Beijing, China; University of Delaware, Newark, DE, USA|Recently, pre-training methods tailored for IR tasks have achieved great success. However, as the mechanisms behind the performance improvement remain under-investigated, the interpretability and robustness of these pre-trained models still need to be improved. Axiomatic IR aims to identify a set of desirable properties expressed mathematically as formal constraints to guide the design of ranking models. Existing studies have already shown that considering certain axioms may help improve the effectiveness and interpretability of IR models. However, there still lack efforts of incorporating these IR axioms into pre-training methodologies. To shed light on this research question, we propose a novel pre-training method with \underlineA xiomatic \underlineRe gularization for ad hoc \underlineS earch (ARES). In the ARES framework, a number of existing IR axioms are re-organized to generate training samples to be fitted in the pre-training process. These training samples then guide neural rankers to learn the desirable ranking properties. Compared to existing pre-training approaches, ARES is more intuitive and explainable. Experimental results on multiple publicly available benchmark datasets have shown the effectiveness of ARES in both full-resource and low-resource (e.g., zero-shot and few-shot) settings. An intuitive case study also indicates that ARES has learned useful knowledge that existing pre-trained models (e.g., BERT and PROP) fail to possess. This work provides insights into improving the interpretability of pre-trained models and the guidance of incorporating IR axioms or human heuristics into pre-training methods.|近年来，针对 IR 任务的预训练方法取得了很大的成功。然而，由于性能改进背后的机制仍然没有得到充分的研究，这些预先训练的模型的可解释性和鲁棒性仍然需要改进。公理 IR 旨在识别一组用数学方法表示为形式约束的理想属性，以指导排序模型的设计。现有的研究已经表明，考虑某些公理可能有助于提高红外模型的有效性和可解释性。然而，仍然缺乏将这些 IR 公理纳入预训练方法的努力。针对这一问题，本文提出了一种基于下划线公理化下划线正则化的自组织下划线搜索(ARES)预训练方法。在 ARES 框架中，对一些现有的信息检索公理进行了重新组织，以生成培训样本，用于培训前进程。这些训练样本然后指导神经排序学习理想的排序属性。与现有的预训练方法相比，ARES 更加直观和易于解释。在多个公开可用的基准数据集上的实验结果显示了 ARES 在全资源和低资源(例如，零拍摄和少拍摄)环境下的有效性。一个直观的案例研究还表明，ARES 已经学到了有用的知识，现有的预训练模型(例如，BERT 和 PROP)不能拥有。这项工作为提高预训练模型的可解释性提供了见解，并指导将 IR 公理或人类启发式融入预训练方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Axiomatically+Regularized+Pre-training+for+Ad+hoc+Search)|0| +|[On the Role of Relevance in Natural Language Processing Tasks](https://doi.org/10.1145/3477495.3532034)|Artsiom Sauchuk, James Thorne, Alon Y. Halevy, Nicola Tonellotto, Fabrizio Silvestri|Cambridge University, London, United Kingdom; University of Pisa, Pisa, Italy; Sapienza University of Rome, Roma, Italy; Meta AI, Menlo Park, CA, USA|Many recent Natural Language Processing (NLP) task formulations, such as question answering and fact verification, are implemented as a two-stage cascading architecture. In the first stage an IR system retrieves "relevant'' documents containing the knowledge, and in the second stage an NLP system performs reasoning to solve the task. Optimizing the IR system for retrieving relevant documents ensures that the NLP system has sufficient information to operate over. These recent NLP task formulations raise interesting and exciting challenges for IR, where the end-user of an IR system is not a human with an information need, but another system exploiting the documents retrieved by the IR system to perform reasoning and address the user information need. Among these challenges, as we will show, is that noise from the IR system, such as retrieving spurious or irrelevant documents, can negatively impact the accuracy of the downstream reasoning module. Hence, there is the need to balance maximizing relevance while minimizing noise in the IR system. This paper presents experimental results on two NLP tasks implemented as a two-stage cascading architecture. We show how spurious or irrelevant retrieved results from the first stage can induce errors in the second stage. We use these results to ground our discussion of the research challenges that the IR community should address in the context of these knowledge-intensive NLP tasks.|近年来，自然语言处理(NLP)的许多任务公式，如问题回答和事实验证，都是作为一个两阶段级联结构实现的。在第一阶段，IR 系统检索包含知识的“相关”文档，在第二阶段，NLP 系统执行推理来解决任务。优化检索相关文件的红外系统，确保自然语言处理系统有足够的信息进行操作。这些最新的 NLP 任务公式为 IR 提出了有趣和令人兴奋的挑战，其中 IR 系统的最终用户不是一个有信息需求的人，而是另一个利用 IR 系统检索到的文档进行推理并满足用户信息需求的系统。在这些挑战中，正如我们将要展示的，来自 IR 系统的噪音，例如检索虚假或不相关的文档，可能会对下游推理模块的准确性产生负面影响。因此，需要在最大相关性和最小噪声之间取得平衡。本文给出了两个实现为两级级联结构的自然语言处理任务的实验结果。我们展示了如何伪造或不相关的检索结果从第一阶段可以导致错误的第二阶段。我们使用这些结果来基础我们的研究挑战的讨论，IR 社区应该在这些知识密集型的自然语言处理任务的背景下解决。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=On+the+Role+of+Relevance+in+Natural+Language+Processing+Tasks)|0| |[Adversarial Graph Perturbations for Recommendations at Scale](https://doi.org/10.1145/3477495.3531763)|Huiyuan Chen, Kaixiong Zhou, KweiHerng Lai, Xia Hu, Fei Wang, Hao Yang|Rice University, Houston, TX, USA; Visa Research, palo alto, CA, USA|Graph Neural Networks (GNNs) provide a class of powerful architectures that are effective for graph-based collaborative filtering. Nevertheless, GNNs are known to be vulnerable to adversarial perturbations. Adversarial training is a simple yet effective way to improve the robustness of neural models. For example, many prior studies inject adversarial perturbations into either node features or hidden layers of GNNs. However, perturbing graph structures has been far less studied in recommendations. To bridge this gap, we propose AdvGraph to model adversarial graph perturbations during the training of GNNs. Our AdvGraph is mainly based on min-max robust optimization, where an universal graph perturbation is obtained through an inner maximization while the outer optimization aims to compute the model parameters of GNNs. However, direct optimizing the inner problem is challenging due to discrete nature of the graph perturbations. To address this issue, an unbiased gradient estimator is further proposed to compute the gradients of discrete variables. Extensive experiments demonstrate that our AdvGraph is able to enhance the generalization performance of GNN-based recommenders.|图形神经网络(GNN)为基于图形的协同过滤提供了一种强大的架构。然而，GNN 是众所周知的脆弱的对抗性扰动。对抗训练是提高神经模型鲁棒性的一种简单而有效的方法。例如，许多先前的研究将对抗扰动注入到 GNN 的节点特征或隐层中。然而，令人不安的图形结构在推荐中却很少被研究。为了弥补这一差距，我们提出了 AdvGraph 来模拟 GNN 训练过程中的对抗图扰动。我们的 AdvGraph 主要是基于最小-最大鲁棒优化，其中通过内部最大化获得通用图摄动，而外部优化的目的是计算 GNN 的模型参数。然而，直接优化的内部问题是具有挑战性的，由于离散性质的图摄动。为了解决这一问题，进一步提出了一种无偏的梯度估计器来计算离散变量的梯度。大量的实验表明，我们的 AdvGraph 能够提高基于 GNN 的推荐器的泛化性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Adversarial+Graph+Perturbations+for+Recommendations+at+Scale)|0| -|[Relevance under the Iceberg: Reasonable Prediction for Extreme Multi-label Classification](https://doi.org/10.1145/3477495.3531767)|JyunYu Jiang, WeiCheng Chang, Jiong Zhang, ChoJui Hsieh, HsiangFu Yu|Amazon Search, Palo Alto, CA, USA; University of California, Los Angeles, Los Angeles, CA, USA|In the era of big data, eXtreme Multi-label Classification (XMC) has already become one of the most essential research tasks to deal with enormous label spaces in machine learning applications. Instead of assessing every individual label, most XMC methods rely on label trees or filters to derive short ranked label lists as prediction, thereby reducing computational overhead. Specifically, existing studies obtain ranked label lists with a fixed length for prediction and evaluation. However, these predictions are unreasonable since data points have varied numbers of relevant labels. The greatly small and large list lengths in evaluation, such as [email protected] and [email protected] , can also lead to the ignorance of other relevant labels or the tolerance of many irrelevant labels. In this paper, we aim to provide reasonable prediction for extreme multi-label classification with dynamic numbers of predicted labels. In particular, we propose a novel framework, Model-Agnostic List Truncation with Ordinal Regression (MALTOR), to leverage the ranking properties and truncate long ranked label lists for better accuracy. Extensive experiments conducted on six large-scale real-world benchmark datasets demonstrate that MALTOR significantly outperforms statistical baseline methods and conventional ranked list truncation methods in ad-hoc retrieval with both linear and deep XMC models. The results of an ablation study also shows the effectiveness of each individual component in our proposed MALTOR.|在大数据时代，极限多标签分类(XMC)已经成为处理机器学习应用中大量标签空间的重要研究课题之一。大多数 XMC 方法不是评估每个单独的标签，而是依靠标签树或过滤器来推导出短排名标签列表作为预测，从而减少计算开销。具体来说，现有的研究获得了具有固定长度的排名标签列表，用于预测和评价。然而，这些预测是不合理的，因为数据点有不同数量的相关标签。评估中的列表长度大大小小，如[ email protected ]和[ email protected ] ，也可能导致对其他相关标签的忽视或许多不相关标签的容忍。在本文中，我们的目的是提供合理的预测与预测标签的动态数量极端多标签分类。特别是，我们提出了一个新的框架，模型不可知列表与有序回归截断(MALTOR) ，以利用排名属性和截断长的排名标签列表，以更好的准确性。在六个大规模真实世界基准数据集上进行的大量实验表明，MALTOR 在线性和深度 XMC 模型的特别检索中显著优于统计基线方法和传统的排序列表截断方法。消融研究的结果也显示了我们提出的 MALTOR 中每个单独组件的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Relevance+under+the+Iceberg:+Reasonable+Prediction+for+Extreme+Multi-label+Classification)|0| +|[Relevance under the Iceberg: Reasonable Prediction for Extreme Multi-label Classification](https://doi.org/10.1145/3477495.3531767)|JyunYu Jiang, WeiCheng Chang, Jiong Zhang, ChoJui Hsieh, HsiangFu Yu|University of California, Los Angeles, Los Angeles, CA, USA; Amazon Search, Palo Alto, CA, USA|In the era of big data, eXtreme Multi-label Classification (XMC) has already become one of the most essential research tasks to deal with enormous label spaces in machine learning applications. Instead of assessing every individual label, most XMC methods rely on label trees or filters to derive short ranked label lists as prediction, thereby reducing computational overhead. Specifically, existing studies obtain ranked label lists with a fixed length for prediction and evaluation. However, these predictions are unreasonable since data points have varied numbers of relevant labels. The greatly small and large list lengths in evaluation, such as [email protected] and [email protected] , can also lead to the ignorance of other relevant labels or the tolerance of many irrelevant labels. In this paper, we aim to provide reasonable prediction for extreme multi-label classification with dynamic numbers of predicted labels. In particular, we propose a novel framework, Model-Agnostic List Truncation with Ordinal Regression (MALTOR), to leverage the ranking properties and truncate long ranked label lists for better accuracy. Extensive experiments conducted on six large-scale real-world benchmark datasets demonstrate that MALTOR significantly outperforms statistical baseline methods and conventional ranked list truncation methods in ad-hoc retrieval with both linear and deep XMC models. The results of an ablation study also shows the effectiveness of each individual component in our proposed MALTOR.|在大数据时代，极限多标签分类(XMC)已经成为处理机器学习应用中大量标签空间的重要研究课题之一。大多数 XMC 方法不是评估每个单独的标签，而是依靠标签树或过滤器来推导出短排名标签列表作为预测，从而减少计算开销。具体来说，现有的研究获得了具有固定长度的排名标签列表，用于预测和评价。然而，这些预测是不合理的，因为数据点有不同数量的相关标签。评估中的列表长度大大小小，如[ email protected ]和[ email protected ] ，也可能导致对其他相关标签的忽视或许多不相关标签的容忍。在本文中，我们的目的是提供合理的预测与预测标签的动态数量极端多标签分类。特别是，我们提出了一个新的框架，模型不可知列表与有序回归截断(MALTOR) ，以利用排名属性和截断长的排名标签列表，以更好的准确性。在六个大规模真实世界基准数据集上进行的大量实验表明，MALTOR 在线性和深度 XMC 模型的特别检索中显著优于统计基线方法和传统的排序列表截断方法。消融研究的结果也显示了我们提出的 MALTOR 中每个单独组件的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Relevance+under+the+Iceberg:+Reasonable+Prediction+for+Extreme+Multi-label+Classification)|0| |[Gating-adapted Wavelet Multiresolution Analysis for Exposure Sequence Modeling in CTR Prediction](https://doi.org/10.1145/3477495.3531771)|Xiaoxiao Xu, Zhiwei Fang, Qian Yu, Ruoran Huang, Chaosheng Fan, Yong Li, Yang He, Changping Peng, Zhangang Lin, Jingping Shao, Non Non|JD Com, Business Growth BU, Beijing, Peoples R China|The exposure sequence is being actively studied for user interest modeling in Click-Through Rate (CTR) prediction. However, the existing methods for exposure sequence modeling bring extensive computational burden and neglect noise problems, resulting in an excessively latency and the limited performance in online recommenders. In this paper, we propose to address the high latency and noise problems via Gating-adapted wavelet multiresolution analysis (Gama), which can effectively denoise the extremely long exposure sequence and adaptively capture the implied multi-dimension user interest with linear computational complexity. This is the first attempt to integrate non-parametric multiresolution analysis technique into deep neural network to model user exposure sequence. Extensive experiments on large scale benchmark dataset and real production dataset confirm the effectiveness of Gama for exposure sequence modeling, especially in cold-start scenarios. Benefited from its low latency and high effecitveness, Gama has been deployed in our real large-scale industrial recommender, successfully serving over hundreds of millions users.|我们正积极研究暴露次序，以建立用户兴趣模式，预测点进率。然而，现有的曝光序列建模方法存在计算量大、忽视噪声等问题，导致在线推荐系统的延迟过长，性能有限。在这篇文章中，我们提出利用门控自适应小波多解析度分析(Gama)来处理高延迟和噪声问题，它可以有效地去除极长曝光序列的噪声，并以线性计算复杂度自适应地捕捉隐含的多维用户兴趣。这是首次尝试将非参数多解析度分析技术与深层神经网络相结合，建立用户暴露序列模型。在大规模基准数据集和实际生产数据集上的大量实验证实了伽马方法对曝光序列建模的有效性，特别是在冷启动情况下。得益于它的低延迟和高效率，伽马已经部署在我们真正的大规模工业推荐，成功地为数亿用户服务。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Gating-adapted+Wavelet+Multiresolution+Analysis+for+Exposure+Sequence+Modeling+in+CTR+Prediction)|0| |[Animating Images to Transfer CLIP for Video-Text Retrieval](https://doi.org/10.1145/3477495.3531776)|Yu Liu, Huai Chen, Lianghua Huang, Di Chen, Bin Wang, Pan Pan, Lisheng Wang|DAMO Academy, Alibaba Group, Hangzhou, China; DAMO Academy, Alibaba Group, Beijing, China; Shanghai Jiao Tong University, Shanghai, China|Recent works show the possibility of transferring the CLIP (Contrastive Language-Image Pretraining) model for video-text retrieval with promising performance. However, due to the domain gap between static images and videos, CLIP-based video-text retrieval models with interaction-based matching perform far worse than models with representation-based matching. In this paper, we propose a novel image animation strategy to transfer the image-text CLIP model to video-text retrieval effectively. By imitating the video shooting components, we convert widely used image-language corpus to synthesized video-text data for pretraining. To reduce the time complexity of interaction matching, we further propose a coarse to fine framework which consists of dual encoders for fast candidates searching and a cross-modality interaction module for fine-grained re-ranking. The coarse to fine framework with the synthesized video-text pretraining provides significant gains in retrieval accuracy while preserving efficiency. Comprehensive experiments conducted on MSR-VTT, MSVD, and VATEX datasets demonstrate the effectiveness of our approach.|最近的研究表明，将对比语言-图像预训练(CLIP)模型应用于视频文本检索具有良好的性能。然而，由于静态图像和视频之间存在领域差异，基于 CLIP 的基于交互匹配的视频文本检索模型的性能远远不如基于表示匹配的模型。本文提出了一种新的图像动画策略，将图像-文本 CLIP 模型有效地转化为视频-文本检索。通过模拟视频拍摄组件，将广泛使用的图像语言语料库转换为合成的视频文本数据进行预训练。为了降低交互匹配的时间复杂度，我们进一步提出了一个由双编码器组成的快速候选搜索框架和一个交叉模式交互模块组成的细粒度重排序框架。综合视频文本预训练的粗细框架在保持检索效率的同时，提高了检索精度。在 MSR-VTT、 MSVD 和 VATEX 数据集上进行的综合实验证明了该方法的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Animating+Images+to+Transfer+CLIP+for+Video-Text+Retrieval)|0| -|[Image-Text Retrieval via Contrastive Learning with Auxiliary Generative Features and Support-set Regularization](https://doi.org/10.1145/3477495.3531783)|Lei Zhang, Min Yang, Chengming Li, Ruifeng Xu|Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China; Shenzhen Institutes of Advanced Technology, The Chinese Academy of Sciences, Shenzhen, China; Sun Yat-sen University, Shenzhen, China; Harbin Institute of Technology, Shenzhen, China|In this paper, we bridge the heterogeneity gap between different modalities and improve image-text retrieval by taking advantage of auxiliary image-to-text and text-to-image generative features with contrastive learning. Concretely, contrastive learning is devised to narrow the distance between the aligned image-text pairs and push apart the distance between the unaligned pairs from both inter- and intra-modality perspectives with the help of cross-modal retrieval features and auxiliary generative features. In addition, we devise a support-set regularization term to further improve contrastive learning by constraining the distance between each image/text and its corresponding cross-modal support-set information contained in the same semantic category. To evaluate the effectiveness of the proposed method, we conduct experiments on three benchmark datasets (i.e., MIRFLICKR-25K, NUS-WIDE, MS COCO). Experimental results show that our model significantly outperforms the strong baselines for cross-modal image-text retrieval. For reproducibility, we submit the code and data publicly at: \urlhttps://github.com/Hambaobao/CRCGS.|本文通过对比学习，利用图像到文本和文本到图像生成的辅助特征，弥补了不同检索方式之间的异质性差距，提高了图像-文本检索的性能。具体来说，对比学习是通过跨模态检索特征和辅助生成特征来缩小图像-文本对之间的距离，并从情态间和情态内的角度分离未对齐图像-文本对之间的距离。此外，我们设计了一个支持集正则化项来进一步改善对比学习，约束图像/文本之间的距离及其相应的跨模态支持集信息包含在同一语义范畴。为了评估该方法的有效性，我们对三个基准数据集(即 MIRFLICKR-25K，NUS-WIDE，MS COCO)进行了实验。实验结果表明，该模型在图像文本检索中的性能明显优于强基线检索。为了重现性，我们公开在 urlhttps:// github.com/hambaobao/crcgs 提交代码和数据。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Image-Text+Retrieval+via+Contrastive+Learning+with+Auxiliary+Generative+Features+and+Support-set+Regularization)|0| -|[Denoising Time Cycle Modeling for Recommendation](https://doi.org/10.1145/3477495.3531785)|Sicong Xie, Qunwei Li, Weidi Xu, Kaiming Shen, Shaohu Chen, Wenliang Zhong|Ant Group, Shanghai, China; Ant Group, Hangzhou, China; Ant Group, Beijing, China|Recently, modeling temporal patterns of user-item interactions have attracted much attention in recommender systems. We argue that existing methods ignore the variety of temporal patterns of user behaviors. We define the subset of user behaviors that are ir- relevant to the target item as noises, which limits the performance of target-related time cycle modeling and affect the recommendation performance. In this paper, we propose Denoising Time Cycle Modeling (DiCycle), a novel approach to denoise user behaviors and select the subset of user behaviors that are highly related to the target item. DiCycle is able to explicitly model diverse time cycle patterns for recommendation. Extensive experiments are conducted on both public benchmarks and a real-world dataset, demonstrating the superior performance of DiCycle over the state-of-the-art recommendation methods.|近年来，推荐系统中用户-项目交互的时间模式建模引起了人们的广泛关注。我们认为现有的方法忽略了用户行为的时间模式的多样性。将与目标项无关的用户行为定义为噪声，限制了目标相关时间周期建模的性能，影响了推荐性能。本文提出了一种新的去噪时间周期建模方法(DiCycle) ，用于去除用户行为的噪声，并选择与目标项高度相关的用户行为子集。DiCycle 能够显式地为推荐建模不同的时间周期模式。在公共基准测试和真实世界数据集上进行了广泛的实验，证明了 DiCycle 优于最先进的推荐方法的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Denoising+Time+Cycle+Modeling+for+Recommendation)|0| +|[Image-Text Retrieval via Contrastive Learning with Auxiliary Generative Features and Support-set Regularization](https://doi.org/10.1145/3477495.3531783)|Lei Zhang, Min Yang, Chengming Li, Ruifeng Xu|Shenzhen Institutes of Advanced Technology, The Chinese Academy of Sciences, Shenzhen, China; Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China; Sun Yat-sen University, Shenzhen, China; Harbin Institute of Technology, Shenzhen, China|In this paper, we bridge the heterogeneity gap between different modalities and improve image-text retrieval by taking advantage of auxiliary image-to-text and text-to-image generative features with contrastive learning. Concretely, contrastive learning is devised to narrow the distance between the aligned image-text pairs and push apart the distance between the unaligned pairs from both inter- and intra-modality perspectives with the help of cross-modal retrieval features and auxiliary generative features. In addition, we devise a support-set regularization term to further improve contrastive learning by constraining the distance between each image/text and its corresponding cross-modal support-set information contained in the same semantic category. To evaluate the effectiveness of the proposed method, we conduct experiments on three benchmark datasets (i.e., MIRFLICKR-25K, NUS-WIDE, MS COCO). Experimental results show that our model significantly outperforms the strong baselines for cross-modal image-text retrieval. For reproducibility, we submit the code and data publicly at: \urlhttps://github.com/Hambaobao/CRCGS.|本文通过对比学习，利用图像到文本和文本到图像生成的辅助特征，弥补了不同检索方式之间的异质性差距，提高了图像-文本检索的性能。具体来说，对比学习是通过跨模态检索特征和辅助生成特征来缩小图像-文本对之间的距离，并从情态间和情态内的角度分离未对齐图像-文本对之间的距离。此外，我们设计了一个支持集正则化项来进一步改善对比学习，约束图像/文本之间的距离及其相应的跨模态支持集信息包含在同一语义范畴。为了评估该方法的有效性，我们对三个基准数据集(即 MIRFLICKR-25K，NUS-WIDE，MS COCO)进行了实验。实验结果表明，该模型在图像文本检索中的性能明显优于强基线检索。为了重现性，我们公开在 urlhttps:// github.com/hambaobao/crcgs 提交代码和数据。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Image-Text+Retrieval+via+Contrastive+Learning+with+Auxiliary+Generative+Features+and+Support-set+Regularization)|0| +|[Denoising Time Cycle Modeling for Recommendation](https://doi.org/10.1145/3477495.3531785)|Sicong Xie, Qunwei Li, Weidi Xu, Kaiming Shen, Shaohu Chen, Wenliang Zhong|Ant Group, Hangzhou, China; Ant Group, Beijing, China; Ant Group, Shanghai, China|Recently, modeling temporal patterns of user-item interactions have attracted much attention in recommender systems. We argue that existing methods ignore the variety of temporal patterns of user behaviors. We define the subset of user behaviors that are ir- relevant to the target item as noises, which limits the performance of target-related time cycle modeling and affect the recommendation performance. In this paper, we propose Denoising Time Cycle Modeling (DiCycle), a novel approach to denoise user behaviors and select the subset of user behaviors that are highly related to the target item. DiCycle is able to explicitly model diverse time cycle patterns for recommendation. Extensive experiments are conducted on both public benchmarks and a real-world dataset, demonstrating the superior performance of DiCycle over the state-of-the-art recommendation methods.|近年来，推荐系统中用户-项目交互的时间模式建模引起了人们的广泛关注。我们认为现有的方法忽略了用户行为的时间模式的多样性。将与目标项无关的用户行为定义为噪声，限制了目标相关时间周期建模的性能，影响了推荐性能。本文提出了一种新的去噪时间周期建模方法(DiCycle) ，用于去除用户行为的噪声，并选择与目标项高度相关的用户行为子集。DiCycle 能够显式地为推荐建模不同的时间周期模式。在公共基准测试和真实世界数据集上进行了广泛的实验，证明了 DiCycle 优于最先进的推荐方法的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Denoising+Time+Cycle+Modeling+for+Recommendation)|0| |[Value Penalized Q-Learning for Recommender Systems](https://doi.org/10.1145/3477495.3531796)|Chengqian Gao, Ke Xu, Kuangqi Zhou, Lanqing Li, Xueqian Wang, Bo Yuan, Peilin Zhao||Scaling reinforcement learning (RL) to recommender systems (RS) is promising since maximizing the expected cumulative rewards for RL agents meets the objective of RS, i.e., improving customers' long-term satisfaction. A key approach to this goal is offline RL, which aims to learn policies from logged data rather than expensive online interactions. In this paper, we propose Value Penalized Q-learning (VPQ), a novel uncertainty-based offline RL algorithm that penalizes the unstable Q-values in the regression target using uncertainty-aware weights, achieving the conservative Q-function without the need of estimating the behavior policy, suitable for RS with a large number of items. Experiments on two real-world datasets show the proposed method serves as a gain plug-in for existing RS models.|由于推荐系统能够最大化推荐系统代理商的预期累积回报，从而达到推荐系统的目标，即提高客户的长期满意度，因此，将推荐系统扩展到推荐系统的强化学习是有前景的。实现这一目标的一个关键方法是离线 RL，它旨在从记录的数据中学习策略，而不是昂贵的在线交互。本文提出了一种新的基于不确定性的离线 RL 算法——价值惩罚 Q 学习算法(VPQ) ，该算法利用不确定性感知权值惩罚回归目标中不稳定的 Q 值，不需要估计行为策略就可以实现保守的 Q 函数，适用于大项目的 RS。在两个实际数据集上的实验表明，该方法可以作为现有 RS 模型的增益插件。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Value+Penalized+Q-Learning+for+Recommender+Systems)|0| -|[From Cluster Ranking to Document Ranking](https://doi.org/10.1145/3477495.3531819)|Egor Markovskiy, Fiana Raiber, Shoham Sabach, Oren Kurland|Technion, Haifa, Israel; Yahoo Research, Haifa, Israel|The common approach of using clusters of similar documents for ad hoc document retrieval is to rank the clusters in response to the query; then, the cluster ranking is transformed to document ranking. We present a novel supervised approach to transform cluster ranking to document ranking. The approach allows to simultaneously utilize different clusterings and the resultant cluster rankings; this helps to improve the modeling of the document similarity space. Empirical evaluation shows that using our approach results in performance that substantially transcends the state-of-the-art in cluster-based document retrieval.|使用类似文档集群进行特别文献检索的常见方法是根据查询对集群进行排序，然后将集群排序转换为文档排序。提出了一种新的监督方法将聚类排序转换为文档排序。该方法允许同时使用不同的聚类和由此产生的聚类排名; 这有助于改进文档相似性空间的建模。经验性的评估表明，使用我们的方法所产生的效果远远超过了基于集群的文献检索的最新水平。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=From+Cluster+Ranking+to+Document+Ranking)|0| -|[ILMART: Interpretable Ranking with Constrained LambdaMART](https://doi.org/10.1145/3477495.3531840)|Claudio Lucchese, Franco Maria Nardini, Salvatore Orlando, Raffaele Perego, Alberto Veneri|Ca' Foscari University of Venice, Venice, Italy; Ca' Foscari University of Venice & ISTI-CNR, Venice, Italy; ISTI-CNR, Pisa, Italy|Interpretable Learning to Rank (LtR) is an emerging field within the research area of explainable AI, aiming at developing intelligible and accurate predictive models. While most of the previous research efforts focus on creating post-hoc explanations, in this paper we investigate how to train effective and intrinsically-interpretable ranking models. Developing these models is particularly challenging and it also requires finding a trade-off between ranking quality and model complexity. State-of-the-art rankers, made of either large ensembles of trees or several neural layers, exploit in fact an unlimited number of feature interactions making them black boxes. Previous approaches on intrinsically-interpretable ranking models address this issue by avoiding interactions between features thus paying a significant performance drop with respect to full-complexity models. Conversely, ILMART, our novel and interpretable LtR solution based on LambdaMART, is able to train effective and intelligible models by exploiting a limited and controlled number of pairwise feature interactions. Exhaustive and reproducible experiments conducted on three publicly-available LtR datasets show that ILMART outperforms the current state-of-the-art solution for interpretable ranking of a large margin with a gain of nDCG of up to 8%.|可解释排序学习是可解释人工智能研究领域中的一个新兴领域，其目标是建立可理解的、准确的预测模型。以往的研究大多侧重于创建事后解释，本文主要研究如何训练有效且内在可解释的排序模型。开发这些模型尤其具有挑战性，而且还需要在排名质量和模型复杂性之间找到平衡。最先进的排名器，由大型的树木集合或几个神经层组成，实际上利用了无限数量的特征交互，使它们成为黑盒子。以前关于内在可解释的排名模型的方法通过避免特性之间的交互来解决这个问题，因此相对于全复杂模型来说，性能下降很大。相反，我们基于 lambdaMART 的新颖且可解释的 ILMART 有限公司解决方案，能够通过利用有限和可控数量的成对特征交互来培训有效和可理解的模型。在三个公开可用的有限责任公司数据集上进行的详尽和可重复的实验表明，ILMART 在可解释的大幅度排名方面优于目前的最先进的解决方案，nDCG 的增益高达8% 。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=ILMART:+Interpretable+Ranking+with+Constrained+LambdaMART)|0| +|[From Cluster Ranking to Document Ranking](https://doi.org/10.1145/3477495.3531819)|Egor Markovskiy, Fiana Raiber, Shoham Sabach, Oren Kurland|Yahoo Research, Haifa, Israel; Technion, Haifa, Israel|The common approach of using clusters of similar documents for ad hoc document retrieval is to rank the clusters in response to the query; then, the cluster ranking is transformed to document ranking. We present a novel supervised approach to transform cluster ranking to document ranking. The approach allows to simultaneously utilize different clusterings and the resultant cluster rankings; this helps to improve the modeling of the document similarity space. Empirical evaluation shows that using our approach results in performance that substantially transcends the state-of-the-art in cluster-based document retrieval.|使用类似文档集群进行特别文献检索的常见方法是根据查询对集群进行排序，然后将集群排序转换为文档排序。提出了一种新的监督方法将聚类排序转换为文档排序。该方法允许同时使用不同的聚类和由此产生的聚类排名; 这有助于改进文档相似性空间的建模。经验性的评估表明，使用我们的方法所产生的效果远远超过了基于集群的文献检索的最新水平。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=From+Cluster+Ranking+to+Document+Ranking)|0| +|[ILMART: Interpretable Ranking with Constrained LambdaMART](https://doi.org/10.1145/3477495.3531840)|Claudio Lucchese, Franco Maria Nardini, Salvatore Orlando, Raffaele Perego, Alberto Veneri|Ca' Foscari University of Venice & ISTI-CNR, Venice, Italy; Ca' Foscari University of Venice, Venice, Italy; ISTI-CNR, Pisa, Italy|Interpretable Learning to Rank (LtR) is an emerging field within the research area of explainable AI, aiming at developing intelligible and accurate predictive models. While most of the previous research efforts focus on creating post-hoc explanations, in this paper we investigate how to train effective and intrinsically-interpretable ranking models. Developing these models is particularly challenging and it also requires finding a trade-off between ranking quality and model complexity. State-of-the-art rankers, made of either large ensembles of trees or several neural layers, exploit in fact an unlimited number of feature interactions making them black boxes. Previous approaches on intrinsically-interpretable ranking models address this issue by avoiding interactions between features thus paying a significant performance drop with respect to full-complexity models. Conversely, ILMART, our novel and interpretable LtR solution based on LambdaMART, is able to train effective and intelligible models by exploiting a limited and controlled number of pairwise feature interactions. Exhaustive and reproducible experiments conducted on three publicly-available LtR datasets show that ILMART outperforms the current state-of-the-art solution for interpretable ranking of a large margin with a gain of nDCG of up to 8%.|可解释排序学习是可解释人工智能研究领域中的一个新兴领域，其目标是建立可理解的、准确的预测模型。以往的研究大多侧重于创建事后解释，本文主要研究如何训练有效且内在可解释的排序模型。开发这些模型尤其具有挑战性，而且还需要在排名质量和模型复杂性之间找到平衡。最先进的排名器，由大型的树木集合或几个神经层组成，实际上利用了无限数量的特征交互，使它们成为黑盒子。以前关于内在可解释的排名模型的方法通过避免特性之间的交互来解决这个问题，因此相对于全复杂模型来说，性能下降很大。相反，我们基于 lambdaMART 的新颖且可解释的 ILMART 有限公司解决方案，能够通过利用有限和可控数量的成对特征交互来培训有效和可理解的模型。在三个公开可用的有限责任公司数据集上进行的详尽和可重复的实验表明，ILMART 在可解释的大幅度排名方面优于目前的最先进的解决方案，nDCG 的增益高达8% 。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=ILMART:+Interpretable+Ranking+with+Constrained+LambdaMART)|0| |[On Extractive Summarization for Profile-centric Neural Expert Search in Academia](https://doi.org/10.1145/3477495.3531713)|Rennan C. Lima, Rodrygo L. T. Santos|Universidade Federal de Minas Gerais, Belo Horizonte, Brazil|Identifying academic experts is crucial for the progress of science, enabling researchers to connect, form networks, and collaborate on the most pressing research problems. A key challenge for ranking experts in response to a query is how to infer their expertise from the publications they coauthored. Profile-centric approaches represent candidate experts by concatenating all their publications into a text-based profile. Despite offering a complete picture of each candidate's scientific output, such lengthy profiles make it inefficient to leverage state-of-the-art neural architectures for inferring expertise. To overcome this limitation, we investigate the suitability of extractive summarization as a mechanism to reduce candidate profiles for semantic encoding using Transformers. Our thorough experiments with a representative academic search test collection demonstrate the benefits of encoding summarized profiles for an improved expertise inference.|识别学术专家对科学进步至关重要，使研究人员能够在最紧迫的研究问题上进行联系、形成网络和协作。对专家进行排名以回答问题的一个关键挑战是如何从他们合著的出版物中推断出他们的专业知识。以配置文件为中心的方法通过将候选专家的所有出版物连接到一个基于文本的配置文件中来代表他们。尽管提供了每个候选人的科学成果的完整图片，这样冗长的档案使得利用最先进的神经结构来推断专业知识效率低下。为了克服这个限制，我们研究了提取摘要作为使用 Transformers 减少语义编码候选配置文件的机制的适用性。我们对一个有代表性的学术搜索测试集合进行了彻底的实验，证明了对概要进行编码以改进专业知识推理的好处。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=On+Extractive+Summarization+for+Profile-centric+Neural+Expert+Search+in+Academia)|0| |[Joint Optimization of Ad Ranking and Creative Selection](https://doi.org/10.1145/3477495.3531855)|Kaiyi Lin, Xiang Zhang, Feng Li, Pengjie Wang, Qingqing Long, Hongbo Deng, Jian Xu, Bo Zheng|Alibaba Group, Beijing, China|In e-commerce, ad creatives play an important role in effectively delivering product information to users. The purpose of online creative selection is to learn users' preferences for ad creatives, and to select the most appealing design for users to maximize Click-Through Rate (CTR). However, the existing common practices in the industry usually place the creative selection after the ad ranking stage, and thus the optimal creative fails to reflect the influence on the ad ranking stage. To address these issues, we propose a novel Cascade Architecture of Creative Selection (CACS), which is built before the ranking stage to joint optimization of intra-ad creative selection and inter-ad ranking. To improve the efficiency, we design a classic two-tower structure and allow creative embeddings of the creative selection stage to share with the ranking stage. To boost the effectiveness, on the one hand, we propose a soft label list-wise ranking distillation method to distill the ranking knowledge from the ranking stage to guide CACS learning; and on the other hand, we also design an adaptive dropout network to encourage the model to probabilistically ignore ID features in favor of content features to learn multi-modal representations of the creative. Most of all, the ranking model obtains the optimal creative information of each ad from our CACS, and uses all available features to improve the performance of the ranking model. We have launched our solution in Taobao advertising platform and have obtained significant improvements both in offline and online evaluations.|在电子商务中，广告创意人员在有效地向用户传递产品信息方面发挥着重要作用。在线创意选择的目的是了解用户对广告创意的偏好，并为用户选择最具吸引力的设计，以最大限度地提高点进率。然而，现有的行业惯例通常将创意选择置于广告排名阶段之后，因此最优创意未能反映出对广告排名阶段的影响。为了解决这些问题，我们提出了一种新颖的创意选择级联体系结构(CACS) ，该体系结构建立在排名阶段之前，以联合优化内部广告创意选择和内部广告排名。为了提高效率，我们设计了一个经典的双塔结构，并允许创造性的嵌入创造性的选择阶段与排名阶段共享。为了提高效率，一方面，我们提出了一种软标签列表式的排序精馏方法，从排序阶段提取排序知识来指导 CACS 学习; 另一方面，我们还设计了一个自适应辍学网络来鼓励模型忽略 ID 特征而有利于内容特征学习创造性的多模态表示。最重要的是，排名模型从我们的 CACS 中获得每个广告的最佳创意信息，并利用所有可用的功能来改善排名模型的性能。我们已经在淘宝广告平台上推出了我们的解决方案，并且在线下和在线评估方面都取得了显著的进步。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Joint+Optimization+of+Ad+Ranking+and+Creative+Selection)|0| |[Long Document Re-ranking with Modular Re-ranker](https://doi.org/10.1145/3477495.3531860)|Luyu Gao, Jamie Callan|Carnegie Mellon University, Pittsburgh, PA, USA|Long document re-ranking has been a challenging problem for neural re-rankers based on deep language models like BERT. Early work breaks the documents into short passage-like chunks. These chunks are independently mapped to scalar scores or latent vectors, which are then pooled into a final relevance score. These encode-and-pool methods however inevitably introduce an information bottleneck: the low dimension representations. In this paper, we propose instead to model full query-to-document interaction, leveraging the attention operation and modular Transformer re-ranker framework. First, document chunks are encoded independently with an encoder module. An interaction module then encodes the query and performs joint attention from the query to all document chunk representations. We demonstrate that the model can use this new degree of freedom to aggregate important information from the entire document. Our experiments show that this design produces effective re-ranking on two classical IR collections Robust04 and ClueWeb09, and a large-scale supervised collection MS-MARCO document ranking.|长文档重新排序一直是基于 BERT 等深层语言模型的神经网络重新排序的一个具有挑战性的问题。早期的工作将文档分解成短小的段落。这些块被独立映射到标量分数或潜在向量，然后将它们汇集到最终的相关分数中。然而，这些编码和池方法不可避免地引入了一个信息瓶颈: 低维表示。在本文中，我们提出了利用注意力操作和模块化的 formerre-rank 框架来建立完全的查询到文档的交互模型。首先，文档块使用编码器模块进行独立编码。然后，交互模块对查询进行编码，并执行从查询到所有文档块表示的联合注意。我们证明该模型可以使用这种新的自由度来聚合整个文档中的重要信息。我们的实验表明，这种设计产生了有效的重新排序的两个经典的红外收集鲁棒04和 ClueWeb09，以及一个大规模的监督收集 MS-MARCO 文件排序。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Long+Document+Re-ranking+with+Modular+Re-ranker)|0| -|[Relation Extraction as Open-book Examination: Retrieval-enhanced Prompt Tuning](https://doi.org/10.1145/3477495.3531746)|Xiang Chen, Lei Li, Ningyu Zhang, Chuanqi Tan, Fei Huang, Luo Si, Huajun Chen|Alibaba Group, Hangzhou, China; Zhejiang University, Hangzhou, China; Zhejiang University, Hangzhou, Chile|Pre-trained language models have contributed significantly to relation extraction by demonstrating remarkable few-shot learning abilities. However, prompt tuning methods for relation extraction may still fail to generalize to those rare or hard patterns. Note that the previous parametric learning paradigm can be viewed as memorization regarding training data as a book and inference as the close-book test. Those long-tailed or hard patterns can hardly be memorized in parameters given few-shot instances. To this end, we regard RE as an open-book examination and propose a new semiparametric paradigm of retrieval-enhanced prompt tuning for relation extraction. We construct an open-book datastore for retrieval regarding prompt-based instance representations and corresponding relation labels as memorized key-value pairs. During inference, the model can infer relations by linearly interpolating the base output of PLM with the non-parametric nearest neighbor distribution over the datastore. In this way, our model not only infers relation through knowledge stored in the weights during training but also assists decision-making by unwinding and querying examples in the open-book datastore. Extensive experiments on benchmark datasets show that our method can achieve state-of-the-art in both standard supervised and few-shot settings|预训练的语言模型通过显示出显著的短镜头学习能力，对关系抽取做出了重要贡献。然而，关系抽取的快速调优方法可能仍然无法推广到那些罕见的或难以实现的模式。请注意，以前的参数学习范式可以被视为记忆的训练数据作为一本书和推论作为关闭书测试。这些长尾或硬模式几乎不能被记忆在参数中，因为实例很少。为此，我们将 RE 视为一个开放式的考试，并提出了一个新的检索半参数范式——关系抽取的增强型提示调优。我们构造了一个开放式数据存储，用于检索基于提示的实例表示和相应的关系标签作为记忆的键值对。在推理过程中，该模型可以通过对 PLM 的基本输出与数据存储上的非参数最近邻分布进行线性插值来推断关系。这样，我们的模型不仅可以通过训练过程中权重中存储的知识来推断关系，而且可以通过展开和查询开卷数据库中的实例来辅助决策。对基准数据集的大量实验表明，该方法可以在标准监督和少镜头设置下达到最先进的水平|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Relation+Extraction+as+Open-book+Examination:+Retrieval-enhanced+Prompt+Tuning)|0| +|[Relation Extraction as Open-book Examination: Retrieval-enhanced Prompt Tuning](https://doi.org/10.1145/3477495.3531746)|Xiang Chen, Lei Li, Ningyu Zhang, Chuanqi Tan, Fei Huang, Luo Si, Huajun Chen|Alibaba Group, Hangzhou, China; Zhejiang University, Hangzhou, Chile; Zhejiang University, Hangzhou, China|Pre-trained language models have contributed significantly to relation extraction by demonstrating remarkable few-shot learning abilities. However, prompt tuning methods for relation extraction may still fail to generalize to those rare or hard patterns. Note that the previous parametric learning paradigm can be viewed as memorization regarding training data as a book and inference as the close-book test. Those long-tailed or hard patterns can hardly be memorized in parameters given few-shot instances. To this end, we regard RE as an open-book examination and propose a new semiparametric paradigm of retrieval-enhanced prompt tuning for relation extraction. We construct an open-book datastore for retrieval regarding prompt-based instance representations and corresponding relation labels as memorized key-value pairs. During inference, the model can infer relations by linearly interpolating the base output of PLM with the non-parametric nearest neighbor distribution over the datastore. In this way, our model not only infers relation through knowledge stored in the weights during training but also assists decision-making by unwinding and querying examples in the open-book datastore. Extensive experiments on benchmark datasets show that our method can achieve state-of-the-art in both standard supervised and few-shot settings|预训练的语言模型通过显示出显著的短镜头学习能力，对关系抽取做出了重要贡献。然而，关系抽取的快速调优方法可能仍然无法推广到那些罕见的或难以实现的模式。请注意，以前的参数学习范式可以被视为记忆的训练数据作为一本书和推论作为关闭书测试。这些长尾或硬模式几乎不能被记忆在参数中，因为实例很少。为此，我们将 RE 视为一个开放式的考试，并提出了一个新的检索半参数范式——关系抽取的增强型提示调优。我们构造了一个开放式数据存储，用于检索基于提示的实例表示和相应的关系标签作为记忆的键值对。在推理过程中，该模型可以通过对 PLM 的基本输出与数据存储上的非参数最近邻分布进行线性插值来推断关系。这样，我们的模型不仅可以通过训练过程中权重中存储的知识来推断关系，而且可以通过展开和查询开卷数据库中的实例来辅助决策。对基准数据集的大量实验表明，该方法可以在标准监督和少镜头设置下达到最先进的水平|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Relation+Extraction+as+Open-book+Examination:+Retrieval-enhanced+Prompt+Tuning)|0| |[End-to-end Distantly Supervised Information Extraction with Retrieval Augmentation](https://doi.org/10.1145/3477495.3531876)|Yue Zhang, Hongliang Fei, Ping Li|Baidu, Bellevue, WA, USA|Distant supervision (DS) has been a prevalent approach to generating labeled data for information extraction (IE) tasks. However, DS often suffers from noisy label problems, where the labels are extracted from the knowledge base (KB), regardless of the input context. Many efforts have been devoted to designing denoising mechanisms. However, most strategies are only designed for one specific task and cannot be directly adapted to other tasks. We propose a general paradigm (Dasiera) to resolve issues in KB-based DS. Labels from KB can be viewed as universal labels of a target entity or an entity pair. While the given context for an IE task may only contain partial/zero information about the target entities, or the entailed information may be vague. Hence the mismatch between the given context and KB labels, i.e., the given context has insufficient information to infer DS labels, can happen in IE training datasets. To solve the problem, during training, Dasiera leverages a retrieval-augmentation mechanism to complete missing information of the given context, where we seamlessly integrate a neural retriever and a general predictor in an end-to-end framework. During inference, we can keep/remove the retrieval component based on whether we want to predict solely on the given context. We have evaluated Dasiera on two IE tasks under the DS setting: named entity typing and relation extraction. Experimental results show Dasiera's superiority to other baselines in both tasks.|远程监控(DS)已经成为一种普遍的方法来为信息抽取(IE)任务生成标记数据。然而，DS 经常遇到噪声标签问题，这些标签是从知识库(KB)中提取出来的，与输入上下文无关。人们在设计去噪机制方面付出了很多努力。然而，大多数策略只针对一个特定任务设计，不能直接适应其他任务。我们提出了一个通用范例(Dasiera)来解决基于知识库的 DS 中的问题。来自 KB 的标签可以被视为目标实体或实体对的通用标签。IE 任务的给定上下文可能只包含关于目标实体的部分/零信息，或者所涉及的信息可能是模糊的。因此，给定上下文和知识库标签之间的不匹配，即给定上下文没有足够的信息来推断 DS 标签，可能发生在 IE 训练数据集中。为了解决这个问题，在训练期间，Dasiera 利用检索增强机制来完成给定上下文的缺失信息，在这里我们无缝地将神经检索器和通用预测器集成在一个端到端框架中。在推理过程中，我们可以保留/删除检索组件，这取决于我们是否希望仅根据给定的上下文进行预测。我们在 DS 设置下对 Dasiera 的两个 IE 任务进行了评估: 命名实体类型和关系提取。实验结果表明，Dasiera 在这两个任务中都优于其他基线。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=End-to-end+Distantly+Supervised+Information+Extraction+with+Retrieval+Augmentation)|0| -|[Assessing Scientific Research Papers with Knowledge Graphs](https://doi.org/10.1145/3477495.3531879)|Kexuan Sun, Zhiqiang Qiu, Abel Salinas, Yuzhong Huang, DongHo Lee, Daniel Benjamin, Fred Morstatter, Xiang Ren, Kristina Lerman, Jay Pujara|Nova Southeastern University, Fort Lauderdale, CA, USA; University of Southern California, Los Angeles, CA, USA|In recent decades, the growing scale of scientific research has led to numerous novel findings. Reproducing these findings is the foundation of future research. However, due to the complexity of experiments, manually assessing scientific research is laborious and time-intensive, especially in social and behavioral sciences. Although increasing reproducibility studies have garnered increased attention in the research community, there is still a lack of systematic ways for evaluating scientific research at scale. In this paper, we propose a novel approach towards automatically assessing scientific publications by constructing a knowledge graph (KG) that captures a holistic view of the research contributions. Specifically, during the KG construction, we combine information from two different perspectives: micro-level features that capture knowledge from published articles such as sample sizes, effect sizes, and experimental models, and macro-level features that comprise relationships between entities such as authorship and reference information. We then learn low-dimensional representations using language models and knowledge graph embeddings for entities (nodes in KGs), which are further used for the assessments. A comprehensive set of experiments on two benchmark datasets shows the usefulness of leveraging KGs for scoring scientific research.|近几十年来，科学研究的规模不断扩大，产生了许多新的发现。复制这些发现是未来研究的基础。然而，由于实验的复杂性，人工评估科学研究是费时费力的，尤其是在社会科学和行为科学领域。虽然越来越多的重复性研究已经引起了研究界越来越多的关注，但仍然缺乏系统的方法来评价科学研究的规模。在本文中，我们提出了一种新的方法来自动评估科学出版物，通过构造一个知识图(KG) ，捕获了研究贡献的整体观点。具体而言，在 KG 构建过程中，我们从两个不同的角度组合信息: 从已发表的文章(如样本量，效应量和实验模型)中捕获知识的微观层面特征，以及包含实体(如作者和参考信息)之间关系的宏观层面特征。然后，我们学习低维表示使用语言模型和知识图嵌入的实体(节点在幼稚园) ，这是进一步用于评估。在两个基准数据集上的一组综合实验表明了利用幼儿园评分科学研究的有用性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Assessing+Scientific+Research+Papers+with+Knowledge+Graphs)|0| -|[A Content Recommendation Policy for Gaining Subscribers](https://doi.org/10.1145/3477495.3531885)|Konstantinos Theocharidis, Manolis Terrovitis, Spiros Skiadopoulos, Panagiotis Karras|Aarhus University, Aarhus, Denmark; University of the Peloponnese & Information Management Systems Institute, Athena Research Center, Tripoli & Athens, Greece; Information Management Systems Institute, Athena Research Center, Athens, Greece; University of the Peloponnese, Tripoli, Greece|How can we recommend content for a brand agent to use over a series of rounds so as to gain new subscribers to its social network page? The Influence Maximization (IM) problem seeks a set of~k users, and its content-aware variants seek a set of~k post features, that achieve, in both cases, an objective of expected influence in a social network. However, apart from raw influence, it is also relevant to study gain in subscribers, as long-term success rests on the subscribers of a brand page; classic IM may select~k users from the subscriber set, and content-aware IM starts the post's propagation from that subscriber set. In this paper, we propose a novel content recommendation policy to a brand agent for Gaining Subscribers by Messaging (GSM) over many rounds. In each round, the brand agent messages a fixed number of social network users and invites them to visit the brand page aiming to gain their subscription, while its most recently published content consists of features that intensely attract the preferences of the invited users. To solve GSM, we find, in each round, which content features to publish and which users to notify aiming to maximize the cumulative subscription gain over all rounds. We deploy three GSM solvers, named \sR, \sSC, and \sSU, and we experimentally evaluate their performance based on VKontakte (VK) posts by considering different user sets and feature sets. Our experimental results show that \sSU provides the best solution, as it is significantly more efficient than \sSC with a minor loss of efficacy and clearly more efficacious than \sR with competitive efficiency.|我们怎样才能向品牌代理商推荐一系列的内容，从而吸引新的用户访问其社交网络页面？影响力最大化(IM)问题寻找一组 ~ k 用户，其内容感知变体寻找一组 ~ k 帖子特征，在这两种情况下，都实现了社交网络中预期影响力的目标。然而，除了原始的影响力之外，研究订阅者的收益也是相关的，因为长期的成功取决于品牌页面的订阅者; 传统的 IM 可能从订阅者集合中选择 ~ k 用户，而内容感知的 IM 从订阅者集合中开始发布信息。在本文中，我们提出了一个新的内容推荐策略的品牌代理商获得用户的消息(GSM)多轮。在每一轮中，品牌代理人给固定数量的社交网络用户发信息，并邀请他们访问品牌页面以获得订阅，而其最近发布的内容包括强烈吸引受邀用户偏好的功能。为了解决 GSM 问题，我们发现，在每一轮中，哪些内容特性要发布，哪些用户要通知，目的是在所有轮次中最大化累积订阅收益。我们部署了三个 GSM 解决方案，分别命名为 sR、 sSC 和 sSU，通过考虑不同的用户集和特性集，实验性地评估了它们基于 VKontakte (VK)帖子的性能。我们的实验结果表明，sSU 提供了最佳的解决方案，因为它明显地比 sSC 更有效，具有较小的效率损失，并且明显地比具有竞争效率的 sR 更有效。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Content+Recommendation+Policy+for+Gaining+Subscribers)|0| -|[MM-Rec: Visiolinguistic Model Empowered Multimodal News Recommendation](https://doi.org/10.1145/3477495.3531896)|Chuhan Wu, Fangzhao Wu, Tao Qi, Chao Zhang, Yongfeng Huang, Tong Xu|Tsinghua University, Beijing, China; Shandong University, Jinan, China; University of Science and Technology of China, Hefei, China; Microsoft Research Asia, Beijing, China|News representation is critical for news recommendation. Most existing methods learn news representations only from news texts while ignoring the visual information of news. In fact, users may click news not only due to the interest in news titles but also the attraction of news images. Thus, images are useful for representing news and predicting news clicks. Pretrained visiolinguistic models are powerful in multi-modal understanding, which can represent news from both textual and visual contents. In this paper, we propose a multimodal news recommendation method that can incorporate both textual and visual information of news to learn multimodal news representations. We first extract region-of-interests (ROIs) from news images via object detection. We then use a pre-trained visiolinguistic model to encode both news texts and image ROIs and model their inherent relatedness using co-attentional Transformers. In addition, we propose a crossmodal candidate-aware attention network to select relevant historical clicked news for the accurate modeling of user interest in candidate news. Experiments validate that incorporating multimodal news information can effectively improve the performance of news recommendation.|新闻表达对新闻推荐至关重要。现有的大多数方法只从新闻文本中学习新闻表征，而忽视了新闻的视觉信息。事实上，用户之所以会点击新闻，不仅是因为他们对新闻标题感兴趣，还因为新闻图片的吸引力。因此，图像对于表示新闻和预测新闻点击是非常有用的。预先训练的视觉语言模型在多模态理解中具有很强的表现能力，可以从文本和视觉两个方面表现新闻。在本文中，我们提出了一种多通道新闻推荐方法，它可以结合新闻的文本信息和视觉信息来学习多通道新闻表示。我们首先通过目标检测从新闻图像中提取感兴趣区域(ROI)。然后，我们使用一个预先训练的视觉语言学模型来编码新闻文本和图像 ROI，并使用共注意转换器来模拟它们之间的内在联系。此外，我们提出了一个跨模式的候选人感知注意网络来选择相关的历史点击新闻，以准确建模用户对候选人新闻的兴趣。实验证明，融合多模态新闻信息可以有效地提高新闻推荐的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=MM-Rec:+Visiolinguistic+Model+Empowered+Multimodal+News+Recommendation)|0| +|[Assessing Scientific Research Papers with Knowledge Graphs](https://doi.org/10.1145/3477495.3531879)|Kexuan Sun, Zhiqiang Qiu, Abel Salinas, Yuzhong Huang, DongHo Lee, Daniel Benjamin, Fred Morstatter, Xiang Ren, Kristina Lerman, Jay Pujara|University of Southern California, Los Angeles, CA, USA; Nova Southeastern University, Fort Lauderdale, CA, USA|In recent decades, the growing scale of scientific research has led to numerous novel findings. Reproducing these findings is the foundation of future research. However, due to the complexity of experiments, manually assessing scientific research is laborious and time-intensive, especially in social and behavioral sciences. Although increasing reproducibility studies have garnered increased attention in the research community, there is still a lack of systematic ways for evaluating scientific research at scale. In this paper, we propose a novel approach towards automatically assessing scientific publications by constructing a knowledge graph (KG) that captures a holistic view of the research contributions. Specifically, during the KG construction, we combine information from two different perspectives: micro-level features that capture knowledge from published articles such as sample sizes, effect sizes, and experimental models, and macro-level features that comprise relationships between entities such as authorship and reference information. We then learn low-dimensional representations using language models and knowledge graph embeddings for entities (nodes in KGs), which are further used for the assessments. A comprehensive set of experiments on two benchmark datasets shows the usefulness of leveraging KGs for scoring scientific research.|近几十年来，科学研究的规模不断扩大，产生了许多新的发现。复制这些发现是未来研究的基础。然而，由于实验的复杂性，人工评估科学研究是费时费力的，尤其是在社会科学和行为科学领域。虽然越来越多的重复性研究已经引起了研究界越来越多的关注，但仍然缺乏系统的方法来评价科学研究的规模。在本文中，我们提出了一种新的方法来自动评估科学出版物，通过构造一个知识图(KG) ，捕获了研究贡献的整体观点。具体而言，在 KG 构建过程中，我们从两个不同的角度组合信息: 从已发表的文章(如样本量，效应量和实验模型)中捕获知识的微观层面特征，以及包含实体(如作者和参考信息)之间关系的宏观层面特征。然后，我们学习低维表示使用语言模型和知识图嵌入的实体(节点在幼稚园) ，这是进一步用于评估。在两个基准数据集上的一组综合实验表明了利用幼儿园评分科学研究的有用性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Assessing+Scientific+Research+Papers+with+Knowledge+Graphs)|0| +|[A Content Recommendation Policy for Gaining Subscribers](https://doi.org/10.1145/3477495.3531885)|Konstantinos Theocharidis, Manolis Terrovitis, Spiros Skiadopoulos, Panagiotis Karras|Aarhus University, Aarhus, Denmark; University of the Peloponnese & Information Management Systems Institute, Athena Research Center, Tripoli & Athens, Greece; University of the Peloponnese, Tripoli, Greece; Information Management Systems Institute, Athena Research Center, Athens, Greece|How can we recommend content for a brand agent to use over a series of rounds so as to gain new subscribers to its social network page? The Influence Maximization (IM) problem seeks a set of~k users, and its content-aware variants seek a set of~k post features, that achieve, in both cases, an objective of expected influence in a social network. However, apart from raw influence, it is also relevant to study gain in subscribers, as long-term success rests on the subscribers of a brand page; classic IM may select~k users from the subscriber set, and content-aware IM starts the post's propagation from that subscriber set. In this paper, we propose a novel content recommendation policy to a brand agent for Gaining Subscribers by Messaging (GSM) over many rounds. In each round, the brand agent messages a fixed number of social network users and invites them to visit the brand page aiming to gain their subscription, while its most recently published content consists of features that intensely attract the preferences of the invited users. To solve GSM, we find, in each round, which content features to publish and which users to notify aiming to maximize the cumulative subscription gain over all rounds. We deploy three GSM solvers, named \sR, \sSC, and \sSU, and we experimentally evaluate their performance based on VKontakte (VK) posts by considering different user sets and feature sets. Our experimental results show that \sSU provides the best solution, as it is significantly more efficient than \sSC with a minor loss of efficacy and clearly more efficacious than \sR with competitive efficiency.|我们怎样才能向品牌代理商推荐一系列的内容，从而吸引新的用户访问其社交网络页面？影响力最大化(IM)问题寻找一组 ~ k 用户，其内容感知变体寻找一组 ~ k 帖子特征，在这两种情况下，都实现了社交网络中预期影响力的目标。然而，除了原始的影响力之外，研究订阅者的收益也是相关的，因为长期的成功取决于品牌页面的订阅者; 传统的 IM 可能从订阅者集合中选择 ~ k 用户，而内容感知的 IM 从订阅者集合中开始发布信息。在本文中，我们提出了一个新的内容推荐策略的品牌代理商获得用户的消息(GSM)多轮。在每一轮中，品牌代理人给固定数量的社交网络用户发信息，并邀请他们访问品牌页面以获得订阅，而其最近发布的内容包括强烈吸引受邀用户偏好的功能。为了解决 GSM 问题，我们发现，在每一轮中，哪些内容特性要发布，哪些用户要通知，目的是在所有轮次中最大化累积订阅收益。我们部署了三个 GSM 解决方案，分别命名为 sR、 sSC 和 sSU，通过考虑不同的用户集和特性集，实验性地评估了它们基于 VKontakte (VK)帖子的性能。我们的实验结果表明，sSU 提供了最佳的解决方案，因为它明显地比 sSC 更有效，具有较小的效率损失，并且明显地比具有竞争效率的 sR 更有效。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Content+Recommendation+Policy+for+Gaining+Subscribers)|0| +|[MM-Rec: Visiolinguistic Model Empowered Multimodal News Recommendation](https://doi.org/10.1145/3477495.3531896)|Chuhan Wu, Fangzhao Wu, Tao Qi, Chao Zhang, Yongfeng Huang, Tong Xu|Tsinghua University, Beijing, China; University of Science and Technology of China, Hefei, China; Microsoft Research Asia, Beijing, China; Shandong University, Jinan, China|News representation is critical for news recommendation. Most existing methods learn news representations only from news texts while ignoring the visual information of news. In fact, users may click news not only due to the interest in news titles but also the attraction of news images. Thus, images are useful for representing news and predicting news clicks. Pretrained visiolinguistic models are powerful in multi-modal understanding, which can represent news from both textual and visual contents. In this paper, we propose a multimodal news recommendation method that can incorporate both textual and visual information of news to learn multimodal news representations. We first extract region-of-interests (ROIs) from news images via object detection. We then use a pre-trained visiolinguistic model to encode both news texts and image ROIs and model their inherent relatedness using co-attentional Transformers. In addition, we propose a crossmodal candidate-aware attention network to select relevant historical clicked news for the accurate modeling of user interest in candidate news. Experiments validate that incorporating multimodal news information can effectively improve the performance of news recommendation.|新闻表达对新闻推荐至关重要。现有的大多数方法只从新闻文本中学习新闻表征，而忽视了新闻的视觉信息。事实上，用户之所以会点击新闻，不仅是因为他们对新闻标题感兴趣，还因为新闻图片的吸引力。因此，图像对于表示新闻和预测新闻点击是非常有用的。预先训练的视觉语言模型在多模态理解中具有很强的表现能力，可以从文本和视觉两个方面表现新闻。在本文中，我们提出了一种多通道新闻推荐方法，它可以结合新闻的文本信息和视觉信息来学习多通道新闻表示。我们首先通过目标检测从新闻图像中提取感兴趣区域(ROI)。然后，我们使用一个预先训练的视觉语言学模型来编码新闻文本和图像 ROI，并使用共注意转换器来模拟它们之间的内在联系。此外，我们提出了一个跨模式的候选人感知注意网络来选择相关的历史点击新闻，以准确建模用户对候选人新闻的兴趣。实验证明，融合多模态新闻信息可以有效地提高新闻推荐的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=MM-Rec:+Visiolinguistic+Model+Empowered+Multimodal+News+Recommendation)|0| |[Towards Personalized Bundle Creative Generation with Contrastive Non-Autoregressive Decoding](https://doi.org/10.1145/3477495.3531909)|Penghui Wei, Shaoguo Liu, Xuanhua Yang, Liang Wang, Bo Zheng|Alibaba Group, Beijing, China|Current bundle generation studies focus on generating a combination of items to improve user experience. In real-world applications, there is also a great need to produce bundle creatives that consist of mixture types of objects (e.g., items, slogans and templates) for achieving better promotion effect. We study a new problem named bundle creative generation: for given users, the goal is to generate personalized bundle creatives that the users will be interested in. To take both quality and efficiency into account, we propose a contrastive non-autoregressive model that captures user preferences with ingenious decoding objective. Experiments on large-scale real-world datasets verify that our proposed model shows significant advantages in terms of creative quality and generation speed.|当前的捆绑包生成研究侧重于生成项目的组合，以改善用户体验。在实际应用中，为了达到更好的促销效果，还需要产生由混合类型的对象(例如，项目、标语和模板)组成的捆绑创意。我们研究了一个新的问题——捆绑包创意生成: 对于给定的用户，目标是生成用户感兴趣的个性化捆绑包创意。为了同时考虑质量和效率，我们提出了一个对比的非自回归模型，捕捉用户的喜好与巧妙的解码目标。在大规模真实世界数据集上的实验表明，我们提出的模型在创新质量和生成速度方面具有显著的优势。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Towards+Personalized+Bundle+Creative+Generation+with+Contrastive+Non-Autoregressive+Decoding)|0| |[Another Look at Information Retrieval as Statistical Translation](https://doi.org/10.1145/3477495.3531717)|Yuqi Liu, Chengcheng Hu, Jimmy Lin|University of Waterloo, Waterloo, ON, Canada|Over two decades ago, Berger and Lafferty proposed "information retrieval as statistical translation" (IRST), a simple and elegant method for ad hoc retrieval based on the noisy channel model. At the time, they lacked the large-scale human-annotated datasets necessary to properly train their models. In this paper, we ask the simple question: What if Berger and Lafferty had access to datasets such as the MS MARCO passage ranking dataset that we take for granted today? The answer to this question tells us how much of recent improvements in ranking can be solely attributed to having more data available, as opposed to improvements in models (e.g., pretrained transformers) and optimization techniques (e.g., contrastive loss). In fact, Boytsov and Kolter recently began to answer this question with a replication of Berger and Lafferty's model, and this work can be viewed as another independent replication effort, with generalizations to additional conditions not previously explored, including replacing the sum of translation probabilities with ColBERT's MaxSim operator. We confirm that while neural models (particularly pretrained transformers) have indeed led to great advances in retrieval effectiveness, the IRST model proposed decades ago is quite effective if provided sufficient training data.|二十多年前，伯杰和拉弗蒂提出了“信息检索作为统计翻译”(IRST) ，这是一种基于噪声信道模型的简单而优雅的自组织检索方法。当时，他们缺乏必要的大规模人工注释数据集来适当地训练他们的模型。在本文中，我们提出一个简单的问题: 如果 Berger 和 Lafferty 能够访问数据集，比如我们今天认为理所当然的 MS MARCO 通道排名数据集，那会怎样？这个问题的答案告诉我们，最近排名的改善在多大程度上可以完全归因于有更多的数据可用，而不是模型(例如，预先训练的变压器)和优化技术(例如，对比损失)的改进。事实上，Boytsov 和 Kolter 最近开始通过复制 Berger 和 Lafferty 的模型来回答这个问题，这项工作可以被看作是另一个独立的复制努力，对以前没有探索过的其他条件进行推广，包括用 ColBERT 的 MaxSim 运算符替换翻译概率的总和。我们证实，虽然神经模型(特别是预先训练的变压器)确实导致了检索效率的巨大进步，几十年前提出的 IRST 模型是相当有效的，如果提供足够的训练数据。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Another+Look+at+Information+Retrieval+as+Statistical+Translation)|0| -|[ACORDAR: A Test Collection for Ad Hoc Content-Based (RDF) Dataset Retrieval](https://doi.org/10.1145/3477495.3531729)|Tengteng Lin, Qiaosheng Chen, Gong Cheng, Ahmet Soylu, Basil Ell, Ruoqi Zhao, Qing Shi, Xiaxia Wang, Yu Gu, Evgeny Kharlamov|The Ohio State University, Columbus, OH, USA; Bosch Center for Artificial Intelligence & University of Oslo, Renningen, Germany; Bielefeld University & University of Oslo, Bielefeld, Germany; OsloMet -- Oslo Metropolitan University & Norwegian University of Science and Technology, Oslo, Norway; Nanjing University, Nanjing, China|Ad hoc dataset retrieval is a trending topic in IR research. Methods and systems are evolving from metadata-based to content-based ones which exploit the data itself for improving retrieval accuracy but thus far lack a specialized test collection. In this paper, we build and release the first test collection for ad hoc content-based dataset retrieval, where content-oriented dataset queries and content-based relevance judgments are annotated by human experts who are assisted with a dashboard designed specifically for comprehensively and conveniently browsing both the metadata and data of a dataset. We conduct extensive experiments on the test collection to analyze its difficulty and provide insights into the underlying task.|自组织数据集检索是信息检索领域的一个研究热点。方法和系统正在从基于元数据向基于内容的方法和系统演变，这些方法和系统利用数据本身来提高检索的准确性，但迄今为止还缺乏专门的测试集合。本文构建并发布了第一个基于特定内容的数据集检索测试集合，其中面向内容的数据集查询和基于内容的相关性判断由人类专家进行注释，并辅以一个专门为全面方便地浏览数据集的元数据和数据而设计的仪表板。我们进行了广泛的实验测试收集，以分析其难度，并提供深入了解潜在的任务。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=ACORDAR:+A+Test+Collection+for+Ad+Hoc+Content-Based+(RDF)+Dataset+Retrieval)|0| -|[RELISON: A Framework for Link Recommendation in Social Networks](https://doi.org/10.1145/3477495.3531730)|Javier SanzCruzado, Pablo Castells|University of Glasgow, Glasgow, United Kingdom; Universidad Autónoma de Madrid, Madrid, Spain|Link recommendation is an important and compelling problem at the intersection of recommender systems and online social networks. Given a user, link recommenders identify people in the platform the user might be interested in interacting with. We present RELISON, an extensible framework for running link recommendation experiments. The library provides a wide range of algorithms, along with tools for evaluating the produced recommendations. RELISON includes algorithms and metrics that consider the potential effect of recommendations on the properties of online social networks. For this reason, the library also implements network structure analysis metrics, community detection algorithms, and network diffusion simulation functionalities. The library code and documentation is available at https://github.com/ir-uam/RELISON.|在推荐系统和在线社交网络的交叉点上，链接推荐是一个重要且引人注目的问题。给定一个用户，链接推荐器会识别出平台中用户可能感兴趣的交互对象。我们提出了 RELISON，一个运行链路推荐实验的可扩展框架。该库提供了广泛的算法，以及用于评估生成的建议的工具。RELISON 包括一些算法和度量标准，这些算法和度量标准考虑了推荐对在线社交网络属性的潜在影响。出于这个原因，该库还实现了网络结构分析度量、社区检测算法和网络扩散模拟功能。图书馆代码及文件可于 https://github.com/ir-uam/relison 索取。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=RELISON:+A+Framework+for+Link+Recommendation+in+Social+Networks)|0| -|[The Istella22 Dataset: Bridging Traditional and Neural Learning to Rank Evaluation](https://doi.org/10.1145/3477495.3531740)|Domenico Dato, Sean MacAvaney, Franco Maria Nardini, Raffaele Perego, Nicola Tonellotto|University of Glasgow, Glasgow, United Kingdom; University of Pisa, Pisa, Italy; ISTI-CNR, Pisa, Italy; Istella, Milano, Italy|Neural approaches that use pre-trained language models are effective at various ranking tasks, such as question answering and ad-hoc document ranking. However, their effectiveness compared to feature-based Learning-to-Rank (LtR) methods has not yet been well-established. A major reason for this is because present LtR benchmarks that contain query-document feature vectors do not contain the raw query and document text needed for neural models. On the other hand, the benchmarks often used for evaluating neural models, e.g., MS MARCO, TREC Robust, etc., provide text but do not provide query-document feature vectors. In this paper, we present Istella22, a new dataset that enables such comparisons by providing both query/document text and strong query-document feature vectors used by an industrial search engine. The dataset consists of a comprehensive corpus of 8.4M web documents, a collection of query-document pairs including 220 hand-crafted features, relevance judgments on a 5-graded scale, and a set of 2,198 textual queries used for testing purposes. Istella22 enables a fair evaluation of traditional learning-to-rank and transfer ranking techniques on the same data. LtR models exploit the feature-based representations of training samples while pre-trained transformer-based neural rankers can be evaluated on the corresponding textual content of queries and documents. Through preliminary experiments on Istella22, we find that neural re-ranking approaches lag behind LtR models in terms of effectiveness. However, LtR models identify the scores from neural models as strong signals.|使用预训练语言模型的神经网络方法可以有效地完成各种排序任务，例如问题回答和即席文档排序。然而，与基于特征的学习到等级(LT)方法相比，它们的有效性还没有得到很好的证实。其中一个主要原因是，现有的包含查询文档特征向量的 LITR 基准测试不包含神经模型所需的原始查询和文档文本。另一方面，常用于评估神经模型的基准，如 MS MARCO、 TREC 鲁棒性等，提供文本但不提供查询文档特征向量。在本文中，我们介绍了新的数据集 Istella22，它通过提供工业搜索引擎使用的查询/文档文本和强查询-文档特征向量来实现这种比较。该数据集包括840万份网络文档的综合语料库、包括220个手工制作的功能的查询-文档对的集合、5级量表的相关性判断，以及用于测试目的的2198个文本查询。Istella22可以对传统的学习排序和转移排序技术在相同数据上进行公平的评估。LTR 模型利用训练样本的基于特征的表示，而预训练的基于变压器的神经排序器可以根据查询和文档的相应文本内容进行评估。通过在 Istella22上的初步实验，我们发现神经重新排序方法在有效性方面落后于 LTR 模型。然而，LTR 模型将神经模型的分数识别为强信号。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=The+Istella22+Dataset:+Bridging+Traditional+and+Neural+Learning+to+Rank+Evaluation)|0| +|[ACORDAR: A Test Collection for Ad Hoc Content-Based (RDF) Dataset Retrieval](https://doi.org/10.1145/3477495.3531729)|Tengteng Lin, Qiaosheng Chen, Gong Cheng, Ahmet Soylu, Basil Ell, Ruoqi Zhao, Qing Shi, Xiaxia Wang, Yu Gu, Evgeny Kharlamov|Bosch Center for Artificial Intelligence & University of Oslo, Renningen, Germany; OsloMet -- Oslo Metropolitan University & Norwegian University of Science and Technology, Oslo, Norway; Bielefeld University & University of Oslo, Bielefeld, Germany; The Ohio State University, Columbus, OH, USA; Nanjing University, Nanjing, China|Ad hoc dataset retrieval is a trending topic in IR research. Methods and systems are evolving from metadata-based to content-based ones which exploit the data itself for improving retrieval accuracy but thus far lack a specialized test collection. In this paper, we build and release the first test collection for ad hoc content-based dataset retrieval, where content-oriented dataset queries and content-based relevance judgments are annotated by human experts who are assisted with a dashboard designed specifically for comprehensively and conveniently browsing both the metadata and data of a dataset. We conduct extensive experiments on the test collection to analyze its difficulty and provide insights into the underlying task.|自组织数据集检索是信息检索领域的一个研究热点。方法和系统正在从基于元数据向基于内容的方法和系统演变，这些方法和系统利用数据本身来提高检索的准确性，但迄今为止还缺乏专门的测试集合。本文构建并发布了第一个基于特定内容的数据集检索测试集合，其中面向内容的数据集查询和基于内容的相关性判断由人类专家进行注释，并辅以一个专门为全面方便地浏览数据集的元数据和数据而设计的仪表板。我们进行了广泛的实验测试收集，以分析其难度，并提供深入了解潜在的任务。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=ACORDAR:+A+Test+Collection+for+Ad+Hoc+Content-Based+(RDF)+Dataset+Retrieval)|0| +|[RELISON: A Framework for Link Recommendation in Social Networks](https://doi.org/10.1145/3477495.3531730)|Javier SanzCruzado, Pablo Castells|Universidad Autónoma de Madrid, Madrid, Spain; University of Glasgow, Glasgow, United Kingdom|Link recommendation is an important and compelling problem at the intersection of recommender systems and online social networks. Given a user, link recommenders identify people in the platform the user might be interested in interacting with. We present RELISON, an extensible framework for running link recommendation experiments. The library provides a wide range of algorithms, along with tools for evaluating the produced recommendations. RELISON includes algorithms and metrics that consider the potential effect of recommendations on the properties of online social networks. For this reason, the library also implements network structure analysis metrics, community detection algorithms, and network diffusion simulation functionalities. The library code and documentation is available at https://github.com/ir-uam/RELISON.|在推荐系统和在线社交网络的交叉点上，链接推荐是一个重要且引人注目的问题。给定一个用户，链接推荐器会识别出平台中用户可能感兴趣的交互对象。我们提出了 RELISON，一个运行链路推荐实验的可扩展框架。该库提供了广泛的算法，以及用于评估生成的建议的工具。RELISON 包括一些算法和度量标准，这些算法和度量标准考虑了推荐对在线社交网络属性的潜在影响。出于这个原因，该库还实现了网络结构分析度量、社区检测算法和网络扩散模拟功能。图书馆代码及文件可于 https://github.com/ir-uam/relison 索取。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=RELISON:+A+Framework+for+Link+Recommendation+in+Social+Networks)|0| +|[The Istella22 Dataset: Bridging Traditional and Neural Learning to Rank Evaluation](https://doi.org/10.1145/3477495.3531740)|Domenico Dato, Sean MacAvaney, Franco Maria Nardini, Raffaele Perego, Nicola Tonellotto|University of Pisa, Pisa, Italy; University of Glasgow, Glasgow, United Kingdom; Istella, Milano, Italy; ISTI-CNR, Pisa, Italy|Neural approaches that use pre-trained language models are effective at various ranking tasks, such as question answering and ad-hoc document ranking. However, their effectiveness compared to feature-based Learning-to-Rank (LtR) methods has not yet been well-established. A major reason for this is because present LtR benchmarks that contain query-document feature vectors do not contain the raw query and document text needed for neural models. On the other hand, the benchmarks often used for evaluating neural models, e.g., MS MARCO, TREC Robust, etc., provide text but do not provide query-document feature vectors. In this paper, we present Istella22, a new dataset that enables such comparisons by providing both query/document text and strong query-document feature vectors used by an industrial search engine. The dataset consists of a comprehensive corpus of 8.4M web documents, a collection of query-document pairs including 220 hand-crafted features, relevance judgments on a 5-graded scale, and a set of 2,198 textual queries used for testing purposes. Istella22 enables a fair evaluation of traditional learning-to-rank and transfer ranking techniques on the same data. LtR models exploit the feature-based representations of training samples while pre-trained transformer-based neural rankers can be evaluated on the corresponding textual content of queries and documents. Through preliminary experiments on Istella22, we find that neural re-ranking approaches lag behind LtR models in terms of effectiveness. However, LtR models identify the scores from neural models as strong signals.|使用预训练语言模型的神经网络方法可以有效地完成各种排序任务，例如问题回答和即席文档排序。然而，与基于特征的学习到等级(LT)方法相比，它们的有效性还没有得到很好的证实。其中一个主要原因是，现有的包含查询文档特征向量的 LITR 基准测试不包含神经模型所需的原始查询和文档文本。另一方面，常用于评估神经模型的基准，如 MS MARCO、 TREC 鲁棒性等，提供文本但不提供查询文档特征向量。在本文中，我们介绍了新的数据集 Istella22，它通过提供工业搜索引擎使用的查询/文档文本和强查询-文档特征向量来实现这种比较。该数据集包括840万份网络文档的综合语料库、包括220个手工制作的功能的查询-文档对的集合、5级量表的相关性判断，以及用于测试目的的2198个文本查询。Istella22可以对传统的学习排序和转移排序技术在相同数据上进行公平的评估。LTR 模型利用训练样本的基于特征的表示，而预训练的基于变压器的神经排序器可以根据查询和文档的相应文本内容进行评估。通过在 Istella22上的初步实验，我们发现神经重新排序方法在有效性方面落后于 LTR 模型。然而，LTR 模型将神经模型的分数识别为强信号。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=The+Istella22+Dataset:+Bridging+Traditional+and+Neural+Learning+to+Rank+Evaluation)|0| |[Axiomatic Retrieval Experimentation with ir_axioms](https://doi.org/10.1145/3477495.3531743)|Alexander Bondarenko, Maik Fröbe, Jan Heinrich Reimer, Benno Stein, Michael Völske, Matthias Hagen|Bauhaus-Universität Weimar, Weimar, Germany; Martin-Luther-Universität Halle-Wittenberg, Halle, Germany|Axiomatic approaches to information retrieval have played a key role in determining basic constraints that characterize good retrieval models. Beyond their importance in retrieval theory, axioms have been operationalized to improve an initial ranking, to "guide" retrieval, or to explain some model's rankings. However, recent open-source retrieval frameworks like PyTerrier and Pyserini, which made it easy to experiment with sparse and dense retrieval models, have not included any retrieval axiom support so far. To fill this gap, we propose ir_axioms, an open-source Python framework that integrates retrieval axioms with common retrieval frameworks. We include reference implementations for 25 retrieval axioms, as well as components for preference aggregation, re-ranking, and evaluation. New axioms can easily be defined by implementing an abstract data type or by intuitively combining existing axioms with Python operators or regression. Integration with PyTerrier and ir_datasets makes standard retrieval models, corpora, topics, and relevance judgments---including those used at TREC---immediately accessible for axiomatic experimentation. Our experiments on the TREC Deep Learning tracks showcase some potential research questions that ir_axioms can help to address.|公理化的信息检索检索方法在确定优秀检索模型的基本约束条件方面发挥了关键作用。除了在检索理论中的重要性，公理已经被用来改进初始排名、“指导”检索或解释某些模型的排名。然而，最近的开放源代码检索框架，如 PyTerrier 和 Pyserini，使得对稀疏和密集的检索模型进行试验变得容易，到目前为止还没有包含任何检索公理支持。为了填补这个空白，我们提出 ir _ xioms，这是一个开放源码的 Python 框架，它将检索公理与通用检索框架集成在一起。我们包括用于25个检索公理的参考实现，以及用于偏好聚合、重新排序和评估的组件。通过实现一个抽象数据类型或直观地将现有公理与 Python 运算符或回归相结合，可以很容易地定义新公理。与 PyTerrier 和 ir _ data 集合的集成使得标准检索模型、语料库、主题和相关性判断——包括 TREC 使用的那些——可以立即用于公理化实验。我们在 TREC 深度学习轨道上的实验展示了一些 ir _ axioms 可以帮助解决的潜在研究问题。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Axiomatic+Retrieval+Experimentation+with+ir_axioms)|0| |[Knowledge Graph Question Answering Datasets and Their Generalizability: Are They Enough for Future Research?](https://doi.org/10.1145/3477495.3531751)|Longquan Jiang, Ricardo Usbeck|University Hamburg, Hamburg, Germany|Existing approaches on Question Answering over Knowledge Graphs (KGQA) have weak generalizability. That is often due to the standard i.i.d. assumption on the underlying dataset. Recently, three levels of generalization for KGQA were defined, namely i.i.d., compositional, zero-shot. We analyze 25 well-known KGQA datasets for 5 different Knowledge Graphs (KGs). We show that according to this definition many existing and online available KGQA datasets are either not suited to train a generalizable KGQA system or that the datasets are based on discontinued and out-dated KGs. Generating new datasets is a costly process and, thus, is not an alternative to smaller research groups and companies. In this work, we propose a mitigation method for re-splitting available KGQA datasets to enable their applicability to evaluate generalization, without any cost and manual effort. We test our hypothesis on three KGQA datasets, i.e., LC-QuAD, LC-QuAD 2.0 and QALD-9). Experiments on re-splitted KGQA datasets demonstrate its effectiveness towards generalizability. The code and a unified way to access 18 available datasets is online at https://github.com/semantic-systems/KGQA-datasets as well as https://github.com/semantic-systems/KGQA-datasets-generalization.|现有的知识图问答方法具有较弱的泛化能力。这通常是由于基础数据集上的标准 i.id 假设造成的。最近，定义了 KGQA 的三个推广水平，即标识、合成和零拍。我们分析了5个不同的知识图表(KG)的25个著名的 KGQA 数据集。我们表明，根据这个定义，许多现有的和在线可用的 KGQA 数据集要么不适合训练一个可推广的 KGQA 系统，要么数据集基于不连续的和过时的 KG。生成新的数据集是一个昂贵的过程，因此，不能替代较小的研究团体和公司。在这项工作中，我们提出了一个缓解方法，重新分裂可用的 KGQA 数据集，使其适用性评估一般化，没有任何成本和人工的努力。我们在三个 KGQA 数据集上检验我们的假设，即 LC-QuAD，LC-QuAD 2.0和 QALD-9)。通过对 KGQA 数据集的重新分割实验，证明了该算法的有效性。该代码和一个统一的方式访问18个可用的数据集是在线的 https://github.com/semantic-systems/kgqa-datasets 和 https://github.com/semantic-systems/kgqa-datasets-generalization。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Knowledge+Graph+Question+Answering+Datasets+and+Their+Generalizability:+Are+They+Enough+for+Future+Research?)|0| |[Golden Retriever: A Real-Time Multi-Modal Text-Image Retrieval System with the Ability to Focus](https://doi.org/10.1145/3477495.3531666)|Florian Schneider, Chris Biemann|Universität Hamburg, Hamburg, Germany|In this work, we present the Golden Retriever, a system leveraging state-of-the-art visio-linguistic models (VLMs) for real-time text-image retrieval. The unique feature of our system is that it can focus on words contained in the textual query, i.e., locate and high-light them within retrieved images. An efficient two-stage process implements real-time capability and the ability to focus. Therefore, we first drastically reduce the number of images processed by a VLM. Then, in the second stage, we rank the images and highlight the focussed word using the outputs of a VLM. Further, we introduce a new and efficient algorithm based on the idea of TF-IDF to retrieve images for short textual queries. One of multiple use cases where we employ the Golden Retriever is a language learner scenario, where visual cues for "difficult" words within sentences are provided to improve a user's reading comprehension. However, since the backend is completely decoupled from the frontend, the system can be integrated into any other application where images must be retrieved fast. We demonstrate the Golden Retriever with screenshots of a minimalistic user interface.|在这项工作中，我们介绍了金毛寻回犬，一个利用最先进的视觉语言模型(vlm)进行实时文本图像检索的系统。我们的系统的独特之处在于它可以专注于文本查询中包含的单词，也就是说，在检索到的图像中定位并高亮显示它们。一个有效的两阶段过程实现实时能力和集中能力。因此，我们首先大幅度减少图像处理的 VLM 数量。然后，在第二阶段，我们使用 VLM 的输出对图像进行排序并突出显示聚焦词。在此基础上，提出了一种基于 TF-IDF 思想的短文本查询图像检索算法。我们使用金毛寻回犬的多个用例之一是语言学习者场景，其中提供句子中“难”词的视觉提示，以提高用户的阅读理解。但是，由于后端与前端完全解耦，因此系统可以集成到任何其他必须快速检索图像的应用程序中。我们用一个极简的用户界面截图来演示这个金毛寻回犬。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Golden+Retriever:+A+Real-Time+Multi-Modal+Text-Image+Retrieval+System+with+the+Ability+to+Focus)|0| -|[ZeroMatcher: A Cost-Off Entity Matching System](https://doi.org/10.1145/3477495.3531661)|Congcong Ge, Xiaocan Zeng, Lu Chen, Yunjun Gao|Zhejiang University, Ningbo, China; Zhejiang University, Hangzhou, China|Entity Matching (EM) aims to find data instances from different sources that refer to the same real-world entity. The existing EM techniques can be either costly or tailored for a specific data type. We present ZeroMatcher, a cost-off entity matching system, which supports (i) handling EM tasks with different data types, including relational tables and knowledge graphs; (ii) keeping its EM performance always competitive by enabling the sub-modules to be updated in a lightweight manner, thus reducing development costs; and (iii) performing EM without human annotations to further slash the labor costs. First, ZeroMatcher automatically suggests users a set of appropriate modules for EM according to the data types of the input datasets. Users could specify the modules for the subsequent EM process according to their preferences. Alternatively, users are able to customize the modules of ZeroMatcher. Then, the system proceeds to the EM task, where users can track the entire EM process and monitor the memory usage changes in real-time. When the EM process is completed, ZeroMatcher visualizes the EM results from different aspects to ease the understanding for users. Finally, ZeroMatcher provides EM results evaluation, enabling users to compare the effectiveness among different parameter settings.|实体匹配(Entity Matching，EM)的目标是从引用相同实体的不同数据源中找到数据实例。现有的 EM 技术要么成本高昂，要么针对特定的数据类型进行量身定制。我们提出了 ZeroMatcher，一个成本实体匹配系统，它支持(i)处理不同数据类型的 EM 任务，包括关系表和知识图; (ii)保持其 EM 性能始终具有竞争力，使子模块以轻量级的方式更新，从而降低开发成本; 以及(iii)执行 EM 而不需要人工注释，以进一步降低劳动力成本。首先，ZeroMatcher 根据输入数据集的数据类型自动为用户建议一组适合 EM 的模块。用户可以根据自己的偏好为后续 EM 过程指定模块。或者，用户可以自定义 ZeroMatcher 的模块。然后，系统继续执行 EM 任务，在这个任务中，用户可以跟踪整个 EM 进程并实时监视内存使用的变化。当 EM 过程完成后，ZeroMatcher 从不同方面可视化 EM 结果，以便于用户理解。最后，ZeroMatcher 提供 EM 结果评估，使用户能够比较不同参数设置之间的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=ZeroMatcher:+A+Cost-Off+Entity+Matching+System)|0| +|[ZeroMatcher: A Cost-Off Entity Matching System](https://doi.org/10.1145/3477495.3531661)|Congcong Ge, Xiaocan Zeng, Lu Chen, Yunjun Gao|Zhejiang University, Hangzhou, China; Zhejiang University, Ningbo, China|Entity Matching (EM) aims to find data instances from different sources that refer to the same real-world entity. The existing EM techniques can be either costly or tailored for a specific data type. We present ZeroMatcher, a cost-off entity matching system, which supports (i) handling EM tasks with different data types, including relational tables and knowledge graphs; (ii) keeping its EM performance always competitive by enabling the sub-modules to be updated in a lightweight manner, thus reducing development costs; and (iii) performing EM without human annotations to further slash the labor costs. First, ZeroMatcher automatically suggests users a set of appropriate modules for EM according to the data types of the input datasets. Users could specify the modules for the subsequent EM process according to their preferences. Alternatively, users are able to customize the modules of ZeroMatcher. Then, the system proceeds to the EM task, where users can track the entire EM process and monitor the memory usage changes in real-time. When the EM process is completed, ZeroMatcher visualizes the EM results from different aspects to ease the understanding for users. Finally, ZeroMatcher provides EM results evaluation, enabling users to compare the effectiveness among different parameter settings.|实体匹配(Entity Matching，EM)的目标是从引用相同实体的不同数据源中找到数据实例。现有的 EM 技术要么成本高昂，要么针对特定的数据类型进行量身定制。我们提出了 ZeroMatcher，一个成本实体匹配系统，它支持(i)处理不同数据类型的 EM 任务，包括关系表和知识图; (ii)保持其 EM 性能始终具有竞争力，使子模块以轻量级的方式更新，从而降低开发成本; 以及(iii)执行 EM 而不需要人工注释，以进一步降低劳动力成本。首先，ZeroMatcher 根据输入数据集的数据类型自动为用户建议一组适合 EM 的模块。用户可以根据自己的偏好为后续 EM 过程指定模块。或者，用户可以自定义 ZeroMatcher 的模块。然后，系统继续执行 EM 任务，在这个任务中，用户可以跟踪整个 EM 进程并实时监视内存使用的变化。当 EM 过程完成后，ZeroMatcher 从不同方面可视化 EM 结果，以便于用户理解。最后，ZeroMatcher 提供 EM 结果评估，使用户能够比较不同参数设置之间的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=ZeroMatcher:+A+Cost-Off+Entity+Matching+System)|0| |[QFinder: A Framework for Quantity-centric Ranking](https://doi.org/10.1145/3477495.3531672)|Satya Almasian, Milena Bruseva, Michael Gertz|Heidelberg University, Heidelberg, Germany|Quantities shape our understanding of measures and values, and they are an important means to communicate the properties of objects. Often, search queries contain numbers as retrieval units, e.g., "iPhone that costs less than 800 Euros''. Yet, modern search engines lack a proper understanding of numbers and units. In queries and documents, search engines handle them as normal keywords and therefore are ignorant of relative conditions between numbers, such as greater than or less than, or, more generally, the numerical proximity of quantities. In this work, we demonstrate QFinder, our quantity-centric framework for ranking search results for queries with quantity constraints. We also open-source our new ranking method as an Elasticsearch plug-in for future use. Our demo is available at: https://qfinder.ifi.uni-heidelberg.de/|数量塑造了我们对度量和价值的理解，它们是沟通对象属性的重要手段。通常，搜索查询包含数字作为检索单位，例如，“成本低于800欧元的 iPhone”。然而，现代搜索引擎缺乏对数字和单位的正确理解。在查询和文档中，搜索引擎将它们作为普通关键字处理，因此不知道数字之间的相对条件，例如大于或小于，或者更一般地说，数量的数值接近性。在这项工作中，我们展示了 QFinder，我们的数量为中心的框架排序查询搜索结果的数量约束。我们还将我们的新排名方法作为一个 Elasticsearch 插件开源以供将来使用。我们的演示可以在 https://qfinder.ifi.uni-heidelberg.de/下载|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=QFinder:+A+Framework+for+Quantity-centric+Ranking)|0| -|[CHERCHE: A New Tool to Rapidly Implement Pipelines in Information Retrieval](https://doi.org/10.1145/3477495.3531695)|Raphaël Sourty, José G. Moreno, Lynda Tamine, FrançoisPaul Servant|Université Paul Sabatier, IRIT, Toulouse, France; Université Paul Sabatier, IRIT & Renault, Toulouse, France; Renault, Boulogne-Billancourt, France|In this demo paper, we present a new open-source python module for building information retrieval pipelines with transformers namely CHERCHE. Our aim is to propose an easy to plug tool capable to execute, simple but strong, state-of-the-art information retrieval models. To do so, we have integrated classical models based on lexical matching but also recent models based on semantic matching. Indeed, a large number of models available on public hubs can be now tested on information retrieval tasks with only a few lines. CHERCHE is oriented to newcomers into the neural information retrieval field that want to use transformer-based models in small collections without struggling with heavy tools. The code and documentation of CHERCHE is public available at https://github.com/raphaelsty/cherche|在本演示文件中，我们提出了一个新的开源 python 模块，用于建设带有变压器的信息检索管道，即 CHERCHE。我们的目标是提出一个容易插入的工具，能够执行，简单但强大，国家的最先进的信息检索模型。为此，我们集成了基于词汇匹配的经典模型和基于语义匹配的最新模型。事实上，在公共集线器上提供的大量模型，现在只需几行代码就可以在信息检索任务上进行测试。CHERCHE 面向的是神经信息检索领域的新手，他们希望在小型集合中使用基于变压器的模型，而无需使用笨重的工具。CHERCHE 的代码和文档可在 https://github.com/raphaelsty/CHERCHE 查阅|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=CHERCHE:+A+New+Tool+to+Rapidly+Implement+Pipelines+in+Information+Retrieval)|0| +|[CHERCHE: A New Tool to Rapidly Implement Pipelines in Information Retrieval](https://doi.org/10.1145/3477495.3531695)|Raphaël Sourty, José G. Moreno, Lynda Tamine, FrançoisPaul Servant|Université Paul Sabatier, IRIT & Renault, Toulouse, France; Université Paul Sabatier, IRIT, Toulouse, France; Renault, Boulogne-Billancourt, France|In this demo paper, we present a new open-source python module for building information retrieval pipelines with transformers namely CHERCHE. Our aim is to propose an easy to plug tool capable to execute, simple but strong, state-of-the-art information retrieval models. To do so, we have integrated classical models based on lexical matching but also recent models based on semantic matching. Indeed, a large number of models available on public hubs can be now tested on information retrieval tasks with only a few lines. CHERCHE is oriented to newcomers into the neural information retrieval field that want to use transformer-based models in small collections without struggling with heavy tools. The code and documentation of CHERCHE is public available at https://github.com/raphaelsty/cherche|在本演示文件中，我们提出了一个新的开源 python 模块，用于建设带有变压器的信息检索管道，即 CHERCHE。我们的目标是提出一个容易插入的工具，能够执行，简单但强大，国家的最先进的信息检索模型。为此，我们集成了基于词汇匹配的经典模型和基于语义匹配的最新模型。事实上，在公共集线器上提供的大量模型，现在只需几行代码就可以在信息检索任务上进行测试。CHERCHE 面向的是神经信息检索领域的新手，他们希望在小型集合中使用基于变压器的模型，而无需使用笨重的工具。CHERCHE 的代码和文档可在 https://github.com/raphaelsty/CHERCHE 查阅|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=CHERCHE:+A+New+Tool+to+Rapidly+Implement+Pipelines+in+Information+Retrieval)|0| |[Arm: Efficient Learning of Neural Retrieval Models with Desired Accuracy by Automatic Knowledge Amalgamation](https://doi.org/10.1145/3477495.3531664)|Linzhu Yu, Dawei Jiang, Ke Chen, Lidan Shou|Zhejiang University, Hangzhou, China|In recent years, there has been increasing interest in adopting published neural retrieval models learned from corpora for text retrieval. Although these models achieve excellent retrieval performance, in terms of popular accuracy metrics, on datasets they have been trained, their performance on new text data might degrade. To obtain the desired retrieval performance on both the data used in training and the latest data collected after training, the simple approach of learning a new model from both datasets is not always feasible since the annotated dataset used in training is often not published along with the learned model. Knowledge amalgamation (KA) is an emerging technique to deal with this problem of inaccessibility of data used in previous training. KA learns a new model (called a student model) from new data by reusing (called amalgamating) a number of trained models (called teacher models) instead of accessing the teachers' original training data. However, in order to efficiently learn an accurate student model, the classical KA approach requires manual selection of an appropriate subset of teacher models for amalgamation. This manual procedure for selecting teacher models prevents the classical KA from being scaled to retrieval tasks for which a large number of candidate teacher models are ready to be reused. This paper presents Arm, an intelligent system for efficiently learning a neural retrieval model with the desired accuracy on incoming data by automatically amalgamating a subset of teacher models (called a teacher model combination or simply combination ) among a large number of teacher models. o filter combinations that fail to produce accurate student models, Arm employs Bayesian optimization to derive an accuracy prediction model based on sampled amalgamation tasks. Then, Arm uses the derived prediction model to exclude unqualified combinations without training the rest combinations. To speed up training, Arm introduces a cost model that picks the teacher model combination with the minimal training cost among all qualified teacher model combinations to produce the final student model. This paper will demonstrate the major workflow of Arm and present the produced student models to users.|近年来，从语料库中学习的已发表的神经检索模型越来越多地被用于文本检索。尽管这些模型在它们已经训练过的数据集上获得了优异的检索性能，但是它们在新文本数据上的性能可能会下降。为了获得对训练中使用的数据和训练后收集的最新数据的期望检索性能，从两个数据集中学习新模型的简单方法并不总是可行的，因为训练中使用的注释数据集通常不与学习模型一起发布。知识融合(KA)是一种新兴的技术，以解决这一问题的数据无法访问以往的训练使用。KA 从新的数据中学习新的模型(称为学生模型) ，方法是重用(称为合并)一些经过训练的模型(称为教师模型) ，而不是访问教师的原始培训数据。然而，为了有效地学习一个准确的学生模型，经典的 KA 方法需要手动选择合适的教师模型子集进行合并。这个选择教师模型的手工程序阻止了经典的 KA 被缩放到检索任务，其中大量的候选教师模型可以被重用。本文提出了一个智能系统 Arm，它通过在大量的教师模型中自动合并一个教师模型子集(称为教师模型组合或简单组合)来有效地学习一个神经检索模型，该模型对输入数据具有期望的准确性。O 过滤器组合不能产生准确的学生模型，Arm 使用贝叶斯优化得到一个基于采样融合任务的精度预测模型。然后，利用导出的预测模型排除不合格组合，而不对剩余组合进行训练。为了加速培训，Arm 引入了一个成本模型，在所有合格的教师模型组合中选择教师模型组合和最小的培训成本，从而产生最终的学生模型。本文将演示 Arm 的主要工作流程，并将生成的学生模型提供给用户。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Arm:+Efficient+Learning+of+Neural+Retrieval+Models+with+Desired+Accuracy+by+Automatic+Knowledge+Amalgamation)|0| |[An Auto Encoder-based Dimensionality Reduction Technique for Efficient Entity Linking in Business Phone Conversations](https://doi.org/10.1145/3477495.3536322)|Md. Tahmid Rahman Laskar, Cheng Chen, Jonathan Johnston, XueYong Fu, Shashi Bhushan TN, Simon CorstonOliver|Dialpad Canada Inc., Vancouver, BC, Canada|An entity linking system links named entities in a text to their corresponding entries in a knowledge base. In recent years, building an entity linking system that leverages the transformer architecture has gained lots of attention. However, deploying a transformer-based neural entity linking system in industrial production environments in a limited resource setting is a challenging task. In this work, we present an entity linking system that leverages a transformer-based BERT encoder (the BLINK model) to connect the product and organization type entities in business phone conversations to their corresponding Wikipedia entries. We propose a dimensionality reduction technique via utilizing an auto encoder that can effectively compress the dimension of the pre-trained BERT embeddings to 256 from the original size of 1024. This allows our entity linking system to significantly optimize the space requirement when deployed in a resource limited cloud machine while reducing the inference time along with retaining high accuracy.|连接系统的实体将文本中的命名实体链接到知识库中相应的条目。近年来，利用变压器体系结构构建实体连接系统引起了人们的广泛关注。然而，在资源有限的工业生产环境中部署一个基于变压器的神经实体连接系统是一个具有挑战性的任务。在这项工作中，我们提出了一个实体链接系统，该系统利用基于转换器的 BERT 编码器(BLINK 模型)将商务电话会话中的产品和组织类型实体连接到相应的 Wikipedia 条目。我们提出了一种降维技术，通过使用自动编码器，可以有效地将预先训练的 BERT 嵌入的尺寸从原来的1024压缩到256。这使得我们的实体连接系统在部署在资源有限的云计算机中时，能够显著优化空间需求，同时减少推理时间，并保持高精度。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=An+Auto+Encoder-based+Dimensionality+Reduction+Technique+for+Efficient+Entity+Linking+in+Business+Phone+Conversations)|0| |[Applications and Future of Dense Retrieval in Industry](https://doi.org/10.1145/3477495.3536324)|Yubin Kim|Etsy, Inc., Brooklyn, NY, USA|Large-scale search engines are often designed as tiered systems with at least two layers. The L1 candidate retrieval layer efficiently generates a subset of potentially relevant documents (typically ~1000 documents) from a corpus many orders of magnitude larger in size. L1 systems emphasize efficiency and are designed to maximize recall. The L2 re-ranking layer uses a more computationally expensive, but more accurate model (e.g. learning-to-rank or neural model) to re-rank the candidates generated by L1 in order to maximize precision of the final result list. Traditionally, candidate retrieval was performed with an inverted index data structure, with exact lexical matching. Candidates are ordered by a dot-product-like scoring function f(q,d) where q and d are sparse vectors containing token weights, typically derived from the token's frequency in the document/query and corpus. The inverted index enables sub-linear ranking of the documents. Due to the sparse vector representation of the documents and queries, lexical match retrieval systems have also been called sparse retrieval. To contrast, dense retrieval represents queries and documents by embedding the text into lower dimensional dense vectors. Candidate documents are scored based on the distance between the query and document embedding vectors. Practically, the similarity computations are made efficiently with approximate k-nearest neighbours (ANN) systems. In this panel, we bring together experts in dense retrieval across multiple industry applications, including web search, enterprise and personal search, e-commerce, and out-of-domain retrieval.|大型搜索引擎通常被设计成至少有两层的分层系统。第一语言候选检索层有效地从一个数量级更大的语料库中生成一个可能相关的文档子集(通常是1000个文档)。L1系统强调效率，旨在最大限度地提高召回率。L2重新排序层使用一个计算更加昂贵，但更加精确的模型(例如学习到排序或神经模型)来重新排序 L1产生的候选人，以最大限度地提高最终结果列表的精确度。传统的候选检索采用倒排索引数据结构，并且采用精确的词法匹配。候选者按照类似点乘积的评分函数 f (q，d)排序，其中 q 和 d 是包含令牌权重的稀疏向量，通常来自文档/查询和语料库中令牌的频率。倒排索引允许对文档进行次线性排序。由于文档和查询的稀疏向量表示，词汇匹配检索系统也被称为稀疏检索。相比之下，密集检索通过将文本嵌入低维密集向量来表示查询和文档。候选文档根据查询和文档嵌入向量之间的距离进行评分。在实际应用中，利用近似 k 最近邻(ANN)系统可以有效地进行相似度计算。在这个专题讨论小组中，我们汇集了跨多个行业应用的密集检索方面的专家，包括网络搜索、企业和个人搜索、电子商务和域外检索。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Applications+and+Future+of+Dense+Retrieval+in+Industry)|0| -|[Flipping the Script: Inverse Information Seeking Dialogues for Market Research](https://doi.org/10.1145/3477495.3536326)|Josh Seltzer, Kathy Cheng, Shi Zong, Jimmy Lin|Nexxt Intelligence, Toronto, Canada; University of Waterloo, Waterloo, Canada|Information retrieval has traditionally been framed in terms of searching and extracting information from mostly static resources. Interactive information retrieval (IIR) has widened the scope, with interactive dialogues largely playing the role of clarifying (i.e., making explicit, and/or refining) the information search space. Informed by market research practices, we seek to reframe IIR as a process of eliciting novel information from human interlocutors, with a chatbot-inspired virtual agent playing the role of an interviewer. This reframing flips conventional IIR into what we call an inverse information seeking dialogue, wherein the virtual agent recurrently extracts information from human utterances and poses questions intended to elicit related information. In this work, we introduce and provide a formal definition of an inverse information seeking agent, outline some of its unique challenges, and propose our novel framework to tackle this problem based on techniques from natural language processing (NLP) and IIR.|传统上，信息检索的框架是从大多数静态资源中搜索和提取信息。交互式信息检索(IIR)扩大了搜索范围，交互式对话主要扮演澄清(即明确和/或完善)信息搜索空间的角色。通过市场研究实践，我们试图将 IIR 重新定义为从人类对话者那里获取新信息的过程，由聊天机器人启发的虚拟代理扮演访问者的角色。这种重构将传统的 IIR 翻转为我们所说的反向信息寻求对话，在这种对话中，虚拟代理不断地从人类的话语中提取信息，并提出旨在引出相关信息的问题。本文首先介绍并给出了反向信息搜索代理的形式化定义，概述了它所面临的一些独特挑战，并提出了基于自然语言处理(NLP)和 IIR 技术的反向信息搜索代理框架。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Flipping+the+Script:+Inverse+Information+Seeking+Dialogues+for+Market+Research)|0| +|[Flipping the Script: Inverse Information Seeking Dialogues for Market Research](https://doi.org/10.1145/3477495.3536326)|Josh Seltzer, Kathy Cheng, Shi Zong, Jimmy Lin|University of Waterloo, Waterloo, Canada; Nexxt Intelligence, Toronto, Canada|Information retrieval has traditionally been framed in terms of searching and extracting information from mostly static resources. Interactive information retrieval (IIR) has widened the scope, with interactive dialogues largely playing the role of clarifying (i.e., making explicit, and/or refining) the information search space. Informed by market research practices, we seek to reframe IIR as a process of eliciting novel information from human interlocutors, with a chatbot-inspired virtual agent playing the role of an interviewer. This reframing flips conventional IIR into what we call an inverse information seeking dialogue, wherein the virtual agent recurrently extracts information from human utterances and poses questions intended to elicit related information. In this work, we introduce and provide a formal definition of an inverse information seeking agent, outline some of its unique challenges, and propose our novel framework to tackle this problem based on techniques from natural language processing (NLP) and IIR.|传统上，信息检索的框架是从大多数静态资源中搜索和提取信息。交互式信息检索(IIR)扩大了搜索范围，交互式对话主要扮演澄清(即明确和/或完善)信息搜索空间的角色。通过市场研究实践，我们试图将 IIR 重新定义为从人类对话者那里获取新信息的过程，由聊天机器人启发的虚拟代理扮演访问者的角色。这种重构将传统的 IIR 翻转为我们所说的反向信息寻求对话，在这种对话中，虚拟代理不断地从人类的话语中提取信息，并提出旨在引出相关信息的问题。本文首先介绍并给出了反向信息搜索代理的形式化定义，概述了它所面临的一些独特挑战，并提出了基于自然语言处理(NLP)和 IIR 技术的反向信息搜索代理框架。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Flipping+the+Script:+Inverse+Information+Seeking+Dialogues+for+Market+Research)|0| |[Information Ecosystem Threats in Minoritized Communities: Challenges, Open Problems and Research Directions](https://doi.org/10.1145/3477495.3536327)|Shiri DoriHacohen, Scott A. Hale|Meedan & University of Oxford, San Francisco, CA, USA; University of Connecticut & AuCoDe, Storrs, CT, USA|Journalists, fact-checkers, academics, and community media are overwhelmed in their attempts to support communities suffering from gender-, race- and ethnicity-targeted information ecosystem threats, including but not limited to misinformation, hate speech, weaponized controversy and online-to-offline harassment. Yet, for a plethora of reasons, minoritized groups are underserved by current approaches to combat such threats. In this panel, we will present and discuss the challenges and open problems facing such communities and the researchers hoping to serve them. We will also discuss the current state-of-the-art as well as the most promising future directions, both within IR specifically, across Computer Science more broadly, as well as that requiring transdisciplinary and cross-sectoral collaborations. The panel will attract both IR practitioners and researchers and include at least one panelist outside of IR, with unique expertise in this space.|记者、事实核查员、学者和社区媒体在试图支持遭受针对性别、种族和民族的信息生态系统威胁的社区时，不堪重负，这些威胁包括但不限于错误信息、仇恨言论、武器化的争议和线上到线下的骚扰。然而，由于种种原因，少数群体在目前应对这些威胁的方法中得不到充分的服务。在这个小组中，我们将介绍和讨论这些社区面临的挑战和公开的问题，以及研究人员希望为他们服务的问题。我们还将讨论当前最先进的技术，以及最有前途的未来方向，既包括在信息研究领域，也包括更广泛的计算机科学领域，还包括需要跨学科和跨部门合作的领域。该小组将吸引国际关系从业人员和研究人员，并包括至少一个国际关系以外的小组成员，在这个领域具有独特的专业知识。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Information+Ecosystem+Threats+in+Minoritized+Communities:+Challenges,+Open+Problems+and+Research+Directions)|0| |[Extractive Search for Analysis of Biomedical Texts](https://doi.org/10.1145/3477495.3536328)|Daniel Clothiaux, Ravi Starzl|Bioplx & Carnegie Mellon University, Boulder, CO, USA|Extractive search has been used to create datasets matching queries and syntactic patterns, but less attention has been paid on what to do with those datasets. We present a two-stage system targeted towards biomedical texts. First, it creates custom datasets using a powerful mix of keyword and syntactic matching. We then return lists of related words, provide semantic search, train a large language model, a synthetic data based QA model, a summarization model over those results, and so on. These are then used in downstream biomedical work.|提取搜索已被用于创建匹配查询和语法模式的数据集，但对如何处理这些数据集的关注较少。我们提出了一个针对生物医学文本的两阶段系统。首先，它使用关键字和语法匹配的强大组合创建自定义数据集。然后，我们返回相关词汇的列表，提供语义搜索，训练一个大型语言模型，一个基于综合数据的 QA 模型，这些结果的汇总模型，等等。这些然后用于下游的生物医学工作。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Extractive+Search+for+Analysis+of+Biomedical+Texts)|0| |[Recent Advances in Retrieval-Augmented Text Generation](https://doi.org/10.1145/3477495.3532682)|Deng Cai, Yan Wang, Lemao Liu, Shuming Shi|The Chinese University of Hong Kong, Hong Kong, China; Tencent AI Lab, Shenzhen, China|Recently retrieval-augmented text generation has achieved state-of-the-art performance in many NLP tasks and has attracted increasing attention of the NLP and IR community, this tutorial thereby aims to present recent advances in retrieval-augmented text generation comprehensively and comparatively. It firstly highlights the generic paradigm of retrieval-augmented text generation, then reviews notable works for different text generation tasks including dialogue generation, machine translation, and other generation tasks, and finally points out some limitations and shortcomings to facilitate future research.|近年来，检索增强文本生成技术在许多自然语言处理任务中都取得了很好的效果，引起了自然语言处理和信息检索领域的广泛关注，本教程旨在全面、比较地介绍检索增强文本生成技术的最新进展。首先介绍了检索增强文本生成的一般范式，然后回顾了不同文本生成任务(包括对话生成、机器翻译和其他生成任务)中值得注意的工作，最后指出了一些局限性和不足，以利于今后的研究。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Recent+Advances+in+Retrieval-Augmented+Text+Generation)|0| @@ -375,52 +375,52 @@ |[Pre-Training for Mathematics-Aware Retrieval](https://doi.org/10.1145/3477495.3531680)|Anja Reusch|Technische Universität Dresden, Dresden, Germany|Mathematical formulas are an important tool to concisely communicate ideas in science and education, used to clarify descriptions, calculations or derivations. When searching in scientific literature, mathematical notation, which is often written using the LATEX notation, therefore plays a crucial role that should not be neglected. The task of mathematics-aware information retrieval is to retrieve relevant passages given a query or question, which both can include natural language and mathematical formulas. As in many domains that rely on Natural Language Understanding, transformer-based models are now dominating the field of information retrieval [3]. Apart from their size and the transformerencoder architecture, pre-training is considered to be a key factor for the high performance of these models. It has also been shown that domain-adaptive pre-training improves their performance on down-stream tasks even further [2] especially when the vocabulary overlap between pre-training and in-domain data is low. This is also the case for the domain of mathematical documents.|数学公式是在科学和教育中简明地传达思想的重要工具，用于澄清描述、计算或推导。在搜索科学文献时，数学符号(通常使用 LATEX 符号书写)起着不容忽视的关键作用。具有数学意识的信息检索的任务是检索给出查询或问题的相关段落，这些段落既可以包括自然语言，也可以包括数学公式。正如许多依赖于自然语言理解的领域一样，基于转换器的模型目前在信息检索领域占据主导地位。除了它们的大小和变压器编码器的架构，预训练被认为是这些模型的高性能的一个关键因素。研究还表明，领域自适应预训练可以进一步提高他们在下游任务中的表现，尤其是当预训练和领域内数据的词汇重叠度较低时。数学文献领域也是如此。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Pre-Training+for+Mathematics-Aware+Retrieval)|0| |[Explainable Conversational Question Answering over Heterogeneous Sources](https://doi.org/10.1145/3477495.3531688)|Philipp Christmann|Max Planck Institute for Informatics & Saarland University, Saarbrücken, Germany|State-of-the-art conversational question answering (ConvQA) operates over homogeneous sources of information: either a knowledge base (KB), or a text corpus, or a collection of tables. This inherently limits the answer coverage of ConvQA systems. Therefore, during my PhD, we would like to tap into heterogeneous sources for answering conversational questions. Further, we plan to investigate the explainability of such ConvQA systems, to identify what helps users in understanding the answer derivation process.|最先进的会话问答(ConvQA)操作于同质的信息源: 知识库(KB)、文本语料库或表格集合。这固有地限制了 ConvQA 系统的应答覆盖范围。因此，在我攻读博士学位期间，我们希望利用不同的资源来回答会话中的问题。此外，我们计划调查这样的 ConvQA 系统的可解释性，以确定什么有助于用户理解的答案推导过程。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Explainable+Conversational+Question+Answering+over+Heterogeneous+Sources)|0| |[KA-Recsys: Patient Focused Knowledge Appropriate Health Recommender System](https://doi.org/10.1145/3477495.3531687)|Khushboo Thaker||||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=KA-Recsys:+Patient+Focused+Knowledge+Appropriate+Health+Recommender+System)|0| -|[Bilateral Self-unbiased Learning from Biased Implicit Feedback](https://doi.org/10.1145/3477495.3531946)|Jaewoong Lee, Seongmin Park, Joonseok Lee, Jongwuk Lee|Sungkyunkwan Univ, Dept Elect & Comp Engn, Seoul, South Korea; Sungkyunkwan Univ, Dept Artificial Intelligence, Seoul, South Korea|Implicit feedback has been widely used to build commercial recommender systems. Because observed feedback represents users' click logs, there is a semantic gap between true relevance and observed feedback. More importantly, observed feedback is usually biased towards popular items, thereby overestimating the actual relevance of popular items. Although existing studies have developed unbiased learning methods using inverse propensity weighting (IPW) or causal reasoning, they solely focus on eliminating the popularity bias of items. In this paper, we propose a novel unbiased recommender learning model, namely BIlateral SElf-unbiased Recommender (BISER), to eliminate the exposure bias of items caused by recommender models. Specifically, BISER consists of two key components: (i) self-inverse propensity weighting (SIPW) to gradually mitigate the bias of items without incurring high computational costs; and (ii) bilateral unbiased learning (BU) to bridge the gap between two complementary models in model predictions, i.e., user- and item-based autoencoders, alleviating the high variance of SIPW. Extensive experiments show that BISER consistently outperforms state-of-the-art unbiased recommender models over several datasets, including Coat, Yahoo! R3, MovieLens, and CiteULike.|隐式反馈已被广泛用于构建商业推荐系统。因为观察到的反馈代表用户的点击日志，所以在真正的相关性和观察到的反馈之间存在语义差距。更重要的是，观察到的反馈通常偏向于流行项目，从而高估了流行项目的实际相关性。虽然现有的研究已经开发出使用反倾向加权(IPW)或因果推理的无偏学习方法，但他们只关注于消除项目的流行偏差。本文提出了一种新的无偏推荐学习模型，即双边自我无偏推荐模型(BISER) ，以消除由推荐模型引起的项目暴露偏差。具体而言，BISER 由两个关键组成部分组成: (i)自逆倾向加权(SIPW) ，以逐渐减轻项目的偏差，而不会产生高计算成本; (ii)双边无偏学习(BU) ，以弥补模型预测中两个互补模型之间的差距，即用户和基于项目的自动编码器，减轻 SIPW 的高方差。大量的实验表明，BISER 始终优于几个数据集，包括 Coat，Yahoo! R3，MovieLens 和 CiteULike 的最先进的无偏推荐模型。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Bilateral+Self-unbiased+Learning+from+Biased+Implicit+Feedback)|0| -|[Why do Semantically Unrelated Categories Appear in the Same Session?: A Demand-aware Method](https://doi.org/10.1145/3477495.3531806)|Liqi Yang, Linhao Luo, Xiaofeng Zhang, Fengxin Li, Xinni Zhang, Zelin Jiang, Shuai Tang|Harbin Institute of Technology, Shenzhen, Shenzhen, China; China Merchants Securities Co., Ltd, Shenzhen, China|Session-based recommendation has recently attracted more and more research efforts. Most existing approaches are intuitively proposed to discover users' potential preferences or interests from the anonymous session data. This apparently ignores the fact that these sequential behavior data usually reflect session user's potential demand, i.e., a semantic level factor, and therefore how to estimate underlying demands from a session has become a challenging task. To tackle the aforementioned issue, this paper proposes a novel demand-aware graph neural network model. Particularly, a demand modeling component is designed to extract the underlying multiple demands of each session. Then, the demand-aware graph neural network is designed to first construct session demand graphs and then learn the demand-aware item embeddings to make the recommendation. The mutual information loss is further designed to enhance the quality of the learnt embeddings. Extensive experiments have been performed on two real-world datasets and the proposed model achieves the SOTA model performance.|近年来，基于会话的推荐引起了越来越多的研究者的关注。现有的大多数方法都是直观地从匿名会话数据中发现用户的潜在偏好或兴趣。这显然忽略了这样一个事实，即这些顺序行为数据通常反映会话用户的潜在需求，即语义级因素，因此如何估计会话的潜在需求已经成为一个具有挑战性的任务。为了解决上述问题，本文提出了一种新的需求感知图神经网络模型。特别地，需求建模组件被设计用于提取每个会话的底层多个需求。然后，设计需求感知图神经网络，首先构造会话需求图，然后学习需求感知项嵌入，进行推荐。进一步设计了互信息损失，提高了学习嵌入的质量。在两个实际数据集上进行了广泛的实验，所提出的模型达到了 SOTA 模型的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Why+do+Semantically+Unrelated+Categories+Appear+in+the+Same+Session?:+A+Demand-aware+Method)|0| +|[Bilateral Self-unbiased Learning from Biased Implicit Feedback](https://doi.org/10.1145/3477495.3531946)|Jaewoong Lee, Seongmin Park, Joonseok Lee, Jongwuk Lee|Sungkyunkwan Univ, Dept Artificial Intelligence, Seoul, South Korea; Sungkyunkwan Univ, Dept Elect & Comp Engn, Seoul, South Korea|Implicit feedback has been widely used to build commercial recommender systems. Because observed feedback represents users' click logs, there is a semantic gap between true relevance and observed feedback. More importantly, observed feedback is usually biased towards popular items, thereby overestimating the actual relevance of popular items. Although existing studies have developed unbiased learning methods using inverse propensity weighting (IPW) or causal reasoning, they solely focus on eliminating the popularity bias of items. In this paper, we propose a novel unbiased recommender learning model, namely BIlateral SElf-unbiased Recommender (BISER), to eliminate the exposure bias of items caused by recommender models. Specifically, BISER consists of two key components: (i) self-inverse propensity weighting (SIPW) to gradually mitigate the bias of items without incurring high computational costs; and (ii) bilateral unbiased learning (BU) to bridge the gap between two complementary models in model predictions, i.e., user- and item-based autoencoders, alleviating the high variance of SIPW. Extensive experiments show that BISER consistently outperforms state-of-the-art unbiased recommender models over several datasets, including Coat, Yahoo! R3, MovieLens, and CiteULike.|隐式反馈已被广泛用于构建商业推荐系统。因为观察到的反馈代表用户的点击日志，所以在真正的相关性和观察到的反馈之间存在语义差距。更重要的是，观察到的反馈通常偏向于流行项目，从而高估了流行项目的实际相关性。虽然现有的研究已经开发出使用反倾向加权(IPW)或因果推理的无偏学习方法，但他们只关注于消除项目的流行偏差。本文提出了一种新的无偏推荐学习模型，即双边自我无偏推荐模型(BISER) ，以消除由推荐模型引起的项目暴露偏差。具体而言，BISER 由两个关键组成部分组成: (i)自逆倾向加权(SIPW) ，以逐渐减轻项目的偏差，而不会产生高计算成本; (ii)双边无偏学习(BU) ，以弥补模型预测中两个互补模型之间的差距，即用户和基于项目的自动编码器，减轻 SIPW 的高方差。大量的实验表明，BISER 始终优于几个数据集，包括 Coat，Yahoo! R3，MovieLens 和 CiteULike 的最先进的无偏推荐模型。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Bilateral+Self-unbiased+Learning+from+Biased+Implicit+Feedback)|0| +|[Why do Semantically Unrelated Categories Appear in the Same Session?: A Demand-aware Method](https://doi.org/10.1145/3477495.3531806)|Liqi Yang, Linhao Luo, Xiaofeng Zhang, Fengxin Li, Xinni Zhang, Zelin Jiang, Shuai Tang|China Merchants Securities Co., Ltd, Shenzhen, China; Harbin Institute of Technology, Shenzhen, Shenzhen, China|Session-based recommendation has recently attracted more and more research efforts. Most existing approaches are intuitively proposed to discover users' potential preferences or interests from the anonymous session data. This apparently ignores the fact that these sequential behavior data usually reflect session user's potential demand, i.e., a semantic level factor, and therefore how to estimate underlying demands from a session has become a challenging task. To tackle the aforementioned issue, this paper proposes a novel demand-aware graph neural network model. Particularly, a demand modeling component is designed to extract the underlying multiple demands of each session. Then, the demand-aware graph neural network is designed to first construct session demand graphs and then learn the demand-aware item embeddings to make the recommendation. The mutual information loss is further designed to enhance the quality of the learnt embeddings. Extensive experiments have been performed on two real-world datasets and the proposed model achieves the SOTA model performance.|近年来，基于会话的推荐引起了越来越多的研究者的关注。现有的大多数方法都是直观地从匿名会话数据中发现用户的潜在偏好或兴趣。这显然忽略了这样一个事实，即这些顺序行为数据通常反映会话用户的潜在需求，即语义级因素，因此如何估计会话的潜在需求已经成为一个具有挑战性的任务。为了解决上述问题，本文提出了一种新的需求感知图神经网络模型。特别地，需求建模组件被设计用于提取每个会话的底层多个需求。然后，设计需求感知图神经网络，首先构造会话需求图，然后学习需求感知项嵌入，进行推荐。进一步设计了互信息损失，提高了学习嵌入的质量。在两个实际数据集上进行了广泛的实验，所提出的模型达到了 SOTA 模型的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Why+do+Semantically+Unrelated+Categories+Appear+in+the+Same+Session?:+A+Demand-aware+Method)|0| |[Scalable User Interface Optimization Using Combinatorial Bandits](https://doi.org/10.1145/3477495.3536325)|Ioannis Kangas, Maud Schwoerer, Lucas Bernardi|Booking.com, Amsterdam, Netherlands|The mission of major e-commerce platforms is to enable their customers to find the best products for their needs. In the common case of large inventories, complex User Interfaces (UIs) are required to allow a seamless navigation. However, as UIs often contain many widgets of different relevance, the task of constructing an optimal layout arises in order to improve the customer's experience. This is a challenging task, especially in the typical industrial setup where multiple independent teams conflict by adding and modifying UI widgets. It becomes even more challenging due to the customer preferences evolving over time, bringing the need for adaptive solutions. In a previous work [6], we addressed this task by introducing a UI governance framework powered by Machine Learning (ML) algorithms that automatically and continuously search for the optimal layout. Nevertheless, we highlighted that naive algorithmic choices exhibit several issues when implemented in the industry, such as widget dependency, combinatorial solution space and cold start problem. In this work, we demonstrate how we deal with these issues using Combinatorial Bandits, an extension of Multi-Armed Bandits (MAB) where the agent selects not only one but multiple arms at the same time. We develop two novel approaches to model combinatorial bandits, inspired by the Natural Language Processing (NLP) and the Evolutionary Algorithms (EA) fields and present their ability to enable scalable UI optimization.|主要电子商务平台的使命是使其客户能够找到满足其需要的最佳产品。在大量存货的常见情况下，需要复杂的用户界面(UI)来实现无缝导航。然而，由于 UI 通常包含许多不同相关性的小部件，因此需要构建最佳布局以改善客户体验。这是一项具有挑战性的任务，特别是在典型的工业设置中，多个独立团队通过添加和修改 UI 小部件而发生冲突。由于客户的偏好随着时间的推移而不断变化，因此对适应性解决方案的需求变得更加具有挑战性。在之前的工作[6]中，我们通过引入一个由机器学习(ML)算法支持的 UI 治理框架来解决这个问题，该框架可以自动并持续地搜索最佳布局。然而，我们强调，幼稚的算法选择表现出几个问题，如小部件依赖，组合解决方案空间和冷启动问题。在这项工作中，我们展示了如何处理这些问题使用组合强盗，一个扩展的多臂强盗(MAB) ，其中代理人选择不仅一个，但多个武器在同一时间。受自然语言处理(NLP)和进化算法(EA)领域的启发，我们开发了两种新的组合强盗建模方法，并展示了它们实现可扩展 UI 优化的能力。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Scalable+User+Interface+Optimization+Using+Combinatorial+Bandits)|0| |[Users: Can't Work With Them, Can't Work Without Them?](https://doi.org/10.1145/3477495.3532787)|Alistair Moffat|The University of Melbourne, Melbourne, VIC, Australia|If we could design the ideal IR "effectiveness" experiment (as distinct from an IR "efficiency" experiment), what would it look like? It would probably be a lab-based observational study [3] involving multiple search systems masked behind a uniform interface, and with hundreds (or thousands) of users each progressing some "real" search activity they were interested in. And we'd plan to (non-intrusively, somehow) capture per-snippet, per-document, per-SERP, and per-session annotations and satisfaction responses. The collected data could then be compared against a range of measured "task completion quality" indicators, and also against search effectiveness metric scores computed from the elements contained in the SERPs that were served by the systems. That's a tremendously big ask! So we often use offline evaluation techniques instead, employing test collections, static qrels sets, and effectiveness metrics [6]. We abstract the user into a deterministic evaluation script, supposing for pragmatic reasons that we know what query they would issue, and at the same time assuming that we can apply an effectiveness metric to calculate how much usefulness (or satisfaction) they will derive from any given SERP. The great advantage of this approach is that aside from the process of collecting the qrels, it is free of the need for users, meaning that it is repeatable. Indeed, we often do repeat, iterating to set parameters (and to rectify programming errors). Then, once metric scores have been computed, we carry out one or more paired statistical tests and draw conclusions as to relative system effectiveness.|如果我们可以设计一个理想的红外“有效性”实验(不同于红外“有效性”实验) ，它会是什么样子？这可能是一个基于实验室的观察性研究，在一个统一的界面背后隐藏着多个搜索系统，数百(或数千)用户每个人都在进行一些他们感兴趣的“真实”搜索活动。我们计划(非侵入性地，以某种方式)捕获每个片段、每个文档、每个 SERP 和每个会话的注释和满意度响应。然后，收集的数据可以与一系列测量的“任务完成质量”指标进行比较，也可以与搜索效率指标得分进行比较，这些得分是从系统提供的 SERP 中包含的元素计算出来的。这个要求太过分了！因此，我们经常使用离线评估技术，使用测试集合、静态 qrel 集和有效性度量[6]。我们将用户抽象成一个确定性的评估脚本，假设出于实用原因，我们知道他们会发出什么样的查询，同时假设我们可以应用一个有效性度量来计算他们将从任何给定的 SERP 中获得多少有用性(或满意度)。这种方法的最大优点是，除了收集 qrel 的过程之外，它不需要用户，这意味着它是可重复的。实际上，我们经常重复，迭代以设置参数(并纠正编程错误)。然后，一旦计算出度量分数，我们进行一个或多个成对的统计检验，并得出相对系统有效性的结论。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Users:+Can't+Work+With+Them,+Can't+Work+Without+Them?)|0| -|[Interacting with Non-Cooperative User: A New Paradigm for Proactive Dialogue Policy](https://doi.org/10.1145/3477495.3532001)|Wenqiang Lei, Yao Zhang, Feifan Song, Hongru Liang, Jiaxin Mao, Jiancheng Lv, Zhenglu Yang, TatSeng Chua|Peking University, Beijing, China; National University of Singapore, Singapore, Singapore; Sichuan University, Chengdu, China; Renmin University of China, Beijing, China; Nankai University, Tianjin, China|Proactive dialogue system is able to lead the conversation to a goal topic and has advantaged potential in bargain, persuasion, and negotiation. Current corpus-based learning manner limits its practical application in real-world scenarios. To this end, we contribute to advancing the study of the proactive dialogue policy to a more natural and challenging setting, i.e., interacting dynamically with users. Further, we call attention to the non-cooperative user behavior - the user talks about off-path topics when he/she is not satisfied with the previous topics introduced by the agent. We argue that the targets of reaching the goal topic quickly and maintaining a high user satisfaction are not always converged, because the topics close to the goal and the topics user preferred may not be the same. Towards this issue, we propose a new solution named I-Pro that can learn Proactive policy in the Interactive setting. Specifically, we learn the trade-off via a learned goal weight, which consists of four factors (dialogue turn, goal completion difficulty, user satisfaction estimation, and cooperative degree). The experimental results demonstrate I-Pro significantly outperforms baselines in terms of effectiveness and interpretability.|积极主动的对话系统能够将对话引向一个目标话题，并且在讨价还价、说服和谈判方面具有优势。目前基于语料库的学习方式限制了其在现实情景中的实际应用。为此，我们致力于将积极对话政策的研究推向一个更加自然和富有挑战性的环境，即与用户进行动态互动。此外，我们还提请注意非合作用户行为——当用户对代理引入的前一个主题不满意时，他/她会谈论偏离路径的主题。我们认为，快速达到目标主题和保持高用户满意度的目标并不总是趋同的，因为接近目标的主题和用户喜欢的主题可能不一样。针对这个问题，我们提出了一个新的解决方案，称为 I-Pro，可以学习积极的政策在互动的设置。具体来说，我们通过一个学习的目标权重来学习权衡，这个权重包括四个因素(对话转向、目标完成难度、用户满意度估计和合作程度)。实验结果表明，I-Pro 在有效性和可解释性方面明显优于基线。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Interacting+with+Non-Cooperative+User:+A+New+Paradigm+for+Proactive+Dialogue+Policy)|0| +|[Interacting with Non-Cooperative User: A New Paradigm for Proactive Dialogue Policy](https://doi.org/10.1145/3477495.3532001)|Wenqiang Lei, Yao Zhang, Feifan Song, Hongru Liang, Jiaxin Mao, Jiancheng Lv, Zhenglu Yang, TatSeng Chua|National University of Singapore, Singapore, Singapore; Peking University, Beijing, China; Sichuan University, Chengdu, China; Renmin University of China, Beijing, China; Nankai University, Tianjin, China|Proactive dialogue system is able to lead the conversation to a goal topic and has advantaged potential in bargain, persuasion, and negotiation. Current corpus-based learning manner limits its practical application in real-world scenarios. To this end, we contribute to advancing the study of the proactive dialogue policy to a more natural and challenging setting, i.e., interacting dynamically with users. Further, we call attention to the non-cooperative user behavior - the user talks about off-path topics when he/she is not satisfied with the previous topics introduced by the agent. We argue that the targets of reaching the goal topic quickly and maintaining a high user satisfaction are not always converged, because the topics close to the goal and the topics user preferred may not be the same. Towards this issue, we propose a new solution named I-Pro that can learn Proactive policy in the Interactive setting. Specifically, we learn the trade-off via a learned goal weight, which consists of four factors (dialogue turn, goal completion difficulty, user satisfaction estimation, and cooperative degree). The experimental results demonstrate I-Pro significantly outperforms baselines in terms of effectiveness and interpretability.|积极主动的对话系统能够将对话引向一个目标话题，并且在讨价还价、说服和谈判方面具有优势。目前基于语料库的学习方式限制了其在现实情景中的实际应用。为此，我们致力于将积极对话政策的研究推向一个更加自然和富有挑战性的环境，即与用户进行动态互动。此外，我们还提请注意非合作用户行为——当用户对代理引入的前一个主题不满意时，他/她会谈论偏离路径的主题。我们认为，快速达到目标主题和保持高用户满意度的目标并不总是趋同的，因为接近目标的主题和用户喜欢的主题可能不一样。针对这个问题，我们提出了一个新的解决方案，称为 I-Pro，可以学习积极的政策在互动的设置。具体来说，我们通过一个学习的目标权重来学习权衡，这个权重包括四个因素(对话转向、目标完成难度、用户满意度估计和合作程度)。实验结果表明，I-Pro 在有效性和可解释性方面明显优于基线。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Interacting+with+Non-Cooperative+User:+A+New+Paradigm+for+Proactive+Dialogue+Policy)|0| |[ADPL: Adversarial Prompt-based Domain Adaptation for Dialogue Summarization with Knowledge Disentanglement](https://doi.org/10.1145/3477495.3531933)|Lulu Zhao, Fujia Zheng, Weihao Zeng, Keqing He, Ruotong Geng, Huixing Jiang, Wei Wu, Weiran Xu|Beijing University of Posts and Telecommunications, Beijing, China; Meituan Group, Beijing, China|Traditional dialogue summarization models rely on a large-scale manually-labeled corpus, lacking generalization ability to new domains, and domain adaptation from a labeled source domain to an unlabeled target domain is important in practical summarization scenarios. However, existing domain adaptation works in dialogue summarization generally require large-scale pre-training using extensive external data. To explore the lightweight fine-tuning methods, in this paper, we propose an efficient Adversarial Disentangled Prompt Learning (ADPL) model for domain adaptation in dialogue summarization. We introduce three kinds of prompts including domain-invariant prompt (DIP), domain-specific prompt (DSP), and task-oriented prompt (TOP). DIP aims to disentangle and transfer the shared knowledge from the source domain and target domain in an adversarial way, which improves the accuracy of prediction about domain-invariant information and enhances the ability for generalization to new domains. DSP is designed to guide our model to focus on domain-specific knowledge using domain-related features. TOP is to capture task-oriented knowledge to generate high-quality summaries. Instead of fine-tuning the whole pre-trained language model (PLM), we only update the prompt networks but keep PLM fixed. Experimental results on the zero-shot setting show that the novel design of prompts can yield more coherent, faithful, and relevant summaries than baselines using the prefix-tuning, and perform at par with fine-tuning while being more efficient. Overall, our work introduces a prompt-based perspective to the zero-shot learning for dialogue summarization task and provides valuable findings and insights for future research.|传统的对话摘要模型依赖于大规模的人工标记语料库，缺乏对新领域的泛化能力，在实际的摘要场景中，从标记的源领域到未标记的目标领域的领域适应性是非常重要的。然而，现有的对话摘要领域适应工作一般需要使用大量的外部数据进行大规模的预训练。为了探索轻量级微调方法，本文提出了一种有效的对话摘要领域自适应对抗性分离提示学习(ADPL)模型。介绍了领域不变提示(DIP)、领域特定提示(DSP)和面向任务提示(TOP)三种提示方式。DIP 旨在对源域和目标域的共享知识进行对抗性的分离和转移，提高了领域不变信息预测的准确性，增强了对新领域的推广能力。DSP 被设计用来指导我们的模型使用领域相关的特征来关注领域特定的知识。TOP 是一种以任务为导向的知识获取方法，用于生成高质量的摘要。我们没有对整个预先训练好的语言模型(PLM)进行微调，而是只更新提示网络，但保持 PLM 不变。零镜头设置的实验结果表明，新颖的提示符设计比基线前缀调整更能产生连贯、忠实和相关的总结，并且在更有效率的同时表现出与微调相当的效果。总的来说，我们的工作为对话总结任务的零拍学习引入了一个基于及时性的视角，并为未来的研究提供了有价值的发现和见解。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=ADPL:+Adversarial+Prompt-based+Domain+Adaptation+for+Dialogue+Summarization+with+Knowledge+Disentanglement)|0| |[IR Evaluation and Learning in the Presence of Forbidden Documents](https://doi.org/10.1145/3477495.3532006)|David Carmel, Nachshon Cohen, Amir Ingber, Elad Kravi|Pinecone Systems, Haifa, Israel; Amazon, Haifa, Israel|Many IR collections contain forbidden documents (F-docs), i.e. documents that should not be retrieved to the searcher. In an ideal scenario F-docs are clearly flagged, hence the ranker can filter them out, guaranteeing that no F-doc will be exposed. However, in real-world scenarios, filtering algorithms are prone to errors. Therefore, an IR evaluation system should also measure filtering quality in addition to ranking quality. Typically, filtering is considered as a classification task and is evaluated independently of the ranking quality. However, due to the mutual affinity between the two, it is desirable to evaluate ranking quality while filtering decisions are being made. In this work we propose nDCGf, a novel extension of the nDCGmin metric[14], which measures both ranking and filtering quality of the search results. We show both theoretically and empirically that while nDCGmin is not suitable for the simultaneous ranking and filtering task, nDCGf is a reliable metric in this case. We experiment with three datasets for which ranking and filtering are both required. In the PR dataset our task is to rank product reviews while filtering those marked as spam. Similarly, in the CQA dataset our task is to rank a list of human answers per question while filtering bad answers. We also experiment with the TREC web-track datasets, where F-docs are explicitly labeled, sorting participant runs according to their ranking and filtering quality, demonstrating the stability, sensitivity, and reliability of nDCGf for this task. We propose a learning to rank and filter (LTRF) framework that is specifically designed to optimize nDCGf, by learning a ranking model and optimizing a filtering threshold used for discarding documents with lower scores. We experiment with several loss functions demonstrating their success in learning an effective LTRF model for the simultaneous learning and filtering task.|许多 IR 集合包含禁用的文档(F-docs) ，即不应该被检索到搜索器的文档。在一个理想的场景中，F-doc 被清楚地标记，因此排名者可以过滤掉它们，保证没有 F-doc 被暴露。然而，在真实场景中，过滤算法很容易出错。因此，一个红外评价系统除了评级质量外，还应该衡量过滤质量。通常，过滤被认为是一个分类任务，独立于排序质量进行评估。然而，由于两者之间的相互亲和性，在做出过滤决策的同时评估排名质量是可取的。在这项工作中，我们提出了 nDCGf，nDCGmin 度量的一个新的扩展[14] ，它衡量搜索结果的排名和过滤质量。我们从理论和实验两方面证明了，虽然 nDCGmin 不适合同时进行排序和过滤任务，但在这种情况下，nDCGf 是一个可靠的度量。我们对三个数据集进行了实验，这三个数据集都需要排名和过滤。在公关数据集中，我们的任务是对产品评论进行排序，同时过滤那些被标记为垃圾邮件的评论。类似地，在 CQA 数据集中，我们的任务是对每个问题的人工答案列表进行排序，同时过滤错误答案。我们还对 TREC 网络跟踪数据集进行了实验，其中 F-docs 被明确标记，根据其排名和过滤质量对参与者进行排序，证明了 nDCGf 用于该任务的稳定性，灵敏度和可靠性。我们提出了一个学习排序和过滤(LTRF)框架，专门设计优化 nDCGf，通过学习排序模型和优化过滤阈值用于丢弃分数较低的文档。我们用几个损失函数进行了实验，证明了它们在同时学习和过滤任务中学习一个有效的 LTRF 模型是成功的。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=IR+Evaluation+and+Learning+in+the+Presence+of+Forbidden+Documents)|0| -|[Human Preferences as Dueling Bandits](https://doi.org/10.1145/3477495.3531991)|Xinyi Yan, Chengxi Luo, Charles L. A. Clarke, Nick Craswell, Ellen M. Voorhees, Pablo Castells|University of Waterloo, Waterloo, ON, Canada; Universidad Autónoma de Madrid, Madrid, Spain; National Institute of Standards and Technology, Gaithersburg, MD, USA; Microsoft, Bellevue, WA, USA|The dramatic improvements in core information retrieval tasks engendered by neural rankers create a need for novel evaluation methods. If every ranker returns highly relevant items in the top ranks, it becomes difficult to recognize meaningful differences between them and to build reusable test collections. Several recent papers explore pairwise preference judgments as an alternative to traditional graded relevance assessments. Rather than viewing items one at a time, assessors view items side-by-side and indicate the one that provides the better response to a query, allowing fine-grained distinctions. If we employ preference judgments to identify the probably best items for each query, we can measure rankers by their ability to place these items as high as possible. We frame the problem of finding best items as a dueling bandits problem. While many papers explore dueling bandits for online ranker evaluation via interleaving, they have not been considered as a framework for offline evaluation via human preference judgments. We review the literature for possible solutions. For human preference judgments, any usable algorithm must tolerate ties, since two items may appear nearly equal to assessors, and it must minimize the number of judgments required for any specific pair, since each such comparison requires an independent assessor. Since the theoretical guarantees provided by most algorithms depend on assumptions that are not satisfied by human preference judgments, we simulate selected algorithms on representative test cases to provide insight into their practical utility. Based on these simulations, one algorithm stands out for its potential. Our simulations suggest modifications to further improve its performance. Using the modified algorithm, we collect over 10,000 preference judgments for pools derived from submissions to the TREC 2021 Deep Learning Track, confirming its suitability. We test the idea of best-item evaluation and suggest ideas for further theoretical and practical progress.|由神经排序引起的核心信息检索任务的显著改进创造了对新的评估方法的需求。如果每个排名都返回顶级排名中高度相关的项目，那么就很难识别它们之间有意义的差异，也很难构建可重用的测试集合。最近的几篇论文探讨了成对偏好判断作为传统分级相关性评估的替代方法。评估员不是一次查看一个项，而是并排查看项，并指出哪个项能够对查询提供更好的响应，从而允许细粒度的区分。如果我们使用偏好判断来确定每个查询可能最好的项目，我们可以通过他们将这些项目放在尽可能高的位置的能力来衡量排名。我们把寻找最佳物品的问题框定为一个决斗土匪问题。尽管许多论文通过交错的方式探索了在线排名评价中的决斗强盗，但是它们并没有被认为是一个通过人类偏好判断进行离线评价的框架。我们回顾了可能的解决方案的文献。对于人类偏好判断，任何可用的算法都必须容忍关系，因为两个项目可能看起来几乎等于评估者，它必须最小化任何特定对所需的判断数量，因为每个这样的比较需要一个独立的评估者。由于大多数算法提供的理论保证依赖于人类偏好判断不能满足的假设，我们在代表性测试案例上模拟选定的算法，以深入了解它们的实际效用。基于这些模拟，一种算法因其潜力而脱颖而出。我们的模拟结果表明，改进可以进一步提高它的性能。使用修改后的算法，我们收集了超过10,000个来自 TREC 2021深度学习跟踪提交的池的偏好判断，确认了它的适用性。我们检验了最佳项目评价的思想，并为进一步的理论和实践进展提出了建议。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Human+Preferences+as+Dueling+Bandits)|0| +|[Human Preferences as Dueling Bandits](https://doi.org/10.1145/3477495.3531991)|Xinyi Yan, Chengxi Luo, Charles L. A. Clarke, Nick Craswell, Ellen M. Voorhees, Pablo Castells|National Institute of Standards and Technology, Gaithersburg, MD, USA; University of Waterloo, Waterloo, ON, Canada; Universidad Autónoma de Madrid, Madrid, Spain; Microsoft, Bellevue, WA, USA|The dramatic improvements in core information retrieval tasks engendered by neural rankers create a need for novel evaluation methods. If every ranker returns highly relevant items in the top ranks, it becomes difficult to recognize meaningful differences between them and to build reusable test collections. Several recent papers explore pairwise preference judgments as an alternative to traditional graded relevance assessments. Rather than viewing items one at a time, assessors view items side-by-side and indicate the one that provides the better response to a query, allowing fine-grained distinctions. If we employ preference judgments to identify the probably best items for each query, we can measure rankers by their ability to place these items as high as possible. We frame the problem of finding best items as a dueling bandits problem. While many papers explore dueling bandits for online ranker evaluation via interleaving, they have not been considered as a framework for offline evaluation via human preference judgments. We review the literature for possible solutions. For human preference judgments, any usable algorithm must tolerate ties, since two items may appear nearly equal to assessors, and it must minimize the number of judgments required for any specific pair, since each such comparison requires an independent assessor. Since the theoretical guarantees provided by most algorithms depend on assumptions that are not satisfied by human preference judgments, we simulate selected algorithms on representative test cases to provide insight into their practical utility. Based on these simulations, one algorithm stands out for its potential. Our simulations suggest modifications to further improve its performance. Using the modified algorithm, we collect over 10,000 preference judgments for pools derived from submissions to the TREC 2021 Deep Learning Track, confirming its suitability. We test the idea of best-item evaluation and suggest ideas for further theoretical and practical progress.|由神经排序引起的核心信息检索任务的显著改进创造了对新的评估方法的需求。如果每个排名都返回顶级排名中高度相关的项目，那么就很难识别它们之间有意义的差异，也很难构建可重用的测试集合。最近的几篇论文探讨了成对偏好判断作为传统分级相关性评估的替代方法。评估员不是一次查看一个项，而是并排查看项，并指出哪个项能够对查询提供更好的响应，从而允许细粒度的区分。如果我们使用偏好判断来确定每个查询可能最好的项目，我们可以通过他们将这些项目放在尽可能高的位置的能力来衡量排名。我们把寻找最佳物品的问题框定为一个决斗土匪问题。尽管许多论文通过交错的方式探索了在线排名评价中的决斗强盗，但是它们并没有被认为是一个通过人类偏好判断进行离线评价的框架。我们回顾了可能的解决方案的文献。对于人类偏好判断，任何可用的算法都必须容忍关系，因为两个项目可能看起来几乎等于评估者，它必须最小化任何特定对所需的判断数量，因为每个这样的比较需要一个独立的评估者。由于大多数算法提供的理论保证依赖于人类偏好判断不能满足的假设，我们在代表性测试案例上模拟选定的算法，以深入了解它们的实际效用。基于这些模拟，一种算法因其潜力而脱颖而出。我们的模拟结果表明，改进可以进一步提高它的性能。使用修改后的算法，我们收集了超过10,000个来自 TREC 2021深度学习跟踪提交的池的偏好判断，确认了它的适用性。我们检验了最佳项目评价的思想，并为进一步的理论和实践进展提出了建议。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Human+Preferences+as+Dueling+Bandits)|0| |[IAOTP: An Interactive End-to-End Solution for Aspect-Opinion Term Pairs Extraction](https://doi.org/10.1145/3477495.3532085)|Ambreen Nazir, Yuan Rao|Xi'an Jiaotong University, Xi'an, China|Recently, the aspect-opinion term pairs (AOTP) extraction task has gained substantial importance in the domain of aspect-based sentiment analysis. It intends to extract the potential pair of each aspect term with its corresponding opinion term present in a user review. Some existing studies heavily relied on the annotated aspect terms and/or opinion terms, or adopted external knowledge/resources to figure out the task. Therefore, in this study, we propose a novel end-to-end solution, called an Interactive AOTP (IAOTP) model, for exploring AOTP. The IAOTP model first tracks the boundary of each token in given aspect-specific and opinion-specific representations through a span-based operation. Next, it generates the candidate AOTP by formulating the dyadic relations between tokens through the Biaffine transformation. Then, it computes the positioning information to capture the significant distance relationship that each candidate pair holds. And finally, it jointly models collaborative interactions and prediction of AOTP through a 2D self-attention. Besides the IAOTP model, this study also proposes an independent aspect/opinion encoding model (a RS model) that formulates relational semantics to obtain aspect-specific and opinion-specific representations that can effectively perform the extraction of aspect and opinion terms. Detailed experiments conducted on the publicly available benchmark datasets for AOTP, aspect terms, and opinion terms extraction tasks, clearly demonstrate the significantly improved performance of our models relative to other competitive state-of-the-art baselines.|近年来，在基于方面的情绪分析领域，方面-意见词对(AOTP)提取任务得到了广泛的重视。它打算提取每个方面术语与其相应的意见术语在用户评论中出现的潜在对。一些现有的研究严重依赖于注释的方面术语和/或意见术语，或者采用外部知识/资源来完成任务。因此，在这项研究中，我们提出了一个新的端到端解决方案，称为交互式 AOTP (IAOTP)模型，用于探索 AOTP。IAOTP 模型首先通过基于跨度的操作跟踪给定方面特定表示和意见特定表示中每个令牌的边界。其次，通过双仿射变换建立令牌之间的并元关系，生成候选 AOTP。然后，通过计算定位信息来捕获每个候选对所持有的重要距离关系。最后，通过二维自我注意共同建立 AOTP 协作交互和预测模型。除了 IAOTP 模型外，本研究还提出了一个独立的方面/意见编码模型(RS 模型) ，该模型通过建立关系语义来获得方面特定和意见特定的表示，从而有效地提取方面和意见术语。在 AOTP，方面术语和意见术语提取任务的公开可用基准数据集上进行的详细实验清楚地表明，相对于其他竞争性的最先进的基线，我们的模型的性能显着改善。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=IAOTP:+An+Interactive+End-to-End+Solution+for+Aspect-Opinion+Term+Pairs+Extraction)|0| -|[Exploring Heterogeneous Data Lake based on Unified Canonical Graphs](https://doi.org/10.1145/3477495.3531759)|Qin Yuan, Ye Yuan, Zhenyu Wen, He Wang, Chen Chen, Guoren Wang|Zhejiang University of Technology, Hangzhou, China; Beijing Institute of Technology, Beijing, China|A data lake is a repository for massive raw and heterogeneous data, which includes multiple data models with different data schemas and query interfaces. Keyword search can extract valuable information for users without the knowledge of underlying schemas and query languages. However, conventional keyword searches are restricted to a certain data model and cannot easily adapt to a data lake. In this paper, we study a novel keyword search. To achieve high accuracy and efficiency, we introduce canonical graphs and then integrate semantically related vertices based on vertex representations. A matching entity based keyword search algorithm is presented to find answers across multiple data sources. Finally, extensive experimental study shows the effectiveness and efficiency of our solution.|数据湖是大量原始和异构数据的存储库，其中包括具有不同数据模式和查询接口的多个数据模型。关键字搜索可以在不了解基础模式和查询语言的情况下为用户提取有价值的信息。然而，传统的关键字搜索仅限于特定的数据模型，不能很容易地适应数据湖。本文研究了一种新的关键词搜索方法。为了实现高精度和高效率，我们引入了规范图，然后基于顶点表示对语义相关的顶点进行集成。提出了一种基于匹配实体的关键字搜索算法，用于在多个数据源之间寻找答案。最后，广泛的实验研究表明了我们的解决方案的有效性和效率。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Exploring+Heterogeneous+Data+Lake+based+on+Unified+Canonical+Graphs)|0| -|[Distilling Knowledge on Text Graph for Social Media Attribute Inference](https://doi.org/10.1145/3477495.3531968)|Quan Li, Xiaoting Li, Lingwei Chen, Dinghao Wu|Wright State University, Dayton, OH, USA; Visa Research, Palo Alto, CA, USA; The Pennsylvania State University, State College, PA, USA|The popularization of social media generates a large amount of user-oriented data, where text data especially attracts researchers and speculators to infer user attributes (e.g., age, gender) for fulfilling their intents. Generally, this line of work casts attribute inference as a text classification problem, and starts to leverage graph neural networks for higher-level text representations. However, these text graphs are constructed on words, suffering from high memory consumption and ineffectiveness on few labeled texts. To address this challenge, we design a text-graph-based few-shot learning model for social media attribute inferences. Our model builds a text graph with texts as nodes and edges learned from current text representations via manifold learning and message passing. To further use unlabeled texts to improve few-shot performance, a knowledge distillation is devised to optimize the problem. This offers a trade-off between expressiveness and complexity. Experiments on social media datasets demonstrate the state-of-the-art performance of our model on attribute inferences with considerably fewer labeled texts.|社交媒体的普及产生了大量以用户为导向的数据，其中文本数据尤其吸引研究人员和投机者推断用户属性(如年龄、性别)以实现其意图。一般来说，这一行的工作将属性推理作为一个文本分类问题，并开始利用图神经网络进行更高级别的文本表示。然而，这些文本图形是建立在单词上的，由于高记忆消耗和对少数带标签的文本无效。为了解决这个问题，我们设计了一个基于文本图形的社会媒体属性推理的少镜头学习模型。我们的模型通过流形学习和消息传递从当前的文本表示中学习文本作为节点和边来构建一个文本图。为了进一步利用未标记文本来提高短镜头性能，设计了一种知识提取方法来优化问题。这在表达性和复杂性之间提供了一种权衡。在社会媒体数据集上的实验证明了我们的模型在标记文本相当少的情况下对属性推理的最新性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Distilling+Knowledge+on+Text+Graph+for+Social+Media+Attribute+Inference)|0| -|[A Simple Meta-learning Paradigm for Zero-shot Intent Classification with Mixture Attention Mechanism](https://doi.org/10.1145/3477495.3531803)|Han Liu, Siyang Zhao, Xiaotong Zhang, Feng Zhang, Junjie Sun, Hong Yu, Xianchao Zhang|Peking University, Beijing, China; Dalian University of Technology, Dalian, China|Zero-shot intent classification is a vital and challenging task in dialogue systems, which aims to deal with numerous fast-emerging unacquainted intents without annotated training data. To obtain more satisfactory performance, the crucial points lie in two aspects: extracting better utterance features and strengthening the model generalization ability. In this paper, we propose a simple yet effective meta-learning paradigm for zero-shot intent classification. To learn better semantic representations for utterances, we introduce a new mixture attention mechanism, which encodes the pertinent word occurrence patterns by leveraging the distributional signature attention and multi-layer perceptron attention simultaneously. To strengthen the transfer ability of the model from seen classes to unseen classes, we reformulate zero-shot intent classification with a meta-learning strategy, which trains the model by simulating multiple zero-shot classification tasks on seen categories, and promotes the model generalization ability with a meta-adapting procedure on mimic unseen categories. Extensive experiments on two real-world dialogue datasets in different languages show that our model outperforms other strong baselines on both standard and generalized zero-shot intent classification tasks.|零射击意图分类是对话系统中的一项重要而具有挑战性的任务，其目的是在没有注释训练数据的情况下处理大量快速出现的不熟悉意图。要获得更满意的表现，关键在于提取更好的话语特征和增强模型泛化能力两个方面。在本文中，我们提出了一个简单而有效的元学习范式的零射击意图分类。为了更好地学习话语的语义表征，我们引入了一种新的混合注意机制，该机制同时利用分布特征注意和多层感知器注意对相关词语出现模式进行编码。为了增强模型从可见类到不可见类的迁移能力，我们采用元学习策略重新构建了零射击意图分类模型，通过模拟可见类的多个零射击分类任务来训练模型，并通过模拟不可见类的元自适应过程来提高模型的泛化能力。在两个不同语言的真实世界对话数据集上的大量实验表明，我们的模型在标准和广义零射击意图分类任务上都优于其他强基线。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Simple+Meta-learning+Paradigm+for+Zero-shot+Intent+Classification+with+Mixture+Attention+Mechanism)|0| -|[Analyzing the Support Level for Tips Extracted from Product Reviews](https://doi.org/10.1145/3477495.3531805)|Miriam Farber, David Carmel, Lital Kuchy, Avihai Mejer|Faebook, Haifa, Israel; Amazon, Haifa, Israel|Useful tips extracted from product reviews assist customers to take a more informed purchase decision, as well as making a better, easier, and safer usage of the product. In this work we argue that extracted tips should be examined based on the amount of support and opposition they receive from all product reviews. A classifier, developed for this purpose, determines the degree to which a tip is supported or contradicted by a single review sentence. These support-levels are then aggregated over all review sentences, providing a global support score, and a global contradiction score, reflecting the support-level of all reviews to the given tip, thus improving the customer confidence in the tip validity. By analyzing a large set of tips extracted from product reviews, we propose a novel taxonomy for categorizing tips as highly-supported, highly-contradicted, controversial (supported and contradicted), and anecdotal (neither supported nor contradicted).|从产品评论中提取的有用的技巧可以帮助客户做出更明智的购买决定，以及更好、更容易和更安全地使用产品。在这项工作中，我们认为提取的技巧应该基于他们从所有产品评审中得到的支持和反对的数量进行检查。为此目的开发的分类器决定了一个复习句支持或反驳一个提示的程度。然后将这些支持级别聚合到所有评论句中，提供全局支持评分和全局矛盾评分，反映所有评论对给定提示的支持级别，从而提高客户对提示有效性的信心。通过分析从产品评论中提取的大量技巧，我们提出了一种新的分类方法，将技巧分为高支持、高矛盾、有争议(支持和矛盾)和轶事(既不支持也不矛盾)。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Analyzing+the+Support+Level+for+Tips+Extracted+from+Product+Reviews)|0| -|[UserBERT: Pre-training User Model with Contrastive Self-supervision](https://doi.org/10.1145/3477495.3531810)|Chuhan Wu, Fangzhao Wu, Tao Qi, Yongfeng Huang|Microsoft Research Asia, Beijing, China; Department of Electronic Engineering, Tsinghua University, Beijing, China|User modeling is critical for personalization. Existing methods usually train user models from task-specific labeled data, which may be insufficient. In fact, there are usually abundant unlabeled user behavior data that encode rich universal user information, and pre-training user models on them can empower user modeling in many downstream tasks. In this paper, we propose a user model pre-training method named UserBERT to learn universal user models on unlabeled user behavior data with two contrastive self-supervision tasks. The first one is masked behavior prediction and discrimination, aiming to model the contexts of user behaviors. The second one is behavior sequence matching, aiming to capture user interest stable in different periods. Besides, we propose a medium-hard negative sampling framework to select informative negative samples for better contrastive pre-training. Extensive experiments validate the effectiveness of UserBERT in user model pre-training.|用户建模对个性化至关重要。现有的方法通常根据特定于任务的标记数据训练用户模型，这可能是不够的。事实上，通常存在大量未标记的用户行为数据，这些数据编码了丰富的通用用户信息，并且对这些数据进行预训练的用户模型可以在许多下游任务中增强用户建模能力。本文提出了一种用户模型预训练方法 UserBERT，通过两个对比的自我监督任务来学习未标记用户行为数据的通用用户模型。第一种是隐蔽行为预测和识别，旨在对用户行为的上下文进行建模。第二种是行为序列匹配，旨在捕获不同时期用户兴趣的稳定性。此外，我们提出了一个中等硬度的负面抽样框架来选择信息量大的负面样本，以便更好地进行对比预训练。大量实验验证了 UserBERT 在用户模型预训练中的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=UserBERT:+Pre-training+User+Model+with+Contrastive+Self-supervision)|0| -|[Modern Baselines for SPARQL Semantic Parsing](https://doi.org/10.1145/3477495.3531841)|Debayan Banerjee, Pranav Ajit Nair, Jivat Neet Kaur, Ricardo Usbeck, Chris Biemann|Universität Hamburg, Hamburg, Germany; Microsoft Research, Bengaluru, India; Indian Institute of Technology (BHU), Varanasi, India|In this work, we focus on the task of generating SPARQL queries from natural language questions, which can then be executed on Knowledge Graphs (KGs). We assume that gold entity and relations have been provided, and the remaining task is to arrange them in the right order along with SPARQL vocabulary, and input tokens to produce the correct SPARQL query. Pre-trained Language Models (PLMs) have not been explored in depth on this task so far, so we experiment with BART, T5 and PGNs (Pointer Generator Networks) with BERT embeddings, looking for new baselines in the PLM era for this task, on DBpedia and Wikidata KGs. We show that T5 requires special input tokenisation, but produces state of the art performance on LC-QuAD 1.0 and LC-QuAD 2.0 datasets, and outperforms task-specific models from previous works. Moreover, the methods enable semantic parsing for questions where a part of the input needs to be copied to the output query, thus enabling a new paradigm in KG semantic parsing.|在这项工作中，我们将重点放在从自然语言问题生成 SPARQL 查询的任务上，然后可以在知识图(Knowledge Graphs，KG)上执行这些查询。我们假设已经提供了 gold 实体和关系，剩下的任务是将它们与 SPARQL 词汇表一起按正确的顺序排列，并输入令牌以生成正确的 SPARQL 查询。预先训练的语言模型(PLM)到目前为止还没有被深入探索，所以我们在 BART，T5和 PGNs (指针生成器网络)中嵌入 BERT 进行试验，在 DBpedia 和 Wikidata KG 上为这项任务寻找 PLM 时代的新基线。我们展示了 T5需要特殊的输入标记，但是在 LC-QuAD 1.0和 LC-QuAD 2.0数据集上产生了最先进的性能，并且优于以前作品中的任务特定模型。此外，这些方法还支持对需要将部分输入复制到输出查询的问题进行语义解析，从而为 KG 语义解析提供了一个新的范例。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Modern+Baselines+for+SPARQL+Semantic+Parsing)|0| +|[Exploring Heterogeneous Data Lake based on Unified Canonical Graphs](https://doi.org/10.1145/3477495.3531759)|Qin Yuan, Ye Yuan, Zhenyu Wen, He Wang, Chen Chen, Guoren Wang|Beijing Institute of Technology, Beijing, China; Zhejiang University of Technology, Hangzhou, China|A data lake is a repository for massive raw and heterogeneous data, which includes multiple data models with different data schemas and query interfaces. Keyword search can extract valuable information for users without the knowledge of underlying schemas and query languages. However, conventional keyword searches are restricted to a certain data model and cannot easily adapt to a data lake. In this paper, we study a novel keyword search. To achieve high accuracy and efficiency, we introduce canonical graphs and then integrate semantically related vertices based on vertex representations. A matching entity based keyword search algorithm is presented to find answers across multiple data sources. Finally, extensive experimental study shows the effectiveness and efficiency of our solution.|数据湖是大量原始和异构数据的存储库，其中包括具有不同数据模式和查询接口的多个数据模型。关键字搜索可以在不了解基础模式和查询语言的情况下为用户提取有价值的信息。然而，传统的关键字搜索仅限于特定的数据模型，不能很容易地适应数据湖。本文研究了一种新的关键词搜索方法。为了实现高精度和高效率，我们引入了规范图，然后基于顶点表示对语义相关的顶点进行集成。提出了一种基于匹配实体的关键字搜索算法，用于在多个数据源之间寻找答案。最后，广泛的实验研究表明了我们的解决方案的有效性和效率。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Exploring+Heterogeneous+Data+Lake+based+on+Unified+Canonical+Graphs)|0| +|[Distilling Knowledge on Text Graph for Social Media Attribute Inference](https://doi.org/10.1145/3477495.3531968)|Quan Li, Xiaoting Li, Lingwei Chen, Dinghao Wu|Visa Research, Palo Alto, CA, USA; Wright State University, Dayton, OH, USA; The Pennsylvania State University, State College, PA, USA|The popularization of social media generates a large amount of user-oriented data, where text data especially attracts researchers and speculators to infer user attributes (e.g., age, gender) for fulfilling their intents. Generally, this line of work casts attribute inference as a text classification problem, and starts to leverage graph neural networks for higher-level text representations. However, these text graphs are constructed on words, suffering from high memory consumption and ineffectiveness on few labeled texts. To address this challenge, we design a text-graph-based few-shot learning model for social media attribute inferences. Our model builds a text graph with texts as nodes and edges learned from current text representations via manifold learning and message passing. To further use unlabeled texts to improve few-shot performance, a knowledge distillation is devised to optimize the problem. This offers a trade-off between expressiveness and complexity. Experiments on social media datasets demonstrate the state-of-the-art performance of our model on attribute inferences with considerably fewer labeled texts.|社交媒体的普及产生了大量以用户为导向的数据，其中文本数据尤其吸引研究人员和投机者推断用户属性(如年龄、性别)以实现其意图。一般来说，这一行的工作将属性推理作为一个文本分类问题，并开始利用图神经网络进行更高级别的文本表示。然而，这些文本图形是建立在单词上的，由于高记忆消耗和对少数带标签的文本无效。为了解决这个问题，我们设计了一个基于文本图形的社会媒体属性推理的少镜头学习模型。我们的模型通过流形学习和消息传递从当前的文本表示中学习文本作为节点和边来构建一个文本图。为了进一步利用未标记文本来提高短镜头性能，设计了一种知识提取方法来优化问题。这在表达性和复杂性之间提供了一种权衡。在社会媒体数据集上的实验证明了我们的模型在标记文本相当少的情况下对属性推理的最新性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Distilling+Knowledge+on+Text+Graph+for+Social+Media+Attribute+Inference)|0| +|[A Simple Meta-learning Paradigm for Zero-shot Intent Classification with Mixture Attention Mechanism](https://doi.org/10.1145/3477495.3531803)|Han Liu, Siyang Zhao, Xiaotong Zhang, Feng Zhang, Junjie Sun, Hong Yu, Xianchao Zhang|Dalian University of Technology, Dalian, China; Peking University, Beijing, China|Zero-shot intent classification is a vital and challenging task in dialogue systems, which aims to deal with numerous fast-emerging unacquainted intents without annotated training data. To obtain more satisfactory performance, the crucial points lie in two aspects: extracting better utterance features and strengthening the model generalization ability. In this paper, we propose a simple yet effective meta-learning paradigm for zero-shot intent classification. To learn better semantic representations for utterances, we introduce a new mixture attention mechanism, which encodes the pertinent word occurrence patterns by leveraging the distributional signature attention and multi-layer perceptron attention simultaneously. To strengthen the transfer ability of the model from seen classes to unseen classes, we reformulate zero-shot intent classification with a meta-learning strategy, which trains the model by simulating multiple zero-shot classification tasks on seen categories, and promotes the model generalization ability with a meta-adapting procedure on mimic unseen categories. Extensive experiments on two real-world dialogue datasets in different languages show that our model outperforms other strong baselines on both standard and generalized zero-shot intent classification tasks.|零射击意图分类是对话系统中的一项重要而具有挑战性的任务，其目的是在没有注释训练数据的情况下处理大量快速出现的不熟悉意图。要获得更满意的表现，关键在于提取更好的话语特征和增强模型泛化能力两个方面。在本文中，我们提出了一个简单而有效的元学习范式的零射击意图分类。为了更好地学习话语的语义表征，我们引入了一种新的混合注意机制，该机制同时利用分布特征注意和多层感知器注意对相关词语出现模式进行编码。为了增强模型从可见类到不可见类的迁移能力，我们采用元学习策略重新构建了零射击意图分类模型，通过模拟可见类的多个零射击分类任务来训练模型，并通过模拟不可见类的元自适应过程来提高模型的泛化能力。在两个不同语言的真实世界对话数据集上的大量实验表明，我们的模型在标准和广义零射击意图分类任务上都优于其他强基线。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Simple+Meta-learning+Paradigm+for+Zero-shot+Intent+Classification+with+Mixture+Attention+Mechanism)|0| +|[Analyzing the Support Level for Tips Extracted from Product Reviews](https://doi.org/10.1145/3477495.3531805)|Miriam Farber, David Carmel, Lital Kuchy, Avihai Mejer|Amazon, Haifa, Israel; Faebook, Haifa, Israel|Useful tips extracted from product reviews assist customers to take a more informed purchase decision, as well as making a better, easier, and safer usage of the product. In this work we argue that extracted tips should be examined based on the amount of support and opposition they receive from all product reviews. A classifier, developed for this purpose, determines the degree to which a tip is supported or contradicted by a single review sentence. These support-levels are then aggregated over all review sentences, providing a global support score, and a global contradiction score, reflecting the support-level of all reviews to the given tip, thus improving the customer confidence in the tip validity. By analyzing a large set of tips extracted from product reviews, we propose a novel taxonomy for categorizing tips as highly-supported, highly-contradicted, controversial (supported and contradicted), and anecdotal (neither supported nor contradicted).|从产品评论中提取的有用的技巧可以帮助客户做出更明智的购买决定，以及更好、更容易和更安全地使用产品。在这项工作中，我们认为提取的技巧应该基于他们从所有产品评审中得到的支持和反对的数量进行检查。为此目的开发的分类器决定了一个复习句支持或反驳一个提示的程度。然后将这些支持级别聚合到所有评论句中，提供全局支持评分和全局矛盾评分，反映所有评论对给定提示的支持级别，从而提高客户对提示有效性的信心。通过分析从产品评论中提取的大量技巧，我们提出了一种新的分类方法，将技巧分为高支持、高矛盾、有争议(支持和矛盾)和轶事(既不支持也不矛盾)。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Analyzing+the+Support+Level+for+Tips+Extracted+from+Product+Reviews)|0| +|[UserBERT: Pre-training User Model with Contrastive Self-supervision](https://doi.org/10.1145/3477495.3531810)|Chuhan Wu, Fangzhao Wu, Tao Qi, Yongfeng Huang|Department of Electronic Engineering, Tsinghua University, Beijing, China; Microsoft Research Asia, Beijing, China|User modeling is critical for personalization. Existing methods usually train user models from task-specific labeled data, which may be insufficient. In fact, there are usually abundant unlabeled user behavior data that encode rich universal user information, and pre-training user models on them can empower user modeling in many downstream tasks. In this paper, we propose a user model pre-training method named UserBERT to learn universal user models on unlabeled user behavior data with two contrastive self-supervision tasks. The first one is masked behavior prediction and discrimination, aiming to model the contexts of user behaviors. The second one is behavior sequence matching, aiming to capture user interest stable in different periods. Besides, we propose a medium-hard negative sampling framework to select informative negative samples for better contrastive pre-training. Extensive experiments validate the effectiveness of UserBERT in user model pre-training.|用户建模对个性化至关重要。现有的方法通常根据特定于任务的标记数据训练用户模型，这可能是不够的。事实上，通常存在大量未标记的用户行为数据，这些数据编码了丰富的通用用户信息，并且对这些数据进行预训练的用户模型可以在许多下游任务中增强用户建模能力。本文提出了一种用户模型预训练方法 UserBERT，通过两个对比的自我监督任务来学习未标记用户行为数据的通用用户模型。第一种是隐蔽行为预测和识别，旨在对用户行为的上下文进行建模。第二种是行为序列匹配，旨在捕获不同时期用户兴趣的稳定性。此外，我们提出了一个中等硬度的负面抽样框架来选择信息量大的负面样本，以便更好地进行对比预训练。大量实验验证了 UserBERT 在用户模型预训练中的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=UserBERT:+Pre-training+User+Model+with+Contrastive+Self-supervision)|0| +|[Modern Baselines for SPARQL Semantic Parsing](https://doi.org/10.1145/3477495.3531841)|Debayan Banerjee, Pranav Ajit Nair, Jivat Neet Kaur, Ricardo Usbeck, Chris Biemann|Universität Hamburg, Hamburg, Germany; Indian Institute of Technology (BHU), Varanasi, India; Microsoft Research, Bengaluru, India|In this work, we focus on the task of generating SPARQL queries from natural language questions, which can then be executed on Knowledge Graphs (KGs). We assume that gold entity and relations have been provided, and the remaining task is to arrange them in the right order along with SPARQL vocabulary, and input tokens to produce the correct SPARQL query. Pre-trained Language Models (PLMs) have not been explored in depth on this task so far, so we experiment with BART, T5 and PGNs (Pointer Generator Networks) with BERT embeddings, looking for new baselines in the PLM era for this task, on DBpedia and Wikidata KGs. We show that T5 requires special input tokenisation, but produces state of the art performance on LC-QuAD 1.0 and LC-QuAD 2.0 datasets, and outperforms task-specific models from previous works. Moreover, the methods enable semantic parsing for questions where a part of the input needs to be copied to the output query, thus enabling a new paradigm in KG semantic parsing.|在这项工作中，我们将重点放在从自然语言问题生成 SPARQL 查询的任务上，然后可以在知识图(Knowledge Graphs，KG)上执行这些查询。我们假设已经提供了 gold 实体和关系，剩下的任务是将它们与 SPARQL 词汇表一起按正确的顺序排列，并输入令牌以生成正确的 SPARQL 查询。预先训练的语言模型(PLM)到目前为止还没有被深入探索，所以我们在 BART，T5和 PGNs (指针生成器网络)中嵌入 BERT 进行试验，在 DBpedia 和 Wikidata KG 上为这项任务寻找 PLM 时代的新基线。我们展示了 T5需要特殊的输入标记，但是在 LC-QuAD 1.0和 LC-QuAD 2.0数据集上产生了最先进的性能，并且优于以前作品中的任务特定模型。此外，这些方法还支持对需要将部分输入复制到输出查询的问题进行语义解析，从而为 KG 语义解析提供了一个新的范例。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Modern+Baselines+for+SPARQL+Semantic+Parsing)|0| |[Posterior Probability Matters: Doubly-Adaptive Calibration for Neural Predictions in Online Advertising](https://doi.org/10.1145/3477495.3531911)|Penghui Wei, Weimin Zhang, Ruijie Hou, Jinquan Liu, Shaoguo Liu, Liang Wang, Bo Zheng|Alibaba Group, Beijing, China|Predicting user response probabilities is vital for ad ranking and bidding. We hope that predictive models can produce accurate probabilistic predictions that reflect true likelihoods. Calibration techniques aims to post-process model predictions to posterior probabilities. Field-level calibration -- which performs calibration w.r.t. to a specific field value -- is fine-grained and more practical. In this paper we propose a doubly-adaptive approach AdaCalib. It learns an isotonic function family to calibrate model predictions with the guidance of posterior statistics, and field-adaptive mechanisms are designed to ensure that the posterior is appropriate for the field value to be calibrated. Experiments verify that AdaCalib achieves significant improvement on calibration performance. It has been deployed online and beats previous approach.|预测用户响应概率对广告排名和投标至关重要。我们希望预测模型能够产生反映真实可能性的准确概率预测。校正技术旨在对模型预测进行后期处理，以便后验概率。现场级校准——对特定的现场值进行校准。现场级校准更加细粒度和实用。在本文中，我们提出了一个双重自适应的方法 AdaCalib。它学习了一个等调函数族，以后验统计学为指导校准模型预测，并设计了场自适应机制，以确保后验适合于被校准的场值。实验证明，AdaCalib 在校准性能方面取得了显著的改善。它已经在网上部署，打破了以前的做法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Posterior+Probability+Matters:+Doubly-Adaptive+Calibration+for+Neural+Predictions+in+Online+Advertising)|0| |[Table Enrichment System for Machine Learning](https://doi.org/10.1145/3477495.3531678)|Yuyang Dong, Masafumi Oyamada|NEC Corporation, Kawasaki, Japan|Data scientists are constantly facing the problem of how to improve prediction accuracy with insufficient tabular data. We propose a table enrichment system that enriches a query table by adding external attributes (columns) from data lakes and improves the accuracy of machine learning predictive models. Our system has four stages, join row search, task-related table selection, row and column alignment, and feature selection and evaluation, to efficiently create an enriched table for a given query table and a specified machine learning task. We demonstrate our system with a web UI to show the use cases of table enrichment.|如何在表格数据不足的情况下提高预测的准确性，是数据科学家不断面临的问题。我们提出了一个表增加系统，通过增加数据湖的外部属性(列)来丰富查询表，提高机器学习预测模型的准确性。我们的系统分为四个阶段: 连接行搜索、任务相关的表选择、行和列对齐以及特征选择和评估，以有效地为给定的查询表和指定的机器学习任务创建一个丰富的表。我们用一个 web UI 来演示我们的系统，以显示表充实的用例。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Table+Enrichment+System+for+Machine+Learning)|0| |[LawNet-Viz: A Web-based System to Visually Explore Networks of Law Article References](https://doi.org/10.1145/3477495.3531668)|Lucio La Cava, Andrea Simeri, Andrea Tagarelli|University of Calabria, Rende (CS), Italy|We present LawNet-Viz, a web-based tool for the modeling, analysis and visualization of law reference networks extracted from a statute law corpus. LawNet-Viz is designed to support legal research tasks and help legal professionals as well as laymen visually exploring the article connections built upon the explicit law references detected in the article contents. To demonstrate LawNet-Viz, we show its application to the Italian Civil Code (ICC), which exploits a recent BERT-based model fine-tuned on the ICC. LawNet-Viz is a system prototype that is planned for product development.|我们介绍了 LawNet-Viz，这是一个基于网络的工具，用于对从成文法语料库中提取的法律参考网络进行建模、分析和可视化。LawNet-Viz 旨在支持法律研究任务，帮助法律专业人士以及外行人士在文章内容中发现的明确的法律参考文献的基础上，直观地探索文章之间的联系。为了演示 LawNet-Viz，我们展示了它在意大利民法典(ICC)中的应用，该法典利用了最近在 ICC 上微调的基于 BERT 的模型。LawNet-Viz 是计划用于产品开发的系统原型。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=LawNet-Viz:+A+Web-based+System+to+Visually+Explore+Networks+of+Law+Article+References)|0| -|[Quote Erat Demonstrandum: A Web Interface for Exploring the Quotebank Corpus](https://doi.org/10.1145/3477495.3531696)|Vuk Vukovic, Akhil Arora, HuanCheng Chang, Andreas Spitz, Robert West|University of Konstanz, Konstanz, Germany; EPFL, Lausanne, Switzerland|The use of attributed quotes is the most direct and least filtered pathway of information propagation in news. Consequently, quotes play a central role in the conception, reception, and analysis of news stories. Since quotes provide a more direct window into a speaker's mind than regular reporting, they are a valuable resource for journalists and researchers alike. While substantial research efforts have been devoted to methods for the automated extraction of quotes from news and their attribution to speakers, few comprehensive corpora of attributed quotes from contemporary sources are available to the public. Here, we present an adaptive web interface for searching Quotebank, a massive collection of quotes from the news, which we make available at https://quotebank.dlab.tools.|引用属性是新闻信息传播中最直接、过滤最少的途径。因此，引文在新闻故事的概念、接受和分析中起着核心作用。由于引语比常规报道提供了一个更直接的窗口来了解演讲者的思想，因此它们对记者和研究人员都是一种宝贵的资源。虽然大量的研究工作致力于从新闻中自动摘录引语及其对发言者的归属的方法，但公众很少能够获得来自当代来源的归属引语的综合语料库。在这里，我们提供了一个自适应的网络界面，用于搜索“报价银行”(Quotebank) ，这是一个来自新闻的大量引用集合，我们可以在 https://Quotebank.dlab.tools 上找到它。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Quote+Erat+Demonstrandum:+A+Web+Interface+for+Exploring+the+Quotebank+Corpus)|0| +|[Quote Erat Demonstrandum: A Web Interface for Exploring the Quotebank Corpus](https://doi.org/10.1145/3477495.3531696)|Vuk Vukovic, Akhil Arora, HuanCheng Chang, Andreas Spitz, Robert West|EPFL, Lausanne, Switzerland; University of Konstanz, Konstanz, Germany|The use of attributed quotes is the most direct and least filtered pathway of information propagation in news. Consequently, quotes play a central role in the conception, reception, and analysis of news stories. Since quotes provide a more direct window into a speaker's mind than regular reporting, they are a valuable resource for journalists and researchers alike. While substantial research efforts have been devoted to methods for the automated extraction of quotes from news and their attribution to speakers, few comprehensive corpora of attributed quotes from contemporary sources are available to the public. Here, we present an adaptive web interface for searching Quotebank, a massive collection of quotes from the news, which we make available at https://quotebank.dlab.tools.|引用属性是新闻信息传播中最直接、过滤最少的途径。因此，引文在新闻故事的概念、接受和分析中起着核心作用。由于引语比常规报道提供了一个更直接的窗口来了解演讲者的思想，因此它们对记者和研究人员都是一种宝贵的资源。虽然大量的研究工作致力于从新闻中自动摘录引语及其对发言者的归属的方法，但公众很少能够获得来自当代来源的归属引语的综合语料库。在这里，我们提供了一个自适应的网络界面，用于搜索“报价银行”(Quotebank) ，这是一个来自新闻的大量引用集合，我们可以在 https://Quotebank.dlab.tools 上找到它。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Quote+Erat+Demonstrandum:+A+Web+Interface+for+Exploring+the+Quotebank+Corpus)|0| |[Unsupervised Product Offering Title Quality Scores](https://doi.org/10.1145/3477495.3536333)|Henry S. Vieira|LuizaLabs, São Paulo, Brazil|The title of a product offering is the consolidation of a product's characteristics in textual format for user consumption. The low quality of the textual content of a product's title can negatively influence the entire shopping experience. The negative experience can start with the impossibility of discovering a desired product, going from problems in identifying a product and its characteristics up to the purchase of an unwanted item. A solution to this problem is to establish an indicator that automatically describes the quality of the product title. With this assessment, it is possible to notify sellers who have registered products with poor quality titles and encourage revisions or suggest improvements. The focus of this work is to show how it is possible to assign a score that indicates the descriptive quality of product offers in an e-commerce marketplace environment using unsupervised methods.|产品提供的标题是以文本格式整合产品的特征以供用户使用。产品标题文字内容的低质量会对整个购物体验产生负面影响。消极的体验可以从发现想要的产品的不可能性开始，从识别产品及其特征的问题到购买不想要的产品。解决这个问题的一个办法是建立一个自动描述产品名称质量的指标。通过这种评估，可以通知注册产品质量低劣的卖方，并鼓励修改或提出改进建议。这项工作的重点是展示如何可以指定一个分数，表明在电子商务市场环境中的产品提供的描述性质量使用无监督的方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Unsupervised+Product+Offering+Title+Quality+Scores)|0| |[Few-shot Information Extraction is Here: Pre-train, Prompt and Entail](https://doi.org/10.1145/3477495.3532786)|Eneko Agirre|University of the Basque Country UPV/EHU, Donostia, Spain|Deep Learning has made tremendous progress in Natural Language Processing (NLP), where large pre-trained language models (PLM) fine-tuned on the target task have become the predominant tool. More recently, in a process called prompting, NLP tasks are rephrased as natural language text, allowing us to better exploit linguistic knowledge learned by PLMs and resulting in significant improvements. Still, PLMs have limited inference ability. In the Textual Entailment task, systems need to output whether the truth of a certain textual hypothesis follows from the given premise text. Manually annotated entailment datasets covering multiple inference phenomena have been used to infuse inference capabilities to PLMs. This talk will review these recent developments, and will present an approach that combines prompts and PLMs fine-tuned for textual entailment that yields state-of-the-art results on Information Extraction (IE) using only a small fraction of the annotations. The approach has additional benefits, like the ability to learn from different schemas and inference datasets. These developments enable a new paradigm for IE where the expert can define the domain-specific schema using natural language and directly run those specifications, annotating a handful of examples in the process. A user interface based on this new paradigm will also be presented. Beyond IE, inference capabilities could be extended, acquired and applied from other tasks, opening a new research avenue where entailment and downstream task performance improve in tandem.|深度学习在自然语言处理(NLP)领域取得了巨大的进步，其中针对目标任务进行微调的大型预训练语言模型(PLM)已成为主要的工具。最近，在一个叫做“提示”的过程中，NLP 任务被重新定义为自然语言文本，使我们能够更好地利用 PLM 学到的语言知识，从而带来显著的改进。不过，PLM 的推理能力有限。在文字蕴涵任务中，系统需要输出某个文本假设的真实性是否来自给定的前提文本。包含多种推理现象的人工注释的蕴涵数据集已经被用来为 PLM 注入推理能力。这个演讲将回顾这些最近的发展，并将提出一个方法，结合提示和 PLM 微调的文字蕴涵，产生最先进的结果在信息抽取(IE)使用只有一小部分的注释。这种方法还有其他好处，比如可以从不同的模式和推断数据集中学习。这些开发为 IE 提供了一个新的范例，在这个范例中，专家可以使用自然语言定义特定于领域的模式，并直接运行这些规范，在过程中注释一些示例。本文还将介绍一个基于这种新范例的用户界面。在 IE 之外，推理能力可以从其他任务中得到扩展、获取和应用，开辟了一条新的研究途径，其中蕴含和下游任务绩效同步提高。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Few-shot+Information+Extraction+is+Here:+Pre-train,+Prompt+and+Entail)|0| |[Improving Implicit Alternating Least Squares with Ring-based Regularization](https://doi.org/10.1145/3477495.3531995)|Rui Fan, Jin Chen, Jin Zhang, Defu Lian, Enhong Chen|University of Electronic Science and Technology of China, Chengdu, China; University of Science and Technology of China, Hefei, China|Due to the widespread presence of implicit feedback, recommendation based on them has been a long-standing research problem in academia and industry. However, it suffers from the extremely-sparse problem, since each user only interacts with a few items. One well-known and good-performing method is to treat each user's all uninteracted items as negative with low confidence. The method intrinsically imposes an implicit regularization to penalize large deviation of each user's preferences for uninteracted items from a constant. However, these methods have to assume a constant-rating prior to uninteracted items, which may be questionable. In this paper, we propose a novel ring-based regularization to penalize significant differences of each user's preferences between each item and some other items. The ring structure, described by an item graph, determines which other items are selected for each item in the regularization. The regularization not only averts the introduction of the prior ratings but also implicitly penalizes the remarkable preference differences for all items according to theoretical analysis. However, optimizing the recommenders with the regularization still suffers from computational challenges, so we develop a scalable alternating least square algorithm by carefully designing gradient computation. Therefore, as long as connecting each item with a sublinear/constant number of other items in the item graph, the overall learning algorithm could be comparably efficient to the existing algorithms. The proposed regularization is extensively evaluated with several public recommendation datasets, where the results show that the regularization could lead to considerable improvements in recommendation performance.|由于内隐反馈的广泛存在，基于内隐反馈的推荐已经成为学术界和业界长期研究的课题。但是，由于每个用户只与少数几个项目交互，因此它遇到了极其稀疏的问题。一个众所周知的好方法是将每个用户的所有未交互的项目视为负面的，并且信心不足。这种方法本质上强加了一种隐式正则化，以惩罚每个用户对常量中未交互项偏好的巨大偏差。但是，这些方法必须在未交互的项目之前假设一个常量评级，这可能是值得怀疑的。在本文中，我们提出了一个新的基于环的正则化，以惩罚每个用户的偏好之间的显着差异，每个项目和其他一些项目。由项目图描述的环结构确定为正则化中的每个项目选择哪些其他项目。根据理论分析，规则化不仅避免了先验评分的引入，而且隐含地惩罚了对所有项目的显著偏好差异。然而，正则化推荐算法的优化仍然面临着计算上的挑战，因此我们通过精心设计梯度计算，发展了一种可扩展的交替最小二乘算法。因此，只要将每个项目与项目图中其他项目的次线性/常数连接起来，整个学习算法就可以比现有算法更有效。利用若干公共推荐数据集对拟议的规范化进行了广泛评估，结果表明，规范化可大大改善推荐性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Improving+Implicit+Alternating+Least+Squares+with+Ring-based+Regularization)|0| -|[Target-aware Abstractive Related Work Generation with Contrastive Learning](https://doi.org/10.1145/3477495.3532065)|Xiuying Chen, Hind Alamro, Mingzhe Li, Shen Gao, Rui Yan, Xin Gao, Xiangliang Zhang|KAUST, Jeddah, China; Peking University, Beijing, China; KAUST, Jeddah, Saudi Arabia; Renmin University of China, Beijing, China; University of Notre Dame & KAUST, South Bend, China|The related work section is an important component of a scientific paper, which highlights the contribution of the target paper in the context of the reference papers. Authors can save their time and effort by using the automatically generated related work section as a draft to complete the final related work. Most of the existing related work section generation methods rely on extracting off-the-shelf sentences to make a comparative discussion about the target work and the reference papers. However, such sentences need to be written in advance and are hard to obtain in practice. Hence, in this paper, we propose an abstractive target-aware related work generator (TAG), which can generate related work sections consisting of new sentences. Concretely, we first propose a target-aware graph encoder, which models the relationships between reference papers and the target paper with target-centered attention mechanisms. In the decoding process, we propose a hierarchical decoder that attends to the nodes of different levels in the graph with keyphrases as semantic indicators. Finally, to generate a more informative related work, we propose multi-level contrastive optimization objectives, which aim to maximize the mutual information between the generated related work with the references and minimize that with non-references. Extensive experiments on two public scholar datasets show that the proposed model brings substantial improvements over several strong baselines in terms of automatic and tailored human evaluations.|相关工作部分是科学论文的一个重要组成部分，它突出了目标论文在参考文件中的贡献。作者可以通过使用自动生成的相关工作部分作为草稿来完成最终的相关工作，从而节省时间和精力。现有的相关作品章节生成方法大多依赖于抽取现成的句子，对目标作品和参考文献进行比较讨论。然而，这样的句子需要事先写好，在实践中很难获得。因此，本文提出了一种抽象的目标感知相关工作生成器(TAG) ，它可以生成由新句子组成的相关工作部分。具体地说，我们首先提出了一种目标感知图形编码器，它使用以目标为中心的注意机制来模拟参考文献和目标文献之间的关系。在解码过程中，我们提出了一种以关键词作为语义指标的层次化解码器，它可以处理图中不同层次的节点。最后，为了生成信息量更大的相关作品，我们提出了多层次对比优化目标，目的是最大化生成的相关作品与参考文献之间的相互信息，最小化非参考文献之间的相互信息。对两个公共学者数据集的大量实验表明，该模型在自动化和量身定制的人类评估方面比几个强大的基线带来了实质性的改进。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Target-aware+Abstractive+Related+Work+Generation+with+Contrastive+Learning)|0| +|[Target-aware Abstractive Related Work Generation with Contrastive Learning](https://doi.org/10.1145/3477495.3532065)|Xiuying Chen, Hind Alamro, Mingzhe Li, Shen Gao, Rui Yan, Xin Gao, Xiangliang Zhang|Peking University, Beijing, China; Renmin University of China, Beijing, China; University of Notre Dame & KAUST, South Bend, China; KAUST, Jeddah, Saudi Arabia; KAUST, Jeddah, China|The related work section is an important component of a scientific paper, which highlights the contribution of the target paper in the context of the reference papers. Authors can save their time and effort by using the automatically generated related work section as a draft to complete the final related work. Most of the existing related work section generation methods rely on extracting off-the-shelf sentences to make a comparative discussion about the target work and the reference papers. However, such sentences need to be written in advance and are hard to obtain in practice. Hence, in this paper, we propose an abstractive target-aware related work generator (TAG), which can generate related work sections consisting of new sentences. Concretely, we first propose a target-aware graph encoder, which models the relationships between reference papers and the target paper with target-centered attention mechanisms. In the decoding process, we propose a hierarchical decoder that attends to the nodes of different levels in the graph with keyphrases as semantic indicators. Finally, to generate a more informative related work, we propose multi-level contrastive optimization objectives, which aim to maximize the mutual information between the generated related work with the references and minimize that with non-references. Extensive experiments on two public scholar datasets show that the proposed model brings substantial improvements over several strong baselines in terms of automatic and tailored human evaluations.|相关工作部分是科学论文的一个重要组成部分，它突出了目标论文在参考文件中的贡献。作者可以通过使用自动生成的相关工作部分作为草稿来完成最终的相关工作，从而节省时间和精力。现有的相关作品章节生成方法大多依赖于抽取现成的句子，对目标作品和参考文献进行比较讨论。然而，这样的句子需要事先写好，在实践中很难获得。因此，本文提出了一种抽象的目标感知相关工作生成器(TAG) ，它可以生成由新句子组成的相关工作部分。具体地说，我们首先提出了一种目标感知图形编码器，它使用以目标为中心的注意机制来模拟参考文献和目标文献之间的关系。在解码过程中，我们提出了一种以关键词作为语义指标的层次化解码器，它可以处理图中不同层次的节点。最后，为了生成信息量更大的相关作品，我们提出了多层次对比优化目标，目的是最大化生成的相关作品与参考文献之间的相互信息，最小化非参考文献之间的相互信息。对两个公共学者数据集的大量实验表明，该模型在自动化和量身定制的人类评估方面比几个强大的基线带来了实质性的改进。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Target-aware+Abstractive+Related+Work+Generation+with+Contrastive+Learning)|0| |[Information Need Awareness: An EEG Study](https://doi.org/10.1145/3477495.3531999)|Dominika Michalkova, Mario ParraRodriguez, Yashar Moshfeghi|University of Strathclyde, Glasgow, United Kingdom|A fundamental goal of Information Retrieval (IR) is to satisfy search­ers' information need (IN). Advances in neuroimaging technologies have allowed for interdisciplinary research to investigate the brain activity associated with the realisation of IN. While these studies have been informative, they were not able to capture the cognitive processes underlying the realisation of IN and the interplay between them with a high temporal resolution. This paper aims to investigate this research question by inferring the variability of brain activity based on the contrast of a state of IN with the two other (no-IN) scenarios. To do so, we employed Electroencephalography (EEG) and constructed an Event-Related Potential (ERP) analysis of the brain signals captured while the participants were experiencing the realisation of IN. In particular, the brain signals of 24 healthy participants were captured while performing a Question-Answering (Q/A) Task. Our results show a link between the early stages of processing, corresponding to awareness and the late activity, meaning memory control mechanisms. Our findings also show that participants exhibited early N1-P2 complex indexing awareness processes and indicate, thus, that the realisation of IN is manifested in the brain before it reaches the user's consciousness. This research contributes novel insights into a better understanding of IN and informs the design of IR systems to better satisfy it.|信息检索的一个基本目标是满足用户的信息需求。神经影像技术的进步使得科际整合能够研究与智能网络实现相关的大脑活动。虽然这些研究提供了很多信息，但是他们并没有能够高时间解析度地捕捉到智力认知的认知过程以及它们之间的相互作用。本文旨在通过比较智力状态与其他两种情境(无智力状态)的差异来推断大脑活动的变异性，从而探讨这一研究问题。为了做到这一点，我们使用了脑电图(EEG) ，并构建了一个事件相关电位(ERP)分析，分析参与者在体验智力活动时捕捉到的大脑信号。特别是，24名健康参与者的大脑信号在进行问答(Q/A)任务时被捕获。我们的研究结果显示，早期的加工阶段，相应的意识和晚期的活动，意味着记忆控制机制之间的联系。我们的研究结果还表明，参与者表现出早期的 N1-P2复杂的索引意识过程，并表明，因此，实现 IN 是表现在大脑之前，达到用户的意识。这项研究为更好地理解智能网提供了新的见解，并为设计更好地满足智能网的红外系统提供了参考。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Information+Need+Awareness:+An+EEG+Study)|0| |[Unifying Cross-lingual Summarization and Machine Translation with Compression Rate](https://doi.org/10.1145/3477495.3532071)|Yu Bai, Heyan Huang, Kai Fan, Yang Gao, Yiming Zhu, Jiaao Zhan, Zewen Chi, Boxing Chen||Cross-lingual Summarization (CLS), converting a document into a cross-lingual summary, is highly related to Machine Translation (MT) task. However, MT resources are still underutilized for the CLS task. In this paper, we propose a novel task, Cross-lingual Summarization with Compression rate (CSC), to benefit cross-lingual summarization through large-scale MT corpus. Through introducing compression rate, we regard MT task as a special CLS task with the compression rate of 100%. Hence they can be trained as a unified task, sharing knowledge more effectively. Moreover, to bridge these two tasks smoothly, we propose a simple yet effective data augmentation method to produce document-summary pairs with different compression rates. The proposed method not only improves the performance of CLS task, but also provides controllability to generate summaries in desired lengths. Experiments demonstrate that our method outperforms various strong baselines.|跨语言摘要(CLS)是将文档转换为跨语言摘要的一种技术，它与机器翻译(MT)任务密切相关。然而，用于 CLS 任务的 MT 资源仍未得到充分利用。在本文中，我们提出了一个新的任务，跨语言压缩率摘要(CSC) ，以利于跨语言摘要通过大规模的机器翻译语料库。通过引入压缩率，将机器翻译任务视为一种特殊的 CLS 任务，压缩率为100% 。因此，他们可以被训练成一个统一的任务，更有效地分享知识。此外，为了平滑地连接这两个任务，我们提出了一种简单而有效的数据增强方法来产生不同压缩率的文档-摘要对。该方法不仅提高了 CLS 任务的性能，而且提供了生成所需长度摘要的可控性。实验结果表明，该方法的性能优于各种强基线。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Unifying+Cross-lingual+Summarization+and+Machine+Translation+with+Compression+Rate)|0| -|[What Makes the Story Forward?: Inferring Commonsense Explanations as Prompts for Future Event Generation](https://doi.org/10.1145/3477495.3532080)|Li Lin, Yixin Cao, Lifu Huang, Shuang Li, Xuming Hu, Lijie Wen, Jianmin Wang|Tsinghua University, Beijing, China; Singapore Management University, Singapore, Singapore; Virginia Tech, Blacksburg, VA, USA|Prediction over event sequences is critical for many real-world applications in Information Retrieval and Natural Language Processing. Future Event Generation (FEG) is a challenging task in event sequence prediction because it requires not only fluent text generation but also commonsense reasoning to maintain the logical coherence of the entire event story. In this paper, we propose a novel explainable FEG framework, Coep. It highlights and integrates two types of event knowledge, sequential knowledge of direct event-event relations and inferential knowledge that reflects the intermediate character psychology between events, such as intents, causes, reactions, which intrinsically pushes the story forward. To alleviate the knowledge forgetting issue, we design two modules, IM and GM, for each type of knowledge, which are combined via prompt tuning. First, IM focuses on understanding inferential knowledge to generate commonsense explanations and provide a soft prompt vector for GM. We also design a contrastive discriminator for better generalization ability. Second, GM generates future events by modeling direct sequential knowledge with the guidance of IM. Automatic and human evaluation demonstrate that our approach can generate more coherent, specific, and logical future events.|对事件序列的预测对于信息检索和自然语言处理中的许多实际应用来说是至关重要的。未来事件生成(FEG)是事件序列预测中的一个具有挑战性的任务，因为它不仅需要流畅的文本生成，而且需要常识推理来保持整个事件故事的逻辑一致性。在本文中，我们提出了一个新的可解释的 FEG 框架，Coep。它突出并整合了两种类型的事件知识，即直接事件-事件关系的序贯知识和反映事件之间的中间人物心理的推理知识，这些中间人物心理在本质上推动了故事的发展。为了解决知识遗忘问题，我们针对每种类型的知识设计了两个模块，即 IM 模块和 GM 模块，并通过快速调整将它们结合起来。首先，IM 侧重于理解推理知识，产生常识性的解释，并为 GM 提供一个软提示向量。为了提高泛化能力，我们还设计了一种对比鉴别器。其次，GM 在 IM 的指导下，通过建模直接序列知识生成未来事件。自动和人工评估表明，我们的方法可以生成更加连贯、具体和合乎逻辑的未来事件。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=What+Makes+the+Story+Forward?:+Inferring+Commonsense+Explanations+as+Prompts+for+Future+Event+Generation)|0| +|[What Makes the Story Forward?: Inferring Commonsense Explanations as Prompts for Future Event Generation](https://doi.org/10.1145/3477495.3532080)|Li Lin, Yixin Cao, Lifu Huang, Shuang Li, Xuming Hu, Lijie Wen, Jianmin Wang|Virginia Tech, Blacksburg, VA, USA; Tsinghua University, Beijing, China; Singapore Management University, Singapore, Singapore|Prediction over event sequences is critical for many real-world applications in Information Retrieval and Natural Language Processing. Future Event Generation (FEG) is a challenging task in event sequence prediction because it requires not only fluent text generation but also commonsense reasoning to maintain the logical coherence of the entire event story. In this paper, we propose a novel explainable FEG framework, Coep. It highlights and integrates two types of event knowledge, sequential knowledge of direct event-event relations and inferential knowledge that reflects the intermediate character psychology between events, such as intents, causes, reactions, which intrinsically pushes the story forward. To alleviate the knowledge forgetting issue, we design two modules, IM and GM, for each type of knowledge, which are combined via prompt tuning. First, IM focuses on understanding inferential knowledge to generate commonsense explanations and provide a soft prompt vector for GM. We also design a contrastive discriminator for better generalization ability. Second, GM generates future events by modeling direct sequential knowledge with the guidance of IM. Automatic and human evaluation demonstrate that our approach can generate more coherent, specific, and logical future events.|对事件序列的预测对于信息检索和自然语言处理中的许多实际应用来说是至关重要的。未来事件生成(FEG)是事件序列预测中的一个具有挑战性的任务，因为它不仅需要流畅的文本生成，而且需要常识推理来保持整个事件故事的逻辑一致性。在本文中，我们提出了一个新的可解释的 FEG 框架，Coep。它突出并整合了两种类型的事件知识，即直接事件-事件关系的序贯知识和反映事件之间的中间人物心理的推理知识，这些中间人物心理在本质上推动了故事的发展。为了解决知识遗忘问题，我们针对每种类型的知识设计了两个模块，即 IM 模块和 GM 模块，并通过快速调整将它们结合起来。首先，IM 侧重于理解推理知识，产生常识性的解释，并为 GM 提供一个软提示向量。为了提高泛化能力，我们还设计了一种对比鉴别器。其次，GM 在 IM 的指导下，通过建模直接序列知识生成未来事件。自动和人工评估表明，我们的方法可以生成更加连贯、具体和合乎逻辑的未来事件。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=What+Makes+the+Story+Forward?:+Inferring+Commonsense+Explanations+as+Prompts+for+Future+Event+Generation)|0| |[A Dual-Expert Framework for Event Argument Extraction](https://doi.org/10.1145/3477495.3531923)|Rui Li, Wenlin Zhao, Cheng Yang, Sen Su|Beijing University of Posts and Telecommunications, Beijing, China|Event argument extraction (EAE) is an important information extraction task, which aims to identify the arguments of an event described in a given text and classify the roles played by them. A key characteristic in realistic EAE data is that the instance numbers of different roles follow an obvious long-tail distribution. However, the training and evaluation paradigms of existing EAE models either prone to neglect the performance on "tail roles'', or change the role instance distribution for model training to an unrealistic uniform distribution. Though some generic methods can alleviate the class imbalance in long-tail datasets, they usually sacrifice the performance of "head classes'' as a trade-off. To address the above issues, we propose to train our model on realistic long-tail EAE datasets, and evaluate the average performance over all roles. Inspired by the Mixture of Experts (MOE), we propose a Routing-Balanced Dual Expert Framework (RBDEF), which divides all roles into "head" and "tail" two scopes and assigns the classifications of head and tail roles to two separate experts. In inference, each encoded instance will be allocated to one of the two experts by a routing mechanism. To reduce routing errors caused by the imbalance of role instances, we design a Balanced Routing Mechanism (BRM), which transfers several head roles to the tail expert to balance the load of routing, and employs a tri-filter routing strategy to reduce the misallocation of the tail expert's instances. To enable an effective learning of tail roles with scarce instances, we devise Target-Specialized Meta Learning (TSML) to train the tail expert. Different from other meta learning algorithms that only search a generic parameter initialization equally applying to infinite tasks, TSML can adaptively adjust its search path to obtain a specialized initialization for the tail expert, thereby expanding the benefits to the learning of tail roles. In experiments, RBDEF significantly outperforms the state-of-the-art EAE models and advanced methods for long-tail data.|事件参数提取(EAE)是一项重要的信息抽取任务，其目的是识别给定文本中描述的事件的参数，并对它们所扮演的角色进行分类。实际 EAE 数据的一个关键特征是不同角色的实例数量遵循明显的长尾分布。然而，现有 EAE 模型的训练和评估范式要么忽视了“尾部角色”的表现，要么将模型训练的角色实例分布改变为不现实的统一分布。虽然一些通用的方法可以缓解长尾数据集中的类不平衡，但它们通常牺牲“头类”的性能作为一种权衡。为了解决上述问题，我们建议在实际的长尾 EAE 数据集上训练我们的模型，并评估所有角色的平均性能。受专家混合模型(MOE)的启发，本文提出了一种路由平衡双专家框架(RBDEF) ，该框架将所有角色划分为“头部”和“尾部”两个范围，并将“头部”和“尾部”角色的分类分配给两个独立的专家。在推理中，每个编码实例将通过路由机制分配给两个专家中的一个。为了减少由于角色实例不平衡而引起的路由错误，设计了一种平衡路由机制(BRM) ，将多个主要角色转移给尾部专家以平衡路由负载，并采用三重过滤路由策略来减少尾部专家实例的错误分配。为了能够有效地学习尾部角色与稀少的实例，我们设计了目标专门化元学习(TSML) ，以培训尾部专家。不同于其他元学习算法，只搜索一个通用的参数初始化同样适用于无限的任务，TSML 可以自适应地调整其搜索路径，以获得专门的初始化尾部专家，从而扩大的好处，尾部角色的学习。在实验中，RBDEF 的性能明显优于最先进的 EAE 模型和先进的长尾数据处理方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Dual-Expert+Framework+for+Event+Argument+Extraction)|0| |[CorED: Incorporating Type-level and Instance-level Correlations for Fine-grained Event Detection](https://doi.org/10.1145/3477495.3531956)|Jiawei Sheng, Rui Sun, Shu Guo, Shiyao Cui, Jiangxia Cao, Lihong Wang, Tingwen Liu, Hongbo Xu|National Computer Network Emergency Response Technical Team/Coordination Center of China, Beijing, China; Institute of Information Engineering, Chinese Academy of Sciences & UCAS, Beijing, China; Beihang University, Beijing, China|Event detection (ED) is a pivotal task for information retrieval, which aims at identifying event triggers and classifying them into pre-defined event types. In real-world applications, events are usually annotated with numerous fine-grained types, which often arises long-tail type nature and co-occurrence event nature. Existing studies explore the event correlations without full utilization, which may limit the capability of event detection. This paper simultaneously incorporates both the type-level and instance-level event correlations, and proposes a novel framework, termed as CorED. Specifically, we devise an adaptive graph-based type encoder to capture instance-level correlations, learning type representations not only from their training data but also from their relevant types, thus leading to more informative type representations especially for the low-resource types. Besides, we devise an instance interactive decoder to capture instance-level correlations, which predicts event instance types conditioned on the contextual typed event instances, leveraging co-occurrence events as remarkable evidence in prediction. We conduct experiments on two public benchmarks, MAVEN and ACE-2005 dataset. Empirical results demonstrate the unity of both type-level and instance-level correlations, and the model achieves effectiveness performance on both benchmarks.|事件检测是信息检索的关键任务，其目的是识别事件触发器，并将其分类为预先定义的事件类型。在实际应用程序中，事件通常用许多细粒度类型进行注释，这些类型通常具有长尾类型性质和共现事件性质。现有的研究在未充分利用事件相关性的情况下，可能会限制事件检测的能力。本文同时结合了类型级和实例级的事件相关性，提出了一种新的框架，称为 CoreED。具体来说，我们设计了一种基于自适应图形的类型编码器来捕获实例级相关性，不仅从它们的训练数据中学习类型表示，而且从它们的相关类型中学习类型表示，从而导致更多的信息类型表示，特别是对于低资源类型。此外，我们设计了一个实例交互式解码器来捕获实例级的相关性，该解码器利用共现事件作为预测中的显著证据来预测以上下文类型的事件实例为条件的事件实例类型。我们在 MAVEN 和 ACE-2005两个公共基准数据集上进行了实验。实证结果表明，该模型实现了类型级和实例级相关性的统一，达到了两个基准的有效性表现。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=CorED:+Incorporating+Type-level+and+Instance-level+Correlations+for+Fine-grained+Event+Detection)|0| |[QUASER: Question Answering with Scalable Extractive Rationalization](https://doi.org/10.1145/3477495.3532049)|Asish Ghoshal, Srinivasan Iyer, Bhargavi Paranjape, Kushal Lakhotia, Scott Wentau Yih, Yashar Mehdad|Meta AI, Seattle, WA, USA; University of Washington, Seattle, WA, USA|Designing natural language processing (NLP) models that produce predictions by first extracting a set of relevant input sentences, i.e., rationales, is gaining importance for improving model interpretability and producing supporting evidence for users. Current unsupervised approaches are designed to extract rationales that maximize prediction accuracy, which is invariably obtained by exploiting spurious correlations in datasets, and leads to unconvincing rationales. In this paper, we introduce unsupervised generative models to extract dual-purpose rationales, which must not only be able to support a subsequent answer prediction, but also support a reproduction of the input query. We show that such models can produce more meaningful rationales, that are less influenced by dataset artifacts, and as a result, also achieve the state-of-the-art on rationale extraction metrics on four datasets from the ERASER benchmark, significantly improving upon previous unsupervised methods. Our multi-task model is scalable and enables using state-of-the-art pretrained language models to design explainable question answering systems.|设计自然语言处理(NLP)模型，通过首先提取一组相关的输入句，即基本原理，来产生预测，这对于提高模型的可解释性和为用户提供支持性证据越来越重要。目前的无监督方法旨在提取最大限度地提高预测准确性的理由，这些理由总是通过利用数据集中的虚假相关性来获得，并导致不令人信服的理由。在本文中，我们引入了无监督生成模型来提取双重用途的基本原理，它不仅要能够支持后续的答案预测，而且还要能够支持输入查询的重现。我们表明，这样的模型可以产生更有意义的基本原理，这些基本原理受数据集伪影的影响较小，因此，在 ERASER 基准的四个数据集上也实现了最先进的基本原理提取指标，显着改善了以前的无监督方法。我们的多任务模型是可扩展的，并且能够使用最先进的预先训练的语言模型来设计可解释的问答系统。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=QUASER:+Question+Answering+with+Scalable+Extractive+Rationalization)|0| |[PTAU: Prompt Tuning for Attributing Unanswerable Questions](https://doi.org/10.1145/3477495.3532048)|Jinzhi Liao, Xiang Zhao, Jianming Zheng, Xinyi Li, Fei Cai, Jiuyang Tang|National University of Defense Technology, Changsha, China|Current question answering systems are insufficient when confronting real-life scenarios, as they can hardly be aware of whether a question is answerable given its context. Hence, there is a recent pursuit of unanswerability of a question and its attribution. Attribution of unanswerability requires the system to choose an appropriate cause for an unanswerable question. As the task is sophisticated for even human beings, it is expensive to acquire labeled data, which makes it a low-data regime problem. Moreover, the causes themselves are semantically abstract and complex, and the process of attribution is heavily question- and context-dependent. Thus, a capable model has to carefully appreciate the causes, and then, judiciously contrast the question with its context, in order to cast it into the right cause. In response to the challenges, we present PTAU, which refers to and implements a high-level human reading strategy such that one reads with anticipation. In specific, PTAU leverages the recent prompt-tuning paradigm, and is further enhanced with two innovatively conceived modules: 1) a cause-oriented template module that constructs continuous templates towards certain attributing class in high dimensional vector space; and 2) a semantics-aware label module that exploits label semantics through contrastive learning to render the classes distinguishable. Extensive experiments demonstrate that the proposed design better enlightens not only the attribution model, but also current question answering models, leading to superior performance.|当前的问答系统在面对现实情景时是不够的，因为它们很难意识到一个问题是否可以根据其上下文进行回答。因此，最近有一个追求一个问题及其归属无法回答。无法回答的归因要求系统为无法回答的问题选择合适的原因。由于这项任务甚至对人类来说都是复杂的，因此获取标记数据的成本很高，这使得它成为一个低数据量的系统问题。此外，原因本身具有语义抽象性和复杂性，归因过程严重依赖于问题和上下文。因此，一个有能力的模型必须仔细地鉴别原因，然后，明智地将问题与其上下文进行对比，以便将其投入正确的原因。为了应对这些挑战，我们提出了 PTAU，它提出并实施了一种高水平的人类阅读策略，使人们在阅读时带有预期性。具体而言，PTAU 利用了最近的提示调优范式，并进一步增强了两个创新构想的模块: 1)面向原因的模板模块，在高维向量空间中构建针对特定属性类的连续模板; 2)语义感知的标签模块，通过对比学习利用标签语义来使类可区分。大量实验表明，该设计不仅对归因模型有较好的启发作用，而且对现有的问答模型也有较好的启发作用，具有较好的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=PTAU:+Prompt+Tuning+for+Attributing+Unanswerable+Questions)|0| -|[DGQAN: Dual Graph Question-Answer Attention Networks for Answer Selection](https://doi.org/10.1145/3477495.3532084)|Haitian Yang, Xuan Zhao, Yan Wang, Min Li, Wei Chen, Weiqing Huang|Institute of Information Engineering, Chinese Academy of Sciences & School of Cyber Security, University of Chinese Academy of Sciences, Beijing, China; Shanghai University of Finance and Economics, Shanghai, China; York University, Toronto, Canada|Community question answering (CQA) becomes increasingly prevalent in recent years, providing platforms for users with various backgrounds to obtain information and share knowledge. However, the redundancy and lengthiness issues of crowd-sourced answers limit the performance of answer selection, thus leading to difficulties in reading or even misunderstandings for community users. To solve these problems, we propose the dual graph question-answer attention networks (DGQAN) for answer selection task. Aims to fully understand the internal structure of the question and the corresponding answer, firstly, we construct a dual-CQA concept graph with graph convolution networks using the original question and answer text. Specifically, our CQA concept graph exploits the correlation information between question-answer pairs to construct two sub-graphs (QSubject-Answer and QBody-Answer), respectively. Further, a novel dual attention mechanism is incorporated to model both the internal and external semantic relations among questions and answers. More importantly, we conduct experiment to investigate the impact of each layer in the BERT model. The experimental results show that DGQAN model achieves state-of-the-art performance on three datasets (SemEval-2015, 2016, and 2017), outperforming all the baseline models.|近年来，社区问答越来越普遍，为不同背景的用户提供了获取信息和分享知识的平台。然而，众包答案的冗余和冗长问题限制了答案选择的性能，从而导致阅读困难，甚至对社区用户产生误解。为了解决这些问题，我们提出了双图问答注意网络(DGQAN)的答案选择任务。为了充分理解问题和相应答案的内部结构，首先利用原始问答文本构建了一个具有图卷积网络的双 CQA 概念图。具体来说，我们的 CQA 概念图利用问题-答案对之间的相关信息分别构造了两个子图(问题-答案和问题-答案)。此外，还引入了一种新的双重注意机制来建立问答之间的内部和外部语义关系模型。更重要的是，我们通过实验研究了 BERT 模型中各层的影响。实验结果表明，DGQAN 模型在三个数据集(SemEval-2015,2016和2017)上实现了最先进的性能，优于所有基线模型。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=DGQAN:+Dual+Graph+Question-Answer+Attention+Networks+for+Answer+Selection)|0| +|[DGQAN: Dual Graph Question-Answer Attention Networks for Answer Selection](https://doi.org/10.1145/3477495.3532084)|Haitian Yang, Xuan Zhao, Yan Wang, Min Li, Wei Chen, Weiqing Huang|York University, Toronto, Canada; Institute of Information Engineering, Chinese Academy of Sciences & School of Cyber Security, University of Chinese Academy of Sciences, Beijing, China; Shanghai University of Finance and Economics, Shanghai, China|Community question answering (CQA) becomes increasingly prevalent in recent years, providing platforms for users with various backgrounds to obtain information and share knowledge. However, the redundancy and lengthiness issues of crowd-sourced answers limit the performance of answer selection, thus leading to difficulties in reading or even misunderstandings for community users. To solve these problems, we propose the dual graph question-answer attention networks (DGQAN) for answer selection task. Aims to fully understand the internal structure of the question and the corresponding answer, firstly, we construct a dual-CQA concept graph with graph convolution networks using the original question and answer text. Specifically, our CQA concept graph exploits the correlation information between question-answer pairs to construct two sub-graphs (QSubject-Answer and QBody-Answer), respectively. Further, a novel dual attention mechanism is incorporated to model both the internal and external semantic relations among questions and answers. More importantly, we conduct experiment to investigate the impact of each layer in the BERT model. The experimental results show that DGQAN model achieves state-of-the-art performance on three datasets (SemEval-2015, 2016, and 2017), outperforming all the baseline models.|近年来，社区问答越来越普遍，为不同背景的用户提供了获取信息和分享知识的平台。然而，众包答案的冗余和冗长问题限制了答案选择的性能，从而导致阅读困难，甚至对社区用户产生误解。为了解决这些问题，我们提出了双图问答注意网络(DGQAN)的答案选择任务。为了充分理解问题和相应答案的内部结构，首先利用原始问答文本构建了一个具有图卷积网络的双 CQA 概念图。具体来说，我们的 CQA 概念图利用问题-答案对之间的相关信息分别构造了两个子图(问题-答案和问题-答案)。此外，还引入了一种新的双重注意机制来建立问答之间的内部和外部语义关系模型。更重要的是，我们通过实验研究了 BERT 模型中各层的影响。实验结果表明，DGQAN 模型在三个数据集(SemEval-2015,2016和2017)上实现了最先进的性能，优于所有基线模型。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=DGQAN:+Dual+Graph+Question-Answer+Attention+Networks+for+Answer+Selection)|0| |[Towards Event-level Causal Relation Identification](https://doi.org/10.1145/3477495.3531758)|Chuang Fan, Daoxing Liu, Libo Qin, Yue Zhang, Ruifeng Xu|Harbin Institute of Technology, Harbin, China; Harbin Institute of Technology (Shenzhen), Shenzhen, China; WestLake University, Hangzhou, China|Existing methods usually identify causal relations between events at the mention-level, which takes each event mention pair as a separate input. As a result, they either suffer from conflicts among causal relations predicted separately or require a set of additional constraints to resolve such conflicts. We propose to study this task in a more realistic setting, where event-level causality identification can be made. The advantage is two folds: 1) with modeling different mentions of an event as a single unit, no more conflicts among predicted results, without any extra constraints; 2) with the use of diverse knowledge sources (e.g., co-occurrence and coreference relations), a rich graph-based event structure can be induced from the document for supporting event-level causal inference. Graph convolutional network is used to encode such structural information, which aims to capture the local and non-local dependencies among nodes. Results show that our model achieves the best performance under both mention- and event-level settings, outperforming a number of strong baselines by at least 2.8% on F1 score.|现有的方法通常在提及级别上确定事件之间的因果关系，它将每个事件提及对作为一个单独的输入。因此，它们要么受到单独预测的因果关系之间的冲突的影响，要么需要一套额外的约束来解决这些冲突。我们建议在一个更现实的背景下研究这个任务，在这里可以进行事件级的因果关系识别。其优点有两个方面: 1)将事件的不同提及建模为一个单元，预测结果之间没有冲突，没有任何额外的约束; 2)利用多样化的知识来源(例如，共现关系和共参照关系) ，可以从文档中诱导出丰富的基于图的事件结构，以支持事件级因果推理。图卷积网络用于对这些结构信息进行编码，目的是捕获节点之间的局部和非局部依赖关系。结果表明，我们的模型在提及和事件级别设置下都达到了最佳性能，在 F1得分上比一些强基线至少高出2.8% 。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Towards+Event-level+Causal+Relation+Identification)|0| |[Hierarchical Task-aware Multi-Head Attention Network](https://doi.org/10.1145/3477495.3531781)|Jing Du, Lina Yao, Xianzhi Wang, Bin Guo, Zhiwen Yu|The University of New South Wales, Sydney, NSW, Australia; University of Technology Sydney, Sydney, NSW, Australia; Northwestern Polytechnical University, Xi'an, Shaanxi, China|Neural Multi-task Learning is gaining popularity as a way to learn multiple tasks jointly within a single model. While related research continues to break new ground, two major limitations still remain, including (i) poor generalization to scenarios where tasks are loosely correlated; and (ii) under-investigation on global commonality and local characteristics of tasks. Our aim is to bridge these gaps by presenting a neural multi-task learning model coined Hierarchical Task-aware Multi-headed Attention Network (HTMN). HTMN explicitly distinguishes task-specific features from task-shared features to reduce the impact caused by weak correlation between tasks. The proposed method highlights two parts: Multi-level Task-aware Experts Network that identifies task-shared global features and task-specific local features, and Hierarchical Multi-Head Attention Network that hybridizes global and local features to profile more robust and adaptive representations for each task. Afterwards, each task tower receives its hybrid task-adaptive representation to perform task-specific predictions. Extensive experiments on two real datasets show that HTMN consistently outperforms the compared methods on a variety of prediction tasks.|神经多任务学习作为一种在单个模型中联合学习多个任务的方法越来越受到人们的欢迎。尽管相关研究不断取得新进展，但仍然存在两个主要的局限性，包括(i)对任务松散相关的情景概括不足; 以及(ii)对任务的全局共性和局部特征调查不足。我们的目标是通过提出一个神经多任务学习模型来弥补这些差距，该模型被称为分层任务感知多头注意网络(HTMN)。HTMN 明确区分任务特定特性和任务共享特性，以减少任务之间相关性较弱所造成的影响。提出的方法突出了两个部分: 多级任务感知专家网络，识别任务共享的全局特征和任务特定的局部特征，和分层多头注意网络，混合全局和局部特征，以配置更健壮的和自适应的表示为每个任务。然后，每个任务塔接收它的混合任务自适应表示来执行任务特定的预测。在两个实际数据集上进行的大量实验表明，HTMN 在各种预测任务中的表现均优于比较方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Hierarchical+Task-aware+Multi-Head+Attention+Network)|0| -|[Enhancing Event-Level Sentiment Analysis with Structured Arguments](https://doi.org/10.1145/3477495.3531784)|Qi Zhang, Jie Zhou, Qin Chen, Qingchun Bai, Liang He|Fudan University, Shanghai, China; Shanghai Open University, Shanghai, China; East China Normal University, Shanghai, China|Previous studies about event-level sentiment analysis (SA) usually model the event as a topic, a category or target terms, while the structured arguments (e.g., subject, object, time and location) that have potential effects on the sentiment are not well studied. In this paper, we redefine the task as structured event-level SA and propose an End-to-End Event-level Sentiment Analysis (E3SA) approach to solve this issue. Specifically, we explicitly extract and model the event structure information for enhancing event-level SA. Extensive experiments demonstrate the great advantages of our proposed approach over the state-of-the-art methods. Noting the lack of the dataset, we also release a large-scale real-world dataset with event arguments and sentiment labelling for promoting more researches.|以往关于事件层面情绪分析(SA)的研究通常将事件建模为一个主题、一个类别或目标术语，而对情绪有潜在影响的结构化论证(如主语、客体、时间和地点)则没有得到很好的研究。本文将任务重新定义为结构化事件级情绪分析，并提出了一种端到端事件级情绪分析(E3SA)方法来解决这一问题。具体来说，我们显式地提取和建模事件结构信息，以增强事件级 SA。大量的实验证明了我们提出的方法相对于最先进的方法的巨大优势。注意到数据集的缺乏，我们也发布了一个大规模的现实世界的数据集与事件论点和情绪标签，以促进更多的研究。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Enhancing+Event-Level+Sentiment+Analysis+with+Structured+Arguments)|0| -|[Translation-Based Implicit Annotation Projection for Zero-Shot Cross-Lingual Event Argument Extraction](https://doi.org/10.1145/3477495.3531808)|Chenwei Lou, Jun Gao, Changlong Yu, Wei Wang, Huan Zhao, Weiwei Tu, Ruifeng Xu|The Hong Kong University of Science and Technology, Hong Kong, Hong Kong; 4Paradigm Inc, Beijing, China; Harbin Institute of Technology (Shenzhen), Shenzhen, China; Tsinghua University, Beijing, China|Zero-shot cross-lingual event argument extraction (EAE) is a challenging yet practical problem in Information Extraction. Most previous works heavily rely on external structured linguistic features, which are not easily accessible in real-world scenarios. This paper investigates a translation-based method to implicitly project annotations from the source language to the target language. With the use of translation-based parallel corpora, no additional linguistic features are required during training and inference. As a result, the proposed approach is more cost effective than previous works on zero-shot cross-lingual EAE. Moreover, our implicit annotation projection approach introduces less noises and hence is more effective and robust than explicit ones. Experimental results show that our model achieves the best performance, outperforming a number of competitive baselines. The thorough analysis further demonstrates the effectiveness of our model compared to explicit annotation projection approaches.|零镜头跨语言事件参数提取(EAE)是一个具有挑战性的实际信息抽取问题。以往的大多数作品都严重依赖于外部结构化语言特征，而这些特征在现实世界中并不容易获得。本文研究了一种基于翻译的方法来隐式地将注释从源语言投射到目标语言。使用基于翻译的平行语料库，在训练和推理过程中不需要额外的语言特征。结果表明，本文提出的方法比以往针对零镜头跨语言 EAE 的研究更具有成本效益。此外，我们的隐式注释投影方法引入较少的噪声，因此比显式更有效和鲁棒性。实验结果表明，我们的模型达到了最佳的性能，超过了一些竞争基线。深入的分析进一步证明了我们的模型与显式注释投影方法相比的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Translation-Based+Implicit+Annotation+Projection+for+Zero-Shot+Cross-Lingual+Event+Argument+Extraction)|0| +|[Enhancing Event-Level Sentiment Analysis with Structured Arguments](https://doi.org/10.1145/3477495.3531784)|Qi Zhang, Jie Zhou, Qin Chen, Qingchun Bai, Liang He|East China Normal University, Shanghai, China; Fudan University, Shanghai, China; Shanghai Open University, Shanghai, China|Previous studies about event-level sentiment analysis (SA) usually model the event as a topic, a category or target terms, while the structured arguments (e.g., subject, object, time and location) that have potential effects on the sentiment are not well studied. In this paper, we redefine the task as structured event-level SA and propose an End-to-End Event-level Sentiment Analysis (E3SA) approach to solve this issue. Specifically, we explicitly extract and model the event structure information for enhancing event-level SA. Extensive experiments demonstrate the great advantages of our proposed approach over the state-of-the-art methods. Noting the lack of the dataset, we also release a large-scale real-world dataset with event arguments and sentiment labelling for promoting more researches.|以往关于事件层面情绪分析(SA)的研究通常将事件建模为一个主题、一个类别或目标术语，而对情绪有潜在影响的结构化论证(如主语、客体、时间和地点)则没有得到很好的研究。本文将任务重新定义为结构化事件级情绪分析，并提出了一种端到端事件级情绪分析(E3SA)方法来解决这一问题。具体来说，我们显式地提取和建模事件结构信息，以增强事件级 SA。大量的实验证明了我们提出的方法相对于最先进的方法的巨大优势。注意到数据集的缺乏，我们也发布了一个大规模的现实世界的数据集与事件论点和情绪标签，以促进更多的研究。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Enhancing+Event-Level+Sentiment+Analysis+with+Structured+Arguments)|0| +|[Translation-Based Implicit Annotation Projection for Zero-Shot Cross-Lingual Event Argument Extraction](https://doi.org/10.1145/3477495.3531808)|Chenwei Lou, Jun Gao, Changlong Yu, Wei Wang, Huan Zhao, Weiwei Tu, Ruifeng Xu|The Hong Kong University of Science and Technology, Hong Kong, Hong Kong; Tsinghua University, Beijing, China; Harbin Institute of Technology (Shenzhen), Shenzhen, China; 4Paradigm Inc, Beijing, China|Zero-shot cross-lingual event argument extraction (EAE) is a challenging yet practical problem in Information Extraction. Most previous works heavily rely on external structured linguistic features, which are not easily accessible in real-world scenarios. This paper investigates a translation-based method to implicitly project annotations from the source language to the target language. With the use of translation-based parallel corpora, no additional linguistic features are required during training and inference. As a result, the proposed approach is more cost effective than previous works on zero-shot cross-lingual EAE. Moreover, our implicit annotation projection approach introduces less noises and hence is more effective and robust than explicit ones. Experimental results show that our model achieves the best performance, outperforming a number of competitive baselines. The thorough analysis further demonstrates the effectiveness of our model compared to explicit annotation projection approaches.|零镜头跨语言事件参数提取(EAE)是一个具有挑战性的实际信息抽取问题。以往的大多数作品都严重依赖于外部结构化语言特征，而这些特征在现实世界中并不容易获得。本文研究了一种基于翻译的方法来隐式地将注释从源语言投射到目标语言。使用基于翻译的平行语料库，在训练和推理过程中不需要额外的语言特征。结果表明，本文提出的方法比以往针对零镜头跨语言 EAE 的研究更具有成本效益。此外，我们的隐式注释投影方法引入较少的噪声，因此比显式更有效和鲁棒性。实验结果表明，我们的模型达到了最佳的性能，超过了一些竞争基线。深入的分析进一步证明了我们的模型与显式注释投影方法相比的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Translation-Based+Implicit+Annotation+Projection+for+Zero-Shot+Cross-Lingual+Event+Argument+Extraction)|0| |[Understanding Long Programming Languages with Structure-Aware Sparse Attention](https://doi.org/10.1145/3477495.3531811)|Tingting Liu, Chengyu Wang, Cen Chen, Ming Gao, Aoying Zhou|Alibaba Group, Hangzhou, China; East China Normal University, Shanghai, China|Programming-based Pre-trained Language Models (PPLMs) such as CodeBERT have achieved great success in many downstream code-related tasks. Since the memory and computational complexity of self-attention in the Transformer grow quadratically with the sequence length, PPLMs typically limit the code length to 512. However, codes in real-world applications are generally long, such as code searches, which cannot be processed efficiently by existing PPLMs. To solve this problem, in this paper, we present SASA, a Structure-Aware Sparse Attention mechanism, which reduces the complexity and improves performance for long code understanding tasks. The key components in SASA are top-k sparse attention and Abstract Syntax Tree (AST)-based structure-aware attention. With top-k sparse attention, the most crucial attention relation can be obtained with a lower computational cost. As the code structure represents the logic of the code statements, which is a complement to the code sequence characteristics, we further introduce AST structures into attention. Extensive experiments on CodeXGLUE tasks show that SASA achieves better performance than the competing baselines.|基于编程的预训练语言模型(PPLM) ，如 CodeBERT，在许多下游代码相关的任务中取得了巨大的成功。由于变压器中自注意的内存和计算复杂度随序列长度的二次增长而增长，PPLM 通常将码长限制在512。然而，实际应用中的代码通常都很长，比如代码搜索，现有的 PPLM 无法有效地处理这些代码。为了解决这一问题，本文提出了一种基于结构感知的稀疏注意机制 SASA，该机制降低了长代码理解任务的复杂度，提高了性能。SASA 的关键组成部分是 top-k 稀疏注意和基于抽象语法树(AST)的结构感知注意。使用 top-k 稀疏注意，可以以较低的计算代价得到最关键的注意关系。由于代码结构代表了代码语句的逻辑，是对代码序列特性的补充，因此我们进一步引入了 AST 结构。在 CodeXGLUE 任务上的大量实验表明，SASA 比竞争基线获得了更好的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Understanding+Long+Programming+Languages+with+Structure-Aware+Sparse+Attention)|0| -|[Dialogue Topic Segmentation via Parallel Extraction Network with Neighbor Smoothing](https://doi.org/10.1145/3477495.3531817)|Jinxiong Xia, Cao Liu, Jiansong Chen, Yuchen Li, Fan Yang, Xunliang Cai, Guanglu Wan, Houfeng Wang|Peking University, Beijing, China; Meituan, Beijing, China|Dialogue topic segmentation is a challenging task in which dialogues are split into segments with pre-defined topics. Existing works on topic segmentation adopt a two-stage paradigm, including text segmentation and segment labeling. However, such methods tend to focus on the local context in segmentation, and the inter-segment dependency is not well captured. Besides, the ambiguity and labeling noise in dialogue segment bounds bring further challenges to existing models. In this work, we propose the Parallel Extraction Network with Neighbor Smoothing (PEN-NS) to address the above issues. Specifically, we propose the parallel extraction network to perform segment extractions, optimizing the bipartite matching cost of segments to capture inter-segment dependency. Furthermore, we propose neighbor smoothing to handle the segment-bound noise and ambiguity. Experiments on a dialogue-based and a document-based topic segmentation dataset show that PEN-NS outperforms state-the-of-art models significantly.|对话主题分割是一个具有挑战性的任务，其中对话分割成具有预定义的主题片段。现有的主题切分研究采用两阶段模式，包括文本切分和段标注。然而，这些方法在分割过程中往往只关注局部上下文，而且不能很好地捕获分段间的依赖关系。此外，对话段边界的模糊性和标注噪声也给现有的模型带来了进一步的挑战。针对上述问题，本文提出了一种基于邻域平滑的并行抽取网络(PEN-NS)。具体来说，我们提出并行提取网络来执行分段提取，优化分段的二部匹配代价来捕获分段间的依赖关系。此外，我们还提出了邻域平滑法来处理分段定界噪声和模糊度。在基于对话和基于文档的主题分割数据集上进行的实验表明，PEN-NS 模型的性能明显优于目前最先进的模型。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Dialogue+Topic+Segmentation+via+Parallel+Extraction+Network+with+Neighbor+Smoothing)|0| -|[Expression Syntax Information Bottleneck for Math Word Problems](https://doi.org/10.1145/3477495.3531824)|Jing Xiong, Chengming Li, Min Yang, Xiping Hu, Bin Hu|Lanzhou University, Lanzhou, China; Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China; Sun Yat-sen University, Shenzhen, China|Math Word Problems (MWP) aims to automatically solve mathematical questions given in texts. Previous studies tend to design complex models to capture additional information in the original text so as to enable the model to gain more comprehensive features. In this paper, we turn our attention in the opposite direction, and work on how to discard redundant features containing spurious correlations for MWP. To this end, we design an Expression Syntax Information Bottleneck method for MWP (called ESIB) based on variational information bottleneck, which extracts essential features of the expression syntax tree while filtering latent-specific redundancy containing syntax-irrelevant features. The key idea of ESIB is to encourage multiple models to predict the same expression syntax tree for different problem representations of the same problem by mutual learning so as to capture consistent information of expression syntax tree and discard latent-specific redundancy. To improve the generalization ability of the model and generate more diverse expressions, we design a self-distillation loss to encourage the model to rely more on the expression syntax information in the latent space. Experimental results on two large-scale benchmarks show that our model not only achieves state-of-the-art results but also generates more diverse solutions.|数学词汇问题(MWP)旨在自动解决课文中给出的数学问题。以往的研究倾向于设计复杂的模型来捕捉原始文本中的附加信息，从而使模型获得更全面的特征。本文从相反的角度出发，研究了如何去除含有虚假相关的冗余特征。为此，我们设计了一种基于变分信息瓶颈的 MWP 表达式语法信息瓶颈方法(称为 ESIB) ，该方法在过滤包含语法无关特征的潜在特定冗余的同时，提取表达式语法树的基本特征。ESIB 的核心思想是鼓励多个模型通过相互学习对同一问题的不同问题表示预测相同的表达式语法树，从而获取表达式语法树的一致性信息，去除潜在的特定冗余。为了提高模型的泛化能力，生成更多不同的表达式，我们设计了一个自蒸馏损失，以鼓励模型更多地依赖潜在空间中的表达式语法信息。在两个大规模基准上的实验结果表明，该模型不仅取得了最佳的结果，而且产生了更加多样化的解决方案。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Expression+Syntax+Information+Bottleneck+for+Math+Word+Problems)|0| -|[Masking and Generation: An Unsupervised Method for Sarcasm Detection](https://doi.org/10.1145/3477495.3531825)|Rui Wang, Qianlong Wang, Bin Liang, Yi Chen, Zhiyuan Wen, Bing Qin, Ruifeng Xu|Harbin Institute of Technology, Harbin, China; Harbin Institute of Technology & Peng Cheng Laboratory, Shenzhen, China; Harbin Institute of Technology, Shenzhen, China|Existing approaches for sarcasm detection are mainly based on supervised learning, in which the promising performance largely depends on a considerable amount of labeled data or extra information. In the real world scenario, however, the abundant labeled data or extra information requires high labor cost, not to mention that sufficient annotated data is unavailable in many low-resource conditions. To alleviate this dilemma, we investigate sarcasm detection from an unsupervised perspective, in which we explore a masking and generation paradigm in the context to extract the context incongruities for learning sarcastic expression. Further, to improve the feature representations of the sentences, we use unsupervised contrastive learning to improve the sentence representation based on the standard dropout. Experimental results on six perceived sarcasm detection benchmark datasets show that our approach outperforms baselines. Simultaneously, our unsupervised method obtains comparative performance with supervised methods for the intended sarcasm dataset.|现有的挖苦检测方法主要基于监督式学习，其有效性很大程度上取决于大量的标记数据或额外信息。然而，在现实世界的场景中，大量的标记数据或额外的信息需要很高的人工成本，更不用说在许多资源不足的情况下没有足够的注释数据了。为了缓解这一困境，我们从无监督的角度研究了讽刺语的检测问题，探索了语境中的掩蔽和生成范式，以提取语境中的不一致性，从而学习讽刺语的表达。此外，为了改善句子的特征表示，我们使用无监督对比学习来改善基于标准辍学的句子表示。实验结果表明，该方法的性能优于基准测试。同时，我们的无监督方法获得了比较性能的监督方法为预期讽刺数据集。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Masking+and+Generation:+An+Unsupervised+Method+for+Sarcasm+Detection)|0| +|[Dialogue Topic Segmentation via Parallel Extraction Network with Neighbor Smoothing](https://doi.org/10.1145/3477495.3531817)|Jinxiong Xia, Cao Liu, Jiansong Chen, Yuchen Li, Fan Yang, Xunliang Cai, Guanglu Wan, Houfeng Wang|Meituan, Beijing, China; Peking University, Beijing, China|Dialogue topic segmentation is a challenging task in which dialogues are split into segments with pre-defined topics. Existing works on topic segmentation adopt a two-stage paradigm, including text segmentation and segment labeling. However, such methods tend to focus on the local context in segmentation, and the inter-segment dependency is not well captured. Besides, the ambiguity and labeling noise in dialogue segment bounds bring further challenges to existing models. In this work, we propose the Parallel Extraction Network with Neighbor Smoothing (PEN-NS) to address the above issues. Specifically, we propose the parallel extraction network to perform segment extractions, optimizing the bipartite matching cost of segments to capture inter-segment dependency. Furthermore, we propose neighbor smoothing to handle the segment-bound noise and ambiguity. Experiments on a dialogue-based and a document-based topic segmentation dataset show that PEN-NS outperforms state-the-of-art models significantly.|对话主题分割是一个具有挑战性的任务，其中对话分割成具有预定义的主题片段。现有的主题切分研究采用两阶段模式，包括文本切分和段标注。然而，这些方法在分割过程中往往只关注局部上下文，而且不能很好地捕获分段间的依赖关系。此外，对话段边界的模糊性和标注噪声也给现有的模型带来了进一步的挑战。针对上述问题，本文提出了一种基于邻域平滑的并行抽取网络(PEN-NS)。具体来说，我们提出并行提取网络来执行分段提取，优化分段的二部匹配代价来捕获分段间的依赖关系。此外，我们还提出了邻域平滑法来处理分段定界噪声和模糊度。在基于对话和基于文档的主题分割数据集上进行的实验表明，PEN-NS 模型的性能明显优于目前最先进的模型。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Dialogue+Topic+Segmentation+via+Parallel+Extraction+Network+with+Neighbor+Smoothing)|0| +|[Expression Syntax Information Bottleneck for Math Word Problems](https://doi.org/10.1145/3477495.3531824)|Jing Xiong, Chengming Li, Min Yang, Xiping Hu, Bin Hu|Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China; Lanzhou University, Lanzhou, China; Sun Yat-sen University, Shenzhen, China|Math Word Problems (MWP) aims to automatically solve mathematical questions given in texts. Previous studies tend to design complex models to capture additional information in the original text so as to enable the model to gain more comprehensive features. In this paper, we turn our attention in the opposite direction, and work on how to discard redundant features containing spurious correlations for MWP. To this end, we design an Expression Syntax Information Bottleneck method for MWP (called ESIB) based on variational information bottleneck, which extracts essential features of the expression syntax tree while filtering latent-specific redundancy containing syntax-irrelevant features. The key idea of ESIB is to encourage multiple models to predict the same expression syntax tree for different problem representations of the same problem by mutual learning so as to capture consistent information of expression syntax tree and discard latent-specific redundancy. To improve the generalization ability of the model and generate more diverse expressions, we design a self-distillation loss to encourage the model to rely more on the expression syntax information in the latent space. Experimental results on two large-scale benchmarks show that our model not only achieves state-of-the-art results but also generates more diverse solutions.|数学词汇问题(MWP)旨在自动解决课文中给出的数学问题。以往的研究倾向于设计复杂的模型来捕捉原始文本中的附加信息，从而使模型获得更全面的特征。本文从相反的角度出发，研究了如何去除含有虚假相关的冗余特征。为此，我们设计了一种基于变分信息瓶颈的 MWP 表达式语法信息瓶颈方法(称为 ESIB) ，该方法在过滤包含语法无关特征的潜在特定冗余的同时，提取表达式语法树的基本特征。ESIB 的核心思想是鼓励多个模型通过相互学习对同一问题的不同问题表示预测相同的表达式语法树，从而获取表达式语法树的一致性信息，去除潜在的特定冗余。为了提高模型的泛化能力，生成更多不同的表达式，我们设计了一个自蒸馏损失，以鼓励模型更多地依赖潜在空间中的表达式语法信息。在两个大规模基准上的实验结果表明，该模型不仅取得了最佳的结果，而且产生了更加多样化的解决方案。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Expression+Syntax+Information+Bottleneck+for+Math+Word+Problems)|0| +|[Masking and Generation: An Unsupervised Method for Sarcasm Detection](https://doi.org/10.1145/3477495.3531825)|Rui Wang, Qianlong Wang, Bin Liang, Yi Chen, Zhiyuan Wen, Bing Qin, Ruifeng Xu|Harbin Institute of Technology, Harbin, China; Harbin Institute of Technology, Shenzhen, China; Harbin Institute of Technology & Peng Cheng Laboratory, Shenzhen, China|Existing approaches for sarcasm detection are mainly based on supervised learning, in which the promising performance largely depends on a considerable amount of labeled data or extra information. In the real world scenario, however, the abundant labeled data or extra information requires high labor cost, not to mention that sufficient annotated data is unavailable in many low-resource conditions. To alleviate this dilemma, we investigate sarcasm detection from an unsupervised perspective, in which we explore a masking and generation paradigm in the context to extract the context incongruities for learning sarcastic expression. Further, to improve the feature representations of the sentences, we use unsupervised contrastive learning to improve the sentence representation based on the standard dropout. Experimental results on six perceived sarcasm detection benchmark datasets show that our approach outperforms baselines. Simultaneously, our unsupervised method obtains comparative performance with supervised methods for the intended sarcasm dataset.|现有的挖苦检测方法主要基于监督式学习，其有效性很大程度上取决于大量的标记数据或额外信息。然而，在现实世界的场景中，大量的标记数据或额外的信息需要很高的人工成本，更不用说在许多资源不足的情况下没有足够的注释数据了。为了缓解这一困境，我们从无监督的角度研究了讽刺语的检测问题，探索了语境中的掩蔽和生成范式，以提取语境中的不一致性，从而学习讽刺语的表达。此外，为了改善句子的特征表示，我们使用无监督对比学习来改善基于标准辍学的句子表示。实验结果表明，该方法的性能优于基准测试。同时，我们的无监督方法获得了比较性能的监督方法为预期讽刺数据集。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Masking+and+Generation:+An+Unsupervised+Method+for+Sarcasm+Detection)|0| |[Learned Token Pruning in Contextualized Late Interaction over BERT (ColBERT)](https://doi.org/10.1145/3477495.3531835)|Carlos Lassance, Maroua Maachou, Joohee Park, Stéphane Clinchant|Naver, Seoul, Republic of Korea; Naver Labs Europe, Meylan, France|BERT-based rankers have been shown very effective as rerankers in information retrieval tasks. In order to extend these models to full-ranking scenarios, the ColBERT model has been recently proposed, which adopts a late interaction mechanism. This mechanism allows for the representation of documents to be precomputed in advance. However, the late-interaction mechanism leads to large index size, as one needs to save a representation for each token of every document. In this work, we focus on token pruning techniques in order to mitigate this problem. We test four methods, ranging from simpler ones to the use of a single layer of attention mechanism to select the tokens to keep at indexing time. Our experiments show that for the MS MARCO-passages collection, indexes can be pruned up to 70% of their original size, without a significant drop in performance. We also evaluate on the MS MARCO-documents collection and the BEIR benchmark, which reveals some challenges for the proposed mechanism.|以 BERT 为基础的排名已经被证明在信息检索任务中非常有效。为了将这些模型扩展到完全排序的场景，最近提出了 ColBERT 模型，该模型采用了一种后交互机制。这种机制允许预先计算文档的表示形式。但是，后期交互机制会导致索引大小增加，因为需要为每个文档的每个标记保存表示形式。在这项工作中，我们重点关注令牌剪枝技术，以减轻这个问题。我们测试了四种方法，从简单的方法到使用单层注意机制在索引时选择要保留的令牌。我们的实验表明，对于 MS MARCO 段收集，索引可以修剪高达原始大小的70% ，性能没有明显下降。我们还对 MS MARCO 文档集和 BEIR 基准进行了评估，揭示了该机制面临的一些挑战。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Learned+Token+Pruning+in+Contextualized+Late+Interaction+over+BERT+(ColBERT))|0| |[GraFN: Semi-Supervised Node Classification on Graph with Few Labels via Non-Parametric Distribution Assignment](https://doi.org/10.1145/3477495.3531838)|Junseok Lee, Yunhak Oh, Yeonjun In, Namkyeong Lee, Dongmin Hyun, Chanyoung Park|KAIST, Daejeon, Republic of Korea; POSTECH, Pohang, Republic of Korea|Despite the success of Graph Neural Networks (GNNs) on various applications, GNNs encounter significant performance degradation when the amount of supervision signals, i.e., number of labeled nodes, is limited, which is expected as GNNs are trained solely based on the supervision obtained from the labeled nodes. On the other hand, recent self-supervised learning paradigm aims to train GNNs by solving pretext tasks that do not require any labeled nodes, and it has shown to even outperform GNNs trained with few labeled nodes. However, a major drawback of self-supervised methods is that they fall short of learning class discriminative node representations since no labeled information is utilized during training. To this end, we propose a novel semi-supervised method for graphs, GraFN, that leverages few labeled nodes to ensure nodes that belong to the same class to be grouped together, thereby achieving the best of both worlds of semi-supervised and self-supervised methods. Specifically, GraFN randomly samples support nodes from labeled nodes and anchor nodes from the entire graph. Then, it minimizes the difference between two predicted class distributions that are non-parametrically assigned by anchor-supports similarity from two differently augmented graphs. We experimentally show that GraFN surpasses both the semi-supervised and self-supervised methods in terms of node classification on real-world graphs.|尽管图形神经网络(GNN)在各种应用中取得了成功，但是当监督信号(即标记节点的数量)受到限制时，GNN 会遇到显著的性能下降，这是预期的，因为 GNN 仅仅基于从标记节点获得的监督进行训练。另一方面，最近的自我监督学习范式旨在通过解决不需要任何标记节点的托辞任务来训练 GNN，并且已经证明它的表现甚至优于用少量标记节点训练的 GNN。然而，自监督方法的一个主要缺点是，由于在训练过程中没有使用标记信息，因此它们不能很好地表示学习类的判别节点。为此，我们提出了一种新的图的半监督方法，GraFN，它利用少量的标记节点来确保属于同一类的节点被分组在一起，从而实现了半监督和自监督方法的最佳结合。具体来说，GraFN 随机采样支持来自标记节点的节点和来自整个图的锚节点。然后，从两个不同的增广图中最小化由锚支持相似性非参数赋值的两个预测类分布之间的差异。实验结果表明，GraFN 在实际图的节点分类方面优于半监督和自监督方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=GraFN:+Semi-Supervised+Node+Classification+on+Graph+with+Few+Labels+via+Non-Parametric+Distribution+Assignment)|0| -|[Which Discriminator for Cooperative Text Generation?](https://doi.org/10.1145/3477495.3531858)|Antoine Chaffin, Thomas Scialom, Sylvain Lamprier, Jacopo Staiano, Benjamin Piwowarski, Ewa Kijak, Vincent Claveau|CNRS, ISIR - Sorbonne Université, Paris, France; Université Rennes, IRISA, Rennes, France; ISIR - Sorbonne Université, Paris, France; IRISA, IMATAG, Rennes, France; reciTAL, Paris, France; ISIR - Sorbonne Université, reciTAL, Paris, France; CNRS, IRISA, Rennes, France|Language models generate texts by successively predicting probability distributions for next tokens given past ones. A growing field of interest tries to leverage external information in the decoding process so that the generated texts have desired properties, such as being more natural, non toxic, faithful, or having a specific writing style. A solution is to use a classifier at each generation step, resulting in a cooperative environment where the classifier guides the decoding of the language model distribution towards relevant texts for the task at hand. In this paper, we examine three families of (transformer-based) discriminators for this specific task of cooperative decoding: bidirectional, left-to-right and generative ones. We evaluate the pros and cons of these different types of discriminators for cooperative generation, exploring respective accuracy on classification tasks along with their impact on the resulting sample quality and computational performances. We also provide the code of a batched implementation of the powerful cooperative decoding strategy used for our experiments, the Monte Carlo Tree Search, working with each discriminator for Natural Language Generation.|语言模型通过依次预测给定过去标记的下一个标记的概率分布来生成文本。越来越多的研究领域试图在解码过程中利用外部信息，使生成的文本具有期望的特性，如更自然、无毒、忠实或具有特定的写作风格。一个解决方案是在每个生成步骤中使用一个分类器，从而形成一个合作环境，在这个环境中，分类器将语言模型分布的解码引导到手头任务的相关文本中。在这篇论文中，我们针对这个特定的合作解码任务，研究了三类(基于变压器的)鉴别器: 双向的，从左到右的和生成的。我们评估了这些不同类型的鉴别器在合作生成中的优缺点，探讨了它们在分类任务中各自的准确性及其对所得样本质量和计算性能的影响。我们还提供了一个批处理实现的强大的合作解码策略，用于我们的实验，蒙特卡罗树搜索，与自然语言生成的每个鉴别器工作的代码。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Which+Discriminator+for+Cooperative+Text+Generation?)|0| +|[Which Discriminator for Cooperative Text Generation?](https://doi.org/10.1145/3477495.3531858)|Antoine Chaffin, Thomas Scialom, Sylvain Lamprier, Jacopo Staiano, Benjamin Piwowarski, Ewa Kijak, Vincent Claveau|ISIR - Sorbonne Université, reciTAL, Paris, France; reciTAL, Paris, France; Université Rennes, IRISA, Rennes, France; CNRS, ISIR - Sorbonne Université, Paris, France; ISIR - Sorbonne Université, Paris, France; IRISA, IMATAG, Rennes, France; CNRS, IRISA, Rennes, France|Language models generate texts by successively predicting probability distributions for next tokens given past ones. A growing field of interest tries to leverage external information in the decoding process so that the generated texts have desired properties, such as being more natural, non toxic, faithful, or having a specific writing style. A solution is to use a classifier at each generation step, resulting in a cooperative environment where the classifier guides the decoding of the language model distribution towards relevant texts for the task at hand. In this paper, we examine three families of (transformer-based) discriminators for this specific task of cooperative decoding: bidirectional, left-to-right and generative ones. We evaluate the pros and cons of these different types of discriminators for cooperative generation, exploring respective accuracy on classification tasks along with their impact on the resulting sample quality and computational performances. We also provide the code of a batched implementation of the powerful cooperative decoding strategy used for our experiments, the Monte Carlo Tree Search, working with each discriminator for Natural Language Generation.|语言模型通过依次预测给定过去标记的下一个标记的概率分布来生成文本。越来越多的研究领域试图在解码过程中利用外部信息，使生成的文本具有期望的特性，如更自然、无毒、忠实或具有特定的写作风格。一个解决方案是在每个生成步骤中使用一个分类器，从而形成一个合作环境，在这个环境中，分类器将语言模型分布的解码引导到手头任务的相关文本中。在这篇论文中，我们针对这个特定的合作解码任务，研究了三类(基于变压器的)鉴别器: 双向的，从左到右的和生成的。我们评估了这些不同类型的鉴别器在合作生成中的优缺点，探讨了它们在分类任务中各自的准确性及其对所得样本质量和计算性能的影响。我们还提供了一个批处理实现的强大的合作解码策略，用于我们的实验，蒙特卡罗树搜索，与自然语言生成的每个鉴别器工作的代码。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Which+Discriminator+for+Cooperative+Text+Generation?)|0| |[Topological Analysis of Contradictions in Text](https://doi.org/10.1145/3477495.3531881)|Xiangcheng Wu, Xi Niu, Ruhani Rahman|University of North Carolina at Charlotte, Charlotte, NC, USA|Automatically finding contradictions from text is a fundamental yet under-studied problem in natural language understanding and information retrieval. Recently, topology, a branch of mathematics concerned with the properties of geometric shapes, has been shown useful to understand semantics of text. This study presents a topological approach to enhancing deep learning models in detecting contradictions in text. In addition, in order to better understand contradictions, we propose a classification with six types of contradictions. Following that, the topologically enhanced models are evaluated with different contradictions types, as well as different text genres. Overall we have demonstrated the usefulness of topological features in finding contradictions, especially the more latent and more complex contradictions in text.|自动从文本中找出矛盾是自然语言理解和信息检索中一个基本但尚未得到充分研究的问题。近年来，拓扑学作为一门研究 Unicode几何图形列表性质的数学分支，在理解文本语义方面发挥了重要作用。本研究提出了一种拓扑方法来增强深度学习模型在文本矛盾检测中的应用。此外，为了更好地理解矛盾，我们提出了六种类型的矛盾分类。然后，利用不同的矛盾类型和不同的文本类型对拓扑增强模型进行评估。总的来说，我们已经证明了拓扑特征在发现矛盾，特别是在文本中更多的潜在和更复杂的矛盾方面的有用性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Topological+Analysis+of+Contradictions+in+Text)|0| -|[Dual Pseudo Supervision for Semi-Supervised Text Classification with a Reliable Teacher](https://doi.org/10.1145/3477495.3531887)|Shujie Li, Min Yang, Chengming Li, Ruifeng Xu|Harbin Institute of Technology & Peng Cheng Lab, Shenzhen, China; Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China; University of Science and Technology of China, Hefei, China; Sun Yat-sen University, Shenzhen, China|In this paper, we study the semi-supervised text classification (SSTC) by exploring both labeled and extra unlabeled data. One of the most popular SSTC techniques is pseudo-labeling which assigns pseudo labels for unlabeled data via a teacher classifier trained on labeled data. These pseudo labeled data is then applied to train a student classifier. However, when the pseudo labels are inaccurate, the student classifier will learn from inaccurate data and get even worse performance than the teacher. To mitigate this issue, we propose a simple yet efficient pseudo-labeling framework called Dual Pseudo Supervision (DPS), which exploits the feedback signal from the student to guide the teacher to generate better pseudo labels. In particular, we alternately update the student based on the pseudo labeled data annotated by the teacher and optimize the teacher based on the student's performance via meta learning. In addition, we also design a consistency regularization term to further improve the stability of the teacher. With the above two strategies, the learned reliable teacher can provide more accurate pseudo-labels to the student and thus improve the overall performance of text classification. We conduct extensive experiments on three benchmark datasets (i.e., AG News, Yelp and Yahoo) to verify the effectiveness of our DPS method. Experimental results show that our approach achieves substantially better performance than the strong competitors. For reproducibility, we will release our code and data of this paper publicly at https://github.com/GRIT621/DPS.|本文通过对已标记和未标记数据的分析，研究了半监督文本分类算法(SSTC)。最流行的 SSTC 技术之一是伪标记技术，它通过对标记数据进行训练的教师分类器为未标记的数据分配伪标记。然后应用这些伪标记数据训练学生分类器。然而，当伪标签不准确时，学生分类器会从不准确的数据中学习，得到比教师更差的性能。为了解决这个问题，我们提出了一个简单而有效的伪标签框架，称为双伪监督(DPS) ，它利用学生的反馈信号来指导教师生成更好的伪标签。特别是，我们交替更新学生的基础上伪标记数据的教师注释和优化教师的基础上学生的表现通过元学习。此外，我们还设计了一个一致性正则项，以进一步提高教师的稳定性。通过以上两种策略，学习可靠的教师可以为学生提供更准确的伪标签，从而提高文本分类的整体性能。我们对三个基准数据集(即 AG News、 Yelp 和 Yahoo)进行了广泛的实验，以验证我们的 DPS 方法的有效性。实验结果表明，我们的方法实现了大大优于强竞争对手的性能。为确保重复性，我们会在 https://github.com/grit621/dps 公开发布本文件的代码和数据。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Dual+Pseudo+Supervision+for+Semi-Supervised+Text+Classification+with+a+Reliable+Teacher)|0| +|[Dual Pseudo Supervision for Semi-Supervised Text Classification with a Reliable Teacher](https://doi.org/10.1145/3477495.3531887)|Shujie Li, Min Yang, Chengming Li, Ruifeng Xu|Sun Yat-sen University, Shenzhen, China; Harbin Institute of Technology & Peng Cheng Lab, Shenzhen, China; University of Science and Technology of China, Hefei, China; Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China|In this paper, we study the semi-supervised text classification (SSTC) by exploring both labeled and extra unlabeled data. One of the most popular SSTC techniques is pseudo-labeling which assigns pseudo labels for unlabeled data via a teacher classifier trained on labeled data. These pseudo labeled data is then applied to train a student classifier. However, when the pseudo labels are inaccurate, the student classifier will learn from inaccurate data and get even worse performance than the teacher. To mitigate this issue, we propose a simple yet efficient pseudo-labeling framework called Dual Pseudo Supervision (DPS), which exploits the feedback signal from the student to guide the teacher to generate better pseudo labels. In particular, we alternately update the student based on the pseudo labeled data annotated by the teacher and optimize the teacher based on the student's performance via meta learning. In addition, we also design a consistency regularization term to further improve the stability of the teacher. With the above two strategies, the learned reliable teacher can provide more accurate pseudo-labels to the student and thus improve the overall performance of text classification. We conduct extensive experiments on three benchmark datasets (i.e., AG News, Yelp and Yahoo) to verify the effectiveness of our DPS method. Experimental results show that our approach achieves substantially better performance than the strong competitors. For reproducibility, we will release our code and data of this paper publicly at https://github.com/GRIT621/DPS.|本文通过对已标记和未标记数据的分析，研究了半监督文本分类算法(SSTC)。最流行的 SSTC 技术之一是伪标记技术，它通过对标记数据进行训练的教师分类器为未标记的数据分配伪标记。然后应用这些伪标记数据训练学生分类器。然而，当伪标签不准确时，学生分类器会从不准确的数据中学习，得到比教师更差的性能。为了解决这个问题，我们提出了一个简单而有效的伪标签框架，称为双伪监督(DPS) ，它利用学生的反馈信号来指导教师生成更好的伪标签。特别是，我们交替更新学生的基础上伪标记数据的教师注释和优化教师的基础上学生的表现通过元学习。此外，我们还设计了一个一致性正则项，以进一步提高教师的稳定性。通过以上两种策略，学习可靠的教师可以为学生提供更准确的伪标签，从而提高文本分类的整体性能。我们对三个基准数据集(即 AG News、 Yelp 和 Yahoo)进行了广泛的实验，以验证我们的 DPS 方法的有效性。实验结果表明，我们的方法实现了大大优于强竞争对手的性能。为确保重复性，我们会在 https://github.com/grit621/dps 公开发布本文件的代码和数据。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Dual+Pseudo+Supervision+for+Semi-Supervised+Text+Classification+with+a+Reliable+Teacher)|0| |[An Efficient Fusion Mechanism for Multimodal Low-resource Setting](https://doi.org/10.1145/3477495.3531900)|Dushyant Singh Chauhan, Asif Ekbal, Pushpak Bhattacharyya|Indian Institute of Technology Patna, Patna, India|The effective fusion of multiple modalities (i.e., text, acoustic, and visual) is a non-trivial task, as these modalities often carry specific and diverse information and do not contribute equally. The fusion of different modalities could even be more challenging under the low-resource setting, where we have fewer samples for training. This paper proposes a multi-representative fusion mechanism that generates diverse fusions with multiple modalities and then chooses the best fusion among them. To achieve this, we first apply convolution filters on multimodal inputs to generate different and diverse representations of modalities. We then fuse pairwise modalities with multiple representations to get the multiple fusions. Finally, we propose an attention mechanism that only selects the most appropriate fusion, which eventually helps resolve the noise problem by ignoring the noisy fusions. We evaluate our proposed approach on three low-resource multimodal sentiment analysis datasets, i.e., YouTube, MOUD, and ICT-MMMO. Experimental results show the effectiveness of our proposed approach with the accuracies of 59.3%, 83.0%, and 84.1% for the YouTube, MOUD, and ICT-MMMO datasets, respectively.|多种模式(即文本、声学和视觉)的有效融合是一项非常重要的任务，因为这些模式往往携带特定的、多样化的信息，并且不能平等地作出贡献。在资源匮乏的情况下，不同模式的融合可能更具挑战性，因为我们的培训样本较少。提出了一种多代表性融合机制，该机制通过多种融合方式生成多种融合，然后从中选择最佳融合方式。为了实现这一点，我们首先应用卷积滤波器的多模式输入，以产生不同的和不同的表示形式。然后，我们融合成对模式与多重表示，以获得多重融合。最后，我们提出了一种注意机制，只选择最适当的融合，这最终有助于解决噪声问题，而忽略了噪声融合。我们在三个低资源多模态情绪分析数据集(即 YouTube、 MOUD 和 ICT-MMMO)上评估了我们提出的方法。实验结果表明，该方法对 YouTube、 MOUD 和 ICT-MMMO 数据集的准确率分别为59.3% 、83.0% 和84.1% 。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=An+Efficient+Fusion+Mechanism+for+Multimodal+Low-resource+Setting)|0| -|[PST: Measuring Skill Proficiency in Programming Exercise Process via Programming Skill Tracing](https://doi.org/10.1145/3477495.3531903)|Ruixin Li, Yu Yin, Le Dai, Shuanghong Shen, Xin Lin, Yu Su, Enhong Chen|Hefei Normal University & Hefei Comprehensive National Science Center, Hefei, Anhui, China; School of Data Science, University of Science and Technology of China & State Key Laboratory of Cognitive Intelligence, Hefei, Anhui, China; School of Computer Science and Technology, University of Science and Technology of China & State Key Laboratory of Cognitive Intelligence, Hefei, Anhui, China; Institute of Advanced Technology, University of Science and Technology of China & State Key Laboratory of Cognitive Intelligence, Hefei, Anhui, China|Programming has become an important skill for individuals nowadays. For the demand to improve personal programming skill, tracking programming skill proficiency is getting more and more important. However, few researchers pay attention to measuring the programming skill of learners. Most of existing studies on learner capability portrait only made use of the exercise results, while the rich behavioral information contained in programming exercise process remains unused. Therefore, we propose a model that measures skill proficiency in programming exercise process named Programming Skill Tracing (PST). We designed Code Information Graph (CIG) to represent the feature of learners' solution code, and Code Tracing Graph (CTG) to measure the changes between the adjacent submissions. Furthermore, we divided programming skill into programming knowledge and coding ability to get more fine-grained assessment. Finally, we conducted various experiments to verify the effectiveness and interpretability of our PST model.|编程已经成为当今个人的一项重要技能。对于提高个人编程技能的需求，跟踪编程技能熟练程度变得越来越重要。然而，很少有研究者注意测量学习者的编程技能。现有的关于学习者能力描述的研究大多只是利用了习题结果，而编程习题过程中所包含的丰富的行为信息却没有得到充分的利用。因此，我们提出了一个测量编程技能熟练程度的模型，命名为编程技能跟踪(PST)。我们设计了代码信息图(CIG)来表示学习者解决方案代码的特征，代码跟踪图(CTG)来度量相邻提交的代码之间的变化。此外，将编程技能分为编程知识和编码能力两部分，以获得更细粒度的评价。最后，我们进行了各种实验来验证我们的 PST 模型的有效性和可解释性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=PST:+Measuring+Skill+Proficiency+in+Programming+Exercise+Process+via+Programming+Skill+Tracing)|0| +|[PST: Measuring Skill Proficiency in Programming Exercise Process via Programming Skill Tracing](https://doi.org/10.1145/3477495.3531903)|Ruixin Li, Yu Yin, Le Dai, Shuanghong Shen, Xin Lin, Yu Su, Enhong Chen|Hefei Normal University & Hefei Comprehensive National Science Center, Hefei, Anhui, China; School of Data Science, University of Science and Technology of China & State Key Laboratory of Cognitive Intelligence, Hefei, Anhui, China; Institute of Advanced Technology, University of Science and Technology of China & State Key Laboratory of Cognitive Intelligence, Hefei, Anhui, China; School of Computer Science and Technology, University of Science and Technology of China & State Key Laboratory of Cognitive Intelligence, Hefei, Anhui, China|Programming has become an important skill for individuals nowadays. For the demand to improve personal programming skill, tracking programming skill proficiency is getting more and more important. However, few researchers pay attention to measuring the programming skill of learners. Most of existing studies on learner capability portrait only made use of the exercise results, while the rich behavioral information contained in programming exercise process remains unused. Therefore, we propose a model that measures skill proficiency in programming exercise process named Programming Skill Tracing (PST). We designed Code Information Graph (CIG) to represent the feature of learners' solution code, and Code Tracing Graph (CTG) to measure the changes between the adjacent submissions. Furthermore, we divided programming skill into programming knowledge and coding ability to get more fine-grained assessment. Finally, we conducted various experiments to verify the effectiveness and interpretability of our PST model.|编程已经成为当今个人的一项重要技能。对于提高个人编程技能的需求，跟踪编程技能熟练程度变得越来越重要。然而，很少有研究者注意测量学习者的编程技能。现有的关于学习者能力描述的研究大多只是利用了习题结果，而编程习题过程中所包含的丰富的行为信息却没有得到充分的利用。因此，我们提出了一个测量编程技能熟练程度的模型，命名为编程技能跟踪(PST)。我们设计了代码信息图(CIG)来表示学习者解决方案代码的特征，代码跟踪图(CTG)来度量相邻提交的代码之间的变化。此外，将编程技能分为编程知识和编码能力两部分，以获得更细粒度的评价。最后，我们进行了各种实验来验证我们的 PST 模型的有效性和可解释性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=PST:+Measuring+Skill+Proficiency+in+Programming+Exercise+Process+via+Programming+Skill+Tracing)|0| |[MuchSUM: Multi-channel Graph Neural Network for Extractive Summarization](https://doi.org/10.1145/3477495.3531906)|Qianren Mao, Hongdong Zhu, Junnan Liu, Cheng Ji, Hao Peng, Jianxin Li, Lihong Wang, Zheng Wang|University of Leeds, Leeds, West Yorkshire, United Kingdom; CNCERT, Beijing, China; Beihang University, Beijing, China|Recent studies of extractive text summarization have leveraged BERT for document encoding with breakthrough performance. However, when using a pre-trained BERT-based encoder, existing approaches for selecting representative sentences for text summarization are inadequate since the encoder is not explicitly trained for representing sentences. Simply providing the BERT-initialized sentences to cross-sentential graph-based neural networks (GNNs) to encode semantic features of the sentences is not ideal because doing so fail to integrate other summary-worthy features like sentence importance and positions. This paper presents MuchSUM, a better approach for extractive text summarization. MuchSUM is a multi-channel graph convolutional network designed to explicitly incorporate multiple salient summary-worthy features. Specifically, we introduce three specific graph channels to encode the node textual features, node centrality features, and node position features, respectively, under bipartite word-sentence heterogeneous graphs. Then, a cross-channel convolution operation is designed to distill the common graph representations shared by different channels. Finally, the sentence representations of each channel are fused for extractive summarization. We also investigate three weighted graphs in each channel to infuse edge features for graph-based summarization modeling. Experimental results demonstrate our model can achieve considerable performance compared with some BERT-initialized graph-based extractive summarization systems.|提取文本摘要的最新研究已经利用 BERT 技术实现了突破性的文档编码。然而，当使用预先训练的 BERT 编码器时，现有的选择文本摘要代表性句子的方法是不够的，因为编码器没有明确地训练代表性句子。简单地将 BERT 初始化的句子提供给基于跨句图的神经网络(GNN)来编码句子的语义特征是不理想的，因为这样做不能整合其他有总结价值的特征，如句子的重要性和位置。提出了一种更好的文本摘要提取方法 MuchSUM。MuchSUM 是一个多通道图卷积网络，旨在明确合并多个突出的值得总结的功能。具体来说，我们引入了三个特定的图通道，分别对二分词-句子异质图下的结点文本特征、结点中心特征和结点位置特征进行编码。然后，设计一个跨信道卷积运算来提取不同信道共享的公共图表示。最后，对每个通道的句子表示进行融合，进行提取摘要。我们还研究了每个通道中的三个加权图，为基于图的摘要建模注入边缘特征。实验结果表明，与一些 BERT 初始化的基于图的抽取摘要系统相比，该模型具有较好的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=MuchSUM:+Multi-channel+Graph+Neural+Network+for+Extractive+Summarization)|0| |[Multi-label Masked Language Modeling on Zero-shot Code-switched Sentiment Analysis](https://doi.org/10.1145/3477495.3531914)|Zhi Li, Xing Gao, Ji Zhang, Yin Zhang|Alibaba Group, Hangzhou, China; Zhejiang University, Hangzhou, China|In multilingual communities, code-switching is a common phenomenon and code-switched tasks have become a crucial area of research in natural language processing (NLP) applications. Existing approaches mainly focus on supervised learning. However, it is expensive to annotate a sufficient amount of code-switched data. In this paper, we consider zero-shot setting and improve model performance on code-switched tasks via monolingual language datasets, unlabeled code-switched datasets, and semantic dictionaries. Inspired by the mechanism of code-switching itself, we propose multi-label masked language modeling and predict both the masked word and its synonyms in other languages. Experimental results show that compared with baselines, our method can further improve the pretrained multilingual model's performance on code-switched sentiment analysis datasets.|在多语言社区中，语码转换是一种常见的现象，语码转换任务已经成为自然语言处理(NLP)应用研究的一个重要领域。现有方法主要侧重于监督式学习。然而，对足够数量的代码切换数据进行注释是昂贵的。本文通过单语种语言数据集、未标记的语码转换数据集和语义词典，考虑了零拍设置，提高了语码转换任务的模型性能。受语码转换本身机制的启发，我们提出了多标签隐藏语言模型，并对其他语言中的隐藏词及其同义词进行了预测。实验结果表明，与基线方法相比，该方法可以进一步提高预训练多语言模型在编码切换情感分析数据集上的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Multi-label+Masked+Language+Modeling+on+Zero-shot+Code-switched+Sentiment+Analysis)|0| |[Extractive Elementary Discourse Units for Improving Abstractive Summarization](https://doi.org/10.1145/3477495.3531916)|Ye Xiong, Teeradaj Racharak, Minh Le Nguyen|Japan Advanced Institute of Science and Technology, Nomi,Ishikawa, Japan|Abstractive summarization focuses on generating concise and fluent text from an original document while maintaining the original intent and containing the new words that do not appear in the original document. Recent studies point out that rewriting extractive summaries help improve the performance with a more concise and comprehensible output summary, which uses a sentence as a textual unit. However, a single document sentence normally cannot supply sufficient information. In this paper, we apply elementary discourse unit (EDU) as textual unit of content selection. In order to utilize EDU for generating a high quality summary, we propose a novel summarization model that first designs an EDU selector to choose salient content. Then, the generator model rewrites the selected EDUs as the final summary. To determine the relevancy of each EDU on the entire document, we choose to apply group tag embedding, which can establish the connection between summary sentences and relevant EDUs, so that our generator does not only focus on selected EDUs, but also ingest the entire original document. Extensive experiments on the CNN/Daily Mail dataset have demonstrated the effectiveness of our model.|抽象摘要的重点是从原始文档中生成简洁流畅的文本，同时保持原始意图并包含原始文档中没有出现的新词。最近的研究指出，重写提取摘要有助于提高性能与更简明易懂的输出摘要，使用一个句子作为文本单位。然而，一个单独的文档句子通常不能提供足够的信息。本文采用基本语篇单位(EDU)作为内容选择的语篇单位。为了利用 EDU 生成高质量的摘要，我们提出了一种新的摘要模型，首先设计一个 EDU 选择器来选择显著的内容。然后，生成器模型重写所选的 EDU 作为最终摘要。为了确定每个 EDU 对整个文档的相关性，我们选择使用组标签嵌入，它可以建立摘要句子和相关 EDU 之间的连接，因此我们的生成器不仅关注选定的 EDU，而且摄取整个原始文档。在 CNN/Daily Mail 数据集上的大量实验已经证明了我们模型的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Extractive+Elementary+Discourse+Units+for+Improving+Abstractive+Summarization)|0| diff --git a/papers/sigir/sigir2023.md b/papers/sigir/sigir2023.md index 7dc0d761..39532528 100644 --- a/papers/sigir/sigir2023.md +++ b/papers/sigir/sigir2023.md @@ -6,27 +6,27 @@ |[Frequency Enhanced Hybrid Attention Network for Sequential Recommendation](https://doi.org/10.1145/3539618.3591689)|Xinyu Du, Huanhuan Yuan, Pengpeng Zhao, Jianfeng Qu, Fuzhen Zhuang, Guanfeng Liu, Yanchi Liu, Victor S. Sheng||The self-attention mechanism, which equips with a strong capability of modeling long-range dependencies, is one of the extensively used techniques in the sequential recommendation field. However, many recent studies represent that current self-attention based models are low-pass filters and are inadequate to capture high-frequency information. Furthermore, since the items in the user behaviors are intertwined with each other, these models are incomplete to distinguish the inherent periodicity obscured in the time domain. In this work, we shift the perspective to the frequency domain, and propose a novel Frequency Enhanced Hybrid Attention Network for Sequential Recommendation, namely FEARec. In this model, we firstly improve the original time domain self-attention in the frequency domain with a ramp structure to make both low-frequency and high-frequency information could be explicitly learned in our approach. Moreover, we additionally design a similar attention mechanism via auto-correlation in the frequency domain to capture the periodic characteristics and fuse the time and frequency level attention in a union model. Finally, both contrastive learning and frequency regularization are utilized to ensure that multiple views are aligned in both the time domain and frequency domain. Extensive experiments conducted on four widely used benchmark datasets demonstrate that the proposed model performs significantly better than the state-of-the-art approaches.|自注意机制具有很强的远程依赖建模能力，是顺序推荐领域中广泛应用的技术之一。然而，许多最近的研究表明，目前基于自我注意的模型是低通滤波器，不足以捕获高频信息。此外，由于用户行为中的项目相互交织在一起，这些模型不能完全区分隐藏在时域中的固有周期性。在这项工作中，我们将视角转移到频域，并提出了一种新的频率增强的混合注意网络的顺序推荐，即 FEARec。在这个模型中，我们首先在频率域改进了原有的时域自注意，使得低频和高频信息都可以在我们的方法中显式地学习。此外，我们还设计了一个类似的注意机制，通过在频域中的自相关来捕捉周期特性，并将时间和频率水平的注意融合到一个联合模型中。最后，利用对比学习和频率正则化技术保证了多视图在时域和频域的对齐。在四个广泛使用的基准数据集上进行的大量实验表明，所提出的模型的性能明显优于最先进的方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Frequency+Enhanced+Hybrid+Attention+Network+for+Sequential+Recommendation)|1| |[Reformulating CTR Prediction: Learning Invariant Feature Interactions for Recommendation](https://doi.org/10.1145/3539618.3591755)|Yang Zhang, Tianhao Shi, Fuli Feng, Wenjie Wang, Dingxian Wang, Xiangnan He, Yongdong Zhang||Click-Through Rate (CTR) prediction plays a core role in recommender systems, serving as the final-stage filter to rank items for a user. The key to addressing the CTR task is learning feature interactions that are useful for prediction, which is typically achieved by fitting historical click data with the Empirical Risk Minimization (ERM) paradigm. Representative methods include Factorization Machines and Deep Interest Network, which have achieved wide success in industrial applications. However, such a manner inevitably learns unstable feature interactions, i.e., the ones that exhibit strong correlations in historical data but generalize poorly for future serving. In this work, we reformulate the CTR task -- instead of pursuing ERM on historical data, we split the historical data chronologically into several periods (a.k.a, environments), aiming to learn feature interactions that are stable across periods. Such feature interactions are supposed to generalize better to predict future behavior data. Nevertheless, a technical challenge is that existing invariant learning solutions like Invariant Risk Minimization are not applicable, since the click data entangles both environment-invariant and environment-specific correlations. To address this dilemma, we propose Disentangled Invariant Learning (DIL) which disentangles feature embeddings to capture the two types of correlations separately. To improve the modeling efficiency, we further design LightDIL which performs the disentanglement at the higher level of the feature field. Extensive experiments demonstrate the effectiveness of DIL in learning stable feature interactions for CTR. We release the code at https://github.com/zyang1580/DIL.|点进率(ctrl)预测在推荐系统中扮演着核心角色，充当为用户排序项目的最终过滤器。解决 CTR 任务的关键是学习对预测有用的特征交互，这通常是通过将历史点击数据与经验风险最小化(ERM)范式相匹配来实现的。代表性的方法包括因子分解机和深度兴趣网络，它们在工业应用中取得了广泛的成功。然而，这种方式不可避免地会学习到不稳定的特征交互，即在历史数据中表现出强相关性的特征交互，但对于未来服务的推广很差。在这项工作中，我们重新规划了 CTR 任务——而不是追求历史数据的 ERM，我们按时间顺序将历史数据分成几个时期(也就是环境) ，目的是学习跨时期稳定的特性交互。这样的特征交互被认为可以更好地推广以预测未来的行为数据。然而，一个技术挑战是，现有的不变学习解决方案，如不变风险最小化是不适用的，因为点击数据纠缠环境不变和环境特定的相关性。为了解决这一难题，我们提出了解除特征嵌入的不变学习(DIL)算法，分别捕获两种类型的相关性。为了提高建模效率，我们进一步设计了 LightDIL，它在特征字段的更高层次上执行分离。大量的实验证明了 DIL 在 CTR 中学习稳定特征交互的有效性。我们在 https://github.com/zyang1580/dil 公布密码。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Reformulating+CTR+Prediction:+Learning+Invariant+Feature+Interactions+for+Recommendation)|1| |[How Well do Offline Metrics Predict Online Performance of Product Ranking Models?](https://doi.org/10.1145/3539618.3591865)|Xiaojie Wang, Ruoyuan Gao, Anoop Jain, Graham Edge, Sachin Ahuja|Amazon.com, Palo Alto, CA, USA; Amazon.com, Seattle, WA, USA|Online evaluation techniques are widely adopted by industrial search engines to determine which ranking models perform better under a certain business metric. However, online evaluation can only evaluate a small number of rankers and people resort to offline evaluation to select rankers that are likely to yield good online performance. To use offline metrics for effective model selection, a major challenge is to understand how well offline metrics predict which ranking models perform better in online experiments. This paper aims to address this challenge in product search ranking. Towards this end, we collect gold data in the form of preferences over ranker pairs under a business metric in e-commerce search engine. For the first time, we use such gold data to evaluate offline metrics in terms of directional agreement with the business metric. Furthermore, we analyze offline metrics in terms of discriminative power through paired sample t-test and rank correlations among offline metrics. Through extensive online and offline experiments, we studied 36 offline metrics and observed that: (1) Offline metrics align well with online metrics: they agree on which one of two ranking models is better up to 97% of times; (2) Offline metrics are highly discriminative on large-scale search ranking data, especially NDCG (Normalized Discounted Cumulative Gain) which has a discriminative power over 99%.|在线评估技术被工业搜索引擎广泛采用，以确定哪些排名模型在一定的业务指标下表现更好。然而，在线评价只能评价少量的排名者，人们通过离线评价来选择有可能产生良好在线表现的排名者。为了使用离线指标进行有效的模型选择，一个主要的挑战是了解离线指标如何很好地预测哪些排名模型在在线实验中表现得更好。本文旨在解决这一挑战的产品搜索排名。为此，我们在电子商务搜索引擎的商业度量下，以优先于排名对的形式收集黄金数据。我们第一次使用这样的黄金数据来评估与业务度量方向一致的离线度量。此外，我们还通过配对样本 t 检验和离线指标之间的等级相关性来分析离线指标的判别能力。通过大量的在线和离线实验，我们研究了36个离线指标，并观察到: (1)离线指标与在线指标很好地一致，他们对两个排名模型中的哪一个更好达到97% 的时间; (2)离线指标对大规模搜索排名数据具有高度歧视性，特别是 NDCG (标准化折扣累积收益) ，其歧视性超过99% 。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=How+Well+do+Offline+Metrics+Predict+Online+Performance+of+Product+Ranking+Models?)|1| -|[pybool_ir: A Toolkit for Domain-Specific Search Experiments](https://doi.org/10.1145/3539618.3591819)|Harrisen Scells, Martin Potthast|Leipzig University and ScaDS.AI, Leipzig, Germany; Leipzig University, Leipzig, Germany|Undertaking research in domain-specific scenarios such as systematic review literature search, legal search, and patent search can often have a high barrier of entry due to complicated indexing procedures and complex Boolean query syntax. Indexing and searching document collections like PubMed in off-the-shelf tools such as Elasticsearch and Lucene often yields less accurate (and less effective) results than the PubMed search engine, i.e., retrieval results do not match what would be retrieved if one issued the same query to PubMed. Furthermore, off-the-shelf tools have their own nuanced query languages and do not allow directly using the often large and complicated Boolean queries seen in domain-specific search scenarios. The pybool_ir toolkit aims to address these problems and to lower the barrier to entry for developing new methods for domain-specific search. The toolkit is an open source package available at https://github.com/hscells/pybool_ir.|由于复杂的索引程序和复杂的布尔查询语法，在特定领域进行研究，如系统综述文献检索、法律检索和专利检索，往往会遇到很高的入门门槛。在诸如 Elasticsearch 和 Lucene 这样的现成工具中，对像 PubMed 这样的文档集合进行索引和搜索，通常会得到比 PubMed 搜索引擎更不准确(也更不有效)的结果，也就是说，如果向 PubMed 发出同样的查询，检索结果与检索结果不匹配。此外，现成的工具有自己的微妙查询语言，不允许直接使用特定领域搜索场景中常见的大型和复杂的布尔查询。Pybool _ ir 工具包旨在解决这些问题，降低开发特定领域搜索新方法的门槛。该工具包是一个开源软件包，可在 https://github.com/hscells/pybool_ir 下载。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=pybool_ir:+A+Toolkit+for+Domain-Specific+Search+Experiments)|1| +|[pybool_ir: A Toolkit for Domain-Specific Search Experiments](https://doi.org/10.1145/3539618.3591819)|Harrisen Scells, Martin Potthast|Leipzig University, Leipzig, Germany; Leipzig University and ScaDS.AI, Leipzig, Germany|Undertaking research in domain-specific scenarios such as systematic review literature search, legal search, and patent search can often have a high barrier of entry due to complicated indexing procedures and complex Boolean query syntax. Indexing and searching document collections like PubMed in off-the-shelf tools such as Elasticsearch and Lucene often yields less accurate (and less effective) results than the PubMed search engine, i.e., retrieval results do not match what would be retrieved if one issued the same query to PubMed. Furthermore, off-the-shelf tools have their own nuanced query languages and do not allow directly using the often large and complicated Boolean queries seen in domain-specific search scenarios. The pybool_ir toolkit aims to address these problems and to lower the barrier to entry for developing new methods for domain-specific search. The toolkit is an open source package available at https://github.com/hscells/pybool_ir.|由于复杂的索引程序和复杂的布尔查询语法，在特定领域进行研究，如系统综述文献检索、法律检索和专利检索，往往会遇到很高的入门门槛。在诸如 Elasticsearch 和 Lucene 这样的现成工具中，对像 PubMed 这样的文档集合进行索引和搜索，通常会得到比 PubMed 搜索引擎更不准确(也更不有效)的结果，也就是说，如果向 PubMed 发出同样的查询，检索结果与检索结果不匹配。此外，现成的工具有自己的微妙查询语言，不允许直接使用特定领域搜索场景中常见的大型和复杂的布尔查询。Pybool _ ir 工具包旨在解决这些问题，降低开发特定领域搜索新方法的门槛。该工具包是一个开源软件包，可在 https://github.com/hscells/pybool_ir 下载。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=pybool_ir:+A+Toolkit+for+Domain-Specific+Search+Experiments)|1| |[Lexically-Accelerated Dense Retrieval](https://doi.org/10.1145/3539618.3591715)|Hrishikesh Kulkarni, Sean MacAvaney, Nazli Goharian, Ophir Frieder||Retrieval approaches that score documents based on learned dense vectors (i.e., dense retrieval) rather than lexical signals (i.e., conventional retrieval) are increasingly popular. Their ability to identify related documents that do not necessarily contain the same terms as those appearing in the user's query (thereby improving recall) is one of their key advantages. However, to actually achieve these gains, dense retrieval approaches typically require an exhaustive search over the document collection, making them considerably more expensive at query-time than conventional lexical approaches. Several techniques aim to reduce this computational overhead by approximating the results of a full dense retriever. Although these approaches reasonably approximate the top results, they suffer in terms of recall -- one of the key advantages of dense retrieval. We introduce 'LADR' (Lexically-Accelerated Dense Retrieval), a simple-yet-effective approach that improves the efficiency of existing dense retrieval models without compromising on retrieval effectiveness. LADR uses lexical retrieval techniques to seed a dense retrieval exploration that uses a document proximity graph. We explore two variants of LADR: a proactive approach that expands the search space to the neighbors of all seed documents, and an adaptive approach that selectively searches the documents with the highest estimated relevance in an iterative fashion. Through extensive experiments across a variety of dense retrieval models, we find that LADR establishes a new dense retrieval effectiveness-efficiency Pareto frontier among approximate k nearest neighbor techniques. Further, we find that when tuned to take around 8ms per query in retrieval latency on our hardware, LADR consistently achieves both precision and recall that are on par with an exhaustive search on standard benchmarks.|基于学习密集向量(即密集检索)而非词汇信号(即常规检索)对文档进行评分的检索方法越来越流行。它们能够识别出与用户查询中出现的不一定包含相同术语的相关文档(从而提高召回率) ，这是它们的主要优势之一。然而，为了实际获得这些收益，密集检索方法通常需要对文档集进行彻底搜索，这使得它们在查询时比传统的词法方法昂贵得多。一些技术旨在通过近似一个完全密集的检索器的结果来减少这种计算开销。尽管这些方法可以合理地接近最高的结果，但它们在回忆方面受到影响——这是密集检索的关键优势之一。我们介绍了“ LADR”(词汇加速密集检索) ，一个简单而有效的方法，提高了现有的密集检索模型的效率，而不影响检索的有效性。LADR 使用词汇检索技术来引导使用文档接近图的密集检索探索。我们探索了 LADR 的两种变体: 一种积极主动的方法，将搜索空间扩展到所有种子文档的邻居，以及一种自适应的方法，以迭代的方式选择性地搜索具有最高估计相关性的文档。通过对各种密集检索模型的大量实验，我们发现 LADR 在近似 k 最近邻技术中建立了一种新的密集检索有效性——帕累托前沿。此外，我们发现，当硬件上的检索延迟调整到每个查询大约需要8毫秒时，LADR 始终如一地实现了这两个准确率召回率，与标准基准的详尽搜索相当。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Lexically-Accelerated+Dense+Retrieval)|1| |[Safe Deployment for Counterfactual Learning to Rank with Exposure-Based Risk Minimization](https://doi.org/10.1145/3539618.3591760)|Shashank Gupta, Harrie Oosterhuis, Maarten de Rijke||Counterfactual learning to rank (CLTR) relies on exposure-based inverse propensity scoring (IPS), a LTR-specific adaptation of IPS to correct for position bias. While IPS can provide unbiased and consistent estimates, it often suffers from high variance. Especially when little click data is available, this variance can cause CLTR to learn sub-optimal ranking behavior. Consequently, existing CLTR methods bring significant risks with them, as naively deploying their models can result in very negative user experiences. We introduce a novel risk-aware CLTR method with theoretical guarantees for safe deployment. We apply a novel exposure-based concept of risk regularization to IPS estimation for LTR. Our risk regularization penalizes the mismatch between the ranking behavior of a learned model and a given safe model. Thereby, it ensures that learned ranking models stay close to a trusted model, when there is high uncertainty in IPS estimation, which greatly reduces the risks during deployment. Our experimental results demonstrate the efficacy of our proposed method, which is effective at avoiding initial periods of bad performance when little data is available, while also maintaining high performance at convergence. For the CLTR field, our novel exposure-based risk minimization method enables practitioners to adopt CLTR methods in a safer manner that mitigates many of the risks attached to previous methods.|反事实学习排名(CLTR)依赖于基于暴露的逆倾向评分(IPS) ，IPS 的一种 LTR 特异性适应，以纠正位置偏差。虽然 IPS 可以提供无偏和一致的估计，但它经常受到高方差的影响。特别是当很少的点击数据可用时，这种方差会导致 CLTR 学习次优排序行为。因此，现有的 CLTR 方法带来了巨大的风险，因为天真地部署它们的模型可能会导致非常负面的用户体验。我们介绍了一种新的风险意识的 CLTR 方法与安全部署的理论保证。我们将一种新的基于暴露的风险正则化概念应用于长期寿命周期的 IPS 估计。我们的风险正则化惩罚了学习模型的排序行为和给定的安全模型之间的不匹配。因此，当 IPS 估计存在较高的不确定性时，学习的排序模型能够保持接近可信模型，从而大大降低了部署过程中的风险。实验结果证明了该方法的有效性，该方法在保证收敛性能的同时，能有效地避免初始阶段性能不佳的情况。对于 CLTR 领域，我们新颖的基于暴露的风险最小化方法使从业者能够以更安全的方式采用 CLTR 方法，从而减轻了许多与以前的方法相关的风险。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Safe+Deployment+for+Counterfactual+Learning+to+Rank+with+Exposure-Based+Risk+Minimization)|1| |[Disentangled Contrastive Collaborative Filtering](https://doi.org/10.1145/3539618.3591665)|Xubin Ren, Lianghao Xia, Jiashu Zhao, Dawei Yin, Chao Huang||Recent studies show that graph neural networks (GNNs) are prevalent to model high-order relationships for collaborative filtering (CF). Towards this research line, graph contrastive learning (GCL) has exhibited powerful performance in addressing the supervision label shortage issue by learning augmented user and item representations. While many of them show their effectiveness, two key questions still remain unexplored: i) Most existing GCL-based CF models are still limited by ignoring the fact that user-item interaction behaviors are often driven by diverse latent intent factors (e.g., shopping for family party, preferred color or brand of products); ii) Their introduced non-adaptive augmentation techniques are vulnerable to noisy information, which raises concerns about the model's robustness and the risk of incorporating misleading self-supervised signals. In light of these limitations, we propose a Disentangled Contrastive Collaborative Filtering framework (DCCF) to realize intent disentanglement with self-supervised augmentation in an adaptive fashion. With the learned disentangled representations with global context, our DCCF is able to not only distill finer-grained latent factors from the entangled self-supervision signals but also alleviate the augmentation-induced noise. Finally, the cross-view contrastive learning task is introduced to enable adaptive augmentation with our parameterized interaction mask generator. Experiments on various public datasets demonstrate the superiority of our method compared to existing solutions. Our model implementation is released at the link https://github.com/HKUDS/DCCF.|最近的研究表明，图形神经网络(GNN)普遍用于模拟协同过滤(CF)的高阶关系。针对这一研究方向，图形对比学习(GCL)通过学习增强用户和项目表示，在解决监督标签短缺问题方面表现出强大的性能。虽然其中许多显示了它们的有效性，但有两个关键问题仍然没有得到探索: i)大多数现有的基于 GCL 的 CF 模型仍然受到限制，因为忽略了用户项目交互行为通常由不同的潜在意图因素驱动(例如，为家庭聚会，首选颜色或产品品牌) ; ii)它们引入的非自适应增强技术易受噪声信息的影响，这引起了对模型的稳健性和纳入误导性自我监督信号的风险的担忧。鉴于这些局限性，我们提出了一个自适应增强的协同过滤对比度分离框架(dCCF)来实现意图分离。利用学习的全局解纠缠表示，我们的 DCCF 不仅能够从纠缠的自我监督信号中提取出更细粒度的潜在因子，而且能够减轻增强引起的噪声。最后，引入横向视图对比学习任务，利用参数化交互掩模生成器实现自适应增强。在各种公共数据集上的实验表明了该方法相对于现有解的优越性。我们的模型实现在链接 https://github.com/hkuds/dccf 发布。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Disentangled+Contrastive+Collaborative+Filtering)|1| |[A Unified Generative Retriever for Knowledge-Intensive Language Tasks via Prompt Learning](https://doi.org/10.1145/3539618.3591631)|Jiangui Chen, Ruqing Zhang, Jiafeng Guo, Maarten de Rijke, Yiqun Liu, Yixing Fan, Xueqi Cheng||Knowledge-intensive language tasks (KILTs) benefit from retrieving high-quality relevant contexts from large external knowledge corpora. Learning task-specific retrievers that return relevant contexts at an appropriate level of semantic granularity, such as a document retriever, passage retriever, sentence retriever, and entity retriever, may help to achieve better performance on the end-to-end task. But a task-specific retriever usually has poor generalization ability to new domains and tasks, and it may be costly to deploy a variety of specialised retrievers in practice. We propose a unified generative retriever (UGR) that combines task-specific effectiveness with robust performance over different retrieval tasks in KILTs. To achieve this goal, we make two major contributions: (i) To unify different retrieval tasks into a single generative form, we introduce an n-gram-based identifier for relevant contexts at different levels of granularity in KILTs. And (ii) to address different retrieval tasks with a single model, we employ a prompt learning strategy and investigate three methods to design prompt tokens for each task. In this way, the proposed UGR model can not only share common knowledge across tasks for better generalization, but also perform different retrieval tasks effectively by distinguishing task-specific characteristics. We train UGR on a heterogeneous set of retrieval corpora with well-designed prompts in a supervised and multi-task fashion. Experimental results on the KILT benchmark demonstrate the effectiveness of UGR on in-domain datasets, out-of-domain datasets, and unseen tasks.|知识密集型语言任务(KILT)受益于从大型外部知识库中检索高质量的相关上下文。学习以适当的语义粒度返回相关上下文的特定任务检索器，如文档检索器、文章检索器、句子检索器和实体检索器，可能有助于在端到端任务中获得更好的性能。但是特定于任务的检索器通常对新领域和任务的泛化能力较差，而且在实践中部署各种专门的检索器可能成本较高。我们提出了一个统一的生成检索器(UGR) ，它结合了任务特定的有效性和鲁棒性能对不同的检索任务在 KILT。为了实现这一目标，我们做出了两个主要贡献: (i)为了将不同的检索任务统一到一个单一的生成形式，我们在 KILT 中引入了一个基于 n-gram 的标识符来标识不同粒度级别的相关上下文。为了解决不同的检索任务，我们采用了快速学习策略，并研究了三种方法为每个任务设计快速令牌。这样，该模型不仅可以实现任务之间的共同知识共享以便更好地泛化，而且可以通过区分任务特定的特征来有效地执行不同的检索任务。我们在一个异构的检索语料集上训练 UGR，这些检索语料集以监督和多任务的方式使用精心设计的提示。在 KILT 基准上的实验结果证明了 UGR 在域内数据集、域外数据集和未知任务上的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Unified+Generative+Retriever+for+Knowledge-Intensive+Language+Tasks+via+Prompt+Learning)|1| |[ADL: Adaptive Distribution Learning Framework for Multi-Scenario CTR Prediction](https://doi.org/10.1145/3539618.3591944)|Jinyun Li, Huiwen Zheng, Yuanlin Liu, Minfang Lu, Lixia Wu, Haoyuan Hu|Cainiao Network, Hangzhou, China|Large-scale commercial platforms usually involve numerous business scenarios for diverse business strategies. To provide click-through rate (CTR) predictions for multiple scenarios simultaneously, existing promising multi-scenario models explicitly construct scenario-specific networks by manually grouping scenarios based on particular business strategies. Nonetheless, this pre-defined data partitioning process heavily relies on prior knowledge, and it may neglect the underlying data distribution of each scenario, hence limiting the model's representation capability. Regarding the above issues, we propose Adaptive Distribution Learning (ADL): an end-to-end optimization distribution framework which is composed of a clustering process and classification process. Specifically, we design a distribution adaptation module with a customized dynamic routing mechanism. Instead of introducing prior knowledge for pre-defined data allocation, this routing algorithm adaptively provides a distribution coefficient for each sample to determine which cluster it belongs to. Each cluster corresponds to a particular distribution so that the model can sufficiently capture the commonalities and distinctions between these distinct clusters. Our results on both public and large-scale industrial datasets show the effectiveness and efficiency of ADL: the model yields impressive prediction accuracy with more than 50% reduction in time cost during the training phase when compared to other methods.|大型商业平台通常涉及多种不同业务策略的多个业务场景。为了同时提供多个场景的点进率预测，现有有前途的多场景模型通过基于特定业务策略的手动分组场景，明确地构建特定场景的网络。尽管如此，这个预定义的数据分区过程严重依赖于先前的知识，并且它可能会忽略每个场景的底层数据分布，从而限制模型的表示能力。针对以上问题，本文提出了自适应分布学习(ADL) : 一种由聚类过程和分类过程组成的端到端优化分布框架。具体来说，我们设计了一个具有自定义动态路由机制的分布式自适应模块。这种路由算法不需要为预定义的数据分配引入先验知识，而是自适应地为每个样本提供一个分布系数，以确定它属于哪个集群。每个集群对应于一个特定的分布，以便模型能够充分捕获这些不同集群之间的共性和区别。我们在公共和大规模工业数据集上的结果显示了 ADL 的有效性和效率: 与其他方法相比，该模型在训练阶段的时间成本降低了50% 以上，预测精度令人印象深刻。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=ADL:+Adaptive+Distribution+Learning+Framework+for+Multi-Scenario+CTR+Prediction)|1| -|[FINAL: Factorized Interaction Layer for CTR Prediction](https://doi.org/10.1145/3539618.3591988)|Jieming Zhu, Qinglin Jia, Guohao Cai, Quanyu Dai, Jingjie Li, Zhenhua Dong, Ruiming Tang, Rui Zhang|ruizhang.info, Shenzhen, China; Huawei Noah's Ark Lab, Shenzhen, China|Multi-layer perceptron (MLP) serves as a core component in many deep models for click-through rate (CTR) prediction. However, vanilla MLP networks are inefficient in learning multiplicative feature interactions, making feature interaction learning an essential topic for CTR prediction. Existing feature interaction networks are effective in complementing the learning of MLPs, but they often fall short of the performance of MLPs when applied alone. Thus, their integration with MLP networks is necessary to achieve improved performance. This situation motivates us to explore a better alternative to the MLP backbone that could potentially replace MLPs. Inspired by factorization machines, in this paper, we propose FINAL, a factorized interaction layer that extends the widely-used linear layer and is capable of learning 2nd-order feature interactions. Similar to MLPs, multiple FINAL layers can be stacked into a FINAL block, yielding feature interactions with an exponential degree growth. We unify feature interactions and MLPs into a single FINAL block and empirically show its effectiveness as a replacement for the MLP block. Furthermore, we explore the ensemble of two FINAL blocks as an enhanced two-stream CTR model, setting a new state-of-the-art on open benchmark datasets. FINAL can be easily adopted as a building block and has achieved business metric gains in multiple applications at Huawei. Our source code will be made available at MindSpore/models and FuxiCTR/model_zoo.|多层感知器(MLP)是许多深度点进率(CTR)预测模型的核心部件。然而，普通 MLP 网络在学习乘法特征交互方面效率低下，使得特征交互学习成为 CTR 预测的重要课题。现有的特征交互网络在补充 MLP 学习方面是有效的，但在单独应用时往往不能满足 MLP 的性能要求。因此，它们与 MLP 网络的集成对于提高性能是必要的。这种情况促使我们探索可能取代 MLP 的更好的替代 MLP 骨干。受因子分解机器的启发，本文提出了一种新的因子分解交互层 FINAL，它扩展了广泛使用的线性层，能够学习二阶特征交互。与 MLP 类似，多个 FINAL 层可以堆叠成一个 FINAL 块，产生指数级增长的特征交互。我们将特征交互和 MLP 统一到一个 FINAL 块中，并通过实验证明了它作为 MLP 块的替代品的有效性。此外，我们探讨了两个 FINAL 块的集成作为一个增强的双流 CTR 模型，设置了一个新的国家的最先进的开放基准数据集。FINAL 可以很容易地作为一个组成部分使用，并且在华为的多个应用程序中取得了业务指标收益。我们的源代码将在 MindSpore/model 和 FuxiCTR/model _ zoo 提供。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=FINAL:+Factorized+Interaction+Layer+for+CTR+Prediction)|1| -|[The Dark Side of Explanations: Poisoning Recommender Systems with Counterfactual Examples](https://doi.org/10.1145/3539618.3592070)|Ziheng Chen, Fabrizio Silvestri, Jia Wang, Yongfeng Zhang, Gabriele Tolomei|Sapienza University of Rome, ROME, Italy; Xi'an Jiaotong-Liverpool University, Suzhou, China; Stony Brook University, Stony Brook, NY, USA; Rutgers University, New Brunswick, NJ, USA|Deep learning-based recommender systems have become an integral part of several online platforms. However, their black-box nature emphasizes the need for explainable artificial intelligence (XAI) approaches to provide human-understandable reasons why a specific item gets recommended to a given user. One such method is counterfactual explanation (CF). While CFs can be highly beneficial for users and system designers, malicious actors may also exploit these explanations to undermine the system's security. In this work, we propose H-CARS, a novel strategy to poison recommender systems via CFs. Specifically, we first train a logical-reasoning-based surrogate model on training data derived from counterfactual explanations. By reversing the learning process of the recommendation model, we thus develop a proficient greedy algorithm to generate fabricated user profiles and their associated interaction records for the aforementioned surrogate model. Our experiments, which employ a well-known CF generation method and are conducted on two distinct datasets, show that H-CARS yields significant and successful attack performance.|基于深度学习的推荐系统已经成为几个在线平台的组成部分。然而，它们的黑匣子特性强调了对可解释的人工智能(XAI)方法的需要，以便提供人类可以理解的理由，说明为什么某个特定项目会被推荐给给定的用户。其中一种方法是反事实解释(CF)。虽然 CF 对用户和系统设计人员非常有益，但恶意参与者也可能利用这些解释来破坏系统的安全性。在这项工作中，我们提出了 H-CARS，一种新的策略，毒害推荐系统通过 CFs。具体来说，我们首先训练了一个基于逻辑推理的代理模型的训练数据来源于反事实的解释。通过逆推荐模型的学习过程，我们开发了一个熟练的贪婪算法来为上述代理模型生成虚构的用户配置文件及其相关的交互记录。我们的实验采用了著名的 CF 生成方法，并在两个不同的数据集上进行，结果表明 H-CARS 产生了显著的成功的攻击性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=The+Dark+Side+of+Explanations:+Poisoning+Recommender+Systems+with+Counterfactual+Examples)|1| +|[FINAL: Factorized Interaction Layer for CTR Prediction](https://doi.org/10.1145/3539618.3591988)|Jieming Zhu, Qinglin Jia, Guohao Cai, Quanyu Dai, Jingjie Li, Zhenhua Dong, Ruiming Tang, Rui Zhang|Huawei Noah's Ark Lab, Shenzhen, China; ruizhang.info, Shenzhen, China|Multi-layer perceptron (MLP) serves as a core component in many deep models for click-through rate (CTR) prediction. However, vanilla MLP networks are inefficient in learning multiplicative feature interactions, making feature interaction learning an essential topic for CTR prediction. Existing feature interaction networks are effective in complementing the learning of MLPs, but they often fall short of the performance of MLPs when applied alone. Thus, their integration with MLP networks is necessary to achieve improved performance. This situation motivates us to explore a better alternative to the MLP backbone that could potentially replace MLPs. Inspired by factorization machines, in this paper, we propose FINAL, a factorized interaction layer that extends the widely-used linear layer and is capable of learning 2nd-order feature interactions. Similar to MLPs, multiple FINAL layers can be stacked into a FINAL block, yielding feature interactions with an exponential degree growth. We unify feature interactions and MLPs into a single FINAL block and empirically show its effectiveness as a replacement for the MLP block. Furthermore, we explore the ensemble of two FINAL blocks as an enhanced two-stream CTR model, setting a new state-of-the-art on open benchmark datasets. FINAL can be easily adopted as a building block and has achieved business metric gains in multiple applications at Huawei. Our source code will be made available at MindSpore/models and FuxiCTR/model_zoo.|多层感知器(MLP)是许多深度点进率(CTR)预测模型的核心部件。然而，普通 MLP 网络在学习乘法特征交互方面效率低下，使得特征交互学习成为 CTR 预测的重要课题。现有的特征交互网络在补充 MLP 学习方面是有效的，但在单独应用时往往不能满足 MLP 的性能要求。因此，它们与 MLP 网络的集成对于提高性能是必要的。这种情况促使我们探索可能取代 MLP 的更好的替代 MLP 骨干。受因子分解机器的启发，本文提出了一种新的因子分解交互层 FINAL，它扩展了广泛使用的线性层，能够学习二阶特征交互。与 MLP 类似，多个 FINAL 层可以堆叠成一个 FINAL 块，产生指数级增长的特征交互。我们将特征交互和 MLP 统一到一个 FINAL 块中，并通过实验证明了它作为 MLP 块的替代品的有效性。此外，我们探讨了两个 FINAL 块的集成作为一个增强的双流 CTR 模型，设置了一个新的国家的最先进的开放基准数据集。FINAL 可以很容易地作为一个组成部分使用，并且在华为的多个应用程序中取得了业务指标收益。我们的源代码将在 MindSpore/model 和 FuxiCTR/model _ zoo 提供。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=FINAL:+Factorized+Interaction+Layer+for+CTR+Prediction)|1| +|[The Dark Side of Explanations: Poisoning Recommender Systems with Counterfactual Examples](https://doi.org/10.1145/3539618.3592070)|Ziheng Chen, Fabrizio Silvestri, Jia Wang, Yongfeng Zhang, Gabriele Tolomei|Sapienza University of Rome, ROME, Italy; Xi'an Jiaotong-Liverpool University, Suzhou, China; Rutgers University, New Brunswick, NJ, USA; Stony Brook University, Stony Brook, NY, USA|Deep learning-based recommender systems have become an integral part of several online platforms. However, their black-box nature emphasizes the need for explainable artificial intelligence (XAI) approaches to provide human-understandable reasons why a specific item gets recommended to a given user. One such method is counterfactual explanation (CF). While CFs can be highly beneficial for users and system designers, malicious actors may also exploit these explanations to undermine the system's security. In this work, we propose H-CARS, a novel strategy to poison recommender systems via CFs. Specifically, we first train a logical-reasoning-based surrogate model on training data derived from counterfactual explanations. By reversing the learning process of the recommendation model, we thus develop a proficient greedy algorithm to generate fabricated user profiles and their associated interaction records for the aforementioned surrogate model. Our experiments, which employ a well-known CF generation method and are conducted on two distinct datasets, show that H-CARS yields significant and successful attack performance.|基于深度学习的推荐系统已经成为几个在线平台的组成部分。然而，它们的黑匣子特性强调了对可解释的人工智能(XAI)方法的需要，以便提供人类可以理解的理由，说明为什么某个特定项目会被推荐给给定的用户。其中一种方法是反事实解释(CF)。虽然 CF 对用户和系统设计人员非常有益，但恶意参与者也可能利用这些解释来破坏系统的安全性。在这项工作中，我们提出了 H-CARS，一种新的策略，毒害推荐系统通过 CFs。具体来说，我们首先训练了一个基于逻辑推理的代理模型的训练数据来源于反事实的解释。通过逆推荐模型的学习过程，我们开发了一个熟练的贪婪算法来为上述代理模型生成虚构的用户配置文件及其相关的交互记录。我们的实验采用了著名的 CF 生成方法，并在两个不同的数据集上进行，结果表明 H-CARS 产生了显著的成功的攻击性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=The+Dark+Side+of+Explanations:+Poisoning+Recommender+Systems+with+Counterfactual+Examples)|1| |[Weakly-Supervised Scientific Document Classification via Retrieval-Augmented Multi-Stage Training](https://doi.org/10.1145/3539618.3592085)|Ran Xu, Yue Yu, Joyce C. Ho, Carl Yang|Emory University, Atlanta, USA; Georgia Institute of Technology, Atlanta, USA|Scientific document classification is a critical task for a wide range of applications, but the cost of obtaining massive amounts of human-labeled data can be prohibitive. To address this challenge, we propose a weakly-supervised approach for scientific document classification using label names only. In scientific domains, label names often include domain-specific concepts that may not appear in the document corpus, making it difficult to match labels and documents precisely. To tackle this issue, we propose WANDER, which leverages dense retrieval to perform matching in the embedding space to capture the semantics of label names. We further design the label name expansion module to enrich the label name representations. Lastly, a self-training step is used to refine the predictions. The experiments on three datasets show that WANDER outperforms the best baseline by 11.9% on average. Our code will be published at https://github.com/ritaranx/wander.|科学文档分类对于广泛的应用来说是一个关键的任务，但是获取大量的人类标记数据的成本可能是高昂的。为了应对这一挑战，我们提出了一种弱监督的方法，用于只使用标签名称的科学文档分类。在科学领域，标签名称往往包括特定领域的概念，这些概念可能不会出现在文档语料库中，因此难以精确匹配标签和文档。为了解决这个问题，我们提出了 WANDER，它利用密集检索在嵌入空间中执行匹配以捕获标签名的语义。进一步设计了标签名扩展模块，丰富了标签名表示。最后，使用一个自我训练步骤来完善预测。在三个数据集上的实验结果表明，WANDER 平均比最佳基准线高出11.9% 。我们的代码会在 https://github.com/ritaranx/wander 公布。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Weakly-Supervised+Scientific+Document+Classification+via+Retrieval-Augmented+Multi-Stage+Training)|1| |[Click-Conversion Multi-Task Model with Position Bias Mitigation for Sponsored Search in eCommerce](https://doi.org/10.1145/3539618.3591963)|Yibo Wang, Yanbing Xue, Bo Liu, Musen Wen, Wenting Zhao, Stephen Guo, Philip S. Yu||Position bias, the phenomenon whereby users tend to focus on higher-ranked items of the search result list regardless of the actual relevance to queries, is prevailing in many ranking systems. Position bias in training data biases the ranking model, leading to increasingly unfair item rankings, click-through-rate (CTR), and conversion rate (CVR) predictions. To jointly mitigate position bias in both item CTR and CVR prediction, we propose two position-bias-free CTR and CVR prediction models: Position-Aware Click-Conversion (PACC) and PACC via Position Embedding (PACC-PE). PACC is built upon probability decomposition and models position information as a probability. PACC-PE utilizes neural networks to model product-specific position information as embedding. Experiments on the E-commerce sponsored product search dataset show that our proposed models have better ranking effectiveness and can greatly alleviate position bias in both CTR and CVR prediction.|排名偏差是指用户倾向于关注搜索结果列表中排名较高的项目，而不考虑与查询的实际相关性，这种现象在许多排名系统中普遍存在。训练数据中的位置偏差使排名模型产生偏差，导致项目排名、点击率(CTR)和转换率(CVR)预测日益不公平。为了共同减轻项目 CTR 和 CVR 预测中的位置偏差，我们提出了两种无位置偏差的 CTR 和 CVR 预测模型: 位置感知点击转换(PACC)和通过位置嵌入(PACC-PE)的 PACC。PACC 是建立在概率分解和模型位置信息作为一个概率。PACC-PE 利用神经网络将特定产品的位置信息作为嵌入信息进行建模。对电子商务赞助商产品搜索数据集的实验结果表明，该模型具有较好的排序效果，可以大大减轻 CTR 和 CVR 预测中的位置偏差。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Click-Conversion+Multi-Task+Model+with+Position+Bias+Mitigation+for+Sponsored+Search+in+eCommerce)|0| -|[Beyond Two-Tower Matching: Learning Sparse Retrievable Cross-Interactions for Recommendation](https://doi.org/10.1145/3539618.3591643)|Liangcai Su, Fan Yan, Jieming Zhu, Xi Xiao, Haoyi Duan, Zhou Zhao, Zhenhua Dong, Ruiming Tang|Zhejiang University, Hangzhou, China; Huawei Noah's Ark Lab, Shenzhen, China; Shenzhen International Graduate School, Tsinghua University, Shenzhen, China|Two-tower models are a prevalent matching framework for recommendation, which have been widely deployed in industrial applications. The success of two-tower matching attributes to its efficiency in retrieval among a large number of items, since the item tower can be precomputed and used for fast Approximate Nearest Neighbor (ANN) search. However, it suffers two main challenges, including limited feature interaction capability and reduced accuracy in online serving. Existing approaches attempt to design novel late interactions instead of dot products, but they still fail to support complex feature interactions or lose retrieval efficiency. To address these challenges, we propose a new matching paradigm named SparCode, which supports not only sophisticated feature interactions but also efficient retrieval. Specifically, SparCode introduces an all-to-all interaction module to model fine-grained query-item interactions. Besides, we design a discrete code-based sparse inverted index jointly trained with the model to achieve effective and efficient model inference. Extensive experiments have been conducted on open benchmark datasets to demonstrate the superiority of our framework. The results show that SparCode significantly improves the accuracy of candidate item matching while retaining the same level of retrieval efficiency with two-tower models.|双塔模型是一种流行的推荐匹配框架，在工业应用中得到了广泛的应用。双塔匹配属性的成功在于它能够有效地检索大量的项目，因为项目塔可以预先计算并用于快速近似最近邻(ANN)搜索。然而，它面临两个主要的挑战，包括有限的特征交互能力和降低在线服务的准确性。现有的方法试图设计新的后期交互而不是点积，但仍然不能支持复杂的特征交互或失去检索效率。为了应对这些挑战，我们提出了一种新的匹配范例 SparCode，它不仅支持复杂的特征交互，而且支持高效的检索。具体来说，SparCode 引入了一个全对全交互模块来对细粒度的查询项交互进行建模。此外，我们设计了一个基于离散编码的稀疏倒排索引与模型联合训练，以实现有效和高效的模型推理。在开放的基准数据集上进行了大量的实验，验证了该框架的优越性。结果表明，SparCode 在保持双塔模型检索效率不变的情况下，显著提高了候选项匹配的准确率。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Beyond+Two-Tower+Matching:+Learning+Sparse+Retrievable+Cross-Interactions+for+Recommendation)|0| +|[Beyond Two-Tower Matching: Learning Sparse Retrievable Cross-Interactions for Recommendation](https://doi.org/10.1145/3539618.3591643)|Liangcai Su, Fan Yan, Jieming Zhu, Xi Xiao, Haoyi Duan, Zhou Zhao, Zhenhua Dong, Ruiming Tang|Huawei Noah's Ark Lab, Shenzhen, China; Zhejiang University, Hangzhou, China; Shenzhen International Graduate School, Tsinghua University, Shenzhen, China|Two-tower models are a prevalent matching framework for recommendation, which have been widely deployed in industrial applications. The success of two-tower matching attributes to its efficiency in retrieval among a large number of items, since the item tower can be precomputed and used for fast Approximate Nearest Neighbor (ANN) search. However, it suffers two main challenges, including limited feature interaction capability and reduced accuracy in online serving. Existing approaches attempt to design novel late interactions instead of dot products, but they still fail to support complex feature interactions or lose retrieval efficiency. To address these challenges, we propose a new matching paradigm named SparCode, which supports not only sophisticated feature interactions but also efficient retrieval. Specifically, SparCode introduces an all-to-all interaction module to model fine-grained query-item interactions. Besides, we design a discrete code-based sparse inverted index jointly trained with the model to achieve effective and efficient model inference. Extensive experiments have been conducted on open benchmark datasets to demonstrate the superiority of our framework. The results show that SparCode significantly improves the accuracy of candidate item matching while retaining the same level of retrieval efficiency with two-tower models.|双塔模型是一种流行的推荐匹配框架，在工业应用中得到了广泛的应用。双塔匹配属性的成功在于它能够有效地检索大量的项目，因为项目塔可以预先计算并用于快速近似最近邻(ANN)搜索。然而，它面临两个主要的挑战，包括有限的特征交互能力和降低在线服务的准确性。现有的方法试图设计新的后期交互而不是点积，但仍然不能支持复杂的特征交互或失去检索效率。为了应对这些挑战，我们提出了一种新的匹配范例 SparCode，它不仅支持复杂的特征交互，而且支持高效的检索。具体来说，SparCode 引入了一个全对全交互模块来对细粒度的查询项交互进行建模。此外，我们设计了一个基于离散编码的稀疏倒排索引与模型联合训练，以实现有效和高效的模型推理。在开放的基准数据集上进行了大量的实验，验证了该框架的优越性。结果表明，SparCode 在保持双塔模型检索效率不变的情况下，显著提高了候选项匹配的准确率。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Beyond+Two-Tower+Matching:+Learning+Sparse+Retrievable+Cross-Interactions+for+Recommendation)|0| |[News Popularity Beyond the Click-Through-Rate for Personalized Recommendations](https://doi.org/10.1145/3539618.3591741)|Ashutosh Nayak, Mayur Garg, Rajasekhara Reddy Duvvuru Muni|Samsung R&D Institute, Bangalore, India|Popularity detection of news articles is critical for making relevant recommendations for users and drive user engagement for maximum business value. Among several well-known metrics such as likes, shares, comments, Click-Through-Rate (CTR) has evolved as a default metric of popularity. However, CTR is highly influenced by the probability of news articles getting an impression, which in turn depends on the recommendation algorithm. Furthermore, it does not consider the age of the news articles, which are highly perishable and also misses out on human contextual behavioral preferences towards news. Here, we use the MIND dataset, open sourced by Microsoft to investigate the existing metrics of popularity and propose six new metrics. Our aim is to create awareness about the different perspectives of measuring popularity while discussing the advantages and disadvantages of the proposed metrics with respect to the human click behavior. We evaluated the predictability of the proposed metrics in comparison to CTR prediction. We further evaluated the utility of the proposed metrics through different test cases. Our results indicate that by using appropriate popularity metrics, we can reduce the initial news corpus (item set) by 50% and still could achieve 99% of the total clicks as compared to unfiltered news corpus based recommender systems. Similarly, our results show that we can reduce the effective number of articles recommended per impression that could improve user experience with the news platforms. The metrics proposed in this paper can be useful in other contexts, especially in recommenders with perishable items e.g. video reels or blogs.|新闻文章的流行度检测对于向用户提供相关建议和推动用户参与以获得最大商业价值至关重要。在一些众所周知的指标，如喜欢，分享，评论，点击率(CTR)已经发展成为一个默认的流行度量。然而，点击率受新闻文章获得印象的概率的影响很大，而这又取决于推荐算法。此外，它没有考虑到新闻文章的年龄，这些文章是高度容易腐烂的，也错过了人类对新闻的语境行为偏好。在这里，我们使用由微软开源的 MIND 数据集来调查现有的流行度量标准，并提出六个新的度量标准。我们的目标是在讨论与人类点击行为相关的度量标准的优缺点时，建立对测量流行度的不同视角的认识。与 CTR 预测相比，我们评估了提出的指标的可预测性。我们通过不同的测试用例进一步评估了提出的度量标准的效用。我们的研究结果表明，通过使用合适的流行度指标，我们可以减少50% 的初始新闻语料库(项目集) ，仍然可以实现99% 的总点击量相比，未过滤的新闻语料库为基础的推荐系统。同样，我们的研究结果表明，我们可以减少有效数量的文章推荐每个印象，可以改善用户体验的新闻平台。本文中提出的度量标准在其他情况下也是有用的，特别是对于那些容易变质的项目(如视频卷轴或博客)的推荐者。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=News+Popularity+Beyond+the+Click-Through-Rate+for+Personalized+Recommendations)|0| |[Learning Query-aware Embedding Index for Improving E-commerce Dense Retrieval](https://doi.org/10.1145/3539618.3591834)|Mingming Li, Chunyuan Yuan, Binbin Wang, Jingwei Zhuo, Songlin Wang, Lin Liu, Sulong Xu|JD.com, Beijing, China|The embedding index has become an essential part of the dense retrieval (DR) system, which enables a fast search for billion of items in online E-commerce applications. To accelerate the retrieval process in industrial scenarios, most of the previous studies only utilize item embeddings. However, the product quantization process without query embeddings will lead to inconsistency between queries and items. A straightforward solution is to put query embedding into the product quantization process. But we found that the distance of the positive query and item embedding pairs is too large, which means the query and item embeddings learned by the two-tower are not fully aligned. This problem would lead to performance decay when directly putting query embeddings into the product quantization. In this paper, we propose a novel query-aware embedding Index framework, which aligns the query and item embedding space to reduce the distance between positive pairs, thereby mixing the query and item embeddings to learn better cluster centers for product quantization. Specifically, we first propose s symmetric loss to train a better two-tower to achieve space alignment. Subsequently, we propose a mixed quantization strategy to put the query embeddings into the product quantization process for bridging the gap between queries and compressed item embeddings. Extensive experiments show that our framework significantly outperforms previous models on a real-world dataset, which demonstrates the superiority and effectiveness of the framework.|嵌入索引已经成为密集检索(DR)系统的重要组成部分，可以实现在线电子商务应用中对数十亿条目的快速搜索。为了加速工业场景中的检索过程，以往的研究大多只使用项目嵌入。但是，没有嵌入查询的产品量化过程会导致查询和项目之间的不一致。一个直接的解决方案是将查询嵌入到产品量化过程中。但是我们发现正查询和项目嵌入对之间的距离太大，这意味着双塔学习的查询和项目嵌入没有完全对齐。当直接将查询嵌入到产品量化中时，这个问题会导致性能下降。本文提出了一种新的查询感知嵌入索引框架，该框架通过对查询和项目嵌入空间进行对齐来减少正对之间的距离，从而混合查询和项目嵌入来学习更好的产品量化聚类中心。具体来说，我们首先提出了对称损失训练一个更好的双塔实现空间对准。随后，我们提出了一种混合量化策略，将查询嵌入放入产品量化过程中，以缩小查询与压缩项嵌入之间的差距。大量实验表明，该框架在实际数据集上的性能明显优于以往的模型，证明了该框架的优越性和有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Learning+Query-aware+Embedding+Index+for+Improving+E-commerce+Dense+Retrieval)|0| |[Exploiting Simulated User Feedback for Conversational Search: Ranking, Rewriting, and Beyond](https://doi.org/10.1145/3539618.3591683)|Paul Owoicho, Ivan Sekulic, Mohammad Aliannejadi, Jeffrey Dalton, Fabio Crestani||This research aims to explore various methods for assessing user feedback in mixed-initiative conversational search (CS) systems. While CS systems enjoy profuse advancements across multiple aspects, recent research fails to successfully incorporate feedback from the users. One of the main reasons for that is the lack of system-user conversational interaction data. To this end, we propose a user simulator-based framework for multi-turn interactions with a variety of mixed-initiative CS systems. Specifically, we develop a user simulator, dubbed ConvSim, that, once initialized with an information need description, is capable of providing feedback to a system's responses, as well as answering potential clarifying questions. Our experiments on a wide variety of state-of-the-art passage retrieval and neural re-ranking models show that effective utilization of user feedback can lead to 16% retrieval performance increase in terms of nDCG@3. Moreover, we observe consistent improvements as the number of feedback rounds increases (35% relative improvement in terms of nDCG@3 after three rounds). This points to a research gap in the development of specific feedback processing modules and opens a potential for significant advancements in CS. To support further research in the topic, we release over 30,000 transcripts of system-simulator interactions based on well-established CS datasets.|本研究旨在探讨混合主动式会话搜寻系统中评估使用者反馈的各种方法。虽然 CS 系统在多个方面取得了长足的进步，但最近的研究未能成功地整合来自用户的反馈。其中一个主要原因是缺乏系统-用户交互数据。为此，我们提出了一个基于用户模拟器的框架，用于多个混合主动 CS 系统的多回合交互。具体来说，我们开发了一个用户模拟器，称为 ConvSim，一旦用信息需求描述初始化，就能够为系统的响应提供反馈，并回答潜在的澄清问题。我们在各种最先进的文章检索和神经元重排序模型上的实验表明，有效利用用户反馈可以提高16% 的检索性能。此外，我们观察到随着反馈回合数量的增加，一致的改善(三个回合后 nDCG@3的相对改善率为35%)。这指出了在开发特定的反馈处理模块方面的研究差距，并为 CS 的重大进展开辟了潜力。为了支持本课题的进一步研究，我们发布了超过30,000份基于已建立的 CS 数据集的系统-模拟器交互的文本。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Exploiting+Simulated+User+Feedback+for+Conversational+Search:+Ranking,+Rewriting,+and+Beyond)|0| |[Modeling Orders of User Behaviors via Differentiable Sorting: A Multi-task Framework to Predicting User Post-click Conversion](https://doi.org/10.1145/3539618.3592023)|Menghan Wang, Jinming Yang, Yuchen Guo, Yuming Shen, Mengying Zhu, Yanlin Wang||User post-click conversion prediction is of high interest to researchers and developers. Recent studies employ multi-task learning to tackle the selection bias and data sparsity problem, two severe challenges in post-click behavior prediction, by incorporating click data. However, prior works mainly focused on pointwise learning and the orders of labels (i.e., click and post-click) are not well explored, which naturally poses a listwise learning problem. Inspired by recent advances on differentiable sorting, in this paper, we propose a novel multi-task framework that leverages orders of user behaviors to predict user post-click conversion in an end-to-end approach. Specifically, we define an aggregation operator to combine predicted outputs of different tasks to a unified score, then we use the computed scores to model the label relations via differentiable sorting. Extensive experiments on public and industrial datasets show the superiority of our proposed model against competitive baselines.|用户点击后的转换预测是研究人员和开发人员非常感兴趣的。最近的研究采用多任务学习来解决选择偏差和数据稀疏问题，这两个严峻的挑战后点击行为预测，通过整合点击数据。然而，以前的作品主要集中在点式学习和标签的顺序(即，点击和后点击)没有很好的探索，这自然造成了一个列表式学习问题。受可微分排序技术最新进展的启发，本文提出了一种新的多任务框架，该框架利用用户行为的顺序来预测端到端的用户点击后转换。具体来说，我们定义了一个聚合算子，将不同任务的预测输出结合成一个统一的得分，然后使用计算出的得分通过可微排序来建模标签关系。在公共和工业数据集上的大量实验表明了我们提出的模型对竞争基线的优越性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Modeling+Orders+of+User+Behaviors+via+Differentiable+Sorting:+A+Multi-task+Framework+to+Predicting+User+Post-click+Conversion)|0| -|[Semantic-enhanced Modality-asymmetric Retrieval for Online E-commerce Search](https://doi.org/10.1145/3539618.3591863)|Zhigong Zhou, Ning Ding, Xiaochuan Fan, Yue Shang, Yiming Qiu, Jingwei Zhuo, Zhiwei Ge, Songlin Wang, Lin Liu, Sulong Xu, Han Zhang|JD.com, California, China; JD.com, Beijing, China|Semantic retrieval, which retrieves semantically matched items given a textual query, has been an essential component to enhance system effectiveness in e-commerce search. In this paper, we study the multimodal retrieval problem, where the visual information (e.g, image) of item is leveraged as supplementary of textual information to enrich item representation and further improve retrieval performance. Though learning from cross-modality data has been studied extensively in tasks such as visual question answering or media summarization, multimodal retrieval remains a non-trivial and unsolved problem especially in the asymmetric scenario where the query is unimodal while the item is multimodal. In this paper, we propose a novel model named SMAR, which stands for Semantic-enhanced Modality-Asymmetric Retrieval, to tackle the problem of modality fusion and alignment in this kind of asymmetric scenario. Extensive experimental results on an industrial dataset show that the proposed model outperforms baseline models significantly in retrieval accuracy. We have open sourced our industrial dataset for the sake of reproducibility and future research works.|语义检索是提高电子商务搜索系统效率的重要组成部分，它检索给定文本查询的语义匹配项。本文研究了利用项目的视觉信息(如图像)作为文本信息的补充来丰富项目表示并进一步提高检索性能的多模态检索问题。尽管在视觉问题回答或媒体摘要等任务中对跨模态数据的学习已经进行了广泛的研究，但是多模态检索仍然是一个非常重要的未解决的问题，尤其是在查询为单模态而项目为多模态的非对称情况下。本文提出了一种新的模型 SMAR，即语义增强的情态-非对称检索模型，以解决这种非对称情景下的情态融合和对齐问题。在工业数据集上的大量实验结果表明，该模型在检索精度上明显优于基线模型。为了重现性和未来的研究工作，我们已经开源了我们的工业数据集。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Semantic-enhanced+Modality-asymmetric+Retrieval+for+Online+E-commerce+Search)|0| +|[Semantic-enhanced Modality-asymmetric Retrieval for Online E-commerce Search](https://doi.org/10.1145/3539618.3591863)|Zhigong Zhou, Ning Ding, Xiaochuan Fan, Yue Shang, Yiming Qiu, Jingwei Zhuo, Zhiwei Ge, Songlin Wang, Lin Liu, Sulong Xu, Han Zhang|JD.com, Beijing, China; JD.com, California, China|Semantic retrieval, which retrieves semantically matched items given a textual query, has been an essential component to enhance system effectiveness in e-commerce search. In this paper, we study the multimodal retrieval problem, where the visual information (e.g, image) of item is leveraged as supplementary of textual information to enrich item representation and further improve retrieval performance. Though learning from cross-modality data has been studied extensively in tasks such as visual question answering or media summarization, multimodal retrieval remains a non-trivial and unsolved problem especially in the asymmetric scenario where the query is unimodal while the item is multimodal. In this paper, we propose a novel model named SMAR, which stands for Semantic-enhanced Modality-Asymmetric Retrieval, to tackle the problem of modality fusion and alignment in this kind of asymmetric scenario. Extensive experimental results on an industrial dataset show that the proposed model outperforms baseline models significantly in retrieval accuracy. We have open sourced our industrial dataset for the sake of reproducibility and future research works.|语义检索是提高电子商务搜索系统效率的重要组成部分，它检索给定文本查询的语义匹配项。本文研究了利用项目的视觉信息(如图像)作为文本信息的补充来丰富项目表示并进一步提高检索性能的多模态检索问题。尽管在视觉问题回答或媒体摘要等任务中对跨模态数据的学习已经进行了广泛的研究，但是多模态检索仍然是一个非常重要的未解决的问题，尤其是在查询为单模态而项目为多模态的非对称情况下。本文提出了一种新的模型 SMAR，即语义增强的情态-非对称检索模型，以解决这种非对称情景下的情态融合和对齐问题。在工业数据集上的大量实验结果表明，该模型在检索精度上明显优于基线模型。为了重现性和未来的研究工作，我们已经开源了我们的工业数据集。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Semantic-enhanced+Modality-asymmetric+Retrieval+for+Online+E-commerce+Search)|0| |[Intent-aware Ranking Ensemble for Personalized Recommendation](https://doi.org/10.1145/3539618.3591702)|Jiayu Li, Peijie Sun, Zhefan Wang, Weizhi Ma, Yangkun Li, Min Zhang, Zhoutian Feng, Daiyue Xue||Ranking ensemble is a critical component in real recommender systems. When a user visits a platform, the system will prepare several item lists, each of which is generally from a single behavior objective recommendation model. As multiple behavior intents, e.g., both clicking and buying some specific item category, are commonly concurrent in a user visit, it is necessary to integrate multiple single-objective ranking lists into one. However, previous work on rank aggregation mainly focused on fusing homogeneous item lists with the same objective while ignoring ensemble of heterogeneous lists ranked with different objectives with various user intents. In this paper, we treat a user's possible behaviors and the potential interacting item categories as the user's intent. And we aim to study how to fuse candidate item lists generated from different objectives aware of user intents. To address such a task, we propose an Intent-aware ranking Ensemble Learning~(IntEL) model to fuse multiple single-objective item lists with various user intents, in which item-level personalized weights are learned. Furthermore, we theoretically prove the effectiveness of IntEL with point-wise, pair-wise, and list-wise loss functions via error-ambiguity decomposition. Experiments on two large-scale real-world datasets also show significant improvements of IntEL on multiple behavior objectives simultaneously compared to previous ranking ensemble models.|等级集成是实际推荐系统中的一个重要组成部分。当用户访问一个平台时，系统将准备几个项目列表，每个项目列表通常来自一个行为客观推荐模型。由于多种行为意图，例如点击和购买某个特定的商品类别，在用户访问中通常是并发的，因此有必要将多个单目标排名列表集成到一个列表中。然而，以往的排名聚合研究主要集中在同一目标下的同类项目列表的融合，而忽略了不同目标下不同用户意图的异类项目列表的集合。在本文中，我们将用户的可能行为和潜在的交互项目类别视为用户的意图。研究了如何根据用户意图对不同目标生成的候选项目表进行融合。为了解决这个问题，我们提出了一个意图感知排名集成学习模型(IntEL) ，该模型将多个单目标项目列表与不同的用户意图相融合，其中项目级别的个性化权重被学习。此外，我们还通过误差模糊度分解从理论上证明了对点损耗、对损耗和列表损耗函数的有效性。在两个大规模真实世界数据集上的实验也表明，与以前的排序集合模型相比，IntEL 在同时处理多个行为目标上也有显著的改善。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Intent-aware+Ranking+Ensemble+for+Personalized+Recommendation)|0| -|[A Model-Agnostic Popularity Debias Training Framework for Click-Through Rate Prediction in Recommender System](https://doi.org/10.1145/3539618.3591939)|Fan Zhang, Qijie Shen|Shopee, Shanghai, China; Alibaba Group, Hangzhou, China|Recommender system (RS) is widely applied in a multitude of scenarios to aid individuals obtaining the information they require efficiently. At the same time, the prevalence of popularity bias in such systems has become a widely acknowledged issue. To address this challenge, we propose a novel method named Model-Agnostic Popularity Debias Training Framework (MDTF). It consists of two basic modules including 1) General Ranking Model (GRM), which is model-agnostic and can be implemented as any ranking models; and 2) Popularity Debias Module (PDM), which estimates the impact of the competitiveness and popularity of candidate items on the CTR, by utilizing the feedback of cold-start users to re-weigh the loss in GRM. MDTF seamlessly integrates these two modules in an end-to-end multi-task learning framework. Extensive experiments on both real-world offline dataset and online A/B test demonstrate its superiority over state-of-the-art methods.|推荐系统(RS)在多种情况下被广泛应用，以帮助个人有效地获得他们所需要的信息。与此同时，这类系统中普遍存在的受欢迎程度偏差已经成为一个广为人知的问题。为了应对这一挑战，我们提出了一种新的方法称为模型不可知流行性偏差训练框架(MDTF)。它包括两个基本模块: 1)模型无关的通用排名模型(GRM) ，可以作为任何排名模型实现; 2)流行度偏差模型(PDM) ，利用冷启动用户的反馈重新权衡排名模型中的损失，估计候选项的竞争力和流行程度对 CTR 的影响。MDTF 在端到端多任务学习框架中无缝地集成了这两个模块。通过对现实世界离线数据集和在线 A/B 测试的大量实验，证明了该方法优于最先进的方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Model-Agnostic+Popularity+Debias+Training+Framework+for+Click-Through+Rate+Prediction+in+Recommender+System)|0| +|[A Model-Agnostic Popularity Debias Training Framework for Click-Through Rate Prediction in Recommender System](https://doi.org/10.1145/3539618.3591939)|Fan Zhang, Qijie Shen|Alibaba Group, Hangzhou, China; Shopee, Shanghai, China|Recommender system (RS) is widely applied in a multitude of scenarios to aid individuals obtaining the information they require efficiently. At the same time, the prevalence of popularity bias in such systems has become a widely acknowledged issue. To address this challenge, we propose a novel method named Model-Agnostic Popularity Debias Training Framework (MDTF). It consists of two basic modules including 1) General Ranking Model (GRM), which is model-agnostic and can be implemented as any ranking models; and 2) Popularity Debias Module (PDM), which estimates the impact of the competitiveness and popularity of candidate items on the CTR, by utilizing the feedback of cold-start users to re-weigh the loss in GRM. MDTF seamlessly integrates these two modules in an end-to-end multi-task learning framework. Extensive experiments on both real-world offline dataset and online A/B test demonstrate its superiority over state-of-the-art methods.|推荐系统(RS)在多种情况下被广泛应用，以帮助个人有效地获得他们所需要的信息。与此同时，这类系统中普遍存在的受欢迎程度偏差已经成为一个广为人知的问题。为了应对这一挑战，我们提出了一种新的方法称为模型不可知流行性偏差训练框架(MDTF)。它包括两个基本模块: 1)模型无关的通用排名模型(GRM) ，可以作为任何排名模型实现; 2)流行度偏差模型(PDM) ，利用冷启动用户的反馈重新权衡排名模型中的损失，估计候选项的竞争力和流行程度对 CTR 的影响。MDTF 在端到端多任务学习框架中无缝地集成了这两个模块。通过对现实世界离线数据集和在线 A/B 测试的大量实验，证明了该方法优于最先进的方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Model-Agnostic+Popularity+Debias+Training+Framework+for+Click-Through+Rate+Prediction+in+Recommender+System)|0| |[U-NEED: A Fine-grained Dataset for User Needs-Centric E-commerce Conversational Recommendation](https://doi.org/10.1145/3539618.3591878)|Yuanxing Liu, Weinan Zhang, Baohua Dong, Yan Fan, Hang Wang, Fan Feng, Yifan Chen, Ziyu Zhuang, Hengbin Cui, Yongbin Li, Wanxiang Che||Conversational recommender systems (CRSs) aim to understand the information needs and preferences expressed in a dialogue to recommend suitable items to the user. Most of the existing conversational recommendation datasets are synthesized or simulated with crowdsourcing, which has a large gap with real-world scenarios. To bridge the gap, previous work contributes a dataset E-ConvRec, based on pre-sales dialogues between users and customer service staff in E-commerce scenarios. However, E-ConvRec only supplies coarse-grained annotations and general tasks for making recommendations in pre-sales dialogues. Different from that, we use real user needs as a clue to explore the E-commerce conversational recommendation in complex pre-sales dialogues, namely user needs-centric E-commerce conversational recommendation (UNECR). In this paper, we construct a user needs-centric E-commerce conversational recommendation dataset (U-NEED) from real-world E-commerce scenarios. U-NEED consists of 3 types of resources: (i) 7,698 fine-grained annotated pre-sales dialogues in 5 top categories (ii) 333,879 user behaviors and (iii) 332,148 product knowledge tuples. To facilitate the research of UNECR, we propose 5 critical tasks: (i) pre-sales dialogue understanding (ii) user needs elicitation (iii) user needs-based recommendation (iv) pre-sales dialogue generation and (v) pre-sales dialogue evaluation. We establish baseline methods and evaluation metrics for each task. We report experimental results of 5 tasks on U-NEED. We also report results in 3 typical categories. Experimental results indicate that the challenges of UNECR in various categories are different.|会话推荐系统(CRS)旨在理解对话中表达的信息需求和偏好，从而向用户推荐合适的项目。现有的会话推荐数据集大多是通过众包合成或模拟的，与现实情景有很大的差距。为了弥补这一差距，以前的工作提供了一个数据集 E-ConvRec，该数据集基于电子商务场景中用户和客户服务人员之间的售前对话。然而，E-ConvRec 只提供粗粒度的注释和一般性任务，用于在售前对话中提出建议。与此不同的是，我们以用户真实需求为线索，探索复杂的售前对话中的电子商务会话推荐，即以用户需求为中心的电子商务会话推荐。本文从现实电子商务场景出发，构建了一个以用户需求为中心的电子商务会话推荐数据集(U-Need)。U-Need 由3种类型的资源组成: (i)在5个顶级类别中的7,698个细粒度注释的售前对话(ii)333,879个用户行为和(iii)332,148个产品知识元组。为了促进 UNECR 的研究，我们提出了5个关键任务: (i)售前对话理解(ii)用户需求启发(iii)基于用户需求的推荐(iv)售前对话生成和(v)售前对话评估。我们为每个任务建立基线方法和评估指标。我们报告了5个任务的实验结果。我们还报告了3个典型类别的结果。实验结果表明，联合国难民事务高级专员办事处在不同类别中面临的挑战是不同的。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=U-NEED:+A+Fine-grained+Dataset+for+User+Needs-Centric+E-commerce+Conversational+Recommendation)|0| |[Continuous Input Embedding Size Search For Recommender Systems](https://doi.org/10.1145/3539618.3591653)|Yunke Qu, Tong Chen, Xiangyu Zhao, Lizhen Cui, Kai Zheng, Hongzhi Yin||Latent factor models are the most popular backbones for today's recommender systems owing to their prominent performance. Latent factor models represent users and items as real-valued embedding vectors for pairwise similarity computation, and all embeddings are traditionally restricted to a uniform size that is relatively large (e.g., 256-dimensional). With the exponentially expanding user base and item catalog in contemporary e-commerce, this design is admittedly becoming memory-inefficient. To facilitate lightweight recommendation, reinforcement learning (RL) has recently opened up opportunities for identifying varying embedding sizes for different users/items. However, challenged by search efficiency and learning an optimal RL policy, existing RL-based methods are restricted to highly discrete, predefined embedding size choices. This leads to a largely overlooked potential of introducing finer granularity into embedding sizes to obtain better recommendation effectiveness under a given memory budget. In this paper, we propose continuous input embedding size search (CIESS), a novel RL-based method that operates on a continuous search space with arbitrary embedding sizes to choose from. In CIESS, we further present an innovative random walk-based exploration strategy to allow the RL policy to efficiently explore more candidate embedding sizes and converge to a better decision. CIESS is also model-agnostic and hence generalizable to a variety of latent factor RSs, whilst experiments on two real-world datasets have shown state-of-the-art performance of CIESS under different memory budgets when paired with three popular recommendation models.|潜在因素模型是当今推荐系统最受欢迎的骨干，因为它们具有突出的性能。潜在因素模型将用户和项目表示为实值嵌入向量进行两两相似性计算，所有的嵌入传统上都限制在一个相对较大的统一大小(例如，256维)。随着当代电子商务中用户数量和商品目录的指数级增长，这种设计无可否认地变得内存效率低下。为了方便轻量级推荐，强化学习(RL)最近为不同用户/项目提供了识别不同嵌入大小的机会。然而，由于受到搜索效率和学习最优 RL 策略的挑战，现有的基于 RL 的方法仅限于高度离散的、预定义的嵌入大小选择。这导致在给定的内存预算下，为了获得更好的推荐效果，在嵌入大小中引入更细粒度的可能性被很大程度上忽略了。本文提出了连续输入嵌入大小搜索(CIESS) ，这是一种新的基于 RL 的方法，可以在任意嵌入大小的连续搜索空间上进行选择。在 CIESS，我们进一步提出了一个创新的基于随机漫步的探索策略，使 RL 策略能够有效地探索更多的候选嵌入规模，并收敛到一个更好的决策。CIESS 也是模型无关的，因此可以推广到各种潜在因素 RS，而在两个真实世界数据集上的实验显示，当与三种流行的推荐模型配对时，CIESS 在不同内存预算下的最先进性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Continuous+Input+Embedding+Size+Search+For+Recommender+Systems)|0| -|[A Geometric Framework for Query Performance Prediction in Conversational Search](https://doi.org/10.1145/3539618.3591625)|Guglielmo Faggioli, Nicola Ferro, Cristina Ioana Muntean, Raffaele Perego, Nicola Tonellotto|University of Pisa, Pisa, Italy; ISTI-CNR, Pisa, Italy; University of Padova, Padova, Italy|Thanks to recent advances in IR and NLP, the way users interact with search engines is evolving rapidly, with multi-turn conversations replacing traditional one-shot textual queries. Given its interactive nature, Conversational Search (CS) is one of the scenarios that can benefit the most from Query Performance Prediction (QPP) techniques. QPP for the CS domain is a relatively new field and lacks proper framing. In this study, we address this gap by proposing a framework for the application of QPP in the CS domain and use it to evaluate the performance of predictors. We characterize what it means to predict the performance in the CS scenario, where information needs are not independent queries but a series of closely related utterances. We identify three main ways to use QPP models in the CS domain: as a diagnostic tool, as a way to adjust the system's behaviour during a conversation, or as a way to predict the system's performance on the next utterance. Due to the lack of established evaluation procedures for QPP in the CS domain, we propose a protocol to evaluate QPPs for each of the use cases. Additionally, we introduce a set of spatial-based QPP models designed to work the best in the conversational search domain, where dense neural retrieval models are the most common approaches and query cutoffs are typically small. We show how the proposed QPP approaches improve significantly the predictive performance over the state-of-the-art in different scenarios and collections.|由于最近在 IR 和 NLP 方面的进步，用户与搜索引擎交互的方式正在迅速发展，多回合会话取代了传统的一次性文本查询。鉴于其交互性，会话搜索(Conversational Search，CS)是可以从查询性能预测(Query Performance Prevention，QPP)技术中获益最多的场景之一。CS 领域的 QPP 是一个相对较新的领域，缺乏合适的框架。在这项研究中，我们通过提出一个在 CS 领域应用 QPP 的框架来弥补这一差距，并用它来评估预测因子的性能。我们描述了在 CS 场景中预测性能意味着什么，在 CS 场景中，信息需求不是独立的查询，而是一系列密切相关的语句。我们确定了在 CS 领域使用 QPP 模型的三种主要方法: 作为一种诊断工具，作为一种在谈话中调整系统行为的方法，或者作为一种预测系统在下一次谈话中的表现的方法。由于在 CS 领域缺乏已建立的 QPP 评估程序，我们提出了一个协议来评估每个用例的 QPP。此外，我们还介绍了一套基于空间的 QPP 模型，该模型旨在在会话搜索领域中发挥最佳作用，其中密集神经检索模型是最常用的方法，而查询截断通常很小。我们展示了所提出的 QPP 方法如何在不同的场景和集合中显著提高最先进的预测性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Geometric+Framework+for+Query+Performance+Prediction+in+Conversational+Search)|0| +|[A Geometric Framework for Query Performance Prediction in Conversational Search](https://doi.org/10.1145/3539618.3591625)|Guglielmo Faggioli, Nicola Ferro, Cristina Ioana Muntean, Raffaele Perego, Nicola Tonellotto|University of Pisa, Pisa, Italy; University of Padova, Padova, Italy; ISTI-CNR, Pisa, Italy|Thanks to recent advances in IR and NLP, the way users interact with search engines is evolving rapidly, with multi-turn conversations replacing traditional one-shot textual queries. Given its interactive nature, Conversational Search (CS) is one of the scenarios that can benefit the most from Query Performance Prediction (QPP) techniques. QPP for the CS domain is a relatively new field and lacks proper framing. In this study, we address this gap by proposing a framework for the application of QPP in the CS domain and use it to evaluate the performance of predictors. We characterize what it means to predict the performance in the CS scenario, where information needs are not independent queries but a series of closely related utterances. We identify three main ways to use QPP models in the CS domain: as a diagnostic tool, as a way to adjust the system's behaviour during a conversation, or as a way to predict the system's performance on the next utterance. Due to the lack of established evaluation procedures for QPP in the CS domain, we propose a protocol to evaluate QPPs for each of the use cases. Additionally, we introduce a set of spatial-based QPP models designed to work the best in the conversational search domain, where dense neural retrieval models are the most common approaches and query cutoffs are typically small. We show how the proposed QPP approaches improve significantly the predictive performance over the state-of-the-art in different scenarios and collections.|由于最近在 IR 和 NLP 方面的进步，用户与搜索引擎交互的方式正在迅速发展，多回合会话取代了传统的一次性文本查询。鉴于其交互性，会话搜索(Conversational Search，CS)是可以从查询性能预测(Query Performance Prevention，QPP)技术中获益最多的场景之一。CS 领域的 QPP 是一个相对较新的领域，缺乏合适的框架。在这项研究中，我们通过提出一个在 CS 领域应用 QPP 的框架来弥补这一差距，并用它来评估预测因子的性能。我们描述了在 CS 场景中预测性能意味着什么，在 CS 场景中，信息需求不是独立的查询，而是一系列密切相关的语句。我们确定了在 CS 领域使用 QPP 模型的三种主要方法: 作为一种诊断工具，作为一种在谈话中调整系统行为的方法，或者作为一种预测系统在下一次谈话中的表现的方法。由于在 CS 领域缺乏已建立的 QPP 评估程序，我们提出了一个协议来评估每个用例的 QPP。此外，我们还介绍了一套基于空间的 QPP 模型，该模型旨在在会话搜索领域中发挥最佳作用，其中密集神经检索模型是最常用的方法，而查询截断通常很小。我们展示了所提出的 QPP 方法如何在不同的场景和集合中显著提高最先进的预测性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Geometric+Framework+for+Query+Performance+Prediction+in+Conversational+Search)|0| |[Neighborhood-based Hard Negative Mining for Sequential Recommendation](https://doi.org/10.1145/3539618.3591995)|Lu Fan, Jiashu Pu, Rongsheng Zhang, XiaoMing Wu||Negative sampling plays a crucial role in training successful sequential recommendation models. Instead of merely employing random negative sample selection, numerous strategies have been proposed to mine informative negative samples to enhance training and performance. However, few of these approaches utilize structural information. In this work, we observe that as training progresses, the distributions of node-pair similarities in different groups with varying degrees of neighborhood overlap change significantly, suggesting that item pairs in distinct groups may possess different negative relationships. Motivated by this observation, we propose a Graph-based Negative sampling approach based on Neighborhood Overlap (GNNO) to exploit structural information hidden in user behaviors for negative mining. GNNO first constructs a global weighted item transition graph using training sequences. Subsequently, it mines hard negative samples based on the degree of overlap with the target item on the graph. Furthermore, GNNO employs curriculum learning to control the hardness of negative samples, progressing from easy to difficult. Extensive experiments on three Amazon benchmarks demonstrate GNNO's effectiveness in consistently enhancing the performance of various state-of-the-art models and surpassing existing negative sampling strategies. The code will be released at \url{https://github.com/floatSDSDS/GNNO}.|负抽样对顺序推荐模型的建立起着至关重要的作用。为了提高训练效果，人们提出了许多挖掘负样本信息的策略，而不仅仅是采用随机的负样本选择。然而，这些方法很少利用结构信息。本研究发现，随着训练的进行，邻域重叠程度不同的不同群体中节点对相似性的分布发生了显著的变化，提示不同群体中的项目对可能具有不同的负相关关系。在此基础上，本文提出了一种基于邻域重叠(GNNO)的负向采样方法，利用用户行为中隐藏的结构信息进行负向挖掘。GNNO 首先使用训练序列构造一个全局加权项目转移图。然后，根据图上目标项的重叠程度挖掘硬负样本。此外，GNNO 使用课程学习来控制负面样本的硬度，从容易进展到难。在三个 Amazon 基准上的大量实验证明了 GNNO 在持续提高各种最先进模型的性能和超越现有的负采样策略方面的有效性。代码将在 url { https://github.com/floatsdsds/gnno }发布。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Neighborhood-based+Hard+Negative+Mining+for+Sequential+Recommendation)|0| |[Query Performance Prediction: From Ad-hoc to Conversational Search](https://doi.org/10.1145/3539618.3591919)|Chuan Meng, Negar Arabzadeh, Mohammad Aliannejadi, Maarten de Rijke||Query performance prediction (QPP) is a core task in information retrieval. The QPP task is to predict the retrieval quality of a search system for a query without relevance judgments. Research has shown the effectiveness and usefulness of QPP for ad-hoc search. Recent years have witnessed considerable progress in conversational search (CS). Effective QPP could help a CS system to decide an appropriate action to be taken at the next turn. Despite its potential, QPP for CS has been little studied. We address this research gap by reproducing and studying the effectiveness of existing QPP methods in the context of CS. While the task of passage retrieval remains the same in the two settings, a user query in CS depends on the conversational history, introducing novel QPP challenges. In particular, we seek to explore to what extent findings from QPP methods for ad-hoc search generalize to three CS settings: (i) estimating the retrieval quality of different query rewriting-based retrieval methods, (ii) estimating the retrieval quality of a conversational dense retrieval method, and (iii) estimating the retrieval quality for top ranks vs. deeper-ranked lists. Our findings can be summarized as follows: (i) supervised QPP methods distinctly outperform unsupervised counterparts only when a large-scale training set is available; (ii) point-wise supervised QPP methods outperform their list-wise counterparts in most cases; and (iii) retrieval score-based unsupervised QPP methods show high effectiveness in assessing the conversational dense retrieval method, ConvDR.|查询性能预测是信息检索的核心任务。QPP 任务是在没有相关性判断的情况下预测查询搜索系统的检索质量。研究表明 QPP 在自组织搜索中的有效性和实用性。近年来，会话搜索取得了长足的进步。有效的质量保证计划可以帮助 CS 系统决定下一轮要采取的适当行动。尽管 QPP 具有很大的潜力，但是对它的研究还很少。我们通过再现和研究现有的 QPP 方法在 CS 背景下的有效性来弥补这一研究差距。虽然在这两种情况下，文章检索的任务是相同的，但用户在 CS 中的查询依赖于会话历史，引入了新的 QPP 挑战。特别是，我们试图探索用于特别搜索的 QPP 方法的结果在多大程度上概括为三种 CS 设置: (i)估计不同基于查询重写的检索方法的检索质量，(ii)估计会话密集检索方法的检索质量，以及(iii)估计顶级与更深级列表的检索质量。我们的研究结果可以总结如下: (i)监督 QPP 方法只有在大规模训练集可用时才明显优于无监督的对应方法; (ii)点式监督 QPP 方法在大多数情况下优于其列表式对应方法; 和(iii)基于检索评分的无监督 QPP 方法在评估会话密集检索方法，ConvDR 方面显示出高效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Query+Performance+Prediction:+From+Ad-hoc+to+Conversational+Search)|0| |[Integrity and Junkiness Failure Handling for Embedding-based Retrieval: A Case Study in Social Network Search](https://doi.org/10.1145/3539618.3591831)|Wenping Wang, Yunxi Guo, Chiyao Shen, Shuai Ding, Guangdeng Liao, Hao Fu, Pramodh Karanth Prabhakar||Embedding based retrieval has seen its usage in a variety of search applications like e-commerce, social networking search etc. While the approach has demonstrated its efficacy in tasks like semantic matching and contextual search, it is plagued by the problem of uncontrollable relevance. In this paper, we conduct an analysis of embedding-based retrieval launched in early 2021 on our social network search engine, and define two main categories of failures introduced by it, integrity and junkiness. The former refers to issues such as hate speech and offensive content that can severely harm user experience, while the latter includes irrelevant results like fuzzy text matching or language mismatches. Efficient methods during model inference are further proposed to resolve the issue, including indexing treatments and targeted user cohort treatments, etc. Though being simple, we show the methods have good offline NDCG and online A/B tests metrics gain in practice. We analyze the reasons for the improvements, pointing out that our methods are only preliminary attempts to this important but challenging problem. We put forward potential future directions to explore.|嵌入式检索在电子商务、社交网络搜索等多种搜索应用中得到了广泛的应用。虽然该方法在语义匹配和上下文搜索等任务中表现出了很好的效果，但是相关性不可控的问题仍然困扰着该方法。本文对2021年初在我国社交网络搜索引擎上推出的嵌入式检索进行了分析，并定义了嵌入式检索引入的两类主要失败: 完整性和垃圾性。前者指的是可能严重损害用户体验的仇恨言论和冒犯性内容等问题，而后者包括模糊文本匹配或语言不匹配等不相关的结果。进一步提出了有效的模型推理方法，包括索引处理和目标用户队列处理等。实践表明，该方法简单易行，具有良好的离线 NDCG 和在线 A/B 测试指标收益。我们分析了改进的原因，指出我们的方法只是对这个重要但具有挑战性的问题的初步尝试。我们提出了潜在的未来发展方向。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Integrity+and+Junkiness+Failure+Handling+for+Embedding-based+Retrieval:+A+Case+Study+in+Social+Network+Search)|0| @@ -36,31 +36,31 @@ |[DMBIN: A Dual Multi-behavior Interest Network for Click-Through Rate Prediction via Contrastive Learning](https://doi.org/10.1145/3539618.3591669)|Tianqi He, Kaiyuan Li, Shan Chen, Haitao Wang, Qiang Liu, Xingxing Wang, Dong Wang|Meituan.com, Beijing, China|Click-through rate (CTR) prediction plays a critical role in various online applications, aiming to estimate the user's click probability. User interest modeling from various interactive behaviors(e.g., click, add-to-cart, order) is becoming a mainstream approach to CTR prediction. We argue that the various user behaviors contain two important intrinsic characteristics: 1) The discrepancy in various behaviors reveals different aspects of user's behavior-specific interests. For example, one may click out of need but pay more attention to the rating when purchasing. 2) The consistency of various behaviors contains user's behavior-invariant interest. For example, the user prefers interacted items rather than other items. Therefore, it is necessary to disentangle the discrepancy and consistency signals from the massive behavior information. Unfortunately, previous methods have yet to study this phenomenon well, which limits the recommendation performance. To tackle this challenge, we propose a novel Dual Multi-Behavior Interest Network (DMBIN for short) to disentangle behavior-specific and behavioral-invariant interests from various behaviors for a better recommendation. Specifically, DMBIN formalizethe discrepancy and consistency characteristics among various behaviors. Extensive experiments and empirical analysis on two real-world datasets demonstrate that DMBIN significantly outperforms the state-of-the-art methods. Moreover, DMBIN is also deployed in the online sponsored search advertising system in Meituan and achieves 2.11% and 2.76% improvement on CTR and CPM, respectively.s the dismantlement task as two contrastive learning tasks of multi-behavior interests extracted through the Multi-behavior Interest Module: Multi-behavior Interest Contrast(MIC) task and Multi-behavior Interest Alignment(MIA) task. These two tasks focus on extracting|点进率点击率(ctrl)预测在各种在线应用程序中扮演着重要的角色，旨在估计用户的点击概率。来自各种交互行为的用户兴趣建模(例如，单击、添加到购物车、订单)正在成为 CTR 预测的主流方法。我们认为，不同的用户行为包含两个重要的内在特征: 1)不同行为的差异揭示了用户行为特定兴趣的不同方面。例如，一个可能点击需要，但更注意评级时，购买。2)各种行为的一致性包含用户行为不变的兴趣。例如，用户更喜欢交互式项目而不是其他项目。因此，有必要从海量的行为信息中分离出差异性和一致性信号。不幸的是，以前的方法还没有很好地研究这种现象，这限制了推荐性能。为了应对这一挑战，我们提出了一种新的双重多行为兴趣网络(简称 DMBIN) ，将行为特异性兴趣和行为不变性兴趣从各种行为中分离出来，以获得更好的推荐。具体来说，DMBIN 形式化了各种行为之间的差异性和一致性特征。对两个实际数据集的大量实验和实证分析表明，DMBIN 显著优于最先进的方法。此外，DMBIN 也被应用于在线赞助商搜索广告系统中，在点击率和美团成本方面分别取得了2.11% 和2.76% 的提高。拆分任务是通过多行为兴趣模块提取的两个多行为兴趣对比学习任务: 多行为兴趣对比任务(MIC)和多行为兴趣对齐任务(MIA)。这两个任务的重点是提取|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=DMBIN:+A+Dual+Multi-behavior+Interest+Network+for+Click-Through+Rate+Prediction+via+Contrastive+Learning)|0| |[Ensemble Modeling with Contrastive Knowledge Distillation for Sequential Recommendation](https://doi.org/10.1145/3539618.3591679)|Hanwen Du, Huanhuan Yuan, Pengpeng Zhao, Fuzhen Zhuang, Guanfeng Liu, Lei Zhao, Yanchi Liu, Victor S. Sheng||Sequential recommendation aims to capture users' dynamic interest and predicts the next item of users' preference. Most sequential recommendation methods use a deep neural network as sequence encoder to generate user and item representations. Existing works mainly center upon designing a stronger sequence encoder. However, few attempts have been made with training an ensemble of networks as sequence encoders, which is more powerful than a single network because an ensemble of parallel networks can yield diverse prediction results and hence better accuracy. In this paper, we present Ensemble Modeling with contrastive Knowledge Distillation for sequential recommendation (EMKD). Our framework adopts multiple parallel networks as an ensemble of sequence encoders and recommends items based on the output distributions of all these networks. To facilitate knowledge transfer between parallel networks, we propose a novel contrastive knowledge distillation approach, which performs knowledge transfer from the representation level via Intra-network Contrastive Learning (ICL) and Cross-network Contrastive Learning (CCL), as well as Knowledge Distillation (KD) from the logits level via minimizing the Kullback-Leibler divergence between the output distributions of the teacher network and the student network. To leverage contextual information, we train the primary masked item prediction task alongside the auxiliary attribute prediction task as a multi-task learning scheme. Extensive experiments on public benchmark datasets show that EMKD achieves a significant improvement compared with the state-of-the-art methods. Besides, we demonstrate that our ensemble method is a generalized approach that can also improve the performance of other sequential recommenders. Our code is available at this link: https://github.com/hw-du/EMKD.|序贯推荐旨在捕捉用户的动态兴趣，预测用户的下一项偏好。大多数顺序推荐方法使用深层神经网络作为序列编码器来生成用户和项目表示。现有的工作主要集中在设计一个更强的序列编码器。然而，很少有人尝试将一个网络集合训练成序列编码器，它比单个网络更强大，因为一个并行网络集合可以产生不同的预测结果，从而提高准确性。本文提出了一种基于对比知识提取的序贯推荐系统集成建模方法。我们的框架采用多个并行网络作为序列编码器的集成，并根据这些网络的输出分布推荐项目。为了促进并行网络之间的知识转移，提出了一种新的对比知识提取方法，该方法通过网络内对比学习(ICL)和跨网络对比学习(CCL)从表示层进行知识转移，通过最小化教师网络和学生网络输出分布之间的 Kullback-Leibler 差异，从 logits 层进行知识提取。为了充分利用上下文信息，我们将主要隐藏项目预测任务与辅助属性预测任务一起训练为一个多任务学习方案。在公共基准数据集上进行的大量实验表明，EMKD 方法与最新的方法相比，取得了显著的改进。此外，我们证明了我们的集成方法是一种通用的方法，也可以改善其他顺序推荐器的性能。我们的代码可在以下连结找到: https://github.com/hw-du/emkd。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Ensemble+Modeling+with+Contrastive+Knowledge+Distillation+for+Sequential+Recommendation)|0| |[HDNR: A Hyperbolic-Based Debiased Approach for Personalized News Recommendation](https://doi.org/10.1145/3539618.3591693)|Shicheng Wang, Shu Guo, Lihong Wang, Tingwen Liu, Hongbo Xu|Institute of Information Engineering, Chinese Academy of Sciences & School of Cyber Security, University of Chinese Academy of Sciences, Beijing, China; National Computer Network Emergency Response Technical Team/Coordination Center, Beijing, China|Personalized news recommendation aims to recommend candidate news to the target user, according to the clicked news history. The user-news interaction data exhibits power-law distribution, however, existing works usually learn representations in Euclidean space which makes inconsistent capacities between data space and embedding space, leading to severe representation distortion problem. Besides, the existence of conformity bias, a potential cause of power-law distribution, may introduce biased guidance to learn user representations. In this paper, we propose a novel debiased method based on hyperbolic space, named HDNR, to tackle the above problems. Specifically, first, we employ hyperboloid model with exponential growth capacity to conduct user and news modeling, in order to solve inconsistent space capacities problem and obtain low distortion representations. Second, we design a re-weighting aggregation module to further mitigate conformity bias in data distribution, through considering local importance of the clicked news among contextual history and its global popularity degree simultaneously. Finally, we calculate the relevance score between target user and candidate news representations. We conduct experiments on two real-world news recommendation datasets MIND-Large, MIND-Small and empirical results demonstrate the effectiveness of our approach from multiple perspectives.|个性化新闻推荐的目的是根据点击新闻的历史记录向目标用户推荐候选新闻。用户-新闻交互数据呈幂律分布，但现有的作品通常在欧氏空间中学习表示，使得数据空间与嵌入空间的容量不一致，从而导致严重的表示失真问题。此外，幂律分布的一个潜在原因——从众偏差的存在，可能会引入偏差指导来学习用户表征。在本文中，我们提出了一种新的基于双曲空间的去偏方法，命名为 HDNR，以解决上述问题。具体来说，首先，我们使用具有双曲面模型容量的指数增长来进行用户和新闻建模，以解决空间容量不一致的问题，并获得低失真表示。其次，通过同时考虑点击新闻在上下文历史中的局部重要性及其全球受欢迎程度，设计了一个重新加权聚合模块，进一步减轻数据分布中的一致性偏差。最后，计算目标用户与候选新闻表征之间的相关度得分。我们对两个真实世界的新闻推荐数据集 MIND-Large、 MIND-Small 和实证结果进行了实验，从多个角度证明了我们的方法的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=HDNR:+A+Hyperbolic-Based+Debiased+Approach+for+Personalized+News+Recommendation)|0| -|[LinRec: Linear Attention Mechanism for Long-term Sequential Recommender Systems](https://doi.org/10.1145/3539618.3591717)|Langming Liu, Liu Cai, Chi Zhang, Xiangyu Zhao, Jingtong Gao, Wanyu Wang, Yifu Lv, Wenqi Fan, Yiqi Wang, Ming He, Zitao Liu, Qing Li|City University of Hong Kong, Hong Kong, Hong Kong; Harbin Engineering University, Harbin, China; The Hong Kong Polytechnic University, Hong Kong, Hong Kong; AI Lab, Lenovo Research, Beijing, China; City University of Hong Kong, Hong kong, Hong Kong; National University of Defense Technology, Changsha, China; Ant Group, Beijing, China; Guangdong Institute of Smart Education, Jinan University, Guangdong, China|Transformer models have achieved remarkable success in sequential recommender systems (SRSs). However, computing the attention matrix in traditional dot-product attention mechanisms results in a quadratic complexity with sequence lengths, leading to high computational costs for long-term sequential recommendation. Motivated by the above observation, we propose a novel L2-Normalized Linear Attention for the Transformer-based Sequential Recommender Systems (LinRec), which theoretically improves efficiency while preserving the learning capabilities of the traditional dot-product attention. Specifically, by thoroughly examining the equivalence conditions of efficient attention mechanisms, we show that LinRec possesses linear complexity while preserving the property of attention mechanisms. In addition, we reveal its latent efficiency properties by interpreting the proposed LinRec mechanism through a statistical lens. Extensive experiments are conducted based on two public benchmark datasets, demonstrating that the combination of LinRec and Transformer models achieves comparable or even superior performance than state-of-the-art Transformer-based SRS models while significantly improving time and memory efficiency. The implementation code is available online at https://github.com/Applied-Machine-Learning-Lab/LinRec.>|变压器模型在顺序推荐系统(SRS)中取得了显著的成功。然而，在传统的点积注意机制中，注意矩阵的计算会导致序列长度的二次复杂性，从而导致长期序列推荐的计算成本较高。在此基础上，我们提出了一种新的基于变压器的顺序推荐系统(LinRec)的 L2归一化线性注意算法，该算法在保留传统点积注意学习能力的同时，从理论上提高了效率。具体来说，通过对有效注意机制等价条件的深入研究，我们证明了 LinRec 具有线性复杂性，同时保留了注意机制的性质。此外，我们通过统计透镜解释所提出的 LinRec 机制，揭示了其潜在的效率特性。基于两个公开的基准数据集进行了大量的实验，结果表明，LinRec 模型和 Transformer 模型的组合在显著提高时间和内存效率的同时，实现了与基于最新变压器的 SRS 模型相当甚至更好的性能。实施守则可于网上 https://github.com/applied-machine-learning-lab/linrec 下载。 >|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=LinRec:+Linear+Attention+Mechanism+for+Long-term+Sequential+Recommender+Systems)|0| -|[Personalized Retrieval over Millions of Items](https://doi.org/10.1145/3539618.3591749)|Hemanth Vemuri, Sheshansh Agrawal, Shivam Mittal, Deepak Saini, Akshay Soni, Abhinav V. Sambasivan, Wenhao Lu, Yajun Wang, Mehul Parsana, Purushottam Kar, Manik Varma|Microsoft Research, Bengaluru, India; LinkedIn Corporation, Sunnyvale, CA, USA; Google, Mountain View, CA, USA; Microsoft, Mountain View, CA, USA; Microsoft, Bengaluru, India; Microsoft, Redmond, WA, USA; IIT Kanpur, Kanpur, India|Personalized retrieval seeks to retrieve items relevant to a user event (e.g. a page visit or a query) that are adapted to the user's personal preferences. For example, two users who happen to perform the same event such as visiting the same product page or asking the same query should receive potentially distinct recommendations adapted to their individual tastes. Personalization is seldom attempted over catalogs of millions of items since the cost of existing personalization routines scale linearly in the number of candidate items. For example, performing two-sided personalized retrieval (with both event and item embeddings personalized to the user) incurs prohibitive storage and compute costs. Instead, it is common to use non-personalized retrieval to obtain a small shortlist of items over which personalized re-ranking can be done quickly. Despite being scalable, this strategy risks losing items uniquely relevant to a user that fail to get shortlisted during non-personalized retrieval. This paper bridges this gap by developing the XPERT algorithm that identifies a form of two-sided personalization that can be scalably implemented over millions of items and hundreds of millions of users. Key to overcoming the computational challenges of personalized retrieval is a novel concept of morph operators that can be used with arbitrary encoder architectures, completely avoids the steep memory overheads of two-sided personalization, provides millisecond-time inference and offers multi-intent retrieval. On multiple public and proprietary datasets, XPERT offered upto 5% superior recall and AUC than state-of-the-art techniques. Code for XPERT is available at https://github.com/personalizedretrieval/xpert.|个性化检索寻求检索与用户事件相关的项目(例如页面访问或查询) ，这些项目适合用户的个人喜好。例如，碰巧执行相同事件(如访问同一产品页面或询问同一查询)的两个用户应该会收到适合他们各自口味的潜在不同的推荐。由于现有个性化程序的成本在候选项的数量上呈线性规模，因此很少尝试对数百万个项目的目录进行个性化处理。例如，执行双边个性化检索(同时嵌入针对用户的个性化事件和项目)会带来令人望而却步的存储和计算成本。相反，通常使用非个性化检索来获得一个小的项目短名单，可以对其进行快速的个性化重新排名。尽管这种策略具有可伸缩性，但如果用户在非个性化检索过程中未能进入决选名单，则可能丢失与其唯一相关的项目。本文通过开发 XPERT 算法来弥补这一差距，该算法确定了一种双边个性化的形式，这种形式可以在数百万条目和数亿用户中可伸缩地实现。克服个性化检索的计算挑战的关键是一种新颖的变形运算符概念，它可以用于任意编码器架构，完全避免双边个性化的高昂内存开销，提供毫秒级推理和提供多意图检索。在多个公共和专有数据集上，XPERT 比最先进的技术提供高达5% 的优越召回率和 AUC。XPERT 代码可在 https://github.com/personalizedretrieval/XPERT 下载。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Personalized+Retrieval+over+Millions+of+Items)|0| -|[BKD: A Bridge-based Knowledge Distillation Method for Click-Through Rate Prediction](https://doi.org/10.1145/3539618.3591958)|Yin Deng, Yingxin Chen, Xin Dong, Lingchao Pan, Hai Li, Lei Cheng, Linjian Mo|Ant Group, Shanghai, China; Ant Group, Hangzhou, China|Prediction models for click-through rate (CTR) learn feature interactions underlying user behaviors, which are crucial in recommendation systems. Due to their size and complexity, existing approaches have a limited range of applications. In order to decrease inference delay, knowledge distillation techniques have been used in recommendation systems. Due to the student model's lower capacity, the knowledge distillation process is less effective when there is a significant difference in the complexity of the network architecture between the teacher model and the student model. We present a novel knowledge distillation approach called Bridge-based Knowledge Distillation (BKD), which employs a bridge model to facilitate the student model's learning from the teacher model's latent representations. The bridge model is based on Graph Neural Networks (GNNs), and leverages the edges of GNNs to identify significant feature interaction relationships, while simultaneously reducing redundancy for improved efficiency. To further enhance the efficiency of knowledge distillation, we decoupled the extracted knowledge and transferred each component separately to the student model, aiming to improve the distillation sufficiency of each module. Extensive experimental results show that our proposed BKD approach outperforms state-of-the-art competitors on various tasks.|点进率预测模型(CTR)学习用户行为背后的特征交互，这在推荐系统中是至关重要的。由于其规模和复杂性，现有方法的应用范围有限。为了减少推理时延，在推荐系统中引入了知识提取技术。由于学生模型的容量较小，当教师模型和学生模型的网络结构复杂度存在显著差异时，知识提取过程的有效性较差。本文提出了一种新的知识提取方法——基于桥梁的知识提取方法(BKD) ，该方法采用一种桥梁模型来促进学生模型从教师模型的潜在表征中学习。该桥梁模型基于图形神经网络(GNN) ，利用 GNN 的边缘来识别重要的特征交互关系，同时减少冗余以提高效率。为了进一步提高知识提取的效率，对提取的知识进行解耦，并将每个组件分别转移到学生模型中，以提高每个模块的知识提取充分性。大量的实验结果表明，我们提出的 BKD 方法在各种任务上都优于最先进的竞争对手。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=BKD:+A+Bridge-based+Knowledge+Distillation+Method+for+Click-Through+Rate+Prediction)|0| +|[LinRec: Linear Attention Mechanism for Long-term Sequential Recommender Systems](https://doi.org/10.1145/3539618.3591717)|Langming Liu, Liu Cai, Chi Zhang, Xiangyu Zhao, Jingtong Gao, Wanyu Wang, Yifu Lv, Wenqi Fan, Yiqi Wang, Ming He, Zitao Liu, Qing Li|City University of Hong Kong, Hong Kong, Hong Kong; AI Lab, Lenovo Research, Beijing, China; Guangdong Institute of Smart Education, Jinan University, Guangdong, China; City University of Hong Kong, Hong kong, Hong Kong; Harbin Engineering University, Harbin, China; National University of Defense Technology, Changsha, China; The Hong Kong Polytechnic University, Hong Kong, Hong Kong; Ant Group, Beijing, China|Transformer models have achieved remarkable success in sequential recommender systems (SRSs). However, computing the attention matrix in traditional dot-product attention mechanisms results in a quadratic complexity with sequence lengths, leading to high computational costs for long-term sequential recommendation. Motivated by the above observation, we propose a novel L2-Normalized Linear Attention for the Transformer-based Sequential Recommender Systems (LinRec), which theoretically improves efficiency while preserving the learning capabilities of the traditional dot-product attention. Specifically, by thoroughly examining the equivalence conditions of efficient attention mechanisms, we show that LinRec possesses linear complexity while preserving the property of attention mechanisms. In addition, we reveal its latent efficiency properties by interpreting the proposed LinRec mechanism through a statistical lens. Extensive experiments are conducted based on two public benchmark datasets, demonstrating that the combination of LinRec and Transformer models achieves comparable or even superior performance than state-of-the-art Transformer-based SRS models while significantly improving time and memory efficiency. The implementation code is available online at https://github.com/Applied-Machine-Learning-Lab/LinRec.>|变压器模型在顺序推荐系统(SRS)中取得了显著的成功。然而，在传统的点积注意机制中，注意矩阵的计算会导致序列长度的二次复杂性，从而导致长期序列推荐的计算成本较高。在此基础上，我们提出了一种新的基于变压器的顺序推荐系统(LinRec)的 L2归一化线性注意算法，该算法在保留传统点积注意学习能力的同时，从理论上提高了效率。具体来说，通过对有效注意机制等价条件的深入研究，我们证明了 LinRec 具有线性复杂性，同时保留了注意机制的性质。此外，我们通过统计透镜解释所提出的 LinRec 机制，揭示了其潜在的效率特性。基于两个公开的基准数据集进行了大量的实验，结果表明，LinRec 模型和 Transformer 模型的组合在显著提高时间和内存效率的同时，实现了与基于最新变压器的 SRS 模型相当甚至更好的性能。实施守则可于网上 https://github.com/applied-machine-learning-lab/linrec 下载。 >|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=LinRec:+Linear+Attention+Mechanism+for+Long-term+Sequential+Recommender+Systems)|0| +|[Personalized Retrieval over Millions of Items](https://doi.org/10.1145/3539618.3591749)|Hemanth Vemuri, Sheshansh Agrawal, Shivam Mittal, Deepak Saini, Akshay Soni, Abhinav V. Sambasivan, Wenhao Lu, Yajun Wang, Mehul Parsana, Purushottam Kar, Manik Varma|IIT Kanpur, Kanpur, India; Microsoft Research, Bengaluru, India; LinkedIn Corporation, Sunnyvale, CA, USA; Google, Mountain View, CA, USA; Microsoft, Redmond, WA, USA; Microsoft, Mountain View, CA, USA; Microsoft, Bengaluru, India|Personalized retrieval seeks to retrieve items relevant to a user event (e.g. a page visit or a query) that are adapted to the user's personal preferences. For example, two users who happen to perform the same event such as visiting the same product page or asking the same query should receive potentially distinct recommendations adapted to their individual tastes. Personalization is seldom attempted over catalogs of millions of items since the cost of existing personalization routines scale linearly in the number of candidate items. For example, performing two-sided personalized retrieval (with both event and item embeddings personalized to the user) incurs prohibitive storage and compute costs. Instead, it is common to use non-personalized retrieval to obtain a small shortlist of items over which personalized re-ranking can be done quickly. Despite being scalable, this strategy risks losing items uniquely relevant to a user that fail to get shortlisted during non-personalized retrieval. This paper bridges this gap by developing the XPERT algorithm that identifies a form of two-sided personalization that can be scalably implemented over millions of items and hundreds of millions of users. Key to overcoming the computational challenges of personalized retrieval is a novel concept of morph operators that can be used with arbitrary encoder architectures, completely avoids the steep memory overheads of two-sided personalization, provides millisecond-time inference and offers multi-intent retrieval. On multiple public and proprietary datasets, XPERT offered upto 5% superior recall and AUC than state-of-the-art techniques. Code for XPERT is available at https://github.com/personalizedretrieval/xpert.|个性化检索寻求检索与用户事件相关的项目(例如页面访问或查询) ，这些项目适合用户的个人喜好。例如，碰巧执行相同事件(如访问同一产品页面或询问同一查询)的两个用户应该会收到适合他们各自口味的潜在不同的推荐。由于现有个性化程序的成本在候选项的数量上呈线性规模，因此很少尝试对数百万个项目的目录进行个性化处理。例如，执行双边个性化检索(同时嵌入针对用户的个性化事件和项目)会带来令人望而却步的存储和计算成本。相反，通常使用非个性化检索来获得一个小的项目短名单，可以对其进行快速的个性化重新排名。尽管这种策略具有可伸缩性，但如果用户在非个性化检索过程中未能进入决选名单，则可能丢失与其唯一相关的项目。本文通过开发 XPERT 算法来弥补这一差距，该算法确定了一种双边个性化的形式，这种形式可以在数百万条目和数亿用户中可伸缩地实现。克服个性化检索的计算挑战的关键是一种新颖的变形运算符概念，它可以用于任意编码器架构，完全避免双边个性化的高昂内存开销，提供毫秒级推理和提供多意图检索。在多个公共和专有数据集上，XPERT 比最先进的技术提供高达5% 的优越召回率和 AUC。XPERT 代码可在 https://github.com/personalizedretrieval/XPERT 下载。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Personalized+Retrieval+over+Millions+of+Items)|0| +|[BKD: A Bridge-based Knowledge Distillation Method for Click-Through Rate Prediction](https://doi.org/10.1145/3539618.3591958)|Yin Deng, Yingxin Chen, Xin Dong, Lingchao Pan, Hai Li, Lei Cheng, Linjian Mo|Ant Group, Hangzhou, China; Ant Group, Shanghai, China|Prediction models for click-through rate (CTR) learn feature interactions underlying user behaviors, which are crucial in recommendation systems. Due to their size and complexity, existing approaches have a limited range of applications. In order to decrease inference delay, knowledge distillation techniques have been used in recommendation systems. Due to the student model's lower capacity, the knowledge distillation process is less effective when there is a significant difference in the complexity of the network architecture between the teacher model and the student model. We present a novel knowledge distillation approach called Bridge-based Knowledge Distillation (BKD), which employs a bridge model to facilitate the student model's learning from the teacher model's latent representations. The bridge model is based on Graph Neural Networks (GNNs), and leverages the edges of GNNs to identify significant feature interaction relationships, while simultaneously reducing redundancy for improved efficiency. To further enhance the efficiency of knowledge distillation, we decoupled the extracted knowledge and transferred each component separately to the student model, aiming to improve the distillation sufficiency of each module. Extensive experimental results show that our proposed BKD approach outperforms state-of-the-art competitors on various tasks.|点进率预测模型(CTR)学习用户行为背后的特征交互，这在推荐系统中是至关重要的。由于其规模和复杂性，现有方法的应用范围有限。为了减少推理时延，在推荐系统中引入了知识提取技术。由于学生模型的容量较小，当教师模型和学生模型的网络结构复杂度存在显著差异时，知识提取过程的有效性较差。本文提出了一种新的知识提取方法——基于桥梁的知识提取方法(BKD) ，该方法采用一种桥梁模型来促进学生模型从教师模型的潜在表征中学习。该桥梁模型基于图形神经网络(GNN) ，利用 GNN 的边缘来识别重要的特征交互关系，同时减少冗余以提高效率。为了进一步提高知识提取的效率，对提取的知识进行解耦，并将每个组件分别转移到学生模型中，以提高每个模块的知识提取充分性。大量的实验结果表明，我们提出的 BKD 方法在各种任务上都优于最先进的竞争对手。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=BKD:+A+Bridge-based+Knowledge+Distillation+Method+for+Click-Through+Rate+Prediction)|0| |[Hierarchical Type Enhanced Negative Sampling for Knowledge Graph Embedding](https://doi.org/10.1145/3539618.3591996)|Zhenzhou Lin, Zishuo Zhao, Jingyou Xie, Ying Shen|Sun Yat-sen University, Shenzhen, China|Knowledge graph embedding aims at modeling knowledge by projecting entities and relations into a low-dimensional semantic space. Most of the works on knowledge graph embedding construct negative samples by negative sampling as knowledge graphs typically only contain positive facts. Although substantial progress has been made by dynamic distribution based sampling methods, selecting plausible and prior information-engaged negative samples still poses many challenges. Inspired by type constraint methods, we propose Hierarchical Type Enhanced Negative Sampling (HTENS) which leverages hierarchical entity type information and entity-relation cooccurrence information to optimize the sampling probability distribution of negative samples. The experiments performed on the link prediction task demonstrate the effectiveness of HTENS. Additionally, HTENS shows its superiority in versatility and can be integrated into scalable systems with enhanced negative sampling.|知识图嵌入的目的是将实体和关系投影到低维语义空间中，对知识进行建模。大多数关于知识图嵌入的工作都是通过负抽样来构造负样本，因为知识图通常只包含正事实。尽管基于动态分布的抽样方法已经取得了实质性的进展，但是选择合理的和事先信息参与的负样本仍然带来许多挑战。受到类型约束方法的启发，我们提出了层次类型增强负面抽样(HTENS) ，它利用层次实体类型信息和实体关系共现信息来优化负面样本的抽样概率分布。在链路预测任务上的实验验证了 HTENS 的有效性。此外，HTENS 显示了其通用性的优势，可以集成到增强负采样的可扩展系统中。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Hierarchical+Type+Enhanced+Negative+Sampling+for+Knowledge+Graph+Embedding)|0| |[LOVF: Layered Organic View Fusion for Click-through Rate Prediction in Online Advertising](https://doi.org/10.1145/3539618.3592014)|Lingwei Kong, Lu Wang, Xiwei Zhao, Junsheng Jin, Zhangang Lin, Jinghe Hu, Jingping Shao|JD.com, Beijing, China|Organic recommendation and advertising recommendation usually coexist on e-commerce platforms. In this paper, we study the problem of utilizing data from organic recommendation to reinforce click-through rate prediction in advertising scenarios from a multi-view learning perspective. We propose a novel method, termed LOVF (Layered Organic View Fusion). LOVF implements a multi-view fusion mechanism - for each advertising instance, LOVF derives deep representations layer-by-layer from the organic recommendation view and these deep representations are then fused into the corresponding vanilla representations of the advertising view. Extensive experiments across a variety of backbones demonstrate LOVF's generality, effectiveness and efficiency on a new real-world production dataset. The dataset encompasses data from both the organic recommendation and advertising scenarios. Notably, LOVF has been successfully deployed in the advertising recommender system of JD.com, which is one of the world's largest e-commerce platforms; online A/B testing shows that LOVF achieves impressive improvement on advertising clicks and revenue. Our code and dataset are available at https://github.com/adsturing/lovf for facilitating further research.|在电子商务平台上，有机推荐和广告推荐通常并存。在本文中，我们从多视角学习的角度来研究利用有机推荐的数据来加强广告场景中的点进率预测的问题。我们提出了一种新的方法，称为 LOVF (分层有机视图融合)。LOVF 实现了一种多视图融合机制——对于每个广告实例，LOVF 从有机推荐视图中逐层获得深度表示，然后将这些深度表示融合到相应的普通广告视图中。横跨各种骨干的大量实验证明了 LOVF 在一个新的真实世界生产数据集上的通用性、有效性和效率。数据集包含来自有机推荐和广告场景的数据。值得注意的是，在世界最大的电子商务平台之一京东的广告推荐系统中，LOVF 已经成功部署，在线 A/B 测试表明，LOVF 在广告点击量和收入方面取得了令人印象深刻的进步。我们的代码和数据集可供 https://github.com/adsturing/lovf 使用，以方便进一步的研究。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=LOVF:+Layered+Organic+View+Fusion+for+Click-through+Rate+Prediction+in+Online+Advertising)|0| -|[Optimizing Reciprocal Rank with Bayesian Average for improved Next Item Recommendation](https://doi.org/10.1145/3539618.3592033)|Xiangkui Lu, Jun Wu, Jianbo Yuan|Beijing Jiaotong University & Engineering Research Center of Integration and Application of Digital Learning Technology,Ministry of Education, Beijing, China; ByteDance, Bellevue, WA, USA; Beijing Jiaotong University, Beijing, China|Next item recommendation is a crucial task of session-based recommendation. However, the gap between the optimization objective (Binary Cross Entropy) and the ranking metric (Mean Reciprocal Rank) has not been well-explored, resulting in sub-optimal recommendations. In this paper, we propose a novel objective function, namely Adjusted-RR, to directly optimize Mean Reciprocal Rank. Specifically, Adjusted-RR adopts Bayesian Average to adjust Reciprocal Rank loss with Normal Rank loss by creating position-aware weights between them. Adjusted-RR is a plug-and-play objective that is compatible with various models. We apply Adjusted-RR on two base models and two datasets, and experimental results show that it makes a significant improvement in the next item recommendation.|下一个项目推荐是基于会话的推荐的关键任务。然而，优化目标(二元交叉熵)和排名指标(平均倒数排名)之间的差距还没有得到很好的探索，导致次优建议。在本文中，我们提出了一个新的目标函数，即调整后的平均倒数排名，直接优化。具体来说，调整后的 RR 采用贝叶斯平均法，通过在两者之间建立位置感知权重来调整相互秩损失和正常秩损失。调整后的 RR 是一个即插即用的目标，它与各种模型兼容。我们在两个基本模型和两个数据集上应用了调整后的 RR，实验结果表明它对下一个项目的推荐有显著的改善。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Optimizing+Reciprocal+Rank+with+Bayesian+Average+for+improved+Next+Item+Recommendation)|0| +|[Optimizing Reciprocal Rank with Bayesian Average for improved Next Item Recommendation](https://doi.org/10.1145/3539618.3592033)|Xiangkui Lu, Jun Wu, Jianbo Yuan|Beijing Jiaotong University, Beijing, China; ByteDance, Bellevue, WA, USA; Beijing Jiaotong University & Engineering Research Center of Integration and Application of Digital Learning Technology,Ministry of Education, Beijing, China|Next item recommendation is a crucial task of session-based recommendation. However, the gap between the optimization objective (Binary Cross Entropy) and the ranking metric (Mean Reciprocal Rank) has not been well-explored, resulting in sub-optimal recommendations. In this paper, we propose a novel objective function, namely Adjusted-RR, to directly optimize Mean Reciprocal Rank. Specifically, Adjusted-RR adopts Bayesian Average to adjust Reciprocal Rank loss with Normal Rank loss by creating position-aware weights between them. Adjusted-RR is a plug-and-play objective that is compatible with various models. We apply Adjusted-RR on two base models and two datasets, and experimental results show that it makes a significant improvement in the next item recommendation.|下一个项目推荐是基于会话的推荐的关键任务。然而，优化目标(二元交叉熵)和排名指标(平均倒数排名)之间的差距还没有得到很好的探索，导致次优建议。在本文中，我们提出了一个新的目标函数，即调整后的平均倒数排名，直接优化。具体来说，调整后的 RR 采用贝叶斯平均法，通过在两者之间建立位置感知权重来调整相互秩损失和正常秩损失。调整后的 RR 是一个即插即用的目标，它与各种模型兼容。我们在两个基本模型和两个数据集上应用了调整后的 RR，实验结果表明它对下一个项目的推荐有显著的改善。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Optimizing+Reciprocal+Rank+with+Bayesian+Average+for+improved+Next+Item+Recommendation)|0| |[uCTRL: Unbiased Contrastive Representation Learning via Alignment and Uniformity for Collaborative Filtering](https://doi.org/10.1145/3539618.3592076)|Jaewoong Lee, Seongmin Park, Mincheol Yoon, Jongwuk Lee||Because implicit user feedback for the collaborative filtering (CF) models is biased toward popular items, CF models tend to yield recommendation lists with popularity bias. Previous studies have utilized inverse propensity weighting (IPW) or causal inference to mitigate this problem. However, they solely employ pointwise or pairwise loss functions and neglect to adopt a contrastive loss function for learning meaningful user and item representations. In this paper, we propose Unbiased ConTrastive Representation Learning (uCTRL), optimizing alignment and uniformity functions derived from the InfoNCE loss function for CF models. Specifically, we formulate an unbiased alignment function used in uCTRL. We also devise a novel IPW estimation method that removes the bias of both users and items. Despite its simplicity, uCTRL equipped with existing CF models consistently outperforms state-of-the-art unbiased recommender models, up to 12.22% for Recall@20 and 16.33% for NDCG@20 gains, on four benchmark datasets.|由于协同过滤(CF)模型的隐式用户反馈偏向于流行项目，CF 模型倾向于产生带有流行偏见的推荐列表。以往的研究采用逆倾向加权(IPW)或因果推断来缓解这一问题。然而，他们仅仅使用逐点损失函数或成对损失函数，而忽略了采用对比损失函数来学习有意义的用户和项目表示。本文针对 CF 模型，提出了无偏对比表示学习(uCTRL)、优化对齐和由 InfoNCE 损失函数导出的均匀性函数。具体来说，我们制定了一个无偏对齐函数在 uCTRL 中使用。我们还设计了一种新的 IPW 估计方法，消除了用户和项目的偏差。尽管简单，配备现有 CF 模型的 uCTRL 始终优于最先进的无偏差推荐模型，在四个基准数据集上，Recall@20最高为12.22% ，NDCG@20最高为16.33% 。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=uCTRL:+Unbiased+Contrastive+Representation+Learning+via+Alignment+and+Uniformity+for+Collaborative+Filtering)|0| -|[Unsupervised Query Performance Prediction for Neural Models with Pairwise Rank Preferences](https://doi.org/10.1145/3539618.3592082)|Ashutosh Singh, Debasis Ganguly, Suchana Datta, Craig MacDonald|University of Glasgow, Glasgow, United Kingdom; University College Dublin, Dublin, Ireland|A query performance prediction (QPP) method predicts the effectiveness of an IR system for a given query. While unsupervised approaches have been shown to work well for statistical IR models, it is likely that these approaches would yield limited effectiveness for neural ranking models (NRMs) because the retrieval scores of these models lie within a short range unlike their statistical counterparts. In this work, we propose to leverage a pairwise inference-based NRM's (specifically, DuoT5) output to accumulate evidences on the pairwise believes of one document ranked above the other. We hypothesize that the more consistent these pairwise likelihoods are, the higher is the likelihood of the retrieval to be of better quality, thus yielding a higher QPP score. We conduct our experiments on the TREC-DL dataset leveraging pairwise likelihoods from an auxiliary model DuoT5. Our experiments demonstrate that the proposed method called Pairwise Rank Preference-based QPP (QPP-PRP) leads to significantly better results than a number of standard unsupervised QPP baselines on several NRMs.|一种查询性能预测(QPP)方法预测一个给定查询的红外系统的有效性。虽然无监督方法已被证明对于统计 IR 模型工作得很好，但是这些方法可能对神经排序模型(NRM)产生有限的有效性，因为这些模型的检索分数在与其统计对应物不同的短范围内。在这项工作中，我们建议利用基于成对推理的 NRM (特别是 DuoT5)输出来积累证据，证明一个文档的成对信念高于另一个文档。我们假设这些成对可能性越一致，检索的质量越好的可能性就越高，从而产生更高的 QPP 评分。我们利用来自辅助模型 DuoT5的成对可能性在 TREC-DL 数据集上进行实验。我们的实验表明，所提出的方法称为成对秩优先的 QPP (QPP-PRP)导致明显更好的结果比一些标准的无监督 QPP 基线上的几个 NRM。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Unsupervised+Query+Performance+Prediction+for+Neural+Models+with+Pairwise+Rank+Preferences)|0| +|[Unsupervised Query Performance Prediction for Neural Models with Pairwise Rank Preferences](https://doi.org/10.1145/3539618.3592082)|Ashutosh Singh, Debasis Ganguly, Suchana Datta, Craig MacDonald|University College Dublin, Dublin, Ireland; University of Glasgow, Glasgow, United Kingdom|A query performance prediction (QPP) method predicts the effectiveness of an IR system for a given query. While unsupervised approaches have been shown to work well for statistical IR models, it is likely that these approaches would yield limited effectiveness for neural ranking models (NRMs) because the retrieval scores of these models lie within a short range unlike their statistical counterparts. In this work, we propose to leverage a pairwise inference-based NRM's (specifically, DuoT5) output to accumulate evidences on the pairwise believes of one document ranked above the other. We hypothesize that the more consistent these pairwise likelihoods are, the higher is the likelihood of the retrieval to be of better quality, thus yielding a higher QPP score. We conduct our experiments on the TREC-DL dataset leveraging pairwise likelihoods from an auxiliary model DuoT5. Our experiments demonstrate that the proposed method called Pairwise Rank Preference-based QPP (QPP-PRP) leads to significantly better results than a number of standard unsupervised QPP baselines on several NRMs.|一种查询性能预测(QPP)方法预测一个给定查询的红外系统的有效性。虽然无监督方法已被证明对于统计 IR 模型工作得很好，但是这些方法可能对神经排序模型(NRM)产生有限的有效性，因为这些模型的检索分数在与其统计对应物不同的短范围内。在这项工作中，我们建议利用基于成对推理的 NRM (特别是 DuoT5)输出来积累证据，证明一个文档的成对信念高于另一个文档。我们假设这些成对可能性越一致，检索的质量越好的可能性就越高，从而产生更高的 QPP 评分。我们利用来自辅助模型 DuoT5的成对可能性在 TREC-DL 数据集上进行实验。我们的实验表明，所提出的方法称为成对秩优先的 QPP (QPP-PRP)导致明显更好的结果比一些标准的无监督 QPP 基线上的几个 NRM。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Unsupervised+Query+Performance+Prediction+for+Neural+Models+with+Pairwise+Rank+Preferences)|0| |[Repetition and Exploration in Sequential Recommendation](https://doi.org/10.1145/3539618.3591914)|Ming Li, Ali Vardasbi, Andrew Yates, Maarten de Rijke|University of Amsterdam, Amsterdam, Netherlands|In several recommendation scenarios, including next basket recommendation, the importance of repetition and exploration has been discovered and studied. Sequential recommenders (SR) aim to infer a user's preferences and suggest the next item for them to interact with based on their historical interaction sequences. There has not been a systematic analysis of sequential recommenders from the perspective of repetition and exploration. As a result, it is unclear how these models, that are typically optimized for accuracy, perform in terms of repetition and exploration, as well as the potential drawbacks of deploying them in real applications. In this paper, we examine whether repetition and exploration are important dimensions in the sequential recommendation scenario. We consider this generalizability question both from a user-centered and an item-centered perspective. Towards the latter, we define item repeat exposure and item explore exposure and examine the recommendation performance of sequential recommendation models in terms of both accuracy and exposure from the perspective of repetition and exploration. We find that (i) there is an imbalance in accuracy and difficulty w.r.t. repetition and exploration in SR scenarios, (ii) using the conventional average overall accuracy with a significance test does not fully represent a model's recommendation accuracy, and (iii) accuracy-oriented sequential recommendation models may suffer from less/zero item explore exposure issue, where items are mostly (or even only) recommended to their repeat users and fail to reach their potential new users. To analyze our findings, we remove repeat samples from the dataset, that often act as easy shortcuts, and focus on a pure exploration SR scenario. We find that (i) removing the repetition shortcut increases the recommendation novelty and helps users who prefer to consume novel items next, (ii) neural-based models fail to learn the basic characteristics of this pure exploration scenario and suffer from an inherent repetitive bias issue, (iii) using shared item embeddings in the prediction layer may skew recommendations to repeat items, and (iv) removing all repeat items to post-processing recommendation results leads to a substantial improvement on top of several SR methods.|在几个推荐场景中，包括下一个篮子推荐，重复和探索的重要性已经被发现和研究。顺序推荐(SR)旨在推断用户的偏好，并根据用户的历史交互顺序建议下一个用户交互的项目。从重复和探索的角度对序贯推荐进行了系统的分析。因此，目前还不清楚这些通常为精确性而优化的模型在重复和探索方面如何执行，以及在实际应用中部署它们的潜在缺点。在本文中，我们研究了重复和探索是否是顺序推荐场景中的重要维度。我们从以用户为中心和以项目为中心的角度来考虑这个可推广性问题。对于后者，我们定义了项目重复暴露和项目探索暴露，并从重复和探索的角度考察了序贯推荐模型在准确性和暴露性方面的推荐绩效。我们发现(i)在 SR 情景下重复和探索的准确性和难度存在不平衡，(ii)使用传统的平均整体准确性和显着性测试并不完全代表模型的推荐准确性，以及(iii)面向准确性的顺序推荐模型可能遭受较少/零项目探索暴露问题，其中项目大部分(甚至只有)被推荐给其重复用户，并且未能达到其潜在的新用户。为了分析我们的发现，我们从数据集中移除了重复的样本，这些样本通常作为简单的捷径，并专注于一个纯粹的探索 SR 场景。我们发现(i)删除重复快捷方式增加了推荐的新颖性，并帮助喜欢接下来消费新颖项目的用户，(ii)基于神经的模型未能学习这种纯探索场景的基本特征，并遭受固有的重复偏差问题，(iii)在预测层中使用共享项嵌入可能使推荐偏向重复项目，以及(iv)删除所有重复项目以后处理推荐结果导致几种 SR 方法之上的实质性改进。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Repetition+and+Exploration+in+Sequential+Recommendation)|0| |[JDsearch: A Personalized Product Search Dataset with Real Queries and Full Interactions](https://doi.org/10.1145/3539618.3591900)|Jiongnan Liu, Zhicheng Dou, Guoyu Tang, Sulong Xu||Recently, personalized product search attracts great attention and many models have been proposed. To evaluate the effectiveness of these models, previous studies mainly utilize the simulated Amazon recommendation dataset, which contains automatically generated queries and excludes cold users and tail products. We argue that evaluating with such a dataset may yield unreliable results and conclusions, and deviate from real user satisfaction. To overcome these problems, in this paper, we release a personalized product search dataset comprised of real user queries and diverse user-product interaction types (clicking, adding to cart, following, and purchasing) collected from JD.com, a popular Chinese online shopping platform. More specifically, we sample about 170,000 active users on a specific date, then record all their interacted products and issued queries in one year, without removing any tail users and products. This finally results in roughly 12,000,000 products, 9,400,000 real searches, and 26,000,000 user-product interactions. We study the characteristics of this dataset from various perspectives and evaluate representative personalization models to verify its feasibility. The dataset can be publicly accessed at Github: https://github.com/rucliujn/JDsearch.|近年来，个性化产品搜索引起了人们的广泛关注，提出了许多个性化产品搜索模型。为了评估这些模型的有效性，以往的研究主要利用模拟亚马逊推荐数据集，其中包含自动生成的查询，并排除了冷用户和尾部产品。我们认为，使用这样的数据集进行评估可能产生不可靠的结果和结论，并偏离真正的用户满意度。为了克服这些问题，本文发布了一个个性化产品搜索数据集，该数据集由真实用户查询和不同的用户-产品交互类型(点击、添加到购物车、跟随和购买)组成，收集自中国流行的在线购物平台京东(JD.com)。更具体地说，我们在一个特定的日期抽样约170,000个活跃用户，然后记录他们所有的交互产品，并在一年内发布查询，而不删除任何尾用户和产品。这最终产生了大约12,000,000个产品，9,400,000个实际搜索，以及26,000,000个用户-产品交互。我们从不同的角度研究了这个数据集的特点，并评价了具有代表性的个性化模型，以验证其可行性。该数据集可以在 Github: https://Github.com/rucliujn/jdsearch 上公开访问。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=JDsearch:+A+Personalized+Product+Search+Dataset+with+Real+Queries+and+Full+Interactions)|0| |[FedAds: A Benchmark for Privacy-Preserving CVR Estimation with Vertical Federated Learning](https://doi.org/10.1145/3539618.3591909)|Penghui Wei, Hongjian Dou, Shaoguo Liu, Rongjun Tang, Li Liu, Liang Wang, Bo Zheng||Conversion rate (CVR) estimation aims to predict the probability of conversion event after a user has clicked an ad. Typically, online publisher has user browsing interests and click feedbacks, while demand-side advertising platform collects users' post-click behaviors such as dwell time and conversion decisions. To estimate CVR accurately and protect data privacy better, vertical federated learning (vFL) is a natural solution to combine two sides' advantages for training models, without exchanging raw data. Both CVR estimation and applied vFL algorithms have attracted increasing research attentions. However, standardized and systematical evaluations are missing: due to the lack of standardized datasets, existing studies adopt public datasets to simulate a vFL setting via hand-crafted feature partition, which brings challenges to fair comparison. We introduce FedAds, the first benchmark for CVR estimation with vFL, to facilitate standardized and systematical evaluations for vFL algorithms. It contains a large-scale real world dataset collected from Alibaba's advertising platform, as well as systematical evaluations for both effectiveness and privacy aspects of various vFL algorithms. Besides, we also explore to incorporate unaligned data in vFL to improve effectiveness, and develop perturbation operations to protect privacy well. We hope that future research work in vFL and CVR estimation benefits from the FedAds benchmark.|转换率(CVR)估计的目的是预测用户点击广告后发生转换事件的概率。通常，在线出版商有用户浏览兴趣和点击反馈，而需求侧广告平台收集用户的点击后行为，如停留时间和转换决定。为了更准确地估计 CVR，更好地保护数据隐私，垂直联邦学习(vFL)是一种自然而然的解决方案，它结合了双方在训练模型方面的优势，不需要交换原始数据。CVR 估计和应用的 vFL 算法都受到了越来越多的研究关注。然而，由于缺乏标准化的数据集，现有的研究采用公共数据集通过手工特征划分来模拟 vFL 设置，这给公平比较带来了挑战。我们引入 FedAds，第一个用 vFL 进行 CVR 估计的基准，以促进 vFL 算法的标准化和系统化评估。它包含从阿里巴巴广告平台收集的大规模真实世界数据集，以及对各种 vfL 算法的有效性和隐私方面的系统评估。此外，我们还探讨了在 vFL 中加入未对齐数据以提高效率，并开发扰动操作以更好地保护隐私。我们希望未来在 vFL 和 CVR 估计方面的研究工作能够从 FedAds 基准中受益。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=FedAds:+A+Benchmark+for+Privacy-Preserving+CVR+Estimation+with+Vertical+Federated+Learning)|0| |[TMML: Text-Guided MuliModal Product Location For Alleviating Retrieval Inconsistency in E-Commerce](https://doi.org/10.1145/3539618.3591836)|Youhua Tang, Xiong Xiong, Siyang Sun, Baoliang Cui, Yun Zheng, Haihong Tang|Alibaba Group, Hangzhou, China|Image retrieval system (IRS) is commonly used in E-Commerce platforms for a wide range of applications such as price comparison and commodity recommendation. However, customers may experience inconsistent retrieval problems. Although the retrieved image contains the query object, the main product of the retrieved image is not associated with the query product. This is caused by the wrong product instance location when building the product image retrieval library. We can easily determine which product is on sale through the hint of the title, so we propose Text-Guided MuliModal Product Location (TMML) to use additional product titles to assist in locating the actual selling product instance. We design a weakly-aligned region-text data collection method to generate region-text pseudo-label by utilizing the IRS and user behavior from the E-commerce platform. To mitigate the impact of data noise, we propose a Mutual-Aware Contrastive Loss. Our results show that the proposed TMML outperforms the state-of-the-art method GLIP [11] by 3.95% in top-1 precision on our multi-objects test set, and 2.53% error located images in AliExpress has been corrected, which greatly alleviates the retrieval inconsistencies in IRS.|图像检索系统(IRS)广泛应用于电子商务平台的价格比较和商品推荐等领域。但是，客户可能会遇到不一致的检索问题。虽然检索到的图像包含查询对象，但是检索到的图像的主产品不与查询产品关联。这是由于构建产品图像检索库时产品实例位置错误造成的。我们可以通过标题的提示轻松地确定正在销售的产品，因此我们建议使用文本导向的 MuliModal Product Location (TMML)来使用附加的产品标题来帮助定位实际的销售产品实例。设计了一种弱对齐区域文本数据采集方法，利用电子商务平台的 IRS 和用户行为生成区域文本伪标签。为了减轻数据噪声的影响，我们提出了一种相互感知的对比度损失。实验结果表明，本文提出的 TMML 算法在多目标测试集上的检索精度比最先进的 GLIP [11]算法提高了3.95% ，并且对 AliExpress 中2.53% 的错误定位图像进行了校正，大大减轻了 IRS 中的检索不一致性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=TMML:+Text-Guided+MuliModal+Product+Location+For+Alleviating+Retrieval+Inconsistency+in+E-Commerce)|0| |[Alleviating Matching Bias in Marketing Recommendations](https://doi.org/10.1145/3539618.3591854)|Junpeng Fang, Qing Cui, Gongduo Zhang, Caizhi Tang, Lihong Gu, Longfei Li, Jinjie Gu, Jun Zhou, Fei Wu|Ant Group, Hangzhou, China; Zhejiang University, Hangzhou, China|In marketing recommendations, the campaign organizers will distribute coupons to users to encourage consumption. In general, a series of strategies are employed to interfere with the coupon distribution process, leading to a growing imbalance between user-coupon interactions, resulting in a bias in the estimation of conversion probabilities. We refer to the estimation bias as the matching bias. In this paper, we explore how to alleviate the matching bias from the causal-effect perspective. We regard the historical distributions of users and coupons over each other as confounders and characterize the matching bias as a confounding effect to reveal and eliminate the spurious correlations between user-coupon representations and conversion probabilities. Then we propose a new training paradigm named De-Matching Bias Recommendation (DMBR) to remove the confounding effects during model training via the backdoor adjustment. We instantiate DMBR on two representative models: DNN and MMOE, and conduct extensive offline and online experiments to demonstrate the effectiveness of our proposed paradigm.|在营销建议中，活动组织者将向用户分发优惠券以鼓励消费。一般来说，一系列的策略被用来干扰优惠券的分配过程，导致用户-优惠券之间的交互日益不平衡，导致在估计转换概率方面的偏差。我们把估计偏差称为匹配偏差。本文从因果关系的角度探讨如何减轻匹配偏差。我们将用户和优惠券的历史分布视为混杂因素，并将匹配偏差描述为混杂效应，以揭示和消除用户-优惠券表示和转换概率之间的虚假相关性。然后提出了一种新的训练范式——去匹配偏差推荐(DMBR) ，通过后门调整消除模型训练过程中的混杂效应。我们在两个代表性的模型上实例化了 DMBR: DNN 和 MMOE，并进行了大量的离线和在线实验来证明我们提出的范例的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Alleviating+Matching+Bias+in+Marketing+Recommendations)|0| -|[Implicit Query Parsing at Amazon Product Search](https://doi.org/10.1145/3539618.3591858)|Chen Luo, Rahul Goutam, Haiyang Zhang, Chao Zhang, Yangqiu Song, Bing Yin|Gatech, Atlanta, GA, USA; Amazon Search, Palo Alto, CA, USA; HKUST, Hong Kong, Hong Kong|Query Parsing aims to extract product attributes, such as color, brand, and product type, from search queries. These attributes play a crucial role in search engines for tasks such as matching, ranking, and recommendation. There are two types of attributes: explicit attributes that are mentioned explicitly in the search query, and implicit attributes that are mentioned implicitly. Existing works on query parsing do not differentiate between explicit query parsing and implicit query parsing, which limits their performance in product search engines. In this work, we demonstrate the critical importance of implicit attributes in real-world product search engines. We then present our solution for implicit query parsing at Amazon Search, which is a unified framework combining recent advancements in knowledge graph technologies and customer behavior analysis. We demonstrate the effectiveness of our proposal through offline experiments on Amazon search log data. We also show how to deploy and use the framework on Amazon search to improve customers' shopping experiences.|查询解析旨在从搜索查询中提取产品属性，如颜色、品牌和产品类型。这些属性在搜索引擎的任务(如匹配、排名和推荐)中起着至关重要的作用。有两种类型的属性: 在搜索查询中显式提到的显式属性和隐式提到的隐式属性。现有的查询解析工作没有区分显式查询解析和隐式查询解析，这限制了它们在产品搜索引擎中的性能。在这项工作中，我们证明了隐式属性在现实世界的产品搜索引擎中的重要性。然后，我们提出了我们的解决方案隐式查询解析在亚马逊搜索，这是一个统一的框架相结合的最新进展，知识图技术和客户行为分析。我们通过亚马逊搜索日志数据的离线实验来证明我们提议的有效性。我们还展示了如何部署和使用 Amazon 搜索框架来改善客户的购物体验。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Implicit+Query+Parsing+at+Amazon+Product+Search)|0| +|[Implicit Query Parsing at Amazon Product Search](https://doi.org/10.1145/3539618.3591858)|Chen Luo, Rahul Goutam, Haiyang Zhang, Chao Zhang, Yangqiu Song, Bing Yin|HKUST, Hong Kong, Hong Kong; Gatech, Atlanta, GA, USA; Amazon Search, Palo Alto, CA, USA|Query Parsing aims to extract product attributes, such as color, brand, and product type, from search queries. These attributes play a crucial role in search engines for tasks such as matching, ranking, and recommendation. There are two types of attributes: explicit attributes that are mentioned explicitly in the search query, and implicit attributes that are mentioned implicitly. Existing works on query parsing do not differentiate between explicit query parsing and implicit query parsing, which limits their performance in product search engines. In this work, we demonstrate the critical importance of implicit attributes in real-world product search engines. We then present our solution for implicit query parsing at Amazon Search, which is a unified framework combining recent advancements in knowledge graph technologies and customer behavior analysis. We demonstrate the effectiveness of our proposal through offline experiments on Amazon search log data. We also show how to deploy and use the framework on Amazon search to improve customers' shopping experiences.|查询解析旨在从搜索查询中提取产品属性，如颜色、品牌和产品类型。这些属性在搜索引擎的任务(如匹配、排名和推荐)中起着至关重要的作用。有两种类型的属性: 在搜索查询中显式提到的显式属性和隐式提到的隐式属性。现有的查询解析工作没有区分显式查询解析和隐式查询解析，这限制了它们在产品搜索引擎中的性能。在这项工作中，我们证明了隐式属性在现实世界的产品搜索引擎中的重要性。然后，我们提出了我们的解决方案隐式查询解析在亚马逊搜索，这是一个统一的框架相结合的最新进展，知识图技术和客户行为分析。我们通过亚马逊搜索日志数据的离线实验来证明我们提议的有效性。我们还展示了如何部署和使用 Amazon 搜索框架来改善客户的购物体验。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Implicit+Query+Parsing+at+Amazon+Product+Search)|0| |[Delving into E-Commerce Product Retrieval with Vision-Language Pre-training](https://doi.org/10.1145/3539618.3591859)|Xiaoyang Zheng, Fuyu Lv, Zilong Wang, Qingwen Liu, Xiaoyi Zeng||E-commerce search engines comprise a retrieval phase and a ranking phase, where the first one returns a candidate product set given user queries. Recently, vision-language pre-training, combining textual information with visual clues, has been popular in the application of retrieval tasks. In this paper, we propose a novel V+L pre-training method to solve the retrieval problem in Taobao Search. We design a visual pre-training task based on contrastive learning, outperforming common regression-based visual pre-training tasks. In addition, we adopt two negative sampling schemes, tailored for the large-scale retrieval task. Besides, we introduce the details of the online deployment of our proposed method in real-world situations. Extensive offline/online experiments demonstrate the superior performance of our method on the retrieval task. Our proposed method is employed as one retrieval channel of Taobao Search and serves hundreds of millions of users in real time.|电子商务搜索引擎包括检索阶段和排名阶段，其中第一个搜索引擎返回给定用户查询的候选产品集。近年来，将文本信息与视觉线索相结合的视觉语言预训练方法在检索任务中的应用越来越普遍。本文针对淘宝搜索中的检索问题，提出了一种新的 V + L 预训练方法。我们设计了一个基于对比学习的视觉预训练任务，其表现优于一般的基于回归的视觉预训练任务。此外，我们还采用了两种负抽样方案，以适应大规模的检索任务。此外，我们还详细介绍了我们提出的方法在现实环境中的在线部署。大量的离线/在线实验证明了该方法在检索任务中的优越性能。我们提出的方法被用作淘宝搜索的一个检索通道，为数亿用户提供实时服务。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Delving+into+E-Commerce+Product+Retrieval+with+Vision-Language+Pre-training)|0| |[Contrastive Box Embedding for Collaborative Reasoning](https://doi.org/10.1145/3539618.3591654)|Tingting Liang, Yuanqing Zhang, Qianhui Di, Congying Xia, Youhuizi Li, Yuyu Yin|Hangzhou Dianzi University, Hangzhou, China; Salesforce AI Research, Palo Alto, USA|Most of the existing personalized recommendation methods predict the probability that one user might interact with the next item by matching their representations in the latent space. However, as a cognitive task, it is essential for an impressive recommender system to acquire the cognitive capacity rather than to decide the users' next steps by learning the pattern from the historical interactions through matching-based objectives. Therefore, in this paper, we propose to model the recommendation as a logical reasoning task which is more in line with an intelligent recommender system. Different from the prior works, we embed each query as a box rather than a single point in the vector space, which is able to model sets of users or items enclosed and logical operators (e.g., intersection) over boxes in a more natural manner. Although modeling the logical query with box embedding significantly improves the previous work of reasoning-based recommendation, there still exist two intractable issues including aggregation of box embeddings and training stalemate in critical point of boxes. To tackle these two limitations, we propose a Contrastive Box learning framework for Collaborative Reasoning (CBox4CR). Specifically, CBox4CR combines a smoothed box volume-based contrastive learning objective with the logical reasoning objective to learn the distinctive box representations for the user's preference and the logical query based on the historical interaction sequence. Extensive experiments conducted on four publicly available datasets demonstrate the superiority of our CBox4CR over the state-of-the-art models in recommendation task.|现有的大多数个性化推荐方法通过匹配用户在潜在空间中的表示来预测用户与下一个项目交互的概率。然而，作为一项认知任务，一个令人印象深刻的推荐系统必须获得认知能力，而不是通过基于匹配的目标从历史交互中学习模式来决定用户的下一步行动。因此，在本文中，我们建议将推荐建模为一个更符合智能逻辑推理的推荐系统任务。与之前的工作不同，我们将每个查询嵌入一个框，而不是向量空间中的一个点，这样就能够以更自然的方式在框之上建模用户集或被封闭的项以及逻辑运算符集(例如，交叉)。框嵌入逻辑查询建模虽然显著改善了以往基于推理的推荐工作，但仍然存在框嵌入的聚合和框关键点的训练僵局等难题。为了解决这两个限制，我们提出了一个用于协作推理的对比框学习框架(CBox4CR)。具体来说，cbox4CR 结合了基于光滑盒子体积的对比学习目标和逻辑推理目标，学习用户偏好的独特盒子表示和基于历史交互序列的逻辑查询。在四个公开数据集上进行的大量实验证明了我们的 CBox4CR 在推荐任务中优于最先进的模型。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Contrastive+Box+Embedding+for+Collaborative+Reasoning)|0| -|[Dual Contrastive Transformer for Hierarchical Preference Modeling in Sequential Recommendation](https://doi.org/10.1145/3539618.3591672)|Chengkai Huang, Shoujin Wang, Xianzhi Wang, Lina Yao|CSIRO's Data 61 and UNSW, Sydney, NSW, Australia; The University of New South Wales, Sydney, NSW, Australia; University of Technology Sydney, Sydney, NSW, Australia|Sequential recommender systems (SRSs) aim to predict the subsequent items which may interest users via comprehensively modeling users' complex preference embedded in the sequence of user-item interactions. However, most of existing SRSs often model users' single low-level preference based on item ID information while ignoring the high-level preference revealed by item attribute information, such as item category. Furthermore, they often utilize limited sequence context information to predict the next item while overlooking richer inter-item semantic relations. To this end, in this paper, we proposed a novel hierarchical preference modeling framework to substantially model the complex low- and high-level preference dynamics for accurate sequential recommendation. Specifically, in the framework, a novel dual-transformer module and a novel dual contrastive learning scheme have been designed to discriminatively learn users' low- and high-level preference and to effectively enhance both low- and high-level preference learning respectively. In addition, a novel semantics-enhanced context embedding module has been devised to generate more informative context embedding for further improving the recommendation performance. Extensive experiments on six real-world datasets have demonstrated both the superiority of our proposed method over the state-of-the-art ones and the rationality of our design.|序贯推荐系统通过对用户交互序列中的复杂偏好进行综合建模，对用户可能感兴趣的后续项目进行预测。然而，现有的 SRS 往往基于项目 ID 信息对用户的单一低层偏好进行建模，而忽略了项目属性信息(如项目类别)所揭示的高层偏好。此外，他们往往利用有限的序列上下文信息来预测下一个项目，而忽略了更丰富的项目间语义关系。为此，本文提出了一种新的层次偏好建模框架，对复杂的低层和高层偏好动态进行实质性建模，以实现精确的顺序推荐。在该框架中，设计了一种新的双变压器模块和一种新的双对比学习方案，以区分学习用户的低级和高级偏好，并分别有效地增强低级和高级偏好学习。此外，还设计了一种新的语义增强的上下文嵌入模块，以生成更多的信息，从而进一步提高推荐性能。在六个实际数据集上的大量实验证明了该方法的优越性和设计的合理性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Dual+Contrastive+Transformer+for+Hierarchical+Preference+Modeling+in+Sequential+Recommendation)|0| +|[Dual Contrastive Transformer for Hierarchical Preference Modeling in Sequential Recommendation](https://doi.org/10.1145/3539618.3591672)|Chengkai Huang, Shoujin Wang, Xianzhi Wang, Lina Yao|The University of New South Wales, Sydney, NSW, Australia; University of Technology Sydney, Sydney, NSW, Australia; CSIRO's Data 61 and UNSW, Sydney, NSW, Australia|Sequential recommender systems (SRSs) aim to predict the subsequent items which may interest users via comprehensively modeling users' complex preference embedded in the sequence of user-item interactions. However, most of existing SRSs often model users' single low-level preference based on item ID information while ignoring the high-level preference revealed by item attribute information, such as item category. Furthermore, they often utilize limited sequence context information to predict the next item while overlooking richer inter-item semantic relations. To this end, in this paper, we proposed a novel hierarchical preference modeling framework to substantially model the complex low- and high-level preference dynamics for accurate sequential recommendation. Specifically, in the framework, a novel dual-transformer module and a novel dual contrastive learning scheme have been designed to discriminatively learn users' low- and high-level preference and to effectively enhance both low- and high-level preference learning respectively. In addition, a novel semantics-enhanced context embedding module has been devised to generate more informative context embedding for further improving the recommendation performance. Extensive experiments on six real-world datasets have demonstrated both the superiority of our proposed method over the state-of-the-art ones and the rationality of our design.|序贯推荐系统通过对用户交互序列中的复杂偏好进行综合建模，对用户可能感兴趣的后续项目进行预测。然而，现有的 SRS 往往基于项目 ID 信息对用户的单一低层偏好进行建模，而忽略了项目属性信息(如项目类别)所揭示的高层偏好。此外，他们往往利用有限的序列上下文信息来预测下一个项目，而忽略了更丰富的项目间语义关系。为此，本文提出了一种新的层次偏好建模框架，对复杂的低层和高层偏好动态进行实质性建模，以实现精确的顺序推荐。在该框架中，设计了一种新的双变压器模块和一种新的双对比学习方案，以区分学习用户的低级和高级偏好，并分别有效地增强低级和高级偏好学习。此外，还设计了一种新的语义增强的上下文嵌入模块，以生成更多的信息，从而进一步提高推荐性能。在六个实际数据集上的大量实验证明了该方法的优越性和设计的合理性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Dual+Contrastive+Transformer+for+Hierarchical+Preference+Modeling+in+Sequential+Recommendation)|0| |[Meta-optimized Contrastive Learning for Sequential Recommendation](https://doi.org/10.1145/3539618.3591727)|Xiuyuan Qin, Huanhuan Yuan, Pengpeng Zhao, Junhua Fang, Fuzhen Zhuang, Guanfeng Liu, Yanchi Liu, Victor S. Sheng||Contrastive Learning (CL) performances as a rising approach to address the challenge of sparse and noisy recommendation data. Although having achieved promising results, most existing CL methods only perform either hand-crafted data or model augmentation for generating contrastive pairs to find a proper augmentation operation for different datasets, which makes the model hard to generalize. Additionally, since insufficient input data may lead the encoder to learn collapsed embeddings, these CL methods expect a relatively large number of training data (e.g., large batch size or memory bank) to contrast. However, not all contrastive pairs are always informative and discriminative enough for the training processing. Therefore, a more general CL-based recommendation model called Meta-optimized Contrastive Learning for sequential Recommendation (MCLRec) is proposed in this work. By applying both data augmentation and learnable model augmentation operations, this work innovates the standard CL framework by contrasting data and model augmented views for adaptively capturing the informative features hidden in stochastic data augmentation. Moreover, MCLRec utilizes a meta-learning manner to guide the updating of the model augmenters, which helps to improve the quality of contrastive pairs without enlarging the amount of input data. Finally, a contrastive regularization term is considered to encourage the augmentation model to generate more informative augmented views and avoid too similar contrastive pairs within the meta updating. The experimental results on commonly used datasets validate the effectiveness of MCLRec.|对比学习(CL)作为一种新兴的方法来解决稀疏和噪声推荐数据的挑战。虽然已经取得了很好的效果，但是现有的 CL 方法只能通过手工数据或者模型增强来生成对比对，以便为不同的数据集找到合适的增强操作，这使得模型很难推广。此外，由于输入数据不足可能导致编码器学习折叠嵌入，这些 CL 方法期望相对大量的训练数据(例如，大批量或内存库)进行对比。然而，并非所有的对比对都具有足够的信息量和判别力来进行训练处理。因此，本文提出了一种更为通用的基于 CL 的推荐模型，称为序贯推荐元优化对比学习(MCLRec)。通过应用数据增强和可学习模型增强操作，对比数据和模型增强视图，创新标准 CL 框架，自适应地捕捉随机数据增强中隐藏的信息特征。此外，MCLRec 利用元学习方式来指导模型增强器的更新，这有助于提高对比对的质量，而不需要扩大输入数据的数量。最后，考虑了一个对比正则化项，以鼓励增强模型生成更多的信息增强视图，避免元更新中出现过于相似的对比对。在常用数据集上的实验结果验证了 MCLRec 算法的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Meta-optimized+Contrastive+Learning+for+Sequential+Recommendation)|0| |[A Personalized Dense Retrieval Framework for Unified Information Access](https://doi.org/10.1145/3539618.3591626)|Hansi Zeng, Surya Kallumadi, Zaid Alibadi, Rodrigo Frassetto Nogueira, Hamed Zamani||Developing a universal model that can efficiently and effectively respond to a wide range of information access requests -- from retrieval to recommendation to question answering -- has been a long-lasting goal in the information retrieval community. This paper argues that the flexibility, efficiency, and effectiveness brought by the recent development in dense retrieval and approximate nearest neighbor search have smoothed the path towards achieving this goal. We develop a generic and extensible dense retrieval framework, called \framework, that can handle a wide range of (personalized) information access requests, such as keyword search, query by example, and complementary item recommendation. Our proposed approach extends the capabilities of dense retrieval models for ad-hoc retrieval tasks by incorporating user-specific preferences through the development of a personalized attentive network. This allows for a more tailored and accurate personalized information access experience. Our experiments on real-world e-commerce data suggest the feasibility of developing universal information access models by demonstrating significant improvements even compared to competitive baselines specifically developed for each of these individual information access tasks. This work opens up a number of fundamental research directions for future exploration.|开发一个通用的模型，可以有效率和有效地响应广泛的信息访问请求——从检索到推荐到问答——一直是信息检索社区的长期目标。本文认为，最近在密集检索和近似最近邻搜索方面的发展所带来的灵活性、效率和有效性为实现这一目标铺平了道路。我们开发了一个通用的和可扩展的密集检索框架，称为框架，可以处理广泛的(个性化的)信息访问请求，如关键字搜索，按例查询，补充项目推荐。我们提出的方法扩展了密集检索模型的能力，通过合并用户特定的偏好，通过开发一个个性化的注意网络的特定检索任务。这样可以提供更加量身定制和准确的个性化信息访问体验。我们对现实世界电子商务数据的实验表明，即使与为这些个人信息获取任务中的每一项专门制定的竞争性基线相比，通用信息获取模型的开发也是可行的。这项工作为今后的探索开辟了一些基础性的研究方向。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Personalized+Dense+Retrieval+Framework+for+Unified+Information+Access)|0| |[One Blade for One Purpose: Advancing Math Information Retrieval using Hybrid Search](https://doi.org/10.1145/3539618.3591746)|Wei Zhong, ShengChieh Lin, JhengHong Yang, Jimmy Lin|University of Waterloo, Waterloo, Canada|Neural retrievers have been shown to be effective for math-aware search. Their ability to cope with math symbol mismatches, to represent highly contextualized semantics, and to learn effective representations are critical to improving math information retrieval. However, the most effective retriever for math remains impractical as it depends on token-level dense representations for each math token, which leads to prohibitive storage demands, especially considering that math content generally consumes more tokens. In this work, we try to alleviate this efficiency bottleneck while boosting math information retrieval effectiveness via hybrid search. To this end, we propose MABOWDOR, a Math-Aware Bestof-Worlds Domain Optimized Retriever, which has an unsupervised structure search component, a dense retriever, and optionally a sparse retriever on top of a domain-adapted backbone learned by context-enhanced pretraining, each addressing a different need in retrieving heterogeneous data from math documents. Our hybrid search outperforms the previous state-of-the-art math IR system while eliminating efficiency bottlenecks. Our system is available at https://github.com/approach0/pya0.|神经检索器已被证明对数学感知搜索是有效的。他们处理数学符号不匹配的能力，表达高度上下文化语义的能力，以及学习有效表达的能力对于提高数学信息检索至关重要。然而，最有效的数学检索仍然不切实际，因为它依赖于每个数学令牌的令牌级密集表示，这导致了令人望而却步的存储需求，特别是考虑到数学内容通常消耗更多的令牌。在这项工作中，我们试图通过混合搜索提高数学信息检索的效率，同时缓解这一效率瓶颈。为此，我们提出了 MABOWDOR，一种数学意识最强的领域优化检索器，它具有无监督的结构搜索组件，密集检索器，以及可选的在通过上下文增强预训练学习的领域适应主干上的稀疏检索器，每个都解决了从数学文档中检索异构数据的不同需求。我们的混合搜索优于以前的最先进的数学红外系统，同时消除了效率瓶颈。我们的系统 https://github.com/approach0/pya0可用。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=One+Blade+for+One+Purpose:+Advancing+Math+Information+Retrieval+using+Hybrid+Search)|0| -|[Poisoning Self-supervised Learning Based Sequential Recommendations](https://doi.org/10.1145/3539618.3591751)|Yanling Wang, Yuchen Liu, Qian Wang, Cong Wang, Chenliang Li|Wuhan University, Wuhan, China; Wuhan University & City University of Hong Kong, Wuhan; Hong Kong, China; City University of Hong Kong, Hong Kong, China|Self-supervised learning (SSL) has been recently applied to sequential recommender systems to provide high-quality user representations. However, while facilitating the learning process recommender systems, SSL is not without security threats: carefully crafted inputs can poison the pre-trained models driven by SSL, thus reducing the effectiveness of the downstream recommendation model. This work shows that poisoning attacks against the pre-training stage threaten sequential recommender systems. Without any background knowledge of the model architecture and parameters, nor any API queries, our strategy proves the feasibility of poisoning attacks on mainstream SSL-based recommender schemes as well as on commonly used datasets. By injecting only a tiny amount of fake users, we get the target item recommended to real users more than thousands of times as before, demonstrating that recommender systems have a new attack surface due to SSL. We further show our attack is challenging for recommendation platforms to detect and defend. Our work highlights the weakness of self-supervised recommender systems and shows the necessity for researchers to be aware of this security threat. Our source code is available at https://github.com/CongGroup/Poisoning-SSL-based-RS.|为了提供高质量的用户表示，自监督学习(SSL)最近被应用到顺序推荐系统中。然而，在促进学习过程推荐系统的同时，SSL 也不是没有安全威胁: 精心设计的输入可能毒害由 SSL 驱动的预先训练的模型，从而降低下游推荐模型的有效性。研究结果表明，针对训练前阶段的中毒攻击会对顺序推荐系统造成威胁。在没有任何模型体系结构和参数的背景知识，也没有任何 API 查询的情况下，我们的策略证明了对主流的基于 SSL 的推荐方案以及常用数据集进行中毒攻击的可行性。通过注入少量的虚假用户，我们可以像以前一样将目标条目推荐给真正的用户数千次，这表明推荐系统由于 SSL 而具有了一个新的攻击面。我们进一步表明，我们的攻击对推荐平台的检测和防御是具有挑战性的。我们的工作突出了自我监督推荐系统的弱点，并表明了研究人员意识到这种安全威胁的必要性。我们的源代码可以在 https://github.com/conggroup/poisoning-ssl-based-rs 找到。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Poisoning+Self-supervised+Learning+Based+Sequential+Recommendations)|0| +|[Poisoning Self-supervised Learning Based Sequential Recommendations](https://doi.org/10.1145/3539618.3591751)|Yanling Wang, Yuchen Liu, Qian Wang, Cong Wang, Chenliang Li|City University of Hong Kong, Hong Kong, China; Wuhan University, Wuhan, China; Wuhan University & City University of Hong Kong, Wuhan; Hong Kong, China|Self-supervised learning (SSL) has been recently applied to sequential recommender systems to provide high-quality user representations. However, while facilitating the learning process recommender systems, SSL is not without security threats: carefully crafted inputs can poison the pre-trained models driven by SSL, thus reducing the effectiveness of the downstream recommendation model. This work shows that poisoning attacks against the pre-training stage threaten sequential recommender systems. Without any background knowledge of the model architecture and parameters, nor any API queries, our strategy proves the feasibility of poisoning attacks on mainstream SSL-based recommender schemes as well as on commonly used datasets. By injecting only a tiny amount of fake users, we get the target item recommended to real users more than thousands of times as before, demonstrating that recommender systems have a new attack surface due to SSL. We further show our attack is challenging for recommendation platforms to detect and defend. Our work highlights the weakness of self-supervised recommender systems and shows the necessity for researchers to be aware of this security threat. Our source code is available at https://github.com/CongGroup/Poisoning-SSL-based-RS.|为了提供高质量的用户表示，自监督学习(SSL)最近被应用到顺序推荐系统中。然而，在促进学习过程推荐系统的同时，SSL 也不是没有安全威胁: 精心设计的输入可能毒害由 SSL 驱动的预先训练的模型，从而降低下游推荐模型的有效性。研究结果表明，针对训练前阶段的中毒攻击会对顺序推荐系统造成威胁。在没有任何模型体系结构和参数的背景知识，也没有任何 API 查询的情况下，我们的策略证明了对主流的基于 SSL 的推荐方案以及常用数据集进行中毒攻击的可行性。通过注入少量的虚假用户，我们可以像以前一样将目标条目推荐给真正的用户数千次，这表明推荐系统由于 SSL 而具有了一个新的攻击面。我们进一步表明，我们的攻击对推荐平台的检测和防御是具有挑战性的。我们的工作突出了自我监督推荐系统的弱点，并表明了研究人员意识到这种安全威胁的必要性。我们的源代码可以在 https://github.com/conggroup/poisoning-ssl-based-rs 找到。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Poisoning+Self-supervised+Learning+Based+Sequential+Recommendations)|0| |[Graph Masked Autoencoder for Sequential Recommendation](https://doi.org/10.1145/3539618.3591692)|Yaowen Ye, Lianghao Xia, Chao Huang|University of Hong Kong, Hong Kong, Hong Kong|While some powerful neural network architectures (e.g., Transformer, Graph Neural Networks) have achieved improved performance in sequential recommendation with high-order item dependency modeling, they may suffer from poor representation capability in label scarcity scenarios. To address the issue of insufficient labels, Contrastive Learning (CL) has attracted much attention in recent methods to perform data augmentation through embedding contrasting for self-supervision. However, due to the hand-crafted property of their contrastive view generation strategies, existing CL-enhanced models i) can hardly yield consistent performance on diverse sequential recommendation tasks; ii) may not be immune to user behavior data noise. In light of this, we propose a simple yet effective graph masked autoencoder that adaptively and dynamically distills global item transitional information for self-supervised augmentation. It naturally avoids the above issue of heavy reliance on constructing high-quality embedding contrastive views. Instead, an adaptive data reconstruction paradigm is designed to be integrated with the long-range item dependency modeling, for informative augmentation in sequential recommendation. Extensive experiments demonstrate that our method significantly outperforms state-of-the-art baseline models and can learn more accurate representations against data noise and sparsity. Our implemented model code is available at https://github.com/HKUDS/GMRec.|虽然一些强大的神经网络结构(例如，Transformer、 Graph 神经网络)已经通过高阶项依赖建模实现了序贯推荐的性能改善，但是在标签稀缺的情况下，它们可能会受到表示能力较差的影响。为了解决标签不足的问题，近年来，对比学习(CL)通过嵌入对比度进行自我监督来实现数据增强的方法受到了广泛关注。然而，由于其对比视图生成策略的手工特性，现有的 CL 增强模型 i)很难在不同的顺序推荐任务上产生一致的性能; ii)可能不能免疫用户行为数据噪声。鉴于此，我们提出了一种简单而有效的图掩码自动编码器，自适应和动态提取全局项目过渡信息的自监督增强。它自然而然地避免了上述严重依赖于构造高质量嵌入对比视图的问题。相反，设计了一种自适应数据重建范式，将其与长程项目依赖性建模相结合，用于顺序推荐中的信息增强。广泛的实验表明，我们的方法明显优于国家的最先进的基线模型，可以学习更准确的表示对数据噪音和稀疏。我们已实现的模型代码可在 https://github.com/hkuds/gmrec 获得。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Graph+Masked+Autoencoder+for+Sequential+Recommendation)|0| -|[A Generic Learning Framework for Sequential Recommendation with Distribution Shifts](https://doi.org/10.1145/3539618.3591624)|Zhengyi Yang, Xiangnan He, Jizhi Zhang, Jiancan Wu, Xin Xin, Jiawei Chen, Xiang Wang|Shandong University, Qingdao, China; University of Science and Technology of China, Hefei, China; Zhejiang University, Hangzhou, China|Leading sequential recommendation (SeqRec) models adopt empirical risk minimization (ERM) as the learning framework, which inherently assumes that the training data (historical interaction sequences) and the testing data (future interactions) are drawn from the same distribution. However, such i.i.d. assumption hardly holds in practice, due to the online serving and dynamic nature of recommender system.For example, with the streaming of new data, the item popularity distribution would change, and the user preference would evolve after consuming some items. Such distribution shifts could undermine the ERM framework, hurting the model's generalization ability for future online serving. In this work, we aim to develop a generic learning framework to enhance the generalization of recommenders in the dynamic environment. Specifically, on top of ERM, we devise a Distributionally Robust Optimization mechanism for SeqRec (DROS). At its core is our carefully-designed distribution adaption paradigm, which considers the dynamics of data distribution and explores possible distribution shifts between training and testing. Through this way, we can endow the backbone recommenders with better generalization ability.It is worth mentioning that DROS is an effective model-agnostic learning framework, which is applicable to general recommendation scenarios.Theoretical analyses show that DROS enables the backbone recommenders to achieve robust performance in future testing data.Empirical studies verify the effectiveness against dynamic distribution shifts of DROS. Codes are anonymously open-sourced at https://github.com/YangZhengyi98/DROS.|先导序贯推荐(SeqRec)模型采用经验风险最小化(ERM)作为学习框架，内在假设训练数据(历史交互序列)和测试数据(未来交互)来自同一分布。然而，由于网上服务及推荐系统的动态性，这种身份证假设在实际上难以成立。例如，随着新数据的流动，项目受欢迎程度的分布将发生变化，并且用户偏好将在使用某些项目之后发生变化。这种分销转变可能会破坏 ERM 框架，损害该模型对未来在线服务的推广能力。在这项工作中，我们的目标是开发一个通用的学习框架，以提高推荐系统在动态环境中的推广能力。具体来说，在 ERM 的基础上，我们为 SeqRec (DROS)设计了一种分布式鲁棒优化机制。其核心是我们精心设计的分布适应范例，它考虑到了数据分布的动态性，并探索了培训和测试之间可能的分布变化。通过这种方式，我们可以赋予骨干推荐系统更好的泛化能力。值得一提的是，DROS 是一种有效的模型无关学习框架，适用于一般推荐场景。理论分析表明，DROS 可以使骨干推荐器在未来的测试数据中获得稳健的性能。实证研究验证了该方法对 DROS 动态分布移位的有效性。代码在 https://github.com/yangzhengyi98/dros 是匿名开源的。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Generic+Learning+Framework+for+Sequential+Recommendation+with+Distribution+Shifts)|0| +|[A Generic Learning Framework for Sequential Recommendation with Distribution Shifts](https://doi.org/10.1145/3539618.3591624)|Zhengyi Yang, Xiangnan He, Jizhi Zhang, Jiancan Wu, Xin Xin, Jiawei Chen, Xiang Wang|University of Science and Technology of China, Hefei, China; Zhejiang University, Hangzhou, China; Shandong University, Qingdao, China|Leading sequential recommendation (SeqRec) models adopt empirical risk minimization (ERM) as the learning framework, which inherently assumes that the training data (historical interaction sequences) and the testing data (future interactions) are drawn from the same distribution. However, such i.i.d. assumption hardly holds in practice, due to the online serving and dynamic nature of recommender system.For example, with the streaming of new data, the item popularity distribution would change, and the user preference would evolve after consuming some items. Such distribution shifts could undermine the ERM framework, hurting the model's generalization ability for future online serving. In this work, we aim to develop a generic learning framework to enhance the generalization of recommenders in the dynamic environment. Specifically, on top of ERM, we devise a Distributionally Robust Optimization mechanism for SeqRec (DROS). At its core is our carefully-designed distribution adaption paradigm, which considers the dynamics of data distribution and explores possible distribution shifts between training and testing. Through this way, we can endow the backbone recommenders with better generalization ability.It is worth mentioning that DROS is an effective model-agnostic learning framework, which is applicable to general recommendation scenarios.Theoretical analyses show that DROS enables the backbone recommenders to achieve robust performance in future testing data.Empirical studies verify the effectiveness against dynamic distribution shifts of DROS. Codes are anonymously open-sourced at https://github.com/YangZhengyi98/DROS.|先导序贯推荐(SeqRec)模型采用经验风险最小化(ERM)作为学习框架，内在假设训练数据(历史交互序列)和测试数据(未来交互)来自同一分布。然而，由于网上服务及推荐系统的动态性，这种身份证假设在实际上难以成立。例如，随着新数据的流动，项目受欢迎程度的分布将发生变化，并且用户偏好将在使用某些项目之后发生变化。这种分销转变可能会破坏 ERM 框架，损害该模型对未来在线服务的推广能力。在这项工作中，我们的目标是开发一个通用的学习框架，以提高推荐系统在动态环境中的推广能力。具体来说，在 ERM 的基础上，我们为 SeqRec (DROS)设计了一种分布式鲁棒优化机制。其核心是我们精心设计的分布适应范例，它考虑到了数据分布的动态性，并探索了培训和测试之间可能的分布变化。通过这种方式，我们可以赋予骨干推荐系统更好的泛化能力。值得一提的是，DROS 是一种有效的模型无关学习框架，适用于一般推荐场景。理论分析表明，DROS 可以使骨干推荐器在未来的测试数据中获得稳健的性能。实证研究验证了该方法对 DROS 动态分布移位的有效性。代码在 https://github.com/yangzhengyi98/dros 是匿名开源的。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Generic+Learning+Framework+for+Sequential+Recommendation+with+Distribution+Shifts)|0| |[Single-shot Feature Selection for Multi-task Recommendations](https://doi.org/10.1145/3539618.3591767)|Yejing Wang, Zhaocheng Du, Xiangyu Zhao, Bo Chen, Huifeng Guo, Ruiming Tang, Zhenhua Dong|Huawei Noah's Ark Lab, Shen Zhen, China; City University of Hong Kong, Hong Kong, Hong Kong|Multi-task Recommender Systems (MTRSs) has become increasingly prevalent in a variety of real-world applications due to their exceptional training efficiency and recommendation quality. However, conventional MTRSs often input all relevant feature fields without distinguishing their contributions to different tasks, which can lead to confusion and a decline in performance. Existing feature selection methods may neglect task relations or require significant computation during model training in multi-task setting. To this end, this paper proposes a novel Single-shot Feature Selection framework for MTRSs, referred to as MultiSFS, which is capable of selecting feature fields for each task while considering task relations in a single-shot manner. Specifically, MultiSFS first efficiently obtains task-specific feature importance through a single forward-backward pass. Then, a data-task bipartite graph is constructed to learn field-level task relations. Subsequently, MultiSFS merges the feature importance according to task relations and selects feature fields for different tasks. To demonstrate the effectiveness and properties of MultiSFS, we integrate it with representative MTRS models and evaluate on three real-world datasets. The implementation code is available online to ease reproducibility.|多任务推荐系统由于其出色的培训效率和推荐质量，在各种实际应用中越来越普遍。然而，传统的中期审查系统往往输入所有相关特征领域，而不区分它们对不同任务的贡献，这可能导致混乱和业绩下降。现有的特征选择方法在多任务环境下进行模型训练时，可能会忽略任务间的关系，或者需要进行大量的计算。为此，本文提出了一种新的单镜头特征选择框架 MultiSFS，该框架能够在考虑单镜头任务关系的情况下为每个任务选择特征域。具体来说，MultiSFS 首先通过单个向前向后传递有效地获得特定于任务的特征重要性。然后，构造一个数据-任务二分图来学习场级任务关系。随后，MultiSFS 根据任务关系对特征重要度进行合并，并为不同的任务选择特征域。为了验证 MultiSFS 的有效性和特性，我们将其与具有代表性的中期业绩预测模型相结合，并在三个实际数据集上进行了评估。实现代码可在线获得，以减轻重复性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Single-shot+Feature+Selection+for+Multi-task+Recommendations)|0| -|[Fine-Grained Preference-Aware Personalized Federated POI Recommendation with Data Sparsity](https://doi.org/10.1145/3539618.3591688)|Xiao Zhang, Ziming Ye, Jianfeng Lu, Fuzhen Zhuang, Yanwei Zheng, Dongxiao Yu|Beihang University, Zhongguancun Laboratory, Beijing, China; Shandong University, Qingdao, China; Wuhan University of Science and Technology, Wuhan, China|With the raised privacy concerns and rigorous data regulations, federated learning has become a hot collaborative learning paradigm for the recommendation model without sharing the highly sensitive POI data. However, the time-sensitive, heterogeneous, and limited POI records seriously restrict the development of federated POI recommendation. To this end, in this paper, we design the fine-grained preference-aware personalized federated POI recommendation framework, namely PrefFedPOI, under extremely sparse historical trajectories to address the above challenges. In details, PrefFedPOI extracts the fine-grained preference of current time slot by combining historical recent preferences and periodic preferences within each local client. Due to the extreme lack of POI data in some time slots, a data amount aware selective strategy is designed for model parameters uploading. Moreover, a performance enhanced clustering mechanism with reinforcement learning is proposed to capture the preference relatedness among all clients to encourage the positive knowledge sharing. Furthermore, a clustering teacher network is designed for improving efficiency by clustering guidance. Extensive experiments are conducted on two diverse real-world datasets to demonstrate the effectiveness of proposed PrefFedPOI comparing with state-of-the-arts. In particular, personalized PrefFedPOI can achieve 7% accuracy improvement on average among data-sparsity clients.|随着隐私问题的提高和严格的数据监管，联合学习已经成为推荐模型的一个热门合作学习，而不需要共享高度敏感的 POI 数据。然而，时间敏感、异构、有限的 POI 记录严重制约了联邦 POI 推荐的发展。为此，本文在极其稀疏的历史轨迹下，设计了细粒度偏好感知的个性化联邦 POI 推荐框架 PrefFedPOI，以解决上述挑战。具体来说，PrefFedPOI 通过组合每个本地客户机内的历史最近首选项和周期首选项来提取当前时隙的细粒度首选项。针对某些时隙中 POI 数据极度缺乏的情况，设计了一种数据量感知的模型参数选择策略。此外，我们亦建议采用一个有强化学习的表现增强聚类机制，以捕捉所有客户之间的偏好关系，从而鼓励他们积极分享知识。为了提高聚类指导的效率，设计了一个聚类教师网络。在两个不同的真实世界数据集上进行了广泛的实验，以验证所提出的 PrefFedPOI 与最新技术相比的有效性。特别是，在数据稀疏的客户端中，个性化 PrefFedPOI 可以平均提高7% 的准确性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Fine-Grained+Preference-Aware+Personalized+Federated+POI+Recommendation+with+Data+Sparsity)|0| +|[Fine-Grained Preference-Aware Personalized Federated POI Recommendation with Data Sparsity](https://doi.org/10.1145/3539618.3591688)|Xiao Zhang, Ziming Ye, Jianfeng Lu, Fuzhen Zhuang, Yanwei Zheng, Dongxiao Yu|Beihang University, Zhongguancun Laboratory, Beijing, China; Wuhan University of Science and Technology, Wuhan, China; Shandong University, Qingdao, China|With the raised privacy concerns and rigorous data regulations, federated learning has become a hot collaborative learning paradigm for the recommendation model without sharing the highly sensitive POI data. However, the time-sensitive, heterogeneous, and limited POI records seriously restrict the development of federated POI recommendation. To this end, in this paper, we design the fine-grained preference-aware personalized federated POI recommendation framework, namely PrefFedPOI, under extremely sparse historical trajectories to address the above challenges. In details, PrefFedPOI extracts the fine-grained preference of current time slot by combining historical recent preferences and periodic preferences within each local client. Due to the extreme lack of POI data in some time slots, a data amount aware selective strategy is designed for model parameters uploading. Moreover, a performance enhanced clustering mechanism with reinforcement learning is proposed to capture the preference relatedness among all clients to encourage the positive knowledge sharing. Furthermore, a clustering teacher network is designed for improving efficiency by clustering guidance. Extensive experiments are conducted on two diverse real-world datasets to demonstrate the effectiveness of proposed PrefFedPOI comparing with state-of-the-arts. In particular, personalized PrefFedPOI can achieve 7% accuracy improvement on average among data-sparsity clients.|随着隐私问题的提高和严格的数据监管，联合学习已经成为推荐模型的一个热门合作学习，而不需要共享高度敏感的 POI 数据。然而，时间敏感、异构、有限的 POI 记录严重制约了联邦 POI 推荐的发展。为此，本文在极其稀疏的历史轨迹下，设计了细粒度偏好感知的个性化联邦 POI 推荐框架 PrefFedPOI，以解决上述挑战。具体来说，PrefFedPOI 通过组合每个本地客户机内的历史最近首选项和周期首选项来提取当前时隙的细粒度首选项。针对某些时隙中 POI 数据极度缺乏的情况，设计了一种数据量感知的模型参数选择策略。此外，我们亦建议采用一个有强化学习的表现增强聚类机制，以捕捉所有客户之间的偏好关系，从而鼓励他们积极分享知识。为了提高聚类指导的效率，设计了一个聚类教师网络。在两个不同的真实世界数据集上进行了广泛的实验，以验证所提出的 PrefFedPOI 与最新技术相比的有效性。特别是，在数据稀疏的客户端中，个性化 PrefFedPOI 可以平均提高7% 的准确性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Fine-Grained+Preference-Aware+Personalized+Federated+POI+Recommendation+with+Data+Sparsity)|0| |[Model-Agnostic Decentralized Collaborative Learning for On-Device POI Recommendation](https://doi.org/10.1145/3539618.3591733)|Jing Long, Tong Chen, Quoc Viet Hung Nguyen, Guandong Xu, Kai Zheng, Hongzhi Yin||As an indispensable personalized service in Location-based Social Networks (LBSNs), the next Point-of-Interest (POI) recommendation aims to help people discover attractive and interesting places. Currently, most POI recommenders are based on the conventional centralized paradigm that heavily relies on the cloud to train the recommendation models with large volumes of collected users' sensitive check-in data. Although a few recent works have explored on-device frameworks for resilient and privacy-preserving POI recommendations, they invariably hold the assumption of model homogeneity for parameters/gradients aggregation and collaboration. However, users' mobile devices in the real world have various hardware configurations (e.g., compute resources), leading to heterogeneous on-device models with different architectures and sizes. In light of this, We propose a novel on-device POI recommendation framework, namely Model-Agnostic Collaborative learning for on-device POI recommendation (MAC), allowing users to customize their own model structures (e.g., dimension \& number of hidden layers). To counteract the sparsity of on-device user data, we propose to pre-select neighbors for collaboration based on physical distances, category-level preferences, and social networks. To assimilate knowledge from the above-selected neighbors in an efficient and secure way, we adopt the knowledge distillation framework with mutual information maximization. Instead of sharing sensitive models/gradients, clients in MAC only share their soft decisions on a preloaded reference dataset. To filter out low-quality neighbors, we propose two sampling strategies, performance-triggered sampling and similarity-based sampling, to speed up the training process and obtain optimal recommenders. In addition, we design two novel approaches to generate more effective reference datasets while protecting users' privacy.|作为基于位置的社交网络(LBSNs)中不可或缺的个性化服务，下一个兴趣点(POI)推荐旨在帮助人们发现有吸引力和有趣的地方。目前，大多数 POI 推荐系统都是基于传统的集中式模式，这种模式严重依赖于云来训练推荐模型，并收集了大量用户的敏感签入数据。虽然最近的一些工作已经探索了弹性和保护隐私的 POI 建议在设备上的框架，他们总是持有参数/梯度聚合和协作的模型同质性的假设。然而，在现实世界中，用户的移动设备有各种各样的硬件配置(例如，计算资源) ，导致了具有不同体系结构和大小的异构设备上模型。鉴于此，我们提出了一个新的设备上 POI 推荐框架，即设备上 POI 推荐的模型不可知合作学习(model-Agnotic) ，允许用户定制自己的模型结构(例如，尺寸和隐藏层数)。为了弥补设备上用户数据的稀缺性，我们建议根据物理距离、类别级别偏好和社交网络预先选择合作的邻居。为了有效、安全地吸收上述邻居的知识，我们采用了具有互信息最大化的知识提取框架。MAC 中的客户端不共享敏感的模型/梯度，而只在预加载的参考数据集上共享他们的软决策。为了过滤掉低质量的邻居，我们提出了两种抽样策略: 性能触发抽样和基于相似度的抽样，以加快训练过程，并获得最优的推荐。此外，我们设计了两种新的方法来生成更有效的参考数据集，同时保护用户的隐私。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Model-Agnostic+Decentralized+Collaborative+Learning+for+On-Device+POI+Recommendation)|0| |[Multi-view Hypergraph Contrastive Policy Learning for Conversational Recommendation](https://doi.org/10.1145/3539618.3591737)|Sen Zhao, Wei Wei, XianLing Mao, Shuai Zhu, Minghui Yang, Zujie Wen, Dangyang Chen, Feida Zhu||Conversational recommendation systems (CRS) aim to interactively acquire user preferences and accordingly recommend items to users. Accurately learning the dynamic user preferences is of crucial importance for CRS. Previous works learn the user preferences with pairwise relations from the interactive conversation and item knowledge, while largely ignoring the fact that factors for a relationship in CRS are multiplex. Specifically, the user likes/dislikes the items that satisfy some attributes (Like/Dislike view). Moreover social influence is another important factor that affects user preference towards the item (Social view), while is largely ignored by previous works in CRS. The user preferences from these three views are inherently different but also correlated as a whole. The user preferences from the same views should be more similar than that from different views. The user preferences from Like View should be similar to Social View while different from Dislike View. To this end, we propose a novel model, namely Multi-view Hypergraph Contrastive Policy Learning (MHCPL). Specifically, MHCPL timely chooses useful social information according to the interactive history and builds a dynamic hypergraph with three types of multiplex relations from different views. The multiplex relations in each view are successively connected according to their generation order.|会话推荐系统(CRS)旨在交互式地获取用户偏好，从而向用户推荐项目。准确地了解动态用户偏好对 CRS 至关重要。以往的研究主要是从交互式会话和项目知识中了解成对关系的用户偏好，而忽略了 CRS 中影响关系的因素是多重的这一事实。具体来说，用户喜欢/不喜欢满足某些属性的项(Like/Dislike 视图)。此外，社会影响力是影响用户对商品偏好的另一个重要因素(社会视图) ，而以往的研究大多忽略了社会影响力。来自这三个视图的用户首选项在本质上是不同的，但作为一个整体也是相关的。来自相同视图的用户首选项应该比来自不同视图的用户首选项更加相似。来自 Like View 的用户首选项应该类似于 Social View，而不同于 Dislike View。为此，我们提出了一个新的模型，即多视图超图对比策略学习(MHCPL)。具体来说，MHCPL 根据交互历史及时选择有用的社会信息，从不同角度构建了三类多元关系的动态超图。每个视图中的多路复用关系根据它们的生成顺序依次连接。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Multi-view+Hypergraph+Contrastive+Policy+Learning+for+Conversational+Recommendation)|0| |[Learnable Pillar-based Re-ranking for Image-Text Retrieval](https://doi.org/10.1145/3539618.3591712)|Leigang Qu, Meng Liu, Wenjie Wang, Zhedong Zheng, Liqiang Nie, TatSeng Chua||Image-text retrieval aims to bridge the modality gap and retrieve cross-modal content based on semantic similarities. Prior work usually focuses on the pairwise relations (i.e., whether a data sample matches another) but ignores the higher-order neighbor relations (i.e., a matching structure among multiple data samples). Re-ranking, a popular post-processing practice, has revealed the superiority of capturing neighbor relations in single-modality retrieval tasks. However, it is ineffective to directly extend existing re-ranking algorithms to image-text retrieval. In this paper, we analyze the reason from four perspectives, i.e., generalization, flexibility, sparsity, and asymmetry, and propose a novel learnable pillar-based re-ranking paradigm. Concretely, we first select top-ranked intra- and inter-modal neighbors as pillars, and then reconstruct data samples with the neighbor relations between them and the pillars. In this way, each sample can be mapped into a multimodal pillar space only using similarities, ensuring generalization. After that, we design a neighbor-aware graph reasoning module to flexibly exploit the relations and excavate the sparse positive items within a neighborhood. We also present a structure alignment constraint to promote cross-modal collaboration and align the asymmetric modalities. On top of various base backbones, we carry out extensive experiments on two benchmark datasets, i.e., Flickr30K and MS-COCO, demonstrating the effectiveness, superiority, generalization, and transferability of our proposed re-ranking paradigm.|图像-文本检索旨在弥合情态差异，基于语义相似性检索跨情态内容。先前的工作通常侧重于成对关系(例如，一个数据样本是否匹配另一个) ，但忽略了高阶邻居关系(例如，多个数据样本之间的匹配结构)。重排序是一种流行的后处理实践，它揭示了在单模态检索任务中捕获邻居关系的优越性。然而，将现有的重新排序算法直接推广到图像文本检索是无效的。本文从概括性、灵活性、稀疏性和不对称性四个方面分析了排序问题产生的原因，并提出了一种新的可学习的基于支柱的排序范式。具体来说，我们首先选择排名最高的模态内邻居和模态间邻居作为支柱，然后利用它们与支柱之间的邻居关系重构数据样本。通过这种方式，每个样本只需使用相似性即可映射到多模态支柱空间，从而确保泛化。然后设计邻域感知图推理模块，灵活地利用邻域间的关系，挖掘邻域内的稀疏正项。我们还提出了一个结构调整约束，以促进跨模式协作和调整不对称的模式。在各种基础骨干之上，我们对 Flickr30K 和 MS-COCO 这两个基准数据集进行了广泛的实验，证明了我们提出的重新排序范式的有效性、优越性、通用性和可转移性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Learnable+Pillar-based+Re-ranking+for+Image-Text+Retrieval)|0| @@ -69,7 +69,7 @@ |[Candidate-aware Graph Contrastive Learning for Recommendation](https://doi.org/10.1145/3539618.3591647)|Wei He, Guohao Sun, Jinhu Lu, Xiu Susie Fang|Donghua University, Shanghai, China|Recently, Graph Neural Networks (GNNs) have become a mainstream recommender system method, where it captures high-order collaborative signals between nodes by performing convolution operations on the user-item interaction graph to predict user preferences for different items. However, in real scenarios, the user-item interaction graph is extremely sparse, which means numerous users only interact with a small number of items, resulting in the inability of GNN in learning high-quality node embeddings. To alleviate this problem, the Graph Contrastive Learning (GCL)-based recommender system method is proposed. GCL improves embedding quality by maximizing the similarity of the positive pair and minimizing the similarity of the negative pair. However, most GCL-based methods use heuristic data augmentation methods, i.e., random node/edge drop and attribute masking, to construct contrastive pairs, resulting in the loss of important information. To solve the problems in GCL-based methods, we propose a novel method, Candidate-aware Graph Contrastive Learning for Recommendation, called CGCL. In CGCL, we explore the relationship between the user and the candidate item in the embedding at different layers and use similar semantic embeddings to construct contrastive pairs. By our proposed CGCL, we construct structural neighbor contrastive learning objects, candidate contrastive learning objects, and candidate structural neighbor contrastive learning objects to obtain high-quality node embeddings. To validate the proposed model, we conducted extensive experiments on three publicly available datasets. Compared with various state-of-the-art DNN-, GNN- and GCL-based methods, our proposed CGCL achieved significant improvements in all indicators.|最近，图形神经网络(GNN)已经成为一种主流的推荐系统方法，它通过在用户-项目交互图上执行卷积操作来预测用户对不同项目的偏好，从而捕获节点之间的高阶协作信号。然而，在实际场景中，用户-项目交互图是非常稀疏的，这意味着大量的用户只与少量的项目交互，导致 GNN 无法学习高质量的节点嵌入。为了解决这个问题，我们提出了基于图形对比学习(gCL)的推荐系统学习方法。GCL 通过最大化正对的相似度和最小化负对的相似度来提高嵌入质量。然而，大多数基于 GCL 的方法使用启发式数据增强方法，即随机节点/边降和属性掩蔽来构造对比对，导致重要信息的丢失。为了解决基于 GCL 的方法中存在的问题，我们提出了一种新的方法，候选感知的图形对比推荐学习，称为 CGCL。在 CGCL 中，我们在不同层次的嵌入中探索用户与候选项之间的关系，并利用相似语义嵌入构造对比对。通过我们提出的 CGCL，我们构造了结构化邻域对比学习对象、候选对比学习对象和候选结构化邻域对比学习对象，以获得高质量的节点嵌入。为了验证所提出的模型，我们在三个公开的数据集上进行了广泛的实验。与基于 DNN、 GNN 和 GCL 的各种最新方法相比，我们提出的 CGCL 在所有指标上都取得了显著的改善。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Candidate-aware+Graph+Contrastive+Learning+for+Recommendation)|0| |[Attention Mixtures for Time-Aware Sequential Recommendation](https://doi.org/10.1145/3539618.3591951)|VietAnh Tran, Guillaume SalhaGalvan, Bruno Sguerra, Romain Hennequin||Transformers emerged as powerful methods for sequential recommendation. However, existing architectures often overlook the complex dependencies between user preferences and the temporal context. In this short paper, we introduce MOJITO, an improved Transformer sequential recommender system that addresses this limitation. MOJITO leverages Gaussian mixtures of attention-based temporal context and item embedding representations for sequential modeling. Such an approach permits to accurately predict which items should be recommended next to users depending on past actions and the temporal context. We demonstrate the relevance of our approach, by empirically outperforming existing Transformers for sequential recommendation on several real-world datasets.|变压器作为强大的顺序推荐方法出现了。然而，现有的体系结构往往忽略了用户首选项和时态上下文之间的复杂依赖关系。在这篇简短的文章中，我们介绍了 MOJITO，一个改进的 formers顺序推荐系统，它解决了这个问题。MOJITO 利用基于注意的时间上下文和项目嵌入表示的高斯混合序列建模。这种方法允许根据过去的行为和时间上下文准确地预测哪些项目应该被推荐给用户。我们证明了我们的方法的相关性，通过经验优于现有的变形金刚顺序推荐几个真实世界的数据集。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Attention+Mixtures+for+Time-Aware+Sequential+Recommendation)|0| |[Augmenting Passage Representations with Query Generation for Enhanced Cross-Lingual Dense Retrieval](https://doi.org/10.1145/3539618.3591952)|Shengyao Zhuang, Linjun Shou, Guido Zuccon||Effective cross-lingual dense retrieval methods that rely on multilingual pre-trained language models (PLMs) need to be trained to encompass both the relevance matching task and the cross-language alignment task. However, cross-lingual data for training is often scarcely available. In this paper, rather than using more cross-lingual data for training, we propose to use cross-lingual query generation to augment passage representations with queries in languages other than the original passage language. These augmented representations are used at inference time so that the representation can encode more information across the different target languages. Training of a cross-lingual query generator does not require additional training data to that used for the dense retriever. The query generator training is also effective because the pre-training task for the generator (T5 text-to-text training) is very similar to the fine-tuning task (generation of a query). The use of the generator does not increase query latency at inference and can be combined with any cross-lingual dense retrieval method. Results from experiments on a benchmark cross-lingual information retrieval dataset show that our approach can improve the effectiveness of existing cross-lingual dense retrieval methods. Implementation of our methods, along with all generated query files are made publicly available at https://github.com/ielab/xQG4xDR.|有效的跨语言密集检索方法依赖于多语言预训练语言模型(PLM)需要培训，以涵盖相关性匹配任务和跨语言对齐任务。然而，用于培训的跨语言数据往往很少。在本文中，我们不使用更多的跨语言数据进行训练，而是提出使用跨语言查询生成来增强非原始语言中的查询的段落表示。这些增强表示在推理时使用，以便表示可以跨不同的目标语言编码更多的信息。跨语言查询生成器的训练不需要比密集检索器所使用的训练数据更多的训练数据。查询生成器训练也很有效，因为生成器的预训练任务(T5文本到文本训练)与微调任务(生成查询)非常相似。该生成器的使用不会增加推断时的查询延迟，并且可以与任何跨语言密集检索方法结合使用。对基准跨语言信息检索数据集的实验结果表明，我们的方法可以提高现有跨语言密集检索方法的有效性。我们方法的实现，以及所有生成的查询文件都可以在 https://github.com/ielab/xqg4xdr 上公开获得。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Augmenting+Passage+Representations+with+Query+Generation+for+Enhanced+Cross-Lingual+Dense+Retrieval)|0| -|[CEC: Towards Learning Global Optimized Recommendation through Causality Enhanced Conversion Model](https://doi.org/10.1145/3539618.3591962)|Ran Le, Guoqing Jiang, Xiufeng Shu, Ruidong Han, Qianzhong Li, Yacheng Li, Xiang Li, Wei Lin|Unaffiliated, Beijing, China; Meituan, Beijing, China|Most e-commerce platforms consist of multiple entries (e.g., recommendation, search, shopping cart and etc.) for users to purchase their liked items. Among the research on the recommendation entry, most of them focus on improving the conversion volumes merely in the recommendation entry. However, such way could not ensure an increase in the global conversion volumes of the e-commerce platform. To achieve this goal by optimizing the recommendation entry only, in this paper, we focus on modeling the causality between the recommendation-entry-impression and the conversion by proposing the two-stage Causality Enhanced Conversion (CEC) model. In the first stage, we define the recommendation-entry-impression as treatment, then we estimate the conversion rate conditioned on the inclusion or exclusion of treatment respectively and calculate the corresponding individual treatment effect (ITE). In the second stage, we propose a propensity-normalization (PN) based method to transform the learned ITE to a weight term for instance weighting in the conversion loss. Extensive offline and online experiments on a large-scale food e-commerce scenario demonstrate that the CEC model could focus more on those conversed instances that can improve the global conversion volumes of the platform.|大多数电子商务平台由多个条目组成(例如，推荐、搜索、购物车等) ，用户可以购买他们喜欢的商品。在对推荐条目的研究中，大多数研究仅仅关注于提高推荐条目的转化率。然而，这种方式并不能保证增加电子商务平台的全球转换量。为了通过优化推荐条目来实现这一目标，本文提出了两阶段因果关系增强转换(CEC)模型，着重对推荐条目印象与转换之间的因果关系进行建模。在第一阶段，我们将推荐入口印象定义为治疗，然后分别估计以包含或排除治疗为条件的转化率，并计算相应的个体治疗效果(ITE)。在第二阶段，我们提出了一个基于倾向标准化(PN)的方法，将学习到的 ITE 转换为一个权重项，例如在转换损失中的权重。大规模食品电子商务情景的大量离线和在线实验表明，CEC 模型可以更多地关注那些可以提高平台全球转换量的转换实例。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=CEC:+Towards+Learning+Global+Optimized+Recommendation+through+Causality+Enhanced+Conversion+Model)|0| +|[CEC: Towards Learning Global Optimized Recommendation through Causality Enhanced Conversion Model](https://doi.org/10.1145/3539618.3591962)|Ran Le, Guoqing Jiang, Xiufeng Shu, Ruidong Han, Qianzhong Li, Yacheng Li, Xiang Li, Wei Lin|Meituan, Beijing, China; Unaffiliated, Beijing, China|Most e-commerce platforms consist of multiple entries (e.g., recommendation, search, shopping cart and etc.) for users to purchase their liked items. Among the research on the recommendation entry, most of them focus on improving the conversion volumes merely in the recommendation entry. However, such way could not ensure an increase in the global conversion volumes of the e-commerce platform. To achieve this goal by optimizing the recommendation entry only, in this paper, we focus on modeling the causality between the recommendation-entry-impression and the conversion by proposing the two-stage Causality Enhanced Conversion (CEC) model. In the first stage, we define the recommendation-entry-impression as treatment, then we estimate the conversion rate conditioned on the inclusion or exclusion of treatment respectively and calculate the corresponding individual treatment effect (ITE). In the second stage, we propose a propensity-normalization (PN) based method to transform the learned ITE to a weight term for instance weighting in the conversion loss. Extensive offline and online experiments on a large-scale food e-commerce scenario demonstrate that the CEC model could focus more on those conversed instances that can improve the global conversion volumes of the platform.|大多数电子商务平台由多个条目组成(例如，推荐、搜索、购物车等) ，用户可以购买他们喜欢的商品。在对推荐条目的研究中，大多数研究仅仅关注于提高推荐条目的转化率。然而，这种方式并不能保证增加电子商务平台的全球转换量。为了通过优化推荐条目来实现这一目标，本文提出了两阶段因果关系增强转换(CEC)模型，着重对推荐条目印象与转换之间的因果关系进行建模。在第一阶段，我们将推荐入口印象定义为治疗，然后分别估计以包含或排除治疗为条件的转化率，并计算相应的个体治疗效果(ITE)。在第二阶段，我们提出了一个基于倾向标准化(PN)的方法，将学习到的 ITE 转换为一个权重项，例如在转换损失中的权重。大规模食品电子商务情景的大量离线和在线实验表明，CEC 模型可以更多地关注那些可以提高平台全球转换量的转换实例。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=CEC:+Towards+Learning+Global+Optimized+Recommendation+through+Causality+Enhanced+Conversion+Model)|0| |[ConQueR: Contextualized Query Reduction using Search Logs](https://doi.org/10.1145/3539618.3591966)|Hyeyoung Kim, Minjin Choi, Sunkyung Lee, Eunseong Choi, YoungIn Song, Jongwuk Lee||Query reformulation is a key mechanism to alleviate the linguistic chasm of query in ad-hoc retrieval. Among various solutions, query reduction effectively removes extraneous terms and specifies concise user intent from long queries. However, it is challenging to capture hidden and diverse user intent. This paper proposes Contextualized Query Reduction (ConQueR) using a pre-trained language model (PLM). Specifically, it reduces verbose queries with two different views: core term extraction and sub-query selection. One extracts core terms from an original query at the term level, and the other determines whether a sub-query is a suitable reduction for the original query at the sequence level. Since they operate at different levels of granularity and complement each other, they are finally aggregated in an ensemble manner. We evaluate the reduction quality of ConQueR on real-world search logs collected from a commercial web search engine. It achieves up to 8.45% gains in exact match scores over the best competing model.|查询重构是自组织检索中缓解查询语言鸿沟的关键机制。在各种解决方案中，查询缩减有效地去除了多余的术语，并从长查询中指定了简洁的用户意图。然而，捕捉隐藏的和多样化的用户意图是一个挑战。提出了一种基于预训练语言模型(PLM)的上下文查询约简(ConQueR)算法。具体来说，它使用两种不同的视图来减少冗长查询: 核心术语提取和子查询选择。一个在术语级别从原始查询中提取核心术语，另一个在序列级别确定子查询对于原始查询是否是合适的简化。由于它们在不同的粒度级别上运行并相互补充，因此最终以集成的方式进行聚合。我们对从商业网络搜索引擎收集的真实世界搜索日志中的 ConQueR 的还原质量进行评估。与最佳竞争模型相比，它在精确匹配得分上获得了高达8.45% 的增益。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=ConQueR:+Contextualized+Query+Reduction+using+Search+Logs)|0| |[Explain Like I am BM25: Interpreting a Dense Model's Ranked-List with a Sparse Approximation](https://doi.org/10.1145/3539618.3591982)|Michael Llordes, Debasis Ganguly, Sumit Bhatia, Chirag Agarwal||Neural retrieval models (NRMs) have been shown to outperform their statistical counterparts owing to their ability to capture semantic meaning via dense document representations. These models, however, suffer from poor interpretability as they do not rely on explicit term matching. As a form of local per-query explanations, we introduce the notion of equivalent queries that are generated by maximizing the similarity between the NRM's results and the result set of a sparse retrieval system with the equivalent query. We then compare this approach with existing methods such as RM3-based query expansion and contrast differences in retrieval effectiveness and in the terms generated by each approach.|神经检索模型(NRM)由于能够通过密集的文档表示来捕获语义信息，因此其性能优于统计检索模型。然而，这些模型的可解释性很差，因为它们不依赖于明确的术语匹配。作为局部查询解释的一种形式，我们引入了等价查询的概念，这些等价查询是通过最大化 NRM 的结果与具有等价查询的稀疏检索系统的结果集之间的相似性而生成的。然后，我们比较了这种方法与现有的方法，如基于 RM3的查询扩展和对比差异的检索效果和术语生成的每种方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Explain+Like+I+am+BM25:+Interpreting+a+Dense+Model's+Ranked-List+with+a+Sparse+Approximation)|0| |[Graph Collaborative Signals Denoising and Augmentation for Recommendation](https://doi.org/10.1145/3539618.3591994)|Ziwei Fan, Ke Xu, Zhang Dong, Hao Peng, Jiawei Zhang, Philip S. Yu||Graph collaborative filtering (GCF) is a popular technique for capturing high-order collaborative signals in recommendation systems. However, GCF's bipartite adjacency matrix, which defines the neighbors being aggregated based on user-item interactions, can be noisy for users/items with abundant interactions and insufficient for users/items with scarce interactions. Additionally, the adjacency matrix ignores user-user and item-item correlations, which can limit the scope of beneficial neighbors being aggregated. In this work, we propose a new graph adjacency matrix that incorporates user-user and item-item correlations, as well as a properly designed user-item interaction matrix that balances the number of interactions across all users. To achieve this, we pre-train a graph-based recommendation method to obtain users/items embeddings, and then enhance the user-item interaction matrix via top-K sampling. We also augment the symmetric user-user and item-item correlation components to the adjacency matrix. Our experiments demonstrate that the enhanced user-item interaction matrix with improved neighbors and lower density leads to significant benefits in graph-based recommendation. Moreover, we show that the inclusion of user-user and item-item correlations can improve recommendations for users with both abundant and insufficient interactions. The code is in \url{https://github.com/zfan20/GraphDA}.|图形协同过滤(gCF)是在推荐系统中捕获高阶协作信号的一种流行技术。然而，绿色气候基金的二分邻接矩阵定义了基于用户-项目交互聚合的邻居，这对于交互丰富的用户/项目来说可能是嘈杂的，对于交互稀少的用户/项目来说则是不足的。此外，邻接矩阵忽略了用户-用户和项目-项目之间的相关性，这可能会限制有益邻居被聚合的范围。在这项工作中，我们提出了一个新的图形邻接矩阵，包括用户-用户和项目-项目的相关性，以及一个适当设计的用户-项目交互矩阵，平衡所有用户之间的交互数量。为了实现这一目标，我们预先训练了一种基于图的推荐方法来获得用户/项目的嵌入，然后通过 top-K 抽样来增强用户-项目的交互矩阵。我们还增加了对称的用户-用户和项目-项目相关组件的邻接矩阵。我们的实验表明，增强的用户项目交互矩阵与改进的邻居和较低的密度导致显着的好处，在图的推荐。此外，我们表明，包含用户-用户和项目-项目的相关性可以改善对交互丰富和不充分的用户的推荐。代码在 url { https://github.com/zfan20/graphda }中。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Graph+Collaborative+Signals+Denoising+and+Augmentation+for+Recommendation)|0| @@ -80,7 +80,7 @@ |[Measuring Service-Level Learning Effects in Search Via Query-Randomized Experiments](https://doi.org/10.1145/3539618.3592020)|Paul Musgrave, Cuize Han, Parth Gupta|Amazon Search, Palo Alto, CA, USA|In order to determine the relevance of a given item to a query, most modern search ranking systems make use of features which aggregate prior user behavior for that item and query (e.g. click rate). For practical reasons, when running A/B tests on ranking systems, these features are generally shared between all treatments. For the most common experiment designs, which randomize traffic by user or by session, this creates a pathway by which the behavior of units in one treatment can effect the outcomes for units in other treatments, violating the Stable Unit Treatment Value Assumption (SUTVA) and biasing measured outcomes. Moreover, for experiments targeting improvements to the behavior data available to such features (e.g. online exploration), this pathway is precisely the one we are trying to affect; if such changes occur identically in treatment and control, then they cannot be measured. To address this, we propose the use of experiments which instead randomize traffic based on the search query. To validate our approach, we perform a pair of A/B tests on an explore-exploit framework in the Amazon search page: one under query randomization, and one under user randomization. In line with the theoretical predictions, we find that the query-randomized iteration is able to measure a statistically significant effect (+0.66% Purchases, p=0.001) where the user-randomized iteration does not (-0.02% Purchases, p=0.851).|为了确定给定条目与查询的相关性，大多数现代搜索排名系统使用的特征是聚合该条目和查询之前的用户行为(例如点击率)。出于实际原因，在排名系统上运行 A/B 测试时，这些特性通常在所有处理之间共享。对于最常见的实验设计，其按用户或会话随机化流量，这创建了一个途径，通过这个途径，一个治疗中的单位的行为可以影响其他治疗中的单位的结果，违反稳定单位治疗价值假设(SUTVA)和偏倚测量结果。此外，对于针对改善这些特征的行为数据(例如在线探索)的实验，这条路径正是我们试图影响的路径; 如果这些变化在治疗和控制中发生相同，那么它们就无法被测量。为了解决这个问题，我们提出用实验代替基于搜索查询的流量随机化。为了验证我们的方法，我们在 Amazon 搜索页面中的探索开发框架上执行了两个 A/B 测试: 一个在查询随机化下，另一个在用户随机化下。与理论预测一致，我们发现查询随机迭代能够测量统计学显着效应(+ 0.66% 购买，p = 0.001) ，而用户随机迭代不能(- 0.02% 购买，p = 0.851)。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Measuring+Service-Level+Learning+Effects+in+Search+Via+Query-Randomized+Experiments)|0| |[Personalized Dynamic Recommender System for Investors](https://doi.org/10.1145/3539618.3592035)|Takehiro Takayanagi, ChungChi Chen, Kiyoshi Izumi|The University of Tokyo, Tokyo, Japan; National Institute of Advanced Industrial Science and Technology, Tokyo, Japan|With the development of online platforms, people can share and obtain opinions quickly. It also makes individuals' preferences change dynamically and rapidly because they may change their minds when getting convincing opinions from other users. Unlike representative areas of recommendation research such as e-commerce platforms where items' features are fixed, in investment scenarios financial instruments' features such as stock price, also change dynamically over time. To capture these dynamic features and provide a better-personalized recommendation for amateur investors, this study proposes a Personalized Dynamic Recommender System for Investors, PDRSI. The proposed PDRSI considers two investor's personal features: dynamic preferences and historical interests, and two temporal environmental properties: recent discussions on the social media platform and the latest market information. The experimental results support the usefulness of the proposed PDRSI, and the ablation studies show the effect of each module. For reproduction, we follow Twitter's developer policy to share our dataset for future work.|随着在线平台的发展，人们可以快速地分享和获取意见。它还使个人的喜好动态而迅速地改变，因为当他们从其他用户那里得到令人信服的意见时，他们可能会改变自己的想法。不像有代表性的推荐研究领域，如电子商务平台，项目的特征是固定的，在投资情景下，金融工具的特征，如股票价格，也随着时间的推移而动态变化。为了捕捉这些动态特征，并为业余投资者提供更好的个性化推荐，本研究提出了一个投资者个性化动态推荐系统 PDRSI。提出的 PDRSI 考虑了两个投资者的个人特征: 动态偏好和历史兴趣，以及两个时间环境属性: 最近在社交媒体平台上的讨论和最新的市场信息。实验结果支持了所提出的 PDRSI 的有效性，并且消融研究显示了每个模块的效果。为了复制，我们遵循 Twitter 的开发者政策来共享我们的数据集，以备将来使用。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Personalized+Dynamic+Recommender+System+for+Investors)|0| |[Personalized Showcases: Generating Multi-Modal Explanations for Recommendations](https://doi.org/10.1145/3539618.3592036)|An Yan, Zhankui He, Jiacheng Li, Tianyang Zhang, Julian John McAuley||Existing explanation models generate only text for recommendations but still struggle to produce diverse contents. In this paper, to further enrich explanations, we propose a new task named personalized showcases, in which we provide both textual and visual information to explain our recommendations. Specifically, we first select a personalized image set that is the most relevant to a user's interest toward a recommended item. Then, natural language explanations are generated accordingly given our selected images. For this new task, we collect a large-scale dataset from Google Local (i.e.,~maps) and construct a high-quality subset for generating multi-modal explanations. We propose a personalized multi-modal framework which can generate diverse and visually-aligned explanations via contrastive learning. Experiments show that our framework benefits from different modalities as inputs, and is able to produce more diverse and expressive explanations compared to previous methods on a variety of evaluation metrics.|现有的解释模型只生成建议的文本，但仍然难以产生不同的内容。在本文中，为了进一步丰富解释，我们提出了一个新的任务称为个性化展示，其中我们提供文本和视觉信息来解释我们的建议。具体来说，我们首先选择与用户对推荐项目的兴趣最相关的个性化图像集。然后，根据所选图像生成自然语言解释。对于这个新任务，我们从 Google Local (即 ~ map)收集了一个大规模的数据集，并构建了一个高质量的子集来生成多模态解释。我们提出了一个个性化的多模态框架，它可以通过对比学习产生多样化和视觉一致的解释。实验表明，我们的框架受益于不同的模式作为输入，并能够产生更多的多样性和表达的解释比以前的方法在各种评价指标。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Personalized+Showcases:+Generating+Multi-Modal+Explanations+for+Recommendations)|0| -|[PersonalTM: Transformer Memory for Personalized Retrieval](https://doi.org/10.1145/3539618.3592037)|Ruixue Lian, Sixing Lu, Clint Solomon, Gustavo Aguilar, Pragaash Ponnusamy, Jialong Han, Chengyuan Ma, Chenlei Guo|Amazon Alexa AI, Seattle, WA, USA; University of Wisconsin-Madison, Madison, WI, USA|The Transformer Memory as a Differentiable Search Index (DSI) has been proposed as a new information retrieval paradigm, which aims to address the limitations of dual-encoder retrieval framework based on the similarity score. The DSI framework outperforms strong baselines by directly generating relevant document identifiers from queries without relying on an explicit index. The memorization power of DSI framework makes it suitable for personalized retrieval tasks. Therefore, we propose a Personal Transformer Memory (PersonalTM) architecture for personalized text retrieval. PersonalTM incorporates user-specific profiles and contextual user click behaviors, and introduces hierarchical loss in the decoding process to align with the hierarchical assignment of document identifier. Additionally, PersonalTM also employs an adapter architecture to improve the scalability for index updates and reduce computation costs, compared to the vanilla DSI. Experiments show that PersonalTM outperforms the DSI baseline, BM25, fine-tuned dual-encoder, and other personalized models in terms of precision at top 1st and 10th positions and Mean Reciprocal Rank (MRR). Specifically, PersonalTM improves p@1 by 58%, 49%, and 12% compared to BM25, Dual-encoder, and DSI, respectively.|作为一种新的信息检索检索范式，变压器内存作为一种可微检索索引(DSI)已被提出，其目的是解决基于相似性得分的双编码器检索框架的局限性。DSI 框架通过直接从查询中生成相关文档标识符而不依赖于显式索引，从而优于强大的基线。DSI 框架的记忆能力使其适用于个性化检索任务。因此，我们提出了一种个性化文本检索的个人变压器内存(PersonalTM)体系结构。PersonalTM 融合了用户特定的配置文件和上下文用户点击行为，并在解码过程中引入了层次性丢失，以便与文档标识符的层次性分配保持一致。此外，PersonalTM 还采用了适配器架构，以提高索引更新的可伸缩性并降低计算成本。实验表明，PersonalTM 在位置和平均倒数排名(MRR)的精度方面优于 DSI 基线、 BM25、微调双编码器和其他个性化模型。具体来说，PersonalTM 比 BM25、双编码器和 DSI 分别提高了58% 、49% 和12% 。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=PersonalTM:+Transformer+Memory+for+Personalized+Retrieval)|0| +|[PersonalTM: Transformer Memory for Personalized Retrieval](https://doi.org/10.1145/3539618.3592037)|Ruixue Lian, Sixing Lu, Clint Solomon, Gustavo Aguilar, Pragaash Ponnusamy, Jialong Han, Chengyuan Ma, Chenlei Guo|University of Wisconsin-Madison, Madison, WI, USA; Amazon Alexa AI, Seattle, WA, USA|The Transformer Memory as a Differentiable Search Index (DSI) has been proposed as a new information retrieval paradigm, which aims to address the limitations of dual-encoder retrieval framework based on the similarity score. The DSI framework outperforms strong baselines by directly generating relevant document identifiers from queries without relying on an explicit index. The memorization power of DSI framework makes it suitable for personalized retrieval tasks. Therefore, we propose a Personal Transformer Memory (PersonalTM) architecture for personalized text retrieval. PersonalTM incorporates user-specific profiles and contextual user click behaviors, and introduces hierarchical loss in the decoding process to align with the hierarchical assignment of document identifier. Additionally, PersonalTM also employs an adapter architecture to improve the scalability for index updates and reduce computation costs, compared to the vanilla DSI. Experiments show that PersonalTM outperforms the DSI baseline, BM25, fine-tuned dual-encoder, and other personalized models in terms of precision at top 1st and 10th positions and Mean Reciprocal Rank (MRR). Specifically, PersonalTM improves p@1 by 58%, 49%, and 12% compared to BM25, Dual-encoder, and DSI, respectively.|作为一种新的信息检索检索范式，变压器内存作为一种可微检索索引(DSI)已被提出，其目的是解决基于相似性得分的双编码器检索框架的局限性。DSI 框架通过直接从查询中生成相关文档标识符而不依赖于显式索引，从而优于强大的基线。DSI 框架的记忆能力使其适用于个性化检索任务。因此，我们提出了一种个性化文本检索的个人变压器内存(PersonalTM)体系结构。PersonalTM 融合了用户特定的配置文件和上下文用户点击行为，并在解码过程中引入了层次性丢失，以便与文档标识符的层次性分配保持一致。此外，PersonalTM 还采用了适配器架构，以提高索引更新的可伸缩性并降低计算成本。实验表明，PersonalTM 在位置和平均倒数排名(MRR)的精度方面优于 DSI 基线、 BM25、微调双编码器和其他个性化模型。具体来说，PersonalTM 比 BM25、双编码器和 DSI 分别提高了58% 、49% 和12% 。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=PersonalTM:+Transformer+Memory+for+Personalized+Retrieval)|0| |[Prediction then Correction: An Abductive Prediction Correction Method for Sequential Recommendation](https://doi.org/10.1145/3539618.3592040)|Yulong Huang, Yang Zhang, Qifan Wang, Chenxu Wang, Fuli Feng||Sequential recommender models typically generate predictions in a single step during testing, without considering additional prediction correction to enhance performance as humans would. To improve the accuracy of these models, some researchers have attempted to simulate human analogical reasoning to correct predictions for testing data by drawing analogies with the prediction errors of similar training data. However, there are inherent gaps between testing and training data, which can make this approach unreliable. To address this issue, we propose an \textit{Abductive Prediction Correction} (APC) framework for sequential recommendation. Our approach simulates abductive reasoning to correct predictions. Specifically, we design an abductive reasoning task that infers the most probable historical interactions from the future interactions predicted by a recommender, and minimizes the discrepancy between the inferred and true historical interactions to adjust the predictions.We perform the abductive inference and adjustment using a reversed sequential model in the forward and backward propagation manner of neural networks. Our APC framework is applicable to various differentiable sequential recommender models. We implement it on three backbone models and demonstrate its effectiveness. We release the code at https://github.com/zyang1580/APC.|顺序推荐模型通常在测试期间在单一步骤中生成预测，而不像人类那样考虑额外的预测修正以提高性能。为了提高这些模型的准确性，一些研究人员尝试模拟人类的类比推理，通过与类似训练数据的预测错误进行类比来修正测试数据的预测。然而，测试和训练数据之间存在固有的差距，这使得这种方法不可靠。为了解决这个问题，我们提出了一个文本{溯因预测修正}(APC)框架的顺序推荐。我们的方法模拟溯因推理来修正预测。具体来说，我们设计了一个溯因推理任务，从推荐者预测的未来相互作用中推断出最可能的历史相互作用，并最小化推断的和真实的历史相互作用之间的差异来调整预测。我们使用逆序模型在神经网络的前向和后向传播方式中执行溯因推理和调整。我们的 APC 框架适用于各种可微顺序推荐模型。我们在三个骨干模型上实现了它，并验证了它的有效性。我们在 https://github.com/zyang1580/apc 公布密码。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Prediction+then+Correction:+An+Abductive+Prediction+Correction+Method+for+Sequential+Recommendation)|0| |[Priming and Actions: An Analysis in Conversational Search Systems](https://doi.org/10.1145/3539618.3592041)|Xiao Fu, Aldo Lipani|University College London, London, United Kingdom|In order to accurately simulate users in conversational systems, it is essential to comprehend the factors that influence their behaviour. This is a critical challenge for the Information Retrieval (IR) field, as conventional methods are not well-suited for the interactive and unique sequential structure of conversational contexts. In this study, we employed the concept of Priming effects from the Psychology literature to identify core stimuli for each abstracted effect. We then examined these stimuli on various datasets to investigate their correlations with users' actions. Finally, we trained Logistic Regression (LR) models based on these stimuli to anticipate users' actions. Our findings offer a basis for creating more realistic user models and simulators, as we identified the subset of stimuli with strong relationships with users' actions. Additionally, we built a model that can predict users' actions.|为了准确地模拟会话系统中的用户，有必要了解影响用户行为的因素。这对于信息检索研究领域来说是一个严峻的挑战，因为传统的研究方法并不适用于交互式和独特的会话语境顺序结构。在本研究中，我们运用心理学文献中启动效应的概念来识别每个抽象效应的核心刺激。然后，我们在不同的数据集上检查这些刺激，以调查它们与用户行为的相关性。最后，我们训练了基于这些刺激的 Logit模型模型来预测用户的行为。我们的研究结果为创建更加真实的用户模型和模拟器提供了基础，因为我们确定了与用户行为有密切关系的刺激子集。此外，我们还建立了一个能够预测用户行为的模型。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Priming+and+Actions:+An+Analysis+in+Conversational+Search+Systems)|0| |[Quantifying Ranker Coverage of Different Query Subspaces](https://doi.org/10.1145/3539618.3592045)|Negar Arabzadeh, Amin Bigdeli, Radin Hamidi Rad, Ebrahim Bagheri|University of Waterloo, Waterloo, ON, Canada; Toronto Metropolitan University, Toronto, ON, Canada|The information retrieval community has observed significant performance improvements over various tasks due to the introduction of neural architectures. However, such improvements do not necessarily seem to have happened uniformly across a range of queries. As we will empirically show in this paper, the performance of neural rankers follow a long-tail distribution where there are many subsets of queries, which are not effectively satisfied by neural methods. Despite this observation, performance is often reported using standard retrieval metrics, such as MRR or nDCG, which capture average performance over all queries. As such, it is not clear whether reported improvements are due to incremental boost on a small subset of already well-performing queries or addressing queries that have been difficult to address by existing methods. In this paper, we propose the Task Subspace Coverage (TaSC /tAHsk/) metric, which systematically quantifies whether and to what extent improvements in retrieval effectiveness happen on similar or disparate query subspaces for different rankers. Our experiments show that the consideration of our proposed TaSC metric in conjunction with existing ranking metrics provides deeper insight into ranker performance and their contribution to overall advances on a given task.|由于引入了神经结构，信息检索社区已经观察到在各种任务中的表现有了显著的改善。然而，这种改进似乎并不一定在一系列查询中一致发生。正如我们将在本文中实证表明，神经排序器的性能遵循长尾分布，其中有许多子集的查询，这是不能有效地满足神经方法。尽管如此，性能通常使用标准检索指标(如 MRR 或 nDCG)报告，这些指标捕获所有查询的平均性能。因此，目前尚不清楚所报告的改进是否是由于对一小部分已经运行良好的查询进行了增量提升，或者是由于解决了现有方法难以解决的查询。在本文中，我们提出了任务子空间覆盖度量(TaSC/tAHsk/) ，该度量系统地量化是否和在什么程度上提高检索效率发生在相似或不同的查询子空间对不同的排名。我们的实验表明，考虑我们提出的 TaSC 度量结合现有的排名度量提供了更深入的洞察排名器的表现和他们的贡献，对整体进步的给定的任务。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Quantifying+Ranker+Coverage+of+Different+Query+Subspaces)|0| @@ -94,7 +94,7 @@ |[DECAF: A Modular and Extensible Conversational Search Framework](https://doi.org/10.1145/3539618.3591913)|Marco Alessio, Guglielmo Faggioli, Nicola Ferro|University of Padova, Padova, Italy|The Conversational Search (CS) paradigm allows for an intuitive interaction between the user and the system through natural language sentences and it is increasingly being adopted in various scenarios. However, its widespread experimentation has led to the birth of a multitude of conversational search systems with custom implementations and variants of information retrieval models. This exacerbates the reproducibility crisis already observed in several research areas, including Information Retrieval (IR). To address this issue, we propose DECAF: a modular and extensible conversational search framework designed for fast prototyping and development of conversational agents. Our framework integrates all the components that characterize a modern conversational search system and allows for the seamless integration of Machine Learning (ML) and Large Language Models (LLMs)-based techniques. Furthermore, thanks to its uniform interface, DECAF allows for experiments characterized by a high degree of reproducibility. DECAF contains several state-of-the-art components including query rewriting, search functions under BoW and dense paradigms, and re-ranking functions. Our framework is tested on two well-known conversational collections: TREC CAsT 2019 and TREC CAsT 2020 and the results can be used by future practitioners as baselines. Our contributions include the identification of a series of state-of-the-art components for the conversational search task and the definition of a modular framework for its implementation.|会话搜索(CS)范式允许用户和系统之间通过自然语言句子进行直观的交互，并且越来越多地被各种场景所采用。然而，它的广泛实验导致了大量会话搜索系统的诞生，这些系统有自定义实现和信息检索模型的变体。这加剧了包括信息检索(IR)在内的一些研究领域已经观察到的可重复性危机。为了解决这个问题，我们提出了 DECAF: 一个模块化和可扩展的会话搜索框架，用于会话代理的快速原型设计和开发。我们的框架集成了现代会话搜索系统的所有组件，并且允许基于机器学习(ML)和大语言模型(LLM)的技术的无缝集成。此外，由于其统一的界面，DECAF 允许实验拥有属性的高度重现性。DECAF 包含几个最先进的组件，包括查询重写、 BW 和稠密范例下的搜索函数以及重新排序函数。我们的框架在两个著名的会话集合上进行了测试: TREC CAsT 2019和 TREC CAsT 2020，结果可以被未来的从业者用作基线。我们的贡献包括为会话搜索任务确定了一系列最先进的组件，并为其实现定义了一个模块化框架。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=DECAF:+A+Modular+and+Extensible+Conversational+Search+Framework)|0| |[LongEval-Retrieval: French-English Dynamic Test Collection for Continuous Web Search Evaluation](https://doi.org/10.1145/3539618.3591921)|Petra Galuscáková, Romain Deveaud, Gabriela González Sáez, Philippe Mulhem, Lorraine Goeuriot, Florina Piroi, Martin Popel||LongEval-Retrieval is a Web document retrieval benchmark that focuses on continuous retrieval evaluation. This test collection is intended to be used to study the temporal persistence of Information Retrieval systems and will be used as the test collection in the Longitudinal Evaluation of Model Performance Track (LongEval) at CLEF 2023. This benchmark simulates an evolving information system environment - such as the one a Web search engine operates in - where the document collection, the query distribution, and relevance all move continuously, while following the Cranfield paradigm for offline evaluation. To do that, we introduce the concept of a dynamic test collection that is composed of successive sub-collections each representing the state of an information system at a given time step. In LongEval-Retrieval, each sub-collection contains a set of queries, documents, and soft relevance assessments built from click models. The data comes from Qwant, a privacy-preserving Web search engine that primarily focuses on the French market. LongEval-Retrieval also provides a 'mirror' collection: it is initially constructed in the French language to benefit from the majority of Qwant's traffic, before being translated to English. This paper presents the creation process of LongEval-Retrieval and provides baseline runs and analysis.|LongEval-Retrieval 是一个关注于持续检索评估的 Web 文献检索基准。这个测试集旨在用于研究信息检索系统的时间持续性，并将作为 CLEF 2023年模型性能跟踪纵向评估(LongEval)的测试集。这个基准测试模拟了一个不断发展的信息系统环境——比如 Web 搜索引擎运行的环境——其中文档收集、查询分发和相关性都在不断变化，同时遵循 Cranfield 离线评估范例。为此，我们引入了动态测试集合的概念，该集合由连续的子集合组成，每个子集合表示给定时间步骤中信息系统的状态。在 LongEval-Retrieval，每个子集包含一组查询、文档和基于点击模型的软相关性评估。这些数据来自 Qwant，一个主要关注法国市场的保护隐私的网络搜索引擎。LongEval-Retrieval 还提供了一个“镜像”集合: 它最初是用法语构建的，以便从 Qwant 的大部分流量中受益，然后才被翻译成英语。本文介绍了 LongEval-Retrieval 的创建过程，并提供了基线运行和分析。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=LongEval-Retrieval:+French-English+Dynamic+Test+Collection+for+Continuous+Web+Search+Evaluation)|0| |[OpenMatch-v2: An All-in-one Multi-Modality PLM-based Information Retrieval Toolkit](https://doi.org/10.1145/3539618.3591813)|Shi Yu, Zhenghao Liu, Chenyan Xiong, Zhiyuan Liu|Tsinghua University, Beijing, China; Northeastern University, Shenyang, China; Microsoft Research, Seattle, WA, USA|Pre-trained language models (PLMs) have emerged as the foundation of the most advanced Information Retrieval (IR) models. Powered by PLMs, the latest IR research has proposed novel models, new domain adaptation algorithms as well as enlarged datasets. In this paper, we present a Python-based IR toolkit OpenMatch-v2. As a full upgrade of OpenMatch proposed in 2021, OpenMatch-v2 incorporates the most recent advancements of PLM-based IR research, providing support for new, cross-modality models and enhanced domain adaptation techniques with a streamlined, optimized infrastructure. The code of OpenMatch is publicly available at https://github.com/OpenMatch/OpenMatch.|预先训练的语言模型(PLM)已经成为最先进的信息检索模型(IR)的基础。在 PLM 的推动下，最新的 IR 研究提出了新的模型、新的领域自适应算法以及扩大的数据集。在本文中，我们介绍了一个基于 Python 的 IR 工具包 OpenMatch-v2。作为2021年提出的 OpenMatch 的全面升级，OpenMatch-v2整合了基于 PLM 的 IR 研究的最新进展，为新的跨模式模型和增强的领域适应技术提供支持，同时提供简化、优化的基础设施。OpenMatch 的代码可在 https://github.com/OpenMatch/OpenMatch 公开查阅。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=OpenMatch-v2:+An+All-in-one+Multi-Modality+PLM-based+Information+Retrieval+Toolkit)|0| -|[Bootstrapping Query Suggestions in Spotify's Instant Search System](https://doi.org/10.1145/3539618.3591827)|Alva Liu, Humberto Jesús Corona Pampin, Enrico Palumbo|Spotify, Amsterdam, Netherlands; Spotify, Stockholm, Sweden; Spotify, Turin, Italy|Instant search systems present results to the user at every keystroke. This type of search system works best when the query ambiguity is low, the catalog is limited, and users know what they are looking for. However, Spotify's catalog is large and diverse, leading some users to struggle when formulating search intents. Query suggestions can be a powerful tool that helps users to express intents and explore content from the long-tail of the catalog. In this paper, we explain how we introduce query suggestions in Spotify's instant search system--a system that connects hundreds of millions of users with billions of items in our audio catalog. Specifically, we describe how we: (1) generate query suggestions from instant search logs, which largely contains in-complete prefix queries that cannot be directly applied as suggestions; (2) experiment with the generated suggestions in a specific UI feature, Related Searches; and (3) develop new metrics to measure whether the feature helps users to express search intent and formulate exploratory queries.|即时搜索系统在每次按键时向用户显示结果。这种类型的搜索系统在查询模糊度低、目录有限、用户知道他们在寻找什么的情况下工作得最好。然而，Spotify 的目录规模庞大且多样化，导致一些用户在制定搜索意图时举步维艰。查询建议是一个强大的工具，可以帮助用户表达意图并从目录的长尾部分查看内容。在本文中，我们将解释如何在 Spotify 的即时搜索系统中引入查询建议——该系统将数亿用户与我们的音频目录中的数十亿条目联系起来。具体来说，我们描述了我们如何: (1)从即时搜索日志生成查询建议，其中大部分包含不完整的前缀查询，不能直接作为建议应用; (2)实验生成的建议在一个特定的用户界面功能，相关搜索; (3)开发新的指标，以衡量功能是否有助于用户表达搜索意图和制定探索性查询。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Bootstrapping+Query+Suggestions+in+Spotify's+Instant+Search+System)|0| +|[Bootstrapping Query Suggestions in Spotify's Instant Search System](https://doi.org/10.1145/3539618.3591827)|Alva Liu, Humberto Jesús Corona Pampin, Enrico Palumbo|Spotify, Turin, Italy; Spotify, Stockholm, Sweden; Spotify, Amsterdam, Netherlands|Instant search systems present results to the user at every keystroke. This type of search system works best when the query ambiguity is low, the catalog is limited, and users know what they are looking for. However, Spotify's catalog is large and diverse, leading some users to struggle when formulating search intents. Query suggestions can be a powerful tool that helps users to express intents and explore content from the long-tail of the catalog. In this paper, we explain how we introduce query suggestions in Spotify's instant search system--a system that connects hundreds of millions of users with billions of items in our audio catalog. Specifically, we describe how we: (1) generate query suggestions from instant search logs, which largely contains in-complete prefix queries that cannot be directly applied as suggestions; (2) experiment with the generated suggestions in a specific UI feature, Related Searches; and (3) develop new metrics to measure whether the feature helps users to express search intent and formulate exploratory queries.|即时搜索系统在每次按键时向用户显示结果。这种类型的搜索系统在查询模糊度低、目录有限、用户知道他们在寻找什么的情况下工作得最好。然而，Spotify 的目录规模庞大且多样化，导致一些用户在制定搜索意图时举步维艰。查询建议是一个强大的工具，可以帮助用户表达意图并从目录的长尾部分查看内容。在本文中，我们将解释如何在 Spotify 的即时搜索系统中引入查询建议——该系统将数亿用户与我们的音频目录中的数十亿条目联系起来。具体来说，我们描述了我们如何: (1)从即时搜索日志生成查询建议，其中大部分包含不完整的前缀查询，不能直接作为建议应用; (2)实验生成的建议在一个特定的用户界面功能，相关搜索; (3)开发新的指标，以衡量功能是否有助于用户表达搜索意图和制定探索性查询。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Bootstrapping+Query+Suggestions+in+Spotify's+Instant+Search+System)|0| |[Long-Form Information Retrieval for Enterprise Matchmaking](https://doi.org/10.1145/3539618.3591833)|Pengyuan Li, GuangJie Ren, Anna Lisa Gentile, Chad DeLuca, Daniel Tan, Sandeep Gopisetty|IBM Research - Almaden, San Jose, CA, USA|Understanding customer requirements is a key success factor for both business-to-consumer (B2C) and business-to-business (B2B) enterprises. In a B2C context, most requirements are directly related to products and therefore expressed in keyword-based queries. In comparison, B2B requirements contain more information about customer needs and as such the queries are often in a longer form. Such long-form queries pose significant challenges to the information retrieval task in B2B context. In this work, we address the long-form information retrieval challenges by proposing a combination of (i) traditional retrieval methods, to leverage the lexical match from the query, and (ii) state-of-the-art sentence transformers, to capture the rich context in the long queries. We compare our method against traditional TF-IDF and BM25 models on an internal dataset of 12,368 pairs of long-form requirements and products sold. The evaluation shows promising results and provides directions for future work.|理解客户需求是 B2C 和 B2B 企业成功的关键因素。在 B2C 上下文中，大多数需求与产品直接相关，因此以基于关键字的查询表示。相比之下，B2B 需求包含更多关于客户需求的信息，因此查询通常是较长的形式。这种长形式的查询对 B2B 环境下的信息检索任务构成了重大挑战。在这项工作中，我们通过提出一个组合来解决长信息检索的挑战: (i)传统检索方法，利用查询中的词汇匹配; (ii)最先进的句子转换器，在长查询中捕捉丰富的上下文。我们比较了我们的方法与传统的 TF-IDF 和 BM25模型的内部数据集的12,368对长形式的要求和产品销售。评价结果显示了有希望的结果，并为今后的工作提供了方向。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Long-Form+Information+Retrieval+for+Enterprise+Matchmaking)|0| |[Facebook Content Search: Efficient and Effective Adapting Search on A Large Scale](https://doi.org/10.1145/3539618.3591840)|Xiangyu Niu, YuWei Wu, Xiao Lu, Gautam Nagpal, Philip Pronin, Kecheng Hao, Zhen Liao, Guangdeng Liao|Meta Platforms, Inc, Menlo Park, CA, USA|Facebook content search is a critical channel that enables people to discover the best content to deepen their engagement with friends and family, creators, and communities. Building a highly personalized search engine to serve billions of daily active users to find the best results from a large scale of candidates is a challenging task. The search engine must take multiple dimensions into consideration, including different content types, different query intents, and user social graph, etc. In this paper, we discuss the challenges of Facebook content search in depth, and then describe our novel approach to efficiently handling a massive number of documents with advanced query understanding, retrieval, and machine learning techniques. The proposed system has been fully verified and applied to the production system of Facebook Search, which serves billions of users.|Facebook 内容搜索是一个重要的渠道，它使人们能够发现最好的内容，从而加深他们与朋友、家人、创作者和社区的联系。构建一个高个性化检索的引擎，为数十亿日常活跃用户提供服务，从大量候选人中找到最佳结果，是一项具有挑战性的任务。搜索引擎必须考虑多个维度，包括不同的内容类型，不同的查询意图，用户社会图等。在本文中，我们深入讨论了 Facebook 内容搜索的挑战，然后描述了我们的新方法，有效地处理大量具有先进的查询理解，检索和机器学习技术的文档。该系统已经得到充分验证，并应用于 Facebook 搜索的生产系统，该系统服务于数十亿用户。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Facebook+Content+Search:+Efficient+and+Effective+Adapting+Search+on+A+Large+Scale)|0| |[Personalized Stock Recommendation with Investors' Attention and Contextual Information](https://doi.org/10.1145/3539618.3591850)|Takehiro Takayanagi, Kiyoshi Izumi, Atsuo Kato, Naoyuki Tsunedomi, Yukina Abe|The University of Tokyo, Tokyo, Japan; CONNECT Co.Ltd., Tokyo, Japan; Daiwa Securities Group Inc., Tokyo, Japan; Daiwa Institute of Research Ltd., Tokyo, Japan|The personalized stock recommendation is a task to recommend suitable stocks for each investor. The personalized recommendations are valuable, especially in investment decision making as the objective of building a portfolio varies by each retail investor. In this paper, we propose a Personalized Stock Recommendation with Investors' Attention and Contextual Information (PSRIC). PSRIC aims to incorporate investors' financial decision-making process into a stock recommendation, and it consists of an investor modeling module and a context module. The investor modeling module models the investor's attention toward various stock information. The context module incorporates stock dynamics and investor profiles. The result shows that the proposed model outperforms the baseline models and verifies the usefulness of both modules in ablation studies.|个性化股票推荐是为每个投资者推荐合适的股票的任务。个性化的建议是有价值的，特别是在投资决策中，因为建立一个投资组合的目标因每个散户投资者而异。本文提出了一种基于投资者注意力和上下文信息的个性化股票推荐方法。PSRIC 旨在将投资者的财务决策过程融入到股票推荐中，它由投资者建模模块和上下文模块组成。投资者建模模块模拟投资者对各种股票信息的关注。上下文模块包括股票动态和投资者概况。结果表明，该模型的性能优于基线模型，验证了两个模块在烧蚀研究中的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Personalized+Stock+Recommendation+with+Investors'+Attention+and+Contextual+Information)|0| @@ -115,9 +115,9 @@ |[A Preference Learning Decoupling Framework for User Cold-Start Recommendation](https://doi.org/10.1145/3539618.3591627)|Chunyang Wang, Yanmin Zhu, Aixin Sun, Zhaobo Wang, Ke Wang|Shanghai Jiao Tong University, Shanghai, China; Nanyang Technological University, Singapore, Singapore|The issue of user cold-start poses a long-standing challenge to recommendation systems, due to the scarce interactions of new users. Recently, meta-learning based studies treat each cold-start user as a user-specific few-shot task and then derive meta-knowledge about fast model adaptation across training users. However, existing solutions mostly do not clearly distinguish the concept of new users and the concept of novel preferences, leading to over-reliance on meta-learning based adaptability to novel patterns. In addition, we also argue that the existing meta-training task construction inherently suffers from the memorization overfitting issue, which inevitably hinders meta-generalization to new users. In response to the aforementioned issues, we propose a preference learning decoupling framework, which is enhanced with meta-augmentation (PDMA), for user cold-start recommendation. To rescue the meta-learning from unnecessary adaptation to common patterns, our framework decouples preference learning for a cold-start user into two complementary aspects: common preference transfer, and novel preference adaptation. To handle the memorization overfitting issue, we further propose to augment meta-training users by injecting attribute-based noises, to achieve mutually-exclusive tasks. Extensive experiments on benchmark datasets demonstrate that our framework achieves superior performance improvements against state-of-the-art methods. We also show that our proposed framework is effective in alleviating memorization overfitting.|由于新用户之间的交互很少，用户冷启动问题对推荐系统提出了长期的挑战。最近，基于元学习的研究将每个冷启动用户视为一个特定于用户的短暂任务，然后得到关于跨培训用户快速模型适应的元知识。然而，现有的解决方案大多没有明确区分新用户的概念和新偏好的概念，导致过度依赖基于元学习的新模式适应性。此外，我们还认为，现有的元训练任务结构本质上存在记忆过拟合问题，这不可避免地阻碍了对新用户的元概括。针对上述问题，本文提出了一种基于元增强(PDMA)的偏好学习解耦框架，用于用户冷启动推荐。为了将元学习从不必要的对共同模式的适应中解救出来，我们的框架将冷启动用户的偏好学习分解为两个互补的方面: 共同偏好转移和新的偏好适应。为了解决记忆过拟合问题，我们进一步提出通过注入基于属性的噪声来增加元训练用户，以实现相互排斥的任务。在基准数据集上的大量实验表明，与最先进的方法相比，我们的框架实现了更好的性能改进。我们还表明，我们提出的框架在缓解记忆过度拟合方面是有效的。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Preference+Learning+Decoupling+Framework+for+User+Cold-Start+Recommendation)|0| |[Online Conversion Rate Prediction via Neural Satellite Networks in Delayed Feedback Advertising](https://doi.org/10.1145/3539618.3591747)|Qiming Liu, Haoming Li, Xiang Ao, Yuyao Guo, Zhihong Dong, Ruobing Zhang, Qiong Chen, Jianfeng Tong, Qing He|Tencent, Beijing, China; Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China|The delayed feedback is becoming one of the main obstacles in online advertising due to the pervasive deployment of the cost-per-conversion display strategy requesting a real-time conversion rate (CVR) prediction. It makes the observed data contain a large number of fake negatives that temporarily have no feedback but will convert later. Training on such biased data distribution would severely harm the performance of models. Prevailing approaches wait for a set period of time to see if samples convert before training on them, but solutions to guaranteeing data freshness remain under-explored by current research. In this work, we propose Delayed Feed-back modeling via neural Satellite Networks (DFSN for short) for online CVR prediction. It tackles the issue of data freshness to permit adaptive waiting windows. We first assign a long waiting window for our main model to cover most of conversions and greatly reduce fake negatives. Meanwhile, two kinds of satellite models are devised to learn from the latest data, and online transfer learning techniques are utilized to sufficiently exploit their knowledge. With information from satellites, our main model can deal with the issue of data freshness, achieving better performance than previous methods. Extensive experiments on two real-world advertising datasets demonstrate the superiority of our model.|延迟反馈正在成为网络广告的主要障碍之一，这是由于广泛采用了要求实时转换率(CVR)预测的按转换成本计费的显示策略。它使观测数据包含大量的假阴性，暂时没有反馈，但将在以后转换。关于这种有偏见的数据分发的培训将严重损害模型的性能。目前流行的方法要等一段时间才能看到样本在训练之前是否转换，但是目前的研究仍然没有探索出保证数据新鲜的解决方案。本文提出了基于神经网络的延迟反馈模型(简称 DFSN)用于 CVR 在线预测。它解决了数据新鲜性的问题，允许自适应的等待窗口。我们首先为我们的主要模型指定一个长的等待窗口，以覆盖大部分转换，并大大减少假否定。同时，设计了两种卫星模型来学习最新的数据，并利用在线迁移学习技术来充分利用他们的知识。利用卫星信息，我们的主模型可以处理数据的新鲜性问题，比以前的方法获得更好的性能。在两个实际广告数据集上的大量实验证明了该模型的优越性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Online+Conversion+Rate+Prediction+via+Neural+Satellite+Networks+in+Delayed+Feedback+Advertising)|0| |[M2GNN: Metapath and Multi-interest Aggregated Graph Neural Network for Tag-based Cross-domain Recommendation](https://doi.org/10.1145/3539618.3591720)|Zepeng Huai, Yuji Yang, Mengdi Zhang, Zhongyi Zhang, Yichun Li, Wei Wu||Cross-domain recommendation (CDR) is an effective way to alleviate the data sparsity problem. Content-based CDR is one of the most promising branches since most kinds of products can be described by a piece of text, especially when cold-start users or items have few interactions. However, two vital issues are still under-explored: (1) From the content modeling perspective, sufficient long-text descriptions are usually scarce in a real recommender system, more often the light-weight textual features, such as a few keywords or tags, are more accessible, which is improperly modeled by existing methods. (2) From the CDR perspective, not all inter-domain interests are helpful to infer intra-domain interests. Caused by domain-specific features, there are part of signals benefiting for recommendation in the source domain but harmful for that in the target domain. Therefore, how to distill useful interests is crucial. To tackle the above two problems, we propose a metapath and multi-interest aggregated graph neural network (M2GNN). Specifically, to model the tag-based contents, we construct a heterogeneous information network to hold the semantic relatedness between users, items, and tags in all domains. The metapath schema is predefined according to domain-specific knowledge, with one metapath for one domain. User representations are learned by GNN with a hierarchical aggregation framework, where the intra-metapath aggregation firstly filters out trivial tags and the inter-metapath aggregation further filters out useless interests. Offline experiments and online A/B tests demonstrate that M2GNN achieves significant improvements over the state-of-the-art methods and current industrial recommender system in Dianping, respectively. Further analysis shows that M2GNN offers an interpretable recommendation.|跨域推荐(CDR)是解决数据稀疏问题的有效方法。基于内容的 CDR 是最有前途的分支之一，因为大多数类型的产品都可以用文本描述，特别是当冷启动用户或项目几乎没有交互时。然而，有两个重要的问题仍然没有得到充分的探索: (1)从内容建模的角度来看，在真实的推荐系统中，充足的长文本描述通常是稀缺的，更常见的是轻量级的文本特征，如一些关键字或标签，是更容易获得的，这是不适当的建模现有方法。(2)从 CDR 的角度来看，并非所有域间利益都有助于推断域内利益。由于特定于领域的特性，有一部分信号在源域中有利于推荐，但在目标域中有害于推荐。因此，如何提取有用的利益至关重要。为了解决上述两个问题，我们提出了一种元路径和多兴趣聚合图神经网络(M2GNN)。具体来说，为了对基于标签的内容进行建模，我们构建了一个异构的信息网络来保持所有领域中用户、项目和标签之间的语义关系。元路径模式是根据特定于领域的知识预定义的，对于一个领域只有一个元路径。GNN 利用层次聚合框架学习用户表示，其中元路径内聚合首先过滤掉平凡的标签，元路径间聚合进一步过滤掉无用的兴趣。离线实验和在线 A/B 测试表明，M2GNN 在最先进的方法和 Dianping 目前的工业推荐系统上分别取得了显著的改进。进一步的分析表明，M2GNN 提供了一个可解释的建议。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=M2GNN:+Metapath+and+Multi-interest+Aggregated+Graph+Neural+Network+for+Tag-based+Cross-domain+Recommendation)|0| -|[Beyond the Overlapping Users: Cross-Domain Recommendation via Adaptive Anchor Link Learning](https://doi.org/10.1145/3539618.3591642)|Yi Zhao, Chaozhuo Li, Jiquan Peng, Xiaohan Fang, Feiran Huang, Senzhang Wang, Xing Xie, Jibing Gong|Yanshan University, Qinhuangdao, China; Jinan University, Guangzhou, China; Central South University, Changsha, China; Yanshan University & The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province, Qinhuangdao, China; Microsoft Research Asia, Beijing, China|Cross-Domain Recommendation (CDR) is capable of incorporating auxiliary information from multiple domains to advance recommendation performance. Conventional CDR methods primarily rely on overlapping users, whereby knowledge is conveyed between the source and target identities belonging to the same natural person. However, such a heuristic assumption is not universally applicable due to an individual may exhibit distinct or even conflicting preferences in different domains, leading to potential noises. In this paper, we view the anchor links between users of various domains as the learnable parameters to learn the task-relevant cross-domain correlations. A novel optimal transport based model ALCDR is further proposed to precisely infer the anchor links and deeply aggregate collaborative signals from the perspectives of intra-domain and inter-domain. Our proposal is extensively evaluated over real-world datasets, and experimental results demonstrate its superiority.|跨域推荐(CDR)能够结合来自多个域的辅助信息来提高推荐性能。传统的 CDR 方法主要依赖于重叠的用户，即在属于同一自然人的来源和目标身份之间传递知识。然而，这种启发式假设并不普遍适用，因为个体在不同的领域可能表现出不同的甚至相互冲突的偏好，导致潜在的噪音。本文将不同领域的用户之间的锚链视为学习任务相关的跨领域关联的可学习参数。进一步提出了一种新的基于最优传输的 ALCDR 模型，从域内和域间两个角度精确推断锚链和深度聚合协作信号。我们的建议是广泛的评估在现实世界的数据集，实验结果表明其优越性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Beyond+the+Overlapping+Users:+Cross-Domain+Recommendation+via+Adaptive+Anchor+Link+Learning)|0| +|[Beyond the Overlapping Users: Cross-Domain Recommendation via Adaptive Anchor Link Learning](https://doi.org/10.1145/3539618.3591642)|Yi Zhao, Chaozhuo Li, Jiquan Peng, Xiaohan Fang, Feiran Huang, Senzhang Wang, Xing Xie, Jibing Gong|Central South University, Changsha, China; Jinan University, Guangzhou, China; Yanshan University & The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province, Qinhuangdao, China; Yanshan University, Qinhuangdao, China; Microsoft Research Asia, Beijing, China|Cross-Domain Recommendation (CDR) is capable of incorporating auxiliary information from multiple domains to advance recommendation performance. Conventional CDR methods primarily rely on overlapping users, whereby knowledge is conveyed between the source and target identities belonging to the same natural person. However, such a heuristic assumption is not universally applicable due to an individual may exhibit distinct or even conflicting preferences in different domains, leading to potential noises. In this paper, we view the anchor links between users of various domains as the learnable parameters to learn the task-relevant cross-domain correlations. A novel optimal transport based model ALCDR is further proposed to precisely infer the anchor links and deeply aggregate collaborative signals from the perspectives of intra-domain and inter-domain. Our proposal is extensively evaluated over real-world datasets, and experimental results demonstrate its superiority.|跨域推荐(CDR)能够结合来自多个域的辅助信息来提高推荐性能。传统的 CDR 方法主要依赖于重叠的用户，即在属于同一自然人的来源和目标身份之间传递知识。然而，这种启发式假设并不普遍适用，因为个体在不同的领域可能表现出不同的甚至相互冲突的偏好，导致潜在的噪音。本文将不同领域的用户之间的锚链视为学习任务相关的跨领域关联的可学习参数。进一步提出了一种新的基于最优传输的 ALCDR 模型，从域内和域间两个角度精确推断锚链和深度聚合协作信号。我们的建议是广泛的评估在现实世界的数据集，实验结果表明其优越性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Beyond+the+Overlapping+Users:+Cross-Domain+Recommendation+via+Adaptive+Anchor+Link+Learning)|0| |[Manipulating Federated Recommender Systems: Poisoning with Synthetic Users and Its Countermeasures](https://doi.org/10.1145/3539618.3591722)|Wei Yuan, Quoc Viet Hung Nguyen, Tieke He, Liang Chen, Hongzhi Yin||Federated Recommender Systems (FedRecs) are considered privacy-preserving techniques to collaboratively learn a recommendation model without sharing user data. Since all participants can directly influence the systems by uploading gradients, FedRecs are vulnerable to poisoning attacks of malicious clients. However, most existing poisoning attacks on FedRecs are either based on some prior knowledge or with less effectiveness. To reveal the real vulnerability of FedRecs, in this paper, we present a new poisoning attack method to manipulate target items' ranks and exposure rates effectively in the top-$K$ recommendation without relying on any prior knowledge. Specifically, our attack manipulates target items' exposure rate by a group of synthetic malicious users who upload poisoned gradients considering target items' alternative products. We conduct extensive experiments with two widely used FedRecs (Fed-NCF and Fed-LightGCN) on two real-world recommendation datasets. The experimental results show that our attack can significantly improve the exposure rate of unpopular target items with extremely fewer malicious users and fewer global epochs than state-of-the-art attacks. In addition to disclosing the security hole, we design a novel countermeasure for poisoning attacks on FedRecs. Specifically, we propose a hierarchical gradient clipping with sparsified updating to defend against existing poisoning attacks. The empirical results demonstrate that the proposed defending mechanism improves the robustness of FedRecs.|联邦推荐系统(FedRecs)被认为是一种保护隐私的技术，可以在不共享用户数据的情况下协同学习推荐模型。由于所有参与者都可以通过上传梯度直接影响系统，因此 FedRecs 很容易受到恶意客户端的中毒攻击。然而，大多数现有的对联邦卫生委员会的中毒攻击要么是基于一些先前的知识，要么效果较差。为了揭示 FedRecs 的真实脆弱性，本文提出了一种新的中毒攻击方法，在不依赖任何先验知识的情况下，有效地操纵目标项目的等级和暴露率。具体来说，我们的攻击通过一组合成的恶意用户操纵目标项目的暴露率，这些用户上传有毒梯度，并考虑目标项目的替代产品。我们使用两个广泛使用的 FedRecs (Fed-NCF 和 Fed-LightGCN)在两个真实世界的推荐数据集上进行了广泛的实验。实验结果表明，我们的攻击可以显著提高暴露率的不受欢迎的目标项目与极少的恶意用户和更少的全球纪元比最先进的攻击。除了揭示安全漏洞，我们还设计了一种新的对策来防止对 FedRecs 的中毒攻击。具体来说，我们提出了一个分层梯度剪裁与稀疏更新，以防御现有的中毒攻击。实验结果表明，该防御机制提高了 FedRecs 的鲁棒性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Manipulating+Federated+Recommender+Systems:+Poisoning+with+Synthetic+Users+and+Its+Countermeasures)|0| -|[Behavior Modeling for Point of Interest Search](https://doi.org/10.1145/3539618.3591955)|Haitian Chen, Qingyao Ai, Zhijing Wu, Zhihong Wang, Yiqun Liu, Min Zhang, Shaoping Ma, Juan Hu, Naiqiang Tan, Hua Chai|Tsinghua University, Beijing, China; Didi Chuxing, Beijing, China; Beijing Institute of Technology, Beijing, China|With the increasing popularity of location-based services, the point-of-interest (POI) search has received considerable attention in recent years. Existing studies on POI search mostly focus on how to construct better retrieval models to retrieve the relevant POI based on query-POI matching. However, user behavior in POI search, i.e., how users examine the search engine result page (SERP), is mostly underexplored. A good understanding of user behavior is well-recognized as a key to develop effective user models and retrieval models to improve the search quality. Therefore, in this paper, we propose to investigate user behavior in POI search with a lab study in which users' eye movements and their implicit feedback on the SERP are collected. Based on the collected data, we analyze (1) query-level user behavior patterns in POI search, i.e., examination and interactions on SERP; (2) session-level user behavior patterns in POI search, i.e., query reformulation, termination of search, etc. Our work sheds light on user behavior in POI search and could potentially benefit future studies on related research topics.|随着基于位置服务的日益普及，感兴趣点(POI)搜索近年来受到了广泛的关注。现有的 POI 检索研究主要集中在如何构造更好的检索模型来检索基于查询-POI 匹配的相关 POI。然而，POI 搜索中的用户行为，即用户如何检查搜索引擎结果页面(SERP) ，大多数都没有得到充分的研究。对用户行为的深入了解是建立有效的用户模型和检索模型以提高检索质量的关键。因此，在本文中，我们提出了一个实验室研究 POI 搜索中的用户行为，其中用户的眼球运动和他们对 SERP 的内隐反馈收集。基于收集到的数据，本文分析了(1) POI 搜索中的查询级用户行为模式，即在 SERP 上的检查和交互; (2) POI 搜索中的会话级用户行为模式，即查询重构、搜索终止等。我们的工作揭示了 POI 搜索中的用户行为，并可能有利于未来相关研究主题的研究。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Behavior+Modeling+for+Point+of+Interest+Search)|0| +|[Behavior Modeling for Point of Interest Search](https://doi.org/10.1145/3539618.3591955)|Haitian Chen, Qingyao Ai, Zhijing Wu, Zhihong Wang, Yiqun Liu, Min Zhang, Shaoping Ma, Juan Hu, Naiqiang Tan, Hua Chai|Beijing Institute of Technology, Beijing, China; Tsinghua University, Beijing, China; Didi Chuxing, Beijing, China|With the increasing popularity of location-based services, the point-of-interest (POI) search has received considerable attention in recent years. Existing studies on POI search mostly focus on how to construct better retrieval models to retrieve the relevant POI based on query-POI matching. However, user behavior in POI search, i.e., how users examine the search engine result page (SERP), is mostly underexplored. A good understanding of user behavior is well-recognized as a key to develop effective user models and retrieval models to improve the search quality. Therefore, in this paper, we propose to investigate user behavior in POI search with a lab study in which users' eye movements and their implicit feedback on the SERP are collected. Based on the collected data, we analyze (1) query-level user behavior patterns in POI search, i.e., examination and interactions on SERP; (2) session-level user behavior patterns in POI search, i.e., query reformulation, termination of search, etc. Our work sheds light on user behavior in POI search and could potentially benefit future studies on related research topics.|随着基于位置服务的日益普及，感兴趣点(POI)搜索近年来受到了广泛的关注。现有的 POI 检索研究主要集中在如何构造更好的检索模型来检索基于查询-POI 匹配的相关 POI。然而，POI 搜索中的用户行为，即用户如何检查搜索引擎结果页面(SERP) ，大多数都没有得到充分的研究。对用户行为的深入了解是建立有效的用户模型和检索模型以提高检索质量的关键。因此，在本文中，我们提出了一个实验室研究 POI 搜索中的用户行为，其中用户的眼球运动和他们对 SERP 的内隐反馈收集。基于收集到的数据，本文分析了(1) POI 搜索中的查询级用户行为模式，即在 SERP 上的检查和交互; (2) POI 搜索中的会话级用户行为模式，即查询重构、搜索终止等。我们的工作揭示了 POI 搜索中的用户行为，并可能有利于未来相关研究主题的研究。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Behavior+Modeling+for+Point+of+Interest+Search)|0| |[Disentangling User Conversations with Voice Assistants for Online Shopping](https://doi.org/10.1145/3539618.3591974)|Nikhita Vedula, Marcus D. Collins, Oleg Rokhlenko|Amazon, Seattle, USA|Conversation disentanglement aims to identify and group utterances from a conversation into separate threads. Existing methods primarily focus on disentangling multi-party conversations with three or more speakers, explicitly or implicitly incorporating speaker-related feature signals to disentangle. Most existing models require a large amount of human annotated data for model training, and often focus on pairwise relations between utterances, not accounting much for the conversational context. In this work, we propose a multi-task learning approach with a contrastive learning objective, DiSC, to disentangle conversations between two speakers -- a user and a virtual speech assistant, for a novel domain of e-commerce. We analyze multiple ways and granularities to define conversation "threads''. DiSC jointly learns the relation between pairs of utterances, as well as between utterances and their respective thread context. We train and evaluate our models on multiple multi-threaded conversation datasets that were automatically created, without any human labeling effort. Experimental results on public datasets as well as real-world shopping conversations from a commercial speech assistant show that DiSC outperforms state-of-the-art baselines by at least 3%, across both automatic and human evaluation metrics. We also demonstrate how DiSC improves downstream dialog response generation in the shopping domain.|会话分离旨在将会话中的话语识别并分组为不同的线程。现有的方法主要集中在与三个或三个以上说话人的多方会话的分离，明确或隐含地结合说话人相关的特征信号来分离。大多数现有的模型需要大量的人工注释数据来进行模型训练，并且往往侧重于话语之间的成对关系，而不考虑会话语境。在这项工作中，我们提出了一个多任务的学习方法与对比学习的目标，DiSC，以分离两个说话人之间的会话-一个用户和虚拟语音助手，为一个新的领域的电子商务。我们分析了定义会话“线程”的多种方式和粒度。DiSC 共同学习话语对之间的关系，以及话语和它们各自的线索上下文之间的关系。我们在多个自动创建的多线程会话数据集上训练和评估我们的模型，而不需要任何人工标记。对公共数据集以及来自商业语音助手的真实世界购物会话的实验结果表明，DiSC 在自动和人工评估指标方面的表现至少比最先进的基线水平高出3% 。我们还演示了 DiSC 如何改进购物领域中的下游对话响应生成。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Disentangling+User+Conversations+with+Voice+Assistants+for+Online+Shopping)|0| |[MaxSimE: Explaining Transformer-based Semantic Similarity via Contextualized Best Matching Token Pairs](https://doi.org/10.1145/3539618.3592017)|Eduardo Brito, Henri Iser|Fraunhofer Institute for Intelligent Analysis and Information Systems IAIS & Lamarr Institute for Machine Learning and Artificial Intelligence, Sankt Augustin, Germany|Current semantic search approaches rely on black-box language models, such as BERT, which limit their interpretability and transparency. In this work, we propose MaxSimE, an explanation method for language models applied to measure semantic similarity. Our approach is inspired by the explainable-by-design ColBERT architecture and generates explanations by matching contextualized query tokens to the most similar tokens from the retrieved document according to the cosine similarity of their embeddings. Unlike existing post-hoc explanation methods, which may lack fidelity to the model and thus fail to provide trustworthy explanations in critical settings, we demonstrate that MaxSimE can generate faithful explanations under certain conditions and how it improves the interpretability of semantic search results on ranked documents from the LoTTe benchmark, showing its potential for trustworthy information retrieval.|当前的语义搜索方法依赖于黑盒语言模型，比如 BERT，这限制了它们的可解释性和透明性。本文提出了一种用于语义相似度测量的语言模型解释方法 MaxSimE。我们的方法受到设计中可解释的 ColBERT 架构的启发，通过根据嵌入的余弦距离将上下文相关的查询标记与检索到的文档中最相似的标记匹配来生成解释。与现有的事后解释方法不同，这些方法可能对模型缺乏保真度，因此无法在关键环境下提供可信的解释，我们证明 MaxSimE 可以在某些条件下产生可信的解释，以及它如何提高对来自 LotTe 基准的排名文档的语义搜索结果的可解释性，显示其潜在的可信信息检索。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=MaxSimE:+Explaining+Transformer-based+Semantic+Similarity+via+Contextualized+Best+Matching+Token+Pairs)|0| |[Searching for Products in Virtual Reality: Understanding the Impact of Context and Result Presentation on User Experience](https://doi.org/10.1145/3539618.3592057)|Austin R. Ward, Sandeep Avula, Hao Fei Cheng, Sheikh Muhammad Sarwar, Vanessa Murdock, Eugene Agichtein|UNC Chapel Hill, Chapel Hill, NC, USA; Amazon, Seattle, WA, USA|Immersive technologies such as virtual reality (VR) and head-mounted displays (HMD) have seen increased adoption in recent years. In this work, we study two factors that influence users' experience when shopping in VR through voice queries: (1) context alignment of the search environment and (2) the level of detail on the Search Engine Results Page (SERP). To this end, we developed a search system for VR and conducted a within-subject exploratory study (N=18) to understand the impact of the two experimental conditions. Our results suggest that both context alignment and SERP are important factors for information-seeking in VR, which present unique opportunities and challenges. More specifically, based on our findings, we suggest that search systems for VR must be able to: (1) provide cues for information-seeking in both the VR environment and SERP, (2) distribute attention between the VR environment and the search interface, (3) reduce distractions in the VR environment and (4) provide a ''sense of control'' to search in the VR environment.|近年来，虚拟现实(VR)和头戴式显示器(HMD)等沉浸式技术得到了越来越多的采用。在这项工作中，我们研究了两个因素，影响用户的体验，当在虚拟现实购物时，通过语音查询: (1)上下文对齐的搜索环境和(2)搜索引擎结果页面(SERP)的详细程度。为此，我们开发了一个虚拟现实搜索系统，并进行了一个主题内探索性研究(N = 18) ，以了解这两个实验条件的影响。我们的研究结果表明，上下文对齐和 SERP 都是虚拟现实中信息搜索的重要因素，它们提供了独特的机遇和挑战。更具体地说，根据我们的研究结果，我们建议 VR 搜索系统必须能够: (1)为 VR 环境和 SERP 中的信息搜索提供线索; (2)在 VR 环境和搜索界面之间分配注意力; (3)减少 VR 环境中的干扰; (4)提供在 VR 环境中搜索的“控制感”。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Searching+for+Products+in+Virtual+Reality:+Understanding+the+Impact+of+Context+and+Result+Presentation+on+User+Experience)|0| @@ -134,56 +134,56 @@ |[Multi-behavior Self-supervised Learning for Recommendation](https://doi.org/10.1145/3539618.3591734)|Jingcao Xu, Chaokun Wang, Cheng Wu, Yang Song, Kai Zheng, Xiaowei Wang, Changping Wang, Guorui Zhou, Kun Gai||Modern recommender systems often deal with a variety of user interactions, e.g., click, forward, purchase, etc., which requires the underlying recommender engines to fully understand and leverage multi-behavior data from users. Despite recent efforts towards making use of heterogeneous data, multi-behavior recommendation still faces great challenges. Firstly, sparse target signals and noisy auxiliary interactions remain an issue. Secondly, existing methods utilizing self-supervised learning (SSL) to tackle the data sparsity neglect the serious optimization imbalance between the SSL task and the target task. Hence, we propose a Multi-Behavior Self-Supervised Learning (MBSSL) framework together with an adaptive optimization method. Specifically, we devise a behavior-aware graph neural network incorporating the self-attention mechanism to capture behavior multiplicity and dependencies. To increase the robustness to data sparsity under the target behavior and noisy interactions from auxiliary behaviors, we propose a novel self-supervised learning paradigm to conduct node self-discrimination at both inter-behavior and intra-behavior levels. In addition, we develop a customized optimization strategy through hybrid manipulation on gradients to adaptively balance the self-supervised learning task and the main supervised recommendation task. Extensive experiments on five real-world datasets demonstrate the consistent improvements obtained by MBSSL over ten state-of-the art (SOTA) baselines. We release our model implementation at: https://github.com/Scofield666/MBSSL.git.|现代推荐系统经常处理各种用户交互，例如点击、转发、购买等，这需要底层推荐引擎完全理解和利用来自用户的多行为数据。尽管最近努力利用异构数据，多行为推荐仍然面临巨大的挑战。首先，稀疏目标信号和噪声辅助交互作用仍然是一个问题。其次，现有的利用自监督学习(SSL)解决数据稀疏问题的方法忽略了 SSL 任务与目标任务之间存在严重的优化不平衡。因此，我们提出了一个多行为自我监督学习(MBSSL)框架和自适应优化方法。具体来说，我们设计了一个行为感知图形神经网络结合自我注意机制，以捕捉行为的多样性和依赖性。为了提高目标行为和辅助行为引起的噪声干扰下对数据稀疏性的鲁棒性，提出了一种新的自监督学习范式，在行为间和行为内两个层次上进行节点自辨识。此外，通过对梯度的混合操作，我们开发了一个定制的优化策略，以自适应地平衡自监督学习任务和主要的监督推荐任务。在五个真实世界数据集上的大量实验证明了 MBSSL 在超过十个最先进的(SOTA)基线上获得的一致性改进。我们在以下 https://github.com/scofield666/mbssl.git 发布我们的模型实现。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Multi-behavior+Self-supervised+Learning+for+Recommendation)|0| |[LOAM: Improving Long-tail Session-based Recommendation via Niche Walk Augmentation and Tail Session Mixup](https://doi.org/10.1145/3539618.3591718)|Heeyoon Yang, YunSeok Choi, Gahyung Kim, JeeHyong Lee|Sungkyunkwan University, Suwon, Republic of Korea|Session-based recommendation aims to predict the user's next action based on anonymous sessions without using side information. Most of the real-world session datasets are sparse and have long-tail item distribution. Although long-tail item recommendation plays a crucial role in improving user satisfaction, only a few methods have been proposed to take the long-tail session recommendation into consideration. Previous works in handling data sparsity problems are mostly limited to self-supervised learning techniques with heuristic augmentation which can ruin the original characteristic of session datasets, sequential and co-occurrences, and make noisier short sessions by dropping items and cropping sequences. We propose a novel method, LOAM, improving LOng-tail session-based recommendation via niche walk Augmentation and tail session Mixup, that alleviates popularity bias and enhances long-tail recommendation performance. LOAM consists of two modules, Niche Walk Augmentation (NWA) and Tail Session Mixup (TSM). NWA can generate synthetic sessions considering long-tail distribution which are likely to be found in original datasets, unlike previous heuristic methods, and expose a recommender model to various item transitions with global information. This improves the item coverage of recommendations. TSM makes the model more generalized and robust by interpolating sessions at the representation level. It encourages the recommender system to predict niche items with more diversity and relevance. We conduct extensive experiments with four real-world datasets and verify that our methods greatly improve tail performance while balancing overall performance.|基于会话的推荐系统旨在基于匿名会话预测用户的下一步操作，而不使用侧信息。大多数真实世界的会话数据集是稀疏的，并且具有长尾项分布。尽管长尾条目推荐在提高用户满意度方面发挥着关键作用，但只有少数几种方法被提出来考虑长尾会话推荐。以往处理数据稀疏问题的工作大多局限于采用启发式增强的自监督学习技术，这种方法会破坏会话数据集的原有特性，使得会话数据具有连续性和共现性，并且通过丢弃项目和剪切序列使得会话变得更加嘈杂。我们提出了一种新的方法 LOAM，通过小生境步行增强和尾部会话混合来改进基于长尾会话的推荐，减少了流行偏差，提高了长尾会话的推荐性能。LOAM 包括两个模块，小生境步行增强(NWA)和尾会话混合(TSM)。与以往的启发式方法不同，NWA 可以考虑原始数据集中可能出现的长尾分布，生成综合会话，并将推荐模型暴露给具有全局信息的各种项目转换。这提高了建议的项目覆盖率。TSM 通过在表示层次上插入会话，使模型更加通用和健壮。它鼓励推荐系统预测更具多样性和相关性的利基项目。我们对四个实际数据集进行了广泛的实验，验证了我们的方法在平衡整体性能的同时大大提高了尾部性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=LOAM:+Improving+Long-tail+Session-based+Recommendation+via+Niche+Walk+Augmentation+and+Tail+Session+Mixup)|0| |[An Offline Metric for the Debiasedness of Click Models](https://doi.org/10.1145/3539618.3591639)|Romain Deffayet, Philipp Hager, JeanMichel Renders, Maarten de Rijke||A well-known problem when learning from user clicks are inherent biases prevalent in the data, such as position or trust bias. Click models are a common method for extracting information from user clicks, such as document relevance in web search, or to estimate click biases for downstream applications such as counterfactual learning-to-rank, ad placement, or fair ranking. Recent work shows that the current evaluation practices in the community fail to guarantee that a well-performing click model generalizes well to downstream tasks in which the ranking distribution differs from the training distribution, i.e., under covariate shift. In this work, we propose an evaluation metric based on conditional independence testing to detect a lack of robustness to covariate shift in click models. We introduce the concept of debiasedness and a metric for measuring it. We prove that debiasedness is a necessary condition for recovering unbiased and consistent relevance scores and for the invariance of click prediction under covariate shift. In extensive semi-synthetic experiments, we show that our proposed metric helps to predict the downstream performance of click models under covariate shift and is useful in an off-policy model selection setting.|当从用户点击中学习时，一个众所周知的问题是数据中普遍存在的固有偏差，如位置偏差或信任偏差。点击模型是一种从用户点击中提取信息的常用方法，比如网络搜索中的文档相关性，或者评估下游应用的点击偏差，比如反事实学习排名、广告位置或公平排名。最近的研究表明，目前社区中的评估实践不能保证一个良好的点击模型很好地推广到下游任务，其中排名分布不同于训练分布，即在协变量转移。在这项工作中，我们提出了一个基于条件独立测试的评估指标来检测点击模型中缺乏协变量转移的稳健性。我们介绍了偏差的概念和一个衡量它的度量。我们证明了偏差是恢复无偏和一致相关分数的必要条件，以及点击预测在协变量移动下的不变性。在广泛的半综合实验中，我们表明，我们提出的度量有助于预测下游性能的点击模型下的协变量移动，是有用的非策略模型选择设置。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=An+Offline+Metric+for+the+Debiasedness+of+Click+Models)|0| -|[InceptionXML: A Lightweight Framework with Synchronized Negative Sampling for Short Text Extreme Classification](https://doi.org/10.1145/3539618.3591699)|Siddhant Kharbanda, Atmadeep Banerjee, Devaansh Gupta, Akash Palrecha, Rohit Babbar|Aalto University & Microsoft Corporation, Espoo, Finland; Aalto University, Espoo, Finland; Aalto University & BITS Pilani, Espoo, Finland; Aalto University & University of Bath, Espoo, Finland|Automatic annotation of short-text data to a large number of target labels, referred to as Short Text Extreme Classification, has found numerous applications including prediction of related searches and product recommendation. In this paper, we propose a convolutional architecture InceptionXML which is light-weight, yet powerful, and robust to the inherent lack of word-order in short-text queries encountered in search and recommendation. We demonstrate the efficacy of applying convolutions by recasting the operation along the embedding dimension instead of the word dimension as applied in conventional CNNs for text classification. Towards scaling our model to datasets with millions of labels, we also propose SyncXML pipeline which improves upon the shortcomings of the recently proposed dynamic hard-negative mining technique for label shortlisting by synchronizing the label-shortlister and extreme classifier. SyncXML not only reduces the inference time to half but is also an order of magnitude smaller than state-of-the-art Astec in terms of model size. Through a comprehensive empirical comparison, we show that not only can InceptionXML outperform existing approaches on benchmark datasets but also the transformer baselines requiring only 2% FLOPs. The code for InceptionXML is available at https://github.com/xmc-aalto.|将短文本数据自动标注到大量的目标标签，即短文本极端分类，已经发现了许多应用程序，包括相关搜索的预测和产品推荐。在本文中，我们提出了一个卷积体系结构 InceptionXML，它是轻量级的，但强大的，和健壮的固有缺乏词序的短文本查询遇到的搜索和推荐。我们通过重铸嵌入维数的卷积运算来代替传统的词维数用于文本分类，从而证明了卷积运算在文本分类中的有效性。为了将我们的模型扩展到具有数百万个标签的数据集，我们还提出了 SyncXML 流水线，通过同步标签短列表和极端分类器，改进了最近提出的标签短列表动态硬负面挖掘技术的缺点。SyncXML 不仅将推理时间减少了一半，而且在模型大小方面也比最先进的 Astec 数量级小。通过全面的实证比较，我们发现 InceptionXML 不仅在基准数据集上优于现有的方法，而且在只需要2% FLOP 的转换器基线上也优于现有的方法。有关 InceptionXML 的代码可于 https://github.com/xmc-aalto 下载。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=InceptionXML:+A+Lightweight+Framework+with+Synchronized+Negative+Sampling+for+Short+Text+Extreme+Classification)|0| +|[InceptionXML: A Lightweight Framework with Synchronized Negative Sampling for Short Text Extreme Classification](https://doi.org/10.1145/3539618.3591699)|Siddhant Kharbanda, Atmadeep Banerjee, Devaansh Gupta, Akash Palrecha, Rohit Babbar|Aalto University & University of Bath, Espoo, Finland; Aalto University, Espoo, Finland; Aalto University & Microsoft Corporation, Espoo, Finland; Aalto University & BITS Pilani, Espoo, Finland|Automatic annotation of short-text data to a large number of target labels, referred to as Short Text Extreme Classification, has found numerous applications including prediction of related searches and product recommendation. In this paper, we propose a convolutional architecture InceptionXML which is light-weight, yet powerful, and robust to the inherent lack of word-order in short-text queries encountered in search and recommendation. We demonstrate the efficacy of applying convolutions by recasting the operation along the embedding dimension instead of the word dimension as applied in conventional CNNs for text classification. Towards scaling our model to datasets with millions of labels, we also propose SyncXML pipeline which improves upon the shortcomings of the recently proposed dynamic hard-negative mining technique for label shortlisting by synchronizing the label-shortlister and extreme classifier. SyncXML not only reduces the inference time to half but is also an order of magnitude smaller than state-of-the-art Astec in terms of model size. Through a comprehensive empirical comparison, we show that not only can InceptionXML outperform existing approaches on benchmark datasets but also the transformer baselines requiring only 2% FLOPs. The code for InceptionXML is available at https://github.com/xmc-aalto.|将短文本数据自动标注到大量的目标标签，即短文本极端分类，已经发现了许多应用程序，包括相关搜索的预测和产品推荐。在本文中，我们提出了一个卷积体系结构 InceptionXML，它是轻量级的，但强大的，和健壮的固有缺乏词序的短文本查询遇到的搜索和推荐。我们通过重铸嵌入维数的卷积运算来代替传统的词维数用于文本分类，从而证明了卷积运算在文本分类中的有效性。为了将我们的模型扩展到具有数百万个标签的数据集，我们还提出了 SyncXML 流水线，通过同步标签短列表和极端分类器，改进了最近提出的标签短列表动态硬负面挖掘技术的缺点。SyncXML 不仅将推理时间减少了一半，而且在模型大小方面也比最先进的 Astec 数量级小。通过全面的实证比较，我们发现 InceptionXML 不仅在基准数据集上优于现有的方法，而且在只需要2% FLOP 的转换器基线上也优于现有的方法。有关 InceptionXML 的代码可于 https://github.com/xmc-aalto 下载。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=InceptionXML:+A+Lightweight+Framework+with+Synchronized+Negative+Sampling+for+Short+Text+Extreme+Classification)|0| |[Unsupervised Story Discovery from Continuous News Streams via Scalable Thematic Embedding](https://doi.org/10.1145/3539618.3591782)|Susik Yoon, Dongha Lee, Yunyi Zhang, Jiawei Han||Unsupervised discovery of stories with correlated news articles in real-time helps people digest massive news streams without expensive human annotations. A common approach of the existing studies for unsupervised online story discovery is to represent news articles with symbolic- or graph-based embedding and incrementally cluster them into stories. Recent large language models are expected to improve the embedding further, but a straightforward adoption of the models by indiscriminately encoding all information in articles is ineffective to deal with text-rich and evolving news streams. In this work, we propose a novel thematic embedding with an off-the-shelf pretrained sentence encoder to dynamically represent articles and stories by considering their shared temporal themes. To realize the idea for unsupervised online story discovery, a scalable framework USTORY is introduced with two main techniques, theme- and time-aware dynamic embedding and novelty-aware adaptive clustering, fueled by lightweight story summaries. A thorough evaluation with real news data sets demonstrates that USTORY achieves higher story discovery performances than baselines while being robust and scalable to various streaming settings.|不受监督地实时发现与相关新闻文章相关的故事，可以帮助人们消化大量的新闻流，而不需要昂贵的人工注释。现有的无监督在线故事发现研究的一种常用方法是使用基于符号或图形的嵌入方法来表示新闻文章，并逐步地将它们聚类到故事中。最近的大型语言模型预计将进一步改善嵌入，但直接采用模型，不加区分地编码文章中的所有信息，对处理文本丰富和不断变化的新闻流是无效的。在这项工作中，我们提出了一个新的主题嵌入与现成的预先训练的句子编码器，以动态表示文章和故事，考虑其共享的时间主题。为了实现无监督在线故事发现的思想，引入了一个可扩展框架 USTORY，该框架采用了两种主要技术: 主题和时间感知的动态嵌入和新颖感知的自适应聚类，并以轻量级故事摘要为基础。使用真实新闻数据集进行的全面评估表明，USTORY 实现了比基线更高的故事发现性能，同时对各种流设置具有鲁棒性和可扩展性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Unsupervised+Story+Discovery+from+Continuous+News+Streams+via+Scalable+Thematic+Embedding)|0| |[When Newer is Not Better: Does Deep Learning Really Benefit Recommendation From Implicit Feedback?](https://doi.org/10.1145/3539618.3591785)|Yushun Dong, Jundong Li, Tobias Schnabel||In recent years, neural models have been repeatedly touted to exhibit state-of-the-art performance in recommendation. Nevertheless, multiple recent studies have revealed that the reported state-of-the-art results of many neural recommendation models cannot be reliably replicated. A primary reason is that existing evaluations are performed under various inconsistent protocols. Correspondingly, these replicability issues make it difficult to understand how much benefit we can actually gain from these neural models. It then becomes clear that a fair and comprehensive performance comparison between traditional and neural models is needed. Motivated by these issues, we perform a large-scale, systematic study to compare recent neural recommendation models against traditional ones in top-n recommendation from implicit data. We propose a set of evaluation strategies for measuring memorization performance, generalization performance, and subgroup-specific performance of recommendation models. We conduct extensive experiments with 13 popular recommendation models (including two neural models and 11 traditional ones as baselines) on nine commonly used datasets. Our experiments demonstrate that even with extensive hyper-parameter searches, neural models do not dominate traditional models in all aspects, e.g., they fare worse in terms of average HitRate. We further find that there are areas where neural models seem to outperform non-neural models, for example, in recommendation diversity and robustness between different subgroups of users and items. Our work illuminates the relative advantages and disadvantages of neural models in recommendation and is therefore an important step towards building better recommender systems.|近年来，神经模型一再被吹捧为具有最先进性能的推荐模型。然而，最近的多项研究表明，许多神经推荐模型的报告的最新结果不能可靠地复制。一个主要原因是现有的评估是在各种不一致的协议下执行的。相应地，这些可复制性问题使我们很难理解我们实际上能从这些神经模型中获得多少好处。因此，很明显，需要在传统模型和神经模型之间进行公平和全面的性能比较。在这些问题的激励下，我们进行了一项大规模的系统研究，以比较最近的神经推荐模型和传统的顶部 n 推荐模型的隐含数据。我们提出了一套评估策略，用于测量记忆性能、概括性能和推荐模型的子组特定性能。我们在9个常用的数据集上对13个流行的推荐模型(包括2个神经模型和11个传统模型作为基线)进行了广泛的实验。我们的实验表明，即使有广泛的超参数搜索，神经模型也不能在所有方面支配传统模型，例如，它们在平均 HitRate 方面的表现更差。我们进一步发现，在一些领域，神经模型似乎比非神经模型表现得更好，例如，在不同的用户和项目子群之间的推荐多样性和鲁棒性。我们的工作阐明了神经模型在推荐中的相对优缺点，因此是建立更好的推荐系统的重要一步。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=When+Newer+is+Not+Better:+Does+Deep+Learning+Really+Benefit+Recommendation+From+Implicit+Feedback?)|0| -|[Session Search with Pre-trained Graph Classification Model](https://doi.org/10.1145/3539618.3591766)|Shengjie Ma, Chong Chen, Jiaxin Mao, Qi Tian, Xuhui Jiang|Renmin University of China, Beijing, China; Huawei Cloud BU, Shenzhen, China; Huawei Cloud BU, Beijing, China; Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China|Session search is a widely adopted technique in search engines that seeks to leverage the complete interaction history of a search session to better understand the information needs of users and provide more relevant ranking results. The vast majority of existing methods model a search session as a sequence of queries and previously clicked documents. However, if we simply represent a search session as a sequence we will lose the topological information in the original search session. It is non-trivial to model the intra-session interactions and complicated structural patterns among the previously issued queries, clicked documents, as well as the terms or entities that appeared in them. To solve this problem, in this paper, we propose a novel Session Search with Graph Classification Model (SSGC), which regards session search as a graph classification task on a heterogeneous graph that represents the search history in each session. To improve the performance of the graph classification, we design a specific pre-training strategy for our proposed GNN-based classification model. Extensive experiments on two public session search datasets demonstrate the effectiveness of our model in the session search task.|会话搜索是搜索引擎中广泛采用的一种技术，旨在利用搜索会话的完整交互历史，更好地理解用户的信息需求，并提供更相关的排名结果。绝大多数现有方法将搜索会话建模为一系列查询和先前单击的文档。但是，如果我们仅仅将一个搜索会话表示为一个序列，我们将丢失原始搜索会话中的拓扑信息。对以前发出的查询、单击的文档以及它们中出现的术语或实体之间的会话内交互和复杂的结构模式建模是非常重要的。为了解决这一问题，本文提出了一种新的基于图分类模型的会话搜索(SSGC)方法，该方法将会话搜索作为异构图上的一个图分类任务，表示每个会话中的搜索历史。为了提高图的分类性能，我们针对所提出的基于 GNN 的分类模型设计了一种特定的预训练策略。在两个公开会话搜索数据集上的大量实验证明了该模型在会话搜索任务中的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Session+Search+with+Pre-trained+Graph+Classification+Model)|0| -|[Leveraging Transferable Knowledge Concept Graph Embedding for Cold-Start Cognitive Diagnosis](https://doi.org/10.1145/3539618.3591774)|Weibo Gao, Hao Wang, Qi Liu, Fei Wang, Xin Lin, Linan Yue, Zheng Zhang, Rui Lv, Shijin Wang|iFLYTEK CO., LTD, Hefei, China; University of Science and Technology of China, Hefei, China|Cognitive diagnosis (CD) aims to reveal the proficiency of students on specific knowledge concepts and traits of test exercises (e.g., difficulty). It plays a critical role in intelligent education systems by supporting personalized learning guidance. However, recent developments in CD mostly concentrate on improving the accuracy of diagnostic results and often overlook the important and practical task: domain-level zero-shot cognitive diagnosis (DZCD). The primary challenge of DZCD is the deficiency of student behavior data in the target domain due to the absence of student-exercise interactions or unavailability of exercising records for training purposes. To tackle the cold-start issue, we propose a two-stage solution named TechCD (Transferable knowledgE Concept grapH embedding framework for Cognitive Diagnosis). The fundamental notion involves utilizing a pedagogical knowledge concept graph (KCG) as a mediator to connect disparate domains, allowing the transmission of student cognitive signals from established domains to the zero-shot cold-start domain. Specifically, a naive yet effective graph convolutional network (GCN) with the bottom-layer discarding operation is initially employed over the KCG to learn transferable student cognitive states and domain-specific exercise traits. Moreover, we give three implementations of the general TechCD framework following the typical cognitive diagnosis solutions. Finally, extensive experiments on real-world datasets not only prove that Tech can effectively perform zero-shot diagnosis, but also give some popular applications such as exercise recommendation.|认知诊断(CD)旨在揭示学生对特定知识概念的熟练程度和测验题的特点(例如难度)。它通过支持个性化学习指导在智能教育系统中发挥着关键作用。然而，近年来 CD 的发展主要集中在提高诊断结果的准确性上，而往往忽视了领域级零点认知诊断(DZCD)这一重要而实际的任务。DZCD 的主要挑战是由于缺乏学生与练习的交互作用，或者缺乏用于训练目的的练习记录，导致目标领域的学生行为数据不足。为了解决冷启动问题，我们提出了一个两阶段的解决方案 TechCD (用于认知诊断的可转移知识概念图嵌入框架)。基本概念包括利用教学知识概念图(KCG)作为中介连接不同的领域，允许学生的认知信号从建立的领域传输到零拍冷启动领域。具体来说，在 KCG 上首先使用一个具有底层丢弃操作的幼稚而有效的图卷积网络(GCN)来学习可转移的学生认知状态和领域特定的练习特征。此外，在典型的认知诊断解决方案的基础上，我们给出了三种通用 TechCD 框架的实现。最后，在实际数据集上进行了大量的实验，不仅证明了 Tech 可以有效地进行零点诊断，而且给出了一些常用的应用，如运动推荐。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Leveraging+Transferable+Knowledge+Concept+Graph+Embedding+for+Cold-Start+Cognitive+Diagnosis)|0| +|[Session Search with Pre-trained Graph Classification Model](https://doi.org/10.1145/3539618.3591766)|Shengjie Ma, Chong Chen, Jiaxin Mao, Qi Tian, Xuhui Jiang|Huawei Cloud BU, Shenzhen, China; Renmin University of China, Beijing, China; Huawei Cloud BU, Beijing, China; Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China|Session search is a widely adopted technique in search engines that seeks to leverage the complete interaction history of a search session to better understand the information needs of users and provide more relevant ranking results. The vast majority of existing methods model a search session as a sequence of queries and previously clicked documents. However, if we simply represent a search session as a sequence we will lose the topological information in the original search session. It is non-trivial to model the intra-session interactions and complicated structural patterns among the previously issued queries, clicked documents, as well as the terms or entities that appeared in them. To solve this problem, in this paper, we propose a novel Session Search with Graph Classification Model (SSGC), which regards session search as a graph classification task on a heterogeneous graph that represents the search history in each session. To improve the performance of the graph classification, we design a specific pre-training strategy for our proposed GNN-based classification model. Extensive experiments on two public session search datasets demonstrate the effectiveness of our model in the session search task.|会话搜索是搜索引擎中广泛采用的一种技术，旨在利用搜索会话的完整交互历史，更好地理解用户的信息需求，并提供更相关的排名结果。绝大多数现有方法将搜索会话建模为一系列查询和先前单击的文档。但是，如果我们仅仅将一个搜索会话表示为一个序列，我们将丢失原始搜索会话中的拓扑信息。对以前发出的查询、单击的文档以及它们中出现的术语或实体之间的会话内交互和复杂的结构模式建模是非常重要的。为了解决这一问题，本文提出了一种新的基于图分类模型的会话搜索(SSGC)方法，该方法将会话搜索作为异构图上的一个图分类任务，表示每个会话中的搜索历史。为了提高图的分类性能，我们针对所提出的基于 GNN 的分类模型设计了一种特定的预训练策略。在两个公开会话搜索数据集上的大量实验证明了该模型在会话搜索任务中的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Session+Search+with+Pre-trained+Graph+Classification+Model)|0| +|[Leveraging Transferable Knowledge Concept Graph Embedding for Cold-Start Cognitive Diagnosis](https://doi.org/10.1145/3539618.3591774)|Weibo Gao, Hao Wang, Qi Liu, Fei Wang, Xin Lin, Linan Yue, Zheng Zhang, Rui Lv, Shijin Wang|University of Science and Technology of China, Hefei, China; iFLYTEK CO., LTD, Hefei, China|Cognitive diagnosis (CD) aims to reveal the proficiency of students on specific knowledge concepts and traits of test exercises (e.g., difficulty). It plays a critical role in intelligent education systems by supporting personalized learning guidance. However, recent developments in CD mostly concentrate on improving the accuracy of diagnostic results and often overlook the important and practical task: domain-level zero-shot cognitive diagnosis (DZCD). The primary challenge of DZCD is the deficiency of student behavior data in the target domain due to the absence of student-exercise interactions or unavailability of exercising records for training purposes. To tackle the cold-start issue, we propose a two-stage solution named TechCD (Transferable knowledgE Concept grapH embedding framework for Cognitive Diagnosis). The fundamental notion involves utilizing a pedagogical knowledge concept graph (KCG) as a mediator to connect disparate domains, allowing the transmission of student cognitive signals from established domains to the zero-shot cold-start domain. Specifically, a naive yet effective graph convolutional network (GCN) with the bottom-layer discarding operation is initially employed over the KCG to learn transferable student cognitive states and domain-specific exercise traits. Moreover, we give three implementations of the general TechCD framework following the typical cognitive diagnosis solutions. Finally, extensive experiments on real-world datasets not only prove that Tech can effectively perform zero-shot diagnosis, but also give some popular applications such as exercise recommendation.|认知诊断(CD)旨在揭示学生对特定知识概念的熟练程度和测验题的特点(例如难度)。它通过支持个性化学习指导在智能教育系统中发挥着关键作用。然而，近年来 CD 的发展主要集中在提高诊断结果的准确性上，而往往忽视了领域级零点认知诊断(DZCD)这一重要而实际的任务。DZCD 的主要挑战是由于缺乏学生与练习的交互作用，或者缺乏用于训练目的的练习记录，导致目标领域的学生行为数据不足。为了解决冷启动问题，我们提出了一个两阶段的解决方案 TechCD (用于认知诊断的可转移知识概念图嵌入框架)。基本概念包括利用教学知识概念图(KCG)作为中介连接不同的领域，允许学生的认知信号从建立的领域传输到零拍冷启动领域。具体来说，在 KCG 上首先使用一个具有底层丢弃操作的幼稚而有效的图卷积网络(GCN)来学习可转移的学生认知状态和领域特定的练习特征。此外，在典型的认知诊断解决方案的基础上，我们给出了三种通用 TechCD 框架的实现。最后，在实际数据集上进行了大量的实验，不仅证明了 Tech 可以有效地进行零点诊断，而且给出了一些常用的应用，如运动推荐。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Leveraging+Transferable+Knowledge+Concept+Graph+Embedding+for+Cold-Start+Cognitive+Diagnosis)|0| |[Editable User Profiles for Controllable Text Recommendations](https://doi.org/10.1145/3539618.3591677)|Sheshera Mysore, Mahmood Jasim, Andrew McCallum, Hamed Zamani||Methods for making high-quality recommendations often rely on learning latent representations from interaction data. These methods, while performant, do not provide ready mechanisms for users to control the recommendation they receive. Our work tackles this problem by proposing LACE, a novel concept value bottleneck model for controllable text recommendations. LACE represents each user with a succinct set of human-readable concepts through retrieval given user-interacted documents and learns personalized representations of the concepts based on user documents. This concept based user profile is then leveraged to make recommendations. The design of our model affords control over the recommendations through a number of intuitive interactions with a transparent user profile. We first establish the quality of recommendations obtained from LACE in an offline evaluation on three recommendation tasks spanning six datasets in warm-start, cold-start, and zero-shot setups. Next, we validate the controllability of LACE under simulated user interactions. Finally, we implement LACE in an interactive controllable recommender system and conduct a user study to demonstrate that users are able to improve the quality of recommendations they receive through interactions with an editable user profile.|提出高质量建议的方法通常依赖于从交互数据中学习潜在的表示。这些方法虽然具有良好的性能，但是并没有为用户提供现成的机制来控制他们收到的推荐。我们的工作解决了这个问题，提出了 LACE，一个新的概念价值瓶颈模型的可控文本推荐。通过检索给定的用户交互文档，LACE 用一组简洁的人类可读的概念表示每个用户，并学习基于用户文档的概念的个性化表示。然后利用这个基于概念的用户配置文件来提出建议。我们的模型的设计通过与透明用户配置文件的直观交互提供了对推荐的控制。我们首先建立从 LACE 获得的建议的质量，在一个离线评估中，对三个建议任务进行评估，这三个任务跨越六个数据集，分别是暖启动、冷启动和零启动设置。接下来，我们验证了 LACE 在模拟用户交互下的可控性。最后，我们以交互式可控推荐系统实施 LACE，并进行用户研究，以证明用户能够通过与可编辑的用户资料进行交互，提高他们收到的建议的质量。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Editable+User+Profiles+for+Controllable+Text+Recommendations)|0| |[Keyword-Based Diverse Image Retrieval by Semantics-aware Contrastive Learning and Transformer](https://doi.org/10.1145/3539618.3591705)|Minyi Zhao, Jinpeng Wang, Dongliang Liao, Yiru Wang, Huanzhong Duan, Shuigeng Zhou||In addition to relevance, diversity is an important yet less studied performance metric of cross-modal image retrieval systems, which is critical to user experience. Existing solutions for diversity-aware image retrieval either explicitly post-process the raw retrieval results from standard retrieval systems or try to learn multi-vector representations of images to represent their diverse semantics. However, neither of them is good enough to balance relevance and diversity. On the one hand, standard retrieval systems are usually biased to common semantics and seldom exploit diversity-aware regularization in training, which makes it difficult to promote diversity by post-processing. On the other hand, multi-vector representation methods are not guaranteed to learn robust multiple projections. As a result, irrelevant images and images of rare or unique semantics may be projected inappropriately, which degrades the relevance and diversity of the results generated by some typical algorithms like top-k. To cope with these problems, this paper presents a new method called CoLT that tries to generate much more representative and robust representations for accurately classifying images. Specifically, CoLT first extracts semantics-aware image features by enhancing the preliminary representations of an existing one-to-one cross-modal system with semantics-aware contrastive learning. Then, a transformer-based token classifier is developed to subsume all the features into their corresponding categories. Finally, a post-processing algorithm is designed to retrieve images from each category to form the final retrieval result. Extensive experiments on two real-world datasets Div400 and Div150Cred show that CoLT can effectively boost diversity, and outperforms the existing methods as a whole (with a higher F1 score).|除了相关性之外，多样性是跨模态图像检索系统中一个重要但研究较少的性能指标，它对用户体验至关重要。现有的基于多样性的图像检索解决方案要么对标准检索系统的原始检索结果进行显式的后处理，要么尝试学习图像的多向量表示来表示图像的多样性语义。然而，它们都不足以平衡相关性和多样性。一方面，标准检索系统往往偏向于通用语义，在训练中很少采用多样性感知的正则化方法，这使得后处理难以提高多样性。另一方面，多向量表示方法不能保证学习鲁棒的多重投影。因此，不相关的图像和罕见或唯一语义的图像可能会被不适当地投影，从而降低了由 top-k 等典型算法产生的结果的相关性和多样性。为了解决这些问题，本文提出了一种称为 CoLT 的新方法，该方法试图生成更具代表性和鲁棒性的图像表示，从而实现图像的准确分类。具体来说，CoLT 首先提取语义感知的图像特征，通过增强现有的具有语义感知对比学习的一对一交叉模式系统的初步表示。然后，开发了一个基于转换器的令牌分类器，将所有特征归入相应的类别。最后，设计了一个后处理算法来检索每个类别的图像，形成最终的检索结果。在 Div400和 Div150Cred 两个实际数据集上的大量实验表明，CoLT 能够有效地提高多样性，并且整体上优于现有的方法(具有更高的 F1分数)。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Keyword-Based+Diverse+Image+Retrieval+by+Semantics-aware+Contrastive+Learning+and+Transformer)|0| |[Next Basket Recommendation with Intent-aware Hypergraph Adversarial Network](https://doi.org/10.1145/3539618.3591742)|Ran Li, Liang Zhang, Guannan Liu, Junjie Wu|Nanyang Technological University, Singapore, Singapore; Beihang University & Key Laboratory of Data Intelligence and Management, MIIT, Beiing, China; Beihang University & Key Laboratory of Data Intelligence and Management, MIIT, Bejing, China; Beihang University, Beijing, China|Next Basket Recommendation (NBR) that recommends a basket of items to users has become a promising promotion artifice for online businesses. The key challenge of NBR is rooted in the complicated relations of items that are dependent on one another in a same basket with users' diverse purchasing intentions, which goes far beyond the pairwise item relations in traditional recommendation tasks, and yet has not been well addressed by existing NBR methods that mostly model the inter-basket item relations only. To that end, in this paper, we construct a hypergraph from basket-wise purchasing records and probe the inter-basket and intra-basket item relations behind the hyperedges. In particular, we combine the strength of HyperGraph Neural Network with disentangled representation learning to derive the intent-aware representations of hyperedges for characterizing the nuances of user purchasing patterns. Moreover, considering the information loss in traditional item-wise optimization, we propose a novel basket-wise optimization scheme via an adversarial network to generate high-quality negative baskets. Extensive experiments conducted on four different data sets demonstrate the superior performances over the state-of-the-art NBR methods. Notably, our method is shown to strike a good balance in recommending both repeated and explorative items as a basket.|下一个篮子推荐(NBR)向用户推荐一篮子商品已经成为在线商务的一个有前途的促销手段。NBR 的主要挑战来自于同一篮子中相互依赖的商品之间的复杂关系，以及用户不同的购买意图，这种复杂关系远远超出了传统推荐任务中的成对商品关系，但是现有的 NBR 方法并没有很好地解决这个问题，因为大多数 NBR 方法只是模拟篮子间的商品关系。为此，在本文中，我们从篮子购买记录构造了一个超图，并探讨了超边背后的篮子间和篮子内的项目关系。特别是，我们结合超图神经网络的力量和分离表示学习来推导超边界的意图感知表示，以表征用户购买模式的细微差别。此外，考虑到传统项目优化中的信息损失，本文提出了一种新的基于对抗网络的篮子优化方案来生成高质量的负篮子。在四个不同的数据集上进行的大量实验表明，该方法的性能优于目前最先进的 NBR 方法。值得注意的是，我们的方法在推荐重复项目和探索性项目作为一个篮子方面取得了很好的平衡。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Next+Basket+Recommendation+with+Intent-aware+Hypergraph+Adversarial+Network)|0| |[AutoTransfer: Instance Transfer for Cross-Domain Recommendations](https://doi.org/10.1145/3539618.3591701)|Jingtong Gao, Xiangyu Zhao, Bo Chen, Fan Yan, Huifeng Guo, Ruiming Tang|Huawei Noah's Ark Lab, Shenzhen, China; City University of Hong Kong, Hong Kong, China|Cross-Domain Recommendation (CDR) is a widely used approach for leveraging information from domains with rich data to assist domains with insufficient data. A key challenge of CDR research is the effective and efficient transfer of helpful information from source domain to target domain. Currently, most existing CDR methods focus on extracting implicit information from the source domain to enhance the target domain. However, the hidden structure of the extracted implicit information is highly dependent on the specific CDR model, and is therefore not easily reusable or transferable. Additionally, the extracted implicit information only appears within the intermediate substructure of specific CDRs during training and is thus not easily retained for more use. In light of these challenges, this paper proposes AutoTransfer, with an Instance Transfer Policy Network, to selectively transfers instances from source domain to target domain for improved recommendations. Specifically, AutoTransfer acts as an agent that adaptively selects a subset of informative and transferable instances from the source domain. Notably, the selected subset possesses extraordinary re-utilization property that can be saved for improving model training of various future RS models in target domain. Experimental results on two public CDR benchmark datasets demonstrate that the proposed method outperforms state-of-the-art CDR baselines and classic Single-Domain Recommendation (SDR) approaches. The implementation code is available for easy reproduction.|跨域推荐(CDR)是一种广泛使用的方法，用于利用来自具有丰富数据的域的信息来帮助数据不足的域。CDR 研究的一个关键挑战是有效和高效地将有用的信息从源域传递到目标域。目前，大多数 CDR 方法主要从源域中提取隐式信息来增强目标域。然而，提取的隐含信息的隐藏结构高度依赖于特定的 CDR 模型，因此不容易重用或转移。此外，提取的隐含信息只出现在训练期间特定 CDR 的中间子结构中，因此不容易保留以供更多使用。针对这些挑战，本文提出使用实例传输策略网络的自动传输，选择性地将实例从源域传输到目标域，以获得改进的建议。具体来说，AutoTransfer 充当一个代理，自适应地从源域中选择信息丰富和可转移实例的子集。值得注意的是，所选择的子集具有非凡的再利用特性，可以用来改进目标域中各种未来 RS 模型的模型训练。在两个公共 CDR 基准数据集上的实验结果表明，该方法的性能优于现有的 CDR 基准和经典的单域推荐(SDR)方法。实现代码可以很容易地复制。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=AutoTransfer:+Instance+Transfer+for+Cross-Domain+Recommendations)|0| -|[Distillation-Enhanced Graph Masked Autoencoders for Bundle Recommendation](https://doi.org/10.1145/3539618.3591666)|Yuyang Ren, Haonan Zhang, Luoyi Fu, Xinbing Wang, Chenghu Zhou|; Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China; Shanghai Jiao Tong University, Shanghai, China|Bundle recommendation aims to recommend a bundle of items to users as a whole with user-bundle (U-B) interaction information, and auxiliary user-item (U-I) interaction and bundle-item affiliation information. Recent methods usually use two graph neural networks (GNNs) to model user's bundle preferences separately from the U-B graph (bundle view) and U-I graph (item view). However, by conducting statistical analysis, we find that the auxiliary U-I information is far underexplored due to the following reasons: 1) Loosely combining the predicted results cannot well synthesize the knowledge from both views. 2) The local U-B and U-I collaborative relations might not be consistent, leading to GNN's inaccurate modeling of user's bundle preference from the U-I graph. 3) The U-I interactions are usually modeled equally while the significant ones corresponding to user's bundle preference are less emphasized. Based on these analyses, we propose a Distillation-enhanced Graph Masked AutoEncoder (DGMAE) for bundle recommendation. Our framework extracts the knowledge of first- and higher-order U-B relations from the U-B graph and injects it into a well-designed graph masked autoencoder (student model). The student model is built with two key designs to jointly capture significant local and global U-I relations from the U-I graph. In specific, we design a transformer-enhanced GNN encoder for global relation learning, which increases the model's representational power of depicting user's bundle preferences. Meanwhile, an adaptive edge masking strategy and reconstruction target are designed on the significant U-I edges to guide the student model to identify the potential ones suggesting user's bundle preferences. Extensive experiments on benchmark datasets show the significant improvements of DGMAE over the SOTA methods.|Bundle 推荐的目的是向用户作为一个整体推荐一个包，包含用户包(U-B)交互信息、辅助用户项(U-I)交互信息和包项关联信息。最近的方法通常使用两个图神经网络(GNN)来模型用户的捆绑偏好分别从 U-B 图(捆绑视图)和 U-I 图(项目视图)。然而，通过统计分析发现，由于以下原因，辅助 U-I 信息的开发远远不够: 1)松散地结合预测结果不能很好地综合两种观点的知识。2)本地 U-B 和 U-I 协作关系可能不一致，导致 GNN 从 U-I 图中对用户捆绑偏好建模不准确。3) U-I 交互通常是均匀建模的，而与用户捆绑包偏好相对应的重要交互通常较少被强调。基于这些分析，我们提出了一种用于捆绑推荐的蒸馏增强型图掩码自动编码器(DGMAE)。我们的框架从 U-B 图中提取出一阶和高阶 U-B 关系的知识，并将其注入到一个设计良好的图掩码自动编码器(学生模型)中。学生模型由两个关键设计构建，从 U-I 图中共同捕获重要的本地和全球 U-I 关系。具体来说，我们设计了一个用于全局关系学习的变压器增强型 GNN 编码器，增强了模型描述用户束偏好的表示能力。同时，在显著的 U-I 边缘上设计了自适应边缘掩蔽策略和重构目标，以指导学生模型识别提示用户捆绑偏好的潜在边缘。在基准数据集上的大量实验表明，DGMAE 方法比 SOTA 方法有明显的改进。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Distillation-Enhanced+Graph+Masked+Autoencoders+for+Bundle+Recommendation)|0| +|[Distillation-Enhanced Graph Masked Autoencoders for Bundle Recommendation](https://doi.org/10.1145/3539618.3591666)|Yuyang Ren, Haonan Zhang, Luoyi Fu, Xinbing Wang, Chenghu Zhou|; Shanghai Jiao Tong University, Shanghai, China; Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China|Bundle recommendation aims to recommend a bundle of items to users as a whole with user-bundle (U-B) interaction information, and auxiliary user-item (U-I) interaction and bundle-item affiliation information. Recent methods usually use two graph neural networks (GNNs) to model user's bundle preferences separately from the U-B graph (bundle view) and U-I graph (item view). However, by conducting statistical analysis, we find that the auxiliary U-I information is far underexplored due to the following reasons: 1) Loosely combining the predicted results cannot well synthesize the knowledge from both views. 2) The local U-B and U-I collaborative relations might not be consistent, leading to GNN's inaccurate modeling of user's bundle preference from the U-I graph. 3) The U-I interactions are usually modeled equally while the significant ones corresponding to user's bundle preference are less emphasized. Based on these analyses, we propose a Distillation-enhanced Graph Masked AutoEncoder (DGMAE) for bundle recommendation. Our framework extracts the knowledge of first- and higher-order U-B relations from the U-B graph and injects it into a well-designed graph masked autoencoder (student model). The student model is built with two key designs to jointly capture significant local and global U-I relations from the U-I graph. In specific, we design a transformer-enhanced GNN encoder for global relation learning, which increases the model's representational power of depicting user's bundle preferences. Meanwhile, an adaptive edge masking strategy and reconstruction target are designed on the significant U-I edges to guide the student model to identify the potential ones suggesting user's bundle preferences. Extensive experiments on benchmark datasets show the significant improvements of DGMAE over the SOTA methods.|Bundle 推荐的目的是向用户作为一个整体推荐一个包，包含用户包(U-B)交互信息、辅助用户项(U-I)交互信息和包项关联信息。最近的方法通常使用两个图神经网络(GNN)来模型用户的捆绑偏好分别从 U-B 图(捆绑视图)和 U-I 图(项目视图)。然而，通过统计分析发现，由于以下原因，辅助 U-I 信息的开发远远不够: 1)松散地结合预测结果不能很好地综合两种观点的知识。2)本地 U-B 和 U-I 协作关系可能不一致，导致 GNN 从 U-I 图中对用户捆绑偏好建模不准确。3) U-I 交互通常是均匀建模的，而与用户捆绑包偏好相对应的重要交互通常较少被强调。基于这些分析，我们提出了一种用于捆绑推荐的蒸馏增强型图掩码自动编码器(DGMAE)。我们的框架从 U-B 图中提取出一阶和高阶 U-B 关系的知识，并将其注入到一个设计良好的图掩码自动编码器(学生模型)中。学生模型由两个关键设计构建，从 U-I 图中共同捕获重要的本地和全球 U-I 关系。具体来说，我们设计了一个用于全局关系学习的变压器增强型 GNN 编码器，增强了模型描述用户束偏好的表示能力。同时，在显著的 U-I 边缘上设计了自适应边缘掩蔽策略和重构目标，以指导学生模型识别提示用户捆绑偏好的潜在边缘。在基准数据集上的大量实验表明，DGMAE 方法比 SOTA 方法有明显的改进。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Distillation-Enhanced+Graph+Masked+Autoencoders+for+Bundle+Recommendation)|0| |[Popularity Debiasing from Exposure to Interaction in Collaborative Filtering](https://doi.org/10.1145/3539618.3591947)|Yuanhao Liu, Qi Cao, Huawei Shen, Yunfan Wu, Shuchang Tao, Xueqi Cheng||Recommender systems often suffer from popularity bias, where popular items are overly recommended while sacrificing unpopular items. Existing researches generally focus on ensuring the number of recommendations exposure of each item is equal or proportional, using inverse propensity weighting, causal intervention, or adversarial training. However, increasing the exposure of unpopular items may not bring more clicks or interactions, resulting in skewed benefits and failing in achieving real reasonable popularity debiasing. In this paper, we propose a new criterion for popularity debiasing, i.e., in an unbiased recommender system, both popular and unpopular items should receive Interactions Proportional to the number of users who Like it, namely IPL criterion. Under the guidance of the criterion, we then propose a debiasing framework with IPL regularization term which is theoretically shown to achieve a win-win situation of both popularity debiasing and recommendation performance. Experiments conducted on four public datasets demonstrate that when equipping two representative collaborative filtering models with our framework, the popularity bias is effectively alleviated while maintaining the recommendation performance.|推荐系统经常受到受欢迎程度偏差的影响，受欢迎的项目被过度推荐，而牺牲了不受欢迎的项目。现有的研究通常集中在确保每个项目的建议暴露数量相等或成比例，使用反倾向权重，因果干预，或对抗性训练。然而，增加不受欢迎的项目的曝光可能不会带来更多的点击或互动，导致扭曲的利益和未能实现真正合理的流行去偏见。在这篇文章中，我们提出了一个新的减低受欢迎程度的准则，即在一个不偏不倚的推荐系统下，受欢迎和不受欢迎的项目都应该接受与喜欢它的用户数成比例的互动，即 IPL 准则。在该准则的指导下，我们提出了一个带有 IPL 正则项的去偏框架，理论上证明了该框架可以实现人气去偏和推荐性能的双赢。在四个公共数据集上进行的实验表明，当在我们的框架中安装两个具有代表性的协同过滤模型时，在保持推荐性能的同时有效地减少了流行偏差。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Popularity+Debiasing+from+Exposure+to+Interaction+in+Collaborative+Filtering)|0| |[AutoDPQ: Automated Differentiable Product Quantization for Embedding Compression](https://doi.org/10.1145/3539618.3591953)|Xin Gan, Yuhao Wang, Xiangyu Zhao, Wanyu Wang, Yiqi Wang, Zitao Liu|National University of Defense Technology, Changsha, China; Guangdong Institute of Smart Education, Jinan University, Guangzhou, China; City University of Hong Kong, Hong Kong, China|Deep recommender systems typically involve numerous feature fields for users and items, with a large number of low-frequency features. These low-frequency features would reduce the prediction accuracy with large storage space due to their vast quantity and inadequate training. Some pioneering studies have explored embedding compression techniques to address this issue of the trade-off between storage space and model predictability. However, these methods have difficulty compacting the embedding of low-frequency features in various feature fields due to the high demand for human experience and computing resources during hyper-parameter searching. In this paper, we propose the AutoDPQ framework, which automatically compacts low-frequency feature embeddings for each feature field to an adaptive magnitude. Experimental results indicate that AutoDPQ can significantly reduce the parameter space while improving recommendation accuracy. Moreover, AutoDPQ is compatible with various deep CTR models by improving their performance significantly with high efficiency.|深度推荐系统通常涉及用户和项目的大量特性字段，以及大量低频特性。这些低频特征由于其数量庞大，训练不足，在存储空间较大的情况下会降低预测精度。一些开创性的研究探索了嵌入式压缩技术来解决存储空间和模型可预测性之间的权衡问题。然而，由于在超参数搜索过程中对人的经验和计算资源的要求很高，这些方法很难将低频特征嵌入到各种特征域中。在本文中，我们提出了 AutoDPQ 框架，它自动压缩每个特征字段的低频特征嵌入到一个自适应的大小。实验结果表明，AutoDPQ 在提高推荐精度的同时，可以显著减少参数空间。此外，AutoDPQ 与各种深度 CTR 模型兼容，大大提高了它们的性能和效率。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=AutoDPQ:+Automated+Differentiable+Product+Quantization+for+Embedding+Compression)|0| |[Can Generative LLMs Create Query Variants for Test Collections? An Exploratory Study](https://doi.org/10.1145/3539618.3591960)|Marwah Alaofi, Luke Gallagher, Mark Sanderson, Falk Scholer, Paul Thomas|Microsoft, Adelaide, SA, Australia; RMIT University, Melbourne, VIC, Australia|This paper explores the utility of a Large Language Model (LLM) to automatically generate queries and query variants from a description of an information need. Given a set of information needs described as backstories, we explore how similar the queries generated by the LLM are to those generated by humans. We quantify the similarity using different metrics and examine how the use of each set would contribute to document pooling when building test collections. Our results show potential in using LLMs to generate query variants. While they may not fully capture the wide variety of human-generated variants, they generate similar sets of relevant documents, reaching up to 71.1% overlap at a pool depth of 100.|本文探讨了大语言模型(LLM)的实用性，该模型根据对信息需求的描述自动生成查询和查询变体。给定一组被描述为背景故事的信息需求，我们探索 LLM 生成的查询与人工生成的查询有多相似。我们使用不同的度量标准量化相似性，并检查在构建测试集合时，每个集合的使用如何有助于文档池。我们的结果显示了使用 LLM 生成查询变量的潜力。虽然它们可能不能完全捕获各种各样的人类产生的变体，但它们生成了类似的相关文档集，在池深度为100的情况下，重叠率高达71.1% 。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Can+Generative+LLMs+Create+Query+Variants+for+Test+Collections?+An+Exploratory+Study)|0| |[Causal Disentangled Variational Auto-Encoder for Preference Understanding in Recommendation](https://doi.org/10.1145/3539618.3591961)|Siyu Wang, Xiaocong Chen, Quan Z. Sheng, Yihong Zhang, Lina Yao||Recommendation models are typically trained on observational user interaction data, but the interactions between latent factors in users' decision-making processes lead to complex and entangled data. Disentangling these latent factors to uncover their underlying representation can improve the robustness, interpretability, and controllability of recommendation models. This paper introduces the Causal Disentangled Variational Auto-Encoder (CaD-VAE), a novel approach for learning causal disentangled representations from interaction data in recommender systems. The CaD-VAE method considers the causal relationships between semantically related factors in real-world recommendation scenarios, rather than enforcing independence as in existing disentanglement methods. The approach utilizes structural causal models to generate causal representations that describe the causal relationship between latent factors. The results demonstrate that CaD-VAE outperforms existing methods, offering a promising solution for disentangling complex user behavior data in recommendation systems.|推荐模型通常基于观测用户交互数据进行训练，但用户决策过程中潜在因素之间的相互作用会导致复杂和纠缠的数据。分离这些潜在因素，揭示它们的基底形式，可以提高推荐模型的稳健性、可解释性和可控性。介绍了一种从推荐系统中的交互数据中学习因果解缠表示的新方法——因果解缠变分自动编码器(CaD-VAE)。在现实推荐场景中，CaD-VAE 方法考虑了语义相关因素之间的因果关系，而不是像现有的分离方法那样强制独立性。该方法利用结构性因果模型来产生描述潜在因素之间因果关系的因果表示。结果表明，CaD-VAE 方法的性能优于现有方法，为推荐系统中复杂用户行为数据的分离提供了一种有前途的解决方案。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Causal+Disentangled+Variational+Auto-Encoder+for+Preference+Understanding+in+Recommendation)|0| |[Connecting Unseen Domains: Cross-Domain Invariant Learning in Recommendation](https://doi.org/10.1145/3539618.3591965)|Yang Zhang, Yue Shen, Dong Wang, Jinjie Gu, Guannan Zhang|Ant Group, Hangzhou, China|As web applications continue to expand and diversify their services, user interactions exist in different scenarios. To leverage this wealth of information, cross-domain recommendation (CDR) has gained significant attention in recent years. However, existing CDR approaches mostly focus on information transfer between observed domains, with little attention paid to generalizing to unseen domains. Although recent research on invariant learning can help for the purpose of generalization, relying only on invariant preference may be overly conservative and result in mediocre performance when the unseen domain shifts slightly. In this paper, we present a novel framework that considers both CDR and domain generalization through a united causal invariant view. We assume that user interactions are determined by domain-invariant preference and domain-specific preference. The proposed approach differentiates the invariant preference and the specific preference from observational behaviors in a way of adversarial learning. Additionally, a novel domain routing module is designed to connect unseen domains to observed domains. Extensive experiments on public and industry datasets have proved the effectiveness of the proposed approach under both CDR and domain generalization settings.|随着 Web 应用程序不断扩展和使其服务多样化，用户交互存在于不同的场景中。为了充分利用这些丰富的信息，跨域推荐(CDR)近年来受到了广泛的关注。然而，现有的 CDR 方法大多侧重于观察域之间的信息传递，很少关注对未知域的推广。虽然近年来对不变学习的研究有助于推广，但仅依赖不变偏好可能会过于保守，当不可见领域发生轻微变化时，学习效果平平。在本文中，我们提出了一个新的框架，通过一个统一的因果不变量的观点，同时考虑 CDR 和领域推广。我们假设用户交互是由领域不变偏好和领域特定偏好决定的。该方法以对抗学习的方式区分不变偏好和特定偏好与观察行为。此外，一个新颖的域路由模组被设计来连接看不见的域到观察到的域。在公共和行业数据集上的大量实验证明了该方法在 CDR 和领域泛化设置下的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Connecting+Unseen+Domains:+Cross-Domain+Invariant+Learning+in+Recommendation)|0| -|[Event-Aware Adaptive Clustering Uplift Network for Insurance Creative Ranking](https://doi.org/10.1145/3539618.3591980)|Wanjie Tao, Huihui Liu, Xuqi Li, Qun Dai, Hong Wen, Zulong Chen|Nanjing University of Aeronautics and Astronautics, Nanjing, China; Alibaba Group, Hangzhou, China|In the classical e-commerce platforms, the personalized product-tying recommendation has proven to be of great added value, which improves users' purchase willingness to product-tying by displaying the suitable marketing creative. In this paper, we present a new recommendation problem, i.e., the Pop-up One-time Marketing (POM), where the product-tying marketing creative only pops up one time when the user pays for the main item. POM has become a ubiquitous application in e-commerce platforms, e.g., buy the mobile tying mobile case and buy flight ticket tying insurance. However, many existing recommendation methods are sub-optimal for the creative marketing in the POM scenario due to unconsidering the unique characteristics in the scenario. To tackle this problem, we propose a novel framework named Event-aware Adaptive Clustering Uplift Network (EACU-Net) for the POM scenario, which is to our best knowledge the first attempt along this line. EACU-Net contains three modules: (1) the event-aware graph cascading learning, which employs a heterogeneous graph network to comprehensively learn the embedding for the user attributes, event categories, and creative elements by stage. (2) an adaptive clustering uplift network, which learns the sensitivity of users to creatives under the same context. (3) an event-aware information gain network to learn more information from samples with event affection. Extensive offline and online evaluations on a real-world e-commerce platform demonstrate the superior performance of the proposed model compared with the state-of-the-art method.|在传统的电子商务平台中，个性化产品绑定推荐被证明具有很大的附加价值，它通过展示合适的营销创意来提高用户的产品绑定购买意愿。本文提出了一个新的推荐问题，即弹出式一次性营销(POM) ，即当用户为主要商品付费时，产品绑定营销创意只会弹出一次。POM 已经成为电子商务平台上无处不在的应用程序，例如，购买移动绑定手机套和购买机票绑定保险。然而，许多现有的推荐方法对于 POM 场景中的创造性营销来说是次优的，因为没有考虑到场景中的独特特征。为了解决这个问题，我们提出了一种新的 POM 场景的事件感知自适应聚类提升网络(EACU-Net)框架，这是我们所知道的沿着这条路线的第一次尝试。EACU-Net 包含三个模块: (1)事件感知图级联学习，该学习采用异构图网络，分阶段全面学习用户属性、事件类别和创新元素的嵌入。(2)自适应聚类提升网络，学习用户在相同情境下对创新的敏感性。(事件感知信息获取网络从具有事件影响的样本中获取更多的信息。在一个真实世界的电子商务平台上进行的大量离线和在线评估表明，与最先进的方法相比，所提出的模型具有更好的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Event-Aware+Adaptive+Clustering+Uplift+Network+for+Insurance+Creative+Ranking)|0| +|[Event-Aware Adaptive Clustering Uplift Network for Insurance Creative Ranking](https://doi.org/10.1145/3539618.3591980)|Wanjie Tao, Huihui Liu, Xuqi Li, Qun Dai, Hong Wen, Zulong Chen|Alibaba Group, Hangzhou, China; Nanjing University of Aeronautics and Astronautics, Nanjing, China|In the classical e-commerce platforms, the personalized product-tying recommendation has proven to be of great added value, which improves users' purchase willingness to product-tying by displaying the suitable marketing creative. In this paper, we present a new recommendation problem, i.e., the Pop-up One-time Marketing (POM), where the product-tying marketing creative only pops up one time when the user pays for the main item. POM has become a ubiquitous application in e-commerce platforms, e.g., buy the mobile tying mobile case and buy flight ticket tying insurance. However, many existing recommendation methods are sub-optimal for the creative marketing in the POM scenario due to unconsidering the unique characteristics in the scenario. To tackle this problem, we propose a novel framework named Event-aware Adaptive Clustering Uplift Network (EACU-Net) for the POM scenario, which is to our best knowledge the first attempt along this line. EACU-Net contains three modules: (1) the event-aware graph cascading learning, which employs a heterogeneous graph network to comprehensively learn the embedding for the user attributes, event categories, and creative elements by stage. (2) an adaptive clustering uplift network, which learns the sensitivity of users to creatives under the same context. (3) an event-aware information gain network to learn more information from samples with event affection. Extensive offline and online evaluations on a real-world e-commerce platform demonstrate the superior performance of the proposed model compared with the state-of-the-art method.|在传统的电子商务平台中，个性化产品绑定推荐被证明具有很大的附加价值，它通过展示合适的营销创意来提高用户的产品绑定购买意愿。本文提出了一个新的推荐问题，即弹出式一次性营销(POM) ，即当用户为主要商品付费时，产品绑定营销创意只会弹出一次。POM 已经成为电子商务平台上无处不在的应用程序，例如，购买移动绑定手机套和购买机票绑定保险。然而，许多现有的推荐方法对于 POM 场景中的创造性营销来说是次优的，因为没有考虑到场景中的独特特征。为了解决这个问题，我们提出了一种新的 POM 场景的事件感知自适应聚类提升网络(EACU-Net)框架，这是我们所知道的沿着这条路线的第一次尝试。EACU-Net 包含三个模块: (1)事件感知图级联学习，该学习采用异构图网络，分阶段全面学习用户属性、事件类别和创新元素的嵌入。(2)自适应聚类提升网络，学习用户在相同情境下对创新的敏感性。(事件感知信息获取网络从具有事件影响的样本中获取更多的信息。在一个真实世界的电子商务平台上进行的大量离线和在线评估表明，与最先进的方法相比，所提出的模型具有更好的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Event-Aware+Adaptive+Clustering+Uplift+Network+for+Insurance+Creative+Ranking)|0| |[Exploiting Cluster-Skipping Inverted Index for Semantic Place Retrieval](https://doi.org/10.1145/3539618.3591983)|Enes Recep Cinar, Ismail Sengor Altingovde|Middle East Technical University, Ankara, Turkey|Semantic place retrieval aims to find the top-k place entities, which are both textually relevant and spatially close to a given query, from a knowledge graph. In this work, our contribution toward improving the efficiency of semantic place retrieval is two-fold. First, we show that by applying an ad hoc yet intuitive restriction on the depth of search on the knowledge graph, it is possible to adopt IR-tree indexing scheme [7], which has been introduced for processing spatial keyword queries, for the semantic place retrieval scenario. Secondly, as a novel solution to this problem, we adapt the idea of cluster-skipping inverted index (CS-IIS) [1, 4], which has been originally proposed for retrieval over topically clustered document collections. Our experiments show that CS-IIS is comparable to IR-tree in terms of CPU time, while it yields substantial efficiency gains in terms of I/O time during query processing.|语义位置检索的目的是从知识图中找到文本相关且在空间上接近于给定查询的前 k 位置实体。在这项工作中，我们对提高语义位置检索的效率的贡献是双重的。首先，我们表明，通过对知识图上的搜索深度应用一个即兴但直观的限制，可以采用 IR-tree 索引方案[7] ，这是为处理空间关键字查询而引入的，用于语义位置检索场景。其次，作为这个问题的一个新的解决方案，我们采用了跳跃聚类倒排索引(CS-IIS)[1,4]的思想，这是最初提出的主题聚类文档集合的检索。我们的实验表明，CS-IIS 在 CPU 时间方面与 IR 树相当，而在查询处理过程中，它在 I/O 时间方面产生了显著的效率提高。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Exploiting+Cluster-Skipping+Inverted+Index+for+Semantic+Place+Retrieval)|0| |[Friend Ranking in Online Games via Pre-training Edge Transformers](https://doi.org/10.1145/3539618.3591990)|Liang Yao, Jiazhen Peng, Shenggong Ji, Qiang Liu, Hongyun Cai, Feng He, Xu Cheng||Friend recall is an important way to improve Daily Active Users (DAU) in online games. The problem is to generate a proper lost friend ranking list essentially. Traditional friend recall methods focus on rules like friend intimacy or training a classifier for predicting lost players' return probability, but ignore feature information of (active) players and historical friend recall events. In this work, we treat friend recall as a link prediction problem and explore several link prediction methods which can use features of both active and lost players, as well as historical events. Furthermore, we propose a novel Edge Transformer model and pre-train the model via masked auto-encoders. Our method achieves state-of-the-art results in the offline experiments and online A/B Tests of three Tencent games.|朋友回忆是提高网络游戏日常活跃用户(DAU)水平的重要途径。问题是生成一个适当的失去朋友排名名单本质上。传统的朋友回忆方法侧重于朋友间的亲密关系或训练一个分类器来预测失去的玩家的返回概率等规则，而忽略了(活跃的)玩家的特征信息和历史的朋友回忆事件。本文将好友回忆视为一个链接预测问题，探索了几种既能利用主动玩家特征又能利用丢失玩家特征以及历史事件特征的链接预测方法。此外，我们提出了一个新的边缘变压器模型和预训练的掩码自动编码器的模型。我们的方法在三个腾讯游戏的离线实验和在线 A/B 测试中取得了最先进的结果。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Friend+Ranking+in+Online+Games+via+Pre-training+Edge+Transformers)|0| |[Generative Relevance Feedback with Large Language Models](https://doi.org/10.1145/3539618.3591992)|Iain Mackie, Shubham Chatterjee, Jeffrey Dalton||Current query expansion models use pseudo-relevance feedback to improve first-pass retrieval effectiveness; however, this fails when the initial results are not relevant. Instead of building a language model from retrieved results, we propose Generative Relevance Feedback (GRF) that builds probabilistic feedback models from long-form text generated from Large Language Models. We study the effective methods for generating text by varying the zero-shot generation subtasks: queries, entities, facts, news articles, documents, and essays. We evaluate GRF on document retrieval benchmarks covering a diverse set of queries and document collections, and the results show that GRF methods significantly outperform previous PRF methods. Specifically, we improve MAP between 5-19% and NDCG@10 17-24% compared to RM3 expansion, and achieve the best R@1k effectiveness on all datasets compared to state-of-the-art sparse, dense, and expansion models.|当前的查询扩展模型使用伪相关反馈来提高首次检索的有效性，但是，当初始结果不相关时，这种方法就会失败。我们建议使用生成关联反馈(Generative) ，从大型语言模型生成的长形文本中建立概率反馈模型，而不是根据检索到的结果建立语言模型。我们研究了通过改变零镜头生成子任务(查询、实体、事实、新闻文章、文档和随笔)来生成文本的有效方法。我们根据涵盖不同查询和文档集合的文献检索基准对 GRF 进行评估，结果显示 GRF 方法明显优于以前的 PRF 方法。具体而言，与 RM3扩展相比，我们改善了5-19% 和 NDCG@1017-24% 之间的 MAP，并且与最先进的稀疏、密集和扩展模型相比，在所有数据集上实现了最佳的 R@1k 效率。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Generative+Relevance+Feedback+with+Large+Language+Models)|0| |[Improved Vector Quantization For Dense Retrieval with Contrastive Distillation](https://doi.org/10.1145/3539618.3592001)|James O'Neill, Sourav Dutta|Huawei, Dublin, Ireland|Recent work has identified that distillation can be used to create vector quantization based ANN indexes by learning the inverted file index and product quantization. The argued advantage of using a fixed teacher encoder for queries and documents is that the scores produced by the teacher can be used instead of the label judgements that are required when using traditional supervised learning, such as contrastive learning. However, current work only distills the teacher encoder outputs of dot products between quantized query embedddings and product quantized document embeddings. Our work combines the benefits of contrastive learning and distillation by using contrastive distillation whereby the teacher outputs contrastive scores that the student learns from. Our experimental results on MSMARCO passage retrieval and NQ open question answering datasets show that contrastive distillation improves over current state of the art for vector quantized dense retrieval.|最近的研究表明，蒸馏可以通过学习倒排文件索引和产品量化来创建基于向量量化的人工神经网络索引。使用固定的教师编码器进行查询和文档编码的一个有争议的优势是，教师所得的分数可以用来代替使用传统监督式学习(如对比学习)时所需的标签判断。然而，目前的工作只是提取量化查询嵌入和量化产品文档嵌入之间的点积的教师编码器输出。我们的工作结合了对比学习和蒸馏的好处，通过使用对比蒸馏，教师输出对比分数，学生学习。我们在 MSMARCO 段落检索和 NQ 开放式问题回答数据集上的实验结果表明，对比精馏方法在矢量量化密集检索方面优于现有技术。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Improved+Vector+Quantization+For+Dense+Retrieval+with+Contrastive+Distillation)|0| |[LogicRec: Recommendation with Users' Logical Requirements](https://doi.org/10.1145/3539618.3592012)|Zhenwei Tang, Griffin Floto, Armin Toroghi, Shichao Pei, Xiangliang Zhang, Scott Sanner||Users may demand recommendations with highly personalized requirements involving logical operations, e.g., the intersection of two requirements, where such requirements naturally form structured logical queries on knowledge graphs (KGs). To date, existing recommender systems lack the capability to tackle users' complex logical requirements. In this work, we formulate the problem of recommendation with users' logical requirements (LogicRec) and construct benchmark datasets for LogicRec. Furthermore, we propose an initial solution for LogicRec based on logical requirement retrieval and user preference retrieval, where we face two challenges. First, KGs are incomplete in nature. Therefore, there are always missing true facts, which entails that the answers to logical requirements can not be completely found in KGs. In this case, item selection based on the answers to logical queries is not applicable. We thus resort to logical query embedding (LQE) to jointly infer missing facts and retrieve items based on logical requirements. Second, answer sets are under-exploited. Existing LQE methods can only deal with query-answer pairs, where queries in our case are the intersected user preferences and logical requirements. However, the logical requirements and user preferences have different answer sets, offering us richer knowledge about the requirements and preferences by providing requirement-item and preference-item pairs. Thus, we design a multi-task knowledge-sharing mechanism to exploit these answer sets collectively. Extensive experimental results demonstrate the significance of the LogicRec task and the effectiveness of our proposed method.|用户可能需要具有高度个性化需求的推荐，这些需求涉及逻辑操作，例如，两个需求的交叉点，这些需求自然而然地在知识图(KG)上形成结构化的逻辑查询。迄今为止，现有的推荐系统缺乏处理用户复杂逻辑需求的能力。在这项工作中，我们提出了与用户的逻辑需求(LogicRec)的推荐问题，并构造了基准数据集的 LogicRec。在此基础上，提出了基于逻辑需求检索和用户偏好检索的 LogicRec 初步解决方案。首先，幼稚园本质上是不完整的。因此，总是缺少真正的事实，这就意味着逻辑要求的答案不能完全在幼稚园中找到。在这种情况下，基于逻辑查询答案的项选择是不适用的。因此，我们使用逻辑查询嵌入(LQE)来联合推断丢失的事实并根据逻辑需求检索项。其次，答案集未得到充分利用。现有的 LQE 方法只能处理查询-答案对，在我们的例子中，查询是交叉的用户首选项和逻辑需求。然而，逻辑需求和用户偏好有不同的答案集，通过提供需求项和偏好项对，为我们提供了关于需求和偏好的更丰富的知识。因此，我们设计了一个多任务知识共享机制来共同利用这些答案集。大量的实验结果表明了 LogicRec 任务的重要性和提出的方法的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=LogicRec:+Recommendation+with+Users'+Logical+Requirements)|0| -|[Matching Point of Interests and Travel Blog with Multi-view Information Fusion](https://doi.org/10.1145/3539618.3592016)|Shuokai Li, Jingbo Zhou, Jizhou Huang, Hao Chen, Fuzhen Zhuang, Qing He, Dejing Dou|Baidu Inc., Beijing, China; Beihang University, Beijing, China; Baidu Research, Beijing, China; Institute of Computing Technology, Chinese Academy of Sciences & University of Chinese Academy of Sciences, Beijing, China; BCG X, Beijing, China|The past few years have witnessed an explosive growth of user-generated POI-centric travel blogs, which can provide a comprehensive understanding of a POI for people. However, evaluating the quality of the POI-centric travel blogs and ranking the blogs is not a simple task without domain knowledge or actual travel experience on the target POI. Nevertheless, our insight is that the user search behavior related to the target POI on the online map service can partly valid the rationality of the POIs appearing in the travel blogs, which helps for travel blogs ranking. To this end, in this paper, we propose a novel end-to-end framework for travel blogs ranking, coined Matching POI and Travel Blogs with Multi-view InFormation (MOTIF). Concretely, we first construct two POI graphs as multi-view information: (1) the search-level POI graph which reflects the user behaviors on the online map service; and (2) the document-level POI graph which shows the POI co-occurrence frequency in travel blogs. Then, to better model the intrinsic correlation of the two graphs, we adopt Mutual Information Maximization to align the search-level and document-level semantic spaces. Moreover, we leverage a pair-wise ranking loss for POI-document relevance scoring. Extensive experiments on two real-world datasets demonstrate the superiority of our method.|在过去的几年里，以用户为中心的旅游博客呈爆炸式增长，它可以为人们提供对 POI 的全面理解。然而，评估以 POI 为中心的旅游博客的质量并对这些博客进行排名并不是一项简单的任务，因为在目标 POI 上没有领域知识或实际的旅游经验。然而，我们的研究发现，在线地图服务中与目标 POI 相关的用户搜索行为可以部分验证出现在旅游博客中的 POI 的合理性，这有助于旅游博客的排名。为此，在本文中，我们提出了一个新颖的端到端的旅游博客排名框架，创造匹配 POI 和旅游博客多视图信息(MOTIF)。具体来说，我们首先构建两个 POI 图作为多视图信息: (1)反映在线地图服务中用户行为的搜索级 POI 图; (2)显示旅游博客中 POI 共现频率的文档级 POI 图。然后，为了更好地建立两个图的内在相关性模型，我们采用互信息最大化来对齐搜索级和文档级语义空间。此外，我们利用一对 POI 文档相关性评分的排名损失。在两个实际数据集上的大量实验证明了该方法的优越性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Matching+Point+of+Interests+and+Travel+Blog+with+Multi-view+Information+Fusion)|0| +|[Matching Point of Interests and Travel Blog with Multi-view Information Fusion](https://doi.org/10.1145/3539618.3592016)|Shuokai Li, Jingbo Zhou, Jizhou Huang, Hao Chen, Fuzhen Zhuang, Qing He, Dejing Dou|BCG X, Beijing, China; Baidu Research, Beijing, China; Beihang University, Beijing, China; Institute of Computing Technology, Chinese Academy of Sciences & University of Chinese Academy of Sciences, Beijing, China; Baidu Inc., Beijing, China|The past few years have witnessed an explosive growth of user-generated POI-centric travel blogs, which can provide a comprehensive understanding of a POI for people. However, evaluating the quality of the POI-centric travel blogs and ranking the blogs is not a simple task without domain knowledge or actual travel experience on the target POI. Nevertheless, our insight is that the user search behavior related to the target POI on the online map service can partly valid the rationality of the POIs appearing in the travel blogs, which helps for travel blogs ranking. To this end, in this paper, we propose a novel end-to-end framework for travel blogs ranking, coined Matching POI and Travel Blogs with Multi-view InFormation (MOTIF). Concretely, we first construct two POI graphs as multi-view information: (1) the search-level POI graph which reflects the user behaviors on the online map service; and (2) the document-level POI graph which shows the POI co-occurrence frequency in travel blogs. Then, to better model the intrinsic correlation of the two graphs, we adopt Mutual Information Maximization to align the search-level and document-level semantic spaces. Moreover, we leverage a pair-wise ranking loss for POI-document relevance scoring. Extensive experiments on two real-world datasets demonstrate the superiority of our method.|在过去的几年里，以用户为中心的旅游博客呈爆炸式增长，它可以为人们提供对 POI 的全面理解。然而，评估以 POI 为中心的旅游博客的质量并对这些博客进行排名并不是一项简单的任务，因为在目标 POI 上没有领域知识或实际的旅游经验。然而，我们的研究发现，在线地图服务中与目标 POI 相关的用户搜索行为可以部分验证出现在旅游博客中的 POI 的合理性，这有助于旅游博客的排名。为此，在本文中，我们提出了一个新颖的端到端的旅游博客排名框架，创造匹配 POI 和旅游博客多视图信息(MOTIF)。具体来说，我们首先构建两个 POI 图作为多视图信息: (1)反映在线地图服务中用户行为的搜索级 POI 图; (2)显示旅游博客中 POI 共现频率的文档级 POI 图。然后，为了更好地建立两个图的内在相关性模型，我们采用互信息最大化来对齐搜索级和文档级语义空间。此外，我们利用一对 POI 文档相关性评分的排名损失。在两个实际数据集上的大量实验证明了该方法的优越性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Matching+Point+of+Interests+and+Travel+Blog+with+Multi-view+Information+Fusion)|0| |[One-Shot Labeling for Automatic Relevance Estimation](https://doi.org/10.1145/3539618.3592032)|Sean MacAvaney, Luca Soldaini||Dealing with unjudged documents ("holes") in relevance assessments is a perennial problem when evaluating search systems with offline experiments. Holes can reduce the apparent effectiveness of retrieval systems during evaluation and introduce biases in models trained with incomplete data. In this work, we explore whether large language models can help us fill such holes to improve offline evaluations. We examine an extreme, albeit common, evaluation setting wherein only a single known relevant document per query is available for evaluation. We then explore various approaches for predicting the relevance of unjudged documents with respect to a query and the known relevant document, including nearest neighbor, supervised, and prompting techniques. We find that although the predictions of these One-Shot Labelers (1SLs) frequently disagree with human assessments, the labels they produce yield a far more reliable ranking of systems than the single labels do alone. Specifically, the strongest approaches can consistently reach system ranking correlations of over 0.85 with the full rankings over a variety of measures. Meanwhile, the approach substantially reduces the false positive rate of t-tests due to holes in relevance assessments (from 15-30% down to under 5%), giving researchers more confidence in results they find to be significant.|在用离线实验评估搜索系统时，处理相关性评估中未经判断的文档(“漏洞”)是一个长期存在的问题。漏洞会降低检索系统在评价过程中的表观有效性，并在不完全数据训练的模型中引入偏差。在这项工作中，我们探讨是否大型语言模型可以帮助我们填补这些漏洞，以改善离线评估。我们检查一个极端的，尽管是常见的，评估设置，其中每个查询只有一个已知的相关文档可用于评估。然后，我们探讨了预测未判断文档与查询和已知相关文档相关性的各种方法，包括最近邻、监督和提示技术。我们发现，尽管这些一次性标签(1SLs)的预测经常与人类的评估不一致，但是他们生产的标签产生的系统排名比单独的标签产生的系统排名可靠得多。具体来说，最强的方法可以始终达到系统排名相关性超过0.85与完整的排名在各种措施。同时，这种方法大大降低了由于相关性评估漏洞(从15-30% 下降到5% 以下)而导致的 t 检验的假阳性率，使研究人员对他们发现的重要结果更有信心。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=One-Shot+Labeling+for+Automatic+Relevance+Estimation)|0| |[Quantifying and Leveraging User Fatigue for Interventions in Recommender Systems](https://doi.org/10.1145/3539618.3592044)|Hitesh Sagtani, Madan Gopal Jhawar, Akshat Gupta, Rishabh Mehrotra|Sharechat, Bengaluru, India|Predicting churn and designing intervention strategies are crucial for online platforms to maintain user engagement. We hypothesize that predicting churn, i.e. users leaving from the system without further return, is often a delayed act, and it might get too late for the system to intervene. We propose detecting early signs of users losing interest, allowing time for intervention, and introduce a new formulation ofuser fatigue as short-term dissatisfaction, providing early signals to predict long-term churn. We identify behavioral signals predicting fatigue and develop models for fatigue prediction. Furthermore, we leverage the predicted fatigue estimates to develop fatigue-aware ad-load balancing intervention strategy that reduces churn, improving short- and long-term user retention. Results from deployed recommendation system and multiple live A/B tests across over 80 million users generating over 200 million sessions highlight gains for user engagement and platform strategic metrics.|预测流失和设计干预策略对于在线平台维持用户参与度至关重要。我们假设预测流失，也就是说用户没有进一步返回就离开系统，通常是一个延迟行为，系统可能会为时已晚进行干预。我们建议检测用户失去兴趣的早期迹象，为干预留出时间，并引入一种新的用户疲劳公式作为短期不满，为预测长期流失提供早期信号。我们识别预测疲劳的行为信号，并建立疲劳预测模型。此外，我们利用预测的疲劳估计来开发具有疲劳意识的广告负载平衡干预策略，以减少流失，提高短期和长期用户保留率。已部署的推荐系统和超过8,000万用户的多个现场 A/B 测试的结果产生了超过2亿次会议，突出显示了用户参与和平台战略指标的收益。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Quantifying+and+Leveraging+User+Fatigue+for+Interventions+in+Recommender+Systems)|0| |[Rating Prediction in Conversational Task Assistants with Behavioral and Conversational-Flow Features](https://doi.org/10.1145/3539618.3592048)|Rafael Ferreira, David Semedo, João Magalhães|NOVA University of Lisbon, Lisbon, Portugal|Predicting the success of Conversational Task Assistants (CTA) can be critical to understand user behavior and act accordingly. In this paper, we propose TB-Rater, a Transformer model which combines conversational-flow features with user behavior features for predicting user ratings in a CTA scenario. In particular, we use real human-agent conversations and ratings collected in the Alexa TaskBot challenge, a novel multimodal and multi-turn conversational context. Our results show the advantages of modeling both the conversational-flow and behavioral aspects of the conversation in a single model for offline rating prediction. Additionally, an analysis of the CTA-specific behavioral features brings insights into this setting and can be used to bootstrap future systems.|预测会话任务助理(CTA)的成功对于理解用户行为并据此采取行动至关重要。在本文中，我们提出了 TB-Rater 模型，它结合了会话流特征和用户行为特征来预测 CTA 场景中的用户评分。特别是，我们使用真实的人类代理会话和评分收集在 Alexa TaskBot 挑战，一个新颖的多模式和多回合会话环境。我们的研究结果显示了在一个单一的离线评分预测模型中同时建模会话流和会话的行为方面的优势。此外，对 CTA 特定行为特征的分析可以深入了解这种设置，并可用于引导未来的系统。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Rating+Prediction+in+Conversational+Task+Assistants+with+Behavioral+and+Conversational-Flow+Features)|0| |[Retrieval-Enhanced Generative Model for Large-Scale Knowledge Graph Completion](https://doi.org/10.1145/3539618.3592052)|Donghan Yu, Yiming Yang|Carnegie Mellon University, Pittsburgh, PA, USA|The task of knowledge graph completion (KGC) is of great importance. To achieve scalability when dealing with large-scale knowledge graphs, recent works formulate KGC as a sequence-to-sequence process, where the incomplete triplet (input) and the missing entity (output) are both verbalized as text sequences. However, inference with these methods relies solely on the model parameters for implicit reasoning and neglects the use of KG itself, which limits the performance since the model lacks the capacity to memorize a vast number of triplets. To tackle this issue, we introduce ReSKGC, a Retrieval-enhanced Seq2seq KGC model, which selects semantically relevant triplets from the KG and uses them as evidence to guide output generation with explicit reasoning. Our method has demonstrated state-of-the-art performance on benchmark datasets Wikidata5M and WikiKG90Mv2, which contain about 5M and 90M entities, respectively.|知识图完成任务(KGC)具有重要意义。为了在处理大规模知识图时实现可伸缩性，最近的工作将 KGC 表述为一个序列到序列的过程，其中不完整的三元组(输入)和缺失的实体(输出)都表述为文本序列。然而，这些方法的推理完全依赖于模型参数的隐式推理，而忽略了 KG 本身的使用，这限制了性能，因为模型缺乏记忆大量三联体的能力。为了解决这个问题，我们引入了 ReSKGC，一种检索增强的 Seq2seq KGC 模型，它从 KG 中选择语义相关的三联体，并使用它们作为证据来指导显式推理的输出生成。我们的方法已经在基准数据集 Wikidata5M 和 WikiKG90Mv2上演示了最先进的性能，它们分别包含大约5M 和90M 实体。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Retrieval-Enhanced+Generative+Model+for+Large-Scale+Knowledge+Graph+Completion)|0| -|[Review-based Multi-intention Contrastive Learning for Recommendation](https://doi.org/10.1145/3539618.3592053)|Wei Yang, Tengfei Huo, Zhiqiang Liu, Chi Lu|Institute of Computing Technology, Chinese Academy of Sciences & University of Chinese Academy of Sciences, Beijing, China; Institute of Automation, Chinese Academy of Sciences & University of Chinese Academy of Sciences, Beijing, China; Kuaishou Technology, Beijing, China|Real recommendation systems contain various features, which are often high-dimensional, sparse, and difficult to learn effectively. In addition to numerical features, user reviews contain rich semantic information including user preferences, which are used as auxiliary features by researchers. The methods of supplementing data features based on reviews have certain effects. However, most of them simply concatenate review representations and other features together, without considering that the text representation contains a lot of noise information. In addition, the important intentions contained in user reviews are not modeled effectively. In order to solve the above problems, we propose a novel Review-based Multi-intention Contrastive Learning (RMCL) method. In detail, RMCL proposes an intention representation method based on mixed Gaussian distribution hypothesis. Further, RMCL adopts a multi-intention contrastive strategy, which establishes a fine-grained connection between user reviews and item reviews. Extensive experiments on five real-world datasets demonstrate significant improvements of our proposed RMCL model over the state-of-the-art methods.|真正的推荐系统包含各种各样的特性，这些特性通常是高维的、稀疏的，并且难以有效地学习。除了数字特征之外，用户评论还包含丰富的语义信息，包括用户偏好，研究人员将其作为辅助特征。基于评论的数据特征补充方法具有一定的效果。然而，它们中的大多数只是简单地将评论表示和其他特性连接在一起，而没有考虑到文本表示包含大量噪声信息。此外，包含在用户评论中的重要意图没有被有效地建模。为了解决上述问题，我们提出了一种新的基于复习的多意图对比学习(RMCL)方法。具体来说，RMCL 提出了一种基于混合正态分布假设的意图表示方法。此外，RMCL 还采用了多目的对比策略，在用户评论和项目评论之间建立了细粒度的联系。在五个真实世界数据集上的大量实验表明，我们提出的 RMCL 模型比最先进的方法有显著的改进。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Review-based+Multi-intention+Contrastive+Learning+for+Recommendation)|0| -|[Rows or Columns? Minimizing Presentation Bias When Comparing Multiple Recommender Systems](https://doi.org/10.1145/3539618.3592056)|Patrik Dokoupil, Ladislav Peska, Ludovico Boratto|University of Cagliari, Cagliari, Italy; Faculty of Mathematics and Physics, Charles University, Prague, Czech Rep|Going beyond accuracy in the evaluation of a recommender system is an aspect that is receiving more and more attention. Among the many perspectives that can be considered, the impact of presentation bias is of central importance. Under presentation bias, the attention of the users to the items in a recommendation list changes, thus affecting their possibility to be considered and the effectiveness of a model. Page-wise within-subject studies are widely employed in the recommender systems literature to compare algorithms by displaying their results in parallel. However, no study has ever been performed to assess the impact of presentation bias in this context. In this paper, we characterize how presentation bias affects different layout options, which present the results in column- or row-wise fashion. Concretely, we present a user study where six layout variants are proposed to the users in a page-wise within-subject setting, so as to evaluate their perception of the displayed recommendations. Results show that presentation bias impacts users clicking behavior (low-level feedback), but not so much the perceived performance of a recommender system (high-level feedback). Source codes and raw results are available at https://tinyurl.com/PresBiasSIGIR2023.|除了评估推荐系统的准确性之外，这方面也越来越受到重视。在许多可以考虑的观点中，表达偏见的影响是非常重要的。在列报偏差的情况下，用户对推荐清单中项目的注意力发生变化，从而影响其被考虑的可能性和模式的有效性。在推荐系统文献中，主题内的逐页研究被广泛用于通过并行显示算法的结果来比较算法。然而，还没有研究评估在这种情况下呈现偏差的影响。在本文中，我们描述了表示偏差如何影响不同的布局选项，它们以列或行的方式呈现结果。具体来说，我们提出了一个用户研究，其中六个布局变量提出了用户在一个页面明确的主题设置，以评估他们的感知显示的建议。结果显示，呈现偏差影响用户的点击行为(低级别反馈) ，但对推荐系统的感知表现(高级别反馈)影响不大。源代码和原始结果可在 https://tinyurl.com/presbiassigir2023下载。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Rows+or+Columns?+Minimizing+Presentation+Bias+When+Comparing+Multiple+Recommender+Systems)|0| -|[Simpler is Much Faster: Fair and Independent Inner Product Search](https://doi.org/10.1145/3539618.3592061)|Kazuyoshi Aoyama, Daichi Amagata, Sumio Fujita, Takahiro Hara|Osaka University, Suita, Osaka, Japan; Osaka University, Chiyodaku, Tokyo, Japan|The problem of inner product search (IPS) is important in many fields. Although maximum inner product search (MIPS) is often considered, its result is usually skewed and static. Users are hence hard to obtain diverse and/or new items by using the MIPS problem. Motivated by this, we formulate a new problem, namely the fair and independent IPS problem. Given a query, a threshold, and an output size k, this problem randomly samples k items from a set of items such that the inner product of the query and item is not less than the threshold. For each item that satisfies the threshold, this problem is fair, because the probability that such an item is outputted is equal to that for each other item. This fairness can yield diversity and novelty, but this problem faces a computational challenge. Some existing (M)IPS techniques can be employed in this problem, but they require O(n) or o(n) time, where n is the dataset size. To scale well to large datasets, we propose a simple yet efficient algorithm that runs in O(log n + k) expected time. We conduct experiments using real datasets, and the results demonstrate that our algorithm is up to 330 times faster than baselines.|内积搜索问题在许多领域都具有重要意义。尽管经常考虑最大内积搜索(MIPS) ，但其结果通常是倾斜和静态的。因此，用户很难通过使用 MIPS 问题来获得不同的和/或新的项目。在此基础上，我们提出了一个新的问题，即公平独立的知识产权问题。给定一个查询、一个阈值和一个输出大小 k，该问题从一组项中随机抽样 k 项，使得查询和项的内积不小于阈值。对于每个满足阈值的项目，这个问题是公平的，因为这样一个项目输出的概率等于其他项目的概率。这种公平性可以产生多样性和新颖性，但是这个问题面临着计算上的挑战。一些现有的(M) IPS 技术可以用于这个问题，但是它们需要 O (n)或 o (n)时间，其中 n 是数据集大小。为了能够很好地扩展到大数据集，我们提出了一个简单而有效的算法，该算法在 O (log n + k)期望时间内运行。我们使用真实数据集进行实验，结果表明我们的算法比基线快330倍。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Simpler+is+Much+Faster:+Fair+and+Independent+Inner+Product+Search)|0| +|[Review-based Multi-intention Contrastive Learning for Recommendation](https://doi.org/10.1145/3539618.3592053)|Wei Yang, Tengfei Huo, Zhiqiang Liu, Chi Lu|Institute of Automation, Chinese Academy of Sciences & University of Chinese Academy of Sciences, Beijing, China; Institute of Computing Technology, Chinese Academy of Sciences & University of Chinese Academy of Sciences, Beijing, China; Kuaishou Technology, Beijing, China|Real recommendation systems contain various features, which are often high-dimensional, sparse, and difficult to learn effectively. In addition to numerical features, user reviews contain rich semantic information including user preferences, which are used as auxiliary features by researchers. The methods of supplementing data features based on reviews have certain effects. However, most of them simply concatenate review representations and other features together, without considering that the text representation contains a lot of noise information. In addition, the important intentions contained in user reviews are not modeled effectively. In order to solve the above problems, we propose a novel Review-based Multi-intention Contrastive Learning (RMCL) method. In detail, RMCL proposes an intention representation method based on mixed Gaussian distribution hypothesis. Further, RMCL adopts a multi-intention contrastive strategy, which establishes a fine-grained connection between user reviews and item reviews. Extensive experiments on five real-world datasets demonstrate significant improvements of our proposed RMCL model over the state-of-the-art methods.|真正的推荐系统包含各种各样的特性，这些特性通常是高维的、稀疏的，并且难以有效地学习。除了数字特征之外，用户评论还包含丰富的语义信息，包括用户偏好，研究人员将其作为辅助特征。基于评论的数据特征补充方法具有一定的效果。然而，它们中的大多数只是简单地将评论表示和其他特性连接在一起，而没有考虑到文本表示包含大量噪声信息。此外，包含在用户评论中的重要意图没有被有效地建模。为了解决上述问题，我们提出了一种新的基于复习的多意图对比学习(RMCL)方法。具体来说，RMCL 提出了一种基于混合正态分布假设的意图表示方法。此外，RMCL 还采用了多目的对比策略，在用户评论和项目评论之间建立了细粒度的联系。在五个真实世界数据集上的大量实验表明，我们提出的 RMCL 模型比最先进的方法有显著的改进。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Review-based+Multi-intention+Contrastive+Learning+for+Recommendation)|0| +|[Rows or Columns? Minimizing Presentation Bias When Comparing Multiple Recommender Systems](https://doi.org/10.1145/3539618.3592056)|Patrik Dokoupil, Ladislav Peska, Ludovico Boratto|Faculty of Mathematics and Physics, Charles University, Prague, Czech Rep; University of Cagliari, Cagliari, Italy|Going beyond accuracy in the evaluation of a recommender system is an aspect that is receiving more and more attention. Among the many perspectives that can be considered, the impact of presentation bias is of central importance. Under presentation bias, the attention of the users to the items in a recommendation list changes, thus affecting their possibility to be considered and the effectiveness of a model. Page-wise within-subject studies are widely employed in the recommender systems literature to compare algorithms by displaying their results in parallel. However, no study has ever been performed to assess the impact of presentation bias in this context. In this paper, we characterize how presentation bias affects different layout options, which present the results in column- or row-wise fashion. Concretely, we present a user study where six layout variants are proposed to the users in a page-wise within-subject setting, so as to evaluate their perception of the displayed recommendations. Results show that presentation bias impacts users clicking behavior (low-level feedback), but not so much the perceived performance of a recommender system (high-level feedback). Source codes and raw results are available at https://tinyurl.com/PresBiasSIGIR2023.|除了评估推荐系统的准确性之外，这方面也越来越受到重视。在许多可以考虑的观点中，表达偏见的影响是非常重要的。在列报偏差的情况下，用户对推荐清单中项目的注意力发生变化，从而影响其被考虑的可能性和模式的有效性。在推荐系统文献中，主题内的逐页研究被广泛用于通过并行显示算法的结果来比较算法。然而，还没有研究评估在这种情况下呈现偏差的影响。在本文中，我们描述了表示偏差如何影响不同的布局选项，它们以列或行的方式呈现结果。具体来说，我们提出了一个用户研究，其中六个布局变量提出了用户在一个页面明确的主题设置，以评估他们的感知显示的建议。结果显示，呈现偏差影响用户的点击行为(低级别反馈) ，但对推荐系统的感知表现(高级别反馈)影响不大。源代码和原始结果可在 https://tinyurl.com/presbiassigir2023下载。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Rows+or+Columns?+Minimizing+Presentation+Bias+When+Comparing+Multiple+Recommender+Systems)|0| +|[Simpler is Much Faster: Fair and Independent Inner Product Search](https://doi.org/10.1145/3539618.3592061)|Kazuyoshi Aoyama, Daichi Amagata, Sumio Fujita, Takahiro Hara|Osaka University, Chiyodaku, Tokyo, Japan; Osaka University, Suita, Osaka, Japan|The problem of inner product search (IPS) is important in many fields. Although maximum inner product search (MIPS) is often considered, its result is usually skewed and static. Users are hence hard to obtain diverse and/or new items by using the MIPS problem. Motivated by this, we formulate a new problem, namely the fair and independent IPS problem. Given a query, a threshold, and an output size k, this problem randomly samples k items from a set of items such that the inner product of the query and item is not less than the threshold. For each item that satisfies the threshold, this problem is fair, because the probability that such an item is outputted is equal to that for each other item. This fairness can yield diversity and novelty, but this problem faces a computational challenge. Some existing (M)IPS techniques can be employed in this problem, but they require O(n) or o(n) time, where n is the dataset size. To scale well to large datasets, we propose a simple yet efficient algorithm that runs in O(log n + k) expected time. We conduct experiments using real datasets, and the results demonstrate that our algorithm is up to 330 times faster than baselines.|内积搜索问题在许多领域都具有重要意义。尽管经常考虑最大内积搜索(MIPS) ，但其结果通常是倾斜和静态的。因此，用户很难通过使用 MIPS 问题来获得不同的和/或新的项目。在此基础上，我们提出了一个新的问题，即公平独立的知识产权问题。给定一个查询、一个阈值和一个输出大小 k，该问题从一组项中随机抽样 k 项，使得查询和项的内积不小于阈值。对于每个满足阈值的项目，这个问题是公平的，因为这样一个项目输出的概率等于其他项目的概率。这种公平性可以产生多样性和新颖性，但是这个问题面临着计算上的挑战。一些现有的(M) IPS 技术可以用于这个问题，但是它们需要 O (n)或 o (n)时间，其中 n 是数据集大小。为了能够很好地扩展到大数据集，我们提出了一个简单而有效的算法，该算法在 O (log n + k)期望时间内运行。我们使用真实数据集进行实验，结果表明我们的算法比基线快330倍。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Simpler+is+Much+Faster:+Fair+and+Independent+Inner+Product+Search)|0| |[Simplifying Content-Based Neural News Recommendation: On User Modeling and Training Objectives](https://doi.org/10.1145/3539618.3592062)|Andreea Iana, Goran Glavas, Heiko Paulheim||The advent of personalized news recommendation has given rise to increasingly complex recommender architectures. Most neural news recommenders rely on user click behavior and typically introduce dedicated user encoders that aggregate the content of clicked news into user embeddings (early fusion). These models are predominantly trained with standard point-wise classification objectives. The existing body of work exhibits two main shortcomings: (1) despite general design homogeneity, direct comparisons between models are hindered by varying evaluation datasets and protocols; (2) it leaves alternative model designs and training objectives vastly unexplored. In this work, we present a unified framework for news recommendation, allowing for a systematic and fair comparison of news recommenders across several crucial design dimensions: (i) candidate-awareness in user modeling, (ii) click behavior fusion, and (iii) training objectives. Our findings challenge the status quo in neural news recommendation. We show that replacing sizable user encoders with parameter-efficient dot products between candidate and clicked news embeddings (late fusion) often yields substantial performance gains. Moreover, our results render contrastive training a viable alternative to point-wise classification objectives.|个性化新闻推荐的出现引发了日益复杂的推荐体系结构。大多数神经新闻推荐器依赖于用户的点击行为，通常引入专用的用户编码器，将点击新闻的内容聚合到用户嵌入(早期融合)中。这些模型主要用标准的逐点分类目标进行训练。现有的工作主体表现出两个主要缺点: (1)尽管总体设计同质化，但不同的评估数据集和协议阻碍了模型之间的直接比较; (2)留下了大量未被探索的替代模型设计和培训目标。在这项工作中，我们提出了一个新闻推荐的统一框架，允许在几个关键的设计维度上对新闻推荐进行系统和公平的比较: (i)用户建模中的候选人意识，(ii)点击行为融合，以及(iii)培训目标。我们的发现挑战了神经新闻推荐的现状。我们表明，在候选和点击新闻嵌入(后期融合)之间用参数有效的点积替换大型用户编码器通常会产生显著的性能提高。此外，我们的结果使对比训练成为一个可行的替代点分类目标。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Simplifying+Content-Based+Neural+News+Recommendation:+On+User+Modeling+and+Training+Objectives)|0| -|[SimTDE: Simple Transformer Distillation for Sentence Embeddings](https://doi.org/10.1145/3539618.3592063)|Jian Xie, Xin He, Jiyang Wang, Zimeng Qiu, Ali Kebarighotbi, Farhad Ghassemi|Amazon Alexa, Cambridge, MA, USA; Amazon Alexa, Bellevue, WA, USA; University of Central Florida, Orlando, FL, USA|In this paper we introduce SimTDE, a simple knowledge distillation framework to compress sentence embeddings transformer models with minimal performance loss and significant size and latency reduction. SimTDE effectively distills large and small transformers via a compact token embedding block and a shallow encoding block, connected with a projection layer, relaxing dimension match requirement. SimTDE simplifies distillation loss to focus only on token embedding and sentence embedding. We evaluate on standard semantic textual similarity (STS) tasks and entity resolution (ER) tasks. It achieves 99.94% of the state-of-the-art (SOTA) SimCSE-Bert-Base performance with 3 times size reduction and 96.99% SOTA performance with 12 times size reduction on STS tasks. It also achieves 99.57% of teacher's performance on multi-lingual ER data with a tiny transformer student model of 1.4M parameters and 5.7MB size. Moreover, compared to other distilled transformers SimTDE is 2 times faster at inference given similar size and still 1.17 times faster than a model 33% smaller (e.g. MiniLM). The easy-to-adopt framework, strong accuracy and low latency of SimTDE can widely enable runtime deployment of SOTA sentence embeddings.|本文介绍了一个简单的知识提取框架 SimTDE，它可以以最小的性能损失、最大的规模和最小的延迟来压缩句子嵌入转换器模型。SimTDE 通过一个紧凑的令牌嵌入块和一个与投影层连接的浅层编码块，有效地提取大型和小型变压器，放松了尺寸匹配要求。SimTDE 简化了蒸馏损失，只关注标记嵌入和句子嵌入。我们对标准语义文本相似度(STS)任务和实体解析(ER)任务进行了评估。它实现了99.94% 的最先进的(SOTA) SimCSE-Bert-Base 性能与3倍的大小减少和96.99% 的 SOTA 性能与12倍的大小减少的 STS 任务。采用1.4 M 参数、5.7 MB 大小的微型变压器学生模型，实现了99.57% 的教师多语种 ER 数据处理效果。此外，与其他蒸馏变压器相比，SimTDE 在相似尺寸下的推断速度是其他变压器的2倍，仍然比小33% 的型号(例如 MiniLM)快1.17倍。SimTDE 的框架易于采用、准确性强、延迟低，可以广泛应用于 SOTA 句子嵌入的运行时部署。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=SimTDE:+Simple+Transformer+Distillation+for+Sentence+Embeddings)|0| +|[SimTDE: Simple Transformer Distillation for Sentence Embeddings](https://doi.org/10.1145/3539618.3592063)|Jian Xie, Xin He, Jiyang Wang, Zimeng Qiu, Ali Kebarighotbi, Farhad Ghassemi|University of Central Florida, Orlando, FL, USA; Amazon Alexa, Bellevue, WA, USA; Amazon Alexa, Cambridge, MA, USA|In this paper we introduce SimTDE, a simple knowledge distillation framework to compress sentence embeddings transformer models with minimal performance loss and significant size and latency reduction. SimTDE effectively distills large and small transformers via a compact token embedding block and a shallow encoding block, connected with a projection layer, relaxing dimension match requirement. SimTDE simplifies distillation loss to focus only on token embedding and sentence embedding. We evaluate on standard semantic textual similarity (STS) tasks and entity resolution (ER) tasks. It achieves 99.94% of the state-of-the-art (SOTA) SimCSE-Bert-Base performance with 3 times size reduction and 96.99% SOTA performance with 12 times size reduction on STS tasks. It also achieves 99.57% of teacher's performance on multi-lingual ER data with a tiny transformer student model of 1.4M parameters and 5.7MB size. Moreover, compared to other distilled transformers SimTDE is 2 times faster at inference given similar size and still 1.17 times faster than a model 33% smaller (e.g. MiniLM). The easy-to-adopt framework, strong accuracy and low latency of SimTDE can widely enable runtime deployment of SOTA sentence embeddings.|本文介绍了一个简单的知识提取框架 SimTDE，它可以以最小的性能损失、最大的规模和最小的延迟来压缩句子嵌入转换器模型。SimTDE 通过一个紧凑的令牌嵌入块和一个与投影层连接的浅层编码块，有效地提取大型和小型变压器，放松了尺寸匹配要求。SimTDE 简化了蒸馏损失，只关注标记嵌入和句子嵌入。我们对标准语义文本相似度(STS)任务和实体解析(ER)任务进行了评估。它实现了99.94% 的最先进的(SOTA) SimCSE-Bert-Base 性能与3倍的大小减少和96.99% 的 SOTA 性能与12倍的大小减少的 STS 任务。采用1.4 M 参数、5.7 MB 大小的微型变压器学生模型，实现了99.57% 的教师多语种 ER 数据处理效果。此外，与其他蒸馏变压器相比，SimTDE 在相似尺寸下的推断速度是其他变压器的2倍，仍然比小33% 的型号(例如 MiniLM)快1.17倍。SimTDE 的框架易于采用、准确性强、延迟低，可以广泛应用于 SOTA 句子嵌入的运行时部署。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=SimTDE:+Simple+Transformer+Distillation+for+Sentence+Embeddings)|0| |[TAML: Time-Aware Meta Learning for Cold-Start Problem in News Recommendation](https://doi.org/10.1145/3539618.3592068)|Jingyuan Li, Yue Zhang, Xuan Lin, Xinxing Yang, Ge Zhou, Longfei Li, Hong Chen, Jun Zhou|Ant Group, Hangzhou, China|Meta-learning has become a widely used method for the user cold-start problem in recommendation systems, as it allows the model to learn from similar learning tasks and transfer the knowledge to new tasks. However, most existing meta-learning methods do not consider the temporal factor of users' preferences, which is crucial for news recommendation scenarios where news streams change dynamically over time. In this paper, we propose Time-Aware Meta-Learning (TAML), a novel framework that focuses on cold-start users in news recommendation systems. TAML factorizes user preferences into time-specifc and time-shift representations that jointly affect users' news preferences. These temporal factors are further incorporated into the meta-learning framework to achieve accurate and timely cold-start recommendations. Extensive experiments are conducted on two real-world datasets, demonstrating the superior performance of TAML over state-of-the-art methods.|在推荐系统中，元学习使得模型能够从相似的学习任务中学习知识并将知识转移到新的任务中，因而成为解决用户冷启动问题的一种广泛应用的方法。然而，大多数现有的元学习方法都没有考虑用户偏好的时间因素，这对于新闻流随时间动态变化的新闻推荐场景来说是至关重要的。在本文中，我们提出了时间感知元学习(TAML) ，一个新的框架，侧重于冷启动用户在新闻推荐系统。TAML 将用户偏好分解为特定时间和时移表示，共同影响用户的新闻偏好。这些时间因素进一步纳入元学习框架，以实现准确和及时的冷启动建议。在两个真实世界的数据集上进行了广泛的实验，证明了 TAML 优于最先进的方法的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=TAML:+Time-Aware+Meta+Learning+for+Cold-Start+Problem+in+News+Recommendation)|0| -|[Uncertainty-based Heterogeneous Privileged Knowledge Distillation for Recommendation System](https://doi.org/10.1145/3539618.3592079)|Ang Li, Jian Hu, Ke Ding, Xiaolu Zhang, Jun Zhou, Yong He, Xu Min|Queen Mary University of London, London, United Kingdom; Ant Group, Hangzhou, China; Ant Group, Beijing, China|In industrial recommendation systems, both data sizes and computational resources vary across different scenarios. For scenarios with limited data, data sparsity can lead to a decrease in model performance. Heterogeneous knowledge distillation-based transfer learning can be used to transfer knowledge from models in data-rich domains. However, in recommendation systems, the target domain possesses specific privileged features that significantly contribute to the model. While existing knowledge distillation methods have not taken these features into consideration, leading to suboptimal transfer weights. To overcome this limitation, we propose a novel algorithm called Uncertainty-based Heterogeneous Privileged Knowledge Distillation (UHPKD). Our method aims to quantify the knowledge of both the source and target domains, which represents the uncertainty of the models. This approach allows us to derive transfer weights based on the knowledge gain, which captures the difference in knowledge between the source and target domains. Experiments conducted on both public and industrial datasets demonstrate the superiority of our UHPKD algorithm compared to other state-of-the-art methods.|在工业推荐系统中，数据大小和计算资源在不同的场景中都有所不同。对于数据有限的场景，数据稀疏会导致模型性能下降。基于异构知识提取的转移学习可以用来从数据丰富的领域的模型中转移知识。然而，在推荐系统中，目标域具有特定的特权特性，这些特性对模型有很大的贡献。现有的知识提取方法没有考虑到这些特点，导致传递权重不够优化。为了克服这一局限性，我们提出了一种基于不确定性的异构特权知识提取(UHPKD)算法。我们的方法旨在量化源域和目标域的知识，这代表了模型的不确定性。这种方法可以根据知识增益推导出传递权重，从而获取源域和目标域之间的知识差异。在公共数据集和工业数据集上进行的实验表明，与其他最先进的算法相比，我们的 UHPKD 算法具有优越性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Uncertainty-based+Heterogeneous+Privileged+Knowledge+Distillation+for+Recommendation+System)|0| +|[Uncertainty-based Heterogeneous Privileged Knowledge Distillation for Recommendation System](https://doi.org/10.1145/3539618.3592079)|Ang Li, Jian Hu, Ke Ding, Xiaolu Zhang, Jun Zhou, Yong He, Xu Min|Ant Group, Hangzhou, China; Ant Group, Beijing, China; Queen Mary University of London, London, United Kingdom|In industrial recommendation systems, both data sizes and computational resources vary across different scenarios. For scenarios with limited data, data sparsity can lead to a decrease in model performance. Heterogeneous knowledge distillation-based transfer learning can be used to transfer knowledge from models in data-rich domains. However, in recommendation systems, the target domain possesses specific privileged features that significantly contribute to the model. While existing knowledge distillation methods have not taken these features into consideration, leading to suboptimal transfer weights. To overcome this limitation, we propose a novel algorithm called Uncertainty-based Heterogeneous Privileged Knowledge Distillation (UHPKD). Our method aims to quantify the knowledge of both the source and target domains, which represents the uncertainty of the models. This approach allows us to derive transfer weights based on the knowledge gain, which captures the difference in knowledge between the source and target domains. Experiments conducted on both public and industrial datasets demonstrate the superiority of our UHPKD algorithm compared to other state-of-the-art methods.|在工业推荐系统中，数据大小和计算资源在不同的场景中都有所不同。对于数据有限的场景，数据稀疏会导致模型性能下降。基于异构知识提取的转移学习可以用来从数据丰富的领域的模型中转移知识。然而，在推荐系统中，目标域具有特定的特权特性，这些特性对模型有很大的贡献。现有的知识提取方法没有考虑到这些特点，导致传递权重不够优化。为了克服这一局限性，我们提出了一种基于不确定性的异构特权知识提取(UHPKD)算法。我们的方法旨在量化源域和目标域的知识，这代表了模型的不确定性。这种方法可以根据知识增益推导出传递权重，从而获取源域和目标域之间的知识差异。在公共数据集和工业数据集上进行的实验表明，与其他最先进的算法相比，我们的 UHPKD 算法具有优越性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Uncertainty-based+Heterogeneous+Privileged+Knowledge+Distillation+for+Recommendation+System)|0| |[WSFE: Wasserstein Sub-graph Feature Encoder for Effective User Segmentation in Collaborative Filtering](https://doi.org/10.1145/3539618.3592089)|Yankai Chen, Yifei Zhang, Menglin Yang, Zixing Song, Chen Ma, Irwin King||Maximizing the user-item engagement based on vectorized embeddings is a standard procedure of recent recommender models. Despite the superior performance for item recommendations, these methods however implicitly deprioritize the modeling of user-wise similarity in the embedding space; consequently, identifying similar users is underperforming, and additional processing schemes are usually required otherwise. To avoid thorough model re-training, we propose WSFE, a model-agnostic and training-free representation encoder, to be flexibly employed on the fly for effective user segmentation. Underpinned by the optimal transport theory, the encoded representations from WSFE present a matched user-wise similarity/distance measurement between the realistic and embedding space. We incorporate WSFE into six state-of-the-art recommender models and conduct extensive experiments on six real-world datasets. The empirical analyses well demonstrate the superiority and generality of WSFE to fuel multiple downstream tasks with diverse underlying targets in recommendation.|最大化基于向量化嵌入的用户项目参与是最近推荐模型的一个标准过程。尽管项目推荐的性能优越，但是这些方法隐含地剥夺了嵌入空间中用户相似性的建模优先级; 因此，识别相似的用户表现不佳，并且通常需要额外的处理方案。为了避免彻底的模型再训练，我们提出了 WSFE，一种模型不可知和无训练的表示编码器，可以灵活地应用于有效的用户分割。在最优传输理论的支持下，来自 WSFE 的编码表示提供了现实空间和嵌入空间之间匹配的用户相似度/距离度量。我们将 WSFE 整合到六个最先进的推荐模型中，并在六个真实世界的数据集上进行广泛的实验。实证分析很好地证明了 WSFE 在推荐目标不同的多下游任务方面的优越性和普遍性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=WSFE:+Wasserstein+Sub-graph+Feature+Encoder+for+Effective+User+Segmentation+in+Collaborative+Filtering)|0| |[Balanced Topic Aware Sampling for Effective Dense Retriever: A Reproducibility Study](https://doi.org/10.1145/3539618.3591915)|Shuai Wang, Guido Zuccon|The University of Queensland, Brisbane, Australia|Knowledge distillation plays a key role in boosting the effectiveness of rankers based on pre-trained language models (PLMs); this is achieved using an effective but inefficient large model to teach a more efficient student model. In the context of knowledge distillation for a student dense passage retriever, the balanced topic-aware sampling method has been shown to provide state-of-the-art effectiveness. This method intervenes in the creation of the training batches by creating batches that contain positive-negative pairs of passages from the same topic, and balancing the pairwise margins of the positive and negative passages. In this paper, we reproduce the balanced topic-aware sampling method; we do so for both the dataset used for evaluation in the original work (MS MARCO) and for a dataset in a different domain, that of product search (Amazon shopping queries dataset) to study whether the original results generalize to a different context. We show that while we could not replicate the exact results from the original paper, we do confirm the original findings in terms of trends: balanced topic-aware sampling indeed leads to highly effective dense retrievers. These results partially generalize to the other search task we investigate, product search: although we observe the improvements are less significant compared to MS MARCO. In addition to reproducing the original results and studying how the method generalizes to a different dataset, we also investigate a key aspect that influences the effectiveness of the method: the use of a hard margin threshold for negative sampling. This aspect was not studied in the original paper. With respect to hard margins, we find that while setting different hard margin values significantly influences the effectiveness of the student model, this impact is dataset-dependent -- and indeed, it does depend on the score distributions exhibited by retrieval models on the dataset at hand. Our reproducibility code is available at https://github.com/ielab/TAS-B-Reproduction.|知识提取在提高基于预训练语言模型(PLM)的排名的有效性方面起着关键作用，这是通过使用一个有效但低效的大模型来教授一个更有效的学生模型来实现的。在对学生密集通道检索器进行知识提取的背景下，平衡主题感知抽样方法已被证明可以提供最先进的有效性。这种方法通过创建包含来自同一主题的正负段落对的批处理，以及平衡正负段落的成对边距来干预训练批处理的创建。在本文中，我们重现了平衡主题感知抽样方法; 我们这样做的数据集用于评估在原始工作(MS MARCO)和在不同领域的数据集，产品搜索(亚马逊购物查询数据集) ，以研究是否原始结果概括到不同的背景。我们表明，虽然我们不能复制从原始论文的确切结果，我们确实证实了原始发现的趋势: 平衡的主题感知抽样确实导致高效的密集检索。这些结果部分推广到我们调查的其他搜索任务，产品搜索: 虽然我们观察到的改进不太显着相比微软 MARCO。除了重现原始结果和研究该方法如何推广到不同的数据集，我们还研究了影响该方法有效性的一个关键方面: 对负采样使用硬边界阈值。这方面的研究没有在原来的文章。关于硬边际值，我们发现当设置不同的硬边际值显著影响学生模型的有效性时，这种影响是依赖于数据集的——事实上，它确实依赖于手边数据集的检索模型显示的分数分布。我们的重复性代码可以在 https://github.com/ielab/tas-b-reproduction 上找到。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Balanced+Topic+Aware+Sampling+for+Effective+Dense+Retriever:+A+Reproducibility+Study)|0| |[T2Ranking: A Large-scale Chinese Benchmark for Passage Ranking](https://doi.org/10.1145/3539618.3591874)|Xiaohui Xie, Qian Dong, Bingning Wang, Feiyang Lv, Ting Yao, Weinan Gan, Zhijing Wu, Xiangsheng Li, Haitao Li, Yiqun Liu, Jin Ma||Passage ranking involves two stages: passage retrieval and passage re-ranking, which are important and challenging topics for both academics and industries in the area of Information Retrieval (IR). However, the commonly-used datasets for passage ranking usually focus on the English language. For non-English scenarios, such as Chinese, the existing datasets are limited in terms of data scale, fine-grained relevance annotation and false negative issues. To address this problem, we introduce T2Ranking, a large-scale Chinese benchmark for passage ranking. T2Ranking comprises more than 300K queries and over 2M unique passages from real-world search engines. Expert annotators are recruited to provide 4-level graded relevance scores (fine-grained) for query-passage pairs instead of binary relevance judgments (coarse-grained). To ease the false negative issues, more passages with higher diversities are considered when performing relevance annotations, especially in the test set, to ensure a more accurate evaluation. Apart from the textual query and passage data, other auxiliary resources are also provided, such as query types and XML files of documents which passages are generated from, to facilitate further studies. To evaluate the dataset, commonly used ranking models are implemented and tested on T2Ranking as baselines. The experimental results show that T2Ranking is challenging and there is still scope for improvement. The full data and all codes are available at https://github.com/THUIR/T2Ranking/|短文排序包括短文检索和短文重排两个阶段，这两个阶段对于信息检索领域的学术界和工业界来说都是非常重要和具有挑战性的课题。然而，常用于文章排名的数据集通常集中在英语语言上。对于非英语情景，如中文，现有的数据集在数据规模、细粒度相关性标注和虚假否定问题等方面存在局限性。为了解决这个问题，我们引入了 T2Ranking，一个大规模的中文通过排名基准。T2Rank 包含超过30万个查询和超过200万个来自现实世界搜索引擎的独特段落。招募专家注释者为查询-通过对提供4级分级相关分数(细粒度) ，而不是二进制相关判断(粗粒度)。为了减少错误的否定性问题，在进行相关注释时，特别是在测试集中，需要考虑更多多样性较高的段落，以确保更准确的评价。除了文字查询和段落资料外，还提供其他辅助资源，例如查询类型和产生段落的文件的 XML 档案，以方便进一步研究。为了评估数据集，常用的排名模型被实现，并在 T2Ranking 上作为基线进行测试。实验结果表明，T2Ranking 是具有挑战性的，仍然有改进的空间。完整的数据和所有的代码都可以在 https://github.com/thuir/t2ranking/找到|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=T2Ranking:+A+Large-scale+Chinese+Benchmark+for+Passage+Ranking)|0| |[Towards a More User-Friendly and Easy-to-Use Benchmark Library for Recommender Systems](https://doi.org/10.1145/3539618.3591889)|Lanling Xu, Zhen Tian, Gaowei Zhang, Junjie Zhang, Lei Wang, Bowen Zheng, Yifan Li, Jiakai Tang, Zeyu Zhang, Yupeng Hou, Xingyu Pan, Wayne Xin Zhao, Xu Chen, JiRong Wen|Renmin University of China, Beijing, China|In recent years, the reproducibility of recommendation models has become a severe concern in recommender systems. In light of this challenge, we have previously released a unified, comprehensive and efficient recommendation library called RecBole, attracting much attention from the research community. With the increasing number of users, we have received a number of suggestions and update requests. This motivates us to make further improvements on our library, so as to meet the user requirements and contribute to the research community. In this paper, we present a significant update of RecBole, making it more user-friendly and easy-to-use as a comprehensive benchmark library for recommendation. More specifically, the highlights of this update are summarized as: (1) we include more benchmark models and datasets, improve the benchmark framework in terms of data processing, training and evaluation, and release reproducible configurations to benchmark the recommendation models; (2) we upgrade the user friendliness of our library by providing more detailed documentation and well-organized frequently asked questions, and (3) we propose several development guidelines for the open-source library developers. These extensions make it much easier to reproduce the benchmark results and stay up-to-date with the recent advances on recommender systems. Our update is released at the link: https://github.com/RUCAIBox/RecBole.|近年来，推荐模型的可重复性已经成为推荐系统中的一个重要问题。鉴于这一挑战，我们以前发布了一个统一、全面和高效的推荐库，名为 RecBole，吸引了研究界的广泛关注。随着用户数量的增加，我们收到了一些建议和更新请求。这促使我们进一步改善我们的图书馆，以满足用户的要求，并为研究社区作出贡献。在本文中，我们提出了一个重大的更新 RecBole，使其更加友好的用户和易于使用作为一个综合的基准库推荐。更具体地说，这次更新的亮点总结如下: (1)我们包括了更多的基准模型和数据集，改进了数据处理、培训和评估方面的基准框架，并发布了可重复的配置来作为推荐模型的基准; (2)我们通过提供更详细的文档和组织良好的常见问题来提升我们图书馆的用户友好性; (3)我们为开源图书馆开发者提出了几条开发指导方针。这些扩展使得复制基准测试结果和保持最新的推荐系统的最新进展变得更加容易。我们的更新发布在链接: https://github.com/rucaibox/recbole。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Towards+a+More+User-Friendly+and+Easy-to-Use+Benchmark+Library+for+Recommender+Systems)|0| |[RiverText: A Python Library for Training and Evaluating Incremental Word Embeddings from Text Data Streams](https://doi.org/10.1145/3539618.3591908)|Gabriel IturraBocaz, Felipe BravoMarquez|University of Chile, Santiago, Chile|Word embeddings have become essential components in various information retrieval and natural language processing tasks, such as ranking, document classification, and question answering. However, despite their widespread use, traditional word embedding models present a limitation in their static nature, which hampers their ability to adapt to the constantly evolving language patterns that emerge in sources such as social media and the web (e.g., new hashtags or brand names). To overcome this problem, incremental word embedding algorithms are introduced, capable of dynamically updating word representations in response to new language patterns and processing continuous data streams. This paper presents RiverText, a Python library for training and evaluating incremental word embeddings from text data streams. Our tool is a resource for the information retrieval and natural language processing communities that work with word embeddings in streaming scenarios, such as analyzing social media. The library implements different incremental word embedding techniques, such as Skip-gram, Continuous Bag of Words, and Word Context Matrix, in a standardized framework. In addition, it uses PyTorch as its backend for neural network training. We have implemented a module that adapts existing intrinsic static word embedding evaluation tasks for word similarity and word categorization to a streaming setting. Finally, we compare the implemented methods with different hyperparameter settings and discuss the results. Our open-source library is available at https://github.com/dccuchile/rivertext.|单词嵌入已经成为各种信息检索和自然语言处理任务的重要组成部分，例如排序、文档分类和问答。然而，尽管它们被广泛使用，传统的单词嵌入模型在其静态本质上存在局限性，这阻碍了它们适应不断变化的语言模式的能力，这些模式出现在社交媒体和网络等资源中(例如，新的标签或品牌名称)。为了克服这一问题，引入了增量式词嵌入算法，该算法能够根据新的语言模式动态更新词表示并处理连续的数据流。本文介绍了 RiverText，一个用于训练和评估文本数据流中增量单词嵌入的 Python 库。我们的工具是一个为信息检索和自然语言处理社区提供的资源，这些社区使用流媒体场景中的单词嵌入，比如分析社交媒体。该库在一个标准化的框架内实现了不同的增量式单词嵌入技术，如跳跃图、连续单词袋和单词上下文矩阵。此外，它使用 PyTorch 作为神经网络训练的后端。我们实现了一个模块，适应现有的内在静态词语嵌入评价任务的词语相似性和词语分类的流设置。最后，我们比较了不同超参数设置下的实现方法并讨论了结果。我们的开源图书馆可以在 https://github.com/dccuchile/rivertext 上使用。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=RiverText:+A+Python+Library+for+Training+and+Evaluating+Incremental+Word+Embeddings+from+Text+Data+Streams)|0| -|[HeteroCS: A Heterogeneous Community Search System With Semantic Explanation](https://doi.org/10.1145/3539618.3591812)|Weibin Cai, Fanwei Zhu, Zemin Liu, Minghui Wu|Hangzhou City University, Hangzhou, China; Hangzhou City University, Singapore, China|Community search, which looks for query-dependent communities in a graph, is an important task in graph analysis. Existing community search studies address the problem by finding a densely-connected subgraph containing the query. However, many real-world networks are heterogeneous with rich semantics. Queries in heterogeneous networks generally involve in multiple communities with different semantic connections, while returning a single community with mixed semantics has limited applications. In this paper, we revisit the community search problem on heterogeneous networks and introduce a novel paradigm of heterogeneous community search and ranking. We propose to automatically discover the query semantics to enable the search of different semantic communities and develop a comprehensive community evaluation model to support the ranking of results. We build HeteroCS, a heterogeneous community search system with semantic explanation, upon our semantic community model, and deploy it on two real-world graphs. We present a demonstration case to illustrate the novelty and effectiveness of the system.|社区搜索是图分析中的一项重要任务，它在图中寻找与查询相关的社区。现有的社区搜索研究通过查找包含查询的密集连接子图来解决这个问题。然而，许多现实世界的网络具有丰富的语义异构性。异构网络中的查询通常涉及具有不同语义连接的多个社区，而返回具有混合语义的单个社区的应用程序有限。本文重新讨论了异构网络上的社区搜索问题，提出了一种新的异构社区搜索和排序方法。我们提出自动发现查询语义，以便搜索不同的语义社区，并开发一个综合的社区评价模型，以支持结果的排序。我们在语义社区模型的基础上构建了一个具有语义解释的异构社区搜索系统，并将其部署在两个真实世界的图中。通过一个实例说明了该系统的新颖性和有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=HeteroCS:+A+Heterogeneous+Community+Search+System+With+Semantic+Explanation)|0| +|[HeteroCS: A Heterogeneous Community Search System With Semantic Explanation](https://doi.org/10.1145/3539618.3591812)|Weibin Cai, Fanwei Zhu, Zemin Liu, Minghui Wu|Hangzhou City University, Singapore, China; Hangzhou City University, Hangzhou, China|Community search, which looks for query-dependent communities in a graph, is an important task in graph analysis. Existing community search studies address the problem by finding a densely-connected subgraph containing the query. However, many real-world networks are heterogeneous with rich semantics. Queries in heterogeneous networks generally involve in multiple communities with different semantic connections, while returning a single community with mixed semantics has limited applications. In this paper, we revisit the community search problem on heterogeneous networks and introduce a novel paradigm of heterogeneous community search and ranking. We propose to automatically discover the query semantics to enable the search of different semantic communities and develop a comprehensive community evaluation model to support the ranking of results. We build HeteroCS, a heterogeneous community search system with semantic explanation, upon our semantic community model, and deploy it on two real-world graphs. We present a demonstration case to illustrate the novelty and effectiveness of the system.|社区搜索是图分析中的一项重要任务，它在图中寻找与查询相关的社区。现有的社区搜索研究通过查找包含查询的密集连接子图来解决这个问题。然而，许多现实世界的网络具有丰富的语义异构性。异构网络中的查询通常涉及具有不同语义连接的多个社区，而返回具有混合语义的单个社区的应用程序有限。本文重新讨论了异构网络上的社区搜索问题，提出了一种新的异构社区搜索和排序方法。我们提出自动发现查询语义，以便搜索不同的语义社区，并开发一个综合的社区评价模型，以支持结果的排序。我们在语义社区模型的基础上构建了一个具有语义解释的异构社区搜索系统，并将其部署在两个真实世界的图中。通过一个实例说明了该系统的新颖性和有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=HeteroCS:+A+Heterogeneous+Community+Search+System+With+Semantic+Explanation)|0| |[ranxhub: An Online Repository for Information Retrieval Runs](https://doi.org/10.1145/3539618.3591823)|Elias Bassani|University of Milano-Bicocca, Milan, Italy|ranxhub is an online repository for sharing artifacts deriving from the evaluation of Information Retrieval systems. Specifically, we provide a platform for sharing pre-computed runs: the ranked lists of documents retrieved for a specific set of queries by a retrieval model. We also extend ranx, a Python library for the evaluation and comparison of Information Retrieval runs, adding functionalities to integrate the usage of ranxhub seamlessly, allowing the user to compare the results of multiple systems in just a few lines of code. In this paper, we first outline the many advantages and implications that an online repository for sharing runs can bring to the table. Then, we introduce ranxhub and its integration with ranx, showing its very simple usage. Finally, we discuss some use cases for which ranxhub can be highly valuable for the research community.|Ranxhub 是一个在线资源库，用于共享信息检索系统评估产生的工件。具体来说，我们提供了一个共享预计算运行的平台: 通过检索模型为特定查询集检索的文档排序列表。我们还扩展了 ranx，一个用于评估和比较信息检索运行的 Python 库，增加了无缝集成 ranxhub 使用的功能，允许用户在几行代码中比较多个系统的结果。在本文中，我们首先概述了用于共享运行的在线存储库可以带来的许多优势和影响。然后介绍了 ranxhub 及其与 ranx 的集成，说明了它的简单用法。最后，我们讨论了一些使用实例，其中 ranxhub 对于研究社区非常有价值。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=ranxhub:+An+Online+Repository+for+Information+Retrieval+Runs)|0| |[Exploratory Visualization Tool for the Continuous Evaluation of Information Retrieval Systems](https://doi.org/10.1145/3539618.3591825)|Gabriela González Sáez, Petra Galuscáková, Romain Deveaud, Lorraine Goeuriot, Philippe Mulhem|Université Grenoble Alpes, Grenoble, France; Qwant, Paris, France|This paper introduces a novel visualization tool that facilitates the exploratory analysis of continuous evaluation for information retrieval systems. We base our analysis on score standardization and meta-analysis techniques applied to Information Retrieval evaluation. We present three functionalities: evaluation overview, delta evaluation, and meta-analysis applied to three perspectives: evaluation rounds, queries, and systems. To illustrate the use of the tool, we provide an example using the TREC-COVID test collection.|本文介绍一种新颖的可视化工具，可以方便地对信息检索系统的连续评估进行探索性分析。我们的分析基于分数标准化和应用于信息检索评估的荟萃分析技术。我们提出了三种功能: 评估概述、增量评估和应用于三个视角的元分析: 评估轮、查询和系统。为了说明该工具的使用，我们提供了一个使用 TREC-COVID 测试集合的示例。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Exploratory+Visualization+Tool+for+the+Continuous+Evaluation+of+Information+Retrieval+Systems)|0| |[COUPA: An Industrial Recommender System for Online to Offline Service Platforms](https://doi.org/10.1145/3539618.3591828)|Sicong Xie, Binbin Hu, Fengze Li, Ziqi Liu, Zhiqiang Zhang, Wenliang Zhong, Jun Zhou||Aiming at helping users locally discovery retail services (e.g., entertainment and dinning), Online to Offline (O2O) service platforms have become popular in recent years, which greatly challenge current recommender systems. With the real data in Alipay, a feeds-like scenario for O2O services, we find that recurrence based temporal patterns and position biases commonly exist in our scenarios, which seriously threaten the recommendation effectiveness. To this end, we propose COUPA, an industrial system targeting for characterizing user preference with following two considerations: (1) Time aware preference: we employ the continuous time aware point process equipped with an attention mechanism to fully capture temporal patterns for recommendation. (2) Position aware preference: a position selector component equipped with a position personalization module is elaborately designed to mitigate position bias in a personalized manner. Finally, we carefully implement and deploy COUPA on Alipay with a cooperation of edge, streaming and batch computing, as well as a two-stage online serving mode, to support several popular recommendation scenarios. We conduct extensive experiments to demonstrate that COUPA consistently achieves superior performance and has potential to provide intuitive evidences for recommendation|为了帮助用户在本地发现零售服务(如娱乐和餐饮) ，Online To Offline线上到线下服务平台近年来越来越流行，这极大地挑战了现有的推荐系统。利用支付宝中的实际数据，我们发现在 O2O 服务中，基于循环的时间模式和位置偏差在我们的场景中普遍存在，这严重威胁了推荐的有效性。为此，我们提出了 COUPA，这是一个针对用户偏好特征的工业系统，具有以下两个考虑因素: (1)时间感知偏好: 我们采用连续的时间感知点过程，配备注意机制，以充分捕获推荐的时间模式。(2)位置感知偏好: 精心设计的配有位置个性化模块的位置选择元件，以个性化的方式减轻位置偏差。最后，我们在支付宝上小心地实现和部署了 COUPA，并结合了边缘计算、流计算和批处理计算，以及两阶段的在线服务模式，以支持多种流行的推荐场景。我们进行了广泛的实验，以证明 COUPA 始终如一地实现卓越的性能，并有潜力为推荐提供直观的证据|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=COUPA:+An+Industrial+Recommender+System+for+Online+to+Offline+Service+Platforms)|0| |[A Practical Online Allocation Framework at Industry-scale in Constrained Recommendation](https://doi.org/10.1145/3539618.3591835)|Daohong Jian, Yang Bao, Jun Zhou, Hua Wu|AntGroup, Beijing, China|Online allocation is a critical challenge in constrained recommendation systems, where the distribution of goods, ads, vouchers, and other content to users with limited resources needs to be managed effectively. While the existing literature has made significant progress in improving recommendation algorithms for various scenarios, less attention has been given to developing and deploying industry-scale online allocation system in an efficient manner. To address this issue, this paper introduces an integrated and efficient learning framework in constrained recommendation scenarios at Alipay. The framework has been tested through experiments, demonstrating its superiority over other state-of-the-art methods.|在有限的推荐系统中，在线分配是一个关键的挑战，需要有效地管理向资源有限的用户分发商品、广告、凭证和其他内容。虽然现有文献在改进各种情景下的推荐算法方面取得了重大进展，但对于高效开发和部署行业规模的在线分配系统的关注较少。为了解决这个问题，本文在支付宝的受限推荐场景中引入了一个集成的、高效的学习框架。该框架已通过实验进行了测试，证明了其优于其他最先进的方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Practical+Online+Allocation+Framework+at+Industry-scale+in+Constrained+Recommendation)|0| |[A Transformer-Based Substitute Recommendation Model Incorporating Weakly Supervised Customer Behavior Data](https://doi.org/10.1145/3539618.3591847)|Wenting Ye, Hongfei Yang, Shuai Zhao, Haoyang Fang, Xingjian Shi, Naveen Neppalli||The substitute-based recommendation is widely used in E-commerce to provide better alternatives to customers. However, existing research typically uses the customer behavior signals like co-view and view-but-purchase-another to capture the substitute relationship. Despite its intuitive soundness, we find that such an approach might ignore the functionality and characteristics of products. In this paper, we adapt substitute recommendation into language matching problem by taking product title description as model input to consider product functionality. We design a new transformation method to de-noise the signals derived from production data. In addition, we consider multilingual support from the engineering point of view. Our proposed end-to-end transformer-based model achieves both successes from offline and online experiments. The proposed model has been deployed in a large-scale E-commerce website for 11 marketplaces in 6 languages. Our proposed model is demonstrated to increase revenue by 19% based on an online A/B experiment.|基于替代品的推荐广泛应用于电子商务中，为客户提供更好的替代品。然而，现有的研究通常使用顾客行为信号，如共同查看和查看-但购买-另一个来捕捉替代关系。尽管这种方法直观可靠，但我们发现它可能会忽略产品的功能和特性。本文以产品名称描述作为模型输入，考虑产品功能，将替代推荐应用到语言匹配问题中。我们设计了一种新的变换方法来去除从生产数据中得到的信号的噪声。此外，我们从工程的角度考虑多语言支持。我们提出的基于端到端变压器的模型在离线和在线实验中都取得了成功。拟议的模式已经在一个大型电子商务网站上用6种语言为11个市场部署。基于在线 A/B 实验，我们提出的模型被证明可以增加19% 的收入。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Transformer-Based+Substitute+Recommendation+Model+Incorporating+Weakly+Supervised+Customer+Behavior+Data)|0| -|[DCBT: A Simple But Effective Way for Unified Warm and Cold Recommendation](https://doi.org/10.1145/3539618.3591856)|Jieyu Yang, Liang Zhang, Yong He, Ke Ding, Zhaoxin Huan, Xiaolu Zhang, Linjian Mo|Ant Group, Shanghai, China; Ant Group, Hangzhou, China|The cold-start problem of conversion rate prediction is a common challenge in online advertising systems. To alleviate this problem, a large number of methods either use content information or uncertainty methods, or use meta-learning based methods to improve the ranking performance of cold-start items. However, they can work for cold-start scenarios but fail to adaptively unify warm and cold recommendations into one model, requiring additional human efforts or knowledge to adapt to different scenarios. Meanwhile, none of them pay attention to the discrepancy between model predictions and true likelihoods of cold items, while over- or under-estimation is harmful to the ROI (Return on Investment) of advertising placements. In this paper, in order to address the above issues, we propose a framework called Distribution-Constrained Batch Transformer (DCBT). Specifically, the framework introduces a Transformer module into the batch dimension to automatically choose proper information from warm samples to enhance the representation of cold samples and preserve the property of warm samples. In addition, to avoid the distribution of cold samples being affected by the warm samples, the framework adds MMD loss to constrain the sample distribution before and after feeding into the Transformer module. Extensive offline experiments on two real-world datasets show that our proposed method attains state-of-the-art performance in AUC and PCOC (Predicted CVR over CVR) for cold items and warm items. An online A/B test demonstrates that the DCBT model obtained a 20.08% improvement in CVR and a 13.21% increase in GMV (Gross Merchandise Volume).|转化率预测的冷启动问题是在线广告系统中普遍存在的问题。为了缓解这一问题，大量的方法或者使用内容信息或不确定性方法，或者使用基于元学习的方法来改善冷启动项目的排序性能。然而，它们可以适用于冷启动的情况，但不能适应性地将温暖和冷的建议统一到一个模型中，需要额外的人力或知识来适应不同的情况。同时，他们都没有注意到模型预测与真实可能性之间的差异，而过高或过低的估计对广告投放的投资回报率(ROI)是有害的。为了解决上述问题，本文提出了一种分布约束间歇变压器(DCBT)框架。具体来说，该框架引入了变压器模块，自动选择适当的信息从热样本，以增强表示冷样本和保持性能的热样本。此外，为了避免冷样品的分布受到温样品的影响，该框架增加了 MMD 损耗，以约束送入变压器模块前后的样品分布。在两个实际数据集上的大量离线实验表明，我们提出的方法在冷项目和温项目的 AUC 和 PCOC (预测 CVR)方面达到了最先进的性能。在线 A/B 测试表明，DCBT 模型获得了20.08% 的改善 CVR 和13.21% 的增加 GMV (总商品量)。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=DCBT:+A+Simple+But+Effective+Way+for+Unified+Warm+and+Cold+Recommendation)|0| +|[DCBT: A Simple But Effective Way for Unified Warm and Cold Recommendation](https://doi.org/10.1145/3539618.3591856)|Jieyu Yang, Liang Zhang, Yong He, Ke Ding, Zhaoxin Huan, Xiaolu Zhang, Linjian Mo|Ant Group, Hangzhou, China; Ant Group, Shanghai, China|The cold-start problem of conversion rate prediction is a common challenge in online advertising systems. To alleviate this problem, a large number of methods either use content information or uncertainty methods, or use meta-learning based methods to improve the ranking performance of cold-start items. However, they can work for cold-start scenarios but fail to adaptively unify warm and cold recommendations into one model, requiring additional human efforts or knowledge to adapt to different scenarios. Meanwhile, none of them pay attention to the discrepancy between model predictions and true likelihoods of cold items, while over- or under-estimation is harmful to the ROI (Return on Investment) of advertising placements. In this paper, in order to address the above issues, we propose a framework called Distribution-Constrained Batch Transformer (DCBT). Specifically, the framework introduces a Transformer module into the batch dimension to automatically choose proper information from warm samples to enhance the representation of cold samples and preserve the property of warm samples. In addition, to avoid the distribution of cold samples being affected by the warm samples, the framework adds MMD loss to constrain the sample distribution before and after feeding into the Transformer module. Extensive offline experiments on two real-world datasets show that our proposed method attains state-of-the-art performance in AUC and PCOC (Predicted CVR over CVR) for cold items and warm items. An online A/B test demonstrates that the DCBT model obtained a 20.08% improvement in CVR and a 13.21% increase in GMV (Gross Merchandise Volume).|转化率预测的冷启动问题是在线广告系统中普遍存在的问题。为了缓解这一问题，大量的方法或者使用内容信息或不确定性方法，或者使用基于元学习的方法来改善冷启动项目的排序性能。然而，它们可以适用于冷启动的情况，但不能适应性地将温暖和冷的建议统一到一个模型中，需要额外的人力或知识来适应不同的情况。同时，他们都没有注意到模型预测与真实可能性之间的差异，而过高或过低的估计对广告投放的投资回报率(ROI)是有害的。为了解决上述问题，本文提出了一种分布约束间歇变压器(DCBT)框架。具体来说，该框架引入了变压器模块，自动选择适当的信息从热样本，以增强表示冷样本和保持性能的热样本。此外，为了避免冷样品的分布受到温样品的影响，该框架增加了 MMD 损耗，以约束送入变压器模块前后的样品分布。在两个实际数据集上的大量离线实验表明，我们提出的方法在冷项目和温项目的 AUC 和 PCOC (预测 CVR)方面达到了最先进的性能。在线 A/B 测试表明，DCBT 模型获得了20.08% 的改善 CVR 和13.21% 的增加 GMV (总商品量)。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=DCBT:+A+Simple+But+Effective+Way+for+Unified+Warm+and+Cold+Recommendation)|0| |[OFAR: A Multimodal Evidence Retrieval Framework for Illegal Live-streaming Identification](https://doi.org/10.1145/3539618.3591864)|Dengtian Lin, Yang Ma, Yuhong Li, Xuemeng Song, Jianlong Wu, Liqiang Nie||Illegal live-streaming identification, which aims to help live-streaming platforms immediately recognize the illegal behaviors in the live-streaming, such as selling precious and endangered animals, plays a crucial role in purifying the network environment. Traditionally, the live-streaming platform needs to employ some professionals to manually identify the potential illegal live-streaming. Specifically, the professional needs to search for related evidence from a large-scale knowledge database for evaluating whether a given live-streaming clip contains illegal behavior, which is time-consuming and laborious. To address this issue, in this work, we propose a multimodal evidence retrieval system, named OFAR, to facilitate the illegal live-streaming identification. OFAR consists of three modules: Query Encoder, Document Encoder, and MaxSim-based Contrastive Late Intersection. Both query encoder and document encoder are implemented with the advanced OFA encoder, which is pretrained on a large-scale multimodal dataset. In the last module, we introduce contrastive learning on the basis of the MaxiSim-based late intersection, to enhance the model's ability of query-document matching. The proposed framework achieves significant improvement on our industrial dataset TaoLive, demonstrating the advances of our scheme.|非法流媒体识别是帮助流媒体平台及时识别流媒体中的非法行为，如出售珍稀濒危动物等，对净化网络环境起着至关重要的作用。传统上，直播平台需要雇佣一些专业人员来手动识别潜在的非法直播。具体来说，专业人员需要从大规模的知识库中搜索相关证据，以评估给定的直播剪辑是否包含违法行为，这是一项既费时又费力的工作。为了解决这个问题，本文提出了一个多模态证据检索系统 OFAR，以方便非法流媒体证据的识别。OFAR 由三个模块组成: 查询编码器、文档编码器和基于 MaxSim 的对比晚交。查询编码器和文档编码器都是用先进的 OFA 编码器实现的，该编码器是在大规模多模态数据集上预先训练好的。在最后一个模块中，我们引入了基于 MaxiSim 的后期交集对比学习，以提高模型的查询-文档匹配能力。所提出的框架对我们的工业数据集 TaoLive 进行了重大改进，展示了我们方案的进步。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=OFAR:+A+Multimodal+Evidence+Retrieval+Framework+for+Illegal+Live-streaming+Identification)|0| |[Complex Item Set Recommendation](https://doi.org/10.1145/3539618.3594248)|Mozhdeh Ariannezhad, Ming Li, Sami Jullien, Maarten de Rijke|University of Amsterdam, Amsterdam, Netherlands|In this tutorial, we aim to shed light on the task of recommending a set of multiple items at once. In this scenario, historical interaction data between users and items could also be in the form of a sequence of interactions with sets of items. Complex sets of items being recommended together occur in different and diverse domains, such as grocery shopping with so-called baskets and fashion set recommendation with a focus on outfits rather than individual clothing items. We describe the current landscape of research and expose our participants to real-world examples of item set recommendation. We further provide our audience with hands-on experience via a notebook session. Finally, we describe open challenges and call for further research in the area, which we hope will inspire both early stage and more experienced researchers.|在本教程中，我们的目标是阐明一次推荐多个项目的任务。在这个场景中，用户和项之间的历史交互数据也可以是与项集的交互序列的形式。复杂的项目集被推荐一起出现在不同的和多样化的领域，例如杂货店购物与所谓的篮子和时尚集推荐的重点是服装而不是个别的衣服项目。我们描述了当前的研究状况，并让我们的参与者接触到项目集推荐的现实世界的例子。我们进一步提供我们的观众通过笔记本会议的实践经验。最后，我们描述了开放的挑战，并呼吁在该领域进一步的研究，我们希望这将激励早期阶段和更有经验的研究人员。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Complex+Item+Set+Recommendation)|0| |[Recent Advances in the Foundations and Applications of Unbiased Learning to Rank](https://doi.org/10.1145/3539618.3594247)|Shashank Gupta, Philipp Hager, Jin Huang, Ali Vardasbi, Harrie Oosterhuis||Since its inception, the field of unbiased learning to rank (ULTR) has remained very active and has seen several impactful advancements in recent years. This tutorial provides both an introduction to the core concepts of the field and an overview of recent advancements in its foundations along with several applications of its methods. The tutorial is divided into four parts: Firstly, we give an overview of the different forms of bias that can be addressed with ULTR methods. Secondly, we present a comprehensive discussion of the latest estimation techniques in the ULTR field. Thirdly, we survey published results of ULTR in real-world applications. Fourthly, we discuss the connection between ULTR and fairness in ranking. We end by briefly reflecting on the future of ULTR research and its applications. This tutorial is intended to benefit both researchers and industry practitioners who are interested in developing new ULTR solutions or utilizing them in real-world applications.|自成立以来，无偏学习排名(ULTR)领域一直非常活跃，近年来取得了一些有影响力的进展。本教程介绍了该领域的核心概念，概述了该领域基础方面的最新进展及其方法的若干应用。本教程分为四个部分: 首先，我们概述了不同形式的偏倚，可以用 ULTR 方法处理。其次，我们对 ULTR 领域中的最新估计技术进行了全面的讨论。第三，我们调查了已发表的 ULTR 在实际应用中的结果。第四，我们讨论了 ULTR 与排名公平性之间的关系。最后，我们简要地回顾了 ULTR 研究及其应用的未来。本教程旨在使那些对开发新的 ULTR 解决方案或在实际应用中使用它们感兴趣的研究人员和行业从业人员受益。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Recent+Advances+in+the+Foundations+and+Applications+of+Unbiased+Learning+to+Rank)|0| |[Large-Scale Data Processing for Information Retrieval Applications](https://doi.org/10.1145/3539618.3591797)|Pooya Khandel|University of Amsterdam, Amsterdam, Netherlands|Developing Information Retrieval (IR) applications such as search engines and recommendation systems require training of models that are growing in complexity and size with immense collections of data that contain multiple dimensions (documents/items text, user profiles, and interactions). Much of the research in IR concentrates on improving the performance of ranking models; however, given the high training time and high computational resources required to improve the performance by designing new models, it is crucial to address efficiency aspects of the design and deployment of IR applications at large-scale. In my thesis, I aim to improve the training efficiency of IR applications and speed up the development phase of new models, by applying dataset distillation approaches to reduce the dataset size while preserving the ranking quality and employing efficient High-Performance Computing (HPC) solutions to increase the processing speed.|开发像搜索引擎和推荐系统这样的信息检索(IR)应用需要对模型进行培训，这些模型的复杂性和规模都在不断增长，其中包含大量的数据，这些数据包含多个维度(文档/项目文本、用户配置文件和交互)。信息检索领域的大部分研究集中在提高排序模型的性能上; 然而，考虑到通过设计新模型来提高性能所需的高训练时间和高计算资源，在大规模设计和部署信息检索应用时，解决效率方面的问题是至关重要的。在本文中，我的目标是提高红外应用程序的训练效率，加快新模型的开发阶段，通过应用数据集精馏方法来减少数据集的大小，同时保持排序质量，并使用高效的高性能计算(HPC)解决方案来提高处理速度。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Large-Scale+Data+Processing+for+Information+Retrieval+Applications)|0| -|[Generative Information Retrieval](https://doi.org/10.1145/3539618.3591871)|Marc Najork|Walmart Labs, Sunnyvale, CA, USA; Instacart, San Francisco, CA, USA|ABSTRACTIn the relatively short history of machine learning, the subtle balance between engineering and theoretical progress has been proved critical at various stages. The most recent wave of AI has brought to the IR community powerful techniques, particularly for pattern recognition. While many benefits from the burst of ideas as numerous tasks become algorithmically feasible, the balance is tilting toward the application side. The existing theoretical tools in IR can no longer explain, guide, and justify the newly-established methodologies. With no choices, we have to bet our design on black-box mechanisms that we only empirically understand. The consequences can be suffering: in stark contrast to how the IR industry has envisioned modern AI making life easier, many are experiencing increased confusion and costs in data manipulation, model selection, monitoring, censoring, and decision making. This reality is not surprising: without handy theoretical tools, we often lack principled knowledge of the pattern recognition model's expressivity, optimization property, generalization guarantee, and our decision-making process has to rely on over-simplified assumptions and human judgments from time to time. Facing all the challenges, we started researching advanced theoretical tools emerging from various domains that can potentially resolve modern IR problems. We encountered many impactful ideas and made several independent publications emphasizing different pieces. Time is now to bring the community a systematic tutorial on how we successfully adapt those tools and make significant progress in understanding, designing, and eventually productionize impactful IR systems. We emphasize systematicity because IR is a comprehensive discipline that touches upon particular aspects of learning, causal inference analysis, interactive (online) decision-making, etc. It thus requires systematic calibrations to render the actual usefulness of the imported theoretical tools to serve IR problems, as they usually exhibit unique structures and definitions. Therefore, we plan this tutorial to systematically demonstrate our learning and successful experience of using advanced theoretical tools for understanding and designing IR systems.|在机器学习相对较短的历史中，工程与理论进步之间的微妙平衡在不同的阶段被证明是至关重要的。最近的人工智能浪潮给红外社区带来了强大的技术，特别是模式识别。虽然随着大量任务在算法上变得可行，思想的迸发带来了许多好处，但是平衡正在向应用程序方面倾斜。现有的 IR 理论工具已经不能解释、指导和证明新建立的方法论。由于别无选择，我们不得不把我们的设计押在我们只能凭经验理解的黑盒机制上。其结果可能是痛苦的: 与红外行业设想的现代人工智能如何使生活变得更容易形成鲜明对比的是，许多人在数据操作、模型选择、监控、审查和决策方面正经历着越来越多的混乱和成本。这一现实并不令人惊讶: 没有方便的理论工具，我们往往缺乏模式识别模型的表达能力、优化特性、泛化保证的原则性知识，我们的决策过程不得不依赖于过于简化的假设和人为判断。面对这些挑战，我们开始研究来自不同领域的先进理论工具，这些工具可以解决现代国际关系问题。我们遇到了许多有影响力的想法，并作出了几个独立的出版物，强调不同的作品。现在是时候给社区带来一个系统的教程，告诉他们我们如何成功地调整这些工具，并在理解、设计和最终生产有影响力的 IR 系统方面取得重大进展。我们强调系统性，因为国际关系是一个综合性的学科，涉及到学习的特定方面，因果推理分析，互动(在线)决策，等等。因此，需要进行系统的校准，以提供进口的理论工具的实际用途，以服务红外问题，因为他们通常表现出独特的结构和定义。因此，我们计划本教程系统地展示我们使用先进的理论工具来理解和设计 IR 系统的学习和成功经验。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Generative+Information+Retrieval)|0| +|[Generative Information Retrieval](https://doi.org/10.1145/3539618.3591871)|Marc Najork|Instacart, San Francisco, CA, USA; Walmart Labs, Sunnyvale, CA, USA|ABSTRACTIn the relatively short history of machine learning, the subtle balance between engineering and theoretical progress has been proved critical at various stages. The most recent wave of AI has brought to the IR community powerful techniques, particularly for pattern recognition. While many benefits from the burst of ideas as numerous tasks become algorithmically feasible, the balance is tilting toward the application side. The existing theoretical tools in IR can no longer explain, guide, and justify the newly-established methodologies. With no choices, we have to bet our design on black-box mechanisms that we only empirically understand. The consequences can be suffering: in stark contrast to how the IR industry has envisioned modern AI making life easier, many are experiencing increased confusion and costs in data manipulation, model selection, monitoring, censoring, and decision making. This reality is not surprising: without handy theoretical tools, we often lack principled knowledge of the pattern recognition model's expressivity, optimization property, generalization guarantee, and our decision-making process has to rely on over-simplified assumptions and human judgments from time to time. Facing all the challenges, we started researching advanced theoretical tools emerging from various domains that can potentially resolve modern IR problems. We encountered many impactful ideas and made several independent publications emphasizing different pieces. Time is now to bring the community a systematic tutorial on how we successfully adapt those tools and make significant progress in understanding, designing, and eventually productionize impactful IR systems. We emphasize systematicity because IR is a comprehensive discipline that touches upon particular aspects of learning, causal inference analysis, interactive (online) decision-making, etc. It thus requires systematic calibrations to render the actual usefulness of the imported theoretical tools to serve IR problems, as they usually exhibit unique structures and definitions. Therefore, we plan this tutorial to systematically demonstrate our learning and successful experience of using advanced theoretical tools for understanding and designing IR systems.|在机器学习相对较短的历史中，工程与理论进步之间的微妙平衡在不同的阶段被证明是至关重要的。最近的人工智能浪潮给红外社区带来了强大的技术，特别是模式识别。虽然随着大量任务在算法上变得可行，思想的迸发带来了许多好处，但是平衡正在向应用程序方面倾斜。现有的 IR 理论工具已经不能解释、指导和证明新建立的方法论。由于别无选择，我们不得不把我们的设计押在我们只能凭经验理解的黑盒机制上。其结果可能是痛苦的: 与红外行业设想的现代人工智能如何使生活变得更容易形成鲜明对比的是，许多人在数据操作、模型选择、监控、审查和决策方面正经历着越来越多的混乱和成本。这一现实并不令人惊讶: 没有方便的理论工具，我们往往缺乏模式识别模型的表达能力、优化特性、泛化保证的原则性知识，我们的决策过程不得不依赖于过于简化的假设和人为判断。面对这些挑战，我们开始研究来自不同领域的先进理论工具，这些工具可以解决现代国际关系问题。我们遇到了许多有影响力的想法，并作出了几个独立的出版物，强调不同的作品。现在是时候给社区带来一个系统的教程，告诉他们我们如何成功地调整这些工具，并在理解、设计和最终生产有影响力的 IR 系统方面取得重大进展。我们强调系统性，因为国际关系是一个综合性的学科，涉及到学习的特定方面，因果推理分析，互动(在线)决策，等等。因此，需要进行系统的校准，以提供进口的理论工具的实际用途，以服务红外问题，因为他们通常表现出独特的结构和定义。因此，我们计划本教程系统地展示我们使用先进的理论工具来理解和设计 IR 系统的学习和成功经验。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Generative+Information+Retrieval)|0| |[Tasks, Copilots, and the Future of Search](https://doi.org/10.1145/3539618.3593069)|Ryen W. White|Microsoft Research, Redmond, WA, USA|Tasks are central to information retrieval (IR) and drive interactions with search systems [2, 4, 10]. Understanding and modeling tasks helps these systems better support user needs [8, 9, 11]. This keynote focuses on search tasks, the emergence of generative artificial intelligence (AI), and the implications of recent work at their intersection for the future of search. Recent estimates suggest that half of Web search queries go unanswered, many of them connected to complex search tasks that are ill-defined or multi-step and span several queries[6]. AI copilots, e.g., ChatGPT and Bing Chat, are emerging to address complex search tasks and many other challenges. These copilots are built on large foundation models such as GPT-4 and are being extended with skills and plugins. Copilots broaden the surface of tasks achievable via search, moving toward creation not just finding (e.g., interview preparation, email composition), and can make searchers more efficient and more successful. Users currently engage with AI copilots via natural language queries and dialog and the copilots generate answers with source attribution [7]. However, in delegating responsibility for answer generation, searchers also lose some control over aspects of the search process, such as directly manipulating queries and examining lists of search results [1]. The efficiency gains from auto-generating a single, synthesized answer may also reduce opportunities for user learning and serendipity. A wholesale move to copilots for all search tasks is neither practical nor necessary: model inference is expensive, conversational interfaces are unfamiliar to many users in a search context, and traditional search already excels for many types of task. Instead, experiences that unite search and chat are becoming more common, enabling users to adjust the modality and other aspects (e.g., answer tone) based on the task. The rise of AI copilots creates many opportunities for IR, including aligning generated answers with user intent, tasks, and applications via human feedback [3]; understanding copilot usage, including functional fixedness [5]; using context and data to tailor responses to people and situations (e.g., grounding, personalization); new search experiences (e.g., unifying search and chat); reliability and safety (e.g., accuracy, bias); understanding impacts on user learning and agency; and evaluation (e.g., model-based feedback, searcher simulations [12] repeatability). Research in these and related areas will enable search systems to more effectively utilize new copilot technologies together with traditional search to help searchers better tackle a wider variety of tasks.|任务是信息检索(IR)的核心，并驱动与搜索系统的交互[2,4,10]。理解和建模任务有助于这些系统更好地支持用户需求[8,9,11]。本演讲的重点是搜索任务，生成性人工智能(AI)的出现，以及最近的工作在其交叉点对搜索的未来的影响。最近的估计表明，一半的网络搜索查询没有得到回答，其中许多与复杂的搜索任务相关，这些任务定义不明确或多步骤，跨越多个查询[6]。人工智能副驾驶员，如 ChatGPT 和 Bing Chat，正在出现，以解决复杂的搜索任务和许多其他挑战。这些副驾驶员是建立在大型基础模型，如 GPT-4，并正在扩展技能和插件。副驾驶员拓宽了通过搜索可以完成的任务的表面，不仅仅是创造性的搜索(例如，面试准备，电子邮件写作) ，而且可以使搜索者更有效率和更成功。用户目前通过自然语言查询和对话与人工智能副驾驶员交流，副驾驶员通过源代码归属生成答案[7]。然而，在分配生成答案的责任时，搜索者也失去了对搜索过程某些方面的控制，比如直接操作查询和检查搜索结果列表[1]。自动生成一个单一的综合答案所带来的效率也可能减少用户学习和意外发现的机会。对于所有搜索任务而言，大规模转向副驾驶员既不实际，也不必要: 模型推理成本高昂，对于搜索上下文中的许多用户而言，会话界面并不熟悉，而且传统搜索已经在许多类型的任务中表现出色。相反，将搜索和聊天结合在一起的体验正变得越来越普遍，使用户能够根据任务调整模式和其他方面(例如，回答语气)。人工智能副驾驶员的崛起为信息检索创造了许多机会，包括通过人工反馈将生成的答案与用户意图、任务和应用程序对齐[3] ; 理解副驾驶员的使用，包括功能固着[5] ; 使用上下文和数据来调整对人和情况的反应(例如，禁足，个性化) ; 新的搜索体验(例如，统一搜索和聊天) ; 可靠性和安全性(例如，准确性，偏见) ; 理解对用户学习和代理的影响; 以及评估(例如，基于模型的反馈，搜索器模拟[。这些领域和相关领域的研究将使搜索系统能够更有效地利用新的副驾驶技术和传统搜索，以帮助搜索人员更好地处理更广泛的各种任务。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Tasks,+Copilots,+and+the+Future+of+Search)|0| |[Learning to Re-rank with Constrained Meta-Optimal Transport](https://doi.org/10.1145/3539618.3591714)|Andrés Hoyos Idrobo||Many re-ranking strategies in search systems rely on stochastic ranking policies, encoded as Doubly-Stochastic (DS) matrices, that satisfy desired ranking constraints in expectation, e.g., Fairness of Exposure (FOE). These strategies are generally two-stage pipelines: \emph{i)} an offline re-ranking policy construction step and \emph{ii)} an online sampling of rankings step. Building a re-ranking policy requires repeatedly solving a constrained optimization problem, one for each issued query. Thus, it is necessary to recompute the optimization procedure for any new/unseen query. Regarding sampling, the Birkhoff-von-Neumann decomposition (BvND) is the favored approach to draw rankings from any DS-based policy. However, the BvND is too costly to compute online. Hence, the BvND as a sampling solution is memory-consuming as it can grow as $\gO(N\, n^2)$ for $N$ queries and $n$ documents. This paper offers a novel, fast, lightweight way to predict fair stochastic re-ranking policies: Constrained Meta-Optimal Transport (CoMOT). This method fits a neural network shared across queries like a learning-to-rank system. We also introduce Gumbel-Matching Sampling (GumMS), an online sampling approach from DS-based policies. Our proposed pipeline, CoMOT + GumMS, only needs to store the parameters of a single model, and it generalizes to unseen queries. We empirically evaluated our pipeline on the TREC 2019 and 2020 datasets under FOE constraints. Our experiments show that CoMOT rapidly predicts fair re-ranking policies on held-out data, with a speed-up proportional to the average number of documents per query. It also displays fairness and ranking performance similar to the original optimization-based policy. Furthermore, we empirically validate the effectiveness of GumMS to approximate DS-based policies in expectation.|搜索系统中的许多重排序策略都依赖于随机排序策略，这些策略被编码为双随机(DS)矩阵，满足期望的排序约束，例如，公平曝光(FOE)。这些策略通常是两个阶段的管道: emph { i }离线重新排序策略构建步骤和 emph { ii }在线排序步骤抽样。构建一个重新排序策略需要重复解决一个受限制的最佳化问题，每个发出的查询一个。因此，有必要重新计算任何新的/未见查询的优化过程。关于抽样，Birkhoff-von-Neumann 分解(BvND)是从任何基于 DS 的政策中提取排名的最受欢迎的方法。然而，在线计算 BvND 的成本太高。因此，作为抽样解决方案的 BvND 占用内存，因为对于 $N $查询和 $n $document，它可以增长为 $gO (N，n ^ 2) $。本文提出了一种新颖、快速、轻量级的预测公平随机重排策略的方法: 约束元最优运输(CoMOT)。该方法适用于跨查询共享的神经网络，如学习排序系统。我们还介绍了 Gumbel 匹配抽样(GumMS) ，一种基于 DS 策略的在线抽样方法。我们提出的流水线 CoMOT + GumMS 只需要存储单个模型的参数，并且它可以推广到不可见的查询。我们在 FOE 约束下对 TREC 2019和2020数据集的管道进行了实证评估。我们的实验表明，CoMOT 能够快速地预测对被拒绝的数据进行公平的重新排序的策略，其速度与每个查询的平均文档数成正比。它还显示公平性和排名性能类似于原来的优化为基础的政策。此外，我们还实验验证了 GumMS 在预期情况下逼近基于 DS 策略的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Learning+to+Re-rank+with+Constrained+Meta-Optimal+Transport)|0| |[Constructing Tree-based Index for Efficient and Effective Dense Retrieval](https://doi.org/10.1145/3539618.3591651)|Haitao Li, Qingyao Ai, Jingtao Zhan, Jiaxin Mao, Yiqun Liu, Zheng Liu, Zhao Cao||Recent studies have shown that Dense Retrieval (DR) techniques can significantly improve the performance of first-stage retrieval in IR systems. Despite its empirical effectiveness, the application of DR is still limited. In contrast to statistic retrieval models that rely on highly efficient inverted index solutions, DR models build dense embeddings that are difficult to be pre-processed with most existing search indexing systems. To avoid the expensive cost of brute-force search, the Approximate Nearest Neighbor (ANN) algorithm and corresponding indexes are widely applied to speed up the inference process of DR models. Unfortunately, while ANN can improve the efficiency of DR models, it usually comes with a significant price on retrieval performance. To solve this issue, we propose JTR, which stands for Joint optimization of TRee-based index and query encoding. Specifically, we design a new unified contrastive learning loss to train tree-based index and query encoder in an end-to-end manner. The tree-based negative sampling strategy is applied to make the tree have the maximum heap property, which supports the effectiveness of beam search well. Moreover, we treat the cluster assignment as an optimization problem to update the tree-based index that allows overlapped clustering. We evaluate JTR on numerous popular retrieval benchmarks. Experimental results show that JTR achieves better retrieval performance while retaining high system efficiency compared with widely-adopted baselines. It provides a potential solution to balance efficiency and effectiveness in neural retrieval system designs.|近年来的研究表明，密集检索(DR)技术可以显著提高红外系统第一阶段检索的性能。尽管 DR 在实证研究中取得了一定的成效，但其应用仍然有限。与依赖于高效率倒排索引解决方案的统计检索模型相比，DR 模型构建了密集的嵌入，这些嵌入很难在大多数现有的搜索索引系统中进行预处理。为了避免昂贵的暴力搜索法成本，近似最近邻(ANN)算法和相应的索引被广泛应用于加快 DR 模型的推理过程。遗憾的是，尽管人工神经网络可以提高 DR 模型的效率，但它通常会给检索性能带来巨大的代价。为了解决这个问题，我们提出了 JTR，它代表了基于树的索引和查询编码的联合优化。具体来说，我们设计了一种新的统一对比学习丢失算法，用于以端到端的方式训练基于树的索引和查询编码器。采用基于树的负采样策略，使树具有最大的堆性质，很好地支持了波束搜索的有效性。此外，我们把集群分配当作一个最佳化问题，以更新允许重叠集群的基于树的索引。我们在许多流行的检索基准上评估 JTR。实验结果表明，与广泛采用的基线相比，JTR 在保持较高系统效率的同时，获得了较好的检索性能。它提供了一个潜在的解决方案，以平衡效率和有效的神经检索系统设计。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Constructing+Tree-based+Index+for+Efficient+and+Effective+Dense+Retrieval)|0| @@ -192,26 +192,26 @@ |[Alleviating Matthew Effect of Offline Reinforcement Learning in Interactive Recommendation](https://doi.org/10.1145/3539618.3591636)|Chongming Gao, Kexin Huang, Jiawei Chen, Yuan Zhang, Biao Li, Peng Jiang, Shiqi Wang, Zhong Zhang, Xiangnan He||Offline reinforcement learning (RL), a technology that offline learns a policy from logged data without the need to interact with online environments, has become a favorable choice in decision-making processes like interactive recommendation. Offline RL faces the value overestimation problem. To address it, existing methods employ conservatism, e.g., by constraining the learned policy to be close to behavior policies or punishing the rarely visited state-action pairs. However, when applying such offline RL to recommendation, it will cause a severe Matthew effect, i.e., the rich get richer and the poor get poorer, by promoting popular items or categories while suppressing the less popular ones. It is a notorious issue that needs to be addressed in practical recommender systems. In this paper, we aim to alleviate the Matthew effect in offline RL-based recommendation. Through theoretical analyses, we find that the conservatism of existing methods fails in pursuing users' long-term satisfaction. It inspires us to add a penalty term to relax the pessimism on states with high entropy of the logging policy and indirectly penalizes actions leading to less diverse states. This leads to the main technical contribution of the work: Debiased model-based Offline RL (DORL) method. Experiments show that DORL not only captures user interests well but also alleviates the Matthew effect. The implementation is available via https://github.com/chongminggao/DORL-codes.|离线强化学习(off-line)是一种无需与在线环境交互就可以从已记录的数据中学习策略的技术，已经成为诸如交互式推荐等决策过程中的一个有利选择。脱机 RL 面临价值高估问题。为了解决这个问题，现有的方法采用了保守主义，例如，通过限制学习政策接近行为政策或惩罚很少访问的国家行动对。然而，当把这种线下 RL 应用于推荐时，它会产生一种严重的马太效应，也就是说，富人变得更富，穷人变得更穷，通过推销受欢迎的项目或类别，同时压制不太受欢迎的项目或类别。这是一个臭名昭著的问题，需要解决的实际推荐系统。本文旨在减轻基于 RL 的离线推荐中的马太效应。通过理论分析，我们发现现有方法的保守性不足以追求用户的长期满意度。它启发我们增加一个惩罚项，以放松对伐木政策的高熵状态的悲观情绪，并间接惩罚导致较少多样性状态的行动。这导致了这项工作的主要技术贡献: 基于消偏模型的离线 RL (DORL)方法。实验表明，DORL 不仅能够很好地捕获用户兴趣，而且能够减轻马太效应。有关实施方案可透过 https://github.com/chongminggao/dorl-codes 提供。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Alleviating+Matthew+Effect+of+Offline+Reinforcement+Learning+in+Interactive+Recommendation)|0| |[Distributionally Robust Sequential Recommnedation](https://doi.org/10.1145/3539618.3591668)|Rui Zhou, Xian Wu, Zhaopeng Qiu, Yefeng Zheng, Xu Chen|Renmin University of China, Beijing, China; Tencent Jarvis Lab, Shenzhen, China|Modeling user sequential behaviors have been demonstrated to be effective in promoting the recommendation performance. While previous work has achieved remarkable successes, they mostly assume that the training and testing distributions are consistent, which may contradict with the diverse and complex user preferences, and limit the recommendation performance in real-world scenarios. To alleviate this problem, in this paper, we propose a robust sequential recommender framework to overcome the potential distribution shift between the training and testing sets. In specific, we firstly simulate different training distributions via sample reweighting. Then, we minimize the largest loss induced by these distributions to optimize the 'worst-case' loss for improving the model robustness. Considering that there can be too many sample weights, which may introduce too much flexibility and be hard to optimize, we cluster the training samples based on both hard and soft strategies, and assign each cluster with a unified weight. At last, we analyze our framework by presenting the generalization error bound of the above minimax objective, which help us to better understand the proposed framework from the theoretical perspective. We conduct extensive experiments based on three real-world datasets to demonstrate the effectiveness of our proposed framework. To reproduce our experiments and promote this research direction, we have released our project at https://anonymousrsr.github.io/RSR/.|建立用户序列行为模型可以有效地提高推荐性能。虽然以前的工作已经取得了显著的成功，他们大多假设培训和测试分布是一致的，这可能与多样化和复杂的用户偏好相矛盾，并限制了推荐性能在现实世界的场景。为了解决这一问题，本文提出了一种鲁棒的顺序推荐框架来克服训练集和测试集之间潜在的分布偏移。具体地说，我们首先通过样本重权重模拟不同的训练分布。然后，我们最小化由这些分布引起的最大损失，优化“最坏情况”的损失，以提高模型的鲁棒性。针对训练样本权重过多、灵活性大、难以优化等问题，采用软硬策略相结合的方法对训练样本进行聚类，并对每个聚类进行统一权重分配。最后，我们通过提出上述极大极小目标的泛化误差界限来分析我们的框架，这有助于我们从理论的角度更好地理解提出的框架。我们进行了广泛的实验基于三个真实世界的数据集，以证明我们提出的框架的有效性。为了重现我们的实验并推动这一研究方向，我们已经在 https://anonymousrsr.github.io/rsr/上发布了我们的项目。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Distributionally+Robust+Sequential+Recommnedation)|0| |[Knowledge-refined Denoising Network for Robust Recommendation](https://doi.org/10.1145/3539618.3591707)|Xinjun Zhu, Yuntao Du, Yuren Mao, Lu Chen, Yujia Hu, Yunjun Gao||Knowledge graph (KG), which contains rich side information, becomes an essential part to boost the recommendation performance and improve its explainability. However, existing knowledge-aware recommendation methods directly perform information propagation on KG and user-item bipartite graph, ignoring the impacts of \textit{task-irrelevant knowledge propagation} and \textit{vulnerability to interaction noise}, which limits their performance. To solve these issues, we propose a robust knowledge-aware recommendation framework, called \textit{Knowledge-refined Denoising Network} (KRDN), to prune the task-irrelevant knowledge associations and noisy implicit feedback simultaneously. KRDN consists of an adaptive knowledge refining strategy and a contrastive denoising mechanism, which are able to automatically distill high-quality KG triplets for aggregation and prune noisy implicit feedback respectively. Besides, we also design the self-adapted loss function and the gradient estimator for model optimization. The experimental results on three benchmark datasets demonstrate the effectiveness and robustness of KRDN over the state-of-the-art knowledge-aware methods like KGIN, MCCLK, and KGCL, and also outperform robust recommendation models like SGL and SimGCL.|知识图(KG)包含了丰富的边信息，是提高推荐性能、增强推荐可解释性的重要组成部分。然而，现有的知识感知推荐方法直接在 KG 和用户项二分图上进行信息传播，忽略了文本{任务无关知识传播}和文本{交互噪声脆弱性}的影响，从而限制了它们的性能。为了解决这些问题，我们提出了一个鲁棒的知识感知推荐框架，称为 texttit { Knowledge-finedDenoisingNetwork }(KRDN) ，它可以同时修剪与任务无关的知识关联和有噪隐式反馈。KRDN 由自适应知识精炼策略和对比去噪机制两部分组成，它们分别能够自动提取高质量的 KG 三元组用于聚集和删除含噪隐式反馈。此外，我们还设计了自适应损失函数和模型优化的梯度估计器。在三个基准数据集上的实验结果显示了 KRDN 相对于最先进的知识感知方法(如 KGIN、 MCCLK 和 KGCL)的有效性和稳健性，也优于稳健的推荐模型(如 SGL 和 SimGCL)。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Knowledge-refined+Denoising+Network+for+Robust+Recommendation)|0| -|[Mixed-Curvature Manifolds Interaction Learning for Knowledge Graph-aware Recommendation](https://doi.org/10.1145/3539618.3591730)|Jihu Wang, Yuliang Shi, Han Yu, Xinjun Wang, Zhongmin Yan, Fanyu Kong|Shandong University, Jinan, China; Shandong University & Dareway Software Co., Ltd, Jinan, China; Nanyang Technological University, Singapore, Singapore|As auxiliary collaborative signals, the entity connectivity and relation semanticity beneath knowledge graph (KG) triples can alleviate the data sparsity and cold-start issues of recommendation tasks. Thus many works consider obtaining user and item representations via information aggregation on graph-structured data within Euclidean space. However, the scale-free graphs (e.g., KGs) inherently exhibit non-Euclidean geometric topologies, such as tree-like and circle-like structures. The existing recommendation models built in a single type of embedding space do not have enough capacity to embrace various geometric patterns, consequently, resulting in suboptimal performance. To address this limitation, we propose a KG-aware recommendation model with mixed-curvature manifolds interaction learning, namely CurvRec. On the one hand, it aims to preserve various global geometric structures in KG with mixed-curvature manifold spaces as the backbone. On the other hand, we integrate Ricci curvature into graph convolutional networks (GCNs) to capture local geometric structural properties when aggregating neighbor nodes. Besides, to exploit the expressive spatial features in KG, we incorporate interaction learning to ensure the geometric message passing between curved manifolds. Specifically, we adopt curvature-aware geodesic distance metrics to maximize the mutual information between Euclidean space and non-Euclidean spaces. Through extensive experiments, we demonstrate that the proposed CurvRec outperforms state-of-the-art baselines.|知识图(KG)三元组下的实体连通性和关系语义作为辅助协同信号，可以缓解推荐任务的数据稀疏性和冷启动问题。因此，许多工作考虑通过在欧几里得空间中对图形结构数据进行信息聚合来获得用户和项目表示。然而，无标度图(例如 KGs)本质上表现出非欧几里德几何拓扑，例如树状结构和圆形结构。现有的建立在单一类型嵌入空间中的推荐模型没有足够的容量来包含各种几何模式，从而导致性能不理想。针对这一局限性，提出了一种基于 KG 的混合曲率流形交互学习推荐模型，即 CurvRec。一方面，以混合曲率流形空间为骨架，保留了 KG 中的各种整体几何结构;。另一方面，我们将 Ricci 曲率集成到图卷积网络(GCNs)中，以便在聚集邻居节点时捕获局部的几何结构性质。此外，为了利用 KG 中的表达空间特征，我们引入了交互学习来保证曲面流形之间的几何信息传递。具体来说，我们采用曲率感知的测地距离度量来最大化欧氏空间和非欧氏空间之间的互信息。通过大量的实验，我们证明了所提出的 CurvRec 优于最先进的基线。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Mixed-Curvature+Manifolds+Interaction+Learning+for+Knowledge+Graph-aware+Recommendation)|0| -|[EEDN: Enhanced Encoder-Decoder Network with Local and Global Context Learning for POI Recommendation](https://doi.org/10.1145/3539618.3591678)|Xinfeng Wang, Fumiyo Fukumoto, Jin Cui, Yoshimi Suzuki, Jiyi Li, Dongjin Yu|University of Yamanashi, Kofu, Japan; Hangzhou Dianzi University, Hangzhou, China|The point-of-interest (POI) recommendation predicts users' destinations, which might be of interest to users and has attracted considerable attention as one of the major applications in location-based social networks (LBSNs). Recent work on graph-based neural networks (GNN) or matrix factorization-based (MF) approaches has resulted in better representations of users and POIs to forecast users' latent preferences. However, they still suffer from the implicit feedback and cold-start problems of check-in data, as they cannot capture both local and global graph-based relations among users (or POIs) simultaneously, and the cold-start neighbors are not handled properly during graph convolution in GNN. In this paper, we propose an enhanced encoder-decoder network (EEDN) to exploit rich latent features between users, POIs, and interactions between users and POIs for POI recommendation. The encoder of EEDN utilizes a hybrid hypergraph convolution to enhance the aggregation ability of each graph convolution step and learns to derive more robust cold-start-aware user representations. In contrast, the decoder mines local and global interactions by both graph- and sequential-based patterns for modeling implicit feedback, especially to alleviate exposure bias. Extensive experiments in three public real-world datasets demonstrate that EEDN outperforms state-of-the-art methods. Our source codes and data are released at https://github.com/WangXFng/EEDN|兴趣点(POI)推荐可以预测用户的目的地，这可能是用户感兴趣的，并且作为基于位置的社交网络(LBSNs)的主要应用之一已经引起了相当大的关注。基于图的神经网络(GNN)或基于矩阵分解(MF)的方法已经导致了用户和 POI 更好的表示，以预测用户的潜在偏好。然而，由于它们不能同时捕获用户之间基于局部和全局图的关系(或 POI) ，并且在 GNN 中的图卷积过程中没有正确处理冷启动邻居，因此仍然存在签入数据的隐式反馈和冷启动问题。本文提出了一种增强型编解码网络(EEDN) ，利用用户间、用户与 POI 之间的交互以及用户与 POI 之间丰富的潜在特征进行 POI 推荐。EEDN 的编码器利用混合超图卷积来增强每个图卷积步骤的聚合能力，并学习推导出更健壮的冷启动感知用户表示。相比之下，解码器通过基于图和序列的模式挖掘局部和全局的交互作用来建模隐式反馈，特别是为了减轻暴露偏差。在三个公开的真实世界数据集中的大量实验表明，EEDN 的性能优于最先进的方法。我们的源代码和数据 https://github.com/wangxfng/eedn 公布|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=EEDN:+Enhanced+Encoder-Decoder+Network+with+Local+and+Global+Context+Learning+for+POI+Recommendation)|0| +|[Mixed-Curvature Manifolds Interaction Learning for Knowledge Graph-aware Recommendation](https://doi.org/10.1145/3539618.3591730)|Jihu Wang, Yuliang Shi, Han Yu, Xinjun Wang, Zhongmin Yan, Fanyu Kong|Shandong University & Dareway Software Co., Ltd, Jinan, China; Shandong University, Jinan, China; Nanyang Technological University, Singapore, Singapore|As auxiliary collaborative signals, the entity connectivity and relation semanticity beneath knowledge graph (KG) triples can alleviate the data sparsity and cold-start issues of recommendation tasks. Thus many works consider obtaining user and item representations via information aggregation on graph-structured data within Euclidean space. However, the scale-free graphs (e.g., KGs) inherently exhibit non-Euclidean geometric topologies, such as tree-like and circle-like structures. The existing recommendation models built in a single type of embedding space do not have enough capacity to embrace various geometric patterns, consequently, resulting in suboptimal performance. To address this limitation, we propose a KG-aware recommendation model with mixed-curvature manifolds interaction learning, namely CurvRec. On the one hand, it aims to preserve various global geometric structures in KG with mixed-curvature manifold spaces as the backbone. On the other hand, we integrate Ricci curvature into graph convolutional networks (GCNs) to capture local geometric structural properties when aggregating neighbor nodes. Besides, to exploit the expressive spatial features in KG, we incorporate interaction learning to ensure the geometric message passing between curved manifolds. Specifically, we adopt curvature-aware geodesic distance metrics to maximize the mutual information between Euclidean space and non-Euclidean spaces. Through extensive experiments, we demonstrate that the proposed CurvRec outperforms state-of-the-art baselines.|知识图(KG)三元组下的实体连通性和关系语义作为辅助协同信号，可以缓解推荐任务的数据稀疏性和冷启动问题。因此，许多工作考虑通过在欧几里得空间中对图形结构数据进行信息聚合来获得用户和项目表示。然而，无标度图(例如 KGs)本质上表现出非欧几里德几何拓扑，例如树状结构和圆形结构。现有的建立在单一类型嵌入空间中的推荐模型没有足够的容量来包含各种几何模式，从而导致性能不理想。针对这一局限性，提出了一种基于 KG 的混合曲率流形交互学习推荐模型，即 CurvRec。一方面，以混合曲率流形空间为骨架，保留了 KG 中的各种整体几何结构;。另一方面，我们将 Ricci 曲率集成到图卷积网络(GCNs)中，以便在聚集邻居节点时捕获局部的几何结构性质。此外，为了利用 KG 中的表达空间特征，我们引入了交互学习来保证曲面流形之间的几何信息传递。具体来说，我们采用曲率感知的测地距离度量来最大化欧氏空间和非欧氏空间之间的互信息。通过大量的实验，我们证明了所提出的 CurvRec 优于最先进的基线。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Mixed-Curvature+Manifolds+Interaction+Learning+for+Knowledge+Graph-aware+Recommendation)|0| +|[EEDN: Enhanced Encoder-Decoder Network with Local and Global Context Learning for POI Recommendation](https://doi.org/10.1145/3539618.3591678)|Xinfeng Wang, Fumiyo Fukumoto, Jin Cui, Yoshimi Suzuki, Jiyi Li, Dongjin Yu|Hangzhou Dianzi University, Hangzhou, China; University of Yamanashi, Kofu, Japan|The point-of-interest (POI) recommendation predicts users' destinations, which might be of interest to users and has attracted considerable attention as one of the major applications in location-based social networks (LBSNs). Recent work on graph-based neural networks (GNN) or matrix factorization-based (MF) approaches has resulted in better representations of users and POIs to forecast users' latent preferences. However, they still suffer from the implicit feedback and cold-start problems of check-in data, as they cannot capture both local and global graph-based relations among users (or POIs) simultaneously, and the cold-start neighbors are not handled properly during graph convolution in GNN. In this paper, we propose an enhanced encoder-decoder network (EEDN) to exploit rich latent features between users, POIs, and interactions between users and POIs for POI recommendation. The encoder of EEDN utilizes a hybrid hypergraph convolution to enhance the aggregation ability of each graph convolution step and learns to derive more robust cold-start-aware user representations. In contrast, the decoder mines local and global interactions by both graph- and sequential-based patterns for modeling implicit feedback, especially to alleviate exposure bias. Extensive experiments in three public real-world datasets demonstrate that EEDN outperforms state-of-the-art methods. Our source codes and data are released at https://github.com/WangXFng/EEDN|兴趣点(POI)推荐可以预测用户的目的地，这可能是用户感兴趣的，并且作为基于位置的社交网络(LBSNs)的主要应用之一已经引起了相当大的关注。基于图的神经网络(GNN)或基于矩阵分解(MF)的方法已经导致了用户和 POI 更好的表示，以预测用户的潜在偏好。然而，由于它们不能同时捕获用户之间基于局部和全局图的关系(或 POI) ，并且在 GNN 中的图卷积过程中没有正确处理冷启动邻居，因此仍然存在签入数据的隐式反馈和冷启动问题。本文提出了一种增强型编解码网络(EEDN) ，利用用户间、用户与 POI 之间的交互以及用户与 POI 之间丰富的潜在特征进行 POI 推荐。EEDN 的编码器利用混合超图卷积来增强每个图卷积步骤的聚合能力，并学习推导出更健壮的冷启动感知用户表示。相比之下，解码器通过基于图和序列的模式挖掘局部和全局的交互作用来建模隐式反馈，特别是为了减轻暴露偏差。在三个公开的真实世界数据集中的大量实验表明，EEDN 的性能优于最先进的方法。我们的源代码和数据 https://github.com/wangxfng/eedn 公布|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=EEDN:+Enhanced+Encoder-Decoder+Network+with+Local+and+Global+Context+Learning+for+POI+Recommendation)|0| |[Adaptive Graph Representation Learning for Next POI Recommendation](https://doi.org/10.1145/3539618.3591634)|Zhaobo Wang, Yanmin Zhu, Chunyang Wang, Wenze Ma, Bo Li, Jiadi Yu|Hong Kong University of Science and Technology, Hong Kong, Hong Kong; Shanghai Jiao Tong University, Shanghai, China|Next Point-of-Interest (POI) recommendation is an essential part of the flourishing location-based applications, where the demands of users are not only conditioned by their recent check-in behaviors but also by the critical influence stemming from geographical dependencies among POIs. Existing methods leverage Graph Neural Networks with the aid of pre-defined POI graphs to capture such indispensable correlations for modeling user preferences, assuming that the appropriate geographical dependencies among POIs could be pre-determined. However, the pre-defined graph structures are always far from the optimal graph topology due to noise and adaptability issues, which may decrease the expressivity of learned POI representations as well as the credibility of modeling user preferences. In this paper, we propose a novel Adaptive Graph Representation-enhanced Attention Network (AGRAN) for next POI recommendation, which explores the utilization of graph structure learning to replace the pre-defined static graphs for learning more expressive representations of POIs. In particular, we develop an adaptive POI graph matrix and learn it via similarity learning with POI embeddings, automatically capturing the underlying geographical dependencies for representation learning. Afterward, we incorporate the learned representations of POIs and personalized spatial-temporal information with an extension to the self-attention mechanism for capturing dynamic user preferences. Extensive experiments conducted on two real-world datasets validate the superior performance of our proposed method over state-of-the-art baselines.|下一个兴趣点(POI)推荐是蓬勃发展的基于位置的应用程序的重要组成部分，其中用户的需求不仅受到他们最近的签入行为的制约，而且受到来自 POI 之间的地理依赖性的关键影响。现有的方法利用图形神经网络与预定义的 POI 图的帮助，以捕获这种不可或缺的相关性建模用户偏好，假设适当的地理依赖关系之间的 POI 可以预先确定。然而，由于噪声和适应性问题，预定义的图结构往往远离最优的图拓扑结构，这可能会降低所学习的 POI 表示的表达能力以及建模用户偏好的可信度。本文提出了一种新的自适应图表示增强注意力网络(AGRAN) ，探讨了利用图结构学习代替预定义的静态图学习更具表现力的 POI 表示。特别地，我们开发了一个自适应的 POI 图形矩阵，并通过 POI 嵌入的相似性学习来学习它，自动捕捉潜在的地理依赖性来进行表示学习。然后，将 POI 的学习表示和个性化的时空信息结合起来，扩展了自我注意机制来获取动态用户偏好。在两个真实世界数据集上进行的大量实验验证了我们提出的方法优于最先进的基线的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Adaptive+Graph+Representation+Learning+for+Next+POI+Recommendation)|0| -|[Spatio-Temporal Hypergraph Learning for Next POI Recommendation](https://doi.org/10.1145/3539618.3591770)|Xiaodong Yan, Tengwei Song, Yifeng Jiao, Jianshan He, Jiaotuan Wang, Ruopeng Li, Wei Chu|Ant Group & Beihang University, Beijing, China; Ant Group, Hangzhou, China; Ant Group, Beijing, China|Next Point-of-Interest (POI) recommendation task focuses on predicting the immediate next position a user would visit, thus providing appealing location advice. In light of this, graph neural networks (GNNs) based models have recently been emerging as breakthroughs for this task due to their ability to learn global user preferences and alleviate cold-start challenges. Nevertheless, most existing methods merely focus on the relations between POIs, neglecting the higher-order information including user trajectories and the collaborative relations among trajectories. In this paper, we propose the Spatio-Temporal HyperGraph Convolutional Network (STHGCN). This model leverages a hypergraph to capture the trajectory-grain information and learn from user's historical trajectories (intra-user) as well as collaborative trajectories from other users (inter-user). Furthermore, a novel hypergraph transformer is introduced to effectively combine the hypergraph structure encoding with spatio-temporal information. Extensive experiments on real-world datasets demonstrate that our model outperforms the existing state-of-the-art methods and further analysis confirms the effectiveness in alleviating cold-start issues and achieving improved performance for both short and long trajectories.|下一个兴趣点(POI)推荐任务的重点是预测用户即将访问的下一个位置，从而提供有吸引力的位置建议。有鉴于此，基于图形神经网络(GNN)的模型由于能够学习全球用户偏好和缓解冷启动挑战，最近已经成为这项任务的突破点。然而，现有的方法大多只关注 POI 之间的关系，而忽略了包括用户轨迹在内的高阶信息以及轨迹之间的协同关系。本文提出了时空超图卷积网络(STHGCN)。该模型利用一个超图来捕获轨迹信息，并从用户的历史轨迹(内部用户)以及其他用户(内部用户)的协作轨迹中学习。此外，引入了一种新的超图变换器，将超图结构编码与时空信息有效地结合起来。对真实世界数据集的大量实验表明，我们的模型优于现有的最先进的方法，进一步的分析证实了在缓解冷启动问题和实现短期和长期轨迹性能改善方面的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Spatio-Temporal+Hypergraph+Learning+for+Next+POI+Recommendation)|0| +|[Spatio-Temporal Hypergraph Learning for Next POI Recommendation](https://doi.org/10.1145/3539618.3591770)|Xiaodong Yan, Tengwei Song, Yifeng Jiao, Jianshan He, Jiaotuan Wang, Ruopeng Li, Wei Chu|Ant Group, Hangzhou, China; Ant Group, Beijing, China; Ant Group & Beihang University, Beijing, China|Next Point-of-Interest (POI) recommendation task focuses on predicting the immediate next position a user would visit, thus providing appealing location advice. In light of this, graph neural networks (GNNs) based models have recently been emerging as breakthroughs for this task due to their ability to learn global user preferences and alleviate cold-start challenges. Nevertheless, most existing methods merely focus on the relations between POIs, neglecting the higher-order information including user trajectories and the collaborative relations among trajectories. In this paper, we propose the Spatio-Temporal HyperGraph Convolutional Network (STHGCN). This model leverages a hypergraph to capture the trajectory-grain information and learn from user's historical trajectories (intra-user) as well as collaborative trajectories from other users (inter-user). Furthermore, a novel hypergraph transformer is introduced to effectively combine the hypergraph structure encoding with spatio-temporal information. Extensive experiments on real-world datasets demonstrate that our model outperforms the existing state-of-the-art methods and further analysis confirms the effectiveness in alleviating cold-start issues and achieving improved performance for both short and long trajectories.|下一个兴趣点(POI)推荐任务的重点是预测用户即将访问的下一个位置，从而提供有吸引力的位置建议。有鉴于此，基于图形神经网络(GNN)的模型由于能够学习全球用户偏好和缓解冷启动挑战，最近已经成为这项任务的突破点。然而，现有的方法大多只关注 POI 之间的关系，而忽略了包括用户轨迹在内的高阶信息以及轨迹之间的协同关系。本文提出了时空超图卷积网络(STHGCN)。该模型利用一个超图来捕获轨迹信息，并从用户的历史轨迹(内部用户)以及其他用户(内部用户)的协作轨迹中学习。此外，引入了一种新的超图变换器，将超图结构编码与时空信息有效地结合起来。对真实世界数据集的大量实验表明，我们的模型优于现有的最先进的方法，进一步的分析证实了在缓解冷启动问题和实现短期和长期轨迹性能改善方面的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Spatio-Temporal+Hypergraph+Learning+for+Next+POI+Recommendation)|0| |[Mining Stable Preferences: Adaptive Modality Decorrelation for Multimedia Recommendation](https://doi.org/10.1145/3539618.3591729)|Jinghao Zhang, Qiang Liu, Shu Wu, Liang Wang||Multimedia content is of predominance in the modern Web era. In real scenarios, multiple modalities reveal different aspects of item attributes and usually possess different importance to user purchase decisions. However, it is difficult for models to figure out users' true preference towards different modalities since there exists strong statistical correlation between modalities. Even worse, the strong statistical correlation might mislead models to learn the spurious preference towards inconsequential modalities. As a result, when data (modal features) distribution shifts, the learned spurious preference might not guarantee to be as effective on the inference set as on the training set. We propose a novel MOdality DEcorrelating STable learning framework, MODEST for brevity, to learn users' stable preference. Inspired by sample re-weighting techniques, the proposed method aims to estimate a weight for each item, such that the features from different modalities in the weighted distribution are decorrelated. We adopt Hilbert Schmidt Independence Criterion (HSIC) as independence testing measure which is a kernel-based method capable of evaluating the correlation degree between two multi-dimensional and non-linear variables. Our method could be served as a play-and-plug module for existing multimedia recommendation backbones. Extensive experiments on four public datasets and four state-of-the-art multimedia recommendation backbones unequivocally show that our proposed method can improve the performances by a large margin.|在现代网络时代，多媒体内容占有主导地位。在实际场景中，多种模式揭示了商品属性的不同方面，对用户的购买决策具有不同的重要性。然而，由于模式之间存在很强的统计相关性，模型很难计算出用户对不同模式的真实偏好。更糟糕的是，这种强烈的统计相关性可能会误导模型，使其学会对无关紧要的模式的虚假偏好。因此，当数据(模态特征)分布发生变化时，学习到的虚假偏好可能不能保证在推理集上和在训练集上同样有效。我们提出了一个新的模态解相关稳定学习框架，MODEST 为简洁，学习用户的稳定偏好。该方法受样本重新加权技术的启发，旨在估计每个项目的权重，使不同方式的特征在加权分布中不相关。我们采用 Hilbert Schmidt 独立准则(HSIC)作为独立性测度，这是一种基于核的方法，能够评估两个多维和非线性变量之间的相关程度。我们的方法可以作为现有多媒体推荐主干的播放和即插即用模块。对四个公共数据集和四个最先进的多媒体推荐骨干网的大量实验表明，我们提出的方法可以大幅度提高性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Mining+Stable+Preferences:+Adaptive+Modality+Decorrelation+for+Multimedia+Recommendation)|0| |[MEME: Multi-Encoder Multi-Expert Framework with Data Augmentation for Video Retrieval](https://doi.org/10.1145/3539618.3591726)|SeongMin Kang, YoonSik Cho|Chung-Ang University, Seoul, Republic of Korea|Text-to-video(T2V) retrieval aims to find relevant videos from text queries. The recently introduced Contrastive Language Image Pretraining (CLIP), a pretrained language-vision model trained on large-scale image and caption pairs, has been extensively studied in the literature for this task. Existing studies on T2V task have aimed to transfer the CLIP knowledge and focus on enhancing retrieval performance through fine-grained representation learning. While fine-grained contrast has achieved some remarkable results, less attention has been paid to coarse-grained contrasts. To this end, we propose a method called Graph Patch Spreading (GPS) to aggregate patches across frames at the coarse-grained level. We apply GPS to our proposed framework called Multi-Encoder Multi-Expert (MEME) framework. Our proposed scheme is general enough to be applied to any existing CLIP-based video-text retrieval models. We demonstrate the effectiveness of our method on existing models over the benchmark datasets MSR-VTT, MSVD, and LSMDC datasets. Our code can be found at https://github.com/kang7734/MEME__.|文本到视频(T2V)检索旨在从文本查询中查找相关视频。最近引入的对比语言图像预训练(CLIP)是一种基于大规模图像和字幕对的预训练语言视觉模型。现有关于 T2V 任务的研究主要集中在传递 CLIP 知识和通过细粒度表征学习提高检索性能方面。虽然细粒度对比度取得了一些显著的成果，但对粗粒度对比度的研究较少。为此，我们提出了一种称为图形补丁扩展(GPS)的方法，在粗粒度层次上聚集跨帧的补丁。我们将 GPS 应用到我们提出的多编码器多专家(MEME)框架中。我们提出的方案是通用的，足以应用于任何现有的基于 CLIP 的视频文本检索模型。在 MSR-VTT、 MSVD 和 LSMDC 基准数据集上验证了该方法在现有模型上的有效性。我们的代码可以在 https://github.com/kang7734/meme__ 找到。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=MEME:+Multi-Encoder+Multi-Expert+Framework+with+Data+Augmentation+for+Video+Retrieval)|0| |[Multi-Scenario Ranking with Adaptive Feature Learning](https://doi.org/10.1145/3539618.3591736)|Yu Tian, Bofang Li, Si Chen, Xubin Li, Hongbo Deng, Jian Xu, Bo Zheng, Qian Wang, Chenliang Li||Recently, Multi-Scenario Learning (MSL) is widely used in recommendation and retrieval systems in the industry because it facilitates transfer learning from different scenarios, mitigating data sparsity and reducing maintenance cost. These efforts produce different MSL paradigms by searching more optimal network structure, such as Auxiliary Network, Expert Network, and Multi-Tower Network. It is intuitive that different scenarios could hold their specific characteristics, activating the user's intents quite differently. In other words, different kinds of auxiliary features would bear varying importance under different scenarios. With more discriminative feature representations refined in a scenario-aware manner, better ranking performance could be easily obtained without expensive search for the optimal network structure. Unfortunately, this simple idea is mainly overlooked but much desired in real-world systems.Further analysis also validates the rationality of adaptive feature learning under a multi-scenario scheme. Moreover, our A/B test results on the Alibaba search advertising platform also demonstrate that Maria is superior in production environments.|近年来，多场景学习(Multi-Scenario Learning，MSL)技术被广泛应用于业界的推荐和检索系统中，因为它可以方便地从不同场景中进行转移学习，减少数据稀疏性，降低维护成本。这些努力通过寻找更多的最优网络结构，如辅助网络，专家网络和多塔网络，产生不同的 MSL 范例。直观地说，不同的场景可以保持其特定的特征，激活用户的意图完全不同。换句话说，不同的辅助特征在不同的场景下具有不同的重要性。随着更多的区分性特征表示在场景感知方式精化，可以很容易地获得更好的排序性能，而不需要对最优网络结构进行昂贵的搜索。不幸的是，这个简单的想法在现实系统中基本上被忽视了，但是却是非常需要的。进一步的分析也验证了多场景方案下自适应特征学习的合理性。此外，我们在阿里巴巴搜索广告平台的 A/B 测试结果也表明，玛丽亚在生产环境方面更胜一筹。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Multi-Scenario+Ranking+with+Adaptive+Feature+Learning)|0| |[Curse of "Low" Dimensionality in Recommender Systems](https://doi.org/10.1145/3539618.3591659)|Naoto Ohsaka, Riku Togashi||Beyond accuracy, there are a variety of aspects to the quality of recommender systems, such as diversity, fairness, and robustness. We argue that many of the prevalent problems in recommender systems are partly due to low-dimensionality of user and item embeddings, particularly when dot-product models, such as matrix factorization, are used. In this study, we showcase empirical evidence suggesting the necessity of sufficient dimensionality for user/item embeddings to achieve diverse, fair, and robust recommendation. We then present theoretical analyses of the expressive power of dot-product models. Our theoretical results demonstrate that the number of possible rankings expressible under dot-product models is exponentially bounded by the dimension of item factors. We empirically found that the low-dimensionality contributes to a popularity bias, widening the gap between the rank positions of popular and long-tail items; we also give a theoretical justification for this phenomenon.|除了准确性之外，推荐系统的质量还有很多方面，比如多样性、公平性和鲁棒性。我们认为，推荐系统中许多普遍存在的问题，部分是由于用户和项目嵌入的维度较低，特别是当使用像矩阵分解这样的点产品模型时。在这项研究中，我们展示了一些经验证明，它们表明了用户/项目嵌入需要足够的维度来实现多样化、公平和强大的推荐。然后，我们提出了理论分析的表达能力的点积模式。我们的理论结果表明，在点乘模型下可表达的排名的数量是指数约束的项目因素的维度。实证研究发现，低维度导致了流行偏差，扩大了流行项目和长尾项目的排名差距，并对这一现象进行了理论解释。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Curse+of+"Low"+Dimensionality+in+Recommender+Systems)|0| -|[Subgraph Search over Neural-Symbolic Graphs](https://doi.org/10.1145/3539618.3591773)|Ye Yuan, Delong Ma, Anbiao Wu, Jianbin Qin|Northeastern University, China, Shenyang, China; Shenzhen University, Shenzhen, China; Beijing Institute of Technology, Beijing, China|In this paper, we propose neural-symbolic graph databases (NSGDs) that extends traditional graph data with content and structural embeddings in every node. The content embeddings can represent unstructured data (e.g., images, videos, and texts), while structural embeddings can be used to deal with incomplete graphs. We can advocate machine learning models (e.g., deep learning) to transform unstructured data and graph nodes to these embeddings. NSGDs can support a wide range of applications (e.g., online recommendation and natural language question answering) in social-media networks, multi-modal knowledge graphs and etc. As a typical search over graphs, we study subgraph search over a large NSGD, called neural-symbolic subgraph matching (NSMatch) that includes a novel ranking search function. Specifically, we develop a general algorithmic framework to process NSMatch efficiently. Using real-life multi-modal graphs, we experimentally verify the effectiveness, scalability and efficiency of NSMatch.|在本文中，我们提出了神经符号图数据库(NSGD) ，它扩展了传统图数据的内容和结构嵌入在每个节点。内容嵌入可以表示非结构化数据(如图像、视频和文本) ，而结构嵌入可以用来处理不完整的图形。我们可以提倡机器学习模型(例如深度学习) ，将非结构化数据和图形节点转换为这些嵌入。NSGD 可以支持社交媒体网络、多模式知识图表等的广泛应用(例如，在线推荐和自然语言问答)。作为一个典型的图搜索，我们研究了一个大型 NSGD 上的子图搜索，称为神经符号子图匹配(NSMatch) ，它包含一个新的排序搜索函数。具体来说，我们开发了一个通用的算法框架来有效地处理 NSMatch。利用现实生活中的多模态图，实验验证了 NSMatch 的有效性、可扩展性和效率。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Subgraph+Search+over+Neural-Symbolic+Graphs)|0| +|[Subgraph Search over Neural-Symbolic Graphs](https://doi.org/10.1145/3539618.3591773)|Ye Yuan, Delong Ma, Anbiao Wu, Jianbin Qin|Beijing Institute of Technology, Beijing, China; Shenzhen University, Shenzhen, China; Northeastern University, China, Shenyang, China|In this paper, we propose neural-symbolic graph databases (NSGDs) that extends traditional graph data with content and structural embeddings in every node. The content embeddings can represent unstructured data (e.g., images, videos, and texts), while structural embeddings can be used to deal with incomplete graphs. We can advocate machine learning models (e.g., deep learning) to transform unstructured data and graph nodes to these embeddings. NSGDs can support a wide range of applications (e.g., online recommendation and natural language question answering) in social-media networks, multi-modal knowledge graphs and etc. As a typical search over graphs, we study subgraph search over a large NSGD, called neural-symbolic subgraph matching (NSMatch) that includes a novel ranking search function. Specifically, we develop a general algorithmic framework to process NSMatch efficiently. Using real-life multi-modal graphs, we experimentally verify the effectiveness, scalability and efficiency of NSMatch.|在本文中，我们提出了神经符号图数据库(NSGD) ，它扩展了传统图数据的内容和结构嵌入在每个节点。内容嵌入可以表示非结构化数据(如图像、视频和文本) ，而结构嵌入可以用来处理不完整的图形。我们可以提倡机器学习模型(例如深度学习) ，将非结构化数据和图形节点转换为这些嵌入。NSGD 可以支持社交媒体网络、多模式知识图表等的广泛应用(例如，在线推荐和自然语言问答)。作为一个典型的图搜索，我们研究了一个大型 NSGD 上的子图搜索，称为神经符号子图匹配(NSMatch) ，它包含一个新的排序搜索函数。具体来说，我们开发了一个通用的算法框架来有效地处理 NSMatch。利用现实生活中的多模态图，实验验证了 NSMatch 的有效性、可扩展性和效率。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Subgraph+Search+over+Neural-Symbolic+Graphs)|0| |[Explainable Conversational Question Answering over Heterogeneous Sources via Iterative Graph Neural Networks](https://doi.org/10.1145/3539618.3591682)|Philipp Christmann, Rishiraj Saha Roy, Gerhard Weikum||In conversational question answering, users express their information needs through a series of utterances with incomplete context. Typical ConvQA methods rely on a single source (a knowledge base (KB), or a text corpus, or a set of tables), thus being unable to benefit from increased answer coverage and redundancy of multiple sources. Our method EXPLAIGNN overcomes these limitations by integrating information from a mixture of sources with user-comprehensible explanations for answers. It constructs a heterogeneous graph from entities and evidence snippets retrieved from a KB, a text corpus, web tables, and infoboxes. This large graph is then iteratively reduced via graph neural networks that incorporate question-level attention, until the best answers and their explanations are distilled. Experiments show that EXPLAIGNN improves performance over state-of-the-art baselines. A user study demonstrates that derived answers are understandable by end users.|在会话问答中，用户通过一系列语境不完整的话语来表达自己的信息需求。典型的 ConvQA 方法依赖于单个源(知识库(KB)、文本语料库或一组表) ，因此无法从增加的答案覆盖率和多个源的冗余中获益。我们的方法 EXPLAIGNN 克服了这些限制，整合了来自各种来源的信息和用户可理解的答案解释。它通过从知识库、文本语料库、 Web 表格和信息框中检索到的实体和证据片段构造一个异构图。然后通过包含问题级注意力的图形神经网络迭代地缩减这个大图，直到最佳答案及其解释被提炼出来。实验表明，EXPLAIGNN 提高了性能超过最先进的基线。用户研究表明，最终用户可以理解派生的答案。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Explainable+Conversational+Question+Answering+over+Heterogeneous+Sources+via+Iterative+Graph+Neural+Networks)|0| -|[Data-Aware Proxy Hashing for Cross-modal Retrieval](https://doi.org/10.1145/3539618.3591660)|RongCheng Tu, XianLing Mao, Wenjin Ji, Wei Wei, Heyan Huang|Beijing Institute of Technology, Wuhan, China; Beijing Institute of Technology, Beijing, China|Recently, numerous proxy hash code based methods, which sufficiently exploit the label information of data to supervise the training of hashing models, have been proposed. Although these methods have made impressive progress, their generating processes of proxy hash codes are based only on the class information of the dataset or labels of data but do not take the data themselves into account. Therefore, these methods will probably generate some inappropriate proxy hash codes, thus damaging the retrieval performance of the hash models. To solve the aforementioned problem, we propose a novel Data-Aware Proxy Hashing for cross-modal retrieval, called DAPH. Specifically, our proposed method first train a data-aware proxy network that takes the data points, label vectors of data, and the class vectors of the dataset as inputs to generate class-based data-aware proxy hash codes, label-fused image-aware proxy hash codes and label-fused text-aware proxy hash codes. Then, we propose a novel hash loss that exploits the three types of data-aware proxy hash codes to supervise the training of modality-specific hashing networks. After training, DAPH is able to generate discriminate hash codes with the semantic information preserved adequately. Extensive experiments on three benchmark datasets show that the proposed DAPH outperforms the state-of-the-art baselines in cross-modal retrieval tasks.|近年来，人们提出了许多基于代理哈希码的方法，充分利用数据的标签信息来监督哈希模型的训练。虽然这些方法已经取得了令人印象深刻的进展，但它们的代理哈希码的生成过程只是基于数据集的类信息或数据标签，而没有考虑数据本身。因此，这些方法可能会产生一些不适当的代理哈希码，从而损害哈希模型的检索性能。为了解决上述问题，我们提出了一种新的跨模态检索的数据感知代理哈希算法，称为 DAPH。具体来说，我们提出的方法首先训练一个数据感知代理网络，该网络以数据点、数据标签向量和数据集的类向量作为输入，生成基于类的数据感知代理哈希码、标签融合图像感知代理哈希码和标签融合文本感知代理哈希码。然后，提出了一种新的哈希丢失算法，该算法利用三种数据感知代理哈希码来监控特定模态哈希网络的训练。经过训练后，DAPH 能够在充分保留语义信息的情况下生成有区别的哈希码。在三个基准数据集上的大量实验表明，所提出的 DAPH 算法在跨模态检索任务中的性能优于最先进的基准算法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Data-Aware+Proxy+Hashing+for+Cross-modal+Retrieval)|0| +|[Data-Aware Proxy Hashing for Cross-modal Retrieval](https://doi.org/10.1145/3539618.3591660)|RongCheng Tu, XianLing Mao, Wenjin Ji, Wei Wei, Heyan Huang|Beijing Institute of Technology, Beijing, China; Beijing Institute of Technology, Wuhan, China|Recently, numerous proxy hash code based methods, which sufficiently exploit the label information of data to supervise the training of hashing models, have been proposed. Although these methods have made impressive progress, their generating processes of proxy hash codes are based only on the class information of the dataset or labels of data but do not take the data themselves into account. Therefore, these methods will probably generate some inappropriate proxy hash codes, thus damaging the retrieval performance of the hash models. To solve the aforementioned problem, we propose a novel Data-Aware Proxy Hashing for cross-modal retrieval, called DAPH. Specifically, our proposed method first train a data-aware proxy network that takes the data points, label vectors of data, and the class vectors of the dataset as inputs to generate class-based data-aware proxy hash codes, label-fused image-aware proxy hash codes and label-fused text-aware proxy hash codes. Then, we propose a novel hash loss that exploits the three types of data-aware proxy hash codes to supervise the training of modality-specific hashing networks. After training, DAPH is able to generate discriminate hash codes with the semantic information preserved adequately. Extensive experiments on three benchmark datasets show that the proposed DAPH outperforms the state-of-the-art baselines in cross-modal retrieval tasks.|近年来，人们提出了许多基于代理哈希码的方法，充分利用数据的标签信息来监督哈希模型的训练。虽然这些方法已经取得了令人印象深刻的进展，但它们的代理哈希码的生成过程只是基于数据集的类信息或数据标签，而没有考虑数据本身。因此，这些方法可能会产生一些不适当的代理哈希码，从而损害哈希模型的检索性能。为了解决上述问题，我们提出了一种新的跨模态检索的数据感知代理哈希算法，称为 DAPH。具体来说，我们提出的方法首先训练一个数据感知代理网络，该网络以数据点、数据标签向量和数据集的类向量作为输入，生成基于类的数据感知代理哈希码、标签融合图像感知代理哈希码和标签融合文本感知代理哈希码。然后，提出了一种新的哈希丢失算法，该算法利用三种数据感知代理哈希码来监控特定模态哈希网络的训练。经过训练后，DAPH 能够在充分保留语义信息的情况下生成有区别的哈希码。在三个基准数据集上的大量实验表明，所提出的 DAPH 算法在跨模态检索任务中的性能优于最先进的基准算法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Data-Aware+Proxy+Hashing+for+Cross-modal+Retrieval)|0| |[Hear Me Out: A Study on the Use of the Voice Modality for Crowdsourced Relevance Assessments](https://doi.org/10.1145/3539618.3591694)|Nirmal Roy, Agathe Balayn, David Maxwell, Claudia Hauff||The creation of relevance assessments by human assessors (often nowadays crowdworkers) is a vital step when building IR test collections. Prior works have investigated assessor quality & behaviour, though into the impact of a document's presentation modality on assessor efficiency and effectiveness. Given the rise of voice-based interfaces, we investigate whether it is feasible for assessors to judge the relevance of text documents via a voice-based interface. We ran a user study (n = 49) on a crowdsourcing platform where participants judged the relevance of short and long documents sampled from the TREC Deep Learning corpus-presented to them either in the text or voice modality. We found that: (i) participants are equally accurate in their judgements across both the text and voice modality; (ii) with increased document length it takes participants significantly longer (for documents of length > 120 words it takes almost twice as much time) to make relevance judgements in the voice condition; and (iii) the ability of assessors to ignore stimuli that are not relevant (i.e., inhibition) impacts the assessment quality in the voice modality-assessors with higher inhibition are significantly more accurate than those with lower inhibition. Our results indicate that we can reliably leverage the voice modality as a means to effectively collect relevance labels from crowdworkers.|由人工评估员(通常是现在的众包工作者)创建相关性评估是构建 IR 测试集的关键步骤。以前的工作已经调查了评估员的质量和行为，尽管文件的呈现方式对评估员的效率和有效性的影响。鉴于基于语音的接口的兴起，我们研究了评估者通过基于语音的接口来判断文本文档的相关性是否可行。我们在一个众包平台上运行了一个用户研究(n = 49) ，参与者判断从 TREC 深度学习语料库中取样的短文档和长文档的相关性——以文本或语音形式呈现给他们。我们发现: (i)参与者在文本和语音模式中的判断同样准确; (ii)随着文件长度的增加，参与者在语音条件下做出相关判断的时间显著延长(对于长度 > 120个单词的文件，需要几乎两倍的时间) ; 以及(iii)评估者忽略不相关刺激(即抑制)的能力影响语音模式中的评估质量-抑制程度较高的评估者比抑制程度较低的评估者明显更准确。我们的研究结果表明，我们可以可靠地利用语音模式作为一种手段，有效地收集相关标签从众工作者。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Hear+Me+Out:+A+Study+on+the+Use+of+the+Voice+Modality+for+Crowdsourced+Relevance+Assessments)|0| -|[Asymmetric Hashing for Fast Ranking via Neural Network Measures](https://doi.org/10.1145/3539618.3591640)|Khoa D. Doan, Shulong Tan, Weijie Zhao, Ping Li|Rochester Institute of Technology, Rochester, NY, USA; Linkedin Ads, Seattle, WA, USA; Coupang, Inc., Mountain View, CA, USA; VinUniversity, Hanoi, Vietnam|Fast item ranking is an important task in recommender systems. In previous works, graph-based Approximate Nearest Neighbor (ANN) approaches have demonstrated good performance on item ranking tasks with generic searching/matching measures (including complex measures such as neural network measures). However, since these ANN approaches must go through the neural measures several times during ranking, the computation is not practical if the neural measure is a large network. On the other hand, fast item ranking using existing hashing-based approaches, such as Locality Sensitive Hashing (LSH), only works with a limited set of measures. Previous learning-to-hash approaches are also not suitable to solve the fast item ranking problem since they can take a significant amount of time and computation to train the hash functions. Hashing approaches, however, are attractive because they provide a principle and efficient way to retrieve candidate items. In this paper, we propose a simple and effective learning-to-hash approach for the fast item ranking problem that can be used for any type of measure, including neural network measures. Specifically, we solve this problem with an asymmetric hashing framework based on discrete inner product fitting. We learn a pair of related hash functions that map heterogeneous objects (e.g., users and items) into a common discrete space where the inner product of their binary codes reveals their true similarity defined via the original searching measure. The fast ranking problem is reduced to an ANN search via this asymmetric hashing scheme. Then, we propose a sampling strategy to efficiently select relevant and contrastive samples to train the hashing model. We empirically validate the proposed method against the existing state-of-the-art fast item ranking methods in several combinations of non-linear searching functions and prominent datasets.|项目快速排序是推荐系统中的一项重要任务。在以往的工作中，基于图的近似最近邻(ANN)方法已经证明了良好的性能项目排序任务与一般的搜索/匹配措施(包括复杂的措施，如神经网络措施)。然而，由于这些神经网络方法在排序过程中必须经过多次神经测度，如果神经测度是一个大型网络，计算是不切实际的。另一方面，使用现有的基于哈希的方法(如区域敏感哈希(LSH))进行快速项目排序，只能在有限的度量集合下进行。以往的哈希学习方法也不适合解决快速项目排序问题，因为它们需要大量的时间和计算来训练哈希函数。然而，散列方法很有吸引力，因为它们提供了检索候选项的原则和有效方法。在本文中，我们提出了一个简单而有效的学习-哈希方法，用于快速项目排序问题，可以用于任何类型的测度，包括神经网络测度。具体地说，我们采用基于离散内积拟合的非对称散列框架来解决这个问题。我们学习了一对相关的散列函数，它们将异构对象(例如，用户和项)映射到一个公共的离散空间中，在这个空间中，它们的二进制码的内积揭示了它们通过原始搜索度量定义的真实相似性。通过这种非对称散列方法，将快速排序问题简化为人工神经网络搜索问题。然后，我们提出了一种抽样策略，有效地选择相关和对比的样本来训练散列模型。针对现有的快速项目排序方法，在多种非线性搜索函数和突出数据集的组合下，对该方法进行了实验验证。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Asymmetric+Hashing+for+Fast+Ranking+via+Neural+Network+Measures)|0| -|[Wisdom of Crowds and Fine-Grained Learning for Serendipity Recommendations](https://doi.org/10.1145/3539618.3591787)|Zhe Fu, Xi Niu, Li Yu|University of North Carolina at Charlotte, Charlotte, USA; Renmin University of China, Beijing, China|Serendipity is a notion that means an unexpected but valuable discovery. Due to its elusive and subjective nature, serendipity is difficult to study even with today's advances in machine learning and deep learning techniques. Both ground truth data collecting and model developing are the open research questions. This paper addresses both the data and the model challenges for identifying serendipity in recommender systems. For the ground truth data collecting, it proposes a new and scalable approach by using both user generated reviews and a crowd sourcing method. The result is a large-scale ground truth data on serendipity. For model developing, it designed a self-enhanced module to learn the fine-grained facets of serendipity in order to mitigate the inherent data sparsity problem in any serendipity ground truth dataset. The self-enhanced module is general enough to be applied with many base deep learning models for serendipity. A series of experiments have been conducted. As the result, a base deep learning model trained on our collected ground truth data, as well as with the help of the self-enhanced module, outperforms the state-of-the-art baseline models in predicting serendipity.|意外的发现意味着一个意想不到但有价值的发现。由于其难以捉摸和主观的性质，即使在今天的机器学习和深度学习技术的进步，意外发现是难以研究。地面真实数据采集和模型开发都是开放性的研究课题。本文讨论了在推荐系统中识别意外发现的数据和模型挑战。对于地面真相数据的收集，它提出了一种新的和可扩展的方法，通过使用用户生成的评论和众包的方法。其结果是一个关于意外发现的大规模地面真相数据。在模型开发方面，设计了一个自增强模块来学习机遇的细粒度方面，以减轻任何机遇地面真实数据集中固有的数据稀疏问题。该自增强模块具有较高的通用性，可以应用于许多基础深度学习模型中。进行了一系列的实验。因此，基于我们收集的地面真相数据的基础深度学习模型，以及在自我增强模块的帮助下，在预测偶然性方面优于最先进的基线模型。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Wisdom+of+Crowds+and+Fine-Grained+Learning+for+Serendipity+Recommendations)|0| -|[An Effective Framework for Enhancing Query Answering in a Heterogeneous Data Lake](https://doi.org/10.1145/3539618.3591637)|Qin Yuan, Ye Yuan, Zhenyu Wen, He Wang, Shiyuan Tang|Beijing Institute of Technology, Hangzhou, China; Beijing Institute of Technology, Beijing, China|There has been a growing interest in cross-source searching to gain rich knowledge in recent years. A data lake collects massive raw and heterogeneous data with different data schemas and query interfaces. Many real-life applications require query answering over the heterogeneous data lake, such as e-commerce, bioinformatics and healthcare. In this paper, we propose LakeAns that semantically integrates heterogeneous data schemas of the lake to enhance the semantics of query answers. To this end, we propose a novel framework to efficiently and effectively perform the cross-source searching. The framework exploits a reinforcement learning method to semantically integrate the data schemas and further create a global relational schema for the heterogeneous data. It then performs a query answering algorithm based on the global schema to find answers across multiple data sources. We conduct extensive experimental evaluations using real-life data to verify that our approach outperforms existing solutions in terms of effectiveness and efficiency.|近年来，人们对跨源搜索以获取丰富的知识越来越感兴趣。数据湖通过不同的数据模式和查询接口收集大量的原始和异构数据。许多实际应用程序需要在异构数据湖上进行查询回答，例如电子商务、生物信息学和医疗保健。在本文中，我们提出了一种基于 LakeAns 的方法，该方法在语义上集成了湖中的异构数据模式，从而增强了查询答案的语义。为此，我们提出了一个新的框架，以高效和有效地执行跨源搜索。该框架采用了一种强化学习方法，从语义上集成数据模式，并进一步为异构数据创建一个全局关系模式。然后，它根据全局模式执行查询应答算法，以跨多个数据源查找答案。我们使用实际数据进行广泛的实验评估，以验证我们的方法在有效性和效率方面优于现有的解决方案。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=An+Effective+Framework+for+Enhancing+Query+Answering+in+a+Heterogeneous+Data+Lake)|0| +|[Asymmetric Hashing for Fast Ranking via Neural Network Measures](https://doi.org/10.1145/3539618.3591640)|Khoa D. Doan, Shulong Tan, Weijie Zhao, Ping Li|VinUniversity, Hanoi, Vietnam; Rochester Institute of Technology, Rochester, NY, USA; Linkedin Ads, Seattle, WA, USA; Coupang, Inc., Mountain View, CA, USA|Fast item ranking is an important task in recommender systems. In previous works, graph-based Approximate Nearest Neighbor (ANN) approaches have demonstrated good performance on item ranking tasks with generic searching/matching measures (including complex measures such as neural network measures). However, since these ANN approaches must go through the neural measures several times during ranking, the computation is not practical if the neural measure is a large network. On the other hand, fast item ranking using existing hashing-based approaches, such as Locality Sensitive Hashing (LSH), only works with a limited set of measures. Previous learning-to-hash approaches are also not suitable to solve the fast item ranking problem since they can take a significant amount of time and computation to train the hash functions. Hashing approaches, however, are attractive because they provide a principle and efficient way to retrieve candidate items. In this paper, we propose a simple and effective learning-to-hash approach for the fast item ranking problem that can be used for any type of measure, including neural network measures. Specifically, we solve this problem with an asymmetric hashing framework based on discrete inner product fitting. We learn a pair of related hash functions that map heterogeneous objects (e.g., users and items) into a common discrete space where the inner product of their binary codes reveals their true similarity defined via the original searching measure. The fast ranking problem is reduced to an ANN search via this asymmetric hashing scheme. Then, we propose a sampling strategy to efficiently select relevant and contrastive samples to train the hashing model. We empirically validate the proposed method against the existing state-of-the-art fast item ranking methods in several combinations of non-linear searching functions and prominent datasets.|项目快速排序是推荐系统中的一项重要任务。在以往的工作中，基于图的近似最近邻(ANN)方法已经证明了良好的性能项目排序任务与一般的搜索/匹配措施(包括复杂的措施，如神经网络措施)。然而，由于这些神经网络方法在排序过程中必须经过多次神经测度，如果神经测度是一个大型网络，计算是不切实际的。另一方面，使用现有的基于哈希的方法(如区域敏感哈希(LSH))进行快速项目排序，只能在有限的度量集合下进行。以往的哈希学习方法也不适合解决快速项目排序问题，因为它们需要大量的时间和计算来训练哈希函数。然而，散列方法很有吸引力，因为它们提供了检索候选项的原则和有效方法。在本文中，我们提出了一个简单而有效的学习-哈希方法，用于快速项目排序问题，可以用于任何类型的测度，包括神经网络测度。具体地说，我们采用基于离散内积拟合的非对称散列框架来解决这个问题。我们学习了一对相关的散列函数，它们将异构对象(例如，用户和项)映射到一个公共的离散空间中，在这个空间中，它们的二进制码的内积揭示了它们通过原始搜索度量定义的真实相似性。通过这种非对称散列方法，将快速排序问题简化为人工神经网络搜索问题。然后，我们提出了一种抽样策略，有效地选择相关和对比的样本来训练散列模型。针对现有的快速项目排序方法，在多种非线性搜索函数和突出数据集的组合下，对该方法进行了实验验证。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Asymmetric+Hashing+for+Fast+Ranking+via+Neural+Network+Measures)|0| +|[Wisdom of Crowds and Fine-Grained Learning for Serendipity Recommendations](https://doi.org/10.1145/3539618.3591787)|Zhe Fu, Xi Niu, Li Yu|Renmin University of China, Beijing, China; University of North Carolina at Charlotte, Charlotte, USA|Serendipity is a notion that means an unexpected but valuable discovery. Due to its elusive and subjective nature, serendipity is difficult to study even with today's advances in machine learning and deep learning techniques. Both ground truth data collecting and model developing are the open research questions. This paper addresses both the data and the model challenges for identifying serendipity in recommender systems. For the ground truth data collecting, it proposes a new and scalable approach by using both user generated reviews and a crowd sourcing method. The result is a large-scale ground truth data on serendipity. For model developing, it designed a self-enhanced module to learn the fine-grained facets of serendipity in order to mitigate the inherent data sparsity problem in any serendipity ground truth dataset. The self-enhanced module is general enough to be applied with many base deep learning models for serendipity. A series of experiments have been conducted. As the result, a base deep learning model trained on our collected ground truth data, as well as with the help of the self-enhanced module, outperforms the state-of-the-art baseline models in predicting serendipity.|意外的发现意味着一个意想不到但有价值的发现。由于其难以捉摸和主观的性质，即使在今天的机器学习和深度学习技术的进步，意外发现是难以研究。地面真实数据采集和模型开发都是开放性的研究课题。本文讨论了在推荐系统中识别意外发现的数据和模型挑战。对于地面真相数据的收集，它提出了一种新的和可扩展的方法，通过使用用户生成的评论和众包的方法。其结果是一个关于意外发现的大规模地面真相数据。在模型开发方面，设计了一个自增强模块来学习机遇的细粒度方面，以减轻任何机遇地面真实数据集中固有的数据稀疏问题。该自增强模块具有较高的通用性，可以应用于许多基础深度学习模型中。进行了一系列的实验。因此，基于我们收集的地面真相数据的基础深度学习模型，以及在自我增强模块的帮助下，在预测偶然性方面优于最先进的基线模型。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Wisdom+of+Crowds+and+Fine-Grained+Learning+for+Serendipity+Recommendations)|0| +|[An Effective Framework for Enhancing Query Answering in a Heterogeneous Data Lake](https://doi.org/10.1145/3539618.3591637)|Qin Yuan, Ye Yuan, Zhenyu Wen, He Wang, Shiyuan Tang|Beijing Institute of Technology, Beijing, China; Beijing Institute of Technology, Hangzhou, China|There has been a growing interest in cross-source searching to gain rich knowledge in recent years. A data lake collects massive raw and heterogeneous data with different data schemas and query interfaces. Many real-life applications require query answering over the heterogeneous data lake, such as e-commerce, bioinformatics and healthcare. In this paper, we propose LakeAns that semantically integrates heterogeneous data schemas of the lake to enhance the semantics of query answers. To this end, we propose a novel framework to efficiently and effectively perform the cross-source searching. The framework exploits a reinforcement learning method to semantically integrate the data schemas and further create a global relational schema for the heterogeneous data. It then performs a query answering algorithm based on the global schema to find answers across multiple data sources. We conduct extensive experimental evaluations using real-life data to verify that our approach outperforms existing solutions in terms of effectiveness and efficiency.|近年来，人们对跨源搜索以获取丰富的知识越来越感兴趣。数据湖通过不同的数据模式和查询接口收集大量的原始和异构数据。许多实际应用程序需要在异构数据湖上进行查询回答，例如电子商务、生物信息学和医疗保健。在本文中，我们提出了一种基于 LakeAns 的方法，该方法在语义上集成了湖中的异构数据模式，从而增强了查询答案的语义。为此，我们提出了一个新的框架，以高效和有效地执行跨源搜索。该框架采用了一种强化学习方法，从语义上集成数据模式，并进一步为异构数据创建一个全局关系模式。然后，它根据全局模式执行查询应答算法，以跨多个数据源查找答案。我们使用实际数据进行广泛的实验评估，以验证我们的方法在有效性和效率方面优于现有的解决方案。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=An+Effective+Framework+for+Enhancing+Query+Answering+in+a+Heterogeneous+Data+Lake)|0| |[BeamQA: Multi-hop Knowledge Graph Question Answering with Sequence-to-Sequence Prediction and Beam Search](https://doi.org/10.1145/3539618.3591698)|Farah Atif, Ola El Khatib, Djellel Eddine Difallah|NYU Abu Dhabi, Abu Dhabi, UAE|Knowledge Graph Question Answering (KGQA) is a task that aims to answer natural language queries by extracting facts from a knowledge graph. Current state-of-the-art techniques for KGQA rely on text-based information from graph entity and relations labels, as well as external textual corpora. By reasoning over multiple edges in the graph, these can accurately rank and return the most relevant entities. However, one of the limitations of these methods is that they cannot handle the inherent incompleteness of real-world knowledge graphs and may lead to inaccurate answers due to missing edges. To address this issue, recent advances in graph representation learning have led to the development of systems that can use link prediction techniques to handle missing edges probabilistically, allowing the system to reason with incomplete information. However, existing KGQA frameworks that use such techniques often depend on learning a transformation from the query representation to the graph embedding space, which requires access to a large training dataset. We present BeamQA, an approach that overcomes these limitations by combining a sequence-to-sequence prediction model with beam search execution in the embedding space. Our model uses a pre-trained large language model and synthetic question generation. Our experiments demonstrate the effectiveness of BeamQA when compared to other KGQA methods on two knowledge graph question-answering datasets.|知识图问题回答(KGQA)是一个通过从知识图中提取事实来回答自然语言查询的任务。KGQA 目前最先进的技术依赖于来自图形实体和关系标签以及外部文本语料库的基于文本的信息。通过对图中的多条边进行推理，这些边可以准确地对最相关的实体进行排序并返回。然而，这些方法的局限性之一是它们不能处理真实世界知识图固有的不完备性，并可能由于缺少边而导致不准确的答案。为了解决这个问题，图表示学习的最新进展导致了系统的发展，可以使用链接预测技术来处理缺失的边概率，使系统推理不完整的信息。然而，使用这些技术的现有 KGQA 框架通常依赖于学习从查询表示到图嵌入空间的转换，这需要访问大型训练数据集。我们提出了一种 BeamQA 方法，该方法将序列到序列预测模型与嵌入空间中的波束搜索执行相结合，克服了这些局限性。我们的模型使用预先训练的大型语言模型和综合问题生成。实验结果表明，BeamQA 方法与其他 KGQA 方法相比，在两个知识图问答数据集上具有较好的效果。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=BeamQA:+Multi-hop+Knowledge+Graph+Question+Answering+with+Sequence-to-Sequence+Prediction+and+Beam+Search)|0| |[Time-interval Aware Share Recommendation via Bi-directional Continuous Time Dynamic Graphs](https://doi.org/10.1145/3539618.3591775)|Ziwei Zhao, Xi Zhu, Tong Xu, Aakas Lizhiyu, Yu Yu, Xueying Li, Zikai Yin, Enhong Chen|Alibaba Group, Hangzhou, China; University of Science and Technology of China, Hefei, China|Dynamic share recommendation, which aims at recommending a friend who would like to share a particular item at a certain timestamp, has emerged as a novel task for social-oriented e-commerce platforms. Different from traditional graph-based recommendation tasks, with integrating the interconnected social interactions and fine-grained temporal information from historical share records, this novel task may encounter one unique challenge, i.e., how to deal with the dynamic social connections and asymmetric share interactions. Even worse, users may keep inactive during some periods, which results in difficulties in updating personalized profiles. To address the above challenges, in this paper, we propose a dynamic graph share recommendation model called DynShare. Specifically, we first divide each user embedding into two parts, namely the invitation embedding and vote embedding to show the tendencies of sending and receiving items, respectively. Then, temporal graph attention networks (TGATs) based on bi-directional continuous time dynamic graphs (CTDGs) are leveraged to encode temporal neighbor information from different directions. Afterward, to estimate how different users perceive the time intervals after the last interaction, we further design a time-interval aware personalized projection operator on the foundation of temporal point processes (TPPs) to project user embedding for the next-time share prediction. Extensive experiments on a real-world e-commerce share dataset have demonstrated that our proposed DynShare can achieve better results compared with state-of-the-art baseline methods. And our code is available on the project website: https://github.com/meteor-gif/DynShare.|动态分享推荐，目的是推荐一个朋友谁愿意分享一个特定的项目在一定的时间戳，已经成为一个新的任务的社会导向的电子商务平台。与传统的基于图的推荐任务不同，该任务融合了互联的社会交互和历史共享记录中的细粒度时间信息，可能会遇到一个独特的挑战，即如何处理动态的社会关系和非对称的共享交互。更糟糕的是，用户可能会在某些时期保持不活跃，这导致难以更新个性化配置文件。为了解决上述问题，本文提出了一种动态图形共享推荐模型 DynShare。具体来说，我们首先将每个用户嵌入分为邀请嵌入和投票嵌入两部分，分别表示发送和接收条目的趋势。然后，利用基于双向连续时间动态图(CTDGs)的时间图注意网络(TGAT)对不同方向的时间邻居信息进行编码。然后，为了估计不同用户在最后一次交互后对时间间隔的感知程度，我们进一步设计了一个基于时间点过程(TPP)的时间间隔感知个性化投影算子，以投影用户嵌入来预测下一次共享。在一个真实的电子商务共享数据集上的大量实验表明，我们提出的 DynShare 可以取得比最先进的基线方法更好的结果。我们的代码可以在项目网站上找到: https://github.com/meteor-gif/dynshare。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Time-interval+Aware+Share+Recommendation+via+Bi-directional+Continuous+Time+Dynamic+Graphs)|0| |[Diffusion Recommender Model](https://doi.org/10.1145/3539618.3591663)|Wenjie Wang, Yiyan Xu, Fuli Feng, Xinyu Lin, Xiangnan He, TatSeng Chua||Generative models such as Generative Adversarial Networks (GANs) and Variational Auto-Encoders (VAEs) are widely utilized to model the generative process of user interactions. However, these generative models suffer from intrinsic limitations such as the instability of GANs and the restricted representation ability of VAEs. Such limitations hinder the accurate modeling of the complex user interaction generation procedure, such as noisy interactions caused by various interference factors. In light of the impressive advantages of Diffusion Models (DMs) over traditional generative models in image synthesis, we propose a novel Diffusion Recommender Model (named DiffRec) to learn the generative process in a denoising manner. To retain personalized information in user interactions, DiffRec reduces the added noises and avoids corrupting users' interactions into pure noises like in image synthesis. In addition, we extend traditional DMs to tackle the unique challenges in practical recommender systems: high resource costs for large-scale item prediction and temporal shifts of user preference. To this end, we propose two extensions of DiffRec: L-DiffRec clusters items for dimension compression and conducts the diffusion processes in the latent space; and T-DiffRec reweights user interactions based on the interaction timestamps to encode temporal information. We conduct extensive experiments on three datasets under multiple settings (e.g. clean training, noisy training, and temporal training). The empirical results and in-depth analysis validate the superiority of DiffRec with two extensions over competitive baselines.|生成模型如生成对抗网络(GANs)和变分自动编码器(VAE)被广泛地用于模拟用户交互的生成过程。然而，这些生成模型受到 GAN 的不稳定性和 VAE 表示能力的限制等内在的局限性。这些局限性阻碍了复杂用户交互生成过程的精确建模，例如由各种干扰因素引起的噪声交互。鉴于扩散模型在图像合成中相对于传统生成模型的显著优势，我们提出了一种新的扩散推荐模型。为了在用户交互中保留个性化信息，区分反射降低了附加的噪声，避免了在图像合成中将用户交互变成纯粹的噪声。此外，我们还扩展了传统的数据模型，以解决实际推荐系统中面临的独特挑战: 大规模项目预测的高资源成本和用户偏好的时间变化。为此，我们提出了区分反射的两个扩展: L-DiffRec 聚类项用于维数压缩并在潜空间中进行扩散过程; T-DiffRec 基于交互时间戳重新加权用户交互以编码时间信息。我们在三个数据集上进行了多重设置下的广泛实验(例如干净训练、噪声训练和时间训练)。实证结果和深入的分析验证了区分共享的优越性与两个扩展的竞争基线。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Diffusion+Recommender+Model)|0| |[Relation-Aware Multi-Positive Contrastive Knowledge Graph Completion with Embedding Dimension Scaling](https://doi.org/10.1145/3539618.3591756)|Bin Shang, Yinliang Zhao, Di Wang, Jun Liu|Xi'dian University, Xi'an, China; Xi'an Jiaotong University, Xi'an, China|Recently, a large amount of work has emerged for knowledge graph completion (KGC), which aims to reason over known facts and to infer the missing links. Meanwhile, contrastive learning has been applied to the KGC tasks, which can improve the representation quality of entities and relations. However, existing KGC approaches tend to improve their performance with high-dimensional embeddings and complex models, which make them suffer from large storage space and high training costs. Furthermore, contrastive loss with single positive sample learns little structural and semantic information in knowledge graphs due to the complex relation types. To address these challenges, we propose a novel knowledge graph completion model named ConKGC with the embedding dimension scaling and a relation-aware multi-positive contrastive loss. In order to achieve both space consumption reduction and model performance improvement, a new scoring function is proposed to map the raw low-dimensional embeddings of entities and relations to high-dimensional embedding space, and predict low-dimensional tail entities with latent semantic information of high-dimensional embeddings. In addition, ConKGC designs a multiple weak positive samples based contrastive loss under different relation types to maintain two important training targets, Alignment and Uniformity. This loss function and few parameters of the model ensure that ConKGC performs best and has fast convergence speed. Extensive experiments on three standard datasets confirm the effectiveness of our innovations, and the performance of ConKGC is significantly improved compared to the state-of-the-art methods.|近年来，知识图完成(KGC)领域出现了大量的工作，其目的是对已知事实进行推理，并推断出缺失的环节。同时，将对比学习应用到 KGC 任务中，提高了实体和关系的表示质量。然而，现有的 KGC 方法往往通过高维嵌入和复杂模型来提高性能，这使得它们存储空间大，培训成本高。此外，由于复杂的关系类型，单个正样本的对比损失对知识图的结构和语义信息影响很小。为了解决这些问题，我们提出了一种新的知识图完成模型 ConKGC，该模型具有嵌入维度缩放和关系感知的多正对比度损失。为了同时降低空间消耗和提高模型性能，提出了一种新的评分函数，将实体的原始低维嵌入和关系映射到高维嵌入空间，并预测具有高维嵌入潜在语义信息的低维尾实体。此外，ConKGC 在不同的关系类型下设计了多个基于对比度损失的弱阳性样本，以维持校准和均匀性这两个重要训练目标。该损失函数和模型参数较少，保证了 ConKGC 算法的最佳性能和较快的收敛速度。在三个标准数据集上的大量实验证实了我们创新的有效性，并且与最先进的方法相比，ConKGC 的性能得到了显著的提高。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Relation-Aware+Multi-Positive+Contrastive+Knowledge+Graph+Completion+with+Embedding+Dimension+Scaling)|0| -|[Weighted Knowledge Graph Embedding](https://doi.org/10.1145/3539618.3591784)|Zhao Zhang, Zhanpeng Guan, Fuwei Zhang, Fuzhen Zhuang, Zhulin An, Fei Wang, Yongjun Xu|Institute of Computing Technology, Chinese Academy of Sciences & University of Chinese Academy of Sciences, Beijing, China; Institute of Artificial Intelligence, Beihang University & Zhongguancun Laboratory, Beijing, China; Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China|Knowledge graph embedding (KGE) aims to project both entities and relations in a knowledge graph (KG) into low-dimensional vectors. Indeed, existing KGs suffer from the data imbalance issue, i.e., entities and relations conform to a long-tail distribution, only a small portion of entities and relations occur frequently, while the vast majority of entities and relations only have a few training samples. Existing KGE methods assign equal weights to each entity and relation during the training process. Under this setting, long-tail entities and relations are not fully trained during training, leading to unreliable representations. In this paper, we propose WeightE, which attends differentially to different entities and relations. Specifically, WeightE is able to endow lower weights to frequent entities and relations, and higher weights to infrequent ones. In such manner, WeightE is capable of increasing the weights of long-tail entities and relations, and learning better representations for them. In particular, WeightE tailors bilevel optimization for the KGE task, where the inner level aims to learn reliable entity and relation embeddings, and the outer level attempts to assign appropriate weights for each entity and relation. Moreover, it is worth noting that our technique of applying weights to different entities and relations is general and flexible, which can be applied to a number of existing KGE models. Finally, we extensively validate the superiority of WeightE against various state-of-the-art baselines.|知识图嵌入(KGE)是将知识图中的实体和关系投影到低维向量中。实际上，现有的幼儿园存在数据不平衡问题，即实体和关系符合长尾分布，只有一小部分实体和关系频繁出现，而绝大多数实体和关系只有少数训练样本。现有的 KGE 方法在训练过程中对每个实体和关系赋予相同的权重。在这种情况下，长尾实体和关系在训练期间没有得到充分的训练，导致不可靠的表示。在本文中，我们提出了权重 E，它区别对待不同的实体和关系。具体来说，WeightE 能够赋予频繁实体和关系较低的权重，赋予不频繁实体和关系较高的权重。通过这种方式，WeightE 能够增加长尾实体和关系的权重，并为它们学习更好的表示。特别是，WeightE 为 KGE 任务定制了两层优化，其中内层的目标是学习可靠的实体和关系嵌入，外层的目标是为每个实体和关系分配适当的权重。此外，值得注意的是，我们的技术应用权重不同的实体和关系是通用的和灵活的，这可以适用于现有的 KGE 模型。最后，我们广泛地验证了 WeightE 对于各种最先进的基线的优越性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Weighted+Knowledge+Graph+Embedding)|0| +|[Weighted Knowledge Graph Embedding](https://doi.org/10.1145/3539618.3591784)|Zhao Zhang, Zhanpeng Guan, Fuwei Zhang, Fuzhen Zhuang, Zhulin An, Fei Wang, Yongjun Xu|Institute of Artificial Intelligence, Beihang University & Zhongguancun Laboratory, Beijing, China; Institute of Computing Technology, Chinese Academy of Sciences & University of Chinese Academy of Sciences, Beijing, China; Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China|Knowledge graph embedding (KGE) aims to project both entities and relations in a knowledge graph (KG) into low-dimensional vectors. Indeed, existing KGs suffer from the data imbalance issue, i.e., entities and relations conform to a long-tail distribution, only a small portion of entities and relations occur frequently, while the vast majority of entities and relations only have a few training samples. Existing KGE methods assign equal weights to each entity and relation during the training process. Under this setting, long-tail entities and relations are not fully trained during training, leading to unreliable representations. In this paper, we propose WeightE, which attends differentially to different entities and relations. Specifically, WeightE is able to endow lower weights to frequent entities and relations, and higher weights to infrequent ones. In such manner, WeightE is capable of increasing the weights of long-tail entities and relations, and learning better representations for them. In particular, WeightE tailors bilevel optimization for the KGE task, where the inner level aims to learn reliable entity and relation embeddings, and the outer level attempts to assign appropriate weights for each entity and relation. Moreover, it is worth noting that our technique of applying weights to different entities and relations is general and flexible, which can be applied to a number of existing KGE models. Finally, we extensively validate the superiority of WeightE against various state-of-the-art baselines.|知识图嵌入(KGE)是将知识图中的实体和关系投影到低维向量中。实际上，现有的幼儿园存在数据不平衡问题，即实体和关系符合长尾分布，只有一小部分实体和关系频繁出现，而绝大多数实体和关系只有少数训练样本。现有的 KGE 方法在训练过程中对每个实体和关系赋予相同的权重。在这种情况下，长尾实体和关系在训练期间没有得到充分的训练，导致不可靠的表示。在本文中，我们提出了权重 E，它区别对待不同的实体和关系。具体来说，WeightE 能够赋予频繁实体和关系较低的权重，赋予不频繁实体和关系较高的权重。通过这种方式，WeightE 能够增加长尾实体和关系的权重，并为它们学习更好的表示。特别是，WeightE 为 KGE 任务定制了两层优化，其中内层的目标是学习可靠的实体和关系嵌入，外层的目标是为每个实体和关系分配适当的权重。此外，值得注意的是，我们的技术应用权重不同的实体和关系是通用的和灵活的，这可以适用于现有的 KGE 模型。最后，我们广泛地验证了 WeightE 对于各种最先进的基线的优越性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Weighted+Knowledge+Graph+Embedding)|0| |[Contrastive State Augmentations for Reinforcement Learning-Based Recommender Systems](https://doi.org/10.1145/3539618.3591656)|Zhaochun Ren, Na Huang, Yidan Wang, Pengjie Ren, Jun Ma, Jiahuan Lei, Xinlei Shi, Hengliang Luo, Joemon M. Jose, Xin Xin||Learning reinforcement learning (RL)-based recommenders from historical user-item interaction sequences is vital to generate high-reward recommendations and improve long-term cumulative benefits. However, existing RL recommendation methods encounter difficulties (i) to estimate the value functions for states which are not contained in the offline training data, and (ii) to learn effective state representations from user implicit feedback due to the lack of contrastive signals. In this work, we propose contrastive state augmentations (CSA) for the training of RL-based recommender systems. To tackle the first issue, we propose four state augmentation strategies to enlarge the state space of the offline data. The proposed method improves the generalization capability of the recommender by making the RL agent visit the local state regions and ensuring the learned value functions are similar between the original and augmented states. For the second issue, we propose introducing contrastive signals between augmented states and the state randomly sampled from other sessions to improve the state representation learning further. To verify the effectiveness of the proposed CSA, we conduct extensive experiments on two publicly accessible datasets and one dataset collected from a real-life e-commerce platform. We also conduct experiments on a simulated environment as the online evaluation setting. Experimental results demonstrate that CSA can effectively improve recommendation performance.|从历史用户项目交互序列中学习基于强化学习的推荐对于产生高回报的推荐和提高长期累积效益至关重要。然而，现有的 RL 推荐方法遇到了困难(i)估计不包含在离线训练数据中的状态的值函数，以及(ii)由于缺乏对比信号而从用户隐式反馈中学习有效的状态表示。在这项工作中，我们提出了对比状态增强(CSA)的训练基于 RL 的推荐系统。针对第一个问题，我们提出了四种状态增强策略来扩大离线数据的状态空间。该方法通过使 RL 代理访问局部状态区域，保证学习值函数在原状态和增广状态之间相似，提高了推荐器的泛化能力。对于第二个问题，我们提出在增广状态和从其他会话中随机采样的状态之间引入对比信号，以进一步改进状态表示学习。为了验证所提出的 CSA 的有效性，我们对从现实生活中的电子商务平台收集的两个公开可访问的数据集和一个数据集进行了广泛的实验。我们还进行了模拟环境的实验，作为在线评价设置。实验结果表明，CSA 能有效提高推荐性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Contrastive+State+Augmentations+for+Reinforcement+Learning-Based+Recommender+Systems)|0| |[Multi-order Matched Neighborhood Consistent Graph Alignment in a Union Vector Space](https://doi.org/10.1145/3539618.3591735)|Wei Tang, Haifeng Sun, Jingyu Wang, Qi Qi, Jing Wang, Hao Yang, Shimin Tao|Beijing University of Posts and Telecommunications, Beijing, China; Huawei, Beijing, China|In this paper, we study the unsupervised plain graph alignment problem, which aims to find node correspondences across two graphs without any side information. The majority of previous works addressed UPGA based on structural information, which will inevitably lead to subgraph isomorphism issues. That is, unaligned nodes could take similar local structural information. To mitigate this issue, we present the Multi-order Matched Neighborhood Consistent (MMNC) which tries to match nodes by aligning the learned node embeddings with only a small number of pseudo alignment seeds. In particular, we extend matched neighborhood consistency (MNC) to vector space and further develop embedding-based MNC (EMNC). By minimizing the EMNC-based loss function, we can utilize the limited pseudo alignment seeds to approximate the orthogonal transformation matrix between two groups of node embeddings with high efficiency and accuracy. Through extensive experiments on public benchmarks, we show that the proposed methods achieve a good balance between alignment accuracy and speed over multiple datasets compared with existing methods.|本文研究了无监督平面图对齐问题，目的是在没有任何边信息的情况下寻找两个图之间的节点对应。以往的大多数研究都是基于结构信息的 UPGA，这必然会导致子图同构问题。也就是说，未对齐的节点可以采用类似的本地结构信息。为了解决这个问题，我们提出了多阶匹配邻域一致性(MMNC)算法，该算法通过只使用少量伪对齐种子对齐学习节点嵌入来匹配节点。特别地，我们将匹配邻域一致性(MNC)扩展到向量空间，并进一步发展了基于嵌入的 MNC (EMNC)。通过最小化基于 EMNC 的损耗函数，我们可以利用有限的伪对齐种子来逼近两组节点嵌入之间的正交变换矩阵，从而提高效率和准确性。通过对公共基准的大量实验表明，与现有方法相比，本文提出的方法在多个数据集的对准精度和速度之间取得了良好的平衡。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Multi-order+Matched+Neighborhood+Consistent+Graph+Alignment+in+a+Union+Vector+Space)|0| |[Personalized Federated Relation Classification over Heterogeneous Texts](https://doi.org/10.1145/3539618.3591748)|Ning Pang, Xiang Zhao, Weixin Zeng, Ji Wang, Weidong Xiao|National University of Defense Technology, Changsha, China|Relation classification detects the semantic relation between two annotated entities from a piece of text, which is a useful tool for structurization of knowledge. Recently, federated learning has been introduced to train relation classification models in decentralized settings. Current methods strive for a strong server model by decoupling the model training at server from direct access to texts at clients while taking advantage of them. Nevertheless, they overlook the fact that clients have heterogeneous texts (i.e., texts with diversely skewed distribution of relations), which renders existing methods less practical. In this paper, we propose to investigate personalized federated relation classification, in which strong client models adapted to their own data are desired. To further meet the challenges brought by heterogeneous texts, we present a novel framework, namely pf-RC, with several optimized designs. It features a knowledge aggregation method that exploits a relation-wise weighting mechanism, and a feature augmentation method that leverages prototypes to adaptively enhance the representations of instances of long-tail relations. We experimentally validate the superiority of pf-RC against competing baselines in various settings, and the results suggest that the tailored techniques mitigate the challenges.|关系分类从一段文本中检测两个注释实体之间的语义关系，是知识结构化的有效工具。最近，联邦学习被引入到分散环境中训练关系分类模型。目前的方法力求建立一个强大的服务器模型，它将服务器上的模型训练与客户端直接访问文本分离开来，同时利用这些优势。尽管如此，他们忽视了这样一个事实: 客户的文本是异质的(即，文本具有不同的偏态分布的关系) ，这使得现有的方法不太实用。在本文中，我们提出研究个性化的联邦关系分类，其中需要强大的客户端模型适应自己的数据。为了进一步迎接异构文本带来的挑战，我们提出了一个新的框架，即 pf-RC，并进行了一些优化设计。提出了一种利用关系加权机制的知识聚合方法和一种利用原型自适应增强长尾关系实例表示的特征增强方法。我们通过实验验证了 pf-RC 在不同环境下对抗竞争基线的优越性，结果表明量身定制的技术可以缓解这一挑战。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Personalized+Federated+Relation+Classification+over+Heterogeneous+Texts)|0| @@ -223,15 +223,15 @@ |[Generative-Contrastive Graph Learning for Recommendation](https://doi.org/10.1145/3539618.3591691)|Yonghui Yang, Zhengwei Wu, Le Wu, Kun Zhang, Richang Hong, Zhiqiang Zhang, Jun Zhou, Meng Wang||By treating users' interactions as a user-item graph, graph learning models have been widely deployed in Collaborative Filtering(CF) based recommendation. Recently, researchers have introduced Graph Contrastive Learning(GCL) techniques into CF to alleviate the sparse supervision issue, which first constructs contrastive views by data augmentations and then provides self-supervised signals by maximizing the mutual information between contrastive views. Despite the effectiveness, we argue that current GCL-based recommendation models are still limited as current data augmentation techniques, either structure augmentation or feature augmentation. First, structure augmentation randomly dropout nodes or edges, which is easy to destroy the intrinsic nature of the user-item graph. Second, feature augmentation imposes the same scale noise augmentation on each node, which neglects the unique characteristics of nodes on the graph. To tackle the above limitations, we propose a novel Variational Graph Generative-Contrastive Learning(VGCL) framework for recommendation. Specifically, we leverage variational graph reconstruction to estimate a Gaussian distribution of each node, then generate multiple contrastive views through multiple samplings from the estimated distributions, which builds a bridge between generative and contrastive learning. Besides, the estimated variances are tailored to each node, which regulates the scale of contrastive loss for each node on optimization. Considering the similarity of the estimated distributions, we propose a cluster-aware twofold contrastive learning, a node-level to encourage consistency of a node's contrastive views and a cluster-level to encourage consistency of nodes in a cluster. Finally, extensive experimental results on three public datasets clearly demonstrate the effectiveness of the proposed model.|通过将用户交互作为一个用户项目图，图形学习模型已经广泛应用于基于协同过滤(CF)的推荐中。近年来，研究人员将图形对比学习(Graph Contrative Learning，GCL)技术引入到 CF 中，解决了对比视图稀疏监督问题。尽管有效，我们认为目前基于 GCL 的推荐模型仍然是有限的，作为当前的数据增强技术，无论是结构增强或特征增强。首先，结构扩展随机丢弃节点或边，这很容易破坏用户项图的内在本质。其次，特征增强对每个节点进行相同尺度的噪声增强，忽略了图上节点的独特性。针对上述局限性，本文提出了一种新的变分图生成对比学习(VGCL)框架。具体来说，我们利用变分图重建来估计每个节点的正态分布，然后从估计的分布中通过多个样本生成多个对比视图，这在生成学习和对比学习之间架起了一座桥梁。此外，估计的方差是针对每个节点量身定制的，它调节了每个节点在优化时的对比损失规模。考虑到估计分布的相似性，我们提出了一种基于聚类的双重对比学习方法，一个节点级别用于增强节点对比视图的一致性，一个聚类级别用于增强聚类中节点的一致性。最后，在三个公共数据集上的大量实验结果清楚地表明了该模型的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Generative-Contrastive+Graph+Learning+for+Recommendation)|0| |[Hydrus: Improving Personalized Quality of Experience in Short-form Video Services](https://doi.org/10.1145/3539618.3591696)|Zhiyu Yuan, Kai Ren, Gang Wang, Xin Miao|Tsinghua University, Beijing, China; Tsinghua University & Kuaishou, Beijing, China; Kuaishou, Beijing, China|Traditional approaches to improving users' quality of experience (QoE) focus on minimizing the latency on the server side. Through an analysis of 15 million users, however, we find that for short-form video apps, user experience depends on both response latency and recommendation accuracy. This observation brings a dilemma to service providers since improving recommendation accuracy requires adopting complex strategies that demand heavy computation, which substantially increases response latency. Our motivation is that users' sensitivity to response latency and recommendation accuracy varies greatly. In other words, some users would accept a 20ms increase in latency to enjoy higher-quality videos, while others prioritize minimizing lag above all else. Inspired by this, we present Hydrus, a novel resource allocation system that delivers the best possible personalized QoE by making tradeoffs between response latency and recommendation accuracy. Specifically, we formulate the resource allocation problem as a utility maximization problem, and Hydrus is guaranteed to solve the problem within a few milliseconds. We demonstrate the effectiveness of Hydrus through offline simulation and online experiments in Kuaishou, a massively popular video app with hundreds of millions of users worldwide. The results show that Hydrus can increase QoE by 35.6% with the same latency or reduce the latency by 10.1% with the same QoE. Furthermore, Hydrus can achieve 54.5% higher throughput without a decrease in QoE. In online A/B testing, Hydrus significantly improves click-through rate (CTR) and watch time; it can also reduce system resource costs without sacrificing QoE.|提高用户体验质量(QoE)的传统方法侧重于最小化服务器端的延迟。然而，通过对1500万用户的分析，我们发现，对于短格式视频应用程序，用户体验既取决于响应时间，也取决于推荐的准确性。这种现象给服务提供商带来了一个难题，因为提高推荐的准确性需要采用复杂的策略，这些策略需要大量的计算，这大大增加了响应延迟。我们的动机是，用户对响应延迟和推荐准确性的敏感性差异很大。换句话说，一些用户会接受延迟增加20毫秒来享受更高质量的视频，而其他人则优先考虑最小化延迟。受此启发，我们提出了 Hydrus，一种新的资源分配系统，通过在响应延迟和推荐准确性之间进行权衡，提供尽可能最好的个性化 QoE。具体来说，我们把资源分配问题当作一个效用最大化，Hydrus 保证能在几毫秒内解决这个问题。我们通过在 Kuaishou 的离线模拟和在线实验来展示 Hydrus 的有效性，这是一个在全世界拥有数亿用户的非常流行的视频应用程序。结果表明，在相同的延迟时间下，Hydrus 可以提高35.6% 的 QoE，而在相同的延迟时间下，Hydrus 可以降低10.1% 的 QoE。此外，Hydrus 可以在不降低 QoE 的情况下提高54.5% 的吞吐量。在线 A/B 测试中，Hydrus 显著提高了点进率和观看时间，它还可以在不牺牲 QoE 的情况下降低系统资源成本。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Hydrus:+Improving+Personalized+Quality+of+Experience+in+Short-form+Video+Services)|0| |[Topic-enhanced Graph Neural Networks for Extraction-based Explainable Recommendation](https://doi.org/10.1145/3539618.3591776)|Jie Shuai, Le Wu, Kun Zhang, Peijie Sun, Richang Hong, Meng Wang|Tsinghua University, Beijing, China; Hefei University of Technology, Hefei, China|Review information has been demonstrated beneficial for the explainable recommendation. It can be treated as training corpora for generation-based methods or knowledge bases for extraction-based models. However, for generation-based methods, the sparsity of user-generated reviews and the high complexity of generative language models lead to a lack of personalization and adaptability. For extraction-based methods, focusing only on relevant attributes makes them invalid in situations where explicit attribute words are absent, limiting the potential of extraction-based models. To this end, in this paper, we focus on the explicit and implicit analysis of review information simultaneously and propose novel a Topic-enhanced Graph Neural Networks (TGNN) to fully explore review information for better explainable recommendations. To be specific, we first use a pre-trained topic model to analyze reviews at the topic level, and design a sentence-enhanced topic graph to model user preference explicitly, where topics are intermediate nodes between users and items. Corresponding sentences serve as edge features. Thus, the requirement of explicit attribute words can be mitigated. Meanwhile, we leverage a review-enhanced rating graph to model user preference implicitly, where reviews are also considered as edge features for fine-grained user-item interaction modeling. Next, user and item representations from two graphs are used for final rating prediction and explanation extraction. Extensive experiments on three real-world datasets demonstrate the superiority of our proposed TGNN with both recommendation accuracy and explanation quality.|评审信息已被证明对解释性建议有益。它可以作为基于生成的方法的训练语料库，也可以作为基于抽取的模型的知识库。然而，对于基于生成的方法来说，用户生成评论的稀少性和生成语言模型的高度复杂性导致了个性化和适应性的缺乏。对于基于抽取的方法，只关注相关属性会使它们在没有显式属性词的情况下失效，从而限制了基于抽取的模型的潜力。为此，本文集中研究了复习信息的显性和隐性分析，并提出了一种新颖的主题增强图形神经网络(TGNN)来充分挖掘复习信息，以获得更好的解释性建议。具体来说，我们首先使用一个预先训练好的主题模型来分析主题层面的评论，然后设计一个句子增强的主题图来显式地模拟用户偏好，其中主题是用户和项目之间的中间节点。相应的句子作为边缘特征。因此，可以减少显式属性词的需求。同时，我们利用评论增强的评分图来隐式地建模用户偏好，其中评论也被认为是细粒度用户项交互建模的边缘特征。接下来，使用来自两个图的用户和项目表示来进行最终评分预测和解释提取。在三个实际数据集上的大量实验表明，我们提出的 TGNN 在推荐精度和解释质量方面都具有优势。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Topic-enhanced+Graph+Neural+Networks+for+Extraction-based+Explainable+Recommendation)|0| -|[Strategy-aware Bundle Recommender System](https://doi.org/10.1145/3539618.3591771)|Yinwei Wei, Xiaohao Liu, Yunshan Ma, Xiang Wang, Liqiang Nie, TatSeng Chua|Harbin Institute of Technology (Shenzhen), Shenzhen, China; University of Science and Technology of China, Hefei, China; University of Chinese Academy of Sciences, Beijing, China; National University of Singapore, Singapore, Singapore|A bundle is a group of items that provides improved services to users and increased profits for sellers. However, locating the desired bundles that match the users' tastes still challenges us, due to the sparsity issue. Despite the remarkable performance of existing approaches, we argue that they seldom consider the bundling strategy (i.e., how the items within a bundle are associated with each other) in the bundle recommendation, resulting in the suboptimal user and bundle representations for their interaction prediction. Therefore, we propose to model the strategy-aware user and bundle representations for the bundle recommendation. Towards this end, we develop a new model for bundle recommendation, termed Bundle Graph Transformer (BundleGT), which consists of the token embedding layer, hierarchical graph transformer (HGT) layer, and prediction layer. Specifically, in the token embedding layer, we take the items within bundles as tokens and represent them with items' id embedding learned from user-item interactions. Having the input tokens, the HGT layer can simultaneously model the strategy-aware bundle and user representations. Therein, we encode the prior knowledge of bundling strategy from the well-designed bundles and incorporate it with tokens' embeddings to model the bundling strategy and learn the strategy-aware bundle representations. Meanwhile, upon the correlation between bundles consumed by the same user, we further learn the user preference on bundling strategy. Jointly considering it with the user preference on the item content, we can learn the strategy-aware user representation for user-bundle interaction prediction. Conducting extensive experiments on Youshu, ifashion, and Netease datasets, we demonstrate that our proposed model outperforms the state-of-the-art baselines (e.g., BundelNet [7] Net, BGCN [3] BGCN, and CrossCBR [22]), justifying the effectiveness of our proposed model. Moreover, in HGT layer, our devised light self-attention block improves not only the accuracy performance but efficiency of BundleGT. Our code is publicly available at: https://github.com/Xiaohao-Liu/BundleGT.|捆绑销售是指向用户提供更好的服务并为卖家增加利润的一组商品。然而，由于稀缺性问题，找到符合用户口味的所需捆绑包仍然是一个挑战。尽管现有的方法表现出色，我们认为他们很少考虑捆绑推荐中的捆绑策略(即，一个捆绑包中的项目是如何相互关联的) ，导致用户和捆绑包的交互预测表示次优。因此，我们建议为捆绑推荐建立策略感知用户和捆绑表示的模型。为此，我们提出了一种新的捆绑式推荐模型——捆绑图转换器(Bundle Graph former，BundleGT) ，该模型由令牌嵌入层、层次图转换器(HGT)层和预测层组成。具体来说，在令牌嵌入层中，我们将捆绑包中的项目作为令牌，并使用从用户-项目交互中学到的项目 id 嵌入来表示它们。有了输入令牌，HGT 层可以同时对战略感知捆绑包和用户表示进行建模。其中，我们从设计良好的捆绑包中编码捆绑策略的先验知识，并将其与令牌的嵌入相结合，对捆绑策略进行建模，学习策略感知的捆绑表示。同时，根据同一用户使用的捆绑包之间的相关性，进一步了解用户对捆绑策略的偏好。结合用户对项目内容的偏好，我们可以学习用于用户绑定交互预测的策略感知用户表示。在有书，时尚和网易数据集上进行广泛的实验，我们证明我们提出的模型优于最先进的基线(例如 BundelNet [7] Net，BGCN [3] BGCN 和 CrossCBR [22]) ，证明了我们提出的模型的有效性。此外，在 HGT 层，我们设计的光自注意块不仅提高了 BundleGT 的精度性能，而且提高了效率。我们的代码可以在以下 https://github.com/xiaohao-liu/bundlegt 公开获得。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Strategy-aware+Bundle+Recommender+System)|0| +|[Strategy-aware Bundle Recommender System](https://doi.org/10.1145/3539618.3591771)|Yinwei Wei, Xiaohao Liu, Yunshan Ma, Xiang Wang, Liqiang Nie, TatSeng Chua|Harbin Institute of Technology (Shenzhen), Shenzhen, China; National University of Singapore, Singapore, Singapore; University of Science and Technology of China, Hefei, China; University of Chinese Academy of Sciences, Beijing, China|A bundle is a group of items that provides improved services to users and increased profits for sellers. However, locating the desired bundles that match the users' tastes still challenges us, due to the sparsity issue. Despite the remarkable performance of existing approaches, we argue that they seldom consider the bundling strategy (i.e., how the items within a bundle are associated with each other) in the bundle recommendation, resulting in the suboptimal user and bundle representations for their interaction prediction. Therefore, we propose to model the strategy-aware user and bundle representations for the bundle recommendation. Towards this end, we develop a new model for bundle recommendation, termed Bundle Graph Transformer (BundleGT), which consists of the token embedding layer, hierarchical graph transformer (HGT) layer, and prediction layer. Specifically, in the token embedding layer, we take the items within bundles as tokens and represent them with items' id embedding learned from user-item interactions. Having the input tokens, the HGT layer can simultaneously model the strategy-aware bundle and user representations. Therein, we encode the prior knowledge of bundling strategy from the well-designed bundles and incorporate it with tokens' embeddings to model the bundling strategy and learn the strategy-aware bundle representations. Meanwhile, upon the correlation between bundles consumed by the same user, we further learn the user preference on bundling strategy. Jointly considering it with the user preference on the item content, we can learn the strategy-aware user representation for user-bundle interaction prediction. Conducting extensive experiments on Youshu, ifashion, and Netease datasets, we demonstrate that our proposed model outperforms the state-of-the-art baselines (e.g., BundelNet [7] Net, BGCN [3] BGCN, and CrossCBR [22]), justifying the effectiveness of our proposed model. Moreover, in HGT layer, our devised light self-attention block improves not only the accuracy performance but efficiency of BundleGT. Our code is publicly available at: https://github.com/Xiaohao-Liu/BundleGT.|捆绑销售是指向用户提供更好的服务并为卖家增加利润的一组商品。然而，由于稀缺性问题，找到符合用户口味的所需捆绑包仍然是一个挑战。尽管现有的方法表现出色，我们认为他们很少考虑捆绑推荐中的捆绑策略(即，一个捆绑包中的项目是如何相互关联的) ，导致用户和捆绑包的交互预测表示次优。因此，我们建议为捆绑推荐建立策略感知用户和捆绑表示的模型。为此，我们提出了一种新的捆绑式推荐模型——捆绑图转换器(Bundle Graph former，BundleGT) ，该模型由令牌嵌入层、层次图转换器(HGT)层和预测层组成。具体来说，在令牌嵌入层中，我们将捆绑包中的项目作为令牌，并使用从用户-项目交互中学到的项目 id 嵌入来表示它们。有了输入令牌，HGT 层可以同时对战略感知捆绑包和用户表示进行建模。其中，我们从设计良好的捆绑包中编码捆绑策略的先验知识，并将其与令牌的嵌入相结合，对捆绑策略进行建模，学习策略感知的捆绑表示。同时，根据同一用户使用的捆绑包之间的相关性，进一步了解用户对捆绑策略的偏好。结合用户对项目内容的偏好，我们可以学习用于用户绑定交互预测的策略感知用户表示。在有书，时尚和网易数据集上进行广泛的实验，我们证明我们提出的模型优于最先进的基线(例如 BundelNet [7] Net，BGCN [3] BGCN 和 CrossCBR [22]) ，证明了我们提出的模型的有效性。此外，在 HGT 层，我们设计的光自注意块不仅提高了 BundleGT 的精度性能，而且提高了效率。我们的代码可以在以下 https://github.com/xiaohao-liu/bundlegt 公开获得。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Strategy-aware+Bundle+Recommender+System)|0| |[Soft Prompt Decoding for Multilingual Dense Retrieval](https://doi.org/10.1145/3539618.3591769)|Zhiqi Huang, Hansi Zeng, Hamed Zamani, James Allan||In this work, we explore a Multilingual Information Retrieval (MLIR) task, where the collection includes documents in multiple languages. We demonstrate that applying state-of-the-art approaches developed for cross-lingual information retrieval to MLIR tasks leads to sub-optimal performance. This is due to the heterogeneous and imbalanced nature of multilingual collections -- some languages are better represented in the collection and some benefit from large-scale training data. To address this issue, we present KD-SPD, a novel soft prompt decoding approach for MLIR that implicitly "translates" the representation of documents in different languages into the same embedding space. To address the challenges of data scarcity and imbalance, we introduce a knowledge distillation strategy. The teacher model is trained on rich English retrieval data, and by leveraging bi-text data, our distillation framework transfers its retrieval knowledge to the multilingual document encoder. Therefore, our approach does not require any multilingual retrieval training data. Extensive experiments on three MLIR datasets with a total of 15 languages demonstrate that KD-SPD significantly outperforms competitive baselines in all cases. We conduct extensive analyses to show that our method has less language bias and better zero-shot transfer ability towards new languages.|在这项工作中，我们探索了一个多语言信息检索(mLIR)任务，其中收集包括多种语言的文档。我们证明，将为跨语言信息检索开发的最先进的方法应用于 mLIR 任务，会导致性能欠佳。这是由于多语种集合的异质性和不平衡性——有些语言在集合中得到更好的表示，有些则得益于大规模的培训数据。为了解决这个问题，我们提出了一种新的针对 MLIR 的软提示解码方法 KD-SPD，它将不同语言的文档表示隐式地“翻译”到相同的嵌入空间中。为了解决数据稀缺和不平衡的挑战，我们引入了一种知识提取策略。教师模型是在丰富的英语检索数据上进行训练的，并且通过利用双文本数据，我们的精馏框架将其检索知识传递给多语言文档编码器。因此，我们的方法不需要任何多语言检索训练数据。在三个共有15种语言的 MLIR 数据集上的广泛实验表明，KD-SPD 在所有情况下都显著优于竞争性基线。我们进行了广泛的分析表明，我们的方法具有较少的语言偏见和更好的零-镜头转移能力的新语言。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Soft+Prompt+Decoding+for+Multilingual+Dense+Retrieval)|0| |[Rethinking Benchmarks for Cross-modal Image-text Retrieval](https://doi.org/10.1145/3539618.3591758)|Weijing Chen, Linli Yao, Qin Jin||Image-text retrieval, as a fundamental and important branch of information retrieval, has attracted extensive research attentions. The main challenge of this task is cross-modal semantic understanding and matching. Some recent works focus more on fine-grained cross-modal semantic matching. With the prevalence of large scale multimodal pretraining models, several state-of-the-art models (e.g. X-VLM) have achieved near-perfect performance on widely-used image-text retrieval benchmarks, i.e. MSCOCO-Test-5K and Flickr30K-Test-1K. In this paper, we review the two common benchmarks and observe that they are insufficient to assess the true capability of models on fine-grained cross-modal semantic matching. The reason is that a large amount of images and texts in the benchmarks are coarse-grained. Based on the observation, we renovate the coarse-grained images and texts in the old benchmarks and establish the improved benchmarks called MSCOCO-FG and Flickr30K-FG. Specifically, on the image side, we enlarge the original image pool by adopting more similar images. On the text side, we propose a novel semi-automatic renovation approach to refine coarse-grained sentences into finer-grained ones with little human effort. Furthermore, we evaluate representative image-text retrieval models on our new benchmarks to demonstrate the effectiveness of our method. We also analyze the capability of models on fine-grained semantic comprehension through extensive experiments. The results show that even the state-of-the-art models have much room for improvement in fine-grained semantic understanding, especially in distinguishing attributes of close objects in images. Our code and improved benchmark datasets are publicly available at: https://github.com/cwj1412/MSCOCO-Flikcr30K_FG, which we hope will inspire further in-depth research on cross-modal retrieval.|图像-文本检索作为信息检索学的一个重要分支，已经引起了广泛的研究关注。这项任务的主要挑战是跨模态语义理解和匹配。最近的一些工作更多地关注于细粒度的跨情态语义匹配。随着大规模多模式预训练模型的普及，一些最先进的模型(例如 X-VLM)在广泛使用的图像-文本检索基准(例如 MSCOCO-Test-5K 和 Flickr30K-Test-1K)上取得了近乎完美的性能。在本文中，我们回顾了这两个常用的基准测试，发现它们不足以评估模型在细粒度跨模态语义匹配方面的真正能力。原因是基准测试中的大量图像和文本是粗粒度的。在此基础上，对原有基准测试中的粗粒度图像和文本进行了改进，建立了改进后的基准测试 MSCOCO-FG 和 Flickr30K-FG。具体地说，在图像方面，我们通过采用更多的相似图像来扩大原始图像池。在文本方面，我们提出了一种新的半自动更新方法，以较少的人工精力将粗粒度的句子细化为细粒度的句子。在此基础上，我们对具有代表性的图像-文本检索模型进行了评估，以验证该方法的有效性。通过大量实验，分析了模型对细粒度语义理解的能力。结果表明，即使是最先进的模型，在细粒度的语义理解方面，尤其是在图像中近似对象的属性识别方面，仍有很大的改进空间。我们的代码和改进的基准数据集可以在以下 https://github.com/cwj1412/mscoco-flikcr30k_fg 公开获得，我们希望这将激发对跨模式检索的进一步深入研究。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Rethinking+Benchmarks+for+Cross-modal+Image-text+Retrieval)|0| |[From Region to Patch: Attribute-Aware Foreground-Background Contrastive Learning for Fine-Grained Fashion Retrieval](https://doi.org/10.1145/3539618.3591690)|Jianfeng Dong, Xiaoman Peng, Zhe Ma, Daizong Liu, Xiaoye Qu, Xun Yang, Jixiang Zhu, Baolong Liu||Attribute-specific fashion retrieval (ASFR) is a challenging information retrieval task, which has attracted increasing attention in recent years. Different from traditional fashion retrieval which mainly focuses on optimizing holistic similarity, the ASFR task concentrates on attribute-specific similarity, resulting in more fine-grained and interpretable retrieval results. As the attribute-specific similarity typically corresponds to the specific subtle regions of images, we propose a Region-to-Patch Framework (RPF) that consists of a region-aware branch and a patch-aware branch to extract fine-grained attribute-related visual features for precise retrieval in a coarse-to-fine manner. In particular, the region-aware branch is first to be utilized to locate the potential regions related to the semantic of the given attribute. Then, considering that the located region is coarse and still contains the background visual contents, the patch-aware branch is proposed to capture patch-wise attribute-related details from the previous amplified region. Such a hybrid architecture strikes a proper balance between region localization and feature extraction. Besides, different from previous works that solely focus on discriminating the attribute-relevant foreground visual features, we argue that the attribute-irrelevant background features are also crucial for distinguishing the detailed visual contexts in a contrastive manner. Therefore, a novel E-InfoNCE loss based on the foreground and background representations is further proposed to improve the discrimination of attribute-specific representation. Extensive experiments on three datasets demonstrate the effectiveness of our proposed framework, and also show a decent generalization of our RPF on out-of-domain fashion images. Our source code is available at https://github.com/HuiGuanLab/RPF.|特定属性的时尚检索(asFR)是一项具有挑战性的信息检索检索任务，近年来受到越来越多的关注。与以优化整体相似度为核心的传统时尚检索不同，ASFR 任务集中于特定属性的相似度，使得检索结果更加细粒度和可解释性。由于特定属性的相似性通常对应于图像的特定细微区域，因此我们提出了一种由区域感知分支和补丁感知分支组成的区域到补丁框架(Regional-to-Patch Framework，RPF)来提取细粒度的属性相关视觉特征，以便以粗到细的方式进行精确检索。特别地，区域感知分支首先被用来定位与给定属性的语义相关的潜在区域。然后，考虑到所定位的区域比较粗糙，并且仍然包含背景视觉内容，提出了基于补丁感知的分支来从前面的放大区域中获取与补丁相关的属性细节。这种混合结构在区域定位和特征提取之间取得了适当的平衡。此外，与以往单纯侧重于识别与属性相关的前景视觉特征的作品不同，我们认为与属性无关的背景特征对于以对比的方式识别详细的视觉背景也是至关重要的。因此，本文进一步提出了一种基于前景和背景表征的新型 E-InfoNCE 损失算法，以改善特定属性表征的识别能力。在三个数据集上的大量实验证明了我们提出的框架的有效性，同时也显示了我们的 RPF 在域外时尚图像上的良好推广。我们的源代码可以在 https://github.com/huiguanlab/rpf 找到。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=From+Region+to+Patch:+Attribute-Aware+Foreground-Background+Contrastive+Learning+for+Fine-Grained+Fashion+Retrieval)|0| |[Multi-view Multi-aspect Neural Networks for Next-basket Recommendation](https://doi.org/10.1145/3539618.3591738)|Zhiying Deng, Jianjun Li, Zhiqiang Guo, Wei Liu, Li Zou, Guohui Li|Huazhong University of Science and Technology, Wuhan, China|Next-basket recommendation (NBR) is a type of recommendation that aims to recommend a set of items to users according to their historical basket sequences. Existing NBR methods suffer from two limitations: (1) overlooking low-level item correlations, which results in coarse-grained item representation; and (2) failing to consider spurious interests in repeated behaviors, leading to suboptimal user interest learning. To address these limitations, we propose a novel solution named Multi-view Multi-aspect Neural Recommendation (MMNR) for NBR, which first normalizes the interactions from both the user-side and item-side, respectively, aiming to remove the spurious interests, and utilizes them as weights for items from different views to construct differentiated representations for each interaction item, enabling comprehensive user interest learning. Then, to capture low-level item correlations, MMNR models different aspects of items to obtain disentangled representations of items, thereby fully capturing multiple user interests. Extensive experiments on real-world datasets demonstrate the effectiveness of MMNR, showing that it consistently outperforms several state-of-the-art NBR methods.|下一个篮子推荐(NBR)是一种推荐类型，旨在根据用户的历史篮子序列向用户推荐一组项目。现有的 NBR 方法存在两个局限性: (1)忽视低层次的项目相关性，导致粗粒度的项目表示; (2)未能考虑重复行为中的虚假兴趣，导致次优的用户兴趣学习。针对这些局限性，本文提出了一种基于 NBR 的多视角多方面神经网络推荐方法(Multi-view Multi-spectNearn汪汪推荐方法) ，该方法首先对用户方和项目方的交互进行规范化处理，消除虚假兴趣，并利用这些兴趣作为不同视角项目的权重，为每个交互项目构建差异化表示，从而实现用户兴趣的全面学习。然后，为了捕获低层次的项目相关性，MMNR 对项目的不同方面进行建模，以获得项目的分离表示，从而充分捕获多个用户兴趣。在真实世界数据集上的大量实验证明了 MMNR 的有效性，表明它始终优于几种最先进的 NBR 方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Multi-view+Multi-aspect+Neural+Networks+for+Next-basket+Recommendation)|0| |[EulerNet: Adaptive Feature Interaction Learning via Euler's Formula for CTR Prediction](https://doi.org/10.1145/3539618.3591681)|Zhen Tian, Ting Bai, Wayne Xin Zhao, JiRong Wen, Zhao Cao||Learning effective high-order feature interactions is very crucial in the CTR prediction task. However, it is very time-consuming to calculate high-order feature interactions with massive features in online e-commerce platforms. Most existing methods manually design a maximal order and further filter out the useless interactions from them. Although they reduce the high computational costs caused by the exponential growth of high-order feature combinations, they still suffer from the degradation of model capability due to the suboptimal learning of the restricted feature orders. The solution to maintain the model capability and meanwhile keep it efficient is a technical challenge, which has not been adequately addressed. To address this issue, we propose an adaptive feature interaction learning model, named as EulerNet, in which the feature interactions are learned in a complex vector space by conducting space mapping according to Euler's formula. EulerNet converts the exponential powers of feature interactions into simple linear combinations of the modulus and phase of the complex features, making it possible to adaptively learn the high-order feature interactions in an efficient way. Furthermore, EulerNet incorporates the implicit and explicit feature interactions into a unified architecture, which achieves the mutual enhancement and largely boosts the model capabilities. Such a network can be fully learned from data, with no need of pre-designed form or order for feature interactions. Extensive experiments conducted on three public datasets have demonstrated the effectiveness and efficiency of our approach. Our code is available at: https://github.com/RUCAIBox/EulerNet.|在 CTR 预测任务中，学习有效的高阶特征交互是非常关键的。然而，在线电子商务平台中计算具有大量特征的高阶特征交互是非常耗时的。大多数现有的方法手工设计一个最大顺序，并进一步从中筛选出无用的交互。虽然它们降低了高阶特征组合的指数增长所造成的高计算成本，但是由于受限特征阶次的次优学习，它们仍然受到模型能力退化的影响。保持模型能力并同时保持其有效性的解决方案是一个技术挑战，尚未得到充分解决。针对这一问题，提出了一种自适应特征交互学习模型 EulerNet。EulerNet 将特征交互的指数幂转化为复杂特征模量和相位的简单线性组合，使得高阶特征交互的自适应学习成为可能。此外，EulerNet 将隐式和显式的特征交互融合到一个统一的体系结构中，实现了相互增强，大大提高了模型的性能。这样的网络可以完全从数据中学习，不需要预先设计的形式或特征交互的顺序。在三个公共数据集上进行的大量实验已经证明了我们方法的有效性和效率。我们的代码可以在以下 https://github.com/rucaibox/eulernet 找到。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=EulerNet:+Adaptive+Feature+Interaction+Learning+via+Euler's+Formula+for+CTR+Prediction)|0| -|[FiD-Light: Efficient and Effective Retrieval-Augmented Text Generation](https://doi.org/10.1145/3539618.3591687)|Sebastian Hofstätter, Jiecao Chen, Karthik Raman, Hamed Zamani|Technische Universität Wien; Google; University of Massachusetts, Amherst|Retrieval-augmented generation models offer many benefits over standalone language models: besides a textual answer to a given query they provide provenance items retrieved from an updateable knowledge base. However, they are also more complex systems and need to handle long inputs. In this work, we introduce FiD-Light to strongly increase the efficiency of the state-of-the-art retrieval-augmented FiD model, while maintaining the same level of effectiveness. Our FiD-Light model constrains the information flow from the encoder (which encodes passages separately) to the decoder (using concatenated encoded representations). Furthermore, we adapt FiD-Light with re-ranking capabilities through textual source pointers, to improve the top-ranked provenance precision. Our experiments on a diverse set of seven knowledge intensive tasks (KILT) show FiD-Light consistently improves the Pareto frontier between query latency and effectiveness. FiD-Light with source pointing sets substantial new state-of-the-art results on six KILT tasks for combined text generation and provenance retrieval evaluation, while maintaining reasonable efficiency.|与独立语言模型相比，检索增强生成模型提供了许多好处: 除了对给定查询的文本回答之外，它们还提供了从可更新的知识库中检索到的出处项。然而，它们也是更复杂的系统，需要处理长输入。在这项工作中，我们引入了 FiD-Light，在保持相同的效率水平的同时，强有力地提高了最先进的检索增强 FiD 模型的效率。我们的 FiD-Light 模型约束了从编码器(分别编码通道)到解码器(使用级联编码表示)的信息流。此外，我们通过文本源指针改编具有重新排序能力的 FiD-Light，以提高顶级来源精度。我们在七个不同的知识密集型任务(KILT)上的实验表明，FiD-Light 一致地改善了查询延迟和有效性之间的帕累托边界。带源点的 FiD-Light 在六个 KILT 任务上设置了大量的最新结果，用于组合文本生成和出处检索评估，同时保持合理的效率。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=FiD-Light:+Efficient+and+Effective+Retrieval-Augmented+Text+Generation)|0| -|[PLATE: A Prompt-Enhanced Paradigm for Multi-Scenario Recommendations](https://doi.org/10.1145/3539618.3591750)|Yuhao Wang, Xiangyu Zhao, Bo Chen, Qidong Liu, Huifeng Guo, Huanshuo Liu, Yichao Wang, Rui Zhang, Ruiming Tang|Sun Yat-sen University, Guangzhou, China; Huawei Noah's Ark Lab, Shenzhen, China; Huawei Noah's Ark Lab, Shanghai, China; ruizhang.info, Shenzhen, China; City University of Hong Kong, Hong Kong, China|With the explosive growth of commercial applications of recommender systems, multi-scenario recommendation (MSR) has attracted considerable attention, which utilizes data from multiple domains to improve their recommendation performance simultaneously. However, training a unified deep recommender system (DRS) may not explicitly comprehend the commonality and difference among domains, whereas training an individual model for each domain neglects the global information and incurs high computation costs. Likewise, fine-tuning on each domain is inefficient, and recent advances that apply the prompt tuning technique to improve fine-tuning efficiency rely solely on large-sized transformers. In this work, we propose a novel prompt-enhanced paradigm for multi-scenario recommendation. Specifically, a unified DRS backbone model is first pre-trained using data from all the domains in order to capture the commonality across domains. Then, we conduct prompt tuning with two novel prompt modules, capturing the distinctions among various domains and users. Our experiments on Douban, Amazon, and Ali-CCP datasets demonstrate the effectiveness of the proposed paradigm with two noticeable strengths: (i) its great compatibility with various DRS backbone models, and (ii) its high computation and storage efficiency with only 6% trainable parameters in prompt tuning phase. The implementation code is available for easy reproduction.|随着推荐系统商业应用的迅猛发展，多场景推荐(MSR)技术引起了人们的广泛关注，它利用来自多个领域的数据来同时提高推荐性能。然而，训练一个统一的深度推荐系统(DRS)可能无法明确理解领域之间的共性和差异，而为每个领域训练一个单独的模型会忽略全局信息，并导致高计算成本。同样，每个领域的微调效率都不高，最近应用快速调优技术来提高微调效率的进展完全依赖于大型变压器。在这项工作中，我们提出了一个新的提示增强的多情景推荐范式。具体来说，一个统一的 DRS 骨干模型是首先使用来自所有领域的数据进行预训练，以便捕获跨领域的共性。然后，我们用两个新颖的提示模块进行提示调优，捕捉不同领域和用户之间的差异。我们在 Douban、亚马逊和阿里-CCP 数据集上的实验证明了这种模式的有效性，它有两个显著的优势: (i)它与各种 DRS 骨干模型的极大兼容性，(ii)它的高计算和存储效率，在及时调整阶段只有6% 的可训练参数。实现代码可以很容易地复制。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=PLATE:+A+Prompt-Enhanced+Paradigm+for+Multi-Scenario+Recommendations)|0| -|[LightGT: A Light Graph Transformer for Multimedia Recommendation](https://doi.org/10.1145/3539618.3591716)|Yinwei Wei, Wenqi Liu, Fan Liu, Xiang Wang, Liqiang Nie, TatSeng Chua|Harbin Institute of Technology (Shenzhen), Shenzhen, China; Shandong University, Qingdao, China; University of Science and Technology of China, Hefei, China; National University of Singapore, Singapore, Singapore|Multimedia recommendation methods aim to discover the user preference on the multi-modal information to enhance the collaborative filtering (CF) based recommender system. Nevertheless, they seldom consider the impact of feature extraction on the user preference modeling and prediction of the user-item interaction, as the extracted features contain excessive information irrelevant to the recommendation. To capture the informative features from the extracted ones, we resort to Transformer model to establish the correlation between the items historically interacted by the same user. Considering its challenges in effectiveness and efficiency, we propose a novel Transformer-based recommendation model, termed as Light Graph Transformer model (LightGT). Therein, we develop a modal-specific embedding and a layer-wise position encoder for the effective similarity measurement, and present a light self-attention block to improve the efficiency of self-attention scoring. Based on these designs, we can effectively and efficiently learn the user preference from the off-the-shelf items' features to predict the user-item interactions. Conducting extensive experiments on Movielens, Tiktok and Kwai datasets, we demonstrate that LigthGT significantly outperforms the state-of-the-art baselines with less time. Our code is publicly available at: https://github.com/Liuwq-bit/LightGT.|多媒体推荐方法旨在发现用户对多模式信息的偏好，以增强基于协同过滤(CF)的推荐系统。然而，他们很少考虑特征提取对用户偏好建模和用户项目交互预测的影响，因为提取的特征包含了与推荐无关的过多信息。为了从提取的特征中获取信息性特征，我们使用 Transformer 模型来建立历史上由同一用户交互的项之间的相关性。考虑到其在有效性和效率方面的挑战，我们提出了一种新的基于变压器的推荐模型，称为光图形变压器模型(LightGT)。在此基础上，提出了一种基于模态特异性的嵌入方法和分层位置编码器，以提高自我注意评分的效率。基于这些设计，我们可以有效地从现成商品的特征中学习用户偏好，从而预测用户与商品的交互。在 Movielens，Tiktok 和葵数据集上进行了广泛的实验，我们证明 LigthGT 在更短的时间内显著优于最先进的基线。我们的代码可以在以下 https://github.com/liuwq-bit/lightgt 公开获得。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=LightGT:+A+Light+Graph+Transformer+for+Multimedia+Recommendation)|0| +|[FiD-Light: Efficient and Effective Retrieval-Augmented Text Generation](https://doi.org/10.1145/3539618.3591687)|Sebastian Hofstätter, Jiecao Chen, Karthik Raman, Hamed Zamani|Google; University of Massachusetts, Amherst; Technische Universität Wien|Retrieval-augmented generation models offer many benefits over standalone language models: besides a textual answer to a given query they provide provenance items retrieved from an updateable knowledge base. However, they are also more complex systems and need to handle long inputs. In this work, we introduce FiD-Light to strongly increase the efficiency of the state-of-the-art retrieval-augmented FiD model, while maintaining the same level of effectiveness. Our FiD-Light model constrains the information flow from the encoder (which encodes passages separately) to the decoder (using concatenated encoded representations). Furthermore, we adapt FiD-Light with re-ranking capabilities through textual source pointers, to improve the top-ranked provenance precision. Our experiments on a diverse set of seven knowledge intensive tasks (KILT) show FiD-Light consistently improves the Pareto frontier between query latency and effectiveness. FiD-Light with source pointing sets substantial new state-of-the-art results on six KILT tasks for combined text generation and provenance retrieval evaluation, while maintaining reasonable efficiency.|与独立语言模型相比，检索增强生成模型提供了许多好处: 除了对给定查询的文本回答之外，它们还提供了从可更新的知识库中检索到的出处项。然而，它们也是更复杂的系统，需要处理长输入。在这项工作中，我们引入了 FiD-Light，在保持相同的效率水平的同时，强有力地提高了最先进的检索增强 FiD 模型的效率。我们的 FiD-Light 模型约束了从编码器(分别编码通道)到解码器(使用级联编码表示)的信息流。此外，我们通过文本源指针改编具有重新排序能力的 FiD-Light，以提高顶级来源精度。我们在七个不同的知识密集型任务(KILT)上的实验表明，FiD-Light 一致地改善了查询延迟和有效性之间的帕累托边界。带源点的 FiD-Light 在六个 KILT 任务上设置了大量的最新结果，用于组合文本生成和出处检索评估，同时保持合理的效率。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=FiD-Light:+Efficient+and+Effective+Retrieval-Augmented+Text+Generation)|0| +|[PLATE: A Prompt-Enhanced Paradigm for Multi-Scenario Recommendations](https://doi.org/10.1145/3539618.3591750)|Yuhao Wang, Xiangyu Zhao, Bo Chen, Qidong Liu, Huifeng Guo, Huanshuo Liu, Yichao Wang, Rui Zhang, Ruiming Tang|Huawei Noah's Ark Lab, Shenzhen, China; Sun Yat-sen University, Guangzhou, China; ruizhang.info, Shenzhen, China; City University of Hong Kong, Hong Kong, China; Huawei Noah's Ark Lab, Shanghai, China|With the explosive growth of commercial applications of recommender systems, multi-scenario recommendation (MSR) has attracted considerable attention, which utilizes data from multiple domains to improve their recommendation performance simultaneously. However, training a unified deep recommender system (DRS) may not explicitly comprehend the commonality and difference among domains, whereas training an individual model for each domain neglects the global information and incurs high computation costs. Likewise, fine-tuning on each domain is inefficient, and recent advances that apply the prompt tuning technique to improve fine-tuning efficiency rely solely on large-sized transformers. In this work, we propose a novel prompt-enhanced paradigm for multi-scenario recommendation. Specifically, a unified DRS backbone model is first pre-trained using data from all the domains in order to capture the commonality across domains. Then, we conduct prompt tuning with two novel prompt modules, capturing the distinctions among various domains and users. Our experiments on Douban, Amazon, and Ali-CCP datasets demonstrate the effectiveness of the proposed paradigm with two noticeable strengths: (i) its great compatibility with various DRS backbone models, and (ii) its high computation and storage efficiency with only 6% trainable parameters in prompt tuning phase. The implementation code is available for easy reproduction.|随着推荐系统商业应用的迅猛发展，多场景推荐(MSR)技术引起了人们的广泛关注，它利用来自多个领域的数据来同时提高推荐性能。然而，训练一个统一的深度推荐系统(DRS)可能无法明确理解领域之间的共性和差异，而为每个领域训练一个单独的模型会忽略全局信息，并导致高计算成本。同样，每个领域的微调效率都不高，最近应用快速调优技术来提高微调效率的进展完全依赖于大型变压器。在这项工作中，我们提出了一个新的提示增强的多情景推荐范式。具体来说，一个统一的 DRS 骨干模型是首先使用来自所有领域的数据进行预训练，以便捕获跨领域的共性。然后，我们用两个新颖的提示模块进行提示调优，捕捉不同领域和用户之间的差异。我们在 Douban、亚马逊和阿里-CCP 数据集上的实验证明了这种模式的有效性，它有两个显著的优势: (i)它与各种 DRS 骨干模型的极大兼容性，(ii)它的高计算和存储效率，在及时调整阶段只有6% 的可训练参数。实现代码可以很容易地复制。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=PLATE:+A+Prompt-Enhanced+Paradigm+for+Multi-Scenario+Recommendations)|0| +|[LightGT: A Light Graph Transformer for Multimedia Recommendation](https://doi.org/10.1145/3539618.3591716)|Yinwei Wei, Wenqi Liu, Fan Liu, Xiang Wang, Liqiang Nie, TatSeng Chua|Harbin Institute of Technology (Shenzhen), Shenzhen, China; National University of Singapore, Singapore, Singapore; University of Science and Technology of China, Hefei, China; Shandong University, Qingdao, China|Multimedia recommendation methods aim to discover the user preference on the multi-modal information to enhance the collaborative filtering (CF) based recommender system. Nevertheless, they seldom consider the impact of feature extraction on the user preference modeling and prediction of the user-item interaction, as the extracted features contain excessive information irrelevant to the recommendation. To capture the informative features from the extracted ones, we resort to Transformer model to establish the correlation between the items historically interacted by the same user. Considering its challenges in effectiveness and efficiency, we propose a novel Transformer-based recommendation model, termed as Light Graph Transformer model (LightGT). Therein, we develop a modal-specific embedding and a layer-wise position encoder for the effective similarity measurement, and present a light self-attention block to improve the efficiency of self-attention scoring. Based on these designs, we can effectively and efficiently learn the user preference from the off-the-shelf items' features to predict the user-item interactions. Conducting extensive experiments on Movielens, Tiktok and Kwai datasets, we demonstrate that LigthGT significantly outperforms the state-of-the-art baselines with less time. Our code is publicly available at: https://github.com/Liuwq-bit/LightGT.|多媒体推荐方法旨在发现用户对多模式信息的偏好，以增强基于协同过滤(CF)的推荐系统。然而，他们很少考虑特征提取对用户偏好建模和用户项目交互预测的影响，因为提取的特征包含了与推荐无关的过多信息。为了从提取的特征中获取信息性特征，我们使用 Transformer 模型来建立历史上由同一用户交互的项之间的相关性。考虑到其在有效性和效率方面的挑战，我们提出了一种新的基于变压器的推荐模型，称为光图形变压器模型(LightGT)。在此基础上，提出了一种基于模态特异性的嵌入方法和分层位置编码器，以提高自我注意评分的效率。基于这些设计，我们可以有效地从现成商品的特征中学习用户偏好，从而预测用户与商品的交互。在 Movielens，Tiktok 和葵数据集上进行了广泛的实验，我们证明 LigthGT 在更短的时间内显著优于最先进的基线。我们的代码可以在以下 https://github.com/liuwq-bit/lightgt 公开获得。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=LightGT:+A+Light+Graph+Transformer+for+Multimedia+Recommendation)|0| |[Law Article-Enhanced Legal Case Matching: A Causal Learning Approach](https://doi.org/10.1145/3539618.3591709)|Zhongxiang Sun, Jun Xu, Xiao Zhang, Zhenhua Dong, JiRong Wen||Legal case matching, which automatically constructs a model to estimate the similarities between the source and target cases, has played an essential role in intelligent legal systems. Semantic text matching models have been applied to the task where the source and target legal cases are considered as long-form text documents. These general-purpose matching models make the predictions solely based on the texts in the legal cases, overlooking the essential role of the law articles in legal case matching. In the real world, the matching results (e.g., relevance labels) are dramatically affected by the law articles because the contents and the judgments of a legal case are radically formed on the basis of law. From the causal sense, a matching decision is affected by the mediation effect from the cited law articles by the legal cases, and the direct effect of the key circumstances (e.g., detailed fact descriptions) in the legal cases. In light of the observation, this paper proposes a model-agnostic causal learning framework called Law-Match, under which the legal case matching models are learned by respecting the corresponding law articles. Given a pair of legal cases and the related law articles, Law-Match considers the embeddings of the law articles as instrumental variables (IVs), and the embeddings of legal cases as treatments. Using IV regression, the treatments can be decomposed into law-related and law-unrelated parts, respectively reflecting the mediation and direct effects. These two parts are then combined with different weights to collectively support the final matching prediction. We show that the framework is model-agnostic, and a number of legal case matching models can be applied as the underlying models. Comprehensive experiments show that Law-Match can outperform state-of-the-art baselines on three public datasets.|法律案件匹配是一种自动构建判断源案件与目标案件相似性的模型，在智能法律系统中发挥着重要作用。本文将语义文本匹配模型应用于源、目标法律案件作为长文本文档的任务中。这些通用匹配模型仅根据法律案件的案文进行预测，忽视了法律条款在法律案件匹配中的重要作用。在现实世界中，由于案件的内容和判决是在法律的基础上从根本上形成的，所以匹配结果(如关联标签)受到法律条文的巨大影响。从因果关系的角度来看，法律案件引用的法律条文所产生的调解效果，以及法律案件中关键情节(如详细的事实描述)的直接效果，都会影响匹配决策。基于这种观察，本文提出了一个模型无关的因果学习框架——法律匹配，在此框架下，通过尊重相应的法律条款来学习法律案例匹配模型。在给定一对法律案例和相关法律条文的情况下，Law-Match 将法律条文的嵌入视为工具变量，将法律案例的嵌入视为处理方法。利用四元回归，可以将处理分解为与法律相关的部分和与法律无关的部分，分别反映调解效果和直接效果。然后将这两部分与不同的权重相结合，共同支持最终的匹配预测。我们表明，该框架是模型无关的，一些法律案件匹配模型可以作为基础模型应用。综合实验表明，Law-Match 在三个公共数据集上的表现优于最先进的基线。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Law+Article-Enhanced+Legal+Case+Matching:+A+Causal+Learning+Approach)|0| |[Multimodal Counterfactual Learning Network for Multimedia-based Recommendation](https://doi.org/10.1145/3539618.3591739)|Shuaiyang Li, Dan Guo, Kang Liu, Richang Hong, Feng Xue|Hefei University of Technology & Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, China; Hefei University of Technology, Hefei, China|Multimedia-based recommendation (MMRec) utilizes multimodal content (images, textual descriptions, etc.) as auxiliary information on historical interactions to determine user preferences. Most MMRec approaches predict user interests by exploiting a large amount of multimodal contents of user-interacted items, ignoring the potential effect of multimodal content of user-uninteracted items. As a matter of fact, there is a small portion of user preference-irrelevant features in the multimodal content of user-interacted items, which may be a kind of spurious correlation with user preferences, thereby degrading the recommendation performance. In this work, we argue that the multimodal content of user-uninteracted items can be further exploited to identify and eliminate the user preference-irrelevant portion inside user-interacted multimodal content, for example by counterfactual inference of causal theory. Going beyond multimodal user preference modeling only using interacted items, we propose a novel model called Multimodal Counterfactual Learning Network (MCLN), in which user-uninteracted items' multimodal content is additionally exploited to further purify the representation of user preference-relevant multimodal content that better matches the user's interests, yielding state-of-the-art performance. Extensive experiments are conducted to validate the effectiveness and rationality of MCLN. We release the complete codes of MCLN at https://github.com/hfutmars/MCLN.|基于多媒体的推荐(MMRec)利用多模态内容(图像、文本描述等)作为历史交互的辅助信息，以确定用户偏好。大多数 MMRec 方法通过利用用户交互项目的大量多通道内容来预测用户的兴趣，而忽略了用户未交互项目的多通道内容的潜在影响。事实上，在用户交互项目的多通道内容中，有一小部分与用户偏好无关的功能，这可能是一种与用户偏好有关的伪相关，从而降低了推荐性能。本文认为，可以进一步利用用户未交互项目的多通道内容来识别和消除用户交互多通道内容中与用户偏好无关的部分，如通过因果理论的反事实推断。超越了仅使用交互项目的多模态用户偏好建模，我们提出了一种新的模型，称为多模态反事实学习网络(MultimodalCounterfact Learning Network，MCLN) ，其中用户未交互项目的多模态内容被进一步利用，以进一步纯化与用户偏好相关的多模态内容的表示，更好地匹配用户的兴趣，产生最先进的性能。通过大量实验验证了 MCLN 算法的有效性和合理性。我们会在 https://github.com/hfutmars/MCLN 公布 MCLN 的完整代码。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Multimodal+Counterfactual+Learning+Network+for+Multimedia-based+Recommendation)|0| |[Dynamic Graph Evolution Learning for Recommendation](https://doi.org/10.1145/3539618.3591674)|Haoran Tang, Shiqing Wu, Guandong Xu, Qing Li|University of Technology Sydney, Sydney, Australia; The Hong Kong Polytechnic University, Hong Kong, China|Graph neural network (GNN) based algorithms have achieved superior performance in recommendation tasks due to their advanced capability of exploiting high-order connectivity between users and items. However, most existing GNN-based recommendation models ignore the dynamic evolution of nodes, where users will continuously interact with items over time, resulting in rapid changes in the environment (e.g., neighbor and structure). Moreover, the heuristic normalization of embeddings in dynamic recommendation is de-coupled with the model learning process, making the whole system suboptimal. In this paper, we propose a novel framework for generating satisfying recommendations in dynamic environments, called Dynamic Graph Evolution Learning (DGEL). First, we design three efficient real-time update learning methods for nodes from the perspectives of inherent interaction potential, time-decay neighbor augmentation, and symbiotic local structure learning. Second, we construct the re-scaling enhancement networks for dynamic embeddings to adaptively and automatically bridge the normalization process with model learning. Third, we leverage the interaction matching task and the future prediction task together for joint training to further improve performance. Extensive experiments on three real-world datasets demonstrate the effectiveness and improvements of our proposed DGEL. The code is available at https://github.com/henrictang/DGEL.|基于图神经网络(GNN)的推荐算法由于具有利用用户与项目之间高阶连通性的先进能力，在推荐任务中取得了较好的性能。然而，大多数现有的基于 GNN 的推荐模型忽略了节点的动态演化，即用户将随着时间的推移不断地与项目交互，从而导致环境(如邻居和结构)的快速变化。此外，将动态推荐中嵌入的启发式规范化与模型学习过程解耦，使整个系统处于次优状态。在本文中，我们提出了一个在动态环境中生成满意建议的新框架，称为动态图进化学习(DGEL)。首先，我们从固有交互势、时间衰减邻域增强和共生局部结构学习的角度设计了三种有效的节点实时更新学习方法。其次，构造了动态嵌入的可重缩放增强网络，自适应地、自动地连接规范化过程和模型学习。第三，我们将交互匹配任务和未来预测任务结合起来进行联合训练，以进一步提高性能。在三个实际数据集上的大量实验证明了我们提出的 DGEL 的有效性和改进。密码可在 https://github.com/henrictang/dgel 查阅。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Dynamic+Graph+Evolution+Learning+for+Recommendation)|0| @@ -245,68 +245,68 @@ |[A Static Pruning Study on Sparse Neural Retrievers](https://doi.org/10.1145/3539618.3591941)|Carlos Lassance, Simon Lupart, Hervé Déjean, Stéphane Clinchant, Nicola Tonellotto||Sparse neural retrievers, such as DeepImpact, uniCOIL and SPLADE, have been introduced recently as an efficient and effective way to perform retrieval with inverted indexes. They aim to learn term importance and, in some cases, document expansions, to provide a more effective document ranking compared to traditional bag-of-words retrieval models such as BM25. However, these sparse neural retrievers have been shown to increase the computational costs and latency of query processing compared to their classical counterparts. To mitigate this, we apply a well-known family of techniques for boosting the efficiency of query processing over inverted indexes: static pruning. We experiment with three static pruning strategies, namely document-centric, term-centric and agnostic pruning, and we assess, over diverse datasets, that these techniques still work with sparse neural retrievers. In particular, static pruning achieves $2\times$ speedup with negligible effectiveness loss ($\leq 2\%$ drop) and, depending on the use case, even $4\times$ speedup with minimal impact on the effectiveness ($\leq 8\%$ drop). Moreover, we show that neural rerankers are robust to candidates from statically pruned indexes.|稀疏神经检索器，如 DeepImpact、 uniCOIL 和 SPLADE，作为一种有效的反向索引检索方法，近年来得到了广泛的应用。他们的目标是学习词汇的重要性，并在某些情况下，文档扩展，以提供更有效的文档排序相比，传统的单词袋检索模型，如 BM25。然而，这些稀疏的神经检索已被证明增加了计算成本和查询处理的延迟相比，他们的经典对应物。为了缓解这种情况，我们应用了一系列众所周知的技术来提高反向索引上的查询处理效率: 静态剪枝。我们试验了三种静态修剪策略，即以文档为中心的、以术语为中心的和不可知的修剪，并且我们在不同的数据集上评估，这些技术仍然适用于稀疏的神经检索器。特别是，静态修剪可以达到 $2倍的加速效果，而且效果损失可以忽略不计($leq 2% $drop) ，根据用例的不同，甚至可以达到 $4倍的加速效果，而且对效果的影响最小($leq 8% $drop)。此外，我们证明了神经重新排序器对静态剪枝索引的候选者是鲁棒的。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Static+Pruning+Study+on+Sparse+Neural+Retrievers)|0| |[Adapting Learned Sparse Retrieval for Long Documents](https://doi.org/10.1145/3539618.3591943)|Thong Nguyen, Sean MacAvaney, Andrew Yates||Learned sparse retrieval (LSR) is a family of neural retrieval methods that transform queries and documents into sparse weight vectors aligned with a vocabulary. While LSR approaches like Splade work well for short passages, it is unclear how well they handle longer documents. We investigate existing aggregation approaches for adapting LSR to longer documents and find that proximal scoring is crucial for LSR to handle long documents. To leverage this property, we proposed two adaptations of the Sequential Dependence Model (SDM) to LSR: ExactSDM and SoftSDM. ExactSDM assumes only exact query term dependence, while SoftSDM uses potential functions that model the dependence of query terms and their expansion terms (i.e., terms identified using a transformer's masked language modeling head). Experiments on the MSMARCO Document and TREC Robust04 datasets demonstrate that both ExactSDM and SoftSDM outperform existing LSR aggregation approaches for different document length constraints. Surprisingly, SoftSDM does not provide any performance benefits over ExactSDM. This suggests that soft proximity matching is not necessary for modeling term dependence in LSR. Overall, this study provides insights into handling long documents with LSR, proposing adaptations that improve its performance.|学习稀疏检索(LSR)是一类将查询和文档转换为与词汇表对齐的稀疏权重向量的神经检索方法。虽然像 Splade 这样的 LSR 方法在处理短文时效果很好，但是它们在处理较长文档时效果如何还不清楚。我们研究了现有的将 LSR 适应于较长文档的聚合方法，发现近似评分对于 LSR 处理较长文档至关重要。为了利用这个特性，我们提出了序列依赖模型(SDM)对 LSR 的两种适应: ExactSDM 和 SoftSDM。ExactSDM 只假设精确的查询术语依赖性，而 SoftSDM 使用潜在函数对查询术语及其扩展术语的依赖性进行建模(即，使用转换器的掩蔽语言建模头标识的术语)。在 MSMARCO Document 和 TREC Robust04数据集上的实验表明，ExactSDM 和 SoftSDM 在不同文档长度约束下的性能都优于现有的 LSR 聚合方法。令人惊讶的是，与 ExactSDM 相比，SoftSDM 并没有提供任何性能优势。这表明软接近匹配对于 LSR 中的项依赖建模是不必要的。总的来说，这项研究为使用 LSR 处理长文档提供了见解，并提出了改进其性能的建议。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Adapting+Learned+Sparse+Retrieval+for+Long+Documents)|0| |[Affective Relevance: Inferring Emotional Responses via fNIRS Neuroimaging](https://doi.org/10.1145/3539618.3591946)|Tuukka Ruotsalo, Kalle Mäkelä, Michiel M. A. Spapé, Luis A. Leiva|University of Helsinki, Helsinki, Finland; University of Copenhagen and University of Helsinki, Helsinki, Finland; University of Luxembourg, Luxembourg, Luxembourg|Information retrieval (IR) relies on a general notion of relevance, which is used as the principal foundation for ranking and evaluation methods. However, IR does not account for more a nuanced affective experience. Here, we consider the emotional response decoded directly from the human brain as an alternative dimension of relevance. We report an experiment covering seven different scenarios in which we measure and predict how users emotionally respond to visual image contents by using functional near-infrared spectroscopy (fNIRS) neuroimaging on two commonly used affective dimensions: valence (negativity and positivity) and arousal (boredness and excitedness). Our results show that affective states can be successfully decoded using fNIRS, and utilized to complement the present notion of relevance in IR studies. For example, we achieved 0.39 Balanced accuracy and 0.61 AUC in 4-class classification of affective states (vs. 0.25 Balanced accuracy and 0.5 AUC of a random classifier). Likewise, we achieved 0.684 Precision@20 when retrieving high-arousal images. Our work opens new avenues for incorporating emotional states in IR evaluation, affective feedback, and information filtering.|信息检索(IR)依赖于一个普遍的相关性概念，这个概念被用作排名和评估方法的主要基础。然而，IR 并不能解释更细微的情感体验。在这里，我们认为情绪反应解码直接从人类大脑作为一个替代维度的相关性。我们报告了一个实验，在这个实验中，我们测量和预测了使用者对视觉图像内容的情绪反应，通过使用功能近红外光谱技术(fNIRS)神经成像技术，在两个常用的情感维度上进行测量和预测: 效价(消极和积极)和唤醒(无聊和兴奋)。我们的研究结果表明，情感状态可以成功地解码使用 fNIRS，并利用补充现有的概念的相关性在国际关系研究。例如，在情感状态的4类分类中，我们实现了0.39平衡准确性和0.61 AUC (相对于随机分类器的0.25平衡准确性和0.5 AUC)。同样，我们在检索高唤醒图像时也达到了0.684 Precision@20。我们的工作开辟了将情绪状态纳入 IR 评估、情感反馈和信息过滤的新途径。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Affective+Relevance:+Inferring+Emotional+Responses+via+fNIRS+Neuroimaging)|0| -|[Attacking Pre-trained Recommendation](https://doi.org/10.1145/3539618.3591949)|Yiqing Wu, Ruobing Xie, Zhao Zhang, Yongchun Zhu, Fuzhen Zhuang, Jie Zhou, Yongjun Xu, Qing He|Institute of Artificial Intelligence, Beihang University, Beijing, China; WeChat, Tencent & Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China; WeChat, Tencent, Beijing, China; Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China|Recently, a series of pioneer studies have shown the potency of pre-trained models in sequential recommendation, illuminating the path of building an omniscient unified pre-trained recommendation model for different downstream recommendation tasks. Despite these advancements, the vulnerabilities of classical recommender systems also exist in pre-trained recommendation in a new form, while the security of pre-trained recommendation model is still unexplored, which may threaten its widely practical applications. In this study, we propose a novel framework for backdoor attacking in pre-trained recommendation. We demonstrate the provider of the pre-trained model can easily insert a backdoor in pre-training, thereby increasing the exposure rates of target items to target user groups. Specifically, we design two novel and effective backdoor attacks: basic replacement and prompt-enhanced, under various recommendation pre-training usage scenarios. Experimental results on real-world datasets show that our proposed attack strategies significantly improve the exposure rates of target items to target users by hundreds of times in comparison to the clean model.|最近，一系列的先驱研究已经显示了预训练模型在顺序推荐中的效力，阐明了为不同的下游推荐任务建立一个全知的统一预训练推荐模型的途径。尽管取得了这些进展，但传统推荐系统的脆弱性还存在于新形式的预训练推荐中，而预训练推荐模型的安全性仍未得到探索，这可能威胁到其广泛的实际应用。在这项研究中，我们提出了一个新的框架，用于后门攻击的预训练推荐。我们证明预训练模型的提供者可以很容易地在预训练中插入一个后门，从而增加目标项目对目标用户群的暴露率。具体来说，我们设计了两种新颖而有效的后门攻击: 基本替换和提示增强，在不同的推荐预训练使用场景下。在实际数据集上的实验结果表明，与清洁模型相比，我们提出的攻击策略显著提高了目标项对目标用户的暴露率。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Attacking+Pre-trained+Recommendation)|0| +|[Attacking Pre-trained Recommendation](https://doi.org/10.1145/3539618.3591949)|Yiqing Wu, Ruobing Xie, Zhao Zhang, Yongchun Zhu, Fuzhen Zhuang, Jie Zhou, Yongjun Xu, Qing He|WeChat, Tencent & Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China; Institute of Artificial Intelligence, Beihang University, Beijing, China; WeChat, Tencent, Beijing, China; Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China|Recently, a series of pioneer studies have shown the potency of pre-trained models in sequential recommendation, illuminating the path of building an omniscient unified pre-trained recommendation model for different downstream recommendation tasks. Despite these advancements, the vulnerabilities of classical recommender systems also exist in pre-trained recommendation in a new form, while the security of pre-trained recommendation model is still unexplored, which may threaten its widely practical applications. In this study, we propose a novel framework for backdoor attacking in pre-trained recommendation. We demonstrate the provider of the pre-trained model can easily insert a backdoor in pre-training, thereby increasing the exposure rates of target items to target user groups. Specifically, we design two novel and effective backdoor attacks: basic replacement and prompt-enhanced, under various recommendation pre-training usage scenarios. Experimental results on real-world datasets show that our proposed attack strategies significantly improve the exposure rates of target items to target users by hundreds of times in comparison to the clean model.|最近，一系列的先驱研究已经显示了预训练模型在顺序推荐中的效力，阐明了为不同的下游推荐任务建立一个全知的统一预训练推荐模型的途径。尽管取得了这些进展，但传统推荐系统的脆弱性还存在于新形式的预训练推荐中，而预训练推荐模型的安全性仍未得到探索，这可能威胁到其广泛的实际应用。在这项研究中，我们提出了一个新的框架，用于后门攻击的预训练推荐。我们证明预训练模型的提供者可以很容易地在预训练中插入一个后门，从而增加目标项目对目标用户群的暴露率。具体来说，我们设计了两种新颖而有效的后门攻击: 基本替换和提示增强，在不同的推荐预训练使用场景下。在实际数据集上的实验结果表明，与清洁模型相比，我们提出的攻击策略显著提高了目标项对目标用户的暴露率。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Attacking+Pre-trained+Recommendation)|0| |[Always Strengthen Your Strengths: A Drift-Aware Incremental Learning Framework for CTR Prediction](https://doi.org/10.1145/3539618.3591948)|Congcong Liu, Fei Teng, Xiwei Zhao, Zhangang Lin, Jinghe Hu, Jingping Shao|JD.COM, Beijing, China|Click-through rate (CTR) prediction is of great importance in recommendation systems and online advertising platforms. When served in industrial scenarios, the user-generated data observed by the CTR model typically arrives as a stream. Streaming data has the characteristic that the underlying distribution drifts over time and may recur. This can lead to catastrophic forgetting if the model simply adapts to new data distribution all the time. Also, it's inefficient to relearn distribution that has been occurred. Due to memory constraints and diversity of data distributions in large-scale industrial applications, conventional strategies for catastrophic forgetting such as replay, parameter isolation, and knowledge distillation are difficult to be deployed. In this work, we design a novel drift-aware incremental learning framework based on ensemble learning to address catastrophic forgetting in CTR prediction. With explicit error-based drift detection on streaming data, the framework further strengthens well-adapted ensembles and freezes ensembles that do not match the input distribution avoiding catastrophic interference. Both evaluations on offline experiments and A/B test shows that our method outperforms all baselines considered.|在推荐系统和在线广告平台中，点进率预测非常重要。在工业场景中服务时，CTR 模型观察到的用户生成的数据通常以流的形式到达。流数据的特征是底层分布随时间漂移并可能重复出现。如果模型只是一直适应新的数据分布，这可能导致灾难性遗忘。此外，重新学习已经发生的分发是低效的。在大规模工业应用中，由于存储约束和数据分布的多样性，传统的灾难遗忘策略如重播、参数隔离和知识提取等难以实现。在这项工作中，我们设计了一个新的基于在线机机器学习的漂移感知集成学习框架，以解决 ctrr 预测中的灾难性遗忘问题。通过对流式数据进行明确的基于错误的漂移检测，该框架进一步强化了适应性良好的集合，并冻结了与输入分布不匹配的集合，以避免出现灾难性干扰。对离线实验和 A/B 测试的评估都表明，我们的方法优于所考虑的所有基线。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Always+Strengthen+Your+Strengths:+A+Drift-Aware+Incremental+Learning+Framework+for+CTR+Prediction)|0| |[Attention-guided Multi-step Fusion: A Hierarchical Fusion Network for Multimodal Recommendation](https://doi.org/10.1145/3539618.3591950)|Yan Zhou, Jie Guo, Hao Sun, Bin Song, Fei Richard Yu|Shenzhen University, Shenzhen, China; Xidian University, Xi'an, China|The main idea of multimodal recommendation is the rational utilization of the item's multimodal information to improve the recommendation performance. Previous works directly integrate item multimodal features with item ID embeddings, ignoring the inherent semantic relations contained in the multimodal features. In this paper, we propose a novel and effective aTtention-guided Multi-step FUsion Network for multimodal recommendation, named TMFUN. Specifically, our model first constructs modality feature graph and item feature graph to model the latent item-item semantic structures. Then, we use the attention module to identify inherent connections between user-item interaction data and multimodal data, evaluate the impact of multimodal data on different interactions, and achieve early-step fusion of item features. Furthermore, our model optimizes item representation through the attention-guided multi-step fusion strategy and contrastive learning to improve recommendation performance. The extensive experiments on three real-world datasets show that our model has superior performance compared to the state-of-the-art models.|多通道推荐的主要思想是合理利用项目的多通道信息来提高推荐性能。以往的研究直接将项目多模态特征与项目 ID 嵌入相结合，忽略了项目多模态特征所包含的内在语义关系。在本文中，我们提出了一种新颖而有效的注意力引导多步融合网络 TMFUN。具体来说，我们的模型首先构造情态特征图和项目特征图，对潜在的项目-项目语义结构进行建模。然后，利用注意模块识别用户项目交互数据与多模态数据之间的内在联系，评估多模态数据对不同交互的影响，实现项目特征的早期融合。此外，我们的模型通过注意引导的多步融合策略和对比学习优化项目表示，以提高推荐性能。在三个真实世界数据集上的大量实验表明，我们的模型比最先进的模型具有更好的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Attention-guided+Multi-step+Fusion:+A+Hierarchical+Fusion+Network+for+Multimodal+Recommendation)|0| |[Benchmarking Middle-Trained Language Models for Neural Search](https://doi.org/10.1145/3539618.3591956)|Hervé Déjean, Stéphane Clinchant, Carlos Lassance, Simon Lupart, Thibault Formal|Naver Labs Europe, Meylan, France|Middle training methods aim to bridge the gap between the Masked Language Model (MLM) pre-training and the final finetuning for retrieval. Recent models such as CoCondenser, RetroMAE, and LexMAE argue that the MLM task is not sufficient enough to pre-train a transformer network for retrieval and hence propose various tasks to do so. Intrigued by those novel methods, we noticed that all these models used different finetuning protocols, making it hard to assess the benefits of middle training. We propose in this paper a benchmark of CoCondenser, RetroMAE, and LexMAE, under the same finetuning conditions. We compare both dense and sparse approaches under various finetuning protocols and middle training on different collections (MS MARCO, Wikipedia or Tripclick). We use additional middle training baselines, such as a standard MLM finetuning on the retrieval collection, optionally augmented by a CLS predicting the passage term frequency. For the sparse approach, our study reveals that there is almost no statistical difference between those methods: the more effective the finetuning procedure is, the less difference there is between those models. For the dense approach, RetroMAE using MS MARCO as middle-training collection shows excellent results in almost all the settings. Finally, we show that middle training on the retrieval collection, thus adapting the language model to it, is a critical factor. Overall, a better experimental setup should be adopted to evaluate middle training methods. Code available at https://github.com/naver/splade/tree/benchmarch-SIGIR23|中间训练方法旨在弥补蒙版语言模型(MLM)预训练和检索的最终微调之间的差距。最近的一些模型，如 CoCondenser，RotMAE 和 LexMAE 认为传销任务不足以预先训练一个变压器网络进行检索，因此提出了各种各样的任务来这样做。被这些新奇的方法所吸引，我们注意到所有这些模型使用不同的微调协议，使得评估中间训练的好处变得困难。在本文中，我们提出了一个基准的协同凝聚器，反向 MAE 和 LexMAE，在相同的微调条件下。我们比较了在各种微调协议和不同集合(MS MARCO，Wikipedia 或 Tripclick)的中间培训下的密集和稀疏方法。我们使用额外的中间训练基线，例如在检索集合上的标准 MLM 微调，可选地通过预测通过项频率的 CLS 增强。对于稀疏方法，我们的研究表明，这些方法之间几乎没有统计上的差异: 微调过程越有效，这些模型之间的差异就越小。对于密集的方法，使用 MS MARCO 作为中间训练收集在几乎所有的设置中都显示出优异的结果。最后，我们表明，中间训练的检索集，从而使语言模型适应它，是一个关键因素。总的来说，应该采用更好的实验设置来评价中间训练方法。Https://github.com/naver/splade/tree/benchmarch-sigir23提供密码|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Benchmarking+Middle-Trained+Language+Models+for+Neural+Search)|0| -|[Computational Versus Perceived Popularity Miscalibration in Recommender Systems](https://doi.org/10.1145/3539618.3591964)|Oleg Lesota, Gustavo Escobedo, Yashar Deldjoo, Bruce Ferwerda, Simone Kopeinik, Elisabeth Lex, Navid Rekabsaz, Markus Schedl|Johannes Kepler University Linz and Linz Institute of Technology, Linz, Austria; Polytechnic University of Bari, Bari, Italy; Jönköping University, Jönköping, Sweden; Johannes Kepler University Linz, Linz, Austria; Graz University of Technology, Graz, Austria; Know-Center GmbH, Graz, Austria|Popularity bias in recommendation lists refers to over-representation of popular content and is a challenge for many recommendation algorithms. Previous research has suggested several offline metrics to quantify popularity bias, which commonly relate the popularity of items in users' recommendation lists to the popularity of items in their interaction history. Discrepancies between these two factors are referred to as popularity miscalibration. While popularity metrics provide a straightforward and well-defined means to measure popularity bias, it is unknown whether they actually reflect users' perception of popularity bias. To address this research gap, we conduct a crowd-sourced user study on Prolific, involving 56 participants, to (1) investigate whether the level of perceived popularity miscalibration differs between common recommendation algorithms, (2) assess the correlation between perceived popularity miscalibration and its corresponding quantification according to a common offline metric. We conduct our study in a well-defined and important domain, namely music recommendation using the standardized LFM-2b dataset, and quantify popularity miscalibration of five recommendation algorithms by utilizing Jensen-Shannon distance (JSD). Challenging the findings of previous studies, we observe that users generally do perceive significant differences in terms of popularity bias between algorithms if this bias is framed as popularity miscalibration. In addition, JSD correlates moderately with users' perception of popularity, but not with their perception of unpopularity.|推荐列表中的流行度偏差指的是流行内容的过度表现，是许多推荐算法面临的一个挑战。先前的研究已经提出了一些离线指标来量化流行偏差，这些指标通常将用户推荐列表中项目的流行程度与其交互历史中项目的流行程度联系起来。这两个因素之间的差异被称为流行度误差。虽然流行指标提供了一个直接和明确的方法来衡量流行偏见，但它们是否真正反映了用户对流行偏见的感知尚不清楚。为了解决这一研究差距，我们对 Prolific 进行了一项涉及56名参与者的众包用户研究，以(1)调查常见推荐算法之间的感知流行度误差水平是否不同，(2)根据常见的离线度量评估感知流行度误差与其相应的量化之间的相关性。我们在一个定义明确的重要领域进行研究，即使用标准化的 LFM-2b 数据集进行音乐推荐，并通过使用 Jensen-Shannon 距离(JSD)量化五种推荐算法的流行性误差。挑战以往的研究结果，我们观察到，如果这种偏差被定义为流行性误差，用户通常会感觉到算法之间的流行性偏差方面的显著差异。此外，JSD 与用户的受欢迎程度有一定的相关性，但与他们的不受欢迎程度无关。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Computational+Versus+Perceived+Popularity+Miscalibration+in+Recommender+Systems)|0| +|[Computational Versus Perceived Popularity Miscalibration in Recommender Systems](https://doi.org/10.1145/3539618.3591964)|Oleg Lesota, Gustavo Escobedo, Yashar Deldjoo, Bruce Ferwerda, Simone Kopeinik, Elisabeth Lex, Navid Rekabsaz, Markus Schedl|Graz University of Technology, Graz, Austria; Johannes Kepler University Linz, Linz, Austria; Johannes Kepler University Linz and Linz Institute of Technology, Linz, Austria; Polytechnic University of Bari, Bari, Italy; Jönköping University, Jönköping, Sweden; Know-Center GmbH, Graz, Austria|Popularity bias in recommendation lists refers to over-representation of popular content and is a challenge for many recommendation algorithms. Previous research has suggested several offline metrics to quantify popularity bias, which commonly relate the popularity of items in users' recommendation lists to the popularity of items in their interaction history. Discrepancies between these two factors are referred to as popularity miscalibration. While popularity metrics provide a straightforward and well-defined means to measure popularity bias, it is unknown whether they actually reflect users' perception of popularity bias. To address this research gap, we conduct a crowd-sourced user study on Prolific, involving 56 participants, to (1) investigate whether the level of perceived popularity miscalibration differs between common recommendation algorithms, (2) assess the correlation between perceived popularity miscalibration and its corresponding quantification according to a common offline metric. We conduct our study in a well-defined and important domain, namely music recommendation using the standardized LFM-2b dataset, and quantify popularity miscalibration of five recommendation algorithms by utilizing Jensen-Shannon distance (JSD). Challenging the findings of previous studies, we observe that users generally do perceive significant differences in terms of popularity bias between algorithms if this bias is framed as popularity miscalibration. In addition, JSD correlates moderately with users' perception of popularity, but not with their perception of unpopularity.|推荐列表中的流行度偏差指的是流行内容的过度表现，是许多推荐算法面临的一个挑战。先前的研究已经提出了一些离线指标来量化流行偏差，这些指标通常将用户推荐列表中项目的流行程度与其交互历史中项目的流行程度联系起来。这两个因素之间的差异被称为流行度误差。虽然流行指标提供了一个直接和明确的方法来衡量流行偏见，但它们是否真正反映了用户对流行偏见的感知尚不清楚。为了解决这一研究差距，我们对 Prolific 进行了一项涉及56名参与者的众包用户研究，以(1)调查常见推荐算法之间的感知流行度误差水平是否不同，(2)根据常见的离线度量评估感知流行度误差与其相应的量化之间的相关性。我们在一个定义明确的重要领域进行研究，即使用标准化的 LFM-2b 数据集进行音乐推荐，并通过使用 Jensen-Shannon 距离(JSD)量化五种推荐算法的流行性误差。挑战以往的研究结果，我们观察到，如果这种偏差被定义为流行性误差，用户通常会感觉到算法之间的流行性偏差方面的显著差异。此外，JSD 与用户的受欢迎程度有一定的相关性，但与他们的不受欢迎程度无关。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Computational+Versus+Perceived+Popularity+Miscalibration+in+Recommender+Systems)|0| |[Context-Aware Modeling via Simulated Exposure Page for CTR Prediction](https://doi.org/10.1145/3539618.3591967)|Xiang Li, Shuwei Chen, Jian Dong, Jin Zhang, Yongkang Wang, Xingxing Wang, Dong Wang|Meituan, Beijing, China|Click-through rate (CTR) prediction plays a crucial role in industrial recommendation and advertising systems, which generate and expose multiple items for each user request. Although the user's click action on an item will be affected by the other exposed items (called contextual items), current CTR prediction methods do not exploit this context because CTR prediction is performed before the contextual items are generated. This paper introduces a solution Contextual Items Simulation and Modeling (CISM) to tackle this limitation. Specifically, we propose a near-line Context Simulation Center to simulate exposure page without affecting online service latency, and an online Context Modeling Transformer to learn user-wise context from the simulated results w.r.t. the candidate item. In addition, knowledge distillation is introduced to further improve CTR prediction. Extensive experiments on both public and industrial datasets demonstrate the effectiveness of CISM. Currently, CISM has been deployed in the online display advertising system of Meituan Waimai, serving the main traffic.|在工业推荐和广告系统中，点进率(ctrl)预测起着至关重要的作用，它为每个用户请求生成和公开多个条目。虽然用户对一个项目的点击操作会受到其他公开项目(称为上下文项目)的影响，但是当前的 CTR 预测方法不会利用这个上下文，因为 CTR 预测是在生成上下文项目之前执行的。本文介绍了一种解决上下文项目模拟与建模(CISM)的方法来解决这一局限性。具体来说，我们提出了一个近线上下文模拟中心来模拟暴露页面而不影响在线服务延迟，以及一个在线上下文建模转换器来从模拟结果中学习用户明智的上下文。此外，引入知识提取技术，进一步改进了 CTR 预测。在公共和工业数据集上的大量实验证明了 CISM 的有效性。目前，CISM 已经部署在美团外卖的在线显示广告系统中，服务于主要流量。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Context-Aware+Modeling+via+Simulated+Exposure+Page+for+CTR+Prediction)|0| |[Contrastive Learning for Conversion Rate Prediction](https://doi.org/10.1145/3539618.3591968)|Wentao Ouyang, Rui Dong, Xiuwu Zhang, Chaofeng Guo, Jinmei Luo, Xiangzheng Liu, Yanlong Du|Alibaba Group, Beijing, China|Conversion rate (CVR) prediction plays an important role in advertising systems. Recently, supervised deep neural network-based models have shown promising performance in CVR prediction. However, they are data hungry and require an enormous amount of training data. In online advertising systems, although there are millions to billions of ads, users tend to click only a small set of them and to convert on an even smaller set. This data sparsity issue restricts the power of these deep models. In this paper, we propose the Contrastive Learning for CVR prediction (CL4CVR) framework. It associates the supervised CVR prediction task with a contrastive learning task, which can learn better data representations exploiting abundant unlabeled data and improve the CVR prediction performance. To tailor the contrastive learning task to the CVR prediction problem, we propose embedding masking (EM), rather than feature masking, to create two views of augmented samples. We also propose a false negative elimination (FNE) component to eliminate samples with the same feature as the anchor sample, to account for the natural property in user behavior data. We further propose a supervised positive inclusion (SPI) component to include additional positive samples for each anchor sample, in order to make full use of sparse but precious user conversion events. Experimental results on two real-world conversion datasets demonstrate the superior performance of CL4CVR. The source code is available at https://github.com/DongRuiHust/CL4CVR.|转化率(CVR)预测在广告系统中起着重要作用。近年来，基于监督深层神经网络的 CVR 预测模型在 CVR 预测中表现出了良好的性能。然而，它们需要大量的数据，并且需要大量的训练数据。在在线广告系统中，尽管有数百万到数十亿的广告，用户往往只点击其中的一小部分，然后转换成更小的一部分。这种数据稀疏问题限制了这些深度模型的能力。本文提出了 CVR 预测的对比学习(CL4CVR)框架。该方法将有监督的 CVR 预测任务与对比学习任务相结合，利用大量未标记数据学习更好的数据表示，提高 CVR 预测性能。为了使对比学习任务适用于 CVR 预测问题，我们提出了嵌入掩蔽(EM)方法，而不是特征掩蔽方法，来创建增广样本的两个视图。我们还提出了一种假阴性消除(FNE)组件来消除具有与锚样本相同特征的样本，以解释用户行为数据的自然属性。我们进一步提出了一个监督正包含(SPI)组件，为每个锚样本包含额外的正样本，以充分利用稀疏但宝贵的用户转换事件。在两个实际转换数据集上的实验结果表明，CL4CVR 具有较好的性能。源代码可在 https://github.com/dongruihust/cl4cvr 下载。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Contrastive+Learning+for+Conversion+Rate+Prediction)|0| |[Curriculum Modeling the Dependence among Targets with Multi-task Learning for Financial Marketing](https://doi.org/10.1145/3539618.3591969)|Yunpeng Weng, Xing Tang, Liang Chen, Xiuqiang He|FiT, Tencent, Shenzhen, China|Multi-task learning for various real-world applications usually involves tasks with logical sequential dependence. For example, in online marketing, the cascade behavior pattern of $impression \rightarrow click \rightarrow conversion$ is usually modeled as multiple tasks in a multi-task manner, where the sequential dependence between tasks is simply connected with an explicitly defined function or implicitly transferred information in current works. These methods alleviate the data sparsity problem for long-path sequential tasks as the positive feedback becomes sparser along with the task sequence. However, the error accumulation and negative transfer will be a severe problem for downstream tasks. Especially, at the beginning stage of training, the optimization for parameters of former tasks is not converged yet, and thus the information transferred to downstream tasks is negative. In this paper, we propose a prior information merged model (\textbf{PIMM}), which explicitly models the logical dependence among tasks with a novel prior information merged (\textbf{PIM}) module for multiple sequential dependence task learning in a curriculum manner. Specifically, the PIM randomly selects the true label information or the prior task prediction with a soft sampling strategy to transfer to the downstream task during the training. Following an easy-to-difficult curriculum paradigm, we dynamically adjust the sampling probability to ensure that the downstream task will get the effective information along with the training. The offline experimental results on both public and product datasets verify that PIMM outperforms state-of-the-art baselines. Moreover, we deploy the PIMM in a large-scale FinTech platform, and the online experiments also demonstrate the effectiveness of PIMM.|实际应用中的多任务学习通常涉及逻辑顺序依赖的任务。例如，在网络营销中，右键点击右键转换的级联行为模式通常被建模为多任务的多任务方式，其中任务之间的顺序依赖只是与一个明确定义的功能或当前作品中隐式传递的信息相关联。这些方法缓解了长路顺序任务的数据稀疏性问题，因为正反馈随着任务顺序的减少而变得稀疏。然而，对于下游任务来说，错误积累和负迁移将是一个严重的问题。特别是在训练的初始阶段，前期任务的参数优化还没有收敛，因此传递给后期任务的信息是负的。本文提出了一种先验信息合并模型(textbf { PIMM }) ，该模型使用一种新的先验信息合并模型(textbf { PIM })对任务间的逻辑依赖关系进行显式建模，并以课程的形式进行多个顺序依赖任务的学习。具体来说，PIM 通过软抽样策略随机选择真实的标签信息或先前的任务预测，在训练过程中传递给下游任务。采用易难课程范式，动态调整抽样概率，确保下游任务在训练过程中获得有效信息。在公共数据集和产品数据集上的离线实验结果验证了 PIMM 的性能优于最先进的基线。在此基础上，将 PIMM 应用于大型金融技术平台，并通过在线实验验证了 PIMM 的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Curriculum+Modeling+the+Dependence+among+Targets+with+Multi-task+Learning+for+Financial+Marketing)|0| -|[Denoise to Protect: A Method to Robustify Visual Recommenders from Adversaries](https://doi.org/10.1145/3539618.3591971)|Felice Antonio Merra, Vito Walter Anelli, Tommaso Di Noia, Daniele Malitesta, Alberto Carlo Maria Mancino|Amazon, Berlin, Germany; Politecnico di Bari, Bari, Italy|While the integration of product images enhances the recommendation performance of visual-based recommender systems (VRSs), this can make the model vulnerable to adversaries that can produce noised images capable to alter the recommendation behavior. Recently, stronger and stronger adversarial attacks have emerged to raise awareness of these risks; however, effective defense methods are still an urgent open challenge. In this work, we propose "Adversarial Image Denoiser" (AiD), a novel defense method that cleans up the item images by malicious perturbations. In particular, we design a training strategy whose denoising objective is to minimize both the visual differences between clean and adversarial images and preserve the ranking performance in authentic settings. We perform experiments to evaluate the efficacy of AiD using three state-of-the-art adversarial attacks mounted against standard VRSs. Code and datasets at https://github.com/sisinflab/Denoise-to-protect-VRS.|虽然产品图像的集成增强了基于可视化的推荐系统(VRS)的推荐性能，但是这会使模型容易受到能够产生噪声图像的对手的攻击，而这些噪声图像能够改变推荐行为。近年来，越来越强大的对抗性攻击已经出现，以提高人们对这些风险的认识，但是，有效的防御方法仍然是一个迫切的公开挑战。在这项工作中，我们提出了“对抗性图像去噪器”(AiD) ，一种新的防御方法，清除项目图像的恶意扰动。特别地，我们设计了一个训练策略，其去噪目标是最小化干净图像和对手图像之间的视觉差异，并保持在真实环境中的排名性能。我们进行实验来评估艾滋病的功效使用三个国家的最先进的敌对攻击安装对标准的 VRS。Https://github.com/sisinflab/denoise-to-protect-vrs 代码和数据集。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Denoise+to+Protect:+A+Method+to+Robustify+Visual+Recommenders+from+Adversaries)|0| -|[Edge-cloud Collaborative Learning with Federated and Centralized Features](https://doi.org/10.1145/3539618.3591976)|Zexi Li, Qunwei Li, Yi Zhou, Wenliang Zhong, Guannan Zhang, Chao Wu|The University of Utah, Salt Lake City, UT, USA; Ant Group, Hangzhou, China; Zhejiang University, Hangzhou, China|Federated learning (FL) is a popular way of edge computing that doesn't compromise users' privacy. Current FL paradigms assume that data only resides on the edge, while cloud servers only perform model averaging. However, in real-life situations such as recommender systems, the cloud server has the ability to store historical and interactive features. In this paper, our proposed Edge-Cloud Collaborative Knowledge Transfer Framework (ECCT) bridges the gap between the edge and cloud, enabling bi-directional knowledge transfer between both, sharing feature embeddings and prediction logits. ECCT consolidates various benefits, including enhancing personalization, enabling model heterogeneity, tolerating training asynchronization, and relieving communication burdens. Extensive experiments on public and industrial datasets demonstrate ECCT's effectiveness and potential for use in academia and industry.|联邦学习(FL)是一种流行的边缘计算方法，它不会损害用户的隐私。当前的 FL 范例假设数据只停留在边缘，而云服务器只执行模型平均。但是，在推荐系统等实际情况下，云服务器具有存储历史和交互特性的能力。本文提出的边缘-云协同知识转移框架(ECCT)弥补了边缘和云之间的差距，实现了边缘和云之间的双向知识转移，共享特征嵌入和预测逻辑。ECCT 巩固了各种好处，包括增强个性化、支持模型异构性、容忍培训异步以及减轻通信负担。在公共和工业数据集上的大量实验证明了 ECCT 的有效性和在学术界和工业界使用的潜力。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Edge-cloud+Collaborative+Learning+with+Federated+and+Centralized+Features)|0| +|[Denoise to Protect: A Method to Robustify Visual Recommenders from Adversaries](https://doi.org/10.1145/3539618.3591971)|Felice Antonio Merra, Vito Walter Anelli, Tommaso Di Noia, Daniele Malitesta, Alberto Carlo Maria Mancino|Politecnico di Bari, Bari, Italy; Amazon, Berlin, Germany|While the integration of product images enhances the recommendation performance of visual-based recommender systems (VRSs), this can make the model vulnerable to adversaries that can produce noised images capable to alter the recommendation behavior. Recently, stronger and stronger adversarial attacks have emerged to raise awareness of these risks; however, effective defense methods are still an urgent open challenge. In this work, we propose "Adversarial Image Denoiser" (AiD), a novel defense method that cleans up the item images by malicious perturbations. In particular, we design a training strategy whose denoising objective is to minimize both the visual differences between clean and adversarial images and preserve the ranking performance in authentic settings. We perform experiments to evaluate the efficacy of AiD using three state-of-the-art adversarial attacks mounted against standard VRSs. Code and datasets at https://github.com/sisinflab/Denoise-to-protect-VRS.|虽然产品图像的集成增强了基于可视化的推荐系统(VRS)的推荐性能，但是这会使模型容易受到能够产生噪声图像的对手的攻击，而这些噪声图像能够改变推荐行为。近年来，越来越强大的对抗性攻击已经出现，以提高人们对这些风险的认识，但是，有效的防御方法仍然是一个迫切的公开挑战。在这项工作中，我们提出了“对抗性图像去噪器”(AiD) ，一种新的防御方法，清除项目图像的恶意扰动。特别地，我们设计了一个训练策略，其去噪目标是最小化干净图像和对手图像之间的视觉差异，并保持在真实环境中的排名性能。我们进行实验来评估艾滋病的功效使用三个国家的最先进的敌对攻击安装对标准的 VRS。Https://github.com/sisinflab/denoise-to-protect-vrs 代码和数据集。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Denoise+to+Protect:+A+Method+to+Robustify+Visual+Recommenders+from+Adversaries)|0| +|[Edge-cloud Collaborative Learning with Federated and Centralized Features](https://doi.org/10.1145/3539618.3591976)|Zexi Li, Qunwei Li, Yi Zhou, Wenliang Zhong, Guannan Zhang, Chao Wu|Ant Group, Hangzhou, China; Zhejiang University, Hangzhou, China; The University of Utah, Salt Lake City, UT, USA|Federated learning (FL) is a popular way of edge computing that doesn't compromise users' privacy. Current FL paradigms assume that data only resides on the edge, while cloud servers only perform model averaging. However, in real-life situations such as recommender systems, the cloud server has the ability to store historical and interactive features. In this paper, our proposed Edge-Cloud Collaborative Knowledge Transfer Framework (ECCT) bridges the gap between the edge and cloud, enabling bi-directional knowledge transfer between both, sharing feature embeddings and prediction logits. ECCT consolidates various benefits, including enhancing personalization, enabling model heterogeneity, tolerating training asynchronization, and relieving communication burdens. Extensive experiments on public and industrial datasets demonstrate ECCT's effectiveness and potential for use in academia and industry.|联邦学习(FL)是一种流行的边缘计算方法，它不会损害用户的隐私。当前的 FL 范例假设数据只停留在边缘，而云服务器只执行模型平均。但是，在推荐系统等实际情况下，云服务器具有存储历史和交互特性的能力。本文提出的边缘-云协同知识转移框架(ECCT)弥补了边缘和云之间的差距，实现了边缘和云之间的双向知识转移，共享特征嵌入和预测逻辑。ECCT 巩固了各种好处，包括增强个性化、支持模型异构性、容忍培训异步以及减轻通信负担。在公共和工业数据集上的大量实验证明了 ECCT 的有效性和在学术界和工业界使用的潜力。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Edge-cloud+Collaborative+Learning+with+Federated+and+Centralized+Features)|0| |[Evaluating Cross-modal Generative Models Using Retrieval Task](https://doi.org/10.1145/3539618.3591979)|Shivangi Bithel, Srikanta Bedathur|IIT Delhi, New Delhi, India|Generative models have taken the world by storm -- image generative models such as Stable Diffusion and DALL-E generate photo-realistic images, whereas image captioning models such as BLIP, GIT, ClipCap, and ViT-GPT2 generate descriptive and informative captions. While it may be true that these models produce remarkable results, their systematic evaluation is missing, making it hard to advance the research further. Currently, heuristic metrics such as the Inception Score and the Fréchet Inception Distance are the most prevalent metrics for the image generation task, while BLEU, CIDEr, SPICE, METEOR, BERTScore, and CLIPScore are common for the image captioning task. Unfortunately, these are poorly interpretable and are not based on the solid user-behavior model that the Information Retrieval community has worked towards. In this paper, we present a novel cross-modal retrieval framework to evaluate the effectiveness of cross-modal (image-to-text and text-to-image) generative models using reference text and images. We propose the use of scoring models based on user-behavior, such as Normalized Discounted Cumulative Gain (nDCG'@K ) and Rank-Biased Precision (RBP'@K) adjusted for incomplete judgments. Experiments using ECCV Caption and Flickr8k-EXPERTS benchmark datasets demonstrate the effectiveness of various image captioning and image generation models for the proposed retrieval task. Results also indicate that the nDCG'@K and RBP'@K scores are consistent with heuristics-driven metrics, excluding CLIPScore, in model selection.|生成模型已经席卷世界——图像生成模型，如稳定扩散和 DALL-E 生成逼真的图像，而图像字幕模型，如 BLIP、 GIT、 ClipCap 和 ViT-GPT2生成描述性和信息性的字幕。尽管这些模型确实产生了显著的结果，但它们缺乏系统的评估，使得进一步的研究难以推进。目前，启发式指标，如先启评分和弗雷谢先启距离是最普遍的图像生成任务的指标，而 BLEU，CIDEr，SPICE，METEOR，BERTScore 和 CLIPScore 是常见的图像字幕任务。不幸的是，这些都是很难解释的，而且不是基于信息检索社区已经努力建立的可靠的用户行为模型。在本文中，我们提出了一个新的跨模态检索框架来评估跨模态(图像到文本和文本到图像)生成模型的有效性使用参考文本和图像。我们建议使用基于用户行为的评分模型，如对不完全判断进行调整的标准化贴现累积增益(nDCG’@K)和秩偏差精度(RBP’@K)。使用 ECCV 字幕和 Flickr8k-EXPERTS 基准数据集的实验证明了各种图像字幕和图像生成模型对提出的检索任务的有效性。结果还表明，在模型选择中，nDCG’@K 和 RBP’@K 得分与启发式驱动的指标(不包括 CLIPScore)一致。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Evaluating+Cross-modal+Generative+Models+Using+Retrieval+Task)|0| |[SLIM: Sparsified Late Interaction for Multi-Vector Retrieval with Inverted Indexes](https://doi.org/10.1145/3539618.3591977)|Minghan Li, ShengChieh Lin, Xueguang Ma, Jimmy Lin|University of Waterloo, Waterloo, ON, Canada|This paper introduces a method called Sparsified Late Interaction for Multi-vector retrieval with inverted indexes (SLIM). Although multi-vector models have demonstrated their effectiveness in various information retrieval tasks, most of their pipelines require custom optimization to be efficient in both time and space. Among them, ColBERT is probably the most established method which is based on the late interaction of contextualized token embeddings of pre-trained language models. Unlike ColBERT where all its token embeddings are low-dimensional and dense, SLIM projects each token embedding into a high-dimensional, sparse lexical space before performing late interaction. In practice, we further propose to approximate SLIM using the lower- and upper-bound of the late interaction to reduce latency and storage. In this way, the sparse outputs can be easily incorporated into an inverted search index and are fully compatible with off-the-shelf search tools such as Pyserini and Elasticsearch. SLIM has competitive accuracy on information retrieval benchmarks such as MS MARCO Passages and BEIR compared to ColBERT while being much smaller and faster on CPUs. Source code and data will be available at https://github.com/castorini/pyserini/blob/master/docs/experiments-slim.md.|本文介绍了一种具有倒排索引的多向量检索方法(SLIM)。尽管多向量模型已经证明了它们在各种信息检索任务中的有效性，但是它们的大多数流水线都需要定制的优化，以便在时间和空间上都有效率。其中，ColBERT 可能是最成熟的方法，它是基于预训练语言模型的上下文化标记嵌入的后期交互。与 ColBERT 中的所有令牌嵌入都是低维和密集的不同，SLIM 在执行后期交互之前将每个令牌嵌入到一个高维、稀疏的词法空间中。在实践中，我们进一步提出使用后期交互的上下界来近似 SLIM，以减少延迟和存储。通过这种方式，稀疏输出可以很容易地合并到反向搜索索引中，并且与 Pyserini 和 Elasticsearch 等现成的搜索工具完全兼容。与 ColBERT 相比，SLIM 在微软 MARCO Passages 和 BEIR 等信息检索基准测试上具有竞争性的准确性，而在 CPU 上则更小、更快。源代码和数据将在 https://github.com/castorini/pyserini/blob/master/docs/experiments-slim.md 提供。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=SLIM:+Sparsified+Late+Interaction+for+Multi-Vector+Retrieval+with+Inverted+Indexes)|0| -|[Forget Me Now: Fast and Exact Unlearning in Neighborhood-based Recommendation](https://doi.org/10.1145/3539618.3591989)|Sebastian Schelter, Mozhdeh Ariannezhad, Maarten de Rijke|University of Amsterdam, Amsterdam, Netherlands; AIRLab, University of Amsterdam, Amsterdam, Netherlands|Modern search and recommendation systems are optimized using logged interaction data. There is increasing societal pressure to enable users of such systems to have some of their data deleted from those systems. This paper focuses on "unlearning" such user data from neighborhood-based recommendation models on sparse, high-dimensional datasets. We present caboose, a custom top-k index for such models, which enables fast and exact deletion of user interactions. We experimentally find that caboose provides competitive index building times, makes sub-second unlearning possible (even for a large index built from one million users and 256 million interactions), and, when integrated into three state-of-the-art next-basket recommendation models, allows users to effectively adjust their predictions to remove sensitive items.|现代搜索和推荐系统使用日志交互数据进行优化。越来越多的社会压力要求这些系统的用户从这些系统中删除一些数据。本文主要研究在稀疏、高维数据集上从基于邻域的推荐模型中“去除”这类用户数据。我们提出的守护，这种模型的自定义 top-k 索引，它使快速和准确的删除用户交互。我们通过实验发现，caboose 提供了具有竞争力的索引构建时间，使得亚秒级的忘却成为可能(即使是对于一个由一百万用户和2.56亿互动构建的大型索引) ，并且，当整合到三个最先进的下一篮子推荐模型中时，允许用户有效地调整他们的预测以删除敏感项目。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Forget+Me+Now:+Fast+and+Exact+Unlearning+in+Neighborhood-based+Recommendation)|0| +|[Forget Me Now: Fast and Exact Unlearning in Neighborhood-based Recommendation](https://doi.org/10.1145/3539618.3591989)|Sebastian Schelter, Mozhdeh Ariannezhad, Maarten de Rijke|AIRLab, University of Amsterdam, Amsterdam, Netherlands; University of Amsterdam, Amsterdam, Netherlands|Modern search and recommendation systems are optimized using logged interaction data. There is increasing societal pressure to enable users of such systems to have some of their data deleted from those systems. This paper focuses on "unlearning" such user data from neighborhood-based recommendation models on sparse, high-dimensional datasets. We present caboose, a custom top-k index for such models, which enables fast and exact deletion of user interactions. We experimentally find that caboose provides competitive index building times, makes sub-second unlearning possible (even for a large index built from one million users and 256 million interactions), and, when integrated into three state-of-the-art next-basket recommendation models, allows users to effectively adjust their predictions to remove sensitive items.|现代搜索和推荐系统使用日志交互数据进行优化。越来越多的社会压力要求这些系统的用户从这些系统中删除一些数据。本文主要研究在稀疏、高维数据集上从基于邻域的推荐模型中“去除”这类用户数据。我们提出的守护，这种模型的自定义 top-k 索引，它使快速和准确的删除用户交互。我们通过实验发现，caboose 提供了具有竞争力的索引构建时间，使得亚秒级的忘却成为可能(即使是对于一个由一百万用户和2.56亿互动构建的大型索引) ，并且，当整合到三个最先进的下一篮子推荐模型中时，允许用户有效地调整他们的预测以删除敏感项目。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Forget+Me+Now:+Fast+and+Exact+Unlearning+in+Neighborhood-based+Recommendation)|0| |[Gradient Coordination for Quantifying and Maximizing Knowledge Transference in Multi-Task Learning](https://doi.org/10.1145/3539618.3591993)|Xuanhua Yang, Jianxin Zhao, Shaoguo Liu, Liang Wang, Bo Zheng|Alibaba Group, Beijing, China|Multi-task learning (MTL) has been widely applied in online advertising and recommender systems. To address the negative transfer issue, recent studies have proposed optimization methods that thoroughly focus on the gradient alignment of directions or magnitudes. However, since prior study has proven that both general and specific knowledge exist in the limited shared capacity, overemphasizing on gradient alignment may crowd out task-specific knowledge, and vice versa. In this paper, we propose a transference-driven approach CoGrad that adaptively maximizes knowledge transference via Coordinated Gradient modification. We explicitly quantify the transference as loss reduction from one task to another, and then derive an auxiliary gradient from optimizing it. We perform the optimization by incorporating this gradient into original task gradients, making the model automatically maximize inter-task transfer and minimize individual losses. Thus, CoGrad can harmonize between general and specific knowledge to boost overall performance. Besides, we introduce an efficient approximation of the Hessian matrix, making CoGrad computationally efficient and simple to implement. Both offline and online experiments verify that CoGrad significantly outperforms previous methods.|多任务学习(MTL)在网络广告和推荐系统中得到了广泛的应用。为了解决负迁移问题，最近的研究提出了一些优化方法，这些方法完全集中在方向或大小的梯度对齐上。然而，由于先前的研究已经证明，一般知识和具体知识都存在于有限的共享能力中，过分强调梯度调整可能会排挤任务特定的知识，反之亦然。本文提出了一种基于转移驱动的 CoGrad 方法，该方法通过协调梯度修正自适应地最大化知识转移。我们明确地量化转移作为损失减少从一个任务到另一个，然后推导出一个辅助梯度优化它。我们通过将梯度合并到原始任务梯度中来执行优化，使模型自动最大化任务间的转移并最小化个体损失。因此，CoGrad 可以协调一般知识和具体知识，以提高整体性能。此外，我们还引入了 Hessian 矩阵的一种有效逼近，使 CoGrad 的计算变得简单有效。线下和线上实验都证实了 CoGrad 显著优于以前的方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Gradient+Coordination+for+Quantifying+and+Maximizing+Knowledge+Transference+in+Multi-Task+Learning)|0| |[Model-free Reinforcement Learning with Stochastic Reward Stabilization for Recommender Systems](https://doi.org/10.1145/3539618.3592022)|Tianchi Cai, Shenliao Bao, Jiyan Jiang, Shiji Zhou, Wenpeng Zhang, Lihong Gu, Jinjie Gu, Guannan Zhang|Ant Group, Hangzhou, China; Ant Group, Beijing, China|Model-free RL-based recommender systems have recently received increasing research attention due to their capability to handle partial feedback and long-term rewards. However, most existing research has ignored a critical feature in recommender systems: one user's feedback on the same item at different times is random. The stochastic rewards property essentially differs from that in classic RL scenarios with deterministic rewards, which makes RL-based recommender systems much more challenging. In this paper, we first demonstrate in a simulator environment where using direct stochastic feedback results in a significant drop in performance. Then to handle the stochastic feedback more efficiently, we design two stochastic reward stabilization frameworks that replace the direct stochastic feedback with that learned by a supervised model. Both frameworks are model-agnostic, i.e., they can effectively utilize various supervised models. We demonstrate the superiority of the proposed frameworks over different RL-based recommendation baselines with extensive experiments on a recommendation simulator as well as an industrial-level recommender system.|基于无模型 RL 的推荐系统由于能够处理部分反馈和长期奖励，近年来受到越来越多的研究关注。然而，大多数现有的研究忽略了推荐系统的一个关键特征: 一个用户在不同时间对同一条目的反馈是随机的。随机奖励属性与传统的确定性奖励的 RL 场景不同，这使得基于 RL 的推荐系统更具挑战性。在本文中，我们首先在一个模拟器环境中演示，使用直接随机反馈会导致性能的显著下降。然后，为了更有效地处理随机反馈，我们设计了两个随机报酬稳定化框架，用监督模型学习的随机反馈代替直接的随机反馈。这两种框架都是模型无关的，也就是说，它们可以有效地利用各种监督模型。我们在推荐模拟器和工业级推荐系统上进行了广泛的实验，证明了所提出的框架相对于不同的基于 RL 的推荐基线的优越性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Model-free+Reinforcement+Learning+with+Stochastic+Reward+Stabilization+for+Recommender+Systems)|0| |[Multi-Grained Topological Pre-Training of Language Models in Sponsored Search](https://doi.org/10.1145/3539618.3592024)|Zhoujin Tian, Chaozhuo Li, Zhiqiang Zuo, Zengxuan Wen, Xinyue Hu, Xiao Han, Haizhen Huang, Senzhang Wang, Weiwei Deng, Xing Xie, Qi Zhang|Central South University, Changsha, China; Microsoft Research Asia, Beijing, China; Microsoft, Beijing, China|Relevance models measure the semantic closeness between queries and the candidate ads, widely recognized as the nucleus of sponsored search systems. Conventional relevance models solely rely on the textual data within the queries and ads, whose performance is hindered by the scarce semantic information in these short texts. Recently, user behavior graphs have been incorporated to provide complementary information beyond pure textual semantics.Despite the promising performance, behavior-enhanced models suffer from exhausting resource costs due to the extra computations introduced by explicit topological aggregations. In this paper, we propose a novel Multi-Grained Topological Pre-Training paradigm, MGTLM, to teach language models to understand multi-grained topological information in behavior graphs, which contributes to eliminating explicit graph aggregations and avoiding information loss. Extensive experimental results over online and offline settings demonstrate the superiority of our proposal.|关联模型测量查询和候选广告之间的语义接近度，被广泛认为是赞助商搜索系统的核心。传统的关联模型仅仅依赖于查询和广告中的文本数据，而这些数据的表现受到这些短文本中缺乏语义信息的限制。最近，用户行为图被用来提供超越纯文本语义的互补信息。尽管性能良好，但由于显式拓扑聚合引入了额外的计算，行为增强模型会耗尽资源成本。本文提出了一种新的多粒度拓扑预训练模型 MGTLM，用于教语言模型理解行为图中的多粒度拓扑信息，有助于消除显性图集合，避免信息丢失。在在线和离线环境下的大量实验结果证明了我们方案的优越性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Multi-Grained+Topological+Pre-Training+of+Language+Models+in+Sponsored+Search)|0| |[Multi-grained Representation Learning for Cross-modal Retrieval](https://doi.org/10.1145/3539618.3592025)|Shengwei Zhao, Linhai Xu, Yuying Liu, Shaoyi Du|Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, Xian, China|The purpose of audio-text retrieval is to learn a cross-modal similarity function between audio and text, enabling a given audio/text to find similar text/audio from a candidate set. Recent audio-text retrieval models aggregate multi-modal features into a single-grained representation. However, single-grained representation is difficult to solve the situation that an audio is described by multiple texts of different granularity levels, because the association pattern between audio and text is complex. Therefore, we propose an adaptive aggregation strategy to automatically find the optimal pool function to aggregate the features into a comprehensive representation, so as to learn valuable multi-grained representation. And multi-grained comparative learning is carried out in order to focus on the complex correlation between audio and text in different granularity. Meanwhile, text-guided token interaction is used to reduce the impact of redundant audio clips. We evaluated our proposed method on two audio-text retrieval benchmark datasets of Audiocaps and Clotho, achieving the state-of-the-art results in text-to-audio and audio-to-text retrieval. Our findings emphasize the importance of learning multi-modal multi-grained representation.|音频文本检索的目的是学习音频和文本之间的跨模式相似度函数，使给定的音频/文本能够从候选集中找到相似的文本/音频。最近的音频文本检索模型将多模态特征聚合为单粒度表示。然而，由于音频和文本之间的关联模式非常复杂，单粒度表示很难解决由不同粒度级别的多个文本描述音频的问题。为此，提出了一种自适应聚合策略，自动寻找最优池函数，将特征聚合成一个综合表示，从而学习有价值的多粒度表示。针对不同粒度的音频和文本之间复杂的相关性，进行了多粒度比较学习。同时，采用文本引导的令牌交互方式来减少冗余音频片段的影响。我们在 Audiocaps 和 Clotho 两个音频文本检索基准数据集上对所提出的方法进行了评估，在文本到音频和音频到文本的检索中取得了最先进的结果。我们的研究结果强调了学习多模态多粒度表示的重要性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Multi-grained+Representation+Learning+for+Cross-modal+Retrieval)|0| |[On the Effects of Regional Spelling Conventions in Retrieval Models](https://doi.org/10.1145/3539618.3592030)|Andreas Chari, Sean MacAvaney, Iadh Ounis|University of Glasgow, Glasgow, United Kingdom|One advantage of neural ranking models is that they are meant to generalise well in situations of synonymity i.e. where two words have similar or identical meanings. In this paper, we investigate and quantify how well various ranking models perform in a clear-cut case of synonymity: when words are simply expressed in different surface forms due to regional differences in spelling conventions (e.g., color vs colour). We first explore the prevalence of American and British English spelling conventions in datasets used for the pre-training, training and evaluation of neural retrieval methods, and find that American spelling conventions are far more prevalent. Despite these biases in the training data, we find that retrieval models often generalise well in this case of synonymity. We explore the effect of document spelling normalisation in retrieval and observe that all models are affected by normalising the document's spelling. While they all experience a drop in performance when normalised to a different spelling convention than that of the query, we observe varied behaviour when the document is normalised to share the query spelling convention: lexical models show improvements, dense retrievers remain unaffected, and re-rankers exhibit contradictory behaviour.|神经排序模型的一个优点是，它们可以很好地概括同义词的情况，即两个词有相似或相同的意思。在本文中，我们调查和量化各种排名模型在一个明确的同义性情况下表现如何: 当单词只是表示在不同的表面形式，由于拼写惯例的区域差异(例如，颜色与颜色)。我们首先探讨了美国和英国英语拼写惯例在用于神经检索方法的预训练、培训和评估的数据集中的普遍性，发现美国的拼写惯例更为普遍。尽管在训练数据中存在这些偏差，我们发现在这种同义性的情况下，检索模型往往能够很好地推广。我们探讨了文档拼写规范化在检索中的作用，并观察到所有模型都受到文档拼写规范化的影响。虽然他们都经历了性能下降时，规范化的不同拼写约定比查询，我们观察到不同的行为时，文档规范化，共享查询拼写约定: 词汇模型显示改进，密集检索仍然没有受到影响，重新排序表现出矛盾的行为。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=On+the+Effects+of+Regional+Spelling+Conventions+in+Retrieval+Models)|0| -|[Patterns of Gender-Specializing Query Reformulation](https://doi.org/10.1145/3539618.3592034)|Amifa Raj, Bhaskar Mitra, Nick Craswell, Michael D. Ekstrand|Microsoft Research, Montreal, USA; Microsoft, Redmond, USA; Boise State University, Boise, USA|Users of search systems often reformulate their queries by adding query terms to reflect their evolving information need or to more precisely express their information need when the system fails to surface relevant content. Analyzing these query reformulations can inform us about both system and user behavior. In this work, we study a special category of query reformulations that involve specifying demographic group attributes, such as gender, as part of the reformulated query (e.g., "olympic 2021 soccer results" to "olympic 2021 women's soccer results"). There are many ways a query, the search results, and a demographic attribute such as gender may relate, leading us to hypothesize different causes for these reformulation patterns, such as under-representation on the original result page or based on the linguistic theory of markedness. This paper reports on an observational study of gender-specializing query reformulations -- their contexts and effects -- as a lens on the relationship between system results and gender, based on large-scale search log data from Bing. We find that these reformulations sometimes correct for and other times reinforce gender representation on the original result page, but typically yield better access to the ultimately-selected results. The prevalence of these reformulations -- and which gender they skew towards -- differ by topical context. However, we do not find evidence that either group under-representation or markedness alone adequately explains these reformulations. We hope that future research will use such reformulations as a probe for deeper investigation into gender (and other demographic) representation on the search result page.|搜索系统的用户往往通过添加查询词语来重新表述其查询，以反映其不断变化的信息需求，或在系统未能显示相关内容时更准确地表达其信息需求。分析这些查询重构可以让我们了解系统和用户行为。在这项工作中，我们研究了一个特殊类别的查询重构，涉及指定人口组属性，如性别，作为重构查询的一部分(例如，“2021年奥运会足球结果”到“2021年奥运会女子足球结果”)。查询、搜索结果和性别等人口统计学特征可能有多种关联，这导致我们假设这些重构模式的不同原因，例如原始结果页面上的表示不足或基于标记性语言学理论。这篇论文报道了一系列针对性别的查询观察性研究——它们的背景和效果——作为系统结果和性别之间关系的一个透镜，基于 Bing 的大规模搜索日志数据。我们发现，这些重新编排有时正确，其他时候加强原始结果页面上的性别代表性，但通常产生更好的访问最终选定的结果。这些重新拟订的普遍程度——以及它们倾向于哪一性别——因主题背景的不同而有所不同。然而，我们没有发现证据表明，无论是集团代表性不足或标记单独充分解释这些重新编排。我们希望未来的研究将使用这样的重新编排作为一种探索，对搜索结果页面上的性别(和其他人口统计学)表征进行更深入的调查。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Patterns+of+Gender-Specializing+Query+Reformulation)|0| +|[Patterns of Gender-Specializing Query Reformulation](https://doi.org/10.1145/3539618.3592034)|Amifa Raj, Bhaskar Mitra, Nick Craswell, Michael D. Ekstrand|Microsoft, Redmond, USA; Boise State University, Boise, USA; Microsoft Research, Montreal, USA|Users of search systems often reformulate their queries by adding query terms to reflect their evolving information need or to more precisely express their information need when the system fails to surface relevant content. Analyzing these query reformulations can inform us about both system and user behavior. In this work, we study a special category of query reformulations that involve specifying demographic group attributes, such as gender, as part of the reformulated query (e.g., "olympic 2021 soccer results" to "olympic 2021 women's soccer results"). There are many ways a query, the search results, and a demographic attribute such as gender may relate, leading us to hypothesize different causes for these reformulation patterns, such as under-representation on the original result page or based on the linguistic theory of markedness. This paper reports on an observational study of gender-specializing query reformulations -- their contexts and effects -- as a lens on the relationship between system results and gender, based on large-scale search log data from Bing. We find that these reformulations sometimes correct for and other times reinforce gender representation on the original result page, but typically yield better access to the ultimately-selected results. The prevalence of these reformulations -- and which gender they skew towards -- differ by topical context. However, we do not find evidence that either group under-representation or markedness alone adequately explains these reformulations. We hope that future research will use such reformulations as a probe for deeper investigation into gender (and other demographic) representation on the search result page.|搜索系统的用户往往通过添加查询词语来重新表述其查询，以反映其不断变化的信息需求，或在系统未能显示相关内容时更准确地表达其信息需求。分析这些查询重构可以让我们了解系统和用户行为。在这项工作中，我们研究了一个特殊类别的查询重构，涉及指定人口组属性，如性别，作为重构查询的一部分(例如，“2021年奥运会足球结果”到“2021年奥运会女子足球结果”)。查询、搜索结果和性别等人口统计学特征可能有多种关联，这导致我们假设这些重构模式的不同原因，例如原始结果页面上的表示不足或基于标记性语言学理论。这篇论文报道了一系列针对性别的查询观察性研究——它们的背景和效果——作为系统结果和性别之间关系的一个透镜，基于 Bing 的大规模搜索日志数据。我们发现，这些重新编排有时正确，其他时候加强原始结果页面上的性别代表性，但通常产生更好的访问最终选定的结果。这些重新拟订的普遍程度——以及它们倾向于哪一性别——因主题背景的不同而有所不同。然而，我们没有发现证据表明，无论是集团代表性不足或标记单独充分解释这些重新编排。我们希望未来的研究将使用这样的重新编排作为一种探索，对搜索结果页面上的性别(和其他人口统计学)表征进行更深入的调查。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Patterns+of+Gender-Specializing+Query+Reformulation)|0| |[PiTL: Cross-modal Retrieval with Weakly-supervised Vision-language Pre-training via Prompting](https://doi.org/10.1145/3539618.3592038)|Zixin Guo, TzuJui Julius Wang, Selen Pehlivan, Abduljalil Radman, Jorma Laaksonen|Aalto University, Espoo, Finland|Vision-language (VL) Pre-training (VLP) has shown to well generalize VL models over a wide range of VL downstream tasks, especially for cross-modal retrieval. However, it hinges on a huge amount of image-text pairs, which requires tedious and costly curation. On the contrary, weakly-supervised VLP (W-VLP) explores means with object tags generated by a pre-trained object detector (OD) from images. Yet, they still require paired information, i.e. images and object-level annotations, as supervision to train an OD. To further reduce the amount of supervision, we propose Prompts-in-The-Loop (PiTL) that prompts knowledge from large language models (LLMs) to describe images. Concretely, given a category label of an image, e.g. refinery, the knowledge, e.g. a refinery could be seen with large storage tanks, pipework, and ..., extracted by LLMs is used as the language counterpart. The knowledge supplements, e.g. the common relations among entities most likely appearing in a scene. We create IN14K, a new VL dataset of 9M images and 1M descriptions of 14K categories from ImageNet21K with PiTL. Empirically, the VL models pre-trained with PiTL-generated pairs are strongly favored over other W-VLP works on image-to-text (I2T) and text-to-image (T2I) retrieval tasks, with less supervision. The results reveal the effectiveness of PiTL-generated pairs for VLP.|视觉语言(VL)预训练(VLP)已被证明可以在 VL 下游任务的广泛范围内很好地推广 VL 模型，特别是在跨模式检索中。然而，它依赖于大量的图像-文本对，这需要乏味和昂贵的管理。相反，弱监督 VLP (W-VLP)利用预先训练的目标检测器(OD)从图像中生成目标标记来探索目标检测方法。然而，他们仍然需要成对的信息，即图像和对象级注释，作为培训 OD 的监督。为了进一步减少监督的数量，我们提出了循环提示(Prompts-in-The-Loop，PiTL) ，它提示来自大型语言模型(LLM)的知识来描述图像。具体来说，给定一个图像的类别标签，例如精炼厂，知识，例如精炼厂可以看到大型存储罐，管道系统，和... ，由 LLM 提取被用作语言对应物。知识的补充，例如最可能出现在场景中的实体之间的共同关系。我们使用 PiTL 从 ImageNet21K 创建了 IN14K，这是一个包含9M 图像和1M 描述14K 类别的新的 VL 数据集。经验表明，在图像到文本(I2T)和文本到图像(T2I)的检索任务中，预先使用 PiTL 生成对训练的 VL 模型比其他 W-VLP 模型更受青睐，而且监督更少。结果表明，PiTL 生成的对对于 VLP 是有效的。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=PiTL:+Cross-modal+Retrieval+with+Weakly-supervised+Vision-language+Pre-training+via+Prompting)|0| -|[Query-specific Variable Depth Pooling via Query Performance Prediction](https://doi.org/10.1145/3539618.3592046)|Debasis Ganguly, Emine Yilmaz|University of Glasgow, Glasgow, United Kingdom; University College London, London, United Kingdom|Due to the massive size of test collections, a standard practice in IR evaluation is to construct a 'pool' of candidate relevant documents comprised of the top-k documents retrieved by a wide range of different retrieval systems - a process called depth-k pooling. A standard practice is to set the depth (k) to a constant value for each query constituting the benchmark set. However, in this paper we argue that the annotation effort can be substantially reduced if the depth of the pool is made a variable quantity for each query, the rationale being that the number of documents relevant to the information need can widely vary across queries. Our hypothesis is that a lower depth for the former class of queries and a higher depth for the latter can potentially reduce the annotation effort without a significant change in retrieval effectiveness evaluation. We make use of standard query performance prediction (QPP) techniques to estimate the number of potentially relevant documents for each query, which is then used to determine the depth of the pool. Our experiments conducted on standard test collections demonstrate that this proposed method of employing query-specific variable depths is able to adequately reflect the relative effectiveness of IR systems with a substantially smaller annotation effort.|由于测试集的规模庞大，IR 评估的一个标准实践是构建一个由各种不同检索系统检索到的 top-k 文档组成的候选相关文档“池”——这个过程被称为深度 k 池。标准实践是将构成基准集的每个查询的深度(k)设置为常量值。然而，在本文中，我们认为，如果对于每个查询，池的深度都是可变的，那么注释工作可以大大减少，其基本原理是，与信息需求相关的文档数量可以在不同的查询之间有很大的差异。我们的假设是，对于前一类查询，较低的深度和对于后一类查询，较高的深度可以潜在地减少注释工作，而不会显著改变检索有效性评估。我们使用标准查询性能预测(QPP)技术来估计每个查询可能相关的文档的数量，然后使用这些文档来确定池的深度。我们在标准测试集合上进行的实验表明，这种使用特定于查询的变量深度的方法能够充分反映 IR 系统的相对有效性，而且注释工作要少得多。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Query-specific+Variable+Depth+Pooling+via+Query+Performance+Prediction)|0| -|[RankT5: Fine-Tuning T5 for Text Ranking with Ranking Losses](https://doi.org/10.1145/3539618.3592047)|Honglei Zhuang, Zhen Qin, Rolf Jagerman, Kai Hui, Ji Ma, Jing Lu, Jianmo Ni, Xuanhui Wang, Michael Bendersky|Google Research, Mountain View, USA; Google Research, London, United Kingdom; Google Research, New York, USA; Google Research, Amsterdam, Netherlands|Recently, substantial progress has been made in text ranking based on pretrained language models such as BERT. However, there are limited studies on how to leverage more powerful sequence-to-sequence models such as T5. Existing attempts usually formulate text ranking as classification and rely on postprocessing to obtain a ranked list. In this paper, we propose RankT5 and study two T5-based ranking model structures, an encoder-decoder and an encoder-only one, so that they not only can directly output ranking scores for each query-document pair, but also can be fine-tuned with "pairwise" or "listwise" ranking losses to optimize ranking performances. Our experiments show that the proposed models with ranking losses can achieve substantial ranking performance gains on different public text ranking data sets. Moreover, when fine-tuned with listwise ranking losses, the ranking model appears to have better zero-shot ranking performance on out-of-domain data sets compared to the model fine-tuned with classification losses.|近年来，基于 BERT 等预训练语言模型的文本排序研究取得了长足的进展。然而，关于如何利用更强大的序列到序列模型(如 T5)的研究很有限。现有的尝试通常将文本排序公式化为分类，并依赖于后处理来获得排序列表。本文提出了 RankT5，并研究了两种基于 T5的排序模型结构: 编码-解码器结构和编码器-纯编码器结构，使它们不仅可以直接输出每个查询-文档对的排序得分，而且可以通过“成对”或“列表”排序损失进行微调，以优化排序性能。我们的实验表明，在不同的公共文本排序数据集上，所提出的带有排序损失的模型可以获得较大的排序性能增益。此外，当用列表排序损失进行微调时，与用分类损失进行微调的模型相比，排序模型在域外数据集上似乎具有更好的零点排序性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=RankT5:+Fine-Tuning+T5+for+Text+Ranking+with+Ranking+Losses)|0| +|[Query-specific Variable Depth Pooling via Query Performance Prediction](https://doi.org/10.1145/3539618.3592046)|Debasis Ganguly, Emine Yilmaz|University College London, London, United Kingdom; University of Glasgow, Glasgow, United Kingdom|Due to the massive size of test collections, a standard practice in IR evaluation is to construct a 'pool' of candidate relevant documents comprised of the top-k documents retrieved by a wide range of different retrieval systems - a process called depth-k pooling. A standard practice is to set the depth (k) to a constant value for each query constituting the benchmark set. However, in this paper we argue that the annotation effort can be substantially reduced if the depth of the pool is made a variable quantity for each query, the rationale being that the number of documents relevant to the information need can widely vary across queries. Our hypothesis is that a lower depth for the former class of queries and a higher depth for the latter can potentially reduce the annotation effort without a significant change in retrieval effectiveness evaluation. We make use of standard query performance prediction (QPP) techniques to estimate the number of potentially relevant documents for each query, which is then used to determine the depth of the pool. Our experiments conducted on standard test collections demonstrate that this proposed method of employing query-specific variable depths is able to adequately reflect the relative effectiveness of IR systems with a substantially smaller annotation effort.|由于测试集的规模庞大，IR 评估的一个标准实践是构建一个由各种不同检索系统检索到的 top-k 文档组成的候选相关文档“池”——这个过程被称为深度 k 池。标准实践是将构成基准集的每个查询的深度(k)设置为常量值。然而，在本文中，我们认为，如果对于每个查询，池的深度都是可变的，那么注释工作可以大大减少，其基本原理是，与信息需求相关的文档数量可以在不同的查询之间有很大的差异。我们的假设是，对于前一类查询，较低的深度和对于后一类查询，较高的深度可以潜在地减少注释工作，而不会显著改变检索有效性评估。我们使用标准查询性能预测(QPP)技术来估计每个查询可能相关的文档的数量，然后使用这些文档来确定池的深度。我们在标准测试集合上进行的实验表明，这种使用特定于查询的变量深度的方法能够充分反映 IR 系统的相对有效性，而且注释工作要少得多。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Query-specific+Variable+Depth+Pooling+via+Query+Performance+Prediction)|0| +|[RankT5: Fine-Tuning T5 for Text Ranking with Ranking Losses](https://doi.org/10.1145/3539618.3592047)|Honglei Zhuang, Zhen Qin, Rolf Jagerman, Kai Hui, Ji Ma, Jing Lu, Jianmo Ni, Xuanhui Wang, Michael Bendersky|Google Research, New York, USA; Google Research, Amsterdam, Netherlands; Google Research, London, United Kingdom; Google Research, Mountain View, USA|Recently, substantial progress has been made in text ranking based on pretrained language models such as BERT. However, there are limited studies on how to leverage more powerful sequence-to-sequence models such as T5. Existing attempts usually formulate text ranking as classification and rely on postprocessing to obtain a ranked list. In this paper, we propose RankT5 and study two T5-based ranking model structures, an encoder-decoder and an encoder-only one, so that they not only can directly output ranking scores for each query-document pair, but also can be fine-tuned with "pairwise" or "listwise" ranking losses to optimize ranking performances. Our experiments show that the proposed models with ranking losses can achieve substantial ranking performance gains on different public text ranking data sets. Moreover, when fine-tuned with listwise ranking losses, the ranking model appears to have better zero-shot ranking performance on out-of-domain data sets compared to the model fine-tuned with classification losses.|近年来，基于 BERT 等预训练语言模型的文本排序研究取得了长足的进展。然而，关于如何利用更强大的序列到序列模型(如 T5)的研究很有限。现有的尝试通常将文本排序公式化为分类，并依赖于后处理来获得排序列表。本文提出了 RankT5，并研究了两种基于 T5的排序模型结构: 编码-解码器结构和编码器-纯编码器结构，使它们不仅可以直接输出每个查询-文档对的排序得分，而且可以通过“成对”或“列表”排序损失进行微调，以优化排序性能。我们的实验表明，在不同的公共文本排序数据集上，所提出的带有排序损失的模型可以获得较大的排序性能增益。此外，当用列表排序损失进行微调时，与用分类损失进行微调的模型相比，排序模型在域外数据集上似乎具有更好的零点排序性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=RankT5:+Fine-Tuning+T5+for+Text+Ranking+with+Ranking+Losses)|0| |[Representation Sparsification with Hybrid Thresholding for Fast SPLADE-based Document Retrieval](https://doi.org/10.1145/3539618.3592051)|Yifan Qiao, Yingrui Yang, Shanxiu He, Tao Yang|University of California, Santa Barbara, Santa Barbara, CA, USA|Learned sparse document representations using a transformer-based neural model has been found to be attractive in both relevance effectiveness and time efficiency. This paper describes a representation sparsification scheme based on hard and soft thresholding with an inverted index approximation for faster SPLADE-based document retrieval. It provides analytical and experimental results on the impact of this learnable hybrid thresholding scheme.|使用基于变换器的神经模型学习稀疏文档表示已经被发现在相关性效率和时间效率方面具有吸引力。本文描述了一种基于硬阈值和软阈值的表示稀疏化方案，该方案采用倒排索引近似来实现更快的基于 SPLADE 的文献检索。文中给出了分析和实验结果的影响，这种学习的混合阈值方案。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Representation+Sparsification+with+Hybrid+Thresholding+for+Fast+SPLADE-based+Document+Retrieval)|0| -|[Robust Causal Inference for Recommender System to Overcome Noisy Confounders](https://doi.org/10.1145/3539618.3592055)|Zhiheng Zhang, Quanyu Dai, Xu Chen, Zhenhua Dong, Ruiming Tang|Gaoling School of Artificial Intelligence, Renmin University of China, Shenzhen, China; Huawei Noah's Ark Lab, Shenzhen, China; Institute for Interdisciplinary Information Sciences (IIIS), Tsinghua University, Beijing, China|Recently, there has been growing interest in integrating causal inference into recommender systems to answer the hypothetical question: "what would be the potential feedback when a user is recommended a product?" Various unbiased estimators, including Inverse Propensity Score (IPS) and Doubly Robust (DR), have been proposed to address this question. However, these estimators often assume that confounders are precisely observable, which is not always the case in real-world scenarios. To address this challenge, we propose a novel method called Adversarial Training-based IPS (AT-IPS), which uses adversarial training to handle noisy confounders. The proposed method defines a feasible region for the confounders, obtains the worst-case noise (adversarial noise) within the region, and jointly trains the propensity model and the prediction model against such noise to improve their robustness. We provide a theoretical analysis of the accuracy-robustness tradeoff of AT-IPS and demonstrate its superior performance compared to other popular estimators on both real-world and semi-synthetic datasets.|最近，人们越来越有兴趣将因果推理集成到推荐系统中，以回答这样一个假设性问题: “当用户被推荐一个产品时，潜在的反馈是什么?”针对这一问题，人们提出了各种无偏估计，包括反倾向评分(IPS)和双稳健估计(DR)。然而，这些评估者通常假设混杂因素是可以精确观察到的，而在现实世界的场景中并不总是如此。为了应对这一挑战，我们提出了一种新的方法称为基于对抗性训练的 IPS (AT-IPS) ，它使用对抗性训练来处理噪声混杂因素。该方法为混杂因子定义了一个可行区域，得到了该区域内的最坏情况噪声(对抗噪声) ，并对倾向模型和预测模型进行了联合训练，以提高其鲁棒性。本文从理论上分析了 AT-IPS 算法的精度-鲁棒性折衷问题，并在实际数据集和半合成数据集上证明了该算法与其他常用估计算法相比具有更好的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Robust+Causal+Inference+for+Recommender+System+to+Overcome+Noisy+Confounders)|0| +|[Robust Causal Inference for Recommender System to Overcome Noisy Confounders](https://doi.org/10.1145/3539618.3592055)|Zhiheng Zhang, Quanyu Dai, Xu Chen, Zhenhua Dong, Ruiming Tang|Huawei Noah's Ark Lab, Shenzhen, China; Institute for Interdisciplinary Information Sciences (IIIS), Tsinghua University, Beijing, China; Gaoling School of Artificial Intelligence, Renmin University of China, Shenzhen, China|Recently, there has been growing interest in integrating causal inference into recommender systems to answer the hypothetical question: "what would be the potential feedback when a user is recommended a product?" Various unbiased estimators, including Inverse Propensity Score (IPS) and Doubly Robust (DR), have been proposed to address this question. However, these estimators often assume that confounders are precisely observable, which is not always the case in real-world scenarios. To address this challenge, we propose a novel method called Adversarial Training-based IPS (AT-IPS), which uses adversarial training to handle noisy confounders. The proposed method defines a feasible region for the confounders, obtains the worst-case noise (adversarial noise) within the region, and jointly trains the propensity model and the prediction model against such noise to improve their robustness. We provide a theoretical analysis of the accuracy-robustness tradeoff of AT-IPS and demonstrate its superior performance compared to other popular estimators on both real-world and semi-synthetic datasets.|最近，人们越来越有兴趣将因果推理集成到推荐系统中，以回答这样一个假设性问题: “当用户被推荐一个产品时，潜在的反馈是什么?”针对这一问题，人们提出了各种无偏估计，包括反倾向评分(IPS)和双稳健估计(DR)。然而，这些评估者通常假设混杂因素是可以精确观察到的，而在现实世界的场景中并不总是如此。为了应对这一挑战，我们提出了一种新的方法称为基于对抗性训练的 IPS (AT-IPS) ，它使用对抗性训练来处理噪声混杂因素。该方法为混杂因子定义了一个可行区域，得到了该区域内的最坏情况噪声(对抗噪声) ，并对倾向模型和预测模型进行了联合训练，以提高其鲁棒性。本文从理论上分析了 AT-IPS 算法的精度-鲁棒性折衷问题，并在实际数据集和半合成数据集上证明了该算法与其他常用估计算法相比具有更好的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Robust+Causal+Inference+for+Recommender+System+to+Overcome+Noisy+Confounders)|0| |[Sharpness-Aware Graph Collaborative Filtering](https://doi.org/10.1145/3539618.3592059)|Huiyuan Chen, ChinChia Michael Yeh, Yujie Fan, Yan Zheng, Junpeng Wang, Vivian Lai, Mahashweta Das, Hao Yang|Visa Research, Palo Alto, USA|Graph Neural Networks (GNNs) have achieved impressive performance in collaborative filtering. However, GNNs tend to yield inferior performance when the distributions of training and test data are not aligned well. Also, training GNNs requires optimizing non-convex neural networks with an abundance of local and global minima, which may differ widely in their performance at test time. Thus, it is essential to choose the minima carefully. Here we propose an effective training schema, called {gSAM}, under the principle that the \textit{flatter} minima has a better generalization ability than the \textit{sharper} ones. To achieve this goal, gSAM regularizes the flatness of the weight loss landscape by forming a bi-level optimization: the outer problem conducts the standard model training while the inner problem helps the model jump out of the sharp minima. Experimental results show the superiority of our gSAM.|图形神经网络(GNN)在协同过滤方面取得了令人印象深刻的成就。然而，当训练和测试数据的分布不一致时，GNN 往往会产生较差的性能。此外，训练 GNN 需要优化具有丰富的局部和全局最小值的非凸神经网络，这可能在测试时间的性能差异很大。因此，仔细选择最小值是非常必要的。在这里，我们提出了一个有效的训练模式，称为{ gSAM } ，根据文本{平坦}极小比文本{锐利}极小具有更好的泛化能力的原则。为了实现这一目标，gSAM 通过形成一个双层优化来调整减肥景观的平面性: 外部问题进行标准模型训练，而内部问题帮助模型跳出尖锐的极小值。实验结果表明了我们的 gSAM 的优越性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Sharpness-Aware+Graph+Collaborative+Filtering)|0| |[ExaRanker: Synthetic Explanations Improve Neural Rankers](https://doi.org/10.1145/3539618.3592067)|Fernando Ferraretto, Thiago Laitz, Roberto de Alencar Lotufo, Rodrigo Frassetto Nogueira|UNICAMP, Campinas, UNK, Brazil|Recent work has shown that incorporating explanations into the output generated by large language models (LLMs) can significantly enhance performance on a broad spectrum of reasoning tasks. Our study extends these findings by demonstrating the benefits of explanations for neural rankers. By utilizing LLMs such as GPT-3.5 to enrich retrieval datasets with explanations, we trained a sequence-to-sequence ranking model, dubbed ExaRanker, to generate relevance labels and explanations for query-document pairs. The ExaRanker model, finetuned on a limited number of examples and synthetic explanations, exhibits performance comparable to models finetuned on three times more examples, but without explanations. Moreover, incorporating explanations imposes no additional computational overhead into the reranking step and allows for on-demand explanation generation. The codebase and datasets used in this study will be available at https://github.com/unicamp-dl/ExaRanker|最近的研究表明，在大型语言模型(LLM)产生的输出中加入解释能够显著提高大范围推理任务的性能。我们的研究扩展了这些发现，证明了神经排序解释的好处。通过使用诸如 GPT-3.5这样的 LLM 来丰富检索数据集的解释，我们训练了一个序列到序列的排序模型，称为 ExaRanker，以生成查询文档对的相关标签和解释。ExaRanker 模型在有限数量的例子和综合解释的基础上进行了微调，其性能与在三倍以上的例子上进行微调的模型相当，但没有解释。此外，合并解释不会给重新排序步骤带来额外的计算开销，并允许按需生成解释。这项研究所使用的代码库和数据集将在 https://github.com/unicamp-dl/exaranker 提供|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=ExaRanker:+Synthetic+Explanations+Improve+Neural+Rankers)|0| -|[Text-to-Motion Retrieval: Towards Joint Understanding of Human Motion Data and Natural Language](https://doi.org/10.1145/3539618.3592069)|Nicola Messina, Jan Sedmidubský, Fabrizio Falchi, Tomás Rebok|ISTI-CNR, Pisa, Italy; Masaryk University, Brno, Czech Rep|Due to recent advances in pose-estimation methods, human motion can be extracted from a common video in the form of 3D skeleton sequences. Despite wonderful application opportunities, effective and efficient content-based access to large volumes of such spatio-temporal skeleton data still remains a challenging problem. In this paper, we propose a novel content-based text-to-motion retrieval task, which aims at retrieving relevant motions based on a specified natural-language textual description. To define baselines for this uncharted task, we employ the BERT and CLIP language representations to encode the text modality and successful spatio-temporal models to encode the motion modality. We additionally introduce our transformer-based approach, called Motion Transformer (MoT), which employs divided space-time attention to effectively aggregate the different skeleton joints in space and time. Inspired by the recent progress in text-to-image/video matching, we experiment with two widely-adopted metric-learning loss functions. Finally, we set up a common evaluation protocol by defining qualitative metrics for assessing the quality of the retrieved motions, targeting the two recently-introduced KIT Motion-Language and HumanML3D datasets. The code for reproducing our results is available at https://github.com/mesnico/text-to-motion-retrieval.|由于近年来姿态估计方法的发展，人体运动可以从常见的视频中以三维骨架序列的形式提取出来。尽管有很好的应用机会，有效和高效的基于内容的访问大量这样的时空骨架数据仍然是一个具有挑战性的问题。本文提出了一种新的基于内容的文本-运动检索任务，该任务旨在基于特定的自然语言文本描述来检索相关运动。为了定义这个未知任务的基线，我们使用 BERT 和 CLIP 语言表示来编码文本模态，使用成功的时空模型来编码运动模态。此外，我们还介绍了我们的基于变压器的方法，称为运动变压器(MoT) ，它使用分割的时空注意力来有效地聚合不同的骨骼关节在时间和空间上。受到文本-图像/视频匹配技术的启发，我们实验了两种广泛采用的度量学习损失函数。最后，针对最近引入的两个 KIT Motion-Language 和 HumanML3D 数据集，通过定义定性指标来评估检索到的运动质量，建立了一个通用的评估协议。复制我们结果的代码可在 https://github.com/mesnico/text-to-motion-retrieval 找到。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Text-to-Motion+Retrieval:+Towards+Joint+Understanding+of+Human+Motion+Data+and+Natural+Language)|0| +|[Text-to-Motion Retrieval: Towards Joint Understanding of Human Motion Data and Natural Language](https://doi.org/10.1145/3539618.3592069)|Nicola Messina, Jan Sedmidubský, Fabrizio Falchi, Tomás Rebok|Masaryk University, Brno, Czech Rep; ISTI-CNR, Pisa, Italy|Due to recent advances in pose-estimation methods, human motion can be extracted from a common video in the form of 3D skeleton sequences. Despite wonderful application opportunities, effective and efficient content-based access to large volumes of such spatio-temporal skeleton data still remains a challenging problem. In this paper, we propose a novel content-based text-to-motion retrieval task, which aims at retrieving relevant motions based on a specified natural-language textual description. To define baselines for this uncharted task, we employ the BERT and CLIP language representations to encode the text modality and successful spatio-temporal models to encode the motion modality. We additionally introduce our transformer-based approach, called Motion Transformer (MoT), which employs divided space-time attention to effectively aggregate the different skeleton joints in space and time. Inspired by the recent progress in text-to-image/video matching, we experiment with two widely-adopted metric-learning loss functions. Finally, we set up a common evaluation protocol by defining qualitative metrics for assessing the quality of the retrieved motions, targeting the two recently-introduced KIT Motion-Language and HumanML3D datasets. The code for reproducing our results is available at https://github.com/mesnico/text-to-motion-retrieval.|由于近年来姿态估计方法的发展，人体运动可以从常见的视频中以三维骨架序列的形式提取出来。尽管有很好的应用机会，有效和高效的基于内容的访问大量这样的时空骨架数据仍然是一个具有挑战性的问题。本文提出了一种新的基于内容的文本-运动检索任务，该任务旨在基于特定的自然语言文本描述来检索相关运动。为了定义这个未知任务的基线，我们使用 BERT 和 CLIP 语言表示来编码文本模态，使用成功的时空模型来编码运动模态。此外，我们还介绍了我们的基于变压器的方法，称为运动变压器(MoT) ，它使用分割的时空注意力来有效地聚合不同的骨骼关节在时间和空间上。受到文本-图像/视频匹配技术的启发，我们实验了两种广泛采用的度量学习损失函数。最后，针对最近引入的两个 KIT Motion-Language 和 HumanML3D 数据集，通过定义定性指标来评估检索到的运动质量，建立了一个通用的评估协议。复制我们结果的代码可在 https://github.com/mesnico/text-to-motion-retrieval 找到。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Text-to-Motion+Retrieval:+Towards+Joint+Understanding+of+Human+Motion+Data+and+Natural+Language)|0| |[TripSafe: Retrieving Safety-related Abnormal Trips in Real-time with Trajectory Data](https://doi.org/10.1145/3539618.3592074)|Yueyang Su, Di Yao, Xiaolei Zhou, Yuxuan Zhang, Yunxia Fan, Lu Bai, Jingping Bi|DiDi Global Inc., beijing, China; Institute of Computing Technology Chinese Academy of Sciences, beijing, China|Nowadays safety has become one of the most critical factors for ride-hailing service. Ride-hailing platforms have conducted meticulous background checks for drivers to minimize the risk of abnormal trips, e.g. violence and sexual assault. However, current methods are labor-consuming and highly rely on the personal information of drivers, which may harm the fairness of the order dispatching system. In this paper, we utilize the trip trajectories as inputs and propose a dual variational auto-encoder(VAE) framework, namely TripSafe, to estimate the probability of abnormal safety incidents. Specifically, TripSafe models the moving behavior and route information, as two independent components and employs VAEs to pre-train generative models for normal trips. Then, a fusion network is adopted to fine-tune the whole model with a few labeled samples. In practice, TripSafe monitors the data update and calculate the anomaly score of partial-observed trips in real-time. Experiments on real ridehailing data show that TripSafe is superior to the state-of-the-art baselines with about 14.2%~28.9% improvements on F1 score.|如今，安全已经成为叫车服务最关键的因素之一。叫车平台对司机进行严格的背景调查，以尽量减少不正常行程的风险，例如暴力和性侵犯。然而，目前的方法耗费大量人力资源，对驾驶员个人信息的依赖程度较高，可能会损害订单调度系统的公平性。本文利用出行轨迹作为输入，提出了一种双变分自动编码器(VAE)框架，即 TripSafe，来估计异常安全事件的发生概率。具体来说，TripSafe 将移动行为和路径信息建模为两个独立的组件，并使用 VAE 预训练正常旅行的生成模型。然后，采用融合网络对整个模型进行微调，并加入少量标记样本。在实践中，TripSafe 监视数据更新并实时计算部分观测行程的异常评分。对真实叫车数据的实验表明，TripSafe 优于最先进的基线，F1得分提高了约14.2% ~ 28.9% 。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=TripSafe:+Retrieving+Safety-related+Abnormal+Trips+in+Real-time+with+Trajectory+Data)|0| |[Unsupervised Dense Retrieval Training with Web Anchors](https://doi.org/10.1145/3539618.3592080)|Yiqing Xie, Xiao Liu, Chenyan Xiong|Microsoft, Redmond, WA, USA; Carnegie Mellon University, Pittsburgh, PA, USA|In this work, we present an unsupervised retrieval method with contrastive learning on web anchors. The anchor text describes the content that is referenced from the linked page. This shows similarities to search queries that aim to retrieve pertinent information from relevant documents. Based on their commonalities, we train an unsupervised dense retriever, Anchor-DR, with a contrastive learning task that matches the anchor text and the linked document. To filter out uninformative anchors (such as ``homepage'' or other functional anchors), we present a novel filtering technique to only select anchors that contain similar types of information as search queries. Experiments show that Anchor-DR outperforms state-of-the-art methods on unsupervised dense retrieval by a large margin (e.g., by 5.3% NDCG@10 on MSMARCO). The gain of our method is especially significant for search and question answering tasks. Our analysis further reveals that the pattern of anchor-document pairs is similar to that of search query-document pairs. Code available at https://github.com/Veronicium/AnchorDR.|本文提出了一种基于对比学习的无监督检索方法。锚文本描述从链接页面引用的内容。这显示了与旨在从相关文档中检索相关信息的搜索查询的相似性。基于它们的共性，我们训练了一个无监督的密集检索器 Anchor-DR，它具有匹配锚文本和链接文档的对比学习任务。为了过滤掉没有信息的锚(如“主页”或其他功能性锚) ，我们提出了一种新的过滤技术，只选择包含与搜索查询类似类型信息的锚。实验表明，Anchor-DR 在无监督密集检索方面的性能大大优于最先进的方法(例如 MSMARCO 上的5.3% NDCG@10)。对于搜索和问答任务，该方法的增益尤为显著。我们的分析进一步揭示了锚-文档对的模式与搜索查询-文档对的模式相似。Https://github.com/veronicium/anchordr 提供密码。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Unsupervised+Dense+Retrieval+Training+with+Web+Anchors)|0| |[When the Music Stops: Tip-of-the-Tongue Retrieval for Music](https://doi.org/10.1145/3539618.3592086)|Samarth Bhargav, Anne Schuth, Claudia Hauff||We present a study of Tip-of-the-tongue (ToT) retrieval for music, where a searcher is trying to find an existing music entity, but is unable to succeed as they cannot accurately recall important identifying information. ToT information needs are characterized by complexity, verbosity, uncertainty, and possible false memories. We make four contributions. (1) We collect a dataset - $ToT_{Music}$ - of 2,278 information needs and ground truth answers. (2) We introduce a schema for these information needs and show that they often involve multiple modalities encompassing several Music IR subtasks such as lyric search, audio-based search, audio fingerprinting, and text search. (3) We underscore the difficulty of this task by benchmarking a standard text retrieval approach on this dataset. (4) We investigate the efficacy of query reformulations generated by a large language model (LLM), and show that they are not as effective as simply employing the entire information need as a query - leaving several open questions for future research.|我们提出了一个音乐舌尖(ToT)检索的研究，其中一个搜索者试图找到一个现有的音乐实体，但不能成功，因为他们不能准确地回忆重要的识别信息。时间信息的需求是拥有属性的复杂性、冗长性、不确定性和可能的错误记忆。我们做了四笔捐款。(1)我们收集了2278个信息需求和基本事实答案的数据集。(2)针对这些信息需求，我们引入了一个模式，表明它们通常涉及多种形式，包括多个 Music IR 子任务，如歌词搜索、基于音频的搜索、音频指纹识别和文本搜索。(3)我们强调这项任务的困难，通过基准测试的标准文本检索方法对这个数据集。(4)我们研究了大语言模型(LLM)生成的查询重构的有效性，结果表明它们不如简单地将整个信息需求作为一个查询来得有效——为未来的研究留下了一些未解决的问题。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=When+the+Music+Stops:+Tip-of-the-Tongue+Retrieval+for+Music)|0| -|[Reproducibility, Replicability, and Insights into Dense Multi-Representation Retrieval Models: from ColBERT to Col](https://doi.org/10.1145/3539618.3591916)|Xiao Wang, Craig Macdonald, Nicola Tonellotto, Iadh Ounis|University of Glasgow, Glasgow, United Kingdom; University of Pisa, Pisa, Italy|Dense multi-representation retrieval models, exemplified as ColBERT, estimate the relevance between a query and a document based on the similarity of their contextualised token-level embeddings. Indeed, by using contextualised token embeddings, dense retrieval, conducted as either exact or semantic matches, can result in increased effectiveness for both in-domain and out-of-domain retrieval tasks, indicating that it is an important model to study. However, the exact role that these semantic matches play is not yet well investigated. For instance, although tokenisation is one of the crucial design choices for various pretrained language models, its impact on the matching behaviour has not been examined in detail. In this work, we inspect the reproducibility and replicability of the contextualised late interaction mechanism by extending ColBERT to Col⋆ which implements the late interaction mechanism across various pretrained models and different types of tokenisers. As different tokenisation methods can directly impact the matching behaviour within the late interaction mechanism, we study the nature of matches occurring in different Col⋆ models, and further quantify the contribution of lexical and semantic matching on retrieval effectiveness. Overall, our experiments successfully reproduce the performance of ColBERT on various query sets, and replicate the late interaction mechanism upon different pretrained models with different tokenisers. Moreover, our experimental results yield new insights, such as: (i) semantic matching behaviour varies across different tokenisers; (ii) more specifically, high-frequency tokens tend to perform semantic matching than other token families; (iii) late interaction mechanism benefits more from lexical matching than semantic matching; (iv) special tokens, such as [CLS], play a very important role in late interaction.|以 ColBERT 为例的稠密多表示检索模型，基于上下文化标记级嵌入的相似性来估计查询与文档之间的相关性。事实上，通过使用上下文化的令牌嵌入，以精确匹配或语义匹配的方式进行的密集检索可以提高域内和域外检索任务的有效性，这表明它是一个重要的研究模型。然而，这些语义匹配所起的确切作用还没有得到很好的研究。例如，虽然标记化是各种预先训练的语言模型的关键设计选择之一，但它对匹配行为的影响尚未得到详细研究。在这项工作中，我们检查了上下文化的后期交互机制的可重复性和可复制性，通过将 ColBERT 扩展到 Col something，实现了跨各种预先训练的模型和不同类型的标记器的后期交互机制。由于不同的标记化方法可以直接影响后期交互机制中的匹配行为，本文研究了不同 Col 模型中匹配的性质，并进一步量化了词汇和语义匹配对检索效率的贡献。总的来说，我们的实验成功地再现了 ColBERT 在不同查询集上的性能，并且在不同的预训练模型上复制了后期交互机制。此外，我们的实验结果产生了新的见解，如: (i)语义匹配行为不同的标记; (ii)更具体地说，高频标记倾向于执行语义匹配比其他标记家族; (iii)后期交互机制受益于词汇匹配比语义匹配更多; (iv)特殊标记，如[ CLS ] ，在后期交互中发挥非常重要的作用。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Reproducibility,+Replicability,+and+Insights+into+Dense+Multi-Representation+Retrieval+Models:+from+ColBERT+to+Col)|0| -|[On Stance Detection in Image Retrieval for Argumentation](https://doi.org/10.1145/3539618.3591917)|Miriam Louise Carnot, Lorenz Heinemann, Jan Braker, Tobias Schreieder, Johannes Kiesel, Maik Fröbe, Martin Potthast, Benno Stein|Bauhaus-Universität Weimar, Weimar, Germany; Leipzig University and ScaDS.AI, Leipzig, Germany; Leipzig University, Leipzig, Germany; Martin-Luther-Universität Halle Wittenberg, Halle, Germany|Given a text query on a controversial topic, the task of Image Retrieval for Argumentation is to rank images according to how well they can be used to support a discussion on the topic. An important subtask therein is to determine the stance of the retrieved images, i.e., whether an image supports the pro or con side of the topic. In this paper, we conduct a comprehensive reproducibility study of the state of the art as represented by the CLEF'22 Touché lab and an in-house extension of it. Based on the submitted approaches, we developed a unified and modular retrieval process and reimplemented the submitted approaches according to this process. Through this unified reproduction (which also includes models not previously considered), we achieve an effectiveness improvement in argumentative image detection of up to 0.832 precision@10. However, despite this reproduction success, our study also revealed a previously unknown negative result: for stance detection, none of the reproduced or new approaches can convincingly beat a random baseline. To understand the apparent challenges inherent to image stance detection, we conduct a thorough error analysis and provide insight into potential new ways to approach this task.|给定一个关于有争议话题的文本查询，图像检索用于论证的任务是根据图像在多大程度上可以用于支持关于这个话题的讨论来对它们进行排序。其中一个重要的子任务是确定检索图像的立场，即图像是否支持主题的正面或反面。在本文中，我们进行了一个全面的再现性研究的状态所代表的 CLEF’22 Touché 实验室和它的内部扩展。在提交方法的基础上，我们开发了一个统一的模块化检索流程，并根据该流程重新实现了提交的方法。通过这种统一的再现(其中还包括以前未考虑的模型) ，我们实现了高达0.832精度@10的议论图像检测的有效性改进。然而，尽管复制成功，我们的研究也揭示了一个以前未知的负面结果: 对于姿势检测，没有一种复制或新的方法能够令人信服地超过随机基线。为了理解图像姿态检测固有的明显挑战，我们进行了一个彻底的错误分析，并提供洞察潜在的新方法来处理这一任务。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=On+Stance+Detection+in+Image+Retrieval+for+Argumentation)|0| +|[Reproducibility, Replicability, and Insights into Dense Multi-Representation Retrieval Models: from ColBERT to Col](https://doi.org/10.1145/3539618.3591916)|Xiao Wang, Craig Macdonald, Nicola Tonellotto, Iadh Ounis|University of Pisa, Pisa, Italy; University of Glasgow, Glasgow, United Kingdom|Dense multi-representation retrieval models, exemplified as ColBERT, estimate the relevance between a query and a document based on the similarity of their contextualised token-level embeddings. Indeed, by using contextualised token embeddings, dense retrieval, conducted as either exact or semantic matches, can result in increased effectiveness for both in-domain and out-of-domain retrieval tasks, indicating that it is an important model to study. However, the exact role that these semantic matches play is not yet well investigated. For instance, although tokenisation is one of the crucial design choices for various pretrained language models, its impact on the matching behaviour has not been examined in detail. In this work, we inspect the reproducibility and replicability of the contextualised late interaction mechanism by extending ColBERT to Col⋆ which implements the late interaction mechanism across various pretrained models and different types of tokenisers. As different tokenisation methods can directly impact the matching behaviour within the late interaction mechanism, we study the nature of matches occurring in different Col⋆ models, and further quantify the contribution of lexical and semantic matching on retrieval effectiveness. Overall, our experiments successfully reproduce the performance of ColBERT on various query sets, and replicate the late interaction mechanism upon different pretrained models with different tokenisers. Moreover, our experimental results yield new insights, such as: (i) semantic matching behaviour varies across different tokenisers; (ii) more specifically, high-frequency tokens tend to perform semantic matching than other token families; (iii) late interaction mechanism benefits more from lexical matching than semantic matching; (iv) special tokens, such as [CLS], play a very important role in late interaction.|以 ColBERT 为例的稠密多表示检索模型，基于上下文化标记级嵌入的相似性来估计查询与文档之间的相关性。事实上，通过使用上下文化的令牌嵌入，以精确匹配或语义匹配的方式进行的密集检索可以提高域内和域外检索任务的有效性，这表明它是一个重要的研究模型。然而，这些语义匹配所起的确切作用还没有得到很好的研究。例如，虽然标记化是各种预先训练的语言模型的关键设计选择之一，但它对匹配行为的影响尚未得到详细研究。在这项工作中，我们检查了上下文化的后期交互机制的可重复性和可复制性，通过将 ColBERT 扩展到 Col something，实现了跨各种预先训练的模型和不同类型的标记器的后期交互机制。由于不同的标记化方法可以直接影响后期交互机制中的匹配行为，本文研究了不同 Col 模型中匹配的性质，并进一步量化了词汇和语义匹配对检索效率的贡献。总的来说，我们的实验成功地再现了 ColBERT 在不同查询集上的性能，并且在不同的预训练模型上复制了后期交互机制。此外，我们的实验结果产生了新的见解，如: (i)语义匹配行为不同的标记; (ii)更具体地说，高频标记倾向于执行语义匹配比其他标记家族; (iii)后期交互机制受益于词汇匹配比语义匹配更多; (iv)特殊标记，如[ CLS ] ，在后期交互中发挥非常重要的作用。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Reproducibility,+Replicability,+and+Insights+into+Dense+Multi-Representation+Retrieval+Models:+from+ColBERT+to+Col)|0| +|[On Stance Detection in Image Retrieval for Argumentation](https://doi.org/10.1145/3539618.3591917)|Miriam Louise Carnot, Lorenz Heinemann, Jan Braker, Tobias Schreieder, Johannes Kiesel, Maik Fröbe, Martin Potthast, Benno Stein|Martin-Luther-Universität Halle Wittenberg, Halle, Germany; Bauhaus-Universität Weimar, Weimar, Germany; Leipzig University, Leipzig, Germany; Leipzig University and ScaDS.AI, Leipzig, Germany|Given a text query on a controversial topic, the task of Image Retrieval for Argumentation is to rank images according to how well they can be used to support a discussion on the topic. An important subtask therein is to determine the stance of the retrieved images, i.e., whether an image supports the pro or con side of the topic. In this paper, we conduct a comprehensive reproducibility study of the state of the art as represented by the CLEF'22 Touché lab and an in-house extension of it. Based on the submitted approaches, we developed a unified and modular retrieval process and reimplemented the submitted approaches according to this process. Through this unified reproduction (which also includes models not previously considered), we achieve an effectiveness improvement in argumentative image detection of up to 0.832 precision@10. However, despite this reproduction success, our study also revealed a previously unknown negative result: for stance detection, none of the reproduced or new approaches can convincingly beat a random baseline. To understand the apparent challenges inherent to image stance detection, we conduct a thorough error analysis and provide insight into potential new ways to approach this task.|给定一个关于有争议话题的文本查询，图像检索用于论证的任务是根据图像在多大程度上可以用于支持关于这个话题的讨论来对它们进行排序。其中一个重要的子任务是确定检索图像的立场，即图像是否支持主题的正面或反面。在本文中，我们进行了一个全面的再现性研究的状态所代表的 CLEF’22 Touché 实验室和它的内部扩展。在提交方法的基础上，我们开发了一个统一的模块化检索流程，并根据该流程重新实现了提交的方法。通过这种统一的再现(其中还包括以前未考虑的模型) ，我们实现了高达0.832精度@10的议论图像检测的有效性改进。然而，尽管复制成功，我们的研究也揭示了一个以前未知的负面结果: 对于姿势检测，没有一种复制或新的方法能够令人信服地超过随机基线。为了理解图像姿态检测固有的明显挑战，我们进行了一个彻底的错误分析，并提供洞察潜在的新方法来处理这一任务。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=On+Stance+Detection+in+Image+Retrieval+for+Argumentation)|0| |[Take a Fresh Look at Recommender Systems from an Evaluation Standpoint](https://doi.org/10.1145/3539618.3591931)|Aixin Sun|Nanyang Technological University, Singapore, Singapore|Recommendation has become a prominent area of research in the field of Information Retrieval (IR). Evaluation is also a traditional research topic in this community. Motivated by a few counter-intuitive observations reported in recent studies, this perspectives paper takes a fresh look at recommender systems from an evaluation standpoint. Rather than examining metrics like recall, hit rate, or NDCG, or perspectives like novelty and diversity, the key focus here is on how these metrics are calculated when evaluating a recommender algorithm. Specifically, the commonly used train/test data splits and their consequences are re-examined. We begin by examining common data splitting methods, such as random split or leave-one-out, and discuss why the popularity baseline is poorly defined under such splits. We then move on to explore the two implications of neglecting a global timeline during evaluation: data leakage and oversimplification of user preference modeling. Afterwards, we present new perspectives on recommender systems, including techniques for evaluating algorithm performance that more accurately reflect real-world scenarios, and possible approaches to consider decision contexts in user preference modeling.|推荐已经成为信息检索领域的一个重要研究领域。评价也是这个社区的一个传统研究课题。受近期研究中一些反直觉观察的启发，本文从评估的角度重新审视推荐系统。与其检查诸如召回率、命中率或 NDCG 之类的指标，或者新颖性和多样性之类的观点，这里的关键焦点是在评估推荐算法时如何计算这些指标。特别是，常用的列车/测试数据分割及其后果被重新检查。我们首先研究常见的数据分割方法，比如随机分割或者漏掉一个，然后讨论为什么在这样的分割下流行基线定义得很差。然后，我们继续探讨在评估过程中忽略全局时间线的两个含义: 数据泄漏和用户偏好建模的过度简化。随后，我们提出了推荐系统的新观点，包括评估算法性能的技术，更准确地反映真实世界的场景，以及可能的方法，以考虑决策上下文在用户偏好建模。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Take+a+Fresh+Look+at+Recommender+Systems+from+an+Evaluation+Standpoint)|0| -|[Where to Go Next for Recommender Systems? ID- vs. Modality-based Recommender Models Revisited](https://doi.org/10.1145/3539618.3591932)|Zheng Yuan, Fajie Yuan, Yu Song, Youhua Li, Junchen Fu, Fei Yang, Yunzhu Pan, Yongxin Ni|Zhejiang Lab, Hangzhou, China; Westlake University, Hangzhou, China|Recommendation models that utilize unique identities (IDs) to represent distinct users and items have been state-of-the-art (SOTA) and dominated the recommender systems (RS) literature for over a decade. Meanwhile, the pre-trained modality encoders, such as BERT and ViT, have become increasingly powerful in modeling the raw modality features of an item, such as text and images. Given this, a natural question arises: can a purely modality-based recommendation model (MoRec) outperforms or matches a pure ID-based model (IDRec) by replacing the itemID embedding with a SOTA modality encoder? In fact, this question was answered ten years ago when IDRec beats MoRec by a strong margin in both recommendation accuracy and efficiency. We aim to revisit this `old' question and systematically study MoRec from several aspects. Specifically, we study several sub-questions: (i) which recommendation paradigm, MoRec or IDRec, performs better in practical scenarios, especially in the general setting and warm item scenarios where IDRec has a strong advantage? does this hold for items with different modality features? (ii) can the latest technical advances from other communities (i.e., natural language processing and computer vision) translate into accuracy improvement for MoRec? (iii) how to effectively utilize item modality representation, can we use it directly or do we have to adjust it with new data? (iv) are there some key challenges for MoRec to be solved in practical applications? To answer them, we conduct rigorous experiments for item recommendations with two popular modalities, i.e., text and vision. We provide the first empirical evidence that MoRec is already comparable to its IDRec counterpart with an expensive end-to-end training method, even for warm item recommendation. Our results potentially imply that the dominance of IDRec in the RS field may be greatly challenged in the future.|推荐模型利用独特的身份(ID)来代表不同的用户和项目已经是最先进的(SOTA) ，并主导推荐系统(RS)文献超过十年。同时，经过训练的情态编码器，如 BERT 和 ViT，在对文本和图像等项目的原始情态特征进行建模方面已经变得越来越强大。鉴于此，一个自然的问题出现了: 通过用 SOTA 模式编码器替换嵌入的 itemID，纯基于模式的推荐模型(MoRec)是否优于或匹配纯基于 ID 的模型(IDRec) ？事实上，这个问题在十年前就得到了回答，当时 IDRec 在推荐的准确性和效率上都大大超过了 MoRec。我们的目标是重新审视这个“老”问题，并从几个方面系统地研究 MoRec。具体来说，我们研究了几个子问题: (i)哪个推荐范例，MoRec 或 IDRec，在实际场景中表现更好，特别是在一般设置和暖项目场景中，IDRec 有很强的优势？对于具有不同形态特征的物品是否适用？(ii)来自其他社区的最新技术进步(即自然语言处理和计算机视觉)能否转化为 MoRec 的准确性改进？(iii)如何有效地利用项目形态表征，是直接使用还是用新数据进行调整？(iv) MoRec 在实际应用中是否存在一些需要解决的关键挑战？为了回答这些问题，我们用两种流行的模式，即文本和视觉，对项目推荐进行了严格的实验。我们提供的第一个经验证明是，MoRec 已经可以与 IDrec 相媲美，它采用了一种昂贵的端到端培训方法，即使是暖项推荐也是如此。我们的研究结果可能意味着 IDRec 在遥感领域的主导地位在未来可能会受到很大的挑战。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Where+to+Go+Next+for+Recommender+Systems?+ID-+vs.+Modality-based+Recommender+Models+Revisited)|0| +|[Where to Go Next for Recommender Systems? ID- vs. Modality-based Recommender Models Revisited](https://doi.org/10.1145/3539618.3591932)|Zheng Yuan, Fajie Yuan, Yu Song, Youhua Li, Junchen Fu, Fei Yang, Yunzhu Pan, Yongxin Ni|Westlake University, Hangzhou, China; Zhejiang Lab, Hangzhou, China|Recommendation models that utilize unique identities (IDs) to represent distinct users and items have been state-of-the-art (SOTA) and dominated the recommender systems (RS) literature for over a decade. Meanwhile, the pre-trained modality encoders, such as BERT and ViT, have become increasingly powerful in modeling the raw modality features of an item, such as text and images. Given this, a natural question arises: can a purely modality-based recommendation model (MoRec) outperforms or matches a pure ID-based model (IDRec) by replacing the itemID embedding with a SOTA modality encoder? In fact, this question was answered ten years ago when IDRec beats MoRec by a strong margin in both recommendation accuracy and efficiency. We aim to revisit this `old' question and systematically study MoRec from several aspects. Specifically, we study several sub-questions: (i) which recommendation paradigm, MoRec or IDRec, performs better in practical scenarios, especially in the general setting and warm item scenarios where IDRec has a strong advantage? does this hold for items with different modality features? (ii) can the latest technical advances from other communities (i.e., natural language processing and computer vision) translate into accuracy improvement for MoRec? (iii) how to effectively utilize item modality representation, can we use it directly or do we have to adjust it with new data? (iv) are there some key challenges for MoRec to be solved in practical applications? To answer them, we conduct rigorous experiments for item recommendations with two popular modalities, i.e., text and vision. We provide the first empirical evidence that MoRec is already comparable to its IDRec counterpart with an expensive end-to-end training method, even for warm item recommendation. Our results potentially imply that the dominance of IDRec in the RS field may be greatly challenged in the future.|推荐模型利用独特的身份(ID)来代表不同的用户和项目已经是最先进的(SOTA) ，并主导推荐系统(RS)文献超过十年。同时，经过训练的情态编码器，如 BERT 和 ViT，在对文本和图像等项目的原始情态特征进行建模方面已经变得越来越强大。鉴于此，一个自然的问题出现了: 通过用 SOTA 模式编码器替换嵌入的 itemID，纯基于模式的推荐模型(MoRec)是否优于或匹配纯基于 ID 的模型(IDRec) ？事实上，这个问题在十年前就得到了回答，当时 IDRec 在推荐的准确性和效率上都大大超过了 MoRec。我们的目标是重新审视这个“老”问题，并从几个方面系统地研究 MoRec。具体来说，我们研究了几个子问题: (i)哪个推荐范例，MoRec 或 IDRec，在实际场景中表现更好，特别是在一般设置和暖项目场景中，IDRec 有很强的优势？对于具有不同形态特征的物品是否适用？(ii)来自其他社区的最新技术进步(即自然语言处理和计算机视觉)能否转化为 MoRec 的准确性改进？(iii)如何有效地利用项目形态表征，是直接使用还是用新数据进行调整？(iv) MoRec 在实际应用中是否存在一些需要解决的关键挑战？为了回答这些问题，我们用两种流行的模式，即文本和视觉，对项目推荐进行了严格的实验。我们提供的第一个经验证明是，MoRec 已经可以与 IDrec 相媲美，它采用了一种昂贵的端到端培训方法，即使是暖项推荐也是如此。我们的研究结果可能意味着 IDRec 在遥感领域的主导地位在未来可能会受到很大的挑战。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Where+to+Go+Next+for+Recommender+Systems?+ID-+vs.+Modality-based+Recommender+Models+Revisited)|0| |[How Important is Periodic Model update in Recommender System?](https://doi.org/10.1145/3539618.3591934)|Hyunsung Lee, Sungwook Yoo, Dongjun Lee, Jaekwang Kim|Sungkyunkwan University, Suwon, Republic of Korea; Kakao Corporation, Seongnam, Republic of Korea|In real-world recommender model deployments, the models are typically retrained and deployed repeatedly. It is the rule-of-thumb to periodically retrain recommender models to capture up-to-date user behavior and item trends. However, the harm caused by delayed model updates has not been investigated extensively yet. in this perspective paper, we formulate the delayed model update problem and quantitatively demonstrate the delayed model update actually harms the model performance by increasing the number of cold users and cold items increase and decreasing overall model performances. These effects vary across different domains having different characteristics. Upon these findings, we further argue that although the delayed model update has negative effects on online recommender model deployment, yet it has not gathered enough attention from research communities. We argue our verification of the relationship between the model update cycle and model performance calls for further research such as faster model training, and more efficient data pipelines to keep the model more up-to-date with the latest user behaviors and item trends.|在现实世界的推荐模型部署中，模型通常被重复训练和部署。经验法则是定期重新训练推荐模型，以捕获最新的用户行为和项目趋势。然而，延迟模型更新所造成的危害还没有得到广泛的研究。本文提出了模型延迟更新问题，定量地证明了模型延迟更新通过增加冷用户数量、增加冷条目、降低模型整体性能而对模型性能造成损害。这些效应在具有不同特征的不同领域中有所不同。基于这些发现，我们进一步认为，虽然延迟模型更新对在线推荐模型的部署有负面影响，但是它还没有引起研究界足够的重视。我们认为模型更新周期和模型性能之间的关系的验证需要进一步的研究，如更快的模型训练，更有效的数据管道，以保持模型更新与最新的用户行为和项目趋势。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=How+Important+is+Periodic+Model+update+in+Recommender+System?)|0| -|[Metric-agnostic Ranking Optimization](https://doi.org/10.1145/3539618.3591935)|Qingyao Ai, Xuanhui Wang, Michael Bendersky|Tsinghua University, Beijing, China; Google Research, Mountain View, CA, USA|Ranking is at the core of Information Retrieval. Classic ranking optimization studies often treat ranking as a sorting problem with the assumption that the best performance of ranking would be achieved if we rank items according to their individual utility. Accordingly, considerable ranking metrics have been developed and learning-to-rank algorithms that have been designed to optimize these simple performance metrics have been widely used in modern IR systems. As applications evolve, however, people's need for information retrieval have shifted from simply retrieving relevant documents to more advanced information services that satisfy their complex working and entertainment needs. Thus, more complicated and user-centric objectives such as user satisfaction and engagement have been adopted to evaluate modern IR systems today. Those objectives, unfortunately, are difficult to be optimized under existing learning-to-rank frameworks as they are subject to great variance and complicated structures that cannot be explicitly explained or formulated with math equations like those simple performance metrics. This leads to the following research question -- how to optimize result ranking for complex ranking metrics without knowing their internal structures? To address this question, we conduct formal analysis on the limitation of existing ranking optimization techniques and describe three research tasks in \textit{Metric-agnostic Ranking Optimization}. Through the discussion of potential solutions to these tasks, we hope to encourage more people to look into the problem of ranking optimization in complex search and recommendation scenarios.|排名是信息检索的核心。经典的排序优化研究往往把排序当作一个排序问题，假设如果我们根据项目的个别效用进行排序，就可以获得最佳的排序效果。因此，相当多的排序指标已经开发和学习到排序算法的设计，以优化这些简单的性能指标已被广泛应用于现代红外系统。然而，随着应用程序的发展，人们对信息检索的需求已经从简单地检索相关文档转向更先进的信息服务，以满足他们复杂的工作和娱乐需求。因此，更复杂和以用户为中心的目标，如用户满意度和参与度已被采用来评估现代 IR 系统。不幸的是，这些目标很难在现有的学习排名框架下进行优化，因为它们会受到巨大的变化和复杂的结构的影响，而这些变化和结构无法用简单的性能指标这样的数学方程来明确解释或表述。这就引出了下面的研究问题——如何在不知道其内部结构的情况下优化复杂排序指标的结果排序？为了解决这个问题，我们对现有排序优化技术的局限性进行了形式化分析，并描述了《度量无关排序优化》中的三个研究任务。通过讨论这些任务的潜在解决方案，我们希望鼓励更多的人研究复杂搜索和推荐场景中的排序优化问题。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Metric-agnostic+Ranking+Optimization)|0| -|[Recipe-MPR: A Test Collection for Evaluating Multi-aspect Preference-based Natural Language Retrieval](https://doi.org/10.1145/3539618.3591880)|Haochen Zhang, Anton Korikov, Parsa Farinneya, Mohammad Mahdi Abdollah Pour, Manasa Bharadwaj, Ali Pesaranghader, Xi Yu Huang, Yi Xin Lok, Zhaoqi Wang, Nathan Jones, Scott Sanner|University of Toronto & Vector Institute of Artificial Intelligence, Toronto, ON, Canada; University of Toronto, Toronto, ON, Canada; LG Electronics, Toronto AI Lab, Toronto, ON, Canada|The rise of interactive recommendation assistants has led to a novel domain of natural language (NL) recommendation that would benefit from improved multi-aspect reasoning to retrieve relevant items based on NL statements of preference. Such preference statements often involve multiple aspects, e.g., "I would like meat lasagna but I'm watching my weight". Unfortunately, progress in this domain is slowed by the lack of annotated data. To address this gap, we curate a novel dataset which captures logical reasoning over multi-aspect, NL preference-based queries and a set of multiple-choice, multi-aspect item descriptions. We focus on the recipe domain in which multi-aspect preferences are often encountered due to the complexity of the human diet. The goal of publishing our dataset is to provide a benchmark for joint progress in three key areas: 1) structured, multi-aspect NL reasoning with a variety of properties (e.g., level of specificity, presence of negation, and the need for commonsense, analogical, and/or temporal inference), 2) the ability of recommender systems to respond to NL preference utterances, and 3) explainable NL recommendation facilitated by aspect extraction and reasoning. We perform experiments using a variety of methods (sparse and dense retrieval, zero- and few-shot reasoning with large language models) in two settings: a monolithic setting which uses the full query and an aspect-based setting which isolates individual query aspects and aggregates the results. GPT-3 results in much stronger performance than other methods with 73% zero-shot accuracy and 83% few-shot accuracy in the monolithic setting. Aspect-based GPT-3, which facilitates structured explanations, also shows promise with 68% zero-shot accuracy. These results establish baselines for future research into explainable recommendations via multi-aspect preference-based NL reasoning.|交互式推荐助手的兴起导致了自然语言(NL)推荐的一个新领域，它将受益于改进的多方面推理来检索基于 NL 偏好语句的相关项目。这样的偏好陈述往往涉及多个方面，例如，“我想吃肉千层面，但我在控制体重”。不幸的是，由于缺乏注释数据，这个领域的进展缓慢。为了解决这个问题，我们建立了一个新的数据集，它能够捕获多方面的逻辑推理，基于 NL 偏好的查询，以及一组多选择、多方面的项目描述。我们关注的食谱领域，其中多方面的偏好往往遇到由于人类饮食的复杂性。发布我们的数据集的目标是为三个关键领域的联合进展提供基准: 1)具有各种属性的结构化，多方面的 NL 推理(例如，特异性水平，否定的存在，以及对常识，类比和/或时间推理的需要) ，2)推荐系统响应 NL 偏好话语的能力，和3)通过方面提取和推理促进的可解释的 NL 推荐。我们使用各种方法(稀疏和密集检索，零和大型语言模型的少镜头推理)在两种设置下进行实验: 一种是使用完整查询的整体设置，另一种是基于方面的设置，隔离各个查询方面并聚合结果。GPT-3比其他方法具有更强的性能，在单片机设置中，零拍准确率为73% ，少拍准确率为83% 。基于方面的 GPT-3促进了结构化解释，也显示出68% 的零拍准确率。这些结果为今后通过基于多方面偏好的 NL 推理研究可解释的建议奠定了基础。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Recipe-MPR:+A+Test+Collection+for+Evaluating+Multi-aspect+Preference-based+Natural+Language+Retrieval)|0| -|[The Information Retrieval Experiment Platform](https://doi.org/10.1145/3539618.3591888)|Maik Fröbe, Jan Heinrich Reimer, Sean MacAvaney, Niklas Deckers, Simon Reich, Janek Bevendorff, Benno Stein, Matthias Hagen, Martin Potthast|Leipzig University and ScaDS.AI, Leipzig, Germany; Bauhaus-Universität Weimar, Weimar, Germany; Friedrich-Schiller-Universität Jena, Jena, Germany; Leipzig University, Leipzig, Germany; University of Glasgow, Glasgow, United Kingdom|We integrate ir_datasets, ir_measures, and PyTerrier with TIRA in the Information Retrieval Experiment Platform (TIREx) to promote more standardized, reproducible, scalable, and even blinded retrieval experiments. Standardization is achieved when a retrieval approach implements PyTerrier's interfaces and the input and output of an experiment are compatible with ir_datasets and ir_measures. However, none of this is a must for reproducibility and scalability, as TIRA can run any dockerized software locally or remotely in a cloud-native execution environment. Version control and caching ensure efficient (re)execution. TIRA allows for blind evaluation when an experiment runs on a remote server or cloud not under the control of the experimenter. The test data and ground truth are then hidden from public access, and the retrieval software has to process them in a sandbox that prevents data leaks. We currently host an instance of TIREx with 15 corpora (1.9 billion documents) on which 32 shared retrieval tasks are based. Using Docker images of 50 standard retrieval approaches, we automatically evaluated all approaches on all tasks (50 $\cdot$ 32 = 1,600~runs) in less than a week on a midsize cluster (1,620 CPU cores and 24 GPUs). This instance of TIREx is open for submissions and will be integrated with the IR Anthology, as well as released open source.|我们在信息检索实验平台(TIREx)中整合了 ir _ data set，ir _ 径和 PyTerrier 与 TIRA，以促进更标准化、可重复、可扩展甚至盲法检索实验。当检索方法实现了 PyTerrier 的接口，并且实验的输入和输出与 ir _ data 集和 ir _ 径兼容时，就实现了标准化。然而，所有这些对于可重复性和可伸缩性来说都不是必须的，因为 TIRA 可以在本地或远程运行任何经过停靠的软件，在一个云本地执行环境中。版本控制和缓存确保有效(重新)执行。当实验在远程服务器或不受实验者控制的云上运行时，TIRA 允许盲目评估。然后，测试数据和地面真相被隐藏起来，不为公众所知，检索软件必须在防止数据泄露的沙箱中处理它们。我们目前托管了一个包含15个语料库(19亿个文档)的 TIREx 实例，其中有32个共享检索任务。使用50种标准检索方法的 Docker 图像，我们在不到一周的时间内在一个中型集群(1620个 CPU 核和24个 GPU)上对所有任务(50 $cdot $32 = 1600 ~ run)的所有方法进行自动评估。这个 TIREx 实例对提交者开放，并将与 IR 选集集成，以及发布的开放源码。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=The+Information+Retrieval+Experiment+Platform)|0| -|[RecStudio: Towards a Highly-Modularized Recommender System](https://doi.org/10.1145/3539618.3591894)|Defu Lian, Xu Huang, Xiaolong Chen, Jin Chen, Xingmei Wang, Yankai Wang, Haoran Jin, Rui Fan, Zheng Liu, Le Wu, Enhong Chen|Hefei University of Technology, Hefei, China; University of Science and Technology of China, Hefei, China; Huawei, Beijing, China; University of Electronics Science and Technology of China, Chengdu, China|A dozen recommendation libraries have recently been developed to accommodate popular recommendation algorithms for reproducibility. However, they are almost simply a collection of algorithms, overlooking the modularization of recommendation algorithms and their usage in practical scenarios. Algorithmic modularization has the following advantages: 1) helps to understand the effectiveness of each algorithm; 2) easily assembles new algorithms with well-performed modules by either drag-and-drop programming or automatic machine learning; 3) enables reinforcement between algorithms since one algorithm may act as a module of another algorithm. To this end, we develop a highly-modularized recommender system -- RecStudio, in which any recommendation algorithm is categorized into either a ranker or a retriever. In the RecStudio library, we implement 90 recommendation algorithms with the pure Pytorch, covering both common algorithms in other libraries and complex algorithms involving multiple recommendation models. RecStudio is featured from several perspectives, such as index-supported efficient recommendation and evaluation, GPU-accelerated negative sampling, hyperparameter learning on the validation, and cooperation between the retriever and ranker. RecStudio is also equipped with a web service, where the recommendation pipeline can be quickly established and visually evaluated on selected datasets, and the evaluation results are automatically archived and visualized in a leaderboard. The project and documents are released at http://recstudio.org.cn.|最近已经开发了十几个推荐库来适应流行的推荐算法以实现可重复性。然而，它们几乎只是算法的集合，忽略了推荐算法的模块化及其在实际场景中的应用。算法模块化具有以下优点: 1)有助于理解每个算法的有效性; 2)通过拖放编程或自动机器学习，可以轻松地将表现良好的模块与新算法组合在一起; 3)可以在算法之间进行强化，因为一个算法可以作为另一个算法的模块。为此，我们开发了一个高度模块化的推荐系统—— RecStudio，其中任何推荐算法都可以分为排名算法和检索算法。在 RecStudio 库中，我们使用纯 Python 实现了90个推荐算法，涵盖了其他库中的常见算法和涉及多个推荐模型的复杂算法。RecStudio 的特点主要体现在以下几个方面: 索引支持的高效推荐和评价、 GPU 加速的负抽样、验证过程中的超参数学习以及检索者与排名者之间的协作。RecStudio 还配备了一个网络服务，可以快速建立推荐渠道，并对选定的数据集进行可视化评估，评估结果将自动存档，并在排行榜上可视化。项目和文件将在 http://recstudio.org.cn 公布。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=RecStudio:+Towards+a+Highly-Modularized+Recommender+System)|0| -|[MR2: A Benchmark for Multimodal Retrieval-Augmented Rumor Detection in Social Media](https://doi.org/10.1145/3539618.3591896)|Xuming Hu, Zhijiang Guo, Junzhe Chen, Lijie Wen, Philip S. Yu|Tsinghua University, Beijing, China; University of Illinois at Chicago, Chicago, IL, USA; University of Cambridge, Cambridge, United Kingdom|As social media platforms are evolving from text-based forums into multi-modal environments, the nature of misinformation in social media is also transforming accordingly. Misinformation spreaders have recently targeted contextual connections between the modalities e.g., text and image. However, existing datasets for rumor detection mainly focus on a single modality i.e., text. To bridge this gap, we construct MR2, a multimodal multilingual retrieval-augmented dataset for rumor detection. The dataset covers rumors with images and texts, and provides evidence from both modalities that are retrieved from the Internet. Further, we develop established baselines and conduct a detailed analysis of the systems evaluated on the dataset. Extensive experiments show that MR2 will provide a challenging testbed for developing rumor detection systems designed to retrieve and reason over social media posts. Source code and data are available at: https://github.com/THU-BPM/MR2.|随着社交媒体平台从基于文本的论坛演变为多模式的环境，社交媒体中错误信息的性质也在相应地发生变化。错误信息传播者最近将目标对准了模式之间的上下文联系，例如文本和图像。然而，现有的谣言检测数据集主要集中在一个单一的模式，即文本。为了弥合这一差距，我们构建了 MR2，一个用于谣言检测的多模态多语言检索增强数据集。该数据集用图像和文本覆盖谣言，并提供从互联网检索到的两种形式的证据。此外，我们建立了基线，并对数据集上评估的系统进行了详细的分析。大量的实验表明，MR2将为开发旨在检索和推理社交媒体帖子的谣言检测系统提供一个具有挑战性的试验平台。源代码和数据可在以下 https://github.com/thu-bpm/mr2获得:。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=MR2:+A+Benchmark+for+Multimodal+Retrieval-Augmented+Rumor+Detection+in+Social+Media)|0| +|[Metric-agnostic Ranking Optimization](https://doi.org/10.1145/3539618.3591935)|Qingyao Ai, Xuanhui Wang, Michael Bendersky|Google Research, Mountain View, CA, USA; Tsinghua University, Beijing, China|Ranking is at the core of Information Retrieval. Classic ranking optimization studies often treat ranking as a sorting problem with the assumption that the best performance of ranking would be achieved if we rank items according to their individual utility. Accordingly, considerable ranking metrics have been developed and learning-to-rank algorithms that have been designed to optimize these simple performance metrics have been widely used in modern IR systems. As applications evolve, however, people's need for information retrieval have shifted from simply retrieving relevant documents to more advanced information services that satisfy their complex working and entertainment needs. Thus, more complicated and user-centric objectives such as user satisfaction and engagement have been adopted to evaluate modern IR systems today. Those objectives, unfortunately, are difficult to be optimized under existing learning-to-rank frameworks as they are subject to great variance and complicated structures that cannot be explicitly explained or formulated with math equations like those simple performance metrics. This leads to the following research question -- how to optimize result ranking for complex ranking metrics without knowing their internal structures? To address this question, we conduct formal analysis on the limitation of existing ranking optimization techniques and describe three research tasks in \textit{Metric-agnostic Ranking Optimization}. Through the discussion of potential solutions to these tasks, we hope to encourage more people to look into the problem of ranking optimization in complex search and recommendation scenarios.|排名是信息检索的核心。经典的排序优化研究往往把排序当作一个排序问题，假设如果我们根据项目的个别效用进行排序，就可以获得最佳的排序效果。因此，相当多的排序指标已经开发和学习到排序算法的设计，以优化这些简单的性能指标已被广泛应用于现代红外系统。然而，随着应用程序的发展，人们对信息检索的需求已经从简单地检索相关文档转向更先进的信息服务，以满足他们复杂的工作和娱乐需求。因此，更复杂和以用户为中心的目标，如用户满意度和参与度已被采用来评估现代 IR 系统。不幸的是，这些目标很难在现有的学习排名框架下进行优化，因为它们会受到巨大的变化和复杂的结构的影响，而这些变化和结构无法用简单的性能指标这样的数学方程来明确解释或表述。这就引出了下面的研究问题——如何在不知道其内部结构的情况下优化复杂排序指标的结果排序？为了解决这个问题，我们对现有排序优化技术的局限性进行了形式化分析，并描述了《度量无关排序优化》中的三个研究任务。通过讨论这些任务的潜在解决方案，我们希望鼓励更多的人研究复杂搜索和推荐场景中的排序优化问题。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Metric-agnostic+Ranking+Optimization)|0| +|[Recipe-MPR: A Test Collection for Evaluating Multi-aspect Preference-based Natural Language Retrieval](https://doi.org/10.1145/3539618.3591880)|Haochen Zhang, Anton Korikov, Parsa Farinneya, Mohammad Mahdi Abdollah Pour, Manasa Bharadwaj, Ali Pesaranghader, Xi Yu Huang, Yi Xin Lok, Zhaoqi Wang, Nathan Jones, Scott Sanner|LG Electronics, Toronto AI Lab, Toronto, ON, Canada; University of Toronto & Vector Institute of Artificial Intelligence, Toronto, ON, Canada; University of Toronto, Toronto, ON, Canada|The rise of interactive recommendation assistants has led to a novel domain of natural language (NL) recommendation that would benefit from improved multi-aspect reasoning to retrieve relevant items based on NL statements of preference. Such preference statements often involve multiple aspects, e.g., "I would like meat lasagna but I'm watching my weight". Unfortunately, progress in this domain is slowed by the lack of annotated data. To address this gap, we curate a novel dataset which captures logical reasoning over multi-aspect, NL preference-based queries and a set of multiple-choice, multi-aspect item descriptions. We focus on the recipe domain in which multi-aspect preferences are often encountered due to the complexity of the human diet. The goal of publishing our dataset is to provide a benchmark for joint progress in three key areas: 1) structured, multi-aspect NL reasoning with a variety of properties (e.g., level of specificity, presence of negation, and the need for commonsense, analogical, and/or temporal inference), 2) the ability of recommender systems to respond to NL preference utterances, and 3) explainable NL recommendation facilitated by aspect extraction and reasoning. We perform experiments using a variety of methods (sparse and dense retrieval, zero- and few-shot reasoning with large language models) in two settings: a monolithic setting which uses the full query and an aspect-based setting which isolates individual query aspects and aggregates the results. GPT-3 results in much stronger performance than other methods with 73% zero-shot accuracy and 83% few-shot accuracy in the monolithic setting. Aspect-based GPT-3, which facilitates structured explanations, also shows promise with 68% zero-shot accuracy. These results establish baselines for future research into explainable recommendations via multi-aspect preference-based NL reasoning.|交互式推荐助手的兴起导致了自然语言(NL)推荐的一个新领域，它将受益于改进的多方面推理来检索基于 NL 偏好语句的相关项目。这样的偏好陈述往往涉及多个方面，例如，“我想吃肉千层面，但我在控制体重”。不幸的是，由于缺乏注释数据，这个领域的进展缓慢。为了解决这个问题，我们建立了一个新的数据集，它能够捕获多方面的逻辑推理，基于 NL 偏好的查询，以及一组多选择、多方面的项目描述。我们关注的食谱领域，其中多方面的偏好往往遇到由于人类饮食的复杂性。发布我们的数据集的目标是为三个关键领域的联合进展提供基准: 1)具有各种属性的结构化，多方面的 NL 推理(例如，特异性水平，否定的存在，以及对常识，类比和/或时间推理的需要) ，2)推荐系统响应 NL 偏好话语的能力，和3)通过方面提取和推理促进的可解释的 NL 推荐。我们使用各种方法(稀疏和密集检索，零和大型语言模型的少镜头推理)在两种设置下进行实验: 一种是使用完整查询的整体设置，另一种是基于方面的设置，隔离各个查询方面并聚合结果。GPT-3比其他方法具有更强的性能，在单片机设置中，零拍准确率为73% ，少拍准确率为83% 。基于方面的 GPT-3促进了结构化解释，也显示出68% 的零拍准确率。这些结果为今后通过基于多方面偏好的 NL 推理研究可解释的建议奠定了基础。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Recipe-MPR:+A+Test+Collection+for+Evaluating+Multi-aspect+Preference-based+Natural+Language+Retrieval)|0| +|[The Information Retrieval Experiment Platform](https://doi.org/10.1145/3539618.3591888)|Maik Fröbe, Jan Heinrich Reimer, Sean MacAvaney, Niklas Deckers, Simon Reich, Janek Bevendorff, Benno Stein, Matthias Hagen, Martin Potthast|Leipzig University, Leipzig, Germany; University of Glasgow, Glasgow, United Kingdom; Leipzig University and ScaDS.AI, Leipzig, Germany; Friedrich-Schiller-Universität Jena, Jena, Germany; Bauhaus-Universität Weimar, Weimar, Germany|We integrate ir_datasets, ir_measures, and PyTerrier with TIRA in the Information Retrieval Experiment Platform (TIREx) to promote more standardized, reproducible, scalable, and even blinded retrieval experiments. Standardization is achieved when a retrieval approach implements PyTerrier's interfaces and the input and output of an experiment are compatible with ir_datasets and ir_measures. However, none of this is a must for reproducibility and scalability, as TIRA can run any dockerized software locally or remotely in a cloud-native execution environment. Version control and caching ensure efficient (re)execution. TIRA allows for blind evaluation when an experiment runs on a remote server or cloud not under the control of the experimenter. The test data and ground truth are then hidden from public access, and the retrieval software has to process them in a sandbox that prevents data leaks. We currently host an instance of TIREx with 15 corpora (1.9 billion documents) on which 32 shared retrieval tasks are based. Using Docker images of 50 standard retrieval approaches, we automatically evaluated all approaches on all tasks (50 $\cdot$ 32 = 1,600~runs) in less than a week on a midsize cluster (1,620 CPU cores and 24 GPUs). This instance of TIREx is open for submissions and will be integrated with the IR Anthology, as well as released open source.|我们在信息检索实验平台(TIREx)中整合了 ir _ data set，ir _ 径和 PyTerrier 与 TIRA，以促进更标准化、可重复、可扩展甚至盲法检索实验。当检索方法实现了 PyTerrier 的接口，并且实验的输入和输出与 ir _ data 集和 ir _ 径兼容时，就实现了标准化。然而，所有这些对于可重复性和可伸缩性来说都不是必须的，因为 TIRA 可以在本地或远程运行任何经过停靠的软件，在一个云本地执行环境中。版本控制和缓存确保有效(重新)执行。当实验在远程服务器或不受实验者控制的云上运行时，TIRA 允许盲目评估。然后，测试数据和地面真相被隐藏起来，不为公众所知，检索软件必须在防止数据泄露的沙箱中处理它们。我们目前托管了一个包含15个语料库(19亿个文档)的 TIREx 实例，其中有32个共享检索任务。使用50种标准检索方法的 Docker 图像，我们在不到一周的时间内在一个中型集群(1620个 CPU 核和24个 GPU)上对所有任务(50 $cdot $32 = 1600 ~ run)的所有方法进行自动评估。这个 TIREx 实例对提交者开放，并将与 IR 选集集成，以及发布的开放源码。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=The+Information+Retrieval+Experiment+Platform)|0| +|[RecStudio: Towards a Highly-Modularized Recommender System](https://doi.org/10.1145/3539618.3591894)|Defu Lian, Xu Huang, Xiaolong Chen, Jin Chen, Xingmei Wang, Yankai Wang, Haoran Jin, Rui Fan, Zheng Liu, Le Wu, Enhong Chen|Hefei University of Technology, Hefei, China; Huawei, Beijing, China; University of Science and Technology of China, Hefei, China; University of Electronics Science and Technology of China, Chengdu, China|A dozen recommendation libraries have recently been developed to accommodate popular recommendation algorithms for reproducibility. However, they are almost simply a collection of algorithms, overlooking the modularization of recommendation algorithms and their usage in practical scenarios. Algorithmic modularization has the following advantages: 1) helps to understand the effectiveness of each algorithm; 2) easily assembles new algorithms with well-performed modules by either drag-and-drop programming or automatic machine learning; 3) enables reinforcement between algorithms since one algorithm may act as a module of another algorithm. To this end, we develop a highly-modularized recommender system -- RecStudio, in which any recommendation algorithm is categorized into either a ranker or a retriever. In the RecStudio library, we implement 90 recommendation algorithms with the pure Pytorch, covering both common algorithms in other libraries and complex algorithms involving multiple recommendation models. RecStudio is featured from several perspectives, such as index-supported efficient recommendation and evaluation, GPU-accelerated negative sampling, hyperparameter learning on the validation, and cooperation between the retriever and ranker. RecStudio is also equipped with a web service, where the recommendation pipeline can be quickly established and visually evaluated on selected datasets, and the evaluation results are automatically archived and visualized in a leaderboard. The project and documents are released at http://recstudio.org.cn.|最近已经开发了十几个推荐库来适应流行的推荐算法以实现可重复性。然而，它们几乎只是算法的集合，忽略了推荐算法的模块化及其在实际场景中的应用。算法模块化具有以下优点: 1)有助于理解每个算法的有效性; 2)通过拖放编程或自动机器学习，可以轻松地将表现良好的模块与新算法组合在一起; 3)可以在算法之间进行强化，因为一个算法可以作为另一个算法的模块。为此，我们开发了一个高度模块化的推荐系统—— RecStudio，其中任何推荐算法都可以分为排名算法和检索算法。在 RecStudio 库中，我们使用纯 Python 实现了90个推荐算法，涵盖了其他库中的常见算法和涉及多个推荐模型的复杂算法。RecStudio 的特点主要体现在以下几个方面: 索引支持的高效推荐和评价、 GPU 加速的负抽样、验证过程中的超参数学习以及检索者与排名者之间的协作。RecStudio 还配备了一个网络服务，可以快速建立推荐渠道，并对选定的数据集进行可视化评估，评估结果将自动存档，并在排行榜上可视化。项目和文件将在 http://recstudio.org.cn 公布。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=RecStudio:+Towards+a+Highly-Modularized+Recommender+System)|0| +|[MR2: A Benchmark for Multimodal Retrieval-Augmented Rumor Detection in Social Media](https://doi.org/10.1145/3539618.3591896)|Xuming Hu, Zhijiang Guo, Junzhe Chen, Lijie Wen, Philip S. Yu|University of Cambridge, Cambridge, United Kingdom; Tsinghua University, Beijing, China; University of Illinois at Chicago, Chicago, IL, USA|As social media platforms are evolving from text-based forums into multi-modal environments, the nature of misinformation in social media is also transforming accordingly. Misinformation spreaders have recently targeted contextual connections between the modalities e.g., text and image. However, existing datasets for rumor detection mainly focus on a single modality i.e., text. To bridge this gap, we construct MR2, a multimodal multilingual retrieval-augmented dataset for rumor detection. The dataset covers rumors with images and texts, and provides evidence from both modalities that are retrieved from the Internet. Further, we develop established baselines and conduct a detailed analysis of the systems evaluated on the dataset. Extensive experiments show that MR2 will provide a challenging testbed for developing rumor detection systems designed to retrieve and reason over social media posts. Source code and data are available at: https://github.com/THU-BPM/MR2.|随着社交媒体平台从基于文本的论坛演变为多模式的环境，社交媒体中错误信息的性质也在相应地发生变化。错误信息传播者最近将目标对准了模式之间的上下文联系，例如文本和图像。然而，现有的谣言检测数据集主要集中在一个单一的模式，即文本。为了弥合这一差距，我们构建了 MR2，一个用于谣言检测的多模态多语言检索增强数据集。该数据集用图像和文本覆盖谣言，并提供从互联网检索到的两种形式的证据。此外，我们建立了基线，并对数据集上评估的系统进行了详细的分析。大量的实验表明，MR2将为开发旨在检索和推理社交媒体帖子的谣言检测系统提供一个具有挑战性的试验平台。源代码和数据可在以下 https://github.com/thu-bpm/mr2获得:。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=MR2:+A+Benchmark+for+Multimodal+Retrieval-Augmented+Rumor+Detection+in+Social+Media)|0| |[RL4RS: A Real-World Dataset for Reinforcement Learning based Recommender System](https://doi.org/10.1145/3539618.3591899)|Kai Wang, Zhene Zou, Minghao Zhao, Qilin Deng, Yue Shang, Yile Liang, Runze Wu, Xudong Shen, Tangjie Lyu, Changjie Fan|NetEase Fuxi AI Lab, Hangzhou, China|Reinforcement learning based recommender systems (RL-based RS) aim at learning a good policy from a batch of collected data, by casting recommendations to multi-step decision-making tasks. However, current RL-based RS research commonly has a large reality gap. In this paper, we introduce the first open-source real-world dataset, RL4RS, hoping to replace the artificial datasets and semi-simulated RS datasets previous studies used due to the resource limitation of the RL-based RS domain. Unlike academic RL research, RL-based RS suffers from the difficulties of being well-validated before deployment. We attempt to propose a new systematic evaluation framework, including evaluation of environment simulation, evaluation on environments, counterfactual policy evaluation, and evaluation on environments built from test set. In summary, the RL4RS (Reinforcement Learning for Recommender Systems), a new resource with special concerns on the reality gaps, contains two real-world datasets, data understanding tools, tuned simulation environments, related advanced RL baselines, batch RL baselines, and counterfactual policy evaluation algorithms. The RL4RS suite can be found at https://github.com/fuxiAIlab/RL4RS. In addition to the RL-based recommender systems, we expect the resource to contribute to research in applied reinforcement learning.|基于强化学习的推荐系统(RL-based RS)旨在从一批收集到的数据中学习一个好的政策，将推荐应用到多步决策任务中。然而，目前基于 RL 的遥感研究普遍存在较大的现实差距。本文介绍了第一个开源的真实世界数据集 RL4RS，希望能够取代以往由于基于 RL 的 RS 域资源有限而使用的人工数据集和半模拟 RS 数据集。与学术上的 RL 研究不同，基于 RL 的 RS 在部署前很难得到很好的验证。本文尝试提出一个新的系统评价框架，包括环境模拟评价、环境评价、反事实政策评价和基于测试集的环境评价。总之，RL4RS (推荐系统的强化学习) ，一个特别关注现实差距的新资源，包含两个真实世界的数据集，数据理解工具，调优的仿真环境，相关的高级 RL 基线，批量 RL 基线，和反事实政策评估算法。Rl4RS 套房可在 https://github.com/fuxiailab/RL4RS 找到。除了基于 RL 的推荐系统，我们希望这些资源能够为应用强化学习的研究做出贡献。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=RL4RS:+A+Real-World+Dataset+for+Reinforcement+Learning+based+Recommender+System)|0| |[iQPP: A Benchmark for Image Query Performance Prediction](https://doi.org/10.1145/3539618.3591901)|Eduard Poesina, Radu Tudor Ionescu, Josiane Mothe|INSPE, IRIT UMR5505 CNRS, Université Toulouse Jean-Jaurès, Toulouse, France; University of Bucharest, Bucharest, Romania|To date, query performance prediction (QPP) in the context of content-based image retrieval remains a largely unexplored task, especially in the query-by-example scenario, where the query is an image. To boost the exploration of the QPP task in image retrieval, we propose the first benchmark for image query performance prediction (iQPP). First, we establish a set of four data sets (PASCAL VOC 2012, Caltech-101, ROxford5k and RParis6k) and estimate the ground-truth difficulty of each query as the average precision or the precision@k, using two state-of-the-art image retrieval models. Next, we propose and evaluate novel pre-retrieval and post-retrieval query performance predictors, comparing them with existing or adapted (from text to image) predictors. The empirical results show that most predictors do not generalize across evaluation scenarios. Our comprehensive experiments indicate that iQPP is a challenging benchmark, revealing an important research gap that needs to be addressed in future work. We release our code and data as open source at https://github.com/Eduard6421/iQPP, to foster future research.|迄今为止，在基于内容的图像检索上下文中的查询性能预测(QPP)仍然是一个很大程度上尚未探索的任务，特别是在逐例查询场景中，其中查询是一个图像。为了促进 QPP 任务在图像检索中的应用，我们提出了第一个图像查询性能预测基准(iQPP)。首先，我们建立了一组四个数据集(PASCAL VOC 2012，Caltech-101，ROxford5k 和 RParis6k) ，并使用两种最先进的图像检索模型将每个查询的地面真实度难度估计为平均精度或精度@k。接下来，我们提出并评估新的检索前和检索后的查询性能预测因子，并将它们与现有的或改编的(从文本到图像)预测因子进行比较。经验结果表明，大多数预测因素并不能一概而论。我们的全面实验表明，iQPP 是一个具有挑战性的基准，揭示了一个重要的研究差距，需要在未来的工作中解决。我们将代码和数据作为开源 https://github.com/eduard6421/iqpp 发布，以促进未来的研究。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=iQPP:+A+Benchmark+for+Image+Query+Performance+Prediction)|0| |[SPRINT: A Unified Toolkit for Evaluating and Demystifying Zero-shot Neural Sparse Retrieval](https://doi.org/10.1145/3539618.3591902)|Nandan Thakur, Kexin Wang, Iryna Gurevych, Jimmy Lin|University of Waterloo, Waterloo, ON, Canada; Technical University of Darmstadt, Darmstadt, Germany|Traditionally, sparse retrieval systems relied on lexical representations to retrieve documents, such as BM25, dominated information retrieval tasks. With the onset of pre-trained transformer models such as BERT, neural sparse retrieval has led to a new paradigm within retrieval. Despite the success, there has been limited software supporting different sparse retrievers running in a unified, common environment. This hinders practitioners from fairly comparing different sparse models and obtaining realistic evaluation results. Another missing piece is, that a majority of prior work evaluates sparse retrieval models on in-domain retrieval, i.e. on a single dataset: MS MARCO. However, a key requirement in practical retrieval systems requires models that can generalize well to unseen out-of-domain, i.e. zero-shot retrieval tasks. In this work, we provide SPRINT, a unified Python toolkit based on Pyserini and Lucene, supporting a common interface for evaluating neural sparse retrieval. The toolkit currently includes five built-in models: uniCOIL, DeepImpact, SPARTA, TILDEv2 and SPLADEv2. Users can also easily add customized models by defining their term weighting method. Using our toolkit, we establish strong and reproducible zero-shot sparse retrieval baselines across the well-acknowledged benchmark, BEIR. Our results demonstrate that SPLADEv2 achieves the best average score of 0.470 nDCG@10 on BEIR amongst all neural sparse retrievers. In this work, we further uncover the reasons behind its performance gain. We show that SPLADEv2 produces sparse representations with a majority of tokens outside of the original query and document which is often crucial for its performance gains, i.e. a limitation among its other sparse counterparts. We provide our SPRINT toolkit, models, and data used in our experiments publicly here at https://github.com/thakur-nandan/sprint.|传统上，稀疏检索系统依赖于词汇表示来检索文档，比如 BM25，这是主要的信息检索任务。随着像 BERT 这样的预训练变压器模型的出现，神经元稀疏检索在检索中引入了一种新的范式。尽管取得了成功，但支持不同稀疏检索器在统一、通用环境中运行的软件有限。这妨碍了从业人员公平地比较不同的稀疏模型和获得现实的评估结果。另一个缺失的部分是，大多数先前的工作评估稀疏的检索模型在域内检索，即在一个单一的数据集: MS MARCO。然而，实际检索系统中的一个关键要求是模型能够很好地推广到看不见的域外，即零镜头检索任务。在这项工作中，我们提供了 SPRINT，一个基于 Pyserini 和 Lucene 的统一的 Python 工具包，支持一个评估神经稀疏检索的通用接口。该工具包目前包括五个内置模型: uniCOIL、 DeepImpact、 SPARTA、 TILDEv2和 SPLADEv2。用户还可以通过定义术语加权方法轻松添加自定义模型。使用我们的工具包，我们建立强大的和可重现的零拍稀疏检索基线跨公认的基准，BEIR。结果表明，在所有神经稀疏检索器中，SPLADEv2在 BEIR 上平均得分最高，为0.470 nDCG@10。在这项工作中，我们进一步揭示背后的原因，其性能增益。我们展示了 SPLADEv2在原始查询和文档之外使用大部分令牌产生稀疏表示，这对其性能提高通常是至关重要的，即其他稀疏对应方之间的一个限制。我们提供了我们的 SPRINT 工具包，模型和数据用于我们的实验公开在这里的 https://github.com/thakur-nandan/SPRINT。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=SPRINT:+A+Unified+Toolkit+for+Evaluating+and+Demystifying+Zero-shot+Neural+Sparse+Retrieval)|0| -|[AToMiC: An Image/Text Retrieval Test Collection to Support Multimedia Content Creation](https://doi.org/10.1145/3539618.3591903)|JhengHong Yang, Carlos Lassance, Rafael Sampaio de Rezende, Krishna Srinivasan, Miriam Redi, Stéphane Clinchant, Jimmy Lin|Google Research, San Francisco, CA, USA; Naver Labs Europe, Grenoble, France; Wikimedia Foundation, London, United Kingdom; University of Waterloo, Waterloo, Canada|This paper presents the AToMiC (Authoring Tools for Multimedia Content) dataset, designed to advance research in image/text cross-modal retrieval. While vision-language pretrained transformers have led to significant improvements in retrieval effectiveness, existing research has relied on image-caption datasets that feature only simplistic image-text relationships and underspecified user models of retrieval tasks. To address the gap between these oversimplified settings and real-world applications for multimedia content creation, we introduce a new approach for building retrieval test collections. We leverage hierarchical structures and diverse domains of texts, styles, and types of images, as well as large-scale image-document associations embedded in Wikipedia. We formulate two tasks based on a realistic user model and validate our dataset through retrieval experiments using baseline models. AToMiC offers a testbed for scalable, diverse, and reproducible multimedia retrieval research. Finally, the dataset provides the basis for a dedicated track at the 2023 Text Retrieval Conference (TREC), and is publicly available at https://github.com/TREC-AToMiC/AToMiC.|本文介绍了 AToMiC (多媒体内容创作工具)数据集，旨在促进图像/文本跨模式检索的研究。虽然视觉语言预先训练的转换器已经导致检索效率的显著提高，但现有的研究依赖于图像标题数据集，这些数据集只具有简单的图像-文本关系和检索任务的用户模型不明确的特点。为了解决这些过于简化的设置与多媒体内容创建的现实应用程序之间的差距，我们引入了一种构建检索测试集的新方法。我们利用层次结构和文本、风格和图像类型的不同领域，以及嵌入在 Wikipedia 中的大规模图像-文档关联。我们基于一个真实的用户模型制定两个任务，并通过基线模型的检索实验验证我们的数据集。AToMiC 为可扩展、多样化和可重复的多媒体检索研究提供了一个试验平台。最后，该数据集为2023年文本检索会议(TREC)的专门跟踪提供了基础，并且可以在 https://github.com/TREC-atomic/atomic 上公开获得。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=AToMiC:+An+Image/Text+Retrieval+Test+Collection+to+Support+Multimedia+Content+Creation)|0| +|[AToMiC: An Image/Text Retrieval Test Collection to Support Multimedia Content Creation](https://doi.org/10.1145/3539618.3591903)|JhengHong Yang, Carlos Lassance, Rafael Sampaio de Rezende, Krishna Srinivasan, Miriam Redi, Stéphane Clinchant, Jimmy Lin|Wikimedia Foundation, London, United Kingdom; University of Waterloo, Waterloo, Canada; Google Research, San Francisco, CA, USA; Naver Labs Europe, Grenoble, France|This paper presents the AToMiC (Authoring Tools for Multimedia Content) dataset, designed to advance research in image/text cross-modal retrieval. While vision-language pretrained transformers have led to significant improvements in retrieval effectiveness, existing research has relied on image-caption datasets that feature only simplistic image-text relationships and underspecified user models of retrieval tasks. To address the gap between these oversimplified settings and real-world applications for multimedia content creation, we introduce a new approach for building retrieval test collections. We leverage hierarchical structures and diverse domains of texts, styles, and types of images, as well as large-scale image-document associations embedded in Wikipedia. We formulate two tasks based on a realistic user model and validate our dataset through retrieval experiments using baseline models. AToMiC offers a testbed for scalable, diverse, and reproducible multimedia retrieval research. Finally, the dataset provides the basis for a dedicated track at the 2023 Text Retrieval Conference (TREC), and is publicly available at https://github.com/TREC-AToMiC/AToMiC.|本文介绍了 AToMiC (多媒体内容创作工具)数据集，旨在促进图像/文本跨模式检索的研究。虽然视觉语言预先训练的转换器已经导致检索效率的显著提高，但现有的研究依赖于图像标题数据集，这些数据集只具有简单的图像-文本关系和检索任务的用户模型不明确的特点。为了解决这些过于简化的设置与多媒体内容创建的现实应用程序之间的差距，我们引入了一种构建检索测试集的新方法。我们利用层次结构和文本、风格和图像类型的不同领域，以及嵌入在 Wikipedia 中的大规模图像-文档关联。我们基于一个真实的用户模型制定两个任务，并通过基线模型的检索实验验证我们的数据集。AToMiC 为可扩展、多样化和可重复的多媒体检索研究提供了一个试验平台。最后，该数据集为2023年文本检索会议(TREC)的专门跟踪提供了基础，并且可以在 https://github.com/TREC-atomic/atomic 上公开获得。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=AToMiC:+An+Image/Text+Retrieval+Test+Collection+to+Support+Multimedia+Content+Creation)|0| |[MobileRec: A Large Scale Dataset for Mobile Apps Recommendation](https://doi.org/10.1145/3539618.3591906)|Muhammad Hasan Maqbool, Umar Farooq, Adib Mosharrof, A. B. Siddique, Hassan Foroosh||Recommender systems have become ubiquitous in our digital lives, from recommending products on e-commerce websites to suggesting movies and music on streaming platforms. Existing recommendation datasets, such as Amazon Product Reviews and MovieLens, greatly facilitated the research and development of recommender systems in their respective domains. While the number of mobile users and applications (aka apps) has increased exponentially over the past decade, research in mobile app recommender systems has been significantly constrained, primarily due to the lack of high-quality benchmark datasets, as opposed to recommendations for products, movies, and news. To facilitate research for app recommendation systems, we introduce a large-scale dataset, called MobileRec. We constructed MobileRec from users' activity on the Google play store. MobileRec contains 19.3 million user interactions (i.e., user reviews on apps) with over 10K unique apps across 48 categories. MobileRec records the sequential activity of a total of 0.7 million distinct users. Each of these users has interacted with no fewer than five distinct apps, which stands in contrast to previous datasets on mobile apps that recorded only a single interaction per user. Furthermore, MobileRec presents users' ratings as well as sentiments on installed apps, and each app contains rich metadata such as app name, category, description, and overall rating, among others. We demonstrate that MobileRec can serve as an excellent testbed for app recommendation through a comparative study of several state-of-the-art recommendation approaches. The quantitative results can act as a baseline for other researchers to compare their results against. The MobileRec dataset is available at https://huggingface.co/datasets/recmeapp/mobilerec.|在我们的数字生活中，推荐系统已经变得无处不在，从在电子商务网站上推荐产品到在流媒体平台上推荐电影和音乐。现有的推荐数据集，如 Amazon Product Reviews 和 MovieLens，极大地促进了各自领域推荐系统的研究和开发。虽然移动用户和应用程序(又名应用程序)的数量在过去十年中呈指数级增长，但是对移动应用程序推荐系统的研究受到了严重的限制，主要是由于缺乏高质量的基准数据集，而不是对产品、电影和新闻的推荐。为了促进应用程序推荐系统的研究，我们引入了一个大规模的数据集，称为 MobileRec。我们根据用户在 Google 游戏商店的活动构建了 MobileRec。MobileRec 包含1930万用户交互(例如，应用程序上的用户评论) ，有超过10000个独特的应用程序，分为48个类别。MobileRec 记录了总共70万不同用户的连续活动。这些用户中的每一个都与不少于5个不同的应用程序进行了交互，这与之前的移动应用程序数据集形成了鲜明对比，之前的数据集只记录了每个用户的一次交互。此外，MobileRec 还提供了用户的评分以及对已安装应用程序的看法，每个应用程序都包含丰富的元数据，如应用程序名称、类别、描述和总体评分等。我们通过对几种最先进的推荐方法的比较研究，证明 MobileRec 可以作为一个优秀的应用程序推荐试验台。定量的结果可以作为其他研究人员比较他们的结果的基准。MobileRec 数据集可在 https://huggingface.co/datasets/recmeapp/MobileRec 下载。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=MobileRec:+A+Large+Scale+Dataset+for+Mobile+Apps+Recommendation)|0| |[LibVQ: A Toolkit for Optimizing Vector Quantization and Efficient Neural Retrieval](https://doi.org/10.1145/3539618.3591799)|Chaofan Li, Zheng Liu, Shitao Xiao, Yingxia Shao, Defu Lian, Zhao Cao|Huawei, Beijing, China|Vector quantization is one of the critical techniques which enables dense retrieval for realtime applications. The recent study shows that vanilla vector quantization methods, like those implemented by FAISS [8], are lossy and prone to limited retrieval performances when large acceleration ratios are needed [14, 16, 18]. Besides, there have also been multiple algorithms which make the retriever and VQ better collaborated to alleviate such a loss. On top of these progresses, we develop LibVQ, which optimizes vector quantization for efficient dense retrieval. Our toolkit is highlighted for three advantages. 1. Effectiveness. The retrieval quality can be substantially improved over the vanilla implementations of VQ. 2. Simplicity. The optimization can be conducted in a lowcode fashion, and the optimization results can be easily loaded to ANN indexes to support downstream applications. 3. Universality. The optimization is agnostic to the embedding's learning process, and may accommodate different input conditions and ANN back-ends with little modification of the workflow. LibVQ may also support rich applications beyond dense retrieval, e.g., embedding compression, topic modeling, and de-duplication. In this demo, we provide comprehensive hand-on examples and evaluations for LibVQ. The toolkit is publicly released at: https://github.com/staoxiao/LibVQ/tree/demo.|向量量化检索是实现实时应用密集检索的关键技术之一。最近的研究表明，香草向量量化方法，就像 FAISS [8]实现的那样，是有损耗的，当需要大的加速比时，容易受到有限的检索性能的限制[14,16,18]。此外，还有多种算法可以使检索器和 VQ 更好地协作，以减轻这种损失。在这些进展的基础上，我们开发了 libvQ，它可以优化向量量化，实现高效的密集检索。我们的工具包突出显示了三个优点。1.有效性。与普通的 VQ 实现相比，可以大大提高检索质量。2.简单。优化可以以低代码的方式进行，优化结果可以很容易地加载到 ANN 索引以支持下游应用程序。3.普遍性。该优化算法对嵌入式的学习过程是不可知的，可以适应不同的输入条件和神经网络后端，对工作流的修改很少。除了密集检索之外，LibVQ 还可以支持丰富的应用程序，例如嵌入式压缩、主题建模和去复制。在这个演示中，我们为 LibVQ 提供了全面的实际例子和评估。该工具包在以下 https://github.com/staoxiao/libvq/tree/demo 公开发布:。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=LibVQ:+A+Toolkit+for+Optimizing+Vector+Quantization+and+Efficient+Neural+Retrieval)|0| -|[VoMBaT: A Tool for Visualising Evaluation Measure Behaviour in High-Recall Search Tasks](https://doi.org/10.1145/3539618.3591802)|Wojciech Kusa, Aldo Lipani, Petr Knoth, Allan Hanbury|TU Wien, Vienna, Austria; The Open University, Milton Keynes, United Kingdom; University College London, London, United Kingdom|The objective of High-Recall Information Retrieval (HRIR) is to retrieve as many relevant documents as possible for a given search topic. One approach to HRIR is Technology-Assisted Review (TAR), which uses information retrieval and machine learning techniques to aid the review of large document collections. TAR systems are commonly used in legal eDiscovery and systematic literature reviews. Successful TAR systems are able to find the majority of relevant documents using the least number of assessments. Commonly used retrospective evaluation assumes that the system achieves a specific, fixed recall level first, and then measures the precision or work saved (e.g., precision at r% recall). This approach can cause problems related to understanding the behaviour of evaluation measures in a fixed recall setting. It is also problematic when estimating time and money savings during technology-assisted reviews. This paper presents a new visual analytics tool to explore the dynamics of evaluation measures depending on recall level. We implemented 18 evaluation measures based on the confusion matrix terms, both from general IR tasks and specific to TAR. The tool allows for a comparison of the behaviour of these measures in a fixed recall evaluation setting. It can also simulate savings in time and money and a count of manual vs automatic assessments for different datasets depending on the model quality. The tool is open-source, and the demo is available under the following URL: https://vombat.streamlit.app.|高召回信息检索(HRIR)的目的是为一个给定的搜索主题检索尽可能多的相关文档。HRIR 的一种方法是技术辅助审查(technology-Assistant Review，TAR) ，它使用信息检索和机器学习技术来帮助审查大型文档集合。TAR 系统通常用于法律电子发现和系统文献综述。成功的第三次评估系统能够使用最少的评估数量找到大多数相关文件。常用的回顾性评估假设系统首先达到一个特定的、固定的召回水平，然后测量精度或节省的工作(例如，在 r% 召回时的精度)。这种方法可能会导致与理解固定召回环境下评估措施的行为有关的问题。在技术辅助审查期间估计节省的时间和金钱也是有问题的。本文提出了一种新的可视化分析工具，以探索评估措施的动态性取决于召回水平。我们实施了18项基于混淆矩阵的评估措施，包括一般 IR 任务和特定于 TAR 的评估措施。该工具允许在一个固定的召回评估环境中比较这些措施的行为。它还可以模拟节省时间和金钱，以及根据模型质量对不同数据集进行人工评估和自动评估的次数。该工具是开源的，演示可以通过以下网址获得: https://vombat.streamlit.app。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=VoMBaT:+A+Tool+for+Visualising+Evaluation+Measure+Behaviour+in+High-Recall+Search+Tasks)|0| +|[VoMBaT: A Tool for Visualising Evaluation Measure Behaviour in High-Recall Search Tasks](https://doi.org/10.1145/3539618.3591802)|Wojciech Kusa, Aldo Lipani, Petr Knoth, Allan Hanbury|University College London, London, United Kingdom; The Open University, Milton Keynes, United Kingdom; TU Wien, Vienna, Austria|The objective of High-Recall Information Retrieval (HRIR) is to retrieve as many relevant documents as possible for a given search topic. One approach to HRIR is Technology-Assisted Review (TAR), which uses information retrieval and machine learning techniques to aid the review of large document collections. TAR systems are commonly used in legal eDiscovery and systematic literature reviews. Successful TAR systems are able to find the majority of relevant documents using the least number of assessments. Commonly used retrospective evaluation assumes that the system achieves a specific, fixed recall level first, and then measures the precision or work saved (e.g., precision at r% recall). This approach can cause problems related to understanding the behaviour of evaluation measures in a fixed recall setting. It is also problematic when estimating time and money savings during technology-assisted reviews. This paper presents a new visual analytics tool to explore the dynamics of evaluation measures depending on recall level. We implemented 18 evaluation measures based on the confusion matrix terms, both from general IR tasks and specific to TAR. The tool allows for a comparison of the behaviour of these measures in a fixed recall evaluation setting. It can also simulate savings in time and money and a count of manual vs automatic assessments for different datasets depending on the model quality. The tool is open-source, and the demo is available under the following URL: https://vombat.streamlit.app.|高召回信息检索(HRIR)的目的是为一个给定的搜索主题检索尽可能多的相关文档。HRIR 的一种方法是技术辅助审查(technology-Assistant Review，TAR) ，它使用信息检索和机器学习技术来帮助审查大型文档集合。TAR 系统通常用于法律电子发现和系统文献综述。成功的第三次评估系统能够使用最少的评估数量找到大多数相关文件。常用的回顾性评估假设系统首先达到一个特定的、固定的召回水平，然后测量精度或节省的工作(例如，在 r% 召回时的精度)。这种方法可能会导致与理解固定召回环境下评估措施的行为有关的问题。在技术辅助审查期间估计节省的时间和金钱也是有问题的。本文提出了一种新的可视化分析工具，以探索评估措施的动态性取决于召回水平。我们实施了18项基于混淆矩阵的评估措施，包括一般 IR 任务和特定于 TAR 的评估措施。该工具允许在一个固定的召回评估环境中比较这些措施的行为。它还可以模拟节省时间和金钱，以及根据模型质量对不同数据集进行人工评估和自动评估的次数。该工具是开源的，演示可以通过以下网址获得: https://vombat.streamlit.app。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=VoMBaT:+A+Tool+for+Visualising+Evaluation+Measure+Behaviour+in+High-Recall+Search+Tasks)|0| |[Searching the ACL Anthology with Math Formulas and Text](https://doi.org/10.1145/3539618.3591803)|Bryan Amador, Matt Langsenkamp, Abhisek Dey, Ayush Kumar Shah, Richard Zanibbi|Rochester Institute of Technology, Rochester, NY, USA|Mathematical notation is a key analytical resource for science and technology. Unfortunately, current math-aware search engines require LATEX or template palettes to construct formulas, which can be challenging for non-experts. Also, their indexed collections are primarily web pages where formulas are represented explicitly in machine-readable formats (e.g., LATEX, Presentation MathML). The new MathDeck system searches PDF documents in a portion of the ACL Anthology using both formulas and text, and shows matched words and formulas along with other extracted formulas in-context. In PDF, formulas are not demarcated: a new indexing module extracts formulas using PDF vector graphics information and computer vision techniques. For non-expert users and visual editing, a central design feature of MathDeck's interface is formula 'chips' usable in formula creation, search, reuse, and annotation with titles and descriptions in cards. For experts, LATEX is supported in the text query box and the visual formula editor. MathDeck is open-source, and our demo is available online.|数学符号是科学和技术的重要分析资源。不幸的是，目前的数学感知搜索引擎需要 LATEX 或模板调色板来构造公式，这对非专业人士可能是一个挑战。此外，他们的索引集合主要是网页，其中公式以机器可读的格式显式表示(例如，LATEX，PresentationMathML)。新的 MathDeck 系统使用公式和文本在 ACL 选集的一部分中搜索 PDF 文档，并在上下文中显示匹配的单词和公式以及其他提取的公式。在 PDF 中，公式没有标定: 一个新的索引模块利用 PDF 矢量图形信息和计算机视觉技术提取公式。对于非专业用户和可视化编辑，MathDeck 界面的一个核心设计特征是可用于公式创建、搜索、重用和标题和卡片描述注释的公式“芯片”。对于专家来说，在文本查询框和可视化公式编辑器中支持 LATEX。MathDeck 是开源的，我们的演示可以在线获得。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Searching+the+ACL+Anthology+with+Math+Formulas+and+Text)|0| |[Tevatron: An Efficient and Flexible Toolkit for Neural Retrieval](https://doi.org/10.1145/3539618.3591805)|Luyu Gao, Xueguang Ma, Jimmy Lin, Jamie Callan|University of Waterloo, Waterloo, ON, Canada; Carnegie Mellon University, Pittsburgh, PA, USA|Recent rapid advances in deep pre-trained language models and the introduction of large datasets have powered research in embedding-based neural retrieval. While many excellent research papers have emerged, most of them come with their own implementations, which are typically optimized for some particular research goals instead of efficiency or code organization. In this paper, we introduce Tevatron, a neural retrieval toolkit that is optimized for efficiency, flexibility, and code simplicity. Tevatron enables model training and evaluation for a variety of ranking components such as dense retrievers, sparse retrievers, and rerankers. It also provides a standardized pipeline that includes text processing, model training, corpus/query encoding, and search. In addition, Tevatron incorporates well-studied methods for improving retriever effectiveness such as hard negative mining and knowledge distillation. We provide an overview of Tevatron in this paper, demonstrating its effectiveness and efficiency on multiple IR and QA datasets. We highlight Tevatron's flexible design, which enables easy generalization across datasets, model architectures, and accelerator platforms (GPUs and TPUs). Overall, we believe that Tevatron can serve as a solid software foundation for research on neural retrieval systems, including their design, modeling, and optimization.|深度预训练语言模型的快速发展和大数据集的引入为基于嵌入的神经检索研究提供了动力。虽然已经出现了许多优秀的研究论文，但大多数论文都有自己的实现，它们通常是针对某些特定的研究目标而优化的，而不是针对效率或代码组织。在本文中，我们介绍了 Tevatron，一个针对效率、灵活性和代码简单性进行优化的神经检索工具包。Tevatron 支持对各种排序组件进行模型训练和评估，例如稠密检索器、稀疏检索器和重新排序器。它还提供了一个标准化的流水线，包括文本处理、模型训练、语料库/查询编码和搜索。此外，Tevatron 还采用了经过充分研究的方法来提高回收效率，如硬负面挖掘和知识提取。我们在本文中提供了 Tevatron 的概述，展示了它在多个 IR 和 QA 数据集上的有效性和效率。我们着重介绍 Tevatron 的灵活设计，它使得跨数据集、模型架构和加速器平台(GPU 和 TPU)的通用化变得容易。总的来说，我们相信 Tevatron 可以作为研究神经检索系统的坚实的软件基础，包括它们的设计、建模和优化。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Tevatron:+An+Efficient+and+Flexible+Toolkit+for+Neural+Retrieval)|0| -|[SciHarvester: Searching Scientific Documents for Numerical Values](https://doi.org/10.1145/3539618.3591808)|Maciej Rybinski, Stephen Wan, Sarvnaz Karimi, Cécile Paris, Brian Jin, Neil I. Huth, Peter J. Thorburn, Dean P. Holzworth|CSIRO, Brisbane, VIC, Australia; CSIRO, Toowoomba, Australia; CSIRO, Sydney, NSW, Australia|A challenge for search technologies is to support scientific literature surveys that present overviews of the reported numerical values documented for specific physical properties. We present SciHarvester, a system tailored to address this problem for agronomic science. It provides an interface to search PubAg documents, allowing complex queries involving restrictions on numerical values. SciHarvester identifies relevant documents and generates overview of reported parameter values. The system allows interrogation of the results to explain the system's performance. Our evaluations demonstrate the promise of incorporating information extraction techniques with the use of neural scoring mechanisms.|搜索技术面临的一个挑战是支持科学文献调查，这些调查概述了为具体物理特性记录的报告数值。我们介绍 SciHarvester，一个专门为农学科学解决这个问题的系统。它提供了一个用于搜索 PubAg 文档的界面，允许包含数值限制的复杂查询。SciHarvester 识别相关文档并生成报告参数值的概述。系统允许询问结果来解释系统的性能。我们的评估显示了将信息抽取技术与神经评分机制相结合的前景。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=SciHarvester:+Searching+Scientific+Documents+for+Numerical+Values)|0| +|[SciHarvester: Searching Scientific Documents for Numerical Values](https://doi.org/10.1145/3539618.3591808)|Maciej Rybinski, Stephen Wan, Sarvnaz Karimi, Cécile Paris, Brian Jin, Neil I. Huth, Peter J. Thorburn, Dean P. Holzworth|CSIRO, Brisbane, VIC, Australia; CSIRO, Sydney, NSW, Australia; CSIRO, Toowoomba, Australia|A challenge for search technologies is to support scientific literature surveys that present overviews of the reported numerical values documented for specific physical properties. We present SciHarvester, a system tailored to address this problem for agronomic science. It provides an interface to search PubAg documents, allowing complex queries involving restrictions on numerical values. SciHarvester identifies relevant documents and generates overview of reported parameter values. The system allows interrogation of the results to explain the system's performance. Our evaluations demonstrate the promise of incorporating information extraction techniques with the use of neural scoring mechanisms.|搜索技术面临的一个挑战是支持科学文献调查，这些调查概述了为具体物理特性记录的报告数值。我们介绍 SciHarvester，一个专门为农学科学解决这个问题的系统。它提供了一个用于搜索 PubAg 文档的界面，允许包含数值限制的复杂查询。SciHarvester 识别相关文档并生成报告参数值的概述。系统允许询问结果来解释系统的性能。我们的评估显示了将信息抽取技术与神经评分机制相结合的前景。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=SciHarvester:+Searching+Scientific+Documents+for+Numerical+Values)|0| |[A Retrieval System for Images and Videos based on Aesthetic Assessment of Visuals](https://doi.org/10.1145/3539618.3591817)|Daniel Vera Nieto, Saikishore Kalloori, Fabio Zund, Clara FernandezLabrador, Marc Willhaus, Severin Klingler, Markus H. Gross|ETH Zürich, Zurich, Switzerland|Attractive images or videos are the visual backbones of journalism and social media to gain the user's attention. From trailers to teaser images to image galleries, appealing visuals have only grown in importance over the years. However, selecting eye-catching shots from a video or the perfect image from large image collections is a challenging and time-consuming task. We present our tool that can assess image and video content from an aesthetic standpoint. We discovered that it is possible to perform such an assessment by combining expert knowledge with data-driven information. We combine the relevant aesthetic features and machine learning algorithms into an aesthetics retrieval system, which enables users to sort uploaded visuals based on an aesthetic score and interact with additional photographic, cinematic, and person-specific features.|吸引人的图片或视频是新闻业和社交媒体吸引用户注意力的视觉支柱。从预告片到预告图片再到图片画廊，吸引人的视觉效果在这些年里变得越来越重要。然而，从视频中选择引人注目的镜头或从大型图像集合中选择完美的图像是一项具有挑战性和耗时的任务。我们提出了我们的工具，可以评估图像和视频内容从美学的立场。我们发现，通过将专家知识与数据驱动的信息相结合来进行这种评估是可能的。我们将相关的美学特征和机器学习算法结合到一个美学检索系统中，使用户能够根据美学评分对上传的图像进行分类，并与其他照片、电影和特定人物的特征进行交互。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Retrieval+System+for+Images+and+Videos+based+on+Aesthetic+Assessment+of+Visuals)|0| |[XpmIR: A Modular Library for Learning to Rank and Neural IR Experiments](https://doi.org/10.1145/3539618.3591818)|Yuxuan Zong, Benjamin Piwowarski|Sorbonne Université, CNRS, ISIR, Paris, France; CNRS, Sorbonne Université, ISIR, Paris, France|During past years, several frameworks for (Neural) Information Retrieval have been proposed. However, while they allow reproducing already published results, it is still very hard to re-use some parts of the learning pipelines, such as for instance the pre-training, sampling strategy, or a loss in newly developed models. It is also difficult to use new training techniques with old models, which makes it more difficult to assess the usefulness of ideas on various neural IR models. This slows the adoption of new techniques, and in turn, the development of the IR field. In this paper, we present XpmIR, a Python library defining a reusable set of experimental components. The library already contains state-of-the-art models and indexation techniques and is integrated with the HuggingFace hub.|在过去的几年中，人们提出了几种神经信息检索的框架。然而，尽管它们允许复制已经发表的结果，但仍然很难重复使用学习管道的某些部分，例如预训练、抽样策略或新开发的模型中的损失。新的训练技术也很难与旧的模型一起使用，这使得评估各种神经 IR 模型思想的有用性变得更加困难。这就减缓了新技术的采用，进而也延缓了红外领域的发展。在本文中，我们介绍了 XpmIR，它是一个 Python 库，定义了一组可重用的实验组件。该库已经包含了最先进的模型和索引技术，并与 HuggingFace 集成在一起。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=XpmIR:+A+Modular+Library+for+Learning+to+Rank+and+Neural+IR+Experiments)|0| |[Searching for Reliable Facts over a Medical Knowledge Base](https://doi.org/10.1145/3539618.3591822)|Fabio Giachelle, Stefano Marchesin, Gianmaria Silvello, Omar Alonso|University of Padua, Padua, Italy; Amazon, Santa Clara, CA, USA|This work presents CoreKB, a Web platform for searching reliable facts over gene expression-cancer associations Knowledge Base (KB). It provides search capabilities over an RDF graph using natural language queries, structured facets, and autocomplete. CoreKB is designed to be intuitive and easy to use for healthcare professionals, medical researchers, and clinicians. The system offers the user a comprehensive overview of the scientific evidence supporting a medical fact. It provides a quantitative comparison between the possible gene-cancer associations a particular fact can reflect.|这项工作介绍了 CoreKB，一个在基因表达-癌症关联知识库(KB)上搜索可靠事实的网络平台。它通过使用自然语言查询、结构化方面和自动完成在 RDF 图上提供搜索功能。CoreKB 的设计是直观和易于使用的医疗保健专业人员，医学研究人员和临床医生。该系统为用户提供了支持医学事实的科学证据的全面概述。它提供了一个可能的基因之间的定量比较癌症的关联，一个特定的事实可以反映。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Searching+for+Reliable+Facts+over+a+Medical+Knowledge+Base)|0| -|[Interactive Recommendation System for Meituan Waimai](https://doi.org/10.1145/3539618.3591830)|Chen Ji, Yacheng Li, Rui Li, Fei Jiang, Xiang Li, Wei Lin, Chenglong Zhang, Wei Wang, Shuyang Wang|Independent, Beijing, China; Meituan, Beijing, China|As the largest local retail & instant delivery platform in China, Meituan Waimai has deployed a personalized recommender system on server and recommend nearby stores to users through APP homepage. To capture real-time intention of users and flexibly adjust the recommendation results on the homepage, we further add an interactive recommender system. The existing interactive recommender systems in the industry mainly capture intention of users based on their feedback on a specific UI of questions. However, we find that it will undermine use fluency and increase use complexity by rashly inserting a new question UI when users browse the homepage. Therefore, we develop an Embedded Interactive Recommender System (EIRS) that directly infers users' intention according to their click behaviors on the homepage and dynamically inserts a new recommendation result into the homepage1. To demonstrate the effectiveness of EIRS, we conduct systematic online A/B Tests, where click-through & conversion rate of the inserted EIRS result is 132% higher than that of the initial result on the homepage, and the overall gross merchandise volume is effectively enhanced by 0.43%.|作为中国最大的本地零售和即时送货平台，美团外卖已经在服务器上部署了个性化的推荐系统，并通过 APP 主页向用户推荐附近的商店。为了捕捉用户的实时意图，并灵活调整网页上的推荐结果，我们进一步加入了互动推荐系统。业界现有的交互式推荐系统主要根据用户对特定用户界面问题的反馈来捕捉用户的意图。然而，我们发现，当用户浏览主页时，它会草率地插入一个新的问题用户界面，从而破坏使用的流畅性并增加使用的复杂性。因此，我们开发了一个嵌入式交互式推荐系统(eIRS) ，它可以根据用户在主页上的点击行为直接推断用户的意图，并动态地将一个新的推荐结果插入到主页1中。为了证明 EIRS 的有效性，我们进行了有系统的网上 A/B 测试，其中插入的 EIRS 结果的点击率和转换率比首页上的初步结果高出132% ，而整体商品销售量有效地提高了0.43% 。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Interactive+Recommendation+System+for+Meituan+Waimai)|0| -|[Practice and Challenges in Building a Business-oriented Search Engine Quality Metric](https://doi.org/10.1145/3539618.3591841)|Nuo Chen, Donghyun Park, Hyungae Park, Kijun Choi, Tetsuya Sakai, Jinyoung Kim|Waseda University, Japan, Japan; Waseda University & Naver Corp., Tokyo, Japan; Naver Corp., Seoul, Republic of Korea; Naver Corp., Belmont, CA, USA|One of the most challenging aspects of operating a large-scale web search engine is to accurately evaluate and monitor the search engine's result quality regardless of search types. From a business perspective, in the face of such challenges, it is important to establish a universal search quality metric that can be easily understood by the entire organisation. In this paper, we introduce a model-based quality metric using Explainable Boosting Machine as the classifier and online user behaviour signals as features to predict search quality. The proposed metric takes into account a variety of search types and has good interpretability. To examine the performance of the metric, we constructed a large dataset of user behaviour on search engine results pages (SERPs) with SERP quality ratings from professional annotators. We compared the performance of the model in our metric to those of other black-box machine learning models on the dataset. We also share a few experiences within our company for the org-wide adoption of this metric relevant to metric design.|运行大规模网络搜索引擎最具挑战性的方面之一是准确评估和监控搜索引擎的结果质量，而不管搜索类型如何。从商业的角度来看，面对这些挑战，建立一个通用的搜索质量度量标准是非常重要的，它可以很容易地被整个组织所理解。本文提出了一种基于模型的质量度量方法，该方法以可解释增强机作为分类器，以在线用户行为信号作为特征来预测搜索质量。所提出的度量标准考虑到各种搜索类型，具有良好的可解释性。为了检验这个指标的性能，我们构建了一个大型的搜索引擎结果页面(SERP)用户行为数据集，其中包括来自专业注释者的 SERP 质量评级。我们将我们度量中的模型的性能与数据集上其他黑盒机器学习模型的性能进行了比较。我们还在公司内部分享了一些与度量设计相关的度量在组织范围内采用的经验。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Practice+and+Challenges+in+Building+a+Business-oriented+Search+Engine+Quality+Metric)|0| +|[Interactive Recommendation System for Meituan Waimai](https://doi.org/10.1145/3539618.3591830)|Chen Ji, Yacheng Li, Rui Li, Fei Jiang, Xiang Li, Wei Lin, Chenglong Zhang, Wei Wang, Shuyang Wang|Meituan, Beijing, China; Independent, Beijing, China|As the largest local retail & instant delivery platform in China, Meituan Waimai has deployed a personalized recommender system on server and recommend nearby stores to users through APP homepage. To capture real-time intention of users and flexibly adjust the recommendation results on the homepage, we further add an interactive recommender system. The existing interactive recommender systems in the industry mainly capture intention of users based on their feedback on a specific UI of questions. However, we find that it will undermine use fluency and increase use complexity by rashly inserting a new question UI when users browse the homepage. Therefore, we develop an Embedded Interactive Recommender System (EIRS) that directly infers users' intention according to their click behaviors on the homepage and dynamically inserts a new recommendation result into the homepage1. To demonstrate the effectiveness of EIRS, we conduct systematic online A/B Tests, where click-through & conversion rate of the inserted EIRS result is 132% higher than that of the initial result on the homepage, and the overall gross merchandise volume is effectively enhanced by 0.43%.|作为中国最大的本地零售和即时送货平台，美团外卖已经在服务器上部署了个性化的推荐系统，并通过 APP 主页向用户推荐附近的商店。为了捕捉用户的实时意图，并灵活调整网页上的推荐结果，我们进一步加入了互动推荐系统。业界现有的交互式推荐系统主要根据用户对特定用户界面问题的反馈来捕捉用户的意图。然而，我们发现，当用户浏览主页时，它会草率地插入一个新的问题用户界面，从而破坏使用的流畅性并增加使用的复杂性。因此，我们开发了一个嵌入式交互式推荐系统(eIRS) ，它可以根据用户在主页上的点击行为直接推断用户的意图，并动态地将一个新的推荐结果插入到主页1中。为了证明 EIRS 的有效性，我们进行了有系统的网上 A/B 测试，其中插入的 EIRS 结果的点击率和转换率比首页上的初步结果高出132% ，而整体商品销售量有效地提高了0.43% 。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Interactive+Recommendation+System+for+Meituan+Waimai)|0| +|[Practice and Challenges in Building a Business-oriented Search Engine Quality Metric](https://doi.org/10.1145/3539618.3591841)|Nuo Chen, Donghyun Park, Hyungae Park, Kijun Choi, Tetsuya Sakai, Jinyoung Kim|Naver Corp., Belmont, CA, USA; Naver Corp., Seoul, Republic of Korea; Waseda University, Japan, Japan; Waseda University & Naver Corp., Tokyo, Japan|One of the most challenging aspects of operating a large-scale web search engine is to accurately evaluate and monitor the search engine's result quality regardless of search types. From a business perspective, in the face of such challenges, it is important to establish a universal search quality metric that can be easily understood by the entire organisation. In this paper, we introduce a model-based quality metric using Explainable Boosting Machine as the classifier and online user behaviour signals as features to predict search quality. The proposed metric takes into account a variety of search types and has good interpretability. To examine the performance of the metric, we constructed a large dataset of user behaviour on search engine results pages (SERPs) with SERP quality ratings from professional annotators. We compared the performance of the model in our metric to those of other black-box machine learning models on the dataset. We also share a few experiences within our company for the org-wide adoption of this metric relevant to metric design.|运行大规模网络搜索引擎最具挑战性的方面之一是准确评估和监控搜索引擎的结果质量，而不管搜索类型如何。从商业的角度来看，面对这些挑战，建立一个通用的搜索质量度量标准是非常重要的，它可以很容易地被整个组织所理解。本文提出了一种基于模型的质量度量方法，该方法以可解释增强机作为分类器，以在线用户行为信号作为特征来预测搜索质量。所提出的度量标准考虑到各种搜索类型，具有良好的可解释性。为了检验这个指标的性能，我们构建了一个大型的搜索引擎结果页面(SERP)用户行为数据集，其中包括来自专业注释者的 SERP 质量评级。我们将我们度量中的模型的性能与数据集上其他黑盒机器学习模型的性能进行了比较。我们还在公司内部分享了一些与度量设计相关的度量在组织范围内采用的经验。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Practice+and+Challenges+in+Building+a+Business-oriented+Search+Engine+Quality+Metric)|0| |[Building a Graph-Based Patent Search Engine](https://doi.org/10.1145/3539618.3591842)|Sebastian Björkqvist, Juho Kallio|IPRally Technologies Oy, Helsinki, Finland|Performing prior art searches is an essential step in both patent drafting and invalidation. The task is challenging due to the large number of existing patent documents and the domain knowledge required to analyze the documents. We present a graph-based patent search engine that tries to mimic the work done by a professional patent examiner. Each patent document is converted to a graph that describes the parts of the invention and the relations between the parts. The search engine is powered by a graph neural network that learns to find prior art by using novelty citation data from patent office search reports where citations are compiled by human patent examiners. We show that a graph-based approach is an efficient way to perform searches on technical documents and demonstrate it in the context of patent searching.|进行现有技术检索是专利起草和宣告无效的一个重要步骤。这项任务是具有挑战性的，因为大量的现有专利文件和领域知识需要分析的文件。我们提出了一个基于图表的专利搜索引擎，试图模仿专业专利审查员所做的工作。每个专利文件被转换成描述发明的各部分以及各部分之间的关系的图形。搜索引擎由一个图形神经网络驱动，该网络通过使用专利局搜索报告中的新颖引文数据来学习发现先前的技术，这些引文是由人类专利审查员汇编的。我们证明了基于图的方法是一种有效的方法来执行对技术文档的检索，并在专利检索的背景下进行论证。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Building+a+Graph-Based+Patent+Search+Engine)|0| |[Graph Enhanced BERT for Query Understanding](https://doi.org/10.1145/3539618.3591845)|Juanhui Li, Wei Zeng, Suqi Cheng, Yao Ma, Jiliang Tang, Shuaiqiang Wang, Dawei Yin|; Michigan State University, East Lansing, USA; Baidu Inc., Beijing, China|Query understanding plays a key role in exploring users' search intents and facilitating users to locate their most desired information. However, it is inherently challenging since it needs to capture semantic information from short and ambiguous queries and often requires massive task-specific labeled data. In recent years, pre-trained language models (PLMs) have advanced various natural language processing tasks because they can extract general semantic information from large-scale corpora. Therefore, there are unprecedented opportunities to adopt PLMs for query understanding. However, there is a gap between the goal of query understanding and existing pre-training strategies -- the goal of query understanding is to boost search performance while existing strategies rarely consider this goal. Thus, directly applying them to query understanding is sub-optimal. On the other hand, search logs contain user clicks between queries and urls that provide rich users' search behavioral information on queries beyond their content. Therefore, in this paper, we aim to fill this gap by exploring search logs. In particular, to incorporate search logs into pre-training, we first construct a query graph where nodes are queries and two queries are connected if they lead to clicks on the same urls. Then we propose a novel graph-enhanced pre-training framework, GE-BERT, which can leverage both query content and the query graph. In other words, GE-BERT can capture both the semantic information and the users' search behavioral information of queries. Extensive experiments on various query understanding tasks have demonstrated the effectiveness of the proposed framework.|查询理解在探索用户的搜索意图和帮助用户定位最需要的信息方面起着关键作用。然而，这本身就具有挑战性，因为它需要从短而模糊的查询中捕获语义信息，并且经常需要大量特定于任务的标记数据。近年来，预训练语言模型(pre-training language model，PLM)已经推进了各种自然语言处理任务，因为它们可以从大规模语料库中提取一般语义信息。因此，采用 PLM 来理解查询有着前所未有的机会。然而，在查询理解的目标和现有的预训练策略之间存在差距——查询理解的目标是提高搜索性能，而现有的策略很少考虑这个目标。因此，直接将它们应用于查询理解是次优的。另一方面，搜索日志包含用户在查询和 URL 之间的点击，这些点击提供了丰富用户在其内容之外的查询上的搜索行为信息。因此，在本文中，我们的目标是通过探索搜索日志来填补这一空白。特别是，为了将搜索日志合并到预训练中，我们首先构造一个查询图，其中节点是查询，如果两个查询导致点击相同的网址，则两个查询是连接的。然后提出了一种新的图增强预训练框架 GE-BERT，该框架可以同时利用查询内容和查询图。换句话说，GE-BERT 可以同时捕捉语义信息和用户的搜索行为信息。在各种查询理解任务上的大量实验证明了该框架的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Graph+Enhanced+BERT+for+Query+Understanding)|0| -|[Neural Methods for Cross-Language Information Retrieval](https://doi.org/10.1145/3539618.3594244)|Eugene Yang, Dawn J. Lawrie, James Mayfield, Suraj Nair, Douglas W. Oard|University of Maryland, College Park, MD, USA; Johns Hopkins University, Baltimore, MD, USA|This half day tutorial introduces the participant to the basic concepts underlying neural Cross-Language Information Retrieval (CLIR). It discusses the most common algorithmic approaches to CLIR, focusing on modern neural methods; the history of CLIR; where to find and how to use CLIR training collections, test collections and baseline systems; how CLIR training and test collections are constructed; and open research questions in CLIR.|这个半天的教程向参与者介绍神经跨语检索的基本概念。它讨论了 CLIR 最常见的算法方法，重点是现代神经学方法; CLIR 的历史; 在哪里找到和如何使用 CLIR 训练集、测试集和基线系统; CLIR 训练集和测试集是如何构建的; 以及 CLIR 中的开放研究问题。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Neural+Methods+for+Cross-Language+Information+Retrieval)|0| -|[Causal Recommendation: Progresses and Future Directions](https://doi.org/10.1145/3539618.3594245)|Wenjie Wang, Yang Zhang, Haoxuan Li, Peng Wu, Fuli Feng, Xiangnan He|University of Science and Technology of China, Hefei, China; University of Science and Technology of China, Singapore, China; University of Science and Technology of China, Beijing, China|Data-driven recommender systems have demonstrated great success in various Web applications owing to the extraordinary ability of machine learning models to recognize patterns (ie correlation) from users' behaviors. However, they still suffer from several issues such as biases and unfairness due to spurious correlations. Considering the causal mechanism behind data can avoid the influences of such spurious correlations. In this light, embracing causal recommender modeling is an exciting and promising direction. In this tutorial, we aim to introduce the key concepts in causality and provide a systemic review of existing work on causal recommendation. We will introduce existing methods from two different causal frameworks --- the potential outcome (PO) framework and the structural causal model (SCM). We will give examples and discussions regarding how to utilize different causal tools under these two frameworks to model and solve problems in recommendation. Moreover, we will summarize and compare the paradigms of PO-based and SCM-based recommendation. Besides, we identify some open challenges and potential future directions for this area. We hope this tutorial could stimulate more ideas on this topic and facilitate the development of causality-aware recommender systems.|由于机器学习模型能够从用户行为中识别模式(即相关性) ，数据驱动的推荐系统在各种 Web 应用程序中取得了巨大的成功。然而，由于虚假的相关性，他们仍然受到一些问题的困扰，如偏见和不公平。考虑数据背后的因果机制可以避免这种虚假相关性的影响。从这个角度来看，采用因果推荐建模是一个令人兴奋和有前途的方向。在本教程中，我们的目的是介绍因果关系的关键概念，并提供一个系统的回顾，现有的工作在因果推荐。我们将介绍来自两个不同因果框架的现有方法——潜在结果(PO)框架和结构因果模型(SCM)。我们将举例讨论如何在这两个框架下利用不同的因果工具来建模和解决推荐中的问题。此外，我们还将总结和比较基于 PO 和基于 SCM 的推荐模式。此外，我们确定了这一领域的一些开放性挑战和潜在的未来方向。我们希望本教程可以激发更多关于这个主题的想法，并促进因果关系推荐系统的开发。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Causal+Recommendation:+Progresses+and+Future+Directions)|0| -|[Neuro-Symbolic Representations for Information Retrieval](https://doi.org/10.1145/3539618.3594246)|Laura Dietz, Hannah Bast, Shubham Chatterjee, Jeffrey Dalton, JianYun Nie, Rodrigo Frassetto Nogueira|University of Freiburg, Freiburg, Germany; University of Montreal, Montreal, PQ, Canada; University of New Hampshire, Durham, NH, USA; State University of Campinas, Campinas, Brazil; University of Glasgow, Glasgow, United Kingdom|This tutorial will provide an overview of recent advances on neuro-symbolic approaches for information retrieval. A decade ago, knowledge graphs and semantic annotations technology led to active research on how to best leverage symbolic knowledge. At the same time, neural methods have demonstrated to be versatile and highly effective. From a neural network perspective, the same representation approach can service document ranking or knowledge graph reasoning. End-to-end training allows to optimize complex methods for downstream tasks. We are at the point where both the symbolic and the neural research advances are coalescing into neuro-symbolic approaches. The underlying research questions are how to best combine symbolic and neural approaches, what kind of symbolic/neural approaches are most suitable for which use case, and how to best integrate both ideas to advance the state of the art in information retrieval. Materials are available online: https://github.com/laura-dietz/neurosymbolic-representations-for-IR|这个教程将提供一个关于信息检索神经符号方法的最新进展的概述。十年前，知识图表和语义注释技术导致了对如何最好地利用符号知识的积极研究。与此同时，神经学方法已被证明是通用的和高度有效的。从神经网络的角度来看，同样的表示方法可以服务文档排序或知识图推理。端到端培训允许为下游任务优化复杂的方法。我们正处于这样一个时刻，符号学和神经学的研究进展正在结合成神经符号学的方法。潜在的研究问题是如何最好地结合符号和神经方法，什么样的符号/神经方法最适合哪种用例，以及如何最好地整合这两种思想以提高信息检索的艺术水平。资料可在网上查阅: https://github.com/laura-dietz/neurosymbolic-representations-for-ir|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Neuro-Symbolic+Representations+for+Information+Retrieval)|0| -|[Explainable Information Retrieval](https://doi.org/10.1145/3539618.3594249)|Avishek Anand, Procheta Sen, Sourav Saha, Manisha Verma, Mandar Mitra|Delft University of Technology, Delft, Netherlands; Indian Statistical Institute, Kolkata, India; University of Liverpool, Liverpool, United Kingdom; Amazon New York, New York, USA|This tutorial presents explainable information retrieval (ExIR), an emerging area focused on fostering responsible and trustworthy deployment of machine learning systems in the context of information retrieval. As the field has rapidly evolved in the past 4-5 years, numerous approaches have been proposed that focus on different access modes, stakeholders, and model development stages. This tutorial aims to introduce IR-centric notions, classification, and evaluation styles in ExIR, while focusing on IR-specific tasks such as ranking, text classification, and learning-to-rank systems. We will delve into method families and their adaptations to IR, extensively covering post-hoc methods, axiomatic and probing approaches, and recent advances in interpretability-by-design approaches. We will also discuss ExIR applications for different stakeholders, such as researchers, practitioners, and end-users, in contexts like web search, patent and legal search, and high-stakes decision-making tasks. To facilitate practical understanding, we will provide a hands-on session on applying ExIR methods, reducing the entry barrier for students, researchers, and practitioners alike.|本教程介绍了可解释信息检索(exIR) ，这是一个新兴的领域，专注于在信息检索环境中培养机器学习系统的负责任和可靠的部署。随着该领域在过去4-5年中的迅速发展，已经提出了许多方法，重点关注不同的访问模式、涉众和模型开发阶段。本教程旨在介绍 ExIR 中以 IR 为中心的概念、分类和评估风格，同时关注特定于 IR 的任务，如排名、文本分类和学习到排名系统。我们将深入研究方法族及其对 IR 的适应性，广泛地涵盖事后方法、公理化方法和探索性方法，以及设计可解释性方法的最新进展。我们还将讨论不同利益相关者(如研究人员、从业人员和最终用户)在网络搜索、专利和法律搜索以及高风险决策任务等上下文中的 ExIR 应用程序。为了便于实际理解，我们将提供一个应用 ExIR 方法的实践会议，减少学生，研究人员和从业人员的进入障碍。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Explainable+Information+Retrieval)|0| -|[Proactive Conversational Agents in the Post-ChatGPT World](https://doi.org/10.1145/3539618.3594250)|Lizi Liao, Grace Hui Yang, Chirag Shah|Georgetown University, Washington DC, USA; Singapore Management University, Singapore, Singapore; University of Washington, Seattle, USA|ChatGPT and similar large language model (LLM) based conversational agents have brought shock waves to the research world. Although astonished by their human-like performance, we find they share a significant weakness with many other existing conversational agents in that they all take a passive approach in responding to user queries. This limits their capacity to understand the users and the task better and to offer recommendations based on a broader context than a given conversation. Proactiveness is still missing in these agents, including their ability to initiate a conversation, shift topics, or offer recommendations that take into account a more extensive context. To address this limitation, this tutorial reviews methods for equipping conversational agents with proactive interaction abilities. The full-day tutorial is divided into four parts, including multiple interactive exercises. We will begin the tutorial with an interactive exercise and cover the design of existing conversational systems architecture and challenges. The content includes coverage of LLM-based recent advancements such as ChatGPT and Bard, along with reinforcement learning with human feedback (RLHF) technique. Then we will introduce the concept of proactive conversation agents and preset recent advancements in proactiveness of conversational agents, including actively driving conversations by asking questions, topic shifting, and methods that support strategic planning of conversation. Next, we will discuss important issues in conversational responses' quality control, including safety, appropriateness, language detoxication, hallucination, and alignment. Lastly, we will launch another interactive exercise and discussion with the audience to arrive at concluding remarks, prospecting open challenges and new directions. By exploring new techniques for enhancing conversational agents' proactive behavior to improve user engagement, this tutorial aims to help researchers and practitioners develop more effective conversational agents that can better understand and respond to user needs proactively and safely.|基于 ChatGPT 和类似大语言模型(LLM)的会话代理给研究领域带来了冲击波。虽然它们的人性化表现令人惊讶，但我们发现它们与许多其他现有的会话代理有一个明显的弱点，即它们都采用被动的方式来响应用户查询。这限制了他们更好地理解用户和任务的能力，也限制了他们基于比特定对话更广泛的背景提供建议的能力。这些代理仍然缺乏主动性，包括他们发起对话、转移话题或提供考虑到更广泛背景的建议的能力。为了解决这个限制，本教程回顾了使会话代理具有主动交互能力的方法。全天教程分为四个部分，包括多个互动练习。我们将以一个交互式练习开始本教程，并讨论现有会话系统体系结构和挑战的设计。内容包括基于 LLM 的最新进展，如 ChatGPT 和巴德，以及人类反馈(rlHF)技术的强化学习。然后，我们将介绍主动会话代理的概念，并预设会话代理主动性的最新进展，包括通过提问积极推动会话，话题转移，以及支持会话策略规划的方法。接下来，我们将讨论会话反应质量控制中的重要问题，包括安全性、恰当性、语言排毒、幻觉和对齐。最后，我们会展开另一项互动活动，与听众进行讨论，以达致总结意见、探讨公开挑战和新方向。通过探索增强会话代理主动行为的新技术，以提高用户参与度，本教程旨在帮助研究人员和从业人员开发更有效的会话代理，可以更好地理解和回应用户的需求，主动和安全。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Proactive+Conversational+Agents+in+the+Post-ChatGPT+World)|0| +|[Neural Methods for Cross-Language Information Retrieval](https://doi.org/10.1145/3539618.3594244)|Eugene Yang, Dawn J. Lawrie, James Mayfield, Suraj Nair, Douglas W. Oard|Johns Hopkins University, Baltimore, MD, USA; University of Maryland, College Park, MD, USA|This half day tutorial introduces the participant to the basic concepts underlying neural Cross-Language Information Retrieval (CLIR). It discusses the most common algorithmic approaches to CLIR, focusing on modern neural methods; the history of CLIR; where to find and how to use CLIR training collections, test collections and baseline systems; how CLIR training and test collections are constructed; and open research questions in CLIR.|这个半天的教程向参与者介绍神经跨语检索的基本概念。它讨论了 CLIR 最常见的算法方法，重点是现代神经学方法; CLIR 的历史; 在哪里找到和如何使用 CLIR 训练集、测试集和基线系统; CLIR 训练集和测试集是如何构建的; 以及 CLIR 中的开放研究问题。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Neural+Methods+for+Cross-Language+Information+Retrieval)|0| +|[Causal Recommendation: Progresses and Future Directions](https://doi.org/10.1145/3539618.3594245)|Wenjie Wang, Yang Zhang, Haoxuan Li, Peng Wu, Fuli Feng, Xiangnan He|University of Science and Technology of China, Singapore, China; University of Science and Technology of China, Hefei, China; University of Science and Technology of China, Beijing, China|Data-driven recommender systems have demonstrated great success in various Web applications owing to the extraordinary ability of machine learning models to recognize patterns (ie correlation) from users' behaviors. However, they still suffer from several issues such as biases and unfairness due to spurious correlations. Considering the causal mechanism behind data can avoid the influences of such spurious correlations. In this light, embracing causal recommender modeling is an exciting and promising direction. In this tutorial, we aim to introduce the key concepts in causality and provide a systemic review of existing work on causal recommendation. We will introduce existing methods from two different causal frameworks --- the potential outcome (PO) framework and the structural causal model (SCM). We will give examples and discussions regarding how to utilize different causal tools under these two frameworks to model and solve problems in recommendation. Moreover, we will summarize and compare the paradigms of PO-based and SCM-based recommendation. Besides, we identify some open challenges and potential future directions for this area. We hope this tutorial could stimulate more ideas on this topic and facilitate the development of causality-aware recommender systems.|由于机器学习模型能够从用户行为中识别模式(即相关性) ，数据驱动的推荐系统在各种 Web 应用程序中取得了巨大的成功。然而，由于虚假的相关性，他们仍然受到一些问题的困扰，如偏见和不公平。考虑数据背后的因果机制可以避免这种虚假相关性的影响。从这个角度来看，采用因果推荐建模是一个令人兴奋和有前途的方向。在本教程中，我们的目的是介绍因果关系的关键概念，并提供一个系统的回顾，现有的工作在因果推荐。我们将介绍来自两个不同因果框架的现有方法——潜在结果(PO)框架和结构因果模型(SCM)。我们将举例讨论如何在这两个框架下利用不同的因果工具来建模和解决推荐中的问题。此外，我们还将总结和比较基于 PO 和基于 SCM 的推荐模式。此外，我们确定了这一领域的一些开放性挑战和潜在的未来方向。我们希望本教程可以激发更多关于这个主题的想法，并促进因果关系推荐系统的开发。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Causal+Recommendation:+Progresses+and+Future+Directions)|0| +|[Neuro-Symbolic Representations for Information Retrieval](https://doi.org/10.1145/3539618.3594246)|Laura Dietz, Hannah Bast, Shubham Chatterjee, Jeffrey Dalton, JianYun Nie, Rodrigo Frassetto Nogueira|University of Freiburg, Freiburg, Germany; University of New Hampshire, Durham, NH, USA; University of Montreal, Montreal, PQ, Canada; University of Glasgow, Glasgow, United Kingdom; State University of Campinas, Campinas, Brazil|This tutorial will provide an overview of recent advances on neuro-symbolic approaches for information retrieval. A decade ago, knowledge graphs and semantic annotations technology led to active research on how to best leverage symbolic knowledge. At the same time, neural methods have demonstrated to be versatile and highly effective. From a neural network perspective, the same representation approach can service document ranking or knowledge graph reasoning. End-to-end training allows to optimize complex methods for downstream tasks. We are at the point where both the symbolic and the neural research advances are coalescing into neuro-symbolic approaches. The underlying research questions are how to best combine symbolic and neural approaches, what kind of symbolic/neural approaches are most suitable for which use case, and how to best integrate both ideas to advance the state of the art in information retrieval. Materials are available online: https://github.com/laura-dietz/neurosymbolic-representations-for-IR|这个教程将提供一个关于信息检索神经符号方法的最新进展的概述。十年前，知识图表和语义注释技术导致了对如何最好地利用符号知识的积极研究。与此同时，神经学方法已被证明是通用的和高度有效的。从神经网络的角度来看，同样的表示方法可以服务文档排序或知识图推理。端到端培训允许为下游任务优化复杂的方法。我们正处于这样一个时刻，符号学和神经学的研究进展正在结合成神经符号学的方法。潜在的研究问题是如何最好地结合符号和神经方法，什么样的符号/神经方法最适合哪种用例，以及如何最好地整合这两种思想以提高信息检索的艺术水平。资料可在网上查阅: https://github.com/laura-dietz/neurosymbolic-representations-for-ir|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Neuro-Symbolic+Representations+for+Information+Retrieval)|0| +|[Explainable Information Retrieval](https://doi.org/10.1145/3539618.3594249)|Avishek Anand, Procheta Sen, Sourav Saha, Manisha Verma, Mandar Mitra|Delft University of Technology, Delft, Netherlands; University of Liverpool, Liverpool, United Kingdom; Indian Statistical Institute, Kolkata, India; Amazon New York, New York, USA|This tutorial presents explainable information retrieval (ExIR), an emerging area focused on fostering responsible and trustworthy deployment of machine learning systems in the context of information retrieval. As the field has rapidly evolved in the past 4-5 years, numerous approaches have been proposed that focus on different access modes, stakeholders, and model development stages. This tutorial aims to introduce IR-centric notions, classification, and evaluation styles in ExIR, while focusing on IR-specific tasks such as ranking, text classification, and learning-to-rank systems. We will delve into method families and their adaptations to IR, extensively covering post-hoc methods, axiomatic and probing approaches, and recent advances in interpretability-by-design approaches. We will also discuss ExIR applications for different stakeholders, such as researchers, practitioners, and end-users, in contexts like web search, patent and legal search, and high-stakes decision-making tasks. To facilitate practical understanding, we will provide a hands-on session on applying ExIR methods, reducing the entry barrier for students, researchers, and practitioners alike.|本教程介绍了可解释信息检索(exIR) ，这是一个新兴的领域，专注于在信息检索环境中培养机器学习系统的负责任和可靠的部署。随着该领域在过去4-5年中的迅速发展，已经提出了许多方法，重点关注不同的访问模式、涉众和模型开发阶段。本教程旨在介绍 ExIR 中以 IR 为中心的概念、分类和评估风格，同时关注特定于 IR 的任务，如排名、文本分类和学习到排名系统。我们将深入研究方法族及其对 IR 的适应性，广泛地涵盖事后方法、公理化方法和探索性方法，以及设计可解释性方法的最新进展。我们还将讨论不同利益相关者(如研究人员、从业人员和最终用户)在网络搜索、专利和法律搜索以及高风险决策任务等上下文中的 ExIR 应用程序。为了便于实际理解，我们将提供一个应用 ExIR 方法的实践会议，减少学生，研究人员和从业人员的进入障碍。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Explainable+Information+Retrieval)|0| +|[Proactive Conversational Agents in the Post-ChatGPT World](https://doi.org/10.1145/3539618.3594250)|Lizi Liao, Grace Hui Yang, Chirag Shah|University of Washington, Seattle, USA; Singapore Management University, Singapore, Singapore; Georgetown University, Washington DC, USA|ChatGPT and similar large language model (LLM) based conversational agents have brought shock waves to the research world. Although astonished by their human-like performance, we find they share a significant weakness with many other existing conversational agents in that they all take a passive approach in responding to user queries. This limits their capacity to understand the users and the task better and to offer recommendations based on a broader context than a given conversation. Proactiveness is still missing in these agents, including their ability to initiate a conversation, shift topics, or offer recommendations that take into account a more extensive context. To address this limitation, this tutorial reviews methods for equipping conversational agents with proactive interaction abilities. The full-day tutorial is divided into four parts, including multiple interactive exercises. We will begin the tutorial with an interactive exercise and cover the design of existing conversational systems architecture and challenges. The content includes coverage of LLM-based recent advancements such as ChatGPT and Bard, along with reinforcement learning with human feedback (RLHF) technique. Then we will introduce the concept of proactive conversation agents and preset recent advancements in proactiveness of conversational agents, including actively driving conversations by asking questions, topic shifting, and methods that support strategic planning of conversation. Next, we will discuss important issues in conversational responses' quality control, including safety, appropriateness, language detoxication, hallucination, and alignment. Lastly, we will launch another interactive exercise and discussion with the audience to arrive at concluding remarks, prospecting open challenges and new directions. By exploring new techniques for enhancing conversational agents' proactive behavior to improve user engagement, this tutorial aims to help researchers and practitioners develop more effective conversational agents that can better understand and respond to user needs proactively and safely.|基于 ChatGPT 和类似大语言模型(LLM)的会话代理给研究领域带来了冲击波。虽然它们的人性化表现令人惊讶，但我们发现它们与许多其他现有的会话代理有一个明显的弱点，即它们都采用被动的方式来响应用户查询。这限制了他们更好地理解用户和任务的能力，也限制了他们基于比特定对话更广泛的背景提供建议的能力。这些代理仍然缺乏主动性，包括他们发起对话、转移话题或提供考虑到更广泛背景的建议的能力。为了解决这个限制，本教程回顾了使会话代理具有主动交互能力的方法。全天教程分为四个部分，包括多个互动练习。我们将以一个交互式练习开始本教程，并讨论现有会话系统体系结构和挑战的设计。内容包括基于 LLM 的最新进展，如 ChatGPT 和巴德，以及人类反馈(rlHF)技术的强化学习。然后，我们将介绍主动会话代理的概念，并预设会话代理主动性的最新进展，包括通过提问积极推动会话，话题转移，以及支持会话策略规划的方法。接下来，我们将讨论会话反应质量控制中的重要问题，包括安全性、恰当性、语言排毒、幻觉和对齐。最后，我们会展开另一项互动活动，与听众进行讨论，以达致总结意见、探讨公开挑战和新方向。通过探索增强会话代理主动行为的新技术，以提高用户参与度，本教程旨在帮助研究人员和从业人员开发更有效的会话代理，可以更好地理解和回应用户的需求，主动和安全。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Proactive+Conversational+Agents+in+the+Post-ChatGPT+World)|0| |[FLIRT: Federated Learning for Information Retrieval](https://doi.org/10.1145/3539618.3591926)|Fabio Pinelli, Gabriele Tolomei, Giovanni Trappolini|Sapienza University of Rome, Rome, Italy; IMT Lucca, Lucca, Italy|A wide range of core information retrieval (IR) tasks, such as searching, ranking, and filtering, to name a few, have seen tremendous improvements thanks to machine learning (ML) and artificial intelligence (AI). The traditional centralized approach to training AI/ML models is still predominant: large volumes of data generated by end users must be transferred from their origins and shared with remote locations for processing. However, this centralized paradigm suffers from significant privacy issues and does not take full advantage of the computing power of client devices like modern smartphones. A possible answer to this need is provided by federated learning (FL), which enables collaborative training of predictive models among a set of cooperating edge devices without disclosing any private local data. Unfortunately, FL is still far from being fully exploited in the IR ecosystem. In this workshop proposal, we have the ambition to start filling this gap. More specifically, the first workshop on ''Federated Learning for Information ReTrieval'' (FLIRT) is willing to provide an open forum for researchers and practitioners where they can exchange ideas, identify key challenges, and define the roadmap toward a successful application of FL in the broad IR area.|由于机器学习(ML)和人工智能(AI)的出现，搜索、排名和过滤等核心信息检索(IR)任务都得到了巨大的改善。传统的集中式人工智能/机器学习模型培训方法仍然占主导地位: 最终用户生成的大量数据必须从他们的来源转移，并与远程位置共享进行处理。然而，这种集中式模式存在严重的隐私问题，并且没有充分利用现代智能手机等客户端设备的计算能力。联邦学习(FL)为这一需求提供了一个可能的答案，它能够在一组协作的边缘设备之间协作训练预测模型，而不需要公开任何私有的本地数据。不幸的是，FL 在红外生态系统中还远远没有得到充分利用。在本次研讨会的提案中，我们有雄心开始填补这一空白。更具体地说，第一个关于“信息检索联合学习”的研讨会(FLIRT)愿意为研究人员和实践者提供一个开放的论坛，在这里他们可以交流想法，确定关键的挑战，并确定在广泛的信息共享领域成功应用联合学习的路线图。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=FLIRT:+Federated+Learning+for+Information+Retrieval)|0| |[Quantifying and Advancing Information Retrieval System Explainability](https://doi.org/10.1145/3539618.3591792)|Catherine Chen|Brown University, Providence, RI, USA|As information retrieval (IR) systems, such as search engines and conversational agents, become ubiquitous in various domains, the need for transparent and explainable systems grows to ensure accountability, fairness, and unbiased results. Despite many recent advances toward explainable AI and IR techniques, there is no consensus on what it means for a system to be explainable. Although a growing body of literature suggests that explainability is comprised of multiple subfactors [2, 5, 6], virtually all existing approaches treat it as a singular notion. Additionally, while neural retrieval models (NRMs) have become popular for their ability to achieve high performance[3, 4, 7, 8], research on the explainability of NRMs has been largely unexplored until recent years. Numerous questions remain unanswered regarding the most effective means of comprehending how these intricate models arrive at their decisions and the extent to which these methods will function efficiently for both developers and end-users. This research aims to develop effective methods to evaluate and advance explainable retrieval systems toward the broader research field goal of creating techniques to make potential biases more identifiable. Specifically, I aim to investigate the following: RQ1: How do we quantitatively measure explainability? RQ2: How can we develop a set of inherently explainable NRMs using feature attributions that are robust across different retrieval domain contexts? RQ3: How can we leverage knowledge about influential training instances to better understand NRMs and promote more efficient search practices? In future work, I plan to address RQ2 and RQ3 by investigating two avenues of attribution methods, feature-based and instance-based, to develop a suite of explainable NRMs. While much work has been done on investigating the interpretability of deep neural network architectures in the general ML field, particularly in vision and language domains, creating inherently explainable neural architectures remains largely unexplored in IR. Thus, I intend to draw on previous work in the broader fields of NLP and ML to develop methods that offer deeper insights into the inner workings of NRMs and how ranking decisions are made. By developing explainable IR systems, we can facilitate users' comprehension of the intricate, non-linear mechanisms that link their search queries to highly ranked content. If applied correctly, this research has the potential to benefit society in a broad range of applications, such as disinformation detection and clinical decision support. Given their critical importance in modern society, these areas demand robust solutions to combat the escalating dissemination of false information. By enhancing the transparency and accountability of these systems, explainable systems can play a crucial role in curbing this trend.|随着信息检索(IR)系统，如搜索引擎和会话代理，在各个领域变得无处不在，对透明和可解释的系统的需求增长，以确保问责制，公平性和无偏见的结果。尽管在可解释的人工智能和红外技术方面取得了许多最新进展，但对于系统可解释意味着什么还没有达成共识。虽然越来越多的文献表明，可解释性是由多个子因素组成的[2,5,6] ，几乎所有现有的方法都把它作为一个单一的概念。此外，虽然神经检索模型(NRM)因其获得高性能的能力而受到欢迎[3,4,7,8] ，但是对 NRM 的可解释性的研究直到最近几年才被大量探索。关于如何最有效地理解这些错综复杂的模型是如何作出决定的，以及这些方法在多大程度上能够有效地为开发人员和最终用户发挥作用，还有许多问题没有得到解答。本研究旨在发展有效的方法来评估和推进可解释的检索系统，以达到更广泛的研究领域目标，即创造技术，使潜在的偏差更容易识别。具体来说，我的目标是调查以下问题: RQ1: 我们如何定量衡量可解释性？RQ2: 我们如何使用在不同检索领域上下文中具有鲁棒性的特征属性来开发一组固有的可解释的 NRM？RQ3: 我们如何利用关于有影响力的培训实例的知识来更好地理解 NRM 并促进更有效的搜索实践？在未来的工作中，我计划通过研究基于特征和基于实例的两种归因方法来解决 RQ2和 RQ3问题，以开发一套可解释的 NRM。尽管在一般机器学习领域，特别是在视觉和语言领域，已经做了很多工作来研究深层神经网络结构的可解释性，但是在 IR 领域，创建内在可解释的神经网络结构仍然很大程度上没有被探索。因此，我打算利用以前在 NLP 和机器学习这两个更广泛领域的工作，来开发能够提供对 NRM 内部工作原理以及排序决策是如何做出的更深层次见解的方法。通过开发可解释的 IR 系统，我们可以帮助用户理解复杂的、非线性的机制，这些机制将用户的搜索查询与排名较高的内容联系起来。如果应用得当，这项研究有可能在广泛的应用领域造福社会，如虚假信息检测和临床决策支持。鉴于这些领域在现代社会中至关重要，需要有力的解决办法来打击不断升级的虚假信息传播。通过提高这些系统的透明度和问责制，可解释的系统可以在遏制这一趋势方面发挥关键作用。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Quantifying+and+Advancing+Information+Retrieval+System+Explainability)|0| |[Multimodal Named Entity Recognition and Relation Extraction with Retrieval-Augmented Strategy](https://doi.org/10.1145/3539618.3591790)|Xuming Hu|Tsinghua University, Beijing, China|Multimodal Named Entity Recognition (MNER) and Multimodal Relation Extraction (MRE) are tasks in information retrieval that aim to recognize entities and extract relations among them using information from multiple modalities, such as text and images. Although current methods have attempted a variety of modality fusion approaches to enhance the information in text, a large amount of readily available internet retrieval data has not been considered. Therefore, we attempt to retrieve real-world text related to images, objects, and entire sentences from the internet and use this retrieved text as input for cross-modal fusion to improve the performance of entity and relation extraction tasks in the text.|多模态命名实体识别(mNER)和多模态关系提取(MRE)是信息检索中的任务，目的是利用文本和图像等多种模态的信息来识别实体并提取它们之间的关系。尽管目前的方法已经尝试了各种模态融合方法来增强文本中的信息，但是大量现成的网络检索数据还没有得到充分考虑。因此，我们尝试从互联网上检索与图像、物体和整个句子相关的真实文本，并将检索到的文本作为跨模态融合的输入，以提高文本中实体和关系抽取任务的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Multimodal+Named+Entity+Recognition+and+Relation+Extraction+with+Retrieval-Augmented+Strategy)|0| @@ -315,152 +315,152 @@ |[Neural Architectures for Searching Subgraph Structures](https://doi.org/10.1145/3539618.3591791)|Radin Hamidi Rad|Toronto Metropolitan University, Toronto, ON, Canada|With the development of new neural network architectures for graph learning in recent years, the use of graphs to store, represent and process data become more trendy. Nowadays, graphs as a rich structured form of data representation are used in many real-world projects. In these projects, objects are often defined in terms of their connections to other things. There are many practical applications in areas such as antibacterial discovery, physics simulations, fake news detection, traffic prediction and recommendation systems. While there are a growing number of neural graph representation learning techniques that allow one to learn effective graph representations [1-3, 5], they may not necessarily be appropriate for the task of searching over graphs for several reasons: (1) nodes within a graph consist of a set of attributes, which are the subject of the search process. However, the number of unique attributes on the graph compared to the number of attributes on each node is extremely sparse; therefore, this makes it very difficult to learn effective graph representations for such sparse information; (2) graph neural networks are capable of generating rich embedding representations for nodes and entities in graph however, depending on the downstream task, the embedding vectors may perform not well as expected. Therefore, researchers need to apply solutions such as custom loss functions or pre-training tasks to adapt mentioned architectures for their specific task. Search in the graph as a downstream task follows the same trend and therefore a tailored graph neural network representation is needed to particularly address the need for a rich embedding representation for the sole purpose of subgraph search. My work focuses on searching for subgraph structures over both complete and incomplete heterogeneous graphs. The significance of my research direction lies in the fact that exact subgraph search is an NP-hard problem and as such existing methods are either accurate but impractically slow, or efficient yet suffering from low effectiveness. With a focus on learning robust neural representations for complete and incomplete graphs, my research focuses on developing search methods that are both effective and efficient. Specifically, my research addresses the following research questions: RQ1) Would it be possible to design and develop graph representation learning methods for heterogeneous graphs that can generate effective embedding vectors from a heterogeneous graph and support effective and efficient subgraph search? RQ2) Whether it would be possible to address the issue of graphs with varying degrees of missing values. Incomplete graphs suffer from missing attributes and/or missing edges. I explore the design of robust graph representation learning models capable of effectively searching in light of missing information. RQ3) Can efficient and effective derivations of my subgraph search methods be used to address practical applications in real-world domains such as team formation, and keyword search over knowledge graphs? Research conducted as a part of my Ph.D. has so far focused on identifying and retrieving subgraphs over complete heterogeneous graphs. I have used the Team Formation problem as a case study in order to evaluate my work [4]. As the next step, I am planning to focus on expanding my research to include incomplete graphs and investigate methodologies to craft optimized graph representations for the specific task of searching on graphs.|近年来，随着用于图形学习的新型神经网络结构的发展，利用图形来存储、表示和处理数据成为一种趋势。目前，图作为一种丰富的结构化数据表示形式，已经广泛应用于实际工程中。在这些项目中，对象通常是根据它们与其他事物的连接来定义的。在抗菌发现、物理模拟、假新闻检测、流量预测和推荐系统等领域有许多实际应用。虽然有越来越多的神经图表示学习技术，允许一个人学习有效的图表示[1-3,5] ，但它们可能不一定适合于在图中搜索的任务，原因有几个: (1)图中的节点由一组属性组成，这是搜索过程的主题。然而，与每个节点上的属性数量相比，图上的唯一属性数量是非常稀疏的，因此，这使得学习这种稀疏信息的有效图表示变得非常困难; (2)图神经网络能够为图中的节点和实体生成丰富的嵌入表示，然而，根据下游任务，嵌入向量的表现可能不如预期。因此，研究人员需要应用解决方案，如自定义丢失功能或预训练任务，以适应提到的架构，他们的具体任务。图中的搜索作为一个下游任务遵循同样的趋势，因此需要一个量身定制的图神经网络表示来特别满足子图搜索对丰富嵌入表示的需求。我的工作重点是在完全和不完全异构图上寻找子图结构。本文研究方向的意义在于精确子图搜索是一个 NP 难问题，因此现有的方法要么是精确但不切实际的慢，要么是有效但效率低的。针对完全图和不完全图的鲁棒神经表示学习，我的研究侧重于开发既有效又高效的搜索方法。具体来说，我的研究解决了以下研究问题: RQ1)是否有可能设计和开发异构图的图表示学习方法，可以从异构图中生成有效的嵌入向量，并支持有效和高效的子图搜索？RQ2)是否有可能解决具有不同程度缺失值的图的问题。不完全图缺少属性和/或缺少边。探讨了一种能够在缺失信息情况下进行有效搜索的鲁棒图表示学习模型的设计方法。RQ3)我的子图搜索方法的高效和有效的推导能否用于解决实际领域中的实际应用，如团队组建和知识图上的关键字搜索？到目前为止，作为我博士学位的一部分进行的研究主要集中在识别和检索完整异构图的子图。为了评估我的工作，我使用了团队形成问题作为案例研究[4]。作为下一步，我计划把重点放在扩大我的研究，包括不完整的图和调查方法，以工艺优化图表示的具体任务，搜索图表。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Neural+Architectures+for+Searching+Subgraph+Structures)|0| |[Dense Passage Retrieval: Architectures and Augmentation Methods](https://doi.org/10.1145/3539618.3591796)|Thilina Rajapakse|University of Amsterdam, Amsterdam, Netherlands|The dual-encoder model is a dense retrieval architecture, consisting of two encoder models, that has surpassed traditional sparse retrieval methods for open-domain retrieval [1]. But, room exists for improvement, particularly when dense retrievers are exposed to unseen passages or queries. Considering out-of-domain queries, i.e., queries originating from domains other than the one the model was trained on, the loss in accuracy may be significant. A main factor for this is the mismatch in the information available to the context encoder and the query encoder during training. Common retireval training datasets contain an overwhelming majority of passages with one query from a passage. I hypothesize that this could lead the dual-encoder model, particularly the passage encoder, to overfit to a single potential query from a given passage to the detriment of out-of-domain performance. Based on this, I seek to answer the following research question: (RQ1.1) Does training a DPR model on data containing multiple queries per passage improve the generalizability of the model? To answer RQ1.1, I build generated datasets that have multiple queries for most passages, and compare dense passage retriever models trained on these datasets against models trained on (mostly) single query per passage datasets. I show that training on passages with multiple queries leads to models that generalize better to out-of-distribution and out-of-domain test datasets [2]. Language can be considered another domain in the context of a dense retrieval. Training a dense retrieval model is especially challenging in languages other than English due to the scarcity of training data. I propose a novel training technique, clustered training, aimed at improving the retrieval quality of dense retrievers, especially in out-of-distribution and zero-shot settings. I address the following research questions: (RQ2.1)Does clustered training improve the effectiveness of multilingual DPR models on in-distribution data? (RQ2.2) Does clustered training improve the effectiveness of multilingual DPR models on out-of-distribution data from languages that it is trained on? (RQ2.2 Does clustered training improve the effectiveness of multilingual DPR models on out-of-distribution data from languages that it is trained on? (RQ2.3) Does clustered training help multilingual DPR models to generalize to new languages (zero-shot)? I show that clustered training improves the out-of-distribution and zero-shot performance of a DPR model without a clear loss in in-distribution performance using the Mr. TyDi [3] dataset. Finally, I propose a modified dual-encoder architecture that can perform both retrieval and reranking with the same model in a single forward pass. While dual encoder models can surpass traditional sparse retrieval methods, they lag behind two stage retrieval pipelines in retrieval quality. I propose a modification to the dual encoder model where a second representation is used to rerank the passages retrieved using the first representation. Here, a second stage model is not required and both representations are generated in a single forward pass from the dual encoder. I aim to answer the following research questions in this work: (RQ3.1), Can the same model be trained to effectively generate two representations intended for two uses? RQ3.2 Can the retrieval quality of the model be improved by simultaneously performing retrieval and reranking? (RQ3.3 What is the tradeoff between retrieval quality vs. latency and compute resource efficiency for the proposed method vs. a two stage retriever? I expect that my proposed architecture would improve the dual encoder retrieval quality without sacrificing throughput or needing more computational resources.|双编码器模型是一个密集的检索体系结构，由两个编码器模型组成，超越了传统的开放域检索稀疏检索方法[1]。但是，还有改进的空间，特别是当密集的检索器暴露于看不见的通道或查询时。考虑域外查询，即来自模型所在域以外的其他域的查询，准确性的损失可能是显著的。造成这种情况的一个主要因素是在培训期间上下文编码器和查询编码器可用的信息不匹配。常见的退休训练数据集包含绝大多数段落，其中只有一个来自段落的查询。我假设，这可能导致双编码器模型，特别是通道编码器，过分适合从给定通道的单个潜在查询，从而损害域外性能。在此基础上，我试图回答以下研究问题: (RQ1.1)在每篇文章包含多个查询的数据上训练 DPR 模型是否提高了模型的通用性？为了回答 RQ1.1的问题，我构建了对大多数段落有多个查询的生成数据集，并将在这些数据集上训练的密集段落检索器模型与(大多数)每个段落数据集上的单个查询训练的模型进行了比较。我展示了对具有多个查询的段落进行训练可以得到更好地推广到分布以外和域外测试数据集的模型[2]。在密集检索的上下文中，语言可以被认为是另一个领域。由于训练数据的稀缺性，在英语以外的语言中训练一个密集的检索模型尤其具有挑战性。本文提出了一种新的训练技术——聚类训练，旨在提高密集型检索器的检索质量，特别是在分布不均匀和零射击的情况下。我提出了以下研究问题: (RQ2.1)集群训练是否提高了多语言 DPR 模型在分布式数据上的有效性？(RQ2.2)集群培训是否提高了多语言 DPR 模型对来自其所受培训语言的分布外数据的有效性？(RQ2.2集群培训是否提高了多语言 DPR 模型对来自所受培训语言的分布外数据的有效性？(RQ2.3)集群训练是否有助于多语言 DPR 模型推广到新语言(零射击) ？我展示了使用 TyDi 先生[3]数据集的聚类训练改善了 DPR 模型的分布外性能和零射性能，而没有明显的分布内性能损失。最后，我提出了一个改进的双编码器体系结构，可以执行检索和重新排序与同一模型在一个前进通道。双编码器模型虽然可以超越传统的稀疏检索方法，但在检索质量上却落后于两个阶段的检索流程。我提议对双重编码器模型进行修改，其中使用第二种表示重新排列使用第一种表示检索的段落。在这里，不需要第二阶段模型，两种表示都是在双重编码器的一次前向传递中生成的。我的目标是在这项工作中回答以下的研究问题: (RQ3.1) ，能否训练同一个模型有效地生成两个表示意图两个用途？RQ3.2通过同时执行检索和重新排序可以提高模型的检索质量吗？(RQ3.3检索质量与延迟和计算资源效率之间的权衡是什么，提出的方法与一个两阶段检索？我希望我提出的架构能够在不牺牲吞吐量或者不需要更多计算资源的情况下提高双编码器的检索质量。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Dense+Passage+Retrieval:+Architectures+and+Augmentation+Methods)|0| |[Towards Trustworthy Recommender System: A Faithful and Responsible Recommendation Perspective](https://doi.org/10.1145/3539618.3591798)|Yang Zhang|University of Science and Technology of China, Hefei, China|Recommender systems (RecSys) become increasingly prevalent in modern society, offering personalized information filtering to alleviate information overload and significantly impacting various human online activities. Machine learning-based recommendation methods have been extensively developed in recent years to achieve more accurate recommendations, with some of these approaches having been extensively deployed in industrial applications, such as the Deep Interest Network (DIN). Despite their widespread use, researchers and practitioners have highlighted various trustworthiness issues inherent in these systems, including bias and promoting polarization issues. In order to better serve users and comply with regulations pertaining to recommendation algorithms established by different countries, it is essential to consider the trustworthiness issues of recommender systems. This research focuses on trustworthiness in recommendation from two perspectives of user-centered principles: faithfulness and responsibility. On the one hand, collected recommendation data may not faithfully reflect user preferences, especially those of the service stage, due to bias[2, 3] and temporal effects,[4,5]etc. Achieving faithful recommendations with such data is crucial to ensure user satisfaction, i.e., making recommendations faithfully reflect user preferences during the testing. On the other hand, recommender systems could not only cater to user preferences [1] but also unconsciously and unintentionally affect (or even manipulate) user preferences. In the recommendation process, controlling the influence of recommender systems, such as avoiding potential opinion polarization, to provide responsible recommendations is also an important aspect of building trustworthy recommender systems. Consequently, there raise four research questions on the two aspects: RQ1: How can we model genuine user preferences when training data fails to faithfully reflect the user's current preferences? RQ2: How can we ensure that recommender models faithfully match the user's future preferences? RQ3: How can we quantify and evaluate the impact of a recommender system on user preferences? RQ4: How can we control the impact of a recommender system on user preferences to avoid negative side effects? Our objective is to achieve faithful and responsible recommendations for users while addressing these research questions. We attribute unfaithful recommendation to the discrepancies between the training data and the service objectives, which we formulate as different data shift problems (RQ1 and RQ2). We provide systematic analyses for these data shift problems from causal perspectives and develop several causality-inspired solutions to enhance recommendation faithfulness. In pursuit of responsible recommendations, we investigate the effect of recommender systems on users from a causal perspective. We develop a causal effect evaluation and adjustment framework to quantify and control the influence of recommender systems on user preferences (RQ3 and RQ4).|推荐系统在现代社会越来越普遍，它提供个性化的信息过滤以减轻信息超载，并对各种人类在线活动产生重大影响。基于机器学习的推荐方法近年来得到了广泛的发展，以实现更准确的推荐，其中一些方法已经广泛应用于工业应用，如深度兴趣网络(DIN)。尽管它们被广泛使用，研究人员和从业人员还是强调了这些系统固有的各种可信问题，包括偏见和促进两极分化的问题。为了更好地为用户服务，遵守不同国家制定的关于推荐算法的条例，必须考虑推荐系统的可信度问题。本研究从以用户为中心的忠实性和责任性两个角度对推荐的可信性进行了研究。一方面，由于偏差[2,3]和时间效应[4,5]等原因，收集的推荐数据可能不能忠实地反映用户的偏好，尤其是服务阶段的偏好。使用这些数据实现忠实的推荐对于确保用户满意度至关重要，也就是说，在测试期间使推荐忠实地反映用户的偏好。另一方面，推荐系统不仅可以迎合用户的偏好，而且还可以无意识地影响(甚至操纵)用户的偏好。在推荐过程中，控制推荐系统的影响，如避免潜在的意见分化，提供负责任的推荐，也是建立可信推荐系统的一个重要方面。因此，在这两个方面提出了四个研究问题: RQ1: 当训练数据不能如实反映用户当前的偏好时，我们如何建立真实的用户偏好模型？RQ2: 我们如何确保推荐模型忠实地匹配用户未来的偏好？RQ3: 我们如何量化和评估推荐系统对用户偏好的影响？RQ4: 我们如何控制推荐系统对用户偏好的影响以避免负面影响？我们的目标是在解决这些研究问题的同时，为用户提供忠实和负责任的建议。我们将不忠实的推荐归因于培训数据和服务目标之间的差异，我们将这些差异表述为不同的数据转移问题(RQ1和 RQ2)。我们从因果关系的角度对这些数据转移问题进行了系统的分析，并提出了一些基于因果关系的解决方案，以提高推荐的忠实度。为了追求负责任的推荐，我们从因果关系的角度研究推荐系统对用户的影响。我们开发了一个因果效应评估和调整框架来量化和控制推荐系统对用户偏好的影响(RQ3和 RQ4)。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Towards+Trustworthy+Recommender+System:+A+Faithful+and+Responsible+Recommendation+Perspective)|0| -|[Adversarial Meta Prompt Tuning for Open Compound Domain Adaptive Intent Detection](https://doi.org/10.1145/3539618.3591945)|Feiteng Fang, Min Yang, Chengming Li, Ruifeng Xu|Harbin Institute of Technology (Shenzhen), Shenzhen, China; University of Science and Technology of China, Hefei, China; SIAT, Chinese Academy of Sciences, Shenzhen, China; Shenzhen MSU-BIT University, Shenzhen, China|Intent detection plays an essential role in dialogue systems. This paper takes the lead to study open compound domain adaptation (OCDA) for intent detection, which brings the advantage of improved generalization to unseen domains. OCDA for intent detection is indeed a more realistic domain adaptation setting, which learns an intent classifier from labeled source domains and adapts it to unlabeled compound target domains containing different intent classes with the source domains. At inference time, we test the intent classifier in open domains that contain previously unseen intent classes. To this end, we propose an Adversarial Meta Prompt Tuning method (called AMPT) for open compound domain adaptive intent detection. Concretely, we propose a meta prompt tuning method, which utilizes language prompts to elicit rich knowledge from large-scale pre-trained language models (PLMs) and automatically finds better prompt initialization that facilitates fast adaptation via meta learning. Furthermore, we leverage a domain adversarial training technique to acquire domain-invariant representations of diverse domains. By taking advantage of the collaborative effect of meta learning, prompt tuning, and adversarial training, we can learn an intent classifier that can effectively generalize to unseen open domains. Experimental results on two benchmark datasets (i.e., HWU64 and CLINC) show that our model can learn substantially better-generalized representations for unseen domains compared with strong competitors.|意图检测在对话系统中起着至关重要的作用。本文率先研究了开放式复合域自适应(OCDA)的意图检测技术，该技术具有对不可见域进行改进泛化的优点。用于意图检测的 OCDA 确实是一种更加现实的域适应设置，它从标记的源域中学习意图分类器，并将其适应于包含不同意图类别的未标记的源域复合目标域。在推理时，我们在包含以前看不到的意图类的开放域中测试意图分类器。为此，我们提出了一种用于开放复合域自适应意图检测的对抗元提示调整方法(AMPT)。具体地说，我们提出了一种元提示调优方法，该方法利用语言提示从大规模预训练语言模型(PLM)中获取丰富的知识，并自动发现更好的提示初始化，通过元学习促进快速适应。此外，我们利用领域对抗性训练技术来获得不同领域的领域不变表示。通过利用元学习、及时调优和对抗性训练的协作效应，我们可以学习一个意图分类器，它可以有效地推广到看不见的开放领域。在两个基准数据集(即 HWU64和 CLINC)上的实验结果表明，与强竞争对手相比，我们的模型能够更好地学习未知领域的广义表示。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Adversarial+Meta+Prompt+Tuning+for+Open+Compound+Domain+Adaptive+Intent+Detection)|0| +|[Adversarial Meta Prompt Tuning for Open Compound Domain Adaptive Intent Detection](https://doi.org/10.1145/3539618.3591945)|Feiteng Fang, Min Yang, Chengming Li, Ruifeng Xu|Shenzhen MSU-BIT University, Shenzhen, China; Harbin Institute of Technology (Shenzhen), Shenzhen, China; University of Science and Technology of China, Hefei, China; SIAT, Chinese Academy of Sciences, Shenzhen, China|Intent detection plays an essential role in dialogue systems. This paper takes the lead to study open compound domain adaptation (OCDA) for intent detection, which brings the advantage of improved generalization to unseen domains. OCDA for intent detection is indeed a more realistic domain adaptation setting, which learns an intent classifier from labeled source domains and adapts it to unlabeled compound target domains containing different intent classes with the source domains. At inference time, we test the intent classifier in open domains that contain previously unseen intent classes. To this end, we propose an Adversarial Meta Prompt Tuning method (called AMPT) for open compound domain adaptive intent detection. Concretely, we propose a meta prompt tuning method, which utilizes language prompts to elicit rich knowledge from large-scale pre-trained language models (PLMs) and automatically finds better prompt initialization that facilitates fast adaptation via meta learning. Furthermore, we leverage a domain adversarial training technique to acquire domain-invariant representations of diverse domains. By taking advantage of the collaborative effect of meta learning, prompt tuning, and adversarial training, we can learn an intent classifier that can effectively generalize to unseen open domains. Experimental results on two benchmark datasets (i.e., HWU64 and CLINC) show that our model can learn substantially better-generalized representations for unseen domains compared with strong competitors.|意图检测在对话系统中起着至关重要的作用。本文率先研究了开放式复合域自适应(OCDA)的意图检测技术，该技术具有对不可见域进行改进泛化的优点。用于意图检测的 OCDA 确实是一种更加现实的域适应设置，它从标记的源域中学习意图分类器，并将其适应于包含不同意图类别的未标记的源域复合目标域。在推理时，我们在包含以前看不到的意图类的开放域中测试意图分类器。为此，我们提出了一种用于开放复合域自适应意图检测的对抗元提示调整方法(AMPT)。具体地说，我们提出了一种元提示调优方法，该方法利用语言提示从大规模预训练语言模型(PLM)中获取丰富的知识，并自动发现更好的提示初始化，通过元学习促进快速适应。此外，我们利用领域对抗性训练技术来获得不同领域的领域不变表示。通过利用元学习、及时调优和对抗性训练的协作效应，我们可以学习一个意图分类器，它可以有效地推广到看不见的开放领域。在两个基准数据集(即 HWU64和 CLINC)上的实验结果表明，与强竞争对手相比，我们的模型能够更好地学习未知领域的广义表示。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Adversarial+Meta+Prompt+Tuning+for+Open+Compound+Domain+Adaptive+Intent+Detection)|0| |[Tahaqqaq: A Real-Time System for Assisting Twitter Users in Arabic Claim Verification](https://doi.org/10.1145/3539618.3591815)|Zien Sheikh Ali, Watheq Mansour, Fatima Haouari, Maram Hasanain, Tamer Elsayed, Abdulaziz AlAli|Qatar University, Doha, Qatar|Over the past years, notable progress has been made towards fighting misinformation spread over social media, encouraging the development of many fact-checking systems. However, systems that operate over Arabic content are scarce. In this work, we bridge this gap by proposing Tahaqqaq (Verify), an Arabic real-time system that helps users verify claims over Twitter with several functionalities, such as identifying check-worthy claims, estimating credibility of users in terms of spreading fake news, and finding authoritative accounts. Tahaqqaq has a friendly online Web interface that supports various real-time user scenarios. In the same breath, we enable public access to Tahaqqaq services through a handy RESTful API. Finally, in terms of performance, multiple components of Tahaqqaq outperform the state-of-the-art models on Arabic datasets.|过去几年来，在打击社交媒体上传播的错误信息方面取得了显著进展，促进了许多事实核查系统的发展。然而，在阿拉伯文内容上运行的系统很少。在这项工作中，我们通过提出 Tahaqqaq (验证)来弥补这一差距，这是一个阿拉伯语实时系统，可以帮助用户通过 Twitter 验证一些功能，比如识别值得检查的声明，评估用户在传播假新闻方面的可信度，以及查找权威账户。Tahaqqaq 有一个友好的在线 Web 界面，支持各种实时用户场景。同时，我们通过一个方便的 RESTful API 使公众能够访问 Tahaqqaq 服务。最后，在性能方面，Tahaqqaq 的多个组件优于阿拉伯数据集上的最先进模型。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Tahaqqaq:+A+Real-Time+System+for+Assisting+Twitter+Users+in+Arabic+Claim+Verification)|0| |[Building K-Anonymous User Cohorts with Consecutive Consistent Weighted Sampling (CCWS)](https://doi.org/10.1145/3539618.3591857)|Xinyi Zheng, Weijie Zhao, Xiaoyun Li, Ping Li|LinkedIn, Mountain View, USA|To retrieve personalized campaigns and creatives while protecting user privacy, digital advertising is shifting from member-based identity to cohort-based identity. Under such identity regime, an accurate and efficient cohort building algorithm is desired to group users with similar characteristics. In this paper, we propose a scalable $K$-anonymous cohort building algorithm called {\em consecutive consistent weighted sampling} (CCWS). The proposed method combines the spirit of the ($p$-powered) consistent weighted sampling and hierarchical clustering, so that the $K$-anonymity is ensured by enforcing a lower bound on the size of cohorts. Evaluations on a LinkedIn dataset consisting of $>70$M users and ads campaigns demonstrate that CCWS achieves substantial improvements over several hashing-based methods including sign random projections (SignRP), minwise hashing (MinHash), as well as the vanilla CWS.|为了在保护用户隐私的同时检索个性化活动和创意，数字广告正在从基于成员的身份转向基于队列的身份。在这种身份体制下，需要一种准确有效的队列生成算法来对具有相似特征的用户进行分组。本文提出了一种可扩展的 $K $匿名队列构建算法，称为{ em 连续一致加权抽样}(CCWS)。该方法结合了一致性加权抽样和层次聚类的思想，通过对队列大小的下界限制来保证 $K $匿名性。对一个由7000万美元以上的用户和广告活动组成的 LinkedIn 数据集的评估表明，CCWS 比几种基于散列的方法(包括符号随机投影(SignRP) ，minwise 散列(MinHash)以及普通的 CWS)取得了实质性的改进。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Building+K-Anonymous+User+Cohorts+with+Consecutive+Consistent+Weighted+Sampling+(CCWS))|0| |[On the "Rough Use" of Machine Learning Techniques](https://doi.org/10.1145/3539618.3591872)|ChihJen Lin|National Taiwan University & MBZUAI, Abu Dhabi & Taipei, Taiwan Roc|Machine learning is everywhere, but unfortunately, we are not experts of every method. Sometimes we "inappropriately'' use machine learning techniques. Examples include reporting training instead of test performance and comparing two methods without suitable hyper-parameter searches. However, the reality is that there are more sophisticated or more subtle examples, which we broadly call the "rough use'' of machine learning techniques. The setting may be roughly fine, but seriously speaking, is inappropriate. We briefly discuss two intriguing examples. - In the topic of graph representation learning, to evaluate the quality of the obtained representations, the multi-label problem of node classification is often considered. An unrealistic setting was used in almost the entire area by assuming that the number of labels of each test instance is known in the prediction stage. In practice, such ground truth information is rarely available. Details of this interesting story are in Lin et al. (2021). - In training deep neural networks, the optimization process often relies on the validation performance for termination or selecting the best epoch. Thus in many public repositories, training, validation, and test sets are explicitly provided. Many think this setting is standard in applying any machine learning technique. However, except that the test set should be completely independent, users can do whatever the best setting on all the available labeled data (i.e., training and validation sets combined). Through real stories, we show that many did not clearly see the relation between training, validation, and test sets. The rough use of machine learning methods is common and sometimes unavoidable. The reason is that nothing is called a perfect use of a machine learning method. Further, it is not easy to assess the seriousness of the situation. We argue that having high-quality and easy-to-use software is an important way to improve the practical use of machine learning techniques.|机器学习无处不在，但不幸的是，我们并不是每种方法的专家。有时我们“不恰当地”使用机器学习技术。例子包括报告训练而不是测试性能和比较两种方法没有合适的超参数搜索。然而，现实情况是，有更复杂或更微妙的例子，我们广泛地称为“粗略使用”的机器学习技术。设置可能大致没问题，但严肃地说，是不合适的。我们简要讨论两个有趣的例子。在图表示学习中，为了评价所获得表示的质量，经常考虑节点分类的多标号问题。通过假设每个测试实例的标签数量在预测阶段已知，在几乎整个区域中使用了不现实的设置。在实践中，这样的地面真相信息很少是可用的。这个有趣故事的细节在 Lin et al。(2021)。- 在训练深层神经网络时，优化过程往往取决于终止或选择最佳时期的验证性能。因此，在许多公共存储库中，显式地提供了培训、验证和测试集。许多人认为这种设置是应用任何机器学习技术的标准。然而，除了测试集应该是完全独立的以外，用户可以对所有可用的标记数据进行任何最佳设置(例如，训练集和验证集的组合)。通过真实的故事，我们发现许多人没有清楚地看到训练、验证和测试集之间的关系。粗略使用机器学习方法是常见的，有时也是不可避免的。原因在于，没有什么能称为机器学习方法的完美运用。此外，要评估形势的严重性并不容易。我们认为拥有高质量和易于使用的软件是提高机器学习技术实际应用的一个重要途径。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=On+the+"Rough+Use"+of+Machine+Learning+Techniques)|0| -|[Adapting Generative Pretrained Language Model for Open-domain Multimodal Sentence Summarization](https://doi.org/10.1145/3539618.3591633)|Dengtian Lin, Liqiang Jing, Xuemeng Song, Meng Liu, Teng Sun, Liqiang Nie|Shandong University, Qingdao, China; Harbin Institute of Technology (Shenzhen), Shenzhen, China; Shandong Jianzhu University, Jinan, China|Multimodal sentence summarization, aiming to generate a brief summary of the source sentence and image, is a new yet challenging task. Although existing methods have achieved compelling success, they still suffer from two key limitations: 1) lacking the adaptation of generative pre-trained language models for open-domain MMSS, and 2) lacking the explicit critical information modeling. To address these limitations, we propose a BART-MMSS framework, where BART is adopted as the backbone. To be specific, we propose a prompt-guided image encoding module to extract the source image feature. It leverages several soft to-be-learned prompts for image patch embedding, which facilitates the visual content injection to BART for open-domain MMSS tasks. Thereafter, we devise an explicit source critical token learning module to directly capture the critical tokens of the source sentence with the reference of the source image, where we incorporate explicit supervision to improve performance. Extensive experiments on a public dataset fully validate the superiority of our proposed method. In addition, the predicted tokens by the vision-guided key-token highlighting module can be easily understood by humans and hence improve the interpretability of our model.|多模态句子摘要是一项新的具有挑战性的任务，其目的是对源语句和意象进行简要的概括。虽然现有的方法已经取得了令人瞩目的成功，但它们仍然存在两个关键的局限性: 1)缺乏适应开放领域 MMSS 的生成性预训练语言模型，2)缺乏明确的关键信息建模。为了解决这些限制，我们提出了一个 BART-MMSS 框架，其中采用 BART 作为骨干。具体来说，我们提出了一个提示指导的图像编码模块来提取源图像的特征。它利用了几个软的要学习的提示为图像补丁嵌入，这促进了可视化内容注入到 BART 的开放域 MMSS 任务。然后，我们设计了一个显式的源关键标记学习模块，通过引用源图像直接捕获源语句的关键标记，在这个模块中我们加入了显式监督以提高性能。在一个公共数据集上的大量实验充分验证了该方法的优越性。此外，视觉引导的密钥标记高亮模块预测的标记可以很容易地被人类理解，从而提高了我们的模型的可解释性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Adapting+Generative+Pretrained+Language+Model+for+Open-domain+Multimodal+Sentence+Summarization)|0| +|[Adapting Generative Pretrained Language Model for Open-domain Multimodal Sentence Summarization](https://doi.org/10.1145/3539618.3591633)|Dengtian Lin, Liqiang Jing, Xuemeng Song, Meng Liu, Teng Sun, Liqiang Nie|Shandong Jianzhu University, Jinan, China; Harbin Institute of Technology (Shenzhen), Shenzhen, China; Shandong University, Qingdao, China|Multimodal sentence summarization, aiming to generate a brief summary of the source sentence and image, is a new yet challenging task. Although existing methods have achieved compelling success, they still suffer from two key limitations: 1) lacking the adaptation of generative pre-trained language models for open-domain MMSS, and 2) lacking the explicit critical information modeling. To address these limitations, we propose a BART-MMSS framework, where BART is adopted as the backbone. To be specific, we propose a prompt-guided image encoding module to extract the source image feature. It leverages several soft to-be-learned prompts for image patch embedding, which facilitates the visual content injection to BART for open-domain MMSS tasks. Thereafter, we devise an explicit source critical token learning module to directly capture the critical tokens of the source sentence with the reference of the source image, where we incorporate explicit supervision to improve performance. Extensive experiments on a public dataset fully validate the superiority of our proposed method. In addition, the predicted tokens by the vision-guided key-token highlighting module can be easily understood by humans and hence improve the interpretability of our model.|多模态句子摘要是一项新的具有挑战性的任务，其目的是对源语句和意象进行简要的概括。虽然现有的方法已经取得了令人瞩目的成功，但它们仍然存在两个关键的局限性: 1)缺乏适应开放领域 MMSS 的生成性预训练语言模型，2)缺乏明确的关键信息建模。为了解决这些限制，我们提出了一个 BART-MMSS 框架，其中采用 BART 作为骨干。具体来说，我们提出了一个提示指导的图像编码模块来提取源图像的特征。它利用了几个软的要学习的提示为图像补丁嵌入，这促进了可视化内容注入到 BART 的开放域 MMSS 任务。然后，我们设计了一个显式的源关键标记学习模块，通过引用源图像直接捕获源语句的关键标记，在这个模块中我们加入了显式监督以提高性能。在一个公共数据集上的大量实验充分验证了该方法的优越性。此外，视觉引导的密钥标记高亮模块预测的标记可以很容易地被人类理解，从而提高了我们的模型的可解释性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Adapting+Generative+Pretrained+Language+Model+for+Open-domain+Multimodal+Sentence+Summarization)|0| |[SciMine: An Efficient Systematic Prioritization Model Based on Richer Semantic Information](https://doi.org/10.1145/3539618.3591764)|Fang Guo, Yun Luo, Linyi Yang, Yue Zhang|Westlake University, Hangzhou, China|Systematic review is a crucial method that has been widely used. by scholars from different research domains. However, screening for relevant scientific literature from paper candidates remains an extremely time-consuming process so the task of screening prioritization has been established to reduce the human workload. Various methods under the human-in-the-loop fashion are proposed to solve this task by using lexical features. These methods, even though achieving better performance than more sophisticated feature-based models such as BERT, omit rich and essential semantic information, therefore suffered from feature bias. In this study, we propose a novel framework SciMine to accelerate this screening process by capturing semantic feature representations from both background and the corpus. In particular, based on contextual representation learned from the pre-trained language models, our approach utilizes an autoencoder-based classifier and a feature-dependent classification module to extract general document-level and phrase-level information. Then a ranking ensemble strategy is used to combine these two complementary pieces of information. Experiments on five real-world datasets demonstrate that SciMine achieves state-of-the-art performance and comprehensive analysis further shows the efficacy of SciMine to solve feature bias.|系统综述是一种被广泛使用的关键方法。来自不同研究领域的学者。然而，从论文候选人中筛选相关科学文献仍然是一个极其耗时的过程，因此确定了筛选优先次序的任务，以减少人的工作量。针对这一问题，本文提出了多种基于人在回路的方法，利用词汇特征来解决这一问题。这些方法即使比基于特征的更复杂的模型(如 BERT)获得了更好的性能，但是省略了丰富和必要的语义信息，因此受到了特征偏差的影响。在这项研究中，我们提出了一个新的框架 SciMine，通过从背景和语料库中获取语义特征来加速这个筛选过程。特别是基于预训练语言模型的上下文表示，该方法利用基于自动编码器的分类器和特征相关的分类模块来提取一般文档级和短语级信息。然后采用排序集成策略将这两个互补的信息片段进行组合。在五个实际数据集上的实验表明，SciMine 的性能达到了最高水平，综合分析进一步证明了 SciMine 解决特征偏差问题的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=SciMine:+An+Efficient+Systematic+Prioritization+Model+Based+on+Richer+Semantic+Information)|0| |[Towards Multi-Interest Pre-training with Sparse Capsule Network](https://doi.org/10.1145/3539618.3591778)|Zuoli Tang, Lin Wang, Lixin Zou, Xiaolu Zhang, Jun Zhou, Chenliang Li|Ant Group, Hangzhou, China; Wuhan University, Wuhan, China|The pre-training paradigm, i.e., learning universal knowledge across a wide spectrum of domains, has increasingly become a new de-facto practice in many fields, especially for transferring to new domains. The recent progress includes universal pre-training solutions for recommendation. However, we argue that the common treatment utilizing the masked language modeling or simple data augmentation via contrastive learning is not sufficient for pre-training a recommender system, since a user's intent could be more complex than predicting the next word or item. It is more intuitive to go a step further by devising the multi-interest driven pre-training framework for universal user understanding. Nevertheless, incorporating multi-interest modeling in recommender system pre-training is non-trivial due to the dynamic, contextual, and temporary nature of the user interests, particularly when the users are from different domains. The limited effort on this line has greatly rendered it as an open question. In this paper, we propose a novel Multi-Interest Pre-training with Sparse Capsule framework (named Miracle). Miracle performs a universal multi-interest modeling with a sparse capsule network and an interest-aware pre-training task. Specifically, we utilize a text-aware item embedding module, including an MoE adaptor and a deeply-contextual encoding component, to model contextual and transferable item representations. Then, we propose a sparse interest activation mechanism coupled with a position-aware capsule network for adaptive interest extraction. Furthermore, an interest-level contrastive pre-training task is introduced to guide the sparse capsule network to learn universal interests precisely. We conduct extensive experiments on eleven real-world datasets and eight baselines. The results show that our method significantly outperforms a series of SOTA on these benchmark datasets. The code is available at https://github.com/WHUIR/Miracle.|培训前范式，即学习广泛领域的普遍知识，已日益成为许多领域，特别是转移到新的领域的一种新的事实上的做法。最近的进展包括通用的培训前推荐解决方案。然而，我们认为使用掩蔽语言建模或通过对比学习进行简单数据增强的常规处理不足以预先训练推荐系统，因为用户的意图可能比预测下一个单词或项目更复杂。更直观的做法是设计多兴趣驱动的通用用户理解预训练框架。尽管如此，由于用户兴趣的动态性、上下文关联性和临时性，特别是当用户来自不同的领域时，将多推荐系统模型结合到预先培训中是非常重要的。在这方面的有限努力使它成为一个悬而未决的问题。本文提出了一种新的基于稀疏胶囊框架的多兴趣预训练算法(称为奇迹算法)。Miracle 使用稀疏胶囊网络和感兴趣的预训练任务执行通用的多兴趣建模。具体来说，我们利用文本感知项嵌入模块(包括 MoE 适配器和深度上下文编码组件)来建模上下文和可转移的项表示。然后，我们提出了一个稀疏兴趣激活机制耦合位置感知胶囊网络的自适应兴趣提取。引入兴趣级对比预训练任务，引导稀疏胶囊网络精确学习普遍兴趣。我们在十一个真实世界的数据集和8个基线上进行广泛的实验。结果表明，我们的方法在这些基准数据集上的性能明显优于一系列 SOTA。密码可在 https://github.com/whuir/miracle 查阅。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Towards+Multi-Interest+Pre-training+with+Sparse+Capsule+Network)|0| |[A Scalable Framework for Automatic Playlist Continuation on Music Streaming Services](https://doi.org/10.1145/3539618.3591628)|Walid Bendada, Guillaume SalhaGalvan, Thomas Bouabça, Tristan Cazenave|Université Paris Dauphine & PSL, CNRS, Paris, France; Deezer Research, Paris, France; Deezer Research & Université Paris Dauphine - PSL, Paris, France|Music streaming services often aim to recommend songs for users to extend the playlists they have created on these services. However, extending playlists while preserving their musical characteristics and matching user preferences remains a challenging task, commonly referred to as Automatic Playlist Continuation (APC). Besides, while these services often need to select the best songs to recommend in real-time and among large catalogs with millions of candidates, recent research on APC mainly focused on models with few scalability guarantees and evaluated on relatively small datasets. In this paper, we introduce a general framework to build scalable yet effective APC models for large-scale applications. Based on a represent-then-aggregate strategy, it ensures scalability by design while remaining flexible enough to incorporate a wide range of representation learning and sequence modeling techniques, e.g., based on Transformers. We demonstrate the relevance of this framework through in-depth experimental validation on Spotify's Million Playlist Dataset (MPD), the largest public dataset for APC. We also describe how, in 2022, we successfully leveraged this framework to improve APC in production on Deezer. We report results from a large-scale online A/B test on this service, emphasizing the practical impact of our approach in such a real-world application.|音乐流媒体服务通常旨在向用户推荐歌曲，以扩展他们在这些服务上创建的播放列表。然而，扩展播放列表，同时保留其音乐特征和匹配用户喜好仍然是一个具有挑战性的任务，通常被称为自动播放列表延续(APC)。此外，虽然这些服务往往需要在实时推荐和有数百万候选人的大型目录中选择最好的歌曲，但最近对 APC 的研究主要集中在几乎没有可扩展性保证的模型上，并在相对较小的数据集上进行评估。在本文中，我们介绍了一个通用的框架，以建立可扩展但有效的 APC 模型的大规模应用。基于先表示后聚合的策略，它通过设计来确保可扩展性，同时保持足够的灵活性，以结合广泛的表示学习和序列建模技术，例如，基于 Transformers。我们通过对 Spotify 的百万播放列表数据集(MPD)—— APC 最大的公共数据集——进行深入的实验验证，证明了该框架的相关性。我们还描述了，在2022年，我们如何成功地利用这个框架来改进 Deezer 上的 APC 生产。我们在这个服务上报告了大规模在线 A/B 测试的结果，强调了我们的方法在这样一个实际应用中的实际影响。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Scalable+Framework+for+Automatic+Playlist+Continuation+on+Music+Streaming+Services)|0| |[BotMoE: Twitter Bot Detection with Community-Aware Mixtures of Modal-Specific Experts](https://doi.org/10.1145/3539618.3591646)|Yuhan Liu, Zhaoxuan Tan, Heng Wang, Shangbin Feng, Qinghua Zheng, Minnan Luo|University of Washington, Seattle, WA, USA; Xi'an Jiaotong University, Xi'an, China|Twitter bot detection has become a crucial task in efforts to combat online misinformation, mitigate election interference, and curb malicious propaganda. However, advanced Twitter bots often attempt to mimic the characteristics of genuine users through feature manipulation and disguise themselves to fit in diverse user communities, posing challenges for existing Twitter bot detection models. To this end, we propose BotMoE, a Twitter bot detection framework that jointly utilizes multiple user information modalities (metadata, textual content, network structure) to improve the detection of deceptive bots. Furthermore, BotMoE incorporates a community-aware Mixture-of-Experts (MoE) layer to improve domain generalization and adapt to different Twitter communities. Specifically, BotMoE constructs modal-specific encoders for metadata features, textual content, and graphical structure, which jointly model Twitter users from three modal-specific perspectives. We then employ a community-aware MoE layer to automatically assign users to different communities and leverage the corresponding expert networks. Finally, user representations from metadata, text, and graph perspectives are fused with an expert fusion layer, combining all three modalities while measuring the consistency of user information. Extensive experiments demonstrate that BotMoE significantly advances the state-of-the-art on three Twitter bot detection benchmarks. Studies also confirm that BotMoE captures advanced and evasive bots, alleviates the reliance on training data, and better generalizes to new and previously unseen user communities.|在打击网络虚假信息、减轻选举干扰和遏制恶意宣传的努力中，Twitter 机器人检测已成为一项关键任务。然而，先进的 Twitter 机器人往往试图通过特征操作来模仿真实用户的特征，并伪装自己以适应不同的用户群体，这对现有的 Twitter 机器人检测模型构成了挑战。为此，我们提出了 BotMoE，这是一个 Twitter 机器人检测框架，它联合利用多种用户信息模式(元数据、文本内容、网络结构)来改善对欺骗性机器人的检测。此外，BotMoE 还包含了一个社区感知的专家混合(Miture-of-Expert，MoE)层，以提高域泛化能力并适应不同的 Twitter 社区。具体来说，BotMoE 为元数据特性、文本内容和图形结构构建了特定于模式的编码器，这些编码器从三个特定于模式的视角共同为 Twitter 用户建模。然后，我们使用一个社区感知的 MoE 层来自动将用户分配到不同的社区，并利用相应的专家网络。最后，将来自元数据、文本和图形视角的用户表示与专家融合层融合，在测量用户信息一致性的同时结合所有三种模式。大量的实验表明，BotMoE 在三个 Twitter 机器人检测基准上显著提高了最先进的水平。研究还证实，BotMoE 捕获先进和规避机器人，减轻对训练数据的依赖，并更好地推广到新的和以前未见的用户社区。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=BotMoE:+Twitter+Bot+Detection+with+Community-Aware+Mixtures+of+Modal-Specific+Experts)|0| |[Decoupled Hyperbolic Graph Attention Network for Cross-domain Named Entity Recognition](https://doi.org/10.1145/3539618.3591662)|Jingyun Xu, Yi Cai|South China University of Technology, Guangzhou, China|To address the scarcity of massive labeled data, cross-domain named entity recognition (cross-domain NER) attracts increasing attention. Recent studies focus on decomposing NER into two separate tasks (i.e., entity span detection and entity type classification) to reduce the complexity of the cross-domain transfer. Despite the promising results, there still exists room for improvement. In particular, the rich domain-shared syntactic and semantic information, which are respectively important for entity span detection and entity type classification, are still underutilized. In light of these two challenges, we propose applying graph attention networks (GATs) to encode the above two kinds of information. Moreover, considering that GATs mainly operate in the Euclidean space, which may fail to capture the latent hierarchical relations among words for learning high-quality word representations, we further propose to embed words into Hyperbolic spaces. Finally, a decouple hyperbolic graph attention network (DH-GAT) is introduced for cross-domain NER. Empirical results on 10 domain pairs show that DH-GAT achieves state-of-the-art performance on several standard metrics, and further analyses are presented to better understand each component's effectiveness.|为了解决海量标记数据稀缺的问题，跨域命名实体识别(cross-domain NER)越来越受到人们的关注。最近的研究集中于将 NER 分解为两个独立的任务(即实体跨度检测和实体类型分类) ，以降低跨域传输的复杂性。尽管取得了令人鼓舞的成果，但仍有改进的余地。特别是对于实体跨度检测和实体类型分类来说分别重要的丰富的域共享语法和语义信息仍然没有得到充分利用。针对这两个挑战，我们提出应用图注意网络(GAT)对上述两种信息进行编码。此外，考虑到 GAT 主要在欧几里德空间中运作，可能无法捕捉到词之间潜在的层次关系来学习高质量的词表示，我们进一步建议将词嵌入双曲空间。最后，针对跨域 NER，提出了一种解耦双曲图注意网络(DH-GAT)。对10个域对的实证结果表明，DH-GAT 在几个标准指标上达到了最佳性能，并进一步分析以更好地了解每个组件的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Decoupled+Hyperbolic+Graph+Attention+Network+for+Cross-domain+Named+Entity+Recognition)|0| -|[StreamE: Learning to Update Representations for Temporal Knowledge Graphs in Streaming Scenarios](https://doi.org/10.1145/3539618.3591772)|Jiasheng Zhang, Jie Shao, Bin Cui|Shenzhen Institute for Advanced Study, University of Electronic Science and Technology of China, Shenzhen, China; Peking University, Beijing, China; University of Electronic Science and Technology of China, Chengdu, China|Learning representations for temporal knowledge graphs (TKGs) is a fundamental task. Most existing methods regard TKG as a sequence of static snapshots and recurrently learn representations by retracing the previous snapshots. However, new knowledge can be continuously accrued to TKGs as streams. These methods either cannot handle new entities or fail to update representations in real time, making them unfeasible to adapt to the streaming scenarios. In this paper, we propose a lightweight framework called StreamE towards the efficient generation of TKG representations in streaming scenarios. To reduce the parameter size, entity representations in StreamE are decoupled from the model training to serve as the memory module to store the historical information of entities. To achieve efficient update and generation, the process of generating representations is decoupled as two functions in StreamE. An update function is learned to incrementally update entity representations based on the newly-arrived knowledge and a read function is learned to predict the future semantics of entity representations. The update function avoids the recurrent modeling paradigm and thus gains high efficiency while the read function considers multiple semantic change properties. We further propose a joint training strategy with two temporal regularizations to effectively optimize the framework. Experimental results show that StreamE can achieve better performance than baseline methods with 100x faster in inference, 25x faster in training, and only 1/5 parameter size, which demonstrates its superiority. Code is available at https://github.com/zjs123/StreamE.|时态知识图(TKG)的学习表示是一个基本的任务。大多数现有的方法将 TKG 视为一系列静态快照，并通过回溯以前的快照来反复学习表示法。然而，新的知识可以不断累积到 TKG 作为流。这些方法要么不能处理新的实体，要么不能实时更新表示，使它们无法适应流场景。在本文中，我们提出了一个称为 StreamE 的轻量级框架，用于在流场景中有效地生成 TKG 表示。为了减少参数的大小，StreamE 中的实体表示与模型训练解耦，作为存储实体历史信息的内存模块。为了实现有效的更新和生成，生成表示的过程被解耦为 StreamE 中的两个函数。学习更新函数以根据新到达的知识增量更新实体表示，学习读取函数以预测实体表示的未来语义。更新函数避免了重复建模范式，因此在读取函数考虑多种语义变化属性的情况下，获得了高效率。我们进一步提出了一个具有两个时间规则的联合训练策略，以有效地优化框架。实验结果表明，StreamE 方法的推理速度比基线方法快100倍，训练速度比基线方法快25倍，参数只有基线方法的1/5，表明了该方法的优越性。密码可于 https://github.com/zjs123/streame 索取。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=StreamE:+Learning+to+Update+Representations+for+Temporal+Knowledge+Graphs+in+Streaming+Scenarios)|0| -|[Understand the Dynamic World: An End-to-End Knowledge Informed Framework for Open Domain Entity State Tracking](https://doi.org/10.1145/3539618.3591781)|Mingchen Li, Lifu Huang|Georgia State University, ATLANTA, GA, USA; Virginia Tech, Blacksburg, VA, USA|Open domain entity state tracking aims to predict reasonable state changes of entities (i.e., [attribute] of [entity] was [before_state] and [after_state] afterwards) given the action descriptions. It's important to many reasoning tasks to support human everyday activities. However, it's challenging as the model needs to predict an arbitrary number of entity state changes caused by the action while most of the entities are implicitly relevant to the actions and their attributes as well as states are from open vocabularies. To tackle these challenges, we propose a novel end-to-end Knowledge Informed framework for open domain Entity State Tracking, namely KIEST, which explicitly retrieves the relevant entities and attributes from external knowledge graph (i.e., ConceptNet) and incorporates them to autoregressively generate all the entity state changes with a novel dynamic knowledge grained encoder-decoder framework. To enforce the logical coherence among the predicted entities, attributes, and states, we design a new constraint decoding strategy and employ a coherence reward to improve the decoding process. Experimental results show that our proposed KIEST framework significantly outperforms the strong baselines on the public benchmark dataset OpenPI.|开放域实体状态跟踪旨在根据行为描述预测实体的合理状态变化(即，[实体]的[属性]是[前 _ 状态]和[后 _ 状态])。对于许多推理任务来说，支持人类的日常活动是非常重要的。然而，这是具有挑战性的，因为模型需要预测由操作引起的任意数量的实体状态变化，而大多数实体都与操作隐式相关，并且它们的属性以及状态都来自开放词汇表。为了应对这些挑战，我们提出了一个新的开放领域实体状态跟踪端到端知识信息框架，即 KIEST，它显式地从外部知识图(即概念网)中检索相关实体和属性，并将它们合并在一个新的动态知识粒度编解码框架中自动回归生成所有实体状态变化。为了增强预测实体、属性和状态之间的逻辑一致性，我们设计了一种新的约束译码策略，并采用一致性奖励来改善译码过程。实验结果表明，我们提出的 KIEST 框架明显优于公共基准数据集 OpenPI 的强基线。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Understand+the+Dynamic+World:+An+End-to-End+Knowledge+Informed+Framework+for+Open+Domain+Entity+State+Tracking)|0| -|[BLADE: Combining Vocabulary Pruning and Intermediate Pretraining for Scaleable Neural CLIR](https://doi.org/10.1145/3539618.3591644)|Suraj Nair, Eugene Yang, Dawn J. Lawrie, James Mayfield, Douglas W. Oard|University of Maryland, College Park, MD, USA; Johns Hopkins University, Baltimore, MD, USA|Learning sparse representations using pretrained language models enhances the monolingual ranking effectiveness. Such representations are sparse vectors in the vocabulary of a language model projected from document terms. Extending such approaches to Cross-Language Information Retrieval (CLIR) using multilingual pretrained language models poses two challenges. First, the larger vocabularies of multilingual models affect both training and inference efficiency. Second, the representations of terms from different languages with similar meanings might not be sufficiently similar. To address these issues, we propose a learned sparse representation model, BLADE, combining vocabulary pruning with intermediate pre-training based on cross-language supervision. Our experiments reveal BLADE significantly reduces indexing time compared to its monolingual counterpart, SPLADE, on machine-translated documents, and it generates rankings with strengths complementary to those of other efficient CLIR methods.|使用预训练语言模型学习稀疏表示增强了单语言排序的有效性。这种表示是从文档术语投影出来的语言模型的词汇表中的稀疏向量。使用多语言预先训练的语言模型将这些方法扩展到跨语检索(CLIR)会带来两个挑战。首先，多语言模型的词汇量越大，训练效率和推理效率越高。其次，来自不同语言的具有相似意义的术语的表示可能不够相似。为了解决这些问题，我们提出了一种学习型稀疏表示模型 BLADE，该模型将词汇修剪和基于跨语言监控的中级预训结合起来。我们的实验显示，与单语言版本的 SPLADE 相比，BLADE 在机器翻译文档上大大减少了索引时间，而且它产生的排名与其他有效的 CLIR 方法相互补充。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=BLADE:+Combining+Vocabulary+Pruning+and+Intermediate+Pretraining+for+Scaleable+Neural+CLIR)|0| -|[Cross-Market Product-Related Question Answering](https://doi.org/10.1145/3539618.3591658)|Negin Ghasemi, Mohammad Aliannejadi, Hamed R. Bonab, Evangelos Kanoulas, Arjen P. de Vries, James Allan, Djoerd Hiemstra|Amazon Inc., Seattle, WA, USA; Radboud University, Nijmegen, Netherlands; University of Massachusetts Amherst, Amherst, MA, USA; University of Amsterdam, Amsterdam, Netherlands|Online shops such as Amazon, eBay, and Etsy continue to expand their presence in multiple countries, creating new resource-scarce marketplaces with thousands of items. We consider a marketplace to be resource-scarce when only limited user-generated data is available about the products (e.g., ratings, reviews, and product-related questions). In such a marketplace, an information retrieval system is less likely to help users find answers to their questions about the products. As a result, questions posted online may go unanswered for extended periods. This study investigates the impact of using available data in a resource-rich marketplace to answer new questions in a resource-scarce marketplace, a new problem we call cross-market question answering. To study this problem's potential impact, we collect and annotate a new dataset, XMarket-QA, from Amazon's UK (resource-scarce) and US (resource-rich) local marketplaces. We conduct a data analysis to understand the scope of the cross-market question-answering task. This analysis shows a temporal gap of almost one year between the first question answered in the UK marketplace and the US marketplace. Also, it shows that the first question about a product is posted in the UK marketplace only when 28 questions, on average, have already been answered about the same product in the US marketplace. Human annotations demonstrate that, on average, 65% of the questions in the UK marketplace can be answered within the US marketplace, supporting the concept of cross-market question answering. Inspired by these findings, we develop a new method, CMJim, which utilizes product similarities across marketplaces in the training phase for retrieving answers from the resource-rich marketplace that can be used to answer a question in the resource-scarce marketplace. Our evaluations show CMJim's significant improvement compared to competitive baselines.|亚马逊、 eBay 和 Etsy 等在线商店继续在多个国家扩张业务，创造了拥有数千种商品的新的资源稀缺市场。我们认为市场是资源稀缺的，当只有有限的用户生成的数据可用的产品(例如，评级，评论，和产品相关的问题)。在这样一个市场中，信息检索系统不太可能帮助用户找到有关产品的问题的答案。因此，网上发布的问题可能会长时间得不到回答。这项研究调查了在资源丰富的市场中使用可用数据来回答资源稀缺的市场中的新问题的影响，这个新问题我们称之为跨市场问题回答。为了研究这个问题的潜在影响，我们从亚马逊的英国(资源稀缺)和美国(资源丰富)本地市场收集并注释了一个新的数据集 XMarket-QA。我们进行数据分析，以了解跨市场问答任务的范围。这一分析表明，在英国市场回答的第一个问题与美国市场回答的第一个问题之间存在近一年的时间差。此外，它还显示，只有当平均28个关于同一产品的问题在美国市场上已经得到回答时，关于该产品的第一个问题才会在英国市场上发布。人工注释表明，平均65% 的问题在英国市场可以回答在美国市场，支持跨市场问题回答的概念。受这些发现的启发，我们开发了一种新的方法，CMJim，它利用培训阶段不同市场的产品相似性，从资源丰富的市场检索答案，这些答案可以用来回答资源稀缺的市场中的一个问题。我们的评估显示，与竞争基线相比，CMJim 的进步显著。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Cross-Market+Product-Related+Question+Answering)|0| +|[StreamE: Learning to Update Representations for Temporal Knowledge Graphs in Streaming Scenarios](https://doi.org/10.1145/3539618.3591772)|Jiasheng Zhang, Jie Shao, Bin Cui|Shenzhen Institute for Advanced Study, University of Electronic Science and Technology of China, Shenzhen, China; University of Electronic Science and Technology of China, Chengdu, China; Peking University, Beijing, China|Learning representations for temporal knowledge graphs (TKGs) is a fundamental task. Most existing methods regard TKG as a sequence of static snapshots and recurrently learn representations by retracing the previous snapshots. However, new knowledge can be continuously accrued to TKGs as streams. These methods either cannot handle new entities or fail to update representations in real time, making them unfeasible to adapt to the streaming scenarios. In this paper, we propose a lightweight framework called StreamE towards the efficient generation of TKG representations in streaming scenarios. To reduce the parameter size, entity representations in StreamE are decoupled from the model training to serve as the memory module to store the historical information of entities. To achieve efficient update and generation, the process of generating representations is decoupled as two functions in StreamE. An update function is learned to incrementally update entity representations based on the newly-arrived knowledge and a read function is learned to predict the future semantics of entity representations. The update function avoids the recurrent modeling paradigm and thus gains high efficiency while the read function considers multiple semantic change properties. We further propose a joint training strategy with two temporal regularizations to effectively optimize the framework. Experimental results show that StreamE can achieve better performance than baseline methods with 100x faster in inference, 25x faster in training, and only 1/5 parameter size, which demonstrates its superiority. Code is available at https://github.com/zjs123/StreamE.|时态知识图(TKG)的学习表示是一个基本的任务。大多数现有的方法将 TKG 视为一系列静态快照，并通过回溯以前的快照来反复学习表示法。然而，新的知识可以不断累积到 TKG 作为流。这些方法要么不能处理新的实体，要么不能实时更新表示，使它们无法适应流场景。在本文中，我们提出了一个称为 StreamE 的轻量级框架，用于在流场景中有效地生成 TKG 表示。为了减少参数的大小，StreamE 中的实体表示与模型训练解耦，作为存储实体历史信息的内存模块。为了实现有效的更新和生成，生成表示的过程被解耦为 StreamE 中的两个函数。学习更新函数以根据新到达的知识增量更新实体表示，学习读取函数以预测实体表示的未来语义。更新函数避免了重复建模范式，因此在读取函数考虑多种语义变化属性的情况下，获得了高效率。我们进一步提出了一个具有两个时间规则的联合训练策略，以有效地优化框架。实验结果表明，StreamE 方法的推理速度比基线方法快100倍，训练速度比基线方法快25倍，参数只有基线方法的1/5，表明了该方法的优越性。密码可于 https://github.com/zjs123/streame 索取。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=StreamE:+Learning+to+Update+Representations+for+Temporal+Knowledge+Graphs+in+Streaming+Scenarios)|0| +|[Understand the Dynamic World: An End-to-End Knowledge Informed Framework for Open Domain Entity State Tracking](https://doi.org/10.1145/3539618.3591781)|Mingchen Li, Lifu Huang|Virginia Tech, Blacksburg, VA, USA; Georgia State University, ATLANTA, GA, USA|Open domain entity state tracking aims to predict reasonable state changes of entities (i.e., [attribute] of [entity] was [before_state] and [after_state] afterwards) given the action descriptions. It's important to many reasoning tasks to support human everyday activities. However, it's challenging as the model needs to predict an arbitrary number of entity state changes caused by the action while most of the entities are implicitly relevant to the actions and their attributes as well as states are from open vocabularies. To tackle these challenges, we propose a novel end-to-end Knowledge Informed framework for open domain Entity State Tracking, namely KIEST, which explicitly retrieves the relevant entities and attributes from external knowledge graph (i.e., ConceptNet) and incorporates them to autoregressively generate all the entity state changes with a novel dynamic knowledge grained encoder-decoder framework. To enforce the logical coherence among the predicted entities, attributes, and states, we design a new constraint decoding strategy and employ a coherence reward to improve the decoding process. Experimental results show that our proposed KIEST framework significantly outperforms the strong baselines on the public benchmark dataset OpenPI.|开放域实体状态跟踪旨在根据行为描述预测实体的合理状态变化(即，[实体]的[属性]是[前 _ 状态]和[后 _ 状态])。对于许多推理任务来说，支持人类的日常活动是非常重要的。然而，这是具有挑战性的，因为模型需要预测由操作引起的任意数量的实体状态变化，而大多数实体都与操作隐式相关，并且它们的属性以及状态都来自开放词汇表。为了应对这些挑战，我们提出了一个新的开放领域实体状态跟踪端到端知识信息框架，即 KIEST，它显式地从外部知识图(即概念网)中检索相关实体和属性，并将它们合并在一个新的动态知识粒度编解码框架中自动回归生成所有实体状态变化。为了增强预测实体、属性和状态之间的逻辑一致性，我们设计了一种新的约束译码策略，并采用一致性奖励来改善译码过程。实验结果表明，我们提出的 KIEST 框架明显优于公共基准数据集 OpenPI 的强基线。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Understand+the+Dynamic+World:+An+End-to-End+Knowledge+Informed+Framework+for+Open+Domain+Entity+State+Tracking)|0| +|[BLADE: Combining Vocabulary Pruning and Intermediate Pretraining for Scaleable Neural CLIR](https://doi.org/10.1145/3539618.3591644)|Suraj Nair, Eugene Yang, Dawn J. Lawrie, James Mayfield, Douglas W. Oard|Johns Hopkins University, Baltimore, MD, USA; University of Maryland, College Park, MD, USA|Learning sparse representations using pretrained language models enhances the monolingual ranking effectiveness. Such representations are sparse vectors in the vocabulary of a language model projected from document terms. Extending such approaches to Cross-Language Information Retrieval (CLIR) using multilingual pretrained language models poses two challenges. First, the larger vocabularies of multilingual models affect both training and inference efficiency. Second, the representations of terms from different languages with similar meanings might not be sufficiently similar. To address these issues, we propose a learned sparse representation model, BLADE, combining vocabulary pruning with intermediate pre-training based on cross-language supervision. Our experiments reveal BLADE significantly reduces indexing time compared to its monolingual counterpart, SPLADE, on machine-translated documents, and it generates rankings with strengths complementary to those of other efficient CLIR methods.|使用预训练语言模型学习稀疏表示增强了单语言排序的有效性。这种表示是从文档术语投影出来的语言模型的词汇表中的稀疏向量。使用多语言预先训练的语言模型将这些方法扩展到跨语检索(CLIR)会带来两个挑战。首先，多语言模型的词汇量越大，训练效率和推理效率越高。其次，来自不同语言的具有相似意义的术语的表示可能不够相似。为了解决这些问题，我们提出了一种学习型稀疏表示模型 BLADE，该模型将词汇修剪和基于跨语言监控的中级预训结合起来。我们的实验显示，与单语言版本的 SPLADE 相比，BLADE 在机器翻译文档上大大减少了索引时间，而且它产生的排名与其他有效的 CLIR 方法相互补充。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=BLADE:+Combining+Vocabulary+Pruning+and+Intermediate+Pretraining+for+Scaleable+Neural+CLIR)|0| +|[Cross-Market Product-Related Question Answering](https://doi.org/10.1145/3539618.3591658)|Negin Ghasemi, Mohammad Aliannejadi, Hamed R. Bonab, Evangelos Kanoulas, Arjen P. de Vries, James Allan, Djoerd Hiemstra|Radboud University, Nijmegen, Netherlands; University of Massachusetts Amherst, Amherst, MA, USA; Amazon Inc., Seattle, WA, USA; University of Amsterdam, Amsterdam, Netherlands|Online shops such as Amazon, eBay, and Etsy continue to expand their presence in multiple countries, creating new resource-scarce marketplaces with thousands of items. We consider a marketplace to be resource-scarce when only limited user-generated data is available about the products (e.g., ratings, reviews, and product-related questions). In such a marketplace, an information retrieval system is less likely to help users find answers to their questions about the products. As a result, questions posted online may go unanswered for extended periods. This study investigates the impact of using available data in a resource-rich marketplace to answer new questions in a resource-scarce marketplace, a new problem we call cross-market question answering. To study this problem's potential impact, we collect and annotate a new dataset, XMarket-QA, from Amazon's UK (resource-scarce) and US (resource-rich) local marketplaces. We conduct a data analysis to understand the scope of the cross-market question-answering task. This analysis shows a temporal gap of almost one year between the first question answered in the UK marketplace and the US marketplace. Also, it shows that the first question about a product is posted in the UK marketplace only when 28 questions, on average, have already been answered about the same product in the US marketplace. Human annotations demonstrate that, on average, 65% of the questions in the UK marketplace can be answered within the US marketplace, supporting the concept of cross-market question answering. Inspired by these findings, we develop a new method, CMJim, which utilizes product similarities across marketplaces in the training phase for retrieving answers from the resource-rich marketplace that can be used to answer a question in the resource-scarce marketplace. Our evaluations show CMJim's significant improvement compared to competitive baselines.|亚马逊、 eBay 和 Etsy 等在线商店继续在多个国家扩张业务，创造了拥有数千种商品的新的资源稀缺市场。我们认为市场是资源稀缺的，当只有有限的用户生成的数据可用的产品(例如，评级，评论，和产品相关的问题)。在这样一个市场中，信息检索系统不太可能帮助用户找到有关产品的问题的答案。因此，网上发布的问题可能会长时间得不到回答。这项研究调查了在资源丰富的市场中使用可用数据来回答资源稀缺的市场中的新问题的影响，这个新问题我们称之为跨市场问题回答。为了研究这个问题的潜在影响，我们从亚马逊的英国(资源稀缺)和美国(资源丰富)本地市场收集并注释了一个新的数据集 XMarket-QA。我们进行数据分析，以了解跨市场问答任务的范围。这一分析表明，在英国市场回答的第一个问题与美国市场回答的第一个问题之间存在近一年的时间差。此外，它还显示，只有当平均28个关于同一产品的问题在美国市场上已经得到回答时，关于该产品的第一个问题才会在英国市场上发布。人工注释表明，平均65% 的问题在英国市场可以回答在美国市场，支持跨市场问题回答的概念。受这些发现的启发，我们开发了一种新的方法，CMJim，它利用培训阶段不同市场的产品相似性，从资源丰富的市场检索答案，这些答案可以用来回答资源稀缺的市场中的一个问题。我们的评估显示，与竞争基线相比，CMJim 的进步显著。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Cross-Market+Product-Related+Question+Answering)|0| |[ErrorCLR: Semantic Error Classification, Localization and Repair for Introductory Programming Assignments](https://doi.org/10.1145/3539618.3591680)|Siqi Han, Yu Wang, Xuesong Lu|East China Normal University, Shanghai, China|Programming education at scale increasingly relies on automated feedback to help students learn to program. An important form of feedback is to point out semantic errors in student programs and provide hints for program repair. Such automated feedback depends essentially on solving the tasks of classification, localization and repair of semantic errors. Although there are datasets for the tasks, we observe that they do not have the annotations supporting all three tasks. As such, existing approaches for semantic error feedback treat error classification, localization and repair as independent tasks, resulting in sub-optimal performance on each task. Moreover, existing datasets either contain few programming assignments or have few programs for each assignment. Therefore, existing approaches often leverage rule-based methods and evaluate them with a small number of programming assignments. To tackle the problems, we first describe the creation of a new dataset COJ2022 that contains 5,914 C programs with semantic errors submitted to 498 different assignments in an introductory programming course, where each program is annotated with the error types and locations and is coupled with the repaired program submitted by the same student. We show the advantages of COJ2022 over existing datasets on various aspects. Second, we treat semantic error classification, localization and repair as dependent tasks, and propose a novel two-stage method ErrorCLR to solve them. Specifically, in the first stage we train a model based on graph matching networks to jointly classify and localize potential semantic errors in student programs, and in the second stage we mask error spans in buggy programs using information of error types and locations and train a CodeT5 model to predict correct spans. The predicted spans replace the error spans to form repaired programs. Experimental results show that ErrorCLR remarkably outperforms the comparative methods for all three tasks on COJ2022 and other public datasets. We also conduct a case study to visualize and interpret what is learned by the graph matching network in ErrorCLR. We have released the source code and COJ2022 at https://github.com/DaSESmartEdu/ErrorCLR.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=ErrorCLR:+Semantic+Error+Classification,+Localization+and+Repair+for+Introductory+Programming+Assignments)|0| -|[Dual Semantic Knowledge Composed Multimodal Dialog Systems](https://doi.org/10.1145/3539618.3591673)|Xiaolin Chen, Xuemeng Song, Yinwei Wei, Liqiang Nie, TatSeng Chua|School of Software, Joint SDU-NTU Centre for Artificial Intelligence Research, Shandong University, Jinan, China; School of Computer Science and Technology, Shandong University, Qingdao, China; School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen), Shenzhen, Singapore; School of Computing, National University of Singapore, Singapore, Singapore|Textual response generation is an essential task for multimodal task-oriented dialog systems.Although existing studies have achieved fruitful progress, they still suffer from two critical limitations: 1) focusing on the attribute knowledge but ignoring the relation knowledge that can reveal the correlations between different entities and hence promote the response generation}, and 2) only conducting the cross-entropy loss based output-level supervision but lacking the representation-level regularization. To address these limitations, we devise a novel multimodal task-oriented dialog system (named MDS-S2). Specifically, MDS-S2 first simultaneously acquires the context related attribute and relation knowledge from the knowledge base, whereby the non-intuitive relation knowledge is extracted by the n-hop graph walk. Thereafter, considering that the attribute knowledge and relation knowledge can benefit the responding to different levels of questions, we design a multi-level knowledge composition module in MDS-S2 to obtain the latent composed response representation. Moreover, we devise a set of latent query variables to distill the semantic information from the composed response representation and the ground truth response representation, respectively, and thus conduct the representation-level semantic regularization. Extensive experiments on a public dataset have verified the superiority of our proposed MDS-S2. We have released the codes and parameters to facilitate the research community.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Dual+Semantic+Knowledge+Composed+Multimodal+Dialog+Systems)|0| +|[Dual Semantic Knowledge Composed Multimodal Dialog Systems](https://doi.org/10.1145/3539618.3591673)|Xiaolin Chen, Xuemeng Song, Yinwei Wei, Liqiang Nie, TatSeng Chua|School of Computer Science and Technology, Shandong University, Qingdao, China; School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen), Shenzhen, Singapore; School of Software, Joint SDU-NTU Centre for Artificial Intelligence Research, Shandong University, Jinan, China; School of Computing, National University of Singapore, Singapore, Singapore|Textual response generation is an essential task for multimodal task-oriented dialog systems.Although existing studies have achieved fruitful progress, they still suffer from two critical limitations: 1) focusing on the attribute knowledge but ignoring the relation knowledge that can reveal the correlations between different entities and hence promote the response generation}, and 2) only conducting the cross-entropy loss based output-level supervision but lacking the representation-level regularization. To address these limitations, we devise a novel multimodal task-oriented dialog system (named MDS-S2). Specifically, MDS-S2 first simultaneously acquires the context related attribute and relation knowledge from the knowledge base, whereby the non-intuitive relation knowledge is extracted by the n-hop graph walk. Thereafter, considering that the attribute knowledge and relation knowledge can benefit the responding to different levels of questions, we design a multi-level knowledge composition module in MDS-S2 to obtain the latent composed response representation. Moreover, we devise a set of latent query variables to distill the semantic information from the composed response representation and the ground truth response representation, respectively, and thus conduct the representation-level semantic regularization. Extensive experiments on a public dataset have verified the superiority of our proposed MDS-S2. We have released the codes and parameters to facilitate the research community.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Dual+Semantic+Knowledge+Composed+Multimodal+Dialog+Systems)|0| |[Mixup-based Unified Framework to Overcome Gender Bias Resurgence](https://doi.org/10.1145/3539618.3591938)|Liu Yu, Yuzhou Mao, Jin Wu, Fan Zhou|University of Electronic Science and Technology of China & Kashi Institute of Electronics and Information Industry, Chengdu, China; University of Electronic Science and Technology of China, Chengdu, China|Unwanted social biases are usually encoded in pretrained language models (PLMs). Recent efforts are devoted to mitigating intrinsic bias encoded in PLMs. However, the separate fine-tuning on applications is detrimental to intrinsic debiasing. A bias resurgence issue arises when fine-tuning the debiased PLMs on downstream tasks. To eliminate undesired stereotyped associations in PLMs during fine-tuning, we present a mixup-based framework Mix-Debias from a new unified perspective, which directly combines debiasing PLMs with fine-tuning applications. The key to Mix-Debias is applying mixup-based linear interpolation on counterfactually augmented downstream datasets, with expanded pairs from external corpora. Besides, we devised an alignment regularizer to ensure original augmented pairs and gender-balanced counterparts are spatially closer. Experimental results show that Mix-Debias can reduce biases in PLMs while maintaining a promising performance in applications.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Mixup-based+Unified+Framework+to+Overcome+Gender+Bias+Resurgence)|0| -|[Calibration Learning for Few-shot Novel Product Description](https://doi.org/10.1145/3539618.3591959)|Zheng Liu, Mingjing Wu, Bo Peng, Yichao Liu, Qi Peng, Chong Zou|Newcastle University, Newcastle upon Tyne, United Kingdom; China Institute of Atomic Energy, Beijing, China; University College London, London, United Kingdom; Nanyang Technological University, Singapore, Singapore|In the field of E-commerce, the rapid introduction of new products poses challenges for product description generation. Traditional approaches rely on large labelled datasets, which are often unavailable for novel products with limited data. To address this issue, we propose a calibration learning approach for few-shot novel product description. Our method leverages a small amount of labelled data for calibration and utilizes the novel product's semantic representation as prompts to generate accurate and informative descriptions. We evaluate our approach on three large-scale e-commerce datasets of novel products and demonstrate its effectiveness in significantly improving the quality of generated product descriptions compared to existing methods, especially when only limited data is available. We also conduct the analysis to understand the impact of different modules on the performance.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Calibration+Learning+for+Few-shot+Novel+Product+Description)|0| -|[Decomposing Logits Distillation for Incremental Named Entity Recognition](https://doi.org/10.1145/3539618.3591970)|Duzhen Zhang, Yahan Yu, Feilong Chen, Xiuyi Chen|Baidu Inc., Beijing, China; Huawei Inc., Beijing, China|Incremental Named Entity Recognition (INER) aims to continually train a model with new data, recognizing emerging entity types without forgetting previously learned ones. Prior INER methods have shown that Logits Distillation (LD), which involves preserving predicted logits via knowledge distillation, effectively alleviates this challenging issue. In this paper, we discover that a predicted logit can be decomposed into two terms that measure the likelihood of an input token belonging to a specific entity type or not. However, the traditional LD only preserves the sum of these two terms without considering the change in each component. To explicitly constrain each term, we propose a novel Decomposing Logits Distillation (DLD) method, enhancing the model's ability to retain old knowledge and mitigate catastrophic forgetting. Moreover, DLD is model-agnostic and easy to implement. Extensive experiments show that DLD consistently improves the performance of state-of-the-art INER methods across ten INER settings in three datasets.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Decomposing+Logits+Distillation+for+Incremental+Named+Entity+Recognition)|0| -|[Examining the Impact of Uncontrolled Variables on Physiological Signals in User Studies for Information Processing Activities](https://doi.org/10.1145/3539618.3591981)|Kaixin Ji, Damiano Spina, Danula Hettiachchi, Flora Dilys Salim, Falk Scholer|RMIT University, Melbourne, VIC, Australia; The University of New South Wales, Sydney, NSW, Australia|Physiological signals can potentially be applied as objective measures to understand the behavior and engagement of users interacting with information access systems. However, the signals are highly sensitive, and many controls are required in laboratory user studies. To investigate the extent to which controlled or uncontrolled (i.e., confounding) variables such as task sequence or duration influence the observed signals, we conducted a pilot study where each participant completed four types of information-processing activities (READ, LISTEN, SPEAK, and WRITE). Meanwhile, we collected data on blood volume pulse, electrodermal activity, and pupil responses. We then used machine learning approaches as a mechanism to examine the influence of controlled and uncontrolled variables that commonly arise in user studies. Task duration was found to have a substantial effect on the model performance, suggesting it represents individual differences rather than giving insight into the target variables. This work contributes to our understanding of such variables in using physiological signals in information retrieval user studies.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Examining+the+Impact+of+Uncontrolled+Variables+on+Physiological+Signals+in+User+Studies+for+Information+Processing+Activities)|0| -|[Fairness for both Readers and Authors: Evaluating Summaries of User Generated Content](https://doi.org/10.1145/3539618.3591986)|Garima Chhikara, Kripabandhu Ghosh, Saptarshi Ghosh, Abhijnan Chakraborty|Indian Institute of Technology, Delhi Technological University, New Delhi, India; Indian Institute of Science Education and Research Kolkata, Mohanpur, India; Indian Institute of Technology Kharagpur, Kharagpur, India; Indian Institute of Technology Delhi, New Delhi, India|Summarization of textual content has many applications, ranging from summarizing long documents to recent efforts towards summarizing user generated text (e.g., tweets, Facebook or Reddit posts). Traditionally, the focus of summarization has been to generate summaries which can best satisfy the readers. In this work, we look at summarization of user-generated content as a two-sided problem where satisfaction of both readers and authors is crucial. Through three surveys, we show that for user-generated content, traditional evaluation approach of measuring similarity between reference summaries and algorithmic summaries cannot capture author satisfaction. We propose an author satisfaction-based evaluation metric CROSSEM which, we show empirically, can potentially complement the current evaluation paradigm. We further propose the idea of inequality in satisfaction, to account for individual fairness amongst readers and authors. To our knowledge, this is the first attempt towards developing a fair summary evaluation framework for user generated content, and is likely to spawn lot of future research in this space.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Fairness+for+both+Readers+and+Authors:+Evaluating+Summaries+of+User+Generated+Content)|0| +|[Calibration Learning for Few-shot Novel Product Description](https://doi.org/10.1145/3539618.3591959)|Zheng Liu, Mingjing Wu, Bo Peng, Yichao Liu, Qi Peng, Chong Zou|University College London, London, United Kingdom; China Institute of Atomic Energy, Beijing, China; Newcastle University, Newcastle upon Tyne, United Kingdom; Nanyang Technological University, Singapore, Singapore|In the field of E-commerce, the rapid introduction of new products poses challenges for product description generation. Traditional approaches rely on large labelled datasets, which are often unavailable for novel products with limited data. To address this issue, we propose a calibration learning approach for few-shot novel product description. Our method leverages a small amount of labelled data for calibration and utilizes the novel product's semantic representation as prompts to generate accurate and informative descriptions. We evaluate our approach on three large-scale e-commerce datasets of novel products and demonstrate its effectiveness in significantly improving the quality of generated product descriptions compared to existing methods, especially when only limited data is available. We also conduct the analysis to understand the impact of different modules on the performance.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Calibration+Learning+for+Few-shot+Novel+Product+Description)|0| +|[Decomposing Logits Distillation for Incremental Named Entity Recognition](https://doi.org/10.1145/3539618.3591970)|Duzhen Zhang, Yahan Yu, Feilong Chen, Xiuyi Chen|Huawei Inc., Beijing, China; Baidu Inc., Beijing, China|Incremental Named Entity Recognition (INER) aims to continually train a model with new data, recognizing emerging entity types without forgetting previously learned ones. Prior INER methods have shown that Logits Distillation (LD), which involves preserving predicted logits via knowledge distillation, effectively alleviates this challenging issue. In this paper, we discover that a predicted logit can be decomposed into two terms that measure the likelihood of an input token belonging to a specific entity type or not. However, the traditional LD only preserves the sum of these two terms without considering the change in each component. To explicitly constrain each term, we propose a novel Decomposing Logits Distillation (DLD) method, enhancing the model's ability to retain old knowledge and mitigate catastrophic forgetting. Moreover, DLD is model-agnostic and easy to implement. Extensive experiments show that DLD consistently improves the performance of state-of-the-art INER methods across ten INER settings in three datasets.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Decomposing+Logits+Distillation+for+Incremental+Named+Entity+Recognition)|0| +|[Examining the Impact of Uncontrolled Variables on Physiological Signals in User Studies for Information Processing Activities](https://doi.org/10.1145/3539618.3591981)|Kaixin Ji, Damiano Spina, Danula Hettiachchi, Flora Dilys Salim, Falk Scholer|The University of New South Wales, Sydney, NSW, Australia; RMIT University, Melbourne, VIC, Australia|Physiological signals can potentially be applied as objective measures to understand the behavior and engagement of users interacting with information access systems. However, the signals are highly sensitive, and many controls are required in laboratory user studies. To investigate the extent to which controlled or uncontrolled (i.e., confounding) variables such as task sequence or duration influence the observed signals, we conducted a pilot study where each participant completed four types of information-processing activities (READ, LISTEN, SPEAK, and WRITE). Meanwhile, we collected data on blood volume pulse, electrodermal activity, and pupil responses. We then used machine learning approaches as a mechanism to examine the influence of controlled and uncontrolled variables that commonly arise in user studies. Task duration was found to have a substantial effect on the model performance, suggesting it represents individual differences rather than giving insight into the target variables. This work contributes to our understanding of such variables in using physiological signals in information retrieval user studies.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Examining+the+Impact+of+Uncontrolled+Variables+on+Physiological+Signals+in+User+Studies+for+Information+Processing+Activities)|0| +|[Fairness for both Readers and Authors: Evaluating Summaries of User Generated Content](https://doi.org/10.1145/3539618.3591986)|Garima Chhikara, Kripabandhu Ghosh, Saptarshi Ghosh, Abhijnan Chakraborty|Indian Institute of Science Education and Research Kolkata, Mohanpur, India; Indian Institute of Technology, Delhi Technological University, New Delhi, India; Indian Institute of Technology Kharagpur, Kharagpur, India; Indian Institute of Technology Delhi, New Delhi, India|Summarization of textual content has many applications, ranging from summarizing long documents to recent efforts towards summarizing user generated text (e.g., tweets, Facebook or Reddit posts). Traditionally, the focus of summarization has been to generate summaries which can best satisfy the readers. In this work, we look at summarization of user-generated content as a two-sided problem where satisfaction of both readers and authors is crucial. Through three surveys, we show that for user-generated content, traditional evaluation approach of measuring similarity between reference summaries and algorithmic summaries cannot capture author satisfaction. We propose an author satisfaction-based evaluation metric CROSSEM which, we show empirically, can potentially complement the current evaluation paradigm. We further propose the idea of inequality in satisfaction, to account for individual fairness amongst readers and authors. To our knowledge, this is the first attempt towards developing a fair summary evaluation framework for user generated content, and is likely to spawn lot of future research in this space.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Fairness+for+both+Readers+and+Authors:+Evaluating+Summaries+of+User+Generated+Content)|0| |[Limitations of Open-Domain Question Answering Benchmarks for Document-level Reasoning](https://doi.org/10.1145/3539618.3592011)|Ehsan Kamalloo, Charles L. A. Clarke, Davood Rafiei|University of Waterloo, Waterloo, ON, Canada; University of Alberta, Edmonton, AB, Canada|Many recent QA models retrieve answers from passages, rather than whole documents, due to the limitations of deep learning models with limited context size. However, this approach ignores important document-level cues that can be crucial in answering questions. This paper reviews three open-domain QA benchmarks from a document-level perspective and finds that they are biased towards passage-level information. Out of 17,000 assessed questions, 82 were identified as requiring document-level reasoning and could not be answered by passage-based models. Document-level retrieval (BM25) outperformed both dense and sparse passage-level retrieval on these questions, highlighting the need for more evaluation of models' ability to understand documents, an often-overlooked challenge in open-domain QA.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Limitations+of+Open-Domain+Question+Answering+Benchmarks+for+Document-level+Reasoning)|0| |[MDDL: A Framework for Reinforcement Learning-based Position Allocation in Multi-Channel Feed](https://doi.org/10.1145/3539618.3592018)|Xiaowen Shi, Ze Wang, Yuanying Cai, Xiaoxu Wu, Fan Yang, Guogang Liao, Yongkang Wang, Xingxing Wang, Dong Wang|Meituan, Beijing, China; Tsinghua Universing, Beijing, China|Nowadays, the mainstream approach in position allocation system is to utilize a reinforcement learning model to allocate appropriate locations for items in various channels and then mix them into the feed. There are two types of data employed to train reinforcement learning (RL) model for position allocation, named strategy data and random data. Strategy data is collected from the current online model, it suffers from an imbalanced distribution of state-action pairs, resulting in severe overestimation problems during training. On the other hand, random data offers a more uniform distribution of state-action pairs, but is challenging to obtain in industrial scenarios as it could negatively impact platform revenue and user experience due to random exploration. As the two types of data have different distributions, designing an effective strategy to leverage both types of data to enhance the efficacy of the RL model training has become a highly challenging problem. In this study, we propose a framework named Multi-Distribution Data Learning (MDDL) to address the challenge of effectively utilizing both strategy and random data for training RL models on mixed multi-distribution data. Specifically, MDDL incorporates a novel imitation learning signal to mitigate overestimation problems in strategy data and maximizes the RL signal for random data to facilitate effective learning. In our experiments, we evaluated the proposed MDDL framework in a real-world position allocation system and demonstrated its superior performance compared to the previous baseline. MDDL has been fully deployed on the Meituan food delivery platform and currently serves over 300 million users.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=MDDL:+A+Framework+for+Reinforcement+Learning-based+Position+Allocation+in+Multi-Channel+Feed)|0| |[On Answer Position Bias in Transformers for Question Answering](https://doi.org/10.1145/3539618.3592029)|Rafael Glater, Rodrygo L. T. Santos|Universidade Federal de Minas Gerais, Belo Horizonte, Brazil|Extractive Transformer-based models for question answering (QA) are trained to predict the start and end position of the answer in a candidate paragraph. However, the true answer position can bias these models when its distribution in the training data is highly skewed. That is, models trained only with the answer at the beginning of the paragraph will perform poorly on test instances with the answer at the end. Many studies have focused on countering answer position bias but have yet to deepen our understanding of how such bias manifests in the main components of the Transformer. In this paper, we analyze the self-attention and embedding generation components of five Transformer-based models with different architectures and position embedding strategies. Our analysis shows that models tend to map position bias in their attention matrices, generating embeddings that correlate the answer and its biased position, ultimately compromising model generalization.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=On+Answer+Position+Bias+in+Transformers+for+Question+Answering)|0| |[Prompt Learning to Mitigate Catastrophic Forgetting in Cross-lingual Transfer for Open-domain Dialogue Generation](https://doi.org/10.1145/3539618.3592043)|Lei Liu, Jimmy Xiangji Huang|York University, Toronto, ON, Canada|Dialogue systems for non-English languages have long been under-explored. In this paper, we take the first step to investigate few-shot cross-lingual transfer learning (FS-XLT) and multitask learning (MTL) in the context of open-domain dialogue generation for non-English languages with limited data. We observed catastrophic forgetting in both FS-XLT and MTL for all 6 languages in our preliminary experiments. To mitigate the issue, we propose a simple yet effective prompt learning approach that can preserve the multilinguality of multilingual pre-trained language model (mPLM) in FS-XLT and MTL by bridging the gap between pre-training and fine-tuning with Fixed-prompt LM Tuning and our hand-crafted prompts. Experimental results on all 6 languages in terms of both automatic and human evaluations demonstrate the effectiveness of our approach. Our code is available at https://github.com/JeremyLeiLiu/XLinguDial.|长期以来，对非英语语言的对话系统探索不足。本文首先研究了数据有限的非英语语言开放领域对话生成过程中的短镜头跨语言迁移学习(few-shot cross-language Transfer learning，FS-XLT)和多任务学习(multitask learning，MTL)。在我们的初步实验中，我们观察到所有6种语言在 FS-XLT 和 MTL 中的灾难性遗忘。为了缓解这一问题，我们提出了一种简单而有效的快速学习方法，可以通过固定提示 LM 调优和我们手工制作的提示来弥合预训练和微调之间的差距，从而保持 FS-XLT 和 MTL 中多语言预训练语言模型(mPLM)的多语言性。在所有6种语言的自动和人工评估方面的实验结果证明了我们的方法的有效性。我们的代码可以在 https://github.com/jeremyleiliu/xlingudial 找到。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Prompt+Learning+to+Mitigate+Catastrophic+Forgetting+in+Cross-lingual+Transfer+for+Open-domain+Dialogue+Generation)|0| -|[Reducing Spurious Correlations for Relation Extraction by Feature Decomposition and Semantic Augmentation](https://doi.org/10.1145/3539618.3592050)|Tianshu Yu, Min Yang, Chengming Li, Ruifeng Xu|Harbin Institute of Technology (Shenzhen), Shenzhen, China; SIAT, Chinese Academy of Sciences, Shenzhen, China; Shenzhen MSU-BIT University, Shenzhen, China|Deep neural models have become mainstream in relation extraction (RE), yielding state-of-the-art performance. However, most existing neural models are prone to spurious correlations between input features and prediction labels, making the models suffer from low robustness and generalization.In this paper, we propose a spurious correlation reduction method for RE via feature decomposition and semantic augmentation (denoted as FDSA). First, we decompose the original sentence representation into class-related features and context-related features. To obtain better context-related features, we devise a contrastive learning method to pull together the context-related features of the anchor sentence and its augmented sentences, and push away the context-related features of different anchor sentences. In addition, we propose gradient-based semantic augmentation on context-related features in order to improve the robustness of the RE model. Experiments on four datasets show that our model outperforms the strong competitors.|深层神经模型已经成为关系抽取(RE)的主流，具有最先进的性能。然而，现有的神经模型往往存在输入特征与预测标签之间的虚假关联，使得模型的鲁棒性和 generalization.in 性较差。本文提出了一种基于特征分解和语义增强的伪相关约简方法(简称 FDSA)。首先，我们将原始句子表示分解为与类相关的特征和与上下文相关的特征。为了获得更好的上下文相关特征，我们设计了一种对比学习方法，将锚语句及其增强句的上下文相关特征整合起来，去除不同锚语句的上下文相关特征。此外，为了提高 RE 模型的鲁棒性，提出了基于梯度的上下文相关特征语义增强方法。在四个数据集上的实验表明，我们的模型优于强有力的竞争对手。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Reducing+Spurious+Correlations+for+Relation+Extraction+by+Feature+Decomposition+and+Semantic+Augmentation)|0| +|[Reducing Spurious Correlations for Relation Extraction by Feature Decomposition and Semantic Augmentation](https://doi.org/10.1145/3539618.3592050)|Tianshu Yu, Min Yang, Chengming Li, Ruifeng Xu|Shenzhen MSU-BIT University, Shenzhen, China; Harbin Institute of Technology (Shenzhen), Shenzhen, China; SIAT, Chinese Academy of Sciences, Shenzhen, China|Deep neural models have become mainstream in relation extraction (RE), yielding state-of-the-art performance. However, most existing neural models are prone to spurious correlations between input features and prediction labels, making the models suffer from low robustness and generalization.In this paper, we propose a spurious correlation reduction method for RE via feature decomposition and semantic augmentation (denoted as FDSA). First, we decompose the original sentence representation into class-related features and context-related features. To obtain better context-related features, we devise a contrastive learning method to pull together the context-related features of the anchor sentence and its augmented sentences, and push away the context-related features of different anchor sentences. In addition, we propose gradient-based semantic augmentation on context-related features in order to improve the robustness of the RE model. Experiments on four datasets show that our model outperforms the strong competitors.|深层神经模型已经成为关系抽取(RE)的主流，具有最先进的性能。然而，现有的神经模型往往存在输入特征与预测标签之间的虚假关联，使得模型的鲁棒性和 generalization.in 性较差。本文提出了一种基于特征分解和语义增强的伪相关约简方法(简称 FDSA)。首先，我们将原始句子表示分解为与类相关的特征和与上下文相关的特征。为了获得更好的上下文相关特征，我们设计了一种对比学习方法，将锚语句及其增强句的上下文相关特征整合起来，去除不同锚语句的上下文相关特征。此外，为了提高 RE 模型的鲁棒性，提出了基于梯度的上下文相关特征语义增强方法。在四个数据集上的实验表明，我们的模型优于强有力的竞争对手。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Reducing+Spurious+Correlations+for+Relation+Extraction+by+Feature+Decomposition+and+Semantic+Augmentation)|0| |[EmoUS: Simulating User Emotions in Task-Oriented Dialogues](https://doi.org/10.1145/3539618.3592092)|HsienChin Lin, Shutong Feng, Christian Geishauser, Nurul Lubis, Carel van Niekerk, Michael Heck, Benjamin Matthias Ruppik, Renato Vukovic, Milica Gasic|Heinrich-Heine-Universität Düsseldorf, Düsseldorf, Germany|Existing user simulators (USs) for task-oriented dialogue systems only model user behaviour on semantic and natural language levels without considering the user persona and emotions. Optimising dialogue systems with generic user policies, which cannot model diverse user behaviour driven by different emotional states, may result in a high drop-off rate when deployed in the real world. Thus, we present EmoUS, a user simulator that learns to simulate user emotions alongside user behaviour. EmoUS generates user emotions, semantic actions, and natural language responses based on the user goal, the dialogue history, and the user persona. By analysing what kind of system behaviour elicits what kind of user emotions, we show that EmoUS can be used as a probe to evaluate a variety of dialogue systems and in particular their effect on the user's emotional state. Developing such methods is important in the age of large language model chat-bots and rising ethical concerns.|现有的面向任务对话系统的用户模拟器(USs)只是在语义和自然语言层面上对用户行为进行建模，而没有考虑用户的角色和情感。使用通用用户策略优化对话系统，不能模拟由不同情绪状态驱动的不同用户行为，在现实世界中部署时可能导致较高的下降率。因此，我们提出了 emoUS，一个用户模拟器，学习模拟用户的情绪以及用户的行为。基于用户目标、对话历史和用户角色，EmoUS 产生用户情感、语义动作和自然语言反应。通过分析什么样的系统行为引发了什么样的用户情绪，我们表明，情绪美可以作为一个探测器来评估各种对话系统，特别是他们对用户的情绪状态的影响。在大型语言模型聊天机器人时代，开发这样的方法非常重要，同时也引起了越来越多的道德关注。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=EmoUS:+Simulating+User+Emotions+in+Task-Oriented+Dialogues)|0| -|[BizGraphQA: A Dataset for Image-based Inference over Graph-structured Diagrams from Business Domains](https://doi.org/10.1145/3539618.3591875)|Petr Babkin, William Watson, Zhiqiang Ma, Lucas Cecchi, Natraj Raman, Armineh Nourbakhsh, Sameena Shah|J.P. Morgan AI Research, London, United Kingdom; J.P. Morgan AI Research, New York, NY, USA; J.P. Morgan AI Research, Palo Alto, CA, USA|Graph-structured diagrams, such as enterprise ownership charts or management hierarchies, are a challenging medium for deep learning models as they not only require the capacity to model language and spatial relations but also the topology of links between entities and the varying semantics of what those links represent. Devising Question Answering models that automatically process and understand such diagrams have vast applications to many enterprise domains, and can move the state-of-the-art on multimodal document understanding to a new frontier. Curating real-world datasets to train these models can be difficult, due to scarcity and confidentiality of the documents where such diagrams are included. Recently released synthetic datasets are often prone to repetitive structures that can be memorized or tackled using heuristics. In this paper, we present a collection of 10,000 synthetic graphs that faithfully reflect properties of real graphs in four business domains, and are realistically rendered within a PDF document with varying styles and layouts. In addition, we have generated over 130,000 question instances that target complex graphical relationships specific to each domain. We hope this challenge will encourage the development of models capable of robust reasoning about graph structured images, which are ubiquitous in numerous sectors in business and across scientific disciplines.|图形结构图，如企业所有权图或管理层次结构图，是深度学习模型的一个具有挑战性的媒介，因为它们不仅需要建模语言和空间关系的能力，而且还需要实体之间链接的拓扑结构以及这些链接所代表的不同语义。设计自动处理和理解这些图表的问题回答模型在许多企业领域有着广泛的应用，并且可以将多模式文档理解的最新技术推向一个新的前沿。管理真实世界的数据集来训练这些模型可能是困难的，因为包含这些图表的文档是稀缺的和机密的。最近发布的合成数据集往往倾向于重复结构，可以通过启发式方法记忆或处理。在本文中，我们提出了一个10,000个合成图的集合，它忠实地反映了四个业务领域中真实图的属性，并且在一个具有不同样式和布局的 PDF 文档中真实地呈现。此外，我们已经生成了超过130,000个问题实例，这些实例针对每个领域特定的复杂图形关系。我们希望这一挑战将鼓励开发能够对图形结构图像进行强有力推理的模型，这些图形结构图像在商业和科学分支的许多领域中无处不在。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=BizGraphQA:+A+Dataset+for+Image-based+Inference+over+Graph-structured+Diagrams+from+Business+Domains)|0| -|[Introducing MBIB - The First Media Bias Identification Benchmark Task and Dataset Collection](https://doi.org/10.1145/3539618.3591882)|Martin Wessel, Tomás Horych, Terry Ruas, Akiko Aizawa, Bela Gipp, Timo Spinde|University of Konstanz, Konstanz, Germany; University of Göttingen, Göttingen, Germany; National Institute of Informatics, Tokyo, Japan; Czech Technical University, Prague, Czech Rep|Although media bias detection is a complex multi-task problem, there is, to date, no unified benchmark grouping these evaluation tasks. We introduce the Media Bias Identification Benchmark (MBIB), a comprehensive benchmark that groups different types of media bias (e.g., linguistic, cognitive, political) under a common framework to test how prospective detection techniques generalize. After reviewing 115 datasets, we select nine tasks and carefully propose 22 associated datasets for evaluating media bias detection techniques. We evaluate MBIB using state-of-the-art Transformer techniques (e.g., T5, BART). Our results suggest that while hate speech, racial bias, and gender bias are easier to detect, models struggle to handle certain bias types, e.g., cognitive and political bias. However, our results show that no single technique can outperform all the others significantly. We also find an uneven distribution of research interest and resource allocation to the individual tasks in media bias. A unified benchmark encourages the development of more robust systems and shifts the current paradigm in media bias detection evaluation towards solutions that tackle not one but multiple media bias types simultaneously.|尽管媒介偏差检测是一个复杂的多任务问题，但迄今为止，还没有统一的基准对这些评估任务进行分组。我们介绍了媒介偏见识别基准(MBIB) ，这是一个综合性的基准，它将不同类型的媒介偏见(例如，语言的，认知的，政治的)归类在一个共同的框架下，以测试如何前瞻性检测技术一般化。在回顾了115个数据集之后，我们选择了9个任务并仔细提出了22个相关的数据集用于评估媒介偏倚检测技术。我们评估 MBIB 使用最先进的变压器技术(例如，T5，BART)。我们的研究结果表明，尽管仇恨言论、种族偏见和性别偏见更容易被发现，但模特们很难处理某些偏见类型，例如认知和政治偏见。然而，我们的研究结果表明，没有任何一种技术可以显著地优于所有其他技术。我们还发现，在媒介偏见下，研究兴趣和资源分配对个体任务的分配是不均衡的。统一的基准鼓励开发更健全的系统，并将目前媒体偏差检测评价的范式转向同时处理不是一种而是多种媒体偏差类型的解决方案。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Introducing+MBIB+-+The+First+Media+Bias+Identification+Benchmark+Task+and+Dataset+Collection)|0| -|[MetroScope: An Advanced System for Real-Time Detection and Analysis of Metro-Related Threats and Events via Twitter](https://doi.org/10.1145/3539618.3591807)|Jianfeng He, SyuanYing Wu, Abdulaziz Alhamadani, ChihFang Chen, WenFang Lu, ChangTien Lu, David Solnick, Yanlin Li|Washington Metropolitan Area Transit Authority, Washington, DC, USA; Virginia Tech, Falls Church, VA, USA|Metro systems are vital to our daily lives, but they face safety or reliability challenges, such as criminal activities or infrastructure disruptions, respectively. Real-time threat detection and analysis are crucial to ensure their safety and reliability. Although many existing systems use Twitter to detect metro-related threats or events in real-time, they have limitations in event analysis and system maintenance. Specifically, they cannot analyze event development, or prioritize events from numerous tweets. Besides, their users are required to continuously monitor system notifications, use inefficient content retrieval methods, and perform detailed system maintenance. We addressed those issues by developing the MetroScope system, a real-time threat/event detection system applied to Washington D.C. metro system. MetroScope can automatically analyze event development, prioritize events based on urgency, send emergency notifications via emails, provide efficient content retrieval, and self-maintain the system. Our MetroScope system is now available at http://orion.nvc.cs.vt.edu:5000/, with a video (https://www.youtube.com/watch?v=vKIK9M60-J8) introducing its features and instructions. MetroScope is a significant advancement in enhancing the safety and reliability of metro systems.|地铁系统对我们的日常生活至关重要，但它们分别面临安全或可靠性方面的挑战，例如犯罪活动或基础设施中断。实时的威胁检测和分析是保证其安全性和可靠性的关键。尽管许多现有系统使用 Twitter 实时检测与地铁相关的威胁或事件，但它们在事件分析和系统维护方面存在局限性。具体来说，它们不能分析事件开发，也不能对来自大量 tweet 的事件进行优先排序。此外，它们还要求用户不断监视系统通知，使用效率低下的内容检索方法，并执行详细的系统维护。我们通过开发 MetroScope 系统解决了这些问题，该系统是一个应用于华盛顿特区地铁系统的实时威胁/事件检测系统。MetroScope 可以自动分析事件发展，根据紧急情况对事件进行优先排序，通过电子邮件发送紧急通知，提供有效的内容检索，并自我维护系统。我们的 MetroScope 系统现在可以在 http://orion.nvc.cs.vt.edu:5000/上使用，并附有介绍其特性和使用说明的视频( https://www.youtube.com/watch?v=vkik9m60-j8)。MetroScope 在提高地铁系统的安全性和可靠性方面取得了重大进展。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=MetroScope:+An+Advanced+System+for+Real-Time+Detection+and+Analysis+of+Metro-Related+Threats+and+Events+via+Twitter)|0| +|[BizGraphQA: A Dataset for Image-based Inference over Graph-structured Diagrams from Business Domains](https://doi.org/10.1145/3539618.3591875)|Petr Babkin, William Watson, Zhiqiang Ma, Lucas Cecchi, Natraj Raman, Armineh Nourbakhsh, Sameena Shah|J.P. Morgan AI Research, Palo Alto, CA, USA; J.P. Morgan AI Research, London, United Kingdom; J.P. Morgan AI Research, New York, NY, USA|Graph-structured diagrams, such as enterprise ownership charts or management hierarchies, are a challenging medium for deep learning models as they not only require the capacity to model language and spatial relations but also the topology of links between entities and the varying semantics of what those links represent. Devising Question Answering models that automatically process and understand such diagrams have vast applications to many enterprise domains, and can move the state-of-the-art on multimodal document understanding to a new frontier. Curating real-world datasets to train these models can be difficult, due to scarcity and confidentiality of the documents where such diagrams are included. Recently released synthetic datasets are often prone to repetitive structures that can be memorized or tackled using heuristics. In this paper, we present a collection of 10,000 synthetic graphs that faithfully reflect properties of real graphs in four business domains, and are realistically rendered within a PDF document with varying styles and layouts. In addition, we have generated over 130,000 question instances that target complex graphical relationships specific to each domain. We hope this challenge will encourage the development of models capable of robust reasoning about graph structured images, which are ubiquitous in numerous sectors in business and across scientific disciplines.|图形结构图，如企业所有权图或管理层次结构图，是深度学习模型的一个具有挑战性的媒介，因为它们不仅需要建模语言和空间关系的能力，而且还需要实体之间链接的拓扑结构以及这些链接所代表的不同语义。设计自动处理和理解这些图表的问题回答模型在许多企业领域有着广泛的应用，并且可以将多模式文档理解的最新技术推向一个新的前沿。管理真实世界的数据集来训练这些模型可能是困难的，因为包含这些图表的文档是稀缺的和机密的。最近发布的合成数据集往往倾向于重复结构，可以通过启发式方法记忆或处理。在本文中，我们提出了一个10,000个合成图的集合，它忠实地反映了四个业务领域中真实图的属性，并且在一个具有不同样式和布局的 PDF 文档中真实地呈现。此外，我们已经生成了超过130,000个问题实例，这些实例针对每个领域特定的复杂图形关系。我们希望这一挑战将鼓励开发能够对图形结构图像进行强有力推理的模型，这些图形结构图像在商业和科学分支的许多领域中无处不在。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=BizGraphQA:+A+Dataset+for+Image-based+Inference+over+Graph-structured+Diagrams+from+Business+Domains)|0| +|[Introducing MBIB - The First Media Bias Identification Benchmark Task and Dataset Collection](https://doi.org/10.1145/3539618.3591882)|Martin Wessel, Tomás Horych, Terry Ruas, Akiko Aizawa, Bela Gipp, Timo Spinde|Czech Technical University, Prague, Czech Rep; National Institute of Informatics, Tokyo, Japan; University of Konstanz, Konstanz, Germany; University of Göttingen, Göttingen, Germany|Although media bias detection is a complex multi-task problem, there is, to date, no unified benchmark grouping these evaluation tasks. We introduce the Media Bias Identification Benchmark (MBIB), a comprehensive benchmark that groups different types of media bias (e.g., linguistic, cognitive, political) under a common framework to test how prospective detection techniques generalize. After reviewing 115 datasets, we select nine tasks and carefully propose 22 associated datasets for evaluating media bias detection techniques. We evaluate MBIB using state-of-the-art Transformer techniques (e.g., T5, BART). Our results suggest that while hate speech, racial bias, and gender bias are easier to detect, models struggle to handle certain bias types, e.g., cognitive and political bias. However, our results show that no single technique can outperform all the others significantly. We also find an uneven distribution of research interest and resource allocation to the individual tasks in media bias. A unified benchmark encourages the development of more robust systems and shifts the current paradigm in media bias detection evaluation towards solutions that tackle not one but multiple media bias types simultaneously.|尽管媒介偏差检测是一个复杂的多任务问题，但迄今为止，还没有统一的基准对这些评估任务进行分组。我们介绍了媒介偏见识别基准(MBIB) ，这是一个综合性的基准，它将不同类型的媒介偏见(例如，语言的，认知的，政治的)归类在一个共同的框架下，以测试如何前瞻性检测技术一般化。在回顾了115个数据集之后，我们选择了9个任务并仔细提出了22个相关的数据集用于评估媒介偏倚检测技术。我们评估 MBIB 使用最先进的变压器技术(例如，T5，BART)。我们的研究结果表明，尽管仇恨言论、种族偏见和性别偏见更容易被发现，但模特们很难处理某些偏见类型，例如认知和政治偏见。然而，我们的研究结果表明，没有任何一种技术可以显著地优于所有其他技术。我们还发现，在媒介偏见下，研究兴趣和资源分配对个体任务的分配是不均衡的。统一的基准鼓励开发更健全的系统，并将目前媒体偏差检测评价的范式转向同时处理不是一种而是多种媒体偏差类型的解决方案。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Introducing+MBIB+-+The+First+Media+Bias+Identification+Benchmark+Task+and+Dataset+Collection)|0| +|[MetroScope: An Advanced System for Real-Time Detection and Analysis of Metro-Related Threats and Events via Twitter](https://doi.org/10.1145/3539618.3591807)|Jianfeng He, SyuanYing Wu, Abdulaziz Alhamadani, ChihFang Chen, WenFang Lu, ChangTien Lu, David Solnick, Yanlin Li|Virginia Tech, Falls Church, VA, USA; Washington Metropolitan Area Transit Authority, Washington, DC, USA|Metro systems are vital to our daily lives, but they face safety or reliability challenges, such as criminal activities or infrastructure disruptions, respectively. Real-time threat detection and analysis are crucial to ensure their safety and reliability. Although many existing systems use Twitter to detect metro-related threats or events in real-time, they have limitations in event analysis and system maintenance. Specifically, they cannot analyze event development, or prioritize events from numerous tweets. Besides, their users are required to continuously monitor system notifications, use inefficient content retrieval methods, and perform detailed system maintenance. We addressed those issues by developing the MetroScope system, a real-time threat/event detection system applied to Washington D.C. metro system. MetroScope can automatically analyze event development, prioritize events based on urgency, send emergency notifications via emails, provide efficient content retrieval, and self-maintain the system. Our MetroScope system is now available at http://orion.nvc.cs.vt.edu:5000/, with a video (https://www.youtube.com/watch?v=vKIK9M60-J8) introducing its features and instructions. MetroScope is a significant advancement in enhancing the safety and reliability of metro systems.|地铁系统对我们的日常生活至关重要，但它们分别面临安全或可靠性方面的挑战，例如犯罪活动或基础设施中断。实时的威胁检测和分析是保证其安全性和可靠性的关键。尽管许多现有系统使用 Twitter 实时检测与地铁相关的威胁或事件，但它们在事件分析和系统维护方面存在局限性。具体来说，它们不能分析事件开发，也不能对来自大量 tweet 的事件进行优先排序。此外，它们还要求用户不断监视系统通知，使用效率低下的内容检索方法，并执行详细的系统维护。我们通过开发 MetroScope 系统解决了这些问题，该系统是一个应用于华盛顿特区地铁系统的实时威胁/事件检测系统。MetroScope 可以自动分析事件发展，根据紧急情况对事件进行优先排序，通过电子邮件发送紧急通知，提供有效的内容检索，并自我维护系统。我们的 MetroScope 系统现在可以在 http://orion.nvc.cs.vt.edu:5000/上使用，并附有介绍其特性和使用说明的视频( https://www.youtube.com/watch?v=vkik9m60-j8)。MetroScope 在提高地铁系统的安全性和可靠性方面取得了重大进展。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=MetroScope:+An+Advanced+System+for+Real-Time+Detection+and+Analysis+of+Metro-Related+Threats+and+Events+via+Twitter)|0| |[FairUP: A Framework for Fairness Analysis of Graph Neural Network-Based User Profiling Models](https://doi.org/10.1145/3539618.3591814)|Mohamed Abdelrazek, Erasmo Purificato, Ludovico Boratto, Ernesto William De Luca|University of Cagliari, Cagliari, Italy; Otto von Guericke University Magdeburg, Magdeburg, Germany; Otto von Guericke University Magdeburg & Leibniz Institute for Educational Media | Georg Eckert Institute, Magdeburg, Germany|Modern user profiling approaches capture different forms of interactions with the data, from user-item to user-user relationships. Graph Neural Networks (GNNs) have become a natural way to model these behaviours and build efficient and effective user profiles. However, each GNN-based user profiling approach has its own way of processing information, thus creating heterogeneity that does not favour the benchmarking of these techniques. To overcome this issue, we present FairUP, a framework that standardises the input needed to run three state-of-the-art GNN-based models for user profiling tasks. Moreover, given the importance that algorithmic fairness is getting in the evaluation of machine learning systems, FairUP includes two additional components to (1) analyse pre-processing and post-processing fairness and (2) mitigate the potential presence of unfairness in the original datasets through three pre-processing debiasing techniques. The framework, while extensible in multiple directions, in its first version, allows the user to conduct experiments on four real-world datasets. The source code is available at https://link.erasmopurif.com/FairUP-source-code, and the web application is available at https://link.erasmopurif.com/FairUP.|现代用户分析方法捕获与数据的不同交互形式，从用户项到用户-用户关系。图形神经网络(GNN)已经成为一种自然的方式来建模这些行为，并建立有效和有效的用户配置文件。然而，每种基于 GNN 的用户分析方法都有自己处理信息的方式，因此产生了不利于这些技术基准测试的异构性。为了克服这个问题，我们提出了 FairUP，这是一个标准化的框架，用于运行三个最先进的基于 GNN 的模型来完成用户分析任务。此外，考虑到算法公平性在机器学习系统评估中的重要性，FairUP 包括两个额外的组成部分: (1)分析预处理和后处理公平性; (2)通过三种预处理去偏技术缓解原始数据集中潜在的不公平性。该框架在第一个版本中虽然可以向多个方向扩展，但允许用户在四个真实世界的数据集上进行实验。源代码可在 https://link.erasmopurif.com/fairup-source-code 下载，web 应用程序可在 https://link.erasmopurif.com/fairup 下载。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=FairUP:+A+Framework+for+Fairness+Analysis+of+Graph+Neural+Network-Based+User+Profiling+Models)|0| |[SONAR: Web-based Tool for Multimodal Exploration of Non-Fungible Token Inspiration Networks](https://doi.org/10.1145/3539618.3591821)|Lucio La Cava, Davide Costa, Andrea Tagarelli|DIMES, University of Calabria, Rende, Italy|In this work, we present SONAR, a web-based tool for multimodal exploration of Non-Fungible Token (NFT) inspiration networks. SONAR is conceived to support both creators and traders in the emerging Web3 by providing an interactive visualization of the inspiration-driven connections between NFTs, at both individual level and collection level. SONAR can hence be useful to identify new investment opportunities as well as anomalous inspirations. To demonstrate SONAR's capabilities, we present an application to the largest and most representative dataset concerning the NFT landscape to date, showing how our proposed tool can scale and ensure high-level user experience up to millions of edges.|在这项工作中，我们提出了声纳，一个基于网络的工具，多模态探索非可替换令牌(NFT)的灵感网络。SONAR 被设计用来支持新兴的 Web3中的创建者和交易者，通过在个人层面和集合层面上提供一个交互式可视化的 NFT 之间的灵感驱动的连接。因此，声纳可以用来识别新的投资机会以及异常的灵感。为了展示 SONAR 的能力，我们展示了一个迄今为止最大和最具代表性的 NFT 数据集，展示了我们提议的工具如何扩展和确保高水平的用户体验达到数以百万计的边缘。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=SONAR:+Web-based+Tool+for+Multimodal+Exploration+of+Non-Fungible+Token+Inspiration+Networks)|0| -|[MDI: A Debiasing Method Combining Unbiased and Biased Data](https://doi.org/10.1145/3539618.3591838)|Han Zhao, Qing Cui, Xinyu Li, Rongzhou Bao, Longfei Li, Jun Zhou, Zhehao Liu, Jinghua Feng|Peking University, Beijing, China; Ant Group, Hangzhou, China|In recent years, many methods have been proposed to alleviate the biases in recommender systems by combining biased data and unbiased data. Among these methods, data imputation method is effective, but previous works only employ a straightforward model to generate imputed data, which can not fully characterize the data. In this paper, we propose a novel data imputation approach that combines an unbiased model and a debiasing model with adaptively learnt weights. We conduct extensive experiments on two public recommendation datasets and one production dataset to demonstrate the effectiveness and robustness of the proposed method.|近年来，通过有偏数据和无偏数据相结合的方法来减少推荐系统中的偏差已经被提出了许多方法。在这些方法中，数据插补方法是有效的，但以往的工作只采用一个简单的模型来生成插补数据，不能充分表征数据。在本文中，我们提出了一种新的数据插补方法，结合无偏模型和去偏模型与自适应学习权重。为了验证该方法的有效性和鲁棒性，我们对两个公共推荐数据集和一个生产数据集进行了广泛的实验。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=MDI:+A+Debiasing+Method+Combining+Unbiased+and+Biased+Data)|0| +|[MDI: A Debiasing Method Combining Unbiased and Biased Data](https://doi.org/10.1145/3539618.3591838)|Han Zhao, Qing Cui, Xinyu Li, Rongzhou Bao, Longfei Li, Jun Zhou, Zhehao Liu, Jinghua Feng|Ant Group, Hangzhou, China; Peking University, Beijing, China|In recent years, many methods have been proposed to alleviate the biases in recommender systems by combining biased data and unbiased data. Among these methods, data imputation method is effective, but previous works only employ a straightforward model to generate imputed data, which can not fully characterize the data. In this paper, we propose a novel data imputation approach that combines an unbiased model and a debiasing model with adaptively learnt weights. We conduct extensive experiments on two public recommendation datasets and one production dataset to demonstrate the effectiveness and robustness of the proposed method.|近年来，通过有偏数据和无偏数据相结合的方法来减少推荐系统中的偏差已经被提出了许多方法。在这些方法中，数据插补方法是有效的，但以往的工作只采用一个简单的模型来生成插补数据，不能充分表征数据。在本文中，我们提出了一种新的数据插补方法，结合无偏模型和去偏模型与自适应学习权重。为了验证该方法的有效性和鲁棒性，我们对两个公共推荐数据集和一个生产数据集进行了广泛的实验。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=MDI:+A+Debiasing+Method+Combining+Unbiased+and+Biased+Data)|0| |[A Data-centric Solution to Improve Online Performance of Customer Service Bots](https://doi.org/10.1145/3539618.3591843)|Sen Hu, Changlin Yang, Junjie Wang, Siye Liu, Teng Xu, Wangshu Zhang, Jing Zheng|Ant Group, Beijing, China|The online performance of customer service bots is often less than satisfactory because of the gap between limited training data and real-world user questions. As a straightforward way to improve online performance, model iteration and re-deployment are time consuming and labor-intensive, and therefore difficult to sustain. To fix badcases and improve online performance of chatbots in a timely and continuous manner, we propose a data-centric solution consisting of three main modules: badcase detection, bad case correction, and answer extraction. By making full use of online model signals, implicit user feedback and artificial customer service log, the proposed solution can fix online badcases automatically. Our solution has been deployed and bringing consistently positive impacts for hundreds of customer service bots used by Alipay app.|客户服务机器人的在线性能往往不能令人满意，因为有限的训练数据和现实世界的用户问题之间的差距。作为提高在线性能的直接方法，模型迭代和重新部署耗费时间和人力，因此难以维持。为了及时、持续地修复恶例并提高聊天机器人的在线性能，提出了一种以数据为中心的解决方案，该方案由恶例检测、恶例修正和答案提取三个主要模块组成。该解决方案充分利用在线模型信号、隐式用户反馈和人工客户服务日志，实现了在线坏包的自动修复。我们的解决方案已经部署，并为支付宝应用程序使用的数百个客户服务机器人带来了持续的积极影响。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Data-centric+Solution+to+Improve+Online+Performance+of+Customer+Service+Bots)|0| -|[KATIE: A System for Key Attributes Identification in Product Knowledge Graph Construction](https://doi.org/10.1145/3539618.3591846)|Btissam Er Rahmadi, Arturo Oncevay, Yuanyi Ji, Jeff Z. Pan|Huawei Technologies R&D UK, Edinburgh, United Kingdom; The University of Edinburgh, Edinburgh, United Kingdom; Huawei Technologies R&D UK & The University of Edinburgh, Edinburgh, United Kingdom|We present part of Huawei's efforts in building a Product Knowledge Graph (PKG). We want to identify which product attributes (i.e. properties) are relevant and important in terms of shopping decisions to product categories (i.e. classes). This is particularly challenging when the attributes and their values are mined from online product catalogues, i.e. HTML pages. These web pages contain semi-structured data, which do not follow a concerted format and use diverse vocabulary to designate the same features. We propose a system for key attribute identification (KATIE) based on fine-tuning pre-trained models (e.g., DistilBERT) to predict the applicability and importance of an attribute to a category. We also propose an attribute synonyms identification module that allows us to discover synonymous attributes by considering not only their labels' similarities but also the similarity of their values sets. We have evaluated our approach to Huawei categories taxonomy and a set of internally mined attributes from web pages. KATIE guarantees promising performance results compared to the most recent baselines.|我们介绍华为在建立产品知识图表方面的部分工作。我们希望确定哪些产品属性(即属性)与产品类别(即类别)的购物决策相关且重要。当从在线产品目录(即 HTML 页面)中挖掘属性及其值时，这尤其具有挑战性。这些网页包含半结构化的数据，这些数据没有采用统一的格式，而是使用不同的词汇来表示相同的特征。提出了一种基于微调预训练模型(例如 DistilBERT)的关键属性识别系统(KATIE) ，用于预测属性对类别的适用性和重要性。我们还提出了一个属性同义词识别模块，该模块不仅考虑了同义属性标签的相似性，而且还考虑了同义属性值集的相似性，从而发现同义属性。我们已经评估了我们对华为分类法的方法，以及从网页中挖掘出来的一组内部属性。与最近的基线相比，KATIE 保证了有希望的性能结果。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=KATIE:+A+System+for+Key+Attributes+Identification+in+Product+Knowledge+Graph+Construction)|0| -|[Synerise Monad: A Foundation Model for Behavioral Event Data](https://doi.org/10.1145/3539618.3591851)|Barbara Rychalska, Szymon Lukasik, Jacek Dabrowski|Synerise & Warsaw University of Technology, Warsaw, Poland; Synerise & AGH University of Science and Technology, Krakow, Poland; Synerise, Warsaw, Poland|The complexity of industry-grade event-based datalakes grows dynamically each passing hour. Companies actively gather behavioral information on their customers, recording multiple types of events, such as clicks, likes, page views, card transactions, add-to-basket, or purchase events. In response to this, the Synerise Monad platform has been proposed. The primary focus of Monad is to produce Universal Behavioral Representations (UBRs) - large vectors encapsulating the behavioral patterns of each user. UBRs do not lose knowledge about individual events, in contrast to aggregated features or averaged embeddings. They are based on award-winning algorithms developed at Synerise - Cleora and EMDE - and allow to process real-life datasets composed of billions of events in record time. In this paper, we introduce a new aspect of Monad: private foundation models for behavioral data, trained on top of UBRs. The foundation models are trained in purely self-supervised manner and allow to exploit general knowledge about human behavior, which proves especially useful when multiple downstream models must be trained and time constraints are tight, or when labeled data is scarce. Experimental results show that the Monad foundation models can cut training time in half and require 3x less data to reach optimal results, often achieving state-of-the-art results.|基于事件的工业级数据湖的复杂性随着时间的推移而动态增长。公司积极地收集客户的行为信息，记录多种类型的事件，比如点击、点赞、页面浏览、卡片交易、添加到购物篮或购物事件。为了应对这种情况，Synerise Monad 平台已经被提出。Monad 的主要焦点是产生通用行为表征(UBRs)——封装每个用户行为模式的大型向量。与聚合特性或平均嵌入相比，UBRs 不会丢失关于单个事件的知识。它们基于 Synerise-Cleora 和 EMDE 开发的获奖算法，可以在创纪录的时间内处理由数十亿事件组成的实际数据集。在本文中，我们介绍了 Monad 的一个新的方面: 在 UBRs 之上训练的行为数据的私有基础模型。基础模型是纯自我监督的训练方式，并允许利用人类行为的一般知识，这证明了特别有用的时候，多个下游模型必须训练和时间约束是紧张的，或标记数据是稀缺的。实验结果表明，Monad 基础模型可以将训练时间减少一半，所需数据减少3倍，达到最佳结果，往往达到最先进的结果。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Synerise+Monad:+A+Foundation+Model+for+Behavioral+Event+Data)|0| +|[KATIE: A System for Key Attributes Identification in Product Knowledge Graph Construction](https://doi.org/10.1145/3539618.3591846)|Btissam Er Rahmadi, Arturo Oncevay, Yuanyi Ji, Jeff Z. Pan|Huawei Technologies R&D UK & The University of Edinburgh, Edinburgh, United Kingdom; The University of Edinburgh, Edinburgh, United Kingdom; Huawei Technologies R&D UK, Edinburgh, United Kingdom|We present part of Huawei's efforts in building a Product Knowledge Graph (PKG). We want to identify which product attributes (i.e. properties) are relevant and important in terms of shopping decisions to product categories (i.e. classes). This is particularly challenging when the attributes and their values are mined from online product catalogues, i.e. HTML pages. These web pages contain semi-structured data, which do not follow a concerted format and use diverse vocabulary to designate the same features. We propose a system for key attribute identification (KATIE) based on fine-tuning pre-trained models (e.g., DistilBERT) to predict the applicability and importance of an attribute to a category. We also propose an attribute synonyms identification module that allows us to discover synonymous attributes by considering not only their labels' similarities but also the similarity of their values sets. We have evaluated our approach to Huawei categories taxonomy and a set of internally mined attributes from web pages. KATIE guarantees promising performance results compared to the most recent baselines.|我们介绍华为在建立产品知识图表方面的部分工作。我们希望确定哪些产品属性(即属性)与产品类别(即类别)的购物决策相关且重要。当从在线产品目录(即 HTML 页面)中挖掘属性及其值时，这尤其具有挑战性。这些网页包含半结构化的数据，这些数据没有采用统一的格式，而是使用不同的词汇来表示相同的特征。提出了一种基于微调预训练模型(例如 DistilBERT)的关键属性识别系统(KATIE) ，用于预测属性对类别的适用性和重要性。我们还提出了一个属性同义词识别模块，该模块不仅考虑了同义属性标签的相似性，而且还考虑了同义属性值集的相似性，从而发现同义属性。我们已经评估了我们对华为分类法的方法，以及从网页中挖掘出来的一组内部属性。与最近的基线相比，KATIE 保证了有希望的性能结果。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=KATIE:+A+System+for+Key+Attributes+Identification+in+Product+Knowledge+Graph+Construction)|0| +|[Synerise Monad: A Foundation Model for Behavioral Event Data](https://doi.org/10.1145/3539618.3591851)|Barbara Rychalska, Szymon Lukasik, Jacek Dabrowski|Synerise, Warsaw, Poland; Synerise & AGH University of Science and Technology, Krakow, Poland; Synerise & Warsaw University of Technology, Warsaw, Poland|The complexity of industry-grade event-based datalakes grows dynamically each passing hour. Companies actively gather behavioral information on their customers, recording multiple types of events, such as clicks, likes, page views, card transactions, add-to-basket, or purchase events. In response to this, the Synerise Monad platform has been proposed. The primary focus of Monad is to produce Universal Behavioral Representations (UBRs) - large vectors encapsulating the behavioral patterns of each user. UBRs do not lose knowledge about individual events, in contrast to aggregated features or averaged embeddings. They are based on award-winning algorithms developed at Synerise - Cleora and EMDE - and allow to process real-life datasets composed of billions of events in record time. In this paper, we introduce a new aspect of Monad: private foundation models for behavioral data, trained on top of UBRs. The foundation models are trained in purely self-supervised manner and allow to exploit general knowledge about human behavior, which proves especially useful when multiple downstream models must be trained and time constraints are tight, or when labeled data is scarce. Experimental results show that the Monad foundation models can cut training time in half and require 3x less data to reach optimal results, often achieving state-of-the-art results.|基于事件的工业级数据湖的复杂性随着时间的推移而动态增长。公司积极地收集客户的行为信息，记录多种类型的事件，比如点击、点赞、页面浏览、卡片交易、添加到购物篮或购物事件。为了应对这种情况，Synerise Monad 平台已经被提出。Monad 的主要焦点是产生通用行为表征(UBRs)——封装每个用户行为模式的大型向量。与聚合特性或平均嵌入相比，UBRs 不会丢失关于单个事件的知识。它们基于 Synerise-Cleora 和 EMDE 开发的获奖算法，可以在创纪录的时间内处理由数十亿事件组成的实际数据集。在本文中，我们介绍了 Monad 的一个新的方面: 在 UBRs 之上训练的行为数据的私有基础模型。基础模型是纯自我监督的训练方式，并允许利用人类行为的一般知识，这证明了特别有用的时候，多个下游模型必须训练和时间约束是紧张的，或标记数据是稀缺的。实验结果表明，Monad 基础模型可以将训练时间减少一半，所需数据减少3倍，达到最佳结果，往往达到最先进的结果。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Synerise+Monad:+A+Foundation+Model+for+Behavioral+Event+Data)|0| |[Contextual Multilingual Spellchecker for User Queries](https://doi.org/10.1145/3539618.3591861)|Sanat Sharma, Josep VallsVargas, Tracy Holloway King, François Guerin, Chirag Arora|Adobe Inc., San Jose, CA, USA|Spellchecking is one of the most fundamental and widely used search features. Correcting incorrectly spelled user queries not only enhances the user experience but is expected by the user. However, most widely available spellchecking solutions are either lower accuracy than state-of-the-art solutions or too slow to be used for search use cases where latency is a key requirement. Furthermore, most innovative recent architectures focus on English and are not trained in a multilingual fashion and are trained for spell correction in longer text, which is a different paradigm from spell correction for user queries, where context is sparse (most queries are 1-2 words long). Finally, since most enterprises have unique vocabularies such as product names, off-the-shelf spelling solutions fall short of users' needs. In this work, we build a multilingual spellchecker that is extremely fast and scalable and that adapts its vocabulary and hence speller output based on a specific product's needs. Furthermore, our speller out-performs general purpose spellers by a wide margin on in-domain datasets. Our multilingual speller is used in search in Adobe products, powering autocomplete in various applications.|拼写检查是最基本和最广泛使用的搜索特性之一。纠正拼写错误的用户查询不仅增强了用户体验，而且是用户所期望的。然而，大多数广泛使用的拼写检查解决方案要么比最先进的解决方案准确率低，要么太慢，无法用于延迟是关键要求的搜索用例。此外，最近大多数创新的体系结构侧重于英语，没有以多语言方式进行培训，而是针对较长文本的拼写校正进行培训，这与用户查询的拼写校正是不同的范例，用户查询的上下文很少(大多数查询只有1-2个单词)。最后，由于大多数企业都有独特的词汇，如产品名称，现成的拼写解决方案不能满足用户的需求。在这项工作中，我们构建了一个多语言拼写检查器，它非常快速，可伸缩，并根据特定产品的需要调整其词汇表和拼写输出。此外，我们的拼写器性能优于通用拼写器在很大程度上的领域内数据集。我们的多语言拼写器用于 Adobe 产品的搜索，支持各种应用程序的自动完成。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Contextual+Multilingual+Spellchecker+for+User+Queries)|0| -|[Exploring 360-Degree View of Customers for Lookalike Modeling](https://doi.org/10.1145/3539618.3591862)|Md. Mostafizur Rahman, Daisuke Kikuta, Satyen Abrol, Yu Hirate, Toyotaro Suzumura, Pablo Loyola, Takuma Ebisu, Manoj Kondapaka|The University of Tokyo, Tokyo, Japan; Rakuten Institute of Technology, Tokyo, Japan; Rakuten Institute of Technology, Bengaluru, India|Lookalike models are based on the assumption that user similarity plays an important role towards product selling and enhancing the existing advertising campaigns from a very large user base. Challenges associated to these models reside on the heterogeneity of the user base and its sparsity. In this work, we propose a novel framework that unifies the customers different behaviors or features such as demographics, buying behaviors on different platforms, customer loyalty behaviors and build a lookalike model to improve customer targeting for Rakuten Group, Inc. Extensive experiments on real e-commerce and travel datasets demonstrate the effectiveness of our proposed lookalike model for user targeting task.|相似模型是基于这样一个假设，即用户相似性对产品销售起着重要作用，并从一个非常大的用户基础上增强现有的广告活动。与这些模型相关的挑战在于用户基础的异质性及其稀疏性。在本文中，我们提出了一个新的框架，统一客户不同的行为或特征，如人口统计学，在不同的平台上的购买行为，客户忠诚行为和建立一个相似的模型，以改善客户的乐天集团，公司的目标顾客。在实际电子商务和旅游数据集上的大量实验证明了我们提出的相似模型对用户定位任务的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Exploring+360-Degree+View+of+Customers+for+Lookalike+Modeling)|0| +|[Exploring 360-Degree View of Customers for Lookalike Modeling](https://doi.org/10.1145/3539618.3591862)|Md. Mostafizur Rahman, Daisuke Kikuta, Satyen Abrol, Yu Hirate, Toyotaro Suzumura, Pablo Loyola, Takuma Ebisu, Manoj Kondapaka|The University of Tokyo, Tokyo, Japan; Rakuten Institute of Technology, Bengaluru, India; Rakuten Institute of Technology, Tokyo, Japan|Lookalike models are based on the assumption that user similarity plays an important role towards product selling and enhancing the existing advertising campaigns from a very large user base. Challenges associated to these models reside on the heterogeneity of the user base and its sparsity. In this work, we propose a novel framework that unifies the customers different behaviors or features such as demographics, buying behaviors on different platforms, customer loyalty behaviors and build a lookalike model to improve customer targeting for Rakuten Group, Inc. Extensive experiments on real e-commerce and travel datasets demonstrate the effectiveness of our proposed lookalike model for user targeting task.|相似模型是基于这样一个假设，即用户相似性对产品销售起着重要作用，并从一个非常大的用户基础上增强现有的广告活动。与这些模型相关的挑战在于用户基础的异质性及其稀疏性。在本文中，我们提出了一个新的框架，统一客户不同的行为或特征，如人口统计学，在不同的平台上的购买行为，客户忠诚行为和建立一个相似的模型，以改善客户的乐天集团，公司的目标顾客。在实际电子商务和旅游数据集上的大量实验证明了我们提出的相似模型对用户定位任务的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Exploring+360-Degree+View+of+Customers+for+Lookalike+Modeling)|0| |[Evaluating Task-oriented Dialogue Systems with Users](https://doi.org/10.1145/3539618.3591788)|Clemencia Siro|University of Amsterdam, Amsterdam, Netherlands|Evaluation is one of the major concerns when developing information retrieval systems. Especially in the field of conversational AI, this topic has been heavily studied in the setting of both non-task and task-oriented conversational agents (dialogue systems).[1] Recently, several automatic metrics e.g., BLEU and ROUGE, proposed for the evaluation of dialogue systems, have shown poor correlation with human judgment and are thus ineffective for the evaluation of dialogue systems. As a consequence, a significant amount of research relies on human evaluation to estimate the effectiveness of dialogue systems[1, 4}. An emerging approach for evaluating task-oriented dialogue systems (TDS) is to estimate a user's overall satisfaction with the system from explicit and implicit user interaction signals [2, 3]. Though useful and effective, overall user satisfaction does not necessarily give insights into what aspects or dimensions a TDS is performing well on. Understanding why a user is satisfied or dissatisfied helps the TDS recover from an error and optimize towards an individual aspect to avoid total dissatisfaction during an interaction session. Understanding a user's satisfaction with TDS is crucial, mainly for two reasons. First, it allows system designers to understand different user perceptions regarding satisfaction, which in turn leads to better user personalization. Secondly, it can be used to avoid total dialogue failure by the system by deploying adaptive conversational approaches, such as failure recovery or switching topics. And, thus, fine-grained evaluation of TDS gives the system an opportunity to learn an individual user's interaction preferences leading to a fulfilled user goal. Therefore in this research, we take the first initiative toward understanding user satisfaction with TDS. We mainly focus on the fine-grained evaluation of conversational systems in a task-oriented setting.|在开发信息检索系统时，评估是主要关注的问题之一。特别是在会话人工智能领域，该课题在非任务和面向任务的会话代理(对话系统)的设置中得到了广泛的研究。[1]近年来，一些用于对话系统评估的自动指标，如 BLEU 和 ROUGE，与人类判断的相关性较差，因此对于对话系统的评估是无效的。因此，大量的研究依赖于人类评估来估计对话系统的有效性[1,4]。评估任务导向对话系统(TDS)的一种新兴方法是通过显性和隐性用户交互信号估计用户对系统的总体满意度[2,3]。虽然有用且有效，但总体用户满意度并不一定能够洞察 TDS 在哪些方面或维度上表现良好。理解用户满意或不满意的原因有助于 TDS 从错误中恢复，并针对个别方面进行优化，以避免在交互会话期间出现完全不满意的情况。理解用户对 TDS 的满意度是至关重要的，主要有两个原因。首先，它允许系统设计人员理解不同的用户对满意度的看法，这反过来又会导致更好的用户个性化。其次，它可以通过部署自适应的会话方法，如故障恢复或话题切换，来避免系统的全面对话失败。因此，TDS 的细粒度评估使系统有机会了解个人用户的交互偏好，从而实现用户目标。因此，在本研究中，我们率先了解使用者对 TDS 的满意度。本文主要研究任务导向环境下会话系统的细粒度评价问题。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Evaluating+Task-oriented+Dialogue+Systems+with+Users)|0| |[Bridging Quantitative and Qualitative Digital Experience Testing](https://doi.org/10.1145/3539618.3591873)|Ranjitha Kumar|University of Illinois at Urbana-Champaign & UserTesting, Inc., Champaign & San Francisco, CA, USA|Digital user experiences are a mainstay of modern communication and commerce; multi-billion dollar industries have arisen around optimizing digital design. Usage analytics and A/B testing solutions allow growth hackers to quantitatively compute conversion over key user journeys, while user experience (UX) testing platforms enable UX researchers to qualitatively analyze usability and brand perception. Although these workflows are in pursuit of the same objective - producing better UX - the gulf between quantitative and qualitative testing is wide: they involve different stakeholders, and rely on disparate methodologies, budget, data streams, and software tools. This gap belies the opportunity to create a single platform that optimizes digital experiences holistically: using quantitative methods to uncover what and how much and qualitative analysis to understand why. Such a platform could monitor conversion funnels, identify ano­malous behaviors, intercept live users exhibiting those behaviors, and solicit explicit feedback in situ. This feedback could take many forms: survey responses, screen recordings of participants performing tasks, think-aloud audio, and more. By combining data from multiple users and correlating across feedback types, the platform could surface not just insights that a particular conversion funnel had been affected, but hypotheses about what had caused the change in user behavior. The platform could then rank these insights by how often the observed behavior occurred in the wild, using large-scale analytics to contextualize the results from small-scale UX tests. To this end, a decade of research has focused on interaction mining: a set of techniques for capturing interaction and design data from digital artifacts, and aggregating these multimodal data streams into structured representations bridging quantitative and qualitative experience testing[1-4]. During user sessions, interaction mining systems record user interactions (e.g., clicks, scrolls, text input), screen captures, and render-time data structures (e.g., website DOMs, native app view hierarchies). Once captured, these data streams are aligned and combined into user traces, sequences of user interactions semanticized by the design data of their UI targets [5]. The structure of these traces affords new workflows for composing quantitative and qualitative methods, building toward a unified platform for optimizing digital experiences.|数字用户体验是现代通信和商业的支柱; 数十亿美元的产业已经围绕优化数字设计而兴起。使用分析和 A/B 测试解决方案允许增长黑客定量计算关键用户旅程的转化率，而用户体验(UX)测试平台允许用户体验研究人员定性分析可用性和品牌认知。尽管这些工作流追求同一个目标——产生更好的用户体验——但定量测试和定性测试之间的鸿沟是巨大的: 它们涉及不同的利益相关者，并且依赖于不同的方法、预算、数据流和软件工具。这个差距掩盖了创建一个单一平台，从整体上优化数字体验的机会: 使用定量方法揭示什么和多少，定性分析理解为什么。这样的平台可以监控转换漏斗，识别异常行为，拦截表现出异常行为的现场用户，并在现场获得明确的反馈。这种反馈可以采取多种形式: 调查反馈、参与者执行任务的屏幕录音、有声思考音频等等。通过组合来自多个用户的数据和跨反馈类型的相关性，该平台不仅可以提供某个特定转换漏斗受到影响的见解，还可以提供有关导致用户行为变化的原因的假设。然后，该平台可以根据观察到的行为在野外发生的频率对这些见解进行排名，使用大规模分析来将小规模用户体验测试的结果联系起来。为此，十年的研究集中在交互挖掘: 一套从数字工件中获取交互和设计数据的技术，并将这些多模态数据流聚合成结构化表示，连接定量和定性经验测试[1-4]。在用户会话期间，交互挖掘系统记录用户交互(例如，点击、滚动、文本输入)、屏幕截图和呈现时间数据结构(例如，网站 DOM、本地应用程序视图层次结构)。一旦捕获，这些数据流就被对齐并组合成用户跟踪，用户交互的序列被其 UI 目标的设计数据语义化[5]。这些轨迹的结构为定量和定性方法的组合提供了新的工作流程，为优化数字体验建立了统一的平台。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Bridging+Quantitative+and+Qualitative+Digital+Experience+Testing)|0| -|[MGeo: Multi-Modal Geographic Language Model Pre-Training](https://doi.org/10.1145/3539618.3591728)|Ruixue Ding, Boli Chen, Pengjun Xie, Fei Huang, Xin Li, Qiang Zhang, Yao Xu|Damo Academy, Alibaba Group, Hangzhou, China; Gaode Map, Alibaba Group, Beijing, China|Query and point of interest (POI) matching is a core task in location-based services~(LBS), e.g., navigation maps. It connects users' intent with real-world geographic information. Lately, pre-trained language models (PLMs) have made notable advancements in many natural language processing (NLP) tasks. To overcome the limitation that generic PLMs lack geographic knowledge for query-POI matching, related literature attempts to employ continued pre-training based on domain-specific corpus. However, a query generally describes the geographic context (GC) about its destination and contains mentions of multiple geographic objects like nearby roads and regions of interest (ROIs). These diverse geographic objects and their correlations are pivotal to retrieving the most relevant POI. Text-based single-modal PLMs can barely make use of the important GC and are therefore limited. In this work, we propose a novel method for query-POI matching, namely Multi-modal Geographic language model (MGeo), which comprises a geographic encoder and a multi-modal interaction module. Representing GC as a new modality, MGeo is able to fully extract multi-modal correlations to perform accurate query-POI matching. Moreover, there exists no publicly available query-POI matching benchmark. Intending to facilitate further research, we build a new open-source large-scale benchmark for this topic, i.e., Geographic TExtual Similarity (GeoTES). The POIs come from an open-source geographic information system (GIS) and the queries are manually generated by annotators to prevent privacy issues. Compared with several strong baselines, the extensive experiment results and detailed ablation analyses demonstrate that our proposed multi-modal geographic pre-training method can significantly improve the query-POI matching capability of PLMs with or without users' locations. Our code and benchmark are publicly available at https://github.com/PhantomGrapes/MGeo.|查询和感兴趣点(POI)匹配是基于位置服务 ~ (LBS)的核心任务，例如导航地图。它将用户的意图与现实世界的地理信息联系起来。最近，预训练语言模型(PLM)在许多自然语言处理(NLP)任务中取得了显著的进展。为了克服通用 PLM 在查询-POI 匹配方面缺乏地理知识的局限性，相关文献试图采用基于领域特定语料库的持续预训练。但是，查询通常描述其目的地的地理上下文(GC) ，并包含多个地理对象，如附近的道路和感兴趣的区域(ROI)。这些不同的地理对象及其相关性是检索最相关的 POI 的关键。基于文本的单模式 PLM 几乎不能利用重要的 GC，因此是有限的。本文提出了一种新的查询-POI 匹配方法，即多模态地理语言模型(MGeo) ，该模型由一个地理编码器和一个多模态交互模块组成。代表 GC 作为一种新的模式，MGeo 能够充分提取多模态相关性，以执行准确的查询-POI 匹配。此外，不存在公开可用的查询-POI 匹配基准。为了促进进一步的研究，我们为这个主题建立了一个新的开源大规模基准，即地理文本相似度(GeoTES)。POI 来自一个开源的地理信息系统(GIS) ，查询由注释器手动生成，以防止隐私问题。与几个强基线相比，大量的实验结果和详细的烧蚀分析表明，我们提出的多模态地理预训练方法可以显著提高有或无用户位置的 PLM 的查询-POI 匹配能力。我们的代码和基准已经在 https://github.com/phantomgrapes/mgeo 上公开发布。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=MGeo:+Multi-Modal+Geographic+Language+Model+Pre-Training)|0| +|[MGeo: Multi-Modal Geographic Language Model Pre-Training](https://doi.org/10.1145/3539618.3591728)|Ruixue Ding, Boli Chen, Pengjun Xie, Fei Huang, Xin Li, Qiang Zhang, Yao Xu|Gaode Map, Alibaba Group, Beijing, China; Damo Academy, Alibaba Group, Hangzhou, China|Query and point of interest (POI) matching is a core task in location-based services~(LBS), e.g., navigation maps. It connects users' intent with real-world geographic information. Lately, pre-trained language models (PLMs) have made notable advancements in many natural language processing (NLP) tasks. To overcome the limitation that generic PLMs lack geographic knowledge for query-POI matching, related literature attempts to employ continued pre-training based on domain-specific corpus. However, a query generally describes the geographic context (GC) about its destination and contains mentions of multiple geographic objects like nearby roads and regions of interest (ROIs). These diverse geographic objects and their correlations are pivotal to retrieving the most relevant POI. Text-based single-modal PLMs can barely make use of the important GC and are therefore limited. In this work, we propose a novel method for query-POI matching, namely Multi-modal Geographic language model (MGeo), which comprises a geographic encoder and a multi-modal interaction module. Representing GC as a new modality, MGeo is able to fully extract multi-modal correlations to perform accurate query-POI matching. Moreover, there exists no publicly available query-POI matching benchmark. Intending to facilitate further research, we build a new open-source large-scale benchmark for this topic, i.e., Geographic TExtual Similarity (GeoTES). The POIs come from an open-source geographic information system (GIS) and the queries are manually generated by annotators to prevent privacy issues. Compared with several strong baselines, the extensive experiment results and detailed ablation analyses demonstrate that our proposed multi-modal geographic pre-training method can significantly improve the query-POI matching capability of PLMs with or without users' locations. Our code and benchmark are publicly available at https://github.com/PhantomGrapes/MGeo.|查询和感兴趣点(POI)匹配是基于位置服务 ~ (LBS)的核心任务，例如导航地图。它将用户的意图与现实世界的地理信息联系起来。最近，预训练语言模型(PLM)在许多自然语言处理(NLP)任务中取得了显著的进展。为了克服通用 PLM 在查询-POI 匹配方面缺乏地理知识的局限性，相关文献试图采用基于领域特定语料库的持续预训练。但是，查询通常描述其目的地的地理上下文(GC) ，并包含多个地理对象，如附近的道路和感兴趣的区域(ROI)。这些不同的地理对象及其相关性是检索最相关的 POI 的关键。基于文本的单模式 PLM 几乎不能利用重要的 GC，因此是有限的。本文提出了一种新的查询-POI 匹配方法，即多模态地理语言模型(MGeo) ，该模型由一个地理编码器和一个多模态交互模块组成。代表 GC 作为一种新的模式，MGeo 能够充分提取多模态相关性，以执行准确的查询-POI 匹配。此外，不存在公开可用的查询-POI 匹配基准。为了促进进一步的研究，我们为这个主题建立了一个新的开源大规模基准，即地理文本相似度(GeoTES)。POI 来自一个开源的地理信息系统(GIS) ，查询由注释器手动生成，以防止隐私问题。与几个强基线相比，大量的实验结果和详细的烧蚀分析表明，我们提出的多模态地理预训练方法可以显著提高有或无用户位置的 PLM 的查询-POI 匹配能力。我们的代码和基准已经在 https://github.com/phantomgrapes/mgeo 上公开发布。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=MGeo:+Multi-Modal+Geographic+Language+Model+Pre-Training)|0| |[Large Language Models are Versatile Decomposers: Decomposing Evidence and Questions for Table-based Reasoning](https://doi.org/10.1145/3539618.3591708)|Yunhu Ye, Binyuan Hui, Min Yang, Binhua Li, Fei Huang, Yongbin Li|Alibaba Group, Beijing, China; University of Science and Technology of China, Hefei, China; SIAT, Chinese Academy of Sciences, Shenzhen, China|Table-based reasoning has shown remarkable progress in combining deep models with discrete reasoning, which requires reasoning over both free-form natural language (NL) questions and structured tabular data. However, previous table-based reasoning solutions usually suffer from significant performance degradation on huge evidence (tables). In addition, most existing methods struggle to reason over complex questions since the required information is scattered in different places. To alleviate the above challenges, we exploit large language models (LLMs) as decomposers for effective table-based reasoning, which (i) decompose huge evidence (a huge table) into sub-evidence (a small table) to mitigate the interference of useless information for table reasoning; and (ii) decompose complex questions into simpler sub-questions for text reasoning. Specifically, we first use the LLMs to break down the evidence (tables) involved in the current question, retaining the relevant evidence and excluding the remaining irrelevant evidence from the huge table. In addition, we propose a "parsing-execution-filling" strategy to alleviate the hallucination dilemma of the chain of thought by decoupling logic and numerical computation in each step. Extensive experiments show that our method can effectively leverage decomposed evidence and questions and outperforms the strong baselines on TabFact, WikiTableQuestion, and FetaQA datasets. Notably, our model outperforms human performance for the first time on the TabFact dataset.|基于表格的推理在深度模型与离散推理相结合方面取得了显著的进展，这需要对自由形式的自然语言(NL)问题和结构化表格数据进行推理。然而，以前的基于表的推理解决方案通常会在大量证据(表)上出现严重的性能下降。此外，由于所需的信息分散在不同的地方，大多数现有方法都难以对复杂问题进行推理。为了缓解上述挑战，我们利用大语言模型(LLM)作为有效的基于表的推理的分解器，它(i)分解巨大的证据(一个巨大的表)为子证据(一个小的表) ，以减轻无用的信息对表推理的干扰; (ii)分解复杂的问题为文本推理的简单的子问题。具体来说，我们首先使用 LLM 来分解当前问题中涉及的证据(表格) ，保留相关的证据，并从庞大的表格中排除其余不相关的证据。此外，我们还提出了一种“解析-执行-填充”策略，通过在每个步骤中解耦逻辑和数值计算来缓解思维链的幻觉困境。大量的实验表明，我们的方法可以有效地利用分解的证据和问题，并优于 TabFact、 WikiTablequestions 和 FetaQA 数据集的强基线。值得注意的是，我们的模型第一次在 TabFact 数据集上优于人类性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Large+Language+Models+are+Versatile+Decomposers:+Decomposing+Evidence+and+Questions+for+Table-based+Reasoning)|0| -|[DisCover: Disentangled Music Representation Learning for Cover Song Identification](https://doi.org/10.1145/3539618.3591664)|Jiahao Xun, Shengyu Zhang, Yanting Yang, Jieming Zhu, Liqun Deng, Zhou Zhao, Zhenhua Dong, Ruiqi Li, Lichao Zhang, Fei Wu|Zhejiang University, Hangzhou, China; Huawei Noah's Ark Lab, Shenzhen, China|In the field of music information retrieval (MIR), cover song identification (CSI) is a challenging task that aims to identify cover versions of a query song from a massive collection. Existing works still suffer from high intra-song variances and inter-song correlations, due to the entangled nature of version-specific and version-invariant factors in their modeling. In this work, we set the goal of disentangling version-specific and version-invariant factors, which could make it easier for the model to learn invariant music representations for unseen query songs. We analyze the CSI task in a disentanglement view with the causal graph technique, and identify the intra-version and inter-version effects biasing the invariant learning. To block these effects, we propose the disentangled music representation learning framework (DisCover) for CSI. DisCover consists of two critical components: (1) Knowledge-guided Disentanglement Module (KDM) and (2) Gradient-based Adversarial Disentanglement Module (GADM), which block intra-version and inter-version biased effects, respectively. KDM minimizes the mutual information between the learned representations and version-variant factors that are identified with prior domain knowledge. GADM identifies version-variant factors by simulating the representation transitions between intra-song versions, and exploits adversarial distillation for effect blocking. Extensive comparisons with best-performing methods and in-depth analysis demonstrate the effectiveness of DisCover and the and necessity of disentanglement for CSI.|在音乐信息检索(MIR)领域，翻唱歌曲识别(CSI)是一项具有挑战性的任务，其目标是从大量的收藏中识别出一首查询歌曲的翻唱版本。现有作品由于版本特异性因素和版本不变性因素的纠缠性，仍然存在着高度的内部歌曲变异和内部歌曲相关性。本文设定了版本不变因子和版本不变因子的分离目标，使得模型更容易学习未知查询歌曲的不变音乐表示。本文运用因果图技术对 CSI 任务进行了解缠分析，识别出内版本效应和内版本效应对不变学习的影响。为了阻止这些效应，我们提出了一个用于 CSI 的分离音乐表征学习框架(DisCover)。DisCover 由两个关键组成部分组成: (1)知识引导的解缠模块(KDM)和(2)基于梯度的对抗性解缠模块(GADM) ，它们分别阻止内部版本效应和内部版本效应。KDM 最小化了学习表示和版本变异因子之间的相互信息，这些因子与先验领域知识一致。GADM 通过模拟歌曲内部版本之间的表示转换来识别版本变异因素，并利用对抗精馏来阻断效果。通过与最佳方法的广泛比较和深入分析，证明了 DisCover 的有效性以及 CSI 解纠缠的必要性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=DisCover:+Disentangled+Music+Representation+Learning+for+Cover+Song+Identification)|0| +|[DisCover: Disentangled Music Representation Learning for Cover Song Identification](https://doi.org/10.1145/3539618.3591664)|Jiahao Xun, Shengyu Zhang, Yanting Yang, Jieming Zhu, Liqun Deng, Zhou Zhao, Zhenhua Dong, Ruiqi Li, Lichao Zhang, Fei Wu|Huawei Noah's Ark Lab, Shenzhen, China; Zhejiang University, Hangzhou, China|In the field of music information retrieval (MIR), cover song identification (CSI) is a challenging task that aims to identify cover versions of a query song from a massive collection. Existing works still suffer from high intra-song variances and inter-song correlations, due to the entangled nature of version-specific and version-invariant factors in their modeling. In this work, we set the goal of disentangling version-specific and version-invariant factors, which could make it easier for the model to learn invariant music representations for unseen query songs. We analyze the CSI task in a disentanglement view with the causal graph technique, and identify the intra-version and inter-version effects biasing the invariant learning. To block these effects, we propose the disentangled music representation learning framework (DisCover) for CSI. DisCover consists of two critical components: (1) Knowledge-guided Disentanglement Module (KDM) and (2) Gradient-based Adversarial Disentanglement Module (GADM), which block intra-version and inter-version biased effects, respectively. KDM minimizes the mutual information between the learned representations and version-variant factors that are identified with prior domain knowledge. GADM identifies version-variant factors by simulating the representation transitions between intra-song versions, and exploits adversarial distillation for effect blocking. Extensive comparisons with best-performing methods and in-depth analysis demonstrate the effectiveness of DisCover and the and necessity of disentanglement for CSI.|在音乐信息检索(MIR)领域，翻唱歌曲识别(CSI)是一项具有挑战性的任务，其目标是从大量的收藏中识别出一首查询歌曲的翻唱版本。现有作品由于版本特异性因素和版本不变性因素的纠缠性，仍然存在着高度的内部歌曲变异和内部歌曲相关性。本文设定了版本不变因子和版本不变因子的分离目标，使得模型更容易学习未知查询歌曲的不变音乐表示。本文运用因果图技术对 CSI 任务进行了解缠分析，识别出内版本效应和内版本效应对不变学习的影响。为了阻止这些效应，我们提出了一个用于 CSI 的分离音乐表征学习框架(DisCover)。DisCover 由两个关键组成部分组成: (1)知识引导的解缠模块(KDM)和(2)基于梯度的对抗性解缠模块(GADM) ，它们分别阻止内部版本效应和内部版本效应。KDM 最小化了学习表示和版本变异因子之间的相互信息，这些因子与先验领域知识一致。GADM 通过模拟歌曲内部版本之间的表示转换来识别版本变异因素，并利用对抗精馏来阻断效果。通过与最佳方法的广泛比较和深入分析，证明了 DisCover 的有效性以及 CSI 解纠缠的必要性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=DisCover:+Disentangled+Music+Representation+Learning+for+Cover+Song+Identification)|0| |[Augmenting Low-Resource Text Classification with Graph-Grounded Pre-training and Prompting](https://doi.org/10.1145/3539618.3591641)|Zhihao Wen, Yuan Fang|Singapore Management University, Singapore, Singapore|Text classification is a fundamental problem in information retrieval with many real-world applications, such as predicting the topics of online articles and the categories of e-commerce product descriptions. However, low-resource text classification, with few or no labeled samples, poses a serious concern for supervised learning. Meanwhile, many text data are inherently grounded on a network structure, such as a hyperlink/citation network for online articles, and a user-item purchase network for e-commerce products. These graph structures capture rich semantic relationships, which can potentially augment low-resource text classification. In this paper, we propose a novel model called Graph-Grounded Pre-training and Prompting (G2P2) to address low-resource text classification in a two-pronged approach. During pre-training, we propose three graph interaction-based contrastive strategies to jointly pre-train a graph-text model; during downstream classification, we explore prompting for the jointly pre-trained model to achieve low-resource classification. Extensive experiments on four real-world datasets demonstrate the strength of G2P2 in zero- and few-shot low-resource text classification tasks.|文本分类是许多实际应用程序的基本信息检索，例如预测在线文章的主题和电子商务产品说明的类别。然而，低资源的文本分类，很少或没有标记的样本，提出了一个严重的问题，监督式学习。与此同时，许多文本数据内在地基于网络结构，如在线文章的超链接/引用网络，以及电子商务产品的用户项目购买网络。这些图结构捕获了丰富的语义关系，可以潜在地增强低资源文本分类。本文提出了一种新的基于图的预训练和提示(G2P2)模型，采用双管齐下的方法解决低资源文本分类问题。在预训练阶段，我们提出了三种基于图形交互的对比策略来联合预训练一个图文模型; 在下游分类阶段，我们探索了联合预训练模型来实现低资源分类的提示。在四个真实世界数据集上的大量实验证明了 G2P2在零镜头和少镜头低资源文本分类任务中的优势。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Augmenting+Low-Resource+Text+Classification+with+Graph-Grounded+Pre-training+and+Prompting)|0| |[Smooth Operators for Effective Systematic Review Queries](https://doi.org/10.1145/3539618.3591768)|Harrisen Scells, Ferdinand Schlatt, Martin Potthast|Leipzig University & ScaDS.AI, Leipzig, Germany; Friedrich-Schiller-Universität, Jena, Germany; Leipzig University, Leipzig, Germany|Effective queries are crucial to minimising the time and cost of medical systematic reviews, as all retrieved documents must be judged for relevance. Boolean queries, developed by expert librarians, are the standard for systematic reviews. They guarantee reproducible and verifiable retrieval and more control than free-text queries. However, the result sets of Boolean queries are unranked and difficult to control due to the strict Boolean operators. We address these problems in a single unified retrieval model by formulating a class of smooth operators that are compatible with and extend existing Boolean operators. Our smooth operators overcome several shortcomings of previous extensions of the Boolean retrieval model. In particular, our operators are independent of the underlying ranking function, so that exact-match and large language model rankers can be combined in the same query. We found that replacing Boolean operators with equivalent or similar smooth operators often improves the effectiveness of queries. Their properties make tuning a query to precision or recall intuitive and allow greater control over how documents are retrieved. This additional control leads to more effective queries and reduces the cost of systematic reviews.|有效的查询对于最大限度地减少医疗系统评价的时间和成本至关重要，因为所有检索到的文档都必须判断其相关性。由专家图书馆员开发的布尔查询是系统评审的标准。它们保证了可重复和可验证的检索，并且比自由文本查询更具控制性。然而，由于严格的布尔运算符，布尔查询的结果集是无序的并且难以控制。我们通过构造一类与现有布尔算子兼容并扩展的平滑算子，在一个统一的检索模型中解决了这些问题。我们的平滑算子克服了以前布尔检索模型扩展的一些缺点。特别地，我们的运算符独立于底层的排序函数，因此精确匹配和大型语言模型排序器可以组合在同一个查询中。我们发现，用等效或类似的平滑运算符替换布尔运算符通常会提高查询的有效性。它们的属性使得将查询调优到精确或召回非常直观，并允许对如何检索文档进行更大的控制。这种额外的控制导致更有效的查询，并降低了系统评审的成本。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Smooth+Operators+for+Effective+Systematic+Review+Queries)|0| -|[Do-GOOD: Towards Distribution Shift Evaluation for Pre-Trained Visual Document Understanding Models](https://doi.org/10.1145/3539618.3591670)|Jiabang He, Yi Hu, Lei Wang, Xing Xu, Ning Liu, Hui Liu, Heng Tao Shen|Beijing Forestry University, Beijing, China; Beijing Rongda Technology Co., Ltd., Beijing, China; University of Electronic Science and Technology of China, Chengdu, China; University of Electronic Science and Technology of China & Peng Cheng Laboratory, Chengdu & Shenzhen, China; Singapore Management University, Singapore , Singapore|Numerous pre-training techniques for visual document understanding (VDU) have recently shown substantial improvements in performance across a wide range of document tasks. However, these pre-trained VDU models cannot guarantee continued success when the distribution of test data differs from the distribution of training data. In this paper, to investigate how robust existing pre-trained VDU models are to various distribution shifts, we first develop an out-of-distribution (OOD) benchmark termed Do-GOOD for the fine-Grained analysis on Document image-related tasks specifically. The Do-GOOD benchmark defines the underlying mechanisms that result in different distribution shifts and contains 9 OOD datasets covering 3 VDU related tasks, e.g., document information extraction, classification and question answering. We then evaluate the robustness and perform a fine-grained analysis of 5 latest VDU pre-trained models and 2 typical OOD generalization algorithms on these OOD datasets. Results from the experiments demonstrate that there is a significant performance gap between the in-distribution (ID) and OOD settings for document images, and that fine-grained analysis of distribution shifts can reveal the brittle nature of existing pre-trained VDU models and OOD generalization algorithms. The code and datasets for our Do-GOOD benchmark can be found at https://github.com/MAEHCM/Do-GOOD.|许多可视化文档理解(VDU)的预训练技术最近显示了在广泛的文档任务中性能的实质性改进。然而，当测试数据的分布与训练数据的分布不同时，这些预先训练的 VDU 模型不能保证连续的成功。为了研究现有的预先训练的 VDU 模型对不同分布转移的鲁棒性，我们首先开发了一个名为 Do-Good 的分布外(OOD)基准，专门用于文档图像相关任务的细粒度分析。Do-GOOD 基准定义了导致不同分配转移的基本机制，包含9个面向对象数据集，涵盖3个与视觉数据单相关的任务，例如文档信息抽取、分类和问题回答。然后，我们评估了稳健性，并对这些 OOD 数据集上的5个最新的 VDU 预训练模型和2个典型的 OOD 泛化算法进行了细粒度分析。实验结果表明，文档图像的内分布(ID)和面向对象设置(OOD)之间存在显著的性能差距，对分布偏移的细粒度分析可以揭示现有预先训练的 VDU 模型和面向对象设置泛化算法的脆弱性。我们的 Do-GOOD 基准的代码和数据集可以在 https://github.com/maehcm/Do-GOOD 找到。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Do-GOOD:+Towards+Distribution+Shift+Evaluation+for+Pre-Trained+Visual+Document+Understanding+Models)|0| -|[Continual Learning on Dynamic Graphs via Parameter Isolation](https://doi.org/10.1145/3539618.3591652)|Peiyan Zhang, Yuchen Yan, Chaozhuo Li, Senzhang Wang, Xing Xie, Guojie Song, Sunghun Kim|Hong Kong University of Science and Technology, Hong Kong, Hong Kong; School of Computer Science and Engineering, Central South University, Changsha, China; Microsoft Research Asia, Beijing, China; Hong Kong University of Science and Technology, Beijing, China; School of Intelligence Science and Technology, Peking University, Beijing, China|Many real-world graph learning tasks require handling dynamic graphs where new nodes and edges emerge. Dynamic graph learning methods commonly suffer from the catastrophic forgetting problem, where knowledge learned for previous graphs is overwritten by updates for new graphs. To alleviate the problem, continual graph learning methods are proposed. However, existing continual graph learning methods aim to learn new patterns and maintain old ones with the same set of parameters of fixed size, and thus face a fundamental tradeoff between both goals. In this paper, we propose Parameter Isolation GNN (PI-GNN) for continual learning on dynamic graphs that circumvents the tradeoff via parameter isolation and expansion. Our motivation lies in that different parameters contribute to learning different graph patterns. Based on the idea, we expand model parameters to continually learn emerging graph patterns. Meanwhile, to effectively preserve knowledge for unaffected patterns, we find parameters that correspond to them via optimization and freeze them to prevent them from being rewritten. Experiments on eight real-world datasets corroborate the effectiveness of PI-GNN compared to state-of-the-art baselines.|许多真实世界的图形学习任务需要处理出现新节点和边的动态图形。动态图学习方法通常受到灾难性遗忘问题的困扰，在这个问题中，先前图的知识被新图的更新所覆盖。为了解决这一问题，提出了连续图学习方法。然而，现有的连续图学习方法的目的是学习新的模式和保持固定大小的相同的参数集的旧模式，因此面临着两个目标之间的基本权衡。本文提出了参数隔离 GNN (PI-GNN) ，用于动态图的连续学习，避免了通过参数隔离和扩展进行折衷的问题。我们的动机在于不同的参数有助于学习不同的图形模式。基于这一思想，我们扩展了模型参数来不断学习新兴的图形模式。同时，为了有效地保存未受影响的模式的知识，我们通过优化找到与之对应的参数，并冻结它们以防止它们被重写。在八个真实世界数据集上的实验证实了 PI-GNN 与最先进的基线相比的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Continual+Learning+on+Dynamic+Graphs+via+Parameter+Isolation)|0| -|[An Effective, Efficient, and Scalable Confidence-based Instance Selection Framework for Transformer-Based Text Classification](https://doi.org/10.1145/3539618.3591638)|Washington Cunha, Celso França, Guilherme Fonseca, Leonardo Rocha, Marcos André Gonçalves|Federal University of São João del Rei, São João del Rei, Brazil; Federal University of Minas Gerais, Belo Horizonte, Brazil|Transformer-based deep learning is currently the state-of-the-art in many NLP and IR tasks. However, fine-tuning such Transformers for specific tasks, especially in scenarios of ever-expanding volumes of data with constant re-training requirements and budget constraints, is costly (computationally and financially) and energy-consuming. In this paper, we focus on Instance Selection (IS) - a set of methods focused on selecting the most representative documents for training, aimed at maintaining (or improving) classification effectiveness while reducing total time for training (or fine-tuning). We propose E2SC-IS -- Effective, Efficient, and Scalable Confidence-Based IS -- a two-step framework with a particular focus on Transformers and large datasets. E2SC-IS estimates the probability of each instance being removed from the training set based on scalable, fast, and calibrated weak classifiers. E2SC-IS also exploits iterative heuristics to estimate a near-optimal reduction rate. Our solution can reduce the training sets by 29% on average while maintaining the effectiveness in all datasets, with speedup gains up to 70%, scaling for very large datasets (something that the baselines cannot do).|基于变压器的深度学习是目前许多自然语言处理和红外任务中的最新技术。然而，针对特定任务对这些变形金刚进行微调，特别是在不断扩大的数据量、不断的再培训要求和预算限制的情况下，成本(计算和财务)和能源消耗都很高。在本文中，我们重点讨论实例选择(IS)-一组方法集中在选择最有代表性的文档进行培训，旨在保持(或提高)分类的有效性，同时减少总的培训时间(或微调)。我们提出了 E2SC-IS ——高效、高效和可扩展的基于置信度的 IS ——一个两步框架，特别关注变压器和大型数据集。基于可伸缩、快速和校准的弱分类器，E2SC-IS 估计每个实例从训练集中移除的概率。E2SC-IS 还利用迭代启发式算法来估计接近最优的减速率。我们的解决方案可以平均减少29% 的训练集，同时保持所有数据集的有效性，加速增益高达70% ，适用于非常大的数据集(这是基线无法做到的)。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=An+Effective,+Efficient,+and+Scalable+Confidence-based+Instance+Selection+Framework+for+Transformer-Based+Text+Classification)|0| -|[EDIndex: Enabling Fast Data Queries in Edge Storage Systems](https://doi.org/10.1145/3539618.3591676)|Qiang He, Siyu Tan, Feifei Chen, Xiaolong Xu, Lianyong Qi, Xinhong Hei, Hai Jin, Yun Yang|Southeast University, Nanjing, China; Nanjing University of Information Science and Technology, Nanjing, China; China University of Petroleum, Qingdao, China; Huazhong University of Science and Technology, Wuhan, China; Swinburne University of Technology, Hawthorn, VIC, Australia; Huazhong University of Science and Technology & Swinburne University of Technology, Wuhan, China; Xi'an University of Technology, Xi'an, China; Deakin University, Burwood, VIC, Australia|In an edge storage system, popular data can be stored on edge servers to enable low-latency data retrieval for nearby users. Suffering from constrained storage capacities, edge servers must process users' data requests collaboratively. For sourcing data, it is essential to find out which edge servers in the system have the requested data. In this paper, we make the first attempt to study this edge data query (EDQ) problem and present EDIndex, a distributed Edge Data Indexing system to enable fast data queries at the edge. First, we introduce a new index structure named Counting Bloom Filter (CBF) tree for facilitating edge data queries. Then, to improve query performance, we enhance EDIndex with a novel index structure named hierarchical Counting Bloom Filter (HCBF) tree. In EDIndex, each edge server maintains an HCBF tree that indexes the data stored on nearby edge servers to facilitate data sourcing between edge servers at the edge. The results of extensive experiments conducted on an edge storage system comprised of 90 edge servers demonstrate that EDIndex 1) takes up to 8.8x less time to answer edge data queries compared with state-of-the-art edge indexing systems; and 2) can be implemented in practice with a high query accuracy at low initialization and maintenance overheads.|在边缘存储系统中，流行的数据可以存储在边缘服务器上，以便对附近的用户进行低延迟的数据检索。由于存储容量有限，边缘服务器必须协同处理用户的数据请求。为了获取数据，必须找出系统中哪些边缘服务器具有所请求的数据。本文首次尝试研究边缘数据查询(EDQ)问题，提出了一种分布式边缘数据索引系统 EDIndex，该系统可以实现边缘数据的快速查询。首先，我们引入了一种新的索引结构，称为 Counting Bloom Filter (CBF)树，以方便边缘数据查询。然后，为了提高查询性能，我们提出了一种新的索引结构——分层计数布鲁姆过滤(HCBF)树来增强 EDIndex。在 EDIndex 中，每个边缘服务器维护一个 HCBF 树，该树对存储在附近边缘服务器上的数据进行索引，以促进边缘服务器之间的数据源。在由90台边缘服务器组成的边缘存储系统上进行的大量实验结果表明，EDIndex 1)与最先进的边缘索引系统相比，回答边缘数据查询所需的时间少了8.8倍; 以及2)可以在实践中以低初始化和维护开销的高查询精度实现。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=EDIndex:+Enabling+Fast+Data+Queries+in+Edge+Storage+Systems)|0| +|[Do-GOOD: Towards Distribution Shift Evaluation for Pre-Trained Visual Document Understanding Models](https://doi.org/10.1145/3539618.3591670)|Jiabang He, Yi Hu, Lei Wang, Xing Xu, Ning Liu, Hui Liu, Heng Tao Shen|University of Electronic Science and Technology of China, Chengdu, China; Singapore Management University, Singapore , Singapore; University of Electronic Science and Technology of China & Peng Cheng Laboratory, Chengdu & Shenzhen, China; Beijing Forestry University, Beijing, China; Beijing Rongda Technology Co., Ltd., Beijing, China|Numerous pre-training techniques for visual document understanding (VDU) have recently shown substantial improvements in performance across a wide range of document tasks. However, these pre-trained VDU models cannot guarantee continued success when the distribution of test data differs from the distribution of training data. In this paper, to investigate how robust existing pre-trained VDU models are to various distribution shifts, we first develop an out-of-distribution (OOD) benchmark termed Do-GOOD for the fine-Grained analysis on Document image-related tasks specifically. The Do-GOOD benchmark defines the underlying mechanisms that result in different distribution shifts and contains 9 OOD datasets covering 3 VDU related tasks, e.g., document information extraction, classification and question answering. We then evaluate the robustness and perform a fine-grained analysis of 5 latest VDU pre-trained models and 2 typical OOD generalization algorithms on these OOD datasets. Results from the experiments demonstrate that there is a significant performance gap between the in-distribution (ID) and OOD settings for document images, and that fine-grained analysis of distribution shifts can reveal the brittle nature of existing pre-trained VDU models and OOD generalization algorithms. The code and datasets for our Do-GOOD benchmark can be found at https://github.com/MAEHCM/Do-GOOD.|许多可视化文档理解(VDU)的预训练技术最近显示了在广泛的文档任务中性能的实质性改进。然而，当测试数据的分布与训练数据的分布不同时，这些预先训练的 VDU 模型不能保证连续的成功。为了研究现有的预先训练的 VDU 模型对不同分布转移的鲁棒性，我们首先开发了一个名为 Do-Good 的分布外(OOD)基准，专门用于文档图像相关任务的细粒度分析。Do-GOOD 基准定义了导致不同分配转移的基本机制，包含9个面向对象数据集，涵盖3个与视觉数据单相关的任务，例如文档信息抽取、分类和问题回答。然后，我们评估了稳健性，并对这些 OOD 数据集上的5个最新的 VDU 预训练模型和2个典型的 OOD 泛化算法进行了细粒度分析。实验结果表明，文档图像的内分布(ID)和面向对象设置(OOD)之间存在显著的性能差距，对分布偏移的细粒度分析可以揭示现有预先训练的 VDU 模型和面向对象设置泛化算法的脆弱性。我们的 Do-GOOD 基准的代码和数据集可以在 https://github.com/maehcm/Do-GOOD 找到。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Do-GOOD:+Towards+Distribution+Shift+Evaluation+for+Pre-Trained+Visual+Document+Understanding+Models)|0| +|[Continual Learning on Dynamic Graphs via Parameter Isolation](https://doi.org/10.1145/3539618.3591652)|Peiyan Zhang, Yuchen Yan, Chaozhuo Li, Senzhang Wang, Xing Xie, Guojie Song, Sunghun Kim|Hong Kong University of Science and Technology, Hong Kong, Hong Kong; Hong Kong University of Science and Technology, Beijing, China; School of Intelligence Science and Technology, Peking University, Beijing, China; School of Computer Science and Engineering, Central South University, Changsha, China; Microsoft Research Asia, Beijing, China|Many real-world graph learning tasks require handling dynamic graphs where new nodes and edges emerge. Dynamic graph learning methods commonly suffer from the catastrophic forgetting problem, where knowledge learned for previous graphs is overwritten by updates for new graphs. To alleviate the problem, continual graph learning methods are proposed. However, existing continual graph learning methods aim to learn new patterns and maintain old ones with the same set of parameters of fixed size, and thus face a fundamental tradeoff between both goals. In this paper, we propose Parameter Isolation GNN (PI-GNN) for continual learning on dynamic graphs that circumvents the tradeoff via parameter isolation and expansion. Our motivation lies in that different parameters contribute to learning different graph patterns. Based on the idea, we expand model parameters to continually learn emerging graph patterns. Meanwhile, to effectively preserve knowledge for unaffected patterns, we find parameters that correspond to them via optimization and freeze them to prevent them from being rewritten. Experiments on eight real-world datasets corroborate the effectiveness of PI-GNN compared to state-of-the-art baselines.|许多真实世界的图形学习任务需要处理出现新节点和边的动态图形。动态图学习方法通常受到灾难性遗忘问题的困扰，在这个问题中，先前图的知识被新图的更新所覆盖。为了解决这一问题，提出了连续图学习方法。然而，现有的连续图学习方法的目的是学习新的模式和保持固定大小的相同的参数集的旧模式，因此面临着两个目标之间的基本权衡。本文提出了参数隔离 GNN (PI-GNN) ，用于动态图的连续学习，避免了通过参数隔离和扩展进行折衷的问题。我们的动机在于不同的参数有助于学习不同的图形模式。基于这一思想，我们扩展了模型参数来不断学习新兴的图形模式。同时，为了有效地保存未受影响的模式的知识，我们通过优化找到与之对应的参数，并冻结它们以防止它们被重写。在八个真实世界数据集上的实验证实了 PI-GNN 与最先进的基线相比的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Continual+Learning+on+Dynamic+Graphs+via+Parameter+Isolation)|0| +|[An Effective, Efficient, and Scalable Confidence-based Instance Selection Framework for Transformer-Based Text Classification](https://doi.org/10.1145/3539618.3591638)|Washington Cunha, Celso França, Guilherme Fonseca, Leonardo Rocha, Marcos André Gonçalves|Federal University of Minas Gerais, Belo Horizonte, Brazil; Federal University of São João del Rei, São João del Rei, Brazil|Transformer-based deep learning is currently the state-of-the-art in many NLP and IR tasks. However, fine-tuning such Transformers for specific tasks, especially in scenarios of ever-expanding volumes of data with constant re-training requirements and budget constraints, is costly (computationally and financially) and energy-consuming. In this paper, we focus on Instance Selection (IS) - a set of methods focused on selecting the most representative documents for training, aimed at maintaining (or improving) classification effectiveness while reducing total time for training (or fine-tuning). We propose E2SC-IS -- Effective, Efficient, and Scalable Confidence-Based IS -- a two-step framework with a particular focus on Transformers and large datasets. E2SC-IS estimates the probability of each instance being removed from the training set based on scalable, fast, and calibrated weak classifiers. E2SC-IS also exploits iterative heuristics to estimate a near-optimal reduction rate. Our solution can reduce the training sets by 29% on average while maintaining the effectiveness in all datasets, with speedup gains up to 70%, scaling for very large datasets (something that the baselines cannot do).|基于变压器的深度学习是目前许多自然语言处理和红外任务中的最新技术。然而，针对特定任务对这些变形金刚进行微调，特别是在不断扩大的数据量、不断的再培训要求和预算限制的情况下，成本(计算和财务)和能源消耗都很高。在本文中，我们重点讨论实例选择(IS)-一组方法集中在选择最有代表性的文档进行培训，旨在保持(或提高)分类的有效性，同时减少总的培训时间(或微调)。我们提出了 E2SC-IS ——高效、高效和可扩展的基于置信度的 IS ——一个两步框架，特别关注变压器和大型数据集。基于可伸缩、快速和校准的弱分类器，E2SC-IS 估计每个实例从训练集中移除的概率。E2SC-IS 还利用迭代启发式算法来估计接近最优的减速率。我们的解决方案可以平均减少29% 的训练集，同时保持所有数据集的有效性，加速增益高达70% ，适用于非常大的数据集(这是基线无法做到的)。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=An+Effective,+Efficient,+and+Scalable+Confidence-based+Instance+Selection+Framework+for+Transformer-Based+Text+Classification)|0| +|[EDIndex: Enabling Fast Data Queries in Edge Storage Systems](https://doi.org/10.1145/3539618.3591676)|Qiang He, Siyu Tan, Feifei Chen, Xiaolong Xu, Lianyong Qi, Xinhong Hei, Hai Jin, Yun Yang|Southeast University, Nanjing, China; Nanjing University of Information Science and Technology, Nanjing, China; Xi'an University of Technology, Xi'an, China; Huazhong University of Science and Technology, Wuhan, China; Swinburne University of Technology, Hawthorn, VIC, Australia; China University of Petroleum, Qingdao, China; Huazhong University of Science and Technology & Swinburne University of Technology, Wuhan, China; Deakin University, Burwood, VIC, Australia|In an edge storage system, popular data can be stored on edge servers to enable low-latency data retrieval for nearby users. Suffering from constrained storage capacities, edge servers must process users' data requests collaboratively. For sourcing data, it is essential to find out which edge servers in the system have the requested data. In this paper, we make the first attempt to study this edge data query (EDQ) problem and present EDIndex, a distributed Edge Data Indexing system to enable fast data queries at the edge. First, we introduce a new index structure named Counting Bloom Filter (CBF) tree for facilitating edge data queries. Then, to improve query performance, we enhance EDIndex with a novel index structure named hierarchical Counting Bloom Filter (HCBF) tree. In EDIndex, each edge server maintains an HCBF tree that indexes the data stored on nearby edge servers to facilitate data sourcing between edge servers at the edge. The results of extensive experiments conducted on an edge storage system comprised of 90 edge servers demonstrate that EDIndex 1) takes up to 8.8x less time to answer edge data queries compared with state-of-the-art edge indexing systems; and 2) can be implemented in practice with a high query accuracy at low initialization and maintenance overheads.|在边缘存储系统中，流行的数据可以存储在边缘服务器上，以便对附近的用户进行低延迟的数据检索。由于存储容量有限，边缘服务器必须协同处理用户的数据请求。为了获取数据，必须找出系统中哪些边缘服务器具有所请求的数据。本文首次尝试研究边缘数据查询(EDQ)问题，提出了一种分布式边缘数据索引系统 EDIndex，该系统可以实现边缘数据的快速查询。首先，我们引入了一种新的索引结构，称为 Counting Bloom Filter (CBF)树，以方便边缘数据查询。然后，为了提高查询性能，我们提出了一种新的索引结构——分层计数布鲁姆过滤(HCBF)树来增强 EDIndex。在 EDIndex 中，每个边缘服务器维护一个 HCBF 树，该树对存储在附近边缘服务器上的数据进行索引，以促进边缘服务器之间的数据源。在由90台边缘服务器组成的边缘存储系统上进行的大量实验结果表明，EDIndex 1)与最先进的边缘索引系统相比，回答边缘数据查询所需的时间少了8.8倍; 以及2)可以在实践中以低初始化和维护开销的高查询精度实现。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=EDIndex:+Enabling+Fast+Data+Queries+in+Edge+Storage+Systems)|0| |[Extending Label Aggregation Models with a Gaussian Process to Denoise Crowdsourcing Labels](https://doi.org/10.1145/3539618.3591685)|Dan Li, Maarten de Rijke|University of Amsterdam & Elsevier, Amsterdam, Netherlands; University of Amsterdam, Amsterdam, Netherlands|Label aggregation (LA) is the task of inferring a high-quality label for an example from multiple noisy labels generated by either human annotators or model predictions. Existing work on LA assumes a label generation process and designs a probabilistic graphical model (PGM) to learn latent true labels from observed crowd labels. However, the performance of PGM-based LA models is easily affected by the noise of the crowd labels. As a consequence, the performance of LA models differs on different datasets and no single LA model outperforms the rest on all datasets. We extend PGM-based LA models by integrating a GP prior on the true labels. The advantage of LA models extended with a GP prior is that they can take as input crowd labels, example features, and existing pre-trained label prediction models to infer the true labels, while the original LA can only leverage crowd labels. Experimental results on both synthetic and real datasets show that any LA models extended with a GP prior and a suitable mean function achieves better performance than the underlying LA models, demonstrating the effectiveness of using a GP prior.|标签聚合(LA)是从人类注释者或模型预测产生的多个噪声标签中推断出一个高质量标签的任务。现有的 LA 工作假设标签生成过程，并设计了一个概率图形模型(PGM)来从观察到的人群标签中学习潜在的真实标签。然而，基于 PGM 的 LA 模型的性能很容易受到人群标签噪声的影响。因此，LA 模型在不同数据集上的性能是不同的，没有一个 LA 模型在所有数据集上的性能优于其他模型。我们扩展了基于 PGM 的 LA 模型通过集成一个 GP 先验的真实标签。利用 GP 先验扩展的 LA 模型的优势在于，它可以将人群标签、示例特征和已有的预训练标签预测模型作为输入，从而推断出真实的标签，而原始的 LA 模型只能利用人群标签。在合成和实际数据集上的实验结果表明，任何具有 GP 先验和适当均值函数的 LA 模型都比基本 LA 模型具有更好的性能，证明了使用 GP 先验的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Extending+Label+Aggregation+Models+with+a+Gaussian+Process+to+Denoise+Crowdsourcing+Labels)|0| |[Dataset Preparation for Arbitrary Object Detection: An Automatic Approach based on Web Information in English](https://doi.org/10.1145/3539618.3591661)|Shucheng Li, Boyu Chang, Bo Yang, Hao Wu, Sheng Zhong, Fengyuan Xu|National Key Lab for Novel Software Technology, Nanjing University, Nanjing, China|Automatic dataset preparation can help users avoid labor-intensive and costly manual data annotations. The difficulty in preparing a high-quality dataset for object detection involves three key aspects: relevance, naturality, and balance, which are not addressed by existing works. In this paper, we leverage information from the web, and propose a fully-automatic dataset preparation mechanism without any human annotation, which can automatically prepare a high-quality training dataset for the detection task with English text terms describing target objects. It contains three key designs, i.e., keyword expansion, data de-noising, and data balancing. Our experiments demonstrate that the object detectors trained with auto-prepared data are comparable to those trained with benchmark datasets and outperform other baselines. We also demonstrate the effectiveness of our approach in several more challenging real-world object categories that are not included in the benchmark datasets.|自动数据集准备可以帮助用户避免劳动密集型和昂贵的手工数据注释。为目标检测准备一个高质量的数据集的难度涉及三个关键方面: 相关性、自然性和平衡性，这些都是现有作品没有涉及到的。本文利用网络信息，提出了一种无需人工注释的全自动数据集准备机制，该机制能够自动准备高质量的训练数据集，用于目标物体的检测任务。它包含三个关键设计，即关键字扩展、数据去噪和数据平衡。实验结果表明，用自动准备数据训练的目标检测器与用基准数据训练的目标检测器具有可比性，其检测性能优于其他基准检测器。我们还展示了我们的方法在几个更具挑战性的实际对象类别中的有效性，这些对象类别不包括在基准数据集中。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Dataset+Preparation+for+Arbitrary+Object+Detection:+An+Automatic+Approach+based+on+Web+Information+in+English)|0| |[Leader-Generator Net: Dividing Skill and Implicitness for Conquering FairytaleQA](https://doi.org/10.1145/3539618.3591710)|Wei Peng, Wanshui Li, Yue Hu|University College London, London, United Kingdom; Institute of Information Engineering, Chinese Academy of Sciences & University of Chinese Academy of Sciences, Beijing, China|Machine reading comprehension requires systems to understand the given passage and answer questions. Previous methods mainly focus on the interaction between the question and passage. However, they ignore the deep exploration of cognitive elements behind questions, such as fine-grained reading skills (this paper focuses on narrative comprehension skills) and implicitness or explicitness of the question (whether the answer can be found in the passage). Grounded in prior literature on reading comprehension, the understanding of a question is a complex process where human beings need to understand the semantics of the question, use different reading skills for different questions, and then judge the implicitness of the question. To this end, a simple but effective Leader-Generator Network is proposed to explicitly separate and extract fine-grained reading skills and the implicitness or explicitness of the question. Specifically, the proposed skill leader accurately captures the semantic representation of fine-grained reading skills with contrastive learning. And the implicitness-aware pointer-generator adaptively extracts or generates the answer based on the implicitness or explicitness of the question. Furthermore, to validate the generalizability of the methodology, we annotate a new dataset named NarrativeQA 1.1. Experiments on the FairytaleQA and NarrativeQA 1.1 show that the proposed model achieves the state-of-the-art performance (about 5% gain on Rouge-L) on the question answering task. Our annotated data and code are available at https://github.com/pengwei-iie/Leader-Generator-Net.|机器阅读理解要求系统理解给定的文章并回答问题。以往的研究方法主要集中在问句和短文之间的交互作用上。然而，他们忽视了对问题背后认知因素的深入探索，如细粒度阅读技巧(本文侧重于叙事理解技巧)和问题的隐含性或明确性(是否能在文章中找到答案)。根据以往关于阅读理解的文献，理解问题是一个复杂的过程，人们需要理解问题的语义，对不同的问题使用不同的阅读技巧，然后判断问题的隐含性。为此，本文提出了一种简单而有效的领导者-发生器网络，以明确地区分和提取细粒度阅读技能和问题的隐含性或明显性。具体来说，提出的技能领导者准确地捕获了语义表示的细粒度阅读技能与对比学习。而隐式感知指针生成器则根据问题的隐式性或显式性自适应地提取或生成答案。此外，为了验证该方法的普遍性，我们注释了一个名为 NarratveQA 1.1的新数据集。在 FairytaleQA 和 NarratveQA 1.1上的实验表明，该模型在问答任务上达到了最先进的性能(在 Rouge-L 上大约增加了5%)。我们的注释数据和代码可以在 https://github.com/pengwei-iie/leader-generator-net 找到。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Leader-Generator+Net:+Dividing+Skill+and+Implicitness+for+Conquering+FairytaleQA)|0| -|[BiTimeBERT: Extending Pre-Trained Language Representations with Bi-Temporal Information](https://doi.org/10.1145/3539618.3591686)|Jiexin Wang, Adam Jatowt, Masatoshi Yoshikawa, Yi Cai|University of Innsbruck, Innsbruck, Austria; Kyoto University, Kyoto, Japan; South China University of Technology, Guangzhou, China|Time is an important aspect of documents and is used in a range of NLP and IR tasks. In this work, we investigate methods for incorporating temporal information during pre-training to further improve the performance on time-related tasks. Compared with common pre-trained language models like BERT which utilize synchronic document collections (e.g., BookCorpus and Wikipedia) as the training corpora, we use long-span temporal news article collection for building word representations. We introduce BiTimeBERT, a novel language representation model trained on a temporal collection of news articles via two new pre-training tasks, which harnesses two distinct temporal signals to construct time-aware language representations. The experimental results show that BiTimeBERT consistently outperforms BERT and other existing pre-trained models with substantial gains on different downstream NLP tasks and applications for which time is of importance (e.g., the accuracy improvement over BERT is 155\% on the event time estimation task).|时间是文档的一个重要方面，用于一系列自然语言处理(NLP)和信息检索(IR)任务。在这项工作中，我们研究的方法，合并时间信息在预训练，以进一步提高绩效的时间相关的任务。与使用同步文档集合(如 BookCorpus 和 Wikipedia)作为训练语料库的常见的预训练语言模型 BERT 相比，我们使用大跨度的时态新闻文章集合来构建单词表示。我们介绍了一种新的语言表示模型 BiTimeBERT，该模型通过两个新的预训练任务训练新闻文章的时间集合，利用两个不同的时间信号来构造具有时间意识的语言表示。实验结果表明，BiTimeBERT 在不同的下游 NLP 任务和重要时间应用(例如，在事件时间估计任务上，误码率比 BERT 提高了155%)上，始终优于 BERT 和其他已有的预训练模型，获得了实质性的提高。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=BiTimeBERT:+Extending+Pre-Trained+Language+Representations+with+Bi-Temporal+Information)|0| +|[BiTimeBERT: Extending Pre-Trained Language Representations with Bi-Temporal Information](https://doi.org/10.1145/3539618.3591686)|Jiexin Wang, Adam Jatowt, Masatoshi Yoshikawa, Yi Cai|South China University of Technology, Guangzhou, China; Kyoto University, Kyoto, Japan; University of Innsbruck, Innsbruck, Austria|Time is an important aspect of documents and is used in a range of NLP and IR tasks. In this work, we investigate methods for incorporating temporal information during pre-training to further improve the performance on time-related tasks. Compared with common pre-trained language models like BERT which utilize synchronic document collections (e.g., BookCorpus and Wikipedia) as the training corpora, we use long-span temporal news article collection for building word representations. We introduce BiTimeBERT, a novel language representation model trained on a temporal collection of news articles via two new pre-training tasks, which harnesses two distinct temporal signals to construct time-aware language representations. The experimental results show that BiTimeBERT consistently outperforms BERT and other existing pre-trained models with substantial gains on different downstream NLP tasks and applications for which time is of importance (e.g., the accuracy improvement over BERT is 155\% on the event time estimation task).|时间是文档的一个重要方面，用于一系列自然语言处理(NLP)和信息检索(IR)任务。在这项工作中，我们研究的方法，合并时间信息在预训练，以进一步提高绩效的时间相关的任务。与使用同步文档集合(如 BookCorpus 和 Wikipedia)作为训练语料库的常见的预训练语言模型 BERT 相比，我们使用大跨度的时态新闻文章集合来构建单词表示。我们介绍了一种新的语言表示模型 BiTimeBERT，该模型通过两个新的预训练任务训练新闻文章的时间集合，利用两个不同的时间信号来构造具有时间意识的语言表示。实验结果表明，BiTimeBERT 在不同的下游 NLP 任务和重要时间应用(例如，在事件时间估计任务上，误码率比 BERT 提高了155%)上，始终优于 BERT 和其他已有的预训练模型，获得了实质性的提高。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=BiTimeBERT:+Extending+Pre-Trained+Language+Representations+with+Bi-Temporal+Information)|0| |[Incorporating Structured Sentences with Time-enhanced BERT for Fully-inductive Temporal Relation Prediction](https://doi.org/10.1145/3539618.3591700)|Zhongwu Chen, Chengjin Xu, Fenglong Su, Zhen Huang, Yong Dou|International Digital Economy Academy, Shenzhen, China; National University of Defense Technology, Changsha, China|Temporal relation prediction in incomplete temporal knowledge graphs (TKGs) is a popular temporal knowledge graph completion (TKGC) problem in both transductive and inductive settings. Traditional embedding-based TKGC models (TKGE) rely on structured connections and can only handle a fixed set of entities, i.e., the transductive setting. In the inductive setting where test TKGs contain emerging entities, the latest methods are based on symbolic rules or pre-trained language models (PLMs). However, they suffer from being inflexible and not time-specific, respectively. In this work, we extend the fully-inductive setting, where entities in the training and test sets are totally disjoint, into TKGs and take a further step towards a more flexible and time-sensitive temporal relation prediction approach SST-BERT, incorporating Structured Sentences with Time-enhanced BERT. Our model can obtain the entity history and implicitly learn rules in the semantic space by encoding structured sentences, solving the problem of inflexibility. We propose to use a time masking MLM task to pre-train BERT in a corpus rich in temporal tokens specially generated for TKGs, enhancing the time sensitivity of SST-BERT. To compute the probability of occurrence of a target quadruple, we aggregate all its structured sentences from both temporal and semantic perspectives into a score. Experiments on the transductive datasets and newly generated fully-inductive benchmarks show that SST-BERT successfully improves over state-of-the-art baselines.|不完备时态知识图(TKGs)中的时态关系预测问题是一个普遍存在的时态知识图补全问题。传统的基于嵌入的 TKGC 模型(TKGE)依赖于结构化连接，只能处理一组固定的实体，即传导性设置。在归纳环境中，测试 TKG 包含新兴的实体，最新的方法是基于符号规则或预训练语言模型(PLM)。然而，它们分别受到缺乏灵活性和不具体时间的影响。在这项工作中，我们将训练集和测试集中的实体完全分离的完全归纳环境扩展到 TKG，并进一步朝着更加灵活和时间敏感的时间关系预测方法 SST-BERT 迈进了一步，结合结构化句子和时间增强的 BERT。该模型通过对结构化句子进行编码，在语义空间中获取实体历史，并隐式地学习规则，解决了不灵活性问题。我们建议使用时间掩蔽 MLM 任务来预训练 BERT 在一个丰富的时间令牌为 TKG 专门生成的语料库中，提高 SST-BERT 的时间敏感性。为了计算目标四元组出现的概率，我们从时间和语义两个角度将其所有结构化句子聚合成一个分数。对传导数据集和新生成的全归纳基准测试的实验表明，SST-BERT 算法成功地提高了基准测试的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Incorporating+Structured+Sentences+with+Time-enhanced+BERT+for+Fully-inductive+Temporal+Relation+Prediction)|0| |[Normalizing Flow-based Neural Process for Few-Shot Knowledge Graph Completion](https://doi.org/10.1145/3539618.3591743)|Linhao Luo, YuanFang Li, Gholamreza Haffari, Shirui Pan|Griffith University, Gold Coast, SQ, Australia; Monash University, Melbourne, VIC, Australia|Knowledge graphs (KGs), as a structured form of knowledge representation, have been widely applied in the real world. Recently, few-shot knowledge graph completion (FKGC), which aims to predict missing facts for unseen relations with few-shot associated facts, has attracted increasing attention from practitioners and researchers. However, existing FKGC methods are based on metric learning or meta-learning, which often suffer from the out-of-distribution and overfitting problems. Meanwhile, they are incompetent at estimating uncertainties in predictions, which is critically important as model predictions could be very unreliable in few-shot settings. Furthermore, most of them cannot handle complex relations and ignore path information in KGs, which largely limits their performance. In this paper, we propose a normalizing flow-based neural process for few-shot knowledge graph completion (NP-FKGC). Specifically, we unify normalizing flows and neural processes to model a complex distribution of KG completion functions. This offers a novel way to predict facts for few-shot relations while estimating the uncertainty. Then, we propose a stochastic ManifoldE decoder to incorporate the neural process and handle complex relations in few-shot settings. To further improve performance, we introduce an attentive relation path-based graph neural network to capture path information in KGs. Extensive experiments on three public datasets demonstrate that our method significantly outperforms the existing FKGC methods and achieves state-of-the-art performance. Code is available at https://github.com/RManLuo/NP-FKGC.git.|知识图作为一种结构化的知识表示形式，在现实世界中得到了广泛的应用。近年来，用少镜头关联事实预测未知关系中缺失事实的少镜头知识图完成(FKGC)越来越受到从业人员和研究人员的关注。然而，现有的 FKGC 方法都是基于度量学习或元学习的，这些方法往往存在分布不均衡和过拟合的问题。与此同时，他们无法估计预测中的不确定性，这是至关重要的，因为模型预测可能是非常不可靠的几个镜头的设置。此外，大多数幼儿园学生不能处理复杂的关系，忽视路径信息，这在很大程度上限制了幼儿园学生的学习成绩。本文提出了一种基于规范化流的少镜头知识图补全神经过程(NP-FKGC)。具体地说，我们将归一化流和神经过程相结合，建立了 KG 完备函数的复杂分布模型。这为在估计不确定性的同时预测少镜头关系的事实提供了一种新颖的方法。然后，我们提出了一个随机流形 E 解码器，结合神经过程和处理复杂关系的少镜头设置。为了进一步提高性能，我们引入了一种基于注意关系路径的图神经网络来捕获幼儿园的路径信息。在三个公共数据集上的大量实验表明，该方法明显优于现有的 FKGC 方法，达到了最先进的性能。密码可于 https://github.com/rmanluo/np-fkgc.git 索取。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Normalizing+Flow-based+Neural+Process+for+Few-Shot+Knowledge+Graph+Completion)|0| -|[Schema-aware Reference as Prompt Improves Data-Efficient Knowledge Graph Construction](https://doi.org/10.1145/3539618.3591763)|Yunzhi Yao, Shengyu Mao, Ningyu Zhang, Xiang Chen, Shumin Deng, Xi Chen, Huajun Chen|Tencent, Shenzhen, China; Zhejiang University, Hangzhou, China; National University of Singapore, Singapore, Singapore|With the development of pre-trained language models, many prompt-based approaches to data-efficient knowledge graph construction have been proposed and achieved impressive performance. However, existing prompt-based learning methods for knowledge graph construction are still susceptible to several potential limitations: (i) semantic gap between natural language and output structured knowledge with pre-defined schema, which means model cannot fully exploit semantic knowledge with the constrained templates; (ii) representation learning with locally individual instances limits the performance given the insufficient features, which are unable to unleash the potential analogical capability of pre-trained language models. Motivated by these observations, we propose a retrieval-augmented approach, which retrieves schema-aware Reference As Prompt (RAP), for data-efficient knowledge graph construction. It can dynamically leverage schema and knowledge inherited from human-annotated and weak-supervised data as a prompt for each sample, which is model-agnostic and can be plugged into widespread existing approaches. Experimental results demonstrate that previous methods integrated with RAP can achieve impressive performance gains in low-resource settings on five datasets of relational triple extraction and event extraction for knowledge graph construction. Code is available in https://github.com/zjunlp/RAP.|随着预训练语言模型的发展，许多基于提示的数据高效知识图构造方法被提出，并取得了令人印象深刻的性能。然而，现有的基于提示的知识图构建学习方法仍然容易受到一些潜在的限制: (i)自然语言和具有预定义模式的输出结构化知识之间的语义差距，这意味着模型不能充分利用具有约束模板的语义知识; (ii)具有局部个体实例的表示学习限制了性能，因为功能不足，无法释放预训练语言模型的潜在类比能力。基于这些观察结果，我们提出了一种检索增强方法，检索模式感知的参考文献作为提示(RAP) ，用于数据高效的知识图构造。它可以动态地利用从人工注释和弱监督数据继承的模式和知识作为每个样本的提示，这是模型无关的，并且可以插入到广泛存在的方法中。实验结果表明，先前的 RAP 方法在低资源环境下，可以在五个数据集的关系三重提取和事件提取中取得显著的性能提高。代码可在 https://github.com/zjunlp/rap 下载。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Schema-aware+Reference+as+Prompt+Improves+Data-Efficient+Knowledge+Graph+Construction)|0| -|[ML-LJP: Multi-Law Aware Legal Judgment Prediction](https://doi.org/10.1145/3539618.3591731)|Yifei Liu, Yiquan Wu, Yating Zhang, Changlong Sun, Weiming Lu, Fei Wu, Kun Kuang|Alibaba Group, Hangzhou, China; Zhejiang University, Hangzhou, China; Alibaba Group & Zhejiang University, Hangzhou, China|Legal judgment prediction (LJP) is a significant task in legal intelligence, which aims to assist the judges and determine the judgment result based on the case's fact description. The judgment result consists of law articles, charge, and prison term. The law articles serve as the basis for the charge and the prison term, which can be divided into two types, named as charge-related law article and term-related law article, respectively. Recently, many methods have been proposed and made tremendous progress in LJP. However, the existing methods only focus on the prediction of the charge-related law articles, ignoring the term-related law articles (e.g., laws about lenient treatment), which limits the performance in the prison term prediction. In this paper, following the actual legal process, we expand the law article prediction as a multi-label classification task that includes both the charge-related law articles and term-related law articles and propose a novel multi-law aware LJP (ML-LJP) method to improve the performance of LJP. Given the case's fact description, firstly, the label (e.g., law article and charge) definitions in the Code of Law are used to transform the representation of the fact into several label-specific representations and make the prediction of the law articles and the charge. To distinguish the similar content of different label definitions, contrastive learning is conducted in the training. Then, a graph attention network (GAT) is applied to learn the interactions among the multiple law articles for the prediction of the prison term. Since numbers (e.g., amount of theft and weight of drugs) are important for LJP but often ignored by conventional encoders, we design a corresponding number representation method to locate and better represent these effective numbers. Extensive experiments on real-world dataset show that our method achieves the best results compared to the state-of-the-art models, especially in the task of prison term prediction where ML-LJP achieves a 10.07% relative improvement over the best baseline.|法律判决预测是法律情报工作中的一项重要任务，其目的在于帮助法官根据案件的事实描述确定判决结果。判决结果由法律条文、罪名、刑期构成。法律条文作为定罪依据和刑期，可分为定罪相关法律条文和刑期相关法律条文两类。近年来，许多方法被提出并取得了巨大的进展。然而，现有的预测方法只注重罪刑法定条款的预测，而忽视了罪刑法定条款(如宽严相济法)的预测，限制了刑期预测的效果。本文根据实际的法律过程，将法律条款预测扩展为一个包含收费相关法律条款和术语相关法律条款的多标签分类任务，提出了一种新的多法律感知 LJP (ML-LJP)方法，以提高 LJP 的性能。在给定案件事实描述的基础上，首先利用《法典》中的标签(如法律条文和罪名)定义，将事实的表述转化为若干特定标签的表述，并对法律条文和罪名进行预测。为了区分不同标签定义的相似内容，在训练中进行了对比学习。然后，应用图注意网络(GAT)学习多个法律条文之间的相互作用，以预测刑期。由于数字(例如，盗窃量和药品重量)对 LJP 很重要，但常常被常规编码器忽略，因此我们设计了一种相应的数字表示方法来定位和更好地表示这些有效的数字。在实际数据集上的大量实验表明，与最先进的模型相比，我们的方法获得了最好的结果，特别是在监狱刑期预测任务中，ML-LJP 比最佳基线达到了10.07% 的相对改进。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=ML-LJP:+Multi-Law+Aware+Legal+Judgment+Prediction)|0| -|[Creating a Silver Standard for Patent Simplification](https://doi.org/10.1145/3539618.3591657)|Silvia Casola, Alberto Lavelli, Horacio Saggion|Universitat Pompeu Fabra, Barcelona, Spain; Fondazione Bruno Kessler, Trento, Italy; University of Padua & Fondazione Bruno Kessler, Padua, Italy|Patents are legal documents that aim at protecting inventions on the one hand and at making technical knowledge circulate on the other. Their complex style -- a mix of legal, technical, and extremely vague language -- makes their content hard to access for humans and machines and poses substantial challenges to the information retrieval community. This paper proposes an approach to automatically simplify patent text through rephrasing. Since no in-domain parallel simplification data exist, we propose a method to automatically generate a large-scale silver standard for patent sentences. To obtain candidates, we use a general-domain paraphrasing system; however, the process is error-prone and difficult to control. Thus, we pair it with proper filters and construct a cleaner corpus that can successfully be used to train a simplification system. Human evaluation of the synthetic silver corpus shows that it is considered grammatical, adequate, and contains simple sentences.|专利是一种法律文件，一方面是为了保护发明，另一方面是为了使技术知识流通。它们复杂的风格——混合了法律、技术和极其模糊的语言——使得人类和机器很难访问它们的内容，并给信息检索社区带来了巨大的挑战。本文提出了一种通过重新措辞来自动简化专利文本的方法。由于不存在域内并行简化数据，本文提出了一种自动生成大规模专利语句银标准的方法。为了获得候选者，我们使用一个通用的领域解释系统，但是，这个过程是容易出错和难以控制。因此，我们将它与适当的过滤器配对，构造一个更清晰的语料库，可以成功地用于训练一个简化系统。人们对合成银语料库的评价表明，它被认为是合乎语法的，充分的，并包含简单的句子。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Creating+a+Silver+Standard+for+Patent+Simplification)|0| +|[Schema-aware Reference as Prompt Improves Data-Efficient Knowledge Graph Construction](https://doi.org/10.1145/3539618.3591763)|Yunzhi Yao, Shengyu Mao, Ningyu Zhang, Xiang Chen, Shumin Deng, Xi Chen, Huajun Chen|National University of Singapore, Singapore, Singapore; Zhejiang University, Hangzhou, China; Tencent, Shenzhen, China|With the development of pre-trained language models, many prompt-based approaches to data-efficient knowledge graph construction have been proposed and achieved impressive performance. However, existing prompt-based learning methods for knowledge graph construction are still susceptible to several potential limitations: (i) semantic gap between natural language and output structured knowledge with pre-defined schema, which means model cannot fully exploit semantic knowledge with the constrained templates; (ii) representation learning with locally individual instances limits the performance given the insufficient features, which are unable to unleash the potential analogical capability of pre-trained language models. Motivated by these observations, we propose a retrieval-augmented approach, which retrieves schema-aware Reference As Prompt (RAP), for data-efficient knowledge graph construction. It can dynamically leverage schema and knowledge inherited from human-annotated and weak-supervised data as a prompt for each sample, which is model-agnostic and can be plugged into widespread existing approaches. Experimental results demonstrate that previous methods integrated with RAP can achieve impressive performance gains in low-resource settings on five datasets of relational triple extraction and event extraction for knowledge graph construction. Code is available in https://github.com/zjunlp/RAP.|随着预训练语言模型的发展，许多基于提示的数据高效知识图构造方法被提出，并取得了令人印象深刻的性能。然而，现有的基于提示的知识图构建学习方法仍然容易受到一些潜在的限制: (i)自然语言和具有预定义模式的输出结构化知识之间的语义差距，这意味着模型不能充分利用具有约束模板的语义知识; (ii)具有局部个体实例的表示学习限制了性能，因为功能不足，无法释放预训练语言模型的潜在类比能力。基于这些观察结果，我们提出了一种检索增强方法，检索模式感知的参考文献作为提示(RAP) ，用于数据高效的知识图构造。它可以动态地利用从人工注释和弱监督数据继承的模式和知识作为每个样本的提示，这是模型无关的，并且可以插入到广泛存在的方法中。实验结果表明，先前的 RAP 方法在低资源环境下，可以在五个数据集的关系三重提取和事件提取中取得显著的性能提高。代码可在 https://github.com/zjunlp/rap 下载。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Schema-aware+Reference+as+Prompt+Improves+Data-Efficient+Knowledge+Graph+Construction)|0| +|[ML-LJP: Multi-Law Aware Legal Judgment Prediction](https://doi.org/10.1145/3539618.3591731)|Yifei Liu, Yiquan Wu, Yating Zhang, Changlong Sun, Weiming Lu, Fei Wu, Kun Kuang|Alibaba Group & Zhejiang University, Hangzhou, China; Alibaba Group, Hangzhou, China; Zhejiang University, Hangzhou, China|Legal judgment prediction (LJP) is a significant task in legal intelligence, which aims to assist the judges and determine the judgment result based on the case's fact description. The judgment result consists of law articles, charge, and prison term. The law articles serve as the basis for the charge and the prison term, which can be divided into two types, named as charge-related law article and term-related law article, respectively. Recently, many methods have been proposed and made tremendous progress in LJP. However, the existing methods only focus on the prediction of the charge-related law articles, ignoring the term-related law articles (e.g., laws about lenient treatment), which limits the performance in the prison term prediction. In this paper, following the actual legal process, we expand the law article prediction as a multi-label classification task that includes both the charge-related law articles and term-related law articles and propose a novel multi-law aware LJP (ML-LJP) method to improve the performance of LJP. Given the case's fact description, firstly, the label (e.g., law article and charge) definitions in the Code of Law are used to transform the representation of the fact into several label-specific representations and make the prediction of the law articles and the charge. To distinguish the similar content of different label definitions, contrastive learning is conducted in the training. Then, a graph attention network (GAT) is applied to learn the interactions among the multiple law articles for the prediction of the prison term. Since numbers (e.g., amount of theft and weight of drugs) are important for LJP but often ignored by conventional encoders, we design a corresponding number representation method to locate and better represent these effective numbers. Extensive experiments on real-world dataset show that our method achieves the best results compared to the state-of-the-art models, especially in the task of prison term prediction where ML-LJP achieves a 10.07% relative improvement over the best baseline.|法律判决预测是法律情报工作中的一项重要任务，其目的在于帮助法官根据案件的事实描述确定判决结果。判决结果由法律条文、罪名、刑期构成。法律条文作为定罪依据和刑期，可分为定罪相关法律条文和刑期相关法律条文两类。近年来，许多方法被提出并取得了巨大的进展。然而，现有的预测方法只注重罪刑法定条款的预测，而忽视了罪刑法定条款(如宽严相济法)的预测，限制了刑期预测的效果。本文根据实际的法律过程，将法律条款预测扩展为一个包含收费相关法律条款和术语相关法律条款的多标签分类任务，提出了一种新的多法律感知 LJP (ML-LJP)方法，以提高 LJP 的性能。在给定案件事实描述的基础上，首先利用《法典》中的标签(如法律条文和罪名)定义，将事实的表述转化为若干特定标签的表述，并对法律条文和罪名进行预测。为了区分不同标签定义的相似内容，在训练中进行了对比学习。然后，应用图注意网络(GAT)学习多个法律条文之间的相互作用，以预测刑期。由于数字(例如，盗窃量和药品重量)对 LJP 很重要，但常常被常规编码器忽略，因此我们设计了一种相应的数字表示方法来定位和更好地表示这些有效的数字。在实际数据集上的大量实验表明，与最先进的模型相比，我们的方法获得了最好的结果，特别是在监狱刑期预测任务中，ML-LJP 比最佳基线达到了10.07% 的相对改进。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=ML-LJP:+Multi-Law+Aware+Legal+Judgment+Prediction)|0| +|[Creating a Silver Standard for Patent Simplification](https://doi.org/10.1145/3539618.3591657)|Silvia Casola, Alberto Lavelli, Horacio Saggion|Fondazione Bruno Kessler, Trento, Italy; Universitat Pompeu Fabra, Barcelona, Spain; University of Padua & Fondazione Bruno Kessler, Padua, Italy|Patents are legal documents that aim at protecting inventions on the one hand and at making technical knowledge circulate on the other. Their complex style -- a mix of legal, technical, and extremely vague language -- makes their content hard to access for humans and machines and poses substantial challenges to the information retrieval community. This paper proposes an approach to automatically simplify patent text through rephrasing. Since no in-domain parallel simplification data exist, we propose a method to automatically generate a large-scale silver standard for patent sentences. To obtain candidates, we use a general-domain paraphrasing system; however, the process is error-prone and difficult to control. Thus, we pair it with proper filters and construct a cleaner corpus that can successfully be used to train a simplification system. Human evaluation of the synthetic silver corpus shows that it is considered grammatical, adequate, and contains simple sentences.|专利是一种法律文件，一方面是为了保护发明，另一方面是为了使技术知识流通。它们复杂的风格——混合了法律、技术和极其模糊的语言——使得人类和机器很难访问它们的内容，并给信息检索社区带来了巨大的挑战。本文提出了一种通过重新措辞来自动简化专利文本的方法。由于不存在域内并行简化数据，本文提出了一种自动生成大规模专利语句银标准的方法。为了获得候选者，我们使用一个通用的领域解释系统，但是，这个过程是容易出错和难以控制。因此，我们将它与适当的过滤器配对，构造一个更清晰的语料库，可以成功地用于训练一个简化系统。人们对合成银语料库的评价表明，它被认为是合乎语法的，充分的，并包含简单的句子。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Creating+a+Silver+Standard+for+Patent+Simplification)|0| |[Cone: Unsupervised Contrastive Opinion Extraction](https://doi.org/10.1145/3539618.3591650)|Runcong Zhao, Lin Gui, Yulan He|King's College London, London, United Kingdom|Contrastive opinion extraction aims to extract a structured summary or key points organised as positive and negative viewpoints towards a common aspect or topic. Most recent works for unsupervised key point extraction is largely built on sentence clustering or opinion summarisation based on the popularity of opinions expressed in text. However, these methods tend to generate aspect clusters with incoherent sentences, conflicting viewpoints, redundant aspects. To address these problems, we propose a novel unsupervised Contrastive OpinioN Extraction model, called Cone, which learns disentangled latent aspect and sentiment representations based on pseudo aspect and sentiment labels by combining contrastive learning with iterative aspect/sentiment clustering refinement. Apart from being able to extract contrastive opinions, it is also able to quantify the relative popularity of aspects and their associated sentiment distributions. The model has been evaluated on both a hotel review dataset and a Twitter dataset about COVID vaccines. The results show that despite using no label supervision or aspect-denoted seed words, Cone outperforms a number of competitive baselines on contrastive opinion extraction. The results of Cone can be used to offer a better recommendation of products and services online.|对比意见提取旨在提取一个结构化的总结或关键点组织为积极和消极的观点对一个共同的方面或主题。最近大多数无监督的关键点提取工作主要是建立在句子聚类或意见摘要的基础上表达的意见在文本中的流行。然而，这些方法往往会产生不连贯的句子，相互冲突的观点，冗余的方面聚类。为了解决这些问题，我们提出了一种新的无监督的对比观点提取模型——锥形模型，该模型将对比学习与迭代方面/情感聚类细化相结合，学习基于伪方面和情感标签的分离潜在方面和情感表示。除了能够提取对比意见，它还能够量化方面的相对受欢迎程度及其相关的情绪分布。该模型已经通过酒店评论数据集和关于冠状病毒疾病疫苗的 Twitter 数据集进行了评估。结果表明，尽管没有使用标签监督或方面表示的种子词，锥表现优于一些竞争基线的意见提取对比。Cone 的结果可以用来在线提供更好的产品和服务推荐。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Cone:+Unsupervised+Contrastive+Opinion+Extraction)|0| -|[Representation and Labeling Gap Bridging for Cross-lingual Named Entity Recognition](https://doi.org/10.1145/3539618.3591757)|Xinghua Zhang, Bowen Yu, Jiangxia Cao, Quangang Li, Xuebin Wang, Tingwen Liu, Hongbo Xu|Institute of Information Engineering, Chinese Academy of Sciences & School of Cyber Security, UCAS, Beijing, China; DAMO Academy, Alibaba Group, Beijing, China; Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China|Cross-lingual Named Entity Recognition (NER) aims to address the challenge of data scarcity in low-resource languages by leveraging knowledge from high-resource languages. Most current work relies on general multilingual language models to represent text, and then uses classic combined tagging (e.g., B-ORG) to annotate entities; However, this approach neglects the lack of cross-lingual alignment of entity representations in language models, and also ignores the fact that entity spans and types have varying levels of labeling difficulty in terms of transferability. To address these challenges, we propose a novel framework, referred to as DLBri, which addresses the issues of representation and labeling simultaneously. Specifically, the proposed framework utilizes progressive contrastive learning with source-to-target oriented sentence pairs to pre-finetune the language model, resulting in improved cross-lingual entity-aware representations. Additionally, a decomposition-then-combination procedure is proposed, which separately transfers entity span and type, and then combines their information, to reduce the difficulty of cross-lingual entity labeling. Extensive experiments on 13 diverse language pairs confirm the effectiveness of DLBri.|跨语言命名实体识别(NER)旨在通过利用高资源语言的知识来解决低资源语言中的数据稀缺性问题。目前大多数工作依赖于一般的多语言模型来表示文本，然后使用经典的组合标注(例如，B-ORG)来注释实体; 然而，这种方法忽视了实体表示在语言模型中缺乏跨语言对齐，也忽略了实体跨度和类型在可迁移性方面有不同程度的标注困难这一事实。为了应对这些挑战，我们提出了一个新的框架，称为 DLBri，它同时解决了表示和标签的问题。具体来说，该框架利用面向源-目标句子对的逐步对比学习来预调整语言模型，从而改进跨语言实体感知表示。此外，本文还提出了一种分解-组合过程，该过程分别传递实体的跨度和类型，然后组合它们的信息，以减少跨语言实体标注的难度。对13个不同语言对的大量实验证实了 DLBri 的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Representation+and+Labeling+Gap+Bridging+for+Cross-lingual+Named+Entity+Recognition)|0| +|[Representation and Labeling Gap Bridging for Cross-lingual Named Entity Recognition](https://doi.org/10.1145/3539618.3591757)|Xinghua Zhang, Bowen Yu, Jiangxia Cao, Quangang Li, Xuebin Wang, Tingwen Liu, Hongbo Xu|Institute of Information Engineering, Chinese Academy of Sciences & School of Cyber Security, UCAS, Beijing, China; Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China; DAMO Academy, Alibaba Group, Beijing, China|Cross-lingual Named Entity Recognition (NER) aims to address the challenge of data scarcity in low-resource languages by leveraging knowledge from high-resource languages. Most current work relies on general multilingual language models to represent text, and then uses classic combined tagging (e.g., B-ORG) to annotate entities; However, this approach neglects the lack of cross-lingual alignment of entity representations in language models, and also ignores the fact that entity spans and types have varying levels of labeling difficulty in terms of transferability. To address these challenges, we propose a novel framework, referred to as DLBri, which addresses the issues of representation and labeling simultaneously. Specifically, the proposed framework utilizes progressive contrastive learning with source-to-target oriented sentence pairs to pre-finetune the language model, resulting in improved cross-lingual entity-aware representations. Additionally, a decomposition-then-combination procedure is proposed, which separately transfers entity span and type, and then combines their information, to reduce the difficulty of cross-lingual entity labeling. Extensive experiments on 13 diverse language pairs confirm the effectiveness of DLBri.|跨语言命名实体识别(NER)旨在通过利用高资源语言的知识来解决低资源语言中的数据稀缺性问题。目前大多数工作依赖于一般的多语言模型来表示文本，然后使用经典的组合标注(例如，B-ORG)来注释实体; 然而，这种方法忽视了实体表示在语言模型中缺乏跨语言对齐，也忽略了实体跨度和类型在可迁移性方面有不同程度的标注困难这一事实。为了应对这些挑战，我们提出了一个新的框架，称为 DLBri，它同时解决了表示和标签的问题。具体来说，该框架利用面向源-目标句子对的逐步对比学习来预调整语言模型，从而改进跨语言实体感知表示。此外，本文还提出了一种分解-组合过程，该过程分别传递实体的跨度和类型，然后组合它们的信息，以减少跨语言实体标注的难度。对13个不同语言对的大量实验证实了 DLBri 的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Representation+and+Labeling+Gap+Bridging+for+Cross-lingual+Named+Entity+Recognition)|0| |[Unsupervised Readability Assessment via Learning from Weak Readability Signals](https://doi.org/10.1145/3539618.3591695)|Yuliang Liu, Zhiwei Jiang, Yafeng Yin, Cong Wang, Sheng Chen, Zhaoling Chen, Qing Gu|Nanjing University, Nanjing, China|Unsupervised readability assessment aims to evaluate the reading difficulty of text without any manually-labeled data for model training. This is a challenging task because the absence of labeled data makes it difficult for the model to understand what readability is. In this paper, we propose a novel framework to Learn a neural model from Weak Readability Signals (LWRS). Instead of relying on labeled data, LWRS utilizes a set of heuristic signals that specialize in describing text readability from different aspects to guide the model in outputting readability scores for ranking. Specifically, to effectively use multiple heuristic weak signals for model training, we build a multi-signal learning model that ranks the unlabeled texts from multiple readability-related aspects based on intra- and inter-signal learning. We also adopt the pairwise ranking paradigm to reduce the cascade coupling among partial-order pairs. Furthermore, we propose identifying the most representative signal based on the batch-level consensus distribution of all signals. This strategy helps identify the predicted signal that is most correlated with readability in the absence of ground-truth labels. We conduct experiments on three public readability assessment datasets. The experimental results demonstrate that our LWRS outperforms each heuristic signal and their combinations significantly, and can even perform comparably with some supervised methods. Additionally, our LWRS trained on one dataset can be effectively transferred to other datasets, including those in other languages, which indicates its good generalization and potential for wide application.|非监督可读性评估的目的是评估文本的阅读难度，没有任何手工标记的数据进行模型训练。这是一个具有挑战性的任务，因为缺少标记数据使得模型难以理解什么是可读性。本文提出了一种从弱可读性信号(LWRS)中学习神经模型的新框架。LWRS 不依赖于标记数据，而是利用一组专门从不同方面描述文本可读性的启发式信号来指导模型输出可读性分数以进行排名。为了有效地利用多个启发式弱信号进行模型训练，我们建立了一个基于信号内和信号间学习的多信号学习模型，从多个可读性相关方面对未标记文本进行排序。我们还采用成对排序范式来减少偏序对之间的级联耦合。此外，我们提出了基于所有信号的批级一致分布来识别最有代表性的信号。这种策略有助于识别预测的信号，是最相关的可读性在没有地面真相标签。我们在三个公共可读性评估数据集上进行了实验。实验结果表明，我们的 LWRS 方法的性能明显优于各种启发式信号及其组合，甚至可以与一些监督方法相媲美。此外，我们在一个数据集上训练的 LWRS 可以有效地转移到其他数据集，包括其他语言的数据集，这表明它具有良好的通用性和广泛的应用潜力。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Unsupervised+Readability+Assessment+via+Learning+from+Weak+Readability+Signals)|0| |[What If: Generating Code to Answer Simulation Questions in Chemistry Texts](https://doi.org/10.1145/3539618.3591783)|Gal Peretz, Mousa Arraf, Kira Radinsky|Technion - Israel Institute of Technology, Haifa, Israel|Many texts, especially in chemistry and biology, describe complex processes. We focus on texts that describe a chemical reaction process and questions that ask about the process's outcome under different environmental conditions. To answer questions about such processes, one needs to understand the interactions between the different entities involved in the process and simulate their state transitions during the process execution under other conditions. We hypothesize that generating code and executing it to simulate the process will allow answering such questions. We, therefore, define a domain-specific language (DSL) to represent processes. We contribute to the community a unique dataset curated by chemists and annotated by computer scientists. The dataset is composed of process texts, simulation questions, and their corresponding computer codes represented by the DSL. We propose a neural program synthesis approach based on reinforcement learning with a novel state-transition semantic reward. The novel reward is based on the run-time semantic similarity between the predicted code and the reference code. This allows simulating complex process transitions and thus answering simulation questions. Our approach yields a significant boost in accuracy for simulation questions: we achieved 88% accuracy as opposed to 83% accuracy of the state-of-the-art neural program synthesis approaches and 54% accuracy of state-of-the-art end-to-end text-based approaches.|许多文本，特别是在化学和生物学，描述了复杂的过程。我们重点关注描述化学反应过程的文本，以及在不同环境条件下询问过程结果的问题。要回答有关这些流程的问题，需要理解流程中涉及的不同实体之间的交互，并在其他条件下模拟流程执行期间的状态转换。我们假设生成代码并执行它来模拟过程将允许回答这些问题。因此，我们定义一个领域特定语言(DSL)来表示过程。我们为社区贡献了一个独特的数据集，由化学家管理，并由计算机科学家注释。该数据集由过程文本、仿真问题及其相应的由 DSL 表示的计算机代码组成。我们提出了一种基于强化学习的神经程序综合方法，它具有一种新的状态转换语义奖励。新的奖励基于预测代码和引用代码之间的运行时语义相似度。这允许模拟复杂的过程转换，从而回答模拟问题。我们的方法在模拟问题的准确性方面产生了显著的提高: 我们实现了88% 的准确性，而最先进的神经程序综合方法的准确性为83% ，最先进的端到端文本方法的准确性为54% 。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=What+If:+Generating+Code+to+Answer+Simulation+Questions+in+Chemistry+Texts)|0| -|[A Topic-aware Summarization Framework with Different Modal Side Information](https://doi.org/10.1145/3539618.3591630)|Xiuying Chen, Mingzhe Li, Shen Gao, Xin Cheng, Qiang Yang, Qishen Zhang, Xin Gao, Xiangliang Zhang|Peking University, Beijing, China; Ant Group, Beijing, China; KAUST, Jeddah, Saudi Arabia; Shandong university, Qingdao, China; University of Notre Dame & KAUST, Notre Dame, IL, USA|Automatic summarization plays an important role in the exponential document growth on the Web. On content websites such as CNN.com and WikiHow.com, there often exist various kinds of side information along with the main document for attention attraction and easier understanding, such as videos, images, and queries. Such information can be used for better summarization, as they often explicitly or implicitly mention the essence of the article. However, most of the existing side-aware summarization methods are designed to incorporate either single-modal or multi-modal side information, and cannot effectively adapt to each other. In this paper, we propose a general summarization framework, which can flexibly incorporate various modalities of side information. The main challenges in designing a flexible summarization model with side information include: (1) the side information can be in textual or visual format, and the model needs to align and unify it with the document into the same semantic space, (2) the side inputs can contain information from various aspects, and the model should recognize the aspects useful for summarization. To address these two challenges, we first propose a unified topic encoder, which jointly discovers latent topics from the document and various kinds of side information. The learned topics flexibly bridge and guide the information flow between multiple inputs in a graph encoder through a topic-aware interaction. We secondly propose a triplet contrastive learning mechanism to align the single-modal or multi-modal information into a unified semantic space, where the summary quality is enhanced by better understanding the document and side information. Results show that our model significantly surpasses strong baselines on three public single-modal or multi-modal benchmark summarization datasets.|自动汇总在网络文档的指数增长中扮演着重要的角色。在像 CNN.com 和 WikiHow.com 这样的内容网站上，为了吸引注意力和更容易理解，经常会有各种各样的附属信息和主要文档，比如视频、图片和查询。这些信息可以用于更好的总结，因为它们通常显式或隐式地提到文章的实质。然而，现有的侧面感知摘要方法大多是针对单模态侧面信息或多模态侧面信息而设计的，不能有效地相互适应。本文提出了一个通用的汇总框架，它可以灵活地整合各种形式的侧信息。设计具有侧信息的灵活摘要模型面临的主要挑战包括: (1)侧信息可以是文本格式或可视化格式，模型需要将侧信息与文档对齐统一到相同的语义空间中; (2)侧输入可以包含各个方面的信息，模型应该识别对摘要有用的方面。为了解决这两个问题，我们首先提出了一种统一的主题编码器，它可以联合发现文档中的潜在主题和各种侧信息。所学习的主题通过主题感知交互灵活地连接和引导图形编码器中多个输入之间的信息流。其次，我们提出了一种三元对比学习机制，将单模态或多模态信息整合到一个统一的语义空间中，通过更好地理解文档和侧面信息来提高摘要的质量。结果表明，我们的模型显著超过了三个公共的单模式或多模式基准汇总数据集的强基线。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Topic-aware+Summarization+Framework+with+Different+Modal+Side+Information)|0| -|[RHB-Net: A Relation-aware Historical Bridging Network for Text2SQL Auto-Completion](https://doi.org/10.1145/3539618.3591759)|Bolong Zheng, Lei Bi, Ruijie Xi, Lu Chen, Yunjun Gao, Xiaofang Zhou, Christian S. Jensen|Zhejiang University, Hangzhou, China; Aalborg University, Aalborg, Denmark; Huazhong University of Science and Technology, Wuhan, China; Hong Kong University of Science and Technology, Hong Kong, China|Test2SQL, a natural language interface to database querying, has seen considerable improvement, in part due to advances in deep learning. However, despite recent improvement, existing Text2SQL proposals allow only input in the form of complete questions. This leaves behind users who struggle to formulate complete questions, e.g., because they lack database expertise or are unfamiliar with the underlying database schema. To address this shortcoming, we study the novel problem of Text2SQL Auto-Completion (TSAC) that extends Text2SQL to also take partial or incomplete questions as input. Specifically, the TSAC problem is to predict the complete, executable SQL query. To solve the problem, we propose a novel Relation-aware Historical Bridging Network (RHB-Net) that consists of a relation-aware union encoder and an extraction-generation sensitive decoder. RHB-Net models relations between questions and database schemas and predicts the ambiguous intents expressed in partial queries. We also propose two optimization strategies: historical query bridging that fuses historical database queries, and a dynamic context construction that prevents repeated generation of the same SQL elements. Extensive experiments with real-world data offer evidence that RHB-Net is capable of outperforming baseline algorithms.|Test2SQL 是一种用于数据库查询的自然语言接口，已经取得了相当大的进步，部分原因是由于深度学习的进步。然而，尽管最近有所改进，现有的 Text2SQL 建议只允许以完整问题的形式输入。这使得用户很难形成完整的问题，例如，因为他们缺乏数据库专业知识或者不熟悉底层的数据库模式。为了解决这个问题，我们研究了 Text2SQL 自动完成(TSAC)的新问题，该问题扩展了 Text2SQL，同时将部分或不完整的问题作为输入。具体来说，TSAC 问题是预测完整的、可执行的 SQL 查询。为了解决这个问题，我们提出了一种新的关系感知历史桥接网络(RHB-Net) ，它由一个关系感知联合编码器和一个抽取生成敏感解码器组成。RHB-Net 模拟问题与数据库模式之间的关系，预测部分查询中表达的歧义意图。我们还提出了两种优化策略: 融合历史数据库查询的历史查询桥接和防止重复生成相同 SQL 元素的动态上下文构造。广泛的实验与真实世界的数据提供的证据表明，RHB-Net 是能够优于基线算法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=RHB-Net:+A+Relation-aware+Historical+Bridging+Network+for+Text2SQL+Auto-Completion)|0| -|[MAMO: Fine-Grained Vision-Language Representations Learning with Masked Multimodal Modeling](https://doi.org/10.1145/3539618.3591721)|Zijia Zhao, Longteng Guo, Xingjian He, Shuai Shao, Zehuan Yuan, Jing Liu|Bytedance Inc., Beijing, China; Institute of Automation, Chinese Academy of Sciences & University of Chinese Academy of Sciences, Beijing, China|Multimodal representation learning has shown promising improvements on various vision-language tasks (e.g., image-text retrieval, visual question answering, etc) and has significantly advanced the development of multimedia information systems. Most existing methods excel at building global-level alignment between vision and language while lacking effective fine-grained image-text interaction. In this paper, we propose a jointly masked multimodal modeling method to learn fine-grained multimodal representations. Our method performs joint masking on image-text input and integrates both implicit and explicit targets for the masked signals to recover. The implicit target provides a unified and debiased objective for vision and language, where the model predicts latent multimodal representations of the unmasked input. The explicit target further enriches the multimodal representations by recovering high-level and semantically meaningful information: momentum visual features of image patches and concepts of word tokens. Through such a masked modeling process, our model not only learns fine-grained multimodal interaction, but also avoids the semantic gap between high-level representations and low-or mid-level prediction targets (e.g., image pixels, discrete vision tokens), thus producing semantically rich multimodal representations that perform well on both zero-shot and fine-tuned settings. Our pre-trained model (named MAMO) achieves state-of-the-art performance on various downstream vision-language tasks, including image-text retrieval, visual question answering, visual reasoning, and weakly-supervised visual grounding.|多模态表示学习在各种视觉语言任务(如图像-文本检索、视觉问题回答等)中表现出良好的改进效果，极大地推动了多媒体信息系统的发展。现有的大多数方法都擅长于在视觉和语言之间建立全局一致性，但缺乏有效的细粒度图像-文本交互。本文提出了一种联合掩蔽多模态建模方法来学习细粒度多模态表示。该方法对图像-文本输入进行联合掩蔽，并将隐式和显式目标相结合进行掩蔽信号的恢复。隐式目标为视觉和语言提供了一个统一的、无偏的目标，该模型预测未掩盖的输入的潜在多模态表示。显式目标通过恢复高层次的、语义上有意义的信息，即图像块的动量视觉特征和词标记的概念，进一步丰富了多模式表示。通过这样一个隐藏的建模过程，我们的模型不仅学习细粒度的多模态交互，而且还避免了高级表示和低级或中级预测目标(例如，图像像素，离散视觉标记)之间的语义差距，从而产生语义丰富的多模态表示，在零拍摄和微调设置上表现良好。我们的预训练模型(命名为 MAMO)在各种下游视觉语言任务中实现了最先进的性能，包括图像文本检索、视觉问题回答、视觉推理和弱监督视觉接地。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=MAMO:+Fine-Grained+Vision-Language+Representations+Learning+with+Masked+Multimodal+Modeling)|0| +|[A Topic-aware Summarization Framework with Different Modal Side Information](https://doi.org/10.1145/3539618.3591630)|Xiuying Chen, Mingzhe Li, Shen Gao, Xin Cheng, Qiang Yang, Qishen Zhang, Xin Gao, Xiangliang Zhang|Peking University, Beijing, China; KAUST, Jeddah, Saudi Arabia; Shandong university, Qingdao, China; Ant Group, Beijing, China; University of Notre Dame & KAUST, Notre Dame, IL, USA|Automatic summarization plays an important role in the exponential document growth on the Web. On content websites such as CNN.com and WikiHow.com, there often exist various kinds of side information along with the main document for attention attraction and easier understanding, such as videos, images, and queries. Such information can be used for better summarization, as they often explicitly or implicitly mention the essence of the article. However, most of the existing side-aware summarization methods are designed to incorporate either single-modal or multi-modal side information, and cannot effectively adapt to each other. In this paper, we propose a general summarization framework, which can flexibly incorporate various modalities of side information. The main challenges in designing a flexible summarization model with side information include: (1) the side information can be in textual or visual format, and the model needs to align and unify it with the document into the same semantic space, (2) the side inputs can contain information from various aspects, and the model should recognize the aspects useful for summarization. To address these two challenges, we first propose a unified topic encoder, which jointly discovers latent topics from the document and various kinds of side information. The learned topics flexibly bridge and guide the information flow between multiple inputs in a graph encoder through a topic-aware interaction. We secondly propose a triplet contrastive learning mechanism to align the single-modal or multi-modal information into a unified semantic space, where the summary quality is enhanced by better understanding the document and side information. Results show that our model significantly surpasses strong baselines on three public single-modal or multi-modal benchmark summarization datasets.|自动汇总在网络文档的指数增长中扮演着重要的角色。在像 CNN.com 和 WikiHow.com 这样的内容网站上，为了吸引注意力和更容易理解，经常会有各种各样的附属信息和主要文档，比如视频、图片和查询。这些信息可以用于更好的总结，因为它们通常显式或隐式地提到文章的实质。然而，现有的侧面感知摘要方法大多是针对单模态侧面信息或多模态侧面信息而设计的，不能有效地相互适应。本文提出了一个通用的汇总框架，它可以灵活地整合各种形式的侧信息。设计具有侧信息的灵活摘要模型面临的主要挑战包括: (1)侧信息可以是文本格式或可视化格式，模型需要将侧信息与文档对齐统一到相同的语义空间中; (2)侧输入可以包含各个方面的信息，模型应该识别对摘要有用的方面。为了解决这两个问题，我们首先提出了一种统一的主题编码器，它可以联合发现文档中的潜在主题和各种侧信息。所学习的主题通过主题感知交互灵活地连接和引导图形编码器中多个输入之间的信息流。其次，我们提出了一种三元对比学习机制，将单模态或多模态信息整合到一个统一的语义空间中，通过更好地理解文档和侧面信息来提高摘要的质量。结果表明，我们的模型显著超过了三个公共的单模式或多模式基准汇总数据集的强基线。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Topic-aware+Summarization+Framework+with+Different+Modal+Side+Information)|0| +|[RHB-Net: A Relation-aware Historical Bridging Network for Text2SQL Auto-Completion](https://doi.org/10.1145/3539618.3591759)|Bolong Zheng, Lei Bi, Ruijie Xi, Lu Chen, Yunjun Gao, Xiaofang Zhou, Christian S. Jensen|Hong Kong University of Science and Technology, Hong Kong, China; Huazhong University of Science and Technology, Wuhan, China; Aalborg University, Aalborg, Denmark; Zhejiang University, Hangzhou, China|Test2SQL, a natural language interface to database querying, has seen considerable improvement, in part due to advances in deep learning. However, despite recent improvement, existing Text2SQL proposals allow only input in the form of complete questions. This leaves behind users who struggle to formulate complete questions, e.g., because they lack database expertise or are unfamiliar with the underlying database schema. To address this shortcoming, we study the novel problem of Text2SQL Auto-Completion (TSAC) that extends Text2SQL to also take partial or incomplete questions as input. Specifically, the TSAC problem is to predict the complete, executable SQL query. To solve the problem, we propose a novel Relation-aware Historical Bridging Network (RHB-Net) that consists of a relation-aware union encoder and an extraction-generation sensitive decoder. RHB-Net models relations between questions and database schemas and predicts the ambiguous intents expressed in partial queries. We also propose two optimization strategies: historical query bridging that fuses historical database queries, and a dynamic context construction that prevents repeated generation of the same SQL elements. Extensive experiments with real-world data offer evidence that RHB-Net is capable of outperforming baseline algorithms.|Test2SQL 是一种用于数据库查询的自然语言接口，已经取得了相当大的进步，部分原因是由于深度学习的进步。然而，尽管最近有所改进，现有的 Text2SQL 建议只允许以完整问题的形式输入。这使得用户很难形成完整的问题，例如，因为他们缺乏数据库专业知识或者不熟悉底层的数据库模式。为了解决这个问题，我们研究了 Text2SQL 自动完成(TSAC)的新问题，该问题扩展了 Text2SQL，同时将部分或不完整的问题作为输入。具体来说，TSAC 问题是预测完整的、可执行的 SQL 查询。为了解决这个问题，我们提出了一种新的关系感知历史桥接网络(RHB-Net) ，它由一个关系感知联合编码器和一个抽取生成敏感解码器组成。RHB-Net 模拟问题与数据库模式之间的关系，预测部分查询中表达的歧义意图。我们还提出了两种优化策略: 融合历史数据库查询的历史查询桥接和防止重复生成相同 SQL 元素的动态上下文构造。广泛的实验与真实世界的数据提供的证据表明，RHB-Net 是能够优于基线算法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=RHB-Net:+A+Relation-aware+Historical+Bridging+Network+for+Text2SQL+Auto-Completion)|0| +|[MAMO: Fine-Grained Vision-Language Representations Learning with Masked Multimodal Modeling](https://doi.org/10.1145/3539618.3591721)|Zijia Zhao, Longteng Guo, Xingjian He, Shuai Shao, Zehuan Yuan, Jing Liu|Institute of Automation, Chinese Academy of Sciences & University of Chinese Academy of Sciences, Beijing, China; Bytedance Inc., Beijing, China|Multimodal representation learning has shown promising improvements on various vision-language tasks (e.g., image-text retrieval, visual question answering, etc) and has significantly advanced the development of multimedia information systems. Most existing methods excel at building global-level alignment between vision and language while lacking effective fine-grained image-text interaction. In this paper, we propose a jointly masked multimodal modeling method to learn fine-grained multimodal representations. Our method performs joint masking on image-text input and integrates both implicit and explicit targets for the masked signals to recover. The implicit target provides a unified and debiased objective for vision and language, where the model predicts latent multimodal representations of the unmasked input. The explicit target further enriches the multimodal representations by recovering high-level and semantically meaningful information: momentum visual features of image patches and concepts of word tokens. Through such a masked modeling process, our model not only learns fine-grained multimodal interaction, but also avoids the semantic gap between high-level representations and low-or mid-level prediction targets (e.g., image pixels, discrete vision tokens), thus producing semantically rich multimodal representations that perform well on both zero-shot and fine-tuned settings. Our pre-trained model (named MAMO) achieves state-of-the-art performance on various downstream vision-language tasks, including image-text retrieval, visual question answering, visual reasoning, and weakly-supervised visual grounding.|多模态表示学习在各种视觉语言任务(如图像-文本检索、视觉问题回答等)中表现出良好的改进效果，极大地推动了多媒体信息系统的发展。现有的大多数方法都擅长于在视觉和语言之间建立全局一致性，但缺乏有效的细粒度图像-文本交互。本文提出了一种联合掩蔽多模态建模方法来学习细粒度多模态表示。该方法对图像-文本输入进行联合掩蔽，并将隐式和显式目标相结合进行掩蔽信号的恢复。隐式目标为视觉和语言提供了一个统一的、无偏的目标，该模型预测未掩盖的输入的潜在多模态表示。显式目标通过恢复高层次的、语义上有意义的信息，即图像块的动量视觉特征和词标记的概念，进一步丰富了多模式表示。通过这样一个隐藏的建模过程，我们的模型不仅学习细粒度的多模态交互，而且还避免了高级表示和低级或中级预测目标(例如，图像像素，离散视觉标记)之间的语义差距，从而产生语义丰富的多模态表示，在零拍摄和微调设置上表现良好。我们的预训练模型(命名为 MAMO)在各种下游视觉语言任务中实现了最先进的性能，包括图像文本检索、视觉问题回答、视觉推理和弱监督视觉接地。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=MAMO:+Fine-Grained+Vision-Language+Representations+Learning+with+Masked+Multimodal+Modeling)|0| |[Learn from Relational Correlations and Periodic Events for Temporal Knowledge Graph Reasoning](https://doi.org/10.1145/3539618.3591711)|Ke Liang, Lingyuan Meng, Meng Liu, Yue Liu, Wenxuan Tu, Siwei Wang, Sihang Zhou, Xinwang Liu|National University of Defense Technology, Changsha, China|Reasoning on temporal knowledge graphs (TKGR), aiming to infer missing events along the timeline, has been widely studied to alleviate incompleteness issues in TKG, which is composed of a series of KG snapshots at different timestamps. Two types of information, i.e., intra-snapshot structural information and inter-snapshot temporal interactions, mainly contribute to the learned representations for reasoning in previous models. However, these models fail to leverage (1) semantic correlations between relationships for the former information and (2) the periodic temporal patterns along the timeline for the latter one. Thus, such insufficient mining manners hinder expressive ability, leading to sub-optimal performances. To address these limitations, we propose a novel reasoning model, termed RPC, which sufficiently mines the information underlying the Relational correlations and Periodic patterns via two novel Correspondence units, i.e., relational correspondence unit (RCU) and periodic correspondence unit (PCU). Concretely, relational graph convolutional network (RGCN) and RCU are used to encode the intra-snapshot graph structural information for entities and relations, respectively. Besides, the gated recurrent units (GRU) and PCU are designed for sequential and periodic inter-snapshot temporal interactions, separately. Moreover, the model-agnostic time vectors are generated by time2vector encoders to guide the time-dependent decoder for fact scoring. Extensive experiments on six benchmark datasets show that RPC outperforms the state-of-the-art TKGR models, and also demonstrate the effectiveness of two novel strategies in our model.|时间知识图(TKGR)的推理旨在推断时间轴上的缺失事件，已被广泛研究，以减轻 TKG 中的不完备性问题，TKG 由一系列不同时间戳的 KG 快照组成。快照内部结构信息和快照间的时间相互作用是以往模型推理学习表征的主要方式。然而，这些模型未能利用(1)前者信息的关系之间的语义相关性和(2)后者信息的时间轴上的周期性时间模式。因此，这种不充分的采矿方式阻碍了表达能力，导致次优性能。为了解决这些局限性，我们提出了一种新的推理模型，称为 RPC，它通过两个新的通信单元，即关系通信单元(RCU)和周期通信单元(PCU) ，充分挖掘关系相关性和周期模式的信息。具体地，分别采用关系图卷积网络(RGCN)和 RCU 对实体和关系的快照内图结构信息进行编码。此外，本文还分别针对快照间的时间相互作用设计了门控回归单元(GRU)和 PCU。此外，时间向量编码器产生模型无关的时间向量，以指导时间相关的解码器进行事实评分。在六个基准数据集上的大量实验表明，RPC 算法的性能优于最先进的 TKGR 模型，并且证明了两种新策略在我们的模型中的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Learn+from+Relational+Correlations+and+Periodic+Events+for+Temporal+Knowledge+Graph+Reasoning)|0| |[Dynamic Mixed Membership Stochastic Block Model for Weighted Labeled Networks](https://doi.org/10.1145/3539618.3591675)|Gaël PouxMédard, Julien Velcin, Sabine Loudcher|Université de Lyon, Lyon 2, UR 3083, Lyon, France|Most real-world networks evolve over time. Existing literature proposes models for dynamic networks that are either unlabeled or assumed to have a single membership structure. On the other hand, a new family of Mixed Membership Stochastic Block Models (MMSBM) allows to model static labeled networks under the assumption of mixed-membership clustering. In this work, we propose to extend this later class of models to infer dynamic labeled networks under a mixed membership assumption. Our approach takes the form of a temporal prior on the model's parameters. It relies on the single assumption that dynamics are not abrupt. We show that our method significantly differs from existing approaches, and allows to model more complex systems --dynamic labeled networks. We demonstrate the robustness of our method with several experiments on both synthetic and real-world datasets. A key interest of our approach is that it needs very few training data to yield good results. The performance gain under challenging conditions broadens the variety of possible applications of automated learning tools --as in social sciences, which comprise many fields where small datasets are a major obstacle to the introduction of machine learning methods.|大多数现实世界的网络都是随着时间而演变的。现有的文献提出了动态网络的模型，这些模型要么是未标记的，要么是假定有一个单一的成员结构。另一方面，一类新的混合成员随机块模型(MMSBM)允许在混合成员聚类的假设下对静态标记网络进行建模。在这项工作中，我们建议扩展这类模型，以推断动态标记网络下的混合成员假设。我们的方法采用模型参数的时间先验的形式。它依赖于一个单一的假设，即动态不是突然的。我们展示了我们的方法与现有方法的显著不同，并且允许对更复杂的系统进行建模——动态标记网络。我们通过在合成数据集和真实数据集上的几个实验证明了该方法的鲁棒性。我们的方法的一个关键兴趣是它只需要很少的训练数据就能产生良好的结果。在具有挑战性的条件下，性能的提高扩大了自动学习工具的各种可能应用——如在社会科学领域，其中包括许多领域，小型数据集是引入机器学习方法的主要障碍。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Dynamic+Mixed+Membership+Stochastic+Block+Model+for+Weighted+Labeled+Networks)|0| |[DREAM: Adaptive Reinforcement Learning based on Attention Mechanism for Temporal Knowledge Graph Reasoning](https://doi.org/10.1145/3539618.3591671)|Shangfei Zheng, Hongzhi Yin, Tong Chen, Quoc Viet Hung Nguyen, Wei Chen, Lei Zhao|The University of Queensland, Brisbane, QLD, Australia; Griffith University, Gold Coast, Australia; Soochow University, Suzhou, China|Temporal knowledge graphs (TKGs) model the temporal evolution of events and have recently attracted increasing attention. Since TKGs are intrinsically incomplete, it is necessary to reason out missing elements. Although existing TKG reasoning methods have the ability to predict missing future events, they fail to generate explicit reasoning paths and lack explainability. As reinforcement learning (RL) for multi-hop reasoning on traditional knowledge graphs starts showing superior explainability and performance in recent advances, it has opened up opportunities for exploring RL techniques on TKG reasoning. However, the performance of RL-based TKG reasoning methods is limited due to: (1) lack of ability to capture temporal evolution and semantic dependence jointly; (2) excessive reliance on manually designed rewards. To overcome these challenges, we propose an adaptive reinforcement learning model based on attention mechanism (DREAM) to predict missing elements in the future. Specifically, the model contains two components: (1) a multi-faceted attention representation learning method that captures semantic dependence and temporal evolution jointly; (2) an adaptive RL framework that conducts multi-hop reasoning by adaptively learning the reward functions. Experimental results demonstrate DREAM outperforms state-of-the-art models on public dataset|时间知识图(TKGs)模拟事件的时间演化过程，近年来受到越来越多的关注。由于 TKG 本质上是不完整的，因此有必要对缺失的元素进行推理。现有的 TKG 推理方法虽然具有预测未来事件缺失的能力，但不能产生明确的推理路径，缺乏可解释性。由于传统知识图的多跳推理强化学习(rL)在最近的进展中显示出优越的可解释性和性能，它为探索传统知识图的多跳推理技术开辟了机会。然而，基于 RL 的 TKG 推理方法的性能受到以下因素的限制: (1)缺乏联合捕获时间演化和语义依赖的能力; (2)过度依赖手工设计的奖励。为了克服这些挑战，我们提出了一个基于注意力机制(dREAM)的自适应强化学习模型来预测未来缺失的元素。具体来说，该模型包含两个部分: (1)一个多方面的注意表征学习方法，它能够联合捕捉语义依赖和时间进化; (2)一个自适应 RL 框架，通过自适应学习奖励函数来进行多跳推理。实验结果表明，DREAM 在公共数据集上的性能优于最先进的模型|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=DREAM:+Adaptive+Reinforcement+Learning+based+on+Attention+Mechanism+for+Temporal+Knowledge+Graph+Reasoning)|0| |[SCHash: Speedy Simplicial Complex Neural Networks via Randomized Hashing](https://doi.org/10.1145/3539618.3591762)|Xuan Tan, Wei Wu, Chuan Luo|Central South University, Changsha, China; Beihang University, Beijing, China|Graphs, as a non-linear data structure, are ubiquitous in practice, and efficient graph analysis can benefit important information retrieval applications in the era of big data. Currently, one of the fundamental graph mining problems is graph embedding, which aims to represent the graph as a low-dimensional feature vector with the content and structural information in the graph preserved. Although the graph embedding technique has evolved considerably, traditional methods mainly focus on node pairwise relationship in graphs, which makes the representational power of such schemes limited. Recently, a number of works have explored the simplicial complexes, which describe the higher-order interactions between nodes in the graphs, and further proposed several Graph Neural Network (GNN) algorithms based on simplicial complexes. However, these GNN approaches are highly inefficient in terms of running time and space, due to massive parameter learning. In this paper, we propose a simple and speedy graph embedding algorithm dubbed SCHash. Through adopting the Locality Sensitive Hashing (LSH) technique, SCHash captures the higher-order information derived from the simplicial complex in the GNN framework, and it can achieve a good balance between accuracy and efficiency. Our extensive experiments clearly show that, in terms of accuracy, the performance of our proposed SCHash algorithm is comparable to that of state-of-the-art GNN algorithms; also, SCHash achieves higher accuracy than the existing LSH algorithms. In terms of efficiency, SCHash runs faster than GNN algorithms by 2 ~ 4 orders of magnitude, and is more efficient than the existing LSH algorithms.|图形作为一种非线性数据结构，在实践中无处不在，有效的图形分析可以使大数据时代的重要信息检索应用受益。目前，图挖掘的基本问题之一是图嵌入，其目的是将图表示为一个低维特征向量，并保留图中的内容和结构信息。虽然图嵌入技术已经有了很大的发展，但是传统的方法主要集中在图中的节点成对关系上，这使得这种方案的表示能力受到了限制。近年来，人们对描述图中节点之间高阶相互作用的单纯复形进行了研究，并进一步提出了几种基于单纯复形的图神经网络(GNN)算法。然而，由于大量的参数学习，这些 GNN 方法在运行时间和空间上都是非常低效的。本文提出了一种简单快速的图嵌入算法 SCHash。通过采用位置敏感哈希(Locality Sensor hash，LSH)技术，SCHash 捕获来自 GNN 框架中单纯复形的高阶信息，可以在准确性和效率之间取得良好的平衡。我们的大量实验清楚地表明，在准确性方面，我们提出的 SCHash 算法的性能与最先进的 GNN 算法相当; 而且，SCHash 比现有的 LSH 算法获得更高的准确性。在效率方面，SCHash 的运行速度比 GNN 算法快2 ~ 4个数量级，而且比现有的 LSH 算法更有效率。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=SCHash:+Speedy+Simplicial+Complex+Neural+Networks+via+Randomized+Hashing)|0| |[A Critical Reexamination of Intra-List Distance and Dispersion](https://doi.org/10.1145/3539618.3591623)|Naoto Ohsaka, Riku Togashi|CyberAgent, Inc., Tokyo, Japan|Diversification of recommendation results is a promising approach for coping with the uncertainty associated with users' information needs. Of particular importance in diversified recommendation is to define and optimize an appropriate diversity objective. In this study, we revisit the most popular diversity objective called intra-list distance (ILD), defined as the average pairwise distance between selected items, and a similar but lesser known objective called dispersion, which is the minimum pairwise distance. Owing to their simplicity and flexibility, ILD and dispersion have been used in a plethora of diversified recommendation research. Nevertheless, we do not actually know what kind of items are preferred by them. We present a critical reexamination of ILD and dispersion from theoretical and experimental perspectives. Our theoretical results reveal that these objectives have potential drawbacks: ILD may select duplicate items that are very close to each other, whereas dispersion may overlook distant item pairs. As a competitor to ILD and dispersion, we design a diversity objective called Gaussian ILD, which can interpolate between ILD and dispersion by tuning the bandwidth parameter. We verify our theoretical results by experimental results using real-world data and confirm the extreme behavior of ILD and dispersion in practice.|推荐结果的多样化是解决与用户信息需求相关的不确定性的一种有前途的方法。在多样化推荐中，定义和优化适当的多样化目标尤为重要。在这项研究中，我们重新审视了最流行的多样性目标，称为列表内距离(ILD) ，定义为选定项目之间的平均配对距离，以及一个类似但不太为人所知的目标，称为离散度，即最小配对距离。由于它们的简单性和灵活性，ILD 和分散性已经被用于各种各样的推荐研究中。尽管如此，我们实际上并不知道他们喜欢什么样的项目。我们从理论和实验的角度对 ILD 和离散度进行了批判性的重新审视。我们的理论结果揭示了这些目标具有潜在的缺点: ILD 可能会选择彼此非常接近的重复项目，而分散可能会忽略远距离项目对。作为 ILD 和色散的竞争对手，我们设计了一个分集目标——高斯 ILD，它可以通过调整带宽参数在 ILD 和色散之间进行插值。我们利用实际数据对理论结果进行了验证，并在实际应用中验证了 ILD 和色散的极端行为。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Critical+Reexamination+of+Intra-List+Distance+and+Dispersion)|0| |[Contrastive Learning for Signed Bipartite Graphs](https://doi.org/10.1145/3539618.3591655)|Zeyu Zhang, Jiamou Liu, Kaiqi Zhao, Song Yang, Xianda Zheng, Yifei Wang|The University of Auckland, Auckland, New Zealand; University of Electronic Science and Technology of China & The University of Auckland, Chengdu, China|This paper is the first to use contrastive learning to improve the robustness of graph representation learning for signed bipartite graphs, which are commonly found in social networks, recommender systems, and paper review platforms. Existing contrastive learning methods for signed graphs cannot capture implicit relations between nodes of the same type in signed bipartite graphs, which have two types of nodes and edges only connect nodes of different types. We propose a Signed Bipartite Graph Contrastive Learning (SBGCL) method to learn robust node representation while retaining the implicit relations between nodes of the same type. SBGCL augments a signed bipartite graph with a novel two-level graph augmentation method. At the top level, we maintain two perspectives of the signed bipartite graph, one presents the original interactions between nodes of different types, and the other presents the implicit relations between nodes of the same type. At the bottom level, we employ stochastic perturbation strategies to create two perturbed graphs in each perspective. Then, we construct positive and negative samples from the perturbed graphs and design a multi-perspective contrastive loss to unify the node presentations learned from the two perspectives. Results show proposed model is effective over state-of-the-art methods on real-world datasets.|本文首次使用对比学习方法来提高有符号二部图的图表示学习的鲁棒性，这种学习方法在社交网络、推荐系统和论文评论平台中很常见。现有的有符号图的对比学习方法不能捕捉有符号二部图中同类节点之间的隐式关系，有两类节点，边只连接不同类型的节点。本文提出了一种符号二部图对比学习(SBGCL)方法来学习鲁棒节点表示，同时保留同类节点之间的隐式关系。SBGCL 用一种新的两层图增强方法对符号二部图进行增强。在顶层，我们保持了符号二部图的两个透视图，一个透视图表示不同类型节点之间的原始交互，另一个透视图表示同类型节点之间的隐式关系。在底层，我们使用随机扰动策略在每个透视图中创建两个扰动图。然后，我们从受扰图中构造正样本和负样本，并设计一个多视角对比损失来统一从这两个视角学到的节点表示。实验结果表明，该模型对于真实数据集是有效的。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Contrastive+Learning+for+Signed+Bipartite+Graphs)|0| -|[Uncertainty Quantification for Extreme Classification](https://doi.org/10.1145/3539618.3591780)|JyunYu Jiang, WeiCheng Chang, Jiong Zhang, ChoJui Hsieh, HsiangFu Yu|Amazon Search, Palo Alto, CA, USA; University of California, Los Angeles & Amazon Search, Los Angeles, CA, USA|Uncertainty quantification is one of the most crucial tasks to obtain trustworthy and reliable machine learning models for decision making. However, most research in this domain has only focused on problems with small label spaces and ignored eXtreme Multi-label Classification (XMC), which is an essential task in the era of big data for web-scale machine learning applications. Moreover, enormous label spaces could also lead to noisy retrieval results and intractable computational challenges for uncertainty quantification. In this paper, we aim to investigate general uncertainty quantification approaches for tree-based XMC models with a probabilistic ensemble-based framework. In particular, we analyze label-level and instance-level uncertainty in XMC, and propose a general approximation framework based on beam search to efficiently estimate the uncertainty with a theoretical guarantee under long-tail XMC predictions. Empirical studies on six large-scale real-world datasets show that our framework not only outperforms single models in predictive performance, but also can serve as strong uncertainty-based baselines for label misclassification and out-of-distribution detection, with significant speedup. Besides, our framework can further yield better state-of-the-art results based on deep XMC models with uncertainty quantification.|不确定性量化是获得可信可靠的决策机器学习模型的关键任务之一。然而，这一领域的大多数研究只关注于小标签空间的问题，而忽视了 eXtreme Multi-label 分类(XMC) ，这是当今大数据时代的一个重要任务，适用于网络规模的机器学习应用。此外，巨大的标签空间也可能导致噪声检索结果和棘手的计算不确定性量化的挑战。在本文中，我们的目的是研究一般的不确定性量化方法的树为基础的 XMC 模型与概率集成的框架。特别地，我们分析了 XMC 中的标签级和实例级不确定性，并提出了一个基于束搜索的通用近似框架，以有效地估计在长尾 XMC 预测下的不确定性和理论保证。对6个大规模实际数据集的实证研究表明，该框架不仅在预测性能上优于单个模型，而且可以作为标签错误分类和分布外检测的强不确定性基线，具有显著的加速效果。此外，我们的框架可以进一步产生更好的国家的最先进的结果基于深入的 XMC 模型与不确定性量化。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Uncertainty+Quantification+for+Extreme+Classification)|0| -|[A Mathematical Word Problem Generator with Structure Planning and Knowledge Enhancement](https://doi.org/10.1145/3539618.3591937)|Longhu Qin, Jiayu Liu, Zhenya Huang, Kai Zhang, Qi Liu, Binbin Jin, Enhong Chen|Anhui Province Key Laboratory of Big Data Analysis and Application, University of Science and Technology of China & State Key Laboratory of Cognitive Intelligence, Hefei, China; School of Computer Science and Technology, University of Science and Technology of China & State Key Laboratory of Cognitive Intelligence, Hefei, China; School of Data Science, University of Science and Technology of China & State Key Laboratory of Cognitive Intelligence, Hefei, China; Huawei Cloud Computing Technologies Co., Ltd., Hangzhou, China|Automatically generating controllable and diverse mathematical word problems (MWPs) which conform to equations and topics is a crucial task in information retrieval and natural language generation. Recent deep learning models mainly focus on improving the problem readability but overlook the mathematical logic coherence, which tends to generate unsolvable problems. In this paper, we draw inspiration from the human problem-designing process and propose a Mathematical structure Planning and Knowledge enhanced Generation model (MaPKG), following the "plan-then-generate" steps. Specifically, we propose a novel dynamic planning module to make sentence-level equation plans and a dual-attention mechanism for word-level generation, incorporating equation structure representation and external commonsense knowledge. Extensive experiments on two MWP datasets show our model can guarantee more solvable, high-quality, and diverse problems. Our code is available at https://github.com/KenelmQLH/MaPKG.git|自动生成符合公式和主题的可控的多样化数学问题(MWPs)是信息检索和自然语言生成中的一项关键任务。目前的深度学习模式主要侧重于提高问题的可读性，而忽视了数学逻辑的一致性，这往往会产生无法解决的问题。本文从人类问题设计过程中得到的启示，按照“先规划后生成”的步骤，提出了一种数学结构规划与知识增强生成模型(MaPKG)。具体来说，我们提出了一个新的动态规划模块来制定句子水平的方程计划和一个双重注意机制的词水平生成，结合方程结构表示和外部常识知识。在两个 MWP 数据集上的大量实验表明，我们的模型可以保证更多可解决的、高质量的和多样化的问题。我们的代码可以在 https://github.com/kenelmqlh/mapkg.git 找到|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Mathematical+Word+Problem+Generator+with+Structure+Planning+and+Knowledge+Enhancement)|0| +|[Uncertainty Quantification for Extreme Classification](https://doi.org/10.1145/3539618.3591780)|JyunYu Jiang, WeiCheng Chang, Jiong Zhang, ChoJui Hsieh, HsiangFu Yu|University of California, Los Angeles & Amazon Search, Los Angeles, CA, USA; Amazon Search, Palo Alto, CA, USA|Uncertainty quantification is one of the most crucial tasks to obtain trustworthy and reliable machine learning models for decision making. However, most research in this domain has only focused on problems with small label spaces and ignored eXtreme Multi-label Classification (XMC), which is an essential task in the era of big data for web-scale machine learning applications. Moreover, enormous label spaces could also lead to noisy retrieval results and intractable computational challenges for uncertainty quantification. In this paper, we aim to investigate general uncertainty quantification approaches for tree-based XMC models with a probabilistic ensemble-based framework. In particular, we analyze label-level and instance-level uncertainty in XMC, and propose a general approximation framework based on beam search to efficiently estimate the uncertainty with a theoretical guarantee under long-tail XMC predictions. Empirical studies on six large-scale real-world datasets show that our framework not only outperforms single models in predictive performance, but also can serve as strong uncertainty-based baselines for label misclassification and out-of-distribution detection, with significant speedup. Besides, our framework can further yield better state-of-the-art results based on deep XMC models with uncertainty quantification.|不确定性量化是获得可信可靠的决策机器学习模型的关键任务之一。然而，这一领域的大多数研究只关注于小标签空间的问题，而忽视了 eXtreme Multi-label 分类(XMC) ，这是当今大数据时代的一个重要任务，适用于网络规模的机器学习应用。此外，巨大的标签空间也可能导致噪声检索结果和棘手的计算不确定性量化的挑战。在本文中，我们的目的是研究一般的不确定性量化方法的树为基础的 XMC 模型与概率集成的框架。特别地，我们分析了 XMC 中的标签级和实例级不确定性，并提出了一个基于束搜索的通用近似框架，以有效地估计在长尾 XMC 预测下的不确定性和理论保证。对6个大规模实际数据集的实证研究表明，该框架不仅在预测性能上优于单个模型，而且可以作为标签错误分类和分布外检测的强不确定性基线，具有显著的加速效果。此外，我们的框架可以进一步产生更好的国家的最先进的结果基于深入的 XMC 模型与不确定性量化。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Uncertainty+Quantification+for+Extreme+Classification)|0| +|[A Mathematical Word Problem Generator with Structure Planning and Knowledge Enhancement](https://doi.org/10.1145/3539618.3591937)|Longhu Qin, Jiayu Liu, Zhenya Huang, Kai Zhang, Qi Liu, Binbin Jin, Enhong Chen|Huawei Cloud Computing Technologies Co., Ltd., Hangzhou, China; School of Computer Science and Technology, University of Science and Technology of China & State Key Laboratory of Cognitive Intelligence, Hefei, China; School of Data Science, University of Science and Technology of China & State Key Laboratory of Cognitive Intelligence, Hefei, China; Anhui Province Key Laboratory of Big Data Analysis and Application, University of Science and Technology of China & State Key Laboratory of Cognitive Intelligence, Hefei, China|Automatically generating controllable and diverse mathematical word problems (MWPs) which conform to equations and topics is a crucial task in information retrieval and natural language generation. Recent deep learning models mainly focus on improving the problem readability but overlook the mathematical logic coherence, which tends to generate unsolvable problems. In this paper, we draw inspiration from the human problem-designing process and propose a Mathematical structure Planning and Knowledge enhanced Generation model (MaPKG), following the "plan-then-generate" steps. Specifically, we propose a novel dynamic planning module to make sentence-level equation plans and a dual-attention mechanism for word-level generation, incorporating equation structure representation and external commonsense knowledge. Extensive experiments on two MWP datasets show our model can guarantee more solvable, high-quality, and diverse problems. Our code is available at https://github.com/KenelmQLH/MaPKG.git|自动生成符合公式和主题的可控的多样化数学问题(MWPs)是信息检索和自然语言生成中的一项关键任务。目前的深度学习模式主要侧重于提高问题的可读性，而忽视了数学逻辑的一致性，这往往会产生无法解决的问题。本文从人类问题设计过程中得到的启示，按照“先规划后生成”的步骤，提出了一种数学结构规划与知识增强生成模型(MaPKG)。具体来说，我们提出了一个新的动态规划模块来制定句子水平的方程计划和一个双重注意机制的词水平生成，结合方程结构表示和外部常识知识。在两个 MWP 数据集上的大量实验表明，我们的模型可以保证更多可解决的、高质量的和多样化的问题。我们的代码可以在 https://github.com/kenelmqlh/mapkg.git 找到|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Mathematical+Word+Problem+Generator+with+Structure+Planning+and+Knowledge+Enhancement)|0| |[A Simple yet Effective Framework for Few-Shot Aspect-Based Sentiment Analysis](https://doi.org/10.1145/3539618.3591940)|Zengzhi Wang, Qiming Xie, Rui Xia|Nanjing University of Science and Technology, Nanjing, China|The pre-training and fine-tuning paradigm has become the main-stream framework in the field of Aspect-Based Sentiment Analysis (ABSA). Although it has achieved sound performance in the domains containing enough fine-grained aspect-sentiment annotations, it is still challenging to conduct few-shot ABSA in domains where manual annotations are scarce. In this work, we argue that two kinds of gaps, i.e., domain gap and objective gap, hinder the transfer of knowledge from pre-training language models (PLMs) to ABSA tasks. To address this issue, we introduce a simple yet effective framework called FS-ABSA, which involves domain-adaptive pre-training and text-infilling fine-tuning. We approach the End-to-End ABSA task as a text-infilling problem and perform domain-adaptive pre-training with the text-infilling objective, narrowing the two gaps and consequently facilitating the knowledge transfer. Experiments show that the resulting model achieves more compelling performance than baselines under the few-shot setting while driving the state-of-the-art performance to a new level across datasets under the fully-supervised setting. Moreover, we apply our framework to two non-English low-resource languages to demonstrate its generality and effectiveness.|预训练和微调范式已经成为基于方面的情绪分析(ABSA)领域的主流框架。尽管它已经在包含足够细粒度的方面-情感注释的领域中取得了良好的性能，但是在缺乏手动注释的领域中进行少量的 ABSA 仍然具有挑战性。在本研究中，我们认为领域差距和目标差距两种差距阻碍了知识从培训前语言模型(PLM)到 ABSA 任务的转移。为了解决这个问题，我们引入了一个简单而有效的框架 FS-ABSA，它包括领域自适应预训练和文本填充微调。将端到端 ABSA 任务作为文本填充问题进行处理，并针对文本填充目标进行领域自适应预训练，缩小两者之间的差距，从而促进知识转移。实验结果表明，该模型比基准模型在少镜头情况下的性能更好，同时在全监督情况下将数据集间的性能提高到了一个新的水平。此外，我们将该框架应用于两种非英语低资源语言，以证明其通用性和有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Simple+yet+Effective+Framework+for+Few-Shot+Aspect-Based+Sentiment+Analysis)|0| |[A Unified Formulation for the Frequency Distribution of Word Frequencies using the Inverse Zipf's Law](https://doi.org/10.1145/3539618.3591942)|Can Özbey, Talha Çolakoglu, M. Safak Bilici, Ekin Can Erkus|Huawei Turkey R&D Center, Istanbul, Turkey|The power-law approximation for the frequency distribution of words postulated by Zipf has been extensively studied for decades, which led to many variations on the theme. However, comparatively less attention has been paid to the investigation of the case of word frequencies. In this paper, we derive its analytical expression from the inverse of the underlying rank-size distribution as a function of total word count, vocabulary size and the shape parameter, thereby providing a unified framework to explain the nonlinear behavior of low frequencies on the log-log scale. We also present an efficient method based on relative entropy minimization for a robust estimation of the shape parameter using a small number of empirical low-frequency probabilities. Experiments were carried out for a selected set of languages with varying degrees of inflection in order to demonstrate the effectiveness of the proposed approach.|Zipf 假设的词频分布的幂律近似已经被广泛地研究了几十年，这导致了主题的许多变化。然而，对词频的研究却相对较少。本文从底层秩-大小分布与词汇总量、词汇量和形状参数的反向关系出发，推导出其解析表达式，从而为低频非线性行为在对数尺度上的解释提供了一个统一的框架。我们还提出了一种基于相对熵最小化的有效方法，使用少量的经验低频概率对形状参数进行稳健估计。为了验证该方法的有效性，对一组具有不同屈折度的选定语言进行了实验。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Unified+Formulation+for+the+Frequency+Distribution+of+Word+Frequencies+using+the+Inverse+Zipf's+Law)|0| |[Bayesian Knowledge-driven Critiquing with Indirect Evidence](https://doi.org/10.1145/3539618.3591954)|Armin Toroghi, Griffin Floto, Zhenwei Tang, Scott Sanner|University of Toronto, Toronto, ON, Canada|Conversational recommender systems (CRS) enhance the expressivity and personalization of recommendations through multiple turns of user-system interaction. Critiquing is a well-known paradigm for CRS that allows users to iteratively refine recommendations by providing feedback about attributes of recommended items. While existing critiquing methodologies utilize direct attributes of items to address user requests such as 'I prefer Western movies', the opportunity of incorporating richer contextual and side information about items stored in Knowledge Graphs (KG) into the critiquing paradigm has been overlooked. Employing this substantial knowledge together with a well-established reasoning methodology paves the way for critique-based recommenders to allow for complex knowledge-based feedback (e.g., 'I like movies featuring war side effects on veterans') which may arise in natural user-system conversations. In this work, we aim to increase the flexibility of critique-based recommendation by integrating KGs and propose a novel Bayesian inference framework that enables reasoning with relational knowledge-based feedback. We study and formulate the framework considering a Gaussian likelihood and evaluate it on two well-known recommendation datasets with KGs. Our evaluations demonstrate the effectiveness of our framework in leveraging indirect KG-based feedback (i.e., preferred relational properties of items rather than preferred items themselves), often improving personalized recommendations over a one-shot recommender by more than 15%. This work enables a new paradigm for using rich knowledge content and reasoning over indirect evidence as a mechanism for critiquing interactions with CRS.|会话推荐系统(CRS)通过多轮用户系统交互增强推荐的表达能力和个性化。批评是 CRS 的一个众所周知的范例，它允许用户通过提供关于推荐项目属性的反馈来迭代地完善推荐。虽然现有的批评方法利用项目的直接属性来满足用户的要求，例如“我更喜欢西部电影”，但是将知识图表(KG)中存储的项目的更丰富的上下文和侧面信息纳入批评范式的机会被忽视了。使用这些实质性的知识和一个完善的推理方法为基于评论的推荐者铺平了道路，以允许复杂的基于知识的反馈(例如，“我喜欢有退伍军人战争副作用的电影”) ，这可能出现在自然的用户系统对话中。在这项工作中，我们的目标是通过整合幼稚园来增加基于批判的推荐的灵活性，并提出一个新的贝叶斯推断框架，使推理与关系知识为基础的反馈。我们研究并制定了考虑高斯似然的框架，并在两个著名的 KG 推荐数据集上进行了评估。我们的评估表明，我们的框架在利用间接的基于 KG 的反馈(即，项目的首选关系属性，而不是首选项本身)方面的有效性，通常比一次性推荐提高个性化推荐超过15% 。这项工作为利用丰富的知识内容和推理间接证据作为一种机制批判与 CRS 的相互作用提供了一个新的范例。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Bayesian+Knowledge-driven+Critiquing+with+Indirect+Evidence)|0| |[BioAug: Conditional Generation based Data Augmentation for Low-Resource Biomedical NER](https://doi.org/10.1145/3539618.3591957)|Sreyan Ghosh, Utkarsh Tyagi, Sonal Kumar, Dinesh Manocha|University of Maryland College Park, USA, College Park, MD, USA|Biomedical Named Entity Recognition (BioNER) is the fundamental task of identifying named entities from biomedical text. However, BioNER suffers from severe data scarcity and lacks high-quality labeled data due to the highly specialized and expert knowledge required for annotation. Though data augmentation has shown to be highly effective for low-resource NER in general, existing data augmentation techniques fail to produce factual and diverse augmentations for BioNER. In this paper, we present BioAug, a novel data augmentation framework for low-resource BioNER. BioAug, built on BART, is trained to solve a novel text reconstruction task based on selective masking and knowledge augmentation. Post training, we perform conditional generation and generate diverse augmentations conditioning BioAug on selectively corrupted text similar to the training stage. We demonstrate the effectiveness of BioAug on 5 benchmark BioNER datasets and show that BioAug outperforms all our baselines by a significant margin (1.5%-21.5% absolute improvement) and is able to generate augmentations that are both more factual and diverse. Code: https://github.com/Sreyan88/BioAug.|生物医学命名实体识别(BioNER)是从生物医学文本中识别命名实体的基础性工作。然而，由于注释所需的高度专业化和专家知识，BioNER 存在严重的数据稀缺性，并且缺乏高质量的标记数据。虽然数据增强已被证明对于低资源 NER 一般来说是非常有效的，但现有的数据增强技术不能为 BioNER 产生实际的和多样的增强。在本文中，我们提出了 BioAug，一个新的低资源 BioNER 数据增强框架。基于 BART 的 BioAug 被训练来解决一个基于选择性掩蔽和知识增强的文本重建任务。训练后，我们执行条件生成和生成不同的增强条件 BioAug 对选择性损坏的文本类似于训练阶段。我们证明了 BioAug 在5个基准 BioNER 数据集上的有效性，并显示 BioAug 比我们所有的基线都有显着的提高(1.5% -21.5% 的绝对改善) ，并且能够产生更加实际和多样化的增强。密码: https://github.com/sreyan88/bioaug。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=BioAug:+Conditional+Generation+based+Data+Augmentation+for+Low-Resource+Biomedical+NER)|0| -|[Dimension-Prompts Boost Commonsense Consolidation](https://doi.org/10.1145/3539618.3591973)|Jiazhan Feng, Chongyang Tao, Tao Shen, Chang Liu, Dongyan Zhao|Peking University, Beijing, China; Microsoft Corporation, Beijing, China; University of Technology Sydney, Sydney, NSW, Australia; Peking University & National Key Laboratory of General Artificial Intelligence, BIGAI, Beijing, China|Neural knowledge models emerged and advanced common-sense-centric knowledge grounding. They parameterize a small seed curated commonsense knowledge graph (CS-KG) in a language model to generalize more. A current trend is to scale the seed up by directly mixing multiple sources of CS-KG (e.g., ATOMIC, ConceptNet) into one model. But, such brute-force mixing inevitably hinders effective knowledge consolidation due to i) ambiguous, polysemic, and/or inconsistent relations across sources and ii) knowledge learned in an entangled manner despite distinct types (e.g., causal, temporal). To mitigate this, we adopt a concept of commonsense knowledge dimension and propose a brand-new dimension-disentangled knowledge model (D2KM) learning paradigm with multiple sources. That is, a generative language model with dimension-specific soft prompts is trained to disentangle knowledge acquisitions along with different dimensions and facilitate potential intra-dimension consolidation across CS-KG sources. Experiments show our knowledge model outperforms its baselines in both standard and zero-shot scenarios.|神经知识模型的出现和先进的常识为中心的知识基础。他们在一个语言模型中参数化一个小的种子策划的常识知识图(CS-KG) ，以便进一步推广。目前的趋势是通过直接将 CS-KG 的多个来源(例如 ATOMIC、 ConcepeptNet)混合到一个模型中来扩大种子的规模。但是，这种蛮力混合不可避免地阻碍了有效的知识整合，因为 i)模棱两可，多义和/或来源之间的不一致关系，以及 ii)尽管有不同的类型(例如因果关系，时间关系) ，但以纠缠方式学习的知识。为了解决这一问题，我们采用常识知识维度的概念，提出了一种全新的多源维度分离知识模型(D2KM)学习范式。也就是说，训练一个具有特定维度的软提示的生成语言模型，以便将知识获取与不同维度一起分离，并促进跨 CS-KG 源的潜在维度内整合。实验表明，我们的知识模型在标准场景和零射击场景中都优于其基线。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Dimension-Prompts+Boost+Commonsense+Consolidation)|0| +|[Dimension-Prompts Boost Commonsense Consolidation](https://doi.org/10.1145/3539618.3591973)|Jiazhan Feng, Chongyang Tao, Tao Shen, Chang Liu, Dongyan Zhao|Microsoft Corporation, Beijing, China; University of Technology Sydney, Sydney, NSW, Australia; Peking University, Beijing, China; Peking University & National Key Laboratory of General Artificial Intelligence, BIGAI, Beijing, China|Neural knowledge models emerged and advanced common-sense-centric knowledge grounding. They parameterize a small seed curated commonsense knowledge graph (CS-KG) in a language model to generalize more. A current trend is to scale the seed up by directly mixing multiple sources of CS-KG (e.g., ATOMIC, ConceptNet) into one model. But, such brute-force mixing inevitably hinders effective knowledge consolidation due to i) ambiguous, polysemic, and/or inconsistent relations across sources and ii) knowledge learned in an entangled manner despite distinct types (e.g., causal, temporal). To mitigate this, we adopt a concept of commonsense knowledge dimension and propose a brand-new dimension-disentangled knowledge model (D2KM) learning paradigm with multiple sources. That is, a generative language model with dimension-specific soft prompts is trained to disentangle knowledge acquisitions along with different dimensions and facilitate potential intra-dimension consolidation across CS-KG sources. Experiments show our knowledge model outperforms its baselines in both standard and zero-shot scenarios.|神经知识模型的出现和先进的常识为中心的知识基础。他们在一个语言模型中参数化一个小的种子策划的常识知识图(CS-KG) ，以便进一步推广。目前的趋势是通过直接将 CS-KG 的多个来源(例如 ATOMIC、 ConcepeptNet)混合到一个模型中来扩大种子的规模。但是，这种蛮力混合不可避免地阻碍了有效的知识整合，因为 i)模棱两可，多义和/或来源之间的不一致关系，以及 ii)尽管有不同的类型(例如因果关系，时间关系) ，但以纠缠方式学习的知识。为了解决这一问题，我们采用常识知识维度的概念，提出了一种全新的多源维度分离知识模型(D2KM)学习范式。也就是说，训练一个具有特定维度的软提示的生成语言模型，以便将知识获取与不同维度一起分离，并促进跨 CS-KG 源的潜在维度内整合。实验表明，我们的知识模型在标准场景和零射击场景中都优于其基线。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Dimension-Prompts+Boost+Commonsense+Consolidation)|0| |[DeviceGPT: A Generative Pre-Training Transformer on the Heterogenous Graph for Internet of Things](https://doi.org/10.1145/3539618.3591972)|Yimo Ren, Jinfa Wang, Hong Li, Hongsong Zhu, Limin Sun|University of Chinese Academy of Science & Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China|Recently, Graph neural networks (GNNs) have been adopted to model a wide range of structured data from academic and industry fields. With the rapid development of Internet technology, there are more and more meaningful applications for Internet devices, including device identification, geolocation and others, whose performance needs improvement. To replicate the several claimed successes of GNNs, this paper proposes DeviceGPT based on a generative pre-training transformer on a heterogeneous graph via self-supervised learning to learn interactions-rich information of devices from its large-scale databases well. The experiments on the dataset constructed from the real world show DeviceGPT could achieve competitive results in multiple Internet applications.|近年来，图神经网络(GNN)已被广泛应用于学术和工业领域的结构化数据建模。随着互联网技术的飞速发展，互联网设备在设备识别、地理定位等方面的应用越来越多，其性能需要进一步提高。为了复制 GNN 的几个成功案例，本文提出了一种基于异构图上生成式预训练转换器的 DeviceGPT，通过自监督学习从其大规模数据库中很好地学习设备的交互信息。实验表明，DeviceGPT 可以在多种互联网应用中取得有竞争力的效果。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=DeviceGPT:+A+Generative+Pre-Training+Transformer+on+the+Heterogenous+Graph+for+Internet+of+Things)|0| -|[DocGraphLM: Documental Graph Language Model for Information Extraction](https://doi.org/10.1145/3539618.3591975)|Dongsheng Wang, Zhiqiang Ma, Armineh Nourbakhsh, Kang Gu, Sameena Shah|JPMorgan AI Research, New York, NY, USA; JPMorgan AI Research, London, United Kingdom; Dartmouth College, Hanover, NH, USA|Advances in Visually Rich Document Understanding (VrDU) have enabled information extraction and question answering over documents with complex layouts. Two tropes of architectures have emerged-transformer-based models inspired by LLMs, and Graph Neural Networks. In this paper, we introduce DocGraphLM, a novel framework that combines pre-trained language models with graph semantics. To achieve this, we propose 1) a joint encoder architecture to represent documents, and 2) a novel link prediction approach to reconstruct document graphs. DocGraphLM predicts both directions and distances between nodes using a convergent joint loss function that prioritizes neighborhood restoration and downweighs distant node detection. Our experiments on three SotA datasets show consistent improvement on IE and QA tasks with the adoption of graph features. Moreover, we report that adopting the graph features accelerates convergence in the learning process druing training, despite being solely constructed through link prediction.|视觉丰富文档理解技术的进步(vrdU)使得信息抽取和问题解答能够在复杂布局的文档上进行。出现了两种类型的体系结构——受 LLM 启发的基于变压器的模型和图形神经网络。在本文中，我们介绍了 DocGraphLM，这是一个结合了预训练语言模型和图语义的新框架。为了实现这一点，我们提出了1)联合编码器体系结构来表示文档，2)一种新的链接预测方法来重建文档图形。DocGraphLM 使用收敛的联合损失函数预测节点之间的方向和距离，该函数优先考虑邻域恢复并降低远程节点检测。我们在三个 SotA 数据集上的实验表明，随着图形特征的采用，IE 和 QA 任务得到了一致的改善。此外，我们报告采用图形特征加速收敛的学习过程中的训练，尽管只是通过链接预测构造。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=DocGraphLM:+Documental+Graph+Language+Model+for+Information+Extraction)|0| +|[DocGraphLM: Documental Graph Language Model for Information Extraction](https://doi.org/10.1145/3539618.3591975)|Dongsheng Wang, Zhiqiang Ma, Armineh Nourbakhsh, Kang Gu, Sameena Shah|JPMorgan AI Research, New York, NY, USA; Dartmouth College, Hanover, NH, USA; JPMorgan AI Research, London, United Kingdom|Advances in Visually Rich Document Understanding (VrDU) have enabled information extraction and question answering over documents with complex layouts. Two tropes of architectures have emerged-transformer-based models inspired by LLMs, and Graph Neural Networks. In this paper, we introduce DocGraphLM, a novel framework that combines pre-trained language models with graph semantics. To achieve this, we propose 1) a joint encoder architecture to represent documents, and 2) a novel link prediction approach to reconstruct document graphs. DocGraphLM predicts both directions and distances between nodes using a convergent joint loss function that prioritizes neighborhood restoration and downweighs distant node detection. Our experiments on three SotA datasets show consistent improvement on IE and QA tasks with the adoption of graph features. Moreover, we report that adopting the graph features accelerates convergence in the learning process druing training, despite being solely constructed through link prediction.|视觉丰富文档理解技术的进步(vrdU)使得信息抽取和问题解答能够在复杂布局的文档上进行。出现了两种类型的体系结构——受 LLM 启发的基于变压器的模型和图形神经网络。在本文中，我们介绍了 DocGraphLM，这是一个结合了预训练语言模型和图语义的新框架。为了实现这一点，我们提出了1)联合编码器体系结构来表示文档，2)一种新的链接预测方法来重建文档图形。DocGraphLM 使用收敛的联合损失函数预测节点之间的方向和距离，该函数优先考虑邻域恢复并降低远程节点检测。我们在三个 SotA 数据集上的实验表明，随着图形特征的采用，IE 和 QA 任务得到了一致的改善。此外，我们报告采用图形特征加速收敛的学习过程中的训练，尽管只是通过链接预测构造。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=DocGraphLM:+Documental+Graph+Language+Model+for+Information+Extraction)|0| |[Exploiting Ubiquitous Mentions for Document-Level Relation Extraction](https://doi.org/10.1145/3539618.3591984)|Ruoyu Zhang, Yanzeng Li, Minhao Zhang, Lei Zou|Peking University, Beijing, China|Recent years have witnessed the transition from sentence-level to document-level in relation extraction (RE), with new formulation, new methods and new insights. Yet, the fundamental concept, mention, is not well-considered and well-defined. Current datasets usually use automatically-detected named entities as mentions, which leads to the missing reference problem. We show that such phenomenon hinders models' reasoning abilities. To address it, we propose to incorporate coreferences (e.g. pronouns and common nouns) into mentions, based on which we refine and re-annotate the widely-used DocRED benchmark as R-DocRED. We evaluate various methods and conduct thorough experiments to demonstrate the efficacy of our formula. Specifically, the results indicate that incorporating coreferences helps reduce the long-term dependencies, further improving models' robustness and generalization under adversarial and low-resource settings. The new dataset is made publicly available for future research.|近年来，关系抽取技术经历了从句子层次到文档层次的转变，出现了新的表述方式、新的方法和新的见解。然而，这里提到的基本概念并没有经过深思熟虑和明确定义。当前数据集通常使用自动检测到的命名实体作为提及，这导致缺少引用问题。我们发现这种现象阻碍了模型的推理能力。为了解决这个问题，我们建议将共引用(例如代词和普通名词)合并到提及中，在此基础上，我们将广泛使用的 DocRED 基准改进并重新注释为 R-DocRED。我们评估各种方法，并进行彻底的实验，以证明我们的公式的功效。具体地说，结果表明，合并参考文献有助于减少长期依赖，进一步提高模型的健壮性和泛化的对抗性和低资源设置。这个新的数据集可以公开用于未来的研究。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Exploiting+Ubiquitous+Mentions+for+Document-Level+Relation+Extraction)|0| |[Faster Dynamic Pruning via Reordering of Documents in Inverted Indexes](https://doi.org/10.1145/3539618.3591987)|Erman Yafay, Ismail Sengor Altingovde|Middle East Technical University, Ankara, Turkey|Widely used dynamic pruning algorithms (such as MaxScore, WAND and BMW) keep track of the k-th highest score (i.e., heap threshold) among the documents that are scored so far, to avoid scoring the documents that cannot get into the top-k result list. Obviously, the faster the heap threshold converges to its final value, the larger will be the number of skipped documents and hence, the efficiency gains of the pruning algorithms. In this paper, we tailor approaches that reorder the documents in the inverted index based on their access counts and ranks for previous queries. By storing such frequently retrieved documents at front of the postings lists, we aim to compute the heap threshold earlier during the query processing. Our approach yields substantial speedups (up to 1.33x) for all three dynamic pruning algorithms and outperforms two strong baselines that have been employed for document reordering in the literature.|广泛使用的动态修剪算法(如 MaxScore、 WAND 和 BMW)跟踪目前为止得分最高的文档中的 k-th 最高得分(即堆阈值) ，以避免对无法进入 top-k 结果列表的文档进行得分。显然，堆阈值收敛到最终值的速度越快，跳过的文档数量就越多，因此，剪枝算法的效率也就越高。在本文中，我们针对以前的查询，根据文档的访问次数和排名，对倒排索引中的文档进行重新排序。通过将这些经常检索到的文档存储在发布列表的前面，我们的目标是在查询处理的早期计算堆阈值。我们的方法为所有三种动态剪枝算法提供了可观的加速(最高1.33 x) ，并且优于文献中用于文档重排序的两个强基线。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Faster+Dynamic+Pruning+via+Reordering+of+Documents+in+Inverted+Indexes)|0| |[Gated Attention with Asymmetric Regularization for Transformer-based Continual Graph Learning](https://doi.org/10.1145/3539618.3591991)|Hongxiang Lin, Ruiqi Jia, Xiaoqing Lyu|Wangxuan Institute of Computer Technology, Peking University, Beijing, China|Continual graph learning (CGL) aims to mitigate the topological-feature-induced catastrophic forgetting problem (TCF) in graph neural networks, which plays an essential role in the field of information retrieval. The TCF is mainly caused by the forgetting of node features of old tasks and the forgetting of topological features shared by old and new tasks. Existing CGL methods do not pay enough attention to the forgetting of topological features shared between different tasks. In this paper, we propose a transformer-based CGL method (Trans-CGL), thereby taking full advantage of the transformer's properties to mitigate the TCF problem. Specifically, to alleviate forgetting of node features, we introduce a gated attention mechanism for Trans-CGL based on parameter isolation that allows the model to be independent of each other when learning old and new tasks. Furthermore, to address the forgetting of shared parameters that store topological information between different tasks, we propose an asymmetric mask attention regularization module to constrain the shared attention parameters ensuring that the shared topological information is preserved. Comparative experiments show that the method achieves competitive performance on four real-world datasets.|连续图学习(CGL)的目的是缓解图神经网络中由拓扑特征引起的灾难性遗忘问题(TCF) ，这在信息检索领域中起着至关重要的作用。TCF 主要是由于遗忘旧任务的节点特征和新旧任务共享的拓扑特征造成的。现有的 CGL 方法对不同任务之间共享的拓扑特征的遗忘没有给予足够的重视。本文提出了一种基于变压器的 CGL 方法(Trans-CGL) ，从而充分利用变压器的特性来缓解 TCF 问题。为了减少节点特征的遗忘，本文提出了一种基于参数隔离的 Trans-CGL 门控注意机制，该机制允许模型在学习新旧任务时相互独立。此外，为了解决存储不同任务间拓扑信息的共享参数的遗忘问题，本文提出了一种非对称掩码注意正则化模型来约束共享注意参数，以保证共享拓扑信息的保留。对比实验表明，该方法在四个实际数据集上都取得了较好的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Gated+Attention+with+Asymmetric+Regularization+for+Transformer-based+Continual+Graph+Learning)|0| -|[HiPrompt: Few-Shot Biomedical Knowledge Fusion via Hierarchy-Oriented Prompting](https://doi.org/10.1145/3539618.3591997)|Jiaying Lu, Jiaming Shen, Bo Xiong, Wenjing Ma, Steffen Staab, Carl Yang|University of Stuttgart, Stuttgart, Germany; Emory University, Atlanta, GA, USA; University of Stuttgart; University of Southampton, Stuttgart, Germany; Google Research, New York, NY, USA|Medical decision-making processes can be enhanced by comprehensive biomedical knowledge bases, which require fusing knowledge graphs constructed from different sources via a uniform index system. The index system often organizes biomedical terms in a hierarchy to provide the aligned entities with fine-grained granularity. To address the challenge of scarce supervision in the biomedical knowledge fusion (BKF) task, researchers have proposed various unsupervised methods. However, these methods heavily rely on ad-hoc lexical and structural matching algorithms, which fail to capture the rich semantics conveyed by biomedical entities and terms. Recently, neural embedding models have proved effective in semantic-rich tasks, but they rely on sufficient labeled data to be adequately trained. To bridge the gap between the scarce-labeled BKF and neural embedding models, we propose HiPrompt, a supervision-efficient knowledge fusion framework that elicits the few-shot reasoning ability of large language models through hierarchy-oriented prompts. Empirical results on the collected KG-Hi-BKF benchmark datasets demonstrate the effectiveness of HiPrompt.|综合生物医学知识库可以增强医学决策过程，它需要通过统一的指标体系融合不同来源的知识图。索引系统通常将生物医学术语组织在一个层次结构中，以提供细粒度的对齐实体。为了解决生物医学知识融合(BKF)任务中缺乏监督的问题，研究人员提出了各种无监督方法。然而，这些方法严重依赖于特定的词法和结构匹配算法，无法捕获生物医学实体和术语所传达的丰富语义。最近，神经嵌入模型被证明在语义丰富的任务中是有效的，但是它们依赖于足够的标记数据来进行充分的训练。为了弥合稀缺标记 BKF 模型和神经嵌入模型之间的差距，我们提出了 HiPrompt，一个监督高效的知识融合框架，通过面向层次的提示来激发大型语言模型的少镜头推理能力。在 KG-Hi-BKF 基准数据集上的实证结果证明了 HiPrompt 的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=HiPrompt:+Few-Shot+Biomedical+Knowledge+Fusion+via+Hierarchy-Oriented+Prompting)|0| -|[How Significant Attributes are in the Community Detection of Attributed Multiplex Networks](https://doi.org/10.1145/3539618.3591998)|Junwei Cheng, Chaobo He, Kunlin Han, Wenjie Ma, Yong Tang|China Mobile Group Zhejiang Company Limited, Ningbo, China; South China Normal University, Guangzhou, China; University of Southern California, Los Angeles, CA, USA|Existing community detection methods for attributed multiplex networks focus on exploiting the complementary information from different topologies, while they are paying little attention to the role of attributes. However, we observe that real attributed multiplex networks exhibit two unique features, namely, consistency and homogeneity of node attributes. Therefore, in this paper, we propose a novel method, called ACDM, which is based on these two characteristics of attributes, to detect communities on attributed multiplex networks. Specifically, we extract commonality representation of nodes through the consistency of attributes. The collaboration between the homogeneity of attributes and topology information reveals the particularity representation of nodes. The comprehensive experimental results on real attributed multiplex networks well validate that our method outperforms state-of-the-art methods in most networks.|现有的属性复用网络社区检测方法主要是利用不同拓扑的互补信息，而对属性的作用关注不够。然而，我们观察到实际的属性化多路复用网络表现出两个独特的特征，即节点属性的一致性和同质性。因此，本文提出了一种基于属性的两个特征的 ACDM 方法来检测属性复用网络上的社区。具体来说，我们通过属性的一致性来提取节点的公共性表示。属性的同质性和拓扑信息的协同作用揭示了节点的特殊性表示。在实际属性化多路复用网络上的综合实验结果验证了该方法在大多数网络中的性能优于现有的方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=How+Significant+Attributes+are+in+the+Community+Detection+of+Attributed+Multiplex+Networks)|0| -|[HyperFormer: Learning Expressive Sparse Feature Representations via Hypergraph Transformer](https://doi.org/10.1145/3539618.3591999)|Kaize Ding, Albert Jiongqian Liang, Bryan Perozzi, Ting Chen, Ruoxi Wang, Lichan Hong, Ed H. Chi, Huan Liu, Derek Zhiyuan Cheng|Google, Tempe, USA; Google, Mountain View, USA|Learning expressive representations for high-dimensional yet sparse features has been a longstanding problem in information retrieval. Though recent deep learning methods can partially solve the problem, they often fail to handle the numerous sparse features, particularly those tail feature values with infrequent occurrences in the training data. Worse still, existing methods cannot explicitly leverage the correlations among different instances to help further improve the representation learning on sparse features since such relational prior knowledge is not provided. To address these challenges, in this paper, we tackle the problem of representation learning on feature-sparse data from a graph learning perspective. Specifically, we propose to model the sparse features of different instances using hypergraphs where each node represents a data instance and each hyperedge denotes a distinct feature value. By passing messages on the constructed hypergraphs based on our Hypergraph Transformer (HyperFormer), the learned feature representations capture not only the correlations among different instances but also the correlations among features. Our experiments demonstrate that the proposed approach can effectively improve feature representation learning on sparse features.|长期以来，学习高维稀疏特征的表达方式一直是信息检索领域的一个难题。目前的深度学习方法虽然能够部分解决这一问题，但往往无法处理大量稀疏特征，尤其是训练数据中不常出现的尾部特征值。更糟糕的是，现有的方法不能明确地利用不同实例之间的相关性来帮助进一步改善稀疏特征的表示学习，因为这样的关系先验知识是没有提供的。为了解决这些问题，本文从图学习的角度研究了特征稀疏数据的表示学习问题。具体来说，我们建议使用超图对不同实例的稀疏特征进行建模，其中每个节点代表一个数据实例，每个超边表示一个不同的特征值。通过在基于超图变换的构造的超图上传递消息，学习的特征表示不仅能够捕获不同实例之间的相关性，而且能够捕获特征之间的相关性。实验结果表明，该方法能够有效地改善稀疏特征的特征表示学习。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=HyperFormer:+Learning+Expressive+Sparse+Feature+Representations+via+Hypergraph+Transformer)|0| +|[HiPrompt: Few-Shot Biomedical Knowledge Fusion via Hierarchy-Oriented Prompting](https://doi.org/10.1145/3539618.3591997)|Jiaying Lu, Jiaming Shen, Bo Xiong, Wenjing Ma, Steffen Staab, Carl Yang|Google Research, New York, NY, USA; Emory University, Atlanta, GA, USA; University of Stuttgart, Stuttgart, Germany; University of Stuttgart; University of Southampton, Stuttgart, Germany|Medical decision-making processes can be enhanced by comprehensive biomedical knowledge bases, which require fusing knowledge graphs constructed from different sources via a uniform index system. The index system often organizes biomedical terms in a hierarchy to provide the aligned entities with fine-grained granularity. To address the challenge of scarce supervision in the biomedical knowledge fusion (BKF) task, researchers have proposed various unsupervised methods. However, these methods heavily rely on ad-hoc lexical and structural matching algorithms, which fail to capture the rich semantics conveyed by biomedical entities and terms. Recently, neural embedding models have proved effective in semantic-rich tasks, but they rely on sufficient labeled data to be adequately trained. To bridge the gap between the scarce-labeled BKF and neural embedding models, we propose HiPrompt, a supervision-efficient knowledge fusion framework that elicits the few-shot reasoning ability of large language models through hierarchy-oriented prompts. Empirical results on the collected KG-Hi-BKF benchmark datasets demonstrate the effectiveness of HiPrompt.|综合生物医学知识库可以增强医学决策过程，它需要通过统一的指标体系融合不同来源的知识图。索引系统通常将生物医学术语组织在一个层次结构中，以提供细粒度的对齐实体。为了解决生物医学知识融合(BKF)任务中缺乏监督的问题，研究人员提出了各种无监督方法。然而，这些方法严重依赖于特定的词法和结构匹配算法，无法捕获生物医学实体和术语所传达的丰富语义。最近，神经嵌入模型被证明在语义丰富的任务中是有效的，但是它们依赖于足够的标记数据来进行充分的训练。为了弥合稀缺标记 BKF 模型和神经嵌入模型之间的差距，我们提出了 HiPrompt，一个监督高效的知识融合框架，通过面向层次的提示来激发大型语言模型的少镜头推理能力。在 KG-Hi-BKF 基准数据集上的实证结果证明了 HiPrompt 的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=HiPrompt:+Few-Shot+Biomedical+Knowledge+Fusion+via+Hierarchy-Oriented+Prompting)|0| +|[How Significant Attributes are in the Community Detection of Attributed Multiplex Networks](https://doi.org/10.1145/3539618.3591998)|Junwei Cheng, Chaobo He, Kunlin Han, Wenjie Ma, Yong Tang|University of Southern California, Los Angeles, CA, USA; South China Normal University, Guangzhou, China; China Mobile Group Zhejiang Company Limited, Ningbo, China|Existing community detection methods for attributed multiplex networks focus on exploiting the complementary information from different topologies, while they are paying little attention to the role of attributes. However, we observe that real attributed multiplex networks exhibit two unique features, namely, consistency and homogeneity of node attributes. Therefore, in this paper, we propose a novel method, called ACDM, which is based on these two characteristics of attributes, to detect communities on attributed multiplex networks. Specifically, we extract commonality representation of nodes through the consistency of attributes. The collaboration between the homogeneity of attributes and topology information reveals the particularity representation of nodes. The comprehensive experimental results on real attributed multiplex networks well validate that our method outperforms state-of-the-art methods in most networks.|现有的属性复用网络社区检测方法主要是利用不同拓扑的互补信息，而对属性的作用关注不够。然而，我们观察到实际的属性化多路复用网络表现出两个独特的特征，即节点属性的一致性和同质性。因此，本文提出了一种基于属性的两个特征的 ACDM 方法来检测属性复用网络上的社区。具体来说，我们通过属性的一致性来提取节点的公共性表示。属性的同质性和拓扑信息的协同作用揭示了节点的特殊性表示。在实际属性化多路复用网络上的综合实验结果验证了该方法在大多数网络中的性能优于现有的方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=How+Significant+Attributes+are+in+the+Community+Detection+of+Attributed+Multiplex+Networks)|0| +|[HyperFormer: Learning Expressive Sparse Feature Representations via Hypergraph Transformer](https://doi.org/10.1145/3539618.3591999)|Kaize Ding, Albert Jiongqian Liang, Bryan Perozzi, Ting Chen, Ruoxi Wang, Lichan Hong, Ed H. Chi, Huan Liu, Derek Zhiyuan Cheng|Google, Mountain View, USA; Google, Tempe, USA|Learning expressive representations for high-dimensional yet sparse features has been a longstanding problem in information retrieval. Though recent deep learning methods can partially solve the problem, they often fail to handle the numerous sparse features, particularly those tail feature values with infrequent occurrences in the training data. Worse still, existing methods cannot explicitly leverage the correlations among different instances to help further improve the representation learning on sparse features since such relational prior knowledge is not provided. To address these challenges, in this paper, we tackle the problem of representation learning on feature-sparse data from a graph learning perspective. Specifically, we propose to model the sparse features of different instances using hypergraphs where each node represents a data instance and each hyperedge denotes a distinct feature value. By passing messages on the constructed hypergraphs based on our Hypergraph Transformer (HyperFormer), the learned feature representations capture not only the correlations among different instances but also the correlations among features. Our experiments demonstrate that the proposed approach can effectively improve feature representation learning on sparse features.|长期以来，学习高维稀疏特征的表达方式一直是信息检索领域的一个难题。目前的深度学习方法虽然能够部分解决这一问题，但往往无法处理大量稀疏特征，尤其是训练数据中不常出现的尾部特征值。更糟糕的是，现有的方法不能明确地利用不同实例之间的相关性来帮助进一步改善稀疏特征的表示学习，因为这样的关系先验知识是没有提供的。为了解决这些问题，本文从图学习的角度研究了特征稀疏数据的表示学习问题。具体来说，我们建议使用超图对不同实例的稀疏特征进行建模，其中每个节点代表一个数据实例，每个超边表示一个不同的特征值。通过在基于超图变换的构造的超图上传递消息，学习的特征表示不仅能够捕获不同实例之间的相关性，而且能够捕获特征之间的相关性。实验结果表明，该方法能够有效地改善稀疏特征的特征表示学习。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=HyperFormer:+Learning+Expressive+Sparse+Feature+Representations+via+Hypergraph+Transformer)|0| |[Best Prompts for Text-to-Image Models and How to Find Them](https://doi.org/10.1145/3539618.3592000)|Nikita Pavlichenko, Dmitry Ustalov||Recent progress in generative models, especially in text-guided diffusion models, has enabled the production of aesthetically-pleasing imagery resembling the works of professional human artists. However, one has to carefully compose the textual description, called the prompt, and augment it with a set of clarifying keywords. Since aesthetics are challenging to evaluate computationally, human feedback is needed to determine the optimal prompt formulation and keyword combination. In this paper, we present a human-in-the-loop approach to learning the most useful combination of prompt keywords using a genetic algorithm. We also show how such an approach can improve the aesthetic appeal of images depicting the same descriptions.|最近在生成模型，特别是在文本引导的扩散模型的进展，已经能够生产美观的形象类似于专业人类艺术家的作品。但是，必须仔细编写文本描述(称为提示符) ，并使用一组澄清关键字对其进行扩充。由于美学是具有挑战性的评价计算，人类反馈需要确定最佳的及时公式和关键字组合。在本文中，我们提出了一个人在循环的方法来学习最有用的组合提示关键字使用遗传算法。我们还展示了这种方法如何能够提高描绘相同描述的图像的审美吸引力。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Best+Prompts+for+Text-to-Image+Models+and+How+to+Find+Them)|0| -|[LAPCA: Language-Agnostic Pretraining with Cross-Lingual Alignment](https://doi.org/10.1145/3539618.3592006)|Dmitry Abulkhanov, Nikita Sorokin, Sergey Nikolenko, Valentin Malykh|Ivannikov Institute for System Programming of the RAS & Steklov Institute of Mathematics of the RAS, Moscow, Russian Fed.; Huawei Noah's Ark Lab, Moscow, Russian Fed.|Data collection and mining is a crucial bottleneck for cross-lingual information retrieval (CLIR). While previous works used machine translation and iterative training, we present a novel approach to cross-lingual pretraining called LAPCA (language-agnostic pretraining with cross-lingual alignment). We train the LAPCA-LM model based on XLM-RoBERTa and łexa that significantly improves cross-lingual knowledge transfer for question answering and sentence retrieval on, e.g., XOR-TyDi and Mr. TyDi datasets, and in the zero-shot cross-lingual scenario performs on par with supervised methods, outperforming many of them on MKQA.|数据收集和挖掘是跨语言信息检索(CLIR)的一个关键瓶颈。虽然以前的工作使用机器翻译和迭代训练，我们提出了一种新的方法来跨语言预训练称为 LAPCA (语言无关预训练跨语言对齐)。我们训练了基于 XLM-RoBERTa 和 exa 的 LAPCA-LM 模型，该模型显着改善了问题回答和句子检索的跨语言知识转移，例如 XOR-TyDi 和 TyDi 先生数据集，并且在零击跨语言场景中表现与监督方法相当，在 MKQA 上优于其中许多方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=LAPCA:+Language-Agnostic+Pretraining+with+Cross-Lingual+Alignment)|0| +|[LAPCA: Language-Agnostic Pretraining with Cross-Lingual Alignment](https://doi.org/10.1145/3539618.3592006)|Dmitry Abulkhanov, Nikita Sorokin, Sergey Nikolenko, Valentin Malykh|Huawei Noah's Ark Lab, Moscow, Russian Fed.; Ivannikov Institute for System Programming of the RAS & Steklov Institute of Mathematics of the RAS, Moscow, Russian Fed.|Data collection and mining is a crucial bottleneck for cross-lingual information retrieval (CLIR). While previous works used machine translation and iterative training, we present a novel approach to cross-lingual pretraining called LAPCA (language-agnostic pretraining with cross-lingual alignment). We train the LAPCA-LM model based on XLM-RoBERTa and łexa that significantly improves cross-lingual knowledge transfer for question answering and sentence retrieval on, e.g., XOR-TyDi and Mr. TyDi datasets, and in the zero-shot cross-lingual scenario performs on par with supervised methods, outperforming many of them on MKQA.|数据收集和挖掘是跨语言信息检索(CLIR)的一个关键瓶颈。虽然以前的工作使用机器翻译和迭代训练，我们提出了一种新的方法来跨语言预训练称为 LAPCA (语言无关预训练跨语言对齐)。我们训练了基于 XLM-RoBERTa 和 exa 的 LAPCA-LM 模型，该模型显着改善了问题回答和句子检索的跨语言知识转移，例如 XOR-TyDi 和 TyDi 先生数据集，并且在零击跨语言场景中表现与监督方法相当，在 MKQA 上优于其中许多方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=LAPCA:+Language-Agnostic+Pretraining+with+Cross-Lingual+Alignment)|0| |[Learning from Crowds with Annotation Reliability](https://doi.org/10.1145/3539618.3592007)|Zhi Cao, Enhong Chen, Ye Huang, Shuanghong Shen, Zhenya Huang|Anhui Province Key Laboratory of Big Data Analysis and Application, School of Computer Science and Technology, University of Science and Technology of China & State Key Laboratory of Cognitive Intelligence, Hefei, China|Crowdsourcing provides a practical approach for obtaining annotated data to train supervised learning models. However, since the crowd annotators may have different expertise domain and cannot always guarantee the high-quality annotations, learning from crowds generally suffers from the problem of unreliable results of introducing some noises, which makes it hard to achieve satisfying performance. In this work, we investigate the reliability of annotations to improve learning from crowds. Specifically, we first project annotator and data instance to factor vectors and model the complex interaction between annotator expertise and instance difficulty to predict annotation reliability. The learned reliability can be used to evaluate the quality of crowdsourced data directly. Then, we construct a new annotation, namely soft annotation, which serves as the gold label during the training. To recognize the different strengths of annotators, we model each annotator's confusion in an end-to-end manner. Extensive experimental results on three real-world datasets demonstrate the effectiveness of our method.|众包为获取注释数据以训练监督式学习模型提供了一种实用的方法。然而，由于人群注释者的专业领域不同，并不能保证注释的质量，因此向人群学习通常存在引入一些噪声的结果不可靠的问题，难以达到令人满意的效果。在这项工作中，我们调查注释的可靠性，以改善从群体学习。具体来说，我们首先将注释者和数据实例投影到因子向量中，建立注释者专业知识和实例难度之间的复杂交互模型来预测注释的可靠性。学习可靠性可以直接用来评价众包数据的质量。然后，我们构建了一个新的注释，即软注释，作为培训过程中的金标签。为了识别注释者的不同优势，我们以端到端的方式对每个注释者的混淆进行建模。在三个实际数据集上的大量实验结果证明了该方法的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Learning+from+Crowds+with+Annotation+Reliability)|0| -|[Learning Through Interpolative Augmentation of Dynamic Curvature Spaces](https://doi.org/10.1145/3539618.3592008)|Parth Chhabra, Atula Tejaswi Neerkaje, Shivam Agarwal, Ramit Sawhney, Megh Thakkar, Preslav Nakov, Sudheer Chava|Manipal Institute of Technology, Manipal, India; Georgia Institute of Technology, Atlanta, GA, USA; BITS, Pilani, Pilani, India; Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, UAE; Indraprastha Institute of Information Technology, Delhi, Delhi, India; University of Illinois at Urbana-Champaign, Urbana, IL, USA|Mixup is an efficient data augmentation technique, which improves generalization by interpolating random examples. While numerous approaches have been developed for Mixup in the Euclidean and in the hyperbolic space, they do not fully use the intrinsic properties of the examples, i.e., they manually set the geometry (Euclidean or hyperbolic) based on the overall dataset, which may be sub-optimal since each example may require a different geometry. We propose DynaMix, a framework that automatically selects an example-specific geometry and performs Mixup between the different geometries to improve training dynamics and generalization. Through extensive experiments in image and text modalities we show that DynaMix outperforms state-of-the-art methods over six downstream applications. We find that DynaMix is more useful in low-resource and semi-supervised settings likely because it displays a probabilistic view of the geometry.|混合是一种有效的数据增强技术，它通过插值随机样本来提高泛化能力。虽然在欧几里得和双曲空间中已经开发了很多种方法用于 Mixup，但它们并没有充分利用示例的内在属性，即，它们基于整个数据集手动设置几何(欧几里得或双曲线) ，这可能是次优的，因为每个示例可能需要不同的几何。我们提出了 DynaMix，这是一个自动选择特定于示例的几何图形并在不同几何图形之间执行 Mixup 的框架，以改善训练动态性和泛化性。通过在图像和文本模式中的大量实验，我们显示 DynaMix 在六个下游应用程序中优于最先进的方法。我们发现 DynaMix 在低资源和半监督设置中更有用，因为它显示了几何的概率视图。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Learning+Through+Interpolative+Augmentation+of+Dynamic+Curvature+Spaces)|0| +|[Learning Through Interpolative Augmentation of Dynamic Curvature Spaces](https://doi.org/10.1145/3539618.3592008)|Parth Chhabra, Atula Tejaswi Neerkaje, Shivam Agarwal, Ramit Sawhney, Megh Thakkar, Preslav Nakov, Sudheer Chava|Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, UAE; University of Illinois at Urbana-Champaign, Urbana, IL, USA; Indraprastha Institute of Information Technology, Delhi, Delhi, India; BITS, Pilani, Pilani, India; Manipal Institute of Technology, Manipal, India; Georgia Institute of Technology, Atlanta, GA, USA|Mixup is an efficient data augmentation technique, which improves generalization by interpolating random examples. While numerous approaches have been developed for Mixup in the Euclidean and in the hyperbolic space, they do not fully use the intrinsic properties of the examples, i.e., they manually set the geometry (Euclidean or hyperbolic) based on the overall dataset, which may be sub-optimal since each example may require a different geometry. We propose DynaMix, a framework that automatically selects an example-specific geometry and performs Mixup between the different geometries to improve training dynamics and generalization. Through extensive experiments in image and text modalities we show that DynaMix outperforms state-of-the-art methods over six downstream applications. We find that DynaMix is more useful in low-resource and semi-supervised settings likely because it displays a probabilistic view of the geometry.|混合是一种有效的数据增强技术，它通过插值随机样本来提高泛化能力。虽然在欧几里得和双曲空间中已经开发了很多种方法用于 Mixup，但它们并没有充分利用示例的内在属性，即，它们基于整个数据集手动设置几何(欧几里得或双曲线) ，这可能是次优的，因为每个示例可能需要不同的几何。我们提出了 DynaMix，这是一个自动选择特定于示例的几何图形并在不同几何图形之间执行 Mixup 的框架，以改善训练动态性和泛化性。通过在图像和文本模式中的大量实验，我们显示 DynaMix 在六个下游应用程序中优于最先进的方法。我们发现 DynaMix 在低资源和半监督设置中更有用，因为它显示了几何的概率视图。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Learning+Through+Interpolative+Augmentation+of+Dynamic+Curvature+Spaces)|0| |[Learning to Ask Clarification Questions with Spatial Reasoning](https://doi.org/10.1145/3539618.3592009)|Yang Deng, Shuaiyi Li, Wai Lam|The Chinese University of Hong Kong, Hong Kong, Hong Kong|Asking clarifying questions has become a key element of various conversational systems, allowing for an effective resolution of ambiguity and uncertainty through natural language questions. Despite the extensive applications of spatial information grounded dialogues, it remains an understudied area on learning to ask clarification questions with the capability of spatial reasoning. In this work, we propose a novel method, named SpatialCQ, for this problem. Specifically, we first align the representation space between textual and spatial information by encoding spatial states with textual descriptions. Then a multi-relational graph is constructed to capture the spatial relations and enable spatial reasoning with relational graph attention networks. Finally, a unified encoder is adopted to fuse the multimodal information for asking clarification questions. Experimental results on the latest IGLU dataset show the superiority of the proposed method over existing approaches.|提出明确的问题已经成为各种会话系统的一个关键因素，允许通过自然语言问题有效地解决歧义和不确定性。尽管基于空间信息的对话得到了广泛的应用，但是在学习提出具有空间推理能力的澄清问题方面，它仍然是一个尚未被研究的领域。在这项工作中，我们提出了一个新的方法，称为空间 CQ，为这个问题。具体来说，我们首先通过文本描述对空间状态进行编码，从而在文本信息和空间信息之间对齐表示空间。然后构造一个多关系图来捕获空间关系，并利用关系图注意网络进行空间推理。最后，采用统一的编码器对多模态信息进行融合，提出澄清问题。在最新的 IGLU 数据集上的实验结果表明了该方法相对于现有方法的优越性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Learning+to+Ask+Clarification+Questions+with+Spatial+Reasoning)|0| |[Learning to Ask Questions for Zero-shot Dialogue State Tracking](https://doi.org/10.1145/3539618.3592010)|Diogo Tavares, David Semedo, Alexander Rudnicky, João Magalhães|Carnegie Mellon University, Pittsburgh, PA, USA; NOVA University of Lisbon, Lisbon, Portugal|We present a method for performing zero-shot Dialogue State Tracking (DST) by casting the task as a learning-to-ask-questions framework. The framework learns to pair the best question generation (QG) strategy with in-domain question answering (QA) methods to extract slot values from a dialogue without any human intervention. A novel self-supervised QA pretraining step using in-domain data is essential to learn the structure without requiring any slot-filling annotations. Moreover, we show that QG methods need to be aligned with the same grammatical person used in the dialogue. Empirical evaluation on the MultiWOZ 2.1 dataset demonstrates that our approach, when used alongside robust QA models, outperforms existing zero-shot methods in the challenging task of zero-shot cross domain adaptation-given a comparable amount of domain knowledge during data creation. Finally, we analyze the impact of the types of questions used, and demonstrate that the algorithmic approach outperforms template-based question generation.|提出了一种将任务作为学习-提问框架进行对话状态跟踪(DST)的方法。该框架学习将最佳问题生成(QG)策略与领域内问题回答(QA)方法结合起来，在不需要任何人工干预的情况下从对话中提取插槽值。一个新的自监督 QA 预训练步骤使用域内数据是必不可少的结构学习不需要任何插槽填充注释。此外，我们表明 QG 方法需要与对话中使用的相同人称保持一致。对 MultiWOZ 2.1数据集的实证评估表明，我们的方法，当与健壮的 QA 模型一起使用时，在零拍跨域适应的挑战性任务中优于现有的零拍方法-在数据创建期间给定相当数量的领域知识。最后，我们分析了所使用问题类型的影响，并证明了算法的方法优于基于模板的问题生成。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Learning+to+Ask+Questions+for+Zero-shot+Dialogue+State+Tracking)|0| |[Look Ahead: Improving the Accuracy of Time-Series Forecasting by Previewing Future Time Features](https://doi.org/10.1145/3539618.3592013)|Seonmin Kim, DongKyu Chae|Hanyang University, Seoul, Republic of Korea|Time-series forecasting has been actively studied and adopted in various real-world domains. Recently there have been two research mainstreams in this area: building Transformer-based architectures such as Informer, Autoformer and Reformer, and developing time-series representation learning frameworks based on contrastive learning such as TS2Vec and CoST. Both efforts have greatly improved the performance of time series forecasting. In this paper, we investigate a novel direction towards improving the forecasting performance even more, which is orthogonal to the aforementioned mainstreams as a model-agnostic scheme. We focus on time stamp embeddings that has been less-focused in the literature. Our idea is simple-yet-effective: based on given current time stamp, we predict embeddings of its near future time stamp and utilize the predicted embeddings in the time-series (value) forecasting task. We believe that if such future time information can be previewed at the time of prediction, they can be utilized by any time-series forecasting models as useful additional information. Our experimental results confirmed that our method consistently and significantly improves the accuracy of the recent Transformer-based models and time-series representation learning frameworks. Our code is available at: https://github.com/sunsunmin/Look_Ahead|时间序列预测在现实世界的各个领域都得到了积极的研究和应用。目前该领域的研究主流主要有两种: 一种是构建基于变压器的体系结构，如 Informer、 Autoformer 和 Reformer; 另一种是开发基于对比学习的时间序列表示学习框架，如 TS2Vec 和 CoST。这两种方法都大大提高了时间序列预测的性能。在本文中，我们研究了一个新的方向来提高预测性能，这是正交的上述主流作为一个模型不可知方案。我们关注的是时间戳嵌入，这在文献中已经很少被关注。我们的思想是简单而有效的: 在给定的当前时间戳的基础上，我们预测其近期时间戳的嵌入，并利用预测嵌入的时间序列(值)预测任务。我们相信，如果这种未来时间信息可以在预测时预测，它们可以被任何时间序列预测模型作为有用的附加信息加以利用。我们的实验结果证实，我们的方法一致和显著地提高了最近的变压器为基础的模型和时间序列表示学习框架的准确性。我们的代码可以在以下 https://github.com/sunsunmin/look_ahead 找到|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Look+Ahead:+Improving+the+Accuracy+of+Time-Series+Forecasting+by+Previewing+Future+Time+Features)|0| -|[MA-MRC: A Multi-answer Machine Reading Comprehension Dataset](https://doi.org/10.1145/3539618.3592015)|Zhiang Yue, Jingping Liu, Cong Zhang, Chao Wang, Haiyun Jiang, Yue Zhang, Xianyang Tian, Zhedong Cen, Yanghua Xiao, Tong Ruan|East China University of Science and Technology, Shanghai, China; Shanghai University, Shanghai, China; Fudan University, Shanghai, China; AECC Sichuan Gas Turbine Establishment, Mianyang City, China; Tencent AI Lab, Shenzhen, China|Machine reading comprehension (MRC) is an essential task for many question-answering applications. However, existing MRC datasets mainly focus on data with single answer and overlook multiple answers, which are common in the real world. In this paper, we aim to construct an MRC dataset with both data of single answer and multiple answers. To achieve this purpose, we design a novel pipeline method: data collection, data cleaning, question generation and test set annotation. Based on these procedures, we construct a high-quality multi-answer MRC dataset (MA-MRC) with 129K question-answer-context samples. We implement a sequence of baselines and carry out extensive experiments on MA-MRC. According to the experimental results, MA-MRC is a challenging dataset, which can facilitate the future research on the multi-answer MRC task.|机器阅读理解(MRC)是许多问答应用程序的基本任务。然而，现有的 MRC 数据集主要集中在单答案数据上，忽略了现实世界中常见的多答案数据。本文旨在构建一个同时包含单个答案和多个答案的 MRC 数据集。为此，我们设计了一种新的流水线方法: 数据采集、数据清理、问题生成和测试集注释。在此基础上，我们构建了一个129K 问答背景样本的高质量多答案 MRC 数据集(MA-MRC)。我们实现了一系列的基线，并在 MA-MRC 上进行了广泛的实验。实验结果表明，MA-MRC 是一个具有挑战性的数据集，有利于多答案 MRC 任务的进一步研究。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=MA-MRC:+A+Multi-answer+Machine+Reading+Comprehension+Dataset)|0| -|[Multiple Topics Community Detection in Attributed Networks](https://doi.org/10.1145/3539618.3592026)|Chaobo He, Junwei Cheng, Guohua Chen, Yong Tang|South China Normal University, Guangzhou, China; South China Normal University & Pazhou Lab, Guangzhou, China|Since existing methods are often not effective to detect communities with multiple topics in attributed networks, we propose a method named SSAGCN via Autoencoder-style self-supervised learning. SSAGCN firstly designs an adaptive graph convolutional network (AGCN), which is treated as the encoder for fusing topology information and attribute information automatically, and then utilizes a dual decoder to simultaneously reconstruct network topology and attributes. By further introducing the modularity maximization and the joint optimization strategies, SSAGCN can detect communities with multiple topics in an end-to-end manner. Experimental results show that SSAGCN outperforms state-of-the-art approaches, and also can be used to conduct topic analysis well.|由于现有的方法往往不能有效地检测属性网络中具有多个主题的社区，我们提出了一种基于自动编码的自监督学习方法 SSAGCN。SSAGCN 首先设计一个自适应图卷积网络(AGCN) ，将其作为自动融合拓扑信息和属性信息的编码器，然后利用一个双解码器同时重构网络拓扑和属性。通过进一步引入模块化最大化和联合优化策略，SSAGCN 能够以端到端的方式检测具有多个主题的社区。实验结果表明，SSAGCN 方法的性能优于目前最先进的方法，并且可以很好地用于主题分析。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Multiple+Topics+Community+Detection+in+Attributed+Networks)|0| -|[NC2T: Novel Curriculum Learning Approaches for Cross-Prompt Trait Scoring](https://doi.org/10.1145/3539618.3592027)|Yejin Lee, Seokwon Jeong, Hongjin Kim, Taeil Kim, SungWon Choi, Harksoo Kim|Kangwon National University, Gangwon-do, Republic of Korea; Naver, Gyeonggi-do, Republic of Korea; Konkuk University, Seoul, Republic of Korea; Konkuk University, Seoul, South Korea|Automated essay scoring (AES) is a crucial research area with potential applications in education and beyond. However, recent studies have primarily focused on AES models that evaluate essays within a specific domain or using a holistic score, leaving a gap in research and resources for more generalized models capable of assessing essays with detailed items from multiple perspectives. As evaluating and scoring essays based on complex traits is costly and time-consuming, datasets for such AES evaluations are limited. To address these issues, we developed a cross-prompt trait scoring AES model and proposed a suitable curriculum learning (CL) design. By devising difficulty scores and introducing the key curriculum method, we demonstrated its effectiveness compared to existing CL strategies in natural language understanding tasks.|自动论文评分(AES)是一个重要的研究领域，在教育和其他方面有潜在的应用。然而，最近的研究主要集中在 AES 模型评估论文在一个特定的领域或使用一个整体评分，留下了研究和资源的差距，更广泛的模型能够评估论文的详细项目从多个角度。由于基于复杂性状的论文评估和评分是昂贵的和耗时的，用于这种 AES 评估的数据集是有限的。为了解决这些问题，我们开发了一个交叉提示特质评分 AES 模型，并提出了一个合适的课程学习(CL)设计。在自然语言理解任务中，通过设计难度分数和引入关键课程方法，验证了该策略与现有的 CL 策略相比的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=NC2T:+Novel+Curriculum+Learning+Approaches+for+Cross-Prompt+Trait+Scoring)|0| +|[MA-MRC: A Multi-answer Machine Reading Comprehension Dataset](https://doi.org/10.1145/3539618.3592015)|Zhiang Yue, Jingping Liu, Cong Zhang, Chao Wang, Haiyun Jiang, Yue Zhang, Xianyang Tian, Zhedong Cen, Yanghua Xiao, Tong Ruan|Shanghai University, Shanghai, China; East China University of Science and Technology, Shanghai, China; Tencent AI Lab, Shenzhen, China; Fudan University, Shanghai, China; AECC Sichuan Gas Turbine Establishment, Mianyang City, China|Machine reading comprehension (MRC) is an essential task for many question-answering applications. However, existing MRC datasets mainly focus on data with single answer and overlook multiple answers, which are common in the real world. In this paper, we aim to construct an MRC dataset with both data of single answer and multiple answers. To achieve this purpose, we design a novel pipeline method: data collection, data cleaning, question generation and test set annotation. Based on these procedures, we construct a high-quality multi-answer MRC dataset (MA-MRC) with 129K question-answer-context samples. We implement a sequence of baselines and carry out extensive experiments on MA-MRC. According to the experimental results, MA-MRC is a challenging dataset, which can facilitate the future research on the multi-answer MRC task.|机器阅读理解(MRC)是许多问答应用程序的基本任务。然而，现有的 MRC 数据集主要集中在单答案数据上，忽略了现实世界中常见的多答案数据。本文旨在构建一个同时包含单个答案和多个答案的 MRC 数据集。为此，我们设计了一种新的流水线方法: 数据采集、数据清理、问题生成和测试集注释。在此基础上，我们构建了一个129K 问答背景样本的高质量多答案 MRC 数据集(MA-MRC)。我们实现了一系列的基线，并在 MA-MRC 上进行了广泛的实验。实验结果表明，MA-MRC 是一个具有挑战性的数据集，有利于多答案 MRC 任务的进一步研究。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=MA-MRC:+A+Multi-answer+Machine+Reading+Comprehension+Dataset)|0| +|[Multiple Topics Community Detection in Attributed Networks](https://doi.org/10.1145/3539618.3592026)|Chaobo He, Junwei Cheng, Guohua Chen, Yong Tang|South China Normal University & Pazhou Lab, Guangzhou, China; South China Normal University, Guangzhou, China|Since existing methods are often not effective to detect communities with multiple topics in attributed networks, we propose a method named SSAGCN via Autoencoder-style self-supervised learning. SSAGCN firstly designs an adaptive graph convolutional network (AGCN), which is treated as the encoder for fusing topology information and attribute information automatically, and then utilizes a dual decoder to simultaneously reconstruct network topology and attributes. By further introducing the modularity maximization and the joint optimization strategies, SSAGCN can detect communities with multiple topics in an end-to-end manner. Experimental results show that SSAGCN outperforms state-of-the-art approaches, and also can be used to conduct topic analysis well.|由于现有的方法往往不能有效地检测属性网络中具有多个主题的社区，我们提出了一种基于自动编码的自监督学习方法 SSAGCN。SSAGCN 首先设计一个自适应图卷积网络(AGCN) ，将其作为自动融合拓扑信息和属性信息的编码器，然后利用一个双解码器同时重构网络拓扑和属性。通过进一步引入模块化最大化和联合优化策略，SSAGCN 能够以端到端的方式检测具有多个主题的社区。实验结果表明，SSAGCN 方法的性能优于目前最先进的方法，并且可以很好地用于主题分析。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Multiple+Topics+Community+Detection+in+Attributed+Networks)|0| +|[NC2T: Novel Curriculum Learning Approaches for Cross-Prompt Trait Scoring](https://doi.org/10.1145/3539618.3592027)|Yejin Lee, Seokwon Jeong, Hongjin Kim, Taeil Kim, SungWon Choi, Harksoo Kim|Naver, Gyeonggi-do, Republic of Korea; Kangwon National University, Gangwon-do, Republic of Korea; Konkuk University, Seoul, South Korea; Konkuk University, Seoul, Republic of Korea|Automated essay scoring (AES) is a crucial research area with potential applications in education and beyond. However, recent studies have primarily focused on AES models that evaluate essays within a specific domain or using a holistic score, leaving a gap in research and resources for more generalized models capable of assessing essays with detailed items from multiple perspectives. As evaluating and scoring essays based on complex traits is costly and time-consuming, datasets for such AES evaluations are limited. To address these issues, we developed a cross-prompt trait scoring AES model and proposed a suitable curriculum learning (CL) design. By devising difficulty scores and introducing the key curriculum method, we demonstrated its effectiveness compared to existing CL strategies in natural language understanding tasks.|自动论文评分(AES)是一个重要的研究领域，在教育和其他方面有潜在的应用。然而，最近的研究主要集中在 AES 模型评估论文在一个特定的领域或使用一个整体评分，留下了研究和资源的差距，更广泛的模型能够评估论文的详细项目从多个角度。由于基于复杂性状的论文评估和评分是昂贵的和耗时的，用于这种 AES 评估的数据集是有限的。为了解决这些问题，我们开发了一个交叉提示特质评分 AES 模型，并提出了一个合适的课程学习(CL)设计。在自然语言理解任务中，通过设计难度分数和引入关键课程方法，验证了该策略与现有的 CL 策略相比的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=NC2T:+Novel+Curriculum+Learning+Approaches+for+Cross-Prompt+Trait+Scoring)|0| |[On the Impact of Data Quality on Image Classification Fairness](https://doi.org/10.1145/3539618.3592031)|Aki Barry, Lei Han, Gianluca Demartini|The University of Queensland, Brisbane, QLD, Australia|With the proliferation of algorithmic decision-making, increased scrutiny has been placed on these systems. This paper explores the relationship between the quality of the training data and the overall fairness of the models trained with such data in the context of supervised classification. We measure key fairness metrics across a range of algorithms over multiple image classification datasets that have a varying level of noise in both the labels and the training data itself. We describe noise in the labels as inaccuracies in the labelling of the data in the training set and noise in the data as distortions in the data, also in the training set. By adding noise to the original datasets, we can explore the relationship between the quality of the training data and the fairness of the output of the models trained on that data.|随着算法决策的激增，对这些系统的审查也越来越多。本文探讨了在有监督分类的情况下，训练数据的质量与用这些数据训练的模型的总体公平性之间的关系。我们在多个图像分类数据集上测量一系列算法的关键公平性指标，这些图像分类数据在标签和训练数据本身中都有不同程度的噪声。我们将标签中的噪声描述为训练集中数据标注的不准确性，将数据中的噪声描述为数据中的畸变，同时也将数据中的噪声描述为训练集中的畸变。通过在原始数据集中添加噪声，我们可以探索训练数据的质量与模型输出的公平性之间的关系。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=On+the+Impact+of+Data+Quality+on+Image+Classification+Fairness)|0| |[Power Norm Based Lifelong Learning for Paraphrase Generations](https://doi.org/10.1145/3539618.3592039)|Dingcheng Li, Peng Yang, Yue Zhang, Ping Li|Baidu Research, Sammamish, WA, USA; Baidu Research, Bellevue, WA, USA|Lifelong seq2seq language generation models are trained with multiple domains in a lifelong learning manner, with data from each domain being observed in an online fashion. It is a well-known problem that lifelong learning suffers from the catastrophic forgetting (CF). To handle this challenge, existing works have leveraged experience replay or dynamic architecture to consolidate the past knowledge, which however result in incremental memory space or high computational cost. In this work, we propose a novel framework name "power norm based lifelong learning" (PNLLL), which aims to remedy the catastrophic forgetting issues with a power normalization on NLP transformer models. Specifically, PNLLL leverages power norm to achieve a better balance between past experience rehearsal and new knowledge acquisition. These designs enable the knowledge adaptation onto new tasks while memorizing the experience of past tasks. Our experiments on paraphrase generation tasks show that PNLLL not only outperforms SOTA models by a considerable margin and but also largely alleviates forgetting.|终身 seq2seq 语言生成模型以终身学习的方式对多个域进行训练，每个域的数据以在线方式进行观察。众所周知，终身学习患有灾难性遗忘症(CF)。为了应对这一挑战，现有的工作已经利用经验重放或动态架构来巩固过去的知识，但这导致增加的内存空间或高计算成本。在这项工作中，我们提出了一个新的框架名称“基于功率规范的终身学习”(PNLL) ，旨在补救灾难性遗忘问题的功率规范化的自然语言处理变压器模型。具体来说，PNLL 利用权力规范，在过去的经验复习和新的知识获取之间实现更好的平衡。这些设计使知识适应新的任务，同时记忆过去的任务经验。我们在释义生成任务上的实验表明，PNLLL 不仅比 SOTA 模型有相当大的优势，而且在很大程度上减轻了遗忘。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Power+Norm+Based+Lifelong+Learning+for+Paraphrase+Generations)|0| -|[Private Meeting Summarization Without Performance Loss](https://doi.org/10.1145/3539618.3592042)|Seolhwa Lee, Anders Søgaard|Technical University of Darmstadt, Darmstadt, Germany; University of Copenhagen, Copenhagen, Denmark|Meeting summarization has an enormous business potential, but in addition to being a hard problem, roll-out is challenged by privacy concerns. We explore the problem of meeting summarization under differential privacy constraints and find, to our surprise, that while differential privacy leads to slightly lower performance on in-sample data, differential privacy improves performance when evaluated on unseen meeting types. Since meeting summarization systems will encounter a great variety of meeting types in practical employment scenarios, this observation makes safe meeting summarization seem much more feasible. We perform extensive error analysis and identify potential risks in meeting summarization under differential privacy, including a faithfulness analysis.|会议摘要具有巨大的商业潜力，但除了成为一个难题之外，它的推出还受到隐私问题的挑战。我们研究了在差分隐私约束下会议总结的问题，令我们惊讶的是，虽然差分隐私会导致样本内数据的性能稍微下降，但是当在看不见的会议类型上进行评估时，差分隐私会提高性能。由于会议摘要系统在实际的工作场景中会遇到各种各样的会议类型，这种观察使得安全的会议摘要看起来更加可行。我们进行广泛的错误分析，并识别在差分隐私下会议摘要的潜在风险，包括忠实性分析。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Private+Meeting+Summarization+Without+Performance+Loss)|0| -|[Read it Twice: Towards Faithfully Interpretable Fact Verification by Revisiting Evidence](https://doi.org/10.1145/3539618.3592049)|Xuming Hu, Zhaochen Hong, Zhijiang Guo, Lijie Wen, Philip S. Yu|Tsinghua University, Beijing, China; University of Illinois at Chicago, Chicago, IL, USA; University of Cambridge, Cambridgeshire, England UK|Real-world fact verification task aims to verify the factuality of a claim by retrieving evidence from the source document. The quality of the retrieved evidence plays an important role in claim verification. Ideally, the retrieved evidence should be faithful (reflecting the model's decision-making process in claim verification) and plausible (convincing to humans), and can improve the accuracy of verification task. Although existing approaches leverage the similarity measure of semantic or surface form between claims and documents to retrieve evidence, they all rely on certain heuristics that prevent them from satisfying all three requirements. In light of this, we propose a fact verification model named ReRead to retrieve evidence and verify claim that: (1) Train the evidence retriever to obtain interpretable evidence (i.e., faithfulness and plausibility criteria); (2) Train the claim verifier to revisit the evidence retrieved by the optimized evidence retriever to improve the accuracy. The proposed system is able to achieve significant improvements upon best-reported models under different settings.|现实世界中的事实验证任务是通过从原始文档中检索证据来验证索赔的真实性。检索到的证据的质量在索赔核实中起着重要作用。理想情况下，检索到的证据应该是忠实的(反映模型的决策过程中的索赔验证)和合理的(令人信服) ，并可以提高验证任务的准确性。尽管现有的方法利用声明和文档之间的语义或表面形式的相似性度量来检索证据，但是它们都依赖于某些启发式方法，这些启发式方法使它们无法满足所有三个要求。针对这种情况，我们提出了一种名为 ReRead 的事实验证模型来检索证据并验证索赔: (1)训练证据检索者获得可解释的证据(即忠实性和合理性标准) ; (2)训练索赔验证者重新检索优化后的证据检索者检索的证据以提高准确性。拟议的系统能够在不同环境下对最佳报告模型作出重大改进。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Read+it+Twice:+Towards+Faithfully+Interpretable+Fact+Verification+by+Revisiting+Evidence)|0| -|[RewardTLG: Learning to Temporally Language Grounding from Flexible Reward](https://doi.org/10.1145/3539618.3592054)|Yawen Zeng, Keyu Pan, Ning Han|ByteDance AI Lab, China, China; Xiangtan University, China, China|Given a textual sentence provided by a user, the Temporal Language Grounding (TLG) task is defined as the process of finding a semantically relevant video moment or clip from an untrimmed video. In recent years, localization-based TLG methods have been explored, which adopt reinforcement learning to locate a clip from the video. However, these methods are not stable enough due to the stochastic exploration mechanism of reinforcement learning, which is sensitive to the reward. Therefore, providing a more flexible and reasonable reward has become a focus of attention for both academia and industry. Inspired by the training process of chatGPT, we innovatively adopt a vision-language pre-training (VLP) model as a reward model, which provides flexible rewards to help the localization-based TLG task converge. Specifically, a reinforcement learning-based localization module is introduced to predict the start and end timestamps in multi-modal scenarios. Thereafter, we fine-tune a reward model based on a VLP model, even introducing some human feedback, which provides a flexible reward score for the localization module. In this way, our model is able to capture subtle differences of the untrimmed video. Extensive experiments on two datasets have well verified the effectiveness of our proposed solution.|给定一个用户提供的文本句子，时态语言基础(TLG)任务被定义为从未修剪的视频中寻找语义相关的视频片段或剪辑的过程。近年来，人们开始探索基于本地化的 TLG 方法，即采用强化学习来定位视频片段。然而，由于强化学习的随机勘探机制对报酬非常敏感，这些方法不够稳定。因此，提供更加灵活合理的报酬成为学术界和业界关注的焦点。本文受到聊天 GPT 训练过程的启发，创新性地采用视觉语言预训练(VLP)模型作为奖励模型，提供灵活的奖励，帮助基于定位的 TLG 任务收敛。特别地，引入了一个基于强化学习的定位模块来预测多模态场景中的开始和结束时间戳。在此基础上，对基于 VLP 模型的奖励模型进行了微调，甚至引入了一些人工反馈，为定位模块提供了灵活的奖励评分。通过这种方式，我们的模型能够捕捉未修剪视频的细微差别。在两个数据集上的大量实验已经很好地验证了我们提出的解决方案的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=RewardTLG:+Learning+to+Temporally+Language+Grounding+from+Flexible+Reward)|0| -|[SelfLRE: Self-refining Representation Learning for Low-resource Relation Extraction](https://doi.org/10.1145/3539618.3592058)|Xuming Hu, Junzhe Chen, Shiao Meng, Lijie Wen, Philip S. Yu|Tsinghua University, Beijing, China; University of Illinois at Chicago, Chicago, IL, USA|Low-resource relation extraction (LRE) aims to extract potential relations from limited labeled corpus to handle the problem of scarcity of human annotations. Previous works mainly consist of two categories of methods: (1) Self-training methods, which improve themselves through the models' predictions, thus suffering from confirmation bias when the predictions are wrong. (2) Self-ensembling methods, which learn task-agnostic representations, therefore, generally do not work well for specific tasks. In our work, we propose a novel LRE architecture named SelfLRE, which leverages two complementary modules, one module uses self-training to obtain pseudo-labels for unlabeled data, and the other module uses self-ensembling learning to obtain the task-agnostic representations, and leverages the existing pseudo-labels to refine the better task-specific representations on unlabeled data. The two models are jointly trained through multi-task learning to iteratively improve the effect of LRE task. Experiments on three public datasets show that SelfLRE achieves 1.81% performance gain over the SOTA baseline. Source code is available at: https://github.com/THU-BPM/SelfLRE.|低资源关系抽取(LRE)的目的是从有限的标记语料库中提取潜在的关系，以解决人工标注稀缺的问题。以往的研究主要包括两类方法: (1)自我训练方法，它通过模型的预测来改善自身，当预测错误时就会产生确认偏差。(2)学习任务不可知表征的自组合方法对特定任务通常不起作用。在我们的工作中，我们提出了一种新的 LRE 体系结构 SelfLRE，它利用两个互补模块，一个模块使用自我训练来获得未标记数据的伪标签，另一个模块使用自我集成学习来获得任务无关的表示，并利用现有的伪标签来改进未标记数据的更好的任务特定表示。两种模型通过多任务联合训练，迭代地提高 LRE 任务的效果。在三个公共数据集上的实验表明，SelfLRE 在 SOTA 基线上获得了1.81% 的性能增益。源代码可在以下 https://github.com/thu-bpm/selflre 找到:。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=SelfLRE:+Self-refining+Representation+Learning+for+Low-resource+Relation+Extraction)|0| -|[Simple Approach for Aspect Sentiment Triplet Extraction Using Span-Based Segment Tagging and Dual Extractors](https://doi.org/10.1145/3539618.3592060)|Dongxu Li, Zhihao Yang, Yuquan Lan, Yunqi Zhang, Hui Zhao, Gang Zhao|Shanghai Key Laboratory of Trustworthy Computing, Shanghai, China; Microsoft, Beijing, China; East China Normal University, Shanghai, China|Aspect sentiment triplet extraction (ASTE) is a task which extracts aspect terms, opinion terms, and sentiment polarities as triplets from review sentences. Existing approaches have developed bidirectional structures for term interaction. Sentiment polarities are subsequently extracted from aspect-opinion pairs. These solutions suffer from: 1) high dependency on custom bidirectional structures, 2) inadequate representation of the information through existing tagging schemes, and 3) insufficient usage of all available sentiment data. To address the above issues, we propose a simple span-based solution named SimSTAR with Segment Tagging And dual extRactors. SimSTAR does not introduce any additional bidirectional mechanism. The segment tagging scheme is capable to indicate all possible cases of spans and reveals more information through negative labels. Dual extractors are employed to make the sentiment extraction independent of the term extraction. We evaluate our model on four ASTE datasets. The experimental results show that our simple method achieves state-of-the-art performance.|方面情感三联提取(ASTE)是一项从复习句中提取方面词、意见词和情感极性的任务。现有的方法已经开发了用于术语交互的双向结构。情感极性随后从方面-意见对中提取出来。这些解决方案受到以下因素的影响: 1)高度依赖定制的双向结构，2)通过现有的标记方案不能充分表示信息，3)不能充分利用所有可用的情感数据。为了解决上述问题，我们提出了一个简单的基于跨度的解决方案，名为 SimSTAR，它具有段标记和双提取器。SimSTAR 不引入任何额外的双向机制。分段标记方案能够指示所有可能的跨度情况，并通过负标记显示更多的信息。采用双抽取器使情感抽取独立于术语抽取。我们在四个 ASTE 数据集上评估我们的模型。实验结果表明，我们的简单方法达到了最先进的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Simple+Approach+for+Aspect+Sentiment+Triplet+Extraction+Using+Span-Based+Segment+Tagging+and+Dual+Extractors)|0| +|[Private Meeting Summarization Without Performance Loss](https://doi.org/10.1145/3539618.3592042)|Seolhwa Lee, Anders Søgaard|University of Copenhagen, Copenhagen, Denmark; Technical University of Darmstadt, Darmstadt, Germany|Meeting summarization has an enormous business potential, but in addition to being a hard problem, roll-out is challenged by privacy concerns. We explore the problem of meeting summarization under differential privacy constraints and find, to our surprise, that while differential privacy leads to slightly lower performance on in-sample data, differential privacy improves performance when evaluated on unseen meeting types. Since meeting summarization systems will encounter a great variety of meeting types in practical employment scenarios, this observation makes safe meeting summarization seem much more feasible. We perform extensive error analysis and identify potential risks in meeting summarization under differential privacy, including a faithfulness analysis.|会议摘要具有巨大的商业潜力，但除了成为一个难题之外，它的推出还受到隐私问题的挑战。我们研究了在差分隐私约束下会议总结的问题，令我们惊讶的是，虽然差分隐私会导致样本内数据的性能稍微下降，但是当在看不见的会议类型上进行评估时，差分隐私会提高性能。由于会议摘要系统在实际的工作场景中会遇到各种各样的会议类型，这种观察使得安全的会议摘要看起来更加可行。我们进行广泛的错误分析，并识别在差分隐私下会议摘要的潜在风险，包括忠实性分析。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Private+Meeting+Summarization+Without+Performance+Loss)|0| +|[Read it Twice: Towards Faithfully Interpretable Fact Verification by Revisiting Evidence](https://doi.org/10.1145/3539618.3592049)|Xuming Hu, Zhaochen Hong, Zhijiang Guo, Lijie Wen, Philip S. Yu|University of Illinois at Chicago, Chicago, IL, USA; Tsinghua University, Beijing, China; University of Cambridge, Cambridgeshire, England UK|Real-world fact verification task aims to verify the factuality of a claim by retrieving evidence from the source document. The quality of the retrieved evidence plays an important role in claim verification. Ideally, the retrieved evidence should be faithful (reflecting the model's decision-making process in claim verification) and plausible (convincing to humans), and can improve the accuracy of verification task. Although existing approaches leverage the similarity measure of semantic or surface form between claims and documents to retrieve evidence, they all rely on certain heuristics that prevent them from satisfying all three requirements. In light of this, we propose a fact verification model named ReRead to retrieve evidence and verify claim that: (1) Train the evidence retriever to obtain interpretable evidence (i.e., faithfulness and plausibility criteria); (2) Train the claim verifier to revisit the evidence retrieved by the optimized evidence retriever to improve the accuracy. The proposed system is able to achieve significant improvements upon best-reported models under different settings.|现实世界中的事实验证任务是通过从原始文档中检索证据来验证索赔的真实性。检索到的证据的质量在索赔核实中起着重要作用。理想情况下，检索到的证据应该是忠实的(反映模型的决策过程中的索赔验证)和合理的(令人信服) ，并可以提高验证任务的准确性。尽管现有的方法利用声明和文档之间的语义或表面形式的相似性度量来检索证据，但是它们都依赖于某些启发式方法，这些启发式方法使它们无法满足所有三个要求。针对这种情况，我们提出了一种名为 ReRead 的事实验证模型来检索证据并验证索赔: (1)训练证据检索者获得可解释的证据(即忠实性和合理性标准) ; (2)训练索赔验证者重新检索优化后的证据检索者检索的证据以提高准确性。拟议的系统能够在不同环境下对最佳报告模型作出重大改进。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Read+it+Twice:+Towards+Faithfully+Interpretable+Fact+Verification+by+Revisiting+Evidence)|0| +|[RewardTLG: Learning to Temporally Language Grounding from Flexible Reward](https://doi.org/10.1145/3539618.3592054)|Yawen Zeng, Keyu Pan, Ning Han|Xiangtan University, China, China; ByteDance AI Lab, China, China|Given a textual sentence provided by a user, the Temporal Language Grounding (TLG) task is defined as the process of finding a semantically relevant video moment or clip from an untrimmed video. In recent years, localization-based TLG methods have been explored, which adopt reinforcement learning to locate a clip from the video. However, these methods are not stable enough due to the stochastic exploration mechanism of reinforcement learning, which is sensitive to the reward. Therefore, providing a more flexible and reasonable reward has become a focus of attention for both academia and industry. Inspired by the training process of chatGPT, we innovatively adopt a vision-language pre-training (VLP) model as a reward model, which provides flexible rewards to help the localization-based TLG task converge. Specifically, a reinforcement learning-based localization module is introduced to predict the start and end timestamps in multi-modal scenarios. Thereafter, we fine-tune a reward model based on a VLP model, even introducing some human feedback, which provides a flexible reward score for the localization module. In this way, our model is able to capture subtle differences of the untrimmed video. Extensive experiments on two datasets have well verified the effectiveness of our proposed solution.|给定一个用户提供的文本句子，时态语言基础(TLG)任务被定义为从未修剪的视频中寻找语义相关的视频片段或剪辑的过程。近年来，人们开始探索基于本地化的 TLG 方法，即采用强化学习来定位视频片段。然而，由于强化学习的随机勘探机制对报酬非常敏感，这些方法不够稳定。因此，提供更加灵活合理的报酬成为学术界和业界关注的焦点。本文受到聊天 GPT 训练过程的启发，创新性地采用视觉语言预训练(VLP)模型作为奖励模型，提供灵活的奖励，帮助基于定位的 TLG 任务收敛。特别地，引入了一个基于强化学习的定位模块来预测多模态场景中的开始和结束时间戳。在此基础上，对基于 VLP 模型的奖励模型进行了微调，甚至引入了一些人工反馈，为定位模块提供了灵活的奖励评分。通过这种方式，我们的模型能够捕捉未修剪视频的细微差别。在两个数据集上的大量实验已经很好地验证了我们提出的解决方案的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=RewardTLG:+Learning+to+Temporally+Language+Grounding+from+Flexible+Reward)|0| +|[SelfLRE: Self-refining Representation Learning for Low-resource Relation Extraction](https://doi.org/10.1145/3539618.3592058)|Xuming Hu, Junzhe Chen, Shiao Meng, Lijie Wen, Philip S. Yu|University of Illinois at Chicago, Chicago, IL, USA; Tsinghua University, Beijing, China|Low-resource relation extraction (LRE) aims to extract potential relations from limited labeled corpus to handle the problem of scarcity of human annotations. Previous works mainly consist of two categories of methods: (1) Self-training methods, which improve themselves through the models' predictions, thus suffering from confirmation bias when the predictions are wrong. (2) Self-ensembling methods, which learn task-agnostic representations, therefore, generally do not work well for specific tasks. In our work, we propose a novel LRE architecture named SelfLRE, which leverages two complementary modules, one module uses self-training to obtain pseudo-labels for unlabeled data, and the other module uses self-ensembling learning to obtain the task-agnostic representations, and leverages the existing pseudo-labels to refine the better task-specific representations on unlabeled data. The two models are jointly trained through multi-task learning to iteratively improve the effect of LRE task. Experiments on three public datasets show that SelfLRE achieves 1.81% performance gain over the SOTA baseline. Source code is available at: https://github.com/THU-BPM/SelfLRE.|低资源关系抽取(LRE)的目的是从有限的标记语料库中提取潜在的关系，以解决人工标注稀缺的问题。以往的研究主要包括两类方法: (1)自我训练方法，它通过模型的预测来改善自身，当预测错误时就会产生确认偏差。(2)学习任务不可知表征的自组合方法对特定任务通常不起作用。在我们的工作中，我们提出了一种新的 LRE 体系结构 SelfLRE，它利用两个互补模块，一个模块使用自我训练来获得未标记数据的伪标签，另一个模块使用自我集成学习来获得任务无关的表示，并利用现有的伪标签来改进未标记数据的更好的任务特定表示。两种模型通过多任务联合训练，迭代地提高 LRE 任务的效果。在三个公共数据集上的实验表明，SelfLRE 在 SOTA 基线上获得了1.81% 的性能增益。源代码可在以下 https://github.com/thu-bpm/selflre 找到:。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=SelfLRE:+Self-refining+Representation+Learning+for+Low-resource+Relation+Extraction)|0| +|[Simple Approach for Aspect Sentiment Triplet Extraction Using Span-Based Segment Tagging and Dual Extractors](https://doi.org/10.1145/3539618.3592060)|Dongxu Li, Zhihao Yang, Yuquan Lan, Yunqi Zhang, Hui Zhao, Gang Zhao|Shanghai Key Laboratory of Trustworthy Computing, Shanghai, China; East China Normal University, Shanghai, China; Microsoft, Beijing, China|Aspect sentiment triplet extraction (ASTE) is a task which extracts aspect terms, opinion terms, and sentiment polarities as triplets from review sentences. Existing approaches have developed bidirectional structures for term interaction. Sentiment polarities are subsequently extracted from aspect-opinion pairs. These solutions suffer from: 1) high dependency on custom bidirectional structures, 2) inadequate representation of the information through existing tagging schemes, and 3) insufficient usage of all available sentiment data. To address the above issues, we propose a simple span-based solution named SimSTAR with Segment Tagging And dual extRactors. SimSTAR does not introduce any additional bidirectional mechanism. The segment tagging scheme is capable to indicate all possible cases of spans and reveals more information through negative labels. Dual extractors are employed to make the sentiment extraction independent of the term extraction. We evaluate our model on four ASTE datasets. The experimental results show that our simple method achieves state-of-the-art performance.|方面情感三联提取(ASTE)是一项从复习句中提取方面词、意见词和情感极性的任务。现有的方法已经开发了用于术语交互的双向结构。情感极性随后从方面-意见对中提取出来。这些解决方案受到以下因素的影响: 1)高度依赖定制的双向结构，2)通过现有的标记方案不能充分表示信息，3)不能充分利用所有可用的情感数据。为了解决上述问题，我们提出了一个简单的基于跨度的解决方案，名为 SimSTAR，它具有段标记和双提取器。SimSTAR 不引入任何额外的双向机制。分段标记方案能够指示所有可能的跨度情况，并通过负标记显示更多的信息。采用双抽取器使情感抽取独立于术语抽取。我们在四个 ASTE 数据集上评估我们的模型。实验结果表明，我们的简单方法达到了最先进的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Simple+Approach+for+Aspect+Sentiment+Triplet+Extraction+Using+Span-Based+Segment+Tagging+and+Dual+Extractors)|0| |[Surprise: Result List Truncation via Extreme Value Theory](https://doi.org/10.1145/3539618.3592066)|Dara Bahri, Che Zheng, Yi Tay, Donald Metzler, Andrew Tomkins|Google Research, Mountain View, CA, USA|Work in information retrieval has largely been centered around ranking and relevance: given a query, return some number of results ordered by relevance to the user. The problem of result list truncation, or where to truncate the ranked list of results, however, has received less attention despite being crucial in a variety of applications. Such truncation is a balancing act between the overall relevance, or usefulness of the results, with the user cost of processing more results. Result list truncation can be challenging because relevance scores are often not well-calibrated. This is particularly true in large-scale IR systems where documents and queries are embedded in the same metric space and a query's nearest document neighbors are returned during inference. Here, relevance is inversely proportional to the distance between the query and candidate document, but what distance constitutes relevance varies from query to query and changes dynamically as more documents are added to the index. In this work, we propose Surprise scoring, a statistical method that leverages the Generalized Pareto distribution that arises in extreme value theory to produce interpretable and calibrated relevance scores at query time using nothing more than the ranked scores. We demonstrate its effectiveness on the result list truncation task across image, text, and IR datasets and compare it to both classical and recent baselines. We draw connections to hypothesis testing and $p$-values.|信息检索的工作主要围绕着排名和相关性: 给出一个查询，返回一些根据用户相关性排序的结果。然而，尽管结果列表截断问题在各种应用中都是至关重要的，但是它在结果排序列表中的截断位置却没有得到足够的重视。这种截断是在结果的整体相关性或有用性与处理更多结果的用户成本之间的一种平衡行为。结果列表的截断可能是具有挑战性的，因为相关性得分往往不能很好地校准。在大规模 IR 系统中尤其如此，其中文档和查询嵌入在相同的度量空间中，并且在推断过程中返回查询的最近文档邻居。在这里，相关性与查询和候选文档之间的距离成反比，但是构成相关性的距离因查询而异，并且随着向索引添加更多文档而动态变化。在这项工作中，我们提出了惊喜得分，一种统计方法，利用极值理论中出现的广义帕累托分布，在查询时仅仅使用排名得分就可以产生可解释和校准的相关得分。我们证明了它在跨图像、文本和红外数据集的结果列表截断任务中的有效性，并将其与经典和最近的基线进行了比较。我们把假设检验和 $p $- 值联系起来。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Surprise:+Result+List+Truncation+via+Extreme+Value+Theory)|0| |[The Tale of Two MSMARCO - and Their Unfair Comparisons](https://doi.org/10.1145/3539618.3592071)|Carlos Lassance, Stéphane Clinchant|Naver Labs Europe, Meylan, France|The MS MARCO-passage dataset has been the main large-scale dataset open to the IR community and it has fostered successfully the development of novel neural retrieval models over the years. But, it turns out that two different corpora of MS MARCO are used in the literature, the official one and a second one where passages were augmented with titles, mostly due to the introduction of the Tevatron code base. However, the addition of titles actually leaks relevance information, while breaking the original guidelines of the MS MARCO-passage dataset. In this work, we investigate the differences between the two corpora and demonstrate empirically that they make a significant difference when evaluating a new method. In other words, we show that if a paper does not properly report which version is used, reproducing fairly its results is basically impossible. Furthermore, given the current status of reviewing, where monitoring state-of-the-art results is of great importance, having two different versions of a dataset is a large problem. This is why this paper aims to report the importance of this issue so that researchers can be made aware of this problem and appropriately report their results.|多年来，MS MARCO 通道数据集已成为向国际关系界开放的主要大规模数据集，并成功地促进了新型神经检索模型的发展。但是，事实证明，文献中使用了两个不同的 MS MARCO 语料库，第一个是官方语料库，第二个语料库中的段落增加了标题，这主要是由于 Tevatron 代码库的引入。然而，标题的添加实际上泄漏了相关信息，同时打破了 MS MARCO 通过数据集的原始指导方针。在这项工作中，我们调查了两个语料库之间的差异，并实证表明，它们在评价一种新的方法时有显著的差异。换句话说，我们表明，如果一篇论文没有正确地报告使用了哪个版本，公平地复制它的结果基本上是不可能的。此外，考虑到当前的审查状态(其中监视最先进的结果非常重要) ，拥有一个数据集的两个不同版本是一个大问题。这就是为什么本文旨在报告这个问题的重要性，使研究人员可以意识到这个问题，并适当地报告他们的结果。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=The+Tale+of+Two+MSMARCO+-+and+Their+Unfair+Comparisons)|0| -|[Towards Robust Knowledge Tracing Models via k-Sparse Attention](https://doi.org/10.1145/3539618.3592073)|Shuyan Huang, Zitao Liu, Xiangyu Zhao, Weiqi Luo, Jian Weng|City University of Hong Kong, Hong Kong, China; TAL Education Group, Beijing, China; Jinan University, Guangzhou, China|Knowledge tracing (KT) is the problem of predicting students' future performance based on their historical interaction sequences. With the advanced capability of capturing contextual long-term dependency, attention mechanism becomes one of the essential components in many deep learning based KT (DLKT) models. In spite of the impressive performance achieved by these attentional DLKT models, many of them are often vulnerable to run the risk of overfitting, especially on small-scale educational datasets. Therefore, in this paper, we propose sparseKT, a simple yet effective framework to improve the robustness and generalization of the attention based DLKT approaches. Specifically, we incorporate a k-selection module to only pick items with the highest attention scores. We propose two sparsification heuristics: (1) soft-thresholding sparse attention and (2) top-K sparse attention. We show that our sparseKT is able to help attentional KT models get rid of irrelevant student interactions and improve the predictive performance when compared to 11 state-of-the-art KT models on three publicly available real-world educational datasets. To encourage reproducible research, we make our data and code publicly available at https://github.com/pykt-team/pykt-toolkit1..|知识追踪(KT)是根据学生的历史交互序列预测其未来表现的问题。注意机制是深度学习 KT (DLKT)模型的重要组成部分，具有捕捉上下文长期依赖的能力。尽管这些注意力 DLKT 模型取得了令人印象深刻的性能，但其中许多模型往往容易出现过度拟合的风险，尤其是在小规模的教育数据集上。因此，在本文中，我们提出了稀疏 KT，一个简单而有效的框架，以提高基于注意的 DLKT 方法的鲁棒性和泛化能力。具体来说，我们合并了一个 k 选择模块，只选择注意力得分最高的项目。提出了两种稀疏化启发式算法: (1)软阈值稀疏注意和(2) top-K 稀疏注意。我们表明，我们的稀疏 KT 能够帮助注意 KT 模型摆脱不相关的学生互动，并提高预测性能相比，在三个公开可用的现实世界教育数据集的11个国家的最先进的 KT 模型。为了鼓励可重复的研究，我们将我们的数据和代码在 https://github.com/pykt-team/pykt-toolkit1公开。.|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Towards+Robust+Knowledge+Tracing+Models+via+k-Sparse+Attention)|0| -|[Think Rationally about What You See: Continuous Rationale Extraction for Relation Extraction](https://doi.org/10.1145/3539618.3592072)|Xuming Hu, Zhaochen Hong, Chenwei Zhang, Irwin King, Philip S. Yu|Tsinghua University, Beijing, China; University of Illinois at Chicago, Chicago, IL, USA; The Chinese University of Hong Kong, Hong Kong, Hong Kong; Amazon, Seattle, WA, USA|Relation extraction (RE) aims to extract potential relations according to the context of two entities, thus, deriving rational contexts from sentences plays an important role. Previous works either focus on how to leverage the entity information (e.g., entity types, entity verbalization) to inference relations, but ignore context-focused content, or use counterfactual thinking to remove the model's bias of potential relations in entities, but the relation reasoning process will still be hindered by irrelevant content. Therefore, how to preserve relevant content and remove noisy segments from sentences is a crucial task. In addition, retained content needs to be fluent enough to maintain semantic coherence and interpretability. In this work, we propose a novel rationale extraction framework named RE2, which leverages two continuity and sparsity factors to obtain relevant and coherent rationales from sentences. To solve the problem that the gold rationales are not labeled, RE2 applies an optimizable binary mask to each token in the sentence, and adjust the rationales that need to be selected according to the relation label. Experiments on four datasets show that RE2 surpasses baselines.|关系抽取(RE)的目的是根据两个实体的语境提取潜在的关系，因此，从句子中抽取合理的语境起着重要作用。以往的研究主要集中在如何利用实体信息(如实体类型、实体语言化)来推理关系，而忽视了关注上下文的内容，或者运用反事实思维来消除模型对实体中潜在关系的偏见，但是关系推理过程仍然会受到不相关内容的阻碍。因此，如何在句子中保留相关内容和去除噪声片段是一个非常关键的问题。此外，保留的内容需要足够流畅，以保持语义连贯性和可解释性。在这项工作中，我们提出了一个新的理论基础抽取框架，称为 RE2，它利用两个连续性和稀疏性因素来获得相关和连贯的理论基础从句子。为了解决黄金基本原理没有标记的问题，RE2对句子中的每个标记应用一个可优化的二进制掩码，并根据关系标签调整需要选择的基本原理。在四个数据集上的实验表明，RE2超过了基线。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Think+Rationally+about+What+You+See:+Continuous+Rationale+Extraction+for+Relation+Extraction)|0| -|[TrustSGCN: Learning Trustworthiness on Edge Signs for Effective Signed Graph Convolutional Networks](https://doi.org/10.1145/3539618.3592075)|MinJeong Kim, YeonChang Lee, SangWook Kim|Hanyang University, Seoul, Republic of Korea; Georgia Institute of Technology, Atlanta, USA|The problem of signed network embedding (SNE) aims to represent nodes in a given signed network as low-dimensional vectors. While several SNE methods based on graph convolutional networks (GCN) have been proposed, we point out that they significantly rely on the assumption that the decades-old balance theory always holds in the real world. To address this limitation, we propose a novel GCN-based SNE approach, named as TrustSGCN, which measures the trustworthiness on edge signs for high-order relationships inferred by balance theory and corrects incorrect embedding propagation based on the trustworthiness. The experiments on four real-world signed network datasets demonstrate that TrustSGCN consistently outperforms five state-of-the-art GCN-based SNE methods. The code is available at https://github.com/kmj0792/TrustSGCN.|有符号网络嵌入(SNE)问题旨在将给定有符号网络中的节点表示为低维向量。提出了几种基于图卷积网络(GCN)的 SNE 方法，指出它们主要依赖于几十年前的平衡理论在现实世界中仍然适用的假设。针对这一局限性，提出了一种新的基于 GCN 的 SNE 方法 TrustSGCN，该方法通过平衡理论对高阶关系的边缘符号进行可信度度量，并基于可信度校正不正确的嵌入传播。在四个真实世界签名网络数据集上的实验表明，TrustSGCN 始终优于基于 GCN 的五种最新的 SNE 方法。密码可在 https://github.com/kmj0792/trustsgcn 查阅。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=TrustSGCN:+Learning+Trustworthiness+on+Edge+Signs+for+Effective+Signed+Graph+Convolutional+Networks)|0| -|[Unsupervised Dialogue Topic Segmentation with Topic-aware Contrastive Learning](https://doi.org/10.1145/3539618.3592081)|Haoyu Gao, Rui Wang, TingEn Lin, Yuchuan Wu, Min Yang, Fei Huang, Yongbin Li|University of Science and Technology of China & Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, HeFei, China; Alibaba Group, Beijing, China; Alibaba Group, New York, China; Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, ShenZhen, China|Dialogue Topic Segmentation (DTS) plays an essential role in a variety of dialogue modeling tasks. Previous DTS methods either focus on semantic similarity or dialogue coherence to assess topic similarity for unsupervised dialogue segmentation. However, the topic similarity cannot be fully identified via semantic similarity or dialogue coherence. In addition, the unlabeled dialogue data, which contains useful clues of utterance relationships, remains underexploited. In this paper, we propose a novel unsupervised DTS framework, which learns topic-aware utterance representations from unlabeled dialogue data through neighboring utterance matching and pseudo-segmentation. Extensive experiments on two benchmark datasets (i.e., DialSeg711 and Doc2Dial) demonstrate that our method significantly outperforms the strong baseline methods. For reproducibility, we provide our code and data at: https://github.com/AlibabaResearch/DAMO-ConvAI/tree/main/dial-start.|对话主题分割(DTS)在各种对话建模任务中起着至关重要的作用。以往的 DTS 方法都是基于语义相似性或对话连贯性来评估无监督对话分割的主题相似性。然而，主题相似性不能通过语义相似性或对话连贯来完全识别。此外，未标记的对话数据，其中包含有用的话语关系的线索，仍然没有得到充分利用。本文提出了一种新的无监督 DTS 框架，它通过邻近话语匹配和伪分割从未标记的对话数据中学习主题感知的话语表征。对两个基准数据集(即，DialSeg711和 Doc2Dial)的大量实验表明，我们的方法明显优于强基准方法。为了可重复性，我们在以下 https://github.com/alibabaresearch/damo-convai/tree/main/dial-start 提供代码和数据。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Unsupervised+Dialogue+Topic+Segmentation+with+Topic-aware+Contrastive+Learning)|0| -|[Which Matters Most in Making Fund Investment Decisions? A Multi-granularity Graph Disentangled Learning Framework](https://doi.org/10.1145/3539618.3592088)|Chunjing Gan, Binbin Hu, Bo Huang, Tianyu Zhao, Yingru Lin, Wenliang Zhong, Zhiqiang Zhang, Jun Zhou, Chuan Shi|Ant Group, Shanghai, China; Beijing University of Posts and Telecommunications, Beijing, China; Ant Group, Hangzhou, China; Ant Group, Beijing, China|In this paper, we highlight that both conformity and risk preference matter in making fund investment decisions beyond personal interest and seek to jointly characterize these aspects in a disentangled manner. Consequently, we develop a novel Multi-granularity Graph Disentangled Learning framework named MGDL to effectively perform intelligent matching of fund investment products. Benefiting from the well-established fund graph and the attention module, multi-granularity user representations are derived from historical behaviors to separately express personal interest, conformity and risk preference in a fine-grained way. To attain stronger disentangled representations with specific semantics, MGDL explicitly involve two self-supervised signals, ie fund type based contrasts and fund popularity. Extensive experiments in offline and online environments verify the effectiveness of MGDL.|在本文中，我们强调了一致性和风险偏好在基金投资决策中的重要性，超越了个人利益，并试图以一种分离的方式共同描述这些方面。因此，我们提出了一个新的多粒度图解缠学习框架 MGDL，以有效地进行基金投资产品的智能匹配。利用已建立的基金图和注意模型，从历史行为中分离出多粒度的用户表征，以细粒度的方式分别表达个人兴趣、整合和风险偏好。为了获得具有特定语义的更强的分离表征，MGDL 明确涉及两个自我监督信号，即基金类型对比和基金受欢迎程度。在离线和在线环境中的大量实验验证了 MGDL 的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Which+Matters+Most+in+Making+Fund+Investment+Decisions?+A+Multi-granularity+Graph+Disentangled+Learning+Framework)|0| -|[Where Does Your News Come From? Predicting Information Pathways in Social Media](https://doi.org/10.1145/3539618.3592087)|Alexander K. Taylor, Nuan Wen, PoNien Kung, Jiaao Chen, Violet Peng, Wei Wang|University Southern California, Los Angeles, CA, USA; University of California, Los Angeles, Los Angeles, CA, USA; Georgia Tech, Atlanta, GA, USA|As social networks become further entrenched in modern society, it becomes increasingly important to understand and predict how information (e.g., news coverage of a given event) is propagated across social media (i.e., information pathway), which helps the understandings of the impact of real-world information. Thus, in this paper, we propose a novel task, Information Pathway Prediction (IPP), which depicts the propagation paths of a given passage as a community tree (rooted at the information source) on constructed community interaction graphs where we first aggregate individual users into communities formed around news sources and influential users, and then elucidate the patterns of information dissemination across media based on such community nodes. We argue that this is an important and useful task because, on one hand, community-level interactions offer more stability than those at the user level; on the other hand, individual users are often influenced by their community, and modeling community-level information propagation will help the traditional link-prediction problem. To tackle the IPP task, we introduce Lightning, a novel content-aware link prediction GNN model and demonstrate using a large Twitter dataset consisting of all COVID related tweets that Lightning outperforms state-of-the-art link prediction baselines by a significant margin.|随着社交网络在现代社会中越来越根深蒂固，理解和预测信息(例如，特定事件的新闻报道)如何通过社交媒体(例如，信息路径)传播变得越来越重要，这有助于理解现实世界信息的影响。因此，我们提出了一个新的任务，信息路径预测(IPP) ，它描述了一个给定的文章的传播路径作为一个社区树(根源于信息来源)在构建的社区互动图中，首先聚集个人用户周围新闻来源和有影响力的用户形成的社区，然后阐明基于这些社区节点的跨媒体信息传播模式。我们认为这是一个重要而有用的任务，因为一方面，社区层面的交互比用户层面的交互提供了更多的稳定性; 另一方面，个人用户经常受到他们的社区的影响，建模社区层面的信息传播将有助于传统的链接预测问题。为了解决 IPP 任务，我们引入了“闪电”，一个新颖的内容感知链接预测 GNN 模型，并使用一个包含所有与冠状病毒疾病相关的 tweet 的大型 Twitter 数据集演示了“闪电”比最先进的链接预测基线表现出了显著的优势。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Where+Does+Your+News+Come+From?+Predicting+Information+Pathways+in+Social+Media)|0| -|[Automated Medical Coding on MIMIC-III and MIMIC-IV: A Critical Review and Replicability Study](https://doi.org/10.1145/3539618.3591918)|Joakim Edin, Alexander Junge, Jakob D. Havtorn, Lasse Borgholt, Maria Maistro, Tuukka Ruotsalo, Lars Maaløe|University of Copenhagen & Corti, Copenhagen, Denmark; Technical University of Denmark & Corti, Copenhagen, Denmark; Corti, Copenhagen, Denmark; University of Copenhagen & University of Helsinki, Copenhagen, Denmark; University of Aalborg & Corti, Copenhagen, Denmark; University of Copenhagen, Copenhagen, Denmark|Medical coding is the task of assigning medical codes to clinical free-text documentation. Healthcare professionals manually assign such codes to track patient diagnoses and treatments. Automated medical coding can considerably alleviate this administrative burden. In this paper, we reproduce, compare, and analyze state-of-the-art automated medical coding machine learning models. We show that several models underperform due to weak configurations, poorly sampled train-test splits, and insufficient evaluation. In previous work, the macro F1 score has been calculated sub-optimally, and our correction doubles it. We contribute a revised model comparison using stratified sampling and identical experimental setups, including hyperparameters and decision boundary tuning. We analyze prediction errors to validate and falsify assumptions of previous works. The analysis confirms that all models struggle with rare codes, while long documents only have a negligible impact. Finally, we present the first comprehensive results on the newly released MIMIC-IV dataset using the reproduced models. We release our code, model parameters, and new MIMIC-III and MIMIC-IV training and evaluation pipelines to accommodate fair future comparisons.|医学编码是为临床自由文本文档分配医学编码的任务。医疗保健专业人员手动指定这样的代码来跟踪患者的诊断和治疗。自动化医疗编码可以大大减轻这种管理负担。在本文中，我们再现，比较和分析国家的最先进的自动医疗编码机学习模型。我们表明，几个模型表现不佳，由于配置薄弱，采样列车测试分裂，和不充分的评估。在以前的工作中，宏 F1得分已经计算次优，我们的修正是它的两倍。我们使用分层抽样和相同的实验装置，包括超参数和决策边界调整，提供了一个修订后的模型比较。我们分析预测误差，以验证和证伪以往工作的假设。分析证实，所有的模型都与罕见的代码作斗争，而长文档只有微不足道的影响。最后，我们使用复制的模型对新发布的 MIMIC-IV 数据集提出了第一个全面的结果。我们发布我们的代码，模型参数，以及新的 MIMIC-III 和 MIMIC-IV 培训和评估管道，以适应公平的未来比较。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Automated+Medical+Coding+on+MIMIC-III+and+MIMIC-IV:+A+Critical+Review+and+Replicability+Study)|0| +|[Towards Robust Knowledge Tracing Models via k-Sparse Attention](https://doi.org/10.1145/3539618.3592073)|Shuyan Huang, Zitao Liu, Xiangyu Zhao, Weiqi Luo, Jian Weng|TAL Education Group, Beijing, China; City University of Hong Kong, Hong Kong, China; Jinan University, Guangzhou, China|Knowledge tracing (KT) is the problem of predicting students' future performance based on their historical interaction sequences. With the advanced capability of capturing contextual long-term dependency, attention mechanism becomes one of the essential components in many deep learning based KT (DLKT) models. In spite of the impressive performance achieved by these attentional DLKT models, many of them are often vulnerable to run the risk of overfitting, especially on small-scale educational datasets. Therefore, in this paper, we propose sparseKT, a simple yet effective framework to improve the robustness and generalization of the attention based DLKT approaches. Specifically, we incorporate a k-selection module to only pick items with the highest attention scores. We propose two sparsification heuristics: (1) soft-thresholding sparse attention and (2) top-K sparse attention. We show that our sparseKT is able to help attentional KT models get rid of irrelevant student interactions and improve the predictive performance when compared to 11 state-of-the-art KT models on three publicly available real-world educational datasets. To encourage reproducible research, we make our data and code publicly available at https://github.com/pykt-team/pykt-toolkit1..|知识追踪(KT)是根据学生的历史交互序列预测其未来表现的问题。注意机制是深度学习 KT (DLKT)模型的重要组成部分，具有捕捉上下文长期依赖的能力。尽管这些注意力 DLKT 模型取得了令人印象深刻的性能，但其中许多模型往往容易出现过度拟合的风险，尤其是在小规模的教育数据集上。因此，在本文中，我们提出了稀疏 KT，一个简单而有效的框架，以提高基于注意的 DLKT 方法的鲁棒性和泛化能力。具体来说，我们合并了一个 k 选择模块，只选择注意力得分最高的项目。提出了两种稀疏化启发式算法: (1)软阈值稀疏注意和(2) top-K 稀疏注意。我们表明，我们的稀疏 KT 能够帮助注意 KT 模型摆脱不相关的学生互动，并提高预测性能相比，在三个公开可用的现实世界教育数据集的11个国家的最先进的 KT 模型。为了鼓励可重复的研究，我们将我们的数据和代码在 https://github.com/pykt-team/pykt-toolkit1公开。.|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Towards+Robust+Knowledge+Tracing+Models+via+k-Sparse+Attention)|0| +|[Think Rationally about What You See: Continuous Rationale Extraction for Relation Extraction](https://doi.org/10.1145/3539618.3592072)|Xuming Hu, Zhaochen Hong, Chenwei Zhang, Irwin King, Philip S. Yu|University of Illinois at Chicago, Chicago, IL, USA; Tsinghua University, Beijing, China; The Chinese University of Hong Kong, Hong Kong, Hong Kong; Amazon, Seattle, WA, USA|Relation extraction (RE) aims to extract potential relations according to the context of two entities, thus, deriving rational contexts from sentences plays an important role. Previous works either focus on how to leverage the entity information (e.g., entity types, entity verbalization) to inference relations, but ignore context-focused content, or use counterfactual thinking to remove the model's bias of potential relations in entities, but the relation reasoning process will still be hindered by irrelevant content. Therefore, how to preserve relevant content and remove noisy segments from sentences is a crucial task. In addition, retained content needs to be fluent enough to maintain semantic coherence and interpretability. In this work, we propose a novel rationale extraction framework named RE2, which leverages two continuity and sparsity factors to obtain relevant and coherent rationales from sentences. To solve the problem that the gold rationales are not labeled, RE2 applies an optimizable binary mask to each token in the sentence, and adjust the rationales that need to be selected according to the relation label. Experiments on four datasets show that RE2 surpasses baselines.|关系抽取(RE)的目的是根据两个实体的语境提取潜在的关系，因此，从句子中抽取合理的语境起着重要作用。以往的研究主要集中在如何利用实体信息(如实体类型、实体语言化)来推理关系，而忽视了关注上下文的内容，或者运用反事实思维来消除模型对实体中潜在关系的偏见，但是关系推理过程仍然会受到不相关内容的阻碍。因此，如何在句子中保留相关内容和去除噪声片段是一个非常关键的问题。此外，保留的内容需要足够流畅，以保持语义连贯性和可解释性。在这项工作中，我们提出了一个新的理论基础抽取框架，称为 RE2，它利用两个连续性和稀疏性因素来获得相关和连贯的理论基础从句子。为了解决黄金基本原理没有标记的问题，RE2对句子中的每个标记应用一个可优化的二进制掩码，并根据关系标签调整需要选择的基本原理。在四个数据集上的实验表明，RE2超过了基线。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Think+Rationally+about+What+You+See:+Continuous+Rationale+Extraction+for+Relation+Extraction)|0| +|[TrustSGCN: Learning Trustworthiness on Edge Signs for Effective Signed Graph Convolutional Networks](https://doi.org/10.1145/3539618.3592075)|MinJeong Kim, YeonChang Lee, SangWook Kim|Georgia Institute of Technology, Atlanta, USA; Hanyang University, Seoul, Republic of Korea|The problem of signed network embedding (SNE) aims to represent nodes in a given signed network as low-dimensional vectors. While several SNE methods based on graph convolutional networks (GCN) have been proposed, we point out that they significantly rely on the assumption that the decades-old balance theory always holds in the real world. To address this limitation, we propose a novel GCN-based SNE approach, named as TrustSGCN, which measures the trustworthiness on edge signs for high-order relationships inferred by balance theory and corrects incorrect embedding propagation based on the trustworthiness. The experiments on four real-world signed network datasets demonstrate that TrustSGCN consistently outperforms five state-of-the-art GCN-based SNE methods. The code is available at https://github.com/kmj0792/TrustSGCN.|有符号网络嵌入(SNE)问题旨在将给定有符号网络中的节点表示为低维向量。提出了几种基于图卷积网络(GCN)的 SNE 方法，指出它们主要依赖于几十年前的平衡理论在现实世界中仍然适用的假设。针对这一局限性，提出了一种新的基于 GCN 的 SNE 方法 TrustSGCN，该方法通过平衡理论对高阶关系的边缘符号进行可信度度量，并基于可信度校正不正确的嵌入传播。在四个真实世界签名网络数据集上的实验表明，TrustSGCN 始终优于基于 GCN 的五种最新的 SNE 方法。密码可在 https://github.com/kmj0792/trustsgcn 查阅。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=TrustSGCN:+Learning+Trustworthiness+on+Edge+Signs+for+Effective+Signed+Graph+Convolutional+Networks)|0| +|[Unsupervised Dialogue Topic Segmentation with Topic-aware Contrastive Learning](https://doi.org/10.1145/3539618.3592081)|Haoyu Gao, Rui Wang, TingEn Lin, Yuchuan Wu, Min Yang, Fei Huang, Yongbin Li|University of Science and Technology of China & Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, HeFei, China; Alibaba Group, Beijing, China; Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, ShenZhen, China; Alibaba Group, New York, China|Dialogue Topic Segmentation (DTS) plays an essential role in a variety of dialogue modeling tasks. Previous DTS methods either focus on semantic similarity or dialogue coherence to assess topic similarity for unsupervised dialogue segmentation. However, the topic similarity cannot be fully identified via semantic similarity or dialogue coherence. In addition, the unlabeled dialogue data, which contains useful clues of utterance relationships, remains underexploited. In this paper, we propose a novel unsupervised DTS framework, which learns topic-aware utterance representations from unlabeled dialogue data through neighboring utterance matching and pseudo-segmentation. Extensive experiments on two benchmark datasets (i.e., DialSeg711 and Doc2Dial) demonstrate that our method significantly outperforms the strong baseline methods. For reproducibility, we provide our code and data at: https://github.com/AlibabaResearch/DAMO-ConvAI/tree/main/dial-start.|对话主题分割(DTS)在各种对话建模任务中起着至关重要的作用。以往的 DTS 方法都是基于语义相似性或对话连贯性来评估无监督对话分割的主题相似性。然而，主题相似性不能通过语义相似性或对话连贯来完全识别。此外，未标记的对话数据，其中包含有用的话语关系的线索，仍然没有得到充分利用。本文提出了一种新的无监督 DTS 框架，它通过邻近话语匹配和伪分割从未标记的对话数据中学习主题感知的话语表征。对两个基准数据集(即，DialSeg711和 Doc2Dial)的大量实验表明，我们的方法明显优于强基准方法。为了可重复性，我们在以下 https://github.com/alibabaresearch/damo-convai/tree/main/dial-start 提供代码和数据。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Unsupervised+Dialogue+Topic+Segmentation+with+Topic-aware+Contrastive+Learning)|0| +|[Which Matters Most in Making Fund Investment Decisions? A Multi-granularity Graph Disentangled Learning Framework](https://doi.org/10.1145/3539618.3592088)|Chunjing Gan, Binbin Hu, Bo Huang, Tianyu Zhao, Yingru Lin, Wenliang Zhong, Zhiqiang Zhang, Jun Zhou, Chuan Shi|Ant Group, Hangzhou, China; Ant Group, Beijing, China; Beijing University of Posts and Telecommunications, Beijing, China; Ant Group, Shanghai, China|In this paper, we highlight that both conformity and risk preference matter in making fund investment decisions beyond personal interest and seek to jointly characterize these aspects in a disentangled manner. Consequently, we develop a novel Multi-granularity Graph Disentangled Learning framework named MGDL to effectively perform intelligent matching of fund investment products. Benefiting from the well-established fund graph and the attention module, multi-granularity user representations are derived from historical behaviors to separately express personal interest, conformity and risk preference in a fine-grained way. To attain stronger disentangled representations with specific semantics, MGDL explicitly involve two self-supervised signals, ie fund type based contrasts and fund popularity. Extensive experiments in offline and online environments verify the effectiveness of MGDL.|在本文中，我们强调了一致性和风险偏好在基金投资决策中的重要性，超越了个人利益，并试图以一种分离的方式共同描述这些方面。因此，我们提出了一个新的多粒度图解缠学习框架 MGDL，以有效地进行基金投资产品的智能匹配。利用已建立的基金图和注意模型，从历史行为中分离出多粒度的用户表征，以细粒度的方式分别表达个人兴趣、整合和风险偏好。为了获得具有特定语义的更强的分离表征，MGDL 明确涉及两个自我监督信号，即基金类型对比和基金受欢迎程度。在离线和在线环境中的大量实验验证了 MGDL 的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Which+Matters+Most+in+Making+Fund+Investment+Decisions?+A+Multi-granularity+Graph+Disentangled+Learning+Framework)|0| +|[Where Does Your News Come From? Predicting Information Pathways in Social Media](https://doi.org/10.1145/3539618.3592087)|Alexander K. Taylor, Nuan Wen, PoNien Kung, Jiaao Chen, Violet Peng, Wei Wang|University of California, Los Angeles, Los Angeles, CA, USA; Georgia Tech, Atlanta, GA, USA; University Southern California, Los Angeles, CA, USA|As social networks become further entrenched in modern society, it becomes increasingly important to understand and predict how information (e.g., news coverage of a given event) is propagated across social media (i.e., information pathway), which helps the understandings of the impact of real-world information. Thus, in this paper, we propose a novel task, Information Pathway Prediction (IPP), which depicts the propagation paths of a given passage as a community tree (rooted at the information source) on constructed community interaction graphs where we first aggregate individual users into communities formed around news sources and influential users, and then elucidate the patterns of information dissemination across media based on such community nodes. We argue that this is an important and useful task because, on one hand, community-level interactions offer more stability than those at the user level; on the other hand, individual users are often influenced by their community, and modeling community-level information propagation will help the traditional link-prediction problem. To tackle the IPP task, we introduce Lightning, a novel content-aware link prediction GNN model and demonstrate using a large Twitter dataset consisting of all COVID related tweets that Lightning outperforms state-of-the-art link prediction baselines by a significant margin.|随着社交网络在现代社会中越来越根深蒂固，理解和预测信息(例如，特定事件的新闻报道)如何通过社交媒体(例如，信息路径)传播变得越来越重要，这有助于理解现实世界信息的影响。因此，我们提出了一个新的任务，信息路径预测(IPP) ，它描述了一个给定的文章的传播路径作为一个社区树(根源于信息来源)在构建的社区互动图中，首先聚集个人用户周围新闻来源和有影响力的用户形成的社区，然后阐明基于这些社区节点的跨媒体信息传播模式。我们认为这是一个重要而有用的任务，因为一方面，社区层面的交互比用户层面的交互提供了更多的稳定性; 另一方面，个人用户经常受到他们的社区的影响，建模社区层面的信息传播将有助于传统的链接预测问题。为了解决 IPP 任务，我们引入了“闪电”，一个新颖的内容感知链接预测 GNN 模型，并使用一个包含所有与冠状病毒疾病相关的 tweet 的大型 Twitter 数据集演示了“闪电”比最先进的链接预测基线表现出了显著的优势。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Where+Does+Your+News+Come+From?+Predicting+Information+Pathways+in+Social+Media)|0| +|[Automated Medical Coding on MIMIC-III and MIMIC-IV: A Critical Review and Replicability Study](https://doi.org/10.1145/3539618.3591918)|Joakim Edin, Alexander Junge, Jakob D. Havtorn, Lasse Borgholt, Maria Maistro, Tuukka Ruotsalo, Lars Maaløe|University of Copenhagen, Copenhagen, Denmark; Corti, Copenhagen, Denmark; Technical University of Denmark & Corti, Copenhagen, Denmark; University of Aalborg & Corti, Copenhagen, Denmark; University of Copenhagen & Corti, Copenhagen, Denmark; University of Copenhagen & University of Helsinki, Copenhagen, Denmark|Medical coding is the task of assigning medical codes to clinical free-text documentation. Healthcare professionals manually assign such codes to track patient diagnoses and treatments. Automated medical coding can considerably alleviate this administrative burden. In this paper, we reproduce, compare, and analyze state-of-the-art automated medical coding machine learning models. We show that several models underperform due to weak configurations, poorly sampled train-test splits, and insufficient evaluation. In previous work, the macro F1 score has been calculated sub-optimally, and our correction doubles it. We contribute a revised model comparison using stratified sampling and identical experimental setups, including hyperparameters and decision boundary tuning. We analyze prediction errors to validate and falsify assumptions of previous works. The analysis confirms that all models struggle with rare codes, while long documents only have a negligible impact. Finally, we present the first comprehensive results on the newly released MIMIC-IV dataset using the reproduced models. We release our code, model parameters, and new MIMIC-III and MIMIC-IV training and evaluation pipelines to accommodate fair future comparisons.|医学编码是为临床自由文本文档分配医学编码的任务。医疗保健专业人员手动指定这样的代码来跟踪患者的诊断和治疗。自动化医疗编码可以大大减轻这种管理负担。在本文中，我们再现，比较和分析国家的最先进的自动医疗编码机学习模型。我们表明，几个模型表现不佳，由于配置薄弱，采样列车测试分裂，和不充分的评估。在以前的工作中，宏 F1得分已经计算次优，我们的修正是它的两倍。我们使用分层抽样和相同的实验装置，包括超参数和决策边界调整，提供了一个修订后的模型比较。我们分析预测误差，以验证和证伪以往工作的假设。分析证实，所有的模型都与罕见的代码作斗争，而长文档只有微不足道的影响。最后，我们使用复制的模型对新发布的 MIMIC-IV 数据集提出了第一个全面的结果。我们发布我们的代码，模型参数，以及新的 MIMIC-III 和 MIMIC-IV 培训和评估管道，以适应公平的未来比较。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Automated+Medical+Coding+on+MIMIC-III+and+MIMIC-IV:+A+Critical+Review+and+Replicability+Study)|0| |[An Empirical Comparison of Web Content Extraction Algorithms](https://doi.org/10.1145/3539618.3591920)|Janek Bevendorff, Sanket Gupta, Johannes Kiesel, Benno Stein|Bauhaus-Universität Weimar, Weimar, Germany|Main content extraction from web pages-sometimes also called boilerplate removal-has been a research topic for over two decades. Yet despite web pages being delivered in a machine-readable markup format, extracting the actual content is still a challenge today. Even with the latest HTML5 standard, which defines many semantic elements to mark content areas, web page authors do not always use semantic markup correctly or to its full potential, making it hard for automated systems to extract the relevant information. A high-precision, high-recall content extraction is crucial for downstream applications such as search engines, AI language tools, distraction-free reader modes in users' browsers, and other general assistive technologies. For such a fundamental task, however, surprisingly few openly available extraction systems or training and benchmarking datasets exist. Even less research has gone into the rigorous evaluation and a true apples-to-apples comparison of the few extraction systems that do exist. To get a better grasp on the current state of the art in the field, we combine and clean eight existing human-labeled web content extraction datasets. On the combined dataset, we evaluate 14~competitive main content extraction systems and five baseline approaches. Finally, we build three ensembles as new state-of-the-art extraction baselines. We find that the performance of existing systems is quite genre-dependent and no single extractor performs best on all types of web pages.|从网页中提取主要内容——有时也称为样板删除——是二十多年来的一个研究课题。然而，尽管网页是以机器可读的标记格式发布的，提取实际内容在今天仍然是一个挑战。即使使用最新的 HTML5标准，定义了许多语义元素来标记内容区域，网页作者并不总是正确地使用语义标记或充分发挥其潜力，这使得自动化系统很难提取相关信息。一个高精度，高召回率的内容提取是至关重要的下游应用程序，如搜索引擎，人工智能语言工具，无干扰阅读模式在用户的浏览器，以及其他通用的辅助技术。然而，对于这样一个基本任务，令人惊讶的是，很少有公开可用的提取系统或培训和基准测试数据集存在。甚至更少的研究进入了严格的评估和一个真正的苹果对苹果的比较，几个提取系统是存在的。为了更好地掌握该领域的现状，我们组合并清理了8个现有的人类标记的 Web 内容提取数据集。在组合数据集上，我们评估了14种具有竞争力的主要内容提取系统和5种基线方法。最后，我们构建三个集合作为新的最先进的提取基线。我们发现，现有系统的性能是相当类型依赖，没有一个单一的提取器表现最好的所有类型的网页。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=An+Empirical+Comparison+of+Web+Content+Extraction+Algorithms)|0| -|[Multimodal Neural Databases](https://doi.org/10.1145/3539618.3591930)|Giovanni Trappolini, Andrea Santilli, Emanuele Rodolà, Alon Y. Halevy, Fabrizio Silvestri|Sapienza University & ISTI-CNR, Rome, Italy; Meta AI, Menlo Park, CA, USA; Sapienza University, Rome, Italy|The rise in loosely-structured data available through text, images, and other modalities has called for new ways of querying them. Multimedia Information Retrieval has filled this gap and has witnessed exciting progress in recent years. Tasks such as search and retrieval of extensive multimedia archives have undergone massive performance improvements, driven to a large extent by recent developments in multimodal deep learning. However, methods in this field remain limited in the kinds of queries they support and, in particular, their inability to answer database-like queries. For this reason, inspired by recent work on neural databases, we propose a new framework, which we name Multimodal Neural Databases (MMNDBs). MMNDBs can answer complex database-like queries that involve reasoning over different input modalities, such as text and images, at scale. In this paper, we present the first architecture able to fulfill this set of requirements and test it with several baselines, showing the limitations of currently available models. The results show the potential of these new techniques to process unstructured data coming from different modalities, paving the way for future research in the area. Code to replicate the experiments will be released at https://github.com/GiovanniTRA/MultimodalNeuralDatabases|随着通过文本、图像和其他方式获得的松散结构数据的增加，需要用新的方式来查询这些数据。多媒体信息检索填补了这一空白，近年来取得了令人兴奋的进展。搜索和检索大量多媒体档案等任务在很大程度上受到多模式深度学习的最新发展的推动，已经取得了巨大的性能改进。但是，这个领域的方法仍然受到它们支持的查询类型的限制，特别是它们无法回答类似数据库的查询。基于这个原因，受最近神经数据库研究的启发，我们提出了一个新的框架，命名为多模态神经数据库(MMNDB)。MMNDB 可以回答复杂的数据库查询，这些查询涉及对不同输入模式(如文本和图像)进行大规模推理。在本文中，我们提出了第一个能够满足这组需求的体系结构，并用几个基线对其进行了测试，显示了当前可用模型的局限性。研究结果显示了这些新技术处理来自不同模式的非结构化数据的潜力，为该领域未来的研究铺平了道路。复制实验的代码将在 https://github.com/giovannitra/multimodalneuraldatabases 公布|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Multimodal+Neural+Databases)|0| -|[SocialDial: A Benchmark for Socially-Aware Dialogue Systems](https://doi.org/10.1145/3539618.3591877)|Haolan Zhan, Zhuang Li, Yufei Wang, Linhao Luo, Tao Feng, Xiaoxi Kang, Yuncheng Hua, Lizhen Qu, LayKi Soon, Suraj Sharma, Ingrid Zukerman, Zhaleh SemnaniAzad, Gholamreza Haffari|Monash University, Melbourne, VIC, Australia; California State University, Northridge, Northridge, CA, USA; Monash University, Subang Jaya, Malaysia|Dialogue systems have been widely applied in many scenarios and are now more powerful and ubiquitous than ever before. With large neural models and massive available data, current dialogue systems have access to more knowledge than any people in their life. However, current dialogue systems still do not perform at a human level. One major gap between conversational agents and humans lies in their abilities to be aware of social norms. The development of socially-aware dialogue systems is impeded due to the lack of resources. In this paper, we present the first socially-aware dialogue corpus - SocialDial, based on Chinese social culture. SocialDial consists of two parts: 1,563 multi-turn dialogues between two human speakers with fine-grained labels, and 4,870 synthetic conversations generated by ChatGPT. The human corpus covers five categories of social norms, which have 14 sub-categories in total. Specifically, it contains social factor annotations including social relation, context, social distance, and social norms. However, collecting sufficient socially-aware dialogues is costly. Thus, we harness the power of ChatGPT and devise an ontology-based synthetic data generation framework. This framework is able to generate synthetic data at scale. To ensure the quality of synthetic dialogues, we design several mechanisms for quality control during data collection. Finally, we evaluate our dataset using several pre-trained models, such as BERT and RoBERTa. Comprehensive empirical results based on state-of-the-art neural models demonstrate that modeling of social norms for dialogue systems is a promising research direction. To the best of our knowledge, SocialDial is the first socially-aware dialogue dataset that covers multiple social factors and has fine-grained labels.|对话系统在许多情况下得到了广泛应用，现在比以往任何时候都更加强大和普遍。通过大型神经模型和大量可用数据，目前的对话系统比他们生活中的任何人都能获得更多的知识。然而，目前的对话系统仍然不能在人类的水平上运行。会话主体和人类之间的一个主要差距在于他们意识到社会规范的能力。由于缺乏资源，社会意识对话系统的发展受到阻碍。本文以中国社会文化为基础，提出了第一个具有社会意识的对话语料库——社会拨号。SocialDial 由两部分组成: 1,563个具有细粒度标签的人类说话者之间的多回合对话，以及由 ChatGPT 生成的4,870个合成对话。人类主体包括五类社会规范，共有14个子类。具体来说，它包含了社会因素的诠释，包括社会关系、语境、社会距离和社会规范。然而，收集足够的具有社会意识的对话是昂贵的。因此，我们利用 ChatGPT 的功能，设计了一个基于本体的综合数据生成框架。这个框架能够生成大规模的综合数据。为了保证综合对话的质量，我们设计了数据采集过程中的质量控制机制。最后，我们使用一些预先训练的模型来评估我们的数据集，例如 BERT 和 RoBERTa。基于最新神经模型的综合实证结果表明，对话系统的社会规范建模是一个有前途的研究方向。据我们所知，SocialDial 是第一个具有社会意识的对话数据集，它涵盖了多种社会因素，并有细粒度的标签。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=SocialDial:+A+Benchmark+for+Socially-Aware+Dialogue+Systems)|0| +|[Multimodal Neural Databases](https://doi.org/10.1145/3539618.3591930)|Giovanni Trappolini, Andrea Santilli, Emanuele Rodolà, Alon Y. Halevy, Fabrizio Silvestri|Sapienza University & ISTI-CNR, Rome, Italy; Sapienza University, Rome, Italy; Meta AI, Menlo Park, CA, USA|The rise in loosely-structured data available through text, images, and other modalities has called for new ways of querying them. Multimedia Information Retrieval has filled this gap and has witnessed exciting progress in recent years. Tasks such as search and retrieval of extensive multimedia archives have undergone massive performance improvements, driven to a large extent by recent developments in multimodal deep learning. However, methods in this field remain limited in the kinds of queries they support and, in particular, their inability to answer database-like queries. For this reason, inspired by recent work on neural databases, we propose a new framework, which we name Multimodal Neural Databases (MMNDBs). MMNDBs can answer complex database-like queries that involve reasoning over different input modalities, such as text and images, at scale. In this paper, we present the first architecture able to fulfill this set of requirements and test it with several baselines, showing the limitations of currently available models. The results show the potential of these new techniques to process unstructured data coming from different modalities, paving the way for future research in the area. Code to replicate the experiments will be released at https://github.com/GiovanniTRA/MultimodalNeuralDatabases|随着通过文本、图像和其他方式获得的松散结构数据的增加，需要用新的方式来查询这些数据。多媒体信息检索填补了这一空白，近年来取得了令人兴奋的进展。搜索和检索大量多媒体档案等任务在很大程度上受到多模式深度学习的最新发展的推动，已经取得了巨大的性能改进。但是，这个领域的方法仍然受到它们支持的查询类型的限制，特别是它们无法回答类似数据库的查询。基于这个原因，受最近神经数据库研究的启发，我们提出了一个新的框架，命名为多模态神经数据库(MMNDB)。MMNDB 可以回答复杂的数据库查询，这些查询涉及对不同输入模式(如文本和图像)进行大规模推理。在本文中，我们提出了第一个能够满足这组需求的体系结构，并用几个基线对其进行了测试，显示了当前可用模型的局限性。研究结果显示了这些新技术处理来自不同模式的非结构化数据的潜力，为该领域未来的研究铺平了道路。复制实验的代码将在 https://github.com/giovannitra/multimodalneuraldatabases 公布|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Multimodal+Neural+Databases)|0| +|[SocialDial: A Benchmark for Socially-Aware Dialogue Systems](https://doi.org/10.1145/3539618.3591877)|Haolan Zhan, Zhuang Li, Yufei Wang, Linhao Luo, Tao Feng, Xiaoxi Kang, Yuncheng Hua, Lizhen Qu, LayKi Soon, Suraj Sharma, Ingrid Zukerman, Zhaleh SemnaniAzad, Gholamreza Haffari|Monash University, Subang Jaya, Malaysia; California State University, Northridge, Northridge, CA, USA; Monash University, Melbourne, VIC, Australia|Dialogue systems have been widely applied in many scenarios and are now more powerful and ubiquitous than ever before. With large neural models and massive available data, current dialogue systems have access to more knowledge than any people in their life. However, current dialogue systems still do not perform at a human level. One major gap between conversational agents and humans lies in their abilities to be aware of social norms. The development of socially-aware dialogue systems is impeded due to the lack of resources. In this paper, we present the first socially-aware dialogue corpus - SocialDial, based on Chinese social culture. SocialDial consists of two parts: 1,563 multi-turn dialogues between two human speakers with fine-grained labels, and 4,870 synthetic conversations generated by ChatGPT. The human corpus covers five categories of social norms, which have 14 sub-categories in total. Specifically, it contains social factor annotations including social relation, context, social distance, and social norms. However, collecting sufficient socially-aware dialogues is costly. Thus, we harness the power of ChatGPT and devise an ontology-based synthetic data generation framework. This framework is able to generate synthetic data at scale. To ensure the quality of synthetic dialogues, we design several mechanisms for quality control during data collection. Finally, we evaluate our dataset using several pre-trained models, such as BERT and RoBERTa. Comprehensive empirical results based on state-of-the-art neural models demonstrate that modeling of social norms for dialogue systems is a promising research direction. To the best of our knowledge, SocialDial is the first socially-aware dialogue dataset that covers multiple social factors and has fine-grained labels.|对话系统在许多情况下得到了广泛应用，现在比以往任何时候都更加强大和普遍。通过大型神经模型和大量可用数据，目前的对话系统比他们生活中的任何人都能获得更多的知识。然而，目前的对话系统仍然不能在人类的水平上运行。会话主体和人类之间的一个主要差距在于他们意识到社会规范的能力。由于缺乏资源，社会意识对话系统的发展受到阻碍。本文以中国社会文化为基础，提出了第一个具有社会意识的对话语料库——社会拨号。SocialDial 由两部分组成: 1,563个具有细粒度标签的人类说话者之间的多回合对话，以及由 ChatGPT 生成的4,870个合成对话。人类主体包括五类社会规范，共有14个子类。具体来说，它包含了社会因素的诠释，包括社会关系、语境、社会距离和社会规范。然而，收集足够的具有社会意识的对话是昂贵的。因此，我们利用 ChatGPT 的功能，设计了一个基于本体的综合数据生成框架。这个框架能够生成大规模的综合数据。为了保证综合对话的质量，我们设计了数据采集过程中的质量控制机制。最后，我们使用一些预先训练的模型来评估我们的数据集，例如 BERT 和 RoBERTa。基于最新神经模型的综合实证结果表明，对话系统的社会规范建模是一个有前途的研究方向。据我们所知，SocialDial 是第一个具有社会意识的对话数据集，它涵盖了多种社会因素，并有细粒度的标签。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=SocialDial:+A+Benchmark+for+Socially-Aware+Dialogue+Systems)|0| |[End-to-End Multimodal Fact-Checking and Explanation Generation: A Challenging Dataset and Models](https://doi.org/10.1145/3539618.3591879)|Barry Menglong Yao, Aditya Shah, Lichao Sun, JinHee Cho, Lifu Huang|Lehigh University, Bethlehem, PA, USA; Virginia Tech, Blacksburg, VA, USA|We propose the end-to-end multimodal fact-checking and explanation generation, where the input is a claim and a large collection of web sources, including articles, images, videos, and tweets, and the goal is to assess the truthfulness of the claim by retrieving relevant evidence and predicting a truthfulness label (i.e., support, refute and not enough information), and generate a rationalization statement to explain the reasoning and ruling process. To support this research, we construct Mocheg, a large-scale dataset that consists of 21,184 claims where each claim is assigned with a truthfulness label and ruling statement, with 58,523 evidence in the form of text and images. To establish baseline performances on Mocheg, we experiment with several state-of-the-art neural architectures on the three pipelined subtasks: multimodal evidence retrieval, claim verification, and explanation generation, and demonstrate the current state-of-the-art performance of end-to-end multimodal fact-checking is still far from satisfying. To the best of our knowledge, we are the first to build the benchmark dataset and solutions for end-to-end multimodal fact-checking and justification.|我们提出端到端的多模态事实检查和解释生成，其中输入是一个声明和大量的网络来源，包括文章，图像，视频和 tweet，目标是通过检索相关证据和预测真实性标签(即，支持，反驳和不足的信息)来评估声明的真实性，并生成合理化陈述来解释推理和裁决过程。为了支持这项研究，我们构建了 Mocheg，一个包含21,184个索赔的大型数据集，其中每个索赔都被分配了一个真实性标签和裁决声明，以文本和图像的形式提供了58,523个证据。为了建立 Mocheg 的基准表现，我们在三个流水线子任务(多模态证据检索、索赔验证和解释生成)上试验了几种最先进的神经结构，并证明目前端到端多模态事实检查的最先进表现仍然远远不能令人满意。据我们所知，我们是第一个建立基准数据集和解决方案的端到端多模式事实检查和论证。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=End-to-End+Multimodal+Fact-Checking+and+Explanation+Generation:+A+Challenging+Dataset+and+Models)|0| -|[The JOKER Corpus: English-French Parallel Data for Multilingual Wordplay Recognition](https://doi.org/10.1145/3539618.3591885)|Liana Ermakova, AnneGwenn Bosser, Adam Jatowt, Tristan Miller|Université de Bretagne Occidentale, Brest, France; École Nationale d'Ingénieurs de Brest, Plouzané, France; University of Innsbruck, Innsbruck, Austria; Austrian Research Institute for Artificial Intelligence, Vienna, Austria|Despite recent advances in information retrieval and natural language processing, rhetorical devices that exploit ambiguity or subvert linguistic rules remain a challenge for such systems. However, corpus-based analysis of wordplay has been a perennial topic of scholarship in the humanities, including literary criticism, language education, and translation studies. The immense data-gathering effort required for these studies points to the need for specialized text retrieval and classification technology, and consequently for appropriate test collections. In this paper, we introduce and analyze a new dataset for research and applications in the retrieval and processing of wordplay. Developed for the JOKER track at CLEF 2023, our annotated corpus extends and improves upon past English wordplay detection datasets in several ways. First, we introduce hundreds of additional positive examples of wordplay; second, we provide French translations for the examples; and third, we provide negative examples of non-wordplay with characteristics closely matching those of the positive examples. This last feature helps ensure that AI models learn to effectively distinguish wordplay from non-wordplay, and not simply texts differing in length, style, or vocabulary. Our test collection represents then a step towards wordplay-aware multilingual information retrieval.|尽管最近在信息检索和自然语言处理方面取得了进展，但利用歧义或颠覆语言规则的修辞手段仍然是这类系统面临的一个挑战。然而，基于语料库的文字游戏分析一直是人文学科研究的一个长期课题，包括文学批评、语言教育和翻译研究。这些研究需要大量的数据收集工作，这表明需要专门的文本检索和分类技术，因此需要适当的测试收集。本文介绍并分析了一个新的数据集，以便在文字游戏的检索和处理中进行研究和应用。在 CLEF 2023年为 JOKER 轨道开发，我们的注释语料库在几个方面扩展和改进了过去的英语文字游戏检测数据集。首先，我们介绍了数以百计的文字游戏的正面例子; 其次，我们提供了法语翻译的例子; 第三，我们提供了非文字游戏的负面例子，其特点与正面例子非常相似。最后一个特性有助于确保人工智能模型学会有效地区分文字游戏和非文字游戏，而不仅仅是文本的长度、风格或词汇不同。我们的测试集代表着向着支持文字游戏的多语言信息检索迈进了一步。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=The+JOKER+Corpus:+English-French+Parallel+Data+for+Multilingual+Wordplay+Recognition)|0| -|[Form-NLU: Dataset for the Form Natural Language Understanding](https://doi.org/10.1145/3539618.3591886)|Yihao Ding, Siqu Long, Jiabin Huang, Kaixuan Ren, Xingxiang Luo, Hyunsuk Chung, Soyeon Caren Han|FortifyEdge, Sydney, NSW, Australia; The University of Sydney, Sydney, NSW, Australia; The University of Western Australia, Perth, WA, Australia|Compared to general document analysis tasks, form document structure understanding and retrieval are challenging. Form documents are typically made by two types of authors; A form designer, who develops the form structure and keys, and a form user, who fills out form values based on the provided keys. Hence, the form values may not be aligned with the form designer's intention (structure and keys) if a form user gets confused. In this paper, we introduce Form-NLU, the first novel dataset for form structure understanding and its key and value information extraction, interpreting the form designer's intent and the alignment of user-written value on it. It consists of 857 form images, 6k form keys and values, and 4k table keys and values. Our dataset also includes three form types: digital, printed, and handwritten, which cover diverse form appearances and layouts. We propose a robust positional and logical relation-based form key-value information extraction framework. Using this dataset, Form-NLU, we first examine strong object detection models for the form layout understanding, then evaluate the key information extraction task on the dataset, providing fine-grained results for different types of forms and keys. Furthermore, we examine it with the off-the-shelf pdf layout extraction tool and prove its feasibility in real-world cases.|与一般的文档分析任务相比，表单文档结构的理解和检索具有挑战性。表单文档通常由两种类型的作者创建: 一种是开发表单结构和键的表单设计人员，另一种是根据提供的键填写表单值的表单用户。因此，如果表单用户弄混了，表单值可能与表单设计器的意图(结构和键)不一致。在本文中，我们将介绍第一个用于表单结构理解的新型数据集 Form-NLU 及其关键和价值信息抽取，解释表单设计者的意图以及用户书面价值的对齐。它由857个表单映像、6k 个表单键和值以及4k 个表单键和值组成。我们的数据集还包括三种形式类型: 数字、印刷和手写，涵盖了不同形式的外观和布局。我们提出了一个健壮的基于位置和逻辑关系的表单键值信息抽取框架。使用这个数据集 Form-nlU，我们首先检查表单布局理解的强目标检测模型，然后评估数据集上的关键信息抽取任务，为不同类型的表单和关键字提供细粒度的结果。此外，我们还利用现有的 pdf 版面抽取工具对其进行了检验，并在实际案例中证明了其可行性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Form-NLU:+Dataset+for+the+Form+Natural+Language+Understanding)|0| +|[The JOKER Corpus: English-French Parallel Data for Multilingual Wordplay Recognition](https://doi.org/10.1145/3539618.3591885)|Liana Ermakova, AnneGwenn Bosser, Adam Jatowt, Tristan Miller|Austrian Research Institute for Artificial Intelligence, Vienna, Austria; École Nationale d'Ingénieurs de Brest, Plouzané, France; Université de Bretagne Occidentale, Brest, France; University of Innsbruck, Innsbruck, Austria|Despite recent advances in information retrieval and natural language processing, rhetorical devices that exploit ambiguity or subvert linguistic rules remain a challenge for such systems. However, corpus-based analysis of wordplay has been a perennial topic of scholarship in the humanities, including literary criticism, language education, and translation studies. The immense data-gathering effort required for these studies points to the need for specialized text retrieval and classification technology, and consequently for appropriate test collections. In this paper, we introduce and analyze a new dataset for research and applications in the retrieval and processing of wordplay. Developed for the JOKER track at CLEF 2023, our annotated corpus extends and improves upon past English wordplay detection datasets in several ways. First, we introduce hundreds of additional positive examples of wordplay; second, we provide French translations for the examples; and third, we provide negative examples of non-wordplay with characteristics closely matching those of the positive examples. This last feature helps ensure that AI models learn to effectively distinguish wordplay from non-wordplay, and not simply texts differing in length, style, or vocabulary. Our test collection represents then a step towards wordplay-aware multilingual information retrieval.|尽管最近在信息检索和自然语言处理方面取得了进展，但利用歧义或颠覆语言规则的修辞手段仍然是这类系统面临的一个挑战。然而，基于语料库的文字游戏分析一直是人文学科研究的一个长期课题，包括文学批评、语言教育和翻译研究。这些研究需要大量的数据收集工作，这表明需要专门的文本检索和分类技术，因此需要适当的测试收集。本文介绍并分析了一个新的数据集，以便在文字游戏的检索和处理中进行研究和应用。在 CLEF 2023年为 JOKER 轨道开发，我们的注释语料库在几个方面扩展和改进了过去的英语文字游戏检测数据集。首先，我们介绍了数以百计的文字游戏的正面例子; 其次，我们提供了法语翻译的例子; 第三，我们提供了非文字游戏的负面例子，其特点与正面例子非常相似。最后一个特性有助于确保人工智能模型学会有效地区分文字游戏和非文字游戏，而不仅仅是文本的长度、风格或词汇不同。我们的测试集代表着向着支持文字游戏的多语言信息检索迈进了一步。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=The+JOKER+Corpus:+English-French+Parallel+Data+for+Multilingual+Wordplay+Recognition)|0| +|[Form-NLU: Dataset for the Form Natural Language Understanding](https://doi.org/10.1145/3539618.3591886)|Yihao Ding, Siqu Long, Jiabin Huang, Kaixuan Ren, Xingxiang Luo, Hyunsuk Chung, Soyeon Caren Han|The University of Western Australia, Perth, WA, Australia; The University of Sydney, Sydney, NSW, Australia; FortifyEdge, Sydney, NSW, Australia|Compared to general document analysis tasks, form document structure understanding and retrieval are challenging. Form documents are typically made by two types of authors; A form designer, who develops the form structure and keys, and a form user, who fills out form values based on the provided keys. Hence, the form values may not be aligned with the form designer's intention (structure and keys) if a form user gets confused. In this paper, we introduce Form-NLU, the first novel dataset for form structure understanding and its key and value information extraction, interpreting the form designer's intent and the alignment of user-written value on it. It consists of 857 form images, 6k form keys and values, and 4k table keys and values. Our dataset also includes three form types: digital, printed, and handwritten, which cover diverse form appearances and layouts. We propose a robust positional and logical relation-based form key-value information extraction framework. Using this dataset, Form-NLU, we first examine strong object detection models for the form layout understanding, then evaluate the key information extraction task on the dataset, providing fine-grained results for different types of forms and keys. Furthermore, we examine it with the off-the-shelf pdf layout extraction tool and prove its feasibility in real-world cases.|与一般的文档分析任务相比，表单文档结构的理解和检索具有挑战性。表单文档通常由两种类型的作者创建: 一种是开发表单结构和键的表单设计人员，另一种是根据提供的键填写表单值的表单用户。因此，如果表单用户弄混了，表单值可能与表单设计器的意图(结构和键)不一致。在本文中，我们将介绍第一个用于表单结构理解的新型数据集 Form-NLU 及其关键和价值信息抽取，解释表单设计者的意图以及用户书面价值的对齐。它由857个表单映像、6k 个表单键和值以及4k 个表单键和值组成。我们的数据集还包括三种形式类型: 数字、印刷和手写，涵盖了不同形式的外观和布局。我们提出了一个健壮的基于位置和逻辑关系的表单键值信息抽取框架。使用这个数据集 Form-nlU，我们首先检查表单布局理解的强目标检测模型，然后评估数据集上的关键信息抽取任务，为不同类型的表单和关键字提供细粒度的结果。此外，我们还利用现有的 pdf 版面抽取工具对其进行了检验，并在实际案例中证明了其可行性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Form-NLU:+Dataset+for+the+Form+Natural+Language+Understanding)|0| |[MMEAD: MS MARCO Entity Annotations and Disambiguations](https://doi.org/10.1145/3539618.3591887)|Chris Kamphuis, Aileen Lin, Siwen Yang, Jimmy Lin, Arjen P. de Vries, Faegheh Hasibi|Radboud University, Nijmegen, Netherlands; University of Waterloo, Waterloo, Canada|MMEAD, or MS MARCO Entity Annotations and Disambiguations, is a resource for entity links for the MS MARCO datasets. We specify a format to store and share links for both document and passage collections of MS MARCO. Following this specification, we release entity links to Wikipedia for documents and passages in both MS MARCO collections (v1 and v2). Entity links have been produced by the REL and BLINK systems. MMEAD is an easy-to-install Python package, allowing users to load the link data and entity embeddings effortlessly. Using MMEAD takes only a few lines of code. Finally, we show how MMEAD can be used for IR research that uses entity information. We show how to improve recall@1000 and MRR@10 on more complex queries on the MS MARCO v1 passage dataset by using this resource. We also demonstrate how entity expansions can be used for interactive search applications.|MMEAD，或 MS MARCO 实体注释和消除歧义，是一个为 MS MARCO 数据集实体链接的资源。我们指定了一个格式，以存储和共享的文件和文章的微软 MARCO 集合链接。遵循此规范，我们发布到 Wikipedia 的实体链接，以获取 MS MARCO 集合(v1和 v2)中的文档和段落。实体链接已由 REL 和 BLINK 系统生成。MMEAD 是一个易于安装的 Python 包，允许用户毫不费力地加载链接数据和实体嵌入。使用 MMEAD 只需要几行代码。最后，我们展示了 MMEAD 如何用于使用实体信息的 IR 研究。我们展示了如何通过使用这个资源来提高对 MS MARCO v1通道数据集的更复杂查询的召回@1000和 MRR@10。我们还演示了如何将实体扩展用于交互式搜索应用程序。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=MMEAD:+MS+MARCO+Entity+Annotations+and+Disambiguations)|0| -|[GammaGL: A Multi-Backend Library for Graph Neural Networks](https://doi.org/10.1145/3539618.3591891)|Yaoqi Liu, Cheng Yang, Tianyu Zhao, Hui Han, Siyuan Zhang, Jing Wu, Guangyu Zhou, Hai Huang, Hui Wang, Chuan Shi|Beijing University of Posts and Telecommunications & Peng Cheng Laboratory, Beijing, Shenzhen, China; Beijing University of Posts and Telecommunications, Beijing, China; Beijing University of Posts and Telecommunications & Peng Cheng Laboratory, Beijing, shenzhen, China; Peng Cheng Laboratory, Shenzhen, China|Graph Neural Networks (GNNs) have shown their superiority in modeling graph-structured data, and gained much attention over the last five years. Though traditional deep learning frameworks such as TensorFlow and PyTorch provide convenient tools for implementing neural network algorithms, they do not support the key operations of GNNs well, e.g., the message passing computation based on sparse matrices. To address this issue, GNN libraries such as PyG are proposed by introducing rich Application Programming Interfaces (APIs) specialized for GNNs. However, most current GNN libraries only support a specific deep learning framework as the backend, e.g., PyG is tied up with PyTorch. In practice, users usually need to combine GNNs with other neural network components, which may come from their co-workers or open-source codes with different deep-learning backends. Consequently, users have to be familiar with various GNN libraries, and rewrite their GNNs with corresponding APIs. To provide a more convenient user experience, we present Gamma Graph Library (GammaGL), a GNN library that supports multiple deep learning frameworks as backends. GammaGL uses a framework-agnostic design that allows users to easily switch between deep learning backends on top of existing components with a single line of code change. Following the tensor-centric design idea, GammaGL splits the graph data into several key tensors, and abstracts GNN computational processes (such as message passing and graph mini-batch operations) into a few key functions. We develop many efficient operators in GammaGL for acceleration. So far, GammaGL has provided more than 40 GNN examples that can be applied to a variety of downstream tasks. GammaGL also provides tools for heterogeneous graph neural networks and recommendations to facilitate research in related fields. We present the performance of models implemented by GammaGL and the time consumption of our optimized operators to show the efficiency. Our library is available at https://github.com/BUPT-GAMMA/GammaGL.|图形神经网络(GNN)在建立图形结构数据模型方面显示出其优越性，近五年来受到了广泛的关注。尽管传统的深度学习框架如 TensorFlow 和 PyTorch 为神经网络算法的实现提供了方便的工具，但它们并不能很好地支持 GNN 的关键操作，例如基于稀疏矩阵的消息传递计算。为了解决这个问题，通过引入专门用于 GNN 的丰富的应用程序编程接口(API) ，提出了像 PyG 这样的 GNN 库。然而，大多数当前的 GNN 库只支持特定的深度学习框架作为后端，例如，PyG 与 PyTorch 绑定在一起。在实践中，用户通常需要将 GNN 与其他神经网络组件结合起来，这些组件可能来自他们的同事或具有不同深度学习后端的开源代码。因此，用户必须熟悉各种 GNN 库，并用相应的 API 重写 GNN。为了提供更方便的用户体验，我们介绍了 GammaGraph Library (GammaGL) ，一个支持多个深度学习框架作为后端的 GNN 库。GammaGL 使用了一种与框架无关的设计，允许用户通过一行代码更改就可以轻松地在现有组件上的深度学习后端之间进行切换。遵循以张量为中心的设计思想，GammaGL 将图形数据分解为几个关键张量，并将 GNN 计算过程(如消息传递和图形小批处理操作)抽象为几个关键函数。我们在 GammaGL 中开发了许多有效的加速算子。到目前为止，GammaGL 已经提供了40多个 GNN 示例，可以应用于各种下游任务。GammaGL 还为异构图形神经网络提供了工具，并为相关领域的研究提供了建议。我们展示了由 GammaGL 实现的模型的性能和我们的优化算子的时间消耗，以显示效率。我们的图书馆 https://github.com/bupt-gamma/gammagl 有售。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=GammaGL:+A+Multi-Backend+Library+for+Graph+Neural+Networks)|0| +|[GammaGL: A Multi-Backend Library for Graph Neural Networks](https://doi.org/10.1145/3539618.3591891)|Yaoqi Liu, Cheng Yang, Tianyu Zhao, Hui Han, Siyuan Zhang, Jing Wu, Guangyu Zhou, Hai Huang, Hui Wang, Chuan Shi|Beijing University of Posts and Telecommunications & Peng Cheng Laboratory, Beijing, shenzhen, China; Beijing University of Posts and Telecommunications, Beijing, China; Beijing University of Posts and Telecommunications & Peng Cheng Laboratory, Beijing, Shenzhen, China; Peng Cheng Laboratory, Shenzhen, China|Graph Neural Networks (GNNs) have shown their superiority in modeling graph-structured data, and gained much attention over the last five years. Though traditional deep learning frameworks such as TensorFlow and PyTorch provide convenient tools for implementing neural network algorithms, they do not support the key operations of GNNs well, e.g., the message passing computation based on sparse matrices. To address this issue, GNN libraries such as PyG are proposed by introducing rich Application Programming Interfaces (APIs) specialized for GNNs. However, most current GNN libraries only support a specific deep learning framework as the backend, e.g., PyG is tied up with PyTorch. In practice, users usually need to combine GNNs with other neural network components, which may come from their co-workers or open-source codes with different deep-learning backends. Consequently, users have to be familiar with various GNN libraries, and rewrite their GNNs with corresponding APIs. To provide a more convenient user experience, we present Gamma Graph Library (GammaGL), a GNN library that supports multiple deep learning frameworks as backends. GammaGL uses a framework-agnostic design that allows users to easily switch between deep learning backends on top of existing components with a single line of code change. Following the tensor-centric design idea, GammaGL splits the graph data into several key tensors, and abstracts GNN computational processes (such as message passing and graph mini-batch operations) into a few key functions. We develop many efficient operators in GammaGL for acceleration. So far, GammaGL has provided more than 40 GNN examples that can be applied to a variety of downstream tasks. GammaGL also provides tools for heterogeneous graph neural networks and recommendations to facilitate research in related fields. We present the performance of models implemented by GammaGL and the time consumption of our optimized operators to show the efficiency. Our library is available at https://github.com/BUPT-GAMMA/GammaGL.|图形神经网络(GNN)在建立图形结构数据模型方面显示出其优越性，近五年来受到了广泛的关注。尽管传统的深度学习框架如 TensorFlow 和 PyTorch 为神经网络算法的实现提供了方便的工具，但它们并不能很好地支持 GNN 的关键操作，例如基于稀疏矩阵的消息传递计算。为了解决这个问题，通过引入专门用于 GNN 的丰富的应用程序编程接口(API) ，提出了像 PyG 这样的 GNN 库。然而，大多数当前的 GNN 库只支持特定的深度学习框架作为后端，例如，PyG 与 PyTorch 绑定在一起。在实践中，用户通常需要将 GNN 与其他神经网络组件结合起来，这些组件可能来自他们的同事或具有不同深度学习后端的开源代码。因此，用户必须熟悉各种 GNN 库，并用相应的 API 重写 GNN。为了提供更方便的用户体验，我们介绍了 GammaGraph Library (GammaGL) ，一个支持多个深度学习框架作为后端的 GNN 库。GammaGL 使用了一种与框架无关的设计，允许用户通过一行代码更改就可以轻松地在现有组件上的深度学习后端之间进行切换。遵循以张量为中心的设计思想，GammaGL 将图形数据分解为几个关键张量，并将 GNN 计算过程(如消息传递和图形小批处理操作)抽象为几个关键函数。我们在 GammaGL 中开发了许多有效的加速算子。到目前为止，GammaGL 已经提供了40多个 GNN 示例，可以应用于各种下游任务。GammaGL 还为异构图形神经网络提供了工具，并为相关领域的研究提供了建议。我们展示了由 GammaGL 实现的模型的性能和我们的优化算子的时间消耗，以显示效率。我们的图书馆 https://github.com/bupt-gamma/gammagl 有售。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=GammaGL:+A+Multi-Backend+Library+for+Graph+Neural+Networks)|0| |[tieval: An Evaluation Framework for Temporal Information Extraction Systems](https://doi.org/10.1145/3539618.3591892)|Hugo Sousa, Ricardo Campos, Alípio Mário Jorge|; INESC TEC & University of Porto, Porto, Portugal|Temporal information extraction (TIE) has attracted a great deal of interest over the last two decades, leading to the development of a significant number of datasets. Despite its benefits, having access to a large volume of corpora makes it difficult when it comes to benchmark TIE systems. On the one hand, different datasets have different annotation schemes, thus hindering the comparison between competitors across different corpora. On the other hand, the fact that each corpus is commonly disseminated in a different format requires a considerable engineering effort for a researcher/practitioner to develop parsers for all of them. This constraint forces researchers to select a limited amount of datasets to evaluate their systems which consequently limits the comparability of the systems. Yet another obstacle that hinders the comparability of the TIE systems is the evaluation metric employed. While most research works adopt traditional metrics such as precision, recall, and $F_1$, a few others prefer temporal awareness -- a metric tailored to be more comprehensive on the evaluation of temporal systems. Although the reason for the absence of temporal awareness in the evaluation of most systems is not clear, one of the factors that certainly weights this decision is the necessity to implement the temporal closure algorithm in order to compute temporal awareness, which is not straightforward to implement neither is currently easily available. All in all, these problems have limited the fair comparison between approaches and consequently, the development of temporal extraction systems. To mitigate these problems, we have developed tieval, a Python library that provides a concise interface for importing different corpora and facilitates system evaluation. In this paper, we present the first public release of tieval and highlight its most relevant features.|时间信息抽取(TIE)在过去的二十年中引起了人们的极大兴趣，导致了大量数据集的发展。尽管有这些好处，但是当涉及到基准 TIE 系统时，访问大量的语料库会变得困难。一方面，不同的数据集有不同的注释方案，从而阻碍了不同语料库的竞争对手之间的比较。另一方面，由于每个语料库通常以不同的格式传播，因此研究人员/从业人员需要付出大量的工程努力，为所有语料库开发解析器。这种约束迫使研究人员选择有限数量的数据集来评估他们的系统，从而限制了系统的可比性。妨碍 TIE 系统可比性的另一个障碍是所使用的评价标准。虽然大多数研究工作采用传统的度量方法，如精确度、召回率和 $F _ 1 $，但也有一些研究工作更喜欢时间感知——一种更全面地评估时间系统的度量方法。尽管在大多数系统的评估中缺乏时间感知的原因尚不清楚，但是确定这一决策权重的因素之一是为了计算时间感知而实现时间闭包算法的必要性，这种算法不容易实现，目前也不容易获得。总之，这些问题限制了各种方法之间的公平比较，从而限制了时间提取系统的发展。为了缓解这些问题，我们开发了 tieval，这是一个 Python 库，它为导入不同的语料库提供了一个简洁的接口，并有助于系统评估。在本文中，我们提出了第一次公开发布的时间和突出其最相关的特点。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=tieval:+An+Evaluation+Framework+for+Temporal+Information+Extraction+Systems)|0| -|[HC3: A Suite of Test Collections for CLIR Evaluation over Informal Text](https://doi.org/10.1145/3539618.3591893)|Dawn J. Lawrie, James Mayfield, Douglas W. Oard, Eugene Yang, Suraj Nair, Petra Galuscáková|University of Maryland, College Park, MD, USA; University of Maryland, College Park, MD, France; Johns Hopkins University, Baltimore, MD, USA; Université Grenoble Alpes, Grenoble, France|While there are many test collections for Cross-Language Information Retrieval (CLIR), none of the large public test collections focus on short informal text documents. This paper introduces a new pair of CLIR test collections with millions of Chinese or Persian Tweets or Tweet threads as documents, sixty event-motivated topics written both in English and in each of the two document languages, and three-point graded relevance judgments constructed using interactive search and active learning. The design and construction of these new test collections are described, and baseline results are presented that demonstrate the utility of the collections for system evaluation. Shallow pooling is used to assess the efficacy of active learning to select documents for judgment.|虽然有很多跨语检索测试集合(CLIR) ，但是没有一个大型的公共测试集合关注于简短的非正式文本文档。本文介绍了一组新的 CLIR 测试集，其中包括数百万条中文或波斯语推文或推文，六十个以事件为动机的英文和两种文档语言的主题，以及使用交互式搜索和主动学习构建的三点分级相关判断。描述了这些新的测试集合的设计和构造，并给出了基线结果，说明了这些集合用于系统评估的效用。浅池用于评估主动学习选择判断文档的效力。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=HC3:+A+Suite+of+Test+Collections+for+CLIR+Evaluation+over+Informal+Text)|0| +|[HC3: A Suite of Test Collections for CLIR Evaluation over Informal Text](https://doi.org/10.1145/3539618.3591893)|Dawn J. Lawrie, James Mayfield, Douglas W. Oard, Eugene Yang, Suraj Nair, Petra Galuscáková|Johns Hopkins University, Baltimore, MD, USA; University of Maryland, College Park, MD, France; Université Grenoble Alpes, Grenoble, France; University of Maryland, College Park, MD, USA|While there are many test collections for Cross-Language Information Retrieval (CLIR), none of the large public test collections focus on short informal text documents. This paper introduces a new pair of CLIR test collections with millions of Chinese or Persian Tweets or Tweet threads as documents, sixty event-motivated topics written both in English and in each of the two document languages, and three-point graded relevance judgments constructed using interactive search and active learning. The design and construction of these new test collections are described, and baseline results are presented that demonstrate the utility of the collections for system evaluation. Shallow pooling is used to assess the efficacy of active learning to select documents for judgment.|虽然有很多跨语检索测试集合(CLIR) ，但是没有一个大型的公共测试集合关注于简短的非正式文本文档。本文介绍了一组新的 CLIR 测试集，其中包括数百万条中文或波斯语推文或推文，六十个以事件为动机的英文和两种文档语言的主题，以及使用交互式搜索和主动学习构建的三点分级相关判断。描述了这些新的测试集合的设计和构造，并给出了基线结果，说明了这些集合用于系统评估的效用。浅池用于评估主动学习选择判断文档的效力。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=HC3:+A+Suite+of+Test+Collections+for+CLIR+Evaluation+over+Informal+Text)|0| |[BioSift: A Dataset for Filtering Biomedical Abstracts for Drug Repurposing and Clinical Meta-Analysis](https://doi.org/10.1145/3539618.3591897)|David Kartchner, Irfan AlHussaini, Haydn Turner, Jennifer Deng, Shubham Lohiya, Prasanth Bathala, Cassie S. Mitchell|Georgia Institute of Technology, Atlanta, GA, USA|This work presents a new, original document classification dataset, BioSift, to expedite the initial selection and labeling of studies for drug repurposing. The dataset consists of 10,000 human-annotated abstracts from scientific articles in PubMed. Each abstract is labeled with up to eight attributes necessary to perform meta-analysis utilizing the popular patient-intervention-comparator-outcome (PICO) method: has human subjects, is clinical trial/cohort, has population size, has target disease, has study drug, has comparator group, has a quantitative outcome, and an "aggregate" label. Each abstract was annotated by 3 different annotators (i.e., biomedical students) and randomly sampled abstracts were reviewed by senior annotators to ensure quality. Data statistics such as reviewer agreement, label co-occurrence, and confidence are shown. Robust benchmark results illustrate neither PubMed advanced filters nor state-of-the-art document classification schemes (e.g., active learning, weak supervision, full supervision) can efficiently replace human annotation. In short, BioSift is a pivotal but challenging document classification task to expedite drug repurposing. The full annotated dataset is publicly available and enables research development of algorithms for document classification that enhance drug repurposing.|这项工作提出了一个新的，原始的文档分类数据集，生物筛选，以加快研究的初步选择和标签药物再利用。这个数据集包含 PubMed 上10,000篇科学文章的人工注释摘要。每个摘要被标记为使用流行的患者-干预-比较-结果(PICO)方法进行荟萃分析所必需的多达8个属性: 具有人类受试者，是临床试验/队列，具有人口大小，具有目标疾病，具有研究药物，具有比较组，具有定量结果和“聚合”标签。每个摘要由3个不同的注释者(即生物医学学生)进行注释，并由高级注释者随机抽取摘要进行审查，以确保质量。数据统计，例如审查者协议，标签共现，和置信度显示。强大的基准测试结果表明，PubMed 高级过滤器和最先进的文档分类计划(例如，主动学习、弱监督、全面监督)都不能有效地取代人工注释。简而言之，BioSift 是加速药物再利用的关键但具有挑战性的文档分类任务。完整的注释数据集是公开的，可以用来研究开发文档分类的算法，从而加强药物再利用。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=BioSift:+A+Dataset+for+Filtering+Biomedical+Abstracts+for+Drug+Repurposing+and+Clinical+Meta-Analysis)|0| |[MoocRadar: A Fine-grained and Multi-aspect Knowledge Repository for Improving Cognitive Student Modeling in MOOCs](https://doi.org/10.1145/3539618.3591898)|Jifan Yu, Mengying Lu, Qingyang Zhong, Zijun Yao, Shangqing Tu, Zhengshan Liao, Xiaoya Li, Manli Li, Lei Hou, HaiTao Zheng, Juanzi Li, Jie Tang|Tsinghua University, Beijing, China; Tsinghua University, Shenzhen, China|Student modeling, the task of inferring a student's learning characteristics through their interactions with coursework, is a fundamental issue in intelligent education. Although the recent attempts from knowledge tracing and cognitive diagnosis propose several promising directions for improving the usability and effectiveness of current models, the existing public datasets are still insufficient to meet the need for these potential solutions due to their ignorance of complete exercising contexts, fine-grained concepts, and cognitive labels. In this paper, we present MoocRadar, a fine-grained, multi-aspect knowledge repository consisting of 2,513 exercise questions, 5,600 knowledge concepts, and over 12 million behavioral records. Specifically, we propose a framework to guarantee a high-quality and comprehensive annotation of fine-grained concepts and cognitive labels. The statistical and experimental results indicate that our dataset provides the basis for the future improvements of existing methods. Moreover, to support the convenient usage for researchers, we release a set of tools for data querying, model adaption, and even the extension of our repository, which are now available at https://github.com/THU-KEG/MOOC-Radar.|学生建模是智能教育中的一个基本问题，它是通过学生与课程的互动来推断学生学习特点的任务。尽管最近来自知识跟踪和认知诊断的尝试为改善当前模型的可用性和有效性提出了几个有希望的方向，但现有的公共数据集仍然不足以满足这些潜在解决方案的需要，因为它们对完整的锻炼背景，细粒度的概念和认知标签的无知。在本文中，我们介绍了 MoocRadar，它是一个细粒度的、多方面的知识库，由2,513个练习题、5,600个知识概念和超过1200万个行为记录组成。具体来说，我们提出了一个框架，以保证高质量和全面的细粒度概念和认知标签的注释。统计和实验结果表明，我们的数据集为未来现有方法的改进提供了基础。此外，为了支持研究人员方便的使用，我们发布了一系列工具，用于数据查询、模型适应，甚至扩展我们的知识库，这些工具现在都可以在 https://github.com/thu-keg/mooc-radar 上使用。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=MoocRadar:+A+Fine-grained+and+Multi-aspect+Knowledge+Repository+for+Improving+Cognitive+Student+Modeling+in+MOOCs)|0| |[DICE: a Dataset of Italian Crime Event news](https://doi.org/10.1145/3539618.3591904)|Giovanni Bonisoli, Maria Pia di Buono, Laura Po, Federica Rollo|University of Modena and Reggio Emilia, Modena, Italy; University of Napoli, Napoli, Italy|Extracting events from news stories as the aim of several Natural Language Processing (NLP) applications (e.g., question answering, news recommendation, news summarization) is not a trivial task, due to the complexity of natural language and the fact that news reporting is characterized by journalistic style and norms. Those aspects entail scattering an event description over several sentences within one document (or more documents), applying a mechanism of gradual specification of event-related information. This implies a widespread use of co-reference relations among the textual elements, conveying non-linear temporal information. In addition to this, despite the achievement of state-of-the-art results in several tasks, high-quality training datasets for non-English languages are rarely available. This paper presents our preliminary study to develop an annotated Dataset for Italian Crime Event news (DICE). The contribution of the paper are: (1) the creation of a corpus of 10,395 crime news; (2) the annotation schema; (3) a dataset of 10,395 news with automatic annotations; (4) a preliminary manual annotation using the proposed schema of 1000 documents. The first tests on DICE have compared the performance of a manual annotator with that of single-span and multi-span question answering models and shown there is still a gap in the models, especially when dealing with more complex annotation tasks and limited training data. This underscores the importance of investing in the creation of high-quality annotated datasets like DICE, which can provide a solid foundation for training and testing a wide range of NLP models.|从新闻故事中提取事件作为几个自然语言处理(nLP)应用程序的目标(例如，问题回答，新闻推荐，新闻摘要)并不是一项微不足道的任务，因为自然语言的复杂性以及新闻报道是拥有属性的新闻风格和规范。这些方面需要将事件描述分散在一个文档(或多个文档)中的几个句子中，应用逐步规范事件相关信息的机制。这意味着文本成分之间广泛使用共指关系，传递非线性时间信息。除此之外，尽管在几项任务中取得了最先进的成果，但是很少有非英语语言的高质量培训数据集。本文对意大利犯罪事件新闻(DICE)注释数据集的开发进行了初步研究。本文的主要贡献是: (1)建立了10395条犯罪新闻的语料库; (2)注释模式; (3)10395条新闻的自动注释数据集; (4)使用提出的1000个文档模式进行初步的手工注释。在 DICE 上进行的首次测试比较了手动注释器与单跨和多跨问答模型的性能，发现两者之间仍存在差距，尤其是在处理较复杂的注释任务和有限的训练数据时。这强调了投资于创建高质量注释数据集(如 DICE)的重要性，它可以为广泛的 NLP 模型的培训和测试提供坚实的基础。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=DICE:+a+Dataset+of+Italian+Crime+Event+news)|0| |[BDI-Sen: A Sentence Dataset for Clinical Symptoms of Depression](https://doi.org/10.1145/3539618.3591905)|Anxo Pérez, Javier Parapar, Álvaro Barreiro, Silvia LopezLarrosa|Universidade da Coruña, A Coruña, Spain|People tend to consider social platforms as convenient media for expressing their concerns and emotional struggles. With their widespread use, researchers could access and analyze user-generated content related to mental states. Computational models that exploit that data show promising results in detecting at-risk users based on engineered features or deep learning models. However, recent works revealed that these approaches have a limited capacity for generalization and interpretation when considering clinical settings. Grounding the models' decisions on clinical and recognized symptoms can help to overcome these limitations. In this paper, we introduce BDI-Sen, a symptom-annotated sentence dataset for depressive disorder. BDI-Sen covers all the symptoms present in the Beck Depression Inventory-II (BDI-II), a reliable questionnaire used for detecting and measuring depression. The annotations in the collection reflect whether a statement about the specific symptom is informative (i.e., exposes traces about the individual's state regarding that symptom). We thoroughly analyze this resource and explore linguistic style, emotional attribution, and other psycholinguistic markers. Additionally, we conduct a series of experiments investigating the utility of BDI-Sen for various tasks, including the detection and severity classification of symptoms. We also examine their generalization when considering symptoms from other mental diseases. BDI-Sen may aid the development of future models that consider trustworthy and valuable depression markers.|人们往往认为社交平台是表达自己关切和情感挣扎的便利媒体。随着它们的广泛使用，研究人员可以访问和分析与心理状态有关的用户生成内容。利用这些数据的计算模型在基于工程特征或深度学习模型的高风险用户检测方面显示出有希望的结果。然而，最近的工作表明，这些方法在考虑临床情况时，其概括和解释能力有限。建立模型对临床和已知症状的决策基础可以帮助克服这些局限性。在本文中，我们介绍了 BDI-Sen，一个抑郁症的症状注释句子数据集。BDI-Sen 涵盖了 Beck 抑郁量表 II (BDI-II)中的所有症状，这是一个用于检测和测量抑郁的可靠问卷。集合中的注释反映关于特定症状的语句是否具有信息性(即，公开关于该症状的个人状态的跟踪)。我们深入分析了这一资源，并探讨了语言风格，情感归因和其他心理语言标记。此外，我们还进行了一系列实验，调查 BDI-Sen 在各种任务中的效用，包括症状的检测和严重程度分类。当我们考虑其他精神疾病的症状时，我们也检查它们的概括性。BDI-Sen 可能有助于开发未来的模型，认为值得信赖和有价值的抑郁症标志物。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=BDI-Sen:+A+Sentence+Dataset+for+Clinical+Symptoms+of+Depression)|0| -|[REFinD: Relation Extraction Financial Dataset](https://doi.org/10.1145/3539618.3591911)|Simerjot Kaur, Charese Smiley, Akshat Gupta, Joy Sain, Dongsheng Wang, Suchetha Siddagangappa, Toyin Aguda, Sameena Shah|JPMorgan Chase and Co, London, United Kingdom; JPMorgan Chase and Co, New York, NY, USA; JPMorgan Chase and Co, Palo Alto, CA, USA; JPMorgan Chase and Co, Chicago, IL, USA; Wright State University, Dayton, OH, USA|A number of datasets for Relation Extraction (RE) have been created to aide downstream tasks such as information retrieval, semantic search, question answering and textual entailment. However, these datasets fail to capture financial-domain specific challenges since most of these datasets are compiled using general knowledge sources such as Wikipedia, web-based text and news articles, hindering real-life progress and adoption within the financial world. To address this limitation, we propose REFinD, the first large-scale annotated dataset of relations, with $\sim$29K instances and 22 relations amongst 8 types of entity pairs, generated entirely over financial documents. We also provide an empirical evaluation with various state-of-the-art models as benchmarks for the RE task and highlight the challenges posed by our dataset. We observed that various state-of-the-art deep learning models struggle with numeric inference, relational and directional ambiguity.|我们已经创建了一系列关系提取数据集，用于辅助下游任务，比如信息检索、语义搜索、问题回答和文字蕴涵。然而，这些数据集未能捕捉到金融领域的具体挑战，因为大多数这些数据集是使用一般知识来源(如维基百科、基于网络的文本和新闻文章)编译的，阻碍了金融领域的实际进展和采用。为了解决这一局限性，我们提出了 REFinD，第一个大规模的注释关系数据集，$sim $29K 实例和8种类型的实体对中的22个关系，完全在财务文档上生成。我们还提供了一个实证评估与各种国家的最先进的模型作为基准的可再生能源任务，并突出了我们的数据集所带来的挑战。我们观察到各种最先进的深度学习模型都在与数字推理、关系模糊和方向模糊做斗争。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=REFinD:+Relation+Extraction+Financial+Dataset)|0| +|[REFinD: Relation Extraction Financial Dataset](https://doi.org/10.1145/3539618.3591911)|Simerjot Kaur, Charese Smiley, Akshat Gupta, Joy Sain, Dongsheng Wang, Suchetha Siddagangappa, Toyin Aguda, Sameena Shah|Wright State University, Dayton, OH, USA; JPMorgan Chase and Co, Palo Alto, CA, USA; JPMorgan Chase and Co, London, United Kingdom; JPMorgan Chase and Co, New York, NY, USA; JPMorgan Chase and Co, Chicago, IL, USA|A number of datasets for Relation Extraction (RE) have been created to aide downstream tasks such as information retrieval, semantic search, question answering and textual entailment. However, these datasets fail to capture financial-domain specific challenges since most of these datasets are compiled using general knowledge sources such as Wikipedia, web-based text and news articles, hindering real-life progress and adoption within the financial world. To address this limitation, we propose REFinD, the first large-scale annotated dataset of relations, with $\sim$29K instances and 22 relations amongst 8 types of entity pairs, generated entirely over financial documents. We also provide an empirical evaluation with various state-of-the-art models as benchmarks for the RE task and highlight the challenges posed by our dataset. We observed that various state-of-the-art deep learning models struggle with numeric inference, relational and directional ambiguity.|我们已经创建了一系列关系提取数据集，用于辅助下游任务，比如信息检索、语义搜索、问题回答和文字蕴涵。然而，这些数据集未能捕捉到金融领域的具体挑战，因为大多数这些数据集是使用一般知识来源(如维基百科、基于网络的文本和新闻文章)编译的，阻碍了金融领域的实际进展和采用。为了解决这一局限性，我们提出了 REFinD，第一个大规模的注释关系数据集，$sim $29K 实例和8种类型的实体对中的22个关系，完全在财务文档上生成。我们还提供了一个实证评估与各种国家的最先进的模型作为基准的可再生能源任务，并突出了我们的数据集所带来的挑战。我们观察到各种最先进的深度学习模型都在与数字推理、关系模糊和方向模糊做斗争。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=REFinD:+Relation+Extraction+Financial+Dataset)|0| |[The BETTER Cross-Language Datasets](https://doi.org/10.1145/3539618.3591910)|Ian Soboroff|National Institute of Standards and Technology, Gaithersburg, MD, USA|The IARPA BETTER (Better Extraction from Text Through Enhanced Retrieval) program held three evaluations of information retrieval (IR) and information extraction (IE). For both tasks, the only training data available was in English, but systems had to perform cross-language retrieval and extraction from Arabic, Farsi, Chinese, Russian, and Korean. Pooled assessment and information extraction annotation were used to create reusable IR test collections. These datasets are freely available to researchers working in cross-language retrieval, information extraction, or the conjunction of IR and IE. This paper describes the datasets, how they were constructed, and how they might be used by researchers.|IARPA 的“更好的文本提取(通过增强检索)”项目对信息检索(IR)和信息抽取(IE)进行了三次评估。对于这两个任务，唯一可用的训练数据是英语，但系统必须执行跨语言检索和提取阿拉伯语、波斯语、中文、俄语和韩语。汇总评估和信息抽取注释用于创建可重用的 IR 测试集合。这些数据集免费提供给从事跨语言检索、信息抽取或 IR 和 IE 联合工作的研究人员。本文描述了数据集，它们是如何构建的，以及研究人员如何使用它们。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=The+BETTER+Cross-Language+Datasets)|0| -|[Linked-DocRED - Enhancing DocRED with Entity-Linking to Evaluate End-To-End Document-Level Information Extraction Pipelines](https://doi.org/10.1145/3539618.3591912)|PierreYves Genest, PierreEdouard Portier, Elöd EgyedZsigmond, Martino Lovisetto|Univ Lyon, INSA Lyon, CNRS, UCBL, LIRIS, UMR5205, Villeurbanne, France; Alteca, Lyon, France; Alteca & Univ Lyon, INSA Lyon, CNRS, UCBL, LIRIS, UMR5205, Lyon, France|Information Extraction (IE) pipelines aim to extract meaningful entities and relations from documents and structure them into a knowledge graph that can then be used in downstream applications. Training and evaluating such pipelines requires a dataset annotated with entities, coreferences, relations, and entity-linking. However, existing datasets either lack entity-linking labels, are too small, not diverse enough, or automatically annotated (that is, without a strong guarantee of the correction of annotations). Therefore, we propose Linked-DocRED, to the best of our knowledge, the first manually-annotated, large-scale, document-level IE dataset. We enhance the existing and widely-used DocRED dataset with entity-linking labels that are generated thanks to a semi-automatic process that guarantees high-quality annotations. In particular, we use hyperlinks in Wikipedia articles to provide disambiguation candidates. We also propose a complete framework of metrics to benchmark end-to-end IE pipelines, and we define an entity-centric metric to evaluate entity-linking. The evaluation of a baseline shows promising results while highlighting the challenges of an end-to-end IE pipeline. Linked-DocRED, the source code for the entity-linking, the baseline, and the metrics are distributed under an open-source license and can be downloaded from a public repository.|信息抽取(IE)管道旨在从文档中提取有意义的实体和关系，并将它们组织成一个知识图，然后用于下游应用程序。训练和评估这样的管道需要一个数据集，该数据集使用实体、共引用、关系和实体链接进行注释。然而，现有的数据集要么缺乏实体链接标签，要么太小，不够多样化，要么自动注释(也就是说，没有强有力的注释修正保证)。因此，我们提出了 Linked-DocRED，据我们所知，第一个手动注释的大型文档级 IE 数据集。我们使用实体链接标签来增强现有的和广泛使用的 DocRED 数据集，这些标签是通过一个半自动的过程生成的，这个过程保证了高质量的注释。特别是，我们在 Wikipedia 文章中使用超链接来提供消除歧义的候选者。我们还提出了一个完整的度量框架来基准端到端 IE 管道，并定义了一个以实体为中心的度量来评估实体链接。对基线的评估显示了有希望的结果，同时突出了端到端 IE 管道的挑战。LinkedDocRED，实体链接、基线和度量的源代码在开源许可下分发，可以从公共存储库下载。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Linked-DocRED+-+Enhancing+DocRED+with+Entity-Linking+to+Evaluate+End-To-End+Document-Level+Information+Extraction+Pipelines)|0| +|[Linked-DocRED - Enhancing DocRED with Entity-Linking to Evaluate End-To-End Document-Level Information Extraction Pipelines](https://doi.org/10.1145/3539618.3591912)|PierreYves Genest, PierreEdouard Portier, Elöd EgyedZsigmond, Martino Lovisetto|Alteca & Univ Lyon, INSA Lyon, CNRS, UCBL, LIRIS, UMR5205, Lyon, France; Univ Lyon, INSA Lyon, CNRS, UCBL, LIRIS, UMR5205, Villeurbanne, France; Alteca, Lyon, France|Information Extraction (IE) pipelines aim to extract meaningful entities and relations from documents and structure them into a knowledge graph that can then be used in downstream applications. Training and evaluating such pipelines requires a dataset annotated with entities, coreferences, relations, and entity-linking. However, existing datasets either lack entity-linking labels, are too small, not diverse enough, or automatically annotated (that is, without a strong guarantee of the correction of annotations). Therefore, we propose Linked-DocRED, to the best of our knowledge, the first manually-annotated, large-scale, document-level IE dataset. We enhance the existing and widely-used DocRED dataset with entity-linking labels that are generated thanks to a semi-automatic process that guarantees high-quality annotations. In particular, we use hyperlinks in Wikipedia articles to provide disambiguation candidates. We also propose a complete framework of metrics to benchmark end-to-end IE pipelines, and we define an entity-centric metric to evaluate entity-linking. The evaluation of a baseline shows promising results while highlighting the challenges of an end-to-end IE pipeline. Linked-DocRED, the source code for the entity-linking, the baseline, and the metrics are distributed under an open-source license and can be downloaded from a public repository.|信息抽取(IE)管道旨在从文档中提取有意义的实体和关系，并将它们组织成一个知识图，然后用于下游应用程序。训练和评估这样的管道需要一个数据集，该数据集使用实体、共引用、关系和实体链接进行注释。然而，现有的数据集要么缺乏实体链接标签，要么太小，不够多样化，要么自动注释(也就是说，没有强有力的注释修正保证)。因此，我们提出了 Linked-DocRED，据我们所知，第一个手动注释的大型文档级 IE 数据集。我们使用实体链接标签来增强现有的和广泛使用的 DocRED 数据集，这些标签是通过一个半自动的过程生成的，这个过程保证了高质量的注释。特别是，我们在 Wikipedia 文章中使用超链接来提供消除歧义的候选者。我们还提出了一个完整的度量框架来基准端到端 IE 管道，并定义了一个以实体为中心的度量来评估实体链接。对基线的评估显示了有希望的结果，同时突出了端到端 IE 管道的挑战。LinkedDocRED，实体链接、基线和度量的源代码在开源许可下分发，可以从公共存储库下载。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Linked-DocRED+-+Enhancing+DocRED+with+Entity-Linking+to+Evaluate+End-To-End+Document-Level+Information+Extraction+Pipelines)|0| |[A Preference Judgment Tool for Authoritative Assessment](https://doi.org/10.1145/3539618.3591801)|Mahsa Seifikar, Linh Nhi Phan Minh, Negar Arabzadeh, Charles L. A. Clarke, Mark D. Smucker|University of Waterloo, Waterloo, ON, Canada|Preference judgments have been established as an effective method for offline evaluation of information retrieval systems with advantages to graded or binary relevance judgments. Graded judgments assign each document a pre-defined grade level, while preference judgments involve assessing a pair of items presented side by side and indicating which is better. However, leveraging preference judgments may require a more extensive number of judgments, and there are limitations in terms of evaluation measures. In this study, we present a new preference judgment tool called JUDGO, designed for expert assessors and researchers. The tool is supported by a new heap-like preference judgment algorithm that assumes transitivity and allows for ties. An earlier version of the tool was employed by NIST to determine up to the top-10 best items for each of the 38 topics for the TREC 2022 Health Misinformation track, with over 2,200 judgments collected. The current version has been applied in a separate research study to collect almost 10,000 judgments, with multiple assessors completing each topic. The code and resources are available at https://judgo-system.github.io.|偏好判断已被确立为一种有效的方法，用于对信息检索系统进行离线评估，这种方法优于分级或二元相关判断。分级判断为每份文件分配一个预定义的等级水平，而偏好判断涉及评估一对项目并排出现，并指出哪一个更好。然而，利用偏好判断可能需要更广泛的判断数量，并且在评估措施方面存在局限性。在这项研究中，我们提出了一个新的偏好判断工具称为 JUDGO，专为专家评估员和研究人员设计。该工具由一个新的类似堆的偏好判断算法支持，该算法假定传递性并允许关系。NIST 使用了该工具的早期版本，为 TREC 2022健康错误信息跟踪的38个主题中的每个主题确定最多10个最佳项目，收集了超过2200个判断。目前的版本已经应用在一个单独的研究中，收集了近10,000个判断，由多个评估人员完成每个主题。代码和资源可在 https://judgo-system.github.io 查阅。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Preference+Judgment+Tool+for+Authoritative+Assessment)|0| |[One Stop Shop for Question-Answering Dataset Selection](https://doi.org/10.1145/3539618.3591804)|Chang Nian Chuy, Qinmin Vivian Hu, Chen Ding|Toronto Metropolitan University, Toronto, ON, Canada|In this paper, we offer a new visualization tool -- Dataset Statistical View (DSV), to lower the barrier of research entry by providing easy access to the question-answering (QA) datasets that researchers can build their work upon. Our target users are new researchers to the QA domain with no prior knowledge nor programming skills. The system is populated with multiple QA datasets, which covers a wide range of QA tasks. It allows researchers to explore and compare existing QA datasets at a one-stop website. The system shows statistical graphs for each QA dataset to offer an overview and a visual comparison between datasets. Although this paper focuses mainly at the syntactic level comparison, integrating bias and semantic level analysis is our ongoing work. We believe our DSV system is a valuable contribution to the advancement of the QA field, as it provides a solid starting point for new researchers and practitioners. An overview of the framework is demonstrated in this paper and the introduction of the application system is available at https://cnchuy.github.io/images/demo.mp4.|本文提出了一种新的可视化工具——数据集统计视图(Dataset Statistics View，DSV) ，通过提供易于访问的问答(QA)数据集来降低研究进入的障碍，研究人员可以在此基础上开展工作。我们的目标用户是 QA 领域的新研究人员，既没有先前的知识，也没有编程技能。该系统由多个 QA 数据集填充，这些数据集涵盖了广泛的 QA 任务。它允许研究人员在一站式网站上探索和比较现有的质量保证数据集。该系统显示每个质量保证数据集的统计图表，以提供数据集之间的概述和可视化比较。虽然本文主要集中在句法层面的比较，但是整合偏倚和语义层面的分析是我们正在进行的工作。我们相信，我们的 DSV 系统是一个宝贵的贡献，提高质量保证领域，因为它提供了一个坚实的起点，新的研究人员和从业人员。本文概述这个架构，并介绍应用系统的 https://cnchuy.github.io/images/demo.mp4。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=One+Stop+Shop+for+Question-Answering+Dataset+Selection)|0| |[Profiling and Visualizing Dynamic Pruning Algorithms](https://doi.org/10.1145/3539618.3591806)|Zhixuan Li, Joel Mackenzie|The University of Queensland, Brisbane, QLD, Australia|Efficiently retrieving the top-k documents for a given query is a fundamental operation in many search applications. Dynamic pruning algorithms accelerate top-k retrieval over inverted indexes by skipping documents that are not able to enter the current set of results. However, the performance of these algorithms depends on a number of variables such as the ranking function, the order of documents within the index, and the number of documents to be retrieved. In this paper, we propose a diagnostic framework, Dyno, for profiling and visualizing the performance of dynamic pruning algorithms. Our framework captures processing traces during retrieval, allowing the operations of the index traversal algorithm to be visualized. These visualizations support both query-level and system-to-system comparisons, enabling performance characteristics to be readily understood for different systems. Dyno benefits both academics and practitioners by furthering our understanding of the behavior of dynamic pruning algorithms, allowing better design choices to be made during experimentation and deployment.|有效地检索给定查询的 top-k 文档是许多搜索应用程序中的基本操作。动态剪枝算法通过跳过无法输入当前结果集的文档来加速对倒排索引的 top-k 检索。然而，这些算法的性能取决于许多变量，例如排名函数、索引中文档的顺序以及要检索的文档数量。在本文中，我们提出了一个诊断框架，Dyno，用于分析和可视化动态剪枝算法的性能。我们的框架在检索期间捕获处理跟踪，允许可视化索引遍历算法的操作。这些可视化支持查询级别和系统到系统的比较，从而能够容易地理解不同系统的性能特征。Dyno 通过加深我们对动态修剪算法行为的理解，使学者和实践者受益匪浅，从而允许在实验和部署过程中做出更好的设计选择。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Profiling+and+Visualizing+Dynamic+Pruning+Algorithms)|0| -|[NeuralKG-ind: A Python Library for Inductive Knowledge Graph Representation Learning](https://doi.org/10.1145/3539618.3591809)|Wen Zhang, Zhen Yao, Mingyang Chen, Zhiwei Huang, Huajun Chen|Zhejiang University, Ningbo, China; Zhejiang University, Hangzhou, China|Since the dynamic characteristics of knowledge graphs, many inductive knowledge graph representation learning (KGRL) works have been proposed in recent years, focusing on enabling prediction over new entities. NeuralKG-ind is the first library of inductive KGRL as an important update of NeuralKG library. It includes standardized processes, rich existing methods, decoupled modules, and comprehensive evaluation metrics. With NeuralKG-ind, it is easy for researchers and engineers to reproduce, redevelop, and compare inductive KGRL methods. The library, experimental methodologies, and model re-implementing results of NeuralKG-ind are all publicly released at https://github.com/zjukg/NeuralKG/tree/ind .|由于知识图的动态特性，近年来提出了许多归纳知识图表示学习(KGRL)的工作，重点是实现对新实体的预测。NeuralKG-ind 是第一个归纳 KGRL 文库，是 NeuralKG 文库的重要更新。它包括标准化过程、丰富的现有方法、解耦模块和全面的评估指标。使用 NeuralKG-ind，研究人员和工程师可以很容易地再现、重新开发和比较归纳 KGRL 方法。NeuralKG-ind 的数据库、实验方法和模型重新实现结果都在 https://github.com/zjukg/neuralkg/tree/ind 公开发布。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=NeuralKG-ind:+A+Python+Library+for+Inductive+Knowledge+Graph+Representation+Learning)|0| +|[NeuralKG-ind: A Python Library for Inductive Knowledge Graph Representation Learning](https://doi.org/10.1145/3539618.3591809)|Wen Zhang, Zhen Yao, Mingyang Chen, Zhiwei Huang, Huajun Chen|Zhejiang University, Hangzhou, China; Zhejiang University, Ningbo, China|Since the dynamic characteristics of knowledge graphs, many inductive knowledge graph representation learning (KGRL) works have been proposed in recent years, focusing on enabling prediction over new entities. NeuralKG-ind is the first library of inductive KGRL as an important update of NeuralKG library. It includes standardized processes, rich existing methods, decoupled modules, and comprehensive evaluation metrics. With NeuralKG-ind, it is easy for researchers and engineers to reproduce, redevelop, and compare inductive KGRL methods. The library, experimental methodologies, and model re-implementing results of NeuralKG-ind are all publicly released at https://github.com/zjukg/NeuralKG/tree/ind .|由于知识图的动态特性，近年来提出了许多归纳知识图表示学习(KGRL)的工作，重点是实现对新实体的预测。NeuralKG-ind 是第一个归纳 KGRL 文库，是 NeuralKG 文库的重要更新。它包括标准化过程、丰富的现有方法、解耦模块和全面的评估指标。使用 NeuralKG-ind，研究人员和工程师可以很容易地再现、重新开发和比较归纳 KGRL 方法。NeuralKG-ind 的数据库、实验方法和模型重新实现结果都在 https://github.com/zjukg/neuralkg/tree/ind 公开发布。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=NeuralKG-ind:+A+Python+Library+for+Inductive+Knowledge+Graph+Representation+Learning)|0| |[AMICA: Alleviating Misinformation for Chinese Americans](https://doi.org/10.1145/3539618.3591810)|Xiaoxiao Shang, Ye Chen, Yi Fang, Yuhong Liu, Subramaniam Vincent|Santa Clara University, Santa Clara, CA, USA|The increasing popularity of social media promotes the proliferation of misinformation, especially in the communities of Chinese-speaking diasporas, which has caused significant negative societal impacts. In addition, most of the existing efforts on misinformation mitigation have focused on English and other western languages, which makes numerous overseas Chinese a very vulnerable population to online disinformation campaigns. In this paper, we present AMICA, an information retrieval system for alleviating misinformation for Chinese Americans. AMICA dynamically collects data from popular social media platforms for Chinese Americans, including WeChat, Twitter, YouTube, and Chinese forums. The data are stored and indexed in Elasticsearch to provide advanced search functionalities. Given a user query, the ranking of social media posts considers both topical relevance and the likelihood of being misinformation.|社交媒体的日益普及促进了错误信息的扩散，特别是在讲华语的侨民社区，这已经造成了重大的负面社会影响。此外，现有的减少虚假信息的努力大多集中在英语和其他西方语言，这使得许多海外华人成为网络虚假信息活动的一个非常脆弱的群体。在这篇文章中，我们介绍了 AMICA，这是一个信息检索系统，用于减少对华裔美国人的错误信息。AMICA 为华裔美国人动态收集流行社交媒体平台的数据，包括微信、 Twitter、 YouTube 和中国论坛。这些数据在 Elasticsearch 存储和编制索引，以提供高级搜索功能。如果用户提出疑问，社交媒体帖子的排名既考虑了主题相关性，也考虑了错误信息的可能性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=AMICA:+Alleviating+Misinformation+for+Chinese+Americans)|0| |[PEPO: Petition Executing Processing Optimizer Based on Natural Language Processing](https://doi.org/10.1145/3539618.3591811)|YinWei Chiu, HsiaoChing Huang, ChengJu Lee, HsunPing Hsieh|National Cheng Kung University, Tainan, Taiwan Roc|In this paper, we propose "Petition Executing Process Optimizer (PEPO)," an AI-based petition processing system that features three components, (a) Department Classification, (b) Importance Assessment, and (c) Response Generation for improving the Public Work Bureau (PWB) 1999 Hotline petitions handling process in Taiwan. Our Department Classification algorithm has been evaluated with NDCG, achieving an impressive score of 86.48%, while the Important Assessment function has an accuracy rate of 85%. Besides, Response Generation enhances communication efficiency between the government and citizens. The PEPO system has been deployed as an online web service for the Public Works Bureau of the Tainan City Government. With PEPO, the PWB benefits greatly from the effectiveness and efficiency of handling citizens' petitions.|在本文中，我们提出了“请愿执行程序优化器(PEPO)”，一个基于人工智能的请愿处理系统，包括三个部分: (a)部门分类，(b)重要性评估，(c)响应生成，以改善台湾公共工程局(PWB)1999年热线请愿处理程序。我们的部门分类算法已经评估与 NDCG，取得了令人印象深刻的分数为86.48% ，而重要的评估功能的准确率为85% 。此外，响应生成提高了政府与市民之间的沟通效率。公共服务电子化系统是为台南市政府工务局提供的网上服务。有了 PEPO，工务局可以从处理公民请愿的效率和效力中获益匪浅。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=PEPO:+Petition+Executing+Processing+Optimizer+Based+on+Natural+Language+Processing)|0| -|[SEA: A Scalable Entity Alignment System](https://doi.org/10.1145/3539618.3591816)|Junyang Wu, Tianyi Li, Lu Chen, Yunjun Gao, Ziheng Wei|Aalborg University, Aalborg, Denmark; Huawei, Hangzhou, China; Zhejiang University, Hangzhou, China|Entity alignment (EA) aims to find equivalent entities in different knowledge graphs (KGs). State-of-the-art EA approaches generally use Graph Neural Networks (GNNs) to encode entities. However, most of them train the models and evaluate the results in a fullbatch fashion, which prohibits EA from being scalable on largescale datasets. To enhance the usability of GNN-based EA models in real-world applications, we present SEA, a scalable entity alignment system that enables to (i) train large-scale GNNs for EA, (ii) speed up the normalization and the evaluation process, and (iii) report clear results for users to estimate different models and parameter settings. SEA can be run on a computer with merely one graphic card. Moreover, SEA encompasses six state-of-the-art EA models and provides access for users to quickly establish and evaluate their own models. Thus, SEA allows users to perform EA without being involved in tedious implementations, such as negative sampling and GPU-accelerated evaluation. With SEA, users can gain a clear view of the model performance. In the demonstration, we show that SEA is user-friendly and is of high scalability even on computers with limited computational resources.|实体对齐(EA)的目的是在不同的知识图中寻找等价的实体。最先进的 EA 方法通常使用图形神经网络(GNN)对实体进行编码。然而，他们中的大多数以全批方式训练模型并评估结果，这阻碍了 EA 在大规模数据集上的可伸缩性。为了提高基于 GNN 的 EA 模型在实际应用中的可用性，我们提出了 SEA，这是一个可扩展的实体对齐系统，它能够(i)为 EA 训练大规模的 GNN，(ii)加速归一化和评估过程，以及(iii)报告清晰的结果，供用户估计不同的模型和参数设置。SEA 可以在一台计算机上运行，只需要一张图形卡。此外，SEA 还包括六种最先进的 EA 模型，并为用户提供了快速建立和评估自己模型的途径。因此，SEA 允许用户执行 EA，而不必参与繁琐的实现，例如负采样和 GPU 加速的评估。通过 SEA，用户可以清楚地看到模型的性能。在演示中，我们证明了 SEA 是用户友好的，即使在计算资源有限的计算机上也具有很高的可伸缩性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=SEA:+A+Scalable+Entity+Alignment+System)|0| +|[SEA: A Scalable Entity Alignment System](https://doi.org/10.1145/3539618.3591816)|Junyang Wu, Tianyi Li, Lu Chen, Yunjun Gao, Ziheng Wei|Aalborg University, Aalborg, Denmark; Zhejiang University, Hangzhou, China; Huawei, Hangzhou, China|Entity alignment (EA) aims to find equivalent entities in different knowledge graphs (KGs). State-of-the-art EA approaches generally use Graph Neural Networks (GNNs) to encode entities. However, most of them train the models and evaluate the results in a fullbatch fashion, which prohibits EA from being scalable on largescale datasets. To enhance the usability of GNN-based EA models in real-world applications, we present SEA, a scalable entity alignment system that enables to (i) train large-scale GNNs for EA, (ii) speed up the normalization and the evaluation process, and (iii) report clear results for users to estimate different models and parameter settings. SEA can be run on a computer with merely one graphic card. Moreover, SEA encompasses six state-of-the-art EA models and provides access for users to quickly establish and evaluate their own models. Thus, SEA allows users to perform EA without being involved in tedious implementations, such as negative sampling and GPU-accelerated evaluation. With SEA, users can gain a clear view of the model performance. In the demonstration, we show that SEA is user-friendly and is of high scalability even on computers with limited computational resources.|实体对齐(EA)的目的是在不同的知识图中寻找等价的实体。最先进的 EA 方法通常使用图形神经网络(GNN)对实体进行编码。然而，他们中的大多数以全批方式训练模型并评估结果，这阻碍了 EA 在大规模数据集上的可伸缩性。为了提高基于 GNN 的 EA 模型在实际应用中的可用性，我们提出了 SEA，这是一个可扩展的实体对齐系统，它能够(i)为 EA 训练大规模的 GNN，(ii)加速归一化和评估过程，以及(iii)报告清晰的结果，供用户估计不同的模型和参数设置。SEA 可以在一台计算机上运行，只需要一张图形卡。此外，SEA 还包括六种最先进的 EA 模型，并为用户提供了快速建立和评估自己模型的途径。因此，SEA 允许用户执行 EA，而不必参与繁琐的实现，例如负采样和 GPU 加速的评估。通过 SEA，用户可以清楚地看到模型的性能。在演示中，我们证明了 SEA 是用户友好的，即使在计算资源有限的计算机上也具有很高的可伸缩性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=SEA:+A+Scalable+Entity+Alignment+System)|0| |[TIB AV-Analytics: A Web-based Platform for Scholarly Video Analysis and Film Studies](https://doi.org/10.1145/3539618.3591820)|Matthias Springstein, Markos Stamatakis, Margret Plank, Julian Sittel, Roman Mauer, Oksana Bulgakowa, Ralph Ewerth, Eric MüllerBudack|TIB - Leibniz Information Centre for Science and Technology & Leibniz University Hannover, Hannover, Germany; TIB - Leibniz Information Centre for Science and Technology, Hannover, Germany; Johannes Gutenberg University Mainz, Mainz, Germany|Video analysis platforms that integrate automatic solutions for multimedia and information retrieval enable various applications in many disciplines including film and media studies, communication science, and education. However, current platforms for video analysis either focus on manual annotations or include only a few tools for automatic content analysis. In this paper, we present a novel web-based video analysis platform called TIB AV-Analytics (TIB-AV-A). Unlike previous platforms, TIB-AV-A integrates state-of-the-art approaches in the fields of computer vision, audio analysis, and natural language processing for many relevant video analysis tasks. To facilitate future extensions and to ensure interoperability with existing tools, the video analysis approaches are implemented in a plugin structure with appropriate interfaces and import-export functions. TIB-AV-A leverages modern web technologies to provide users with a responsive and interactive web interface that enables manual annotation and provides access to powerful deep learning tools without a requirement for specific hardware dependencies. Source code and demo are publicly available at: https://service.tib.eu/tibava.|集成了多媒体和信息检索自动解决方案的视频分析平台，使许多学科(包括电影和媒体研究、通信科学和教育)的各种应用成为可能。然而，目前用于视频分析的平台要么侧重于手动注释，要么只包括一些用于自动内容分析的工具。本文提出了一种新的基于网络的视频分析平台 TIB AV-Analytics (TIB-AV-A)。与以前的平台不同，TIB-AV-A 集成了计算机视觉、音频分析和自然语言处理领域的最先进的方法，用于许多相关的视频分析任务。为了促进未来的扩展，并确保与现有工具的互操作性，视频分析方法是在一个具有适当接口和导入导出功能的插件结构中实现的。TIB-AV-A 利用现代网络技术为用户提供响应和交互式的网络界面，支持手动注释，并提供访问强大的深度学习工具，而不需要特定的硬件依赖。源代码和演示可以在以下 https://service.tib.eu/tibava 公开获得。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=TIB+AV-Analytics:+A+Web-based+Platform+for+Scholarly+Video+Analysis+and+Film+Studies)|0| |[A Consumer Compensation System in Ride-hailing Service](https://doi.org/10.1145/3539618.3591829)|Zhe Yu, Chi Xia, Shaosheng Cao, Lin Zhou, Haibin Huang|DiDi Chuxing, Hangzhou, China|In the ride-hailing business, compensation is mostly used to motivate consumers to place more orders and grow the market scale. However, most of the previous studies focus on car-hailing services. Few works investigate localized smart transportation innovations, such as intra-city freight logistics and designated driving. In addition, satisfying consumer fairness and improving consumer surplus, with the objective of maximizing revenue, are also important. In this paper, we propose a consumer compensation system, where a transfer learning enhanced uplift modeling is designed to measure the elasticity, and a model predictive control based optimization is formulated to control the budget accurately. Our implementation is effective and can keep the online environment lightweight. The proposed system has been deployed in the production environment of the real-world ride-hailing platform for 300 days, which outperforms the expert strategy by using 0.5% less subsidy and achieving 14.4% more revenue.|在叫车业务中，薪酬主要用于激励消费者下更多的订单，扩大市场规模。然而，以前的大多数研究都集中在叫车服务上。很少有作品研究本地化的智能交通创新，如城市内的货运物流和指定驾驶。此外，满足消费者公平和改善以收益最大化为目标的消费者剩余也很重要。在这篇文章中，我们提出了一个消费者补偿系统，其中转移学习增强提升模型被设计来测量弹性，并且基于模型预估计控制的优化被制定来精确地控制预算。我们的实现是有效的，可以保持在线环境的轻量级。该系统已经在现实世界的叫车平台生产环境中部署了300天，比专家策略节省了0.5% 的补贴，获得了14.4% 的收入。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Consumer+Compensation+System+in+Ride-hailing+Service)|0| -|[Dialog-to-Actions: Building Task-Oriented Dialogue System via Action-Level Generation](https://doi.org/10.1145/3539618.3591832)|Yuncheng Hua, Xiangyu Xi, Zheng Jiang, Guanwei Zhang, Chaobo Sun, Guanglu Wan, Wei Ye|Peking University, Beijing, China; Meituan Group, Beijing, China|End-to-end generation-based approaches have been investigated and applied in task-oriented dialogue systems. However, in industrial scenarios, existing methods face the bottlenecks of controllability (e.g., domain-inconsistent responses, repetition problem, etc) and efficiency (e.g., long computation time, etc). In this paper, we propose a task-oriented dialogue system via action-level generation. Specifically, we first construct dialogue actions from large-scale dialogues and represent each natural language (NL) response as a sequence of dialogue actions. Further, we train a Sequence-to-Sequence model which takes the dialogue history as input and outputs sequence of dialogue actions. The generated dialogue actions are transformed into verbal responses. Experimental results show that our light-weighted method achieves competitive performance, and has the advantage of controllability and efficiency.|研究了基于端到端生成的方法，并将其应用于面向任务的对话系统。然而，在工业场景中，现有的方法面临着可控性(例如，领域不一致的响应、重复问题等)和效率(例如，长计算时间等)的瓶颈。在本文中，我们提出了一个面向任务的对话系统，通过行为级生成。具体来说，我们首先从大规模对话中构建对话行为，并将每个自然语言(NL)响应表示为一系列对话行为。进一步，我们训练了一个序列到序列模型，该模型以对话历史为输入，输出对话动作的序列。生成的对话动作转化为语言反应。实验结果表明，本文提出的轻量化方法具有良好的性能，并且具有可控性和高效性的优点。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Dialog-to-Actions:+Building+Task-Oriented+Dialogue+System+via+Action-Level+Generation)|0| -|[Context-Aware Classification of Legal Document Pages](https://doi.org/10.1145/3539618.3591839)|Pavlos Fragkogiannis, Martina Forster, Grace E. Lee, Dell Zhang|Thomson Reuters Labs, London, United Kingdom; Thomson Reuters Labs, Zug, Switzerland|For many business applications that require the processing, indexing, and retrieval of professional documents such as legal briefs (in PDF format etc.), it is often essential to classify the pages of any given document into their corresponding types beforehand. Most existing studies in the field of document image classification either focus on single-page documents or treat multiple pages in a document independently. Although in recent years a few techniques have been proposed to exploit the context information from neighboring pages to enhance document page classification, they typically cannot be utilized with large pre-trained language models due to the constraint on input length. In this paper, we present a simple but effective approach that overcomes the above limitation. Specifically, we enhance the input with extra tokens carrying sequential information about previous pages - introducing recurrence - which enables the usage of pre-trained Transformer models like BERT for context-aware page classification. Our experiments conducted on two legal datasets in English and Portuguese respectively show that the proposed approach can significantly improve the performance of document page classification compared to the non-recurrent setup as well as the other context-aware baselines.|对于许多需要处理、索引和检索专业文档(如法律摘要(PDF 格式等))的业务应用程序，通常必须事先将任何给定文档的页面分类为相应的类型。现有的文档图像分类研究大多集中于单页文档，或者单独处理多页文档。尽管近年来提出了一些利用相邻页面的上下文信息来提高文档页面分类的技术，但由于输入长度的限制，这些技术通常不能用于大型预训练语言模型。在本文中，我们提出了一个简单而有效的方法，克服了上述限制。具体来说，我们使用额外的标记来增强输入，这些标记携带关于前面页面的连续信息——引入循环——这使得可以使用像 BERT 这样的预先训练的 Transformer 模型来进行上下文感知的页面分类。我们分别在英文和葡萄牙文的两个法律数据集上进行的实验表明，与非经常性设置和其他上下文感知基线相比，该方法可以显著提高文档页面分类的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Context-Aware+Classification+of+Legal+Document+Pages)|0| -|[Enhancing Dynamic Image Advertising with Vision-Language Pre-training](https://doi.org/10.1145/3539618.3591844)|Zhoufutu Wen, Xinyu Zhao, Zhipeng Jin, Yi Yang, Wei Jia, Xiaodong Chen, Shuanglong Li, Lin Liu|Baidu Inc., Beijing, China; Peking University, Beijing, China|In the multimedia era, image is an effective medium in search advertising. Dynamic Image Advertising (DIA), a system that matches queries with ad images and generates multimodal ads, is introduced to improve user experience and ad revenue. The core of DIA is a query-image matching module performing ad image retrieval and relevance modeling. Current query-image matching suffers from limited and inconsistent data, and insufficient cross-modal interaction. Also, the separate optimization of retrieval and relevance models affects overall performance. To address this issue, we propose a vision-language framework consisting of two parts. First, we train a base model on large-scale image-text pairs to learn general multimodal representation. Then, we fine-tune the base model on advertising business data, unifying relevance modeling and retrieval through multi-objective learning. Our framework has been implemented in Baidu search advertising system "Phoneix Nest". Online evaluation shows that it improves cost per mille (CPM) and click-through rate (CTR) by 1.04% and 1.865%.|在多媒体时代，图像是搜索广告的有效媒介。为了提高用户体验和广告收入，引入了动态图像广告(DIA)系统，将查询与广告图像进行匹配，生成多模式广告。DIA 的核心是一个查询-图像匹配模块，用于进行广告图像检索和相关性建模。当前的查询-图像匹配存在数据有限、不一致以及跨模式交互不足的问题。此外，检索和相关性模型的单独优化也会影响整体性能。为了解决这个问题，我们提出了一个由两部分组成的视觉语言框架。首先，我们训练一个基于大规模图像-文本对的模型来学习一般的多模态表示。然后，对广告业务数据的基础模型进行微调，通过多目标学习将相关性建模与检索统一起来。我们的架构已在百度搜寻广告系统“ Phoneix Nest”中实施。在线评估显示，它使每公里成本(CPM)和点进率(CTR)分别提高了1.04% 和1.865% 。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Enhancing+Dynamic+Image+Advertising+with+Vision-Language+Pre-training)|0| +|[Dialog-to-Actions: Building Task-Oriented Dialogue System via Action-Level Generation](https://doi.org/10.1145/3539618.3591832)|Yuncheng Hua, Xiangyu Xi, Zheng Jiang, Guanwei Zhang, Chaobo Sun, Guanglu Wan, Wei Ye|Meituan Group, Beijing, China; Peking University, Beijing, China|End-to-end generation-based approaches have been investigated and applied in task-oriented dialogue systems. However, in industrial scenarios, existing methods face the bottlenecks of controllability (e.g., domain-inconsistent responses, repetition problem, etc) and efficiency (e.g., long computation time, etc). In this paper, we propose a task-oriented dialogue system via action-level generation. Specifically, we first construct dialogue actions from large-scale dialogues and represent each natural language (NL) response as a sequence of dialogue actions. Further, we train a Sequence-to-Sequence model which takes the dialogue history as input and outputs sequence of dialogue actions. The generated dialogue actions are transformed into verbal responses. Experimental results show that our light-weighted method achieves competitive performance, and has the advantage of controllability and efficiency.|研究了基于端到端生成的方法，并将其应用于面向任务的对话系统。然而，在工业场景中，现有的方法面临着可控性(例如，领域不一致的响应、重复问题等)和效率(例如，长计算时间等)的瓶颈。在本文中，我们提出了一个面向任务的对话系统，通过行为级生成。具体来说，我们首先从大规模对话中构建对话行为，并将每个自然语言(NL)响应表示为一系列对话行为。进一步，我们训练了一个序列到序列模型，该模型以对话历史为输入，输出对话动作的序列。生成的对话动作转化为语言反应。实验结果表明，本文提出的轻量化方法具有良好的性能，并且具有可控性和高效性的优点。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Dialog-to-Actions:+Building+Task-Oriented+Dialogue+System+via+Action-Level+Generation)|0| +|[Context-Aware Classification of Legal Document Pages](https://doi.org/10.1145/3539618.3591839)|Pavlos Fragkogiannis, Martina Forster, Grace E. Lee, Dell Zhang|Thomson Reuters Labs, Zug, Switzerland; Thomson Reuters Labs, London, United Kingdom|For many business applications that require the processing, indexing, and retrieval of professional documents such as legal briefs (in PDF format etc.), it is often essential to classify the pages of any given document into their corresponding types beforehand. Most existing studies in the field of document image classification either focus on single-page documents or treat multiple pages in a document independently. Although in recent years a few techniques have been proposed to exploit the context information from neighboring pages to enhance document page classification, they typically cannot be utilized with large pre-trained language models due to the constraint on input length. In this paper, we present a simple but effective approach that overcomes the above limitation. Specifically, we enhance the input with extra tokens carrying sequential information about previous pages - introducing recurrence - which enables the usage of pre-trained Transformer models like BERT for context-aware page classification. Our experiments conducted on two legal datasets in English and Portuguese respectively show that the proposed approach can significantly improve the performance of document page classification compared to the non-recurrent setup as well as the other context-aware baselines.|对于许多需要处理、索引和检索专业文档(如法律摘要(PDF 格式等))的业务应用程序，通常必须事先将任何给定文档的页面分类为相应的类型。现有的文档图像分类研究大多集中于单页文档，或者单独处理多页文档。尽管近年来提出了一些利用相邻页面的上下文信息来提高文档页面分类的技术，但由于输入长度的限制，这些技术通常不能用于大型预训练语言模型。在本文中，我们提出了一个简单而有效的方法，克服了上述限制。具体来说，我们使用额外的标记来增强输入，这些标记携带关于前面页面的连续信息——引入循环——这使得可以使用像 BERT 这样的预先训练的 Transformer 模型来进行上下文感知的页面分类。我们分别在英文和葡萄牙文的两个法律数据集上进行的实验表明，与非经常性设置和其他上下文感知基线相比，该方法可以显著提高文档页面分类的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Context-Aware+Classification+of+Legal+Document+Pages)|0| +|[Enhancing Dynamic Image Advertising with Vision-Language Pre-training](https://doi.org/10.1145/3539618.3591844)|Zhoufutu Wen, Xinyu Zhao, Zhipeng Jin, Yi Yang, Wei Jia, Xiaodong Chen, Shuanglong Li, Lin Liu|Peking University, Beijing, China; Baidu Inc., Beijing, China|In the multimedia era, image is an effective medium in search advertising. Dynamic Image Advertising (DIA), a system that matches queries with ad images and generates multimodal ads, is introduced to improve user experience and ad revenue. The core of DIA is a query-image matching module performing ad image retrieval and relevance modeling. Current query-image matching suffers from limited and inconsistent data, and insufficient cross-modal interaction. Also, the separate optimization of retrieval and relevance models affects overall performance. To address this issue, we propose a vision-language framework consisting of two parts. First, we train a base model on large-scale image-text pairs to learn general multimodal representation. Then, we fine-tune the base model on advertising business data, unifying relevance modeling and retrieval through multi-objective learning. Our framework has been implemented in Baidu search advertising system "Phoneix Nest". Online evaluation shows that it improves cost per mille (CPM) and click-through rate (CTR) by 1.04% and 1.865%.|在多媒体时代，图像是搜索广告的有效媒介。为了提高用户体验和广告收入，引入了动态图像广告(DIA)系统，将查询与广告图像进行匹配，生成多模式广告。DIA 的核心是一个查询-图像匹配模块，用于进行广告图像检索和相关性建模。当前的查询-图像匹配存在数据有限、不一致以及跨模式交互不足的问题。此外，检索和相关性模型的单独优化也会影响整体性能。为了解决这个问题，我们提出了一个由两部分组成的视觉语言框架。首先，我们训练一个基于大规模图像-文本对的模型来学习一般的多模态表示。然后，对广告业务数据的基础模型进行微调，通过多目标学习将相关性建模与检索统一起来。我们的架构已在百度搜寻广告系统“ Phoneix Nest”中实施。在线评估显示，它使每公里成本(CPM)和点进率(CTR)分别提高了1.04% 和1.865% 。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Enhancing+Dynamic+Image+Advertising+with+Vision-Language+Pre-training)|0| |[Modeling Spoken Information Queries for Virtual Assistants: Open Problems, Challenges and Opportunities](https://doi.org/10.1145/3539618.3591849)|Christophe Van Gysel|Apple, Cambridge, MA, USA|Virtual assistants are becoming increasingly important speech-driven Information Retrieval platforms that assist users with various tasks. We discuss open problems and challenges with respect to modeling spoken information queries for virtual assistants, and list opportunities where Information Retrieval methods and research can be applied to improve the quality of virtual assistant speech recognition. We discuss how query domain classification, knowledge graphs and user interaction data, and query personalization can be helpful to improve the accurate recognition of spoken information domain queries. Finally, we also provide a brief overview of current problems and challenges in speech recognition.|虚拟助手正在成为越来越重要的语音驱动信息检索平台，帮助用户完成各种任务。我们讨论了在虚拟助理语音信息查询建模方面存在的问题和挑战，并列出了可以应用信息检索方法和研究来提高虚拟助理语音识别质量的机会。讨论了查询域分类、知识图和用户交互数据以及查询个性化如何有助于提高对语音信息域查询的准确识别。最后，我们还简要概述了当前语音识别中存在的问题和挑战。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Modeling+Spoken+Information+Queries+for+Virtual+Assistants:+Open+Problems,+Challenges+and+Opportunities)|0| -|[Extracting Complex Named Entities in Legal Documents via Weakly Supervised Object Detection](https://doi.org/10.1145/3539618.3591852)|HsiuWei Yang, Abhinav Agrawal|Thomson Reuters Labs, Toronto, ON, Canada; Thomson Reuters Labs, Bangalore, India|Accurate Named Entity Recognition (NER) is crucial for various information retrieval tasks in industry. However, despite significant progress in traditional NER methods, the extraction of Complex Named Entities remains a relatively unexplored area. In this paper, we propose a novel system that combines object detection for Document Layout Analysis (DLA) with weakly supervised learning to address the challenge of extracting discontinuous complex named entities in legal documents. Notably, to the best of our knowledge, this is the first work to apply weak supervision to DLA. Our experimental results show that the model trained solely on pseudo labels outperforms the supervised baseline when gold-standard data is limited, highlighting the effectiveness of our proposed approach in reducing the dependency on annotated data.|精确命名实体识别(NER)对于工业中的各种信息检索任务都是至关重要的。然而，尽管在传统的 NER 方法中取得了重大进展，但是复杂命名实体的提取仍然是一个相对未开发的领域。在本文中，我们提出了一个新的系统，它结合了文档布局分析(DLA)的目标检测和弱监督式学习，以解决在法律文档中提取不连续的复杂命名实体的难题。值得注意的是，据我们所知，这是第一项对 DLA 实施薄弱监管的工作。我们的实验结果表明，当金标准数据有限时，仅用伪标签训练的模型优于监督基线，突出了我们提出的方法在减少对注释数据的依赖方面的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Extracting+Complex+Named+Entities+in+Legal+Documents+via+Weakly+Supervised+Object+Detection)|0| +|[Extracting Complex Named Entities in Legal Documents via Weakly Supervised Object Detection](https://doi.org/10.1145/3539618.3591852)|HsiuWei Yang, Abhinav Agrawal|Thomson Reuters Labs, Bangalore, India; Thomson Reuters Labs, Toronto, ON, Canada|Accurate Named Entity Recognition (NER) is crucial for various information retrieval tasks in industry. However, despite significant progress in traditional NER methods, the extraction of Complex Named Entities remains a relatively unexplored area. In this paper, we propose a novel system that combines object detection for Document Layout Analysis (DLA) with weakly supervised learning to address the challenge of extracting discontinuous complex named entities in legal documents. Notably, to the best of our knowledge, this is the first work to apply weak supervision to DLA. Our experimental results show that the model trained solely on pseudo labels outperforms the supervised baseline when gold-standard data is limited, highlighting the effectiveness of our proposed approach in reducing the dependency on annotated data.|精确命名实体识别(NER)对于工业中的各种信息检索任务都是至关重要的。然而，尽管在传统的 NER 方法中取得了重大进展，但是复杂命名实体的提取仍然是一个相对未开发的领域。在本文中，我们提出了一个新的系统，它结合了文档布局分析(DLA)的目标检测和弱监督式学习，以解决在法律文档中提取不连续的复杂命名实体的难题。值得注意的是，据我们所知，这是第一项对 DLA 实施薄弱监管的工作。我们的实验结果表明，当金标准数据有限时，仅用伪标签训练的模型优于监督基线，突出了我们提出的方法在减少对注释数据的依赖方面的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Extracting+Complex+Named+Entities+in+Legal+Documents+via+Weakly+Supervised+Object+Detection)|0| |[Improving Programming Q&A with Neural Generative Augmentation](https://doi.org/10.1145/3539618.3591860)|Suthee Chaidaroon, Xiao Zhang, Shruti Subramaniyam, Jeffrey Svajlenko, Tanya Shourya, Iman Keivanloo, Ria Joy|Amazon Web Service, Seattle, WA, USA|Knowledge-intensive programming Q&A is an active research area in industry. Its application boosts developer productivity by aiding developers in quickly finding programming answers from the vast amount of information on the Internet. In this study, we propose ProQANS and its variants ReProQANS and ReAugProQANS to tackle programming Q&A. ProQANS is a neural search approach that leverages unlabeled data on the Internet (such as StackOverflow) to mitigate the cold-start problem. ReProQANS extends ProQANS by utilizing reformulated queries with a novel triplet loss. We further use an auxiliary generative model to augment the training queries, and design a novel dual triplet loss function to adapt these generated queries, to build another variant of ReProQANS termed as ReAugProQANS. In our empirical experiments, we show ReProQANS has the best performance when evaluated on the in-domain test set, while ReAugProQANS has the superior performance on the out-of-domain real programming questions, by outperforming the state-of-the-art model by up to 477% lift on the MRR metric respectively. The results suggest their robustness to previously unseen questions and its wide application to real programming questions.|知识密集型规划问答是工业界一个活跃的研究领域。它的应用程序通过帮助开发人员从互联网上的大量信息中快速找到编程答案来提高开发人员的生产力。在这项研究中，我们提出 ProQANS 及其变体 ReProQANS 和 ReAugProQANS 来解决编程问答。ProQANS 是一种神经搜索方法，它利用 Internet 上的未标记数据(如 StackOverflow)来缓解冷启动问题。ReProQANS 通过使用具有新的三联体损失的重新制定的查询来扩展 ProQANS。我们进一步使用一个辅助生成模型来增加训练查询，并设计一个新颖的双三重丢失函数来适应这些生成的查询，以构建另一种称为 ReaugproQANS 的 ReProQANS。在我们的实证实验中，我们发现 ReProQANS 在域内测试集上的表现最好，而 ReAugProQANS 在域外实际编程问题上的表现更好，在 MRR 指标上的表现分别比最先进的模型高出477% 。结果表明，它们对以前看不见的问题的鲁棒性及其对真正的编程问题的广泛应用。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Improving+Programming+Q&A+with+Neural+Generative+Augmentation)|0| -|[Uncertainty Quantification for Text Classification](https://doi.org/10.1145/3539618.3594243)|Dell Zhang, Murat Sensoy, Masoud Makrehchi, Bilyana TanevaPopova, Lin Gui, Yulan He|Thomson Reuters Labs, Toronto, ON, Canada; Amazon Alexa AI, London, United Kingdom; King's College London, London, United Kingdom; King's College London & The Alan Turing Institute, London, United Kingdom; Thomson Reuters Labs, London, United Kingdom; Thomson Reuters Labs, Zug, Switzerland|This full-day tutorial introduces modern techniques for practical uncertainty quantification specifically in the context of multi-class and multi-label text classification. First, we explain the usefulness of estimating aleatoric uncertainty and epistemic uncertainty for text classification models. Then, we describe several state-of-the-art approaches to uncertainty quantification and analyze their scalability to big text data: Virtual Ensemble in GBDT, Bayesian Deep Learning (including Deep Ensemble, Monte-Carlo Dropout, Bayes by Backprop, and their generalization Epistemic Neural Networks), Evidential Deep Learning (including Prior Networks and Posterior Networks), as well as Distance Awareness (including Spectral-normalized Neural Gaussian Process and Deep Deterministic Uncertainty). Next, we talk about the latest advances in uncertainty quantification for pre-trained language models (including asking language models to express their uncertainty, interpreting uncertainties of text classifiers built on large-scale language models, uncertainty estimation in text generation, calibration of language models, and calibration for in-context learning). After that, we discuss typical application scenarios of uncertainty quantification in text classification (including in-domain calibration, cross-domain robustness, and novel class detection). Finally, we list popular performance metrics for the evaluation of uncertainty quantification effectiveness in text classification. Practical hands-on examples/exercises are provided to the attendees for them to experiment with different uncertainty quantification methods on a few real-world text classification datasets such as CLINC150.|本教程介绍了在多类和多标签文本分类的背景下实用的不确定性量化的现代技术。首先，我们解释了文本分类模型中估计随机不确定性和认知不确定性的有用性。然后，我们描述了几种最先进的不确定性量化方法，并分析了它们对大文本数据的可伸缩性: GBDT 中的虚拟集合，贝叶斯深度学习(包括深度集合，Monte-Carlo 辍学，Bayes by Backsupport 及其泛化认知神经网络) ，证据深度学习(包括先验网络和后验网络) ，以及距离感知(包括谱归一化神经高斯过程和深度确定性不确定性)。接下来，我们将讨论预训练语言模型不确定性量化的最新进展(包括要求语言模型表达它们的不确定性，解释建立在大规模语言模型上的文本分类器的不确定性，文本生成中的不确定性估计，语言模型的校准，以及在上下文中学习的校准)。然后，我们讨论了不确定性量化在文本分类中的典型应用场景(包括域内校准、跨域鲁棒性和新的类检测)。最后，我们列出了文本分类中用于评估不确定性量化效果的常用性能指标。我们提供实际的例子/练习给与会者，让他们在一些真实世界的文本分类数据集(例如 CLINC150)上尝试不同的不确定性量化方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Uncertainty+Quantification+for+Text+Classification)|0| -|[Offline Pseudo Relevance Feedback for Efficient and Effective Single-pass Dense Retrieval](https://doi.org/10.1145/3539618.3592028)|Xueru Wen, Xiaoyang Chen, Xuanang Chen, Ben He, Le Sun|Institute of Software, Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences & Institute of Software, Chinese Academy of Sciences, Beijing, China|Dense retrieval has made significant advancements in information retrieval (IR) by achieving high levels of effectiveness while maintaining online efficiency during a single-pass retrieval process. However, the application of pseudo relevance feedback (PRF) to further enhance retrieval effectiveness results in a doubling of online latency. To address this challenge, this paper presents a single-pass dense retrieval framework that shifts the PRF process offline through the utilization of pre-generated pseudo-queries. As a result, online retrieval is reduced to a single matching with the pseudo-queries, hence providing faster online retrieval. The effectiveness of the proposed approach is evaluated on the standard TREC DL and HARD datasets, and the results demonstrate its promise. Our code is openly available at https://github.com/Rosenberg37/OPRF.|密集检索通过在一次性检索过程中保持在线效率的同时实现高水平的有效性，在信息检索(IR)方面取得了重大进展。然而，应用伪关联反馈(PRF)进一步提高检索效率会导致在线延迟加倍。为了解决这个问题，本文提出了一个单通道密集检索框架，通过利用预生成的伪查询将 PRF 过程转移到离线状态。因此，在线检索减少为与伪查询的单一匹配，从而提供更快的在线检索。在标准 TREC DL 和 HARD 数据集上对该方法的有效性进行了评估，结果表明了该方法的有效性。我们的代码在 https://github.com/rosenberg37/oprf 公开可用。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Offline+Pseudo+Relevance+Feedback+for+Efficient+and+Effective+Single-pass+Dense+Retrieval)|-1| -|[Exploring Scenarios of Uncertainty about the Users' Preferences in Interactive Recommendation Systems](https://doi.org/10.1145/3539618.3591684)|Nícollas Silva, Thiago Silva, Henrique Hott, Yan Ribeiro, Adriano C. M. Pereira, Leonardo Rocha|Federal University of São João Del Rei, São João del-Rei, Brazil; Universidade Federal de Minas Gerais, Belo Horizonte, Brazil|Interactive Recommender Systems have played a crucial role in distinct entertainment domains through a Contextual Bandit model. Despite the current advances, their personalisation level is still directly related to the information previously available about the users. However, there are at least two scenarios of uncertainty about the users' preferences over their journey: (1) when the user joins for the first time and (2) when the system continually makes wrong recommendations because of prior misleading assumptions. In this work, we introduce concepts from the Active Learning theory to mitigate the impact of such scenarios. We modify three traditional bandits to recommend items with a higher potential to get more user information without decreasing the model's accuracy when an uncertain scenario is observed. Our experiments show that the modified models outperform all baselines by increasing the cumulative reward in the long run. Moreover, a counterfactual evaluation validates that such improvements were not simply achieved due to the bias of offline datasets.|交互式推荐系统通过上下文强盗模式在不同的娱乐领域发挥了重要作用。尽管目前的进步，他们的个性化水平仍然直接相关的信息以前可用的用户。然而，至少存在两种不确定性情景: (1)当用户第一次加入时; (2)当系统由于先前的误导性假设而不断提出错误的建议时。在这项工作中，我们引入了主动学习理论的概念，以减轻这种情景的影响。在不确定情景下，通过修改三个传统的土匪推荐模型，在不降低模型精度的前提下，获得更多的用户信息。我们的实验表明，修改后的模型通过增加长期的累积报酬优于所有的基线。此外，一个反事实的评估验证了这样的改进不仅仅是由于离线数据集的偏见而实现的。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Exploring+Scenarios+of+Uncertainty+about+the+Users'+Preferences+in+Interactive+Recommendation+Systems)|-1| +|[Uncertainty Quantification for Text Classification](https://doi.org/10.1145/3539618.3594243)|Dell Zhang, Murat Sensoy, Masoud Makrehchi, Bilyana TanevaPopova, Lin Gui, Yulan He|King's College London, London, United Kingdom; Thomson Reuters Labs, London, United Kingdom; King's College London & The Alan Turing Institute, London, United Kingdom; Thomson Reuters Labs, Toronto, ON, Canada; Thomson Reuters Labs, Zug, Switzerland; Amazon Alexa AI, London, United Kingdom|This full-day tutorial introduces modern techniques for practical uncertainty quantification specifically in the context of multi-class and multi-label text classification. First, we explain the usefulness of estimating aleatoric uncertainty and epistemic uncertainty for text classification models. Then, we describe several state-of-the-art approaches to uncertainty quantification and analyze their scalability to big text data: Virtual Ensemble in GBDT, Bayesian Deep Learning (including Deep Ensemble, Monte-Carlo Dropout, Bayes by Backprop, and their generalization Epistemic Neural Networks), Evidential Deep Learning (including Prior Networks and Posterior Networks), as well as Distance Awareness (including Spectral-normalized Neural Gaussian Process and Deep Deterministic Uncertainty). Next, we talk about the latest advances in uncertainty quantification for pre-trained language models (including asking language models to express their uncertainty, interpreting uncertainties of text classifiers built on large-scale language models, uncertainty estimation in text generation, calibration of language models, and calibration for in-context learning). After that, we discuss typical application scenarios of uncertainty quantification in text classification (including in-domain calibration, cross-domain robustness, and novel class detection). Finally, we list popular performance metrics for the evaluation of uncertainty quantification effectiveness in text classification. Practical hands-on examples/exercises are provided to the attendees for them to experiment with different uncertainty quantification methods on a few real-world text classification datasets such as CLINC150.|本教程介绍了在多类和多标签文本分类的背景下实用的不确定性量化的现代技术。首先，我们解释了文本分类模型中估计随机不确定性和认知不确定性的有用性。然后，我们描述了几种最先进的不确定性量化方法，并分析了它们对大文本数据的可伸缩性: GBDT 中的虚拟集合，贝叶斯深度学习(包括深度集合，Monte-Carlo 辍学，Bayes by Backsupport 及其泛化认知神经网络) ，证据深度学习(包括先验网络和后验网络) ，以及距离感知(包括谱归一化神经高斯过程和深度确定性不确定性)。接下来，我们将讨论预训练语言模型不确定性量化的最新进展(包括要求语言模型表达它们的不确定性，解释建立在大规模语言模型上的文本分类器的不确定性，文本生成中的不确定性估计，语言模型的校准，以及在上下文中学习的校准)。然后，我们讨论了不确定性量化在文本分类中的典型应用场景(包括域内校准、跨域鲁棒性和新的类检测)。最后，我们列出了文本分类中用于评估不确定性量化效果的常用性能指标。我们提供实际的例子/练习给与会者，让他们在一些真实世界的文本分类数据集(例如 CLINC150)上尝试不同的不确定性量化方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Uncertainty+Quantification+for+Text+Classification)|0| +|[Offline Pseudo Relevance Feedback for Efficient and Effective Single-pass Dense Retrieval](https://doi.org/10.1145/3539618.3592028)|Xueru Wen, Xiaoyang Chen, Xuanang Chen, Ben He, Le Sun|University of Chinese Academy of Sciences & Institute of Software, Chinese Academy of Sciences, Beijing, China; Institute of Software, Chinese Academy of Sciences, Beijing, China|Dense retrieval has made significant advancements in information retrieval (IR) by achieving high levels of effectiveness while maintaining online efficiency during a single-pass retrieval process. However, the application of pseudo relevance feedback (PRF) to further enhance retrieval effectiveness results in a doubling of online latency. To address this challenge, this paper presents a single-pass dense retrieval framework that shifts the PRF process offline through the utilization of pre-generated pseudo-queries. As a result, online retrieval is reduced to a single matching with the pseudo-queries, hence providing faster online retrieval. The effectiveness of the proposed approach is evaluated on the standard TREC DL and HARD datasets, and the results demonstrate its promise. Our code is openly available at https://github.com/Rosenberg37/OPRF.|密集检索通过在一次性检索过程中保持在线效率的同时实现高水平的有效性，在信息检索(IR)方面取得了重大进展。然而，应用伪关联反馈(PRF)进一步提高检索效率会导致在线延迟加倍。为了解决这个问题，本文提出了一个单通道密集检索框架，通过利用预生成的伪查询将 PRF 过程转移到离线状态。因此，在线检索减少为与伪查询的单一匹配，从而提供更快的在线检索。在标准 TREC DL 和 HARD 数据集上对该方法的有效性进行了评估，结果表明了该方法的有效性。我们的代码在 https://github.com/rosenberg37/oprf 公开可用。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Offline+Pseudo+Relevance+Feedback+for+Efficient+and+Effective+Single-pass+Dense+Retrieval)|-1| +|[Exploring Scenarios of Uncertainty about the Users' Preferences in Interactive Recommendation Systems](https://doi.org/10.1145/3539618.3591684)|Nícollas Silva, Thiago Silva, Henrique Hott, Yan Ribeiro, Adriano C. M. Pereira, Leonardo Rocha|Universidade Federal de Minas Gerais, Belo Horizonte, Brazil; Federal University of São João Del Rei, São João del-Rei, Brazil|Interactive Recommender Systems have played a crucial role in distinct entertainment domains through a Contextual Bandit model. Despite the current advances, their personalisation level is still directly related to the information previously available about the users. However, there are at least two scenarios of uncertainty about the users' preferences over their journey: (1) when the user joins for the first time and (2) when the system continually makes wrong recommendations because of prior misleading assumptions. In this work, we introduce concepts from the Active Learning theory to mitigate the impact of such scenarios. We modify three traditional bandits to recommend items with a higher potential to get more user information without decreasing the model's accuracy when an uncertain scenario is observed. Our experiments show that the modified models outperform all baselines by increasing the cumulative reward in the long run. Moreover, a counterfactual evaluation validates that such improvements were not simply achieved due to the bias of offline datasets.|交互式推荐系统通过上下文强盗模式在不同的娱乐领域发挥了重要作用。尽管目前的进步，他们的个性化水平仍然直接相关的信息以前可用的用户。然而，至少存在两种不确定性情景: (1)当用户第一次加入时; (2)当系统由于先前的误导性假设而不断提出错误的建议时。在这项工作中，我们引入了主动学习理论的概念，以减轻这种情景的影响。在不确定情景下，通过修改三个传统的土匪推荐模型，在不降低模型精度的前提下，获得更多的用户信息。我们的实验表明，修改后的模型通过增加长期的累积报酬优于所有的基线。此外，一个反事实的评估验证了这样的改进不仅仅是由于离线数据集的偏见而实现的。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Exploring+Scenarios+of+Uncertainty+about+the+Users'+Preferences+in+Interactive+Recommendation+Systems)|-1| |[Exploring the Spatiotemporal Features of Online Food Recommendation Service](https://doi.org/10.1145/3539618.3591853)|Shaochuan Lin, Jiayan Pei, Taotao Zhou, Hengxu He, Jia Jia, Ning Hu||Online Food Recommendation Service (OFRS) has remarkable spatiotemporal characteristics and the advantage of being able to conveniently satisfy users' needs in a timely manner. There have been a variety of studies that have begun to explore its spatiotemporal properties, but a comprehensive and in-depth analysis of the OFRS spatiotemporal features is yet to be conducted. Therefore, this paper studies the OFRS based on three questions: how spatiotemporal features play a role; why self-attention cannot be used to model the spatiotemporal sequences of OFRS; and how to combine spatiotemporal features to improve the efficiency of OFRS. Firstly, through experimental analysis, we systemically extracted the spatiotemporal features of OFRS, identified the most valuable features and designed an effective combination method. Secondly, we conducted a detailed analysis of the spatiotemporal sequences, which revealed the shortcomings of self-attention in OFRS, and proposed a more optimized spatiotemporal sequence method for replacing self-attention. In addition, we also designed a Dynamic Context Adaptation Model to further improve the efficiency and performance of OFRS. Through the offline experiments on two large datasets and online experiments for a week, the feasibility and superiority of our model were proven.|在线食品推荐服务(OFRS)具有显著的时空特征和能够方便、及时地满足用户需求的优势。已经有各种各样的研究开始探索它的时空特性，但是对 OFRS 时空特性的全面和深入的分析还有待进行。为此，本文从时空特征如何发挥作用、为什么自我注意不能用于 OFRS 时空序列建模以及如何结合时空特征提高 OFRS 效率三个方面对 OFRS 进行了研究。首先，通过实验分析，系统地提取了 OFRS 的时空特征，识别出最有价值的特征，并设计了一种有效的组合方法。其次，我们对时空序列进行了详细的分析，揭示了 OFRS 中自我注意的缺点，并提出了一种更优化的时空序列替代自我注意的方法。此外，我们还设计了一个动态上下文适应模型来进一步提高 OFRS 的效率和性能。通过两个大型数据集的离线实验和为期一周的在线实验，验证了该模型的可行性和优越性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Exploring+the+Spatiotemporal+Features+of+Online+Food+Recommendation+Service)|-1| -|[MDKG: Graph-Based Medical Knowledge-Guided Dialogue Generation](https://doi.org/10.1145/3539618.3592019)|Usman Naseem, Surendrabikram Thapa, Qi Zhang, Liang Hu, Mehwish Nasim|The University of Western Australia & Flinders University, Perth, WA, Australia; University of Sydney, Sydney, NSW, Australia; Virginia Tech, Blacksburg, VA, USA; Tongji University, Shanghai, China|Medical dialogue systems (MDS) have shown promising abilities to diagnose through a conversation with a patient like a human doctor would. However, current systems are mostly based on sequence modeling, which does not account for medical knowledge. This makes the systems more prone to misdiagnosis in case of diseases with limited information. To overcome this issue, we present MDKG, an end-to-end dialogue system for medical dialogue generation (MDG) specifically designed to adapt to new diseases by quickly learning and evolving a meta-knowledge graph that allows it to reason about disease-symptom correlations. Our approach relies on a medical knowledge graph to extract disease-symptom relationships and uses a dynamic graph-based meta-learning framework to learn how to evolve the given knowledge graph to reason about disease-symptom correlations. Our approach incorporates medical knowledge and hence reduces the need for a large number of dialogues. Evaluations show that our system outperforms existing approaches when tested on benchmark datasets.|医学对话系统(MDS)已经显示出通过与病人交谈来诊断的有前途的能力，就像人类医生会做的那样。然而，目前的系统大多基于序列建模，没有考虑到医学知识。这使得系统在信息有限的情况下更容易误诊疾病。为了克服这个问题，我们提出了 MDKG，一个用于医疗对话生成(MDG)的端到端对话系统，专门设计用于通过快速学习和发展元知识图来适应新的疾病，使其能够推理疾病-症状相关性。我们的方法依赖于医学知识图来提取疾病-症状关系，并使用基于动态图的元学习框架来学习如何进化给定的知识图以推断疾病-症状相关性。我们的方法结合了医学知识，因此减少了对大量对话的需要。评估表明，当在基准数据集上进行测试时，我们的系统优于现有的方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=MDKG:+Graph-Based+Medical+Knowledge-Guided+Dialogue+Generation)|-1| +|[MDKG: Graph-Based Medical Knowledge-Guided Dialogue Generation](https://doi.org/10.1145/3539618.3592019)|Usman Naseem, Surendrabikram Thapa, Qi Zhang, Liang Hu, Mehwish Nasim|University of Sydney, Sydney, NSW, Australia; Virginia Tech, Blacksburg, VA, USA; Tongji University, Shanghai, China; The University of Western Australia & Flinders University, Perth, WA, Australia|Medical dialogue systems (MDS) have shown promising abilities to diagnose through a conversation with a patient like a human doctor would. However, current systems are mostly based on sequence modeling, which does not account for medical knowledge. This makes the systems more prone to misdiagnosis in case of diseases with limited information. To overcome this issue, we present MDKG, an end-to-end dialogue system for medical dialogue generation (MDG) specifically designed to adapt to new diseases by quickly learning and evolving a meta-knowledge graph that allows it to reason about disease-symptom correlations. Our approach relies on a medical knowledge graph to extract disease-symptom relationships and uses a dynamic graph-based meta-learning framework to learn how to evolve the given knowledge graph to reason about disease-symptom correlations. Our approach incorporates medical knowledge and hence reduces the need for a large number of dialogues. Evaluations show that our system outperforms existing approaches when tested on benchmark datasets.|医学对话系统(MDS)已经显示出通过与病人交谈来诊断的有前途的能力，就像人类医生会做的那样。然而，目前的系统大多基于序列建模，没有考虑到医学知识。这使得系统在信息有限的情况下更容易误诊疾病。为了克服这个问题，我们提出了 MDKG，一个用于医疗对话生成(MDG)的端到端对话系统，专门设计用于通过快速学习和发展元知识图来适应新的疾病，使其能够推理疾病-症状相关性。我们的方法依赖于医学知识图来提取疾病-症状关系，并使用基于动态图的元学习框架来学习如何进化给定的知识图以推断疾病-症状相关性。我们的方法结合了医学知识，因此减少了对大量对话的需要。评估表明，当在基准数据集上进行测试时，我们的系统优于现有的方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=MDKG:+Graph-Based+Medical+Knowledge-Guided+Dialogue+Generation)|-1| diff --git a/papers/sigir/sigir2024.md b/papers/sigir/sigir2024.md index ddcbf693..45db255c 100644 --- a/papers/sigir/sigir2024.md +++ b/papers/sigir/sigir2024.md @@ -3,192 +3,192 @@ |论文|作者|组织|摘要|翻译|代码|引用数| |---|---|---|---|---|---|---| |[Optimization Methods for Personalizing Large Language Models through Retrieval Augmentation](https://doi.org/10.1145/3626772.3657783)|Alireza Salemi, Surya Kallumadi, Hamed Zamani|Lowe's Companies, Inc.; University of Massachusetts Amherst|This paper studies retrieval-augmented approaches for personalizing large language models (LLMs), which potentially have a substantial impact on various applications and domains. We propose the first attempt to optimize the retrieval models that deliver a limited number of personal documents to large language models for the purpose of personalized generation. We develop two optimization algorithms that solicit feedback from the downstream personalized generation tasks for retrieval optimization–one based on reinforcement learning whose reward function is defined using any arbitrary metric for personalized generation and another based on knowledge distillation from the downstream LLM to the retrieval model. This paper also introduces a pre- and post-generation retriever selection model that decides what retriever to choose for each LLM input. Extensive experiments on diverse tasks from the language model personalization (LaMP) benchmark reveal statistically significant improvements in six out of seven datasets.|本文研究了个性化大型语言模型(LLM)的检索增强方法，这些方法可能对各种应用和领域产生重大影响。我们首次尝试优化检索模型，将有限数量的个人文档提供给大型语言模型，以实现个性化生成。我们开发了两个优化算法，从下游的个性化生成任务中寻求反馈进行检索优化-一个基于强化学习，其奖励函数是定义使用任意指标的个性化生成和另一个基于知识提取从下游 LLM 到检索模型。本文还介绍了一个前生成和后生成的检索器选择模型，该模型决定检索器为每个 LLM 输入选择什么。从语言模型个性化(LaMP)基准对不同任务的广泛实验显示，七个数据集中有六个在统计学上有显著的改善。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Optimization+Methods+for+Personalizing+Large+Language+Models+through+Retrieval+Augmentation)|4| -|[On Generative Agents in Recommendation](https://doi.org/10.1145/3626772.3657844)|An Zhang, Yuxin Chen, Leheng Sheng, Xiang Wang, TatSeng Chua|Tsinghua University; Recommendation System, Large Language Model; National University of Singapore|Recommender systems are the cornerstone of today's information dissemination,yet a disconnect between offline metrics and online performance greatly hinderstheir development. Addressing this challenge, we envision a recommendationsimulator, capitalizing on recent breakthroughs in human-level intelligenceexhibited by Large Language Models (LLMs). We propose Agent4Rec, a usersimulator in recommendation, leveraging LLM-empowered generative agentsequipped with user profile, memory, and actions modules specifically tailoredfor the recommender system. In particular, these agents' profile modules areinitialized using real-world datasets (e.g. MovieLens, Steam, Amazon-Book),capturing users' unique tastes and social traits; memory modules log bothfactual and emotional memories and are integrated with an emotion-drivenreflection mechanism; action modules support a wide variety of behaviors,spanning both taste-driven and emotion-driven actions. Each agent interactswith personalized recommender models in a page-by-page manner, relying on apre-implemented collaborative filtering-based recommendation algorithm. Wedelve into both the capabilities and limitations of Agent4Rec, aiming toexplore an essential research question: “To what extent can LLM-empoweredgenerative agents faithfully simulate the behavior of real, autonomous humansin recommender systems?” Extensive and multi-faceted evaluations of Agent4Rechighlight both the alignment and deviation between agents and user-personalizedpreferences. Beyond mere performance comparison, we explore insightfulexperiments, such as emulating the filter bubble effect and discovering theunderlying causal relationships in recommendation tasks. Our codes areavailable at https://github.com/LehengTHU/Agent4Rec.|推荐系统是当今信息传播的基石，然而离线指标和在线性能之间的脱节严重阻碍了它们的发展。为了应对这一挑战，我们设想了一个推荐模拟器，利用大型语言模型(LLM)在人类智力水平方面的最新突破。我们推荐 Agent4Rec，一个用户模拟器，利用 LLM 授权的生成代理，配备用户配置文件、内存和专门为推荐系统量身定制的操作模块。特别是，这些代理的个人资料模块是使用真实世界的数据集(如 MovieLens，Stream，Amazon-Book)初始化的，捕捉用户独特的品味和社会特征; 记忆模块记录事实和情感记忆，并与情感驱动的反射机制相结合; 行动模块支持各种各样的行为，跨越品味驱动和情感驱动的行为。每个代理以逐页的方式与个性化推荐模型进行交互，依赖于先前实现的基于协同过滤的推荐算法。探讨 Agent4Rec 的能力和局限性，旨在探索一个基本的研究问题: “ LLM 授权的生成代理能在多大程度上忠实地模拟真实的、自主的人类推荐系统的行为?”Agent4Rechight 的广泛和多方面的评估突出了代理和用户个性化偏好之间的一致性和偏差。除了单纯的性能比较，我们还探索了一些有洞察力的实验，比如模拟过滤泡效应和发现推荐任务中潜在的因果关系。我们的密码可以在 https://github.com/lehengthu/agent4rec 找到。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=On+Generative+Agents+in+Recommendation)|4| -|[C-Pack: Packed Resources For General Chinese Embeddings](https://doi.org/10.1145/3626772.3657878)|Shitao Xiao, Zheng Liu, Peitian Zhang, Niklas Muennighoff, Defu Lian, JianYun Nie|UTSC, Hefei, China; University of Montreal, Montreal, Canada; Beijing Academy of AI, Beijing, China; Renmin University of China, Beijing, China; HuggingFace, Beijing, China|We introduce C-Pack, a package of resources that significantly advances the field of general text embeddings for Chinese. C-Pack includes three critical resources. 1) C-MTP is a massive training dataset for text embedding, which is based on the curation of vast unlabeled corpora and the integration of high-quality labeled corpora. 2) C-MTEB is a comprehensive benchmark for Chinese text embeddings covering 6 tasks and 35 datasets. 3) BGE is a family of embedding models covering multiple sizes. Our models outperform all prior Chinese text embeddings on C-MTEB by more than +10% upon the time of the release. We also integrate and optimize the entire suite of training methods for BGE. Along with our resources on general Chinese embedding, we release our data and models for English text embeddings. The English models also achieve state-of-the-art performance on the MTEB benchmark; meanwhile, our released English data is 2 times larger than the Chinese data. Both Chinese and English datasets are the largest public release of training data for text embeddings. All these resources are made publicly available at https://github.com/FlagOpen/FlagEmbedding.|我们介绍了 C-Pack，这是一个资源包，它极大地推动了中文通用文本嵌入领域的发展。C-Pack 包括三个关键资源。1) C-MTP 是一个大规模的文本嵌入训练数据集，它建立在对大量未标记语料库的管理和对高质量标记语料库的集成的基础之上。2) C-MTEB 是一个涵盖6个任务和35个数据集的中文文本嵌入的综合基准。3) BGE 是一个多尺度嵌入模型家族。我们的模型比 C-MTEB 之前的所有中文文本嵌入在发布时的表现都要好10% 以上。我们还整合和优化了 BGE 的整套培训方法。随着我们的一般中文嵌入资源，我们发布了我们的数据和英文文本嵌入模型。英语模型在 MTEB 基准上也达到了最先进的性能，同时，我们发布的英语数据是中文数据的2倍。中文和英文数据集是最大的文本嵌入训练数据的公开发布。所有这些资源都可以在 https://github.com/flagopen/flagembedding 上公开获得。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=C-Pack:+Packed+Resources+For+General+Chinese+Embeddings)|3| +|[On Generative Agents in Recommendation](https://doi.org/10.1145/3626772.3657844)|An Zhang, Yuxin Chen, Leheng Sheng, Xiang Wang, TatSeng Chua|Recommendation System, Large Language Model; National University of Singapore; Tsinghua University|Recommender systems are the cornerstone of today's information dissemination,yet a disconnect between offline metrics and online performance greatly hinderstheir development. Addressing this challenge, we envision a recommendationsimulator, capitalizing on recent breakthroughs in human-level intelligenceexhibited by Large Language Models (LLMs). We propose Agent4Rec, a usersimulator in recommendation, leveraging LLM-empowered generative agentsequipped with user profile, memory, and actions modules specifically tailoredfor the recommender system. In particular, these agents' profile modules areinitialized using real-world datasets (e.g. MovieLens, Steam, Amazon-Book),capturing users' unique tastes and social traits; memory modules log bothfactual and emotional memories and are integrated with an emotion-drivenreflection mechanism; action modules support a wide variety of behaviors,spanning both taste-driven and emotion-driven actions. Each agent interactswith personalized recommender models in a page-by-page manner, relying on apre-implemented collaborative filtering-based recommendation algorithm. Wedelve into both the capabilities and limitations of Agent4Rec, aiming toexplore an essential research question: “To what extent can LLM-empoweredgenerative agents faithfully simulate the behavior of real, autonomous humansin recommender systems?” Extensive and multi-faceted evaluations of Agent4Rechighlight both the alignment and deviation between agents and user-personalizedpreferences. Beyond mere performance comparison, we explore insightfulexperiments, such as emulating the filter bubble effect and discovering theunderlying causal relationships in recommendation tasks. Our codes areavailable at https://github.com/LehengTHU/Agent4Rec.|推荐系统是当今信息传播的基石，然而离线指标和在线性能之间的脱节严重阻碍了它们的发展。为了应对这一挑战，我们设想了一个推荐模拟器，利用大型语言模型(LLM)在人类智力水平方面的最新突破。我们推荐 Agent4Rec，一个用户模拟器，利用 LLM 授权的生成代理，配备用户配置文件、内存和专门为推荐系统量身定制的操作模块。特别是，这些代理的个人资料模块是使用真实世界的数据集(如 MovieLens，Stream，Amazon-Book)初始化的，捕捉用户独特的品味和社会特征; 记忆模块记录事实和情感记忆，并与情感驱动的反射机制相结合; 行动模块支持各种各样的行为，跨越品味驱动和情感驱动的行为。每个代理以逐页的方式与个性化推荐模型进行交互，依赖于先前实现的基于协同过滤的推荐算法。探讨 Agent4Rec 的能力和局限性，旨在探索一个基本的研究问题: “ LLM 授权的生成代理能在多大程度上忠实地模拟真实的、自主的人类推荐系统的行为?”Agent4Rechight 的广泛和多方面的评估突出了代理和用户个性化偏好之间的一致性和偏差。除了单纯的性能比较，我们还探索了一些有洞察力的实验，比如模拟过滤泡效应和发现推荐任务中潜在的因果关系。我们的密码可以在 https://github.com/lehengthu/agent4rec 找到。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=On+Generative+Agents+in+Recommendation)|4| +|[C-Pack: Packed Resources For General Chinese Embeddings](https://doi.org/10.1145/3626772.3657878)|Shitao Xiao, Zheng Liu, Peitian Zhang, Niklas Muennighoff, Defu Lian, JianYun Nie|University of Montreal, Montreal, Canada; Renmin University of China, Beijing, China; UTSC, Hefei, China; Beijing Academy of AI, Beijing, China; HuggingFace, Beijing, China|We introduce C-Pack, a package of resources that significantly advances the field of general text embeddings for Chinese. C-Pack includes three critical resources. 1) C-MTP is a massive training dataset for text embedding, which is based on the curation of vast unlabeled corpora and the integration of high-quality labeled corpora. 2) C-MTEB is a comprehensive benchmark for Chinese text embeddings covering 6 tasks and 35 datasets. 3) BGE is a family of embedding models covering multiple sizes. Our models outperform all prior Chinese text embeddings on C-MTEB by more than +10% upon the time of the release. We also integrate and optimize the entire suite of training methods for BGE. Along with our resources on general Chinese embedding, we release our data and models for English text embeddings. The English models also achieve state-of-the-art performance on the MTEB benchmark; meanwhile, our released English data is 2 times larger than the Chinese data. Both Chinese and English datasets are the largest public release of training data for text embeddings. All these resources are made publicly available at https://github.com/FlagOpen/FlagEmbedding.|我们介绍了 C-Pack，这是一个资源包，它极大地推动了中文通用文本嵌入领域的发展。C-Pack 包括三个关键资源。1) C-MTP 是一个大规模的文本嵌入训练数据集，它建立在对大量未标记语料库的管理和对高质量标记语料库的集成的基础之上。2) C-MTEB 是一个涵盖6个任务和35个数据集的中文文本嵌入的综合基准。3) BGE 是一个多尺度嵌入模型家族。我们的模型比 C-MTEB 之前的所有中文文本嵌入在发布时的表现都要好10% 以上。我们还整合和优化了 BGE 的整套培训方法。随着我们的一般中文嵌入资源，我们发布了我们的数据和英文文本嵌入模型。英语模型在 MTEB 基准上也达到了最先进的性能，同时，我们发布的英语数据是中文数据的2倍。中文和英文数据集是最大的文本嵌入训练数据的公开发布。所有这些资源都可以在 https://github.com/flagopen/flagembedding 上公开获得。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=C-Pack:+Packed+Resources+For+General+Chinese+Embeddings)|3| |[Large Language Models can Accurately Predict Searcher Preferences](https://doi.org/10.1145/3626772.3657707)|Paul Thomas, Seth Spielman, Nick Craswell, Bhaskar Mitra|Microsoft|Much of the evaluation and tuning of a search system relies on relevance labels---annotations that say whether a document is useful for a given search and searcher. Ideally these come from real searchers, but it is hard to collect this data at scale, so typical experiments rely on third-party labellers who may or may not produce accurate annotations. Label quality is managed with ongoing auditing, training, and monitoring. We discuss an alternative approach. We take careful feedback from real searchers and use this to select a large language model (LLM), and prompt, that agrees with this feedback; the LLM can then produce labels at scale. Our experiments show LLMs are as accurate as human labellers and as useful for finding the best systems and hardest queries. LLM performance varies with prompt features, but also varies unpredictably with simple paraphrases. This unpredictability reinforces the need for high-quality "gold" labels.|搜索系统的大部分评估和调整都依赖于相关标签——说明文档是否对给定的搜索和搜索者有用的注释。理想情况下，这些数据来自真正的搜索者，但是很难大规模地收集这些数据，所以典型的实验依赖于第三方标注者，他们可能会或可能不会产生准确的注释。通过持续的审核、培训和监控来管理标签质量。我们讨论另一种方法。我们从真正的搜索者那里获得仔细的反馈，并使用它来选择一个大型语言模型(LLM) ，并提示符合这个反馈; LLM 然后可以按比例生成标签。我们的实验表明 LLM 和人工标记器一样精确，对于寻找最好的系统和最难的查询也同样有用。LLM 的性能随着提示特征的不同而不同，但也随着简单的转述而不可预测地变化。这种不可预测性加强了对高质量“黄金”标签的需求。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Large+Language+Models+can+Accurately+Predict+Searcher+Preferences)|3| -|[Data-efficient Fine-tuning for LLM-based Recommendation](https://doi.org/10.1145/3626772.3657807)|Xinyu Lin, Wenjie Wang, Yongqi Li, Shuo Yang, Fuli Feng, Yinwei Wei, TatSeng Chua|Monash University; The Hong Kong Polytechnic University; University of Science and Technology of China; National University of Singapore; University of Technology Sydney|Leveraging Large Language Models (LLMs) for recommendation has recentlygarnered considerable attention, where fine-tuning plays a key role in LLMs'adaptation. However, the cost of fine-tuning LLMs on rapidly expandingrecommendation data limits their practical application. To address thischallenge, few-shot fine-tuning offers a promising approach to quickly adaptLLMs to new recommendation data. We propose the task of data pruning forefficient LLM-based recommendation, aimed at identifying representative samplestailored for LLMs' few-shot fine-tuning. While coreset selection is closelyrelated to the proposed task, existing coreset selection methods often rely onsuboptimal heuristic metrics or entail costly optimization on large-scalerecommendation data. To tackle these issues, we introduce two objectives for the data pruning taskin the context of LLM-based recommendation: 1) high accuracy aims to identifythe influential samples that can lead to high overall performance; and 2) highefficiency underlines the low costs of the data pruning process. To pursue thetwo objectives, we propose a novel data pruning method based on two scores,i.e., influence score and effort score, to efficiently identify the influentialsamples. Particularly, the influence score is introduced to accurately estimatethe influence of sample removal on the overall performance. To achieve lowcosts of the data pruning process, we use a small-sized surrogate model toreplace LLMs to obtain the influence score. Considering the potential gapbetween the surrogate model and LLMs, we further propose an effort score toprioritize some hard samples specifically for LLMs. Empirical results on threereal-world datasets validate the effectiveness of our proposed method. Inparticular, the proposed method uses only 2fine-tuning, reducing time costs by 97|最近，利用大型语言模型(LLM)进行推荐引起了相当大的关注，其中微调在 LLM 的适应过程中起着关键作用。然而，在快速扩展的推荐数据上微调 LLM 的成本限制了它们的实际应用。为了应对这一挑战，少量微调提供了一种有希望的方法来快速适应新的推荐数据 LLM。我们提出了基于 LLM 的有效数据剪枝推荐的任务，旨在识别具有代表性的样本，为 LLM 的少镜头微调定制。虽然协同复位选择与所提出的任务密切相关，但现有的协同复位选择方法往往依赖于次优的启发式度量，或者需要对大规模推荐数据进行代价高昂的优化。为了解决这些问题，我们在基于 LLM 的推荐的背景下为数据修剪任务引入了两个目标: 1)高精度旨在确定可以导致高总体性能的有影响的样本; 2)高效率突出了数据修剪过程的低成本。为了实现这两个目标，我们提出了一种新的基于两个分数的数据修剪方法，即影响分数和努力分数，以有效地识别有影响力的样本。特别地，引入影响分数来准确估计样本去除对整体性能的影响。为了降低数据剪枝过程的成本，我们使用一个小型的代理模型来代替 LLM 来获得影响分数。考虑到代理模型和 LLM 之间的潜在差距，我们进一步提出了一个努力分数，优先考虑一些硬样本，特别是 LLM。在三个实际数据集上的实验结果验证了该方法的有效性。特别是，提出的方法只使用2个微调，减少了97个时间成本|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Data-efficient+Fine-tuning+for+LLM-based+Recommendation)|2| -|[The Power of Noise: Redefining Retrieval for RAG Systems](https://doi.org/10.1145/3626772.3657834)|Florin Cuconasu, Giovanni Trappolini, Federico Siciliano, Simone Filice, Cesare Campagnano, Yoelle Maarek, Nicola Tonellotto, Fabrizio Silvestri|University of Pisa; Sapienza University of Rome; Technology Innovation Institute|Retrieval-Augmented Generation (RAG) systems represent a significantadvancement over traditional Large Language Models (LLMs). RAG systems enhancetheir generation ability by incorporating external data retrieved through anInformation Retrieval (IR) phase, overcoming the limitations of standard LLMs,which are restricted to their pre-trained knowledge and limited context window.Most research in this area has predominantly concentrated on the generativeaspect of LLMs within RAG systems. Our study fills this gap by thoroughly andcritically analyzing the influence of IR components on RAG systems. This paperanalyzes which characteristics a retriever should possess for an effectiveRAG's prompt formulation, focusing on the type of documents that should beretrieved. We evaluate various elements, such as the relevance of the documentsto the prompt, their position, and the number included in the context. Ourfindings reveal, among other insights, that including irrelevant documents canunexpectedly enhance performance by more than 30our initial assumption of diminished quality. These findings call fordeveloping specialized approaches tailored to the specific demands ofintegrating retrieval with language generation models and pave the way forfuture research. These results underscore the need for developing specializedstrategies to integrate retrieval with language generation models, therebylaying the groundwork for future research in this field.|检索增强生成(RAG)系统代表了对传统大语言模型(LLM)的重大进步。RAG 系统通过合并通过信息检索(IR)阶段检索的外部数据，克服了标准 LLM 的局限性，提高了系统的生成能力，标准 LLM 局限于其预先训练的知识和有限的上下文窗口。这一领域的大多数研究主要集中在 RAG 系统中 LLM 的生成方面。我们的研究通过彻底和严格地分析红外成分对 RAG 系统的影响来填补这一空白。本文分析了搜索引擎应该具备哪些特征才能有效地快速制定 RAG，重点分析了应该检索的文档类型。我们评估各种因素，例如文件与提示的相关性、它们的位置以及包含在上下文中的数量。我们的研究结果显示，除了其他见解外，包括不相关的文档可以意外地提高超过30个我们最初假设的质量下降的性能。这些研究结果呼吁发展专门的方法，以适应整合检索与语言生成模型的具体要求，并为未来的研究铺平道路。这些结果强调需要制定专门的策略来整合检索和语言生成模型，从而为该领域的未来研究奠定基础。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=The+Power+of+Noise:+Redefining+Retrieval+for+RAG+Systems)|2| -|[LLaRA: Large Language-Recommendation Assistant](https://doi.org/10.1145/3626772.3657690)|Jiayi Liao, Sihang Li, Zhengyi Yang, Jiancan Wu, Yancheng Yuan, Xiang Wang, Xiangnan He|The Hong Kong Polytechnic University; University of Science and Technology of China|Sequential recommendation aims to predict users' next interaction with itemsbased on their past engagement sequence. Recently, the advent of Large LanguageModels (LLMs) has sparked interest in leveraging them for sequentialrecommendation, viewing it as language modeling. Previous studies representitems within LLMs' input prompts as either ID indices or textual metadata.However, these approaches often fail to either encapsulate comprehensive worldknowledge or exhibit sufficient behavioral understanding. To combine thecomplementary strengths of conventional recommenders in capturing behavioralpatterns of users and LLMs in encoding world knowledge about items, weintroduce Large Language-Recommendation Assistant (LLaRA). Specifically, ituses a novel hybrid prompting method that integrates ID-based item embeddingslearned by traditional recommendation models with textual item features.Treating the "sequential behaviors of users" as a distinct modality beyondtexts, we employ a projector to align the traditional recommender's IDembeddings with the LLM's input space. Moreover, rather than directly exposingthe hybrid prompt to LLMs, a curriculum learning strategy is adopted togradually ramp up training complexity. Initially, we warm up the LLM usingtext-only prompts, which better suit its inherent language modeling ability.Subsequently, we progressively transition to the hybrid prompts, training themodel to seamlessly incorporate the behavioral knowledge from the traditionalsequential recommender into the LLM. Empirical results validate theeffectiveness of our proposed framework. Codes are available athttps://github.com/ljy0ustc/LLaRA.|顺序推荐的目的是根据用户过去的参与顺序来预测用户下一次与商品的交互。最近，大型语言模型(LLM)的出现引起了人们对利用它们进行顺序推荐的兴趣，并将其视为语言建模。以前的研究将 LLM 输入提示符中的代表项表示为 ID 索引或文本元数据。然而，这些方法往往不能封装全面的世界知识或表现出足够的行为理解。为了结合传统推荐系统在捕捉用户行为模式方面的互补优势，以及在编码关于项目的世界知识方面的 LLM，我们引入了大型语言推荐助手(LLaRA)。具体来说，它采用了一种新的混合提示方法，将传统推荐模型中基于 ID 的项目嵌入技术与文本项目特征相结合。将“用户的顺序行为”作为文本之外的一种独特模式，我们使用一个投影仪将传统推荐器的 IDembedding 与 LLM 的输入空间对齐。此外，采用课程学习策略来逐渐增加训练的复杂性，而不是直接将混合提示暴露给 LLM。最初，我们使用纯文本提示对 LLM 进行预热，这更适合其固有的语言建模能力。随后，我们逐步过渡到混合提示，训练模型无缝地将来自传统的顺序推荐的行为知识合并到 LLM 中。实证结果验证了我们提出的框架的有效性。代码可通过 https:// github.com/ljy0ustc/llara 查询。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=LLaRA:+Large+Language-Recommendation+Assistant)|2| -|[Let Me Do It For You: Towards LLM Empowered Recommendation via Tool Learning](https://doi.org/10.1145/3626772.3657828)|Yuyue Zhao, Jiancan Wu, Xiang Wang, Wei Tang, Dingxian Wang, Maarten de Rijke|University of Science and Technology of China; University of Science and Technology of China School of Data Science; University of Technology Sydney; University of Amsterdam; University of Science and Technology of China, University of Amsterdam|Conventional recommender systems (RSs) face challenges in precisely capturingusers' fine-grained preferences. Large language models (LLMs) have showncapabilities in commonsense reasoning and leveraging external tools that mayhelp address these challenges. However, existing LLM-based RSs suffer fromhallucinations, misalignment between the semantic space of items and thebehavior space of users, or overly simplistic control strategies (e.g., whetherto rank or directly present existing results). To bridge these gap, weintroduce ToolRec, a framework for LLM-empowered recommendations via toollearning that uses LLMs as surrogate users, thereby guiding the recommendationprocess and invoking external tools to generate a recommendation list thataligns closely with users' nuanced preferences. We formulate the recommendation process as a process aimed at exploring userinterests in attribute granularity. The process factors in the nuances of thecontext and user preferences. The LLM then invokes external tools based on auser's attribute instructions and probes different segments of the item pool.We consider two types of attribute-oriented tools: rank tools and retrievaltools. Through the integration of LLMs, ToolRec enables conventionalrecommender systems to become external tools with a natural language interface.Extensive experiments verify the effectiveness of ToolRec, particularly inscenarios that are rich in semantic content.|传统的推荐系统(RS)面临着精确捕捉用户细粒度偏好的挑战。大型语言模型(LLM)具有常识推理和利用外部工具的能力，这些工具可能有助于解决这些挑战。然而，现有的基于 LLM 的 RSS 存在幻觉，项目的语义空间和用户的行为空间之间的不一致，或者过于简单的控制策略(例如，是否排名或直接呈现现有的结果)。为了弥合这些差距，我们引入了 ToolRec，这是一个通过使用 LLM 作为替代用户的工具学习来提供 LLM 授权推荐的框架，从而指导推荐过程并调用外部工具来生成一个与用户微妙偏好密切相关的推荐列表。我们将推荐过程描述为一个在属性粒度上探索用户兴趣的过程。这个过程影响到上下文和用户偏好的细微差别。然后 LLM 根据用户的属性指令调用外部工具，并探测项目池的不同部分。我们考虑两种面向属性的工具: 排名工具和检索工具。通过对 LLM 的集成，ToolRec 使传统的推荐系统成为具有自然语言界面的外部工具。大量的实验验证了 ToolRec 的有效性，特别是在语义内容丰富的场景中。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Let+Me+Do+It+For+You:+Towards+LLM+Empowered+Recommendation+via+Tool+Learning)|2| +|[Data-efficient Fine-tuning for LLM-based Recommendation](https://doi.org/10.1145/3626772.3657807)|Xinyu Lin, Wenjie Wang, Yongqi Li, Shuo Yang, Fuli Feng, Yinwei Wei, TatSeng Chua|University of Technology Sydney; The Hong Kong Polytechnic University; University of Science and Technology of China; Monash University; National University of Singapore|Leveraging Large Language Models (LLMs) for recommendation has recentlygarnered considerable attention, where fine-tuning plays a key role in LLMs'adaptation. However, the cost of fine-tuning LLMs on rapidly expandingrecommendation data limits their practical application. To address thischallenge, few-shot fine-tuning offers a promising approach to quickly adaptLLMs to new recommendation data. We propose the task of data pruning forefficient LLM-based recommendation, aimed at identifying representative samplestailored for LLMs' few-shot fine-tuning. While coreset selection is closelyrelated to the proposed task, existing coreset selection methods often rely onsuboptimal heuristic metrics or entail costly optimization on large-scalerecommendation data. To tackle these issues, we introduce two objectives for the data pruning taskin the context of LLM-based recommendation: 1) high accuracy aims to identifythe influential samples that can lead to high overall performance; and 2) highefficiency underlines the low costs of the data pruning process. To pursue thetwo objectives, we propose a novel data pruning method based on two scores,i.e., influence score and effort score, to efficiently identify the influentialsamples. Particularly, the influence score is introduced to accurately estimatethe influence of sample removal on the overall performance. To achieve lowcosts of the data pruning process, we use a small-sized surrogate model toreplace LLMs to obtain the influence score. Considering the potential gapbetween the surrogate model and LLMs, we further propose an effort score toprioritize some hard samples specifically for LLMs. Empirical results on threereal-world datasets validate the effectiveness of our proposed method. Inparticular, the proposed method uses only 2fine-tuning, reducing time costs by 97|最近，利用大型语言模型(LLM)进行推荐引起了相当大的关注，其中微调在 LLM 的适应过程中起着关键作用。然而，在快速扩展的推荐数据上微调 LLM 的成本限制了它们的实际应用。为了应对这一挑战，少量微调提供了一种有希望的方法来快速适应新的推荐数据 LLM。我们提出了基于 LLM 的有效数据剪枝推荐的任务，旨在识别具有代表性的样本，为 LLM 的少镜头微调定制。虽然协同复位选择与所提出的任务密切相关，但现有的协同复位选择方法往往依赖于次优的启发式度量，或者需要对大规模推荐数据进行代价高昂的优化。为了解决这些问题，我们在基于 LLM 的推荐的背景下为数据修剪任务引入了两个目标: 1)高精度旨在确定可以导致高总体性能的有影响的样本; 2)高效率突出了数据修剪过程的低成本。为了实现这两个目标，我们提出了一种新的基于两个分数的数据修剪方法，即影响分数和努力分数，以有效地识别有影响力的样本。特别地，引入影响分数来准确估计样本去除对整体性能的影响。为了降低数据剪枝过程的成本，我们使用一个小型的代理模型来代替 LLM 来获得影响分数。考虑到代理模型和 LLM 之间的潜在差距，我们进一步提出了一个努力分数，优先考虑一些硬样本，特别是 LLM。在三个实际数据集上的实验结果验证了该方法的有效性。特别是，提出的方法只使用2个微调，减少了97个时间成本|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Data-efficient+Fine-tuning+for+LLM-based+Recommendation)|2| +|[The Power of Noise: Redefining Retrieval for RAG Systems](https://doi.org/10.1145/3626772.3657834)|Florin Cuconasu, Giovanni Trappolini, Federico Siciliano, Simone Filice, Cesare Campagnano, Yoelle Maarek, Nicola Tonellotto, Fabrizio Silvestri|Technology Innovation Institute; University of Pisa; Sapienza University of Rome|Retrieval-Augmented Generation (RAG) systems represent a significantadvancement over traditional Large Language Models (LLMs). RAG systems enhancetheir generation ability by incorporating external data retrieved through anInformation Retrieval (IR) phase, overcoming the limitations of standard LLMs,which are restricted to their pre-trained knowledge and limited context window.Most research in this area has predominantly concentrated on the generativeaspect of LLMs within RAG systems. Our study fills this gap by thoroughly andcritically analyzing the influence of IR components on RAG systems. This paperanalyzes which characteristics a retriever should possess for an effectiveRAG's prompt formulation, focusing on the type of documents that should beretrieved. We evaluate various elements, such as the relevance of the documentsto the prompt, their position, and the number included in the context. Ourfindings reveal, among other insights, that including irrelevant documents canunexpectedly enhance performance by more than 30our initial assumption of diminished quality. These findings call fordeveloping specialized approaches tailored to the specific demands ofintegrating retrieval with language generation models and pave the way forfuture research. These results underscore the need for developing specializedstrategies to integrate retrieval with language generation models, therebylaying the groundwork for future research in this field.|检索增强生成(RAG)系统代表了对传统大语言模型(LLM)的重大进步。RAG 系统通过合并通过信息检索(IR)阶段检索的外部数据，克服了标准 LLM 的局限性，提高了系统的生成能力，标准 LLM 局限于其预先训练的知识和有限的上下文窗口。这一领域的大多数研究主要集中在 RAG 系统中 LLM 的生成方面。我们的研究通过彻底和严格地分析红外成分对 RAG 系统的影响来填补这一空白。本文分析了搜索引擎应该具备哪些特征才能有效地快速制定 RAG，重点分析了应该检索的文档类型。我们评估各种因素，例如文件与提示的相关性、它们的位置以及包含在上下文中的数量。我们的研究结果显示，除了其他见解外，包括不相关的文档可以意外地提高超过30个我们最初假设的质量下降的性能。这些研究结果呼吁发展专门的方法，以适应整合检索与语言生成模型的具体要求，并为未来的研究铺平道路。这些结果强调需要制定专门的策略来整合检索和语言生成模型，从而为该领域的未来研究奠定基础。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=The+Power+of+Noise:+Redefining+Retrieval+for+RAG+Systems)|2| +|[LLaRA: Large Language-Recommendation Assistant](https://doi.org/10.1145/3626772.3657690)|Jiayi Liao, Sihang Li, Zhengyi Yang, Jiancan Wu, Yancheng Yuan, Xiang Wang, Xiangnan He|University of Science and Technology of China; The Hong Kong Polytechnic University|Sequential recommendation aims to predict users' next interaction with itemsbased on their past engagement sequence. Recently, the advent of Large LanguageModels (LLMs) has sparked interest in leveraging them for sequentialrecommendation, viewing it as language modeling. Previous studies representitems within LLMs' input prompts as either ID indices or textual metadata.However, these approaches often fail to either encapsulate comprehensive worldknowledge or exhibit sufficient behavioral understanding. To combine thecomplementary strengths of conventional recommenders in capturing behavioralpatterns of users and LLMs in encoding world knowledge about items, weintroduce Large Language-Recommendation Assistant (LLaRA). Specifically, ituses a novel hybrid prompting method that integrates ID-based item embeddingslearned by traditional recommendation models with textual item features.Treating the "sequential behaviors of users" as a distinct modality beyondtexts, we employ a projector to align the traditional recommender's IDembeddings with the LLM's input space. Moreover, rather than directly exposingthe hybrid prompt to LLMs, a curriculum learning strategy is adopted togradually ramp up training complexity. Initially, we warm up the LLM usingtext-only prompts, which better suit its inherent language modeling ability.Subsequently, we progressively transition to the hybrid prompts, training themodel to seamlessly incorporate the behavioral knowledge from the traditionalsequential recommender into the LLM. Empirical results validate theeffectiveness of our proposed framework. Codes are available athttps://github.com/ljy0ustc/LLaRA.|顺序推荐的目的是根据用户过去的参与顺序来预测用户下一次与商品的交互。最近，大型语言模型(LLM)的出现引起了人们对利用它们进行顺序推荐的兴趣，并将其视为语言建模。以前的研究将 LLM 输入提示符中的代表项表示为 ID 索引或文本元数据。然而，这些方法往往不能封装全面的世界知识或表现出足够的行为理解。为了结合传统推荐系统在捕捉用户行为模式方面的互补优势，以及在编码关于项目的世界知识方面的 LLM，我们引入了大型语言推荐助手(LLaRA)。具体来说，它采用了一种新的混合提示方法，将传统推荐模型中基于 ID 的项目嵌入技术与文本项目特征相结合。将“用户的顺序行为”作为文本之外的一种独特模式，我们使用一个投影仪将传统推荐器的 IDembedding 与 LLM 的输入空间对齐。此外，采用课程学习策略来逐渐增加训练的复杂性，而不是直接将混合提示暴露给 LLM。最初，我们使用纯文本提示对 LLM 进行预热，这更适合其固有的语言建模能力。随后，我们逐步过渡到混合提示，训练模型无缝地将来自传统的顺序推荐的行为知识合并到 LLM 中。实证结果验证了我们提出的框架的有效性。代码可通过 https:// github.com/ljy0ustc/llara 查询。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=LLaRA:+Large+Language-Recommendation+Assistant)|2| +|[Let Me Do It For You: Towards LLM Empowered Recommendation via Tool Learning](https://doi.org/10.1145/3626772.3657828)|Yuyue Zhao, Jiancan Wu, Xiang Wang, Wei Tang, Dingxian Wang, Maarten de Rijke|University of Amsterdam; University of Technology Sydney; University of Science and Technology of China; University of Science and Technology of China School of Data Science; University of Science and Technology of China, University of Amsterdam|Conventional recommender systems (RSs) face challenges in precisely capturingusers' fine-grained preferences. Large language models (LLMs) have showncapabilities in commonsense reasoning and leveraging external tools that mayhelp address these challenges. However, existing LLM-based RSs suffer fromhallucinations, misalignment between the semantic space of items and thebehavior space of users, or overly simplistic control strategies (e.g., whetherto rank or directly present existing results). To bridge these gap, weintroduce ToolRec, a framework for LLM-empowered recommendations via toollearning that uses LLMs as surrogate users, thereby guiding the recommendationprocess and invoking external tools to generate a recommendation list thataligns closely with users' nuanced preferences. We formulate the recommendation process as a process aimed at exploring userinterests in attribute granularity. The process factors in the nuances of thecontext and user preferences. The LLM then invokes external tools based on auser's attribute instructions and probes different segments of the item pool.We consider two types of attribute-oriented tools: rank tools and retrievaltools. Through the integration of LLMs, ToolRec enables conventionalrecommender systems to become external tools with a natural language interface.Extensive experiments verify the effectiveness of ToolRec, particularly inscenarios that are rich in semantic content.|传统的推荐系统(RS)面临着精确捕捉用户细粒度偏好的挑战。大型语言模型(LLM)具有常识推理和利用外部工具的能力，这些工具可能有助于解决这些挑战。然而，现有的基于 LLM 的 RSS 存在幻觉，项目的语义空间和用户的行为空间之间的不一致，或者过于简单的控制策略(例如，是否排名或直接呈现现有的结果)。为了弥合这些差距，我们引入了 ToolRec，这是一个通过使用 LLM 作为替代用户的工具学习来提供 LLM 授权推荐的框架，从而指导推荐过程并调用外部工具来生成一个与用户微妙偏好密切相关的推荐列表。我们将推荐过程描述为一个在属性粒度上探索用户兴趣的过程。这个过程影响到上下文和用户偏好的细微差别。然后 LLM 根据用户的属性指令调用外部工具，并探测项目池的不同部分。我们考虑两种面向属性的工具: 排名工具和检索工具。通过对 LLM 的集成，ToolRec 使传统的推荐系统成为具有自然语言界面的外部工具。大量的实验验证了 ToolRec 的有效性，特别是在语义内容丰富的场景中。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Let+Me+Do+It+For+You:+Towards+LLM+Empowered+Recommendation+via+Tool+Learning)|2| |[Evaluating Retrieval Quality in Retrieval-Augmented Generation](https://doi.org/10.1145/3626772.3657957)|Alireza Salemi, Hamed Zamani|University of Massachusetts Amherst|Evaluating retrieval-augmented generation (RAG) presents challenges,particularly for retrieval models within these systems. Traditional end-to-endevaluation methods are computationally expensive. Furthermore, evaluation ofthe retrieval model's performance based on query-document relevance labelsshows a small correlation with the RAG system's downstream performance. Wepropose a novel evaluation approach, eRAG, where each document in the retrievallist is individually utilized by the large language model within the RAGsystem. The output generated for each document is then evaluated based on thedownstream task ground truth labels. In this manner, the downstream performancefor each document serves as its relevance label. We employ various downstreamtask metrics to obtain document-level annotations and aggregate them usingset-based or ranking metrics. Extensive experiments on a wide range of datasetsdemonstrate that eRAG achieves a higher correlation with downstream RAGperformance compared to baseline methods, with improvements in Kendall's τcorrelation ranging from 0.168 to 0.494. Additionally, eRAG offers significantcomputational advantages, improving runtime and consuming up to 50 times lessGPU memory than end-to-end evaluation.|评估检索增强生成(RAG)提出了挑战，特别是在这些系统中的检索模型。传统的端到端评价方法计算量很大。此外，基于查询文档相关标签的检索模型性能评估与 RAG 系统的下游性能相关性较小。我们提出了一种新的评估方法，eRAG，其中检索列表中的每个文档都由 RAG 系统中的大型语言模型单独使用。然后根据下游任务地面真相标签对每个文档生成的输出进行评估。以这种方式，每个文档的下游性能作为其相关标签。我们使用各种下游任务度量来获取文档级注释，并使用基于集合或排名度量来聚合它们。在广泛的数据集上进行的大量实验表明，与基线方法相比，eRAG 与下游 RAG 性能具有更高的相关性，Kendall 的 τ 相关性从0.168到0.494不等。此外，eRAG 提供了显著的计算优势，改善了运行时，并且比端到端计算消耗了多达50倍的 GPU 内存。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Evaluating+Retrieval+Quality+in+Retrieval-Augmented+Generation)|2| |[Stochastic RAG: End-to-End Retrieval-Augmented Generation through Expected Utility Maximization](https://doi.org/10.1145/3626772.3657923)|Hamed Zamani, Michael Bendersky|Google; University of Massachusetts Amherst|This paper introduces Stochastic RAG--a novel approach for end-to-end optimization of retrieval-augmented generation (RAG) models that relaxes the simplifying assumptions of marginalization and document independence, made in most prior work. Stochastic RAG casts the retrieval process in RAG as a stochastic sampling without replacement process. Through this formulation, we employ straight-through Gumbel-top-k that provides a differentiable approximation for sampling without replacement and enables effective end-to-end optimization for RAG. We conduct extensive experiments on seven diverse datasets on a wide range of tasks, from open-domain question answering to fact verification to slot-filling for relation extraction and to dialogue systems. By applying this optimization method to a recent and effective RAG model, we advance state-of-the-art results on six out of seven datasets.|本文介绍了随机RAG——一种用于检索增强生成（RAG）模型端到端优化的创新方法，该方法放松了大多数先前工作中所做的边缘化和文档独立性的简化假设。随机RAG将RAG中的检索过程视为一个无放回的随机抽样过程。通过这一表述，我们采用了直接通过Gumbel-top-k方法，该方法为无放回抽样提供了一个可微分的近似，并实现了对RAG的有效端到端优化。我们在七个多样化的数据集上进行了广泛的实验，涵盖了从开放领域问答、事实验证、关系抽取的槽填充到对话系统等一系列任务。通过将这种优化方法应用于一个最新且有效的RAG模型，我们在七个数据集中的六个上取得了最先进的结果。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Stochastic+RAG:+End-to-End+Retrieval-Augmented+Generation+through+Expected+Utility+Maximization)|2| |[What do Users Really Ask Large Language Models? An Initial Log Analysis of Google Bard Interactions in the Wild](https://doi.org/10.1145/3626772.3657914)|Johanne R. Trippas, Sara Fahad Dawood Al Lawati, Joel Mackenzie, Luke Gallagher||||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=What+do+Users+Really+Ask+Large+Language+Models?+An+Initial+Log+Analysis+of+Google+Bard+Interactions+in+the+Wild)|2| |[GraphGPT: Graph Instruction Tuning for Large Language Models](https://doi.org/10.1145/3626772.3657775)|Jiabin Tang, Yuhao Yang, Wei Wei, Lei Shi, Lixin Su, Suqi Cheng, Dawei Yin, Chao Huang||||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=GraphGPT:+Graph+Instruction+Tuning+for+Large+Language+Models)|2| |[UniSAR: Modeling User Transition Behaviors between Search and Recommendation](https://doi.org/10.1145/3626772.3657811)|Teng Shi, Zihua Si, Jun Xu, Xiao Zhang, Xiaoxue Zang, Kai Zheng, Dewei Leng, Yanan Niu, Yang Song|Kuaishou Technology Co., Ltd.; Renmin University of China Gaoling School of Artificial Intelligence|Nowadays, many platforms provide users with both search and recommendation services as important tools for accessing information. The phenomenon has led to a correlation between user search and recommendation behaviors, providing an opportunity to model user interests in a fine-grained way. Existing approaches either model user search and recommendation behaviors separately or overlook the different transitions between user search and recommendation behaviors. In this paper, we propose a framework named UniSAR that effectively models the different types of fine-grained behavior transitions for providing users a Unified Search And Recommendation service. Specifically, UniSAR models the user transition behaviors between search and recommendation through three steps: extraction, alignment, and fusion, which are respectively implemented by transformers equipped with pre-defined masks, contrastive learning that aligns the extracted fine-grained user transitions, and cross-attentions that fuse different transitions. To provide users with a unified service, the learned representations are fed into the downstream search and recommendation models. Joint learning on both search and recommendation data is employed to utilize the knowledge and enhance each other. Experimental results on two public datasets demonstrated the effectiveness of UniSAR in terms of enhancing both search and recommendation simultaneously. The experimental analysis further validates that UniSAR enhances the results by successfully modeling the user transition behaviors between search and recommendation.|目前，许多平台为用户提供搜索和推荐服务，作为获取信息的重要工具。这种现象导致了用户搜索和推荐行为之间的相关性，为用户兴趣的细粒度建模提供了机会。现有的方法或者分别模拟用户搜索和推荐行为，或者忽略用户搜索和推荐行为之间的不同转换。在本文中，我们提出一个名为 UniSAR 的框架，有效地模拟不同类型的细粒度行为转换，为用户提供一个统一的搜索和推荐服务。具体而言，UniSAR 通过三个步骤对搜索和推荐之间的用户转换行为进行建模: 提取、对齐和融合，这些步骤分别由配备预定义掩码的变压器实现，对比学习将提取的细粒度用户转换对齐，交叉注意融合不同的转换。为了向用户提供统一的服务，学习表示被反馈到下游搜索和推荐模型中。对搜索数据和推荐数据进行联合学习，以利用知识并相互增强。在两个公共数据集上的实验结果证明了 UniSAR 在同时提高搜索和推荐能力方面的有效性。实验分析进一步验证了 UniSAR 通过成功地模拟用户在搜索和推荐之间的转换行为，提高了结果的准确性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=UniSAR:+Modeling+User+Transition+Behaviors+between+Search+and+Recommendation)|1| -|[Poisoning Decentralized Collaborative Recommender System and Its Countermeasures](https://doi.org/10.1145/3626772.3657814)|Ruiqi Zheng, Liang Qu, Tong Chen, Kai Zheng, Yuhui Shi, Hongzhi Yin|The University of Queensland; The University of Queensland School of Electrical Engineering and Computer Science; Southern University of Science and Technology; University of Electronic Science and Technology of China|To make room for privacy and efficiency, the deployment of many recommender systems is experiencing a shift from central servers to personal devices, where the federated recommender systems (FedRecs) and decentralized collaborative recommender systems (DecRecs) are arguably the two most representative paradigms. While both leverage knowledge (e.g., gradients) sharing to facilitate learning local models, FedRecs rely on a central server to coordinate the optimization process, yet in DecRecs, the knowledge sharing directly happens between clients. Knowledge sharing also opens a backdoor for model poisoning attacks, where adversaries disguise themselves as benign clients and disseminate polluted knowledge to achieve malicious goals like promoting an item's exposure rate. Although research on such poisoning attacks provides valuable insights into finding security loopholes and corresponding countermeasures, existing attacks mostly focus on FedRecs, and are either inapplicable or ineffective for DecRecs. Compared with FedRecs where the tampered information can be universally distributed to all clients once uploaded to the cloud, each adversary in DecRecs can only communicate with neighbor clients of a small size, confining its impact to a limited range. To fill the gap, we present a novel attack method named Poisoning with Adaptive Malicious Neighbors (PAMN). With item promotion in top-K recommendation as the attack objective, PAMN effectively boosts target items' ranks with several adversaries that emulate benign clients and transfers adaptively crafted gradients conditioned on each adversary's neighbors. Moreover, with the vulnerabilities of DecRecs uncovered, a dedicated defensive mechanism based on user-level gradient clipping with sparsified updating is proposed. Extensive experiments demonstrate the effectiveness of the poisoning attack and the robustness of our defensive mechanism.|为了给隐私和效率腾出空间，许多推荐系统的部署正在经历从中央服务器到个人设备的转变，其中联邦推荐系统(FedRecs)和分散式协作推荐系统(DecRecs)可以说是两个最具代表性的范例。虽然两者都利用知识共享(例如，梯度)来促进本地模型的学习，FedRecs 依赖于一个中央服务器来协调优化过程，但在 DecRecs 中，知识共享直接发生在客户之间。知识共享还为模型中毒攻击打开了一个后门，在这种攻击中，对手把自己伪装成良性的客户，传播受污染的知识，以达到恶意目的，比如提高项目的曝光率。尽管对这类中毒攻击的研究为发现安全漏洞和相应的对策提供了有价值的见解，但现有的攻击主要集中在 FedRecs 上，对 DecRecs 要么不适用，要么无效。与 FedRecs 相比，DecRecs 中的每个对手只能与小规模的邻居客户机通信，将其影响限制在有限的范围内。为了填补这一空白，我们提出了一种新的攻击方法，称为自适应恶意邻居中毒(PAMN)。通过在 top-K 推荐中的物品推广作为攻击目标，PAMN 可以有效地提高目标物品的等级，其中有几个对手可以模仿良性客户，并根据每个对手的邻居传输自适应的精心制作的渐变。此外，针对 DecRecs 的漏洞，提出了一种基于用户级梯度裁剪和稀疏更新的专用防御机制。大量的实验证明了中毒攻击的有效性和我们防御机制的鲁棒性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Poisoning+Decentralized+Collaborative+Recommender+System+and+Its+Countermeasures)|1| -|[Resources for Combining Teaching and Research in Information Retrieval Coursework](https://doi.org/10.1145/3626772.3657886)|Maik Fröbe, Harrisen Scells, Theresa Elstner, Christopher Akiki, Lukas Gienapp, Jan Heinrich Reimer, Sean MacAvaney, Benno Stein, Matthias Hagen, Martin Potthast|Informatik, Leipzig University, Leipzig, Germany; University of Kassel, hessian.AI, and ScaDS.AI, Kassel, Germany; Institute for Computer Science, Friedrich-Schiller-Universität Jena, Jena, Germany; Friedrich-Schiller-Universität Jena, Jena, Germany; Leipzig University, Leipzig, Germany; University of Glasgow, Glasgow, UK, United Kingdom; Bauhaus-Universität Weimar, imar, Germany|The first International Workshop on Open Web Search (WOWS) was held on Thursday, March 28th, at ECIR 2024 in Glasgow, UK. The full-day workshop had two calls for contributions: the first call aimed at scientific contributions to building, operating, and evaluating search engines cooperatively and the cooperative use of the web as a resource for researchers and innovators. The second call for implementations of retrieval components aimed to gain practical experience with joint, cooperative evaluation of search engines and their components. In total, 2~papers were accepted for the first call, and 11~software components were submitted for the second. The workshop ended with breakout sessions on how the OpenWebSearch.eu project can incorporate collaborative evaluations and a hub of search engines.|首届开放网络搜索(WOWS)国际研讨会于3月28日(星期四)在英国格拉斯哥的 ECIR 2024上举行。为期一天的讲习班有两项要求作出贡献的呼吁: 第一项呼吁旨在对合作建立、运营和评价搜索引擎作出科学贡献，以及合作利用网络作为研究人员和创新者的资源。第二个实施检索组件的呼吁旨在通过联合、合作评估搜索引擎及其组件获得实际经验。第一次调用共接受2篇论文，第二次调用提交了11个软件组件。研讨会最后分组讨论了 openwebsearch.eu 项目如何将协作评估和搜索引擎中心结合起来。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Resources+for+Combining+Teaching+and+Research+in+Information+Retrieval+Coursework)|1| +|[Poisoning Decentralized Collaborative Recommender System and Its Countermeasures](https://doi.org/10.1145/3626772.3657814)|Ruiqi Zheng, Liang Qu, Tong Chen, Kai Zheng, Yuhui Shi, Hongzhi Yin|Southern University of Science and Technology; The University of Queensland School of Electrical Engineering and Computer Science; University of Electronic Science and Technology of China; The University of Queensland|To make room for privacy and efficiency, the deployment of many recommender systems is experiencing a shift from central servers to personal devices, where the federated recommender systems (FedRecs) and decentralized collaborative recommender systems (DecRecs) are arguably the two most representative paradigms. While both leverage knowledge (e.g., gradients) sharing to facilitate learning local models, FedRecs rely on a central server to coordinate the optimization process, yet in DecRecs, the knowledge sharing directly happens between clients. Knowledge sharing also opens a backdoor for model poisoning attacks, where adversaries disguise themselves as benign clients and disseminate polluted knowledge to achieve malicious goals like promoting an item's exposure rate. Although research on such poisoning attacks provides valuable insights into finding security loopholes and corresponding countermeasures, existing attacks mostly focus on FedRecs, and are either inapplicable or ineffective for DecRecs. Compared with FedRecs where the tampered information can be universally distributed to all clients once uploaded to the cloud, each adversary in DecRecs can only communicate with neighbor clients of a small size, confining its impact to a limited range. To fill the gap, we present a novel attack method named Poisoning with Adaptive Malicious Neighbors (PAMN). With item promotion in top-K recommendation as the attack objective, PAMN effectively boosts target items' ranks with several adversaries that emulate benign clients and transfers adaptively crafted gradients conditioned on each adversary's neighbors. Moreover, with the vulnerabilities of DecRecs uncovered, a dedicated defensive mechanism based on user-level gradient clipping with sparsified updating is proposed. Extensive experiments demonstrate the effectiveness of the poisoning attack and the robustness of our defensive mechanism.|为了给隐私和效率腾出空间，许多推荐系统的部署正在经历从中央服务器到个人设备的转变，其中联邦推荐系统(FedRecs)和分散式协作推荐系统(DecRecs)可以说是两个最具代表性的范例。虽然两者都利用知识共享(例如，梯度)来促进本地模型的学习，FedRecs 依赖于一个中央服务器来协调优化过程，但在 DecRecs 中，知识共享直接发生在客户之间。知识共享还为模型中毒攻击打开了一个后门，在这种攻击中，对手把自己伪装成良性的客户，传播受污染的知识，以达到恶意目的，比如提高项目的曝光率。尽管对这类中毒攻击的研究为发现安全漏洞和相应的对策提供了有价值的见解，但现有的攻击主要集中在 FedRecs 上，对 DecRecs 要么不适用，要么无效。与 FedRecs 相比，DecRecs 中的每个对手只能与小规模的邻居客户机通信，将其影响限制在有限的范围内。为了填补这一空白，我们提出了一种新的攻击方法，称为自适应恶意邻居中毒(PAMN)。通过在 top-K 推荐中的物品推广作为攻击目标，PAMN 可以有效地提高目标物品的等级，其中有几个对手可以模仿良性客户，并根据每个对手的邻居传输自适应的精心制作的渐变。此外，针对 DecRecs 的漏洞，提出了一种基于用户级梯度裁剪和稀疏更新的专用防御机制。大量的实验证明了中毒攻击的有效性和我们防御机制的鲁棒性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Poisoning+Decentralized+Collaborative+Recommender+System+and+Its+Countermeasures)|1| +|[Resources for Combining Teaching and Research in Information Retrieval Coursework](https://doi.org/10.1145/3626772.3657886)|Maik Fröbe, Harrisen Scells, Theresa Elstner, Christopher Akiki, Lukas Gienapp, Jan Heinrich Reimer, Sean MacAvaney, Benno Stein, Matthias Hagen, Martin Potthast|Leipzig University, Leipzig, Germany; University of Glasgow, Glasgow, UK, United Kingdom; Informatik, Leipzig University, Leipzig, Germany; University of Kassel, hessian.AI, and ScaDS.AI, Kassel, Germany; Bauhaus-Universität Weimar, imar, Germany; Institute for Computer Science, Friedrich-Schiller-Universität Jena, Jena, Germany; Friedrich-Schiller-Universität Jena, Jena, Germany|The first International Workshop on Open Web Search (WOWS) was held on Thursday, March 28th, at ECIR 2024 in Glasgow, UK. The full-day workshop had two calls for contributions: the first call aimed at scientific contributions to building, operating, and evaluating search engines cooperatively and the cooperative use of the web as a resource for researchers and innovators. The second call for implementations of retrieval components aimed to gain practical experience with joint, cooperative evaluation of search engines and their components. In total, 2~papers were accepted for the first call, and 11~software components were submitted for the second. The workshop ended with breakout sessions on how the OpenWebSearch.eu project can incorporate collaborative evaluations and a hub of search engines.|首届开放网络搜索(WOWS)国际研讨会于3月28日(星期四)在英国格拉斯哥的 ECIR 2024上举行。为期一天的讲习班有两项要求作出贡献的呼吁: 第一项呼吁旨在对合作建立、运营和评价搜索引擎作出科学贡献，以及合作利用网络作为研究人员和创新者的资源。第二个实施检索组件的呼吁旨在通过联合、合作评估搜索引擎及其组件获得实际经验。第一次调用共接受2篇论文，第二次调用提交了11个软件组件。研讨会最后分组讨论了 openwebsearch.eu 项目如何将协作评估和搜索引擎中心结合起来。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Resources+for+Combining+Teaching+and+Research+in+Information+Retrieval+Coursework)|1| |[Leveraging LLMs for Unsupervised Dense Retriever Ranking](https://doi.org/10.1145/3626772.3657798)|Ekaterina Khramtsova, Shengyao Zhuang, Mahsa Baktashmotlagh, Guido Zuccon||This paper introduces a novel unsupervised technique that utilizes large language models (LLMs) to determine the most suitable dense retriever for a specific test(target) corpus. Selecting the appropriate dense retriever is vital for numerous IR applications that employ these retrievers, trained on public datasets, to encode or conduct searches within a new private target corpus. The effectiveness of a dense retriever can significantly diminish when applied to a target corpus that diverges in domain or task from the original training set. The problem becomes more pronounced in cases where the target corpus is unlabeled, e.g. in zero-shot scenarios, rendering direct evaluation of the model's effectiveness on the target corpus unattainable. Therefore, the unsupervised selection of an optimally pre-trained dense retriever, especially under conditions of domain shift, emerges as a critical challenge. Existing methodologies for ranking dense retrievers fall short in addressing these domain shift scenarios. To tackle this, our method capitalizes on LLMs to create pseudo-relevant queries, labels, and reference lists by analyzing a subset of documents from the target corpus. This allows for the ranking of dense retrievers based on their performance with these pseudo-relevant signals. Significantly, this strategy is the first to depend exclusively on the target corpus data, removing the necessity for training data and test labels. We assessed the effectiveness of our approach by compiling a comprehensive pool of cutting-edge dense retrievers and comparing our method against traditional dense retriever selection benchmarks. The findings reveal that our proposed solution surpasses the existing benchmarks in both the selection and ranking of dense retrievers.|本文介绍了一种新的无监督检索技术，该技术利用大语言模型(LLM)来确定特定测试(目标)语料库中最适合的密集检索器。选择合适的密集检索器对于许多 IR 应用程序至关重要，这些应用程序使用这些检索器，在公共数据集上进行培训，以便在新的私有目标语料库中进行编码或搜索。密集检索器的有效性可以显著降低时，应用于目标语料库的领域或任务偏离原来的训练集。在目标语料没有标记的情况下，这个问题变得更加明显，例如，在零射击情况下，无法直接评估模型对目标语料的有效性。因此，无监督选择最佳预训练密集检索，特别是在领域移动的条件下，出现了一个关键的挑战。现有的密集检索器排名方法在解决这些领域转移场景方面存在不足。为了解决这个问题，我们的方法利用 LLM 通过分析目标语料库中的文档子集来创建伪相关查询、标签和引用列表。这允许根据这些伪相关信号的性能对密集检索器进行排名。值得注意的是，这个策略是第一个完全依赖于目标语料库数据的策略，它消除了培训数据和测试标签的必要性。我们评估了我们的方法的有效性，编制了一个全面的前沿密集检索器库，并将我们的方法与传统的密集检索器选择基准进行了比较。研究结果表明，我们提出的解决方案在密集检索器的选择和排序方面都超过了现有的基准。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Leveraging+LLMs+for+Unsupervised+Dense+Retriever+Ranking)|1| -|[Large Language Models for Intent-Driven Session Recommendations](https://doi.org/10.1145/3626772.3657688)|Zhu Sun, Hongyang Liu, Xinghua Qu, Kaidong Feng, Yan Wang, Yew Soon Ong|Agency for Science, Technology and Research; A*STAR Centre for Frontier AI Research and Nanyang Technological University; Macquarie University; Yanshan University; Shanda Group AI Lab|Intent-aware session recommendation (ISR) is pivotal in discerning user intents within sessions for precise predictions. Traditional approaches, however, face limitations due to their presumption of a uniform number of intents across all sessions. This assumption overlooks the dynamic nature of user sessions, where the number and type of intentions can significantly vary. In addition, these methods typically operate in latent spaces, thus hinder the model's transparency.Addressing these challenges, we introduce a novel ISR approach, utilizing the advanced reasoning capabilities of large language models (LLMs). First, this approach begins by generating an initial prompt that guides LLMs to predict the next item in a session, based on the varied intents manifested in user sessions. Then, to refine this process, we introduce an innovative prompt optimization mechanism that iteratively self-reflects and adjusts prompts. Furthermore, our prompt selection module, built upon the LLMs' broad adaptability, swiftly selects the most optimized prompts across diverse domains. This new paradigm empowers LLMs to discern diverse user intents at a semantic level, leading to more accurate and interpretable session recommendations. Our extensive experiments on three real-world datasets demonstrate the effectiveness of our method, marking a significant advancement in ISR systems.|意图感知会话推荐(ISR)在识别会话中的用户意图以进行精确预测方面非常关键。然而，传统的方法由于假定所有会话的意图数量一致而面临局限性。这个假设忽略了用户会话的动态特性，其中意图的数量和类型可能有很大的不同。此外，这些方法通常在潜在的空间操作，从而阻碍了模型的透明度。针对这些挑战，我们引入了一种新的 ISR 方法，利用大型语言模型(LLM)的高级推理能力。首先，这种方法首先生成一个初始提示，指导 LLM 根据用户会话中显示的不同意图预测会话中的下一个项目。然后，为了完善这个过程，我们引入了一个创新的提示优化机制，它可以迭代地自我反映和调整提示。此外，我们的提示选择模块，建立在 LLM 的广泛适应性，迅速选择最优化的提示跨不同的领域。这种新的范式使 LLM 能够在语义层次上识别不同的用户意图，从而产生更加准确和可解释的会话建议。我们在三个实际数据集上的广泛实验证明了我们方法的有效性，标志着 ISR 系统的重大进步。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Large+Language+Models+for+Intent-Driven+Session+Recommendations)|1| -|[Scalable Community Search over Large-scale Graphs based on Graph Transformer](https://doi.org/10.1145/3626772.3657771)|Yuxiang Wang, Xiaoxuan Gou, Xiaoliang Xu, Yuxia Geng, Xiangyu Ke, Tianxing Wu, Zhiyuan Yu, Runhuai Chen, Xiangying Wu|Hangzhou Dianzi University, Hangzhou, China; Hangzhou Dianzi University, Zhejiang, China; Zhejiang University, Zhejiang, China; Southeast University, Jiangsu, China|Given a graph G and a query node q, community search (CS) aims to find a structurally cohesive subgraph from G that contains q. CS is widely used in many real-world applications, such as online recommendation and expert finding. Recently, the rise of learning-based CS methods has garnered extensive research interests, showcasing the promising potential of neural solutions. However, there remains room for optimization: (1) They initialize node features via classical methods, e.g., one-hot, random, and position encoding, which may fall short in capturing valuable community cohesiveness-related features. (2) The reliance on GCN or GCN-like models poses challenges in scaling to large graphs. (3) Existing methods do not adapt well to dynamic graphs, often requiring retraining from scratch. To handle this, we present CSFormer, a scalable CS based on Graph Transformer. First, we present a novel l-hop neighborhood community vector based on n-order h-index to represent each node's community features, generating a sequence of feature vectors by varying the neighborhood scope l. Then, we build a Transformer backbone to learn a good graph embedding that carries rich community features, based on which we perform a prediction-filtering-based online CS to efficiently return a community of q. We extend CSFormer to dynamic graphs and various community models. Extensive experiments on seven real-world graphs show our solution's superiority on effectiveness, e.g., we attain an average improvement of 20.6% in F1-score compared to the latest competitors.|给定一个图 G 和一个查询节点 q，社区搜索(Community Search，CS)旨在从 G 中找到一个结构内聚的子图。最近，基于学习的 CS 方法的兴起引起了广泛的研究兴趣，展示了神经解决方案的潜力。然而，仍有优化的空间: (1)他们通过传统的方法，如一个热点，随机和位置编码初始化节点特征，这可能不能捕获有价值的社区凝聚力相关的特征。(2)对 GCN 或类似 GCN 的模型的依赖对扩展到大图形提出了挑战。(3)现有方法不能很好地适应动态图，往往需要从头开始重新训练。为了处理这个问题，我们提出了 CSForm，一种基于图形转换器的可伸缩 CS。首先，我们提出了一种新的基于 n 阶 h 指数的 l-hop 邻域社区向量来表示每个节点的社区特征，通过改变邻域范围生成一系列的特征向量。然后，我们构建了一个主干变压器来学习一个具有丰富社区特征的良好的图嵌入，在此基础上，我们进行了一个基于预测滤波的在线 CS 来有效地返回一个 q 的社区。我们将 CSForm 扩展到动态图和各种社区模型。在七个现实世界图表上的大量实验表明我们的解决方案在有效性方面的优势，例如，我们在 F1得分上比最新的竞争对手平均提高了20.6% 。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Scalable+Community+Search+over+Large-scale+Graphs+based+on+Graph+Transformer)|1| +|[Large Language Models for Intent-Driven Session Recommendations](https://doi.org/10.1145/3626772.3657688)|Zhu Sun, Hongyang Liu, Xinghua Qu, Kaidong Feng, Yan Wang, Yew Soon Ong|A*STAR Centre for Frontier AI Research and Nanyang Technological University; Yanshan University; Agency for Science, Technology and Research; Shanda Group AI Lab; Macquarie University|Intent-aware session recommendation (ISR) is pivotal in discerning user intents within sessions for precise predictions. Traditional approaches, however, face limitations due to their presumption of a uniform number of intents across all sessions. This assumption overlooks the dynamic nature of user sessions, where the number and type of intentions can significantly vary. In addition, these methods typically operate in latent spaces, thus hinder the model's transparency.Addressing these challenges, we introduce a novel ISR approach, utilizing the advanced reasoning capabilities of large language models (LLMs). First, this approach begins by generating an initial prompt that guides LLMs to predict the next item in a session, based on the varied intents manifested in user sessions. Then, to refine this process, we introduce an innovative prompt optimization mechanism that iteratively self-reflects and adjusts prompts. Furthermore, our prompt selection module, built upon the LLMs' broad adaptability, swiftly selects the most optimized prompts across diverse domains. This new paradigm empowers LLMs to discern diverse user intents at a semantic level, leading to more accurate and interpretable session recommendations. Our extensive experiments on three real-world datasets demonstrate the effectiveness of our method, marking a significant advancement in ISR systems.|意图感知会话推荐(ISR)在识别会话中的用户意图以进行精确预测方面非常关键。然而，传统的方法由于假定所有会话的意图数量一致而面临局限性。这个假设忽略了用户会话的动态特性，其中意图的数量和类型可能有很大的不同。此外，这些方法通常在潜在的空间操作，从而阻碍了模型的透明度。针对这些挑战，我们引入了一种新的 ISR 方法，利用大型语言模型(LLM)的高级推理能力。首先，这种方法首先生成一个初始提示，指导 LLM 根据用户会话中显示的不同意图预测会话中的下一个项目。然后，为了完善这个过程，我们引入了一个创新的提示优化机制，它可以迭代地自我反映和调整提示。此外，我们的提示选择模块，建立在 LLM 的广泛适应性，迅速选择最优化的提示跨不同的领域。这种新的范式使 LLM 能够在语义层次上识别不同的用户意图，从而产生更加准确和可解释的会话建议。我们在三个实际数据集上的广泛实验证明了我们方法的有效性，标志着 ISR 系统的重大进步。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Large+Language+Models+for+Intent-Driven+Session+Recommendations)|1| +|[Scalable Community Search over Large-scale Graphs based on Graph Transformer](https://doi.org/10.1145/3626772.3657771)|Yuxiang Wang, Xiaoxuan Gou, Xiaoliang Xu, Yuxia Geng, Xiangyu Ke, Tianxing Wu, Zhiyuan Yu, Runhuai Chen, Xiangying Wu|Hangzhou Dianzi University, Hangzhou, China; Hangzhou Dianzi University, Zhejiang, China; Southeast University, Jiangsu, China; Zhejiang University, Zhejiang, China|Given a graph G and a query node q, community search (CS) aims to find a structurally cohesive subgraph from G that contains q. CS is widely used in many real-world applications, such as online recommendation and expert finding. Recently, the rise of learning-based CS methods has garnered extensive research interests, showcasing the promising potential of neural solutions. However, there remains room for optimization: (1) They initialize node features via classical methods, e.g., one-hot, random, and position encoding, which may fall short in capturing valuable community cohesiveness-related features. (2) The reliance on GCN or GCN-like models poses challenges in scaling to large graphs. (3) Existing methods do not adapt well to dynamic graphs, often requiring retraining from scratch. To handle this, we present CSFormer, a scalable CS based on Graph Transformer. First, we present a novel l-hop neighborhood community vector based on n-order h-index to represent each node's community features, generating a sequence of feature vectors by varying the neighborhood scope l. Then, we build a Transformer backbone to learn a good graph embedding that carries rich community features, based on which we perform a prediction-filtering-based online CS to efficiently return a community of q. We extend CSFormer to dynamic graphs and various community models. Extensive experiments on seven real-world graphs show our solution's superiority on effectiveness, e.g., we attain an average improvement of 20.6% in F1-score compared to the latest competitors.|给定一个图 G 和一个查询节点 q，社区搜索(Community Search，CS)旨在从 G 中找到一个结构内聚的子图。最近，基于学习的 CS 方法的兴起引起了广泛的研究兴趣，展示了神经解决方案的潜力。然而，仍有优化的空间: (1)他们通过传统的方法，如一个热点，随机和位置编码初始化节点特征，这可能不能捕获有价值的社区凝聚力相关的特征。(2)对 GCN 或类似 GCN 的模型的依赖对扩展到大图形提出了挑战。(3)现有方法不能很好地适应动态图，往往需要从头开始重新训练。为了处理这个问题，我们提出了 CSForm，一种基于图形转换器的可伸缩 CS。首先，我们提出了一种新的基于 n 阶 h 指数的 l-hop 邻域社区向量来表示每个节点的社区特征，通过改变邻域范围生成一系列的特征向量。然后，我们构建了一个主干变压器来学习一个具有丰富社区特征的良好的图嵌入，在此基础上，我们进行了一个基于预测滤波的在线 CS 来有效地返回一个 q 的社区。我们将 CSForm 扩展到动态图和各种社区模型。在七个现实世界图表上的大量实验表明我们的解决方案在有效性方面的优势，例如，我们在 F1得分上比最新的竞争对手平均提高了20.6% 。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Scalable+Community+Search+over+Large-scale+Graphs+based+on+Graph+Transformer)|1| |[LLM-Ensemble: Optimal Large Language Model Ensemble Method for E-commerce Product Attribute Value Extraction](https://doi.org/10.1145/3626772.3661357)|Chenhao Fang, Xiaohan Li, Zezhong Fan, Jianpeng Xu, Kaushiki Nag, Evren Körpeoglu, Sushant Kumar, Kannan Achan|Walmart Global Tech; University of Wisconsin-Madison|Product attribute value extraction is a pivotal component in Natural LanguageProcessing (NLP) and the contemporary e-commerce industry. The provision ofprecise product attribute values is fundamental in ensuring high-qualityrecommendations and enhancing customer satisfaction. The recently emergingLarge Language Models (LLMs) have demonstrated state-of-the-art performance innumerous attribute extraction tasks, without the need for domain-specifictraining data. Nevertheless, varying strengths and weaknesses are exhibited bydifferent LLMs due to the diversity in data, architectures, andhyperparameters. This variation makes them complementary to each other, with nosingle LLM dominating all others. Considering the diverse strengths andweaknesses of LLMs, it becomes necessary to develop an ensemble method thatleverages their complementary potentials. In this paper, we propose a novelalgorithm called LLM-ensemble to ensemble different LLMs' outputs for attributevalue extraction. We iteratively learn the weights for different LLMs toaggregate the labels with weights to predict the final attribute value. Notonly can our proposed method be proven theoretically optimal, but it alsoensures efficient computation, fast convergence, and safe deployment. We havealso conducted extensive experiments with various state-of-the-art LLMs,including Llama2-13B, Llama2-70B, PaLM-2, GPT-3.5, and GPT-4, on Walmart'sinternal data. Our offline metrics demonstrate that the LLM-ensemble methodoutperforms all the state-of-the-art single LLMs on Walmart's internal dataset.This method has been launched in several production models, leading to improvedGross Merchandise Volume (GMV), Click-Through Rate (CTR), Conversion Rate(CVR), and Add-to-Cart Rate (ATC).|产品属性值抽取是自然语言处理(NLP)和当代电子商务产业的关键组成部分。提供精确的产品属性值是确保高质量推荐和提高客户满意度的基础。最近出现的大型语言模型(LLM)已经展示了无数属性提取任务的最新性能，而不需要特定于领域的训练数据。然而，由于数据、体系结构和超参数的多样性，不同的 LLM 表现出不同的优缺点。这种变化使它们相互补充，没有单一的 LLM 支配所有其他。考虑到 LLM 的不同优缺点，有必要发展一种利用它们互补潜力的集成方法。本文提出了一种新的 LLM- 集成算法，用于集成不同 LLM 的输出，从而实现属性值的提取。我们迭代学习不同 LLM 的权重，以聚合权重的标签来预测最终的属性值。该方法不仅可以在理论上证明是最优的，而且可以保证计算效率、收敛速度和安全部署。我们还对各种最先进的 LLM 进行了广泛的实验，包括 Llama2-13B、 Llama2-70B、 PaLM-2、 GPT-3.5和 GPT-4，这些都是基于沃尔玛的内部数据。我们的离线指标表明，在沃尔玛的内部数据集上，LLM 集成方法优于所有最先进的单一 LLM。这种方法已经在多种生产模式中推出，从而改善了商品总量(GMV)、点进率(CTR)、转化率(CVR)和购物车率(ATC)。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=LLM-Ensemble:+Optimal+Large+Language+Model+Ensemble+Method+for+E-commerce+Product+Attribute+Value+Extraction)|1| |[Question Suggestion for Conversational Shopping Assistants Using Product Metadata](https://doi.org/10.1145/3626772.3661371)|Nikhita Vedula, Oleg Rokhlenko, Shervin Malmasi|Amazon|Digital assistants have become ubiquitous in e-commerce applications,following the recent advancements in Information Retrieval (IR), NaturalLanguage Processing (NLP) and Generative Artificial Intelligence (AI). However,customers are often unsure or unaware of how to effectively converse with theseassistants to meet their shopping needs. In this work, we emphasize theimportance of providing customers a fast, easy to use, and natural way tointeract with conversational shopping assistants. We propose a framework thatemploys Large Language Models (LLMs) to automatically generate contextual,useful, answerable, fluent and diverse questions about products, via in-contextlearning and supervised fine-tuning. Recommending these questions to customersas helpful suggestions or hints to both start and continue a conversation canresult in a smoother and faster shopping experience with reduced conversationoverhead and friction. We perform extensive offline evaluations, and discuss indetail about potential customer impact, and the type, length and latency of ourgenerated product questions if incorporated into a real-world shoppingassistant.|随着信息检索(IR)、自然语言处理(NLP)和生成人工智能(AI)的最新进展，数字助理已经成为电子商务应用程序中无处不在的一部分。然而，顾客往往不确定或不知道如何有效地与这些助理交谈，以满足他们的购物需求。在这项工作中，我们强调的重要性，为客户提供一个快速，易于使用，自然的方式与会话购物助理互动。我们提出了一个框架，使用大语言模型(LLM)自动生成上下文，有用的，可回答的，流畅的和多样化的产品问题，通过上下文内学习和监督微调。将这些问题推荐给顾客，作为开始和继续谈话的有用建议或提示，可以使购物体验更加顺畅和快捷，减少谈话开销和摩擦。我们进行广泛的离线评估，并详细讨论潜在的客户影响，以及我们生成的产品问题的类型，长度和延迟，如果纳入一个现实世界的购物助理。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Question+Suggestion+for+Conversational+Shopping+Assistants+Using+Product+Metadata)|1| -|[A Setwise Approach for Effective and Highly Efficient Zero-shot Ranking with Large Language Models](https://doi.org/10.1145/3626772.3657813)|Shengyao Zhuang, Honglei Zhuang, Bevan Koopman, Guido Zuccon|CSIRO; The University of Queensland; Google Research|We propose a novel zero-shot document ranking approach based on Large Language Models (LLMs): the Setwise prompting approach. Our approach complements existing prompting approaches for LLM-based zero-shot ranking: Pointwise, Pairwise, and Listwise. Through the first-of-its-kind comparative evaluation within a consistent experimental framework and considering factors like model size, token consumption, latency, among others, we show that existing approaches are inherently characterised by trade-offs between effectiveness and efficiency. We find that while Pointwise approaches score high on efficiency, they suffer from poor effectiveness. Conversely, Pairwise approaches demonstrate superior effectiveness but incur high computational overhead. Our Setwise approach, instead, reduces the number of LLM inferences and the amount of prompt token consumption during the ranking procedure, compared to previous methods. This significantly improves the efficiency of LLM-based zero-shot ranking, while also retaining high zero-shot ranking effectiveness. We make our code and results publicly available at .|提出了一种新的基于大语言模型(LLM)的零拍文档排序方法: Setwise 提示方法。我们的方法补充了现有的基于 LLM 的零拍排名的提示方法: Pointwise、 Pairwise 和 Listwise。通过在一致的实验框架内进行首次比较评估，并考虑模型大小、令牌消耗、延迟等因素，我们发现现有方法的内在特征是在效率和效益之间进行权衡。我们发现，虽然 Pointwise 方法在效率上得分较高，但它们的效率较低。相反，成对方法显示出更好的有效性，但是会产生较高的计算开销。相反，与以前的方法相比，我们的 Setwise 方法减少了 LLM 推断的数量和排序过程中的提示令牌消耗量。这显著提高了基于 LLM 的零拍排序的效率，同时也保持了较高的零拍排序效率。我们将我们的代码和结果公开地提供给。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Setwise+Approach+for+Effective+and+Highly+Efficient+Zero-shot+Ranking+with+Large+Language+Models)|1| -|[Ranked List Truncation for Large Language Model-based Re-Ranking](https://doi.org/10.1145/3626772.3657864)|Chuan Meng, Negar Arabzadeh, Arian Askari, Mohammad Aliannejadi, Maarten de Rijke|Leiden University; University of Waterloo; University of Amsterdam|We study ranked list truncation (RLT) from a novel "retrieve-then-re-rank"perspective, where we optimize re-ranking by truncating the retrieved list(i.e., trim re-ranking candidates). RLT is crucial for re-ranking as it canimprove re-ranking efficiency by sending variable-length candidate lists to are-ranker on a per-query basis. It also has the potential to improve re-rankingeffectiveness. Despite its importance, there is limited research into applyingRLT methods to this new perspective. To address this research gap, we reproduceexisting RLT methods in the context of re-ranking, especially newly emergedlarge language model (LLM)-based re-ranking. In particular, we examine to whatextent established findings on RLT for retrieval are generalizable to the"retrieve-then-re-rank" setup from three perspectives: (i) assessing RLTmethods in the context of LLM-based re-ranking with lexical first-stageretrieval, (ii) investigating the impact of different types of first-stageretrievers on RLT methods, and (iii) investigating the impact of differenttypes of re-rankers on RLT methods. We perform experiments on the TREC 2019 and2020 deep learning tracks, investigating 8 RLT methods for pipelines involving3 retrievers and 2 re-rankers. We reach new insights into RLT methods in thecontext of re-ranking.|我们从一个新的“检索-然后重新排名”的角度研究排名列表截断(RLT) ，其中我们通过截断检索列表(即，修剪重新排名的候选人)来优化重新排名。RLT 是重新排序的关键，因为它可以提高重新排序的效率发送可变长度的候选人名单是排名的每个查询的基础上。它还具有提高重新排名效率的潜力。尽管它的重要性，有限的研究应用 RLT 方法到这个新的角度。为了解决这一研究差距，我们在重新排序的背景下重现了现有的 RLT 方法，特别是新出现的基于大语言模型(LLM)的重新排序方法。具体而言，我们从三个角度研究了 RLT 检索的既定发现在多大程度上可以推广到“检索然后重新排名”的设置: (i)在基于 LLM 的词汇第一阶段重新排名的背景下评估 RLTmethod，(ii)调查不同类型的第一阶段检索者对 RLT 方法的影响，以及(iii)调查不同类型的重新排名对 RLT 方法的影响。我们在 TREC 2019年和2020年的深度学习轨道上进行了实验，研究了8种涉及3个检索器和2个重新排序器的管道 RLT 方法。在重新排名的背景下，我们对 RLT 方法有了新的认识。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Ranked+List+Truncation+for+Large+Language+Model-based+Re-Ranking)|1| -|[Fine-grained Textual Inversion Network for Zero-Shot Composed Image Retrieval](https://doi.org/10.1145/3626772.3657831)|Haoqiang Lin, Haokun Wen, Xuemeng Song, Meng Liu, Yupeng Hu, Liqiang Nie|Harbin Institute of Technology (Shenzhen), Shenzhen, Guangdong, China; Shandong Jianzhu University, Jinan, Shandong, China; Shandong University, Qingdao, Shandong, China; Shandong University, Jinan, Shandong, China|Composed Image Retrieval (CIR) allows users to search target images with a multimodal query, comprising a reference image and a modification text that describes the user's modification demand over the reference image. Nevertheless, due to the expensive labor cost of training data annotation, recent researchers have shifted to the challenging task of zero-shot CIR (ZS-CIR), which targets fulfilling CIR without annotated triplets. The pioneer ZS-CIR studies focus on converting the CIR task into a standard text-to-image retrieval task by pre-training a textual inversion network that can map a given image into a single pseudo-word token. Despite their significant progress, their coarse-grained textual inversion may be insufficient to capture the full content of the image accurately. To overcome this issue, in this work, we propose a novel Fine-grained Textual Inversion Network for ZS-CIR, named FTI4CIR. In particular, FTI4CIR comprises two main components: fine-grained pseudo-word token mapping and tri-wise caption-based semantic regularization. The former maps the image into a subject-oriented pseudo-word token and several attribute-oriented pseudo-word tokens to comprehensively express the image in the textual form, while the latter works on jointly aligning the fine-grained pseudo-word tokens to the real-word token embedding space based on a BLIP-generated image caption template. Extensive experiments conducted on three benchmark datasets demonstrate the superiority of our proposed method.|复合图像检索(CIR)允许用户通过多模态查询搜索目标图像，包括参考图像和描述用户对参考图像的修改要求的修改文本。然而，由于培训数据注释的昂贵人工成本，最近的研究人员已经转向具有挑战性的任务零射击 CIR (ZS-CIR) ，其目标是实现没有注释三联体的 CIR。先驱的 ZS-CIR 研究集中在通过预训练文本反演网络将给定的图像映射为单个伪单词标记，将 CIR 任务转换为标准的文本-图像检索任务。尽管取得了显著的进展，但粗粒度的文本反演可能不足以准确地捕捉图像的全部内容。为了克服这一问题，本文提出了一种新的 ZS-CIR 细粒度文本反演网络 FTI4CIR。具体而言，FTI4CIR 包括两个主要组成部分: 细粒度伪词标记映射和基于三分字幕的语义正则化。前者将图像映射为一个面向主题的伪词标记和若干个面向属性的伪词标记，以文本形式综合表示图像，后者基于 BLIP 生成的图像标题模板，将细粒度的伪词标记与实词标记嵌入空间联合对齐。在三个基准数据集上进行的大量实验表明了该方法的优越性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Fine-grained+Textual+Inversion+Network+for+Zero-Shot+Composed+Image+Retrieval)|1| -|[Denoising Diffusion Recommender Model](https://doi.org/10.1145/3626772.3657825)|Jujia Zhao, Wenjie Wang, Yiyan Xu, Teng Sun, Fuli Feng, TatSeng Chua|Shandong University; University of Science and Technology of China; National University of Singapore School of Computing; University of Science and Technology of China School of Data Science; National University of Singapore; Leiden University|Recommender systems often grapple with noisy implicit feedback. Most studiesalleviate the noise issues from data cleaning perspective such as dataresampling and reweighting, but they are constrained by heuristic assumptions.Another denoising avenue is from model perspective, which proactively injectsnoises into user-item interactions and enhances the intrinsic denoising abilityof models. However, this kind of denoising process poses significant challengesto the recommender model's representation capacity to capture noise patterns. To address this issue, we propose Denoising Diffusion Recommender Model(DDRM), which leverages multi-step denoising process of diffusion models torobustify user and item embeddings from any recommender models. DDRM injectscontrolled Gaussian noises in the forward process and iteratively removesnoises in the reverse denoising process, thereby improving embedding robustnessagainst noisy feedback. To achieve this target, the key lies in offeringappropriate guidance to steer the reverse denoising process and providing aproper starting point to start the forward-reverse process during inference. Inparticular, we propose a dedicated denoising module that encodes collaborativeinformation as denoising guidance. Besides, in the inference stage, DDRMutilizes the average embeddings of users' historically liked items as thestarting point rather than using pure noise since pure noise lackspersonalization, which increases the difficulty of the denoising process.Extensive experiments on three datasets with three representative backendrecommender models demonstrate the effectiveness of DDRM.|推荐系统经常与含噪隐式反馈作斗争。大多数研究从数据清理的角度缓解噪声问题，如数据采样和重新加权，但他们受到启发式假设的约束。另一种去噪方法是从模型的角度出发，主动地将噪声注入到用户-项目的交互中，提高模型的内在去噪能力。然而，这种去噪过程对推荐模型捕获噪声模式的表示能力提出了严峻的挑战。为了解决这一问题，我们提出了去噪扩散推荐模型(DDRM) ，它利用扩散模型的多步去噪过程来模糊任何推荐模型中的用户和项目嵌入。DDRM 在正向过程中注入受控的高斯噪声，在反向过程中迭代去除噪声，从而提高了对噪声反馈的嵌入鲁棒性。要实现这一目标，关键在于提供适当的指导来引导反向去噪过程，并在推理过程中提供适当的起点来启动正向反向去噪过程。特别是，我们提出了一个专用的去噪模块，编码协作信息作为去噪指导。此外，在推理阶段，由于纯噪声缺乏个性化，DDRM 利用用户历史喜好项的平均嵌入作为起点，而非纯噪声，增加了去噪过程的难度。通过对三个具有代表性的后向推荐模型的三个数据集的大量实验，证明了 DDRM 的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Denoising+Diffusion+Recommender+Model)|1| -|[Systematic Evaluation of Neural Retrieval Models on the Touché 2020 Argument Retrieval Subset of BEIR](https://doi.org/10.1145/3626772.3657861)|Nandan Thakur, Luiz Bonifacio, Maik Fröbe, Alexander Bondarenko, Ehsan Kamalloo, Martin Potthast, Matthias Hagen, Jimmy Lin|Friedrich-Schiller-Universität Jena; UNICAMP, University of Waterloo; University of Waterloo; Leipzig University and ScaDS.AI|The zero-shot effectiveness of neural retrieval models is often evaluated on the BEIR benchmark – a combination of different IR evaluation datasets. Interestingly, previous studies found that particularly on the BEIR subset Touché 2020, an argument retrieval task, neural retrieval models are considerably less effective than BM25. Still, so far, no further investigation has been conducted on what makes argument retrieval so "special". To more deeply analyze the respective potential limits of neural retrieval models, we run a reproducibility study on the Touché 2020 data. In our study, we focus on two experiments: (i) a black-box evaluation (i.e., no model retraining), incorporating a theoretical exploration using retrieval axioms, and (ii) a data denoising evaluation involving post-hoc relevance judgments. Our black-box evaluation reveals an inherent bias of neural models towards retrieving short passages from the Touché 2020 data, and we also find that quite a few of the neural models' results are unjudged in the Touché 2020 data. As many of the short Touché passages are not argumentative and thus non-relevant per se, and as the missing judgments complicate fair comparison, we denoise the Touché 2020 data by excluding very short passages (less than 20 words) and by augmenting the unjudged data with post-hoc judgments following the Touché guidelines. On the denoised data, the effectiveness of the neural models improves by up to 0.52 in nDCG@10, but BM25 is still more effective. Our code and the augmented Touché 2020 dataset are available at .|神经检索模型的零点效应通常是在 BEIR 基准上进行评估的，BEIR 基准是不同的 IR 评估数据集的组合。有趣的是，以往的研究发现，特别是在 BEIR 子集 Touché 2020(一个论点检索任务)上，神经检索模型的有效性明显低于 BM25。尽管如此，到目前为止，还没有进一步的调查，使论点检索如此“特殊”。为了更深入地分析神经检索模型各自的潜在局限性，我们对 Touché 2020数据进行了重复性研究。在我们的研究中，我们侧重于两个实验: (i)黑盒评估(即，没有模型再训练) ，结合使用检索公理的理论探索，和(ii)涉及事后相关性判断的数据去噪评估。我们的黑匣子评估揭示了神经模型对从 Touché 2020数据中检索短文本的固有偏见，并且我们还发现相当多的神经模型的结果在 Touché 2020数据中是未经判断的。由于许多简短的 Touché 段落并不具有争议性，因此本身并不相关，并且由于缺失的判断使公平比较复杂化，我们通过排除非常短的段落(少于20个单词)以及按照 Touché 指南用事后判断增加未经判断的数据来降低 Touché 2020数据的噪声。在去噪数据中，神经模型的有效性在 nDCG@10中提高了0.52，但 BM25仍然更有效。我们的代码和增强的 Touché 2020数据集可以在。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Systematic+Evaluation+of+Neural+Retrieval+Models+on+the+Touché+2020+Argument+Retrieval+Subset+of+BEIR)|1| -|[Generative Retrieval as Multi-Vector Dense Retrieval](https://doi.org/10.1145/3626772.3657697)|Shiguang Wu, Wenda Wei, Mengqi Zhang, Zhumin Chen, Jun Ma, Zhaochun Ren, Maarten de Rijke, Pengjie Ren|Shandong University; Leiden University; University of Amsterdam|Generative retrieval generates identifiers of relevant documents in anend-to-end manner using a sequence-to-sequence architecture for a given query.The relation between generative retrieval and other retrieval methods,especially those based on matching within dense retrieval models, is not yetfully comprehended. Prior work has demonstrated that generative retrieval withatomic identifiers is equivalent to single-vector dense retrieval. Accordingly,generative retrieval exhibits behavior analogous to hierarchical search withina tree index in dense retrieval when using hierarchical semantic identifiers.However, prior work focuses solely on the retrieval stage without consideringthe deep interactions within the decoder of generative retrieval. In this paper, we fill this gap by demonstrating that generative retrievaland multi-vector dense retrieval share the same framework for measuring therelevance to a query of a document. Specifically, we examine the attentionlayer and prediction head of generative retrieval, revealing that generativeretrieval can be understood as a special case of multi-vector dense retrieval.Both methods compute relevance as a sum of products of query and documentvectors and an alignment matrix. We then explore how generative retrievalapplies this framework, employing distinct strategies for computing documenttoken vectors and the alignment matrix. We have conducted experiments to verifyour conclusions and show that both paradigms exhibit commonalities of termmatching in their alignment matrix.|生成检索使用给定查询的序列到序列体系结构以端到端的方式生成相关文档的标识符。生成检索与其他检索方法之间的关系，特别是那些基于密集检索模型中匹配的检索方法之间的关系，还没有得到充分的理解。先前的工作已经证明，具有原子标识符的生成检索等价于单向量密集检索。相应地，在使用层次语义标识符进行密集检索时，生成检索表现出类似于树索引层次检索的行为。然而，先前的工作仅仅集中在检索阶段，而没有考虑生成检索解码器内部的深层交互作用。本文通过证明生成检索和多向量密集检索在测量文档查询的相关性方面具有相同的框架来填补这一空白。具体来说，我们考察了生成检索的注意层和预测头，发现生成检索可以理解为多向量密集检索的一个特例。这两种方法都将相关性计算为查询、文档向量和对齐矩阵的乘积之和。然后，我们探讨如何生成检索应用这个框架，使用不同的策略计算文档令牌向量和对齐矩阵。我们已经进行了实验来验证你的结论，并表明这两种范例在它们的对齐矩阵中表现出术语匹配的共性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Generative+Retrieval+as+Multi-Vector+Dense+Retrieval)|1| +|[A Setwise Approach for Effective and Highly Efficient Zero-shot Ranking with Large Language Models](https://doi.org/10.1145/3626772.3657813)|Shengyao Zhuang, Honglei Zhuang, Bevan Koopman, Guido Zuccon|Google Research; The University of Queensland; CSIRO|We propose a novel zero-shot document ranking approach based on Large Language Models (LLMs): the Setwise prompting approach. Our approach complements existing prompting approaches for LLM-based zero-shot ranking: Pointwise, Pairwise, and Listwise. Through the first-of-its-kind comparative evaluation within a consistent experimental framework and considering factors like model size, token consumption, latency, among others, we show that existing approaches are inherently characterised by trade-offs between effectiveness and efficiency. We find that while Pointwise approaches score high on efficiency, they suffer from poor effectiveness. Conversely, Pairwise approaches demonstrate superior effectiveness but incur high computational overhead. Our Setwise approach, instead, reduces the number of LLM inferences and the amount of prompt token consumption during the ranking procedure, compared to previous methods. This significantly improves the efficiency of LLM-based zero-shot ranking, while also retaining high zero-shot ranking effectiveness. We make our code and results publicly available at .|提出了一种新的基于大语言模型(LLM)的零拍文档排序方法: Setwise 提示方法。我们的方法补充了现有的基于 LLM 的零拍排名的提示方法: Pointwise、 Pairwise 和 Listwise。通过在一致的实验框架内进行首次比较评估，并考虑模型大小、令牌消耗、延迟等因素，我们发现现有方法的内在特征是在效率和效益之间进行权衡。我们发现，虽然 Pointwise 方法在效率上得分较高，但它们的效率较低。相反，成对方法显示出更好的有效性，但是会产生较高的计算开销。相反，与以前的方法相比，我们的 Setwise 方法减少了 LLM 推断的数量和排序过程中的提示令牌消耗量。这显著提高了基于 LLM 的零拍排序的效率，同时也保持了较高的零拍排序效率。我们将我们的代码和结果公开地提供给。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Setwise+Approach+for+Effective+and+Highly+Efficient+Zero-shot+Ranking+with+Large+Language+Models)|1| +|[Ranked List Truncation for Large Language Model-based Re-Ranking](https://doi.org/10.1145/3626772.3657864)|Chuan Meng, Negar Arabzadeh, Arian Askari, Mohammad Aliannejadi, Maarten de Rijke|University of Amsterdam; University of Waterloo; Leiden University|We study ranked list truncation (RLT) from a novel "retrieve-then-re-rank"perspective, where we optimize re-ranking by truncating the retrieved list(i.e., trim re-ranking candidates). RLT is crucial for re-ranking as it canimprove re-ranking efficiency by sending variable-length candidate lists to are-ranker on a per-query basis. It also has the potential to improve re-rankingeffectiveness. Despite its importance, there is limited research into applyingRLT methods to this new perspective. To address this research gap, we reproduceexisting RLT methods in the context of re-ranking, especially newly emergedlarge language model (LLM)-based re-ranking. In particular, we examine to whatextent established findings on RLT for retrieval are generalizable to the"retrieve-then-re-rank" setup from three perspectives: (i) assessing RLTmethods in the context of LLM-based re-ranking with lexical first-stageretrieval, (ii) investigating the impact of different types of first-stageretrievers on RLT methods, and (iii) investigating the impact of differenttypes of re-rankers on RLT methods. We perform experiments on the TREC 2019 and2020 deep learning tracks, investigating 8 RLT methods for pipelines involving3 retrievers and 2 re-rankers. We reach new insights into RLT methods in thecontext of re-ranking.|我们从一个新的“检索-然后重新排名”的角度研究排名列表截断(RLT) ，其中我们通过截断检索列表(即，修剪重新排名的候选人)来优化重新排名。RLT 是重新排序的关键，因为它可以提高重新排序的效率发送可变长度的候选人名单是排名的每个查询的基础上。它还具有提高重新排名效率的潜力。尽管它的重要性，有限的研究应用 RLT 方法到这个新的角度。为了解决这一研究差距，我们在重新排序的背景下重现了现有的 RLT 方法，特别是新出现的基于大语言模型(LLM)的重新排序方法。具体而言，我们从三个角度研究了 RLT 检索的既定发现在多大程度上可以推广到“检索然后重新排名”的设置: (i)在基于 LLM 的词汇第一阶段重新排名的背景下评估 RLTmethod，(ii)调查不同类型的第一阶段检索者对 RLT 方法的影响，以及(iii)调查不同类型的重新排名对 RLT 方法的影响。我们在 TREC 2019年和2020年的深度学习轨道上进行了实验，研究了8种涉及3个检索器和2个重新排序器的管道 RLT 方法。在重新排名的背景下，我们对 RLT 方法有了新的认识。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Ranked+List+Truncation+for+Large+Language+Model-based+Re-Ranking)|1| +|[Fine-grained Textual Inversion Network for Zero-Shot Composed Image Retrieval](https://doi.org/10.1145/3626772.3657831)|Haoqiang Lin, Haokun Wen, Xuemeng Song, Meng Liu, Yupeng Hu, Liqiang Nie|Shandong University, Qingdao, Shandong, China; Harbin Institute of Technology (Shenzhen), Shenzhen, Guangdong, China; Shandong Jianzhu University, Jinan, Shandong, China; Shandong University, Jinan, Shandong, China|Composed Image Retrieval (CIR) allows users to search target images with a multimodal query, comprising a reference image and a modification text that describes the user's modification demand over the reference image. Nevertheless, due to the expensive labor cost of training data annotation, recent researchers have shifted to the challenging task of zero-shot CIR (ZS-CIR), which targets fulfilling CIR without annotated triplets. The pioneer ZS-CIR studies focus on converting the CIR task into a standard text-to-image retrieval task by pre-training a textual inversion network that can map a given image into a single pseudo-word token. Despite their significant progress, their coarse-grained textual inversion may be insufficient to capture the full content of the image accurately. To overcome this issue, in this work, we propose a novel Fine-grained Textual Inversion Network for ZS-CIR, named FTI4CIR. In particular, FTI4CIR comprises two main components: fine-grained pseudo-word token mapping and tri-wise caption-based semantic regularization. The former maps the image into a subject-oriented pseudo-word token and several attribute-oriented pseudo-word tokens to comprehensively express the image in the textual form, while the latter works on jointly aligning the fine-grained pseudo-word tokens to the real-word token embedding space based on a BLIP-generated image caption template. Extensive experiments conducted on three benchmark datasets demonstrate the superiority of our proposed method.|复合图像检索(CIR)允许用户通过多模态查询搜索目标图像，包括参考图像和描述用户对参考图像的修改要求的修改文本。然而，由于培训数据注释的昂贵人工成本，最近的研究人员已经转向具有挑战性的任务零射击 CIR (ZS-CIR) ，其目标是实现没有注释三联体的 CIR。先驱的 ZS-CIR 研究集中在通过预训练文本反演网络将给定的图像映射为单个伪单词标记，将 CIR 任务转换为标准的文本-图像检索任务。尽管取得了显著的进展，但粗粒度的文本反演可能不足以准确地捕捉图像的全部内容。为了克服这一问题，本文提出了一种新的 ZS-CIR 细粒度文本反演网络 FTI4CIR。具体而言，FTI4CIR 包括两个主要组成部分: 细粒度伪词标记映射和基于三分字幕的语义正则化。前者将图像映射为一个面向主题的伪词标记和若干个面向属性的伪词标记，以文本形式综合表示图像，后者基于 BLIP 生成的图像标题模板，将细粒度的伪词标记与实词标记嵌入空间联合对齐。在三个基准数据集上进行的大量实验表明了该方法的优越性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Fine-grained+Textual+Inversion+Network+for+Zero-Shot+Composed+Image+Retrieval)|1| +|[Denoising Diffusion Recommender Model](https://doi.org/10.1145/3626772.3657825)|Jujia Zhao, Wenjie Wang, Yiyan Xu, Teng Sun, Fuli Feng, TatSeng Chua|University of Science and Technology of China; Shandong University; University of Science and Technology of China School of Data Science; Leiden University; National University of Singapore School of Computing; National University of Singapore|Recommender systems often grapple with noisy implicit feedback. Most studiesalleviate the noise issues from data cleaning perspective such as dataresampling and reweighting, but they are constrained by heuristic assumptions.Another denoising avenue is from model perspective, which proactively injectsnoises into user-item interactions and enhances the intrinsic denoising abilityof models. However, this kind of denoising process poses significant challengesto the recommender model's representation capacity to capture noise patterns. To address this issue, we propose Denoising Diffusion Recommender Model(DDRM), which leverages multi-step denoising process of diffusion models torobustify user and item embeddings from any recommender models. DDRM injectscontrolled Gaussian noises in the forward process and iteratively removesnoises in the reverse denoising process, thereby improving embedding robustnessagainst noisy feedback. To achieve this target, the key lies in offeringappropriate guidance to steer the reverse denoising process and providing aproper starting point to start the forward-reverse process during inference. Inparticular, we propose a dedicated denoising module that encodes collaborativeinformation as denoising guidance. Besides, in the inference stage, DDRMutilizes the average embeddings of users' historically liked items as thestarting point rather than using pure noise since pure noise lackspersonalization, which increases the difficulty of the denoising process.Extensive experiments on three datasets with three representative backendrecommender models demonstrate the effectiveness of DDRM.|推荐系统经常与含噪隐式反馈作斗争。大多数研究从数据清理的角度缓解噪声问题，如数据采样和重新加权，但他们受到启发式假设的约束。另一种去噪方法是从模型的角度出发，主动地将噪声注入到用户-项目的交互中，提高模型的内在去噪能力。然而，这种去噪过程对推荐模型捕获噪声模式的表示能力提出了严峻的挑战。为了解决这一问题，我们提出了去噪扩散推荐模型(DDRM) ，它利用扩散模型的多步去噪过程来模糊任何推荐模型中的用户和项目嵌入。DDRM 在正向过程中注入受控的高斯噪声，在反向过程中迭代去除噪声，从而提高了对噪声反馈的嵌入鲁棒性。要实现这一目标，关键在于提供适当的指导来引导反向去噪过程，并在推理过程中提供适当的起点来启动正向反向去噪过程。特别是，我们提出了一个专用的去噪模块，编码协作信息作为去噪指导。此外，在推理阶段，由于纯噪声缺乏个性化，DDRM 利用用户历史喜好项的平均嵌入作为起点，而非纯噪声，增加了去噪过程的难度。通过对三个具有代表性的后向推荐模型的三个数据集的大量实验，证明了 DDRM 的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Denoising+Diffusion+Recommender+Model)|1| +|[Systematic Evaluation of Neural Retrieval Models on the Touché 2020 Argument Retrieval Subset of BEIR](https://doi.org/10.1145/3626772.3657861)|Nandan Thakur, Luiz Bonifacio, Maik Fröbe, Alexander Bondarenko, Ehsan Kamalloo, Martin Potthast, Matthias Hagen, Jimmy Lin|University of Waterloo; Leipzig University and ScaDS.AI; UNICAMP, University of Waterloo; Friedrich-Schiller-Universität Jena|The zero-shot effectiveness of neural retrieval models is often evaluated on the BEIR benchmark – a combination of different IR evaluation datasets. Interestingly, previous studies found that particularly on the BEIR subset Touché 2020, an argument retrieval task, neural retrieval models are considerably less effective than BM25. Still, so far, no further investigation has been conducted on what makes argument retrieval so "special". To more deeply analyze the respective potential limits of neural retrieval models, we run a reproducibility study on the Touché 2020 data. In our study, we focus on two experiments: (i) a black-box evaluation (i.e., no model retraining), incorporating a theoretical exploration using retrieval axioms, and (ii) a data denoising evaluation involving post-hoc relevance judgments. Our black-box evaluation reveals an inherent bias of neural models towards retrieving short passages from the Touché 2020 data, and we also find that quite a few of the neural models' results are unjudged in the Touché 2020 data. As many of the short Touché passages are not argumentative and thus non-relevant per se, and as the missing judgments complicate fair comparison, we denoise the Touché 2020 data by excluding very short passages (less than 20 words) and by augmenting the unjudged data with post-hoc judgments following the Touché guidelines. On the denoised data, the effectiveness of the neural models improves by up to 0.52 in nDCG@10, but BM25 is still more effective. Our code and the augmented Touché 2020 dataset are available at .|神经检索模型的零点效应通常是在 BEIR 基准上进行评估的，BEIR 基准是不同的 IR 评估数据集的组合。有趣的是，以往的研究发现，特别是在 BEIR 子集 Touché 2020(一个论点检索任务)上，神经检索模型的有效性明显低于 BM25。尽管如此，到目前为止，还没有进一步的调查，使论点检索如此“特殊”。为了更深入地分析神经检索模型各自的潜在局限性，我们对 Touché 2020数据进行了重复性研究。在我们的研究中，我们侧重于两个实验: (i)黑盒评估(即，没有模型再训练) ，结合使用检索公理的理论探索，和(ii)涉及事后相关性判断的数据去噪评估。我们的黑匣子评估揭示了神经模型对从 Touché 2020数据中检索短文本的固有偏见，并且我们还发现相当多的神经模型的结果在 Touché 2020数据中是未经判断的。由于许多简短的 Touché 段落并不具有争议性，因此本身并不相关，并且由于缺失的判断使公平比较复杂化，我们通过排除非常短的段落(少于20个单词)以及按照 Touché 指南用事后判断增加未经判断的数据来降低 Touché 2020数据的噪声。在去噪数据中，神经模型的有效性在 nDCG@10中提高了0.52，但 BM25仍然更有效。我们的代码和增强的 Touché 2020数据集可以在。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Systematic+Evaluation+of+Neural+Retrieval+Models+on+the+Touché+2020+Argument+Retrieval+Subset+of+BEIR)|1| +|[Generative Retrieval as Multi-Vector Dense Retrieval](https://doi.org/10.1145/3626772.3657697)|Shiguang Wu, Wenda Wei, Mengqi Zhang, Zhumin Chen, Jun Ma, Zhaochun Ren, Maarten de Rijke, Pengjie Ren|University of Amsterdam; Shandong University; Leiden University|Generative retrieval generates identifiers of relevant documents in anend-to-end manner using a sequence-to-sequence architecture for a given query.The relation between generative retrieval and other retrieval methods,especially those based on matching within dense retrieval models, is not yetfully comprehended. Prior work has demonstrated that generative retrieval withatomic identifiers is equivalent to single-vector dense retrieval. Accordingly,generative retrieval exhibits behavior analogous to hierarchical search withina tree index in dense retrieval when using hierarchical semantic identifiers.However, prior work focuses solely on the retrieval stage without consideringthe deep interactions within the decoder of generative retrieval. In this paper, we fill this gap by demonstrating that generative retrievaland multi-vector dense retrieval share the same framework for measuring therelevance to a query of a document. Specifically, we examine the attentionlayer and prediction head of generative retrieval, revealing that generativeretrieval can be understood as a special case of multi-vector dense retrieval.Both methods compute relevance as a sum of products of query and documentvectors and an alignment matrix. We then explore how generative retrievalapplies this framework, employing distinct strategies for computing documenttoken vectors and the alignment matrix. We have conducted experiments to verifyour conclusions and show that both paradigms exhibit commonalities of termmatching in their alignment matrix.|生成检索使用给定查询的序列到序列体系结构以端到端的方式生成相关文档的标识符。生成检索与其他检索方法之间的关系，特别是那些基于密集检索模型中匹配的检索方法之间的关系，还没有得到充分的理解。先前的工作已经证明，具有原子标识符的生成检索等价于单向量密集检索。相应地，在使用层次语义标识符进行密集检索时，生成检索表现出类似于树索引层次检索的行为。然而，先前的工作仅仅集中在检索阶段，而没有考虑生成检索解码器内部的深层交互作用。本文通过证明生成检索和多向量密集检索在测量文档查询的相关性方面具有相同的框架来填补这一空白。具体来说，我们考察了生成检索的注意层和预测头，发现生成检索可以理解为多向量密集检索的一个特例。这两种方法都将相关性计算为查询、文档向量和对齐矩阵的乘积之和。然后，我们探讨如何生成检索应用这个框架，使用不同的策略计算文档令牌向量和对齐矩阵。我们已经进行了实验来验证你的结论，并表明这两种范例在它们的对齐矩阵中表现出术语匹配的共性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Generative+Retrieval+as+Multi-Vector+Dense+Retrieval)|1| |[A Workbench for Autograding Retrieve/Generate Systems](https://doi.org/10.1145/3626772.3657871)|Laura Dietz|University of New Hampshire|This resource paper addresses the challenge of evaluating Information Retrieval (IR) systems in the era of autoregressive Large Language Models (LLMs). Traditional methods relying on passage-level judgments are no longer effective due to the diversity of responses generated by LLM-based systems. We provide a workbench to explore several alternative evaluation approaches to judge the relevance of a system's response that incorporate LLMs: 1. Asking an LLM whether the response is relevant; 2. Asking the LLM which set of nuggets (i.e., relevant key facts) is covered in the response; 3. Asking the LLM to answer a set of exam questions with the response. This workbench aims to facilitate the development of new, reusable test collections. Researchers can manually refine sets of nuggets and exam questions, observing their impact on system evaluation and leaderboard rankings. Resource available at https://github.com/TREMA-UNH/rubric-grading-workbench|本资源文件阐述了在自回归大语言模型(LLM)时代评估信息检索(IR)系统所面临的挑战。由于基于 LLM 的系统所产生的响应的多样性，传统的依赖于通道级判断的方法已不再有效。我们提供了一个工作台来探索几种替代的评估方法，以判断一个系统的响应的相关性，包括 LLM: 1。询问 LLM 的响应是否相关; 2。询问 LLM 回答中包含了哪些重要信息(即相关的关键事实) ; 3。要求 LLM 使用响应来回答一组考试问题。这个工作台旨在促进新的、可重用的测试集合的开发。研究人员可以手动完善成套的金块和考试问题，观察它们对系统评估和排行榜的影响。Https://github.com/trema-unh/rubric-grading-workbench 可提供的资源|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Workbench+for+Autograding+Retrieve/Generate+Systems)|1| -|[Evaluating Generative Ad Hoc Information Retrieval](https://doi.org/10.1145/3626772.3657849)|Lukas Gienapp, Harrisen Scells, Niklas Deckers, Janek Bevendorff, Shuai Wang, Johannes Kiesel, Shahbaz Syed, Maik Fröbe, Guido Zuccon, Benno Stein, Matthias Hagen, Martin Potthast|; University of Kassel & hessian.AI, Kassel, Germany; Bauhaus-Universität Weimar, Weimar, Germany; Leipzig University & ScaDS.AI, Leipzig, Germany; Friedrich-Schiller-Universität Jena, Jena, Germany; Leipzig University, Leipzig, Germany|Recent advances in large language models have enabled the development of viable generative information retrieval systems. A generative retrieval system returns a grounded generated text in response to an information need instead of the traditional document ranking. Quantifying the utility of these types of responses is essential for evaluating generative retrieval systems. As the established evaluation methodology for ranking-based ad hoc retrieval may seem unsuitable for generative retrieval, new approaches for reliable, repeatable, and reproducible experimentation are required. In this paper, we survey the relevant information retrieval and natural language processing literature, identify search tasks and system architectures in generative retrieval, develop a corresponding user model, and study its operationalization. This theoretical analysis provides a foundation and new insights for the evaluation of generative ad hoc retrieval systems.|大型语言模型的最新进展使得可行的生成信息检索系统的开发成为可能。一个生成检索系统返回一个接地生成的文本，以响应信息需求，而不是传统的文档排序。量化这些类型的响应的效用对于评估生成检索系统是必不可少的。由于已建立的基于排序的特别检索的评估方法似乎不适合于生成性检索，因此需要可靠的、可重复的和可重现的实验的新方法。在本文中，我们调查了相关的信息检索和自然语言处理文献，确定了生成检索中的搜索任务和系统架构，开发了相应的用户模型，并研究了它的操作主义。这一理论分析为生成式自组织检索系统的评估提供了基础和新的视角。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Evaluating+Generative+Ad+Hoc+Information+Retrieval)|1| -|[Embark on DenseQuest: A System for Selecting the Best Dense Retriever for a Custom Collection](https://doi.org/10.1145/3626772.3657674)|Ekaterina Khramtsova, Teerapong Leelanupab, Shengyao Zhuang, Mahsa Baktashmotlagh, Guido Zuccon|University of Queensland; The University of Queensland|In this demo we present a web-based application for selecting an effective pre-trained dense retriever to use on a private collection. Our system, DenseQuest, provides unsupervised selection and ranking capabilities to predict the best dense retriever among a pool of available dense retrievers, tailored to an uploaded target collection. DenseQuest implements a number of existing approaches, including a recent, highly effective method powered by Large Language Models (LLMs), which requires neither queries nor relevance judgments. The system is designed to be intuitive and easy to use for those information retrieval engineers and researchers who need to identify a general-purpose dense retrieval model to encode or search a new private target collection. Our demonstration illustrates conceptual architecture and the different use case scenarios of the system implemented on the cloud, enabling universal access and use. DenseQuest is available at https://densequest.ielab.io.|在本演示中，我们展示了一个基于网络的应用程序，用于在私有数据集上选择有效的预训练密集检索器。我们的系统DenseQuest提供了无监督的选择和排序功能，以预测在一组可用密集检索器中，最适合上传目标集合的最佳密集检索器。DenseQuest实现了多种现有方法，包括一种由大型语言模型（LLMs）驱动的新近高效方法，该方法既不需要查询也不需要相关性判断。该系统设计得直观且易于使用，适用于那些需要为新的私有目标集合编码或搜索而识别通用密集检索模型的信息检索工程师和研究人员。我们的演示展示了系统的概念架构以及在云上实现的不同的使用案例场景，实现了普遍的访问和使用。DenseQuest可通过https://densequest.ielab.io访问。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Embark+on+DenseQuest:+A+System+for+Selecting+the+Best+Dense+Retriever+for+a+Custom+Collection)|1| -|[QuanTemp: A real-world open-domain benchmark for fact-checking numerical claims](https://doi.org/10.1145/3626772.3657874)|Venktesh V, Abhijit Anand, Avishek Anand, Vinay Setty|University of Stavanger; TU Delft; L3S Research Institute|With the growth of misinformation on the web, automated fact checking has garnered immense interest for detecting growing misinformation and disinformation. Current systems have made significant advancements in handling synthetic claims sourced from Wikipedia, and noteworthy progress has been achieved in addressing real-world claims that are verified by fact-checking organizations as well. We compile and release QuanTemp, a diverse, multi-domain dataset focused exclusively on numerical claims, encompassing comparative, statistical, interval, and temporal aspects, with detailed metadata and an accompanying evidence collection. This addresses the challenge of verifying real-world numerical claims, which are complex and often lack precise information, a gap not filled by existing works that mainly focus on synthetic claims. We evaluate and quantify these gaps in existing solutions for the task of verifying numerical claims. We also evaluate claim decomposition based methods, numerical understanding based natural language inference (NLI) models and our best baselines achieves a macro-F1 of 58.32. This demonstrates that QuanTemp serves as a challenging evaluation set for numerical claim verification.|随着网络上错误信息的增多，自动化事实核查因其在检测不断增长的虚假信息和误导信息方面的潜力而引起了极大的关注。当前的系统在处理源自维基百科的合成声明方面取得了显著进展，同时在解决由事实核查组织验证的真实世界声明方面也取得了值得注意的进步。我们编制并发布了QuanTemp，这是一个专注于数值声明的多领域、多样化的数据集，涵盖了比较性、统计性、区间性和时间性方面，并附有详细的元数据和相应的证据集合。这一数据集解决了验证真实世界数值声明的挑战，这些声明通常复杂且缺乏精确信息，而现有的工作主要集中在合成声明上，未能填补这一空白。我们评估并量化了现有解决方案在验证数值声明任务中的差距。我们还评估了基于声明分解的方法、基于数值理解的自然语言推理（NLI）模型，以及我们最佳基线模型的宏F1得分为58.32。这表明QuanTemp作为数值声明验证的具有挑战性的评估集。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=QuanTemp:+A+real-world+open-domain+benchmark+for+fact-checking+numerical+claims)|1| +|[Evaluating Generative Ad Hoc Information Retrieval](https://doi.org/10.1145/3626772.3657849)|Lukas Gienapp, Harrisen Scells, Niklas Deckers, Janek Bevendorff, Shuai Wang, Johannes Kiesel, Shahbaz Syed, Maik Fröbe, Guido Zuccon, Benno Stein, Matthias Hagen, Martin Potthast|; Leipzig University & ScaDS.AI, Leipzig, Germany; Leipzig University, Leipzig, Germany; University of Kassel & hessian.AI, Kassel, Germany; Friedrich-Schiller-Universität Jena, Jena, Germany; Bauhaus-Universität Weimar, Weimar, Germany|Recent advances in large language models have enabled the development of viable generative information retrieval systems. A generative retrieval system returns a grounded generated text in response to an information need instead of the traditional document ranking. Quantifying the utility of these types of responses is essential for evaluating generative retrieval systems. As the established evaluation methodology for ranking-based ad hoc retrieval may seem unsuitable for generative retrieval, new approaches for reliable, repeatable, and reproducible experimentation are required. In this paper, we survey the relevant information retrieval and natural language processing literature, identify search tasks and system architectures in generative retrieval, develop a corresponding user model, and study its operationalization. This theoretical analysis provides a foundation and new insights for the evaluation of generative ad hoc retrieval systems.|大型语言模型的最新进展使得可行的生成信息检索系统的开发成为可能。一个生成检索系统返回一个接地生成的文本，以响应信息需求，而不是传统的文档排序。量化这些类型的响应的效用对于评估生成检索系统是必不可少的。由于已建立的基于排序的特别检索的评估方法似乎不适合于生成性检索，因此需要可靠的、可重复的和可重现的实验的新方法。在本文中，我们调查了相关的信息检索和自然语言处理文献，确定了生成检索中的搜索任务和系统架构，开发了相应的用户模型，并研究了它的操作主义。这一理论分析为生成式自组织检索系统的评估提供了基础和新的视角。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Evaluating+Generative+Ad+Hoc+Information+Retrieval)|1| +|[Embark on DenseQuest: A System for Selecting the Best Dense Retriever for a Custom Collection](https://doi.org/10.1145/3626772.3657674)|Ekaterina Khramtsova, Teerapong Leelanupab, Shengyao Zhuang, Mahsa Baktashmotlagh, Guido Zuccon|The University of Queensland; University of Queensland|In this demo we present a web-based application for selecting an effective pre-trained dense retriever to use on a private collection. Our system, DenseQuest, provides unsupervised selection and ranking capabilities to predict the best dense retriever among a pool of available dense retrievers, tailored to an uploaded target collection. DenseQuest implements a number of existing approaches, including a recent, highly effective method powered by Large Language Models (LLMs), which requires neither queries nor relevance judgments. The system is designed to be intuitive and easy to use for those information retrieval engineers and researchers who need to identify a general-purpose dense retrieval model to encode or search a new private target collection. Our demonstration illustrates conceptual architecture and the different use case scenarios of the system implemented on the cloud, enabling universal access and use. DenseQuest is available at https://densequest.ielab.io.|在本演示中，我们展示了一个基于网络的应用程序，用于在私有数据集上选择有效的预训练密集检索器。我们的系统DenseQuest提供了无监督的选择和排序功能，以预测在一组可用密集检索器中，最适合上传目标集合的最佳密集检索器。DenseQuest实现了多种现有方法，包括一种由大型语言模型（LLMs）驱动的新近高效方法，该方法既不需要查询也不需要相关性判断。该系统设计得直观且易于使用，适用于那些需要为新的私有目标集合编码或搜索而识别通用密集检索模型的信息检索工程师和研究人员。我们的演示展示了系统的概念架构以及在云上实现的不同的使用案例场景，实现了普遍的访问和使用。DenseQuest可通过https://densequest.ielab.io访问。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Embark+on+DenseQuest:+A+System+for+Selecting+the+Best+Dense+Retriever+for+a+Custom+Collection)|1| +|[QuanTemp: A real-world open-domain benchmark for fact-checking numerical claims](https://doi.org/10.1145/3626772.3657874)|Venktesh V, Abhijit Anand, Avishek Anand, Vinay Setty|University of Stavanger; L3S Research Institute; TU Delft|With the growth of misinformation on the web, automated fact checking has garnered immense interest for detecting growing misinformation and disinformation. Current systems have made significant advancements in handling synthetic claims sourced from Wikipedia, and noteworthy progress has been achieved in addressing real-world claims that are verified by fact-checking organizations as well. We compile and release QuanTemp, a diverse, multi-domain dataset focused exclusively on numerical claims, encompassing comparative, statistical, interval, and temporal aspects, with detailed metadata and an accompanying evidence collection. This addresses the challenge of verifying real-world numerical claims, which are complex and often lack precise information, a gap not filled by existing works that mainly focus on synthetic claims. We evaluate and quantify these gaps in existing solutions for the task of verifying numerical claims. We also evaluate claim decomposition based methods, numerical understanding based natural language inference (NLI) models and our best baselines achieves a macro-F1 of 58.32. This demonstrates that QuanTemp serves as a challenging evaluation set for numerical claim verification.|随着网络上错误信息的增多，自动化事实核查因其在检测不断增长的虚假信息和误导信息方面的潜力而引起了极大的关注。当前的系统在处理源自维基百科的合成声明方面取得了显著进展，同时在解决由事实核查组织验证的真实世界声明方面也取得了值得注意的进步。我们编制并发布了QuanTemp，这是一个专注于数值声明的多领域、多样化的数据集，涵盖了比较性、统计性、区间性和时间性方面，并附有详细的元数据和相应的证据集合。这一数据集解决了验证真实世界数值声明的挑战，这些声明通常复杂且缺乏精确信息，而现有的工作主要集中在合成声明上，未能填补这一空白。我们评估并量化了现有解决方案在验证数值声明任务中的差距。我们还评估了基于声明分解的方法、基于数值理解的自然语言推理（NLI）模型，以及我们最佳基线模型的宏F1得分为58.32。这表明QuanTemp作为数值声明验证的具有挑战性的评估集。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=QuanTemp:+A+real-world+open-domain+benchmark+for+fact-checking+numerical+claims)|1| |[Instruction-based Hypergraph Pretraining](https://doi.org/10.1145/3626772.3657715)|Mingdai Yang, Zhiwei Liu, Liangwei Yang, Xiaolong Liu, Chen Wang, Hao Peng, Philip S. Yu||||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Instruction-based+Hypergraph+Pretraining)|1| |[Characterizing Information Seeking Processes with Multiple Physiological Signals](https://doi.org/10.1145/3626772.3657793)|Kaixin Ji, Danula Hettiachchi, Flora D. Salim, Falk Scholer, Damiano Spina||||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Characterizing+Information+Seeking+Processes+with+Multiple+Physiological+Signals)|1| |[Resources for Brewing BEIR: Reproducible Reference Models and Statistical Analyses](https://doi.org/10.1145/3626772.3657862)|Ehsan Kamalloo, Nandan Thakur, Carlos Lassance, Xueguang Ma, JhengHong Yang, Jimmy Lin|University of Waterloo; Naver Labs Europe|BEIR is a benchmark dataset originally designed for zero-shot evaluation of retrieval models across 18 different domain/task combinations. In recent years, we have witnessed the growing popularity of models based on representation learning, which naturally begs the question: How effective are these models when presented with queries and documents that differ from the training data? While BEIR was designed to answer this question, our work addresses two shortcomings that prevent the benchmark from achieving its full potential: First, the sophistication of modern neural methods and the complexity of current software infrastructure create barriers to entry for newcomers. To this end, we provide reproducible reference implementations that cover learned dense and sparse models. Second, comparisons on BEIR are performed by reducing scores from heterogeneous datasets into a single average that is difficult to interpret. To remedy this, we present meta-analyses focusing on effect sizes across datasets that are able to accurately quantify model differences. By addressing both shortcomings, our work facilitates future explorations in a range of interesting research questions.|BEIR是一个基准数据集，最初设计用于对跨18个不同领域/任务组合的检索模型进行零样本评估。近年来，我们见证了基于表示学习的模型日益流行，这自然引出了一个问题：当面对与训练数据不同的查询和文档时，这些模型的效果如何？尽管BEIR旨在回答这个问题，但我们的工作针对该基准存在的两个缺陷，这两个缺陷阻碍了其充分发挥潜力：首先，现代神经方法的复杂性和当前软件基础设施的复杂性为新进入者设置了门槛。为此，我们提供了涵盖学习型密集和稀疏模型的可重现参考实现。其次，在BEIR上的比较是通过将来自异构数据集的分数简化为一个难以解释的单一平均值来进行的。为了解决这个问题，我们提出了专注于跨数据集效应大小的元分析，这些分析能够准确量化模型差异。通过解决这两个缺陷，我们的工作有助于未来在各种有趣的研究问题上的探索。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Resources+for+Brewing+BEIR:+Reproducible+Reference+Models+and+Statistical+Analyses)|1| -|[On the Evaluation of Machine-Generated Reports](https://doi.org/10.1145/3626772.3657846)|James Mayfield, Eugene Yang, Dawn J. Lawrie, Sean MacAvaney, Paul McNamee, Douglas W. Oard, Luca Soldaini, Ian Soboroff, Orion Weller, Efsun Selin Kayi, Kate Sanders, Marc Mason, Noah Hibbler|Allen Institute for AI; University of Glasgow; University of Maryland; NIST; Johns Hopkins University|Large Language Models (LLMs) have enabled new ways to satisfy information needs. Although great strides have been made in applying them to settings like document ranking and short-form text generation, they still struggle to compose complete, accurate, and verifiable long-form reports. Reports with these qualities are necessary to satisfy the complex, nuanced, or multi-faceted information needs of users. In this perspective paper, we draw together opinions from industry and academia, and from a variety of related research areas, to present our vision for automatic report generation, and---critically---a flexible framework by which such reports can be evaluated. In contrast with other summarization tasks, automatic report generation starts with a detailed description of an information need, stating the necessary background, requirements, and scope of the report. Further, the generated reports should be complete, accurate, and verifiable. These qualities, which are desirable---if not required---in many analytic report-writing settings, require rethinking how to build and evaluate systems that exhibit these qualities. To foster new efforts in building these systems, we present an evaluation framework that draws on ideas found in various evaluations. To test completeness and accuracy, the framework uses nuggets of information, expressed as questions and answers, that need to be part of any high-quality generated report. Additionally, evaluation of citations that map claims made in the report to their source documents ensures verifiability.|大型语言模型（LLMs）已开辟了满足信息需求的新途径。尽管在将它们应用于文档排序和短文本生成等领域取得了显著进展，但它们在撰写完整、准确且可验证的长篇报告方面仍面临挑战。这些特性的报告对于满足用户复杂、微妙或多方面的信息需求是必要的。在这篇观点论文中，我们汇集了来自行业和学术界以及各种相关研究领域的意见，提出了我们对自动报告生成的愿景，并——关键的是——提出了一个灵活的评估框架，用于评估这些报告。与其它摘要任务不同，自动报告生成始于对信息需求的详细描述，明确必要的背景、要求和报告范围。此外，生成的报告应具备完整性、准确性和可验证性。这些特性，虽然在许多分析报告撰写场景中是可取的，甚至是必需的，但需要重新思考如何构建和评估展现这些特性的系统。为了促进构建这些系统的新努力，我们提出了一个评估框架，借鉴了各种评估中的理念。为了测试完整性和准确性，该框架使用了信息片段，这些片段以问答形式表达，需要成为任何高质量生成报告的一部分。此外，对报告中引用的评估，即将报告中的主张与其来源文档对应起来，确保了可验证性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=On+the+Evaluation+of+Machine-Generated+Reports)|1| +|[On the Evaluation of Machine-Generated Reports](https://doi.org/10.1145/3626772.3657846)|James Mayfield, Eugene Yang, Dawn J. Lawrie, Sean MacAvaney, Paul McNamee, Douglas W. Oard, Luca Soldaini, Ian Soboroff, Orion Weller, Efsun Selin Kayi, Kate Sanders, Marc Mason, Noah Hibbler|NIST; Johns Hopkins University; Allen Institute for AI; University of Glasgow; University of Maryland|Large Language Models (LLMs) have enabled new ways to satisfy information needs. Although great strides have been made in applying them to settings like document ranking and short-form text generation, they still struggle to compose complete, accurate, and verifiable long-form reports. Reports with these qualities are necessary to satisfy the complex, nuanced, or multi-faceted information needs of users. In this perspective paper, we draw together opinions from industry and academia, and from a variety of related research areas, to present our vision for automatic report generation, and---critically---a flexible framework by which such reports can be evaluated. In contrast with other summarization tasks, automatic report generation starts with a detailed description of an information need, stating the necessary background, requirements, and scope of the report. Further, the generated reports should be complete, accurate, and verifiable. These qualities, which are desirable---if not required---in many analytic report-writing settings, require rethinking how to build and evaluate systems that exhibit these qualities. To foster new efforts in building these systems, we present an evaluation framework that draws on ideas found in various evaluations. To test completeness and accuracy, the framework uses nuggets of information, expressed as questions and answers, that need to be part of any high-quality generated report. Additionally, evaluation of citations that map claims made in the report to their source documents ensures verifiability.|大型语言模型（LLMs）已开辟了满足信息需求的新途径。尽管在将它们应用于文档排序和短文本生成等领域取得了显著进展，但它们在撰写完整、准确且可验证的长篇报告方面仍面临挑战。这些特性的报告对于满足用户复杂、微妙或多方面的信息需求是必要的。在这篇观点论文中，我们汇集了来自行业和学术界以及各种相关研究领域的意见，提出了我们对自动报告生成的愿景，并——关键的是——提出了一个灵活的评估框架，用于评估这些报告。与其它摘要任务不同，自动报告生成始于对信息需求的详细描述，明确必要的背景、要求和报告范围。此外，生成的报告应具备完整性、准确性和可验证性。这些特性，虽然在许多分析报告撰写场景中是可取的，甚至是必需的，但需要重新思考如何构建和评估展现这些特性的系统。为了促进构建这些系统的新努力，我们提出了一个评估框架，借鉴了各种评估中的理念。为了测试完整性和准确性，该框架使用了信息片段，这些片段以问答形式表达，需要成为任何高质量生成报告的一部分。此外，对报告中引用的评估，即将报告中的主张与其来源文档对应起来，确保了可验证性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=On+the+Evaluation+of+Machine-Generated+Reports)|1| |[Are Large Language Models Good at Utility Judgments?](https://doi.org/10.1145/3626772.3657784)|Hengran Zhang, Ruqing Zhang, Jiafeng Guo, Maarten de Rijke, Yixing Fan, Xueqi Cheng||||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Are+Large+Language+Models+Good+at+Utility+Judgments?)|1| -|[CWRCzech: 100M Query-Document Czech Click Dataset and Its Application to Web Relevance Ranking](https://doi.org/10.1145/3626772.3657851)|Josef Vonásek, Milan Straka, Rostislav Krc, Lenka Lasonová, Ekaterina Egorova, Jana Straková, Jakub Náplava|Institute of Formal and Applied Linguistics, Charles University; Seznam.cz|We present CWRCzech, Click Web Ranking dataset for Czech, a 100M query-document Czech click dataset for relevance ranking with user behavior data collected from search engine logs of Seznam.cz. To the best of our knowledge, CWRCzech is the largest click dataset with raw text published so far. It provides document positions in the search results as well as information about user behavior: 27.6M clicked documents and 10.8M dwell times. In addition, we also publish a manually annotated Czech test for the relevance task, containing nearly 50k query-document pairs, each annotated by at least 2 annotators. Finally, we analyze how the user behavior data improve relevance ranking and show that models trained on data automatically harnessed at sufficient scale can surpass the performance of models trained on human annotated data. CWRCzech is published under an academic non-commercial license and is available to the research community at https://github.com/seznam/CWRCzech.|我们为捷克提供了一个100M 的查询文档捷克点击数据集，用于从 Seznam.cz 的搜索引擎日志中收集的用户行为数据进行相关性排名。据我们所知，CWR 捷克是迄今为止发布原始文本的最大的点击数据集。它提供了搜索结果中的文档位置以及关于用户行为的信息: 27.6 M 的单击文档和10.8 M 的停留时间。此外，我们还为相关任务发布了一个手动注释的捷克测试，包含近50k 个查询-文档对，每个查询-文档对至少由2个注释者进行注释。最后，我们分析了用户行为数据如何提高相关性排名，并表明在足够大的规模上自动利用数据训练的模型可以超过在人类注释数据上训练的模型的性能。捷克语研究中心以学术非商业许可证的形式发表论文，研究团体可以在 https://github.com/seznam/CWRCzech 获得该论文。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=CWRCzech:+100M+Query-Document+Czech+Click+Dataset+and+Its+Application+to+Web+Relevance+Ranking)|0| +|[CWRCzech: 100M Query-Document Czech Click Dataset and Its Application to Web Relevance Ranking](https://doi.org/10.1145/3626772.3657851)|Josef Vonásek, Milan Straka, Rostislav Krc, Lenka Lasonová, Ekaterina Egorova, Jana Straková, Jakub Náplava|Seznam.cz; Institute of Formal and Applied Linguistics, Charles University|We present CWRCzech, Click Web Ranking dataset for Czech, a 100M query-document Czech click dataset for relevance ranking with user behavior data collected from search engine logs of Seznam.cz. To the best of our knowledge, CWRCzech is the largest click dataset with raw text published so far. It provides document positions in the search results as well as information about user behavior: 27.6M clicked documents and 10.8M dwell times. In addition, we also publish a manually annotated Czech test for the relevance task, containing nearly 50k query-document pairs, each annotated by at least 2 annotators. Finally, we analyze how the user behavior data improve relevance ranking and show that models trained on data automatically harnessed at sufficient scale can surpass the performance of models trained on human annotated data. CWRCzech is published under an academic non-commercial license and is available to the research community at https://github.com/seznam/CWRCzech.|我们为捷克提供了一个100M 的查询文档捷克点击数据集，用于从 Seznam.cz 的搜索引擎日志中收集的用户行为数据进行相关性排名。据我们所知，CWR 捷克是迄今为止发布原始文本的最大的点击数据集。它提供了搜索结果中的文档位置以及关于用户行为的信息: 27.6 M 的单击文档和10.8 M 的停留时间。此外，我们还为相关任务发布了一个手动注释的捷克测试，包含近50k 个查询-文档对，每个查询-文档对至少由2个注释者进行注释。最后，我们分析了用户行为数据如何提高相关性排名，并表明在足够大的规模上自动利用数据训练的模型可以超过在人类注释数据上训练的模型的性能。捷克语研究中心以学术非商业许可证的形式发表论文，研究团体可以在 https://github.com/seznam/CWRCzech 获得该论文。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=CWRCzech:+100M+Query-Document+Czech+Click+Dataset+and+Its+Application+to+Web+Relevance+Ranking)|0| |[A Unified Search and Recommendation Framework Based on Multi-Scenario Learning for Ranking in E-commerce](https://doi.org/10.1145/3626772.3661356)|Jinhan Liu, Qiyu Chen, Junjie Xu, Junjie Li, Baoli Li, Sulong Xu|JD; JD or JD.com|Search and recommendation (S R) are the two most important scenarios in e-commerce. The majority of users typically interact with products in S R scenarios, indicating the need and potential for joint modeling. Traditional multi-scenario models use shared parameters to learn the similarity of multiple tasks, and task-specific parameters to learn the divergence of individual tasks. This coarse-grained modeling approach does not effectively capture the differences between S R scenarios. Furthermore, this approach does not sufficiently exploit the information across the global label space. These issues can result in the suboptimal performance of multi-scenario models in handling both S R scenarios. To address these issues, we propose an effective and universal framework for Unified Search and Recommendation (USR), designed with S R Views User Interest Extractor Layer (IE) and S R Views Feature Generator Layer (FG) to separately generate user interests and scenario-agnostic feature representations for S R. Next, we introduce a Global Label Space Multi-Task Layer (GLMT) that uses global labels as supervised signals of auxiliary tasks and jointly models the main task and auxiliary tasks using conditional probability. Extensive experimental evaluations on real-world industrial datasets show that USR can be applied to various multi-scenario models and significantly improve their performance. Online A/B testing also indicates substantial performance gains across multiple metrics. Currently, USR has been successfully deployed in the 7Fresh App.|搜索和推荐(S R)是电子商务中最重要的两种情况。大多数用户通常在 S R 场景中与产品交互，这表明了联合建模的需要和潜力。传统的多场景模型使用共享参数来学习多个任务的相似性，使用特定任务的参数来学习单个任务的差异性。这种粗粒度建模方法不能有效地捕获 S R 场景之间的差异。此外，这种方法不能充分利用全局标签空间中的信息。这些问题可能导致多场景模型在处理两个 S R 场景时的次优性能。为了解决这些问题，我们提出了一个有效和通用的统一搜索和推荐(USR)框架，该框架使用 S R 视图用户兴趣提取层(IE)和 S R 视图特征生成层(FG)分别生成用户兴趣和场景无关的特征表示。接下来，我们引入了一个全局标签空间多任务层(GLMT) ，它使用全局标签作为辅助任务的监督信号，并使用条件概率联合建模主要任务和辅助任务。对现实世界工业数据集的大量实验评估表明，USR 可以应用于各种多场景模型，并显著提高其性能。在线 A/B 测试还表明跨多个指标的性能显著提高。目前，USR 已经成功地部署在7Fresh 应用程序中。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Unified+Search+and+Recommendation+Framework+Based+on+Multi-Scenario+Learning+for+Ranking+in+E-commerce)|0| |[Improving Embedding-Based Retrieval in Friend Recommendation with ANN Query Expansion](https://doi.org/10.1145/3626772.3661367)|Pau PerngHwa Kung, Zihao Fan, Tong Zhao, Yozen Liu, Zhixin Lai, Jiahui Shi, Yan Wu, Jun Yu, Neil Shah, Ganesh Venkataraman|Snap|Embedding-based retrieval in graph-based recommendation has shown great improvements over traditional graph walk retrieval methods, and has been adopted in large-scale industry applications such as friend recommendations [16]. However, it is not without its challenges: retraining graph embeddings frequently due to changing data is slow and costly, and producing high recall of approximate nearest neighbor search (ANN) on such embeddings is challenging due to the power law distribution of the indexed users. In this work, we address theses issues by introducing a simple query expansion method in ANN, called FriendSeedSelection, where for each node query, we construct a set of 1-hop embeddings and run ANN search. We highlight our approach does not require any model-level tuning, and is inferred from the data at test-time. This design choice effectively enables our recommendation system to adapt to the changing graph distribution without frequent heavy model retraining. We also discuss how we design our system to efficiently construct such queries online to support 10k+ QPS. For friend recommendation, our method shows improvements of recall, and 11% relative friend reciprocated communication metric gains, now serving over 800 million monthly active users at Snapchat.|在基于图的推荐中嵌入式检索已经显示出对传统的图步检索方法的巨大改进，并且已经被大规模的工业应用如好友推荐所采用[16]。然而，这并非没有挑战: 由于数据的变化而频繁地重新训练图嵌入是缓慢和昂贵的，并且由于索引用户的幂律分布，在这种嵌入上产生高召回的近似最近邻搜索(ANN)是具有挑战性的。在这项工作中，我们通过在人工神经网络中引入一个简单的查询扩展方法，称为 FriendSeedSelection，对于每个节点查询，我们构造一组1跳嵌入并运行人工神经网络搜索来解决这些问题。我们强调我们的方法不需要任何模型级别的调优，并且是从测试时的数据中推断出来的。这种设计选择有效地使我们的推荐系统能够适应变化的图形分布，而不需要频繁的重模型再训练。我们还讨论了如何设计我们的系统，以有效地构建这样的查询在线支持10k + QPS。对于朋友推荐，我们的方法显示了回忆的改进，11% 的亲属朋友回馈了通信指标的收益，现在为 Snapchat 超过8亿的月活跃用户提供服务。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Improving+Embedding-Based+Retrieval+in+Friend+Recommendation+with+ANN+Query+Expansion)|0| -|[Doing Personal LAPS: LLM-Augmented Dialogue Construction for Personalized Multi-Session Conversational Search](https://doi.org/10.1145/3626772.3657815)|Hideaki Joko, Shubham Chatterjee, Andrew Ramsay, Arjen P. de Vries, Jeff Dalton, Faegheh Hasibi|University of Glasgow; Radboud University; University of Edinburgh|The future of conversational agents will provide users with personalized information responses. However, a significant challenge in developing models is the lack of large-scale dialogue datasets that span multiple sessions and reflect real-world user preferences. Previous approaches rely on experts in a wizard-of-oz setup that is difficult to scale, particularly for personalized tasks. Our method, LAPS, addresses this by using large language models (LLMs) to guide a single human worker in generating personalized dialogues. This method has proven to speed up the creation process and improve quality. LAPS can collect large-scale, human-written, multi-session, and multi-domain conversations, including extracting user preferences. When compared to existing datasets, LAPS-produced conversations are as natural and diverse as expert-created ones, which stays in contrast with fully synthetic methods. The collected dataset is suited to train preference extraction and personalized response generation. Our results show that responses generated explicitly using extracted preferences better match user's actual preferences, highlighting the value of using extracted preferences over simple dialogue history. Overall, LAPS introduces a new method to leverage LLMs to create realistic personalized conversational data more efficiently and effectively than previous methods.|会话代理的未来将为用户提供个性化的信息响应。然而，在开发模型方面的一个重大挑战是缺乏跨越多个会议并反映真实世界用户偏好的大规模对话数据集。以前的方法依赖于难以伸缩的绿色向导设置中的专家，特别是对于个性化任务。我们的方法 LAPS 通过使用大型语言模型(LLM)来指导单个人类工作者生成个性化对话来解决这个问题。这种方法已被证明可以加快创作过程，提高质量。LAPS 可以收集大规模、人工编写、多会话和多域会话，包括提取用户首选项。与现有的数据集相比，LAPS 产生的对话和专家创建的对话一样自然和多样化，这与完全合成的方法形成了对比。采集的数据集适合于训练偏好提取和个性化响应生成。我们的研究结果表明，明确使用提取的偏好生成的响应更好地匹配用户的实际偏好，突出了使用提取的偏好的价值超过简单的对话历史。总的来说，LAPS 引入了一种新的方法，利用 LLM 创建真实的个性化会话数据，比以前的方法更有效率和效果。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Doing+Personal+LAPS:+LLM-Augmented+Dialogue+Construction+for+Personalized+Multi-Session+Conversational+Search)|0| +|[Doing Personal LAPS: LLM-Augmented Dialogue Construction for Personalized Multi-Session Conversational Search](https://doi.org/10.1145/3626772.3657815)|Hideaki Joko, Shubham Chatterjee, Andrew Ramsay, Arjen P. de Vries, Jeff Dalton, Faegheh Hasibi|University of Glasgow; University of Edinburgh; Radboud University|The future of conversational agents will provide users with personalized information responses. However, a significant challenge in developing models is the lack of large-scale dialogue datasets that span multiple sessions and reflect real-world user preferences. Previous approaches rely on experts in a wizard-of-oz setup that is difficult to scale, particularly for personalized tasks. Our method, LAPS, addresses this by using large language models (LLMs) to guide a single human worker in generating personalized dialogues. This method has proven to speed up the creation process and improve quality. LAPS can collect large-scale, human-written, multi-session, and multi-domain conversations, including extracting user preferences. When compared to existing datasets, LAPS-produced conversations are as natural and diverse as expert-created ones, which stays in contrast with fully synthetic methods. The collected dataset is suited to train preference extraction and personalized response generation. Our results show that responses generated explicitly using extracted preferences better match user's actual preferences, highlighting the value of using extracted preferences over simple dialogue history. Overall, LAPS introduces a new method to leverage LLMs to create realistic personalized conversational data more efficiently and effectively than previous methods.|会话代理的未来将为用户提供个性化的信息响应。然而，在开发模型方面的一个重大挑战是缺乏跨越多个会议并反映真实世界用户偏好的大规模对话数据集。以前的方法依赖于难以伸缩的绿色向导设置中的专家，特别是对于个性化任务。我们的方法 LAPS 通过使用大型语言模型(LLM)来指导单个人类工作者生成个性化对话来解决这个问题。这种方法已被证明可以加快创作过程，提高质量。LAPS 可以收集大规模、人工编写、多会话和多域会话，包括提取用户首选项。与现有的数据集相比，LAPS 产生的对话和专家创建的对话一样自然和多样化，这与完全合成的方法形成了对比。采集的数据集适合于训练偏好提取和个性化响应生成。我们的研究结果表明，明确使用提取的偏好生成的响应更好地匹配用户的实际偏好，突出了使用提取的偏好的价值超过简单的对话历史。总的来说，LAPS 引入了一种新的方法，利用 LLM 创建真实的个性化会话数据，比以前的方法更有效率和效果。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Doing+Personal+LAPS:+LLM-Augmented+Dialogue+Construction+for+Personalized+Multi-Session+Conversational+Search)|0| |[Towards a Search Engine for Machines: Unified Ranking for Multiple Retrieval-Augmented Large Language Models](https://doi.org/10.1145/3626772.3657733)|Alireza Salemi, Hamed Zamani|University of Massachusetts Amherst|This paper introduces uRAG–a framework with a unified retrieval engine that serves multiple downstream retrieval-augmented generation (RAG) systems. Each RAG system consumes the retrieval results for a unique purpose, such as open-domain question answering, fact verification, entity linking, and relation extraction. We introduce a generic training guideline that standardizes the communication between the search engine and the downstream RAG systems that engage in optimizing the retrieval model. This lays the groundwork for us to build a large-scale experimentation ecosystem consisting of 18 RAG systems that engage in training and 18 unknown RAG systems that use the uRAG as the new users of the search engine. Using this experimentation ecosystem, we answer a number of fundamental research questions that improve our understanding of promises and challenges in developing search engines for machines.|本文介绍了一个统一检索引擎的框架 uRAG，它可以为多个下游检索增强生成(RAG)系统提供服务。每个 RAG 系统都为一个独特的目的使用检索结果，例如开放域问题回答、事实验证、实体链接和关系提取。我们引入了一个通用的培训指导方针，它标准化了搜索引擎与下游 RAG 系统之间的通信，这些 RAG 系统参与优化检索模型。这为我们建立一个大规模实验生态系统奠定了基础，该系统包括18个参与培训的 RAG 系统和18个使用 uRAG 作为搜索引擎新用户的未知 RAG 系统。利用这个实验生态系统，我们回答了许多基础研究问题，这些问题提高了我们对开发机器搜索引擎的承诺和挑战的理解。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Towards+a+Search+Engine+for+Machines:+Unified+Ranking+for+Multiple+Retrieval-Augmented+Large+Language+Models)|0| -|[Sequential Recommendation with Collaborative Explanation via Mutual Information Maximization](https://doi.org/10.1145/3626772.3657770)|Yi Yu, Kazunari Sugiyama, Adam Jatowt|Kyoto University; University of Innsbruck; Osaka Seikei University|Current research on explaining sequential recommendations lacks reliable benchmarks and quantitative metrics, making it difficult to compare explanation performance between different models. In this work, we propose a new explanation type, namely, collaborative explanation, into sequential recommendation, allowing a unified approach for modeling user actions and assessing the performance of both recommendation and explanation. We accomplish this by framing the problem as a joint sequential prediction task, which takes a sequence of user's past item-explanation pairs and predicts the next item along with its associated explanation. We propose a pipeline that comprises data preparation and a model adaptation framework called Sequential recommendation with Collaborative Explanation (SCE). This framework can be flexibly applied to any sequential recommendation model for this problem. Furthermore, to address the issue of inconsistency between item and explanation representations when learning both sub-tasks, we propose Sequential recommendation with Collaborative Explanation via Mutual Information Maximization (SCEMIM). Our extensive experiments demonstrate that: (i) SCE framework is effective in enabling sequential models to make recommendations and provide accurate explanations. (ii) Importantly, SCEMIM enhances the consistency between recommendations and explanations, leading to further improvements in the performance of both sub-tasks.|目前关于解释顺序推荐的研究缺乏可靠的基准和量化指标，因此难以比较不同模型之间的解释性能。在这项工作中，我们提出了一个新的解释类型，即协作解释，到顺序推荐，允许一个统一的方法来建模用户的行为和评估两者的性能的推荐和解释。我们通过将问题框架为一个联合的顺序预测任务来完成这个任务，该任务采用用户过去的项目解释对的序列，并预测下一个项目及其相关的解释。我们提出了一个流水线，包括数据准备和模型适应框架称为顺序推荐与协作解释(SCE)。该框架可以灵活地应用于该问题的任何顺序推荐模型。此外，为了解决两个子任务学习过程中项目表征与解释表征不一致的问题，本文提出了基于互信息最大化的协同解释的序贯推荐方法。我们的大量实验表明: (i) SCE 框架能够有效地使序贯模型提出建议并提供准确的解释。(ii)重要的是，SCEMIM 加强了建议和解释之间的一致性，从而进一步改善了这两个子任务的表现。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Sequential+Recommendation+with+Collaborative+Explanation+via+Mutual+Information+Maximization)|0| -|[A Learning-to-Rank Formulation of Clustering-Based Approximate Nearest Neighbor Search](https://doi.org/10.1145/3626772.3657931)|Thomas Vecchiato, Claudio Lucchese, Franco Maria Nardini, Sebastian Bruch|Pinecone; ISTI-CNR; Ca' Foscari University of Venice|A critical piece of the modern information retrieval puzzle is approximate nearest neighbor search. Its objective is to return a set of k data points that are closest to a query point, with its accuracy measured by the proportion of exact nearest neighbors captured in the returned set. One popular approach to this question is clustering: The indexing algorithm partitions data points into non-overlapping subsets and represents each partition by a point such as its centroid. The query processing algorithm first identifies the nearest clusters – a process known as routing – then performs a nearest neighbor search over those clusters only. In this work, we make a simple observation: The routing function solves a ranking problem. Its quality can therefore be assessed with a ranking metric, making the function amenable to learning-to-rank. Interestingly, ground-truth is often freely available: Given a query distribution in a top-k configuration, the ground-truth is the set of clusters that contain the exact top-k vectors. We develop this insight and apply it to Maximum Inner Product Search (MIPS). As we demonstrate empirically on various datasets, learning a simple linear function consistently improves the accuracy of clustering-based MIPS.|现代信息检索难题的一个关键部分是近似最近邻搜索。它的目标是返回一组最接近查询点的 k 个数据点，其精度由返回集中捕获的精确最近邻点的比例来衡量。解决这个问题的一种流行方法是聚类: 索引算法将数据分割成不重叠的子集，并用一个点(如其质心)表示每个分区。查询处理算法首先识别最近的集群——一个称为路由的过程——然后仅对这些集群执行最近邻搜索。在这项工作中，我们做了一个简单的观察: 路由函数解决了一个排序问题。因此，它的质量可以评估与排名度量，使功能适合学习到排名。有趣的是，地面真相通常是免费提供的: 给定 top-k 配置中的查询分布，地面真相是包含精确 top-k 向量的集合。我们开发了这种洞察力，并将其应用于最大内部产品搜索(MIPS)。正如我们在各种数据集上的经验证明，学习一个简单的线性函数可以持续地提高基于聚类的 MIPS 的准确性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Learning-to-Rank+Formulation+of+Clustering-Based+Approximate+Nearest+Neighbor+Search)|0| +|[Sequential Recommendation with Collaborative Explanation via Mutual Information Maximization](https://doi.org/10.1145/3626772.3657770)|Yi Yu, Kazunari Sugiyama, Adam Jatowt|University of Innsbruck; Osaka Seikei University; Kyoto University|Current research on explaining sequential recommendations lacks reliable benchmarks and quantitative metrics, making it difficult to compare explanation performance between different models. In this work, we propose a new explanation type, namely, collaborative explanation, into sequential recommendation, allowing a unified approach for modeling user actions and assessing the performance of both recommendation and explanation. We accomplish this by framing the problem as a joint sequential prediction task, which takes a sequence of user's past item-explanation pairs and predicts the next item along with its associated explanation. We propose a pipeline that comprises data preparation and a model adaptation framework called Sequential recommendation with Collaborative Explanation (SCE). This framework can be flexibly applied to any sequential recommendation model for this problem. Furthermore, to address the issue of inconsistency between item and explanation representations when learning both sub-tasks, we propose Sequential recommendation with Collaborative Explanation via Mutual Information Maximization (SCEMIM). Our extensive experiments demonstrate that: (i) SCE framework is effective in enabling sequential models to make recommendations and provide accurate explanations. (ii) Importantly, SCEMIM enhances the consistency between recommendations and explanations, leading to further improvements in the performance of both sub-tasks.|目前关于解释顺序推荐的研究缺乏可靠的基准和量化指标，因此难以比较不同模型之间的解释性能。在这项工作中，我们提出了一个新的解释类型，即协作解释，到顺序推荐，允许一个统一的方法来建模用户的行为和评估两者的性能的推荐和解释。我们通过将问题框架为一个联合的顺序预测任务来完成这个任务，该任务采用用户过去的项目解释对的序列，并预测下一个项目及其相关的解释。我们提出了一个流水线，包括数据准备和模型适应框架称为顺序推荐与协作解释(SCE)。该框架可以灵活地应用于该问题的任何顺序推荐模型。此外，为了解决两个子任务学习过程中项目表征与解释表征不一致的问题，本文提出了基于互信息最大化的协同解释的序贯推荐方法。我们的大量实验表明: (i) SCE 框架能够有效地使序贯模型提出建议并提供准确的解释。(ii)重要的是，SCEMIM 加强了建议和解释之间的一致性，从而进一步改善了这两个子任务的表现。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Sequential+Recommendation+with+Collaborative+Explanation+via+Mutual+Information+Maximization)|0| +|[A Learning-to-Rank Formulation of Clustering-Based Approximate Nearest Neighbor Search](https://doi.org/10.1145/3626772.3657931)|Thomas Vecchiato, Claudio Lucchese, Franco Maria Nardini, Sebastian Bruch|Pinecone; Ca' Foscari University of Venice; ISTI-CNR|A critical piece of the modern information retrieval puzzle is approximate nearest neighbor search. Its objective is to return a set of k data points that are closest to a query point, with its accuracy measured by the proportion of exact nearest neighbors captured in the returned set. One popular approach to this question is clustering: The indexing algorithm partitions data points into non-overlapping subsets and represents each partition by a point such as its centroid. The query processing algorithm first identifies the nearest clusters – a process known as routing – then performs a nearest neighbor search over those clusters only. In this work, we make a simple observation: The routing function solves a ranking problem. Its quality can therefore be assessed with a ranking metric, making the function amenable to learning-to-rank. Interestingly, ground-truth is often freely available: Given a query distribution in a top-k configuration, the ground-truth is the set of clusters that contain the exact top-k vectors. We develop this insight and apply it to Maximum Inner Product Search (MIPS). As we demonstrate empirically on various datasets, learning a simple linear function consistently improves the accuracy of clustering-based MIPS.|现代信息检索难题的一个关键部分是近似最近邻搜索。它的目标是返回一组最接近查询点的 k 个数据点，其精度由返回集中捕获的精确最近邻点的比例来衡量。解决这个问题的一种流行方法是聚类: 索引算法将数据分割成不重叠的子集，并用一个点(如其质心)表示每个分区。查询处理算法首先识别最近的集群——一个称为路由的过程——然后仅对这些集群执行最近邻搜索。在这项工作中，我们做了一个简单的观察: 路由函数解决了一个排序问题。因此，它的质量可以评估与排名度量，使功能适合学习到排名。有趣的是，地面真相通常是免费提供的: 给定 top-k 配置中的查询分布，地面真相是包含精确 top-k 向量的集合。我们开发了这种洞察力，并将其应用于最大内部产品搜索(MIPS)。正如我们在各种数据集上的经验证明，学习一个简单的线性函数可以持续地提高基于聚类的 MIPS 的准确性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Learning-to-Rank+Formulation+of+Clustering-Based+Approximate+Nearest+Neighbor+Search)|0| |[A Surprisingly Simple yet Effective Multi-Query Rewriting Method for Conversational Passage Retrieval](https://doi.org/10.1145/3626772.3657933)|Ivica Kostric, Krisztian Balog|University of Stavanger; University of Stavanger & Google Research|Conversational passage retrieval is challenging as it often requires the resolution of references to previous utterances and needs to deal with the complexities of natural language, such as coreference and ellipsis. To address these challenges, pre-trained sequence-to-sequence neural query rewriters are commonly used to generate a single de-contextualized query based on conversation history. Previous research shows that combining multiple query rewrites for the same user utterance has a positive effect on retrieval performance. We propose the use of a neural query rewriter to generate multiple queries and show how to integrate those queries in the passage retrieval pipeline efficiently. The main strength of our approach lies in its simplicity: it leverages how the beam search algorithm works and can produce multiple query rewrites at no additional cost. Our contributions further include devising ways to utilize multi-query rewrites in both sparse and dense first-pass retrieval. We demonstrate that applying our approach on top of a standard passage retrieval pipeline delivers state-of-the-art performance without sacrificing efficiency.|会话短文检索是一个具有挑战性的问题，因为它往往需要解决对以前话语的引用，并需要处理自然语言的复杂性，如共引和省略。为了应对这些挑战，预先训练的序列到序列神经查询重写器通常用于生成基于会话历史的单个去上下文化查询。以往的研究表明，对同一用户语句进行多次查询重写对检索性能有积极的影响。我们提出使用神经查询重写器来生成多个查询，并说明如何有效地将这些查询集成到文章检索流水线中。我们方法的主要优点在于它的简单性: 它利用了束搜索算法的工作方式，并且可以在不增加成本的情况下产生多个查询重写。我们的贡献还包括设计在稀疏和密集首通检索中利用多查询重写的方法。我们演示了将我们的方法应用于标准通道检索流水线之上，可以在不牺牲效率的情况下提供最先进的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Surprisingly+Simple+yet+Effective+Multi-Query+Rewriting+Method+for+Conversational+Passage+Retrieval)|0| -|[Memory-Efficient Deep Recommender Systems using Approximate Rotary Compositional Embedding](https://doi.org/10.1145/3626772.3657953)|Dongning Ma, Xun Jiao|Villanova University Electrical and Computer Engineering; Villanova University ECE|Embedding tables in deep recommender systems (DRS) process categorical data, which can be memory-intensive due to the high feature cardinality. In this paper, we propose Approximate Rotary Compositional Embedding (ARCE), which intentionally trades off performance to aggressively reduce the size of the embedding tables. Specifically, ARCE uses compositional embedding to split large embedding tables into smaller compositions and replaces index look-ups with vector rotations. To regain the performance loss of this trade-off, ARCE features an input approximation where one index is mapped into multiple indices, creating a larger space for a potential increased learning capability. Experimental results show that using ARCE can reduce the memory overhead of embedding tables in DRS by more than 1000x with less than 3% performance loss, highlighting the potential of using ARCE for less memory intensive DRS designs. We open-source ARCE at https://github.com/VU-DETAIL/arce.|深度推荐系统(DRS)中嵌入表处理分类数据，由于特征基数高，可能会占用大量内存。在本文中，我们提出了近似旋转组合嵌入(ARCE) ，它有意地牺牲性能以积极地减少嵌入表的大小。具体来说，ARCE 使用组合嵌入将大型嵌入表拆分为较小的组合，并用向量旋转替换索引查找。为了重新获得这种折衷的性能损失，ARCE 采用了一种输入近似，其中一个索引映射到多个索引中，为潜在的增强的学习能力创造了更大的空间。实验结果表明，使用 ARCE 可以将 DRS 中嵌入表的内存开销减少1000倍以上，性能损失小于3% ，突出了使用 ARCE 进行内存密集型 DRS 设计的潜力。我们开源的 ARCE https://github.com/vu-detail/ARCE。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Memory-Efficient+Deep+Recommender+Systems+using+Approximate+Rotary+Compositional+Embedding)|0| +|[Memory-Efficient Deep Recommender Systems using Approximate Rotary Compositional Embedding](https://doi.org/10.1145/3626772.3657953)|Dongning Ma, Xun Jiao|Villanova University ECE; Villanova University Electrical and Computer Engineering|Embedding tables in deep recommender systems (DRS) process categorical data, which can be memory-intensive due to the high feature cardinality. In this paper, we propose Approximate Rotary Compositional Embedding (ARCE), which intentionally trades off performance to aggressively reduce the size of the embedding tables. Specifically, ARCE uses compositional embedding to split large embedding tables into smaller compositions and replaces index look-ups with vector rotations. To regain the performance loss of this trade-off, ARCE features an input approximation where one index is mapped into multiple indices, creating a larger space for a potential increased learning capability. Experimental results show that using ARCE can reduce the memory overhead of embedding tables in DRS by more than 1000x with less than 3% performance loss, highlighting the potential of using ARCE for less memory intensive DRS designs. We open-source ARCE at https://github.com/VU-DETAIL/arce.|深度推荐系统(DRS)中嵌入表处理分类数据，由于特征基数高，可能会占用大量内存。在本文中，我们提出了近似旋转组合嵌入(ARCE) ，它有意地牺牲性能以积极地减少嵌入表的大小。具体来说，ARCE 使用组合嵌入将大型嵌入表拆分为较小的组合，并用向量旋转替换索引查找。为了重新获得这种折衷的性能损失，ARCE 采用了一种输入近似，其中一个索引映射到多个索引中，为潜在的增强的学习能力创造了更大的空间。实验结果表明，使用 ARCE 可以将 DRS 中嵌入表的内存开销减少1000倍以上，性能损失小于3% ，突出了使用 ARCE 进行内存密集型 DRS 设计的潜力。我们开源的 ARCE https://github.com/vu-detail/ARCE。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Memory-Efficient+Deep+Recommender+Systems+using+Approximate+Rotary+Compositional+Embedding)|0| |[Retrieval-Augmented Conversational Recommendation with Prompt-based Semi-Structured Natural Language State Tracking](https://doi.org/10.1145/3626772.3657670)|Sara Kemper, Justin Cui, Kai Dicarlantonio, Kathy Lin, Danjie Tang, Anton Korikov, Scott Sanner|University of Waterloo; University of Toronto|Conversational recommendation (ConvRec) systems must understand rich and diverse natural language (NL) expressions of user preferences and intents, often communicated in an indirect manner (e.g., "I'm watching my weight"). Such complex utterances make retrieving relevant items challenging, especially if only using often incomplete or out-of-date metadata. Fortunately, many domains feature rich item reviews that cover standard metadata categories and offer complex opinions that might match a user's interests (e.g., "classy joint for a date"). However, only recently have large language models (LLMs) let us unlock the commonsense connections between user preference utterances and complex language in user-generated reviews. Further, LLMs enable novel paradigms for semi-structured dialogue state tracking, complex intent and preference understanding, and generating recommendations, explanations, and question answers. We thus introduce a novel technology RA-Rec, a Retrieval-Augmented, LLM-driven dialogue state tracking system for ConvRec, showcased with a video, open source GitHub repository, and interactive Google Colab notebook.|会话推荐系统必须理解用户偏好和意图的丰富多样的自然语言(NL)表达，通常以间接的方式进行沟通(例如，“我在减肥”)。这种复杂的语句使得检索相关项目变得具有挑战性，特别是如果仅仅使用不完整或过时的元数据。幸运的是，许多域名都有丰富的项目评论，涵盖标准的元数据类别，并提供可能符合用户兴趣的复杂意见(例如，“优雅的约会联合”)。然而，直到最近才有了大型语言模型(LLM) ，让我们能够在用户生成的评论中解开用户偏好话语和复杂语言之间的常识性联系。此外，LLM 为半结构化对话状态跟踪、复杂意图和偏好理解以及生成建议、解释和问题答案提供了新的范例。因此，我们引入了一种新技术 RA-Rec，这是一种用于 ConvRec 的恢复增强的 LLM 驱动的对话状态跟踪系统，通过一个视频、开源 GitHub 仓库和交互式 Google Colab 笔记本进行了展示。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Retrieval-Augmented+Conversational+Recommendation+with+Prompt-based+Semi-Structured+Natural+Language+State+Tracking)|0| |[LLMGR: Large Language Model-based Generative Retrieval in Alipay Search](https://doi.org/10.1145/3626772.3661364)|Chen Wei, Yixin Ji, Zeyuan Chen, Jia Xu, Zhongyi Liu|Ant Group; Ant Group Search Recommendation Technology Department; Soochow University School of Computer Science & Technology|The search system aims to help users quickly find items according to queries they enter, which includes the retrieval and ranking modules. Traditional retrieval is a multi-stage process, including indexing and sorting, which cannot be optimized end-to-end. With the real data about mini-apps in the Alipay search, we find that many complex queries fail to display the relevant mini-apps, seriously threatening users' search experience. To address the challenges, we propose a Large Language Model-based Generative Retrieval (LLMGR) approach for retrieving mini-app candidates. The information of the mini-apps is encoded into the large model, and the title of the mini-app is directly generated. Through the online A/B test in Alipay search, LLMGR as a supplementary source has statistically significant improvements in the Click-Through Rate (CTR) of the search system compared to traditional methods. In this paper, we have deployed a novel retrieval method for the Alipay search system and demonstrated that generative retrieval methods based on LLM can improve the performance of search system, particularly for complex queries, which have an average increase of 0.2% in CTR.|该搜索系统旨在帮助用户根据输入的查询快速查找项目，其中包括检索和排序模块。传统的检索是一个多阶段的过程，包括索引和排序，不能实现端到端的优化。通过对支付宝搜索中迷你应用的真实数据进行分析，我们发现许多复杂的查询都无法显示相关的迷你应用，严重威胁了用户的搜索体验。为了应对这些挑战，我们提出了一种基于大语言模型的生成检索(LLMGR)方法来检索迷你应用程序候选者。迷你应用程序的信息被编码到大模型中，并直接生成迷你应用程序的标题。通过支付宝搜索中的在线 A/B 测试，作为补充来源的 LLMGR 在统计学上显著改善了搜索系统的点进率(ctrr) ，而不是传统方法。本文针对支付宝搜索系统提出了一种新的检索方法，并证明了基于 LLM 的生成式检索方法可以提高搜索系统的性能，尤其是对于平均点击率提高0.2% 的复杂查询。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=LLMGR:+Large+Language+Model-based+Generative+Retrieval+in+Alipay+Search)|0| |[Optimizing E-commerce Search: Toward a Generalizable and Rank-Consistent Pre-Ranking Model](https://doi.org/10.1145/3626772.3661343)|Enqiang Xu, Yiming Qiu, Junyang Bai, Ping Zhang, Dadong Miao, Songlin Wang, Guoyu Tang, Lin Liu, Mingming Li|JD.com|In large e-commerce platforms, search systems are typically composed of a series of modules, including recall, pre-ranking, and ranking phases. The pre-ranking phase, serving as a lightweight module, is crucial for filtering out the bulk of products in advance for the downstream ranking module. Industrial efforts on optimizing the pre-ranking model have predominantly focused on enhancing ranking consistency, model structure, and generalization towards long-tail items. Beyond these optimizations, meeting the system performance requirements presents a significant challenge. Contrasting with existing industry works, we propose a novel method: a Generalizable and RAnk-ConsistEnt Pre-Ranking Model (GRACE), which achieves: 1) Ranking consistency by introducing multiple binary classification tasks that predict whether a product is within the top-k results as estimated by the ranking model, which facilitates the addition of learning objectives on common point-wise ranking models; 2) Generalizability through contrastive learning of representation for all products by pre-training on a subset of ranking product embeddings; 3) Ease of implementation in feature construction and online deployment. Our extensive experiments demonstrate significant improvements in both offline metrics and online A/B test: a 0.75 increase in CVR.|在大型电子商务平台中，搜索系统通常由一系列模块组成，包括召回、预排序和排序阶段。预排序阶段作为一个轻量级模块，对于提前过滤掉下游排序模块的大部分产品至关重要。优化预排序模型的工业努力主要集中在增强排序一致性、模型结构和对长尾项目的推广。除了这些优化之外，满足系统性能需求也是一个重大的挑战。与现有的行业工作相比，我们提出了一种新的方法: 一个一般化和排名一致的预排名模型(GRACE) ，它实现了: 1)排名一致性通过引入多个二进制分类任务，预测一个产品是否在由排名模型估计的前 k 结果之内，这有助于增加学习目标的共同点明智的排名模型; 2)通过对比学习的表示对所有产品的一个排名产品嵌入子集的预训练的一般化; 3)易于实施的功能构建和在线部署。我们的大量实验表明，在离线指标和在线 A/B 测试方面都有显著改善: CVR 增加了0.75。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Optimizing+E-commerce+Search:+Toward+a+Generalizable+and+Rank-Consistent+Pre-Ranking+Model)|0| |[A Preference-oriented Diversity Model Based on Mutual-information in Re-ranking for E-commerce Search](https://doi.org/10.1145/3626772.3661359)|Huimu Wang, Mingming Li, Dadong Miao, Songlin Wang, Guoyu Tang, Lin Liu, Sulong Xu, Jinghe Hu|JD.com; JD or JD.com|Re-ranking is a process of rearranging ranking list to more effectively meet user demands by accounting for the interrelationships between items. Existing methods predominantly enhance the precision of search results, often at the expense of diversity, leading to outcomes that may not fulfill the varied needs of users. Conversely, methods designed to promote diversity might compromise the precision of the results, failing to satisfy the users' requirements for accuracy. To alleviate the above problems, this paper proposes a Preference-oriented Diversity Model Based on Mutual-information (PODM-MI), which consider both accuracy and diversity in the re-ranking process. Specifically, PODM-MI adopts Multidimensional Gaussian distributions based on variational inference to capture users' diversity preferences with uncertainty. Then we maximize the mutual information between the diversity preferences of the users and the candidate items using the maximum variational inference lower bound to enhance their correlations. Subsequently, we derive a utility matrix based on the correlations, enabling the adaptive ranking of items in line with user preferences and establishing a balance between the aforementioned objectives. Experimental results on real-world online e-commerce systems demonstrate the significant improvements of PODM-MI, and we have successfully deployed PODM-MI on an e-commerce search platform.|重新排序是通过考虑项目之间的相互关系来重新安排排序列表以更有效地满足用户需求的过程。现有的方法主要是提高搜索结果的精确度，往往以牺牲多样性为代价，导致结果可能无法满足用户的不同需求。相反，旨在促进多样性的方法可能会损害结果的精确性，不能满足用户对精确性的要求。针对上述问题，本文提出了一种基于互信息的偏好导向多样性模型(PODM-MI) ，该模型在重排序过程中同时考虑了准确性和多样性。具体来说，PODM-MI 采用基于变分推理的多维高斯分布来捕获具有不确定性的用户多样性偏好。然后利用最大变分推理下界，最大化用户多样性偏好与候选项之间的相互信息，以增强它们之间的相关性。随后，我们推导出一个基于相关性的效用矩阵，使项目的自适应排序符合用户偏好，并建立上述目标之间的平衡。在实际的在线电子商务系统上的实验结果表明，PODM-MI 算法得到了显著的改进，并成功地在电子商务搜索平台上部署了 PODM-MI 算法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Preference-oriented+Diversity+Model+Based+on+Mutual-information+in+Re-ranking+for+E-commerce+Search)|0| |[Query Performance Prediction for Conversational Search and Beyond](https://doi.org/10.1145/3626772.3657658)|Chuan Meng|University of Amsterdam|Query performance prediction (QPP) is a key task in information retrieval (IR) [1]. The QPP task is to estimate the retrieval quality of a search system for a query without human relevance judgments. In summary, I aim to solve 4 limitations identified in previous QPP studies: I have published 3 papers that address 3 of these limitations, while the remaining one is the focus of my future work. While extensively explored for traditional ad-hoc search, QPP for conversational search (CS) [4] has been little studied. I have identified limitation 1 in previous QPP studies: There is a lack of a comprehensive investigation into how well existing QPP methods designed for ad-hoc search perform in the context of CS. To fill this research gap, I have conducted a comprehensive reproducibility study [5], where I examined various QPP methods that were designed for ad-hoc search in the CS setting. I have made the code and data publicly available on https://github.com/ChuanMeng/QPP4CS. Moreover, I have identified limitation 2 in previous studies on QPP for CS: There is a lack of research in investigating and leveraging the CS-specific features that do not exist in ad-hoc search to improve QPP quality for CS. I have authored a paper to fill this research gap [3]. Specifically, my empirical analysis indicates a correlation between query rewriting quality in CS and the actual retrieval quality. Based on this finding, I have proposed a perplexity-based pre-retrieval QPP framework (PPL-QPP) for CS, which integrates query rewriting quality into existing QPP methods. Experimental results show that PPL-QPP improves QPP quality. Beyond the scope of QPP for CS, I have identified drawbacks in general QPP methods. Existing QPP methods typically return a single scalar value that indicates the retrieval quality, which results in two issues: (i) relying on a single value to represent different IR metrics leads to a "one size fits all" issue, and (ii) a single value constraints the interpretability of QPP. Thus, I have identified limitation 3: there is a shortage of QPP methods that are capable of effectively predicting various IR evaluation metrics while maintaining interpretability. To address the limitation, I have proposed a QPP framework using automatically generated reevance judgments (QPP-GenRE); it decomposes QPP into independent subtasks of judging the relevance of each item in a ranked list to a given query [6]. QPP-GenRE enables the prediction of any IR metric using generated relevance judgments as pseudo-labels, and enables the interpretation of predicted IR metrics based on generated judgments. I have fine-tuned an open-source large language model (LLM) for judging relevance. Experimental results show that QPP-GenRE achieves state-of-the-art QPP quality; my fine-tuned LLM demonstrates a high relevance judgment agreement with human assessors. I have made the code and data publicly available on https://github.com/ChuanMeng/QPP-GenRE. As part of my future work, I plan to solve limitation 4: No study has explored the application of QPP in retrieval-augmented generation (RAG) to predict when not to rely on low-quality retrieved items that have the potential to hurt RAG's text generation.|查询性能预测(QPP)是信息检索(IR)[1]中的一项关键任务。QPP 任务是在没有人类相关性判断的情况下，对查询搜索系统的检索质量进行评估。总之，我的目标是解决在以前的 QPP 研究中发现的4个局限性: 我已经发表了3篇论文，解决了其中的3个局限性，而其余的一个是我未来工作的重点。虽然对传统的自组织搜索进行了广泛的研究，但是对会话搜索的 QPP 研究却很少。我已经在以前的 QPP 研究中确定了局限性1: 缺乏一个全面的调查，以了解现有的 QPP 方法设计的特别搜索在 CS 的情况下表现如何。为了填补这个研究空白，我进行了一个全面的重复性研究[5] ，其中我检查了各种 QPP 方法，这些方法是为在 CS 设置中的特别搜索而设计的。我已经把代码和数据公布在 https://github.com/chuanmeng/qpp4cs 上了。此外，我已经在以前的 CS QPP 研究中确定了局限性2: 缺乏研究调查和利用 CS 特定的特征，这些特征在特别搜索中不存在，以提高 CS 的 QPP 质量。我已经写了一篇论文来填补这个研究空白[3]。具体来说，本文的实证分析表明了 CS 中查询重写质量与实际检索质量之间的相关性。基于这一发现，我提出了一个基于实例的检索前 QPP 框架(PPL-QPP) ，该框架将查询重写质量与现有的 QPP 方法相结合。实验结果表明，PPL-QPP 提高了 QPP 的质量。除了 CS 的 QPP 范围，我已经确定了一般 QPP 方法的缺点。现有的 QPP 方法通常返回指示检索质量的单个标量值，这导致两个问题: (i)依赖于单个值来表示不同的 IR 指标导致“一种尺寸适合所有”问题，以及(ii)单个值限制了 QPP 的可解释性。因此，我已经确定了局限性3: 缺乏能够有效预测各种 IR 评估指标同时保持可解释性的 QPP 方法。为了解决这个局限性，我提出了一个 QPP 框架，它使用了自动的 < u > gen ated < u > re 事件判断(QPP-GenRE) ; 它将 QPP 分解为独立的子任务，判断排序列表中的每个项目与给定查询的相关性[6]。QPP-GenRE 能够使用生成的相关性判断作为伪标签来预测任何 IR 度量，并且能够基于生成的判断来解释预测的 IR 度量。我已经微调了一个用于判断相关性的开源大型语言模型(LLM)。实验结果表明，QPP-GenRE 实现了最先进的 QPP 质量，我的微调 LLM 与人类评估者的相关性判断一致性很高。我已经把代码和数据公布在 https://github.com/chuanmeng/qpp-genre 上了。作为我未来工作的一部分，我计划解决局限性4: 还没有研究探索 QPP 在检索增强生成(RAG)中的应用，以预测何时不依赖于有可能损害 RAG 文本生成的低质量检索项。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Query+Performance+Prediction+for+Conversational+Search+and+Beyond)|0| -|[Unbiased Learning to Rank Meets Reality: Lessons from Baidu's Large-Scale Search Dataset](https://doi.org/10.1145/3626772.3657892)|Philipp Hager, Romain Deffayet, JeanMichel Renders, Onno Zoeter, Maarten de Rijke|Naver Labs Europe; Booking.com; University of Amsterdam|Unbiased learning-to-rank (ULTR) is a well-established framework for learning from user clicks, which are often biased by the ranker collecting the data. While theoretically justified and extensively tested in simulation, ULTR techniques lack empirical validation, especially on modern search engines. The dataset released for the WSDM Cup 2023, collected from Baidu's search engine, offers a rare opportunity to assess the real-world performance of prominent ULTR techniques. Despite multiple submissions during the WSDM Cup 2023 and the subsequent NTCIR ULTRE-2 task, it remains unclear whether the observed improvements stem from applying ULTR or other learning techniques. We revisit and extend the available experiments. We find that unbiased learning-to-rank techniques do not bring clear performance improvements, especially compared to the stark differences brought by the choice of ranking loss and query-document features. Our experiments reveal that ULTR robustly improves click prediction. However, these gains in click prediction do not translate to enhanced ranking performance on expert relevance annotations, implying that conclusions strongly depend on how success is measured in this benchmark.|无偏学习排名(ULTR)是一个从用户点击中学习的成熟的框架，用户点击往往受到排名收集数据的影响。ULTR 技术虽然在理论上得到了验证，并在仿真中得到了广泛的测试，但缺乏经验验证，特别是在现代搜索引擎上。从百度搜索引擎收集的2023年 WSDM 杯的数据集提供了一个难得的机会来评估突出的 ULTR 技术在现实世界中的表现。尽管在2023年 WSDM 杯和随后的 NTCIR ULTRE-2任务期间提交了多份申请，但目前尚不清楚观察到的改善是否源于应用 ULTR 或其他学习技术。我们重新审视并扩展现有的实验。我们发现，无偏见的学习排序技术并不能带来明显的性能改善，尤其是与排序丢失和查询文档特性的选择所带来的明显差异相比。我们的实验表明，ULTR 强有力地改善点击预测。然而，在点击预测方面取得的这些进展并不能转化为专家相关性注释排名表现的提高，这意味着结论在很大程度上取决于如何在这一基准中衡量成功。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Unbiased+Learning+to+Rank+Meets+Reality:+Lessons+from+Baidu's+Large-Scale+Search+Dataset)|0| -|[CMCLRec: Cross-modal Contrastive Learning for User Cold-start Sequential Recommendation](https://doi.org/10.1145/3626772.3657839)|Xiaolong Xu, Hongsheng Dong, Lianyong Qi, Xuyun Zhang, Haolong Xiang, Xiaoyu Xia, Yanwei Xu, Wanchun Dou|RMIT University; China University of Petroleum; Nanjing University of Information Science and Technology; Macquarle Unnversity; Macquarie University; College of Intelligence and Computing, Tianjin University; Nanjing University|Sequential recommendation models generate embeddings for items through the analysis of historical user-item interactions and utilize the acquired embeddings to predict user preferences. Despite being effective in revealing personalized preferences for users, these models heavily rely on user-item interactions. However, due to the lack of interaction information, new users face challenges when utilizing sequential recommendation models for predictions, which is recognized as the cold-start problem. Recent studies, while addressing this problem within specific structures, often neglect the compatibility with existing sequential recommendation models, making seamless integration into existing models unfeasible.To address this challenge, we propose CMCLRec, a Cross-Modal Contrastive Learning framework for user cold-start RECommendation. This approach aims to solve the user cold-start problem by customizing inputs for cold-start users that align with the requirements of sequential recommendation models in a cross-modal manner. Specifically, CMCLRec adopts cross-modal contrastive learning to construct a mapping from user features to user-item interactions based on warm user data. It then generates a simulated behavior sequence for each cold-start user in turn for recommendation purposes. In this way, CMCLRec is theoretically compatible with any extant sequential recommendation model. Comprehensive experiments conducted on real-world datasets substantiate that, compared with state-of-the-art baseline models, CMCLRec markedly enhances the performance of conventional sequential recommendation models, particularly for cold-start users.|序贯推荐模型通过分析历史上的用户-项目交互，生成项目的嵌入，并利用获得的嵌入来预测用户偏好。尽管这些模型能够有效地向用户展示个性化偏好，但它们严重依赖于用户项目交互。然而，由于缺乏交互信息，新用户在使用顺序推荐模型进行预测时面临着挑战，这被认为是冷启动问题。最近的研究虽然在特定的结构内解决了这个问题，但往往忽视了与现有顺序推荐模型的兼容性，使得与现有模型的无缝集成变得不可行。为了应对这一挑战，我们提出了 CMCLRec，一个用于用户冷启动推荐的跨模态对比学习框架。这种方法旨在解决用户冷启动问题，为冷启动用户定制输入，以跨模式的方式符合顺序推荐模型的要求。具体来说，CMCLRec 采用跨模态对比学习方法，构建了基于暖用户数据的用户特征到用户项交互的映射关系。然后，它为每个冷启动用户依次生成一个模拟的行为序列，用于推荐目的。这样，CMCLRec 在理论上与任何现存的顺序推荐模型兼容。在真实世界数据集上进行的综合实验证实，与最先进的基线模型相比，CMCLRec 显著提高了传统顺序推荐模型的性能，特别是对于冷启动用户。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=CMCLRec:+Cross-modal+Contrastive+Learning+for+User+Cold-start+Sequential+Recommendation)|0| -|[Sequential Recommendation for Optimizing Both Immediate Feedback and Long-term Retention](https://doi.org/10.1145/3626772.3657829)|Ziru Liu, Shuchang Liu, Zijian Zhang, Qingpeng Cai, Xiangyu Zhao, Kesen Zhao, Lantao Hu, Peng Jiang, Kun Gai|City University of Hong Kong School of Data Science; Kuaishou Technology Strategy Algorithm Department; Unaffiliated; City University of Hong Kong; Kuaishou Technology|In the landscape of Recommender System (RS) applications, reinforcement learning (RL) has recently emerged as a powerful tool, primarily due to its proficiency in optimizing long-term rewards. Nevertheless, it suffers from instability in the learning process, stemming from the intricate interactions among bootstrapping, off-policy training, and function approximation. Moreover, in multi-reward recommendation scenarios, designing a proper reward setting that reconciles the inner dynamics of various tasks is quite intricate. In response to these challenges, we introduce DT4IER, an advanced decision transformer-based recommendation model that is engineered to not only elevate the effectiveness of recommendations but also to achieve a harmonious balance between immediate user engagement and long-term retention. The DT4IER applies an innovative multi-reward design that adeptly balances short and long-term rewards with user-specific attributes, which serve to enhance the contextual richness of the reward sequence ensuring a more informed and personalized recommendation process. To enhance its predictive capabilities, DT4IER incorporates a high-dimensional encoder, skillfully designed to identify and leverage the intricate interrelations across diverse tasks. Furthermore, we integrate a contrastive learning approach within the action embedding predictions, a strategy that significantly boosts the model's overall performance. Experiments on three real-world datasets demonstrate the effectiveness of DT4IER against state-of-the-art Sequential Recommender Systems (SRSs) and Multi-Task Learning (MTL) models in terms of both prediction accuracy and effectiveness in specific tasks. The source code is accessible online to facilitate replication|在推荐系统(RS)应用领域，强化学习(rL)最近已经成为一种强大的工具，这主要是由于它在优化长期回报方面的熟练程度。尽管如此，由于自学、非政策培训和函数逼近之间错综复杂的相互作用，它在学习过程中存在不稳定性。此外，在多奖励推荐场景中，设计一个适当的奖励设置来协调各种任务的内部动态是相当复杂的。为了应对这些挑战，我们引入了 DT4IER，这是一种基于决策转换器的高级推荐模型，不仅旨在提高推荐的有效性，而且还旨在实现直接用户参与和长期保留之间的和谐平衡。DT4IER 采用了一种创新的多奖励设计，能够巧妙地平衡短期和长期奖励与用户特定属性之间的关系，这有助于增强奖励序列的上下文丰富性，确保推荐过程更加知情和个性化。为了增强其预测能力，DT4IER 采用了高维编码器，巧妙地设计识别和利用不同任务之间错综复杂的相互关系。此外，我们在嵌入预测的动作中整合了一种对比学习方法，这种策略显著地提高了模型的整体性能。在三个实际数据集上的实验证明了 DT4IER 对最先进的顺序推荐系统(SRS)和多任务学习(MTL)模型在特定任务的预测准确性和有效性方面的有效性。可以联机访问源代码，以便于复制|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Sequential+Recommendation+for+Optimizing+Both+Immediate+Feedback+and+Long-term+Retention)|0| +|[Unbiased Learning to Rank Meets Reality: Lessons from Baidu's Large-Scale Search Dataset](https://doi.org/10.1145/3626772.3657892)|Philipp Hager, Romain Deffayet, JeanMichel Renders, Onno Zoeter, Maarten de Rijke|University of Amsterdam; Naver Labs Europe; Booking.com|Unbiased learning-to-rank (ULTR) is a well-established framework for learning from user clicks, which are often biased by the ranker collecting the data. While theoretically justified and extensively tested in simulation, ULTR techniques lack empirical validation, especially on modern search engines. The dataset released for the WSDM Cup 2023, collected from Baidu's search engine, offers a rare opportunity to assess the real-world performance of prominent ULTR techniques. Despite multiple submissions during the WSDM Cup 2023 and the subsequent NTCIR ULTRE-2 task, it remains unclear whether the observed improvements stem from applying ULTR or other learning techniques. We revisit and extend the available experiments. We find that unbiased learning-to-rank techniques do not bring clear performance improvements, especially compared to the stark differences brought by the choice of ranking loss and query-document features. Our experiments reveal that ULTR robustly improves click prediction. However, these gains in click prediction do not translate to enhanced ranking performance on expert relevance annotations, implying that conclusions strongly depend on how success is measured in this benchmark.|无偏学习排名(ULTR)是一个从用户点击中学习的成熟的框架，用户点击往往受到排名收集数据的影响。ULTR 技术虽然在理论上得到了验证，并在仿真中得到了广泛的测试，但缺乏经验验证，特别是在现代搜索引擎上。从百度搜索引擎收集的2023年 WSDM 杯的数据集提供了一个难得的机会来评估突出的 ULTR 技术在现实世界中的表现。尽管在2023年 WSDM 杯和随后的 NTCIR ULTRE-2任务期间提交了多份申请，但目前尚不清楚观察到的改善是否源于应用 ULTR 或其他学习技术。我们重新审视并扩展现有的实验。我们发现，无偏见的学习排序技术并不能带来明显的性能改善，尤其是与排序丢失和查询文档特性的选择所带来的明显差异相比。我们的实验表明，ULTR 强有力地改善点击预测。然而，在点击预测方面取得的这些进展并不能转化为专家相关性注释排名表现的提高，这意味着结论在很大程度上取决于如何在这一基准中衡量成功。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Unbiased+Learning+to+Rank+Meets+Reality:+Lessons+from+Baidu's+Large-Scale+Search+Dataset)|0| +|[CMCLRec: Cross-modal Contrastive Learning for User Cold-start Sequential Recommendation](https://doi.org/10.1145/3626772.3657839)|Xiaolong Xu, Hongsheng Dong, Lianyong Qi, Xuyun Zhang, Haolong Xiang, Xiaoyu Xia, Yanwei Xu, Wanchun Dou|College of Intelligence and Computing, Tianjin University; RMIT University; Nanjing University; Macquarie University; Nanjing University of Information Science and Technology; Macquarle Unnversity; China University of Petroleum|Sequential recommendation models generate embeddings for items through the analysis of historical user-item interactions and utilize the acquired embeddings to predict user preferences. Despite being effective in revealing personalized preferences for users, these models heavily rely on user-item interactions. However, due to the lack of interaction information, new users face challenges when utilizing sequential recommendation models for predictions, which is recognized as the cold-start problem. Recent studies, while addressing this problem within specific structures, often neglect the compatibility with existing sequential recommendation models, making seamless integration into existing models unfeasible.To address this challenge, we propose CMCLRec, a Cross-Modal Contrastive Learning framework for user cold-start RECommendation. This approach aims to solve the user cold-start problem by customizing inputs for cold-start users that align with the requirements of sequential recommendation models in a cross-modal manner. Specifically, CMCLRec adopts cross-modal contrastive learning to construct a mapping from user features to user-item interactions based on warm user data. It then generates a simulated behavior sequence for each cold-start user in turn for recommendation purposes. In this way, CMCLRec is theoretically compatible with any extant sequential recommendation model. Comprehensive experiments conducted on real-world datasets substantiate that, compared with state-of-the-art baseline models, CMCLRec markedly enhances the performance of conventional sequential recommendation models, particularly for cold-start users.|序贯推荐模型通过分析历史上的用户-项目交互，生成项目的嵌入，并利用获得的嵌入来预测用户偏好。尽管这些模型能够有效地向用户展示个性化偏好，但它们严重依赖于用户项目交互。然而，由于缺乏交互信息，新用户在使用顺序推荐模型进行预测时面临着挑战，这被认为是冷启动问题。最近的研究虽然在特定的结构内解决了这个问题，但往往忽视了与现有顺序推荐模型的兼容性，使得与现有模型的无缝集成变得不可行。为了应对这一挑战，我们提出了 CMCLRec，一个用于用户冷启动推荐的跨模态对比学习框架。这种方法旨在解决用户冷启动问题，为冷启动用户定制输入，以跨模式的方式符合顺序推荐模型的要求。具体来说，CMCLRec 采用跨模态对比学习方法，构建了基于暖用户数据的用户特征到用户项交互的映射关系。然后，它为每个冷启动用户依次生成一个模拟的行为序列，用于推荐目的。这样，CMCLRec 在理论上与任何现存的顺序推荐模型兼容。在真实世界数据集上进行的综合实验证实，与最先进的基线模型相比，CMCLRec 显著提高了传统顺序推荐模型的性能，特别是对于冷启动用户。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=CMCLRec:+Cross-modal+Contrastive+Learning+for+User+Cold-start+Sequential+Recommendation)|0| +|[Sequential Recommendation for Optimizing Both Immediate Feedback and Long-term Retention](https://doi.org/10.1145/3626772.3657829)|Ziru Liu, Shuchang Liu, Zijian Zhang, Qingpeng Cai, Xiangyu Zhao, Kesen Zhao, Lantao Hu, Peng Jiang, Kun Gai|City University of Hong Kong; City University of Hong Kong School of Data Science; Kuaishou Technology Strategy Algorithm Department; Kuaishou Technology; Unaffiliated|In the landscape of Recommender System (RS) applications, reinforcement learning (RL) has recently emerged as a powerful tool, primarily due to its proficiency in optimizing long-term rewards. Nevertheless, it suffers from instability in the learning process, stemming from the intricate interactions among bootstrapping, off-policy training, and function approximation. Moreover, in multi-reward recommendation scenarios, designing a proper reward setting that reconciles the inner dynamics of various tasks is quite intricate. In response to these challenges, we introduce DT4IER, an advanced decision transformer-based recommendation model that is engineered to not only elevate the effectiveness of recommendations but also to achieve a harmonious balance between immediate user engagement and long-term retention. The DT4IER applies an innovative multi-reward design that adeptly balances short and long-term rewards with user-specific attributes, which serve to enhance the contextual richness of the reward sequence ensuring a more informed and personalized recommendation process. To enhance its predictive capabilities, DT4IER incorporates a high-dimensional encoder, skillfully designed to identify and leverage the intricate interrelations across diverse tasks. Furthermore, we integrate a contrastive learning approach within the action embedding predictions, a strategy that significantly boosts the model's overall performance. Experiments on three real-world datasets demonstrate the effectiveness of DT4IER against state-of-the-art Sequential Recommender Systems (SRSs) and Multi-Task Learning (MTL) models in terms of both prediction accuracy and effectiveness in specific tasks. The source code is accessible online to facilitate replication|在推荐系统(RS)应用领域，强化学习(rL)最近已经成为一种强大的工具，这主要是由于它在优化长期回报方面的熟练程度。尽管如此，由于自学、非政策培训和函数逼近之间错综复杂的相互作用，它在学习过程中存在不稳定性。此外，在多奖励推荐场景中，设计一个适当的奖励设置来协调各种任务的内部动态是相当复杂的。为了应对这些挑战，我们引入了 DT4IER，这是一种基于决策转换器的高级推荐模型，不仅旨在提高推荐的有效性，而且还旨在实现直接用户参与和长期保留之间的和谐平衡。DT4IER 采用了一种创新的多奖励设计，能够巧妙地平衡短期和长期奖励与用户特定属性之间的关系，这有助于增强奖励序列的上下文丰富性，确保推荐过程更加知情和个性化。为了增强其预测能力，DT4IER 采用了高维编码器，巧妙地设计识别和利用不同任务之间错综复杂的相互关系。此外，我们在嵌入预测的动作中整合了一种对比学习方法，这种策略显著地提高了模型的整体性能。在三个实际数据集上的实验证明了 DT4IER 对最先进的顺序推荐系统(SRS)和多任务学习(MTL)模型在特定任务的预测准确性和有效性方面的有效性。可以联机访问源代码，以便于复制|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Sequential+Recommendation+for+Optimizing+Both+Immediate+Feedback+and+Long-term+Retention)|0| |[Invisible Relevance Bias: Text-Image Retrieval Models Prefer AI-Generated Images](https://doi.org/10.1145/3626772.3657750)|Shicheng Xu, Danyang Hou, Liang Pang, Jingcheng Deng, Jun Xu, Huawei Shen, Xueqi Cheng|Institute of Computing Technology, Chinese Academy of Sciences; Gaoling School of Artificial Intelligence, Renmin University of China|With the advancement of generation models, AI-generated content (AIGC) is becoming more realistic, flooding the Internet. A recent study suggests that this phenomenon causes source bias in text retrieval for web search. Specifically, neural retrieval models tend to rank generated texts higher than human-written texts. In this paper, we extend the study of this bias to cross-modal retrieval. Firstly, we successfully construct a suitable benchmark to explore the existence of the bias. Subsequent extensive experiments on this benchmark reveal that AI-generated images introduce an invisible relevance bias to text-image retrieval models. Specifically, our experiments show that text-image retrieval models tend to rank the AI-generated images higher than the real images, even though the AI-generated images do not exhibit more visually relevant features to the query than real images. This invisible relevance bias is prevalent across retrieval models with varying training data and architectures. Furthermore, our subsequent exploration reveals that the inclusion of AI-generated images in the training data of the retrieval models exacerbates the invisible relevance bias. The above phenomenon triggers a vicious cycle, which makes the invisible relevance bias become more and more serious. To elucidate the potential causes of invisible relevance and address the aforementioned issues, we introduce an effective training method aimed at alleviating the invisible relevance bias. Subsequently, we apply our proposed debiasing method to retroactively identify the causes of invisible relevance, revealing that the AI-generated images induce the image encoder to embed additional information into their representation. This information exhibits a certain consistency across generated images with different semantics and can make the retriever estimate a higher relevance score.|随着生成模型的进步，人工智能生成的内容(AIGC)正变得越来越现实，充斥着互联网。最近的一项研究表明，这种现象造成源偏见的文本检索的网络搜索。具体来说，神经检索模型对生成文本的排名往往高于人写文本。在本文中，我们将这种偏差的研究扩展到跨模态检索。首先，我们成功地构建了一个合适的基准来研究这种偏差的存在。随后在这个基准上进行的大量实验表明，人工智能生成的图像给文本图像检索模型带来了不可见的相关性偏差。具体来说，我们的实验表明，文本图像检索模型对人工智能生成的图像的排序往往高于真实图像，即使人工智能生成的图像并没有表现出更多的视觉相关特征的查询比真实图像。这种看不见的相关性偏差在具有不同训练数据和结构的检索模型中普遍存在。此外，我们随后的研究表明，在检索模型的训练数据中包含人工智能生成的图像加剧了不可见的相关性偏差。上述现象引发了一个恶性循环，使得无形的关联偏差越来越严重。为了阐明隐性相关产生的潜在原因并解决上述问题，我们引入了一种有效的训练方法来缓解隐性相关偏差。随后，我们应用我们提出的去偏方法来追溯识别不可见相关性的原因，揭示了人工智能生成的图像诱导图像编码器嵌入额外的信息到他们的表示。这些信息在生成的具有不同语义的图像之间表现出一定的一致性，并且可以使检索器估计出更高的相关性得分。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Invisible+Relevance+Bias:+Text-Image+Retrieval+Models+Prefer+AI-Generated+Images)|0| -|[Fair Sequential Recommendation without User Demographics](https://doi.org/10.1145/3626772.3657703)|Huimin Zeng, Zhankui He, Zhenrui Yue, Julian J. McAuley, Dong Wang|University of Illinois at Urbana-Champaign; University of Illinois Urbana-Champaign; University of California, San Diego|Much existing literature on fair recommendation (i.e., group fairness) leverages users' demographic attributes (e.g., gender) to develop fair recommendation methods. However, in real-world scenarios, due to privacy concerns and convenience considerations, users may not be willing to share their demographic information with the system, which limits the application of many existing methods. Moreover, sequential recommendation (SR) models achieve state-of-the-art performance compared to traditional collaborative filtering (CF) recommenders, and can represent users solely using user-item interactions (user-free). This leaves a wrong impression that SR models are free from group unfairness by design. In this work, we explore a critical question: how can we build a fair sequential recommendation system without even knowing user demographics? To address this problem, we propose Agnostic FairSeqRec (A-FSR): a model-agnostic and demographic-agnostic debiasing framework for sequential recommendation without requiring users' demographic attributes. Firstly, A-FSR reduces the correlation between the potential stereotypical patterns in the input sequences and final recommendations via Dirichlet neighbor smoothing. Secondly, A-FSR estimates an under-represented group of sequences via a gradient-based heuristic, and implicitly moves training focus towards the under-represented group by minimizing a distributionally robust optimization (DRO) based objective. Results on real-world datasets show that A-FSR achieves significant improvements on group fairness in sequential recommendation, while outperforming other state-of-the-art baselines.|关于公平推荐(即群体公平)的许多现有文献利用用户的人口统计特征(如性别)来发展公平推荐方法。然而，在现实世界的情况下，由于隐私问题和方便的考虑，用户可能不愿意与系统共享他们的人口统计信息，这限制了许多现有方法的应用。此外，序贯推荐(SR)模型与传统的协同过滤推荐(CF)模型相比，可以实现最先进的性能，并且可以完全使用用户项交互(用户自由)来代表用户。这就给人留下了一个错误的印象，认为 SR 模型在设计上不存在群体不公平。在这项工作中，我们探讨了一个关键问题: 我们如何建立一个公平的顺序推荐系统，甚至不知道用户的人口统计？为了解决这个问题，我们提出了不可知的 FairSeqRec (A-FSR) : 一个不需要用户人口统计属性的模型不可知和人口统计不可知的连续推荐消偏框架。首先，A-FSR 通过 Dirichlet 邻域平滑降低了输入序列中潜在的常规模式与最终推荐值之间的相关性。其次，A-FSR 通过基于梯度的启发式算法估计一组未被充分表示的序列，并通过最小化基于分布鲁棒优化(DRO)的目标隐式地将训练焦点移向未被充分表示的序列。实际数据集的结果表明，A-FSR 在顺序推荐方面取得了显著的改善，同时优于其他最先进的基线。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Fair+Sequential+Recommendation+without+User+Demographics)|0| +|[Fair Sequential Recommendation without User Demographics](https://doi.org/10.1145/3626772.3657703)|Huimin Zeng, Zhankui He, Zhenrui Yue, Julian J. McAuley, Dong Wang|University of Illinois Urbana-Champaign; University of Illinois at Urbana-Champaign; University of California, San Diego|Much existing literature on fair recommendation (i.e., group fairness) leverages users' demographic attributes (e.g., gender) to develop fair recommendation methods. However, in real-world scenarios, due to privacy concerns and convenience considerations, users may not be willing to share their demographic information with the system, which limits the application of many existing methods. Moreover, sequential recommendation (SR) models achieve state-of-the-art performance compared to traditional collaborative filtering (CF) recommenders, and can represent users solely using user-item interactions (user-free). This leaves a wrong impression that SR models are free from group unfairness by design. In this work, we explore a critical question: how can we build a fair sequential recommendation system without even knowing user demographics? To address this problem, we propose Agnostic FairSeqRec (A-FSR): a model-agnostic and demographic-agnostic debiasing framework for sequential recommendation without requiring users' demographic attributes. Firstly, A-FSR reduces the correlation between the potential stereotypical patterns in the input sequences and final recommendations via Dirichlet neighbor smoothing. Secondly, A-FSR estimates an under-represented group of sequences via a gradient-based heuristic, and implicitly moves training focus towards the under-represented group by minimizing a distributionally robust optimization (DRO) based objective. Results on real-world datasets show that A-FSR achieves significant improvements on group fairness in sequential recommendation, while outperforming other state-of-the-art baselines.|关于公平推荐(即群体公平)的许多现有文献利用用户的人口统计特征(如性别)来发展公平推荐方法。然而，在现实世界的情况下，由于隐私问题和方便的考虑，用户可能不愿意与系统共享他们的人口统计信息，这限制了许多现有方法的应用。此外，序贯推荐(SR)模型与传统的协同过滤推荐(CF)模型相比，可以实现最先进的性能，并且可以完全使用用户项交互(用户自由)来代表用户。这就给人留下了一个错误的印象，认为 SR 模型在设计上不存在群体不公平。在这项工作中，我们探讨了一个关键问题: 我们如何建立一个公平的顺序推荐系统，甚至不知道用户的人口统计？为了解决这个问题，我们提出了不可知的 FairSeqRec (A-FSR) : 一个不需要用户人口统计属性的模型不可知和人口统计不可知的连续推荐消偏框架。首先，A-FSR 通过 Dirichlet 邻域平滑降低了输入序列中潜在的常规模式与最终推荐值之间的相关性。其次，A-FSR 通过基于梯度的启发式算法估计一组未被充分表示的序列，并通过最小化基于分布鲁棒优化(DRO)的目标隐式地将训练焦点移向未被充分表示的序列。实际数据集的结果表明，A-FSR 在顺序推荐方面取得了显著的改善，同时优于其他最先进的基线。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Fair+Sequential+Recommendation+without+User+Demographics)|0| |[Negative Sampling Techniques for Dense Passage Retrieval in a Multilingual Setting](https://doi.org/10.1145/3626772.3657854)|Thilina Chaturanga Rajapakse, Andrew Yates, Maarten de Rijke|University of Amsterdam|The bi-encoder transformer architecture has become popular in open-domain retrieval, surpassing traditional sparse retrieval methods. Using hard negatives during training can improve the effectiveness of dense retrievers, and various techniques have been proposed to generate these hard negatives. We investigate the effectiveness of multiple negative sampling methods based on lexical methods (BM25), clustering, and periodically updated dense indices. We examine techniques that were introduced for finding hard negatives in a monolingual setting and reproduce them in a multilingual setting. We discover a gap amongst these techniques that we fill by proposing a novel clustered training method. Specifically, we focus on monolingual retrieval using multilingual dense retrievers across a broad set of diverse languages. We find that negative sampling based on BM25 negatives is surprisingly effective in an in-distribution setting, but this finding does not generalize to out-of-distribution and zero-shot settings, where the newly proposed method achieves the best results. We conclude with recommendations on which negative sampling methods may be the most effective given different multilingual retrieval scenarios.|双编码器变压器结构已经成为开放域检索中的热点，超越了传统的稀疏检索方法。在训练过程中使用硬负片可以提高密集型检索器的效率，人们提出了各种技术来产生这些硬负片。我们研究了基于词汇方法(BM25)、聚类和周期性更新密集指数的多重负抽样方法的有效性。我们研究了在单语环境下寻找硬负面的技术，并在多语环境下重现这些技术。我们通过提出一种新的聚类训练方法来填补这些技术之间的空白。具体来说，我们的重点是使用多语言密集检索器跨多种语言的单语言检索。我们发现基于 BM25负值的负采样在分布内环境中有惊人的效果，但是这一发现并没有推广到分布外环境和零拍环境中，在这两种环境中，新提出的方法取得了最好的效果。最后，我们给出了在不同的多语言检索场景下，哪种负抽样方法可能是最有效的建议。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Negative+Sampling+Techniques+for+Dense+Passage+Retrieval+in+a+Multilingual+Setting)|0| |[M3oE: Multi-Domain Multi-Task Mixture-of Experts Recommendation Framework](https://doi.org/10.1145/3626772.3657686)|Zijian Zhang, Shuchang Liu, Jiaao Yu, Qingpeng Cai, Xiangyu Zhao, Chunxu Zhang, Ziru Liu, Qidong Liu, Hongwei Zhao, Lantao Hu, Peng Jiang, Kun Gai||Multi-domain recommendation and multi-task recommendation have demonstrated their effectiveness in leveraging common information from different domains and objectives for comprehensive user modeling. Nonetheless, the practical recommendation usually faces multiple domains and tasks simultaneously, which cannot be well-addressed by current methods. To this end, we introduce M3oE, an adaptive multi-domain multi-task mixture-of-experts recommendation framework. M3oE integrates multi-domain information, maps knowledge across domains and tasks, and optimizes multiple objectives. We leverage three mixture-of-experts modules to learn common, domain-aspect, and task-aspect user preferences respectively to address the complex dependencies among multiple domains and tasks in a disentangled manner. Additionally, we design a two-level fusion mechanism for precise control over feature extraction and fusion across diverse domains and tasks. The framework's adaptability is further enhanced by applying AutoML technique, which allows dynamic structure optimization. To the best of the authors' knowledge, our M3oE is the first effort to solve multi-domain multi-task recommendation self-adaptively. Extensive experiments on two benchmark datasets against diverse baselines demonstrate M3oE's superior performance. The implementation code is available to ensure reproducibility.|多领域推荐和多任务推荐在利用来自不同领域和目标的公共信息进行全面的用户建模方面展示了它们的有效性。尽管如此，实际的推荐通常同时面对多个领域和任务，而这些领域和任务不能被当前的方法很好地处理。为此，我们介绍了一个自适应的多领域多任务混合专家推荐框架 M3oE。M3oE 集成了多领域信息，映射了跨领域和任务的知识，并优化了多个目标。我们利用三个专家混合模块分别学习通用、领域方面和任务方面的用户偏好，以解决多个领域和任务之间的复杂依赖关系。此外，我们设计了一个两级融合机制，用于精确控制不同领域和任务的特征提取和融合。通过应用 AutoML 技术，进一步提高了框架的适应性，实现了动态结构优化。据作者所知，我们的 M3oE 首次尝试自适应地解决多领域多任务推荐问题。针对不同基线的两个基准数据集的大量实验证明了 M3oE 的优越性能。实现代码可用于确保可重复性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=M3oE:+Multi-Domain+Multi-Task+Mixture-of+Experts+Recommendation+Framework)|0| |[NFARec: A Negative Feedback-Aware Recommender Model](https://doi.org/10.1145/3626772.3657809)|Xinfeng Wang, Fumiyo Fukumoto, Jin Cui, Yoshimi Suzuki, Dongjin Yu|Graduate Faculty of Interdisciplinary Research, University of Yamanashi; School of Computer Science and Technology, Hangzhou Dianzi University; Faculty of Engineering, Integrated Graduate School of Medicine, Engineering, and Agricultural Sciences|Graph neural network (GNN)-based models have been extensively studied for recommendations, as they can extract high-order collaborative signals accurately which is required for high-quality recommender systems. However, they neglect the valuable information gained through negative feedback in two aspects: (1) different users might hold opposite feedback on the same item, which hampers optimal information propagation in GNNs, and (2) even when an item vastly deviates from users' preferences, they might still choose it and provide a negative rating. In this paper, we propose a negative feedback-aware recommender model (NFARec) that maximizes the leverage of negative feedback. To transfer information to multi-hop neighbors along an optimal path effectively, NFARec adopts a feedback-aware correlation that guides hypergraph convolutions (HGCs) to learn users' structural representations. Moreover, NFARec incorporates an auxiliary task - predicting the feedback sentiment polarity (i.e., positive or negative) of the next interaction - based on the Transformer Hawkes Process. The task is beneficial for understanding users by learning the sentiment expressed in their previous sequential feedback patterns and predicting future interactions. Extensive experiments demonstrate that NFARec outperforms competitive baselines. Our source code and data are released at https://github.com/WangXFng/NFARec.|基于图形神经网络(GNN)的推荐系统模型能够准确地提取高阶协同信号，是高质量推荐系统所必需的。然而，他们忽视了通过负面反馈获得的有价值的信息在两个方面: (1)不同的用户可能对同一个项目持有相反的反馈，这阻碍了最佳信息在 GNN 中的传播，和(2)即使一个项目大大偏离用户的喜好，他们仍然可能选择它，并提供一个负面评价。在本文中，我们提出了一个负反馈感知的推荐模型(NFARec) ，最大限度地利用负反馈。NFARec 采用反馈感知关联算法，引导超图卷积(HGC)学习用户的结构表示，有效地将信息沿着最优路径传递给多跳邻居。此外，NFARec 还包含了一个辅助任务——预测下一次交互的反馈情绪极性(即正极或负极)——基于变压器霍克斯过程。这项任务有利于了解用户的情绪表达在他们以前的顺序反馈模式和预测未来的交互。大量的实验表明，NFARec 的表现优于竞争基线。我们的源代码和数据在 https://github.com/wangxfng/nfarec 公布。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=NFARec:+A+Negative+Feedback-Aware+Recommender+Model)|0| -|[Modeling User Fatigue for Sequential Recommendation](https://doi.org/10.1145/3626772.3657802)|Nian Li, Xin Ban, Cheng Ling, Chen Gao, Lantao Hu, Peng Jiang, Kun Gai, Yong Li, Qingmin Liao|Independent; Shenzhen International Graduate School, Tsinghua University; Tsinghua University; Kuaishou Inc.; Department of Electronic Engineering, Tsinghua University|Recommender systems filter out information that meets user interests. However, users may be tired of the recommendations that are too similar to the content they have been exposed to in a short historical period, which is the so-called user fatigue. Despite the significance for a better user experience, user fatigue is seldom explored by existing recommenders. In fact, there are three main challenges to be addressed for modeling user fatigue, including what features support it, how it influences user interests, and how its explicit signals are obtained. In this paper, we propose to model user Fatigue in interest learning for sequential Recommendations (FRec). To address the first challenge, based on a multi-interest framework, we connect the target item with historical items and construct an interest-aware similarity matrix as features to support fatigue modeling. Regarding the second challenge, built upon feature cross, we propose a fatigue-enhanced multi-interest fusion to capture long-term interest. In addition, we develop a fatigue-gated recurrent unit for short-term interest learning, with temporal fatigue representations as important inputs for constructing update and reset gates. For the last challenge, we propose a novel sequence augmentation to obtain explicit fatigue signals for contrastive learning. We conduct extensive experiments on real-world datasets, including two public datasets and one large-scale industrial dataset. Experimental results show that FRec can improve AUC and GAUC up to 0.026 and 0.019 compared with state-of-the-art models, respectively. Moreover, large-scale online experiments demonstrate the effectiveness of FRec for fatigue reduction. Our codes are released at https://github.com/tsinghua-fib-lab/SIGIR24-FRec.|推荐系统过滤出符合用户兴趣的信息。然而，用户可能会厌倦那些与他们在很短的历史时期内接触到的内容过于相似的推荐，这就是所谓的用户疲劳。尽管这对于更好的用户体验意义重大，但是现有的推荐者很少探讨用户疲劳问题。实际上，建立用户疲劳模型需要解决三个主要问题，包括哪些特性支持用户疲劳，它如何影响用户兴趣，以及如何获得用户疲劳的显性信号。在本文中，我们提出了模型用户疲劳的兴趣学习顺序推荐(FRec)。为了解决第一个问题，我们基于一个多兴趣框架，将目标项目与历史项目连接起来，构造一个感兴趣的相似矩阵作为特征来支持疲劳建模。针对第二个挑战，建立在特征交叉的基础上，我们提出了一种疲劳增强的多兴趣融合来捕获长期兴趣。此外，我们开发了一个用于短期兴趣学习的疲劳门控循环单元，以时间疲劳表示作为构造更新门和复位门的重要输入。针对最后一个挑战，我们提出了一种新的序列增强方法，用于获得用于对比学习的显式疲劳信号。我们对真实世界的数据集进行了广泛的实验，包括两个公共数据集和一个大规模的工业数据集。实验结果表明，与现有模型相比，FRec 可以提高 AUC 和 GAUC，分别达到0.026和0.019。此外，大规模的在线实验证明了 FRec 对疲劳减振的有效性。我们的密码在 https://github.com/tsinghua-fib-lab/sigir24-frec 公布。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Modeling+User+Fatigue+for+Sequential+Recommendation)|0| -|[DDPO: Direct Dual Propensity Optimization for Post-Click Conversion Rate Estimation](https://doi.org/10.1145/3626772.3657817)|Hongzu Su, Lichao Meng, Lei Zhu, Ke Lu, Jingjing Li|University of Electronic Science and Technology of China; Tongji University|In online advertising, the sample selection bias problem is a major cause of inaccurate conversion rate estimates. Current mainstream solutions only perform causality-based optimization in the click space since the conversion labels in the non-click space are absent. However, optimization for unclicked samples is equally essential because the non-click space contains more samples and user characteristics than the click space. To exploit the unclicked samples, we propose a Direct Dual Propensity Optimization (DDPO) framework to optimize the model directly in impression space with both clicked and unclicked samples. In this framework, we specifically design a click propensity network and a conversion propensity network. The click propensity network is dedicated to ensuring that optimization in the click space is unbiased. The conversion propensity network is designed to generate pseudo-conversion labels for unclicked samples, thus overcoming the challenge of absent labels in non-click space. With these two propensity networks, we are able to perform causality-based optimization in both click space and non-click space. In addition, to strengthen the causal relationship, we design two causal transfer modules for the conversion rate prediction model with the attention mechanism. The proposed framework is evaluated on five real-world public datasets and one private Tencent advertising dataset. Experimental results verify that our method is able to improve the prediction performance significantly. For instance, our method outperforms the previous state-of-the-art method by 7.0% in terms of the Area Under the Curve on the Ali-CCP dataset.|在网络广告中，样本选择偏差问题是导致转化率估计不准确的主要原因。当前的主流解决方案只在点击空间中执行基于因果关系的优化，因为非点击空间中没有转换标签。然而，对未点击样本的优化同样重要，因为非点击空间比点击空间包含更多的样本和用户特征。为了利用未点击样本，我们提出了一个直接双倾向优化(DDPO)框架，直接在印象空间中对点击样本和未点击样本进行优化。在这个框架中，我们具体设计了一个点击倾向网络和一个转换倾向网络。点击倾向网络致力于确保点击空间的优化是无偏的。转换倾向网络的设计目的是为未点击样本生成伪转换标签，从而克服非点击空间中标签缺失的困难。有了这两个倾向网络，我们就能够在点击空间和非点击空间进行基于因果关系的优化。此外，为了加强因果关系，我们设计了两个具有注意机制的因果传递模块用于转化率预测模型。建议的框架是根据五个真实世界的公共数据集和一个私人腾讯广告数据集进行评估的。实验结果表明，该方法能够显著提高预测性能。例如，在 Ali-CCP 数据集的曲线下面积方面，我们的方法比以前最先进的方法高出7.0% 。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=DDPO:+Direct+Dual+Propensity+Optimization+for+Post-Click+Conversion+Rate+Estimation)|0| +|[Modeling User Fatigue for Sequential Recommendation](https://doi.org/10.1145/3626772.3657802)|Nian Li, Xin Ban, Cheng Ling, Chen Gao, Lantao Hu, Peng Jiang, Kun Gai, Yong Li, Qingmin Liao|Department of Electronic Engineering, Tsinghua University; Shenzhen International Graduate School, Tsinghua University; Kuaishou Inc.; Tsinghua University; Independent|Recommender systems filter out information that meets user interests. However, users may be tired of the recommendations that are too similar to the content they have been exposed to in a short historical period, which is the so-called user fatigue. Despite the significance for a better user experience, user fatigue is seldom explored by existing recommenders. In fact, there are three main challenges to be addressed for modeling user fatigue, including what features support it, how it influences user interests, and how its explicit signals are obtained. In this paper, we propose to model user Fatigue in interest learning for sequential Recommendations (FRec). To address the first challenge, based on a multi-interest framework, we connect the target item with historical items and construct an interest-aware similarity matrix as features to support fatigue modeling. Regarding the second challenge, built upon feature cross, we propose a fatigue-enhanced multi-interest fusion to capture long-term interest. In addition, we develop a fatigue-gated recurrent unit for short-term interest learning, with temporal fatigue representations as important inputs for constructing update and reset gates. For the last challenge, we propose a novel sequence augmentation to obtain explicit fatigue signals for contrastive learning. We conduct extensive experiments on real-world datasets, including two public datasets and one large-scale industrial dataset. Experimental results show that FRec can improve AUC and GAUC up to 0.026 and 0.019 compared with state-of-the-art models, respectively. Moreover, large-scale online experiments demonstrate the effectiveness of FRec for fatigue reduction. Our codes are released at https://github.com/tsinghua-fib-lab/SIGIR24-FRec.|推荐系统过滤出符合用户兴趣的信息。然而，用户可能会厌倦那些与他们在很短的历史时期内接触到的内容过于相似的推荐，这就是所谓的用户疲劳。尽管这对于更好的用户体验意义重大，但是现有的推荐者很少探讨用户疲劳问题。实际上，建立用户疲劳模型需要解决三个主要问题，包括哪些特性支持用户疲劳，它如何影响用户兴趣，以及如何获得用户疲劳的显性信号。在本文中，我们提出了模型用户疲劳的兴趣学习顺序推荐(FRec)。为了解决第一个问题，我们基于一个多兴趣框架，将目标项目与历史项目连接起来，构造一个感兴趣的相似矩阵作为特征来支持疲劳建模。针对第二个挑战，建立在特征交叉的基础上，我们提出了一种疲劳增强的多兴趣融合来捕获长期兴趣。此外，我们开发了一个用于短期兴趣学习的疲劳门控循环单元，以时间疲劳表示作为构造更新门和复位门的重要输入。针对最后一个挑战，我们提出了一种新的序列增强方法，用于获得用于对比学习的显式疲劳信号。我们对真实世界的数据集进行了广泛的实验，包括两个公共数据集和一个大规模的工业数据集。实验结果表明，与现有模型相比，FRec 可以提高 AUC 和 GAUC，分别达到0.026和0.019。此外，大规模的在线实验证明了 FRec 对疲劳减振的有效性。我们的密码在 https://github.com/tsinghua-fib-lab/sigir24-frec 公布。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Modeling+User+Fatigue+for+Sequential+Recommendation)|0| +|[DDPO: Direct Dual Propensity Optimization for Post-Click Conversion Rate Estimation](https://doi.org/10.1145/3626772.3657817)|Hongzu Su, Lichao Meng, Lei Zhu, Ke Lu, Jingjing Li|Tongji University; University of Electronic Science and Technology of China|In online advertising, the sample selection bias problem is a major cause of inaccurate conversion rate estimates. Current mainstream solutions only perform causality-based optimization in the click space since the conversion labels in the non-click space are absent. However, optimization for unclicked samples is equally essential because the non-click space contains more samples and user characteristics than the click space. To exploit the unclicked samples, we propose a Direct Dual Propensity Optimization (DDPO) framework to optimize the model directly in impression space with both clicked and unclicked samples. In this framework, we specifically design a click propensity network and a conversion propensity network. The click propensity network is dedicated to ensuring that optimization in the click space is unbiased. The conversion propensity network is designed to generate pseudo-conversion labels for unclicked samples, thus overcoming the challenge of absent labels in non-click space. With these two propensity networks, we are able to perform causality-based optimization in both click space and non-click space. In addition, to strengthen the causal relationship, we design two causal transfer modules for the conversion rate prediction model with the attention mechanism. The proposed framework is evaluated on five real-world public datasets and one private Tencent advertising dataset. Experimental results verify that our method is able to improve the prediction performance significantly. For instance, our method outperforms the previous state-of-the-art method by 7.0% in terms of the Area Under the Curve on the Ali-CCP dataset.|在网络广告中，样本选择偏差问题是导致转化率估计不准确的主要原因。当前的主流解决方案只在点击空间中执行基于因果关系的优化，因为非点击空间中没有转换标签。然而，对未点击样本的优化同样重要，因为非点击空间比点击空间包含更多的样本和用户特征。为了利用未点击样本，我们提出了一个直接双倾向优化(DDPO)框架，直接在印象空间中对点击样本和未点击样本进行优化。在这个框架中，我们具体设计了一个点击倾向网络和一个转换倾向网络。点击倾向网络致力于确保点击空间的优化是无偏的。转换倾向网络的设计目的是为未点击样本生成伪转换标签，从而克服非点击空间中标签缺失的困难。有了这两个倾向网络，我们就能够在点击空间和非点击空间进行基于因果关系的优化。此外，为了加强因果关系，我们设计了两个具有注意机制的因果传递模块用于转化率预测模型。建议的框架是根据五个真实世界的公共数据集和一个私人腾讯广告数据集进行评估的。实验结果表明，该方法能够显著提高预测性能。例如，在 Ali-CCP 数据集的曲线下面积方面，我们的方法比以前最先进的方法高出7.0% 。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=DDPO:+Direct+Dual+Propensity+Optimization+for+Post-Click+Conversion+Rate+Estimation)|0| |[A Generic Behavior-Aware Data Augmentation Framework for Sequential Recommendation](https://doi.org/10.1145/3626772.3657682)|Jing Xiao, Weike Pan, Zhong Ming|Shenzhen University|Multi-behavior sequential recommendation (MBSR), which models multi-behavior sequentiality and heterogeneity to better learn users' multifaceted intentions has achieved remarkable success. Though effective, the performance of these approaches may be limited due to the sparsity inherent in a real-world data. Existing data augmentation methods in recommender systems focus solely on a single type of behavior, overlooking the variations in expressing user preferences via different types of behaviors. During the augmentation of samples, it is easy to introduce excessive disturbance or noise, which may mislead the next-item recommendation. To address this limitation, we propose a novel generic framework called multi-behavior data augmentation for sequential recommendation (MBASR). Specifically, we design three behavior-aware data augmentation operations to construct rich training samples. Each augmentation operation takes into account the correlations between behaviors and aligns with the users' behavior patterns. In addition, we introduce a position-based sampling strategy that can effectively reduce the perturbation brought by the augmentation operations to the original data. Note that our model is data-oriented and can thus be embedded in different downstream MBSR models, so the overall framework is generic. Extensive experiments on three real-world datasets demonstrate the effectiveness of our MBASR and its applicability to a wide variety of mainstream MBSR models. Our source code is available at https://github.com/XiaoJing-C/MBASR.|多行为顺序推荐(MBRR)模型对多行为顺序性和异构性进行建模，以更好地了解用户的多方面意图，已取得了显著的成功。尽管这些方法有效，但由于真实世界数据中固有的稀疏性，它们的性能可能会受到限制。推荐系统中现有的数据增强方法只关注单一类型的行为，忽略了通过不同类型的行为表达用户偏好的差异。在样本的增大过程中，容易引入过多的干扰或噪声，从而误导下一项的推荐。为了解决这个问题，我们提出了一种新的通用框架，称为序贯推荐的多行为数据增强(MBASR)。具体来说，我们设计了三个行为感知的数据增强操作来构造丰富的训练样本。每个增强操作都考虑到行为之间的相关性，并与用户的行为模式保持一致。此外，我们还引入了一种基于位置的采样策略，可以有效地减少增广操作对原始数据的干扰。注意，我们的模型是面向数据的，因此可以嵌入到不同的下游 MBSR 模型中，所以总体框架是通用的。在三个实际数据集上的大量实验证明了我们的 MBASR 的有效性及其对各种主流 MBSR 模型的适用性。我们的源代码可以在 https://github.com/xiaojing-c/mbasr 找到。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Generic+Behavior-Aware+Data+Augmentation+Framework+for+Sequential+Recommendation)|0| -|[FineRec: Exploring Fine-grained Sequential Recommendation](https://doi.org/10.1145/3626772.3657761)|Xiaokun Zhang, Bo Xu, Youlin Wu, Yuan Zhong, Hongfei Lin, Fenglong Ma|Pennsylvania State University; Dalian University of Technology|Sequential recommendation is dedicated to offering items of interest for users based on their history behaviors. The attribute-opinion pairs, expressed by users in their reviews for items, provide the potentials to capture user preferences and item characteristics at a fine-grained level. To this end, we propose a novel framework FineRec that explores the attribute-opinion pairs of reviews to finely handle sequential recommendation. Specifically, we utilize a large language model to extract attribute-opinion pairs from reviews. For each attribute, a unique attribute-specific user-opinion-item graph is created, where corresponding opinions serve as the edges linking heterogeneous user and item nodes. Afterwards, we devise a diversity-aware convolution operation to aggregate information within the graphs, enabling attribute-specific user and item representation learning. Ultimately, we present an interaction-driven fusion mechanism to integrate attribute-specific user/item representations across all attributes for generating recommendations. Extensive experiments conducted on several real-world datasets demonstrate the superiority of our FineRec over existing state-ofthe-art methods. Further analysis also verifies the effectiveness of our fine-grained manner in handling the task.|序列推荐致力于根据用户的历史行为为他们提供感兴趣的项目。由用户在项目评论中表达的属性-意见对提供了在细粒度水平上捕获用户偏好和项目特征的潜力。为此，我们提出了一个新的框架 FineRec，探索评论的属性-意见对，以精细处理顺序推荐。具体来说，我们利用一个大型的语言模型来从评论中提取属性-意见对。对于每个属性，创建一个惟一的特定于属性的用户意见项图，其中相应的意见作为连接异构用户和项目节点的边。然后，我们设计一个多样性感知的卷积运算来聚集图中的信息，使特定属性的用户和项目表示学习。最后，我们提出了一种交互驱动的融合机制，用于跨所有属性集成特定于属性的用户/项表示，以生成建议。在几个真实世界数据集上进行的大量实验证明了我们的 FineRec 相对于现有最先进的方法的优越性。进一步的分析还验证了我们处理任务的细粒度方式的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=FineRec:+Exploring+Fine-grained+Sequential+Recommendation)|0| -|[ReFer: Retrieval-Enhanced Vertical Federated Recommendation for Full Set User Benefit](https://doi.org/10.1145/3626772.3657763)|Wenjie Li, Zhongren Wang, Jinpeng Wang, Shutao Xia, Jile Zhu, Mingjian Chen, Jiangke Fan, Jia Cheng, Jun Lei|Meituan; Tsinghua University|As an emerging privacy-preserving approach to leveraging cross-platform user interactions, vertical federated learning (VFL) has been increasingly applied in recommender systems. However, vanilla VFL is only applicable to overlapped users, ignoring potential universal interest patterns hidden among non-overlapped users and suffers from limited user group benefits, which hinders its application in real-world recommenders. In this paper, we extend the traditional vertical federated recommendation problem (VFR) to a more realistic Fully-Vertical federated recommendation setting (Fully-VFR) which aims to utilize all available data and serve full user groups. To tackle challenges in implementing Fully-VFR, we propose a Retrieval-enhanced Vertical Federated recommender (ReFer), a groundbreaking initiative that explores retrieval-enhanced machine learning approaches in VFL. Specifically, we establish a general "retrieval-and-utilization" algorithm to enhance the quality of representations across all parties. We design a flexible federated retrieval augmentation (RA) mechanism for VFL: (i) Cross-RA to complement field missing and (ii) Local-RA to promote mutual understanding between user groups. We conduct extensive experiments on both public and industry datasets. Results on both sequential and non-sequential CTR prediction tasks demonstrate that our method achieves significant performance improvements over baselines and is beneficial for all user groups.|作为一种新兴的利用跨平台用户交互的隐私保护方法，垂直联邦学习(VFL)在推荐系统中得到了越来越多的应用。然而，普通的 VFL 只适用于重叠用户，忽略了隐藏在非重叠用户之间的潜在通用兴趣模式，并且受到用户组好处的限制，这阻碍了它在实际推荐中的应用。本文将传统的垂直联邦推荐问题(VFR)扩展到一个更加现实的全垂直联邦推荐设置(Full-VFR) ，其目的是利用所有可用的数据，为全用户组提供服务。为了解决在实施完全 VFR 的挑战，我们提出了一个检索增强垂直联邦推荐(参考) ，一个突破性的倡议，探索检索增强机器学习方法在 VFL。具体来说，我们建立了一个通用的“检索和利用”算法，以提高所有各方的表示质量。我们设计了一个灵活的 VFL 联邦检索增强(RA)机制: (i)交叉 RA 来补充字段缺失; (ii)本地 RA 来促进用户组之间的相互理解。我们在公共和行业数据集上进行广泛的实验。在顺序和非顺序 CTR 预测任务中的结果表明，我们的方法比基线性能有了显著的提高，并且对所有用户组都有利。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=ReFer:+Retrieval-Enhanced+Vertical+Federated+Recommendation+for+Full+Set+User+Benefit)|0| -|[Pacer and Runner: Cooperative Learning Framework between Single- and Cross-Domain Sequential Recommendation](https://doi.org/10.1145/3626772.3657710)|Chung Park, Taesan Kim, Hyungjun Yoon, Junui Hong, Yelim Yu, Mincheol Cho, Minsung Choi, Jaegul Choo|SK Telelcom / KAIST; Korea Advanced Institute of Science and Technology; SK Telecom / KAIST; SK Telelcom|Cross-Domain Sequential Recommendation (CDSR) improves recommendation performance by utilizing information from multiple domains, which contrasts with Single-Domain Sequential Recommendation (SDSR) that relies on a historical interaction within a specific domain. However, CDSR may underperform compared to the SDSR approach in certain domains due to negative transfer, which occurs when there is a lack of relation between domains or different levels of data sparsity. To address the issue of negative transfer, our proposed CDSR model estimates the degree of negative transfer of each domain and adaptively assigns it as a weight factor to the prediction loss, to control gradient flows through domains with significant negative transfer. To this end, our model compares the performance of a model trained on multiple domains (CDSR) with a model trained solely on the specific domain (SDSR) to evaluate the negative transfer of each domain using our asymmetric cooperative network. In addition, to facilitate the transfer of valuable cues between the SDSR and CDSR tasks, we developed an auxiliary loss that maximizes the mutual information between the representation pairs from both tasks on a per-domain basis. This cooperative learning between SDSR and CDSR tasks is similar to the collaborative dynamics between pacers and runners in a marathon. Our model outperformed numerous previous works in extensive experiments on two real-world industrial datasets across ten service domains. We also have deployed our model in the recommendation system of our personal assistant app service, resulting in 21.4% increase in click-through rate compared to existing models, which is valuable to real-world business1.|跨域序列推荐(CDSR)通过利用来自多个域的信息来提高推荐性能，这与依赖于特定域内的历史交互的单域序列推荐(SDSR)形成了鲜明的对比。然而，CDSR 方法在某些领域的表现可能不如 SDSR 方法，这是由于负迁移，这种负迁移发生在领域之间缺乏联系或不同层次的数据稀疏时。为了解决负迁移问题，我们提出的 CDSR 模型估计每个域的负迁移程度，并自适应地将其作为预测损失的权重因子，以控制梯度流通过具有显著负迁移的域。为此，我们的模型比较了在多域(CDSR)训练的模型和单独在特定域(SDSR)训练的模型的性能，以评估使用我们的非对称合作网络的每个域的负迁移。此外，为了促进 SDSR 和 CDSR 任务之间有价值线索的传递，我们开发了一个辅助损失模型，该模型在每个领域的基础上最大化两个任务表征对之间的相互信息。SDSR 和 CDSR 任务之间的协作学习类似于马拉松中步行者和跑步者之间的协作动力学。我们的模型在十个服务领域的两个实际工业数据集上进行了广泛的实验，其性能优于以前的许多工作。我们也在个人助理应用程序服务的推荐系统中使用了我们的模型，与现有模型相比，点进率增加了21.4% ，这对于现实世界的商业来说是很有价值的。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Pacer+and+Runner:+Cooperative+Learning+Framework+between+Single-+and+Cross-Domain+Sequential+Recommendation)|0| -|[Aiming at the Target: Filter Collaborative Information for Cross-Domain Recommendation](https://doi.org/10.1145/3626772.3657713)|Hanyu Li, Weizhi Ma, Peijie Sun, Jiayu Li, Cunxiang Yin, Yancheng He, Guoqiang Xu, Min Zhang, Shaoping Ma|Tencent; Tsinghua University|Cross-domain recommender (CDR) systems aim to enhance the performance of the target domain by utilizing data from other related domains. However, irrelevant information from the source domain may instead degrade target domain performance, which is known as the negative transfer problem. There have been some attempts to address this problem, mostly by designing adaptive representations for overlapped users. Whereas, representation adaptions solely rely on the expressive capacity of the CDR model, lacking explicit constraint to filter the irrelevant source-domain collaborative information for the target domain. In this paper, we propose a novel Collaborative information regularized User Transformation (CUT) framework to tackle the negative transfer problem by directly filtering users' collaborative information. In CUT, user similarity in the target domain is adopted as a constraint for user transformation learning to filter the user collaborative information from the source domain. CUT first learns user similarity relationships from the target domain. Then, source-target information transfer is guided by the user similarity, where we design a user transformation layer to learn target-domain user representations and a contrastive loss to supervise the user collaborative information transferred. The results show significant performance improvement of CUT compared with SOTA single and cross-domain methods. Further analysis of the target-domain results illustrates that CUT can effectively alleviate the negative transfer problem.|跨域推荐(CDR)系统旨在通过利用其他相关域的数据来提高目标域的性能。然而，来自源域的不相关信息反而会降低目标域的性能，这就是所谓的负迁移问题。已经有一些尝试来解决这个问题，主要是通过为重叠用户设计自适应表示。然而，表示适配仅仅依赖于 CDR 模型的表达能力，缺乏明确的约束来过滤不相关的源域协同信息。本文提出了一种新的协同信息规范化用户转换(CUT)框架，通过直接过滤用户的协同信息来解决负迁移问题。在 CUT 中，采用目标域中的用户相似度作为用户转换学习的约束条件，对源域中的用户协作信息进行过滤。CUT 首先从目标域学习用户相似性关系。然后，以用户相似性为指导，设计了用户转换层来学习目标域用户表示，并通过对比度损失来监督用户协同信息的传输。结果表明，与 SOTA 单域和跨域方法相比，CUT 的性能有了显著的提高。对目标域结果的进一步分析表明，CUT 可以有效地缓解负迁移问题。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Aiming+at+the+Target:+Filter+Collaborative+Information+for+Cross-Domain+Recommendation)|0| +|[FineRec: Exploring Fine-grained Sequential Recommendation](https://doi.org/10.1145/3626772.3657761)|Xiaokun Zhang, Bo Xu, Youlin Wu, Yuan Zhong, Hongfei Lin, Fenglong Ma|Dalian University of Technology; Pennsylvania State University|Sequential recommendation is dedicated to offering items of interest for users based on their history behaviors. The attribute-opinion pairs, expressed by users in their reviews for items, provide the potentials to capture user preferences and item characteristics at a fine-grained level. To this end, we propose a novel framework FineRec that explores the attribute-opinion pairs of reviews to finely handle sequential recommendation. Specifically, we utilize a large language model to extract attribute-opinion pairs from reviews. For each attribute, a unique attribute-specific user-opinion-item graph is created, where corresponding opinions serve as the edges linking heterogeneous user and item nodes. Afterwards, we devise a diversity-aware convolution operation to aggregate information within the graphs, enabling attribute-specific user and item representation learning. Ultimately, we present an interaction-driven fusion mechanism to integrate attribute-specific user/item representations across all attributes for generating recommendations. Extensive experiments conducted on several real-world datasets demonstrate the superiority of our FineRec over existing state-ofthe-art methods. Further analysis also verifies the effectiveness of our fine-grained manner in handling the task.|序列推荐致力于根据用户的历史行为为他们提供感兴趣的项目。由用户在项目评论中表达的属性-意见对提供了在细粒度水平上捕获用户偏好和项目特征的潜力。为此，我们提出了一个新的框架 FineRec，探索评论的属性-意见对，以精细处理顺序推荐。具体来说，我们利用一个大型的语言模型来从评论中提取属性-意见对。对于每个属性，创建一个惟一的特定于属性的用户意见项图，其中相应的意见作为连接异构用户和项目节点的边。然后，我们设计一个多样性感知的卷积运算来聚集图中的信息，使特定属性的用户和项目表示学习。最后，我们提出了一种交互驱动的融合机制，用于跨所有属性集成特定于属性的用户/项表示，以生成建议。在几个真实世界数据集上进行的大量实验证明了我们的 FineRec 相对于现有最先进的方法的优越性。进一步的分析还验证了我们处理任务的细粒度方式的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=FineRec:+Exploring+Fine-grained+Sequential+Recommendation)|0| +|[ReFer: Retrieval-Enhanced Vertical Federated Recommendation for Full Set User Benefit](https://doi.org/10.1145/3626772.3657763)|Wenjie Li, Zhongren Wang, Jinpeng Wang, Shutao Xia, Jile Zhu, Mingjian Chen, Jiangke Fan, Jia Cheng, Jun Lei|Tsinghua University; Meituan|As an emerging privacy-preserving approach to leveraging cross-platform user interactions, vertical federated learning (VFL) has been increasingly applied in recommender systems. However, vanilla VFL is only applicable to overlapped users, ignoring potential universal interest patterns hidden among non-overlapped users and suffers from limited user group benefits, which hinders its application in real-world recommenders. In this paper, we extend the traditional vertical federated recommendation problem (VFR) to a more realistic Fully-Vertical federated recommendation setting (Fully-VFR) which aims to utilize all available data and serve full user groups. To tackle challenges in implementing Fully-VFR, we propose a Retrieval-enhanced Vertical Federated recommender (ReFer), a groundbreaking initiative that explores retrieval-enhanced machine learning approaches in VFL. Specifically, we establish a general "retrieval-and-utilization" algorithm to enhance the quality of representations across all parties. We design a flexible federated retrieval augmentation (RA) mechanism for VFL: (i) Cross-RA to complement field missing and (ii) Local-RA to promote mutual understanding between user groups. We conduct extensive experiments on both public and industry datasets. Results on both sequential and non-sequential CTR prediction tasks demonstrate that our method achieves significant performance improvements over baselines and is beneficial for all user groups.|作为一种新兴的利用跨平台用户交互的隐私保护方法，垂直联邦学习(VFL)在推荐系统中得到了越来越多的应用。然而，普通的 VFL 只适用于重叠用户，忽略了隐藏在非重叠用户之间的潜在通用兴趣模式，并且受到用户组好处的限制，这阻碍了它在实际推荐中的应用。本文将传统的垂直联邦推荐问题(VFR)扩展到一个更加现实的全垂直联邦推荐设置(Full-VFR) ，其目的是利用所有可用的数据，为全用户组提供服务。为了解决在实施完全 VFR 的挑战，我们提出了一个检索增强垂直联邦推荐(参考) ，一个突破性的倡议，探索检索增强机器学习方法在 VFL。具体来说，我们建立了一个通用的“检索和利用”算法，以提高所有各方的表示质量。我们设计了一个灵活的 VFL 联邦检索增强(RA)机制: (i)交叉 RA 来补充字段缺失; (ii)本地 RA 来促进用户组之间的相互理解。我们在公共和行业数据集上进行广泛的实验。在顺序和非顺序 CTR 预测任务中的结果表明，我们的方法比基线性能有了显著的提高，并且对所有用户组都有利。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=ReFer:+Retrieval-Enhanced+Vertical+Federated+Recommendation+for+Full+Set+User+Benefit)|0| +|[Pacer and Runner: Cooperative Learning Framework between Single- and Cross-Domain Sequential Recommendation](https://doi.org/10.1145/3626772.3657710)|Chung Park, Taesan Kim, Hyungjun Yoon, Junui Hong, Yelim Yu, Mincheol Cho, Minsung Choi, Jaegul Choo|SK Telelcom; SK Telecom / KAIST; Korea Advanced Institute of Science and Technology; SK Telelcom / KAIST|Cross-Domain Sequential Recommendation (CDSR) improves recommendation performance by utilizing information from multiple domains, which contrasts with Single-Domain Sequential Recommendation (SDSR) that relies on a historical interaction within a specific domain. However, CDSR may underperform compared to the SDSR approach in certain domains due to negative transfer, which occurs when there is a lack of relation between domains or different levels of data sparsity. To address the issue of negative transfer, our proposed CDSR model estimates the degree of negative transfer of each domain and adaptively assigns it as a weight factor to the prediction loss, to control gradient flows through domains with significant negative transfer. To this end, our model compares the performance of a model trained on multiple domains (CDSR) with a model trained solely on the specific domain (SDSR) to evaluate the negative transfer of each domain using our asymmetric cooperative network. In addition, to facilitate the transfer of valuable cues between the SDSR and CDSR tasks, we developed an auxiliary loss that maximizes the mutual information between the representation pairs from both tasks on a per-domain basis. This cooperative learning between SDSR and CDSR tasks is similar to the collaborative dynamics between pacers and runners in a marathon. Our model outperformed numerous previous works in extensive experiments on two real-world industrial datasets across ten service domains. We also have deployed our model in the recommendation system of our personal assistant app service, resulting in 21.4% increase in click-through rate compared to existing models, which is valuable to real-world business1.|跨域序列推荐(CDSR)通过利用来自多个域的信息来提高推荐性能，这与依赖于特定域内的历史交互的单域序列推荐(SDSR)形成了鲜明的对比。然而，CDSR 方法在某些领域的表现可能不如 SDSR 方法，这是由于负迁移，这种负迁移发生在领域之间缺乏联系或不同层次的数据稀疏时。为了解决负迁移问题，我们提出的 CDSR 模型估计每个域的负迁移程度，并自适应地将其作为预测损失的权重因子，以控制梯度流通过具有显著负迁移的域。为此，我们的模型比较了在多域(CDSR)训练的模型和单独在特定域(SDSR)训练的模型的性能，以评估使用我们的非对称合作网络的每个域的负迁移。此外，为了促进 SDSR 和 CDSR 任务之间有价值线索的传递，我们开发了一个辅助损失模型，该模型在每个领域的基础上最大化两个任务表征对之间的相互信息。SDSR 和 CDSR 任务之间的协作学习类似于马拉松中步行者和跑步者之间的协作动力学。我们的模型在十个服务领域的两个实际工业数据集上进行了广泛的实验，其性能优于以前的许多工作。我们也在个人助理应用程序服务的推荐系统中使用了我们的模型，与现有模型相比，点进率增加了21.4% ，这对于现实世界的商业来说是很有价值的。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Pacer+and+Runner:+Cooperative+Learning+Framework+between+Single-+and+Cross-Domain+Sequential+Recommendation)|0| +|[Aiming at the Target: Filter Collaborative Information for Cross-Domain Recommendation](https://doi.org/10.1145/3626772.3657713)|Hanyu Li, Weizhi Ma, Peijie Sun, Jiayu Li, Cunxiang Yin, Yancheng He, Guoqiang Xu, Min Zhang, Shaoping Ma|Tsinghua University; Tencent|Cross-domain recommender (CDR) systems aim to enhance the performance of the target domain by utilizing data from other related domains. However, irrelevant information from the source domain may instead degrade target domain performance, which is known as the negative transfer problem. There have been some attempts to address this problem, mostly by designing adaptive representations for overlapped users. Whereas, representation adaptions solely rely on the expressive capacity of the CDR model, lacking explicit constraint to filter the irrelevant source-domain collaborative information for the target domain. In this paper, we propose a novel Collaborative information regularized User Transformation (CUT) framework to tackle the negative transfer problem by directly filtering users' collaborative information. In CUT, user similarity in the target domain is adopted as a constraint for user transformation learning to filter the user collaborative information from the source domain. CUT first learns user similarity relationships from the target domain. Then, source-target information transfer is guided by the user similarity, where we design a user transformation layer to learn target-domain user representations and a contrastive loss to supervise the user collaborative information transferred. The results show significant performance improvement of CUT compared with SOTA single and cross-domain methods. Further analysis of the target-domain results illustrates that CUT can effectively alleviate the negative transfer problem.|跨域推荐(CDR)系统旨在通过利用其他相关域的数据来提高目标域的性能。然而，来自源域的不相关信息反而会降低目标域的性能，这就是所谓的负迁移问题。已经有一些尝试来解决这个问题，主要是通过为重叠用户设计自适应表示。然而，表示适配仅仅依赖于 CDR 模型的表达能力，缺乏明确的约束来过滤不相关的源域协同信息。本文提出了一种新的协同信息规范化用户转换(CUT)框架，通过直接过滤用户的协同信息来解决负迁移问题。在 CUT 中，采用目标域中的用户相似度作为用户转换学习的约束条件，对源域中的用户协作信息进行过滤。CUT 首先从目标域学习用户相似性关系。然后，以用户相似性为指导，设计了用户转换层来学习目标域用户表示，并通过对比度损失来监督用户协同信息的传输。结果表明，与 SOTA 单域和跨域方法相比，CUT 的性能有了显著的提高。对目标域结果的进一步分析表明，CUT 可以有效地缓解负迁移问题。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Aiming+at+the+Target:+Filter+Collaborative+Information+for+Cross-Domain+Recommendation)|0| |[On the Negative Perception of Cross-domain Recommendations and Explanations](https://doi.org/10.1145/3626772.3657735)|Denis Kotkov, Alan Medlar, Yang Liu, Dorota Glowacka|University of Helsinki|Recommender systems typically operate within a single domain, for example, recommending books based on users' reading habits. If such data is unavailable, it may be possible to make cross-domain recommendations and recommend books based on user preferences from another domain, such as movies. However, despite considerable research on cross-domain recommendations, no studies have investigated their impact on users' behavioural intentions or system perceptions compared to single-domain recommendations. Similarly, while single-domain explanations have been shown to improve users' perceptions of recommendations, there are no comparable studies for the cross-domain case. In this article, we present a between-subject study (N=237) of users' behavioural intentions and perceptions of book recommendations. The study was designed to disentangle the effects of whether recommendations were single- or cross-domain from whether explanations were present or not. Our results show that cross-domain recommendations have lower trust and interest than single-domain recommendations, regardless of their quality. While these negative effects can be ameliorated by cross-domain explanations, they are still perceived as inferior to single-domain recommendations without explanations. Last, we show that explanations decrease interest in the single-domain case, but increase perceived transparency and scrutability in both single- and cross-domain recommendations. Our findings offer valuable insights into the impact of recommendation provenance on user experience and could inform the future development of cross-domain recommender systems.|推荐系统通常在单一领域内运作，例如，根据用户的阅读习惯推荐书籍。如果这样的数据是不可用的，它可能会作出跨领域的建议，并推荐书籍的基础上用户喜好从另一个领域，如电影。然而，尽管对跨领域建议进行了大量的研究，但没有研究调查它们对用户行为意图或系统感知的影响，与单领域建议相比。同样，虽然单一领域的解释已被证明可以改善用户对推荐的看法，但是对于跨领域的案例没有可比较的研究。在这篇文章中，我们提出了一个主题间的研究(N = 237)用户的行为意图和感知的书籍推荐。这项研究的目的是将建议是单一还是跨领域的影响与解释是否存在区分开来。我们的研究结果表明，无论其质量如何，跨域建议比单域建议具有更低的信任度和兴趣。虽然这些负面影响可以通过跨领域的解释得到改善，但它们仍然被认为不如没有解释的单领域建议。最后，我们表明，解释降低兴趣的单一领域的情况下，但增加感知的透明度和审查在单一和跨领域的建议。我们的研究结果为推荐来源对用户体验的影响提供了有价值的见解，并且可以为跨域推荐系统的未来发展提供信息。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=On+the+Negative+Perception+of+Cross-domain+Recommendations+and+Explanations)|0| -|[Multi-Domain Sequential Recommendation via Domain Space Learning](https://doi.org/10.1145/3626772.3657685)|Junyoung Hwang, Hyunjun Ju, SeongKu Kang, Sanghwan Jang, Hwanjo Yu|Pohang University of Science and Technology; 42dot; University of Illinois Urbana-Champaign|This paper explores Multi-Domain Sequential Recommendation (MDSR), an advancement of Multi-Domain Recommendation that incorporates sequential context. Recent MDSR approach exploits domain-specific sequences, decoupled from mixed-domain histories, to model domain-specific sequential preference, and use mixeddomain histories to model domain-shared sequential preference. However, the approach faces challenges in accurately obtaining domain-specific sequential preferences in the target domain, especially when users only occasionally engage with it. In such cases, the history of users in the target domain is limited or not recent, leading the sequential recommender system to capture inaccurate domain-specific sequential preferences. To address this limitation, this paper introduces Multi-Domain Sequential Recommendation via Domain Space Learning (MDSR-DSL). Our approach utilizes cross-domain items to supplement missing sequential context in domain-specific sequences. It involves creating a "domain space" to maintain and utilize the unique characteristics of each domain and a domain-to-domain adaptation mechanism to transform item representations across domain spaces. To validate the effectiveness of MDSR-DSL, this paper extensively compares it with state-of-the-art MD(S)R methods and provides detailed analyses.|多域顺序推荐(MDSR)是结合顺序上下文的多域推荐的一种进步。最近的 MDSR 方法利用领域特定的序列，从混合领域历史解耦，建模领域特定的顺序偏好，并使用混合领域历史建模领域共享的顺序偏好。然而，该方法在准确获取目标域中特定于领域的顺序首选项时面临挑战，特别是当用户只是偶尔使用它时。在这种情况下，目标域的用户历史是有限的或不是最近的，导致顺序推荐系统捕获不准确的领域特定的顺序首选项。针对这一局限性，本文引入了基于领域空间学习的多领域序贯推荐(MDSR-DSL)。我们的方法利用跨领域的项目来补充领域特定序列中缺少的顺序上下文。它包括创建一个“域空间”来维护和利用每个域的独特特征，以及一个域到域的适应机制来跨域空间转换项表示。为了验证 MDSR-DSL 的有效性，本文将其与最新的 MD (S) R 方法进行了广泛的比较，并给出了详细的分析。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Multi-Domain+Sequential+Recommendation+via+Domain+Space+Learning)|0| +|[Multi-Domain Sequential Recommendation via Domain Space Learning](https://doi.org/10.1145/3626772.3657685)|Junyoung Hwang, Hyunjun Ju, SeongKu Kang, Sanghwan Jang, Hwanjo Yu|Pohang University of Science and Technology; University of Illinois Urbana-Champaign; 42dot|This paper explores Multi-Domain Sequential Recommendation (MDSR), an advancement of Multi-Domain Recommendation that incorporates sequential context. Recent MDSR approach exploits domain-specific sequences, decoupled from mixed-domain histories, to model domain-specific sequential preference, and use mixeddomain histories to model domain-shared sequential preference. However, the approach faces challenges in accurately obtaining domain-specific sequential preferences in the target domain, especially when users only occasionally engage with it. In such cases, the history of users in the target domain is limited or not recent, leading the sequential recommender system to capture inaccurate domain-specific sequential preferences. To address this limitation, this paper introduces Multi-Domain Sequential Recommendation via Domain Space Learning (MDSR-DSL). Our approach utilizes cross-domain items to supplement missing sequential context in domain-specific sequences. It involves creating a "domain space" to maintain and utilize the unique characteristics of each domain and a domain-to-domain adaptation mechanism to transform item representations across domain spaces. To validate the effectiveness of MDSR-DSL, this paper extensively compares it with state-of-the-art MD(S)R methods and provides detailed analyses.|多域顺序推荐(MDSR)是结合顺序上下文的多域推荐的一种进步。最近的 MDSR 方法利用领域特定的序列，从混合领域历史解耦，建模领域特定的顺序偏好，并使用混合领域历史建模领域共享的顺序偏好。然而，该方法在准确获取目标域中特定于领域的顺序首选项时面临挑战，特别是当用户只是偶尔使用它时。在这种情况下，目标域的用户历史是有限的或不是最近的，导致顺序推荐系统捕获不准确的领域特定的顺序首选项。针对这一局限性，本文引入了基于领域空间学习的多领域序贯推荐(MDSR-DSL)。我们的方法利用跨领域的项目来补充领域特定序列中缺少的顺序上下文。它包括创建一个“域空间”来维护和利用每个域的独特特征，以及一个域到域的适应机制来跨域空间转换项表示。为了验证 MDSR-DSL 的有效性，本文将其与最新的 MD (S) R 方法进行了广泛的比较，并给出了详细的分析。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Multi-Domain+Sequential+Recommendation+via+Domain+Space+Learning)|0| |[Behavior Alignment: A New Perspective of Evaluating LLM-based Conversational Recommendation Systems](https://doi.org/10.1145/3626772.3657924)|Dayu Yang, Fumian Chen, Hui Fang|University of Delaware|Large Language Models (LLMs) have demonstrated great potential in Conversational Recommender Systems (CRS). However, the application of LLMs to CRS has exposed a notable discrepancy in behavior between LLM-based CRS and human recommenders: LLMs often appear inflexible and passive, frequently rushing to complete the recommendation task without sufficient inquiry.This behavior discrepancy can lead to decreased accuracy in recommendations and lower user satisfaction. Despite its importance, existing studies in CRS lack a study about how to measure such behavior discrepancy. To fill this gap, we propose Behavior Alignment, a new evaluation metric to measure how well the recommendation strategies made by a LLM-based CRS are consistent with human recommenders'. Our experiment results show that the new metric is better aligned with human preferences and can better differentiate how systems perform than existing evaluation metrics. As Behavior Alignment requires explicit and costly human annotations on the recommendation strategies, we also propose a classification-based method to implicitly measure the Behavior Alignment based on the responses. The evaluation results confirm the robustness of the method.|大语言模型(LLM)在会话推荐系统(CRS)中显示出巨大的潜力。然而，LLM 在 CRS 中的应用暴露了基于 LLM 的 CRS 和人类推荐者之间显着的行为差异: LLM 往往显得不灵活和被动，经常在没有充分询问的情况下匆忙完成推荐任务。这种行为差异可能导致推荐的准确性下降和用户满意度降低。尽管 CRS 具有重要意义，但是现有的研究缺乏如何测量这种行为差异的研究。为了填补这个空白，我们提出了行为校准，一个新的评估指标，以衡量如何以 LLM 为基础的 CRS 的推荐策略是一致的人类推荐者的。我们的实验结果表明，与现有的评估指标相比，新的指标更符合人类的偏好，能够更好地区分系统的执行情况。由于行为对齐需要对推荐策略进行明确而昂贵的人工注释，我们还提出了一种基于分类的方法来隐式地度量基于响应的行为对齐。评价结果证实了该方法的鲁棒性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Behavior+Alignment:+A+New+Perspective+of+Evaluating+LLM-based+Conversational+Recommendation+Systems)|0| |[Bi-Objective Negative Sampling for Sensitivity-Aware Search](https://doi.org/10.1145/3626772.3657895)|Jack McKechnie, Graham McDonald, Craig Macdonald|University of Glasgow|Cross-encoders leverage fine-grained interactions between documents and queries for effective relevance ranking. Such ranking models are typically trained to satisfy the single objective of providing relevant information to the users. However, not all information should be made available. For example, documents containing sensitive information, such as personal or confidential information, should not be returned in the search results. Sensitivity-aware search (SAS) aims to develop retrieval models that can satisfy two objectives, namely: (1) providing the user with relevant search results, while (2) ensuring that no documents that contain sensitive information are included in the ranking. In this work, we propose three novel negative sampling strategies that enable cross-encoders to be trained to satisfy the bi-objective task of SAS. Additionally, we investigate and compare with filtering sensitive documents in ranking pipelines. Our experiments on a collection labelled for sensitivity show that our proposed negative sampling strategies lead to a ~37% increase in terms of cost-sensitive nDCG (nCSDCG) for SAS.|交叉编码器利用文档和查询之间的细粒度交互来进行有效的相关性排序。这种排名模型通常经过训练，以满足向用户提供相关信息的单一目标。然而，并非所有的信息都应该提供。例如，包含敏感信息(如个人或机密信息)的文档不应在搜索结果中返回。敏感性搜索(SAS)旨在开发能够满足两个目标的检索模型，即: (1)为用户提供相关的搜索结果，同时(2)确保没有包含敏感信息的文档被包含在排名中。在这项工作中，我们提出了三种新颖的负采样策略，使交叉编码器的训练，以满足 SAS 的双目标任务。此外，我们还研究和比较了在排序管道中过滤敏感文档的方法。我们对标记为敏感性的集合的实验表明，我们提出的阴性采样策略导致 SAS 的成本敏感性 nDCG (nCSDCG)增加约37% 。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Bi-Objective+Negative+Sampling+for+Sensitivity-Aware+Search)|0| |[Relevance Feedback Method For Patent Searching Using Vector Subspaces](https://doi.org/10.1145/3626772.3661365)|Sebastian Björkqvist|IPRally Technologies Oy|Searching for novelty-destroying prior art is an important part of patent application drafting and invalidation. The task is challenging due to the detailed information needed to determine whether a document is novelty-destroying or simply closely related, resulting in the original search results not always being fully on target. Allowing the user to provide feedback on the relevance of the initial search results and iterating on the search may thus improve the results significantly. We present a relevance feedback method based on computing the affine vector subspace spanned by the relevant document vectors. The method can be used with any dense retrieval system, and we demonstrate its effectiveness in improving recall in prior art searches. We compare the subspace-based method to the Rocchio algorithm and show that the method is less sensitive to changes in hyperparameters when the number of relevant documents increases.|查找毁新技术是专利申请起草和失效的重要组成部分。这项任务具有挑战性，因为确定一份文件是否具有新颖性或仅仅是密切相关所需的详细信息，导致原始搜索结果并不总是完全符合目标。因此，允许用户就初始搜索结果的相关性提供反馈并对搜索进行迭代，可以大大改进搜索结果。我们提出了一种基于计算相关文档向量所跨越的仿射向量子空间的关联反馈方法。该方法可以应用于任何密集检索系统，并证明了该方法在提高现有技术检索中的召回率方面的有效性。我们比较了基于子空间的方法和 Rocchio 算法，发现当相关文档数量增加时，该方法对超参数的变化不太敏感。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Relevance+Feedback+Method+For+Patent+Searching+Using+Vector+Subspaces)|0| -|[Efficient Inverted Indexes for Approximate Retrieval over Learned Sparse Representations](https://doi.org/10.1145/3626772.3657769)|Sebastian Bruch, Franco Maria Nardini, Cosimo Rulli, Rossano Venturini|Pinecone; ISTI-CNR; Dipartimento di Informatica, Università di Pisa|Learned sparse representations form an attractive class of contextual embeddings for text retrieval. That is so because they are effective models of relevance and are interpretable by design. Despite their apparent compatibility with inverted indexes, however, retrieval over sparse embeddings remains challenging. That is due to the distributional differences between learned embeddings and term frequency-based lexical models of relevance such as BM25. Recognizing this challenge, a great deal of research has gone into, among other things, designing retrieval algorithms tailored to the properties of learned sparse representations, including approximate retrieval systems. In fact, this task featured prominently in the latest BigANN Challenge at NeurIPS 2023, where approximate algorithms were evaluated on a large benchmark dataset by throughput and recall. In this work, we propose a novel organization of the inverted index that enables fast yet effective approximate retrieval over learned sparse embeddings. Our approach organizes inverted lists into geometrically-cohesive blocks, each equipped with a summary vector. During query processing, we quickly determine if a block must be evaluated using the summaries. As we show experimentally, single-threaded query processing using our method, Seismic, reaches sub-millisecond per-query latency on various sparse embeddings of the MS MARCO dataset while maintaining high recall. Our results indicate that Seismic is one to two orders of magnitude faster than state-of-the-art inverted index-based solutions and further outperforms the winning (graph-based) submissions to the BigANN Challenge by a significant margin.|学习的稀疏表示形成了一类有吸引力的文本检索上下文嵌入。之所以如此，是因为它们是有效的相关性模型，可以通过设计加以解释。然而，尽管它们与反向索引具有明显的兼容性，但是通过稀疏嵌入进行检索仍然具有挑战性。这是由于学习嵌入和基于词汇频率的关联词汇模型(如 BM25)之间的分布差异造成的。认识到这一挑战，大量的研究已经进入，除其他事项外，设计检索算法适合于学习稀疏表示的属性，包括近似检索系统。事实上，这项任务在 NeurIPS 2023最新的 BigANN 挑战中占有显著地位，在这个挑战中，通过吞吐量和召回率对大型基准数据集上的近似算法进行了评估。在这项工作中，我们提出了一种新的组织倒排索引，使快速而有效的近似检索学习稀疏嵌入。我们的方法将倒排的列表组织成具有几何内聚性的块，每个块配备一个汇总向量。在查询处理过程中，我们快速确定是否必须使用摘要计算块。正如我们的实验表明，使用我们的方法，地震，单线程查询处理达到亚毫秒每查询延迟各种稀疏嵌入的 MS MARCO 数据集，同时保持高召回率。我们的研究结果表明，地震数量级比最先进的基于倒排索引的解决方案快一到两倍，并进一步优于 BigANN 挑战赛的获胜者(基于图表的)。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Efficient+Inverted+Indexes+for+Approximate+Retrieval+over+Learned+Sparse+Representations)|0| +|[Efficient Inverted Indexes for Approximate Retrieval over Learned Sparse Representations](https://doi.org/10.1145/3626772.3657769)|Sebastian Bruch, Franco Maria Nardini, Cosimo Rulli, Rossano Venturini|Pinecone; Dipartimento di Informatica, Università di Pisa; ISTI-CNR|Learned sparse representations form an attractive class of contextual embeddings for text retrieval. That is so because they are effective models of relevance and are interpretable by design. Despite their apparent compatibility with inverted indexes, however, retrieval over sparse embeddings remains challenging. That is due to the distributional differences between learned embeddings and term frequency-based lexical models of relevance such as BM25. Recognizing this challenge, a great deal of research has gone into, among other things, designing retrieval algorithms tailored to the properties of learned sparse representations, including approximate retrieval systems. In fact, this task featured prominently in the latest BigANN Challenge at NeurIPS 2023, where approximate algorithms were evaluated on a large benchmark dataset by throughput and recall. In this work, we propose a novel organization of the inverted index that enables fast yet effective approximate retrieval over learned sparse embeddings. Our approach organizes inverted lists into geometrically-cohesive blocks, each equipped with a summary vector. During query processing, we quickly determine if a block must be evaluated using the summaries. As we show experimentally, single-threaded query processing using our method, Seismic, reaches sub-millisecond per-query latency on various sparse embeddings of the MS MARCO dataset while maintaining high recall. Our results indicate that Seismic is one to two orders of magnitude faster than state-of-the-art inverted index-based solutions and further outperforms the winning (graph-based) submissions to the BigANN Challenge by a significant margin.|学习的稀疏表示形成了一类有吸引力的文本检索上下文嵌入。之所以如此，是因为它们是有效的相关性模型，可以通过设计加以解释。然而，尽管它们与反向索引具有明显的兼容性，但是通过稀疏嵌入进行检索仍然具有挑战性。这是由于学习嵌入和基于词汇频率的关联词汇模型(如 BM25)之间的分布差异造成的。认识到这一挑战，大量的研究已经进入，除其他事项外，设计检索算法适合于学习稀疏表示的属性，包括近似检索系统。事实上，这项任务在 NeurIPS 2023最新的 BigANN 挑战中占有显著地位，在这个挑战中，通过吞吐量和召回率对大型基准数据集上的近似算法进行了评估。在这项工作中，我们提出了一种新的组织倒排索引，使快速而有效的近似检索学习稀疏嵌入。我们的方法将倒排的列表组织成具有几何内聚性的块，每个块配备一个汇总向量。在查询处理过程中，我们快速确定是否必须使用摘要计算块。正如我们的实验表明，使用我们的方法，地震，单线程查询处理达到亚毫秒每查询延迟各种稀疏嵌入的 MS MARCO 数据集，同时保持高召回率。我们的研究结果表明，地震数量级比最先进的基于倒排索引的解决方案快一到两倍，并进一步优于 BigANN 挑战赛的获胜者(基于图表的)。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Efficient+Inverted+Indexes+for+Approximate+Retrieval+over+Learned+Sparse+Representations)|0| |[Can We Trust Recommender System Fairness Evaluation? The Role of Fairness and Relevance](https://doi.org/10.1145/3626772.3657832)|Theresia Veronika Rampisela, Tuukka Ruotsalo, Maria Maistro, Christina Lioma|University of Copenhagen|Relevance and fairness are two major objectives of recommender systems (RSs). Recent work proposes measures of RS fairness that are either independent from relevance (fairness-only) or conditioned on relevance (joint measures). While fairness-only measures have been studied extensively, we look into whether joint measures can be trusted. We collect all joint evaluation measures of RS relevance and fairness, and ask: How much do they agree with each other? To what extent do they agree with relevance/fairness measures? How sensitive are they to changes in rank position, or to increasingly fair and relevant recommendations? We empirically study for the first time the behaviour of these measures across 4 real-world datasets and 4 recommenders. We find that most of these measures: i) correlate weakly with one another and even contradict each other at times; ii) are less sensitive to rank position changes than relevance- and fairness-only measures, meaning that they are less granular than traditional RS measures; and iii) tend to compress scores at the low end of their range, meaning that they are not very expressive. We counter the above limitations with a set of guidelines on the appropriate usage of such measures, i.e., they should be used with caution due to their tendency to contradict each other and of having a very small empirical range.|相关性和公平性是推荐系统的两个主要目标。最近的研究提出了 RS 公平性的测量方法，这些测量方法要么独立于相关性(仅仅是公平性) ，要么以相关性(联合测量)为条件。虽然只有公平的措施已经得到了广泛的研究，但是我们研究的是联合措施是否可以信任。我们收集了所有 RS 相关性和公平性的联合评价指标，并问: 它们之间有多大程度的一致性？它们在多大程度上同意相关性/公平性措施？他们对职位的变化，或者对越来越公平和相关的建议有多敏感？我们首次实证研究了这些措施的行为在4个真实世界的数据集和4个推荐。我们发现这些测量中的大多数: i)彼此之间相关性很弱，有时甚至相互矛盾; ii)对排名位置变化的敏感性低于相关性和公平性测量，这意味着它们比传统的 RS 测量粒度更小; iii)倾向于压缩其范围的低端分数，这意味着它们不是非常具有表现力。针对上述限制，我们制定了一套关于适当使用此类措施的指导方针，即应谨慎使用这些措施，因为它们往往相互矛盾，而且经验范围很小。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Can+We+Trust+Recommender+System+Fairness+Evaluation?+The+Role+of+Fairness+and+Relevance)|0| -|[Sequential Recommendation with Latent Relations based on Large Language Model](https://doi.org/10.1145/3626772.3657762)|Shenghao Yang, Weizhi Ma, Peijie Sun, Qingyao Ai, Yiqun Liu, Mingchen Cai, Min Zhang|Meituan; Tsinghua University|Sequential recommender systems predict items that may interest users by modeling their preferences based on historical interactions. Traditional sequential recommendation methods rely on capturing implicit collaborative filtering signals among items. Recent relation-aware sequential recommendation models have achieved promising performance by explicitly incorporating item relations into the modeling of user historical sequences, where most relations are extracted from knowledge graphs. However, existing methods rely on manually predefined relations and suffer the sparsity issue, limiting the generalization ability in diverse scenarios with varied item relations. In this paper, we propose a novel relation-aware sequential recommendation framework with Latent Relation Discovery (LRD). Different from previous relation-aware models that rely on predefined rules, we propose to leverage the Large Language Model (LLM) to provide new types of relations and connections between items. The motivation is that LLM contains abundant world knowledge, which can be adopted to mine latent relations of items for recommendation. Specifically, inspired by that humans can describe relations between items using natural language, LRD harnesses the LLM that has demonstrated human-like knowledge to obtain language knowledge representations of items. These representations are fed into a latent relation discovery module based on the discrete state variational autoencoder (DVAE). Then the self-supervised relation discovery tasks and recommendation tasks are jointly optimized. Experimental results on multiple public datasets demonstrate our proposed latent relations discovery method can be incorporated with existing relation-aware sequential recommendation models and significantly improve the performance. Further analysis experiments indicate the effectiveness and reliability of the discovered latent relations.|顺序推荐系统通过基于历史交互对用户偏好进行建模来预测用户可能感兴趣的项目。传统的顺序推荐方法依赖于捕捉项目之间隐含的协同过滤信号。最近的关系感知序列推荐模型已经取得了良好的性能，明确地结合项目关系到用户历史序列的建模，其中大多数关系是从知识图提取。然而，现有的方法依赖于人工预定义的关系，并且存在稀疏性问题，限制了在不同项目关系的不同场景中的泛化能力。本文提出了一种新的基于潜在关系发现(LRD)的关系感知序列推荐框架。与以前依赖于预定义规则的关系感知模型不同，我们建议利用大语言模型(LLM)来提供新类型的关系和项之间的连接。其动机是 LLM 包含了丰富的世界知识，可以用来挖掘推荐项目的潜在关系。具体来说，受到人类可以使用自然语言描述项目之间关系的启发，LRD 利用已经证明类似于人类的知识的 LLM 来获得项目的语言知识表示。这些表示被反馈到基于离散状态变分自动编码器(DVAE)的潜在关系发现模块中。然后对自监督关系发现任务和推荐任务进行联合优化。在多个公共数据集上的实验结果表明，本文提出的潜在关系发现方法可以与现有的关系感知顺序推荐模型相结合，从而显著提高推荐性能。进一步的分析实验表明了所发现的潜在关系的有效性和可靠性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Sequential+Recommendation+with+Latent+Relations+based+on+Large+Language+Model)|0| +|[Sequential Recommendation with Latent Relations based on Large Language Model](https://doi.org/10.1145/3626772.3657762)|Shenghao Yang, Weizhi Ma, Peijie Sun, Qingyao Ai, Yiqun Liu, Mingchen Cai, Min Zhang|Tsinghua University; Meituan|Sequential recommender systems predict items that may interest users by modeling their preferences based on historical interactions. Traditional sequential recommendation methods rely on capturing implicit collaborative filtering signals among items. Recent relation-aware sequential recommendation models have achieved promising performance by explicitly incorporating item relations into the modeling of user historical sequences, where most relations are extracted from knowledge graphs. However, existing methods rely on manually predefined relations and suffer the sparsity issue, limiting the generalization ability in diverse scenarios with varied item relations. In this paper, we propose a novel relation-aware sequential recommendation framework with Latent Relation Discovery (LRD). Different from previous relation-aware models that rely on predefined rules, we propose to leverage the Large Language Model (LLM) to provide new types of relations and connections between items. The motivation is that LLM contains abundant world knowledge, which can be adopted to mine latent relations of items for recommendation. Specifically, inspired by that humans can describe relations between items using natural language, LRD harnesses the LLM that has demonstrated human-like knowledge to obtain language knowledge representations of items. These representations are fed into a latent relation discovery module based on the discrete state variational autoencoder (DVAE). Then the self-supervised relation discovery tasks and recommendation tasks are jointly optimized. Experimental results on multiple public datasets demonstrate our proposed latent relations discovery method can be incorporated with existing relation-aware sequential recommendation models and significantly improve the performance. Further analysis experiments indicate the effectiveness and reliability of the discovered latent relations.|顺序推荐系统通过基于历史交互对用户偏好进行建模来预测用户可能感兴趣的项目。传统的顺序推荐方法依赖于捕捉项目之间隐含的协同过滤信号。最近的关系感知序列推荐模型已经取得了良好的性能，明确地结合项目关系到用户历史序列的建模，其中大多数关系是从知识图提取。然而，现有的方法依赖于人工预定义的关系，并且存在稀疏性问题，限制了在不同项目关系的不同场景中的泛化能力。本文提出了一种新的基于潜在关系发现(LRD)的关系感知序列推荐框架。与以前依赖于预定义规则的关系感知模型不同，我们建议利用大语言模型(LLM)来提供新类型的关系和项之间的连接。其动机是 LLM 包含了丰富的世界知识，可以用来挖掘推荐项目的潜在关系。具体来说，受到人类可以使用自然语言描述项目之间关系的启发，LRD 利用已经证明类似于人类的知识的 LLM 来获得项目的语言知识表示。这些表示被反馈到基于离散状态变分自动编码器(DVAE)的潜在关系发现模块中。然后对自监督关系发现任务和推荐任务进行联合优化。在多个公共数据集上的实验结果表明，本文提出的潜在关系发现方法可以与现有的关系感知顺序推荐模型相结合，从而显著提高推荐性能。进一步的分析实验表明了所发现的潜在关系的有效性和可靠性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Sequential+Recommendation+with+Latent+Relations+based+on+Large+Language+Model)|0| |[Enhancing Sequential Recommenders with Augmented Knowledge from Aligned Large Language Models](https://doi.org/10.1145/3626772.3657782)|Yankun Ren, Zhongde Chen, Xinxing Yang, Longfei Li, Cong Jiang, Lei Cheng, Bo Zhang, Linjian Mo, Jun Zhou|Ant Group|Recommender systems are widely used in various online platforms. In the context of sequential recommendation, it is essential to accurately capture the chronological patterns in user activities to generate relevant recommendations. Conventional ID-based sequential recommenders have shown promise but lack comprehensive real-world knowledge about items, limiting their effectiveness. Recent advancements in Large Language Models (LLMs) offer the potential to bridge this gap by leveraging the extensive real-world knowledge encapsulated in LLMs. However, integrating LLMs into sequential recommender systems comes with its own challenges, including inadequate representation of sequential behavior patterns and long inference latency. In this paper, we propose SeRALM (Enhancing Sequential Recommenders with Augmented Knowledge from Aligned Large Language Models) to address these challenges. SeRALM integrates LLMs with conventional ID-based sequential recommenders for sequential recommendation tasks. We combine text-format knowledge generated by LLMs with item IDs and feed this enriched data into ID-based recommenders, benefitting from the strengths of both paradigms. Moreover, we develop a theoretically underpinned alignment training method to refine LLMs' generation using feedback from ID-based recommenders for better knowledge augmentation. We also present an asynchronous technique to expedite the alignment training process. Experimental results on public benchmarks demonstrate that SeRALM significantly improves the performances of ID-based sequential recommenders. Further, a series of ablation studies and analyses corroborate SeRALM's proficiency in steering LLMs to generate more pertinent and advantageous knowledge across diverse scenarios.|推荐系统广泛应用于各种在线平台。在顺序推荐的背景下，准确地捕获用户活动中的顺序模式以生成相关的推荐是至关重要的。传统的基于 ID 的顺序推荐已经显示出希望，但是缺乏关于项目的全面的现实世界知识，限制了它们的有效性。大型语言模型(LLM)中的最新进展提供了通过利用 LLM 中封装的广泛的现实世界知识来弥补这一差距的潜力。然而，将 LLM 集成到顺序推荐系统中也有其自身的挑战，包括顺序行为模式的不充分表示和长的推理延迟。在这篇论文中，我们提出了 SeRALM (增强 < u > Se 量 < u > R 从 < u > A 线性大 < u > L 语言 < u > M 模型的增强知识推荐)来解决这些挑战。SerRALM 将 LLM 与传统的基于 ID 的顺序推荐器集成在一起，用于顺序推荐任务。我们将 LLM 生成的文本格式知识与项目 ID 结合起来，并将这些丰富的数据提供给基于 ID 的推荐程序，这两种范例的优势使我们受益匪浅。此外，我们开发了一个理论上支持的对齐训练方法来细化 LLM 的生成，使用基于 ID 的推荐者的反馈来更好地增强知识。我们还提出了一种异步技术，以加快对准训练过程。对公共基准测试的实验结果表明，基于 ID 的顺序推荐算法的性能得到了明显的改善。此外，一系列的消融研究和分析证实了 SerRALM 在指导 LLM 方面的能力，以便在不同的情况下产生更相关和更有利的知识。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Enhancing+Sequential+Recommenders+with+Augmented+Knowledge+from+Aligned+Large+Language+Models)|0| |[Adaptive Fair Representation Learning for Personalized Fairness in Recommendations via Information Alignment](https://doi.org/10.1145/3626772.3657709)|Xinyu Zhu, Lilin Zhang, Ning Yang|Sichuan University|Personalized fairness in recommendations has been attracting increasing attention from researchers. The existing works often treat a fairness requirement, represented as a collection of sensitive attributes, as a hyper-parameter, and pursue extreme fairness by completely removing information of sensitive attributes from the learned fair embedding, which suffer from two challenges: huge training cost incurred by the explosion of attribute combinations, and the suboptimal trade-off between fairness and accuracy. In this paper, we propose a novel Adaptive Fair Representation Learning (AFRL) model, which achieves a real personalized fairness due to its advantage of training only one model to adaptively serve different fairness requirements during inference phase. Particularly, AFRL treats fairness requirements as inputs and can learn an attribute-specific embedding for each attribute from the unfair user embedding, which endows AFRL with the adaptability during inference phase to determine the non-sensitive attributes under the guidance of the user's unique fairness requirement. To achieve a better trade-off between fairness and accuracy in recommendations, AFRL conducts a novel Information Alignment to exactly preserve discriminative information of non-sensitive attributes and incorporate a debiased collaborative embedding into the fair embedding to capture attribute-independent collaborative signals, without loss of fairness. Finally, the extensive experiments conducted on real datasets together with the sound theoretical analysis demonstrate the superiority of AFRL.|推荐的个性化公平性越来越受到研究者的关注。现有的公平需求表示为一组敏感属性，是一个超参数，通过从学习公平嵌入中完全去除敏感属性的信息来追求极端公平，这种方法面临着两个挑战: 属性组合爆炸所带来的巨大训练成本，以及公平性和准确性之间的次优权衡。本文提出了一种新的自适应公平表示学习(AFRL)模型，该模型由于在推理阶段只训练一个模型来适应不同的公平需求，从而实现了真正的个性化公平。特别地，AFRL 将公平性要求视为输入，可以从不公平的用户嵌入中学习每个属性的特定属性嵌入，从而赋予 AFRL 在推理阶段在用户唯一公平性要求指导下确定非敏感属性的适应性。为了在推荐的公平性和准确性之间取得更好的平衡，AFRL 进行了一种新的信息对齐，以精确地保留非敏感属性的区分信息，并在公平嵌入中加入去偏见的协作嵌入，以捕获与属性无关的协作信号，而不会损失公平性。最后，在实际数据集上进行了广泛的实验，结合可靠的理论分析，验证了 AFRL 的优越性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Adaptive+Fair+Representation+Learning+for+Personalized+Fairness+in+Recommendations+via+Information+Alignment)|0| -|[MealRec+: A Meal Recommendation Dataset with Meal-Course Affiliation for Personalization and Healthiness](https://doi.org/10.1145/3626772.3657857)|Ming Li, Lin Li, Xiaohui Tao, Jimmy Xiangji Huang|Wuhan University of Technology; University of Southern Queensland; York University|Meal recommendation, as a typical health-related recommendation task, contains complex relationships between users, courses, and meals. Among them, meal-course affiliation associates user-meal and user-course interactions. However, an extensive literature review demonstrates that there is a lack of publicly available meal recommendation datasets including meal-course affiliation. Meal recommendation research has been constrained in exploring the impact of cooperation between two levels of interaction on personalization and healthiness. To pave the way for meal recommendation research, we introduce a new benchmark dataset called MealRec^+. Due to constraints related to user health privacy and meal scenario characteristics, the collection of data that includes both meal-course affiliation and two levels of interactions is impeded. Therefore, a simulation method is adopted to derive meal-course affiliation and user-meal interaction from the user's dining sessions simulated based on user-course interaction data. Then, two well-known nutritional standards are used to calculate the healthiness scores of meals. Moreover, we experiment with several baseline models, including separate and cooperative interaction learning methods. Our experiment demonstrates that cooperating the two levels of interaction in appropriate ways is beneficial for meal recommendations. Furthermore, in response to the less healthy recommendation phenomenon found in the experiment, we explore methods to enhance the healthiness of meal recommendations. The dataset is available on GitHub (https://github.com/WUT-IDEA/MealRecPlus).|膳食推荐作为一项典型的与健康相关的推荐任务，包含用户、课程和膳食之间的复杂关系。其中，用餐过程的联系将用户-用餐和用户-过程的交互联系起来。然而，一个广泛的文献回顾表明，有缺乏公开可用的膳食推荐数据集，包括膳食过程的联系。饮食推荐研究在探讨两个互动水平之间的合作对个性化和健康的影响方面受到了限制。为了为膳食推荐研究铺平道路，我们引入了一个新的基准数据集 MealRec ^ + 。由于与用户健康隐私和用餐场景特征有关的限制，收集包括用餐过程关联和两个层次的互动的数据受到阻碍。为此，采用一种仿真方法，从基于用户-过程交互数据的用户用餐会话模拟中，推导出用户-过程关联关系和用户-用餐交互关系。然后，使用两个众所周知的营养标准来计算膳食的健康评分。此外，我们还实验了几个基线模型，包括分离式和合作式交互学习方法。我们的实验表明，以适当的方式协调两个层次的互动对于推荐用餐是有益的。此外，针对实验中发现的不太健康的推荐现象，我们探讨了提高膳食推荐健康性的方法。该数据集可在 GitHub ( https://GitHub.com/wut-idea/mealrecplus )上获得。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=MealRec+:+A+Meal+Recommendation+Dataset+with+Meal-Course+Affiliation+for+Personalization+and+Healthiness)|0| -|[IISAN: Efficiently Adapting Multimodal Representation for Sequential Recommendation with Decoupled PEFT](https://doi.org/10.1145/3626772.3657725)|Junchen Fu, Xuri Ge, Xin Xin, Alexandros Karatzoglou, Ioannis Arapakis, Jie Wang, Joemon M. Jose|Shandong University; Telefonica Research; University of Glasgow; Amazon; University of Glasgow school pf computing science|Multimodal foundation models are transformative in sequential recommender systems, leveraging powerful representation learning capabilities. While Parameter-efficient Fine-tuning (PEFT) is commonly used to adapt foundation models for recommendation tasks, most research prioritizes parameter efficiency, often overlooking critical factors like GPU memory efficiency and training speed. Addressing this gap, our paper introduces IISAN (Intra- and Inter-modal Side Adapted Network for Multimodal Representation), a simple plug-and-play architecture using a Decoupled PEFT structure and exploiting both intra- and inter-modal adaptation. IISAN matches the performance of full fine-tuning (FFT) and state-of-the-art PEFT. More importantly, it significantly reduces GPU memory usage - from 47GB to just 3GB for multimodal sequential recommendation tasks. Additionally, it accelerates training time per epoch from 443s to 22s compared to FFT. This is also a notable improvement over the Adapter and LoRA, which require 37-39 GB GPU memory and 350-380 seconds per epoch for training. Furthermore, we propose a new composite efficiency metric, TPME (Training-time, Parameter, and GPU Memory Efficiency) to alleviate the prevalent misconception that "parameter efficiency represents overall efficiency". TPME provides more comprehensive insights into practical efficiency comparisons between different methods. Besides, we give an accessible efficiency analysis of all PEFT and FFT approaches, which demonstrate the superiority of IISAN. We release our codes and other materials at https://github.com/GAIR-Lab/IISAN.|多模态基础模型在顺序推荐系统中具有变革性，利用了强大的表示学习能力。虽然参数有效微调(PEFT)通常用于为推荐任务调整基础模型，但大多数研究优先考虑参数有效性，往往忽略了 GPU 内存效率和训练速度等关键因素。针对这一差距，本文介绍了 IISAN (Intra-and Inter-modal Side Adapted Network for Multimodal Reform) ，这是一个简单的即插即用的结构，采用了解耦 PEFT 结构，同时利用了模式内和模式间的自适应。IISAN 匹配全微调(FFT)和最先进的 PEFT 的性能。更重要的是，它显著降低了 GPU 内存使用量——对于多通道顺序推荐任务，从47GB 降至仅3GB。此外，与 FFT 相比，它将每个历元的训练时间从443秒提高到22秒。与 Adapter 和 LoRA 相比，这也是一个显著的改进，后者需要37-39 GB 的 GPU 内存和350-380秒每个纪元的训练时间。此外，我们提出了一个新的组合效率度量，TPME (训练时间，参数和 GPU 内存效率) ，以缓解流行的误解“参数效率代表整体效率”。TPME 为不同方法之间的实际效率比较提供了更全面的见解。此外，我们还对所有 PEFT 和 FFT 方法进行了有效性分析，从而验证了 IISAN 方法的优越性。我们在 https://github.com/gair-lab/iisan 公布我们的代码和其他材料。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=IISAN:+Efficiently+Adapting+Multimodal+Representation+for+Sequential+Recommendation+with+Decoupled+PEFT)|0| -|[FeB4RAG: Evaluating Federated Search in the Context of Retrieval Augmented Generation](https://doi.org/10.1145/3626772.3657853)|Shuai Wang, Ekaterina Khramtsova, Shengyao Zhuang, Guido Zuccon|CSIRO; The University of Queensland ITEE; The University of Queensland School of Information Technology and Electrical Engineering|Federated search systems aggregate results from multiple search engines, selecting appropriate sources to enhance result quality and align with user intent. With the increasing uptake of Retrieval-Augmented Generation (RAG) pipelines, federated search can play a pivotal role in sourcing relevant information across heterogeneous data sources to generate informed responses. However, existing datasets, such as those developed in the past TREC FedWeb tracks, predate the RAG paradigm shift and lack representation of modern information retrieval challenges. To bridge this gap, we present FeB4RAG, a novel dataset specifically designed for federated search within RAG frameworks. This dataset, derived from 16 sub-collections of the widely used benchmarking collection, includes 790 information requests (akin to conversational queries) tailored for chatbot applications, along with top results returned by each resource and associated LLM-derived relevance judgements. Additionally, to support the need for this collection, we demonstrate the impact on response generation of a high quality federated search system for RAG compared to a naive approach to federated search. We do so by comparing answers generated through the RAG pipeline through a qualitative side-by-side comparison. Our collection fosters and supports the development and evaluation of new federated search methods, especially in the context of RAG pipelines.|联邦搜索系统聚合来自多个搜索引擎的结果，选择适当的来源，以提高结果质量，并与用户意图保持一致。随着检索增强生成(RAG)流水线的日益普及，联邦搜索在跨异构数据源获取相关信息以产生知情响应方面可以发挥关键作用。然而，现有的数据集，比如在过去 TREC 联邦网络跟踪中开发的数据集，早于 RAG 范式转变，缺乏对现代信息检索挑战的描述。为了弥补这一差距，我们提出了 FeB4RAG，一个专门为 RAG 框架内的联邦搜索而设计的新型数据集。该数据集来源于广泛使用的基准测试集合的16个子集，包括为聊天机器人应用程序量身定制的790个信息请求(类似于对话查询) ，以及每个资源返回的最高结果和相关的 LLM 衍生的相关性判断。此外，为了支持对这个集合的需求，我们演示了 RAG 的高质量联邦搜索系统与联邦搜索的简单方法相比对响应生成的影响。我们通过定性的并行比较来比较通过 RAG 管道产生的答案。我们的集合支持开发和评估新的联邦搜索方法，特别是在 RAG 管道上下文中。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=FeB4RAG:+Evaluating+Federated+Search+in+the+Context+of+Retrieval+Augmented+Generation)|0| -|[Dynamic Demonstration Retrieval and Cognitive Understanding for Emotional Support Conversation](https://doi.org/10.1145/3626772.3657695)|Zhe Xu, Daoyuan Chen, Jiayi Kuang, Zihao Yi, Yaliang Li, Ying Shen|Sun Yat-sen University; Alibaba group; Alibaba Group|Emotional Support Conversation (ESC) systems are pivotal in providing empathetic interactions, aiding users through negative emotional states by understanding and addressing their unique experiences. In this paper, we tackle two key challenges in ESC: enhancing contextually relevant and empathetic response generation through dynamic demonstration retrieval, and advancing cognitive understanding to grasp implicit mental states comprehensively. We introduce Dynamic Demonstration Retrieval and Cognitive-Aspect Situation Understanding (), a novel approach that synergizes these elements to improve the quality of support provided in ESCs. By leveraging in-context learning and persona information, we introduce an innovative retrieval mechanism that selects informative and personalized demonstration pairs. We also propose a cognitive understanding module that utilizes four cognitive relationships from the ATOMIC knowledge source to deepen situational awareness of help-seekers' mental states. Our supportive decoder integrates information from diverse knowledge sources, underpinning response generation that is both empathetic and cognitively aware. The effectiveness of is demonstrated through extensive automatic and human evaluations, revealing substantial improvements over numerous state-of-the-art models, with up to 13.79% enhancement in overall performance of ten metrics. Our codes are available for public access to facilitate further research and development.|情绪支持对话(ESC)系统是提供移情互动的关键，帮助用户通过理解和处理他们独特的经验的消极情绪状态。本文研究了 ESC 中的两个关键问题: 通过动态实证检索来提高情境相关性和同理心反应的产生; 通过提高认知理解来全面掌握内隐心理状态。我们介绍了动态演示检索和认知方面情境理解() ，一种新的方法，协同这些要素，以提高质量的支持提供在胚胎干细胞。通过利用上下文学习和人物角色信息，我们引入了一种创新的检索机制，选择信息丰富和个性化的演示对。我们还提出了一个认知理解模块，该模块利用来自 ATOMIC 知识源的四种认知关系来加深求助者心理状态的情势察觉。我们的支持性解码器整合了来自不同知识来源的信息，支持同理心和认知意识的反应生成。其有效性通过广泛的自动和人工评估得到了证实，显示出在许多最先进的模型上有了实质性的改进，10个指标的总体性能提高了13.79% 。我们的守则可供公众查阅，以促进进一步的研究和发展。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Dynamic+Demonstration+Retrieval+and+Cognitive+Understanding+for+Emotional+Support+Conversation)|0| -|[Broadening the View: Demonstration-augmented Prompt Learning for Conversational Recommendation](https://doi.org/10.1145/3626772.3657755)|Huy Dao, Yang Deng, Dung D. Le, Lizi Liao|College of Engineering and Computer Science, VinUniversity; Singapore Management University; National University of Singapore|Conversational Recommender Systems (CRSs) leverage natural language dialogues to provide tailored recommendations. Traditional methods in this field primarily focus on extracting user preferences from isolated dialogues. It often yields responses with a limited perspective, confined to the scope of individual conversations. Recognizing the potential in collective dialogue examples, our research proposes an expanded approach for CRS models, utilizing selective analogues from dialogue histories and responses to enrich both generation and recommendation processes. This introduces significant research challenges, including: (1) How to secure high-quality collections of recommendation dialogue exemplars? (2) How to effectively leverage these exemplars to enhance CRS models? To tackle these challenges, we introduce a novel Demonstration-enhanced Conversational Recommender System (DCRS), which aims to strengthen its understanding on the given dialogue contexts by retrieving and learning from demonstrations. In particular, we first propose a knowledge-aware contrastive learning method that adeptly taps into the mentioned entities and the dialogue's contextual essence for pretraining the demonstration retriever. Subsequently, we further develop two adaptive demonstration-augmented prompt learning approaches, involving contextualized prompt learning and knowledge-enriched prompt learning, to bridge the gap between the retrieved demonstrations and the two end tasks of CRS, i.e., response generation and item recommendation, respectively. Rigorous evaluations on two established benchmark datasets underscore DCRS's superior performance over existing CRS methods in both item recommendation and response generation.|会话推荐系统(CRS)利用自然语言对话提供量身定制的推荐。该领域的传统方法主要侧重于从孤立对话中提取用户首选项。它常常产生一个有限的视角的回应，局限于个人对话的范围。认识到集体对话实例的潜力，我们的研究提出了一种扩展的 CRS 模型方法，利用对话历史和回应中的选择性类比来丰富生成和推荐过程。这引入了重大的研究挑战，包括: (1)如何保证高质量的推荐对话样本集？(2)如何有效地利用这些范例来增强 CRS 模型？为了应对这些挑战，我们引入了一个新颖的示范增强会话推荐系统(dCRS) ，目的是通过检索和学习示范来加强对特定对话背景的理解。特别地，我们首先提出了一种知识感知的对比学习方法，该方法能够很好地利用上述实体和对话的语境本质来预先训练示范检索器。随后，我们进一步开发了两种适应性示范增强的及时学习方法，包括上下文化的及时学习和知识丰富的及时学习，以弥合检索的示范和 CRS 的两个最终任务之间的差距，即响应生成和项目推荐。对两个已建立的基准数据集的严格评估强调了 DCRS 在项目推荐和响应生成方面优于现有 CRS 方法的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Broadening+the+View:+Demonstration-augmented+Prompt+Learning+for+Conversational+Recommendation)|0| +|[MealRec+: A Meal Recommendation Dataset with Meal-Course Affiliation for Personalization and Healthiness](https://doi.org/10.1145/3626772.3657857)|Ming Li, Lin Li, Xiaohui Tao, Jimmy Xiangji Huang|University of Southern Queensland; Wuhan University of Technology; York University|Meal recommendation, as a typical health-related recommendation task, contains complex relationships between users, courses, and meals. Among them, meal-course affiliation associates user-meal and user-course interactions. However, an extensive literature review demonstrates that there is a lack of publicly available meal recommendation datasets including meal-course affiliation. Meal recommendation research has been constrained in exploring the impact of cooperation between two levels of interaction on personalization and healthiness. To pave the way for meal recommendation research, we introduce a new benchmark dataset called MealRec^+. Due to constraints related to user health privacy and meal scenario characteristics, the collection of data that includes both meal-course affiliation and two levels of interactions is impeded. Therefore, a simulation method is adopted to derive meal-course affiliation and user-meal interaction from the user's dining sessions simulated based on user-course interaction data. Then, two well-known nutritional standards are used to calculate the healthiness scores of meals. Moreover, we experiment with several baseline models, including separate and cooperative interaction learning methods. Our experiment demonstrates that cooperating the two levels of interaction in appropriate ways is beneficial for meal recommendations. Furthermore, in response to the less healthy recommendation phenomenon found in the experiment, we explore methods to enhance the healthiness of meal recommendations. The dataset is available on GitHub (https://github.com/WUT-IDEA/MealRecPlus).|膳食推荐作为一项典型的与健康相关的推荐任务，包含用户、课程和膳食之间的复杂关系。其中，用餐过程的联系将用户-用餐和用户-过程的交互联系起来。然而，一个广泛的文献回顾表明，有缺乏公开可用的膳食推荐数据集，包括膳食过程的联系。饮食推荐研究在探讨两个互动水平之间的合作对个性化和健康的影响方面受到了限制。为了为膳食推荐研究铺平道路，我们引入了一个新的基准数据集 MealRec ^ + 。由于与用户健康隐私和用餐场景特征有关的限制，收集包括用餐过程关联和两个层次的互动的数据受到阻碍。为此，采用一种仿真方法，从基于用户-过程交互数据的用户用餐会话模拟中，推导出用户-过程关联关系和用户-用餐交互关系。然后，使用两个众所周知的营养标准来计算膳食的健康评分。此外，我们还实验了几个基线模型，包括分离式和合作式交互学习方法。我们的实验表明，以适当的方式协调两个层次的互动对于推荐用餐是有益的。此外，针对实验中发现的不太健康的推荐现象，我们探讨了提高膳食推荐健康性的方法。该数据集可在 GitHub ( https://GitHub.com/wut-idea/mealrecplus )上获得。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=MealRec+:+A+Meal+Recommendation+Dataset+with+Meal-Course+Affiliation+for+Personalization+and+Healthiness)|0| +|[IISAN: Efficiently Adapting Multimodal Representation for Sequential Recommendation with Decoupled PEFT](https://doi.org/10.1145/3626772.3657725)|Junchen Fu, Xuri Ge, Xin Xin, Alexandros Karatzoglou, Ioannis Arapakis, Jie Wang, Joemon M. Jose|Amazon; Shandong University; Telefonica Research; University of Glasgow; University of Glasgow school pf computing science|Multimodal foundation models are transformative in sequential recommender systems, leveraging powerful representation learning capabilities. While Parameter-efficient Fine-tuning (PEFT) is commonly used to adapt foundation models for recommendation tasks, most research prioritizes parameter efficiency, often overlooking critical factors like GPU memory efficiency and training speed. Addressing this gap, our paper introduces IISAN (Intra- and Inter-modal Side Adapted Network for Multimodal Representation), a simple plug-and-play architecture using a Decoupled PEFT structure and exploiting both intra- and inter-modal adaptation. IISAN matches the performance of full fine-tuning (FFT) and state-of-the-art PEFT. More importantly, it significantly reduces GPU memory usage - from 47GB to just 3GB for multimodal sequential recommendation tasks. Additionally, it accelerates training time per epoch from 443s to 22s compared to FFT. This is also a notable improvement over the Adapter and LoRA, which require 37-39 GB GPU memory and 350-380 seconds per epoch for training. Furthermore, we propose a new composite efficiency metric, TPME (Training-time, Parameter, and GPU Memory Efficiency) to alleviate the prevalent misconception that "parameter efficiency represents overall efficiency". TPME provides more comprehensive insights into practical efficiency comparisons between different methods. Besides, we give an accessible efficiency analysis of all PEFT and FFT approaches, which demonstrate the superiority of IISAN. We release our codes and other materials at https://github.com/GAIR-Lab/IISAN.|多模态基础模型在顺序推荐系统中具有变革性，利用了强大的表示学习能力。虽然参数有效微调(PEFT)通常用于为推荐任务调整基础模型，但大多数研究优先考虑参数有效性，往往忽略了 GPU 内存效率和训练速度等关键因素。针对这一差距，本文介绍了 IISAN (Intra-and Inter-modal Side Adapted Network for Multimodal Reform) ，这是一个简单的即插即用的结构，采用了解耦 PEFT 结构，同时利用了模式内和模式间的自适应。IISAN 匹配全微调(FFT)和最先进的 PEFT 的性能。更重要的是，它显著降低了 GPU 内存使用量——对于多通道顺序推荐任务，从47GB 降至仅3GB。此外，与 FFT 相比，它将每个历元的训练时间从443秒提高到22秒。与 Adapter 和 LoRA 相比，这也是一个显著的改进，后者需要37-39 GB 的 GPU 内存和350-380秒每个纪元的训练时间。此外，我们提出了一个新的组合效率度量，TPME (训练时间，参数和 GPU 内存效率) ，以缓解流行的误解“参数效率代表整体效率”。TPME 为不同方法之间的实际效率比较提供了更全面的见解。此外，我们还对所有 PEFT 和 FFT 方法进行了有效性分析，从而验证了 IISAN 方法的优越性。我们在 https://github.com/gair-lab/iisan 公布我们的代码和其他材料。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=IISAN:+Efficiently+Adapting+Multimodal+Representation+for+Sequential+Recommendation+with+Decoupled+PEFT)|0| +|[FeB4RAG: Evaluating Federated Search in the Context of Retrieval Augmented Generation](https://doi.org/10.1145/3626772.3657853)|Shuai Wang, Ekaterina Khramtsova, Shengyao Zhuang, Guido Zuccon|The University of Queensland ITEE; CSIRO; The University of Queensland School of Information Technology and Electrical Engineering|Federated search systems aggregate results from multiple search engines, selecting appropriate sources to enhance result quality and align with user intent. With the increasing uptake of Retrieval-Augmented Generation (RAG) pipelines, federated search can play a pivotal role in sourcing relevant information across heterogeneous data sources to generate informed responses. However, existing datasets, such as those developed in the past TREC FedWeb tracks, predate the RAG paradigm shift and lack representation of modern information retrieval challenges. To bridge this gap, we present FeB4RAG, a novel dataset specifically designed for federated search within RAG frameworks. This dataset, derived from 16 sub-collections of the widely used benchmarking collection, includes 790 information requests (akin to conversational queries) tailored for chatbot applications, along with top results returned by each resource and associated LLM-derived relevance judgements. Additionally, to support the need for this collection, we demonstrate the impact on response generation of a high quality federated search system for RAG compared to a naive approach to federated search. We do so by comparing answers generated through the RAG pipeline through a qualitative side-by-side comparison. Our collection fosters and supports the development and evaluation of new federated search methods, especially in the context of RAG pipelines.|联邦搜索系统聚合来自多个搜索引擎的结果，选择适当的来源，以提高结果质量，并与用户意图保持一致。随着检索增强生成(RAG)流水线的日益普及，联邦搜索在跨异构数据源获取相关信息以产生知情响应方面可以发挥关键作用。然而，现有的数据集，比如在过去 TREC 联邦网络跟踪中开发的数据集，早于 RAG 范式转变，缺乏对现代信息检索挑战的描述。为了弥补这一差距，我们提出了 FeB4RAG，一个专门为 RAG 框架内的联邦搜索而设计的新型数据集。该数据集来源于广泛使用的基准测试集合的16个子集，包括为聊天机器人应用程序量身定制的790个信息请求(类似于对话查询) ，以及每个资源返回的最高结果和相关的 LLM 衍生的相关性判断。此外，为了支持对这个集合的需求，我们演示了 RAG 的高质量联邦搜索系统与联邦搜索的简单方法相比对响应生成的影响。我们通过定性的并行比较来比较通过 RAG 管道产生的答案。我们的集合支持开发和评估新的联邦搜索方法，特别是在 RAG 管道上下文中。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=FeB4RAG:+Evaluating+Federated+Search+in+the+Context+of+Retrieval+Augmented+Generation)|0| +|[Dynamic Demonstration Retrieval and Cognitive Understanding for Emotional Support Conversation](https://doi.org/10.1145/3626772.3657695)|Zhe Xu, Daoyuan Chen, Jiayi Kuang, Zihao Yi, Yaliang Li, Ying Shen|Alibaba group; Sun Yat-sen University; Alibaba Group|Emotional Support Conversation (ESC) systems are pivotal in providing empathetic interactions, aiding users through negative emotional states by understanding and addressing their unique experiences. In this paper, we tackle two key challenges in ESC: enhancing contextually relevant and empathetic response generation through dynamic demonstration retrieval, and advancing cognitive understanding to grasp implicit mental states comprehensively. We introduce Dynamic Demonstration Retrieval and Cognitive-Aspect Situation Understanding (), a novel approach that synergizes these elements to improve the quality of support provided in ESCs. By leveraging in-context learning and persona information, we introduce an innovative retrieval mechanism that selects informative and personalized demonstration pairs. We also propose a cognitive understanding module that utilizes four cognitive relationships from the ATOMIC knowledge source to deepen situational awareness of help-seekers' mental states. Our supportive decoder integrates information from diverse knowledge sources, underpinning response generation that is both empathetic and cognitively aware. The effectiveness of is demonstrated through extensive automatic and human evaluations, revealing substantial improvements over numerous state-of-the-art models, with up to 13.79% enhancement in overall performance of ten metrics. Our codes are available for public access to facilitate further research and development.|情绪支持对话(ESC)系统是提供移情互动的关键，帮助用户通过理解和处理他们独特的经验的消极情绪状态。本文研究了 ESC 中的两个关键问题: 通过动态实证检索来提高情境相关性和同理心反应的产生; 通过提高认知理解来全面掌握内隐心理状态。我们介绍了动态演示检索和认知方面情境理解() ，一种新的方法，协同这些要素，以提高质量的支持提供在胚胎干细胞。通过利用上下文学习和人物角色信息，我们引入了一种创新的检索机制，选择信息丰富和个性化的演示对。我们还提出了一个认知理解模块，该模块利用来自 ATOMIC 知识源的四种认知关系来加深求助者心理状态的情势察觉。我们的支持性解码器整合了来自不同知识来源的信息，支持同理心和认知意识的反应生成。其有效性通过广泛的自动和人工评估得到了证实，显示出在许多最先进的模型上有了实质性的改进，10个指标的总体性能提高了13.79% 。我们的守则可供公众查阅，以促进进一步的研究和发展。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Dynamic+Demonstration+Retrieval+and+Cognitive+Understanding+for+Emotional+Support+Conversation)|0| +|[Broadening the View: Demonstration-augmented Prompt Learning for Conversational Recommendation](https://doi.org/10.1145/3626772.3657755)|Huy Dao, Yang Deng, Dung D. Le, Lizi Liao|Singapore Management University; National University of Singapore; College of Engineering and Computer Science, VinUniversity|Conversational Recommender Systems (CRSs) leverage natural language dialogues to provide tailored recommendations. Traditional methods in this field primarily focus on extracting user preferences from isolated dialogues. It often yields responses with a limited perspective, confined to the scope of individual conversations. Recognizing the potential in collective dialogue examples, our research proposes an expanded approach for CRS models, utilizing selective analogues from dialogue histories and responses to enrich both generation and recommendation processes. This introduces significant research challenges, including: (1) How to secure high-quality collections of recommendation dialogue exemplars? (2) How to effectively leverage these exemplars to enhance CRS models? To tackle these challenges, we introduce a novel Demonstration-enhanced Conversational Recommender System (DCRS), which aims to strengthen its understanding on the given dialogue contexts by retrieving and learning from demonstrations. In particular, we first propose a knowledge-aware contrastive learning method that adeptly taps into the mentioned entities and the dialogue's contextual essence for pretraining the demonstration retriever. Subsequently, we further develop two adaptive demonstration-augmented prompt learning approaches, involving contextualized prompt learning and knowledge-enriched prompt learning, to bridge the gap between the retrieved demonstrations and the two end tasks of CRS, i.e., response generation and item recommendation, respectively. Rigorous evaluations on two established benchmark datasets underscore DCRS's superior performance over existing CRS methods in both item recommendation and response generation.|会话推荐系统(CRS)利用自然语言对话提供量身定制的推荐。该领域的传统方法主要侧重于从孤立对话中提取用户首选项。它常常产生一个有限的视角的回应，局限于个人对话的范围。认识到集体对话实例的潜力，我们的研究提出了一种扩展的 CRS 模型方法，利用对话历史和回应中的选择性类比来丰富生成和推荐过程。这引入了重大的研究挑战，包括: (1)如何保证高质量的推荐对话样本集？(2)如何有效地利用这些范例来增强 CRS 模型？为了应对这些挑战，我们引入了一个新颖的示范增强会话推荐系统(dCRS) ，目的是通过检索和学习示范来加强对特定对话背景的理解。特别地，我们首先提出了一种知识感知的对比学习方法，该方法能够很好地利用上述实体和对话的语境本质来预先训练示范检索器。随后，我们进一步开发了两种适应性示范增强的及时学习方法，包括上下文化的及时学习和知识丰富的及时学习，以弥合检索的示范和 CRS 的两个最终任务之间的差距，即响应生成和项目推荐。对两个已建立的基准数据集的严格评估强调了 DCRS 在项目推荐和响应生成方面优于现有 CRS 方法的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Broadening+the+View:+Demonstration-augmented+Prompt+Learning+for+Conversational+Recommendation)|0| |[ProCIS: A Benchmark for Proactive Retrieval in Conversations](https://doi.org/10.1145/3626772.3657869)|Chris Samarinas, Hamed Zamani|University of Massachusetts Amherst|The field of conversational information seeking, which is rapidly gaining interest in both academia and industry, is changing how we interact with search engines through natural language interactions. Existing datasets and methods are mostly evaluating reactive conversational information seeking systems that solely provide response to every query from the user. We identify a gap in building and evaluating proactive conversational information seeking systems that can monitor a multi-party human conversation and proactively engage in the conversation at an opportune moment by retrieving useful resources and suggestions. In this paper, we introduce a large-scale dataset for proactive document retrieval that consists of over 2.8 million conversations. We conduct crowdsourcing experiments to obtain high-quality and relatively complete relevance judgments through depth-k pooling. We also collect annotations related to the parts of the conversation that are related to each document, enabling us to evaluate proactive retrieval systems. We introduce normalized proactive discounted cumulative gain (npDCG) for evaluating these systems, and further provide benchmark results for a wide range of models, including a novel model we developed for this task. We believe that the developed dataset, called ProCIS, paves the path towards developing proactive conversational information seeking systems.|会话信息搜索领域正在迅速引起学术界和工业界的兴趣，它正在改变我们通过自然语言交互与搜索引擎进行互动的方式。现有的数据集和方法主要是评估反应式会话信息搜索系统，这种系统只对用户的每个查询提供响应。我们发现在建立和评估积极主动的会话信息搜索系统方面存在差距，这种系统可以监控多方的人类会话，并通过检索有用的资源和建议，在适当的时候积极主动地参与会话。在这篇文章中，我们介绍了一个大型的主动文献检索数据集，包括超过280万次对话。我们进行众包实验，以获得高质量和相对完整的相关性判断通过深度 k 池。我们还收集与会话中与每个文档相关的部分相关的注释，使我们能够评估主动检索系统。我们引入标准化的前瞻性折扣累积增益(npDCG)来评估这些系统，并进一步提供基准结果的范围广泛的模型，包括一个新的模型，我们开发的这项任务。我们相信，所开发的数据集，称为 ProCIS，铺平了发展前瞻性会话信息搜索系统的道路。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=ProCIS:+A+Benchmark+for+Proactive+Retrieval+in+Conversations)|0| |[An Empirical Analysis on Multi-turn Conversational Recommender Systems](https://doi.org/10.1145/3626772.3657893)|Lu Zhang, Chen Li, Yu Lei, Zhu Sun, Guanfeng Liu|Macquarie University; Agency for Science, Technology and Research, Singapore; Yanshan University; Chengdu University of Information Technology|The rise of conversational recommender systems (CRSs) brings the evolution of the recommendation paradigm, which enables users to interact with the system and achieve dynamic recommendations. As one essential branch, multi-turn CRSs, built on the user simulator paradigm, have attracted great attention due to their powerful ability to accomplish recommendations without real dialogue resources. Recent multi-turn CRS models, equipped with various delicately designed components (e.g., conversation module), achieve state-of-the-art (SOTA) performance. We, for the first time, propose a comprehensive experimental evaluation for existing SOTA multi-turn CRSs to investigate three research questions: (1) reproducibility - are the designed components beneficial to target multi-turn CRSs? (2) scenario-specific adaptability - how do these components perform in various scenarios? and (3) generality - can the effective components from the target CRS be effectively transferred to other multi-turn CRSs? To answer these questions, we design and conduct experiments under different settings, including carefully selected SOTA baselines, components of CRSs, datasets, and evaluation metrics, thus providing an experimental aspect overview of multi-turn CRSs. As a result, we derive several significant insights whereby effective guidelines are provided for future multi-turn CRS model designs across diverse scenarios.|会话推荐系统(CRS)的兴起带来了推荐范式的演变，使得用户能够与系统进行交互，实现动态推荐。作为一个重要的分支，建立在用户模拟器范式之上的多回合 CRS 由于其在没有真实对话资源的情况下完成推荐的强大能力而引起了人们的极大关注。最近的多回转 CRS 模型，配备了各种精心设计的组件(例如，会话模块) ，实现了最先进的(SOTA)性能。我们首次对现有的 SOTA 多回转 CRS 进行了全面的实验评价，以探讨三个研究问题: (1)可重复性——所设计的部件是否有利于靶向多回转 CRS？(2)特定场景的适应性——这些组件在各种场景中如何执行？(3)通用性——目标 CRS 的有效部件能否有效地转移到其他多回路 CRS 上？为了回答这些问题，我们在不同的设置下设计和进行实验，包括精心选择的 SOTA 基线，CRS 的组成部分，数据集和评估指标，从而提供多回合 CRS 的实验方面的概述。因此，我们得出了几个重要的见解，从而为未来多回合 CRS 模型设计提供了有效的指导方针，跨越不同的情景。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=An+Empirical+Analysis+on+Multi-turn+Conversational+Recommender+Systems)|0| |[SM-RS: Single- and Multi-Objective Recommendations with Contextual Impressions and Beyond-Accuracy Propensity Scores](https://doi.org/10.1145/3626772.3657863)|Patrik Dokoupil, Ladislav Peska, Ludovico Boratto|Faculty of Mathematics and Physics, Charles University, Prague, Czechia; University of Cagliari|Recommender systems (RS) rely on interaction data between users and items to generate effective results. Historically, RS aimed to deliver the most consistent (i.e., accurate) items to the trained user profiles. However, the attention towards additional (beyond-accuracy) quality criteria has increased tremendously in recent years. Both the research and applied models are being optimized for diversity, novelty, or fairness, to name a few. Naturally, the proper functioning of such optimization methods depends on the knowledge of users' propensities towards interacting with recommendations having certain quality criteria. However, so far, no dataset that captures such propensities exists. To bridge this research gap, we present SM-RS (single-objective + multi-objective recommendations dataset) that links users' self-declared propensity toward relevance, novelty, and diversity criteria with impressions and corresponding item selections. After presenting the dataset's collection procedure and basic statistics, we propose three tasks that are rarely available to conduct using existing RS datasets: impressions-aware click prediction, users' propensity scores prediction, and construction of recommendations proportional to the users' propensity scores. For each task, we also provide detailed evaluation procedures and competitive baselines. The dataset is available at https://osf.io/hkzje/.|推荐系统(RS)依赖于用户和项目之间的交互数据来生成有效的结果。从历史上看，RS 的目标是向训练有素的用户配置文件提供最一致(即准确)的条目。然而，对于额外的(超精确度)质量标准的关注在最近几年已经大大增加。研究和应用模型都在为多样性、新颖性或公平性而进行优化。当然，这种优化方法的正确功能取决于用户对具有某些质量标准的建议的交互倾向的了解。然而，到目前为止，还没有数据集能够捕捉到这种倾向。为了弥合这一研究差距，我们提出了 SM-RS (单目标 + 多目标推荐数据集) ，将用户自我声明的相关性，新颖性和多样性标准与印象和相应的项目选择联系起来。在介绍了数据集的收集过程和基本统计数据之后，我们提出了三个使用现有 RS 数据集很少可用的任务: 印象感知的点击预测，用户倾向得分预测，以及与用户倾向得分成比例的建议的构建。对于每项任务，我们还提供了详细的评估程序和竞争基线。数据集可在 https://osf.io/hkzje/下载。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=SM-RS:+Single-+and+Multi-Objective+Recommendations+with+Contextual+Impressions+and+Beyond-Accuracy+Propensity+Scores)|0| -|[To Search or to Recommend: Predicting Open-App Motivation with Neural Hawkes Process](https://doi.org/10.1145/3626772.3657732)|Zhongxiang Sun, Zihua Si, Xiao Zhang, Xiaoxue Zang, Yang Song, Hongteng Xu, Jun Xu|Kuaishou Technology Co., Ltd. Recommendation; Renmin University of China Gaoling School of Artificial Intelligence; Renmin Unversity of China Gaoling School of Artificial Intelligence; Kuaishou Technology Co., Ltd.; Renmin Unversity of China|Incorporating Search and Recommendation (S R) services within a singularapplication is prevalent in online platforms, leading to a new task termedopen-app motivation prediction, which aims to predict whether users initiatethe application with the specific intent of information searching, or toexplore recommended content for entertainment. Studies have shown thatpredicting users' motivation to open an app can help to improve user engagementand enhance performance in various downstream tasks. However, accuratelypredicting open-app motivation is not trivial, as it is influenced byuser-specific factors, search queries, clicked items, as well as their temporaloccurrences. Furthermore, these activities occur sequentially and exhibitintricate temporal dependencies. Inspired by the success of the Neural HawkesProcess (NHP) in modeling temporal dependencies in sequences, this paperproposes a novel neural Hawkes process model to capture the temporaldependencies between historical user browsing and querying actions. The model,referred to as Neural Hawkes Process-based Open-App Motivation prediction model(NHP-OAM), employs a hierarchical transformer and a novel intensity function toencode multiple factors, and open-app motivation prediction layer to integratetime and user-specific information for predicting users' open-app motivations.To demonstrate the superiority of our NHP-OAM model and construct a benchmarkfor the Open-App Motivation Prediction task, we not only extend the public S Rdataset ZhihuRec but also construct a new real-world Open-App MotivationDataset (OAMD). Experiments on these two datasets validate NHP-OAM'ssuperiority over baseline models. Further downstream application experimentsdemonstrate NHP-OAM's effectiveness in predicting users' Open-App Motivation,highlighting the immense application value of NHP-OAM.|将搜索和推荐(S R)服务整合到一个单一的应用程序中在在线平台中非常普遍，这导致了一个新的任务，即开放应用程序动机预测，其目的是预测用户是以信息搜索的特定意图启动应用程序，还是为娱乐探索推荐的内容。研究表明，预测用户打开应用程序的动机有助于提高用户参与度，并提高各种下游任务的性能。然而，准确预测开放应用程序的动机并非易事，因为它受到用户特定因素、搜索查询、点击项以及它们的时间出现的影响。此外，这些活动发生顺序和表现出复杂的时间依赖性。受到神经霍克斯过程(NHP)在序列时间依赖性建模方面的成功启发，提出了一种新的神经霍克斯过程模型来捕捉历史用户浏览和查询操作之间的时间依赖性。该模型被称为基于神经霍克斯过程的开放应用动机预测模型(NHP-OAM) ，采用分层变换器和新颖的强度函数对多个因素进行编码，并使用开放应用动机预测层整合时间和用户特定信息来预测用户的开放应用动机。为了证明我们的 NHP-OAM 模型的优越性，构建开放应用动机预测任务的基准，我们不仅扩展了公共 S 数据集 ZhhuRec，而且构建了一个新的现实世界的开放应用动机数据集(OAMD)。在这两个数据集上的实验验证了 NHP-OAM 算法相对于基线模型的优越性。进一步的下游应用实验证明了 NHP-OAM 在预测用户开放应用动机方面的有效性，突出了 NHP-OAM 的巨大应用价值。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=To+Search+or+to+Recommend:+Predicting+Open-App+Motivation+with+Neural+Hawkes+Process)|0| -|[Counterfactual Ranking Evaluation with Flexible Click Models](https://doi.org/10.1145/3626772.3657810)|Alexander Buchholz, Ben London, Giuseppe Di Benedetto, Jan Malte Lichtenberg, Yannik Stein, Thorsten Joachims|Amazon Music, Ithaca, NY, NY, USA; Amazon Music, Berlin, Germany; Amazon Music, Seattle, WA, USA|Evaluating a new ranking policy using data logged by a previously deployed policy requires a counterfactual (off-policy) estimator that corrects for presentation and selection biases. Some estimators (e.g., the position-based model) perform this correction by making strong assumptions about user behavior, which can lead to high bias if the assumptions are not met. Other estimators (e.g., the item-position model) rely on randomization to avoid these assumptions, but they often suffer from high variance. In this paper, we develop a new counterfactual estimator, called Interpol, that provides a tunable trade-off in the assumptions it makes, thus providing a novel ability to optimize the bias-variance trade-off. We analyze the bias of our estimator, both theoretically and empirically, and show that it achieves lower error than both the position-based model and the item-position model, on both synthetic and real datasets. This improvement in accuracy not only benefits offline evaluation of ranking policies, we also find that Interpol improves learning of new ranking policies when used as the training objective for learning-to-rank.|使用先前部署的策略记录的数据来评估新的排序策略需要一个反事实(非策略)估计器来纠正表示和选择偏差。一些估计量(例如，基于位置的模型)通过对用户行为做出强有力的假设来执行这种修正，如果假设不能满足，就会导致高偏差。其他估计量(例如，项目位置模型)依赖于随机化来避免这些假设，但是它们经常受到高方差的影响。在本文中，我们发展了一个新的反事实估计器，称为国际刑警组织，它提供了一个可调的权衡，它所做的假设，从而提供了一个新的能力，优化偏差-方差权衡。从理论和实证两个方面分析了估计器的误差，结果表明，无论是在合成数据集上还是在实际数据集上，该估计器都比基于位置的模型和项目位置的模型具有更低的误差。这种准确性的提高不仅有利于排序策略的离线评估，我们还发现国际刑警组织在将新的排序策略作为学习排序的培训目标时改善了学习效果。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Counterfactual+Ranking+Evaluation+with+Flexible+Click+Models)|0| -|[Deep Pattern Network for Click-Through Rate Prediction](https://doi.org/10.1145/3626772.3657777)|Hengyu Zhang, Junwei Pan, Dapeng Liu, Jie Jiang, Xiu Li|Tsinghua University; Tencent; Tsinghua University|Click-through rate (CTR) prediction tasks play a pivotal role in real-worldapplications, particularly in recommendation systems and online advertising. Asignificant research branch in this domain focuses on user behavior modeling.Current research predominantly centers on modeling co-occurrence relationshipsbetween the target item and items previously interacted with by users in theirhistorical data. However, this focus neglects the intricate modeling of userbehavior patterns. In reality, the abundance of user interaction recordsencompasses diverse behavior patterns, indicative of a spectrum of habitualparadigms. These patterns harbor substantial potential to significantly enhanceCTR prediction performance. To harness the informational potential within userbehavior patterns, we extend Target Attention (TA) to Target Pattern Attention(TPA) to model pattern-level dependencies. Furthermore, three criticalchallenges demand attention: the inclusion of unrelated items within behaviorpatterns, data sparsity in behavior patterns, and computational complexityarising from numerous patterns. To address these challenges, we introduce theDeep Pattern Network (DPN), designed to comprehensively leverage informationfrom user behavior patterns. DPN efficiently retrieves target-related userbehavior patterns using a target-aware attention mechanism. Additionally, itcontributes to refining user behavior patterns through a pre-training paradigmbased on self-supervised learning while promoting dependency learning withinsparse patterns. Our comprehensive experiments, conducted across three publicdatasets, substantiate the superior performance and broad compatibility of DPN.|点进率(ctrl)预测任务在现实世界的应用程序中扮演着关键角色，特别是在推荐系统和在线广告中。该领域的一个重要研究分支是用户行为建模。目前的研究主要集中在建模共现关系之间的目标项目和项目以前互动的用户在他们的历史数据。然而，这种关注忽略了用户行为模式的复杂建模。实际上，用户交互记录的丰富性包含了不同的行为模式，表明了一系列的习惯范式。这些模式具有显著提高 CTR 预测性能的巨大潜力。为了利用用户行为模式中的信息潜力，我们将目标注意力(TA)扩展到目标模式注意力(TPA) ，以建立模式级别的依赖关系。此外，三个关键的挑战需要注意: 在行为模式中包含不相关的项目，行为模式中的数据稀疏，以及由许多模式引起的计算复杂性。为了应对这些挑战，我们引入了深度模式网络(Deep Pattern Network，DPN) ，它旨在全面利用来自用户行为模式的信息。DPN 使用目标感知注意机制有效地检索与目标相关的用户行为模式。此外，它有助于细化用户的行为模式，通过预训练范式的基础上自我监督学习，同时促进稀疏模式的依赖性学习。我们在三个公共数据集上进行的全面实验证实了 DPN 的优越性能和广泛的兼容性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Deep+Pattern+Network+for+Click-Through+Rate+Prediction)|0| -|[AFDGCF: Adaptive Feature De-correlation Graph Collaborative Filtering for Recommendations](https://doi.org/10.1145/3626772.3657724)|Wei Wu, Chao Wang, Dazhong Shen, Chuan Qin, Liyi Chen, Hui Xiong|The Hong Kong University of Science and Technology (Guangzhou); University of Science and Technology of China; BOSS Zhipin; HKUST Fok Ying Tung Research Institute, The Hong Kong University of Science and Technology (Guangzhou); Shanghai Artificial Intelligence Laboratory|Collaborative filtering methods based on graph neural networks (GNNs) havewitnessed significant success in recommender systems (RS), capitalizing ontheir ability to capture collaborative signals within intricate user-itemrelationships via message-passing mechanisms. However, these GNN-based RSinadvertently introduce excess linear correlation between user and itemembeddings, contradicting the goal of providing personalized recommendations.While existing research predominantly ascribes this flaw to the over-smoothingproblem, this paper underscores the critical, often overlooked role of theover-correlation issue in diminishing the effectiveness of GNN representationsand subsequent recommendation performance. Up to now, the over-correlationissue remains unexplored in RS. Meanwhile, how to mitigate the impact ofover-correlation while preserving collaborative filtering signals is asignificant challenge. To this end, this paper aims to address theaforementioned gap by undertaking a comprehensive study of the over-correlationissue in graph collaborative filtering models. Firstly, we present empiricalevidence to demonstrate the widespread prevalence of over-correlation in thesemodels. Subsequently, we dive into a theoretical analysis which establishes apivotal connection between the over-correlation and over-smoothing issues.Leveraging these insights, we introduce the Adaptive Feature De-correlationGraph Collaborative Filtering (AFDGCF) framework, which dynamically appliescorrelation penalties to the feature dimensions of the representation matrix,effectively alleviating both over-correlation and over-smoothing issues. Theefficacy of the proposed framework is corroborated through extensiveexperiments conducted with four representative graph collaborative filteringmodels across four publicly available datasets.|基于图形神经网络(GNN)的协同过滤方法在推荐系统(RS)中取得了巨大的成功，利用了它们通过消息传递机制在复杂的用户-项目关系中捕获协作信号的能力。然而，这些基于 GNN 的 RSN 无意中在用户和项目嵌入之间引入了过多的线性相关性，与提供个性化推荐的目标相矛盾。虽然现有的研究主要把这个缺陷归因于过度平滑问题，但本文强调了过度相关问题在降低 GNN 表示的有效性和随后的推荐性能方面的关键作用，往往被忽视。到目前为止，过度相关性问题在 RS 中仍然没有得到探讨。同时，如何在保留协同过滤信号的同时减轻过度相关的影响是一个重大挑战。为此，本文旨在通过对图形协同过滤模型中的过度相关问题进行全面研究来弥补上述差距。首先，我们提出的经验证据表明，在这些模型中过度相关的广泛流行。随后，我们深入进行了理论分析，建立了过度相关和过度平滑问题之间的关键联系。利用这些见解，我们引入了自适应特征去相关图协同过滤(AFDGCF)框架，该框架动态地将相关惩罚应用于表示矩阵的特征维度，有效地缓解了过度相关和过度平滑的问题。该框架的有效性通过四个具有代表性的图形协同过滤模型在四个公开数据集上进行的广泛实验得到了证实。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=AFDGCF:+Adaptive+Feature+De-correlation+Graph+Collaborative+Filtering+for+Recommendations)|0| -|[TransGNN: Harnessing the Collaborative Power of Transformers and Graph Neural Networks for Recommender Systems](https://doi.org/10.1145/3626772.3657721)|Peiyan Zhang, Yuchen Yan, Xi Zhang, Chaozhuo Li, Senzhang Wang, Feiran Huang, Sunghun Kim|Central South University; Peking University School of Intelligence Science and Technology; Jinan University; Microsoft Research Asia; Hong Kong University of Science and Technology; Fuzhou University Interdisciplinary Institute for Medical Engineering|Graph Neural Networks (GNNs) have emerged as promising solutions forcollaborative filtering (CF) through the modeling of user-item interactiongraphs. The nucleus of existing GNN-based recommender systems involvesrecursive message passing along user-item interaction edges to refine encodedembeddings. Despite their demonstrated effectiveness, current GNN-based methodsencounter challenges of limited receptive fields and the presence of noisy"interest-irrelevant" connections. In contrast, Transformer-based methods excelin aggregating information adaptively and globally. Nevertheless, theirapplication to large-scale interaction graphs is hindered by inherentcomplexities and challenges in capturing intricate, entangled structuralinformation. In this paper, we propose TransGNN, a novel model that integratesTransformer and GNN layers in an alternating fashion to mutually enhance theircapabilities. Specifically, TransGNN leverages Transformer layers to broadenthe receptive field and disentangle information aggregation from edges, whichaggregates information from more relevant nodes, thereby enhancing the messagepassing of GNNs. Additionally, to capture graph structure informationeffectively, positional encoding is meticulously designed and integrated intoGNN layers to encode such structural knowledge into node attributes, thusenhancing the Transformer's performance on graphs. Efficiency considerationsare also alleviated by proposing the sampling of the most relevant nodes forthe Transformer, along with two efficient sample update strategies to reducecomplexity. Furthermore, theoretical analysis demonstrates that TransGNN offersincreased expressiveness compared to GNNs, with only a marginal increase inlinear complexity. Extensive experiments on five public datasets validate theeffectiveness and efficiency of TransGNN.|图形神经网络(GNN)通过对用户项交互图的建模，成为协同过滤(CF)的有效解决方案。现有的基于 GNN 的推荐系统的核心涉及递归消息传递沿用户项交互边缘细化编码解码。尽管已经证明了这些方法的有效性，但是目前基于 GNN 的方法遇到了接受域有限和存在噪声“兴趣无关”连接的挑战。相比之下，基于 Transform- 的方法优于自适应和全局聚合信息。然而，在获取错综复杂、纠缠不清的结构信息方面，它们在大尺度相互作用图中的应用受到了固有的复杂性和挑战性的阻碍。在本文中，我们提出了 TransGNN，一个新颖的模型，集成变压器和 GNN 层在一个交替的方式，以相互增强他们的能力。具体来说，TransGNN 利用 TransGNN 层来扩展接收字段，并从边界中分离信息聚合，从而从更相关的节点聚合信息，从而增强 GNN 的消息传递。此外，为了有效地获取图结构信息，位置编码被精心设计并集成到 GNN 层中，将这些结构知识编码到节点属性中，从而提高了变压器在图上的性能。通过提出变压器最相关节点的抽样，以及两种有效的样本更新策略来降低复杂性，也减轻了效率方面的考虑。此外，理论分析表明，与 GNN 相比，TransGNN 提供了更高的表达能力，只是略微增加了非线性复杂度。通过对五个公共数据集的大量实验验证了 TransGNN 的有效性和高效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=TransGNN:+Harnessing+the+Collaborative+Power+of+Transformers+and+Graph+Neural+Networks+for+Recommender+Systems)|0| +|[To Search or to Recommend: Predicting Open-App Motivation with Neural Hawkes Process](https://doi.org/10.1145/3626772.3657732)|Zhongxiang Sun, Zihua Si, Xiao Zhang, Xiaoxue Zang, Yang Song, Hongteng Xu, Jun Xu|Renmin Unversity of China; Kuaishou Technology Co., Ltd. Recommendation; Renmin Unversity of China Gaoling School of Artificial Intelligence; Kuaishou Technology Co., Ltd.; Renmin University of China Gaoling School of Artificial Intelligence|Incorporating Search and Recommendation (S R) services within a singularapplication is prevalent in online platforms, leading to a new task termedopen-app motivation prediction, which aims to predict whether users initiatethe application with the specific intent of information searching, or toexplore recommended content for entertainment. Studies have shown thatpredicting users' motivation to open an app can help to improve user engagementand enhance performance in various downstream tasks. However, accuratelypredicting open-app motivation is not trivial, as it is influenced byuser-specific factors, search queries, clicked items, as well as their temporaloccurrences. Furthermore, these activities occur sequentially and exhibitintricate temporal dependencies. Inspired by the success of the Neural HawkesProcess (NHP) in modeling temporal dependencies in sequences, this paperproposes a novel neural Hawkes process model to capture the temporaldependencies between historical user browsing and querying actions. The model,referred to as Neural Hawkes Process-based Open-App Motivation prediction model(NHP-OAM), employs a hierarchical transformer and a novel intensity function toencode multiple factors, and open-app motivation prediction layer to integratetime and user-specific information for predicting users' open-app motivations.To demonstrate the superiority of our NHP-OAM model and construct a benchmarkfor the Open-App Motivation Prediction task, we not only extend the public S Rdataset ZhihuRec but also construct a new real-world Open-App MotivationDataset (OAMD). Experiments on these two datasets validate NHP-OAM'ssuperiority over baseline models. Further downstream application experimentsdemonstrate NHP-OAM's effectiveness in predicting users' Open-App Motivation,highlighting the immense application value of NHP-OAM.|将搜索和推荐(S R)服务整合到一个单一的应用程序中在在线平台中非常普遍，这导致了一个新的任务，即开放应用程序动机预测，其目的是预测用户是以信息搜索的特定意图启动应用程序，还是为娱乐探索推荐的内容。研究表明，预测用户打开应用程序的动机有助于提高用户参与度，并提高各种下游任务的性能。然而，准确预测开放应用程序的动机并非易事，因为它受到用户特定因素、搜索查询、点击项以及它们的时间出现的影响。此外，这些活动发生顺序和表现出复杂的时间依赖性。受到神经霍克斯过程(NHP)在序列时间依赖性建模方面的成功启发，提出了一种新的神经霍克斯过程模型来捕捉历史用户浏览和查询操作之间的时间依赖性。该模型被称为基于神经霍克斯过程的开放应用动机预测模型(NHP-OAM) ，采用分层变换器和新颖的强度函数对多个因素进行编码，并使用开放应用动机预测层整合时间和用户特定信息来预测用户的开放应用动机。为了证明我们的 NHP-OAM 模型的优越性，构建开放应用动机预测任务的基准，我们不仅扩展了公共 S 数据集 ZhhuRec，而且构建了一个新的现实世界的开放应用动机数据集(OAMD)。在这两个数据集上的实验验证了 NHP-OAM 算法相对于基线模型的优越性。进一步的下游应用实验证明了 NHP-OAM 在预测用户开放应用动机方面的有效性，突出了 NHP-OAM 的巨大应用价值。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=To+Search+or+to+Recommend:+Predicting+Open-App+Motivation+with+Neural+Hawkes+Process)|0| +|[Counterfactual Ranking Evaluation with Flexible Click Models](https://doi.org/10.1145/3626772.3657810)|Alexander Buchholz, Ben London, Giuseppe Di Benedetto, Jan Malte Lichtenberg, Yannik Stein, Thorsten Joachims|Amazon Music, Seattle, WA, USA; Amazon Music, Berlin, Germany; Amazon Music, Ithaca, NY, NY, USA|Evaluating a new ranking policy using data logged by a previously deployed policy requires a counterfactual (off-policy) estimator that corrects for presentation and selection biases. Some estimators (e.g., the position-based model) perform this correction by making strong assumptions about user behavior, which can lead to high bias if the assumptions are not met. Other estimators (e.g., the item-position model) rely on randomization to avoid these assumptions, but they often suffer from high variance. In this paper, we develop a new counterfactual estimator, called Interpol, that provides a tunable trade-off in the assumptions it makes, thus providing a novel ability to optimize the bias-variance trade-off. We analyze the bias of our estimator, both theoretically and empirically, and show that it achieves lower error than both the position-based model and the item-position model, on both synthetic and real datasets. This improvement in accuracy not only benefits offline evaluation of ranking policies, we also find that Interpol improves learning of new ranking policies when used as the training objective for learning-to-rank.|使用先前部署的策略记录的数据来评估新的排序策略需要一个反事实(非策略)估计器来纠正表示和选择偏差。一些估计量(例如，基于位置的模型)通过对用户行为做出强有力的假设来执行这种修正，如果假设不能满足，就会导致高偏差。其他估计量(例如，项目位置模型)依赖于随机化来避免这些假设，但是它们经常受到高方差的影响。在本文中，我们发展了一个新的反事实估计器，称为国际刑警组织，它提供了一个可调的权衡，它所做的假设，从而提供了一个新的能力，优化偏差-方差权衡。从理论和实证两个方面分析了估计器的误差，结果表明，无论是在合成数据集上还是在实际数据集上，该估计器都比基于位置的模型和项目位置的模型具有更低的误差。这种准确性的提高不仅有利于排序策略的离线评估，我们还发现国际刑警组织在将新的排序策略作为学习排序的培训目标时改善了学习效果。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Counterfactual+Ranking+Evaluation+with+Flexible+Click+Models)|0| +|[Deep Pattern Network for Click-Through Rate Prediction](https://doi.org/10.1145/3626772.3657777)|Hengyu Zhang, Junwei Pan, Dapeng Liu, Jie Jiang, Xiu Li|Tsinghua University; Tsinghua University; Tencent|Click-through rate (CTR) prediction tasks play a pivotal role in real-worldapplications, particularly in recommendation systems and online advertising. Asignificant research branch in this domain focuses on user behavior modeling.Current research predominantly centers on modeling co-occurrence relationshipsbetween the target item and items previously interacted with by users in theirhistorical data. However, this focus neglects the intricate modeling of userbehavior patterns. In reality, the abundance of user interaction recordsencompasses diverse behavior patterns, indicative of a spectrum of habitualparadigms. These patterns harbor substantial potential to significantly enhanceCTR prediction performance. To harness the informational potential within userbehavior patterns, we extend Target Attention (TA) to Target Pattern Attention(TPA) to model pattern-level dependencies. Furthermore, three criticalchallenges demand attention: the inclusion of unrelated items within behaviorpatterns, data sparsity in behavior patterns, and computational complexityarising from numerous patterns. To address these challenges, we introduce theDeep Pattern Network (DPN), designed to comprehensively leverage informationfrom user behavior patterns. DPN efficiently retrieves target-related userbehavior patterns using a target-aware attention mechanism. Additionally, itcontributes to refining user behavior patterns through a pre-training paradigmbased on self-supervised learning while promoting dependency learning withinsparse patterns. Our comprehensive experiments, conducted across three publicdatasets, substantiate the superior performance and broad compatibility of DPN.|点进率(ctrl)预测任务在现实世界的应用程序中扮演着关键角色，特别是在推荐系统和在线广告中。该领域的一个重要研究分支是用户行为建模。目前的研究主要集中在建模共现关系之间的目标项目和项目以前互动的用户在他们的历史数据。然而，这种关注忽略了用户行为模式的复杂建模。实际上，用户交互记录的丰富性包含了不同的行为模式，表明了一系列的习惯范式。这些模式具有显著提高 CTR 预测性能的巨大潜力。为了利用用户行为模式中的信息潜力，我们将目标注意力(TA)扩展到目标模式注意力(TPA) ，以建立模式级别的依赖关系。此外，三个关键的挑战需要注意: 在行为模式中包含不相关的项目，行为模式中的数据稀疏，以及由许多模式引起的计算复杂性。为了应对这些挑战，我们引入了深度模式网络(Deep Pattern Network，DPN) ，它旨在全面利用来自用户行为模式的信息。DPN 使用目标感知注意机制有效地检索与目标相关的用户行为模式。此外，它有助于细化用户的行为模式，通过预训练范式的基础上自我监督学习，同时促进稀疏模式的依赖性学习。我们在三个公共数据集上进行的全面实验证实了 DPN 的优越性能和广泛的兼容性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Deep+Pattern+Network+for+Click-Through+Rate+Prediction)|0| +|[AFDGCF: Adaptive Feature De-correlation Graph Collaborative Filtering for Recommendations](https://doi.org/10.1145/3626772.3657724)|Wei Wu, Chao Wang, Dazhong Shen, Chuan Qin, Liyi Chen, Hui Xiong|HKUST Fok Ying Tung Research Institute, The Hong Kong University of Science and Technology (Guangzhou); University of Science and Technology of China; The Hong Kong University of Science and Technology (Guangzhou); Shanghai Artificial Intelligence Laboratory; BOSS Zhipin|Collaborative filtering methods based on graph neural networks (GNNs) havewitnessed significant success in recommender systems (RS), capitalizing ontheir ability to capture collaborative signals within intricate user-itemrelationships via message-passing mechanisms. However, these GNN-based RSinadvertently introduce excess linear correlation between user and itemembeddings, contradicting the goal of providing personalized recommendations.While existing research predominantly ascribes this flaw to the over-smoothingproblem, this paper underscores the critical, often overlooked role of theover-correlation issue in diminishing the effectiveness of GNN representationsand subsequent recommendation performance. Up to now, the over-correlationissue remains unexplored in RS. Meanwhile, how to mitigate the impact ofover-correlation while preserving collaborative filtering signals is asignificant challenge. To this end, this paper aims to address theaforementioned gap by undertaking a comprehensive study of the over-correlationissue in graph collaborative filtering models. Firstly, we present empiricalevidence to demonstrate the widespread prevalence of over-correlation in thesemodels. Subsequently, we dive into a theoretical analysis which establishes apivotal connection between the over-correlation and over-smoothing issues.Leveraging these insights, we introduce the Adaptive Feature De-correlationGraph Collaborative Filtering (AFDGCF) framework, which dynamically appliescorrelation penalties to the feature dimensions of the representation matrix,effectively alleviating both over-correlation and over-smoothing issues. Theefficacy of the proposed framework is corroborated through extensiveexperiments conducted with four representative graph collaborative filteringmodels across four publicly available datasets.|基于图形神经网络(GNN)的协同过滤方法在推荐系统(RS)中取得了巨大的成功，利用了它们通过消息传递机制在复杂的用户-项目关系中捕获协作信号的能力。然而，这些基于 GNN 的 RSN 无意中在用户和项目嵌入之间引入了过多的线性相关性，与提供个性化推荐的目标相矛盾。虽然现有的研究主要把这个缺陷归因于过度平滑问题，但本文强调了过度相关问题在降低 GNN 表示的有效性和随后的推荐性能方面的关键作用，往往被忽视。到目前为止，过度相关性问题在 RS 中仍然没有得到探讨。同时，如何在保留协同过滤信号的同时减轻过度相关的影响是一个重大挑战。为此，本文旨在通过对图形协同过滤模型中的过度相关问题进行全面研究来弥补上述差距。首先，我们提出的经验证据表明，在这些模型中过度相关的广泛流行。随后，我们深入进行了理论分析，建立了过度相关和过度平滑问题之间的关键联系。利用这些见解，我们引入了自适应特征去相关图协同过滤(AFDGCF)框架，该框架动态地将相关惩罚应用于表示矩阵的特征维度，有效地缓解了过度相关和过度平滑的问题。该框架的有效性通过四个具有代表性的图形协同过滤模型在四个公开数据集上进行的广泛实验得到了证实。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=AFDGCF:+Adaptive+Feature+De-correlation+Graph+Collaborative+Filtering+for+Recommendations)|0| +|[TransGNN: Harnessing the Collaborative Power of Transformers and Graph Neural Networks for Recommender Systems](https://doi.org/10.1145/3626772.3657721)|Peiyan Zhang, Yuchen Yan, Xi Zhang, Chaozhuo Li, Senzhang Wang, Feiran Huang, Sunghun Kim|Microsoft Research Asia; Hong Kong University of Science and Technology; Central South University; Peking University School of Intelligence Science and Technology; Jinan University; Fuzhou University Interdisciplinary Institute for Medical Engineering|Graph Neural Networks (GNNs) have emerged as promising solutions forcollaborative filtering (CF) through the modeling of user-item interactiongraphs. The nucleus of existing GNN-based recommender systems involvesrecursive message passing along user-item interaction edges to refine encodedembeddings. Despite their demonstrated effectiveness, current GNN-based methodsencounter challenges of limited receptive fields and the presence of noisy"interest-irrelevant" connections. In contrast, Transformer-based methods excelin aggregating information adaptively and globally. Nevertheless, theirapplication to large-scale interaction graphs is hindered by inherentcomplexities and challenges in capturing intricate, entangled structuralinformation. In this paper, we propose TransGNN, a novel model that integratesTransformer and GNN layers in an alternating fashion to mutually enhance theircapabilities. Specifically, TransGNN leverages Transformer layers to broadenthe receptive field and disentangle information aggregation from edges, whichaggregates information from more relevant nodes, thereby enhancing the messagepassing of GNNs. Additionally, to capture graph structure informationeffectively, positional encoding is meticulously designed and integrated intoGNN layers to encode such structural knowledge into node attributes, thusenhancing the Transformer's performance on graphs. Efficiency considerationsare also alleviated by proposing the sampling of the most relevant nodes forthe Transformer, along with two efficient sample update strategies to reducecomplexity. Furthermore, theoretical analysis demonstrates that TransGNN offersincreased expressiveness compared to GNNs, with only a marginal increase inlinear complexity. Extensive experiments on five public datasets validate theeffectiveness and efficiency of TransGNN.|图形神经网络(GNN)通过对用户项交互图的建模，成为协同过滤(CF)的有效解决方案。现有的基于 GNN 的推荐系统的核心涉及递归消息传递沿用户项交互边缘细化编码解码。尽管已经证明了这些方法的有效性，但是目前基于 GNN 的方法遇到了接受域有限和存在噪声“兴趣无关”连接的挑战。相比之下，基于 Transform- 的方法优于自适应和全局聚合信息。然而，在获取错综复杂、纠缠不清的结构信息方面，它们在大尺度相互作用图中的应用受到了固有的复杂性和挑战性的阻碍。在本文中，我们提出了 TransGNN，一个新颖的模型，集成变压器和 GNN 层在一个交替的方式，以相互增强他们的能力。具体来说，TransGNN 利用 TransGNN 层来扩展接收字段，并从边界中分离信息聚合，从而从更相关的节点聚合信息，从而增强 GNN 的消息传递。此外，为了有效地获取图结构信息，位置编码被精心设计并集成到 GNN 层中，将这些结构知识编码到节点属性中，从而提高了变压器在图上的性能。通过提出变压器最相关节点的抽样，以及两种有效的样本更新策略来降低复杂性，也减轻了效率方面的考虑。此外，理论分析表明，与 GNN 相比，TransGNN 提供了更高的表达能力，只是略微增加了非线性复杂度。通过对五个公共数据集的大量实验验证了 TransGNN 的有效性和高效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=TransGNN:+Harnessing+the+Collaborative+Power+of+Transformers+and+Graph+Neural+Networks+for+Recommender+Systems)|0| |[Lightweight Embeddings for Graph Collaborative Filtering](https://doi.org/10.1145/3626772.3657820)|Xurong Liang, Tong Chen, Lizhen Cui, Yang Wang, Meng Wang, Hongzhi Yin|Shandong University; Hefei University of Technology; The University of Queensland School of Electrical Engineering and Computer Science|Graph neural networks (GNNs) are currently one of the most performantcollaborative filtering methods. Meanwhile, owing to the use of an embeddingtable to represent each user/item as a distinct vector, GNN-based recommendershave inherited the long-standing defect of parameter inefficiency. As a commonpractice for scalable embeddings, parameter sharing enables the use of fewerembedding vectors (i.e., meta-embeddings). When assigning meta-embeddings, mostexisting methods are a heuristically designed, predefined mapping from eachuser's/item's ID to the corresponding meta-embedding indexes, thus simplifyingthe optimization problem into learning only the meta-embeddings. However, inthe context of GNN-based collaborative filtering, such a fixed mapping omitsthe semantic correlations between entities that are evident in the user-iteminteraction graph, leading to suboptimal recommendation performance. To thisend, we propose Lightweight Embeddings for Graph Collaborative Filtering(LEGCF), a parameter-efficient embedding framework dedicated to GNN-basedrecommenders. LEGCF innovatively introduces an assignment matrix as an extralearnable component on top of meta-embeddings. To jointly optimize these twoheavily entangled components, aside from learning the meta-embeddings byminimizing the recommendation loss, LEGCF further performs efficient assignmentupdate by enforcing a novel semantic similarity constraint and finding itsclosed-form solution based on matrix pseudo-inverse. The meta-embeddings andassignment matrix are alternately updated, where the latter is sparsified onthe fly to ensure negligible storage overhead. Extensive experiments on threebenchmark datasets have verified LEGCF's smallest trade-off between size andperformance, with consistent accuracy gain over state-of-the-art baselines. Thecodebase of LEGCF is available in https://github.com/xurong-liang/LEGCF.|图神经网络(GNN)是目前性能最好的协同过滤方法之一。同时，由于使用嵌入表将每个用户/项目表示为一个独立的向量，基于 GNN 的推荐器继承了长期以来参数低效的缺陷。作为可伸缩嵌入的常见实践，参数共享使嵌入向量的使用更少(即元嵌入)。当分配元嵌入时，大多数现有的方法都是启发式设计的，预定义的从每个用户/项目的 ID 到相应元嵌入索引的映射，从而简化了最佳化问题，只学习元嵌入。然而，在基于 GNN 的协同过滤中，这种固定的映射忽略了在用户-项目/交互图中显而易见的实体之间的语义相关性，导致了次优的推荐性能。为此，我们提出了图形协同过滤的轻量级嵌入(legCF) ，这是一个专门针对基于 GNN 的推荐程序的参数高效嵌入框架。LEGCF 在元嵌入的基础上创新地引入了指派矩阵作为可学习的组件。为了联合优化这两个高度纠缠的组件，LEGCF 除了通过最小化推荐丢失来学习元嵌入之外，还通过强制执行一种新的语义相似性约束并基于矩阵伪逆寻找其封闭形式解来进一步执行有效的赋值更新。元嵌入和分配矩阵交替更新，其中后者被动态稀疏化，以确保可以忽略存储开销。在三个基准数据集上的大量实验已经证实了 LEGCF 在大小和性能之间的最小权衡，在最先进的基准上有一致的准确性增益。LEgCF 的代码库有 https://github.com/xurong-liang/LEGCF。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Lightweight+Embeddings+for+Graph+Collaborative+Filtering)|0| |[Graded Relevance Scoring of Written Essays with Dense Retrieval](https://doi.org/10.1145/3626772.3657744)|Salam Albatarni, Sohaila Eltanbouly, Tamer Elsayed|Qatar University Computer Science and Engineering Department|Automated Essay Scoring automates the grading process of essays, providing agreat advantage for improving the writing proficiency of students. Whileholistic essay scoring research is prevalent, a noticeable gap exists inscoring essays for specific quality traits. In this work, we focus on therelevance trait, which measures the ability of the student to stay on-topicthroughout the entire essay. We propose a novel approach for graded relevancescoring of written essays that employs dense retrieval encoders. Denserepresentations of essays at different relevance levels then form clusters inthe embeddings space, such that their centroids are potentially separate enoughto effectively represent their relevance levels. We hence use the simple1-Nearest-Neighbor classification over those centroids to determine therelevance level of an unseen essay. As an effective unsupervised dense encoder,we leverage Contriever, which is pre-trained with contrastive learning anddemonstrated comparable performance to supervised dense retrieval models. Wetested our approach on both task-specific (i.e., training and testing on sametask) and cross-task (i.e., testing on unseen task) scenarios using the widelyused ASAP++ dataset. Our method establishes a new state-of-the-art performancein the task-specific scenario, while its extension for the cross-task scenarioexhibited a performance that is on par with the state-of-the-art model for thatscenario. We also analyzed the performance of our approach in a more practicalfew-shot scenario, showing that it can significantly reduce the labeling costwhile sacrificing only 10|自动化论文评分自动化了论文的评分过程，为提高学生的写作水平提供了巨大的优势。虽然整体论文评分研究是普遍存在的，一个明显的差距存在评分论文的具体质量特征。在这项工作中，我们关注的是关联特质，它衡量的是学生在整篇文章中始终保持主题的能力。我们提出了一种新的方法分级相关护航的书面文章，使用密集检索编码器。随后，在嵌入空间中，不同关联水平的密集表示形成聚类，这样它们的质心可能足够分离，以有效地表示它们的关联水平。因此，我们使用这些质心上的简单1-最近邻分类来确定一篇看不见的文章的关联水平。作为一个有效的无监督密集编码器，我们利用捐助者，这是预先训练与对比学习，并显示了可比性能的监督密集检索模型。使用广泛使用的 ASAP + + 数据集，对我们的方法进行了任务特定(即同一任务的培训和测试)和跨任务(即未知任务的测试)场景的测试。我们的方法在任务特定场景中建立了一种新的最先进的表现，而它在跨任务场景中的扩展表现出了与该场景的最先进模型相当的表现。我们还分析了我们的方法在一个更实用的少镜头场景中的性能，表明它可以显著降低标签成本，同时只牺牲10|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Graded+Relevance+Scoring+of+Written+Essays+with+Dense+Retrieval)|0| |[Axiomatic Causal Interventions for Reverse Engineering Relevance Computation in Neural Retrieval Models](https://doi.org/10.1145/3626772.3657841)|Catherine Chen, Jack Merullo, Carsten Eickhoff|University of Tübingen; Brown University|Neural models have demonstrated remarkable performance across diverse rankingtasks. However, the processes and internal mechanisms along which theydetermine relevance are still largely unknown. Existing approaches foranalyzing neural ranker behavior with respect to IR properties rely either onassessing overall model behavior or employing probing methods that may offer anincomplete understanding of causal mechanisms. To provide a more granularunderstanding of internal model decision-making processes, we propose the useof causal interventions to reverse engineer neural rankers, and demonstrate howmechanistic interpretability methods can be used to isolate componentssatisfying term-frequency axioms within a ranking model. We identify a group ofattention heads that detect duplicate tokens in earlier layers of the model,then communicate with downstream heads to compute overall document relevance.More generally, we propose that this style of mechanistic analysis opens upavenues for reverse engineering the processes neural retrieval models use tocompute relevance. This work aims to initiate granular interpretability effortsthat will not only benefit retrieval model development and training, butultimately ensure safer deployment of these models.|神经模型在不同的排序任务中表现出了显著的性能。然而，它们决定相关性的过程和内部机制在很大程度上仍然是未知的。现有的方法分析神经排序行为方面的 IR 特性依赖于评估整体模型行为或使用探测方法，可能提供不完整的因果机制的理解。为了提供对内部模型决策过程的更细粒度的理解，我们提出使用因果干预来逆向工程神经排序器，并演示如何使用机械可解释性方法在排序模型中分离满足术语频率公理的组件。我们识别出一组注意头，它们检测模型早期层中的重复标记，然后与下游头通信以计算整个文档的相关性。更一般地说，我们认为这种类型的机械分析为神经检索模型用于计算相关性的过程开辟了逆向工程。这项工作的目的是启动细粒度的可解释性工作，这不仅有利于检索模型的开发和培训，而且最终确保这些模型的更安全的部署。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Axiomatic+Causal+Interventions+for+Reverse+Engineering+Relevance+Computation+in+Neural+Retrieval+Models)|0| -|[Optimizing Learning-to-Rank Models for Ex-Post Fair Relevance](https://doi.org/10.1145/3626772.3657751)|Sruthi Gorantla, Eshaan Bhansali, Amit Deshpande, Anand Louis|Indian Institute of Science Bangalore; Indian Institute of Science; Microsoft; University of Wisconsin-Madison|Learning-to-rank (LTR) models rank items based on specific features, aiming to maximize ranking utility by prioritizing highly relevant items. However, optimizing only for ranking utility can lead to representational harm and may fail to address implicit bias in relevance scores. Prior studies introduced algorithms to train stochastic ranking models, such as the Plackett-Luce ranking model, that maximize expected ranking utility while achieving fairness in expectation (ex-ante fairness). Still, every sampled ranking may not satisfy group fairness (ex-post fairness). Post-processing methods ensure ex-post fairness; however, the LTR model lacks awareness of this step, creating a mismatch between the objective function the LTR model optimizes and the one it is supposed to optimize. In this paper, we first propose a novel objective where the relevance (or the expected ranking utility) is computed over only those rankings that satisfy given representation constraints for groups of items. We call this the ex-post fair relevance. We then give a framework for training Group-Fair LTR models to maximize our proposed ranking objective. Leveraging an efficient sampler for ex-post group-fair rankings and efficient algorithms to train the Plackett-Luce LTR model, we demonstrate their use in training the Group-Fair Plackett-Luce model in our framework. Experiments on MovieLens and Kiva datasets reveal improved fairness and relevance with our group-fair Plackett-Luce model compared to post-processing. In scenarios with implicit bias, our algorithm generally outperforms existing LTR baselines in both fairness and relevance.|学习排序(LTR)模型根据特定的特征对项目进行排序，目的是通过对高度相关的项目进行优先排序，使排序效用最大化。然而，仅仅为了排名效用而优化可能会导致代表性损害，并且可能无法解决相关性得分中的隐性偏差。先前的研究介绍了训练随机排名模型的算法，例如 Plackett-Luce 排名模型，这种算法在实现预期公平(事前公平)的同时最大化预期排名效用。尽管如此，每个抽样排名可能不满足组公平性(事后公平性)。后处理方法确保了事后公平性，但是 LTR 模型缺乏对这一步骤的意识，使得 LTR 模型优化的目标函数与其应该优化的目标函数不匹配。在本文中，我们首先提出一个新的目标，其中的相关性(或期望的排名效用)是计算只有那些满足给定的表示约束的项目组的排名。我们称之为事后公平相关。然后，我们给出了一个训练集团公平的长期资产负债率模型的框架，以最大限度地提出我们的排名目标。我们利用一个有效的后集团公平排名采样器和有效的算法来训练 Plackett-Luce LTR 模型，在我们的框架中演示了它们在训练集团公平 Plackett-Luce 模型中的应用。在 MovieLens 和 Kiva 数据集上进行的实验表明，与后处理相比，我们的组公平 Plackett-Luce 模型提高了公平性和相关性。在存在隐性偏差的情况下，我们的算法通常在公平性和相关性方面都优于现有的 LTR 基线。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Optimizing+Learning-to-Rank+Models+for+Ex-Post+Fair+Relevance)|0| +|[Optimizing Learning-to-Rank Models for Ex-Post Fair Relevance](https://doi.org/10.1145/3626772.3657751)|Sruthi Gorantla, Eshaan Bhansali, Amit Deshpande, Anand Louis|Indian Institute of Science; Indian Institute of Science Bangalore; University of Wisconsin-Madison; Microsoft|Learning-to-rank (LTR) models rank items based on specific features, aiming to maximize ranking utility by prioritizing highly relevant items. However, optimizing only for ranking utility can lead to representational harm and may fail to address implicit bias in relevance scores. Prior studies introduced algorithms to train stochastic ranking models, such as the Plackett-Luce ranking model, that maximize expected ranking utility while achieving fairness in expectation (ex-ante fairness). Still, every sampled ranking may not satisfy group fairness (ex-post fairness). Post-processing methods ensure ex-post fairness; however, the LTR model lacks awareness of this step, creating a mismatch between the objective function the LTR model optimizes and the one it is supposed to optimize. In this paper, we first propose a novel objective where the relevance (or the expected ranking utility) is computed over only those rankings that satisfy given representation constraints for groups of items. We call this the ex-post fair relevance. We then give a framework for training Group-Fair LTR models to maximize our proposed ranking objective. Leveraging an efficient sampler for ex-post group-fair rankings and efficient algorithms to train the Plackett-Luce LTR model, we demonstrate their use in training the Group-Fair Plackett-Luce model in our framework. Experiments on MovieLens and Kiva datasets reveal improved fairness and relevance with our group-fair Plackett-Luce model compared to post-processing. In scenarios with implicit bias, our algorithm generally outperforms existing LTR baselines in both fairness and relevance.|学习排序(LTR)模型根据特定的特征对项目进行排序，目的是通过对高度相关的项目进行优先排序，使排序效用最大化。然而，仅仅为了排名效用而优化可能会导致代表性损害，并且可能无法解决相关性得分中的隐性偏差。先前的研究介绍了训练随机排名模型的算法，例如 Plackett-Luce 排名模型，这种算法在实现预期公平(事前公平)的同时最大化预期排名效用。尽管如此，每个抽样排名可能不满足组公平性(事后公平性)。后处理方法确保了事后公平性，但是 LTR 模型缺乏对这一步骤的意识，使得 LTR 模型优化的目标函数与其应该优化的目标函数不匹配。在本文中，我们首先提出一个新的目标，其中的相关性(或期望的排名效用)是计算只有那些满足给定的表示约束的项目组的排名。我们称之为事后公平相关。然后，我们给出了一个训练集团公平的长期资产负债率模型的框架，以最大限度地提出我们的排名目标。我们利用一个有效的后集团公平排名采样器和有效的算法来训练 Plackett-Luce LTR 模型，在我们的框架中演示了它们在训练集团公平 Plackett-Luce 模型中的应用。在 MovieLens 和 Kiva 数据集上进行的实验表明，与后处理相比，我们的组公平 Plackett-Luce 模型提高了公平性和相关性。在存在隐性偏差的情况下，我们的算法通常在公平性和相关性方面都优于现有的 LTR 基线。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Optimizing+Learning-to-Rank+Models+for+Ex-Post+Fair+Relevance)|0| |[Scaling Sequential Recommendation Models with Transformers](https://doi.org/10.1145/3626772.3657816)|Pablo Zivic, Hernán Ceferino Vázquez, Jorge Sánchez|Mercado Libre Inc., Córdoba, Argentina; Mercado Libre Inc., Buenos Aires, Argentina|Modeling user preferences has been mainly addressed by looking at users' interaction history with the different elements available in the system. Tailoring content to individual preferences based on historical data is the main goal of sequential recommendation. The nature of the problem, as well as the good performance observed across various domains, has motivated the use of the transformer architecture, which has proven effective in leveraging increasingly larger amounts of training data when accompanied by an increase in the number of model parameters. This scaling behavior has brought a great deal of attention, as it provides valuable guidance in the design and training of even larger models. Taking inspiration from the scaling laws observed in training large language models, we explore similar principles for sequential recommendation. Addressing scalability in this context requires special considerations as some particularities of the problem depart from the language modeling case. These particularities originate in the nature of the content catalogs, which are significantly larger than the vocabularies used for language and might change over time. In our case, we start from a well-known transformer-based model from the literature and make two crucial modifications. First, we pivot from the traditional representation of catalog items as trainable embeddings to representations computed with a trainable feature extractor, making the parameter count independent of the number of items in the catalog. Second, we propose a contrastive learning formulation that provides us with a better representation of the catalog diversity. We demonstrate that, under this setting, we can train our models effectively on increasingly larger datasets under a common experimental setup. We use the full Amazon Product Data dataset, which has only been partially explored in other studies, and reveal scaling behaviors similar to those found in language models. Compute-optimal training is possible but requires a careful analysis of the compute-performance trade-offs specific to the application. We also show that performance scaling translates to downstream tasks by fine-tuning larger pre-trained models on smaller task-specific domains. Our approach and findings provide a strategic roadmap for model training and deployment in real high-dimensional preference spaces, facilitating better training and inference efficiency. We hope this paper bridges the gap between the potential of transformers and the intrinsic complexities of high-dimensional sequential recommendation in real-world recommender systems. Code and models can be found at https://github.com/mercadolibre/srt.|建模用户偏好主要通过查看用户的交互历史和系统中可用的不同元素来解决。根据历史数据为个人偏好定制内容是顺序推荐的主要目标。这一问题的性质以及在各个领域观察到的良好性能促使使用了变压器结构，事实证明，在模型参数数量增加的同时，变压器结构有效地利用了越来越多的培训数据。这种缩放行为引起了广泛的关注，因为它为更大型模型的设计和训练提供了有价值的指导。从训练大型语言模型中观察到的比例规律中获得启发，我们探索了顺序推荐的相似原则。在这种情况下处理可伸缩性需要特别的考虑，因为问题的一些特殊性与语言建模情况不同。这些特殊性源于内容目录的性质，它们明显大于用于语言的词汇表，并且可能随着时间的推移而变化。在我们的例子中，我们从文献中的一个著名的基于变压器的模型开始，并进行了两个关键的修改。首先，我们将目录项的传统表示从可训练的嵌入转向使用可训练的特征提取器计算的表示，使得参数计数独立于目录项的数量。其次，我们提出了一个对比学习公式，为我们提供了一个更好的表示目录多样性。我们证明，在这种设置下，我们可以在一个通用的实验设置下，在越来越大的数据集上有效地训练我们的模型。我们使用完整的亚马逊产品数据集，这只是在其他研究中部分探索，并揭示了类似于在语言模型中发现的缩放行为。计算优化训练是可能的，但是需要对特定于应用程序的计算性能权衡进行仔细分析。我们还表明，通过在较小的特定于任务的领域上微调较大的预先训练的模型，性能伸缩转化为下游任务。我们的方法和研究结果提供了一个战略路线图的模型训练和部署在真正的高维偏好空间，促进更好的训练和推理效率。我们希望本文能够弥补现实推荐系统中变压器的潜力和高维顺序推荐的内在复杂性之间的差距。代码和模型可以在 https://github.com/mercadolibre/srt 找到。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Scaling+Sequential+Recommendation+Models+with+Transformers)|0| |[SelfGNN: Self-Supervised Graph Neural Networks for Sequential Recommendation](https://doi.org/10.1145/3626772.3657716)|Yuxi Liu, Lianghao Xia, Chao Huang|; University of Hong Kong|Sequential recommendation effectively addresses information overload bymodeling users' temporal and sequential interaction patterns. To overcome thelimitations of supervision signals, recent approaches have adoptedself-supervised learning techniques in recommender systems. However, there arestill two critical challenges that remain unsolved. Firstly, existingsequential models primarily focus on long-term modeling of individualinteraction sequences, overlooking the valuable short-term collaborativerelationships among the behaviors of different users. Secondly, real-world dataoften contain noise, particularly in users' short-term behaviors, which canarise from temporary intents or misclicks. Such noise negatively impacts theaccuracy of both graph and sequence models, further complicating the modelingprocess. To address these challenges, we propose a novel framework calledSelf-Supervised Graph Neural Network (SelfGNN) for sequential recommendation.The SelfGNN framework encodes short-term graphs based on time intervals andutilizes Graph Neural Networks (GNNs) to learn short-term collaborativerelationships. It captures long-term user and item representations at multiplegranularity levels through interval fusion and dynamic behavior modeling.Importantly, our personalized self-augmented learning structure enhances modelrobustness by mitigating noise in short-term graphs based on long-term userinterests and personal stability. Extensive experiments conducted on fourreal-world datasets demonstrate that SelfGNN outperforms variousstate-of-the-art baselines. Our model implementation codes are available athttps://github.com/HKUDS/SelfGNN.|顺序推荐通过建模用户的时间和顺序交互模式有效地解决了信息超载问题。为了克服监督信号的局限性，最近的方法在推荐系统中采用了自监督学习技术。然而，仍然有两个关键的挑战没有得到解决。首先，现有的序贯模型主要侧重于个体交互序列的长期建模，忽视了不同用户行为之间有价值的短期协作关系。其次，真实世界的数据往往包含噪音，特别是在用户的短期行为，这可能是由于临时意图或错误点击。这种噪声对图模型和序列模型的精度都有负面影响，使得建模过程更加复杂。为了应对这些挑战，我们提出了一个新的框架，称为自我监督图神经网络(SelfGNN)的顺序推荐。自组织神经网络(SelfGNN)框架根据时间间隔对短期图形进行编码，并利用图形神经网络(GNN)学习短期协作关系。它通过区间融合和动态行为建模，在多粒度级别捕获长期用户和项目表示。重要的是，我们的个性化自增强学习结构通过减少基于长期用户兴趣和个人稳定性的短期图中的噪声来增强模型鲁棒性。在四个真实世界数据集上进行的大量实验表明，SelfGNN 的性能优于各种最先进的基线。我们的模型实现代码可以通过 https:// github.com/hkuds/selfgnn 获得。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=SelfGNN:+Self-Supervised+Graph+Neural+Networks+for+Sequential+Recommendation)|0| |[Revisit Targeted Model Poisoning on Federated Recommendation: Optimize via Multi-objective Transport](https://doi.org/10.1145/3626772.3657764)|Jiajie Su, Chaochao Chen, Weiming Liu, Zibin Lin, Shuheng Shen, Weiqiang Wang, Xiaolin Zheng|Ant Group, Hangzhou, China; Zhejiang University, Hangzhou, China|Federated Recommendation (FedRec) is popularly investigated in personalized recommenders for preserving user privacy. However, due to the distributed training paradigm, FedRec is vulnerable to model poisoning attacks. In this paper, we focus on the targeted model poisoning attack against FedRec, which aims at effectively attacking the FedRec via uploading poisoned gradients to raise the exposure ratio of a multi-target item set. Previous attack methods excel with fewer target items but suffer performance decline as the amount of target items increases, which reveals two perennially neglected issues: (i) The simple promotion of prediction scores without considering intrinsic collaborations between users and items is ineffective in multi-target cases. (ii) Target items are heterogeneous, which requires discriminative attacking users and strategies for different targets. To address the issues, we propose a novel Heterogeneous Multi-target Transfer Attack framework named HMTA which consists of two stages, i.e., (1) diverse user agent generation and (2) optimal multi-target transport attack. The former stage leverages collaboration-aware manifold learning to extract latent associations among users and items, and develops a differentiable contrastive sorting to generate user agents from both difficulty and diversity scale. The latter stage conducts poisoning in a fine-grained and distinguishing way, which first completes distribution mapping from target items to generated user agents and then achieves a hybrid multi-target attack. Extensive experiments on benchmark datasets demonstrate the effectiveness of HMTA.|为了保护用户隐私，联邦推荐(FedRec)在个性化推荐中得到了广泛的应用。然而，由于分布式训练范例，FedRec 容易受到模型中毒攻击。本文研究了针对 FedRec 的目标模型中毒攻击，目的是通过上传中毒梯度来有效地攻击 FedRec，以提高多目标项目集的暴露率。以往的攻击方法优于较少的目标项目，但随着目标项目数量的增加性能下降，这揭示了两个长期被忽视的问题: (i)在多目标情况下，不考虑用户和项目之间的内在协作的简单预测得分的提升是无效的。(ii)目标项是异构的，需要针对不同目标的区分性攻击用户和策略。针对这一问题，提出了一种新的异构多目标传输攻击框架 HMTA，该框架由两个阶段组成，即(1)多用户代理生成阶段和(2)最优多目标传输攻击阶段。前一阶段利用协作感知流形学习来提取用户和项目之间的潜在关联，并开发了一种可微对比排序方法来从难度和多样性尺度生成用户代理。后一阶段采用细粒度和区分的方式进行中毒，首先完成目标项到生成用户代理的分布映射，然后实现混合多目标攻击。在基准数据集上的大量实验证明了 HMTA 算法的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Revisit+Targeted+Model+Poisoning+on+Federated+Recommendation:+Optimize+via+Multi-objective+Transport)|0| |[LoRec: Combating Poisons with Large Language Model for Robust Sequential Recommendation](https://doi.org/10.1145/3626772.3657684)|Kaike Zhang, Qi Cao, Yunfan Wu, Fei Sun, Huawei Shen, Xueqi Cheng|Institute of Computing Technology, CAS|Sequential recommender systems stand out for their ability to capture users' dynamic interests and the patterns of item transitions. However, the inherent openness of sequential recommender systems renders them vulnerable to poisoning attacks, where fraudsters are injected into the training data to manipulate learned patterns. Traditional defense methods predominantly depend on predefined assumptions or rules extracted from specific known attacks, limiting their generalizability to unknown attacks. To solve the above problems, considering the rich open-world knowledge encapsulated in Large Language Models (LLMs), we attempt to introduce LLMs into defense methods to broaden the knowledge beyond limited known attacks. We propose LoRec, an innovative framework that employs LLM-Enhanced Calibration to strengthen the robustness of sequential Recommender systems against poisoning attacks. LoRec integrates an LLM-enhanced CalibraTor (LCT) that refines the training process of sequential recommender systems with knowledge derived from LLMs, applying a user-wise reweighting to diminish the impact of attacks. Incorporating LLMs' open-world knowledge, the LCT effectively converts the limited, specific priors or rules into a more general pattern of fraudsters, offering improved defenses against poisons. Our comprehensive experiments validate that LoRec, as a general framework, significantly strengthens the robustness of sequential recommender systems.|顺序推荐系统因其捕捉用户动态兴趣和项目转换模式的能力而脱颖而出。然而，顺序推荐系统固有的开放性使它们容易受到中毒攻击，欺诈者被注入培训数据以操纵学到的模式。传统的防御方法主要依赖于预定义的假设或从特定的已知攻击中提取的规则，限制了它们对未知攻击的普遍性。为了解决上述问题，考虑到大语言模型(LLM)中包含的丰富的开放世界知识，我们尝试将 LLM 引入防御方法中，以扩展已知攻击范围之外的知识。我们提出 LoRec，一个创新的框架，使用 LLM 增强校准，以加强顺序推荐系统对中毒攻击的健壮性。LoRec 集成了一个 LLM 增强的 CalibraTor (LCT) ，它利用从 LLM 获得的知识完善了顺序推荐系统的训练过程，应用了用户明智的重新加权来减少攻击的影响。结合 LLM 的开放世界知识，LCT 有效地将有限的、特定的前科或规则转化为更普遍的欺诈者模式，提供了针对有毒物质的更好的防御。我们的综合实验验证了 LoRec 作为一个通用框架，显著增强了顺序推荐系统的鲁棒性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=LoRec:+Combating+Poisons+with+Large+Language+Model+for+Robust+Sequential+Recommendation)|0| -|[Treatment Effect Estimation for User Interest Exploration on Recommender Systems](https://doi.org/10.1145/3626772.3657736)|Jiaju Chen, Wenjie Wang, Chongming Gao, Peng Wu, Jianxiong Wei, Qingsong Hua|Meituan; University of Science and Technology of China; National University of Singapore; Beijing Technology and Business University|Recommender systems learn personalized user preferences from user feedback like clicks. However, user feedback is usually biased towards partially observed interests, leaving many users' hidden interests unexplored. Existing approaches typically mitigate the bias, increase recommendation diversity, or use bandit algorithms to balance exploration-exploitation trade-offs. Nevertheless, they fail to consider the potential rewards of recommending different categories of items and lack the global scheduling of allocating top-N recommendations to categories, leading to suboptimal exploration. In this work, we propose an Uplift model-based Recommender (UpliftRec) framework, which regards top-N recommendation as a treatment optimization problem. UpliftRec estimates the treatment effects, i.e., the click-through rate (CTR) under different category exposure ratios, by using observational user feedback. UpliftRec calculates group-level treatment effects to discover users' hidden interests with high CTR rewards and leverages inverse propensity weighting to alleviate confounder bias. Thereafter, UpliftRec adopts a dynamic programming method to calculate the optimal treatment for overall CTR maximization. We implement UpliftRec on different backend models and conduct extensive experiments on three datasets. The empirical results validate the effectiveness of UpliftRec in discovering users' hidden interests while achieving superior recommendation accuracy.|推荐系统通过用户反馈(如点击)学习个性化用户偏好。然而，用户反馈通常偏向于部分观察到的兴趣，使得许多用户的隐藏兴趣未被探索。现有的方法通常会减轻偏差，增加推荐多样性，或者使用盗贼算法来平衡勘探与开发的权衡。然而，他们没有考虑推荐不同类别项目的潜在回报，缺乏将前 N 项推荐分配给类别的全球时间表，导致次优探索。在这项工作中，我们提出了一个基于 UpliftRec (UpliftRec)模型的推荐框架，它将 top-N 推荐作为一个治疗最佳化问题。UpliftRec 通过观察用户反馈来估计治疗效果，即不同类别暴露比例下的点进率。UpliftRec 计算组级治疗效果，以发现用户的高点击率奖励隐藏的兴趣，并利用反倾向加权，以减轻混杂偏见。然后，UpliftRec 采用动态规划方法来计算总体 CTR 最大化的最优处理。我们在不同的后端模型上实现 UpliftRec，并在三个数据集上进行了广泛的实验。实证结果验证了 UpliftRec 在发现用户隐藏兴趣的同时达到更高的推荐准确率的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Treatment+Effect+Estimation+for+User+Interest+Exploration+on+Recommender+Systems)|0| +|[Treatment Effect Estimation for User Interest Exploration on Recommender Systems](https://doi.org/10.1145/3626772.3657736)|Jiaju Chen, Wenjie Wang, Chongming Gao, Peng Wu, Jianxiong Wei, Qingsong Hua|University of Science and Technology of China; National University of Singapore; Beijing Technology and Business University; Meituan|Recommender systems learn personalized user preferences from user feedback like clicks. However, user feedback is usually biased towards partially observed interests, leaving many users' hidden interests unexplored. Existing approaches typically mitigate the bias, increase recommendation diversity, or use bandit algorithms to balance exploration-exploitation trade-offs. Nevertheless, they fail to consider the potential rewards of recommending different categories of items and lack the global scheduling of allocating top-N recommendations to categories, leading to suboptimal exploration. In this work, we propose an Uplift model-based Recommender (UpliftRec) framework, which regards top-N recommendation as a treatment optimization problem. UpliftRec estimates the treatment effects, i.e., the click-through rate (CTR) under different category exposure ratios, by using observational user feedback. UpliftRec calculates group-level treatment effects to discover users' hidden interests with high CTR rewards and leverages inverse propensity weighting to alleviate confounder bias. Thereafter, UpliftRec adopts a dynamic programming method to calculate the optimal treatment for overall CTR maximization. We implement UpliftRec on different backend models and conduct extensive experiments on three datasets. The empirical results validate the effectiveness of UpliftRec in discovering users' hidden interests while achieving superior recommendation accuracy.|推荐系统通过用户反馈(如点击)学习个性化用户偏好。然而，用户反馈通常偏向于部分观察到的兴趣，使得许多用户的隐藏兴趣未被探索。现有的方法通常会减轻偏差，增加推荐多样性，或者使用盗贼算法来平衡勘探与开发的权衡。然而，他们没有考虑推荐不同类别项目的潜在回报，缺乏将前 N 项推荐分配给类别的全球时间表，导致次优探索。在这项工作中，我们提出了一个基于 UpliftRec (UpliftRec)模型的推荐框架，它将 top-N 推荐作为一个治疗最佳化问题。UpliftRec 通过观察用户反馈来估计治疗效果，即不同类别暴露比例下的点进率。UpliftRec 计算组级治疗效果，以发现用户的高点击率奖励隐藏的兴趣，并利用反倾向加权，以减轻混杂偏见。然后，UpliftRec 采用动态规划方法来计算总体 CTR 最大化的最优处理。我们在不同的后端模型上实现 UpliftRec，并在三个数据集上进行了广泛的实验。实证结果验证了 UpliftRec 在发现用户隐藏兴趣的同时达到更高的推荐准确率的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Treatment+Effect+Estimation+for+User+Interest+Exploration+on+Recommender+Systems)|0| |[Disentangling Instructive Information from Ranked Multiple Candidates for Multi-Document Scientific Summarization](https://doi.org/10.1145/3626772.3657705)|Pancheng Wang, Shasha Li, Dong Li, Kehan Long, Jintao Tang, Ting Wang|National University of Defense Technology|Automatically condensing multiple topic-related scientific papers into asuccinct and concise summary is referred to as Multi-Document ScientificSummarization (MDSS). Currently, while commonly used abstractive MDSS methodscan generate flexible and coherent summaries, the difficulty in handling globalinformation and the lack of guidance during decoding still make it challengingto generate better summaries. To alleviate these two shortcomings, this paperintroduces summary candidates into MDSS, utilizing the global information ofthe document set and additional guidance from the summary candidates to guidethe decoding process. Our insights are twofold: Firstly, summary candidates canprovide instructive information from both positive and negative perspectives,and secondly, selecting higher-quality candidates from multiple optionscontributes to producing better summaries. Drawing on the insights, we proposea summary candidates fusion framework – Disentangling Instructive informationfrom Ranked candidates (DIR) for MDSS. Specifically, DIR first uses aspecialized pairwise comparison method towards multiple candidates to pick outthose of higher quality. Then DIR disentangles the instructive information ofsummary candidates into positive and negative latent variables with ConditionalVariational Autoencoder. These variables are further incorporated into thedecoder to guide generation. We evaluate our approach with three differenttypes of Transformer-based models and three different types of candidates, andconsistently observe noticeable performance improvements according to automaticand human evaluation. More analyses further demonstrate the effectiveness ofour model in handling global information and enhancing decodingcontrollability.|将多篇与主题相关的科学论文自动压缩成简洁、简洁的摘要，称为多文档科学摘要(MDSS)。目前，虽然常用的抽象 MDSS 方法可以生成灵活和连贯的摘要，但全球信息处理的困难和解码过程中缺乏指导仍然使得生成更好的摘要具有挑战性。为了克服这两个缺点，本文将摘要候选集引入 MDSS，利用文档集的全局信息和摘要候选集的额外指导来指导解码过程。我们的见解是双重的: 首先，总结候选人可以从正面和负面的角度提供有益的信息，其次，从多个选项中选择更高质量的候选人有助于产生更好的总结。在此基础上，我们提出了一个综合候选人融合框架——从排名候选人(DIR)中分离指导性信息，用于 MDSS。具体来说，DIR 首先对多个候选人使用专门的成对比较方法来挑选那些质量较高的候选人。然后用条件变分自动编码器将总结候选人的指导信息分解为正变量和负变量。这些变量进一步合并到解码器中以指导生成。我们评估我们的方法与三个不同类型的变压器为基础的模型和三个不同类型的候选人，并一致地观察显着的性能改善，根据自动和人工评估。更多的分析进一步证明了该模型在处理全局信息和提高译码可控性方面的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Disentangling+Instructive+Information+from+Ranked+Multiple+Candidates+for+Multi-Document+Scientific+Summarization)|0| |[Boosting Conversational Question Answering with Fine-Grained Retrieval-Augmentation and Self-Check](https://doi.org/10.1145/3626772.3657980)|Linhao Ye, Zhikai Lei, Jianghao Yin, Qin Chen, Jie Zhou, Liang He|East China Normal University|Retrieval-Augmented Generation (RAG) aims to generate more reliable andaccurate responses, by augmenting large language models (LLMs) with theexternal vast and dynamic knowledge. Most previous work focuses on using RAGfor single-round question answering, while how to adapt RAG to the complexconversational setting wherein the question is interdependent on the precedingcontext is not well studied. In this paper, we propose a conversation-level RAGapproach, which incorporates fine-grained retrieval augmentation and self-checkfor conversational question answering (CQA). In particular, our approachconsists of three components, namely conversational question refiner,fine-grained retriever and self-check based response generator, which workcollaboratively for question understanding and relevant information acquisitionin conversational settings. Extensive experiments demonstrate the greatadvantages of our approach over the state-of-the-art baselines. Moreover, wealso release a Chinese CQA dataset with new features including reformulatedquestion, extracted keyword, retrieved paragraphs and their helpfulness, whichfacilitates further researches in RAG enhanced CQA.|检索增强生成(RAG)旨在通过扩充大型语言模型(LLM)和外部海量动态知识来产生更可靠、更准确的响应。以往的研究主要集中在 RAG 在单轮问答中的应用，而如何将 RAG 应用到复杂的会话环境中，使问题相互依赖于前面的上下文，这方面的研究还不多。在本文中，我们提出了一种会话级的 RAG 方法，该方法结合了细粒度检索增强和会话问题回答(CQA)的自我检查。特别是，我们的方法由三个部分组成，即会话问题细化，细粒度检索和基于自我检查的响应生成器，它们协同工作的问题理解和相关信息的获取在会话环境中。大量的实验证明了我们的方法相对于最先进的基线的巨大优势。此外，我们还发布了一个中文 CQA 数据集，该数据集具有重构问题、提取关键词、检索段落及其有用性等新特征，为进一步研究 RAG 增强 CQA 提供了方便。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Boosting+Conversational+Question+Answering+with+Fine-Grained+Retrieval-Augmentation+and+Self-Check)|0| -|[Can Query Expansion Improve Generalization of Strong Cross-Encoder Rankers?](https://doi.org/10.1145/3626772.3657979)|Minghan Li, Honglei Zhuang, Kai Hui, Zhen Qin, Jimmy Lin, Rolf Jagerman, Xuanhui Wang, Michael Bendersky|University of Waterloo; Google Research|Query expansion has been widely used to improve the search results offirst-stage retrievers, yet its influence on second-stage, cross-encoderrankers remains under-explored. A recent work of Weller et al. [44] shows thatcurrent expansion techniques benefit weaker models such as DPR and BM25 butharm stronger rankers such as MonoT5. In this paper, we re-examine thisconclusion and raise the following question: Can query expansion improvegeneralization of strong cross-encoder rankers? To answer this question, wefirst apply popular query expansion methods to state-of-the-art cross-encoderrankers and verify the deteriorated zero-shot performance. We identify twovital steps for cross-encoders in the experiment: high-quality keywordgeneration and minimal-disruptive query modification. We show that it ispossible to improve the generalization of a strong neural ranker, by promptengineering and aggregating the ranking results of each expanded query viafusion. Specifically, we first call an instruction-following language model togenerate keywords through a reasoning chain. Leveraging self-consistency andreciprocal rank weighting, we further combine the ranking results of eachexpanded query dynamically. Experiments on BEIR and TREC Deep Learning2019/2020 show that the nDCG@10 scores of both MonoT5 and RankT5 followingthese steps are improved, which points out a direction for applying queryexpansion to strong cross-encoder rankers.|查询扩展已被广泛用于改善第一阶段检索者的搜索结果，但其对第二阶段交叉编码者的影响尚未得到充分研究。Weller 等人最近的一项工作[44]表明，目前的扩展技术有利于较弱的模型，如 DPR 和 BM25，但损害较强的排名，如 MonoT5。在本文中，我们重新审视这个结论，并提出以下问题: 查询扩展能否改善强交叉编码器排序器的泛化？为了回答这个问题，我们首先将流行的查询扩展方法应用于最先进的交叉编码器，并验证恶化的零射击性能。在实验中，我们确定了交叉编码器的两个重要步骤: 高质量的关键字生成和最小干扰的查询修改。我们表明，通过提示工程和聚合每个扩展查询的排序结果，提高一个强神经排序器的泛化是可能的。具体来说，我们首先调用一个指令遵循语言模型来通过一个推理链生成关键字。利用自相容性和相互排序加权，进一步动态组合每个扩展查询的排序结果。BEIR 和 TREC Deep Learning2019/2020的实验表明，遵循这些步骤的 MonoT5和 RankT5的 nDCG@10分数得到了改善，这为将查询扩展应用于强交叉编码器排序器指明了方向。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Can+Query+Expansion+Improve+Generalization+of+Strong+Cross-Encoder+Rankers?)|0| +|[Can Query Expansion Improve Generalization of Strong Cross-Encoder Rankers?](https://doi.org/10.1145/3626772.3657979)|Minghan Li, Honglei Zhuang, Kai Hui, Zhen Qin, Jimmy Lin, Rolf Jagerman, Xuanhui Wang, Michael Bendersky|Google Research; University of Waterloo|Query expansion has been widely used to improve the search results offirst-stage retrievers, yet its influence on second-stage, cross-encoderrankers remains under-explored. A recent work of Weller et al. [44] shows thatcurrent expansion techniques benefit weaker models such as DPR and BM25 butharm stronger rankers such as MonoT5. In this paper, we re-examine thisconclusion and raise the following question: Can query expansion improvegeneralization of strong cross-encoder rankers? To answer this question, wefirst apply popular query expansion methods to state-of-the-art cross-encoderrankers and verify the deteriorated zero-shot performance. We identify twovital steps for cross-encoders in the experiment: high-quality keywordgeneration and minimal-disruptive query modification. We show that it ispossible to improve the generalization of a strong neural ranker, by promptengineering and aggregating the ranking results of each expanded query viafusion. Specifically, we first call an instruction-following language model togenerate keywords through a reasoning chain. Leveraging self-consistency andreciprocal rank weighting, we further combine the ranking results of eachexpanded query dynamically. Experiments on BEIR and TREC Deep Learning2019/2020 show that the nDCG@10 scores of both MonoT5 and RankT5 followingthese steps are improved, which points out a direction for applying queryexpansion to strong cross-encoder rankers.|查询扩展已被广泛用于改善第一阶段检索者的搜索结果，但其对第二阶段交叉编码者的影响尚未得到充分研究。Weller 等人最近的一项工作[44]表明，目前的扩展技术有利于较弱的模型，如 DPR 和 BM25，但损害较强的排名，如 MonoT5。在本文中，我们重新审视这个结论，并提出以下问题: 查询扩展能否改善强交叉编码器排序器的泛化？为了回答这个问题，我们首先将流行的查询扩展方法应用于最先进的交叉编码器，并验证恶化的零射击性能。在实验中，我们确定了交叉编码器的两个重要步骤: 高质量的关键字生成和最小干扰的查询修改。我们表明，通过提示工程和聚合每个扩展查询的排序结果，提高一个强神经排序器的泛化是可能的。具体来说，我们首先调用一个指令遵循语言模型来通过一个推理链生成关键字。利用自相容性和相互排序加权，进一步动态组合每个扩展查询的排序结果。BEIR 和 TREC Deep Learning2019/2020的实验表明，遵循这些步骤的 MonoT5和 RankT5的 nDCG@10分数得到了改善，这为将查询扩展应用于强交叉编码器排序器指明了方向。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Can+Query+Expansion+Improve+Generalization+of+Strong+Cross-Encoder+Rankers?)|0| |[EASE-DR: Enhanced Sentence Embeddings for Dense Retrieval](https://doi.org/10.1145/3626772.3657925)|Xixi Zhou, Yang Gao, Xin Jie, Xiaoxu Cai, Jiajun Bu, Haishuai Wang|Zhejiang University, Hangzhou, China|Recent neural information retrieval models using dense text representations generated by pre-trained models commonly face two issues. First, a pre-trained model (e.g., BERT) usually truncates a long document before giving its representation, which may cause the loss of some important semantic information. Second, although pre-training models like BERT have been widely used in generating sentence embeddings, a substantial body of literature has shown that the pre-training models often represent sentence embeddings in a homogeneous and narrow space, known as the problem of representation anisotropy, which hurts the quality of dense vector retrieval. In this paper, we split the query and the document in information retrieval into two sets of natural sentences and generate their sentence embeddings with BERT, the most popular pre-trained model. Before aggregating the sentence embeddings to get the entire embedding representations of the input query and document, to alleviate the usual representation degeneration problem of sentence embeddings from BERT, we sample the variational auto-encoder's latent space distribution to obtain isotropic sentence embeddings and utilize supervised contrastive learning to uniform the distribution of these sentence embeddings in the representation space. Our proposed model undergoes training optimization for both the query and the document in the abovementioned aspects. Our model performs well in evaluating three extensively researched neural information retrieval datasets.|最近的神经信息检索模型使用由预先训练的模型产生的密集文本表示，通常面临两个问题。首先，预先训练的模型(例如 BERT)在给出表示之前通常会截断一个长文档，这可能会导致一些重要语义信息的丢失。其次，尽管像 BERT 这样的预训练模型已经被广泛应用于生成句子嵌入，但是大量的文献表明，预训练模型往往代表同质和狭窄空间中的句子嵌入，即所谓的表示各向异性问题，这损害了密集向量检索的质量。在本文中，我们将查询和信息检索中的文档分成两组自然句子，并使用 BERT (最流行的预训练模型)生成它们的句子嵌入。在对输入查询和文档的句子嵌入进行聚合得到完整的嵌入表示之前，为了缓解 BERT 中常见的句子嵌入表示退化问题，采用变分自动编码器的潜空间分布来获得各向同性的句子嵌入，并利用监督对比学习来统一这些句子嵌入在表示空间中的分布。我们提出的模型在上述方面对查询和文档进行了训练优化。我们的模型在评估三个广泛研究的神经信息检索数据集方面表现良好。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=EASE-DR:+Enhanced+Sentence+Embeddings+for+Dense+Retrieval)|0| |[Explainable Uncertainty Attribution for Sequential Recommendation](https://doi.org/10.1145/3626772.3657900)|Carles Balsells Rodas, Fan Yang, Zhishen Huang, Yan Gao|Imperial College London, London, United Kingdom; Amazon.com Inc, Seattle, WA, USA|Sequential recommendation systems suggest products based on users' historical behaviours. The inherent sparsity of user-item interactions in a vast product space often leads to unreliable recommendations. Recent research addresses this challenge by leveraging auxiliary product relations to mitigate recommendation uncertainty, and quantifying uncertainty in recommendation scores to modify the candidates selection. However, such approaches may not be efficient due to the requirement of additional side information or providing suboptimal recommendations. To enhance sequential recommendation performance by leveraging uncertainty information, we introduce Explainable Uncertainty Attribution (ExUA). We employ gradient-based saliency attribution to identify sources of uncertainty stemming from sequential interactions. Experimental findings on Amazon and MovieLens datasets demonstrate ExUA's effectiveness in identifying interactions that induce uncertainty, resulting in a 6%+ improvement in NDCG@20 scores when the uncertainty information is integrated into a post-hoc training phase.|连续推荐系统根据用户的历史行为推荐产品。在广阔的产品空间中，用户项交互的固有稀疏性常常导致不可靠的建议。最近的研究通过利用辅助产品关系来减少推荐的不确定性，并量化推荐分数的不确定性来修改候选人的选择，从而解决了这一挑战。然而，由于需要额外的辅助信息或提供次优的建议，这种方法可能不是有效的。为了利用不确定性信息提高序贯推荐的性能，我们引入了可解释的不确定性归因(ExUA)。我们使用基于梯度的显著性归因来识别源于序列相互作用的不确定性。Amazon 和 MovieLens 数据集上的实验结果证明了 ExUA 在识别诱导不确定性的相互作用方面的有效性，当不确定性信息被整合到事后训练阶段时，导致 NDCG@20分数提高6% + 。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Explainable+Uncertainty+Attribution+for+Sequential+Recommendation)|0| |[FedUD: Exploiting Unaligned Data for Cross-Platform Federated Click-Through Rate Prediction](https://doi.org/10.1145/3626772.3657941)|Wentao Ouyang, Rui Dong, Ri Tao, Xiangzheng Liu|Alibaba Group, Beijing, China|Click-through rate (CTR) prediction plays an important role in online advertising platforms. Most existing methods use data from the advertising platform itself for CTR prediction. As user behaviors also exist on many other platforms, e.g., media platforms, it is beneficial to further exploit such complementary information for better modeling user interest and for improving CTR prediction performance. However, due to privacy concerns, data from different platforms cannot be uploaded to a server for centralized model training. Vertical federated learning (VFL) provides a possible solution which is able to keep the raw data on respective participating parties and learn a collaborative model in a privacy-preserving way. However, traditional VFL methods only utilize aligned data with common keys across parties, which strongly restricts their application scope. In this paper, we propose FedUD, which is able to exploit unaligned data, in addition to aligned data, for more accurate federated CTR prediction. FedUD contains two steps. In the first step, FedUD utilizes aligned data across parties like traditional VFL, but it additionally includes a knowledge distillation module. This module distills useful knowledge from the guest party's high-level representations and guides the learning of a representation transfer network. In the second step, FedUD applies the learned knowledge to enrich the representations of the host party's unaligned data such that both aligned and unaligned data can contribute to federated model training. Experiments on two real-world datasets demonstrate the superior performance of FedUD for federated CTR prediction.|点进率预测在在线广告平台中扮演着重要的角色。大多数现有的方法使用来自广告平台本身的数据进行点击率预测。由于用户行为也存在于许多其他平台上，如媒体平台，因此进一步利用这些互补信息有利于更好地建立用户兴趣模型和提高 CTR 预测性能。然而，由于隐私问题，不同平台的数据不能上传到服务器进行集中的模型培训。垂直联邦学习(VFL)提供了一种可能的解决方案，它能够保存各参与方的原始数据，并以保护隐私的方式学习协作模型。然而，传统的 VFL 方法只利用跨各方公共密钥的对齐数据，这严重限制了它们的应用范围。在本文中，我们提出了 FedUD，它能够利用未对齐的数据，除了对齐的数据，更准确的联邦点击率预测。FedUD 包含两个步骤。在第一个步骤中，FedUD 利用传统 VFL 等各方之间的对齐数据，但是它还包括一个知识提取模块。该模块从客方的高层次表示中提取有用的知识，并指导表示传递网络的学习。在第二步中，FedUD 应用所学到的知识来丰富主机方的未对齐数据的表示，这样对齐和未对齐的数据都可以有助于联邦模型的训练。在两个实际数据集上的实验表明，FedUD 在联邦 CTR 预测方面具有优越的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=FedUD:+Exploiting+Unaligned+Data+for+Cross-Platform+Federated+Click-Through+Rate+Prediction)|0| -|[Generalizable Tip-of-the-Tongue Retrieval with LLM Re-ranking](https://doi.org/10.1145/3626772.3657917)|Luís Borges, Rohan Jha, Jamie Callan, Bruno Martins|Instituto Superior Técnico and INESC-ID, Lisbon, Portugal; The University of Texas at Austin, Austin, Texas, USA; Carnegie Mellon University, Pittsburgh, Pennsylvania, USA|Tip-of-the-Tongue (ToT) retrieval is challenging for search engines because the queries are usually natural-language, verbose, and contain uncertain and inaccurate information. This paper studies the generalization capabilities of existing retrieval methods with ToT queries in multiple domains. We curate a multi-domain dataset and evaluate the effectiveness of recall-oriented first-stage retrieval methods across the different domains, considering in-domain, out-of-domain, and multi-domain training settings. We further explore the use of a Large Language Model (LLM), i.e. GPT-4, for zero-shot re-ranking in various ToT domains, relying solely on the item titles. Results show that multi-domain training enhances recall, and that LLMs are strong zero-shot re-rankers, especially for popular items, outperforming direct GPT-4 prompting without first-stage retrieval. Datasets and code can be found on GitHub https://github.com/LuisPB7/TipTongue|舌尖检索(ToT)对于搜索引擎来说是一个挑战，因为查询通常是自然语言的，冗长的，并且包含不确定和不准确的信息。本文研究了现有的多领域 ToT 查询检索方法的泛化能力。我们策划一个多领域的数据集，并评估面向召回的第一阶段检索方法在不同领域的有效性，考虑域内，域外和多领域的训练设置。我们进一步探索了大型语言模型(LLM)的使用，即 GPT-4，用于在各种 ToT 域中重新排序，仅仅依赖于项目标题。结果表明，多领域训练有助于提高记忆力，LLM 具有较强的零击重排能力，尤其是对于热门项目，其表现优于没有第一阶段提取的直接 GPT-4提示。数据集和代码可以在 gitHub https://GitHub.com/luispb7/tiptongue 上找到|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Generalizable+Tip-of-the-Tongue+Retrieval+with+LLM+Re-ranking)|0| -|[Grasping Both Query Relevance and Essential Content for Query-focused Summarization](https://doi.org/10.1145/3626772.3657958)|Ye Xiong, Hidetaka Kamigaito, Soichiro Murakami, Peinan Zhang, Hiroya Takamura, Manabu Okumura|CyberAgent, Inc., Tokyo, Japan; Tokyo Institute of Technology, Tokyo, Japan|Numerous effective methods have been developed to improve query-focused summarization (QFS) performance, e.g., pre-trained model-based and query-answer relevance-based methods. However, these methods still suffer from missing or redundant information due to the inability to capture and effectively utilize the interrelationship between the query and the source document, as well as between the source document and its generated summary, resulting in the summary being unable to answer the query or containing additional unrequired information. To mitigate this problem, we propose an end-to-end hierarchical two-stage summarization model, that first predicts essential content, and then generates a summary by emphasizing the predicted important sentences while maintaining separate encodings for the query and the source, so that it can comprehend not only the query itself but also the essential information in the source. We evaluated the proposed model on two QFS datasets, and the results indicated its overall effectiveness and that of each component.|为了提高查询聚焦摘要(QFS)的性能，已经开发了许多有效的方法，例如基于预训练模型的方法和基于查询-回答相关性的方法。然而，由于无法捕获和有效利用查询与源文档之间以及源文档与其生成的摘要之间的相互关系，这些方法仍然存在信息缺失或多余的问题，导致摘要无法回答查询或包含额外的不必要信息。为了解决这个问题，我们提出了一种端到端的层次化两阶段摘要模型，该模型首先对重要内容进行预测，然后通过强调预测的重要句子来生成摘要，同时对查询和源代码保持单独的编码，从而不仅能够理解查询本身，而且能够理解源代码中的重要信息。我们在两个 QFS 数据集上对所提出的模型进行了评估，结果表明了该模型的整体有效性和每个组件的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Grasping+Both+Query+Relevance+and+Essential+Content+for+Query-focused+Summarization)|0| +|[Generalizable Tip-of-the-Tongue Retrieval with LLM Re-ranking](https://doi.org/10.1145/3626772.3657917)|Luís Borges, Rohan Jha, Jamie Callan, Bruno Martins|The University of Texas at Austin, Austin, Texas, USA; Carnegie Mellon University, Pittsburgh, Pennsylvania, USA; Instituto Superior Técnico and INESC-ID, Lisbon, Portugal|Tip-of-the-Tongue (ToT) retrieval is challenging for search engines because the queries are usually natural-language, verbose, and contain uncertain and inaccurate information. This paper studies the generalization capabilities of existing retrieval methods with ToT queries in multiple domains. We curate a multi-domain dataset and evaluate the effectiveness of recall-oriented first-stage retrieval methods across the different domains, considering in-domain, out-of-domain, and multi-domain training settings. We further explore the use of a Large Language Model (LLM), i.e. GPT-4, for zero-shot re-ranking in various ToT domains, relying solely on the item titles. Results show that multi-domain training enhances recall, and that LLMs are strong zero-shot re-rankers, especially for popular items, outperforming direct GPT-4 prompting without first-stage retrieval. Datasets and code can be found on GitHub https://github.com/LuisPB7/TipTongue|舌尖检索(ToT)对于搜索引擎来说是一个挑战，因为查询通常是自然语言的，冗长的，并且包含不确定和不准确的信息。本文研究了现有的多领域 ToT 查询检索方法的泛化能力。我们策划一个多领域的数据集，并评估面向召回的第一阶段检索方法在不同领域的有效性，考虑域内，域外和多领域的训练设置。我们进一步探索了大型语言模型(LLM)的使用，即 GPT-4，用于在各种 ToT 域中重新排序，仅仅依赖于项目标题。结果表明，多领域训练有助于提高记忆力，LLM 具有较强的零击重排能力，尤其是对于热门项目，其表现优于没有第一阶段提取的直接 GPT-4提示。数据集和代码可以在 gitHub https://GitHub.com/luispb7/tiptongue 上找到|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Generalizable+Tip-of-the-Tongue+Retrieval+with+LLM+Re-ranking)|0| +|[Grasping Both Query Relevance and Essential Content for Query-focused Summarization](https://doi.org/10.1145/3626772.3657958)|Ye Xiong, Hidetaka Kamigaito, Soichiro Murakami, Peinan Zhang, Hiroya Takamura, Manabu Okumura|Tokyo Institute of Technology, Tokyo, Japan; CyberAgent, Inc., Tokyo, Japan|Numerous effective methods have been developed to improve query-focused summarization (QFS) performance, e.g., pre-trained model-based and query-answer relevance-based methods. However, these methods still suffer from missing or redundant information due to the inability to capture and effectively utilize the interrelationship between the query and the source document, as well as between the source document and its generated summary, resulting in the summary being unable to answer the query or containing additional unrequired information. To mitigate this problem, we propose an end-to-end hierarchical two-stage summarization model, that first predicts essential content, and then generates a summary by emphasizing the predicted important sentences while maintaining separate encodings for the query and the source, so that it can comprehend not only the query itself but also the essential information in the source. We evaluated the proposed model on two QFS datasets, and the results indicated its overall effectiveness and that of each component.|为了提高查询聚焦摘要(QFS)的性能，已经开发了许多有效的方法，例如基于预训练模型的方法和基于查询-回答相关性的方法。然而，由于无法捕获和有效利用查询与源文档之间以及源文档与其生成的摘要之间的相互关系，这些方法仍然存在信息缺失或多余的问题，导致摘要无法回答查询或包含额外的不必要信息。为了解决这个问题，我们提出了一种端到端的层次化两阶段摘要模型，该模型首先对重要内容进行预测，然后通过强调预测的重要句子来生成摘要，同时对查询和源代码保持单独的编码，从而不仅能够理解查询本身，而且能够理解源代码中的重要信息。我们在两个 QFS 数据集上对所提出的模型进行了评估，结果表明了该模型的整体有效性和每个组件的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Grasping+Both+Query+Relevance+and+Essential+Content+for+Query-focused+Summarization)|0| |[MoME: Mixture-of-Masked-Experts for Efficient Multi-Task Recommendation](https://doi.org/10.1145/3626772.3657922)|Jiahui Xu, Lu Sun, Dengji Zhao|ShanghaiTech University, Shanghai, China|Multi-task learning techniques have attracted great attention in recommendation systems because they can meet the needs of modeling multiple perspectives simultaneously and improve recommendation performance. As promising multi-task recommendation system models, Mixture-of-Experts (MoE) and related methods use an ensemble of expert sub-networks to improve generalization and have achieved significant success in practical applications. However, they still face key challenges in efficient parameter sharing and resource utilization, especially when they are applied to real-world datasets and resource-constrained devices. In this paper, we propose a novel framework called Mixture-of-Masked-Experts (MoME) to address the challenges. Unlike MoE, expert sub-networks in MoME are extracted from an identical over-parameterized base network by learning binary masks. It utilizes a binary mask learning mechanism composed of neuron-level model masking and weight-level expert masking to achieve coarse-grained base model pruning and fine-grained expert pruning, respectively. Compared to existing MoE-based models, MoME achieves efficient parameter sharing and requires significantly less sub-network storage since it actually only trains a base network and a mixture of partially overlapped binary expert masks. Experimental results on real-world datasets demonstrate the superior performance of MoME in terms of recommendation accuracy and computational efficiency. Our code is available at https://https://github.com/Xjh0327/MoME.|多任务学习技术能够满足同时建立多视角模型的需要，提高推荐系统的性能，因而受到推荐系统的广泛关注。专家混合推荐系统作为一种有前途的多任务推荐系统模型，利用专家子网络集成技术提高推荐系统的泛化能力，在实际应用中取得了显著的成功。然而，它们在有效的参数共享和资源利用方面仍然面临着关键的挑战，特别是当它们应用于真实世界的数据集和资源受限的设备时。在本文中，我们提出了一个新的框架，称为蒙版专家混合(MoME) ，以解决这一挑战。与 MoE 不同的是，MoME 中的专家子网络是通过学习二进制掩码从相同的过参数化基网络中提取出来的。该方法利用神经元级模型掩蔽和权重级专家掩蔽组成的二元掩蔽学习机制，分别实现了粗粒度基模型和细粒度专家模型的修剪。与现有的基于 MoE 的模型相比，MoME 实现了有效的参数共享，并且需要的子网存储量明显减少，因为它实际上只训练一个基本网络和部分重叠的二进制专家掩码的混合。在实际数据集上的实验结果表明，MoME 在推荐精度和计算效率方面具有优越的性能。我们的代码可以在 https://https://github.com/xjh0327/mome 找到。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=MoME:+Mixture-of-Masked-Experts+for+Efficient+Multi-Task+Recommendation)|0| -|[Multi-Layer Ranking with Large Language Models for News Source Recommendation](https://doi.org/10.1145/3626772.3657966)|Wenjia Zhang, Lin Gui, Rob Procter, Yulan He|University of Warwick; The University of Warwick; King's College London|To seek reliable information sources for news events, we introduce a noveltask of expert recommendation, which aims to identify trustworthy sources basedon their previously quoted statements. To achieve this, we built a noveldataset, called NewsQuote, consisting of 23,571 quote-speaker pairs sourcedfrom a collection of news articles. We formulate the recommendation task as theretrieval of experts based on their likelihood of being associated with a givenquery. We also propose a multi-layer ranking framework employing Large LanguageModels to improve the recommendation performance. Our results show thatemploying an in-context learning based LLM ranker and a multi-layerranking-based filter significantly improve both the predictive quality andbehavioural quality of the recommender system.|为了为新闻事件寻找可靠的信息来源，本文提出了一种新颖的专家推荐任务，该任务的目的是根据新闻事件的可靠信息来源的先前引用的陈述来确定其可靠性。为了实现这一点，我们建立了一个名为 NewsQuote 的新颖数据集，其中包含23,571对引用说话者，这些引用说话者来自一系列新闻文章。我们根据专家与特定查询相关联的可能性来制定推荐任务。我们还提出了一个使用大型语言模型的多层次排序框架，以提高推荐性能。我们的研究结果表明，使用基于上下文学习的 LLM 排名器和基于多层次排名的过滤器可以显著提高推荐系统的预测质量和行为质量。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Multi-Layer+Ranking+with+Large+Language+Models+for+News+Source+Recommendation)|0| -|[Neural Click Models for Recommender Systems](https://doi.org/10.1145/3626772.3657939)|Mikhail Shirokikh, Ilya Shenbin, Anton Alekseev, Anna Volodkevich, Alexey Vasilev, Andrey V. Savchenko, Sergey I. Nikolenko|St. Petersburg State University, St. Petersburg, Russian Federation; Sber AI Lab; PDMI RAS & St. Petersburg University, St. Petersburg, Russian Federation; Sber AI Lab, Moscow, Russian Federation; PDMI RAS, St. Petersburg, Russian Federation; Steklov Mathematical Institute, St. Petersburg|We develop and evaluate neural architectures to model the user behavior in recommender systems (RS) inspired by click models for Web search but going beyond standard click models. Proposed architectures include recurrent networks, Transformer-based models that alleviate the quadratic complexity of self-attention, adversarial and hierarchical architectures. Our models outperform baselines on the ContentWise and RL4RS datasets and can be used in RS simulators to model user response for RS evaluation and pretraining.|我们开发和评估神经结构，以模拟推荐系统(RS)中的用户行为，该系统受到 Web 搜索的点击模型的启发，但超越了标准的点击模型。建议的体系结构包括循环网络，基于变压器的模型，减轻自我注意的二次复杂性，对手和分层体系结构。我们的模型优于 ContentWise 和 RL4RS 数据集的基线，可以用于 RS 模拟器，为 RS 评估和预训练建立用户响应模型。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Neural+Click+Models+for+Recommender+Systems)|0| +|[Multi-Layer Ranking with Large Language Models for News Source Recommendation](https://doi.org/10.1145/3626772.3657966)|Wenjia Zhang, Lin Gui, Rob Procter, Yulan He|King's College London; The University of Warwick; University of Warwick|To seek reliable information sources for news events, we introduce a noveltask of expert recommendation, which aims to identify trustworthy sources basedon their previously quoted statements. To achieve this, we built a noveldataset, called NewsQuote, consisting of 23,571 quote-speaker pairs sourcedfrom a collection of news articles. We formulate the recommendation task as theretrieval of experts based on their likelihood of being associated with a givenquery. We also propose a multi-layer ranking framework employing Large LanguageModels to improve the recommendation performance. Our results show thatemploying an in-context learning based LLM ranker and a multi-layerranking-based filter significantly improve both the predictive quality andbehavioural quality of the recommender system.|为了为新闻事件寻找可靠的信息来源，本文提出了一种新颖的专家推荐任务，该任务的目的是根据新闻事件的可靠信息来源的先前引用的陈述来确定其可靠性。为了实现这一点，我们建立了一个名为 NewsQuote 的新颖数据集，其中包含23,571对引用说话者，这些引用说话者来自一系列新闻文章。我们根据专家与特定查询相关联的可能性来制定推荐任务。我们还提出了一个使用大型语言模型的多层次排序框架，以提高推荐性能。我们的研究结果表明，使用基于上下文学习的 LLM 排名器和基于多层次排名的过滤器可以显著提高推荐系统的预测质量和行为质量。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Multi-Layer+Ranking+with+Large+Language+Models+for+News+Source+Recommendation)|0| +|[Neural Click Models for Recommender Systems](https://doi.org/10.1145/3626772.3657939)|Mikhail Shirokikh, Ilya Shenbin, Anton Alekseev, Anna Volodkevich, Alexey Vasilev, Andrey V. Savchenko, Sergey I. Nikolenko|PDMI RAS, St. Petersburg, Russian Federation; St. Petersburg State University, St. Petersburg, Russian Federation; Sber AI Lab; PDMI RAS & St. Petersburg University, St. Petersburg, Russian Federation; Steklov Mathematical Institute, St. Petersburg; Sber AI Lab, Moscow, Russian Federation|We develop and evaluate neural architectures to model the user behavior in recommender systems (RS) inspired by click models for Web search but going beyond standard click models. Proposed architectures include recurrent networks, Transformer-based models that alleviate the quadratic complexity of self-attention, adversarial and hierarchical architectures. Our models outperform baselines on the ContentWise and RL4RS datasets and can be used in RS simulators to model user response for RS evaluation and pretraining.|我们开发和评估神经结构，以模拟推荐系统(RS)中的用户行为，该系统受到 Web 搜索的点击模型的启发，但超越了标准的点击模型。建议的体系结构包括循环网络，基于变压器的模型，减轻自我注意的二次复杂性，对手和分层体系结构。我们的模型优于 ContentWise 和 RL4RS 数据集的基线，可以用于 RS 模拟器，为 RS 评估和预训练建立用户响应模型。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Neural+Click+Models+for+Recommender+Systems)|0| |[SpherE: Expressive and Interpretable Knowledge Graph Embedding for Set Retrieval](https://doi.org/10.1145/3626772.3657910)|Zihao Li, Yuyi Ao, Jingrui He|University of Illinois at Urbana-Champaign|Knowledge graphs (KGs), which store an extensive number of relational facts(head, relation, tail), serve various applications. While many downstream taskshighly rely on the expressive modeling and predictive embedding of KGs, most ofthe current KG representation learning methods, where each entity is embeddedas a vector in the Euclidean space and each relation is embedded as atransformation, follow an entity ranking protocol. On one hand, such anembedding design cannot capture many-to-many relations. On the other hand, inmany retrieval cases, the users wish to get an exact set of answers without anyranking, especially when the results are expected to be precise, e.g., whichgenes cause an illness. Such scenarios are commonly referred to as "setretrieval". This work presents a pioneering study on the KG set retrievalproblem. We show that the set retrieval highly depends on expressive modelingof many-to-many relations, and propose a new KG embedding model SpherE toaddress this problem. SpherE is based on rotational embedding methods, but eachentity is embedded as a sphere instead of a vector. While inheriting the highinterpretability of rotational-based models, our SpherE can more expressivelymodel one-to-many, many-to-one, and many-to-many relations. Through extensiveexperiments, we show that our SpherE can well address the set retrieval problemwhile still having a good predictive ability to infer missing facts. The codeis available at https://github.com/Violet24K/SpherE.|知识图(KGs)存储了大量的关系事实(头部、关系、尾部) ，服务于各种应用。虽然许多下游任务高度依赖于 KG 的表达式建模和预测嵌入，但目前大多数 KG 表示学习方法都遵循实体排序协议，其中每个实体嵌入在欧几里德空间中作为一个向量，每个关系嵌入作为变换。一方面，这样的嵌入式设计不能捕获多对多的关系。另一方面，在许多检索案例中，用户希望在没有任何排名的情况下得到一组精确的答案，特别是当结果被期望是精确的时候，例如，哪些基因导致疾病。这样的场景通常被称为“设置检索”。本文对 KG 集检索问题进行了开创性的研究。我们证明了集合检索高度依赖于多对多关系的表达式建模，并提出了一种新的 KG 嵌入模型 SpherE 来解决这一问题。球面嵌入是基于旋转嵌入的方法，但是每个实体都是球面嵌入而不是向量嵌入。在继承了基于旋转的模型的高可解释性的同时，我们的 SpherE 可以更有表现力地建立一对多、多对一和多对多的关系模型。通过大量的实验表明，我们的 SphereE 能够很好地解决集合检索问题，同时仍然具有很好的预测能力来推断丢失的事实。密码可以在 https://github.com/violet24k/sphere 找到。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=SpherE:+Expressive+and+Interpretable+Knowledge+Graph+Embedding+for+Set+Retrieval)|0| |[CoSearchAgent: A Lightweight Collaborative Search Agent with Large Language Models](https://doi.org/10.1145/3626772.3657672)|Peiyuan Gong, Jiamian Li, Jiaxin Mao|Renmin University of China Gaoling School of Artificial Intelligence|Collaborative search supports multiple users working together to accomplish aspecific search task. Research has found that designing lightweightcollaborative search plugins within instant messaging platforms aligns betterwith users' collaborative habits. However, due to the complexity of multi-userinteraction scenarios, it is challenging to implement a fully functioninglightweight collaborative search system. Therefore, previous studies onlightweight collaborative search had to rely on the Wizard of Oz paradigm. Inrecent years, large language models (LLMs) have been demonstrated to interactnaturally with users and achieve complex information-seeking tasks throughLLM-based agents. Hence, to better support the research in collaborativesearch, in this demo, we propose CoSearchAgent, a lightweight collaborativesearch agent powered by LLMs. CoSearchAgent is designed as a Slack plugin thatcan support collaborative search during multi-party conversations on thisplatform. Equipped with the capacity to understand the queries and context inmulti-user conversations and the ability to search the Web for relevantinformation via APIs, CoSearchAgent can respond to user queries with answersgrounded on the relevant search results. It can also ask clarifying questionswhen the information needs are unclear. The proposed CoSearchAgent is highlyflexible and would be useful for supporting further research on collaborativesearch. The code and demo video are accessible.|协同搜索支持多个用户协同工作来完成特定的搜索任务。研究发现，在即时通讯平台上设计轻量级/协作式搜索插件更符合用户的协作习惯。然而，由于多用户交互场景的复杂性，实现一个全功能的轻量级协同搜索系统是一个挑战。因此，之前对轻量级协作搜索的研究必须依赖于绿野仙踪范式。近年来，大型语言模型(LLM)已被证明可以与用户进行自然交互，并通过基于 LLM 的代理实现复杂的信息搜索任务。因此，为了更好地支持协作搜索的研究，在本演示中，我们提出了 CoSearchAgent，一个由 LLM 支持的轻量级协作搜索代理。CoSearchAgent 被设计成一个 Slack 插件，可以在这个平台上支持多方对话的协作搜索。CoSearchAgent 具有理解多用户对话中的查询和上下文的能力，并能够通过 API 在网上搜索相关信息，CoSearchAgent 可以根据相关搜索结果回答用户的查询。当信息需求不明确时，它也可以提出澄清问题。提出的 CoSearchAgent 具有高度灵活性，将有助于支持协作研究的进一步研究。代码和演示视频是可访问的。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=CoSearchAgent:+A+Lightweight+Collaborative+Search+Agent+with+Large+Language+Models)|0| |[MeMemo: On-device Retrieval Augmentation for Private and Personalized Text Generation](https://doi.org/10.1145/3626772.3657662)|Zijie J. Wang, Duen Horng Chau|Georgia Tech|Retrieval-augmented text generation (RAG) addresses the common limitations of large language models (LLMs), such as hallucination, by retrieving information from an updatable external knowledge base. However, existing approaches often require dedicated backend servers for data storage and retrieval, thereby limiting their applicability in use cases that require strict data privacy, such as personal finance, education, and medicine. To address the pressing need for client-side dense retrieval, we introduce MeMemo, the first open-source JavaScript toolkit that adapts the state-of-the-art approximate nearest neighbor search technique HNSW to browser environments. Developed with modern and native Web technologies, such as IndexedDB and Web Workers, our toolkit leverages client-side hardware capabilities to enable researchers and developers to efficiently search through millions of high-dimensional vectors in the browser. MeMemo enables exciting new design and research opportunities, such as private and personalized content creation and interactive prototyping, as demonstrated in our example application RAG Playground. Reflecting on our work, we discuss the opportunities and challenges for on-device dense retrieval. MeMemo is available at https://github.com/poloclub/mememo.|检索增强型文本生成(RAG)通过从可更新的外部知识库中检索信息，解决了大型语言模型(LLM)的常见局限性，如幻觉。然而，现有的方法通常需要专用的后端服务器来存储和检索数据，从而限制了它们在需要严格数据隐私的用例中的适用性，例如个人理财、教育和医疗。为了满足客户端密集检索的迫切需求，我们引入了 MeMemo，这是第一个开源 JavaScript 工具包，它将最先进的近似最近邻搜索技术 HNSW 应用于浏览器环境。我们的工具包利用 IndexedDB 和 Web Workers 等现代和本地 Web 技术开发，利用客户端硬件能力，使研究人员和开发人员能够在浏览器中有效地搜索数百万个高维向量。MeMemo 提供了令人兴奋的新设计和研究机会，如私人和个性化的内容创建和交互式原型制作，如我们的示例应用程序 RAG Playground 所示。回顾我们的工作，我们讨论了设备上密集检索的机会和挑战。备忘录可在 https://github.com/poloclub/MeMemo 下载。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=MeMemo:+On-device+Retrieval+Augmentation+for+Private+and+Personalized+Text+Generation)|0| -|[Monitoring the Evolution of Behavioural Embeddings in Social Media Recommendation](https://doi.org/10.1145/3626772.3661368)|Srijan Saket, Olivier Jeunen, Md. Danish Kalim|ShareChat; Sharechat|Emerging short-video platforms like TikTok, Instagram Reels, and ShareChatpresent unique challenges for recommender systems, primarily originating from acontinuous stream of new content. ShareChat alone receives approximately 2million pieces of fresh content daily, complicating efforts to assess quality,learn effective latent representations, and accurately match content with theappropriate user base, especially given limited user feedback. Embedding-basedapproaches are a popular choice for industrial recommender systems because theycan learn low-dimensional representations of items, leading to effectiverecommendation that can easily scale to millions of items and users. Our work characterizes the evolution of such embeddings in short-videorecommendation systems, comparing the effect of batch and real-time updates tocontent embeddings. We investigate how embeddings change with subsequentupdates, explore the relationship between embeddings and popularity bias, andhighlight their impact on user engagement metrics. Our study unveils thecontrast in the number of interactions needed to achieve mature embeddings in abatch learning setup versus a real-time one, identifies the point of highestinformation updates, and explores the distribution of ℓ_2-norms across thetwo competing learning modes. Utilizing a production system deployed on alarge-scale short-video app with over 180 million users, our findings offerinsights into designing effective recommendation systems and enhancing usersatisfaction and engagement in short-video applications.|新兴的短视频平台，如 TikTok、 Instagram Reels 和 ShareChat.com 对推荐系统提出了独特的挑战，这些平台主要来源于源源不断的新内容。ShareChat 每天接收大约200万条新内容，这使得评估质量、学习有效的潜在表现形式以及将内容与合适的用户群准确匹配的工作变得复杂，特别是在用户反馈有限的情况下。基于嵌入的方法是工业推荐系统的一个流行选择，因为它们可以学习项目的低维表示，导致有效的推荐，可以轻松地扩展到数百万个项目和用户。我们的工作描述了这种嵌入在短视频推荐系统中的演变，比较了批量和实时更新对内容嵌入的影响。我们调查嵌入如何随着后续更新而改变，探索嵌入与流行偏差之间的关系，并强调它们对用户参与度量的影响。我们的研究揭示了在批量学习设置中实现成熟嵌入与实时嵌入所需的交互数量的对比，确定了最高信息更新的点，并探索了在两种竞争学习模式中 l _ 2-规范的分布。我们的研究结果利用一个部署在拥有超过1.8亿用户的大规模短视频应用程序上的生产系统，为设计有效的推荐系统、提高用户满意度和参与短视频应用程序提供了见解。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Monitoring+the+Evolution+of+Behavioural+Embeddings+in+Social+Media+Recommendation)|0| +|[Monitoring the Evolution of Behavioural Embeddings in Social Media Recommendation](https://doi.org/10.1145/3626772.3661368)|Srijan Saket, Olivier Jeunen, Md. Danish Kalim|Sharechat; ShareChat|Emerging short-video platforms like TikTok, Instagram Reels, and ShareChatpresent unique challenges for recommender systems, primarily originating from acontinuous stream of new content. ShareChat alone receives approximately 2million pieces of fresh content daily, complicating efforts to assess quality,learn effective latent representations, and accurately match content with theappropriate user base, especially given limited user feedback. Embedding-basedapproaches are a popular choice for industrial recommender systems because theycan learn low-dimensional representations of items, leading to effectiverecommendation that can easily scale to millions of items and users. Our work characterizes the evolution of such embeddings in short-videorecommendation systems, comparing the effect of batch and real-time updates tocontent embeddings. We investigate how embeddings change with subsequentupdates, explore the relationship between embeddings and popularity bias, andhighlight their impact on user engagement metrics. Our study unveils thecontrast in the number of interactions needed to achieve mature embeddings in abatch learning setup versus a real-time one, identifies the point of highestinformation updates, and explores the distribution of ℓ_2-norms across thetwo competing learning modes. Utilizing a production system deployed on alarge-scale short-video app with over 180 million users, our findings offerinsights into designing effective recommendation systems and enhancing usersatisfaction and engagement in short-video applications.|新兴的短视频平台，如 TikTok、 Instagram Reels 和 ShareChat.com 对推荐系统提出了独特的挑战，这些平台主要来源于源源不断的新内容。ShareChat 每天接收大约200万条新内容，这使得评估质量、学习有效的潜在表现形式以及将内容与合适的用户群准确匹配的工作变得复杂，特别是在用户反馈有限的情况下。基于嵌入的方法是工业推荐系统的一个流行选择，因为它们可以学习项目的低维表示，导致有效的推荐，可以轻松地扩展到数百万个项目和用户。我们的工作描述了这种嵌入在短视频推荐系统中的演变，比较了批量和实时更新对内容嵌入的影响。我们调查嵌入如何随着后续更新而改变，探索嵌入与流行偏差之间的关系，并强调它们对用户参与度量的影响。我们的研究揭示了在批量学习设置中实现成熟嵌入与实时嵌入所需的交互数量的对比，确定了最高信息更新的点，并探索了在两种竞争学习模式中 l _ 2-规范的分布。我们的研究结果利用一个部署在拥有超过1.8亿用户的大规模短视频应用程序上的生产系统，为设计有效的推荐系统、提高用户满意度和参与短视频应用程序提供了见解。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Monitoring+the+Evolution+of+Behavioural+Embeddings+in+Social+Media+Recommendation)|0| |[Embedding Based Deduplication in E-commerce AutoComplete](https://doi.org/10.1145/3626772.3661373)|Shaodan Zhai, Yuwei Chen, Yixue Li|Coupang Inc., Mountain View, CA, USA; Coupang Inc., Mountain View, USA|Query AutoComplete (QAC) is an important feature in e-commerce search engines, aimed at enhancing user experience by offering relevant query suggestions. However, these suggestions often include semantically duplicate entries derived from user logs. While the existing literature has made significant progress in query similarity learning for e-commerce applications, the specific challenge of query deduplication has received less attention. To address this issue, this paper presents a new industry-scale framework for QAC deduplication at Coupang, utilizing diverse data augmentation techniques to enhance deduplication accuracy effectively. Our results reveal that this approach substantially outperforms existing query similarity methods, providing valuable insights into the utility of various pre-trained models and data augmentation strategies. Online A/B testing further validates the significant impact of our deduplication framework on improving the e-commerce search experience, highlighting the importance of addressing semantic duplicates in QAC suggestions and offering a practical solution with proven effectiveness in a live e-commerce environment.|查询自动完成(Query AutoComplete，QAC)是电子商务搜索引擎的一个重要特性，旨在通过提供相关的查询建议来增强用户体验。但是，这些建议通常包括来自用户日志的语义重复条目。虽然现有的文献在电子商务应用中的查询相似性学习方面取得了显著的进展，但是查询重复数据删除这一具体挑战却没有得到足够的重视。为了解决这个问题，本文提出了一个新的行业规模的质保局在 Coupang 重复数据删除框架，利用不同的数据增强技术，以有效地提高重复数据删除的准确性。我们的研究结果表明，这种方法大大优于现有的查询相似性方法，为各种预先训练的模型和数据增强策略的实用性提供了有价值的见解。在线 A/B 测试进一步验证了我们的重复数据删除框架对改善电子商务搜索体验的重要影响，突出了解决质量保证委员会建议中的语义重复的重要性，并提供了一个实用的解决方案，在实时电子商务环境中被证明是有效的。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Embedding+Based+Deduplication+in+E-commerce+AutoComplete)|0| -|[Using and Evaluating Quantum Computing for Information Retrieval and Recommender Systems](https://doi.org/10.1145/3626772.3661378)|Maurizio Ferrari Dacrema, Andrea Pasin, Paolo Cremonesi, Nicola Ferro|Università degli Studi di Padova, Padova, Italy; Politecnico di Milano, Milano, Italy|The field of Quantum Computing (QC) has gained significant popularity in recent years, due to its potential to provide benefits in terms of efficiency and effectiveness when employed to solve certain computationally intensive tasks. In both Information Retrieval (IR) and Recommender Systems (RS) we are required to build methods that apply complex processing on large and heterogeneous datasets, it is natural therefore to wonder whether QC could also be applied to boost their performance. The tutorial aims to provide first an introduction to QC for an audience that is not familiar with the technology, then to show how to apply the QC paradigm of Quantum Annealing (QA) to solve practical problems that are currently faced by IR and RS systems. During the tutorial, participants will be provided with the fundamentals required to understand QC and to apply it in practice by using a real D-Wave quantum annealer through APIs.|近年来，量子计算(QC)因其在解决某些计算密集型任务时在效率和有效性方面的潜力而广受欢迎。在信息检索(IR)和推荐系统(RS)中，我们都被要求建立在大型异构数据集上应用复杂处理的方法，因此很自然地想知道是否也可以应用质量控制来提高它们的性能。本教程旨在首先为不熟悉这项技术的观众介绍质量控制，然后展示如何应用质量控制量子退火(QA)范例来解决当前 IR 和 RS 系统所面临的实际问题。在本教程期间，参与者将提供必要的基本知识，以了解质量控制，并应用它在实践中使用一个真正的 D-Wave 量子退火器通过 API。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Using+and+Evaluating+Quantum+Computing+for+Information+Retrieval+and+Recommender+Systems)|0| -|[Reinforcing Long-Term Performance in Recommender Systems with User-Oriented Exploration Policy](https://doi.org/10.1145/3626772.3657714)|Changshuo Zhang, Sirui Chen, Xiao Zhang, Sunhao Dai, Weijie Yu, Jun Xu|Gaoling School of AI, Renmin University of China, Beijing, China; University of Illinois at Urbana-Champaign, Champaign, USA; University of International Business and Economics School of Information Technology and Management|Reinforcement learning (RL) has gained popularity in recommender systems for improving long-term performance by effectively exploring users' interests. However, modern recommender systems face the challenge of different user behavioral patterns among millions of items, making exploration more difficult. For example, users with varying activity levels require different exploration intensities. Unfortunately, previous studies often overlook this aspect and apply a uniform exploration strategy to all users, which ultimately hampers long-term user experiences. To tackle these challenges, we propose User-Oriented Exploration Policy (UOEP), a novel approach that enables fine-grained exploration among user groups. We first construct a distributional critic that allows policy optimization based on varying quantile levels of cumulative reward feedback from users, representing user groups with different activity levels. Using this critic as a guide, we design a population of distinct actors dedicated to effective and fine-grained exploration within their respective user groups. To simultaneously enhance diversity and stability during the exploration process, we also introduce a population-level diversity regularization term and a supervision module. Experimental results on public recommendation datasets validate the effectiveness of our approach, as it outperforms all other baselines in terms of long-term performance. Moreover, further analyses reveal the benefits of our approach, including improved performance for low-activity users and increased fairness among users.|在推荐系统中，强化学习(RL)通过有效地探索用户的兴趣来改善长期表现，这种做法已经越来越受欢迎。然而，现代推荐系统面临着数以百万计的项目中不同的用户行为模式的挑战，使得探索变得更加困难。例如，活动级别不同的用户需要不同的探索强度。不幸的是，以往的研究往往忽视了这一方面，对所有用户采用统一的探索策略，这最终阻碍了长期的用户体验。为了应对这些挑战，我们提出了面向用户的探索策略(UOEP) ，这是一种新颖的方法，能够在用户组之间进行细粒度的探索。我们首先构造一个分布式批评，允许基于来自用户的累积奖励反馈的不同分位数级别的策略优化，代表具有不同活动级别的用户组。以这个批评家为指导，我们设计了一组不同的参与者，致力于在他们各自的用户群中进行有效和细粒度的探索。为了在勘探过程中同时增强多样性和稳定性，我们还引入了一个种群级多样性正则化项和一个监督模块。对公共推荐数据集的实验结果验证了我们方法的有效性，因为它在长期性能方面优于所有其他基线。此外，进一步的分析揭示了我们的方法的好处，包括改善低活动用户的性能和增加用户之间的公平性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Reinforcing+Long-Term+Performance+in+Recommender+Systems+with+User-Oriented+Exploration+Policy)|0| -|[Unsupervised Cross-Domain Image Retrieval with Semantic-Attended Mixture-of-Experts](https://doi.org/10.1145/3626772.3657826)|Kai Wang, Jiayang Liu, Xing Xu, Jingkuan Song, Xin Liu, Heng Tao Shen|University of Electronic Science and Technology of China; College of Electronic and Information Engineering, Tongji University, Shanghai, China; College of Computer Science and Technology, Huaqiao University, Xiamen, China|Unsupervised cross-domain image retrieval is designed to facilitate the retrieval between images in different domains in an unsupervised way. Without the guidance of labels, both intra-domain semantic learning and inter-domain semantic alignment pose significant challenges to the model's learning process. The resolution of these challenges relies on the accurate capture of domain-invariant semantic features by the model. Based on this consideration, we propose our Semantic-Attended Mixture of Experts (SA-MoE) model. Leveraging the proficiency of MoE network in capturing visual features, we enhance the model's focus on semantically relevant features through a series of strategies. We first utilize the self-attention mechanism of Vision Transformer to adaptively collect information with different weights on instances from different domains. In addition, we introduce contextual semantic association metrics to more accurately measure the semantic relatedness between instances. By utilizing the association metrics, secondary clustering is performed in the feature space to reinforce semantic relationships. Finally, we employ the metrics for information selection on the fused data to remove the semantic noise. We conduct extensive experiments on three widely used datasets. The consistent comparison results with existing methods indicate that our model possesses the state-of-the-art performance.|无监督跨域图像检索是为了方便不同域间图像的无监督检索而设计的。在没有标签指导的情况下，域内语义学习和域间语义对齐都给模型的学习过程带来了巨大的挑战。这些挑战的解决依赖于模型对领域不变语义特征的准确捕获。基于这一考虑，我们提出了语义参与的专家混合模型(SA-MoE)。利用 MoE 网络捕获视觉特征的能力，我们通过一系列策略提高了模型对语义相关特征的关注度。首先利用视觉变压器的自注意机制对不同领域的实例进行不同权重的信息自适应采集。此外，我们引入上下文语义关联度量来更准确地度量实例之间的语义关联。利用关联度量，在特征空间中进行二次聚类以增强语义关系。最后，利用融合数据的信息选择度量去除语义噪声。我们在三个广泛使用的数据集上进行了广泛的实验。与现有方法的一致性比较结果表明，我们的模型具有最先进的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Unsupervised+Cross-Domain+Image+Retrieval+with+Semantic-Attended+Mixture-of-Experts)|0| -|[Multilingual Meta-Distillation Alignment for Semantic Retrieval](https://doi.org/10.1145/3626772.3657812)|Meryem M'hamdi, Jonathan May, Franck Dernoncourt, Trung Bui, Seunghyun Yoon|Microsoft & University of Southern California, Redmond, WA, USA; Adobe Research, Seattle, WA, USA; Adobe Research, San Jose, CA, USA; University of Southern California, Los Angeles, CA, USA|Multilingual semantic retrieval involves retrieving semantically relevant content to a query irrespective of the language. Compared to monolingual and bilingual semantic retrieval, multilingual semantic retrieval requires a stronger alignment approach to pull the contents to be retrieved close to the representation of their corresponding queries, no matter their language combinations. Traditionally, this is achieved through more supervision in the form of multilingual parallel resources, which are expensive to obtain, especially for low-resource languages. In this work, on top of an optimization-based Model-Agnostic Meta-Learner (MAML), we propose a data-efficient meta-distillation approach: MAML-Align,1 specifically for low-resource multilingual semantic retrieval. Our approach simulates a gradual feedback loop from monolingual to bilingual and from bilingual to multilingual semantic retrieval. We systematically compare multilingual meta-distillation learning to different baselines and conduct ablation studies on the role of different sampling approaches in the meta-task construction. We show that MAML-Align's gradual feedback loop boosts the generalization to different languages, including zero-shot ones, better than naive fine-tuning and vanilla MAML.|多语言语义检索包括检索与查询语言无关的语义相关内容。与单语言和双语语义检索相比，多语言语义检索需要一种更强的对齐方法来将要检索的内容拉近其相应查询的表示，而不管它们的语言组合如何。传统上，这是通过以多语言并行资源的形式进行更多的监督来实现的，这些资源是昂贵的，特别是对于资源较少的语言。本文在基于优化的模型不可知元学习器(MAML)的基础上，提出了一种数据高效的元精馏方法: MAML-Align，1，专门用于低资源的多语言语义检索。我们的方法模拟了一个从单语到双语，从双语到多语的语义检索的渐进反馈循环。我们系统地比较了不同基线的多语言元精馏学习，并对不同抽样方法在元任务构建中的作用进行了消融研究。我们展示了 MAML-Align 的渐进反馈循环提高了对不同语言的泛化能力，包括0-shot 语言，这比单纯的微调和普通的 MAML 要好。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Multilingual+Meta-Distillation+Alignment+for+Semantic+Retrieval)|0| -|[Dataset and Models for Item Recommendation Using Multi-Modal User Interactions](https://doi.org/10.1145/3626772.3657881)|Simone Borg Bruun, Krisztian Balog, Maria Maistro|PhD; Tenure Track Assistant Professor; Dr. Scient.|While recommender systems with multi-modal item representations (image,audio, and text), have been widely explored, learning recommendations frommulti-modal user interactions (e.g., clicks and speech) remains an openproblem. We study the case of multi-modal user interactions in a setting whereusers engage with a service provider through multiple channels (website andcall center). In such cases, incomplete modalities naturally occur, since notall users interact through all the available channels. To address thesechallenges, we publish a real-world dataset that allows progress in thisunder-researched area. We further present and benchmark various methods forleveraging multi-modal user interactions for item recommendations, and proposea novel approach that specifically deals with missing modalities by mappinguser interactions to a common feature space. Our analysis reveals importantinteractions between the different modalities and that a frequently occurringmodality can enhance learning from a less frequent one.|虽然具有多模态项目表示(图像、音频和文本)的推荐系统已经得到了广泛的探索，但是从多模态用户交互(例如点击和语音)中学习推荐仍然是一个尚未解决的问题。我们研究的情况下，多模式的用户交互设置，其中用户与服务提供商通过多个渠道(网站和呼叫中心)。在这种情况下，不完整的模式自然会出现，因为没有用户通过所有可用的渠道进行交互。为了应对这些挑战，我们发布了一个真实世界的数据集，允许在这个研究不足的领域取得进展。我们进一步介绍和基准利用多模态用户交互的项目推荐的各种方法，并提出新的方法，具体处理缺失的模式映射到一个共同的功能空间的用户交互。我们的分析揭示了不同模式之间的重要相互作用，一个频繁出现的模式可以增强从一个不太频繁的学习。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Dataset+and+Models+for+Item+Recommendation+Using+Multi-Modal+User+Interactions)|0| -|[Behavior-Contextualized Item Preference Modeling for Multi-Behavior Recommendation](https://doi.org/10.1145/3626772.3657696)|Mingshi Yan, Fan Liu, Jing Sun, Fuming Sun, Zhiyong Cheng, Yahong Han|Dalian Minzu University; Hefei University of Technology; National University of Singapore; Tianjin University|In recommender systems, multi-behavior methods have demonstrated theireffectiveness in mitigating issues like data sparsity, a common challenge intraditional single-behavior recommendation approaches. These methods typicallyinfer user preferences from various auxiliary behaviors and apply them to thetarget behavior for recommendations. However, this direct transfer canintroduce noise to the target behavior in recommendation, due to variations inuser attention across different behaviors. To address this issue, this paperintroduces a novel approach, Behavior-Contextualized Item Preference Modeling(BCIPM), for multi-behavior recommendation. Our proposedBehavior-Contextualized Item Preference Network discerns and learns users'specific item preferences within each behavior. It then considers only thosepreferences relevant to the target behavior for final recommendations,significantly reducing noise from auxiliary behaviors. These auxiliarybehaviors are utilized solely for training the network parameters, therebyrefining the learning process without compromising the accuracy of the targetbehavior recommendations. To further enhance the effectiveness of BCIPM, weadopt a strategy of pre-training the initial embeddings. This step is crucialfor enriching the item-aware preferences, particularly in scenarios where datarelated to the target behavior is sparse. Comprehensive experiments conductedon four real-world datasets demonstrate BCIPM's superior performance comparedto several leading state-of-the-art models, validating the robustness andefficiency of our proposed approach.|在推荐系统中，多行为方法已经证明了它们在缓解诸如数据稀疏等问题上的有效性，这是传统的单行为推荐方法所面临的共同挑战。这些方法通常从各种辅助行为推断出用户偏好，并将其应用于目标行为以获得推荐。然而，由于不同行为的用户注意力的差异，这种直接转移会给推荐中的目标行为带来噪声。为了解决这个问题，本文提出了一种新的方法，行为上下文项目偏好建模(BCIPM) ，用于多行为推荐。我们提出的上下文化项目偏好网络识别和学习用户的特定项目偏好在每个行为。然后它只考虑那些与目标行为相关的偏好作为最终建议，显著减少辅助行为的噪音。这些辅助行为仅用于训练网络参数，从而在不影响目标行为建议准确性的前提下完善学习过程。为了进一步提高 BCIPM 的有效性，我们采用了预先训练初始嵌入的策略。这一步对于丰富项目感知偏好是至关重要的，特别是在与目标行为相关的数据稀少的情况下。在四个真实世界数据集上进行的综合实验表明，BCIPM 的性能优于几个领先的最先进的模型，验证了我们提出的方法的鲁棒性和有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Behavior-Contextualized+Item+Preference+Modeling+for+Multi-Behavior+Recommendation)|0| +|[Using and Evaluating Quantum Computing for Information Retrieval and Recommender Systems](https://doi.org/10.1145/3626772.3661378)|Maurizio Ferrari Dacrema, Andrea Pasin, Paolo Cremonesi, Nicola Ferro|Politecnico di Milano, Milano, Italy; Università degli Studi di Padova, Padova, Italy|The field of Quantum Computing (QC) has gained significant popularity in recent years, due to its potential to provide benefits in terms of efficiency and effectiveness when employed to solve certain computationally intensive tasks. In both Information Retrieval (IR) and Recommender Systems (RS) we are required to build methods that apply complex processing on large and heterogeneous datasets, it is natural therefore to wonder whether QC could also be applied to boost their performance. The tutorial aims to provide first an introduction to QC for an audience that is not familiar with the technology, then to show how to apply the QC paradigm of Quantum Annealing (QA) to solve practical problems that are currently faced by IR and RS systems. During the tutorial, participants will be provided with the fundamentals required to understand QC and to apply it in practice by using a real D-Wave quantum annealer through APIs.|近年来，量子计算(QC)因其在解决某些计算密集型任务时在效率和有效性方面的潜力而广受欢迎。在信息检索(IR)和推荐系统(RS)中，我们都被要求建立在大型异构数据集上应用复杂处理的方法，因此很自然地想知道是否也可以应用质量控制来提高它们的性能。本教程旨在首先为不熟悉这项技术的观众介绍质量控制，然后展示如何应用质量控制量子退火(QA)范例来解决当前 IR 和 RS 系统所面临的实际问题。在本教程期间，参与者将提供必要的基本知识，以了解质量控制，并应用它在实践中使用一个真正的 D-Wave 量子退火器通过 API。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Using+and+Evaluating+Quantum+Computing+for+Information+Retrieval+and+Recommender+Systems)|0| +|[Reinforcing Long-Term Performance in Recommender Systems with User-Oriented Exploration Policy](https://doi.org/10.1145/3626772.3657714)|Changshuo Zhang, Sirui Chen, Xiao Zhang, Sunhao Dai, Weijie Yu, Jun Xu|University of International Business and Economics School of Information Technology and Management; Gaoling School of AI, Renmin University of China, Beijing, China; University of Illinois at Urbana-Champaign, Champaign, USA|Reinforcement learning (RL) has gained popularity in recommender systems for improving long-term performance by effectively exploring users' interests. However, modern recommender systems face the challenge of different user behavioral patterns among millions of items, making exploration more difficult. For example, users with varying activity levels require different exploration intensities. Unfortunately, previous studies often overlook this aspect and apply a uniform exploration strategy to all users, which ultimately hampers long-term user experiences. To tackle these challenges, we propose User-Oriented Exploration Policy (UOEP), a novel approach that enables fine-grained exploration among user groups. We first construct a distributional critic that allows policy optimization based on varying quantile levels of cumulative reward feedback from users, representing user groups with different activity levels. Using this critic as a guide, we design a population of distinct actors dedicated to effective and fine-grained exploration within their respective user groups. To simultaneously enhance diversity and stability during the exploration process, we also introduce a population-level diversity regularization term and a supervision module. Experimental results on public recommendation datasets validate the effectiveness of our approach, as it outperforms all other baselines in terms of long-term performance. Moreover, further analyses reveal the benefits of our approach, including improved performance for low-activity users and increased fairness among users.|在推荐系统中，强化学习(RL)通过有效地探索用户的兴趣来改善长期表现，这种做法已经越来越受欢迎。然而，现代推荐系统面临着数以百万计的项目中不同的用户行为模式的挑战，使得探索变得更加困难。例如，活动级别不同的用户需要不同的探索强度。不幸的是，以往的研究往往忽视了这一方面，对所有用户采用统一的探索策略，这最终阻碍了长期的用户体验。为了应对这些挑战，我们提出了面向用户的探索策略(UOEP) ，这是一种新颖的方法，能够在用户组之间进行细粒度的探索。我们首先构造一个分布式批评，允许基于来自用户的累积奖励反馈的不同分位数级别的策略优化，代表具有不同活动级别的用户组。以这个批评家为指导，我们设计了一组不同的参与者，致力于在他们各自的用户群中进行有效和细粒度的探索。为了在勘探过程中同时增强多样性和稳定性，我们还引入了一个种群级多样性正则化项和一个监督模块。对公共推荐数据集的实验结果验证了我们方法的有效性，因为它在长期性能方面优于所有其他基线。此外，进一步的分析揭示了我们的方法的好处，包括改善低活动用户的性能和增加用户之间的公平性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Reinforcing+Long-Term+Performance+in+Recommender+Systems+with+User-Oriented+Exploration+Policy)|0| +|[Unsupervised Cross-Domain Image Retrieval with Semantic-Attended Mixture-of-Experts](https://doi.org/10.1145/3626772.3657826)|Kai Wang, Jiayang Liu, Xing Xu, Jingkuan Song, Xin Liu, Heng Tao Shen|College of Electronic and Information Engineering, Tongji University, Shanghai, China; College of Computer Science and Technology, Huaqiao University, Xiamen, China; University of Electronic Science and Technology of China|Unsupervised cross-domain image retrieval is designed to facilitate the retrieval between images in different domains in an unsupervised way. Without the guidance of labels, both intra-domain semantic learning and inter-domain semantic alignment pose significant challenges to the model's learning process. The resolution of these challenges relies on the accurate capture of domain-invariant semantic features by the model. Based on this consideration, we propose our Semantic-Attended Mixture of Experts (SA-MoE) model. Leveraging the proficiency of MoE network in capturing visual features, we enhance the model's focus on semantically relevant features through a series of strategies. We first utilize the self-attention mechanism of Vision Transformer to adaptively collect information with different weights on instances from different domains. In addition, we introduce contextual semantic association metrics to more accurately measure the semantic relatedness between instances. By utilizing the association metrics, secondary clustering is performed in the feature space to reinforce semantic relationships. Finally, we employ the metrics for information selection on the fused data to remove the semantic noise. We conduct extensive experiments on three widely used datasets. The consistent comparison results with existing methods indicate that our model possesses the state-of-the-art performance.|无监督跨域图像检索是为了方便不同域间图像的无监督检索而设计的。在没有标签指导的情况下，域内语义学习和域间语义对齐都给模型的学习过程带来了巨大的挑战。这些挑战的解决依赖于模型对领域不变语义特征的准确捕获。基于这一考虑，我们提出了语义参与的专家混合模型(SA-MoE)。利用 MoE 网络捕获视觉特征的能力，我们通过一系列策略提高了模型对语义相关特征的关注度。首先利用视觉变压器的自注意机制对不同领域的实例进行不同权重的信息自适应采集。此外，我们引入上下文语义关联度量来更准确地度量实例之间的语义关联。利用关联度量，在特征空间中进行二次聚类以增强语义关系。最后，利用融合数据的信息选择度量去除语义噪声。我们在三个广泛使用的数据集上进行了广泛的实验。与现有方法的一致性比较结果表明，我们的模型具有最先进的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Unsupervised+Cross-Domain+Image+Retrieval+with+Semantic-Attended+Mixture-of-Experts)|0| +|[Multilingual Meta-Distillation Alignment for Semantic Retrieval](https://doi.org/10.1145/3626772.3657812)|Meryem M'hamdi, Jonathan May, Franck Dernoncourt, Trung Bui, Seunghyun Yoon|University of Southern California, Los Angeles, CA, USA; Microsoft & University of Southern California, Redmond, WA, USA; Adobe Research, San Jose, CA, USA; Adobe Research, Seattle, WA, USA|Multilingual semantic retrieval involves retrieving semantically relevant content to a query irrespective of the language. Compared to monolingual and bilingual semantic retrieval, multilingual semantic retrieval requires a stronger alignment approach to pull the contents to be retrieved close to the representation of their corresponding queries, no matter their language combinations. Traditionally, this is achieved through more supervision in the form of multilingual parallel resources, which are expensive to obtain, especially for low-resource languages. In this work, on top of an optimization-based Model-Agnostic Meta-Learner (MAML), we propose a data-efficient meta-distillation approach: MAML-Align,1 specifically for low-resource multilingual semantic retrieval. Our approach simulates a gradual feedback loop from monolingual to bilingual and from bilingual to multilingual semantic retrieval. We systematically compare multilingual meta-distillation learning to different baselines and conduct ablation studies on the role of different sampling approaches in the meta-task construction. We show that MAML-Align's gradual feedback loop boosts the generalization to different languages, including zero-shot ones, better than naive fine-tuning and vanilla MAML.|多语言语义检索包括检索与查询语言无关的语义相关内容。与单语言和双语语义检索相比，多语言语义检索需要一种更强的对齐方法来将要检索的内容拉近其相应查询的表示，而不管它们的语言组合如何。传统上，这是通过以多语言并行资源的形式进行更多的监督来实现的，这些资源是昂贵的，特别是对于资源较少的语言。本文在基于优化的模型不可知元学习器(MAML)的基础上，提出了一种数据高效的元精馏方法: MAML-Align，1，专门用于低资源的多语言语义检索。我们的方法模拟了一个从单语到双语，从双语到多语的语义检索的渐进反馈循环。我们系统地比较了不同基线的多语言元精馏学习，并对不同抽样方法在元任务构建中的作用进行了消融研究。我们展示了 MAML-Align 的渐进反馈循环提高了对不同语言的泛化能力，包括0-shot 语言，这比单纯的微调和普通的 MAML 要好。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Multilingual+Meta-Distillation+Alignment+for+Semantic+Retrieval)|0| +|[Dataset and Models for Item Recommendation Using Multi-Modal User Interactions](https://doi.org/10.1145/3626772.3657881)|Simone Borg Bruun, Krisztian Balog, Maria Maistro|Dr. Scient.; PhD; Tenure Track Assistant Professor|While recommender systems with multi-modal item representations (image,audio, and text), have been widely explored, learning recommendations frommulti-modal user interactions (e.g., clicks and speech) remains an openproblem. We study the case of multi-modal user interactions in a setting whereusers engage with a service provider through multiple channels (website andcall center). In such cases, incomplete modalities naturally occur, since notall users interact through all the available channels. To address thesechallenges, we publish a real-world dataset that allows progress in thisunder-researched area. We further present and benchmark various methods forleveraging multi-modal user interactions for item recommendations, and proposea novel approach that specifically deals with missing modalities by mappinguser interactions to a common feature space. Our analysis reveals importantinteractions between the different modalities and that a frequently occurringmodality can enhance learning from a less frequent one.|虽然具有多模态项目表示(图像、音频和文本)的推荐系统已经得到了广泛的探索，但是从多模态用户交互(例如点击和语音)中学习推荐仍然是一个尚未解决的问题。我们研究的情况下，多模式的用户交互设置，其中用户与服务提供商通过多个渠道(网站和呼叫中心)。在这种情况下，不完整的模式自然会出现，因为没有用户通过所有可用的渠道进行交互。为了应对这些挑战，我们发布了一个真实世界的数据集，允许在这个研究不足的领域取得进展。我们进一步介绍和基准利用多模态用户交互的项目推荐的各种方法，并提出新的方法，具体处理缺失的模式映射到一个共同的功能空间的用户交互。我们的分析揭示了不同模式之间的重要相互作用，一个频繁出现的模式可以增强从一个不太频繁的学习。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Dataset+and+Models+for+Item+Recommendation+Using+Multi-Modal+User+Interactions)|0| +|[Behavior-Contextualized Item Preference Modeling for Multi-Behavior Recommendation](https://doi.org/10.1145/3626772.3657696)|Mingshi Yan, Fan Liu, Jing Sun, Fuming Sun, Zhiyong Cheng, Yahong Han|National University of Singapore; Hefei University of Technology; Tianjin University; Dalian Minzu University|In recommender systems, multi-behavior methods have demonstrated theireffectiveness in mitigating issues like data sparsity, a common challenge intraditional single-behavior recommendation approaches. These methods typicallyinfer user preferences from various auxiliary behaviors and apply them to thetarget behavior for recommendations. However, this direct transfer canintroduce noise to the target behavior in recommendation, due to variations inuser attention across different behaviors. To address this issue, this paperintroduces a novel approach, Behavior-Contextualized Item Preference Modeling(BCIPM), for multi-behavior recommendation. Our proposedBehavior-Contextualized Item Preference Network discerns and learns users'specific item preferences within each behavior. It then considers only thosepreferences relevant to the target behavior for final recommendations,significantly reducing noise from auxiliary behaviors. These auxiliarybehaviors are utilized solely for training the network parameters, therebyrefining the learning process without compromising the accuracy of the targetbehavior recommendations. To further enhance the effectiveness of BCIPM, weadopt a strategy of pre-training the initial embeddings. This step is crucialfor enriching the item-aware preferences, particularly in scenarios where datarelated to the target behavior is sparse. Comprehensive experiments conductedon four real-world datasets demonstrate BCIPM's superior performance comparedto several leading state-of-the-art models, validating the robustness andefficiency of our proposed approach.|在推荐系统中，多行为方法已经证明了它们在缓解诸如数据稀疏等问题上的有效性，这是传统的单行为推荐方法所面临的共同挑战。这些方法通常从各种辅助行为推断出用户偏好，并将其应用于目标行为以获得推荐。然而，由于不同行为的用户注意力的差异，这种直接转移会给推荐中的目标行为带来噪声。为了解决这个问题，本文提出了一种新的方法，行为上下文项目偏好建模(BCIPM) ，用于多行为推荐。我们提出的上下文化项目偏好网络识别和学习用户的特定项目偏好在每个行为。然后它只考虑那些与目标行为相关的偏好作为最终建议，显著减少辅助行为的噪音。这些辅助行为仅用于训练网络参数，从而在不影响目标行为建议准确性的前提下完善学习过程。为了进一步提高 BCIPM 的有效性，我们采用了预先训练初始嵌入的策略。这一步对于丰富项目感知偏好是至关重要的，特别是在与目标行为相关的数据稀少的情况下。在四个真实世界数据集上进行的综合实验表明，BCIPM 的性能优于几个领先的最先进的模型，验证了我们提出的方法的鲁棒性和有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Behavior-Contextualized+Item+Preference+Modeling+for+Multi-Behavior+Recommendation)|0| |[Exploring the Individuality and Collectivity of Intents behind Interactions for Graph Collaborative Filtering](https://doi.org/10.1145/3626772.3657738)|Yi Zhang, Lei Sang, Yiwen Zhang|Anhui University|Intent modeling has attracted widespread attention in recommender systems. Asthe core motivation behind user selection of items, intent is crucial forelucidating recommendation results. The current mainstream modeling method isto abstract the intent into unknowable but learnable shared or non-sharedparameters. Despite considerable progress, we argue that it still confronts thefollowing challenges: firstly, these methods only capture the coarse-grainedaspects of intent, ignoring the fact that user-item interactions will beaffected by collective and individual factors (e.g., a user may choose a moviebecause of its high box office or because of his own unique preferences);secondly, modeling believable intent is severely hampered by implicit feedback,which is incredibly sparse and devoid of true semantics. To address thesechallenges, we propose a novel recommendation framework designated as BilateralIntent-guided Graph Collaborative Filtering (BIGCF). Specifically, we take acloser look at user-item interactions from a causal perspective and put forththe concepts of individual intent-which signifies private preferences-andcollective intent-which denotes overall awareness. To counter the sparsity ofimplicit feedback, the feature distributions of users and items are encoded viaa Gaussian-based graph generation strategy, and we implement the recommendationprocess through bilateral intent-guided graph reconstruction re-sampling.Finally, we propose graph contrastive regularization for both interaction andintent spaces to uniformize users, items, intents, and interactions in aself-supervised and non-augmented paradigm. Experimental results on threereal-world datasets demonstrate the effectiveness of BIGCF compared withexisting solutions.|意图建模在推荐系统中引起了广泛的关注。作为用户选择项目背后的核心动机，意图是否定推荐结果的关键。目前主流的建模方法是将意图抽象为不可知但可学的共享或非共享参数。尽管取得了相当大的进步，我们认为它仍然面临以下挑战: 首先，这些方法只捕获意图的粗粒度方面，忽略了用户项目的交互将受到集体和个人因素的影响(例如，用户可能会选择一部电影，因为它的高票房或因为他自己的独特偏好) ; 其次，建模可信的意图是严重阻碍隐式反馈，这是令人难以置信的稀疏和缺乏真正的语义。为了应对这些挑战，我们提出了一个新的推荐框架，命名为双边/意图引导的图形协同过滤(bIGCF)。具体来说，我们从因果关系的角度来看待用户-项目的交互，提出了个人意图的概念——表示私人偏好——和集体意图——表示整体意识。针对隐式反馈的稀疏性，采用基于高斯的图生成策略对用户和项目的特征分布进行编码，并通过双边意图引导的图重构重采样实现推荐过程。最后，我们提出了交互空间和意图空间的图形对比正则化，在自监督和非增强的范式中统一用户、项目、意图和交互。在三个实际数据集上的实验结果表明了 BIGCF 方法与现有方法相比的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Exploring+the+Individuality+and+Collectivity+of+Intents+behind+Interactions+for+Graph+Collaborative+Filtering)|0| |[Content-based Graph Reconstruction for Cold-start Item Recommendation](https://doi.org/10.1145/3626772.3657801)|Jinri Kim, Eungi Kim, Kwangeun Yeo, Yujin Jeon, Chanwoo Kim, Sewon Lee, Joonseok Lee|Seoul National Univ., Seoul, Republic of Korea|Graph convolutions have been successfully applied to recommendation systems, utilizing high-order collaborative signals present in the user-item interaction graph. This idea, however, has not been applicable to the cold-start items, since cold nodes are isolated in the graph and thus do not take advantage of information exchange from neighboring nodes. Recently, there have been a few attempts to utilize graph convolutions on item-item or user-user attribute graphs to capture high-order collaborative signals for cold-start cases, but these approaches are still limited in that the item-item or user-user graph falls short in capturing the dynamics of user-item interactions, as their edges are constructed based on arbitrary and heuristic attribute similarity. In this paper, we introduce Content-based Graph Reconstruction for Cold-start item recommendation (CGRC), employing a masked graph autoencoder structure and multimodal contents to directly incorporate interaction-based high-order connectivity, applicable even in cold-start scenarios. To address the cold-start items directly on the interaction graph, our approach trains the model to reconstruct plausible user-item interactions from masked edges of randomly chosen cold items, simulating fresh items without connection to users. This strategy enables the model to infer potential edges for unseen cold-start nodes. Extensive experiments on real-world datasets demonstrate the superiority of our model.|图卷积已成功应用于推荐系统，利用高阶协作信号存在于用户项交互图中。然而，这种思想并不适用于冷启动项，因为冷节点在图中是孤立的，因此不利用相邻节点之间的信息交换。近年来，利用项目卷积或用户-用户属性图来获取冷启动情况下的高阶协作信号的方法已经有了一些尝试，但这些方法仍然受到项目卷积或用户-用户图在获取用户-项目交互动态方面的局限，因为它们的边是基于任意和启发式属性相似性构造的。本文介绍了基于内容的图重构技术在冷启动项目推荐中的应用，该技术采用屏蔽图自动编码器结构和多模态内容直接结合基于交互的高阶连通性，适用于冷启动项目推荐。为了直接处理交互图上的冷启动项目，我们的方法训练模型从随机选择的冷启动项目的掩盖边缘重建合理的用户-项目交互，模拟与用户没有联系的新鲜项目。该策略使模型能够推断出未知冷启动节点的潜在边。在实际数据集上的大量实验证明了该模型的优越性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Content-based+Graph+Reconstruction+for+Cold-start+Item+Recommendation)|0| -|[Unbiased Learning-to-Rank Needs Unconfounded Propensity Estimation](https://doi.org/10.1145/3626772.3657772)|Dan Luo, Lixin Zou, Qingyao Ai, Zhiyu Chen, Chenliang Li, Dawei Yin, Brian D. Davison|Amazon.com, Inc., Seattle, WA, USA; Baidu Inc., Beijing, China; Tsinghua University, Beijing, China; Wuhan University, Wuhan, China; Lehigh University, Bethlehem, PA, USA|The logs of the use of a search engine provide sufficient data to train a better ranker. However, it is well known that such implicit feedback reflects biases, and in particular a presentation bias that favors higher-ranked results. Unbiased Learning-to-Rank (ULTR) methods attempt to optimize performance by jointly modeling this bias along with the ranker so that the bias can be removed. Such methods have been shown to provide theoretical soundness, and promise superior performance and low deployment costs. However, existing ULTR methods don't recognize that query-document relevance is a confounder -- it affects both the likelihood of a result being clicked because of relevance and the likelihood of the result being ranked high by the base ranker. Moreover, the performance guarantees of existing ULTR methods assume the use of a weak ranker -- one that does a poor job of ranking documents based on relevance to a query. In practice, of course, commercial search engines use highly tuned rankers, and desire to improve upon them using the implicit judgments in search logs. This results in a significant correlation between position and relevance, which leads existing ULTR methods to overestimate click propensities in highly ranked results, reducing ULTR's effectiveness. This paper is the first to demonstrate the problem of propensity overestimation by ULTR algorithms, based on a causal analysis. We develop a new learning objective based on a backdoor adjustment. In addition, we introduce the Logging-Policy-aware Propensity (LPP) model that can jointly learn LPP and a more accurate ranker. We extensively test our approach on two public benchmark tasks and show that our proposal is effective, practical and significantly outperforms the state of the art.|使用搜索引擎的日志提供了足够的数据来训练一个更好的排名。然而，众所周知，这种隐性反馈反映了偏见，特别是偏向于排名较高的结果的表示偏见。无偏学习排序(ULTR)方法试图通过将这种偏差与排序器联合建模来优化性能，从而消除这种偏差。这样的方法已被证明提供了理论上的可靠性，并承诺优越的性能和低部署成本。然而，现有的 ULTR 方法没有认识到查询文档的相关性是一个混杂因素——它影响因为相关性而被点击的结果的可能性和基础排名结果被排名高的可能性。此外，现有 ULTR 方法的性能保证假设使用了一个弱排名器——一个根据与查询的相关性对文档进行排名的工作做得很糟糕的排名器。当然，在实践中，商业搜索引擎使用高度调整的排名，并希望利用搜索日志中隐含的判断来改进它们。这导致了位置和相关性之间的显著相关性，这导致现有的 ULTR 方法高估了高排名结果中的点击倾向，降低了 ULTR 的有效性。本文首次在分析因果关系的基础上，论证了 ULTR 算法存在的倾向高估问题。我们开发了一个新的学习目标的基础上后门调整。此外，我们还介绍了日志策略感知倾向(LPP)模型，该模型可以联合学习 LPP 和一个更准确的排名。我们在两个公共基准任务上广泛测试了我们的方法，并表明我们的建议是有效的、实用的，而且明显优于最先进的水平。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Unbiased+Learning-to-Rank+Needs+Unconfounded+Propensity+Estimation)|0| -|[Scenario-Adaptive Fine-Grained Personalization Network: Tailoring User Behavior Representation to the Scenario Context](https://doi.org/10.1145/3626772.3657803)|Moyu Zhang, Yongxiang Tang, Jinxin Hu, Yu Zhang|Lazada Group; Unaffiliated|Existing methods often adjust representations adaptively only after aggregating user behavior sequences. This coarse-grained approach to re-weighting the entire user sequence hampers the model's ability to accurately model the user interest migration across different scenarios. To enhance the model's capacity to capture user interests from historical behavior sequences in each scenario, we develop a ranking framework named the Scenario-Adaptive Fine-Grained Personalization Network (SFPNet), which designs a kind of fine-grained method for multi-scenario personalized recommendations. Specifically, SFPNet comprises a series of blocks named as Scenario-Tailoring Block, stacked sequentially. Each block initially deploys a parameter personalization unit to integrate scenario information at a coarse-grained level by redefining fundamental features. Subsequently, we consolidate scenario-adaptively adjusted feature representations to serve as context information. By employing residual connection, we incorporate this context into the representation of each historical behavior, allowing for context-aware fine-grained customization of the behavior representations at the scenario-level, which in turn supports scenario-aware user interest modeling.|现有的方法通常只在聚合用户行为序列之后才自适应地调整表示。这种重新加权整个用户序列的粗粒度方法阻碍了模型在不同场景间精确建模用户兴趣迁移的能力。为了提高模型从每个场景中的历史行为序列中获取用户兴趣的能力，提出了一种基于场景-自适应细粒度个性化网络(Scenario-AdaptiveFine-GrainedPersonalization Network，SFPNet)的排序框架，该框架设计了一种用于多场景个性化推荐的细粒度方法。具体来说，SFPNet 包含一系列名为 Scenario-Tailoring Block 的块，按顺序堆叠。每个块最初部署一个参数个性化单元，通过重新定义基本特性在粗粒度级别集成场景信息。随后，我们整合场景-自适应调整的特征表示作为上下文信息。通过使用剩余连接，我们将这个上下文整合到每个历史行为的表示中，允许在场景级别对行为表示进行上下文感知的细粒度定制，这反过来又支持场景感知的用户兴趣建模。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Scenario-Adaptive+Fine-Grained+Personalization+Network:+Tailoring+User+Behavior+Representation+to+the+Scenario+Context)|0| +|[Unbiased Learning-to-Rank Needs Unconfounded Propensity Estimation](https://doi.org/10.1145/3626772.3657772)|Dan Luo, Lixin Zou, Qingyao Ai, Zhiyu Chen, Chenliang Li, Dawei Yin, Brian D. Davison|Amazon.com, Inc., Seattle, WA, USA; Tsinghua University, Beijing, China; Lehigh University, Bethlehem, PA, USA; Wuhan University, Wuhan, China; Baidu Inc., Beijing, China|The logs of the use of a search engine provide sufficient data to train a better ranker. However, it is well known that such implicit feedback reflects biases, and in particular a presentation bias that favors higher-ranked results. Unbiased Learning-to-Rank (ULTR) methods attempt to optimize performance by jointly modeling this bias along with the ranker so that the bias can be removed. Such methods have been shown to provide theoretical soundness, and promise superior performance and low deployment costs. However, existing ULTR methods don't recognize that query-document relevance is a confounder -- it affects both the likelihood of a result being clicked because of relevance and the likelihood of the result being ranked high by the base ranker. Moreover, the performance guarantees of existing ULTR methods assume the use of a weak ranker -- one that does a poor job of ranking documents based on relevance to a query. In practice, of course, commercial search engines use highly tuned rankers, and desire to improve upon them using the implicit judgments in search logs. This results in a significant correlation between position and relevance, which leads existing ULTR methods to overestimate click propensities in highly ranked results, reducing ULTR's effectiveness. This paper is the first to demonstrate the problem of propensity overestimation by ULTR algorithms, based on a causal analysis. We develop a new learning objective based on a backdoor adjustment. In addition, we introduce the Logging-Policy-aware Propensity (LPP) model that can jointly learn LPP and a more accurate ranker. We extensively test our approach on two public benchmark tasks and show that our proposal is effective, practical and significantly outperforms the state of the art.|使用搜索引擎的日志提供了足够的数据来训练一个更好的排名。然而，众所周知，这种隐性反馈反映了偏见，特别是偏向于排名较高的结果的表示偏见。无偏学习排序(ULTR)方法试图通过将这种偏差与排序器联合建模来优化性能，从而消除这种偏差。这样的方法已被证明提供了理论上的可靠性，并承诺优越的性能和低部署成本。然而，现有的 ULTR 方法没有认识到查询文档的相关性是一个混杂因素——它影响因为相关性而被点击的结果的可能性和基础排名结果被排名高的可能性。此外，现有 ULTR 方法的性能保证假设使用了一个弱排名器——一个根据与查询的相关性对文档进行排名的工作做得很糟糕的排名器。当然，在实践中，商业搜索引擎使用高度调整的排名，并希望利用搜索日志中隐含的判断来改进它们。这导致了位置和相关性之间的显著相关性，这导致现有的 ULTR 方法高估了高排名结果中的点击倾向，降低了 ULTR 的有效性。本文首次在分析因果关系的基础上，论证了 ULTR 算法存在的倾向高估问题。我们开发了一个新的学习目标的基础上后门调整。此外，我们还介绍了日志策略感知倾向(LPP)模型，该模型可以联合学习 LPP 和一个更准确的排名。我们在两个公共基准任务上广泛测试了我们的方法，并表明我们的建议是有效的、实用的，而且明显优于最先进的水平。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Unbiased+Learning-to-Rank+Needs+Unconfounded+Propensity+Estimation)|0| +|[Scenario-Adaptive Fine-Grained Personalization Network: Tailoring User Behavior Representation to the Scenario Context](https://doi.org/10.1145/3626772.3657803)|Moyu Zhang, Yongxiang Tang, Jinxin Hu, Yu Zhang|Unaffiliated; Lazada Group|Existing methods often adjust representations adaptively only after aggregating user behavior sequences. This coarse-grained approach to re-weighting the entire user sequence hampers the model's ability to accurately model the user interest migration across different scenarios. To enhance the model's capacity to capture user interests from historical behavior sequences in each scenario, we develop a ranking framework named the Scenario-Adaptive Fine-Grained Personalization Network (SFPNet), which designs a kind of fine-grained method for multi-scenario personalized recommendations. Specifically, SFPNet comprises a series of blocks named as Scenario-Tailoring Block, stacked sequentially. Each block initially deploys a parameter personalization unit to integrate scenario information at a coarse-grained level by redefining fundamental features. Subsequently, we consolidate scenario-adaptively adjusted feature representations to serve as context information. By employing residual connection, we incorporate this context into the representation of each historical behavior, allowing for context-aware fine-grained customization of the behavior representations at the scenario-level, which in turn supports scenario-aware user interest modeling.|现有的方法通常只在聚合用户行为序列之后才自适应地调整表示。这种重新加权整个用户序列的粗粒度方法阻碍了模型在不同场景间精确建模用户兴趣迁移的能力。为了提高模型从每个场景中的历史行为序列中获取用户兴趣的能力，提出了一种基于场景-自适应细粒度个性化网络(Scenario-AdaptiveFine-GrainedPersonalization Network，SFPNet)的排序框架，该框架设计了一种用于多场景个性化推荐的细粒度方法。具体来说，SFPNet 包含一系列名为 Scenario-Tailoring Block 的块，按顺序堆叠。每个块最初部署一个参数个性化单元，通过重新定义基本特性在粗粒度级别集成场景信息。随后，我们整合场景-自适应调整的特征表示作为上下文信息。通过使用剩余连接，我们将这个上下文整合到每个历史行为的表示中，允许在场景级别对行为表示进行上下文感知的细粒度定制，这反过来又支持场景感知的用户兴趣建模。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Scenario-Adaptive+Fine-Grained+Personalization+Network:+Tailoring+User+Behavior+Representation+to+the+Scenario+Context)|0| |[EulerFormer: Sequential User Behavior Modeling with Complex Vector Attention](https://doi.org/10.1145/3626772.3657805)|Zhen Tian, Wayne Xin Zhao, Changwang Zhang, Xin Zhao, Zhongrui Ma, JiRong Wen|Renmin University of China; Huawei|To capture user preference, transformer models have been widely applied tomodel sequential user behavior data. The core of transformer architecture liesin the self-attention mechanism, which computes the pairwise attention scoresin a sequence. Due to the permutation-equivariant nature, positional encodingis used to enhance the attention between token representations. In thissetting, the pairwise attention scores can be derived by both semanticdifference and positional difference. However, prior studies often model thetwo kinds of difference measurements in different ways, which potentiallylimits the expressive capacity of sequence modeling. To address this issue,this paper proposes a novel transformer variant with complex vector attention,named EulerFormer, which provides a unified theoretical framework to formulateboth semantic difference and positional difference. The EulerFormer involvestwo key technical improvements. First, it employs a new transformation functionfor efficiently transforming the sequence tokens into polar-form complexvectors using Euler's formula, enabling the unified modeling of both semanticand positional information in a complex rotation form.Secondly, it develops adifferential rotation mechanism, where the semantic rotation angles can becontrolled by an adaptation function, enabling the adaptive integration of thesemantic and positional information according to the semanticcontexts.Furthermore, a phase contrastive learning task is proposed to improvethe anisotropy of contextual representations in EulerFormer. Our theoreticalframework possesses a high degree of completeness and generality. It is morerobust to semantic variations and possesses moresuperior theoretical propertiesin principle. Extensive experiments conducted on four public datasetsdemonstrate the effectiveness and efficiency of our approach.|为了捕获用户偏好，转换器模型已被广泛应用于模型顺序用户行为数据。变压器结构的核心是自注意机制，它计算一个序列的成对注意得分。由于置换等变的特性，位置编码被用来增强标记表示之间的注意力。在这种情况下，成对的注意分数可以通过语义差异和位置差异得出。然而，先前的研究往往以不同的方式对这两种差异测量进行建模，这可能限制了序列建模的表达能力。为了解决这一问题，本文提出了一种新的具有复矢量注意力的变换器变体，称为欧拉变换器，它提供了一个统一的理论框架来描述语义差异和位置差异。欧拉前者包括两个关键的技术改进。首先，利用欧拉公式有效地将序列标记转换为极形复合向量，使得语义和位置信息以复杂的旋转形式统一建模成为可能。其次，提出了一种差分旋转机制，该机制通过自适应函数控制语义旋转角度，实现了语义和位置信息根据语义上下文的自适应集成。此外，本文还提出了一个相位对比学习任务来改善 EulerForm 中上下文表示的各向异性。我们的理论框架具有较高的完整性和普遍性。它对语义变异具有更强的鲁棒性，在原则上具有更优越的理论性。在四个公共数据集上进行的大量实验证明了我们方法的有效性和效率。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=EulerFormer:+Sequential+User+Behavior+Modeling+with+Complex+Vector+Attention)|0| |[Breaking the Length Barrier: LLM-Enhanced CTR Prediction in Long Textual User Behaviors](https://doi.org/10.1145/3626772.3657974)|Binzong Geng, Zhaoxin Huan, Xiaolu Zhang, Yong He, Liang Zhang, Fajie Yuan, Jun Zhou, Linjian Mo|Ant Group; Westlake University|With the rise of large language models (LLMs), recent works have leveragedLLMs to improve the performance of click-through rate (CTR) prediction.However, we argue that a critical obstacle remains in deploying LLMs forpractical use: the efficiency of LLMs when processing long textual userbehaviors. As user sequences grow longer, the current efficiency of LLMs isinadequate for training on billions of users and items. To break through theefficiency barrier of LLMs, we propose Behavior Aggregated HierarchicalEncoding (BAHE) to enhance the efficiency of LLM-based CTR modeling.Specifically, BAHE proposes a novel hierarchical architecture that decouplesthe encoding of user behaviors from inter-behavior interactions. Firstly, toprevent computational redundancy from repeated encoding of identical userbehaviors, BAHE employs the LLM's pre-trained shallow layers to extractembeddings of the most granular, atomic user behaviors from extensive usersequences and stores them in the offline database. Subsequently, the deeper,trainable layers of the LLM facilitate intricate inter-behavior interactions,thereby generating comprehensive user embeddings. This separation allows thelearning of high-level user representations to be independent of low-levelbehavior encoding, significantly reducing computational complexity. Finally,these refined user embeddings, in conjunction with correspondingly processeditem embeddings, are incorporated into the CTR model to compute the CTR scores.Extensive experimental results show that BAHE reduces training time and memoryby five times for CTR models using LLMs, especially with longer user sequences.BAHE has been deployed in a real-world system, allowing for daily updates of 50million CTR data on 8 A100 GPUs, making LLMs practical for industrial CTRprediction.|随着大型语言模型(LLM)的兴起，最近的工作已经利用 LLM 来提高点进率预测(CTR)的性能。然而，我们认为在实际应用中部署 LLM 仍然存在一个关键的障碍: 当处理长文本用户行为时 LLM 的效率。随着用户序列的增长，LLM 目前的效率不足以对数十亿用户和项目进行培训。为了突破 LLM 的效率障碍，我们提出了行为聚合层次编码(BAHE)来提高基于 LLM 的 CTR 建模的效率。具体来说，BAHE 提出了一种新的层次结构，将用户行为的编码与行为间的交互分离开来。首先，为了防止计算冗余重复编码相同的用户行为，BAHE 使用 LLM 预先训练的浅层来从大量的用户序列中提取最细粒度的原子用户行为，并将它们存储在离线数据库中。随后，LLM 的更深层、可训练的层促进了复杂的行为间交互，从而产生了全面的用户嵌入。这种分离使得高级用户表示的学习独立于低级行为编码，显著降低了计算复杂度。最后，这些改进的用户嵌入，结合相应的处理过的项目嵌入，被合并到 CTR 模型中来计算 CTR 分数。大量的实验结果表明，BAHE 使用 LLM 将 CTR 模型的训练时间和记忆降低了5倍，特别是对于更长的用户序列。 BAHE 已经部署在现实世界的系统中，允许在8个 A100图形处理器上每天更新5000万个 CTR 数据，使 LLM 用于工业 CTR 预测变得实用。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Breaking+the+Length+Barrier:+LLM-Enhanced+CTR+Prediction+in+Long+Textual+User+Behaviors)|0| -|[Multi-intent-aware Session-based Recommendation](https://doi.org/10.1145/3626772.3657928)|Minjin Choi, Hyeyoung Kim, Hyunsouk Cho, Jongwuk Lee|Sungkyunkwan University Artificial intelligence; Ajou University; Sungkyunkwan University|Session-based recommendation (SBR) aims to predict the following item a userwill interact with during an ongoing session. Most existing SBR models focus ondesigning sophisticated neural-based encoders to learn a sessionrepresentation, capturing the relationship among session items. However, theytend to focus on the last item, neglecting diverse user intents that may existwithin a session. This limitation leads to significant performance drops,especially for longer sessions. To address this issue, we propose a novel SBRmodel, called Multi-intent-aware Session-based Recommendation Model (MiaSRec).It adopts frequency embedding vectors indicating the item frequency in sessionto enhance the information about repeated items. MiaSRec represents varioususer intents by deriving multiple session representations centered on each itemand dynamically selecting the important ones. Extensive experimental resultsshow that MiaSRec outperforms existing state-of-the-art SBR models on sixdatasets, particularly those with longer average session length, achieving upto 6.27https://github.com/jin530/MiaSRec.|基于会话的推荐(SBR)旨在预测用户将在正在进行的会话期间与以下项目进行交互。大多数现有的 SBR 模型侧重于设计复杂的基于神经的编码器来学习会话表示，捕获会话项之间的关系。然而，他们倾向于关注最后一项，忽略了会话中可能存在的不同用户意图。这种限制会导致显著的性能下降，特别是对于较长的会话。为了解决这个问题，我们提出了一种新的 SBR 模型，称为多意图感知的基于会话的推荐模型(MiaSRec)。采用频率嵌入向量表示会话中项目的频率，增强重复项目的信息。MiaSRec 通过派生以每个项为中心的多个会话表示并动态选择重要的会话表示来表示各种用户意图。大量的实验结果表明，MiaSrec 在6个数据集上优于现有的最先进的 SBR 模型，特别是那些平均会话长度更长的模型，达到了6.27的 https://github.com/jin530/MiaSRec。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Multi-intent-aware+Session-based+Recommendation)|0| +|[Multi-intent-aware Session-based Recommendation](https://doi.org/10.1145/3626772.3657928)|Minjin Choi, Hyeyoung Kim, Hyunsouk Cho, Jongwuk Lee|Ajou University; Sungkyunkwan University; Sungkyunkwan University Artificial intelligence|Session-based recommendation (SBR) aims to predict the following item a userwill interact with during an ongoing session. Most existing SBR models focus ondesigning sophisticated neural-based encoders to learn a sessionrepresentation, capturing the relationship among session items. However, theytend to focus on the last item, neglecting diverse user intents that may existwithin a session. This limitation leads to significant performance drops,especially for longer sessions. To address this issue, we propose a novel SBRmodel, called Multi-intent-aware Session-based Recommendation Model (MiaSRec).It adopts frequency embedding vectors indicating the item frequency in sessionto enhance the information about repeated items. MiaSRec represents varioususer intents by deriving multiple session representations centered on each itemand dynamically selecting the important ones. Extensive experimental resultsshow that MiaSRec outperforms existing state-of-the-art SBR models on sixdatasets, particularly those with longer average session length, achieving upto 6.27https://github.com/jin530/MiaSRec.|基于会话的推荐(SBR)旨在预测用户将在正在进行的会话期间与以下项目进行交互。大多数现有的 SBR 模型侧重于设计复杂的基于神经的编码器来学习会话表示，捕获会话项之间的关系。然而，他们倾向于关注最后一项，忽略了会话中可能存在的不同用户意图。这种限制会导致显著的性能下降，特别是对于较长的会话。为了解决这个问题，我们提出了一种新的 SBR 模型，称为多意图感知的基于会话的推荐模型(MiaSRec)。采用频率嵌入向量表示会话中项目的频率，增强重复项目的信息。MiaSRec 通过派生以每个项为中心的多个会话表示并动态选择重要的会话表示来表示各种用户意图。大量的实验结果表明，MiaSrec 在6个数据集上优于现有的最先进的 SBR 模型，特别是那些平均会话长度更长的模型，达到了6.27的 https://github.com/jin530/MiaSRec。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Multi-intent-aware+Session-based+Recommendation)|0| |[PLAID SHIRTTT for Large-Scale Streaming Dense Retrieval](https://doi.org/10.1145/3626772.3657964)|Dawn J. Lawrie, Efsun Selin Kayi, Eugene Yang, James Mayfield, Douglas W. Oard|University of Maryland; Johns Hopkins University HLTCOE|PLAID, an efficient implementation of the ColBERT late interaction bi-encoderusing pretrained language models for ranking, consistently achievesstate-of-the-art performance in monolingual, cross-language, and multilingualretrieval. PLAID differs from ColBERT by assigning terms to clusters andrepresenting those terms as cluster centroids plus compressed residual vectors.While PLAID is effective in batch experiments, its performance degrades instreaming settings where documents arrive over time because representations ofnew tokens may be poorly modeled by the earlier tokens used to select clustercentroids. PLAID Streaming Hierarchical Indexing that Runs on Terabytes ofTemporal Text (PLAID SHIRTTT) addresses this concern using multi-phaseincremental indexing based on hierarchical sharding. Experiments on ClueWeb09and the multilingual NeuCLIR collection demonstrate the effectiveness of thisapproach both for the largest collection indexed to date by the ColBERTarchitecture and in the multilingual setting, respectively.|PLAID 是 ColBERT 后期交互双编码器的有效实现，使用预先训练的语言模型进行排名，始终在单语言、跨语言和多语言检索方面取得最佳性能。PLAID 不同于 ColBERT，它将术语分配给聚类，并将这些术语表示为聚类质心加上压缩的残差向量。虽然 PLAID 在批处理实验中是有效的，但它的性能降低了文档随时间到达的流化设置，因为用于选择集群中心的早期令牌可能对新令牌的表示建模不足。基于 T 级时态文本的 PLAID 流式分层索引(PLAID SHIRTTT)使用基于分层分片的多阶段增量索引解决了这个问题。在 ClueWeb09和多语言 NeuCLIR 集合上的实验分别证明了这种方法对 ColBERT 架构迄今为止索引的最大集合和多语言设置的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=PLAID+SHIRTTT+for+Large-Scale+Streaming+Dense+Retrieval)|0| |[Towards Ethical Item Ranking: A Paradigm Shift from User-Centric to Item-Centric Approaches](https://doi.org/10.1145/3626772.3657977)|Guilherme Ramos, Mirko Marras, Ludovico Boratto|University of Cagliari, Cagliari, Italy; Instituto de Telecomunicações, and Instituto Superior Técnico, ULisboa, Lisbon, Portugal|Ranking systems are instrumental in shaping user experiences by determining the relevance and order of presented items. However, current approaches, particularly those revolving around user-centric reputation scoring, raise ethical concerns associated with scoring individuals. To counter such issues, in this paper, we introduce a novel item ranking system approach that strategically transitions its emphasis from scoring users to calculating item rankings relying exclusively on items' ratings information, to achieve the same objective. Experiments on three datasets show that our approach achieves higher effectiveness and efficiency than state-of-the-art baselines. Furthermore, the resulting rankings are more robust to spam and resistant to bribery, contributing to a novel and ethically sound direction for item ranking systems.|排名系统有助于通过确定呈现项目的相关性和顺序来塑造用户体验。然而，目前的方法，特别是那些围绕以用户为中心的声誉评分的方法，提出了与个人评分相关的伦理问题。为了解决这些问题，本文提出了一种新的项目排名系统方法，该方法从对用户进行评分转变为完全依靠项目的评分信息来计算项目排名，以达到同样的目的。在三个数据集上的实验表明，该方法比最先进的基线方法具有更高的效率和效果。此外，由此产生的排名更强大的垃圾邮件和抵抗贿赂，有助于一个新颖和道德上健全的项目排名系统的方向。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Towards+Ethical+Item+Ranking:+A+Paradigm+Shift+from+User-Centric+to+Item-Centric+Approaches)|0| |[A Large-scale Offer Alignment Model for Partitioning Filtering and Matching Product Offers](https://doi.org/10.1145/3626772.3661351)|Wenyu Huang, André Melo, Jeff Z. Pan|The University of Edinburgh, Edinburgh, United Kingdom; Huawei Technologies R&D, Edinburgh, United Kingdom|Offer alignment is a key step in a product knowledge graph construction pipeline. It aims to align retailer offers of the same product for better coverage of product details. With the rapid development of online shopping services, the offer alignment task is applied in ever larger datasets. This work aims to build an offer alignment system that can efficiently be used in large-scale offer data. The key components of this system include: 1) common offer encoders for encoding text offer data into representations; 2) trainable LSH partitioning module to divide similar offers into small blocks; 3) lightweight sophisticated late-interactions for efficient filtering and scoring of offer alignment candidate pairs. We evaluate the system on public WDC offer alignment dataset, as well as DBLP-Scholar and DBLP-ACM.|报价对齐是产品知识图构建流程中的一个关键步骤。它旨在使零售商提供的同一产品，以更好地覆盖产品的细节。随着网上购物服务的迅速发展，报价对齐任务在越来越大的数据集中得到了广泛的应用。本文旨在建立一个能够有效应用于大规模报价数据的报价对齐系统。该系统的关键组成部分包括: 1)用于编码文本的通用报价编码器将数据提供到表示中; 2)可训练的 LSH 分区模块将类似的报价划分为小块; 3)轻量级复杂的后期交互，以便对报价对齐候选对进行有效的过滤和评分。我们在公共 WDC 提供的比对数据集上以及 DBLP-Scholar 和 DBLP-ACM 上对该系统进行了评估。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Large-scale+Offer+Alignment+Model+for+Partitioning+Filtering+and+Matching+Product+Offers)|0| |[Interest Clock: Time Perception in Real-Time Streaming Recommendation System](https://doi.org/10.1145/3626772.3661369)|Yongchun Zhu, Jingwu Chen, Ling Chen, Yitan Li, Feng Zhang, Zuotao Liu|ByteDance|User preferences follow a dynamic pattern over a day, e.g., at 8 am, a usermight prefer to read news, while at 8 pm, they might prefer to watch movies.Time modeling aims to enable recommendation systems to perceive time changes tocapture users' dynamic preferences over time, which is an important andchallenging problem in recommendation systems. Especially, streamingrecommendation systems in the industry, with only available samples of thecurrent moment, present greater challenges for time modeling. There is still alack of effective time modeling methods for streaming recommendation systems.In this paper, we propose an effective and universal method Interest Clock toperceive time information in recommendation systems. Interest Clock firstencodes users' time-aware preferences into a clock (hour-level personalizedfeatures) and then uses Gaussian distribution to smooth and aggregate them intothe final interest clock embedding according to the current time for the finalprediction. By arming base models with Interest Clock, we conduct online A/Btests, obtaining +0.509duration respectively. Besides, the extended offline experiments showimprovements as well. Interest Clock has been deployed on Douyin Music App.|用户偏好在一天中遵循一种动态模式，例如，在早上8点，用户可能更喜欢阅读新闻，而在晚上8点，他们可能更喜欢看电影。时间建模的目的是使推荐系统能够感知时间的变化，捕获用户随时间变化的动态偏好，这是推荐系统中的一个重要而又具有挑战性的问题。特别是，业界的流式推荐系统，只有当前时刻的可用样本，对时间建模提出了更大的挑战。针对流媒体推荐系统中时间建模方法的不足，本文提出了一种有效而通用的时间建模方法——兴趣时钟法。兴趣时钟首先将用户的时间感知偏好编码成一个时钟(小时级别的个性化功能) ，然后使用正态分布来平滑和聚合它们，根据当前的时间嵌入到最终的兴趣时钟中，进行最终的预测。通过使用兴趣时钟武装基本模型，我们进行在线 A/Btest，分别获得 + 0.509的持续时间。此外，延长的离线实验也有所改善。兴趣时钟已经在抖音音乐应用程序上部署。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Interest+Clock:+Time+Perception+in+Real-Time+Streaming+Recommendation+System)|0| -|[Unsupervised Large Language Model Alignment for Information Retrieval via Contrastive Feedback](https://doi.org/10.1145/3626772.3657689)|Qian Dong, Yiding Liu, Qingyao Ai, Zhijing Wu, Haitao Li, Yiqun Liu, Shuaiqiang Wang, Dawei Yin, Shaoping Ma|; Baidu Inc. Search Science; Baidu Inc.; Beijing Institute of Technology; Tsinghua University; Tsinghua University Department of Computer Science and Technology; Tsinghua University Computer Science and Technology|Large language models (LLMs) have demonstrated remarkable capabilities acrossvarious research domains, including the field of Information Retrieval (IR).However, the responses generated by off-the-shelf LLMs tend to be generic,i.e., cannot capture the distinctiveness of each document with similar content.This limits the performance of LLMs in IR because finding and distinguishingrelevant documents from substantial similar documents is a typical problem inmany IR tasks. To address this issue, we propose an unsupervised alignmentmethod, namely Reinforcement Learning from Contrastive Feedback (RLCF),empowering LLMs to generate both high-quality and context-specific responses.Our approach constructs unsupervised contrastive feedback signals based onsimilar document groups, and adopts a reward function, named group-wisereciprocal rank, to optimize LLMs within a standard Proximal PolicyOptimization. We conduct extensive experiments to evaluate the effectiveness ofRLCF on LLMs built with different languages and parameter sizes on multipledownstream IR applications. RLCF significantly outperforms existing alignmentmethods, and RLCF-optimized LLMs demonstrate considerable improvement ingenerating responses with distinctiveness.|大型语言模型(LLM)已经在各个研究领域展示了卓越的能力，包括信息检索领域(IR)。然而，现成 LLM 生成的响应往往是通用的，即不能捕获具有相似内容的每个文档的独特性。这限制了 LLM 在 IR 中的性能，因为在许多 IR 任务中，查找和区分相关文档和大量类似文档是一个典型的问题。为了解决这个问题，我们提出了一种无监督的对齐方法，即从对比反馈(强化学习) ，授权 LLM 生成高质量和上下文特定的反应。该方法基于相似文档组构造无监督对比反馈信号，并采用一种称为组-智力互惠秩的奖励函数，在标准的近似策略优化中优化 LLM。我们进行了广泛的实验来评估 RLCF 在不同语言构建的 LLM 上的有效性，以及在多下游 IR 应用中的参数大小。RLCF 明显优于现有的比对方法，并且 RLCF 优化的 LLM 显示出相当大的改进，产生独特的响应。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Unsupervised+Large+Language+Model+Alignment+for+Information+Retrieval+via+Contrastive+Feedback)|0| -|[Amazon-KG: A Knowledge Graph Enhanced Cross-Domain Recommendation Dataset](https://doi.org/10.1145/3626772.3657880)|Yuhan Wang, Qing Xie, Mengzi Tang, Lin Li, Jingling Yuan, Yongjian Liu|Wuhan University Of Technology; Wuhan University of Technology|Cross-domain recommendation (CDR) aims to utilize the information from relevant domains to guide the recommendation task in the target domain, and shows great potential in alleviating the data sparsity and cold-start problems of recommender systems. Most existing methods utilize the interaction information (e.g., ratings and clicks) or consider auxiliary information (e.g., tags and comments) to analyze the users' cross-domain preferences, but such kinds of information ignore the intrinsic semantic relationship of different domains. In order to effectively explore the inter-domain correlations, encyclopedic knowledge graphs (KG) involving different domains are highly desired in cross-domain recommendation tasks because they contain general information covering various domains with structured data format. However, there are few datasets containing KG information for CDR tasks, so in order to enrich the available data resource, we build a KG-enhanced cross-domain recommendation dataset, named Amazon-KG, based on the widely used Amazon dataset for CDR and the well-known KG DBpedia. In this work, we analyze the potential of KG applying in cross-domain recommendations, and describe the construction process of our dataset in detail. Finally, we perform quantitative statistical analysis on the dataset. We believe that datasets like Amazon-KG contribute to the development of knowledge-aware cross-domain recommender systems. Our dataset has been released at https://github.com/WangYuhan-0520/Amazon-KG-v2.0-dataset.|跨域推荐技术是利用相关领域的信息来指导目标领域的推荐任务，在缓解推荐系统的数据稀疏性和冷启动问题方面显示出巨大的潜力。大多数现有的方法利用交互信息(如评分和点击)或考虑辅助信息(如标签和评论)来分析用户的跨域偏好，但这类信息忽视了不同域的内在语义关系。为了有效地探索域间相关性，涉及不同领域的百科知识图(KG)是跨领域推荐任务中非常需要的，因为它包含了覆盖不同领域的结构化数据格式的一般信息。然而，包含用于 CDR 任务的 KG 信息的数据集很少，因此为了丰富可用的数据资源，我们基于广泛使用的用于 CDR 的 Amazon 数据集和著名的 KG DBpedia，构建了一个 KG 增强的跨域推荐数据集，命名为 Amazon-KG。在这项工作中，我们分析了 KG 在跨领域推荐中的应用潜力，并详细描述了我们的数据集的构建过程。最后，对数据集进行定量统计分析。我们相信像 Amazon-KG 这样的数据集有助于开发知识感知的跨领域推荐系统。我们的数据 https://github.com/wangyuhan-0520/amazon-kg-v2.0-dataset 已经发布了。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Amazon-KG:+A+Knowledge+Graph+Enhanced+Cross-Domain+Recommendation+Dataset)|0| +|[Unsupervised Large Language Model Alignment for Information Retrieval via Contrastive Feedback](https://doi.org/10.1145/3626772.3657689)|Qian Dong, Yiding Liu, Qingyao Ai, Zhijing Wu, Haitao Li, Yiqun Liu, Shuaiqiang Wang, Dawei Yin, Shaoping Ma|; Tsinghua University Computer Science and Technology; Tsinghua University Department of Computer Science and Technology; Beijing Institute of Technology; Baidu Inc.; Tsinghua University; Baidu Inc. Search Science|Large language models (LLMs) have demonstrated remarkable capabilities acrossvarious research domains, including the field of Information Retrieval (IR).However, the responses generated by off-the-shelf LLMs tend to be generic,i.e., cannot capture the distinctiveness of each document with similar content.This limits the performance of LLMs in IR because finding and distinguishingrelevant documents from substantial similar documents is a typical problem inmany IR tasks. To address this issue, we propose an unsupervised alignmentmethod, namely Reinforcement Learning from Contrastive Feedback (RLCF),empowering LLMs to generate both high-quality and context-specific responses.Our approach constructs unsupervised contrastive feedback signals based onsimilar document groups, and adopts a reward function, named group-wisereciprocal rank, to optimize LLMs within a standard Proximal PolicyOptimization. We conduct extensive experiments to evaluate the effectiveness ofRLCF on LLMs built with different languages and parameter sizes on multipledownstream IR applications. RLCF significantly outperforms existing alignmentmethods, and RLCF-optimized LLMs demonstrate considerable improvement ingenerating responses with distinctiveness.|大型语言模型(LLM)已经在各个研究领域展示了卓越的能力，包括信息检索领域(IR)。然而，现成 LLM 生成的响应往往是通用的，即不能捕获具有相似内容的每个文档的独特性。这限制了 LLM 在 IR 中的性能，因为在许多 IR 任务中，查找和区分相关文档和大量类似文档是一个典型的问题。为了解决这个问题，我们提出了一种无监督的对齐方法，即从对比反馈(强化学习) ，授权 LLM 生成高质量和上下文特定的反应。该方法基于相似文档组构造无监督对比反馈信号，并采用一种称为组-智力互惠秩的奖励函数，在标准的近似策略优化中优化 LLM。我们进行了广泛的实验来评估 RLCF 在不同语言构建的 LLM 上的有效性，以及在多下游 IR 应用中的参数大小。RLCF 明显优于现有的比对方法，并且 RLCF 优化的 LLM 显示出相当大的改进，产生独特的响应。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Unsupervised+Large+Language+Model+Alignment+for+Information+Retrieval+via+Contrastive+Feedback)|0| +|[Amazon-KG: A Knowledge Graph Enhanced Cross-Domain Recommendation Dataset](https://doi.org/10.1145/3626772.3657880)|Yuhan Wang, Qing Xie, Mengzi Tang, Lin Li, Jingling Yuan, Yongjian Liu|Wuhan University of Technology; Wuhan University Of Technology|Cross-domain recommendation (CDR) aims to utilize the information from relevant domains to guide the recommendation task in the target domain, and shows great potential in alleviating the data sparsity and cold-start problems of recommender systems. Most existing methods utilize the interaction information (e.g., ratings and clicks) or consider auxiliary information (e.g., tags and comments) to analyze the users' cross-domain preferences, but such kinds of information ignore the intrinsic semantic relationship of different domains. In order to effectively explore the inter-domain correlations, encyclopedic knowledge graphs (KG) involving different domains are highly desired in cross-domain recommendation tasks because they contain general information covering various domains with structured data format. However, there are few datasets containing KG information for CDR tasks, so in order to enrich the available data resource, we build a KG-enhanced cross-domain recommendation dataset, named Amazon-KG, based on the widely used Amazon dataset for CDR and the well-known KG DBpedia. In this work, we analyze the potential of KG applying in cross-domain recommendations, and describe the construction process of our dataset in detail. Finally, we perform quantitative statistical analysis on the dataset. We believe that datasets like Amazon-KG contribute to the development of knowledge-aware cross-domain recommender systems. Our dataset has been released at https://github.com/WangYuhan-0520/Amazon-KG-v2.0-dataset.|跨域推荐技术是利用相关领域的信息来指导目标领域的推荐任务，在缓解推荐系统的数据稀疏性和冷启动问题方面显示出巨大的潜力。大多数现有的方法利用交互信息(如评分和点击)或考虑辅助信息(如标签和评论)来分析用户的跨域偏好，但这类信息忽视了不同域的内在语义关系。为了有效地探索域间相关性，涉及不同领域的百科知识图(KG)是跨领域推荐任务中非常需要的，因为它包含了覆盖不同领域的结构化数据格式的一般信息。然而，包含用于 CDR 任务的 KG 信息的数据集很少，因此为了丰富可用的数据资源，我们基于广泛使用的用于 CDR 的 Amazon 数据集和著名的 KG DBpedia，构建了一个 KG 增强的跨域推荐数据集，命名为 Amazon-KG。在这项工作中，我们分析了 KG 在跨领域推荐中的应用潜力，并详细描述了我们的数据集的构建过程。最后，对数据集进行定量统计分析。我们相信像 Amazon-KG 这样的数据集有助于开发知识感知的跨领域推荐系统。我们的数据 https://github.com/wangyuhan-0520/amazon-kg-v2.0-dataset 已经发布了。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Amazon-KG:+A+Knowledge+Graph+Enhanced+Cross-Domain+Recommendation+Dataset)|0| |[Contrast then Memorize: Semantic Neighbor Retrieval-Enhanced Inductive Multimodal Knowledge Graph Completion](https://doi.org/10.1145/3626772.3657838)|Yu Zhao, Ying Zhang, Baohang Zhou, Xinying Qian, Kehui Song, Xiangrui Cai|Nankai University College of Computer Science, VCIP, TMCC, TBI Center; Tiangong University School of Software|A large number of studies have emerged for Multimodal Knowledge Graph Completion (MKGC) to predict the missing links in MKGs. However, fewer studies have been proposed to study the inductive MKGC (IMKGC) involving emerging entities unseen during training. Existing inductive approaches focus on learning textual entity representations, which neglect rich semantic information in visual modality. Moreover, they focus on aggregating structural neighbors from existing KGs, which of emerging entities are usually limited. However, the semantic neighbors are decoupled from the topology linkage and usually imply the true target entity. In this paper, we propose the IMKGC task and a semantic neighbor retrieval-enhanced IMKGC framework CMR, where the contrast brings the helpful semantic neighbors close, and then the memorize supports semantic neighbor retrieval to enhance inference. Specifically, we first propose a unified cross-modal contrastive learning to simultaneously capture the textual-visual and textual-textual correlations of query-entity pairs in a unified representation space. The contrastive learning increases the similarity of positive query-entity pairs, therefore making the representations of helpful semantic neighbors close. Then, we explicitly memorize the knowledge representations to support the semantic neighbor retrieval. At test time, we retrieve the nearest semantic neighbors and interpolate them to the query-entity similarity distribution to augment the final prediction. Extensive experiments validate the effectiveness of CMR on three inductive MKGC datasets. Codes are available at https://github.com/OreOZhao/CMR.|大量的研究已经出现的多模态知识图完成(MKGC) ，以预测缺失的环节，在 MKG。然而，很少有研究提出研究诱导性 MKGC (IMKGC)涉及新兴实体在培训期间看不见。现有的归纳法侧重于学习文本实体表征，忽略了视觉形态的丰富语义信息。此外，他们的重点是聚集结构性邻居从现有的幼儿园，这些新兴实体通常是有限的。然而，语义邻居与拓扑链接是解耦的，通常意味着真正的目标实体。在本文中，我们提出了 IMKGC 任务和一个语义邻居检索增强的 IMKGC 框架 CMR，其中对比度使有用的语义邻居更加接近，然后记忆支持语义邻居检索增强推理。具体来说，我们首先提出一种统一的跨模态对比学习方法，在统一的表示空间中同时捕获查询实体对的文本-视觉和文本-文本关联。对比学习增加了正向查询实体对的相似性，从而使有用语义邻居的表示更加紧密。然后，我们显式地记忆知识表示，以支持语义邻居检索。在测试时，我们检索最近的语义邻居，并将它们插值到查询实体的相似度分布中，以增强最终的预测。大量的实验验证了 CMR 在三个归纳 MKGC 数据集上的有效性。密码可在 https://github.com/oreozhao/cmr 索取。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Contrast+then+Memorize:+Semantic+Neighbor+Retrieval-Enhanced+Inductive+Multimodal+Knowledge+Graph+Completion)|0| -|[The Treatment of Ties in Rank-Biased Overlap](https://doi.org/10.1145/3626772.3657700)|Matteo Corsi, Julián Urbano|TU Delft; Delft University of Technology|Rank-Biased Overlap (RBO) is a similarity measure for indefinite rankings: itis top-weighted, and can be computed when only a prefix of the rankings isknown or when they have only some items in common. It is widely used forinstance to analyze differences between search engines by comparing therankings of documents they retrieve for the same queries. In these situations,though, it is very frequent to find tied documents that have the same score.Unfortunately, the treatment of ties in RBO remains superficial and incomplete,in the sense that it is not clear how to calculate it from the ranking prefixesonly. In addition, the existing way of dealing with ties is very different fromthe one traditionally followed in the field of Statistics, most notably foundin rank correlation coefficients such as Kendall's and Spearman's. In thispaper we propose a generalized formulation for RBO to handle ties, thanks towhich we complete the original definitions by showing how to perform prefixevaluation. We also use it to fully develop two variants that align with theones found in the Statistics literature: one when there is a reference rankingto compare to, and one when there is not. Overall, these three variants provideresearchers with flexibility when comparing rankings with RBO, by clearlydetermining what ties mean, and how they should be treated. Finally, using bothsynthetic and TREC data, we demonstrate the use of these new tie-aware RBOmeasures. We show that the scores may differ substantially from the originaltie-unaware RBO measure, where ties had to be broken at random or by arbitrarycriteria such as by document ID. Overall, these results evidence the need for aproper account of ties in rank similarity measures such as RBO.|排名偏差重叠(RBO)是对不确定排名的一种相似度量: 它是最权重的，可以在只知道排名的前缀或者只有一些共同点的情况下计算出来。它被广泛用于分析搜索引擎之间的差异，例如通过比较它们为相同的查询检索的文档的排名。但是，在这些情况下，经常会发现具有相同分数的绑定文档。不幸的是，RBO 中的关系处理仍然是肤浅和不完整的，因为不清楚如何仅仅从排名前缀来计算它。此外，现有的处理关系的方法与统计学领域中传统的方法有很大的不同，最明显的是发现级别相关系数，如肯德尔的和斯皮尔曼的。在本文中，我们提出了一个处理关系的广义 RBO 公式，由此我们完成了原来的定义，通过展示如何执行前缀评价。我们也使用它来完全开发两个变体，与在统计文献中发现的变体一致: 一个当有一个参考排名进行比较时，一个当没有时。总的来说，这三个变量为研究人员提供了灵活性，当比较排名与 RBO，通过清楚地确定什么是联系意味着，以及他们应该如何处理。最后，使用综合数据和 TREC 数据，我们演示了这些新的领带感知 RBO 措施的使用。我们表明，分数可能大不相同的原始意识 RBO 措施，其中关系必须打破随机或任意标准，如文件 ID。总的来说，这些结果证明了在 RBO 等等级相似性度量中需要适当考虑关系。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=The+Treatment+of+Ties+in+Rank-Biased+Overlap)|0| -|[What Matters in a Measure? A Perspective from Large-Scale Search Evaluation](https://doi.org/10.1145/3626772.3657845)|Paul Thomas, Gabriella Kazai, Nick Craswell, Seth Spielman|Microsoft, Seattle, WA, USA; Microsoft, Adelaide, Australia; Amazon, London, United Kingdom; Microsoft, Boulder, CO, USA|Information retrieval (IR) has a large literature on evaluation, dating back decades and forming a central part of the research culture. The largest proportion of this literature discusses techniques to turn a sequence of relevance labels into a single number, reflecting the system's performance: precision or cumulative gain, for example, or dozens of alternatives. Those techniques-metrics-are themselves evaluated, commonly by reference to sensitivity and validity. In our experience measuring search in industrial settings, a measurement regime needs many other qualities to be practical. For example, we must also consider how much a metric costs; how robust it is to the happenstance of sampling; whether it is debuggable; and what activities are incentivised when a metric is taken as a goal. In this perspective paper we discuss what makes a search metric successful in large-scale settings, including factors which are not often canvassed in IR research but which are important in "real-world" use. We illustrate this with examples, including from industrial settings, and offer suggestions for metrics as part of a working system.|信息检索(IR)有大量关于评估的文献，可以追溯到几十年前，是研究文化的核心部分。文献中最大的部分讨论了将一系列相关标签转化为单个数字的技术，这些技术反映了系统的性能: 例如，精度或累积增益，或者几十种替代方案。这些技术-度量-本身是评估，通常参照敏感性和有效性。根据我们在工业环境中测量搜索的经验，测量制度需要许多其他的质量才能实用。例如，我们还必须考虑一个度量标准的成本有多高; 它对抽样的偶然性有多强大; 它是否可调试; 以及当一个度量标准被作为一个目标时，激励什么活动。在这篇透视文章中，我们讨论了是什么使得搜索度量在大规模环境中成功，包括那些在 IR 研究中不经常被提及但在“现实世界”使用中非常重要的因素。我们用示例(包括来自工业设置的示例)来说明这一点，并提供作为工作系统一部分的指标建议。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=What+Matters+in+a+Measure?+A+Perspective+from+Large-Scale+Search+Evaluation)|0| +|[The Treatment of Ties in Rank-Biased Overlap](https://doi.org/10.1145/3626772.3657700)|Matteo Corsi, Julián Urbano|Delft University of Technology; TU Delft|Rank-Biased Overlap (RBO) is a similarity measure for indefinite rankings: itis top-weighted, and can be computed when only a prefix of the rankings isknown or when they have only some items in common. It is widely used forinstance to analyze differences between search engines by comparing therankings of documents they retrieve for the same queries. In these situations,though, it is very frequent to find tied documents that have the same score.Unfortunately, the treatment of ties in RBO remains superficial and incomplete,in the sense that it is not clear how to calculate it from the ranking prefixesonly. In addition, the existing way of dealing with ties is very different fromthe one traditionally followed in the field of Statistics, most notably foundin rank correlation coefficients such as Kendall's and Spearman's. In thispaper we propose a generalized formulation for RBO to handle ties, thanks towhich we complete the original definitions by showing how to perform prefixevaluation. We also use it to fully develop two variants that align with theones found in the Statistics literature: one when there is a reference rankingto compare to, and one when there is not. Overall, these three variants provideresearchers with flexibility when comparing rankings with RBO, by clearlydetermining what ties mean, and how they should be treated. Finally, using bothsynthetic and TREC data, we demonstrate the use of these new tie-aware RBOmeasures. We show that the scores may differ substantially from the originaltie-unaware RBO measure, where ties had to be broken at random or by arbitrarycriteria such as by document ID. Overall, these results evidence the need for aproper account of ties in rank similarity measures such as RBO.|排名偏差重叠(RBO)是对不确定排名的一种相似度量: 它是最权重的，可以在只知道排名的前缀或者只有一些共同点的情况下计算出来。它被广泛用于分析搜索引擎之间的差异，例如通过比较它们为相同的查询检索的文档的排名。但是，在这些情况下，经常会发现具有相同分数的绑定文档。不幸的是，RBO 中的关系处理仍然是肤浅和不完整的，因为不清楚如何仅仅从排名前缀来计算它。此外，现有的处理关系的方法与统计学领域中传统的方法有很大的不同，最明显的是发现级别相关系数，如肯德尔的和斯皮尔曼的。在本文中，我们提出了一个处理关系的广义 RBO 公式，由此我们完成了原来的定义，通过展示如何执行前缀评价。我们也使用它来完全开发两个变体，与在统计文献中发现的变体一致: 一个当有一个参考排名进行比较时，一个当没有时。总的来说，这三个变量为研究人员提供了灵活性，当比较排名与 RBO，通过清楚地确定什么是联系意味着，以及他们应该如何处理。最后，使用综合数据和 TREC 数据，我们演示了这些新的领带感知 RBO 措施的使用。我们表明，分数可能大不相同的原始意识 RBO 措施，其中关系必须打破随机或任意标准，如文件 ID。总的来说，这些结果证明了在 RBO 等等级相似性度量中需要适当考虑关系。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=The+Treatment+of+Ties+in+Rank-Biased+Overlap)|0| +|[What Matters in a Measure? A Perspective from Large-Scale Search Evaluation](https://doi.org/10.1145/3626772.3657845)|Paul Thomas, Gabriella Kazai, Nick Craswell, Seth Spielman|Microsoft, Seattle, WA, USA; Microsoft, Boulder, CO, USA; Microsoft, Adelaide, Australia; Amazon, London, United Kingdom|Information retrieval (IR) has a large literature on evaluation, dating back decades and forming a central part of the research culture. The largest proportion of this literature discusses techniques to turn a sequence of relevance labels into a single number, reflecting the system's performance: precision or cumulative gain, for example, or dozens of alternatives. Those techniques-metrics-are themselves evaluated, commonly by reference to sensitivity and validity. In our experience measuring search in industrial settings, a measurement regime needs many other qualities to be practical. For example, we must also consider how much a metric costs; how robust it is to the happenstance of sampling; whether it is debuggable; and what activities are incentivised when a metric is taken as a goal. In this perspective paper we discuss what makes a search metric successful in large-scale settings, including factors which are not often canvassed in IR research but which are important in "real-world" use. We illustrate this with examples, including from industrial settings, and offer suggestions for metrics as part of a working system.|信息检索(IR)有大量关于评估的文献，可以追溯到几十年前，是研究文化的核心部分。文献中最大的部分讨论了将一系列相关标签转化为单个数字的技术，这些技术反映了系统的性能: 例如，精度或累积增益，或者几十种替代方案。这些技术-度量-本身是评估，通常参照敏感性和有效性。根据我们在工业环境中测量搜索的经验，测量制度需要许多其他的质量才能实用。例如，我们还必须考虑一个度量标准的成本有多高; 它对抽样的偶然性有多强大; 它是否可调试; 以及当一个度量标准被作为一个目标时，激励什么活动。在这篇透视文章中，我们讨论了是什么使得搜索度量在大规模环境中成功，包括那些在 IR 研究中不经常被提及但在“现实世界”使用中非常重要的因素。我们用示例(包括来自工业设置的示例)来说明这一点，并提供作为工作系统一部分的指标建议。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=What+Matters+in+a+Measure?+A+Perspective+from+Large-Scale+Search+Evaluation)|0| |[CaDRec: Contextualized and Debiased Recommender Model](https://doi.org/10.1145/3626772.3657799)|Xinfeng Wang, Fumiyo Fukumoto, Jin Cui, Yoshimi Suzuki, Jiyi Li, Dongjin Yu|Graduate Faculty of Interdisciplinary Research, University of Yamanashi; School of Computer Science and Technology, Hangzhou Dianzi University; Faculty of Engineering, Integrated Graduate School of Medicine, Engineering, and Agricultural Sciences|Recommender models aimed at mining users' behavioral patterns have raisedgreat attention as one of the essential applications in daily life. Recent workon graph neural networks (GNNs) or debiasing methods has attained remarkablegains. However, they still suffer from (1) over-smoothing node embeddingscaused by recursive convolutions with GNNs, and (2) the skewed distribution ofinteractions due to popularity and user-individual biases. This paper proposesa contextualized and debiased recommender model (CaDRec). To overcome theover-smoothing issue, we explore a novel hypergraph convolution operator thatcan select effective neighbors during convolution by introducing bothstructural context and sequential context. To tackle the skewed distribution,we propose two strategies for disentangling interactions: (1) modelingindividual biases to learn unbiased item embeddings, and (2) incorporating itempopularity with positional encoding. Moreover, we mathematically show that theimbalance of the gradients to update item embeddings exacerbates the popularitybias, thus adopting regularization and weighting schemes as solutions.Extensive experiments on four datasets demonstrate the superiority of theCaDRec against state-of-the-art (SOTA) methods. Our source code and data arereleased at https://github.com/WangXFng/CaDRec.|以挖掘用户行为模式为目标的推荐模型作为其在日常生活中的重要应用之一，引起了人们的广泛关注。近年来研究的图形神经网络(GNN)或消偏方法取得了显著的进展。然而，他们仍然遭受(1)过度平滑的节点嵌入造成的递归卷积与 GNN，(2)由于流行和用户个人偏见的交互分布偏斜。本文提出了一种情境化和无偏的推荐模型(CaDRec)。为了克服过平滑问题，我们通过引入结构上下文和序列上下文，提出了一种新的超图卷积算子，它可以在卷积过程中选择有效的邻居。为了解决偏态分布问题，我们提出了两种分离交互作用的策略: (1)建立个体偏好模型来学习无偏项嵌入，(2)将项流行性与位置编码相结合。此外，我们从数学上证明了更新项目嵌入的梯度不平衡加剧了流行偏差，因此采用正则化和加权方案作为解决方案。在四个数据集上的大量实验证明了 CaDRec 与最先进的(SOTA)方法相比的优越性。我们的源代码和数据已经在 https://github.com/wangxfng/cadrec 公布了。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=CaDRec:+Contextualized+and+Debiased+Recommender+Model)|0| -|[Going Beyond Popularity and Positivity Bias: Correcting for Multifactorial Bias in Recommender Systems](https://doi.org/10.1145/3626772.3657749)|Jin Huang, Harrie Oosterhuis, Masoud Mansoury, Herke van Hoof, Maarten de Rijke|Radboud University; University of Amsterdam|Two typical forms of bias in user interaction data with recommender systems(RSs) are popularity bias and positivity bias, which manifest themselves as theover-representation of interactions with popular items or items that usersprefer, respectively. Debiasing methods aim to mitigate the effect of selectionbias on the evaluation and optimization of RSs. However, existing debiasingmethods only consider single-factor forms of bias, e.g., only the item(popularity) or only the rating value (positivity). This is in stark contrastwith the real world where user selections are generally affected by multiplefactors at once. In this work, we consider multifactorial selection bias inRSs. Our focus is on selection bias affected by both item and rating valuefactors, which is a generalization and combination of popularity and positivitybias. While the concept of multifactorial bias is intuitive, it brings a severepractical challenge as it requires substantially more data for accurate biasestimation. As a solution, we propose smoothing and alternating gradientdescent techniques to reduce variance and improve the robustness of itsoptimization. Our experimental results reveal that, with our proposedtechniques, multifactorial bias corrections are more effective and robust thansingle-factor counterparts on real-world and synthetic datasets.|用户与推荐系统交互数据中两种典型的偏差形式是流行偏差和正向偏差，它们分别表现为与流行项目或用户喜欢的项目交互的过度表现。去偏方法旨在减轻选择性偏差对 RS 评价和优化的影响。然而，现有的去偏方法只考虑单因素形式的偏差，例如，只有项目(受欢迎程度)或只有评分值(积极性)。这与现实世界形成了鲜明的对比，在现实世界中，用户的选择通常同时受到多种因素的影响。在这项工作中，我们考虑了多因素选择偏差。我们的重点是选择偏差影响项目和评价价值因素，这是一个普遍性和积极性偏差的综合。虽然多因素偏差的概念是直观的，但它带来了严峻的实际挑战，因为它需要大量的数据进行准确的偏差估计。作为解决方案，我们提出了平滑和交替梯度下降技术，以减少方差和提高其优化的鲁棒性。我们的实验结果表明，我们提出的技术，多因素偏差修正是更有效的和鲁棒性的单因素对应的真实世界和合成数据集。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Going+Beyond+Popularity+and+Positivity+Bias:+Correcting+for+Multifactorial+Bias+in+Recommender+Systems)|0| -|[Configurable Fairness for New Item Recommendation Considering Entry Time of Items](https://doi.org/10.1145/3626772.3657694)|Huizhong Guo, Dongxia Wang, Zhu Sun, Haonan Zhang, Jinfeng Li, Jie Zhang|Singapore University of Technology and Design, Singapore, Singapore; Zhejiang University, Hangzhou, Zhejiang, China, China; Alibaba Group, Hangzhou, China; Zhejiang University, Hangzhou, China; Nanyang Technological University, Singapore, Singapore|Recommender systems tend to excessively expose longer-standing items, resulting in significant unfairness to new items with little interaction records, despite they may possess potential to attract considerable amount of users. The existing fairness-based solutions do not specifically consider the exposure fairness of new items, for which a systematic definition also lacks, discouraging the promotion of new items or contents. In this work, we introduce a multi-degree new-item exposure fairness definition, which considers item entry-time, and also is configurable regarding different fairness requirements. We then propose a configurable new-item fairness-aware framework named CNIF, which employs two-stage training where fairness degrees are incorporated for guidance. Extensive experiments on multiple popular datasets and backbone models demonstrate that CNIF can effectively enhance fairness of the existing models regarding the exposure resources of new items (including the brand-new items with no interaction). Specifically, CNIF demonstrates a substantial advancement with a 65.59% improvement in fairness metric and a noteworthy 9.97% improvement in recommendation accuracy compared to backbone models on the KuaiRec dataset. In comparison to various fairness-based solutions, it stands out by achieving the best trade-off between fairness and recommendation accuracy, surpassing the best baseline by 14.20%.|推荐系统往往过分暴露存放时间较长的项目，导致对交互记录较少的新项目的严重不公平，尽管这些项目可能具有吸引大量用户的潜力。现有的基于公平的解决方案没有特别考虑新项目的曝光公平性，对此也缺乏系统的定义，从而阻碍了新项目或内容的推广。在本文中，我们引入了一个多级新项目曝光公平性的定义，它考虑了项目进入时间，并且可以根据不同的公平性需求进行配置。然后，我们提出了一个可配置的新项目公平感知框架 CNIF，该框架采用两阶段的训练，其中包含公平度作为指导。在多个流行数据集和骨干模型上的大量实验表明，CNIF 能够有效地提高现有模型对新项目(包括没有交互的全新项目)曝光资源的公平性。具体而言，与 KuaiRec 数据集上的主干模型相比，CNIF 显示出实质性的进步，公平性指标提高了65.59% ，推荐准确率提高了9.97% 。与各种基于公平的解决方案相比，它在公平性和推荐准确性之间取得了最佳的平衡，比最佳基线高出14.20% 。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Configurable+Fairness+for+New+Item+Recommendation+Considering+Entry+Time+of+Items)|0| -|[Generative Retrieval via Term Set Generation](https://doi.org/10.1145/3626772.3657797)|Peitian Zhang, Zheng Liu, Yujia Zhou, Zhicheng Dou, Fangchao Liu, Zhao Cao|Beijing Academy of Artificial Intelligence NLP; Renmin University of China Gaoling School of Artificial Intelligence; Huawei Poisson Lab|Recently, generative retrieval emerges as a promising alternative totraditional retrieval paradigms. It assigns each document a unique identifier,known as DocID, and employs a generative model to directly generate therelevant DocID for the input query. A common choice for DocID is one or severalnatural language sequences, e.g. the title or n-grams, so that the pre-trainedknowledge of the generative model can be utilized. However, a sequence isgenerated token by token, where only the most likely candidates are kept andthe rest are pruned at each decoding step, thus, retrieval fails if any tokenwithin the relevant DocID is falsely pruned. What's worse, during decoding, themodel can only perceive preceding tokens in DocID while being blind tosubsequent ones, hence is prone to make such errors. To address this problem,we present a novel framework for generative retrieval, dubbed Term-SetGeneration (TSGen). Instead of sequences, we use a set of terms as DocID, whichare automatically selected to concisely summarize the document's semantics anddistinguish it from others. On top of the term-set DocID, we propose apermutation-invariant decoding algorithm, with which the term set can begenerated in any permutation yet will always lead to the correspondingdocument. Remarkably, TSGen perceives all valid terms rather than only thepreceding ones at each decoding step. Given the constant decoding space, it canmake more reliable decisions due to the broader perspective. TSGen is alsoresilient to errors: the relevant DocID will not be pruned as long as thedecoded term belongs to it. Lastly, we design an iterative optimizationprocedure to incentivize the model to generate the relevant term set in itsfavorable permutation. We conduct extensive experiments on popular benchmarks,which validate the effectiveness, the generalizability, the scalability, andthe efficiency of TSGen.|近年来，生成检索成为传统检索模式的一个有前途的替代方案。它为每个文档分配一个唯一标识符，称为 DocID，并使用一个生成模型直接为输入查询生成相关的 DocID。DocID 的一个常见选择是一个或几个自然语言序列，例如标题或 n-gram，这样就可以利用生成模型的预先训练的知识。然而，一个序列是按令牌生成的，其中只保留最有可能的候选者，其余的在每个解码步骤中被删除，因此，如果相关 DocID 中的任何令牌被错误地删除，则检索失败。更糟糕的是，在解码过程中，模型只能感知 DocID 中的前一个标记，而不能感知后一个标记，因此容易出现这样的错误。为了解决这个问题，我们提出了一种新的生成检索框架，称为术语设置生成(Term-SetGeneration，TSGen)。我们不使用序列，而是使用一组术语作为 DocID，这些术语被自动选择以简明地总结文档的语义并与其他术语区分开来。在术语集 DocID 的基础上，提出了一种不变译码算法，该算法可以在任意置换情况下生成术语集，并且始终生成相应的文档。值得注意的是，TSGen 在每个解码步骤中感知所有有效的术语，而不仅仅是前面的术语。给定不变的解码空间，它可以做出更可靠的决定，由于更广泛的角度。TSGen 对错误也很有弹性: 只要解码的术语属于它，相关的 DocID 就不会被删除。最后，我们设计了一个迭代优化过程来激励模型以其有利的排列生成相关的条件集。我们在流行的基准上进行了广泛的实验，验证了 TSGen 的有效性、通用性、可扩展性和效率。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Generative+Retrieval+via+Term+Set+Generation)|0| -|[MIRROR: A Multi-View Reciprocal Recommender System for Online Recruitment](https://doi.org/10.1145/3626772.3657776)|Zhi Zheng, Xiao Hu, Shanshan Gao, Hengshu Zhu, Hui Xiong|Career Science Lab, BOSS Zhipin, Beijing, China; The Hong Kong University of Science and Technology (Guangzhou); University of Science and Technology of China; BOSS Zhipin, Beijing, China; Career Science Lab, Boss Zhipin, Beijing, China|Reciprocal Recommender Systems (RRSs) which aim to satisfy the preferences of both service providers and seekers simultaneously has attracted significant research interest in recent years. Existing studies on RRSs mainly focus on modeling the bilateral interactions between the users on both sides to capture the user preferences. However, due to the presence of exposure bias, modeling user preferences solely based on bilateral interactions often lacks precision. Additionally, in RRSs, users may exhibit varying preferences when acting in different roles, and how to effectively model users from multiple perspectives remains a substantial problem. To solve the above challenges, in this paper, we propose a novel MultI-view Reciprocal Recommender system for Online Recruitment (MIRROR). Specifically, we first propose to model the users from three different views, respectively search, active, and passive views, and we further design several Transformer-based sequential models to capture the user representation corresponding to each view. Then, we propose to divide the bilateral matching process into three stages, respectively apply, reply, and match, and a multi-stage output layer is designed based on the above multi-view modeling results. To train our MIRROR model, we first design a multi-task learning loss based on the multi-stage output results. Moreover, to bridge the semantic gap between search queries and user behaviors, we additionally design a supplementary task for next-query prediction. Finally, we conduct both offline experiments on five real-world datasets and online A/B tests, and the experiment results clearly validate the effectiveness of our MIRROR model compared with several state-of-the-art baseline methods.|旨在同时满足服务提供者和寻求者偏好的互惠推荐系统(RRS)近年来引起了人们的广泛研究兴趣。现有的关于 RRS 的研究主要集中在对双方用户之间的双边交互进行建模，以获取用户的偏好。然而，由于暴露偏差的存在，仅仅基于双边交互的用户偏好建模往往缺乏精度。此外，在 RRS 中，用户在扮演不同角色时可能表现出不同的偏好，如何从多个角度有效地为用户建模仍然是一个实质性问题。为了解决上述挑战，在本文中，我们提出了一个新颖的多视图在线招聘互惠推荐系统(mIRROR)。具体来说，我们首先提出从搜索视图、主动视图和被动视图三个不同的视图对用户进行建模，并进一步设计了几个基于 Transformer 的序列模型来捕获每个视图对应的用户表示。然后，我们提出将双边匹配过程分为应用、回复和匹配三个阶段，并根据上述多视图建模结果设计了一个多阶段输出层。为了训练我们的 MIRROR 模型，我们首先设计了一个基于多阶段输出结果的多任务学习丢失模型。此外，为了缩小搜索查询和用户行为之间的语义差距，我们还设计了一个用于下一次查询预测的补充任务。最后，我们在五个实际数据集上进行了离线实验和在线 A/B 测试，实验结果清楚地验证了我们的 MIRROR 模型与几种最先进的基线方法相比的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=MIRROR:+A+Multi-View+Reciprocal+Recommender+System+for+Online+Recruitment)|0| +|[Going Beyond Popularity and Positivity Bias: Correcting for Multifactorial Bias in Recommender Systems](https://doi.org/10.1145/3626772.3657749)|Jin Huang, Harrie Oosterhuis, Masoud Mansoury, Herke van Hoof, Maarten de Rijke|University of Amsterdam; Radboud University|Two typical forms of bias in user interaction data with recommender systems(RSs) are popularity bias and positivity bias, which manifest themselves as theover-representation of interactions with popular items or items that usersprefer, respectively. Debiasing methods aim to mitigate the effect of selectionbias on the evaluation and optimization of RSs. However, existing debiasingmethods only consider single-factor forms of bias, e.g., only the item(popularity) or only the rating value (positivity). This is in stark contrastwith the real world where user selections are generally affected by multiplefactors at once. In this work, we consider multifactorial selection bias inRSs. Our focus is on selection bias affected by both item and rating valuefactors, which is a generalization and combination of popularity and positivitybias. While the concept of multifactorial bias is intuitive, it brings a severepractical challenge as it requires substantially more data for accurate biasestimation. As a solution, we propose smoothing and alternating gradientdescent techniques to reduce variance and improve the robustness of itsoptimization. Our experimental results reveal that, with our proposedtechniques, multifactorial bias corrections are more effective and robust thansingle-factor counterparts on real-world and synthetic datasets.|用户与推荐系统交互数据中两种典型的偏差形式是流行偏差和正向偏差，它们分别表现为与流行项目或用户喜欢的项目交互的过度表现。去偏方法旨在减轻选择性偏差对 RS 评价和优化的影响。然而，现有的去偏方法只考虑单因素形式的偏差，例如，只有项目(受欢迎程度)或只有评分值(积极性)。这与现实世界形成了鲜明的对比，在现实世界中，用户的选择通常同时受到多种因素的影响。在这项工作中，我们考虑了多因素选择偏差。我们的重点是选择偏差影响项目和评价价值因素，这是一个普遍性和积极性偏差的综合。虽然多因素偏差的概念是直观的，但它带来了严峻的实际挑战，因为它需要大量的数据进行准确的偏差估计。作为解决方案，我们提出了平滑和交替梯度下降技术，以减少方差和提高其优化的鲁棒性。我们的实验结果表明，我们提出的技术，多因素偏差修正是更有效的和鲁棒性的单因素对应的真实世界和合成数据集。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Going+Beyond+Popularity+and+Positivity+Bias:+Correcting+for+Multifactorial+Bias+in+Recommender+Systems)|0| +|[Configurable Fairness for New Item Recommendation Considering Entry Time of Items](https://doi.org/10.1145/3626772.3657694)|Huizhong Guo, Dongxia Wang, Zhu Sun, Haonan Zhang, Jinfeng Li, Jie Zhang|Zhejiang University, Hangzhou, Zhejiang, China, China; Singapore University of Technology and Design, Singapore, Singapore; Zhejiang University, Hangzhou, China; Nanyang Technological University, Singapore, Singapore; Alibaba Group, Hangzhou, China|Recommender systems tend to excessively expose longer-standing items, resulting in significant unfairness to new items with little interaction records, despite they may possess potential to attract considerable amount of users. The existing fairness-based solutions do not specifically consider the exposure fairness of new items, for which a systematic definition also lacks, discouraging the promotion of new items or contents. In this work, we introduce a multi-degree new-item exposure fairness definition, which considers item entry-time, and also is configurable regarding different fairness requirements. We then propose a configurable new-item fairness-aware framework named CNIF, which employs two-stage training where fairness degrees are incorporated for guidance. Extensive experiments on multiple popular datasets and backbone models demonstrate that CNIF can effectively enhance fairness of the existing models regarding the exposure resources of new items (including the brand-new items with no interaction). Specifically, CNIF demonstrates a substantial advancement with a 65.59% improvement in fairness metric and a noteworthy 9.97% improvement in recommendation accuracy compared to backbone models on the KuaiRec dataset. In comparison to various fairness-based solutions, it stands out by achieving the best trade-off between fairness and recommendation accuracy, surpassing the best baseline by 14.20%.|推荐系统往往过分暴露存放时间较长的项目，导致对交互记录较少的新项目的严重不公平，尽管这些项目可能具有吸引大量用户的潜力。现有的基于公平的解决方案没有特别考虑新项目的曝光公平性，对此也缺乏系统的定义，从而阻碍了新项目或内容的推广。在本文中，我们引入了一个多级新项目曝光公平性的定义，它考虑了项目进入时间，并且可以根据不同的公平性需求进行配置。然后，我们提出了一个可配置的新项目公平感知框架 CNIF，该框架采用两阶段的训练，其中包含公平度作为指导。在多个流行数据集和骨干模型上的大量实验表明，CNIF 能够有效地提高现有模型对新项目(包括没有交互的全新项目)曝光资源的公平性。具体而言，与 KuaiRec 数据集上的主干模型相比，CNIF 显示出实质性的进步，公平性指标提高了65.59% ，推荐准确率提高了9.97% 。与各种基于公平的解决方案相比，它在公平性和推荐准确性之间取得了最佳的平衡，比最佳基线高出14.20% 。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Configurable+Fairness+for+New+Item+Recommendation+Considering+Entry+Time+of+Items)|0| +|[Generative Retrieval via Term Set Generation](https://doi.org/10.1145/3626772.3657797)|Peitian Zhang, Zheng Liu, Yujia Zhou, Zhicheng Dou, Fangchao Liu, Zhao Cao|Beijing Academy of Artificial Intelligence NLP; Huawei Poisson Lab; Renmin University of China Gaoling School of Artificial Intelligence|Recently, generative retrieval emerges as a promising alternative totraditional retrieval paradigms. It assigns each document a unique identifier,known as DocID, and employs a generative model to directly generate therelevant DocID for the input query. A common choice for DocID is one or severalnatural language sequences, e.g. the title or n-grams, so that the pre-trainedknowledge of the generative model can be utilized. However, a sequence isgenerated token by token, where only the most likely candidates are kept andthe rest are pruned at each decoding step, thus, retrieval fails if any tokenwithin the relevant DocID is falsely pruned. What's worse, during decoding, themodel can only perceive preceding tokens in DocID while being blind tosubsequent ones, hence is prone to make such errors. To address this problem,we present a novel framework for generative retrieval, dubbed Term-SetGeneration (TSGen). Instead of sequences, we use a set of terms as DocID, whichare automatically selected to concisely summarize the document's semantics anddistinguish it from others. On top of the term-set DocID, we propose apermutation-invariant decoding algorithm, with which the term set can begenerated in any permutation yet will always lead to the correspondingdocument. Remarkably, TSGen perceives all valid terms rather than only thepreceding ones at each decoding step. Given the constant decoding space, it canmake more reliable decisions due to the broader perspective. TSGen is alsoresilient to errors: the relevant DocID will not be pruned as long as thedecoded term belongs to it. Lastly, we design an iterative optimizationprocedure to incentivize the model to generate the relevant term set in itsfavorable permutation. We conduct extensive experiments on popular benchmarks,which validate the effectiveness, the generalizability, the scalability, andthe efficiency of TSGen.|近年来，生成检索成为传统检索模式的一个有前途的替代方案。它为每个文档分配一个唯一标识符，称为 DocID，并使用一个生成模型直接为输入查询生成相关的 DocID。DocID 的一个常见选择是一个或几个自然语言序列，例如标题或 n-gram，这样就可以利用生成模型的预先训练的知识。然而，一个序列是按令牌生成的，其中只保留最有可能的候选者，其余的在每个解码步骤中被删除，因此，如果相关 DocID 中的任何令牌被错误地删除，则检索失败。更糟糕的是，在解码过程中，模型只能感知 DocID 中的前一个标记，而不能感知后一个标记，因此容易出现这样的错误。为了解决这个问题，我们提出了一种新的生成检索框架，称为术语设置生成(Term-SetGeneration，TSGen)。我们不使用序列，而是使用一组术语作为 DocID，这些术语被自动选择以简明地总结文档的语义并与其他术语区分开来。在术语集 DocID 的基础上，提出了一种不变译码算法，该算法可以在任意置换情况下生成术语集，并且始终生成相应的文档。值得注意的是，TSGen 在每个解码步骤中感知所有有效的术语，而不仅仅是前面的术语。给定不变的解码空间，它可以做出更可靠的决定，由于更广泛的角度。TSGen 对错误也很有弹性: 只要解码的术语属于它，相关的 DocID 就不会被删除。最后，我们设计了一个迭代优化过程来激励模型以其有利的排列生成相关的条件集。我们在流行的基准上进行了广泛的实验，验证了 TSGen 的有效性、通用性、可扩展性和效率。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Generative+Retrieval+via+Term+Set+Generation)|0| +|[MIRROR: A Multi-View Reciprocal Recommender System for Online Recruitment](https://doi.org/10.1145/3626772.3657776)|Zhi Zheng, Xiao Hu, Shanshan Gao, Hengshu Zhu, Hui Xiong|Career Science Lab, Boss Zhipin, Beijing, China; University of Science and Technology of China; The Hong Kong University of Science and Technology (Guangzhou); Career Science Lab, BOSS Zhipin, Beijing, China; BOSS Zhipin, Beijing, China|Reciprocal Recommender Systems (RRSs) which aim to satisfy the preferences of both service providers and seekers simultaneously has attracted significant research interest in recent years. Existing studies on RRSs mainly focus on modeling the bilateral interactions between the users on both sides to capture the user preferences. However, due to the presence of exposure bias, modeling user preferences solely based on bilateral interactions often lacks precision. Additionally, in RRSs, users may exhibit varying preferences when acting in different roles, and how to effectively model users from multiple perspectives remains a substantial problem. To solve the above challenges, in this paper, we propose a novel MultI-view Reciprocal Recommender system for Online Recruitment (MIRROR). Specifically, we first propose to model the users from three different views, respectively search, active, and passive views, and we further design several Transformer-based sequential models to capture the user representation corresponding to each view. Then, we propose to divide the bilateral matching process into three stages, respectively apply, reply, and match, and a multi-stage output layer is designed based on the above multi-view modeling results. To train our MIRROR model, we first design a multi-task learning loss based on the multi-stage output results. Moreover, to bridge the semantic gap between search queries and user behaviors, we additionally design a supplementary task for next-query prediction. Finally, we conduct both offline experiments on five real-world datasets and online A/B tests, and the experiment results clearly validate the effectiveness of our MIRROR model compared with several state-of-the-art baseline methods.|旨在同时满足服务提供者和寻求者偏好的互惠推荐系统(RRS)近年来引起了人们的广泛研究兴趣。现有的关于 RRS 的研究主要集中在对双方用户之间的双边交互进行建模，以获取用户的偏好。然而，由于暴露偏差的存在，仅仅基于双边交互的用户偏好建模往往缺乏精度。此外，在 RRS 中，用户在扮演不同角色时可能表现出不同的偏好，如何从多个角度有效地为用户建模仍然是一个实质性问题。为了解决上述挑战，在本文中，我们提出了一个新颖的多视图在线招聘互惠推荐系统(mIRROR)。具体来说，我们首先提出从搜索视图、主动视图和被动视图三个不同的视图对用户进行建模，并进一步设计了几个基于 Transformer 的序列模型来捕获每个视图对应的用户表示。然后，我们提出将双边匹配过程分为应用、回复和匹配三个阶段，并根据上述多视图建模结果设计了一个多阶段输出层。为了训练我们的 MIRROR 模型，我们首先设计了一个基于多阶段输出结果的多任务学习丢失模型。此外，为了缩小搜索查询和用户行为之间的语义差距，我们还设计了一个用于下一次查询预测的补充任务。最后，我们在五个实际数据集上进行了离线实验和在线 A/B 测试，实验结果清楚地验证了我们的 MIRROR 模型与几种最先进的基线方法相比的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=MIRROR:+A+Multi-View+Reciprocal+Recommender+System+for+Online+Recruitment)|0| |[Who To Align With: Feedback-Oriented Multi-Modal Alignment in Recommendation Systems](https://doi.org/10.1145/3626772.3657701)|Yang Li, Qi'ao Zhao, Chen Lin, Jinsong Su, Zhilin Zhang|Amazon, Seattle, Washington, USA; Institute of Artificial Intelligence, Xiamen University, Xiamen, China; School of Informatics, Xiamen University, Xiamen, China|Multi-modal Recommendation Systems (MRSs) utilize diverse modalities, such as image and text, to enrich item representations and enhance recommendation accuracy. Current MRSs overlook the large misalignment between multi-modal content features and ID embeddings. While bidirectional alignment between visual and textual modalities has been extensively studied in large multi-modal models, this study suggests that multi-modal alignment in MRSs should be in a one-way direction. A plug-and-play framework is presented, called FEedback-orienTed mulTi-modal aLignmEnt (FETTLE). FETTLE contains three novel solutions: (1) it automatically determines item-level alignment direction between each pair of modalities based on estimated user feedback; (2) it coordinates the alignment directions among multiple modalities; (3) it implements cluster-level alignment from both user and item perspectives for more stable alignments. Extensive experiments on three real datasets demonstrate that FETTLE significantly improves various backbone models. Conventional collaborative filtering models are improved by 24.79%-62.79%, and recent MRSs are improved by 5.91% - 20.11%.|多模态推荐系统(MRS)利用多种模式，如图像和文本，以丰富项目表示和提高推荐的准确性。当前的 MRS 忽视了多模态内容特征和 ID 嵌入之间的大错位。虽然视觉模式和文本模式之间的双向对齐已经在大型多模式模型中得到了广泛的研究，但是本研究认为 MRS 中的多模式对齐应该是单向的。提出了一种即插即用的框架，称为面向反馈的多模态 aLignmEnt (FETTLE)。FETTLE 包含三种新颖的解决方案: (1)基于用户反馈的估计自动确定每对模式之间的项目级对齐方向; (2)协调多个模式之间的对齐方向; (3)从用户和项目的角度实现簇级对齐，以获得更稳定的对齐。在三个实际数据集上的大量实验表明，FETTLE 显著改善了各种骨干模型。传统的协同过滤模型改善了24.79% -62.79% ，最近的 MRS 改善了5.91% -20.11% 。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Who+To+Align+With:+Feedback-Oriented+Multi-Modal+Alignment+in+Recommendation+Systems)|0| -|[EEG-SVRec: An EEG Dataset with User Multidimensional Affective Engagement Labels in Short Video Recommendation](https://doi.org/10.1145/3626772.3657890)|Shaorun Zhang, Zhiyu He, Ziyi Ye, Peijie Sun, Qingyao Ai, Min Zhang, Yiqun Liu|Tsinghua University Department of Computer Science and Technology; Tsinghua University Computer Science and Technology; Tsinghua University|In recent years, short video platforms have gained widespread popularity,making the quality of video recommendations crucial for retaining users.Existing recommendation systems primarily rely on behavioral data, which faceslimitations when inferring user preferences due to issues such as data sparsityand noise from accidental interactions or personal habits. To address thesechallenges and provide a more comprehensive understanding of user affectiveexperience and cognitive activity, we propose EEG-SVRec, the first EEG datasetwith User Multidimensional Affective Engagement Labels in Short VideoRecommendation. The study involves 30 participants and collects 3,657interactions, offering a rich dataset that can be used for a deeper explorationof user preference and cognitive activity. By incorporating selfassessmenttechniques and real-time, low-cost EEG signals, we offer a more detailedunderstanding user affective experiences (valence, arousal, immersion,interest, visual and auditory) and the cognitive mechanisms behind theirbehavior. We establish benchmarks for rating prediction by the recommendationalgorithm, showing significant improvement with the inclusion of EEG signals.Furthermore, we demonstrate the potential of this dataset in gaining insightsinto the affective experience and cognitive activity behind user behaviors inrecommender systems. This work presents a novel perspective for enhancing shortvideo recommendation by leveraging the rich information contained in EEGsignals and multidimensional affective engagement scores, paving the way forfuture research in short video recommendation systems.|近年来，短视频平台得到了广泛的普及，这使得视频推荐的质量对于留住用户至关重要。现有的推荐系统主要依赖于行为数据，由于数据稀疏和意外交互或个人习惯造成的噪音等问题，在推断用户偏好时，行为数据面临限制。为了解决这些问题，提供对用户情感体验和认知活动的更全面的理解，我们提出了 EEG-SVRec，这是第一个在短视频推荐中使用用户多维情感参与标签的 EEG 数据集。这项研究涉及30名参与者，收集了3657次互动，提供了丰富的数据集，可用于更深入地探索用户偏好和认知活动。通过结合自我评估技术和实时、低成本的脑电信号，我们提供了一个更详细的理解用户情感体验(效价，唤醒，沉浸，兴趣，视觉和听觉)和他们行为背后的认知机制。我们通过推荐算法建立了评分预测的基准，在包含脑电信号的情况下有了显著的改善。此外，我们证明了这个数据集的潜力，在获得深入的情感体验和认知活动背后的用户行为的推荐系统。这项工作提出了一个新的视角来提高短视频推荐利用丰富的信息包含在脑电信号和多维情感参与分数，为未来的研究铺平了道路的短视频推荐系统。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=EEG-SVRec:+An+EEG+Dataset+with+User+Multidimensional+Affective+Engagement+Labels+in+Short+Video+Recommendation)|0| -|[Multimodality Invariant Learning for Multimedia-Based New Item Recommendation](https://doi.org/10.1145/3626772.3658596)|Haoyue Bai, Le Wu, Min Hou, Miaomiao Cai, Zhuangzhuang He, Yuyang Zhou, Richang Hong, Meng Wang|Academy of Cyber; Hefei University of Technology|Multimedia-based recommendation provides personalized item suggestions bylearning the content preferences of users. With the proliferation of digitaldevices and APPs, a huge number of new items are created rapidly over time. Howto quickly provide recommendations for new items at the inference time ischallenging. What's worse, real-world items exhibit varying degrees of modalitymissing(e.g., many short videos are uploaded without text descriptions). Thoughmany efforts have been devoted to multimedia-based recommendations, they eithercould not deal with new multimedia items or assumed the modality completenessin the modeling process. In this paper, we highlight the necessity of tackling the modality missingissue for new item recommendation. We argue that users' inherent contentpreference is stable and better kept invariant to arbitrary modality missingenvironments. Therefore, we approach this problem from a novel perspective ofinvariant learning. However, how to construct environments from finite userbehavior training data to generalize any modality missing is challenging. Totackle this issue, we propose a novel Multimodality Invariant LearningreCommendation(a.k.a. MILK) framework. Specifically, MILK first designs across-modality alignment module to keep semantic consistency from pretrainedmultimedia item features. After that, MILK designs multi-modal heterogeneousenvironments with cyclic mixup to augment training data, in order to mimic anymodality missing for invariant user preference learning. Extensive experimentson three real datasets verify the superiority of our proposed framework. Thecode is available at https://github.com/HaoyueBai98/MILK.|基于多媒体的推荐通过学习用户的内容偏好来提供个性化的项目建议。随着数字设备和应用程序的普及，随着时间的推移，大量的新项目被迅速创造出来。如何在推理时间快速提供新项目的建议是具有挑战性的。更糟糕的是，现实世界的项目表现出不同程度的模式缺失(例如，许多短视频上传没有文本描述)。虽然许多努力已致力于基于多媒体的建议，他们要么不能处理新的多媒体项目或承担的模式在建模过程中的完整性。本文强调了解决新项目推荐模式缺失问题的必要性。我们认为用户固有的内容偏好是稳定的，并且能够更好地保持对任意模态缺失环境的不变性。因此，我们从一个新的角度来探讨这个问题的不变学习。然而，如何从有限的用户行为训练数据中构建环境来推广任何模态缺失是一个挑战。为了解决这一问题，我们提出了一种新的多模态不变学习推荐(MILK)框架。具体来说，MILK 首先设计跨模态对齐模块，以保持语义的一致性从预训练的多媒体项目特征。然后，MILK 设计多模态混合异构环境来增加训练数据，以模拟不变用户偏好学习中缺失的任何模态。在三个实际数据集上的大量实验验证了我们提出的框架的优越性。密码可在 https://github.com/haoyuebai98/milk 下载。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Multimodality+Invariant+Learning+for+Multimedia-Based+New+Item+Recommendation)|0| -|[Semi-supervised Prototype Semantic Association Learning for Robust Cross-modal Retrieval](https://doi.org/10.1145/3626772.3657756)|Junsheng Wang, Tiantian Gong, Yan Yan|Nanjing University of Aeronautics and Astronautics, Nanjing, China; Nanjing University of Science and Technology, Nanjing, China; Illinois Institute of Technology, Chicago, IL, USA|Semi-supervised cross-modal retrieval (SS-CMR) aims at learning modality invariance and semantic discrimination from labeled data and unlabeled data, which is crucial for practical applications in the real-world. The key to essentially addressing the SS-CMR task is to solve the semantic association and modality heterogeneity problems. To address these issues, in this paper, we propose a novel semi-supervised cross-modal retrieval method, namely Semi-supervised Prototype Semantic Association Learning (SPAL) for robust cross-modal retrieval. To be specific, we employ shared semantic prototypes to associate labeled and unlabeled data over both modalities to minimize intra-class and maximize inter-class variations, thereby improving discriminative representations on unlabeled data. What is more important is that we propose a novel pseudo-label guided contrastive learning to refine cross-modal representation consistency in the common space, which leverages pseudo-label semantic graph information to constrain cross-modal consistent representations. Meanwhile, multi-modal data inevitably suffers from the cost and difficulty of data collection, resulting in the incomplete multimodal data problem. Thus, to strengthen the robustness of the SS-CMR, we propose a novel prototype propagation method for incomplete data to reconstruct completion representations which preserves the semantic consistency. Extensive evaluations using several baseline methods across four benchmark datasets demonstrate the effectiveness of our method.|半监督跨模态检索(SS-CMR)是从标记数据和未标记数据中学习模态不变性和语义识别的一种检索方法，对于实际应用具有重要意义。从根本上解决 SS-CMR 任务的关键是解决语义关联和情态异质性问题。为了解决这些问题，本文提出了一种新的半监督跨模态检索方法，即半监督原型语义关联学习(SPAL)。具体来说，我们使用共享的语义原型将标记和未标记的数据关联到两种模式上，以最小化类内变量和最大化类间变量，从而改进对未标记数据的区分表示。更重要的是，我们提出了一种新的伪标签引导的对比学习方法来提炼公共空间中的跨模态表示一致性，该方法利用伪标签语义图信息来约束跨模态一致性表示。同时，多模态数据不可避免地受到数据采集成本和难度的影响，导致了多模态数据的不完全性问题。因此，为了增强 SS-CMR 的鲁棒性，我们提出了一种新的不完全数据的原型传播方法来重构完备表示，从而保持了语义的一致性。使用四个基准数据集中的几个基线方法进行广泛的评估，证明了我们方法的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Semi-supervised+Prototype+Semantic+Association+Learning+for+Robust+Cross-modal+Retrieval)|0| +|[EEG-SVRec: An EEG Dataset with User Multidimensional Affective Engagement Labels in Short Video Recommendation](https://doi.org/10.1145/3626772.3657890)|Shaorun Zhang, Zhiyu He, Ziyi Ye, Peijie Sun, Qingyao Ai, Min Zhang, Yiqun Liu|Tsinghua University Computer Science and Technology; Tsinghua University Department of Computer Science and Technology; Tsinghua University|In recent years, short video platforms have gained widespread popularity,making the quality of video recommendations crucial for retaining users.Existing recommendation systems primarily rely on behavioral data, which faceslimitations when inferring user preferences due to issues such as data sparsityand noise from accidental interactions or personal habits. To address thesechallenges and provide a more comprehensive understanding of user affectiveexperience and cognitive activity, we propose EEG-SVRec, the first EEG datasetwith User Multidimensional Affective Engagement Labels in Short VideoRecommendation. The study involves 30 participants and collects 3,657interactions, offering a rich dataset that can be used for a deeper explorationof user preference and cognitive activity. By incorporating selfassessmenttechniques and real-time, low-cost EEG signals, we offer a more detailedunderstanding user affective experiences (valence, arousal, immersion,interest, visual and auditory) and the cognitive mechanisms behind theirbehavior. We establish benchmarks for rating prediction by the recommendationalgorithm, showing significant improvement with the inclusion of EEG signals.Furthermore, we demonstrate the potential of this dataset in gaining insightsinto the affective experience and cognitive activity behind user behaviors inrecommender systems. This work presents a novel perspective for enhancing shortvideo recommendation by leveraging the rich information contained in EEGsignals and multidimensional affective engagement scores, paving the way forfuture research in short video recommendation systems.|近年来，短视频平台得到了广泛的普及，这使得视频推荐的质量对于留住用户至关重要。现有的推荐系统主要依赖于行为数据，由于数据稀疏和意外交互或个人习惯造成的噪音等问题，在推断用户偏好时，行为数据面临限制。为了解决这些问题，提供对用户情感体验和认知活动的更全面的理解，我们提出了 EEG-SVRec，这是第一个在短视频推荐中使用用户多维情感参与标签的 EEG 数据集。这项研究涉及30名参与者，收集了3657次互动，提供了丰富的数据集，可用于更深入地探索用户偏好和认知活动。通过结合自我评估技术和实时、低成本的脑电信号，我们提供了一个更详细的理解用户情感体验(效价，唤醒，沉浸，兴趣，视觉和听觉)和他们行为背后的认知机制。我们通过推荐算法建立了评分预测的基准，在包含脑电信号的情况下有了显著的改善。此外，我们证明了这个数据集的潜力，在获得深入的情感体验和认知活动背后的用户行为的推荐系统。这项工作提出了一个新的视角来提高短视频推荐利用丰富的信息包含在脑电信号和多维情感参与分数，为未来的研究铺平了道路的短视频推荐系统。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=EEG-SVRec:+An+EEG+Dataset+with+User+Multidimensional+Affective+Engagement+Labels+in+Short+Video+Recommendation)|0| +|[Multimodality Invariant Learning for Multimedia-Based New Item Recommendation](https://doi.org/10.1145/3626772.3658596)|Haoyue Bai, Le Wu, Min Hou, Miaomiao Cai, Zhuangzhuang He, Yuyang Zhou, Richang Hong, Meng Wang|Hefei University of Technology; Academy of Cyber|Multimedia-based recommendation provides personalized item suggestions bylearning the content preferences of users. With the proliferation of digitaldevices and APPs, a huge number of new items are created rapidly over time. Howto quickly provide recommendations for new items at the inference time ischallenging. What's worse, real-world items exhibit varying degrees of modalitymissing(e.g., many short videos are uploaded without text descriptions). Thoughmany efforts have been devoted to multimedia-based recommendations, they eithercould not deal with new multimedia items or assumed the modality completenessin the modeling process. In this paper, we highlight the necessity of tackling the modality missingissue for new item recommendation. We argue that users' inherent contentpreference is stable and better kept invariant to arbitrary modality missingenvironments. Therefore, we approach this problem from a novel perspective ofinvariant learning. However, how to construct environments from finite userbehavior training data to generalize any modality missing is challenging. Totackle this issue, we propose a novel Multimodality Invariant LearningreCommendation(a.k.a. MILK) framework. Specifically, MILK first designs across-modality alignment module to keep semantic consistency from pretrainedmultimedia item features. After that, MILK designs multi-modal heterogeneousenvironments with cyclic mixup to augment training data, in order to mimic anymodality missing for invariant user preference learning. Extensive experimentson three real datasets verify the superiority of our proposed framework. Thecode is available at https://github.com/HaoyueBai98/MILK.|基于多媒体的推荐通过学习用户的内容偏好来提供个性化的项目建议。随着数字设备和应用程序的普及，随着时间的推移，大量的新项目被迅速创造出来。如何在推理时间快速提供新项目的建议是具有挑战性的。更糟糕的是，现实世界的项目表现出不同程度的模式缺失(例如，许多短视频上传没有文本描述)。虽然许多努力已致力于基于多媒体的建议，他们要么不能处理新的多媒体项目或承担的模式在建模过程中的完整性。本文强调了解决新项目推荐模式缺失问题的必要性。我们认为用户固有的内容偏好是稳定的，并且能够更好地保持对任意模态缺失环境的不变性。因此，我们从一个新的角度来探讨这个问题的不变学习。然而，如何从有限的用户行为训练数据中构建环境来推广任何模态缺失是一个挑战。为了解决这一问题，我们提出了一种新的多模态不变学习推荐(MILK)框架。具体来说，MILK 首先设计跨模态对齐模块，以保持语义的一致性从预训练的多媒体项目特征。然后，MILK 设计多模态混合异构环境来增加训练数据，以模拟不变用户偏好学习中缺失的任何模态。在三个实际数据集上的大量实验验证了我们提出的框架的优越性。密码可在 https://github.com/haoyuebai98/milk 下载。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Multimodality+Invariant+Learning+for+Multimedia-Based+New+Item+Recommendation)|0| +|[Semi-supervised Prototype Semantic Association Learning for Robust Cross-modal Retrieval](https://doi.org/10.1145/3626772.3657756)|Junsheng Wang, Tiantian Gong, Yan Yan|Illinois Institute of Technology, Chicago, IL, USA; Nanjing University of Science and Technology, Nanjing, China; Nanjing University of Aeronautics and Astronautics, Nanjing, China|Semi-supervised cross-modal retrieval (SS-CMR) aims at learning modality invariance and semantic discrimination from labeled data and unlabeled data, which is crucial for practical applications in the real-world. The key to essentially addressing the SS-CMR task is to solve the semantic association and modality heterogeneity problems. To address these issues, in this paper, we propose a novel semi-supervised cross-modal retrieval method, namely Semi-supervised Prototype Semantic Association Learning (SPAL) for robust cross-modal retrieval. To be specific, we employ shared semantic prototypes to associate labeled and unlabeled data over both modalities to minimize intra-class and maximize inter-class variations, thereby improving discriminative representations on unlabeled data. What is more important is that we propose a novel pseudo-label guided contrastive learning to refine cross-modal representation consistency in the common space, which leverages pseudo-label semantic graph information to constrain cross-modal consistent representations. Meanwhile, multi-modal data inevitably suffers from the cost and difficulty of data collection, resulting in the incomplete multimodal data problem. Thus, to strengthen the robustness of the SS-CMR, we propose a novel prototype propagation method for incomplete data to reconstruct completion representations which preserves the semantic consistency. Extensive evaluations using several baseline methods across four benchmark datasets demonstrate the effectiveness of our method.|半监督跨模态检索(SS-CMR)是从标记数据和未标记数据中学习模态不变性和语义识别的一种检索方法，对于实际应用具有重要意义。从根本上解决 SS-CMR 任务的关键是解决语义关联和情态异质性问题。为了解决这些问题，本文提出了一种新的半监督跨模态检索方法，即半监督原型语义关联学习(SPAL)。具体来说，我们使用共享的语义原型将标记和未标记的数据关联到两种模式上，以最小化类内变量和最大化类间变量，从而改进对未标记数据的区分表示。更重要的是，我们提出了一种新的伪标签引导的对比学习方法来提炼公共空间中的跨模态表示一致性，该方法利用伪标签语义图信息来约束跨模态一致性表示。同时，多模态数据不可避免地受到数据采集成本和难度的影响，导致了多模态数据的不完全性问题。因此，为了增强 SS-CMR 的鲁棒性，我们提出了一种新的不完全数据的原型传播方法来重构完备表示，从而保持了语义的一致性。使用四个基准数据集中的几个基线方法进行广泛的评估，证明了我们方法的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Semi-supervised+Prototype+Semantic+Association+Learning+for+Robust+Cross-modal+Retrieval)|0| |[Hypergraph Convolutional Network for User-Oriented Fairness in Recommender Systems](https://doi.org/10.1145/3626772.3657737)|Zhongxuan Han, Chaochao Chen, Xiaolin Zheng, Li Zhang, Yuyuan Li|Polytechnic Institute, Zhejiang University, Hangzhou, China; Computer Science and Technology, Zhejiang University, Hangzhou, China|The service system involves multiple stakeholders, making it crucial to ensure fairness. In this paper, we take the example of a typical service system, the recommender system, to investigate how to identify and tackle fairness issues within the service system. Recommender systems often exhibit bias towards a small user group, resulting in pronounced unfairness in recommendation performance, specifically the User-Oriented Fairness (UOF) issue. Existing research on UOF faces limitations in addressing two pivotal challenges: CH1: Current methods fall short in addressing the root cause of the UOF issue, stemming from an unfair training process between advantaged and disadvantaged users. CH2: Current methods struggle to unveil compelling correlations among users in sparse datasets. In this paper, we propose a novel Hypergraph Convolutional Network for User-Oriented Fairness, namely HyperUOF, to address the aforementioned challenges. HyperUOF serves as a versatile framework applicable to various backbone recommendation models for achieving UOF. To address CH1, HyperUOF employs an in-processing method that enhances the training process of disadvantaged users during model training. To addressCH2, HyperUOF incorporates a hypergraph-based approach, proven effective in sparse datasets, to explore high-order correlations among users. We conduct extensive experiments on three real-world datasets based on four backbone recommendation models to prove the effectiveness of our proposed HyperUOF.|服务体系涉及多个利益相关者，保证服务的公平性至关重要。本文以一个典型的服务系统——推荐系统为例，探讨如何识别和处理服务系统内的公平问题。推荐系统往往表现出对小用户群体的偏见，导致推荐性能的明显不公平，特别是面向用户的公平性(UOF)问题。现有的 UOF 研究在解决两个关键挑战方面面临着局限性: CH1: 由于优势用户和劣势用户之间的不公平培训过程，目前的方法在解决 UOF 问题的根本原因方面存在缺陷。CH2: 目前的方法很难揭示稀疏数据集中用户之间令人信服的相关性。在本文中，我们提出了一种新的面向用户公平的 Hypergraph 卷积网络，即 HyperUOF，以解决上述挑战。HyperUOF 作为一个多功能的框架，适用于实现 UOF 的各种骨干推荐模型。为了解决 CH1问题，HyperUOF 采用了内处理的方法，在模型训练过程中增强了弱势用户的训练过程。为了解决 CH2问题，HyperUOF 采用了一种基于超图的方法来探索用户之间的高阶相关性，这种方法在稀疏数据集中被证明是有效的。为了验证所提出的 HyperUOF 算法的有效性，我们基于四个骨干推荐模型对三个实际数据集进行了广泛的实验。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Hypergraph+Convolutional+Network+for+User-Oriented+Fairness+in+Recommender+Systems)|0| |[Hierarchical Semantics Alignment for 3D Human Motion Retrieval](https://doi.org/10.1145/3626772.3657804)|Yang Yang, Haoyu Shi, Huaiwen Zhang|Inner Mongolia University, Hohhot, China|Text to 3D human Motion Retrieval (TMR) is a challenging task in information retrieval, aiming to query relevant motion sequences with the natural language description. The conventional approach for TMR is to represent the data instances as point embeddings for alignment. However, in real-world scenarios, multiple motions often co-occur and superimpose on a single avatar. Simply aggregating text and motion sequences into a single global embedding may be inadequate for capturing the intricate semantics of superimposing motions. In addition, most of the motion variations occur locally and subtly, which further presents considerable challenges in precisely aligning motion sequences with their corresponding text. To address the aforementioned challenges, we propose a novel Hierarchical Semantics Alignment (HSA) framework for text-to-3D human motion retrieval. Beyond global alignment, we propose the Probabilistic-based Distribution Alignment (PDA) and a Descriptors-based Fine-grained Alignment (DFA) to achieve precise semantic matching. Specifically, the PDA encodes the text and motion sequences into multidimensional probabilistic distributions, effectively capturing the semantics of superimposing motions. By optimizing the problem of probabilistic distribution alignment, PDA achieves a precise match between superimposing motions and their corresponding text. The DFA first adopts a fine-grained feature gating by selectively filtering to the significant and representative local representations and meanwhile excluding the interferences of meaningless features. Then we adaptively assign local representations from text and motion into a set of cross-modal local aggregated descriptors, enabling local comparison and interaction between fine-grained text and motion features. Extensive experiments on two widely used benchmark datasets, HumanML3D and KIT-ML, demonstrate the effectiveness of the proposed method. It significantly outperforms existing state-of-the-art retrieval methods, achieving Rsum improvements of 24.74% on HumanML3D and 23.08% on KIT-ML.|文本到三维人体运动检索(tMR)是一个具有挑战性的任务在信息检索，旨在查询相关的运动序列与自然语言描述。TMR 的传统方法是将数据实例表示为点嵌入以进行对准。然而，在现实世界的情况下，多个运动往往同时发生，并叠加在一个单一的化身。简单地将文本和运动序列聚合成一个单一的全局嵌入可能不足以捕获叠加运动的复杂语义。此外，大多数运动变化发生在局部和微妙，这进一步提出了相当大的挑战，精确对齐运动序列与其相应的文本。针对上述挑战，我们提出了一种新的文本到三维人体运动检索的层次语义对齐(HSA)框架。在全局对齐的基础上，提出了基于概率的分布对齐(PDA)和基于描述符的细粒度对齐(DFA)来实现精确的语义匹配。具体来说，PDA 将文本和运动序列编码成多维概率分布，有效地捕获了运动叠加的语义。通过优化概率分布对齐问题，PDA 实现了叠加运动与对应文本的精确匹配。DFA 首先采用细粒度特征门控，对有意义和有代表性的局部表征进行选择性滤波，同时排除无意义特征的干扰。然后，我们自适应地从文本和运动中分配局部表示到一组跨模态局部聚合描述符中，从而实现细粒度文本和运动特征之间的局部比较和交互。在两个广泛使用的基准数据集 HumanML3D 和 KIT-ML 上的大量实验证明了该方法的有效性。它明显优于现有的最先进的检索方法，在 HumanML3D 上实现了24.74% 的 Rsum 改进，在 KIT-ML 上实现了23.08% 的 Rsum 改进。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Hierarchical+Semantics+Alignment+for+3D+Human+Motion+Retrieval)|0| -|[A Large Scale Test Corpus for Semantic Table Search](https://doi.org/10.1145/3626772.3657877)|Aristotelis Leventidis, Martin Pekár Christensen, Matteo Lissandrini, Laura Di Rocco, Katja Hose, Renée J. Miller|University of Verona & Aalborg University, Verona, Italy; Aalborg University, Aalborg, Denmark; Technische Universität Wien & Aalborg University, Vienna, Austria; Northeastern University, Boston, MA, USA|Table search aims to answer a query with a ranked list of tables. Unfortunately, current test corpora have focused mostly on needle-in-the-haystack tasks, where only a few tables are expected to exactly match the query intent. Instead, table search tasks often arise in response to the need for retrieving new datasets or augmenting existing ones, e.g., for data augmentation within data science or machine learning pipelines. Existing table repositories and benchmarks are limited in their ability to test retrieval methods for table search tasks. Thus, to close this gap, we introduce a novel dataset for query-by-example Semantic Table Search. This novel dataset consists of two snapshots of the large-scale Wikipedia tables collection from 2013 and 2019 with two important additions: (1) a page and topic aware ground truth relevance judgment and (2) a large-scale DBpedia entity linking annotation. Moreover, we generate a novel set of entity-centric queries that allows testing existing methods under a novel search scenario: semantic exploratory search. The resulting resource consists of 9,296 novel queries, 610,553 query-table relevance annotations, and 238,038 entity-linked tables from the 2013 snapshot. Similarly, on the 2019 snapshot, the resource consists of 2,560 queries, 958,214 relevance annotations, and 457,714 total tables. This makes our resource the largest annotated table-search corpus to date (97 times more queries and 956 times more annotated tables than any existing benchmark). We perform a user study among domain experts and prove that these annotators agree with the automatically generated relevance annotations. As a result, we can re-evaluate some basic assumptions behind existing table search approaches identifying their shortcomings along with promising novel research directions.|表搜索旨在使用表的排序列表来回答查询。不幸的是，目前的测试语料库主要集中在大海捞针的任务上，其中只有少数表格能够精确匹配查询意图。相反，表搜索任务经常出现在需要检索新数据集或扩充现有数据集的时候，例如，在数据科学或机器学习管道中进行数据扩充。现有的表存储库和基准在测试表搜索任务的检索方法方面受到限制。因此，为了弥补这一差距，我们引入了一个新的数据集查询的例子语义表搜索。这个新颖的数据集包括2013年和2019年大规模维基百科表收集的两个快照，其中有两个重要的补充: (1)一个页面和主题感知的基本事实相关性判断和(2)一个大规模的 DBpedia 实体链接注释。此外，我们还生成了一组新的以实体为中心的查询，允许在一个新的搜索场景下测试现有的方法: 语义探索搜索。得到的资源包括来自2013年快照的9,296个新查询、610,553个查询表相关注释和238,038个实体链接表。类似地，在2019年的快照中，资源由2,560个查询、958,214个相关注释和457,714个总表组成。这使我们的资源成为迄今为止最大的带注释的表搜索语料库(比任何现有基准多97倍的查询和956倍的带注释的表)。我们对领域专家进行了用户研究，证明了这些注释者与自动生成的关联注释是一致的。因此，我们可以重新评估现有表搜索方法背后的一些基本假设，识别它们的缺点，以及有希望的新的研究方向。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Large+Scale+Test+Corpus+for+Semantic+Table+Search)|0| +|[A Large Scale Test Corpus for Semantic Table Search](https://doi.org/10.1145/3626772.3657877)|Aristotelis Leventidis, Martin Pekár Christensen, Matteo Lissandrini, Laura Di Rocco, Katja Hose, Renée J. Miller|Northeastern University, Boston, MA, USA; University of Verona & Aalborg University, Verona, Italy; Technische Universität Wien & Aalborg University, Vienna, Austria; Aalborg University, Aalborg, Denmark|Table search aims to answer a query with a ranked list of tables. Unfortunately, current test corpora have focused mostly on needle-in-the-haystack tasks, where only a few tables are expected to exactly match the query intent. Instead, table search tasks often arise in response to the need for retrieving new datasets or augmenting existing ones, e.g., for data augmentation within data science or machine learning pipelines. Existing table repositories and benchmarks are limited in their ability to test retrieval methods for table search tasks. Thus, to close this gap, we introduce a novel dataset for query-by-example Semantic Table Search. This novel dataset consists of two snapshots of the large-scale Wikipedia tables collection from 2013 and 2019 with two important additions: (1) a page and topic aware ground truth relevance judgment and (2) a large-scale DBpedia entity linking annotation. Moreover, we generate a novel set of entity-centric queries that allows testing existing methods under a novel search scenario: semantic exploratory search. The resulting resource consists of 9,296 novel queries, 610,553 query-table relevance annotations, and 238,038 entity-linked tables from the 2013 snapshot. Similarly, on the 2019 snapshot, the resource consists of 2,560 queries, 958,214 relevance annotations, and 457,714 total tables. This makes our resource the largest annotated table-search corpus to date (97 times more queries and 956 times more annotated tables than any existing benchmark). We perform a user study among domain experts and prove that these annotators agree with the automatically generated relevance annotations. As a result, we can re-evaluate some basic assumptions behind existing table search approaches identifying their shortcomings along with promising novel research directions.|表搜索旨在使用表的排序列表来回答查询。不幸的是，目前的测试语料库主要集中在大海捞针的任务上，其中只有少数表格能够精确匹配查询意图。相反，表搜索任务经常出现在需要检索新数据集或扩充现有数据集的时候，例如，在数据科学或机器学习管道中进行数据扩充。现有的表存储库和基准在测试表搜索任务的检索方法方面受到限制。因此，为了弥补这一差距，我们引入了一个新的数据集查询的例子语义表搜索。这个新颖的数据集包括2013年和2019年大规模维基百科表收集的两个快照，其中有两个重要的补充: (1)一个页面和主题感知的基本事实相关性判断和(2)一个大规模的 DBpedia 实体链接注释。此外，我们还生成了一组新的以实体为中心的查询，允许在一个新的搜索场景下测试现有的方法: 语义探索搜索。得到的资源包括来自2013年快照的9,296个新查询、610,553个查询表相关注释和238,038个实体链接表。类似地，在2019年的快照中，资源由2,560个查询、958,214个相关注释和457,714个总表组成。这使我们的资源成为迄今为止最大的带注释的表搜索语料库(比任何现有基准多97倍的查询和956倍的带注释的表)。我们对领域专家进行了用户研究，证明了这些注释者与自动生成的关联注释是一致的。因此，我们可以重新评估现有表搜索方法背后的一些基本假设，识别它们的缺点，以及有希望的新的研究方向。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Large+Scale+Test+Corpus+for+Semantic+Table+Search)|0| |[JDivPS: A Diversified Product Search Dataset](https://doi.org/10.1145/3626772.3657888)|Zhirui Deng, Zhicheng Dou, Yutao Zhu, Xubo Qin, Pengchao Cheng, Jiangxu Wu, Hao Wang|JD.com, Inc., Beijing, China; Gaoling School of Artificial Intelligence, Renmin University of China, Beijing, China|The diversification of product search aims to offer diverse products to satisfy different user intents. Existing diversified product search approaches mainly relied on datasets sourced from online platforms. However, these datasets often present challenges due to their restricted public access and the absence of manually labeled user intents. Such limitations may lead to irreproducible experimental results and unreliable conclusions, restricting the development of this field. To address these problems, this paper introduces a novel dataset JDivPS for diversified product search. To the best of our knowledge, JDivPS is the first publicly accessible dataset with human-annotated user intents. The dataset is collected from JD.com, a major Chinese e-commerce platform. It includes 10,000 queries, around 1,680,000 unique products, and an average of 10 human-labeled user intents for each query. We have extensively evaluated several diversified ranking models using the JDivPS dataset. The results of these models are recorded and presented, serving as a valuable benchmark for future research. More details about the dataset can be found in https://github.com/DengZhirui/JDivPS.|产品搜索的多样化旨在提供多样化的产品，以满足不同的用户意图。现有的多样化产品搜索方法主要依赖于来自在线平台的数据集。然而，由于这些数据集的公开访问受到限制，而且缺乏手动标记的用户意图，因此往往会带来挑战。这些局限性可能导致不可重复的实验结果和不可靠的结论，限制了该领域的发展。为了解决这些问题，本文提出了一种用于多样化产品搜索的新数据集 JDivPS。据我们所知，JDivPS 是第一个具有人工注释用户意图的公开可访问数据集。该数据集是从中国主要的电子商务平台京东(JD.com)收集的。它包括10,000个查询，大约1,680,000个独特的产品，以及每个查询平均10个人工标记的用户意图。我们使用 JDivPS 数据集广泛地评估了几种不同的排名模型。这些模型的结果被记录和呈现，作为未来研究的一个有价值的基准。有关数据集的详细资料，可参阅 https://github.com/dengzhirui/jdivps。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=JDivPS:+A+Diversified+Product+Search+Dataset)|0| -|[An E-Commerce Dataset Revealing Variations during Sales](https://doi.org/10.1145/3626772.3657870)|Jianfu Zhang, Qingtao Yu, Yizhou Chen, Guoliang Zhou, Yawen Liu, Yawei Sun, Chen Liang, Guangda Huzhang, Yabo Ni, Anxiang Zeng, Han Yu|Nanyang Technological University, Singapore, Singapore; Shanghai Jiao Tong University, Shanghai, China; Shopee Pte. Ltd., Singapore, Singapore|Since the development of artificial intelligence technology, E-Commerce has gradually become one of the world's largest commercial markets. Within this domain, sales events, which are based on sociological mechanisms, play a significant role. E-Commerce platforms frequently offer sales and promotions to encourage users to purchase items, leading to significant changes in live environments. Learning-To-Rank (LTR) is a crucial component of E-Commerce search and recommendations, and substantial efforts have been devoted to this area. However, existing methods often assume an independent and identically distributed data setting, which does not account for the evolving distribution of online systems beyond online finetuning strategies. This limitation can lead to inaccurate predictions of user behaviors during sales events, resulting in significant loss of revenue. In addition, models must readjust themselves once sales have concluded in order to eliminate any effects caused by the sales events, leading to further regret. To address these limitations, we introduce a long-term E-Commerce search data set specifically designed to incubate LTR algorithms during such sales events, with the objective of advancing the capabilities of E-Commerce search engines. Our investigation focuses on typical industry practices and aims to identify potential solutions to address these challenges.|自人工智能技术发展以来，电子商务已逐渐成为世界上最大的商业市场之一。在这个领域中，基于社会学机制的销售事件起着重要的作用。电子商务平台经常提供销售和促销，以鼓励用户购买物品，导致生活环境的重大变化。学习排名(LTR)是电子商务搜索和推荐的一个重要组成部分，在这个领域已经投入了大量的努力。然而，现有的方法往往假定一个独立的和同样分布的数据集，这没有考虑到在线系统不断演变的分布，超出了在线微调策略。这种限制可能导致在销售活动期间对用户行为的不准确预测，从而导致收入的显著损失。此外，模型必须调整自己一旦销售结束，以消除任何影响所造成的销售事件，导致进一步的遗憾。为了解决这些局限性，我们引入了一个长期的电子商务搜索数据集，专门设计用于在此类销售活动期间孵化 LTR 算法，目的是提高电子商务搜索引擎的能力。我们的调查集中在典型的行业实践，旨在确定潜在的解决方案，以应对这些挑战。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=An+E-Commerce+Dataset+Revealing+Variations+during+Sales)|0| -|[Exploring Multi-Scenario Multi-Modal CTR Prediction with a Large Scale Dataset](https://doi.org/10.1145/3626772.3657865)|Zhaoxin Huan, Ke Ding, Ang Li, Xiaolu Zhang, Xu Min, Yong He, Liang Zhang, Jun Zhou, Linjian Mo, Jinjie Gu, Zhongyi Liu, Wenliang Zhong, Guannan Zhang, Chenliang Li, Fajie Yuan|Ant Group, Hangzhou, China; Ant Group, Beijing, China; Wuhan University, Wuhan, China; Westlake University, Hangzhou, China|Click-through rate (CTR) prediction plays a crucial role in recommendation systems, with significant impact on user experience and platform revenue generation. Despite the various public CTR datasets available due to increasing interest from both academia and industry, these datasets have limitations. They cover a limited range of scenarios and predominantly focus on ID-based features, neglecting the vital role of multi-modal features for effective multi-scenario CTR prediction. Moreover, their scale is modest compared to real-world industrial datasets, hindering robust and comprehensive evaluation of complex models. To address these challenges, we introduce a large-scale Multi-Scenario Multi-Modal CTR dataset named AntM2 C, built from real industrial data from Alipay. This dataset offers an impressive breadth and depth of information, covering CTR data from four diverse business scenarios, including advertisements, consumer coupons, mini-programs, and videos. Unlike existing datasets, AntM2 C provides not only ID-based features but also five textual features and one image feature for both users and items, supporting more delicate multi-modal CTR prediction. AntM2 C is also substantially larger than existing datasets, comprising 100 million CTR data. This scale allows for robust and comprehensive evaluation and comparison of CTR prediction models. We employ AntM2 C to construct several typical CTR tasks, including multi-scenario modeling, item and user cold-start modeling, and multi-modal modeling. Initial experiments and comparisons with baseline methods have shown that AntM2 C presents both new challenges and opportunities for CTR models, with the potential to significantly advance CTR research. The AntM2 C dataset is available at https://www.atecup.cn/OfficalDataSet.|点进率预测在推荐系统中起着至关重要的作用，对用户体验和平台收入产生重大影响。尽管由于学术界和工业界的兴趣日益增长，各种公共 CTR 数据集可用，但这些数据集有其局限性。它们涵盖的场景范围有限，主要侧重于基于身份的特征，忽视了多模态特征在有效的多场景 CTR 预测中的重要作用。此外，与现实世界的工业数据集相比，它们的规模较小，妨碍了对复杂模型的稳健和全面的评估。为了应对这些挑战，我们引入了一个名为 AntM2C 的大规模多场景多模式 CTR 数据集，该数据集是从支付宝的真实工业数据构建的。这个数据集提供了令人印象深刻的广度和深度的信息，涵盖了来自四个不同商业场景的点击率数据，包括广告、消费者优惠券、迷你程序和视频。与现有的数据集不同，AntM2 C 不仅提供基于 ID 的特征，而且还为用户和项目提供五个文本特征和一个图像特征，支持更精细的多模态 CTR 预测。AntM2C 也远远大于现有的数据集，包括1亿个点击率数据。这个尺度允许对 CTR 预测模型进行稳健和全面的评估和比较。我们使用 AntM2 C 构建了几个典型的 CTR 任务，包括多场景建模、项目和用户冷启动建模以及多模态建模。最初的实验和与基线方法的比较表明，AntM2 C 为 CTR 模型提出了新的挑战和机遇，有可能显著推进 CTR 研究。AntM2 c 数据集可在 https://www.atecup.cn/officaldataset 下载。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Exploring+Multi-Scenario+Multi-Modal+CTR+Prediction+with+a+Large+Scale+Dataset)|0| -|[Dimension Importance Estimation for Dense Information Retrieval](https://doi.org/10.1145/3626772.3657691)|Guglielmo Faggioli, Nicola Ferro, Raffaele Perego, Nicola Tonellotto|University of Pisa, Pisa, Italy; University of Padova, Padua, Italy; University of Padova, Padova, Italy; CNR, Pisa, Italy|Recent advances in Information Retrieval have shown the effectiveness of embedding queries and documents in a latent high-dimensional space to compute their similarity. While operating on such high-dimensional spaces is effective, in this paper, we hypothesize that we can improve the retrieval performance by adequately moving to a query-dependent subspace. More in detail, we formulate the Manifold Clustering (MC) Hypothesis: projecting queries and documents onto a subspace of the original representation space can improve retrieval effectiveness. To empirically validate our hypothesis, we define a novel class of Dimension IMportance Estimators (DIME). Such models aim to determine how much each dimension of a high-dimensional representation contributes to the quality of the final ranking and provide an empirical method to select a subset of dimensions where to project the query and the documents. To support our hypothesis, we propose an oracle DIME, capable of effectively selecting dimensions and almost doubling the retrieval performance. To show the practical applicability of our approach, we then propose a set of DIMEs that do not require any oracular piece of information to estimate the importance of dimensions. These estimators allow us to carry out a dimensionality selection that enables performance improvements of up to +11.5% (moving from 0.675 to 0.752 nDCG@10) compared to the baseline methods using all dimensions. Finally, we show that, with simple and realistic active feedback, such as the user's interaction with a single relevant document, we can design a highly effective DIME, allowing us to outperform the baseline by up to +0.224 nDCG@10 points (+58.6%, moving from 0.384 to 0.608).|信息检索的最新进展表明，将查询和文档嵌入到一个潜在的高维空间来计算它们的相似性是有效的。虽然在这样的高维空间上操作是有效的，但本文假设我们可以通过适当地移动到查询相关的子空间来提高检索性能。更详细地说，我们提出了流形聚类(MC)假说: 将查询和文档投影到原始表示空间的子空间中可以提高检索效率。为了从经验上验证我们的假设，我们定义了一类新的维度重要性估计(DIME)。这些模型旨在确定高维表示的每个维度在多大程度上有助于最终排名的质量，并提供一种经验方法来选择一个维度子集，以投射查询和文档。为了支持我们的假设，我们提出了一个 Oracle DIME，它能够有效地选择维度，并且检索性能几乎翻倍。为了展示我们的方法的实际适用性，我们然后提出一组 DIME，它们不需要任何预言信息来估计维度的重要性。这些估计值使我们能够进行维度选择，与使用所有维度的基线方法相比，性能提高高达11.5% (从0.675移动到0.752 nDCG@10)。最后，我们表明，通过简单和现实的主动反馈，例如用户与单个相关文档的交互，我们可以设计一个高效的 DIME，使我们的表现优于基线最多 + 0.224 nDCG@10分(+ 58.6% ，从0.384移动到0.608)。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Dimension+Importance+Estimation+for+Dense+Information+Retrieval)|0| -|[Large Language Models for Next Point-of-Interest Recommendation](https://doi.org/10.1145/3626772.3657840)|Peibo Li, Maarten de Rijke, Hao Xue, Shuang Ao, Yang Song, Flora D. Salim|The University of New South Wales; University of New South Wales; University of Amsterdam|The next Point of Interest (POI) recommendation task is to predict users'immediate next POI visit given their historical data. Location-Based SocialNetwork (LBSN) data, which is often used for the next POI recommendation task,comes with challenges. One frequently disregarded challenge is how toeffectively use the abundant contextual information present in LBSN data.Previous methods are limited by their numerical nature and fail to address thischallenge. In this paper, we propose a framework that uses pretrained LargeLanguage Models (LLMs) to tackle this challenge. Our framework allows us topreserve heterogeneous LBSN data in its original format, hence avoiding theloss of contextual information. Furthermore, our framework is capable ofcomprehending the inherent meaning of contextual information due to theinclusion of commonsense knowledge. In experiments, we test our framework onthree real-world LBSN datasets. Our results show that the proposed frameworkoutperforms the state-of-the-art models in all three datasets. Our analysisdemonstrates the effectiveness of the proposed framework in using contextualinformation as well as alleviating the commonly encountered cold-start andshort trajectory problems.|下一个兴趣点(POI)推荐任务是根据用户的历史数据预测他们的下一次 POI 访问。基于位置的社交网络(LBSN)数据通常用于下一个 POI 推荐任务，但它也带来了挑战。一个经常被忽视的挑战是如何有效地利用 LBSN 数据中存在的大量上下文信息。以前的方法受到其数字性质的限制，无法解决这一挑战。在本文中，我们提出了一个框架，使用预先训练的大型语言模型(LLM)来应对这一挑战。我们的框架允许我们以原始格式保留异构 LBSN 数据，从而避免了上下文信息的丢失。此外，由于包含了常识性知识，我们的框架能够理解上下文信息的内在含义。在实验中，我们在三个实际的 LBSN 数据集上测试我们的框架。我们的结果表明，所提出的框架在所有三个数据集中都优于最先进的模型。我们的分析证明了该框架在利用上下文信息以及缓解常见的冷启动和短轨道问题方面的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Large+Language+Models+for+Next+Point-of-Interest+Recommendation)|0| -|[The Impact of Group Membership Bias on the Quality and Fairness of Exposure in Ranking](https://doi.org/10.1145/3626772.3657752)|Ali Vardasbi, Maarten de Rijke, Fernando Diaz, Mostafa Dehghani|Carnegie Mellon University; Spotify; Google DeepMind; University of Amsterdam|When learning to rank from user interactions, search and recommender systemsmust address biases in user behavior to provide a high-quality ranking. Onetype of bias that has recently been studied in the ranking literature is whensensitive attributes, such as gender, have an impact on a user's judgment aboutan item's utility. For example, in a search for an expertise area, some usersmay be biased towards clicking on male candidates over female candidates. Wecall this type of bias group membership bias. Increasingly, we seek rankingsthat are fair to individuals and sensitive groups. Merit-based fairnessmeasures rely on the estimated utility of the items. With group membershipbias, the utility of the sensitive groups is under-estimated, hence, withoutcorrecting for this bias, a supposedly fair ranking is not truly fair. In thispaper, first, we analyze the impact of group membership bias on ranking qualityas well as merit-based fairness metrics and show that group membership bias canhurt both ranking and fairness. Then, we provide a correction method for groupbias that is based on the assumption that the utility score of items indifferent groups comes from the same distribution. This assumption has twopotential issues of sparsity and equality-instead-of-equity; we use anamortized approach to address these. We show that our correction method canconsistently compensate for the negative impact of group membership bias onranking quality and fairness metrics.|当学习从用户交互中进行排名时，搜索和推荐系统必须解决用户行为中的偏见，以提供高质量的排名。排名文献中最近研究的一种偏见类型是，当敏感属性，如性别，对用户对一个项目的效用的判断产生影响时。例如，在搜索专业领域时，一些用户可能会偏向于点击男性候选人而不是女性候选人。我们把这种类型的偏见称为群体成员偏见。我们越来越多地寻求对个人和敏感群体公平的排名。基于绩效的公平性衡量依赖于项目的估计效用。由于群体成员偏差，敏感群体的效用被低估，因此，如果不对这种偏差进行修正，所谓的公平排名就不是真正的公平。本文首先分析了群体成员偏差对排名质量的影响，以及基于绩效的公平性指标，指出群体成员偏差会损害排名和公平性。在此基础上，我们提出了一种群体偏差的校正方法，该方法基于不同群体项目的效用得分来自同一分布的假设。这个假设有两个潜在的问题，即稀缺性和公平性，而不是公平性; 我们使用分摊方法来解决这些问题。我们表明，我们的修正方法可以一致地补偿群体成员偏见对排名质量和公平性指标的负面影响。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=The+Impact+of+Group+Membership+Bias+on+the+Quality+and+Fairness+of+Exposure+in+Ranking)|0| +|[An E-Commerce Dataset Revealing Variations during Sales](https://doi.org/10.1145/3626772.3657870)|Jianfu Zhang, Qingtao Yu, Yizhou Chen, Guoliang Zhou, Yawen Liu, Yawei Sun, Chen Liang, Guangda Huzhang, Yabo Ni, Anxiang Zeng, Han Yu|Shanghai Jiao Tong University, Shanghai, China; Nanyang Technological University, Singapore, Singapore; Shopee Pte. Ltd., Singapore, Singapore|Since the development of artificial intelligence technology, E-Commerce has gradually become one of the world's largest commercial markets. Within this domain, sales events, which are based on sociological mechanisms, play a significant role. E-Commerce platforms frequently offer sales and promotions to encourage users to purchase items, leading to significant changes in live environments. Learning-To-Rank (LTR) is a crucial component of E-Commerce search and recommendations, and substantial efforts have been devoted to this area. However, existing methods often assume an independent and identically distributed data setting, which does not account for the evolving distribution of online systems beyond online finetuning strategies. This limitation can lead to inaccurate predictions of user behaviors during sales events, resulting in significant loss of revenue. In addition, models must readjust themselves once sales have concluded in order to eliminate any effects caused by the sales events, leading to further regret. To address these limitations, we introduce a long-term E-Commerce search data set specifically designed to incubate LTR algorithms during such sales events, with the objective of advancing the capabilities of E-Commerce search engines. Our investigation focuses on typical industry practices and aims to identify potential solutions to address these challenges.|自人工智能技术发展以来，电子商务已逐渐成为世界上最大的商业市场之一。在这个领域中，基于社会学机制的销售事件起着重要的作用。电子商务平台经常提供销售和促销，以鼓励用户购买物品，导致生活环境的重大变化。学习排名(LTR)是电子商务搜索和推荐的一个重要组成部分，在这个领域已经投入了大量的努力。然而，现有的方法往往假定一个独立的和同样分布的数据集，这没有考虑到在线系统不断演变的分布，超出了在线微调策略。这种限制可能导致在销售活动期间对用户行为的不准确预测，从而导致收入的显著损失。此外，模型必须调整自己一旦销售结束，以消除任何影响所造成的销售事件，导致进一步的遗憾。为了解决这些局限性，我们引入了一个长期的电子商务搜索数据集，专门设计用于在此类销售活动期间孵化 LTR 算法，目的是提高电子商务搜索引擎的能力。我们的调查集中在典型的行业实践，旨在确定潜在的解决方案，以应对这些挑战。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=An+E-Commerce+Dataset+Revealing+Variations+during+Sales)|0| +|[Exploring Multi-Scenario Multi-Modal CTR Prediction with a Large Scale Dataset](https://doi.org/10.1145/3626772.3657865)|Zhaoxin Huan, Ke Ding, Ang Li, Xiaolu Zhang, Xu Min, Yong He, Liang Zhang, Jun Zhou, Linjian Mo, Jinjie Gu, Zhongyi Liu, Wenliang Zhong, Guannan Zhang, Chenliang Li, Fajie Yuan|Ant Group, Hangzhou, China; Ant Group, Beijing, China; Westlake University, Hangzhou, China; Wuhan University, Wuhan, China|Click-through rate (CTR) prediction plays a crucial role in recommendation systems, with significant impact on user experience and platform revenue generation. Despite the various public CTR datasets available due to increasing interest from both academia and industry, these datasets have limitations. They cover a limited range of scenarios and predominantly focus on ID-based features, neglecting the vital role of multi-modal features for effective multi-scenario CTR prediction. Moreover, their scale is modest compared to real-world industrial datasets, hindering robust and comprehensive evaluation of complex models. To address these challenges, we introduce a large-scale Multi-Scenario Multi-Modal CTR dataset named AntM2 C, built from real industrial data from Alipay. This dataset offers an impressive breadth and depth of information, covering CTR data from four diverse business scenarios, including advertisements, consumer coupons, mini-programs, and videos. Unlike existing datasets, AntM2 C provides not only ID-based features but also five textual features and one image feature for both users and items, supporting more delicate multi-modal CTR prediction. AntM2 C is also substantially larger than existing datasets, comprising 100 million CTR data. This scale allows for robust and comprehensive evaluation and comparison of CTR prediction models. We employ AntM2 C to construct several typical CTR tasks, including multi-scenario modeling, item and user cold-start modeling, and multi-modal modeling. Initial experiments and comparisons with baseline methods have shown that AntM2 C presents both new challenges and opportunities for CTR models, with the potential to significantly advance CTR research. The AntM2 C dataset is available at https://www.atecup.cn/OfficalDataSet.|点进率预测在推荐系统中起着至关重要的作用，对用户体验和平台收入产生重大影响。尽管由于学术界和工业界的兴趣日益增长，各种公共 CTR 数据集可用，但这些数据集有其局限性。它们涵盖的场景范围有限，主要侧重于基于身份的特征，忽视了多模态特征在有效的多场景 CTR 预测中的重要作用。此外，与现实世界的工业数据集相比，它们的规模较小，妨碍了对复杂模型的稳健和全面的评估。为了应对这些挑战，我们引入了一个名为 AntM2C 的大规模多场景多模式 CTR 数据集，该数据集是从支付宝的真实工业数据构建的。这个数据集提供了令人印象深刻的广度和深度的信息，涵盖了来自四个不同商业场景的点击率数据，包括广告、消费者优惠券、迷你程序和视频。与现有的数据集不同，AntM2 C 不仅提供基于 ID 的特征，而且还为用户和项目提供五个文本特征和一个图像特征，支持更精细的多模态 CTR 预测。AntM2C 也远远大于现有的数据集，包括1亿个点击率数据。这个尺度允许对 CTR 预测模型进行稳健和全面的评估和比较。我们使用 AntM2 C 构建了几个典型的 CTR 任务，包括多场景建模、项目和用户冷启动建模以及多模态建模。最初的实验和与基线方法的比较表明，AntM2 C 为 CTR 模型提出了新的挑战和机遇，有可能显著推进 CTR 研究。AntM2 c 数据集可在 https://www.atecup.cn/officaldataset 下载。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Exploring+Multi-Scenario+Multi-Modal+CTR+Prediction+with+a+Large+Scale+Dataset)|0| +|[Dimension Importance Estimation for Dense Information Retrieval](https://doi.org/10.1145/3626772.3657691)|Guglielmo Faggioli, Nicola Ferro, Raffaele Perego, Nicola Tonellotto|University of Pisa, Pisa, Italy; University of Padova, Padova, Italy; University of Padova, Padua, Italy; CNR, Pisa, Italy|Recent advances in Information Retrieval have shown the effectiveness of embedding queries and documents in a latent high-dimensional space to compute their similarity. While operating on such high-dimensional spaces is effective, in this paper, we hypothesize that we can improve the retrieval performance by adequately moving to a query-dependent subspace. More in detail, we formulate the Manifold Clustering (MC) Hypothesis: projecting queries and documents onto a subspace of the original representation space can improve retrieval effectiveness. To empirically validate our hypothesis, we define a novel class of Dimension IMportance Estimators (DIME). Such models aim to determine how much each dimension of a high-dimensional representation contributes to the quality of the final ranking and provide an empirical method to select a subset of dimensions where to project the query and the documents. To support our hypothesis, we propose an oracle DIME, capable of effectively selecting dimensions and almost doubling the retrieval performance. To show the practical applicability of our approach, we then propose a set of DIMEs that do not require any oracular piece of information to estimate the importance of dimensions. These estimators allow us to carry out a dimensionality selection that enables performance improvements of up to +11.5% (moving from 0.675 to 0.752 nDCG@10) compared to the baseline methods using all dimensions. Finally, we show that, with simple and realistic active feedback, such as the user's interaction with a single relevant document, we can design a highly effective DIME, allowing us to outperform the baseline by up to +0.224 nDCG@10 points (+58.6%, moving from 0.384 to 0.608).|信息检索的最新进展表明，将查询和文档嵌入到一个潜在的高维空间来计算它们的相似性是有效的。虽然在这样的高维空间上操作是有效的，但本文假设我们可以通过适当地移动到查询相关的子空间来提高检索性能。更详细地说，我们提出了流形聚类(MC)假说: 将查询和文档投影到原始表示空间的子空间中可以提高检索效率。为了从经验上验证我们的假设，我们定义了一类新的维度重要性估计(DIME)。这些模型旨在确定高维表示的每个维度在多大程度上有助于最终排名的质量，并提供一种经验方法来选择一个维度子集，以投射查询和文档。为了支持我们的假设，我们提出了一个 Oracle DIME，它能够有效地选择维度，并且检索性能几乎翻倍。为了展示我们的方法的实际适用性，我们然后提出一组 DIME，它们不需要任何预言信息来估计维度的重要性。这些估计值使我们能够进行维度选择，与使用所有维度的基线方法相比，性能提高高达11.5% (从0.675移动到0.752 nDCG@10)。最后，我们表明，通过简单和现实的主动反馈，例如用户与单个相关文档的交互，我们可以设计一个高效的 DIME，使我们的表现优于基线最多 + 0.224 nDCG@10分(+ 58.6% ，从0.384移动到0.608)。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Dimension+Importance+Estimation+for+Dense+Information+Retrieval)|0| +|[Large Language Models for Next Point-of-Interest Recommendation](https://doi.org/10.1145/3626772.3657840)|Peibo Li, Maarten de Rijke, Hao Xue, Shuang Ao, Yang Song, Flora D. Salim|University of New South Wales; University of Amsterdam; The University of New South Wales|The next Point of Interest (POI) recommendation task is to predict users'immediate next POI visit given their historical data. Location-Based SocialNetwork (LBSN) data, which is often used for the next POI recommendation task,comes with challenges. One frequently disregarded challenge is how toeffectively use the abundant contextual information present in LBSN data.Previous methods are limited by their numerical nature and fail to address thischallenge. In this paper, we propose a framework that uses pretrained LargeLanguage Models (LLMs) to tackle this challenge. Our framework allows us topreserve heterogeneous LBSN data in its original format, hence avoiding theloss of contextual information. Furthermore, our framework is capable ofcomprehending the inherent meaning of contextual information due to theinclusion of commonsense knowledge. In experiments, we test our framework onthree real-world LBSN datasets. Our results show that the proposed frameworkoutperforms the state-of-the-art models in all three datasets. Our analysisdemonstrates the effectiveness of the proposed framework in using contextualinformation as well as alleviating the commonly encountered cold-start andshort trajectory problems.|下一个兴趣点(POI)推荐任务是根据用户的历史数据预测他们的下一次 POI 访问。基于位置的社交网络(LBSN)数据通常用于下一个 POI 推荐任务，但它也带来了挑战。一个经常被忽视的挑战是如何有效地利用 LBSN 数据中存在的大量上下文信息。以前的方法受到其数字性质的限制，无法解决这一挑战。在本文中，我们提出了一个框架，使用预先训练的大型语言模型(LLM)来应对这一挑战。我们的框架允许我们以原始格式保留异构 LBSN 数据，从而避免了上下文信息的丢失。此外，由于包含了常识性知识，我们的框架能够理解上下文信息的内在含义。在实验中，我们在三个实际的 LBSN 数据集上测试我们的框架。我们的结果表明，所提出的框架在所有三个数据集中都优于最先进的模型。我们的分析证明了该框架在利用上下文信息以及缓解常见的冷启动和短轨道问题方面的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Large+Language+Models+for+Next+Point-of-Interest+Recommendation)|0| +|[The Impact of Group Membership Bias on the Quality and Fairness of Exposure in Ranking](https://doi.org/10.1145/3626772.3657752)|Ali Vardasbi, Maarten de Rijke, Fernando Diaz, Mostafa Dehghani|Spotify; University of Amsterdam; Google DeepMind; Carnegie Mellon University|When learning to rank from user interactions, search and recommender systemsmust address biases in user behavior to provide a high-quality ranking. Onetype of bias that has recently been studied in the ranking literature is whensensitive attributes, such as gender, have an impact on a user's judgment aboutan item's utility. For example, in a search for an expertise area, some usersmay be biased towards clicking on male candidates over female candidates. Wecall this type of bias group membership bias. Increasingly, we seek rankingsthat are fair to individuals and sensitive groups. Merit-based fairnessmeasures rely on the estimated utility of the items. With group membershipbias, the utility of the sensitive groups is under-estimated, hence, withoutcorrecting for this bias, a supposedly fair ranking is not truly fair. In thispaper, first, we analyze the impact of group membership bias on ranking qualityas well as merit-based fairness metrics and show that group membership bias canhurt both ranking and fairness. Then, we provide a correction method for groupbias that is based on the assumption that the utility score of items indifferent groups comes from the same distribution. This assumption has twopotential issues of sparsity and equality-instead-of-equity; we use anamortized approach to address these. We show that our correction method canconsistently compensate for the negative impact of group membership bias onranking quality and fairness metrics.|当学习从用户交互中进行排名时，搜索和推荐系统必须解决用户行为中的偏见，以提供高质量的排名。排名文献中最近研究的一种偏见类型是，当敏感属性，如性别，对用户对一个项目的效用的判断产生影响时。例如，在搜索专业领域时，一些用户可能会偏向于点击男性候选人而不是女性候选人。我们把这种类型的偏见称为群体成员偏见。我们越来越多地寻求对个人和敏感群体公平的排名。基于绩效的公平性衡量依赖于项目的估计效用。由于群体成员偏差，敏感群体的效用被低估，因此，如果不对这种偏差进行修正，所谓的公平排名就不是真正的公平。本文首先分析了群体成员偏差对排名质量的影响，以及基于绩效的公平性指标，指出群体成员偏差会损害排名和公平性。在此基础上，我们提出了一种群体偏差的校正方法，该方法基于不同群体项目的效用得分来自同一分布的假设。这个假设有两个潜在的问题，即稀缺性和公平性，而不是公平性; 我们使用分摊方法来解决这些问题。我们表明，我们的修正方法可以一致地补偿群体成员偏见对排名质量和公平性指标的负面影响。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=The+Impact+of+Group+Membership+Bias+on+the+Quality+and+Fairness+of+Exposure+in+Ranking)|0| |[Grand: A Fast and Accurate Graph Retrieval Framework via Knowledge Distillation](https://doi.org/10.1145/3626772.3657773)|Lin Lan, Pinghui Wang, Rui Shi, Tingqing Liu, Juxiang Zeng, Feiyang Sun, Yang Ren, Jing Tao, Xiaohong Guan|MOE KLINNS Lab, Xi'an Jiaotong University, Xi'an, China; Xi'an Jiaotong University; GaussDB, Huawei Technologies Co Ltd, Shenzhen, China|Graph retrieval aims to find the most similar graphs in a graph database given a query graph, which is a fundamental problem with many real-world applications in chemical engineering, code analysis, etc. To date, existing neural graph retrieval methods generally fall into two categories: Embedding Based Paradigm (Ebp) and Matching Based Paradigm (Mbp). The Ebp models learn an individual vectorial representation for each graph and the retrieval process can be accelerated by pre-computing these representations. The Mbp models learn a neural matching function to compare graphs on a pair-by-pair basis, in which the fine-grained pairwise comparison leads to higher retrieval accuracy but severely degrades retrieval efficiency. In this paper, to combine the advantage of Ebp in retrieval efficiency with that of Mbp in retrieval accuracy, we propose a novel Graph RetrievAl framework via KNowledge Distillation, namely GRAND. The key point is to leverage the idea of knowledge distillation to transfer the fine-grained graph comparison knowledge from an Mbp model to an Ebp model, such that the Ebp model can generate better graph representations and thus yield higher retrieval accuracy. At the same time, we can still pre-compute and index the improved graph representations to retain the retrieval speed of Ebp. Towards this end, we propose to perform knowledge distillation from three perspectives: score, node, and subgraph levels. In addition, we propose to perform mutual two-way knowledge transfer between Mbp and Ebp, such that Mbp and Ebp complement and benefit each other. Extensive experiments on three real-world datasets show that GRAND improves the performance of Ebp by a large margin and the improvement is consistent for different combinations of Ebp and Mbp models. For example, GRAND achieves performance gains of mostly more than 10% and up to 16.88% in terms of Recall@K on different datasets.|图形检索的目的是在给定查询图的图库中找到最相似的图，这是化学工程、代码分析等现实应用中的一个基本问题。迄今为止，现有的神经图检索方法一般分为两类: 基于嵌入的范式(Ebp)和基于匹配的范式(Mbp)。Ebp 模型学习每个图的单个向量表示，并且可以通过预计算这些表示来加速检索过程。Mbp 模型学习了一种神经匹配功能，可以在一对一对的基础上对图进行比较，在这种情况下，细粒度的成对比较导致更高的检索准确性，但严重降低了检索效率。本文结合 Ebp 在检索效率方面的优势和 Mbp 在检索精度方面的优势，提出了一种基于知识提取的图形检索 Al 框架 GRAND。其关键是利用知识精馏的思想，将 Mbp 模型中的细粒度图形比较知识转化为 Ebp 模型，使 Ebp 模型能够生成更好的图形表示，从而提高检索精度。同时，我们还可以对改进后的图表示进行预计算和索引，以保持 Ebp 的检索速度。为此，我们提出从三个角度进行知识提取: 得分、节点和子图层次。此外，我们提出在 Mbp 和 Ebp 之间进行双向知识转移，使 Mbp 和 Ebp 相互补充，相互受益。在三个实际数据集上的大量实验表明，GRAND 算法大大提高了 Ebp 算法的性能，而且对于 Ebp 和 Mbp 模型的不同组合，GRAND 算法的性能改善是一致的。例如，GRAND 在不同的数据集上通过 Recall@K 实现了大多超过10% 和高达16.88% 的性能增益。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Grand:+A+Fast+and+Accurate+Graph+Retrieval+Framework+via+Knowledge+Distillation)|0| |[Unmasking Privacy: A Reproduction and Evaluation Study of Obfuscation-based Perturbation Techniques for Collaborative Filtering](https://doi.org/10.1145/3626772.3657858)|Alex Martinez, Mihnea Tufis, Ludovico Boratto|University of Cagliari, Cagliari, Italy; Eurecat, Technology Centre of Catalonia & Universitat de Barcelona, Barcelona, Catalunya, Spain; Eurecat, Technology Centre of Catalonia, Barcelona, Catalunya, Spain|Recommender systems (RecSys) solve personalisation problems and therefore heavily rely on personal data - demographics, user preferences, user interactions - each baring important privacy risks. It is also widely accepted that in RecSys performance and privacy are at odds, with the increase of one resulting in the decrease of the other. Among the diverse approaches in privacy enhancing technologies (PET) for RecSys, perturbation stands out for its simplicity and computational efficiency. It involves adding noise to sensitive data, thus hiding its real value from an untrusted actor. We reproduce and test a set of four randomization-based perturbation techniques developed by Batmaz and Polat \citebatmaz2016randomization for privacy preserving collaborative filtering. While the framework presents great advantages - low computational requirements, several useful privacy-enhancing parameters - the supporting paper lacks conclusions drawn from empirical evaluation. We address this shortcoming by proposing - in absence of an implementation by the authors - our own implementation of the obfuscation framework. We then develop an evaluation framework to test the main assumption of the reference paper - that RecSys privacy and performance are competing goals. We extend this study to understand how much we can enhance privacy, within reasonable losses of the RecSys performance. We reproduce and test the framework for the more realistic scenario where only implicit feedback is available, using two well-known datasets (MovieLens-1M and Last.fm-1K), and several state-of-the-art recommendation algorithms (NCF and LightGCN from the Microsoft Recommenders public repository).|推荐系统(RecSys)解决个性化问题，因此严重依赖于个人数据——人口统计数据、用户偏好、用户交互——每个都有重要的隐私风险。人们普遍认为，在 RecSys 系统中，性能和隐私是不一致的，一个的增加会导致另一个的减少。在 RecSys 隐私增强技术(PET)的众多方法中，摄动以其简单性和计算效率而著称。它包括向敏感数据添加噪音，从而向不受信任的参与者隐藏其真实值。我们重现并测试了 Batmaz 和 Polat citebatmaz2016为保护隐私而开发的一组基于随机化的扰动技术，这些技术都是用于保护隐私的协同过滤。虽然该框架具有很大的优势——低计算需求，几个有用的隐私增强参数——但是支持文件缺乏从实证评估中得出的结论。我们通过提出——在作者没有实现的情况下——我们自己的模糊框架的实现来解决这个缺陷。然后，我们开发了一个评估框架来检验参考文件的主要假设—— RecSys 的隐私和性能是相互竞争的目标。我们扩展这项研究，以了解我们可以提高多少隐私，在合理损失的 RecSys 性能。我们使用两个众所周知的数据集(MovieLens-1M 和 Last.fm-1K)和几个最先进的推荐算法(来自 Microsoft 推荐公共存储库的 NCF 和 LightGCN) ，重现和测试框架以实现更现实的场景，其中只有隐式反馈是可用的。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Unmasking+Privacy:+A+Reproduction+and+Evaluation+Study+of+Obfuscation-based+Perturbation+Techniques+for+Collaborative+Filtering)|0| -|[GPT4Rec: Graph Prompt Tuning for Streaming Recommendation](https://doi.org/10.1145/3626772.3657720)|Peiyan Zhang, Yuchen Yan, Xi Zhang, Liying Kang, Chaozhuo Li, Feiran Huang, Senzhang Wang, Sunghun Kim|Central South University; Peking University School of Intelligence Science and Technology; Jinan University; Microsoft Research Asia; Hong Kong Polytechnic University; Hong Kong University of Science and Technology; Fuzhou University Interdisciplinary Institute for Medical Engineering|In the realm of personalized recommender systems, the challenge of adaptingto evolving user preferences and the continuous influx of new users and itemsis paramount. Conventional models, typically reliant on a static training-testapproach, struggle to keep pace with these dynamic demands. Streamingrecommendation, particularly through continual graph learning, has emerged as anovel solution. However, existing methods in this area either rely onhistorical data replay, which is increasingly impractical due to stringent dataprivacy regulations; or are inability to effectively address the over-stabilityissue; or depend on model-isolation and expansion strategies. To tackle thesedifficulties, we present GPT4Rec, a Graph Prompt Tuning method for streamingRecommendation. Given the evolving user-item interaction graph, GPT4Rec firstdisentangles the graph patterns into multiple views. After isolating specificinteraction patterns and relationships in different views, GPT4Rec utilizeslightweight graph prompts to efficiently guide the model across varyinginteraction patterns within the user-item graph. Firstly, node-level promptsare employed to instruct the model to adapt to changes in the attributes orproperties of individual nodes within the graph. Secondly, structure-levelprompts guide the model in adapting to broader patterns of connectivity andrelationships within the graph. Finally, view-level prompts are innovativelydesigned to facilitate the aggregation of information from multipledisentangled views. These prompt designs allow GPT4Rec to synthesize acomprehensive understanding of the graph, ensuring that all vital aspects ofthe user-item interactions are considered and effectively integrated.Experiments on four diverse real-world datasets demonstrate the effectivenessand efficiency of our proposal.|在个性化推荐系统领域，适应不断变化的用户偏好和不断涌入的新用户和项目的挑战至关重要。传统的模型，通常依赖于静态的训练-测试方法，很难跟上这些动态的需求。流式推荐，特别是通过持续的图学习，已经成为一种新的解决方案。然而，这一领域的现有方法要么依赖于历史数据重放，由于严格的数据隐私条例，这越来越不切实际; 要么无法有效地解决过度稳定的问题; 要么依赖于模型隔离和扩展策略。为了解决这些问题，我们提出了 GPT4Rec，一种用于流推荐的图形提示调优方法。考虑到不断发展的用户项交互图，GPT4Rec 首先将图形模式分解为多个视图。在不同视图中隔离特定的交互模式和关系之后，GPT4Rec 利用轻量级图形提示有效地指导模型跨用户项图中的各种交互模式。首先，采用节点级提示来指导模型适应图中各个节点的属性或属性的变化。其次，结构级提示指导模型适应图中更广泛的连接和关系模式。最后，视图级提示被创新性地设计，以促进来自多个分离视图的信息的聚合。这些迅速的设计使 GPT4Rec 能够综合对图表的全面理解，确保用户-项目交互的所有重要方面都得到考虑和有效集成。在四个不同的真实世界数据集上的实验证明了我们方案的有效性和高效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=GPT4Rec:+Graph+Prompt+Tuning+for+Streaming+Recommendation)|0| -|[I3: Intent-Introspective Retrieval Conditioned on Instructions](https://doi.org/10.1145/3626772.3657745)|Kaihang Pan, Juncheng Li, Wenjie Wang, Hao Fei, Hongye Song, Wei Ji, Jun Lin, Xiaozhong Liu, TatSeng Chua, Siliang Tang|Zhejiang University; Worcester Polytechnic Institute; National University of Singapore; DAMO Academy, Alibaba Group|Recent studies indicate that dense retrieval models struggle to perform wellon a wide variety of retrieval tasks that lack dedicated training data, asdifferent retrieval tasks often entail distinct search intents. To address thischallenge, in this work we leverage instructions to flexibly describe retrievalintents and introduce I3, a unified retrieval system that performsIntent-Introspective retrieval across various tasks, conditioned onInstructions without any task-specific training. I3 innovatively incorporates apluggable introspector in a parameter-isolated manner to comprehend specificretrieval intents by jointly reasoning over the input query and instruction,and seamlessly integrates the introspected intent into the original retrievalmodel for intent-aware retrieval. Furthermore, we propose progressively-prunedintent learning. It utilizes extensive LLM-generated data to train I3phase-by-phase, embodying two key designs: progressive structure pruning anddrawback extrapolation-based data refinement. Extensive experiments show thatin the BEIR benchmark, I3 significantly outperforms baseline methods designedwith task-specific retrievers, achieving state-of-the-art zero-shot performancewithout any task-specific tuning.|最近的研究表明，密集检索模型难以在缺乏专门训练数据的各种检索任务中表现良好，因为不同的检索任务往往需要不同的搜索意图。为了应对这一挑战，在这项工作中，我们利用指令来灵活地描述检索对象，并引入 I3，一个统一的检索系统，可以在不同任务间执行意图-内省检索，不需要任何特定任务的训练，只需要指令。I3创新地以参数隔离的方式整合了可插入的内省器，通过对输入查询和指令的联合推理来理解特定的检索意图，并且无缝地将内省意图整合到原始的检索模型中用于意图感知检索。此外，我们提出逐步修剪意向学习。它利用大量 LLM 生成的数据对 I3进行逐步训练，包含两个关键设计: 渐进式结构修剪和基于缺陷外推的数据细化。大量的实验表明，在 BEIR 基准测试中，I3明显优于设计有任务特定检索器的基准方法，在没有任何任务特定调整的情况下，实现了最先进的零射击性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=I3:+Intent-Introspective+Retrieval+Conditioned+on+Instructions)|0| -|[Disentangling ID and Modality Effects for Session-based Recommendation](https://doi.org/10.1145/3626772.3657748)|Xiaokun Zhang, Bo Xu, Zhaochun Ren, Xiaochen Wang, Hongfei Lin, Fenglong Ma|Pennsylvania State University; Leiden University; Dalian University of Technology|Session-based recommendation aims to predict intents of anonymous users basedon their limited behaviors. Modeling user behaviors involves two distinctrationales: co-occurrence patterns reflected by item IDs, and fine-grainedpreferences represented by item modalities (e.g., text and images). However,existing methods typically entangle these causes, leading to their failure inachieving accurate and explainable recommendations. To this end, we propose anovel framework DIMO to disentangle the effects of ID and modality in the task.At the item level, we introduce a co-occurrence representation schema toexplicitly incorporate cooccurrence patterns into ID representations.Simultaneously, DIMO aligns different modalities into a unified semantic spaceto represent them uniformly. At the session level, we present a multi-viewself-supervised disentanglement, including proxy mechanism and counterfactualinference, to disentangle ID and modality effects without supervised signals.Leveraging these disentangled causes, DIMO provides recommendations via causalinference and further creates two templates for generating explanations.Extensive experiments on multiple real-world datasets demonstrate theconsistent superiority of DIMO over existing methods. Further analysis alsoconfirms DIMO's effectiveness in generating explanations.|基于会话的推荐旨在根据匿名用户的有限行为来预测用户的意图。建模用户行为涉及两种不同的情况: 由项目 ID 反映的共现模式，以及由项目模式(例如，文本和图像)表示的细粒度偏好。然而，现有的方法通常纠缠这些原因，导致他们无法实现准确和可解释的建议。为此，我们提出了一个新的框架 DIMO，以解决身份和情态在任务中的影响。在项目层面，我们引入了一个共现表示模式，以显式地将共现模式纳入身份表示。同时，DIMO 将不同的模式统一到一个统一的语义空间中，以统一地表示它们。在会话层面，我们提出了一种多视角的自监督解纠缠算法，包括代理机制和反事实推理，用于在没有监督信号的情况下解纠缠 ID 和模态效应。利用这些分离的原因，DIMO 通过因果推理提供建议，并进一步创建两个模板来产生解释。在多个真实世界数据集上的大量实验证明了 DIMO 相对于现有方法的一致优越性。进一步的分析也证实了 DIMO 在产生解释方面的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Disentangling+ID+and+Modality+Effects+for+Session-based+Recommendation)|0| -|[Large Language Models are Learnable Planners for Long-Term Recommendation](https://doi.org/10.1145/3626772.3657683)|Wentao Shi, Xiangnan He, Yang Zhang, Chongming Gao, Xinyue Li, Jizhi Zhang, Qifan Wang, Fuli Feng|Meta; University of Science and Technology of China|Planning for both immediate and long-term benefits becomes increasinglyimportant in recommendation. Existing methods apply Reinforcement Learning (RL)to learn planning capacity by maximizing cumulative reward for long-termrecommendation. However, the scarcity of recommendation data presentschallenges such as instability and susceptibility to overfitting when trainingRL models from scratch, resulting in sub-optimal performance. In this light, wepropose to leverage the remarkable planning capabilities over sparse data ofLarge Language Models (LLMs) for long-term recommendation. The key to achievingthe target lies in formulating a guidance plan following principles ofenhancing long-term engagement and grounding the plan to effective andexecutable actions in a personalized manner. To this end, we propose a Bi-levelLearnable LLM Planner framework, which consists of a set of LLM instances andbreaks down the learning process into macro-learning and micro-learning tolearn macro-level guidance and micro-level personalized recommendationpolicies, respectively. Extensive experiments validate that the frameworkfacilitates the planning ability of LLMs for long-term recommendation. Our codeand data can be found at https://github.com/jizhi-zhang/BiLLP.|计划短期和长期的利益变得越来越重要的建议。现有的方法通过最大化长期推荐的累积回报来应用强化学习(RL)来学习计划能力。然而，推荐数据的稀缺性提出了挑战，如不稳定性和容易过拟合时，从头开始训练 RL 模型，导致次优性能。在这种情况下，我们建议利用大语言模型(LLM)稀疏数据的卓越规划能力来进行长期推荐。实现目标的关键在于制定指导计划，遵循加强长期参与和以个性化方式采取有效和可执行行动的原则。为此，我们提出了一个双层可学习的 LLM 规划框架，该框架由一组 LLM 实例组成，将学习过程分解为宏观学习和微观学习，分别学习宏观层面的指导和微观层面的个性化推荐策略。大量的实验验证了该框架有利于 LLM 的长期推荐规划能力。我们的代码和数据可以在 https://github.com/jizhi-zhang/billp 找到。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Large+Language+Models+are+Learnable+Planners+for+Long-Term+Recommendation)|0| -|[Identifiability of Cross-Domain Recommendation via Causal Subspace Disentanglement](https://doi.org/10.1145/3626772.3657758)|Jing Du, Zesheng Ye, Bin Guo, Zhiwen Yu, Lina Yao|Macquarie University, Sydney, Australia; Northwestern Polytechnical University, Xi'an, China; The University of New South Wales, Sydney, Australia; CSIRO's Data 61 & The University of New South Wales, Sydney, Australia|Cross-Domain Recommendation~(CDR) seeks to enable effective knowledge transfer across domains. Most existing works rely on either representation alignment or transformation bridges, but they come with shortcomings regarding identifiability of domain-shared and domain-specific latent factors. Specifically, while CDR describes user representations as a joint distribution over two domains, these methods fail to account for its joint identifiability as they primarily fixate on the marginal distribution within a particular domain. Such a failure may overlook the conditionality between two domains and how it contributes to latent factor disentanglement, leading to negative transfer when domains are weakly correlated. In this study, we explore what should and should not be transferred in cross-domain user representations from a causality perspective. We propose a Hierarchical causal subspace disentanglement approach to explore the Joint IDentifiability of cross-domain joint distribution, termed HJID, to preserve domain-specific behaviors from domain-shared factors. HJID abides by the feature hierarchy and divides user representations into generic shallow subspace and domain-oriented deep subspaces. We first encode the generic pattern in the shallow subspace by minimizing the Maximum Mean Discrepancy of initial layer activation. Then, to dissect how domain-oriented latent factors are encoded in deeper layers activation, we construct a cross-domain causality-based data generation graph, which identifies cross-domain consistent and domain-specific components, adhering to the Minimal Change principle. This allows HJID to maintain stability whilst discovering unique factors for different domains, all within a generative framework of invertible transformations that guarantee the joint identifiability. With experiments on real-world datasets, we show that HJID outperforms SOTA methods on both strong- and weak-correlation CDR tasks.|跨领域推荐 ~ (CDR)旨在实现跨领域的有效知识转移。现有的工作大多依赖于表示对齐或转换桥梁，但在领域共享和领域特定潜在因素的可识别性方面存在缺陷。具体来说，虽然 CDR 将用户表示描述为两个域的联合分布，但这些方法无法解释其联合可识别性，因为它们主要集中在特定域内的边缘分布。这种失效可能会忽略两个领域之间的条件性，以及它如何促进潜在因素的解纠缠，导致负迁移时，领域是弱相关的。在这项研究中，我们从因果关系的角度探讨什么应该和不应该在跨域用户表示中传递。我们提出了一种分层因果子空间解纠缠方法来探索跨域联合分布的联合可识别性，称为 HJID，以保留领域特定的行为从领域共享因素。HJID 遵循特征层次结构，将用户表示划分为一般的浅子空间和面向领域的深子空间。我们首先通过最小化初始层激活的最大平均差来编码浅子空间中的通用模式。然后，为了剖析面向领域的潜在因素是如何在深层激活中编码的，我们构建了一个基于跨领域因果关系的数据生成图，该图根据最小变化原则识别跨领域一致性和特定领域的组件。这允许 HJID 保持稳定性，同时发现不同领域的独特因素，所有这些都在一个可逆转换的生成框架内，保证了联合可识别性。通过对实际数据集的实验，我们发现 HJID 方法在强相关和弱相关 CDR 任务上都优于 SOTA 方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Identifiability+of+Cross-Domain+Recommendation+via+Causal+Subspace+Disentanglement)|0| -|[DeCoCDR: Deployable Cloud-Device Collaboration for Cross-Domain Recommendation](https://doi.org/10.1145/3626772.3657786)|Yu Li, Yi Zhang, Zimu Zhou, Qiang Li|Algorithm team, WeSure Inc., Shenzhen, China; School of Data Science, City University of Hong Kong, Hong Kong, Hong Kong; College of Computer Science and Technology, Jilin University, Changchun, China|Cross-domain recommendation (CDR) is a widely used methodology in recommender systems to combat data sparsity. It leverages user data across different domains or platforms for providing personalized recommendations. Traditional CDR assumes user preferences and behavior data can be shared freely among cloud and users, which is now impractical due to strict restrictions of data privacy. In this paper, we propose a Deployment-friendly Cloud-Device Collaboration framework for Cross-Domain Recommendation (DeCoCDR). It splits CDR into a two-stage recommendation model through cloud-device collaborations, i.e., item-recall on cloud and item re-ranking on device. This design enables effective CDR while preserving data privacy for both the cloud and the device. Extensive offline and online experiments are conducted to validate the effectiveness of DeCoCDR. In offline experiments, DeCoCDR outperformed the state-of-the-arts in three large datasets. While in real-world deployment, DeCoCDR improved the conversion rate by 45.3% compared with the baseline.|跨域推荐(CDR)是推荐系统中应用最为广泛的一种数据稀疏处理方法。它利用跨不同领域或平台的用户数据来提供个性化的推荐。传统的 CDR 假设用户偏好和行为数据可以在云和用户之间自由共享，但是由于数据隐私的严格限制，这种假设是不切实际的。在本文中，我们提出了一个面向跨域推荐的部署友好的云设备协作框架(DeCoCDR)。它通过云设备协作将 CDR 划分为两个阶段的推荐模型，即云上的项目召回和设备上的项目重新排序。这种设计支持有效的 CDR，同时保护云和设备的数据隐私。为了验证 DeCoCDR 的有效性，进行了大量的离线和在线实验。在离线实验中，DeCoCDR 在三个大型数据集中的表现超过了最先进的水平。在实际部署中，与基线相比，DeCoCDR 将转换率提高了45.3% 。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=DeCoCDR:+Deployable+Cloud-Device+Collaboration+for+Cross-Domain+Recommendation)|0| +|[GPT4Rec: Graph Prompt Tuning for Streaming Recommendation](https://doi.org/10.1145/3626772.3657720)|Peiyan Zhang, Yuchen Yan, Xi Zhang, Liying Kang, Chaozhuo Li, Feiran Huang, Senzhang Wang, Sunghun Kim|Microsoft Research Asia; Hong Kong University of Science and Technology; Central South University; Peking University School of Intelligence Science and Technology; Jinan University; Hong Kong Polytechnic University; Fuzhou University Interdisciplinary Institute for Medical Engineering|In the realm of personalized recommender systems, the challenge of adaptingto evolving user preferences and the continuous influx of new users and itemsis paramount. Conventional models, typically reliant on a static training-testapproach, struggle to keep pace with these dynamic demands. Streamingrecommendation, particularly through continual graph learning, has emerged as anovel solution. However, existing methods in this area either rely onhistorical data replay, which is increasingly impractical due to stringent dataprivacy regulations; or are inability to effectively address the over-stabilityissue; or depend on model-isolation and expansion strategies. To tackle thesedifficulties, we present GPT4Rec, a Graph Prompt Tuning method for streamingRecommendation. Given the evolving user-item interaction graph, GPT4Rec firstdisentangles the graph patterns into multiple views. After isolating specificinteraction patterns and relationships in different views, GPT4Rec utilizeslightweight graph prompts to efficiently guide the model across varyinginteraction patterns within the user-item graph. Firstly, node-level promptsare employed to instruct the model to adapt to changes in the attributes orproperties of individual nodes within the graph. Secondly, structure-levelprompts guide the model in adapting to broader patterns of connectivity andrelationships within the graph. Finally, view-level prompts are innovativelydesigned to facilitate the aggregation of information from multipledisentangled views. These prompt designs allow GPT4Rec to synthesize acomprehensive understanding of the graph, ensuring that all vital aspects ofthe user-item interactions are considered and effectively integrated.Experiments on four diverse real-world datasets demonstrate the effectivenessand efficiency of our proposal.|在个性化推荐系统领域，适应不断变化的用户偏好和不断涌入的新用户和项目的挑战至关重要。传统的模型，通常依赖于静态的训练-测试方法，很难跟上这些动态的需求。流式推荐，特别是通过持续的图学习，已经成为一种新的解决方案。然而，这一领域的现有方法要么依赖于历史数据重放，由于严格的数据隐私条例，这越来越不切实际; 要么无法有效地解决过度稳定的问题; 要么依赖于模型隔离和扩展策略。为了解决这些问题，我们提出了 GPT4Rec，一种用于流推荐的图形提示调优方法。考虑到不断发展的用户项交互图，GPT4Rec 首先将图形模式分解为多个视图。在不同视图中隔离特定的交互模式和关系之后，GPT4Rec 利用轻量级图形提示有效地指导模型跨用户项图中的各种交互模式。首先，采用节点级提示来指导模型适应图中各个节点的属性或属性的变化。其次，结构级提示指导模型适应图中更广泛的连接和关系模式。最后，视图级提示被创新性地设计，以促进来自多个分离视图的信息的聚合。这些迅速的设计使 GPT4Rec 能够综合对图表的全面理解，确保用户-项目交互的所有重要方面都得到考虑和有效集成。在四个不同的真实世界数据集上的实验证明了我们方案的有效性和高效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=GPT4Rec:+Graph+Prompt+Tuning+for+Streaming+Recommendation)|0| +|[I3: Intent-Introspective Retrieval Conditioned on Instructions](https://doi.org/10.1145/3626772.3657745)|Kaihang Pan, Juncheng Li, Wenjie Wang, Hao Fei, Hongye Song, Wei Ji, Jun Lin, Xiaozhong Liu, TatSeng Chua, Siliang Tang|Worcester Polytechnic Institute; National University of Singapore; DAMO Academy, Alibaba Group; Zhejiang University|Recent studies indicate that dense retrieval models struggle to perform wellon a wide variety of retrieval tasks that lack dedicated training data, asdifferent retrieval tasks often entail distinct search intents. To address thischallenge, in this work we leverage instructions to flexibly describe retrievalintents and introduce I3, a unified retrieval system that performsIntent-Introspective retrieval across various tasks, conditioned onInstructions without any task-specific training. I3 innovatively incorporates apluggable introspector in a parameter-isolated manner to comprehend specificretrieval intents by jointly reasoning over the input query and instruction,and seamlessly integrates the introspected intent into the original retrievalmodel for intent-aware retrieval. Furthermore, we propose progressively-prunedintent learning. It utilizes extensive LLM-generated data to train I3phase-by-phase, embodying two key designs: progressive structure pruning anddrawback extrapolation-based data refinement. Extensive experiments show thatin the BEIR benchmark, I3 significantly outperforms baseline methods designedwith task-specific retrievers, achieving state-of-the-art zero-shot performancewithout any task-specific tuning.|最近的研究表明，密集检索模型难以在缺乏专门训练数据的各种检索任务中表现良好，因为不同的检索任务往往需要不同的搜索意图。为了应对这一挑战，在这项工作中，我们利用指令来灵活地描述检索对象，并引入 I3，一个统一的检索系统，可以在不同任务间执行意图-内省检索，不需要任何特定任务的训练，只需要指令。I3创新地以参数隔离的方式整合了可插入的内省器，通过对输入查询和指令的联合推理来理解特定的检索意图，并且无缝地将内省意图整合到原始的检索模型中用于意图感知检索。此外，我们提出逐步修剪意向学习。它利用大量 LLM 生成的数据对 I3进行逐步训练，包含两个关键设计: 渐进式结构修剪和基于缺陷外推的数据细化。大量的实验表明，在 BEIR 基准测试中，I3明显优于设计有任务特定检索器的基准方法，在没有任何任务特定调整的情况下，实现了最先进的零射击性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=I3:+Intent-Introspective+Retrieval+Conditioned+on+Instructions)|0| +|[Disentangling ID and Modality Effects for Session-based Recommendation](https://doi.org/10.1145/3626772.3657748)|Xiaokun Zhang, Bo Xu, Zhaochun Ren, Xiaochen Wang, Hongfei Lin, Fenglong Ma|Dalian University of Technology; Pennsylvania State University; Leiden University|Session-based recommendation aims to predict intents of anonymous users basedon their limited behaviors. Modeling user behaviors involves two distinctrationales: co-occurrence patterns reflected by item IDs, and fine-grainedpreferences represented by item modalities (e.g., text and images). However,existing methods typically entangle these causes, leading to their failure inachieving accurate and explainable recommendations. To this end, we propose anovel framework DIMO to disentangle the effects of ID and modality in the task.At the item level, we introduce a co-occurrence representation schema toexplicitly incorporate cooccurrence patterns into ID representations.Simultaneously, DIMO aligns different modalities into a unified semantic spaceto represent them uniformly. At the session level, we present a multi-viewself-supervised disentanglement, including proxy mechanism and counterfactualinference, to disentangle ID and modality effects without supervised signals.Leveraging these disentangled causes, DIMO provides recommendations via causalinference and further creates two templates for generating explanations.Extensive experiments on multiple real-world datasets demonstrate theconsistent superiority of DIMO over existing methods. Further analysis alsoconfirms DIMO's effectiveness in generating explanations.|基于会话的推荐旨在根据匿名用户的有限行为来预测用户的意图。建模用户行为涉及两种不同的情况: 由项目 ID 反映的共现模式，以及由项目模式(例如，文本和图像)表示的细粒度偏好。然而，现有的方法通常纠缠这些原因，导致他们无法实现准确和可解释的建议。为此，我们提出了一个新的框架 DIMO，以解决身份和情态在任务中的影响。在项目层面，我们引入了一个共现表示模式，以显式地将共现模式纳入身份表示。同时，DIMO 将不同的模式统一到一个统一的语义空间中，以统一地表示它们。在会话层面，我们提出了一种多视角的自监督解纠缠算法，包括代理机制和反事实推理，用于在没有监督信号的情况下解纠缠 ID 和模态效应。利用这些分离的原因，DIMO 通过因果推理提供建议，并进一步创建两个模板来产生解释。在多个真实世界数据集上的大量实验证明了 DIMO 相对于现有方法的一致优越性。进一步的分析也证实了 DIMO 在产生解释方面的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Disentangling+ID+and+Modality+Effects+for+Session-based+Recommendation)|0| +|[Large Language Models are Learnable Planners for Long-Term Recommendation](https://doi.org/10.1145/3626772.3657683)|Wentao Shi, Xiangnan He, Yang Zhang, Chongming Gao, Xinyue Li, Jizhi Zhang, Qifan Wang, Fuli Feng|University of Science and Technology of China; Meta|Planning for both immediate and long-term benefits becomes increasinglyimportant in recommendation. Existing methods apply Reinforcement Learning (RL)to learn planning capacity by maximizing cumulative reward for long-termrecommendation. However, the scarcity of recommendation data presentschallenges such as instability and susceptibility to overfitting when trainingRL models from scratch, resulting in sub-optimal performance. In this light, wepropose to leverage the remarkable planning capabilities over sparse data ofLarge Language Models (LLMs) for long-term recommendation. The key to achievingthe target lies in formulating a guidance plan following principles ofenhancing long-term engagement and grounding the plan to effective andexecutable actions in a personalized manner. To this end, we propose a Bi-levelLearnable LLM Planner framework, which consists of a set of LLM instances andbreaks down the learning process into macro-learning and micro-learning tolearn macro-level guidance and micro-level personalized recommendationpolicies, respectively. Extensive experiments validate that the frameworkfacilitates the planning ability of LLMs for long-term recommendation. Our codeand data can be found at https://github.com/jizhi-zhang/BiLLP.|计划短期和长期的利益变得越来越重要的建议。现有的方法通过最大化长期推荐的累积回报来应用强化学习(RL)来学习计划能力。然而，推荐数据的稀缺性提出了挑战，如不稳定性和容易过拟合时，从头开始训练 RL 模型，导致次优性能。在这种情况下，我们建议利用大语言模型(LLM)稀疏数据的卓越规划能力来进行长期推荐。实现目标的关键在于制定指导计划，遵循加强长期参与和以个性化方式采取有效和可执行行动的原则。为此，我们提出了一个双层可学习的 LLM 规划框架，该框架由一组 LLM 实例组成，将学习过程分解为宏观学习和微观学习，分别学习宏观层面的指导和微观层面的个性化推荐策略。大量的实验验证了该框架有利于 LLM 的长期推荐规划能力。我们的代码和数据可以在 https://github.com/jizhi-zhang/billp 找到。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Large+Language+Models+are+Learnable+Planners+for+Long-Term+Recommendation)|0| +|[Identifiability of Cross-Domain Recommendation via Causal Subspace Disentanglement](https://doi.org/10.1145/3626772.3657758)|Jing Du, Zesheng Ye, Bin Guo, Zhiwen Yu, Lina Yao|CSIRO's Data 61 & The University of New South Wales, Sydney, Australia; Northwestern Polytechnical University, Xi'an, China; Macquarie University, Sydney, Australia; The University of New South Wales, Sydney, Australia|Cross-Domain Recommendation~(CDR) seeks to enable effective knowledge transfer across domains. Most existing works rely on either representation alignment or transformation bridges, but they come with shortcomings regarding identifiability of domain-shared and domain-specific latent factors. Specifically, while CDR describes user representations as a joint distribution over two domains, these methods fail to account for its joint identifiability as they primarily fixate on the marginal distribution within a particular domain. Such a failure may overlook the conditionality between two domains and how it contributes to latent factor disentanglement, leading to negative transfer when domains are weakly correlated. In this study, we explore what should and should not be transferred in cross-domain user representations from a causality perspective. We propose a Hierarchical causal subspace disentanglement approach to explore the Joint IDentifiability of cross-domain joint distribution, termed HJID, to preserve domain-specific behaviors from domain-shared factors. HJID abides by the feature hierarchy and divides user representations into generic shallow subspace and domain-oriented deep subspaces. We first encode the generic pattern in the shallow subspace by minimizing the Maximum Mean Discrepancy of initial layer activation. Then, to dissect how domain-oriented latent factors are encoded in deeper layers activation, we construct a cross-domain causality-based data generation graph, which identifies cross-domain consistent and domain-specific components, adhering to the Minimal Change principle. This allows HJID to maintain stability whilst discovering unique factors for different domains, all within a generative framework of invertible transformations that guarantee the joint identifiability. With experiments on real-world datasets, we show that HJID outperforms SOTA methods on both strong- and weak-correlation CDR tasks.|跨领域推荐 ~ (CDR)旨在实现跨领域的有效知识转移。现有的工作大多依赖于表示对齐或转换桥梁，但在领域共享和领域特定潜在因素的可识别性方面存在缺陷。具体来说，虽然 CDR 将用户表示描述为两个域的联合分布，但这些方法无法解释其联合可识别性，因为它们主要集中在特定域内的边缘分布。这种失效可能会忽略两个领域之间的条件性，以及它如何促进潜在因素的解纠缠，导致负迁移时，领域是弱相关的。在这项研究中，我们从因果关系的角度探讨什么应该和不应该在跨域用户表示中传递。我们提出了一种分层因果子空间解纠缠方法来探索跨域联合分布的联合可识别性，称为 HJID，以保留领域特定的行为从领域共享因素。HJID 遵循特征层次结构，将用户表示划分为一般的浅子空间和面向领域的深子空间。我们首先通过最小化初始层激活的最大平均差来编码浅子空间中的通用模式。然后，为了剖析面向领域的潜在因素是如何在深层激活中编码的，我们构建了一个基于跨领域因果关系的数据生成图，该图根据最小变化原则识别跨领域一致性和特定领域的组件。这允许 HJID 保持稳定性，同时发现不同领域的独特因素，所有这些都在一个可逆转换的生成框架内，保证了联合可识别性。通过对实际数据集的实验，我们发现 HJID 方法在强相关和弱相关 CDR 任务上都优于 SOTA 方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Identifiability+of+Cross-Domain+Recommendation+via+Causal+Subspace+Disentanglement)|0| +|[DeCoCDR: Deployable Cloud-Device Collaboration for Cross-Domain Recommendation](https://doi.org/10.1145/3626772.3657786)|Yu Li, Yi Zhang, Zimu Zhou, Qiang Li|School of Data Science, City University of Hong Kong, Hong Kong, Hong Kong; College of Computer Science and Technology, Jilin University, Changchun, China; Algorithm team, WeSure Inc., Shenzhen, China|Cross-domain recommendation (CDR) is a widely used methodology in recommender systems to combat data sparsity. It leverages user data across different domains or platforms for providing personalized recommendations. Traditional CDR assumes user preferences and behavior data can be shared freely among cloud and users, which is now impractical due to strict restrictions of data privacy. In this paper, we propose a Deployment-friendly Cloud-Device Collaboration framework for Cross-Domain Recommendation (DeCoCDR). It splits CDR into a two-stage recommendation model through cloud-device collaborations, i.e., item-recall on cloud and item re-ranking on device. This design enables effective CDR while preserving data privacy for both the cloud and the device. Extensive offline and online experiments are conducted to validate the effectiveness of DeCoCDR. In offline experiments, DeCoCDR outperformed the state-of-the-arts in three large datasets. While in real-world deployment, DeCoCDR improved the conversion rate by 45.3% compared with the baseline.|跨域推荐(CDR)是推荐系统中应用最为广泛的一种数据稀疏处理方法。它利用跨不同领域或平台的用户数据来提供个性化的推荐。传统的 CDR 假设用户偏好和行为数据可以在云和用户之间自由共享，但是由于数据隐私的严格限制，这种假设是不切实际的。在本文中，我们提出了一个面向跨域推荐的部署友好的云设备协作框架(DeCoCDR)。它通过云设备协作将 CDR 划分为两个阶段的推荐模型，即云上的项目召回和设备上的项目重新排序。这种设计支持有效的 CDR，同时保护云和设备的数据隐私。为了验证 DeCoCDR 的有效性，进行了大量的离线和在线实验。在离线实验中，DeCoCDR 在三个大型数据集中的表现超过了最先进的水平。在实际部署中，与基线相比，DeCoCDR 将转换率提高了45.3% 。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=DeCoCDR:+Deployable+Cloud-Device+Collaboration+for+Cross-Domain+Recommendation)|0| |[Mutual Information-based Preference Disentangling and Transferring for Non-overlapped Multi-target Cross-domain Recommendations](https://doi.org/10.1145/3626772.3657780)|Zhi Li, Daichi Amagata, Yihong Zhang, Takahiro Hara, Shuichiro Haruta, Kei Yonekawa, Mori Kurokawa|KDDI Research, Inc., Fujimino, Saitama, Japan; Osaka University, Suita, Osaka, Japan|Building high-quality recommender systems is challenging for new services and small companies, because of their sparse interactions. Cross-domain recommendations (CDRs) alleviate this issue by transferring knowledge from data in external domains. However, most existing CDRs leverage data from only a single external domain and serve only two domains. CDRs serving multiple domains require domain-shared entities (i.e., users and items) to transfer knowledge, which significantly limits their applications due to the hardness and privacy concerns of finding such entities. We therefore focus on a more general scenario, non-overlapped multi-target CDRs (NO-MTCDRs), which require no domain-shared entities and serve multiple domains. Existing methods require domain-shared users to learn user preferences and cannot work on NO-MTCDRs. We hence propose MITrans, a novel mutual information-based (MI-based) preference disentangling and transferring framework to improve recommendations for all domains. MITrans effectively leverages knowledge from multiple domains as well as learning both domain-shared and domain-specific preferences without using domain-shared users. In MITrans, we devise two novel MI constraints to disentangle domain-shared and domain-specific preferences. Moreover, we introduce a module that fuses domain-shared preferences in different domains and combines them with domain-specific preferences to improve recommendations. Our experimental results on two real-world datasets demonstrate the superiority of MITrans in terms of recommendation quality and application range against state-of-the-art overlapped and non-overlapped CDRs.|构建高质量的推荐系统对于新服务和小公司来说是一个挑战，因为它们的交互很少。跨领域建议(CDR)通过从外部领域的数据中传输知识来缓解这一问题。然而，大多数现有的 CDR 仅利用来自单个外部域的数据，并且仅服务于两个域。服务于多个域的 CDR 需要域共享实体(即用户和项目)来传递知识，由于寻找这些实体的难度和隐私问题，这极大地限制了它们的应用程序。因此，我们关注于一个更一般的场景，非重叠多目标 CDR (NO-MTCDR) ，它不需要域共享实体并服务于多个域。现有的方法要求域共享用户学习用户首选项，并且不能在 NO-MTCDR 上工作。因此，我们提出了一种新的基于互信息(MI-based)的偏好分离和传递框架 MITAN，以改善所有领域的建议。MTrans 有效地利用了来自多个领域的知识，并且在不使用领域共享用户的情况下学习了领域共享和领域特定的偏好。在 MTrans 中，我们设计了两个新的 MI 约束来区分领域共享和领域特定的偏好。此外，我们还引入了一个模块，该模块融合了不同领域中的域共享首选项，并将它们与特定领域的首选项结合起来，以改进建议。我们在两个实际数据集上的实验结果表明，MTrans 在推荐质量和应用范围方面优于最先进的重叠和非重叠 CDR。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Mutual+Information-based+Preference+Disentangling+and+Transferring+for+Non-overlapped+Multi-target+Cross-domain+Recommendations)|0| -|[LeCaRDv2: A Large-Scale Chinese Legal Case Retrieval Dataset](https://doi.org/10.1145/3626772.3657887)|Haitao Li, Yunqiu Shao, Yueyue Wu, Qingyao Ai, Yixiao Ma, Yiqun Liu|Tsinghua university; Tsinghua University|As an important component of intelligent legal systems, legal case retrieval plays a critical role in ensuring judicial justice and fairness. However, the development of legal case retrieval technologies in the Chinese legal system is restricted by three problems in existing datasets: limited data size, narrow definitions of legal relevance, and naive candidate pooling strategies used in data sampling. To alleviate these issues, we introduce LeCaRDv2, a large-scale Legal Case Retrieval Dataset (version 2). It consists of 800 queries and 55,192 candidates extracted from 4.3 million criminal case documents. To the best of our knowledge, LeCaRDv2 is one of the largest Chinese legal case retrieval datasets, providing extensive coverage of criminal charges. Additionally, we enrich the existing relevance criteria by considering three key aspects: characterization, penalty, procedure. This comprehensive criteria enriches the dataset and may provides a more holistic perspective. Furthermore, we propose a two-level candidate set pooling strategy that effectively identify potential candidates for each query case. It's important to note that all cases in the dataset have been annotated by multiple legal experts specializing in criminal law. Their expertise ensures the accuracy and reliability of the annotations. We evaluate several state-of-the-art retrieval models at LeCaRDv2, demonstrating that there is still significant room for improvement in legal case retrieval. The details of LeCaRDv2 can be found at the anonymous website https://github.com/anonymous1113243/LeCaRDv2.|法律案件检索作为智能法律系统的重要组成部分，对于确保司法公正和公平起着至关重要的作用。然而，中国法律体系中法律案例检索技术的发展受到现有数据集存在的三个问题的制约: 数据规模有限、法律相关性定义狭窄以及数据抽样中采用的候选人汇集策略过于简单。为了缓解这些问题，我们引入了 LeCaRDv2，这是一个大型的法律案例检索数据集(版本2)。它包括从430万份刑事案件文件中提取的800个查询和55,192个候选者。据我们所知，LeCaRDv2是中国最大的法律案件检索数据集之一，提供了广泛的刑事指控。此外，我们还考虑了角色塑造、惩罚和程序三个关键因素，丰富了现有的相关标准。这个全面的标准丰富了数据集，并且可以提供一个更全面的视角。此外，我们提出了一个两级候选集合池策略，有效地识别每个查询用例的潜在候选者。值得注意的是，数据集中的所有案例都由多位专门研究刑法的法律专家进行了注释。他们的专业知识确保了注释的准确性和可靠性。我们评估了 LeCaRDv2的几个最先进的检索模型，表明在法律案件检索方面仍有很大的改进空间。有关 Lecardv2的详情，可浏览该匿名网站的 https://github.com/anonymous1113243/LeCaRDv2。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=LeCaRDv2:+A+Large-Scale+Chinese+Legal+Case+Retrieval+Dataset)|0| -|[Behavior Pattern Mining-based Multi-Behavior Recommendation](https://doi.org/10.1145/3626772.3657973)|Haojie Li, Zhiyong Cheng, Xu Yu, Jinhuan Liu, Guanfeng Liu, Junwei Du|College of Computer Science and Technology, China University of Petroleum; Shandong Artifcial Intelligence Institute, Qilu University of Technology (Shandong Academy of Sciences); School of Computing, Macquarie University; College of Data Science, Qingdao University of Science and Technology|Multi-behavior recommendation systems enhance effectiveness by leveraging auxiliary behaviors (such as page views and favorites) to address the limitations of traditional models that depend solely on sparse target behaviors like purchases. Existing approaches to multi-behavior recommendations typically follow one of two strategies: some derive initial node representations from individual behavior subgraphs before integrating them for a comprehensive profile, while others interpret multi-behavior data as a heterogeneous graph, applying graph neural networks to achieve a unified node representation. However, these methods do not adequately explore the intricate patterns of behavior among users and items. To bridge this gap, we introduce a novel algorithm called Behavior Pattern mining-based Multi-behavior Recommendation (BPMR). Our method extensively investigates the diverse interaction patterns between users and items, utilizing these patterns as features for making recommendations. We employ a Bayesian approach to streamline the recommendation process, effectively circumventing the challenges posed by graph neural network algorithms, such as the inability to accurately capture user preferences due to over-smoothing. Our experimental evaluation on three real-world datasets demonstrates that BPMR significantly outperforms existing state-of-the-art algorithms, showing an average improvement of 268.29 in NDCG@10 metrics. The code of our BPMR is openly accessible for use and further research at https://github.com/rookitkitlee/BPMR.|多行为推荐系统通过利用辅助行为(如页面浏览和收藏夹)来解决传统模型的局限性，这些传统模型仅仅依赖于稀疏的目标行为(如购买) ，从而提高了有效性。现有的多行为推荐方法通常遵循以下两种策略之一: 一些方法从个体行为子图中获得初始节点表示，然后将它们整合为一个综合概况; 另一些方法将多行为数据解释为异构图，应用图神经网络实现统一的节点表示。然而，这些方法并没有充分探索用户和项之间复杂的行为模式。为了弥补这一差距，我们引入了一种新的算法，称为基于行为模式挖掘的多行为推荐(BPMR)。我们的方法广泛地调查了用户和项目之间不同的交互模式，利用这些模式作为提出建议的特征。我们使用贝叶斯方法来简化推荐过程，有效地规避了图形神经网络算法带来的挑战，例如由于过度平滑而无法准确地捕获用户偏好。我们对三个真实世界数据集的实验评估表明，BPMR 显著优于现有的最先进的算法，显示 NDCG@10指标的平均改进为268.29。我们的《业务流程核证机关守则》已开放供 https://github.com/rookitkitlee/BPMR 使用和进一步研究。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Behavior+Pattern+Mining-based+Multi-Behavior+Recommendation)|0| -|[Dense Retrieval with Continuous Explicit Feedback for Systematic Review Screening Prioritisation](https://doi.org/10.1145/3626772.3657921)|Xinyu Mao, Shengyao Zhuang, Bevan Koopman, Guido Zuccon|CSIRO; The University of Queensland|The goal of screening prioritisation in systematic reviews is to identify relevant documents with high recall and rank them in early positions for review. This saves reviewing effort if paired with a stopping criterion, and speeds up review completion if performed alongside downstream tasks. Recent studies have shown that neural models have good potential on this task, but their time-consuming fine-tuning and inference discourage their widespread use for screening prioritisation. In this paper, we propose an alternative approach that still relies on neural models, but leverages dense representations and relevance feedback to enhance screening prioritisation, without the need for costly model fine-tuning and inference. This method exploits continuous relevance feedback from reviewers during document screening to efficiently update the dense query representation, which is then applied to rank the remaining documents to be screened. We evaluate this approach across the CLEF TAR datasets for this task. Results suggest that the investigated dense query-driven approach is more efficient than directly using neural models and shows promising effectiveness compared to previous methods developed on the considered datasets. Our code is available at https://github.com/ielab/dense-screening-feedback.|在系统评价中筛选优先级的目标是确定具有高召回率的相关文档，并将其排在早期位置以供审查。如果与停止标准配对，则可以节省审查工作，如果与下游任务一起执行，则可以加快审查完成。最近的研究表明，神经模型在这项任务上有很好的潜力，但是它们耗费时间的微调和推理阻碍了它们在筛选优先级方面的广泛应用。在本文中，我们提出了一种替代方法，这种方法仍然依赖于神经模型，但是利用密集的表示和关联反馈来增强筛选的优先级，而不需要昂贵的模型微调和推断。这种方法利用审阅者在文件筛选过程中的连续关联反馈，有效地更新密集的查询表示，然后应用于对待筛选的其余文件进行排序。我们通过 CLEF TAR 数据集评估这种方法。结果表明，所研究的密集查询驱动的方法比直接使用神经模型更有效，并显示出有希望的有效性比以往的方法开发考虑数据集。我们的代码可以在 https://github.com/ielab/dense-screening-feedback 找到。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Dense+Retrieval+with+Continuous+Explicit+Feedback+for+Systematic+Review+Screening+Prioritisation)|0| -|[Cross-reconstructed Augmentation for Dual-target Cross-domain Recommendation](https://doi.org/10.1145/3626772.3657902)|Qingyang Mao, Qi Liu, Zhi Li, Likang Wu, Bing Lv, Zheng Zhang|University of Science and Technology of China State Key Laboratory of Cognitive Intelligence; University of Science and Technology of China State Key Laboratory of Cognitive Intelligence;Hefei Comprehensive National Science Center Institute of Artificial Intelligence; College of Management and Economics, Tianjin University, Tianjin, China; Shenzhen International Graduate School, Tsinghua University, Shenzhen, China|To alleviate the long-standing data sparsity issue in recommender systems, numerous studies in cross-domain recommendation (CDR) have been conducted to facilitate information transfer processes across domains. In recent years, dual-target CDR has been introduced to gain mutual improvements between two domains through more general bidirectional transfer rather than traditional one-way transit. Existing methods in dual-target CDR focus primarily on designing powerful encoders to learn representative cross-domain information, without tackling the fundamental issue of interaction data shortage. In this paper, we present CrossAug, a novel data augmentation approach to leverage interactions more efficiently in two domains. Specifically, we propose intra-domain and inter-domain augmentations based on cross-reconstructed representations in terms of sampled records. To reduce the harm of domain shift, we project domain-shared representations in two domains into a joint space with Householder transformations and apply center alignments. All these modules boost the utilization of interactions with little influence from negative transfer. Extensive experimental results over public datasets demonstrate the effectiveness of CrossAug and its components in dual-target CDR.|为了缓解推荐系统中长期存在的数据稀疏问题，人们对跨域推荐进行了大量的研究，以促进跨域的信息传递过程。近年来，双目标 CDR 被引入，通过更一般的双向传输而不是传统的单向传输，实现了两个领域之间的相互改进。现有的双目标 CDR 方法主要集中在设计强大的编码器来学习有代表性的跨域信息，而没有解决交互数据短缺的根本问题。在本文中，我们提出 CrossAug，一种新的数据增强方法，以更有效地利用交互作用在两个领域。具体来说，我们提出了域内和域间增强的基础上的交叉重建表示的抽样记录。为了减少域移位的危害，我们将两个域中的域共享表示投影到一个带有 Householder 变换的联合空间中，并应用中心对齐。所有这些模块都提高了交互作用的利用率，而负迁移的影响很小。在公共数据集上的大量实验结果证明了 CrossAug 及其组件在双目标 CDR 中的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Cross-reconstructed+Augmentation+for+Dual-target+Cross-domain+Recommendation)|0| -|[Distillation for Multilingual Information Retrieval](https://doi.org/10.1145/3626772.3657955)|Eugene Yang, Dawn J. Lawrie, James Mayfield|Human Language Technology Center of Excellence, Johns Hopkins University; HLT/COE; Johns Hopkins University|Recent work in cross-language information retrieval (CLIR), where queries anddocuments are in different languages, has shown the benefit of theTranslate-Distill framework that trains a cross-language neural dual-encodermodel using translation and distillation. However, Translate-Distill onlysupports a single document language. Multilingual information retrieval (MLIR),which ranks a multilingual document collection, is harder to train than CLIRbecause the model must assign comparable relevance scores to documents indifferent languages. This work extends Translate-Distill and proposeMultilingual Translate-Distill (MTD) for MLIR. We show that ColBERT-X modelstrained with MTD outperform their counterparts trained ith MultilingualTranslate-Train, which is the previous state-of-the-art training approach, by5robust to the way languages are mixed in training batches. Our implementationis available on GitHub.|最近在跨语检索(CLIR)领域的工作显示了翻译-提取框架的好处，该框架通过翻译和提取训练了一个跨语言的神经双编码器模型。但是，Translate-Distill 只支持一种文档语言。对多语言文档集进行排序的多语言信息检索(mLIR)比 CLIR 更难训练，因为该模型必须为不同语言的文档分配可比较的相关度分数。这项工作扩展了 MLIR 的翻译-蒸馏和提出的多语言翻译-蒸馏(MTD)。我们表明，使用 MTD 训练的 ColBERT-X 模型优于使用多语言翻译训练(这是以前的最先进的训练方法)训练的对应模型，因为它对训练批次中语言的混合方式具有鲁棒性。我们的实现可以在 GitHub 上找到。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Distillation+for+Multilingual+Information+Retrieval)|0| -|[Estimating the Hessian Matrix of Ranking Objectives for Stochastic Learning to Rank with Gradient Boosted Trees](https://doi.org/10.1145/3626772.3657918)|Jingwei Kang, Maarten de Rijke, Harrie Oosterhuis|Radboud University; University of Amsterdam|Stochastic learning to rank (LTR) is a recent branch in the LTR field thatconcerns the optimization of probabilistic ranking models. Their probabilisticbehavior enables certain ranking qualities that are impossible withdeterministic models. For example, they can increase the diversity of displayeddocuments, increase fairness of exposure over documents, and better balanceexploitation and exploration through randomization. A core difficulty in LTR isgradient estimation, for this reason, existing stochastic LTR methods have beenlimited to differentiable ranking models (e.g., neural networks). This is instark contrast with the general field of LTR where Gradient Boosted DecisionTrees (GBDTs) have long been considered the state-of-the-art. In this work, we address this gap by introducing the first stochastic LTRmethod for GBDTs. Our main contribution is a novel estimator for thesecond-order derivatives, i.e., the Hessian matrix, which is a requirement foreffective GBDTs. To efficiently compute both the first and second-orderderivatives simultaneously, we incorporate our estimator into the existingPL-Rank framework, which was originally designed for first-order derivativesonly. Our experimental results indicate that stochastic LTR without the Hessianhas extremely poor performance, whilst the performance is competitive with thecurrent state-of-the-art with our estimated Hessian. Thus, through thecontribution of our novel Hessian estimation method, we have successfullyintroduced GBDTs to stochastic LTR.|随机学习排序(LTR)是 LTR 领域的一个新兴分支，主要研究概率排序模型的优化问题。他们的概率行为使得某些排名质量是不可能的确定性模型。例如，它们可以增加显示文档的多样性，增加文档曝光的公平性，以及通过随机化更好地平衡开发和探索。LTR 的一个核心难点是梯度估计，由于这个原因，现有的随机 LTR 方法仅限于可微排序模型(如神经网络)。这与长期以来一直被认为是最先进的梯度增强决策树(GBDTs)的 LTR 的一般领域形成了鲜明的对比。在这项工作中，我们通过引入第一个随机 LTR- 方法来解决这个差距。我们的主要贡献是一个新的估计器的二阶导数，即，黑森矩阵，这是一个需要有效的 GBDTs。为了同时有效地计算一阶导数和二阶导数，我们将估计量合并到现有的 PL 秩框架中，这个框架最初是为一阶导数而设计的。我们的实验结果表明，随机 LTR 没有黑森有非常差的性能，而性能是竞争性的当前国家的最先进的，我们估计黑森。因此，通过我们新的 Hessian 估计方法的贡献，我们成功地将 GBDTs 引入到随机 LTR 中。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Estimating+the+Hessian+Matrix+of+Ranking+Objectives+for+Stochastic+Learning+to+Rank+with+Gradient+Boosted+Trees)|0| -|[Information Diffusion Prediction via Cascade-Retrieved In-context Learning](https://doi.org/10.1145/3626772.3657909)|Ting Zhong, Jienan Zhang, Zhangtao Cheng, Fan Zhou, Xueqin Chen|University of Electronic Science and Technology of China; Delft University of Technology Faculty of Civil Engineering and Geosciences; University of Electronic Science and Technology of China, Chengdu, China; University of Electronic Science and Technology of China, Chengdu, Sichuan, China|Information diffusion prediction, which aims to infer the infected behavior of individual users during information spread, is critical for understanding the dynamics of information propagation and users' influence on online social media. To date, existing methods either focus on capturing limited contextual information from a single cascade, overlooking the potentially complex dependencies across different cascades, or they are committed to improving model performance by using intricate technologies to extract additional features as supplements to user representations, neglecting the drift of model performance across different platforms. To address these limitations, we propose a novel framework called CARE (CAscade-REtrieved In-Context Learning) inspired by the concept of in-context learning in LLMs. Specifically, CARE first constructs a prompts pool derived from historical cascades, then utilizes ranking-based search engine techniques to retrieve prompts with similar patterns based on the query. Moreover, CARE also introduces two augmentation strategies alongside social relationship enhancement to enrich the input context. Finally, the transformed query-cascade representation from a GPT-type architecture is projected to obtain the prediction. Experiments on real-world datasets from various platforms show that CARE outperforms state-of-the-art baselines in terms of effectiveness and robustness in information diffusion prediction.|信息扩散预测旨在推断个体用户在信息传播过程中的感染行为，对于理解信息传播动态和用户对网络社交媒体的影响至关重要。迄今为止，现有的方法要么侧重于从单个级联捕获有限的上下文信息，忽视不同级联之间潜在的复杂依赖性，要么致力于通过使用复杂的技术提取额外的特征作为用户表示的补充来改善模型性能，忽略了不同平台之间模型性能的漂移。为了解决这些局限性，我们提出了一个新的框架，称为 CARE (级联检索在上下文学习)的启发在 LLM 中的上下文学习的概念。具体来说，CARE 首先构造一个源自历史级联的提示池，然后利用基于排序的搜索引擎技术根据查询检索具有类似模式的提示。此外，CARE 在增强社会关系的同时，还引入了两种增强策略来丰富输入语境。最后，从一个 GPT 类型的体系结构转换的查询级联表示进行投影，以获得预测。对来自不同平台的真实世界数据集的实验表明，CARE 在信息扩散预测的有效性和鲁棒性方面优于最先进的基线。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Information+Diffusion+Prediction+via+Cascade-Retrieved+In-context+Learning)|0| +|[LeCaRDv2: A Large-Scale Chinese Legal Case Retrieval Dataset](https://doi.org/10.1145/3626772.3657887)|Haitao Li, Yunqiu Shao, Yueyue Wu, Qingyao Ai, Yixiao Ma, Yiqun Liu|Tsinghua University; Tsinghua university|As an important component of intelligent legal systems, legal case retrieval plays a critical role in ensuring judicial justice and fairness. However, the development of legal case retrieval technologies in the Chinese legal system is restricted by three problems in existing datasets: limited data size, narrow definitions of legal relevance, and naive candidate pooling strategies used in data sampling. To alleviate these issues, we introduce LeCaRDv2, a large-scale Legal Case Retrieval Dataset (version 2). It consists of 800 queries and 55,192 candidates extracted from 4.3 million criminal case documents. To the best of our knowledge, LeCaRDv2 is one of the largest Chinese legal case retrieval datasets, providing extensive coverage of criminal charges. Additionally, we enrich the existing relevance criteria by considering three key aspects: characterization, penalty, procedure. This comprehensive criteria enriches the dataset and may provides a more holistic perspective. Furthermore, we propose a two-level candidate set pooling strategy that effectively identify potential candidates for each query case. It's important to note that all cases in the dataset have been annotated by multiple legal experts specializing in criminal law. Their expertise ensures the accuracy and reliability of the annotations. We evaluate several state-of-the-art retrieval models at LeCaRDv2, demonstrating that there is still significant room for improvement in legal case retrieval. The details of LeCaRDv2 can be found at the anonymous website https://github.com/anonymous1113243/LeCaRDv2.|法律案件检索作为智能法律系统的重要组成部分，对于确保司法公正和公平起着至关重要的作用。然而，中国法律体系中法律案例检索技术的发展受到现有数据集存在的三个问题的制约: 数据规模有限、法律相关性定义狭窄以及数据抽样中采用的候选人汇集策略过于简单。为了缓解这些问题，我们引入了 LeCaRDv2，这是一个大型的法律案例检索数据集(版本2)。它包括从430万份刑事案件文件中提取的800个查询和55,192个候选者。据我们所知，LeCaRDv2是中国最大的法律案件检索数据集之一，提供了广泛的刑事指控。此外，我们还考虑了角色塑造、惩罚和程序三个关键因素，丰富了现有的相关标准。这个全面的标准丰富了数据集，并且可以提供一个更全面的视角。此外，我们提出了一个两级候选集合池策略，有效地识别每个查询用例的潜在候选者。值得注意的是，数据集中的所有案例都由多位专门研究刑法的法律专家进行了注释。他们的专业知识确保了注释的准确性和可靠性。我们评估了 LeCaRDv2的几个最先进的检索模型，表明在法律案件检索方面仍有很大的改进空间。有关 Lecardv2的详情，可浏览该匿名网站的 https://github.com/anonymous1113243/LeCaRDv2。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=LeCaRDv2:+A+Large-Scale+Chinese+Legal+Case+Retrieval+Dataset)|0| +|[Behavior Pattern Mining-based Multi-Behavior Recommendation](https://doi.org/10.1145/3626772.3657973)|Haojie Li, Zhiyong Cheng, Xu Yu, Jinhuan Liu, Guanfeng Liu, Junwei Du|School of Computing, Macquarie University; Shandong Artifcial Intelligence Institute, Qilu University of Technology (Shandong Academy of Sciences); College of Data Science, Qingdao University of Science and Technology; College of Computer Science and Technology, China University of Petroleum|Multi-behavior recommendation systems enhance effectiveness by leveraging auxiliary behaviors (such as page views and favorites) to address the limitations of traditional models that depend solely on sparse target behaviors like purchases. Existing approaches to multi-behavior recommendations typically follow one of two strategies: some derive initial node representations from individual behavior subgraphs before integrating them for a comprehensive profile, while others interpret multi-behavior data as a heterogeneous graph, applying graph neural networks to achieve a unified node representation. However, these methods do not adequately explore the intricate patterns of behavior among users and items. To bridge this gap, we introduce a novel algorithm called Behavior Pattern mining-based Multi-behavior Recommendation (BPMR). Our method extensively investigates the diverse interaction patterns between users and items, utilizing these patterns as features for making recommendations. We employ a Bayesian approach to streamline the recommendation process, effectively circumventing the challenges posed by graph neural network algorithms, such as the inability to accurately capture user preferences due to over-smoothing. Our experimental evaluation on three real-world datasets demonstrates that BPMR significantly outperforms existing state-of-the-art algorithms, showing an average improvement of 268.29 in NDCG@10 metrics. The code of our BPMR is openly accessible for use and further research at https://github.com/rookitkitlee/BPMR.|多行为推荐系统通过利用辅助行为(如页面浏览和收藏夹)来解决传统模型的局限性，这些传统模型仅仅依赖于稀疏的目标行为(如购买) ，从而提高了有效性。现有的多行为推荐方法通常遵循以下两种策略之一: 一些方法从个体行为子图中获得初始节点表示，然后将它们整合为一个综合概况; 另一些方法将多行为数据解释为异构图，应用图神经网络实现统一的节点表示。然而，这些方法并没有充分探索用户和项之间复杂的行为模式。为了弥补这一差距，我们引入了一种新的算法，称为基于行为模式挖掘的多行为推荐(BPMR)。我们的方法广泛地调查了用户和项目之间不同的交互模式，利用这些模式作为提出建议的特征。我们使用贝叶斯方法来简化推荐过程，有效地规避了图形神经网络算法带来的挑战，例如由于过度平滑而无法准确地捕获用户偏好。我们对三个真实世界数据集的实验评估表明，BPMR 显著优于现有的最先进的算法，显示 NDCG@10指标的平均改进为268.29。我们的《业务流程核证机关守则》已开放供 https://github.com/rookitkitlee/BPMR 使用和进一步研究。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Behavior+Pattern+Mining-based+Multi-Behavior+Recommendation)|0| +|[Dense Retrieval with Continuous Explicit Feedback for Systematic Review Screening Prioritisation](https://doi.org/10.1145/3626772.3657921)|Xinyu Mao, Shengyao Zhuang, Bevan Koopman, Guido Zuccon|The University of Queensland; CSIRO|The goal of screening prioritisation in systematic reviews is to identify relevant documents with high recall and rank them in early positions for review. This saves reviewing effort if paired with a stopping criterion, and speeds up review completion if performed alongside downstream tasks. Recent studies have shown that neural models have good potential on this task, but their time-consuming fine-tuning and inference discourage their widespread use for screening prioritisation. In this paper, we propose an alternative approach that still relies on neural models, but leverages dense representations and relevance feedback to enhance screening prioritisation, without the need for costly model fine-tuning and inference. This method exploits continuous relevance feedback from reviewers during document screening to efficiently update the dense query representation, which is then applied to rank the remaining documents to be screened. We evaluate this approach across the CLEF TAR datasets for this task. Results suggest that the investigated dense query-driven approach is more efficient than directly using neural models and shows promising effectiveness compared to previous methods developed on the considered datasets. Our code is available at https://github.com/ielab/dense-screening-feedback.|在系统评价中筛选优先级的目标是确定具有高召回率的相关文档，并将其排在早期位置以供审查。如果与停止标准配对，则可以节省审查工作，如果与下游任务一起执行，则可以加快审查完成。最近的研究表明，神经模型在这项任务上有很好的潜力，但是它们耗费时间的微调和推理阻碍了它们在筛选优先级方面的广泛应用。在本文中，我们提出了一种替代方法，这种方法仍然依赖于神经模型，但是利用密集的表示和关联反馈来增强筛选的优先级，而不需要昂贵的模型微调和推断。这种方法利用审阅者在文件筛选过程中的连续关联反馈，有效地更新密集的查询表示，然后应用于对待筛选的其余文件进行排序。我们通过 CLEF TAR 数据集评估这种方法。结果表明，所研究的密集查询驱动的方法比直接使用神经模型更有效，并显示出有希望的有效性比以往的方法开发考虑数据集。我们的代码可以在 https://github.com/ielab/dense-screening-feedback 找到。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Dense+Retrieval+with+Continuous+Explicit+Feedback+for+Systematic+Review+Screening+Prioritisation)|0| +|[Cross-reconstructed Augmentation for Dual-target Cross-domain Recommendation](https://doi.org/10.1145/3626772.3657902)|Qingyang Mao, Qi Liu, Zhi Li, Likang Wu, Bing Lv, Zheng Zhang|Shenzhen International Graduate School, Tsinghua University, Shenzhen, China; College of Management and Economics, Tianjin University, Tianjin, China; University of Science and Technology of China State Key Laboratory of Cognitive Intelligence; University of Science and Technology of China State Key Laboratory of Cognitive Intelligence;Hefei Comprehensive National Science Center Institute of Artificial Intelligence|To alleviate the long-standing data sparsity issue in recommender systems, numerous studies in cross-domain recommendation (CDR) have been conducted to facilitate information transfer processes across domains. In recent years, dual-target CDR has been introduced to gain mutual improvements between two domains through more general bidirectional transfer rather than traditional one-way transit. Existing methods in dual-target CDR focus primarily on designing powerful encoders to learn representative cross-domain information, without tackling the fundamental issue of interaction data shortage. In this paper, we present CrossAug, a novel data augmentation approach to leverage interactions more efficiently in two domains. Specifically, we propose intra-domain and inter-domain augmentations based on cross-reconstructed representations in terms of sampled records. To reduce the harm of domain shift, we project domain-shared representations in two domains into a joint space with Householder transformations and apply center alignments. All these modules boost the utilization of interactions with little influence from negative transfer. Extensive experimental results over public datasets demonstrate the effectiveness of CrossAug and its components in dual-target CDR.|为了缓解推荐系统中长期存在的数据稀疏问题，人们对跨域推荐进行了大量的研究，以促进跨域的信息传递过程。近年来，双目标 CDR 被引入，通过更一般的双向传输而不是传统的单向传输，实现了两个领域之间的相互改进。现有的双目标 CDR 方法主要集中在设计强大的编码器来学习有代表性的跨域信息，而没有解决交互数据短缺的根本问题。在本文中，我们提出 CrossAug，一种新的数据增强方法，以更有效地利用交互作用在两个领域。具体来说，我们提出了域内和域间增强的基础上的交叉重建表示的抽样记录。为了减少域移位的危害，我们将两个域中的域共享表示投影到一个带有 Householder 变换的联合空间中，并应用中心对齐。所有这些模块都提高了交互作用的利用率，而负迁移的影响很小。在公共数据集上的大量实验结果证明了 CrossAug 及其组件在双目标 CDR 中的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Cross-reconstructed+Augmentation+for+Dual-target+Cross-domain+Recommendation)|0| +|[Distillation for Multilingual Information Retrieval](https://doi.org/10.1145/3626772.3657955)|Eugene Yang, Dawn J. Lawrie, James Mayfield|HLT/COE; Human Language Technology Center of Excellence, Johns Hopkins University; Johns Hopkins University|Recent work in cross-language information retrieval (CLIR), where queries anddocuments are in different languages, has shown the benefit of theTranslate-Distill framework that trains a cross-language neural dual-encodermodel using translation and distillation. However, Translate-Distill onlysupports a single document language. Multilingual information retrieval (MLIR),which ranks a multilingual document collection, is harder to train than CLIRbecause the model must assign comparable relevance scores to documents indifferent languages. This work extends Translate-Distill and proposeMultilingual Translate-Distill (MTD) for MLIR. We show that ColBERT-X modelstrained with MTD outperform their counterparts trained ith MultilingualTranslate-Train, which is the previous state-of-the-art training approach, by5robust to the way languages are mixed in training batches. Our implementationis available on GitHub.|最近在跨语检索(CLIR)领域的工作显示了翻译-提取框架的好处，该框架通过翻译和提取训练了一个跨语言的神经双编码器模型。但是，Translate-Distill 只支持一种文档语言。对多语言文档集进行排序的多语言信息检索(mLIR)比 CLIR 更难训练，因为该模型必须为不同语言的文档分配可比较的相关度分数。这项工作扩展了 MLIR 的翻译-蒸馏和提出的多语言翻译-蒸馏(MTD)。我们表明，使用 MTD 训练的 ColBERT-X 模型优于使用多语言翻译训练(这是以前的最先进的训练方法)训练的对应模型，因为它对训练批次中语言的混合方式具有鲁棒性。我们的实现可以在 GitHub 上找到。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Distillation+for+Multilingual+Information+Retrieval)|0| +|[Estimating the Hessian Matrix of Ranking Objectives for Stochastic Learning to Rank with Gradient Boosted Trees](https://doi.org/10.1145/3626772.3657918)|Jingwei Kang, Maarten de Rijke, Harrie Oosterhuis|University of Amsterdam; Radboud University|Stochastic learning to rank (LTR) is a recent branch in the LTR field thatconcerns the optimization of probabilistic ranking models. Their probabilisticbehavior enables certain ranking qualities that are impossible withdeterministic models. For example, they can increase the diversity of displayeddocuments, increase fairness of exposure over documents, and better balanceexploitation and exploration through randomization. A core difficulty in LTR isgradient estimation, for this reason, existing stochastic LTR methods have beenlimited to differentiable ranking models (e.g., neural networks). This is instark contrast with the general field of LTR where Gradient Boosted DecisionTrees (GBDTs) have long been considered the state-of-the-art. In this work, we address this gap by introducing the first stochastic LTRmethod for GBDTs. Our main contribution is a novel estimator for thesecond-order derivatives, i.e., the Hessian matrix, which is a requirement foreffective GBDTs. To efficiently compute both the first and second-orderderivatives simultaneously, we incorporate our estimator into the existingPL-Rank framework, which was originally designed for first-order derivativesonly. Our experimental results indicate that stochastic LTR without the Hessianhas extremely poor performance, whilst the performance is competitive with thecurrent state-of-the-art with our estimated Hessian. Thus, through thecontribution of our novel Hessian estimation method, we have successfullyintroduced GBDTs to stochastic LTR.|随机学习排序(LTR)是 LTR 领域的一个新兴分支，主要研究概率排序模型的优化问题。他们的概率行为使得某些排名质量是不可能的确定性模型。例如，它们可以增加显示文档的多样性，增加文档曝光的公平性，以及通过随机化更好地平衡开发和探索。LTR 的一个核心难点是梯度估计，由于这个原因，现有的随机 LTR 方法仅限于可微排序模型(如神经网络)。这与长期以来一直被认为是最先进的梯度增强决策树(GBDTs)的 LTR 的一般领域形成了鲜明的对比。在这项工作中，我们通过引入第一个随机 LTR- 方法来解决这个差距。我们的主要贡献是一个新的估计器的二阶导数，即，黑森矩阵，这是一个需要有效的 GBDTs。为了同时有效地计算一阶导数和二阶导数，我们将估计量合并到现有的 PL 秩框架中，这个框架最初是为一阶导数而设计的。我们的实验结果表明，随机 LTR 没有黑森有非常差的性能，而性能是竞争性的当前国家的最先进的，我们估计黑森。因此，通过我们新的 Hessian 估计方法的贡献，我们成功地将 GBDTs 引入到随机 LTR 中。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Estimating+the+Hessian+Matrix+of+Ranking+Objectives+for+Stochastic+Learning+to+Rank+with+Gradient+Boosted+Trees)|0| +|[Information Diffusion Prediction via Cascade-Retrieved In-context Learning](https://doi.org/10.1145/3626772.3657909)|Ting Zhong, Jienan Zhang, Zhangtao Cheng, Fan Zhou, Xueqin Chen|University of Electronic Science and Technology of China, Chengdu, Sichuan, China; University of Electronic Science and Technology of China, Chengdu, China; University of Electronic Science and Technology of China; Delft University of Technology Faculty of Civil Engineering and Geosciences|Information diffusion prediction, which aims to infer the infected behavior of individual users during information spread, is critical for understanding the dynamics of information propagation and users' influence on online social media. To date, existing methods either focus on capturing limited contextual information from a single cascade, overlooking the potentially complex dependencies across different cascades, or they are committed to improving model performance by using intricate technologies to extract additional features as supplements to user representations, neglecting the drift of model performance across different platforms. To address these limitations, we propose a novel framework called CARE (CAscade-REtrieved In-Context Learning) inspired by the concept of in-context learning in LLMs. Specifically, CARE first constructs a prompts pool derived from historical cascades, then utilizes ranking-based search engine techniques to retrieve prompts with similar patterns based on the query. Moreover, CARE also introduces two augmentation strategies alongside social relationship enhancement to enrich the input context. Finally, the transformed query-cascade representation from a GPT-type architecture is projected to obtain the prediction. Experiments on real-world datasets from various platforms show that CARE outperforms state-of-the-art baselines in terms of effectiveness and robustness in information diffusion prediction.|信息扩散预测旨在推断个体用户在信息传播过程中的感染行为，对于理解信息传播动态和用户对网络社交媒体的影响至关重要。迄今为止，现有的方法要么侧重于从单个级联捕获有限的上下文信息，忽视不同级联之间潜在的复杂依赖性，要么致力于通过使用复杂的技术提取额外的特征作为用户表示的补充来改善模型性能，忽略了不同平台之间模型性能的漂移。为了解决这些局限性，我们提出了一个新的框架，称为 CARE (级联检索在上下文学习)的启发在 LLM 中的上下文学习的概念。具体来说，CARE 首先构造一个源自历史级联的提示池，然后利用基于排序的搜索引擎技术根据查询检索具有类似模式的提示。此外，CARE 在增强社会关系的同时，还引入了两种增强策略来丰富输入语境。最后，从一个 GPT 类型的体系结构转换的查询级联表示进行投影，以获得预测。对来自不同平台的真实世界数据集的实验表明，CARE 在信息扩散预测的有效性和鲁棒性方面优于最先进的基线。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Information+Diffusion+Prediction+via+Cascade-Retrieved+In-context+Learning)|0| |[Masked Graph Transformer for Large-Scale Recommendation](https://doi.org/10.1145/3626772.3657971)|Huiyuan Chen, Zhe Xu, ChinChia Michael Yeh, Vivian Lai, Yan Zheng, Minghua Xu, Hanghang Tong|Visa Inc; University of Illinois Urbana-Champaign|Graph Transformers have garnered significant attention for learninggraph-structured data, thanks to their superb ability to capture long-rangedependencies among nodes. However, the quadratic space and time complexityhinders the scalability of Graph Transformers, particularly for large-scalerecommendation. Here we propose an efficient Masked Graph Transformer, namedMGFormer, capable of capturing all-pair interactions among nodes with a linearcomplexity. To achieve this, we treat all user/item nodes as independenttokens, enhance them with positional embeddings, and feed them into akernelized attention module. Additionally, we incorporate learnable relativedegree information to appropriately reweigh the attentions. Experimentalresults show the superior performance of our MGFormer, even with a singleattention layer.|图形变换器已经获得了学习图形结构化数据的重要关注，由于他们的卓越的能力捕获节点之间的长距离依赖。然而，二次空间和时间的复杂性阻碍了图形变换器的可扩展性，特别是对于大规模推荐。在这里，我们提出了一个有效的屏蔽图形转换器，命名为 MGForm，能够捕获所有对节点之间的交互具有线性复杂度。为了实现这一点，我们将所有用户/项目节点视为独立的令牌，使用位置嵌入来增强它们，并将它们提供给内核化的注意模块。此外，我们结合可学习的相对程度信息，以适当地重新权衡注意力。实验结果表明，即使只有一个注意层，我们设计的 MG 变换器仍具有优越的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Masked+Graph+Transformer+for+Large-Scale+Recommendation)|0| |[Modeling Domains as Distributions with Uncertainty for Cross-Domain Recommendation](https://doi.org/10.1145/3626772.3657930)|Xianghui Zhu, Mengqun Jin, Hengyu Zhang, Chang Meng, Daoxin Zhang, Xiu Li|Xiaohongshu Inc., Shanghai, China; Shenzhen International Graduate School, Tsinghua University, Shenzhen, China|In the field of dual-target Cross-Domain Recommendation (DTCDR), improving the performance in both the information sparse domain and rich domain has been a mainstream research trend. However, prior embedding-based methods are insufficient to adequately describe the dynamics of user actions and items across domains. Moreover, previous efforts frequently lacked a comprehensive investigation of the entire domain distributions. This paper proposes a novel framework entitled Wasserstein Cross-Domain Recommendation (WCDR) that captures uncertainty in Wasserstein space to address above challenges. In this framework, we abstract user/item actions as Elliptical Gaussian distributions and divide them into local-intrinsic and global-domain parts. To further model the domain diversity, we adopt shared-specific pattern for global-domain distributions and present Masked Domain-aware Sub-distribution Aggregation (MDSA) module to produce informative and diversified global-domain distributions, which incorporates attention-based aggregation method and masking strategy that alleviates negative transfer issues. Extensive experiments on two public datasets and one business dataset are conducted. Experimental results demonstrate the superiority of WCDR over state-of-the-art methods.|在双目标跨域推荐(DTCDR)领域，提高信息稀疏域和富域的性能已成为主流研究趋势。但是，先前的基于嵌入的方法不足以充分描述跨领域的用户操作和项的动态。此外，以前的努力经常缺乏对整个领域分布的全面调查。本文提出了一个新的框架，名为沃瑟斯坦跨域建议(WCDR) ，捕捉 Wasserstein 空间的不确定性，以解决上述挑战。在这个框架中，我们将用户/项目行为抽象为椭圆高斯分布，并将其分为局部固有行为和全局域行为。为了进一步对领域多样性进行建模，我们对全局领域分布采用了共享特定模式，并提出了掩蔽领域感知子分布聚合(MDSA)模块，以生成信息丰富且多样化的全局领域分布，该模块结合了基于注意的聚合方法和掩蔽策略，减轻了负迁移问题。对两个公共数据集和一个业务数据集进行了广泛的实验。实验结果表明，WCDR 方法优于目前最先进的方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Modeling+Domains+as+Distributions+with+Uncertainty+for+Cross-Domain+Recommendation)|0| |[SCM4SR: Structural Causal Model-based Data Augmentation for Robust Session-based Recommendation](https://doi.org/10.1145/3626772.3657940)|Muskan Gupta, Priyanka Gupta, Jyoti Narwariya, Lovekesh Vig, Gautam Shroff|TCS Research, Delhi, India|With mounting privacy concerns, and movement towards a cookie-less internet, session-based recommendation (SR) models are gaining increasing popularity. The goal of SR models is to recommend top-K items to a user by utilizing information from past actions within a session. Many deep neural networks (DNN) based SR have been proposed in the literature, however, they experience performance declines in practice due to inherent biases (e.g., popularity bias) present in training data. To alleviate this, we propose an underlying neural-network (NN) based Structural Causal Model (SCM) which comprises an evolving user behavior (simulator) and recommendation model. The causal relations between the two sub-models and variables at consecutive timesteps are defined by a sequence of structural equations, whose parameters are learned using logged data. The learned SCM enables the simulation of a user's response on a counterfactual list of recommended items (slate). For this, we intervene on recommendation slates with counterfactual slates and simulate the user's response through learned SCM thereby generating counterfactual sessions to augment the training data. Through extensive empirical evaluation on simulated and real-world datasets, we show that the augmented data mitigates the impact of sparse training data and improves the performance of the SR models.|随着越来越多的隐私问题，以及向无 cookie 互联网的转变，基于会话的推荐(SR)模型越来越受欢迎。SR 模型的目标是通过利用会话中过去操作的信息向用户推荐 top-K 条目。文献中提出了许多基于深层神经网络(DNN)的 SR 方法，然而，由于训练数据中存在固有的偏差(如流行偏差) ，它们在实际应用中的性能下降。为了解决这一问题，我们提出了一种基于神经网络(NN)的结构性因果模型(SCM) ，该模型包括演化的用户行为模拟器(模拟器)和推荐模型。这两个子模型和变量之间的因果关系在连续的时间步长定义了一系列的结构方程，其参数是利用测井数据学习。学到的 SCM 可以模拟用户对推荐项目(板岩)的反事实列表的响应。为此，我们使用反事实模板介入推荐平台，并通过学习 SCM 模拟用户的反应，从而产生反事实会话来增加训练数据。通过对模拟数据集和真实数据集的大量实证评估，我们发现增强数据减轻了稀疏训练数据的影响，提高了 SR 模型的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=SCM4SR:+Structural+Causal+Model-based+Data+Augmentation+for+Robust+Session-based+Recommendation)|0| |[USimAgent: Large Language Models for Simulating Search Users](https://doi.org/10.1145/3626772.3657963)|Erhan Zhang, Xingzhu Wang, Peiyuan Gong, Yankai Lin, Jiaxin Mao|Renmin University of China|Due to the advantages in the cost-efficiency and reproducibility, usersimulation has become a promising solution to the user-centric evaluation ofinformation retrieval systems. Nonetheless, accurately simulating user searchbehaviors has long been a challenge, because users' actions in search arehighly complex and driven by intricate cognitive processes such as learning,reasoning, and planning. Recently, Large Language Models (LLMs) havedemonstrated remarked potential in simulating human-level intelligence and havebeen used in building autonomous agents for various tasks. However, thepotential of using LLMs in simulating search behaviors has not yet been fullyexplored. In this paper, we introduce a LLM-based user search behaviorsimulator, USimAgent. The proposed simulator can simulate users' querying,clicking, and stopping behaviors during search, and thus, is capable ofgenerating complete search sessions for specific search tasks. Empiricalinvestigation on a real user behavior dataset shows that the proposed simulatoroutperforms existing methods in query generation and is comparable totraditional methods in predicting user clicks and stopping behaviors. Theseresults not only validate the effectiveness of using LLMs for user simulationbut also shed light on the development of a more robust and generic usersimulators.|由于用户仿真具有成本低、重复性好等优点，已成为信息检索系统以用户为中心评估的一种有前途的解决方案。尽管如此，精确模拟用户搜索行为长期以来一直是一个挑战，因为用户在搜索中的行为非常复杂，并受到复杂的认知过程(如学习、推理和计划)的驱动。近年来，大语言模型(LLM)在模拟人类智能水平方面显示出了巨大的潜力，并被用于为各种任务构建自主代理。然而，在模拟搜索行为中使用 LLM 的潜力还没有被充分探索。本文介绍了一个基于 LLM 的用户搜索行为模拟器 USimAgent。该模拟器可以模拟用户在搜索过程中的查询、点击和停止行为，从而能够为特定的搜索任务生成完整的搜索会话。对真实用户行为数据集的实验研究表明，该模拟器在查询生成方面优于现有方法，在预测用户点击和停止行为方面与传统方法具有可比性。研究结果不仅验证了 LLM 用于用户仿真的有效性，而且为开发更加健壮和通用的用户仿真器提供了参考。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=USimAgent:+Large+Language+Models+for+Simulating+Search+Users)|0| |[ECAT: A Entire space Continual and Adaptive Transfer Learning Framework for Cross-Domain Recommendation](https://doi.org/10.1145/3626772.3661348)|Chaoqun Hou, Yuanhang Zhou, Yi Cao, Tong Liu|Alibaba Group|In industrial recommendation systems, there are several mini-apps designed to meet the diverse interests and needs of users. The sample space of them is merely a small subset of the entire space, making it challenging to train an efficient model. In recent years, there have been many excellent studies related to cross-domain recommendation aimed at mitigating the problem of data sparsity. However, few of them have simultaneously considered the adaptability of both sample and representation continual transfer setting to the target task. To overcome the above issue, we propose a Entire space Continual and Adaptive Transfer learning framework called ECAT which includes two core components: First, as for sample transfer, we propose a two-stage method that realizes a coarse-to-fine process. Specifically, we perform an initial selection through a graph-guided method, followed by a fine-grained selection using domain adaptation method. Second, we propose an adaptive knowledge distillation method for continually transferring the representations from a model that is well-trained on the entire space dataset. ECAT enables full utilization of the entire space samples and representations under the supervision of the target task, while avoiding negative migration. Comprehensive experiments on real-world industrial datasets from Taobao show that ECAT advances state-of-the-art performance on offline metrics, and brings +13.6|在工业推荐系统中，有几个微型应用程序可以满足用户的不同兴趣和需求。它们的样本空间只是整个空间的一个小子集，这使得训练一个有效的模型变得非常困难。近年来，针对数据稀疏问题的跨域推荐已经有了很多优秀的研究成果。然而，很少有人同时考虑样本和表征连续迁移设置对目标任务的适应性。为了克服上述问题，我们提出了一个称为 ECAT 的全空间连续自适应传输学习框架，该框架包括两个核心部分: 首先，对于样本传输，我们提出了一个两阶段的方法，实现了从粗到精的过程。具体来说，我们通过图形引导的方法执行初始选择，然后使用领域自适应方法进行细粒度选择。其次，我们提出了一种自适应知识精馏方法，用于连续地从一个对整个空间数据集训练有素的模型中传递表示。ECAT 能够在目标任务的监督下充分利用整个空间样本和表示，同时避免负迁移。对淘宝网真实工业数据集的全面实验表明，ECAT 在离线指标方面提高了最先进的性能，带来了 + 13.6|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=ECAT:+A+Entire+space+Continual+and+Adaptive+Transfer+Learning+Framework+for+Cross-Domain+Recommendation)|0| -|[Minimizing Live Experiments in Recommender Systems: User Simulation to Evaluate Preference Elicitation Policies](https://doi.org/10.1145/3626772.3661358)|ChihWei Hsu, Martin Mladenov, Ofer Meshi, James Pine, Hubert Pham, Shane Li, Xujian Liang, Anton Polishko, Li Yang, Ben Scheetz, Craig Boutilier|YouTube, New York, NY, USA; Google Research, Mountain View, CA, USA; Google, Mountain View, CA, USA|Evaluation of policies in recommender systems typically involves A/B live experiments on real users to assess a new policy's impact on relevant metrics. This "gold standard'' comes at a high cost, however, in terms of cycle time, user cost, and potential user retention. In developing policies for onboarding users, these costs can be especially problematic, since on-boarding occurs only once. In this work, we describe a simulation methodology used to augment (and reduce) the use of live experiments. We illustrate its deployment for the evaluation of preference elicitation algorithms used to onboard new users of the YouTube Music platform. By developing counterfactually robust user behavior models, and a simulation service that couples such models with production infrastructure, we can test new algorithms in a way that reliably predicts their performance on key metrics when deployed live.|推荐系统中的策略评估通常包括对真实用户进行 A/B 现场实验，以评估新策略对相关指标的影响。然而，这种“黄金标准”在周期时间、用户成本和潜在用户保留方面成本很高。在为新入职用户制定政策时，这些成本尤其成问题，因为新入职只发生一次。在这项工作中，我们描述了一种模拟方法，用于增加(和减少)现场实验的使用。我们举例说明它的部署，用于评估用于 YouTube 音乐平台上的新用户的偏好启发算法。通过开发反事实的健壮用户行为模型，以及将这些模型与生产基础设施结合起来的仿真服务，我们可以测试新算法，从而可靠地预测它们在实时部署时的关键指标性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Minimizing+Live+Experiments+in+Recommender+Systems:+User+Simulation+to+Evaluate+Preference+Elicitation+Policies)|0| -|[A Semantic Search Engine for Helping Patients Find Doctors and Locations in a Large Healthcare Organization](https://doi.org/10.1145/3626772.3661349)|Mayank Kejriwal, Hamid Haidarian, MinHsueh Chiu, Andy Xiang, Deep Shrestha, Faizan Javed|University of Southern California, Marina Del Rey, CA, USA; Kaiser Permanente Digital, Oakland, CA, USA; University of Southern California, Marina del Rey, CA, USA|Efficiently finding doctors and locations (FDL) is an important search problem for patients in the healthcare domain, for which traditional information retrieval (IR) methods tend to be sub-optimal. This paper introduces and defines FDL as an important healthcare industry-specific problem in IR. We then propose a semantic search engine as a robust solution to FDL in Kaiser Permanente (KP), a large healthcare organization with 12 million members. Our solution meets practical needs of data security and privacy, scalability, cost-effectiveness, backward compatibility with existing indexes and search infrastructure, and interpretability of outputs for patients. It uses a concept-rich ontology to model raw data from multiple sources as entities, relations, and attributes in a knowledge graph that is stored and indexed in an industry-scale graph database. We evaluate the solution on a real patient-query log and demonstrate its practical utility. The system has been implemented and deployed live to KP customers.|对于医疗领域的患者来说，有效地找到医生和位置(FDL)是一个重要的搜索问题，而传统的信息检索(IR)方法往往不是最佳方法。本文介绍并定义了 FDL 作为医疗保健行业特有的一个重要问题。然后，我们提出了一个语义搜索引擎作为一个健壮的解决方案的 FDL 在凯泽永久(金伯利) ，一个大型医疗保健组织的12万成员。我们的解决方案满足了数据安全和隐私、可扩展性、成本效益、与现有索引和搜索基础设施的向下兼容以及病人输出结果的可解释性的实际需求。它使用概念丰富的本体将来自多个来源的原始数据建模为知识图中的实体、关系和属性，该知识图存储在工业规模的图形数据库中并进行索引。我们在一个真实的病人查询日志上评估了这个解决方案，并演示了它的实用性。该系统已经实施，并部署到金伯利进程客户现场。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Semantic+Search+Engine+for+Helping+Patients+Find+Doctors+and+Locations+in+a+Large+Healthcare+Organization)|0| -|[Clinical Trial Retrieval via Multi-grained Similarity Learning](https://doi.org/10.1145/3626772.3661366)|Junyu Luo, Cheng Qian, Lucas Glass, Fenglong Ma|IQVIA, Chicago, USA; IQVIA, Philadelphia, PA, USA; The Pennsylvania State University, University Park, USA|Clinical trial analysis is one of the main business directions and services in IQVIA, and reviewing past similar studies is one of the most critical steps before starting a commercial clinical trial. The current review process is manual and time-consuming, requiring a clinical trial analyst to manually search through an extensive clinical trial database and then review all candidate studies. Therefore, it is of great interest to develop an automatic retrieval algorithm to select similar studies by giving new study information. To achieve this goal, we propose a novel group-based trial similarity learning network named GTSLNet, consisting of two kinds of similarity learning modules. The pair-wise section-level similarity learning module aims to compare the query trial and the candidate trial from the abstract semantic level via the proposed section transformer. Meanwhile, a word-level similarity learning module uses the word similarly matrix to capture the low-level similarity information. Additionally, an aggregation module combines these similarities. To address potential false negatives and noisy data, we introduce a variance-regularized group distance loss function. Experiment results show that the proposed GTSLNet significantly and consistently outperforms state-of-the-art baselines.|临床试验分析是 IQVIA 主要的商业方向和服务之一，在开始商业临床试验之前，回顾过去的类似研究是最关键的步骤之一。目前的审查过程是手工和耗时的，需要一个临床试验分析人员手工搜索通过一个广泛的临床试验数据库，然后审查所有候选研究。因此，开发一种自动检索算法，通过提供新的研究信息来选择相似的研究，具有重要的意义。为了实现这一目标，我们提出了一种新的基于组的试验相似性学习网络 GTSLNet，该网络由两种相似性学习模块组成。两节级相似性学习模块通过提出的节变换，从抽象语义层面对查询试验和候选试验进行比较。同时，一个词级相似度学习模块利用词相似矩阵来获取低级相似度信息。此外，聚合模块将这些相似性组合在一起。为了处理潜在的假阴性和噪声数据，我们引入了一个方差正则化的群距离损失函数。实验结果表明，所提出的 GTSLNet 性能明显优于最先进的基线。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Clinical+Trial+Retrieval+via+Multi-grained+Similarity+Learning)|0| -|[Search under Uncertainty: Cognitive Biases and Heuristics: A Tutorial on Testing, Mitigating and Accounting for Cognitive Biases in Search Experiments](https://doi.org/10.1145/3626772.3661382)|Jiqun Liu, Leif Azzopardi|The University of Oklahoma, Norman, OK, USA; University of Strathclyde, Glasgow, United Kingdom|Understanding how people interact with search interfaces is core to the field of Interactive Information Retrieval (IIR). While various models have been proposed (e.g., Belkin's ASK, Berry picking, Everyday-life information seeking, Information foraging theory, Economic theory, etc.), they have largely ignored the impact of cognitive biases on search behaviour and performance. A growing body of empirical work exploring how people's cognitive biases influence search and judgments, has led to the development of new models of search that draw upon Behavioural Economics and Psychology. This full day tutorial will provide a starting point for researchers seeking to learn more about information seeking, search and retrieval under uncertainty. The tutorial will be structured into three parts. First, we will provide an introduction of the biases and heuristics program put forward by Tversky and Kahneman [60] (1974) which assumes that people are not always rational. The second part of the tutorial will provide an overview of the types and space of biases in search,[5, 40] before doing a deep dive into several specific examples and the impact of biases on different types of decisions (e.g., health/medical, financial). The third part will focus on a discussion of the practical implication regarding the design and evaluation human-centered IR systems in the light of cognitive biases - where participants will undertake some hands-on exercises.|了解人们如何与搜索界面互动是交互式信息检索(IIR)领域的核心。虽然已经提出了各种各样的模型(例如，Belkin 的 ASK，Berry 拣选，日常生活中的信息搜索，信息搜索理论，经济学理论等) ，但是他们很大程度上忽略了认知偏差对搜索行为和性能的影响。越来越多的实证研究探索人们的认知偏见如何影响搜索和判断，导致了新的搜索模式的发展，这些模式借鉴了行为经济学和心理学。这一整天的教程将提供一个起点，研究人员寻求了解更多的信息搜索，搜索和检索在不确定性下。本教程将分为三个部分。首先，我们将介绍 Tversky 和 Kahneman [60](1974)提出的假设人们不总是理性的偏见和启发式程序。本教程的第二部分将提供一个关于搜索中偏见的类型和空间的概述[5,40] ，然后深入研究几个具体的例子，以及偏见对不同类型决策(例如，健康/医疗，金融)的影响。第三部分将着重讨论基于认知偏差的以人为中心的信息检索系统的设计和评估的实际意义——参与者将进行一些实践练习。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Search+under+Uncertainty:+Cognitive+Biases+and+Heuristics:+A+Tutorial+on+Testing,+Mitigating+and+Accounting+for+Cognitive+Biases+in+Search+Experiments)|0| -|[TRAD: Enhancing LLM Agents with Step-Wise Thought Retrieval and Aligned Decision](https://doi.org/10.1145/3626772.3657788)|Ruiwen Zhou, Yingxuan Yang, Muning Wen, Ying Wen, Wenhao Wang, Chunling Xi, Guoqiang Xu, Yong Yu, Weinan Zhang|Shanghai Jiao Tong University; China Pacific Insurance Company|Numerous large language model (LLM) agents have been built for differenttasks like web navigation and online shopping due to LLM's wide knowledge andtext-understanding ability. Among these works, many of them utilize in-contextexamples to achieve generalization without the need for fine-tuning, while fewof them have considered the problem of how to select and effectively utilizethese examples. Recently, methods based on trajectory-level retrieval with taskmeta-data and using trajectories as in-context examples have been proposed toimprove the agent's overall performance in some sequential decision makingtasks. However, these methods can be problematic due to plausible examplesretrieved without task-specific state transition dynamics and long input withplenty of irrelevant context. In this paper, we propose a novel framework(TRAD) to address these issues. TRAD first conducts Thought Retrieval,achieving step-level demonstration selection via thought matching, leading tomore helpful demonstrations and less irrelevant input noise. Then, TRADintroduces Aligned Decision, complementing retrieved demonstration steps withtheir previous or subsequent steps, which enables tolerance for imperfectthought and provides a choice for balance between more context and less noise.Extensive experiments on ALFWorld and Mind2Web benchmarks show that TRAD notonly outperforms state-of-the-art models but also effectively helps in reducingnoise and promoting generalization. Furthermore, TRAD has been deployed inreal-world scenarios of a global business insurance company and improves thesuccess rate of robotic process automation.|由于大语言模型(LLM)具有广泛的知识和文本理解能力，人们已经为不同的任务建立了大量的大语言模型(LLM)代理，如网络导航和在线购物。在这些工作中，许多人利用上下文中的例子来实现泛化而不需要进行微调，而很少有人考虑如何选择和有效利用这些例子的问题。近年来，人们提出了基于任务元数据的轨迹级检索方法，并将轨迹作为上下文实例来提高代理在一些连续决策任务中的整体性能。然而，这些方法可能是有问题的，因为没有任务特定的状态转换动态检索似乎合理的例子和大量不相关的上下文的长输入。在本文中，我们提出了一个新的框架(TRAD)来解决这些问题。TRAD 首先进行思维检索，通过思维匹配实现阶段性的演示选择，使演示更有帮助，输入噪音更少。然后，TRAD 引入了对齐决策，将检索到的示范步骤与之前或之后的步骤相互补充，这使得对不完美思想的容忍度成为可能，并提供了一个在更多上下文和更少噪音之间取得平衡的选择。在 ALFWorld 和 Mind2Web 基准上的大量实验表明，TRAD 不仅优于最先进的模型，而且有效地帮助降低噪音和促进推广。此外，TRAD 已经部署在一个全球商业保险公司的现实世界场景中，并提高了机器人过程自动化的成功率。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=TRAD:+Enhancing+LLM+Agents+with+Step-Wise+Thought+Retrieval+and+Aligned+Decision)|0| +|[Minimizing Live Experiments in Recommender Systems: User Simulation to Evaluate Preference Elicitation Policies](https://doi.org/10.1145/3626772.3661358)|ChihWei Hsu, Martin Mladenov, Ofer Meshi, James Pine, Hubert Pham, Shane Li, Xujian Liang, Anton Polishko, Li Yang, Ben Scheetz, Craig Boutilier|Google Research, Mountain View, CA, USA; YouTube, New York, NY, USA; Google, Mountain View, CA, USA|Evaluation of policies in recommender systems typically involves A/B live experiments on real users to assess a new policy's impact on relevant metrics. This "gold standard'' comes at a high cost, however, in terms of cycle time, user cost, and potential user retention. In developing policies for onboarding users, these costs can be especially problematic, since on-boarding occurs only once. In this work, we describe a simulation methodology used to augment (and reduce) the use of live experiments. We illustrate its deployment for the evaluation of preference elicitation algorithms used to onboard new users of the YouTube Music platform. By developing counterfactually robust user behavior models, and a simulation service that couples such models with production infrastructure, we can test new algorithms in a way that reliably predicts their performance on key metrics when deployed live.|推荐系统中的策略评估通常包括对真实用户进行 A/B 现场实验，以评估新策略对相关指标的影响。然而，这种“黄金标准”在周期时间、用户成本和潜在用户保留方面成本很高。在为新入职用户制定政策时，这些成本尤其成问题，因为新入职只发生一次。在这项工作中，我们描述了一种模拟方法，用于增加(和减少)现场实验的使用。我们举例说明它的部署，用于评估用于 YouTube 音乐平台上的新用户的偏好启发算法。通过开发反事实的健壮用户行为模型，以及将这些模型与生产基础设施结合起来的仿真服务，我们可以测试新算法，从而可靠地预测它们在实时部署时的关键指标性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Minimizing+Live+Experiments+in+Recommender+Systems:+User+Simulation+to+Evaluate+Preference+Elicitation+Policies)|0| +|[A Semantic Search Engine for Helping Patients Find Doctors and Locations in a Large Healthcare Organization](https://doi.org/10.1145/3626772.3661349)|Mayank Kejriwal, Hamid Haidarian, MinHsueh Chiu, Andy Xiang, Deep Shrestha, Faizan Javed|Kaiser Permanente Digital, Oakland, CA, USA; University of Southern California, Marina del Rey, CA, USA; University of Southern California, Marina Del Rey, CA, USA|Efficiently finding doctors and locations (FDL) is an important search problem for patients in the healthcare domain, for which traditional information retrieval (IR) methods tend to be sub-optimal. This paper introduces and defines FDL as an important healthcare industry-specific problem in IR. We then propose a semantic search engine as a robust solution to FDL in Kaiser Permanente (KP), a large healthcare organization with 12 million members. Our solution meets practical needs of data security and privacy, scalability, cost-effectiveness, backward compatibility with existing indexes and search infrastructure, and interpretability of outputs for patients. It uses a concept-rich ontology to model raw data from multiple sources as entities, relations, and attributes in a knowledge graph that is stored and indexed in an industry-scale graph database. We evaluate the solution on a real patient-query log and demonstrate its practical utility. The system has been implemented and deployed live to KP customers.|对于医疗领域的患者来说，有效地找到医生和位置(FDL)是一个重要的搜索问题，而传统的信息检索(IR)方法往往不是最佳方法。本文介绍并定义了 FDL 作为医疗保健行业特有的一个重要问题。然后，我们提出了一个语义搜索引擎作为一个健壮的解决方案的 FDL 在凯泽永久(金伯利) ，一个大型医疗保健组织的12万成员。我们的解决方案满足了数据安全和隐私、可扩展性、成本效益、与现有索引和搜索基础设施的向下兼容以及病人输出结果的可解释性的实际需求。它使用概念丰富的本体将来自多个来源的原始数据建模为知识图中的实体、关系和属性，该知识图存储在工业规模的图形数据库中并进行索引。我们在一个真实的病人查询日志上评估了这个解决方案，并演示了它的实用性。该系统已经实施，并部署到金伯利进程客户现场。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Semantic+Search+Engine+for+Helping+Patients+Find+Doctors+and+Locations+in+a+Large+Healthcare+Organization)|0| +|[Clinical Trial Retrieval via Multi-grained Similarity Learning](https://doi.org/10.1145/3626772.3661366)|Junyu Luo, Cheng Qian, Lucas Glass, Fenglong Ma|IQVIA, Philadelphia, PA, USA; IQVIA, Chicago, USA; The Pennsylvania State University, University Park, USA|Clinical trial analysis is one of the main business directions and services in IQVIA, and reviewing past similar studies is one of the most critical steps before starting a commercial clinical trial. The current review process is manual and time-consuming, requiring a clinical trial analyst to manually search through an extensive clinical trial database and then review all candidate studies. Therefore, it is of great interest to develop an automatic retrieval algorithm to select similar studies by giving new study information. To achieve this goal, we propose a novel group-based trial similarity learning network named GTSLNet, consisting of two kinds of similarity learning modules. The pair-wise section-level similarity learning module aims to compare the query trial and the candidate trial from the abstract semantic level via the proposed section transformer. Meanwhile, a word-level similarity learning module uses the word similarly matrix to capture the low-level similarity information. Additionally, an aggregation module combines these similarities. To address potential false negatives and noisy data, we introduce a variance-regularized group distance loss function. Experiment results show that the proposed GTSLNet significantly and consistently outperforms state-of-the-art baselines.|临床试验分析是 IQVIA 主要的商业方向和服务之一，在开始商业临床试验之前，回顾过去的类似研究是最关键的步骤之一。目前的审查过程是手工和耗时的，需要一个临床试验分析人员手工搜索通过一个广泛的临床试验数据库，然后审查所有候选研究。因此，开发一种自动检索算法，通过提供新的研究信息来选择相似的研究，具有重要的意义。为了实现这一目标，我们提出了一种新的基于组的试验相似性学习网络 GTSLNet，该网络由两种相似性学习模块组成。两节级相似性学习模块通过提出的节变换，从抽象语义层面对查询试验和候选试验进行比较。同时，一个词级相似度学习模块利用词相似矩阵来获取低级相似度信息。此外，聚合模块将这些相似性组合在一起。为了处理潜在的假阴性和噪声数据，我们引入了一个方差正则化的群距离损失函数。实验结果表明，所提出的 GTSLNet 性能明显优于最先进的基线。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Clinical+Trial+Retrieval+via+Multi-grained+Similarity+Learning)|0| +|[Search under Uncertainty: Cognitive Biases and Heuristics: A Tutorial on Testing, Mitigating and Accounting for Cognitive Biases in Search Experiments](https://doi.org/10.1145/3626772.3661382)|Jiqun Liu, Leif Azzopardi|University of Strathclyde, Glasgow, United Kingdom; The University of Oklahoma, Norman, OK, USA|Understanding how people interact with search interfaces is core to the field of Interactive Information Retrieval (IIR). While various models have been proposed (e.g., Belkin's ASK, Berry picking, Everyday-life information seeking, Information foraging theory, Economic theory, etc.), they have largely ignored the impact of cognitive biases on search behaviour and performance. A growing body of empirical work exploring how people's cognitive biases influence search and judgments, has led to the development of new models of search that draw upon Behavioural Economics and Psychology. This full day tutorial will provide a starting point for researchers seeking to learn more about information seeking, search and retrieval under uncertainty. The tutorial will be structured into three parts. First, we will provide an introduction of the biases and heuristics program put forward by Tversky and Kahneman [60] (1974) which assumes that people are not always rational. The second part of the tutorial will provide an overview of the types and space of biases in search,[5, 40] before doing a deep dive into several specific examples and the impact of biases on different types of decisions (e.g., health/medical, financial). The third part will focus on a discussion of the practical implication regarding the design and evaluation human-centered IR systems in the light of cognitive biases - where participants will undertake some hands-on exercises.|了解人们如何与搜索界面互动是交互式信息检索(IIR)领域的核心。虽然已经提出了各种各样的模型(例如，Belkin 的 ASK，Berry 拣选，日常生活中的信息搜索，信息搜索理论，经济学理论等) ，但是他们很大程度上忽略了认知偏差对搜索行为和性能的影响。越来越多的实证研究探索人们的认知偏见如何影响搜索和判断，导致了新的搜索模式的发展，这些模式借鉴了行为经济学和心理学。这一整天的教程将提供一个起点，研究人员寻求了解更多的信息搜索，搜索和检索在不确定性下。本教程将分为三个部分。首先，我们将介绍 Tversky 和 Kahneman [60](1974)提出的假设人们不总是理性的偏见和启发式程序。本教程的第二部分将提供一个关于搜索中偏见的类型和空间的概述[5,40] ，然后深入研究几个具体的例子，以及偏见对不同类型决策(例如，健康/医疗，金融)的影响。第三部分将着重讨论基于认知偏差的以人为中心的信息检索系统的设计和评估的实际意义——参与者将进行一些实践练习。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Search+under+Uncertainty:+Cognitive+Biases+and+Heuristics:+A+Tutorial+on+Testing,+Mitigating+and+Accounting+for+Cognitive+Biases+in+Search+Experiments)|0| +|[TRAD: Enhancing LLM Agents with Step-Wise Thought Retrieval and Aligned Decision](https://doi.org/10.1145/3626772.3657788)|Ruiwen Zhou, Yingxuan Yang, Muning Wen, Ying Wen, Wenhao Wang, Chunling Xi, Guoqiang Xu, Yong Yu, Weinan Zhang|China Pacific Insurance Company; Shanghai Jiao Tong University|Numerous large language model (LLM) agents have been built for differenttasks like web navigation and online shopping due to LLM's wide knowledge andtext-understanding ability. Among these works, many of them utilize in-contextexamples to achieve generalization without the need for fine-tuning, while fewof them have considered the problem of how to select and effectively utilizethese examples. Recently, methods based on trajectory-level retrieval with taskmeta-data and using trajectories as in-context examples have been proposed toimprove the agent's overall performance in some sequential decision makingtasks. However, these methods can be problematic due to plausible examplesretrieved without task-specific state transition dynamics and long input withplenty of irrelevant context. In this paper, we propose a novel framework(TRAD) to address these issues. TRAD first conducts Thought Retrieval,achieving step-level demonstration selection via thought matching, leading tomore helpful demonstrations and less irrelevant input noise. Then, TRADintroduces Aligned Decision, complementing retrieved demonstration steps withtheir previous or subsequent steps, which enables tolerance for imperfectthought and provides a choice for balance between more context and less noise.Extensive experiments on ALFWorld and Mind2Web benchmarks show that TRAD notonly outperforms state-of-the-art models but also effectively helps in reducingnoise and promoting generalization. Furthermore, TRAD has been deployed inreal-world scenarios of a global business insurance company and improves thesuccess rate of robotic process automation.|由于大语言模型(LLM)具有广泛的知识和文本理解能力，人们已经为不同的任务建立了大量的大语言模型(LLM)代理，如网络导航和在线购物。在这些工作中，许多人利用上下文中的例子来实现泛化而不需要进行微调，而很少有人考虑如何选择和有效利用这些例子的问题。近年来，人们提出了基于任务元数据的轨迹级检索方法，并将轨迹作为上下文实例来提高代理在一些连续决策任务中的整体性能。然而，这些方法可能是有问题的，因为没有任务特定的状态转换动态检索似乎合理的例子和大量不相关的上下文的长输入。在本文中，我们提出了一个新的框架(TRAD)来解决这些问题。TRAD 首先进行思维检索，通过思维匹配实现阶段性的演示选择，使演示更有帮助，输入噪音更少。然后，TRAD 引入了对齐决策，将检索到的示范步骤与之前或之后的步骤相互补充，这使得对不完美思想的容忍度成为可能，并提供了一个在更多上下文和更少噪音之间取得平衡的选择。在 ALFWorld 和 Mind2Web 基准上的大量实验表明，TRAD 不仅优于最先进的模型，而且有效地帮助降低噪音和促进推广。此外，TRAD 已经部署在一个全球商业保险公司的现实世界场景中，并提高了机器人过程自动化的成功率。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=TRAD:+Enhancing+LLM+Agents+with+Step-Wise+Thought+Retrieval+and+Aligned+Decision)|0| |[Representation Learning and Information Retrieval](https://doi.org/10.1145/3626772.3657995)|Yiming Yang||How to best represent words, documents, queries, entities, relations, and other variables in information retrieval (IR) and related applications has been a fundamental research question for decades. Early IR systems relied on the independence assumptions about words and documents for simplicity and scalability, which were clearly sub-optimal from a semantic point of view. The rapid development of deep neural networks in the past decade has revolutionized the representation learning technologies for contextualized word embedding and graph-enhanced document embedding, leading to the new era of dense IR. This talk highlights such impactful shifts in representation learning for IR and related areas, the new challenges coming along and the remedies, including our recent work in large-scale dense IR [1, 9], in graph-based reasoning for knowledge-enhanced predictions [10], in self-refinement of large language models (LLMs) with retrieval augmented generation (RAG)[2,7] and iterative feedback [3,4], in principle-driven self-alignment of LLMs with minimum human supervision [6], etc. More generally, the power of such deep learning goes beyond IR enhancements, e.g., for significantly improving the state-of-the-art solvers for NP-Complete problems in classical computer science [5,8].|如何最好地表示单词、文档、查询、实体、关系以及其他变量在信息检索(IR)和相关应用中一直是几十年来的一个基础研究问题。早期的 IR 系统依赖于关于单词和文档的独立性假设，以实现简单性和可伸缩性，从语义角度来看，这显然是次优的。近十年来深层神经网络的迅速发展，使得上下文化词语嵌入和图增强文档嵌入的表示学习技术发生了革命性的变化，进入了密集红外的新时代。这个演讲强调了 IR 和相关领域表示学习的这种有影响力的转变，新的挑战和补救措施，包括我们最近在大规模稠密 IR [1,9] ，基于图形的知识增强预测推理[10] ，大型语言模型(LLM)的自我完善(RAG)[2,7]和迭代反馈[3,4] ，在原则驱动的 LLM 自我调整与最小人类监督[6]等。更一般地说，这种深度学习的力量超越了 IR 增强，例如，显着提高了经典计算机科学中 NP 完全问题的最先进的解决方案[5,8]。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Representation+Learning+and+Information+Retrieval)|0| |["In-Context Learning" or: How I learned to stop worrying and love "Applied Information Retrieval"](https://doi.org/10.1145/3626772.3657842)|Andrew Parry, Debasis Ganguly, Manish Chandra|University of Glasgow; University of Glasgow School of Computing; University of Glasgow Computing Science|With the increasing ability of large language models (LLMs), in-contextlearning (ICL) has evolved as a new paradigm for natural language processing(NLP), where instead of fine-tuning the parameters of an LLM specific to adownstream task with labeled examples, a small number of such examples isappended to a prompt instruction for controlling the decoder's generationprocess. ICL, thus, is conceptually similar to a non-parametric approach, suchas k-NN, where the prediction for each instance essentially depends on thelocal topology, i.e., on a localised set of similar instances and their labels(called few-shot examples). This suggests that a test instance in ICL isanalogous to a query in IR, and similar examples in ICL retrieved from atraining set relate to a set of documents retrieved from a collection in IR.While standard unsupervised ranking models can be used to retrieve thesefew-shot examples from a training set, the effectiveness of the examples canpotentially be improved by re-defining the notion of relevance specific to itsutility for the downstream task, i.e., considering an example to be relevant ifincluding it in the prompt instruction leads to a correct prediction. With thistask-specific notion of relevance, it is possible to train a supervised rankingmodel (e.g., a bi-encoder or cross-encoder), which potentially learns tooptimally select the few-shot examples. We believe that the recent advances inneural rankers can potentially find a use case for this task of optimallychoosing examples for more effective downstream ICL predictions.|随着大语言模型(LLM)能力的不断提高，上下文内学习(in-context learning，ICL)已经成为自然语言处理(NLP)的一种新范式。因此，ICL 在概念上类似于非参数方法，例如 k-NN，其中每个实例的预测基本上依赖于局部拓扑，即一组局部化的相似实例及其标签(称为极少数例子)。这表明 ICL 中的测试实例类似于 IR 中的查询，并且从训练集中检索的 ICL 中的类似实例与从 IR 中的集合中检索的一组文档有关。虽然标准的无监督排序模型可以用于从训练集中检索这些几个镜头的例子，但是这些例子的有效性可以通过重新定义特定于下游任务的适用性的相关性概念来提高，即，如果在提示指令中包含一个相关的例子，则会导致正确的预测。有了这个任务特定的相关性概念，就有可能训练一个有监督的排名模型(例如，一个双编码器或交叉编码器) ，它可能学会最佳地选择少数镜头的例子。我们相信，最近在神经排序的进展可能会找到这个任务的最佳选择更有效的下游 ICL 预测例子的用例。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q="In-Context+Learning"+or:+How+I+learned+to+stop+worrying+and+love+"Applied+Information+Retrieval")|0| |[LDRE: LLM-based Divergent Reasoning and Ensemble for Zero-Shot Composed Image Retrieval](https://doi.org/10.1145/3626772.3657740)|Zhenyu Yang, Dizhan Xue, Shengsheng Qian, Weiming Dong, Changsheng Xu|State Key Laboratory of Multimodal Artificial Intelligence Systems|Zero-Shot Composed Image Retrieval (ZS-CIR) has garnered increasing interest in recent years, which aims to retrieve a target image based on a query composed of a reference image and a modification text without training samples. Specifically, the modification text describes the distinction between the two images. To conduct ZS-CIR, the prevailing methods employ pre-trained image-to-text models to transform the query image and text into a single text, which is then projected into the common feature space by CLIP to retrieve the target image. However, these methods neglect that ZS-CIR is a typicalfuzzy retrieval task, where the semantics of the target image are not strictly defined by the query image and text. To overcome this limitation, this paper proposes a training-free LLM-based Divergent Reasoning and Ensemble (LDRE) method for ZS-CIR to capture diverse possible semantics of the composed result. Firstly, we employ a pre-trained captioning model to generate dense captions for the reference image, focusing on different semantic perspectives of the reference image. Then, we prompt Large Language Models (LLMs) to conduct divergent compositional reasoning based on the dense captions and modification text, deriving divergent edited captions that cover the possible semantics of the composed target. Finally, we design a divergent caption ensemble to obtain the ensemble caption feature weighted by semantic relevance scores, which is subsequently utilized to retrieve the target image in the CLIP feature space. Extensive experiments on three public datasets demonstrate that our proposed LDRE achieves the new state-of-the-art performance.|近年来，零拍摄合成图像检索(ZS-CIR)越来越受到人们的关注，其目的是基于一个由参考图像和修改文本组成的查询来检索目标图像，而不需要训练样本。具体来说，修改文本描述了这两个图像之间的区别。为了实现 ZS-CIR，常用的方法是使用预先训练好的图文模型将查询图像和文本转换为单个文本，然后通过 CLIP 投影到公共特征空间中检索目标图像。然而，这些方法忽略了 ZS-CIR 是一个典型的模糊检索任务，其中目标图像的语义没有严格地由查询图像和文本来定义。为了克服这一局限性，本文提出了一种基于无训练 LLM 的 ZS-CIR 发散推理与集成(LDRE)方法，用于捕获组合结果的多种可能语义。首先，我们使用一个预先训练的字幕模型为参考图像生成密集的字幕，重点关注参考图像的不同语义视角。然后，我们提示大语言模型(LLM)进行发散的组合推理的基础上密集的标题和修改文本，导出发散的编辑标题，涵盖可能的语义组合的目标。最后，我们设计了一个发散字幕集合来获得以语义相关分数为权重的字幕集合特征，然后利用该特征在 CLIP 特征空间中检索目标图像。在三个公共数据集上的大量实验表明，我们提出的 LDRE 实现了新的最先进的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=LDRE:+LLM-based+Divergent+Reasoning+and+Ensemble+for+Zero-Shot+Composed+Image+Retrieval)|0| -|[EditKG: Editing Knowledge Graph for Recommendation](https://doi.org/10.1145/3626772.3657723)|Gu Tang, Xiaoying Gan, Jinghe Wang, Bin Lu, Lyuwen Wu, Luoyi Fu, Chenghu Zhou|Chinese Academy of Sciences, Beijing, China; Shanghai Jiao Tong University, Shanghai, China|With the enrichment of user-item interactions, Graph Neural Networks (GNNs) are widely used in recommender systems to alleviate information overload. Nevertheless, they still suffer from the cold-start issue. Knowledge Graphs (KGs), providing external information, have been extensively applied in GNN-based methods to mitigate this issue. However, current KG-aware recommendation methods suffer from the knowledge imbalance problem caused by incompleteness of existing KGs. This imbalance is reflected by the long-tail phenomenon of item attributes, i.e., unpopular items usually lack more attributes compared to popular items. To tackle this problem, we propose a novel framework called EditKG: Editing Knowledge Graph for Recommendation, to balance attribute distribution of items via editing KGs. EditKG consists of two key designs: Knowledge Generator and Knowledge Deleter. Knowledge Generator generates attributes for items by exploring their mutual information correlations and semantic correlations. Knowledge Deleter removes the task-irrelevant item attributes according to the parameterized task relevance score, while dropping the spurious item attributes through aligning the attribute scores. Extensive experiments on three benchmark datasets demonstrate that EditKG significantly outperforms state-of-the-art methods, and achieves 8.98% average improvement. The implementations are available at https://github.com/gutang-97/2024SIGIR-EditKG.|随着用户-项目交互的丰富，图形神经网络(GNN)被广泛应用于推荐系统以减轻信息超载。尽管如此，他们仍然受到冷启动问题的困扰。提供外部信息的知识图(KG)已广泛应用于基于 GNN 的方法中，以缓解这一问题。但是，现有的幼儿园知识推荐方法存在不完备性导致的知识不平衡问题。这种不平衡反映在项目属性的长尾现象上，也就是说，不受欢迎的项目通常比受欢迎的项目缺乏更多的属性。为了解决这个问题，我们提出了一个新的框架，即 EditKG: Editing Knowledge Graph for  推荐知识图，通过编辑 KG 来平衡项目的属性分布。EditKG 由两个关键设计组成: 知识生成器和知识删除器。知识生成器通过探索项目之间的信息相关性和语义相关性来生成项目的属性。知识删除器根据参数化的任务相关性得分删除与任务无关的项目属性，同时通过对齐属性得分删除虚假的项目属性。对三个基准数据集的大量实验表明，EditKG 的性能明显优于最先进的方法，平均改进率达到8.98% 。有关实施方案可于 https://github.com/gutang-97/2024sigir-editkg 下载。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=EditKG:+Editing+Knowledge+Graph+for+Recommendation)|0| +|[EditKG: Editing Knowledge Graph for Recommendation](https://doi.org/10.1145/3626772.3657723)|Gu Tang, Xiaoying Gan, Jinghe Wang, Bin Lu, Lyuwen Wu, Luoyi Fu, Chenghu Zhou|Shanghai Jiao Tong University, Shanghai, China; Chinese Academy of Sciences, Beijing, China|With the enrichment of user-item interactions, Graph Neural Networks (GNNs) are widely used in recommender systems to alleviate information overload. Nevertheless, they still suffer from the cold-start issue. Knowledge Graphs (KGs), providing external information, have been extensively applied in GNN-based methods to mitigate this issue. However, current KG-aware recommendation methods suffer from the knowledge imbalance problem caused by incompleteness of existing KGs. This imbalance is reflected by the long-tail phenomenon of item attributes, i.e., unpopular items usually lack more attributes compared to popular items. To tackle this problem, we propose a novel framework called EditKG: Editing Knowledge Graph for Recommendation, to balance attribute distribution of items via editing KGs. EditKG consists of two key designs: Knowledge Generator and Knowledge Deleter. Knowledge Generator generates attributes for items by exploring their mutual information correlations and semantic correlations. Knowledge Deleter removes the task-irrelevant item attributes according to the parameterized task relevance score, while dropping the spurious item attributes through aligning the attribute scores. Extensive experiments on three benchmark datasets demonstrate that EditKG significantly outperforms state-of-the-art methods, and achieves 8.98% average improvement. The implementations are available at https://github.com/gutang-97/2024SIGIR-EditKG.|随着用户-项目交互的丰富，图形神经网络(GNN)被广泛应用于推荐系统以减轻信息超载。尽管如此，他们仍然受到冷启动问题的困扰。提供外部信息的知识图(KG)已广泛应用于基于 GNN 的方法中，以缓解这一问题。但是，现有的幼儿园知识推荐方法存在不完备性导致的知识不平衡问题。这种不平衡反映在项目属性的长尾现象上，也就是说，不受欢迎的项目通常比受欢迎的项目缺乏更多的属性。为了解决这个问题，我们提出了一个新的框架，即 EditKG: Editing Knowledge Graph for  推荐知识图，通过编辑 KG 来平衡项目的属性分布。EditKG 由两个关键设计组成: 知识生成器和知识删除器。知识生成器通过探索项目之间的信息相关性和语义相关性来生成项目的属性。知识删除器根据参数化的任务相关性得分删除与任务无关的项目属性，同时通过对齐属性得分删除虚假的项目属性。对三个基准数据集的大量实验表明，EditKG 的性能明显优于最先进的方法，平均改进率达到8.98% 。有关实施方案可于 https://github.com/gutang-97/2024sigir-editkg 下载。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=EditKG:+Editing+Knowledge+Graph+for+Recommendation)|0| |[GUITAR: Gradient Pruning toward Fast Neural Ranking](https://doi.org/10.1145/3626772.3657728)|Weijie Zhao, Shulong Tan, Ping Li|Rochester Institute of Technology; VecML; Baidu Research USA|With the continuous popularity of deep learning and representation learning,fast vector search becomes a vital task in various ranking/retrieval basedapplications, say recommendation, ads ranking and question answering. Neuralnetwork based ranking is widely adopted due to its powerful capacity inmodeling complex relationships, such as between users and items, questions andanswers. However, it is usually exploited in offline or re-ranking manners forit is time-consuming in computations. Online neural network ranking–so calledfast neural ranking–is considered challenging because neural network measuresare usually non-convex and asymmetric. Traditional Approximate Nearest Neighbor(ANN) search which usually focuses on metric ranking measures, is notapplicable to these advanced measures. In this paper, we introduce a novel graph searching framework to acceleratethe searching in the fast neural ranking problem. The proposed graph searchingalgorithm is bi-level: we first construct a probable candidate set; then weonly evaluate the neural network measure over the probable candidate setinstead of evaluating the neural network over all neighbors. Specifically, wepropose a gradient-based algorithm that approximates the rank of the neuralnetwork matching score to construct the probable candidate set; and we presentan angle-based heuristic procedure to adaptively identify the proper size ofthe probable candidate set. Empirical results on public data confirm theeffectiveness of our proposed algorithms.|随着深度学习和表示学习的不断普及，快速向量搜索成为各种基于排序/检索的应用程序(如推荐、广告排序和问题回答)的重要任务。基于神经网络的排序因其对复杂关系(如用户与项目、问题与答案之间的关系)建模能力强而被广泛采用。然而，它通常是利用离线或重新排序的方式，因为它是耗时的计算。在线神经网络排序-所谓的快速神经排序-被认为是具有挑战性的，因为神经网络测量通常是非凸和不对称的。传统的近似最近邻(ANN)搜索通常侧重于度量排序度量，不适用于这些高级度量。本文提出了一种新的图搜索框架，以加快快速神经排序问题的搜索速度。提出的图搜索算法是双层的: 首先构造一个可能的候选集，然后对可能的候选集进行神经网络测度评估，而不是对所有邻居进行神经网络测度评估。具体来说，我们提出了一个基于梯度的算法，它近似于神经网络匹配得分的秩来构造可能的候选集; 并且我们提出了一个基于角度的启发式过程来自适应地识别可能的候选集的适当大小。公开数据的实验结果证实了算法的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=GUITAR:+Gradient+Pruning+toward+Fast+Neural+Ranking)|0| -|[Revisiting Document Expansion and Filtering for Effective First-Stage Retrieval](https://doi.org/10.1145/3626772.3657850)|Watheq Mansour, Shengyao Zhuang, Guido Zuccon, Joel Mackenzie|The University of Queensland, Brisbane, Australia; CSIRO, Brisbane, Australia|Document expansion is a technique that aims to reduce the likelihood of term mismatch by augmenting documents with related terms or queries. Doc2Query minus minus (Doc2Query-) represents an extension to the expansion process that uses a neural model to identify and remove expansions that may not be relevant to the given document, thereby increasing the quality of the ranking while simultaneously reducing the amount of augmented data. In this work, we conduct a detailed reproducibility study of Doc2Query- to better understand the trade-offs inherent to document expansion and filtering mechanisms. After successfully reproducing the best-performing method from the Doc2Query- family, we show that filtering actually harms recall-based metrics on various test collections. Next, we explore whether the two-stage "generate-then-filter" process can be replaced with a single generation phase via reinforcement learning. Finally, we extend our experimentation to learned sparse retrieval models and demonstrate that filtering is not helpful when term weights can be learned. Overall, our work provides a deeper understanding of the behaviour and characteristics of common document expansion mechanisms, and paves the way for developing more efficient yet effective augmentation models.|文档扩展是一种技术，旨在通过增加文档中的相关术语或查询来减少术语不匹配的可能性。Doc2Query 减号(Doc2Query -)表示扩展过程的一个扩展，它使用一个神经模型来识别和删除可能与给定文档无关的扩展，从而提高排名的质量，同时减少增强数据的数量。在这项工作中，我们对 Doc2Query 进行了详细的可重复性研究，以更好地理解文档扩展和过滤机制所固有的权衡。在成功复制了 Doc2Query 家族中性能最好的方法之后，我们展示了过滤实际上损害了各种测试集合中基于召回的度量。接下来，我们将探讨是否可以通过强化学习将两阶段的“生成-然后过滤”过程替换为单一生成阶段。最后，我们将实验扩展到学习的稀疏检索模型，并证明当词权可以学习时，过滤是没有帮助的。总的来说，我们的工作使人们对共同文件扩展机制的行为和特点有了更深入的了解，并为开发更有效率、更有效力的扩展模型铺平了道路。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Revisiting+Document+Expansion+and+Filtering+for+Effective+First-Stage+Retrieval)|0| -|[Simple but Effective Raw-Data Level Multimodal Fusion for Composed Image Retrieval](https://doi.org/10.1145/3626772.3657727)|Haokun Wen, Xuemeng Song, Xiaolin Chen, Yinwei Wei, Liqiang Nie, TatSeng Chua|Harbin Institute of Technology (Shenzhen) School of Computer Science and Technology; Harbin Institute of Technology (Shenzhen); Shandong University; Monash University; National University of Singapore; Shandong University School of Software, Joint SDU-NTU Centre for Artificial Intelligence Research|Composed image retrieval (CIR) aims to retrieve the target image based on amultimodal query, i.e., a reference image paired with correspondingmodification text. Recent CIR studies leverage vision-language pre-trained(VLP) methods as the feature extraction backbone, and perform nonlinearfeature-level multimodal query fusion to retrieve the target image. Despite thepromising performance, we argue that their nonlinear feature-level multimodalfusion may lead to the fused feature deviating from the original embeddingspace, potentially hurting the retrieval performance. To address this issue, inthis work, we propose shifting the multimodal fusion from the feature level tothe raw-data level to fully exploit the VLP model's multimodal encoding andcross-modal alignment abilities. In particular, we introduce a Dual QueryUnification-based Composed Image Retrieval framework (DQU-CIR), whose backbonesimply involves a VLP model's image encoder and a text encoder. Specifically,DQU-CIR first employs two training-free query unification components:text-oriented query unification and vision-oriented query unification, toderive a unified textual and visual query based on the raw data of themultimodal query, respectively. The unified textual query is derived byconcatenating the modification text with the extracted reference image'stextual description, while the unified visual query is created by writing thekey modification words onto the reference image. Ultimately, to address diversesearch intentions, DQU-CIR linearly combines the features of the two unifiedqueries encoded by the VLP model to retrieve the target image. Extensiveexperiments on four real-world datasets validate the effectiveness of ourproposed method.|复合图像检索(CIR)是一种基于多模态查询的目标图像检索方法。近年来，CIR 研究利用视觉语言预训练(VLP)方法作为特征提取骨干，进行非线性特征级多模态查询融合来检索目标图像。尽管它们具有良好的性能，但是它们的非线性特征级多模融合可能导致融合特征偏离原始嵌入空间，从而影响检索性能。为了解决这一问题，本文提出将多模态融合从特征层次转移到原始数据层次，以充分发挥 VLP 模型的多模态编码和跨模态对齐能力。特别地，我们介绍了一个基于双查询统一的复合图像检索框架(DQU-CIR) ，它的主干包括 VLP 模型的图像编码器和文本编码器。具体来说，DQU-CIR 首先使用两个无需训练的查询统一组件: 面向文本的查询统一和面向视觉的查询统一，分别基于多模态查询的原始数据得到一个统一的文本查询和可视化查询。通过将修改后的文本与提取出的参考图像的文本描述连接起来，得到统一的文本查询; 通过将关键修改词写入参考图像，生成统一的可视化查询。最终，为了解决多样化研究的意图，DQU-CIR 将 VLP 模型编码的两个统一查询的特征线性地结合起来以检索目标图像。通过对四个实际数据集的大量实验验证了本文方法的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Simple+but+Effective+Raw-Data+Level+Multimodal+Fusion+for+Composed+Image+Retrieval)|0| +|[Revisiting Document Expansion and Filtering for Effective First-Stage Retrieval](https://doi.org/10.1145/3626772.3657850)|Watheq Mansour, Shengyao Zhuang, Guido Zuccon, Joel Mackenzie|CSIRO, Brisbane, Australia; The University of Queensland, Brisbane, Australia|Document expansion is a technique that aims to reduce the likelihood of term mismatch by augmenting documents with related terms or queries. Doc2Query minus minus (Doc2Query-) represents an extension to the expansion process that uses a neural model to identify and remove expansions that may not be relevant to the given document, thereby increasing the quality of the ranking while simultaneously reducing the amount of augmented data. In this work, we conduct a detailed reproducibility study of Doc2Query- to better understand the trade-offs inherent to document expansion and filtering mechanisms. After successfully reproducing the best-performing method from the Doc2Query- family, we show that filtering actually harms recall-based metrics on various test collections. Next, we explore whether the two-stage "generate-then-filter" process can be replaced with a single generation phase via reinforcement learning. Finally, we extend our experimentation to learned sparse retrieval models and demonstrate that filtering is not helpful when term weights can be learned. Overall, our work provides a deeper understanding of the behaviour and characteristics of common document expansion mechanisms, and paves the way for developing more efficient yet effective augmentation models.|文档扩展是一种技术，旨在通过增加文档中的相关术语或查询来减少术语不匹配的可能性。Doc2Query 减号(Doc2Query -)表示扩展过程的一个扩展，它使用一个神经模型来识别和删除可能与给定文档无关的扩展，从而提高排名的质量，同时减少增强数据的数量。在这项工作中，我们对 Doc2Query 进行了详细的可重复性研究，以更好地理解文档扩展和过滤机制所固有的权衡。在成功复制了 Doc2Query 家族中性能最好的方法之后，我们展示了过滤实际上损害了各种测试集合中基于召回的度量。接下来，我们将探讨是否可以通过强化学习将两阶段的“生成-然后过滤”过程替换为单一生成阶段。最后，我们将实验扩展到学习的稀疏检索模型，并证明当词权可以学习时，过滤是没有帮助的。总的来说，我们的工作使人们对共同文件扩展机制的行为和特点有了更深入的了解，并为开发更有效率、更有效力的扩展模型铺平了道路。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Revisiting+Document+Expansion+and+Filtering+for+Effective+First-Stage+Retrieval)|0| +|[Simple but Effective Raw-Data Level Multimodal Fusion for Composed Image Retrieval](https://doi.org/10.1145/3626772.3657727)|Haokun Wen, Xuemeng Song, Xiaolin Chen, Yinwei Wei, Liqiang Nie, TatSeng Chua|Shandong University; Monash University; Harbin Institute of Technology (Shenzhen); Shandong University School of Software, Joint SDU-NTU Centre for Artificial Intelligence Research; National University of Singapore; Harbin Institute of Technology (Shenzhen) School of Computer Science and Technology|Composed image retrieval (CIR) aims to retrieve the target image based on amultimodal query, i.e., a reference image paired with correspondingmodification text. Recent CIR studies leverage vision-language pre-trained(VLP) methods as the feature extraction backbone, and perform nonlinearfeature-level multimodal query fusion to retrieve the target image. Despite thepromising performance, we argue that their nonlinear feature-level multimodalfusion may lead to the fused feature deviating from the original embeddingspace, potentially hurting the retrieval performance. To address this issue, inthis work, we propose shifting the multimodal fusion from the feature level tothe raw-data level to fully exploit the VLP model's multimodal encoding andcross-modal alignment abilities. In particular, we introduce a Dual QueryUnification-based Composed Image Retrieval framework (DQU-CIR), whose backbonesimply involves a VLP model's image encoder and a text encoder. Specifically,DQU-CIR first employs two training-free query unification components:text-oriented query unification and vision-oriented query unification, toderive a unified textual and visual query based on the raw data of themultimodal query, respectively. The unified textual query is derived byconcatenating the modification text with the extracted reference image'stextual description, while the unified visual query is created by writing thekey modification words onto the reference image. Ultimately, to address diversesearch intentions, DQU-CIR linearly combines the features of the two unifiedqueries encoded by the VLP model to retrieve the target image. Extensiveexperiments on four real-world datasets validate the effectiveness of ourproposed method.|复合图像检索(CIR)是一种基于多模态查询的目标图像检索方法。近年来，CIR 研究利用视觉语言预训练(VLP)方法作为特征提取骨干，进行非线性特征级多模态查询融合来检索目标图像。尽管它们具有良好的性能，但是它们的非线性特征级多模融合可能导致融合特征偏离原始嵌入空间，从而影响检索性能。为了解决这一问题，本文提出将多模态融合从特征层次转移到原始数据层次，以充分发挥 VLP 模型的多模态编码和跨模态对齐能力。特别地，我们介绍了一个基于双查询统一的复合图像检索框架(DQU-CIR) ，它的主干包括 VLP 模型的图像编码器和文本编码器。具体来说，DQU-CIR 首先使用两个无需训练的查询统一组件: 面向文本的查询统一和面向视觉的查询统一，分别基于多模态查询的原始数据得到一个统一的文本查询和可视化查询。通过将修改后的文本与提取出的参考图像的文本描述连接起来，得到统一的文本查询; 通过将关键修改词写入参考图像，生成统一的可视化查询。最终，为了解决多样化研究的意图，DQU-CIR 将 VLP 模型编码的两个统一查询的特征线性地结合起来以检索目标图像。通过对四个实际数据集的大量实验验证了本文方法的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Simple+but+Effective+Raw-Data+Level+Multimodal+Fusion+for+Composed+Image+Retrieval)|0| |[Browsing and Searching Metadata of TREC](https://doi.org/10.1145/3626772.3657873)|Timo Breuer, Ellen M. Voorhees, Ian Soboroff|National Institute of Standards and Technology, Gaithersburg, MD, USA|Information Retrieval (IR) research is deeply rooted in experimentation and evaluation, and the Text REtrieval Conference (TREC) has been playing a central role in making that possible since its inauguration in 1992. TREC's mission centers around providing the infrastructure and resources to make IR evaluations possible at scale. Over the years, a plethora of different retrieval problems were addressed, culminating in data artifacts that remained as valuable and useful tools for the IR community. Even though the data are largely available from TREC's website, there is currently no resource that facilitates a cohesive way to obtain metadata information about the run file - the IR community's de-facto standard data format for storing rankings of system-oriented IR experiments. To this end, the work at hand introduces a software suite that facilitates access to metadata of experimental resources, resulting from over 30 years of IR experiments and evaluations at TREC. With a particular focus on the run files, the paper motivates the requirements for better access to TREC metadata and details the concepts, the resources, the corresponding implementations, and possible use cases. More specifically, we contribute a web interface to browse former TREC submissions. Besides, we provide the underlying metadatabase and a corresponding RESTful interface for more principled and structured queries about the TREC metadata.|信息检索研究深深植根于实验和评估，而文本检索会议(TREC)自1992年成立以来，一直在实现这一目标方面发挥着核心作用。TREC 的任务主要是提供基础设施和资源，使大规模的 IR 评估成为可能。多年来，大量不同的检索问题得到了解决，最终形成了数据工件，这些工件仍然是 IR 社区宝贵而有用的工具。尽管这些数据大部分可以从 TREC 的网站上获得，但是目前还没有资源能够帮助我们获得关于运行文件的元数据信息——这是 IR 社区用于存储面向系统的 IR 实验排名的事实上的标准数据格式。为此，手头的工作介绍了一个软件套件，促进访问实验资源的元数据，这是在 TREC 超过30年的 IR 实验和评估的结果。本文特别关注运行文件，激发了更好地访问 TREC 元数据的需求，并详细介绍了概念、资源、相应的实现和可能的用例。更具体地说，我们贡献了一个网络界面来浏览以前的 TREC 提交。此外，我们还提供了底层的元数据库和相应的 RESTful 接口，用于针对 TREC 元数据的更原则化和结构化的查询。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Browsing+and+Searching+Metadata+of+TREC)|0| -|[ACORDAR 2.0: A Test Collection for Ad Hoc Dataset Retrieval with Densely Pooled Datasets and Question-Style Queries](https://doi.org/10.1145/3626772.3657866)|Qiaosheng Chen, Weiqing Luo, Zixian Huang, Tengteng Lin, Xiaxia Wang, Ahmet Soylu, Basil Ell, Baifan Zhou, Evgeny Kharlamov, Gong Cheng|Bosch Center for Artificial Intelligence & University of Oslo, Renningen, Germany; Bielefeld University & University of Oslo, Bielefeld, Germany; OsloMet - Oslo Metropolitan University, Oslo, Norway; State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, Jiangsu, China; University of Oxford, Oxford, United Kingdom; OsloMet - Oslo Metropolitan University & University of Oslo, Oslo, Norway|Dataset search, or more specifically, ad hoc dataset retrieval which is a trending specialized IR task, has received increasing attention in both academia and industry. While methods and systems continue evolving, existing test collections for this task exhibit shortcomings, particularly suffering from lexical bias in pooling and limited to keyword-style queries for evaluation. To address these limitations, in this paper, we construct ACORDAR 2.0, a new test collection for this task which is also the largest to date. To reduce lexical bias in pooling, we adapt dense retrieval models to large structured data, using them to find an extended set of semantically relevant datasets to be annotated. To diversify query forms, we employ a large language model to rewrite keyword queries into high-quality question-style queries. We use the test collection to evaluate popular sparse and dense retrieval models to establish a baseline for future studies. The test collection and source code are publicly available.|数据集搜索，或者更具体地说，即特定数据集检索，作为一种趋势性的专业信息检索任务，已经受到学术界和工业界越来越多的关注。虽然方法和系统仍在不断发展，但现有的测试集合显示出缺陷，特别是在池中存在词法偏差，并且仅限于用于评估的关键字样式查询。为了解决这些局限性，在本文中，我们构建了 ACORDAR 2.0，一个新的测试集合来完成这个任务，这也是迄今为止最大的一个测试集合。为了减少池中的词汇偏差，我们将密集检索模型适用于大型结构化数据，使用它们来寻找一组扩展的语义相关数据集来进行注释。为了使查询表单多样化，我们使用了一个大型语言模型来将关键字查询重写为高质量的问题样式查询。我们使用测试集来评估流行的稀疏和密集的检索模型，为未来的研究建立基线。测试集合和源代码是公开可用的。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=ACORDAR+2.0:+A+Test+Collection+for+Ad+Hoc+Dataset+Retrieval+with+Densely+Pooled+Datasets+and+Question-Style+Queries)|0| +|[ACORDAR 2.0: A Test Collection for Ad Hoc Dataset Retrieval with Densely Pooled Datasets and Question-Style Queries](https://doi.org/10.1145/3626772.3657866)|Qiaosheng Chen, Weiqing Luo, Zixian Huang, Tengteng Lin, Xiaxia Wang, Ahmet Soylu, Basil Ell, Baifan Zhou, Evgeny Kharlamov, Gong Cheng|Bosch Center for Artificial Intelligence & University of Oslo, Renningen, Germany; OsloMet - Oslo Metropolitan University & University of Oslo, Oslo, Norway; Bielefeld University & University of Oslo, Bielefeld, Germany; OsloMet - Oslo Metropolitan University, Oslo, Norway; University of Oxford, Oxford, United Kingdom; State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, Jiangsu, China|Dataset search, or more specifically, ad hoc dataset retrieval which is a trending specialized IR task, has received increasing attention in both academia and industry. While methods and systems continue evolving, existing test collections for this task exhibit shortcomings, particularly suffering from lexical bias in pooling and limited to keyword-style queries for evaluation. To address these limitations, in this paper, we construct ACORDAR 2.0, a new test collection for this task which is also the largest to date. To reduce lexical bias in pooling, we adapt dense retrieval models to large structured data, using them to find an extended set of semantically relevant datasets to be annotated. To diversify query forms, we employ a large language model to rewrite keyword queries into high-quality question-style queries. We use the test collection to evaluate popular sparse and dense retrieval models to establish a baseline for future studies. The test collection and source code are publicly available.|数据集搜索，或者更具体地说，即特定数据集检索，作为一种趋势性的专业信息检索任务，已经受到学术界和工业界越来越多的关注。虽然方法和系统仍在不断发展，但现有的测试集合显示出缺陷，特别是在池中存在词法偏差，并且仅限于用于评估的关键字样式查询。为了解决这些局限性，在本文中，我们构建了 ACORDAR 2.0，一个新的测试集合来完成这个任务，这也是迄今为止最大的一个测试集合。为了减少池中的词汇偏差，我们将密集检索模型适用于大型结构化数据，使用它们来寻找一组扩展的语义相关数据集来进行注释。为了使查询表单多样化，我们使用了一个大型语言模型来将关键字查询重写为高质量的问题样式查询。我们使用测试集来评估流行的稀疏和密集的检索模型，为未来的研究建立基线。测试集合和源代码是公开可用的。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=ACORDAR+2.0:+A+Test+Collection+for+Ad+Hoc+Dataset+Retrieval+with+Densely+Pooled+Datasets+and+Question-Style+Queries)|0| |[Reinforcement Learning-based Recommender Systems with Large Language Models for State Reward and Action Modeling](https://doi.org/10.1145/3626772.3657767)|Jie Wang, Alexandros Karatzoglou, Ioannis Arapakis, Joemon M. Jose|University of Glasgow; Telefonica Research; Google|Reinforcement Learning (RL)-based recommender systems have demonstratedpromising performance in meeting user expectations by learning to make accuratenext-item recommendations from historical user-item interactions. However,existing offline RL-based sequential recommendation methods face the challengeof obtaining effective user feedback from the environment. Effectively modelingthe user state and shaping an appropriate reward for recommendation remains achallenge. In this paper, we leverage language understanding capabilities andadapt large language models (LLMs) as an environment (LE) to enhance RL-basedrecommenders. The LE is learned from a subset of user-item interaction data,thus reducing the need for large training data, and can synthesise userfeedback for offline data by: (i) acting as a state model that produces highquality states that enrich the user representation, and (ii) functioning as areward model to accurately capture nuanced user preferences on actions.Moreover, the LE allows to generate positive actions that augment the limitedoffline training data. We propose a LE Augmentation (LEA) method to furtherimprove recommendation performance by optimising jointly the supervisedcomponent and the RL policy, using the augmented actions and historical usersignals. We use LEA, the state and reward models in conjunction withstate-of-the-art RL recommenders and report experimental results on twopublicly available datasets.|基于强化学习(rL)的推荐系统通过学习从历史的用户-项目交互中做出准确的下一项推荐，在满足用户期望方面展示了良好的性能。然而，现有的基于离线 RL 的顺序推荐方法面临着从环境中获得有效用户反馈的挑战。有效地建模用户状态并为推荐建立适当的奖励仍然是一个挑战。在本文中，我们利用语言理解能力并将大型语言模型(LLM)作为一个环境(LE)来增强基于 RL 的推荐器。LE 是从用户项目交互数据的子集中学习的，从而减少了对大量训练数据的需求，并且可以通过以下方式综合用户对离线数据的反馈: (i)作为产生丰富用户表示的高质量状态的状态模型，以及(ii)作为奖励模型来准确捕获细微差别的用户行动偏好。此外，LE 允许产生积极的行动，以增加有限的离线训练数据。提出了一种基于增强动作和历史用户信号的 LEA 方法，通过联合优化被监控组件和 RL 策略，进一步提高推荐性能。我们使用 LEA，状态和奖励模型与最先进的 RL 推荐程序结合使用，并在两个公开可用的数据集上报告实验结果。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Reinforcement+Learning-based+Recommender+Systems+with+Large+Language+Models+for+State+Reward+and+Action+Modeling)|0| |[OpenP5: An Open-Source Platform for Developing, Training, and Evaluating LLM-based Recommender Systems](https://doi.org/10.1145/3626772.3657883)|Shuyuan Xu, Wenyue Hua, Yongfeng Zhang|Rutgers University|In recent years, the integration of Large Language Models (LLMs) intorecommender systems has garnered interest among both practitioners andresearchers. Despite this interest, the field is still emerging, and the lackof open-source R D platforms may impede the exploration of LLM-basedrecommendations. This paper introduces OpenP5, an open-source platform designedas a resource to facilitate the development, training, and evaluation ofLLM-based generative recommender systems for research purposes. The platform isimplemented using encoder-decoder LLMs (e.g., T5) and decoder-only LLMs (e.g.,Llama-2) across 10 widely recognized public datasets, catering to twofundamental recommendation tasks: sequential and straightforwardrecommendations. Recognizing the crucial role of item IDs in LLM-basedrecommendations, we have also incorporated three item indexing methods withinthe OpenP5 platform: random indexing, sequential indexing and collaborativeindexing. Built on the Transformers library, the platform facilitates easycustomization of LLM-based recommendations for users. OpenP5 boasts a range offeatures including extensible data processing, task-centric optimization,comprehensive datasets and checkpoints, efficient acceleration, andstandardized evaluations, making it a valuable tool for the implementation andevaluation of LLM-based recommender systems. The open-source code andpre-trained checkpoints for the OpenP5 library are publicly available athttps://github.com/agiresearch/OpenP5.|近年来，将大语言模型(LLM)集成到推荐系统中引起了从业者和研究者的兴趣。尽管有这样的兴趣，该领域仍然在兴起，缺乏开源的研发平台可能会阻碍对基于 LLM 的建议的探索。本文介绍了 OpenP5，这是一个开源平台，设计了一个资源，用于促进基于 LLM 的生成式推荐系统的开发、培训和评估，以供研究之用。该平台使用编码器-解码器 LLM (例如 T5)和解码器-纯 LLM (例如 Llama-2)在10个广泛认可的公共数据集中实现，满足两个基本的推荐任务: 顺序和直接的推荐。认识到项目 ID 在基于 LLM 的推荐中的关键作用，我们还在 OpenP5平台中引入了三种项目索引方法: 随机索引、顺序索引和协作索引。该平台建立在变形金刚库的基础上，便于用户轻松定制基于 LLM 的推荐。OpenP5拥有一系列功能，包括可扩展的数据处理、以任务为中心的优化、全面的数据集和检查点、高效的加速和标准化的评估，使其成为基于 LLM 的推荐系统的实现和评估的有价值的工具。Openp5库的开源代码和经过预先训练的检查点可以通过 https:// github.com/agiresearch/openp5公开获得。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=OpenP5:+An+Open-Source+Platform+for+Developing,+Training,+and+Evaluating+LLM-based+Recommender+Systems)|0| |[Fair Recommendations with Limited Sensitive Attributes: A Distributionally Robust Optimization Approach](https://doi.org/10.1145/3626772.3657822)|Tianhao Shi, Yang Zhang, Jizhi Zhang, Fuli Feng, Xiangnan He|University of Science and Technology of China; university of science and technology of china|As recommender systems are indispensable in various domains such as jobsearching and e-commerce, providing equitable recommendations to users withdifferent sensitive attributes becomes an imperative requirement. Priorapproaches for enhancing fairness in recommender systems presume theavailability of all sensitive attributes, which can be difficult to obtain dueto privacy concerns or inadequate means of capturing these attributes. Inpractice, the efficacy of these approaches is limited, pushing us toinvestigate ways of promoting fairness with limited sensitive attributeinformation. Toward this goal, it is important to reconstruct missing sensitiveattributes. Nevertheless, reconstruction errors are inevitable due to thecomplexity of real-world sensitive attribute reconstruction problems and legalregulations. Thus, we pursue fair learning methods that are robust toreconstruction errors. To this end, we propose Distributionally Robust FairOptimization (DRFO), which minimizes the worst-case unfairness over allpotential probability distributions of missing sensitive attributes instead ofthe reconstructed one to account for the impact of the reconstruction errors.We provide theoretical and empirical evidence to demonstrate that our methodcan effectively ensure fairness in recommender systems when only limitedsensitive attributes are accessible.|由于推荐系统在求职搜索和电子商务等各个领域都是不可或缺的，因此向具有不同敏感特征的用户提供公平的推荐成为一项必要的要求。提高推荐系统公平性的先验方法假定所有敏感属性的可用性，由于隐私问题或捕获这些属性的手段不足，这些属性可能难以获得。在实践中，这些方法的效果是有限的，促使我们研究的方法，以促进公平与有限的敏感属性信息。为了实现这个目标，重新构建缺失的敏感属性非常重要。然而，由于现实世界敏感属性重构问题和法律规定的复杂性，重构错误是不可避免的。因此，我们追求对重构错误具有鲁棒性的公平学习方法。为此，我们提出了分布式鲁棒公平优化(DRFO)方法，该方法将缺失敏感属性的所有潜在概率分布的最坏情况不公平性最小化，而不是通过重构来考虑重构错误的影响。我们提供了理论和经验证明来证明我们的方法可以有效地确保推荐系统的公平性，当只有有限的敏感属性可以访问时。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Fair+Recommendations+with+Limited+Sensitive+Attributes:+A+Distributionally+Robust+Optimization+Approach)|0| @@ -196,106 +196,106 @@ |[Planning Ahead in Generative Retrieval: Guiding Autoregressive Generation through Simultaneous Decoding](https://doi.org/10.1145/3626772.3657746)|Hansi Zeng, Chen Luo, Hamed Zamani|Amazon; University of Massachusetts Amherst|This paper introduces PAG-a novel optimization and decoding approach that guides autoregressive generation of document identifiers in generative retrieval models through simultaneous decoding. To this aim, PAG constructs a set-based and sequential identifier for each document. Motivated by the bag-of-words assumption in information retrieval, the set-based identifier is built on lexical tokens. The sequential identifier, on the other hand, is obtained via quantizing relevance-based representations of documents. Extensive experiments on MSMARCO and TREC Deep Learning Track data reveal that PAG outperforms the state-of-the-art generative retrieval model by a large margin (e.g., 15.6% MRR improvements on MS MARCO), while achieving 22x speed up in terms of query latency.|本文介绍了一种新的优化和解码方法 PAG，它通过同时解码引导生成检索模型中文档标识符的自回归生成。为此，PAG 为每个文档构造一个基于集合的顺序标识符。由于信息检索中的词袋假设，基于集合的标识符建立在词汇标记之上。序列标识符则是通过量化基于相关性的文档表示来获得的。对 MSMARCO 和 TREC 深度学习跟踪数据的广泛实验表明，PAG 比最先进的生成检索模型有很大优势(例如，对 MS MARCO 的 MRR 改进为15.6%) ，同时在查询延迟方面提高了22倍的速度。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Planning+Ahead+in+Generative+Retrieval:+Guiding+Autoregressive+Generation+through+Simultaneous+Decoding)|0| |[Course Recommender Systems Need to Consider the Job Market](https://doi.org/10.1145/3626772.3657847)|Jibril Frej, Anna Dai, Syrielle Montariol, Antoine Bosselut, Tanja Käser|EPFL|Current course recommender systems primarily leverage learner-courseinteractions, course content, learner preferences, and supplementary coursedetails like instructor, institution, ratings, and reviews, to make theirrecommendation. However, these systems often overlook a critical aspect: theevolving skill demand of the job market. This paper focuses on the perspectiveof academic researchers, working in collaboration with the industry, aiming todevelop a course recommender system that incorporates job market skill demands.In light of the job market's rapid changes and the current state of research incourse recommender systems, we outline essential properties for courserecommender systems to address these demands effectively, includingexplainable, sequential, unsupervised, and aligned with the job market anduser's goals. Our discussion extends to the challenges and research questionsthis objective entails, including unsupervised skill extraction from joblistings, course descriptions, and resumes, as well as predictingrecommendations that align with learner objectives and the job market anddesigning metrics to evaluate this alignment. Furthermore, we introduce aninitial system that addresses some existing limitations of course recommendersystems using large Language Models (LLMs) for skill extraction andReinforcement Learning (RL) for alignment with the job market. We provideempirical results using open-source data to demonstrate its effectiveness.|当前的课程推荐系统主要利用学习者-课程互动、课程内容、学习者偏好和补充课程细节(如教师、机构、评分和评论)来进行推荐。然而，这些系统往往忽略了一个关键方面: 就业市场不断变化的技能需求。本文聚焦于学术研究人员的视角，与行业合作，旨在开发一个包含就业市场技能需求的课程推荐系统。鉴于就业市场的快速变化和研究课程推荐系统的现状，我们概述了课程/推荐系统有效满足这些需求的基本属性，包括可解释的、有序的、无监督的、与就业市场和用户目标一致的。我们的讨论延伸到这一目标所涉及的挑战和研究问题，包括从工作列表中无监督地提取技能，课程描述和简历，以及预测建议，与学习者的目标和就业市场和设计指标来评估这种一致性。此外，我们还介绍了一个初始系统，该系统解决了课程推荐系统的一些现有局限性，它使用大型语言模型(LLM)来提取技能，使用强化学习(RL)来与就业市场保持一致。我们使用开源数据提供了实证结果来证明其有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Course+Recommender+Systems+Need+to+Consider+the+Job+Market)|0| |[Leave No Patient Behind: Enhancing Medication Recommendation for Rare Disease Patients](https://doi.org/10.1145/3626772.3657785)|Zihao Zhao, Yi Jing, Fuli Feng, Jiancan Wu, Chongming Gao, Xiangnan He|University of Science and Technology of China|Medication recommendation systems have gained significant attention inhealthcare as a means of providing tailored and effective drug combinationsbased on patients' clinical information. However, existing approaches oftensuffer from fairness issues, as recommendations tend to be more accurate forpatients with common diseases compared to those with rare conditions. In thispaper, we propose a novel model called Robust and Accurate REcommendations forMedication (RAREMed), which leverages the pretrain-finetune learning paradigmto enhance accuracy for rare diseases. RAREMed employs a transformer encoderwith a unified input sequence approach to capture complex relationships amongdisease and procedure codes. Additionally, it introduces two self-supervisedpre-training tasks, namely Sequence Matching Prediction (SMP) and SelfReconstruction (SR), to learn specialized medication needs and interrelationsamong clinical codes. Experimental results on two real-world datasetsdemonstrate that RAREMed provides accurate drug sets for both rare and commondisease patients, thereby mitigating unfairness in medication recommendationsystems.|药物推荐系统作为一种根据患者临床信息提供量身定制的有效药物组合的手段，在医疗保健领域引起了广泛的关注。然而，现有的治疗方法往往存在公平性问题，因为与罕见疾病患者相比，常见疾病患者的治疗建议往往更为准确。在本文中，我们提出了一个新的模型，称为健壮和准确的推荐用药(RAREMed) ，它利用预训练微调学习范式，以提高准确性的罕见疾病。RAREMed 使用了一个具有统一输入序列方法的变压器编码器来捕获疾病和程序代码之间的复杂关系。此外，还引入了序列匹配预测(SMP)和自我重构(SR)两个自我监督的训练前任务，以了解专业用药需求和临床规范之间的相互关系。在两个真实世界数据集上的实验结果表明，RAREMed 为罕见和常见疾病患者提供了准确的药物组合，从而减轻了药物推荐系统中的不公平性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Leave+No+Patient+Behind:+Enhancing+Medication+Recommendation+for+Rare+Disease+Patients)|0| -|[MIND Your Language: A Multilingual Dataset for Cross-lingual News Recommendation](https://doi.org/10.1145/3626772.3657867)|Andreea Iana, Goran Glavas, Heiko Paulheim|University of Mannheim; University of Würzburg|Digital news platforms use news recommenders as the main instrument to caterto the individual information needs of readers. Despite an increasinglylanguage-diverse online community, in which many Internet users consume news inmultiple languages, the majority of news recommendation focuses on major,resource-rich languages, and English in particular. Moreover, nearly all newsrecommendation efforts assume monolingual news consumption, whereas more andmore users tend to consume information in at least two languages. Accordingly,the existing body of work on news recommendation suffers from a lack ofpublicly available multilingual benchmarks that would catalyze development ofnews recommenders effective in multilingual settings and for low-resourcelanguages. Aiming to fill this gap, we introduce xMIND, an open, multilingualnews recommendation dataset derived from the English MIND dataset using machinetranslation, covering a set of 14 linguistically and geographically diverselanguages, with digital footprints of varying sizes. Using xMIND, wesystematically benchmark several state-of-the-art content-based neural newsrecommenders (NNRs) in both zero-shot (ZS-XLT) and few-shot (FS-XLT)cross-lingual transfer scenarios, considering both monolingual and bilingualnews consumption patterns. Our findings reveal that (i) current NNRs, even whenbased on a multilingual language model, suffer from substantial performancelosses under ZS-XLT and that (ii) inclusion of target-language data in FS-XLTtraining has limited benefits, particularly when combined with a bilingual newsconsumption. Our findings thus warrant a broader research effort inmultilingual and cross-lingual news recommendation. The xMIND dataset isavailable at https://github.com/andreeaiana/xMIND.|数字新闻平台以新闻推荐为主要工具，满足读者的个性化信息需求。尽管网络社区的语言越来越多样化，许多互联网用户使用多种语言阅读新闻，但大多数新闻推荐都集中在主要的、资源丰富的语言上，尤其是英语。此外，几乎所有的新闻推荐都假定消费者只使用一种语言，而越来越多的用户倾向于使用至少两种语言的信息。因此，关于新闻推荐的现有工作缺乏公开提供的多语种基准，这些基准将促进在多语种环境和低资源语言中有效发展新闻推荐人。为了填补这个空白，我们引入了 xMIND，一个开放的，多语言新闻推荐数据集，它来自英语 MIND 数据集，使用机器翻译，涵盖了14种语言和地理上的不同语言，数字足迹大小不一。使用 xMIND，我们系统地基准几个国家的最先进的基于内容的神经新闻推荐器(NNR)在零拍(ZS-XLT)和少拍(FS-XLT)跨语言传输场景，考虑到单语和双语新闻消费模式。我们的研究结果显示，(i)目前的 NNR，即使基于多语言模型，在 ZS-XLT 下也遭受了显着的性能损失，并且(ii)在 FS-XLTtraining 中包含目标语言数据的益处有限，特别是与双语新闻消费相结合时。因此，我们的研究结果值得在多语言和跨语言的新闻推荐更广泛的研究工作。XMIND 数据集可在 https://github.com/andreeaiana/xMIND 下载。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=MIND+Your+Language:+A+Multilingual+Dataset+for+Cross-lingual+News+Recommendation)|0| +|[MIND Your Language: A Multilingual Dataset for Cross-lingual News Recommendation](https://doi.org/10.1145/3626772.3657867)|Andreea Iana, Goran Glavas, Heiko Paulheim|University of Würzburg; University of Mannheim|Digital news platforms use news recommenders as the main instrument to caterto the individual information needs of readers. Despite an increasinglylanguage-diverse online community, in which many Internet users consume news inmultiple languages, the majority of news recommendation focuses on major,resource-rich languages, and English in particular. Moreover, nearly all newsrecommendation efforts assume monolingual news consumption, whereas more andmore users tend to consume information in at least two languages. Accordingly,the existing body of work on news recommendation suffers from a lack ofpublicly available multilingual benchmarks that would catalyze development ofnews recommenders effective in multilingual settings and for low-resourcelanguages. Aiming to fill this gap, we introduce xMIND, an open, multilingualnews recommendation dataset derived from the English MIND dataset using machinetranslation, covering a set of 14 linguistically and geographically diverselanguages, with digital footprints of varying sizes. Using xMIND, wesystematically benchmark several state-of-the-art content-based neural newsrecommenders (NNRs) in both zero-shot (ZS-XLT) and few-shot (FS-XLT)cross-lingual transfer scenarios, considering both monolingual and bilingualnews consumption patterns. Our findings reveal that (i) current NNRs, even whenbased on a multilingual language model, suffer from substantial performancelosses under ZS-XLT and that (ii) inclusion of target-language data in FS-XLTtraining has limited benefits, particularly when combined with a bilingual newsconsumption. Our findings thus warrant a broader research effort inmultilingual and cross-lingual news recommendation. The xMIND dataset isavailable at https://github.com/andreeaiana/xMIND.|数字新闻平台以新闻推荐为主要工具，满足读者的个性化信息需求。尽管网络社区的语言越来越多样化，许多互联网用户使用多种语言阅读新闻，但大多数新闻推荐都集中在主要的、资源丰富的语言上，尤其是英语。此外，几乎所有的新闻推荐都假定消费者只使用一种语言，而越来越多的用户倾向于使用至少两种语言的信息。因此，关于新闻推荐的现有工作缺乏公开提供的多语种基准，这些基准将促进在多语种环境和低资源语言中有效发展新闻推荐人。为了填补这个空白，我们引入了 xMIND，一个开放的，多语言新闻推荐数据集，它来自英语 MIND 数据集，使用机器翻译，涵盖了14种语言和地理上的不同语言，数字足迹大小不一。使用 xMIND，我们系统地基准几个国家的最先进的基于内容的神经新闻推荐器(NNR)在零拍(ZS-XLT)和少拍(FS-XLT)跨语言传输场景，考虑到单语和双语新闻消费模式。我们的研究结果显示，(i)目前的 NNR，即使基于多语言模型，在 ZS-XLT 下也遭受了显着的性能损失，并且(ii)在 FS-XLTtraining 中包含目标语言数据的益处有限，特别是与双语新闻消费相结合时。因此，我们的研究结果值得在多语言和跨语言的新闻推荐更广泛的研究工作。XMIND 数据集可在 https://github.com/andreeaiana/xMIND 下载。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=MIND+Your+Language:+A+Multilingual+Dataset+for+Cross-lingual+News+Recommendation)|0| |[Steering Large Language Models for Cross-lingual Information Retrieval](https://doi.org/10.1145/3626772.3657819)|Ping Guo, Yubing Ren, Yue Hu, Yanan Cao, Yunpeng Li, Heyan Huang|; Institute of Information Engineering, Chinese Academy of Sciences; Beijing Institute of Technology, Beijing, China|In today's digital age, accessing information across language barriers poses a significant challenge, with conventional search systems often struggling to interpret and retrieve multilingual content accurately. Addressing this issue, our study introduces a novel integration of applying Large Language Models (LLMs) as Cross-lingual Readers in information retrieval systems, specifically targeting the complexities of cross-lingual information retrieval (CLIR). We present an innovative approach: Activation Steered Multilingual Retrieval (ASMR) that employs "steering activations''-a method to adjust and direct the LLM's focus-enhancing its ability to understand user queries and generate accurate, language-coherent responses. ASMR adeptly combines a Multilingual Dense Passage Retrieval (mDPR) system with an LLM, overcoming the limitations of traditional search engines in handling diverse linguistic inputs. This approach is particularly effective in managing the nuances and intricacies inherent in various languages. Rigorous testing on established benchmarks such as XOR-TyDi QA, and MKQA demonstrates that ASMR not only meets but surpasses existing standards in CLIR, achieving state-of-the-art performance. The results of our research hold significant implications for understanding the inherent features of how LLMs understand and generate natural languages, offering an attempt towards more inclusive, effective, and linguistically diverse information access on a global scale.|在当今的数字时代，跨越语言障碍获取信息构成了重大挑战，传统的搜索系统往往难以准确地解释和检索多语种内容。针对这个问题，我们的研究介绍了一个新的整合应用大语言模型(LLMs)作为跨语言读者在信息检索系统，特别是针对跨语言信息检索(CLIR)的复杂性。我们提出了一个创新的方法: 激活导向多语言检索(ASMR) ，采用“导向激活”-一种方法来调整和指导 LLM 的重点-增强其能力，理解用户的查询，并产生准确的，语言一致的反应。ASMR 巧妙地将多语言密集通道检索(mDPR)系统与 LLM 相结合，克服了传统搜索引擎在处理多种语言输入时的局限性。这种方法在管理各种语言固有的细微差别和复杂性方面特别有效。对 XOR-tyDi QA 和 MKQA 等既定基准的严格测试表明，ASMR 不仅符合而且超越了 CLIR 现有的标准，实现了最先进的性能。我们的研究结果对于理解 LLM 理解和生成自然语言的内在特征具有重要意义，为在全球范围内获取更具包容性、有效性和语言多样性的信息提供了一种尝试。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Steering+Large+Language+Models+for+Cross-lingual+Information+Retrieval)|0| |[DAC: Quantized Optimal Transport Reward-based Reinforcement Learning Approach to Detoxify Query Auto-Completion](https://doi.org/10.1145/3626772.3657779)|Aishwarya Maheswaran, Kaushal Kumar Maurya, Manish Gupta, Maunendra Sankar Desarkar|Microsoft Corporation, Hyderabad, India; Indian Institute of Technology Hyderabad, Hyderabad, India|Modern Query Auto-Completion (QAC) systems utilize natural language generation (NLG) using large language models (LLM) to achieve remarkable performance. However, these systems are prone to generating biased and toxic completions due to inherent learning biases. Existing detoxification approaches exhibit two key limitations: (1) They primarily focus on mitigating toxicity for grammatically well-formed long sentences but struggle to adapt to the QAC task, where queries are short and structurally different (include spelling errors, do not follow grammatical rules and have relatively flexible word order). (2) These approaches often view detoxification through a binary lens where all text labeled as toxic is undesirable, and non-toxic is considered desirable. To address these limitations, we propose DAC, an intuitive and efficient reinforcement learning-based model to detoxify QAC. With DAC, we introduce an additional perspective of considering the third query class of addressable toxicity. These queries can encompass implicit toxicity, subjective toxicity, or non-toxic queries containing toxic words. We incorporate this three-class query behavior perspective into the proposed model through quantized optimal transport to learn distinctions and generate truly non-toxic completions. We evaluate toxicity levels in the generated completions by DAC across two real-world QAC datasets (Bing and AOL) using two classifiers: a publicly available generic classifier (Detoxify) and a search query-specific classifier, which we develop (TClassify). We find that DAC consistently outperforms all existing baselines on the Bing dataset and achieves competitive performance on the AOL dataset for query detoxification. % providing high quality and low toxicity. We make the code publicly available.|现代查询自动完成(QAC)系统利用自然语言生成(NLG)和大语言模型(LLM)来实现显著的查询性能。然而，由于固有的学习偏差，这些系统容易产生有偏见和有毒的完成。现有的排毒方法表现出两个关键的局限性: (1)它们主要集中在缓解语法结构良好的长句的毒性，但难以适应 QAC 任务，其中查询是短暂的和结构不同的(包括拼写错误，不遵循语法规则，并具有相对灵活的语序)。(2)这些方法通常通过一个二元透镜来看待解毒问题，其中所有标记为有毒的文本都是不可取的，而无毒的文本则被认为是可取的。为了解决这些局限性，我们提出 DAC，一个直观和有效的强化学习为基础的模型来解毒 QAC。使用 DAC，我们引入了考虑可寻址毒性的第三个查询类的另一个视角。这些查询可以包含隐性毒性、主观毒性或包含毒性词的无毒性查询。我们通过量化的最优传输将这三类查询行为视角整合到提出的模型中，以学习区别并产生真正的无毒完成。我们使用两个分类器(公开可用的通用分类器(Detoxify)和我们开发的搜索查询特定分类器(TClassfy))来评估 DAC 在两个实际 QAC 数据集(Bing 和 AOL)中生成的完成中的毒性水平。我们发现 DAC 始终优于 Bing 数据集上所有现有的基线，并在 AOL 数据集上实现了具有竞争力的查询解毒性能。提供高质量和低毒性的百分比。我们公开代码。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=DAC:+Quantized+Optimal+Transport+Reward-based+Reinforcement+Learning+Approach+to+Detoxify+Query+Auto-Completion)|0| -|[IM-RAG: Multi-Round Retrieval-Augmented Generation Through Learning Inner Monologues](https://doi.org/10.1145/3626772.3657760)|Diji Yang, Jinmeng Rao, Kezhen Chen, Xiaoyuan Guo, Yawen Zhang, Jie Yang, Yi Zhang|University of California Santa Cruz; Mineral.ai|Although the Retrieval-Augmented Generation (RAG) paradigms can use externalknowledge to enhance and ground the outputs of Large Language Models (LLMs) tomitigate generative hallucinations and static knowledge base problems, theystill suffer from limited flexibility in adopting Information Retrieval (IR)systems with varying capabilities, constrained interpretability during themulti-round retrieval process, and a lack of end-to-end optimization. Toaddress these challenges, we propose a novel LLM-centric approach, IM-RAG, thatintegrates IR systems with LLMs to support multi-round RAG through learningInner Monologues (IM, i.e., the human inner voice that narrates one'sthoughts). During the IM process, the LLM serves as the core reasoning model(i.e., Reasoner) to either propose queries to collect more information via theRetriever or to provide a final answer based on the conversational context. Wealso introduce a Refiner that improves the outputs from the Retriever,effectively bridging the gap between the Reasoner and IR modules with varyingcapabilities and fostering multi-round communications. The entire IM process isoptimized via Reinforcement Learning (RL) where a Progress Tracker isincorporated to provide mid-step rewards, and the answer prediction is furtherseparately optimized via Supervised Fine-Tuning (SFT). We conduct extensiveexperiments with the HotPotQA dataset, a popular benchmark for retrieval-based,multi-step question-answering. The results show that our approach achievesstate-of-the-art (SOTA) performance while providing high flexibility inintegrating IR modules as well as strong interpretability exhibited in thelearned inner monologues.|虽然检索-增强生成(reeval-augsted Generation，RAG)范式可以使用外部知识来增强和巩固大型语言模型(Large Language model，LLM)的输出，以减少生成幻觉和静态知识库问题，但是它们在采用具有不同功能的信息检索系统方面的灵活性仍然有限，在多轮检索过程中的可解释性受到限制，以及缺乏端到端优化。为了应对这些挑战，我们提出了一种新的以 LLM 为中心的方法，IM-RAG，它将 IR 系统与 LLM 集成在一起，通过学习内部独白(IM，也就是讲述一个人思想的人类内心声音)来支持多轮 RAG。在 IM 过程中，LLM 作为核心推理模型(例如，推理器)提出查询以通过检索器收集更多信息，或者根据会话上下文提供最终答案。我们还引入了一个改善从检索器的输出，有效地弥补差距的推理器和红外模块具有不同的能力，并促进多轮通信。整个即时通讯过程通过强化学习(RL)进行优化，其中包含一个进度跟踪器来提供中间步骤的奖励，而答案预测则通过监督微调(sFT)进一步优化。我们对 HotPotQA 数据集进行了广泛的实验，这是一个基于检索的多步骤问答的流行基准。结果表明，我们的方法实现了最先进的(SOTA)性能，同时提供了高度的灵活性集成的红外模块和强大的解释能力表现在学习的内部独白。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=IM-RAG:+Multi-Round+Retrieval-Augmented+Generation+Through+Learning+Inner+Monologues)|0| -|[Towards Human-centered Proactive Conversational Agents](https://doi.org/10.1145/3626772.3657843)|Yang Deng, Lizi Liao, Zhonghua Zheng, Grace Hui Yang, TatSeng Chua|Georgetown University; Singapore Management University; National University of Singapore; Harbin Institute of Technology, Shenzhen|Recent research on proactive conversational agents (PCAs) mainly focuses onimproving the system's capabilities in anticipating and planning actionsequences to accomplish tasks and achieve goals before users articulate theirrequests. This perspectives paper highlights the importance of moving towardsbuilding human-centered PCAs that emphasize human needs and expectations, andthat considers ethical and social implications of these agents, rather thansolely focusing on technological capabilities. The distinction between aproactive and a reactive system lies in the proactive system'sinitiative-taking nature. Without thoughtful design, proactive systems riskbeing perceived as intrusive by human users. We address the issue byestablishing a new taxonomy concerning three key dimensions of human-centeredPCAs, namely Intelligence, Adaptivity, and Civility. We discuss potentialresearch opportunities and challenges based on this new taxonomy upon the fivestages of PCA system construction. This perspectives paper lays a foundationfor the emerging area of conversational information retrieval research andpaves the way towards advancing human-centered proactive conversationalsystems.|主动会话代理(PCA)的研究主要集中在提高系统预测和计划行为的能力，以便在用户提出要求之前完成任务和实现目标。这份观点文件强调了建立以人为中心的个人协商机制的重要性，这种机制强调人的需求和期望，并考虑到这些机制的道德和社会影响，而不是仅仅关注技术能力。主动系统与被动系统的区别在于主动系统的主动性。如果没有经过深思熟虑的设计，积极主动的系统就有被人类用户认为是侵入性的风险。我们通过建立一个关于以人为中心的 PCA 的三个关键维度的新分类来解决这个问题，即智力、适应性和文明性。我们讨论潜在的研究机会和挑战基于这个新的分类在五个阶段的主成分分析系统建设。这篇观点论文为会话信息检索研究的新兴领域奠定了基础，并为推进以人为中心的主动会话系统铺平了道路。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Towards+Human-centered+Proactive+Conversational+Agents)|0| -|[TREC iKAT 2023: A Test Collection for Evaluating Conversational and Interactive Knowledge Assistants](https://doi.org/10.1145/3626772.3657860)|Mohammad Aliannejadi, Zahra Abbasiantaeb, Shubham Chatterjee, Jeffrey Dalton, Leif Azzopardi|University of Edinburgh; University of Strathclyde; University of Amsterdam|Conversational information seeking has evolved rapidly in the last few yearswith the development of Large Language Models (LLMs), providing the basis forinterpreting and responding in a naturalistic manner to user requests. Theextended TREC Interactive Knowledge Assistance Track (iKAT) collection aims toenable researchers to test and evaluate their Conversational Search Agents(CSA). The collection contains a set of 36 personalized dialogues over 20different topics each coupled with a Personal Text Knowledge Base (PTKB) thatdefines the bespoke user personas. A total of 344 turns with approximately26,000 passages are provided as assessments on relevance, as well as additionalassessments on generated responses over four key dimensions: relevance,completeness, groundedness, and naturalness. The collection challenges CSA toefficiently navigate diverse personal contexts, elicit pertinent personainformation, and employ context for relevant conversations. The integration ofa PTKB and the emphasis on decisional search tasks contribute to the uniquenessof this test collection, making it an essential benchmark for advancingresearch in conversational and interactive knowledge assistants.|近年来，随着大型语言模型(LLM)的发展，会话信息搜索得到了迅速的发展，为用户的自然解释和响应提供了基础。扩展的 TREC 交互式知识援助跟踪(iKAT)集旨在使研究人员能够测试和评估他们的对话搜索代理(CSA)。该集合包含20个不同主题的36个个性化对话集，每个对话集还有一个定义定制用户角色的个人文本知识库(Personal Text Knowledge Base，PTKB)。总共提供了344个回合，大约26,000个段落，作为相关性评估，以及对四个关键维度(相关性，完整性，基础性和自然性)产生的反应的额外评估。这个系列向 CSA 提出了挑战，要求它能够有效地浏览不同的个人背景，获取相关的人物信息，并为相关的对话使用背景。PTKB 的集成和对决策搜索任务的强调有助于该测试集的独特性，使其成为推进会话和交互式知识助手研究的必要基准。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=TREC+iKAT+2023:+A+Test+Collection+for+Evaluating+Conversational+and+Interactive+Knowledge+Assistants)|0| -|[UGNCL: Uncertainty-Guided Noisy Correspondence Learning for Efficient Cross-Modal Matching](https://doi.org/10.1145/3626772.3657806)|Quanxing Zha, Xin Liu, Yiuming Cheung, Xing Xu, Nannan Wang, Jianjia Cao|Xidian University; Hong Kong Baptist University; University of Electronic Science and Technology of China; Huaqiao University|Cross-modal matching has recently gained significant popularity to facilitate retrieval across multi-modal data, and existing works are highly relied on an implicit assumption that the training data pairs are perfectly aligned. However, such an ideal assumption is extremely impossible due to the inevitably mismatched data pairs, a.k.a. noisy correspondence, which can wrongly enforce the mismatched data to be similar and thus induces the performance degradation. Although some recent methods have attempted to address this problem, they still face two challenging issues: 1) unreliable data division for training inefficiency and 2) unstable prediction for matching failure. To address these problems, we propose an efficient Uncertainty-Guided Noisy Correspondence Learning (UGNCL) framework to achieve noise-robust cross-modal matching. Specifically, a novel Uncertainty Guided Division (UGD) algorithm is reliably designed leverage the potential benefits of derived uncertainty to divide the data into clean, noisy and hard partitions, which can effortlessly mitigate the impact of easily-determined noisy pairs. Meanwhile, an efficient Trusted Robust Loss (TRL) is explicitly designed to recast the soft margins, calibrated by confident yet error soft correspondence labels, for the data pairs in the hard partition through the uncertainty, leading to increase/decrease the importance of matched/mismatched pairs and further alleviate the impact of noisy pairs for robustness improvement. Extensive experiments conducted on three public datasets highlight the superiorities of the proposed framework, and show its competitive performance compared with the state-of-the-arts. The code is available at https://github.com/qxzha/UGNCL.|跨模态匹配近年来在多模态数据检索领域得到了广泛的应用，现有的研究大多依赖于训练数据对完美对齐的假设。然而，这种理想的假设是极其不可能的，因为不可避免的不匹配的数据对，也就是噪声对应，会错误地强迫不匹配的数据相似，从而导致性能下降。虽然最近的一些方法已经尝试解决这个问题，但仍然面临两个挑战: 1)不可靠的数据划分训练效率低下和2)不稳定的预测匹配失败。为了解决这些问题，我们提出了一种有效的不确定引导噪声对应学习(UGNCL)框架来实现噪声鲁棒的跨模态匹配。具体地说，一种新的不确定性引导分割(UGD)算法是可靠地设计的，利用导出的不确定性的潜在好处，将数据划分为干净的、有噪声的和硬的分区，这可以毫不费力地减轻容易确定的有噪声对的影响。同时，设计了一种有效的可信鲁棒损失(TRL)算法，通过不确定性重铸硬分区数据对的软边界，使得匹配/不匹配对的重要性增加/减小，进一步减轻噪声对鲁棒性改善的影响。在三个公共数据集上进行的大量实验突出了该框架的优越性，并显示了其与最新技术相比的竞争性能。密码可在 https://github.com/qxzha/ugncl 查阅。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=UGNCL:+Uncertainty-Guided+Noisy+Correspondence+Learning+for+Efficient+Cross-Modal+Matching)|0| +|[IM-RAG: Multi-Round Retrieval-Augmented Generation Through Learning Inner Monologues](https://doi.org/10.1145/3626772.3657760)|Diji Yang, Jinmeng Rao, Kezhen Chen, Xiaoyuan Guo, Yawen Zhang, Jie Yang, Yi Zhang|Mineral.ai; University of California Santa Cruz|Although the Retrieval-Augmented Generation (RAG) paradigms can use externalknowledge to enhance and ground the outputs of Large Language Models (LLMs) tomitigate generative hallucinations and static knowledge base problems, theystill suffer from limited flexibility in adopting Information Retrieval (IR)systems with varying capabilities, constrained interpretability during themulti-round retrieval process, and a lack of end-to-end optimization. Toaddress these challenges, we propose a novel LLM-centric approach, IM-RAG, thatintegrates IR systems with LLMs to support multi-round RAG through learningInner Monologues (IM, i.e., the human inner voice that narrates one'sthoughts). During the IM process, the LLM serves as the core reasoning model(i.e., Reasoner) to either propose queries to collect more information via theRetriever or to provide a final answer based on the conversational context. Wealso introduce a Refiner that improves the outputs from the Retriever,effectively bridging the gap between the Reasoner and IR modules with varyingcapabilities and fostering multi-round communications. The entire IM process isoptimized via Reinforcement Learning (RL) where a Progress Tracker isincorporated to provide mid-step rewards, and the answer prediction is furtherseparately optimized via Supervised Fine-Tuning (SFT). We conduct extensiveexperiments with the HotPotQA dataset, a popular benchmark for retrieval-based,multi-step question-answering. The results show that our approach achievesstate-of-the-art (SOTA) performance while providing high flexibility inintegrating IR modules as well as strong interpretability exhibited in thelearned inner monologues.|虽然检索-增强生成(reeval-augsted Generation，RAG)范式可以使用外部知识来增强和巩固大型语言模型(Large Language model，LLM)的输出，以减少生成幻觉和静态知识库问题，但是它们在采用具有不同功能的信息检索系统方面的灵活性仍然有限，在多轮检索过程中的可解释性受到限制，以及缺乏端到端优化。为了应对这些挑战，我们提出了一种新的以 LLM 为中心的方法，IM-RAG，它将 IR 系统与 LLM 集成在一起，通过学习内部独白(IM，也就是讲述一个人思想的人类内心声音)来支持多轮 RAG。在 IM 过程中，LLM 作为核心推理模型(例如，推理器)提出查询以通过检索器收集更多信息，或者根据会话上下文提供最终答案。我们还引入了一个改善从检索器的输出，有效地弥补差距的推理器和红外模块具有不同的能力，并促进多轮通信。整个即时通讯过程通过强化学习(RL)进行优化，其中包含一个进度跟踪器来提供中间步骤的奖励，而答案预测则通过监督微调(sFT)进一步优化。我们对 HotPotQA 数据集进行了广泛的实验，这是一个基于检索的多步骤问答的流行基准。结果表明，我们的方法实现了最先进的(SOTA)性能，同时提供了高度的灵活性集成的红外模块和强大的解释能力表现在学习的内部独白。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=IM-RAG:+Multi-Round+Retrieval-Augmented+Generation+Through+Learning+Inner+Monologues)|0| +|[Towards Human-centered Proactive Conversational Agents](https://doi.org/10.1145/3626772.3657843)|Yang Deng, Lizi Liao, Zhonghua Zheng, Grace Hui Yang, TatSeng Chua|Singapore Management University; National University of Singapore; Harbin Institute of Technology, Shenzhen; Georgetown University|Recent research on proactive conversational agents (PCAs) mainly focuses onimproving the system's capabilities in anticipating and planning actionsequences to accomplish tasks and achieve goals before users articulate theirrequests. This perspectives paper highlights the importance of moving towardsbuilding human-centered PCAs that emphasize human needs and expectations, andthat considers ethical and social implications of these agents, rather thansolely focusing on technological capabilities. The distinction between aproactive and a reactive system lies in the proactive system'sinitiative-taking nature. Without thoughtful design, proactive systems riskbeing perceived as intrusive by human users. We address the issue byestablishing a new taxonomy concerning three key dimensions of human-centeredPCAs, namely Intelligence, Adaptivity, and Civility. We discuss potentialresearch opportunities and challenges based on this new taxonomy upon the fivestages of PCA system construction. This perspectives paper lays a foundationfor the emerging area of conversational information retrieval research andpaves the way towards advancing human-centered proactive conversationalsystems.|主动会话代理(PCA)的研究主要集中在提高系统预测和计划行为的能力，以便在用户提出要求之前完成任务和实现目标。这份观点文件强调了建立以人为中心的个人协商机制的重要性，这种机制强调人的需求和期望，并考虑到这些机制的道德和社会影响，而不是仅仅关注技术能力。主动系统与被动系统的区别在于主动系统的主动性。如果没有经过深思熟虑的设计，积极主动的系统就有被人类用户认为是侵入性的风险。我们通过建立一个关于以人为中心的 PCA 的三个关键维度的新分类来解决这个问题，即智力、适应性和文明性。我们讨论潜在的研究机会和挑战基于这个新的分类在五个阶段的主成分分析系统建设。这篇观点论文为会话信息检索研究的新兴领域奠定了基础，并为推进以人为中心的主动会话系统铺平了道路。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Towards+Human-centered+Proactive+Conversational+Agents)|0| +|[TREC iKAT 2023: A Test Collection for Evaluating Conversational and Interactive Knowledge Assistants](https://doi.org/10.1145/3626772.3657860)|Mohammad Aliannejadi, Zahra Abbasiantaeb, Shubham Chatterjee, Jeffrey Dalton, Leif Azzopardi|University of Amsterdam; University of Edinburgh; University of Strathclyde|Conversational information seeking has evolved rapidly in the last few yearswith the development of Large Language Models (LLMs), providing the basis forinterpreting and responding in a naturalistic manner to user requests. Theextended TREC Interactive Knowledge Assistance Track (iKAT) collection aims toenable researchers to test and evaluate their Conversational Search Agents(CSA). The collection contains a set of 36 personalized dialogues over 20different topics each coupled with a Personal Text Knowledge Base (PTKB) thatdefines the bespoke user personas. A total of 344 turns with approximately26,000 passages are provided as assessments on relevance, as well as additionalassessments on generated responses over four key dimensions: relevance,completeness, groundedness, and naturalness. The collection challenges CSA toefficiently navigate diverse personal contexts, elicit pertinent personainformation, and employ context for relevant conversations. The integration ofa PTKB and the emphasis on decisional search tasks contribute to the uniquenessof this test collection, making it an essential benchmark for advancingresearch in conversational and interactive knowledge assistants.|近年来，随着大型语言模型(LLM)的发展，会话信息搜索得到了迅速的发展，为用户的自然解释和响应提供了基础。扩展的 TREC 交互式知识援助跟踪(iKAT)集旨在使研究人员能够测试和评估他们的对话搜索代理(CSA)。该集合包含20个不同主题的36个个性化对话集，每个对话集还有一个定义定制用户角色的个人文本知识库(Personal Text Knowledge Base，PTKB)。总共提供了344个回合，大约26,000个段落，作为相关性评估，以及对四个关键维度(相关性，完整性，基础性和自然性)产生的反应的额外评估。这个系列向 CSA 提出了挑战，要求它能够有效地浏览不同的个人背景，获取相关的人物信息，并为相关的对话使用背景。PTKB 的集成和对决策搜索任务的强调有助于该测试集的独特性，使其成为推进会话和交互式知识助手研究的必要基准。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=TREC+iKAT+2023:+A+Test+Collection+for+Evaluating+Conversational+and+Interactive+Knowledge+Assistants)|0| +|[UGNCL: Uncertainty-Guided Noisy Correspondence Learning for Efficient Cross-Modal Matching](https://doi.org/10.1145/3626772.3657806)|Quanxing Zha, Xin Liu, Yiuming Cheung, Xing Xu, Nannan Wang, Jianjia Cao|Huaqiao University; Hong Kong Baptist University; Xidian University; University of Electronic Science and Technology of China|Cross-modal matching has recently gained significant popularity to facilitate retrieval across multi-modal data, and existing works are highly relied on an implicit assumption that the training data pairs are perfectly aligned. However, such an ideal assumption is extremely impossible due to the inevitably mismatched data pairs, a.k.a. noisy correspondence, which can wrongly enforce the mismatched data to be similar and thus induces the performance degradation. Although some recent methods have attempted to address this problem, they still face two challenging issues: 1) unreliable data division for training inefficiency and 2) unstable prediction for matching failure. To address these problems, we propose an efficient Uncertainty-Guided Noisy Correspondence Learning (UGNCL) framework to achieve noise-robust cross-modal matching. Specifically, a novel Uncertainty Guided Division (UGD) algorithm is reliably designed leverage the potential benefits of derived uncertainty to divide the data into clean, noisy and hard partitions, which can effortlessly mitigate the impact of easily-determined noisy pairs. Meanwhile, an efficient Trusted Robust Loss (TRL) is explicitly designed to recast the soft margins, calibrated by confident yet error soft correspondence labels, for the data pairs in the hard partition through the uncertainty, leading to increase/decrease the importance of matched/mismatched pairs and further alleviate the impact of noisy pairs for robustness improvement. Extensive experiments conducted on three public datasets highlight the superiorities of the proposed framework, and show its competitive performance compared with the state-of-the-arts. The code is available at https://github.com/qxzha/UGNCL.|跨模态匹配近年来在多模态数据检索领域得到了广泛的应用，现有的研究大多依赖于训练数据对完美对齐的假设。然而，这种理想的假设是极其不可能的，因为不可避免的不匹配的数据对，也就是噪声对应，会错误地强迫不匹配的数据相似，从而导致性能下降。虽然最近的一些方法已经尝试解决这个问题，但仍然面临两个挑战: 1)不可靠的数据划分训练效率低下和2)不稳定的预测匹配失败。为了解决这些问题，我们提出了一种有效的不确定引导噪声对应学习(UGNCL)框架来实现噪声鲁棒的跨模态匹配。具体地说，一种新的不确定性引导分割(UGD)算法是可靠地设计的，利用导出的不确定性的潜在好处，将数据划分为干净的、有噪声的和硬的分区，这可以毫不费力地减轻容易确定的有噪声对的影响。同时，设计了一种有效的可信鲁棒损失(TRL)算法，通过不确定性重铸硬分区数据对的软边界，使得匹配/不匹配对的重要性增加/减小，进一步减轻噪声对鲁棒性改善的影响。在三个公共数据集上进行的大量实验突出了该框架的优越性，并显示了其与最新技术相比的竞争性能。密码可在 https://github.com/qxzha/ugncl 查阅。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=UGNCL:+Uncertainty-Guided+Noisy+Correspondence+Learning+for+Efficient+Cross-Modal+Matching)|0| |[DHMAE: A Disentangled Hypergraph Masked Autoencoder for Group Recommendation](https://doi.org/10.1145/3626772.3657699)|Yingqi Zhao, Haiwei Zhang, Qijie Bai, Changli Nie, Xiaojie Yuan|Nankai University|Group recommendation aims to suggest items to a group of users that are suitable for the group. Although some existing powerful deep learning models have achieved improved performance, various aspects remain unexplored: (1) Most existing models using contrastive learning tend to rely on high-quality data augmentation which requires precise contrastive view generation; (2) There is multifaceted natural noise in group recommendation, and additional noise is introduced during data augmentation; (3) Most existing hypergraph neural network-based models over-entangle the information of members and items, ignoring their unique characteristics. In light of this, we propose a highly effective Disentangled Hypergraph Masked Auto Encoder-enhanced method for group recommendation (DHMAE), combining a disentangled hypergraph neural network with a graph masked autoencoder. This approach creates self-supervised signals without data augmentation by masking the features of some nodes and hyperedges and then reconstructing them. For the noise problem, we design a masking strategy that relies on pre-computed degree-sensitive probabilities for the process of masking features. Furthermore, we propose a disentangled hypergraph neural network for group recommendation scenarios to extract common messages of members and items and disentangle them during the convolution process. Extensive experiments demonstrate that our method significantly outperforms state-of-the-art models and effectively addresses the noise issue.|群组建议旨在向一组用户推荐适合该群组的项目。虽然现有的一些强大的深度学习模型已经取得了改善的性能，但是各个方面仍然没有得到探索: (1)大多数使用对比学习的现有模型倾向于依赖于高质量的数据增强，这需要精确的对比视图生成; (2)在群体推荐中存在多方面的自然噪声，并且在数据增强过程中引入了额外的噪声; (3)大多数现有的基于超图神经网络的模型过度纠缠成员和项目的信息,。鉴于此，我们提出了一种高效的用于群组推荐(DHMAE)的离散超图掩码自动编码增强方法，该方法将离散超图神经网络与图掩码自动编码器相结合。该方法通过掩盖某些节点和超边缘的特征，然后对其进行重构，在不增加数据量的情况下产生自监督信号。针对噪声问题，我们设计了一种基于预先计算的度敏感概率的掩蔽策略。在此基础上，提出了一种用于群体推荐场景的解缠超图神经网络，用于在卷积过程中提取成员和项目的共同信息并进行解缠。广泛的实验表明，我们的方法显着优于国家的最先进的模型，并有效地解决噪声问题。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=DHMAE:+A+Disentangled+Hypergraph+Masked+Autoencoder+for+Group+Recommendation)|0| -|[Are We Really Achieving Better Beyond-Accuracy Performance in Next Basket Recommendation?](https://doi.org/10.1145/3626772.3657835)|Ming Li, Yuanna Liu, Sami Jullien, Mozhdeh Ariannezhad, Andrew Yates, Mohammad Aliannejadi, Maarten de Rijke|Universiteit van Amsterdam; University of Amsterdam|Next basket recommendation (NBR) is a special type of sequentialrecommendation that is increasingly receiving attention. So far, most NBRstudies have focused on optimizing the accuracy of the recommendation, whereasoptimizing for beyond-accuracy metrics, e.g., item fairness and diversityremains largely unexplored. Recent studies into NBR have found a substantialperformance difference between recommending repeat items and explore items.Repeat items contribute most of the users' perceived accuracy compared withexplore items. Informed by these findings, we identify a potential "short-cut"to optimize for beyond-accuracy metrics while maintaining high accuracy. Toleverage and verify the existence of such short-cuts, we propose aplug-and-play two-step repetition-exploration (TREx) framework that treatsrepeat items and explores items separately, where we design a simple yet highlyeffective repetition module to ensure high accuracy, while two explorationmodules target optimizing only beyond-accuracy metrics. Experiments areperformed on two widely-used datasets w.r.t. a range of beyond-accuracymetrics, viz. five fairness metrics and three diversity metrics. Ourexperimental results verify the effectiveness of TREx. Prima facie, thisappears to be good news: we can achieve high accuracy and improvedbeyond-accuracy metrics at the same time. However, we argue that the real-worldvalue of our algorithmic solution, TREx, is likely to be limited and reflect onthe reasonableness of the evaluation setup. We end up challenging existingevaluation paradigms, particularly in the context of beyond-accuracy metrics,and provide insights for researchers to navigate potential pitfalls anddetermine reasonable metrics to consider when optimizing for accuracy andbeyond-accuracy metrics.|下一个篮子推荐(NBR)是一种特殊类型的顺序推荐，越来越受到关注。到目前为止，大多数 NBRs 研究的重点是优化推荐的准确性，而对于超准确度指标的优化，例如，项目公平性和多样性仍然在很大程度上没有被探索。最近对 NBR 的研究发现，在推荐重复项目和探索项目之间存在显著的绩效差异。与探索项目相比，重复项目贡献了大部分用户的感知准确性。根据这些发现，我们确定了一个潜在的“捷径”来优化超精度度量，同时保持高精度。为了利用和验证这种捷径的存在，我们提出了即插即用的两步重复探索(TREx)框架，它处理重复项目并分别探索项目，其中我们设计了一个简单而高效的重复模块以确保高精度，而两个探索模块的目标仅仅是优化超越精度的度量。实验是在两个广泛使用的数据集 W.R.T。上进行的，这两个数据集包括五个公平性指标和三个多样性指标。实验结果验证了 TREx 的有效性。初步看来，这似乎是个好消息: 我们可以同时实现高精度和改进超精度度量。然而，我们认为，我们的算法解决方案 TREx 的现实价值可能是有限的，并反映了评估设置的合理性。我们最终挑战现有的评估范式，特别是在超精确度指标的背景下，并为研究人员提供洞察力，以导航潜在的陷阱，并确定合理的指标时考虑优化的准确性和超精确度指标。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Are+We+Really+Achieving+Better+Beyond-Accuracy+Performance+in+Next+Basket+Recommendation?)|0| -|[AutoDCS: Automated Decision Chain Selection in Deep Recommender Systems](https://doi.org/10.1145/3626772.3657818)|Dugang Liu, Shenxian Xian, Yuhao Wu, Chaohua Yang, Xing Tang, Xiuqiang He, Zhong Ming|FiT,Tencent; Tencent; College of Computer Science and Software Engineering, Shenzhen University; Shenzhen University|Multi-behavior recommender systems (MBRS) have been commonly deployed on real-world industrial platforms for their superior advantages in understanding user preferences and mitigating data sparsity. However, the cascade graph modeling paradigm adopted in mainstream MBRS usually assumes that users will refer to all types of behavioral knowledge they have when making decisions about target behaviors, i.e., use all types of behavioral interactions indiscriminately when modeling and predicting target behaviors for each user. We call this a full decision chain constraint and argue that it may be too strict by ignoring that different types of behavioral knowledge have varying importance for different users. In this paper, we propose a novel automated decision chain selection (AutoDCS) framework to relax this constraint, which can consider each user's unique decision dependencies and select a reasonable set of behavioral knowledge to activate for the prediction of target behavior. Specifically, AutoDCS first integrates some existing MBRS methods in a base cascade module to obtain a set of behavior-aware embeddings. Then, a bilateral matching gating mechanism is used to select an exclusive set of behaviors for the current user-item pair to form a decision chain, and the corresponding behavior-augmented embeddings are selectively activated. Subsequently, AutoDCS combines the behavior-augmented and original behavior-aware embeddings to predict the target behavior. Finally, we evaluate AutoDCS and demonstrate its effectiveness through experiments over four public multi-behavior benchmarks.|多行为推荐系统(MBRS)因其在理解用户偏好和减少数据稀疏性方面的优越性而广泛应用于现实世界的工业平台。然而，主流 MBRS 采用的级联图建模范式通常假定用户在决策目标行为时会参考他们所拥有的所有类型的行为知识，即在为每个用户建模和预测目标行为时不加区分地使用所有类型的行为交互。我们称之为完全决策链约束，并认为它可能过于严格，忽略了不同类型的行为知识对不同的用户有不同的重要性。本文提出了一种新的自动决策链选择框架(AutoDCS) ，该框架可以考虑每个用户独特的决策依赖关系，并选择一组合理的行为知识来激活目标行为的预测。具体来说，AutoDCS 首先将一些现有的 MBRS 方法集成到一个基本级联模块中，以获得一组行为感知的嵌入。然后，利用双边匹配门控机制为当前的用户项对选择一组排他的行为，形成决策链，并选择性地激活相应的行为增强嵌入。随后，AutoDCS 将行为增强嵌入和原始行为感知嵌入相结合，对目标行为进行预测。最后，我们通过四个公共多行为基准测试对 AutoDCS 进行了评估并验证了其有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=AutoDCS:+Automated+Decision+Chain+Selection+in+Deep+Recommender+Systems)|0| -|[EasyRL4Rec: An Easy-to-use Library for Reinforcement Learning Based Recommender Systems](https://doi.org/10.1145/3626772.3657868)|Yuanqing Yu, Chongming Gao, Jiawei Chen, Heng Tang, Yuefeng Sun, Qian Chen, Weizhi Ma, Min Zhang|Zhejiang University; University of Science and Technology of China; Tsinghua University|Reinforcement Learning (RL)-Based Recommender Systems (RSs) have gainedrising attention for their potential to enhance long-term user engagement.However, research in this field faces challenges, including the lack ofuser-friendly frameworks, inconsistent evaluation metrics, and difficulties inreproducing existing studies. To tackle these issues, we introduce EasyRL4Rec,an easy-to-use code library designed specifically for RL-based RSs. Thislibrary provides lightweight and diverse RL environments based on five publicdatasets and includes core modules with rich options, simplifying modeldevelopment. It provides unified evaluation standards focusing on long-termoutcomes and offers tailored designs for state modeling and actionrepresentation for recommendation scenarios. Furthermore, we share our findingsfrom insightful experiments with current methods. EasyRL4Rec seeks tofacilitate the model development and experimental process in the domain ofRL-based RSs. The library is available for public use.|基于强化学习的推荐系统(RSs)因其增强长期用户参与度的潜力而受到越来越多的关注。然而，该领域的研究面临着挑战，包括缺乏用户友好的框架，不一致的评估指标，以及难以复制现有的研究。为了解决这些问题，我们介绍了 EasyRL4Rec，这是一个专门为基于 RL 的 RSS 设计的易于使用的代码库。该库基于五个 public 数据集提供轻量级和多样化的 RL 环境，并包括具有丰富选项的核心模块，从而简化了模型开发。它提供了注重长期结果的统一评估标准，并为推荐场景的状态建模和行动表示提供了量身定制的设计。此外，我们分享我们的发现，从深刻的实验与目前的方法。EasyRL4Rec 致力于促进基于 RL 的 RSS 领域的模型开发和实验过程。图书馆可供公众使用。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=EasyRL4Rec:+An+Easy-to-use+Library+for+Reinforcement+Learning+Based+Recommender+Systems)|0| +|[Are We Really Achieving Better Beyond-Accuracy Performance in Next Basket Recommendation?](https://doi.org/10.1145/3626772.3657835)|Ming Li, Yuanna Liu, Sami Jullien, Mozhdeh Ariannezhad, Andrew Yates, Mohammad Aliannejadi, Maarten de Rijke|University of Amsterdam; Universiteit van Amsterdam|Next basket recommendation (NBR) is a special type of sequentialrecommendation that is increasingly receiving attention. So far, most NBRstudies have focused on optimizing the accuracy of the recommendation, whereasoptimizing for beyond-accuracy metrics, e.g., item fairness and diversityremains largely unexplored. Recent studies into NBR have found a substantialperformance difference between recommending repeat items and explore items.Repeat items contribute most of the users' perceived accuracy compared withexplore items. Informed by these findings, we identify a potential "short-cut"to optimize for beyond-accuracy metrics while maintaining high accuracy. Toleverage and verify the existence of such short-cuts, we propose aplug-and-play two-step repetition-exploration (TREx) framework that treatsrepeat items and explores items separately, where we design a simple yet highlyeffective repetition module to ensure high accuracy, while two explorationmodules target optimizing only beyond-accuracy metrics. Experiments areperformed on two widely-used datasets w.r.t. a range of beyond-accuracymetrics, viz. five fairness metrics and three diversity metrics. Ourexperimental results verify the effectiveness of TREx. Prima facie, thisappears to be good news: we can achieve high accuracy and improvedbeyond-accuracy metrics at the same time. However, we argue that the real-worldvalue of our algorithmic solution, TREx, is likely to be limited and reflect onthe reasonableness of the evaluation setup. We end up challenging existingevaluation paradigms, particularly in the context of beyond-accuracy metrics,and provide insights for researchers to navigate potential pitfalls anddetermine reasonable metrics to consider when optimizing for accuracy andbeyond-accuracy metrics.|下一个篮子推荐(NBR)是一种特殊类型的顺序推荐，越来越受到关注。到目前为止，大多数 NBRs 研究的重点是优化推荐的准确性，而对于超准确度指标的优化，例如，项目公平性和多样性仍然在很大程度上没有被探索。最近对 NBR 的研究发现，在推荐重复项目和探索项目之间存在显著的绩效差异。与探索项目相比，重复项目贡献了大部分用户的感知准确性。根据这些发现，我们确定了一个潜在的“捷径”来优化超精度度量，同时保持高精度。为了利用和验证这种捷径的存在，我们提出了即插即用的两步重复探索(TREx)框架，它处理重复项目并分别探索项目，其中我们设计了一个简单而高效的重复模块以确保高精度，而两个探索模块的目标仅仅是优化超越精度的度量。实验是在两个广泛使用的数据集 W.R.T。上进行的，这两个数据集包括五个公平性指标和三个多样性指标。实验结果验证了 TREx 的有效性。初步看来，这似乎是个好消息: 我们可以同时实现高精度和改进超精度度量。然而，我们认为，我们的算法解决方案 TREx 的现实价值可能是有限的，并反映了评估设置的合理性。我们最终挑战现有的评估范式，特别是在超精确度指标的背景下，并为研究人员提供洞察力，以导航潜在的陷阱，并确定合理的指标时考虑优化的准确性和超精确度指标。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Are+We+Really+Achieving+Better+Beyond-Accuracy+Performance+in+Next+Basket+Recommendation?)|0| +|[AutoDCS: Automated Decision Chain Selection in Deep Recommender Systems](https://doi.org/10.1145/3626772.3657818)|Dugang Liu, Shenxian Xian, Yuhao Wu, Chaohua Yang, Xing Tang, Xiuqiang He, Zhong Ming|Shenzhen University; FiT,Tencent; College of Computer Science and Software Engineering, Shenzhen University; Tencent|Multi-behavior recommender systems (MBRS) have been commonly deployed on real-world industrial platforms for their superior advantages in understanding user preferences and mitigating data sparsity. However, the cascade graph modeling paradigm adopted in mainstream MBRS usually assumes that users will refer to all types of behavioral knowledge they have when making decisions about target behaviors, i.e., use all types of behavioral interactions indiscriminately when modeling and predicting target behaviors for each user. We call this a full decision chain constraint and argue that it may be too strict by ignoring that different types of behavioral knowledge have varying importance for different users. In this paper, we propose a novel automated decision chain selection (AutoDCS) framework to relax this constraint, which can consider each user's unique decision dependencies and select a reasonable set of behavioral knowledge to activate for the prediction of target behavior. Specifically, AutoDCS first integrates some existing MBRS methods in a base cascade module to obtain a set of behavior-aware embeddings. Then, a bilateral matching gating mechanism is used to select an exclusive set of behaviors for the current user-item pair to form a decision chain, and the corresponding behavior-augmented embeddings are selectively activated. Subsequently, AutoDCS combines the behavior-augmented and original behavior-aware embeddings to predict the target behavior. Finally, we evaluate AutoDCS and demonstrate its effectiveness through experiments over four public multi-behavior benchmarks.|多行为推荐系统(MBRS)因其在理解用户偏好和减少数据稀疏性方面的优越性而广泛应用于现实世界的工业平台。然而，主流 MBRS 采用的级联图建模范式通常假定用户在决策目标行为时会参考他们所拥有的所有类型的行为知识，即在为每个用户建模和预测目标行为时不加区分地使用所有类型的行为交互。我们称之为完全决策链约束，并认为它可能过于严格，忽略了不同类型的行为知识对不同的用户有不同的重要性。本文提出了一种新的自动决策链选择框架(AutoDCS) ，该框架可以考虑每个用户独特的决策依赖关系，并选择一组合理的行为知识来激活目标行为的预测。具体来说，AutoDCS 首先将一些现有的 MBRS 方法集成到一个基本级联模块中，以获得一组行为感知的嵌入。然后，利用双边匹配门控机制为当前的用户项对选择一组排他的行为，形成决策链，并选择性地激活相应的行为增强嵌入。随后，AutoDCS 将行为增强嵌入和原始行为感知嵌入相结合，对目标行为进行预测。最后，我们通过四个公共多行为基准测试对 AutoDCS 进行了评估并验证了其有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=AutoDCS:+Automated+Decision+Chain+Selection+in+Deep+Recommender+Systems)|0| +|[EasyRL4Rec: An Easy-to-use Library for Reinforcement Learning Based Recommender Systems](https://doi.org/10.1145/3626772.3657868)|Yuanqing Yu, Chongming Gao, Jiawei Chen, Heng Tang, Yuefeng Sun, Qian Chen, Weizhi Ma, Min Zhang|University of Science and Technology of China; Tsinghua University; Zhejiang University|Reinforcement Learning (RL)-Based Recommender Systems (RSs) have gainedrising attention for their potential to enhance long-term user engagement.However, research in this field faces challenges, including the lack ofuser-friendly frameworks, inconsistent evaluation metrics, and difficulties inreproducing existing studies. To tackle these issues, we introduce EasyRL4Rec,an easy-to-use code library designed specifically for RL-based RSs. Thislibrary provides lightweight and diverse RL environments based on five publicdatasets and includes core modules with rich options, simplifying modeldevelopment. It provides unified evaluation standards focusing on long-termoutcomes and offers tailored designs for state modeling and actionrepresentation for recommendation scenarios. Furthermore, we share our findingsfrom insightful experiments with current methods. EasyRL4Rec seeks tofacilitate the model development and experimental process in the domain ofRL-based RSs. The library is available for public use.|基于强化学习的推荐系统(RSs)因其增强长期用户参与度的潜力而受到越来越多的关注。然而，该领域的研究面临着挑战，包括缺乏用户友好的框架，不一致的评估指标，以及难以复制现有的研究。为了解决这些问题，我们介绍了 EasyRL4Rec，这是一个专门为基于 RL 的 RSS 设计的易于使用的代码库。该库基于五个 public 数据集提供轻量级和多样化的 RL 环境，并包括具有丰富选项的核心模块，从而简化了模型开发。它提供了注重长期结果的统一评估标准，并为推荐场景的状态建模和行动表示提供了量身定制的设计。此外，我们分享我们的发现，从深刻的实验与目前的方法。EasyRL4Rec 致力于促进基于 RL 的 RSS 领域的模型开发和实验过程。图书馆可供公众使用。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=EasyRL4Rec:+An+Easy-to-use+Library+for+Reinforcement+Learning+Based+Recommender+Systems)|0| |[Explainability for Transparent Conversational Information-Seeking](https://doi.org/10.1145/3626772.3657768)|Weronika Lajewska, Damiano Spina, Johanne Trippas, Krisztian Balog|University of Stavanger; RMIT University|The increasing reliance on digital information necessitates advancements inconversational search systems, particularly in terms of informationtransparency. While prior research in conversational information-seeking hasconcentrated on improving retrieval techniques, the challenge remains ingenerating responses useful from a user perspective. This study exploresdifferent methods of explaining the responses, hypothesizing that transparencyabout the source of the information, system confidence, and limitations canenhance users' ability to objectively assess the response. By exploringtransparency across explanation type, quality, and presentation mode, thisresearch aims to bridge the gap between system-generated responses andresponses verifiable by the user. We design a user study to answer questionsconcerning the impact of (1) the quality of explanations enhancing the responseon its usefulness and (2) ways of presenting explanations to users. Theanalysis of the collected data reveals lower user ratings for noisyexplanations, although these scores seem insensitive to the quality of theresponse. Inconclusive results on the explanations presentation format suggestthat it may not be a critical factor in this setting.|对数字信息的日益依赖使得会话搜索系统的发展成为必然，特别是在信息透明度方面。虽然以前对会话信息搜索的研究集中在提高检索技术，但是挑战仍然是从用户的角度产生有用的反应。本研究探讨了解释响应的不同方法，假设信息来源的透明度、系统信心和局限性可以提高用户客观评估响应的能力。通过探索解释类型、质量和表达模式之间的透明度，本研究旨在弥合系统生成的响应和用户可验证的响应之间的差距。我们设计了一个用户研究来回答以下问题: (1)解释的质量提高了回答的有用性; (2)向用户提供解释的方式。对收集到的数据进行分析后发现，尽管这些分数似乎对回复的质量不敏感，但用户对噪音解释的评分较低。关于解释说明格式的不确定结果表明，它可能不是这种情况下的一个关键因素。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Explainability+for+Transparent+Conversational+Information-Seeking)|0| |[Evaluating Search System Explainability with Psychometrics and Crowdsourcing](https://doi.org/10.1145/3626772.3657796)|Catherine Chen, Carsten Eickhoff|University of Tübingen; Brown University|As information retrieval (IR) systems, such as search engines andconversational agents, become ubiquitous in various domains, the need fortransparent and explainable systems grows to ensure accountability, fairness,and unbiased results. Despite recent advances in explainable AI and IRtechniques, there is no consensus on the definition of explainability. Existingapproaches often treat it as a singular notion, disregarding themultidimensional definition postulated in the literature. In this paper, we usepsychometrics and crowdsourcing to identify human-centered factors ofexplainability in Web search systems and introduce SSE (Search SystemExplainability), an evaluation metric for explainable IR (XIR) search systems.In a crowdsourced user study, we demonstrate SSE's ability to distinguishbetween explainable and non-explainable systems, showing that systems withhigher scores indeed indicate greater interpretability. We hope that aside fromthese concrete contributions to XIR, this line of work will serve as ablueprint for similar explainability evaluation efforts in other domains ofmachine learning and natural language processing.|随着信息检索(IR)系统，如搜索引擎和会话代理，在各个领域变得无处不在，对透明和可解释的系统的需求增长，以确保问责制，公平性和无偏见的结果。尽管可解释性 AI 和 IR 技术最近取得了一些进展，但是对于可解释性的定义还没有达成共识。现有的方法往往把它作为一个单一的概念，无视文献中假定的多维定义。在本文中，我们使用心理测量学和众包来识别网络搜索系统中以人为中心的可解释性因素，并介绍了 SSE (Search SystemExplainability) ，一个可解释的 IR (XIR)搜索系统的评估指标。在众包用户研究中，我们证明了 SSE 区分可解释和不可解释系统的能力，表明分数较高的系统确实表明更大的可解释性。我们希望，除了这些对 XIR 的具体贡献之外，这一系列工作将为机器学习和自然语言处理的其他领域中类似的可解释性评估工作提供蓝图。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Evaluating+Search+System+Explainability+with+Psychometrics+and+Crowdsourcing)|0| |[Enhancing Dataset Search with Compact Data Snippets](https://doi.org/10.1145/3626772.3657837)|Qiaosheng Chen, Jiageng Chen, Xiao Zhou, Gong Cheng|Nanjing University|In light of the growing availability and significance of open data, the problem of dataset search has attracted great attention in the field of information retrieval. Nevertheless, current metadata-based approaches have revealed shortcomings due to the low quality and availability of dataset metadata, while the magnitude and heterogeneity of actual data hindered the development of content-based solutions. To address these challenges, we propose to convert different formats of structured data into a unified form, from which we extract a compact data snippet that indicates the relevance of the whole data. Thanks to its compactness, we feed it into a dense reranker to improve search accuracy. We also convert it back to the original format to be presented for assisting users in relevance judgment. The effectiveness of our approach has been demonstrated by extensive experiments on two test collections for dataset search.|随着开放数据的日益普及和重要性的提高，数据集搜索引起了信息检索领域的广泛关注。尽管如此，目前基于元数据的方法暴露了数据集元数据质量和可用性低的缺点，而实际数据的规模和异质性阻碍了基于内容的解决方案的开发。为了应对这些挑战，我们建议将不同格式的结构化数据转换成统一的形式，从中提取一个表明整个数据相关性的紧凑数据片段。由于它的紧凑性，我们把它输入到一个密集的重新排序，以提高搜索的准确性。我们还将其转换回原来的格式，以协助用户进行相关性判断。我们的方法的有效性已经得到了广泛的实验证明的两个测试收集的数据集搜索。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Enhancing+Dataset+Search+with+Compact+Data+Snippets)|0| -|[When MOE Meets LLMs: Parameter Efficient Fine-tuning for Multi-task Medical Applications](https://doi.org/10.1145/3626772.3657722)|Qidong Liu, Xian Wu, Xiangyu Zhao, Yuanshao Zhu, Derong Xu, Feng Tian, Yefeng Zheng|Xi'an Jiaotong University, City University of Hong Kong; City University of Hong Kong; Xi'an Jiaotong University; University of Science and Technology of China, City University of Hong Kong; Southern University of Science and Technology, City University of Hong Kong; Tencent|The recent surge in Large Language Models (LLMs) has garnered significantattention across numerous fields. Fine-tuning is often required to fit generalLLMs for a specific domain, like the web-based healthcare system. However, twoproblems arise during fine-tuning LLMs for medical applications. One is thetask variety problem, which involves distinct tasks in real-world medicalscenarios. The variety often leads to sub-optimal fine-tuning for dataimbalance and seesaw problems. Besides, the large amount of parameters in LLMsleads to huge time and computation consumption by fine-tuning. To address thesetwo problems, we propose a novel parameter efficient fine-tuning framework formulti-task medical applications, dubbed as MOELoRA. The designed framework aimsto absorb both the benefits of mixture-of-expert (MOE) for multi-task learningand low-rank adaptation (LoRA) for parameter efficient fine-tuning. Forunifying MOE and LoRA, we devise multiple experts as the trainable parameters,where each expert consists of a pair of low-rank matrices to retain the smallsize of trainable parameters. Then, a task-motivated gate function for allMOELoRA layers is proposed, which can control the contributions of each expertand produce distinct parameters for various tasks. We conduct experiments on amulti-task medical dataset, indicating MOELoRA outperforms the existingparameter efficient fine-tuning methods. The code is available online.|最近大型语言模型(LLM)的兴起已经引起了众多领域的广泛关注。通常需要进行微调，以适应特定领域的常规 LLM，比如基于 Web 的医疗保健系统。然而，在微调 LLM 用于医疗应用时会出现两个问题。其中之一是任务多样性问题，它涉及到现实世界医学场景中不同的任务。这种多样性常常导致数据平衡和跷跷板问题的次优微调。此外，LLMS 中的大量参数导致了大量的时间和计算量的消耗。为了解决这两个问题，我们提出了一种新的参数高效微调框架公式多任务医疗应用，称为 MOELoRA。所设计的框架旨在吸收混合专家(MOE)在多任务学习和低秩自适应(LoRA)在参数有效微调方面的优点。结合 MOE 和 LoRA，我们设计了多个专家作为可训练参数，其中每个专家由一对低秩矩阵组成，以保留可训练参数的小尺寸。然后，提出了一种适用于所有 MOELoRA 层的任务驱动门函数，它可以控制每个专家的贡献，并为不同的任务产生不同的参数。在多任务医学数据集上进行了实验，结果表明 MOELoRA 优于现有的参数有效微调方法。代码可以在线获得。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=When+MOE+Meets+LLMs:+Parameter+Efficient+Fine-tuning+for+Multi-task+Medical+Applications)|0| -|[OEHR: An Orthopedic Electronic Health Record Dataset](https://doi.org/10.1145/3626772.3657885)|Yibo Xie, Kaifan Wang, Jiawei Zheng, Feiyan Liu, Xiaoli Wang, Guofeng Huang|School of Informatics, Institute of AI, Xiamen University, Xiamen, China; Xiamen University, Affiliated Southeast Hospital, Zhangzhou, China; School of Informatics, Xiamen University, Xiamen, China|During the past decades, healthcare institutions continually amassed clinical data that is not intended to support research. Despite the increasing number of publicly available electronic health record (EHR) datasets, it is difficult to find publicly available datasets in Orthopedics that can be used to compare and evaluate downstream tasks. This paper presents OEHR, a healthcare benchmark dataset in Orthopedics, sourced from the EHR of real hospitals. Information available includes patient measurements, diagnoses, treatments, clinical notes, and medical images. OEHR is intended to support clinical research. To evaluate the quality of OEHR, we conduct extensive experiments by implementing state-of-the-art methods for performing downstream tasks. The results show that OEHR serves as a valuable extension to existing publicly available EHR datasets. The dataset is available at http://47.94.174.82/.|在过去的几十年里，医疗机构不断地收集临床数据，而这些数据并不是用来支持研究的。尽管公开可用的电子健康记录(EHR)数据集数量不断增加，但很难在骨科中找到可用于比较和评估下游任务的公开可用数据集。本文介绍了 OEHR，一个骨科医疗基准数据集，来源于实际医院的 EHR。可获得的信息包括患者测量、诊断、治疗、临床记录和医学图像。OEHR 旨在支持临床研究。为了评估 OEHR 的质量，我们进行了广泛的实验，采用了最先进的方法来执行下游任务。结果表明，OEHR 作为一个有价值的扩展，现有的公开可用的 EHR 数据集。数据集可在 http://47.94.174.82/下载。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=OEHR:+An+Orthopedic+Electronic+Health+Record+Dataset)|0| -|[SIGformer: Sign-aware Graph Transformer for Recommendation](https://doi.org/10.1145/3626772.3657747)|Sirui Chen, Jiawei Chen, Sheng Zhou, Bohao Wang, Shen Han, Chanfei Su, Yuqing Yuan, Can Wang|Zhejiang University; OPPO Co Ltd; Huazhong Agricultural University|In recommender systems, most graph-based methods focus on positive userfeedback, while overlooking the valuable negative feedback. Integrating bothpositive and negative feedback to form a signed graph can lead to a morecomprehensive understanding of user preferences. However, the existing effortsto incorporate both types of feedback are sparse and face two main limitations:1) They process positive and negative feedback separately, which fails toholistically leverage the collaborative information within the signed graph; 2)They rely on MLPs or GNNs for information extraction from negative feedback,which may not be effective. To overcome these limitations, we introduce SIGformer, a new method thatemploys the transformer architecture to sign-aware graph-based recommendation.SIGformer incorporates two innovative positional encodings that capture thespectral properties and path patterns of the signed graph, enabling the fullexploitation of the entire graph. Our extensive experiments across fivereal-world datasets demonstrate the superiority of SIGformer overstate-of-the-art methods. The code is available athttps://github.com/StupidThree/SIGformer.|在推荐系统中，大多数基于图表的方法侧重于积极的用户反馈，而忽略了有价值的消极反馈。将正反馈和负反馈结合起来形成一个有符号的图形可以更全面地理解用户偏好。然而，现有的将这两种类型的反馈结合起来的努力是稀疏的，并且面临两个主要的限制: 1)他们分别处理正反馈和负反馈，这不能全面地利用签名图表中的协作信息; 2)他们依赖 MLP 或 GNN 从负反馈中获得信息抽取，这可能不是有效的。为了克服这些局限性，我们引入了 SIGformer，这是一种使用转换器结构来实现基于符号感知图的推荐的新方法。 SIGformer 包含了两种创新的位置编码，它们捕获了符号图的光谱特性和路径模式，从而实现了对整个图的充分利用。我们在五维世界数据集上的广泛实验证明了 SIGformer 夸大了最先进的方法的优越性。该代码可以在 https:// github.com/stupidthree/sigformer 上获得。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=SIGformer:+Sign-aware+Graph+Transformer+for+Recommendation)|0| -|[Scaling Laws For Dense Retrieval](https://doi.org/10.1145/3626772.3657743)|Yan Fang, Jingtao Zhan, Qingyao Ai, Jiaxin Mao, Weihang Su, Jia Chen, Yiqun Liu|Renmin University of China; Xiaohongshu Inc; Tsinghua University|Scaling up neural models has yielded significant advancements in a wide arrayof tasks, particularly in language generation. Previous studies have found thatthe performance of neural models frequently adheres to predictable scalinglaws, correlated with factors such as training set size and model size. Thisinsight is invaluable, especially as large-scale experiments grow increasinglyresource-intensive. Yet, such scaling law has not been fully explored in denseretrieval due to the discrete nature of retrieval metrics and complexrelationships between training data and model sizes in retrieval tasks. In thisstudy, we investigate whether the performance of dense retrieval models followsthe scaling law as other neural models. We propose to use contrastivelog-likelihood as the evaluation metric and conduct extensive experiments withdense retrieval models implemented with different numbers of parameters andtrained with different amounts of annotated data. Results indicate that, underour settings, the performance of dense retrieval models follows a precisepower-law scaling related to the model size and the number of annotations.Additionally, we examine scaling with prevalent data augmentation methods toassess the impact of annotation quality, and apply the scaling law to find thebest resource allocation strategy under a budget constraint. We believe thatthese insights will significantly contribute to understanding the scalingeffect of dense retrieval models and offer meaningful guidance for futureresearch endeavors.|放大神经模型已经在大量任务中取得了重大进展，特别是在语言生成方面。以往的研究发现，神经模型的性能往往遵循可预测的标度律，与训练集大小和模型大小等因素相关。这种洞察力是非常宝贵的，特别是在大规模实验日益增长的资源密集型的情况下。然而，由于检索度量的离散性以及检索任务中训练数据和模型大小之间的复杂关系，这种尺度规律在密集检索中还没有得到充分的研究。在这项研究中，我们调查是否密集检索模型的性能遵循标度律作为其他神经模型。我们建议使用对比日志似然作为评估指标，并进行广泛的实验与密集的检索模型实施与不同数量的参数和训练与不同数量的注释数据。结果表明，在我们的设置下，密集检索模型的性能遵循与模型大小和注释数量相关的精确幂律尺度。此外，我们还研究了使用流行的数据增强方法进行缩放来评估注释质量的影响，并应用缩放定律来寻找预算线下的最佳资源分配策略。我们相信，这些见解将显着有助于理解密集检索模型的缩放效应，并为未来的研究工作提供有意义的指导。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Scaling+Laws+For+Dense+Retrieval)|0| +|[When MOE Meets LLMs: Parameter Efficient Fine-tuning for Multi-task Medical Applications](https://doi.org/10.1145/3626772.3657722)|Qidong Liu, Xian Wu, Xiangyu Zhao, Yuanshao Zhu, Derong Xu, Feng Tian, Yefeng Zheng|Xi'an Jiaotong University; City University of Hong Kong; Xi'an Jiaotong University, City University of Hong Kong; University of Science and Technology of China, City University of Hong Kong; Southern University of Science and Technology, City University of Hong Kong; Tencent|The recent surge in Large Language Models (LLMs) has garnered significantattention across numerous fields. Fine-tuning is often required to fit generalLLMs for a specific domain, like the web-based healthcare system. However, twoproblems arise during fine-tuning LLMs for medical applications. One is thetask variety problem, which involves distinct tasks in real-world medicalscenarios. The variety often leads to sub-optimal fine-tuning for dataimbalance and seesaw problems. Besides, the large amount of parameters in LLMsleads to huge time and computation consumption by fine-tuning. To address thesetwo problems, we propose a novel parameter efficient fine-tuning framework formulti-task medical applications, dubbed as MOELoRA. The designed framework aimsto absorb both the benefits of mixture-of-expert (MOE) for multi-task learningand low-rank adaptation (LoRA) for parameter efficient fine-tuning. Forunifying MOE and LoRA, we devise multiple experts as the trainable parameters,where each expert consists of a pair of low-rank matrices to retain the smallsize of trainable parameters. Then, a task-motivated gate function for allMOELoRA layers is proposed, which can control the contributions of each expertand produce distinct parameters for various tasks. We conduct experiments on amulti-task medical dataset, indicating MOELoRA outperforms the existingparameter efficient fine-tuning methods. The code is available online.|最近大型语言模型(LLM)的兴起已经引起了众多领域的广泛关注。通常需要进行微调，以适应特定领域的常规 LLM，比如基于 Web 的医疗保健系统。然而，在微调 LLM 用于医疗应用时会出现两个问题。其中之一是任务多样性问题，它涉及到现实世界医学场景中不同的任务。这种多样性常常导致数据平衡和跷跷板问题的次优微调。此外，LLMS 中的大量参数导致了大量的时间和计算量的消耗。为了解决这两个问题，我们提出了一种新的参数高效微调框架公式多任务医疗应用，称为 MOELoRA。所设计的框架旨在吸收混合专家(MOE)在多任务学习和低秩自适应(LoRA)在参数有效微调方面的优点。结合 MOE 和 LoRA，我们设计了多个专家作为可训练参数，其中每个专家由一对低秩矩阵组成，以保留可训练参数的小尺寸。然后，提出了一种适用于所有 MOELoRA 层的任务驱动门函数，它可以控制每个专家的贡献，并为不同的任务产生不同的参数。在多任务医学数据集上进行了实验，结果表明 MOELoRA 优于现有的参数有效微调方法。代码可以在线获得。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=When+MOE+Meets+LLMs:+Parameter+Efficient+Fine-tuning+for+Multi-task+Medical+Applications)|0| +|[OEHR: An Orthopedic Electronic Health Record Dataset](https://doi.org/10.1145/3626772.3657885)|Yibo Xie, Kaifan Wang, Jiawei Zheng, Feiyan Liu, Xiaoli Wang, Guofeng Huang|School of Informatics, Institute of AI, Xiamen University, Xiamen, China; School of Informatics, Xiamen University, Xiamen, China; Xiamen University, Affiliated Southeast Hospital, Zhangzhou, China|During the past decades, healthcare institutions continually amassed clinical data that is not intended to support research. Despite the increasing number of publicly available electronic health record (EHR) datasets, it is difficult to find publicly available datasets in Orthopedics that can be used to compare and evaluate downstream tasks. This paper presents OEHR, a healthcare benchmark dataset in Orthopedics, sourced from the EHR of real hospitals. Information available includes patient measurements, diagnoses, treatments, clinical notes, and medical images. OEHR is intended to support clinical research. To evaluate the quality of OEHR, we conduct extensive experiments by implementing state-of-the-art methods for performing downstream tasks. The results show that OEHR serves as a valuable extension to existing publicly available EHR datasets. The dataset is available at http://47.94.174.82/.|在过去的几十年里，医疗机构不断地收集临床数据，而这些数据并不是用来支持研究的。尽管公开可用的电子健康记录(EHR)数据集数量不断增加，但很难在骨科中找到可用于比较和评估下游任务的公开可用数据集。本文介绍了 OEHR，一个骨科医疗基准数据集，来源于实际医院的 EHR。可获得的信息包括患者测量、诊断、治疗、临床记录和医学图像。OEHR 旨在支持临床研究。为了评估 OEHR 的质量，我们进行了广泛的实验，采用了最先进的方法来执行下游任务。结果表明，OEHR 作为一个有价值的扩展，现有的公开可用的 EHR 数据集。数据集可在 http://47.94.174.82/下载。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=OEHR:+An+Orthopedic+Electronic+Health+Record+Dataset)|0| +|[SIGformer: Sign-aware Graph Transformer for Recommendation](https://doi.org/10.1145/3626772.3657747)|Sirui Chen, Jiawei Chen, Sheng Zhou, Bohao Wang, Shen Han, Chanfei Su, Yuqing Yuan, Can Wang|Huazhong Agricultural University; Zhejiang University; OPPO Co Ltd|In recommender systems, most graph-based methods focus on positive userfeedback, while overlooking the valuable negative feedback. Integrating bothpositive and negative feedback to form a signed graph can lead to a morecomprehensive understanding of user preferences. However, the existing effortsto incorporate both types of feedback are sparse and face two main limitations:1) They process positive and negative feedback separately, which fails toholistically leverage the collaborative information within the signed graph; 2)They rely on MLPs or GNNs for information extraction from negative feedback,which may not be effective. To overcome these limitations, we introduce SIGformer, a new method thatemploys the transformer architecture to sign-aware graph-based recommendation.SIGformer incorporates two innovative positional encodings that capture thespectral properties and path patterns of the signed graph, enabling the fullexploitation of the entire graph. Our extensive experiments across fivereal-world datasets demonstrate the superiority of SIGformer overstate-of-the-art methods. The code is available athttps://github.com/StupidThree/SIGformer.|在推荐系统中，大多数基于图表的方法侧重于积极的用户反馈，而忽略了有价值的消极反馈。将正反馈和负反馈结合起来形成一个有符号的图形可以更全面地理解用户偏好。然而，现有的将这两种类型的反馈结合起来的努力是稀疏的，并且面临两个主要的限制: 1)他们分别处理正反馈和负反馈，这不能全面地利用签名图表中的协作信息; 2)他们依赖 MLP 或 GNN 从负反馈中获得信息抽取，这可能不是有效的。为了克服这些局限性，我们引入了 SIGformer，这是一种使用转换器结构来实现基于符号感知图的推荐的新方法。 SIGformer 包含了两种创新的位置编码，它们捕获了符号图的光谱特性和路径模式，从而实现了对整个图的充分利用。我们在五维世界数据集上的广泛实验证明了 SIGformer 夸大了最先进的方法的优越性。该代码可以在 https:// github.com/stupidthree/sigformer 上获得。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=SIGformer:+Sign-aware+Graph+Transformer+for+Recommendation)|0| +|[Scaling Laws For Dense Retrieval](https://doi.org/10.1145/3626772.3657743)|Yan Fang, Jingtao Zhan, Qingyao Ai, Jiaxin Mao, Weihang Su, Jia Chen, Yiqun Liu|Renmin University of China; Tsinghua University; Xiaohongshu Inc|Scaling up neural models has yielded significant advancements in a wide arrayof tasks, particularly in language generation. Previous studies have found thatthe performance of neural models frequently adheres to predictable scalinglaws, correlated with factors such as training set size and model size. Thisinsight is invaluable, especially as large-scale experiments grow increasinglyresource-intensive. Yet, such scaling law has not been fully explored in denseretrieval due to the discrete nature of retrieval metrics and complexrelationships between training data and model sizes in retrieval tasks. In thisstudy, we investigate whether the performance of dense retrieval models followsthe scaling law as other neural models. We propose to use contrastivelog-likelihood as the evaluation metric and conduct extensive experiments withdense retrieval models implemented with different numbers of parameters andtrained with different amounts of annotated data. Results indicate that, underour settings, the performance of dense retrieval models follows a precisepower-law scaling related to the model size and the number of annotations.Additionally, we examine scaling with prevalent data augmentation methods toassess the impact of annotation quality, and apply the scaling law to find thebest resource allocation strategy under a budget constraint. We believe thatthese insights will significantly contribute to understanding the scalingeffect of dense retrieval models and offer meaningful guidance for futureresearch endeavors.|放大神经模型已经在大量任务中取得了重大进展，特别是在语言生成方面。以往的研究发现，神经模型的性能往往遵循可预测的标度律，与训练集大小和模型大小等因素相关。这种洞察力是非常宝贵的，特别是在大规模实验日益增长的资源密集型的情况下。然而，由于检索度量的离散性以及检索任务中训练数据和模型大小之间的复杂关系，这种尺度规律在密集检索中还没有得到充分的研究。在这项研究中，我们调查是否密集检索模型的性能遵循标度律作为其他神经模型。我们建议使用对比日志似然作为评估指标，并进行广泛的实验与密集的检索模型实施与不同数量的参数和训练与不同数量的注释数据。结果表明，在我们的设置下，密集检索模型的性能遵循与模型大小和注释数量相关的精确幂律尺度。此外，我们还研究了使用流行的数据增强方法进行缩放来评估注释质量的影响，并应用缩放定律来寻找预算线下的最佳资源分配策略。我们相信，这些见解将显着有助于理解密集检索模型的缩放效应，并为未来的研究工作提供有意义的指导。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Scaling+Laws+For+Dense+Retrieval)|0| |[Diffusion Models for Generative Outfit Recommendation](https://doi.org/10.1145/3626772.3657719)|Yiyan Xu, Wenjie Wang, Fuli Feng, Yunshan Ma, Jizhi Zhang, Xiangnan He|University of Science and Technology of China; National University of Singapore|Outfit Recommendation (OR) in the fashion domain has evolved through twostages: Pre-defined Outfit Recommendation and Personalized Outfit Composition.However, both stages are constrained by existing fashion products, limitingtheir effectiveness in addressing users' diverse fashion needs. Recently, theadvent of AI-generated content provides the opportunity for OR to transcendthese limitations, showcasing the potential for personalized outfit generationand recommendation. To this end, we introduce a novel task called Generative OutfitRecommendation (GOR), aiming to generate a set of fashion images and composethem into a visually compatible outfit tailored to specific users. The keyobjectives of GOR lie in the high fidelity, compatibility, and personalizationof generated outfits. To achieve these, we propose a generative outfitrecommender model named DiFashion, which empowers exceptional diffusion modelsto accomplish the parallel generation of multiple fashion images. To ensurethree objectives, we design three kinds of conditions to guide the parallelgeneration process and adopt Classifier-Free-Guidance to enhance the alignmentbetween the generated images and conditions. We apply DiFashion on bothpersonalized Fill-In-The-Blank and GOR tasks and conduct extensive experimentson iFashion and Polyvore-U datasets. The quantitative and human-involvedqualitative evaluation demonstrate the superiority of DiFashion overcompetitive baselines.|服装推荐(OR)在时尚领域经历了两个阶段: 预先定义的服装推荐和个性化的服装组合。然而，这两个阶段都受到现有时尚产品的限制，限制了它们满足用户不同时尚需求的有效性。最近，人工智能生成内容的出现为 OR 提供了超越这些限制的机会，展示了个性化服装生成和推荐的潜力。为此，我们引入了一个新颖的任务，称为生成性出行推荐(GOR) ，旨在生成一组时尚图像，并将它们组成一个视觉兼容的服装定制给特定的用户。GOR 的关键目标在于高保真度、兼容性和生成服务的个性化。为了实现这些目标，我们提出了一个名为 DiFashion 的生成式服装推荐模型，它授权异常扩散模型来完成多个时尚图像的并行生成。为了保证三个目标，我们设计了三种条件来指导并行生成过程，并采用无分类器导引来增强生成的图像与条件之间的对齐。我们将 DiFashion 应用于个性化的填空和 GOR 任务，并在 iFashion 和 Polyvore-U 数据集上进行了广泛的实验。定量和人为参与的定性评价证明了 DiFashion 过度竞争基线的优越性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Diffusion+Models+for+Generative+Outfit+Recommendation)|0| |[Collaborative Filtering Based on Diffusion Models: Unveiling the Potential of High-Order Connectivity](https://doi.org/10.1145/3626772.3657742)|Yu Hou, JinDuk Park, WonYong Shin|Yonsei University|A recent study has shown that diffusion models are well-suited for modelingthe generative process of user-item interactions in recommender systems due totheir denoising nature. However, existing diffusion model-based recommendersystems do not explicitly leverage high-order connectivities that containcrucial collaborative signals for accurate recommendations. Addressing thisgap, we propose CF-Diff, a new diffusion model-based collaborative filtering(CF) method, which is capable of making full use of collaborative signals alongwith multi-hop neighbors. Specifically, the forward-diffusion process addsrandom noise to user-item interactions, while the reverse-denoising processaccommodates our own learning model, named cross-attention-guided multi-hopautoencoder (CAM-AE), to gradually recover the original user-item interactions.CAM-AE consists of two core modules: 1) the attention-aided AE module,responsible for precisely learning latent representations of user-iteminteractions while preserving the model's complexity at manageable levels, and2) the multi-hop cross-attention module, which judiciously harnesses high-orderconnectivity information to capture enhanced collaborative signals. Throughcomprehensive experiments on three real-world datasets, we demonstrate thatCF-Diff is (a) Superior: outperforming benchmark recommendation methods,achieving remarkable gains up to 7.29Theoretically-validated: reducing computations while ensuring that theembeddings generated by our model closely approximate those from the originalcross-attention, and (c) Scalable: proving the computational efficiency thatscales linearly with the number of users or items.|最近的一项研究表明，由于扩散模型的去噪特性，它非常适合于模拟推荐系统中用户-项目交互的生成过程。然而，现有的基于扩散模型的推荐系统并没有明确地利用包含关键协作信号的高阶连接来获得准确的推荐。为了解决这一问题，我们提出了基于扩散模型的协同过滤(CF)方法 CF-Diff，该方法能够充分利用协作信号和多跳邻居。具体而言，前向扩散过程在用户项目交互中加入随机噪声，而反向去噪过程适应我们自己的学习模型，即交叉注意引导的多跳自动编码器(CAM-AE) ，以逐渐恢复原始的用户项目交互。 CAM-AE 由两个核心模块组成: 1)注意辅助 AE 模块，负责精确学习用户项目交互的潜在表征，同时将模型的复杂性保持在可管理的水平; 2)多跳交叉注意模块，明智地利用高阶连通性信息捕获增强的协作信号。通过对三个真实世界数据集的全面实验，我们证明 CF-Diff 是(a)优越的: 表现优于基准推荐方法，达到显着的收益高达7.29理论验证: 减少计算，同时确保我们的模型生成的嵌入接近那些来自原始交叉注意力，和(c)可伸缩: 证明计算效率与用户或项目的数量成线性关系。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Collaborative+Filtering+Based+on+Diffusion+Models:+Unveiling+the+Potential+of+High-Order+Connectivity)|0| -|[Graph Signal Diffusion Model for Collaborative Filtering](https://doi.org/10.1145/3626772.3657759)|Yunqin Zhu, Chao Wang, Qi Zhang, Hui Xiong|The Hong Kong University of Science and Technology; University of Science and Technology of China; Shanghai AI Laboratory|Collaborative filtering is a critical technique in recommender systems. Amongvarious methods, an increasingly popular paradigm is to reconstruct user-iteminteractions based on the historical observations. This can be viewed as aconditional generative task, where recently developed diffusion modeldemonstrates great potential. However, existing studies on diffusion modelslack effective solutions for modeling implicit feedback data. Particularly, theisotropic nature of the standard diffusion process fails to account for theheterogeneous dependencies among items, leading to a misalignment with thegraphical structure of the interaction space. Meanwhile, random noisedestroying personalized information in interaction vectors, causing difficultyin reverse reconstruction. In this paper, we make novel adaptions of diffusionmodel and propose Graph Signal Diffusion Model for Collaborative Filtering(named GiffCF). To better represent the high-dimensional and sparsedistribution of implicit feedback, we define a generalized form of denoisingdiffusion using heat equation on the item-item similarity graph. Our forwardprocess smooths interaction signals with an advanced family of graph filters.Hence, instead of losing information, it involves item-item similarities asbeneficial prior knowledge for recommendation. To reconstruct high-qualityinteractions, our reverse process iteratively refines and sharpens preferencesignals in a deterministic manner, where the update direction is conditioned onthe user history and computed from a carefully designed two-stage denoiser.Finally, through extensive experiments, we show that GiffCF effectivelyleverages the advantages of both diffusion model and graph signal processing,and achieves state-of-the-art performance on three benchmark datasets.|协同过滤是推荐系统中的一项关键技术。在各种方法中，一个日益流行的范例是基于历史观察重建用户-项目交互。这可以看作是条件生成任务，其中最近开发的扩散模型显示了巨大的潜力。然而，现有的扩散模型松弛有效解的研究隐式反馈数据建模。特别是，标准扩散过程的各向同性性质没有考虑到项目之间的非均匀依赖性，导致与相互作用空间的图形结构不一致。同时，随机噪声破坏交互矢量中的个性化信息，给反向重建带来困难。在本文中，我们对扩散模型进行了新的改进，并提出了协同过滤的图形信号扩散模型(GiffCF)。为了更好地表示隐式反馈的高维稀疏分布，我们在项目-项目相似图上利用热方程定义了一种广义形式的去噪扩散。我们的正向处理平滑交互信号与一个先进的图形滤波器家族。因此，它不仅不会丢失信息，而且涉及项目项相似性作为推荐的有益先验知识。为了重建高质量的交互，我们的反向过程以确定性方式迭代地细化和锐化偏好信号，其中更新方向以用户历史为条件，并从仔细设计的两阶段去噪器计算。最后，通过大量的实验表明，GifffCF 有效地利用了扩散模型和图形信号处理的优势，在三个基准数据集上实现了最先进的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Graph+Signal+Diffusion+Model+for+Collaborative+Filtering)|0| -|[Multi-granular Adversarial Attacks against Black-box Neural Ranking Models](https://doi.org/10.1145/3626772.3657704)|YuAn Liu, Ruqing Zhang, Jiafeng Guo, Maarten de Rijke, Yixing Fan, Xueqi Cheng|Institute of Computing Technology, Chinese Academy of Sciences; University of Amsterdam|Adversarial ranking attacks have gained increasing attention due to theirsuccess in probing vulnerabilities, and, hence, enhancing the robustness, ofneural ranking models. Conventional attack methods employ perturbations at asingle granularity, e.g., word-level or sentence-level, to a target document.However, limiting perturbations to a single level of granularity may reduce theflexibility of creating adversarial examples, thereby diminishing the potentialthreat of the attack. Therefore, we focus on generating high-qualityadversarial examples by incorporating multi-granular perturbations. Achievingthis objective involves tackling a combinatorial explosion problem, whichrequires identifying an optimal combination of perturbations across allpossible levels of granularity, positions, and textual pieces. To address thischallenge, we transform the multi-granular adversarial attack into a sequentialdecision-making process, where perturbations in the next attack step areinfluenced by the perturbed document in the current attack step. Since theattack process can only access the final state without direct intermediatesignals, we use reinforcement learning to perform multi-granular attacks.During the reinforcement learning process, two agents work cooperatively toidentify multi-granular vulnerabilities as attack targets and organizeperturbation candidates into a final perturbation sequence. Experimentalresults show that our attack method surpasses prevailing baselines in bothattack effectiveness and imperceptibility.|由于对抗性排序攻击能够成功地探测漏洞，从而增强了神经排序模型的鲁棒性，因此受到了越来越多的关注。传统的攻击方法采用单一粒度的扰动，例如，单词级或句子级的攻击目标文档。然而，将扰动限制在单一粒度级别可能会降低创建敌对示例的灵活性，从而减少攻击的潜在威胁。因此，我们的重点是通过结合多粒度扰动生成高质量的对抗性例子。实现这个目标需要解决一个组合爆炸问题，这需要在所有可能的粒度、位置和文本块级别上识别出扰动的最佳组合。为了应对这一挑战，我们将多粒度对抗性攻击转化为一个顺序决策过程，在这个过程中，下一个攻击步骤中的扰动会受到当前攻击步骤中受到干扰的文档的影响。由于攻击过程只能在没有直接中间信号的情况下访问最终状态，因此我们使用强化学习来执行多粒度攻击。在强化学习过程中，两个代理协同工作，将多粒度漏洞识别为攻击目标，并将扰动候选者组织成最终的扰动序列。实验结果表明，我们的攻击方法在攻击效果和不可感知性方面都优于现有的基准。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Multi-granular+Adversarial+Attacks+against+Black-box+Neural+Ranking+Models)|0| -|[Optimal Transport Enhanced Cross-City Site Recommendation](https://doi.org/10.1145/3626772.3657757)|Xinhang Li, Xiangyu Zhao, Zihao Wang, Yang Duan, Yong Zhang, Chunxiao Xing|City University of Hong Kong; UIUC; Tsinghua University; HKUST|Site recommendation, which aims at predicting the optimal location for brands to open new branches, has demonstrated an important role in assisting decision-making in modern business. In contrast to traditional recommender systems that can benefit from extensive information, site recommendation starkly suffers from extremely limited information and thus leads to unsatisfactory performance. Therefore, existing site recommendation methods primarily focus on several specific name brands and heavily rely on fine-grained human-crafted features to avoid the data sparsity problem. However, such solutions are not able to fulfill the demand for rapid development in modern business. Therefore, we aim to alleviate the data sparsity problem by effectively utilizing data across multiple cities and thereby propose a novel Optimal Transport enhanced Cross-city (OTC) framework for site recommendation. Specifically, OTC leverages optimal transport (OT) on the learned embeddings of brands and regions separately to project the brands and regions from the source city to the target city. Then, the projected embeddings of brands and regions are utilized to obtain the inference recommendation in the target city. By integrating the original recommendation and the inference recommendations from multiple cities, OTC is able to achieve enhanced recommendation results. The experimental results on the real-world OpenSiteRec dataset, encompassing thousands of brands and regions across four metropolises, demonstrate the effectiveness of our proposed OTC in further improving the performance of site recommendation models.|网站推荐，旨在预测最佳位置的品牌开设新的分支机构，已经显示了在协助决策的重要作用，在现代企业。与可以从大量信息中受益的传统推荐系统不同，网站推荐严重受限于极其有限的信息，从而导致不能令人满意的性能。因此，现有的网站推荐方法主要集中在几个特定的品牌，并严重依赖于细粒度的人工精心制作的功能，以避免数据稀疏问题。然而，这样的解决方案并不能满足现代企业快速发展的需要。因此，我们的目标是通过有效地利用跨多个城市的数据来缓解数据稀疏性问题，从而提出一种新的最优交通增强跨城市(OTC)的网站推荐框架。具体来说，OTC 利用最优运输(OT)对品牌和区域的学习嵌入，分别从源头城市向目标城市投射品牌和区域。然后，利用品牌和区域的投影嵌入，得到目标城市的推理推荐。通过整合来自多个城市的原始推荐和推理推荐，OTC 能够获得更好的推荐结果。在现实世界 OpenSiteRec 数据集上的实验结果，涵盖了四个大都市的数千个品牌和地区，证明了我们提出的 OTC 在进一步提高网站推荐模型性能方面的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Optimal+Transport+Enhanced+Cross-City+Site+Recommendation)|0| -|[Disentangled Contrastive Hypergraph Learning for Next POI Recommendation](https://doi.org/10.1145/3626772.3657726)|Yantong Lai, Yijun Su, Lingwei Wei, Tianqi He, Haitao Wang, Gaode Chen, Daren Zha, Qiang Liu, Xingxing Wang|Institute of Information Engineering, Chinese Academy of Sciences; JD iCity, JD Technology; Meituan|Next point-of-interest (POI) recommendation has been a prominent and trending task to provide next suitable POI suggestions for users. Most existing sequential-based and graph neural network-based methods have explored various approaches to modeling user visiting behaviors and have achieved considerable performances. However, two key issues have received less attention: i) Most previous studies have ignored the fact that user preferences are diverse and constantly changing in terms of various aspects, leading to entangled and suboptimal user representations. ii) Many existing methods have inadequately modeled the crucial cooperative associations between different aspects, hindering the ability to capture complementary recommendation effects during the learning process. To tackle these challenges, we propose a novel framework Disentangled Contrastive Hypergraph Learning (DCHL) for next POI recommendation. Specifically, we design a multi-view disentangled hypergraph learning component to disentangle intrinsic aspects among collaborative, transitional and geographical views with adjusted hypergraph convolutional networks. Additionally, we propose an adaptive fusion method to integrate multi-view information automatically. Finally, cross-view contrastive learning is employed to capture cooperative associations among views and reinforce the quality of user and POI representations based on self-discrimination. Extensive experiments on three real-world datasets validate the superiority of our proposal over various state-of-the-arts. To facilitate future research, our code is available at https://github.com/icmpnorequest/SIGIR2024_DCHL.|下一个感兴趣的点(POI)建议已经成为一个突出和趋势性的任务，为用户提供下一个合适的 POI 建议。现有的基于序列的和基于图神经网络的方法已经探索了各种用户访问行为的建模方法，并取得了可观的性能。然而，有两个关键问题受到的关注较少: i)大多数以前的研究忽略了这样一个事实，即用户偏好是多样的，并在各个方面不断变化，导致纠缠和次优的用户表示。(2)许多现有的方法对不同方面之间的关键合作关系建模不足，影响了学习过程中获取互补推荐效应的能力。为了应对这些挑战，我们提出了一个新的框架，对比超图学习(DCHL)的下一个 POI 建议。具体来说，我们设计了一个多视图分离超图学习组件，用于分离协作视图、过渡视图和地理视图之间的内在关系，并对超图卷积网络进行了调整。此外，本文还提出了一种自适应融合方法来实现多视点信息的自动融合。最后，利用跨视角对比学习来捕捉视图之间的合作关联，提高基于自我歧视的用户和 POI 表示的质量。在三个真实世界数据集上的大量实验验证了我们的方案相对于各种最新技术的优越性。为方便日后进行研究，我们的代码已上载至 https://github.com/icmpnorequest/sigir2024_dchl。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Disentangled+Contrastive+Hypergraph+Learning+for+Next+POI+Recommendation)|0| +|[Graph Signal Diffusion Model for Collaborative Filtering](https://doi.org/10.1145/3626772.3657759)|Yunqin Zhu, Chao Wang, Qi Zhang, Hui Xiong|University of Science and Technology of China; Shanghai AI Laboratory; The Hong Kong University of Science and Technology|Collaborative filtering is a critical technique in recommender systems. Amongvarious methods, an increasingly popular paradigm is to reconstruct user-iteminteractions based on the historical observations. This can be viewed as aconditional generative task, where recently developed diffusion modeldemonstrates great potential. However, existing studies on diffusion modelslack effective solutions for modeling implicit feedback data. Particularly, theisotropic nature of the standard diffusion process fails to account for theheterogeneous dependencies among items, leading to a misalignment with thegraphical structure of the interaction space. Meanwhile, random noisedestroying personalized information in interaction vectors, causing difficultyin reverse reconstruction. In this paper, we make novel adaptions of diffusionmodel and propose Graph Signal Diffusion Model for Collaborative Filtering(named GiffCF). To better represent the high-dimensional and sparsedistribution of implicit feedback, we define a generalized form of denoisingdiffusion using heat equation on the item-item similarity graph. Our forwardprocess smooths interaction signals with an advanced family of graph filters.Hence, instead of losing information, it involves item-item similarities asbeneficial prior knowledge for recommendation. To reconstruct high-qualityinteractions, our reverse process iteratively refines and sharpens preferencesignals in a deterministic manner, where the update direction is conditioned onthe user history and computed from a carefully designed two-stage denoiser.Finally, through extensive experiments, we show that GiffCF effectivelyleverages the advantages of both diffusion model and graph signal processing,and achieves state-of-the-art performance on three benchmark datasets.|协同过滤是推荐系统中的一项关键技术。在各种方法中，一个日益流行的范例是基于历史观察重建用户-项目交互。这可以看作是条件生成任务，其中最近开发的扩散模型显示了巨大的潜力。然而，现有的扩散模型松弛有效解的研究隐式反馈数据建模。特别是，标准扩散过程的各向同性性质没有考虑到项目之间的非均匀依赖性，导致与相互作用空间的图形结构不一致。同时，随机噪声破坏交互矢量中的个性化信息，给反向重建带来困难。在本文中，我们对扩散模型进行了新的改进，并提出了协同过滤的图形信号扩散模型(GiffCF)。为了更好地表示隐式反馈的高维稀疏分布，我们在项目-项目相似图上利用热方程定义了一种广义形式的去噪扩散。我们的正向处理平滑交互信号与一个先进的图形滤波器家族。因此，它不仅不会丢失信息，而且涉及项目项相似性作为推荐的有益先验知识。为了重建高质量的交互，我们的反向过程以确定性方式迭代地细化和锐化偏好信号，其中更新方向以用户历史为条件，并从仔细设计的两阶段去噪器计算。最后，通过大量的实验表明，GifffCF 有效地利用了扩散模型和图形信号处理的优势，在三个基准数据集上实现了最先进的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Graph+Signal+Diffusion+Model+for+Collaborative+Filtering)|0| +|[Multi-granular Adversarial Attacks against Black-box Neural Ranking Models](https://doi.org/10.1145/3626772.3657704)|YuAn Liu, Ruqing Zhang, Jiafeng Guo, Maarten de Rijke, Yixing Fan, Xueqi Cheng|University of Amsterdam; Institute of Computing Technology, Chinese Academy of Sciences|Adversarial ranking attacks have gained increasing attention due to theirsuccess in probing vulnerabilities, and, hence, enhancing the robustness, ofneural ranking models. Conventional attack methods employ perturbations at asingle granularity, e.g., word-level or sentence-level, to a target document.However, limiting perturbations to a single level of granularity may reduce theflexibility of creating adversarial examples, thereby diminishing the potentialthreat of the attack. Therefore, we focus on generating high-qualityadversarial examples by incorporating multi-granular perturbations. Achievingthis objective involves tackling a combinatorial explosion problem, whichrequires identifying an optimal combination of perturbations across allpossible levels of granularity, positions, and textual pieces. To address thischallenge, we transform the multi-granular adversarial attack into a sequentialdecision-making process, where perturbations in the next attack step areinfluenced by the perturbed document in the current attack step. Since theattack process can only access the final state without direct intermediatesignals, we use reinforcement learning to perform multi-granular attacks.During the reinforcement learning process, two agents work cooperatively toidentify multi-granular vulnerabilities as attack targets and organizeperturbation candidates into a final perturbation sequence. Experimentalresults show that our attack method surpasses prevailing baselines in bothattack effectiveness and imperceptibility.|由于对抗性排序攻击能够成功地探测漏洞，从而增强了神经排序模型的鲁棒性，因此受到了越来越多的关注。传统的攻击方法采用单一粒度的扰动，例如，单词级或句子级的攻击目标文档。然而，将扰动限制在单一粒度级别可能会降低创建敌对示例的灵活性，从而减少攻击的潜在威胁。因此，我们的重点是通过结合多粒度扰动生成高质量的对抗性例子。实现这个目标需要解决一个组合爆炸问题，这需要在所有可能的粒度、位置和文本块级别上识别出扰动的最佳组合。为了应对这一挑战，我们将多粒度对抗性攻击转化为一个顺序决策过程，在这个过程中，下一个攻击步骤中的扰动会受到当前攻击步骤中受到干扰的文档的影响。由于攻击过程只能在没有直接中间信号的情况下访问最终状态，因此我们使用强化学习来执行多粒度攻击。在强化学习过程中，两个代理协同工作，将多粒度漏洞识别为攻击目标，并将扰动候选者组织成最终的扰动序列。实验结果表明，我们的攻击方法在攻击效果和不可感知性方面都优于现有的基准。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Multi-granular+Adversarial+Attacks+against+Black-box+Neural+Ranking+Models)|0| +|[Optimal Transport Enhanced Cross-City Site Recommendation](https://doi.org/10.1145/3626772.3657757)|Xinhang Li, Xiangyu Zhao, Zihao Wang, Yang Duan, Yong Zhang, Chunxiao Xing|Tsinghua University; HKUST; City University of Hong Kong; UIUC|Site recommendation, which aims at predicting the optimal location for brands to open new branches, has demonstrated an important role in assisting decision-making in modern business. In contrast to traditional recommender systems that can benefit from extensive information, site recommendation starkly suffers from extremely limited information and thus leads to unsatisfactory performance. Therefore, existing site recommendation methods primarily focus on several specific name brands and heavily rely on fine-grained human-crafted features to avoid the data sparsity problem. However, such solutions are not able to fulfill the demand for rapid development in modern business. Therefore, we aim to alleviate the data sparsity problem by effectively utilizing data across multiple cities and thereby propose a novel Optimal Transport enhanced Cross-city (OTC) framework for site recommendation. Specifically, OTC leverages optimal transport (OT) on the learned embeddings of brands and regions separately to project the brands and regions from the source city to the target city. Then, the projected embeddings of brands and regions are utilized to obtain the inference recommendation in the target city. By integrating the original recommendation and the inference recommendations from multiple cities, OTC is able to achieve enhanced recommendation results. The experimental results on the real-world OpenSiteRec dataset, encompassing thousands of brands and regions across four metropolises, demonstrate the effectiveness of our proposed OTC in further improving the performance of site recommendation models.|网站推荐，旨在预测最佳位置的品牌开设新的分支机构，已经显示了在协助决策的重要作用，在现代企业。与可以从大量信息中受益的传统推荐系统不同，网站推荐严重受限于极其有限的信息，从而导致不能令人满意的性能。因此，现有的网站推荐方法主要集中在几个特定的品牌，并严重依赖于细粒度的人工精心制作的功能，以避免数据稀疏问题。然而，这样的解决方案并不能满足现代企业快速发展的需要。因此，我们的目标是通过有效地利用跨多个城市的数据来缓解数据稀疏性问题，从而提出一种新的最优交通增强跨城市(OTC)的网站推荐框架。具体来说，OTC 利用最优运输(OT)对品牌和区域的学习嵌入，分别从源头城市向目标城市投射品牌和区域。然后，利用品牌和区域的投影嵌入，得到目标城市的推理推荐。通过整合来自多个城市的原始推荐和推理推荐，OTC 能够获得更好的推荐结果。在现实世界 OpenSiteRec 数据集上的实验结果，涵盖了四个大都市的数千个品牌和地区，证明了我们提出的 OTC 在进一步提高网站推荐模型性能方面的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Optimal+Transport+Enhanced+Cross-City+Site+Recommendation)|0| +|[Disentangled Contrastive Hypergraph Learning for Next POI Recommendation](https://doi.org/10.1145/3626772.3657726)|Yantong Lai, Yijun Su, Lingwei Wei, Tianqi He, Haitao Wang, Gaode Chen, Daren Zha, Qiang Liu, Xingxing Wang|Meituan; Institute of Information Engineering, Chinese Academy of Sciences; JD iCity, JD Technology|Next point-of-interest (POI) recommendation has been a prominent and trending task to provide next suitable POI suggestions for users. Most existing sequential-based and graph neural network-based methods have explored various approaches to modeling user visiting behaviors and have achieved considerable performances. However, two key issues have received less attention: i) Most previous studies have ignored the fact that user preferences are diverse and constantly changing in terms of various aspects, leading to entangled and suboptimal user representations. ii) Many existing methods have inadequately modeled the crucial cooperative associations between different aspects, hindering the ability to capture complementary recommendation effects during the learning process. To tackle these challenges, we propose a novel framework Disentangled Contrastive Hypergraph Learning (DCHL) for next POI recommendation. Specifically, we design a multi-view disentangled hypergraph learning component to disentangle intrinsic aspects among collaborative, transitional and geographical views with adjusted hypergraph convolutional networks. Additionally, we propose an adaptive fusion method to integrate multi-view information automatically. Finally, cross-view contrastive learning is employed to capture cooperative associations among views and reinforce the quality of user and POI representations based on self-discrimination. Extensive experiments on three real-world datasets validate the superiority of our proposal over various state-of-the-arts. To facilitate future research, our code is available at https://github.com/icmpnorequest/SIGIR2024_DCHL.|下一个感兴趣的点(POI)建议已经成为一个突出和趋势性的任务，为用户提供下一个合适的 POI 建议。现有的基于序列的和基于图神经网络的方法已经探索了各种用户访问行为的建模方法，并取得了可观的性能。然而，有两个关键问题受到的关注较少: i)大多数以前的研究忽略了这样一个事实，即用户偏好是多样的，并在各个方面不断变化，导致纠缠和次优的用户表示。(2)许多现有的方法对不同方面之间的关键合作关系建模不足，影响了学习过程中获取互补推荐效应的能力。为了应对这些挑战，我们提出了一个新的框架，对比超图学习(DCHL)的下一个 POI 建议。具体来说，我们设计了一个多视图分离超图学习组件，用于分离协作视图、过渡视图和地理视图之间的内在关系，并对超图卷积网络进行了调整。此外，本文还提出了一种自适应融合方法来实现多视点信息的自动融合。最后，利用跨视角对比学习来捕捉视图之间的合作关联，提高基于自我歧视的用户和 POI 表示的质量。在三个真实世界数据集上的大量实验验证了我们的方案相对于各种最新技术的优越性。为方便日后进行研究，我们的代码已上载至 https://github.com/icmpnorequest/sigir2024_dchl。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Disentangled+Contrastive+Hypergraph+Learning+for+Next+POI+Recommendation)|0| |[CLLP: Contrastive Learning Framework Based on Latent Preferences for Next POI Recommendation](https://doi.org/10.1145/3626772.3657730)|Hongli Zhou, Zhihao Jia, Haiyang Zhu, Zhizheng Zhang|Southeast University School of Computer Science and Engineering|Next Point-Of-Interest (POI) recommendation plays an important role in various location-based services. Its main objective is to predict the users' next interested POI based on their previous check-in information. Most existing studies view the next POI recommendation as a sequence prediction problem but pay little attention to the fine-grained latent preferences of users, neglecting the diversity of user motivations on visiting the POIs. In this paper, we propose a contrastive learning framework based on latent preferences (CLLP) for next POI recommendation, which models the latent preference distributions of users at each POI and then yield disentangled latent preference representations. Specifically, we leverage the cross-local and global spatio-temporal contexts to learn POI representations for dynamically modeling user preferences. And we design a novel distillation strategy to make full use of the collaborative signals from other users for representation optimization. Then, we disentangle multiple latent preferences in POI representations using predefined preference prototypes, while leveraging preference-level contrastive learning to encourage independence of different latent preferences by improving the quality of latent preference representation space. Meanwhile, we employ a multi-task training strategy to jointly optimize all parameters. Experimental results on two real-world datasets show that CLLP achieves the state-of-the-art performance and significantly outperforms all existing solutions. Further investigations demonstrate the robustness of CLLP against sparse and noisy data.|下一个兴趣点(POI)推荐在各种基于位置的服务中起着重要作用。它的主要目标是根据用户以前的签入信息预测用户下一个感兴趣的 POI。现有的研究大多将下一个 POI 推荐视为一个序列预测问题，而忽视了用户访问 POI 的细粒度潜在偏好，忽视了用户访问 POI 动机的多样性。本文提出了一个基于潜在偏好的对比学习框架(CLLP) ，用于下一个 POI 推荐，该框架对每个 POI 用户的潜在偏好分布进行建模，然后产生解纠缠的潜在偏好表示。具体来说，我们利用跨局部和全局的时空上下文来学习用于动态建模用户偏好的 POI 表示。并设计了一种新的精馏策略，充分利用其他用户的协同信号进行表示优化。然后，利用预定义的偏好原型对 POI 表示中的多个潜在偏好进行分离，同时利用偏好水平对比学习通过提高潜在偏好表示空间的质量来鼓励不同潜在偏好的独立性。同时，采用多任务训练策略，对各参数进行联合优化。在两个实际数据集上的实验结果表明，CLLP 算法的性能达到了最高水平，明显优于现有的所有解决方案。进一步的研究证明了 CLLP 算法对稀疏和噪声数据的鲁棒性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=CLLP:+Contrastive+Learning+Framework+Based+on+Latent+Preferences+for+Next+POI+Recommendation)|0| |[OpenSiteRec: An Open Dataset for Site Recommendation](https://doi.org/10.1145/3626772.3657875)|Xinhang Li, Xiangyu Zhao, Yejing Wang, Yu Liu, Chong Chen, Cheng Long, Yong Zhang, Chunxiao Xing||As a representative information retrieval task, site recommendation, which aims at predicting the optimal sites for a brand or an institution to open new branches in an automatic data-driven way, is beneficial and crucial for brand development in modern business. However, there is no publicly available dataset so far and most existing approaches are limited to an extremely small scope of brands, which seriously hinders the research on site recommendation. Therefore, we collect, construct and release an open comprehensive dataset, namely OpenSiteRec, to facilitate and promote the research on site recommendation. Specifically, OpenSiteRec leverages a heterogeneous graph schema to represent various types of real-world entities and relations in four international metropolises. To evaluate the performance of the existing general methods on the site recommendation task, we conduct benchmarking experiments of several representative recommendation models on OpenSiteRec. Furthermore, we also highlight the potential application directions to demonstrate the wide applicability of OpenSiteRec. We believe that our OpenSiteRec dataset is significant and anticipated to encourage the development of advanced methods for site recommendation. OpenSiteRec is available online at https://OpenSiteRec.github.io/.|网站推荐作为一项具有代表性的信息检索任务，旨在预测一个品牌或机构以自动数据驱动的方式开设新分支机构的最佳网站，对于现代商业中的品牌发展是有益的，也是至关重要的。然而，到目前为止还没有公开的数据集，大多数现有的方法仅限于极小范围的品牌，这严重阻碍了对网站推荐的研究。因此，我们收集、构建和发布一个开放的综合数据集，即 OpenSiteRec，以促进和推动网站推荐的研究。具体来说，OpenSiteRec 利用异构图模式来表示四个国际大都市中各种类型的现实世界实体和关系。为了评估现有的一般方法在站点推荐任务中的性能，我们在 OpenSiteRec 上对几种有代表性的推荐模型进行了基准测试实验。此外，我们还强调了潜在的应用程序方向，以展示 OpenSiteRec 的广泛适用性。我们相信我们的 OpenSiteRec 数据集是重要的，并且预计将鼓励开发用于网站推荐的高级方法。OpenSiteRec 可于网上 https://OpenSiteRec.github.io/下载。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=OpenSiteRec:+An+Open+Dataset+for+Site+Recommendation)|0| |[Fairness-Aware Exposure Allocation via Adaptive Reranking](https://doi.org/10.1145/3626772.3657794)|Thomas Jänich, Graham McDonald, Iadh Ounis|University of Glasgow|In the first stage of a re-ranking pipeline, an inexpensive ranking model is typically deployed to retrieve a set of documents that are highly likely to be relevant to the user's query. The retrieved documents are then re-ranked by a more effective but expensive ranking model, e.g., a deep neural ranker such as BERT. However, in such a standard pipeline, no new documents are typically discovered after the first stage retrieval. Hence, the amount of exposure that a particular group of documents - e.g., documents from a particular demographic category - can receive is limited by the number of documents that are retrieved in the first stage retrieval. Indeed, if too few documents from a group are retrieved in the first stage retrieval, ensuring that the group receives a fair amount of exposure to the user may become infeasible. Therefore, it is useful to identify more documents from underrepresented groups that are potentially relevant to the query during the re-ranking stage. In this work, we investigate how deploying adaptive re-ranking, which enables the discovery of additional potentially relevant documents in the re-ranking stage, can improve the exposure that a given group of documents receives in the final ranking. We propose six adaptive re-ranking policies that can discover documents from underrepresented groups to increase the disadvantaged groups' exposure in the final ranking. Our experiments on the TREC 2021 and 2022 Fair Ranking Track test collections show that our policies consistently improve the fairness of the exposure distribution in the final ranking, compared to standard adaptive re-ranking approaches, resulting in increases of up to ~13% in Attention Weighted Ranked Fairness (AWRF). Moreover, our best performing policy, Policy 6, consistently maintains and frequently increases the utility of the search results in terms of nDCG.|在重新排序管道的第一阶段，通常会部署一个廉价的排序模型来检索一组很可能与用户的查询相关的文档。然后，检索到的文档通过一个更有效但代价更高的排序模型进行重新排序，例如，一个像 BERT 这样的深度神经排序器。但是，在这样的标准管道中，在第一阶段检索之后通常不会发现新文档。因此，一组特定文件(例如，来自特定人口类别的文件)能够接触的数量受到在第一阶段检索中检索到的文件数量的限制。实际上，如果在第一阶段检索时检索到的来自某个组的文档太少，那么确保该组获得相当数量的用户暴露就可能变得不可行。因此，在重新排序阶段，从代表性不足的群体中识别出更多可能与查询相关的文档是有用的。在这项工作中，我们研究如何部署自适应重新排序，这使得发现额外的潜在相关的文件在重新排序阶段，可以提高曝光，一组文件接收到的最终排名。我们提出了六个适应性重新排序的政策，可以发现文件来自代表性不足的群体，以增加弱势群体的曝光在最终的排名。我们在 TREC 2021和2022公平排名跟踪测试集合上的实验表明，与标准的自适应重新排名方法相比，我们的政策持续改善了最终排名中暴露分布的公平性，导致注意力加权公平性(AWRF)增加了约13% 。此外，我们表现最好的策略，策略6，始终保持并频繁增加搜索结果在 nDCG 方面的效用。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Fairness-Aware+Exposure+Allocation+via+Adaptive+Reranking)|0| |[A Taxation Perspective for Fair Re-ranking](https://doi.org/10.1145/3626772.3657766)|Chen Xu, Xiaopeng Ye, Wenjie Wang, Liang Pang, Jun Xu, TatSeng Chua|Renmin University of China; Institute of Computing Technology; National University of Singapore|Fair re-ranking aims to redistribute ranking slots among items more equitablyto ensure responsibility and ethics. The exploration of redistribution problemshas a long history in economics, offering valuable insights for conceptualizingfair re-ranking as a taxation process. Such a formulation provides us with afresh perspective to re-examine fair re-ranking and inspire the development ofnew methods. From a taxation perspective, we theoretically demonstrate thatmost previous fair re-ranking methods can be reformulated as an item-level taxpolicy. Ideally, a good tax policy should be effective and convenientlycontrollable to adjust ranking resources. However, both empirical andtheoretical analyses indicate that the previous item-level tax policy cannotmeet two ideal controllable requirements: (1) continuity, ensuring minorchanges in tax rates result in small accuracy and fairness shifts; (2)controllability over accuracy loss, ensuring precise estimation of the accuracyloss under a specific tax rate. To overcome these challenges, we introduce anew fair re-ranking method named Tax-rank, which levies taxes based on thedifference in utility between two items. Then, we efficiently optimize such anobjective by utilizing the Sinkhorn algorithm in optimal transport. Upon acomprehensive analysis, Our model Tax-rank offers a superior tax policy forfair re-ranking, theoretically demonstrating both continuity andcontrollability over accuracy loss. Experimental results show that Tax-rankoutperforms all state-of-the-art baselines in terms of effectiveness andefficiency on recommendation and advertising tasks.|公平重新排名的目的是在项目之间更公平地重新分配排名位置，以确保责任和道德。再分配问题的探索在经济学中有着悠久的历史，为将公平再分配概念化为一个税收过程提供了有价值的见解。这样的表述为我们重新审视公平重排提供了新的视角，也启发了新方法的发展。从税收的角度，我们从理论上证明了大多数以前的公平重新排序方法可以重新制定为项目级的税收政策。理想情况下，一个好的税收政策应该是有效的，方便的，可控的，以调整等级资源。然而，实证和理论分析都表明，以往的项目级税收政策不能满足两个理想的可控要求: (1)连续性，确保税率的微小变化导致小的准确性和公平性转移; (2)准确性损失的可控性，确保在特定税率下准确估计准确性损失。为了克服这些挑战，我们引入了一种新的公平重新排序方法——税级法，该方法根据两个项目之间的效用差异来征税。然后，利用 Sinkhorn 算法对该目标进行有效的优化。在综合分析的基础上，我们的税级模型为公平重排提供了一个优越的税收政策，从理论上证明了精度损失的连续性和可控性。实验结果表明，就推荐和广告任务的有效性和效率而言，税收排名优于所有最先进的基线。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Taxation+Perspective+for+Fair+Re-ranking)|0| -|[A Dual-Embedding Based DQN for Worker Recruitment in Spatial Crowdsourcing with Social Network](https://doi.org/10.1145/3626772.3657718)|Yucen Gao, Wei Liu, Jianxiong Guo, Xiaofeng Gao, Guihai Chen|Shanghai Jiao Tong University; Peking University; Beijing Normal University|Spatial Crowdsourcing (SC) is a promising service that incentives workers to finish location-based tasks with high quality by providing rewards. Worker recruitment is a core issue in SC, for which most state-of-the-art algorithms focus on designing incentive mechanisms based on the existing SC worker pool. However, they may fail when the number of SC workers is not enough, especially for the new SC platforms. In recent years, social networks have been found to be helpful for worker recruitment by selecting seed workers to spread the task information so as to inspire more social users to participate, but how to select seed workers remains a challenge. Existing methods typically require numerous iterative searches leading to inefficiency in facing the big picture and failing to cope with dynamic environments. In the paper, we formulate the Effective Coverage Maximization (ECM) problem. We prove that the ECM problem is NP-hard and propose a novel worker recruitment method combined with the dual-embedding and Rainbow Deep Q-network (DQN), which is called DQNSelector. The dual-embedding extracts long-range social influence information from the social network and near-range coverage quality information from the geographic information map using the inner-product method and our proposed efficient Path Increment Iterative Calculation (PIIC) algorithm respectively. We then combine the dual embedding to design a Rainbow DQN-based reinforcement learning model so as to select seed workers. Extensive experiments and ablation studies based on real-world datasets verify the superiority of DQNSelector.|空间众包(SC)是一项很有前途的服务，它通过提供奖励激励员工高质量地完成基于位置的任务。员工招聘是供应链管理的核心问题，目前大多数最先进的算法都是在现有供应链员工库的基础上设计激励机制。然而，当供应链工作者的数量不足时，尤其是对于新的供应链平台而言，供应链管理可能会失败。近年来，人们发现社会网络有助于种子工人的招聘，通过选择种子工人来传播任务信息，以激励更多的社会用户参与，但如何选择种子工人仍然是一个挑战。现有的方法通常需要大量的迭代搜索，这会导致在面对大局和不能处理动态环境时效率低下。本文研究了有效覆盖最大化问题。我们证明了 ECM 问题是 NP 难的，并提出了一种结合双嵌入和彩虹深 Q 网络(DQN)的新的工人招聘方法，称为 DQNSelector。双嵌入算法分别采用内积法和有效的路径增量迭代计算(PIIC)算法从社交网络中提取远程社会影响信息，从地理信息图中提取近程覆盖质量信息。然后，我们结合双嵌入设计了一个基于彩虹 DQN 的强化学习模型，以便选择种子工人。基于实际数据集的大量实验和烧蚀研究验证了 DQNSelector 的优越性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Dual-Embedding+Based+DQN+for+Worker+Recruitment+in+Spatial+Crowdsourcing+with+Social+Network)|0| +|[A Dual-Embedding Based DQN for Worker Recruitment in Spatial Crowdsourcing with Social Network](https://doi.org/10.1145/3626772.3657718)|Yucen Gao, Wei Liu, Jianxiong Guo, Xiaofeng Gao, Guihai Chen|Peking University; Shanghai Jiao Tong University; Beijing Normal University|Spatial Crowdsourcing (SC) is a promising service that incentives workers to finish location-based tasks with high quality by providing rewards. Worker recruitment is a core issue in SC, for which most state-of-the-art algorithms focus on designing incentive mechanisms based on the existing SC worker pool. However, they may fail when the number of SC workers is not enough, especially for the new SC platforms. In recent years, social networks have been found to be helpful for worker recruitment by selecting seed workers to spread the task information so as to inspire more social users to participate, but how to select seed workers remains a challenge. Existing methods typically require numerous iterative searches leading to inefficiency in facing the big picture and failing to cope with dynamic environments. In the paper, we formulate the Effective Coverage Maximization (ECM) problem. We prove that the ECM problem is NP-hard and propose a novel worker recruitment method combined with the dual-embedding and Rainbow Deep Q-network (DQN), which is called DQNSelector. The dual-embedding extracts long-range social influence information from the social network and near-range coverage quality information from the geographic information map using the inner-product method and our proposed efficient Path Increment Iterative Calculation (PIIC) algorithm respectively. We then combine the dual embedding to design a Rainbow DQN-based reinforcement learning model so as to select seed workers. Extensive experiments and ablation studies based on real-world datasets verify the superiority of DQNSelector.|空间众包(SC)是一项很有前途的服务，它通过提供奖励激励员工高质量地完成基于位置的任务。员工招聘是供应链管理的核心问题，目前大多数最先进的算法都是在现有供应链员工库的基础上设计激励机制。然而，当供应链工作者的数量不足时，尤其是对于新的供应链平台而言，供应链管理可能会失败。近年来，人们发现社会网络有助于种子工人的招聘，通过选择种子工人来传播任务信息，以激励更多的社会用户参与，但如何选择种子工人仍然是一个挑战。现有的方法通常需要大量的迭代搜索，这会导致在面对大局和不能处理动态环境时效率低下。本文研究了有效覆盖最大化问题。我们证明了 ECM 问题是 NP 难的，并提出了一种结合双嵌入和彩虹深 Q 网络(DQN)的新的工人招聘方法，称为 DQNSelector。双嵌入算法分别采用内积法和有效的路径增量迭代计算(PIIC)算法从社交网络中提取远程社会影响信息，从地理信息图中提取近程覆盖质量信息。然后，我们结合双嵌入设计了一个基于彩虹 DQN 的强化学习模型，以便选择种子工人。基于实际数据集的大量实验和烧蚀研究验证了 DQNSelector 的优越性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Dual-Embedding+Based+DQN+for+Worker+Recruitment+in+Spatial+Crowdsourcing+with+Social+Network)|0| |[Efficient Community Search Based on Relaxed k-Truss Index](https://doi.org/10.1145/3626772.3657708)|Xiaoqin Xie, Shuangyuan Liu, Jiaqi Zhang, Shuai Han, Wei Wang, Wu Yang|Harbin Engineering University College of Computer Science and Technology|Communities are prevalent in large graphs such as social networks, protein networks, etc. Community search aims to find a cohesive subgraph that contains the query nodes. Existing community search algorithms often adopt community models to find target communities, and k-truss model is a popularly used one that provides structural constraints. However, the structural constraints presented by k-truss is so tight that the searching algorithm often can not find the target communities. There always exist some subgraphs that may not conform to k-truss structure but do have cohesive characteristics to meet users' personalized requirements. Moreover, the k-truss based community search algorithms can not meet users' real-time demands on large graphs. To address the above problems, this paper proposes the relaxed k-truss community search problem for the first time. Then we construct a relaxed k-truss index, which can help to find cohesive communities in linear time and provide flexible searching for nested communities. We also design an index maintenance algorithm to dynamically update the index. Furthermore, a community search algorithm based on the relaxed k-truss index is presented. Extensive experimental results on real datasets prove the effectiveness and efficiency of our model and algorithms.|社区在社会网络、蛋白质网络等大型图表中普遍存在。社区搜索的目的是找到一个包含查询节点的内聚子图。现有的社区搜索算法往往采用社区模型来寻找目标社区，而 k- 桁架模型是一种常用的提供结构约束的模型。然而，由于 k- 桁架所表示的结构约束过于严格，搜索算法往往无法找到目标群落。总是存在一些子图，这些子图可能不符合 k- 桁架结构，但具有内聚特性，以满足用户的个性化需求。此外，基于 k- 桁架的社区搜索算法不能满足用户对大图的实时性要求。针对上述问题，本文首次提出了松弛 k 桁架群体搜索问题。然后构造一个松弛 k- 桁架索引，它可以帮助在线性时间内找到内聚群落，并为嵌套群落提供灵活的搜索。设计了索引维护算法，实现了索引的动态更新。在此基础上，提出了一种基于松弛 k- 桁架索引的社区搜索算法。在实际数据集上的大量实验结果证明了该模型和算法的有效性和高效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Efficient+Community+Search+Based+on+Relaxed+k-Truss+Index)|0| -|[Untargeted Adversarial Attack on Knowledge Graph Embeddings](https://doi.org/10.1145/3626772.3657702)|Tianzhe Zhao, Jiaoyan Chen, Yanchi Ru, Qika Lin, Yuxia Geng, Jun Liu|School of Computer Science and Technology, Xi'an Jiaotong University; National University of Singapore; School of Computer Science, Hangzhou Dianzi University; Xi'an Jiaotong University; Department of Computer Science, The University of Manchester|Knowledge graph embedding (KGE) methods have achieved great success inhandling various knowledge graph (KG) downstream tasks. However, KGE methodsmay learn biased representations on low-quality KGs that are prevalent in thereal world. Some recent studies propose adversarial attacks to investigate thevulnerabilities of KGE methods, but their attackers are target-oriented withthe KGE method and the target triples to predict are given in advance, whichlacks practicability. In this work, we explore untargeted attacks with the aimof reducing the global performances of KGE methods over a set of unknown testtriples and conducting systematic analyses on KGE robustness. Considering logicrules can effectively summarize the global structure of a KG, we developrule-based attack strategies to enhance the attack efficiency. In particular,weconsider adversarial deletion which learns rules, applying the rules to scoretriple importance and delete important triples, and adversarial addition whichcorrupts the learned rules and applies them for negative triples asperturbations. Extensive experiments on two datasets over three representativeclasses of KGE methods demonstrate the effectiveness of our proposed untargetedattacks in diminishing the link prediction results. And we also find thatdifferent KGE methods exhibit different robustness to untargeted attacks. Forexample, the robustness of methods engaged with graph neural networks and logicrules depends on the density of the graph. But rule-based methods like NCRL areeasily affected by adversarial addition attacks to capture negative rules|知识图嵌入(KGE)方法在处理各种知识图(KG)下游任务方面取得了巨大的成功。然而，KGE 方法可能会学习在现实世界中普遍存在的低质量幼儿园的偏见表示。最近的一些研究提出用对抗性攻击来研究 KGE 方法的脆弱性，但是 KGE 方法的攻击者是面向目标的，而且提前给出了预测的目标三元组，缺乏实用性。在这项工作中，我们探讨非目标攻击的目的是降低全局性能的 KGE 方法在一组未知的三元组和进行系统的 KGE 鲁棒性分析。考虑到逻辑规则可以有效地概括 KG 的全局结构，我们开发了基于规则的攻击策略来提高攻击效率。特别地，我们考虑了学习规则的对抗性删除，应用规则来确定重要性并删除重要的三元组，以及对抗性加法，它破坏了学习规则并将其应用于负的三元组扰动。在三种典型的 KGE 方法上对两个数据集进行了大量的实验，证明了我们提出的非目标攻击在减少链路预测结果方面的有效性。我们还发现不同的 KGE 方法对非目标攻击具有不同的鲁棒性。例如，使用图神经网络和逻辑规则的方法的鲁棒性取决于图的密度。但是像 NCRL 这样基于规则的方法很容易受到对抗性加法攻击的影响，以捕获负面规则|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Untargeted+Adversarial+Attack+on+Knowledge+Graph+Embeddings)|0| -|[Drop your Decoder: Pre-training with Bag-of-Word Prediction for Dense Passage Retrieval](https://doi.org/10.1145/3626772.3657792)|Guangyuan Ma, Xing Wu, Zijia Lin, Songlin Hu|Institute of Information Engineering, Chinese Academy of Sciences; Chinese Academy of Sciences; Tsinghua University|Masked auto-encoder pre-training has emerged as a prevalent technique forinitializing and enhancing dense retrieval systems. It generally utilizesadditional Transformer decoder blocks to provide sustainable supervisionsignals and compress contextual information into dense representations.However, the underlying reasons for the effectiveness of such a pre-trainingtechnique remain unclear. The usage of additional Transformer-based decodersalso incurs significant computational costs. In this study, we aim to shedlight on this issue by revealing that masked auto-encoder (MAE) pre-trainingwith enhanced decoding significantly improves the term coverage of input tokensin dense representations, compared to vanilla BERT checkpoints. Building uponthis observation, we propose a modification to the traditional MAE by replacingthe decoder of a masked auto-encoder with a completely simplified Bag-of-Wordprediction task. This modification enables the efficient compression of lexicalsignals into dense representations through unsupervised pre-training.Remarkably, our proposed method achieves state-of-the-art retrieval performanceon several large-scale retrieval benchmarks without requiring any additionalparameters, which provides a 67auto-encoder pre-training with enhanced decoding.|掩模自动编码器预训练已经成为初始化和增强密集检索系统的一种流行技术。它通常使用额外的变压器解码器块来提供可持续的监督信号，并将上下文信息压缩成密集的表示。然而，这种预培训技术有效性的根本原因仍然不清楚。使用额外的基于变压器的解码器也会带来巨大的计算成本。在这项研究中，我们的目标是通过揭示掩码自动编码器(MAE)预训练与增强的解码显着提高输入标记在密集表示中的术语覆盖率来阐明这个问题，与普通的 BERT 检查点相比。在此基础上，我们提出了一种改进的传统 MAE 方法，用一个完全简化的“字包”预测任务来代替掩码自动编码器的解码器。这种修改使得词汇信号能够通过无监督的预训练有效地压缩成密集的表示。值得注意的是，我们提出的方法在不需要任何额外参数的情况下，在几个大规模检索基准上实现了最先进的检索性能，它提供了一个具有增强解码的67自动编码器预训练。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Drop+your+Decoder:+Pre-training+with+Bag-of-Word+Prediction+for+Dense+Passage+Retrieval)|0| -|[M2-RAAP: A Multi-Modal Recipe for Advancing Adaptation-based Pre-training towards Effective and Efficient Zero-shot Video-text Retrieval](https://doi.org/10.1145/3626772.3657833)|Xingning Dong, Zipeng Feng, Chunluan Zhou, Xuzheng Yu, Ming Yang, Qingpei Guo|Ant Group Zhifubao; Ant Group; Ant Group Multimodal Learning|We present a Multi-Modal Recipe for Advancing Adaptation-based Pre-trainingtowards effective and efficient zero-shot video-text retrieval, dubbed M2-RAAP.Upon popular image-text models like CLIP, most current adaptation-basedvideo-text pre-training methods are confronted by three major issues, i.e.,noisy data corpus, time-consuming pre-training, and limited performance gain.Towards this end, we conduct a comprehensive study including four criticalsteps in video-text pre-training. Specifically, we investigate 1) datafiltering and refinement, 2) video input type selection, 3) temporal modeling,and 4) video feature enhancement. We then summarize this empirical study intothe M2-RAAP recipe, where our technical contributions lie in 1) the datafiltering and text re-writing pipeline resulting in 1M high-quality bilingualvideo-text pairs, 2) the replacement of video inputs with key-frames toaccelerate pre-training, and 3) the Auxiliary-Caption-Guided (ACG) strategy toenhance video features. We conduct extensive experiments by adapting threeimage-text foundation models on two refined video-text datasets from differentlanguages, validating the robustness and reproducibility of M2-RAAP foradaptation-based pre-training. Results demonstrate that M2-RAAP yields superiorperformance with significantly reduced data (-90establishing a new SOTA on four English zero-shot retrieval datasets and twoChinese ones. We are preparing our refined bilingual data annotations andcodebase, which will be available athttps://github.com/alipay/Ant-Multi-Modal-Framework/tree/main/prj/M2_RAAP.|我们提出了一个推进适应性的多模态配方，基于有效和高效的零拍视频文本检索预训练，称为 M2-RAAP。在 CLIP 等流行的图像文本预训练模型中，目前大多数基于自适应的视频文本预训练方法都面临着三个主要问题: 噪声数据库、耗时的预训练以及有限的性能增益。为此，我们进行了一个全面的研究，包括四个关键步骤的视频文本预训练。具体来说，我们研究1)数据过滤和细化，2)视频输入类型选择，3)时间建模，和4)视频特征增强。然后，我们将这一实证研究总结为 M2-RAAP 配方，其中我们的技术贡献在于: 1)数据过滤和文本重写流水线，导致1M 高质量的双语视频文本对; 2)用关键帧替换视频输入以加速预训练; 3)辅助字幕引导(ACG)策略以增强视频特性。通过在两个不同语言的视频文本数据集上调整三个基于图像文本的模型，验证了 M2-RAAP 在基于自适应的预训练中的鲁棒性和可重复性。结果表明，M2-RAAP 算法在显著减少数据量(- 90的情况下，对4个英文零镜头检索数据集和2个中文零镜头检索数据集建立了新的 SOTA。我们正在准备我们精心制作的双语数据注释和代码库，可通过 https:// github.com/alipay/ant-multi-modal-framework/tree/main/prj/m2_raap 查阅。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=M2-RAAP:+A+Multi-Modal+Recipe+for+Advancing+Adaptation-based+Pre-training+towards+Effective+and+Efficient+Zero-shot+Video-text+Retrieval)|0| -|[CaLa: Complementary Association Learning for Augmenting Comoposed Image Retrieval](https://doi.org/10.1145/3626772.3657823)|Xintong Jiang, Yaxiong Wang, Mengjian Li, Yujiao Wu, Bingwen Hu, Xueming Qian|Hefei University of Technology; Anhui University of Technology; Xi'an Jiaotong University School of Software; CSRIO; Zhejiang Lab; Xi'an Jiaotong University|Composed Image Retrieval (CIR) involves searching for target images based onan image-text pair query. While current methods treat this as a query-targetmatching problem, we argue that CIR triplets contain additional associationsbeyond this primary relation. In our paper, we identify two new relationswithin triplets, treating each triplet as a graph node. Firstly, we introducethe concept of text-bridged image alignment, where the query text serves as abridge between the query image and the target image. We propose a hinge-basedcross-attention mechanism to incorporate this relation into network learning.Secondly, we explore complementary text reasoning, considering CIR as a form ofcross-modal retrieval where two images compose to reason about complementarytext. To integrate these perspectives effectively, we design a twinattention-based compositor. By combining these complementary associations withthe explicit query pair-target image relation, we establish a comprehensive setof constraints for CIR. Our framework, CaLa (Complementary Association Learningfor Augmenting Composed Image Retrieval), leverages these insights. We evaluateCaLa on CIRR and FashionIQ benchmarks with multiple backbones, demonstratingits superiority in composed image retrieval.|复合图像检索(CIR)涉及到基于图像-文本对查询的目标图像搜索。虽然目前的方法将其视为一个查询-目标匹配问题，但我们认为 CIR 三联包含了超出这个主要关系的其他关联。在本文中，我们确定了两个新的关系在三元组，治疗每个三元组作为一个图节点。首先，我们引入文本桥接图像对齐的概念，其中查询文本作为查询图像和目标图像之间的桥梁。我们提出了一种基于铰链的交叉注意机制，将这种关系纳入网络学习。其次，我们探讨了互补文本推理，考虑到 CIR 作为一种跨模态检索的形式，其中两个图像组成的互补文本的推理。为了有效地整合这些视角，我们设计了一个基于双注意的排序器。通过将这些互补关联与显式查询对目标图像关系相结合，建立了一套完整的 CIR 约束条件。我们的框架 CaLa (增强合成图像检索的互补关联学习)利用了这些见解。我们在 CIRR 和 FashionIQ 基准上对 CaLa 进行了多骨干评价，证明了其在合成图像检索中的优越性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=CaLa:+Complementary+Association+Learning+for+Augmenting+Comoposed+Image+Retrieval)|0| +|[Untargeted Adversarial Attack on Knowledge Graph Embeddings](https://doi.org/10.1145/3626772.3657702)|Tianzhe Zhao, Jiaoyan Chen, Yanchi Ru, Qika Lin, Yuxia Geng, Jun Liu|Xi'an Jiaotong University; School of Computer Science, Hangzhou Dianzi University; School of Computer Science and Technology, Xi'an Jiaotong University; National University of Singapore; Department of Computer Science, The University of Manchester|Knowledge graph embedding (KGE) methods have achieved great success inhandling various knowledge graph (KG) downstream tasks. However, KGE methodsmay learn biased representations on low-quality KGs that are prevalent in thereal world. Some recent studies propose adversarial attacks to investigate thevulnerabilities of KGE methods, but their attackers are target-oriented withthe KGE method and the target triples to predict are given in advance, whichlacks practicability. In this work, we explore untargeted attacks with the aimof reducing the global performances of KGE methods over a set of unknown testtriples and conducting systematic analyses on KGE robustness. Considering logicrules can effectively summarize the global structure of a KG, we developrule-based attack strategies to enhance the attack efficiency. In particular,weconsider adversarial deletion which learns rules, applying the rules to scoretriple importance and delete important triples, and adversarial addition whichcorrupts the learned rules and applies them for negative triples asperturbations. Extensive experiments on two datasets over three representativeclasses of KGE methods demonstrate the effectiveness of our proposed untargetedattacks in diminishing the link prediction results. And we also find thatdifferent KGE methods exhibit different robustness to untargeted attacks. Forexample, the robustness of methods engaged with graph neural networks and logicrules depends on the density of the graph. But rule-based methods like NCRL areeasily affected by adversarial addition attacks to capture negative rules|知识图嵌入(KGE)方法在处理各种知识图(KG)下游任务方面取得了巨大的成功。然而，KGE 方法可能会学习在现实世界中普遍存在的低质量幼儿园的偏见表示。最近的一些研究提出用对抗性攻击来研究 KGE 方法的脆弱性，但是 KGE 方法的攻击者是面向目标的，而且提前给出了预测的目标三元组，缺乏实用性。在这项工作中，我们探讨非目标攻击的目的是降低全局性能的 KGE 方法在一组未知的三元组和进行系统的 KGE 鲁棒性分析。考虑到逻辑规则可以有效地概括 KG 的全局结构，我们开发了基于规则的攻击策略来提高攻击效率。特别地，我们考虑了学习规则的对抗性删除，应用规则来确定重要性并删除重要的三元组，以及对抗性加法，它破坏了学习规则并将其应用于负的三元组扰动。在三种典型的 KGE 方法上对两个数据集进行了大量的实验，证明了我们提出的非目标攻击在减少链路预测结果方面的有效性。我们还发现不同的 KGE 方法对非目标攻击具有不同的鲁棒性。例如，使用图神经网络和逻辑规则的方法的鲁棒性取决于图的密度。但是像 NCRL 这样基于规则的方法很容易受到对抗性加法攻击的影响，以捕获负面规则|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Untargeted+Adversarial+Attack+on+Knowledge+Graph+Embeddings)|0| +|[Drop your Decoder: Pre-training with Bag-of-Word Prediction for Dense Passage Retrieval](https://doi.org/10.1145/3626772.3657792)|Guangyuan Ma, Xing Wu, Zijia Lin, Songlin Hu|Tsinghua University; Institute of Information Engineering, Chinese Academy of Sciences; Chinese Academy of Sciences|Masked auto-encoder pre-training has emerged as a prevalent technique forinitializing and enhancing dense retrieval systems. It generally utilizesadditional Transformer decoder blocks to provide sustainable supervisionsignals and compress contextual information into dense representations.However, the underlying reasons for the effectiveness of such a pre-trainingtechnique remain unclear. The usage of additional Transformer-based decodersalso incurs significant computational costs. In this study, we aim to shedlight on this issue by revealing that masked auto-encoder (MAE) pre-trainingwith enhanced decoding significantly improves the term coverage of input tokensin dense representations, compared to vanilla BERT checkpoints. Building uponthis observation, we propose a modification to the traditional MAE by replacingthe decoder of a masked auto-encoder with a completely simplified Bag-of-Wordprediction task. This modification enables the efficient compression of lexicalsignals into dense representations through unsupervised pre-training.Remarkably, our proposed method achieves state-of-the-art retrieval performanceon several large-scale retrieval benchmarks without requiring any additionalparameters, which provides a 67auto-encoder pre-training with enhanced decoding.|掩模自动编码器预训练已经成为初始化和增强密集检索系统的一种流行技术。它通常使用额外的变压器解码器块来提供可持续的监督信号，并将上下文信息压缩成密集的表示。然而，这种预培训技术有效性的根本原因仍然不清楚。使用额外的基于变压器的解码器也会带来巨大的计算成本。在这项研究中，我们的目标是通过揭示掩码自动编码器(MAE)预训练与增强的解码显着提高输入标记在密集表示中的术语覆盖率来阐明这个问题，与普通的 BERT 检查点相比。在此基础上，我们提出了一种改进的传统 MAE 方法，用一个完全简化的“字包”预测任务来代替掩码自动编码器的解码器。这种修改使得词汇信号能够通过无监督的预训练有效地压缩成密集的表示。值得注意的是，我们提出的方法在不需要任何额外参数的情况下，在几个大规模检索基准上实现了最先进的检索性能，它提供了一个具有增强解码的67自动编码器预训练。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Drop+your+Decoder:+Pre-training+with+Bag-of-Word+Prediction+for+Dense+Passage+Retrieval)|0| +|[M2-RAAP: A Multi-Modal Recipe for Advancing Adaptation-based Pre-training towards Effective and Efficient Zero-shot Video-text Retrieval](https://doi.org/10.1145/3626772.3657833)|Xingning Dong, Zipeng Feng, Chunluan Zhou, Xuzheng Yu, Ming Yang, Qingpei Guo|Ant Group; Ant Group Zhifubao; Ant Group Multimodal Learning|We present a Multi-Modal Recipe for Advancing Adaptation-based Pre-trainingtowards effective and efficient zero-shot video-text retrieval, dubbed M2-RAAP.Upon popular image-text models like CLIP, most current adaptation-basedvideo-text pre-training methods are confronted by three major issues, i.e.,noisy data corpus, time-consuming pre-training, and limited performance gain.Towards this end, we conduct a comprehensive study including four criticalsteps in video-text pre-training. Specifically, we investigate 1) datafiltering and refinement, 2) video input type selection, 3) temporal modeling,and 4) video feature enhancement. We then summarize this empirical study intothe M2-RAAP recipe, where our technical contributions lie in 1) the datafiltering and text re-writing pipeline resulting in 1M high-quality bilingualvideo-text pairs, 2) the replacement of video inputs with key-frames toaccelerate pre-training, and 3) the Auxiliary-Caption-Guided (ACG) strategy toenhance video features. We conduct extensive experiments by adapting threeimage-text foundation models on two refined video-text datasets from differentlanguages, validating the robustness and reproducibility of M2-RAAP foradaptation-based pre-training. Results demonstrate that M2-RAAP yields superiorperformance with significantly reduced data (-90establishing a new SOTA on four English zero-shot retrieval datasets and twoChinese ones. We are preparing our refined bilingual data annotations andcodebase, which will be available athttps://github.com/alipay/Ant-Multi-Modal-Framework/tree/main/prj/M2_RAAP.|我们提出了一个推进适应性的多模态配方，基于有效和高效的零拍视频文本检索预训练，称为 M2-RAAP。在 CLIP 等流行的图像文本预训练模型中，目前大多数基于自适应的视频文本预训练方法都面临着三个主要问题: 噪声数据库、耗时的预训练以及有限的性能增益。为此，我们进行了一个全面的研究，包括四个关键步骤的视频文本预训练。具体来说，我们研究1)数据过滤和细化，2)视频输入类型选择，3)时间建模，和4)视频特征增强。然后，我们将这一实证研究总结为 M2-RAAP 配方，其中我们的技术贡献在于: 1)数据过滤和文本重写流水线，导致1M 高质量的双语视频文本对; 2)用关键帧替换视频输入以加速预训练; 3)辅助字幕引导(ACG)策略以增强视频特性。通过在两个不同语言的视频文本数据集上调整三个基于图像文本的模型，验证了 M2-RAAP 在基于自适应的预训练中的鲁棒性和可重复性。结果表明，M2-RAAP 算法在显著减少数据量(- 90的情况下，对4个英文零镜头检索数据集和2个中文零镜头检索数据集建立了新的 SOTA。我们正在准备我们精心制作的双语数据注释和代码库，可通过 https:// github.com/alipay/ant-multi-modal-framework/tree/main/prj/m2_raap 查阅。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=M2-RAAP:+A+Multi-Modal+Recipe+for+Advancing+Adaptation-based+Pre-training+towards+Effective+and+Efficient+Zero-shot+Video-text+Retrieval)|0| +|[CaLa: Complementary Association Learning for Augmenting Comoposed Image Retrieval](https://doi.org/10.1145/3626772.3657823)|Xintong Jiang, Yaxiong Wang, Mengjian Li, Yujiao Wu, Bingwen Hu, Xueming Qian|Xi'an Jiaotong University; Xi'an Jiaotong University School of Software; Zhejiang Lab; CSRIO; Anhui University of Technology; Hefei University of Technology|Composed Image Retrieval (CIR) involves searching for target images based onan image-text pair query. While current methods treat this as a query-targetmatching problem, we argue that CIR triplets contain additional associationsbeyond this primary relation. In our paper, we identify two new relationswithin triplets, treating each triplet as a graph node. Firstly, we introducethe concept of text-bridged image alignment, where the query text serves as abridge between the query image and the target image. We propose a hinge-basedcross-attention mechanism to incorporate this relation into network learning.Secondly, we explore complementary text reasoning, considering CIR as a form ofcross-modal retrieval where two images compose to reason about complementarytext. To integrate these perspectives effectively, we design a twinattention-based compositor. By combining these complementary associations withthe explicit query pair-target image relation, we establish a comprehensive setof constraints for CIR. Our framework, CaLa (Complementary Association Learningfor Augmenting Composed Image Retrieval), leverages these insights. We evaluateCaLa on CIRR and FashionIQ benchmarks with multiple backbones, demonstratingits superiority in composed image retrieval.|复合图像检索(CIR)涉及到基于图像-文本对查询的目标图像搜索。虽然目前的方法将其视为一个查询-目标匹配问题，但我们认为 CIR 三联包含了超出这个主要关系的其他关联。在本文中，我们确定了两个新的关系在三元组，治疗每个三元组作为一个图节点。首先，我们引入文本桥接图像对齐的概念，其中查询文本作为查询图像和目标图像之间的桥梁。我们提出了一种基于铰链的交叉注意机制，将这种关系纳入网络学习。其次，我们探讨了互补文本推理，考虑到 CIR 作为一种跨模态检索的形式，其中两个图像组成的互补文本的推理。为了有效地整合这些视角，我们设计了一个基于双注意的排序器。通过将这些互补关联与显式查询对目标图像关系相结合，建立了一套完整的 CIR 约束条件。我们的框架 CaLa (增强合成图像检索的互补关联学习)利用了这些见解。我们在 CIRR 和 FashionIQ 基准上对 CaLa 进行了多骨干评价，证明了其在合成图像检索中的优越性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=CaLa:+Complementary+Association+Learning+for+Augmenting+Comoposed+Image+Retrieval)|0| |[CFIR: Fast and Effective Long-Text To Image Retrieval for Large Corpora](https://doi.org/10.1145/3626772.3657741)|Zijun Long, Xuri Ge, Richard McCreadie, Joemon M. Jose|University of Glasgow|Text-to-image retrieval aims to find the relevant images based on a textquery, which is important in various use-cases, such as digital libraries,e-commerce, and multimedia databases. Although Multimodal Large Language Models(MLLMs) demonstrate state-of-the-art performance, they exhibit limitations inhandling large-scale, diverse, and ambiguous real-world needs of retrieval, dueto the computation cost and the injective embeddings they produce. This paperpresents a two-stage Coarse-to-Fine Index-shared Retrieval (CFIR) framework,designed for fast and effective large-scale long-text to image retrieval. Thefirst stage, Entity-based Ranking (ER), adapts to long-text query ambiguity byemploying a multiple-queries-to-multiple-targets paradigm, facilitatingcandidate filtering for the next stage. The second stage, Summary-basedRe-ranking (SR), refines these rankings using summarized queries. We alsopropose a specialized Decoupling-BEiT-3 encoder, optimized for handlingambiguous user needs and both stages, which also enhances computationalefficiency through vector-based similarity inference. Evaluation on the AToMiCdataset reveals that CFIR surpasses existing MLLMs by up to 11.06Recall@1000, while reducing training and retrieval times by 68.75respectively. We will release our code to facilitate future research athttps://github.com/longkukuhi/CFIR.|文本图像检索是基于文本查询的相关图像检索，在数字图书馆、电子商务、多媒体数据库等领域具有重要意义。虽然多模态大语言模型(MLLM)展示了最先进的性能，但由于计算成本和它们产生的内射嵌入，它们在处理大规模、多样化和模糊的现实世界检索需求方面表现出局限性。本文提出了一个两阶段的粗细索引共享检索(CFIR)框架，用于快速有效的大规模长文本图像检索。第一阶段，基于实体的排名(ER) ，适应长文本查询模糊性通过采用多查询多目标范例，促进候选人过滤的下一阶段。第二个阶段，Summary-basedRe-rank (SR) ，使用汇总查询改进这些排名。我们还提出了一个专门的解耦 -BEiT-3编码器，优化处理模糊的用户需求和两个阶段，这也提高了计算效率通过向量相似性推理。对 AToMiC 数据集的评估显示，CFIR 超越现有的 MLLM 多达11.06 Recall@1000，同时分别减少了68.75的训练和检索时间。我们将发布我们的代码，以促进未来的研究 https:// github.com/longkukuhi/cfir。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=CFIR:++Fast+and+Effective+Long-Text+To+Image+Retrieval+for+Large+Corpora)|0| |[CaseLink: Inductive Graph Learning for Legal Case Retrieval](https://doi.org/10.1145/3626772.3657693)|Yanran Tang, Ruihong Qiu, Hongzhi Yin, Xue Li, Zi Huang|The University of Queensland; The University of Queensland School of Electrical Engineering and Computer Science|In case law, the precedents are the relevant cases that are used to supportthe decisions made by the judges and the opinions of lawyers towards a givencase. This relevance is referred to as the case-to-case reference relation. Toefficiently find relevant cases from a large case pool, retrieval tools arewidely used by legal practitioners. Existing legal case retrieval models mainlywork by comparing the text representations of individual cases. Although theyobtain a decent retrieval accuracy, the intrinsic case connectivityrelationships among cases have not been well exploited for case encoding,therefore limiting the further improvement of retrieval performance. In a casepool, there are three types of case connectivity relationships: the casereference relationship, the case semantic relationship, and the case legalcharge relationship. Due to the inductive manner in the task of legal caseretrieval, using case reference as input is not applicable for testing. Thus,in this paper, a CaseLink model based on inductive graph learning is proposedto utilise the intrinsic case connectivity for legal case retrieval, a novelGlobal Case Graph is incorporated to represent both the case semanticrelationship and the case legal charge relationship. A novel contrastiveobjective with a regularisation on the degree of case nodes is proposed toleverage the information carried by the case reference relationship to optimisethe model. Extensive experiments have been conducted on two benchmark datasets,which demonstrate the state-of-the-art performance of CaseLink. The code hasbeen released on https://github.com/yanran-tang/CaseLink.|在判例法中，判例是用来支持法官作出的裁决和律师对判例的意见的相关案例。这种相关性被称为案例对案例的参考关系。为了有效地从大量案例中找到相关案例，检索工具被法律从业者广泛使用。现有的法律案例检索模型主要是通过比较个案的文本表示来实现的。虽然它们获得了不错的检索准确性，但是案例之间的内在案例连通性关系还没有被很好地用于案例编码，因此限制了检索性能的进一步提高。在案例池中，有三种类型的案例连接关系: 案例引用关系、案例语义关系和案例法律费用关系。由于法律案例检索任务中的归纳方式，以案例参考为输入不适用于检验。为此，本文提出了一种基于归纳图学习的案例链接模型，该模型利用内在的案例连通性进行法律案例检索，并引入了一个新的全局案例图来表示案例语义关系和案例法律指控关系。提出了一种新的对比目标，利用案例参考关系所携带的信息对模型进行优化。在两个基准数据集上进行了大量的实验，这些实验证明了 CaseLink 的最新性能。密码已经在 https://github.com/yanran-tang/caselink 上发布了。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=CaseLink:+Inductive+Graph+Learning+for+Legal+Case+Retrieval)|0| |[Explicitly Integrating Judgment Prediction with Legal Document Retrieval: A Law-Guided Generative Approach](https://doi.org/10.1145/3626772.3657717)|Weicong Qin, Zelin Cao, Weijie Yu, Zihua Si, Sirui Chen, Jun Xu|University of International Business and Economics School of Information Technology and Management; University of Illinois at Urbana-Champaign; Renmin University of China Gaoling School of Artificial Intelligence|Legal document retrieval and judgment prediction are crucial tasks inintelligent legal systems. In practice, determining whether two documents sharethe same judgments is essential for establishing their relevance in legalretrieval. However, existing legal retrieval studies either ignore the vitalrole of judgment prediction or rely on implicit training objectives, expectinga proper alignment of legal documents in vector space based on their judgments.Neither approach provides explicit evidence of judgment consistency forrelevance modeling, leading to inaccuracies and a lack of transparency inretrieval. To address this issue, we propose a law-guided method, namely GEAR,within the generative retrieval framework. GEAR explicitly integrates judgmentprediction with legal document retrieval in a sequence-to-sequence manner.Experiments on two Chinese legal case retrieval datasets show the superiorityof GEAR over state-of-the-art methods while maintaining competitive judgmentprediction performance. Moreover, we validate its robustness across languagesand domains on a French statutory article retrieval dataset.|在智能法律系统中，法律文献检索和判决预测是至关重要的任务。在实践中，确定两份文件是否具有相同的判决对于确定它们在法律检索中的相关性至关重要。然而，现有的法律检索研究要么忽视了判断预测的重要作用，要么依赖于隐含的训练目标，期望法律文献在基于判断的向量空间中进行适当的对齐。这两种方法都没有为相关性建模提供明确的判断一致性证据，导致不准确和缺乏透明度检索。为了解决这个问题，我们提出了一个法律引导的方法，即 GEAR，在生成检索框架内。GEAR 明确地将判断预测与法律文献检索按顺序整合在一起。在两个中文案例检索数据集上的实验表明，在保持竞争性判断预测性能的同时，GEAR 方法优于现有的方法。此外，我们验证了它的鲁棒性跨语言和领域的法国法定文章检索数据集。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Explicitly+Integrating+Judgment+Prediction+with+Legal+Document+Retrieval:+A+Law-Guided+Generative+Approach)|0| |[A Persona-Infused Cross-Task Graph Network for Multimodal Emotion Recognition with Emotion Shift Detection in Conversations](https://doi.org/10.1145/3626772.3657944)|Geng Tu, Feng Xiong, Bin Liang, Ruifeng Xu|The Chinese University of Hong Kong, Hong Kong, China; Harbin Institute of Technology, Shenzhen, China|Recent research in Multimodal Emotion Recognition in Conversations (MERC) focuses on multimodal fusion and modeling speaker-sensitive context. In addition to contextual information, personality traits also affect emotional perception. However, current MERC methods solely consider the personality influence of speakers, neglecting speaker-addressee interaction patterns. Additionally, the bottleneck problem of Emotion Shift (ES), where consecutive utterances by the same speaker exhibit different emotions has been long neglected in MERC. Early ES research fails to distinguish diverse shift patterns and simply introduces whether shifts occur as knowledge into the MERC model without considering the complementary nature of the two tasks. Based on this, we propose a Persona-infused Cross-task Graph Network (PCGNet). It first models the speaker-addressee interactive relationships by the persona-infused refinement network. Then, it learns the auxiliary task of ES Detection and the main task of MERC using cross-task connections to capture correlations across two tasks. Finally, we introduce shift-aware contrastive learning to discern diverse shift patterns. Experimental results demonstrate that PCGNet outperforms state-of-the-art methods on two widely used datasets.|会话中多模态情绪识别(MERC)的研究主要集中在多模态融合和建模说话人敏感语境。除了上下文信息，人格特质也影响情感知觉。然而，目前的 MERC 方法只考虑说话人的个性影响，而忽略了说话人与收件人之间的交互模式。此外，情绪转移的瓶颈问题，即同一说话人的连续话语表现出不同的情绪，长期以来一直被人们所忽视。早期的 ES 研究未能区分不同的转移模式，只是简单地将转移是否作为知识发生引入 MERC 模型，而没有考虑两个任务的互补性。在此基础上，提出了一种基于人格的跨任务图形网络(PCGNet)。它首先通过人格注入精化网络建立了说话人与受话人之间的交互关系模型。然后，学习 ES 检测的辅助任务和 MERC 的主要任务，利用跨任务连接捕获两个任务之间的相关性。最后，我们引入移位意识对比学习来识别不同的移位模式。实验结果表明，PCGNet 在两个广泛使用的数据集上优于最先进的方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Persona-Infused+Cross-Task+Graph+Network+for+Multimodal+Emotion+Recognition+with+Emotion+Shift+Detection+in+Conversations)|0| -|[Analyzing and Mitigating Repetitions in Trip Recommendation](https://doi.org/10.1145/3626772.3657970)|Wenzheng Shu, Kangqi Xu, Wenxin Tai, Ting Zhong, Yong Wang, Fan Zhou|University of Electronic Science and Technology of China; Hong Kong University of Science and Technology|Trip recommendation has emerged as a highly sought-after service over the past decade. Although current studies significantly understand human intention consistency, they struggle with undesired repetitive outcomes that need resolution. We make two pivotal discoveries using statistical analyses and experimental designs: (1) The occurrence of repetitions is intricately linked to the models and decoding strategies. (2) During training and decoding, adding perturbations to logits can reduce repetition. Motivated by these observations, we introduce AR-Trip (Anti Repetition for Trip Recommendation), which incorporates a cycle-aware predictor comprising three mechanisms to avoid duplicate Points-of-Interest (POIs) and demonstrates their effectiveness in alleviating repetition. Experiments on four public datasets illustrate that AR-Trip successfully mitigates repetition issues while enhancing precision.|在过去的十年里，旅行推荐已经成为一项非常受欢迎的服务。虽然目前的研究显着了解人类意图的一致性，他们挣扎与不希望的重复结果，需要解决。通过统计分析和实验设计，我们得到了两个关键的发现: (1)重复的发生与模型和解码策略有着密切的联系。(2)在训练和解码过程中，对 logit 加扰动可以减少重复。受这些观察的启发，我们引入了 AR-Trip (反重复行程推荐) ，其中包含一个周期感知预测器，由三个机制组成，以避免重复感兴趣点(POI) ，并证明其在减轻重复方面的有效性。在四个公共数据集上的实验表明，AR-Trip 在提高精度的同时成功地缓解了重复问题。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Analyzing+and+Mitigating+Repetitions+in+Trip+Recommendation)|0| +|[Analyzing and Mitigating Repetitions in Trip Recommendation](https://doi.org/10.1145/3626772.3657970)|Wenzheng Shu, Kangqi Xu, Wenxin Tai, Ting Zhong, Yong Wang, Fan Zhou|Hong Kong University of Science and Technology; University of Electronic Science and Technology of China|Trip recommendation has emerged as a highly sought-after service over the past decade. Although current studies significantly understand human intention consistency, they struggle with undesired repetitive outcomes that need resolution. We make two pivotal discoveries using statistical analyses and experimental designs: (1) The occurrence of repetitions is intricately linked to the models and decoding strategies. (2) During training and decoding, adding perturbations to logits can reduce repetition. Motivated by these observations, we introduce AR-Trip (Anti Repetition for Trip Recommendation), which incorporates a cycle-aware predictor comprising three mechanisms to avoid duplicate Points-of-Interest (POIs) and demonstrates their effectiveness in alleviating repetition. Experiments on four public datasets illustrate that AR-Trip successfully mitigates repetition issues while enhancing precision.|在过去的十年里，旅行推荐已经成为一项非常受欢迎的服务。虽然目前的研究显着了解人类意图的一致性，他们挣扎与不希望的重复结果，需要解决。通过统计分析和实验设计，我们得到了两个关键的发现: (1)重复的发生与模型和解码策略有着密切的联系。(2)在训练和解码过程中，对 logit 加扰动可以减少重复。受这些观察的启发，我们引入了 AR-Trip (反重复行程推荐) ，其中包含一个周期感知预测器，由三个机制组成，以避免重复感兴趣点(POI) ，并证明其在减轻重复方面的有效性。在四个公共数据集上的实验表明，AR-Trip 在提高精度的同时成功地缓解了重复问题。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Analyzing+and+Mitigating+Repetitions+in+Trip+Recommendation)|0| |[Cluster-based Partial Dense Retrieval Fused with Sparse Text Retrieval](https://doi.org/10.1145/3626772.3657972)|Yingrui Yang, Parker Carlson, Shanxiu He, Yifan Qiao, Tao Yang|University of California at Santa Barbara; University of California, Santa Barbara|Previous work has demonstrated the potential to combine document rankings from dense and sparse retrievers for higher relevance effectiveness. This paper proposes a cluster-based partial dense retrieval scheme guided by sparse retrieval results to optimize fusion between dense and sparse retrieval at a low space and CPU-time cost while retaining a competitive relevance. This scheme exploits the overlap of sparse retrieval results and document embedding clusters, and judiciously selects a limited number of clusters to probabilistically guarantee the inclusion of top sparse results. This paper provides an evaluation of this scheme on its in-domain and zero-shot retrieval performance for the MS MARCO and BEIR datasets.|以前的工作已经证明了将密集和稀疏检索器的文档排名结合起来以提高相关性效率的潜力。提出了一种基于聚类的部分密集检索方案，该方案以稀疏检索结果为指导，在保持竞争相关性的同时，以较低的空间和 CPU 时间成本优化密集和稀疏检索之间的融合。该方案利用了稀疏检索结果和文档嵌入聚类的重叠性，并且明智地选择了有限数量的聚类，从概率上保证了顶部稀疏结果的包含。本文对该方案在 MS MARCO 和 BEIR 数据集上的域内检索性能和零镜头检索性能进行了评价。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Cluster-based+Partial+Dense+Retrieval+Fused+with+Sparse+Text+Retrieval)|0| |[Contextualization with SPLADE for High Recall Retrieval](https://doi.org/10.1145/3626772.3657919)|Eugene Yang|Human Language Technology Center of Excellence, Johns Hopkins University|High Recall Retrieval (HRR), such as eDiscovery and medical systematicreview, is a search problem that optimizes the cost of retrieving most relevantdocuments in a given collection. Iterative approaches, such as iterativerelevance feedback and uncertainty sampling, are shown to be effective undervarious operational scenarios. Despite neural models demonstrating success inother text-related tasks, linear models such as logistic regression, ingeneral, are still more effective and efficient in HRR since the model istrained and retrieves documents from the same fixed collection. In this work,we leverage SPLADE, an efficient retrieval model that transforms documents intocontextualized sparse vectors, for HRR. Our approach combines the best of bothworlds, leveraging both the contextualization from pretrained language modelsand the efficiency of linear models. It reduces 10in two HRR evaluation collections under a one-phase review workflow with atarget recall of 80available at https://github.com/eugene-yang/LSR-for-TAR.|高召回检索(HRR) ，如 eDiscovery 和 Medical Systematicreview，是一个优化检索给定集合中大多数相关文档的成本的搜索问题。迭代方法，如迭代相关性反馈和不确定性抽样，被证明是有效的各种操作场景。尽管神经模型在其他与文本相关的任务中取得了成功，但是线性模型，例如 Logit模型模型、通用模型，在 HRR 中仍然更加有效，因为该模型从相同的固定集合中检索文档。在这项工作中，我们利用 SPLADE，一个有效的检索模型，转换文档到上下文稀疏向量，为 HRR。我们的方法结合了两者的优点，既利用了预先训练的语言模型的上下文化，又利用了线性模型的效率。它通过一个单阶段的评审工作流程减少了十分之二的人力资源评估收集，目标召回 https://github.com/eugene-yang/lsr-for-tar 为80个。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Contextualization+with+SPLADE+for+High+Recall+Retrieval)|0| -|[Convex Feature Embedding for Face and Voice Association](https://doi.org/10.1145/3626772.3657975)|Jiwoo Kang, Taewan Kim, YoungHo Park|Dongduk Women's University; Sookmyung Women's University|Face-and-voice association learning poses significant challenges in the field of deep learning. In this paper, we propose a straightforward yet effective approach for cross-modal feature embedding, specifically targeting the correlation between facial and voice association. Previous studies have examined cross-modal association tasks in order to establish the relationship between voice clips and facial images. Previous studies have examined the issue of cross-modal discrimination; however, they have not adequately recognized the importance of managing the heterogeneity in inter-modal features between audio and video. As a result, there is a significant prevalence of false positives and false negatives. To address the issue, the proposed method learns the embeddings of cross-modal features by introducing an additional feature that bridges the gap between these features. This facilitates the embedding of voice and face features belonging to the same individual within a convex hull. Through the utilization of cross-modal feature learning, cross-modal attention particularly reduces inter-class variance, resulting in a notable enhancement of the clustering power. We comprehensively evaluated our approach on cross-modal verification, matching, and retrieval tasks using the large-scale VoxCeleb dataset. Extensive experimental results demonstrate that the proposed method achieves notable improvements over existing state-of-the-art methods.|面语关联学习是深度学习领域的一个重要研究课题。本文提出了一种简单而有效的跨模态特征嵌入方法，特别针对人脸和语音之间的相关性。以往的研究已经检验了跨模式联想任务，以建立语音剪辑和面部图像之间的关系。以前的研究审查了多式联运歧视问题; 但是，它们没有充分认识到管理音频和视频之间多式联运特征异质性的重要性。因此，伪阳性的流行率相当高。为了解决这个问题，本文提出的方法通过引入一个附加的特征来学习交叉模态特征的嵌入，从而弥补这些特征之间的差距。这有利于在凸壳内嵌入属于同一个人的声音和面部特征。通过利用交叉模态特征学习，交叉模态注意特别地减少了类间方差，使聚类能力显著提高。我们使用大规模的 VoxCeleb 数据集对我们的跨模式验证、匹配和检索任务方法进行了全面的评估。大量的实验结果表明，与现有的最新方法相比，该方法取得了显著的改进。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Convex+Feature+Embedding+for+Face+and+Voice+Association)|0| +|[Convex Feature Embedding for Face and Voice Association](https://doi.org/10.1145/3626772.3657975)|Jiwoo Kang, Taewan Kim, YoungHo Park|Sookmyung Women's University; Dongduk Women's University|Face-and-voice association learning poses significant challenges in the field of deep learning. In this paper, we propose a straightforward yet effective approach for cross-modal feature embedding, specifically targeting the correlation between facial and voice association. Previous studies have examined cross-modal association tasks in order to establish the relationship between voice clips and facial images. Previous studies have examined the issue of cross-modal discrimination; however, they have not adequately recognized the importance of managing the heterogeneity in inter-modal features between audio and video. As a result, there is a significant prevalence of false positives and false negatives. To address the issue, the proposed method learns the embeddings of cross-modal features by introducing an additional feature that bridges the gap between these features. This facilitates the embedding of voice and face features belonging to the same individual within a convex hull. Through the utilization of cross-modal feature learning, cross-modal attention particularly reduces inter-class variance, resulting in a notable enhancement of the clustering power. We comprehensively evaluated our approach on cross-modal verification, matching, and retrieval tasks using the large-scale VoxCeleb dataset. Extensive experimental results demonstrate that the proposed method achieves notable improvements over existing state-of-the-art methods.|面语关联学习是深度学习领域的一个重要研究课题。本文提出了一种简单而有效的跨模态特征嵌入方法，特别针对人脸和语音之间的相关性。以往的研究已经检验了跨模式联想任务，以建立语音剪辑和面部图像之间的关系。以前的研究审查了多式联运歧视问题; 但是，它们没有充分认识到管理音频和视频之间多式联运特征异质性的重要性。因此，伪阳性的流行率相当高。为了解决这个问题，本文提出的方法通过引入一个附加的特征来学习交叉模态特征的嵌入，从而弥补这些特征之间的差距。这有利于在凸壳内嵌入属于同一个人的声音和面部特征。通过利用交叉模态特征学习，交叉模态注意特别地减少了类间方差，使聚类能力显著提高。我们使用大规模的 VoxCeleb 数据集对我们的跨模式验证、匹配和检索任务方法进行了全面的评估。大量的实验结果表明，与现有的最新方法相比，该方法取得了显著的改进。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Convex+Feature+Embedding+for+Face+and+Voice+Association)|0| |[Enhancing Criminal Case Matching through Diverse Legal Factors](https://doi.org/10.1145/3626772.3657960)|Jie Zhao, Ziyu Guan, Wei Zhao, Yue Jiang|Xidian University|Criminal case matching endeavors to determine the relevance between differentcriminal cases. Conventional methods predict the relevance solely based oninstance-level semantic features and neglect the diverse legal factors (LFs),which are associated with diverse court judgments. Consequently,comprehensively representing a criminal case remains a challenge for theseapproaches. Moreover, extracting and utilizing these LFs for criminal casematching face two challenges: (1) the manual annotations of LFs rely heavily onspecialized legal knowledge; (2) overlaps among LFs may potentially harm themodel's performance. In this paper, we propose a two-stage framework namedDiverse Legal Factor-enhanced Criminal Case Matching (DLF-CCM). Firstly,DLF-CCM employs a multi-task learning framework to pre-train an LF extractionnetwork on a large-scale legal judgment prediction dataset. In stage two,DLF-CCM introduces an LF de-redundancy module to learn shared LF and exclusiveLFs. Moreover, an entropy-weighted fusion strategy is introduced to dynamicallyfuse the multiple relevance generated by all LFs. Experimental results validatethe effectiveness of DLF-CCM and show its significant improvements overcompetitive baselines. Code: https://github.com/jiezhao6/DLF-CCM.|刑事案件匹配试图确定不同刑事案件之间的相关性。传统的方法仅仅基于实例层次的语义特征来预测相关性，而忽略了与不同法院判决相关的多种法律因素。因此，综合表述一个刑事案件仍然是这些方法的挑战。此外，提取和利用这些逻辑框架进行刑事案件匹配面临两个挑战: (1)逻辑框架的人工注释严重依赖于专门的法律知识; (2)逻辑框架之间的重叠可能会损害模型的性能。在本文中，我们提出了一个两阶段的框架，即多元法律因素增强刑事案件匹配(DLF-CCM)。首先，DLF-CCM 采用多任务学习框架，在大规模法律判决预测数据集上预训练 LF 抽取网络。在第二阶段，DLF-CCM 引入了一个 LF 去冗余模块来学习共享 LF 和排他 LF。此外，引入熵权融合策略，动态融合所有 LFs 产生的多重相关性。实验结果验证了 DLF-CCM 算法的有效性，并显示了其对过度竞争基线的显著改善。密码: https://github.com/jiezhao6/dlf-ccm。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Enhancing+Criminal+Case+Matching+through+Diverse+Legal+Factors)|0| -|[Faster Learned Sparse Retrieval with Block-Max Pruning](https://doi.org/10.1145/3626772.3657906)|Antonio Mallia, Torsten Suel, Nicola Tonellotto|University of Pisa; Pinecone; New York University|Learned sparse retrieval systems aim to combine the effectiveness ofcontextualized language models with the scalability of conventional datastructures such as inverted indexes. Nevertheless, the indexes generated bythese systems exhibit significant deviations from the ones that use traditionalretrieval models, leading to a discrepancy in the performance of existing queryoptimizations that were specifically developed for traditional structures.These disparities arise from structural variations in query and documentstatistics, including sub-word tokenization, leading to longer queries, smallervocabularies, and different score distributions within posting lists. Thispaper introduces Block-Max Pruning (BMP), an innovative dynamic pruningstrategy tailored for indexes arising in learned sparse retrieval environments.BMP employs a block filtering mechanism to divide the document space intosmall, consecutive document ranges, which are then aggregated and sorted on thefly, and fully processed only as necessary, guided by a defined safe earlytermination criterion or based on approximate retrieval requirements. Throughrigorous experimentation, we show that BMP substantially outperforms existingdynamic pruning strategies, offering unparalleled efficiency in safe retrievalcontexts and improved tradeoffs between precision and efficiency in approximateretrieval tasks.|学习型稀疏检索系统旨在将上下文化语言模型的有效性与倒排索引等传统数据结构的可扩展性结合起来。然而，这些系统生成的索引与使用传统检索模型的索引存在显著差异，导致现有查询优化的性能差异，这些优化是专门为传统结构开发的。这些差异源于查询和文档统计(包括子词标记)中的结构变化，导致查询时间更长，词汇量更小，以及发布列表中的分数分布不同。本文介绍了块最大剪枝(Block-Max Pruning，BMP) ，一种适用于学习型稀疏检索环境中索引的创新动态剪枝策略。 BMP 采用块过滤机制，将文档空间划分为小的、连续的文档范围，然后对这些文档进行动态聚合和排序，只有在必要时才进行全面处理，采用定义的安全提前终止标准或基于近似检索要求。通过严格的实验，我们表明 BMP 大大优于现有的动态修剪策略，在安全检索上下文中提供了无与伦比的效率，并改善了近似检索任务中精度和效率之间的权衡。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Faster+Learned+Sparse+Retrieval+with+Block-Max+Pruning)|0| +|[Faster Learned Sparse Retrieval with Block-Max Pruning](https://doi.org/10.1145/3626772.3657906)|Antonio Mallia, Torsten Suel, Nicola Tonellotto|Pinecone; University of Pisa; New York University|Learned sparse retrieval systems aim to combine the effectiveness ofcontextualized language models with the scalability of conventional datastructures such as inverted indexes. Nevertheless, the indexes generated bythese systems exhibit significant deviations from the ones that use traditionalretrieval models, leading to a discrepancy in the performance of existing queryoptimizations that were specifically developed for traditional structures.These disparities arise from structural variations in query and documentstatistics, including sub-word tokenization, leading to longer queries, smallervocabularies, and different score distributions within posting lists. Thispaper introduces Block-Max Pruning (BMP), an innovative dynamic pruningstrategy tailored for indexes arising in learned sparse retrieval environments.BMP employs a block filtering mechanism to divide the document space intosmall, consecutive document ranges, which are then aggregated and sorted on thefly, and fully processed only as necessary, guided by a defined safe earlytermination criterion or based on approximate retrieval requirements. Throughrigorous experimentation, we show that BMP substantially outperforms existingdynamic pruning strategies, offering unparalleled efficiency in safe retrievalcontexts and improved tradeoffs between precision and efficiency in approximateretrieval tasks.|学习型稀疏检索系统旨在将上下文化语言模型的有效性与倒排索引等传统数据结构的可扩展性结合起来。然而，这些系统生成的索引与使用传统检索模型的索引存在显著差异，导致现有查询优化的性能差异，这些优化是专门为传统结构开发的。这些差异源于查询和文档统计(包括子词标记)中的结构变化，导致查询时间更长，词汇量更小，以及发布列表中的分数分布不同。本文介绍了块最大剪枝(Block-Max Pruning，BMP) ，一种适用于学习型稀疏检索环境中索引的创新动态剪枝策略。 BMP 采用块过滤机制，将文档空间划分为小的、连续的文档范围，然后对这些文档进行动态聚合和排序，只有在必要时才进行全面处理，采用定义的安全提前终止标准或基于近似检索要求。通过严格的实验，我们表明 BMP 大大优于现有的动态修剪策略，在安全检索上下文中提供了无与伦比的效率，并改善了近似检索任务中精度和效率之间的权衡。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Faster+Learned+Sparse+Retrieval+with+Block-Max+Pruning)|0| |[Fine-Tuning LLaMA for Multi-Stage Text Retrieval](https://doi.org/10.1145/3626772.3657951)|Xueguang Ma, Liang Wang, Nan Yang, Furu Wei, Jimmy Lin|University of Waterloo; Microsoft Research Asia|While large language models (LLMs) have shown impressive NLP capabilities, existing IR applications mainly focus on prompting LLMs to generate query expansions or generating permutations for listwise reranking. In this study, we leverage LLMs directly to serve as components in the widely used multi-stage text ranking pipeline. Specifically, we fine-tune the open-source LLaMA-2 model as a dense retriever (repLLaMA) and a pointwise reranker (rankLLaMA). This is performed for both passage and document retrieval tasks using the MS MARCO training data. Our study shows that finetuned LLM retrieval models outperform smaller models. They are more effective and exhibit greater generalizability, requiring only a straightforward training strategy. Moreover, our pipeline allows for the fine-tuning of LLMs at each stage of a multi-stage retrieval pipeline. This demonstrates the strong potential for optimizing LLMs to enhance a variety of retrieval tasks. Furthermore, as LLMs are naturally pre-trained with longer contexts, they can directly represent longer documents. This eliminates the need for heuristic segmenting and pooling strategies to rank long documents. On the MS MARCO and BEIR datasets, our repLLaMA-rankLLaMA pipeline demonstrates a high level of effectiveness.|虽然大型语言模型(LLM)已经显示出令人印象深刻的 NLP 功能，但现有的 IR 应用程序主要侧重于提示 LLM 生成查询扩展或生成列表重新排序的排列。在这项研究中，我们利用 LLM 直接作为组件在广泛使用的多阶段文本排序流水线。具体来说，我们将开源 LLaMA-2模型微调为密集检索器(repLLaMA)和点态重分类器(rankLLaMA)。这是在使用微软 MARCO 训练数据的通道和文献检索任务中进行的。我们的研究表明，微调 LLM 检索模型优于较小的模型。它们更加有效，表现出更大的普遍性，只需要一个简单的训练策略。此外，我们的管道允许在多级检索管道的每个阶段对 LLM 进行微调。这表明优化 LLM 以增强各种检索任务的强大潜力。此外，由于 LLM 自然预先训练了较长的上下文，因此它们可以直接表示较长的文档。这消除了启发式分段和池策略对长文档进行排序的需要。在 MS MARCO 和 BEIR 数据集上，我们的 repLLaMA-rankLLaMA 管道显示了高水平的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Fine-Tuning+LLaMA+for+Multi-Stage+Text+Retrieval)|0| |[Graph Diffusive Self-Supervised Learning for Social Recommendation](https://doi.org/10.1145/3626772.3657962)|Jiuqiang Li, Hongjun Wang|Southwest Jiaotong University|Social recommendation aims at augmenting user-item interaction relationships and boosting recommendation quality by leveraging social information. Recently, self-supervised learning (SSL) has gained widespread adoption for social recommender. However, most existing methods exhibit poor robustness when faced with sparse user behavior data and are susceptible to inevitable social noise. To overcome the aforementioned limitations, we introduce a new Graph Diffusive Self-Supervised Learning (GDSSL) paradigm for social recommendation. Our approach involves the introduction of a guided social graph diffusion model that can adaptively mitigate the impact of social relation noise commonly found in real-world scenarios. This model progressively introduces random noise to the initial social graph and then iteratively restores it to recover the original structure. Additionally, to enhance robustness against noise and sparsity, we propose graph diffusive self-supervised learning, which utilizes the denoised social relation graph generated by our diffusion model for contrastive learning. The extensive experimental outcomes consistently indicate that our proposed GDSSL outmatches existing advanced solutions in social recommendation.|社会化推荐旨在通过利用社会化信息增强用户-项目的交互关系，提高推荐质量。近年来，自我监督学习(SSL)在社交推荐中得到了广泛的应用。然而，大多数现有的方法在面对稀疏的用户行为数据时表现出较差的鲁棒性，并且容易受到不可避免的社会噪声的影响。为了克服上述限制，我们引入了一个新的图扩散自我监督学习(GDSSL)范式用于社会推荐。我们的方法包括引入一个引导的社会图扩散模型，可以自适应地减轻在现实世界中常见的社会关系噪音的影响。该模型将随机噪声逐步引入到初始社会图中，然后迭代恢复它以恢复原始结构。此外，为了增强对噪声和稀疏性的鲁棒性，我们提出了图扩散自监督学习，它利用我们的扩散模型生成的去噪社会关系图进行对比学习。广泛的实验结果一致表明，我们提出的 GDSSL 在社会推荐方面优于现有的先进解决方案。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Graph+Diffusive+Self-Supervised+Learning+for+Social+Recommendation)|0| |[Improving In-Context Learning via Sequentially Selection and Preference Alignment for Few-Shot Aspect-Based Sentiment Analysis](https://doi.org/10.1145/3626772.3657932)|Qianlong Wang, Keyang Ding, Xuan Luo, Ruifeng Xu|Harbin Institute of Technology, Shenzhen|In this paper, we leverage in-context learning (ICL) paradigm to handle few-shot aspect-based sentiment analysis (ABSA). Previous works first rank candidate examples by some metrics and then independently retrieve examples similar to test samples. However, their effectiveness may be discounted because of two limitations: in-context example redundancy and example preference misalignment between retriever and LLM. To alleviate them, we propose a novel framework that sequentially retrieves in-context examples. It not only considers which example is useful for the test sample but also prevents its information from being duplicated by already retrieved examples. Subsequently, we exploit the rewards of LLMs on retrieved in-context examples to optimize parameters for bridging preference gaps. Experiments on four ABSA datasets show that our framework is significantly superior to previous works.|本文中，我们利用上下文学习（ICL）范式来处理少样本基于方面的情感分析（ABSA）。先前的研究首先通过某些指标对候选示例进行排序，然后独立检索与测试样本相似的示例。然而，由于上下文示例冗余和检索器与语言模型（LLM）之间的示例偏好不一致，其有效性可能受到影响。为了缓解这些问题，我们提出了一种新颖的框架，该框架顺序检索上下文示例。它不仅考虑哪个示例对测试样本有用，还防止其信息被已检索的示例重复。随后，我们利用LLM在检索到的上下文示例上的奖励来优化参数，以弥合偏好差距。在四个ABSA数据集上的实验表明，我们的框架明显优于先前的研究。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Improving+In-Context+Learning+via+Sequentially+Selection+and+Preference+Alignment+for+Few-Shot+Aspect-Based+Sentiment+Analysis)|0| -|[Language Fairness in Multilingual Information Retrieval](https://doi.org/10.1145/3626772.3657943)|Eugene Yang, Thomas Jänich, James Mayfield, Dawn J. Lawrie|University of Glasgow; Human Language Technology Center of Excellence, Johns Hopkins University; Johns Hopkins University; HLTCOE|Multilingual information retrieval (MLIR) considers the problem of rankingdocuments in several languages for a query expressed in a language that maydiffer from any of those languages. Recent work has observed that approachessuch as combining ranked lists representing a single document language each orusing multilingual pretrained language models demonstrate a preference for onelanguage over others. This results in systematic unfair treatment of documentsin different languages. This work proposes a language fairness metric toevaluate whether documents across different languages are fairly ranked throughstatistical equivalence testing using the Kruskal-Wallis test. In contrast tomost prior work in group fairness, we do not consider any language to be anunprotected group. Thus our proposed measure, PEER (Probability ofEqualExpected Rank), is the first fairness metric specifically designed tocapture the language fairness of MLIR systems. We demonstrate the behavior ofPEER on artificial ranked lists. We also evaluate real MLIR systems on twopublicly available benchmarks and show that the PEER scores align with prioranalytical findings on MLIR fairness. Our implementation is compatible withir-measures and is available at http://github.com/hltcoe/peer_measure.|多语言信息检索（MLIR）关注的是在查询语言可能不同于任何文档语言的情况下，对多种语言的文档进行排序的问题。最近的研究发现，一些方法，如将每种单一文档语言的排序列表组合起来，或使用多语言预训练语言模型，往往对某一种语言表现出偏好，从而导致对不同语言文档的系统性不公平对待。本文提出了一种语言公平性度量标准，通过使用Kruskal-Wallis检验的统计等效性测试，评估不同语言文档的排序是否公平。与大多数以往的群体公平性研究不同，我们不将任何语言视为不受保护的群体。因此，我们提出的度量标准PEER（Equal Expected Rank的概率）是首个专门设计用于捕捉MLIR系统语言公平性的公平性度量标准。我们在人工排序列表上展示了PEER的行为，并在两个公开可用的基准上评估了实际的MLIR系统，结果显示PEER得分与之前对MLIR公平性的分析发现相一致。我们的实现与ir-measures兼容，并可在http://github.com/hltcoe/peer_measure获取。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Language+Fairness+in+Multilingual+Information+Retrieval)|0| -|[Large Language Models Based Stemming for Information Retrieval: Promises, Pitfalls and Failures](https://doi.org/10.1145/3626772.3657949)|Shuai Wang, Shengyao Zhuang, Guido Zuccon|CSIRO; The University of Queensland|Text stemming is a natural language processing technique that is used to reduce words to their base form, also known as the root form. In Information Retrieval (IR), stemming is used in keyword-based matching pipelines to normalise text before indexing and query processing to improve subsequent matching between document and query keywords. The use of stemming has been shown to often improve the effectiveness of keyword-matching models such as BM25. However, traditional stemming methods, focusing solely on individual terms, overlook the richness of contextual information. Recognizing this gap, in this paper, we investigate the promising idea of using large language models (LLMs) to stem words by lever-aging its capability of context understanding. With this respect, we identify three avenues, each characterised by different trade-offs in terms of computational cost, effectiveness and robustness : (1) use LLMs to stem the vocabulary for a collection, i.e., the set of unique words that appear in the collection (vocabulary stemming), (2) use LLMs to stem each document separately (contextual stemming), and (3) use LLMs to extract from each document entities that should not be stemmed, then use vocabulary stemming to stem the rest of the terms (entity-based contextual stemming). Through a series of empirical experiments, we compare the use of LLMs for stemming with that of traditional lexical stemmers such as Porter and Krovetz for English text. We find that while vocabulary stemming and contextual stemming fail to achieve higher effectiveness than traditional stemmers, entity-based contextual stemming can achieve a higher effectiveness than using Porter stemmer alone, under specific conditions. Code and results are made available at https://github.com/ielab/SIGIR-2024-LLM-Stemming.|文本词干提取是一种自然语言处理技术，用于将单词缩减为其基本形式，即词根形式。在信息检索（IR）中，词干提取用于基于关键词的匹配流程中，通过在索引和查询处理之前规范化文本，以改进文档与查询关键词之间的后续匹配。研究表明，词干提取通常能提高如BM25等关键词匹配模型的有效性。然而，传统的词干提取方法仅关注单个词汇，忽略了上下文信息的丰富性。认识到这一差距，本文探讨了利用大型语言模型（LLMs）进行词干提取的有前景的想法，通过利用其理解上下文的能力。就此而言，我们确定了三条途径，每条途径在计算成本、有效性和鲁棒性方面都有不同的权衡：（1）使用LLMs对集合的词汇进行词干提取，即出现在集合中的唯一单词集合（词汇词干提取），（2）使用LLMs对每个文档单独进行词干提取（上下文词干提取），（3）使用LLMs从每个文档中提取不应进行词干提取的实体，然后使用词汇词干提取对其余词汇进行词干提取（基于实体的上下文词干提取）。通过一系列实证实验，我们将使用LLMs进行词干提取与传统的词汇词干提取器（如Porter和Krovetz）在英语文本中的应用进行了比较。我们发现，尽管词汇词干提取和上下文词干提取未能比传统词干提取器实现更高的有效性，但在特定条件下，基于实体的上下文词干提取能够比单独使用Porter词干提取器实现更高的有效性。代码和结果已在https://github.com/ielab/SIGIR-2024-LLM-Stemming公开。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Large+Language+Models+Based+Stemming+for+Information+Retrieval:+Promises,+Pitfalls+and+Failures)|0| +|[Language Fairness in Multilingual Information Retrieval](https://doi.org/10.1145/3626772.3657943)|Eugene Yang, Thomas Jänich, James Mayfield, Dawn J. Lawrie|University of Glasgow; HLTCOE; Human Language Technology Center of Excellence, Johns Hopkins University; Johns Hopkins University|Multilingual information retrieval (MLIR) considers the problem of rankingdocuments in several languages for a query expressed in a language that maydiffer from any of those languages. Recent work has observed that approachessuch as combining ranked lists representing a single document language each orusing multilingual pretrained language models demonstrate a preference for onelanguage over others. This results in systematic unfair treatment of documentsin different languages. This work proposes a language fairness metric toevaluate whether documents across different languages are fairly ranked throughstatistical equivalence testing using the Kruskal-Wallis test. In contrast tomost prior work in group fairness, we do not consider any language to be anunprotected group. Thus our proposed measure, PEER (Probability ofEqualExpected Rank), is the first fairness metric specifically designed tocapture the language fairness of MLIR systems. We demonstrate the behavior ofPEER on artificial ranked lists. We also evaluate real MLIR systems on twopublicly available benchmarks and show that the PEER scores align with prioranalytical findings on MLIR fairness. Our implementation is compatible withir-measures and is available at http://github.com/hltcoe/peer_measure.|多语言信息检索（MLIR）关注的是在查询语言可能不同于任何文档语言的情况下，对多种语言的文档进行排序的问题。最近的研究发现，一些方法，如将每种单一文档语言的排序列表组合起来，或使用多语言预训练语言模型，往往对某一种语言表现出偏好，从而导致对不同语言文档的系统性不公平对待。本文提出了一种语言公平性度量标准，通过使用Kruskal-Wallis检验的统计等效性测试，评估不同语言文档的排序是否公平。与大多数以往的群体公平性研究不同，我们不将任何语言视为不受保护的群体。因此，我们提出的度量标准PEER（Equal Expected Rank的概率）是首个专门设计用于捕捉MLIR系统语言公平性的公平性度量标准。我们在人工排序列表上展示了PEER的行为，并在两个公开可用的基准上评估了实际的MLIR系统，结果显示PEER得分与之前对MLIR公平性的分析发现相一致。我们的实现与ir-measures兼容，并可在http://github.com/hltcoe/peer_measure获取。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Language+Fairness+in+Multilingual+Information+Retrieval)|0| +|[Large Language Models Based Stemming for Information Retrieval: Promises, Pitfalls and Failures](https://doi.org/10.1145/3626772.3657949)|Shuai Wang, Shengyao Zhuang, Guido Zuccon|The University of Queensland; CSIRO|Text stemming is a natural language processing technique that is used to reduce words to their base form, also known as the root form. In Information Retrieval (IR), stemming is used in keyword-based matching pipelines to normalise text before indexing and query processing to improve subsequent matching between document and query keywords. The use of stemming has been shown to often improve the effectiveness of keyword-matching models such as BM25. However, traditional stemming methods, focusing solely on individual terms, overlook the richness of contextual information. Recognizing this gap, in this paper, we investigate the promising idea of using large language models (LLMs) to stem words by lever-aging its capability of context understanding. With this respect, we identify three avenues, each characterised by different trade-offs in terms of computational cost, effectiveness and robustness : (1) use LLMs to stem the vocabulary for a collection, i.e., the set of unique words that appear in the collection (vocabulary stemming), (2) use LLMs to stem each document separately (contextual stemming), and (3) use LLMs to extract from each document entities that should not be stemmed, then use vocabulary stemming to stem the rest of the terms (entity-based contextual stemming). Through a series of empirical experiments, we compare the use of LLMs for stemming with that of traditional lexical stemmers such as Porter and Krovetz for English text. We find that while vocabulary stemming and contextual stemming fail to achieve higher effectiveness than traditional stemmers, entity-based contextual stemming can achieve a higher effectiveness than using Porter stemmer alone, under specific conditions. Code and results are made available at https://github.com/ielab/SIGIR-2024-LLM-Stemming.|文本词干提取是一种自然语言处理技术，用于将单词缩减为其基本形式，即词根形式。在信息检索（IR）中，词干提取用于基于关键词的匹配流程中，通过在索引和查询处理之前规范化文本，以改进文档与查询关键词之间的后续匹配。研究表明，词干提取通常能提高如BM25等关键词匹配模型的有效性。然而，传统的词干提取方法仅关注单个词汇，忽略了上下文信息的丰富性。认识到这一差距，本文探讨了利用大型语言模型（LLMs）进行词干提取的有前景的想法，通过利用其理解上下文的能力。就此而言，我们确定了三条途径，每条途径在计算成本、有效性和鲁棒性方面都有不同的权衡：（1）使用LLMs对集合的词汇进行词干提取，即出现在集合中的唯一单词集合（词汇词干提取），（2）使用LLMs对每个文档单独进行词干提取（上下文词干提取），（3）使用LLMs从每个文档中提取不应进行词干提取的实体，然后使用词汇词干提取对其余词汇进行词干提取（基于实体的上下文词干提取）。通过一系列实证实验，我们将使用LLMs进行词干提取与传统的词汇词干提取器（如Porter和Krovetz）在英语文本中的应用进行了比较。我们发现，尽管词汇词干提取和上下文词干提取未能比传统词干提取器实现更高的有效性，但在特定条件下，基于实体的上下文词干提取能够比单独使用Porter词干提取器实现更高的有效性。代码和结果已在https://github.com/ielab/SIGIR-2024-LLM-Stemming公开。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Large+Language+Models+Based+Stemming+for+Information+Retrieval:+Promises,+Pitfalls+and+Failures)|0| |[MACA: Memory-aided Coarse-to-fine Alignment for Text-based Person Search](https://doi.org/10.1145/3626772.3657915)|Liangxu Su, Rong Quan, Zhiyuan Qi, Jie Qin|Nanjing University of Aeronautics and Astronautics|Text-based person search (TBPS) aims to search for the target person in the full image through textual descriptions. The key to addressing this task is to effectively perform cross-modality alignment between text and images. In this paper, we propose a novel TBPS framework, named Memory-Aided Coarse-to-fine Alignment (MACA), to learn an accurate and reliable alignment between the two modalities. Firstly, we introduce a proposal-based alignment module, which performs contrastive learning to accurately align the textual modality with different pedestrian proposals at a coarse-grained level. Secondly, for the fine-grained alignment, we propose an attribute-based alignment module to mitigate unreliable features by aligning text-wise details with image-wise global features. Moreover, we introduce an intuitive memory bank strategy to supplement useful negative samples for more effective contrastive learning, improving the convergence and generalization ability of the model based on the learned discriminative features. Extensive experiments on CUHK-SYSU-TBPS and PRW-TBPS demonstrate the superiority of MACA over state-of-the-art approaches. The code is available at https://github.com/suliangxu/MACA.|基于文本的人物搜索（TBPS）旨在通过文本描述在完整图像中搜索目标人物。解决这一任务的关键在于有效实现文本与图像之间的跨模态对齐。本文提出了一种新颖的TBPS框架，名为“记忆辅助的由粗到细对齐”（MACA），以学习两种模态之间准确可靠的对齐。首先，我们引入了一个基于提议的对齐模块，通过对比学习在粗粒度层面准确对齐文本模态与不同的行人提议。其次，为了实现细粒度对齐，我们提出了一个基于属性的对齐模块，通过将文本细节与图像全局特征对齐来缓解不可靠特征。此外，我们引入了一种直观的记忆库策略，以补充有用的负样本，从而实现更有效的对比学习，基于学习到的判别特征提升模型的收敛性和泛化能力。在CUHK-SYSU-TBPS和PRW-TBPS数据集上的广泛实验证明了MACA优于现有最先进方法的优越性。代码可在https://github.com/suliangxu/MACA获取。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=MACA:+Memory-aided+Coarse-to-fine+Alignment+for+Text-based+Person+Search)|0| |[Negative as Positive: Enhancing Out-of-distribution Generalization for Graph Contrastive Learning](https://doi.org/10.1145/3626772.3657927)|Zixu Wang, Bingbing Xu, Yige Yuan, Huawei Shen, Xueqi Cheng|Institute of Computing Technology, Chinese Academy of Sciences; Institute of Computing Technology, Chinese Academy of Sciences; University of Chinese Academy of Sciences|Graph contrastive learning (GCL), standing as the dominant paradigm in therealm of graph pre-training, has yielded considerable progress. Nonetheless,its capacity for out-of-distribution (OOD) generalization has been relativelyunderexplored. In this work, we point out that the traditional optimization ofInfoNCE in GCL restricts the cross-domain pairs only to be negative samples,which inevitably enlarges the distribution gap between different domains. Thisviolates the requirement of domain invariance under OOD scenario andconsequently impairs the model's OOD generalization performance. To addressthis issue, we propose a novel strategy "Negative as Positive", where the mostsemantically similar cross-domain negative pairs are treated as positive duringGCL. Our experimental results, spanning a wide array of datasets, confirm thatthis method substantially improves the OOD generalization performance of GCL.|图对比学习（Graph Contrastive Learning, GCL）作为图预训练领域的主导范式，已经取得了显著的进展。然而，其在分布外（Out-of-Distribution, OOD）泛化能力方面的潜力尚未得到充分探索。本文指出，GCL中传统的InfoNCE优化方法限制了跨域对仅作为负样本，这不可避免地扩大了不同域之间的分布差距。这种做法违背了OOD场景下域不变性的要求，从而损害了模型的OOD泛化性能。为解决这一问题，我们提出了一种名为“负样本视为正样本”的新策略，在GCL过程中将语义上最相似的跨域负样本对视为正样本。我们的实验结果跨越多个数据集，证实了这种方法显著提升了GCL的OOD泛化性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Negative+as+Positive:+Enhancing+Out-of-distribution+Generalization+for+Graph+Contrastive+Learning)|0| |[On Backbones and Training Regimes for Dense Retrieval in African Languages](https://doi.org/10.1145/3626772.3657952)|Akintunde Oladipo, Mofetoluwa Adeyemi, Jimmy Lin|University of Waterloo|The effectiveness of dense retrieval models trained with multilingual language models as backbones has been demonstrated in multilingual and cross-lingual information retrieval contexts. The optimal choice of a backbone model for a given retrieval task is dependent on the target retrieval domain as well as the pre-training domain of available language models and their generalization capabilities, the availability of relevance judgements, etc. In this work, we study the impact of these factors on retrieval effectiveness for African languages using three multilingual benchmark datasets: Mr. TyDi, MIRACL, and the newly released CIRAL dataset. We compare the effectiveness of mBERT as a backbone for dense retrieval models against multilingual language models such as AfriBERTa and AfroXLMR, which are specialized for African languages. Furthermore, we examine the impact of different training regimes on the effectiveness of dense retrieval in different domains for African languages. Our findings show that the pre-training domain of the backbone LM plays a huge role in retrieval effectiveness, especially in the absence of retrieval training data. Code artifacts are available at https://github.com/castorini/afridpr_backbones.|多语言语言模型作为骨干训练的密集检索模型的有效性已经在多语言和跨语言信息检索环境中得到了验证。对于给定的检索任务，最佳的骨干模型选择依赖于目标检索领域、可用语言模型的预训练领域及其泛化能力、相关性判断的可用性等因素。在本研究中，我们使用三个多语言基准数据集——Mr. TyDi、MIRACL和最新发布的CIRAL数据集，研究了这些因素对非洲语言检索效果的影响。我们比较了mBERT作为密集检索模型骨干的有效性与专门针对非洲语言的多语言语言模型如AfriBERTa和AfroXLMR的有效性。此外，我们还探讨了不同的训练机制对非洲语言在不同领域密集检索效果的影响。我们的研究结果表明，骨干语言模型的预训练领域在检索效果中起着重要作用，特别是在缺乏检索训练数据的情况下。代码资源可在https://github.com/castorini/afridpr_backbones获取。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=On+Backbones+and+Training+Regimes+for+Dense+Retrieval+in+African+Languages)|0| -|[Predicting Micro-video Popularity via Multi-modal Retrieval Augmentation](https://doi.org/10.1145/3626772.3657929)|Ting Zhong, Jian Lang, Yifan Zhang, Zhangtao Cheng, Kunpeng Zhang, Fan Zhou|University of Electronic Science and Technology of China; University of Maryland, College Park|Accurately predicting the popularity of micro-videos is crucial for real-world applications such as recommender systems and identifying viral marketing opportunities. Existing methods often focus on limited cross-modal information within individual micro-videos, overlooking the potential advantages of exploiting vast repository of past videos. We present MMRA, a multi-modal retrieval-augmented popularity prediction model that enhances prediction accuracy using relevant retrieved information. MMRA first retrieves relevant instances from a multi-modal memory bank, aligning video and text through transformation mechanisms involving a vision model and a text-based retriever. Additionally, a multi-modal interaction network is carefully designed to jointly capture cross-modal correlations within the target video and extract informative knowledge through retrieved instances, ultimately enhancing the prediction. Extensive experiments conducted on the real-world micro-video dataset demonstrate the superiority of MMRA when compared to state-of-the-art models. The code and data are available at https://github.com/ICDM-UESTC/MMRA.|准确预测微视频的受欢迎程度对于推荐系统及识别病毒式营销机会等实际应用至关重要。现有方法通常仅关注单个微视频内的有限跨模态信息，忽视了利用大量过往视频库的潜在优势。我们提出了MMRA，一种多模态检索增强的流行度预测模型，通过使用相关检索信息来提高预测准确性。MMRA首先从多模态记忆库中检索相关实例，通过视觉模型和基于文本的检索器参与的转换机制来对齐视频和文本。此外，精心设计了一个多模态交互网络，以共同捕捉目标视频内的跨模态关联，并通过检索到的实例提取信息性知识，从而最终提升预测效果。在真实世界微视频数据集上进行的广泛实验表明，相较于最先进的模型，MMRA具有优越性。代码和数据可在https://github.com/ICDM-UESTC/MMRA获取。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Predicting+Micro-video+Popularity+via+Multi-modal+Retrieval+Augmentation)|0| -|[Searching for Physical Documents in Archival Repositories](https://doi.org/10.1145/3626772.3657896)|Tokinori Suzuki, Douglas W. Oard, Emi Ishita, Yoichi Tomiura|University of Maryland; Kyushu University|Early retrieval systems were used to search physical media (e.g., paper) using manually created metadata. Modern ranked retrieval techniques are far more capable, but they require that content be either born digital or digitized. For physical content, searching metadata remains the state of the art. This paper seeks to change that, using a textual-edge graph neural network to learn relations between items from available metadata and from any content that has been digitized. Results show that substantial improvement over the best prior method can be achieved.|早期的检索系统用于通过手动创建的元数据搜索物理媒介（如纸质文档）。现代的排序检索技术则更为强大，但它们要求内容要么是数字化原生的，要么已经被数字化。对于物理内容，搜索元数据仍然是当前的技术水平。本文旨在改变这一现状，通过使用文本边缘图神经网络来学习从可用元数据和已数字化内容中提取的项目之间的关系。结果表明，与之前最好的方法相比，可以实现显著的改进。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Searching+for+Physical+Documents+in+Archival+Repositories)|0| -|[Self-Explainable Next POI Recommendation](https://doi.org/10.1145/3626772.3657967)|Kai Yang, Yi Yang, Qiang Gao, Ting Zhong, Yong Wang, Fan Zhou|University of Electronic Science and Technology of China; Southwestern University of Finance and Economics; Hong Kong University of Science and Technology|Point-of-Interest (POI) recommendation involves predicting users' next preferred POI and is becoming increasingly significant in location-based social networks. However, users are often reluctant to trust recommended results due to the lack of transparency in these systems. While recent work on explaining recommender systems has gained attention, prevailing methods only provide post-hoc explanations based on results or rudimentary explanations according to attention scores. Such limitations hinder reliability and applicability in risk-sensitive scenarios. Inspired by the information theory, we propose a self-explainable framework with an ante-hoc view called ExNext for next POI recommendation aimed at overcoming these limitations. Specifically, we endow self-explainability to POI recommender systems through compact representation learning using a variational information bottleneck approach. The learned representation further improves accuracy by reducing redundancy behind massive spatial-temporal trajectories, which, in turn, boosts the recommendation performance. Experiments on three real-world datasets show significant improvements in both model explainability and recommendation performance.|兴趣点（POI）推荐涉及预测用户下一个偏好的POI，在基于位置的社交网络中变得越来越重要。然而，由于这些系统缺乏透明性，用户通常不愿意信任推荐结果。尽管最近关于解释推荐系统的工作引起了关注，但现有方法仅提供基于结果的事后解释或根据注意力分数的基本解释。这些局限性阻碍了在风险敏感场景中的可靠性和适用性。受信息论启发，我们提出了一种名为ExNext的自我解释框架，旨在克服这些局限性，从先验视角出发进行下一个POI推荐。具体而言，我们通过使用变分信息瓶颈方法进行紧凑表示学习，赋予POI推荐系统自我解释能力。所学到的表示通过减少大量时空轨迹背后的冗余，进一步提高了准确性，从而提升了推荐性能。在三个真实世界数据集上的实验表明，模型解释性和推荐性能均显著提升。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Self-Explainable+Next+POI+Recommendation)|0| -|[Synthetic Test Collections for Retrieval Evaluation](https://doi.org/10.1145/3626772.3657942)|Hossein A. Rahmani, Nick Craswell, Emine Yilmaz, Bhaskar Mitra, Daniel Campos|Snowflake; University College London; Microsoft|Test collections play a vital role in evaluation of information retrieval(IR) systems. Obtaining a diverse set of user queries for test collectionconstruction can be challenging, and acquiring relevance judgments, whichindicate the appropriateness of retrieved documents to a query, is often costlyand resource-intensive. Generating synthetic datasets using Large LanguageModels (LLMs) has recently gained significant attention in variousapplications. In IR, while previous work exploited the capabilities of LLMs togenerate synthetic queries or documents to augment training data and improvethe performance of ranking models, using LLMs for constructing synthetic testcollections is relatively unexplored. Previous studies demonstrate that LLMshave the potential to generate synthetic relevance judgments for use in theevaluation of IR systems. In this paper, we comprehensively investigate whetherit is possible to use LLMs to construct fully synthetic test collections bygenerating not only synthetic judgments but also synthetic queries. Inparticular, we analyse whether it is possible to construct reliable synthetictest collections and the potential risks of bias such test collections mayexhibit towards LLM-based models. Our experiments indicate that using LLMs itis possible to construct synthetic test collections that can reliably be usedfor retrieval evaluation.|测试集合在评估信息检索（IR）系统中扮演着至关重要的角色。为构建测试集合获取多样化用户查询可能颇具挑战性，而获取相关性判断，即指示检索文档与查询的适当性，通常成本高昂且资源密集。近年来，利用大型语言模型（LLMs）生成合成数据集在多个应用领域引起了显著关注。在信息检索领域，尽管先前的工作利用了LLMs的能力来生成合成查询或文档以增强训练数据并提升排序模型的性能，但使用LLMs构建合成测试集合的研究相对较少。以往的研究表明，LLMs具有生成用于评估IR系统合成相关性判断的潜力。本文全面探讨了是否可能利用LLMs构建完全合成的测试集合，不仅生成合成判断，还包括合成查询。特别地，我们分析了构建可靠的合成测试集合的可能性及其可能对基于LLM的模型展示出的偏见风险。我们的实验结果表明，使用LLMs可以构建能够可靠用于检索评估的合成测试集合。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Synthetic+Test+Collections+for+Retrieval+Evaluation)|0| -|[SPLATE: Sparse Late Interaction Retrieval](https://doi.org/10.1145/3626772.3657968)|Thibault Formal, Stéphane Clinchant, Hervé Déjean, Carlos Lassance|Cohere; Naver Labs Europe|The late interaction paradigm introduced with ColBERT stands out in theneural Information Retrieval space, offering a compellingeffectiveness-efficiency trade-off across many benchmarks. Efficient lateinteraction retrieval is based on an optimized multi-step strategy, where anapproximate search first identifies a set of candidate documents to re-rankexactly. In this work, we introduce SPLATE, a simple and lightweight adaptationof the ColBERTv2 model which learns an “MLM adapter”, mapping its frozentoken embeddings to a sparse vocabulary space with a partially learned SPLADEmodule. This allows us to perform the candidate generation step in lateinteraction pipelines with traditional sparse retrieval techniques, making itparticularly appealing for running ColBERT in CPU environments. Our SPLATEColBERTv2 pipeline achieves the same effectiveness as the PLAID ColBERTv2engine by re-ranking 50 documents that can be retrieved under 10ms.|ColBERT引入的晚期交互范式在神经信息检索领域中脱颖而出，在多个基准测试中提供了引人注目的效果与效率权衡。高效的晚期交互检索基于一种优化的多步策略，首先通过近似搜索识别一组候选文档，然后进行精确重排序。在这项工作中，我们提出了SPLATE，这是对ColBERTv2模型的一个简单而轻量级的适应，它学习一个“MLM适配器”，将冻结的令牌嵌入映射到一个由部分学习的SPLADE模块构成的稀疏词汇空间。这使得我们能够在晚期交互管道中使用传统的稀疏检索技术执行候选生成步骤，特别适合在CPU环境中运行ColBERT。我们的SPLATE ColBERTv2管道通过重排序50个可以在10毫秒内检索到的文档，实现了与PLAID ColBERTv2引擎相同的效果。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=SPLATE:+Sparse+Late+Interaction+Retrieval)|0| +|[Predicting Micro-video Popularity via Multi-modal Retrieval Augmentation](https://doi.org/10.1145/3626772.3657929)|Ting Zhong, Jian Lang, Yifan Zhang, Zhangtao Cheng, Kunpeng Zhang, Fan Zhou|University of Maryland, College Park; University of Electronic Science and Technology of China|Accurately predicting the popularity of micro-videos is crucial for real-world applications such as recommender systems and identifying viral marketing opportunities. Existing methods often focus on limited cross-modal information within individual micro-videos, overlooking the potential advantages of exploiting vast repository of past videos. We present MMRA, a multi-modal retrieval-augmented popularity prediction model that enhances prediction accuracy using relevant retrieved information. MMRA first retrieves relevant instances from a multi-modal memory bank, aligning video and text through transformation mechanisms involving a vision model and a text-based retriever. Additionally, a multi-modal interaction network is carefully designed to jointly capture cross-modal correlations within the target video and extract informative knowledge through retrieved instances, ultimately enhancing the prediction. Extensive experiments conducted on the real-world micro-video dataset demonstrate the superiority of MMRA when compared to state-of-the-art models. The code and data are available at https://github.com/ICDM-UESTC/MMRA.|准确预测微视频的受欢迎程度对于推荐系统及识别病毒式营销机会等实际应用至关重要。现有方法通常仅关注单个微视频内的有限跨模态信息，忽视了利用大量过往视频库的潜在优势。我们提出了MMRA，一种多模态检索增强的流行度预测模型，通过使用相关检索信息来提高预测准确性。MMRA首先从多模态记忆库中检索相关实例，通过视觉模型和基于文本的检索器参与的转换机制来对齐视频和文本。此外，精心设计了一个多模态交互网络，以共同捕捉目标视频内的跨模态关联，并通过检索到的实例提取信息性知识，从而最终提升预测效果。在真实世界微视频数据集上进行的广泛实验表明，相较于最先进的模型，MMRA具有优越性。代码和数据可在https://github.com/ICDM-UESTC/MMRA获取。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Predicting+Micro-video+Popularity+via+Multi-modal+Retrieval+Augmentation)|0| +|[Searching for Physical Documents in Archival Repositories](https://doi.org/10.1145/3626772.3657896)|Tokinori Suzuki, Douglas W. Oard, Emi Ishita, Yoichi Tomiura|Kyushu University; University of Maryland|Early retrieval systems were used to search physical media (e.g., paper) using manually created metadata. Modern ranked retrieval techniques are far more capable, but they require that content be either born digital or digitized. For physical content, searching metadata remains the state of the art. This paper seeks to change that, using a textual-edge graph neural network to learn relations between items from available metadata and from any content that has been digitized. Results show that substantial improvement over the best prior method can be achieved.|早期的检索系统用于通过手动创建的元数据搜索物理媒介（如纸质文档）。现代的排序检索技术则更为强大，但它们要求内容要么是数字化原生的，要么已经被数字化。对于物理内容，搜索元数据仍然是当前的技术水平。本文旨在改变这一现状，通过使用文本边缘图神经网络来学习从可用元数据和已数字化内容中提取的项目之间的关系。结果表明，与之前最好的方法相比，可以实现显著的改进。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Searching+for+Physical+Documents+in+Archival+Repositories)|0| +|[Self-Explainable Next POI Recommendation](https://doi.org/10.1145/3626772.3657967)|Kai Yang, Yi Yang, Qiang Gao, Ting Zhong, Yong Wang, Fan Zhou|Southwestern University of Finance and Economics; Hong Kong University of Science and Technology; University of Electronic Science and Technology of China|Point-of-Interest (POI) recommendation involves predicting users' next preferred POI and is becoming increasingly significant in location-based social networks. However, users are often reluctant to trust recommended results due to the lack of transparency in these systems. While recent work on explaining recommender systems has gained attention, prevailing methods only provide post-hoc explanations based on results or rudimentary explanations according to attention scores. Such limitations hinder reliability and applicability in risk-sensitive scenarios. Inspired by the information theory, we propose a self-explainable framework with an ante-hoc view called ExNext for next POI recommendation aimed at overcoming these limitations. Specifically, we endow self-explainability to POI recommender systems through compact representation learning using a variational information bottleneck approach. The learned representation further improves accuracy by reducing redundancy behind massive spatial-temporal trajectories, which, in turn, boosts the recommendation performance. Experiments on three real-world datasets show significant improvements in both model explainability and recommendation performance.|兴趣点（POI）推荐涉及预测用户下一个偏好的POI，在基于位置的社交网络中变得越来越重要。然而，由于这些系统缺乏透明性，用户通常不愿意信任推荐结果。尽管最近关于解释推荐系统的工作引起了关注，但现有方法仅提供基于结果的事后解释或根据注意力分数的基本解释。这些局限性阻碍了在风险敏感场景中的可靠性和适用性。受信息论启发，我们提出了一种名为ExNext的自我解释框架，旨在克服这些局限性，从先验视角出发进行下一个POI推荐。具体而言，我们通过使用变分信息瓶颈方法进行紧凑表示学习，赋予POI推荐系统自我解释能力。所学到的表示通过减少大量时空轨迹背后的冗余，进一步提高了准确性，从而提升了推荐性能。在三个真实世界数据集上的实验表明，模型解释性和推荐性能均显著提升。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Self-Explainable+Next+POI+Recommendation)|0| +|[Synthetic Test Collections for Retrieval Evaluation](https://doi.org/10.1145/3626772.3657942)|Hossein A. Rahmani, Nick Craswell, Emine Yilmaz, Bhaskar Mitra, Daniel Campos|University College London; Snowflake; Microsoft|Test collections play a vital role in evaluation of information retrieval(IR) systems. Obtaining a diverse set of user queries for test collectionconstruction can be challenging, and acquiring relevance judgments, whichindicate the appropriateness of retrieved documents to a query, is often costlyand resource-intensive. Generating synthetic datasets using Large LanguageModels (LLMs) has recently gained significant attention in variousapplications. In IR, while previous work exploited the capabilities of LLMs togenerate synthetic queries or documents to augment training data and improvethe performance of ranking models, using LLMs for constructing synthetic testcollections is relatively unexplored. Previous studies demonstrate that LLMshave the potential to generate synthetic relevance judgments for use in theevaluation of IR systems. In this paper, we comprehensively investigate whetherit is possible to use LLMs to construct fully synthetic test collections bygenerating not only synthetic judgments but also synthetic queries. Inparticular, we analyse whether it is possible to construct reliable synthetictest collections and the potential risks of bias such test collections mayexhibit towards LLM-based models. Our experiments indicate that using LLMs itis possible to construct synthetic test collections that can reliably be usedfor retrieval evaluation.|测试集合在评估信息检索（IR）系统中扮演着至关重要的角色。为构建测试集合获取多样化用户查询可能颇具挑战性，而获取相关性判断，即指示检索文档与查询的适当性，通常成本高昂且资源密集。近年来，利用大型语言模型（LLMs）生成合成数据集在多个应用领域引起了显著关注。在信息检索领域，尽管先前的工作利用了LLMs的能力来生成合成查询或文档以增强训练数据并提升排序模型的性能，但使用LLMs构建合成测试集合的研究相对较少。以往的研究表明，LLMs具有生成用于评估IR系统合成相关性判断的潜力。本文全面探讨了是否可能利用LLMs构建完全合成的测试集合，不仅生成合成判断，还包括合成查询。特别地，我们分析了构建可靠的合成测试集合的可能性及其可能对基于LLM的模型展示出的偏见风险。我们的实验结果表明，使用LLMs可以构建能够可靠用于检索评估的合成测试集合。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Synthetic+Test+Collections+for+Retrieval+Evaluation)|0| +|[SPLATE: Sparse Late Interaction Retrieval](https://doi.org/10.1145/3626772.3657968)|Thibault Formal, Stéphane Clinchant, Hervé Déjean, Carlos Lassance|Naver Labs Europe; Cohere|The late interaction paradigm introduced with ColBERT stands out in theneural Information Retrieval space, offering a compellingeffectiveness-efficiency trade-off across many benchmarks. Efficient lateinteraction retrieval is based on an optimized multi-step strategy, where anapproximate search first identifies a set of candidate documents to re-rankexactly. In this work, we introduce SPLATE, a simple and lightweight adaptationof the ColBERTv2 model which learns an “MLM adapter”, mapping its frozentoken embeddings to a sparse vocabulary space with a partially learned SPLADEmodule. This allows us to perform the candidate generation step in lateinteraction pipelines with traditional sparse retrieval techniques, making itparticularly appealing for running ColBERT in CPU environments. Our SPLATEColBERTv2 pipeline achieves the same effectiveness as the PLAID ColBERTv2engine by re-ranking 50 documents that can be retrieved under 10ms.|ColBERT引入的晚期交互范式在神经信息检索领域中脱颖而出，在多个基准测试中提供了引人注目的效果与效率权衡。高效的晚期交互检索基于一种优化的多步策略，首先通过近似搜索识别一组候选文档，然后进行精确重排序。在这项工作中，我们提出了SPLATE，这是对ColBERTv2模型的一个简单而轻量级的适应，它学习一个“MLM适配器”，将冻结的令牌嵌入映射到一个由部分学习的SPLADE模块构成的稀疏词汇空间。这使得我们能够在晚期交互管道中使用传统的稀疏检索技术执行候选生成步骤，特别适合在CPU环境中运行ColBERT。我们的SPLATE ColBERTv2管道通过重排序50个可以在10毫秒内检索到的文档，实现了与PLAID ColBERTv2引擎相同的效果。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=SPLATE:+Sparse+Late+Interaction+Retrieval)|0| |[The Surprising Effectiveness of Rankers trained on Expanded Queries](https://doi.org/10.1145/3626772.3657938)|Abhijit Anand, Venktesh V, Vinay Setty, Avishek Anand|University of Stavanger; L3S Research Institute; TU Delft|An significant challenge in text-ranking systems is handling hard queries that form the tail end of the query distribution. Difficulty may arise due to the presence of uncommon, underspecified, or incomplete queries. In this work, we improve the ranking performance of hard or difficult queries while maintaining the performance of other queries. Firstly, we do LLM-based query enrichment for training queries using relevant documents. Next, a specialized ranker is fine-tuned only on the enriched hard queries instead of the original queries. We combine the relevance scores from the specialized ranker and the base ranker, along with a query performance score estimated for each query. Our approach departs from existing methods that usually employ a single ranker for all queries, which is biased towards easy queries, which form the majority of the query distribution. In our extensive experiments on the DL-Hard dataset, we find that a principled query performance based scoring method using base and specialized ranker offers a significant improvement of up to 48.4% on the document ranking task and up to 25% on the passage ranking task compared to the baseline performance of using original queries, even outperforming SOTA model.|文本排序系统中的一个重要挑战是如何处理构成查询分布尾部的困难查询。困难的出现可能是由于存在不常见、未明确指定或不完整的查询。在这项工作中，我们提升了困难查询的排序性能，同时保持了其他查询的性能。首先，我们使用相关文档对训练查询进行基于大语言模型（LLM）的查询丰富。接着，我们仅在丰富后的困难查询上微调了一个专门的排序器，而非原始查询。我们将来自专门排序器和基础排序器的相关性分数结合起来，并结合每个查询的查询性能分数进行评估。我们的方法不同于现有通常为所有查询使用单一排序器的方法，这些方法偏向于占查询分布大多数的简单查询。在我们对DL-Hard数据集的广泛实验中，我们发现，基于查询性能的评分方法结合基础和专门排序器，在文档排序任务上实现了高达48.4%的显著改进，在段落排序任务上实现了高达25%的改进，相比于使用原始查询的基线性能，甚至超过了当前最先进的模型。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=The+Surprising+Effectiveness+of+Rankers+trained+on+Expanded+Queries)|0| |[Turbo-CF: Matrix Decomposition-Free Graph Filtering for Fast Recommendation](https://doi.org/10.1145/3626772.3657916)|JinDuk Park, YongMin Shin, WonYong Shin|Yonsei University|A series of graph filtering (GF)-based collaborative filtering (CF) showcasesstate-of-the-art performance on the recommendation accuracy by using a low-passfilter (LPF) without a training process. However, conventional GF-based CFapproaches mostly perform matrix decomposition on the item-item similaritygraph to realize the ideal LPF, which results in a non-trivial computationalcost and thus makes them less practical in scenarios where rapidrecommendations are essential. In this paper, we propose Turbo-CF, a GF-basedCF method that is both training-free and matrix decomposition-free. Turbo-CFemploys a polynomial graph filter to circumvent the issue of expensive matrixdecompositions, enabling us to make full use of modern computer hardwarecomponents (i.e., GPU). Specifically, Turbo-CF first constructs an item-itemsimilarity graph whose edge weights are effectively regulated. Then, our ownpolynomial LPFs are designed to retain only low-frequency signals withoutexplicit matrix decompositions. We demonstrate that Turbo-CF is extremely fastyet accurate, achieving a runtime of less than 1 second on real-world benchmarkdatasets while achieving recommendation accuracies comparable to bestcompetitors.|一系列基于图滤波（Graph Filtering, GF）的协同过滤（Collaborative Filtering, CF）方法通过使用无训练过程的低通滤波器（Low-Pass Filter, LPF）展示了最先进的推荐准确性。然而，传统的基于GF的CF方法大多通过对物品-物品相似度图进行矩阵分解来实现理想的LPF，这导致计算成本高昂，从而使得这些方法在需要快速推荐的情况下不太实用。本文提出了一种名为Turbo-CF的基于GF的CF方法，该方法既无需训练也无需矩阵分解。Turbo-CF采用多项式图滤波器来绕过高成本的矩阵分解问题，使我们能够充分利用现代计算机硬件组件（如GPU）。具体而言，Turbo-CF首先构建了一个边权重得到有效调节的物品-物品相似度图，然后设计了自有的多项式LPF来仅保留低频信号，而无需显式的矩阵分解。我们证明，Turbo-CF在速度极快的同时仍保持高准确性，在真实世界基准数据集上的运行时间不到1秒，同时达到了与最佳竞争对手相当的推荐准确性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Turbo-CF:+Matrix+Decomposition-Free+Graph+Filtering+for+Fast+Recommendation)|0| -|[Unifying Graph Retrieval and Prompt Tuning for Graph-Grounded Text Classification](https://doi.org/10.1145/3626772.3657934)|Le Dai, Yu Yin, Enhong Chen, Hui Xiong|University of Science and Technology of China Department of Computer Science and Technology; The Hong Kong University of Science and Technology (Guangzhou); University of Science and Technology of China; University of Science and Technology of China School of Data Science|Text classification has long time been researched as a fundamental problem in information retrieval. Since text data are frequently connected with graph structures, it poses new possibilities for a more accurate and explainable classification. One common approach of this graph-text integration is to consider text as graph attributes and utilize GNNs to conduct a node classification task. While both text and graph data are modeled, GNNs treat text in a rather coarse-grained way, have limitations in preserving the detailed structures of a graph, and are less robust to graph sparsity. In this paper, we propose to take an alternative perspective instead, viewing graph as the context of texts, as enlightened by retrieval augmented generation. We propose a novel framework called Graph Retrieval Prompt Tuning (GRPT), consisting of a Graph Retrieval Module and a Prompt Tuning Module integrated with graph context. For graph retrieval, two retrieval strategies are designed to retrieve node context and path context, preserving both node proximity and detailed connectivity patterns. Extensive experiments on four real-world datasets show the effectiveness of our framework in both standard supervised and sparse settings.|长期以来，文本分类一直被视为信息检索中的一个基础问题。由于文本数据经常与图结构相关联，这为更准确和可解释的分类带来了新的可能性。一种常见的图-文整合方法是将文本视为图的属性，并利用图神经网络（GNN）进行节点分类任务。然而，尽管文本和图数据都被建模，GNN在处理文本时较为粗粒度，难以保留图的详细结构，并且在图的稀疏性方面表现不够稳健。本文中，我们提出了一种替代视角，即将图视为文本的上下文，这一灵感来源于检索增强生成。我们提出了一种名为图检索提示调优（Graph Retrieval Prompt Tuning, GRPT）的新框架，该框架包括一个图检索模块和一个与图上下文集成的提示调优模块。对于图检索，我们设计了两种检索策略来检索节点上下文和路径上下文，既保留了节点接近性，又保留了详细的连接模式。在四个真实世界数据集上的广泛实验表明，我们的框架在标准监督和稀疏设置下均表现出色。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Unifying+Graph+Retrieval+and+Prompt+Tuning+for+Graph-Grounded+Text+Classification)|0| +|[Unifying Graph Retrieval and Prompt Tuning for Graph-Grounded Text Classification](https://doi.org/10.1145/3626772.3657934)|Le Dai, Yu Yin, Enhong Chen, Hui Xiong|University of Science and Technology of China; The Hong Kong University of Science and Technology (Guangzhou); University of Science and Technology of China School of Data Science; University of Science and Technology of China Department of Computer Science and Technology|Text classification has long time been researched as a fundamental problem in information retrieval. Since text data are frequently connected with graph structures, it poses new possibilities for a more accurate and explainable classification. One common approach of this graph-text integration is to consider text as graph attributes and utilize GNNs to conduct a node classification task. While both text and graph data are modeled, GNNs treat text in a rather coarse-grained way, have limitations in preserving the detailed structures of a graph, and are less robust to graph sparsity. In this paper, we propose to take an alternative perspective instead, viewing graph as the context of texts, as enlightened by retrieval augmented generation. We propose a novel framework called Graph Retrieval Prompt Tuning (GRPT), consisting of a Graph Retrieval Module and a Prompt Tuning Module integrated with graph context. For graph retrieval, two retrieval strategies are designed to retrieve node context and path context, preserving both node proximity and detailed connectivity patterns. Extensive experiments on four real-world datasets show the effectiveness of our framework in both standard supervised and sparse settings.|长期以来，文本分类一直被视为信息检索中的一个基础问题。由于文本数据经常与图结构相关联，这为更准确和可解释的分类带来了新的可能性。一种常见的图-文整合方法是将文本视为图的属性，并利用图神经网络（GNN）进行节点分类任务。然而，尽管文本和图数据都被建模，GNN在处理文本时较为粗粒度，难以保留图的详细结构，并且在图的稀疏性方面表现不够稳健。本文中，我们提出了一种替代视角，即将图视为文本的上下文，这一灵感来源于检索增强生成。我们提出了一种名为图检索提示调优（Graph Retrieval Prompt Tuning, GRPT）的新框架，该框架包括一个图检索模块和一个与图上下文集成的提示调优模块。对于图检索，我们设计了两种检索策略来检索节点上下文和路径上下文，既保留了节点接近性，又保留了详细的连接模式。在四个真实世界数据集上的广泛实验表明，我们的框架在标准监督和稀疏设置下均表现出色。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Unifying+Graph+Retrieval+and+Prompt+Tuning+for+Graph-Grounded+Text+Classification)|0| |[Weighted KL-Divergence for Document Ranking Model Refinement](https://doi.org/10.1145/3626772.3657946)|Yingrui Yang, Yifan Qiao, Shanxiu He, Tao Yang||Transformer-based retrieval and reranking models for text document search are often refined through knowledge distillation together with contrastive learning. A tight distribution matching between the teacher and student models can be hard as over-calibration may degrade training effectiveness when a teacher does not perform well. This paper contrastively reweights KL divergence terms to prioritize the alignment between a student and a teacher model for proper separation of positive and negative documents. This paper analyzes and evaluates the proposed loss function on the MS MARCO and BEIR datasets to demonstrate its effectiveness in improving the relevance of tested student models.|基于Transformer的文本文档检索与重排序模型通常通过知识蒸馏与对比学习进行优化。当教师模型表现不佳时，过度校准可能导致训练效果下降，从而使得教师模型与学生模型之间的紧密分布匹配变得困难。本文通过对比重加权KL散度项，优先考虑学生模型与教师模型之间的对齐，以实现正负文档的适当分离。本文在MS MARCO和BEIR数据集上分析并评估了所提出的损失函数，证明了其在提高测试学生模型相关性方面的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Weighted+KL-Divergence+for+Document+Ranking+Model+Refinement)|0| |[Using Large Language Models for Math Information Retrieval](https://doi.org/10.1145/3626772.3657907)|Behrooz Mansouri, Reihaneh Maarefdoust|University of Southern Maine|Large language models, such as Orca-2, have demonstrated notable problem-solving abilities in mathematics. However, their potential to enhance math information retrieval remains largely unexplored. This paper investigates the use of two large language models, LLaMA-2 and Orca-2 for three tasks in math information retrieval. First, the study explores the use of these models for relevance assessment, evaluating the relevance of answers to math questions. Then, the application of these models for math data augmentation is studied. Using the existing math information retrieval test collection, ARQMath, answers of different relevance degrees are generated for each topic. These answers are then used for fine-tuning a cross-encoder re-ranker and are compared against fine-tuning with answers that are manually labeled. Finally, the use of these models for ranking candidate answers to math questions is explored. The experimental results indicate that, while these models may not be effective for relevance assessment and ranking tasks, Orca-2 can be a valuable resource for math data augmentation.|大型语言模型，如Orca-2，在数学问题的解决能力上已显示出显著的效果。然而，它们在提升数学信息检索方面的潜力仍未得到充分探索。本文研究了使用两个大型语言模型——LLaMA-2和Orca-2——在数学信息检索中的三个任务。首先，研究探讨了这些模型用于相关性评估的情况，评估了回答数学问题的答案的相关性。接着，研究了这些模型在数学数据增强中的应用。利用现有的数学信息检索测试集合ARQMath，为每个主题生成了不同相关程度的答案。然后，这些答案被用于微调一个交叉编码器重新排序器，并与使用手动标记的答案进行微调的结果进行比较。最后，探讨了这些模型在为数学问题排序候选答案中的应用。实验结果表明，尽管这些模型在相关性评估和排序任务中可能效果不佳，但Orca-2在数学数据增强方面可以成为一个宝贵的资源。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Using+Large+Language+Models+for+Math+Information+Retrieval)|0| -|[A Question-Answering Assistant over Personal Knowledge Graph](https://doi.org/10.1145/3626772.3657665)|Lingyuan Liu, Huifang Du, Xiaolian Zhang, Mengying Guo, Haofen Wang, Meng Wang|Tongji University; Huawei Technologies Co. Ltd.; Southeast University Southeast University-Monash University Joint Graduate School|We develop a Personal Knowledge Graph Question-Answering (PKGQA) assistant, seamlessly integrating information from multiple mobile applications into a unified and user-friendly query interface to offer users convenient information retrieval and personalized knowledge services. Based on a fine-grained schema customized for PKG, the PKGQA system in this paper comprises Symbolic Semantic Parsing, Frequently Asked Question (FAQ) Semantic Matching, and Neural Semantic Parsing modules, which are designed to take into account both accuracy and efficiency. The PKGQA system achieves high accuracy on the constructed dataset and demonstrates good performance in answering complex questions. Our system is implemented through an Android application, which is shown in https://youtu.be/p732U5KPEq4.|我们开发了一个个人知识图谱问答（PKGQA）助手，该助手能够无缝整合来自多个移动应用程序的信息，并将其集成到一个统一且用户友好的查询界面中，为用户提供便捷的信息检索和个性化知识服务。基于为PKG定制的细粒度模式，本文中的PKGQA系统包括符号语义解析、常见问题（FAQ）语义匹配和神经语义解析模块，这些模块设计时兼顾了准确性和效率。PKGQA系统在构建的数据集上实现了高准确性，并在回答复杂问题方面表现出色。我们的系统通过一个Android应用程序实现，展示视频可在https://youtu.be/p732U5KPEq4查看。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Question-Answering+Assistant+over+Personal+Knowledge+Graph)|0| -|[ConvLogRecaller: Real-Time Conversational Lifelog Recaller](https://doi.org/10.1145/3626772.3657659)|YuanChi Lee, AnZi Yen, HenHsen Huang, HsinHsi Chen|Institute of Information Science, Academia Sinica; National Taiwan University; National Yang Ming Chiao Tung University|The popularization of networks fosters the convenience of communication. People can easily share their life experiences and thoughts with relatives and friends via instant messaging software. As time passes, individuals may forget certain details of life events, leading to difficulties in effectively communicating with others. The propensity of individuals to forget or mix up life events highlights the importance of services aimed at retrieving information about past experiences. This paper presents a conversational information recall system, ConvLogRecaller, which proactively supports real-time memory recall assistance during online conversations. Given a conversation of the user with others, ConvLogRecaller suggests a message if the user forgets the details of the life experiences. The services provided by our system can avoid hesitations or memory lapses that might hinder the efficiency of a conversation.|网络的普及促进了沟通的便利性。人们可以通过即时通讯软件轻松地与亲友分享生活经历和思想。然而，随着时间的推移，个人可能会忘记生活事件的某些细节，从而导致与他人有效沟通的困难。人们倾向于忘记或混淆生活事件的倾向突显了检索过去经历信息服务的重要性。本文介绍了一个对话式信息回忆系统ConvLogRecaller，该系统在在线对话中主动支持实时记忆回忆辅助。在用户与其他人进行对话时，如果用户忘记了生活经历的细节，ConvLogRecaller会提供一条建议消息。本系统提供的服务可以避免因犹豫或记忆缺失而可能阻碍对话效率的情况。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=ConvLogRecaller:+Real-Time+Conversational+Lifelog+Recaller)|0| -|[CLIP-Branches: Interactive Fine-Tuning for Text-Image Retrieval](https://doi.org/10.1145/3626772.3657678)|Christian Lülf, Denis Mayr Lima Martins, Marcos Antonio Vaz Salles, Yongluan Zhou, Fabian Gieseke|University of Münster; University of Copenhagen; Independent Researcher|The advent of text-image models, most notably CLIP, has significantlytransformed the landscape of information retrieval. These models enable thefusion of various modalities, such as text and images. One significant outcomeof CLIP is its capability to allow users to search for images using text as aquery, as well as vice versa. This is achieved via a joint embedding of imagesand text data that can, for instance, be used to search for similar items.Despite efficient query processing techniques such as approximate nearestneighbor search, the results may lack precision and completeness. We introduceCLIP-Branches, a novel text-image search engine built upon the CLIParchitecture. Our approach enhances traditional text-image search engines byincorporating an interactive fine-tuning phase, which allows the user tofurther concretize the search query by iteratively defining positive andnegative examples. Our framework involves training a classification model giventhe additional user feedback and essentially outputs all positively classifiedinstances of the entire data catalog. By building upon recent techniques, thisinference phase, however, is not implemented by scanning the entire datacatalog, but by employing efficient index structures pre-built for the data.Our results show that the fine-tuned results can improve the initial searchoutputs in terms of relevance and accuracy while maintaining swift responsetimes|文本-图像模型的出现，尤其是CLIP，极大地改变了信息检索的格局。这些模型能够融合多种模态，如文本和图像。CLIP的一个重要成果是它允许用户使用文本作为查询来搜索图像，反之亦然。这是通过图像和文本数据的联合嵌入实现的，例如，可以用于搜索相似的项目。尽管有近似最近邻搜索等高效的查询处理技术，但结果可能缺乏精确性和完整性。我们引入了CLIP-Branches，这是一种基于CLIP架构的新型文本-图像搜索引擎。我们的方法通过引入交互式微调阶段来增强传统的文本-图像搜索引擎，该阶段允许用户通过迭代定义正例和负例来进一步具体化搜索查询。我们的框架涉及在给定额外用户反馈的情况下训练分类模型，并基本上输出整个数据目录中所有正分类的实例。通过借鉴最近的技术，这一推理阶段并非通过扫描整个数据目录来实现，而是通过为数据预建的高效索引结构来实现。我们的结果表明，微调后的结果可以在保持快速响应时间的同时，提高初始搜索输出的相关性和准确性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=CLIP-Branches:++Interactive+Fine-Tuning+for+Text-Image+Retrieval)|0| -|[Img2Loc: Revisiting Image Geolocalization using Multi-modality Foundation Models and Image-based Retrieval-Augmented Generation](https://doi.org/10.1145/3626772.3657673)|Zhongliang Zhou, Jielu Zhang, Zihan Guan, Mengxuan Hu, Ni Lao, Lan Mu, Sheng Li, Gengchen Mai|Google Inc; University of Georgia; University of Virginia|Geolocating precise locations from images presents a challenging problem in computer vision and information retrieval. Traditional methods typically employ either classification-dividing the Earth's surface into grid cells and classifying images accordingly, or retrieval-identifying locations by matching images with a database of image-location pairs. However, classification-based approaches are limited by the cell size and cannot yield precise predictions, while retrieval-based systems usually suffer from poor search quality and inadequate coverage of the global landscape at varied scale and aggregation levels. To overcome these drawbacks, we present Img2Loc, a novel system that redefines image geolocalization as a text generation task. This is achieved using cutting-edge large multi-modality models (LMMs) like GPT-4V or LLaVA with retrieval augmented generation. Img2Loc first employs CLIP-based representations to generate an image-based coordinate query database. It then uniquely combines query results with images itself, forming elaborate prompts customized for LMMs. When tested on benchmark datasets such as Im2GPS3k and YFCC4k, Img2Loc not only surpasses the performance of previous state-of-the-art models but does so without any model training. A video demonstration of the system can be accessed via this link https://drive.google.com/file/d/16A6Amc7AyUoKHRH3_WBRToRC13sn7tU/view?usp=sharing|从图像中定位精确位置在计算机视觉和信息检索领域是一个具有挑战性的问题。传统方法通常采用分类法——将地球表面划分为网格单元并对图像进行相应分类，或检索法——通过将图像与图像-位置对数据库匹配来识别位置。然而，基于分类的方法受限于单元格大小，无法产生精确预测，而基于检索的系统通常搜索质量较差，无法在全球范围内以不同尺度和聚合级别充分覆盖地理景观。为了克服这些缺点，我们提出了Img2Loc，这是一个将图像地理定位重新定义为文本生成任务的新系统。该系统利用GPT-4V或LLaVA等前沿的大型多模态模型（LMMs），结合增强的生成检索技术实现这一目标。Img2Loc首先采用基于CLIP的表示方法生成基于图像的坐标查询数据库。然后，它独特地将查询结果与图像本身相结合，形成专为LMMs定制的复杂提示。在Im2GPS3k和YFCC4k等基准数据集上的测试表明，Img2Loc不仅超越了之前最先进模型的性能，而且无需任何模型训练即可实现这一效果。系统的演示视频可通过以下链接访问：https://drive.google.com/file/d/16A6Amc7AyUoKHRH3_WBRToRC13sn7tU/view?usp=sharing。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Img2Loc:+Revisiting+Image+Geolocalization+using+Multi-modality+Foundation+Models+and+Image-based+Retrieval-Augmented+Generation)|0| -|[JPEC: A Novel Graph Neural Network for Competitor Retrieval in Financial Knowledge Graphs](https://doi.org/10.1145/3626772.3657677)|Wanying Ding, Manoj Cherukumalli, Santosh Chikoti, Vinay K. Chaudhri|JPMorgan Chase & Co; JPMorgan Chase & Co.|Knowledge graphs have gained popularity for their ability to organize and analyze complex data effectively. When combined with graph embedding techniques, such as graph neural networks (GNNs), knowledge graphs become a potent tool in providing valuable insights. This study explores the application of graph embedding in identifying competitors from a financial knowledge graph. Existing state-of-the-art(SOTA) models face challenges due to the unique attributes of our knowledge graph, including directed and undirected relationships, attributed nodes, and minimal annotated competitor connections. To address these challenges, we propose a novel graph embedding model, JPEC(JPMorgan Proximity Embedding for Competitor Detection), which utilizes graph neural network to learn from both first-order and second-order node proximity together with vital features for competitor retrieval. JPEC had outperformed most existing models in extensive experiments, showcasing its effectiveness in competitor retrieval.|知识图谱因其有效组织和分析复杂数据的能力而受到广泛关注。当与图嵌入技术（如图神经网络（GNN））结合时，知识图谱成为提供宝贵见解的强大工具。本研究探讨了图嵌入在从金融知识图谱中识别竞争对手的应用。现有的最先进（SOTA）模型面临挑战，因为我们的知识图谱具有独特的属性，包括有向和无向关系、属性节点以及极少量的标注竞争对手连接。为应对这些挑战，我们提出了一种新颖的图嵌入模型——JPEC（摩根大通竞争对手检测的邻近嵌入），该模型利用图神经网络从一阶和二阶节点邻近性以及竞争对手检索的关键特征中学习。在广泛的实验中，JPEC的表现优于大多数现有模型，展示了其在竞争对手检索中的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=JPEC:+A+Novel+Graph+Neural+Network+for+Competitor+Retrieval+in+Financial+Knowledge+Graphs)|0| +|[A Question-Answering Assistant over Personal Knowledge Graph](https://doi.org/10.1145/3626772.3657665)|Lingyuan Liu, Huifang Du, Xiaolian Zhang, Mengying Guo, Haofen Wang, Meng Wang|Southeast University Southeast University-Monash University Joint Graduate School; Huawei Technologies Co. Ltd.; Tongji University|We develop a Personal Knowledge Graph Question-Answering (PKGQA) assistant, seamlessly integrating information from multiple mobile applications into a unified and user-friendly query interface to offer users convenient information retrieval and personalized knowledge services. Based on a fine-grained schema customized for PKG, the PKGQA system in this paper comprises Symbolic Semantic Parsing, Frequently Asked Question (FAQ) Semantic Matching, and Neural Semantic Parsing modules, which are designed to take into account both accuracy and efficiency. The PKGQA system achieves high accuracy on the constructed dataset and demonstrates good performance in answering complex questions. Our system is implemented through an Android application, which is shown in https://youtu.be/p732U5KPEq4.|我们开发了一个个人知识图谱问答（PKGQA）助手，该助手能够无缝整合来自多个移动应用程序的信息，并将其集成到一个统一且用户友好的查询界面中，为用户提供便捷的信息检索和个性化知识服务。基于为PKG定制的细粒度模式，本文中的PKGQA系统包括符号语义解析、常见问题（FAQ）语义匹配和神经语义解析模块，这些模块设计时兼顾了准确性和效率。PKGQA系统在构建的数据集上实现了高准确性，并在回答复杂问题方面表现出色。我们的系统通过一个Android应用程序实现，展示视频可在https://youtu.be/p732U5KPEq4查看。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Question-Answering+Assistant+over+Personal+Knowledge+Graph)|0| +|[ConvLogRecaller: Real-Time Conversational Lifelog Recaller](https://doi.org/10.1145/3626772.3657659)|YuanChi Lee, AnZi Yen, HenHsen Huang, HsinHsi Chen|National Yang Ming Chiao Tung University; National Taiwan University; Institute of Information Science, Academia Sinica|The popularization of networks fosters the convenience of communication. People can easily share their life experiences and thoughts with relatives and friends via instant messaging software. As time passes, individuals may forget certain details of life events, leading to difficulties in effectively communicating with others. The propensity of individuals to forget or mix up life events highlights the importance of services aimed at retrieving information about past experiences. This paper presents a conversational information recall system, ConvLogRecaller, which proactively supports real-time memory recall assistance during online conversations. Given a conversation of the user with others, ConvLogRecaller suggests a message if the user forgets the details of the life experiences. The services provided by our system can avoid hesitations or memory lapses that might hinder the efficiency of a conversation.|网络的普及促进了沟通的便利性。人们可以通过即时通讯软件轻松地与亲友分享生活经历和思想。然而，随着时间的推移，个人可能会忘记生活事件的某些细节，从而导致与他人有效沟通的困难。人们倾向于忘记或混淆生活事件的倾向突显了检索过去经历信息服务的重要性。本文介绍了一个对话式信息回忆系统ConvLogRecaller，该系统在在线对话中主动支持实时记忆回忆辅助。在用户与其他人进行对话时，如果用户忘记了生活经历的细节，ConvLogRecaller会提供一条建议消息。本系统提供的服务可以避免因犹豫或记忆缺失而可能阻碍对话效率的情况。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=ConvLogRecaller:+Real-Time+Conversational+Lifelog+Recaller)|0| +|[CLIP-Branches: Interactive Fine-Tuning for Text-Image Retrieval](https://doi.org/10.1145/3626772.3657678)|Christian Lülf, Denis Mayr Lima Martins, Marcos Antonio Vaz Salles, Yongluan Zhou, Fabian Gieseke|Independent Researcher; University of Copenhagen; University of Münster|The advent of text-image models, most notably CLIP, has significantlytransformed the landscape of information retrieval. These models enable thefusion of various modalities, such as text and images. One significant outcomeof CLIP is its capability to allow users to search for images using text as aquery, as well as vice versa. This is achieved via a joint embedding of imagesand text data that can, for instance, be used to search for similar items.Despite efficient query processing techniques such as approximate nearestneighbor search, the results may lack precision and completeness. We introduceCLIP-Branches, a novel text-image search engine built upon the CLIParchitecture. Our approach enhances traditional text-image search engines byincorporating an interactive fine-tuning phase, which allows the user tofurther concretize the search query by iteratively defining positive andnegative examples. Our framework involves training a classification model giventhe additional user feedback and essentially outputs all positively classifiedinstances of the entire data catalog. By building upon recent techniques, thisinference phase, however, is not implemented by scanning the entire datacatalog, but by employing efficient index structures pre-built for the data.Our results show that the fine-tuned results can improve the initial searchoutputs in terms of relevance and accuracy while maintaining swift responsetimes|文本-图像模型的出现，尤其是CLIP，极大地改变了信息检索的格局。这些模型能够融合多种模态，如文本和图像。CLIP的一个重要成果是它允许用户使用文本作为查询来搜索图像，反之亦然。这是通过图像和文本数据的联合嵌入实现的，例如，可以用于搜索相似的项目。尽管有近似最近邻搜索等高效的查询处理技术，但结果可能缺乏精确性和完整性。我们引入了CLIP-Branches，这是一种基于CLIP架构的新型文本-图像搜索引擎。我们的方法通过引入交互式微调阶段来增强传统的文本-图像搜索引擎，该阶段允许用户通过迭代定义正例和负例来进一步具体化搜索查询。我们的框架涉及在给定额外用户反馈的情况下训练分类模型，并基本上输出整个数据目录中所有正分类的实例。通过借鉴最近的技术，这一推理阶段并非通过扫描整个数据目录来实现，而是通过为数据预建的高效索引结构来实现。我们的结果表明，微调后的结果可以在保持快速响应时间的同时，提高初始搜索输出的相关性和准确性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=CLIP-Branches:++Interactive+Fine-Tuning+for+Text-Image+Retrieval)|0| +|[Img2Loc: Revisiting Image Geolocalization using Multi-modality Foundation Models and Image-based Retrieval-Augmented Generation](https://doi.org/10.1145/3626772.3657673)|Zhongliang Zhou, Jielu Zhang, Zihan Guan, Mengxuan Hu, Ni Lao, Lan Mu, Sheng Li, Gengchen Mai|University of Georgia; Google Inc; University of Virginia|Geolocating precise locations from images presents a challenging problem in computer vision and information retrieval. Traditional methods typically employ either classification-dividing the Earth's surface into grid cells and classifying images accordingly, or retrieval-identifying locations by matching images with a database of image-location pairs. However, classification-based approaches are limited by the cell size and cannot yield precise predictions, while retrieval-based systems usually suffer from poor search quality and inadequate coverage of the global landscape at varied scale and aggregation levels. To overcome these drawbacks, we present Img2Loc, a novel system that redefines image geolocalization as a text generation task. This is achieved using cutting-edge large multi-modality models (LMMs) like GPT-4V or LLaVA with retrieval augmented generation. Img2Loc first employs CLIP-based representations to generate an image-based coordinate query database. It then uniquely combines query results with images itself, forming elaborate prompts customized for LMMs. When tested on benchmark datasets such as Im2GPS3k and YFCC4k, Img2Loc not only surpasses the performance of previous state-of-the-art models but does so without any model training. A video demonstration of the system can be accessed via this link https://drive.google.com/file/d/16A6Amc7AyUoKHRH3_WBRToRC13sn7tU/view?usp=sharing|从图像中定位精确位置在计算机视觉和信息检索领域是一个具有挑战性的问题。传统方法通常采用分类法——将地球表面划分为网格单元并对图像进行相应分类，或检索法——通过将图像与图像-位置对数据库匹配来识别位置。然而，基于分类的方法受限于单元格大小，无法产生精确预测，而基于检索的系统通常搜索质量较差，无法在全球范围内以不同尺度和聚合级别充分覆盖地理景观。为了克服这些缺点，我们提出了Img2Loc，这是一个将图像地理定位重新定义为文本生成任务的新系统。该系统利用GPT-4V或LLaVA等前沿的大型多模态模型（LMMs），结合增强的生成检索技术实现这一目标。Img2Loc首先采用基于CLIP的表示方法生成基于图像的坐标查询数据库。然后，它独特地将查询结果与图像本身相结合，形成专为LMMs定制的复杂提示。在Im2GPS3k和YFCC4k等基准数据集上的测试表明，Img2Loc不仅超越了之前最先进模型的性能，而且无需任何模型训练即可实现这一效果。系统的演示视频可通过以下链接访问：https://drive.google.com/file/d/16A6Amc7AyUoKHRH3_WBRToRC13sn7tU/view?usp=sharing。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Img2Loc:+Revisiting+Image+Geolocalization+using+Multi-modality+Foundation+Models+and+Image-based+Retrieval-Augmented+Generation)|0| +|[JPEC: A Novel Graph Neural Network for Competitor Retrieval in Financial Knowledge Graphs](https://doi.org/10.1145/3626772.3657677)|Wanying Ding, Manoj Cherukumalli, Santosh Chikoti, Vinay K. Chaudhri|JPMorgan Chase & Co.; JPMorgan Chase & Co|Knowledge graphs have gained popularity for their ability to organize and analyze complex data effectively. When combined with graph embedding techniques, such as graph neural networks (GNNs), knowledge graphs become a potent tool in providing valuable insights. This study explores the application of graph embedding in identifying competitors from a financial knowledge graph. Existing state-of-the-art(SOTA) models face challenges due to the unique attributes of our knowledge graph, including directed and undirected relationships, attributed nodes, and minimal annotated competitor connections. To address these challenges, we propose a novel graph embedding model, JPEC(JPMorgan Proximity Embedding for Competitor Detection), which utilizes graph neural network to learn from both first-order and second-order node proximity together with vital features for competitor retrieval. JPEC had outperformed most existing models in extensive experiments, showcasing its effectiveness in competitor retrieval.|知识图谱因其有效组织和分析复杂数据的能力而受到广泛关注。当与图嵌入技术（如图神经网络（GNN））结合时，知识图谱成为提供宝贵见解的强大工具。本研究探讨了图嵌入在从金融知识图谱中识别竞争对手的应用。现有的最先进（SOTA）模型面临挑战，因为我们的知识图谱具有独特的属性，包括有向和无向关系、属性节点以及极少量的标注竞争对手连接。为应对这些挑战，我们提出了一种新颖的图嵌入模型——JPEC（摩根大通竞争对手检测的邻近嵌入），该模型利用图神经网络从一阶和二阶节点邻近性以及竞争对手检索的关键特征中学习。在广泛的实验中，JPEC的表现优于大多数现有模型，展示了其在竞争对手检索中的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=JPEC:+A+Novel+Graph+Neural+Network+for+Competitor+Retrieval+in+Financial+Knowledge+Graphs)|0| |[MACRec: A Multi-Agent Collaboration Framework for Recommendation](https://doi.org/10.1145/3626772.3657669)|Zhefan Wang, Yuanqing Yu, Wendi Zheng, Weizhi Ma, Min Zhang|Tsinghua University Department of Computer Science and Technology; Tsinghua University Institute for AI Industry Research|LLM-based agents have gained considerable attention for their decision-making skills and ability to handle complex tasks. Recognizing the current gap in leveraging agent capabilities for multi-agent collaboration in recommendation systems, we introduce MACRec, a novel framework designed to enhance recommendation systems through multi-agent collaboration. Unlike existing work on using agents for user/item simulation, we aim to deploy multi-agents to tackle recommendation tasks directly. In our framework, recommendation tasks are addressed through the collaborative efforts of various specialized agents, including Manager, User/Item Analyst, Reflector, Searcher, and Task Interpreter, with different working flows. Furthermore, we provide application examples of how developers can easily use MACRec on various recommendation tasks, including rating prediction, sequential recommendation, conversational recommendation, and explanation generation of recommendation results. The framework and demonstration video are publicly available at https://github.com/wzf2000/MACRec.|基于大型语言模型（LLM）的代理因其决策能力和处理复杂任务的能力而受到广泛关注。认识到目前利用代理能力进行推荐系统中多代理协作的不足，我们提出了MACRec，这是一个通过多代理协作来增强推荐系统的新框架。与现有的使用代理进行用户/项目模拟的工作不同，我们的目标是将多代理直接部署来解决推荐任务。在我们的框架中，推荐任务通过各种专门代理的协作努力来解决，这些代理包括管理者、用户/项目分析师、反思者、搜索者和任务解释者，它们具有不同的工作流程。此外，我们提供了开发人员如何轻松地在各种推荐任务中使用MACRec的应用示例，包括评分预测、序列推荐、对话推荐和推荐结果解释生成。该框架和演示视频已在https://github.com/wzf2000/MACRec公开发布。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=MACRec:+A+Multi-Agent+Collaboration+Framework+for+Recommendation)|0| |[ModelGalaxy: A Versatile Model Retrieval Platform](https://doi.org/10.1145/3626772.3657676)|Wenling Zhang, Yixiao Li, Zhaotian Li, Hailong Sun, Xiang Gao, Xudong Liu|buaa|With the growing number of available machine learning models and the emergence of model-sharing platforms, model reuse has become a significant approach to harnessing the power of artificial intelligence. One of the key issues to realizing model reuse resides in efficiently and accurately finding the target models that meet user needs from a model repository. However, the existing popular model-sharing platforms (e.g., Hugging Face) mainly support model retrieval based on model name matching and task filtering. If not familiar with the platform or specific models, users may suffer from low retrieval efficiency and a less user-friendly interaction experience. To address these issues, we have developed ModelGalaxy, a versatile model retrieval platform supporting multiple model retrieval methods, including keyword-based search, dataset-based search, and user-task-centric search. Moreover, ModelGalaxy leverages the power of large language models to provide users with easily retrieving and using models. Our source code is available at https://github.com/zwl906711886/ModelGalaxy.|随着可用机器学习模型数量的增加以及模型共享平台的兴起，模型复用已成为利用人工智能力量的重要途径之一。实现模型复用的关键问题之一在于如何从模型库中高效且准确地找到满足用户需求的模型。然而，现有的主流模型共享平台（如Hugging Face）主要支持基于模型名称匹配和任务过滤的模型检索方式。对于不熟悉平台或特定模型的用户来说，可能会面临检索效率低下和交互体验不佳的问题。为了解决这些问题，我们开发了ModelGalaxy，这是一个支持多种模型检索方法的多功能模型检索平台，包括基于关键词的搜索、基于数据集的搜索以及以用户任务为中心的搜索。此外，ModelGalaxy还利用大型语言模型的能力，为用户提供便捷的模型检索和使用体验。我们的源代码可在https://github.com/zwl906711886/ModelGalaxy获取。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=ModelGalaxy:+A+Versatile+Model+Retrieval+Platform)|0| |[RAG-Ex: A Generic Framework for Explaining Retrieval Augmented Generation](https://doi.org/10.1145/3626772.3657660)|Viju Sudhi, Sinchana Ramakanth Bhat, Max Rudat, Roman Teucher|Fraunhofer IAIS|Owing to their size and complexity, large language models (LLMs) hardly explain why they generate a response. This effectively reduces the trust and confidence of end users in LLM-based applications, including Retrieval Augmented Generation (RAG) for Question Answering (QA) tasks. In this work, we introduce RAG-Ex, a model- and language-agnostic explanation framework that presents approximate explanations to the users revealing why the LLMs possibly generated a piece of text as a response, given the user input. Our framework is compatible with both open-source and proprietary LLMs. We report the significance scores of the approximated explanations from our generic explainer in both English and German QA tasks and also study their correlation with the downstream performance of LLMs. In the extensive user studies, our explainer yields an F1-score of 76.9% against the end user annotations and attains almost on-par performance with model-intrinsic approaches.|由于其规模和复杂性，大型语言模型（LLMs）几乎不解释它们为何生成某个响应。这实际上降低了终端用户对基于LLM的应用程序（包括用于问答任务的检索增强生成（RAG））的信任和信心。在这项工作中，我们介绍了RAG-Ex，这是一个模型和语言无关的解释框架，它向用户展示近似的解释，揭示LLMs在给定用户输入的情况下，为何可能生成一段文本作为响应。我们的框架兼容开源和专有的LLMs。我们在英语和德语问答任务中报告了我们通用解释器的近似解释的显著性分数，并研究了它们与LLMs下游性能的相关性。在广泛的用户研究中，我们的解释器在与终端用户注释的对比中达到了76.9%的F1分数，并且几乎与模型内在方法表现相当。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=RAG-Ex:+A+Generic+Framework+for+Explaining+Retrieval+Augmented+Generation)|0| |[ResumeFlow: An LLM-facilitated Pipeline for Personalized Resume Generation and Refinement](https://doi.org/10.1145/3626772.3657680)|Saurabh Bhausaheb Zinjad, Amrita Bhattacharjee, Amey Bhilegaonkar, Huan Liu|Arizona State University|Crafting the ideal, job-specific resume is a challenging task for many jobapplicants, especially for early-career applicants. While it is highlyrecommended that applicants tailor their resume to the specific role they areapplying for, manually tailoring resumes to job descriptions and role-specificrequirements is often (1) extremely time-consuming, and (2) prone to humanerrors. Furthermore, performing such a tailoring step at scale while applyingto several roles may result in a lack of quality of the edited resumes. Totackle this problem, in this demo paper, we propose ResumeFlow: a LargeLanguage Model (LLM) aided tool that enables an end user to simply providetheir detailed resume and the desired job posting, and obtain a personalizedresume specifically tailored to that specific job posting in the matter of afew seconds. Our proposed pipeline leverages the language understanding andinformation extraction capabilities of state-of-the-art LLMs such as OpenAI'sGPT-4 and Google's Gemini, in order to (1) extract details from a jobdescription, (2) extract role-specific details from the user-provided resume,and then (3) use these to refine and generate a role-specific resume for theuser. Our easy-to-use tool leverages the user-chosen LLM in a completelyoff-the-shelf manner, thus requiring no fine-tuning. We demonstrate theeffectiveness of our tool via a video demo and propose novel task-specificevaluation metrics to control for alignment and hallucination. Our tool isavailable at https://job-aligned-resume.streamlit.app.|为特定职位打造理想的简历对许多求职者来说是一项艰巨的任务，尤其是对于初入职场的求职者。尽管强烈建议求职者根据所申请的具体职位定制简历，但手动根据职位描述和特定职位要求调整简历通常（1）极其耗时，并且（2）容易出现人为错误。此外，在申请多个职位时大规模进行此类定制步骤可能会导致编辑后的简历质量下降。为了解决这一问题，本文展示了一种名为ResumeFlow的工具：这是一个借助大型语言模型（LLM）的工具，使终端用户只需提供详细的个人简历和目标职位招聘信息，便能在几秒钟内获得一份专门针对该职位的个性化简历。我们提出的流程利用了如OpenAI的GPT-4和Google的Gemini等最先进LLM的语言理解和信息提取能力，以（1）从职位描述中提取细节，（2）从用户提供的简历中提取职位相关细节，然后（3）利用这些信息来优化并生成一份针对特定职位的简历。我们的工具易于使用，完全以即插即用的方式利用用户选择的LLM，无需任何微调。我们通过视频演示展示了该工具的有效性，并提出了新的任务特定评估指标来控制对齐和幻觉现象。该工具可访问 https://job-aligned-resume.streamlit.app。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=ResumeFlow:+An+LLM-facilitated+Pipeline+for+Personalized+Resume+Generation+and+Refinement)|0| -|[ScholarNodes: Applying Content-based Filtering to Recommend Interdisciplinary Communities within Scholarly Social Networks](https://doi.org/10.1145/3626772.3657668)|Md Asaduzzaman Noor, Jason A. Clark, John W. Sheppard|Montana State University; Montana State University, Bozeman, MT, USA; Montana State University Library and Information Science|Detecting communities within dynamic academic social networks and connecting these community detection findings to search and retrieval interfaces presents a multifaceted challenge. We explore an information retrieval method that integrates both partition-based and similarity-based network analysis to identify and recommend communities within content-based datasets. Our prototype "ScholarNodes" web interface bridges the gap between community detection algorithms (Louvain, K-means, Spectral clustering) and the BM25 (Best Matching 25) ranking algorithm within a cohesive user interface. From free-text keyword queries, ScholarNodes recommends collaborations, identifies local and external researcher networks, and visualizes an interdisciplinarity graph for individual researchers using the OpenAlex dataset, a global collection of academic papers and authors. Beyond the specific information retrieval use case, we discuss the broader applicability of the methods to generic social network analysis, community detection, and recommender systems. Additionally, we delve into the technical aspects of generating topical terms, community alignment techniques, and interface design considerations for integrating community detection algorithms into a search experience.|识别动态学术社交网络中的社区，并将这些社区检测结果与搜索和检索界面相连接，是一项多层面的挑战。我们探索了一种信息检索方法，该方法结合了基于分区和基于相似性的网络分析，以在基于内容的语料库中识别和推荐社区。我们的原型“ScholarNodes”网络界面弥合了社区检测算法（Louvain、K-means、谱聚类）与BM25（最佳匹配25）排序算法之间的鸿沟，构建了一个连贯的用户界面。通过自由文本关键词查询，ScholarNodes推荐合作机会，识别本地和外部研究者网络，并利用OpenAlex数据集（一个全球性的学术论文和作者集合）为个别研究者绘制跨学科图谱。除了特定的信息检索应用场景，我们还讨论了这些方法在通用社交网络分析、社区检测和推荐系统中的广泛适用性。此外，我们深入探讨了生成主题术语、社区对齐技术以及将社区检测算法整合到搜索体验中的界面设计考虑等技术细节。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=ScholarNodes:+Applying+Content-based+Filtering+to+Recommend+Interdisciplinary+Communities+within+Scholarly+Social+Networks)|0| +|[ScholarNodes: Applying Content-based Filtering to Recommend Interdisciplinary Communities within Scholarly Social Networks](https://doi.org/10.1145/3626772.3657668)|Md Asaduzzaman Noor, Jason A. Clark, John W. Sheppard|Montana State University, Bozeman, MT, USA; Montana State University Library and Information Science; Montana State University|Detecting communities within dynamic academic social networks and connecting these community detection findings to search and retrieval interfaces presents a multifaceted challenge. We explore an information retrieval method that integrates both partition-based and similarity-based network analysis to identify and recommend communities within content-based datasets. Our prototype "ScholarNodes" web interface bridges the gap between community detection algorithms (Louvain, K-means, Spectral clustering) and the BM25 (Best Matching 25) ranking algorithm within a cohesive user interface. From free-text keyword queries, ScholarNodes recommends collaborations, identifies local and external researcher networks, and visualizes an interdisciplinarity graph for individual researchers using the OpenAlex dataset, a global collection of academic papers and authors. Beyond the specific information retrieval use case, we discuss the broader applicability of the methods to generic social network analysis, community detection, and recommender systems. Additionally, we delve into the technical aspects of generating topical terms, community alignment techniques, and interface design considerations for integrating community detection algorithms into a search experience.|识别动态学术社交网络中的社区，并将这些社区检测结果与搜索和检索界面相连接，是一项多层面的挑战。我们探索了一种信息检索方法，该方法结合了基于分区和基于相似性的网络分析，以在基于内容的语料库中识别和推荐社区。我们的原型“ScholarNodes”网络界面弥合了社区检测算法（Louvain、K-means、谱聚类）与BM25（最佳匹配25）排序算法之间的鸿沟，构建了一个连贯的用户界面。通过自由文本关键词查询，ScholarNodes推荐合作机会，识别本地和外部研究者网络，并利用OpenAlex数据集（一个全球性的学术论文和作者集合）为个别研究者绘制跨学科图谱。除了特定的信息检索应用场景，我们还讨论了这些方法在通用社交网络分析、社区检测和推荐系统中的广泛适用性。此外，我们深入探讨了生成主题术语、社区对齐技术以及将社区检测算法整合到搜索体验中的界面设计考虑等技术细节。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=ScholarNodes:+Applying+Content-based+Filtering+to+Recommend+Interdisciplinary+Communities+within+Scholarly+Social+Networks)|0| |[Synthetic Query Generation using Large Language Models for Virtual Assistants](https://doi.org/10.1145/3626772.3661355)|Sonal Sannigrahi, Thiago FragaSilva, Youssef Oualil, Christophe Van Gysel|Apple; Instituto Superior Técnico|Virtual Assistants (VAs) are important Information Retrieval platforms that help users accomplish various tasks through spoken commands. The speech recognition system (speech-to-text) uses query priors, trained solely on text, to distinguish between phonetically confusing alternatives. Hence, the generation of synthetic queries that are similar to existing VA usage can greatly improve upon the VA's abilities-especially for use-cases that do not (yet) occur in paired audio/text data. In this paper, we provide a preliminary exploration of the use of Large Language Models (LLMs) to generate synthetic queries that are complementary to template-based methods. We investigate whether the methods (a) generate queries that are similar to randomly sampled, representative, and anonymized user queries from a popular VA, and (b) whether the generated queries are specific. We find that LLMs generate more verbose queries, compared to template-based methods, and reference aspects specific to the entity. The generated queries are similar to VA user queries, and are specific enough to retrieve the relevant entity. We conclude that queries generated by LLMs and templates are complementary.|虚拟助手（VAs）是重要的信息检索平台，通过语音指令帮助用户完成各种任务。语音识别系统（语音转文字）使用仅基于文本训练的查询先验来区分语音上易混淆的替代选项。因此，生成与现有VA使用情况相似的合成查询可以极大地提升VA的能力，特别是对于那些在配对的音频/文本数据中尚未出现的用例。本文初步探讨了使用大型语言模型（LLMs）生成与基于模板的生成方法互补的合成查询。我们研究了这些方法是否（a）生成的查询与从流行VA中随机抽样、具有代表性且匿名的用户查询相似，以及（b）生成的查询是否具有特异性。我们发现，与基于模板的方法相比，LLMs生成的查询更为冗长，并引用了与实体相关的特定方面。生成的查询与VA用户查询相似，并且足够具体以检索相关实体。我们得出结论，LLMs和模板生成的查询是互补的。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Synthetic+Query+Generation+using+Large+Language+Models+for+Virtual+Assistants)|0| |[A Study on Unsupervised Question and Answer Generation for Legal Information Retrieval and Precedents Understanding](https://doi.org/10.1145/3626772.3661354)|Johny Moreira, Altigran S. da Silva, Edleno Silva de Moura, Leandro Bezerra Marinho|Universidade Federal de Campina Grande; Universidade Federal do Amazonas|Traditional retrieval systems are hardly adequate for Legal Research, mainly because only returning the documents related to a given query is usually insufficient. Legal documents are extensive, and we posit that generating questions about them and detecting the answers provided by these documents help the Legal Research journey. This paper presents a pipeline that relates Legal Questions with documents answering them. We align features generated by Large Language Models with traditional clustering methods to find convergent and divergent answers to the same legal matter. We performed a case study with 50 legal documents on the Brazilian judiciary system. Our pipeline found convergent and divergent answers to 23 major legal questions regarding the case law for daily fines in Civil Procedural Law. The pipeline manual evaluation shows it managed to group diverse similar answers to the same question with an average precision of 0.85. It also managed to detect two divergent legal matters with an average F1 Score of 0.94.|传统的检索系统在法律研究中往往不够充分，主要原因是仅返回与给定查询相关的文档通常是不够的。法律文件篇幅浩繁，我们假设，为这些文件生成问题并检测这些文件所提供的答案，有助于法律研究的过程。本文提出了一种将法律问题与回答这些问题的文档相关联的流程。我们将大型语言模型生成的特征与传统的聚类方法相结合，以找到对同一法律问题的趋同和分歧答案。我们进行了案例研究，使用了50份关于巴西司法系统的法律文件。我们的流程发现了23个主要法律问题，这些问题涉及民事诉讼法中每日罚款的判例法，既有趋同答案也有分歧答案。该流程的手动评估显示，它成功地将多种相似的答案归类到同一问题下，平均精确度为0.85。同时，它还能够检测到两个分歧的法律事项，平均F1得分为0.94。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Study+on+Unsupervised+Question+and+Answer+Generation+for+Legal+Information+Retrieval+and+Precedents+Understanding)|0| -|[Reflections on the Coding Ability of LLMs for Analyzing Market Research Surveys](https://doi.org/10.1145/3626772.3661362)|Shi Zong, Santosh Kolagati, Amit Chaudhary, Josh Seltzer, Jimmy Lin|Nexxt Intelligence; University of Waterloo|The remarkable success of large language models (LLMs) has drawn people's great interest in their deployment in specific domains and downstream applications. In this paper, we present the first systematic study of applying large language models (in our case, GPT-3.5 and GPT-4) for the automatic coding (multi-class classification) problem in market research. Our experimental results show that large language models could achieve a macro F1 score of over 0.5 for all our collected real-world market research datasets in a zero-shot setting. We also provide in-depth analyses of the errors made by the large language models. We hope this study sheds light on the lessons we learn and the open challenges large language models have when adapting to a specific market research domain.|大型语言模型（LLMs）的显著成功引起了人们对其在特定领域和下游应用中部署的极大兴趣。本文首次系统性地研究了将大型语言模型（在我们的案例中是GPT-3.5和GPT-4）应用于市场研究中的自动编码（多类分类）问题。我们的实验结果表明，在零样本设置下，大型语言模型可以在我们收集的所有真实市场研究数据集上实现超过0.5的宏F1分数。我们还深入分析了大型语言模型所犯的错误。我们希望这项研究能够揭示我们在学习过程中获得的启示以及大型语言模型在适应特定市场研究领域时面临的开放挑战。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Reflections+on+the+Coding+Ability+of+LLMs+for+Analyzing+Market+Research+Surveys)|0| -|[Retrieval-Augmented Generation with Knowledge Graphs for Customer Service Question Answering](https://doi.org/10.1145/3626772.3661370)|Zhentao Xu, Mark Jerome Cruz, Matthew Guevara, Tie Wang, Manasi Deshpande, Xiaofeng Wang, Zheng Li|LinkedIn Corporation; LinkedIn Corporation Senior Machine Learning Engineer|In customer service technical support, swiftly and accurately retrieving relevant past issues is critical for efficiently resolving customer inquiries. The conventional retrieval methods in retrieval-augmented generation (RAG) for large language models (LLMs) treat a large corpus of past issue tracking tickets as plain text, ignoring the crucial intra-issue structure and inter-issue relations, which limits performance. We introduce a novel customer service question-answering method that amalgamates RAG with a knowledge graph (KG). Our method constructs a KG from historical issues for use in retrieval, retaining the intra-issue structure and inter-issue relations. During the question-answering phase, our method parses consumer queries and retrieves related sub-graphs from the KG to generate answers. This integration of a KG not only improves retrieval accuracy by preserving customer service structure information but also enhances answering quality by mitigating the effects of text segmentation. Empirical assessments on our benchmark datasets, utilizing key retrieval (MRR, Recall@K, NDCG@K) and text generation (BLEU, ROUGE, METEOR) metrics, reveal that our method outperforms the baseline by 77.6% in MRR and by 0.32 in BLEU. Our method has been deployed within LinkedIn's customer service team for approximately six months and has reduced the median per-issue resolution time by 28.6%.|在客户服务技术支持中，快速准确地检索相关历史问题对于高效解决客户咨询至关重要。传统的检索增强生成（RAG）方法在大语言模型（LLM）中将大量历史问题跟踪票据视为纯文本处理，忽略了问题内部的结构和问题间的关系，这限制了性能。我们提出了一种结合了RAG和知识图谱（KG）的新型客户服务问答方法。我们的方法从历史问题中构建KG用于检索，保留了问题内部的结构和问题间的关系。在问答阶段，我们的方法解析消费者查询并从KG中检索相关子图以生成答案。这种KG的整合不仅通过保留客户服务结构信息提高了检索准确性，还通过减轻文本分割的影响提升了回答质量。在我们基准数据集上的实证评估，使用关键检索（MRR、Recall@K、NDCG@K）和文本生成（BLEU、ROUGE、METEOR）指标，结果显示我们的方法在MRR上比基线高出77.6%，在BLEU上高出0.32。我们的方法已在LinkedIn的客户服务团队中部署了大约六个月，并将每个问题的解决时间中位数减少了28.6%。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Retrieval-Augmented+Generation+with+Knowledge+Graphs+for+Customer+Service+Question+Answering)|0| +|[Reflections on the Coding Ability of LLMs for Analyzing Market Research Surveys](https://doi.org/10.1145/3626772.3661362)|Shi Zong, Santosh Kolagati, Amit Chaudhary, Josh Seltzer, Jimmy Lin|University of Waterloo; Nexxt Intelligence|The remarkable success of large language models (LLMs) has drawn people's great interest in their deployment in specific domains and downstream applications. In this paper, we present the first systematic study of applying large language models (in our case, GPT-3.5 and GPT-4) for the automatic coding (multi-class classification) problem in market research. Our experimental results show that large language models could achieve a macro F1 score of over 0.5 for all our collected real-world market research datasets in a zero-shot setting. We also provide in-depth analyses of the errors made by the large language models. We hope this study sheds light on the lessons we learn and the open challenges large language models have when adapting to a specific market research domain.|大型语言模型（LLMs）的显著成功引起了人们对其在特定领域和下游应用中部署的极大兴趣。本文首次系统性地研究了将大型语言模型（在我们的案例中是GPT-3.5和GPT-4）应用于市场研究中的自动编码（多类分类）问题。我们的实验结果表明，在零样本设置下，大型语言模型可以在我们收集的所有真实市场研究数据集上实现超过0.5的宏F1分数。我们还深入分析了大型语言模型所犯的错误。我们希望这项研究能够揭示我们在学习过程中获得的启示以及大型语言模型在适应特定市场研究领域时面临的开放挑战。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Reflections+on+the+Coding+Ability+of+LLMs+for+Analyzing+Market+Research+Surveys)|0| +|[Retrieval-Augmented Generation with Knowledge Graphs for Customer Service Question Answering](https://doi.org/10.1145/3626772.3661370)|Zhentao Xu, Mark Jerome Cruz, Matthew Guevara, Tie Wang, Manasi Deshpande, Xiaofeng Wang, Zheng Li|LinkedIn Corporation Senior Machine Learning Engineer; LinkedIn Corporation|In customer service technical support, swiftly and accurately retrieving relevant past issues is critical for efficiently resolving customer inquiries. The conventional retrieval methods in retrieval-augmented generation (RAG) for large language models (LLMs) treat a large corpus of past issue tracking tickets as plain text, ignoring the crucial intra-issue structure and inter-issue relations, which limits performance. We introduce a novel customer service question-answering method that amalgamates RAG with a knowledge graph (KG). Our method constructs a KG from historical issues for use in retrieval, retaining the intra-issue structure and inter-issue relations. During the question-answering phase, our method parses consumer queries and retrieves related sub-graphs from the KG to generate answers. This integration of a KG not only improves retrieval accuracy by preserving customer service structure information but also enhances answering quality by mitigating the effects of text segmentation. Empirical assessments on our benchmark datasets, utilizing key retrieval (MRR, Recall@K, NDCG@K) and text generation (BLEU, ROUGE, METEOR) metrics, reveal that our method outperforms the baseline by 77.6% in MRR and by 0.32 in BLEU. Our method has been deployed within LinkedIn's customer service team for approximately six months and has reduced the median per-issue resolution time by 28.6%.|在客户服务技术支持中，快速准确地检索相关历史问题对于高效解决客户咨询至关重要。传统的检索增强生成（RAG）方法在大语言模型（LLM）中将大量历史问题跟踪票据视为纯文本处理，忽略了问题内部的结构和问题间的关系，这限制了性能。我们提出了一种结合了RAG和知识图谱（KG）的新型客户服务问答方法。我们的方法从历史问题中构建KG用于检索，保留了问题内部的结构和问题间的关系。在问答阶段，我们的方法解析消费者查询并从KG中检索相关子图以生成答案。这种KG的整合不仅通过保留客户服务结构信息提高了检索准确性，还通过减轻文本分割的影响提升了回答质量。在我们基准数据集上的实证评估，使用关键检索（MRR、Recall@K、NDCG@K）和文本生成（BLEU、ROUGE、METEOR）指标，结果显示我们的方法在MRR上比基线高出77.6%，在BLEU上高出0.32。我们的方法已在LinkedIn的客户服务团队中部署了大约六个月，并将每个问题的解决时间中位数减少了28.6%。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Retrieval-Augmented+Generation+with+Knowledge+Graphs+for+Customer+Service+Question+Answering)|0| |[Striking the Right Chord: A Comprehensive Approach to Amazon Music Search Spell Correction](https://doi.org/10.1145/3626772.3661344)|Siddharth Sharma, Shiyun Yang, Ajinkya Walimbe, Tarun Sharma, Joaquin Delgado|Amazon Inc|Music and media search spell correction is distinct as it involves named entities like artist, album and podcast names, keywords from track titles and catchy phrases from lyrics. Users often mix artist names and keywords from track title or lyrics making spell correction highly contextual. Data drift in search queries caused during calendar event days or a newly released music album, brings a unique challenge of quickly adapting to new data points. Scalability of the solution is an essential requirement as the Music catalog is extremely large. In this work, we build a multi-stage framework for spell correction solution for music, media and named entity heavy search engines. We offer contextual spelling suggestions using a generative text transformer model and a mechanism to rapidly adapt to data drift as well as different market needs by using parameter efficient based fine tuning techniques. Furthermore, using a reinforcement learning approach our spell correction system can learn from a user's implicit and explicit feedback in real-time. Some key components of this system are being used in search at Amazon Music and showing significant improvements in customer engagement rate and other relevant metrics.|音乐和媒体搜索的拼写校正有其独特性，因为它涉及艺术家、专辑和播客名称等命名实体，以及来自曲目标题和歌词的关键词和吸引人的短语。用户常常混淆艺术家名称和曲目标题或歌词中的关键词，使得拼写校正高度依赖上下文。搜索查询中的数据漂移，尤其是在日历事件日或新音乐专辑发布时，带来了快速适应新数据点的独特挑战。解决方案的可扩展性是一个基本要求，因为音乐目录极其庞大。在这项工作中，我们构建了一个多阶段的框架，用于音乐、媒体和命名实体密集型搜索引擎的拼写校正解决方案。我们使用生成式文本转换器模型提供上下文拼写建议，并通过基于参数高效微调技术，快速适应数据漂移和不同市场的需求。此外，通过强化学习方法，我们的拼写校正系统能够实时从用户的隐式和显式反馈中学习。该系统的一些关键组件已在亚马逊音乐的搜索中使用，并显著提高了客户参与率和其他相关指标。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Striking+the+Right+Chord:+A+Comprehensive+Approach+to+Amazon+Music+Search+Spell+Correction)|0| |[SLH-BIA: Short-Long Hawkes Process for Buy It Again Recommendations at Scale](https://doi.org/10.1145/3626772.3661374)|Rankyung Park, Amit Pande, David Relyea, Pushkar Chennu, Prathyusha Kanmanth Reddy|Target Corporation|Buy It Again (BIA) recommendations are a crucial component in enhancing the customer experience and site engagement for retailers. In this paper, we build a short (S) and long (L) term Hawkes (H) process for each item and use it to obtain BIA recommendations for each customer. The challenges of deploying into a production environment including model scalability, an evolving item catalog, and real-time inference are discussed along with solutions such as model compression, frequency-based item filtering, training data sampling, data parallelization, parallel execution and microservice-based real-time recommendations. We significantly reduced model training time from roughly 250 hours to about 3 hours by applying the solutions, while serving real-time inference with less than 70ms latency. We compare our BIA model against state-of-the-art baselines using three publicly available datasets and provide results from A/B tests with millions of live customers. On 3 public datasets, our model outperforms SOTA baseline models in recall and NDCG metrics by around 85% and 10%, respectively, and in live A/B testing it exhibited more than 30% increase in click-through rate and roughly 30% revenue increase compared to other state of the art models.|"再次购买"（Buy It Again, BIA）推荐是增强零售商客户体验和网站参与度的关键组成部分。本文中，我们为每个商品构建了短期（S）和长期（L）的Hawkes（H）过程，并利用其为每位客户获取BIA推荐。我们讨论了部署到生产环境中的挑战，包括模型可扩展性、不断演变的商品目录以及实时推理，并提出了相应的解决方案，如模型压缩、基于频率的商品过滤、训练数据采样、数据并行化、并行执行以及基于微服务的实时推荐。通过应用这些解决方案，我们将模型训练时间从大约250小时显著减少到约3小时，同时实现了低于70毫秒的实时推理延迟。我们使用三个公开数据集将我们的BIA模型与最先进的基线模型进行了比较，并提供了与数百万实际客户进行的A/B测试结果。在三个公开数据集上，我们的模型在召回率和NDCG指标上分别优于最先进的基线模型约85%和10%，在实际A/B测试中，点击率提高了超过30%，收入增加了约30%，相较于其他最先进的模型。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=SLH-BIA:+Short-Long+Hawkes+Process+for+Buy+It+Again+Recommendations+at+Scale)|0| -|[Are Embeddings Enough? SIRIP Panel on the Future of Embeddings in Industry IR Systems](https://doi.org/10.1145/3626772.3661360)|Jon Degenhardt, Tracy Holloway King|eBay; Adobe|The IR community as a whole is considering whether search and recommendations can move entirely to embedding-based technologies. This SIRIP panel discusses the future of embedding-based technologies in industry search given its broad range of document types, its specific query types, its performance requirements, and the features that accompany search. The panel comprises long-time industry experts and academics with industry ties. The panelists vary as to whether they believe that the industry in practice will move entirely to embeddings or will remain a hybrid domain.|整个信息检索（IR）社区正在探讨搜索和推荐是否可以完全转向基于嵌入（embedding-based）的技术。本次SIRIP专题讨论会聚焦于嵌入技术在工业搜索中的未来，考虑到其广泛的文档类型、特定的查询类型、性能需求以及伴随搜索的特征。讨论会邀请了长期从事工业界的专家学者，这些专家与学术界有着紧密联系。与会者对于工业界是否会完全转向嵌入技术，还是保持混合模式，持有不同观点。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Are+Embeddings+Enough?+SIRIP+Panel+on+the+Future+of+Embeddings+in+Industry+IR+Systems)|0| +|[Are Embeddings Enough? SIRIP Panel on the Future of Embeddings in Industry IR Systems](https://doi.org/10.1145/3626772.3661360)|Jon Degenhardt, Tracy Holloway King|Adobe; eBay|The IR community as a whole is considering whether search and recommendations can move entirely to embedding-based technologies. This SIRIP panel discusses the future of embedding-based technologies in industry search given its broad range of document types, its specific query types, its performance requirements, and the features that accompany search. The panel comprises long-time industry experts and academics with industry ties. The panelists vary as to whether they believe that the industry in practice will move entirely to embeddings or will remain a hybrid domain.|整个信息检索（IR）社区正在探讨搜索和推荐是否可以完全转向基于嵌入（embedding-based）的技术。本次SIRIP专题讨论会聚焦于嵌入技术在工业搜索中的未来，考虑到其广泛的文档类型、特定的查询类型、性能需求以及伴随搜索的特征。讨论会邀请了长期从事工业界的专家学者，这些专家与学术界有着紧密联系。与会者对于工业界是否会完全转向嵌入技术，还是保持混合模式，持有不同观点。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Are+Embeddings+Enough?+SIRIP+Panel+on+the+Future+of+Embeddings+in+Industry+IR+Systems)|0| |[Large Language Model Powered Agents for Information Retrieval](https://doi.org/10.1145/3626772.3661375)|An Zhang, Yang Deng, Yankai Lin, Xu Chen, JiRong Wen, TatSeng Chua|Natl Univ Singapore, Singapore, Singapore; Renmin Univ China, Beijing, Peoples R China|The vital goal of information retrieval today extends beyond merely connecting users with relevant information they search for. It also aims to enrich the diversity, personalization, and interactivity of that connection, ensuring the information retrieval process is as seamless, beneficial, and supportive as possible in the global digital era. Current information retrieval systems often encounter challenges like a constrained understanding of queries, static and inflexible responses, limited personalization, and restricted interactivity. With the advent of large language models (LLMs), there's a transformative paradigm shift as we integrate LLM-powered agents into these systems. These agents bring forth crucial human capabilities like memory and planning to make them behave like humans in completing various tasks, effectively enhancing user engagement and offering tailored interactions. In this tutorial, we delve into the cutting-edge techniques of LLM-powered agents across various information retrieval fields, such as search engines, social networks, recommender systems, and conversational assistants. We will also explore the prevailing challenges in seamlessly incorporating these agents and hint at prospective research avenues that can revolutionize the way of information retrieval.|当今信息检索的重要目标不仅限于将用户与他们搜索的相关信息连接起来，更在于丰富这种连接的多样性、个性化和互动性，确保在全球数字化时代中，信息检索过程尽可能无缝、有益和支持性。目前的信息检索系统常常面临一些挑战，如对查询理解的局限性、静态且不灵活的响应、有限的个性化以及受限的互动性。随着大型语言模型（LLMs）的出现，我们正在经历一个变革性的范式转变，即将LLM赋能的代理整合到这些系统中。这些代理带来了关键的人类能力，如记忆和规划，使它们能够在完成各种任务时表现得像人类一样，从而有效提升用户参与度并提供定制化的互动。在本教程中，我们将深入探讨LLM赋能代理在各个信息检索领域的尖端技术，包括搜索引擎、社交网络、推荐系统和对话助手。我们还将探讨无缝整合这些代理所面临的当前挑战，并暗示可能的研究方向，这些方向有望彻底改变信息检索的方式。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Large+Language+Model+Powered+Agents+for+Information+Retrieval)|0| |[High Recall Retrieval Via Technology-Assisted Review](https://doi.org/10.1145/3626772.3661376)|Lenora Gray, David D. Lewis, Jeremy Pickens, Eugene Yang|Johns Hopkins Univ, HLTCOE, Baltimore, MD USA; Redgrave Data, Chantilly, VA 20151 USA|High Recall Retrieval (HRR) tasks, including eDiscovery in the law, systematic literature reviews, and sunshine law requests focus on efficiently prioritizing relevant documents for human review.Technology-assisted review (TAR) refers to iterative human-in-the-loop workflows that combine human review with IR and AI techniques to minimize both time and manual effort while maximizing recall. This full-day tutorial provides a comprehensive introduction to TAR. The morning session presents an overview of the key technologies and workflow designs used, the basics of practical evaluation methods, and the social and ethical implications of TAR deployment. The afternoon session provides more technical depth on the implications of TAR workflows for supervised learning algorithm design, how generative AI is can be applied in TAR, more sophisticated statistical evaluation techniques, and a wide range of open research questions.|高召回率检索（HRR）任务，包括法律领域的电子发现、系统性文献综述以及阳光法案请求，都致力于高效地优先处理相关文档以供人工审查。技术辅助审查（TAR）指的是结合了人工审查与信息检索（IR）和人工智能（AI）技术的迭代式人机协作工作流程，旨在最小化时间和人力成本的同时最大化召回率。本全天教程全面介绍了TAR。上午的课程概述了关键技术和工作流程设计，介绍了实际评估方法的基础知识，并探讨了TAR部署的社会和伦理影响。下午的课程则深入探讨了TAR工作流程对监督学习算法设计的影响、生成式AI在TAR中的应用、更复杂的统计评估技术，以及一系列开放的研究问题。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=High+Recall+Retrieval+Via+Technology-Assisted+Review)|0| |[Large Language Models for Recommendation: Past, Present, and Future](https://doi.org/10.1145/3626772.3661383)|Keqin Bao, Jizhi Zhang, Xinyu Lin, Yang Zhang, Wenjie Wang, Fuli Feng|Natl Univ Singapore, Singapore, Singapore; Univ Sci & Technol China, Hefei, Peoples R China|Large language models (LLMs) have significantly influenced recommender systems, spurring interest across academia and industry in leveraging LLMs for recommendation tasks. This includes using LLMs for generative item retrieval and ranking, and developing versatile LLMs for various recommendation tasks, potentially leading to a paradigm shift in the field of recommender systems. This tutorial aims to demystify the Large Language Model for Recommendation (LLM4Rec) by reviewing its evolution and delving into cutting-edge research. We will explore how LLMs enhance recommender systems in terms of architecture, learning paradigms, and functionalities such as conversational abilities, generalization, planning, and content generation. The tutorial will shed light on the challenges and open problems in this burgeoning field, including trustworthiness, efficiency, online training, and evaluation of LLM4Rec. We will conclude by summarizing key learnings from existing studies and outlining potential avenues for future research, with the goal of equipping the audience with a comprehensive understanding of LLM4Rec and inspiring further exploration in this transformative domain.|大型语言模型（LLMs）已显著影响了推荐系统，激发了学术界和工业界利用LLMs进行推荐任务的兴趣。这包括使用LLMs进行生成式项目检索和排序，以及开发多功能的LLMs以应对各种推荐任务，这可能引领推荐系统领域发生范式转变。本教程旨在通过回顾其发展历程并深入探讨前沿研究，揭开大型语言模型在推荐系统（LLM4Rec）中的神秘面纱。我们将探讨LLMs如何从架构、学习范式和功能（如对话能力、泛化能力、规划和内容生成）等方面增强推荐系统。教程还将揭示这一新兴领域中的挑战和开放问题，包括可信度、效率、在线训练以及LLM4Rec的评估。最后，我们将总结现有研究的关键发现，并概述未来研究的潜在方向，旨在为听众提供对LLM4Rec的全面理解，并激发在这一变革性领域的进一步探索。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Large+Language+Models+for+Recommendation:+Past,+Present,+and+Future)|0| -|[Recent Advances in Generative Information Retrieval](https://doi.org/10.1145/3626772.3661379)|Yubao Tang, Ruqing Zhang, Zhaochun Ren, Jiafeng Guo, Maarten de Rijke|Leiden Univ, Leiden, Netherlands; Chinese Acad Sci, CAS Key Lab Network Data Sci & Technol, ICT, Beijing, Peoples R China; Univ Amsterdam, Amsterdam, Netherlands|Generative retrieval (GR) has become a highly active area of information retrieval (IR) that has witnessed significant growth recently. Compared to the traditional “index-retrieve-then-rank” pipeline, the GR paradigm aims to consolidate all information within a corpus into a single model. Typically, a sequence-to-sequence model is trained to directly map a query to its relevant document identifiers (i.e., docids). This tutorial offers an introduction to the core concepts of the GR paradigm and a comprehensive overview of recent advances in its foundations and applications. We start by providing preliminary information covering foundational aspects and problem formulations of GR. Then, our focus shifts towards recent progress in docid design, training approaches, inference strategies, and the applications of GR. We end by outlining remaining challenges and issuing a call for future GR research. This tutorial is intended to be beneficial to both researchers and industry practitioners interested in developing novel GR solutions or applying them in real-world scenarios.|生成式检索（GR）已成为信息检索（IR）领域中一个高度活跃且近期取得显著发展的研究方向。与传统的“索引-检索-然后排序”流程不同，GR范式旨在将语料库中的所有信息整合到一个单一模型中。通常，一个序列到序列的模型被训练用于直接将查询映射到相关的文档标识符（即docids）。本教程旨在介绍GR范式的核心概念，并全面概述其在基础理论和应用方面的最新进展。首先，我们将提供关于GR基础方面和问题表述的初步信息。接着，我们将重点转向docid设计、训练方法、推理策略以及GR应用方面的最新进展。最后，我们将概述当前存在的挑战，并呼吁未来在GR研究方面的努力。本教程旨在对那些有兴趣开发新型GR解决方案或在实际场景中应用GR的研究人员和行业从业者有所裨益。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Recent+Advances+in+Generative+Information+Retrieval)|0| +|[Recent Advances in Generative Information Retrieval](https://doi.org/10.1145/3626772.3661379)|Yubao Tang, Ruqing Zhang, Zhaochun Ren, Jiafeng Guo, Maarten de Rijke|Univ Amsterdam, Amsterdam, Netherlands; Leiden Univ, Leiden, Netherlands; Chinese Acad Sci, CAS Key Lab Network Data Sci & Technol, ICT, Beijing, Peoples R China|Generative retrieval (GR) has become a highly active area of information retrieval (IR) that has witnessed significant growth recently. Compared to the traditional “index-retrieve-then-rank” pipeline, the GR paradigm aims to consolidate all information within a corpus into a single model. Typically, a sequence-to-sequence model is trained to directly map a query to its relevant document identifiers (i.e., docids). This tutorial offers an introduction to the core concepts of the GR paradigm and a comprehensive overview of recent advances in its foundations and applications. We start by providing preliminary information covering foundational aspects and problem formulations of GR. Then, our focus shifts towards recent progress in docid design, training approaches, inference strategies, and the applications of GR. We end by outlining remaining challenges and issuing a call for future GR research. This tutorial is intended to be beneficial to both researchers and industry practitioners interested in developing novel GR solutions or applying them in real-world scenarios.|生成式检索（GR）已成为信息检索（IR）领域中一个高度活跃且近期取得显著发展的研究方向。与传统的“索引-检索-然后排序”流程不同，GR范式旨在将语料库中的所有信息整合到一个单一模型中。通常，一个序列到序列的模型被训练用于直接将查询映射到相关的文档标识符（即docids）。本教程旨在介绍GR范式的核心概念，并全面概述其在基础理论和应用方面的最新进展。首先，我们将提供关于GR基础方面和问题表述的初步信息。接着，我们将重点转向docid设计、训练方法、推理策略以及GR应用方面的最新进展。最后，我们将概述当前存在的挑战，并呼吁未来在GR研究方面的努力。本教程旨在对那些有兴趣开发新型GR解决方案或在实际场景中应用GR的研究人员和行业从业者有所裨益。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Recent+Advances+in+Generative+Information+Retrieval)|0| |[Robust Information Retrieval](https://doi.org/10.1145/3626772.3661380)|YuAn Liu, Ruqing Zhang, Jiafeng Guo, Maarten de Rijke|Computer Engineering Department,Muğla Sıtkı Koçman University,Muğla,Turkey; Computer Engineering Department,Eskişehir Technical University,Eskişehir,Turkey|A typical information retrieval (IR) system applies a single retrieval strategy to every information need of users. However, the results of the past IR experiments show that a particular retrieval strategy is in general good at fulfilling some type of information needs while failing to fulfil some other type, i.e., high variation in retrieval effectiveness across information needs. On the other hand, the same results also show that an information need that a particular retrieval strategy failed to fulfil could be fulfilled by one of the other existing retrieval strategies. The challenge in here is therefore to determine in advance what retrieval strategy should be applied to which information need. This challenge is related to the robustness of IR systems in retrieval effectiveness. For an IR system, robustness can be defined as fulfilling every information need of users with an acceptable level of satisfaction. Maintaining robustness in retrieval effectiveness is a long-standing challenge and in this article we propose a simple but powerful method as a remedy. The method is a selective approach to index term weighting and for any given query (i.e., information need) it predicts the "best" term weighting model amongst a set of alternatives, on the basis of the frequency distributions of query terms on a target document collection. To predict the best term weighting model, the method uses the Chi-square statistic, the statistic of the Chi-square goodness-of-fit test. The results of the experiments, performed using the official query sets of the TREC Web track and the Million Query track, reveal in general that the frequency distributions of query terms provide relevant information on the retrieval effectiveness of term weighting models. In particular, the results show that the selective approach proposed in this article is, on average, more effective and more robust than the most effective single term weighting model.|典型的信息检索（IR）系统对用户的每一种信息需求都采用单一的检索策略。然而，过去的IR实验结果表明，特定的检索策略通常擅长满足某些类型的信息需求，而对其他类型的信息需求则表现不佳，即检索效果在不同信息需求间存在较大差异。另一方面，同样的结果也显示，某一特定检索策略未能满足的信息需求，可能可以通过其他现有的检索策略来满足。因此，这里的挑战在于预先确定应将哪种检索策略应用于哪种信息需求。这一挑战与IR系统在检索效果上的鲁棒性相关。对于一个IR系统而言，鲁棒性可以定义为以用户可接受的满意程度满足每一种信息需求。维持检索效果的鲁棒性是一个长期存在的挑战，本文中我们提出了一种简单但强大的方法作为补救措施。该方法是一种选择性的索引词权重分配方法，对于任何给定的查询（即信息需求），它基于目标文档集合中查询词的频率分布，从一组备选方案中预测出“最佳”的词权重模型。为了预测最佳的词权重模型，该方法使用了卡方统计量，即卡方拟合优度检验的统计量。通过使用TREC Web轨道和百万查询轨道的官方查询集进行的实验结果表明，查询词的频率分布通常能够提供关于词权重模型检索效果的相关信息。特别是，结果显示，本文提出的选择性方法在平均效果和鲁棒性方面，均优于最有效的单一词权重模型。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Robust+Information+Retrieval)|0| |[IR-RAG @ SIGIR24: Information Retrieval's Role in RAG Systems](https://doi.org/10.1145/3626772.3657984)|Fabio Petroni, Federico Siciliano, Fabrizio Silvestri, Giovanni Trappolini|Sapienza Univ Rome, Rome, Italy; Samaya AI, London, England|In recent years, Retrieval Augmented Generation (RAG) systems have emerged as a pivotal component in the field of artificial intelligence, gaining significant attention and importance across various domains. These systems, which combine the strengths of information retrieval and generative models, have shown promise in enhancing the capabilities and performance of machine learning applications. However, despite their growing prominence, RAG systems are not without their limitations and continue to be in need of exploration and improvement. This workshop seeks to focus on the critical aspect of information retrieval and its integral role within RAG frameworks. We argue that current efforts have undervalued the role of Information Retrieval (IR) in the RAG and have concentrated their attention on the generative part. As the cornerstone of these systems, IR's effectiveness dramatically influences the overall performance and outcomes of RAG models. We call for papers that will seek to revisit and emphasize the fundamental principles underpinning RAG systems. At the end of the workshop, we aim to have a clearer understanding of how robust information retrieval mechanisms can significantly enhance the capabilities of RAG systems. The workshop will serve as a platform for experts, researchers, and practitioners. We intend to foster discussions, share insights, and encourage research that underscores the vital role of Information Retrieval in the future of generative systems.|近年来，检索增强生成（RAG）系统在人工智能领域崭露头角，成为跨多个领域备受关注和重视的关键组成部分。这些系统结合了信息检索与生成模型的优势，展现出提升机器学习应用能力和性能的潜力。然而，尽管其日益重要，RAG系统仍存在局限性，亟需进一步探索和改进。本次研讨会旨在聚焦信息检索这一关键方面及其在RAG框架中的核心作用。我们主张，当前的研究低估了信息检索（IR）在RAG中的作用，并将重点过多地放在生成部分。作为这些系统的基石，IR的有效性极大地影响着RAG模型的整体性能和结果。我们呼吁提交论文，重新审视并强调支撑RAG系统的基本原理。研讨会结束时，我们期望能更清晰地理解稳健的信息检索机制如何显著增强RAG系统的能力。研讨会将作为专家、研究人员和从业者的交流平台，旨在促进讨论、分享见解，并鼓励强调信息检索在未来生成系统中关键作用的研究。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=IR-RAG+@+SIGIR24:+Information+Retrieval's+Role+in+RAG+Systems)|0| |[A Predictive Framework for Query Reformulation](https://doi.org/10.1145/3626772.3657653)|Reyhaneh Goli|The University of Melbourne|Web search services are widely employed for various purposes. After identifying information needs, users attempt to articulate them in web queries that express their intentions. Then, they submit these queries to the chosen search engine with the hope of obtaining relevant results to meet their needs. In some cases, users may not immediately find precisely what they are seeking, prompting them to rewrite the query to obtain a greater number of relevant results or results that are perhaps more related to their intent. While significant work has been done on developing features such as query auto-completion, query suggestion, and query recommendation, the majority of these efforts were based on query co-occurrence or query similarity by clustering them or constructing query flow graphs to capture query connections. These approaches operate under the assumption that frequently observed follow-up queries are more likely to be submitted by users [1, 2, 4]. In this research, we investigate user query reformulation behavior. To achieve this, we will utilize the Trip Click dataset, a large-scale collection of user click data within the context of a health web search engine [3]. The log data from 2018 to 2020 will be considered, comprising 1,803,493 records representing the clicks that occurred across 527,749 sessions. Specifically, the focus will be on the impact of user interactions with the search result page when forming subsequent queries.|网络搜索服务被广泛应用于各种目的。在识别信息需求后，用户尝试将这些需求表述为网络查询，以表达他们的意图。然后，他们将这些查询提交给所选的搜索引擎，期望获得相关的结果以满足他们的需求。在某些情况下，用户可能无法立即找到他们所寻求的确切内容，从而促使他们重写查询以获取更多相关结果或更符合其意图的结果。尽管在开发查询自动完成、查询建议和查询推荐等功能方面已经取得了显著进展，但大多数这些努力都是基于查询共现或查询相似性，通过聚类或构建查询流图来捕捉查询之间的关联。这些方法的前提是，频繁观察到的后续查询更可能被用户提交[1, 2, 4]。在本研究中，我们探讨了用户查询重构行为。为此，我们将利用Trip Click数据集，这是一个大规模的健康网络搜索引擎用户点击数据集合[3]。我们将考虑2018年至2020年的日志数据，包含1,803,493条记录，代表在527,749个会话中发生的点击。具体而言，我们将重点研究用户与搜索结果页面交互对形成后续查询的影响。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Predictive+Framework+for+Query+Reformulation)|0| -|[Multimodal Representation and Retrieval [MRR 2024]](https://doi.org/10.1145/3626772.3657987)|Xinliang Zhu, Arnab Dhua, Douglas Gray, I. Zeki Yalniz, Tan Yu, Mohamed Elhoseiny, Bryan Plummer|Meta, Menlo Pk, CA USA; King Abdullah Univ Sci & Technol, Thuwal, Saudi Arabia; Boston Univ, Boston, MA USA; Nvidia, Santa Clara, CA USA; Amazon, Palo Alto, CA 94303 USA|Multimodal data is available in many applications like e-commerce production listings, social media posts and short videos. However, existing algorithms dealing with those types of data still focus on uni-modal representation learning by vision-language alignment and cross-modal retrieval. In this workshop, we target to bring a new retrieval problem where both queries and documents are multimodal. With the popularity of vision language modeling, large language models (LLMs), retrieval augmented generation (RAG), and multimodal LLM, we see a lot of new opportunities for multimodal representation and retrieval tasks. This event will be a comprehensive half-day workshop focusing on the subject of multimodal representation and retrieval. The agenda includes keynote speeches, oral presentations, and an interactive panel discussion.|多模态数据在电子商务产品列表、社交媒体帖子和短视频等许多应用中都存在。然而，现有的处理这些数据的算法仍然主要关注通过视觉-语言对齐和跨模态检索的单模态表示学习。在本次研讨会中，我们旨在引入一个新的检索问题，即查询和文档都是多模态的。随着视觉语言建模、大型语言模型（LLMs）、检索增强生成（RAG）和多模态LLM的普及，我们看到多模态表示和检索任务中涌现出许多新的机会。此次活动将是一个全面的多模态表示和检索主题的半天研讨会。议程包括主题演讲、口头报告和互动小组讨论。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Multimodal+Representation+and+Retrieval+[MRR+2024])|0| +|[Multimodal Representation and Retrieval [MRR 2024]](https://doi.org/10.1145/3626772.3657987)|Xinliang Zhu, Arnab Dhua, Douglas Gray, I. Zeki Yalniz, Tan Yu, Mohamed Elhoseiny, Bryan Plummer|Nvidia, Santa Clara, CA USA; Meta, Menlo Pk, CA USA; King Abdullah Univ Sci & Technol, Thuwal, Saudi Arabia; Boston Univ, Boston, MA USA; Amazon, Palo Alto, CA 94303 USA|Multimodal data is available in many applications like e-commerce production listings, social media posts and short videos. However, existing algorithms dealing with those types of data still focus on uni-modal representation learning by vision-language alignment and cross-modal retrieval. In this workshop, we target to bring a new retrieval problem where both queries and documents are multimodal. With the popularity of vision language modeling, large language models (LLMs), retrieval augmented generation (RAG), and multimodal LLM, we see a lot of new opportunities for multimodal representation and retrieval tasks. This event will be a comprehensive half-day workshop focusing on the subject of multimodal representation and retrieval. The agenda includes keynote speeches, oral presentations, and an interactive panel discussion.|多模态数据在电子商务产品列表、社交媒体帖子和短视频等许多应用中都存在。然而，现有的处理这些数据的算法仍然主要关注通过视觉-语言对齐和跨模态检索的单模态表示学习。在本次研讨会中，我们旨在引入一个新的检索问题，即查询和文档都是多模态的。随着视觉语言建模、大型语言模型（LLMs）、检索增强生成（RAG）和多模态LLM的普及，我们看到多模态表示和检索任务中涌现出许多新的机会。此次活动将是一个全面的多模态表示和检索主题的半天研讨会。议程包括主题演讲、口头报告和互动小组讨论。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Multimodal+Representation+and+Retrieval+[MRR+2024])|0| |[Axiomatic Guidance for Efficient and Controlled Neural Search](https://doi.org/10.1145/3626772.3657651)|Andrew Parry|University of Glasgow|Pre-trained language models based on the transformer architecture provide solutions to general ad-hoc search tasks--ranging from news search to question-answering--vastly outperforming statistical approaches in terms of both precision and recall. These models operate over "semantics'', removing the need for bespoke features based on proprietary data (e.g., interaction logs). In doing so, this paradigm may lead to further adoption of the idealised "end-to-end'' retrieval system as an elegant and powerful search solution. However, outside of sanitised benchmarks, these models present exploitable and untrustworthy biases relinquishing any control over inference due to their black-box nature. Such biases threaten the viability of neural models in production. Without greater control over model output, stakeholders could raise concerns hindering the adoption of effective and efficient search. Today, feature-based search systems are still performant relative to state-of-the-art neural search and can adapt to a changing corpus and the needs of system stakeholders. As agency over information access is further reduced via emerging paradigms such as Retrieval-Augmented-Generation, we must retain control over the output of a search system. We consider that bias in neural search systems is an artefact of the training and underlying mechanisms of current pre-trained models but is not present in statistical models. Features such as statistical models are principled and arbitrarily controllable; these features can adapt to a corpus and meet the demands of a given search task. Conversely, the output of a current neural system can only be changed by post hoc constraints or by re-training the underlying model. We posit that by allowing external features to influence the semantic interactions within neural search at inference time, we can not only allow control over system output but reduce the need to model corpus-specific priors, which can instead be modelled by external features, allowing for greater generalisation and training efficiency gains. We aim to reduce the complexity of neural ranker training and inference, applying classical IR principles and systems that align with such principles as a generalisable process as opposed to the ad-hoc constraint of prior work. Such an approach can reduce the need for larger models whilst improving generalisation. Axiomatic signals can guide and control neural ranking models to reduce spurious factors in semantic relevance estimation by compensating for the frozen priors of neural systems whilst still operating over flexible latent space. Given the biases observed in current systems, this may satiate the concerns of multiple stakeholders, leading to broader adoption of the paradigm.|基于Transformer架构的预训练语言模型为各种即席搜索任务（从新闻搜索到问答）提供了解决方案，在精确度和召回率方面远超统计方法。这些模型在“语义”层面上运作，消除了对基于专有数据（如交互日志）的定制特征的需求。这种范式可能会进一步促进理想化的“端到端”检索系统作为优雅而强大的搜索解决方案的采用。然而，在非规范化的基准测试之外，这些模型存在可利用且不可信的偏见，由于其黑箱特性，放弃了任何对推理的控制。这些偏见威胁到神经模型在实际生产中的可行性。如果没有对模型输出的更大控制，利益相关者可能会提出担忧，阻碍有效和高效搜索的采用。目前，基于特征的搜索系统相对于最先进的神经搜索仍然表现出色，并且能够适应不断变化的语料库和系统利益相关者的需求。随着信息访问权通过诸如检索增强生成等新兴范式进一步减少，我们必须保留对搜索系统输出的控制。我们认为，神经搜索系统中的偏见是当前预训练模型训练及其底层机制的产物，但并非存在于统计模型中。统计模型的特征具有原则性且可任意控制；这些特征能够适应语料库并满足特定搜索任务的需求。相反，当前神经系统的输出只能通过事后约束或重新训练底层模型来改变。我们提出，通过允许外部特征在推理时影响神经搜索中的语义交互，我们不仅可以控制系统输出，还可以减少对语料库特定先验知识的建模需求，这些先验知识可以由外部特征建模，从而实现更大的泛化性和训练效率提升。我们的目标是降低神经排序器训练和推理的复杂性，应用与经典信息检索原则相一致的系统作为可泛化的过程，而不是先前工作的即席约束。这种方法可以在减少对更大模型的需求的同时提高泛化性。公理信号可以指导和控制神经排序模型，通过补偿神经系统的冻结先验来减少语义相关性估计中的虚假因素，同时仍操作在灵活的潜在空间上。鉴于当前系统中观察到的偏见，这可能会缓解多个利益相关者的担忧，从而促进该范式的更广泛采用。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Axiomatic+Guidance+for+Efficient+and+Controlled+Neural+Search)|0| |[Personalized Large Language Models through Parameter Efficient Fine-Tuning Techniques](https://doi.org/10.1145/3626772.3657657)|Marco Braga|University of Milano-Bicocca|Personalization of the search experience according to the users and their context is an important topic in Information Retrieval (IR), studied by the research community for a long time. The IR field has witnessed a transformation with the recent availability of pre-trained Large Language Models. Typically, personalization requires the model to incorporate user-specific information, through the definition of an appropriate prompting or injecting user knowledge into the model and then fine-tuning it. However, using prompting, we do not know where and how much the model is personalizing the output. Furthermore, fine-tuning such systems is computationally expensive: since they are characterized by billions of parameters, the fine-tuning process has introduced profound computational challenges. For these reasons, we propose a novel approach that combines personalization and Parameter Efficient Fine-Tuning methods.|根据用户及其上下文个性化搜索体验是信息检索（IR）领域的一个重要课题，长期以来一直受到研究社区的关注。随着预训练大型语言模型的出现，IR领域经历了重大变革。通常，个性化要求模型整合用户特定的信息，通过定义适当的提示或向模型注入用户知识，然后进行微调。然而，使用提示方法时，我们无法确定模型在何处以及在多大程度上对输出进行了个性化处理。此外，微调这类系统在计算上非常昂贵：由于它们具有数十亿个参数，微调过程引入了巨大的计算挑战。因此，我们提出了一种结合个性化与参数高效微调方法的新方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Personalized+Large+Language+Models+through+Parameter+Efficient+Fine-Tuning+Techniques)|0| |[Towards a Framework for Legal Case Retrieval](https://doi.org/10.1145/3626772.3657650)|Tebo LeburuDingalo|University of Botswana|Legal case reports detail the main points of a decided case, findings and decisions of the court. The reports are a fundamental source for Case law, a law which requires judges to align their rulings with previous judicial decisions on similar cases [1]. Timely and reliable access to case reports is thus of critical importance to legal practitioners working on a current case, and laymen interested in the outcome of cases. However, ensuring effective retrieval of previous case reports is still proving a challenge, even with the use of retrieval technologies already proven effective in other Information Retrieval (IR) domains. This has been attributed to factors such as lack of structure, and lengthiness of case report documents, and queries formulated to represent an ongoing case for which the reports are being sought [4]. To address these factors we propose an IR framework that focuses on infusing structure into the documents and queries through the identification of legal rhetorical roles such as arguments and facts in the text. Furthermore, we aim to explore the use of selected groupings of these rhetorical roles as representations for the documents and queries. The benefit of using selected content is illustrated in recent research where for instance segments of documents such as abstracts, case headers, specific paragraphs, and sentences have been used to build effective legal IR systems. We thus hypothesize that we can attain marked improved performance when we build a case retrieval system using only a section of a case report or a query such as arguments or facts. However, in contrast to these studies we posit that utilizing rhetorical role information to extract content will lead to more effective representations that can enhance the performance of case retrieval systems. The proposed framework will consists of a set of components needed to process both query and case report text to firstly infuse structure, extract effective representative content and finally perform retrieval. To aid the development of the framework, several empirical investigations will be conducted on publicly accessible datasets, and a self-curated test collection derived from Botswana legal case reports. Key research questions to assist in our investigation are as follows:: Can we successfully detect the implicit elements of a legal text reflecting rhetorical roles significant to legal case documents?RQ2: In comparison to human formulated queries, do whole case queries give better performance?RQ3: Can we improve retrieval performance by only retaining textual units representing specific rhetorical roles from an entire query text (current case)?RQ4: Does indexing only textual units representing specific rhetorical roles from prior case documents improve retrieval performance?RQ5: Do the selected approaches result in performance improvement for our local corpus in terms of precision, recall and user satisfaction?Some preliminary work has been done and published towards investigating the viability of using summaries in legal case retrieval and identification of rhetorical roles in case documents. We submitted results of a system that utilized expanded summarized queries for an AILA precedent retrieval task competition that outperformed other submissions [2]. Furthermore, our approach that utilized TagCrowd for summarization performed well on a task of Statute retrieval [5]. Towards the feasibility of rhetorically labelling legal text we experimented with the fastText classifier for an AILA organized task. While our methods did not attain state-of-the-art, they gave insights into the performance of the different roles and factors that can affect performance in the task. [3]. : Can we successfully detect the implicit elements of a legal text reflecting rhetorical roles significant to legal case documents? RQ2: In comparison to human formulated queries, do whole case queries give better performance? RQ3: Can we improve retrieval performance by only retaining textual units representing specific rhetorical roles from an entire query text (current case)? RQ4: Does indexing only textual units representing specific rhetorical roles from prior case documents improve retrieval performance? RQ5: Do the selected approaches result in performance improvement for our local corpus in terms of precision, recall and user satisfaction?|法律案件报告详细记录了已决案件的要点、法院的判决和决定。这些报告是判例法的基础来源，判例法要求法官在判决时与先前类似案件的司法决定保持一致[1]。因此，法律从业者在处理当前案件时，以及对案件结果感兴趣的非专业人士，及时且可靠地获取案件报告至关重要。然而，即使使用在其他信息检索（IR）领域已证明有效的检索技术，确保有效检索先前的案件报告仍然是一个挑战。这归因于案件报告文档缺乏结构、篇幅冗长以及用于表示正在进行的案件的查询不够精确等因素[4]。为了应对这些因素，我们提出了一种信息检索框架，该框架通过识别文本中的法律修辞角色（如论点和事实）来为文档和查询注入结构。此外，我们旨在探索使用这些修辞角色的选定组合作为文档和查询的表示形式。最近的研究表明，使用文档的某些部分（如摘要、案件标题、特定段落和句子）可以构建有效的法律信息检索系统。因此，我们假设，仅使用案件报告或查询的一部分（如论点或事实）构建案件检索系统，可以显著提升性能。然而，与这些研究不同，我们认为利用修辞角色信息提取内容将产生更有效的表示，从而增强案件检索系统的性能。所提出的框架将包括一系列组件，用于处理查询和案件报告文本，首先注入结构，提取有效的代表性内容，最后执行检索。为了辅助框架的开发，将在公开可用的数据集和从博茨瓦纳法律案件报告中自选的测试集合上进行多项实证调查。关键的研究问题如下：我们能否成功检测反映法律案件报告重要修辞角色的法律文本的隐含元素？与人工制定的查询相比，整体案件查询是否能提供更好的性能？我们能否通过仅保留整个查询文本（当前案件）中代表特定修辞角色的文本单元来提高检索性能？仅索引先前案件文档中代表特定修辞角色的文本单元是否能提高检索性能？所选方法是否能提高我们本地语料库的性能，包括精确度、召回率和用户满意度？已经进行了一些初步工作，并发表了关于在法律案件检索中使用摘要的可行性以及识别案件文档中修辞角色的研究。我们提交了一个利用扩展摘要查询的系统结果，该系统在AILA先例检索任务竞赛中表现优于其他提交[2]。此外，我们使用TagCrowd进行摘要的方法在法令检索任务中表现良好[5]。为了验证法律文本修辞标注的可行性，我们使用fastText分类器进行了AILA组织的任务实验。尽管我们的方法未达到最先进水平，但它们提供了关于不同角色性能以及可能影响任务性能的因素的洞察[3]。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Towards+a+Framework+for+Legal+Case+Retrieval)|0| |[Rethinking the Evaluation of Dialogue Systems: Effects of User Feedback on Crowdworkers and LLMs](https://doi.org/10.1145/3626772.3657712)|Clemencia Siro, Mohammad Aliannejadi, Maarten de Rijke|University of Amsterdam|In ad-hoc retrieval, evaluation relies heavily on user actions, includingimplicit feedback. In a conversational setting such signals are usuallyunavailable due to the nature of the interactions, and, instead, the evaluationoften relies on crowdsourced evaluation labels. The role of user feedback inannotators' assessment of turns in a conversational perception has been littlestudied. We focus on how the evaluation of task-oriented dialogue systems(TDSs), is affected by considering user feedback, explicit or implicit, asprovided through the follow-up utterance of a turn being evaluated. We exploreand compare two methodologies for assessing TDSs: one includes the user'sfollow-up utterance and one without. We use both crowdworkers and largelanguage models (LLMs) as annotators to assess system responses across fouraspects: relevance, usefulness, interestingness, and explanation quality. Ourfindings indicate that there is a distinct difference in ratings assigned byboth annotator groups in the two setups, indicating user feedback doesinfluence system evaluation. Workers are more susceptible to user feedback onusefulness and interestingness compared to LLMs on interestingness andrelevance. User feedback leads to a more personalized assessment of usefulnessby workers, aligning closely with the user's explicit feedback. Additionally,in cases of ambiguous or complex user requests, user feedback improvesagreement among crowdworkers. These findings emphasize the significance of userfeedback in refining system evaluations and suggest the potential for automatedfeedback integration in future research. We publicly release the annotated datato foster research in this area.|在临时检索中，评估严重依赖于用户行为，包括隐式反馈。在对话环境中，由于互动的性质，这些信号通常不可用，因此评估往往依赖于众包的评估标签。用户反馈在标注者对对话感知中轮次的评估中的作用尚未得到充分研究。我们关注的是，在考虑用户反馈（无论是显式还是隐式）作为被评估轮次的后续话语时，任务导向对话系统（TDSs）的评估如何受到影响。我们探讨并比较了两种评估TDSs的方法：一种包括用户的后续话语，另一种不包括。我们使用众包工作者和大语言模型（LLMs）作为标注者，从相关性、有用性、有趣性和解释质量四个方面评估系统响应。我们的研究结果表明，在这两种设置中，两组标注者给出的评分存在显著差异，表明用户反馈确实影响了系统评估。在有用性和有趣性方面，工作者比LLMs在有趣性和相关性方面更容易受到用户反馈的影响。用户反馈导致工作者对有用性的评估更具个性化，与用户的显式反馈紧密一致。此外，在用户请求模糊或复杂的情况下，用户反馈提高了众包工作者之间的一致性。这些发现强调了用户反馈在改进系统评估中的重要性，并暗示了未来研究中自动化反馈整合的潜力。我们公开发布了标注数据，以促进该领域的研究。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Rethinking+the+Evaluation+of+Dialogue+Systems:+Effects+of+User+Feedback+on+Crowdworkers+and+LLMs)|0| -|[General-Purpose User Modeling with Behavioral Logs: A Snapchat Case Study](https://doi.org/10.1145/3626772.3657908)|Qixiang Fang, Zhihan Zhou, Francesco Barbieri, Yozen Liu, Leonardo Neves, Dong Nguyen, Daniel L. Oberski, Maarten W. Bos, Ron Dotsch|Snap Inc.; Utrecht University and University Medical Center Utrecht; Northwestern University; Utrecht University|Learning general-purpose user representations based on user behavioral logs is an increasingly popular user modeling approach. It benefits from easily available, privacy-friendly yet expressive data, and does not require extensive re-tuning of the upstream user model for different downstream tasks. While this approach has shown promise in search engines and e-commerce applications, its fit for instant messaging platforms, a cornerstone of modern digital communication, remains largely uncharted. We explore this research gap using Snapchat data as a case study. Specifically, we implement a Transformer-based user model with customized training objectives and show that the model can produce high-quality user representations across a broad range of evaluation tasks, among which we introduce three new downstream tasks that concern pivotal topics in user research: user safety, engagement and churn. We also tackle the challenge of efficient extrapolation of long sequences at inference time, by applying a novel positional encoding method.|基于用户行为日志学习通用用户表征是一种日益流行的用户建模方法。这种方法得益于易于获取、隐私友好且表达丰富的数据，并且不需要为不同的下游任务对上游用户模型进行广泛的重新调整。尽管这种方法在搜索引擎和电子商务应用中显示出潜力，但其适用于即时通讯平台——现代数字通信的基石——的情况仍大多未被探索。我们利用Snapchat数据作为案例研究，探讨了这一研究空白。具体而言，我们实现了一个基于Transformer的用户模型，并采用了定制的训练目标，展示了该模型能够在广泛的评估任务中生成高质量的用户表征，其中包括我们引入的三个新的下游任务，这些任务关注用户研究中的关键主题：用户安全、参与度和流失。此外，我们还通过应用一种新颖的位置编码方法，解决了推理时高效外推长序列的挑战。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=General-Purpose+User+Modeling+with+Behavioral+Logs:+A+Snapchat+Case+Study)|0| +|[General-Purpose User Modeling with Behavioral Logs: A Snapchat Case Study](https://doi.org/10.1145/3626772.3657908)|Qixiang Fang, Zhihan Zhou, Francesco Barbieri, Yozen Liu, Leonardo Neves, Dong Nguyen, Daniel L. Oberski, Maarten W. Bos, Ron Dotsch|Utrecht University; Utrecht University and University Medical Center Utrecht; Snap Inc.; Northwestern University|Learning general-purpose user representations based on user behavioral logs is an increasingly popular user modeling approach. It benefits from easily available, privacy-friendly yet expressive data, and does not require extensive re-tuning of the upstream user model for different downstream tasks. While this approach has shown promise in search engines and e-commerce applications, its fit for instant messaging platforms, a cornerstone of modern digital communication, remains largely uncharted. We explore this research gap using Snapchat data as a case study. Specifically, we implement a Transformer-based user model with customized training objectives and show that the model can produce high-quality user representations across a broad range of evaluation tasks, among which we introduce three new downstream tasks that concern pivotal topics in user research: user safety, engagement and churn. We also tackle the challenge of efficient extrapolation of long sequences at inference time, by applying a novel positional encoding method.|基于用户行为日志学习通用用户表征是一种日益流行的用户建模方法。这种方法得益于易于获取、隐私友好且表达丰富的数据，并且不需要为不同的下游任务对上游用户模型进行广泛的重新调整。尽管这种方法在搜索引擎和电子商务应用中显示出潜力，但其适用于即时通讯平台——现代数字通信的基石——的情况仍大多未被探索。我们利用Snapchat数据作为案例研究，探讨了这一研究空白。具体而言，我们实现了一个基于Transformer的用户模型，并采用了定制的训练目标，展示了该模型能够在广泛的评估任务中生成高质量的用户表征，其中包括我们引入的三个新的下游任务，这些任务关注用户研究中的关键主题：用户安全、参与度和流失。此外，我们还通过应用一种新颖的位置编码方法，解决了推理时高效外推长序列的挑战。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=General-Purpose+User+Modeling+with+Behavioral+Logs:+A+Snapchat+Case+Study)|0| |[Neural Passage Quality Estimation for Static Pruning](https://doi.org/10.1145/3626772.3657765)|Xuejun Chang, Debabrata Mishra, Craig Macdonald, Sean MacAvaney|University of Glasgow|Neural networks-especially those that use large, pre-trained language models-have improved search engines in various ways. Most prominently, they can estimate the relevance of a passage or document to a user's query. In this work, we depart from this direction by exploring whether neural networks can effectively predict which of a document's passages are unlikely to be relevant to any query submitted to the search engine.We refer to this query-agnostic estimation of passage relevance as a passage's quality.We find that our novel methods for estimating passage quality allow passage corpora to be pruned considerably while maintaining statistically equivalent effectiveness; our best methods can consistently prune >25% of passages in a corpora, across various retrieval pipelines. Such substantial pruning reduces the operating costs of neural search engines in terms of computing resources, power usage, and carbon footprint-both when processing queries (thanks to a smaller index size) and when indexing (lightweight models can prune low-quality passages prior to the costly dense or learned sparse encoding step). This work sets the stage for developing more advanced neural "learning-what-to-index" methods.|神经网络，尤其是那些使用大规模预训练语言模型的网络，已经在多个方面改进了搜索引擎。最显著的是，它们能够估计一段文本或文档与用户查询的相关性。在这项工作中，我们偏离了这一方向，转而探讨神经网络是否能够有效预测文档中的哪些段落不太可能与搜索引擎接收的任何查询相关。我们将这种与查询无关的段落相关性估计称为段落的质量。我们发现，我们新颖的段落质量估计方法能够在保持统计等效性的同时显著精简段落语料库；我们最优的方法能够在各种检索管道中持续精简超过25%的段落。这种实质性的精简减少了神经搜索引擎在计算资源、电力消耗和碳足迹方面的运营成本——无论是在处理查询时（由于索引规模较小）还是在索引时（轻量级模型可以在昂贵的密集或学习型稀疏编码步骤之前修剪低质量段落）。这项工作为开发更先进的神经“学习索引内容”方法奠定了基础。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Neural+Passage+Quality+Estimation+for+Static+Pruning)|0| -|[COMI: COrrect and MItigate Shortcut Learning Behavior in Deep Neural Networks](https://doi.org/10.1145/3626772.3657729)|Lili Zhao, Qi Liu, Linan Yue, Wei Chen, Liyi Chen, Ruijun Sun, Chao Song|OPPO; University of Science and Technology of China|Deep Neural Networks (DNNs), despite their notable progress across information retrieval tasks, encounter the issues of shortcut learning and struggle with poor generalization due to their reliance on spurious correlations between features and labels. Current research mainly mitigates shortcut learning behavior using augmentation and distillation techniques, but these methods could be laborious and introduce unwarranted biases. To tackle these, in this paper, we propose COMI, a novel method to COrrect and MItigate shortcut learning behavior. Inspired by the ways students solve shortcuts in educational scenarios, we aim to reduce model's reliance on shortcuts and enhance its ability to extract underlying information integrated with standard Empirical Risk Minimization (ERM). Specifically, we first design Correct Habit (CoHa) strategy to retrieve the top.. challenging samples for priority training, which encourages model to rely less on shortcuts in the early training. Then, to extract more meaningful underlying information, the information derived from ERM is separated into task-relevant and task-irrelevant information, the former serves as the primary basis for model predictions, while the latter is considered non-essential. However, within task-relevant information, certain potential shortcuts contribute to overconfident predictions. To mitigate this, we design Deep Mitigation (DeMi) network with shortcut margin loss to adaptively control the feature weights of shortcuts and eliminate their influence. Besides, to counteract unknown shortcut tokens issue in NLP, we adopt locally interpretable module-LIME to help recognize shortcut tokens. Finally, extensive experiments conducted on NLP and CV tasks demonstrate the effectiveness of COMI, which can perform well on both IID and OOD samples.|深度神经网络（DNNs）虽然在信息检索任务中取得了显著进展，但由于其依赖于特征与标签之间的虚假相关性，面临着捷径学习问题，并难以实现良好的泛化。当前的研究主要通过增强和蒸馏技术来缓解捷径学习行为，但这些方法可能既费力又引入不必要的偏见。为了解决这些问题，本文提出了一种名为COMI的新方法，用于纠正和缓解捷径学习行为。受学生在教育场景中解决捷径的方式启发，我们的目标是减少模型对捷径的依赖，并增强其提取深层信息的能力，同时结合标准的经验风险最小化（ERM）。具体而言，我们首先设计了“纠正习惯”（CoHa）策略，以优先训练最具挑战性的样本，从而在早期训练阶段鼓励模型减少对捷径的依赖。接着，为了提取更有意义的深层信息，我们将从ERM中获得的信息分为任务相关和任务无关信息，前者作为模型预测的主要依据，而后者则被视为非必要的。然而，在任务相关信息中，某些潜在的捷径可能导致过度自信的预测。为此，我们设计了“深度缓解”（DeMi）网络，并结合捷径边际损失，以自适应地控制捷径特征的权重并消除其影响。此外，为了应对自然语言处理（NLP）中未知的捷径词问题，我们采用了局部可解释模块LIME来帮助识别这些捷径词。最后，在NLP和计算机视觉（CV）任务上的广泛实验证明了COMI的有效性，该方法在独立同分布（IID）和非独立同分布（OOD）样本上均表现出色。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=COMI:+COrrect+and+MItigate+Shortcut+Learning+Behavior+in+Deep+Neural+Networks)|0| +|[COMI: COrrect and MItigate Shortcut Learning Behavior in Deep Neural Networks](https://doi.org/10.1145/3626772.3657729)|Lili Zhao, Qi Liu, Linan Yue, Wei Chen, Liyi Chen, Ruijun Sun, Chao Song|University of Science and Technology of China; OPPO|Deep Neural Networks (DNNs), despite their notable progress across information retrieval tasks, encounter the issues of shortcut learning and struggle with poor generalization due to their reliance on spurious correlations between features and labels. Current research mainly mitigates shortcut learning behavior using augmentation and distillation techniques, but these methods could be laborious and introduce unwarranted biases. To tackle these, in this paper, we propose COMI, a novel method to COrrect and MItigate shortcut learning behavior. Inspired by the ways students solve shortcuts in educational scenarios, we aim to reduce model's reliance on shortcuts and enhance its ability to extract underlying information integrated with standard Empirical Risk Minimization (ERM). Specifically, we first design Correct Habit (CoHa) strategy to retrieve the top.. challenging samples for priority training, which encourages model to rely less on shortcuts in the early training. Then, to extract more meaningful underlying information, the information derived from ERM is separated into task-relevant and task-irrelevant information, the former serves as the primary basis for model predictions, while the latter is considered non-essential. However, within task-relevant information, certain potential shortcuts contribute to overconfident predictions. To mitigate this, we design Deep Mitigation (DeMi) network with shortcut margin loss to adaptively control the feature weights of shortcuts and eliminate their influence. Besides, to counteract unknown shortcut tokens issue in NLP, we adopt locally interpretable module-LIME to help recognize shortcut tokens. Finally, extensive experiments conducted on NLP and CV tasks demonstrate the effectiveness of COMI, which can perform well on both IID and OOD samples.|深度神经网络（DNNs）虽然在信息检索任务中取得了显著进展，但由于其依赖于特征与标签之间的虚假相关性，面临着捷径学习问题，并难以实现良好的泛化。当前的研究主要通过增强和蒸馏技术来缓解捷径学习行为，但这些方法可能既费力又引入不必要的偏见。为了解决这些问题，本文提出了一种名为COMI的新方法，用于纠正和缓解捷径学习行为。受学生在教育场景中解决捷径的方式启发，我们的目标是减少模型对捷径的依赖，并增强其提取深层信息的能力，同时结合标准的经验风险最小化（ERM）。具体而言，我们首先设计了“纠正习惯”（CoHa）策略，以优先训练最具挑战性的样本，从而在早期训练阶段鼓励模型减少对捷径的依赖。接着，为了提取更有意义的深层信息，我们将从ERM中获得的信息分为任务相关和任务无关信息，前者作为模型预测的主要依据，而后者则被视为非必要的。然而，在任务相关信息中，某些潜在的捷径可能导致过度自信的预测。为此，我们设计了“深度缓解”（DeMi）网络，并结合捷径边际损失，以自适应地控制捷径特征的权重并消除其影响。此外，为了应对自然语言处理（NLP）中未知的捷径词问题，我们采用了局部可解释模块LIME来帮助识别这些捷径词。最后，在NLP和计算机视觉（CV）任务上的广泛实验证明了COMI的有效性，该方法在独立同分布（IID）和非独立同分布（OOD）样本上均表现出色。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=COMI:+COrrect+and+MItigate+Shortcut+Learning+Behavior+in+Deep+Neural+Networks)|0| |[LLM-enhanced Cascaded Multi-level Learning on Temporal Heterogeneous Graphs](https://doi.org/10.1145/3626772.3657731)|Fengyi Wang, Guanghui Zhu, Chunfeng Yuan, Yihua Huang|Nanjing University State Key Laboratory for Novel Software Technology|Learning on temporal heterogeneous graphs (THGs) has attracted substantial attention in applications of information retrieval. Such graphs are ubiquitous in real-world domains like recommender systems and social networks. However, the spatial heterogeneity, rich semantic information, and intricate evolution patterns of THGs make it still difficult to generate high-quality embeddings for graph nodes. In this paper, we focus on two valuable and understudied issues related to THG learning: (a) How to capture the specific evolutionary characteristics of diverse temporal heterogeneous graphs? (b) Due to the heterogeneous nature of the graph, how to capture the unique temporal patterns of different node types? We explore these questions and present our solution by proposing a new method named CasMLN (Cascaded Multi-level Learning Network) for THG learning. Through the multi-level learning structure and aggregation methods specifically designed for different levels, we obtain information of multiple levels and fuse them to improve embedding generation. Additionally, we pioneer the use of large language models (LLMs) in the THG field. By leveraging the universality and powerful capabilities of LLMs, our method introduces LLM-based external knowledge to effectively capture the implicit nature of graphs and node types, which helps to enhance type- and graph-level representations. We evaluate our method on several real-world THG datasets for different downstream tasks. Extensive experimental results show that CasMLN outperforms the state-of-the-art baselines in both accuracy and efficiency.|学习时间异构图（THG）在信息检索应用中引起了广泛关注。这类图在推荐系统和社交网络等现实领域中无处不在。然而，THG的空间异构性、丰富的语义信息和复杂的演化模式使得生成高质量的图节点嵌入仍然具有挑战性。本文重点研究了与THG学习相关的两个有价值且研究不足的问题：（a）如何捕捉不同时间异构图的特定演化特征？（b）由于图的异构性，如何捕捉不同节点类型的独特时间模式？我们探讨了这些问题，并通过提出一种名为CasMLN（级联多层次学习网络）的新方法来解决THG学习问题。通过多层次学习结构和为不同层次专门设计的聚合方法，我们获取了多层次的信息并将其融合，以改进嵌入生成。此外，我们开创性地在THG领域中使用大型语言模型（LLMs）。通过利用LLMs的通用性和强大能力，我们的方法引入了基于LLM的外部知识，有效地捕捉图和节点类型的隐含性质，从而有助于增强类型和图级别的表示。我们在多个真实世界的THG数据集上评估了我们的方法，用于不同的下游任务。广泛的实验结果表明，CasMLN在准确性和效率方面均优于最先进的基线。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=LLM-enhanced+Cascaded+Multi-level+Learning+on+Temporal+Heterogeneous+Graphs)|0| |[Self-Improving Teacher Cultivates Better Student: Distillation Calibration for Multimodal Large Language Models](https://doi.org/10.1145/3626772.3657692)|Xinwei Li, Li Lin, Shuai Wang, Chen Qian|Southeast University; Tsinghua University; Southeast university|Multimodal content generation, which leverages visual information to enhance the comprehension of cross-modal understanding, plays a critical role in Multimodal Information Retrieval. With the development of large language models (LLMs), recent research has adopted visual instruction tuning to inject the knowledge of LLMs into downstream multimodal tasks. The high complexity and great demand for resources urge researchers to study e.cient distillation solutions to transfer the knowledge from pre-trained multimodal models (teachers) to more compact student models. However, the instruction tuning for knowledge distillation in multimodal LLMs is resource-intensive and capability-restricted. The comprehension of students is highly reliant on the teacher models. To address this issue, we propose a novel Multimodal Distillation Calibration framework (MmDC). The main idea is to generate high-quality training instances that challenge student models to comprehend and prompt the teacher to calibrate the knowledge transferred to students, ultimately cultivating a better student model in downstream tasks. This framework comprises two stages: (1) multimodal alignment and (2) knowledge distillation calibration. In the.rst stage, parameter-e.cient.ne-tuning is used to enhance feature alignment between di.erent modalities. In the second stage, we develop a calibration strategy to assess the student model's capability and generate high-quality instances to calibrate knowledge distillation from teacher to student. The experiments on diverse datasets show that our framework e.ciently improves the student model's capabilities. Our 7B-size student model, after three iterations of distillation calibration, outperforms the current state-of-the-art LLaVA-13B model on the ScienceQA and LLaVA Test datasets and also exceeds other strong baselines in a zero-shot setting.|多模态内容生成，通过利用视觉信息来增强跨模态理解，在多模态信息检索中发挥着关键作用。随着大型语言模型（LLMs）的发展，近期研究采用了视觉指令微调来将LLMs的知识注入下游多模态任务。高复杂性和对资源的大量需求促使研究人员研究高效的蒸馏解决方案，将知识从预训练的多模态模型（教师模型）转移到更紧凑的学生模型中。然而，多模态LLMs中的指令微调知识蒸馏资源密集且能力受限，学生模型的理解高度依赖教师模型。为解决这一问题，我们提出了一种新颖的多模态蒸馏校准框架（MmDC）。其主要思想是生成高质量的训练实例，挑战学生模型以理解并促使教师模型校准传递给学生的知识，最终培养出在下游任务中表现更佳的学生模型。该框架包括两个阶段：（1）多模态对齐和（2）知识蒸馏校准。在第一阶段，采用参数高效微调来增强不同模态之间的特征对齐。在第二阶段，我们开发了一种校准策略，评估学生模型的能力并生成高质量实例，以校准从教师到学生的知识蒸馏。在多个数据集上的实验表明，我们的框架有效提升了学生模型的能力。经过三轮蒸馏校准后，我们的7B规模学生模型在ScienceQA和LLaVA测试数据集上超越了当前最先进的LLaVA-13B模型，并在零样本设置下也优于其他强基线模型。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Self-Improving+Teacher+Cultivates+Better+Student:+Distillation+Calibration+for+Multimodal+Large+Language+Models)|0| |[Deep Automated Mechanism Design for Integrating Ad Auction and Allocation in Feed](https://doi.org/10.1145/3626772.3657774)|Xuejian Li, Ze Wang, Bingqi Zhu, Fei He, Yongkang Wang, Xingxing Wang|Meituan|E-commerce platforms usually present an ordered list, mixed with several organic items and an advertisement, in response to each user's page view request. This list, the outcome of ad auction and allocation processes, directly impacts the platform's ad revenue and gross merchandise volume (GMV). Specifically, the ad auction determines which ad is displayed and the corresponding payment, while the ad allocation decides the display positions of the advertisement and organic items. The prevalent methods of segregating the ad auction and allocation into two distinct stages face two problems: 1) Ad auction does not consider externalities, such as the influence of actual display position and context on ad Click-Through Rate (CTR); 2) The ad allocation, which utilizes the auction-winning ad's payment to determine the display position dynamically, fails to maintain incentive compatibility (IC) for the advertisement. For instance, in the auction stage employing the traditional Generalized Second Price (GSP), even if the winning ad increases its bid, its payment remains unchanged. This implies that the advertisement cannot secure a better position and thus loses the opportunity to achieve higher utility in the subsequent ad allocation stage. Previous research often focused on one of the two stages, neglecting the two-stage problem, which may result in suboptimal outcomes. Therefore, this paper proposes a deep automated mechanism that integrates ad auction and allocation, ensuring both IC and Individual Rationality (IR) in the presence of externalities while maximizing revenue and GMV. The mechanism takes candidate ads and the ordered list of organic items as input. For each candidate ad, several candidate allocations are generated by inserting the ad in different positions of the ordered list of organic items. For each candidate allocation, a list-wise model takes the entire allocation as input and outputs the predicted result for each ad and organic item to model the global externalities. Finally, an automated auction mechanism, modeled by deep neural networks, is executed to select the optimal allocation. Consequently, this mechanism simultaneously decides the ranking, payment, and display position of the ad. Furthermore, the proposed mechanism results in higher revenue and GMV than state-of-the-art baselines in offline experiments and online A/B tests.|电子商务平台通常会针对每个用户的页面浏览请求，提供一个有序列表，其中混合了多个有机商品和一个广告。这个列表是广告拍卖和分配过程的结果，直接影响到平台的广告收入和总商品交易额（GMV）。具体来说，广告拍卖决定了展示哪个广告及其相应的支付，而广告分配则决定了广告和有机商品的展示位置。目前将广告拍卖和分配分隔为两个独立阶段的方法面临两个问题：1) 广告拍卖未考虑外部性，如实际展示位置和上下文对广告点击率（CTR）的影响；2) 广告分配使用拍卖胜出的广告支付来动态决定展示位置，无法维持广告的激励兼容性（IC）。例如，在使用传统广义第二价格（GSP）的拍卖阶段，即使胜出的广告提高其出价，其支付仍保持不变，这意味着广告无法获得更好的位置，从而在后续的广告分配阶段失去实现更高效用的机会。以往的研究往往只关注这两个阶段中的一个，忽视了两阶段问题，可能导致次优结果。因此，本文提出了一种深度自动化机制，将广告拍卖和分配整合在一起，在存在外部性的情况下确保IC和个体理性（IR），同时最大化收入和GMV。该机制以候选广告和有机商品的有序列表为输入。对于每个候选广告，通过将广告插入有机商品有序列表的不同位置，生成多个候选分配。对于每个候选分配，一个列表级模型以整个分配为输入，并输出每个广告和有机商品的预测结果，以模拟全局外部性。最后，通过深度神经网络建模的自动化拍卖机制执行，选择最优分配。因此，该机制同时决定了广告的排序、支付和展示位置。此外，在离线实验和在线A/B测试中，所提出的机制在收入和GMV方面均优于最先进的基线。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Deep+Automated+Mechanism+Design+for+Integrating+Ad+Auction+and+Allocation+in+Feed)|0| -|[TGOnline: Enhancing Temporal Graph Learning with Adaptive Online Meta-Learning](https://doi.org/10.1145/3626772.3657791)|Ruijie Wang, Jingyuan Huang, Yutong Zhang, Jinyang Li, Yufeng Wang, Wanyu Zhao, Shengzhong Liu, Charith Mendis, Tarek F. Abdelzaher|Zhejiang University; Stanford University; University of Illinois Urbana-Champaign; Shanghai Jiao Tong University|Temporal graphs, depicting time-evolving node connections through temporal edges, are extensively utilized in domains where temporal connection patterns are essential, such as recommender systems, financial networks, healthcare, and sensor networks. Despite recent advancements in temporal graph representation learning, performance degradation occurs with periodic collections of new temporal edges, owing to their dynamic nature and newly emerging information. This paper investigates online representation learning on temporal graphs, aiming for efficient updates of temporal models to sustain predictive performance during deployment. Unlike costly retraining or exclusive fine-tuning susceptible to catastrophic forgetting, our approach aims to distill information from previous model parameters and adapt it to newly gathered data. To this end, we propose TGOnline, an adaptive online meta-learning framework, tackling two key challenges. First, to distill valuable knowledge from complex temporal parameters, we establish an optimization objective that determines new parameters, either by leveraging global ones or by placing greater reliance on new data, where global parameters are meta-trained across various data collection periods to enhance temporal generalization. Second, to accelerate the online distillation process, we introduce an edge reduction mechanism that skips new edges lacking additional information and a node deduplication mechanism to prevent redundant computation within training batches on new data. Extensive experiments on four real-world temporal graphs demonstrate the effectiveness and efficiency of TGOnline for online representation learning, outperforming 18 state-of-the-art baselines. Notably, TGOnline not only outperforms the commonly utilized retraining strategy but also achieves a significant speedup of ~30x.|描述时间演化节点连接的时态图在多个领域中得到广泛应用，这些领域中时间连接模式至关重要，如推荐系统、金融网络、医疗保健和传感器网络。尽管时态图表示学习的最新进展取得了一些成果，但由于其动态特性和新信息的不断涌现，随着新时态边的周期性收集，性能下降问题依然存在。本文探讨了时态图上的在线表示学习，旨在实现时态模型的高效更新，以在部署期间维持预测性能。与昂贵的重新训练或易受灾难性遗忘影响的专用微调不同，我们的方法旨在从前模型参数中提取信息，并将其适应于新收集的数据。为此，我们提出了TGOnline，一种自适应的在线元学习框架，解决了两个关键挑战。首先，为了从复杂的时态参数中提取有价值的知识，我们建立了一个优化目标，用于确定新参数，既可以利用全局参数，也可以更依赖于新数据，其中全局参数在不同数据收集周期进行元训练，以增强时态泛化能力。其次，为了加速在线提取过程，我们引入了一种边减少机制，跳过缺乏附加信息的新边，以及一种节点去重机制，以防止在新数据训练批次中的冗余计算。在四个真实世界的时态图上的广泛实验证明了TGOnline在在线表示学习中的有效性和高效性，超越了18种最先进的基线方法。值得注意的是，TGOnline不仅优于常用的重新训练策略，还实现了约30倍的显著加速。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=TGOnline:+Enhancing+Temporal+Graph+Learning+with+Adaptive+Online+Meta-Learning)|0| -|[Intent Distribution based Bipartite Graph Representation Learning](https://doi.org/10.1145/3626772.3657739)|Haojie Li, Wei Wei, Guanfeng Liu, Jinhuan Liu, Feng Jiang, Junwei Du|Macquarie University; School of Information Science & Technology, Qingdao University of Science and Technology; College of Data Science, Qingdao University of Science and Technology|Bipartite graph representation learning embeds users and items into a low-dimensional latent space based on observed interactions. Previous studies mainly fall into two categories: one reconstructs the structural relations of the graph through the representations of nodes, while the other aggregates neighboring node information using graph neural networks. However, existing methods only explore the local structural information of nodes during the learning process. This makes it difficult to represent the macroscopic structural information and leaves it easily affected by data sparsity and noise. To address this issue, we propose the Intent Distribution based Bipartite graph Representation learning (IDBR) model, which explicitly integrates node intent distribution information into the representation learning process. Specifically, we obtain node intent distributions through clustering and design an intent distribution based graph convolution neural network to generate node representations. Compared to traditional methods, we expand the scope of node representations, enabling us to obtain more comprehensive representations of global intent. When constructing the intent distributions, we effectively alleviated the issues of data sparsity and noise. Additionally, we enrich the representations of nodes by integrating potential neighboring nodes from both structural and semantic dimensions. Experiments on the link prediction and recommendation tasks illustrate that the proposed approach outperforms existing state-of-the-art methods. The code of IDBR is available at https://github.com/rookitkitlee/IDBR.|二部图表示学习通过观察到的交互作用将用户和物品嵌入到一个低维潜在空间中。先前的研究主要分为两类：一类是通过节点的表示重建图的结构关系，另一类是使用图神经网络聚合相邻节点的信息。然而，现有方法在表示学习过程中仅探索了节点的局部结构信息。这使得宏观结构信息的表示变得困难，并容易受到数据稀疏性和噪声的影响。为了解决这一问题，我们提出了基于意图分布的二部图表示学习（IDBR）模型，该模型明确地将节点意图分布信息整合到表示学习过程中。具体来说，我们通过聚类获得节点意图分布，并设计了一种基于意图分布的图卷积神经网络来生成节点表示。与传统方法相比，我们扩展了节点表示的范围，从而能够获得更全面的全局意图表示。在构建意图分布时，我们有效地缓解了数据稀疏性和噪声问题。此外，我们通过整合来自结构和语义维度的潜在相邻节点来丰富节点的表示。在链接预测和推荐任务上的实验表明，所提出的方法优于现有的最先进方法。IDBR的代码可在https://github.com/rookitkitlee/IDBR获取。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Intent+Distribution+based+Bipartite+Graph+Representation+Learning)|0| -|[MTMS: Multi-teacher Multi-stage Knowledge Distillation for Reasoning-Based Machine Reading Comprehension](https://doi.org/10.1145/3626772.3657824)|Zhuo Zhao, Zhiwen Xie, Guangyou Zhou, Jimmy Xiangji Huang|jhuangyorku.ca; zhuomails.ccnu.edu.cn; gyzhoumail.ccnu.edu.cn; xiezhiwenwhu.edu.cn|As the field of machine reading comprehension (MRC) continues to evolve, it is unlocking enormous potential for its practical application. However, the currently well-performing models predominantly rely on massive pre-trained language models with at least several hundred million or even over one hundred billion parameters. These complex models not only require immense computational power but also extensive storage, presenting challenges for resource-limited environments such as online education.Current research indicates that specific capabilities of larger models can be transferred to smaller models through knowledge distillation. However, prior to our work, there were no small models specifically designed for MRC task with complex reasoning abilities. In light of this, we present a novel multi-teacher multi-stage distillation approach, MTMS. It facilitates the easier deployment of reasoning-based MRC task on resource-constrained devices, thereby enabling effective applications. In this method, we design a multi-teacher distillation framework that includes both a logical teacher and a semantic teacher. This framework allows MTMS to simultaneously extract features from different perspectives of the text, mitigating the limitations inherent in single-teacher information representations. Furthermore, we introduce a multi-stage contrastive learning strategy. Through this strategy, the student model can progressively align with the teacher models, effectively bridging the gap between them. Extensive experimental outcomes on two inference-based datasets from real-world scenarios demonstrate that MTMS requires nearly 10 times fewer parameters compared with the teacher model size while achieving the competitive performance.|随着机器阅读理解（MRC）领域的不断发展，其在实际应用中的潜力正在被不断解锁。然而，目前表现优异的模型主要依赖于大规模预训练语言模型，这些模型至少拥有数亿甚至超过千亿级别的参数。这些复杂的模型不仅需要巨大的计算能力，还需要大量的存储空间，这对于在线教育等资源受限的环境构成了挑战。现有研究表明，大型模型的特定能力可以通过知识蒸馏转移到小型模型上。然而，在我们开展工作之前，尚未有专门为具有复杂推理能力的MRC任务设计的小型模型。鉴于此，我们提出了一种新颖的多教师多阶段蒸馏方法，即MTMS。该方法有助于在资源受限的设备上更轻松地部署基于推理的MRC任务，从而实现有效的应用。在该方法中，我们设计了一个包含逻辑教师和语义教师的多教师蒸馏框架。该框架使MTMS能够同时从文本的不同角度提取特征，缓解了单教师信息表示的固有限制。此外，我们引入了一种多阶段对比学习策略。通过这一策略，学生模型可以逐步与教师模型对齐，有效地缩小了它们之间的差距。在两个来自真实场景的基于推理的数据集上的广泛实验结果表明，MTMS所需的参数数量几乎是教师模型参数量的十分之一，同时达到了相当的性能水平。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=MTMS:+Multi-teacher+Multi-stage+Knowledge+Distillation+for+Reasoning-Based+Machine+Reading+Comprehension)|0| +|[TGOnline: Enhancing Temporal Graph Learning with Adaptive Online Meta-Learning](https://doi.org/10.1145/3626772.3657791)|Ruijie Wang, Jingyuan Huang, Yutong Zhang, Jinyang Li, Yufeng Wang, Wanyu Zhao, Shengzhong Liu, Charith Mendis, Tarek F. Abdelzaher|Stanford University; University of Illinois Urbana-Champaign; Shanghai Jiao Tong University; Zhejiang University|Temporal graphs, depicting time-evolving node connections through temporal edges, are extensively utilized in domains where temporal connection patterns are essential, such as recommender systems, financial networks, healthcare, and sensor networks. Despite recent advancements in temporal graph representation learning, performance degradation occurs with periodic collections of new temporal edges, owing to their dynamic nature and newly emerging information. This paper investigates online representation learning on temporal graphs, aiming for efficient updates of temporal models to sustain predictive performance during deployment. Unlike costly retraining or exclusive fine-tuning susceptible to catastrophic forgetting, our approach aims to distill information from previous model parameters and adapt it to newly gathered data. To this end, we propose TGOnline, an adaptive online meta-learning framework, tackling two key challenges. First, to distill valuable knowledge from complex temporal parameters, we establish an optimization objective that determines new parameters, either by leveraging global ones or by placing greater reliance on new data, where global parameters are meta-trained across various data collection periods to enhance temporal generalization. Second, to accelerate the online distillation process, we introduce an edge reduction mechanism that skips new edges lacking additional information and a node deduplication mechanism to prevent redundant computation within training batches on new data. Extensive experiments on four real-world temporal graphs demonstrate the effectiveness and efficiency of TGOnline for online representation learning, outperforming 18 state-of-the-art baselines. Notably, TGOnline not only outperforms the commonly utilized retraining strategy but also achieves a significant speedup of ~30x.|描述时间演化节点连接的时态图在多个领域中得到广泛应用，这些领域中时间连接模式至关重要，如推荐系统、金融网络、医疗保健和传感器网络。尽管时态图表示学习的最新进展取得了一些成果，但由于其动态特性和新信息的不断涌现，随着新时态边的周期性收集，性能下降问题依然存在。本文探讨了时态图上的在线表示学习，旨在实现时态模型的高效更新，以在部署期间维持预测性能。与昂贵的重新训练或易受灾难性遗忘影响的专用微调不同，我们的方法旨在从前模型参数中提取信息，并将其适应于新收集的数据。为此，我们提出了TGOnline，一种自适应的在线元学习框架，解决了两个关键挑战。首先，为了从复杂的时态参数中提取有价值的知识，我们建立了一个优化目标，用于确定新参数，既可以利用全局参数，也可以更依赖于新数据，其中全局参数在不同数据收集周期进行元训练，以增强时态泛化能力。其次，为了加速在线提取过程，我们引入了一种边减少机制，跳过缺乏附加信息的新边，以及一种节点去重机制，以防止在新数据训练批次中的冗余计算。在四个真实世界的时态图上的广泛实验证明了TGOnline在在线表示学习中的有效性和高效性，超越了18种最先进的基线方法。值得注意的是，TGOnline不仅优于常用的重新训练策略，还实现了约30倍的显著加速。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=TGOnline:+Enhancing+Temporal+Graph+Learning+with+Adaptive+Online+Meta-Learning)|0| +|[Intent Distribution based Bipartite Graph Representation Learning](https://doi.org/10.1145/3626772.3657739)|Haojie Li, Wei Wei, Guanfeng Liu, Jinhuan Liu, Feng Jiang, Junwei Du|Macquarie University; College of Data Science, Qingdao University of Science and Technology; School of Information Science & Technology, Qingdao University of Science and Technology|Bipartite graph representation learning embeds users and items into a low-dimensional latent space based on observed interactions. Previous studies mainly fall into two categories: one reconstructs the structural relations of the graph through the representations of nodes, while the other aggregates neighboring node information using graph neural networks. However, existing methods only explore the local structural information of nodes during the learning process. This makes it difficult to represent the macroscopic structural information and leaves it easily affected by data sparsity and noise. To address this issue, we propose the Intent Distribution based Bipartite graph Representation learning (IDBR) model, which explicitly integrates node intent distribution information into the representation learning process. Specifically, we obtain node intent distributions through clustering and design an intent distribution based graph convolution neural network to generate node representations. Compared to traditional methods, we expand the scope of node representations, enabling us to obtain more comprehensive representations of global intent. When constructing the intent distributions, we effectively alleviated the issues of data sparsity and noise. Additionally, we enrich the representations of nodes by integrating potential neighboring nodes from both structural and semantic dimensions. Experiments on the link prediction and recommendation tasks illustrate that the proposed approach outperforms existing state-of-the-art methods. The code of IDBR is available at https://github.com/rookitkitlee/IDBR.|二部图表示学习通过观察到的交互作用将用户和物品嵌入到一个低维潜在空间中。先前的研究主要分为两类：一类是通过节点的表示重建图的结构关系，另一类是使用图神经网络聚合相邻节点的信息。然而，现有方法在表示学习过程中仅探索了节点的局部结构信息。这使得宏观结构信息的表示变得困难，并容易受到数据稀疏性和噪声的影响。为了解决这一问题，我们提出了基于意图分布的二部图表示学习（IDBR）模型，该模型明确地将节点意图分布信息整合到表示学习过程中。具体来说，我们通过聚类获得节点意图分布，并设计了一种基于意图分布的图卷积神经网络来生成节点表示。与传统方法相比，我们扩展了节点表示的范围，从而能够获得更全面的全局意图表示。在构建意图分布时，我们有效地缓解了数据稀疏性和噪声问题。此外，我们通过整合来自结构和语义维度的潜在相邻节点来丰富节点的表示。在链接预测和推荐任务上的实验表明，所提出的方法优于现有的最先进方法。IDBR的代码可在https://github.com/rookitkitlee/IDBR获取。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Intent+Distribution+based+Bipartite+Graph+Representation+Learning)|0| +|[MTMS: Multi-teacher Multi-stage Knowledge Distillation for Reasoning-Based Machine Reading Comprehension](https://doi.org/10.1145/3626772.3657824)|Zhuo Zhao, Zhiwen Xie, Guangyou Zhou, Jimmy Xiangji Huang|xiezhiwenwhu.edu.cn; zhuomails.ccnu.edu.cn; gyzhoumail.ccnu.edu.cn; jhuangyorku.ca|As the field of machine reading comprehension (MRC) continues to evolve, it is unlocking enormous potential for its practical application. However, the currently well-performing models predominantly rely on massive pre-trained language models with at least several hundred million or even over one hundred billion parameters. These complex models not only require immense computational power but also extensive storage, presenting challenges for resource-limited environments such as online education.Current research indicates that specific capabilities of larger models can be transferred to smaller models through knowledge distillation. However, prior to our work, there were no small models specifically designed for MRC task with complex reasoning abilities. In light of this, we present a novel multi-teacher multi-stage distillation approach, MTMS. It facilitates the easier deployment of reasoning-based MRC task on resource-constrained devices, thereby enabling effective applications. In this method, we design a multi-teacher distillation framework that includes both a logical teacher and a semantic teacher. This framework allows MTMS to simultaneously extract features from different perspectives of the text, mitigating the limitations inherent in single-teacher information representations. Furthermore, we introduce a multi-stage contrastive learning strategy. Through this strategy, the student model can progressively align with the teacher models, effectively bridging the gap between them. Extensive experimental outcomes on two inference-based datasets from real-world scenarios demonstrate that MTMS requires nearly 10 times fewer parameters compared with the teacher model size while achieving the competitive performance.|随着机器阅读理解（MRC）领域的不断发展，其在实际应用中的潜力正在被不断解锁。然而，目前表现优异的模型主要依赖于大规模预训练语言模型，这些模型至少拥有数亿甚至超过千亿级别的参数。这些复杂的模型不仅需要巨大的计算能力，还需要大量的存储空间，这对于在线教育等资源受限的环境构成了挑战。现有研究表明，大型模型的特定能力可以通过知识蒸馏转移到小型模型上。然而，在我们开展工作之前，尚未有专门为具有复杂推理能力的MRC任务设计的小型模型。鉴于此，我们提出了一种新颖的多教师多阶段蒸馏方法，即MTMS。该方法有助于在资源受限的设备上更轻松地部署基于推理的MRC任务，从而实现有效的应用。在该方法中，我们设计了一个包含逻辑教师和语义教师的多教师蒸馏框架。该框架使MTMS能够同时从文本的不同角度提取特征，缓解了单教师信息表示的固有限制。此外，我们引入了一种多阶段对比学习策略。通过这一策略，学生模型可以逐步与教师模型对齐，有效地缩小了它们之间的差距。在两个来自真实场景的基于推理的数据集上的广泛实验结果表明，MTMS所需的参数数量几乎是教师模型参数量的十分之一，同时达到了相当的性能水平。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=MTMS:+Multi-teacher+Multi-stage+Knowledge+Distillation+for+Reasoning-Based+Machine+Reading+Comprehension)|0| |[Exploring the Trade-Off within Visual Information for MultiModal Sentence Summarization](https://doi.org/10.1145/3626772.3657753)|Minghuan Yuan, Shiyao Cui, Xinghua Zhang, Shicheng Wang, Hongbo Xu, Tingwen Liu|Institute of Information Engineering, Chinese Academy of Sciences|MultiModal Sentence Summarization (MMSS) aims to generate a brief summary based on the given source sentence and its associated image. Previous studies on MMSS have achieved success by either selecting the task-relevant visual information or filtering out the task-irrelevant visual information to help the textual modality to generate the summary. However, enhancing from a single perspective usually introduces over-preservation or over-compression problems. To tackle these issues, we resort to Information Bottleneck (IB), which seeks to find a maximally compressed mapping of the input information that preserves as much information about the target as possible. Specifically, we propose a novel method, T(3), which adopts IB to balance the Trade-off between Task-relevant and Task-irrelevant visual information through the variational inference framework. In this way, the task-irrelevant visual information is compressed to the utmost while the task-relevant visual information is maximally retained. With the holistic perspective, the generated summary could maintain as many key elements as possible while discarding the unnecessary ones as far as possible. Extensive experiments on the representative MMSS dataset demonstrate the superiority of our proposed method. Our code is available at https://github.com/YuanMinghuan/T3.|多模态句子摘要（MMSS）旨在根据给定的源句子和相关图像生成简要摘要。以往的MMSS研究通过选择与任务相关的视觉信息或过滤掉与任务无关的视觉信息来帮助文本模态生成摘要，取得了成功。然而，从单一角度进行增强通常会引入过度保留或过度压缩的问题。为了解决这些问题，我们采用了信息瓶颈（IB）方法，该方法旨在找到输入信息的最大压缩映射，同时尽可能多地保留关于目标的信息。具体而言，我们提出了一种新方法T(3)，该方法通过变分推断框架采用IB来平衡与任务相关和与任务无关的视觉信息之间的权衡。通过这种方式，与任务无关的视觉信息被最大限度地压缩，而与任务相关的视觉信息则被最大程度地保留。从整体角度来看，生成的摘要能够尽可能多地保留关键元素，同时尽可能多地剔除不必要的内容。在代表性的MMSS数据集上的广泛实验证明了我们提出的方法的优越性。我们的代码可在https://github.com/YuanMinghuan/T3获取。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Exploring+the+Trade-Off+within+Visual+Information+for+MultiModal+Sentence+Summarization)|0| |[ChroniclingAmericaQA: A Large-scale Question Answering Dataset based on Historical American Newspaper Pages](https://doi.org/10.1145/3626772.3657891)|Bhawna Piryani, Jamshid Mozafari, Adam Jatowt|University of Innsbruck|Question answering (QA) and Machine Reading Comprehension (MRC) tasks have significantly advanced in recent years due to the rapid development of deep learning techniques and, more recently, large language models. At the same time, many benchmark datasets have become available for QA and MRC tasks. However, most existing large-scale benchmark datasets have been created predominantly using synchronous document collections like Wikipedia or the Web. Archival document collections, such as historical newspapers, contain valuable information from the past that is still not widely used to train large language models. To further contribute to advancing QA and MRC tasks and to overcome the limitation of previous datasets, we introduce ChroniclingAmericaQA, a largescale temporal QA dataset with 487K question-answer pairs created based on the historical newspaper collection Chronicling America. Our dataset is constructed from a subset of the Chronicling America newspaper collection spanning 120 years. One of the significant challenges for utilizing digitized historical newspaper collections is the low quality of OCR text. Therefore, to enable realistic testing of QA models, our dataset can be used in three different ways: answering questions from raw and noisy content, answering questions from cleaner, corrected version of the content, as well as answering questions from scanned images of newspaper pages. This and the fact that ChroniclingAmericaQA spans the longest time period among available QA datasets make it quite a unique and useful resource.|近年来，由于深度学习技术的迅速发展和大规模语言模型的出现，问答（QA）和机器阅读理解（MRC）任务取得了显著进展。与此同时，许多用于QA和MRC任务的基准数据集也相继问世。然而，大多数现有的大规模基准数据集主要基于同步文档集合（如维基百科或网络资源）创建。档案文档集合，如历史报纸，包含了大量有价值的历史信息，但这些信息尚未被广泛用于训练大规模语言模型。为了进一步推动QA和MRC任务的发展，并克服以往数据集的局限性，我们推出了ChroniclingAmericaQA，这是一个基于历史报纸集合Chronicling America创建的包含48.7万个问答对的大规模时间性QA数据集。我们的数据集构建自Chronicling America报纸集合的一个子集，跨越了120年的时间。利用数字化历史报纸集合的一个主要挑战是OCR文本的质量较低。因此，为了实现对QA模型的实际测试，我们的数据集可以以三种不同的方式使用：从原始且包含噪声的内容中回答问题，从经过清理和校正的内容版本中回答问题，以及从报纸页面的扫描图像中回答问题。此外，ChroniclingAmericaQA所涵盖的时间跨度在现有的QA数据集中是最长的，这使得它成为一个非常独特且有用的资源。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=ChroniclingAmericaQA:+A+Large-scale+Question+Answering+Dataset+based+on+Historical+American+Newspaper+Pages)|0| |[BRB-KMeans: Enhancing Binary Data Clustering for Binary Product Quantization](https://doi.org/10.1145/3626772.3657898)|Suwon Lee, SangMin Choi|Gyeongsang National University|In Binary Product Quantization (BPQ), where product quantization is applied to binary data, the traditional k-majority method is used for clustering, with centroids determined based on Hamming distance and majority vote for each bit. However, this approach often leads to a degradation in clustering quality, negatively impacting BPQ's performance. To address these challenges, we introduce Binary-to-Real-and-Back K-Means (BRB-KMeans), a novel method that initially transforms binary data into real-valued vectors, performs k-means clustering on these vectors, and then converts the generated centroids back into binary data. This innovative approach significantly enhances clustering quality by leveraging the high clustering quality of k-means in the real-valued vector space, thereby facilitating future quantization for binary data. Through extensive experiments, we demonstrate that BRB-KMeans significantly enhances clustering quality and overall BPQ performance, notably outperforming traditional methods.|在二进制乘积量化（BPQ）中，当乘积量化应用于二进制数据时，传统的方法使用k-多数方法进行聚类，其中质心是基于汉明距离和每个位的多数投票来确定的。然而，这种方法通常会导致聚类质量下降，从而负面影响BPQ的性能。为了应对这些挑战，我们提出了一种新的方法——二进制到实数再返回K均值（BRB-KMeans），该方法首先将二进制数据转换为实值向量，然后在这些向量上执行k均值聚类，最后将生成的质心转换回二进制数据。这种创新方法通过利用实值向量空间中k均值聚类的高质量，显著提高了聚类质量，从而为二进制数据的未来量化提供了便利。通过广泛的实验，我们证明了BRB-KMeans显著提升了聚类质量和整体BPQ性能，明显优于传统方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=BRB-KMeans:+Enhancing+Binary+Data+Clustering+for+Binary+Product+Quantization)|0| @@ -312,7 +312,7 @@ |[GOLF: Goal-Oriented Long-term liFe tasks supported by human-AI collaboration](https://doi.org/10.1145/3626772.3657655)|Ben Wang||||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=GOLF:+Goal-Oriented+Long-term+liFe+tasks+supported+by+human-AI+collaboration)|0| |[CorpusLM: Towards a Unified Language Model on Corpus for Knowledge-Intensive Tasks](https://doi.org/10.1145/3626772.3657778)|Xiaoxi Li, Zhicheng Dou, Yujia Zhou, Fangchao Liu||||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=CorpusLM:+Towards+a+Unified+Language+Model+on+Corpus+for+Knowledge-Intensive+Tasks)|0| |[Transformer-based Reasoning for Learning Evolutionary Chain of Events on Temporal Knowledge Graph](https://doi.org/10.1145/3626772.3657706)|Zhiyu Fang, ShuaiLong Lei, Xiaobin Zhu, Chun Yang, ShiXue Zhang, XuCheng Yin, Jingyan Qin||||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Transformer-based+Reasoning+for+Learning+Evolutionary+Chain+of+Events+on+Temporal+Knowledge+Graph)|0| -|[NativE: Multi-modal Knowledge Graph Completion in the Wild](https://doi.org/10.1145/3626772.3657800)|Yichi Zhang, Zhuo Chen, Lingbing Guo, Yajing Xu, Binbin Hu, Ziqi Liu, Wen Zhang, Huajun Chen|Zhejiang University; Ant Group; Zhejiang Univerisity; Zhejiang UniversityZhejiang University-Ant Group Joint Laboratory of Knowledge Graph|Multi-modal knowledge graph completion (MMKGC) aims to automatically discover the unobserved factual knowledge from a given multi-modal knowledge graph by collaboratively modeling the triple structure and multi-modal information from entities. However, real-world MMKGs present challenges due to their diverse and imbalanced nature, which means that the modality information can span various types (e.g., image, text, numeric, audio, video) but its distribution among entities is uneven, leading to missing modalities for certain entities. Existing works usually focus on common modalities like image and text while neglecting the imbalanced distribution phenomenon of modal information. To address these issues, we propose a comprehensive framework NativE to achieve MMKGC in the wild. NativE proposes a relation-guided dual adaptive fusion module that enables adaptive fusion for any modalities and employs a collaborative modality adversarial training framework to augment the imbalanced modality information. We construct a new benchmark called WildKGC with five datasets to evaluate our method. The empirical results compared with 21 recent baselines confirm the superiority of our method, consistently achieving state-of-the-art performance across different datasets and various scenarios while keeping efficient and generalizable. Our code and data are released at https://github.com/zjukg/NATIVE|多模态知识图谱补全（MMKGC）旨在通过协同建模实体的三元组结构和多模态信息，自动发现给定多模态知识图谱中未观察到的事实知识。然而，现实世界中的多模态知识图谱由于其多样性和不均衡性而面临挑战，这意味着模态信息可以涵盖多种类型（如图像、文本、数值、音频、视频），但其分布在实体间是不均匀的，导致某些实体缺失模态信息。现有的工作通常关注图像和文本等常见模态，而忽视了模态信息的不均衡分布现象。为了解决这些问题，我们提出了一个全面的框架NativE，以实现真实环境中的多模态知识图谱补全。NativE提出了一种关系引导的双重自适应融合模块，该模块能够对任何模态进行自适应融合，并采用协同模态对抗训练框架来增强不均衡的模态信息。我们构建了一个名为WildKGC的新基准，包含五个数据集，用于评估我们的方法。与21个最近的基线方法进行比较的实证结果证实了我们的方法的优越性，在不同数据集和各种场景下始终保持高效和可推广性，并取得了最先进的性能。我们的代码和数据已在https://github.com/zjukg/NATIVE发布。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=NativE:+Multi-modal+Knowledge+Graph+Completion+in+the+Wild)|0| +|[NativE: Multi-modal Knowledge Graph Completion in the Wild](https://doi.org/10.1145/3626772.3657800)|Yichi Zhang, Zhuo Chen, Lingbing Guo, Yajing Xu, Binbin Hu, Ziqi Liu, Wen Zhang, Huajun Chen|Zhejiang UniversityZhejiang University-Ant Group Joint Laboratory of Knowledge Graph; Ant Group; Zhejiang Univerisity; Zhejiang University|Multi-modal knowledge graph completion (MMKGC) aims to automatically discover the unobserved factual knowledge from a given multi-modal knowledge graph by collaboratively modeling the triple structure and multi-modal information from entities. However, real-world MMKGs present challenges due to their diverse and imbalanced nature, which means that the modality information can span various types (e.g., image, text, numeric, audio, video) but its distribution among entities is uneven, leading to missing modalities for certain entities. Existing works usually focus on common modalities like image and text while neglecting the imbalanced distribution phenomenon of modal information. To address these issues, we propose a comprehensive framework NativE to achieve MMKGC in the wild. NativE proposes a relation-guided dual adaptive fusion module that enables adaptive fusion for any modalities and employs a collaborative modality adversarial training framework to augment the imbalanced modality information. We construct a new benchmark called WildKGC with five datasets to evaluate our method. The empirical results compared with 21 recent baselines confirm the superiority of our method, consistently achieving state-of-the-art performance across different datasets and various scenarios while keeping efficient and generalizable. Our code and data are released at https://github.com/zjukg/NATIVE|多模态知识图谱补全（MMKGC）旨在通过协同建模实体的三元组结构和多模态信息，自动发现给定多模态知识图谱中未观察到的事实知识。然而，现实世界中的多模态知识图谱由于其多样性和不均衡性而面临挑战，这意味着模态信息可以涵盖多种类型（如图像、文本、数值、音频、视频），但其分布在实体间是不均匀的，导致某些实体缺失模态信息。现有的工作通常关注图像和文本等常见模态，而忽视了模态信息的不均衡分布现象。为了解决这些问题，我们提出了一个全面的框架NativE，以实现真实环境中的多模态知识图谱补全。NativE提出了一种关系引导的双重自适应融合模块，该模块能够对任何模态进行自适应融合，并采用协同模态对抗训练框架来增强不均衡的模态信息。我们构建了一个名为WildKGC的新基准，包含五个数据集，用于评估我们的方法。与21个最近的基线方法进行比较的实证结果证实了我们的方法的优越性，在不同数据集和各种场景下始终保持高效和可推广性，并取得了最先进的性能。我们的代码和数据已在https://github.com/zjukg/NATIVE发布。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=NativE:+Multi-modal+Knowledge+Graph+Completion+in+the+Wild)|0| |[MetaHKG: Meta Hyperbolic Learning for Few-shot Temporal Reasoning](https://doi.org/10.1145/3626772.3657711)|Ruijie Wang, Yutong Zhang, Jinyang Li, Shengzhong Liu, Dachun Sun, Tianchen Wang, Tianshi Wang, Yizhuo Chen, Denizhan Kara, Tarek F. Abdelzaher||||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=MetaHKG:+Meta+Hyperbolic+Learning+for+Few-shot+Temporal+Reasoning)|0| |[YAGO 4.5: A Large and Clean Knowledge Base with a Rich Taxonomy](https://doi.org/10.1145/3626772.3657876)|Fabian M. Suchanek, Mehwish Alam, Thomas Bonald, Lihu Chen, PierreHenri Paris, Jules Soria||||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=YAGO+4.5:+A+Large+and+Clean+Knowledge+Base+with+a+Rich+Taxonomy)|0| |[Uncontextualized significance considered dangerous](https://doi.org/10.1145/3626772.3657827)|Nicola Ferro, Mark Sanderson||||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Uncontextualized+significance+considered+dangerous)|0| @@ -320,10 +320,10 @@ |[IDGenRec: LLM-RecSys Alignment with Textual ID Learning](https://doi.org/10.1145/3626772.3657821)|Juntao Tan, Shuyuan Xu, Wenyue Hua, Yingqiang Ge, Zelong Li, Yongfeng Zhang||||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=IDGenRec:+LLM-RecSys+Alignment+with+Textual+ID+Learning)|0| |[Enhanced Packed Marker with Entity Information for Aspect Sentiment Triplet Extraction](https://doi.org/10.1145/3626772.3657734)|You Li, Xupeng Zeng, Yixiao Zeng, Yuming Lin||||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Enhanced+Packed+Marker+with+Entity+Information+for+Aspect+Sentiment+Triplet+Extraction)|0| |[Exogenous and Endogenous Data Augmentation for Low-Resource Complex Named Entity Recognition](https://doi.org/10.1145/3626772.3657754)|Xinghua Zhang, Gaode Chen, Shiyao Cui, Jiawei Sheng, Tingwen Liu, Hongbo Xu||||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Exogenous+and+Endogenous+Data+Augmentation+for+Low-Resource+Complex+Named+Entity+Recognition)|0| -|[ACE-2005-PT: Corpus for Event Extraction in Portuguese](https://doi.org/10.1145/3626772.3657872)|Luís Filipe Cunha, Purificação Silvano, Ricardo Campos, Alípio Jorge|FCUP-University of Porto; University of Beira Interior; FLUP-University of Porto|Event extraction is an NLP task that commonly involves identifying the central word (trigger) for an event and its associated arguments in text. ACE-2005 is widely recognised as the standard corpus in this field. While other corpora, like PropBank, primarily focus on annotating predicate-argument structure, ACE-2005 provides comprehensive information about the overall event structure and semantics. However, its limited language coverage restricts its usability. This paper introduces ACE-2005-PT, a corpus created by translating ACE-2005 into Portuguese, with European and Brazilian variants. To speed up the process of obtaining ACE-2005-PT, we rely on automatic translators. This, however, poses some challenges related to automatically identifying the correct alignments between multi-word annotations in the original text and in the corresponding translated sentence. To achieve this, we developed an alignment pipeline that incorporates several alignment techniques: lemmatization, fuzzy matching, synonym matching, multiple translations and a BERT-based word aligner. To measure the alignment effectiveness, a subset of annotations from the ACE-2005-PT corpus was manually aligned by a linguist expert. This subset was then compared against our pipeline results which achieved exact and relaxed match scores of 70.55% and 87.55% respectively. As a result, we successfully generated a Portuguese version of the ACE-2005 corpus, which has been accepted for publication by LDC.|事件提取是自然语言处理（NLP）中的一项任务，通常涉及识别文本中事件的核心词（触发词）及其相关参数。ACE-2005 在这一领域被广泛认可为标准语料库。尽管其他语料库，如 PropBank，主要关注谓词-参数结构的标注，但 ACE-2005 提供了关于事件整体结构和语义的全面信息。然而，其有限的语言覆盖范围限制了其可用性。本文介绍了 ACE-2005-PT，这是一个通过将 ACE-2005 翻译成葡萄牙语（包括欧洲和巴西变体）而创建的语料库。为了加快获取 ACE-2005-PT 的过程，我们依赖于自动翻译工具。然而，这带来了一些挑战，即在原文和相应的翻译句子中自动识别多词标注之间的正确对齐。为此，我们开发了一个对齐流程，该流程结合了多种对齐技术：词形还原、模糊匹配、同义词匹配、多重翻译以及基于 BERT 的词对齐工具。为了衡量对齐效果，我们请语言学专家手动对齐了 ACE-2005-PT 语料库的一个子集。然后将该子集与我们的流程结果进行比较，分别获得了 70.55% 的精确匹配分数和 87.55% 的宽松匹配分数。因此，我们成功生成了葡萄牙语版本的 ACE-2005 语料库，该语料库已被 LDC 接受出版。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=ACE-2005-PT:+Corpus+for+Event+Extraction+in+Portuguese)|0| +|[ACE-2005-PT: Corpus for Event Extraction in Portuguese](https://doi.org/10.1145/3626772.3657872)|Luís Filipe Cunha, Purificação Silvano, Ricardo Campos, Alípio Jorge|FCUP-University of Porto; FLUP-University of Porto; University of Beira Interior|Event extraction is an NLP task that commonly involves identifying the central word (trigger) for an event and its associated arguments in text. ACE-2005 is widely recognised as the standard corpus in this field. While other corpora, like PropBank, primarily focus on annotating predicate-argument structure, ACE-2005 provides comprehensive information about the overall event structure and semantics. However, its limited language coverage restricts its usability. This paper introduces ACE-2005-PT, a corpus created by translating ACE-2005 into Portuguese, with European and Brazilian variants. To speed up the process of obtaining ACE-2005-PT, we rely on automatic translators. This, however, poses some challenges related to automatically identifying the correct alignments between multi-word annotations in the original text and in the corresponding translated sentence. To achieve this, we developed an alignment pipeline that incorporates several alignment techniques: lemmatization, fuzzy matching, synonym matching, multiple translations and a BERT-based word aligner. To measure the alignment effectiveness, a subset of annotations from the ACE-2005-PT corpus was manually aligned by a linguist expert. This subset was then compared against our pipeline results which achieved exact and relaxed match scores of 70.55% and 87.55% respectively. As a result, we successfully generated a Portuguese version of the ACE-2005 corpus, which has been accepted for publication by LDC.|事件提取是自然语言处理（NLP）中的一项任务，通常涉及识别文本中事件的核心词（触发词）及其相关参数。ACE-2005 在这一领域被广泛认可为标准语料库。尽管其他语料库，如 PropBank，主要关注谓词-参数结构的标注，但 ACE-2005 提供了关于事件整体结构和语义的全面信息。然而，其有限的语言覆盖范围限制了其可用性。本文介绍了 ACE-2005-PT，这是一个通过将 ACE-2005 翻译成葡萄牙语（包括欧洲和巴西变体）而创建的语料库。为了加快获取 ACE-2005-PT 的过程，我们依赖于自动翻译工具。然而，这带来了一些挑战，即在原文和相应的翻译句子中自动识别多词标注之间的正确对齐。为此，我们开发了一个对齐流程，该流程结合了多种对齐技术：词形还原、模糊匹配、同义词匹配、多重翻译以及基于 BERT 的词对齐工具。为了衡量对齐效果，我们请语言学专家手动对齐了 ACE-2005-PT 语料库的一个子集。然后将该子集与我们的流程结果进行比较，分别获得了 70.55% 的精确匹配分数和 87.55% 的宽松匹配分数。因此，我们成功生成了葡萄牙语版本的 ACE-2005 语料库，该语料库已被 LDC 接受出版。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=ACE-2005-PT:+Corpus+for+Event+Extraction+in+Portuguese)|0| |[Universal Adversarial Perturbations for Vision-Language Pre-trained Models](https://doi.org/10.1145/3626772.3657781)|PengFei Zhang, Zi Huang, Guangdong Bai||Vision-Language Pre-training (VLP) models have exhibited unprecedented capability in many applications by taking full advantage of the multimodal alignment. However, previous studies have shown they are vulnerable to maliciously crafted adversarial samples. Despite recent success, these methods are generally instance-specific and require generating perturbations for each input sample. In this paper, we reveal that VLP models are also vulnerable to the instance-agnostic universal adversarial perturbation (UAP). Specifically, we design a novel Contrastive-training Perturbation Generator with Cross-modal conditions (C-PGC) to achieve the attack. In light that the pivotal multimodal alignment is achieved through the advanced contrastive learning technique, we devise to turn this powerful weapon against themselves, i.e., employ a malicious version of contrastive learning to train the C-PGC based on our carefully crafted positive and negative image-text pairs for essentially destroying the alignment relationship learned by VLP models. Besides, C-PGC fully utilizes the characteristics of Vision-and-Language (V+L) scenarios by incorporating both unimodal and cross-modal information as effective guidance. Extensive experiments show that C-PGC successfully forces adversarial samples to move away from their original area in the VLP model's feature space, thus essentially enhancing attacks across various victim models and V+L tasks. The GitHub repository is available at https://github.com/ffhibnese/CPGC_VLP_Universal_Attacks.|视觉-语言预训练（VLP）模型通过充分利用多模态对齐，在许多应用中展现了前所未有的能力。然而，先前的研究表明，这些模型对恶意设计的对抗样本表现出脆弱性。尽管近期在这方面取得了成功，但这些方法通常是针对特定实例的，需要为每个输入样本生成扰动。本文揭示了VLP模型同样容易受到实例无关的通用对抗扰动（UAP）的影响。为此，我们设计了一种新颖的对比训练扰动生成器，名为跨模态条件对比训练扰动生成器（C-PGC），以实现攻击。鉴于关键的多模态对齐是通过先进的对比学习技术实现的，我们设计了一种恶意版本的对比学习，利用精心构建的正负图像-文本对来训练C-PGC，从而从根本上破坏VLP模型所学习到的对齐关系。此外，C-PGC充分利用了视觉与语言（V+L）场景的特性，将单模态和跨模态信息作为有效指导。大量实验表明，C-PGC成功地迫使对抗样本在VLP模型的特征空间中远离其原始区域，从而在各种受害模型和V+L任务中实质上增强了攻击效果。相关代码已发布在GitHub上，地址为https://github.com/ffhibnese/CPGC_VLP_Universal_Attacks。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Universal+Adversarial+Perturbations+for+Vision-Language+Pre-trained+Models)|0| |[Adaptive In-Context Learning with Large Language Models for Bundle Generation](https://doi.org/10.1145/3626772.3657808)|Zhu Sun, Kaidong Feng, Jie Yang, Xinghua Qu, Hui Fang, YewSoon Ong, Wenyuan Liu||||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Adaptive+In-Context+Learning+with+Large+Language+Models+for+Bundle+Generation)|0| -|[Beyond Accuracy: Investigating Error Types in GPT-4 Responses to USMLE Questions](https://doi.org/10.1145/3626772.3657882)|Soumyadeep Roy, Aparup Khatua, Fatemeh Ghoochani, Uwe Hadler, Wolfgang Nejdl, Niloy Ganguly|School of Information, University of Michigan; Indian Institute of Technology Kharagpur; L3S Research Center|GPT-4 demonstrates high accuracy in medical QA tasks, leading with an accuracy of 86.70%, followed by Med-PaLM 2 at 86.50%. However, around 14% of errors remain. Additionally, current works use GPT-4 to only predict the correct option without providing any explanation and thus do not provide any insight into the thinking process and reasoning used by GPT-4 or other LLMs. Therefore, we introduce a new domain-specific error taxonomy derived from collaboration with medical students. Our GPT-4 USMLE Error (G4UE) dataset comprises 4153 GPT-4 correct responses and 919 incorrect responses to the United States Medical Licensing Examination (USMLE) respectively. These responses are quite long (258 words on average), containing detailed explanations from GPT-4 justifying the selected option. We then launch a large-scale annotation study using the Potato annotation platform and recruit 44 medical experts through Prolific, a well-known crowdsourcing platform. We annotated 300 out of these 919 incorrect data points at a granular level for different classes and created a multi-label span to identify the reasons behind the error. In our annotated dataset, a substantial portion of GPT-4's incorrect responses is categorized as a "Reasonable response by GPT-4," by annotators. This sheds light on the challenge of discerning explanations that may lead to incorrect options, even among trained medical professionals. We also provide medical concepts and medical semantic predications extracted using the SemRep tool for every data point. We believe that it will aid in evaluating the ability of LLMs to answer complex medical questions. We make the resources available at https://github.com/roysoumya/usmle-gpt4-error-taxonomy .|GPT-4在医学问答任务中表现出色，准确率高达86.70%，领先于Med-PaLM 2的86.50%。然而，仍有约14%的错误存在。此外，当前的研究仅利用GPT-4预测正确选项，并未提供任何解释，因此无法洞察GPT-4或其他大型语言模型（LLMs）的思维过程和推理机制。为此，我们与医学生合作，引入了一种新的领域特定错误分类法。我们的GPT-4 USMLE错误（G4UE）数据集包括4153条GPT-4正确回答和919条错误回答，这些回答均来自美国医学执照考试（USMLE），且每条回答平均长度为258词，包含了GPT-4对所选选项的详细解释。随后，我们通过Potato标注平台启动了一项大规模的标注研究，并从知名众包平台Prolific招募了44位医学专家。我们对这919条错误数据中的300条进行了细粒度的标注，创建了多标签跨度以识别错误原因。在我们的标注数据集中，相当一部分GPT-4的错误回答被标注者归类为“GPT-4的合理响应”。这揭示了即使对于训练有素的医学专业人员，区分可能导致错误选项的解释也具有挑战性。我们还为每个数据点提供了使用SemRep工具提取的医学概念和医学语义预测。我们相信，这将有助于评估LLMs回答复杂医学问题的能力。相关资源已发布在https://github.com/roysoumya/usmle-gpt4-error-taxonomy。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Beyond+Accuracy:+Investigating+Error+Types+in+GPT-4+Responses+to+USMLE+Questions)|0| +|[Beyond Accuracy: Investigating Error Types in GPT-4 Responses to USMLE Questions](https://doi.org/10.1145/3626772.3657882)|Soumyadeep Roy, Aparup Khatua, Fatemeh Ghoochani, Uwe Hadler, Wolfgang Nejdl, Niloy Ganguly|School of Information, University of Michigan; L3S Research Center; Indian Institute of Technology Kharagpur|GPT-4 demonstrates high accuracy in medical QA tasks, leading with an accuracy of 86.70%, followed by Med-PaLM 2 at 86.50%. However, around 14% of errors remain. Additionally, current works use GPT-4 to only predict the correct option without providing any explanation and thus do not provide any insight into the thinking process and reasoning used by GPT-4 or other LLMs. Therefore, we introduce a new domain-specific error taxonomy derived from collaboration with medical students. Our GPT-4 USMLE Error (G4UE) dataset comprises 4153 GPT-4 correct responses and 919 incorrect responses to the United States Medical Licensing Examination (USMLE) respectively. These responses are quite long (258 words on average), containing detailed explanations from GPT-4 justifying the selected option. We then launch a large-scale annotation study using the Potato annotation platform and recruit 44 medical experts through Prolific, a well-known crowdsourcing platform. We annotated 300 out of these 919 incorrect data points at a granular level for different classes and created a multi-label span to identify the reasons behind the error. In our annotated dataset, a substantial portion of GPT-4's incorrect responses is categorized as a "Reasonable response by GPT-4," by annotators. This sheds light on the challenge of discerning explanations that may lead to incorrect options, even among trained medical professionals. We also provide medical concepts and medical semantic predications extracted using the SemRep tool for every data point. We believe that it will aid in evaluating the ability of LLMs to answer complex medical questions. We make the resources available at https://github.com/roysoumya/usmle-gpt4-error-taxonomy .|GPT-4在医学问答任务中表现出色，准确率高达86.70%，领先于Med-PaLM 2的86.50%。然而，仍有约14%的错误存在。此外，当前的研究仅利用GPT-4预测正确选项，并未提供任何解释，因此无法洞察GPT-4或其他大型语言模型（LLMs）的思维过程和推理机制。为此，我们与医学生合作，引入了一种新的领域特定错误分类法。我们的GPT-4 USMLE错误（G4UE）数据集包括4153条GPT-4正确回答和919条错误回答，这些回答均来自美国医学执照考试（USMLE），且每条回答平均长度为258词，包含了GPT-4对所选选项的详细解释。随后，我们通过Potato标注平台启动了一项大规模的标注研究，并从知名众包平台Prolific招募了44位医学专家。我们对这919条错误数据中的300条进行了细粒度的标注，创建了多标签跨度以识别错误原因。在我们的标注数据集中，相当一部分GPT-4的错误回答被标注者归类为“GPT-4的合理响应”。这揭示了即使对于训练有素的医学专业人员，区分可能导致错误选项的解释也具有挑战性。我们还为每个数据点提供了使用SemRep工具提取的医学概念和医学语义预测。我们相信，这将有助于评估LLMs回答复杂医学问题的能力。相关资源已发布在https://github.com/roysoumya/usmle-gpt4-error-taxonomy。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Beyond+Accuracy:+Investigating+Error+Types+in+GPT-4+Responses+to+USMLE+Questions)|0| |[SuicidEmoji: Derived Emoji Dataset and Tasks for Suicide-Related Social Content](https://doi.org/10.1145/3626772.3657852)|Tianlin Zhang, Kailai Yang, Shaoxiong Ji, Boyang Liu, Qianqian Xie, Sophia Ananiadou||||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=SuicidEmoji:+Derived+Emoji+Dataset+and+Tasks+for+Suicide-Related+Social+Content)|0| |[LADy 💃: A Benchmark Toolkit for Latent Aspect Detection Enriched with Backtranslation Augmentation](https://doi.org/10.1145/3626772.3657894)|Farinam Hemmatizadeh, Christine Wong, Alice Yu, Hossein Fani||||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=LADy+💃:+A+Benchmark+Toolkit+for+Latent+Aspect+Detection+Enriched+with+Backtranslation+Augmentation)|0| |[A Reproducibility Study of PLAID](https://doi.org/10.1145/3626772.3657856)|Sean MacAvaney, Nicola Tonellotto|University of Glasgow; University of Pisa|The PLAID (Performance-optimized Late Interaction Driver) algorithm for ColBERTv2 uses clustered term representations to retrieve and progressively prune documents for final (exact) document scoring. In this paper, we reproduce and fill in missing gaps from the original work. By studying the parameters PLAID introduces, we find that its Pareto frontier is formed of a careful balance among its three parameters; deviations beyond the suggested settings can substantially increase latency without necessarily improving its effectiveness. We then compare PLAID with an important baseline missing from the paper: re-ranking a lexical system. We find that applying ColBERTv2 as a re-ranker atop an initial pool of BM25 results provides better efficiency-effectiveness trade-offs in low-latency settings. However, re-ranking cannot reach peak effectiveness at higher latency settings due to limitations in recall of lexical matching and provides a poor approximation of an exhaustive ColBERTv2 search. We find that recently proposed modifications to re-ranking that pull in the neighbors of top-scoring documents overcome this limitation, providing a Pareto frontier across all operational points for ColBERTv2 when evaluated using a well-annotated dataset. Curious about why re-ranking methods are highly competitive with PLAID, we analyze the token representation clusters PLAID uses for retrieval and find that most clusters are predominantly aligned with a single token and vice versa. Given the competitive trade-offs that re-ranking baselines exhibit, this work highlights the importance of carefully selecting pertinent baselines when evaluating the efficiency of retrieval engines.|PLAID（Performance-optimized Late Interaction Driver）算法用于ColBERTv2，利用聚类的术语表示来检索并逐步剪枝文档，以进行最终（精确）的文档评分。本文中，我们重现并填补了原始工作中的缺失部分。通过研究PLAID引入的参数，我们发现其帕累托前沿是由其三个参数之间的精心平衡形成的；超出建议设置的偏差可能会显著增加延迟，而不一定提高其有效性。随后，我们将PLAID与论文中缺失的一个重要基线进行比较：重排序一个词汇系统。我们发现，在低延迟设置下，将ColBERTv2作为初始BM25结果池之上的重排序器，提供了更好的效率-有效性权衡。然而，由于词汇匹配的召回限制，重排序在高延迟设置下无法达到峰值有效性，并且无法很好地近似于全面的ColBERTv2搜索。我们发现，最近提出的修改重排序方法，即引入高分文档的邻居，克服了这一限制，在使用良好注释的数据集评估时，为ColBERTv2在所有操作点上提供了帕累托前沿。由于对重排序方法为何与PLAID高度竞争感到好奇，我们分析了PLAID用于检索的标记表示簇，发现大多数簇主要与单个标记对齐，反之亦然。鉴于重排序基线展现出的竞争性权衡，这项工作强调了在评估检索引擎效率时，精心选择相关基线的重要性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Reproducibility+Study+of+PLAID)|0| diff --git a/papers/wsdm/wsdm2023.md b/papers/wsdm/wsdm2023.md index a3ec9967..ff378d6a 100644 --- a/papers/wsdm/wsdm2023.md +++ b/papers/wsdm/wsdm2023.md @@ -2,160 +2,160 @@ |论文|作者|组织|摘要|翻译|代码|引用数| |---|---|---|---|---|---|---| -|[Heterogeneous Graph Contrastive Learning for Recommendation](https://doi.org/10.1145/3539597.3570484)|Mengru Chen, Chao Huang, Lianghao Xia, Wei Wei, Yong Xu, Ronghua Luo|University of Hong Kong, Hong Kong, China; South China University of Technology, Guangzhou, China|Graph Neural Networks (GNNs) have become powerful tools in modeling graph-structured data in recommender systems. However, real-life recommendation scenarios usually involve heterogeneous relationships (e.g., social-aware user influence, knowledge-aware item dependency) which contains fruitful information to enhance the user preference learning. In this paper, we study the problem of heterogeneous graph-enhanced relational learning for recommendation. Recently, contrastive self-supervised learning has become successful in recommendation. In light of this, we propose a Heterogeneous Graph Contrastive Learning (HGCL), which is able to incorporate heterogeneous relational semantics into the user-item interaction modeling with contrastive learning-enhanced knowledge transfer across different views. However, the influence of heterogeneous side information on interactions may vary by users and items. To move this idea forward, we enhance our heterogeneous graph contrastive learning with meta networks to allow the personalized knowledge transformer with adaptive contrastive augmentation. The experimental results on three real-world datasets demonstrate the superiority of HGCL over state-of-the-art recommendation methods. Through ablation study, key components in HGCL method are validated to benefit the recommendation performance improvement. The source code of the model implementation is available at the link https://github.com/HKUDS/HGCL.|图形神经网络(GNN)已经成为推荐系统中建立图形结构数据模型的有力工具。然而，现实生活中的推荐场景通常涉及异构关系(例如，社会意识的用户影响，知识意识的项目依赖) ，其中包含丰富的信息，以增强用户偏好学习。本文研究了异构图增强关系学习的推荐问题。近年来，对比自监督学习在推荐领域取得了成功。鉴于此，我们提出了一种异构图形对比学习(HGCL) ，它能够将异构关系语义整合到用户项目交互模型中，并通过对比学习增强跨不同视图的知识转移。然而，异构侧信息对交互的影响可能因用户和项目的不同而不同。为了进一步提高这一思想，我们使用元网络来增强异构图的对比学习，以允许个性化的知识转换器通过自适应的对比增强来实现。在三个实际数据集上的实验结果表明，HGCL 比最先进的推荐方法具有更大的优越性。通过消融研究，验证了 HGCL 方法的关键组成部分，有利于推荐性能的提高。模型实现的源代码可在连结 https://github.com/hkuds/hgcl 找到。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Heterogeneous+Graph+Contrastive+Learning+for+Recommendation)|2| -|[Modeling Fine-grained Information via Knowledge-aware Hierarchical Graph for Zero-shot Entity Retrieval](https://doi.org/10.1145/3539597.3570415)|Taiqiang Wu, Xingyu Bai, Weigang Guo, Weijie Liu, Siheng Li, Yujiu Yang|Tsinghua University, Shenzhen, China; Tencent, Shenzhen, China|Zero-shot entity retrieval, aiming to link mentions to candidate entities under the zero-shot setting, is vital for many tasks in Natural Language Processing. Most existing methods represent mentions/entities via the sentence embeddings of corresponding context from the Pre-trained Language Model. However, we argue that such coarse-grained sentence embeddings can not fully model the mentions/entities, especially when the attention scores towards mentions/entities are relatively low. In this work, we propose GER, a \textbf{G}raph enhanced \textbf{E}ntity \textbf{R}etrieval framework, to capture more fine-grained information as complementary to sentence embeddings. We extract the knowledge units from the corresponding context and then construct a mention/entity centralized graph. Hence, we can learn the fine-grained information about mention/entity by aggregating information from these knowledge units. To avoid the graph information bottleneck for the central mention/entity node, we construct a hierarchical graph and design a novel Hierarchical Graph Attention Network~(HGAN). Experimental results on popular benchmarks demonstrate that our proposed GER framework performs better than previous state-of-the-art models. The code has been available at https://github.com/wutaiqiang/GER-WSDM2023.|零镜头实体检索是自然语言处理中的一项重要任务，其目的是在零镜头设置下将提到的内容与候选实体联系起来。大多数现有的方法通过预训练语言模型中相应上下文的句子嵌入来表示提及/实体。然而，我们认为这种粗粒度句子嵌入不能完全模拟提及/实体，特别是当注意力分数相对较低的提及/实体。在这项工作中，我们提出了 GER，一个 textbf { G } raph 增强 textbf { E }实体 textbf { R }检索框架，以捕获更多的细粒度信息作为句子嵌入的补充。我们从相应的上下文中提取知识单元，然后构造一个提及/实体集中图。因此，我们可以通过聚合来自这些知识单元的信息来学习关于提及/实体的细粒度信息。为了避免中心提及/实体节点的图形信息瓶颈，构造了一个层次图，并设计了一个新的层次图注意网络 ~ (HGAN)。对流行基准测试的实验结果表明，我们提出的 GER 框架比以前最先进的模型表现得更好。密码已经在 https://github.com/wutaiqiang/ger-wsdm2023上公布了。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Modeling+Fine-grained+Information+via+Knowledge-aware+Hierarchical+Graph+for+Zero-shot+Entity+Retrieval)|2| +|[Heterogeneous Graph Contrastive Learning for Recommendation](https://doi.org/10.1145/3539597.3570484)|Mengru Chen, Chao Huang, Lianghao Xia, Wei Wei, Yong Xu, Ronghua Luo|South China University of Technology, Guangzhou, China; University of Hong Kong, Hong Kong, China|Graph Neural Networks (GNNs) have become powerful tools in modeling graph-structured data in recommender systems. However, real-life recommendation scenarios usually involve heterogeneous relationships (e.g., social-aware user influence, knowledge-aware item dependency) which contains fruitful information to enhance the user preference learning. In this paper, we study the problem of heterogeneous graph-enhanced relational learning for recommendation. Recently, contrastive self-supervised learning has become successful in recommendation. In light of this, we propose a Heterogeneous Graph Contrastive Learning (HGCL), which is able to incorporate heterogeneous relational semantics into the user-item interaction modeling with contrastive learning-enhanced knowledge transfer across different views. However, the influence of heterogeneous side information on interactions may vary by users and items. To move this idea forward, we enhance our heterogeneous graph contrastive learning with meta networks to allow the personalized knowledge transformer with adaptive contrastive augmentation. The experimental results on three real-world datasets demonstrate the superiority of HGCL over state-of-the-art recommendation methods. Through ablation study, key components in HGCL method are validated to benefit the recommendation performance improvement. The source code of the model implementation is available at the link https://github.com/HKUDS/HGCL.|图形神经网络(GNN)已经成为推荐系统中建立图形结构数据模型的有力工具。然而，现实生活中的推荐场景通常涉及异构关系(例如，社会意识的用户影响，知识意识的项目依赖) ，其中包含丰富的信息，以增强用户偏好学习。本文研究了异构图增强关系学习的推荐问题。近年来，对比自监督学习在推荐领域取得了成功。鉴于此，我们提出了一种异构图形对比学习(HGCL) ，它能够将异构关系语义整合到用户项目交互模型中，并通过对比学习增强跨不同视图的知识转移。然而，异构侧信息对交互的影响可能因用户和项目的不同而不同。为了进一步提高这一思想，我们使用元网络来增强异构图的对比学习，以允许个性化的知识转换器通过自适应的对比增强来实现。在三个实际数据集上的实验结果表明，HGCL 比最先进的推荐方法具有更大的优越性。通过消融研究，验证了 HGCL 方法的关键组成部分，有利于推荐性能的提高。模型实现的源代码可在连结 https://github.com/hkuds/hgcl 找到。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Heterogeneous+Graph+Contrastive+Learning+for+Recommendation)|2| +|[Modeling Fine-grained Information via Knowledge-aware Hierarchical Graph for Zero-shot Entity Retrieval](https://doi.org/10.1145/3539597.3570415)|Taiqiang Wu, Xingyu Bai, Weigang Guo, Weijie Liu, Siheng Li, Yujiu Yang|Tencent, Shenzhen, China; Tsinghua University, Shenzhen, China|Zero-shot entity retrieval, aiming to link mentions to candidate entities under the zero-shot setting, is vital for many tasks in Natural Language Processing. Most existing methods represent mentions/entities via the sentence embeddings of corresponding context from the Pre-trained Language Model. However, we argue that such coarse-grained sentence embeddings can not fully model the mentions/entities, especially when the attention scores towards mentions/entities are relatively low. In this work, we propose GER, a \textbf{G}raph enhanced \textbf{E}ntity \textbf{R}etrieval framework, to capture more fine-grained information as complementary to sentence embeddings. We extract the knowledge units from the corresponding context and then construct a mention/entity centralized graph. Hence, we can learn the fine-grained information about mention/entity by aggregating information from these knowledge units. To avoid the graph information bottleneck for the central mention/entity node, we construct a hierarchical graph and design a novel Hierarchical Graph Attention Network~(HGAN). Experimental results on popular benchmarks demonstrate that our proposed GER framework performs better than previous state-of-the-art models. The code has been available at https://github.com/wutaiqiang/GER-WSDM2023.|零镜头实体检索是自然语言处理中的一项重要任务，其目的是在零镜头设置下将提到的内容与候选实体联系起来。大多数现有的方法通过预训练语言模型中相应上下文的句子嵌入来表示提及/实体。然而，我们认为这种粗粒度句子嵌入不能完全模拟提及/实体，特别是当注意力分数相对较低的提及/实体。在这项工作中，我们提出了 GER，一个 textbf { G } raph 增强 textbf { E }实体 textbf { R }检索框架，以捕获更多的细粒度信息作为句子嵌入的补充。我们从相应的上下文中提取知识单元，然后构造一个提及/实体集中图。因此，我们可以通过聚合来自这些知识单元的信息来学习关于提及/实体的细粒度信息。为了避免中心提及/实体节点的图形信息瓶颈，构造了一个层次图，并设计了一个新的层次图注意网络 ~ (HGAN)。对流行基准测试的实验结果表明，我们提出的 GER 框架比以前最先进的模型表现得更好。密码已经在 https://github.com/wutaiqiang/ger-wsdm2023上公布了。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Modeling+Fine-grained+Information+via+Knowledge-aware+Hierarchical+Graph+for+Zero-shot+Entity+Retrieval)|2| |[Inductive Graph Transformer for Delivery Time Estimation](https://doi.org/10.1145/3539597.3570409)|Xin Zhou, Jinglong Wang, Yong Liu, Xingyu Wu, Zhiqi Shen, Cyril Leung|Alibaba Group, Hangzhou, China; Nanyang Technological University, Singapore, Singapore|Providing accurate estimated time of package delivery on users' purchasing pages for e-commerce platforms is of great importance to their purchasing decisions and post-purchase experiences. Although this problem shares some common issues with the conventional estimated time of arrival (ETA), it is more challenging with the following aspects: 1) Inductive inference. Models are required to predict ETA for orders with unseen retailers and addresses; 2) High-order interaction of order semantic information. Apart from the spatio-temporal features, the estimated time also varies greatly with other factors, such as the packaging efficiency of retailers, as well as the high-order interaction of these factors. In this paper, we propose an inductive graph transformer (IGT) that leverages raw feature information and structural graph data to estimate package delivery time. Different from previous graph transformer architectures, IGT adopts a decoupled pipeline and trains transformer as a regression function that can capture the multiplex information from both raw feature and dense embeddings encoded by a graph neural network (GNN). In addition, we further simplify the GNN structure by removing its non-linear activation and the learnable linear transformation matrix. The reduced parameter search space and linear information propagation in the simplified GNN enable the IGT to be applied in large-scale industrial scenarios. Experiments on real-world logistics datasets show that our proposed model can significantly outperform the state-of-the-art methods on estimation of delivery time. The source code is available at: https://github.com/enoche/IGT-WSDM23.|为电子商务平台的用户提供准确的预计包裹递送时间，对他们的购买决策和购买后体验至关重要。虽然这个问题与传统的估计到达时间(ETA)方法有一些共同之处，但在以下几个方面更具挑战性: 1)归纳推理。模型需要预测具有隐形零售商和地址的订单的预计到达时间; 2)订单语义信息的高阶交互作用。除了时空特征外，包装时间的估计值也随着其他因素的变化而变化，如零售商的包装效率，以及这些因素之间的高阶相互作用。在本文中，我们提出了一个感应图形变换器(IGT) ，它利用原始特征信息和结构图数据来估计包裹递送时间。与以往的图形变换器体系结构不同，IGT 采用解耦流水线和列车变换器作为回归函数，可以从图形神经网络(GNN)编码的原始特征和密集嵌入中获取多路信息。此外，我们进一步简化了 GNN 的结构，去除了它的非线性激活和可学习的线性映射矩阵。简化 GNN 中的参数搜索空间和线性信息传播使 IGT 能够应用于大规模工业场景。在实际物流数据集上的实验结果表明，该模型在估计交货期方面的性能明显优于目前最先进的方法。源代码可在以下 https://github.com/enoche/igt-wsdm23找到:。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Inductive+Graph+Transformer+for+Delivery+Time+Estimation)|2| -|[A Multimodal Framework for the Identification of Vaccine Critical Memes on Twitter](https://doi.org/10.1145/3539597.3570450)|Usman Naseem, Jinman Kim, Matloob Khushi, Adam G. Dunn|Brunel University, London, United Kingdom; University of Sydney, Sydney, NSW, Australia|Memes can be a useful way to spread information because they are funny, easy to share, and can spread quickly and reach further than other forms. With increased interest in COVID-19 vaccines, vaccination-related memes have grown in number and reach. Memes analysis can be difficult because they use sarcasm and often require contextual understanding. Previous research has shown promising results but could be improved by capturing global and local representations within memes to model contextual information. Further, the limited public availability of annotated vaccine critical memes datasets limit our ability to design computational methods to help design targeted interventions and boost vaccine uptake. To address these gaps, we present VaxMeme, which consists of 10,244 manually labelled memes. With VaxMeme, we propose a new multimodal framework designed to improve the memes' representation by learning the global and local representations of memes. The improved memes' representations are then fed to an attentive representation learning module to capture contextual information for classification using an optimised loss function. Experimental results show that our framework outperformed state-of-the-art methods with an F1-Score of 84.2%. We further analyse the transferability and generalisability of our framework and show that understanding both modalities is important to identify vaccine critical memes on Twitter. Finally, we discuss how understanding memes can be useful in designing shareable vaccination promotion, myth debunking memes and monitoring their uptake on social media platforms.|模因可以成为传播信息的一种有用的方式，因为它们很有趣，容易分享，而且可以迅速传播，比其他形式传播得更远。随着人们对2019冠状病毒疾病疫苗兴趣的增加，与疫苗接种相关的文化基因数量和影响范围都在增加。模因分析可能很困难，因为它们使用讽刺，并且经常需要上下文理解。先前的研究已经显示了有希望的结果，但是可以通过捕获模因中的全局和局部表征来模拟上下文信息来改进。此外，注释疫苗关键模因数据集的有限公共可用性限制了我们设计计算方法的能力，以帮助设计有针对性的干预措施和提高疫苗的摄取。为了解决这些差距，我们提出了 VaxMeme，它由10,244个手动标记的模因组成。利用 VaxMeme，我们提出了一种新的多模态框架，该框架通过学习模因的全局和局部表示来改善模因的表示。然后将改进的模因表示提供给注意表示学习模块，使用优化损失函数捕获上下文信息进行分类。实验结果表明，该框架的性能优于最先进的方法，F1-得分为84.2% 。我们进一步分析了我们框架的可转移性和普遍性，并表明了解这两种模式对于在 Twitter 上识别疫苗关键模因是重要的。最后，我们讨论了如何理解模因可以有助于设计共享疫苗促进，神话揭穿模因和监测他们在社交媒体平台上的吸收。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Multimodal+Framework+for+the+Identification+of+Vaccine+Critical+Memes+on+Twitter)|2| -|[DisenPOI: Disentangling Sequential and Geographical Influence for Point-of-Interest Recommendation](https://doi.org/10.1145/3539597.3570408)|Yifang Qin, Yifan Wang, Fang Sun, Wei Ju, Xuyang Hou, Zhe Wang, Jia Cheng, Jun Lei, Ming Zhang|Peking University, Beijing, China; Meituan, Beijing, China|Point-of-Interest (POI) recommendation plays a vital role in various location-aware services. It has been observed that POI recommendation is driven by both sequential and geographical influences. However, since there is no annotated label of the dominant influence during recommendation, existing methods tend to entangle these two influences, which may lead to sub-optimal recommendation performance and poor interpretability. In this paper, we address the above challenge by proposing DisenPOI, a novel Disentangled dual-graph framework for POI recommendation, which jointly utilizes sequential and geographical relationships on two separate graphs and disentangles the two influences with self-supervision. The key novelty of our model compared with existing approaches is to extract disentangled representations of both sequential and geographical influences with contrastive learning. To be specific, we construct a geographical graph and a sequential graph based on the check-in sequence of a user. We tailor their propagation schemes to become sequence-/geo-aware to better capture the corresponding influences. Preference proxies are extracted from check-in sequence as pseudo labels for the two influences, which supervise the disentanglement via a contrastive loss. Extensive experiments on three datasets demonstrate the superiority of the proposed model.|感兴趣点(POI)推荐在各种位置感知服务中起着至关重要的作用。人们注意到，POI 建议受到顺序和地域影响的驱动。然而，由于在推荐过程中没有标注主导影响的标签，现有的方法倾向于将这两种影响纠缠在一起，这可能导致推荐性能次优和可解释性差。本文针对上述挑战，提出了一种新的用于 POI 推荐的分离双图框架 DisenPOI，该框架在两个分离的图上联合利用序列和地理关系，并通过自我监督来分离这两种影响。与现有方法相比，我们的模型的关键新颖之处在于通过对比学习提取序列和地理影响的分离表示。具体来说，我们根据用户的签入顺序构造了一个地理图和一个序列图。我们调整他们的传播方案成为序列/地理感知，以更好地捕捉相应的影响。针对这两种影响，从签入序列中提取偏好代理作为伪标签，通过对比损失来监控解纠缠过程。在三个数据集上的大量实验表明了该模型的优越性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=DisenPOI:+Disentangling+Sequential+and+Geographical+Influence+for+Point-of-Interest+Recommendation)|1| -|[SGCCL: Siamese Graph Contrastive Consensus Learning for Personalized Recommendation](https://doi.org/10.1145/3539597.3570422)|Boyu Li, Ting Guo, Xingquan Zhu, Qian Li, Yang Wang, Fang Chen|Florida Atlantic University, Boca Raton, FL, USA; Curtin University, Perth, WA, Australia; University of Technology Sydney, Sydney, NSW, Australia|Contrastive-learning-based neural networks have recently been introduced to recommender systems, due to their unique advantage of injecting collaborative signals to model deep representations, and the self-supervision nature in the learning process. Existing contrastive learning methods for recommendations are mainly proposed through introducing augmentations to the user-item (U-I) bipartite graphs. Such a contrastive learning process, however, is susceptible to bias towards popular items and users, because higher-degree users/items are subject to more augmentations and their correlations are more captured. In this paper, we advocate a Siamese Graph Contrastive Consensus Learning (SGCCL) framework, to explore intrinsic correlations and alleviate the bias effects for personalized recommendation. Instead of augmenting original U-I networks, we introduce siamese graphs, which are homogeneous relations of user-user (U-U) similarity and item-item (I-I) correlations. A contrastive consensus optimization process is also adopted to learn effective features for user-item ratings, user-user similarity, and item-item correlation. Finally, we employ the self-supervised learning coupled with the siamese item-item/user-user graph relationships, which ensures unpopular users/items are well preserved in the embedding space. Different from existing studies, SGCCL performs well on both overall and debiasing recommendation tasks resulting in a balanced recommender. Experiments on four benchmark datasets demonstrate that SGCCL outperforms state-of-the-art methods with higher accuracy and greater long-tail item/user exposure.|基于对比学习的神经网络最近被引入到推荐系统中，因为它具有注入协作信号来建立深度表征模型的独特优势，以及学习过程中的自我监督性质。现有的推荐对比学习方法主要是通过对用户项(U-I)二部图的增广来实现的。然而，这种对比学习过程容易对流行项目和用户产生偏见，因为高学历用户/项目受到更多的增强，他们之间的相关性更容易被捕捉。本文提出了一个对比性的“ u > C 共识 < u > L 收益(SGCCL)”框架，以探索个性化推荐的内在相关性，减轻个性化推荐的偏差效应。本文引入了用户-用户(U-U)相似度齐次关系和项目-项目(I-I)相关度齐次关系的连体图，取代了原始的 U-I 网络。采用对比一致性优化方法学习用户项目评分、用户相似度和项目相关性的有效特征。最后，采用自监督学习结合连体项目-项目/用户-用户图关系，保证了不受欢迎的用户/项目在嵌入空间中得到很好的保留。与现有的研究不同，SGCCL 在总体推荐任务和降低推荐偏差任务上都表现良好，从而产生了一个平衡的推荐。在四个基准数据集上的实验表明，SGCCL 在更高的精度和更大的长尾项目/用户暴露方面优于最先进的方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=SGCCL:+Siamese+Graph+Contrastive+Consensus+Learning+for+Personalized+Recommendation)|1| +|[A Multimodal Framework for the Identification of Vaccine Critical Memes on Twitter](https://doi.org/10.1145/3539597.3570450)|Usman Naseem, Jinman Kim, Matloob Khushi, Adam G. Dunn|University of Sydney, Sydney, NSW, Australia; Brunel University, London, United Kingdom|Memes can be a useful way to spread information because they are funny, easy to share, and can spread quickly and reach further than other forms. With increased interest in COVID-19 vaccines, vaccination-related memes have grown in number and reach. Memes analysis can be difficult because they use sarcasm and often require contextual understanding. Previous research has shown promising results but could be improved by capturing global and local representations within memes to model contextual information. Further, the limited public availability of annotated vaccine critical memes datasets limit our ability to design computational methods to help design targeted interventions and boost vaccine uptake. To address these gaps, we present VaxMeme, which consists of 10,244 manually labelled memes. With VaxMeme, we propose a new multimodal framework designed to improve the memes' representation by learning the global and local representations of memes. The improved memes' representations are then fed to an attentive representation learning module to capture contextual information for classification using an optimised loss function. Experimental results show that our framework outperformed state-of-the-art methods with an F1-Score of 84.2%. We further analyse the transferability and generalisability of our framework and show that understanding both modalities is important to identify vaccine critical memes on Twitter. Finally, we discuss how understanding memes can be useful in designing shareable vaccination promotion, myth debunking memes and monitoring their uptake on social media platforms.|模因可以成为传播信息的一种有用的方式，因为它们很有趣，容易分享，而且可以迅速传播，比其他形式传播得更远。随着人们对2019冠状病毒疾病疫苗兴趣的增加，与疫苗接种相关的文化基因数量和影响范围都在增加。模因分析可能很困难，因为它们使用讽刺，并且经常需要上下文理解。先前的研究已经显示了有希望的结果，但是可以通过捕获模因中的全局和局部表征来模拟上下文信息来改进。此外，注释疫苗关键模因数据集的有限公共可用性限制了我们设计计算方法的能力，以帮助设计有针对性的干预措施和提高疫苗的摄取。为了解决这些差距，我们提出了 VaxMeme，它由10,244个手动标记的模因组成。利用 VaxMeme，我们提出了一种新的多模态框架，该框架通过学习模因的全局和局部表示来改善模因的表示。然后将改进的模因表示提供给注意表示学习模块，使用优化损失函数捕获上下文信息进行分类。实验结果表明，该框架的性能优于最先进的方法，F1-得分为84.2% 。我们进一步分析了我们框架的可转移性和普遍性，并表明了解这两种模式对于在 Twitter 上识别疫苗关键模因是重要的。最后，我们讨论了如何理解模因可以有助于设计共享疫苗促进，神话揭穿模因和监测他们在社交媒体平台上的吸收。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Multimodal+Framework+for+the+Identification+of+Vaccine+Critical+Memes+on+Twitter)|2| +|[DisenPOI: Disentangling Sequential and Geographical Influence for Point-of-Interest Recommendation](https://doi.org/10.1145/3539597.3570408)|Yifang Qin, Yifan Wang, Fang Sun, Wei Ju, Xuyang Hou, Zhe Wang, Jia Cheng, Jun Lei, Ming Zhang|Meituan, Beijing, China; Peking University, Beijing, China|Point-of-Interest (POI) recommendation plays a vital role in various location-aware services. It has been observed that POI recommendation is driven by both sequential and geographical influences. However, since there is no annotated label of the dominant influence during recommendation, existing methods tend to entangle these two influences, which may lead to sub-optimal recommendation performance and poor interpretability. In this paper, we address the above challenge by proposing DisenPOI, a novel Disentangled dual-graph framework for POI recommendation, which jointly utilizes sequential and geographical relationships on two separate graphs and disentangles the two influences with self-supervision. The key novelty of our model compared with existing approaches is to extract disentangled representations of both sequential and geographical influences with contrastive learning. To be specific, we construct a geographical graph and a sequential graph based on the check-in sequence of a user. We tailor their propagation schemes to become sequence-/geo-aware to better capture the corresponding influences. Preference proxies are extracted from check-in sequence as pseudo labels for the two influences, which supervise the disentanglement via a contrastive loss. Extensive experiments on three datasets demonstrate the superiority of the proposed model.|感兴趣点(POI)推荐在各种位置感知服务中起着至关重要的作用。人们注意到，POI 建议受到顺序和地域影响的驱动。然而，由于在推荐过程中没有标注主导影响的标签，现有的方法倾向于将这两种影响纠缠在一起，这可能导致推荐性能次优和可解释性差。本文针对上述挑战，提出了一种新的用于 POI 推荐的分离双图框架 DisenPOI，该框架在两个分离的图上联合利用序列和地理关系，并通过自我监督来分离这两种影响。与现有方法相比，我们的模型的关键新颖之处在于通过对比学习提取序列和地理影响的分离表示。具体来说，我们根据用户的签入顺序构造了一个地理图和一个序列图。我们调整他们的传播方案成为序列/地理感知，以更好地捕捉相应的影响。针对这两种影响，从签入序列中提取偏好代理作为伪标签，通过对比损失来监控解纠缠过程。在三个数据集上的大量实验表明了该模型的优越性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=DisenPOI:+Disentangling+Sequential+and+Geographical+Influence+for+Point-of-Interest+Recommendation)|1| +|[SGCCL: Siamese Graph Contrastive Consensus Learning for Personalized Recommendation](https://doi.org/10.1145/3539597.3570422)|Boyu Li, Ting Guo, Xingquan Zhu, Qian Li, Yang Wang, Fang Chen|University of Technology Sydney, Sydney, NSW, Australia; Florida Atlantic University, Boca Raton, FL, USA; Curtin University, Perth, WA, Australia|Contrastive-learning-based neural networks have recently been introduced to recommender systems, due to their unique advantage of injecting collaborative signals to model deep representations, and the self-supervision nature in the learning process. Existing contrastive learning methods for recommendations are mainly proposed through introducing augmentations to the user-item (U-I) bipartite graphs. Such a contrastive learning process, however, is susceptible to bias towards popular items and users, because higher-degree users/items are subject to more augmentations and their correlations are more captured. In this paper, we advocate a Siamese Graph Contrastive Consensus Learning (SGCCL) framework, to explore intrinsic correlations and alleviate the bias effects for personalized recommendation. Instead of augmenting original U-I networks, we introduce siamese graphs, which are homogeneous relations of user-user (U-U) similarity and item-item (I-I) correlations. A contrastive consensus optimization process is also adopted to learn effective features for user-item ratings, user-user similarity, and item-item correlation. Finally, we employ the self-supervised learning coupled with the siamese item-item/user-user graph relationships, which ensures unpopular users/items are well preserved in the embedding space. Different from existing studies, SGCCL performs well on both overall and debiasing recommendation tasks resulting in a balanced recommender. Experiments on four benchmark datasets demonstrate that SGCCL outperforms state-of-the-art methods with higher accuracy and greater long-tail item/user exposure.|基于对比学习的神经网络最近被引入到推荐系统中，因为它具有注入协作信号来建立深度表征模型的独特优势，以及学习过程中的自我监督性质。现有的推荐对比学习方法主要是通过对用户项(U-I)二部图的增广来实现的。然而，这种对比学习过程容易对流行项目和用户产生偏见，因为高学历用户/项目受到更多的增强，他们之间的相关性更容易被捕捉。本文提出了一个对比性的“ u > C 共识 < u > L 收益(SGCCL)”框架，以探索个性化推荐的内在相关性，减轻个性化推荐的偏差效应。本文引入了用户-用户(U-U)相似度齐次关系和项目-项目(I-I)相关度齐次关系的连体图，取代了原始的 U-I 网络。采用对比一致性优化方法学习用户项目评分、用户相似度和项目相关性的有效特征。最后，采用自监督学习结合连体项目-项目/用户-用户图关系，保证了不受欢迎的用户/项目在嵌入空间中得到很好的保留。与现有的研究不同，SGCCL 在总体推荐任务和降低推荐偏差任务上都表现良好，从而产生了一个平衡的推荐。在四个基准数据集上的实验表明，SGCCL 在更高的精度和更大的长尾项目/用户暴露方面优于最先进的方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=SGCCL:+Siamese+Graph+Contrastive+Consensus+Learning+for+Personalized+Recommendation)|1| |[DGRec: Graph Neural Network for Recommendation with Diversified Embedding Generation](https://doi.org/10.1145/3539597.3570472)|Liangwei Yang, Shengjie Wang, Yunzhe Tao, Jiankai Sun, Xiaolong Liu, Philip S. Yu, Taiqing Wang|University of Illinois at Chicago, Chicago, IL, USA; ByteDance Inc., Seattle, WA, USA|Graph Neural Network (GNN) based recommender systems have been attracting more and more attention in recent years due to their excellent performance in accuracy. Representing user-item interactions as a bipartite graph, a GNN model generates user and item representations by aggregating embeddings of their neighbors. However, such an aggregation procedure often accumulates information purely based on the graph structure, overlooking the redundancy of the aggregated neighbors and resulting in poor diversity of the recommended list. In this paper, we propose diversifying GNN-based recommender systems by directly improving the embedding generation procedure. Particularly, we utilize the following three modules: submodular neighbor selection to find a subset of diverse neighbors to aggregate for each GNN node, layer attention to assign attention weights for each layer, and loss reweighting to focus on the learning of items belonging to long-tail categories. Blending the three modules into GNN, we present DGRec(Diversified GNN-based Recommender System) for diversified recommendation. Experiments on real-world datasets demonstrate that the proposed method can achieve the best diversity while keeping the accuracy comparable to state-of-the-art GNN-based recommender systems.|基于图形神经网络(GNN)的推荐系统由于其良好的精度性能，近年来越来越受到人们的关注。GNN 模型将用户-项目交互表示为二分图，通过聚合相邻用户的嵌入来生成用户和项目表示。然而，这样的聚合过程往往只是基于图结构积累信息，忽略了聚合邻居的冗余性，导致推荐列表的多样性较差。本文提出通过直接改进嵌入式生成过程来实现基于 GNN 的推荐系统的多样化。特别地，我们利用以下三个模块: 子模块邻居选择来寻找不同邻居的子集来为每个 GNN 节点聚合，分层注意来为每个层分配注意权重，以及损失重新加权来关注属于长尾类别的项目的学习。将这三个模块融合到 GNN 中，我们提出了 DGrec (基于多样化 GNN 的推荐系统)以供多样化推荐。在实际数据集上的实验表明，该方法在保证精度的同时，能够获得最佳的分集效果，与现有的基于 GNN 的推荐系统相当。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=DGRec:+Graph+Neural+Network+for+Recommendation+with+Diversified+Embedding+Generation)|1| -|[Efficiently Leveraging Multi-level User Intent for Session-based Recommendation via Atten-Mixer Network](https://doi.org/10.1145/3539597.3570445)|Peiyan Zhang, Jiayan Guo, Chaozhuo Li, Yueqi Xie, Jaeboum Kim, Yan Zhang, Xing Xie, Haohan Wang, Sunghun Kim|University of Illinois at Urbana-Champaign, Champaign, IL, USA; The Hong Kong University of Science and Technology, Hong Kong, Hong Kong; Microsoft Research Asia, Beijing, China; The Hong Kong University of Science and Technology & Upstage, Yongin-si, Republic of Korea; School of Intelligence Science and Technology, Peking University, Beijing, China|Session-based recommendation (SBR) aims to predict the user's next action based on short and dynamic sessions. Recently, there has been an increasing interest in utilizing various elaborately designed graph neural networks (GNNs) to capture the pair-wise relationships among items, seemingly suggesting the design of more complicated models is the panacea for improving the empirical performance. However, these models achieve relatively marginal improvements with exponential growth in model complexity. In this paper, we dissect the classical GNN-based SBR models and empirically find that some sophisticated GNN propagations are redundant, given the readout module plays a significant role in GNN-based models. Based on this observation, we intuitively propose to remove the GNN propagation part, while the readout module will take on more responsibility in the model reasoning process. To this end, we propose the Multi-Level Attention Mixture Network (Atten-Mixer), which leverages both concept-view and instance-view readouts to achieve multi-level reasoning over item transitions. As simply enumerating all possible high-level concepts is infeasible for large real-world recommender systems, we further incorporate SBR-related inductive biases, i.e., local invariance and inherent priority to prune the search space. Experiments on three benchmarks demonstrate the effectiveness and efficiency of our proposal. We also have already launched the proposed techniques to a large-scale e-commercial online service since April 2021, with significant improvements of top-tier business metrics demonstrated in the online experiments on live traffic.|基于会话的推荐(SBR)旨在基于短期和动态会话预测用户的下一步操作。近年来，人们越来越热衷于利用各种精心设计的图形神经网络(GNN)来捕捉项目之间的配对关系，这似乎表明设计更复杂的模型是提高经验性能的灵丹妙药。然而，这些模型在模型复杂性方面的改进指数增长相对较小。本文剖析了经典的基于 GNN 的 SBR 模型，发现一些复杂的 GNN 传播是冗余的，因为读出模块在基于 GNN 的模型中起着重要的作用。在此基础上，我们直观地提出了去除 GNN 传播部分，而读出模块将在模型推理过程中承担更多的责任。为此，我们提出了多层次注意力混合网络(Atten-Mixer) ，它利用概念视图和实例视图读数来实现项目过渡的多层次推理。由于简单地列举所有可能的高级概念对于大型实际推荐系统是不可行的，因此我们进一步结合 SBR 相关的归纳偏差，即局部不变性和固有优先级来修剪搜索空间。在三个基准测试上的实验证明了该方案的有效性和高效性。自2021年4月以来，我们已经推出了大规模电子商务在线服务的建议技术，在实时流量的在线实验中，顶级业务指标得到了显著改进。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Efficiently+Leveraging+Multi-level+User+Intent+for+Session-based+Recommendation+via+Atten-Mixer+Network)|1| +|[Efficiently Leveraging Multi-level User Intent for Session-based Recommendation via Atten-Mixer Network](https://doi.org/10.1145/3539597.3570445)|Peiyan Zhang, Jiayan Guo, Chaozhuo Li, Yueqi Xie, Jaeboum Kim, Yan Zhang, Xing Xie, Haohan Wang, Sunghun Kim|School of Intelligence Science and Technology, Peking University, Beijing, China; University of Illinois at Urbana-Champaign, Champaign, IL, USA; The Hong Kong University of Science and Technology, Hong Kong, Hong Kong; Microsoft Research Asia, Beijing, China; The Hong Kong University of Science and Technology & Upstage, Yongin-si, Republic of Korea|Session-based recommendation (SBR) aims to predict the user's next action based on short and dynamic sessions. Recently, there has been an increasing interest in utilizing various elaborately designed graph neural networks (GNNs) to capture the pair-wise relationships among items, seemingly suggesting the design of more complicated models is the panacea for improving the empirical performance. However, these models achieve relatively marginal improvements with exponential growth in model complexity. In this paper, we dissect the classical GNN-based SBR models and empirically find that some sophisticated GNN propagations are redundant, given the readout module plays a significant role in GNN-based models. Based on this observation, we intuitively propose to remove the GNN propagation part, while the readout module will take on more responsibility in the model reasoning process. To this end, we propose the Multi-Level Attention Mixture Network (Atten-Mixer), which leverages both concept-view and instance-view readouts to achieve multi-level reasoning over item transitions. As simply enumerating all possible high-level concepts is infeasible for large real-world recommender systems, we further incorporate SBR-related inductive biases, i.e., local invariance and inherent priority to prune the search space. Experiments on three benchmarks demonstrate the effectiveness and efficiency of our proposal. We also have already launched the proposed techniques to a large-scale e-commercial online service since April 2021, with significant improvements of top-tier business metrics demonstrated in the online experiments on live traffic.|基于会话的推荐(SBR)旨在基于短期和动态会话预测用户的下一步操作。近年来，人们越来越热衷于利用各种精心设计的图形神经网络(GNN)来捕捉项目之间的配对关系，这似乎表明设计更复杂的模型是提高经验性能的灵丹妙药。然而，这些模型在模型复杂性方面的改进指数增长相对较小。本文剖析了经典的基于 GNN 的 SBR 模型，发现一些复杂的 GNN 传播是冗余的，因为读出模块在基于 GNN 的模型中起着重要的作用。在此基础上，我们直观地提出了去除 GNN 传播部分，而读出模块将在模型推理过程中承担更多的责任。为此，我们提出了多层次注意力混合网络(Atten-Mixer) ，它利用概念视图和实例视图读数来实现项目过渡的多层次推理。由于简单地列举所有可能的高级概念对于大型实际推荐系统是不可行的，因此我们进一步结合 SBR 相关的归纳偏差，即局部不变性和固有优先级来修剪搜索空间。在三个基准测试上的实验证明了该方案的有效性和高效性。自2021年4月以来，我们已经推出了大规模电子商务在线服务的建议技术，在实时流量的在线实验中，顶级业务指标得到了显著改进。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Efficiently+Leveraging+Multi-level+User+Intent+for+Session-based+Recommendation+via+Atten-Mixer+Network)|1| |[Counterfactual Collaborative Reasoning](https://doi.org/10.1145/3539597.3570464)|Jianchao Ji, Zelong Li, Shuyuan Xu, Max Xiong, Juntao Tan, Yingqiang Ge, Hao Wang, Yongfeng Zhang|Rutgers Preparatory School, Somerset, NJ, USA; Rutgers University, New Brunswick, NJ, USA|Causal reasoning and logical reasoning are two important types of reasoning abilities for human intelligence. However, their relationship has not been extensively explored under machine intelligence context. In this paper, we explore how the two reasoning abilities can be jointly modeled to enhance both accuracy and explainability of machine learning models. More specifically, by integrating two important types of reasoning ability -- counterfactual reasoning and (neural) logical reasoning -- we propose Counterfactual Collaborative Reasoning (CCR), which conducts counterfactual logic reasoning to improve the performance. In particular, we use recommender system as an example to show how CCR alleviate data scarcity, improve accuracy and enhance transparency. Technically, we leverage counterfactual reasoning to generate "difficult" counterfactual training examples for data augmentation, which -- together with the original training examples -- can enhance the model performance. Since the augmented data is model irrelevant, they can be used to enhance any model, enabling the wide applicability of the technique. Besides, most of the existing data augmentation methods focus on "implicit data augmentation" over users' implicit feedback, while our framework conducts "explicit data augmentation" over users explicit feedback based on counterfactual logic reasoning. Experiments on three real-world datasets show that CCR achieves better performance than non-augmented models and implicitly augmented models, and also improves model transparency by generating counterfactual explanations.|因果推理和逻辑推理是人类智力中两种重要的推理能力。然而，在机器智能语境下，它们之间的关系还没有得到广泛的研究。本文探讨如何将两种推理能力联合建模，以提高机器学习模型的准确性和可解释性。更具体地说，通过整合两种重要类型的推理能力——反事实推理和(神经)逻辑推理——我们提出了反事实协作推理，它通过进行反事实逻辑推理来提高性能。特别是，我们以推荐系统为例，展示 CCR 如何缓解数据稀缺、提高准确性和增强透明度。从技术上讲，我们利用反事实推理来生成“困难的”反事实训练示例用于数据增强，这些示例与原始的训练示例一起可以提高模型的性能。由于增强数据与模型无关，因此可以用它们来增强任何模型，从而使该技术具有广泛的适用性。此外，现有的数据增强方法大多侧重于对用户的隐性反馈进行“隐性数据增强”，而我们的框架基于反事实逻辑推理对用户的显性反馈进行“显性数据增强”。在三个实际数据集上的实验表明，CCR 模型比非增广模型和隐式增广模型具有更好的性能，并且通过生成反事实解释来提高模型的透明度。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Counterfactual+Collaborative+Reasoning)|1| |[VRKG4Rec: Virtual Relational Knowledge Graph for Recommendation](https://doi.org/10.1145/3539597.3570482)|Lingyun Lu, Bang Wang, Zizhuo Zhang, Shenghao Liu, Han Xu|Huazhong University of Science and Technology, Wuhan, China|Incorporating knowledge graph as side information has become a new trend in recommendation systems. Recent studies regard items as entities of a knowledge graph and leverage graph neural networks to assist item encoding, yet by considering each relation type individually. However, relation types are often too many and sometimes one relation type involves too few entities. We argue that it is not efficient nor effective to use every relation type for item encoding. In this paper, we propose a VRKG4Rec model (Virtual Relational Knowledge Graphs for Recommendation), which explicitly distinguish the influence of different relations for item representation learning. We first construct virtual relational graphs (VRKGs) by an unsupervised learning scheme. We also design a local weighted smoothing (LWS) mechanism for encoding nodes, which iteratively updates a node embedding only depending on the embedding of its own and its neighbors, but involve no additional training parameters. We also employ the LWS mechanism on a user-item bipartite graph for user representation learning, which utilizes encodings of items with relational knowledge to help training representations of users. Experiment results on two public datasets validate that our VRKG4Rec model outperforms the state-of-the-art methods. The implementations are available at https://github.com/lulu0913/VRKG4Rec.|在推荐系统中引入知识图作为侧面信息已经成为一种新的趋势。最近的研究把项目看作是一个知识图的实体，并利用图神经网络来辅助项目编码，但是要分别考虑每个关系类型。然而，关系类型往往太多，有时一个关系类型涉及的实体太少。我们认为，使用每种关系类型进行项目编码既不高效也不有效。本文提出了一种 VRKG4Rec 模型(虚拟关系知识推荐图) ，该模型能够明确区分不同关系对项目表示学习的影响。我们首先通过一个非监督式学习方案构造虚拟关系图(vrkGs)。我们还设计了一种局部加权平滑(LWS)编码机制，该机制仅根据节点自身及其邻居的嵌入来迭代更新嵌入的节点，而不涉及额外的训练参数。在用户表征学习的用户项二分图上采用了 LWS 机制，该机制利用关系知识对用户表征进行编码，有助于用户表征的训练。在两个公共数据集上的实验结果验证了我们的 VRKG4Rec 模型优于最先进的方法。有关实施方案可于 https://github.com/lulu0913/vrkg4rec 下载。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=VRKG4Rec:+Virtual+Relational+Knowledge+Graph+for+Recommendation)|1| -|[A Bird's-eye View of Reranking: From List Level to Page Level](https://doi.org/10.1145/3539597.3570399)|Yunjia Xi, Jianghao Lin, Weiwen Liu, Xinyi Dai, Weinan Zhang, Rui Zhang, Ruiming Tang, Yong Yu|ruizhang.info, Shenzhen, China; Huawei Noah's Ark Lab, Shenzhen, China; Shanghai Jiao Tong University, Shanghai, China|Reranking, as the final stage of multi-stage recommender systems, refines the initial lists to maximize the total utility. With the development of multimedia and user interface design, the recommendation page has evolved to a multi-list style. Separately employing traditional list-level reranking methods for different lists overlooks the inter-list interactions and the effect of different page formats, thus yielding suboptimal reranking performance. Moreover, simply applying a shared network for all the lists fails to capture the commonalities and distinctions in user behaviors on different lists. To this end, we propose to draw a bird's-eye view of \textbf{page-level reranking} and design a novel Page-level Attentional Reranking (PAR) model. We introduce a hierarchical dual-side attention module to extract personalized intra- and inter-list interactions. A spatial-scaled attention network is devised to integrate the spatial relationship into pairwise item influences, which explicitly models the page format. The multi-gated mixture-of-experts module is further applied to capture the commonalities and differences of user behaviors between different lists. Extensive experiments on a public dataset and a proprietary dataset show that PAR significantly outperforms existing baseline models.|重新排序作为多阶段推荐系统的最后一个阶段，对初始列表进行细化，使总体效用最大化。随着多媒体技术和用户界面设计的发展，推荐页面已经发展成为多列表的风格。对不同的列表单独使用传统的列表级别重新排序方法忽略了列表间的交互作用和不同页面格式的影响，因此产生了次优的重新排序性能。此外，对所有列表简单地应用共享网络无法捕获不同列表上用户行为的共性和区别。为此，我们提出了一种基于 textbf { Page-level rerank }的鸟瞰图，并设计了一种新的页面级注意力重排(PAR)模型。我们引入了一个分层的双侧注意模块来提取个性化的列表内和列表间的交互。设计了一个空间尺度的注意网络，将空间关系整合到成对的项目影响中，并对页面格式进行了明确的建模。进一步应用多门限混合专家模块来捕获不同列表之间用户行为的共性和差异。对公共数据集和专有数据集的大量实验表明，PAR 的性能明显优于现有的基线模型。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Bird's-eye+View+of+Reranking:+From+List+Level+to+Page+Level)|1| -|[Minimum Entropy Principle Guided Graph Neural Networks](https://doi.org/10.1145/3539597.3570467)|Zhenyu Yang, Ge Zhang, Jia Wu, Jian Yang, Quan Z. Sheng, Hao Peng, Angsheng Li, Shan Xue, Jianlin Su|University of Wollongong, Wollongong, Australia; Macquarie University, Sydney, NSW, Australia; Shenzhen Zhuiyi Technology Co., Ltd., Shenzhen, China; Beihang University, Beijing, China|Graph neural networks (GNNs) are now the mainstream method for mining graph-structured data and learning low-dimensional node- and graph-level embeddings to serve downstream tasks. However, limited by the bottleneck of interpretability that deep neural networks present, existing GNNs have ignored the issue of estimating the appropriate number of dimensions for the embeddings. Hence, we propose a novel framework called Minimum Graph Entropy principle-guided Dimension Estimation, i.e. MGEDE, that learns the appropriate embedding dimensions for both node and graph representations. In terms of node-level estimation, a minimum entropy function that counts both structure and attribute entropy, appraises the appropriate number of dimensions. In terms of graph-level estimation, each graph is assigned a customized embedding dimension from a candidate set based on the number of dimensions estimated for the node-level embeddings. Comprehensive experiments with node and graph classification tasks and nine benchmark datasets verify the effectiveness and generalizability of MGEDE.|图神经网络(GNN)是目前挖掘图结构数据和学习低维节点和图级嵌入以服务下游任务的主流方法。然而，受深度神经网络可解释性瓶颈的限制，现有的神经网络忽略了估计适当的嵌入维数的问题。因此，我们提出了一个新的框架，称为最小图熵原理指导的维度估计，即 MGEDE，学习适当的嵌入维度的节点和图表示。在节点级估计方面，通过计算结构熵和属性熵的最小熵函数来估计合适的维数。在图级估计方面，根据节点级嵌入的维数估计，从候选集中为每个图分配一个定制的嵌入维数。通过对节点和图形分类任务以及9个基准数据集的综合实验，验证了 MGEDE 的有效性和通用性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Minimum+Entropy+Principle+Guided+Graph+Neural+Networks)|1| -|[Making Pre-trained Language Models End-to-end Few-shot Learners with Contrastive Prompt Tuning](https://doi.org/10.1145/3539597.3570398)|Ziyun Xu, Chengyu Wang, Minghui Qiu, Fuli Luo, Runxin Xu, Songfang Huang, Jun Huang|Carnegie Mellon University, Pittsburgh, PA, USA; Alibaba Group, Hangzhou, China; Peking University, Beijing, China|Pre-trained Language Models (PLMs) have achieved remarkable performance for various language understanding tasks in IR systems, which require the fine-tuning process based on labeled training data. For low-resource scenarios, prompt-based learning for PLMs exploits prompts as task guidance and turns downstream tasks into masked language problems for effective few-shot fine-tuning. In most existing approaches, the high performance of prompt-based learning heavily relies on handcrafted prompts and verbalizers, which may limit the application of such approaches in real-world scenarios. To solve this issue, we present CP-Tuning, the first end-to-end Contrastive Prompt Tuning framework for fine-tuning PLMs without any manual engineering of task-specific prompts and verbalizers. It is integrated with the task-invariant continuous prompt encoding technique with fully trainable prompt parameters. We further propose the pair-wise cost-sensitive contrastive learning procedure to optimize the model in order to achieve verbalizer-free class mapping and enhance the task-invariance of prompts. It explicitly learns to distinguish different classes and makes the decision boundary smoother by assigning different costs to easy and hard cases. Experiments over a variety of language understanding tasks used in IR systems and different PLMs show that CP-Tuning outperforms state-of-the-art methods.|预训练语言模型(PLM)在信息检索系统的各种语言理解任务中取得了显著的性能，需要基于标记训练数据进行微调。对于低资源的场景，PLM 的基于提示的学习利用提示作为任务指导，并将下游任务转化为隐藏的语言问题，以便进行有效的少量微调。在大多数现有方法中，基于提示的学习的高性能在很大程度上依赖于手工制作的提示和语言表达器，这可能会限制这种方法在现实世界情景中的应用。为了解决这个问题，我们提出了 CP-Tuning，这是第一个端到端的对比提示调优框架，用于对 PLM 进行微调，而不需要任何特定于任务的提示和语言化工程。它集成了任务不变的连续提示编码技术与完全可训练的提示参数。我们进一步提出了成对代价敏感的对比学习过程来优化模型，以实现无语言代码的类映射和增强提示的任务不变性。它明确地学会区分不同的类别，并通过将不同的成本分配给简单和困难的案例，使决策边界更加平滑。在 IR 系统和不同 PLM 中使用的各种语言理解任务的实验表明，CP 调优优于最先进的方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Making+Pre-trained+Language+Models+End-to-end+Few-shot+Learners+with+Contrastive+Prompt+Tuning)|1| +|[A Bird's-eye View of Reranking: From List Level to Page Level](https://doi.org/10.1145/3539597.3570399)|Yunjia Xi, Jianghao Lin, Weiwen Liu, Xinyi Dai, Weinan Zhang, Rui Zhang, Ruiming Tang, Yong Yu|Huawei Noah's Ark Lab, Shenzhen, China; Shanghai Jiao Tong University, Shanghai, China; ruizhang.info, Shenzhen, China|Reranking, as the final stage of multi-stage recommender systems, refines the initial lists to maximize the total utility. With the development of multimedia and user interface design, the recommendation page has evolved to a multi-list style. Separately employing traditional list-level reranking methods for different lists overlooks the inter-list interactions and the effect of different page formats, thus yielding suboptimal reranking performance. Moreover, simply applying a shared network for all the lists fails to capture the commonalities and distinctions in user behaviors on different lists. To this end, we propose to draw a bird's-eye view of \textbf{page-level reranking} and design a novel Page-level Attentional Reranking (PAR) model. We introduce a hierarchical dual-side attention module to extract personalized intra- and inter-list interactions. A spatial-scaled attention network is devised to integrate the spatial relationship into pairwise item influences, which explicitly models the page format. The multi-gated mixture-of-experts module is further applied to capture the commonalities and differences of user behaviors between different lists. Extensive experiments on a public dataset and a proprietary dataset show that PAR significantly outperforms existing baseline models.|重新排序作为多阶段推荐系统的最后一个阶段，对初始列表进行细化，使总体效用最大化。随着多媒体技术和用户界面设计的发展，推荐页面已经发展成为多列表的风格。对不同的列表单独使用传统的列表级别重新排序方法忽略了列表间的交互作用和不同页面格式的影响，因此产生了次优的重新排序性能。此外，对所有列表简单地应用共享网络无法捕获不同列表上用户行为的共性和区别。为此，我们提出了一种基于 textbf { Page-level rerank }的鸟瞰图，并设计了一种新的页面级注意力重排(PAR)模型。我们引入了一个分层的双侧注意模块来提取个性化的列表内和列表间的交互。设计了一个空间尺度的注意网络，将空间关系整合到成对的项目影响中，并对页面格式进行了明确的建模。进一步应用多门限混合专家模块来捕获不同列表之间用户行为的共性和差异。对公共数据集和专有数据集的大量实验表明，PAR 的性能明显优于现有的基线模型。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Bird's-eye+View+of+Reranking:+From+List+Level+to+Page+Level)|1| +|[Minimum Entropy Principle Guided Graph Neural Networks](https://doi.org/10.1145/3539597.3570467)|Zhenyu Yang, Ge Zhang, Jia Wu, Jian Yang, Quan Z. Sheng, Hao Peng, Angsheng Li, Shan Xue, Jianlin Su|Macquarie University, Sydney, NSW, Australia; Shenzhen Zhuiyi Technology Co., Ltd., Shenzhen, China; University of Wollongong, Wollongong, Australia; Beihang University, Beijing, China|Graph neural networks (GNNs) are now the mainstream method for mining graph-structured data and learning low-dimensional node- and graph-level embeddings to serve downstream tasks. However, limited by the bottleneck of interpretability that deep neural networks present, existing GNNs have ignored the issue of estimating the appropriate number of dimensions for the embeddings. Hence, we propose a novel framework called Minimum Graph Entropy principle-guided Dimension Estimation, i.e. MGEDE, that learns the appropriate embedding dimensions for both node and graph representations. In terms of node-level estimation, a minimum entropy function that counts both structure and attribute entropy, appraises the appropriate number of dimensions. In terms of graph-level estimation, each graph is assigned a customized embedding dimension from a candidate set based on the number of dimensions estimated for the node-level embeddings. Comprehensive experiments with node and graph classification tasks and nine benchmark datasets verify the effectiveness and generalizability of MGEDE.|图神经网络(GNN)是目前挖掘图结构数据和学习低维节点和图级嵌入以服务下游任务的主流方法。然而，受深度神经网络可解释性瓶颈的限制，现有的神经网络忽略了估计适当的嵌入维数的问题。因此，我们提出了一个新的框架，称为最小图熵原理指导的维度估计，即 MGEDE，学习适当的嵌入维度的节点和图表示。在节点级估计方面，通过计算结构熵和属性熵的最小熵函数来估计合适的维数。在图级估计方面，根据节点级嵌入的维数估计，从候选集中为每个图分配一个定制的嵌入维数。通过对节点和图形分类任务以及9个基准数据集的综合实验，验证了 MGEDE 的有效性和通用性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Minimum+Entropy+Principle+Guided+Graph+Neural+Networks)|1| +|[Making Pre-trained Language Models End-to-end Few-shot Learners with Contrastive Prompt Tuning](https://doi.org/10.1145/3539597.3570398)|Ziyun Xu, Chengyu Wang, Minghui Qiu, Fuli Luo, Runxin Xu, Songfang Huang, Jun Huang|Alibaba Group, Hangzhou, China; Carnegie Mellon University, Pittsburgh, PA, USA; Peking University, Beijing, China|Pre-trained Language Models (PLMs) have achieved remarkable performance for various language understanding tasks in IR systems, which require the fine-tuning process based on labeled training data. For low-resource scenarios, prompt-based learning for PLMs exploits prompts as task guidance and turns downstream tasks into masked language problems for effective few-shot fine-tuning. In most existing approaches, the high performance of prompt-based learning heavily relies on handcrafted prompts and verbalizers, which may limit the application of such approaches in real-world scenarios. To solve this issue, we present CP-Tuning, the first end-to-end Contrastive Prompt Tuning framework for fine-tuning PLMs without any manual engineering of task-specific prompts and verbalizers. It is integrated with the task-invariant continuous prompt encoding technique with fully trainable prompt parameters. We further propose the pair-wise cost-sensitive contrastive learning procedure to optimize the model in order to achieve verbalizer-free class mapping and enhance the task-invariance of prompts. It explicitly learns to distinguish different classes and makes the decision boundary smoother by assigning different costs to easy and hard cases. Experiments over a variety of language understanding tasks used in IR systems and different PLMs show that CP-Tuning outperforms state-of-the-art methods.|预训练语言模型(PLM)在信息检索系统的各种语言理解任务中取得了显著的性能，需要基于标记训练数据进行微调。对于低资源的场景，PLM 的基于提示的学习利用提示作为任务指导，并将下游任务转化为隐藏的语言问题，以便进行有效的少量微调。在大多数现有方法中，基于提示的学习的高性能在很大程度上依赖于手工制作的提示和语言表达器，这可能会限制这种方法在现实世界情景中的应用。为了解决这个问题，我们提出了 CP-Tuning，这是第一个端到端的对比提示调优框架，用于对 PLM 进行微调，而不需要任何特定于任务的提示和语言化工程。它集成了任务不变的连续提示编码技术与完全可训练的提示参数。我们进一步提出了成对代价敏感的对比学习过程来优化模型，以实现无语言代码的类映射和增强提示的任务不变性。它明确地学会区分不同的类别，并通过将不同的成本分配给简单和困难的案例，使决策边界更加平滑。在 IR 系统和不同 PLM 中使用的各种语言理解任务的实验表明，CP 调优优于最先进的方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Making+Pre-trained+Language+Models+End-to-end+Few-shot+Learners+with+Contrastive+Prompt+Tuning)|1| |[Effective Seed-Guided Topic Discovery by Integrating Multiple Types of Contexts](https://doi.org/10.1145/3539597.3570475)|Yu Zhang, Yunyi Zhang, Martin Michalski, Yucheng Jiang, Yu Meng, Jiawei Han|University of Illinois Urbana-Champaign, Urbana, IL, USA|Instead of mining coherent topics from a given text corpus in a completely unsupervised manner, seed-guided topic discovery methods leverage user-provided seed words to extract distinctive and coherent topics so that the mined topics can better cater to the user's interest. To model the semantic correlation between words and seeds for discovering topic-indicative terms, existing seed-guided approaches utilize different types of context signals, such as document-level word co-occurrences, sliding window-based local contexts, and generic linguistic knowledge brought by pre-trained language models. In this work, we analyze and show empirically that each type of context information has its value and limitation in modeling word semantics under seed guidance, but combining three types of contexts (i.e., word embeddings learned from local contexts, pre-trained language model representations obtained from general-domain training, and topic-indicative sentences retrieved based on seed information) allows them to complement each other for discovering quality topics. We propose an iterative framework, SeedTopicMine, which jointly learns from the three types of contexts and gradually fuses their context signals via an ensemble ranking process. Under various sets of seeds and on multiple datasets, SeedTopicMine consistently yields more coherent and accurate topics than existing seed-guided topic discovery approaches.|种子引导的主题发现方法不是以完全无监督的方式从给定的文本语料库中挖掘连贯的主题，而是利用用户提供的种子词来提取独特和连贯的主题，以便挖掘出的主题能够更好地迎合用户的兴趣。为了建立词与种子之间的语义关系模型，以发现主题指示性词语，现有的种子引导方法利用了不同类型的上下文信号，如文档级词语共现、基于滑动窗口的局部上下文以及预训练语言模型带来的通用语言知识。在本文中，我们通过实证分析和表明，在种子引导下，每种类型的上下文信息在词语语义建模中都有其价值和局限性，但是结合三种类型的上下文(即从局部上下文中学习的词语嵌入、从一般领域训练中获得的预训练语言模型表示和基于种子信息检索的主题指示句) ，它们可以相互补充，发现高质量的主题。我们提出了一个迭代框架 SeedTopicMine，该框架共同学习三种类型的上下文，并通过一个集成排序过程逐步融合它们的上下文信号。在不同的种子集和多个数据集上，SeedTopicMine 始终比现有的种子引导的主题发现方法产生更加连贯和准确的主题。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Effective+Seed-Guided+Topic+Discovery+by+Integrating+Multiple+Types+of+Contexts)|1| -|[CoQEx: Entity Counts Explained](https://doi.org/10.1145/3539597.3573021)|Shrestha Ghosh, Simon Razniewski, Gerhard Weikum|Max Planck Institute for Informatics & Saarland University, Saarbrücken, Germany; Max Planck Institute for Informatics, Saarbrücken, Germany|For open-domain question answering, queries on entity counts, such ashow many languages are spoken in Indonesia, are challenging. Such queries can be answered through succinct contexts with counts:estimated 700 languages, and instances:Javanese and Sundanese. Answer candidates naturally give rise to a distribution, where count contexts denoting the queried entity counts and their semantic subgroups often coexist, while the instances ground the counts in their constituting entities. In this demo we showcase the CoQEx methodology (Count Queries Explained) [5,6], which aggregates and structures explanatory evidence across search snippets, for answering user queries related to entity counts [4]. Given a entity count query, our system CoQEx retrieves search-snippets and provides the user with a distribution-aware prediction prediction, categorizes the count contexts into semantic groups and ranks instances grounding the counts, all in real-time. Our demo can be accessed athttps://nlcounqer.mpi-inf.mpg.de/.|对于开放领域的问题回答，查询实体计数，如有多少语言在印度尼西亚，是具有挑战性的。这样的查询可以通过简洁的上下文来回答，包括计数: 估计有700种语言，以及实例: 爪哇语和巽丹语。应答候选者自然产生一种分布，其中表示被查询实体计数的计数上下文和它们的语义子组常常共存，而实例将计数置于它们的组成实体中。在这个演示中，我们展示了 CoQEx 方法(< u > Co unt < u > Q ueries < u > Ex 明确)[5,6] ，该方法通过搜索片段聚合和构建解释性证据，用于回答与实体计数相关的用户查询[4]。给定一个实体计数查询，我们的系统 CoQEx 检索搜索片段并为用户提供一个分布感知的预测预测，将计数上下文分类为语义组并对计数实例进行排序，所有这些都是实时的。可以访问我们的演示程序 https://nlounqer.mpi-inf.mpg。德/。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=CoQEx:+Entity+Counts+Explained)|1| +|[CoQEx: Entity Counts Explained](https://doi.org/10.1145/3539597.3573021)|Shrestha Ghosh, Simon Razniewski, Gerhard Weikum|Max Planck Institute for Informatics, Saarbrücken, Germany; Max Planck Institute for Informatics & Saarland University, Saarbrücken, Germany|For open-domain question answering, queries on entity counts, such ashow many languages are spoken in Indonesia, are challenging. Such queries can be answered through succinct contexts with counts:estimated 700 languages, and instances:Javanese and Sundanese. Answer candidates naturally give rise to a distribution, where count contexts denoting the queried entity counts and their semantic subgroups often coexist, while the instances ground the counts in their constituting entities. In this demo we showcase the CoQEx methodology (Count Queries Explained) [5,6], which aggregates and structures explanatory evidence across search snippets, for answering user queries related to entity counts [4]. Given a entity count query, our system CoQEx retrieves search-snippets and provides the user with a distribution-aware prediction prediction, categorizes the count contexts into semantic groups and ranks instances grounding the counts, all in real-time. Our demo can be accessed athttps://nlcounqer.mpi-inf.mpg.de/.|对于开放领域的问题回答，查询实体计数，如有多少语言在印度尼西亚，是具有挑战性的。这样的查询可以通过简洁的上下文来回答，包括计数: 估计有700种语言，以及实例: 爪哇语和巽丹语。应答候选者自然产生一种分布，其中表示被查询实体计数的计数上下文和它们的语义子组常常共存，而实例将计数置于它们的组成实体中。在这个演示中，我们展示了 CoQEx 方法(< u > Co unt < u > Q ueries < u > Ex 明确)[5,6] ，该方法通过搜索片段聚合和构建解释性证据，用于回答与实体计数相关的用户查询[4]。给定一个实体计数查询，我们的系统 CoQEx 检索搜索片段并为用户提供一个分布感知的预测预测，将计数上下文分类为语义组并对计数实例进行排序，所有这些都是实时的。可以访问我们的演示程序 https://nlounqer.mpi-inf.mpg。德/。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=CoQEx:+Entity+Counts+Explained)|1| |[Beyond Hard Negatives in Product Search: Semantic Matching Using One-Class Classification (SMOCC)](https://doi.org/10.1145/3539597.3570488)|Arindam Bhattacharya, Ankit Gandhi, Vijay Huddar, Ankith M. S, Aayush Moroney, Atul Saroop, Rahul Bhagat|Amazon, Bengaluru, India; Amazon, Seattle, WA, USA|Semantic matching is an important component of a product search pipeline. Its goal is to capture the semantic intent of the search query as opposed to the syntactic matching performed by a lexical matching system. A semantic matching model captures relationships like synonyms, and also captures common behavioral patterns to retrieve relevant results by generalizing from purchase data. They however suffer from lack of availability of informative negative examples for model training. Various methods have been proposed in the past to address this issue based upon hard-negative mining and contrastive learning. In this work, we propose a novel method for semantic matching based on one-class classification called SMOCC. Given a query and a relevant product, SMOCC generates the representation of an informative negative which is then used to train the model. Our method is based on the idea of generating negatives by using adversarial search in the neighborhood of the positive examples. We also propose a novel approach for selecting the radius to generate adversarial negative products around queries based on the model's understanding of the query. Depending on how we select the radius, we propose two variants of our method: SMOCC-QS, that quantizes the queries using their specificity, and SMOCC-EM, that uses expectation-maximization paradigm to iteratively learn the best radius. We show that our method outperforms the state-of-the-art hard negative mining approaches by increasing the purchase recall by 3 percentage points, and improving the percentage of exacts retrieved by up to 5 percentage points while reducing irrelevant results by 1.8 percentage points.|语义匹配是产品搜索管道的重要组成部分。它的目标是捕获搜索查询的语义意图，而不是词法匹配系统执行的语法匹配。语义匹配模型捕获同义词之类的关系，还捕获常见的行为模式，通过从购买数据归纳来检索相关结果。然而，他们缺乏可用于模型培训的信息丰富的负面例子。基于硬负面挖掘和对比学习，过去人们提出了各种方法来解决这一问题。本文提出了一种新的基于单类分类的语义匹配方法 SMOCC。给定一个查询和一个相关的产品，SMOCC 生成一个信息否定的表示，然后用于训练模型。我们的方法是基于在正例的邻域内使用对抗搜索生成否定的思想。基于模型对查询的理解，我们提出了一种新的半径选择方法来生成查询周围的对抗性否定产品。根据我们如何选择半径，我们提出了我们的方法的两个变体: SMOCC-QS，使用它们的特异性量化查询，和 SMOCC-EM，使用期望最大化范式迭代学习最佳半径。我们表明，我们的方法优于最先进的硬负面挖掘方法，通过增加3个百分点的购买召回，提高了5个百分点的精确检索百分比，同时减少了1.8个百分点的不相关结果。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Beyond+Hard+Negatives+in+Product+Search:+Semantic+Matching+Using+One-Class+Classification+(SMOCC))|0| |[Separating Examination and Trust Bias from Click Predictions for Unbiased Relevance Ranking](https://doi.org/10.1145/3539597.3570393)|Haiyuan Zhao, Jun Xu, Xiao Zhang, Guohao Cai, Zhenhua Dong, JiRong Wen|Renmin University of China, Beijing, China; Noah's Ark Lab, Huawei, Shenzhen, China|Alleviating the examination and trust bias in ranking systems is an important research line in unbiased learning-to-rank (ULTR). Current methods typically use the propensity to correct the biased user clicks and then learn ranking models based on the corrected clicks. Though successes have been achieved, directly modifying the clicks suffers from the inherent high variance because the propensities are usually involved in the denominators of corrected clicks. The problem gets even worse in the situation of mixed examination and trust bias. To address the issue, this paper proposes a novel ULTR method called Decomposed Ranking Debiasing (DRD). DRD is tailored for learning unbiased relevance models with low variance in the existence of examination and trust bias. Unlike existing methods that directly modify the original user clicks, DRD proposes to decompose each click prediction as the combination of a relevance term outputted by the ranking model and other bias terms. The unbiased relevance model, therefore, can be learned by fitting the overall click predictions to the biased user clicks. A joint learning algorithm is developed to learn the relevance and bias models' parameters alternatively. Theoretical analysis showed that, compared with existing methods, DRD has lower variance while retains unbiasedness. Empirical studies indicated that DRD can effectively reduce the variance and outperform the state-of-the-art ULTR baselines.|消除排名系统中的考试和信任偏差是无偏学习排名(ULTR)的一个重要研究方向。当前的方法通常使用倾向于纠正有偏见的用户点击，然后学习排名模型的基础上纠正点击。虽然已经取得了成功，但是直接修改点击受到固有的高变异性的影响，因为倾向性通常涉及修正点击的分母。在混合考试和信任偏差的情况下，问题更加严重。为了解决这个问题，本文提出了一种新的 ULTR 方法，称为分解排序去偏(DRD)。DRD 是为学习无偏相关模型而量身定制的，在考试和信任偏差存在的情况下具有较低的方差。与直接修改原始用户点击的现有方法不同，DRD 建议将每个点击预测分解为由排名模型输出的相关项和其他偏倚项的组合。无偏相关模型，因此，可以通过拟合整体点击预测偏向用户点击。提出了一种联合学习算法，交替学习相关模型和偏差模型的参数。理论分析表明，与现有方法相比，DRD 方差较小，保持了无偏性。实证研究表明，DRD 可以有效地降低方差，优于最先进的 ULTR 基线。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Separating+Examination+and+Trust+Bias+from+Click+Predictions+for+Unbiased+Relevance+Ranking)|0| |[Travel Bird: A Personalized Destination Recommender with TourBERT and Airbnb Experiences](https://doi.org/10.1145/3539597.3573043)|Veronika Arefieva, Roman Egger, Michael Schrefl, Markus Schedl|Johannes Kepler University Linz & Linz Institute of Technology, Linz, Austria; Salzburg University of Applied Sciences, Salzburg, Austria; Johannes Kepler University Linz, Linz, Austria|We present Travel Bird, a novel personalized destination recommendation and exploration interface which allows its users to find their next tourist destination by describing their specific preferences in a narrative form. Unlike other solutions, Travel Bird is based on TourBERT, a novel NLP model we developed, specifically tailored to the tourism domain. Travel Bird creates a two-dimensional personalized destination exploration space from TourBERT embeddings of social media content and the users' textual description of the experience they are looking for. In this demo, we will showcase several use cases for Travel Bird, which are beneficial for consumers and destination management organizations.|我们介绍了旅游鸟，一个新颖的个性化目的地推荐和探索界面，允许其用户找到他们的下一个旅游目的地通过描述他们的具体喜好在叙述形式。与其他解决方案不同的是，Travel Bird 基于 TourBERT，这是我们开发的一种新型 NLP 模型，专门针对旅游领域。Travel Bird 通过 TourBERT 嵌入的社交媒体内容和用户对所寻找体验的文字描述，创建了一个二维的个性化目的地探索空间。在这个演示中，我们将展示 Travel Bird 的几个用例，这些用例对消费者和目的地管理组织都有好处。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Travel+Bird:+A+Personalized+Destination+Recommender+with+TourBERT+and+Airbnb+Experiences)|0| -|[One for All, All for One: Learning and Transferring User Embeddings for Cross-Domain Recommendation](https://doi.org/10.1145/3539597.3570379)|Chenglin Li, Yuanzhen Xie, Chenyun Yu, Bo Hu, Zang Li, Guoqiang Shu, Xiaohu Qie, Di Niu|University of Alberta, Edmonton, AB, Canada; Sun Yat-sen University, Shenzhen, China; Tencent, Shenzhen, China|Cross-domain recommendation is an important method to improve recommender system performance, especially when observations in target domains are sparse. However, most existing techniques focus on single-target or dual-target cross-domain recommendation (CDR) and are hard to be generalized to CDR with multiple target domains. In addition, the negative transfer problem is prevalent in CDR, where the recommendation performance in a target domain may not always be enhanced by knowledge learned from a source domain, especially when the source domain has sparse data. In this study, we propose CAT-ART, a multi-target CDR method that learns to improve recommendations in all participating domains through representation learning and embedding transfer. Our method consists of two parts: a self-supervised Contrastive AuToencoder (CAT) framework to generate global user embeddings based on information from all participating domains, and an Attention-based Representation Transfer (ART) framework which transfers domain-specific user embeddings from other domains to assist with target domain recommendation. CAT-ART boosts the recommendation performance in any target domain through the combined use of the learned global user representation and knowledge transferred from other domains, in addition to the original user embedding in the target domain. We conducted extensive experiments on a collected real-world CDR dataset spanning 5 domains and involving a million users. Experimental results demonstrate the superiority of the proposed method over a range of prior arts. We further conducted ablation studies to verify the effectiveness of the proposed components. Our collected dataset will be open-sourced to facilitate future research in the field of multi-domain recommender systems and user modeling.|跨域推荐是提高推荐系统性能的一种重要方法，特别是在目标域观测稀疏的情况下。然而，现有的技术大多集中于单目标或双目标跨域推荐(CDR) ，很难推广到多目标域的 CDR。此外，负迁移问题在 CDR 中普遍存在，目标领域的推荐性能并不总是通过从源领域学到的知识得到提高，特别是当源领域数据稀少时。在这项研究中，我们提出了 CAT-ART，一种多目标 CDR 方法，它通过表示学习和嵌入转移学习来改进所有参与领域的推荐。该方法由两部分组成: 一部分是基于所有参与域的信息生成全局用户嵌入的自监督对比编码(CAT)框架，另一部分是基于注意力的表示转移(ART)框架，它将来自其他域的特定域用户嵌入转移到目标域推荐中。CAT-ART 通过综合利用学习到的全局用户表示和从其他领域转移的知识，以及原始用户嵌入到目标领域，提高了目标领域的推荐性能。我们进行了广泛的实验收集现实世界的 CDR 数据集跨越5个领域，涉及一百万用户。实验结果表明，该方法优于现有的一系列技术。我们进一步进行了消融研究，以验证所提议的组件的有效性。我们收集的数据集将是开源的，以促进未来在多领域推荐系统和用户建模领域的研究。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=One+for+All,+All+for+One:+Learning+and+Transferring+User+Embeddings+for+Cross-Domain+Recommendation)|0| +|[One for All, All for One: Learning and Transferring User Embeddings for Cross-Domain Recommendation](https://doi.org/10.1145/3539597.3570379)|Chenglin Li, Yuanzhen Xie, Chenyun Yu, Bo Hu, Zang Li, Guoqiang Shu, Xiaohu Qie, Di Niu|Sun Yat-sen University, Shenzhen, China; Tencent, Shenzhen, China; University of Alberta, Edmonton, AB, Canada|Cross-domain recommendation is an important method to improve recommender system performance, especially when observations in target domains are sparse. However, most existing techniques focus on single-target or dual-target cross-domain recommendation (CDR) and are hard to be generalized to CDR with multiple target domains. In addition, the negative transfer problem is prevalent in CDR, where the recommendation performance in a target domain may not always be enhanced by knowledge learned from a source domain, especially when the source domain has sparse data. In this study, we propose CAT-ART, a multi-target CDR method that learns to improve recommendations in all participating domains through representation learning and embedding transfer. Our method consists of two parts: a self-supervised Contrastive AuToencoder (CAT) framework to generate global user embeddings based on information from all participating domains, and an Attention-based Representation Transfer (ART) framework which transfers domain-specific user embeddings from other domains to assist with target domain recommendation. CAT-ART boosts the recommendation performance in any target domain through the combined use of the learned global user representation and knowledge transferred from other domains, in addition to the original user embedding in the target domain. We conducted extensive experiments on a collected real-world CDR dataset spanning 5 domains and involving a million users. Experimental results demonstrate the superiority of the proposed method over a range of prior arts. We further conducted ablation studies to verify the effectiveness of the proposed components. Our collected dataset will be open-sourced to facilitate future research in the field of multi-domain recommender systems and user modeling.|跨域推荐是提高推荐系统性能的一种重要方法，特别是在目标域观测稀疏的情况下。然而，现有的技术大多集中于单目标或双目标跨域推荐(CDR) ，很难推广到多目标域的 CDR。此外，负迁移问题在 CDR 中普遍存在，目标领域的推荐性能并不总是通过从源领域学到的知识得到提高，特别是当源领域数据稀少时。在这项研究中，我们提出了 CAT-ART，一种多目标 CDR 方法，它通过表示学习和嵌入转移学习来改进所有参与领域的推荐。该方法由两部分组成: 一部分是基于所有参与域的信息生成全局用户嵌入的自监督对比编码(CAT)框架，另一部分是基于注意力的表示转移(ART)框架，它将来自其他域的特定域用户嵌入转移到目标域推荐中。CAT-ART 通过综合利用学习到的全局用户表示和从其他领域转移的知识，以及原始用户嵌入到目标领域，提高了目标领域的推荐性能。我们进行了广泛的实验收集现实世界的 CDR 数据集跨越5个领域，涉及一百万用户。实验结果表明，该方法优于现有的一系列技术。我们进一步进行了消融研究，以验证所提议的组件的有效性。我们收集的数据集将是开源的，以促进未来在多领域推荐系统和用户建模领域的研究。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=One+for+All,+All+for+One:+Learning+and+Transferring+User+Embeddings+for+Cross-Domain+Recommendation)|0| |[A Semantic Search Framework for Similar Audit Issue Recommendation in Financial Industry](https://doi.org/10.1145/3539597.3573040)|Chuchu Zhang, Can Song, Samarth Agarwal, Huayu Wu, Xuejie Zhang, John Jianan Lu|DBS Bank, Singapore, Singapore|Audit issues summarize the findings during audit reviews and provide valuable insights of risks and control gaps in a financial institute. Despite the wide use of data analytics and NLP in financial services, due to the diverse coverage and lack of annotations, there are very few use cases that analyze audit issue writing and derive insights from it. In this paper, we propose a deep learning based semantic search framework to search, rank and recommend similar past issues based on new findings. We adopt a two-step approach. First, a TF-IDF based search algorithm and a Bi-Encoder are used to shortlist a set of issue candidates based on the input query. Then a Cross-Encoder will re-rank the candidates and provide the final recommendation. We will also demonstrate how the models are deployed and integrated with the existing workbench to benefit auditors in their daily work.|审计问题总结审计审查期间的结果，并提供有价值的见解的风险和控制差距的金融机构。尽管数据分析和自然语言处理(NLP)在金融服务中得到了广泛的应用，但是由于覆盖面的多样性和注释的缺乏，很少有用例能够分析审计问题的写作并从中获得见解。在本文中，我们提出了一个基于深度学习的语义搜索框架来搜索，排序和推荐过去的类似问题的新发现。我们采取两步走的方法。首先，使用基于 TF-IDF 的搜索算法和双编码器，根据输入查询列出一组候选问题。然后交叉编码器将重新排名的候选人，并提供最终的建议。我们还将演示如何部署这些模型并将其与现有的工作台集成，以使审计人员在日常工作中受益。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Semantic+Search+Framework+for+Similar+Audit+Issue+Recommendation+in+Financial+Industry)|0| -|[Disentangled Negative Sampling for Collaborative Filtering](https://doi.org/10.1145/3539597.3570419)|Riwei Lai, Li Chen, Yuhan Zhao, Rui Chen, Qilong Han|Harbin Engineering University & Hong Kong Baptist University, Harbin; Hong Kong, China; Hong Kong Baptist University, Hong Kong, China; Harbin Engineering University, Harbin, China|Negative sampling is essential for implicit collaborative filtering to generate negative samples from massive unlabeled data. Unlike existing strategies that consider items as a whole when selecting negative items, we argue that normally user interactions are mainly driven by some relevant, but not all, factors of items, leading to a new direction of negative sampling. In this paper, we introduce a novel disentangled negative sampling (DENS) method. We first disentangle the relevant and irrelevant factors of positive and negative items using a hierarchical gating module. Next, we design a factor-aware sampling strategy to identify the best negative samples by contrasting the relevant factors while keeping irrelevant factors similar. To ensure the credibility of the disentanglement, we propose to adopt contrastive learning and introduce four pairwise contrastive tasks, which enable to learn better disentangled representations of the relevant and irrelevant factors and remove the dependency on ground truth. Extensive experiments on five real-world datasets demonstrate the superiority of DENS against several state-of-the-art competitors, achieving over 7% improvement over the strongest baseline in terms of Recall@20 and NDCG@20. Our code is publically available at https://github.com/Riwei-HEU/DENS .|对于隐性协同过滤来说，从大量未标记的数据中产生负样本是至关重要的。不像现有的策略在选择消极项目时将项目作为一个整体来考虑，我们认为通常用户的交互主要是由一些相关的，但不是全部的项目因素驱动的，导致了一个新的消极抽样方向。本文介绍了一种新的解纠缠负采样(DENS)方法。我们首先利用一个层次化的门控模块来分离正项和负项的相关因素和不相关因素。接下来，我们设计了一个因子感知抽样策略，通过对比相关因子，同时保持不相关因子的相似性来识别最佳负样本。为了保证解纠缠的可信度，我们建议采用对比学习并引入四个成对的对比任务，以便更好地学习相关和不相关因素的解纠缠表示，并消除对基本事实的依赖。在五个真实世界数据集上的广泛实验证明了 DENS 相对于几个最先进的竞争对手的优越性，在 Recall@20和 NDCG@20方面比最强的基线提高了7% 以上。我们的代码可以在 https://github.com/riwei-heu/dens 上公开获得。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Disentangled+Negative+Sampling+for+Collaborative+Filtering)|0| +|[Disentangled Negative Sampling for Collaborative Filtering](https://doi.org/10.1145/3539597.3570419)|Riwei Lai, Li Chen, Yuhan Zhao, Rui Chen, Qilong Han|Harbin Engineering University, Harbin, China; Hong Kong Baptist University, Hong Kong, China; Harbin Engineering University & Hong Kong Baptist University, Harbin; Hong Kong, China|Negative sampling is essential for implicit collaborative filtering to generate negative samples from massive unlabeled data. Unlike existing strategies that consider items as a whole when selecting negative items, we argue that normally user interactions are mainly driven by some relevant, but not all, factors of items, leading to a new direction of negative sampling. In this paper, we introduce a novel disentangled negative sampling (DENS) method. We first disentangle the relevant and irrelevant factors of positive and negative items using a hierarchical gating module. Next, we design a factor-aware sampling strategy to identify the best negative samples by contrasting the relevant factors while keeping irrelevant factors similar. To ensure the credibility of the disentanglement, we propose to adopt contrastive learning and introduce four pairwise contrastive tasks, which enable to learn better disentangled representations of the relevant and irrelevant factors and remove the dependency on ground truth. Extensive experiments on five real-world datasets demonstrate the superiority of DENS against several state-of-the-art competitors, achieving over 7% improvement over the strongest baseline in terms of Recall@20 and NDCG@20. Our code is publically available at https://github.com/Riwei-HEU/DENS .|对于隐性协同过滤来说，从大量未标记的数据中产生负样本是至关重要的。不像现有的策略在选择消极项目时将项目作为一个整体来考虑，我们认为通常用户的交互主要是由一些相关的，但不是全部的项目因素驱动的，导致了一个新的消极抽样方向。本文介绍了一种新的解纠缠负采样(DENS)方法。我们首先利用一个层次化的门控模块来分离正项和负项的相关因素和不相关因素。接下来，我们设计了一个因子感知抽样策略，通过对比相关因子，同时保持不相关因子的相似性来识别最佳负样本。为了保证解纠缠的可信度，我们建议采用对比学习并引入四个成对的对比任务，以便更好地学习相关和不相关因素的解纠缠表示，并消除对基本事实的依赖。在五个真实世界数据集上的广泛实验证明了 DENS 相对于几个最先进的竞争对手的优越性，在 Recall@20和 NDCG@20方面比最强的基线提高了7% 以上。我们的代码可以在 https://github.com/riwei-heu/dens 上公开获得。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Disentangled+Negative+Sampling+for+Collaborative+Filtering)|0| |[Meta Policy Learning for Cold-Start Conversational Recommendation](https://doi.org/10.1145/3539597.3570443)|Zhendong Chu, Hongning Wang, Yun Xiao, Bo Long, Lingfei Wu|University of Virginia, Charlottesville, VA, USA; JD.COM Silicon Valley Research Center, Mountain View, CA, USA; JD.COM, Beijing, UNK, China|Conversational recommender systems (CRS) explicitly solicit users' preferences for improved recommendations on the fly. Most existing CRS solutions count on a single policy trained by reinforcement learning for a population of users. However, for users new to the system, such a global policy becomes ineffective to satisfy them, i.e., the cold-start challenge. In this paper, we study CRS policy learning for cold-start users via meta-reinforcement learning. We propose to learn a meta policy and adapt it to new users with only a few trials of conversational recommendations. To facilitate fast policy adaptation, we design three synergetic components. Firstly, we design a meta-exploration policy dedicated to identifying user preferences via a few exploratory conversations, which accelerates personalized policy adaptation from the meta policy. Secondly, we adapt the item recommendation module for each user to maximize the recommendation quality based on the collected conversation states during conversations. Thirdly, we propose a Transformer-based state encoder as the backbone to connect the previous two components. It provides comprehensive state representations by modeling complicated relations between positive and negative feedback during the conversation. Extensive experiments on three datasets demonstrate the advantage of our solution in serving new users, compared with a rich set of state-of-the-art CRS solutions.|会话推荐系统(CRS)明确地在运行中征求用户对改进推荐的偏好。大多数现有的 CRS 解决方案都依赖于强化学习为大量用户培训的单一政策。然而，对于系统的新用户来说，这样的全局策略对于满足他们来说是无效的，也就是说，冷启动的挑战。本文通过元强化学习研究了冷启动用户的 CRS 策略学习。我们建议学习一个元策略，并使其适应新用户，只有几个试验的会话建议。为了促进政策的快速适应，我们设计了三个协同组件。首先，我们设计了一个元探索策略，通过一些探索性的对话来识别用户的偏好，从而加速了元策略的个性化策略调整。其次，根据会话过程中收集到的会话状态，针对每个用户调整项目推荐模块，使推荐质量最大化。第三，我们提出了一个基于变压器的状态编码器作为骨干，连接前两个组件。它通过在会话过程中建立正反馈和负反馈之间的复杂关系来提供全面的状态表示。对三个数据集进行的大量实验表明，与一组丰富的最先进的 CRS 解决方案相比，我们的解决方案在服务新用户方面具有优势。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Meta+Policy+Learning+for+Cold-Start+Conversational+Recommendation)|0| |[Variational Reasoning over Incomplete Knowledge Graphs for Conversational Recommendation](https://doi.org/10.1145/3539597.3570426)|Xiaoyu Zhang, Xin Xin, Dongdong Li, Wenxuan Liu, Pengjie Ren, Zhumin Chen, Jun Ma, Zhaochun Ren|Shandong University, Qingdao, China|Conversational recommender systems (CRSs) often utilize external knowledge graphs (KGs) to introduce rich semantic information and recommend relevant items through natural language dialogues. However, original KGs employed in existing CRSs are often incomplete and sparse, which limits the reasoning capability in recommendation. Moreover, only few of existing studies exploit the dialogue context to dynamically refine knowledge from KGs for better recommendation. To address the above issues, we propose the Variational Reasoning over Incomplete KGs Conversational Recommender (VRICR). Our key idea is to incorporate the large dialogue corpus naturally accompanied with CRSs to enhance the incomplete KGs; and perform dynamic knowledge reasoning conditioned on the dialogue context. Specifically, we denote the dialogue-specific subgraphs of KGs as latent variables with categorical priors for adaptive knowledge graphs refactor. We propose a variational Bayesian method to approximate posterior distributions over dialogue-specific subgraphs, which not only leverages the dialogue corpus for restructuring missing entity relations but also dynamically selects knowledge based on the dialogue context. Finally, we infuse the dialogue-specific subgraphs to decode the recommendation and responses. We conduct experiments on two benchmark CRSs datasets. Experimental results confirm the effectiveness of our proposed method.|会话推荐系统通常利用外部知识图表(KGs)来介绍丰富的语义信息，并通过自然语言对话来推荐相关项目。然而，现有推荐系统使用的原始幼稚园往往不完整、稀疏，限制了推荐系统的推理能力。此外，现有的研究很少利用对话语境来动态提炼幼儿园的知识，从而获得更好的推荐。针对上述问题，我们提出了不完全幼儿园会话推荐系统(VRICR)的变分推理方法。我们的主要想法是将大型对话语料库自然地与学习策略结合在一起，以加强不完整的幼儿园; 并根据对话背景进行动态的知识推理。具体来说，我们表示对话特定的子图的幼稚园作为潜在的变量与分类先验的自适应知识图重构。提出了一种变分贝叶斯方法来逼近对话特定子图的后验分布，该方法不仅利用对话语料重构缺失的实体关系，而且基于对话上下文动态选择知识。最后，我们注入特定于对话的子图来解码推荐和响应。我们在两个基准 CRS 数据集上进行了实验。实验结果证实了该方法的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Variational+Reasoning+over+Incomplete+Knowledge+Graphs+for+Conversational+Recommendation)|0| |[Multi-Intention Oriented Contrastive Learning for Sequential Recommendation](https://doi.org/10.1145/3539597.3570411)|Xuewei Li, Aitong Sun, Mankun Zhao, Jian Yu, Kun Zhu, Di Jin, Mei Yu, Ruiguo Yu|Tianjin University, Tianjin, China|Sequential recommendation aims to capture users' dynamic preferences, in which data sparsity is a key problem. Most contrastive learning models leverage data augmentation to address this problem, but they amplify noises in original sequences. Contrastive learning has the assumption that two views (positive pairs) obtained from the same user behavior sequence must be similar. However, noises typically disturb the user's main intention, which results in the dissimilarity of two views. To address this problem, in this work, we formalize the denoising problem by selecting the user's main intention, and apply contrastive learning for the first time under this topic, i.e., we propose a novel framework, namely Multi-Intention Oriented Contrastive Learning Recommender (IOCRec). In order to create high-quality views with intent-level, we fuse local and global intentions to unify sequential patterns and intent-level self-supervision signals. Specifically, we design the sequence encoder in IOCRec which includes three modules: local module, global module and disentangled module. The global module can capture users' global preferences, which is independent of the local module. The disentangled module can obtain multi-intention behind global and local representations. From a fine-grained perspective, IOCRec separates different intentions to guide the denoising process. Extensive experiments on four widely-used real datasets demonstrate the effectiveness of our new method for sequential recommendation.|序贯推荐的目的是获取用户的动态偏好，其中数据稀疏性是一个关键问题。大多数对比学习模型利用数据增强来解决这个问题，但是它们放大了原始序列中的噪声。对比学习假设从相同的用户行为序列中获得的两个视图(正对)必须相似。然而，噪声通常会干扰用户的主要意图，从而导致两种视图的不同。为了解决这一问题，本文通过选择用户的主要意图将去噪问题形式化，并首次在此课题下应用对比学习，即提出了一种新的框架，即多意图导向对比学习推荐系统(IOCRec)。为了创建具有意图级别的高质量视图，我们融合了局部意图和全局意图，统一了序列模式和意图级别的自我监督信号。具体来说，我们在 IOCRec 中设计了序列编码器，它包括三个模块: 本地模块、全局模块和解纠模块。全局模块可以捕获用户的全局首选项，这与本地模块无关。分离模块可以获得全局和局部表示背后的多意图。从细粒度的角度来看，IOCRec 分离了不同的意图来指导去噪过程。在四个广泛使用的实际数据集上的大量实验证明了我们的新方法对于顺序推荐的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Multi-Intention+Oriented+Contrastive+Learning+for+Sequential+Recommendation)|0| -|[IDNP: Interest Dynamics Modeling Using Generative Neural Processes for Sequential Recommendation](https://doi.org/10.1145/3539597.3570373)|Jing Du, Zesheng Ye, Bin Guo, Zhiwen Yu, Lina Yao|; The University of New South Wales, Sydeny, Australia; The University of New South Wales, Sydney, Australia|Recent sequential recommendation models rely increasingly on consecutive short-term user-item interaction sequences to model user interests. These approaches have, however, raised concerns about both short- and long-term interests. (1) {\it short-term}: interaction sequences may not result from a monolithic interest, but rather from several intertwined interests, even within a short period of time, resulting in their failures to model skip behaviors; (2) {\it long-term}: interaction sequences are primarily observed sparsely at discrete intervals, other than consecutively over the long run. This renders difficulty in inferring long-term interests, since only discrete interest representations can be derived, without taking into account interest dynamics across sequences. In this study, we address these concerns by learning (1) multi-scale representations of short-term interests; and (2) dynamics-aware representations of long-term interests. To this end, we present an \textbf{I}nterest \textbf{D}ynamics modeling framework using generative \textbf{N}eural \textbf{P}rocesses, coined IDNP, to model user interests from a functional perspective. IDNP learns a global interest function family to define each user's long-term interest as a function instantiation, manifesting interest dynamics through function continuity. Specifically, IDNP first encodes each user's short-term interactions into multi-scale representations, which are then summarized as user context. By combining latent global interest with user context, IDNP then reconstructs long-term user interest functions and predicts interactions at upcoming query timestep. Moreover, IDNP can model such interest functions even when interaction sequences are limited and non-consecutive. Extensive experiments on four real-world datasets demonstrate that our model outperforms state-of-the-arts on various evaluation metrics.|最近的顺序推荐模型越来越依赖于连续的短期用户交互序列来模拟用户兴趣。然而，这些做法引发了人们对短期和长期利益的担忧。(1){ it short-term } : 交互序列可能不是由单个兴趣产生的，而是由几个相互交织的兴趣产生的，即使在很短的时间内，也会导致它们无法对跳过行为进行建模; (2){ it long-term } : 交互序列主要是在离散的间隔稀疏地观察到的，而不是在长期连续观察到的。这使得推断长期利益变得困难，因为只有离散的利益表示可以推导出来，而不考虑跨序列的利益动态。在这项研究中，我们通过学习(1)短期兴趣的多尺度表征; (2)长期兴趣的动态感知表征来解决这些问题。为此，我们提出了一个 textbf { I } interest textbf { D }动态建模框架，该框架使用生成 textbf { N }神经 textbf { P }流程，称为 IDNP，从功能的角度对用户兴趣进行建模。IDNP 学习一个全局兴趣函数族，将每个用户的长期兴趣定义为一个函数实例，通过函数连续性体现兴趣动态。具体来说，IDNP 首先将每个用户的短期交互编码为多尺度表示，然后将其总结为用户上下文。通过将潜在的全局兴趣与用户上下文相结合，IDNP 重构长期用户兴趣函数，并在即将到来的查询时间步进行交互预测。而且，IDNP 可以在交互序列有限且非连续的情况下建立这种兴趣函数模型。在四个真实世界数据集上的大量实验表明，我们的模型在各种评估指标上优于最先进的模型。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=IDNP:+Interest+Dynamics+Modeling+Using+Generative+Neural+Processes+for+Sequential+Recommendation)|0| +|[IDNP: Interest Dynamics Modeling Using Generative Neural Processes for Sequential Recommendation](https://doi.org/10.1145/3539597.3570373)|Jing Du, Zesheng Ye, Bin Guo, Zhiwen Yu, Lina Yao|The University of New South Wales, Sydeny, Australia; ; The University of New South Wales, Sydney, Australia|Recent sequential recommendation models rely increasingly on consecutive short-term user-item interaction sequences to model user interests. These approaches have, however, raised concerns about both short- and long-term interests. (1) {\it short-term}: interaction sequences may not result from a monolithic interest, but rather from several intertwined interests, even within a short period of time, resulting in their failures to model skip behaviors; (2) {\it long-term}: interaction sequences are primarily observed sparsely at discrete intervals, other than consecutively over the long run. This renders difficulty in inferring long-term interests, since only discrete interest representations can be derived, without taking into account interest dynamics across sequences. In this study, we address these concerns by learning (1) multi-scale representations of short-term interests; and (2) dynamics-aware representations of long-term interests. To this end, we present an \textbf{I}nterest \textbf{D}ynamics modeling framework using generative \textbf{N}eural \textbf{P}rocesses, coined IDNP, to model user interests from a functional perspective. IDNP learns a global interest function family to define each user's long-term interest as a function instantiation, manifesting interest dynamics through function continuity. Specifically, IDNP first encodes each user's short-term interactions into multi-scale representations, which are then summarized as user context. By combining latent global interest with user context, IDNP then reconstructs long-term user interest functions and predicts interactions at upcoming query timestep. Moreover, IDNP can model such interest functions even when interaction sequences are limited and non-consecutive. Extensive experiments on four real-world datasets demonstrate that our model outperforms state-of-the-arts on various evaluation metrics.|最近的顺序推荐模型越来越依赖于连续的短期用户交互序列来模拟用户兴趣。然而，这些做法引发了人们对短期和长期利益的担忧。(1){ it short-term } : 交互序列可能不是由单个兴趣产生的，而是由几个相互交织的兴趣产生的，即使在很短的时间内，也会导致它们无法对跳过行为进行建模; (2){ it long-term } : 交互序列主要是在离散的间隔稀疏地观察到的，而不是在长期连续观察到的。这使得推断长期利益变得困难，因为只有离散的利益表示可以推导出来，而不考虑跨序列的利益动态。在这项研究中，我们通过学习(1)短期兴趣的多尺度表征; (2)长期兴趣的动态感知表征来解决这些问题。为此，我们提出了一个 textbf { I } interest textbf { D }动态建模框架，该框架使用生成 textbf { N }神经 textbf { P }流程，称为 IDNP，从功能的角度对用户兴趣进行建模。IDNP 学习一个全局兴趣函数族，将每个用户的长期兴趣定义为一个函数实例，通过函数连续性体现兴趣动态。具体来说，IDNP 首先将每个用户的短期交互编码为多尺度表示，然后将其总结为用户上下文。通过将潜在的全局兴趣与用户上下文相结合，IDNP 重构长期用户兴趣函数，并在即将到来的查询时间步进行交互预测。而且，IDNP 可以在交互序列有限且非连续的情况下建立这种兴趣函数模型。在四个真实世界数据集上的大量实验表明，我们的模型在各种评估指标上优于最先进的模型。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=IDNP:+Interest+Dynamics+Modeling+Using+Generative+Neural+Processes+for+Sequential+Recommendation)|0| |[UserSimCRS: A User Simulation Toolkit for Evaluating Conversational Recommender Systems](https://doi.org/10.1145/3539597.3573029)|Jafar Afzali, Aleksander Mark Drzewiecki, Krisztian Balog, Shuo Zhang|University of Stavanger, Stavanger, Norway; Bloomberg, London, United Kingdom|We present an extensible user simulation toolkit to facilitate automatic evaluation of conversational recommender systems. It builds on an established agenda-based approach and extends it with several novel elements, including user satisfaction prediction, persona and context modeling, and conditional natural language generation. We showcase the toolkit with a pre-existing movie recommender system and demonstrate its ability to simulate dialogues that mimic real conversations, while requiring only a handful of manually annotated dialogues as training data.|我们提出了一个可扩展的用户模拟工具包，以促进会话推荐系统的自动评估。它建立在已建立的基于议程的方法之上，并扩展了几个新的元素，包括用户满意度预测、人物和上下文建模以及条件自然语言生成。我们用一个预先存在的电影推荐系统展示了这个工具包，并演示了它模拟模拟真实对话的对话的能力，同时只需要少量手动注释的对话作为训练数据。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=UserSimCRS:+A+User+Simulation+Toolkit+for+Evaluating+Conversational+Recommender+Systems)|0| |[A Synthetic Search Session Generator for Task-Aware Information Seeking and Retrieval](https://doi.org/10.1145/3539597.3573041)|Shawon Sarkar, Chirag Shah|University of Washington, Seattle, WA, USA|For users working on a complex search task, it is common to address different goals at various stages of the task through query iterations. While addressing these goals, users go through different task states as well. Understanding these task states latent under users' interactions is crucial in identifying users' changing intents and search behaviors to simulate and achieve real-time adaptive search recommendations and retrievals. However, the availability of sizeable real-world web search logs is scarce due to various ethical and privacy concerns, thus often challenging to develop generalizable task-aware computation models. Furthermore, session logs with task state labels are rarer. For many researchers who lack the resources to directly and at scale collect data from users and conduct a time-consuming data annotation process, this becomes a considerable bottleneck to furthering their research. Synthetic search sessions have the potential to address this gap. This paper shares a parsimonious model to simulate synthetic web search sessions with task state information, which interactive information retrieval (IIR) and search personalization studies could utilize to develop and evaluate task-based search and retrieval systems.|对于处理复杂搜索任务的用户来说，通常通过查询迭代在任务的不同阶段处理不同的目标。在实现这些目标的同时，用户还要经历不同的任务状态。理解用户交互中潜在的这些任务状态对于识别用户不断变化的意图和搜索行为来模拟和实现实时自适应搜索推荐和检索是至关重要的。然而，由于各种道德和隐私问题，大量真实世界的网络搜索日志的可用性是稀缺的，因此往往具有挑战性的开发普遍的任务感知计算模型。此外，带有任务状态标签的会话日志很少。对于许多缺乏资源直接和大规模地从用户那里收集数据并进行耗时的数据注释过程的研究人员来说，这成为他们进一步研究的一个相当大的瓶颈。合成搜索会话有可能解决这一差距。本文共享一个简约的模型来模拟任务状态信息的合成网络搜索会话，交互式信息检索(IIR)和搜索个性化研究可以利用这些信息来开发和评估基于任务的搜索和检索系统。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Synthetic+Search+Session+Generator+for+Task-Aware+Information+Seeking+and+Retrieval)|0| |[Understanding the Effect of Outlier Items in E-commerce Ranking](https://doi.org/10.1145/3539597.3572992)|Fatemeh Sarvi|University of Amsterdam, Amsterdam, Netherlands|Implicit feedback is an attractive source of training data in Learning to Rank (LTR). However, naively use of this data can produce unfair ranking policies originating from both exogenous and endogenous factors. Exogenous factors comes from biases in the training data, which can lead to rich-get-richer dynamics. Endogenous factors can result in ranking policies that do not allocate exposure among items in a fair way. Item exposure is a common components influencing both endogenous and exogenous factors which depends on not only position but also Inter-item dependencies. In this project, we focus on a specific case of these Inter-item dependencies which is the existence of an outlier in the list. We first define and formalize outlierness in ranking, then study the effects of this phenomenon on endogenous and exogenous factors. We further investigate the visual aspects of presentational features and their impact on item outlierness.|内隐反馈是学习排序(LTR)中一个很有吸引力的训练数据来源。然而，天真地使用这些数据可能会产生源于外生因素和内生因素的不公平排序策略。外生因素来自训练数据的偏差，这可能导致富者越富的动态。内在因素可能导致排名政策不公平地分配项目的风险。项目暴露是影响内生因素和外生因素的共同因素，它不仅取决于位置，而且取决于项目间的依赖性。在这个项目中，我们关注的是这些项目间依赖关系的一个特定情况，即列表中存在一个异常值。我们首先对排名中的异常进行定义和形式化，然后研究这种现象对内生因素和外生因素的影响。我们进一步研究了表象特征的视觉方面及其对项目异常的影响。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Understanding+the+Effect+of+Outlier+Items+in+E-commerce+Ranking)|0| -|[Simplifying Graph-based Collaborative Filtering for Recommendation](https://doi.org/10.1145/3539597.3570451)|Li He, Xianzhi Wang, Dingxian Wang, Haoyuan Zou, Hongzhi Yin, Guandong Xu|The University of Queensland, Brisbane, QLD, Australia; eBay Research America, Seattle, WA, USA; University of Technology Sydney, Sydney, NSW, Australia; Meta Inc., San Diego, CA, USA|Graph Convolutional Networks (GCNs) are a popular type of machine learning models that use multiple layers of convolutional aggregation operations and non-linear activations to represent data. Recent studies apply GCNs to Collaborative Filtering (CF)-based recommender systems (RSs) by modeling user-item interactions as a bipartite graph and achieve superior performance. However, these models face difficulty in training with non-linear activations on large graphs. Besides, most GCN-based models could not model deeper layers due to the over-smoothing effect with the graph convolution operation. In this paper, we improve the GCN-based CF models from two aspects. First, we remove non-linearities to enhance recommendation performance, which is consistent with the theories in simple graph convolutional networks. Second, we obtain the initialization of the embedding for each node in the graph by computing the network embedding on the condensed graph, which alleviates the over smoothing problem in graph convolution aggregation operation with sparse interaction data. The proposed model is a linear model that is easy to train, scalable to large datasets, and shown to yield better efficiency and effectiveness on four real datasets.|图卷积网络(GCNs)是一种流行的机器学习模型，它使用多层卷积聚合操作和非线性激活来表示数据。最近的研究将通用网络控制器应用于基于协同过滤(CF)的推荐系统(RSs) ，通过将用户与项目之间的交互建模为一个二分图，从而获得更好的性能。然而，这些模型面临的困难训练与非线性激活的大型图。此外，大多数基于 GCN 的模型由于图卷积运算的过度平滑效应而无法建立更深层次的模型。本文从两个方面对基于 GCN 的 CF 模型进行了改进。首先，消除非线性，提高推荐性能，这与简单图卷积网络的理论是一致的。其次，通过计算压缩图上的网络嵌入，初始化图中每个节点的嵌入，解决了稀疏交互数据的图卷积聚合操作中的过平滑问题。该模型是一个线性模型，易于训练，可扩展到大型数据集，并表明产生更好的效率和效果的四个实际数据集。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Simplifying+Graph-based+Collaborative+Filtering+for+Recommendation)|0| +|[Simplifying Graph-based Collaborative Filtering for Recommendation](https://doi.org/10.1145/3539597.3570451)|Li He, Xianzhi Wang, Dingxian Wang, Haoyuan Zou, Hongzhi Yin, Guandong Xu|Meta Inc., San Diego, CA, USA; University of Technology Sydney, Sydney, NSW, Australia; The University of Queensland, Brisbane, QLD, Australia; eBay Research America, Seattle, WA, USA|Graph Convolutional Networks (GCNs) are a popular type of machine learning models that use multiple layers of convolutional aggregation operations and non-linear activations to represent data. Recent studies apply GCNs to Collaborative Filtering (CF)-based recommender systems (RSs) by modeling user-item interactions as a bipartite graph and achieve superior performance. However, these models face difficulty in training with non-linear activations on large graphs. Besides, most GCN-based models could not model deeper layers due to the over-smoothing effect with the graph convolution operation. In this paper, we improve the GCN-based CF models from two aspects. First, we remove non-linearities to enhance recommendation performance, which is consistent with the theories in simple graph convolutional networks. Second, we obtain the initialization of the embedding for each node in the graph by computing the network embedding on the condensed graph, which alleviates the over smoothing problem in graph convolution aggregation operation with sparse interaction data. The proposed model is a linear model that is easy to train, scalable to large datasets, and shown to yield better efficiency and effectiveness on four real datasets.|图卷积网络(GCNs)是一种流行的机器学习模型，它使用多层卷积聚合操作和非线性激活来表示数据。最近的研究将通用网络控制器应用于基于协同过滤(CF)的推荐系统(RSs) ，通过将用户与项目之间的交互建模为一个二分图，从而获得更好的性能。然而，这些模型面临的困难训练与非线性激活的大型图。此外，大多数基于 GCN 的模型由于图卷积运算的过度平滑效应而无法建立更深层次的模型。本文从两个方面对基于 GCN 的 CF 模型进行了改进。首先，消除非线性，提高推荐性能，这与简单图卷积网络的理论是一致的。其次，通过计算压缩图上的网络嵌入，初始化图中每个节点的嵌入，解决了稀疏交互数据的图卷积聚合操作中的过平滑问题。该模型是一个线性模型，易于训练，可扩展到大型数据集，并表明产生更好的效率和效果的四个实际数据集。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Simplifying+Graph-based+Collaborative+Filtering+for+Recommendation)|0| |[A Personalized Neighborhood-based Model for Within-basket Recommendation in Grocery Shopping](https://doi.org/10.1145/3539597.3570417)|Mozhdeh Ariannezhad, Ming Li, Sebastian Schelter, Maarten de Rijke|University of Amsterdam, Amsterdam, Netherlands|Users of online shopping platforms typically purchase multiple items at a time in the form of a shopping basket. Personalized within-basket recommendation is the task of recommending items to complete an incomplete basket during a shopping session. In contrast to the related task of session-based recommendation, where the goal is to complete an ongoing anonymous session, we have access to the shopping history of the user in within-basket recommendation. Previous studies have shown the superiority of neighborhood-based models for session-based recommendation and the importance of personal history in the grocery shopping domain. But their applicability in within-basket recommendation remains unexplored. We propose PerNIR, a neighborhood-based model that explicitly models the personal history of users for within-basket recommendation in grocery shopping. The main novelty of PerNIR is in modeling the short-term interests of users, which are represented by the current basket, as well as their long-term interest, which is reflected in their purchasing history. In addition to the personal history, user neighbors are used to capture the collaborative purchase behavior. We evaluate PerNIR on two public and proprietary datasets. The experimental results show that it outperforms 10 state-of-the-art competitors with a significant margin, i.e., with gains of more than 12% in terms of hit rate over the second best performing approach. Additionally, we showcase an optimized implementation of our method, which computes recommendations fast enough for real-world production scenarios.|网上购物平台的用户通常一次购买多种商品，形式是一个购物篮。个性化购物篮内推荐的任务是在购物过程中推荐完成一个不完整的购物篮的项目。与基于会话的推荐的相关任务(其目标是完成正在进行的匿名会话)不同，我们可以访问篮子内推荐中用户的购物历史记录。以往的研究已经表明基于邻域的模型在基于会话的推荐中的优越性，以及个人历史在杂货店购物领域中的重要性。但它们在篮子内推荐中的适用性仍未得到探索。我们提出了 PerNIR 模型，这是一个基于邻域的模型，明确地模拟了购物篮内推荐用户的个人历史。PerNIR 的主要新颖之处在于为用户的短期利益(以当前篮子为代表)以及他们的长期利益(反映在他们的购买历史中)建模。除了个人历史记录，用户邻居还被用来捕获协同购买行为。我们在两个公共数据集和专有数据集上评估 PerNIR。实验结果表明，它比10个最先进的竞争对手有明显的优势，也就是说，在命中率方面比第二个最好的方法提高了12% 以上。此外，我们还展示了我们的方法的优化实现，该方法计算建议的速度足以满足实际生产场景的需要。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Personalized+Neighborhood-based+Model+for+Within-basket+Recommendation+in+Grocery+Shopping)|0| -|[Self-Supervised Group Graph Collaborative Filtering for Group Recommendation](https://doi.org/10.1145/3539597.3570400)|Kang Li, ChangDong Wang, JianHuang Lai, Huaqiang Yuan|Sun Yat-Sen University, Guangzhou, China; Dongguan University of Technology, Dongguan, China|Nowadays, it is more and more convenient for people to participate in group activities. Therefore, providing some recommendations to groups of individuals is indispensable. Group recommendation is the task of suggesting items or events for a group of users in social networks or online communities. In this work, we study group recommendation in a particular scenario, namely occasional group recommendation, which has few or no historical directly interacted items. Existing group recommendation methods mostly adopt attention-based preference aggregation strategies to capture group preferences. However, these models either ignore the complex high-order interactions between groups, users and items or greatly reduce the efficiency by introducing complex data structures. Moreover, occasional group recommendation suffers from the problem of data sparsity due to the lack of historical group-item interactions. In this work, we focus on addressing the aforementioned challenges and propose a novel group recommendation model called Self-Supervised Group Graph Collaborative Filtering (SGGCF). The goal of the model is capturing the high-order interactions between users, items and groups and alleviating the data sparsity issue in an efficient way. First, we explicitly model the complex relationships as a unified user-centered heterogeneous graph and devise a base group recommendation model. Second, we explore self-supervised learning on the graph with two kinds of contrastive learning module to capture the implicit relations between groups and items. At last, we treat the proposed contrastive learning loss as supplementary and apply a multi-task strategy to jointly train the BPR loss and the proposed contrastive learning loss. We conduct extensive experiments on three real-world datasets, and the experimental results demonstrate the superiority of our proposed model in comparison to the state-of-the-art baselines.|现在，人们参加集体活动越来越方便了。因此，向个人群体提供一些建议是必不可少的。群体推荐是为社交网络或在线社区中的一组用户推荐项目或事件的任务。在这项工作中，我们研究了一个特定场景中的群组推荐，即偶尔群组推荐，它很少或没有历史直接相互作用的项目。现有的群体推荐方法大多采用基于注意的偏好聚合策略来捕获群体偏好。然而，这些模型要么忽略了组、用户和项目之间复杂的高阶交互，要么通过引入复杂的数据结构大大降低了效率。此外，由于缺乏历史上的组项目交互，偶尔的组推荐会遇到数据稀疏的问题。在这项工作中，我们致力于解决上述挑战，并提出了一个新颖的群体推荐模型，称为自我监督群体图协同过滤(sggCF)。该模型的目标是捕获用户、项目和组之间的高阶交互，有效地缓解数据稀疏问题。首先，我们明确地将复杂关系建模为一个统一的以用户为中心的异构图，并设计了一个基本的组推荐模型。其次，利用两种对比学习模块探索图上的自我监督学习，以捕捉群体与项目之间的内隐关系。最后，将提出的对比学习损失作为补充，采用多任务策略对 BPR 损失和提出的对比学习损失进行联合训练。我们在三个真实世界的数据集上进行了广泛的实验，实验结果证明了我们提出的模型相对于最先进的基线的优越性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Self-Supervised+Group+Graph+Collaborative+Filtering+for+Group+Recommendation)|0| +|[Self-Supervised Group Graph Collaborative Filtering for Group Recommendation](https://doi.org/10.1145/3539597.3570400)|Kang Li, ChangDong Wang, JianHuang Lai, Huaqiang Yuan|Dongguan University of Technology, Dongguan, China; Sun Yat-Sen University, Guangzhou, China|Nowadays, it is more and more convenient for people to participate in group activities. Therefore, providing some recommendations to groups of individuals is indispensable. Group recommendation is the task of suggesting items or events for a group of users in social networks or online communities. In this work, we study group recommendation in a particular scenario, namely occasional group recommendation, which has few or no historical directly interacted items. Existing group recommendation methods mostly adopt attention-based preference aggregation strategies to capture group preferences. However, these models either ignore the complex high-order interactions between groups, users and items or greatly reduce the efficiency by introducing complex data structures. Moreover, occasional group recommendation suffers from the problem of data sparsity due to the lack of historical group-item interactions. In this work, we focus on addressing the aforementioned challenges and propose a novel group recommendation model called Self-Supervised Group Graph Collaborative Filtering (SGGCF). The goal of the model is capturing the high-order interactions between users, items and groups and alleviating the data sparsity issue in an efficient way. First, we explicitly model the complex relationships as a unified user-centered heterogeneous graph and devise a base group recommendation model. Second, we explore self-supervised learning on the graph with two kinds of contrastive learning module to capture the implicit relations between groups and items. At last, we treat the proposed contrastive learning loss as supplementary and apply a multi-task strategy to jointly train the BPR loss and the proposed contrastive learning loss. We conduct extensive experiments on three real-world datasets, and the experimental results demonstrate the superiority of our proposed model in comparison to the state-of-the-art baselines.|现在，人们参加集体活动越来越方便了。因此，向个人群体提供一些建议是必不可少的。群体推荐是为社交网络或在线社区中的一组用户推荐项目或事件的任务。在这项工作中，我们研究了一个特定场景中的群组推荐，即偶尔群组推荐，它很少或没有历史直接相互作用的项目。现有的群体推荐方法大多采用基于注意的偏好聚合策略来捕获群体偏好。然而，这些模型要么忽略了组、用户和项目之间复杂的高阶交互，要么通过引入复杂的数据结构大大降低了效率。此外，由于缺乏历史上的组项目交互，偶尔的组推荐会遇到数据稀疏的问题。在这项工作中，我们致力于解决上述挑战，并提出了一个新颖的群体推荐模型，称为自我监督群体图协同过滤(sggCF)。该模型的目标是捕获用户、项目和组之间的高阶交互，有效地缓解数据稀疏问题。首先，我们明确地将复杂关系建模为一个统一的以用户为中心的异构图，并设计了一个基本的组推荐模型。其次，利用两种对比学习模块探索图上的自我监督学习，以捕捉群体与项目之间的内隐关系。最后，将提出的对比学习损失作为补充，采用多任务策略对 BPR 损失和提出的对比学习损失进行联合训练。我们在三个真实世界的数据集上进行了广泛的实验，实验结果证明了我们提出的模型相对于最先进的基线的优越性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Self-Supervised+Group+Graph+Collaborative+Filtering+for+Group+Recommendation)|0| |[Visual Matching is Enough for Scene Text Retrieval](https://doi.org/10.1145/3539597.3570428)|Lilong Wen, Yingrong Wang, Dongxiang Zhang, Gang Chen|Zhejiang University, Hangzhou, China|Given a text query, the task of scene text retrieval aims at searching and localizing all the text instances that are contained in an image gallery. The state-of-the-art method learns a cross-modal similarity between the query text and the detected text regions in natural images to facilitate retrieval. However, this cross-modal approach still cannot well bridge the heterogeneity gap between the text and image modalities. In this paper, we propose a new paradigm that converts the task into a single-modality retrieval problem. Unlike previous works that rely on character recognition or embedding, we directly leverage pictorial information by rendering query text into images to learn the glyph feature of each character, which can be utilized to capture the similarity between query and scene text images. With the extracted visual features, we devise a synthetic label image guided feature alignment mechanism that is robust to different scene text styles and layouts. The modules of glyph feature learning, text instance detection, and visual matching are jointly trained in an end-to-end framework. Experimental results show that our proposed paradigm achieves the best performance in multiple benchmark datasets. As a side product, our method can also be easily generalized to support text queries with unseen characters or languages in a zero-shot manner.|给定一个文本查询，场景文本检索任务的目的是搜索和定位包含在图像库中的所有文本实例。最先进的方法学习查询文本和自然图像中检测到的文本区域之间的跨模式相似性，以便于检索。然而，这种跨模式的方法仍然不能很好地弥合文本和图像模式之间的异质性差距。在本文中，我们提出了一个新的范式，将任务转化为一个单模态检索问题。与以往依赖于字符识别或嵌入的作品不同，我们直接利用图像信息，通过将查询文本渲染成图像来学习每个字符的字形特征，这可以用来捕捉查询文本图像和场景文本图像之间的相似性。利用提取的视觉特征，设计了一种综合标签图像引导的特征对齐机制，该机制对不同的场景文本样式和布局具有鲁棒性。字形特征学习、文本实例检测和视觉匹配等模块在端到端的框架下进行联合训练。实验结果表明，我们提出的范式在多个基准数据集中取得了最佳的性能。作为一个副产品，我们的方法也可以很容易地推广到支持文本查询与看不见的字符或语言在零拍方式。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Visual+Matching+is+Enough+for+Scene+Text+Retrieval)|0| |[Slate-Aware Ranking for Recommendation](https://doi.org/10.1145/3539597.3570380)|Yi Ren, Xiao Han, Xu Zhao, Shenzheng Zhang, Yan Zhang|Tencent, Beijing, China|We see widespread adoption of slate recommender systems, where an ordered item list is fed to the user based on the user interests and items' content. For each recommendation, the user can select one or several items from the list for further interaction. In this setting, the significant impact on user behaviors from the mutual influence among the items is well understood. The existing methods add another step of slate re-ranking after the ranking stage of recommender systems, which considers the mutual influence among recommended items to re-rank and generate the recommendation results so as to maximize the expected overall utility. However, to model the complex interaction of multiple recommended items, the re-ranking stage usually can just handle dozens of candidates because of the constraint of limited hardware resource and system latency. Therefore, the ranking stage is still essential for most applications to provide high-quality candidate set for the re-ranking stage. In this paper, we propose a solution named Slate-Aware ranking (SAR) for the ranking stage. By implicitly considering the relations among the slate items, it significantly enhances the quality of the re-ranking stage's candidate set and boosts the relevance and diversity of the overall recommender systems. Both experiments with the public datasets and internal online A/B testing are conducted to verify its effectiveness.|我们看到了平板推荐系统的广泛应用，它根据用户的兴趣和项目的内容向用户提供一个有序的项目列表。对于每个建议，用户可以从列表中选择一个或多个项以进行进一步的交互。在这种设置中，项目之间的相互影响对用户行为的重大影响是可以理解的。现有的方法在推荐系统的排序阶段之后又增加了一个重新排序的步骤，即考虑推荐项目之间的相互影响，重新排序并生成推荐结果，从而使预期的总体效用最大化。然而，为了对多个推荐项目的复杂交互进行建模，由于硬件资源和系统延迟的限制，重新排序阶段通常只能处理几十个候选项。因此，排名阶段仍然是必不可少的大多数应用程序提供高质量的候选人集重新排名阶段。在本文中，我们提出了一个解决方案，石板感知排序(SAR)的排序阶段。通过隐含地考虑石板条目之间的关系，显著地提高了重新排序阶段候选集的质量，增强了整个推荐系统的相关性和多样性。通过公共数据集和内部在线 A/B 测试的实验，验证了该方法的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Slate-Aware+Ranking+for+Recommendation)|0| -|[MUSENET: Multi-Scenario Learning for Repeat-Aware Personalized Recommendation](https://doi.org/10.1145/3539597.3570414)|Senrong Xu, Liangyue Li, Yuan Yao, Zulong Chen, Han Wu, Quan Lu, Hanghang Tong|Alibaba Group, Hangzhou, China; University of Illinois at Urbana-Champaign, Urbana-Champaign, IL, USA; Nanjing University, Nanjing, China|Personalized recommendation has been instrumental in many real applications. Despite the great progress, the underlying multi-scenario characteristics (e.g., users may behave differently under different scenarios) are largely ignored by existing recommender systems. Intuitively, modeling different scenarios properly could significantly improve the recommendation accuracy, and some existing work has explored this direction. However, these work assumes the scenarios are explicitly given, and thus becomes less effective when such information is unavailable. To complicate things further, proper scenario modeling from data is challenging and the recommendation models may easily overfit to some scenarios. In this paper, we propose a multi-scenario learning framework, MUSENET, for personalized recommendation. The key idea of MUSENET is to learn multiple implicit scenarios from the user behaviors, with a careful design inspired by the causal interpretation of recommender systems to avoid the overfitting issue. Additionally, since users' repeat consumptions account for a large part of the user behavior data on many e-commerce platforms, a repeat-aware mechanism is integrated to handle users' repurchase intentions within each scenario. Comprehensive experimental results on both industrial and public datasets demonstrate the effectiveness of the proposed approach compared with the state-of-the-art methods.|个性化推荐在许多实际应用中发挥了重要作用。尽管取得了巨大的进步，但是现有的推荐系统基本上忽略了潜在的多场景特征(例如，用户在不同场景下的行为可能不同)。直观地说，适当地建立不同场景的模型可以显著地提高推荐的准确性，现有的一些工作已经探索了这一方向。然而，这些工作假设场景是显式给出的，因此当这些信息不可用时，工作效率就会降低。更复杂的是，根据数据建立适当的场景模型是具有挑战性的，推荐模型可能很容易过度适应某些场景。在本文中，我们提出了一个多场景学习框架 MUSENET，用于个性化推荐。MUSENET 的关键思想是从用户行为中学习多个隐式场景，并通过对推荐系统的因果解释进行精心设计，以避免过度拟合问题。此外，由于在许多电子商务平台上，用户的重复消费占用了用户行为数据的很大一部分，因此集成了一个重复感知机制来处理每个场景中用户的回购意图。在工业数据集和公共数据集上的综合实验结果表明，与最新的方法相比，该方法是有效的。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=MUSENET:+Multi-Scenario+Learning+for+Repeat-Aware+Personalized+Recommendation)|0| -|[An F-shape Click Model for Information Retrieval on Multi-block Mobile Pages](https://doi.org/10.1145/3539597.3570365)|Lingyue Fu, Jianghao Lin, Weiwen Liu, Ruiming Tang, Weinan Zhang, Rui Zhang, Yong Yu|ruizhang.info, Shenzhen, China; Huawei Noah's Ark Lab, Shenzhen, China; Shanghai Jiao Tong University, Shanghai, China|To provide click simulation or relevance estimation based on users' implicit interaction feedback, click models have been much studied during recent years. Most click models focus on user behaviors towards a single list. However, with the development of user interface (UI) design, the layout of displayed items on a result page tends to be multi-block (i.e., multi-list) style instead of a single list, which requires different assumptions to model user behaviors more accurately. There exist click models for multi-block pages in desktop contexts, but they cannot be directly applied to mobile scenarios due to different interaction manners, result types and especially multi-block presentation styles. In particular, multi-block mobile pages can normally be decomposed into interleavings of basic vertical blocks and horizontal blocks, thus resulting in typically F-shape forms. To mitigate gaps between desktop and mobile contexts for multi-block pages, we conduct a user eye-tracking study, and identify users' sequential browsing, block skip and comparison patterns on F-shape pages. These findings lead to the design of a novel F-shape Click Model (FSCM), which serves as a general solution to multi-block mobile pages. Firstly, we construct a directed acyclic graph (DAG) for each page, where each item is regarded as a vertex and each edge indicates the user's possible examination flow. Secondly, we propose DAG-structured GRUs and a comparison module to model users' sequential (sequential browsing, block skip) and non-sequential (comparison) behaviors respectively. Finally, we combine GRU states and comparison patterns to perform user click predictions. Experiments on a large-scale real-world dataset validate the effectiveness of FSCM on user behavior predictions compared with baseline models.|为了提供基于用户隐性交互反馈的点击模拟或相关性估计，点击模型近年来得到了广泛的研究。大多数单击模型关注的是用户对单个列表的行为。然而，随着用户界面(UI)设计的发展，结果页面上显示项的布局趋向于多块(即多列表)样式，而不是单列表，这就需要不同的假设来更准确地模拟用户行为。桌面环境中存在多块页面的点击模型，但由于不同的交互方式、结果类型，尤其是多块表示方式，这些模型不能直接应用于移动场景。特别是，多块移动页面通常可以分解为基本垂直块和水平块的交错，从而产生典型的 F 形形式。为了缩小多块网页的桌面上下文和移动上下文之间的差距，我们进行了用户眼动跟踪研究，识别了用户在 F 形网页上的顺序浏览、块跳过和比较模式。这些发现导致了一种新颖的 F 形点击模型(FSCM)的设计，它作为一个多块移动页面的通用解决方案。首先，我们为每个页面建立一个有向无环图(DAG) ，其中每个项目被视为一个顶点，每个边表示用户可能的考试流程。其次，提出了 DAG 结构的 GRU 和比较模块，分别对用户的顺序(顺序浏览，块跳过)和非顺序(比较)行为进行建模。最后，我们结合 GRU 状态和比较模式来执行用户单击预测。在一个大规模真实世界数据集上的实验验证了与基线模型相比，FSCM 在用户行为预测方面的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=An+F-shape+Click+Model+for+Information+Retrieval+on+Multi-block+Mobile+Pages)|0| -|[AgAsk: A Conversational Search Agent for Answering Agricultural Questions](https://doi.org/10.1145/3539597.3573034)|Hang Li, Bevan Koopman, Ahmed Mourad, Guido Zuccon|The University of Queensland, Brisbane, Australia; CSIRO, Brisbane, Australia|While large amounts of potentially useful agricultural resources (journal articles, manuals, reports) are available, their value cannot be realised if they cannot be easily searched and presented to the agriculture users in a digestible form.AgAsk is a conversational search system for the agricultural domain, providing tailored answers to growers questions. AgAsk is underpinned by an efficient and effective neural passage ranking model fine-tuned on real world growers' questions. An adaptable, messaging-style user interface is deployed via the Telegram messaging platform, allowing users to ask natural language questions via text or voice, and receive short natural language answers as replies. AgAsk is empirically evaluated on an agricultural passage retrieval test collection. The system provides a single entry point to access the information needed for better growing decisions. Much of the system is domain agnostic and would benefit other domains. AgAsk can be interacted via Telegram; further information about AgAsk, including codebases, instructions and demonstration videos can be accessed at https://ielab.io/publications/agask-agent.|虽然有大量潜在有用的农业资源(期刊文章、手册、报告)可用，但如果它们不能以易于理解的形式被搜索并呈现给农业用户，它们的价值就无法实现。 AgAsk 是一个农业领域的对话搜索系统，为种植者提供量身定制的问题答案。AgAsk 的基础是一个有效的神经通道排序模型，该模型根据真实世界种植者的问题进行了微调。通过 Telegram 消息平台部署了一个可适应的消息类型的用户界面，允许用户通过文本或语音提出自然语言问题，并收到简短的自然语言答复作为答复。AgAsk 在一个农业通道检索测试集上进行了实证评估。该系统提供了一个单一的切入点，以访问更好的增长决策所需的信息。该系统的大部分是领域不可知的，并将有益于其他领域。AgAsk 可以通过 Telegram 进行交互，关于 AgAsk 的更多信息，包括代码库、说明和演示视频可以在 https://ielab.io/publications/AgAsk-agent 上获得。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=AgAsk:+A+Conversational+Search+Agent+for+Answering+Agricultural+Questions)|0| -|[Learning to Distinguish Multi-User Coupling Behaviors for TV Recommendation](https://doi.org/10.1145/3539597.3570374)|Jiarui Qin, Jiachen Zhu, Yankai Liu, Junchao Gao, Jianjie Ying, Chaoxiong Liu, Ding Wang, Junlan Feng, Chao Deng, Xiaozheng Wang, Jian Jiang, Cong Liu, Yong Yu, Haitao Zeng, Weinan Zhang|China Mobile (Zhejiang) Research & Innovation Institute, Hangzhou, China; Digital Brain Lab, Shanghai, China; Shanghai Jiao Tong University, Shanghai, China; China Mobile Research Institute, Beijing, China; China Mobile Zhejiang, Hangzhou, China|This paper is concerned with TV recommendation, where one major challenge is the coupling behavior issue that the behaviors of multiple users are coupled together and not directly distinguishable because the users share the same account. Unable to identify the current watching user and use the coupling behaviors directly could lead to sub-optimal recommendation results due to the noise introduced by the behaviors of other users. Most existing methods deal with this issue either by unsupervised clustering algorithms or depending on latent user representation learning with strong assumptions. However, they neglect to sophisticatedly model the current session behaviors, which carry the information of user identification. Another critical limitation of the existing models is the lack of supervision signal on distinguishing behaviors because they solely depend on the final click label, which is insufficient to provide effective supervision. To address the above problems, we propose the Coupling Sequence Model (COSMO) for TV recommendation. In COSMO, we design a session-aware co-attention mechanism that uses both the candidate item and session behaviors as the query to attend to the historical behaviors in a fine-grained manner. Furthermore, we propose to use the data of accounts with multiple devices (e.g., families with various TV sets), which means the behaviors of one account are generated on different devices. We regard the device information as weak supervision and propose a novel pair-wise attention loss for learning to distinguish the coupling behaviors. Extensive offline experiments and online A/B tests over a commercial TV service provider demonstrate the efficacy of COSMO compared to the existing models.|本文研究的是电视推荐，其中一个主要的挑战是耦合行为问题，即多个用户的行为耦合在一起，而不能直接区分，因为用户共享相同的帐户。由于其他用户的行为所带来的噪声，如果不能直接识别当前监视用户并使用耦合行为，可能会导致推荐结果不理想。大多数现有的方法都是通过无监督聚类算法或依赖于强假设条件下的潜在用户表征学习来解决这个问题。然而，他们忽视了对当前的会话行为进行复杂的建模，因为当前的会话行为携带用户识别信息。现有模型的另一个严重缺陷是缺乏区分行为的监督信号，因为它们仅仅依赖于最终的点击标签，这不足以提供有效的监督。针对上述问题，本文提出了耦合序列模型(COSMO)用于电视推荐。在 COSMO 中，我们设计了一个会话感知的共注意机制，该机制同时使用候选项和会话行为作为查询，以细粒度的方式关注历史行为。此外，我们建议使用多个设备的帐户数据(例如，拥有不同电视机的家庭) ，这意味着一个帐户的行为是在不同的设备上产生的。我们将设备信息视为弱监督，提出了一种新的对注意损失学习方法来区分耦合行为。通过对一家商业电视服务提供商的大量离线实验和在线 A/B 测试，证明了 COSMO 与现有模型相比的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Learning+to+Distinguish+Multi-User+Coupling+Behaviors+for+TV+Recommendation)|0| -|[A Causal View for Item-level Effect of Recommendation on User Preference](https://doi.org/10.1145/3539597.3570461)|Wei Cai, Fuli Feng, Qifan Wang, Tian Yang, Zhenguang Liu, Congfu Xu|Chinese University of Hong Kong, Hong Kong, China; Meta AI, Menlo Park, USA; University of Science and Technology of China, Hefei, China; Zhejiang University, Hangzhou, China|Recommender systems not only serve users but also affect user preferences through personalized recommendations. Recent researches investigate the effects of the entire recommender system on user preferences, i.e., system-level effects, and find that recommendations may lead to problems such as echo chambers and filter bubbles. To properly alleviate the problems, it is necessary to estimate the effects of recommending a specific item on user preferences, i.e., item-level effects. For example, by understanding whether recommending an item aggravates echo chambers, we can better decide whether to recommend it or not. This work designs a method to estimate the item-level effects from the causal perspective. We resort to causal graphs to characterize the average treatment effect of recommending an item on the preference of another item. The key to estimating the effects lies in mitigating the confounding bias of time and user features without the costly randomized control trials. Towards the goal, we estimate the causal effects from historical observations through a method with stratification and matching to address the two confounders, respectively. Nevertheless, directly implementing stratification and matching is intractable, which requires high computational cost due to the large sample size. We thus propose efficient approximations of stratification and matching to reduce the computation complexity. Extensive experimental results on two real-world datasets validate the effectiveness and efficiency of our method. We also show a simple example of using the item-level effects to provide insights for mitigating echo chambers.|推荐系统不仅服务于用户，而且通过个性化推荐影响用户的偏好。最近的研究调查了整个推荐系统对用户偏好的影响，即系统层面的影响，发现建议可能导致回声室和过滤气泡等问题。为了恰当地缓解这些问题，有必要估计推荐一个特定项目对用户偏好的影响，即项目级别的影响。例如，通过了解推荐一个项目是否会加剧回声室，我们可以更好地决定是否推荐它。本文设计了一种从因果关系角度评价项目水平效应的方法。我们使用因果图来描述推荐一个项目对另一个项目偏好的平均治疗效果。估计效果的关键在于减轻时间和用户特征的混杂偏差，而不需要昂贵的随机对照试验。为了实现这一目标，我们通过分层和匹配的方法来分别解决这两个混杂因素，从历史观察中估计因果效应。然而，直接实现分层和匹配是比较困难的，由于样本量较大，需要较高的计算成本。因此，我们提出了分层和匹配的有效近似，以降低计算复杂度。在两个实际数据集上的大量实验结果验证了该方法的有效性和有效性。我们还展示了一个使用条目级效应来提供减轻回声室的见解的简单示例。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Causal+View+for+Item-level+Effect+of+Recommendation+on+User+Preference)|0| -|[Exploiting Explicit and Implicit Item relationships for Session-based Recommendation](https://doi.org/10.1145/3539597.3570432)|Zihao Li, Xianzhi Wang, Chao Yang, Lina Yao, Julian J. McAuley, Guandong Xu|University of Technology Sydney, Sydney, Australia; University of New South Wales, Sydney, Australia; University of California, San Diego, La Jolla, CA, USA|The session-based recommendation aims to predict users' immediate next actions based on their short-term behaviors reflected by past and ongoing sessions. Graph neural networks (GNNs) recently dominated the related studies, yet their performance heavily relies on graph structures, which are often predefined, task-specific, and designed heuristically. Furthermore, existing graph-based methods either neglect implicit correlations among items or consider explicit and implicit relationships altogether in the same graphs. We propose to decouple explicit and implicit relationships among items. As such, we can capture the prior knowledge encapsulated in explicit dependencies and learned implicit correlations among items simultaneously in a flexible and more interpretable manner for effective recommendations. We design a dual graph neural network that leverages the feature representations extracted by two GNNs: a graph neural network with a single gate (SG-GNN) and an adaptive graph neural network (A-GNN). The former models explicit dependencies among items. The latter employs a self-learning strategy to capture implicit correlations among items. Our experiments on four real-world datasets show our model outperforms state-of-the-art methods by a large margin, achieving 18.46% and 70.72% improvement in HR@20, and 49.10% and 115.29% improvement in MRR@20 on Diginetica and LastFM datasets.|基于会话的建议旨在根据用户过去和正在进行的会话所反映的短期行为来预测用户的即时下一步行动。图形神经网络(GNN)是近年来研究的热点，但其性能主要依赖于图形结构，这种结构往往是预定义的、任务特定的、启发式设计的。此外，现有的基于图的方法要么忽略项目之间的隐式关系，要么在同一个图中同时考虑显式和隐式关系。我们建议解耦项目之间的显式和隐式关系。因此，我们可以以一种灵活和更易于解释的方式同时捕获包含在显式依赖和学习的项目之间的隐式相关性中的先验知识，以获得有效的建议。我们设计了一个双图神经网络，利用两个 GNN 提取的特征表示: 一个单门图神经网络(SG-GNN)和一个自适应图神经网络(A-GNN)。前者对项之间的显式依赖关系进行建模。后者采用自我学习策略来捕捉项目之间的内隐相关性。我们在四个真实世界数据集上的实验表明，我们的模型大大优于最先进的方法，HR@20分别提高了18.46% 和70.72% ，在 Diginetica 和 LastFM 数据集上 MRR@20分别提高了49.10% 和115.29% 。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Exploiting+Explicit+and+Implicit+Item+relationships+for+Session-based+Recommendation)|0| +|[MUSENET: Multi-Scenario Learning for Repeat-Aware Personalized Recommendation](https://doi.org/10.1145/3539597.3570414)|Senrong Xu, Liangyue Li, Yuan Yao, Zulong Chen, Han Wu, Quan Lu, Hanghang Tong|University of Illinois at Urbana-Champaign, Urbana-Champaign, IL, USA; Alibaba Group, Hangzhou, China; Nanjing University, Nanjing, China|Personalized recommendation has been instrumental in many real applications. Despite the great progress, the underlying multi-scenario characteristics (e.g., users may behave differently under different scenarios) are largely ignored by existing recommender systems. Intuitively, modeling different scenarios properly could significantly improve the recommendation accuracy, and some existing work has explored this direction. However, these work assumes the scenarios are explicitly given, and thus becomes less effective when such information is unavailable. To complicate things further, proper scenario modeling from data is challenging and the recommendation models may easily overfit to some scenarios. In this paper, we propose a multi-scenario learning framework, MUSENET, for personalized recommendation. The key idea of MUSENET is to learn multiple implicit scenarios from the user behaviors, with a careful design inspired by the causal interpretation of recommender systems to avoid the overfitting issue. Additionally, since users' repeat consumptions account for a large part of the user behavior data on many e-commerce platforms, a repeat-aware mechanism is integrated to handle users' repurchase intentions within each scenario. Comprehensive experimental results on both industrial and public datasets demonstrate the effectiveness of the proposed approach compared with the state-of-the-art methods.|个性化推荐在许多实际应用中发挥了重要作用。尽管取得了巨大的进步，但是现有的推荐系统基本上忽略了潜在的多场景特征(例如，用户在不同场景下的行为可能不同)。直观地说，适当地建立不同场景的模型可以显著地提高推荐的准确性，现有的一些工作已经探索了这一方向。然而，这些工作假设场景是显式给出的，因此当这些信息不可用时，工作效率就会降低。更复杂的是，根据数据建立适当的场景模型是具有挑战性的，推荐模型可能很容易过度适应某些场景。在本文中，我们提出了一个多场景学习框架 MUSENET，用于个性化推荐。MUSENET 的关键思想是从用户行为中学习多个隐式场景，并通过对推荐系统的因果解释进行精心设计，以避免过度拟合问题。此外，由于在许多电子商务平台上，用户的重复消费占用了用户行为数据的很大一部分，因此集成了一个重复感知机制来处理每个场景中用户的回购意图。在工业数据集和公共数据集上的综合实验结果表明，与最新的方法相比，该方法是有效的。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=MUSENET:+Multi-Scenario+Learning+for+Repeat-Aware+Personalized+Recommendation)|0| +|[An F-shape Click Model for Information Retrieval on Multi-block Mobile Pages](https://doi.org/10.1145/3539597.3570365)|Lingyue Fu, Jianghao Lin, Weiwen Liu, Ruiming Tang, Weinan Zhang, Rui Zhang, Yong Yu|Huawei Noah's Ark Lab, Shenzhen, China; Shanghai Jiao Tong University, Shanghai, China; ruizhang.info, Shenzhen, China|To provide click simulation or relevance estimation based on users' implicit interaction feedback, click models have been much studied during recent years. Most click models focus on user behaviors towards a single list. However, with the development of user interface (UI) design, the layout of displayed items on a result page tends to be multi-block (i.e., multi-list) style instead of a single list, which requires different assumptions to model user behaviors more accurately. There exist click models for multi-block pages in desktop contexts, but they cannot be directly applied to mobile scenarios due to different interaction manners, result types and especially multi-block presentation styles. In particular, multi-block mobile pages can normally be decomposed into interleavings of basic vertical blocks and horizontal blocks, thus resulting in typically F-shape forms. To mitigate gaps between desktop and mobile contexts for multi-block pages, we conduct a user eye-tracking study, and identify users' sequential browsing, block skip and comparison patterns on F-shape pages. These findings lead to the design of a novel F-shape Click Model (FSCM), which serves as a general solution to multi-block mobile pages. Firstly, we construct a directed acyclic graph (DAG) for each page, where each item is regarded as a vertex and each edge indicates the user's possible examination flow. Secondly, we propose DAG-structured GRUs and a comparison module to model users' sequential (sequential browsing, block skip) and non-sequential (comparison) behaviors respectively. Finally, we combine GRU states and comparison patterns to perform user click predictions. Experiments on a large-scale real-world dataset validate the effectiveness of FSCM on user behavior predictions compared with baseline models.|为了提供基于用户隐性交互反馈的点击模拟或相关性估计，点击模型近年来得到了广泛的研究。大多数单击模型关注的是用户对单个列表的行为。然而，随着用户界面(UI)设计的发展，结果页面上显示项的布局趋向于多块(即多列表)样式，而不是单列表，这就需要不同的假设来更准确地模拟用户行为。桌面环境中存在多块页面的点击模型，但由于不同的交互方式、结果类型，尤其是多块表示方式，这些模型不能直接应用于移动场景。特别是，多块移动页面通常可以分解为基本垂直块和水平块的交错，从而产生典型的 F 形形式。为了缩小多块网页的桌面上下文和移动上下文之间的差距，我们进行了用户眼动跟踪研究，识别了用户在 F 形网页上的顺序浏览、块跳过和比较模式。这些发现导致了一种新颖的 F 形点击模型(FSCM)的设计，它作为一个多块移动页面的通用解决方案。首先，我们为每个页面建立一个有向无环图(DAG) ，其中每个项目被视为一个顶点，每个边表示用户可能的考试流程。其次，提出了 DAG 结构的 GRU 和比较模块，分别对用户的顺序(顺序浏览，块跳过)和非顺序(比较)行为进行建模。最后，我们结合 GRU 状态和比较模式来执行用户单击预测。在一个大规模真实世界数据集上的实验验证了与基线模型相比，FSCM 在用户行为预测方面的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=An+F-shape+Click+Model+for+Information+Retrieval+on+Multi-block+Mobile+Pages)|0| +|[AgAsk: A Conversational Search Agent for Answering Agricultural Questions](https://doi.org/10.1145/3539597.3573034)|Hang Li, Bevan Koopman, Ahmed Mourad, Guido Zuccon|CSIRO, Brisbane, Australia; The University of Queensland, Brisbane, Australia|While large amounts of potentially useful agricultural resources (journal articles, manuals, reports) are available, their value cannot be realised if they cannot be easily searched and presented to the agriculture users in a digestible form.AgAsk is a conversational search system for the agricultural domain, providing tailored answers to growers questions. AgAsk is underpinned by an efficient and effective neural passage ranking model fine-tuned on real world growers' questions. An adaptable, messaging-style user interface is deployed via the Telegram messaging platform, allowing users to ask natural language questions via text or voice, and receive short natural language answers as replies. AgAsk is empirically evaluated on an agricultural passage retrieval test collection. The system provides a single entry point to access the information needed for better growing decisions. Much of the system is domain agnostic and would benefit other domains. AgAsk can be interacted via Telegram; further information about AgAsk, including codebases, instructions and demonstration videos can be accessed at https://ielab.io/publications/agask-agent.|虽然有大量潜在有用的农业资源(期刊文章、手册、报告)可用，但如果它们不能以易于理解的形式被搜索并呈现给农业用户，它们的价值就无法实现。 AgAsk 是一个农业领域的对话搜索系统，为种植者提供量身定制的问题答案。AgAsk 的基础是一个有效的神经通道排序模型，该模型根据真实世界种植者的问题进行了微调。通过 Telegram 消息平台部署了一个可适应的消息类型的用户界面，允许用户通过文本或语音提出自然语言问题，并收到简短的自然语言答复作为答复。AgAsk 在一个农业通道检索测试集上进行了实证评估。该系统提供了一个单一的切入点，以访问更好的增长决策所需的信息。该系统的大部分是领域不可知的，并将有益于其他领域。AgAsk 可以通过 Telegram 进行交互，关于 AgAsk 的更多信息，包括代码库、说明和演示视频可以在 https://ielab.io/publications/AgAsk-agent 上获得。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=AgAsk:+A+Conversational+Search+Agent+for+Answering+Agricultural+Questions)|0| +|[Learning to Distinguish Multi-User Coupling Behaviors for TV Recommendation](https://doi.org/10.1145/3539597.3570374)|Jiarui Qin, Jiachen Zhu, Yankai Liu, Junchao Gao, Jianjie Ying, Chaoxiong Liu, Ding Wang, Junlan Feng, Chao Deng, Xiaozheng Wang, Jian Jiang, Cong Liu, Yong Yu, Haitao Zeng, Weinan Zhang|China Mobile Research Institute, Beijing, China; Shanghai Jiao Tong University, Shanghai, China; Digital Brain Lab, Shanghai, China; China Mobile Zhejiang, Hangzhou, China; China Mobile (Zhejiang) Research & Innovation Institute, Hangzhou, China|This paper is concerned with TV recommendation, where one major challenge is the coupling behavior issue that the behaviors of multiple users are coupled together and not directly distinguishable because the users share the same account. Unable to identify the current watching user and use the coupling behaviors directly could lead to sub-optimal recommendation results due to the noise introduced by the behaviors of other users. Most existing methods deal with this issue either by unsupervised clustering algorithms or depending on latent user representation learning with strong assumptions. However, they neglect to sophisticatedly model the current session behaviors, which carry the information of user identification. Another critical limitation of the existing models is the lack of supervision signal on distinguishing behaviors because they solely depend on the final click label, which is insufficient to provide effective supervision. To address the above problems, we propose the Coupling Sequence Model (COSMO) for TV recommendation. In COSMO, we design a session-aware co-attention mechanism that uses both the candidate item and session behaviors as the query to attend to the historical behaviors in a fine-grained manner. Furthermore, we propose to use the data of accounts with multiple devices (e.g., families with various TV sets), which means the behaviors of one account are generated on different devices. We regard the device information as weak supervision and propose a novel pair-wise attention loss for learning to distinguish the coupling behaviors. Extensive offline experiments and online A/B tests over a commercial TV service provider demonstrate the efficacy of COSMO compared to the existing models.|本文研究的是电视推荐，其中一个主要的挑战是耦合行为问题，即多个用户的行为耦合在一起，而不能直接区分，因为用户共享相同的帐户。由于其他用户的行为所带来的噪声，如果不能直接识别当前监视用户并使用耦合行为，可能会导致推荐结果不理想。大多数现有的方法都是通过无监督聚类算法或依赖于强假设条件下的潜在用户表征学习来解决这个问题。然而，他们忽视了对当前的会话行为进行复杂的建模，因为当前的会话行为携带用户识别信息。现有模型的另一个严重缺陷是缺乏区分行为的监督信号，因为它们仅仅依赖于最终的点击标签，这不足以提供有效的监督。针对上述问题，本文提出了耦合序列模型(COSMO)用于电视推荐。在 COSMO 中，我们设计了一个会话感知的共注意机制，该机制同时使用候选项和会话行为作为查询，以细粒度的方式关注历史行为。此外，我们建议使用多个设备的帐户数据(例如，拥有不同电视机的家庭) ，这意味着一个帐户的行为是在不同的设备上产生的。我们将设备信息视为弱监督，提出了一种新的对注意损失学习方法来区分耦合行为。通过对一家商业电视服务提供商的大量离线实验和在线 A/B 测试，证明了 COSMO 与现有模型相比的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Learning+to+Distinguish+Multi-User+Coupling+Behaviors+for+TV+Recommendation)|0| +|[A Causal View for Item-level Effect of Recommendation on User Preference](https://doi.org/10.1145/3539597.3570461)|Wei Cai, Fuli Feng, Qifan Wang, Tian Yang, Zhenguang Liu, Congfu Xu|Meta AI, Menlo Park, USA; Chinese University of Hong Kong, Hong Kong, China; University of Science and Technology of China, Hefei, China; Zhejiang University, Hangzhou, China|Recommender systems not only serve users but also affect user preferences through personalized recommendations. Recent researches investigate the effects of the entire recommender system on user preferences, i.e., system-level effects, and find that recommendations may lead to problems such as echo chambers and filter bubbles. To properly alleviate the problems, it is necessary to estimate the effects of recommending a specific item on user preferences, i.e., item-level effects. For example, by understanding whether recommending an item aggravates echo chambers, we can better decide whether to recommend it or not. This work designs a method to estimate the item-level effects from the causal perspective. We resort to causal graphs to characterize the average treatment effect of recommending an item on the preference of another item. The key to estimating the effects lies in mitigating the confounding bias of time and user features without the costly randomized control trials. Towards the goal, we estimate the causal effects from historical observations through a method with stratification and matching to address the two confounders, respectively. Nevertheless, directly implementing stratification and matching is intractable, which requires high computational cost due to the large sample size. We thus propose efficient approximations of stratification and matching to reduce the computation complexity. Extensive experimental results on two real-world datasets validate the effectiveness and efficiency of our method. We also show a simple example of using the item-level effects to provide insights for mitigating echo chambers.|推荐系统不仅服务于用户，而且通过个性化推荐影响用户的偏好。最近的研究调查了整个推荐系统对用户偏好的影响，即系统层面的影响，发现建议可能导致回声室和过滤气泡等问题。为了恰当地缓解这些问题，有必要估计推荐一个特定项目对用户偏好的影响，即项目级别的影响。例如，通过了解推荐一个项目是否会加剧回声室，我们可以更好地决定是否推荐它。本文设计了一种从因果关系角度评价项目水平效应的方法。我们使用因果图来描述推荐一个项目对另一个项目偏好的平均治疗效果。估计效果的关键在于减轻时间和用户特征的混杂偏差，而不需要昂贵的随机对照试验。为了实现这一目标，我们通过分层和匹配的方法来分别解决这两个混杂因素，从历史观察中估计因果效应。然而，直接实现分层和匹配是比较困难的，由于样本量较大，需要较高的计算成本。因此，我们提出了分层和匹配的有效近似，以降低计算复杂度。在两个实际数据集上的大量实验结果验证了该方法的有效性和有效性。我们还展示了一个使用条目级效应来提供减轻回声室的见解的简单示例。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Causal+View+for+Item-level+Effect+of+Recommendation+on+User+Preference)|0| +|[Exploiting Explicit and Implicit Item relationships for Session-based Recommendation](https://doi.org/10.1145/3539597.3570432)|Zihao Li, Xianzhi Wang, Chao Yang, Lina Yao, Julian J. McAuley, Guandong Xu|University of Technology Sydney, Sydney, Australia; University of California, San Diego, La Jolla, CA, USA; University of New South Wales, Sydney, Australia|The session-based recommendation aims to predict users' immediate next actions based on their short-term behaviors reflected by past and ongoing sessions. Graph neural networks (GNNs) recently dominated the related studies, yet their performance heavily relies on graph structures, which are often predefined, task-specific, and designed heuristically. Furthermore, existing graph-based methods either neglect implicit correlations among items or consider explicit and implicit relationships altogether in the same graphs. We propose to decouple explicit and implicit relationships among items. As such, we can capture the prior knowledge encapsulated in explicit dependencies and learned implicit correlations among items simultaneously in a flexible and more interpretable manner for effective recommendations. We design a dual graph neural network that leverages the feature representations extracted by two GNNs: a graph neural network with a single gate (SG-GNN) and an adaptive graph neural network (A-GNN). The former models explicit dependencies among items. The latter employs a self-learning strategy to capture implicit correlations among items. Our experiments on four real-world datasets show our model outperforms state-of-the-art methods by a large margin, achieving 18.46% and 70.72% improvement in HR@20, and 49.10% and 115.29% improvement in MRR@20 on Diginetica and LastFM datasets.|基于会话的建议旨在根据用户过去和正在进行的会话所反映的短期行为来预测用户的即时下一步行动。图形神经网络(GNN)是近年来研究的热点，但其性能主要依赖于图形结构，这种结构往往是预定义的、任务特定的、启发式设计的。此外，现有的基于图的方法要么忽略项目之间的隐式关系，要么在同一个图中同时考虑显式和隐式关系。我们建议解耦项目之间的显式和隐式关系。因此，我们可以以一种灵活和更易于解释的方式同时捕获包含在显式依赖和学习的项目之间的隐式相关性中的先验知识，以获得有效的建议。我们设计了一个双图神经网络，利用两个 GNN 提取的特征表示: 一个单门图神经网络(SG-GNN)和一个自适应图神经网络(A-GNN)。前者对项之间的显式依赖关系进行建模。后者采用自我学习策略来捕捉项目之间的内隐相关性。我们在四个真实世界数据集上的实验表明，我们的模型大大优于最先进的方法，HR@20分别提高了18.46% 和70.72% ，在 Diginetica 和 LastFM 数据集上 MRR@20分别提高了49.10% 和115.29% 。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Exploiting+Explicit+and+Implicit+Item+relationships+for+Session-based+Recommendation)|0| |[Unbiased Knowledge Distillation for Recommendation](https://doi.org/10.1145/3539597.3570477)|Gang Chen, Jiawei Chen, Fuli Feng, Sheng Zhou, Xiangnan He|University of Science and Technology of China, Hefei, China; Zhejiang University, Hangzhou, China|As a promising solution for model compression, knowledge distillation (KD) has been applied in recommender systems (RS) to reduce inference latency. Traditional solutions first train a full teacher model from the training data, and then transfer its knowledge (\ie \textit{soft labels}) to supervise the learning of a compact student model. However, we find such a standard distillation paradigm would incur serious bias issue -- popular items are more heavily recommended after the distillation. This effect prevents the student model from making accurate and fair recommendations, decreasing the effectiveness of RS. In this work, we identify the origin of the bias in KD -- it roots in the biased soft labels from the teacher, and is further propagated and intensified during the distillation. To rectify this, we propose a new KD method with a stratified distillation strategy. It first partitions items into multiple groups according to their popularity, and then extracts the ranking knowledge within each group to supervise the learning of the student. Our method is simple and teacher-agnostic -- it works on distillation stage without affecting the training of the teacher model. We conduct extensive theoretical and empirical studies to validate the effectiveness of our proposal. We release our code at: https://github.com/chengang95/UnKD.|作为一种有前途的模型压缩方法，知识精馏(KD)已经应用于推荐系统(RS)中，以减少推理延迟。传统的解决方案首先从培训数据中训练一个完整的教师模型，然后转移其知识(即文本(软标签))来监督一个紧凑的学生模型的学习。然而，我们发现这样一个标准的蒸馏范式会引起严重的偏见问题-流行的项目是更多地推荐后蒸馏。这种效应阻碍了学生模型做出准确、公正的推荐，降低了 RS 的有效性。在这项工作中，我们找出偏见的根源 KD-它根源于偏见软标签的教师，并进一步传播和加强在蒸馏过程中。为了解决这一问题，我们提出了一种新的分层蒸馏 KD 方法。它首先根据项目的知名度将项目划分为多个组，然后提取每个组内的排名知识，以监督学生的学习。我们的方法是简单的和教师不可知的——它工作在蒸馏阶段，不影响教师模型的训练。我们进行了广泛的理论和实证研究，以验证我们的建议的有效性。我们在 https://github.com/chengang95/unkd 发布代码。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Unbiased+Knowledge+Distillation+for+Recommendation)|0| |[Multimodal Pre-Training with Self-Distillation for Product Understanding in E-Commerce](https://doi.org/10.1145/3539597.3570423)|Shilei Liu, Lin Li, Jun Song, Yonghua Yang, Xiaoyi Zeng|Alibaba Group, Hangzhou, China|Product understanding refers to a series of product-centric tasks, such as classification, alignment and attribute values prediction, which requires fine-grained fusion of various modalities of products. Excellent product modeling ability will enhance the user experience and benefit search and recommendation systems. In this paper, we propose MBSD, a pre-trained vision-and-language model which can integrate the heterogeneous information of product in a single stream BERT-style architecture. Compared with current approaches, MBSD uses a lightweight convolutional neural network instead of a heavy feature extractor for image encoding, which has lower latency. Besides, we cleverly utilize user behavior data to design a two-stage pre-training task to understand products from different perspectives. In addition, there is an underlying imbalanced problem in multimodal pre-training, which will impairs downstream tasks. To this end, we propose a novel self-distillation strategy to transfer the knowledge in dominated modality to weaker modality, so that each modality can be fully tapped during pre-training. Experimental results on several product understanding tasks demonstrate that the performance of MBSD outperforms the competitive baselines.|产品理解是指一系列以产品为中心的任务，如分类、对齐和属性值预测，它需要对产品的各种模式进行细粒度的融合。优秀的产品建模能力将增强用户体验，有利于搜索和推荐系统。在本文中，我们提出了 MBSD，一个预先训练的视觉和语言模型，它可以集成产品的异构信息在一个单流 BERT 风格的体系结构。与目前的方法相比，MBSD 使用了一个轻量级的卷积神经网络，而不是一个沉重的特征提取器来进行图像编码，后者具有更低的延迟。此外，我们巧妙地利用用户行为数据设计了一个两阶段的培训前任务，从不同的角度来理解产品。此外，在多模式预训练中存在一个潜在的不平衡问题，这将损害下游任务。为此，我们提出了一种新的自我提取策略，将主导模式中的知识转移到较弱模式中，以便在预训练过程中充分利用每种模式。在几个产品理解任务上的实验结果表明，MBSD 的性能优于竞争基线。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Multimodal+Pre-Training+with+Self-Distillation+for+Product+Understanding+in+E-Commerce)|0| -|[Towards Universal Cross-Domain Recommendation](https://doi.org/10.1145/3539597.3570366)|Jiangxia Cao, Shaoshuai Li, Bowen Yu, Xiaobo Guo, Tingwen Liu, Bin Wang|MYbank, Ant Group, Beijing, China; DAMO Academy, Alibaba Group, Beijing, China; Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China; Xiaomi AI Lab, Xiaomi Inc., NEED, China|In industry, web platforms such as Alibaba and Amazon often provide diverse services for users. Unsurprisingly, some developed services are data-rich, while some newly started services are data-scarce accompanied by severe data sparsity and cold-start problems. To alleviate the above problems and incubate new services easily, cross-domain recommendation (CDR) has attracted much attention from industrial and academic researchers. Generally, CDR aims to transfer rich user-item interaction information from related source domains (e.g., developed services) to boost recommendation quality of target domains (e.g., newly started services). For different scenarios, previous CDR methods can be roughly divided into two branches: (1) Data sparsity CDR fulfills user preference aided by other domain data to make intra-domain recommendations for users with few interactions, (2) Cold-start CDR projects user preference from other domain to make inter-domain recommendations for users with none interactions. In the past years, many outstanding CDR methods are emerged, however, to the best of our knowledge, none of them attempts to solve the two branches simultaneously. In this paper, we provide a unified framework, namely UniCDR, which can universally model different CDR scenarios by transferring the domain-shared information. Extensive experiments under the above 2 branches on 4 CDR scenarios and 6 public and large-scale industrial datasets demonstrate the effectiveness and universal ability of our UniCDR.|在工业界，阿里巴巴和亚马逊等网络平台往往为用户提供多样化的服务。毫不奇怪，一些已开发的服务数据丰富，而一些新开始的服务数据稀缺，伴随着严重的数据稀缺和冷启动问题。为了解决上述问题，更好地孵化新的服务，跨域推荐已经引起了业界和学术界的广泛关注。一般来说，CDR 旨在从相关的源域(例如已开发的服务)传输丰富的用户项交互信息，以提高目标域(例如新启动的服务)的推荐质量。对于不同的场景，以往的 CDR 方法大致可以分为两个分支: (1)数据稀疏性 CDR 在其他领域数据的辅助下实现用户偏好，为交互较少的用户提供域内推荐; (2)冷启动 CDR 从其他领域投射用户偏好，为没有交互的用户提供域间推荐。在过去的几年中，出现了许多优秀的 CDR 方法，然而，就我们所知，它们都没有尝试同时解决这两个分支。本文提供了一个统一的框架，即 UniCDR，它通过传递领域共享信息，可以对不同的 CDR 场景进行统一建模。在上述两个分支下，对4个 CDR 场景和6个公共和大规模工业数据集进行了广泛的实验，证明了 UniCDR 的有效性和通用性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Towards+Universal+Cross-Domain+Recommendation)|0| -|[Relation Preference Oriented High-order Sampling for Recommendation](https://doi.org/10.1145/3539597.3570424)|Mukun Chen, Xiuwen Gong, YH Jin, Wenbin Hu|School of Computer Science, Wuhan University, Wuhan, China; Center for Evidence-Based and Translational Medicine, Wuhan University, Wuhan, China; The University of Sydney, Sydney, NSW, Austria|The introduction of knowledge graphs (KG) into recommendation systems (RS) has been proven to be effective because KG introduces a variety of relations between items. In fact, users have different relation preferences depending on the relationship in KG. Existing GNN-based models largely adopt random neighbor sampling strategies to process propagation; however, these models cannot aggregate biased relation preference local information for a specific user, and thus cannot effectively reveal the internal relationship between users' preferences. This will reduce the accuracy of recommendations, while also limiting the interpretability of the results. Therefore, we propose a Relation Preference oriented High-order Sampling (RPHS) model to dynamically sample subgraphs based on relation preference and hard negative samples for user-item pairs. We design a path sampling strategy based on relation preference, which can encode the critical paths between specific user-item pairs to sample the paths in the high-order message passing subgraphs. Next, we design a mixed sampling strategy and define a new propagation operation to further enhance RPHS's ability to distinguish negative signals. Through the above sampling strategies, our model can better aggregate local relation preference information and reveal the internal relationship between users' preferences. Experiments show that our model outperforms the state-of-the-art models on three datasets by 14.98%, 5.31%, and 8.65%, and also performs well in terms of interpretability. The codes are available at https://github.com/RPHS/RPHS.git|在推荐系统中引入知识图(KG)已被证明是有效的，因为 KG 引入了项目之间的各种关系。实际上，根据 KG 中的关系，用户有不同的关系偏好。现有的基于 GNN 的模型大多采用随机邻居抽样策略来处理传播过程，但是这些模型不能为特定用户聚合有偏差的关系偏好局部信息，因此不能有效地揭示用户偏好之间的内在关系。这将降低建议的准确性，同时也限制了结果的可解释性。因此，我们提出了一个面向关系偏好的高阶抽样(RPHS)模型来动态抽样基于关系偏好和硬负样本的用户项目对子图。设计了一种基于关系偏好的路径抽样策略，对特定用户-项目对之间的关键路径进行编码，从而对高阶消息传递子图中的路径进行抽样。接下来，我们设计了一个混合采样策略并定义了一个新的传播操作来进一步提高 RPHS 分辨负信号的能力。通过以上的抽样策略，我们的模型可以更好地聚合局部关系偏好信息，揭示用户偏好之间的内在关系。实验表明，我们的模型在三个数据集上的性能分别比最先进的模型高出14.98% 、5.31% 和8.65% ，而且在可解释性方面也表现出良好的性能。密码可以在 https://github.com/rphs/rphs.git 找到|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Relation+Preference+Oriented+High-order+Sampling+for+Recommendation)|0| -|[Knowledge Enhancement for Contrastive Multi-Behavior Recommendation](https://doi.org/10.1145/3539597.3570386)|Hongrui Xuan, Yi Liu, Bohan Li, Hongzhi Yin|Nanjing University of Aeronautics and Astronautics, Nanjing , China; The University of Queensland, Brisbane, Australia; Nanjing University of Aeronautics and Astronautics, Nanjing, China|A well-designed recommender system can accurately capture the attributes of users and items, reflecting the unique preferences of individuals. Traditional recommendation techniques usually focus on modeling the singular type of behaviors between users and items. However, in many practical recommendation scenarios (e.g., social media, e-commerce), there exist multi-typed interactive behaviors in user-item relationships, such as click, tag-as-favorite, and purchase in online shopping platforms. Thus, how to make full use of multi-behavior information for recommendation is of great importance to the existing system, which presents challenges in two aspects that need to be explored: (1) Utilizing users' personalized preferences to capture multi-behavioral dependencies; (2) Dealing with the insufficient recommendation caused by sparse supervision signal for target behavior. In this work, we propose a Knowledge Enhancement Multi-Behavior Contrastive Learning Recommendation (KMCLR) framework, including two Contrastive Learning tasks and three functional modules to tackle the above challenges, respectively. In particular, we design the multi-behavior learning module to extract users' personalized behavior information for user-embedding enhancement, and utilize knowledge graph in the knowledge enhancement module to derive more robust knowledge-aware representations for items. In addition, in the optimization stage, we model the coarse-grained commonalities and the fine-grained differences between multi-behavior of users to further improve the recommendation effect. Extensive experiments and ablation tests on the three real-world datasets indicate our KMCLR outperforms various state-of-the-art recommendation methods and verify the effectiveness of our method.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Knowledge+Enhancement+for+Contrastive+Multi-Behavior+Recommendation)|0| +|[Towards Universal Cross-Domain Recommendation](https://doi.org/10.1145/3539597.3570366)|Jiangxia Cao, Shaoshuai Li, Bowen Yu, Xiaobo Guo, Tingwen Liu, Bin Wang|Xiaomi AI Lab, Xiaomi Inc., NEED, China; DAMO Academy, Alibaba Group, Beijing, China; Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China; MYbank, Ant Group, Beijing, China|In industry, web platforms such as Alibaba and Amazon often provide diverse services for users. Unsurprisingly, some developed services are data-rich, while some newly started services are data-scarce accompanied by severe data sparsity and cold-start problems. To alleviate the above problems and incubate new services easily, cross-domain recommendation (CDR) has attracted much attention from industrial and academic researchers. Generally, CDR aims to transfer rich user-item interaction information from related source domains (e.g., developed services) to boost recommendation quality of target domains (e.g., newly started services). For different scenarios, previous CDR methods can be roughly divided into two branches: (1) Data sparsity CDR fulfills user preference aided by other domain data to make intra-domain recommendations for users with few interactions, (2) Cold-start CDR projects user preference from other domain to make inter-domain recommendations for users with none interactions. In the past years, many outstanding CDR methods are emerged, however, to the best of our knowledge, none of them attempts to solve the two branches simultaneously. In this paper, we provide a unified framework, namely UniCDR, which can universally model different CDR scenarios by transferring the domain-shared information. Extensive experiments under the above 2 branches on 4 CDR scenarios and 6 public and large-scale industrial datasets demonstrate the effectiveness and universal ability of our UniCDR.|在工业界，阿里巴巴和亚马逊等网络平台往往为用户提供多样化的服务。毫不奇怪，一些已开发的服务数据丰富，而一些新开始的服务数据稀缺，伴随着严重的数据稀缺和冷启动问题。为了解决上述问题，更好地孵化新的服务，跨域推荐已经引起了业界和学术界的广泛关注。一般来说，CDR 旨在从相关的源域(例如已开发的服务)传输丰富的用户项交互信息，以提高目标域(例如新启动的服务)的推荐质量。对于不同的场景，以往的 CDR 方法大致可以分为两个分支: (1)数据稀疏性 CDR 在其他领域数据的辅助下实现用户偏好，为交互较少的用户提供域内推荐; (2)冷启动 CDR 从其他领域投射用户偏好，为没有交互的用户提供域间推荐。在过去的几年中，出现了许多优秀的 CDR 方法，然而，就我们所知，它们都没有尝试同时解决这两个分支。本文提供了一个统一的框架，即 UniCDR，它通过传递领域共享信息，可以对不同的 CDR 场景进行统一建模。在上述两个分支下，对4个 CDR 场景和6个公共和大规模工业数据集进行了广泛的实验，证明了 UniCDR 的有效性和通用性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Towards+Universal+Cross-Domain+Recommendation)|0| +|[Relation Preference Oriented High-order Sampling for Recommendation](https://doi.org/10.1145/3539597.3570424)|Mukun Chen, Xiuwen Gong, YH Jin, Wenbin Hu|The University of Sydney, Sydney, NSW, Austria; Center for Evidence-Based and Translational Medicine, Wuhan University, Wuhan, China; School of Computer Science, Wuhan University, Wuhan, China|The introduction of knowledge graphs (KG) into recommendation systems (RS) has been proven to be effective because KG introduces a variety of relations between items. In fact, users have different relation preferences depending on the relationship in KG. Existing GNN-based models largely adopt random neighbor sampling strategies to process propagation; however, these models cannot aggregate biased relation preference local information for a specific user, and thus cannot effectively reveal the internal relationship between users' preferences. This will reduce the accuracy of recommendations, while also limiting the interpretability of the results. Therefore, we propose a Relation Preference oriented High-order Sampling (RPHS) model to dynamically sample subgraphs based on relation preference and hard negative samples for user-item pairs. We design a path sampling strategy based on relation preference, which can encode the critical paths between specific user-item pairs to sample the paths in the high-order message passing subgraphs. Next, we design a mixed sampling strategy and define a new propagation operation to further enhance RPHS's ability to distinguish negative signals. Through the above sampling strategies, our model can better aggregate local relation preference information and reveal the internal relationship between users' preferences. Experiments show that our model outperforms the state-of-the-art models on three datasets by 14.98%, 5.31%, and 8.65%, and also performs well in terms of interpretability. The codes are available at https://github.com/RPHS/RPHS.git|在推荐系统中引入知识图(KG)已被证明是有效的，因为 KG 引入了项目之间的各种关系。实际上，根据 KG 中的关系，用户有不同的关系偏好。现有的基于 GNN 的模型大多采用随机邻居抽样策略来处理传播过程，但是这些模型不能为特定用户聚合有偏差的关系偏好局部信息，因此不能有效地揭示用户偏好之间的内在关系。这将降低建议的准确性，同时也限制了结果的可解释性。因此，我们提出了一个面向关系偏好的高阶抽样(RPHS)模型来动态抽样基于关系偏好和硬负样本的用户项目对子图。设计了一种基于关系偏好的路径抽样策略，对特定用户-项目对之间的关键路径进行编码，从而对高阶消息传递子图中的路径进行抽样。接下来，我们设计了一个混合采样策略并定义了一个新的传播操作来进一步提高 RPHS 分辨负信号的能力。通过以上的抽样策略，我们的模型可以更好地聚合局部关系偏好信息，揭示用户偏好之间的内在关系。实验表明，我们的模型在三个数据集上的性能分别比最先进的模型高出14.98% 、5.31% 和8.65% ，而且在可解释性方面也表现出良好的性能。密码可以在 https://github.com/rphs/rphs.git 找到|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Relation+Preference+Oriented+High-order+Sampling+for+Recommendation)|0| +|[Knowledge Enhancement for Contrastive Multi-Behavior Recommendation](https://doi.org/10.1145/3539597.3570386)|Hongrui Xuan, Yi Liu, Bohan Li, Hongzhi Yin|The University of Queensland, Brisbane, Australia; Nanjing University of Aeronautics and Astronautics, Nanjing , China; Nanjing University of Aeronautics and Astronautics, Nanjing, China|A well-designed recommender system can accurately capture the attributes of users and items, reflecting the unique preferences of individuals. Traditional recommendation techniques usually focus on modeling the singular type of behaviors between users and items. However, in many practical recommendation scenarios (e.g., social media, e-commerce), there exist multi-typed interactive behaviors in user-item relationships, such as click, tag-as-favorite, and purchase in online shopping platforms. Thus, how to make full use of multi-behavior information for recommendation is of great importance to the existing system, which presents challenges in two aspects that need to be explored: (1) Utilizing users' personalized preferences to capture multi-behavioral dependencies; (2) Dealing with the insufficient recommendation caused by sparse supervision signal for target behavior. In this work, we propose a Knowledge Enhancement Multi-Behavior Contrastive Learning Recommendation (KMCLR) framework, including two Contrastive Learning tasks and three functional modules to tackle the above challenges, respectively. In particular, we design the multi-behavior learning module to extract users' personalized behavior information for user-embedding enhancement, and utilize knowledge graph in the knowledge enhancement module to derive more robust knowledge-aware representations for items. In addition, in the optimization stage, we model the coarse-grained commonalities and the fine-grained differences between multi-behavior of users to further improve the recommendation effect. Extensive experiments and ablation tests on the three real-world datasets indicate our KMCLR outperforms various state-of-the-art recommendation methods and verify the effectiveness of our method.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Knowledge+Enhancement+for+Contrastive+Multi-Behavior+Recommendation)|0| |[Improving News Recommendation with Channel-Wise Dynamic Representations and Contrastive User Modeling](https://doi.org/10.1145/3539597.3570447)|Jingkun Wang, Yongtao Jiang, Haochen Li, Wen Zhao|Peking University, Beijing, China|News modeling and user modeling are the two core tasks of news recommendation. Accurate user representation and news representation can enable the recommendation system to provide users with precise recommendation services. Most existing methods use deep learning models such as CNN and Self-Attention to extract text features from news titles and abstracts to generate specific news vectors. However, the CNN-based methods have fixed parameters and cannot extract specific features for different input words, while the Self-Attention-based methods have high computational costs and are difficult to capture local features effectively. In our proposed method, we build a category-based dynamic component to generate suitable parameters for different inputs and extract local features from multiple perspectives. Meanwhile, users will mistakenly click on some news terms they are not interested in, so there will be some interaction noises in the datasets. In order to explore the critical user behaviors in user data and reduce the impact of noise data on user modeling, we adopt a frequency-aware contrastive learning method in user modeling. Experiments on real-world datasets verify the effectiveness of our proposed method.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Improving+News+Recommendation+with+Channel-Wise+Dynamic+Representations+and+Contrastive+User+Modeling)|0| -|[Search Behavior Prediction: A Hypergraph Perspective](https://doi.org/10.1145/3539597.3570403)|Yan Han, Edward W. Huang, Wenqing Zheng, Nikhil Rao, Zhangyang Wang, Karthik Subbian|Amazon, Palo Alto, CA, USA; University of Texas at Austin, Austin, TX, USA|Although the bipartite shopping graphs are straightforward to model search behavior, they suffer from two challenges: 1) The majority of items are sporadically searched and hence have noisy/sparse query associations, leading to a \textit{long-tail} distribution. 2) Infrequent queries are more likely to link to popular items, leading to another hurdle known as \textit{disassortative mixing}. To address these two challenges, we go beyond the bipartite graph to take a hypergraph perspective, introducing a new paradigm that leverages \underline{auxiliary} information from anonymized customer engagement sessions to assist the \underline{main task} of query-item link prediction. This auxiliary information is available at web scale in the form of search logs. We treat all items appearing in the same customer session as a single hyperedge. The hypothesis is that items in a customer session are unified by a common shopping interest. With these hyperedges, we augment the original bipartite graph into a new \textit{hypergraph}. We develop a \textit{\textbf{D}ual-\textbf{C}hannel \textbf{A}ttention-Based \textbf{H}ypergraph Neural Network} (\textbf{DCAH}), which synergizes information from two potentially noisy sources (original query-item edges and item-item hyperedges). In this way, items on the tail are better connected due to the extra hyperedges, thereby enhancing their link prediction performance. We further integrate DCAH with self-supervised graph pre-training and/or DropEdge training, both of which effectively alleviate disassortative mixing. Extensive experiments on three proprietary E-Commerce datasets show that DCAH yields significant improvements of up to \textbf{24.6\% in mean reciprocal rank (MRR)} and \textbf{48.3\% in recall} compared to GNN-based baselines. Our source code is available at \url{https://github.com/amazon-science/dual-channel-hypergraph-neural-network}.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Search+Behavior+Prediction:+A+Hypergraph+Perspective)|0| -|[Directed Acyclic Graph Factorization Machines for CTR Prediction via Knowledge Distillation](https://doi.org/10.1145/3539597.3570384)|Zhen Tian, Ting Bai, Zibin Zhang, Zhiyuan Xu, Kangyi Lin, JiRong Wen, Wayne Xin Zhao|Renmin University of China, Beijing, China; Beijing University of Posts and Telecommunications & Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia, Beijing, China; Tencent, Guangzhou, China; Renmin University of China & Beijing Key Laboratory of Big Data Management and Analysis Methods, Beijing, China|With the growth of high-dimensional sparse data in web-scale recommender systems, the computational cost to learn high-order feature interaction in CTR prediction task largely increases, which limits the use of high-order interaction models in real industrial applications. Some recent knowledge distillation based methods transfer knowledge from complex teacher models to shallow student models for accelerating the online model inference. However, they suffer from the degradation of model accuracy in knowledge distillation process. It is challenging to balance the efficiency and effectiveness of the shallow student models. To address this problem, we propose a Directed Acyclic Graph Factorization Machine (KD-DAGFM) to learn the high-order feature interactions from existing complex interaction models for CTR prediction via Knowledge Distillation. The proposed lightweight student model DAGFM can learn arbitrary explicit feature interactions from teacher networks, which achieves approximately lossless performance and is proved by a dynamic programming algorithm. Besides, an improved general model KD-DAGFM+ is shown to be effective in distilling both explicit and implicit feature interactions from any complex teacher model. Extensive experiments are conducted on four real-world datasets, including a large-scale industrial dataset from WeChat platform with billions of feature dimensions. KD-DAGFM achieves the best performance with less than 21.5% FLOPs of the state-of-the-art method on both online and offline experiments, showing the superiority of DAGFM to deal with the industrial scale data in CTR prediction task. Our implementation code is available at: https://github.com/RUCAIBox/DAGFM.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Directed+Acyclic+Graph+Factorization+Machines+for+CTR+Prediction+via+Knowledge+Distillation)|0| -|[Model-based Unbiased Learning to Rank](https://doi.org/10.1145/3539597.3570395)|Dan Luo, Lixin Zou, Qingyao Ai, Zhiyu Chen, Dawei Yin, Brian D. Davison|Amazon.com, Inc., Seattle, WA, USA; Baidu Inc., Beijing, PA, China; Lehigh University, Bethlehem, PA, USA; Tsinghua University, Beijing, PA, China|Unbiased Learning to Rank (ULTR) that learns to rank documents with biased user feedback data is a well-known challenge in information retrieval. Existing methods in unbiased learning to rank typically rely on click modeling or inverse propensity weighting (IPW). Unfortunately, the search engines are faced with severe long-tail query distribution, where neither click modeling nor IPW can handle well. Click modeling suffers from data sparsity problem since the same query-document pair appears limited times on tail queries; IPW suffers from high variance problem since it is highly sensitive to small propensity score values. Therefore, a general debiasing framework that works well under tail queries is in desperate need. To address this problem, we propose a model-based unbiased learning-to-rank framework. Specifically, we develop a general context-aware user simulator to generate pseudo clicks for unobserved ranked lists to train rankers, which addresses the data sparsity problem. In addition, considering the discrepancy between pseudo clicks and actual clicks, we take the observation of a ranked list as the treatment variable and further incorporate inverse propensity weighting with pseudo labels in a doubly robust way. The derived bias and variance indicate that the proposed model-based method is more robust than existing methods. Finally, extensive experiments on benchmark datasets, including simulated datasets and real click logs, demonstrate that the proposed model-based method consistently performs outperforms state-of-the-art methods in various scenarios. The code is available at https://github.com/rowedenny/MULTR.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Model-based+Unbiased+Learning+to+Rank)|0| -|[Pairwise Fairness in Ranking as a Dissatisfaction Measure](https://doi.org/10.1145/3539597.3570459)|Alessandro Fabris, Gianmaria Silvello, Gian Antonio Susto, Asia J. Biega|Univesity of Padova, Padova, Italy; Max Planck Institute for Security and Privacy, Bochum, Germany|Fairness and equity have become central to ranking problems in information access systems, such as search engines, recommender systems, or marketplaces. To date, several types of fair ranking measures have been proposed, including diversity, exposure, and pairwise fairness measures. Out of those, pairwise fairness is a family of metrics whose normative grounding has not been clearly explicated, leading to uncertainty with respect to the construct that is being measured and how it relates to stakeholders' desiderata. In this paper, we develop a normative and behavioral grounding for pairwise fairness in ranking. Leveraging measurement theory and user browsing models, we derive an interpretation of pairwise fairness centered on the construct of producer dissatisfaction, tying pairwise fairness to perceptions of ranking quality. Highlighting the key limitations of prior pairwise measures, we introduce a set of reformulations that allow us to capture behavioral and practical aspects of ranking systems. These reformulations form the basis for a novel pairwise metric of producer dissatisfaction. Our analytical and empirical study demonstrates the relationship between dissatisfaction, pairwise, and exposure-based fairness metrics, enabling informed adoption of the measures.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Pairwise+Fairness+in+Ranking+as+a+Dissatisfaction+Measure)|0| -|[Reducing Negative Effects of the Biases of Language Models in Zero-Shot Setting](https://doi.org/10.1145/3539597.3570382)|Xiaosu Wang, Yun Xiong, Beichen Kang, Yao Zhang, Philip S. Yu, Yangyong Zhu|Fudan University, Shanghai, China; University of Illinois at Chicago, Chicago, USA|Pre-trained language models (PLMs) such as GPTs have been revealed to be biased towards certain target classes because of the prompt and the model's intrinsic biases. In contrast to the fully supervised scenario where there are a large number of costly labeled samples that can be used to fine-tune model parameters to correct for biases, there are no labeled samples available for the zero-shot setting. We argue that a key to calibrating the biases of a PLM on a target task in zero-shot setting lies in detecting and estimating the biases, which remains a challenge. In this paper, we first construct probing samples with the randomly generated token sequences, which are simple but effective in detecting inputs for stimulating GPTs to show the biases; and we pursue an in-depth research on the plausibility of utilizing class scores for the probing samples to reflect and estimate the biases of GPTs on a downstream target task. Furtherly, in order to effectively utilize the probing samples and thus reduce negative effects of the biases of GPTs, we propose a lightweight model Calibration Adapter (CA) along with a self-guided training strategy that carries out distribution-level optimization, which enables us to take advantage of the probing samples to fine-tune and select only the proposed CA, respectively, while keeping the PLM encoder frozen. To demonstrate the effectiveness of our study, we have conducted extensive experiments, where the results indicate that the calibration ability acquired by CA on the probing samples can be successfully transferred to reduce negative effects of the biases of GPTs on a downstream target task, and our approach can yield better performance than state-of-the-art (SOTA) models in zero-shot settings.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Reducing+Negative+Effects+of+the+Biases+of+Language+Models+in+Zero-Shot+Setting)|0| -|[Multi-queue Momentum Contrast for Microvideo-Product Retrieval](https://doi.org/10.1145/3539597.3570405)|Yali Du, Yinwei Wei, Wei Ji, Fan Liu, Xin Luo, Liqiang Nie|Shandong University, Jinan, China; National University of Singapore, Kent Ridge, Singapore; Harbin Institute of Technology (Shenzhen), Shenzhen, China; Nanjing University, Nanjing, China|The booming development and huge market of micro-videos bring new e-commerce channels for merchants. Currently, more micro-video publishers prefer to embed relevant ads into their micro-videos, which not only provides them with business income but helps the audiences to discover their interesting products. However, due to the micro-video recording by unprofessional equipment, involving various topics and including multiple modalities, it is challenging to locate the products related to micro-videos efficiently, appropriately, and accurately. We formulate the microvideo-product retrieval task, which is the first attempt to explore the retrieval between the multi-modal and multi-modal instances. A novel approach named Multi-Queue Momentum Contrast (MQMC) network is proposed for bidirectional retrieval, consisting of the uni-modal feature and multi-modal instance representation learning. Moreover, a discriminative selection strategy with a multi-queue is used to distinguish the importance of different negatives based on their categories. We collect two large-scale microvideo-product datasets (MVS and MVS-large) for evaluation and manually construct the hierarchical category ontology, which covers sundry products in daily life. Extensive experiments show that MQMC outperforms the state-of-the-art baselines. Our replication package (including code, dataset, etc.) is publicly available at https://github.com/duyali2000/MQMC.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Multi-queue+Momentum+Contrast+for+Microvideo-Product+Retrieval)|0| +|[Search Behavior Prediction: A Hypergraph Perspective](https://doi.org/10.1145/3539597.3570403)|Yan Han, Edward W. Huang, Wenqing Zheng, Nikhil Rao, Zhangyang Wang, Karthik Subbian|University of Texas at Austin, Austin, TX, USA; Amazon, Palo Alto, CA, USA|Although the bipartite shopping graphs are straightforward to model search behavior, they suffer from two challenges: 1) The majority of items are sporadically searched and hence have noisy/sparse query associations, leading to a \textit{long-tail} distribution. 2) Infrequent queries are more likely to link to popular items, leading to another hurdle known as \textit{disassortative mixing}. To address these two challenges, we go beyond the bipartite graph to take a hypergraph perspective, introducing a new paradigm that leverages \underline{auxiliary} information from anonymized customer engagement sessions to assist the \underline{main task} of query-item link prediction. This auxiliary information is available at web scale in the form of search logs. We treat all items appearing in the same customer session as a single hyperedge. The hypothesis is that items in a customer session are unified by a common shopping interest. With these hyperedges, we augment the original bipartite graph into a new \textit{hypergraph}. We develop a \textit{\textbf{D}ual-\textbf{C}hannel \textbf{A}ttention-Based \textbf{H}ypergraph Neural Network} (\textbf{DCAH}), which synergizes information from two potentially noisy sources (original query-item edges and item-item hyperedges). In this way, items on the tail are better connected due to the extra hyperedges, thereby enhancing their link prediction performance. We further integrate DCAH with self-supervised graph pre-training and/or DropEdge training, both of which effectively alleviate disassortative mixing. Extensive experiments on three proprietary E-Commerce datasets show that DCAH yields significant improvements of up to \textbf{24.6\% in mean reciprocal rank (MRR)} and \textbf{48.3\% in recall} compared to GNN-based baselines. Our source code is available at \url{https://github.com/amazon-science/dual-channel-hypergraph-neural-network}.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Search+Behavior+Prediction:+A+Hypergraph+Perspective)|0| +|[Directed Acyclic Graph Factorization Machines for CTR Prediction via Knowledge Distillation](https://doi.org/10.1145/3539597.3570384)|Zhen Tian, Ting Bai, Zibin Zhang, Zhiyuan Xu, Kangyi Lin, JiRong Wen, Wayne Xin Zhao|Renmin University of China, Beijing, China; Tencent, Guangzhou, China; Renmin University of China & Beijing Key Laboratory of Big Data Management and Analysis Methods, Beijing, China; Beijing University of Posts and Telecommunications & Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia, Beijing, China|With the growth of high-dimensional sparse data in web-scale recommender systems, the computational cost to learn high-order feature interaction in CTR prediction task largely increases, which limits the use of high-order interaction models in real industrial applications. Some recent knowledge distillation based methods transfer knowledge from complex teacher models to shallow student models for accelerating the online model inference. However, they suffer from the degradation of model accuracy in knowledge distillation process. It is challenging to balance the efficiency and effectiveness of the shallow student models. To address this problem, we propose a Directed Acyclic Graph Factorization Machine (KD-DAGFM) to learn the high-order feature interactions from existing complex interaction models for CTR prediction via Knowledge Distillation. The proposed lightweight student model DAGFM can learn arbitrary explicit feature interactions from teacher networks, which achieves approximately lossless performance and is proved by a dynamic programming algorithm. Besides, an improved general model KD-DAGFM+ is shown to be effective in distilling both explicit and implicit feature interactions from any complex teacher model. Extensive experiments are conducted on four real-world datasets, including a large-scale industrial dataset from WeChat platform with billions of feature dimensions. KD-DAGFM achieves the best performance with less than 21.5% FLOPs of the state-of-the-art method on both online and offline experiments, showing the superiority of DAGFM to deal with the industrial scale data in CTR prediction task. Our implementation code is available at: https://github.com/RUCAIBox/DAGFM.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Directed+Acyclic+Graph+Factorization+Machines+for+CTR+Prediction+via+Knowledge+Distillation)|0| +|[Model-based Unbiased Learning to Rank](https://doi.org/10.1145/3539597.3570395)|Dan Luo, Lixin Zou, Qingyao Ai, Zhiyu Chen, Dawei Yin, Brian D. Davison|Lehigh University, Bethlehem, PA, USA; Tsinghua University, Beijing, PA, China; Baidu Inc., Beijing, PA, China; Amazon.com, Inc., Seattle, WA, USA|Unbiased Learning to Rank (ULTR) that learns to rank documents with biased user feedback data is a well-known challenge in information retrieval. Existing methods in unbiased learning to rank typically rely on click modeling or inverse propensity weighting (IPW). Unfortunately, the search engines are faced with severe long-tail query distribution, where neither click modeling nor IPW can handle well. Click modeling suffers from data sparsity problem since the same query-document pair appears limited times on tail queries; IPW suffers from high variance problem since it is highly sensitive to small propensity score values. Therefore, a general debiasing framework that works well under tail queries is in desperate need. To address this problem, we propose a model-based unbiased learning-to-rank framework. Specifically, we develop a general context-aware user simulator to generate pseudo clicks for unobserved ranked lists to train rankers, which addresses the data sparsity problem. In addition, considering the discrepancy between pseudo clicks and actual clicks, we take the observation of a ranked list as the treatment variable and further incorporate inverse propensity weighting with pseudo labels in a doubly robust way. The derived bias and variance indicate that the proposed model-based method is more robust than existing methods. Finally, extensive experiments on benchmark datasets, including simulated datasets and real click logs, demonstrate that the proposed model-based method consistently performs outperforms state-of-the-art methods in various scenarios. The code is available at https://github.com/rowedenny/MULTR.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Model-based+Unbiased+Learning+to+Rank)|0| +|[Pairwise Fairness in Ranking as a Dissatisfaction Measure](https://doi.org/10.1145/3539597.3570459)|Alessandro Fabris, Gianmaria Silvello, Gian Antonio Susto, Asia J. Biega|Max Planck Institute for Security and Privacy, Bochum, Germany; Univesity of Padova, Padova, Italy|Fairness and equity have become central to ranking problems in information access systems, such as search engines, recommender systems, or marketplaces. To date, several types of fair ranking measures have been proposed, including diversity, exposure, and pairwise fairness measures. Out of those, pairwise fairness is a family of metrics whose normative grounding has not been clearly explicated, leading to uncertainty with respect to the construct that is being measured and how it relates to stakeholders' desiderata. In this paper, we develop a normative and behavioral grounding for pairwise fairness in ranking. Leveraging measurement theory and user browsing models, we derive an interpretation of pairwise fairness centered on the construct of producer dissatisfaction, tying pairwise fairness to perceptions of ranking quality. Highlighting the key limitations of prior pairwise measures, we introduce a set of reformulations that allow us to capture behavioral and practical aspects of ranking systems. These reformulations form the basis for a novel pairwise metric of producer dissatisfaction. Our analytical and empirical study demonstrates the relationship between dissatisfaction, pairwise, and exposure-based fairness metrics, enabling informed adoption of the measures.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Pairwise+Fairness+in+Ranking+as+a+Dissatisfaction+Measure)|0| +|[Reducing Negative Effects of the Biases of Language Models in Zero-Shot Setting](https://doi.org/10.1145/3539597.3570382)|Xiaosu Wang, Yun Xiong, Beichen Kang, Yao Zhang, Philip S. Yu, Yangyong Zhu|University of Illinois at Chicago, Chicago, USA; Fudan University, Shanghai, China|Pre-trained language models (PLMs) such as GPTs have been revealed to be biased towards certain target classes because of the prompt and the model's intrinsic biases. In contrast to the fully supervised scenario where there are a large number of costly labeled samples that can be used to fine-tune model parameters to correct for biases, there are no labeled samples available for the zero-shot setting. We argue that a key to calibrating the biases of a PLM on a target task in zero-shot setting lies in detecting and estimating the biases, which remains a challenge. In this paper, we first construct probing samples with the randomly generated token sequences, which are simple but effective in detecting inputs for stimulating GPTs to show the biases; and we pursue an in-depth research on the plausibility of utilizing class scores for the probing samples to reflect and estimate the biases of GPTs on a downstream target task. Furtherly, in order to effectively utilize the probing samples and thus reduce negative effects of the biases of GPTs, we propose a lightweight model Calibration Adapter (CA) along with a self-guided training strategy that carries out distribution-level optimization, which enables us to take advantage of the probing samples to fine-tune and select only the proposed CA, respectively, while keeping the PLM encoder frozen. To demonstrate the effectiveness of our study, we have conducted extensive experiments, where the results indicate that the calibration ability acquired by CA on the probing samples can be successfully transferred to reduce negative effects of the biases of GPTs on a downstream target task, and our approach can yield better performance than state-of-the-art (SOTA) models in zero-shot settings.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Reducing+Negative+Effects+of+the+Biases+of+Language+Models+in+Zero-Shot+Setting)|0| +|[Multi-queue Momentum Contrast for Microvideo-Product Retrieval](https://doi.org/10.1145/3539597.3570405)|Yali Du, Yinwei Wei, Wei Ji, Fan Liu, Xin Luo, Liqiang Nie|National University of Singapore, Kent Ridge, Singapore; Harbin Institute of Technology (Shenzhen), Shenzhen, China; Shandong University, Jinan, China; Nanjing University, Nanjing, China|The booming development and huge market of micro-videos bring new e-commerce channels for merchants. Currently, more micro-video publishers prefer to embed relevant ads into their micro-videos, which not only provides them with business income but helps the audiences to discover their interesting products. However, due to the micro-video recording by unprofessional equipment, involving various topics and including multiple modalities, it is challenging to locate the products related to micro-videos efficiently, appropriately, and accurately. We formulate the microvideo-product retrieval task, which is the first attempt to explore the retrieval between the multi-modal and multi-modal instances. A novel approach named Multi-Queue Momentum Contrast (MQMC) network is proposed for bidirectional retrieval, consisting of the uni-modal feature and multi-modal instance representation learning. Moreover, a discriminative selection strategy with a multi-queue is used to distinguish the importance of different negatives based on their categories. We collect two large-scale microvideo-product datasets (MVS and MVS-large) for evaluation and manually construct the hierarchical category ontology, which covers sundry products in daily life. Extensive experiments show that MQMC outperforms the state-of-the-art baselines. Our replication package (including code, dataset, etc.) is publicly available at https://github.com/duyali2000/MQMC.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Multi-queue+Momentum+Contrast+for+Microvideo-Product+Retrieval)|0| |[Improving Cross-lingual Information Retrieval on Low-Resource Languages via Optimal Transport Distillation](https://doi.org/10.1145/3539597.3570468)|Zhiqi Huang, Puxuan Yu, James Allan|University of Massachusetts Amherst, Amherst, MA, USA|Benefiting from transformer-based pre-trained language models, neural ranking models have made significant progress. More recently, the advent of multilingual pre-trained language models provides great support for designing neural cross-lingual retrieval models. However, due to unbalanced pre-training data in different languages, multilingual language models have already shown a performance gap between high and low-resource languages in many downstream tasks. And cross-lingual retrieval models built on such pre-trained models can inherit language bias, leading to suboptimal result for low-resource languages. Moreover, unlike the English-to-English retrieval task, where large-scale training collections for document ranking such as MS MARCO are available, the lack of cross-lingual retrieval data for low-resource language makes it more challenging for training cross-lingual retrieval models. In this work, we propose OPTICAL: Optimal Transport distillation for low-resource Cross-lingual information retrieval. To transfer a model from high to low resource languages, OPTICAL forms the cross-lingual token alignment task as an optimal transport problem to learn from a well-trained monolingual retrieval model. By separating the cross-lingual knowledge from knowledge of query document matching, OPTICAL only needs bitext data for distillation training, which is more feasible for low-resource languages. Experimental results show that, with minimal training data, OPTICAL significantly outperforms strong baselines on low-resource languages, including neural machine translation.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Improving+Cross-lingual+Information+Retrieval+on+Low-Resource+Languages+via+Optimal+Transport+Distillation)|0| |[MeSH Suggester: A Library and System for MeSH Term Suggestion for Systematic Review Boolean Query Construction](https://doi.org/10.1145/3539597.3573025)|Shuai Wang, Hang Li, Guido Zuccon|The University of Queensland, Brisbane, QLD, Australia|Boolean query construction is often critical for medical systematic review literature search. To create an effective Boolean query, systematic review researchers typically spend weeks coming up with effective query terms and combinations. One challenge to creating an effective systematic review Boolean query is the selection of effective MeSH Terms to include in the query. In our previous work, we created neural MeSH term suggestion methods and compared them to state-of-the-art MeSH term suggestion methods. We found neural MeSH term suggestion methods to be highly effective. In this demonstration, we build upon our previous work by creating (1) a Web-based MeSH term suggestion prototype system that allows users to obtain suggestions from a number of underlying methods and (2) a Python library that implements ours and others' MeSH term suggestion methods and that is aimed at researchers who want to further investigate, create or deploy such type of methods. We describe the architecture of the web-based system and how to use it for the MeSH term suggestion task. For the Python library, we describe how the library can be used for advancing further research and experimentation, and we validate the results of the methods contained in the library on standard datasets. Our web-based prototype system is available at http://ielab-mesh-suggest.uqcloud.net, while our Python library is at https://github.com/ielab/meshsuggestlib.|布尔查询结构通常是医学系统综述文献检索的关键。为了创建一个有效的布尔查询，系统综述研究人员通常要花费数周时间来构建有效的查询术语和组合。创建一个有效的系统综述布尔查询的一个挑战是在查询中选择有效的 MeSH 术语。在我们以前的工作中，我们创建了神经 MeSH 术语建议方法，并将它们与最先进的 MeSH 术语建议方法进行了比较。我们发现神经网络术语推荐方法是非常有效的。在这个演示中，我们通过创建(1)一个基于 Web 的 MeSH 术语建议原型系统，允许用户从一些底层方法中获得建议; (2)一个实现我们和其他人的 MeSH 术语建议方法的 Python 库，目标是希望进一步研究、创建或部署这类方法的研究人员。我们描述了基于 Web 的系统的体系结构，以及如何将其用于 MeSH 术语建议任务。对于 Python 库，我们描述了如何使用该库来推进进一步的研究和实验，并验证了该库中包含的标准数据集方法的结果。我们的基于网络的原型系统可以在 http://ielab-mesh-suggest.uqcloud.net 上使用，而我们的 Python 库则处于 https://github.com/ielab/meshsuggestlib。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=MeSH+Suggester:+A+Library+and+System+for+MeSH+Term+Suggestion+for+Systematic+Review+Boolean+Query+Construction)|0| |[Marginal-Certainty-Aware Fair Ranking Algorithm](https://doi.org/10.1145/3539597.3570474)|Tao Yang, Zhichao Xu, Zhenduo Wang, Anh Tran, Qingyao Ai|Tsinghua University, Beijing, China; University of Utah, Salt Lake City, UT, USA|Ranking systems are ubiquitous in modern Internet services, including online marketplaces, social media, and search engines. Traditionally, ranking systems only focus on how to get better relevance estimation. When relevance estimation is available, they usually adopt a user-centric optimization strategy where ranked lists are generated by sorting items according to their estimated relevance. However, such user-centric optimization ignores the fact that item providers also draw utility from ranking systems. It has been shown in existing research that such user-centric optimization will cause much unfairness to item providers, followed by unfair opportunities and unfair economic gains for item providers. To address ranking fairness, many fair ranking methods have been proposed. However, as we show in this paper, these methods could be suboptimal as they directly rely on the relevance estimation without being aware of the uncertainty (i.e., the variance of the estimated relevance). To address this uncertainty, we propose a novel Marginal-Certainty-aware Fair algorithm named MCFair. MCFair jointly optimizes fairness and user utility, while relevance estimation is constantly updated in an online manner. In MCFair, we first develop a ranking objective that includes uncertainty, fairness, and user utility. Then we directly use the gradient of the ranking objective as the ranking score. We theoretically prove that MCFair based on gradients is optimal for the aforementioned ranking objective. Empirically, we find that on semi-synthesized datasets, MCFair is effective and practical and can deliver superior performance compared to state-of-the-art fair ranking methods. To facilitate reproducibility, we release our code https://github.com/Taosheng-ty/WSDM22-MCFair.|排名系统在现代互联网服务中无处不在，包括在线市场、社交媒体和搜索引擎。传统的排序系统只关注如何获得更好的相关性估计。当相关性估计可用时，它们通常采用以用户为中心的优化策略，根据估计的相关性对项目进行排序，从而生成排名列表。然而，这种以用户为中心的优化忽略了一个事实，即项目提供者也从排名系统中获取效用。已有的研究表明，这种以用户为中心的优化会给项目提供者带来很大的不公平，其次是不公平的机会和不公平的经济收益。为了解决排序公平问题，人们提出了许多公平排序方法。然而，正如我们在本文中所展示的，这些方法可能是次优的，因为它们直接依赖于相关性估计而不知道不确定性(即，估计的相关性的方差)。针对这种不确定性，我们提出了一种新的边际确定性公平算法 MCFair。MCFair 共同优化公平性和用户效用，而相关性估计是不断更新的在线方式。在 MCFair 中，我们首先开发一个包含不确定性、公平性和用户效用的排名目标。然后直接使用排序目标的梯度作为排序得分。从理论上证明了基于梯度的 MCFair 对于上述排序目标是最优的。通过实验，我们发现在半合成数据集上，MCFair 是有效和实用的，能够提供比最先进的公平排名方法更好的性能。为了便于重现，我们发布了我们的代码 https://github.com/taosheng-ty/wsdm22-mcfair。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Marginal-Certainty-Aware+Fair+Ranking+Algorithm)|0| |[Learning Stance Embeddings from Signed Social Graphs](https://doi.org/10.1145/3539597.3570401)|John PouguéBiyong, Akshay Gupta, Aria Haghighi, Ahmed ElKishky|University of Oxford, Oxford, United Kingdom; Meta, London, United Kingdom; Twitter Cortex, Seattle, WA, USA|A key challenge in social network analysis is understanding the position, or stance, of people in the graph on a large set of topics. While past work has modeled (dis)agreement in social networks using signed graphs, these approaches have not modeled agreement patterns across a range of correlated topics. For instance, disagreement on one topic may make disagreement(or agreement) more likely for related topics. We propose the Stance Embeddings Model(SEM), which jointly learns embeddings for each user and topic in signed social graphs with distinct edge types for each topic. By jointly learning user and topic embeddings, SEM is able to perform cold-start topic stance detection, predicting the stance of a user on topics for which we have not observed their engagement. We demonstrate the effectiveness of SEM using two large-scale Twitter signed graph datasets we open-source. One dataset, TwitterSG, labels (dis)agreements using engagements between users via tweets to derive topic-informed, signed edges. The other, BirdwatchSG, leverages community reports on misinformation and misleading content. On TwitterSG and BirdwatchSG, SEM shows a 39% and 26% error reduction respectively against strong baselines.|社交网络分析中的一个关键挑战是理解人们在大量主题图中的位置或立场。虽然过去的工作已经在社会网络中使用有符号图表建模(dis)一致性，但是这些方法还没有在一系列相关主题中建模一致性模式。例如，在一个话题上的分歧可能会使相关话题更容易产生分歧(或一致意见)。我们提出了立场嵌入模型(Stance Embeddings Model，SEM) ，它共同学习每个用户和每个主题的不同边缘类型的有符号社会图中的主题的嵌入。通过联合学习用户和话题嵌入，SEM 能够执行冷启动话题姿态检测，预测用户在我们没有观察到他们参与的话题上的立场。我们使用两个开源的大型 Twitter 签名图表数据集来证明 SEM 的有效性。一个数据集 TwitterSG 使用用户之间通过 tweet 的约定来获得主题知情的、有符号的边。另一个是 Birdwatch SG，利用社区关于错误信息和误导性内容的报告。在 TwitterSG 和 Birdwatch SG 上，SEM 显示与强基线相比，错误率分别降低了39% 和26% 。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Learning+Stance+Embeddings+from+Signed+Social+Graphs)|0| |[Range Restricted Route Recommendation Based on Spatial Keyword](https://doi.org/10.1145/3539597.3570434)|Hongwei Tang, Detian Zhang|Soochow University, Suzhou, China|In this paper, we focus on a new route recommendation problem, i.e., when a user gives a keyword and range constraint, the route that contains the maximum number of POIs tagged with the keyword or similar POIs in the range will be returned for him. This is a practical problem when people want to explore a place, e.g., find a route within 2 km containing as many clothing stores as possible. To solve the problem, we first calculate the score of each edge in road networks based on the number and similarity of POIs. Then, we reformulate the problem into finding the path in a graph with the maximum score within the distance constraint problem, which is proved NP-hard. Given this, we not only propose an exact branch and bound (BnB) algorithm, but also devise a more efficient top-k based network expansion (k-NE) algorithm to find the near-optimal solution. Extensive experiments on real datasets not only verify the effectiveness of the proposed route recommendation algorithm, but also show that the efficiency and accuracy of k-NE algorithm are completely acceptable.|本文研究了一个新的路由推荐问题，即当用户给出一个关键字和范围约束时，该路由将返回包含该关键字或范围内相似 POI 标记的最大 POI 数目的路由。这是一个实际的问题，当人们想要探索一个地方，例如，找到一条2公里内的路线，包括尽可能多的服装店。为了解决这个问题，我们首先根据 POI 的个数和相似度计算路网中每个边缘的得分。然后，在距离约束问题中，我们将该问题重新表述为在一个具有最大得分的图中寻找路径，证明了该问题是 NP 难的。鉴于此，我们不仅提出了一种精确的分枝定界(BnB)算法，而且设计了一种更有效的基于 top-k 的网络扩展(k-NE)算法来寻找近似最优解。在实际数据集上的大量实验不仅验证了该算法的有效性，而且表明 k-NE 算法的效率和准确性是完全可以接受的。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Range+Restricted+Route+Recommendation+Based+on+Spatial+Keyword)|0| -|[NGAME: Negative Mining-aware Mini-batching for Extreme Classification](https://doi.org/10.1145/3539597.3570392)|Kunal Dahiya, Nilesh Gupta, Deepak Saini, Akshay Soni, Yajun Wang, Kushal Dave, Jian Jiao, Gururaj K, Prasenjit Dey, Amit Singh, Deepesh Hada, Vidit Jain, Bhawna Paliwal, Anshul Mittal, Sonu Mehta, Ramachandran Ramjee, Sumeet Agarwal, Purushottam Kar, Manik Varma|Microsoft, Bellevue , WA, USA; Microsoft, Sunnyvale , CA, USA; Microsoft Research, Bangalore, India; IIT Delhi, New Delhi, India; Microsoft Research & IIT Delhi, Bangalore, India; UT Austin, Austin, TX, USA; Microsoft, Bangalore, India; IIT Kanpur, Kanpur, India; Linkedin, Sunnyvale , CA, USA|Extreme Classification (XC) seeks to tag data points with the most relevant subset of labels from an extremely large label set. Performing deep XC with dense, learnt representations for data points and labels has attracted much attention due to its superiority over earlier XC methods that used sparse, hand-crafted features. Negative mining techniques have emerged as a critical component of all deep XC methods that allow them to scale to millions of labels. However, despite recent advances, training deep XC models with large encoder architectures such as transformers remains challenging. This paper identifies that memory overheads of popular negative mining techniques often force mini-batch sizes to remain small and slow training down. In response, this paper introduces NGAME, a light-weight mini-batch creation technique that offers provably accurate in-batch negative samples. This allows training with larger mini-batches offering significantly faster convergence and higher accuracies than existing negative sampling techniques. NGAME was found to be up to 16% more accurate than state-of-the-art methods on a wide array of benchmark datasets for extreme classification, as well as 3% more accurate at retrieving search engine queries in response to a user webpage visit to show personalized ads. In live A/B tests on a popular search engine, NGAME yielded up to 23% gains in click-through-rates.|极端分类(XC)试图用来自极大标签集的最相关的标签子集来标记数据点。对数据点和标签进行深层 XC 表示时，由于其优于使用稀疏的手工特性的早期 XC 方法，因此引起了人们的广泛关注。负面挖掘技术已经成为所有深层 XC 方法的关键组成部分，这些方法可以扩展到数百万个标签。然而，尽管最近取得了一些进展，使用大型编码器架构(如变压器)训练深层 XC 模型仍然具有挑战性。本文指出，流行的负面挖掘技术的内存开销往往迫使小批量保持较小的规模和缓慢的训练下来。作为回应，本文介绍了 NGAME，一种轻量级的小批量生成技术，它可以提供可证明的准确的批内阴性样品。与现有的负面采样技术相比，这使得培训可以使用更大的迷你批量，提供更快的收敛速度和更高的准确度。研究发现，NGAME 在一系列基准数据集的极端分类中，比最先进的方法准确率高达16% ，在检索用户访问网页显示个性化广告的搜索引擎查询时，准确率高达3% 。在一个流行搜索引擎的 A/B 测试中，NGAME 的点击率提高了23% 。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=NGAME:+Negative+Mining-aware+Mini-batching+for+Extreme+Classification)|0| -|[Federated Unlearning for On-Device Recommendation](https://doi.org/10.1145/3539597.3570463)|Wei Yuan, Hongzhi Yin, Fangzhao Wu, Shijie Zhang, Tieke He, Hao Wang|Tencent, Shenzhen, China; The University of Queensland, Brisbane, Australia; Alibaba Cloud, Alibaba Group, Hangzhou, China; Microsoft Research Asia, Beijing, China; Nanjing University, Nanjing, China|The increasing data privacy concerns in recommendation systems have made federated recommendations (FedRecs) attract more and more attention. Existing FedRecs mainly focus on how to effectively and securely learn personal interests and preferences from their on-device interaction data. Still, none of them considers how to efficiently erase a user's contribution to the federated training process. We argue that such a dual setting is necessary. First, from the privacy protection perspective, ``the right to be forgotten'' requires that users have the right to withdraw their data contributions. Without the reversible ability, FedRecs risk breaking data protection regulations. On the other hand, enabling a FedRec to forget specific users can improve its robustness and resistance to malicious clients' attacks. To support user unlearning in FedRecs, we propose an efficient unlearning method FRU (Federated Recommendation Unlearning), inspired by the log-based rollback mechanism of transactions in database management systems. It removes a user's contribution by rolling back and calibrating the historical parameter updates and then uses these updates to speed up federated recommender reconstruction. However, storing all historical parameter updates on resource-constrained personal devices is challenging and even infeasible. In light of this challenge, we propose a small-sized negative sampling method to reduce the number of item embedding updates and an importance-based update selection mechanism to store only important model updates. To evaluate the effectiveness of FRU, we propose an attack method to disturb FedRecs via a group of compromised users and use FRU to recover recommenders by eliminating these users' influence. Finally, we conduct experiments on two real-world recommendation datasets with two widely used FedRecs to show the efficiency and effectiveness of our proposed approaches.|推荐系统中日益增长的数据隐私问题使得联邦推荐(FedRecs)越来越受到人们的关注。现有的 FedRecs 主要关注如何有效和安全地从设备上的交互数据中学习个人兴趣和偏好。尽管如此，它们都没有考虑如何有效地删除用户对联合培训过程的贡献。我们认为这种双重设置是必要的。首先，从隐私保护的角度来看，“被遗忘的权利”要求用户有权撤回他们的数据贡献。如果没有这种可逆能力，FedRecs 就有可能违反数据保护规定。另一方面，允许 FedRec 忘记特定用户可以提高其健壮性和对恶意客户端攻击的抵抗力。为了支持联邦推荐系统中的用户去学习，受数据库管理系统中基于日志的事务回滚机制的启发，提出了一种有效的去学习方法 FRU (FederatedRecumentUnlearning)。它通过回滚和校准历史参数更新来消除用户的贡献，然后使用这些更新来加速联邦推荐重建。然而，在资源受限的个人设备上存储所有历史参数更新是具有挑战性的，甚至是不可行的。针对这一挑战，我们提出了一种小规模的负抽样方法来减少嵌入更新项的数量，以及一种基于重要性的更新选择机制来只存储重要的模型更新。为了评估 FRU 的有效性，我们提出了一种通过一组受到攻击的用户来干扰 FedRecs 的攻击方法，并通过消除这些用户的影响，使用 FRU 来恢复推荐信息。最后，我们使用两个广泛使用的 FedRecs 在两个真实世界的推荐数据集上进行实验，以证明我们提出的方法的效率和有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Federated+Unlearning+for+On-Device+Recommendation)|0| +|[NGAME: Negative Mining-aware Mini-batching for Extreme Classification](https://doi.org/10.1145/3539597.3570392)|Kunal Dahiya, Nilesh Gupta, Deepak Saini, Akshay Soni, Yajun Wang, Kushal Dave, Jian Jiao, Gururaj K, Prasenjit Dey, Amit Singh, Deepesh Hada, Vidit Jain, Bhawna Paliwal, Anshul Mittal, Sonu Mehta, Ramachandran Ramjee, Sumeet Agarwal, Purushottam Kar, Manik Varma|Linkedin, Sunnyvale , CA, USA; Microsoft Research, Bangalore, India; IIT Kanpur, Kanpur, India; IIT Delhi, New Delhi, India; Microsoft, Bangalore, India; Microsoft Research & IIT Delhi, Bangalore, India; UT Austin, Austin, TX, USA; Microsoft, Bellevue , WA, USA; Microsoft, Sunnyvale , CA, USA|Extreme Classification (XC) seeks to tag data points with the most relevant subset of labels from an extremely large label set. Performing deep XC with dense, learnt representations for data points and labels has attracted much attention due to its superiority over earlier XC methods that used sparse, hand-crafted features. Negative mining techniques have emerged as a critical component of all deep XC methods that allow them to scale to millions of labels. However, despite recent advances, training deep XC models with large encoder architectures such as transformers remains challenging. This paper identifies that memory overheads of popular negative mining techniques often force mini-batch sizes to remain small and slow training down. In response, this paper introduces NGAME, a light-weight mini-batch creation technique that offers provably accurate in-batch negative samples. This allows training with larger mini-batches offering significantly faster convergence and higher accuracies than existing negative sampling techniques. NGAME was found to be up to 16% more accurate than state-of-the-art methods on a wide array of benchmark datasets for extreme classification, as well as 3% more accurate at retrieving search engine queries in response to a user webpage visit to show personalized ads. In live A/B tests on a popular search engine, NGAME yielded up to 23% gains in click-through-rates.|极端分类(XC)试图用来自极大标签集的最相关的标签子集来标记数据点。对数据点和标签进行深层 XC 表示时，由于其优于使用稀疏的手工特性的早期 XC 方法，因此引起了人们的广泛关注。负面挖掘技术已经成为所有深层 XC 方法的关键组成部分，这些方法可以扩展到数百万个标签。然而，尽管最近取得了一些进展，使用大型编码器架构(如变压器)训练深层 XC 模型仍然具有挑战性。本文指出，流行的负面挖掘技术的内存开销往往迫使小批量保持较小的规模和缓慢的训练下来。作为回应，本文介绍了 NGAME，一种轻量级的小批量生成技术，它可以提供可证明的准确的批内阴性样品。与现有的负面采样技术相比，这使得培训可以使用更大的迷你批量，提供更快的收敛速度和更高的准确度。研究发现，NGAME 在一系列基准数据集的极端分类中，比最先进的方法准确率高达16% ，在检索用户访问网页显示个性化广告的搜索引擎查询时，准确率高达3% 。在一个流行搜索引擎的 A/B 测试中，NGAME 的点击率提高了23% 。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=NGAME:+Negative+Mining-aware+Mini-batching+for+Extreme+Classification)|0| +|[Federated Unlearning for On-Device Recommendation](https://doi.org/10.1145/3539597.3570463)|Wei Yuan, Hongzhi Yin, Fangzhao Wu, Shijie Zhang, Tieke He, Hao Wang|The University of Queensland, Brisbane, Australia; Alibaba Cloud, Alibaba Group, Hangzhou, China; Nanjing University, Nanjing, China; Tencent, Shenzhen, China; Microsoft Research Asia, Beijing, China|The increasing data privacy concerns in recommendation systems have made federated recommendations (FedRecs) attract more and more attention. Existing FedRecs mainly focus on how to effectively and securely learn personal interests and preferences from their on-device interaction data. Still, none of them considers how to efficiently erase a user's contribution to the federated training process. We argue that such a dual setting is necessary. First, from the privacy protection perspective, ``the right to be forgotten'' requires that users have the right to withdraw their data contributions. Without the reversible ability, FedRecs risk breaking data protection regulations. On the other hand, enabling a FedRec to forget specific users can improve its robustness and resistance to malicious clients' attacks. To support user unlearning in FedRecs, we propose an efficient unlearning method FRU (Federated Recommendation Unlearning), inspired by the log-based rollback mechanism of transactions in database management systems. It removes a user's contribution by rolling back and calibrating the historical parameter updates and then uses these updates to speed up federated recommender reconstruction. However, storing all historical parameter updates on resource-constrained personal devices is challenging and even infeasible. In light of this challenge, we propose a small-sized negative sampling method to reduce the number of item embedding updates and an importance-based update selection mechanism to store only important model updates. To evaluate the effectiveness of FRU, we propose an attack method to disturb FedRecs via a group of compromised users and use FRU to recover recommenders by eliminating these users' influence. Finally, we conduct experiments on two real-world recommendation datasets with two widely used FedRecs to show the efficiency and effectiveness of our proposed approaches.|推荐系统中日益增长的数据隐私问题使得联邦推荐(FedRecs)越来越受到人们的关注。现有的 FedRecs 主要关注如何有效和安全地从设备上的交互数据中学习个人兴趣和偏好。尽管如此，它们都没有考虑如何有效地删除用户对联合培训过程的贡献。我们认为这种双重设置是必要的。首先，从隐私保护的角度来看，“被遗忘的权利”要求用户有权撤回他们的数据贡献。如果没有这种可逆能力，FedRecs 就有可能违反数据保护规定。另一方面，允许 FedRec 忘记特定用户可以提高其健壮性和对恶意客户端攻击的抵抗力。为了支持联邦推荐系统中的用户去学习，受数据库管理系统中基于日志的事务回滚机制的启发，提出了一种有效的去学习方法 FRU (FederatedRecumentUnlearning)。它通过回滚和校准历史参数更新来消除用户的贡献，然后使用这些更新来加速联邦推荐重建。然而，在资源受限的个人设备上存储所有历史参数更新是具有挑战性的，甚至是不可行的。针对这一挑战，我们提出了一种小规模的负抽样方法来减少嵌入更新项的数量，以及一种基于重要性的更新选择机制来只存储重要的模型更新。为了评估 FRU 的有效性，我们提出了一种通过一组受到攻击的用户来干扰 FedRecs 的攻击方法，并通过消除这些用户的影响，使用 FRU 来恢复推荐信息。最后，我们使用两个广泛使用的 FedRecs 在两个真实世界的推荐数据集上进行实验，以证明我们提出的方法的效率和有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Federated+Unlearning+for+On-Device+Recommendation)|0| |[Cognition-aware Knowledge Graph Reasoning for Explainable Recommendation](https://doi.org/10.1145/3539597.3570391)|Qingyu Bing, Qiannan Zhu, Zhicheng Dou|Renmin University of China, Beijing, China|Knowledge graphs (KGs) have been widely used in recommendation systems to improve recommendation accuracy and interpretability effectively. Recent research usually endows KG reasoning to find the multi-hop user-item connection paths for explaining why an item is recommended. The existing path-finding process is well designed by logic-driven inference algorithms, while there exists a gap between how algorithms and users perceive the reasoning process. Factually, human thinking is a natural reasoning process that can provide more proper and convincing explanations of why particular decisions are made. Motivated by the Dual Process Theory in cognitive science, we propose a cognition-aware KG reasoning model CogER for Explainable Recommendation, which imitates the human cognition process and designs two modules, i.e., System~1 (making intuitive judgment) and System~2 (conducting explicit reasoning), to generate the actual decision-making process. At each step during the cognition-aware reasoning process, System~1 generates an intuitive estimation of the next-step entity based on the user's historical behavior, and System~2 conducts explicit reasoning and selects the most promising knowledge entities. These two modules work iteratively and are mutually complementary, enabling our model to yield high-quality recommendations and proper reasoning paths. Experiments on three real-world datasets show that our model achieves better recommendation results with explanations compared with previous methods.|知识图在推荐系统中得到了广泛的应用，有效地提高了推荐的准确性和可解释性。最近的研究通常使用 KG 推理来寻找多跳用户-项目的连接路径来解释为什么推荐一个项目。现有的路径寻找过程是由逻辑驱动的推理算法设计的，而算法与用户对推理过程的感知存在差距。事实上，人类的思考是一个自然的推理过程，可以提供更恰当和令人信服的解释为什么做出特定的决定。基于认知科学中的二元过程理论，本文提出了一种模仿人类认知过程的认知知觉 KG 推理模型 CogER for Explainable 汪洋推理模型，设计了系统1(直觉判断)和系统2(显性推理)两个模块来生成实际的决策过程。在认知推理过程的每个步骤中，System ~ 1根据用户的历史行为对下一步实体进行直观的估计，System ~ 2进行显式推理并选择最有前途的知识实体。这两个模块迭代工作，相互补充，使我们的模型能够产生高质量的建议和适当的推理路径。在三个实际数据集上的实验结果表明，与以前的方法相比，该模型在解释方面取得了较好的推荐效果。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Cognition-aware+Knowledge+Graph+Reasoning+for+Explainable+Recommendation)|0| -|[AGREE: Aligning Cross-Modal Entities for Image-Text Retrieval Upon Vision-Language Pre-trained Models](https://doi.org/10.1145/3539597.3570481)|Xiaodan Wang, Lei Li, Zhixu Li, Xuwu Wang, Xiangru Zhu, Chengyu Wang, Jun Huang, Yanghua Xiao|Fudan University, Shanghai, China; Fudan University & Fudan-Aishu Cognitive Intelligence Joint Research Center, Shanghai, China; Alibaba Group, Hangzhou, China; East China Normal University, Shanghai, China|Image-text retrieval is a challenging cross-modal task that arouses much attention. While the traditional methods cannot break down the barriers between different modalities, Vision-Language Pre-trained (VLP) models greatly improve image-text retrieval performance based on massive image-text pairs. Nonetheless, the VLP-based methods are still prone to produce retrieval results that cannot be cross-modal aligned with entities. Recent efforts try to fix this problem at the pre-training stage, which is not only expensive but also unpractical due to the unavailable of full datasets. In this paper, we novelly propose a lightweight and practical approach to align cross-modal entities for image-text retrieval upon VLP models only at the fine-tuning and re-ranking stages. We employ external knowledge and tools to construct extra fine-grained image-text pairs, and then emphasize cross-modal entity alignment through contrastive learning and entity-level mask modeling in fine-tuning. Besides, two re-ranking strategies are proposed, including one specially designed for zero-shot scenarios. Extensive experiments with several VLP models on multiple Chinese and English datasets show that our approach achieves state-of-the-art results in nearly all settings.|图像-文本检索是一个具有挑战性的跨模态任务，引起了人们的广泛关注。传统的检索方法无法突破不同检索模式之间的障碍，而视觉语言预训练(VLP)模型可以大大提高基于海量图像-文本对的图像-文本检索性能。尽管如此，基于 VLP 的方法仍然容易产生不能与实体进行跨模式对齐的检索结果。最近的努力试图在训练前阶段解决这个问题，这不仅昂贵，而且不切实际，因为没有完整的数据集。本文提出了一种轻量级、实用的方法，仅在微调和重新排序阶段对 VLP 模型上的跨模态实体进行对齐。我们利用外部知识和工具构造超细粒度的图像-文本对，然后通过对比学习和实体级掩模建模进行微调，强调跨模态实体对齐。此外，提出了两种重新排序策略，包括一种专门为零射击场景设计的重新排序策略。在多个中文和英文数据集上对多个 VLP 模型进行的大量实验表明，我们的方法在几乎所有的设置中都取得了最先进的结果。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=AGREE:+Aligning+Cross-Modal+Entities+for+Image-Text+Retrieval+Upon+Vision-Language+Pre-trained+Models)|0| +|[AGREE: Aligning Cross-Modal Entities for Image-Text Retrieval Upon Vision-Language Pre-trained Models](https://doi.org/10.1145/3539597.3570481)|Xiaodan Wang, Lei Li, Zhixu Li, Xuwu Wang, Xiangru Zhu, Chengyu Wang, Jun Huang, Yanghua Xiao|Fudan University & Fudan-Aishu Cognitive Intelligence Joint Research Center, Shanghai, China; Alibaba Group, Hangzhou, China; East China Normal University, Shanghai, China; Fudan University, Shanghai, China|Image-text retrieval is a challenging cross-modal task that arouses much attention. While the traditional methods cannot break down the barriers between different modalities, Vision-Language Pre-trained (VLP) models greatly improve image-text retrieval performance based on massive image-text pairs. Nonetheless, the VLP-based methods are still prone to produce retrieval results that cannot be cross-modal aligned with entities. Recent efforts try to fix this problem at the pre-training stage, which is not only expensive but also unpractical due to the unavailable of full datasets. In this paper, we novelly propose a lightweight and practical approach to align cross-modal entities for image-text retrieval upon VLP models only at the fine-tuning and re-ranking stages. We employ external knowledge and tools to construct extra fine-grained image-text pairs, and then emphasize cross-modal entity alignment through contrastive learning and entity-level mask modeling in fine-tuning. Besides, two re-ranking strategies are proposed, including one specially designed for zero-shot scenarios. Extensive experiments with several VLP models on multiple Chinese and English datasets show that our approach achieves state-of-the-art results in nearly all settings.|图像-文本检索是一个具有挑战性的跨模态任务，引起了人们的广泛关注。传统的检索方法无法突破不同检索模式之间的障碍，而视觉语言预训练(VLP)模型可以大大提高基于海量图像-文本对的图像-文本检索性能。尽管如此，基于 VLP 的方法仍然容易产生不能与实体进行跨模式对齐的检索结果。最近的努力试图在训练前阶段解决这个问题，这不仅昂贵，而且不切实际，因为没有完整的数据集。本文提出了一种轻量级、实用的方法，仅在微调和重新排序阶段对 VLP 模型上的跨模态实体进行对齐。我们利用外部知识和工具构造超细粒度的图像-文本对，然后通过对比学习和实体级掩模建模进行微调，强调跨模态实体对齐。此外，提出了两种重新排序策略，包括一种专门为零射击场景设计的重新排序策略。在多个中文和英文数据集上对多个 VLP 模型进行的大量实验表明，我们的方法在几乎所有的设置中都取得了最先进的结果。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=AGREE:+Aligning+Cross-Modal+Entities+for+Image-Text+Retrieval+Upon+Vision-Language+Pre-trained+Models)|0| |[Disentangled Representation for Diversified Recommendations](https://doi.org/10.1145/3539597.3570389)|Xiaoying Zhang, Hongning Wang, Hang Li|Department of Computer Science, University of Virginia, Charlottesville, VA, USA; AI Lab, Bytedance Inc., Beijing, China|Accuracy and diversity have long been considered to be two conflicting goals for recommendations. We point out, however, that as the diversity is typically measured by certain pre-selected item attributes, e.g., category as the most popularly employed one, improved diversity can be achieved without sacrificing recommendation accuracy, as long as the diversification respects the user's preference about the pre-selected attributes. This calls for a fine-grained understanding of a user's preferences over items, where one needs to recognize the user's choice is driven by the quality of the item itself, or the pre-selected attributes of the item. In this work, we focus on diversity defined on item categories. We propose a general diversification framework agnostic to the choice of recommendation algorithms. Our solution disentangles the learnt user representation in the recommendation module into category-independent and category-dependent components to differentiate a user's preference over items from two orthogonal perspectives. Experimental results on three benchmark datasets and online A/B test demonstrate the effectiveness of our solution in improving both recommendation accuracy and diversity. In-depth analysis suggests that the improvement is due to our improved modeling of users' categorical preferences and refined ranking within item categories.|长期以来，准确性和多样性一直被认为是提出建议的两个相互冲突的目标。然而，我们指出，由于多样性通常是通过某些预先选择的项目属性来衡量的，例如，类别作为最受欢迎的一个，只要多样性尊重用户对预先选择的属性的偏好，就可以在不牺牲推荐准确性的情况下实现改善的多样性。这需要对用户对项目的偏好有一个细粒度的理解，在这种情况下，人们需要认识到用户的选择是由项目本身的质量或预先选择的项目属性驱动的。在这项工作中，我们关注的多样性定义的项目类别。我们提出了一个与推荐算法的选择无关的通用多样化框架。我们的解决方案将推荐模块中的学习用户表示分解为与类别无关和与类别相关的组件，从两个正交的角度区分用户对项目的偏好。在三个基准数据集上的实验结果和在线 A/B 测试表明了该方案在提高推荐精度和多样性方面的有效性。深入的分析表明，这种改进是由于我们改进了对用户分类偏好的建模，并在项目类别中进行了精确的排名。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Disentangled+Representation+for+Diversified+Recommendations)|0| -|[Knowledge-Adaptive Contrastive Learning for Recommendation](https://doi.org/10.1145/3539597.3570483)|Hao Wang, Yao Xu, Cheng Yang, Chuan Shi, Xin Li, Ning Guo, Zhiyuan Liu|Tsinghua University, Beijing, China; Beijing University of Posts and Telecommunications, Beijing, China; Researcher, Beijing, China|By jointly modeling user-item interactions and knowledge graph (KG) information, KG-based recommender systems have shown their superiority in alleviating data sparsity and cold start problems. Recently, graph neural networks (GNNs) have been widely used in KG-based recommendation, owing to the strong ability of capturing high-order structural information. However, we argue that existing GNN-based methods have the following two limitations. Interaction domination: the supervision signal of user-item interaction will dominate the model training, and thus the information of KG is barely encoded in learned item representations; Knowledge overload: KG contains much recommendation-irrelevant information, and such noise would be enlarged during the message aggregation of GNNs. The above limitations prevent existing methods to fully utilize the valuable information lying in KG. In this paper, we propose a novel algorithm named Knowledge-Adaptive Contrastive Learning (KACL) to address these challenges. Specifically, we first generate data augmentations from user-item interaction view and KG view separately, and perform contrastive learning across the two views. Our design of contrastive loss will force the item representations to encode information shared by both views, thereby alleviating the interaction domination issue. Moreover, we introduce two learnable view generators to adaptively remove task-irrelevant edges during data augmentation, and help tolerate the noises brought by knowledge overload. Experimental results on three public benchmarks demonstrate that KACL can significantly improve the performance on top-K recommendation compared with state-of-the-art methods.|基于 KG 的推荐系统通过联合建模用户-项目交互和知识图(KG)信息，在缓解数据稀疏和冷启动问题方面显示出其优越性。近年来，图神经网络(GNN)由于具有较强的高阶结构信息捕获能力，在 KG 推荐中得到了广泛的应用。然而，我们认为现有的基于 GNN 的方法有以下两个局限性。交互控制: 用户-项目交互的监控信号将主导模型训练，因此 KG 的信息几乎不被编码到学习项目表示中; 知识超载: KG 包含大量与推荐无关的信息，这种噪声在 GNN 的信息聚合过程中会被放大。上述限制使得现有的方法无法充分利用幼儿园的宝贵信息。本文提出了一种新的知识自适应对比学习(KACL)算法来解决这些问题。具体来说，我们首先分别从用户项交互视图和 KG 视图生成数据增强，并在两个视图之间进行对比学习。对比性损失的设计将迫使项目表征对两种视图共享的信息进行编码，从而缓解交互支配问题。此外，本文还引入了两个可学习的视图生成器来自适应地去除数据增强过程中与任务无关的边缘，并有助于抑制知识过载带来的噪声。在三个公共基准上的实验结果表明，KACL 算法能够显著提高 top-K 推荐的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Knowledge-Adaptive+Contrastive+Learning+for+Recommendation)|0| -|[Calibrated Recommendations as a Minimum-Cost Flow Problem](https://doi.org/10.1145/3539597.3570402)|Himan Abdollahpouri, Zahra Nazari, Alex Gain, Clay Gibson, Maria Dimakopoulou, Jesse Anderton, Benjamin A. Carterette, Mounia Lalmas, Tony Jebara|Spotify, New York, NY, USA; Spotify, London, United Kingdom; Airbnb, San Francisco, CA, USA|Calibration in recommender systems has recently gained significant attention. In the recommended list of items, calibration ensures that the various (past) areas of interest of a user are reflected with their corresponding proportions. For instance, if a user has watched, say, 80 romance movies and 20 action movies, then it is reasonable to expect the recommended list of movies to be comprised of about 80% romance and 20% action movies as well. Calibration is particularly important given that optimizing towards accuracy often leads to the user's minority interests being dominated by their main interests, or by a few overall popular items, in the recommendations they receive. In this paper, we propose a novel approach based on the max flow problem for generating calibrated recommendations. In a series of experiments using two publicly available datasets, we demonstrate the superior performance of our proposed approach compared to the state-of-the-art in generating relevant and calibrated recommendation lists.|推荐系统的校准最近引起了广泛的关注。在推荐的项目列表中，校准确保用户感兴趣的各个(过去的)领域以其相应的比例得到反映。例如，如果一个用户已经看了80部浪漫电影和20部动作片，那么推荐的电影列表应该包括80% 的浪漫电影和20% 的动作片。校准尤其重要，因为对准确性的优化往往导致用户的少数利益被他们的主要利益所主导，或者在他们收到的推荐中被一些整体流行的项目所主导。在本文中，我们提出了一种新的方法基于最大流问题生成校准的建议。在使用两个公开可用数据集的一系列实验中，我们证明了我们提出的方法在生成相关和校准的推荐列表方面的优越性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Calibrated+Recommendations+as+a+Minimum-Cost+Flow+Problem)|0| -|[Generative Slate Recommendation with Reinforcement Learning](https://doi.org/10.1145/3539597.3570412)|Romain Deffayet, Thibaut Thonet, JeanMichel Renders, Maarten de Rijke|Naver Labs Europe, Meylan, France; University of Amsterdam, Amsterdam, Netherlands; Naver Labs Europe & University of Amsterdam, Meylan, France|Recent research has employed reinforcement learning (RL) algorithms to optimize long-term user engagement in recommender systems, thereby avoiding common pitfalls such as user boredom and filter bubbles. They capture the sequential and interactive nature of recommendations, and thus offer a principled way to deal with long-term rewards and avoid myopic behaviors. However, RL approaches are intractable in the slate recommendation scenario - where a list of items is recommended at each interaction turn - due to the combinatorial action space. In that setting, an action corresponds to a slate that may contain any combination of items. While previous work has proposed well-chosen decompositions of actions so as to ensure tractability, these rely on restrictive and sometimes unrealistic assumptions. Instead, in this work we propose to encode slates in a continuous, low-dimensional latent space learned by a variational auto-encoder. Then, the RL agent selects continuous actions in this latent space, which are ultimately decoded into the corresponding slates. By doing so, we are able to (i) relax assumptions required by previous work, and (ii) improve the quality of the action selection by modeling full slates instead of independent items, in particular by enabling diversity. Our experiments performed on a wide array of simulated environments confirm the effectiveness of our generative modeling of slates over baselines in practical scenarios where the restrictive assumptions underlying the baselines are lifted. Our findings suggest that representation learning using generative models is a promising direction towards generalizable RL-based slate recommendation.|最近的研究使用强化学习算法来优化推荐系统中的长期用户参与，从而避免常见的陷阱，如用户厌烦和过滤气泡。它们捕捉到了建议的连续性和互动性，因此提供了一个处理长期奖励和避免短视行为的原则性方法。然而，由于组合操作空间的存在，RL 方法在平板推荐场景(在每个交互回合中推荐一个项目列表)中是难以处理的。在该设置中，一个操作对应于可能包含任何项目组合的石板。虽然以前的工作提出了精心选择的行动分解方法，以确保易于处理，但这些方法依赖于限制性的、有时不切实际的假设。相反，在这项工作中，我们建议编码石板在一个连续的，低维潜在的空间学习变分自动编码器。然后，RL 代理在这个潜在空间中选择连续的动作，这些动作最终被解码到相应的平板中。通过这样做，我们能够(i)放松以前工作所需要的假设，(ii)通过建模完整的板岩而不是独立的项目来提高行动选择的质量，特别是通过支持多样性。我们在大量的模拟环境中进行的实验证实了我们在基线上的板岩生成模型在实际场景中的有效性，在实际场景中基线的限制性假设被取消。我们的研究结果表明，使用生成模型的表示学习是一个有前途的方向，可推广的 RL 为基础的板岩推荐。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Generative+Slate+Recommendation+with+Reinforcement+Learning)|0| +|[Knowledge-Adaptive Contrastive Learning for Recommendation](https://doi.org/10.1145/3539597.3570483)|Hao Wang, Yao Xu, Cheng Yang, Chuan Shi, Xin Li, Ning Guo, Zhiyuan Liu|Researcher, Beijing, China; Beijing University of Posts and Telecommunications, Beijing, China; Tsinghua University, Beijing, China|By jointly modeling user-item interactions and knowledge graph (KG) information, KG-based recommender systems have shown their superiority in alleviating data sparsity and cold start problems. Recently, graph neural networks (GNNs) have been widely used in KG-based recommendation, owing to the strong ability of capturing high-order structural information. However, we argue that existing GNN-based methods have the following two limitations. Interaction domination: the supervision signal of user-item interaction will dominate the model training, and thus the information of KG is barely encoded in learned item representations; Knowledge overload: KG contains much recommendation-irrelevant information, and such noise would be enlarged during the message aggregation of GNNs. The above limitations prevent existing methods to fully utilize the valuable information lying in KG. In this paper, we propose a novel algorithm named Knowledge-Adaptive Contrastive Learning (KACL) to address these challenges. Specifically, we first generate data augmentations from user-item interaction view and KG view separately, and perform contrastive learning across the two views. Our design of contrastive loss will force the item representations to encode information shared by both views, thereby alleviating the interaction domination issue. Moreover, we introduce two learnable view generators to adaptively remove task-irrelevant edges during data augmentation, and help tolerate the noises brought by knowledge overload. Experimental results on three public benchmarks demonstrate that KACL can significantly improve the performance on top-K recommendation compared with state-of-the-art methods.|基于 KG 的推荐系统通过联合建模用户-项目交互和知识图(KG)信息，在缓解数据稀疏和冷启动问题方面显示出其优越性。近年来，图神经网络(GNN)由于具有较强的高阶结构信息捕获能力，在 KG 推荐中得到了广泛的应用。然而，我们认为现有的基于 GNN 的方法有以下两个局限性。交互控制: 用户-项目交互的监控信号将主导模型训练，因此 KG 的信息几乎不被编码到学习项目表示中; 知识超载: KG 包含大量与推荐无关的信息，这种噪声在 GNN 的信息聚合过程中会被放大。上述限制使得现有的方法无法充分利用幼儿园的宝贵信息。本文提出了一种新的知识自适应对比学习(KACL)算法来解决这些问题。具体来说，我们首先分别从用户项交互视图和 KG 视图生成数据增强，并在两个视图之间进行对比学习。对比性损失的设计将迫使项目表征对两种视图共享的信息进行编码，从而缓解交互支配问题。此外，本文还引入了两个可学习的视图生成器来自适应地去除数据增强过程中与任务无关的边缘，并有助于抑制知识过载带来的噪声。在三个公共基准上的实验结果表明，KACL 算法能够显著提高 top-K 推荐的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Knowledge-Adaptive+Contrastive+Learning+for+Recommendation)|0| +|[Calibrated Recommendations as a Minimum-Cost Flow Problem](https://doi.org/10.1145/3539597.3570402)|Himan Abdollahpouri, Zahra Nazari, Alex Gain, Clay Gibson, Maria Dimakopoulou, Jesse Anderton, Benjamin A. Carterette, Mounia Lalmas, Tony Jebara|Airbnb, San Francisco, CA, USA; Spotify, London, United Kingdom; Spotify, New York, NY, USA|Calibration in recommender systems has recently gained significant attention. In the recommended list of items, calibration ensures that the various (past) areas of interest of a user are reflected with their corresponding proportions. For instance, if a user has watched, say, 80 romance movies and 20 action movies, then it is reasonable to expect the recommended list of movies to be comprised of about 80% romance and 20% action movies as well. Calibration is particularly important given that optimizing towards accuracy often leads to the user's minority interests being dominated by their main interests, or by a few overall popular items, in the recommendations they receive. In this paper, we propose a novel approach based on the max flow problem for generating calibrated recommendations. In a series of experiments using two publicly available datasets, we demonstrate the superior performance of our proposed approach compared to the state-of-the-art in generating relevant and calibrated recommendation lists.|推荐系统的校准最近引起了广泛的关注。在推荐的项目列表中，校准确保用户感兴趣的各个(过去的)领域以其相应的比例得到反映。例如，如果一个用户已经看了80部浪漫电影和20部动作片，那么推荐的电影列表应该包括80% 的浪漫电影和20% 的动作片。校准尤其重要，因为对准确性的优化往往导致用户的少数利益被他们的主要利益所主导，或者在他们收到的推荐中被一些整体流行的项目所主导。在本文中，我们提出了一种新的方法基于最大流问题生成校准的建议。在使用两个公开可用数据集的一系列实验中，我们证明了我们提出的方法在生成相关和校准的推荐列表方面的优越性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Calibrated+Recommendations+as+a+Minimum-Cost+Flow+Problem)|0| +|[Generative Slate Recommendation with Reinforcement Learning](https://doi.org/10.1145/3539597.3570412)|Romain Deffayet, Thibaut Thonet, JeanMichel Renders, Maarten de Rijke|Naver Labs Europe, Meylan, France; Naver Labs Europe & University of Amsterdam, Meylan, France; University of Amsterdam, Amsterdam, Netherlands|Recent research has employed reinforcement learning (RL) algorithms to optimize long-term user engagement in recommender systems, thereby avoiding common pitfalls such as user boredom and filter bubbles. They capture the sequential and interactive nature of recommendations, and thus offer a principled way to deal with long-term rewards and avoid myopic behaviors. However, RL approaches are intractable in the slate recommendation scenario - where a list of items is recommended at each interaction turn - due to the combinatorial action space. In that setting, an action corresponds to a slate that may contain any combination of items. While previous work has proposed well-chosen decompositions of actions so as to ensure tractability, these rely on restrictive and sometimes unrealistic assumptions. Instead, in this work we propose to encode slates in a continuous, low-dimensional latent space learned by a variational auto-encoder. Then, the RL agent selects continuous actions in this latent space, which are ultimately decoded into the corresponding slates. By doing so, we are able to (i) relax assumptions required by previous work, and (ii) improve the quality of the action selection by modeling full slates instead of independent items, in particular by enabling diversity. Our experiments performed on a wide array of simulated environments confirm the effectiveness of our generative modeling of slates over baselines in practical scenarios where the restrictive assumptions underlying the baselines are lifted. Our findings suggest that representation learning using generative models is a promising direction towards generalizable RL-based slate recommendation.|最近的研究使用强化学习算法来优化推荐系统中的长期用户参与，从而避免常见的陷阱，如用户厌烦和过滤气泡。它们捕捉到了建议的连续性和互动性，因此提供了一个处理长期奖励和避免短视行为的原则性方法。然而，由于组合操作空间的存在，RL 方法在平板推荐场景(在每个交互回合中推荐一个项目列表)中是难以处理的。在该设置中，一个操作对应于可能包含任何项目组合的石板。虽然以前的工作提出了精心选择的行动分解方法，以确保易于处理，但这些方法依赖于限制性的、有时不切实际的假设。相反，在这项工作中，我们建议编码石板在一个连续的，低维潜在的空间学习变分自动编码器。然后，RL 代理在这个潜在空间中选择连续的动作，这些动作最终被解码到相应的平板中。通过这样做，我们能够(i)放松以前工作所需要的假设，(ii)通过建模完整的板岩而不是独立的项目来提高行动选择的质量，特别是通过支持多样性。我们在大量的模拟环境中进行的实验证实了我们在基线上的板岩生成模型在实际场景中的有效性，在实际场景中基线的限制性假设被取消。我们的研究结果表明，使用生成模型的表示学习是一个有前途的方向，可推广的 RL 为基础的板岩推荐。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Generative+Slate+Recommendation+with+Reinforcement+Learning)|0| |[AutoGen: An Automated Dynamic Model Generation Framework for Recommender System](https://doi.org/10.1145/3539597.3570456)|Chenxu Zhu, Bo Chen, Huifeng Guo, Hang Xu, Xiangyang Li, Xiangyu Zhao, Weinan Zhang, Yong Yu, Ruiming Tang|Huawei Noah's Ark Lab, Shenzhen, China; Shanghai Jiao Tong University, Shanghai, China; City University of Hong Kong, Hong Kong, China|Considering the balance between revenue and resource consumption for industrial recommender systems, intelligent recommendation computing has been emerging recently. Existing solutions deploy the same recommendation model to serve users indiscriminately, which is sub-optimal for total revenue maximization. We propose a multi-model service solution by deploying different-complexity models to serve different-valued users. An automated dynamic model generation framework AutoGen is elaborated to efficiently derive multiple parameter-sharing models with diverse complexities and adequate predictive capabilities. A mixed search space is designed and an importance-aware progressive training scheme is proposed to prevent interference between different architectures, which avoids the model retraining and improves the search efficiency, thereby efficiently deriving multiple models. Extensive experiments are conducted on two public datasets to demonstrate the effectiveness and efficiency of AutoGen.|考虑到工业推荐系统的收益和资源消耗之间的平衡，智能推荐计算近年来兴起。现有的解决方案部署相同的推荐模型来不加区分地为用户服务，这对于总收入最大化是次优的。我们提出了一个多模型服务解决方案，通过部署不同复杂度的模型来服务不同价值的用户。提出了一种自动动态模型生成框架 AutoGen，有效地推导出复杂度不同、预测能力足够的多参数共享模型。设计了一种混合搜索空间，提出了一种重要性感知的渐进训练方案，避免了模型再训练，提高了搜索效率，从而有效地推导出多个模型。为了验证 AutoGen 的有效性和效率，在两个公共数据集上进行了大量的实验。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=AutoGen:+An+Automated+Dynamic+Model+Generation+Framework+for+Recommender+System)|0| -|[Bring Your Own View: Graph Neural Networks for Link Prediction with Personalized Subgraph Selection](https://doi.org/10.1145/3539597.3570407)|Qiaoyu Tan, Xin Zhang, Ninghao Liu, Daochen Zha, Li Li, Rui Chen, SooHyun Choi, Xia Hu|Samsung Electronics, Mountain view, CA, USA; Texas A&M University, College Station, TX, USA; University of Georgia, Athens, GA, USA; The Hong Kong Polytechnic University, Hong Kong, Hong Kong; Samsung Electronics America, Mountain view, CA, USA; Rice University, Houston, TX, USA|Graph neural networks (GNNs) have received remarkable success in link prediction (GNNLP) tasks. Existing efforts first predefine the subgraph for the whole dataset and then apply GNNs to encode edge representations by leveraging the neighborhood structure induced by the fixed subgraph. The prominence of GNNLP methods significantly relies on the adhoc subgraph. Since node connectivity in real-world graphs is complex, one shared subgraph is limited for all edges. Thus, the choices of subgraphs should be personalized to different edges. However, performing personalized subgraph selection is nontrivial since the potential selection space grows exponentially to the scale of edges. Besides, the inference edges are not available during training in link prediction scenarios, so the selection process needs to be inductive. To bridge the gap, we introduce a Personalized Subgraph Selector (PS2) as a plug-and-play framework to automatically, personally, and inductively identify optimal subgraphs for different edges when performing GNNLP. PS2 is instantiated as a bi-level optimization problem that can be efficiently solved differently. Coupling GNNLP models with PS2, we suggest a brand-new angle towards GNNLP training: by first identifying the optimal subgraphs for edges; and then focusing on training the inference model by using the sampled subgraphs. Comprehensive experiments endorse the effectiveness of our proposed method across various GNNLP backbones (GCN, GraphSage, NGCF, LightGCN, and SEAL) and diverse benchmarks (Planetoid, OGB, and Recommendation datasets). Our code is publicly available at \url{https://github.com/qiaoyu-tan/PS2}|图神经网络(GNNs)在链路预测(GNNLP)任务中取得了显著的成功。现有的工作首先为整个数据集预先定义子图，然后利用固定子图产生的邻域结构，应用 GNN 对边缘表示进行编码。GNNLP 方法的突出性在很大程度上依赖于自组织子图。由于实际图中的节点连通性比较复杂，所以对于所有边，一个共享子图是有限的。因此，子图的选择应该针对不同的边进行个性化。然而，执行个性化的子图选择是不平凡的，因为潜在的选择空间成指数增长的尺度的边。此外，在链路预测场景的训练过程中，推理边不可用，因此选择过程需要归纳。为了弥补这一差距，我们引入了一个个性化子图选择器(PS2)作为即插即用的框架，以便在执行 GNNLP 时自动、个性化和归纳地识别不同边的最优子图。PS2被实例化为一个双层最佳化问题，可以通过不同的方式有效地解决。将 GNNLP 模型与 PS2相结合，提出了一种全新的 GNNLP 训练方法: 首先确定最优边缘子图，然后利用抽样子图对推理模型进行训练。综合实验认可了我们提出的方法在各种 GNNLP 骨干网(GCN，GraphSage，NGCF，LightGCN 和 SEAL)和各种基准(Planetoid，OGB 和推荐数据集)中的有效性。我们的代码可以在 url { https://github.com/qiaoyu-tan/ps2}上公开获得|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Bring+Your+Own+View:+Graph+Neural+Networks+for+Link+Prediction+with+Personalized+Subgraph+Selection)|0| +|[Bring Your Own View: Graph Neural Networks for Link Prediction with Personalized Subgraph Selection](https://doi.org/10.1145/3539597.3570407)|Qiaoyu Tan, Xin Zhang, Ninghao Liu, Daochen Zha, Li Li, Rui Chen, SooHyun Choi, Xia Hu|Rice University, Houston, TX, USA; University of Georgia, Athens, GA, USA; Samsung Electronics America, Mountain view, CA, USA; Texas A&M University, College Station, TX, USA; Samsung Electronics, Mountain view, CA, USA; The Hong Kong Polytechnic University, Hong Kong, Hong Kong|Graph neural networks (GNNs) have received remarkable success in link prediction (GNNLP) tasks. Existing efforts first predefine the subgraph for the whole dataset and then apply GNNs to encode edge representations by leveraging the neighborhood structure induced by the fixed subgraph. The prominence of GNNLP methods significantly relies on the adhoc subgraph. Since node connectivity in real-world graphs is complex, one shared subgraph is limited for all edges. Thus, the choices of subgraphs should be personalized to different edges. However, performing personalized subgraph selection is nontrivial since the potential selection space grows exponentially to the scale of edges. Besides, the inference edges are not available during training in link prediction scenarios, so the selection process needs to be inductive. To bridge the gap, we introduce a Personalized Subgraph Selector (PS2) as a plug-and-play framework to automatically, personally, and inductively identify optimal subgraphs for different edges when performing GNNLP. PS2 is instantiated as a bi-level optimization problem that can be efficiently solved differently. Coupling GNNLP models with PS2, we suggest a brand-new angle towards GNNLP training: by first identifying the optimal subgraphs for edges; and then focusing on training the inference model by using the sampled subgraphs. Comprehensive experiments endorse the effectiveness of our proposed method across various GNNLP backbones (GCN, GraphSage, NGCF, LightGCN, and SEAL) and diverse benchmarks (Planetoid, OGB, and Recommendation datasets). Our code is publicly available at \url{https://github.com/qiaoyu-tan/PS2}|图神经网络(GNNs)在链路预测(GNNLP)任务中取得了显著的成功。现有的工作首先为整个数据集预先定义子图，然后利用固定子图产生的邻域结构，应用 GNN 对边缘表示进行编码。GNNLP 方法的突出性在很大程度上依赖于自组织子图。由于实际图中的节点连通性比较复杂，所以对于所有边，一个共享子图是有限的。因此，子图的选择应该针对不同的边进行个性化。然而，执行个性化的子图选择是不平凡的，因为潜在的选择空间成指数增长的尺度的边。此外，在链路预测场景的训练过程中，推理边不可用，因此选择过程需要归纳。为了弥补这一差距，我们引入了一个个性化子图选择器(PS2)作为即插即用的框架，以便在执行 GNNLP 时自动、个性化和归纳地识别不同边的最优子图。PS2被实例化为一个双层最佳化问题，可以通过不同的方式有效地解决。将 GNNLP 模型与 PS2相结合，提出了一种全新的 GNNLP 训练方法: 首先确定最优边缘子图，然后利用抽样子图对推理模型进行训练。综合实验认可了我们提出的方法在各种 GNNLP 骨干网(GCN，GraphSage，NGCF，LightGCN 和 SEAL)和各种基准(Planetoid，OGB 和推荐数据集)中的有效性。我们的代码可以在 url { https://github.com/qiaoyu-tan/ps2}上公开获得|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Bring+Your+Own+View:+Graph+Neural+Networks+for+Link+Prediction+with+Personalized+Subgraph+Selection)|0| |[Heterogeneous Graph-based Context-aware Document Ranking](https://doi.org/10.1145/3539597.3570390)|Shuting Wang, Zhicheng Dou, Yutao Zhu|Renmin University of China, Beijing, China; University of Montreal, Montreal, PQ, Canada|Users' complex information needs usually require consecutive queries, which results in sessions with a series of interactions. Exploiting such contextual interactions has been proven to be favorable for result ranking. However, existing studies mainly model the contextual information independently and sequentially. They neglect the diverse information hidden in different relations and structured information of session elements as well as the valuable signals from other relevant sessions. In this paper, we propose HEXA, a heterogeneous graph-based context-aware document ranking framework. It exploits heterogeneous graphs to organize the contextual information and beneficial search logs for modeling user intents and ranking results. Specifically, we construct two heterogeneous graphs, i.e., a session graph and a query graph. The session graph is built from the current session queries and documents. Meanwhile, we sample the current query's k-layer neighbors from search logs to construct the query graph. Then, we employ heterogeneous graph neural networks and specialized readout functions on the two graphs to capture the user intents from local and global aspects. Finally, the document ranking scores are measured by how well the documents are matched with the two user intents. Results on two large-scale datasets confirm the effectiveness of our model.|用户复杂的信息需求通常需要连续的查询，这会导致一系列交互的会话。利用这样的情境互动已被证明是有利于结果排名。然而，现有的研究主要是依次独立地对语境信息进行建模。它们忽视了隐藏在不同关系中的各种信息、会议要素的结构化信息以及其他相关会议的宝贵信号。本文提出了一种基于异构图的上下文感知文档排序框架 HEXA。它利用异构图来组织上下文信息和有益的搜索日志，以建立用户意图和排序结果。具体来说，我们构造了两个异构图，即会话图和查询图。会话图是根据当前会话查询和文档构建的。同时，从搜索日志中抽取当前查询的 k 层邻居，构造查询图。然后，利用异构图形神经网络和专用读出函数，从局部和全局两个方面获取用户意图。最后，文档排名分数通过文档与两个用户意图的匹配程度来衡量。两个大规模数据集的结果证实了模型的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Heterogeneous+Graph-based+Context-aware+Document+Ranking)|0| |[Graph Summarization via Node Grouping: A Spectral Algorithm](https://doi.org/10.1145/3539597.3570441)|Arpit Merchant, Michael Mathioudakis, Yanhao Wang|University of Helsinki, Helsinki, Finland; East China Normal University, Shanghai, China|Graph summarization via node grouping is a popular method to build concise graph representations by grouping nodes from the original graph into supernodes and encoding edges into superedges such that the loss of adjacency information is minimized. Such summaries have immense applications in large-scale graph analytics due to their small size and high query processing efficiency. In this paper, we reformulate the loss minimization problem for summarization into an equivalent integer maximization problem. By initially allowing relaxed (fractional) solutions for integer maximization, we analytically expose the underlying connections to the spectral properties of the adjacency matrix. Consequently, we design an algorithm called SpecSumm that consists of two phases. In the first phase, motivated by spectral graph theory, we apply k-means clustering on the k largest (in magnitude) eigenvectors of the adjacency matrix to assign nodes to supernodes. In the second phase, we propose a greedy heuristic that updates the initial assignment to further improve summary quality. Finally, via extensive experiments on 11 datasets, we show that SpecSumm efficiently produces high-quality summaries compared to state-of-the-art summarization algorithms and scales to graphs with millions of nodes.|基于节点分组的图摘要是一种流行的图表示方法，它将原始图中的节点分组成超节点，并将边编码成超边，从而使邻接信息的损失最小化。这种摘要由于体积小、查询处理效率高，在大规模图形分析中有着广泛的应用。本文将汇总损失最小化问题重新表述为等价整数最大化问题。通过最初允许整数最大化的松弛(分数)解，我们分析地揭示了邻接矩阵的光谱特性的潜在联系。因此，我们设计了一个称为 SpecSumm 的算法，该算法由两个阶段组成。在第一阶段，由谱图理论驱动，我们应用 K平均算法对邻接矩阵的 k 个最大(大小)特征向量来分配节点到超节点。在第二阶段，我们提出了一个贪婪的启发式算法，更新初始分配以进一步提高汇总质量。最后，通过对11个数据集的大量实验，我们发现 SpecSumm 与最先进的摘要算法相比，能够有效地生成高质量的摘要，并且可以对具有数百万个节点的图进行缩放。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Graph+Summarization+via+Node+Grouping:+A+Spectral+Algorithm)|0| |[Ranking-based Group Identification via Factorized Attention on Social Tripartite Graph](https://doi.org/10.1145/3539597.3570406)|Mingdai Yang, Zhiwei Liu, Liangwei Yang, Xiaolong Liu, Chen Wang, Hao Peng, Philip S. Yu|University of Illinois at Chicago, Chicago, IL, USA; Salesforce AI Research, Palo Alto, CA, USA; Beihang University, Beijing, China|Due to the proliferation of social media, a growing number of users search for and join group activities in their daily life. This develops a need for the study on the ranking-based group identification (RGI) task, i.e., recommending groups to users. The major challenge in this task is how to effectively and efficiently leverage both the item interaction and group participation of users' online behaviors. Though recent developments of Graph Neural Networks (GNNs) succeed in simultaneously aggregating both social and user-item interaction, they however fail to comprehensively resolve this RGI task. In this paper, we propose a novel GNN-based framework named Contextualized Factorized Attention for Group identification (CFAG). We devise tripartite graph convolution layers to aggregate information from different types of neighborhoods among users, groups, and items. To cope with the data sparsity issue, we devise a novel propagation augmentation (PA) layer, which is based on our proposed factorized attention mechanism. PA layers efficiently learn the relatedness of non-neighbor nodes to improve the information propagation to users. Experimental results on three benchmark datasets verify the superiority of CFAG. Additional detailed investigations are conducted to demonstrate the effectiveness of the proposed framework.|随着社交媒体的普及，越来越多的用户在日常生活中搜索和参加小组活动。这就需要对基于排名的群体识别(RGI)任务进行研究，即向用户推荐群体。这项任务的主要挑战是如何有效地利用项目互动和用户在线行为的群体参与。近年来图形神经网络(GNN)虽然成功地同时聚合了社会交互和用户项目交互，但未能全面解决这一 RGI 任务。本文提出了一种新的基于 GNN 的群体识别框架——上下文分解注意(CFAG)。我们设计三部分图卷积层来聚合来自用户、组和项目之间不同类型的邻域的信息。为了解决数据稀疏的问题，我们在分解注意机制的基础上，设计了一种新的传播增强(PA)层。PA 层有效地学习非邻居节点的相关性，提高信息传播给用户的效率。在三个基准数据集上的实验结果验证了 CFAG 算法的优越性。还进行了更多的详细调查，以证明拟议框架的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Ranking-based+Group+Identification+via+Factorized+Attention+on+Social+Tripartite+Graph)|0| -|[Graph Sequential Neural ODE Process for Link Prediction on Dynamic and Sparse Graphs](https://doi.org/10.1145/3539597.3570465)|Linhao Luo, Gholamreza Haffari, Shirui Pan|Monash University, Melbourne, VIC, Australia; Griffith University, Brisbane, QLD, Australia|Link prediction on dynamic graphs is an important task in graph mining. Existing approaches based on dynamic graph neural networks (DGNNs) typically require a significant amount of historical data (interactions over time), which is not always available in practice. The missing links over time, which is a common phenomenon in graph data, further aggravates the issue and thus creates extremely sparse and dynamic graphs. To address this problem, we propose a novel method based on the neural process, called Graph Sequential Neural ODE Process (GSNOP). Specifically, GSNOP combines the advantage of the neural process and neural ordinary differential equation that models the link prediction on dynamic graphs as a dynamic-changing stochastic process. By defining a distribution over functions, GSNOP introduces the uncertainty into the predictions, making it generalize to more situations instead of overfitting to the sparse data. GSNOP is also agnostic to model structures that can be integrated with any DGNN to consider the chronological and geometrical information for link prediction. Extensive experiments on three dynamic graph datasets show that GSNOP can significantly improve the performance of existing DGNNs and outperform other neural process variants.|动态图的链接预测是图挖掘中的一项重要任务。基于动态图神经网络(DGNN)的现有方法通常需要大量的历史数据(随着时间的推移相互作用) ，这在实践中并不总是可用的。随着时间的推移，丢失链接是图形数据中常见的现象，这进一步加剧了问题的严重性，从而产生了极其稀疏和动态的图形。为了解决这一问题，我们提出了一种基于神经过程的图序贯神经 ODE 过程(GSNOP)方法。具体来说，GSNOP 结合了神经过程和神经常微分方程的优势，将动态图表上的链接预测建模为一个动态变化的随机过程。通过定义函数上的分布，GSNOP 将不确定性引入到预测中，使其能够推广到更多的情况，而不是对稀疏数据进行过度拟合。GSNOP 也是不可知的模型结构，可以与任何 DGNN 集成，以考虑时间和几何信息的链路预测。在三个动态图数据集上的大量实验表明，GSNOP 可以显著提高现有 DGNN 的性能，并优于其他神经过程变体。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Graph+Sequential+Neural+ODE+Process+for+Link+Prediction+on+Dynamic+and+Sparse+Graphs)|0| +|[Graph Sequential Neural ODE Process for Link Prediction on Dynamic and Sparse Graphs](https://doi.org/10.1145/3539597.3570465)|Linhao Luo, Gholamreza Haffari, Shirui Pan|Griffith University, Brisbane, QLD, Australia; Monash University, Melbourne, VIC, Australia|Link prediction on dynamic graphs is an important task in graph mining. Existing approaches based on dynamic graph neural networks (DGNNs) typically require a significant amount of historical data (interactions over time), which is not always available in practice. The missing links over time, which is a common phenomenon in graph data, further aggravates the issue and thus creates extremely sparse and dynamic graphs. To address this problem, we propose a novel method based on the neural process, called Graph Sequential Neural ODE Process (GSNOP). Specifically, GSNOP combines the advantage of the neural process and neural ordinary differential equation that models the link prediction on dynamic graphs as a dynamic-changing stochastic process. By defining a distribution over functions, GSNOP introduces the uncertainty into the predictions, making it generalize to more situations instead of overfitting to the sparse data. GSNOP is also agnostic to model structures that can be integrated with any DGNN to consider the chronological and geometrical information for link prediction. Extensive experiments on three dynamic graph datasets show that GSNOP can significantly improve the performance of existing DGNNs and outperform other neural process variants.|动态图的链接预测是图挖掘中的一项重要任务。基于动态图神经网络(DGNN)的现有方法通常需要大量的历史数据(随着时间的推移相互作用) ，这在实践中并不总是可用的。随着时间的推移，丢失链接是图形数据中常见的现象，这进一步加剧了问题的严重性，从而产生了极其稀疏和动态的图形。为了解决这一问题，我们提出了一种基于神经过程的图序贯神经 ODE 过程(GSNOP)方法。具体来说，GSNOP 结合了神经过程和神经常微分方程的优势，将动态图表上的链接预测建模为一个动态变化的随机过程。通过定义函数上的分布，GSNOP 将不确定性引入到预测中，使其能够推广到更多的情况，而不是对稀疏数据进行过度拟合。GSNOP 也是不可知的模型结构，可以与任何 DGNN 集成，以考虑时间和几何信息的链路预测。在三个动态图数据集上的大量实验表明，GSNOP 可以显著提高现有 DGNN 的性能，并优于其他神经过程变体。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Graph+Sequential+Neural+ODE+Process+for+Link+Prediction+on+Dynamic+and+Sparse+Graphs)|0| |[CL4CTR: A Contrastive Learning Framework for CTR Prediction](https://doi.org/10.1145/3539597.3570372)|Fangye Wang, Yingxu Wang, Dongsheng Li, Hansu Gu, Tun Lu, Peng Zhang, Ning Gu|Fudan University, Shanghai, China; Microsoft Research Asia, Shanghai, China; Independent, Seattle, WA, USA|Many Click-Through Rate (CTR) prediction works focused on designing advanced architectures to model complex feature interactions but neglected the importance of feature representation learning, e.g., adopting a plain embedding layer for each feature, which results in sub-optimal feature representations and thus inferior CTR prediction performance. For instance, low frequency features, which account for the majority of features in many CTR tasks, are less considered in standard supervised learning settings, leading to sub-optimal feature representations. In this paper, we introduce self-supervised learning to produce high-quality feature representations directly and propose a model-agnostic Contrastive Learning for CTR (CL4CTR) framework consisting of three self-supervised learning signals to regularize the feature representation learning: contrastive loss, feature alignment, and field uniformity. The contrastive module first constructs positive feature pairs by data augmentation and then minimizes the distance between the representations of each positive feature pair by the contrastive loss. The feature alignment constraint forces the representations of features from the same field to be close, and the field uniformity constraint forces the representations of features from different fields to be distant. Extensive experiments verify that CL4CTR achieves the best performance on four datasets and has excellent effectiveness and compatibility with various representative baselines.|许多点进率预测工作集中于设计先进的体系结构来模拟复杂的特征交互，但忽视了特征表示学习的重要性，例如，对每个特征采用一个普通的嵌入层，这导致了次优的特征表示，从而导致了较差的 CTR 预测性能。例如，在许多点击率任务中占大多数的低频特征，在标准的监督式学习设置中很少被考虑，导致了次优的特征表示。本文引入自监督学习，直接生成高质量的特征表示，提出了一种由三个自监督学习信号组成的模型无关的 CTR 对比学习(CL4CTR)框架，用于规范特征表示学习: 对比度丢失、特征对齐和场均匀性。对比模块首先通过数据增强构造正特征对，然后通过对比损失最小化每个正特征对表示之间的距离。特征对齐约束迫使来自同一域的特征表示相近，而场均匀性约束迫使来自不同域的特征表示相距较远。大量的实验证明，CL4CTR 在四个数据集上取得了最好的性能，并且与各种代表性的基线具有良好的效率和兼容性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=CL4CTR:+A+Contrastive+Learning+Framework+for+CTR+Prediction)|0| |[Telecommunication Traffic Forecasting via Multi-task Learning](https://doi.org/10.1145/3539597.3570440)|Xiaochuan Gou, Xiangliang Zhang|King Abdullah University of Science and Technology, Thuwal, Saudi Arabia; University of Notre Dame & King Abdullah University of Science and Technology, Notre Dame, IN, USA|Accurate telecommunication time series forecasting is critical for smart management systems of cellular networks, and has a special challenge in predicting different types of time series simultaneously at one base station (BS), e.g., the SMS, Calls, and Internet. Unlike the well-studied single target forecasting problem for one BS, this distributed multi-target forecasting problem should take advantage of both the intra-BS dependence of different types of time series at the same BS and the inter-BS dependence of time series at different BS. To this end, we first propose a model to learn the inter-BS dependence by aggregating the multi-view dependence, e.g., from the viewpoint of SMS, Calls, and Internet. To incorporate the interBS dependence in time series forecasting, we then propose a Graph Gate LSTM (GGLSTM) model that includes a graph-based gate mechanism to unite those base stations with a strong dependence on learning a collaboratively strengthened prediction model. We also extract the intra-BS dependence by an attention network and use it in the final prediction. Our proposed approach is evaluated on two real-world datasets. Experiment results demonstrate the effectiveness of our model in predicting multiple types of telecom traffic at the distributed base stations.|准确的电信时间序列预测对于蜂窝网络的智能管理系统至关重要，并且对于在一个基站(BS)同时预测不同类型的时间序列(如短信、呼叫和因特网)具有特殊的挑战。这种分布式多目标预测问题不同于已有研究的单目标预测问题，它既要利用同一目标点上不同类型时间序列的内部相关性，又要利用不同目标点上时间序列的内部相关性。为此，我们首先从短信、呼叫和互联网的角度提出了一个通过聚合多视图依赖来学习基站间依赖的模型。为了在时间序列预测中考虑基站间的相关性，我们提出了一种基于图的门机制的图门 LSTM (GGLSTM)模型，该模型可以将那些强烈依赖于学习协同增强预测模型的基站联合起来。我们还利用一个注意网络提取了 BS 内部的相关性，并将其应用于最终的预测。我们提出的方法是评估两个真实世界的数据集。实验结果表明，该模型能够有效地预测分布式基站的多种电信业务类型。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Telecommunication+Traffic+Forecasting+via+Multi-task+Learning)|0| |[Uncertainty Quantification for Fairness in Two-Stage Recommender Systems](https://doi.org/10.1145/3539597.3570469)|Lequn Wang, Thorsten Joachims|Cornell University, Ithaca, NY, USA|Many large-scale recommender systems consist of two stages. The first stage efficiently screens the complete pool of items for a small subset of promising candidates, from which the second-stage model curates the final recommendations. In this paper, we investigate how to ensure group fairness to the items in this two-stage architecture. In particular, we find that existing first-stage recommenders might select an irrecoverably unfair set of candidates such that there is no hope for the second-stage recommender to deliver fair recommendations. To this end, motivated by recent advances in uncertainty quantification, we propose two threshold-policy selection rules that can provide distribution-free and finite-sample guarantees on fairness in first-stage recommenders. More concretely, given any relevance model of queries and items and a point-wise lower confidence bound on the expected number of relevant items for each threshold-policy, the two rules find near-optimal sets of candidates that contain enough relevant items in expectation from each group of items. To instantiate the rules, we demonstrate how to derive such confidence bounds from potentially partial and biased user feedback data, which are abundant in many large-scale recommender systems. In addition, we provide both finite-sample and asymptotic analyses of how close the two threshold selection rules are to the optimal thresholds. Beyond this theoretical analysis, we show empirically that these two rules can consistently select enough relevant items from each group while minimizing the size of the candidate sets for a wide range of settings.|许多大型推荐系统由两个阶段组成。第一阶段有效地筛选出一小部分有希望的候选人的完整项目库，第二阶段模型从中筛选出最终的建议。在本文中，我们研究了如何在这两个阶段的体系结构中保证项目的群公平性。特别是，我们发现现有的第一阶段推荐人可能会选择一组不可挽回的不公平候选人，以至于第二阶段推荐人没有希望提供公平的推荐。为此，在不确定性量化研究的最新进展的推动下，我们提出了两个门限策略选择规则，它们可以为第一阶段推荐者的公平性提供无分布和有限样本的保证。更具体地说，给定任何查询和项目的相关性模型，以及对每个阈值策略的相关项目预期数量的逐点置信下限，这两个规则发现在每组项目预期中包含足够相关项目的候选集接近最优。为了实例化这些规则，我们演示了如何从潜在的部分和有偏见的用户反馈数据中推导出这样的置信界限，这些数据在许多大规模的推荐系统中都很丰富。此外，我们还提供了有限样本和渐近分析，如何接近两个阈值选择规则的最佳阈值。除了这个理论分析，我们的经验表明，这两个规则可以一致地选择足够的相关项目从每个组，同时最小化候选集的大小为广泛的设置。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Uncertainty+Quantification+for+Fairness+in+Two-Stage+Recommender+Systems)|0| -|[Revisiting Code Search in a Two-Stage Paradigm](https://doi.org/10.1145/3539597.3570383)|Fan Hu, Yanlin Wang, Lun Du, Xirong Li, Hongyu Zhang, Shi Han, Dongmei Zhang|Renmin University of China, Beijing, China; The University of Newcastle, Sydney, NSW, China; Sun Yat-sen University, Zhuhai, China; Microsoft Research, Beijing, China|With a good code search engine, developers can reuse existing code snippets and accelerate software development process. Current code search methods can be divided into two categories: traditional information retrieval (IR) based and deep learning (DL) based approaches. DL-based approaches include the cross-encoder paradigm and the bi-encoder paradigm. However, both approaches have certain limitations. The inference of IR-based and bi-encoder models are fast, however, they are not accurate enough; while cross-encoder models can achieve higher search accuracy but consume more time. In this work, we propose TOSS, a two-stage fusion code search framework that can combine the advantages of different code search methods. TOSS first uses IR-based and bi-encoder models to efficiently recall a small number of top-k code candidates, and then uses fine-grained cross-encoders for finer ranking. Furthermore, we conduct extensive experiments on different code candidate volumes and multiple programming languages to verify the effectiveness of TOSS. We also compare TOSS with six data fusion methods. Experimental results show that TOSS is not only efficient, but also achieves state-of-the-art accuracy with an overall mean reciprocal ranking (MRR) score of 0.763, compared to the best baseline result on the CodeSearchNet benchmark of 0.713.|有了一个好的代码搜索引擎，开发人员可以重用现有的代码片段，并加快软件开发过程。目前的代码检索方法可分为两类: 传统的基于信息检索的方法和基于深度学习的方法。基于 DL 的方法包括交叉编码器范式和双编码器范式。然而，这两种方法都有一定的局限性。基于红外和双编码器模型的推理速度较快，但不够准确，而交叉编码器模型可以实现更高的搜索精度，但需要更多的时间。在这项工作中，我们提出了 TOSS，一个两阶段的融合代码搜索框架，可以结合不同的代码搜索方法的优点。TOSS 首先使用基于 IR 和双编码器的模型来有效地回忆少量的 top-k 代码候选，然后使用细粒度的交叉编码器进行更精细的排序。此外，我们在不同的代码候选卷和多种编程语言上进行了广泛的实验，以验证 TOSS 的有效性。并将 TOSS 与六种数据融合方法进行了比较。实验结果表明，与 CodeSearchNet 基准测试的最佳基线结果0.713相比，TOSS 不仅有效，而且达到了最先进的准确度，总体平均互惠排名(MRR)得分为0.763。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Revisiting+Code+Search+in+a+Two-Stage+Paradigm)|0| +|[Revisiting Code Search in a Two-Stage Paradigm](https://doi.org/10.1145/3539597.3570383)|Fan Hu, Yanlin Wang, Lun Du, Xirong Li, Hongyu Zhang, Shi Han, Dongmei Zhang|Sun Yat-sen University, Zhuhai, China; Renmin University of China, Beijing, China; The University of Newcastle, Sydney, NSW, China; Microsoft Research, Beijing, China|With a good code search engine, developers can reuse existing code snippets and accelerate software development process. Current code search methods can be divided into two categories: traditional information retrieval (IR) based and deep learning (DL) based approaches. DL-based approaches include the cross-encoder paradigm and the bi-encoder paradigm. However, both approaches have certain limitations. The inference of IR-based and bi-encoder models are fast, however, they are not accurate enough; while cross-encoder models can achieve higher search accuracy but consume more time. In this work, we propose TOSS, a two-stage fusion code search framework that can combine the advantages of different code search methods. TOSS first uses IR-based and bi-encoder models to efficiently recall a small number of top-k code candidates, and then uses fine-grained cross-encoders for finer ranking. Furthermore, we conduct extensive experiments on different code candidate volumes and multiple programming languages to verify the effectiveness of TOSS. We also compare TOSS with six data fusion methods. Experimental results show that TOSS is not only efficient, but also achieves state-of-the-art accuracy with an overall mean reciprocal ranking (MRR) score of 0.763, compared to the best baseline result on the CodeSearchNet benchmark of 0.713.|有了一个好的代码搜索引擎，开发人员可以重用现有的代码片段，并加快软件开发过程。目前的代码检索方法可分为两类: 传统的基于信息检索的方法和基于深度学习的方法。基于 DL 的方法包括交叉编码器范式和双编码器范式。然而，这两种方法都有一定的局限性。基于红外和双编码器模型的推理速度较快，但不够准确，而交叉编码器模型可以实现更高的搜索精度，但需要更多的时间。在这项工作中，我们提出了 TOSS，一个两阶段的融合代码搜索框架，可以结合不同的代码搜索方法的优点。TOSS 首先使用基于 IR 和双编码器的模型来有效地回忆少量的 top-k 代码候选，然后使用细粒度的交叉编码器进行更精细的排序。此外，我们在不同的代码候选卷和多种编程语言上进行了广泛的实验，以验证 TOSS 的有效性。并将 TOSS 与六种数据融合方法进行了比较。实验结果表明，与 CodeSearchNet 基准测试的最佳基线结果0.713相比，TOSS 不仅有效，而且达到了最先进的准确度，总体平均互惠排名(MRR)得分为0.763。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Revisiting+Code+Search+in+a+Two-Stage+Paradigm)|0| |[MMBench: The Match Making Benchmark](https://doi.org/10.1145/3539597.3573023)|Yongsheng Liu, Yanxing Qi, Jiangwei Zhang, Connie Kou, Qiaolin Chen|Tencent, Singapore, Singapore; Tencent, Shenzhen, China|Video gaming has gained huge popularity over the last few decades. As reported, there are about 2.9 billion gamers globally. Among all genres, competitive games are one of the most popular ones. Matchmaking is a core problem for competitive games, which determines the player satisfaction, hence influences the game success. Most matchmaking systems group the queuing players into opposing teams with similar skill levels. The key challenge is to accurately rate the players' skills based on their match performances. There has been an increasing amount of effort on developing such rating systems such as Elo, Glicko. However, games with different game-plays might have different game modes, which might require an extensive amount of effort for rating system customization. Even though there are many rating system choices and various customization strategies, there is a clear lack of a systematic framework with which different rating systems can be analysed and compared against each other. Such a framework could help game developers to identify the bottlenecks of their matchmaking systems and enhance the performance of their matchmaking systems. To bridge the gap, we present MMBench, the first benchmark framework for evaluating different rating systems. It serves as a fair means of comparison for different rating systems and enables a deeper understanding of different rating systems. In this paper, we will present how MMBench could benchmark the three major rating systems, Elo, Glicko, Trueskill in the battle modes of 1 vs 1, n vs n, battle royal and teamed battle royal over both real and synthetic datasets.|在过去的几十年里，视频游戏获得了巨大的普及。据报道，全球大约有29亿玩家。在所有类型中，竞技游戏是最受欢迎的游戏之一。匹配是竞技游戏的核心问题，它决定着玩家的满意度，从而影响着游戏的成功。大多数匹配系统将排队的队员分组成技术水平相似的对立队伍。关键的挑战是根据球员的比赛表现来准确地评价他们的技术。在开发诸如 Elo、 Glicko 之类的评级系统方面，人们付出了越来越多的努力。然而，具有不同游戏玩法的游戏可能有不同的游戏模式，这可能需要大量的工作来评定系统定制。尽管有许多评级制度的选择和各种定制战略，但明显缺乏一个系统框架，用以分析和比较不同的评级制度。这样一个框架可以帮助游戏开发人员识别其匹配系统的瓶颈，并提高其匹配系统的性能。为了弥补差距，我们提出了 MMBench，第一个评估不同评级系统的基准框架。它作为一个公平的手段，比较不同的评级制度，并使不同的评级制度更深入的了解。在这篇文章中，我们将介绍 MMBench 如何在真实和合成数据集上对三个主要的评级系统进行基准测试: Elo，Glicko，Trueskill 在1对1，n 对 n 的战斗模式中，Battle royal 和 team fight royal。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=MMBench:+The+Match+Making+Benchmark)|0| |[Trustworthy Algorithmic Ranking Systems](https://doi.org/10.1145/3539597.3572723)|Markus Schedl, Emilia Gómez, Elisabeth Lex|Johannes Kepler University Linz & Linz Institute of Technology, Linz, Austria; Graz University of Technology, Graz, Austria; European Commission, Joint Research Centre & Universitat Pompeu Fabra, Seville and Barcelona, Spain|This tutorial aims at providing its audience an interdisciplinary overview about the topics of fairness and non-discrimination, diversity, and transparency as relevant dimensions of trustworthy AI systems, tailored to algorithmic ranking systems such as search engines and recommender systems. We will equip the mostly technical audience of WSDM with the necessary understanding of the social and ethical implications of their research and development on the one hand, and of recent ethical guidelines and regulatory frameworks addressing the aforementioned dimensions on the other hand. While the tutorial foremost takes a European perspective, starting from the concept of trustworthy AI and discussing EU regulation in this area currently in the implementation stages, we also consider related initiatives worldwide. Since ensuring non-discrimination, diversity, and transparency in retrieval and recommendation systems is an endeavor in which academic institutions and companies in different parts of the world should collaborate, this tutorial is relevant for researchers and practitioners interested in the ethical, social, and legal impact of their work. The tutorial, therefore, targets both academic scholars and practitioners around the globe, by reviewing recent research and providing practical examples addressing these particular trustworthiness aspects, and showcasing how new regulations affect the audience's daily work.|本教程的目的是为其受众提供一个公平和非歧视，多样性和透明度作为值得信赖的人工智能系统的相关维度的主题的跨学科概述，定制的算法排名系统，如搜索引擎和推荐系统。我们将使主要是技术性的 WSDM 受众一方面对其研究和开发的社会和伦理影响有必要的了解，另一方面对处理上述方面的最新伦理准则和管理框架有必要的了解。虽然最重要的教程从欧洲的角度出发，从可信赖的人工智能的概念出发，讨论欧盟在这一领域目前正处于实施阶段的规章制度，但我们也考虑到世界各地的相关举措。由于确保检索和推荐系统的非歧视性、多样性和透明度是世界不同地区的学术机构和公司应该合作的一项努力，本教程适用于对其工作的伦理、社会和法律影响感兴趣的研究人员和从业人员。因此，本教程通过回顾最近的研究，并提供实际例子，解决这些特定的可信赖性方面，以及展示新的法规如何影响观众的日常工作，面向全球学术界学者和从业人员。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Trustworthy+Algorithmic+Ranking+Systems)|0| -|[Proactive Conversational Agents](https://doi.org/10.1145/3539597.3572724)|Lizi Liao, Grace Hui Yang, Chirag Shah|Singapore Management University, Singapore, Singapore; University of Washington, Seattle, WA, USA; Georgetown University, Washington, DC, USA|Conversational agents, or commonly known as dialogue systems, have gained escalating popularity in recent years. Their widespread applications support conversational interactions with users and accomplishing various tasks as personal assistants. However, one key weakness in existing conversational agents is that they only learn to passively answer user queries via training on pre-collected and manually-labeled data. Such passiveness makes the interaction modeling and system-building process relatively easier, but it largely hinders the possibility of being human-like hence lowering the user engagement level. In this tutorial, we introduce and discuss methods to equip conversational agents with the ability to interact with end users in a more proactive way. This three-hour tutorial is divided into three parts and includes two interactive exercises. It reviews and presents recent advancements on the topic, focusing on automatically expanding ontology space, actively driving conversation by asking questions or strategically shifting topics, and retrospectively conducting response quality control.|会话代理，或通常称为对话系统，近年来越来越受欢迎。它们广泛的应用程序支持与用户的对话交互，并作为个人助理完成各种任务。然而，现有会话代理的一个关键弱点是，它们只能通过对预收集和手动标记的数据进行培训，学会被动地回答用户的查询。这种被动性使得交互建模和系统构建过程相对容易，但是它在很大程度上阻碍了人性化的可能性，从而降低了用户参与水平。在本教程中，我们将介绍和讨论使会话代理具备以更主动的方式与最终用户交互的能力的方法。这个三个小时的教程分为三个部分，包括两个互动练习。它回顾并介绍了最近在这一主题上的进展，侧重于自动扩展本体空间，通过提出问题或策略性地转移话题来积极推动会话，以及回顾性地进行回应质量控制。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Proactive+Conversational+Agents)|0| -|[AutoML for Deep Recommender Systems: Fundamentals and Advances](https://doi.org/10.1145/3539597.3572729)|Ruiming Tang, Bo Chen, Yejing Wang, Huifeng Guo, Yong Liu, Wenqi Fan, Xiangyu Zhao|City University of Hong Kong, Hong Kong, Hong Kong; The Hong Kong Polytechnic University, Hong Kong, Hong Kong; Huawei Noah's Ark Lab, Shenzhen, China|Recommender systems have become increasingly important in our daily lives since they play an important role in mitigating the information overload problem, especially in many user-oriented online services. Recommender systems aim to identify a set of items that best match users' explicit or implicit preferences, by utilizing the user and item interactions to improve the accuracy. With the fast advancement of deep neural networks (DNNs) in the past few decades, recommendation techniques have achieved promising performance. However, we still meet three inherent challenges to design deep recommender systems (DRS): 1) the majority of existing DRS are developed based on hand-crafted components, which requires ample expert knowledge recommender systems; 2) human error and bias can lead to suboptimal components, which reduces the recommendation effectiveness; 3) non-trivial time and engineering efforts are usually required to design the task-specific components in different recommendation scenarios. In this tutorial, we aim to give a comprehensive survey on the recent progress of advanced Automated Machine Learning (AutoML) techniques for solving the above problems in deep recommender systems. More specifically, we will present feature selection, feature embedding search, feature interaction search, and whole DRS pipeline model training and comprehensive search for deep recommender systems. In this way, we expect academic researchers and industrial practitioners in related fields can get deep understanding and accurate insight into the spaces, stimulate more ideas and discussions, and promote developments of technologies in recommendations.|推荐系统在我们的日常生活中越来越重要，因为它们在缓解信息超载问题方面发挥着重要作用，特别是在许多以用户为本的网上服务中。推荐系统旨在通过利用用户和项目的交互来提高准确性，从而识别出一组最符合用户显性或隐性偏好的项目。近几十年来，随着深度神经网络(DNN)的快速发展，推荐技术已经取得了令人满意的性能。然而，在设计深度推荐系统时，我们仍然面临三个内在的挑战: 1)现有的深度推荐系统大部分是基于手工制作的组件开发的，这需要大量的专家知识推荐系统; 2)人为错误和偏差可能导致次优组件，从而降低推荐的有效性; 3)在不同的推荐场景中，通常需要花费大量的时间和工程努力来设计任务特定的组件。在本教程中，我们的目的是给出一个全面的综述，最近的进展，先进的自动机器学习(AutoML)技术，以解决上述问题在深度推荐系统。更具体地说，我们将介绍深度推荐系统的特征选择、特征嵌入搜索、特征交互搜索以及整个 DRS 流水线模型训练和综合搜索。通过这种方式，我们期望学术研究人员和相关领域的行业从业人员能够深入理解和准确洞察空间，激发更多的想法和讨论，并在建议中促进技术的发展。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=AutoML+for+Deep+Recommender+Systems:+Fundamentals+and+Advances)|0| -|[DIGMN: Dynamic Intent Guided Meta Network for Differentiated User Engagement Forecasting in Online Professional Social Platforms](https://doi.org/10.1145/3539597.3570420)|Feifan Li, Lun Du, Qiang Fu, Shi Han, Yushu Du, Guangming Lu, Zi Li|LinkedIn Corp., Beijing, China; Dalian University of Technology, Dalian, China; Microsoft Research, Beijing, China|User engagement prediction plays a critical role for designing interaction strategies to grow user engagement and increase revenue in online social platforms. Through the in-depth analysis of the real-world data from the world's largest professional social platforms, i.e., LinkedIn, we find that users expose diverse engagement patterns, and a major reason for the differences in user engagement patterns is that users have different intents. That is, people have different intents when using LinkedIn, e.g., applying for jobs, building connections, or checking notifications, which shows quite different engagement patterns. Meanwhile, user intents and the corresponding engagement patterns may change over time. Although such pattern differences and dynamics are essential for user engagement prediction, differentiating user engagement patterns based on user dynamic intents for better user engagement forecasting has not received enough attention in previous works. In this paper, we proposed a Dynamic Intent Guided Meta Network (DIGMN), which can explicitly model user intent varying with time and perform differentiated user engagement forecasting. Specifically, we derive some interpretable basic user intents as prior knowledge from data mining and introduce prior intents in explicitly modeling dynamic user intent. Furthermore, based on the dynamic user intent representations, we propose a meta predictor to perform differentiated user engagement forecasting. Through a comprehensive evaluation on LinkedIn anonymous user data, our method outperforms state-of-the-art baselines significantly, i.e., 2.96% and 3.48% absolute error reduction, on coarse-grained and fine-grained user engagement prediction tasks, respectively, demonstrating the effectiveness of our method.|用户参与度预测在设计交互策略以增加用户参与度和在线社交平台收入方面起着至关重要的作用。通过对世界上最大的专业社交平台 LinkedIn 的现实数据进行深入分析，我们发现用户暴露了不同的参与模式，用户参与模式不同的一个主要原因是用户有不同的意图。也就是说，人们在使用 LinkedIn 时有不同的意图，比如，申请工作，建立联系，或者查看通知，这些都显示出完全不同的参与模式。同时，用户意图和相应的参与模式可能会随着时间的推移而改变。尽管这些模式差异和动态对于用户参与预测是必不可少的，但是基于用户动态意图区分用户参与模式以获得更好的用户参与预测在以前的工作中没有得到足够的重视。本文提出了一种动态意图引导元网络(DIGMN) ，它可以显式地模拟随时间变化的用户意图，并进行差异化的用户参与预测。具体地说，我们从数据挖掘中推导出一些可解释的基本用户意图作为先验知识，并将先验意图引入到动态用户意图的显式建模中。此外，基于动态用户意图表示，我们提出了一个元预测器来执行差异化的用户参与预测。通过对 LinkedIn 匿名用户数据的综合评价，该方法在粗粒度和细粒度用户参与预测任务上分别显著优于最先进的基线(2.96% 和3.48%) ，证明了该方法的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=DIGMN:+Dynamic+Intent+Guided+Meta+Network+for+Differentiated+User+Engagement+Forecasting+in+Online+Professional+Social+Platforms)|0| +|[Proactive Conversational Agents](https://doi.org/10.1145/3539597.3572724)|Lizi Liao, Grace Hui Yang, Chirag Shah|University of Washington, Seattle, WA, USA; Singapore Management University, Singapore, Singapore; Georgetown University, Washington, DC, USA|Conversational agents, or commonly known as dialogue systems, have gained escalating popularity in recent years. Their widespread applications support conversational interactions with users and accomplishing various tasks as personal assistants. However, one key weakness in existing conversational agents is that they only learn to passively answer user queries via training on pre-collected and manually-labeled data. Such passiveness makes the interaction modeling and system-building process relatively easier, but it largely hinders the possibility of being human-like hence lowering the user engagement level. In this tutorial, we introduce and discuss methods to equip conversational agents with the ability to interact with end users in a more proactive way. This three-hour tutorial is divided into three parts and includes two interactive exercises. It reviews and presents recent advancements on the topic, focusing on automatically expanding ontology space, actively driving conversation by asking questions or strategically shifting topics, and retrospectively conducting response quality control.|会话代理，或通常称为对话系统，近年来越来越受欢迎。它们广泛的应用程序支持与用户的对话交互，并作为个人助理完成各种任务。然而，现有会话代理的一个关键弱点是，它们只能通过对预收集和手动标记的数据进行培训，学会被动地回答用户的查询。这种被动性使得交互建模和系统构建过程相对容易，但是它在很大程度上阻碍了人性化的可能性，从而降低了用户参与水平。在本教程中，我们将介绍和讨论使会话代理具备以更主动的方式与最终用户交互的能力的方法。这个三个小时的教程分为三个部分，包括两个互动练习。它回顾并介绍了最近在这一主题上的进展，侧重于自动扩展本体空间，通过提出问题或策略性地转移话题来积极推动会话，以及回顾性地进行回应质量控制。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Proactive+Conversational+Agents)|0| +|[AutoML for Deep Recommender Systems: Fundamentals and Advances](https://doi.org/10.1145/3539597.3572729)|Ruiming Tang, Bo Chen, Yejing Wang, Huifeng Guo, Yong Liu, Wenqi Fan, Xiangyu Zhao|Huawei Noah's Ark Lab, Shenzhen, China; The Hong Kong Polytechnic University, Hong Kong, Hong Kong; City University of Hong Kong, Hong Kong, Hong Kong|Recommender systems have become increasingly important in our daily lives since they play an important role in mitigating the information overload problem, especially in many user-oriented online services. Recommender systems aim to identify a set of items that best match users' explicit or implicit preferences, by utilizing the user and item interactions to improve the accuracy. With the fast advancement of deep neural networks (DNNs) in the past few decades, recommendation techniques have achieved promising performance. However, we still meet three inherent challenges to design deep recommender systems (DRS): 1) the majority of existing DRS are developed based on hand-crafted components, which requires ample expert knowledge recommender systems; 2) human error and bias can lead to suboptimal components, which reduces the recommendation effectiveness; 3) non-trivial time and engineering efforts are usually required to design the task-specific components in different recommendation scenarios. In this tutorial, we aim to give a comprehensive survey on the recent progress of advanced Automated Machine Learning (AutoML) techniques for solving the above problems in deep recommender systems. More specifically, we will present feature selection, feature embedding search, feature interaction search, and whole DRS pipeline model training and comprehensive search for deep recommender systems. In this way, we expect academic researchers and industrial practitioners in related fields can get deep understanding and accurate insight into the spaces, stimulate more ideas and discussions, and promote developments of technologies in recommendations.|推荐系统在我们的日常生活中越来越重要，因为它们在缓解信息超载问题方面发挥着重要作用，特别是在许多以用户为本的网上服务中。推荐系统旨在通过利用用户和项目的交互来提高准确性，从而识别出一组最符合用户显性或隐性偏好的项目。近几十年来，随着深度神经网络(DNN)的快速发展，推荐技术已经取得了令人满意的性能。然而，在设计深度推荐系统时，我们仍然面临三个内在的挑战: 1)现有的深度推荐系统大部分是基于手工制作的组件开发的，这需要大量的专家知识推荐系统; 2)人为错误和偏差可能导致次优组件，从而降低推荐的有效性; 3)在不同的推荐场景中，通常需要花费大量的时间和工程努力来设计任务特定的组件。在本教程中，我们的目的是给出一个全面的综述，最近的进展，先进的自动机器学习(AutoML)技术，以解决上述问题在深度推荐系统。更具体地说，我们将介绍深度推荐系统的特征选择、特征嵌入搜索、特征交互搜索以及整个 DRS 流水线模型训练和综合搜索。通过这种方式，我们期望学术研究人员和相关领域的行业从业人员能够深入理解和准确洞察空间，激发更多的想法和讨论，并在建议中促进技术的发展。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=AutoML+for+Deep+Recommender+Systems:+Fundamentals+and+Advances)|0| +|[DIGMN: Dynamic Intent Guided Meta Network for Differentiated User Engagement Forecasting in Online Professional Social Platforms](https://doi.org/10.1145/3539597.3570420)|Feifan Li, Lun Du, Qiang Fu, Shi Han, Yushu Du, Guangming Lu, Zi Li|Microsoft Research, Beijing, China; Dalian University of Technology, Dalian, China; LinkedIn Corp., Beijing, China|User engagement prediction plays a critical role for designing interaction strategies to grow user engagement and increase revenue in online social platforms. Through the in-depth analysis of the real-world data from the world's largest professional social platforms, i.e., LinkedIn, we find that users expose diverse engagement patterns, and a major reason for the differences in user engagement patterns is that users have different intents. That is, people have different intents when using LinkedIn, e.g., applying for jobs, building connections, or checking notifications, which shows quite different engagement patterns. Meanwhile, user intents and the corresponding engagement patterns may change over time. Although such pattern differences and dynamics are essential for user engagement prediction, differentiating user engagement patterns based on user dynamic intents for better user engagement forecasting has not received enough attention in previous works. In this paper, we proposed a Dynamic Intent Guided Meta Network (DIGMN), which can explicitly model user intent varying with time and perform differentiated user engagement forecasting. Specifically, we derive some interpretable basic user intents as prior knowledge from data mining and introduce prior intents in explicitly modeling dynamic user intent. Furthermore, based on the dynamic user intent representations, we propose a meta predictor to perform differentiated user engagement forecasting. Through a comprehensive evaluation on LinkedIn anonymous user data, our method outperforms state-of-the-art baselines significantly, i.e., 2.96% and 3.48% absolute error reduction, on coarse-grained and fine-grained user engagement prediction tasks, respectively, demonstrating the effectiveness of our method.|用户参与度预测在设计交互策略以增加用户参与度和在线社交平台收入方面起着至关重要的作用。通过对世界上最大的专业社交平台 LinkedIn 的现实数据进行深入分析，我们发现用户暴露了不同的参与模式，用户参与模式不同的一个主要原因是用户有不同的意图。也就是说，人们在使用 LinkedIn 时有不同的意图，比如，申请工作，建立联系，或者查看通知，这些都显示出完全不同的参与模式。同时，用户意图和相应的参与模式可能会随着时间的推移而改变。尽管这些模式差异和动态对于用户参与预测是必不可少的，但是基于用户动态意图区分用户参与模式以获得更好的用户参与预测在以前的工作中没有得到足够的重视。本文提出了一种动态意图引导元网络(DIGMN) ，它可以显式地模拟随时间变化的用户意图，并进行差异化的用户参与预测。具体地说，我们从数据挖掘中推导出一些可解释的基本用户意图作为先验知识，并将先验意图引入到动态用户意图的显式建模中。此外，基于动态用户意图表示，我们提出了一个元预测器来执行差异化的用户参与预测。通过对 LinkedIn 匿名用户数据的综合评价，该方法在粗粒度和细粒度用户参与预测任务上分别显著优于最先进的基线(2.96% 和3.48%) ，证明了该方法的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=DIGMN:+Dynamic+Intent+Guided+Meta+Network+for+Differentiated+User+Engagement+Forecasting+in+Online+Professional+Social+Platforms)|0| |[BLADE: Biased Neighborhood Sampling based Graph Neural Network for Directed Graphs](https://doi.org/10.1145/3539597.3570430)|Srinivas Virinchi, Anoop Saladi|Amazon, Bengaluru, India|Directed graphs are ubiquitous and have applications across multiple domains including citation, website, social, and traffic networks. Yet, majority of research involving graph neural networks (GNNs) focus on undirected graphs. In this paper, we deal with the problem of node recommendation in directed graphs. Specifically, given a directed graph and query node as input, the goal is to recommend top- nodes that have a high likelihood of a link with the query node. Here we propose BLADE, a novel GNN to model directed graphs. In order to jointly capture link likelihood and link direction, we employ an asymmetric loss function and learn dual embeddings for each node, by appropriately aggregating features from its neighborhood. In order to achieve optimal performance on both low and high-degree nodes, we employ a biased neighborhood sampling scheme that generates locally varying neighborhoods which differ based on a node's connectivity structure. Extensive experimentation on several open-source and proprietary directed graphs show that BLADE outperforms state-of-the-art baselines by 6-230% in terms of HitRate and MRR for the node recommendation task and 10.5% in terms of AUC for the link direction prediction task. We perform ablation study to accentuate the importance of biased neighborhood sampling employed in generating higher quality recommendations for both low-degree and high-degree query nodes. Further, BLADE delivers significant improvement in revenue and sales as measured through an A/B experiment.|有向图是无处不在，并有跨多个领域的应用，包括引用，网站，社会和流量网络。然而，大多数涉及图神经网络(GNN)的研究集中在无向图上。本文研究有向图中的节点推荐问题。具体来说，给定一个有向图和查询节点作为输入，目标是推荐与查询节点具有高度可能性的链接的顶部节点。在这里，我们提出 BLADE，一个新的 GNN 模型有向图。为了联合捕获链路可能性和链路方向，我们采用了一种非对称损失函数，通过适当地从每个节点的邻域聚集特征来学习每个节点的对偶嵌入。为了在低度和高度节点上获得最佳的性能，我们采用了一种有偏的邻域抽样方案，根据节点的连通性结构产生局部变化的邻域。对几个开放源码和专有有向图的广泛实验表明，BLADE 在节点推荐任务的 HitRate 和 MRR 方面比最先进的基线表现高出6-230% ，在链路方向预测任务的 AUC 方面高出10.5% 。我们进行消融研究，以强调有偏的邻域抽样的重要性，使用在产生高质量的建议，无论是低度和高度查询节点。此外，BLADE 通过 A/B 实验，在收入和销售方面取得了显著的改善。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=BLADE:+Biased+Neighborhood+Sampling+based+Graph+Neural+Network+for+Directed+Graphs)|0| |[Mining User-aware Multi-relations for Fake News Detection in Large Scale Online Social Networks](https://doi.org/10.1145/3539597.3570478)|Xing Su, Jian Yang, Jia Wu, Yuchen Zhang|Macquarie University, Sydney, NSW, Australia|Users' involvement in creating and propagating news is a vital aspect of fake news detection in online social networks. Intuitively, credible users are more likely to share trustworthy news, while untrusted users have a higher probability of spreading untrustworthy news. In this paper, we construct a dual-layer graph (i.e., the news layer and the user layer) to extract multiple relations of news and users in social networks to derive rich information for detecting fake news. Based on the dual-layer graph, we propose a fake news detection model named Us-DeFake. It learns the propagation features of news in the news layer and the interaction features of users in the user layer. Through the inter-layer in the graph, Us-DeFake fuses the user signals that contain credibility information into the news features, to provide distinctive user-aware embeddings of news for fake news detection. The training process conducts on multiple dual-layer subgraphs obtained by a graph sampler to scale Us-DeFake in large scale social networks. Extensive experiments on real-world datasets illustrate the superiority of Us-DeFake which outperforms all baselines, and the users' credibility signals learned by interaction relation can notably improve the performance of our model.|用户参与创造和传播新闻是在线社交网络虚假新闻检测的一个重要方面。直观地说，可信的用户更可能分享可信的新闻，而不可信的用户传播不可信的新闻的可能性更大。本文构造了一个双层图(即新闻层和用户层)来提取社交网络中新闻和用户之间的多重关系，从而获取丰富的信息来检测虚假新闻。在双层图的基础上，提出了一种假新闻检测模型 Us-DeFake。它学习新闻层中新闻的传播特征和用户层中用户的交互特征。Us-DeFake 通过图中的中间层，将包含可信信息的用户信号融合到新闻特征中，为假新闻检测提供独特的用户感知新闻嵌入。该训练过程对图采样器获得的多个双层子图进行训练，以在大规模社会网络中对 Us-DeFake 进行标度。在实际数据集上进行的大量实验表明，Us-DeFake 的性能优于所有基线，通过交互关系获得的用户可信度信号可以显著提高我们模型的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Mining+User-aware+Multi-relations+for+Fake+News+Detection+in+Large+Scale+Online+Social+Networks)|0| |[Generating Explainable Product Comparisons for Online Shopping](https://doi.org/10.1145/3539597.3570489)|Nikhita Vedula, Marcus D. Collins, Eugene Agichtein, Oleg Rokhlenko|Amazon, Atlanta, GA, USA; Amazon, Seattle, WA, USA|An essential part of making shopping purchase decisions is to compare and contrast products based on key differentiating features, but doing this manually can be overwhelming. Prior methods offer limited product comparison capabilities, e.g., via pre-defined common attributes that may be difficult to understand, or irrelevant to a particular product or user. Automatically generating an informative, natural-sounding, and factually consistent comparative text for multiple product and attribute types is a challenging research problem. We describe HCPC (Human Centered Product Comparison), to tackle two kinds of comparisons for online shopping: (i) product-specific, to describe and compare products based on their key attributes; and (ii) attribute-specific comparisons, to compare similar products on a specific attribute. To ensure that comparison text is faithful to the input product data, we introduce a novel multi-decoder, multi-task generative language model. One decoder generates product comparison text, and a second one generates supportive, explanatory text in the form of product attribute names and values. The second task imitates a copy mechanism, improving the comparison generator, and its output is used to justify the factual accuracy of the generated comparison text, by training a factual consistency model to detect and correct errors in the generated comparative text. We release a new dataset (https://registry.opendata.aws/) of ~15K human generated sentences, comparing products on one or more attributes (the first such data we know of for product comparison). We demonstrate on this data that HCPC significantly outperforms strong baselines, by ~10% using automatic metrics, and ~5% using human evaluation.|作出购物决定的一个重要部分是比较和对比产品的基础上的关键差异功能，但这样做手动可能是压倒性的。先前的方法提供有限的产品比较能力，例如，通过预定义的公共属性，可能难以理解，或无关的特定产品或用户。为多种产品和属性类型自动生成信息丰富、听起来自然、事实一致的比较文本是一个具有挑战性的研究问题。我们描述了 HCPC (以人为中心的产品比较) ，以解决网上购物的两种比较: (i)产品特定的，基于关键属性描述和比较产品; 和(ii)属性特定的比较，以比较具体属性上的相似产品。为了保证比较文本忠实于输入的产品数据，我们引入了一种新的多解码器、多任务生成语言模型。一个解码器生成产品比较文本，另一个解码器以产品属性名称和值的形式生成支持性的解释性文本。第二个任务模仿复制机制，改进比较生成器，并通过训练一个事实内存一致性模型来检测和纠正生成的比较文本中的错误，将其输出用于证明所生成的比较文本的事实准确性。我们发布了一个新的数据集( https://registry.opendata.aws/) ，包括大约15k 个人类生成的句子，比较产品的一个或多个属性(这是我们所知道的第一个用于产品比较的数据)。我们在这些数据上证明，HCPC 显著优于强基线，使用自动度量的优势约为10% ，使用人工评估的优势约为5% 。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Generating+Explainable+Product+Comparisons+for+Online+Shopping)|0| |[Never Too Late to Learn: Regularizing Gender Bias in Coreference Resolution](https://doi.org/10.1145/3539597.3570473)|Sunyoung Park, Kyuri Choi, Haeun Yu, Youngjoong Ko|Sungkyunkwan University, Suwon-si, Republic of Korea|Leveraging pre-trained language models (PLMs) as initializers for efficient transfer learning has become a universal approach for text-related tasks. However, the models not only learn the language understanding abilities but also reproduce prejudices for certain groups in the datasets used for pre-training. Recent studies show that the biased knowledge acquired from the datasets affects the model predictions on downstream tasks. In this paper, we mitigate and analyze the gender biases in PLMs with coreference resolution, which is one of the natural language understanding (NLU) tasks. PLMs exhibit two types of gender biases: stereotype and skew. The primary causes for the biases are the imbalanced datasets with more male examples and the stereotypical examples on gender roles. While previous studies mainly focused on the skew problem, we aim to mitigate both gender biases in PLMs while maintaining the model's original linguistic capabilities. Our method employs two regularization terms, Stereotype Neutralization (SN) and Elastic Weight Consolidation (EWC). The models trained with the methods show to be neutralized and reduce the biases significantly on the WinoBias dataset compared to the public BERT. We also invented a new gender bias quantification metric called the Stereotype Quantification (SQ) score. In addition to the metrics, embedding visualizations were used to interpret how our methods have successfully debiased the models.|利用预训练语言模型(PLM)作为有效迁移学习的初始化方法已经成为文本相关任务的通用方法。然而，这些模型不仅学习了语言理解能力，而且在用于预训练的数据集中重现了某些群体的偏见。最近的研究表明，从数据集中获得的有偏见的知识会影响对下游任务的模型预测。本文采用共指消解的方法来缓解和分析 PLM 中的性别偏见，这是自然语言理解(NLU)的任务之一。PLM 表现出两种类型的性别偏见: 刻板印象和偏见。造成偏见的主要原因是数据集不平衡，男性例子较多，以及性别角色方面的陈规定型例子。虽然以前的研究主要集中在倾斜问题，我们的目标是减轻 PLM 中的性别偏见，同时保持模型的原始语言能力。我们的方法采用两个正则化项，刻板印象中和(SN)和弹性加权固结(EWC)。与公开的 BERT 相比，用这些方法训练的模型在 WinoBias 数据集上显示出中和和减少了偏差。我们还发明了一种新的性别偏见量化指标，称为刻板印象量化(SQ)评分。除了度量之外，嵌入可视化被用来解释我们的方法是如何成功地去偏模型的。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Never+Too+Late+to+Learn:+Regularizing+Gender+Bias+in+Coreference+Resolution)|0| -|[Learning to Distill Graph Neural Networks](https://doi.org/10.1145/3539597.3570480)|Cheng Yang, Yuxin Guo, Yao Xu, Chuan Shi, Jiawei Liu, Chunchen Wang, Xin Li, Ning Guo, Hongzhi Yin|The University of Queensland, Brisbane, QLD, Australia; Beijing University of Posts and Telecommunications, Beijing, China; Researcher, Beijing, China|Graph Neural Networks (GNNs) can effectively capture both the topology and attribute information of a graph, and have been extensively studied in many domains. Recently, there is an emerging trend that equips GNNs with knowledge distillation for better efficiency or effectiveness. However, to the best of our knowledge, existing knowledge distillation methods applied on GNNs all employed predefined distillation processes, which are controlled by several hyper-parameters without any supervision from the performance of distilled models. Such isolation between distillation and evaluation would lead to suboptimal results. In this work, we aim to propose a general knowledge distillation framework that can be applied on any pretrained GNN models to further improve their performance. To address the isolation problem, we propose to parameterize and learn distillation processes suitable for distilling GNNs. Specifically, instead of introducing a unified temperature hyper-parameter as most previous work did, we will learn node-specific distillation temperatures towards better performance of distilled models. We first parameterize each node's temperature by a function of its neighborhood's encodings and predictions, and then design a novel iterative learning process for model distilling and temperature learning. We also introduce a scalable variant of our method to accelerate model training. Experimental results on five benchmark datasets show that our proposed framework can be applied on five popular GNN models and consistently improve their prediction accuracies with 3.12% relative enhancement on average. Besides, the scalable variant enables 8 times faster training speed at the cost of 1% prediction accuracy.|图神经网络(GNN)能够有效地捕获图的拓扑和属性信息，已经在许多领域得到了广泛的研究。最近，有一个新兴的趋势，装备 GNN 的知识提取更好的效率或有效性。然而，据我们所知，现有的应用于 GNN 的知识蒸馏方法都采用了预定义的蒸馏过程，这些过程由多个超参数控制，没有对蒸馏模型的性能进行任何监督。蒸馏和评价之间的这种隔离将导致次优结果。在这项工作中，我们的目标是提出一个通用的知识提取框架，可以应用于任何预先训练的 GNN 模型，以进一步提高其性能。为了解决隔离问题，我们提出参数化和学习蒸馏过程适合蒸馏 GNN。具体来说，我们不会像以前的大多数工作那样引入统一的温度超参数，我们将学习节点特定的蒸馏温度，以提高蒸馏模型的性能。我们首先根据每个节点的邻域编码和预测的函数来参数化每个节点的温度，然后设计一个新的模型提取和温度学习的迭代学习过程。我们还引入了一种可扩展的方法来加速模型训练。在五个基准数据集上的实验结果表明，我们提出的框架可以应用于五个流行的 GNN 模型上，预测精度持续提高，平均相对提高3.12% 。此外，可扩展变量使8倍更快的训练速度的成本1% 的预测准确性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Learning+to+Distill+Graph+Neural+Networks)|0| -|[S2TUL: A Semi-Supervised Framework for Trajectory-User Linking](https://doi.org/10.1145/3539597.3570410)|Liwei Deng, Hao Sun, Yan Zhao, Shuncheng Liu, Kai Zheng|Peking University, Peking, China; University of Electronic Science and Technology of China, ChengDu, China; Aalborg University, Aalborg, China|Trajectory-User Linking (TUL) aiming to identify users of anonymous trajectories, has recently received increasing attention due to its wide range of applications, such as criminal investigation and personalized recommendation systems. In this paper, we propose a flexible Semi-Supervised framework for Trajectory-User Linking, namely S2TUL, which includes five components: trajectory-level graph construction, trajectory relation modeling, location-level sequential modeling, a classification layer and greedy trajectory-user relinking. The first two components are proposed to model the relationships among trajectories, in which three homogeneous graphs and two heterogeneous graphs are firstly constructed and then delivered into the graph convolutional networks for converting the discrete identities to hidden representations. Since the graph constructions are irrelevant to the corresponding users, the unlabelled trajectories can also be included in the graphs, which enables the framework to be trained in a semi-supervised way. Afterwards, the location-level sequential modeling component is designed to capture fine-grained intra-trajectory information by passing the trajectories into the sequential neural networks. Finally, these two level representations are concatenated into a classification layer to predict the user of the input trajectory. In the testing phase, a greedy trajectory-user relinking method is proposed to assure the linking results satisfy the timespan overlap constraint. We conduct extensive experiments on three public datasets with six representative competitors. The evaluation results demonstrate the effectiveness of the proposed framework.|轨迹用户链接(TUL)是一种旨在识别匿名轨迹用户的技术，由于其广泛的应用，如刑事调查和个性化推荐系统，近年来受到越来越多的关注。本文提出了一个灵活的 S2TUL 管理框架，即 S2TUL，它包括轨迹层图的构造、轨迹关系建模、位置层顺序建模、分类层和贪婪轨迹-用户重联五个部分。首先构造出三个同质图和两个异质图，然后将其传递到图卷积网络中，将离散恒等式转化为隐藏表示，最后利用图卷积网络模型对轨迹间的关系进行建模。由于图的构造与相应的用户无关，所以图中也可以包含未标记的轨迹，这样就可以用半监督的方式对框架进行训练。然后，设计位置级序列建模组件，通过将轨迹传递给序列神经网络来获取细粒度的轨迹内信息。最后，将这两层表示连接到一个分类层，预测用户的输入轨迹。在测试阶段，提出了一种贪婪的轨迹用户重联方法，以保证链接结果满足时间跨度重叠约束。我们在三个公共数据集上与六个有代表性的竞争对手进行了广泛的实验。评价结果表明了该框架的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=S2TUL:+A+Semi-Supervised+Framework+for+Trajectory-User+Linking)|0| +|[Learning to Distill Graph Neural Networks](https://doi.org/10.1145/3539597.3570480)|Cheng Yang, Yuxin Guo, Yao Xu, Chuan Shi, Jiawei Liu, Chunchen Wang, Xin Li, Ning Guo, Hongzhi Yin|Researcher, Beijing, China; Beijing University of Posts and Telecommunications, Beijing, China; The University of Queensland, Brisbane, QLD, Australia|Graph Neural Networks (GNNs) can effectively capture both the topology and attribute information of a graph, and have been extensively studied in many domains. Recently, there is an emerging trend that equips GNNs with knowledge distillation for better efficiency or effectiveness. However, to the best of our knowledge, existing knowledge distillation methods applied on GNNs all employed predefined distillation processes, which are controlled by several hyper-parameters without any supervision from the performance of distilled models. Such isolation between distillation and evaluation would lead to suboptimal results. In this work, we aim to propose a general knowledge distillation framework that can be applied on any pretrained GNN models to further improve their performance. To address the isolation problem, we propose to parameterize and learn distillation processes suitable for distilling GNNs. Specifically, instead of introducing a unified temperature hyper-parameter as most previous work did, we will learn node-specific distillation temperatures towards better performance of distilled models. We first parameterize each node's temperature by a function of its neighborhood's encodings and predictions, and then design a novel iterative learning process for model distilling and temperature learning. We also introduce a scalable variant of our method to accelerate model training. Experimental results on five benchmark datasets show that our proposed framework can be applied on five popular GNN models and consistently improve their prediction accuracies with 3.12% relative enhancement on average. Besides, the scalable variant enables 8 times faster training speed at the cost of 1% prediction accuracy.|图神经网络(GNN)能够有效地捕获图的拓扑和属性信息，已经在许多领域得到了广泛的研究。最近，有一个新兴的趋势，装备 GNN 的知识提取更好的效率或有效性。然而，据我们所知，现有的应用于 GNN 的知识蒸馏方法都采用了预定义的蒸馏过程，这些过程由多个超参数控制，没有对蒸馏模型的性能进行任何监督。蒸馏和评价之间的这种隔离将导致次优结果。在这项工作中，我们的目标是提出一个通用的知识提取框架，可以应用于任何预先训练的 GNN 模型，以进一步提高其性能。为了解决隔离问题，我们提出参数化和学习蒸馏过程适合蒸馏 GNN。具体来说，我们不会像以前的大多数工作那样引入统一的温度超参数，我们将学习节点特定的蒸馏温度，以提高蒸馏模型的性能。我们首先根据每个节点的邻域编码和预测的函数来参数化每个节点的温度，然后设计一个新的模型提取和温度学习的迭代学习过程。我们还引入了一种可扩展的方法来加速模型训练。在五个基准数据集上的实验结果表明，我们提出的框架可以应用于五个流行的 GNN 模型上，预测精度持续提高，平均相对提高3.12% 。此外，可扩展变量使8倍更快的训练速度的成本1% 的预测准确性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Learning+to+Distill+Graph+Neural+Networks)|0| +|[S2TUL: A Semi-Supervised Framework for Trajectory-User Linking](https://doi.org/10.1145/3539597.3570410)|Liwei Deng, Hao Sun, Yan Zhao, Shuncheng Liu, Kai Zheng|University of Electronic Science and Technology of China, ChengDu, China; Peking University, Peking, China; Aalborg University, Aalborg, China|Trajectory-User Linking (TUL) aiming to identify users of anonymous trajectories, has recently received increasing attention due to its wide range of applications, such as criminal investigation and personalized recommendation systems. In this paper, we propose a flexible Semi-Supervised framework for Trajectory-User Linking, namely S2TUL, which includes five components: trajectory-level graph construction, trajectory relation modeling, location-level sequential modeling, a classification layer and greedy trajectory-user relinking. The first two components are proposed to model the relationships among trajectories, in which three homogeneous graphs and two heterogeneous graphs are firstly constructed and then delivered into the graph convolutional networks for converting the discrete identities to hidden representations. Since the graph constructions are irrelevant to the corresponding users, the unlabelled trajectories can also be included in the graphs, which enables the framework to be trained in a semi-supervised way. Afterwards, the location-level sequential modeling component is designed to capture fine-grained intra-trajectory information by passing the trajectories into the sequential neural networks. Finally, these two level representations are concatenated into a classification layer to predict the user of the input trajectory. In the testing phase, a greedy trajectory-user relinking method is proposed to assure the linking results satisfy the timespan overlap constraint. We conduct extensive experiments on three public datasets with six representative competitors. The evaluation results demonstrate the effectiveness of the proposed framework.|轨迹用户链接(TUL)是一种旨在识别匿名轨迹用户的技术，由于其广泛的应用，如刑事调查和个性化推荐系统，近年来受到越来越多的关注。本文提出了一个灵活的 S2TUL 管理框架，即 S2TUL，它包括轨迹层图的构造、轨迹关系建模、位置层顺序建模、分类层和贪婪轨迹-用户重联五个部分。首先构造出三个同质图和两个异质图，然后将其传递到图卷积网络中，将离散恒等式转化为隐藏表示，最后利用图卷积网络模型对轨迹间的关系进行建模。由于图的构造与相应的用户无关，所以图中也可以包含未标记的轨迹，这样就可以用半监督的方式对框架进行训练。然后，设计位置级序列建模组件，通过将轨迹传递给序列神经网络来获取细粒度的轨迹内信息。最后，将这两层表示连接到一个分类层，预测用户的输入轨迹。在测试阶段，提出了一种贪婪的轨迹用户重联方法，以保证链接结果满足时间跨度重叠约束。我们在三个公共数据集上与六个有代表性的竞争对手进行了广泛的实验。评价结果表明了该框架的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=S2TUL:+A+Semi-Supervised+Framework+for+Trajectory-User+Linking)|0| |[Ask "Who", Not "What": Bitcoin Volatility Forecasting with Twitter Data](https://doi.org/10.1145/3539597.3570387)|M. Eren Akbiyik, Mert Erkul, Killian Kämpf, Vaiva Vasiliauskaite, Nino AntulovFantulin|ETH Zurich, Zurich, Switzerland|Understanding the variations in trading price (volatility), and its response to exogenous information, is a well-researched topic in finance. In this study, we focus on finding stable and accurate volatility predictors for a relatively new asset class of cryptocurrencies, in particular Bitcoin, using deep learning representations of public social media data obtained from Twitter. For our experiments, we extracted semantic information and user statistics from over 30 million Bitcoin-related tweets, in conjunction with 15-minute frequency price data over a horizon of 144 days. Using this data, we built several deep learning architectures that utilized different combinations of the gathered information. For each model, we conducted ablation studies to assess the influence of different components and feature sets over the prediction accuracy. We found statistical evidences for the hypotheses that: (i) temporal convolutional networks perform significantly better than both classical autoregressive models and other deep learning-based architectures in the literature, and (ii) tweet author meta-information, even detached from the tweet itself, is a better predictor of volatility than the semantic content and tweet volume statistics. We demonstrate how different information sets gathered from social media can be utilized in different architectures and how they affect the prediction results. As an additional contribution, we make our dataset public for future research.|理解交易价格(波动性)的变化及其对外部信息的响应是金融学研究的热点。在这项研究中，我们的重点是找到稳定和准确的波动性预测相对较新的资产类别的加密货币，特别是比特币，使用从 Twitter 获得的公共社会媒体数据的深度学习表示。在我们的实验中，我们从超过3000万条与比特币相关的推文中提取了语义信息和用户数据，以及144天内15分钟的频率价格数据。使用这些数据，我们构建了几个深度学习架构，它们利用了所收集信息的不同组合。对于每个模型，我们进行了烧蚀研究，以评估不同组成部分和特征集对预测准确性的影响。我们发现统计学证据的假设: (i)时间卷积网络表现显着优于文献中的经典自回归模型和其他基于深度学习的架构，以及(ii) tweet 作者元信息，即使与 tweet 本身分离，是比语义内容和 tweet 量统计更好的波动性预测器。我们展示了如何在不同的架构中使用从社会媒体收集的不同信息集，以及它们如何影响预测结果。作为额外的贡献，我们将我们的数据集公开以供未来的研究使用。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Ask+"Who",+Not+"What":+Bitcoin+Volatility+Forecasting+with+Twitter+Data)|0| -|[Zero to Hero: Exploiting Null Effects to Achieve Variance Reduction in Experiments with One-sided Triggering](https://doi.org/10.1145/3539597.3570413)|Alex Deng, LoHua Yuan, Naoya Kanai, Alexandre SalamaManteau|Airbnb, Seattle, WA, USA; Airbnb, Paris, France; Airbnb, San Francisco, CA, USA|In online experiments where the intervention is only exposed, or "triggered", for a small subset of the population, it is critical to use variance reduction techniques to estimate treatment effects with sufficient precision to inform business decisions. Trigger-dilute analysis is often used in these situations, and reduces the sampling variance of overall intent-to-treat (ITT) effects by an order of magnitude equal to the inverse of the triggering rate; for example, a triggering rate of $5\%$ corresponds to roughly a $20x$ reduction in variance. To apply trigger-dilute analysis, one needs to know experimental subjects' triggering counterfactual statuses, i.e., the counterfactual behavior of subjects under both treatment and control conditions. In this paper, we propose an unbiased ITT estimator with reduced variance applicable for experiments where the triggering counterfactual status is only observed in the treatment group. Our method is based on the efficiency augmentation idea of CUPED and draws upon identification frameworks from the principal stratification and instrumental variables literature. The unbiasedness of our estimation approach relies on a testable assumption that the augmentation term used for covariate adjustment equals zero in expectation. Unlike traditional covariate adjustment or principal score modeling approaches, our estimator can incorporate both pre-experiment and in-experiment observations. We demonstrate through a real-world experiment and simulations that our estimator can remain unbiased and achieve precision improvements as large as if triggering status were fully observed, and in some cases can even outperform trigger-dilute analysis.|在在线实验中，干预只暴露或“触发”一小部分人群，使用方差减少技术以足够的精确度来估计治疗效果，以便为业务决策提供信息是至关重要的。触发稀释分析经常用于这些情况下，并减少整体意向治疗(ITT)效应的抽样方差的数量级等于触发率的反数，例如，5% $的触发率相当于大约 $20 x $的方差减少。应用触发稀释分析，需要了解实验对象的触发反事实状态，即在治疗和控制条件下实验对象的反事实行为。在本文中，我们提出了一个无偏的减少方差的 ITT 估计器，适用于实验中的触发反事实状态仅在治疗组中观察到。我们的方法基于 CUPED 的效率增强思想，并借鉴了主要分层和工具变量文献中的识别框架。我们估计方法的无偏性依赖于一个可检验的假设，即用于协变量平差的增广项等于期望值中的零。与传统的协变量调整或主成分模型方法不同，我们的估计器可以结合实验前和实验中的观察。我们通过一个真实世界的实验和模拟表明，我们的估计器可以保持无偏，并实现精度的提高，如果触发状态得到充分观察，在某些情况下甚至可以优于触发稀释分析。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Zero+to+Hero:+Exploiting+Null+Effects+to+Achieve+Variance+Reduction+in+Experiments+with+One-sided+Triggering)|0| +|[Zero to Hero: Exploiting Null Effects to Achieve Variance Reduction in Experiments with One-sided Triggering](https://doi.org/10.1145/3539597.3570413)|Alex Deng, LoHua Yuan, Naoya Kanai, Alexandre SalamaManteau|Airbnb, Paris, France; Airbnb, Seattle, WA, USA; Airbnb, San Francisco, CA, USA|In online experiments where the intervention is only exposed, or "triggered", for a small subset of the population, it is critical to use variance reduction techniques to estimate treatment effects with sufficient precision to inform business decisions. Trigger-dilute analysis is often used in these situations, and reduces the sampling variance of overall intent-to-treat (ITT) effects by an order of magnitude equal to the inverse of the triggering rate; for example, a triggering rate of $5\%$ corresponds to roughly a $20x$ reduction in variance. To apply trigger-dilute analysis, one needs to know experimental subjects' triggering counterfactual statuses, i.e., the counterfactual behavior of subjects under both treatment and control conditions. In this paper, we propose an unbiased ITT estimator with reduced variance applicable for experiments where the triggering counterfactual status is only observed in the treatment group. Our method is based on the efficiency augmentation idea of CUPED and draws upon identification frameworks from the principal stratification and instrumental variables literature. The unbiasedness of our estimation approach relies on a testable assumption that the augmentation term used for covariate adjustment equals zero in expectation. Unlike traditional covariate adjustment or principal score modeling approaches, our estimator can incorporate both pre-experiment and in-experiment observations. We demonstrate through a real-world experiment and simulations that our estimator can remain unbiased and achieve precision improvements as large as if triggering status were fully observed, and in some cases can even outperform trigger-dilute analysis.|在在线实验中，干预只暴露或“触发”一小部分人群，使用方差减少技术以足够的精确度来估计治疗效果，以便为业务决策提供信息是至关重要的。触发稀释分析经常用于这些情况下，并减少整体意向治疗(ITT)效应的抽样方差的数量级等于触发率的反数，例如，5% $的触发率相当于大约 $20 x $的方差减少。应用触发稀释分析，需要了解实验对象的触发反事实状态，即在治疗和控制条件下实验对象的反事实行为。在本文中，我们提出了一个无偏的减少方差的 ITT 估计器，适用于实验中的触发反事实状态仅在治疗组中观察到。我们的方法基于 CUPED 的效率增强思想，并借鉴了主要分层和工具变量文献中的识别框架。我们估计方法的无偏性依赖于一个可检验的假设，即用于协变量平差的增广项等于期望值中的零。与传统的协变量调整或主成分模型方法不同，我们的估计器可以结合实验前和实验中的观察。我们通过一个真实世界的实验和模拟表明，我们的估计器可以保持无偏，并实现精度的提高，如果触发状态得到充分观察，在某些情况下甚至可以优于触发稀释分析。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Zero+to+Hero:+Exploiting+Null+Effects+to+Achieve+Variance+Reduction+in+Experiments+with+One-sided+Triggering)|0| |[Unbiased and Efficient Self-Supervised Incremental Contrastive Learning](https://doi.org/10.1145/3539597.3570458)|Cheng Ji, Jianxin Li, Hao Peng, Jia Wu, Xingcheng Fu, Qingyun Sun, Philip S. Yu|University of Illinois at Chicago, Chicago, IL, USA; Macquarie University, Sydney, NSW, Australia; Beihang University, Beijing, China|Contrastive Learning (CL) has been proved to be a powerful self-supervised approach for a wide range of domains, including computer vision and graph representation learning. However, the incremental learning issue of CL has rarely been studied, which brings the limitation in applying it to real-world applications. Contrastive learning identifies the samples with the negative ones from the noise distribution that changes in the incremental scenarios. Therefore, only fitting the change of data without noise distribution causes bias, and directly retraining results in low efficiency. To bridge this research gap, we propose a self-supervised Incremental Contrastive Learning (ICL) framework consisting of (i) a novel Incremental InfoNCE (NCE-II) loss function by estimating the change of noise distribution for old data to guarantee no bias with respect to the retraining, (ii) a meta-optimization with deep reinforced Learning Rate Learning (LRL) mechanism which can adaptively learn the learning rate according to the status of the training processes and achieve fast convergence which is critical for incremental learning. Theoretically, the proposed ICL is equivalent to retraining, which is based on solid mathematical derivation. In practice, extensive experiments in different domains demonstrate that, without retraining a new model, ICL achieves up to 16.7x training speedup and 16.8x faster convergence with competitive results.|对比学习(CL)已被证明是一种强大的自我监督方法，适用于包括计算机视觉和图形表示学习在内的广泛领域。然而，关于 CL 的在线机机器学习问题很少被研究，这就限制了它在实际应用中的局限性。对比学习从增量情景中噪声分布的变化中识别出样本与负样本。因此，只对无噪声分布的数据变化进行拟合会产生偏差，直接导致再训练效率低下。为了弥补这一研究空白，我们提出了一个自监督增量对比学习(ICL)框架，该框架包括: (i)一个新的增量信息增量对比学习(nce-II)损失函数，通过估计旧数据噪声分布的变化来保证再训练方面没有偏差; (ii)一个元优化与深度增强学习率学习(LRL)机制，它可以根据训练过程的状态自适应地学习学习率，并实现快速收敛，这对于在线机机器学习来说是至关重要的。理论上，本文提出的 ICL 等价于再训练，它是基于固体数学推导的。在实际应用中，不同领域的大量实验表明，在不对新模型进行再训练的情况下，ICL 的训练加速比可达16.7倍，收敛速度可达16.8倍，具有较强的竞争力。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Unbiased+and+Efficient+Self-Supervised+Incremental+Contrastive+Learning)|0| -|[Reducing the Bias of Visual Objects in Multimodal Named Entity Recognition](https://doi.org/10.1145/3539597.3570485)|Xin Zhang, Jingling Yuan, Lin Li, Jianquan Liu|Wuhan University of Technology & Engineering Research Center of Digital Publishing Intelligent Service Technology, Ministry of Education, Wuhan, China; NEC Corporation, Tokyo, Japan; Wuhan University of Technology, Wuhan, China|Visual information shows to empower accurately named entity recognition in short texts, such as posts from social media. Previous work on multimodal named entity recognition (MNER) often regards an image as a set of visual objects, trying to explicitly align visual objects and entities. However, these methods may suffer the bias introduced by visual objects when they are not identical to entities in quantity and entity type. Different from this kind of explicit alignment, we argue that implicit alignment is effective in optimizing the shared semantic space learning between text and image for improving MNER. To this end, we propose a de-bias contrastive learning based approach for MNER, which studies modality alignment enhanced by cross-modal contrastive learning. Specifically, our contrastive learning adopts a hard sample mining strategy and a debiased contrastive loss to alleviate the bias of quantity and entity type, respectively, which globally learns to align the feature spaces from text and image. Finally, the learned semantic space works with a NER decoder to recognize entities in text. Conducted on two benchmark datasets, experimental results show that our approach outperforms the current state-of-the-art methods.|视觉信息显示，以授权准确命名的实体识别在短文本，如来自社会媒体的帖子。以前关于多模态命名实体识别(MNER)的工作通常将图像视为一组可视对象，试图显式地对齐可视对象和实体。然而，当视觉对象在数量和实体类型上与实体不一致时，这些方法可能会受到视觉对象引入的偏差。与这种显性对齐不同，本文认为隐性对齐可以有效地优化文本与图像之间的共享语义空间学习，从而提高 MNER。为此，我们提出了一种基于去偏差对比学习的 MNER 方法，该方法研究了通过跨模态对比学习增强模态对齐。具体地说，我们的对比学习分别采用硬样本挖掘策略和去偏对比损失策略来减轻数量和实体类型的偏差，从而在全局上学习从文本和图像中对齐特征空间。最后，学习语义空间与 NER 解码器一起工作来识别文本中的实体。在两个基准数据集上进行的实验结果表明，我们的方法优于当前最先进的方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Reducing+the+Bias+of+Visual+Objects+in+Multimodal+Named+Entity+Recognition)|0| +|[Reducing the Bias of Visual Objects in Multimodal Named Entity Recognition](https://doi.org/10.1145/3539597.3570485)|Xin Zhang, Jingling Yuan, Lin Li, Jianquan Liu|Wuhan University of Technology, Wuhan, China; NEC Corporation, Tokyo, Japan; Wuhan University of Technology & Engineering Research Center of Digital Publishing Intelligent Service Technology, Ministry of Education, Wuhan, China|Visual information shows to empower accurately named entity recognition in short texts, such as posts from social media. Previous work on multimodal named entity recognition (MNER) often regards an image as a set of visual objects, trying to explicitly align visual objects and entities. However, these methods may suffer the bias introduced by visual objects when they are not identical to entities in quantity and entity type. Different from this kind of explicit alignment, we argue that implicit alignment is effective in optimizing the shared semantic space learning between text and image for improving MNER. To this end, we propose a de-bias contrastive learning based approach for MNER, which studies modality alignment enhanced by cross-modal contrastive learning. Specifically, our contrastive learning adopts a hard sample mining strategy and a debiased contrastive loss to alleviate the bias of quantity and entity type, respectively, which globally learns to align the feature spaces from text and image. Finally, the learned semantic space works with a NER decoder to recognize entities in text. Conducted on two benchmark datasets, experimental results show that our approach outperforms the current state-of-the-art methods.|视觉信息显示，以授权准确命名的实体识别在短文本，如来自社会媒体的帖子。以前关于多模态命名实体识别(MNER)的工作通常将图像视为一组可视对象，试图显式地对齐可视对象和实体。然而，当视觉对象在数量和实体类型上与实体不一致时，这些方法可能会受到视觉对象引入的偏差。与这种显性对齐不同，本文认为隐性对齐可以有效地优化文本与图像之间的共享语义空间学习，从而提高 MNER。为此，我们提出了一种基于去偏差对比学习的 MNER 方法，该方法研究了通过跨模态对比学习增强模态对齐。具体地说，我们的对比学习分别采用硬样本挖掘策略和去偏对比损失策略来减轻数量和实体类型的偏差，从而在全局上学习从文本和图像中对齐特征空间。最后，学习语义空间与 NER 解码器一起工作来识别文本中的实体。在两个基准数据集上进行的实验结果表明，我们的方法优于当前最先进的方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Reducing+the+Bias+of+Visual+Objects+in+Multimodal+Named+Entity+Recognition)|0| |[Variance-Minimizing Augmentation Logging for Counterfactual Evaluation in Contextual Bandits](https://doi.org/10.1145/3539597.3570452)|Aaron David Tucker, Thorsten Joachims|Cornell University, Ithaca, NY, USA|Methods for offline A/B testing and counterfactual learning are seeing rapid adoption in search and recommender systems, since they allow efficient reuse of existing log data. However, there are fundamental limits to using existing log data alone, since the counterfactual estimators that are commonly used in these methods can have large bias and large variance when the logging policy is very different from the target policy being evaluated. To overcome this limitation, we explore the question of how to design data-gathering policies that most effectively augment an existing dataset of bandit feedback with additional observations for both learning and evaluation. To this effect, this paper introduces Minimum Variance Augmentation Logging (MVAL), a method for constructing logging policies that minimize the variance of the downstream evaluation or learning problem. We explore multiple approaches to computing MVAL policies efficiently, and find that they can be substantially more effective in decreasing the variance of an estimator than naïve approaches.|离线 A/B 测试和反事实学习方法正迅速被搜索和推荐系统采用，因为它们允许有效地重用现有的日志数据。然而，单独使用现有的测井数据存在基本的局限性，因为当测井策略与被评估的目标策略非常不同时，这些方法中常用的反事实估计量可能会有很大的偏差和很大的方差。为了克服这一局限性，我们探讨了如何设计数据收集政策的问题，以便最有效地增加现有的土匪反馈数据集，并为学习和评估提供额外的观察数据。为此，本文介绍了最小方差增强测井(MVAL) ，一种构造测井策略的方法，使下游评估或学习问题的方差最小化。我们探索了多种有效计算 MVAL 策略的方法，发现它们在减少估计量的方差方面比单纯的方法更有效。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Variance-Minimizing+Augmentation+Logging+for+Counterfactual+Evaluation+in+Contextual+Bandits)|0| |[DisKeyword: Tweet Corpora Exploration for Keyword Selection](https://doi.org/10.1145/3539597.3573033)|Sacha Lévy, Reihaneh Rabbany|McGill University & Mila, Montreal, PQ, Canada|How to accelerate the search for relevant topical keywords within a tweet corpus? Computational social scientists conducting topical studies employ large, self-collected or crowdsourced social media datasets such as tweet corpora. Comprehensive sets of relevant keywords are often necessary to sample or analyze these data sources. However, naively skimming through thousands of keywords can quickly become a daunting task. In this study, we present a web-based application to simplify the search for relevant topical hashtags in a tweet corpus. DisKeyword allows users to grasp high-level trends in their dataset, while iteratively labeling keywords recommended based on their links to prior labeled hashtags. We open-source our code under the MIT license.|如何在 tweet 语料库中加快相关主题关键词的搜索？进行专题研究的计算社会科学家使用大型、自我收集或众包的社会媒体数据集，如 tweet 语料库。为了抽样或分析这些数据源，通常需要相关关键字的综合集合。然而，天真地浏览数以千计的关键字很快就会成为一项艰巨的任务。在这项研究中，我们提出了一个网络应用程序来简化在 tweet 语料库中搜索相关话题标签的过程。DisKeyword 允许用户掌握数据集中的高级趋势，同时根据关键字与之前标记的 # 标签的链接反复标记推荐的关键字。我们在 MIT 许可下开源代码。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=DisKeyword:+Tweet+Corpora+Exploration+for+Keyword+Selection)|0| -|[A Tutorial on Domain Generalization](https://doi.org/10.1145/3539597.3572722)|Jindong Wang, Haoliang Li, Sinno Jialin Pan, Xing Xie|City University of Hong Kong, Hong Kong, Hong Kong; Nanyang Technological University, Singapore, Singapore; Microsoft Research, Beijing, China|With the availability of massive labeled training data, powerful machine learning models can be trained. However, the traditional I.I.D. assumption that the training and testing data should follow the same distribution is often violated in reality. While existing domain adaptation approaches can tackle domain shift, it relies on the target samples for training. Domain generalization is a promising technology that aims to train models with good generalization ability to unseen distributions. In this tutorial, we will present the recent advance of domain generalization. Specifically, we introduce the background, formulation, and theory behind this topic. Our primary focus is on the methodology, evaluation, and applications. We hope this tutorial can draw interest of the community and provide a thorough review of this area. Eventually, more robust systems can be built for responsible AI. All tutorial materials and updates can be found online at https://dgresearch.github.io/.|随着海量标记训练数据的可用性，强大的机器学习模型可以训练。然而，传统的 IID 假设训练和测试数据应该遵循相同的分布在现实中经常被违反。虽然现有的域自适应方法可以解决域移位问题，但它依赖于目标样本进行训练。领域推广是一项很有前途的技术，其目标是训练具有良好的对未知分布推广能力的模型。在本教程中，我们将介绍域泛化的最新进展。具体来说，我们将介绍这个主题背后的背景、公式和理论。我们主要关注方法、评估和应用程序。我们希望本教程能够引起社区的兴趣，并提供一个彻底的审查这个领域。最终，可以为负责任的人工智能建立更健壮的系统。所有教程资料及更新可于网上 https://dgresearch.github.io/找到。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Tutorial+on+Domain+Generalization)|0| -|[Compliance Analyses of Australia's Online Household Appliances](https://doi.org/10.1145/3539597.3575788)|Chang How Tan, Vincent C. S. Lee, Jessie Nghiem, Priya Laxman|Monash University, Melbourne, Australia; Energy Safe Victoria, Melbourne, Australia|Commercially sold electrical or gas products must comply with the safety standards imposed within a country and get registered and certified by a regulated body. However, with the increasing transition of businesses to e-commerce platforms, it becomes challenging to govern the compliance status of online products. This can increase the risk of purchasing non-compliant products which may be unsafe to use. Additionally, examining the compliance status before purchasing can be strenuous because the relevant compliance information can be ambiguous and not always directly available. Therefore, we collaborated with a regulated body from Australia, Energy Safe Victoria, and conducted compliance analyses for household appliances sold on multiple online platforms. A fully autonomous method shown in this public repository is also introduced to check the compliance status of any online product. In this talk, we discuss the compliance check process, which incorporates fuzzy logic for textual matching and a Convolutional Neural Network (CNN) model to classify the product listing based on the images listed. Subsequently, we studied the results with the business users and found that many online listings are non-compliant, signifying that online-shopping consumers are highly susceptible to buying unsafe products. We hope this talk can inspire more follow-up works that collaborate with regulated bodies to introduce a user-friendly compliance check platform that assists in educating consumers to purchase compliant products.|商业销售的电气或气体产品必须符合国家规定的安全标准，并获得受管制机构的注册和认证。然而，随着企业向电子商务平台的转型，对在线产品的合规状态进行管理变得越来越具有挑战性。这可能会增加购买不符合要求的产品的风险，因为这些产品使用起来可能不安全。此外，在采购之前检查法规遵循状态可能会很费力，因为相关的法规遵循信息可能含糊不清，并且并不总是直接可用。因此，我们与澳大利亚的能源安全维多利亚监管机构合作，对在多个在线平台上销售的家用电器进行了合规性分析。该公共存储库还引入了一种完全自主的方法来检查任何在线产品的遵从性状态。在这个演讲中，我们讨论了遵从性检查过程，它结合了文本匹配的模糊逻辑和一个卷积神经网络(CNN)模型来根据列出的图像对产品清单进行分类。随后，我们研究了商业用户的结果，发现许多网上列表是不符合的，这意味着网上购物的消费者非常容易购买不安全的产品。我们希望这次讲座可以激发更多跟进工作，与受规管机构合作，推出一个方便用户的合规检查平台，协助教育消费者购买合规产品。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Compliance+Analyses+of+Australia's+Online+Household+Appliances)|0| +|[A Tutorial on Domain Generalization](https://doi.org/10.1145/3539597.3572722)|Jindong Wang, Haoliang Li, Sinno Jialin Pan, Xing Xie|Microsoft Research, Beijing, China; Nanyang Technological University, Singapore, Singapore; City University of Hong Kong, Hong Kong, Hong Kong|With the availability of massive labeled training data, powerful machine learning models can be trained. However, the traditional I.I.D. assumption that the training and testing data should follow the same distribution is often violated in reality. While existing domain adaptation approaches can tackle domain shift, it relies on the target samples for training. Domain generalization is a promising technology that aims to train models with good generalization ability to unseen distributions. In this tutorial, we will present the recent advance of domain generalization. Specifically, we introduce the background, formulation, and theory behind this topic. Our primary focus is on the methodology, evaluation, and applications. We hope this tutorial can draw interest of the community and provide a thorough review of this area. Eventually, more robust systems can be built for responsible AI. All tutorial materials and updates can be found online at https://dgresearch.github.io/.|随着海量标记训练数据的可用性，强大的机器学习模型可以训练。然而，传统的 IID 假设训练和测试数据应该遵循相同的分布在现实中经常被违反。虽然现有的域自适应方法可以解决域移位问题，但它依赖于目标样本进行训练。领域推广是一项很有前途的技术，其目标是训练具有良好的对未知分布推广能力的模型。在本教程中，我们将介绍域泛化的最新进展。具体来说，我们将介绍这个主题背后的背景、公式和理论。我们主要关注方法、评估和应用程序。我们希望本教程能够引起社区的兴趣，并提供一个彻底的审查这个领域。最终，可以为负责任的人工智能建立更健壮的系统。所有教程资料及更新可于网上 https://dgresearch.github.io/找到。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Tutorial+on+Domain+Generalization)|0| +|[Compliance Analyses of Australia's Online Household Appliances](https://doi.org/10.1145/3539597.3575788)|Chang How Tan, Vincent C. S. Lee, Jessie Nghiem, Priya Laxman|Energy Safe Victoria, Melbourne, Australia; Monash University, Melbourne, Australia|Commercially sold electrical or gas products must comply with the safety standards imposed within a country and get registered and certified by a regulated body. However, with the increasing transition of businesses to e-commerce platforms, it becomes challenging to govern the compliance status of online products. This can increase the risk of purchasing non-compliant products which may be unsafe to use. Additionally, examining the compliance status before purchasing can be strenuous because the relevant compliance information can be ambiguous and not always directly available. Therefore, we collaborated with a regulated body from Australia, Energy Safe Victoria, and conducted compliance analyses for household appliances sold on multiple online platforms. A fully autonomous method shown in this public repository is also introduced to check the compliance status of any online product. In this talk, we discuss the compliance check process, which incorporates fuzzy logic for textual matching and a Convolutional Neural Network (CNN) model to classify the product listing based on the images listed. Subsequently, we studied the results with the business users and found that many online listings are non-compliant, signifying that online-shopping consumers are highly susceptible to buying unsafe products. We hope this talk can inspire more follow-up works that collaborate with regulated bodies to introduce a user-friendly compliance check platform that assists in educating consumers to purchase compliant products.|商业销售的电气或气体产品必须符合国家规定的安全标准，并获得受管制机构的注册和认证。然而，随着企业向电子商务平台的转型，对在线产品的合规状态进行管理变得越来越具有挑战性。这可能会增加购买不符合要求的产品的风险，因为这些产品使用起来可能不安全。此外，在采购之前检查法规遵循状态可能会很费力，因为相关的法规遵循信息可能含糊不清，并且并不总是直接可用。因此，我们与澳大利亚的能源安全维多利亚监管机构合作，对在多个在线平台上销售的家用电器进行了合规性分析。该公共存储库还引入了一种完全自主的方法来检查任何在线产品的遵从性状态。在这个演讲中，我们讨论了遵从性检查过程，它结合了文本匹配的模糊逻辑和一个卷积神经网络(CNN)模型来根据列出的图像对产品清单进行分类。随后，我们研究了商业用户的结果，发现许多网上列表是不符合的，这意味着网上购物的消费者非常容易购买不安全的产品。我们希望这次讲座可以激发更多跟进工作，与受规管机构合作，推出一个方便用户的合规检查平台，协助教育消费者购买合规产品。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Compliance+Analyses+of+Australia's+Online+Household+Appliances)|0| |[Learning to Infer Product Attribute Values From Descriptive Texts and Images](https://doi.org/10.1145/3539597.3575786)|Pablo Montalvo, Aghiles Salah|Rakuten Group, Inc., Paris, France|Online marketplaces are able to offer a staggering array of products that no physical store can match. While this makes it more likely for customers to find what they want, in order for online providers to ensure a smooth and efficient user experience, they must maintain well-organized catalogs, which depends greatly on the availability of per-product attribute values such as color, material, brand, to name a few. Unfortunately, such information is often incomplete or even missing in practice, and therefore we have to resort to predictive models as well as other sources of information to impute missing attribute values. In this talk we present the deep learning-based approach that we have developed at Rakuten Group to extract attribute values from product descriptive texts and images. Starting from pretrained architectures to encode textual and visual modalities, we discuss several refinements and improvements that we find necessary to achieve satisfactory performance and meet strict business requirements, namely improving recall while maintaining a high precision (>= 95%). Our methodology is driven by a systematic investigation into several practical research questions surrounding multimodality, which we revisit in this talk. At the heart of our multimodal architecture, is a new method to combine modalities inspired by empirical cross-modality comparisons. We present the latter component in details, point out one of its major limitations, namely exacerbating the issue of modality collapse, i.e., when the model forgets one modality, and describe our mitigation to this problem based on a principled regularization scheme. We present various empirical results on both Rakuten data as well as public benchmark datasets, which provide evidence of the benefits of our approach compared to several strong baselines. We also share some insights to characterise the circumstances in which the proposed model offers the most significant improvements. We conclude this talk by criticising the current model and discussing possible future developments and improvements. Our model is successfully deployed in Rakuten Ichiba - a Rakuten marketplace - and we believe that our investigation into multimodal attribute value extraction for e-commerce will benefit other researchers and practitioners alike embarking on similar journeys.|在线市场能够提供一个惊人的产品阵列，没有实体店可以匹配。虽然这使得客户更有可能找到他们想要的东西，为了在线供应商确保一个顺利和有效的用户体验，他们必须保持良好的组织目录，这在很大程度上取决于每个产品属性值的可用性，如颜色，材料，品牌，等等。不幸的是，这些信息往往是不完整的，甚至在实践中缺失，因此，我们不得不求助于预测模型和其他信息来源，以推定缺失的属性值。在这个演讲中，我们介绍了我们在乐天集团开发的基于深度学习的方法，从产品描述性文本和图像中提取属性值。从预先训练的体系结构开始编码文本和视觉模式，我们讨论了几个我们认为必要的细化和改进，以实现令人满意的性能和满足严格的业务需求，即在保持高精度(> = 95%)的同时提高召回率。我们的方法论是由一个系统的调查，对几个实际的研究问题围绕多模态，我们在这个演讲中重新讨论。在我们的多模态结构的核心，是一种新的方法，结合模式的灵感经验交叉模态比较。我们详细介绍了后一个组成部分，指出其主要的局限性之一，即加剧了模态崩溃的问题，即当模型忘记了一个模态，并描述了我们对这个问题的缓解基于一个原则性的正则化方案。我们展示了乐天数据和公共基准数据集的各种实证结果，它们提供了证据，证明我们的方法相对于几个强基线的好处。我们还分享了一些见解，以描述所提议的模型在哪些情况下提供了最重要的改进。我们通过批评目前的模式和讨论未来可能的发展和改进来结束这次演讲。我们的模型已经成功地应用于乐天市场——乐天市场——我们相信，我们对电子商务多模态属性值提取的研究将有利于其他研究人员和从业人员开始类似的旅程。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Learning+to+Infer+Product+Attribute+Values+From+Descriptive+Texts+and+Images)|0| |[Student Behavior Pattern Mining and Analysis: Towards Smart Campuses](https://doi.org/10.1145/3539597.3575780)|Teng Guo, Feng Xia|Dalian University of Technology, Dalian, China; RMIT University, Melbourne, Australia|Understanding student behavior patterns is fundamental to building smart campuses. However, the diversity of student behavior and the complexity of educational data not only bring great obstacles to the relevant research, but also leads to unstable performance and low reliability of current student behavior analysis systems. The emergence of educational big data and the latest advances in deep learning and representation learning provide unprecedented opportunities to tackle the above problems. In this talk, we introduce how we mine and analyze student behavior patterns by overcoming the complexity of educational data. Specifically, we propose a series of algorithmic frameworks, which take advantage of network science, data mining, and machine learning to form a data-driven system for mining and analyzing student behavior patterns. Our research not only fills the gap in the field of student abnormal behavior warning and student status monitoring, but also provides insights into data-driven smart city construction.|理解学生的行为模式是建设智能校园的基础。然而，学生行为的多样性和教育数据的复杂性不仅给相关研究带来了巨大的障碍，而且也导致了现有学生行为分析系统的不稳定性和可靠性低。教育大数据的出现以及深度学习和表征学习的最新进展为解决上述问题提供了前所未有的机遇。在这个演讲中，我们将介绍如何通过克服教育数据的复杂性来挖掘和分析学生的行为模式。具体来说，我们提出了一系列的算法框架，它们利用网络科学、数据挖掘和机器学习的优势，形成了一个数据驱动的系统，用于挖掘和分析学生的行为模式。本研究不仅填补了学生异常行为预警和学生状态监测领域的空白，而且为数据驱动的智能城市建设提供了有益的启示。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Student+Behavior+Pattern+Mining+and+Analysis:+Towards+Smart+Campuses)|0| |[Local Edge Dynamics and Opinion Polarization](https://doi.org/10.1145/3539597.3570442)|Nikita Bhalla, Adam Lechowicz, Cameron Musco|University of Massachusetts Amherst, Amherst, MA, USA|The proliferation of social media platforms, recommender systems, and their joint societal impacts have prompted significant interest in opinion formation and evolution within social networks. We study how local edge dynamics can drive opinion polarization. In particular, we introduce a variant of the classic Friedkin-Johnsen opinion dynamics, augmented with a simple time-evolving network model. Edges are iteratively added or deleted according to simple rules, modeling decisions based on individual preferences and network recommendations. Via simulations on synthetic and real-world graphs, we find that the combined presence of two dynamics gives rise to high polarization: 1) confirmation bias -- i.e., the preference for nodes to connect to other nodes with similar expressed opinions and 2) friend-of-friend link recommendations, which encourage new connections between closely connected nodes. We show that our model is tractable to theoretical analysis, which helps explain how these local dynamics erode connectivity across opinion groups, affecting polarization and a related measure of disagreement across edges. Finally, we validate our model against real-world data, showing that our edge dynamics drive the structure of arbitrary graphs, including random graphs, to more closely resemble real social networks.|社交媒体平台、推荐系统的扩散，以及它们共同的社会影响，促使人们对社交网络中的舆论形成和演变产生了极大的兴趣。我们研究局部边缘动力学如何驱动意见两极分化。特别地，我们引入了经典 Friedkin-Johnsen 观点动力学的一个变体，并辅以一个简单的随时间演化的网络模型。根据简单的规则、基于个人偏好和网络建议的决策建模，可以迭代地添加或删除边缘。通过对合成和真实世界图形的模拟，我们发现两种动力的结合产生了高度极化: 1)确认偏差——即，偏好节点连接到其他有相似表达意见的节点，2)朋友之间的链接推荐，鼓励紧密连接的节点之间建立新的连接。我们表明，我们的模型是易于理论分析，这有助于解释这些局部动态如何侵蚀连通性的意见集团，影响两极分化和相关措施的分歧跨边缘。最后，我们验证了我们的模型对现实世界的数据，表明我们的边缘动力学驱动任意图的结构，包括随机图，更接近真实的社会网络。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Local+Edge+Dynamics+and+Opinion+Polarization)|0| |[Beyond-Accuracy Goals, Again](https://doi.org/10.1145/3539597.3572332)|Maarten de Rijke|University of Amsterdam, Amsterdam, Netherlands|Improving the performance of information retrieval systems tends to be narrowly scoped. Often, better prediction performance is considered the only metric of improvement. As a result, work on improving information retrieval methods usually focuses on im- proving the methods' accuracy. Such a focus is myopic. Instead, as researchers and practitioners we should adopt a richer perspective measuring the performance of information retrieval systems. I am not the first to make this point (see, e.g., [4]), but I want to highlight dimensions that broaden the scope considered so far and offer a number of examples to illustrate what this would mean for our research agendas. First, trustworthiness is a prerequisite for people, organizations, and societies to use AI-based, and, especially, machine learning- based systems in general, and information retrieval systems in particular. Trust can be gained in an intrinsic manner by revealing the inner workings of an AI-based system, i.e., through explainability. Or it can be gained extrinsically by showing, in a principled or empirical manner, that a system upholds verifiable guarantees. Such guarantees should obtained for the following dimensions (at a minimum): (i) accuracy, including well-defined and explained contexts of usage; (ii) reliability, including exhibiting parity with respect to sensitive attributes; (iii) repeatable and reproducible results, including audit trails; (iv) resilience to adversarial examples, distributional shifts; and (v) safety, including privacy-preserving search and recommendation. Second, in information retrieval, our experiments are mostly conducted in controlled laboratory environments. Extrapolating this information to evaluate the real-world effects often remains a challenge. This is particularly true when measuring the impact of information retrieval systems across broader scales, both temporally and spatially. Conducting controlled experimental trials for evaluating real-world impacts of information retrieval systems can result in depicting a snapshot situation, where systems are tailored towards that specific environment. As society is constantly changing, the requirements set for information retrieval systems are changing as well, resulting in short-term and long-term feedback loops with interactions between society and information retrieval systems.|改善信息检索系统的表现往往局限于狭窄的范围。通常，更好的预测性能被认为是改进的唯一指标。因此，改进信息检索方法的工作通常侧重于提高方法的准确性。这样的关注是短视的。相反，作为研究人员和实践者，我们应该采取更加丰富的视角来衡量信息检索系统的表现。我并不是第一个提出这个观点的人(参见，例如，[4]) ，但是我想强调的是目前为止所考虑的扩大范围的维度，并提供一些例子来说明这对我们的研究议程意味着什么。首先，诚信是个人、组织和社会使用基于人工智能的系统，尤其是基于机器学习的系统，特别是信息检索系统的先决条件。信任可以通过揭示基于人工智能的系统的内部工作方式获得，也就是说，通过可解释性。或者，可以通过原则性或经验性的方式表明，一个系统支持可验证的保证，从而获得外在的保证。这样的保证应该获得以下维度(最低限度) : (i)准确性，包括明确定义和解释的使用背景; (ii)可靠性，包括显示相对于敏感属性的同等性; (iii)可重复和可重复的结果，包括审计跟踪; (iv)对抗性例子的弹性，分布转移; 和(v)安全，包括保护隐私的搜索和推荐。其次，在信息检索中，我们的实验大多是在受控的实验室环境中进行的。外推这些信息以评估现实世界的影响往往仍然是一个挑战。在衡量信息检索系统在时间和空间上的影响时尤其如此。为评估信息检索系统在现实世界中的影响而进行的受控试验，可以描绘出一个快照状态，即系统根据特定环境进行调整。随着社会不断变化，对信息检索系统的要求也在不断变化，导致社会和信息检索系统之间的互动产生短期和长期的反馈循环。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Beyond-Accuracy+Goals,+Again)|0| -|[Towards Autonomous Driving](https://doi.org/10.1145/3539597.3572331)|YaQin Zhang|Institute for Infocomm Research (I2R), A∗STAR, Singapore; CentraleSuplec, France; Singapore University of Technology and Design, Singapore; CVSSP, University of Surrey, UK|With the increasing global popularity of self-driving cars, there is an immediate need for challenging real-world datasets for benchmarking and training various computer vision tasks such as 3D object detection. Existing datasets either represent simple scenarios or provide only day-time data. In this paper, we introduce a new challenging A*3D dataset which consists of RGB images and LiDAR data with a significant diversity of scene, time, and weather. The dataset consists of high-density images (≈ 10 times more than the pioneering KITTI dataset), heavy occlusions, a large number of nighttime frames (≈ 3 times the nuScenes dataset), addressing the gaps in the existing datasets to push the boundaries of tasks in autonomous driving research to more challenging highly diverse environments. The dataset contains 39K frames, 7 classes, and 230K 3D object annotations. An extensive 3D object detection benchmark evaluation on the A*3D dataset for various attributes such as high density, day-time/night-time, gives interesting insights into the advantages and limitations of training and testing 3D object detection in real-world setting.|随着自动驾驶汽车在全球日益普及，我们迫切需要挑战现实世界中的数据集，以便对各种计算机视觉任务(如3D 目标检测)进行基准测试和培训。现有数据集要么表示简单的方案，要么只提供日间数据。本文介绍了一个新的具有挑战性的 A * 3D 数据集，该数据集由 RGB 图像和激光雷达数据组成，具有显著的场景、时间和天气多样性。该数据集由高密度图像(约比先驱 KITTI 数据集多10倍) ，重闭塞，大量夜间帧(约为 nuScenes 数据集的3倍)组成，解决现有数据集中的差距，以推动自主驾驶研究中的任务边界更具挑战性的高度多样化的环境。该数据集包含39K 帧、7个类和230K 3D 对象注释。对于高密度、日间/夜间等各种属性的 a * 3 d 数据集，一个广泛的3 d 目标检测基准评估，让我们对在现实世界中训练和测试3 d 目标检测的优势和局限性有了有趣的见解。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Towards+Autonomous+Driving)|0| -|[Beyond Digital "Echo Chambers": The Role of Viewpoint Diversity in Political Discussion](https://doi.org/10.1145/3539597.3570487)|Rishav Hada, Amir Ebrahimi Fard, Sarah Shugars, Federico Bianchi, Patrícia G. C. Rossini, Dirk Hovy, Rebekah Tromble, Nava Tintarev|George Washington University, Washington, D.C., DC, USA; Microsoft Research India, Bengaluru, India; Stanford University, Stanford, CA, USA; Maastricht University, Maastricht, Netherlands; Rutgers University, New Brunswick, NJ, USA; University of Glasgow, Glasgow, United Kingdom; Bocconi University, Milan, Italy|Increasingly taking place in online spaces, modern political conversations are typically perceived to be unproductively affirming -- siloed in so called ``echo chambers'' of exclusively like-minded discussants. Yet, to date we lack sufficient means to measure viewpoint diversity in conversations. To this end, in this paper, we operationalize two viewpoint metrics proposed for recommender systems and adapt them to the context of social media conversations. This is the first study to apply these two metrics (Representation and Fragmentation) to real world data and to consider the implications for online conversations specifically. We apply these measures to two topics -- daylight savings time (DST), which serves as a control, and the more politically polarized topic of immigration. We find that the diversity scores for both Fragmentation and Representation are lower for immigration than for DST. Further, we find that while pro-immigrant views receive consistent pushback on the platform, anti-immigrant views largely operate within echo chambers. We observe less severe yet similar patterns for DST. Taken together, Representation and Fragmentation paint a meaningful and important new picture of viewpoint diversity.|现代政治对话越来越多地发生在网络空间，人们通常认为这种肯定毫无成效——被孤立在所谓的“回音室”里，里面全是志趣相投的讨论者。然而，到目前为止，我们还缺乏足够的手段来衡量会话中的观点多样性。为此，本文将推荐系统中提出的两个视点度量进行操作化，并将其应用到社交媒体会话的语境中。这是第一个将这两个指标(表示和碎片化)应用于真实世界数据并特别考虑在线对话的含义的研究。我们将这些措施应用于两个主题——日光节约时间(DST) ，作为一种控制手段，以及政治上更加两极分化的移民问题。我们发现，分裂和表征的多样性得分对于移民来说都比对于 DST 来说要低。此外，我们发现支持移民的观点在平台上受到一致的抵制，而反移民的观点在很大程度上是在回音室内运作的。我们观察到不太严重但类似的 DST 模式。综上所述，表示和碎片描绘了一幅有意义的、重要的视点多样性的新图景。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Beyond+Digital+"Echo+Chambers":+The+Role+of+Viewpoint+Diversity+in+Political+Discussion)|0| -|[MM-GNN: Mix-Moment Graph Neural Network towards Modeling Neighborhood Feature Distribution](https://doi.org/10.1145/3539597.3570457)|Wendong Bi, Lun Du, Qiang Fu, Yanlin Wang, Shi Han, Dongmei Zhang|University of Chinese Academy of Sciences, Beijing, China; Microsoft Research Asia, Beijing, China|Graph Neural Networks (GNNs) have shown expressive performance on graph representation learning by aggregating information from neighbors. Recently, some studies have discussed the importance of modeling neighborhood distribution on the graph. However, most existing GNNs aggregate neighbors' features through single statistic (e.g., mean, max, sum), which loses the information related to neighbor's feature distribution and therefore degrades the model performance. In this paper, inspired by the method of moment in statistical theory, we propose to model neighbor's feature distribution with multi-order moments. We design a novel GNN model, namely Mix-Moment Graph Neural Network (MM-GNN), which includes a Multi-order Moment Embedding (MME) module and an Element-wise Attention-based Moment Adaptor module. MM-GNN first calculates the multi-order moments of the neighbors for each node as signatures, and then use an Element-wise Attention-based Moment Adaptor to assign larger weights to important moments for each node and update node representations. We conduct extensive experiments on 15 real-world graphs (including social networks, citation networks and web-page networks etc.) to evaluate our model, and the results demonstrate the superiority of MM-GNN over existing state-of-the-art models.|图形神经网络(GNN)通过聚集邻居的信息，在图表示学习方面表现出很好的表现。最近，一些研究讨论了在图上建立邻域分布模型的重要性。然而，现有的 GNN 通过单一统计量(如均值、最大值、和)聚集邻居的特征，丢失了与邻居特征分布相关的信息，从而降低了模型的性能。本文借鉴统计理论中的矩方法，提出了用多阶矩来模拟邻居的特征分布。设计了一种新的 GNN 模型，即混合矩图神经网络(MM-GNN) ，它包括一个多阶矩嵌入(MME)模块和一个基于元素注意的矩适配器模块。MM-GNN 首先计算每个节点邻居的多阶矩作为签名，然后使用基于元素的注意力矩适配器为每个节点的重要矩赋予更大的权值，并更新节点表示。我们对15个真实世界的图形(包括社交网络、引文网络和网页网络等)进行了广泛的实验，以评估我们的模型，结果表明 MM-GNN 相对于现有的最先进的模型的优越性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=MM-GNN:+Mix-Moment+Graph+Neural+Network+towards+Modeling+Neighborhood+Feature+Distribution)|0| -|[Global Counterfactual Explainer for Graph Neural Networks](https://doi.org/10.1145/3539597.3570376)|Zexi Huang, Mert Kosan, Sourav Medya, Sayan Ranu, Ambuj K. Singh|; University of Illinois Chicago, Chicago, IL, USA; Indian Institute of Technology Delhi, Delhi, India; University of California, Santa Barbara, Santa Barbara, CA, USA|Graph neural networks (GNNs) find applications in various domains such as computational biology, natural language processing, and computer security. Owing to their popularity, there is an increasing need to explain GNN predictions since GNNs are black-box machine learning models. One way to address this is counterfactual reasoning where the objective is to change the GNN prediction by minimal changes in the input graph. Existing methods for counterfactual explanation of GNNs are limited to instance-specific local reasoning. This approach has two major limitations of not being able to offer global recourse policies and overloading human cognitive ability with too much information. In this work, we study the global explainability of GNNs through global counterfactual reasoning. Specifically, we want to find a small set of representative counterfactual graphs that explains all input graphs. Towards this goal, we propose GCFExplainer, a novel algorithm powered by vertex-reinforced random walks on an edit map of graphs with a greedy summary. Extensive experiments on real graph datasets show that the global explanation from GCFExplainer provides important high-level insights of the model behavior and achieves a 46.9% gain in recourse coverage and a 9.5% reduction in recourse cost compared to the state-of-the-art local counterfactual explainers.|图形神经网络(GNN)在计算生物学、自然语言处理和计算机安全等领域有着广泛的应用。由于 GNN 的流行，人们越来越需要解释 GNN 的预测，因为 GNN 是黑盒机器学习模型。解决这个问题的一种方法是反事实推理，其目标是通过输入图中的最小变化来改变 GNN 预测。现有的 GNN 反事实解释方法仅限于实例特定的局部推理。这种方法有两个主要的局限性，即不能提供全球追索政策和过多的信息使人类的认知能力负担过重。本文通过全局反事实推理来研究 GNN 的全局可解性。具体来说，我们希望找到一小组有代表性的反事实图解释所有输入图。为了实现这一目标，我们提出了 GCFExplainer 算法，这是一种新的算法，它通过在带有贪婪摘要的图的编辑地图上顶点增强的随机游动来实现。对实际图形数据集的大量实验表明，GCFExplainer 的全局解释为模型行为提供了重要的高层次见解，与最先进的本地反事实解释者相比，获得了46.9% 的追索覆盖率和9.5% 的追索成本降低。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Global+Counterfactual+Explainer+for+Graph+Neural+Networks)|0| -|[Effective Graph Kernels for Evolving Functional Brain Networks](https://doi.org/10.1145/3539597.3570449)|Xinlei Wang, Jinyi Chen, Bing Tian Dai, Junchang Xin, Yu Gu, Ge Yu|Singapore Management University, Singapore, Singapore; Northeastern University, Shenyang, China|The graph kernel of the functional brain network is an effective method in the field of neuropsychiatric disease diagnosis like Alzheimer's Disease (AD). The traditional static brain networks cannot reflect dynamic changes of brain activities, but evolving brain networks, which are a series of brain networks over time, are able to seize such dynamic changes. As far as we know, the graph kernel method is effective for calculating the differences among networks. Therefore, it has a great potential to understand the dynamic changes of evolving brain networks, which are a series of chronological differences. However, if the conventional graph kernel methods which are built for static networks are applied directly to evolving networks, the evolving information will be lost and accurate diagnostic results will be far from reach. We propose an effective method, called Global Matching based Graph Kernels (GM-GK), which captures dynamic changes of evolving brain networks and significantly improves classification accuracy. At the same time, in order to reflect the natural properties of the brain activity of the evolving brain network neglected by the GM-GK method, we also propose a Local Matching based Graph Kernel (LM-GK), which allows the order of the evolving brain network to be locally fine-tuned. Finally, the experiments are conducted on real data sets and the results show that the proposed methods can significantly improve the neuropsychiatric disease diagnostic accuracy.|功能性脑网络图核是诊断阿尔茨海默病(AD)等神经精神疾病的有效方法。传统的静态大脑网络不能反映大脑活动的动态变化，而进化的大脑网络是一系列随时间变化的大脑网络，能够抓住这种动态变化。据我们所知，图核方法是计算网络间差异的有效方法。因此，研究进化中的大脑网络的动态变化，即一系列的时间差异，具有很大的潜力。然而，如果将静态网络的传统图核方法直接应用于进化网络，则进化信息将会丢失，远不能得到准确的诊断结果。提出了一种有效的基于全局匹配的图核(GM-GK)方法，该方法能够捕捉大脑网络演化过程中的动态变化，显著提高分类精度。同时，为了反映被 GM-GK 方法忽略的进化脑网络的大脑活动的自然属性，我们还提出了一种基于局部匹配的图核(LM-GK) ，它允许对进化脑网络的顺序进行局部微调。最后，在实际数据集上进行了实验，结果表明所提出的方法可以显著提高神经精神疾病的诊断准确率。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Effective+Graph+Kernels+for+Evolving+Functional+Brain+Networks)|0| -|[Self-Supervised Graph Structure Refinement for Graph Neural Networks](https://doi.org/10.1145/3539597.3570455)|Jianan Zhao, Qianlong Wen, Mingxuan Ju, Chuxu Zhang, Yanfang Ye|Brandeis University, Waltham, MA, USA; University of Notre Dame, Notre Dame, IN, USA|Graph structure learning (GSL), which aims to learn the adjacency matrix for graph neural networks (GNNs), has shown great potential in boosting the performance of GNNs. Most existing GSL works apply a joint learning framework where the estimated adjacency matrix and GNN parameters are optimized for downstream tasks. However, as GSL is essentially a link prediction task, whose goal may largely differ from the goal of the downstream task. The inconsistency of these two goals limits the GSL methods to learn the potential optimal graph structure. Moreover, the joint learning framework suffers from scalability issues in terms of time and space during the process of estimation and optimization of the adjacency matrix. To mitigate these issues, we propose a graph structure refinement (GSR) framework with a pretrain-finetune pipeline. Specifically, The pre-training phase aims to comprehensively estimate the underlying graph structure by a multi-view contrastive learning framework with both intra- and inter-view link prediction tasks. Then, the graph structure is refined by adding and removing edges according to the edge probabilities estimated by the pre-trained model. Finally, the fine-tuning GNN is initialized by the pre-trained model and optimized toward downstream tasks. With the refined graph structure remaining static in the fine-tuning space, GSR avoids estimating and optimizing graph structure in the fine-tuning phase which enjoys great scalability and efficiency. Moreover, the fine-tuning GNN is boosted by both migrating knowledge and refining graphs. Extensive experiments are conducted to evaluate the effectiveness (best performance on six benchmark datasets), efficiency, and scalability (13.8x faster using 32.8% GPU memory compared to the best GSL baseline on Cora) of the proposed model.|图结构学习(GSL)旨在学习图神经网络(gnn)的邻接矩阵，在提高 GNN 的性能方面显示出巨大的潜力。大部分现有的政府物流服务工作均采用联合学习架构，为下游工作优化估计的邻接矩阵和 GNN 参数。然而，由于 GSL 本质上是一个链路预测任务，其目标可能与下游任务的目标大不相同。这两个目标的不一致性限制了 GSL 方法学习潜在的最优图结构。此外，联合学习架构在评估和优化邻接矩阵的过程中，在时间和空间方面都存在可扩展性问题。为了缓解这些问题，我们提出了一个图结构细化(GSR)框架与预训练-精细调整流水线。具体来说，预训练阶段的目标是通过一个多视图对比学习框架，同时完成视图内和视图间的链接预测任务，全面估计底层的图结构。然后，根据预训练模型估计的边概率，通过添加和去除边来细化图的结构。最后，通过预训练模型对微调 GNN 进行初始化，并针对下游任务进行优化。由于精化后的图结构在微调空间中保持静态，GSR 避免了在微调阶段对图结构进行估计和优化，具有很强的可扩展性和高效性。此外，知识的迁移和图的精化对微调 GNN 有很大的促进作用。进行了广泛的实验来评估所提出模型的有效性(在六个基准数据集上的最佳性能) ，效率和可伸缩性(使用32.8% GPU 存储器比 Cora 上的最佳 GSL 基线快13.8倍)。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Self-Supervised+Graph+Structure+Refinement+for+Graph+Neural+Networks)|0| -|[Marketing Budget Allocation with Offline Constrained Deep Reinforcement Learning](https://doi.org/10.1145/3539597.3570486)|Tianchi Cai, Jiyan Jiang, Wenpeng Zhang, Shiji Zhou, Xierui Song, Li Yu, Lihong Gu, Xiaodong Zeng, Jinjie Gu, Guannan Zhang|Tsinghua University, Beijing, China; Ant Group, Hangzhou, China; Ant Group, Beijing, China|We study the budget allocation problem in online marketing campaigns that utilize previously collected offline data. We first discuss the long-term effect of optimizing marketing budget allocation decisions in the offline setting. To overcome the challenge, we propose a novel game-theoretic offline value-based reinforcement learning method using mixed policies. The proposed method reduces the need to store infinitely many policies in previous methods to only constantly many policies, which achieves nearly optimal policy efficiency, making it practical and favorable for industrial usage. We further show that this method is guaranteed to converge to the optimal policy, which cannot be achieved by previous value-based reinforcement learning methods for marketing budget allocation. Our experiments on a large-scale marketing campaign with tens-of-millions users and more than one billion budget verify the theoretical results and show that the proposed method outperforms various baseline methods. The proposed method has been successfully deployed to serve all the traffic of this marketing campaign.|我们研究预算分配问题在线营销活动，利用以前收集的离线数据。我们首先讨论了线下环境下优化营销预算分配决策的长期效果。为了克服这一挑战，我们提出了一种新的基于游戏理论的离线价值强化学习的混合策略方法。该方法将以往方法中存储无限多策略的需要减少到只需要不断存储多个策略，达到了接近最优的策略效率，具有实用性，有利于工业应用。我们进一步表明，这种方法能够保证收敛到最优策略，这是以前基于价值的强化学习方法无法实现的。我们在拥有数千万用户和超过十亿预算的大规模营销活动中进行的实验验证了理论结果，并表明所提出的方法优于各种基准方法。所提出的方法已成功地应用于服务的所有流量的这个营销活动。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Marketing+Budget+Allocation+with+Offline+Constrained+Deep+Reinforcement+Learning)|0| -|[Reliable Decision from Multiple Subtasks through Threshold Optimization: Content Moderation in the Wild](https://doi.org/10.1145/3539597.3570439)|Donghyun Son, Byounggyu Lew, Kwanghee Choi, Yongsu Baek, Seungwoo Choi, Beomjun Shin, Sungjoo Ha, Buru Chang|Match Group, Dallas, TX, USA; Hyperconnect, Seoul, Republic of Korea|Social media platforms struggle to protect users from harmful content through content moderation. These platforms have recently leveraged machine learning models to cope with the vast amount of user-generated content daily. Since moderation policies vary depending on countries and types of products, it is common to train and deploy the models per policy. However, this approach is highly inefficient, especially when the policies change, requiring dataset re-labeling and model re-training on the shifted data distribution. To alleviate this cost inefficiency, social media platforms often employ third-party content moderation services that provide prediction scores of multiple subtasks, such as predicting the existence of underage personnel, rude gestures, or weapons, instead of directly providing final moderation decisions. However, making a reliable automated moderation decision from the prediction scores of the multiple subtasks for a specific target policy has not been widely explored yet. In this study, we formulate real-world scenarios of content moderation and introduce a simple yet effective threshold optimization method that searches the optimal thresholds of the multiple subtasks to make a reliable moderation decision in a cost-effective way. Extensive experiments demonstrate that our approach shows better performance in content moderation compared to existing threshold optimization methods and heuristics.|社交媒体平台努力通过内容管制来保护用户免受有害内容的伤害。这些平台最近利用机器学习模型来处理日常大量的用户生成内容。由于适度政策因国家和产品类型的不同而有所不同，因此通常会根据政策对模型进行培训和部署。然而，这种方法是非常低效的，尤其是当策略发生变化时，需要重新标记数据集和对移动数据分布进行模型再训练。为了降低成本效率，社交媒体平台经常使用第三方内容审核服务，提供多个子任务的预测分数，比如预测未成年人、粗鲁的手势或武器的存在，而不是直接提供最终审核决定。然而，从特定目标策略的多个子任务的预测分数中做出可靠的自动调节决策还没有得到广泛的研究。在这项研究中，我们提出了真实世界中的内容审核场景，并介绍了一个简单而有效的阈值优化方法，搜索多个子任务的最佳阈值，以一种成本效益高的方式作出可靠的审核决策。大量的实验表明，与现有的阈值优化方法和启发式算法相比，我们的方法在内容调节方面表现出更好的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Reliable+Decision+from+Multiple+Subtasks+through+Threshold+Optimization:+Content+Moderation+in+the+Wild)|0| +|[Towards Autonomous Driving](https://doi.org/10.1145/3539597.3572331)|YaQin Zhang|Singapore University of Technology and Design, Singapore; Institute for Infocomm Research (I2R), A∗STAR, Singapore; CentraleSuplec, France; CVSSP, University of Surrey, UK|With the increasing global popularity of self-driving cars, there is an immediate need for challenging real-world datasets for benchmarking and training various computer vision tasks such as 3D object detection. Existing datasets either represent simple scenarios or provide only day-time data. In this paper, we introduce a new challenging A*3D dataset which consists of RGB images and LiDAR data with a significant diversity of scene, time, and weather. The dataset consists of high-density images (≈ 10 times more than the pioneering KITTI dataset), heavy occlusions, a large number of nighttime frames (≈ 3 times the nuScenes dataset), addressing the gaps in the existing datasets to push the boundaries of tasks in autonomous driving research to more challenging highly diverse environments. The dataset contains 39K frames, 7 classes, and 230K 3D object annotations. An extensive 3D object detection benchmark evaluation on the A*3D dataset for various attributes such as high density, day-time/night-time, gives interesting insights into the advantages and limitations of training and testing 3D object detection in real-world setting.|随着自动驾驶汽车在全球日益普及，我们迫切需要挑战现实世界中的数据集，以便对各种计算机视觉任务(如3D 目标检测)进行基准测试和培训。现有数据集要么表示简单的方案，要么只提供日间数据。本文介绍了一个新的具有挑战性的 A * 3D 数据集，该数据集由 RGB 图像和激光雷达数据组成，具有显著的场景、时间和天气多样性。该数据集由高密度图像(约比先驱 KITTI 数据集多10倍) ，重闭塞，大量夜间帧(约为 nuScenes 数据集的3倍)组成，解决现有数据集中的差距，以推动自主驾驶研究中的任务边界更具挑战性的高度多样化的环境。该数据集包含39K 帧、7个类和230K 3D 对象注释。对于高密度、日间/夜间等各种属性的 a * 3 d 数据集，一个广泛的3 d 目标检测基准评估，让我们对在现实世界中训练和测试3 d 目标检测的优势和局限性有了有趣的见解。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Towards+Autonomous+Driving)|0| +|[Beyond Digital "Echo Chambers": The Role of Viewpoint Diversity in Political Discussion](https://doi.org/10.1145/3539597.3570487)|Rishav Hada, Amir Ebrahimi Fard, Sarah Shugars, Federico Bianchi, Patrícia G. C. Rossini, Dirk Hovy, Rebekah Tromble, Nava Tintarev|George Washington University, Washington, D.C., DC, USA; Stanford University, Stanford, CA, USA; Rutgers University, New Brunswick, NJ, USA; Maastricht University, Maastricht, Netherlands; University of Glasgow, Glasgow, United Kingdom; Microsoft Research India, Bengaluru, India; Bocconi University, Milan, Italy|Increasingly taking place in online spaces, modern political conversations are typically perceived to be unproductively affirming -- siloed in so called ``echo chambers'' of exclusively like-minded discussants. Yet, to date we lack sufficient means to measure viewpoint diversity in conversations. To this end, in this paper, we operationalize two viewpoint metrics proposed for recommender systems and adapt them to the context of social media conversations. This is the first study to apply these two metrics (Representation and Fragmentation) to real world data and to consider the implications for online conversations specifically. We apply these measures to two topics -- daylight savings time (DST), which serves as a control, and the more politically polarized topic of immigration. We find that the diversity scores for both Fragmentation and Representation are lower for immigration than for DST. Further, we find that while pro-immigrant views receive consistent pushback on the platform, anti-immigrant views largely operate within echo chambers. We observe less severe yet similar patterns for DST. Taken together, Representation and Fragmentation paint a meaningful and important new picture of viewpoint diversity.|现代政治对话越来越多地发生在网络空间，人们通常认为这种肯定毫无成效——被孤立在所谓的“回音室”里，里面全是志趣相投的讨论者。然而，到目前为止，我们还缺乏足够的手段来衡量会话中的观点多样性。为此，本文将推荐系统中提出的两个视点度量进行操作化，并将其应用到社交媒体会话的语境中。这是第一个将这两个指标(表示和碎片化)应用于真实世界数据并特别考虑在线对话的含义的研究。我们将这些措施应用于两个主题——日光节约时间(DST) ，作为一种控制手段，以及政治上更加两极分化的移民问题。我们发现，分裂和表征的多样性得分对于移民来说都比对于 DST 来说要低。此外，我们发现支持移民的观点在平台上受到一致的抵制，而反移民的观点在很大程度上是在回音室内运作的。我们观察到不太严重但类似的 DST 模式。综上所述，表示和碎片描绘了一幅有意义的、重要的视点多样性的新图景。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Beyond+Digital+"Echo+Chambers":+The+Role+of+Viewpoint+Diversity+in+Political+Discussion)|0| +|[MM-GNN: Mix-Moment Graph Neural Network towards Modeling Neighborhood Feature Distribution](https://doi.org/10.1145/3539597.3570457)|Wendong Bi, Lun Du, Qiang Fu, Yanlin Wang, Shi Han, Dongmei Zhang|Microsoft Research Asia, Beijing, China; University of Chinese Academy of Sciences, Beijing, China|Graph Neural Networks (GNNs) have shown expressive performance on graph representation learning by aggregating information from neighbors. Recently, some studies have discussed the importance of modeling neighborhood distribution on the graph. However, most existing GNNs aggregate neighbors' features through single statistic (e.g., mean, max, sum), which loses the information related to neighbor's feature distribution and therefore degrades the model performance. In this paper, inspired by the method of moment in statistical theory, we propose to model neighbor's feature distribution with multi-order moments. We design a novel GNN model, namely Mix-Moment Graph Neural Network (MM-GNN), which includes a Multi-order Moment Embedding (MME) module and an Element-wise Attention-based Moment Adaptor module. MM-GNN first calculates the multi-order moments of the neighbors for each node as signatures, and then use an Element-wise Attention-based Moment Adaptor to assign larger weights to important moments for each node and update node representations. We conduct extensive experiments on 15 real-world graphs (including social networks, citation networks and web-page networks etc.) to evaluate our model, and the results demonstrate the superiority of MM-GNN over existing state-of-the-art models.|图形神经网络(GNN)通过聚集邻居的信息，在图表示学习方面表现出很好的表现。最近，一些研究讨论了在图上建立邻域分布模型的重要性。然而，现有的 GNN 通过单一统计量(如均值、最大值、和)聚集邻居的特征，丢失了与邻居特征分布相关的信息，从而降低了模型的性能。本文借鉴统计理论中的矩方法，提出了用多阶矩来模拟邻居的特征分布。设计了一种新的 GNN 模型，即混合矩图神经网络(MM-GNN) ，它包括一个多阶矩嵌入(MME)模块和一个基于元素注意的矩适配器模块。MM-GNN 首先计算每个节点邻居的多阶矩作为签名，然后使用基于元素的注意力矩适配器为每个节点的重要矩赋予更大的权值，并更新节点表示。我们对15个真实世界的图形(包括社交网络、引文网络和网页网络等)进行了广泛的实验，以评估我们的模型，结果表明 MM-GNN 相对于现有的最先进的模型的优越性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=MM-GNN:+Mix-Moment+Graph+Neural+Network+towards+Modeling+Neighborhood+Feature+Distribution)|0| +|[Global Counterfactual Explainer for Graph Neural Networks](https://doi.org/10.1145/3539597.3570376)|Zexi Huang, Mert Kosan, Sourav Medya, Sayan Ranu, Ambuj K. Singh|; University of California, Santa Barbara, Santa Barbara, CA, USA; University of Illinois Chicago, Chicago, IL, USA; Indian Institute of Technology Delhi, Delhi, India|Graph neural networks (GNNs) find applications in various domains such as computational biology, natural language processing, and computer security. Owing to their popularity, there is an increasing need to explain GNN predictions since GNNs are black-box machine learning models. One way to address this is counterfactual reasoning where the objective is to change the GNN prediction by minimal changes in the input graph. Existing methods for counterfactual explanation of GNNs are limited to instance-specific local reasoning. This approach has two major limitations of not being able to offer global recourse policies and overloading human cognitive ability with too much information. In this work, we study the global explainability of GNNs through global counterfactual reasoning. Specifically, we want to find a small set of representative counterfactual graphs that explains all input graphs. Towards this goal, we propose GCFExplainer, a novel algorithm powered by vertex-reinforced random walks on an edit map of graphs with a greedy summary. Extensive experiments on real graph datasets show that the global explanation from GCFExplainer provides important high-level insights of the model behavior and achieves a 46.9% gain in recourse coverage and a 9.5% reduction in recourse cost compared to the state-of-the-art local counterfactual explainers.|图形神经网络(GNN)在计算生物学、自然语言处理和计算机安全等领域有着广泛的应用。由于 GNN 的流行，人们越来越需要解释 GNN 的预测，因为 GNN 是黑盒机器学习模型。解决这个问题的一种方法是反事实推理，其目标是通过输入图中的最小变化来改变 GNN 预测。现有的 GNN 反事实解释方法仅限于实例特定的局部推理。这种方法有两个主要的局限性，即不能提供全球追索政策和过多的信息使人类的认知能力负担过重。本文通过全局反事实推理来研究 GNN 的全局可解性。具体来说，我们希望找到一小组有代表性的反事实图解释所有输入图。为了实现这一目标，我们提出了 GCFExplainer 算法，这是一种新的算法，它通过在带有贪婪摘要的图的编辑地图上顶点增强的随机游动来实现。对实际图形数据集的大量实验表明，GCFExplainer 的全局解释为模型行为提供了重要的高层次见解，与最先进的本地反事实解释者相比，获得了46.9% 的追索覆盖率和9.5% 的追索成本降低。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Global+Counterfactual+Explainer+for+Graph+Neural+Networks)|0| +|[Effective Graph Kernels for Evolving Functional Brain Networks](https://doi.org/10.1145/3539597.3570449)|Xinlei Wang, Jinyi Chen, Bing Tian Dai, Junchang Xin, Yu Gu, Ge Yu|Northeastern University, Shenyang, China; Singapore Management University, Singapore, Singapore|The graph kernel of the functional brain network is an effective method in the field of neuropsychiatric disease diagnosis like Alzheimer's Disease (AD). The traditional static brain networks cannot reflect dynamic changes of brain activities, but evolving brain networks, which are a series of brain networks over time, are able to seize such dynamic changes. As far as we know, the graph kernel method is effective for calculating the differences among networks. Therefore, it has a great potential to understand the dynamic changes of evolving brain networks, which are a series of chronological differences. However, if the conventional graph kernel methods which are built for static networks are applied directly to evolving networks, the evolving information will be lost and accurate diagnostic results will be far from reach. We propose an effective method, called Global Matching based Graph Kernels (GM-GK), which captures dynamic changes of evolving brain networks and significantly improves classification accuracy. At the same time, in order to reflect the natural properties of the brain activity of the evolving brain network neglected by the GM-GK method, we also propose a Local Matching based Graph Kernel (LM-GK), which allows the order of the evolving brain network to be locally fine-tuned. Finally, the experiments are conducted on real data sets and the results show that the proposed methods can significantly improve the neuropsychiatric disease diagnostic accuracy.|功能性脑网络图核是诊断阿尔茨海默病(AD)等神经精神疾病的有效方法。传统的静态大脑网络不能反映大脑活动的动态变化，而进化的大脑网络是一系列随时间变化的大脑网络，能够抓住这种动态变化。据我们所知，图核方法是计算网络间差异的有效方法。因此，研究进化中的大脑网络的动态变化，即一系列的时间差异，具有很大的潜力。然而，如果将静态网络的传统图核方法直接应用于进化网络，则进化信息将会丢失，远不能得到准确的诊断结果。提出了一种有效的基于全局匹配的图核(GM-GK)方法，该方法能够捕捉大脑网络演化过程中的动态变化，显著提高分类精度。同时，为了反映被 GM-GK 方法忽略的进化脑网络的大脑活动的自然属性，我们还提出了一种基于局部匹配的图核(LM-GK) ，它允许对进化脑网络的顺序进行局部微调。最后，在实际数据集上进行了实验，结果表明所提出的方法可以显著提高神经精神疾病的诊断准确率。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Effective+Graph+Kernels+for+Evolving+Functional+Brain+Networks)|0| +|[Self-Supervised Graph Structure Refinement for Graph Neural Networks](https://doi.org/10.1145/3539597.3570455)|Jianan Zhao, Qianlong Wen, Mingxuan Ju, Chuxu Zhang, Yanfang Ye|University of Notre Dame, Notre Dame, IN, USA; Brandeis University, Waltham, MA, USA|Graph structure learning (GSL), which aims to learn the adjacency matrix for graph neural networks (GNNs), has shown great potential in boosting the performance of GNNs. Most existing GSL works apply a joint learning framework where the estimated adjacency matrix and GNN parameters are optimized for downstream tasks. However, as GSL is essentially a link prediction task, whose goal may largely differ from the goal of the downstream task. The inconsistency of these two goals limits the GSL methods to learn the potential optimal graph structure. Moreover, the joint learning framework suffers from scalability issues in terms of time and space during the process of estimation and optimization of the adjacency matrix. To mitigate these issues, we propose a graph structure refinement (GSR) framework with a pretrain-finetune pipeline. Specifically, The pre-training phase aims to comprehensively estimate the underlying graph structure by a multi-view contrastive learning framework with both intra- and inter-view link prediction tasks. Then, the graph structure is refined by adding and removing edges according to the edge probabilities estimated by the pre-trained model. Finally, the fine-tuning GNN is initialized by the pre-trained model and optimized toward downstream tasks. With the refined graph structure remaining static in the fine-tuning space, GSR avoids estimating and optimizing graph structure in the fine-tuning phase which enjoys great scalability and efficiency. Moreover, the fine-tuning GNN is boosted by both migrating knowledge and refining graphs. Extensive experiments are conducted to evaluate the effectiveness (best performance on six benchmark datasets), efficiency, and scalability (13.8x faster using 32.8% GPU memory compared to the best GSL baseline on Cora) of the proposed model.|图结构学习(GSL)旨在学习图神经网络(gnn)的邻接矩阵，在提高 GNN 的性能方面显示出巨大的潜力。大部分现有的政府物流服务工作均采用联合学习架构，为下游工作优化估计的邻接矩阵和 GNN 参数。然而，由于 GSL 本质上是一个链路预测任务，其目标可能与下游任务的目标大不相同。这两个目标的不一致性限制了 GSL 方法学习潜在的最优图结构。此外，联合学习架构在评估和优化邻接矩阵的过程中，在时间和空间方面都存在可扩展性问题。为了缓解这些问题，我们提出了一个图结构细化(GSR)框架与预训练-精细调整流水线。具体来说，预训练阶段的目标是通过一个多视图对比学习框架，同时完成视图内和视图间的链接预测任务，全面估计底层的图结构。然后，根据预训练模型估计的边概率，通过添加和去除边来细化图的结构。最后，通过预训练模型对微调 GNN 进行初始化，并针对下游任务进行优化。由于精化后的图结构在微调空间中保持静态，GSR 避免了在微调阶段对图结构进行估计和优化，具有很强的可扩展性和高效性。此外，知识的迁移和图的精化对微调 GNN 有很大的促进作用。进行了广泛的实验来评估所提出模型的有效性(在六个基准数据集上的最佳性能) ，效率和可伸缩性(使用32.8% GPU 存储器比 Cora 上的最佳 GSL 基线快13.8倍)。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Self-Supervised+Graph+Structure+Refinement+for+Graph+Neural+Networks)|0| +|[Marketing Budget Allocation with Offline Constrained Deep Reinforcement Learning](https://doi.org/10.1145/3539597.3570486)|Tianchi Cai, Jiyan Jiang, Wenpeng Zhang, Shiji Zhou, Xierui Song, Li Yu, Lihong Gu, Xiaodong Zeng, Jinjie Gu, Guannan Zhang|Ant Group, Hangzhou, China; Tsinghua University, Beijing, China; Ant Group, Beijing, China|We study the budget allocation problem in online marketing campaigns that utilize previously collected offline data. We first discuss the long-term effect of optimizing marketing budget allocation decisions in the offline setting. To overcome the challenge, we propose a novel game-theoretic offline value-based reinforcement learning method using mixed policies. The proposed method reduces the need to store infinitely many policies in previous methods to only constantly many policies, which achieves nearly optimal policy efficiency, making it practical and favorable for industrial usage. We further show that this method is guaranteed to converge to the optimal policy, which cannot be achieved by previous value-based reinforcement learning methods for marketing budget allocation. Our experiments on a large-scale marketing campaign with tens-of-millions users and more than one billion budget verify the theoretical results and show that the proposed method outperforms various baseline methods. The proposed method has been successfully deployed to serve all the traffic of this marketing campaign.|我们研究预算分配问题在线营销活动，利用以前收集的离线数据。我们首先讨论了线下环境下优化营销预算分配决策的长期效果。为了克服这一挑战，我们提出了一种新的基于游戏理论的离线价值强化学习的混合策略方法。该方法将以往方法中存储无限多策略的需要减少到只需要不断存储多个策略，达到了接近最优的策略效率，具有实用性，有利于工业应用。我们进一步表明，这种方法能够保证收敛到最优策略，这是以前基于价值的强化学习方法无法实现的。我们在拥有数千万用户和超过十亿预算的大规模营销活动中进行的实验验证了理论结果，并表明所提出的方法优于各种基准方法。所提出的方法已成功地应用于服务的所有流量的这个营销活动。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Marketing+Budget+Allocation+with+Offline+Constrained+Deep+Reinforcement+Learning)|0| +|[Reliable Decision from Multiple Subtasks through Threshold Optimization: Content Moderation in the Wild](https://doi.org/10.1145/3539597.3570439)|Donghyun Son, Byounggyu Lew, Kwanghee Choi, Yongsu Baek, Seungwoo Choi, Beomjun Shin, Sungjoo Ha, Buru Chang|Hyperconnect, Seoul, Republic of Korea; Match Group, Dallas, TX, USA|Social media platforms struggle to protect users from harmful content through content moderation. These platforms have recently leveraged machine learning models to cope with the vast amount of user-generated content daily. Since moderation policies vary depending on countries and types of products, it is common to train and deploy the models per policy. However, this approach is highly inefficient, especially when the policies change, requiring dataset re-labeling and model re-training on the shifted data distribution. To alleviate this cost inefficiency, social media platforms often employ third-party content moderation services that provide prediction scores of multiple subtasks, such as predicting the existence of underage personnel, rude gestures, or weapons, instead of directly providing final moderation decisions. However, making a reliable automated moderation decision from the prediction scores of the multiple subtasks for a specific target policy has not been widely explored yet. In this study, we formulate real-world scenarios of content moderation and introduce a simple yet effective threshold optimization method that searches the optimal thresholds of the multiple subtasks to make a reliable moderation decision in a cost-effective way. Extensive experiments demonstrate that our approach shows better performance in content moderation compared to existing threshold optimization methods and heuristics.|社交媒体平台努力通过内容管制来保护用户免受有害内容的伤害。这些平台最近利用机器学习模型来处理日常大量的用户生成内容。由于适度政策因国家和产品类型的不同而有所不同，因此通常会根据政策对模型进行培训和部署。然而，这种方法是非常低效的，尤其是当策略发生变化时，需要重新标记数据集和对移动数据分布进行模型再训练。为了降低成本效率，社交媒体平台经常使用第三方内容审核服务，提供多个子任务的预测分数，比如预测未成年人、粗鲁的手势或武器的存在，而不是直接提供最终审核决定。然而，从特定目标策略的多个子任务的预测分数中做出可靠的自动调节决策还没有得到广泛的研究。在这项研究中，我们提出了真实世界中的内容审核场景，并介绍了一个简单而有效的阈值优化方法，搜索多个子任务的最佳阈值，以一种成本效益高的方式作出可靠的审核决策。大量的实验表明，与现有的阈值优化方法和启发式算法相比，我们的方法在内容调节方面表现出更好的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Reliable+Decision+from+Multiple+Subtasks+through+Threshold+Optimization:+Content+Moderation+in+the+Wild)|0| |[Few-shot Node Classification with Extremely Weak Supervision](https://doi.org/10.1145/3539597.3570435)|Song Wang, Yushun Dong, Kaize Ding, Chen Chen, Jundong Li|University of Virginia, Charlottesville, VA, USA; Arizona State University, Phoniex, AZ, USA|Few-shot node classification aims at classifying nodes with limited labeled nodes as references. Recent few-shot node classification methods typically learn from classes with abundant labeled nodes (i.e., meta-training classes) and then generalize to classes with limited labeled nodes (i.e., meta-test classes). Nevertheless, on real-world graphs, it is usually difficult to obtain abundant labeled nodes for many classes. In practice, each meta-training class can only consist of several labeled nodes, known as the extremely weak supervision problem. In few-shot node classification, with extremely limited labeled nodes for meta-training, the generalization gap between meta-training and meta-test will become larger and thus lead to suboptimal performance. To tackle this issue, we study a novel problem of few-shot node classification with extremely weak supervision and propose a principled framework X-FNC under the prevalent meta-learning framework. Specifically, our goal is to accumulate meta-knowledge across different meta-training tasks with extremely weak supervision and generalize such knowledge to meta-test tasks. To address the challenges resulting from extremely scarce labeled nodes, we propose two essential modules to obtain pseudo-labeled nodes as extra references and effectively learn from extremely limited supervision information. We further conduct extensive experiments on four node classification datasets with extremely weak supervision to validate the superiority of our framework compared to the state-of-the-art baselines.|少镜头节点分类旨在将有限标记节点作为参考节点进行分类。最近的少镜头节点分类方法通常学习具有大量标记节点的类(即元训练类) ，然后推广到具有有限标记节点的类(即元测试类)。然而，在现实世界图中，通常很难获得多个类的大量标记节点。在实践中，每个元培训课程只能由几个标记节点组成，这就是所谓的极弱监督问题。在少镜头节点分类中，由于元训练的标记节点非常有限，元训练和元测试之间的泛化差距会变大，从而导致性能的次优。为了解决这个问题，我们研究了一个新的极弱监督的少镜头节点分类问题，并在现有的元学习框架下提出了一个原则框架 X-FNC。具体来说，我们的目标是在监督非常薄弱的情况下，通过不同的元培训任务积累元知识，并将这些知识推广到元测试任务中。为了解决标记节点稀缺带来的挑战，提出了两个基本模块来获取伪标记节点作为额外的参考，并有效地学习极其有限的监督信息。我们进一步在监督极其薄弱的四个节点分类数据集上进行了广泛的实验，以验证我们的框架相对于最先进的基线的优越性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Few-shot+Node+Classification+with+Extremely+Weak+Supervision)|0| |[Adversarial Autoencoder for Unsupervised Time Series Anomaly Detection and Interpretation](https://doi.org/10.1145/3539597.3570371)|Xuanhao Chen, Liwei Deng, Yan Zhao, Kai Zheng|Aalborg University, Aalborg, Denmark; University of Electronic Science and Technology of China, Chengdu, China|In many complex systems, devices are typically monitored and generating massive multivariate time series. However, due to the complex patterns and little useful labeled data, it is a great challenge to detect anomalies from these time series data. Existing methods either rely on less regularizations, or require a large number of labeled data, leading to poor accuracy in anomaly detection. To overcome the limitations, in this paper, we propose an adversarial autoencoder anomaly detection and interpretation framework named DAEMON, which performs robustly for various datasets. The key idea is to use two discriminators to adversarially train an autoencoder to learn the normal pattern of multivariate time series, and thereafter use the reconstruction error to detect anomalies. The robustness of DAEMON is guaranteed by the regularization of hidden variables and reconstructed data using the adversarial generation method. An unsupervised approach used to detect anomalies is proposed. Moreover, in order to help operators better diagnose anomalies, DAEMON provides anomaly interpretation by computing the gradients of anomalous data. An extensive empirical study on real data offers evidence that the framework is capable of outperforming state-of-the-art methods in terms of the overall F1-score and interpretation accuracy for time series anomaly detection.|在许多复杂的系统中，设备通常被监控并产生大量的多变量时间序列。然而，由于这些时间序列数据模式复杂，标记数据不多，检测异常是一个很大的挑战。现有的方法要么依赖较少的规范化，要么需要大量的标记数据，导致异常检测的准确性较差。为了克服这些局限性，在本文中，我们提出了一个对抗性的自动编码器异常检测和解释框架 DAEMON，它可以对各种数据集进行鲁棒的处理。其核心思想是利用两个鉴别器对自动编码器进行逆向训练，使其学习多变量时间序列的正态模式，然后利用重构误差检测异常。利用对抗生成方法对隐变量进行正则化和重构数据，保证了 DAEMON 的鲁棒性。提出了一种无监督的异常检测方法。此外，为了帮助操作人员更好地诊断异常，DAEMON 通过计算异常数据的梯度来提供异常解释。对实际数据的广泛实证研究提供的证据表明，该框架能够在时间序列异常检测的整体 f 1得分和解释准确性方面超越最先进的方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Adversarial+Autoencoder+for+Unsupervised+Time+Series+Anomaly+Detection+and+Interpretation)|0| -|[Simultaneous Linear Multi-view Attributed Graph Representation Learning and Clustering](https://doi.org/10.1145/3539597.3570367)|Chakib Fettal, Lazhar Labiod, Mohamed Nadif|Université Paris Cité & Informatique CDC, Paris, France; Université Paris Cité, Paris, France|Over the last few years, various multi-view graph clustering methods have shown promising performances. However, we argue that these methods can have limitations. In particular, they are often unnecessarily complex, leading to scalability problems that make them prohibitive for most real-world graph applications. Furthermore, many of them can handle only specific types of multi-view graphs. Another limitation is that the process of learning graph representations is separated from the clustering process, and in some cases these methods do not even learn a graph representation, which severely restricts their flexibility and usefulness. In this paper we propose a simple yet effective linear model that addresses the dual tasks of multi-view attributed graph representation learning and clustering in a unified framework. The model starts by performing a first-order neighborhood smoothing step for the different individual views, then gives each one a weight corresponding to its importance. Finally, an iterative process of simultaneous clustering and representation learning is performed w.r.t. the importance of each view, yielding a consensus embedding and partition of the graph. Our model is generic and can deal with any type of multi-view graph. Finally, we show through extensive experimentation that this simple model consistently achieves competitive performances w.r.t. state-of-the-art multi-view attributed graph clustering models, while at the same time having training times that are shorter, in some cases by orders of magnitude.|在过去的几年中，各种多视图图聚类方法已经显示出良好的性能。然而，我们认为这些方法可能有局限性。特别是，它们通常不必要地复杂，导致可伸缩性问题，这使得它们对于大多数真实世界的图形应用程序来说都是禁止的。此外，它们中的许多只能处理特定类型的多视图图形。另一个限制是学习图表示的过程与聚类过程是分离的，在某些情况下这些方法甚至不学习图表示，这严重限制了它们的灵活性和有用性。本文提出了一个简单而有效的线性模型，在一个统一的框架下解决了多视图属性图表示学习和聚类的双重任务。该模型首先对不同的单个视图执行一阶邻域平滑步骤，然后给每个视图一个与其重要性相对应的权重。最后，对每个视图的重要性进行了同时聚类和表示学习的迭代过程，得到了图的一致嵌入和划分。我们的模型是通用的，可以处理任何类型的多视图图形。最后，我们通过大量的实验表明，这个简单的模型一致地获得了具有竞争力的性能。 r.t. 最先进的多视图属性图聚类模型，同时具有较短的训练时间，在某些情况下通过数量级。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Simultaneous+Linear+Multi-view+Attributed+Graph+Representation+Learning+and+Clustering)|0| -|[DeMEtRIS: Counting (near)-Cliques by Crawling](https://doi.org/10.1145/3539597.3570438)|Suman K. Bera, Jayesh Choudhari, Shahrzad Haddadan, Sara Ahmadian|Rutgers Business School and Brown Data Science Initiative, New Brunswick, NJ, USA; Cube Global Ltd., London, United Kingdom; Google Research, Mountain View, CA, USA; Katana Graph, San Jose, CA, USA|We study the problem of approximately counting cliques and near cliques in a graph, where the access to the graph is only available through crawling its vertices; thus typically seeing only a small portion of it. This model, known as the random walk model or the neighborhood query model has been introduced recently and captures real-life scenarios in which the entire graph is too massive to be stored as a whole or be scanned entirely and sampling vertices independently is non-trivial in it. We introduce DeMEtRIS: Dense Motif Estimation through Random Incident Sampling. This method provides a scalable algorithm for clique and near clique counting in the random walk model. We prove the correctness of our algorithm through rigorous mathematical analysis and extensive experiments. Both our theoretical results and our experiments show that DeMEtRIS obtains a high precision estimation by only crawling a sub-linear portion on vertices, thus we demonstrate a significant improvement over previously known results.|我们研究了近似计算图中的团和近团的问题，其中对图的访问只能通过爬行它的顶点，因此通常只能看到它的一小部分。这种模型被称为随机游走模型或邻域查询模型，最近被引入，它捕获了整个图太大而不能作为一个整体存储或完全被扫描的真实场景，并且独立的采样顶点在其中是不平凡的。我们介绍了 DeMEtRIS: 基于随机事件抽样的稠密基序估计。该方法为随机游走模型中的团簇计数和近团簇计数提供了一种可扩展的算法。通过严格的数学分析和大量的实验证明了算法的正确性。我们的理论结果和我们的实验表明，DeMEtRIS 获得了一个高精度的估计，只爬行一个次线性部分的顶点，因此我们证明了一个显着的改进以前已知的结果。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=DeMEtRIS:+Counting+(near)-Cliques+by+Crawling)|0| +|[Simultaneous Linear Multi-view Attributed Graph Representation Learning and Clustering](https://doi.org/10.1145/3539597.3570367)|Chakib Fettal, Lazhar Labiod, Mohamed Nadif|Université Paris Cité, Paris, France; Université Paris Cité & Informatique CDC, Paris, France|Over the last few years, various multi-view graph clustering methods have shown promising performances. However, we argue that these methods can have limitations. In particular, they are often unnecessarily complex, leading to scalability problems that make them prohibitive for most real-world graph applications. Furthermore, many of them can handle only specific types of multi-view graphs. Another limitation is that the process of learning graph representations is separated from the clustering process, and in some cases these methods do not even learn a graph representation, which severely restricts their flexibility and usefulness. In this paper we propose a simple yet effective linear model that addresses the dual tasks of multi-view attributed graph representation learning and clustering in a unified framework. The model starts by performing a first-order neighborhood smoothing step for the different individual views, then gives each one a weight corresponding to its importance. Finally, an iterative process of simultaneous clustering and representation learning is performed w.r.t. the importance of each view, yielding a consensus embedding and partition of the graph. Our model is generic and can deal with any type of multi-view graph. Finally, we show through extensive experimentation that this simple model consistently achieves competitive performances w.r.t. state-of-the-art multi-view attributed graph clustering models, while at the same time having training times that are shorter, in some cases by orders of magnitude.|在过去的几年中，各种多视图图聚类方法已经显示出良好的性能。然而，我们认为这些方法可能有局限性。特别是，它们通常不必要地复杂，导致可伸缩性问题，这使得它们对于大多数真实世界的图形应用程序来说都是禁止的。此外，它们中的许多只能处理特定类型的多视图图形。另一个限制是学习图表示的过程与聚类过程是分离的，在某些情况下这些方法甚至不学习图表示，这严重限制了它们的灵活性和有用性。本文提出了一个简单而有效的线性模型，在一个统一的框架下解决了多视图属性图表示学习和聚类的双重任务。该模型首先对不同的单个视图执行一阶邻域平滑步骤，然后给每个视图一个与其重要性相对应的权重。最后，对每个视图的重要性进行了同时聚类和表示学习的迭代过程，得到了图的一致嵌入和划分。我们的模型是通用的，可以处理任何类型的多视图图形。最后，我们通过大量的实验表明，这个简单的模型一致地获得了具有竞争力的性能。 r.t. 最先进的多视图属性图聚类模型，同时具有较短的训练时间，在某些情况下通过数量级。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Simultaneous+Linear+Multi-view+Attributed+Graph+Representation+Learning+and+Clustering)|0| +|[DeMEtRIS: Counting (near)-Cliques by Crawling](https://doi.org/10.1145/3539597.3570438)|Suman K. Bera, Jayesh Choudhari, Shahrzad Haddadan, Sara Ahmadian|Cube Global Ltd., London, United Kingdom; Katana Graph, San Jose, CA, USA; Rutgers Business School and Brown Data Science Initiative, New Brunswick, NJ, USA; Google Research, Mountain View, CA, USA|We study the problem of approximately counting cliques and near cliques in a graph, where the access to the graph is only available through crawling its vertices; thus typically seeing only a small portion of it. This model, known as the random walk model or the neighborhood query model has been introduced recently and captures real-life scenarios in which the entire graph is too massive to be stored as a whole or be scanned entirely and sampling vertices independently is non-trivial in it. We introduce DeMEtRIS: Dense Motif Estimation through Random Incident Sampling. This method provides a scalable algorithm for clique and near clique counting in the random walk model. We prove the correctness of our algorithm through rigorous mathematical analysis and extensive experiments. Both our theoretical results and our experiments show that DeMEtRIS obtains a high precision estimation by only crawling a sub-linear portion on vertices, thus we demonstrate a significant improvement over previously known results.|我们研究了近似计算图中的团和近团的问题，其中对图的访问只能通过爬行它的顶点，因此通常只能看到它的一小部分。这种模型被称为随机游走模型或邻域查询模型，最近被引入，它捕获了整个图太大而不能作为一个整体存储或完全被扫描的真实场景，并且独立的采样顶点在其中是不平凡的。我们介绍了 DeMEtRIS: 基于随机事件抽样的稠密基序估计。该方法为随机游走模型中的团簇计数和近团簇计数提供了一种可扩展的算法。通过严格的数学分析和大量的实验证明了算法的正确性。我们的理论结果和我们的实验表明，DeMEtRIS 获得了一个高精度的估计，只爬行一个次线性部分的顶点，因此我们证明了一个显着的改进以前已知的结果。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=DeMEtRIS:+Counting+(near)-Cliques+by+Crawling)|0| |[A Multi-graph Fusion Based Spatiotemporal Dynamic Learning Framework](https://doi.org/10.1145/3539597.3570396)|Xu Wang, Lianliang Chen, Hongbo Zhang, Pengkun Wang, Zhengyang Zhou, Yang Wang|University of Science and Technology of China, Hefei, China|Spatiotemporal data forecasting is a fundamental task in the field of graph data mining. Typical spatiotemporal data prediction methods usually capture spatial dependencies by directly aggregating features of local neighboring vertices in a fixed graph. However, this kind of aggregators can only capture localized correlations between vertices, and while been stacked for larger receptive field, they fall into the dilemma of over-smoothing. Additional, in temporal perspective, traditional methods focus on fixed graphs, while the correlations among vertexes can be dynamic. And time series components integrated strategies in traditional spatiotemporal learning methods can hardly handle frequently and drastically changed sequences. To overcome those limitations of existing works, in this paper, we propose a novel multi-graph based dynamic learning framework. First, a novel Dynamic Neighbor Search (DNS) mechanism is introduced to model global dynamic correlations between vertices by constructing a feature graph (FG), where the adjacency matrix is dynamically determined by DNS. Then we further alleviate the over-smoothing issue with our newly designed Adaptive Heterogeneous Representation (AHR) module. Both FG and origin graph (OG) are fed into the AHR modules and fused in our proposed Multi-graph Fusion block. Additionally, we design a Differential Vertex Representation (DVR) module which takes advantage of differential information to model temporal trends. Extensive experiments illustrate the superior forecasting performances of our proposed multi-graph based dynamic learning framework on six real-world spatiotemporal datasets from different cities and domains, and this corroborates the solid effectiveness of our proposed framework and its superior generalization ability.|时空数据预测是图形数据挖掘领域的一项基础性工作。典型的时空数据预测方法通常通过直接聚集固定图中局部相邻顶点的特征来捕获空间依赖性。然而，这种聚合器只能捕获顶点之间的局部相关性，当它们被叠加以获得更大的接收场时，它们就陷入了过度平滑的困境。另外，从时间的角度来看，传统的方法主要集中在固定的图上，而顶点之间的相关性可以是动态的。传统时空学习方法中的时间序列分量集成策略难以处理频繁剧烈变化的序列。为了克服现有工作的局限性，本文提出了一种新的基于多图的动态学习框架。首先，引入一种新的动态邻域搜索(dNS)机制，通过构造一个特征图(FG)来模拟顶点之间的全局动态关联，其中邻接矩阵由 DNS 动态确定。然后通过设计自适应异构表示(AHR)模块进一步缓解了过平滑问题。在 AHR 模块中输入 FG 和原点图(OG) ，然后在我们提出的多图融合模块中进行融合。此外，我们还设计了差分顶点表示(DVR)模块，利用差分信息对时间趋势进行建模。大量的实验表明，我们提出的基于多图的动态学习框架对来自不同城市和领域的6个真实世界的时空数据集具有优越的预测性能，这证实了我们提出的框架的有效性及其优越的泛化能力。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Multi-graph+Fusion+Based+Spatiotemporal+Dynamic+Learning+Framework)|0| |[Self-supervised Graph Representation Learning for Black Market Account Detection](https://doi.org/10.1145/3539597.3570466)|Zequan Xu, Lianyun Li, Hui Li, Qihang Sun, Shaofeng Hu, Rongrong Ji|Xiamen University, Xiamen, China; Tencent Inc., Guangzhou, China|Nowadays, Multi-purpose Messaging Mobile App (MMMA) has become increasingly prevalent. MMMAs attract fraudsters and some cybercriminals provide support for frauds via black market accounts (BMAs). Compared to fraudsters, BMAs are not directly involved in frauds and are more difficult to detect. This paper illustrates our BMA detection system SGRL (Self-supervised Graph Representation Learning) used in WeChat, a representative MMMA with over a billion users. We tailor Graph Neural Network and Graph Self-supervised Learning in SGRL for BMA detection. The workflow of SGRL contains a pretraining phase that utilizes structural information, node attribute information and available human knowledge, and a lightweight detection phase. In offline experiments, SGRL outperforms state-of-the-art methods by 16.06%-58.17% on offline evaluation measures. We deploy SGRL in the online environment to detect BMAs on the billion-scale WeChat graph, and it exceeds the alternative by 7.27% on the online evaluation measure. In conclusion, SGRL can alleviate label reliance, generalize well to unseen data, and effectively detect BMAs in WeChat.|目前，多用途消息移动应用(MMMA)已经变得越来越普遍。MMMA 吸引欺诈者，一些网络犯罪分子通过黑市账户(BMAs)为欺诈提供支持。与欺诈者相比，BMA 不直接参与欺诈，更难以发现。本文介绍了我们的 BMA 检测系统 SGRL (自监督图形表示学习)在微信上的应用。在 SGRL 中，我们将图神经网络和图自监督学习用于 BMA 检测。SGRL 的工作流包括一个利用结构信息、节点属性信息和可用人类知识的预训练阶段和一个轻量级检测阶段。在离线实验中，SGRL 的离线评价指标比最先进的方法提高了16.06% -58.17% 。我们在在线环境中部署 SGRL 来检测十亿规模的微信图像中的 BMA，在线评价指标比其他方法高出7.27% 。总之，SGRL 可以减轻标签依赖，对未知数据进行良好的泛化，有效地检测微信中的 BMA。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Self-supervised+Graph+Representation+Learning+for+Black+Market+Account+Detection)|0| |[Graph Explicit Neural Networks: Explicitly Encoding Graphs for Efficient and Accurate Inference](https://doi.org/10.1145/3539597.3570388)|Yiwei Wang, Bryan Hooi, Yozen Liu, Neil Shah|Snap Inc., Seattle, WA, USA; National University of Singapore, Singapore, Singapore|As the state-of-the-art graph learning models, the message passing based neural networks (MPNNs) implicitly use the graph topology as the "pathways" to propagate node features. This implicit use of graph topology induces the MPNNs' over-reliance on (node) features and high inference latency, which hinders their large-scale applications in industrial contexts. To mitigate these weaknesses, we propose the Graph Explicit Neural Network (GENN) framework. GENN can be flexibly applied to various MPNNs and improves them by providing more efficient and accurate inference that is robust in feature-constrained settings. Specifically, we carefully incorporate recent developments in network embedding methods to efficiently prioritize the graph topology for inference. From this vantage, GENN explicitly encodes the topology as an important source of information to mitigate the reliance on node features. Moreover, by adopting knowledge distillation (KD) techniques, GENN takes an MPNN as the teacher to supervise the training for better effectiveness while avoiding the teacher's high inference latency. Empirical results show that our GENN infers dramatically faster than its MPNN teacher by 40x-78x. In terms of accuracy, GENN yields significant gains (more than 40%) for its MPNN teacher when the node features are limited based on our explicit encoding. Moreover, GENN outperforms the MPNN teacher even in feature-rich settings thanks to our KD design.|作为最先进的图形学习模型，基于消息传递的神经网络(MPNN)隐含地使用图拓扑作为“路径”来传播节点特征。这种图形拓扑的隐含使用导致了 MPNN 对(节点)特征的过度依赖和高的推理延迟，从而阻碍了它们在工业环境中的大规模应用。为了弥补这些不足，我们提出了图显式神经网络(GENN)框架。GENN 可以灵活地应用于各种 MPNN，并通过提供更有效和准确的推理来改进它们，这种推理在特征约束设置中具有鲁棒性。具体来说，我们小心地结合了网络嵌入方法的最新发展，以有效地优先图拓扑推理。从这个优势出发，GENN 显式地将拓扑编码为一个重要的信息源，以减轻对节点特征的依赖。此外，通过采用知识提取(KD)技术，GENN 以 MPNN 为教师，在避免教师高推理潜伏期的同时，监督训练的有效性。实证结果表明，我们的 GENN 推理速度明显快于其 MPNN 教师的40倍 -78倍。在准确性方面，GENN 产生显着的增益(超过40%)的 MPNN 教师时，节点的特点是有限的基于我们的显式编码。而且，由于我们的 KD 设计，GENN 甚至在功能丰富的设置方面优于 MPNN 教师。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Graph+Explicit+Neural+Networks:+Explicitly+Encoding+Graphs+for+Efficient+and+Accurate+Inference)|0| -|[GOOD-D: On Unsupervised Graph Out-Of-Distribution Detection](https://doi.org/10.1145/3539597.3570446)|Yixin Liu, Kaize Ding, Huan Liu, Shirui Pan|Griffith University, Gold Coast, SQ, Australia; Arizona State University, Tempe, AZ, USA; Monash University, Melbourne, VIC, Australia|Most existing deep learning models are trained based on the closed-world assumption, where the test data is assumed to be drawn i.i.d. from the same distribution as the training data, known as in-distribution (ID). However, when models are deployed in an open-world scenario, test samples can be out-of-distribution (OOD) and therefore should be handled with caution. To detect such OOD samples drawn from unknown distribution, OOD detection has received increasing attention lately. However, current endeavors mostly focus on grid-structured data and its application for graph-structured data remains under-explored. Considering the fact that data labeling on graphs is commonly time-expensive and labor-intensive, in this work we study the problem of unsupervised graph OOD detection, aiming at detecting OOD graphs solely based on unlabeled ID data. To achieve this goal, we develop a new graph contrastive learning framework GOOD-D for detecting OOD graphs without using any ground-truth labels. By performing hierarchical contrastive learning on the augmented graphs generated by our perturbation-free graph data augmentation method, GOOD-D is able to capture the latent ID patterns and accurately detect OOD graphs based on the semantic inconsistency in different granularities (i.e., node-level, graph-level, and group-level). As a pioneering work in unsupervised graph-level OOD detection, we build a comprehensive benchmark to compare our proposed approach with different state-of-the-art methods. The experiment results demonstrate the superiority of our approach over different methods on various datasets.|大多数现有的深度学习模型都是基于封闭世界的假设进行训练的，其中测试数据被假设是从与训练数据相同的分布中提取出来的，称为内分布(in-distribution，ID)。然而，当模型部署在开放世界场景中时，测试样本可能会超出分布(OOD) ，因此应该谨慎处理。为了检测这些来自未知分布的 OOD 样品，OOD 检测近年来受到越来越多的关注。然而，目前的研究主要集中在网格结构化数据上，而其在图形结构化数据中的应用还有待进一步探索。考虑到图形数据标注通常耗费大量时间和人力，本文研究了无监督图形 OOD 检测问题，目的是仅仅基于未标记的 ID 数据来检测 OOD 图形。为了实现这一目标，我们开发了一个新的图形对比学习框架 Good-D，用于检测 OOD 图，而不使用任何地面真值标签。通过对我们的无扰动图数据增强方法生成的增强图进行分层对比学习，Good-D 能够捕获潜在的 ID 模式，并基于不同粒度(即节点级，图级和组级)的语义不一致性准确检测 OOD 图。作为无监督图级 OOD 检测的开创性工作，我们建立了一个全面的基准来比较我们提出的方法与不同的国家最先进的方法。实验结果证明了该方法在不同数据集上对不同方法的优越性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=GOOD-D:+On+Unsupervised+Graph+Out-Of-Distribution+Detection)|0| +|[GOOD-D: On Unsupervised Graph Out-Of-Distribution Detection](https://doi.org/10.1145/3539597.3570446)|Yixin Liu, Kaize Ding, Huan Liu, Shirui Pan|Arizona State University, Tempe, AZ, USA; Griffith University, Gold Coast, SQ, Australia; Monash University, Melbourne, VIC, Australia|Most existing deep learning models are trained based on the closed-world assumption, where the test data is assumed to be drawn i.i.d. from the same distribution as the training data, known as in-distribution (ID). However, when models are deployed in an open-world scenario, test samples can be out-of-distribution (OOD) and therefore should be handled with caution. To detect such OOD samples drawn from unknown distribution, OOD detection has received increasing attention lately. However, current endeavors mostly focus on grid-structured data and its application for graph-structured data remains under-explored. Considering the fact that data labeling on graphs is commonly time-expensive and labor-intensive, in this work we study the problem of unsupervised graph OOD detection, aiming at detecting OOD graphs solely based on unlabeled ID data. To achieve this goal, we develop a new graph contrastive learning framework GOOD-D for detecting OOD graphs without using any ground-truth labels. By performing hierarchical contrastive learning on the augmented graphs generated by our perturbation-free graph data augmentation method, GOOD-D is able to capture the latent ID patterns and accurately detect OOD graphs based on the semantic inconsistency in different granularities (i.e., node-level, graph-level, and group-level). As a pioneering work in unsupervised graph-level OOD detection, we build a comprehensive benchmark to compare our proposed approach with different state-of-the-art methods. The experiment results demonstrate the superiority of our approach over different methods on various datasets.|大多数现有的深度学习模型都是基于封闭世界的假设进行训练的，其中测试数据被假设是从与训练数据相同的分布中提取出来的，称为内分布(in-distribution，ID)。然而，当模型部署在开放世界场景中时，测试样本可能会超出分布(OOD) ，因此应该谨慎处理。为了检测这些来自未知分布的 OOD 样品，OOD 检测近年来受到越来越多的关注。然而，目前的研究主要集中在网格结构化数据上，而其在图形结构化数据中的应用还有待进一步探索。考虑到图形数据标注通常耗费大量时间和人力，本文研究了无监督图形 OOD 检测问题，目的是仅仅基于未标记的 ID 数据来检测 OOD 图形。为了实现这一目标，我们开发了一个新的图形对比学习框架 Good-D，用于检测 OOD 图，而不使用任何地面真值标签。通过对我们的无扰动图数据增强方法生成的增强图进行分层对比学习，Good-D 能够捕获潜在的 ID 模式，并基于不同粒度(即节点级，图级和组级)的语义不一致性准确检测 OOD 图。作为无监督图级 OOD 检测的开创性工作，我们建立了一个全面的基准来比较我们提出的方法与不同的国家最先进的方法。实验结果证明了该方法在不同数据集上对不同方法的优越性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=GOOD-D:+On+Unsupervised+Graph+Out-Of-Distribution+Detection)|0| |[Alleviating Structural Distribution Shift in Graph Anomaly Detection](https://doi.org/10.1145/3539597.3570377)|Yuan Gao, Xiang Wang, Xiangnan He, Zhenguang Liu, Huamin Feng, Yongdong Zhang||Graph anomaly detection (GAD) is a challenging binary classification problem due to its different structural distribution between anomalies and normal nodes – abnormal nodes are a minority, therefore holding high heterophily and low homophily compared to normal nodes. Furthermore, due to various time factors and the annotation preferences of human experts, the heterophily and homophily can change across training and testing data, which is called structural distribution shift (SDS) in this paper. The mainstream methods are built on graph neural networks (GNNs), benefiting the classification of normals from aggregating homophilous neighbors, yet ignoring the SDS issue for anomalies and suffering from poor generalization. This work solves the problem from a feature view. We observe that the degree of SDS varies between anomalies and normal nodes. Hence to address the issue, the key lies in resisting high heterophily for anomalies meanwhile benefiting the learning of normals from homophily. We tease out the anomaly features on which we constrain to mitigate the effect of heterophilous neighbors and make them invariant. We term our proposed framework as Graph Decomposition Network (GDN). Extensive experiments are conducted on two benchmark datasets, and the proposed framework achieves a remarkable performance boost in GAD, especially in an SDS environment where anomalies have largely different structural distribution across training and testing environments. Codes are open-sourced in https://github.com/blacksingular/wsdm_GDN.|图形异常检测(GAD)是一个具有挑战性的二元分类问题，因为它在异常节点和正常节点之间的结构分布不同-异常节点是少数，因此与正常节点相比具有高度异质性和低度同质性。此外，由于各种时间因素和人类专家的注释偏好，训练和测试数据之间的异质性和同质性会发生变化，本文称之为结构分布偏移(SDS)。主流的方法都是建立在图神经网络(GNN)之上的，有利于法向量的分类从聚集同调邻居，但忽略了 SDS 问题的异常和普遍性差。这项工作从特征视图解决了这个问题。我们观察到 SDS 的程度在异常节点和正常节点之间变化。因此，要解决这一问题，关键在于抵制异常的高度异质性，同时有利于从同质性中学习规范。我们梳理出异常特征，我们约束，以减轻异质邻居的影响，使他们不变。我们将我们提出的框架称为图分解网络(GDN)。在两个基准数据集上进行了大量的实验，结果表明，该框架在广域设计中取得了显著的性能提升，尤其是在 SDS 环境中，在不同的训练和测试环境中，异常的结构分布有很大的不同。代码在 https://github.com/blacksingular/wsdm_gdn 中是开源的。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Alleviating+Structural+Distribution+Shift+in+Graph+Anomaly+Detection)|0| |[Friendly Conditional Text Generator](https://doi.org/10.1145/3539597.3570364)|Noriaki Kawamae|NTT Comware, Tokyo, Japan|Our goal is to control text generation with more fine-grained conditions at lower computational cost than is possible with current alternatives; these conditions are attributes (i.e., multiple codes and free-text). As large-scale pre-trained language models (PLMs) offer excellent performance in free-form text generation, we explore efficient architectures and training schemes that can best leverage PLMs. Our framework, Friendly Conditional Text Generator (FCTG), introduces a multi-view attention (MVA) mechanism and two training tasks, Masked Attribute Modeling (MAM) and Attribute Linguistic Matching (ALM), to direct various PLMs via modalities between the text and its attributes. The motivation of FCTG is to map texts and attributes into a shared space, and bridge their modality gaps, as the texts and attributes reside in different regions of semantic space. To avoid catastrophic forgetting, modality-free embedded representations are learnt, and used to direct PLMs in this space, FCTG applies MAM to learn attribute representations, maps them in the same space as text through MVA, and optimizes their alignment in this space via ALM. Experiments on publicly available datasets show that FCTG outperforms baselines over higher level conditions at lower computation cost.|我们的目标是用更细粒度的条件来控制文本的生成，这些条件是属性(即多个代码和自由文本) ，计算成本比目前的替代方案更低。由于大规模的预训练语言模型(PLM)在自由形式的文本生成方面提供了出色的性能，我们探索了能够最好地利用 PLM 的高效体系结构和培训方案。我们的框架，友好条件文本生成器(FCTG) ，引入了一个多视图注意(MVA)机制和两个训练任务，掩盖属性建模(MAM)和属性语言匹配(ALM) ，通过文本和属性之间的模式来指导各种 PLM。FCTG 的目的是将文本和属性映射到一个共享的空间中，由于文本和属性位于语义空间的不同区域，从而弥合它们之间的情态差异。为了避免灾难性的遗忘，FCTG 学习了无模态嵌入式表示，并用于指导 PLM 在这个空间中，FCTG 应用 MAM 来学习属性表示，通过 MVA 将它们映射到与文本相同的空间中，并通过 ALM 优化它们在这个空间中的对齐。在公开可用数据集上的实验表明，FCTG 在较低的计算成本下优于较高级别条件下的基线。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Friendly+Conditional+Text+Generator)|0| -|[Can Pre-trained Language Models Understand Chinese Humor?](https://doi.org/10.1145/3539597.3570431)|Yuyan Chen, Zhixu Li, Jiaqing Liang, Yanghua Xiao, Bang Liu, Yunwen Chen|DataGrand Inc., Shanghai, China; Shanghai Key Laboratory of Data Science, School of Computer Science, Fudan University, Shanghai, China; School of Data Science, Fudan University, Shanghai, China; RALI & Mila, Université de Montréal, Montréal, Canada; Shanghai Key Laboratory of Data Science, School of Computer Science, Fudan University & Fudan-Aishu Cognitive Intelligence Joint Research Center, Shanghai, China|Humor understanding is an important and challenging research in natural language processing. As the popularity of pre-trained language models (PLMs), some recent work makes preliminary attempts to adopt PLMs for humor recognition and generation. However, these simple attempts do not substantially answer the question: whether PLMs are capable of humor understanding? This paper is the first work that systematically investigates the humor understanding ability of PLMs. For this purpose, a comprehensive framework with three evaluation steps and four evaluation tasks is designed. We also construct a comprehensive Chinese humor dataset, which can fully meet all the data requirements of the proposed evaluation framework. Our empirical study on the Chinese humor dataset yields some valuable observations, which are of great guiding value for future optimization of PLMs in humor understanding and generation.|幽默理解是自然语言处理领域的一个重要而富有挑战性的研究课题。随着预训练语言模型(PLM)的普及，近年来的一些研究工作对采用 PLM 进行幽默识别和生成进行了初步尝试。然而，这些简单的尝试并没有实质性地回答这个问题: PLM 是否能够理解幽默？本文是第一篇系统研究 PLM 幽默理解能力的论文。为此，设计了一个包含三个评估步骤和四个评估任务的综合框架。我们还构建了一个全面的中文幽默数据集，能够完全满足所提出的评价框架的所有数据需求。我们对中文幽默数据集的实证研究得到了一些有价值的结果，对今后幽默理解和幽默生成的 PLM 优化具有重要的指导意义。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Can+Pre-trained+Language+Models+Understand+Chinese+Humor?)|0| +|[Can Pre-trained Language Models Understand Chinese Humor?](https://doi.org/10.1145/3539597.3570431)|Yuyan Chen, Zhixu Li, Jiaqing Liang, Yanghua Xiao, Bang Liu, Yunwen Chen|Shanghai Key Laboratory of Data Science, School of Computer Science, Fudan University, Shanghai, China; DataGrand Inc., Shanghai, China; School of Data Science, Fudan University, Shanghai, China; RALI & Mila, Université de Montréal, Montréal, Canada; Shanghai Key Laboratory of Data Science, School of Computer Science, Fudan University & Fudan-Aishu Cognitive Intelligence Joint Research Center, Shanghai, China|Humor understanding is an important and challenging research in natural language processing. As the popularity of pre-trained language models (PLMs), some recent work makes preliminary attempts to adopt PLMs for humor recognition and generation. However, these simple attempts do not substantially answer the question: whether PLMs are capable of humor understanding? This paper is the first work that systematically investigates the humor understanding ability of PLMs. For this purpose, a comprehensive framework with three evaluation steps and four evaluation tasks is designed. We also construct a comprehensive Chinese humor dataset, which can fully meet all the data requirements of the proposed evaluation framework. Our empirical study on the Chinese humor dataset yields some valuable observations, which are of great guiding value for future optimization of PLMs in humor understanding and generation.|幽默理解是自然语言处理领域的一个重要而富有挑战性的研究课题。随着预训练语言模型(PLM)的普及，近年来的一些研究工作对采用 PLM 进行幽默识别和生成进行了初步尝试。然而，这些简单的尝试并没有实质性地回答这个问题: PLM 是否能够理解幽默？本文是第一篇系统研究 PLM 幽默理解能力的论文。为此，设计了一个包含三个评估步骤和四个评估任务的综合框架。我们还构建了一个全面的中文幽默数据集，能够完全满足所提出的评价框架的所有数据需求。我们对中文幽默数据集的实证研究得到了一些有价值的结果，对今后幽默理解和幽默生成的 PLM 优化具有重要的指导意义。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Can+Pre-trained+Language+Models+Understand+Chinese+Humor?)|0| |[Robust Training of Graph Neural Networks via Noise Governance](https://doi.org/10.1145/3539597.3570369)|Siyi Qian, Haochao Ying, Renjun Hu, Jingbo Zhou, Jintai Chen, Danny Z. Chen, Jian Wu|University of Notre Dame, Notre Dame, IN, USA; Baidu Research, Beijing, China; Alibaba Group, Hangzhou, China; Zhejiang University, Hangzhou, China|Graph Neural Networks (GNNs) have become widely-used models for semi-supervised learning. However, the robustness of GNNs in the presence of label noise remains a largely under-explored problem. In this paper, we consider an important yet challenging scenario where labels on nodes of graphs are not only noisy but also scarce. In this scenario, the performance of GNNs is prone to degrade due to label noise propagation and insufficient learning. To address these issues, we propose a novel RTGNN (Robust Training of Graph Neural Networks via Noise Governance) framework that achieves better robustness by learning to explicitly govern label noise. More specifically, we introduce self-reinforcement and consistency regularization as supplemental supervision. The self-reinforcement supervision is inspired by the memorization effects of deep neural networks and aims to correct noisy labels. Further, the consistency regularization prevents GNNs from overfitting to noisy labels via mimicry loss in both the inter-view and intra-view perspectives. To leverage such supervisions, we divide labels into clean and noisy types, rectify inaccurate labels, and further generate pseudo-labels on unlabeled nodes. Supervision for nodes with different types of labels is then chosen adaptively. This enables sufficient learning from clean labels while limiting the impact of noisy ones. We conduct extensive experiments to evaluate the effectiveness of our RTGNN framework, and the results validate its consistent superior performance over state-of-the-art methods with two types of label noises and various noise rates.|图形神经网络(GNN)已成为广泛使用的半监督学习模型。然而，在标签噪声的存在下，GNN 的稳健性仍然是一个很大程度上未被探讨的问题。在本文中，我们考虑了一个重要但具有挑战性的场景，其中图的节点上的标签不仅是有噪声的，而且是稀缺的。在这种情况下，由于标签噪声的传播和不充分的学习，GNN 的性能容易下降。为了解决这些问题，我们提出了一种新的 RTGNN (通过噪声治理的图形神经网络的鲁棒训练)框架，通过学习显式地治理标签噪声来实现更好的鲁棒性。更具体地说，我们引入自我强化和一致性规范作为补充监督。自我强化监督的灵感来自深层神经网络的记忆效应，旨在纠正噪声标签。此外，一致性正则化防止 GNN 过度拟合噪声标签通过模仿损失在视图内和视图内的观点。为了利用这种监督，我们将标签划分为干净和嘈杂的类型，纠正不准确的标签，并进一步在未标记的节点上生成伪标签。然后自适应地选择对具有不同类型标签的节点的监视。这样可以从干净的标签中学到足够的知识，同时减少噪音标签的影响。我们进行了广泛的实验来评估我们的 RTGNN 框架的有效性，结果验证了其一致性优于最先进的方法与两种类型的标签噪声和不同的噪声率。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Robust+Training+of+Graph+Neural+Networks+via+Noise+Governance)|0| -|[Cooperative Explanations of Graph Neural Networks](https://doi.org/10.1145/3539597.3570378)|Junfeng Fang, Xiang Wang, An Zhang, Zemin Liu, Xiangnan He, TatSeng Chua|University of Science and Technology of China, Hefei, China; National University of Singapore, Singapore, Singapore|With the growing success of graph neural networks (GNNs), the explainability of GNN is attracting considerable attention. Current explainers mostly leverage feature attribution and selection to explain a prediction. By tracing the importance of input features, they select the salient subgraph as the explanation. However, their explainability is at the granularity of input features only, and cannot reveal the usefulness of hidden neurons. This inherent limitation makes the explainers fail to scrutinize the model behavior thoroughly, resulting in unfaithful explanations. In this work, we explore the explainability of GNNs at the granularity of both input features and hidden neurons. To this end, we propose an explainer-agnostic framework, Cooperative GNN Explanation (CGE) to generate the explanatory subgraph and subnetwork simultaneously, which jointly explain how the GNN model arrived at its prediction. Specifically, it first initializes the importance scores of input features and hidden neurons with masking networks. Then it iteratively retrains the importance scores, refining the salient subgraph and subnetwork by discarding low-scored features and neurons in each iteration. Through such cooperative learning, CGE not only generates faithful and concise explanations, but also exhibits how the salient information flows by activating and deactivating neurons. We conduct extensive experiments on both synthetic and real-world datasets, validating the superiority of CGE over state-of-the-art approaches. Code is available at https://github.com/MangoKiller/CGE_demo.|随着图形神经网络(GNN)的日益成功，GNN 的可解释性引起了人们的广泛关注。当前的解释者大多利用特征归属和选择来解释预测。通过跟踪输入特征的重要性，选择显著子图作为解释。然而，它们的可解释性仅限于输入特征的粒度，不能揭示隐藏神经元的有用性。这种固有的局限性使得解释者无法彻底审视模型行为，导致解释失实。在这项工作中，我们探讨了 GNN 的可解释性在粒度的输入特征和隐藏的神经元。为此，我们提出了一个解释者不可知的框架，即同时生成解释子图和子网络(CGE) ，从而共同解释 GNN 模型是如何达到其预测目的的。具体地说，它首先用掩蔽网络初始化输入特征和隐藏神经元的重要性得分。然后迭代地重新训练重要性得分，通过在每次迭代中丢弃低得分特征和神经元来精化显著子图和子网络。通过这样的合作学习，CGE 不仅产生了忠实、简洁的解释，而且展示了显著信息是如何通过激活和去活化神经元而流动的。我们在合成和真实世界的数据集上进行了广泛的实验，验证了 CGE 相对于最先进的方法的优越性。密码可于 https://github.com/mangokiller/cge_demo 索取。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Cooperative+Explanations+of+Graph+Neural+Networks)|0| +|[Cooperative Explanations of Graph Neural Networks](https://doi.org/10.1145/3539597.3570378)|Junfeng Fang, Xiang Wang, An Zhang, Zemin Liu, Xiangnan He, TatSeng Chua|National University of Singapore, Singapore, Singapore; University of Science and Technology of China, Hefei, China|With the growing success of graph neural networks (GNNs), the explainability of GNN is attracting considerable attention. Current explainers mostly leverage feature attribution and selection to explain a prediction. By tracing the importance of input features, they select the salient subgraph as the explanation. However, their explainability is at the granularity of input features only, and cannot reveal the usefulness of hidden neurons. This inherent limitation makes the explainers fail to scrutinize the model behavior thoroughly, resulting in unfaithful explanations. In this work, we explore the explainability of GNNs at the granularity of both input features and hidden neurons. To this end, we propose an explainer-agnostic framework, Cooperative GNN Explanation (CGE) to generate the explanatory subgraph and subnetwork simultaneously, which jointly explain how the GNN model arrived at its prediction. Specifically, it first initializes the importance scores of input features and hidden neurons with masking networks. Then it iteratively retrains the importance scores, refining the salient subgraph and subnetwork by discarding low-scored features and neurons in each iteration. Through such cooperative learning, CGE not only generates faithful and concise explanations, but also exhibits how the salient information flows by activating and deactivating neurons. We conduct extensive experiments on both synthetic and real-world datasets, validating the superiority of CGE over state-of-the-art approaches. Code is available at https://github.com/MangoKiller/CGE_demo.|随着图形神经网络(GNN)的日益成功，GNN 的可解释性引起了人们的广泛关注。当前的解释者大多利用特征归属和选择来解释预测。通过跟踪输入特征的重要性，选择显著子图作为解释。然而，它们的可解释性仅限于输入特征的粒度，不能揭示隐藏神经元的有用性。这种固有的局限性使得解释者无法彻底审视模型行为，导致解释失实。在这项工作中，我们探讨了 GNN 的可解释性在粒度的输入特征和隐藏的神经元。为此，我们提出了一个解释者不可知的框架，即同时生成解释子图和子网络(CGE) ，从而共同解释 GNN 模型是如何达到其预测目的的。具体地说，它首先用掩蔽网络初始化输入特征和隐藏神经元的重要性得分。然后迭代地重新训练重要性得分，通过在每次迭代中丢弃低得分特征和神经元来精化显著子图和子网络。通过这样的合作学习，CGE 不仅产生了忠实、简洁的解释，而且展示了显著信息是如何通过激活和去活化神经元而流动的。我们在合成和真实世界的数据集上进行了广泛的实验，验证了 CGE 相对于最先进的方法的优越性。密码可于 https://github.com/mangokiller/cge_demo 索取。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Cooperative+Explanations+of+Graph+Neural+Networks)|0| |[Towards Faithful and Consistent Explanations for Graph Neural Networks](https://doi.org/10.1145/3539597.3570421)|Tianxiang Zhao, Dongsheng Luo, Xiang Zhang, Suhang Wang|Florida International University, Miami, FL, USA; The Pennsylvania State University, State College, PA, USA|Uncovering rationales behind predictions of graph neural networks (GNNs) has received increasing attention over recent years. Instance-level GNN explanation aims to discover critical input elements, like nodes or edges, that the target GNN relies upon for making predictions. Though various algorithms are proposed, most of them formalize this task by searching the minimal subgraph which can preserve original predictions. However, an inductive bias is deep-rooted in this framework: several subgraphs can result in the same or similar outputs as the original graphs. Consequently, they have the danger of providing spurious explanations and fail to provide consistent explanations. Applying them to explain weakly-performed GNNs would further amplify these issues. To address this problem, we theoretically examine the predictions of GNNs from the causality perspective. Two typical reasons of spurious explanations are identified: confounding effect of latent variables like distribution shift, and causal factors distinct from the original input. Observing that both confounding effects and diverse causal rationales are encoded in internal representations, we propose a simple yet effective countermeasure by aligning embeddings. Concretely, concerning potential shifts in the high-dimensional space, we design a distribution-aware alignment algorithm based on anchors. This new objective is easy to compute and can be incorporated into existing techniques with no or little effort. Theoretical analysis shows that it is in effect optimizing a more faithful explanation objective in design, which further justifies the proposed approach.|近年来，揭示图神经网络(GNN)预测背后的基本原理越来越受到人们的关注。实例级 GNN 解释旨在发现关键的输入元素，如节点或边，目标 GNN 依赖于这些元素来进行预测。虽然提出了各种算法，但大多数算法都是通过搜索最小子图来形式化这一任务，从而保留了原始的预测。然而，归纳偏差在这个框架中是根深蒂固的: 几个子图可以产生与原始图相同或相似的输出。因此，他们有提供虚假解释的危险，而且无法提供一致的解释。用它们来解释表现不佳的 GNN 将进一步放大这些问题。为了解决这个问题，我们从因果关系的角度对 GNN 的预测进行了理论研究。两个典型的原因虚假的解释被确定: 混杂效应的潜在变量，如分布转移，和因果因素不同于原始输入。观察到混杂效应和不同的因果理由都编码在内部表征中，我们通过排列嵌入提出了一个简单而有效的对策。具体地，针对高维空间中的位移，我们设计了一种基于锚的分布感知对准算法。这个新目标很容易计算，并且可以毫不费力地将其纳入现有技术中。理论分析表明，它实际上是在优化设计中一个更加忠实的解释目标，这进一步证明了所提出的方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Towards+Faithful+and+Consistent+Explanations+for+Graph+Neural+Networks)|0| |[Position-Aware Subgraph Neural Networks with Data-Efficient Learning](https://doi.org/10.1145/3539597.3570429)|Chang Liu, Yuwen Yang, Zhe Xie, Hongtao Lu, Yue Ding|Shanghai Jiao Tong University, Shanghai, China|Data-efficient learning on graphs (GEL) is essential in real-world applications. Existing GEL methods focus on learning useful representations for nodes, edges, or entire graphs with ``small'' labeled data. But the problem of data-efficient learning for subgraph prediction has not been explored. The challenges of this problem lie in the following aspects: 1) It is crucial for subgraphs to learn positional features to acquire structural information in the base graph in which they exist. Although the existing subgraph neural network method is capable of learning disentangled position encodings, the overall computational complexity is very high. 2) Prevailing graph augmentation methods for GEL, including rule-based, sample-based, adaptive, and automated methods, are not suitable for augmenting subgraphs because a subgraph contains fewer nodes but richer information such as position, neighbor, and structure. Subgraph augmentation is more susceptible to undesirable perturbations. 3) Only a small number of nodes in the base graph are contained in subgraphs, which leads to a potential ``bias'' problem that the subgraph representation learning is dominated by these ``hot'' nodes. By contrast, the remaining nodes fail to be fully learned, which reduces the generalization ability of subgraph representation learning. In this paper, we aim to address the challenges above and propose a Position-Aware Data-Efficient Learning framework for subgraph neural networks called PADEL. Specifically, we propose a novel node position encoding method that is anchor-free, and design a new generative subgraph augmentation method based on a diffused variational subgraph autoencoder, and we propose exploratory and exploitable views for subgraph contrastive learning. Extensive experiment results on three real-world datasets show the superiority of our proposed method over state-of-the-art baselines.|图上的数据有效学习(GEL)在现实应用中是必不可少的。现有的 GEL 方法侧重于学习对带有“小”标记数据的节点、边或整个图的有用表示。但子图预测的数据有效学习问题尚未得到研究。这个问题的挑战在于以下几个方面: 1)子图学习位置特征以获取其所在的基图中的结构信息至关重要。虽然现有的子图神经网络方法能够学习分离位置编码，但整体计算复杂度很高。2)基于规则的、基于样本的、自适应的、自动化的 GEL 图增强方法不适用于子图的增强，因为子图包含的节点较少，但位置、邻居和结构等信息较丰富。子图增广更容易受到不良扰动的影响。3)基图中只有少量的节点包含在子图中，这就导致了子图表示学习受这些“热”节点支配的潜在“偏差”问题。相比之下，剩余的节点不能完全学习，从而降低了子图表示学习的泛化能力。本文针对上述挑战，提出了一种子图神经网络的位置感知数据有效学习框架 PADEL。具体地说，我们提出了一种新的无锚节点位置编码方法，设计了一种基于扩散变分子图自动编码器的生成子图增强方法，并提出了子图对比学习的探索性和可开发性观点。在三个实际数据集上的大量实验结果表明了我们提出的方法相对于最先进的基线的优越性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Position-Aware+Subgraph+Neural+Networks+with+Data-Efficient+Learning)|0| |[Graph Neural Networks with Interlayer Feature Representation for Image Super-Resolution](https://doi.org/10.1145/3539597.3570436)|Shenggui Tang, Kaixuan Yao, Jianqing Liang, Zhiqiang Wang, Jiye Liang|Shanxi University, Taiyuan, China|Although deep learning has been extensively studied and achieved remarkable performance on single image super-resolution (SISR), existing convolutional neural networks (CNN) mainly focus on broader and deeper architecture design, ignoring the detailed information of the image itself and the potential relationship between the features. Recently, several attempts have been made to address the SISR with graph representation learning. However, existing GNN-based methods learning to deal with the SISR problem are limited to the information processing of the entire image or the relationship processing between different feature images of the same layer, ignoring the interdependence between the extracted features of different layers, which is not conducive to extracting deeper hierarchical features. In this paper, we propose an interlayer feature representation based graph neural network for image super-resolution (LSGNN), which consists of a layer feature graph representation learning module and a channel spatial attention module. The layer feature graph representation learning module mainly captures the interdependence between the features of different layers, which can learn more fine-grained image detail features. In addition, we also unified a channel attention module and a spatial attention module into our model, which takes into account the channel dimension information and spatial scale information, to improve the expressive ability, and achieve high quality image details. Extensive experiments and ablation studies demonstrate the superiority of the proposed model.|虽然深度学习在单幅图像超分辨率(SISR)方面已经得到了广泛的研究，并取得了显著的效果，但现有的卷积神经网络(CNN)主要集中在更广泛和更深入的结构设计上，忽略了图像本身的详细信息和特征之间的潜在关系。近年来，人们利用图表示学习的方法对 SISR 问题进行了一些尝试。然而，现有的基于 GNN 的 SISR 问题学习方法仅局限于对整幅图像的信息处理或同一层次不同特征图像之间的关系处理，忽略了不同层次提取特征之间的相互依赖性，不利于提取更深层次的特征。本文提出了一种基于层间特征表示的图像超分辨率神经网络(LSGNN) ，它由层间特征图表示学习模块和通道空间注意模块组成。层次特征图表示学习模块主要捕捉不同层次特征之间的相互依赖关系，可以学习更细粒度的图像细节特征。此外，我们还统一了信道注意模块和空间注意模块，该模型考虑了信道尺寸信息和空间尺度信息，以提高表达能力，实现高质量的图像细节。大量的实验和烧蚀研究证明了该模型的优越性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Graph+Neural+Networks+with+Interlayer+Feature+Representation+for+Image+Super-Resolution)|0| |[CLNode: Curriculum Learning for Node Classification](https://doi.org/10.1145/3539597.3570385)|Xiaowen Wei, Xiuwen Gong, Yibing Zhan, Bo Du, Yong Luo, Wenbin Hu|JD Explore Academy, Beijing, China; The University of Sydney, Sydney, Australia; Wuhan University, Wuhan, China|Node classification is a fundamental graph-based task that aims to predict the classes of unlabeled nodes, for which Graph Neural Networks (GNNs) are the state-of-the-art methods. Current GNNs assume that nodes in the training set contribute equally during training. However, the quality of training nodes varies greatly, and the performance of GNNs could be harmed by two types of low-quality training nodes: (1) inter-class nodes situated near class boundaries that lack the typical characteristics of their corresponding classes. Because GNNs are data-driven approaches, training on these nodes could degrade the accuracy. (2) mislabeled nodes. In real-world graphs, nodes are often mislabeled, which can significantly degrade the robustness of GNNs. To mitigate the detrimental effect of the low-quality training nodes, we present CLNode, which employs a selective training strategy to train GNN based on the quality of nodes. Specifically, we first design a multi-perspective difficulty measurer to accurately measure the quality of training nodes. Then, based on the measured qualities, we employ a training scheduler that selects appropriate training nodes to train GNN in each epoch. To evaluate the effectiveness of CLNode, we conduct extensive experiments by incorporating it in six representative backbone GNNs. Experimental results on real-world networks demonstrate that CLNode is a general framework that can be combined with various GNNs to improve their accuracy and robustness.|节点分类是一项基于图的基本任务，其目的是预测未标记节点的类别，图神经网络(GNN)是这方面的最新研究成果。当前的 GNN 假设训练集中的节点在训练期间作出同样的贡献。然而，训练节点的质量差异很大，两种类型的低质量训练节点可能会损害 GNN 的性能: (1)位于类边界附近的类间节点缺乏相应类的典型特征。由于 GNN 是数据驱动的方法，在这些节点上进行训练可能会降低精度。(2)标记错误的节点。在现实图中，节点经常被错误标记，这会显著降低 GNN 的鲁棒性。为了减轻低质量训练节点的不利影响，我们提出了 CLNode，它采用一种基于节点质量的选择性训练策略来训练 GNN。具体来说，我们首先设计了一个多视角的难度度量器来准确测量训练节点的质量。然后，根据测量的质量，采用训练调度器，选择合适的训练节点，在每个时代训练 GNN。为了评估 CLNode 的有效性，我们进行了广泛的实验，将其纳入六个具有代表性的骨干 GNN。在实际网络上的实验结果表明，CLNode 是一种通用的网络结构，可以与各种 GNN 结合使用，提高网络的准确性和鲁棒性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=CLNode:+Curriculum+Learning+for+Node+Classification)|0| -|[Learning and Maximizing Influence in Social Networks Under Capacity Constraints](https://doi.org/10.1145/3539597.3570433)|Pritish Chakraborty, Sayan Ranu, Krishna Sri Ipsit Mantri, Abir De|Indian Institute of Technology, Delhi, New Delhi, India; Indian Institute of Technology, Bombay, Mumbai, India|Influence maximization (IM) refers to the problem of finding a subset of nodes in a network through which we could maximize our reach to other nodes in the network. This set is often called the "seed set", and its constituent nodes maximize the social diffusion process. IM has previously been studied in various settings, including under a time deadline, subject to constraints such as that of budget or coverage, and even subject to measures other than the centrality of nodes. The solution approach has generally been to prove that the objective function is submodular, or has a submodular proxy, and thus has a close greedy approximation. In this paper, we explore a variant of the IM problem where we wish to reach out to and maximize the probability of infection of a small subset of bounded capacity K. We show that this problem does not exhibit the same submodular guarantees as the original IM problem, for which we resort to the theory of gamma-weakly submodular functions. Subsequently, we develop a greedy algorithm that maximizes our objective despite the lack of submodularity. We also develop a suitable learning model that out-competes baselines on the task of predicting the top-K infected nodes, given a seed set as input.|影响最大化(IM)是指在网络中寻找一个节点子集，通过这个子集我们可以最大限度地接触到网络中的其他节点。这个集合通常被称为“种子集合”，它的组成节点使社会扩散过程最大化。IM 以前已经被研究在各种环境下，包括在一个时间期限下，受到限制，如预算或覆盖面，甚至受到措施以外的节点的中心性。求解方法一般是证明目标函数是子模的，或者有一个子模代理，因此有一个近似的贪婪近似。在本文中，我们探讨了 IM 问题的一个变种，其中我们希望达到并最大化有界容量 K 的一个小子集的感染概率。我们证明了这个问题并没有展示出与原 IM 问题相同的子模保证，对于这个问题我们采用伽马弱子模函数理论。随后，我们开发了一个贪婪算法，最大化我们的目标，尽管缺乏子模块。我们还开发了一个合适的学习模型，在预测顶部 K 感染节点的任务竞争基线，给定一个种子集作为输入。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Learning+and+Maximizing+Influence+in+Social+Networks+Under+Capacity+Constraints)|0| +|[Learning and Maximizing Influence in Social Networks Under Capacity Constraints](https://doi.org/10.1145/3539597.3570433)|Pritish Chakraborty, Sayan Ranu, Krishna Sri Ipsit Mantri, Abir De|Indian Institute of Technology, Bombay, Mumbai, India; Indian Institute of Technology, Delhi, New Delhi, India|Influence maximization (IM) refers to the problem of finding a subset of nodes in a network through which we could maximize our reach to other nodes in the network. This set is often called the "seed set", and its constituent nodes maximize the social diffusion process. IM has previously been studied in various settings, including under a time deadline, subject to constraints such as that of budget or coverage, and even subject to measures other than the centrality of nodes. The solution approach has generally been to prove that the objective function is submodular, or has a submodular proxy, and thus has a close greedy approximation. In this paper, we explore a variant of the IM problem where we wish to reach out to and maximize the probability of infection of a small subset of bounded capacity K. We show that this problem does not exhibit the same submodular guarantees as the original IM problem, for which we resort to the theory of gamma-weakly submodular functions. Subsequently, we develop a greedy algorithm that maximizes our objective despite the lack of submodularity. We also develop a suitable learning model that out-competes baselines on the task of predicting the top-K infected nodes, given a seed set as input.|影响最大化(IM)是指在网络中寻找一个节点子集，通过这个子集我们可以最大限度地接触到网络中的其他节点。这个集合通常被称为“种子集合”，它的组成节点使社会扩散过程最大化。IM 以前已经被研究在各种环境下，包括在一个时间期限下，受到限制，如预算或覆盖面，甚至受到措施以外的节点的中心性。求解方法一般是证明目标函数是子模的，或者有一个子模代理，因此有一个近似的贪婪近似。在本文中，我们探讨了 IM 问题的一个变种，其中我们希望达到并最大化有界容量 K 的一个小子集的感染概率。我们证明了这个问题并没有展示出与原 IM 问题相同的子模保证，对于这个问题我们采用伽马弱子模函数理论。随后，我们开发了一个贪婪算法，最大化我们的目标，尽管缺乏子模块。我们还开发了一个合适的学习模型，在预测顶部 K 感染节点的任务竞争基线，给定一个种子集作为输入。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Learning+and+Maximizing+Influence+in+Social+Networks+Under+Capacity+Constraints)|0| |[Beyond Individuals: Modeling Mutual and Multiple Interactions for Inductive Link Prediction between Groups](https://doi.org/10.1145/3539597.3570448)|Gongzhu Yin, Xing Wang, Hongli Zhang, Chao Meng, Yuchen Yang, Kun Lu, Yi Luo|Harbin Institute of Technology, Harbin, China|Link prediction is a core task in graph machine learning with wide applications. However, little attention has been paid to link prediction between two group entities. This limits the application of the current approaches to many real-life problems, such as predicting collaborations between academic groups or recommending bundles of items to group users. Moreover, groups are often ephemeral or emergent, forcing the predicting model to deal with challenging inductive scenes. To fill this gap, we develop a framework composed of a GNN-based encoder and neural-based aggregating networks, namely the Mutual Multi-view Attention Networks (MMAN). First, we adopt GNN-based encoders to model multiple interactions among members and groups through propagating. Then, we develop MMAN to aggregate members' node representations into multi-view group representations and compute the final results by pooling pairwise scores between views. Specifically, several view-guided attention modules are adopted when learning multi-view group representations, thus capturing diversified member weights and multifaceted group characteristics. In this way, MMAN can further mimic the mutual and multiple interactions between groups. We conduct experiments on three datasets, including two academic group link prediction datasets and one bundle-to-group recommendation dataset. The results demonstrate that the proposed approach can achieve superior performance on both tasks compared with plain GNN-based methods and other aggregating methods.|链路预测是图机学习的核心任务，有着广泛的应用。然而，人们很少关注两个群体实体之间的联系预测。这限制了当前方法在许多实际问题上的应用，例如预测学术团体之间的协作，或者向团体用户推荐成捆的项目。此外，群体往往是短暂的或突发的，迫使预测模型处理具有挑战性的归纳场景。为了填补这一空白，我们开发了一个由基于 GNN 的编码器和基于神经元的聚合网络组成的框架，即互多视点注意网络(MMAN)。首先，我们采用基于 GNN 的编码器，通过传播来建模成员和组之间的多重交互。然后，开发 MMAN，将成员的节点表示聚合为多视图组表示，并通过在视图之间汇集成对分数来计算最终结果。具体地说，在学习多视点群体表征时，采用了多个视点引导的注意模块，从而获取多元化的成员权重和多方面的群体特征。这样，MMAN 可以进一步模拟群体之间的相互作用和多重作用。我们在三个数据集上进行实验，包括两个学术群组链接预测数据集和一个捆绑群组推荐数据集。结果表明，与普通的基于 GNN 的聚集方法和其他聚集方法相比，该方法在两种任务上都能获得较好的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Beyond+Individuals:+Modeling+Mutual+and+Multiple+Interactions+for+Inductive+Link+Prediction+between+Groups)|0| -|[Scalable Adversarial Attack Algorithms on Influence Maximization](https://doi.org/10.1145/3539597.3570416)|Lichao Sun, Xiaobin Rui, Wei Chen|Lehigh University, Bethlehem, PA, USA; China University of Mining and Technology, Xuzhou, China; Microsoft Research Asia, Beijing, China|In this paper, we study the adversarial attacks on influence maximization under dynamic influence propagation models in social networks. In particular, given a known seed set S, the problem is to minimize the influence spread from S by deleting a limited number of nodes and edges. This problem reflects many application scenarios, such as blocking virus (e.g. COVID-19) propagation in social networks by quarantine and vaccination, blocking rumor spread by freezing fake accounts, or attacking competitor's influence by incentivizing some users to ignore the information from the competitor. In this paper, under the linear threshold model, we adapt the reverse influence sampling approach and provide efficient algorithms of sampling valid reverse reachable paths to solve the problem. We present three different design choices on reverse sampling, which all guarantee $1/2 - \varepsilon$ approximation (for any small $\varepsilon >0$) and an efficient running time.|本文研究了社会网络中动态影响传播模型下影响最大化的对手攻击问题。特别地，给定一个已知的种子集 S，问题是通过删除有限数量的节点和边来最小化来自 S 的影响。这个问题反映了许多应用场景，比如通过隔离和接种疫苗来阻止病毒(例如2019冠状病毒疾病)在社交网络中的传播，通过冻结虚假账户来阻止谣言的传播，或者通过鼓励一些用户忽略竞争对手的信息来攻击竞争对手的影响力。本文在线性阈值模型下，采用反向影响采样方法，提出了有效的反向可达路径采样算法。我们提出了三种不同的反向采样设计选择，它们都保证了 $1/2-varepsilon $近似(对于任何小于0 $的 varepsilon)和有效的运行时间。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Scalable+Adversarial+Attack+Algorithms+on+Influence+Maximization)|0| -|[S2GAE: Self-Supervised Graph Autoencoders are Generalizable Learners with Graph Masking](https://doi.org/10.1145/3539597.3570404)|Qiaoyu Tan, Ninghao Liu, Xiao Huang, SooHyun Choi, Li Li, Rui Chen, Xia Hu|Texas A&M University, College station, TX, USA; Rice University, Houston, TX, USA; University of Georgia, Athens, GA, USA; Samsung Electronics America, Mountain view, CA, USA; Samsung Electronics, Mountain view, CA, USA; The Hong Kong Polytechnic University, Hong Kong, China|Self-supervised learning (SSL) has been demonstrated to be effective in pre-training models that can be generalized to various downstream tasks. Graph Autoencoder (GAE), an increasingly popular SSL approach on graphs, has been widely explored to learn node representations without ground-truth labels. However, recent studies show that existing GAE methods could only perform well on link prediction tasks, while their performance on classification tasks is rather limited. This limitation casts doubt on the generalizability and adoption of GAE. In this paper, for the first time, we show that GAE can generalize well to both link prediction and classification scenarios, including node-level and graph-level tasks, by redesigning its critical building blocks from the graph masking perspective. Our proposal is called Self-Supervised Graph Autoencoder--S2GAE, which unleashes the power of GAEs with minimal yet nontrivial efforts. Specifically, instead of reconstructing the whole input structure, we randomly mask a portion of edges and learn to reconstruct these missing edges with an effective masking strategy and an expressive decoder network. Moreover, we theoretically prove that S2GAE could be regarded as an edge-level contrastive learning framework, providing insights into why it generalizes well. Empirically, we conduct extensive experiments on 21 benchmark datasets across link prediction and node & graph classification tasks. The results validate the superiority of S2GAE against state-of-the-art generative and contrastive methods. This study demonstrates the potential of GAE as a universal representation learner on graphs. Our code is publicly available at https://github.com/qiaoyu-tan/S2GAE.|自监督学习(SSL)已被证明是有效的预训练模型，可以推广到各种下游任务。图形自动编码器(Graph Autoencoder，GAE)是一种越来越受欢迎的图形 SSL 方法，已经被广泛研究用于学习没有地面真值标签的节点表示。然而，最近的研究表明，现有的 GAE 方法只能在链路预测任务上表现良好，而在分类任务上的表现相当有限。这种局限性使人们对 GAE 的普遍性和采用性产生了怀疑。本文首次从图掩蔽的角度出发，通过重新设计关键模块，证明了 GAE 可以很好地推广到链路预测和分类场景，包括节点级和图级任务。我们的提议被称为自监督图形自动编码器—— S2GAE，它通过最少的努力释放了 GAE 的威力。具体来说，我们不是重建整个输入结构，而是随机掩蔽一部分边缘，并学习用有效的掩蔽策略和表达式解码器网络重建这些缺失的边缘。此外，我们从理论上证明了 S2GAE 可以被看作是一个边缘层次的对比学习框架，从而为 S2GAE 的推广提供了理论依据。通过实验，我们对21个基准数据集进行了跨链路预测和节点与图分类任务的广泛实验。实验结果验证了 S2GAE 算法相对于最新的生成方法和对比方法的优越性。本研究证明了 GAE 作为图形表示学习者的潜力。我们的代码可以在 https://github.com/qiaoyu-tan/s2gae 上公开获取。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=S2GAE:+Self-Supervised+Graph+Autoencoders+are+Generalizable+Learners+with+Graph+Masking)|0| +|[Scalable Adversarial Attack Algorithms on Influence Maximization](https://doi.org/10.1145/3539597.3570416)|Lichao Sun, Xiaobin Rui, Wei Chen|Lehigh University, Bethlehem, PA, USA; Microsoft Research Asia, Beijing, China; China University of Mining and Technology, Xuzhou, China|In this paper, we study the adversarial attacks on influence maximization under dynamic influence propagation models in social networks. In particular, given a known seed set S, the problem is to minimize the influence spread from S by deleting a limited number of nodes and edges. This problem reflects many application scenarios, such as blocking virus (e.g. COVID-19) propagation in social networks by quarantine and vaccination, blocking rumor spread by freezing fake accounts, or attacking competitor's influence by incentivizing some users to ignore the information from the competitor. In this paper, under the linear threshold model, we adapt the reverse influence sampling approach and provide efficient algorithms of sampling valid reverse reachable paths to solve the problem. We present three different design choices on reverse sampling, which all guarantee $1/2 - \varepsilon$ approximation (for any small $\varepsilon >0$) and an efficient running time.|本文研究了社会网络中动态影响传播模型下影响最大化的对手攻击问题。特别地，给定一个已知的种子集 S，问题是通过删除有限数量的节点和边来最小化来自 S 的影响。这个问题反映了许多应用场景，比如通过隔离和接种疫苗来阻止病毒(例如2019冠状病毒疾病)在社交网络中的传播，通过冻结虚假账户来阻止谣言的传播，或者通过鼓励一些用户忽略竞争对手的信息来攻击竞争对手的影响力。本文在线性阈值模型下，采用反向影响采样方法，提出了有效的反向可达路径采样算法。我们提出了三种不同的反向采样设计选择，它们都保证了 $1/2-varepsilon $近似(对于任何小于0 $的 varepsilon)和有效的运行时间。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Scalable+Adversarial+Attack+Algorithms+on+Influence+Maximization)|0| +|[S2GAE: Self-Supervised Graph Autoencoders are Generalizable Learners with Graph Masking](https://doi.org/10.1145/3539597.3570404)|Qiaoyu Tan, Ninghao Liu, Xiao Huang, SooHyun Choi, Li Li, Rui Chen, Xia Hu|Texas A&M University, College station, TX, USA; Rice University, Houston, TX, USA; University of Georgia, Athens, GA, USA; Samsung Electronics America, Mountain view, CA, USA; The Hong Kong Polytechnic University, Hong Kong, China; Samsung Electronics, Mountain view, CA, USA|Self-supervised learning (SSL) has been demonstrated to be effective in pre-training models that can be generalized to various downstream tasks. Graph Autoencoder (GAE), an increasingly popular SSL approach on graphs, has been widely explored to learn node representations without ground-truth labels. However, recent studies show that existing GAE methods could only perform well on link prediction tasks, while their performance on classification tasks is rather limited. This limitation casts doubt on the generalizability and adoption of GAE. In this paper, for the first time, we show that GAE can generalize well to both link prediction and classification scenarios, including node-level and graph-level tasks, by redesigning its critical building blocks from the graph masking perspective. Our proposal is called Self-Supervised Graph Autoencoder--S2GAE, which unleashes the power of GAEs with minimal yet nontrivial efforts. Specifically, instead of reconstructing the whole input structure, we randomly mask a portion of edges and learn to reconstruct these missing edges with an effective masking strategy and an expressive decoder network. Moreover, we theoretically prove that S2GAE could be regarded as an edge-level contrastive learning framework, providing insights into why it generalizes well. Empirically, we conduct extensive experiments on 21 benchmark datasets across link prediction and node & graph classification tasks. The results validate the superiority of S2GAE against state-of-the-art generative and contrastive methods. This study demonstrates the potential of GAE as a universal representation learner on graphs. Our code is publicly available at https://github.com/qiaoyu-tan/S2GAE.|自监督学习(SSL)已被证明是有效的预训练模型，可以推广到各种下游任务。图形自动编码器(Graph Autoencoder，GAE)是一种越来越受欢迎的图形 SSL 方法，已经被广泛研究用于学习没有地面真值标签的节点表示。然而，最近的研究表明，现有的 GAE 方法只能在链路预测任务上表现良好，而在分类任务上的表现相当有限。这种局限性使人们对 GAE 的普遍性和采用性产生了怀疑。本文首次从图掩蔽的角度出发，通过重新设计关键模块，证明了 GAE 可以很好地推广到链路预测和分类场景，包括节点级和图级任务。我们的提议被称为自监督图形自动编码器—— S2GAE，它通过最少的努力释放了 GAE 的威力。具体来说，我们不是重建整个输入结构，而是随机掩蔽一部分边缘，并学习用有效的掩蔽策略和表达式解码器网络重建这些缺失的边缘。此外，我们从理论上证明了 S2GAE 可以被看作是一个边缘层次的对比学习框架，从而为 S2GAE 的推广提供了理论依据。通过实验，我们对21个基准数据集进行了跨链路预测和节点与图分类任务的广泛实验。实验结果验证了 S2GAE 算法相对于最新的生成方法和对比方法的优越性。本研究证明了 GAE 作为图形表示学习者的潜力。我们的代码可以在 https://github.com/qiaoyu-tan/s2gae 上公开获取。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=S2GAE:+Self-Supervised+Graph+Autoencoders+are+Generalizable+Learners+with+Graph+Masking)|0| |[Dependency-aware Self-training for Entity Alignment](https://doi.org/10.1145/3539597.3570370)|Bing Liu, Tiancheng Lan, Wen Hua, Guido Zuccon|The University of Queensland, Brisbane, QLD, Australia|Entity Alignment (EA), which aims to detect entity mappings (i.e. equivalent entity pairs) in different Knowledge Graphs (KGs), is critical for KG fusion. Neural EA methods dominate current EA research but still suffer from their reliance on labelled mappings. To solve this problem, a few works have explored boosting the training of EA models with self-training, which adds confidently predicted mappings into the training data iteratively. Though the effectiveness of self-training can be glimpsed in some specific settings, we still have very limited knowledge about it. One reason is the existing works concentrate on devising EA models and only treat self-training as an auxiliary tool. To fill this knowledge gap, we change the perspective to self-training to shed light on it. In addition, the existing self-training strategies have limited impact because they introduce either much False Positive noise or a low quantity of True Positive pseudo mappings. To improve self-training for EA, we propose exploiting the dependencies between entities, a particularity of EA, to suppress the noise without hurting the recall of True Positive mappings. Through extensive experiments, we show that the introduction of dependency makes the self-training strategy for EA reach a new level. The value of self-training in alleviating the reliance on annotation is actually much higher than what has been realised. Furthermore, we suggest future study on smart data annotation to break the ceiling of EA performance.|实体对齐(EA)是 KG 融合的关键技术，其目的是检测不同知识图(KG)中的实体映射(即等价实体对)。神经电子学方法主导了目前的电子学研究，但仍然受到依赖于标记映射。为了解决这个问题，一些工作已经探索了用自训练的方法来提高 EA 模型的训练效率，这种方法可以在训练数据中迭代地添加可信的预测映射。虽然自我训练的有效性可以在一些特定的环境中看到，但我们对它的了解仍然非常有限。原因之一是现有的工作集中在设计 EA 模型，只把自我训练作为辅助工具。为了填补这一知识空白，我们将视角转向自我训练，以阐明这一点。此外，现有的自训练策略由于引入了大量的假正噪声或少量的真正正伪映射，因而影响有限。为了提高自训练算法的性能，我们提出利用实体之间的依赖关系，即自训练算法的特殊性，在不影响真正正映射召回的前提下抑制噪声。通过大量的实验，我们发现依赖性的引入使得自我训练策略达到了一个新的水平。自我训练在减轻对注释的依赖方面的价值实际上远远高于已经实现的价值。此外，我们建议未来研究智能数据注释，以打破 EA 性能的上限。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Dependency-aware+Self-training+for+Entity+Alignment)|0| |[Weakly Supervised Entity Alignment with Positional Inspiration](https://doi.org/10.1145/3539597.3570394)|Wei Tang, Fenglong Su, Haifeng Sun, Qi Qi, Jingyu Wang, Shimin Tao, Hao Yang|Beijing University of Posts and Telecommunications, Beijing, China; National University of Defense Technology, Changsha, China; Huawei, Beijing, China|The current success of entity alignment (EA) is still mainly based on large-scale labeled anchor links. However, the refined annotation of anchor links still consumes a lot of manpower and material resources. As a result, an increasing number of works based on active learning, few-shot learning, or other deep network learning techniques have been developed to address the performance bottleneck caused by a lack of labeled data. These works focus either on the strategy of choosing more informative labeled data or on the strategy of model training, while it remains opaque why existing popular EA models (e.g., GNN-based models) fail the EA task with limited labeled data. To overcome this issue, this paper analyzes the problem of weakly supervised EA from the perspective of model design and proposes a novel weakly supervised learning framework, Position Enhanced Entity Alignment (PEEA). Besides absorbing structural and relational information, PEEA aims to increase the connections between far-away entities and labeled ones by incorporating positional information into the representation learning with a Position Attention Layer (PAL). To fully utilize the limited anchor links, we further introduce a novel position encoding method that considers both anchor links and relational information from a global view. The proposed position encoding will be fed into PEEA as additional entity features. Extensive experiments on public datasets demonstrate the effectiveness of PEEA.|目前实体对齐(EA)的成功仍然主要基于大规模标记的锚链。然而，锚链的精细标注仍然耗费了大量的人力物力。因此，越来越多的基于主动学习、少镜头学习或其他深度网络学习技术的工作被开发出来，以解决因缺乏标记数据而造成的性能瓶颈。这些工作要么集中在选择更多信息的标记数据的策略，要么集中在模型训练的策略上，而为什么现有的流行的 EA 模型(例如，基于 GNN 的模型)在有限的标记数据下无法完成 EA 任务仍然是不透明的。为了克服这个问题，本文从模型设计的角度分析了弱监督算法的问题，并提出了一种新的弱监督监督式学习框架——位置增强实体对齐算法。除了吸收结构信息和关系信息外，PEEA 的目标是通过位置注意层(PAL)将位置信息整合到表征学习中来增加远距离实体和被标记实体之间的联系。为了充分利用有限的锚链，我们进一步介绍了一种新的位置编码方法，从全局的角度考虑锚链和关系信息。提出的位置编码将作为额外的实体功能输入 PEEA。在公共数据集上的大量实验证明了 PEEA 的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Weakly+Supervised+Entity+Alignment+with+Positional+Inspiration)|0| -|[Hansel: A Chinese Few-Shot and Zero-Shot Entity Linking Benchmark](https://doi.org/10.1145/3539597.3570418)|Zhenran Xu, Zifei Shan, Yuxin Li, Baotian Hu, Bing Qin|Wechat, Tencent, Shanghai, China; Harbin Institute of Technology, Harbin, China; Wechat, Tencent, Shenzhen, China; Harbin Institute of Technology, Shenzhen, China|Modern Entity Linking (EL) systems entrench a popularity bias, yet there is no dataset focusing on tail and emerging entities in languages other than English. We present Hansel, a new benchmark in Chinese that fills the vacancy of non-English few-shot and zero-shot EL challenges. The test set of Hansel is human annotated and reviewed, created with a novel method for collecting zero-shot EL datasets. It covers 10K diverse documents in news, social media posts and other web articles, with Wikidata as its target Knowledge Base. We demonstrate that the existing state-of-the-art EL system performs poorly on Hansel (R@1 of 36.6% on Few-Shot). We then establish a strong baseline that scores a R@1 of 46.2% on Few-Shot and 76.6% on Zero-Shot on our dataset. We also show that our baseline achieves competitive results on TAC-KBP2015 Chinese Entity Linking task.|现代实体链接(EL)系统固化了一种流行偏见，然而除了英语以外，没有关于尾部和新兴实体的数据集。我们提出韩塞尔，一个新的基准在汉语，填补了非英语少射击和零射击 EL 挑战的空缺。Hansel 的测试集是人工注释和评论的，用一种新颖的方法创建用于收集零激发 EL 数据集。它涵盖了新闻、社会媒体帖子和其他网络文章中的10K 不同文档，以 Wikidata 作为其目标知识库。我们证明了现有的最先进的 EL 系统在 Hansel 上表现很差(R@1在 Little-Shot 上表现为36.6%)。然后，我们建立一个强基线，在我们的数据集上，在 Little-Shot 和 Zero-Shot 分别得到46.2% 和76.6% 的 R@1。我们的基线在 TAC-KBP2015中文实体连接任务上也取得了有竞争力的成绩。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Hansel:+A+Chinese+Few-Shot+and+Zero-Shot+Entity+Linking+Benchmark)|0| -|[Self-supervised Multi-view Disentanglement for Expansion of Visual Collections](https://doi.org/10.1145/3539597.3570425)|Nihal Jain, Praneetha Vaddamanu, Paridhi Maheshwari, Vishwa Vinay, Kuldeep Kulkarni|Carnegie Mellon University, Pittsburgh, PA, USA; Stanford University, Stanford, CA, USA; Adobe Research, Bangalore, India|Image search engines enable the retrieval of images relevant to a query image. In this work, we consider the setting where a query for similar images is derived from a collection of images. For visual search, the similarity measurements may be made along multiple axes, or views, such as style and color. We assume access to a set of feature extractors, each of which computes representations for a specific view. Our objective is to design a retrieval algorithm that effectively combines similarities computed over representations from multiple views. To this end, we propose a self-supervised learning method for extracting disentangled view-specific representations for images such that the inter-view overlap is minimized. We show how this allows us to compute the intent of a collection as a distribution over views. We show how effective retrieval can be performed by prioritizing candidate expansion images that match the intent of a query collection. Finally, we present a new querying mechanism for image search enabled by composing multiple collections and perform retrieval under this setting using the techniques presented in this paper.|图像搜索引擎可以检索与查询图像相关的图像。在这项工作中，我们考虑的设置，其中相似的图像查询是从一个图像集合派生。对于可视化搜索，可以沿着多个轴或视图(如样式和颜色)进行相似性度量。我们假设访问一组特征提取器，每个特征提取器计算特定视图的表示。我们的目标是设计一个检索算法，有效地结合相似性计算的表示从多个视图。为此，我们提出了一种自监督学习方法来提取图像的非纠缠视点特定表示，从而使视点间的重叠最小化。我们展示了这是如何允许我们将集合的意图作为视图的分布来计算的。我们展示了如何通过对匹配查询集合意图的候选扩展图像进行优先级排序来执行有效的检索。最后，我们提出了一种新的图像搜索查询机制，该机制通过组合多个集合，并使用本文提出的技术在此设置下进行检索。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Self-supervised+Multi-view+Disentanglement+for+Expansion+of+Visual+Collections)|0| -|[Efficient Integration of Multi-Order Dynamics and Internal Dynamics in Stock Movement Prediction](https://doi.org/10.1145/3539597.3570427)|Thanh Trung Huynh, Minh Hieu Nguyen, Thanh Tam Nguyen, Phi Le Nguyen, Matthias Weidlich, Quoc Viet Hung Nguyen, Karl Aberer|Hanoi University of Science and Technology, Hanoi, Vietnam; Humboldt-Universitãt zu Berlin, Berlin, Germany; Ecole Polytechnique Federale de Lausanne, Lausanne, Switzerland; Griffith University, Gold Coast, Australia|Advances in deep neural network (DNN) architectures have enabled new prediction techniques for stock market data. Unlike other multivariate time-series data, stock markets show two unique characteristics: (i) \emph{multi-order dynamics}, as stock prices are affected by strong non-pairwise correlations (e.g., within the same industry); and (ii) \emph{internal dynamics}, as each individual stock shows some particular behaviour. Recent DNN-based methods capture multi-order dynamics using hypergraphs, but rely on the Fourier basis in the convolution, which is both inefficient and ineffective. In addition, they largely ignore internal dynamics by adopting the same model for each stock, which implies a severe information loss. In this paper, we propose a framework for stock movement prediction to overcome the above issues. Specifically, the framework includes temporal generative filters that implement a memory-based mechanism onto an LSTM network in an attempt to learn individual patterns per stock. Moreover, we employ hypergraph attentions to capture the non-pairwise correlations. Here, using the wavelet basis instead of the Fourier basis, enables us to simplify the message passing and focus on the localized convolution. Experiments with US market data over six years show that our framework outperforms state-of-the-art methods in terms of profit and stability. Our source code and data are available at \url{https://github.com/thanhtrunghuynh93/estimate}.|深度神经网络(DNN)结构的进步使得新的股票市场数据预测技术成为可能。与其他多变量时间序列数据不同，股票市场表现出两个独特的特征: (i)多阶动态效应，因为股票价格受到强烈的非成对相关性(例如，在同一行业内)的影响; (ii)多阶动态效应，因为每只股票都表现出一些特殊的行为。目前基于 DNN 的方法利用超图来捕获多阶动力学，但是依赖于卷积中的傅里叶基，这种方法不仅效率低下，而且效率低下。此外，他们在很大程度上忽略了内部动态，对每只股票采用相同的模型，这意味着严重的信息损失。针对上述问题，本文提出了一个股票走势预测框架。具体来说，该框架包括时间生成过滤器，它在 LSTM 网络上实现基于内存的机制，以便学习每只股票的单个模式。此外，我们使用超图注意力来捕捉非成对的相关性。这里，用小波基代替傅里叶基，使我们能够简化信息的传递，并集中于局部卷积。六年来对美国市场数据的实验表明，我们的框架在利润和稳定性方面优于最先进的方法。我们的源代码和数据可以在 url { https://github.com/thanhtrunghuynh93/estimate }找到。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Efficient+Integration+of+Multi-Order+Dynamics+and+Internal+Dynamics+in+Stock+Movement+Prediction)|0| +|[Hansel: A Chinese Few-Shot and Zero-Shot Entity Linking Benchmark](https://doi.org/10.1145/3539597.3570418)|Zhenran Xu, Zifei Shan, Yuxin Li, Baotian Hu, Bing Qin|Harbin Institute of Technology, Harbin, China; Harbin Institute of Technology, Shenzhen, China; Wechat, Tencent, Shenzhen, China; Wechat, Tencent, Shanghai, China|Modern Entity Linking (EL) systems entrench a popularity bias, yet there is no dataset focusing on tail and emerging entities in languages other than English. We present Hansel, a new benchmark in Chinese that fills the vacancy of non-English few-shot and zero-shot EL challenges. The test set of Hansel is human annotated and reviewed, created with a novel method for collecting zero-shot EL datasets. It covers 10K diverse documents in news, social media posts and other web articles, with Wikidata as its target Knowledge Base. We demonstrate that the existing state-of-the-art EL system performs poorly on Hansel (R@1 of 36.6% on Few-Shot). We then establish a strong baseline that scores a R@1 of 46.2% on Few-Shot and 76.6% on Zero-Shot on our dataset. We also show that our baseline achieves competitive results on TAC-KBP2015 Chinese Entity Linking task.|现代实体链接(EL)系统固化了一种流行偏见，然而除了英语以外，没有关于尾部和新兴实体的数据集。我们提出韩塞尔，一个新的基准在汉语，填补了非英语少射击和零射击 EL 挑战的空缺。Hansel 的测试集是人工注释和评论的，用一种新颖的方法创建用于收集零激发 EL 数据集。它涵盖了新闻、社会媒体帖子和其他网络文章中的10K 不同文档，以 Wikidata 作为其目标知识库。我们证明了现有的最先进的 EL 系统在 Hansel 上表现很差(R@1在 Little-Shot 上表现为36.6%)。然后，我们建立一个强基线，在我们的数据集上，在 Little-Shot 和 Zero-Shot 分别得到46.2% 和76.6% 的 R@1。我们的基线在 TAC-KBP2015中文实体连接任务上也取得了有竞争力的成绩。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Hansel:+A+Chinese+Few-Shot+and+Zero-Shot+Entity+Linking+Benchmark)|0| +|[Self-supervised Multi-view Disentanglement for Expansion of Visual Collections](https://doi.org/10.1145/3539597.3570425)|Nihal Jain, Praneetha Vaddamanu, Paridhi Maheshwari, Vishwa Vinay, Kuldeep Kulkarni|Adobe Research, Bangalore, India; Stanford University, Stanford, CA, USA; Carnegie Mellon University, Pittsburgh, PA, USA|Image search engines enable the retrieval of images relevant to a query image. In this work, we consider the setting where a query for similar images is derived from a collection of images. For visual search, the similarity measurements may be made along multiple axes, or views, such as style and color. We assume access to a set of feature extractors, each of which computes representations for a specific view. Our objective is to design a retrieval algorithm that effectively combines similarities computed over representations from multiple views. To this end, we propose a self-supervised learning method for extracting disentangled view-specific representations for images such that the inter-view overlap is minimized. We show how this allows us to compute the intent of a collection as a distribution over views. We show how effective retrieval can be performed by prioritizing candidate expansion images that match the intent of a query collection. Finally, we present a new querying mechanism for image search enabled by composing multiple collections and perform retrieval under this setting using the techniques presented in this paper.|图像搜索引擎可以检索与查询图像相关的图像。在这项工作中，我们考虑的设置，其中相似的图像查询是从一个图像集合派生。对于可视化搜索，可以沿着多个轴或视图(如样式和颜色)进行相似性度量。我们假设访问一组特征提取器，每个特征提取器计算特定视图的表示。我们的目标是设计一个检索算法，有效地结合相似性计算的表示从多个视图。为此，我们提出了一种自监督学习方法来提取图像的非纠缠视点特定表示，从而使视点间的重叠最小化。我们展示了这是如何允许我们将集合的意图作为视图的分布来计算的。我们展示了如何通过对匹配查询集合意图的候选扩展图像进行优先级排序来执行有效的检索。最后，我们提出了一种新的图像搜索查询机制，该机制通过组合多个集合，并使用本文提出的技术在此设置下进行检索。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Self-supervised+Multi-view+Disentanglement+for+Expansion+of+Visual+Collections)|0| +|[Efficient Integration of Multi-Order Dynamics and Internal Dynamics in Stock Movement Prediction](https://doi.org/10.1145/3539597.3570427)|Thanh Trung Huynh, Minh Hieu Nguyen, Thanh Tam Nguyen, Phi Le Nguyen, Matthias Weidlich, Quoc Viet Hung Nguyen, Karl Aberer|Hanoi University of Science and Technology, Hanoi, Vietnam; Ecole Polytechnique Federale de Lausanne, Lausanne, Switzerland; Griffith University, Gold Coast, Australia; Humboldt-Universitãt zu Berlin, Berlin, Germany|Advances in deep neural network (DNN) architectures have enabled new prediction techniques for stock market data. Unlike other multivariate time-series data, stock markets show two unique characteristics: (i) \emph{multi-order dynamics}, as stock prices are affected by strong non-pairwise correlations (e.g., within the same industry); and (ii) \emph{internal dynamics}, as each individual stock shows some particular behaviour. Recent DNN-based methods capture multi-order dynamics using hypergraphs, but rely on the Fourier basis in the convolution, which is both inefficient and ineffective. In addition, they largely ignore internal dynamics by adopting the same model for each stock, which implies a severe information loss. In this paper, we propose a framework for stock movement prediction to overcome the above issues. Specifically, the framework includes temporal generative filters that implement a memory-based mechanism onto an LSTM network in an attempt to learn individual patterns per stock. Moreover, we employ hypergraph attentions to capture the non-pairwise correlations. Here, using the wavelet basis instead of the Fourier basis, enables us to simplify the message passing and focus on the localized convolution. Experiments with US market data over six years show that our framework outperforms state-of-the-art methods in terms of profit and stability. Our source code and data are available at \url{https://github.com/thanhtrunghuynh93/estimate}.|深度神经网络(DNN)结构的进步使得新的股票市场数据预测技术成为可能。与其他多变量时间序列数据不同，股票市场表现出两个独特的特征: (i)多阶动态效应，因为股票价格受到强烈的非成对相关性(例如，在同一行业内)的影响; (ii)多阶动态效应，因为每只股票都表现出一些特殊的行为。目前基于 DNN 的方法利用超图来捕获多阶动力学，但是依赖于卷积中的傅里叶基，这种方法不仅效率低下，而且效率低下。此外，他们在很大程度上忽略了内部动态，对每只股票采用相同的模型，这意味着严重的信息损失。针对上述问题，本文提出了一个股票走势预测框架。具体来说，该框架包括时间生成过滤器，它在 LSTM 网络上实现基于内存的机制，以便学习每只股票的单个模式。此外，我们使用超图注意力来捕捉非成对的相关性。这里，用小波基代替傅里叶基，使我们能够简化信息的传递，并集中于局部卷积。六年来对美国市场数据的实验表明，我们的框架在利润和稳定性方面优于最先进的方法。我们的源代码和数据可以在 url { https://github.com/thanhtrunghuynh93/estimate }找到。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Efficient+Integration+of+Multi-Order+Dynamics+and+Internal+Dynamics+in+Stock+Movement+Prediction)|0| |[Combining vs. Transferring Knowledge: Investigating Strategies for Improving Demographic Inference in Low Resource Settings](https://doi.org/10.1145/3539597.3570462)|Yaguang Liu, Lisa Singh|Georgetown University, Washington, DC, USA|For some learning tasks, generating a large labeled data set is impractical. Demographic inference using social media data is one such task. While different strategies have been proposed to mitigate this challenge, including transfer learning, data augmentation, and data combination, they have not been explored for the task of user level demographic inference using social media data. This paper explores two of these strategies: data combination and transfer learning. First, we combine labeled training data from multiple data sets of similar size to understand when the combination is valuable and when it is not. Using data set distance, we quantify the relationship between our data sets to help explain the performance of the combination strategy. Then, we consider supervised transfer learning, where we pretrain a model on a larger labeled data set, fine-tune the model on smaller data sets, and incorporate regularization as part of the transfer learning process. We empirically show the strengths and limitations of the proposed techniques on multiple Twitter data sets.|对于一些学习任务，生成一个大的标记数据集是不切实际的。利用社交媒体数据进行人口统计推断就是这样一项任务。虽然已经提出了不同的策略来缓解这一挑战，包括转移学习，数据增强和数据组合，但是还没有探索使用社交媒体数据进行用户级人口推断的任务。本文探讨了其中的两种策略: 数据组合和迁移学习。首先，我们将来自多个大小相似的数据集的标记训练数据进行组合，以了解这种组合在什么时候有价值，什么时候没有价值。利用数据集距离量化数据集之间的关系，有助于解释组合策略的性能。然后，我们考虑有监督的迁移学习，其中我们预训练一个模型在一个较大的标记数据集，微调模型在较小的数据集，并纳入正则化作为迁移学习过程的一部分。我们通过实例展示了在多个 Twitter 数据集上提出的技术的优势和局限性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Combining+vs.+Transferring+Knowledge:+Investigating+Strategies+for+Improving+Demographic+Inference+in+Low+Resource+Settings)|0| |[Active Ensemble Learning for Knowledge Graph Error Detection](https://doi.org/10.1145/3539597.3570368)|Junnan Dong, Qinggang Zhang, Xiao Huang, Qiaoyu Tan, Daochen Zha, Zihao Zhao|Rice University, Houston, TX, USA; Texas A&M University, College Station, TX, USA; The Hong Kong Polytechnic University, Hong Kong, China|Knowledge graphs (KGs) could effectively integrate a large number of real-world assertions, and improve the performance of various applications, such as recommendation and search. KG error detection has been intensively studied since real-world KGs inevitably contain erroneous triples. While existing studies focus on developing a novel algorithm dedicated to one or a few data characteristics, we explore advancing KG error detection by assembling a set of state-of-the-art (SOTA) KG error detectors. However, it is nontrivial to develop a practical ensemble learning framework for KG error detection. Existing ensemble learning models heavily rely on labels, while it is expensive to acquire labeled errors in KGs. Also, KG error detection itself is challenging since triples contain rich semantic information and might be false because of various reasons. To this end, we propose to leverage active learning to minimize human efforts. Our proposed framework - KAEL, could effectively assemble a set of off-the-shelf error detection algorithms, by actively using a limited number of manual annotations. It adaptively updates the ensemble learning policy in each iteration based on active queries, i.e., the answers from experts. After all annotation budget is used, KAEL utilizes the trained policy to identify remaining suspicious triples. Experiments on real-world KGs demonstrate that we can achieve significant improvement when applying KAEL to assemble SOTA error detectors. KAEL also outperforms SOTA ensemble learning baselines significantly.|知识图(KGs)可以有效地集成大量真实世界的断言，并提高各种应用程序(如推荐和搜索)的性能。由于现实生活中的 KG 不可避免地含有错误的三元组，因此对 KG 误差检测进行了深入的研究。现有的研究集中在开发一种新的算法，专门用于一个或几个数据特征，我们探讨了先进的 KG 错误检测组装一套最先进的(SOTA) KG 错误检测器。然而，开发一个实用的 KG 错误检测集成学习框架并非易事。现有的集成学习模型严重依赖于标签，而在幼儿园中获取标签错误的成本很高。此外，由于三元组包含丰富的语义信息，因此 KG 错误检测本身具有挑战性，并且由于各种原因可能出现错误。为此，我们建议利用主动学习来最大限度地减少人类的努力。我们提出的框架—— KAEL，可以通过主动使用有限数量的手动注释，有效地组装一组现成的错误检测算法。它根据活动查询(即专家的回答)自适应地更新每次迭代中的集成学习策略。在使用所有注释预算之后，KAEL 使用经过训练的策略来识别剩余的可疑三元组。在实际 KG 上的实验表明，采用 KAEL 组装 SOTA 误差检测器可以取得明显的改进。KAEL 的表现也明显优于 SOTA 集成学习基准。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Active+Ensemble+Learning+for+Knowledge+Graph+Error+Detection)|0| |[Stochastic Solutions for Dense Subgraph Discovery in Multilayer Networks](https://doi.org/10.1145/3539597.3570444)|Yasushi Kawase, Atsushi Miyauchi, Hanna Sumita|Tokyo Institute of Technology, Meguro-ku, Japan; The University of Tokyo, Bunkyo-ku, Japan|Network analysis has played a key role in knowledge discovery and data mining. In many real-world applications in recent years, we are interested in mining multilayer networks, where we have a number of edge sets called layers, which encode different types of connections and/or time-dependent connections over the same set of vertices. Among many network analysis techniques, dense subgraph discovery, aiming to find a dense component in a network, is an essential primitive with a variety of applications in diverse domains. In this paper, we introduce a novel optimization model for dense subgraph discovery in multilayer networks. Our model aims to find a stochastic solution, i.e., a probability distribution over the family of vertex subsets, rather than a single vertex subset, whereas it can also be used for obtaining a single vertex subset. For our model, we design an LP-based polynomial-time exact algorithm. Moreover, to handle large-scale networks, we also devise a simple, scalable preprocessing algorithm, which often reduces the size of the input networks significantly and results in a substantial speed-up. Computational experiments demonstrate the validity of our model and the effectiveness of our algorithms.|网络分析在知识发现和数据挖掘中发挥了重要作用。在最近几年的许多实际应用中，我们对挖掘多层网络感兴趣，其中我们有许多称为层的边集，它们在同一组顶点上编码不同类型的连接和/或依赖于时间的连接。在众多的网络分析技术中，致密子图发现是一种必要的原语，其目的是在网络中寻找致密组件，在不同的领域有着广泛的应用。本文提出了一种新的多层网络稠密子图发现优化模型。我们的模型的目的是找到一个随机解，即，一个概率分布在顶点子集家族，而不是一个单一的顶点子集，然而它也可以用来获得一个单一的顶点子集。对于我们的模型，我们设计了一个基于 LP 的多项式时间精确算法。此外，为了处理大规模的网络，我们还设计了一个简单的，可扩展的预处理算法，这往往大大减少了输入网络的大小，并导致大幅度的加速。计算实验验证了模型的有效性和算法的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Stochastic+Solutions+for+Dense+Subgraph+Discovery+in+Multilayer+Networks)|0| -|[Differentially Private Temporal Difference Learning with Stochastic Nonconvex-Strongly-Concave Optimization](https://doi.org/10.1145/3539597.3570470)|Canzhe Zhao, Yanjie Ze, Jing Dong, Baoxiang Wang, Shuai Li|The Chinese University of Hong Kong, Shenzhen, Shenzhen, China; Shanghai Jiao Tong University, Shanghai, China|Temporal difference (TD) learning is a widely used method to evaluate policies in reinforcement learning. While many TD learning methods have been developed in recent years, little attention has been paid to preserving privacy and most of the existing approaches might face the concerns of data privacy from users. To enable complex representative abilities of policies, in this paper, we consider preserving privacy in TD learning with nonlinear value function approximation. This is challenging because such a nonlinear problem is usually studied in the formulation of stochastic nonconvex-strongly-concave optimization to gain finite-sample analysis, which would require simultaneously preserving the privacy on primal and dual sides. To this end, we employ a momentum-based stochastic gradient descent ascent to achieve a single-timescale algorithm, and achieve a good trade-off between meaningful privacy and utility guarantees of both the primal and dual sides by perturbing the gradients on both sides using well-calibrated Gaussian noises. As a result, our DPTD algorithm could provide $(\epsilon,\delta)$-differential privacy (DP) guarantee for the sensitive information encoded in transitions and retain the original power of TD learning, with the utility upper bounded by $\widetilde{\mathcal{O}}(\frac{(d\log(1/\delta))^{1/8}}{(n\epsilon)^{1/4}})$ (The tilde in this paper hides the log factor.), where $n$ is the trajectory length and $d$ is the dimension. Extensive experiments conducted in OpenAI Gym show the advantages of our proposed algorithm.|时差学习是一种广泛使用的评估强化学习政策的方法。尽管近年来 TD 学习方法得到了广泛的应用，但对于保护隐私的研究却很少，现有的方法大多面临着用户对数据隐私的关注。为了使策略具有复杂的代表性能力，本文考虑在具有非线性值函数逼近的 TD 学习中保护隐私。这是一个具有挑战性的问题，因为这样的非线性问题通常被研究在随机非凸-强凹优化的公式获得有限样本分析，这将需要同时保留原始和对偶方面的隐私。为此，我们采用基于动量的随机梯度下降上升来实现一个单时间尺度算法，并通过使用校准良好的高斯噪声扰动两侧的梯度，在原始和双侧的有意义的隐私和效用保证之间达到一个良好的平衡。因此，我们的 dPTD 算法可以为过渡过程中编码的敏感信息提供 $(epsilon，delta) $- 差分隐私(DP)保证，并且保留了 TD 学习的原始能力，其效用上界为 $widtilde { mathcal { O }}(frac {(d log (1/delta)) ^ {1/8}}{(n epsilon) ^ {1/4}}) $(本文中的波浪线隐藏了 log 因子)，其中 $n $是轨迹长度，$d $是维数。在 OpenAI 健身房进行的大量实验表明了我们提出的算法的优点。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Differentially+Private+Temporal+Difference+Learning+with+Stochastic+Nonconvex-Strongly-Concave+Optimization)|0| +|[Differentially Private Temporal Difference Learning with Stochastic Nonconvex-Strongly-Concave Optimization](https://doi.org/10.1145/3539597.3570470)|Canzhe Zhao, Yanjie Ze, Jing Dong, Baoxiang Wang, Shuai Li|Shanghai Jiao Tong University, Shanghai, China; The Chinese University of Hong Kong, Shenzhen, Shenzhen, China|Temporal difference (TD) learning is a widely used method to evaluate policies in reinforcement learning. While many TD learning methods have been developed in recent years, little attention has been paid to preserving privacy and most of the existing approaches might face the concerns of data privacy from users. To enable complex representative abilities of policies, in this paper, we consider preserving privacy in TD learning with nonlinear value function approximation. This is challenging because such a nonlinear problem is usually studied in the formulation of stochastic nonconvex-strongly-concave optimization to gain finite-sample analysis, which would require simultaneously preserving the privacy on primal and dual sides. To this end, we employ a momentum-based stochastic gradient descent ascent to achieve a single-timescale algorithm, and achieve a good trade-off between meaningful privacy and utility guarantees of both the primal and dual sides by perturbing the gradients on both sides using well-calibrated Gaussian noises. As a result, our DPTD algorithm could provide $(\epsilon,\delta)$-differential privacy (DP) guarantee for the sensitive information encoded in transitions and retain the original power of TD learning, with the utility upper bounded by $\widetilde{\mathcal{O}}(\frac{(d\log(1/\delta))^{1/8}}{(n\epsilon)^{1/4}})$ (The tilde in this paper hides the log factor.), where $n$ is the trajectory length and $d$ is the dimension. Extensive experiments conducted in OpenAI Gym show the advantages of our proposed algorithm.|时差学习是一种广泛使用的评估强化学习政策的方法。尽管近年来 TD 学习方法得到了广泛的应用，但对于保护隐私的研究却很少，现有的方法大多面临着用户对数据隐私的关注。为了使策略具有复杂的代表性能力，本文考虑在具有非线性值函数逼近的 TD 学习中保护隐私。这是一个具有挑战性的问题，因为这样的非线性问题通常被研究在随机非凸-强凹优化的公式获得有限样本分析，这将需要同时保留原始和对偶方面的隐私。为此，我们采用基于动量的随机梯度下降上升来实现一个单时间尺度算法，并通过使用校准良好的高斯噪声扰动两侧的梯度，在原始和双侧的有意义的隐私和效用保证之间达到一个良好的平衡。因此，我们的 dPTD 算法可以为过渡过程中编码的敏感信息提供 $(epsilon，delta) $- 差分隐私(DP)保证，并且保留了 TD 学习的原始能力，其效用上界为 $widtilde { mathcal { O }}(frac {(d log (1/delta)) ^ {1/8}}{(n epsilon) ^ {1/4}}) $(本文中的波浪线隐藏了 log 因子)，其中 $n $是轨迹长度，$d $是维数。在 OpenAI 健身房进行的大量实验表明了我们提出的算法的优点。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Differentially+Private+Temporal+Difference+Learning+with+Stochastic+Nonconvex-Strongly-Concave+Optimization)|0| |[Feature Missing-aware Routing-and-Fusion Network for Customer Lifetime Value Prediction in Advertising](https://doi.org/10.1145/3539597.3570460)|Xuejiao Yang, Binfeng Jia, Shuangyang Wang, Shijie Zhang|Tencent, Shenzhen, China|Nowadays, customer lifetime value (LTV) plays an important role in mobile game advertising, since it can be beneficial to adjust ad bids and ensure that the games are promoted to the most valuable users. Some neural models are utilized for LTV prediction based on the rich user features. However, in the advertising scenario, due to the privacy settings or limited length of log retention, etc, most of existing approaches suffer from the missing feature problem. Moreover, only a small fraction of purchase behaviours can be observed. The label sparsity inevitably limits model expressiveness. To tackle the aforementioned challenges, we propose a feature missing-aware routing-and-fusion network (MarfNet) to reduce the effect of the missing features while training. Specifically, we calculate the missing states of raw features and feature interactions for each sample. Based on the missing states, two missing-aware layers are designed to route samples into different experts, thus each expert can focus on the real features of samples assigned to it. Finally we get the missing-aware representation by the weighted fusion of the experts. To alleviate the label sparsity, we further propose a batch-in dynamic discrimination enhanced (Bidden) loss weight mechanism, which can automatically assign greater loss weights to difficult samples in the training process. Both offline experiments and online A/B tests have validated the superiority of our proposed Bidden-MarfNet.|目前，客户生命周期价值(LTV)在手机游戏广告中扮演着重要角色，它有利于调整广告投标价格，保证手机游戏向最有价值的用户推广。基于丰富的用户特征，利用一些神经模型对 LTV 进行预测。然而，在广告场景中，由于隐私设置或有限的日志保留时间等原因，大多数现有的方法都存在缺少特性的问题。此外，只有一小部分的购买行为可以观察到。标签的稀疏性不可避免地限制了模型的表达能力。为了解决上述问题，我们提出了一种特征缺失感知的路由融合网络(MarfNet) ，以减少训练过程中特征缺失的影响。具体来说，我们计算每个样本的原始特征和特征交互的缺失状态。在缺失状态的基础上，设计了两个缺失感知层，将样本分配给不同的专家，从而使每个专家能够专注于分配给它的样本的真实特征。最后通过专家加权融合得到缺失感知表示。为了缓解标签稀疏性，本文进一步提出了一种批中动态鉴别增强(Biden)损失权重机制，该机制可以在训练过程中自动为难度较大的样本赋予较大的损失权重。离线实验和在线 A/B 测试都验证了我们提出的 Biden-MarfNet 的优越性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Feature+Missing-aware+Routing-and-Fusion+Network+for+Customer+Lifetime+Value+Prediction+in+Advertising)|0| |[Boosting Advertising Space: Designing Ad Auctions for Augment Advertising](https://doi.org/10.1145/3539597.3570381)|Yangsu Liu, Dagui Chen, Zhenzhe Zheng, Zhilin Zhang, Chuan Yu, Fan Wu, Guihai Chen|Alibaba Group, Beijing, China; Shanghai Jiao Tong University, Shanghai, China; Alibaba Group, Beijing , China|In online e-commerce platforms, sponsored ads are always mixed with non-sponsored organic content (recommended items). To guarantee user experience, online platforms always impose strict limitations on the number of ads displayed, becoming the bottleneck for advertising revenue. To boost advertising space, we introduce a novel advertising business paradigm called Augment Advertising, where once a user clicks on a leading ad on the main page, instead of being shown the corresponding products, a collection of mini-detail ads relevant to the clicked ad is displayed. A key component for augment advertising is to design ad auctions to jointly select leading ads on the main page and mini-detail ads on the augment ad page. In this work, we decouple the ad auction into a two-stage auction, including a leading ad auction and a mini-detail ad auction. We design the Potential Generalized Second Price (PGSP) auction with Symmetric Nash Equilibrium (SNE) for leading ads, and adopt GSP auction for mini-detail ads. We have deployed augment advertising on Taobao advertising platform, and conducted extensive offline evaluations and online A/B tests. The evaluation results show that augment advertising could guarantee user experience while improving the ad revenue and the PGSP auction outperforms baselines in terms of revenue and user experience in augment advertising.|在在线电子商务平台中，赞助商的广告总是与非赞助商的有机内容(推荐商品)混合在一起。为了保证用户体验，在线平台总是对广告的显示数量进行严格的限制，成为广告收入的瓶颈。为了扩大广告空间，我们引入了一种新颖的广告商业模式，称为“增强广告”(Augment Advertising) ，一旦用户点击主页上的一个领先广告，而不是显示相应的产品，就会显示与点击广告相关的一系列小细节广告。增强型广告的一个关键组成部分是设计广告拍卖，联合选择主页上的领先广告和增强型广告页面上的小细节广告。在这个工作中，我们将广告拍卖分解为两个阶段的拍卖，包括领先广告拍卖和微细节广告拍卖。我们为领先广告设计了对称纳什均衡点的潜在广义二价拍卖(PGSP) ，为微细节广告设计了潜在广义二价拍卖(pgSP)。我们在淘宝广告平台上部署了增强型广告，并进行了广泛的线下评估和在线 A/B 测试。评价结果表明，增强广告可以在保证用户体验的同时提高广告收入，PGSP 拍卖在增强广告收入和用户体验方面优于基线。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Boosting+Advertising+Space:+Designing+Ad+Auctions+for+Augment+Advertising)|0| -|[Long-Document Cross-Lingual Summarization](https://doi.org/10.1145/3539597.3570479)|Shaohui Zheng, Zhixu Li, Jiaan Wang, Jianfeng Qu, An Liu, Lei Zhao, Zhigang Chen|Fudan University, Shanghai, China; Jilin Kexun Information Technology Co., Ltd., Jilin, China; Soochow University, Suzhou, China|Cross-Lingual Summarization (CLS) aims at generating summaries in one language for the given documents in another language. CLS has attracted wide research attention due to its practical significance in the multi-lingual world. Though great contributions have been made, existing CLS works typically focus on short documents, such as news articles, short dialogues and guides. Different from these short texts, long documents such as academic articles and business reports usually discuss complicated subjects and consist of thousands of words, making them non-trivial to process and summarize. To promote CLS research on long documents, we construct Perseus, the first long-document CLS dataset which collects about 94K Chinese scientific documents paired with English summaries. The average length of documents in Perseus is more than two thousand tokens. As a preliminary study on long-document CLS, we build and evaluate various CLS baselines, including pipeline and end-to-end methods. Experimental results on Perseus show the superiority of the end-to-end baseline, outperforming the strong pipeline models equipped with sophisticated machine translation systems. Furthermore, to provide a deeper understanding, we manually analyze the model outputs and discuss specific challenges faced by current approaches. We hope that our work could benchmark long-document CLS and benefit future studies.|跨语言摘要(CLS)的目的是用一种语言为给定的文档生成另一种语言的摘要。CLS 因其在多语言世界中的实际意义而引起了广泛的研究关注。虽然已经作出了巨大的贡献，现有的 CLS 工作通常集中在短文档，如新闻文章，短对话和指南。与这些短文不同的是，学术文章、商务报告等长文档通常涉及复杂的主题，由数千字组成，因此处理和总结起来非常重要。为了促进长文献的 CLS 研究，我们构建了第一个长文献 CLS 数据集 Perseus，该数据集收集了约94K 中文科学文献并附有英文摘要。英仙座文件的平均长度超过二千个令牌。作为对长文档 CLS 的初步研究，我们建立和评估各种 CLS 基线，包括流水线和端到端方法。在 Perseus 上的实验结果显示了端到端基线的优越性，优于配备复杂机器翻译系统的强流水线模型。此外，为了提供更深入的理解，我们手动分析模型输出并讨论当前方法面临的具体挑战。我们希望我们的工作可以作为长文档 CLS 的基准，并有利于未来的研究。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Long-Document+Cross-Lingual+Summarization)|0| +|[Long-Document Cross-Lingual Summarization](https://doi.org/10.1145/3539597.3570479)|Shaohui Zheng, Zhixu Li, Jiaan Wang, Jianfeng Qu, An Liu, Lei Zhao, Zhigang Chen|Jilin Kexun Information Technology Co., Ltd., Jilin, China; Fudan University, Shanghai, China; Soochow University, Suzhou, China|Cross-Lingual Summarization (CLS) aims at generating summaries in one language for the given documents in another language. CLS has attracted wide research attention due to its practical significance in the multi-lingual world. Though great contributions have been made, existing CLS works typically focus on short documents, such as news articles, short dialogues and guides. Different from these short texts, long documents such as academic articles and business reports usually discuss complicated subjects and consist of thousands of words, making them non-trivial to process and summarize. To promote CLS research on long documents, we construct Perseus, the first long-document CLS dataset which collects about 94K Chinese scientific documents paired with English summaries. The average length of documents in Perseus is more than two thousand tokens. As a preliminary study on long-document CLS, we build and evaluate various CLS baselines, including pipeline and end-to-end methods. Experimental results on Perseus show the superiority of the end-to-end baseline, outperforming the strong pipeline models equipped with sophisticated machine translation systems. Furthermore, to provide a deeper understanding, we manually analyze the model outputs and discuss specific challenges faced by current approaches. We hope that our work could benchmark long-document CLS and benefit future studies.|跨语言摘要(CLS)的目的是用一种语言为给定的文档生成另一种语言的摘要。CLS 因其在多语言世界中的实际意义而引起了广泛的研究关注。虽然已经作出了巨大的贡献，现有的 CLS 工作通常集中在短文档，如新闻文章，短对话和指南。与这些短文不同的是，学术文章、商务报告等长文档通常涉及复杂的主题，由数千字组成，因此处理和总结起来非常重要。为了促进长文献的 CLS 研究，我们构建了第一个长文献 CLS 数据集 Perseus，该数据集收集了约94K 中文科学文献并附有英文摘要。英仙座文件的平均长度超过二千个令牌。作为对长文档 CLS 的初步研究，我们建立和评估各种 CLS 基线，包括流水线和端到端方法。在 Perseus 上的实验结果显示了端到端基线的优越性，优于配备复杂机器翻译系统的强流水线模型。此外，为了提供更深入的理解，我们手动分析模型输出并讨论当前方法面临的具体挑战。我们希望我们的工作可以作为长文档 CLS 的基准，并有利于未来的研究。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Long-Document+Cross-Lingual+Summarization)|0| |[FineSum: Target-Oriented, Fine-Grained Opinion Summarization](https://doi.org/10.1145/3539597.3570397)|Suyu Ge, Jiaxin Huang, Yu Meng, Jiawei Han|University of Illinois Urbana-Champaign, Urbana, IL, USA|Target-oriented opinion summarization is to profile a target by extracting user opinions from multiple related documents. Instead of simply mining opinion ratings on a target (e.g., a restaurant) or on multiple aspects (e.g., food, service) of a target, it is desirable to go deeper, to mine opinion on fine-grained sub-aspects (e.g., fish). However, it is expensive to obtain high-quality annotations at such fine-grained scale. This leads to our proposal of a new framework, FineSum, which advances the frontier of opinion analysis in three aspects: (1) minimal supervision, where no document-summary pairs are provided, only aspect names and a few aspect/sentiment keywords are available; (2) fine-grained opinion analysis, where sentiment analysis drills down to a specific subject or characteristic within each general aspect; and (3) phrase-based summarization, where short phrases are taken as basic units for summarization, and semantically coherent phrases are gathered to improve the consistency and comprehensiveness of summary. Given a large corpus with no annotation, FineSum first automatically identifies potential spans of opinion phrases, and further reduces the noise in identification results using aspect and sentiment classifiers. It then constructs multiple fine-grained opinion clusters under each aspect and sentiment. Each cluster expresses uniform opinions towards certain sub-aspects (e.g., "fish" in "food" aspect) or characteristics (e.g., "Mexican" in "food" aspect). To accomplish this, we train a spherical word embedding space to explicitly represent different aspects and sentiments. We then distill the knowledge from embedding to a contextualized phrase classifier, and perform clustering using the contextualized opinion-aware phrase embedding. Both automatic evaluations on the benchmark and quantitative human evaluation validate the effectiveness of our approach.|面向目标的意见摘要是从多个相关文档中提取用户意见，对目标进行轮廓分析。与其简单地挖掘目标(如餐馆)或目标的多个方面(如食物、服务)的意见评级，不如更深入地挖掘细粒度的子方面(如鱼)的意见。然而，在如此细粒度的规模下获得高质量的注释是非常昂贵的。这导致我们提出了一个新的框架，FineSum，它在三个方面推进了意见分析的前沿: (1)最小的监督，没有提供文档-摘要对，只有方面名称和一些方面/情绪关键词可用; (2)细粒度的意见分析，情绪分析深入到每个一般方面的特定主题或特征; 和(3)基于短语的摘要，短语被作为基本的总结单元，语义连贯的短语被收集起来，以提高摘要的一致性和全面性。给定一个没有注释的大型语料库，FineSum 首先自动识别意见短语的潜在范围，并使用方面和情感分类器进一步降低识别结果中的噪声。然后在各个方面和情绪下构建多个细粒度的意见聚类。每个群组对某些子方面(例如，“食物”方面的“鱼”)或特征(例如，“食物”方面的“墨西哥”)表达统一的意见。为了达到这个目的，我们训练一个球形词嵌入空间来显式地表示不同的方面和情感。然后将嵌入的知识提取到上下文短语分类器中，利用上下文意见感知短语嵌入进行聚类。基准的自动评估和定量的人工评估都验证了该方法的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=FineSum:+Target-Oriented,+Fine-Grained+Opinion+Summarization)|0| -|[EZInterviewer: To Improve Job Interview Performance with Mock Interview Generator](https://doi.org/10.1145/3539597.3570476)|Mingzhe Li, Xiuying Chen, Weiheng Liao, Yang Song, Tao Zhang, Dongyan Zhao, Rui Yan|KAUST, Jaddah, Saudi Arabia; Peking University, Beijing, China; Made by DATA, Beijing, China; BOSS Zhipin, Beijing, China; Renmin University of China, Beijing, China|Interview has been regarded as one of the most crucial step for recruitment. To fully prepare for the interview with the recruiters, job seekers usually practice with mock interviews between each other. However, such a mock interview with peers is generally far away from the real interview experience: the mock interviewers are not guaranteed to be professional and are not likely to behave like a real interviewer. Due to the rapid growth of online recruitment in recent years, recruiters tend to have online interviews, which makes it possible to collect real interview data from real interviewers. In this paper, we propose a novel application named EZInterviewer, which aims to learn from the online interview data and provides mock interview services to the job seekers. The task is challenging in two ways: (1) the interview data are now available but still of low-resource; (2) to generate meaningful and relevant interview dialogs requires thorough understanding of both resumes and job descriptions. To address the low-resource challenge, EZInterviewer is trained on a very small set of interview dialogs. The key idea is to reduce the number of parameters that rely on interview dialogs by disentangling the knowledge selector and dialog generator so that most parameters can be trained with ungrounded dialogs as well as the resume data that are not low-resource. Evaluation results on a real-world job interview dialog dataset indicate that we achieve promising results to generate mock interviews. With the help of EZInterviewer, we hope to make mock interview practice become easier for job seekers.|面试被认为是招聘中最关键的步骤之一。为了充分准备与招聘人员的面试，求职者通常会在彼此之间进行模拟面试练习。然而，这种与同龄人的模拟面试通常远离真正的面试经验: 模拟面试官不能保证是专业的，也不太可能表现得像一个真正的面试官。由于近年来网络招聘的快速增长，招聘人员往往采用网络面试的方式，这使得从真正的面试官那里收集真实的面试数据成为可能。本文提出了一个新颖的应用程序 EZInterview，旨在从网上面试数据中获取信息，为求职者提供模拟面试服务。这项任务在两个方面具有挑战性: (1)面试数据现在已经可用，但仍然是低资源; (2)产生有意义的和相关的面试对话需要对简历和职位描述都有透彻的理解。为了解决资源不足的问题，EZInterview 接受了一个非常小的面试对话集的培训。其核心思想是通过分离知识选择器和对话生成器，减少依赖于面试对话的参数数量，使得大多数参数可以通过不接地的对话和资源不少的简历数据进行训练。实际工作面试对话数据集的评估结果表明，我们在模拟面试中取得了令人满意的效果。通过 EZInterview 的帮助，我们希望使模拟面试变得对求职者来说更加容易。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=EZInterviewer:+To+Improve+Job+Interview+Performance+with+Mock+Interview+Generator)|0| +|[EZInterviewer: To Improve Job Interview Performance with Mock Interview Generator](https://doi.org/10.1145/3539597.3570476)|Mingzhe Li, Xiuying Chen, Weiheng Liao, Yang Song, Tao Zhang, Dongyan Zhao, Rui Yan|Peking University, Beijing, China; Renmin University of China, Beijing, China; KAUST, Jaddah, Saudi Arabia; Made by DATA, Beijing, China; BOSS Zhipin, Beijing, China|Interview has been regarded as one of the most crucial step for recruitment. To fully prepare for the interview with the recruiters, job seekers usually practice with mock interviews between each other. However, such a mock interview with peers is generally far away from the real interview experience: the mock interviewers are not guaranteed to be professional and are not likely to behave like a real interviewer. Due to the rapid growth of online recruitment in recent years, recruiters tend to have online interviews, which makes it possible to collect real interview data from real interviewers. In this paper, we propose a novel application named EZInterviewer, which aims to learn from the online interview data and provides mock interview services to the job seekers. The task is challenging in two ways: (1) the interview data are now available but still of low-resource; (2) to generate meaningful and relevant interview dialogs requires thorough understanding of both resumes and job descriptions. To address the low-resource challenge, EZInterviewer is trained on a very small set of interview dialogs. The key idea is to reduce the number of parameters that rely on interview dialogs by disentangling the knowledge selector and dialog generator so that most parameters can be trained with ungrounded dialogs as well as the resume data that are not low-resource. Evaluation results on a real-world job interview dialog dataset indicate that we achieve promising results to generate mock interviews. With the help of EZInterviewer, we hope to make mock interview practice become easier for job seekers.|面试被认为是招聘中最关键的步骤之一。为了充分准备与招聘人员的面试，求职者通常会在彼此之间进行模拟面试练习。然而，这种与同龄人的模拟面试通常远离真正的面试经验: 模拟面试官不能保证是专业的，也不太可能表现得像一个真正的面试官。由于近年来网络招聘的快速增长，招聘人员往往采用网络面试的方式，这使得从真正的面试官那里收集真实的面试数据成为可能。本文提出了一个新颖的应用程序 EZInterview，旨在从网上面试数据中获取信息，为求职者提供模拟面试服务。这项任务在两个方面具有挑战性: (1)面试数据现在已经可用，但仍然是低资源; (2)产生有意义的和相关的面试对话需要对简历和职位描述都有透彻的理解。为了解决资源不足的问题，EZInterview 接受了一个非常小的面试对话集的培训。其核心思想是通过分离知识选择器和对话生成器，减少依赖于面试对话的参数数量，使得大多数参数可以通过不接地的对话和资源不少的简历数据进行训练。实际工作面试对话数据集的评估结果表明，我们在模拟面试中取得了令人满意的效果。通过 EZInterview 的帮助，我们希望使模拟面试变得对求职者来说更加容易。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=EZInterviewer:+To+Improve+Job+Interview+Performance+with+Mock+Interview+Generator)|0| |[A Framework for Detecting Frauds from Extremely Few Labels](https://doi.org/10.1145/3539597.3573022)|YaLin Zhang, YiXuan Sun, Fangfang Fan, Meng Li, Yeyu Zhao, Wei Wang, Longfei Li, Jun Zhou, Jinghua Feng|Zheijiang University & Ant Group, Hangzhou, China; Ant Group, Hangzhou, China; Nanjing University, Nanjing, China|In this paper, we present a framework to deal with the fraud detection task with extremely few labeled frauds. We involve human intelligence in the loop in a labor-saving manner and introduce several ingenious designs to the model construction process. Namely, a rule mining module is introduced, and the learned rules will be refined with expert knowledge. The refined rules will be used to relabel the unlabeled samples and get the potential frauds. We further present a model to learn with the reliable frauds, the potential frauds, and the rest normal samples. Note that the label noise problem, class imbalance problem, and confirmation bias problem are all addressed with specific strategies when building the model. Experimental results are reported to demonstrate the effectiveness of the framework.|在本文中，我们提出了一个框架来处理欺诈检测任务的极少标记欺诈。我们以节省人力的方式将人类智能融入到这个循环中，并且在模型的建造过程中引入了几个巧妙的设计。即引入规则挖掘模块，利用专家知识对学习到的规则进行细化。改进后的规则将被用于重新标记未标记的样品，并得到潜在的欺诈行为。我们进一步提出了一个模型来学习与可靠的欺诈，潜在的欺诈，和其余的正常样本。注意，在建立模型时，标签噪声问题、类别不平衡问题和确认偏差问题都是通过特定的策略来解决的。实验结果证明了该框架的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Framework+for+Detecting+Frauds+from+Extremely+Few+Labels)|0| |[Concept-Oriented Transformers for Visual Sentiment Analysis](https://doi.org/10.1145/3539597.3570437)|QuocTuan Truong, Hady W. Lauw|Singapore Management University, Singapore, Singapore; Amazon, Seattle, WA, USA|In the richly multimedia Web, detecting sentiment signals expressed in images would support multiple applications, e.g., measuring customer satisfaction from online reviews, analyzing trends and opinions from social media. Given an image, visual sentiment analysis aims at recognizing positive or negative sentiment, and occasionally neutral sentiment as well. A nascent yet promising direction is Transformer-based models applied to image data, whereby Vision Transformer (ViT) establishes remarkable performance on large-scale vision benchmarks. In addition to investigating the fitness of ViT for visual sentiment analysis, we further incorporate concept orientation into the self-attention mechanism, which is the core component of Transformer. The proposed model captures the relationships between image features and specific concepts. We conduct extensive experiments on Visual Sentiment Ontology (VSO) and Yelp.com online review datasets, showing that not only does the proposed model significantly improve upon the base model ViT in detecting visual sentiment but it also outperforms previous visual sentiment analysis models with narrowly-defined orientations. Additional analyses yield insightful results and better understanding of the concept-oriented self-attention mechanism.|在丰富的多媒体网络中，检测图像中表达的情绪信号将支持多种应用，例如，通过在线评论测量客户满意度，分析来自社交媒体的趋势和意见。给定一个图像，视觉情绪分析的目的是识别积极或消极的情绪，偶尔中立的情绪以及。一个新兴但有前途的方向是应用于图像数据的基于 Transform- 的模型，其中视觉转换器(ViT)在大规模视觉基准上建立了显著的性能。除了研究 ViT 对视觉情绪分析的适应性之外，我们还将概念定向引入到自我注意机制中，这是变压器的核心部分。提出的模型捕捉图像特征和特定概念之间的关系。我们在视觉情感本体(VSO)和 Yelp.com 在线评论数据集上进行了广泛的实验，结果表明，所提出的模型不仅在检测视觉情感方面显着改善了基本模型 ViT，而且在狭义定义的方向上优于以前的视觉情感分析模型。更多的分析产生了深刻的结果和更好的理解概念导向的自我注意机制。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Concept-Oriented+Transformers+for+Visual+Sentiment+Analysis)|0| |[UnCommonSense in Action! Informative Negations for Commonsense Knowledge Bases](https://doi.org/10.1145/3539597.3573027)|Hiba Arnaout, TuanPhong Nguyen, Simon Razniewski, Gerhard Weikum|Max Planck Institute for Informatics, Saarbrücken , Germany|Knowledge bases about commonsense knowledge i.e., CSKBs, are crucial in applications such as search and question answering. Prominent CSKBs mostly focus on positive statements. In this paper we show that materializing important negations increases the usability of CSKBs. We present Uncommonsense, a web portal to explore informative negations about everyday concepts: (i) in a research-focused interface, users get a glimpse into results-per-steps of the methodology; (ii) in a trivia interface, users can browse fun negative trivia about concepts of their choice; and (iii) in a query interface, users can submit triple-pattern queries with explicit negated relations and compare results with significantly less relevant answers from the positive-only baseline. It can be accessed at:https://uncommonsense.mpi-inf.mpg.de/.|关于常识知识的知识库，即 CSKB，在诸如搜索和问题回答等应用中是至关重要的。著名的 CSKB 大多侧重于积极的声明。在本文中，我们表明，物化重要的否定增加 CSKB 的可用性。我们提出 Uncommonsense，一个网络门户网站，探索日常概念的信息否定: (i)在一个研究为重点的界面，用户得到一个结果，每一步的方法; (ii)在一个琐事界面，用户可以浏览有趣的负面琐事，他们选择的概念; (iii)在一个查询界面，用户可以提交三重模式的查询明确否定的关系，并比较结果显着相关性较低的答案，从积极的基线。你可浏览以下 https://uncommonsense.mpi-inf.mpg.de/ :。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=UnCommonSense+in+Action!+Informative+Negations+for+Commonsense+Knowledge+Bases)|0| -|[SoCraft: Advertiser-level Predictive Scoring for Creative Performance on Meta](https://doi.org/10.1145/3539597.3573032)|Alfred Huang, Qi Yang, Sergey I. Nikolenko, Marlo Ongpin, Ilia Gossoudarev, Ngoc Yen Duong, Kirill Lepikhin, Sergey Vishnyakov, YuYi ChuFarseeva, Aleksandr Farseev|ITMO University, Saint Petersburg, United Kingdom; ITMO University, Saint Petersburg, Russian Fed.; SoMin.ai, London, United Kingdom|In this technical demonstration, we present SoCraft, a framework to build an advertiser-level multimedia ad content scoring platform for Meta Ads. The system utilizes a multimodal deep neural architecture to score and evaluate advertised content on Meta using both high- and low-level features of its contextual data such as text, image, targeting, and ad settings. In this demo, we present two deep models, SoDeep and SoWide, and validate the effectiveness of SoCraft with a successful real-world case study in Singapore.|在这个技术演示中，我们介绍了 SoCraft，一个为元广告构建广告客户级多媒体广告内容评分平台的框架。该系统利用一个多模态深度神经结构，使用其上下文数据(如文本、图像、目标和广告设置)的高级和低级特征，对 Meta 上的广告内容进行评分和评估。在这个演示中，我们提出了两个深度模型，SoDeep 和 SoWide，并验证了 SoCraft 在新加坡成功的现实案例研究的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=SoCraft:+Advertiser-level+Predictive+Scoring+for+Creative+Performance+on+Meta)|0| +|[SoCraft: Advertiser-level Predictive Scoring for Creative Performance on Meta](https://doi.org/10.1145/3539597.3573032)|Alfred Huang, Qi Yang, Sergey I. Nikolenko, Marlo Ongpin, Ilia Gossoudarev, Ngoc Yen Duong, Kirill Lepikhin, Sergey Vishnyakov, YuYi ChuFarseeva, Aleksandr Farseev|ITMO University, Saint Petersburg, Russian Fed.; SoMin.ai, London, United Kingdom; ITMO University, Saint Petersburg, United Kingdom|In this technical demonstration, we present SoCraft, a framework to build an advertiser-level multimedia ad content scoring platform for Meta Ads. The system utilizes a multimodal deep neural architecture to score and evaluate advertised content on Meta using both high- and low-level features of its contextual data such as text, image, targeting, and ad settings. In this demo, we present two deep models, SoDeep and SoWide, and validate the effectiveness of SoCraft with a successful real-world case study in Singapore.|在这个技术演示中，我们介绍了 SoCraft，一个为元广告构建广告客户级多媒体广告内容评分平台的框架。该系统利用一个多模态深度神经结构，使用其上下文数据(如文本、图像、目标和广告设置)的高级和低级特征，对 Meta 上的广告内容进行评分和评估。在这个演示中，我们提出了两个深度模型，SoDeep 和 SoWide，并验证了 SoCraft 在新加坡成功的现实案例研究的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=SoCraft:+Advertiser-level+Predictive+Scoring+for+Creative+Performance+on+Meta)|0| |[Privacy Aware Experiments without Cookies](https://doi.org/10.1145/3539597.3573036)|Shiv Shankar, Ritwik Sinha, Saayan Mitra, Viswanathan (Vishy) Swaminathan, Sridhar Mahadevan, Moumita Sinha|Adobe Inc, San Jose, CA, USA; University of Massachusetts, Amherst, MA, USA; Adobe Research, San Jose, CA, USA|Consider two brands that want to jointly test alternate web experiences for their customers with an A/B test. Such collaborative tests are today enabled using \textit{third-party cookies}, where each brand has information on the identity of visitors to another website. With the imminent elimination of third-party cookies, such A/B tests will become untenable. We propose a two-stage experimental design, where the two brands only need to agree on high-level aggregate parameters of the experiment to test the alternate experiences. Our design respects the privacy of customers. We propose an estimater of the Average Treatment Effect (ATE), show that it is unbiased and theoretically compute its variance. Our demonstration describes how a marketer for a brand can design such an experiment and analyze the results. On real and simulated data, we show that the approach provides valid estimate of the ATE with low variance and is robust to the proportion of visitors overlapping across the brands.|考虑两个品牌，它们希望通过 A/B 测试为客户联合测试替代的网络体验。这样的协作测试今天使用的文本{第三方 cookie } ，其中每个品牌有信息的身份访问另一个网站。随着第三方 cookie 即将被淘汰，这种 A/B 测试将变得站不住脚。我们提出了一个两阶段的实验设计，其中两个品牌只需要同意实验的高水平集合参数来测试交替的经验。我们的设计尊重顾客的隐私。我们提出了一个平均处理效应(ATE)的估计器，证明了它是无偏的，并从理论上计算了它的方差。我们的演示描述了一个品牌的营销人员如何设计这样一个实验并分析结果。在实际数据和模拟数据上，我们表明该方法提供了有效的估计 ATE 与低方差，是鲁棒的比例的访问者重叠跨品牌。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Privacy+Aware+Experiments+without+Cookies)|0| -|[ElasticDL: A Kubernetes-native Deep Learning Framework with Fault-tolerance and Elastic Scheduling](https://doi.org/10.1145/3539597.3573037)|Jun Zhou, Ke Zhang, Feng Zhu, Qitao Shi, Wenjing Fang, Lin Wang, Yi Wang|Zhejiang University & Ant Group, Hangzhou, China; Ant Group, Hangzhou, China|The power of artificial intelligence (AI) models originates with sophisticated model architecture as well as the sheer size of the model. These large-scale AI models impose new and challenging system requirements regarding scalability, reliability, and flexibility. One of the most promising solutions in the industry is to train these large-scale models on distributed deep-learning frameworks. With the power of all distributed computations, it is desired to achieve a training process with excellent scalability, elastic scheduling (flexibility), and fault tolerance (reliability). In this paper, we demonstrate the scalability, flexibility, and reliability of our open-source Elastic Deep Learning (ElasticDL) framework. Our ElasticDL utilizes an open-source system, i.e., Kubernetes, for automating deployment, scaling, and management of containerized application features to provide fault tolerance and support elastic scheduling for DL tasks.|人工智能(AI)模型的力量源于复杂的模型体系结构以及模型的庞大规模。这些大规模的人工智能模型对系统的可伸缩性、可靠性和灵活性提出了新的和具有挑战性的要求。行业中最有前途的解决方案之一是在分布式深度学习框架上对这些大规模模型进行培训。利用所有分布式计算的能力，我们希望能够实现一个具有良好可伸缩性、弹性调度(灵活性)和容错性(可靠性)的训练过程。在本文中，我们展示了我们的开源弹性深度学习(ElasticDL)框架的可伸缩性、灵活性和可靠性。我们的 ElasticDL 利用一个开源系统，即 Kubernetes，来自动部署、扩展和管理容器化应用程序特性，以提供容错能力并支持 DL 任务的弹性调度。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=ElasticDL:+A+Kubernetes-native+Deep+Learning+Framework+with+Fault-tolerance+and+Elastic+Scheduling)|0| -|[PiggyBack: Pretrained Visual Question Answering Environment for Backing up Non-deep Learning Professionals](https://doi.org/10.1145/3539597.3573039)|Zhihao Zhang, Siwen Luo, Junyi Chen, Sijia Lai, Siqu Long, Hyunsuk Chung, Soyeon Caren Han|The University of Sydney, Sydney, NSW, Australia; Fortify Edge, Sydney, NSW, Australia; The University of Sydney & The University of Western Australia, Perth, WA, Australia|We propose a PiggyBack, a Visual Question Answering platform that allows users to apply the state-of-the-art visual-language pretrained models easily. The PiggyBack supports the full stack of visual question answering tasks, specifically data processing, model fine-tuning, and result visualisation. We integrate visual-language models, pretrained by HuggingFace, an open-source API platform of deep learning technologies; however, it cannot be runnable without programming skills or deep learning understanding. Hence, our PiggyBack supports an easy-to-use browser-based user interface with several deep learning visual language pretrained models for general users and domain experts. The PiggyBack includes the following benefits: Free availability under the MIT License, Portability due to web-based and thus runs on almost any platform, A comprehensive data creation and processing technique, and ease of use on deep learning-based visual language pretrained models. The demo video is available on YouTube and can be found at https://youtu.be/iz44RZ1lF4s.|我们提出了 PiggyBack，一个可视化问题回答平台，允许用户轻松应用最先进的可视化语言预先训练的模型。PiggyBack 支持完整的可视化问题回答任务堆栈，特别是数据处理、模型微调和结果可视化。我们集成了可视化语言模型，这些模型是由 HuggingFace (深度学习技术的开源 API 平台)预先训练的; 然而，如果没有编程技能或深度学习理解，它就不能运行。因此，PiggyBack 支持一个易于使用的基于浏览器的用户界面，为一般用户和领域专家提供了几个深度学习可视化语言预先训练的模型。PiggyBack 包括以下好处: 麻省理工学院许可证下的免费可用性，基于网络的便携性，因此可以在几乎任何平台上运行，一个全面的数据创建和处理技术，易于使用基于深度学习的可视化语言预训模型。演示视频可以在 YouTube 上找到，也可以在 https://youtu.be/iz44rz1lf4s 上找到。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=PiggyBack:+Pretrained+Visual+Question+Answering+Environment+for+Backing+up+Non-deep+Learning+Professionals)|0| -|[Web of Conferences: A Conference Knowledge Graph](https://doi.org/10.1145/3539597.3573024)|Shuo Yu, Ciyuan Peng, Chengchuan Xu, Chen Zhang, Feng Xia|Chengdu Neusoft University, Chengdu, China; Dalian University of Technology, Dalian, China; RMIT University, Melbourne, Australia|Academic conferences have been proven to be significant in facilitating academic activities. To promote information retrieval specific to academic conferences, building complete, systematic, and professional conference knowledge graphs is a crucial task. However, many related systems mainly focus on general knowledge of overall academic information or concentrate services on specific domains. Aiming at filling this gap, this work demonstrates a novel conference knowledge graph, namely Web of Conferences. The system accommodates detailed conference profiles, conference ranking lists, intelligent conference queries, and personalized conference recommendations. Web of Conferences supports detailed conference information retrieval while providing the ranking of conferences based on the most recent data. Conference queries in the system can be implemented via precise search or fuzzy search. Then, according to users' query conditions, personalized conference recommendations are available. Web of Conferences is demonstrated with a user-friendly visualization interface and can be served as a useful information retrieval system for researchers.|事实证明，学术会议在促进学术活动方面具有重要意义。为了推广学术会议的信息检索，建立完整、系统和专业的会议知识图表是一项至关重要的任务。然而，许多相关的系统主要集中于整体学术信息的一般知识或集中于特定领域的服务。为了填补这一空白，本文展示了一个新颖的会议知识图，即会议网络。该系统可容纳详细的会议概况、会议排名列表、智能会议查询和个性化会议推荐。会议网站支持详细的会议信息检索，同时提供基于最新数据的会议排名。系统中的会议查询可以通过精确搜索或模糊搜索来实现。然后，根据用户的查询条件，提供个性化的会议推荐。会议网络是一个用户友好的可视化界面，可以作为一个有用的信息检索系统供研究人员使用。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Web+of+Conferences:+A+Conference+Knowledge+Graph)|0| -|[Developing and Evaluating Graph Counterfactual Explanation with GRETEL](https://doi.org/10.1145/3539597.3573026)|Mario Alfonso PradoRomero, Bardh Prenkaj, Giovanni Stilo|University of L'Aquila, L'Aquila, Italy; Sapienza University of Rome, Rome, Italy; Gran Sasso Science Institute, L'Aquila, Italy|The black-box nature and the lack of interpretability detract from constant improvements in Graph Neural Networks (GNNs) performance in social network tasks like friendship prediction and community detection. Graph Counterfactual Explanation (GCE) methods aid in understanding the prediction of GNNs by generating counterfactual examples that promote trustworthiness, debiasing, and privacy in social networks. Alas, the literature on GCE lacks standardised definitions, explainers, datasets, and evaluation metrics. To bridge the gap between the performance and interpretability of GNNs in social networks, we discuss GRETEL, a unified framework for GCE methods development and evaluation. We demonstrate how GRETEL comes with fully extensible built-in components that allow users to define ad-hoc explainer methods, generate synthetic datasets, implement custom evaluation metrics, and integrate state-of-the-art prediction models.|黑盒子的特性和缺乏可解释性使得图神经网络(GNN)在社交网络任务(如友谊预测和社区检测)中的性能不断提高。图形反事实解释(GCE)方法通过生成反事实例子来帮助理解 GNN 的预测，这些反事实例子提高了社交网络中的可信度、消除偏见和隐私。遗憾的是，关于 GCE 的文献缺乏标准化的定义、解释者、数据集和评估指标。为了弥合社交网络中 GNN 的性能和可解释性之间的差距，我们讨论 GRETEL，一个用于 GCE 方法开发和评估的统一框架。我们演示 GRETEL 如何带有完全可扩展的内置组件，允许用户定义特别解释器方法，生成合成数据集，实现自定义评估指标，并集成最先进的预测模型。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Developing+and+Evaluating+Graph+Counterfactual+Explanation+with+GRETEL)|0| -|[DistriBayes: A Distributed Platform for Learning, Inference and Attribution on Large Scale Bayesian Network](https://doi.org/10.1145/3539597.3573028)|Yi Ding, Jun Zhou, Qing Cui, Lin Wang, Mengqi Zhang, Yang Dong|Zhejiang University & Ant Group, Hangzhou, China; Ant Group, Beijing, China|To improve the marketing performance in the financial scenario, it is necessary to develop a trustworthy model to analyze and select promotion-sensitive customers. Bayesian Network (BN) is suitable for this task because of its interpretability and flexibility, but it usually suffers the exponentially growing computation complexity as the number of nodes grows. To tackle this problem, we present a comprehensive distributed platform named DistriBayes, which can efficiently learn, infer and attribute on a large-scale BN all-in-one platform. It implements several score-based structure learning methods, loopy belief propagation with backdoor adjustment for inference, and a carefully optimized search procedure for attribution. Leveraging the distributed cluster, DistriBayes can finish the learning and attribution on Bayesian Network with hundreds of nodes and millions of samples in hours.|为了提高财务情景下的营销绩效，有必要建立一个可信赖的模型来分析和选择促销敏感的客户。贝氏网路因其可解释性和灵活性而适合这项任务，但随着节点数目的增加，计算复杂度通常会呈指数级增长。为了解决这一问题，提出了一个综合分布式平台 DireBayes，该平台可以在大规模的 BN 一体化平台上进行高效的学习、推理和归属。它实现了多种基于分数的结构化学习方法、带有后门调整的循环信念传播推理以及精心优化的属性搜索过程。通过利用分布式集群，distyes 可以在数小时内完成数百个节点和数百万个样本的贝氏网路学习和归属。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=DistriBayes:+A+Distributed+Platform+for+Learning,+Inference+and+Attribution+on+Large+Scale+Bayesian+Network)|0| -|[SimSumIoT: A Platform for Simulating the Summarisation from Internet of Things](https://doi.org/10.1145/3539597.3573042)|Wei Emma Zhang, Adnan Mahmood, Lixin Deng, Minhao Zhu|Macquarie University, Sydney, Australia; The University of Adelaide, Adelaide, Australia|Summarising from the Web could be formed as a problem of multi-document Summarisaiton (MDS) from multiple sources. In contrast to the current MDS problem that involves working on benchmark datasets which provide well clustered set of documents, we envisage to build a pipeline for content Summarisaiton from the Web, but narrow down to the Social Internet of Things (SIoT) paradigm, starting at data collection from the IoT objects, then applying natural language processing techniques for grouping and summarising the data, to distributing summaries back to the IoT objects. In this paper, we present our simulation tool, SimSumIoT, that simulates the process of data sharing, receiving, clustering, and Summarisaiton. A Web-based interface is developed for this purpose allowing users to visualize the process through a set of interactions. The Web interface is accessible via http://simsumlot.tk.|从 Web 上进行摘要可以形成一个多文档摘要(MDS)问题。与目前的 MDS 问题相反，我们设想建立一个管道，从网络内容摘要，但缩小到物联网(SIoT)范式，从物联网对象的数据收集开始，然后应用自然语言处理技术分组和总结数据，分发摘要回物联网对象。在本文中，我们提出了我们的模拟工具，模拟数据共享，接收，集群和 Summarisaiton 的过程。为此开发了一个基于 Web 的界面，允许用户通过一组交互将流程可视化。网页界面可透过 http://simsumlot.tk 进入。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=SimSumIoT:+A+Platform+for+Simulating+the+Summarisation+from+Internet+of+Things)|0| -|[AntTS: A Toolkit for Time Series Forecasting in Industrial Scenarios](https://doi.org/10.1145/3539597.3573030)|Jianping Wei, Zhibo Zhu, Ziqi Liu, Zhiqiang Zhang, Jun Zhou|Zhejiang University & Ant Group, Hangzhou, China; Ant Group, Hangzhou, China|Time series forecasting is an important ingredient in the intelligence of business and decision processes. In industrial scenarios, the time series of interest are mostly macroscopic time series that are aggregated from microscopic time series, e.g., the retail sales is aggregated from the sales of different goods, and that are also intervened by certain treatments on the microscopic individuals, e.g., issuing discount coupons on some goods to increase the retail sales. These characteristics are not considered in existing toolkits, which just focus on the "natural" time series forecasting that predicts the future value based on historical data, regardless of the impact of treatments. In this paper, we present AntTS, a time series toolkit paying more attention on the forecasting of the macroscopic time series with underlying microscopic time series and certain treatments, besides the "natural" time series forecasting. AntTS consists of three decoupled modules, namely Clustering module, Natural Forecasting module, and Effect module, which are utilized to study the homogeneous groups of microscopic individuals, the "natural" time series forecasting of homogeneous groups, and the treatment effect estimation of homogeneous groups. With the combinations of different modules, it can exploit the microscopic individuals and the interventions on them, to help the forecasting of macroscopic time series. We show that AntTS helps address many typical tasks in the industry.|时间序列预测是商业和决策过程智能化的重要组成部分。在工业方案中，利息的时间序列大多是宏观时间序列，由微观时间序列聚合而成，例如，零售销售是由不同商品的销售聚合而成，同时也受到某些对微观个体的干预，例如，发行某些商品的折扣券以增加零售销售。现有的工具包没有考虑这些特征，它们只关注基于历史数据预测未来价值的“自然”时间序列预测，而不考虑治疗的影响。本文提出了一个时间序列工具包，除了“自然”时间序列预测之外，它更注重对宏观时间序列的预测，包括底层的微观时间序列和一定的处理方法。AntTS 由聚类模块、自然预测模块和效应模块三个解耦模块组成，用于研究微观个体的同质群体、同质群体的“自然”时间序列预测以及同质群体的治疗效果估计。通过不同模块的组合，可以利用微观个体以及对微观个体的干预，对宏观时间序列进行预测。我们展示了 Ant TS 可以帮助解决行业中的许多典型任务。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=AntTS:+A+Toolkit+for+Time+Series+Forecasting+in+Industrial+Scenarios)|0| +|[ElasticDL: A Kubernetes-native Deep Learning Framework with Fault-tolerance and Elastic Scheduling](https://doi.org/10.1145/3539597.3573037)|Jun Zhou, Ke Zhang, Feng Zhu, Qitao Shi, Wenjing Fang, Lin Wang, Yi Wang|Ant Group, Hangzhou, China; Zhejiang University & Ant Group, Hangzhou, China|The power of artificial intelligence (AI) models originates with sophisticated model architecture as well as the sheer size of the model. These large-scale AI models impose new and challenging system requirements regarding scalability, reliability, and flexibility. One of the most promising solutions in the industry is to train these large-scale models on distributed deep-learning frameworks. With the power of all distributed computations, it is desired to achieve a training process with excellent scalability, elastic scheduling (flexibility), and fault tolerance (reliability). In this paper, we demonstrate the scalability, flexibility, and reliability of our open-source Elastic Deep Learning (ElasticDL) framework. Our ElasticDL utilizes an open-source system, i.e., Kubernetes, for automating deployment, scaling, and management of containerized application features to provide fault tolerance and support elastic scheduling for DL tasks.|人工智能(AI)模型的力量源于复杂的模型体系结构以及模型的庞大规模。这些大规模的人工智能模型对系统的可伸缩性、可靠性和灵活性提出了新的和具有挑战性的要求。行业中最有前途的解决方案之一是在分布式深度学习框架上对这些大规模模型进行培训。利用所有分布式计算的能力，我们希望能够实现一个具有良好可伸缩性、弹性调度(灵活性)和容错性(可靠性)的训练过程。在本文中，我们展示了我们的开源弹性深度学习(ElasticDL)框架的可伸缩性、灵活性和可靠性。我们的 ElasticDL 利用一个开源系统，即 Kubernetes，来自动部署、扩展和管理容器化应用程序特性，以提供容错能力并支持 DL 任务的弹性调度。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=ElasticDL:+A+Kubernetes-native+Deep+Learning+Framework+with+Fault-tolerance+and+Elastic+Scheduling)|0| +|[PiggyBack: Pretrained Visual Question Answering Environment for Backing up Non-deep Learning Professionals](https://doi.org/10.1145/3539597.3573039)|Zhihao Zhang, Siwen Luo, Junyi Chen, Sijia Lai, Siqu Long, Hyunsuk Chung, Soyeon Caren Han|Fortify Edge, Sydney, NSW, Australia; The University of Sydney & The University of Western Australia, Perth, WA, Australia; The University of Sydney, Sydney, NSW, Australia|We propose a PiggyBack, a Visual Question Answering platform that allows users to apply the state-of-the-art visual-language pretrained models easily. The PiggyBack supports the full stack of visual question answering tasks, specifically data processing, model fine-tuning, and result visualisation. We integrate visual-language models, pretrained by HuggingFace, an open-source API platform of deep learning technologies; however, it cannot be runnable without programming skills or deep learning understanding. Hence, our PiggyBack supports an easy-to-use browser-based user interface with several deep learning visual language pretrained models for general users and domain experts. The PiggyBack includes the following benefits: Free availability under the MIT License, Portability due to web-based and thus runs on almost any platform, A comprehensive data creation and processing technique, and ease of use on deep learning-based visual language pretrained models. The demo video is available on YouTube and can be found at https://youtu.be/iz44RZ1lF4s.|我们提出了 PiggyBack，一个可视化问题回答平台，允许用户轻松应用最先进的可视化语言预先训练的模型。PiggyBack 支持完整的可视化问题回答任务堆栈，特别是数据处理、模型微调和结果可视化。我们集成了可视化语言模型，这些模型是由 HuggingFace (深度学习技术的开源 API 平台)预先训练的; 然而，如果没有编程技能或深度学习理解，它就不能运行。因此，PiggyBack 支持一个易于使用的基于浏览器的用户界面，为一般用户和领域专家提供了几个深度学习可视化语言预先训练的模型。PiggyBack 包括以下好处: 麻省理工学院许可证下的免费可用性，基于网络的便携性，因此可以在几乎任何平台上运行，一个全面的数据创建和处理技术，易于使用基于深度学习的可视化语言预训模型。演示视频可以在 YouTube 上找到，也可以在 https://youtu.be/iz44rz1lf4s 上找到。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=PiggyBack:+Pretrained+Visual+Question+Answering+Environment+for+Backing+up+Non-deep+Learning+Professionals)|0| +|[Web of Conferences: A Conference Knowledge Graph](https://doi.org/10.1145/3539597.3573024)|Shuo Yu, Ciyuan Peng, Chengchuan Xu, Chen Zhang, Feng Xia|Dalian University of Technology, Dalian, China; RMIT University, Melbourne, Australia; Chengdu Neusoft University, Chengdu, China|Academic conferences have been proven to be significant in facilitating academic activities. To promote information retrieval specific to academic conferences, building complete, systematic, and professional conference knowledge graphs is a crucial task. However, many related systems mainly focus on general knowledge of overall academic information or concentrate services on specific domains. Aiming at filling this gap, this work demonstrates a novel conference knowledge graph, namely Web of Conferences. The system accommodates detailed conference profiles, conference ranking lists, intelligent conference queries, and personalized conference recommendations. Web of Conferences supports detailed conference information retrieval while providing the ranking of conferences based on the most recent data. Conference queries in the system can be implemented via precise search or fuzzy search. Then, according to users' query conditions, personalized conference recommendations are available. Web of Conferences is demonstrated with a user-friendly visualization interface and can be served as a useful information retrieval system for researchers.|事实证明，学术会议在促进学术活动方面具有重要意义。为了推广学术会议的信息检索，建立完整、系统和专业的会议知识图表是一项至关重要的任务。然而，许多相关的系统主要集中于整体学术信息的一般知识或集中于特定领域的服务。为了填补这一空白，本文展示了一个新颖的会议知识图，即会议网络。该系统可容纳详细的会议概况、会议排名列表、智能会议查询和个性化会议推荐。会议网站支持详细的会议信息检索，同时提供基于最新数据的会议排名。系统中的会议查询可以通过精确搜索或模糊搜索来实现。然后，根据用户的查询条件，提供个性化的会议推荐。会议网络是一个用户友好的可视化界面，可以作为一个有用的信息检索系统供研究人员使用。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Web+of+Conferences:+A+Conference+Knowledge+Graph)|0| +|[Developing and Evaluating Graph Counterfactual Explanation with GRETEL](https://doi.org/10.1145/3539597.3573026)|Mario Alfonso PradoRomero, Bardh Prenkaj, Giovanni Stilo|University of L'Aquila, L'Aquila, Italy; Gran Sasso Science Institute, L'Aquila, Italy; Sapienza University of Rome, Rome, Italy|The black-box nature and the lack of interpretability detract from constant improvements in Graph Neural Networks (GNNs) performance in social network tasks like friendship prediction and community detection. Graph Counterfactual Explanation (GCE) methods aid in understanding the prediction of GNNs by generating counterfactual examples that promote trustworthiness, debiasing, and privacy in social networks. Alas, the literature on GCE lacks standardised definitions, explainers, datasets, and evaluation metrics. To bridge the gap between the performance and interpretability of GNNs in social networks, we discuss GRETEL, a unified framework for GCE methods development and evaluation. We demonstrate how GRETEL comes with fully extensible built-in components that allow users to define ad-hoc explainer methods, generate synthetic datasets, implement custom evaluation metrics, and integrate state-of-the-art prediction models.|黑盒子的特性和缺乏可解释性使得图神经网络(GNN)在社交网络任务(如友谊预测和社区检测)中的性能不断提高。图形反事实解释(GCE)方法通过生成反事实例子来帮助理解 GNN 的预测，这些反事实例子提高了社交网络中的可信度、消除偏见和隐私。遗憾的是，关于 GCE 的文献缺乏标准化的定义、解释者、数据集和评估指标。为了弥合社交网络中 GNN 的性能和可解释性之间的差距，我们讨论 GRETEL，一个用于 GCE 方法开发和评估的统一框架。我们演示 GRETEL 如何带有完全可扩展的内置组件，允许用户定义特别解释器方法，生成合成数据集，实现自定义评估指标，并集成最先进的预测模型。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Developing+and+Evaluating+Graph+Counterfactual+Explanation+with+GRETEL)|0| +|[DistriBayes: A Distributed Platform for Learning, Inference and Attribution on Large Scale Bayesian Network](https://doi.org/10.1145/3539597.3573028)|Yi Ding, Jun Zhou, Qing Cui, Lin Wang, Mengqi Zhang, Yang Dong|Ant Group, Beijing, China; Zhejiang University & Ant Group, Hangzhou, China|To improve the marketing performance in the financial scenario, it is necessary to develop a trustworthy model to analyze and select promotion-sensitive customers. Bayesian Network (BN) is suitable for this task because of its interpretability and flexibility, but it usually suffers the exponentially growing computation complexity as the number of nodes grows. To tackle this problem, we present a comprehensive distributed platform named DistriBayes, which can efficiently learn, infer and attribute on a large-scale BN all-in-one platform. It implements several score-based structure learning methods, loopy belief propagation with backdoor adjustment for inference, and a carefully optimized search procedure for attribution. Leveraging the distributed cluster, DistriBayes can finish the learning and attribution on Bayesian Network with hundreds of nodes and millions of samples in hours.|为了提高财务情景下的营销绩效，有必要建立一个可信赖的模型来分析和选择促销敏感的客户。贝氏网路因其可解释性和灵活性而适合这项任务，但随着节点数目的增加，计算复杂度通常会呈指数级增长。为了解决这一问题，提出了一个综合分布式平台 DireBayes，该平台可以在大规模的 BN 一体化平台上进行高效的学习、推理和归属。它实现了多种基于分数的结构化学习方法、带有后门调整的循环信念传播推理以及精心优化的属性搜索过程。通过利用分布式集群，distyes 可以在数小时内完成数百个节点和数百万个样本的贝氏网路学习和归属。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=DistriBayes:+A+Distributed+Platform+for+Learning,+Inference+and+Attribution+on+Large+Scale+Bayesian+Network)|0| +|[SimSumIoT: A Platform for Simulating the Summarisation from Internet of Things](https://doi.org/10.1145/3539597.3573042)|Wei Emma Zhang, Adnan Mahmood, Lixin Deng, Minhao Zhu|The University of Adelaide, Adelaide, Australia; Macquarie University, Sydney, Australia|Summarising from the Web could be formed as a problem of multi-document Summarisaiton (MDS) from multiple sources. In contrast to the current MDS problem that involves working on benchmark datasets which provide well clustered set of documents, we envisage to build a pipeline for content Summarisaiton from the Web, but narrow down to the Social Internet of Things (SIoT) paradigm, starting at data collection from the IoT objects, then applying natural language processing techniques for grouping and summarising the data, to distributing summaries back to the IoT objects. In this paper, we present our simulation tool, SimSumIoT, that simulates the process of data sharing, receiving, clustering, and Summarisaiton. A Web-based interface is developed for this purpose allowing users to visualize the process through a set of interactions. The Web interface is accessible via http://simsumlot.tk.|从 Web 上进行摘要可以形成一个多文档摘要(MDS)问题。与目前的 MDS 问题相反，我们设想建立一个管道，从网络内容摘要，但缩小到物联网(SIoT)范式，从物联网对象的数据收集开始，然后应用自然语言处理技术分组和总结数据，分发摘要回物联网对象。在本文中，我们提出了我们的模拟工具，模拟数据共享，接收，集群和 Summarisaiton 的过程。为此开发了一个基于 Web 的界面，允许用户通过一组交互将流程可视化。网页界面可透过 http://simsumlot.tk 进入。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=SimSumIoT:+A+Platform+for+Simulating+the+Summarisation+from+Internet+of+Things)|0| +|[AntTS: A Toolkit for Time Series Forecasting in Industrial Scenarios](https://doi.org/10.1145/3539597.3573030)|Jianping Wei, Zhibo Zhu, Ziqi Liu, Zhiqiang Zhang, Jun Zhou|Ant Group, Hangzhou, China; Zhejiang University & Ant Group, Hangzhou, China|Time series forecasting is an important ingredient in the intelligence of business and decision processes. In industrial scenarios, the time series of interest are mostly macroscopic time series that are aggregated from microscopic time series, e.g., the retail sales is aggregated from the sales of different goods, and that are also intervened by certain treatments on the microscopic individuals, e.g., issuing discount coupons on some goods to increase the retail sales. These characteristics are not considered in existing toolkits, which just focus on the "natural" time series forecasting that predicts the future value based on historical data, regardless of the impact of treatments. In this paper, we present AntTS, a time series toolkit paying more attention on the forecasting of the macroscopic time series with underlying microscopic time series and certain treatments, besides the "natural" time series forecasting. AntTS consists of three decoupled modules, namely Clustering module, Natural Forecasting module, and Effect module, which are utilized to study the homogeneous groups of microscopic individuals, the "natural" time series forecasting of homogeneous groups, and the treatment effect estimation of homogeneous groups. With the combinations of different modules, it can exploit the microscopic individuals and the interventions on them, to help the forecasting of macroscopic time series. We show that AntTS helps address many typical tasks in the industry.|时间序列预测是商业和决策过程智能化的重要组成部分。在工业方案中，利息的时间序列大多是宏观时间序列，由微观时间序列聚合而成，例如，零售销售是由不同商品的销售聚合而成，同时也受到某些对微观个体的干预，例如，发行某些商品的折扣券以增加零售销售。现有的工具包没有考虑这些特征，它们只关注基于历史数据预测未来价值的“自然”时间序列预测，而不考虑治疗的影响。本文提出了一个时间序列工具包，除了“自然”时间序列预测之外，它更注重对宏观时间序列的预测，包括底层的微观时间序列和一定的处理方法。AntTS 由聚类模块、自然预测模块和效应模块三个解耦模块组成，用于研究微观个体的同质群体、同质群体的“自然”时间序列预测以及同质群体的治疗效果估计。通过不同模块的组合，可以利用微观个体以及对微观个体的干预，对宏观时间序列进行预测。我们展示了 Ant TS 可以帮助解决行业中的许多典型任务。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=AntTS:+A+Toolkit+for+Time+Series+Forecasting+in+Industrial+Scenarios)|0| |[Unsupervised Question Duplicate and Related Questions Detection in e-learning platforms](https://doi.org/10.1145/3539597.3573035)|Maksimjeet Chowdhary, Sanyam Goyal, Venktesh V, Mukesh K. Mohania, Vikram Goyal|Indraprastha Institute of Information Technology, Delhi, India; IIIT Delhi, Delhi, India|Online learning platforms provide diverse questions to gauge the learners' understanding of different concepts. The repository of questions has to be constantly updated to ensure a diverse pool of questions to conduct assessments for learners. However, it is impossible for the academician to manually skim through the large repository of questions to check for duplicates when onboarding new questions from external sources. Hence, we propose a tool QDup in this paper that can surface near-duplicate and semantically related questions without any supervised data. The proposed tool follows an unsupervised hybrid pipeline of statistical and neural approaches for incorporating different nuances in similarity for the task of question duplicate detection. We demonstrate that QDup can detect near-duplicate questions and also suggest related questions for practice with remarkable accuracy and speed from a large repository of questions. The demo video of the tool can be found at https://www.youtube.com/watch?v=loh0_-7XLW4.|在线学习平台提供各种各样的问题来衡量学习者对不同概念的理解。问题库必须不断更新，以确保为学习者进行评估的问题库多样化。但是，当从外部来源获得新的问题时，学者不可能手动浏览大量的问题库来检查重复的问题。因此，本文提出了一个 QDup 工具，它可以在没有任何监督数据的情况下将近重复和语义相关的问题表面化。提议的工具遵循统计学和神经学方法的无监督混合管道，以便在相似性方面纳入不同的细微差别，从而完成问题重复检测任务。我们证明了 QDup 可以检测接近重复的问题，并且可以从大量的问题库中以显著的准确性和速度提出相关的问题供实践使用。该工具的演示视频可以在 https://www.youtube.com/watch?v=loh0_-7xlw4找到。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Unsupervised+Question+Duplicate+and+Related+Questions+Detection+in+e-learning+platforms)|0| -|["Just To See You Smile": SMILEY, a Voice-Guided GUY GAN](https://doi.org/10.1145/3539597.3573031)|Qi Yang, Christos Tzelepis, Sergey Nikolenko, Ioannis Patras, Aleksandr Farseev|Queen Mary University of London, London, United Kingdom; ITMO University, Saint Petersburg, Russian Fed.; SoMin.ai Research, London, United Kingdom|In this technical demonstration, we present SMILEY, a voice-guided virtual assistant. The system utilizes a deep neural architecture ContraCLIP to manipulate facial attributes using voice instructions, allowing for deeper speaker engagement and smoother customer experience when being used in the "virtual concierge" scenario. We validate the effectiveness of SMILEY and ContraCLIP via a successful real-world case study in Singapore and a large-scale quantitative evaluation.|在这个技术演示中，我们介绍了 SMILEY，一个语音引导的虚拟助手。该系统利用深层神经结构 ContraCLIP，通过语音指令操纵面部属性，当在“虚拟礼宾”场景中使用时，允许更深入的说话者参与和更顺畅的客户体验。通过在新加坡成功的实际案例研究和大规模的定量评估，我们验证了 SMILEY 和 ContraCLIP 的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q="Just+To+See+You+Smile":+SMILEY,+a+Voice-Guided+GUY+GAN)|0| +|["Just To See You Smile": SMILEY, a Voice-Guided GUY GAN](https://doi.org/10.1145/3539597.3573031)|Qi Yang, Christos Tzelepis, Sergey Nikolenko, Ioannis Patras, Aleksandr Farseev|SoMin.ai Research, London, United Kingdom; Queen Mary University of London, London, United Kingdom; ITMO University, Saint Petersburg, Russian Fed.|In this technical demonstration, we present SMILEY, a voice-guided virtual assistant. The system utilizes a deep neural architecture ContraCLIP to manipulate facial attributes using voice instructions, allowing for deeper speaker engagement and smoother customer experience when being used in the "virtual concierge" scenario. We validate the effectiveness of SMILEY and ContraCLIP via a successful real-world case study in Singapore and a large-scale quantitative evaluation.|在这个技术演示中，我们介绍了 SMILEY，一个语音引导的虚拟助手。该系统利用深层神经结构 ContraCLIP，通过语音指令操纵面部属性，当在“虚拟礼宾”场景中使用时，允许更深入的说话者参与和更顺畅的客户体验。通过在新加坡成功的实际案例研究和大规模的定量评估，我们验证了 SMILEY 和 ContraCLIP 的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q="Just+To+See+You+Smile":+SMILEY,+a+Voice-Guided+GUY+GAN)|0| |[DOCoR: Document-level OpenIE with Coreference Resolution](https://doi.org/10.1145/3539597.3573038)|Shan Jie Yong, Kuicai Dong, Aixin Sun|Nanyang Technological University, Singapore, Singapore|Open Information Extraction (OpenIE) extracts relational fact tuples in the form of from text. Most existing OpenIE solutions operate at sentence level and extract relational tuples solely from a sentence. However, many sentences exist as a part of paragraph or a document, where coreferencing is common. In this demonstration, we present a system which refines the semantic tuples generated by OpenIE with the aid of a coreference resolution tool. Specifically, all coreferential mentions across the entire document are identified and grouped into coreferential clusters. Objects and subjects in the extracted tuples from OpenIE which match any coreferential mentions are then resolved with a suitable representative term. In this way, our system is able to resolve both anaphoric and cataphoric references, to achieve Document-level OpenIE with Coreference Resolution (DOCoR). The demonstration video can be viewed at https://youtu.be/o9ZSWCBvlDs|开放式信息抽取(OpenIE)从文本中提取关系事实元组，其格式为 < subject，relations，object > 。大多数现有的 OpenIE 解决方案都是在句子级别上运行的，并且只从一个句子中提取关系元组。然而，许多句子作为段落或文档的一部分存在，其中共同参照是常见的。在这个演示中，我们提出了一个系统，该系统借助于一个共引用解析工具来提炼 OpenIE 生成的语义元组。具体来说，整个文档中的所有共引用提及都被识别并分组为共引用集群。然后，从 OpenIE 中提取的元组中匹配任何相关提及的对象和主题用一个合适的代表性术语进行解析。通过这种方式，我们的系统能够同时解析照应和照应两种指称，从而实现具有指称解析(DOCoR)的文档级 OpenIE。市民可于 https://youtu.be/o9zswcbvlds 浏览示范短片|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=DOCoR:+Document-level+OpenIE+with+Coreference+Resolution)|0| |[Classification of Different Participating Entities in the Rise of Hateful Content in Social Media](https://doi.org/10.1145/3539597.3572985)|Mithun Das|Indian Institute of Technology Kharagpur, Kharagpur, India|Hateful content is a growing concern across different platforms, whether it is a moderated platform or an unmoderated platform. The public expression of hate speech encourages the devaluation of minority members. It has some consequences in the real world as well. In such a scenario, it is necessary to design AI systems that could detect such harmful entities/elements in online social media and take cautionary actions to mitigate the risk/harm they cause to society. The way individuals disseminate content on social media platforms also deviates. The content can be in the form of texts, images, videos, etc. Hence hateful content in all forms should be detected, and further actions should be taken to maintain the civility of the platform. We first introduced two published works addressing the challenges of detecting low-resource multilingual abusive speech and hateful user detection. Finally, we discuss our ongoing work on multimodal hateful content detection.|不管是一个有节制的平台还是一个没有节制的平台，仇恨内容都越来越受到不同平台的关注。公开发表仇恨言论鼓励贬低少数群体成员。它在现实世界中也有一些后果。在这种情况下，有必要设计人工智能系统，以便能够发现在线社交媒体中的这种有害实体/元素，并采取谨慎行动，减轻它们对社会造成的风险/损害。个人在社交媒体平台上传播内容的方式也有偏差。内容可以是文本、图像、视频等形式。因此，应当发现各种形式的仇恨内容，并采取进一步行动维护该平台的文明。我们首先介绍了两个已发表的工作，解决检测低资源多语言辱骂性言论和仇恨用户检测的挑战。最后，我们讨论了我们正在进行的多通道仇恨内容检测工作。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Classification+of+Different+Participating+Entities+in+the+Rise+of+Hateful+Content+in+Social+Media)|0| |[Generalizing Graph Neural Network across Graphs and Time](https://doi.org/10.1145/3539597.3572986)|Zhihao Wen|Singapore Management University, Singapore, Singapore|Graph-structured data widely exist in diverse real-world scenarios, analysis of these graphs can uncover valuable insights about their respective application domains. However, most previous works focused on learning node representation from a single fixed graph, while many real-world scenarios require representations to be quickly generated for unseen nodes, new edges, or entirely new graphs. This inductive ability is essential for high-throughtput machine learning systems. However, this inductive graph representation problem is quite difficult, compared to the transductive setting, for that generalizing to unseen nodes requires new subgraphs containing the new nodes to be aligned to the neural network trained already. Meanwhile, following a message passing framework, graphneural network (GNN) is an inductive and powerful graph representation tool. We further explore inductive GNN from more specific perspectives: (1) generalizing GNN across graphs, in which we tackle with the problem of semi-supervised node classification across graphs; (2) generalizing GNN across time, in which we mainly solve the problem of temporal link prediction; (3) generalizing GNN across tasks; (4) generalizing GNN across locations.|图结构化数据广泛存在于各种不同的现实场景中，对这些图的分析可以揭示关于它们各自应用领域的有价值的见解。然而，大多数以前的工作集中在从一个固定的图学习节点表示，而许多现实世界的场景需要表示为看不见的节点，新的边，或完全新的图快速生成。这种归纳能力对于高吞吐量的机器学习系统是必不可少的。然而，这个归纳图表示问题是相当困难的，相对于传导设置，因为这个泛化到看不见的节点需要新的子图包含新的节点对齐的神经网络已经训练。同时，遵循消息传递框架的图形神经网络(GNN)是一种归纳的、功能强大的图形表示工具。我们进一步从更具体的角度探索归纳 GNN: (1)跨图泛化 GNN，解决跨图的半监督节点分类问题; (2)跨时间泛化 GNN，主要解决时间链接预测问题; (3)跨任务泛化 GNN; (4)跨位置泛化 GNN。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Generalizing+Graph+Neural+Network+across+Graphs+and+Time)|0| @@ -164,24 +164,24 @@ |[From Classic GNNs to Hyper-GNNs for Detecting Camouflaged Malicious Actors](https://doi.org/10.1145/3539597.3572989)|Venus Haghighi|Macquarie University, Sydney, Australia|Graph neural networks (GNNs), which extend deep learning models to graph-structured data, have achieved great success in many applications such as detecting malicious activities. However, GNN-based models are vulnerable to camouflage behavior of malicious actors, i.e., the performance of existing GNN-based models has been hindered significantly. In this research proposal, we follow two research directions to address this challenge. One direction focuses on enhancing the existing GNN-based models and enabling them to identify both camouflaged and non-camouflaged malicious actors. In this regard, we propose to explore an adaptive aggregation strategy, which empowers GNN-based models to handle camouflage behavior of fraudsters. The other research direction concentrates on leveraging hypergraph neural networks (hyper-GNNs) to learn nodes' representation for more effective identification of camouflaged malicious actors.|将深度学习模型扩展到图结构数据的图神经网络(GNN)在检测恶意行为等许多应用中取得了巨大的成功。然而，基于 GNN 的模型容易受到恶意行为者的伪装行为的影响，即现有的基于 GNN 的模型的性能受到了严重的阻碍。在这项研究建议中，我们遵循两个研究方向来应对这一挑战。其中一个方向侧重于增强现有的基于 GNN 的模型，并使它们能够识别伪装和非伪装的恶意行为者。在这方面，我们提出了一种自适应聚合策略，该策略使得基于 GNN 的模型能够处理欺诈者的伪装行为。另一个研究方向集中在利用超图神经网络学习节点的表示，以更有效地识别伪装的恶意行为者。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=From+Classic+GNNs+to+Hyper-GNNs+for+Detecting+Camouflaged+Malicious+Actors)|0| |[Efficient Graph Learning for Anomaly Detection Systems](https://doi.org/10.1145/3539597.3572990)|Falih Gozi Febrinanto|Federation University Australia, Ballarat, Australia|Anomaly detection plays a significant role in preventing from detrimental effects of abnormalities. It brings many benefits in real-world sectors ranging from transportation, finance to cybersecurity. In reality, millions of data do not stand independently, but they might be connected to each other and form graph or network data. A more advanced technique, named graph anomaly detection, is required to model that data type. The current works of graph anomaly detection have achieved state-of-the-art performance compared to regular anomaly detection. However, most models ignore the efficiency aspect, leading to several problems like technical bottlenecks. This project mainly focuses on improving the efficiency aspect of graph anomaly detection while maintaining its performance.|异常检测在预防异常的有害影响方面起着重要作用。它给现实世界带来了许多好处，从交通运输、金融到网络安全。实际上，数以百万计的数据并不是独立存在的，但它们可能相互连接，形成图形或网络数据。需要一种更先进的技术，称为图形异常检测，来对数据类型进行建模。目前图形异常检测的作品与普通异常检测相比已经达到了最先进的水平。然而，大多数模型忽略了效率方面，导致了一些问题，如技术瓶颈。这个项目主要集中在提高图形异常检测的效率方面，同时保持其性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Efficient+Graph+Learning+for+Anomaly+Detection+Systems)|0| |[Self-supervision and Controlling Techniques to Improve Counter Speech Generation](https://doi.org/10.1145/3539597.3572991)|Punyajoy Saha|Indian Institute of Technology Kharagpur, Kharagpur, India|Hate speech is a challenging problem in today's online social media. One of the current solutions followed by different social media platforms is detecting hate speech using human-in-the-loop approaches. After detection, they moderate such hate speech by deleting the posts or suspending the users. While this approach can be a short-term solution for reducing the spread of hate, many researchers argue that it stifles freedom of expression. An alternate strategy that does not hamper freedom of expression is counterspeech. Recently, many studies have tried to create generation models to assist counter speakers by providing counterspeech suggestions for combating the explosive proliferation of online hate. This pipeline has two major challenges 1) How to improve the performance of generation without a large-scale dataset since building the dataset is costly 2) How to add control in the counter speech generation to make it more personalized. In this paper, we present our published and proposed research aimed at solving these two challenges.|仇恨言论在当今的网络社交媒体中是一个具有挑战性的问题。目前不同社交媒体平台采用的解决方案之一是使用人在线的方法来检测仇恨言论。在被发现后，他们通过删除帖子或暂停用户来缓和这种仇恨言论。虽然这种方法可以作为减少仇恨传播的短期解决办法，但许多研究人员认为，它扼杀了言论自由。另一种不妨碍言论自由的策略是反言论。最近，许多研究试图建立生成模型，通过提供反言论建议来打击网络仇恨的爆炸性扩散，从而帮助反言论者。该流水线面临两大挑战: 1)如何在没有大规模数据集的情况下提高生成性能，因为建立数据集的成本较高; 2)如何在反语音生成中增加控制，使其更加个性化。在本文中，我们介绍了我们发表和提出的研究，旨在解决这两个挑战。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Self-supervision+and+Controlling+Techniques+to+Improve+Counter+Speech+Generation)|0| -|[Knowledge-Augmented Methods for Natural Language Processing](https://doi.org/10.1145/3539597.3572720)|Chenguang Zhu, Yichong Xu, Xiang Ren, Bill Yuchen Lin, Meng Jiang, Wenhao Yu|University of Notre Dame, Notre Dame, IN, USA; University of Southern California, Los Angeles, CA, USA; Microsoft Cognitive Services Research, Bellevue, WA, USA|Knowledge in natural language processing (NLP) has been a rising trend especially after the advent of large scale pre-trained models. NLP models with attention to knowledge can i) access unlimited amount of external information; ii) delegate the task of storing knowledge from its parameter space to knowledge sources; iii) obtain up-to-date information; iv) make prediction results more explainable via selected knowledge. In this tutorial, we will introduce the key steps in integrating knowledge into NLP, including knowledge grounding from text, knowledge representation and fusing. In addition, we will introduce recent state-of-the-art applications in fusing knowledge into language understanding, language generation and commonsense reasoning.|自然语言处理(NLP)中的知识已经成为一种新兴的趋势，特别是在大规模预训练模型出现之后。注重知识的 NLP 模型可以 i)访问无限量的外部信息; ii)将存储知识的任务从其参数空间委托给知识源; iii)获取最新信息; iv)通过选择的知识使预测结果更加可解释。在本教程中，我们将介绍将知识整合到自然语言处理中的关键步骤，包括从文本的知识基础，知识表示和融合。此外，我们将介绍最新的技术应用在融合知识到语言理解，语言生成和常识推理。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Knowledge-Augmented+Methods+for+Natural+Language+Processing)|0| +|[Knowledge-Augmented Methods for Natural Language Processing](https://doi.org/10.1145/3539597.3572720)|Chenguang Zhu, Yichong Xu, Xiang Ren, Bill Yuchen Lin, Meng Jiang, Wenhao Yu|Microsoft Cognitive Services Research, Bellevue, WA, USA; University of Notre Dame, Notre Dame, IN, USA; University of Southern California, Los Angeles, CA, USA|Knowledge in natural language processing (NLP) has been a rising trend especially after the advent of large scale pre-trained models. NLP models with attention to knowledge can i) access unlimited amount of external information; ii) delegate the task of storing knowledge from its parameter space to knowledge sources; iii) obtain up-to-date information; iv) make prediction results more explainable via selected knowledge. In this tutorial, we will introduce the key steps in integrating knowledge into NLP, including knowledge grounding from text, knowledge representation and fusing. In addition, we will introduce recent state-of-the-art applications in fusing knowledge into language understanding, language generation and commonsense reasoning.|自然语言处理(NLP)中的知识已经成为一种新兴的趋势，特别是在大规模预训练模型出现之后。注重知识的 NLP 模型可以 i)访问无限量的外部信息; ii)将存储知识的任务从其参数空间委托给知识源; iii)获取最新信息; iv)通过选择的知识使预测结果更加可解释。在本教程中，我们将介绍将知识整合到自然语言处理中的关键步骤，包括从文本的知识基础，知识表示和融合。此外，我们将介绍最新的技术应用在融合知识到语言理解，语言生成和常识推理。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Knowledge-Augmented+Methods+for+Natural+Language+Processing)|0| |[Hate Speech: Detection, Mitigation and Beyond](https://doi.org/10.1145/3539597.3572721)|Punyajoy Saha, Mithun Das, Binny Mathew, Animesh Mukherjee|Indian Institute of Technology, Kharagpur, Kharagpur, India|Social media sites such as Twitter and Facebook have connected billions of people and given the opportunity to the users to share their ideas and opinions instantly. That being said, there are several negative consequences as well such as online harassment, trolling, cyber-bullying, fake news, and hate speech. Out of these, hate speech presents a unique challenge as it is deeply engraved into our society and is often linked with offline violence. Social media platforms rely on human moderators to identify hate speech and take necessary action. However, with the increase in online hate speech, these platforms are turning toward automated hate speech detection and mitigation systems. This shift brings several challenges to the plate, and hence, is an important avenue to explore for the computation social science community. In this tutorial, we present an exposition of hate speech detection and mitigation in three steps. First, we describe the current state of research in the hate speech domain, focusing on different hate speech detection and mitigation systems that have developed over time. Next, we highlight the challenges that these systems might carry like bias and the lack of transparency. The final section concretizes the path ahead, providing clear guidelines for the community working in hate speech and related domains. We also outline the open challenges and research directions for interested researchers.|Twitter 和 Facebook 等社交媒体网站已经连接了数十亿人，并为用户提供了即时分享想法和观点的机会。尽管如此，还是存在一些负面后果，比如网络骚扰、网络钓鱼、网络欺凌、假新闻和仇恨言论。除此之外，仇恨言论是一个独特的挑战，因为它深深地植根于我们的社会，而且往往与线下暴力联系在一起。社交媒体平台依赖人类管理员来识别仇恨言论并采取必要的行动。然而，随着在线仇恨言论的增加，这些平台正在转向自动仇恨言论检测和缓解系统。这种转变给板块带来了一些挑战，因此，是计算社会科学界探索的一个重要途径。在本教程中，我们将分三个步骤阐述仇恨语音检测和缓解。首先，我们描述了仇恨语音领域的研究现状，重点介绍了随着时间推移而发展起来的不同的仇恨语音检测和缓解系统。接下来，我们强调这些系统可能带来的挑战，如偏见和缺乏透明度。最后一部分具体化了前进的道路，为从事仇恨言论和相关领域工作的社区提供了明确的指导方针。我们还概述了开放的挑战和研究方向感兴趣的研究人员。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Hate+Speech:+Detection,+Mitigation+and+Beyond)|0| |[Natural and Artificial Dynamics in GNNs: A Tutorial](https://doi.org/10.1145/3539597.3572726)|Dongqi Fu, Zhe Xu, Hanghang Tong, Jingrui He|University of Illinois at Urbana-Champaign, Urbana, IL, USA|In the big data era, the relationship between entities becomes more complex. Therefore, graph (or network) data attracts increasing research attention for carrying complex relational information. For a myriad of graph mining/learning tasks, graph neural networks (GNNs) have been proven as effective tools for extracting informative node and graph representations, which empowers a broad range of applications such as recommendation, fraud detection, molecule design, and many more. However, real-world scenarios bring pragmatic challenges to GNNs. First, the input graphs are evolving, i.e., the graph structure and node features are time-dependent. Integrating temporal information into the GNNs to enhance their representation power requires additional ingenious designs. Second, the input graphs may be unreliable, noisy, and suboptimal for a variety of downstream graph mining/learning tasks. How could end-users deliberately modify the given graphs (e.g., graph topology and node features) to boost GNNs' utility (e.g., accuracy and robustness)? Inspired by the above two kinds of dynamics, in this tutorial, we focus on topics of natural dynamics and artificial dynamics in GNNs and introduce the related works systematically. After that, we point out some promising but under-explored research problems in the combination of these two dynamics. We hope this tutorial could be beneficial to researchers and practitioners in areas including data mining, machine learning, and general artificial intelligence.|在大数据时代，实体之间的关系变得更加复杂。因此，图形(或网络)数据由于承载复杂的关系信息而越来越受到研究者的关注。对于大量的图形挖掘/学习任务，图形神经网络(GNN)已被证明是提取信息节点和图表示的有效工具，它赋予了广泛的应用，如推荐、欺诈检测、分子设计等等。然而，真实世界的场景给 GNN 带来了实用的挑战。首先，输入图是演化的，即图的结构和节点特征是依赖于时间的。将时间信息整合到 GNN 中以增强它们的表示能力需要额外的巧妙设计。其次，对于各种下游图挖掘/学习任务，输入图可能是不可靠的、有噪声的和次优的。最终用户如何故意修改给定的图(例如，图形拓扑和节点特性)以提高 GNN 的效用(例如，准确性和鲁棒性) ？受上述两种动力学的启发，本教程重点讨论了 GNN 中的自然动力学和人工动力学，并系统地介绍了相关的工作。然后，我们指出了这两种动力学相结合的一些有前途但尚未得到充分探索的研究问题。我们希望本教程能够对数据挖掘、机器学习和一般人工智能等领域的研究人员和从业人员有所帮助。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Natural+and+Artificial+Dynamics+in+GNNs:+A+Tutorial)|0| |[Data Democratisation with Deep Learning: The Anatomy of a Natural Language Data Interface](https://doi.org/10.1145/3539597.3572728)|George KatsogiannisMeimarakis, Mike Xydas, Georgia Koutrika|Athena Research Center, Athens, Greece|In the age of the Digital Revolution, almost all human activities, from industrial and business operations to medical and academic research, are reliant on the constant integration and utilisation of ever-increasing volumes of data. However, the explosive volume and complexity of data makes data querying and exploration challenging even for experts, and makes the need to democratise the access to data, even for non-technical users, all the more evident. It is time to lift all technical barriers, by empowering users to access relational databases through conversation. We consider 3 main research areas that a natural language data interface is based on: Text-to-SQL, SQL-to-Text, and Data-to-Text. The purpose of this tutorial is a deep dive into these areas, covering state-of-the-art techniques and models, and explaining how the progress in the deep learning field has led to impressive advancements. We will present benchmarks that sparked research and competition, and discuss open problems and research opportunities with one of the most important challenges being the integration of these 3 research areas into one conversational system.|在数字革命时代，几乎所有的人类活动，从工业和商业运作到医学和学术研究，都依赖于不断增长的数据量的不断整合和利用。然而，数据的爆炸性数量和复杂性使得数据查询和探索甚至对专家来说都具有挑战性，并使得即使对非技术用户来说也需要使数据的获取更加民主化，这一点更加明显。现在是消除所有技术障碍的时候了，通过授权用户通过对话访问关系数据库。我们考虑了自然语言数据接口所基于的3个主要研究领域: 文本到 SQL、 SQL 到文本和数据到文本。本教程的目的是深入这些领域，涵盖了最先进的技术和模型，并解释了深度学习领域的进展如何导致了令人印象深刻的进步。我们将展示引发研究和竞争的基准，并讨论开放性问题和研究机会，其中最重要的挑战之一是将这三个研究领域整合到一个会话系统中。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Data+Democratisation+with+Deep+Learning:+The+Anatomy+of+a+Natural+Language+Data+Interface)|0| -|[Next-generation Challenges of Responsible Data Integration](https://doi.org/10.1145/3539597.3572727)|Fatemeh Nargesian, Abolfazl Asudeh, H. V. Jagadish|Univ Rochester, Rochester, NY 14627 USA; Univ Illinois, Chicago, IL USA; Univ Michigan, Ann Arbor, MI USA|Data integration has been extensively studied by the data management community and is a core task in the data pre-processing step of ML pipelines. When the integrated data is used for analysis and model training, responsible data science requires addressing concerns about data quality and bias. We present a tutorial on data integration and responsibility, highlighting the existing efforts in responsible data integration along with research opportunities and challenges. In this tutorial, we encourage the community to audit data integration tasks with responsibility measures and develop integration techniques that optimize the requirements of responsible data science. We focus on three critical aspects: (1) the requirements to be considered for evaluating and auditing data integration tasks for quality and bias; (2) the data integration tasks that elicit attention to data responsibility measures and methods to satisfy these requirements; and, (3) techniques, tasks, and open problems in data integration that help achieve data responsibility.|数据集成已经被数据管理界广泛研究，并且是机器学习管道数据预处理的核心任务。当集成数据用于分析和模型训练时，负责任的数据科学需要解决关于数据质量和偏差的问题。我们提供了一个关于数据集成和责任的教程，强调了在负责任的数据集成方面的现有努力以及研究机会和挑战。在本教程中，我们鼓励社区使用责任度量来审计数据集成任务，并开发优化责任数据科学需求的集成技术。我们集中在三个关键的方面: (1)评估和审计数据集成任务的质量和偏差需要考虑的要求; (2)引起注意的数据集成任务的数据责任措施和方法，以满足这些要求; 和(3)技术，任务，以及数据集成中帮助实现数据责任的开放问题。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Next-generation+Challenges+of+Responsible+Data+Integration)|0| -|[Integrity 2023: Integrity in Social Networks and Media](https://doi.org/10.1145/3539597.3572704)|Lluís Garcia Pueyo, Panayiotis Tsaparas, Prathyusha Senthil Kumar, Timos Sellis, Paolo Papotti, Sibel Adali, Giuseppe Manco, Tudor Trufinescu, Gireeja Ranade, James Verbus, Mehmet N. Tek, Anthony McCosker|Archimedes / Athena Research Center, Athens, Greece; Google, Redwood City, CA, USA; Swinburne Social Innovation Research Institute, Melbourne, VIC, Australia; EURECOM, Biot, France; University of Ioannina, Ioannina, Greece; LinkedIn, Sunnyvale, CA, USA; ICAR-CNR, Rende, Italy; Meta, Bellevue, WA, USA; Meta, Menlo Park, CA, USA; UC Berkeley, Berkeley, CA, USA; Rensselaer Polytechnic Institute, Troy, NY, USA|Integrity 2023 is the fourth edition of the successful Workshop on Integrity in Social Networks and Media, held in conjunction with the ACM Conference on Web Search and Data Mining (WSDM) in the past three years. The goal of the workshop is to bring together researchers and practitioners to discuss content and interaction integrity challenges in social networks and social media platforms. The event consists of a combination of invited talks by reputed members of the Integrity community from both academia and industry and peer-reviewed contributed talks and posters solicited via an open call-for-papers.|“诚信2023”是过去三年与 ACM 网络搜索和数据挖掘会议(WSDM)联合举办的第四届成功的社交网络和媒体诚信研讨会。研讨会的目标是让研究人员和从业人员聚集一堂，讨论社交网络和社交媒体平台中的内容和互动完整性挑战。这次活动包括邀请来自学术界和工业界的知名人士进行的演讲，以及通过公开征集论文征集到的经过同行评议的贡献演讲和海报。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Integrity+2023:+Integrity+in+Social+Networks+and+Media)|0| +|[Next-generation Challenges of Responsible Data Integration](https://doi.org/10.1145/3539597.3572727)|Fatemeh Nargesian, Abolfazl Asudeh, H. V. Jagadish|Univ Illinois, Chicago, IL USA; Univ Rochester, Rochester, NY 14627 USA; Univ Michigan, Ann Arbor, MI USA|Data integration has been extensively studied by the data management community and is a core task in the data pre-processing step of ML pipelines. When the integrated data is used for analysis and model training, responsible data science requires addressing concerns about data quality and bias. We present a tutorial on data integration and responsibility, highlighting the existing efforts in responsible data integration along with research opportunities and challenges. In this tutorial, we encourage the community to audit data integration tasks with responsibility measures and develop integration techniques that optimize the requirements of responsible data science. We focus on three critical aspects: (1) the requirements to be considered for evaluating and auditing data integration tasks for quality and bias; (2) the data integration tasks that elicit attention to data responsibility measures and methods to satisfy these requirements; and, (3) techniques, tasks, and open problems in data integration that help achieve data responsibility.|数据集成已经被数据管理界广泛研究，并且是机器学习管道数据预处理的核心任务。当集成数据用于分析和模型训练时，负责任的数据科学需要解决关于数据质量和偏差的问题。我们提供了一个关于数据集成和责任的教程，强调了在负责任的数据集成方面的现有努力以及研究机会和挑战。在本教程中，我们鼓励社区使用责任度量来审计数据集成任务，并开发优化责任数据科学需求的集成技术。我们集中在三个关键的方面: (1)评估和审计数据集成任务的质量和偏差需要考虑的要求; (2)引起注意的数据集成任务的数据责任措施和方法，以满足这些要求; 和(3)技术，任务，以及数据集成中帮助实现数据责任的开放问题。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Next-generation+Challenges+of+Responsible+Data+Integration)|0| +|[Integrity 2023: Integrity in Social Networks and Media](https://doi.org/10.1145/3539597.3572704)|Lluís Garcia Pueyo, Panayiotis Tsaparas, Prathyusha Senthil Kumar, Timos Sellis, Paolo Papotti, Sibel Adali, Giuseppe Manco, Tudor Trufinescu, Gireeja Ranade, James Verbus, Mehmet N. Tek, Anthony McCosker|Meta, Bellevue, WA, USA; ICAR-CNR, Rende, Italy; Google, Redwood City, CA, USA; LinkedIn, Sunnyvale, CA, USA; EURECOM, Biot, France; University of Ioannina, Ioannina, Greece; Swinburne Social Innovation Research Institute, Melbourne, VIC, Australia; Meta, Menlo Park, CA, USA; Rensselaer Polytechnic Institute, Troy, NY, USA; UC Berkeley, Berkeley, CA, USA; Archimedes / Athena Research Center, Athens, Greece|Integrity 2023 is the fourth edition of the successful Workshop on Integrity in Social Networks and Media, held in conjunction with the ACM Conference on Web Search and Data Mining (WSDM) in the past three years. The goal of the workshop is to bring together researchers and practitioners to discuss content and interaction integrity challenges in social networks and social media platforms. The event consists of a combination of invited talks by reputed members of the Integrity community from both academia and industry and peer-reviewed contributed talks and posters solicited via an open call-for-papers.|“诚信2023”是过去三年与 ACM 网络搜索和数据挖掘会议(WSDM)联合举办的第四届成功的社交网络和媒体诚信研讨会。研讨会的目标是让研究人员和从业人员聚集一堂，讨论社交网络和社交媒体平台中的内容和互动完整性挑战。这次活动包括邀请来自学术界和工业界的知名人士进行的演讲，以及通过公开征集论文征集到的经过同行评议的贡献演讲和海报。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Integrity+2023:+Integrity+in+Social+Networks+and+Media)|0| |[Responsible AI for Trusted AI-powered Enterprise Platforms](https://doi.org/10.1145/3539597.3575784)|Steven C. H. Hoi|Salesforce Research Asia, Singapore, Singapore|With the rapidly growing AI market opportunities and the accelerated adoption of AI technologies for a wide range of real-world applications, responsible AI has attracted increasing attention in both academia and industries. In this talk, I will focus on the topics of responsible AI in the industry settings towards building trusted AI-powered enterprise platforms. I will share our efforts and experience of responsible AI for enterprise at Salesforce, from defining the principles to putting them into practice to build trust in AI. Finally, I will also address some emerging challenges and open issues of recent generative AI advances and call for actions of joint responsible AI efforts from academia, industries and governments.|随着人工智能市场机会的迅速增长，以及人工智能技术在现实世界中广泛应用的加速发展，负责任的人工智能已引起学术界和工业界越来越多的关注。在这个演讲中，我将集中讨论在行业环境中建立可信的 AI 驱动的企业平台的负责任的 AI 的主题。我将在 Salesforce 分享我们为企业负责任的人工智能所做的努力和经验，从界定原则到将原则付诸实践，以建立对人工智能的信任。最后，我还将讨论一些新出现的挑战和最近人工智能发展的公开问题，并呼吁学术界、工业界和政府共同采取负责任的人工智能行动。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Responsible+AI+for+Trusted+AI-powered+Enterprise+Platforms)|0| |[Simulating Humans at Scale to Evaluate Voice Interfaces for TVs: the Round-Trip System at Comcast](https://doi.org/10.1145/3539597.3575787)|Breck Baldwin, Lauren Reese, Liming Zhang, Jan Neumann, Taylor Cassidy, Michael Pereira, G. Craig Murray, Kishorekumar Sundararajan, Yidnekachew Endale, Pramod Kadagattor, Paul Wolfe, Brian Aiken, Tony Braskich, Donte Jiggetts, Adam Sloan, Esther Vaturi, Crystal Pender, Ferhan Ture|Comcast Applied AI, Washington, DC, USA|Evaluating large-scale customer-facing voice interfaces involves a variety of challenges, such as data privacy, fairness or unintended bias, and the cost of human labor. Comcast's Xfinity Voice Remote is one such voice interface aimed at users looking to discover content on their TVs. The artificial intelligence (AI) behind the voice remote currently powers multiple voice interfaces, serving tens of millions of requests every day, from users across the globe.In this talk, we introduce a novel Round-Trip system we have built to evaluate the AI serving these voice interfaces in a semi-automated manner, providing a robust and cheap alternative to traditional quality assurance methods. We discuss five specific challenges we have encountered in Round-Trip and describe our solutions in detail.|评估大规模面向客户的语音界面涉及各种挑战，如数据隐私、公平性或意外偏差，以及人力成本。康卡斯特的 Xfinity Voice Remote 就是这样一个语音界面，旨在帮助用户发现电视上的内容。目前，语音遥控器背后的人工智能(AI)为多个语音界面提供动力，每天为全球用户提供数以千万计的请求服务。在这次演讲中，我们介绍了一个新颖的往返系统，我们已经建立了这个系统，以半自动的方式评估服务于这些语音界面的 AI，为传统的质量保证方法提供了一个强大而廉价的替代方案。我们讨论了在往返过程中遇到的五个具体挑战，并详细描述了我们的解决方案。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Simulating+Humans+at+Scale+to+Evaluate+Voice+Interfaces+for+TVs:+the+Round-Trip+System+at+Comcast)|0| |[Considerations for Ethical Speech Recognition Datasets](https://doi.org/10.1145/3539597.3575793)|Orestis Papakyriakopoulos, Alice Xiang|Sony AI, Zurich, Switzerland; Sony AI, Seattle, WA, USA|Speech AI Technologies are largely trained on publicly available datasets or by the massive web-crawling of speech. In both cases, data acquisition focuses on minimizing collection effort, without necessarily taking the data subjects' protection or user needs into consideration. This results to models that are not robust when used on users who deviate from the dominant demographics in the training set, discriminating individuals having different dialects, accents, speaking styles, and disfluencies. In this talk, we use automatic speech recognition as a case study and examine the properties that ethical speech datasets should possess towards responsible AI applications. We showcase diversity issues, inclusion practices, and necessary considerations that can improve trained models, while facilitating model explainability and protecting users and data subjects. We argue for the legal & privacy protection of data subjects, targeted data sampling corresponding to user demographics & needs, appropriate meta data that ensure explainability & accountability in cases of model failure, and the sociotechnical \& situated model design. We hope this talk can inspire researchers \& practitioners to design and use more human-centric datasets in speech technologies and other domains, in ways that empower and respect users, while improving machine learning models' robustness and utility.|语音人工智能技术在很大程度上是通过公开的数据集或大规模的语音网络爬行训练出来的。在这两种情况下，数据采集都侧重于尽可能减少采集工作，而不必考虑数据主体的保护或用户需求。这种结果导致模型不健壮时，使用的用户偏离主导人口统计学在训练集，区分个人有不同的方言，口音，说话风格，和不流利。在这个演讲中，我们使用自动语音识别作为一个案例研究，并检查伦理语音数据集应具有的特性，以负责任的人工智能应用。我们展示了多样性问题、包容实践和必要的考虑因素，这些因素可以改进经过训练的模型，同时促进模型的可解释性并保护用户和数据主体。我们主张数据主体的法律和隐私保护，针对用户人口统计和需求的有针对性的数据抽样，适当的元数据，以确保模型失败的情况下的可解释性和问责制，以及社会技术和情境模型设计。我们希望这次演讲能够激励研究人员和从业人员在语音技术和其他领域设计和使用更多以人为中心的数据集，以授权和尊重用户的方式，同时提高机器学习模型的健壮性和实用性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Considerations+for+Ethical+Speech+Recognition+Datasets)|0| |[Under the Hood of Social Media Advertising: How Do We use AI Responsibly for Advertising Targeting and Creative Evaluation](https://doi.org/10.1145/3539597.3575791)|Aleksandr Farseev|Somin.ai, ITMO University, Singapore, Singapore|Digital Advertising is historically one of the most developed areas where Machine Learning and AI have been applied since its origination. From smart bidding to creative content generation and DCO, AI is well-demanded in the modern digital marketing industry and partially serves as a backbone of most of the state-of-the-art computational advertising systems, making them impossible for the AI tech and the programmatic systems to exist apart from one another. At the same time, given the drastic growth of the available AI technology nowadays, the issue of responsible AI utilization as well as the balance between the opportunity of deploying AI systems and the possible borderline etic and privacy-related consequences are still yet to be discussed comprehensively in both business and research communities. Particularly, an important issue of automatic User Profiling use in modern Programmatic systems like Meta Ads as well as the need for responsible application of the creative assessment models to fit into the business etic guidelines is yet to be described well. Therefore, in this talk, we are going to discuss the technology behind modern programmatic bidding and content scoring systems and the responsible application of AI by SoMin.ai to manage the Advertising targeting and Creative Validation process.|数字广告历来是机器学习和人工智能应用最发达的领域之一。从智能投标到创意内容生成和 DCO，人工智能在现代数字营销行业中的需求很大，并且在一定程度上作为大多数最先进的计算广告系统的骨干，使得人工智能技术和编程系统不可能彼此分离。与此同时，鉴于目前可用的人工智能技术急剧增长，负责任地利用人工智能的问题，以及在部署人工智能系统的机会与可能的边缘病态和与隐私有关的后果之间的平衡问题，仍有待商界和研究界全面讨论。特别是，一个重要的问题，自动用户剖析使用现代编程系统，如元广告，以及需要负责任的应用创造性的评估模型，以符合商业遗传学指南尚未被很好地描述。因此，在本次演讲中，我们将讨论现代程序投标和内容评分系统背后的技术，以及 SoMin.AI 对人工智能负责任的应用，以管理广告定位和创意验证过程。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Under+the+Hood+of+Social+Media+Advertising:+How+Do+We+use+AI+Responsibly+for+Advertising+Targeting+and+Creative+Evaluation)|0| |[An Open-Source Suite of Causal AI Tools and Libraries](https://doi.org/10.1145/3539597.3575789)|Emre Kiciman|Microsoft Research, Redmond, WA, USA|We propose to accelerate use-inspired basic research in causal AI through a suite of causal tools and libraries that simultaneously provides core causal AI functionality to practitioners and creates a platform for research advances to be rapidly deployed. In this presentation, we describe our contributions towards an open-source causal AI suite. We describe some of their applications, the lessons learned from their usage, and what is next.|我们建议通过一套因果工具和库，加速因果 AI 的基础研究，这些工具和库同时为从业者提供核心因果 AI 功能，并为快速部署研究进展创建一个平台。在这个演讲中，我们描述了我们对开源因果 AI 套件的贡献。我们描述了它们的一些应用程序，从使用中学到的经验教训，以及下一步是什么。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=An+Open-Source+Suite+of+Causal+AI+Tools+and+Libraries)|0| -|[Privacy in the Time of Language Models](https://doi.org/10.1145/3539597.3575792)|Charith Peris, Christophe Dupuy, Jimit Majmudar, Rahil Parikh, Sami Smaili, Richard S. Zemel, Rahul Gupta|Amazon Alexa, Cambridge, MA, USA; Columbia University, New York, NY, USA; Amazon Alexa, Toronto, ON, USA; Amazon Alexa, Sunnyvale, CA, USA|Pretrained large language models (LLMs) have consistently shown state-of-the-art performance across multiple natural language processing (NLP) tasks. These models are of much interest for a variety of industrial applications that use NLP as a core component. However, LLMs have also been shown to memorize portions of their training data, which can contain private information. Therefore, when building and deploying LLMs, it is of value to apply privacy-preserving techniques that protect sensitive data. In this talk, we discuss privacy measurement and preservation techniques for LLMs that can be applied in the context of industrial applications and present case studies of preliminary solutions. We discuss select strategies and metrics relevant for measuring memorization in LLMs that can, in turn, be used to measure privacy-risk in these models. We then discuss privacy-preservation techniques that can be applied at different points of the LLM training life-cycle; including our work on an algorithm for fine-tuning LLMs with improved privacy. In addition, we discuss our work on privacy-preserving solutions that can be applied to LLMs during inference and are feasible for use at run time.|经过预先训练的大型语言模型(LLM)在多个自然语言处理(NLP)任务中始终表现出最先进的性能。这些模型对于使用 NLP 作为核心组件的各种工业应用程序非常有意义。然而，LLM 也被证明能够记住它们训练数据的一部分，这些数据可以包含私人信息。因此，在构建和部署 LLM 时，应用保护敏感数据的隐私保护技术是有价值的。在这个演讲中，我们讨论了 LLM 的隐私度量和保护技术，这些技术可以应用于工业应用的背景下，并提出了初步解决方案的案例研究。我们讨论与测量 LLM 记忆相关的选择策略和指标，这些策略和指标反过来又可以用来测量这些模型中的隐私风险。然后，我们将讨论可以应用于 LLM 培训生命周期不同阶段的隐私保护技术; 包括我们在具有改进隐私的 LLM 微调算法方面的工作。此外，我们还讨论了我们在隐私保护解决方案方面的工作，这些解决方案可以在推理期间应用于 LLM，并且在运行时使用是可行的。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Privacy+in+the+Time+of+Language+Models)|0| -|[Incorporating Fairness in Large Scale NLU Systems](https://doi.org/10.1145/3539597.3575785)|Rahul Gupta, Lisa Bauer, KaiWei Chang, Jwala Dhamala, Aram Galstyan, Palash Goyal, Qian Hu, Avni Khatri, Rohit Parimi, Charith Peris, Apurv Verma, Richard S. Zemel, Prem Natarajan|Amazon Alexa, Los Angeles, CA, USA; Amazon Alexa, Cambridge, MA, USA; Amazon Alexa, New York, NY, USA; Amazon Alexa, Sunnyvale, CA, USA|NLU models power several user facing experiences such as conversations agents and chat bots. Building NLU models typically consist of 3 stages: a) building or finetuning a pre-trained model b) distilling or fine-tuning the pre-trained model to build task specific models and, c) deploying the task-specific model to production. In this presentation, we will identify fairness considerations that can be incorporated in the aforementioned three stages in the life-cycle of NLU model building: (i) selection/building of a large scale language model, (ii) distillation/fine-tuning the large model into task specific model and, (iii) deployment of the task specific model. We will present select metrics that can be used to quantify fairness in NLU models and fairness enhancement techniques that can be deployed in each of these stages. Finally, we will share some recommendations to successfully implement fairness considerations when building an industrial scale NLU system.|NLU 模型支持多种用户体验，如会话代理和聊天机器人。构建 NLU 模型通常包括3个阶段: a)构建或调整预先训练的模型 b)提取或调整预先训练的模型以构建特定于任务的模型，c)将特定于任务的模型部署到生产环境中。在这个介绍中，我们将确定公平性的考虑，可以纳入上述三个阶段的 NLU 模型建设的生命周期: (i)选择/建立一个大规模的语言模型，(ii)精馏/微调大型模型到任务特定的模型，以及(iii)部署任务特定的模型。我们将介绍可用于量化 NLU 模型中的公平性的选择指标，以及可在每个阶段部署的公平性增强技术。最后，我们将分享一些建议，以成功实施公平的考虑时，建立一个工业规模的自然语言大学系统。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Incorporating+Fairness+in+Large+Scale+NLU+Systems)|0| +|[Privacy in the Time of Language Models](https://doi.org/10.1145/3539597.3575792)|Charith Peris, Christophe Dupuy, Jimit Majmudar, Rahil Parikh, Sami Smaili, Richard S. Zemel, Rahul Gupta|Columbia University, New York, NY, USA; Amazon Alexa, Toronto, ON, USA; Amazon Alexa, Cambridge, MA, USA; Amazon Alexa, Sunnyvale, CA, USA|Pretrained large language models (LLMs) have consistently shown state-of-the-art performance across multiple natural language processing (NLP) tasks. These models are of much interest for a variety of industrial applications that use NLP as a core component. However, LLMs have also been shown to memorize portions of their training data, which can contain private information. Therefore, when building and deploying LLMs, it is of value to apply privacy-preserving techniques that protect sensitive data. In this talk, we discuss privacy measurement and preservation techniques for LLMs that can be applied in the context of industrial applications and present case studies of preliminary solutions. We discuss select strategies and metrics relevant for measuring memorization in LLMs that can, in turn, be used to measure privacy-risk in these models. We then discuss privacy-preservation techniques that can be applied at different points of the LLM training life-cycle; including our work on an algorithm for fine-tuning LLMs with improved privacy. In addition, we discuss our work on privacy-preserving solutions that can be applied to LLMs during inference and are feasible for use at run time.|经过预先训练的大型语言模型(LLM)在多个自然语言处理(NLP)任务中始终表现出最先进的性能。这些模型对于使用 NLP 作为核心组件的各种工业应用程序非常有意义。然而，LLM 也被证明能够记住它们训练数据的一部分，这些数据可以包含私人信息。因此，在构建和部署 LLM 时，应用保护敏感数据的隐私保护技术是有价值的。在这个演讲中，我们讨论了 LLM 的隐私度量和保护技术，这些技术可以应用于工业应用的背景下，并提出了初步解决方案的案例研究。我们讨论与测量 LLM 记忆相关的选择策略和指标，这些策略和指标反过来又可以用来测量这些模型中的隐私风险。然后，我们将讨论可以应用于 LLM 培训生命周期不同阶段的隐私保护技术; 包括我们在具有改进隐私的 LLM 微调算法方面的工作。此外，我们还讨论了我们在隐私保护解决方案方面的工作，这些解决方案可以在推理期间应用于 LLM，并且在运行时使用是可行的。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Privacy+in+the+Time+of+Language+Models)|0| +|[Incorporating Fairness in Large Scale NLU Systems](https://doi.org/10.1145/3539597.3575785)|Rahul Gupta, Lisa Bauer, KaiWei Chang, Jwala Dhamala, Aram Galstyan, Palash Goyal, Qian Hu, Avni Khatri, Rohit Parimi, Charith Peris, Apurv Verma, Richard S. Zemel, Prem Natarajan|Amazon Alexa, Los Angeles, CA, USA; Amazon Alexa, Cambridge, MA, USA; Amazon Alexa, Sunnyvale, CA, USA; Amazon Alexa, New York, NY, USA|NLU models power several user facing experiences such as conversations agents and chat bots. Building NLU models typically consist of 3 stages: a) building or finetuning a pre-trained model b) distilling or fine-tuning the pre-trained model to build task specific models and, c) deploying the task-specific model to production. In this presentation, we will identify fairness considerations that can be incorporated in the aforementioned three stages in the life-cycle of NLU model building: (i) selection/building of a large scale language model, (ii) distillation/fine-tuning the large model into task specific model and, (iii) deployment of the task specific model. We will present select metrics that can be used to quantify fairness in NLU models and fairness enhancement techniques that can be deployed in each of these stages. Finally, we will share some recommendations to successfully implement fairness considerations when building an industrial scale NLU system.|NLU 模型支持多种用户体验，如会话代理和聊天机器人。构建 NLU 模型通常包括3个阶段: a)构建或调整预先训练的模型 b)提取或调整预先训练的模型以构建特定于任务的模型，c)将特定于任务的模型部署到生产环境中。在这个介绍中，我们将确定公平性的考虑，可以纳入上述三个阶段的 NLU 模型建设的生命周期: (i)选择/建立一个大规模的语言模型，(ii)精馏/微调大型模型到任务特定的模型，以及(iii)部署任务特定的模型。我们将介绍可用于量化 NLU 模型中的公平性的选择指标，以及可在每个阶段部署的公平性增强技术。最后，我们将分享一些建议，以成功实施公平的考虑时，建立一个工业规模的自然语言大学系统。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Incorporating+Fairness+in+Large+Scale+NLU+Systems)|0| |[Social Public Health Infrastructure for a Smart City Citizen Patient: Advances and Opportunities for AI Driven Disruptive Innovation](https://doi.org/10.1145/3539597.3575779)|Ankur Teredesai|University of Washington & CueZen Inc., Seattle, WA, USA|Promoting health, preventing disease, and prolonging life are central to the success of any smart city initiative. Today, wireless communication, data infrastructure, and low-cost sensors such as lifestyle and activity trackers are making it increasingly possible for cities to collect, collate, and innovate on developing a smart infrastructure. Combining this with AI driven disruptions for human behavior change can fundamentally transform delivery of public health for the citizen patient[4]. Most urban development government bodies consider such infrastructure to be a distributed ecosystem consisting of physical infrastructure, institutional infrastructure, social infrastructure and economic infrastructure[1]. In this talk we will focus mostly on the social health infrastructure component and first showcase some of the recent initiatives created with the purpose of addressing health which is a key social goal, as a smart city goal. Specifically we will discuss how technical advances in IoT, recommendation systems, geospatial computing, and digital health therapeutics are creating a new future. Yet, such advances are not addressing the issues of making healthy behavior change sustainable with broad health equity for those that need it most[3]. Overwhelming nature of siloed apps and digital health solutions which often leave the citizens overwhelmed and those most in need underserved[2]. This talk highlights the advances and opportunities created when behavioral economics and public health combine with AI and cloud infrastructure to make smart public health initiatives personalized to each individual citizen patient.|促进健康、预防疾病和延长寿命是任何智慧城市倡议成功的核心。如今，无线通信、数据基础设施以及生活方式和活动跟踪器等低成本传感器使得城市越来越有可能在开发智能基础设施方面进行收集、整理和创新。将这一点与人工智能驱动的人类行为改变的破坏相结合，可以从根本上改变为公民患者提供公共卫生服务[4]。大多数城市发展政府机构认为这些基础设施是一个分布式的生态系统，包括有形基础设施、制度基础设施、社会基础设施和经济基础设施[1]。在这次演讲中，我们将主要侧重于社会卫生基础设施部分，并首先展示最近为解决卫生问题而采取的一些举措，这是一个关键的社会目标，也是一个智慧城市的目标。具体来说，我们将讨论物联网、推荐系统、地理空间计算和数字健康治疗的技术进步是如何创造一个新的未来的。然而，这些进步并没有解决问题，使健康的行为改变可持续与广泛的健康公平，为那些最需要它[3]。孤立的应用程序和数字健康解决方案的压倒性本质，往往让公民不堪重负，最需要帮助的人得不到充分的服务[2]。这次演讲强调了当行为经济学和公共卫生与人工智能和云基础设施相结合，使智能公共卫生倡议个性化到每个公民病人时所创造的进步和机遇。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Social+Public+Health+Infrastructure+for+a+Smart+City+Citizen+Patient:+Advances+and+Opportunities+for+AI+Driven+Disruptive+Innovation)|0| -|[Recent Advances on Deep Learning based Knowledge Tracing](https://doi.org/10.1145/3539597.3575790)|Zitao Liu, Jiahao Chen, Weiqi Luo|Jinan University, Guangzhou, China; TAL Education Group, Beijing, China|Knowledge tracing (KT) is the task of using students' historical learning interaction data to model their knowledge mastery over time so as to make predictions on their future interaction performance. Recently, remarkable progress has been made of using various deep learning techniques to solve the KT problem. However, the success behind deep learning based knowledge tracing (DLKT) approaches is still left somewhat unknown and proper measurement and analysis of these DLKT approaches remain a challenge. In this talk, we will comprehensively review recent developments of applying state-of-the-art deep learning approaches in KT problems, with a focus on those real-world educational data. Beyond introducing the recent advances of various DLKT models, we will discuss how to guarantee valid comparisons across DLKT methods via thorough evaluations on several publicly available datasets. More specifically, we will talk about (1) KT related psychometric theories; (2) the general DLKT modeling framework that covers recently developed DLKT approaches from different categories; (3) the general DLKT benchmark that allows existing approaches comparable on public KT datasets; (4) the broad application of algorithmic assessment and personalized feedback. Participants will learn about recent trends and emerging challenges in this topic, representative tools and learning resources to obtain ready-to-use models, and how related models and techniques benefit real-world KT applications.|知识追踪(KT)是利用学生的历史学习互动数据，建立学生随时间变化的知识掌握模型，从而预测学生未来的互动表现的任务。近年来，利用各种深度学习技术解决 KT 问题的研究取得了显著的进展。然而，基于深度学习的知识跟踪(DLKT)方法的成功仍然是未知的，对这些 DLKT 方法的正确测量和分析仍然是一个挑战。在这个演讲中，我们将全面回顾在 KT 问题中应用最先进的深度学习方法的最新进展，重点放在那些真实世界的教育数据上。除了介绍各种 DLKT 模型的最新进展之外，我们还将讨论如何通过对几个公开数据集的全面评估来保证跨 DLKT 方法的有效比较。更具体地说，我们将讨论(1)与 KT 相关的心理测量学理论; (2)涵盖最近从不同类别开发的 DLKT 方法的通用 DLKT 建模框架; (3)允许现有方法在公共 KT 数据集上具有可比性的通用 DLKT 基准; (4)算法评估和个性化反馈的广泛应用。与会者将了解这一主题的最新趋势和新出现的挑战，获得现成可用模型的代表性工具和学习资源，以及相关模型和技术如何有利于现实世界的 KT 应用。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Recent+Advances+on+Deep+Learning+based+Knowledge+Tracing)|0| -|[SmartCityBus - A Platform for Smart Transportation Systems](https://doi.org/10.1145/3539597.3575781)|Georgios Bouloukakis, Chrysostomos Zeginis, Nikolaos Papadakis, Kostas Magoutis, George Christodoulou, Chrysanthi Kosyfaki, Konstantinos Lampropoulos, Nikos Mamoulis|Télécom SudParis, Institut Polytechnique de Paris, Paris, France; Foundation for Research and Technology - Hellas (FORTH) & University of Crete, Heraklion, Greece; University of Ioannina, Ioannina, Greece; Foundation for Research and Technology - Hellas (FORTH), Heraklion, Greece|With the growth of the Internet of Things (IoT), Smart(er) Cities have been a research goal of researchers, businesses and local authorities willing to adopt IoT technologies to improve their services. Among them, Smart Transportation [7,8], the integrated application of modern technologies and management strategies in transportation systems, refers to the adoption of new IoT solutions to improve urban mobility. These technologies aim to provide innovative solutions related to different modes of transport and traffic management and enable users to be better informed and make safer and 'smarter' use of transport networks. This talk presents SmartCityBus, a data-driven intelligent transportation system (ITS) whose main objective is to use online and offline data in order to provide accurate statistics and predictions and improve public transportation services in the short and medium/long term.|随着物联网的发展，智能城市已成为研究人员、企业和地方政府愿意采用物联网技术改善其服务的研究目标。其中，智能交通[7,8] ，现代技术和管理战略在交通系统的综合应用，指采用新的物联网解决方案，以改善城市流动性。这些技术旨在提供与不同运输和交通管理模式相关的创新解决方案，使用户能够更好地了解情况，更安全、更“智能”地使用运输网络。是次讲座介绍数据驱动的智能交通系统「智慧城市巴士」(SmartCityBus) ，其主要目的是利用在线和离线数据提供准确的统计数据和预测，并在短、中/长期内改善公共交通服务。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=SmartCityBus+-+A+Platform+for+Smart+Transportation+Systems)|0| +|[Recent Advances on Deep Learning based Knowledge Tracing](https://doi.org/10.1145/3539597.3575790)|Zitao Liu, Jiahao Chen, Weiqi Luo|TAL Education Group, Beijing, China; Jinan University, Guangzhou, China|Knowledge tracing (KT) is the task of using students' historical learning interaction data to model their knowledge mastery over time so as to make predictions on their future interaction performance. Recently, remarkable progress has been made of using various deep learning techniques to solve the KT problem. However, the success behind deep learning based knowledge tracing (DLKT) approaches is still left somewhat unknown and proper measurement and analysis of these DLKT approaches remain a challenge. In this talk, we will comprehensively review recent developments of applying state-of-the-art deep learning approaches in KT problems, with a focus on those real-world educational data. Beyond introducing the recent advances of various DLKT models, we will discuss how to guarantee valid comparisons across DLKT methods via thorough evaluations on several publicly available datasets. More specifically, we will talk about (1) KT related psychometric theories; (2) the general DLKT modeling framework that covers recently developed DLKT approaches from different categories; (3) the general DLKT benchmark that allows existing approaches comparable on public KT datasets; (4) the broad application of algorithmic assessment and personalized feedback. Participants will learn about recent trends and emerging challenges in this topic, representative tools and learning resources to obtain ready-to-use models, and how related models and techniques benefit real-world KT applications.|知识追踪(KT)是利用学生的历史学习互动数据，建立学生随时间变化的知识掌握模型，从而预测学生未来的互动表现的任务。近年来，利用各种深度学习技术解决 KT 问题的研究取得了显著的进展。然而，基于深度学习的知识跟踪(DLKT)方法的成功仍然是未知的，对这些 DLKT 方法的正确测量和分析仍然是一个挑战。在这个演讲中，我们将全面回顾在 KT 问题中应用最先进的深度学习方法的最新进展，重点放在那些真实世界的教育数据上。除了介绍各种 DLKT 模型的最新进展之外，我们还将讨论如何通过对几个公开数据集的全面评估来保证跨 DLKT 方法的有效比较。更具体地说，我们将讨论(1)与 KT 相关的心理测量学理论; (2)涵盖最近从不同类别开发的 DLKT 方法的通用 DLKT 建模框架; (3)允许现有方法在公共 KT 数据集上具有可比性的通用 DLKT 基准; (4)算法评估和个性化反馈的广泛应用。与会者将了解这一主题的最新趋势和新出现的挑战，获得现成可用模型的代表性工具和学习资源，以及相关模型和技术如何有利于现实世界的 KT 应用。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Recent+Advances+on+Deep+Learning+based+Knowledge+Tracing)|0| +|[SmartCityBus - A Platform for Smart Transportation Systems](https://doi.org/10.1145/3539597.3575781)|Georgios Bouloukakis, Chrysostomos Zeginis, Nikolaos Papadakis, Kostas Magoutis, George Christodoulou, Chrysanthi Kosyfaki, Konstantinos Lampropoulos, Nikos Mamoulis|Télécom SudParis, Institut Polytechnique de Paris, Paris, France; University of Ioannina, Ioannina, Greece; Foundation for Research and Technology - Hellas (FORTH) & University of Crete, Heraklion, Greece; Foundation for Research and Technology - Hellas (FORTH), Heraklion, Greece|With the growth of the Internet of Things (IoT), Smart(er) Cities have been a research goal of researchers, businesses and local authorities willing to adopt IoT technologies to improve their services. Among them, Smart Transportation [7,8], the integrated application of modern technologies and management strategies in transportation systems, refers to the adoption of new IoT solutions to improve urban mobility. These technologies aim to provide innovative solutions related to different modes of transport and traffic management and enable users to be better informed and make safer and 'smarter' use of transport networks. This talk presents SmartCityBus, a data-driven intelligent transportation system (ITS) whose main objective is to use online and offline data in order to provide accurate statistics and predictions and improve public transportation services in the short and medium/long term.|随着物联网的发展，智能城市已成为研究人员、企业和地方政府愿意采用物联网技术改善其服务的研究目标。其中，智能交通[7,8] ，现代技术和管理战略在交通系统的综合应用，指采用新的物联网解决方案，以改善城市流动性。这些技术旨在提供与不同运输和交通管理模式相关的创新解决方案，使用户能够更好地了解情况，更安全、更“智能”地使用运输网络。是次讲座介绍数据驱动的智能交通系统「智慧城市巴士」(SmartCityBus) ，其主要目的是利用在线和离线数据提供准确的统计数据和预测，并在短、中/长期内改善公共交通服务。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=SmartCityBus+-+A+Platform+for+Smart+Transportation+Systems)|0| |[Towards an Event-Aware Urban Mobility Prediction System](https://doi.org/10.1145/3539597.3575783)|Zhaonan Wang, Renhe Jiang, Zipei Fan, Xuan Song, Ryosuke Shibasaki|The University of Tokyo, Tokyo, Japan|Today, thanks to the rapid developing mobile and sensor networks in IoT (Internet of Things) systems, spatio-temporal big data are being constantly generated. They have brought us a data-driven possibility to sense and understand crowd mobility on a city scale. A fundamental task towards the next-generation mobility services, such as Intelligent Transportation Systems (ITS), Mobility-as-a-Service (MaaS), is spatio-temporal predictive modeling of the geo-sensory signals. There is a recent line of research leveraging deep learning techniques to boost the forecasting performance on such tasks. While simulating the regularity of mobility behaviors (e.g., routines, periodicity) in a more sophisticated way, the existing studies ignore an important part of urban activities, i.e., events. Including holidays, extreme weathers, pandemic, accidents, various urban events happen from time to time and cause non-stationary phenomena, which by nature make the spatio-temporal forecasting task challenging. We thereby envision an event-aware urban mobility prediction model that is capable of fast adapting and making reliable predictions in different scenarios, which is crucial to decision making towards emergency response and urban resilience.|今天，由于物联网系统中快速发展的移动和传感器网络，时空大数据不断产生。他们给我们带来了一种数据驱动的可能性，在城市规模上感知和理解人群的流动性。智能交通系统(ITS)、移动即服务(MaaS)等下一代移动服务的基本任务是对地理感知信号进行时空预测建模。最近有一项研究利用深度学习技术来提高这类任务的预测性能。虽然现有的研究以一种更复杂的方式模拟流动行为的规律性(例如，例行公事，周期性) ，但是忽略了城市活动的一个重要组成部分，即事件。包括节假日、极端天气、流行病、事故、各种城市事件时有发生，引起非平稳现象，使得时空预测工作具有挑战性。因此，我们设想了一个能够意识到事件的城市流动性预测模型，该模型能够在不同的情况下快速适应并作出可靠的预测，这对于决策应对紧急情况和城市复原力至关重要。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Towards+an+Event-Aware+Urban+Mobility+Prediction+System)|0| |[Metropolitan-scale Mobility Digital Twin](https://doi.org/10.1145/3539597.3575782)|Zipei Fan, Renhe Jiang, Ryosuke Shibasaki|University of Tokyo, Kashiwa, Chiba, Japan; University of Tokyo, Kashiwa, Chiba, Japan and Southern University of Science and Technology, Shenzhen, Guangdong, China|Knowing "what is happening" and "what will happen" of the mobility in a city is the building block of a data-driven smart city system. In recent years, mobility digital twin that makes a virtual replication of human mobility and predicting or simulating the fine-grained movements of the subjects in a virtual space at a metropolitan scale in near real-time has shown its great potential in modern urban intelligent systems. However, few studies have provided practical solutions. The main difficulties are four-folds: 1) the daily variation of human mobility is hard to model and predict; 2) the transportation network enforces a complex constraints on human mobility; 3) generating a rational fine-grained human trajectory is challenging for existing machine learning models; and 4) making a fine-grained prediction incurs high computational costs, which is challenging for an online system. Bearing these difficulties in mind, in this paper we propose a two-stage human mobility predictor that stratifies the coarse and fine-grained level predictions. In the first stage, to encode the daily variation of human mobility at a metropolitan level, we automatically extract citywide mobility trends as crowd contexts and predict long-term and long-distance movements at a coarse level. In the second stage, the coarse predictions are resolved to a fine-grained level via a probabilistic trajectory retrieval method, which offloads most of the heavy computations to the offline phase. We tested our method using a real-world mobile phone GPS dataset in the Kanto area in Japan, and achieved good prediction accuracy and a time efficiency of about 2 min in predicting future 1h movements of about 220K mobile phone users on a single machine to support more higher-level analysis of mobility prediction.|了解城市中移动性的“正在发生的事情”和“将要发生的事情”是数据驱动智能城市系统的组成部分。近年来，移动性数字孪生兄弟在现代城市智能系统中显示出巨大的潜力，它可以对人类的移动性进行虚拟复制，并在城市尺度的虚拟空间中近实时地预测或模拟主体的细粒度移动。然而，很少有研究提供切实可行的解决方案。主要的困难有四个方面: 1)人类流动性的日变化很难建模和预测; 2)交通网络对人类流动性施加了复杂的约束; 3)生成一个合理的细粒度人类轨迹对现有的机器学习模型是一个挑战; 4)做出细粒度预测会带来高计算成本，这对在线系统是一个挑战。考虑到这些困难，在本文中，我们提出了一个两阶段的人类流动性预测器，分层粗粒度和细粒度的水平预测。在第一阶段，为了编码城市层面上人口流动的日变化，我们自动提取城市范围内的人口流动趋势作为人群背景，并在一个粗略的层面上预测长期和长距离的流动。在第二阶段，通过概率轨迹检索方法将粗预测分解到细粒度水平，将大部分繁重的计算转移到离线阶段。我们在 Kanto 地区使用一个真实世界的手机 GPS 数据集测试了我们的方法，在一台机器上预测未来1小时内约220k 手机用户的移动时，取得了很好的预测精度和大约2分钟的时间效率，以支持更高层次的移动性预测分析。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Metropolitan-scale+Mobility+Digital+Twin)|0| -|[Interpretable Research Interest Shift Detection with Temporal Heterogeneous Graphs](https://doi.org/10.1145/3539597.3570453)|Qiang Yang, Changsheng Ma, Qiannan Zhang, Xin Gao, Chuxu Zhang, Xiangliang Zhang|Brandeis University, Massachusetts, MA, USA; University of Notre Dame, Indiana, IN, USA; King Abdullah University of Science and Technology, Jeddah, Saudi Arabia|Researchers dedicate themselves to research problems they are interested in and often have evolving research interests in their academic careers. The study of research interest shift detection can help to find facts relevant to scientific training paths, scientific funding trends, and knowledge discovery. Existing methods define specific graph structures like author-conference-topic networks, and co-citing networks to detect research interest shift. They either ignore the temporal factor or miss heterogeneous information characterizing academic activities. More importantly, the detection results lack the interpretations of how research interests change over time, thus reducing the model's credibility. To address these issues, we propose a novel interpretable research interest shift detection model with temporal heterogeneous graphs. We first construct temporal heterogeneous graphs to represent the research interests of the target authors. To make the detection interpretable, we design a deep neural network to parameterize the generation process of interpretation on the predicted results in the form of a weighted sub-graph. Additionally, to improve the training process, we propose a semantic-aware negative data sampling strategy to generate non-interesting auxiliary shift graphs as contrastive samples. Extensive experiments demonstrate that our model outperforms the state-of-the-art baselines on two public academic graph datasets and is capable of producing interpretable results.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Interpretable+Research+Interest+Shift+Detection+with+Temporal+Heterogeneous+Graphs)|-1| +|[Interpretable Research Interest Shift Detection with Temporal Heterogeneous Graphs](https://doi.org/10.1145/3539597.3570453)|Qiang Yang, Changsheng Ma, Qiannan Zhang, Xin Gao, Chuxu Zhang, Xiangliang Zhang|University of Notre Dame, Indiana, IN, USA; Brandeis University, Massachusetts, MA, USA; King Abdullah University of Science and Technology, Jeddah, Saudi Arabia|Researchers dedicate themselves to research problems they are interested in and often have evolving research interests in their academic careers. The study of research interest shift detection can help to find facts relevant to scientific training paths, scientific funding trends, and knowledge discovery. Existing methods define specific graph structures like author-conference-topic networks, and co-citing networks to detect research interest shift. They either ignore the temporal factor or miss heterogeneous information characterizing academic activities. More importantly, the detection results lack the interpretations of how research interests change over time, thus reducing the model's credibility. To address these issues, we propose a novel interpretable research interest shift detection model with temporal heterogeneous graphs. We first construct temporal heterogeneous graphs to represent the research interests of the target authors. To make the detection interpretable, we design a deep neural network to parameterize the generation process of interpretation on the predicted results in the form of a weighted sub-graph. Additionally, to improve the training process, we propose a semantic-aware negative data sampling strategy to generate non-interesting auxiliary shift graphs as contrastive samples. Extensive experiments demonstrate that our model outperforms the state-of-the-art baselines on two public academic graph datasets and is capable of producing interpretable results.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Interpretable+Research+Interest+Shift+Detection+with+Temporal+Heterogeneous+Graphs)|-1| |[Learning to Understand Audio and Multimodal Content](https://doi.org/10.1145/3539597.3572333)|Rosie Jones|Spotify, Boston, MA, USA|Music, podcasts and audiobooks are rich audio content types with strong listener engagement. Search and recommendation across these content types can be greatly enhanced with a deep understanding of their content; across audio, text, and other multimodal content. In this talk, I discuss some of the challenges and opportunities in understanding this content. This deep understanding of content enables us to delight our users and expand the reach of our content creators. As part of enabling wider academic research into podcast content understanding, Spotify Research [1] has released a podcast dataset [2] with 120,000 hours of podcasts in English [3] and Portuguese [4].|音乐、播客和有声读物是丰富的音频内容类型，具有强大的听众参与度。通过深入理解这些内容类型的内容，可以大大增强跨这些内容类型的搜索和推荐; 跨音频、文本和其他多通道内容。在这个演讲中，我讨论了理解这些内容的一些挑战和机遇。这种对内容的深刻理解使我们能够取悦我们的用户，并扩大我们的内容创作者的范围。为了使更广泛的学术研究能够理解播客内容，Spotify Research [1]发布了一个播客数据集[2] ，其中包括120,000小时的英语和葡萄牙语播客[3]。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Learning+to+Understand+Audio+and+Multimodal+Content)|-1| |[Preference-Based Offline Evaluation](https://doi.org/10.1145/3539597.3572725)|Charles L. A. Clarke, Fernando Diaz, Negar Arabzadeh|University of Waterloo, Waterloo, ON, Canada; Google, Montreal, PQ, Canada|A core step in production model research and development involves the offline evaluation of a system before production deployment. Traditional offline evaluation of search, recommender, and other systems involves gathering item relevance labels from human editors. These labels can then be used to assess system performance using offline evaluation metrics. Unfortunately, this approach does not work when evaluating highly effective ranking systems, such as those emerging from the advances in machine learning. Recent work demonstrates that moving away from pointwise item and metric evaluation can be a more effective approach to the offline evaluation of systems. This tutorial, intended for both researchers and practitioners, reviews early work in preference-based evaluation and covers recent developments in detail.|生产模型研究和开发的核心步骤包括在生产部署之前对系统进行离线评估。传统的搜索、推荐和其他系统的离线评估包括从人工编辑器收集项目相关标签。然后可以使用这些标签使用离线评估指标来评估系统性能。不幸的是，这种方法在评估高效的排名系统时不起作用，例如那些来自机器学习进步的排名系统。最近的工作表明，从点态项目和度量评价可以是一个更有效的方法离线评价系统。本教程面向研究人员和从业人员，回顾了基于偏好的评估的早期工作，并详细介绍了最近的发展。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Preference-Based+Offline+Evaluation)|-1| diff --git a/papers/wsdm/wsdm2024.md b/papers/wsdm/wsdm2024.md index 016aa5bb..7a95b621 100644 --- a/papers/wsdm/wsdm2024.md +++ b/papers/wsdm/wsdm2024.md @@ -12,8 +12,8 @@ |[Likelihood-Based Methods Improve Parameter Estimation in Opinion Dynamics Models](https://doi.org/10.1145/3616855.3635785)|Jacopo Lenti, Corrado Monti, Gianmarco De Francisci Morales||We show that a maximum likelihood approach for parameter estimation in agent-based models (ABMs) of opinion dynamics outperforms the typical simulation-based approach. Simulation-based approaches simulate the model repeatedly in search of a set of parameters that generates data similar enough to the observed one. In contrast, likelihood-based approaches derive a likelihood function that connects the unknown parameters to the observed data in a statistically principled way. We compare these two approaches on the well-known bounded-confidence model of opinion dynamics. We do so on three realistic scenarios of increasing complexity depending on data availability: (i) fully observed opinions and interactions, (ii) partially observed interactions, (iii) observed interactions with noisy proxies of the opinions. We highlight how identifying observed and latent variables is fundamental for connecting the model to the data. To realize the likelihood-based approach, we first cast the model into a probabilistic generative guise that supports a proper data likelihood. Then, we describe the three scenarios via probabilistic graphical models and show the nuances that go into translating the model. Finally, we implement the resulting probabilistic models in an automatic differentiation framework (PyTorch). This step enables easy and efficient maximum likelihood estimation via gradient descent. Our experimental results show that the maximum likelihood estimates are up to 4x more accurate and require up to 200x less computational time.|结果表明，基于主体的观点动力学模型参数估计的最大似然方法优于典型的基于仿真的方法。基于仿真的方法反复模拟模型，以寻找一组参数，生成与观测数据足够相似的数据。相比之下，基于似然的方法推导出一个似然函数，用统计学原理将未知参数与观测数据联系起来。我们比较了这两种方法在众所周知的有界置信模型的意见动态。我们这样做是基于三个现实的场景: (i)完全观察到的观点和相互作用，(ii)部分观察到的相互作用，(iii)观察到的与观点的嘈杂代理的相互作用。我们强调识别观察变量和潜在变量是如何将模型与数据联系起来的基础。为了实现基于似然的方法，我们首先将模型转换成一个支持适当数据似然的概率生成伪装。然后，我们通过概率图形模型来描述这三个场景，并展示模型转换过程中的细微差别。最后，我们在一个自动微分框架(PyTorch)中实现最终的概率模型。这个步骤可以简单有效地利用梯度下降法进行最大似然估计。我们的实验结果表明，最大似然估计是高达4倍以上的准确性，需要高达200倍以下的计算时间。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Likelihood-Based+Methods+Improve+Parameter+Estimation+in+Opinion+Dynamics+Models)|1| |[K2: A Foundation Language Model for Geoscience Knowledge Understanding and Utilization](https://doi.org/10.1145/3616855.3635772)|Cheng Deng, Tianhang Zhang, Zhongmou He, Qiyuan Chen, Yuanyuan Shi, Yi Xu, Luoyi Fu, Weinan Zhang, Xinbing Wang, Chenghu Zhou, Zhouhan Lin, Junxian He||Large language models (LLMs) have achieved great success in general domains of natural language processing. In this paper, we bring LLMs to the realm of geoscience with the objective of advancing research and applications in this field. To this end, we present the first-ever LLM in geoscience, K2, alongside a suite of resources developed to further promote LLM research within geoscience. For instance, we have curated the first geoscience instruction tuning dataset, GeoSignal, which aims to align LLM responses to geoscience-related user queries. Additionally, we have established the first geoscience benchmark, GeoBench, to evaluate LLMs in the context of geoscience. In this work, we experiment with a complete recipe to adapt a pre-trained general-domain LLM to the geoscience domain. Specifically, we further train the LLaMA-7B model on 5.5B tokens of geoscience text corpus, including over 1 million pieces of geoscience literature, and utilize GeoSignal's supervised data to fine-tune the model. Moreover, we share a protocol that can efficiently gather domain-specific data and construct domain-supervised data, even in situations where manpower is scarce. Meanwhile, we equip K2 with the abilities of using tools to be a naive geoscience aide. Experiments conducted on the GeoBench demonstrate the effectiveness of our approach and datasets on geoscience knowledge understanding and utilization.We open-source all the training data and K2 model checkpoints at https://github.com/davendw49/k2.|大语言模型(LLM)在自然语言处理的一般领域取得了巨大的成功。本文将 LLM 引入地球科学领域，旨在促进该领域的研究和应用。为此，我们提出了地球科学有史以来第一个 LLM，K2，以及一套资源的开发，以进一步促进地球科学中的 LLM 研究。例如，我们策划了第一个地球科学指令调整数据集 GeoSignal，其目的是使 LLM 响应与地球科学相关的用户查询保持一致。此外，我们还建立了第一个地球科学基准，GeoBench，用于评估地球科学背景下的 LLM。在这项工作中，我们试验了一个完整的配方，以适应预先训练的一般领域 LLM 的地球科学领域。具体而言，我们进一步训练 LLaMA-7B 模型在地学文本语料库的5.5 B 标记上，包括超过100万篇地学文献，并利用 GeoSignal 的监督数据来微调模型。此外，我们共享一个协议，可以有效地收集特定领域的数据和构造领域监督的数据，即使在人力资源紧缺的情况下。同时，我们使 K2具备使用工具的能力，成为一个天真的地球科学助手。在 GeoBench 上进行的实验证明了我们的方法和数据集对地球科学知识的理解和利用的有效性。我们开源了所有的训练数据和 https://github.com/davendw49/k2的 k2模型检查点。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=K2:+A+Foundation+Language+Model+for+Geoscience+Knowledge+Understanding+and+Utilization)|1| |[Collaboration and Transition: Distilling Item Transitions into Multi-Query Self-Attention for Sequential Recommendation](https://doi.org/10.1145/3616855.3635787)|Tianyu Zhu, Yansong Shi, Yuan Zhang, Yihong Wu, Fengran Mo, JianYun Nie||Modern recommender systems employ various sequential modules such as self-attention to learn dynamic user interests. However, these methods are less effective in capturing collaborative and transitional signals within user interaction sequences. First, the self-attention architecture uses the embedding of a single item as the attention query, which is inherently challenging to capture collaborative signals. Second, these methods typically follow an auto-regressive framework, which is unable to learn global item transition patterns. To overcome these limitations, we propose a new method called Multi-Query Self-Attention with Transition-Aware Embedding Distillation (MQSA-TED). First, we propose an $L$-query self-attention module that employs flexible window sizes for attention queries to capture collaborative signals. In addition, we introduce a multi-query self-attention method that balances the bias-variance trade-off in modeling user preferences by combining long and short-query self-attentions. Second, we develop a transition-aware embedding distillation module that distills global item-to-item transition patterns into item embeddings, which enables the model to memorize and leverage transitional signals and serves as a calibrator for collaborative signals. Experimental results on four real-world datasets show the superiority of our proposed method over state-of-the-art sequential recommendation methods.|现代推荐系统采用自我关注等多种顺序模块来学习动态用户兴趣。然而，这些方法在捕获用户交互序列中的协作和过渡信号方面效率较低。首先，自我注意体系结构使用嵌入单个条目作为注意查询，这对于捕获协作信号具有内在的挑战性。其次，这些方法通常遵循一个自动回归框架，该框架不能学习全局项目转换模式。为了克服这些局限性，本文提出了一种新的基于过渡意识的多查询自注意(MQSA-TED)方法。首先，我们提出了一个 $L $- query 自我注意模块，该模块使用灵活的窗口大小来进行注意查询以捕获协作信号。此外，本文还提出了一种结合长查询和短查询自注意的多查询自注意方法，平衡了偏差-方差权衡。其次，我们开发了一个具有过渡意识的嵌入蒸馏模块，该模块将全局项目到项目的过渡模式提取为项目嵌入，使模型能够记忆和利用过渡信号，并作为协作信号的校准器。在四个实际数据集上的实验结果表明，本文提出的方法优于目前最先进的顺序推荐方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Collaboration+and+Transition:+Distilling+Item+Transitions+into+Multi-Query+Self-Attention+for+Sequential+Recommendation)|0| -|[Contextual MAB Oriented Embedding Denoising for Sequential Recommendation](https://doi.org/10.1145/3616855.3635798)|Zhichao Feng, Pengfei Wang, Kaiyuan Li, Chenliang Li, Shangguang Wang|Beijing Univ Posts & Telecommun, Sch Comp Sci, Beijing, Peoples R China; Wuhan Univ, Sch Cyber Sci & Engn, Wuhan, Peoples R China|Deep neural networks now have become the de-facto standard for sequential recommendation. In the existing techniques, an embedding vector is assigned for each item, encoding all the characteristics of the latter in latent space. Then, the recommendation is transferred to devising a similarity metric to recommend user's next behavior. Here, we consider each dimension of an embedding vector as a (latent) feature. Though effective, it is unknown which feature carries what semantics toward the item. Actually, in reality, this merit is highly preferable since a specific group of features could induce a particular relation among the items while the others are in vain. Unfortunately, the previous treatment overlooks the feature semantic learning at such a fine-grained level. When each item contains multiple latent aspects, which however is prevalent in real-world, the relations between items are very complex. The existing solutions are easy to fail on better recommendation performance. It is necessary to disentangle the item embeddings and extract credible features in a context-aware manner. To address this issue, in this work, we present a novel Contextual MAB based Embedding Denoising model (Comed for short) to adaptively identify relevant dimension-level features for a better recommendation. Specifically, Comed formulates the embedding denoising task as a Contextual Multi-armed Bandit problem. For each feature of the item embedding, we assign a two-armed neural bandit to determine whether the constituent semantics should be preserved, rendering the whole process as embedding denoising. By aggregating the denoised embeddings as contextual information, a reward function deduced by the similarity between the historical interaction sequence and the target item is further designed to approximate the maximum expected payoffs of bandits for efficient learning. Considering the possible inefficiency of training the serial operating mechanism, we also design a swift learning strategy to accelerate the co-guidance between the renovated sequential embedding and the parallel actions of neural bandits for a better recommendation. Comprehensive trials conducted on four widely recognized benchmarks substantiate the efficiency and efficacy of our framework.|深层神经网络现在已经成为事实上的顺序推荐标准。在现有的技术中，为每个项目指定一个嵌入向量，将后者的所有特征编码到潜在空间中。然后，将推荐转换为设计一个相似性度量来推荐用户的下一个行为。在这里，我们将嵌入向量的每个维视为一个(潜在的)特征。虽然有效，但是不知道哪个特性对项目承载了什么语义。事实上，在现实中，这种优点是非常可取的，因为一组特定的特征可以在项目之间产生一种特定的关系，而其他的都是徒劳的。不幸的是，以前的处理忽略了特征语义学习在这样一个细粒度的水平。当每个项目包含多个潜在方面时，项目之间的关系是非常复杂的，而这在现实世界中却是普遍存在的。现有的解决方案很容易因为更好的推荐性能而失败。有必要对项目嵌入进行解密，并以上下文感知的方式提取可信特征。为了解决这一问题，本文提出了一种新的基于上下文 MAB 的嵌入式去噪模型(简称 Comed) ，用于自适应地识别相关的维级特征，以获得更好的推荐。具体来说，Comed 将嵌入去噪任务描述为一个上下文多臂老虎机问题。对于项目嵌入的每一个特征，我们分配一个两臂神经元来确定是否应该保留组成语义，将整个过程描述为嵌入去噪。通过将去噪嵌入作为上下文信息进行聚合，进一步设计了一个由历史交互序列与目标项之间的相似性推导出的奖励函数，以逼近土匪有效学习的最大期望收益。考虑到训练串行操作机制可能效率低下，我们还设计了一种快速学习策略，以加速改进的顺序嵌入与神经网络并行机制之间的协同引导，从而得到更好的推荐。根据四项得到广泛承认的基准进行的全面试验证实了我们框架的效率和效力。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Contextual+MAB+Oriented+Embedding+Denoising+for+Sequential+Recommendation)|0| -|[User Behavior Enriched Temporal Knowledge Graphs for Sequential Recommendation](https://doi.org/10.1145/3616855.3635762)|Hengchang Hu, Wei Guo, Xu Liu, Yong Liu, Ruiming Tang, Rui Zhang, MinYen Kan|Huawei Noahs Ark Lab, Singapore, Singapore; Natl Univ Singapore, Singapore, Singapore; Ruizhang Info, Nanjing, Peoples R China|Knowledge Graphs (KGs) enhance recommendations by providing external connectivity between items. However, there is limited research on distilling relevant knowledge in sequential recommendation, where item connections can change over time. To address this, we introduce the Temporal Knowledge Graph (TKG), which incorporates such dynamic features of user behaviors into the original KG while emphasizing sequential relationships. The TKG captures both patterns of entity dynamics (nodes) and structural dynamics (edges). Considering real-world applications with large-scale and rapidly evolving user behavior patterns, we propose an efficient two-phase framework called TKG-SRec, which strengthens Sequential Rec-ommendation with Temporal KGs. In the first phase, we learn dynamic entity embeddings using our novel Knowledge Evolution Network (KEN) that brings together pretrained static knowledge with evolving temporal knowledge. In the second stage, downstream sequential recommender models utilize these time-specific dynamic entity embeddings with compatible neural backbones like GRUs, Transformers, and MLPs. From our extensive experiments over four datasets, TKG-SRec outperforms the current state-of-the-art by a statistically significant 5% on average. Detailed analysis validates that such filtered temporal knowledge better adapts entity embedding for sequential recommendation. In summary, TKG-SRec provides an effective and efficient approach.|知识图表(KGs)通过提供项目之间的外部连接来增强建议。然而，在序贯推荐中提取相关知识的研究很有限，因为项目间的联系会随着时间的推移而改变。为了解决这个问题，我们引入了时态知识图(TKG) ，它在强调顺序关系的同时，将用户行为的动态特征整合到原始知识图中。TKG 捕获了实体动力学(节点)和结构动力学(边)两种模式。考虑到现实世界中具有大规模和快速发展的用户行为模式的应用程序，我们提出了一个有效的两阶段框架 TKG-SRec，该框架使用时态 KG 强化了顺序推荐。在第一阶段，我们使用新的知识进化网络(KEN)学习动态实体嵌入，该网络将预先训练的静态知识与进化的时态知识结合在一起。在第二阶段，下游顺序推荐模型利用这些时间特定的动态实体嵌入与兼容的神经骨干，如 GRU，变压器和 MLP。从我们对四个数据集的广泛实验来看，TKG-SRec 的性能平均比目前的最先进水平高出5% 。详细的分析验证了这种过滤后的时态知识更适合于实体嵌入的顺序推荐。总之，TKG-SRec 提供了一种有效的方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=User+Behavior+Enriched+Temporal+Knowledge+Graphs+for+Sequential+Recommendation)|0| +|[Contextual MAB Oriented Embedding Denoising for Sequential Recommendation](https://doi.org/10.1145/3616855.3635798)|Zhichao Feng, Pengfei Wang, Kaiyuan Li, Chenliang Li, Shangguang Wang|Wuhan Univ, Sch Cyber Sci & Engn, Wuhan, Peoples R China; Beijing Univ Posts & Telecommun, Sch Comp Sci, Beijing, Peoples R China|Deep neural networks now have become the de-facto standard for sequential recommendation. In the existing techniques, an embedding vector is assigned for each item, encoding all the characteristics of the latter in latent space. Then, the recommendation is transferred to devising a similarity metric to recommend user's next behavior. Here, we consider each dimension of an embedding vector as a (latent) feature. Though effective, it is unknown which feature carries what semantics toward the item. Actually, in reality, this merit is highly preferable since a specific group of features could induce a particular relation among the items while the others are in vain. Unfortunately, the previous treatment overlooks the feature semantic learning at such a fine-grained level. When each item contains multiple latent aspects, which however is prevalent in real-world, the relations between items are very complex. The existing solutions are easy to fail on better recommendation performance. It is necessary to disentangle the item embeddings and extract credible features in a context-aware manner. To address this issue, in this work, we present a novel Contextual MAB based Embedding Denoising model (Comed for short) to adaptively identify relevant dimension-level features for a better recommendation. Specifically, Comed formulates the embedding denoising task as a Contextual Multi-armed Bandit problem. For each feature of the item embedding, we assign a two-armed neural bandit to determine whether the constituent semantics should be preserved, rendering the whole process as embedding denoising. By aggregating the denoised embeddings as contextual information, a reward function deduced by the similarity between the historical interaction sequence and the target item is further designed to approximate the maximum expected payoffs of bandits for efficient learning. Considering the possible inefficiency of training the serial operating mechanism, we also design a swift learning strategy to accelerate the co-guidance between the renovated sequential embedding and the parallel actions of neural bandits for a better recommendation. Comprehensive trials conducted on four widely recognized benchmarks substantiate the efficiency and efficacy of our framework.|深层神经网络现在已经成为事实上的顺序推荐标准。在现有的技术中，为每个项目指定一个嵌入向量，将后者的所有特征编码到潜在空间中。然后，将推荐转换为设计一个相似性度量来推荐用户的下一个行为。在这里，我们将嵌入向量的每个维视为一个(潜在的)特征。虽然有效，但是不知道哪个特性对项目承载了什么语义。事实上，在现实中，这种优点是非常可取的，因为一组特定的特征可以在项目之间产生一种特定的关系，而其他的都是徒劳的。不幸的是，以前的处理忽略了特征语义学习在这样一个细粒度的水平。当每个项目包含多个潜在方面时，项目之间的关系是非常复杂的，而这在现实世界中却是普遍存在的。现有的解决方案很容易因为更好的推荐性能而失败。有必要对项目嵌入进行解密，并以上下文感知的方式提取可信特征。为了解决这一问题，本文提出了一种新的基于上下文 MAB 的嵌入式去噪模型(简称 Comed) ，用于自适应地识别相关的维级特征，以获得更好的推荐。具体来说，Comed 将嵌入去噪任务描述为一个上下文多臂老虎机问题。对于项目嵌入的每一个特征，我们分配一个两臂神经元来确定是否应该保留组成语义，将整个过程描述为嵌入去噪。通过将去噪嵌入作为上下文信息进行聚合，进一步设计了一个由历史交互序列与目标项之间的相似性推导出的奖励函数，以逼近土匪有效学习的最大期望收益。考虑到训练串行操作机制可能效率低下，我们还设计了一种快速学习策略，以加速改进的顺序嵌入与神经网络并行机制之间的协同引导，从而得到更好的推荐。根据四项得到广泛承认的基准进行的全面试验证实了我们框架的效率和效力。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Contextual+MAB+Oriented+Embedding+Denoising+for+Sequential+Recommendation)|0| +|[User Behavior Enriched Temporal Knowledge Graphs for Sequential Recommendation](https://doi.org/10.1145/3616855.3635762)|Hengchang Hu, Wei Guo, Xu Liu, Yong Liu, Ruiming Tang, Rui Zhang, MinYen Kan|Natl Univ Singapore, Singapore, Singapore; Huawei Noahs Ark Lab, Singapore, Singapore; Ruizhang Info, Nanjing, Peoples R China|Knowledge Graphs (KGs) enhance recommendations by providing external connectivity between items. However, there is limited research on distilling relevant knowledge in sequential recommendation, where item connections can change over time. To address this, we introduce the Temporal Knowledge Graph (TKG), which incorporates such dynamic features of user behaviors into the original KG while emphasizing sequential relationships. The TKG captures both patterns of entity dynamics (nodes) and structural dynamics (edges). Considering real-world applications with large-scale and rapidly evolving user behavior patterns, we propose an efficient two-phase framework called TKG-SRec, which strengthens Sequential Rec-ommendation with Temporal KGs. In the first phase, we learn dynamic entity embeddings using our novel Knowledge Evolution Network (KEN) that brings together pretrained static knowledge with evolving temporal knowledge. In the second stage, downstream sequential recommender models utilize these time-specific dynamic entity embeddings with compatible neural backbones like GRUs, Transformers, and MLPs. From our extensive experiments over four datasets, TKG-SRec outperforms the current state-of-the-art by a statistically significant 5% on average. Detailed analysis validates that such filtered temporal knowledge better adapts entity embedding for sequential recommendation. In summary, TKG-SRec provides an effective and efficient approach.|知识图表(KGs)通过提供项目之间的外部连接来增强建议。然而，在序贯推荐中提取相关知识的研究很有限，因为项目间的联系会随着时间的推移而改变。为了解决这个问题，我们引入了时态知识图(TKG) ，它在强调顺序关系的同时，将用户行为的动态特征整合到原始知识图中。TKG 捕获了实体动力学(节点)和结构动力学(边)两种模式。考虑到现实世界中具有大规模和快速发展的用户行为模式的应用程序，我们提出了一个有效的两阶段框架 TKG-SRec，该框架使用时态 KG 强化了顺序推荐。在第一阶段，我们使用新的知识进化网络(KEN)学习动态实体嵌入，该网络将预先训练的静态知识与进化的时态知识结合在一起。在第二阶段，下游顺序推荐模型利用这些时间特定的动态实体嵌入与兼容的神经骨干，如 GRU，变压器和 MLP。从我们对四个数据集的广泛实验来看，TKG-SRec 的性能平均比目前的最先进水平高出5% 。详细的分析验证了这种过滤后的时态知识更适合于实体嵌入的顺序推荐。总之，TKG-SRec 提供了一种有效的方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=User+Behavior+Enriched+Temporal+Knowledge+Graphs+for+Sequential+Recommendation)|0| |[Global Heterogeneous Graph and Target Interest Denoising for Multi-behavior Sequential Recommendation](https://doi.org/10.1145/3616855.3635857)|Xuewei Li, Hongwei Chen, Jian Yu, Mankun Zhao, Tianyi Xu, Wenbin Zhang, Mei Yu|Tianjin Univ, Informat & Network Ctr, Tianjin, Peoples R China; Tianjin Univ, Coll Intelligence & Comp, Tianjin, Peoples R China|Multi-behavior sequential recommendation (MBSR) predicts a user's next item of interest based on their interaction history across different behavior types. Although existing studies have proposed capturing the correlation between different types of behavior, two important challenges have not been explored: i) Dealing with heterogeneous item transitions (both global and local perspectives). ii) Mitigating the issue of noise that arises from the incorporation of auxiliary behaviors. To address these issues, we propose a novel solution, Global Heterogeneous Graph and Target Interest Denoising for Multi-behavior Sequential Recommendation (GHTID). In particular, we view the transitions between behavior types of items as different relationships and propose two heterogeneous graphs. By considering the relationship between items under different behavioral types of transformations, we propose two heterogeneous graph convolution modules and explicitly learn heterogeneous item transitions. Moreover, we utilize two attention networks to integrate long-term and short-term interests associated with the target behavior to alleviate the noisy interference of auxiliary behaviors. Extensive experiments on four real-world datasets demonstrate that our method outperforms other state-of-the-art methods.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Global+Heterogeneous+Graph+and+Target+Interest+Denoising+for+Multi-behavior+Sequential+Recommendation)|0| |[Attribute Simulation for Item Embedding Enhancement in Multi-interest Recommendation](https://doi.org/10.1145/3616855.3635841)|Yaokun Liu, Xiaowang Zhang, Minghui Zou, Zhiyong Feng||Although multi-interest recommenders have achieved significant progress in the matching stage, our research reveals that existing models tend to exhibit an under-clustered item embedding space, which leads to a low discernibility between items and hampers item retrieval. This highlights the necessity for item embedding enhancement. However, item attributes, which serve as effective and straightforward side information for enhancement, are either unavailable or incomplete in many public datasets due to the labor-intensive nature of manual annotation tasks. This dilemma raises two meaningful questions: 1. Can we bypass manual annotation and directly simulate complete attribute information from the interaction data? And 2. If feasible, how to simulate attributes with high accuracy and low complexity in the matching stage? In this paper, we first establish an inspiring theoretical feasibility that the item-attribute correlation matrix can be approximated through elementary transformations on the item co-occurrence matrix. Then based on formula derivation, we propose a simple yet effective module, SimEmb (Item Embedding Enhancement via Simulated Attribute), in the multi-interest recommendation of the matching stage to implement our findings. By simulating attributes with the co-occurrence matrix, SimEmb discards the item ID-based embedding and employs the attribute-weighted summation for item embedding enhancement. Comprehensive experiments on four benchmark datasets demonstrate that our approach notably enhances the clustering of item embedding and significantly outperforms SOTA models with an average improvement of 25.59% on Recall@20.|虽然多兴趣推荐系统在匹配阶段已经取得了显著的进展，但是我们的研究发现现有的模型倾向于表现出一种欠聚类的项目嵌入空间，导致项目之间的差异性较低，从而阻碍了项目的检索。这突出了项目嵌入增强的必要性。然而，由于人工注释任务的劳动密集性，在许多公共数据集中，作为有效和直接的增强辅助信息的项属性要么不可用，要么不完整。这个困境提出了两个有意义的问题: 1。我们是否可以绕过手动注释，直接从交互数据模拟完整的属性信息？二。如果可行，如何在匹配阶段模拟高精度、低复杂度的属性？在本文中，我们首先建立了一个鼓舞人心的理论可行性，即项目-属性相关矩阵可以通过项目共现矩阵的初等变换来近似。然后在公式推导的基础上，在匹配阶段的多兴趣推荐中，提出了一个简单而有效的模块——模拟属性项嵌入增强模块(SimEmb) ，以实现我们的研究结果。通过使用共生矩阵模拟属性，SimEmb 放弃了基于项目 ID 的嵌入，采用属性加权和的方法对项目进行嵌入增强。通过对四个基准数据集的综合实验表明，该方法显著提高了项目嵌入的聚类性能，并明显优于 SOTA 模型，在 Recall@20上平均提高了25.59% 。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Attribute+Simulation+for+Item+Embedding+Enhancement+in+Multi-interest+Recommendation)|0| |[Deep Evolutional Instant Interest Network for CTR Prediction in Trigger-Induced Recommendation](https://doi.org/10.1145/3616855.3635829)|Zhibo Xiao, Luwei Yang, Tao Zhang, Wen Jiang, Wei Ning, Yujiu Yang||The recommendation has been playing a key role in many industries, e.g., e-commerce, streaming media, social media, etc. Recently, a new recommendation scenario, called Trigger-Induced Recommendation (TIR), where users are able to explicitly express their instant interests via trigger items, is emerging as an essential role in many e-commerce platforms, e.g., Alibaba.com and Amazon. Without explicitly modeling the user's instant interest, traditional recommendation methods usually obtain sub-optimal results in TIR. Even though there are a few methods considering the trigger and target items simultaneously to solve this problem, they still haven't taken into account temporal information of user behaviors, the dynamic change of user instant interest when the user scrolls down and the interactions between the trigger and target items. To tackle these problems, we propose a novel method – Deep Evolutional Instant Interest Network (DEI2N), for click-through rate prediction in TIR scenarios. Specifically, we design a User Instant Interest Modeling Layer to predict the dynamic change of the intensity of instant interest when the user scrolls down. Temporal information is utilized in user behavior modeling. Moreover, an Interaction Layer is introduced to learn better interactions between the trigger and target items. We evaluate our method on several offline and real-world industrial datasets. Experimental results show that our proposed DEI2N outperforms state-of-the-art baselines. In addition, online A/B testing demonstrates the superiority over the existing baseline in real-world production environments.|推荐在许多行业都发挥了重要作用，例如电子商务、流媒体、社交媒体等。最近，一个新的推荐场景，称为触发诱导推荐(TIR) ，用户可以通过触发条目明确表达他们的即时兴趣，正在成为许多电子商务平台，如阿里巴巴和亚马逊的重要角色。传统的推荐方法在没有明确建立用户即时兴趣模型的情况下，往往在 TIR 中得到次优的推荐结果。针对这一问题，目前虽然有很多方法同时考虑了触发条目和目标条目，但都没有考虑到用户行为的时间信息、用户向下滚动时瞬间兴趣的动态变化以及触发条目和目标条目之间的交互作用。为了解决这些问题，我们提出了一种新的方法-深度进化即时兴趣网络(DEI2N) ，用于 TIR 场景中的点进率预测。具体来说，我们设计了一个用户即时兴趣建模层来预测用户向下滚动时即时兴趣强度的动态变化。时态信息用于用户行为建模。此外，还引入了交互层来学习触发器和目标项之间更好的交互。我们评估我们的方法在几个离线和现实世界的工业数据集。实验结果表明，我们提出的 DEI2N 性能优于最先进的基线。此外，在线 A/B 测试证明了在现实生产环境中优于现有基线的优越性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Deep+Evolutional+Instant+Interest+Network+for+CTR+Prediction+in+Trigger-Induced+Recommendation)|0| @@ -21,10 +21,10 @@ |[User Consented Federated Recommender System Against Personalized Attribute Inference Attack](https://doi.org/10.1145/3616855.3635830)|Qi Hu, Yangqiu Song||Recommender systems can be privacy-sensitive. To protect users' private historical interactions, federated learning has been proposed in distributed learning for user representations. Using federated recommender (FedRec) systems, users can train a shared recommendation model on local devices and prevent raw data transmissions and collections. However, the recommendation model learned by a common FedRec may still be vulnerable to private information leakage risks, particularly attribute inference attacks, which means that the attacker can easily infer users' personal attributes from the learned model. Additionally, traditional FedRecs seldom consider the diverse privacy preference of users, leading to difficulties in balancing the recommendation utility and privacy preservation. Consequently, FedRecs may suffer from unnecessary recommendation performance loss due to over-protection and private information leakage simultaneously. In this work, we propose a novel user-consented federated recommendation system (UC-FedRec) to flexibly satisfy the different privacy needs of users by paying a minimum recommendation accuracy price. UC-FedRec allows users to self-define their privacy preferences to meet various demands and makes recommendations with user consent. Experiments conducted on different real-world datasets demonstrate that our framework is more efficient and flexible compared to baselines.|推荐系统可能对隐私敏感。为了保护用户的私有历史交互，联邦学习被提出用于用户表示的分布式学习。使用联邦推荐(FedRec)系统，用户可以在本地设备上培训共享推荐模型，并防止原始数据传输和收集。然而，一个普通的联邦快递推荐模型可能仍然容易受到私人信息泄露的攻击，特别是属性推理攻击，这意味着攻击者可以很容易地从推荐模型中推断出用户的个人属性。此外，传统的 FedRecs 很少考虑用户的不同隐私偏好，导致难以平衡推荐实用程序和隐私保护。因此，由于过度保护和私人信息泄露，美联储可能同时遭受不必要的推荐性能损失。在本研究中，我们提出一个新的使用者同意的联邦推荐系统(UC-FedRec) ，以支付最低的推荐准确度价格，灵活地满足使用者不同的隐私需求。UC-FedRec 允许用户自定义他们的隐私偏好，以满足不同的需求，并在用户同意的情况下提出建议。在不同的实际数据集上进行的实验表明，我们的框架比基线更有效和灵活。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=User+Consented+Federated+Recommender+System+Against+Personalized+Attribute+Inference+Attack)|0| |[Multi-Intent Attribute-Aware Text Matching in Searching](https://doi.org/10.1145/3616855.3635813)|Mingzhe Li, Xiuying Chen, Jing Xiang, Qishen Zhang, Changsheng Ma, Chenchen Dai, Jinxiong Chang, Zhongyi Liu, Guannan Zhang||Text matching systems have become a fundamental service in most searching platforms. For instance, they are responsible for matching user queries to relevant candidate items, or rewriting the user-input query to a pre-selected high-performing one for a better search experience. In practice, both the queries and items often contain multiple attributes, such as the category of the item and the location mentioned in the query, which represent condensed key information that is helpful for matching. However, most of the existing works downplay the effectiveness of attributes by integrating them into text representations as supplementary information. Hence, in this work, we focus on exploring the relationship between the attributes from two sides. Since attributes from two ends are often not aligned in terms of number and type, we propose to exploit the benefit of attributes by multiple-intent modeling. The intents extracted from attributes summarize the diverse needs of queries and provide rich content of items, which are more refined and abstract, and can be aligned for paired inputs. Concretely, we propose a multi-intent attribute-aware matching model (MIM), which consists of three main components: attribute-aware encoder, multi-intent modeling, and intent-aware matching. In the attribute-aware encoder, the text and attributes are weighted and processed through a scaled attention mechanism with regard to the attributes' importance. Afterward, the multi-intent modeling extracts intents from two ends and aligns them. Herein, we come up with a distribution loss to ensure the learned intents are diverse but concentrated, and a kullback-leibler divergence loss that aligns the learned intents. Finally, in the intent-aware matching, the intents are evaluated by a self-supervised masking task, and then incorporated to output the final matching result.|文本匹配系统已经成为大多数搜索平台的基础服务。例如，它们负责将用户查询与相关候选项匹配，或者将用户输入查询重写为预先选定的高性能查询，以获得更好的搜索体验。实际上，查询和项通常都包含多个属性，例如项的类别和查询中提到的位置，这些属性表示有助于匹配的压缩键信息。然而，现有的大多数作品通过将属性作为补充信息集成到文本表示中来淡化属性的有效性。因此，本文着重从两个方面探讨属性之间的关系。由于来自两端的属性通常在数量和类型方面不一致，我们建议通过多意图建模来利用属性的优势。从属性中提取的意图总结了查询的不同需求，并提供了丰富的项目内容，这些内容更加精炼和抽象，并且可以对成对的输入进行对齐。具体地说，我们提出了一个多意图属性感知匹配模型(MIM) ，它由三个主要部分组成: 属性感知编码器、多意图建模和意图感知匹配。在属性感知编码器中，文本和属性根据属性的重要性通过缩放注意机制进行加权和处理。然后，多意图建模从两端提取意图并对齐它们。在这里，我们提出了一个分布损失，以确保学习意图的多样性，但集中，和一个 kullback-leibler 散度损失，调整了学习意图。最后，在意图感知匹配中，通过自监督掩蔽任务对意图进行评估，然后合并到一起输出最终的匹配结果。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Multi-Intent+Attribute-Aware+Text+Matching+in+Searching)|0| |[Mixed Attention Network for Cross-domain Sequential Recommendation](https://doi.org/10.1145/3616855.3635801)|Guanyu Lin, Chen Gao, Yu Zheng, Jianxin Chang, Yanan Niu, Yang Song, Kun Gai, Zhiheng Li, Depeng Jin, Yong Li, Meng Wang||In modern recommender systems, sequential recommendation leverages chronological user behaviors to make effective next-item suggestions, which suffers from data sparsity issues, especially for new users. One promising line of work is the cross-domain recommendation, which trains models with data across multiple domains to improve the performance in data-scarce domains. Recent proposed cross-domain sequential recommendation models such as PiNet and DASL have a common drawback relying heavily on overlapped users in different domains, which limits their usage in practical recommender systems. In this paper, we propose a Mixed Attention Network (MAN) with local and global attention modules to extract the domain-specific and cross-domain information. Firstly, we propose a local/global encoding layer to capture the domain-specific/cross-domain sequential pattern. Then we propose a mixed attention layer with item similarity attention, sequence-fusion attention, and group-prototype attention to capture the local/global item similarity, fuse the local/global item sequence, and extract the user groups across different domains, respectively. Finally, we propose a local/global prediction layer to further evolve and combine the domain-specific and cross-domain interests. Experimental results on two real-world datasets (each with two domains) demonstrate the superiority of our proposed model. Further study also illustrates that our proposed method and components are model-agnostic and effective, respectively. The code and data are available at https://github.com/Guanyu-Lin/MAN.|在现代推荐系统中，顺序推荐利用按时间顺序排列的用户行为来提出有效的下一项推荐，这种推荐存在数据稀疏问题，尤其是对于新用户。一个很有前途的工作领域是跨域推荐，它用跨多个域的数据来训练模型，以提高数据稀缺域的性能。最近提出的跨域顺序推荐模型，如 PiNet 和 DASL，有一个共同的缺点，即严重依赖于不同领域的重叠用户，这限制了它们在实际推荐系统中的使用。本文提出了一种基于局部和全局注意模块的混合注意网络(MAN)来提取特定领域和跨领域的信息。首先，我们提出了一个局部/全局编码层来捕获域特定/跨域的序列模式。然后提出了一个具有项目相似性注意、序列融合注意和群体原型注意的混合注意层，分别捕获局部/全局项目相似性，融合局部/全局项目序列，提取不同领域的用户群。最后，我们提出了一个局部/全局预测层，以进一步发展和结合特定领域和跨领域的利益。在两个实际数据集上的实验结果表明了该模型的优越性。进一步的研究还表明，我们提出的方法和组件是模型无关的和有效的，分别。代码和数据可在 https://github.com/guanyu-lin/man 查阅。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Mixed+Attention+Network+for+Cross-domain+Sequential+Recommendation)|0| -|[Multi-Sequence Attentive User Representation Learning for Side-information Integrated Sequential Recommendation](https://doi.org/10.1145/3616855.3635815)|Xiaolin Lin, Jinwei Luo, Junwei Pan, Weike Pan, Zhong Ming, Xun Liu, Shudong Huang, Jie Jiang|Shenzhen Univ, Coll Comp Sci & Software Engn, Shenzhen, Peoples R China; Tencent Mus Entertainment, Shenzhen, Peoples R China; Shenzhen Technol Univ, Shenzhen Univ, Guangdong Lab Artificial Intelligence & Digital E, Shenzhen, Peoples R China; Shenzhen Univ, Shenzhen, Peoples R China; Tencent, Shenzhen, Peoples R China|Side-information integrated sequential recommendation incorporates supplementary information to alleviate the issue of data sparsity. The state-of-the-art works mainly leverage some side information to improve the attention calculation to learn user representation more accurately. However, there are still some limitations to be addressed in this topic. Most of them merely learn the user representation at the item level and overlook the association of the item sequence and the side-information sequences when calculating the attentions, which results in the incomprehensive learning of user representation. Some of them learn the user representations at both the item and side-information levels, but they still face the problem of insufficient optimization of multiple user representations. To address these limitations, we propose a novel model, i.e., Multi-Sequence Sequential Recommender (MSSR), which learns the user's multiple representations from diverse sequences. Specifically, we design a multi-sequence integrated attention layer to learn more attentive pairs than the existing works and adaptively fuse these pairs to learn user representation. Moreover, our user representation alignment module constructs the self-supervised signals to optimize the representations. Subsequently, they are further refined by our side information predictor during training. For item prediction, our MSSR extra considers the side information of the candidate item, enabling a comprehensive measurement of the user's preferences. Extensive experiments on four public datasets show that our MSSR outperforms eleven state-of-the-art baselines. Visualization and case study also demonstrate the rationality and interpretability of our MSSR.|边缘信息集成顺序推荐采用补充信息的方式来缓解数据稀疏性问题。最先进的作品主要是利用一些侧面信息来改进注意力计算，从而更准确地学习用户表征。但是，在这个主题中仍然有一些限制需要解决。它们大多只是在项目层次上学习用户表征，在计算注意力时忽略了项目序列与侧信息序列之间的关联，导致用户表征学习的不全面。其中一些在项目和侧信息级别学习用户表示，但是它们仍然面临多个用户表示不够优化的问题。为了解决这些局限性，我们提出了一种新的模型，即多序列序列推荐器(MSSR) ，它从不同的序列中学习用户的多重表示。具体来说，我们设计了一个多序列的集成注意层来学习比现有作品更多的注意对，并自适应地融合这些注意对来学习用户表征。此外，我们的用户表示对齐模块构造自我监督信号来优化表示。随后，在训练过程中通过我们的侧信息预测器对其进行了进一步细化。对于项目预测，我们的 MSSR 额外考虑了候选项目的侧面信息，从而能够全面测量用户的偏好。对四个公共数据集的大量实验表明，我们的微卫星定位系统优于十一个最先进的基准线。可视化和案例分析也证明了我们的 MSSR 的合理性和可解释性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Multi-Sequence+Attentive+User+Representation+Learning+for+Side-information+Integrated+Sequential+Recommendation)|0| +|[Multi-Sequence Attentive User Representation Learning for Side-information Integrated Sequential Recommendation](https://doi.org/10.1145/3616855.3635815)|Xiaolin Lin, Jinwei Luo, Junwei Pan, Weike Pan, Zhong Ming, Xun Liu, Shudong Huang, Jie Jiang|Shenzhen Technol Univ, Shenzhen Univ, Guangdong Lab Artificial Intelligence & Digital E, Shenzhen, Peoples R China; Tencent, Shenzhen, Peoples R China; Shenzhen Univ, Shenzhen, Peoples R China; Shenzhen Univ, Coll Comp Sci & Software Engn, Shenzhen, Peoples R China; Tencent Mus Entertainment, Shenzhen, Peoples R China|Side-information integrated sequential recommendation incorporates supplementary information to alleviate the issue of data sparsity. The state-of-the-art works mainly leverage some side information to improve the attention calculation to learn user representation more accurately. However, there are still some limitations to be addressed in this topic. Most of them merely learn the user representation at the item level and overlook the association of the item sequence and the side-information sequences when calculating the attentions, which results in the incomprehensive learning of user representation. Some of them learn the user representations at both the item and side-information levels, but they still face the problem of insufficient optimization of multiple user representations. To address these limitations, we propose a novel model, i.e., Multi-Sequence Sequential Recommender (MSSR), which learns the user's multiple representations from diverse sequences. Specifically, we design a multi-sequence integrated attention layer to learn more attentive pairs than the existing works and adaptively fuse these pairs to learn user representation. Moreover, our user representation alignment module constructs the self-supervised signals to optimize the representations. Subsequently, they are further refined by our side information predictor during training. For item prediction, our MSSR extra considers the side information of the candidate item, enabling a comprehensive measurement of the user's preferences. Extensive experiments on four public datasets show that our MSSR outperforms eleven state-of-the-art baselines. Visualization and case study also demonstrate the rationality and interpretability of our MSSR.|边缘信息集成顺序推荐采用补充信息的方式来缓解数据稀疏性问题。最先进的作品主要是利用一些侧面信息来改进注意力计算，从而更准确地学习用户表征。但是，在这个主题中仍然有一些限制需要解决。它们大多只是在项目层次上学习用户表征，在计算注意力时忽略了项目序列与侧信息序列之间的关联，导致用户表征学习的不全面。其中一些在项目和侧信息级别学习用户表示，但是它们仍然面临多个用户表示不够优化的问题。为了解决这些局限性，我们提出了一种新的模型，即多序列序列推荐器(MSSR) ，它从不同的序列中学习用户的多重表示。具体来说，我们设计了一个多序列的集成注意层来学习比现有作品更多的注意对，并自适应地融合这些注意对来学习用户表征。此外，我们的用户表示对齐模块构造自我监督信号来优化表示。随后，在训练过程中通过我们的侧信息预测器对其进行了进一步细化。对于项目预测，我们的 MSSR 额外考虑了候选项目的侧面信息，从而能够全面测量用户的偏好。对四个公共数据集的大量实验表明，我们的微卫星定位系统优于十一个最先进的基准线。可视化和案例分析也证明了我们的 MSSR 的合理性和可解释性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Multi-Sequence+Attentive+User+Representation+Learning+for+Side-information+Integrated+Sequential+Recommendation)|0| |[Intent Contrastive Learning with Cross Subsequences for Sequential Recommendation](https://doi.org/10.1145/3616855.3635773)|Xiuyuan Qin, Huanhuan Yuan, Pengpeng Zhao, Guanfeng Liu, Fuzhen Zhuang, Victor S. Sheng||The user purchase behaviors are mainly influenced by their intentions (e.g., buying clothes for decoration, buying brushes for painting, etc.). Modeling a user's latent intention can significantly improve the performance of recommendations. Previous works model users' intentions by considering the predefined label in auxiliary information or introducing stochastic data augmentation to learn purposes in the latent space. However, the auxiliary information is sparse and not always available for recommender systems, and introducing stochastic data augmentation may introduce noise and thus change the intentions hidden in the sequence. Therefore, leveraging user intentions for sequential recommendation (SR) can be challenging because they are frequently varied and unobserved. In this paper, Intent contrastive learning with Cross Subsequences for sequential Recommendation (ICSRec) is proposed to model users' latent intentions. Specifically, ICSRec first segments a user's sequential behaviors into multiple subsequences by using a dynamic sliding operation and takes these subsequences into the encoder to generate the representations for the user's intentions. To tackle the problem of no explicit labels for purposes, ICSRec assumes different subsequences with the same target item may represent the same intention and proposes a coarse-grain intent contrastive learning to push these subsequences closer. Then, fine-grain intent contrastive learning is mentioned to capture the fine-grain intentions of subsequences in sequential behaviors. Extensive experiments conducted on four real-world datasets demonstrate the superior performance of the proposed ICSRec model compared with baseline methods.|用户的购买行为主要受其购买意图的影响(如购买衣服装饰、购买画笔等)。对用户的潜在意图进行建模可以显著提高推荐的性能。以往的研究通过考虑辅助信息中的预定义标签或引入随机数据增量在潜在空间中学习目的来模拟用户的意图。然而，辅助信息是稀疏的，并不总是可用于推荐系统，引入随机数据增强可能会引入噪声，从而改变意图隐藏在序列。因此，利用用户意图进行顺序推荐(SR)可能是具有挑战性的，因为它们经常变化和未被观察到。本文提出了一种基于交叉子序列的序列推荐意图对比学习(ICSRec)方法来模拟用户的潜在意图。具体来说，ICSRec 首先使用动态滑动操作将用户的连续行为分割成多个子序列，并将这些子序列带入编码器以生成用户意图的表示。为了解决目的不明确标签的问题，ICSRec 假设具有相同目标项的不同子序列可以表示相同的意图，并提出了一种粗粒度意图对比学习方法来使这些子序列更加接近。然后，提出细粒度意图对比学习来捕捉序列行为中子序列的细粒度意图。在四个实际数据集上进行的大量实验表明，与基线方法相比，所提出的 ICSRec 模型具有更好的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Intent+Contrastive+Learning+with+Cross+Subsequences+for+Sequential+Recommendation)|0| |[Debiasing Sequential Recommenders through Distributionally Robust Optimization over System Exposure](https://doi.org/10.1145/3616855.3635848)|Jiyuan Yang, Yue Ding, Yidan Wang, Pengjie Ren, Zhumin Chen, Fei Cai, Jun Ma, Rui Zhang, Zhaochun Ren, Xin Xin||Sequential recommendation (SR) models are typically trained on user-item interactions which are affected by the system exposure bias, leading to the user preference learned from the biased SR model not being fully consistent with the true user preference. Exposure bias refers to the fact that user interactions are dependent upon the partial items exposed to the user. Existing debiasing methods do not make full use of the system exposure data and suffer from sub-optimal recommendation performance and high variance. In this paper, we propose to debias sequential recommenders through Distributionally Robust Optimization (DRO) over system exposure data. The key idea is to utilize DRO to optimize the worst-case error over an uncertainty set to safeguard the model against distributional discrepancy caused by the exposure bias. The main challenge to apply DRO for exposure debiasing in SR lies in how to construct the uncertainty set and avoid the overestimation of user preference on biased samples. Moreover, how to evaluate the debiasing effect on biased test set is also an open question. To this end, we first introduce an exposure simulator trained upon the system exposure data to calculate the exposure distribution, which is then regarded as the nominal distribution to construct the uncertainty set of DRO. Then, we introduce a penalty to items with high exposure probability to avoid the overestimation of user preference for biased samples. Finally, we design a debiased self-normalized inverse propensity score (SNIPS) evaluator for evaluating the debiasing effect on the biased offline test set. We conduct extensive experiments on two real-world datasets to verify the effectiveness of the proposed methods. Experimental results demonstrate the superior exposure debiasing performance of proposed methods. Codes and data are available at \url{https://github.com/nancheng58/DebiasedSR_DRO}.|序贯推荐(SR)模型通常针对受系统暴露偏差影响的用户-项目交互进行训练，导致从偏向 SR 模型中学到的用户偏好与真实用户偏好不完全一致。暴露偏差是指用户交互依赖于暴露给用户的部分项目这一事实。现有的去偏方法没有充分利用系统曝光数据，推荐性能不理想，方差较大。本文提出了一种基于系统曝光数据的分布式鲁棒优化(DRO)方法来降低序列推荐器的偏差。其核心思想是利用 DRO 对不确定集上的最坏情况误差进行优化，以保护模型不受曝光偏差引起的分布差异的影响。如何构造不确定性集合，避免偏差样本对用户偏好的过高估计，是应用 DRO 进行 SR 曝光消偏的主要挑战。此外，如何评价偏置测试集的去偏效果也是一个悬而未决的问题。为此，我们首先介绍了一个基于系统曝光数据训练的曝光模拟器来计算曝光分布，然后将其视为标称分布来构造 DRO 的不确定度集。然后，我们引入了一个惩罚项目的高暴露概率，以避免过高估计的用户偏好偏差样本。最后，我们设计了一个去偏的自标准化逆倾向得分(SNIPS)评估器来评估有偏离离线测试集的去偏效果。为了验证该方法的有效性，我们在两个实际数据集上进行了大量的实验。实验结果表明，该方法具有较好的曝光消偏性能。代码和数据可在 url { https://github.com/nancheng58/debiasedsr_dro }获得。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Debiasing+Sequential+Recommenders+through+Distributionally+Robust+Optimization+over+System+Exposure)|0| -|[Applications of LLMs in E-Commerce Search and Product Knowledge Graph: The DoorDash Case Study](https://doi.org/10.1145/3616855.3635738)|Sudeep Das, Raghav Saboo, Chaitanya S. K. Vadrevu, Bruce Wang, Steven Xu|DoorDash Inc, New York, NY USA; DoorDash Inc, San Francisco, CA 94107 USA; DoorDash Inc, Seattle, WA USA|Extracting knowledge from unstructured or semi-structured textual information is essential for the machine learning applications that power DoorDash's search experience, and the development and maintenance of its product knowledge graph. Large language models (LLMs) have opened up new possibilities for utilizing their power in these areas, replacing or complementing traditional natural language processing methods. LLMs are also proving to be useful in the label and annotation generation process, which is critical for these use cases. In this talk, we will provide a high-level overview of how we incorporated LLMs for search relevance and product understanding use cases, as well as the key lessons learned and challenges faced during their practical implementation.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Applications+of+LLMs+in+E-Commerce+Search+and+Product+Knowledge+Graph:+The+DoorDash+Case+Study)|0| +|[Applications of LLMs in E-Commerce Search and Product Knowledge Graph: The DoorDash Case Study](https://doi.org/10.1145/3616855.3635738)|Sudeep Das, Raghav Saboo, Chaitanya S. K. Vadrevu, Bruce Wang, Steven Xu|DoorDash Inc, Seattle, WA USA; DoorDash Inc, New York, NY USA; DoorDash Inc, San Francisco, CA 94107 USA|Extracting knowledge from unstructured or semi-structured textual information is essential for the machine learning applications that power DoorDash's search experience, and the development and maintenance of its product knowledge graph. Large language models (LLMs) have opened up new possibilities for utilizing their power in these areas, replacing or complementing traditional natural language processing methods. LLMs are also proving to be useful in the label and annotation generation process, which is critical for these use cases. In this talk, we will provide a high-level overview of how we incorporated LLMs for search relevance and product understanding use cases, as well as the key lessons learned and challenges faced during their practical implementation.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Applications+of+LLMs+in+E-Commerce+Search+and+Product+Knowledge+Graph:+The+DoorDash+Case+Study)|0| |[AAGenRec: A Novel Approach for Mitigating Inter-task Interference in Multi-task Optimization of Sequential Behavior Modeling](https://doi.org/10.1145/3616855.3635746)|Jiawei Zhang, Shimin Yang, Liang Shen|Meituan, Beijing, Peoples R China|Multi-task optimization is an emerging research field in recommender systems that aims to enhance the recommendation performance across multiple tasks. Various methodologies have been introduced to address aspects like balancing task weights, handling gradient conflicts, and achieving Pareto optimality. These approaches have shown promise in specific contexts, but are not well-suited for real-world scenarios that involve user sequential behaviors. To address this gap, we present AAGenRec, a novel and effective solution for sequential behavior modeling within multi-task recommender systems, inspired by concepts from acoustic attenuation and genetic differences. Specifically, AAGenRec leverages an established genetic distance method to quantify the dissimilarity between tasks, then introduces an impact attenuation mechanism to mitigate the uncertain task interference in multi-task optimization. Extensive experiments conducted on public e-commerce datasets demonstrate the effectiveness of AAGenRec.|多任务优化是推荐系统中一个新兴的研究领域，旨在提高多任务推荐的性能。我们引入了各种方法来解决诸如平衡任务权重、处理梯度冲突和实现帕累托最优等问题。这些方法已经在特定的上下文中显示了希望，但是不太适合涉及用户顺序行为的真实场景。为了解决这一差距，我们提出了 AAGenRec，一个新颖而有效的解决方案，用于多任务推荐系统中的顺序行为建模，灵感来自声衰减和遗传差异的概念。具体来说，AAGenRec 利用已建立的遗传距离方法来量化任务间的差异，然后引入冲击衰减机制来缓解多任务优化中的不确定性任务干扰。在公共电子商务数据集上进行的大量实验证明了 AAGenRec 的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=AAGenRec:+A+Novel+Approach+for+Mitigating+Inter-task+Interference+in+Multi-task+Optimization+of+Sequential+Behavior+Modeling)|0| |[To Copy, or not to Copy; That is a Critical Issue of the Output Softmax Layer in Neural Sequential Recommenders](https://doi.org/10.1145/3616855.3635755)|HawShiuan Chang, Nikhil Agarwal, Andrew McCallum||Recent studies suggest that the existing neural models have difficulty handling repeated items in sequential recommendation tasks. However, our understanding of this difficulty is still limited. In this study, we substantially advance this field by identifying a major source of the problem: the single hidden state embedding and static item embeddings in the output softmax layer. Specifically, the similarity structure of the global item embeddings in the softmax layer sometimes forces the single hidden state embedding to be close to new items when copying is a better choice, while sometimes forcing the hidden state to be close to the items from the input inappropriately. To alleviate the problem, we adapt the recently-proposed softmax alternatives such as softmax-CPR to sequential recommendation tasks and demonstrate that the new softmax architectures unleash the capability of the neural encoder on learning when to copy and when to exclude the items from the input sequence. By only making some simple modifications on the output softmax layer for SASRec and GRU4Rec, softmax-CPR achieves consistent improvement in 12 datasets. With almost the same model size, our best method not only improves the average NDCG@10 of GRU4Rec in 5 datasets with duplicated items by 10% (4%-17% individually) but also improves 7 datasets without duplicated items by 24% (8%-39%)!|最近的研究表明，现有的神经模型难以处理重复项目的顺序推荐任务。然而，我们对这个困难的理解仍然是有限的。在这项研究中，我们通过识别问题的一个主要来源: 单一的隐藏状态嵌入和静态项嵌入在输出 softmax 层大大推进了这个领域。具体来说，Softmax 层中全局项嵌入的相似性结构有时迫使单个隐藏状态嵌入接近新项，而复制是更好的选择，有时迫使隐藏状态不适当地接近来自输入的项。为了缓解这一问题，我们将最近提出的 softmax 替代方案(如 softmax-CPR)适用于顺序推荐任务，并证明新的 softmax 架构释放了神经编码器学习何时复制和何时从输入序列中排除项目的能力。通过对 SASRec 和 GRU4Rec 的输出 softmax 层进行一些简单的修改，softmax-CPR 在12个数据集中实现了一致的改进。在几乎相同的模型大小下，我们的最佳方法不仅将5个重复项目数据集中 GRU4Rec 的平均 NDCG@10提高了10% (单独4%-17%) ，而且将7个没有重复项目的数据集提高了24% (8%-39%) ！|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=To+Copy,+or+not+to+Copy;+That+is+a+Critical+Issue+of+the+Output+Softmax+Layer+in+Neural+Sequential+Recommenders)|0| |[Budgeted Embedding Table For Recommender Systems](https://doi.org/10.1145/3616855.3635778)|Yunke Qu, Tong Chen, Quoc Viet Hung Nguyen, Hongzhi Yin||At the heart of contemporary recommender systems (RSs) are latent factor models that provide quality recommendation experience to users. These models use embedding vectors, which are typically of a uniform and fixed size, to represent users and items. As the number of users and items continues to grow, this design becomes inefficient and hard to scale. Recent lightweight embedding methods have enabled different users and items to have diverse embedding sizes, but are commonly subject to two major drawbacks. Firstly, they limit the embedding size search to optimizing a heuristic balancing the recommendation quality and the memory complexity, where the trade-off coefficient needs to be manually tuned for every memory budget requested. The implicitly enforced memory complexity term can even fail to cap the parameter usage, making the resultant embedding table fail to meet the memory budget strictly. Secondly, most solutions, especially reinforcement learning based ones derive and optimize the embedding size for each each user/item on an instance-by-instance basis, which impedes the search efficiency. In this paper, we propose Budgeted Embedding Table (BET), a novel method that generates table-level actions (i.e., embedding sizes for all users and items) that is guaranteed to meet pre-specified memory budgets. Furthermore, by leveraging a set-based action formulation and engaging set representation learning, we present an innovative action search strategy powered by an action fitness predictor that efficiently evaluates each table-level action. Experiments have shown state-of-the-art performance on two real-world datasets when BET is paired with three popular recommender models under different memory budgets.|当代推荐系统(RS)的核心是为用户提供高质量推荐体验的潜在因素模型。这些模型使用嵌入向量来表示用户和项目，嵌入向量通常具有统一和固定的大小。随着用户和项目数量的持续增长，这种设计变得效率低下且难以扩展。最近的轻量级嵌入方法允许不同的用户和项具有不同的嵌入大小，但是通常有两个主要缺点。首先，它们将嵌入大小搜索限制为优化一种启发式算法，以平衡推荐质量和内存复杂度，其中需要针对每个请求的内存预算手动调整折衷系数。隐式强制的内存复杂度项甚至可能无法限制参数的使用，使得结果嵌入表无法严格满足内存预算。其次，大多数解决方案，尤其是基于强化学习的解决方案，会逐个实例地推导和优化每个用户/条目的嵌入大小，这会影响搜索效率。在本文中，我们提出了预算嵌入表(BET) ，一种新的方法，生成表级的行为(即，嵌入大小的所有用户和项目) ，是保证满足预先指定的内存预算。此外，通过利用基于集合的动作制定和参与集合表示学习，我们提出了一个创新的动作搜索策略，由动作适应性预测器驱动，有效地评估每个表级动作。实验表明，当 BET 与三个流行的推荐模型在不同的内存预算下配对时，在两个真实世界的数据集上有最先进的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Budgeted+Embedding+Table+For+Recommender+Systems)|0| @@ -43,36 +43,36 @@ |[C²DR: Robust Cross-Domain Recommendation based on Causal Disentanglement](https://doi.org/10.1145/3616855.3635809)|Menglin Kong, Jia Wang, Yushan Pan, Haiyang Zhang, Muzhou Hou||||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=C²DR:+Robust+Cross-Domain+Recommendation+based+on+Causal+Disentanglement)|0| |[Inverse Learning with Extremely Sparse Feedback for Recommendation](https://doi.org/10.1145/3616855.3635797)|Guanyu Lin, Chen Gao, Yu Zheng, Yinfeng Li, Jianxin Chang, Yanan Niu, Yang Song, Kun Gai, Zhiheng Li, Depeng Jin, Yong Li||Modern personalized recommendation services often rely on user feedback, either explicit or implicit, to improve the quality of services. Explicit feedback refers to behaviors like ratings, while implicit feedback refers to behaviors like user clicks. However, in the scenario of full-screen video viewing experiences like Tiktok and Reels, the click action is absent, resulting in unclear feedback from users, hence introducing noises in modeling training. Existing approaches on de-noising recommendation mainly focus on positive instances while ignoring the noise in a large amount of sampled negative feedback. In this paper, we propose a meta-learning method to annotate the unlabeled data from loss and gradient perspectives, which considers the noises in both positive and negative instances. Specifically, we first propose an Inverse Dual Loss (IDL) to boost the true label learning and prevent the false label learning. Then we further propose an Inverse Gradient (IG) method to explore the correct updating gradient and adjust the updating based on meta-learning. Finally, we conduct extensive experiments on both benchmark and industrial datasets where our proposed method can significantly improve AUC by 9.25% against state-of-the-art methods. Further analysis verifies the proposed inverse learning framework is model-agnostic and can improve a variety of recommendation backbones. The source code, along with the best hyper-parameter settings, is available at this link: https://github.com/Guanyu-Lin/InverseLearning.|现代个性化推荐服务通常依赖于用户的反馈，无论是显性的还是隐性的，以提高服务质量。显式反馈指的是评分等行为，而隐式反馈指的是用户点击等行为。然而，在像 Tiktok 和 Reels 这样的全屏视频观看体验的场景中，没有点击动作，导致用户的反馈不清晰，因此在建模训练中引入了噪音。现有的去噪推荐方法主要集中在正向实例上，而忽略了大量采样负反馈中的噪声。本文提出了一种从损失和梯度角度对未标记数据进行标注的元学习方法，该方法同时考虑了正负两种情况下的噪声。具体地说，我们首先提出了一种逆双损耗(IDL)算法来提高真标记学习的能力，防止虚标记学习。然后进一步提出了一种基于元学习的逆梯度(IG)方法来探索正确的更新梯度，并对更新进行调整。最后，我们在基准和工业数据集上进行了广泛的实验，其中我们提出的方法与最先进的方法相比，AUC 可以显著提高9.25% 。进一步的分析验证了所提出的逆向学习框架是模型不可知的，可以改善各种推荐骨干。源代码，以及最好的超参数设置，可在以下连结找到: https://github.com/guanyu-lin/inverselearning。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Inverse+Learning+with+Extremely+Sparse+Feedback+for+Recommendation)|0| |[Pre-trained Recommender Systems: A Causal Debiasing Perspective](https://doi.org/10.1145/3616855.3635779)|Ziqian Lin, Hao Ding, Trong Nghia Hoang, Branislav Kveton, Anoop Deoras, Hao Wang||Recent studies on pre-trained vision/language models have demonstrated the practical benefit of a new, promising solution-building paradigm in AI where models can be pre-trained on broad data describing a generic task space and then adapted successfully to solve a wide range of downstream tasks, even when training data is severely limited (e.g., in zero- or few-shot learning scenarios). Inspired by such progress, we investigate in this paper the possibilities and challenges of adapting such a paradigm to the context of recommender systems, which is less investigated from the perspective of pre-trained model. In particular, we propose to develop a generic recommender that captures universal interaction patterns by training on generic user-item interaction data extracted from different domains, which can then be fast adapted to improve few-shot learning performance in unseen new domains (with limited data). However, unlike vision/language data which share strong conformity in the semantic space, universal patterns underlying recommendation data collected across different domains (e.g., different countries or different E-commerce platforms) are often occluded by both in-domain and cross-domain biases implicitly imposed by the cultural differences in their user and item bases, as well as their uses of different e-commerce platforms. As shown in our experiments, such heterogeneous biases in the data tend to hinder the effectiveness of the pre-trained model. To address this challenge, we further introduce and formalize a causal debiasing perspective, which is substantiated via a hierarchical Bayesian deep learning model, named PreRec. Our empirical studies on real-world data show that the proposed model could significantly improve the recommendation performance in zero- and few-shot learning settings under both cross-market and cross-platform scenarios.|最近关于预先训练的视觉/语言模型的研究已经证明了人工智能中新的，有希望的解决方案构建范式的实际益处，其中模型可以在描述通用任务空间的广泛数据上预先训练，然后成功地适应于解决广泛的下游任务，即使训练数据受到严重限制(例如，在零或少数镜头学习场景中)。受到这些进展的启发，本文研究了将这种模式应用到推荐系统中的可能性和挑战，而从预训练模型的角度对这种可能性和挑战的研究较少。具体而言，我们建议开发一个通用推荐器，通过对从不同领域提取的通用用户项交互数据进行训练来捕获通用交互模式，然后可以快速适应以改善在未见的新领域(具有有限的数据)中的少镜头学习性能。然而，与在语义空间中共享强烈一致性的视觉/语言数据不同，跨不同领域(例如，不同国家或不同电子商务平台)收集的推荐数据的普遍模式通常被隐含地由其用户和项目基础的文化差异所施加的领域内和跨领域偏见所遮蔽，以及他们对不同电子商务平台的使用。正如我们的实验所显示的，这种异质偏差的数据往往会阻碍预训练模型的有效性。为了应对这一挑战，我们进一步引入并形式化了一个因果消偏的观点，这是通过一个分层贝叶斯深度学习模型，命名为 PreRec。我们对实际数据的实证研究表明，在跨市场和跨平台的情况下，该模型可以显著提高零镜头和少镜头学习环境下的推荐性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Pre-trained+Recommender+Systems:+A+Causal+Debiasing+Perspective)|0| -|[Interact with the Explanations: Causal Debiased Explainable Recommendation System](https://doi.org/10.1145/3616855.3635855)|Xu Liu, Tong Yu, Kaige Xie, Junda Wu, Shuai Li|Shanghai Jiao Tong Univ, Shanghai, Peoples R China; NYU, New York, NY USA; Georgia Inst Technol, Atlanta, GA 30332 USA; Adobe Res, San Jose, CA USA|In recent years, the field of recommendation systems has witnessed significant advancements, with explainable recommendation systems gaining prominence as a crucial area of research. These systems aim to enhance user experience by providing transparent and compelling recommendations, accompanied by explanations. However, a persistent challenge lies in addressing biases that can influence the recommendations and explanations offered by these systems. Such biases often stem from a tendency to favor popular items and generate explanations that highlight their common attributes, thereby deviating from the objective of delivering personalized recommendations and explanations. While existing debiasing methods have been applied in explainable recommendation systems, they often overlook the model-generated explanations in tackling biases. Consequently, biases in model-generated explanations may persist, potentially compromising system performance and user satisfaction. To address biases in both model-generated explanations and recommended items, we discern the impact of model-generated explanations in recommendation through a formulated causal graph. Inspired by this causal perspective, we propose a novel approach termed Causal Explainable Recommendation System (CERS), which incorporates model-generated explanations into the debiasing process and enacts causal interventions based on user feedback on the explanations. By utilizing model-generated explanations as intermediaries between user-item interactions and recommendation results, we adeptly mitigate the biases via targeted causal interventions. Experimental results demonstrate the efficacy of CERS in reducing popularity bias while simultaneously improving recommendation performance, leading to more personalized and tailored recommendations. Human evaluation further affirms that CERS|近年来，推荐系统领域取得了重大进展，可解释推荐系统作为一个关键的研究领域日益受到重视。这些系统旨在通过提供透明和令人信服的建议以及解释来提高用户体验。然而，一个持续的挑战在于解决可能影响这些系统提供的建议和解释的偏见。这种偏见往往源于一种倾向，即偏爱流行项目，并产生突出其共同属性的解释，从而偏离提供个性化建议和解释的目标。虽然现有的去偏方法已经应用于可解释的推荐系统，但它们在处理偏差时往往忽略了模型产生的解释。因此，模型生成的解释中的偏差可能会持续存在，潜在地损害系统性能和用户满意度。为了解决模型生成的解释和推荐项目中的偏差，我们通过一个公式化的因果图来识别模型生成的解释在推荐中的影响。受到这种因果观点的启发，我们提出了一种称为因果可解释推荐系统(CERS)的新方法，其将模型生成的解释纳入去偏过程，并根据用户对解释的反馈制定因果干预措施。通过利用模型生成的解释作为用户项目交互和推荐结果之间的中介，我们通过有针对性的因果干预来巧妙地减轻偏差。实验结果表明，CERS 在减少流行偏差的同时，提高推荐性能，导致更个性化和量身定制的推荐的功效。人体评估进一步证实 CERS|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Interact+with+the+Explanations:+Causal+Debiased+Explainable+Recommendation+System)|0| +|[Interact with the Explanations: Causal Debiased Explainable Recommendation System](https://doi.org/10.1145/3616855.3635855)|Xu Liu, Tong Yu, Kaige Xie, Junda Wu, Shuai Li|NYU, New York, NY USA; Adobe Res, San Jose, CA USA; Georgia Inst Technol, Atlanta, GA 30332 USA; Shanghai Jiao Tong Univ, Shanghai, Peoples R China|In recent years, the field of recommendation systems has witnessed significant advancements, with explainable recommendation systems gaining prominence as a crucial area of research. These systems aim to enhance user experience by providing transparent and compelling recommendations, accompanied by explanations. However, a persistent challenge lies in addressing biases that can influence the recommendations and explanations offered by these systems. Such biases often stem from a tendency to favor popular items and generate explanations that highlight their common attributes, thereby deviating from the objective of delivering personalized recommendations and explanations. While existing debiasing methods have been applied in explainable recommendation systems, they often overlook the model-generated explanations in tackling biases. Consequently, biases in model-generated explanations may persist, potentially compromising system performance and user satisfaction. To address biases in both model-generated explanations and recommended items, we discern the impact of model-generated explanations in recommendation through a formulated causal graph. Inspired by this causal perspective, we propose a novel approach termed Causal Explainable Recommendation System (CERS), which incorporates model-generated explanations into the debiasing process and enacts causal interventions based on user feedback on the explanations. By utilizing model-generated explanations as intermediaries between user-item interactions and recommendation results, we adeptly mitigate the biases via targeted causal interventions. Experimental results demonstrate the efficacy of CERS in reducing popularity bias while simultaneously improving recommendation performance, leading to more personalized and tailored recommendations. Human evaluation further affirms that CERS|近年来，推荐系统领域取得了重大进展，可解释推荐系统作为一个关键的研究领域日益受到重视。这些系统旨在通过提供透明和令人信服的建议以及解释来提高用户体验。然而，一个持续的挑战在于解决可能影响这些系统提供的建议和解释的偏见。这种偏见往往源于一种倾向，即偏爱流行项目，并产生突出其共同属性的解释，从而偏离提供个性化建议和解释的目标。虽然现有的去偏方法已经应用于可解释的推荐系统，但它们在处理偏差时往往忽略了模型产生的解释。因此，模型生成的解释中的偏差可能会持续存在，潜在地损害系统性能和用户满意度。为了解决模型生成的解释和推荐项目中的偏差，我们通过一个公式化的因果图来识别模型生成的解释在推荐中的影响。受到这种因果观点的启发，我们提出了一种称为因果可解释推荐系统(CERS)的新方法，其将模型生成的解释纳入去偏过程，并根据用户对解释的反馈制定因果干预措施。通过利用模型生成的解释作为用户项目交互和推荐结果之间的中介，我们通过有针对性的因果干预来巧妙地减轻偏差。实验结果表明，CERS 在减少流行偏差的同时，提高推荐性能，导致更个性化和量身定制的推荐的功效。人体评估进一步证实 CERS|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Interact+with+the+Explanations:+Causal+Debiased+Explainable+Recommendation+System)|0| |[Proxy-based Item Representation for Attribute and Context-aware Recommendation](https://doi.org/10.1145/3616855.3635824)|Jinseok Seol, Minseok Gang, Sanggoo Lee, Jaehui Park||Neural network approaches in recommender systems have shown remarkable success by representing a large set of items as a learnable vector embedding table. However, infrequent items may suffer from inadequate training opportunities, making it difficult to learn meaningful representations. We examine that in attribute and context-aware settings, the poorly learned embeddings of infrequent items impair the recommendation accuracy. To address such an issue, we propose a proxy-based item representation that allows each item to be expressed as a weighted sum of learnable proxy embeddings. Here, the proxy weight is determined by the attributes and context of each item and may incorporate bias terms in case of frequent items to further reflect collaborative signals. The proxy-based method calculates the item representations compositionally, ensuring each representation resides inside a well-trained simplex and, thus, acquires guaranteed quality. Additionally, that the proxy embeddings are shared across all items allows the infrequent items to borrow training signals of frequent items in a unified model structure and end-to-end manner. Our proposed method is a plug-and-play model that can replace the item encoding layer of any neural network-based recommendation model, while consistently improving the recommendation performance with much smaller parameter usage. Experiments conducted on real-world recommendation benchmark datasets demonstrate that our proposed model outperforms state-of-the-art models in terms of recommendation accuracy by up to 17% while using only 10% of the parameters.|推荐系统中的神经网络方法通过将大量的项目表示为一个可学习的向量嵌入表，取得了显著的成功。然而，不经常学习的项目可能会受到培训机会不足的影响，从而难以学习有意义的表征。我们研究了在属性和上下文感知的设置中，不常见项目的嵌入会影响推荐的准确性。为了解决这个问题，我们提出了一种基于代理的项表示方法，该方法允许每个项表示为可学习代理嵌入的加权和。在这里，代理权重是由每个项目的属性和上下文决定的，并且在频繁项目的情况下可以加入偏倚项，以进一步反映协作信号。基于代理的方法组合计算项表示，确保每个表示驻留在一个训练有素的单纯形内，从而获得有保证的质量。此外，代理嵌入在所有项目之间共享，允许频繁项目以统一的模型结构和端到端的方式借用频繁项目的训练信号。我们提出的方法是即插即用模型，可以取代任何基于神经网络的推荐模型的项目编码层，同时以更小的参数使用率持续改善推荐性能。在真实世界的推荐基准数据集上进行的实验表明，我们提出的模型在推荐准确率方面比最先进的模型高出17% ，而只使用了10% 的参数。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Proxy-based+Item+Representation+for+Attribute+and+Context-aware+Recommendation)|0| |[LEAD: Liberal Feature-based Distillation for Dense Retrieval](https://doi.org/10.1145/3616855.3635774)|Hao Sun, Xiao Liu, Yeyun Gong, Anlei Dong, Jingwen Lu, Yan Zhang, Linjun Yang, Rangan Majumder, Nan Duan||Knowledge distillation is often used to transfer knowledge from a strong teacher model to a relatively weak student model. Traditional knowledge distillation methods include response-based methods and feature-based methods. Response-based methods are used the most widely but suffer from lower upper limit of model performance, while feature-based methods have constraints on the vocabularies and tokenizers. In this paper, we propose a tokenizer-free method liberal feature-based distillation (LEAD). LEAD aligns the distribution between teacher model and student model, which is effective, extendable, portable and has no requirements on vocabularies, tokenizer, or model architecture. Extensive experiments show the effectiveness of LEAD on several widely-used benchmarks, including MS MARCO Passage, TREC Passage 19, TREC Passage 20, MS MARCO Document, TREC Document 19 and TREC Document 20.|知识提取经常被用来将知识从一个强教师模型转移到一个相对弱的学生模型。传统的知识提取方法包括基于响应的方法和基于特征的方法。基于响应的方法应用最广泛，但模型性能的上限较低，而基于特征的方法对词汇和标记有限制。本文提出了一种基于特征的自由精馏算法(LEAD)。LEAD 调整了教师模型和学生模型之间的分布，它是有效的、可扩展的、可移植的，并且对词汇表、标记器或模型架构没有要求。广泛的实验显示了 LEAD 在几个广泛使用的基准上的有效性，包括 MS MARCO Passage，TREC Passage 19，TREC Passage 20，MS MARCO Document，TREC Document 19和 TREC Document 20。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=LEAD:+Liberal+Feature-based+Distillation+for+Dense+Retrieval)|0| |[Not All Negatives Are Worth Attending to: Meta-Bootstrapping Negative Sampling Framework for Link Prediction](https://doi.org/10.1145/3616855.3635840)|Yakun Wang, Binbin Hu, Shuo Yang, Meiqi Zhu, Zhiqiang Zhang, Qiyang Zhang, Jun Zhou, Guo Ye, Huimei He|Ant Grp, Hangzhou, Peoples R China|The rapid development of graph neural networks (GNNs) encourages the rising of link prediction, achieving promising performance with various applications. Unfortunately, through a comprehensive analysis, we surprisingly find that current link predictors with dynamic negative samplers (DNSs) suffer from the migration phenomenon between "easy" and "hard" samples, which goes against the preference of DNS of choosing "hard" negatives, thus severely hindering capability. Towards this end, we propose the MeBNS framework, serving as a general plugin that can potentially improve current negative sampling based link predictors. In particular, we elaborately devise a Meta-learning Supported Teacher-student GNN (MST-GNN) that is not only built upon teacher-student architecture for alleviating the migration between "easy" and "hard" samples but also equipped with a meta learning based sample reweighting module for helping the student GNN distinguish "hard" samples in a fine-grained manner. To effectively guide the learning of MST-GNN, we prepare a Structure enhanced Training Data Generator (STD-Generator) and an Uncertainty based Meta Data Collector (UMD-Collector) for supporting the teacher and student GNN, respectively. Extensive experiments show that the MeBNS achieves remarkable performance across six link prediction benchmark datasets.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Not+All+Negatives+Are+Worth+Attending+to:+Meta-Bootstrapping+Negative+Sampling+Framework+for+Link+Prediction)|0| -|[Diff-MSR: A Diffusion Model Enhanced Paradigm for Cold-Start Multi-Scenario Recommendation](https://doi.org/10.1145/3616855.3635807)|Yuhao Wang, Ziru Liu, Yichao Wang, Xiangyu Zhao, Bo Chen, Huifeng Guo, Ruiming Tang|City Univ Hong Kong, Hong Kong, Peoples R China; Huawei Noahs Ark Lab, Hong Kong, Peoples R China|With the explosive growth of various commercial scenarios, there is an increasing number of studies on multi-scenario recommendation (MSR) which trains the recommender system with the data from multiple scenarios, aiming to improve the recommendation performance on all these scenarios synchronously. However, due to the large discrepancy in the number of interactions among domains, multi-scenario recommendation models usually suffer from insufficient learning and negative transfer especially on the cold-start scenarios, thus exacerbating the data sparsity issue. To fill this gap, in this work we propose a novel diffusion model enhanced paradigm tailored for the cold-start problem in multi-scenario recommendation in a data-driven generative manner. Specifically, based on all-domain data, we leverage the diffusion model with our newly designed variance schedule and the proposed classifier, which explicitly boosts the recommendation performance on the cold-start scenarios by exploiting the generated high-quality and informative embedding, leveraging the abundance of rich scenarios. Our experiments on Douban and Amazon datasets demonstrate two strengths of the proposed paradigm: (i) its effectiveness with a significant increase of 8.5% and 1% in accuracy on the two datasets, and (ii) its compatibility with various multi-scenario backbone models. The implementation code is available for easy reproduction(1,2).|随着各种商业场景的爆炸性增长，越来越多的研究开始关注多场景推荐(MSR) ，它利用来自多个场景的数据来训练推荐系统，旨在同步提高所有这些场景的推荐性能。然而，由于域间交互数量的巨大差异，多场景推荐模型通常存在学习不足和负迁移的问题，特别是在冷启动情景下，从而加剧了数据稀疏问题。为了填补这一空白，本文提出了一种新的扩散模型增强范式，以数据驱动的生成方式为多场景推荐中的冷启动问题量身定制。具体而言，基于全域数据，我们利用扩散模型和我们新设计的方差计划和提议的分类器，通过利用生成的高质量和信息嵌入，利用丰富的场景，显式提高冷启动场景的推荐性能。我们在豆瓣和亚马逊数据集上的实验证明了所提出的范式的两个优势: (i)其有效性，在两个数据集上的准确性显着提高8.5% 和1% ，以及(ii)其与各种多场景骨干模型的兼容性。该实现代码可用于方便复制(1,2)。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Diff-MSR:+A+Diffusion+Model+Enhanced+Paradigm+for+Cold-Start+Multi-Scenario+Recommendation)|0| +|[Diff-MSR: A Diffusion Model Enhanced Paradigm for Cold-Start Multi-Scenario Recommendation](https://doi.org/10.1145/3616855.3635807)|Yuhao Wang, Ziru Liu, Yichao Wang, Xiangyu Zhao, Bo Chen, Huifeng Guo, Ruiming Tang|Huawei Noahs Ark Lab, Hong Kong, Peoples R China; City Univ Hong Kong, Hong Kong, Peoples R China|With the explosive growth of various commercial scenarios, there is an increasing number of studies on multi-scenario recommendation (MSR) which trains the recommender system with the data from multiple scenarios, aiming to improve the recommendation performance on all these scenarios synchronously. However, due to the large discrepancy in the number of interactions among domains, multi-scenario recommendation models usually suffer from insufficient learning and negative transfer especially on the cold-start scenarios, thus exacerbating the data sparsity issue. To fill this gap, in this work we propose a novel diffusion model enhanced paradigm tailored for the cold-start problem in multi-scenario recommendation in a data-driven generative manner. Specifically, based on all-domain data, we leverage the diffusion model with our newly designed variance schedule and the proposed classifier, which explicitly boosts the recommendation performance on the cold-start scenarios by exploiting the generated high-quality and informative embedding, leveraging the abundance of rich scenarios. Our experiments on Douban and Amazon datasets demonstrate two strengths of the proposed paradigm: (i) its effectiveness with a significant increase of 8.5% and 1% in accuracy on the two datasets, and (ii) its compatibility with various multi-scenario backbone models. The implementation code is available for easy reproduction(1,2).|随着各种商业场景的爆炸性增长，越来越多的研究开始关注多场景推荐(MSR) ，它利用来自多个场景的数据来训练推荐系统，旨在同步提高所有这些场景的推荐性能。然而，由于域间交互数量的巨大差异，多场景推荐模型通常存在学习不足和负迁移的问题，特别是在冷启动情景下，从而加剧了数据稀疏问题。为了填补这一空白，本文提出了一种新的扩散模型增强范式，以数据驱动的生成方式为多场景推荐中的冷启动问题量身定制。具体而言，基于全域数据，我们利用扩散模型和我们新设计的方差计划和提议的分类器，通过利用生成的高质量和信息嵌入，利用丰富的场景，显式提高冷启动场景的推荐性能。我们在豆瓣和亚马逊数据集上的实验证明了所提出的范式的两个优势: (i)其有效性，在两个数据集上的准确性显着提高8.5% 和1% ，以及(ii)其与各种多场景骨干模型的兼容性。该实现代码可用于方便复制(1,2)。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Diff-MSR:+A+Diffusion+Model+Enhanced+Paradigm+for+Cold-Start+Multi-Scenario+Recommendation)|0| |[On the Effectiveness of Unlearning in Session-Based Recommendation](https://doi.org/10.1145/3616855.3635823)|Xin Xin, Liu Yang, Ziqi Zhao, Pengjie Ren, Zhumin Chen, Jun Ma, Zhaochun Ren||Session-based recommendation predicts users' future interests from previous interactions in a session. Despite the memorizing of historical samples, the request of unlearning, i.e., to remove the effect of certain training samples, also occurs for reasons such as user privacy or model fidelity. However, existing studies on unlearning are not tailored for the session-based recommendation. On the one hand, these approaches cannot achieve satisfying unlearning effects due to the collaborative correlations and sequential connections between the unlearning item and the remaining items in the session. On the other hand, seldom work has conducted the research to verify the unlearning effectiveness in the session-based recommendation scenario. In this paper, we propose SRU, a session-based recommendation unlearning framework, which enables high unlearning efficiency, accurate recommendation performance, and improved unlearning effectiveness in session-based recommendation. Specifically, we first partition the training sessions into separate sub-models according to the similarity across the sessions, then we utilize an attention-based aggregation layer to fuse the hidden states according to the correlations between the session and the centroid of the data in the sub-model. To improve the unlearning effectiveness, we further propose three extra data deletion strategies, including collaborative extra deletion (CED), neighbor extra deletion (NED), and random extra deletion (RED). Besides, we propose an evaluation metric that measures whether the unlearning sample can be inferred after the data deletion to verify the unlearning effectiveness. We implement SRU with three representative session-based recommendation models and conduct experiments on three benchmark datasets. Experimental results demonstrate the effectiveness of our methods.|基于会话的推荐可以根据会话中以前的交互预测用户的未来兴趣。除了对历史样本的记忆，忘记的要求，即去除某些训练样本的影响，也出于用户隐私或模型保真等原因。然而，现有的关于忘却的研究并不适合基于会话的建议。一方面，这些方法不能达到令人满意的忘却效果，因为在忘却项目和会话中剩余项目之间存在协作关联和顺序关联。另一方面，在基于会话的推荐场景中，很少有研究验证学习效果。本文提出了一种基于会话的推荐去学习框架 SRU，该框架具有较高的去学习效率、较准确的推荐性能和较高的去学习效率。具体来说，我们首先根据训练会话间的相似性将训练会话划分为不同的子模型，然后利用基于注意力的聚合层根据会话与子模型中数据质心的相关性对隐藏状态进行融合。为了提高学习效率，我们进一步提出了三种额外的数据删除策略，包括协作额外删除(CED)、邻居额外删除(NED)和随机额外删除(RED)。此外，我们提出一个评估指标，测量在删除数据后是否可以推断出忘却样本，以验证忘却的有效性。我们使用三个具有代表性的基于会话的推荐模型实现 SRU，并在三个基准数据集上进行实验。实验结果证明了该方法的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=On+the+Effectiveness+of+Unlearning+in+Session-Based+Recommendation)|0| |[IAI MovieBot 2.0: An Enhanced Research Platform with Trainable Neural Components and Transparent User Modeling](https://doi.org/10.1145/3616855.3635699)|Nolwenn Bernard, Ivica Kostric, Krisztian Balog||While interest in conversational recommender systems has been on the rise, operational systems suitable for serving as research platforms for comprehensive studies are currently lacking. This paper introduces an enhanced version of the IAI MovieBot conversational movie recommender system, aiming to evolve it into a robust and adaptable platform for conducting user-facing experiments. The key highlights of this enhancement include the addition of trainable neural components for natural language understanding and dialogue policy, transparent and explainable modeling of user preferences, along with improvements in the user interface and research infrastructure.|虽然对会话推荐系统的兴趣一直在上升，但目前还缺乏适合作为综合研究的研究平台的操作系统。本文介绍了 IAI MovieBot 会话电影推荐系统的一个增强版本，旨在将其发展成一个健壮的、可适应的平台，用于进行面向用户的实验。这一增强的主要亮点包括增加了可训练的神经组件，用于自然语言理解和对话政策，对用户偏好进行透明和可解释的建模，以及改进用户界面和研究基础设施。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=IAI+MovieBot+2.0:+An+Enhanced+Research+Platform+with+Trainable+Neural+Components+and+Transparent+User+Modeling)|0| |[Domain Level Interpretability: Interpreting Black-box Model with Domain-specific Embedding](https://doi.org/10.1145/3616855.3635688)|YaLin Zhang, Caizhi Tang, Lu Yu, Jun Zhou, Longfei Li, Qing Cui, Fangfang Fan, Linbo Jiang, Xiaosong Zhao|Ant Grp, Hangzhou, Peoples R China|The importance of incorporating interpretability into machine learning models has been increasingly emphasized. While previous literature has typically focused on feature level interpretability, such as analyzing which features are important and how they influence the final decision, real-world applications often require domain level interpretability, which relates to a group of features. Domain-level interpretability holds the potential for enhanced informativeness and comprehensibility. Unfortunately, there has been limited research in this direction. In this paper, we address this issue and introduce our proposed method DIDE, which obtains domain-level interpretability from domain-specific latent embeddings. To enhance the effectiveness of the framework, we draw inspiration from the gradient smooth philosophy and propose noisy injection in the embedding space, resulting in smoothed interpretability. We conduct extensive experiments to validate the effectiveness of DIDE, and demonstrate its applications in assisting daily business tasks in Alipay(1).|将可解释性纳入机器学习模型的重要性日益受到强调。虽然以前的文献主要集中在特征级别的可解释性上，比如分析哪些特征是重要的，以及它们如何影响最终决策，但是现实世界中的应用通常需要领域级别的可解释性，这涉及到一组特征。领域级的可解释性具有增强信息性和可理解性的潜力。不幸的是，这方面的研究有限。在本文中，我们解决了这个问题，并介绍了我们提出的方法 DIDE，它从领域特定的潜在嵌入获得领域级的可解释性。为了提高该框架的有效性，我们从梯度平滑理论中获得灵感，并在嵌入空间中提出噪声注入，从而实现平滑的可解释性。我们进行了广泛的实验，以验证 DIDE 的有效性，并演示其在支付宝(1)中协助日常业务任务的应用。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Domain+Level+Interpretability:+Interpreting+Black-box+Model+with+Domain-specific+Embedding)|0| -|[Unbiased Learning to Rank: On Recent Advances and Practical Applications](https://doi.org/10.1145/3616855.3636451)|Shashank Gupta, Philipp Hager, Jin Huang, Ali Vardasbi, Harrie Oosterhuis|Radboud Univ Nijmegen, Nijmegen, Netherlands; Univ Amsterdam, Amsterdam, Netherlands|Since its inception, the field of unbiased learning to rank (ULTR) has remained very active and has seen several impactful advancements in recent years. This tutorial provides both an introduction to the core concepts of the field and an overview of recent advancements in its foundations, along with several applications of its methods. The tutorial is divided into four parts: Firstly, we give an overview of the different forms of bias that can be addressed with ULTR methods. Secondly, we present a comprehensive discussion of the latest estimation techniques in the ULTR field. Thirdly, we survey published results of ULTR in real-world applications. Fourthly, we discuss the connection between ULTR and fairness in ranking. We end by briefly reflecting on the future of ULTR research and its applications. This tutorial is intended to benefit both researchers and industry practitioners interested in developing new ULTR solutions or utilizing them in real-world applications.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Unbiased+Learning+to+Rank:+On+Recent+Advances+and+Practical+Applications)|0| +|[Unbiased Learning to Rank: On Recent Advances and Practical Applications](https://doi.org/10.1145/3616855.3636451)|Shashank Gupta, Philipp Hager, Jin Huang, Ali Vardasbi, Harrie Oosterhuis|Univ Amsterdam, Amsterdam, Netherlands; Radboud Univ Nijmegen, Nijmegen, Netherlands|Since its inception, the field of unbiased learning to rank (ULTR) has remained very active and has seen several impactful advancements in recent years. This tutorial provides both an introduction to the core concepts of the field and an overview of recent advancements in its foundations, along with several applications of its methods. The tutorial is divided into four parts: Firstly, we give an overview of the different forms of bias that can be addressed with ULTR methods. Secondly, we present a comprehensive discussion of the latest estimation techniques in the ULTR field. Thirdly, we survey published results of ULTR in real-world applications. Fourthly, we discuss the connection between ULTR and fairness in ranking. We end by briefly reflecting on the future of ULTR research and its applications. This tutorial is intended to benefit both researchers and industry practitioners interested in developing new ULTR solutions or utilizing them in real-world applications.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Unbiased+Learning+to+Rank:+On+Recent+Advances+and+Practical+Applications)|0| |[Leveraging User Simulation to Develop and Evaluate Conversational Information Access Agents](https://doi.org/10.1145/3616855.3635730)|Nolwenn Bernard||We observe a change in the way users access information, that is, the rise of conversational information access (CIA) agents. However, the automatic evaluation of these agents remains an open challenge. Moreover, the training of CIA agents is cumbersome as it mostly relies on conversational corpora, expert knowledge, and reinforcement learning. User simulation has been identified as a promising solution to tackle automatic evaluation and has been previously used in reinforcement learning. In this research, we investigate how user simulation can be leveraged in the context of CIA. We organize the work in three parts. We begin with the identification of requirements for user simulators for training and evaluating CIA agents and compare existing types of simulator regarding these. Then, we plan to combine these different types of simulators into a new hybrid simulator. Finally, we aim to extend simulators to handle more complex information seeking scenarios.|我们观察到用户访问信息的方式发生了变化，即会话信息访问(CIA)代理的兴起。然而，这些代理的自动评估仍然是一个开放的挑战。此外，中情局特工的培训非常繁琐，因为它主要依赖于会话语料库、专业知识和强化学习。用户模拟已被确定为解决自动评估的一个有前途的解决方案，并且以前在强化学习中使用过。在这项研究中，我们调查如何用户模拟可以在中央情报局的背景下利用。我们把工作分为三部分。我们首先确定用户模拟器的培训和评估 CIA 代理的需求，并比较现有的模拟器类型。然后，我们计划将这些不同类型的模拟器组合成一个新的混合模拟器。最后，我们的目标是扩展模拟器以处理更复杂的信息搜索场景。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Leveraging+User+Simulation+to+Develop+and+Evaluate+Conversational+Information+Access+Agents)|0| |[Delphic Costs and Benefits in Web Search: A Utilitarian and Historical Analysis](https://doi.org/10.1145/3616855.3638208)|Andrei Z. Broder||We present a new framework to conceptualize and operationalize the total user experience of search, by studying the entirety of a search journey from an utilitarian point of view. Web search engines are widely perceived as "free". But search requires time and effort: in reality there are many intermingled non-monetary costs (e.g. time costs, cognitive costs, interactivity costs) and the benefits may be marred by various impairments, such as misunderstanding and misinformation. This characterization of costs and benefits appears to be inherent to the human search for information within the pursuit of some larger task: most of the costs and impairments can be identified in interactions with any web search engine, interactions with public libraries, and even in interactions with ancient oracles. To emphasize this innate connection, we call these costs and benefits Delphic, in contrast to explicitly financial costs and benefits. Our main thesis is that the users' satisfaction with a search engine mostly depends on their experience of Delphic cost and benefits, in other words on their utility. The consumer utility is correlated with classic measures of search engine quality, such as ranking, precision, recall, etc., but is not completely determined by them. To argue our thesis, we catalog the Delphic costs and benefits and show how the development of search engines over the last quarter century, from classic Information Retrieval roots to the integration of Large Language Models, was driven to a great extent by the quest of decreasing Delphic costs and increasing Delphic benefits. We hope that the Delphic costs framework will engender new ideas and new research for evaluating and improving the web experience for everyone.|我们提出了一个新的框架来概念化和操作的总体用户体验的搜索，通过研究整个搜索旅程从功利的角度来看。网络搜索引擎被广泛认为是“免费的”。但是搜索需要时间和精力: 在现实中有许多混合的非货币成本(例如时间成本、认知成本、互动成本) ，好处可能被各种损害所破坏，例如误解和错误信息。这种成本和收益的角色塑造似乎是人类在追求某种更大的任务时所固有的信息搜索: 大多数成本和损害可以通过与任何网络搜索引擎的互动、与公共图书馆的互动、甚至与古代神谕的互动来识别。为了强调这种内在的联系，我们把这些成本和收益称为德尔菲，与明确的财务成本和收益形成对比。我们的主要论点是，用户对搜索引擎的满意度主要取决于他们对德尔菲成本和收益的体验，换句话说，取决于他们的实用性。消费者效用与搜索引擎质量的经典指标相关，如排名、精确度、召回等，但并不完全由它们决定。为了证明我们的论点，我们列出了德尔菲的成本和收益，并展示了在过去25年里搜索引擎的发展，从传统的信息检索到大型语言模型的整合，在很大程度上是由降低德尔菲成本和增加德尔菲收益的追求所驱动的。我们希望德尔菲成本框架将产生新的想法和新的研究，以评估和改善每个人的网络体验。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Delphic+Costs+and+Benefits+in+Web+Search:+A+Utilitarian+and+Historical+Analysis)|0| |[The Journey to A Knowledgeable Assistant with Retrieval-Augmented Generation (RAG)](https://doi.org/10.1145/3616855.3638207)|Xin Luna Dong||||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=The+Journey+to+A+Knowledgeable+Assistant+with+Retrieval-Augmented+Generation+(RAG))|0| |[LabelCraft: Empowering Short Video Recommendations with Automated Label Crafting](https://doi.org/10.1145/3616855.3635816)|Yimeng Bai, Yang Zhang, Jing Lu, Jianxin Chang, Xiaoxue Zang, Yanan Niu, Yang Song, Fuli Feng||Short video recommendations often face limitations due to the quality of user feedback, which may not accurately depict user interests. To tackle this challenge, a new task has emerged: generating more dependable labels from original feedback. Existing label generation methods rely on manual rules, demanding substantial human effort and potentially misaligning with the desired objectives of the platform. To transcend these constraints, we introduce LabelCraft, a novel automated label generation method explicitly optimizing pivotal operational metrics for platform success. By formulating label generation as a higher-level optimization problem above recommender model optimization, LabelCraft introduces a trainable labeling model for automatic label mechanism modeling. Through meta-learning techniques, LabelCraft effectively addresses the bi-level optimization hurdle posed by the recommender and labeling models, enabling the automatic acquisition of intricate label generation mechanisms.Extensive experiments on real-world datasets corroborate LabelCraft's excellence across varied operational metrics, encompassing usage time, user engagement, and retention. Codes are available at https://github.com/baiyimeng/LabelCraft.|由于用户反馈的质量问题，短视频推荐往往面临着限制，这可能不能准确地描述用户的兴趣。为了应对这一挑战，一项新的任务出现了: 从原始反馈中生成更可靠的标签。现有的标签生成方法依赖于手工规则，需要大量的人工努力，并且可能与平台的期望目标不一致。为了超越这些约束，我们引入了 LabelCraft，一种新的自动标签生成方法，它显式地优化了平台成功的关键操作指标。通过将标签生成作为推荐模型优化之上的一个更高层次的最佳化问题，LabelCraft 为自动标签机制建模引入了一个可训练的标签模型。通过元学习技术，LabelCraft 有效地解决了推荐和标签模型造成的双层优化障碍，使得复杂的标签生成机制的自动获取成为可能。在真实世界数据集上的大量实验证实了 LabelCraft 在各种操作指标上的卓越性，包括使用时间、用户参与度和保持性。密码可在 https://github.com/baiyimeng/labelcraft 索取。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=LabelCraft:+Empowering+Short+Video+Recommendations+with+Automated+Label+Crafting)|0| |[Towards Mitigating Dimensional Collapse of Representations in Collaborative Filtering](https://doi.org/10.1145/3616855.3635832)|Huiyuan Chen, Vivian Lai, Hongye Jin, Zhimeng Jiang, Mahashweta Das, Xia Hu||Contrastive Learning (CL) has shown promising performance in collaborative filtering. The key idea is to generate augmentation-invariant embeddings by maximizing the Mutual Information between different augmented views of the same instance. However, we empirically observe that existing CL models suffer from the dimensional collapse issue, where user/item embeddings only span a low-dimension subspace of the entire feature space. This suppresses other dimensional information and weakens the distinguishability of embeddings. Here we propose a non-contrastive learning objective, named nCL, which explicitly mitigates dimensional collapse of representations in collaborative filtering. Our nCL aims to achieve geometric properties of Alignment and Compactness on the embedding space. In particular, the alignment tries to push together representations of positive-related user-item pairs, while compactness tends to find the optimal coding length of user/item embeddings, subject to a given distortion. More importantly, our nCL does not require data augmentation nor negative sampling during training, making it scalable to large datasets. Experimental results demonstrate the superiority of our nCL.|对比学习(CL)在协同过滤方面表现出色。其核心思想是通过最大化同一实例中不同增强视图之间的互信息来产生增强不变嵌入。然而，我们经验地观察到现有的 CL 模型遭受维度折叠问题，其中用户/项目嵌入只跨越整个特征空间的一个低维子空间。这抑制了其他维度信息，削弱了嵌入的可区分性。在这里，我们提出了一个非对比学习目标，命名为 nCL，它明确地减轻了协同过滤表示的维度崩溃。我们的 nCL 旨在实现嵌入空间上的对齐性和紧性的几何性质。特别是，对齐试图将正相关的用户项对的表示推到一起，而紧凑性倾向于找到用户/项嵌入的最佳编码长度，受到给定的失真。更重要的是，我们的 nCL 在训练期间不需要数据增强或负采样，使其可扩展到大型数据集。实验结果证明了该方法的优越性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Towards+Mitigating+Dimensional+Collapse+of+Representations+in+Collaborative+Filtering)|0| |[CL4DIV: A Contrastive Learning Framework for Search Result Diversification](https://doi.org/10.1145/3616855.3635851)|Zhirui Deng, Zhicheng Dou, Yutao Zhu, JiRong Wen|Renmin Univ China, Gaoling Sch Artificial Intelligence, Beijing, Peoples R China|Search result diversification aims to provide a diversified document ranking list so as to cover as many intents as possible and satisfy the various information needs of different users. Existing approaches usually represented documents by pretrained embeddings (such as doc2vec and Glove). These document representations cannot adequately represent the document's content and are hard to capture the intrinsic user's intent coverage of the given query. Moreover, the limited number of labeled data for search result diversification exacerbates the difficulty of obtaining more efficient document representations. To alleviate these problems and learn more effective document representations, we propose a Contrastive Learning framework for search result DIVersification (CL4DIV). Specifically, we design three contrastive learning tasks from the perspective of subtopics, documents, and candidate document sequences, which correspond to three essential elements in search result diversification. These training tasks are employed to pretrain the document encoder and the document sequence encoder, which are used in the diversified ranking model. Experimental results show that CL4DIV significantly outperforms all existing diversification models. Further analysis demonstrates that our method has wide applicability and can also be used to improve several existing methods.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=CL4DIV:+A+Contrastive+Learning+Framework+for+Search+Result+Diversification)|0| -|[From Second to First: Mixed Censored Multi-Task Learning for Winning Price Prediction](https://doi.org/10.1145/3616855.3635838)|Jiani Huang, Zhenzhe Zheng, Yanrong Kang, Zixiao Wang|Tencent, Advertising & Mkt Serv, Shenzhen, Peoples R China; Shanghai Jiao Tong Univ, Shanghai, Peoples R China|A transformation from second-price auctions (SPA) to first-price auctions (FPA) has been observed in online advertising. The consequential coexistence of mixed FPA and SPA auction types has further led to the problem of mixed censorship, making bid landscape forecasting, the prerequisite for bid shading, more difficult. Our key insight is that the winning price under SPA can be effectively transferred to FPA scenarios if they share similar user groups, advertisers, and bidding environments. The full utilization of winning price under mixed censorship can effectively alleviate the FPA censorship problem and improve the performance of winning price prediction (also called as bid landscape forecasting). In this work, we propose aMulti-taskMixed Censorship Predictor (MMCP) that utilizes multi-task learning to leverage the winning price under SPA as supervised information for FPA. A Double-gate Mixture-of-Experts architecture has been proposed to alleviate the negative transfer problem of multi-task learning in our context. Furthermore, several auxiliary modules including the first-second mapping module and adaptive censorship loss function have been introduced to integrate multi-task learning and winning price prediction. Extensive experiments on two real-world datasets demonstrate the superior performance of the proposed MMCP compared with other state-of-the-art FPA models under various performance metrics. The implementation of the code is available on github(1).||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=From+Second+to+First:+Mixed+Censored+Multi-Task+Learning+for+Winning+Price+Prediction)|0| +|[From Second to First: Mixed Censored Multi-Task Learning for Winning Price Prediction](https://doi.org/10.1145/3616855.3635838)|Jiani Huang, Zhenzhe Zheng, Yanrong Kang, Zixiao Wang|Shanghai Jiao Tong Univ, Shanghai, Peoples R China; Tencent, Advertising & Mkt Serv, Shenzhen, Peoples R China|A transformation from second-price auctions (SPA) to first-price auctions (FPA) has been observed in online advertising. The consequential coexistence of mixed FPA and SPA auction types has further led to the problem of mixed censorship, making bid landscape forecasting, the prerequisite for bid shading, more difficult. Our key insight is that the winning price under SPA can be effectively transferred to FPA scenarios if they share similar user groups, advertisers, and bidding environments. The full utilization of winning price under mixed censorship can effectively alleviate the FPA censorship problem and improve the performance of winning price prediction (also called as bid landscape forecasting). In this work, we propose aMulti-taskMixed Censorship Predictor (MMCP) that utilizes multi-task learning to leverage the winning price under SPA as supervised information for FPA. A Double-gate Mixture-of-Experts architecture has been proposed to alleviate the negative transfer problem of multi-task learning in our context. Furthermore, several auxiliary modules including the first-second mapping module and adaptive censorship loss function have been introduced to integrate multi-task learning and winning price prediction. Extensive experiments on two real-world datasets demonstrate the superior performance of the proposed MMCP compared with other state-of-the-art FPA models under various performance metrics. The implementation of the code is available on github(1).||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=From+Second+to+First:+Mixed+Censored+Multi-Task+Learning+for+Winning+Price+Prediction)|0| |[DiffKG: Knowledge Graph Diffusion Model for Recommendation](https://doi.org/10.1145/3616855.3635850)|Yangqin Jiang, Yuhao Yang, Lianghao Xia, Chao Huang||Knowledge Graphs (KGs) have emerged as invaluable resources for enriching recommendation systems by providing a wealth of factual information and capturing semantic relationships among items. Leveraging KGs can significantly enhance recommendation performance. However, not all relations within a KG are equally relevant or beneficial for the target recommendation task. In fact, certain item-entity connections may introduce noise or lack informative value, thus potentially misleading our understanding of user preferences. To bridge this research gap, we propose a novel knowledge graph diffusion model for recommendation, referred to as DiffKG. Our framework integrates a generative diffusion model with a data augmentation paradigm, enabling robust knowledge graph representation learning. This integration facilitates a better alignment between knowledge-aware item semantics and collaborative relation modeling. Moreover, we introduce a collaborative knowledge graph convolution mechanism that incorporates collaborative signals reflecting user-item interaction patterns, guiding the knowledge graph diffusion process. We conduct extensive experiments on three publicly available datasets, consistently demonstrating the superiority of our DiffKG compared to various competitive baselines. We provide the source code repository of our proposed DiffKG model at the following link: https://github.com/HKUDS/DiffKG.|知识图(KGs)已经成为丰富推荐系统的宝贵资源，它提供了大量的事实信息，捕获了项目之间的语义关系。利用幼稚园可显著提升推荐表现。然而，并非所有幼儿园内部的关系对于目标推荐任务都同样相关或有益。事实上，某些项目-实体连接可能会引入噪音或缺乏信息价值，从而可能误导我们对用户偏好的理解。为了弥补这一研究差距，我们提出了一种新的知识图扩散推荐模型，称为迪夫 KG。我们的框架集成了一个生成扩散模型和一个数据增强范例，支持健壮的知识图表示学习。这种集成促进了知识感知项语义和协作关系建模之间的更好结合。此外，本文还引入了一种协同知识图卷积机制，该机制融合了反映用户-项目交互模式的协同信号，引导知识图的扩散过程。我们在三个公开可用的数据集上进行了广泛的实验，一致地证明了我们的 DiffKG 相对于各种竞争基线的优越性。我们在以下连结提供有关「区分幼稚园」模式的原始码储存库: https://github.com/hkuds/DiffKG。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=DiffKG:+Knowledge+Graph+Diffusion+Model+for+Recommendation)|0| |[Robust Training for Conversational Question Answering Models with Reinforced Reformulation Generation](https://doi.org/10.1145/3616855.3635822)|Magdalena Kaiser, Rishiraj Saha Roy, Gerhard Weikum||Models for conversational question answering (ConvQA) over knowledge graphs (KGs) are usually trained and tested on benchmarks of gold QA pairs. This implies that training is limited to surface forms seen in the respective datasets, and evaluation is on a small set of held-out questions. Through our proposed framework REIGN, we take several steps to remedy this restricted learning setup. First, we systematically generate reformulations of training questions to increase robustness of models to surface form variations. This is a particularly challenging problem, given the incomplete nature of such questions. Second, we guide ConvQA models towards higher performance by feeding it only those reformulations that help improve their answering quality, using deep reinforcement learning. Third, we demonstrate the viability of training major model components on one benchmark and applying them zero-shot to another. Finally, for a rigorous evaluation of robustness for trained models, we use and release large numbers of diverse reformulations generated by prompting GPT for benchmark test sets (resulting in 20x increase in sizes). Our findings show that ConvQA models with robust training via reformulations, significantly outperform those with standard training from gold QA pairs only.|基于知识图的会话问题回答模型通常在黄金问题回答对的基准上进行训练和测试。这意味着训练仅限于在各自的数据集中看到的表面形式，并且评估是针对一小组被拒绝的问题。通过我们提出的框架 REIGN，我们采取了几个步骤来补救这种受限制的学习设置。首先，我们系统地生成训练问题的重新编排，以增加模型对表面形式变化的鲁棒性。鉴于这些问题的不完整性，这是一个特别具有挑战性的问题。其次，我们使用深度强化学习，通过只提供那些有助于提高回答质量的重新编排来引导 convQA 模型获得更高的性能。第三，我们证明了在一个基准上训练主要模型组件并将它们应用到另一个基准上的可行性。最后，为了严格评估训练过的模型的鲁棒性，我们使用并发布了大量不同的重新编排，这些重新编排是通过提示 GPT 测试基准测试集(导致大小增加20倍)而产生的。我们的研究结果表明，使用强大的训练通过重新制定，严格质量保证模型，明显优于那些标准的训练，从黄金质量保证对。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Robust+Training+for+Conversational+Question+Answering+Models+with+Reinforced+Reformulation+Generation)|0| |[MONET: Modality-Embracing Graph Convolutional Network and Target-Aware Attention for Multimedia Recommendation](https://doi.org/10.1145/3616855.3635817)|Yungi Kim, Taeri Kim, WonYong Shin, SangWook Kim||In this paper, we focus on multimedia recommender systems using graph convolutional networks (GCNs) where the multimodal features as well as user-item interactions are employed together. Our study aims to exploit multimodal features more effectively in order to accurately capture users' preferences for items. To this end, we point out following two limitations of existing GCN-based multimedia recommender systems: (L1) although multimodal features of interacted items by a user can reveal her preferences on items, existing methods utilize GCN designed to focus only on capturing collaborative signals, resulting in insufficient reflection of the multimodal features in the final user/item embeddings; (L2) although a user decides whether to prefer the target item by considering its multimodal features, existing methods represent her as only a single embedding regardless of the target item's multimodal features and then utilize her embedding to predict her preference for the target item. To address the above issues, we propose a novel multimedia recommender system, named MONET, composed of following two core ideas: modality-embracing GCN (MeGCN) and target-aware attention. Through extensive experiments using four real-world datasets, we demonstrate i) the significant superiority of MONET over seven state-of-the-art competitors (up to 30.32% higher accuracy in terms of recall@20, compared to the best competitor) and ii) the effectiveness of the two core ideas in MONET. All MONET codes are available at https://github.com/Kimyungi/MONET.|本文主要研究基于图卷积网络(GCNs)的多媒体推荐系统。我们的研究旨在更有效地利用多模态特征，以便准确地捕捉用户对项目的偏好。为此，我们指出了现有的基于 GCN 的多媒体推荐系统的两个局限性: (L1)尽管用户交互项目的多模态特征可以揭示她对项目的偏好，但是现有的方法利用 GCN 设计只关注于捕获协作信号，导致在最终用户/项目嵌入中对多模态特征的反映不足; (L2)尽管用户决定是否通过考虑其多模态特征来选择目标项目，但是现有的方法表示她只是一个单一的嵌入，而不管目标项目的多模态特征如何，然后利用她的嵌入来预测她对目标。为了解决上述问题，我们提出了一个新的多媒体推荐系统，命名为 MONET，它由以下两个核心思想组成: 包含模式的广域网(MeGCN)和目标感知注意。通过使用四个真实世界数据集的广泛实验，我们证明了 i) MONET 相对于七个最先进的竞争对手的显着优势(与最好的竞争对手相比，在召回@20方面高达30.32% 的准确性)和 ii) MONET 中两个核心思想的有效性。所有 MONET 代码均可在 https://github.com/kimyungi/MONET 下载。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=MONET:+Modality-Embracing+Graph+Convolutional+Network+and+Target-Aware+Attention+for+Multimedia+Recommendation)|0| |[Text-Video Retrieval via Multi-Modal Hypergraph Networks](https://doi.org/10.1145/3616855.3635757)|Qian Li, Lixin Su, Jiashu Zhao, Long Xia, Hengyi Cai, Suqi Cheng, Hengzhu Tang, Junfeng Wang, Dawei Yin||Text-video retrieval is a challenging task that aims to identify relevant videos given textual queries. Compared to conventional textual retrieval, the main obstacle for text-video retrieval is the semantic gap between the textual nature of queries and the visual richness of video content. Previous works primarily focus on aligning the query and the video by finely aggregating word-frame matching signals. Inspired by the human cognitive process of modularly judging the relevance between text and video, the judgment needs high-order matching signal due to the consecutive and complex nature of video contents. In this paper, we propose chunk-level text-video matching, where the query chunks are extracted to describe a specific retrieval unit, and the video chunks are segmented into distinct clips from videos. We formulate the chunk-level matching as n-ary correlations modeling between words of the query and frames of the video and introduce a multi-modal hypergraph for n-ary correlation modeling. By representing textual units and video frames as nodes and using hyperedges to depict their relationships, a multi-modal hypergraph is constructed. In this way, the query and the video can be aligned in a high-order semantic space. In addition, to enhance the model's generalization ability, the extracted features are fed into a variational inference component for computation, obtaining the variational representation under the Gaussian distribution. The incorporation of hypergraphs and variational inference allows our model to capture complex, n-ary interactions among textual and visual contents. Experimental results demonstrate that our proposed method achieves state-of-the-art performance on the text-video retrieval task.|文本视频检索是一项具有挑战性的任务，其目的是识别给定文本查询的相关视频。与传统的文本检索相比，文本-视频检索的主要障碍是查询的文本性质与视频内容的视觉丰富性之间的语义差距。以前的工作主要集中在对齐查询和视频通过精细聚合字帧匹配信号。由于视频内容的连续性和复杂性，受人类对文本与视频相关性进行模块化判断的认知过程的启发，需要高阶匹配信号来进行判断。本文提出了块级文本-视频匹配算法，该算法提取查询块来描述特定的检索单元，并将视频块从视频中分割成不同的片段。将块级匹配表述为查询词与视频帧之间的 n 元相关建模，并引入多模态超图进行 n 元相关建模。通过将文本单元和视频帧表示为节点，利用超边界描述它们之间的关系，构造了一个多模态超图。通过这种方式，查询和视频可以在高阶语义空间中对齐。此外，为了提高模型的泛化能力，提取的特征被输入到一个变分推理组件中进行计算，从而获得正态分布下的变分表示。超图和变分推理的结合使我们的模型能够捕获文本和视觉内容之间复杂的 n 元交互。实验结果表明，该方法在文本视频检索任务中取得了较好的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Text-Video+Retrieval+via+Multi-Modal+Hypergraph+Networks)|0| -|[MultiFS: Automated Multi-Scenario Feature Selection in Deep Recommender Systems](https://doi.org/10.1145/3616855.3635859)|Dugang Liu, Chaohua Yang, Xing Tang, Yejing Wang, Fuyuan Lyu, Weihong Luo, Xiuqiang He, Zhong Ming, Xiangyu Zhao|McGill Univ, Montreal, PQ, Canada; City Univ Hong Kong, Hong Kong, Peoples R China; Shenzhen Technol Univ, Shenzhen, Peoples R China; Shenzhen Univ, Guangdong Lab Artificial Intelligence & Digital E, Shenzhen, Peoples R China; Shenzhen Univ, Shenzhen, Peoples R China; Tencent, Shenzhen, Peoples R China|Multi-scenario recommender systems (MSRSs) have been increasingly used in real-world industrial platforms for their excellent advantages in mitigating data sparsity and reducing maintenance costs. However, conventional MSRSs usually use all relevant features indiscriminately and ignore that different kinds of features have varying importance under different scenarios, which may cause confusion and performance degradation. In addition, existing feature selection methods for deep recommender systems may lack the exploration of scenario relations. In this paper, we propose a novel automated multi-scenario feature selection (MultiFS) framework to bridge this gap, which is able to consider scenario relations and utilize a hierarchical gating mechanism to select features for each scenario. Specifically, MultiFS first efficiently obtains feature importance across all the scenarios through a scenario-shared gate. Then, some scenario-specific gate aims to identify feature importance to individual scenarios from a subset of the former with lower importance. Subsequently, MultiFS imposes constraints on the two gates to make the learning mechanism more feasible and combines the two to select exclusive features for different scenarios. We evaluate MultiFS and demonstrate its ability to enhance the multi-scenario model performance through experiments over two public multi-scenario benchmarks.|多场景推荐系统(MSRS)以其在减少数据稀疏性和降低维护成本方面的优越性越来越多地应用于现实世界的工业平台。然而，传统的 MSRS 通常不加区分地使用所有相关特征，而忽视了不同特征在不同场景下具有不同的重要性，这可能导致混淆和性能下降。此外，现有的深度推荐系统的特征选择方法可能缺乏对场景关系的探索。本文提出了一种新的自动多场景特征选择(MultiFS)框架来弥补这一缺陷，该框架能够考虑场景之间的关系，并利用层次化的门限机制为每个场景选择特征。具体来说，MultiFS 首先通过一个场景共享门有效地获得所有场景的特性重要性。然后，一些场景特定门的目的是识别特征重要性的个别场景从前者的子集较低的重要性。随后，MultiFS 对这两个门进行约束，使学习机制更加可行，并结合这两个门来选择不同场景的专有特征。我们通过两个公开的多场景基准测试，评估了 MultiFS 并证明了其增强多场景模型性能的能力。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=MultiFS:+Automated+Multi-Scenario+Feature+Selection+in+Deep+Recommender+Systems)|0| +|[MultiFS: Automated Multi-Scenario Feature Selection in Deep Recommender Systems](https://doi.org/10.1145/3616855.3635859)|Dugang Liu, Chaohua Yang, Xing Tang, Yejing Wang, Fuyuan Lyu, Weihong Luo, Xiuqiang He, Zhong Ming, Xiangyu Zhao|Tencent, Shenzhen, Peoples R China; City Univ Hong Kong, Hong Kong, Peoples R China; Shenzhen Univ, Shenzhen, Peoples R China; Shenzhen Univ, Guangdong Lab Artificial Intelligence & Digital E, Shenzhen, Peoples R China; Shenzhen Technol Univ, Shenzhen, Peoples R China; McGill Univ, Montreal, PQ, Canada|Multi-scenario recommender systems (MSRSs) have been increasingly used in real-world industrial platforms for their excellent advantages in mitigating data sparsity and reducing maintenance costs. However, conventional MSRSs usually use all relevant features indiscriminately and ignore that different kinds of features have varying importance under different scenarios, which may cause confusion and performance degradation. In addition, existing feature selection methods for deep recommender systems may lack the exploration of scenario relations. In this paper, we propose a novel automated multi-scenario feature selection (MultiFS) framework to bridge this gap, which is able to consider scenario relations and utilize a hierarchical gating mechanism to select features for each scenario. Specifically, MultiFS first efficiently obtains feature importance across all the scenarios through a scenario-shared gate. Then, some scenario-specific gate aims to identify feature importance to individual scenarios from a subset of the former with lower importance. Subsequently, MultiFS imposes constraints on the two gates to make the learning mechanism more feasible and combines the two to select exclusive features for different scenarios. We evaluate MultiFS and demonstrate its ability to enhance the multi-scenario model performance through experiments over two public multi-scenario benchmarks.|多场景推荐系统(MSRS)以其在减少数据稀疏性和降低维护成本方面的优越性越来越多地应用于现实世界的工业平台。然而，传统的 MSRS 通常不加区分地使用所有相关特征，而忽视了不同特征在不同场景下具有不同的重要性，这可能导致混淆和性能下降。此外，现有的深度推荐系统的特征选择方法可能缺乏对场景关系的探索。本文提出了一种新的自动多场景特征选择(MultiFS)框架来弥补这一缺陷，该框架能够考虑场景之间的关系，并利用层次化的门限机制为每个场景选择特征。具体来说，MultiFS 首先通过一个场景共享门有效地获得所有场景的特性重要性。然后，一些场景特定门的目的是识别特征重要性的个别场景从前者的子集较低的重要性。随后，MultiFS 对这两个门进行约束，使学习机制更加可行，并结合这两个门来选择不同场景的专有特征。我们通过两个公开的多场景基准测试，评估了 MultiFS 并证明了其增强多场景模型性能的能力。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=MultiFS:+Automated+Multi-Scenario+Feature+Selection+in+Deep+Recommender+Systems)|0| |[ONCE: Boosting Content-based Recommendation with Both Open- and Closed-source Large Language Models](https://doi.org/10.1145/3616855.3635845)|Qijiong Liu, Nuo Chen, Tetsuya Sakai, XiaoMing Wu||Personalized content-based recommender systems have become indispensable tools for users to navigate through the vast amount of content available on platforms like daily news websites and book recommendation services. However, existing recommenders face significant challenges in understanding the content of items. Large language models (LLMs), which possess deep semantic comprehension and extensive knowledge from pretraining, have proven to be effective in various natural language processing tasks. In this study, we explore the potential of leveraging both open- and closed-source LLMs to enhance content-based recommendation. With open-source LLMs, we utilize their deep layers as content encoders, enriching the representation of content at the embedding level. For closed-source LLMs, we employ prompting techniques to enrich the training data at the token level. Through comprehensive experiments, we demonstrate the high effectiveness of both types of LLMs and show the synergistic relationship between them. Notably, we observed a significant relative improvement of up to 19.32% compared to existing state-of-the-art recommendation models. These findings highlight the immense potential of both open- and closed-source of LLMs in enhancing content-based recommendation systems. We will make our code and LLM-generated data available for other researchers to reproduce our results.|基于内容的个性化推荐系统已经成为用户浏览日常新闻网站和图书推荐服务等平台上大量内容不可或缺的工具。但是，现有的推荐程序在理解项目内容方面面临重大挑战。大语言模型(LLM)具有深刻的语义理解能力和广泛的预训知识，已被证明能够有效地处理各种自然语言处理任务。在这项研究中，我们探索了利用开源和闭源 LLM 来增强基于内容的推荐的潜力。使用开源 LLM，我们利用它们的深层作为内容编码器，丰富了内容在嵌入级别的表示。对于闭源 LLM，我们使用提示技术在令牌级别上丰富训练数据。通过综合实验，我们证明了这两类 LLM 的高效性，并显示了它们之间的协同关系。值得注意的是，与现有最先进的推荐模型相比，我们观察到了高达19.32% 的显著相对改善。这些发现突出了开放和封闭的 LLM 来源在加强基于内容的推荐系统方面的巨大潜力。我们将使我们的代码和 LLM 生成的数据可用于其他研究人员重现我们的结果。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=ONCE:+Boosting+Content-based+Recommendation+with+Both+Open-+and+Closed-source+Large+Language+Models)|0| |[Knowledge Graph Context-Enhanced Diversified Recommendation](https://doi.org/10.1145/3616855.3635803)|Xiaolong Liu, Liangwei Yang, Zhiwei Liu, Mingdai Yang, Chen Wang, Hao Peng, Philip S. Yu||The field of Recommender Systems (RecSys) has been extensively studied to enhance accuracy by leveraging users' historical interactions. Nonetheless, this persistent pursuit of accuracy frequently engenders diminished diversity, culminating in the well-recognized "echo chamber" phenomenon. Diversified RecSys has emerged as a countermeasure, placing diversity on par with accuracy and garnering noteworthy attention from academic circles and industry practitioners. This research explores the realm of diversified RecSys within the intricate context of knowledge graphs (KG). These KGs act as repositories of interconnected information concerning entities and items, offering a propitious avenue to amplify recommendation diversity through the incorporation of insightful contextual information. Our contributions include introducing an innovative metric, Entity Coverage, and Relation Coverage, which effectively quantifies diversity within the KG domain. Additionally, we introduce the Diversified Embedding Learning (DEL) module, meticulously designed to formulate user representations that possess an innate awareness of diversity. In tandem with this, we introduce a novel technique named Conditional Alignment and Uniformity (CAU). It adeptly encodes KG item embeddings while preserving contextual integrity. Collectively, our contributions signify a substantial stride towards augmenting the panorama of recommendation diversity within the realm of KG-informed RecSys paradigms.|推荐系统(RecSys)领域已经被广泛研究，通过利用用户的历史交互来提高准确性。尽管如此，这种对准确性的持续追求常常导致多样性的减少，最终导致公认的“回声室”现象。多样化 RecSys 已经成为一种对策，它将多样性与准确性放在同等重要的位置，并引起了学术界和业内人士的关注。本研究探讨复杂的知识图表(KG)背景下多元化的 RecSys 领域。这些幼儿园充当有关实体和项目的相互关联信息的储存库，通过纳入有见地的背景信息，为扩大建议的多样性提供了一个有利的途径。我们的贡献包括引入一个创新的度量标准，实体覆盖率和关系覆盖率，它有效地量化了 KG 领域内的多样性。此外，我们介绍了多样化嵌入学习(DEL)模块，精心设计的用户表示，具有天生的多样性意识。与此同时，我们介绍了一种新的技术，称为条件对齐和一致性(CAU)。该算法在保持上下文完整性的前提下，对 KG 项嵌入进行编码。总的来说，我们的贡献意味着在 KG 知情的 RecSys 范式领域内，在增强推荐多样性的全景方面取得了实质性的进展。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Knowledge+Graph+Context-Enhanced+Diversified+Recommendation)|0| |[SSLRec: A Self-Supervised Learning Framework for Recommendation](https://doi.org/10.1145/3616855.3635814)|Xubin Ren, Lianghao Xia, Yuhao Yang, Wei Wei, Tianle Wang, Xuheng Cai, Chao Huang||Self-supervised learning (SSL) has gained significant interest in recent years as a solution to address the challenges posed by sparse and noisy data in recommender systems. Despite the growing number of SSL algorithms designed to provide state-of-the-art performance in various recommendation scenarios (e.g., graph collaborative filtering, sequential recommendation, social recommendation, KG-enhanced recommendation), there is still a lack of unified frameworks that integrate recommendation algorithms across different domains. Such a framework could serve as the cornerstone for self-supervised recommendation algorithms, unifying the validation of existing methods and driving the design of new ones. To address this gap, we introduce SSLRec, a novel benchmark platform that provides a standardized, flexible, and comprehensive framework for evaluating various SSL-enhanced recommenders. The SSLRec framework features a modular architecture that allows users to easily evaluate state-of-the-art models and a complete set of data augmentation and self-supervised toolkits to help create SSL recommendation models with specific needs. Furthermore, SSLRec simplifies the process of training and evaluating different recommendation models with consistent and fair settings. Our SSLRec platform covers a comprehensive set of state-of-the-art SSL-enhanced recommendation models across different scenarios, enabling researchers to evaluate these cutting-edge models and drive further innovation in the field. Our implemented SSLRec framework is available at the source code repository https://github.com/HKUDS/SSLRec.|自监督学习(SSL)作为一种解决推荐系统中数据稀疏和噪声问题的方法，近年来引起了人们的极大兴趣。尽管有越来越多的 SSL 算法被设计用来在各种推荐场景中提供最先进的性能(例如，图形协同过滤、顺序推荐、社交推荐、 KG 增强推荐) ，但是仍然缺乏统一的框架来整合不同领域的推荐算法。这样一个框架可以作为自监督推荐算法的基石，统一现有方法的验证，并推动新方法的设计。为了弥补这一差距，我们引入了 SSLRec，这是一个新颖的基准平台，它为评估各种 SSL 增强的推荐程序提供了一个标准化、灵活和全面的框架。SSLRec 框架采用模块化架构，允许用户方便地评估最先进的模型，并提供一套完整的数据增强和自我监督工具包，以帮助创建具有特定需求的 SSL 推荐模型。此外，SSLRec 通过一致和公平的设置简化了不同推荐模型的培训和评估过程。我们的 SSLRec 平台涵盖了不同场景下一整套最先进的 SSL 增强推荐模型，使研究人员能够评估这些尖端模型，并推动该领域的进一步创新。我们已实施的 SSlrec 架构可在原始码储存库 https://github.com/hkuds/SSLRec 找到。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=SSLRec:+A+Self-Supervised+Learning+Framework+for+Recommendation)|0| |[Dynamic Sparse Learning: A Novel Paradigm for Efficient Recommendation](https://doi.org/10.1145/3616855.3635780)|Shuyao Wang, Yongduo Sui, Jiancan Wu, Zhi Zheng, Hui Xiong||In the realm of deep learning-based recommendation systems, the increasing computational demands, driven by the growing number of users and items, pose a significant challenge to practical deployment. This challenge is primarily twofold: reducing the model size while effectively learning user and item representations for efficient recommendations. Despite considerable advancements in model compression and architecture search, prevalent approaches face notable constraints. These include substantial additional computational costs from pre-training/re-training in model compression and an extensive search space in architecture design. Additionally, managing complexity and adhering to memory constraints is problematic, especially in scenarios with strict time or space limitations. Addressing these issues, this paper introduces a novel learning paradigm, Dynamic Sparse Learning (DSL), tailored for recommendation models. DSL innovatively trains a lightweight sparse model from scratch, periodically evaluating and dynamically adjusting each weight's significance and the model's sparsity distribution during the training. This approach ensures a consistent and minimal parameter budget throughout the full learning lifecycle, paving the way for "end-to-end" efficiency from training to inference. Our extensive experimental results underline DSL's effectiveness, significantly reducing training and inference costs while delivering comparable recommendation performance.|在基于深度学习的推荐系统领域，由于用户和项目数量的不断增加，计算需求的不断增加对实际部署提出了严峻的挑战。这个挑战主要有两个方面: 减少模型的大小，同时有效地学习用户和项目表示以获得有效的推荐。尽管在模型压缩和体系结构搜索方面取得了相当大的进步，流行的方法仍然面临着显著的限制。其中包括模型压缩中的预训练/再训练带来的大量额外计算成本，以及体系结构设计中的大量搜索空间。此外，管理复杂性和遵守内存约束是有问题的，特别是在有严格时间或空间限制的场景中。针对这些问题，本文介绍了一种新的学习范式，动态稀疏学习(DSL) ，专为推荐模型。DSL 创新地从头开始训练一个轻量级稀疏模型，在训练过程中定期评估和动态调整每个权重的重要性和模型的稀疏分布。这种方法确保了整个学习生命周期中参数预算的一致性和最小化，为从训练到推理的“端到端”效率铺平了道路。我们广泛的实验结果强调了 DSL 的有效性，显著降低了培训和推理成本，同时提供了可比较的推荐性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Dynamic+Sparse+Learning:+A+Novel+Paradigm+for+Efficient+Recommendation)|0| -|[Towards Better Chinese Spelling Check for Search Engines: A New Dataset and Strong Baseline](https://doi.org/10.1145/3616855.3635847)|Yue Wang, Zilong Zheng, Zecheng Tang, Juntao Li, Zhihui Liu, Kunlong Chen, Jinxiong Chang, Qishen Zhang, Zhongyi Liu, Min Zhang|Ant Grp, Beijing, Peoples R China; Ant Grp, Hangzhou, Peoples R China; Soochow Univ, Suzhou, Peoples R China|Misspellings in search engine queries may prevent search engines from returning accurate results. For Chinese mobile search engines, due to the different input methods (e.g., hand-written and T9 input methods), more types of misspellings exist, making this problem more challenging. As an essential module of search engines, Chinese Spelling Check (CSC) models aim to detect and correct misspelled Chinese characters from user-issued queries. Despite the great value of CSC to the search engine, there is no CSC benchmark collected from real-world search engine queries. To fill this blank, we construct and release the Alipay Search Engine Query (AlipaySEQ) spelling check dataset. To the best of our knowledge, AlipaySEQ is the first Chinese Spelling Check dataset collected from the realworld scenario of Chinese mobile search engines. It consists of 15,522 high-quality human annotated and 1,175,151 automatically generated samples. To demonstrate the unique challenges of AlipaySEQ in the era of Large Language Models (LLMs), we conduct a thorough study to analyze the difference between AlipaySEQ and existing SIGHAN benchmarks and compare the performance of various baselines, including existing task-specific methods and LLMs. We observe that all baselines fail to perform satisfactorily due to the over-correction problem. Especially, LLMs exhibit below-par performance on AlipaySEQ, which is rather surprising. Therefore, to alleviate the over-correction problem, we introduce a modelagnostic CSC Self-Refine Framework (SRF) to construct a strong baseline. Comprehensive experiments demonstrate that our proposed SRF, though more effective against existing models on both the AlipaySEQ and SIGHAN15, is still far from achieving satisfactory performance on our real-world dataset. With the newly collected real-world dataset and strong baseline, we hope more progress can be achieved on such a challenging and valuable task.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Towards+Better+Chinese+Spelling+Check+for+Search+Engines:+A+New+Dataset+and+Strong+Baseline)|0| +|[Towards Better Chinese Spelling Check for Search Engines: A New Dataset and Strong Baseline](https://doi.org/10.1145/3616855.3635847)|Yue Wang, Zilong Zheng, Zecheng Tang, Juntao Li, Zhihui Liu, Kunlong Chen, Jinxiong Chang, Qishen Zhang, Zhongyi Liu, Min Zhang|Ant Grp, Hangzhou, Peoples R China; Soochow Univ, Suzhou, Peoples R China; Ant Grp, Beijing, Peoples R China|Misspellings in search engine queries may prevent search engines from returning accurate results. For Chinese mobile search engines, due to the different input methods (e.g., hand-written and T9 input methods), more types of misspellings exist, making this problem more challenging. As an essential module of search engines, Chinese Spelling Check (CSC) models aim to detect and correct misspelled Chinese characters from user-issued queries. Despite the great value of CSC to the search engine, there is no CSC benchmark collected from real-world search engine queries. To fill this blank, we construct and release the Alipay Search Engine Query (AlipaySEQ) spelling check dataset. To the best of our knowledge, AlipaySEQ is the first Chinese Spelling Check dataset collected from the realworld scenario of Chinese mobile search engines. It consists of 15,522 high-quality human annotated and 1,175,151 automatically generated samples. To demonstrate the unique challenges of AlipaySEQ in the era of Large Language Models (LLMs), we conduct a thorough study to analyze the difference between AlipaySEQ and existing SIGHAN benchmarks and compare the performance of various baselines, including existing task-specific methods and LLMs. We observe that all baselines fail to perform satisfactorily due to the over-correction problem. Especially, LLMs exhibit below-par performance on AlipaySEQ, which is rather surprising. Therefore, to alleviate the over-correction problem, we introduce a modelagnostic CSC Self-Refine Framework (SRF) to construct a strong baseline. Comprehensive experiments demonstrate that our proposed SRF, though more effective against existing models on both the AlipaySEQ and SIGHAN15, is still far from achieving satisfactory performance on our real-world dataset. With the newly collected real-world dataset and strong baseline, we hope more progress can be achieved on such a challenging and valuable task.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Towards+Better+Chinese+Spelling+Check+for+Search+Engines:+A+New+Dataset+and+Strong+Baseline)|0| |[Neural Kalman Filtering for Robust Temporal Recommendation](https://doi.org/10.1145/3616855.3635837)|Jiafeng Xia, Dongsheng Li, Hansu Gu, Tun Lu, Peng Zhang, Li Shang, Ning Gu|; Microsoft Res Asia, Shanghai, Peoples R China; Fudan Univ, Shanghai, Peoples R China|Temporal recommendation methods can achieve superior accuracy due to updating user/item embeddings continuously once obtaining new interactions. However, the randomness of user behaviors will introduce noises into the user interactions and cause the deviation in the modeling of user preference, resulting in sub-optimal performance. To this end, we propose NeuFilter, a robust temporal recommendation algorithm based on neural Kalman Filtering, to learn more accurate user and item embeddings with noisy interactions. Classic Kalman Filtering is time-consuming when applied to recommendation due to its covariance matrices. Thus, we propose a neural network solution to Kalman Filtering, so as to realize higher efficiency and stronger expressivity. Specifically, NeuFilter consists of three alternating units: 1) prediction unit, which predicts user and item embeddings based on their historical embeddings; 2) estimation unit, which updates user and item embeddings in a manner similar to Kalman Filtering; 3) correction unit, which corrects the updated user and item embeddings from estimation unit to ensure reliable estimation and accurate update. Experiments on two recommendation tasks show that NeuFilter can achieve higher accuracy compared with the state-of-the-art methods, while achieving high robustness. Moreover, our empirical studies on a node classification task further confirm the importance of handling noises in tasks on temporal graph, shedding a new light on temporal graph modeling.|时态推荐方法在获得新的交互信息后，通过不断更新用户/项目的嵌入信息，可以获得更高的推荐准确率。然而，用户行为的随机性会在用户交互中引入噪声，导致用户偏好建模的偏差，从而导致系统性能的次优。为此，我们提出了一种基于神经卡尔曼滤波的鲁棒时态推荐算法 NeuFilter，以便在有噪声的交互环境下学习更精确的用户和项目嵌入。经典卡尔曼滤波由于其协方差矩阵的特点，在推荐应用中非常耗时。为此，我们提出了一种卡尔曼滤波的神经网络解决方案，以实现更高的效率和更强的表达能力。具体来说，NeuFilter 由三个交替单元组成: 1)预测单元，根据用户和项目的历史嵌入来预测用户和项目的嵌入; 2)估计单元，以类似于卡尔曼过滤的方式更新用户和项目的嵌入; 3)校正单元，从估计单元纠正更新的用户和项目的嵌入，以确保可靠的估计和准确的更新。在两个推荐任务上的实验表明，NeuFilter 算法在获得较高的鲁棒性的同时，能够获得较高的推荐精度。此外，我们对一个节点分类任务的实证研究进一步证实了时间图任务中处理噪声的重要性，为时间图建模提供了新的视角。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Neural+Kalman+Filtering+for+Robust+Temporal+Recommendation)|0| |[Unified Pretraining for Recommendation via Task Hypergraphs](https://doi.org/10.1145/3616855.3635811)|Mingdai Yang, Zhiwei Liu, Liangwei Yang, Xiaolong Liu, Chen Wang, Hao Peng, Philip S. Yu||Although pretraining has garnered significant attention and popularity in recent years, its application in graph-based recommender systems is relatively limited. It is challenging to exploit prior knowledge by pretraining in widely used ID-dependent datasets. On one hand, user-item interaction history in one dataset can hardly be transferred to other datasets through pretraining, where IDs are different. On the other hand, pretraining and finetuning on the same dataset leads to a high risk of overfitting. In this paper, we propose a novel multitask pretraining framework named Unified Pretraining for Recommendation via Task Hypergraphs. For a unified learning pattern to handle diverse requirements and nuances of various pretext tasks, we design task hypergraphs to generalize pretext tasks to hyperedge prediction. A novel transitional attention layer is devised to discriminatively learn the relevance between each pretext task and recommendation. Experimental results on three benchmark datasets verify the superiority of UPRTH. Additional detailed investigations are conducted to demonstrate the effectiveness of the proposed framework.|尽管预训练近年来得到了广泛的关注和普及，但其在基于图形的推荐系统中的应用相对有限。在广泛使用的依赖于身份的数据集中，通过预训练来利用先验知识是一个挑战。一方面，一个数据集中的用户项交互历史很难通过预训练传递给其他数据集，因为 ID 是不同的。另一方面，对同一数据集进行预训练和微调会导致过度拟合的高风险。本文提出了一种新的多任务预训练框架——基于任务超图的推荐统一预训练。为了统一学习模式来处理不同的需求和各种借口任务的细微差别，我们设计了任务超图将借口任务推广到超边缘预测。设计了一个新颖的过渡注意层，用于区分性地学习每个借口任务和推荐之间的相关性。在三个基准数据集上的实验结果验证了 UPRTH 算法的优越性。还进行了更多的详细调查，以证明拟议框架的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Unified+Pretraining+for+Recommendation+via+Task+Hypergraphs)|0| -|[COTER: Conditional Optimal Transport meets Table Retrieval](https://doi.org/10.1145/3616855.3635796)|Xun Yao, Zhixin Zhang, Xinrong Hu, Jie (Jack) Yang, Yi Guo, Daniel (Dianliang) Zhu|Auxilis Pty Ltd, Wollongong, NSW, Australia; Wuhan Text Univ, Sch Comp Sci & Artificial Intelligence, Wuhan, Hubei, Peoples R China; Western Sydney Univ, Sch Comp Data & Math Sci, Parramatta, NSW, Australia; Univ Wollongong, Sch Comp & Informat Technol, Wollongong, NSW, Australia|Ad hoc table retrieval refers to the task of performing semantic matching between given queries and candidate tables. In recent years, the approach to addressing this retrieval task has undergone significant shifts, transitioning from utilizing hand-crafted features to leveraging the power of Pre-trained Language Models (PLMs). However, key challenges arise when candidate tables contain shared items, and/or queries may refer to only a subset of table items rather than the entire one. Existing models often struggle to distinguish the most informative items and fail to accurately identify the relevant items required to match with the query. To bridge this gap, we propose Conditional Optimal Transport based table retrievER (COTER). The proposed algorithm is characterized by simplifying candidate tables, where the semantic meaning of one or several words (from the original table) is enabled to be effectively "transported" to individual words (from the simplified table), under the prior condition of the query. COTER achieves two essential goals simultaneously: minimizing the semantic loss during the table simplification and ensuring that retained items from simplified tables effectively match the given query. Importantly, the theoretical foundation of COTER empowers it to adapt dynamically to different queries and enhances the overall performance of the table retrieval. Experiments on two popular Web-table retrieval benchmarks show that COTER can effectively identify informative table items without sacrificing retrieval accuracy. This leads to the new state-of-the-art with substantial gains of up to 0.48 absolute Mean Average Precision (MAP) points, compared to the previously reported best result.|Ad hoc 表检索是指在给定查询和候选表之间执行语义匹配的任务。近年来，解决这一检索任务的方法经历了重大转变，从利用手工制作的特性过渡到利用预训练语言模型(PLM)的力量。但是，当候选表包含共享项时，会出现关键问题，并且/或查询可能只引用表项的一个子集，而不是整个表项。现有的模型常常难以区分信息量最大的项目，并且无法准确地识别与查询匹配所需的相关项目。为了弥补这一差距，我们提出了基于条件最优传输的表检索器(COTER)。提出的算法拥有属性是简化候选表，其中一个或几个单词(来自原始表)的语义能够在查询的前提条件下有效地“传输”到单个单词(来自简化表)。COTER 同时实现两个基本目标: 最小化表简化过程中的语义丢失，以及确保简化表中保留的项有效地匹配给定的查询。重要的是，COTER 的理论基础使其能够动态地适应不同的查询，并提高表检索的整体性能。在两个常用的 Web 表检索基准上的实验表明，COTER 能够在不牺牲检索精度的前提下有效地识别信息表项。这导致了新的国家的最先进的大幅增益高达0.48绝对平均精度(MAP)点，相比之下，以前报告的最佳结果。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=COTER:+Conditional+Optimal+Transport+meets+Table+Retrieval)|0| -|[IncMSR: An Incremental Learning Approach for Multi-Scenario Recommendation](https://doi.org/10.1145/3616855.3635828)|Kexin Zhang, Yichao Wang, Xiu Li, Ruiming Tang, Rui Zhang|Tsinghua Univ, Tsinghua Shenzhen Int Grad Sch, Shenzhen, Peoples R China; Huawei Noahs Ark Lab, Shenzhen, Peoples R China; Ruizhang Info, Shenzhen, Peoples R China|For better performance and less resource consumption, multi-scenario recommendation (MSR) is proposed to train a unified model to serve all scenarios by leveraging data from multiple scenarios. Current works in MSR focus on designing effective networks for better information transfer among different scenarios. However, they omit two important issues when applying MSR models in industrial situations. The first is the efficiency problem brought by mixed data, which delays the update of models and further leads to performance degradation. The second is that MSR models are insensitive to the changes of distribution over time, resulting in suboptimal effectiveness in the incoming data. In this paper, we propose an incremental learning approach for MSR (IncMSR), which can not only improve the training efficiency but also perceive changes in distribution over time. Specifically, we first quantify the pair-wise distance between representations from scenario, time and time scenario dimensions respectively. Then, we decompose the MSR model into scenario-shared and scenario-specific parts and apply fine-grained constraints on the distances quantified with respect to the two different parts. Finally, all constraints are fused in an elegant way using a metric learning framework as a supplementary penalty term to the original MSR loss function. Offline experiments on two real-world datasets are conducted to demonstrate the superiority and compatibility of our proposed approach.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=IncMSR:+An+Incremental+Learning+Approach+for+Multi-Scenario+Recommendation)|0| +|[COTER: Conditional Optimal Transport meets Table Retrieval](https://doi.org/10.1145/3616855.3635796)|Xun Yao, Zhixin Zhang, Xinrong Hu, Jie (Jack) Yang, Yi Guo, Daniel (Dianliang) Zhu|Western Sydney Univ, Sch Comp Data & Math Sci, Parramatta, NSW, Australia; Wuhan Text Univ, Sch Comp Sci & Artificial Intelligence, Wuhan, Hubei, Peoples R China; Univ Wollongong, Sch Comp & Informat Technol, Wollongong, NSW, Australia; Auxilis Pty Ltd, Wollongong, NSW, Australia|Ad hoc table retrieval refers to the task of performing semantic matching between given queries and candidate tables. In recent years, the approach to addressing this retrieval task has undergone significant shifts, transitioning from utilizing hand-crafted features to leveraging the power of Pre-trained Language Models (PLMs). However, key challenges arise when candidate tables contain shared items, and/or queries may refer to only a subset of table items rather than the entire one. Existing models often struggle to distinguish the most informative items and fail to accurately identify the relevant items required to match with the query. To bridge this gap, we propose Conditional Optimal Transport based table retrievER (COTER). The proposed algorithm is characterized by simplifying candidate tables, where the semantic meaning of one or several words (from the original table) is enabled to be effectively "transported" to individual words (from the simplified table), under the prior condition of the query. COTER achieves two essential goals simultaneously: minimizing the semantic loss during the table simplification and ensuring that retained items from simplified tables effectively match the given query. Importantly, the theoretical foundation of COTER empowers it to adapt dynamically to different queries and enhances the overall performance of the table retrieval. Experiments on two popular Web-table retrieval benchmarks show that COTER can effectively identify informative table items without sacrificing retrieval accuracy. This leads to the new state-of-the-art with substantial gains of up to 0.48 absolute Mean Average Precision (MAP) points, compared to the previously reported best result.|Ad hoc 表检索是指在给定查询和候选表之间执行语义匹配的任务。近年来，解决这一检索任务的方法经历了重大转变，从利用手工制作的特性过渡到利用预训练语言模型(PLM)的力量。但是，当候选表包含共享项时，会出现关键问题，并且/或查询可能只引用表项的一个子集，而不是整个表项。现有的模型常常难以区分信息量最大的项目，并且无法准确地识别与查询匹配所需的相关项目。为了弥补这一差距，我们提出了基于条件最优传输的表检索器(COTER)。提出的算法拥有属性是简化候选表，其中一个或几个单词(来自原始表)的语义能够在查询的前提条件下有效地“传输”到单个单词(来自简化表)。COTER 同时实现两个基本目标: 最小化表简化过程中的语义丢失，以及确保简化表中保留的项有效地匹配给定的查询。重要的是，COTER 的理论基础使其能够动态地适应不同的查询，并提高表检索的整体性能。在两个常用的 Web 表检索基准上的实验表明，COTER 能够在不牺牲检索精度的前提下有效地识别信息表项。这导致了新的国家的最先进的大幅增益高达0.48绝对平均精度(MAP)点，相比之下，以前报告的最佳结果。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=COTER:+Conditional+Optimal+Transport+meets+Table+Retrieval)|0| +|[IncMSR: An Incremental Learning Approach for Multi-Scenario Recommendation](https://doi.org/10.1145/3616855.3635828)|Kexin Zhang, Yichao Wang, Xiu Li, Ruiming Tang, Rui Zhang|Tsinghua Univ, Tsinghua Shenzhen Int Grad Sch, Shenzhen, Peoples R China; Ruizhang Info, Shenzhen, Peoples R China; Huawei Noahs Ark Lab, Shenzhen, Peoples R China|For better performance and less resource consumption, multi-scenario recommendation (MSR) is proposed to train a unified model to serve all scenarios by leveraging data from multiple scenarios. Current works in MSR focus on designing effective networks for better information transfer among different scenarios. However, they omit two important issues when applying MSR models in industrial situations. The first is the efficiency problem brought by mixed data, which delays the update of models and further leads to performance degradation. The second is that MSR models are insensitive to the changes of distribution over time, resulting in suboptimal effectiveness in the incoming data. In this paper, we propose an incremental learning approach for MSR (IncMSR), which can not only improve the training efficiency but also perceive changes in distribution over time. Specifically, we first quantify the pair-wise distance between representations from scenario, time and time scenario dimensions respectively. Then, we decompose the MSR model into scenario-shared and scenario-specific parts and apply fine-grained constraints on the distances quantified with respect to the two different parts. Finally, all constraints are fused in an elegant way using a metric learning framework as a supplementary penalty term to the original MSR loss function. Offline experiments on two real-world datasets are conducted to demonstrate the superiority and compatibility of our proposed approach.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=IncMSR:+An+Incremental+Learning+Approach+for+Multi-Scenario+Recommendation)|0| |[Defense Against Model Extraction Attacks on Recommender Systems](https://doi.org/10.1145/3616855.3635751)|Sixiao Zhang, Hongzhi Yin, Hongxu Chen, Cheng Long||The robustness of recommender systems has become a prominent topic within the research community. Numerous adversarial attacks have been proposed, but most of them rely on extensive prior knowledge, such as all the white-box attacks or most of the black-box attacks which assume that certain external knowledge is available. Among these attacks, the model extraction attack stands out as a promising and practical method, involving training a surrogate model by repeatedly querying the target model. However, there is a significant gap in the existing literature when it comes to defending against model extraction attacks on recommender systems. In this paper, we introduce Gradient-based Ranking Optimization (GRO), which is the first defense strategy designed to counter such attacks. We formalize the defense as an optimization problem, aiming to minimize the loss of the protected target model while maximizing the loss of the attacker's surrogate model. Since top-k ranking lists are non-differentiable, we transform them into swap matrices which are instead differentiable. These swap matrices serve as input to a student model that emulates the surrogate model's behavior. By back-propagating the loss of the student model, we obtain gradients for the swap matrices. These gradients are used to compute a swap loss, which maximizes the loss of the student model. We conducted experiments on three benchmark datasets to evaluate the performance of GRO, and the results demonstrate its superior effectiveness in defending against model extraction attacks.|推荐系统的健壮性已经成为研究领域的一个重要课题。已经提出了许多对抗性攻击，但大多数都依赖于广泛的先验知识，例如所有的白盒攻击或大多数的黑盒攻击都假设某些外部知识是可用的。在这些攻击中，模型提取攻击是一种很有前途和实用的攻击方法，它通过重复查询目标模型来训练代理模型。然而，现有的文献在防御推荐系统的模型抽取攻击方面还存在很大的差距。本文介绍了基于梯度的排序优化(GRO) ，这是第一种针对此类攻击而设计的防御策略。我们将防御形式化为一个最佳化问题，目的是最小化受保护目标模型的丢失，同时最大化攻击者代理模型的丢失。由于 top-k 排序列表是不可微的，所以我们将它们转换成交换矩阵，这些交换矩阵是可微的。这些交换矩阵作为学生模型的输入，模拟代理模型的行为。通过反向传播学生模型的损失，我们得到了交换矩阵的梯度。这些梯度用于计算交换损失，使学生模型的损失最大化。我们在三个基准数据集上进行了实验，对 GRO 的性能进行了评估，结果表明 GRO 在抵御模型提取攻击方面具有优越的效果。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Defense+Against+Model+Extraction+Attacks+on+Recommender+Systems)|0| |[GEMRec: Towards Generative Model Recommendation](https://doi.org/10.1145/3616855.3635700)|Yuanhe Guo, Haoming Liu, Hongyi Wen||Recommender Systems are built to retrieve relevant items to satisfy users' information needs. The candidate corpus usually consists of a finite set of items that are ready to be served, such as videos, products, or articles. With recent advances in Generative AI such as GPT and Diffusion models, a new form of recommendation task is yet to be explored where items are to be created by generative models with personalized prompts. Taking image generation as an example, with a single prompt from the user and access to a generative model, it is possible to generate hundreds of new images in a few minutes. How shall we attain personalization in the presence of "infinite" items? In this preliminary study, we propose a two-stage framework, namely Prompt-Model Retrieval and Generated Item Ranking, to approach this new task formulation. We release GEMRec-18K, a prompt-model interaction dataset with 18K images generated by 200 publicly-available generative models paired with a diverse set of 90 textual prompts. Our findings demonstrate the promise of generative model recommendation as a novel personalization problem and the limitations of existing evaluation metrics. We highlight future directions for the RecSys community to advance towards generative recommender systems. Our code and dataset are available at https://github.com/MAPS-research/GEMRec.|建立推荐系统是为了检索相关项目，以满足用户的信息需求。候选语料库通常由一组有限的可供服务的项目组成，例如视频、产品或文章。随着生成式人工智能(Generative AI)的最新进展，如 GPT 模型和扩散模型，一种新形式的推荐任务尚待探索，其中项目将由具有个性化提示的生成式模型创建。以图像生成为例，用户只需提示一下，就可以访问一个生成模型，在几分钟内就可以生成数百张新图像。我们如何在“无限”的事物面前实现个性化？在这个初步的研究中，我们提出了一个两阶段的框架，即提示模型检索和生成的项目排序，以接近这个新的任务制定。我们发布了 GEMRec-18K，一个提示模型交互数据集，由200个公开可用的生成模型与90个文本提示配对生成18K 图像。我们的发现证明了生成模型推荐作为一个新的个性化问题的前景，以及现有评估指标的局限性。我们强调 RecSys 社区向生成推荐系统发展的未来方向。我们的代码和数据集可在 https://github.com/maps-research/gemrec 下载。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=GEMRec:+Towards+Generative+Model+Recommendation)|0| |[Making Small Language Models Better Multi-task Learners with Mixture-of-Task-Adapters](https://doi.org/10.1145/3616855.3635690)|Yukang Xie, Chengyu Wang, Junbing Yan, Jiyong Zhou, Feiqi Deng, Jun Huang||Recently, Large Language Models (LLMs) have achieved amazing zero-shot learning performance over a variety of Natural Language Processing (NLP) tasks, especially for text generative tasks. Yet, the large size of LLMs often leads to the high computational cost of model training and online deployment. In our work, we present ALTER, a system that effectively builds the multi-tAsk Learners with mixTure-of-task-adaptERs upon small language models (with <1B parameters) to address multiple NLP tasks simultaneously, capturing the commonalities and differences between tasks, in order to support domain-specific applications. Specifically, in ALTER, we propose the Mixture-of-Task-Adapters (MTA) module as an extension to the transformer architecture for the underlying model to capture the intra-task and inter-task knowledge. A two-stage training method is further proposed to optimize the collaboration between adapters at a small computational cost. Experimental results over a mixture of NLP tasks show that our proposed MTA architecture and the two-stage training method achieve good performance. Based on ALTER, we have also produced MTA-equipped language models for various domains.|近年来，大语言模型(LLM)在自然语言处理(NLP)任务中取得了令人惊讶的零拍学习效果，尤其是在文本生成任务中。然而，大规模的 LLM 往往导致高计算成本的模型训练和在线部署。在我们的工作中，我们提出了 ALTER，一个系统，有效地建立多任务学习者与混合任务适配器的小语言模型(小于1B 参数)同时处理多 NLP 任务，捕捉任务之间的共性和差异，以支持领域特定的应用。具体来说，在 ALTER 中，我们提出将任务混合适配器(MTA)模块作为转换器体系结构的扩展，用于底层模型捕获任务内和任务间的知识。进一步提出了一种两阶段训练方法，以较小的计算代价优化适配器之间的协作。实验结果表明，我们提出的 MTA 体系结构和两阶段训练方法取得了良好的性能。基于 ALTER，我们还为不同领域制作了 MTA 语言模型。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Making+Small+Language+Models+Better+Multi-task+Learners+with+Mixture-of-Task-Adapters)|0| @@ -85,7 +85,7 @@ |[Fresh Content Recommendation at Scale: A Multi-funnel Solution and the Potential of LLMs](https://doi.org/10.1145/3616855.3635749)|Jianling Wang, Haokai Lu, Minmin Chen|Google DeepMind, London, England|Recommendation system serves as a conduit connecting users to an incredibly large, diverse and ever growing collection of contents. In practice, missing information on fresh contents needs to be filled in order for them to be exposed and discovered by their audience. In this context, we are delighted to share our success stories in building a dedicated fresh content recommendation stack on a large commercial platform and also shed a light on the utilization of Large Language Models (LLMs) for fresh content recommendations within an industrial framework. To nominate fresh contents, we built a multi-funnel nomination system that combines (i) a two-tower model with strong generalization power for coverage, and (ii) a sequence model with near real-time update on user feedback for relevance, which effectively balances between coverage and relevance. Beyond that, by harnessing the reasoning and generalization capabilities of LLMs, we are presented with exciting prospects to enhance recommendation systems. We share our initial efforts on employing LLMs as data augmenters to bridge the knowledge gap on cold-start items during the training phase. This innovative approach circumvents the costly generation process during inference, presenting a model-agnostic, forward-looking solution for fresh content recommendation.|推荐系统作为一个渠道，连接用户到一个令人难以置信的庞大，多样化和不断增长的内容集合。在实践中，需要填补关于新内容的缺失信息，以便读者能够揭示和发现这些信息。在这种背景下，我们很高兴分享我们在一个大型商业平台上建立一个专门的新内容推荐堆栈的成功故事，同时也为在一个行业框架内利用大型语言模型(LLM)建立新内容推荐提供了一些启示。为了提名新内容，我们建立了一个多漏斗提名系统，该系统结合了(i)具有很强的覆盖泛化能力的双塔模型和(ii)具有近实时更新用户反馈相关性的序列模型，有效地平衡了覆盖和相关性。除此之外，通过利用 LLM 的推理和泛化能力，我们展示了增强推荐系统的令人兴奋的前景。我们分享了我们在使用 LLM 作为数据增强器以弥合培训阶段冷启动项目的知识差距方面的初步努力。这种创新的方法规避了推理过程中代价高昂的生成过程，为新内容推荐提供了一种与模型无关的前瞻性解决方案。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Fresh+Content+Recommendation+at+Scale:+A+Multi-funnel+Solution+and+the+Potential+of+LLMs)|0| |[Lessons Learnt from Building Friend Recommendation Systems](https://doi.org/10.1145/3616855.3635750)|Jun Yu|Snap Inc, Santa Monica, CA 90405 USA|Friend recommendation systems in online social networks such as Snapchat help users find friends and build meaningful connections, leading to heightened user engagement and retention. While friend recommendation systems follow the classical recommendation system paradigm that consists of retrieval and ranking, they pose distinctive challenges different from item recommendation systems (e.g. Youtube videos, Amazon products, Netflix movies), and require special considerations in building one. In this paper, we elucidate the unique challenges encountered and share invaluable insights from developing the friend recommendation system for hundreds of millions of users on Snapchat.|Snapchat 等在线社交网络中的朋友推荐系统可以帮助用户找到朋友，建立有意义的关系，从而提高用户的参与度和保持率。虽然朋友推荐系统遵循由检索和排名组成的传统推荐系统范式，但它们构成了与项目推荐系统(如 YouTube 视频、亚马逊产品、 Netflix 电影)不同的独特挑战，在构建这样一个系统时需要特别的考虑。在本文中，我们阐述了在 Snapchat 上为数亿用户开发朋友推荐系统所遇到的独特挑战，并分享了宝贵的见解。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Lessons+Learnt+from+Building+Friend+Recommendation+Systems)|0| |[Leveraging Multimodal Features and Item-level User Feedback for Bundle Construction](https://doi.org/10.1145/3616855.3635854)|Yunshan Ma, Xiaohao Liu, Yinwei Wei, Zhulin Tao, Xiang Wang, TatSeng Chua||Automatic bundle construction is a crucial prerequisite step in various bundle-aware online services. Previous approaches are mostly designed to model the bundling strategy of existing bundles. However, it is hard to acquire large-scale well-curated bundle dataset, especially for those platforms that have not offered bundle services before. Even for platforms with mature bundle services, there are still many items that are included in few or even zero bundles, which give rise to sparsity and cold-start challenges in the bundle construction models. To tackle these issues, we target at leveraging multimodal features, item-level user feedback signals, and the bundle composition information, to achieve a comprehensive formulation of bundle construction. Nevertheless, such formulation poses two new technical challenges: 1) how to learn effective representations by optimally unifying multiple features, and 2) how to address the problems of modality missing, noise, and sparsity problems induced by the incomplete query bundles. In this work, to address these technical challenges, we propose a Contrastive Learning-enhanced Hierarchical Encoder method (CLHE). Specifically, we use self-attention modules to combine the multimodal and multi-item features, and then leverage both item- and bundle-level contrastive learning to enhance the representation learning, thus to counter the modality missing, noise, and sparsity problems. Extensive experiments on four datasets in two application domains demonstrate that our method outperforms a list of SOTA methods. The code and dataset are available at https://github.com/Xiaohao-Liu/CLHE.|在各种捆绑感知在线服务中，自动捆绑包构造是关键的先决条件。以前的方法主要用于对现有的捆绑包的捆绑策略进行建模。然而，很难获得大规模、组织良好的捆绑数据集，特别是对于那些以前没有提供捆绑服务的平台。即使对于具有成熟捆绑服务的平台来说，仍然有很多项目被包含在很少甚至没有捆绑包中，这在捆绑包构建模型中引起了稀疏性和冷启动的挑战。为了解决这些问题，我们的目标是利用多模态特征、项目级用户反馈信号和捆绑包组合信息，以实现捆绑包构造的全面表述。然而，这样的表述提出了两个新的技术挑战: 1)如何通过最优地统一多个特征来学习有效的表示，2)如何解决由不完整查询包引起的模态缺失、噪声和稀疏问题。在这项工作中，为了解决这些技术挑战，我们提出了一个对比学习增强的层次编码器方法(CLHE)。具体来说，我们使用自我注意模块来结合多模态和多项目特征，然后利用项目和捆绑层的对比学习来增强表征学习，从而克服模态缺失、噪声和稀疏问题。在两个应用程序域中对四个数据集进行的大量实验表明，我们的方法优于 SOTA 方法列表。代码和数据集可在 https://github.com/xiaohao-liu/clhe 下载。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Leveraging+Multimodal+Features+and+Item-level+User+Feedback+for+Bundle+Construction)|0| -|[Cost-Effective Active Learning for Bid Exploration in Online Advertising](https://doi.org/10.1145/3616855.3635839)|Zixiao Wang, Zhenzhe Zheng, Yanrong Kang, Jiani Huang|Tencent, Advertising & Mkt Serv, Shenzhen, Peoples R China; Shanghai Jiao Tong Univ, Shanghai, Peoples R China|As a bid optimization algorithm in the first-price auction (FPA), bid shading is used in online advertising to avoid overpaying for advertisers. However, we find the bid shading approach would incur serious local optima. This effect prevents the advertisers from maximizing long-term surplus. In this work, we identify the reasons behind this local optima - it comes from the lack of winning price information, which results in the conflict between short-term surplus and the winning rate prediction model training, and is further propagated through the over-exploitation of the model. To rectify this problem, we propose a cost-effective active learning strategy, namely CeBE, for bid exploration. Specifically, we comprehensively consider the uncertainty and density of samples to calculate exploration utility, and use a 2 +epsilon-approximation greedy algorithm to control exploration costs. Instead of selecting bid prices that maximize the expected surplus for all bid requests, we employ the bid exploration strategy to determine the bid prices. By trading off a portion of surplus, we can train the model using higher-quality data to enhance its performance, enabling the system to achieve a long-term surplus. Our method is straightforward and applicable to real-world industrial environment: it is effective across various categories of winning rate prediction models. We conducted empirical studies to validate the efficacy of our approach. In comparison to the traditional bid shading system, CeBE can yield an average surplus improvement of 8.16% across various models and datasets.|作为一级价格拍卖(FPA)中的一种投标优化算法，在网络广告中使用了投标遮蔽技术，以避免广告商支付过高的广告费用。然而，我们发现投标阴影的方法会产生严重的局部最优。这种效应阻碍了广告商最大化长期盈余。在本文中，我们找出了这种局部最优的原因-它来自于缺乏中标价格信息，导致短期盈余与中标率预测模型训练之间的冲突，并通过模型的过度开发进一步传播。为了解决这个问题，我们提出了一个具有成本效益的主动学习策略，即 CeBE，用于投标探索。具体来说，我们综合考虑样本的不确定性和密度来计算勘探效用，并使用2 + ε 近似贪婪算法来控制勘探成本。我们不选择使所有投标请求的预期盈余最大化的投标价格，而是采用投标探索策略来确定投标价格。通过权衡一部分盈余，我们可以训练模型使用更高质量的数据，以提高其性能，使系统能够实现长期盈余。我们的方法是直接和适用于现实世界的工业环境: 它是有效的各种类型的中标率预测模型。我们进行了实证研究，以验证我们的方法的有效性。与传统的投标着色系统相比，CeBE 在不同的模型和数据集中可以产生平均8.16% 的剩余改进。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Cost-Effective+Active+Learning+for+Bid+Exploration+in+Online+Advertising)|0| +|[Cost-Effective Active Learning for Bid Exploration in Online Advertising](https://doi.org/10.1145/3616855.3635839)|Zixiao Wang, Zhenzhe Zheng, Yanrong Kang, Jiani Huang|Shanghai Jiao Tong Univ, Shanghai, Peoples R China; Tencent, Advertising & Mkt Serv, Shenzhen, Peoples R China|As a bid optimization algorithm in the first-price auction (FPA), bid shading is used in online advertising to avoid overpaying for advertisers. However, we find the bid shading approach would incur serious local optima. This effect prevents the advertisers from maximizing long-term surplus. In this work, we identify the reasons behind this local optima - it comes from the lack of winning price information, which results in the conflict between short-term surplus and the winning rate prediction model training, and is further propagated through the over-exploitation of the model. To rectify this problem, we propose a cost-effective active learning strategy, namely CeBE, for bid exploration. Specifically, we comprehensively consider the uncertainty and density of samples to calculate exploration utility, and use a 2 +epsilon-approximation greedy algorithm to control exploration costs. Instead of selecting bid prices that maximize the expected surplus for all bid requests, we employ the bid exploration strategy to determine the bid prices. By trading off a portion of surplus, we can train the model using higher-quality data to enhance its performance, enabling the system to achieve a long-term surplus. Our method is straightforward and applicable to real-world industrial environment: it is effective across various categories of winning rate prediction models. We conducted empirical studies to validate the efficacy of our approach. In comparison to the traditional bid shading system, CeBE can yield an average surplus improvement of 8.16% across various models and datasets.|作为一级价格拍卖(FPA)中的一种投标优化算法，在网络广告中使用了投标遮蔽技术，以避免广告商支付过高的广告费用。然而，我们发现投标阴影的方法会产生严重的局部最优。这种效应阻碍了广告商最大化长期盈余。在本文中，我们找出了这种局部最优的原因-它来自于缺乏中标价格信息，导致短期盈余与中标率预测模型训练之间的冲突，并通过模型的过度开发进一步传播。为了解决这个问题，我们提出了一个具有成本效益的主动学习策略，即 CeBE，用于投标探索。具体来说，我们综合考虑样本的不确定性和密度来计算勘探效用，并使用2 + ε 近似贪婪算法来控制勘探成本。我们不选择使所有投标请求的预期盈余最大化的投标价格，而是采用投标探索策略来确定投标价格。通过权衡一部分盈余，我们可以训练模型使用更高质量的数据，以提高其性能，使系统能够实现长期盈余。我们的方法是直接和适用于现实世界的工业环境: 它是有效的各种类型的中标率预测模型。我们进行了实证研究，以验证我们的方法的有效性。与传统的投标着色系统相比，CeBE 在不同的模型和数据集中可以产生平均8.16% 的剩余改进。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Cost-Effective+Active+Learning+for+Bid+Exploration+in+Online+Advertising)|0| |[LMBot: Distilling Graph Knowledge into Language Model for Graph-less Deployment in Twitter Bot Detection](https://doi.org/10.1145/3616855.3635843)|Zijian Cai, Zhaoxuan Tan, Zhenyu Lei, Zifeng Zhu, Hongrui Wang, Qinghua Zheng, Minnan Luo||As malicious actors employ increasingly advanced and widespread bots to disseminate misinformation and manipulate public opinion, the detection of Twitter bots has become a crucial task. Though graph-based Twitter bot detection methods achieve state-of-the-art performance, we find that their inference depends on the neighbor users multi-hop away from the targets, and fetching neighbors is time-consuming and may introduce bias. At the same time, we find that after finetuning on Twitter bot detection, pretrained language models achieve competitive performance and do not require a graph structure during deployment. Inspired by this finding, we propose a novel bot detection framework LMBot that distills the knowledge of graph neural networks (GNNs) into language models (LMs) for graph-less deployment in Twitter bot detection to combat the challenge of data dependency. Moreover, LMBot is compatible with graph-based and graph-less datasets. Specifically, we first represent each user as a textual sequence and feed them into the LM for domain adaptation. For graph-based datasets, the output of LMs provides input features for the GNN, enabling it to optimize for bot detection and distill knowledge back to the LM in an iterative, mutually enhancing process. Armed with the LM, we can perform graph-less inference, which resolves the graph data dependency and sampling bias issues. For datasets without graph structure, we simply replace the GNN with an MLP, which has also shown strong performance. Our experiments demonstrate that LMBot achieves state-of-the-art performance on four Twitter bot detection benchmarks. Extensive studies also show that LMBot is more robust, versatile, and efficient compared to graph-based Twitter bot detection methods.|随着恶意行为者使用日益先进和广泛的机器人传播错误信息和操纵公众舆论，检测 Twitter 机器人已成为一项至关重要的任务。虽然基于图的 Twitter 机器人检测方法取得了很好的性能，但是我们发现它们的推理依赖于离目标多跳的邻居用户，而且提取邻居非常耗时，并且可能会引入偏差。同时，我们发现在 Twitter 机器人检测上进行微调之后，预先训练的语言模型在部署过程中不需要图形结构就可以获得有竞争力的性能。受到这一发现的启发，我们提出了一种新的机器人检测框架 LMBot，该框架将图神经网络(GNN)的知识提取为语言模型(LMs) ，用于 Twitter 机器人检测中的无图部署，以应对数据依赖的挑战。此外，LMBot 还兼容基于图的和无图的数据集。具体来说，我们首先将每个用户表示为一个文本序列，并将它们提供给 LM 以进行域适配。对于基于图形的数据集，LM 的输出为 GNN 提供了输入特性，使其能够优化机器人检测，并在迭代、相互增强的过程中将知识提取回 LM。在 LM 的支持下，我们可以进行无图推理，解决了图数据的依赖性和抽样偏差问题。对于没有图形结构的数据集，我们简单地用 MLP 替换 GNN，这也显示出很强的性能。我们的实验表明，LMBot 在四个 Twitter 机器人检测基准上实现了最先进的性能。大量的研究还表明，与基于图形的 Twitter 机器人检测方法相比，LMBot 更加健壮、通用和高效。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=LMBot:+Distilling+Graph+Knowledge+into+Language+Model+for+Graph-less+Deployment+in+Twitter+Bot+Detection)|0| |[Long-Term Value of Exploration: Measurements, Findings and Algorithms](https://doi.org/10.1145/3616855.3635833)|Yi Su, Xiangyu Wang, Elaine Ya Le, Liang Liu, Yuening Li, Haokai Lu, Benjamin Lipshitz, Sriraj Badam, Lukasz Heldt, Shuchao Bi, Ed H. Chi, Cristos Goodrow, SuLin Wu, Lexi Baugher, Minmin Chen||Effective exploration is believed to positively influence the long-term user experience on recommendation platforms. Determining its exact benefits, however, has been challenging. Regular A/B tests on exploration often measure neutral or even negative engagement metrics while failing to capture its long-term benefits. We here introduce new experiment designs to formally quantify the long-term value of exploration by examining its effects on content corpus, and connecting content corpus growth to the long-term user experience from real-world experiments. Once established the values of exploration, we investigate the Neural Linear Bandit algorithm as a general framework to introduce exploration into any deep learning based ranking systems. We conduct live experiments on one of the largest short-form video recommendation platforms that serves billions of users to validate the new experiment designs, quantify the long-term values of exploration, and to verify the effectiveness of the adopted neural linear bandit algorithm for exploration.|有效的探索被认为会对推荐平台的长期用户体验产生积极的影响。然而，确定它的确切好处却是一个挑战。定期的 A/B 探索测试常常衡量中性甚至负面的参与度量，但未能获得其长期利益。我们在这里引入新的实验设计，通过考察探索对内容语料库的影响，并将内容语料库的增长与现实世界中的长期用户体验联系起来，形式地量化探索的长期价值。一旦确立了探索的价值，我们研究神经线性班迪特算法作为一个一般框架，引入探索任何深度学习的排序系统。我们在一个最大的短格式视频推荐平台上进行现场实验，该平台为数十亿用户提供服务，以验证新的实验设计，量化探索的长期价值，并验证所采用的神经线性土匪算法在探索中的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Long-Term+Value+of+Exploration:+Measurements,+Findings+and+Algorithms)|0| |[Unified Visual Preference Learning for User Intent Understanding](https://doi.org/10.1145/3616855.3635858)|Yihua Wen, Si Chen, Yu Tian, Wanxian Guan, Pengjie Wang, Hongbo Deng, Jian Xu, Bo Zheng, Zihao Li, Lixin Zou, Chenliang Li|Wuhan Univ, Minist Educ, Sch Cyber Sci & Engn, Key Lab Aerosp Informat Secur & Trusted Comp, Wuhan, Peoples R China; Alibaba Grp China, Hangzhou, Peoples R China|In the world of E-Commerce, the core task is to understand the personalized preference from various kinds of heterogeneous information, such as textual reviews, item images and historical behaviors. In current systems, these heterogeneous information are mainly exploited to generate better item or user representations. For example, in scenario of visual search, the importance of modeling query image has been widely acknowledged. But, these existing solutions focus on improving the representation quality of the query image, overlooking the personalized visual preference of the user. Note that the visual features affect the user's decision significantly, e.g., a user could be more likely to click the items with her preferred design. Hence, it is fruitful to exploit the visual preference to deliver better capacity for personalization. To this end, we propose a simple yet effective target-aware visual preference learning framework (named Tavern) for both item recommendation and search. The proposed Tavern works as an individual and generic model that can be smoothly plugged into different downstream systems. Specifically, for visual preference learning, we utilize the image of the target item to derive the visual preference signals for each historical clicked item. This procedure is modeled as a form of representation disentanglement, where the visual preference signals are extracted by taking off the noisy information irrelevant to visual preference from the shared visual information between the target and historical items. During this process, a novel selective orthogonality disentanglement is proposed to avoid the significant information loss. Then, a GRU network is utilized to aggregate these signals to form the final visual preference representation. Extensive experiments over three large-scale real-world datasets covering visual search, product search and recommendation well demonstrate the superiority of our proposed Tavern against existing technical alternatives. Further ablation study also confirms the validity of each design choice|电子商务的核心任务是从文本评论、商品图像和历史行为等各种异质信息中理解个性化偏好。在当前的系统中，这些异构信息主要被用来生成更好的条目或用户表示。例如，在可视化搜索场景中，对查询图像进行建模的重要性得到了广泛的认可。但是，现有的解决方案侧重于提高查询图像的表示质量，忽视了用户的个性化视觉偏好。请注意，视觉特性对用户的决定有显著影响，例如，用户可能更有可能按照自己喜欢的设计单击项目。因此，利用视觉偏好来提供更好的个性化能力是有成效的。为此，我们提出了一个简单而有效的目标感知的视觉偏好学习框架(名为 Tavern)项目推荐和搜索。建议的酒馆作为一个独立的和通用的模式，可以顺利地插入到不同的下游系统。具体来说，对于视觉偏好学习，我们利用目标项的图像来获得每个历史单击项的视觉偏好信号。这个过程被建模为一种表示分离的形式，通过从目标和历史项目之间共享的视觉信息中去除与视觉偏好无关的噪声信息来提取视觉偏好信号。在此过程中，提出了一种新的选择性正交解纠缠算法，以避免重大的信息损失。然后，利用 GRU 网络对这些信号进行聚合，形成最终的视觉偏好表示。通过对包括视觉搜索、产品搜索和推荐在内的三个大规模现实世界数据集的大量实验，充分证明了我们提出的 Tavern 相对于现有技术选择的优越性。进一步的消融研究也证实了每个设计选择的有效性|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Unified+Visual+Preference+Learning+for+User+Intent+Understanding)|0| @@ -93,7 +93,7 @@ |[IDoFew: Intermediate Training Using Dual-Clustering in Language Models for Few Labels Text Classification](https://doi.org/10.1145/3616855.3635849)|Abdullah Alsuhaibani, Hamad Zogan, Imran Razzak, Shoaib Jameel, Guandong Xu||Language models such as Bidirectional Encoder Representations from Transformers (BERT) have been very effective in various Natural Language Processing (NLP) and text mining tasks including text classification. However, some tasks still pose challenges for these models, including text classification with limited labels. This can result in a cold-start problem. Although some approaches have attempted to address this problem through single-stage clustering as an intermediate training step coupled with a pre-trained language model, which generates pseudo-labels to improve classification, these methods are often error-prone due to the limitations of the clustering algorithms. To overcome this, we have developed a novel two-stage intermediate clustering with subsequent fine-tuning that models the pseudo-labels reliably, resulting in reduced prediction errors. The key novelty in our model, IDoFew, is that the two-stage clustering coupled with two different clustering algorithms helps exploit the advantages of the complementary algorithms that reduce the errors in generating reliable pseudo-labels for fine-tuning. Our approach has shown significant improvements compared to strong comparative models.|语言模型，例如变换器的双向编码器表示(BERT) ，在各种自然语言处理(NLP)和文本挖掘任务(包括文本分类)中都是非常有效的。然而，一些任务仍然对这些模型构成挑战，包括使用有限标签的文本分类。这可能导致冷启动问题。虽然一些方法试图通过单阶段聚类作为一个中间训练步骤加上预训练语言模型来解决这个问题，从而产生伪标签来改善分类，但由于聚类算法的局限性，这些方法往往容易出错。为了克服这个问题，我们开发了一个新的两阶段中间聚类和随后的微调，可靠地建模伪标签，从而减少预测误差。在我们的模型中，IDofew 的关键新颖之处在于，两阶段聚类和两种不同的聚类算法相结合，有助于利用互补算法的优势，减少生成可靠的伪标签进行微调时的错误。与强大的比较模型相比，我们的方法已经显示出显著的改进。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=IDoFew:+Intermediate+Training+Using+Dual-Clustering+in+Language+Models+for+Few+Labels+Text+Classification)|0| |[MAD: Multi-Scale Anomaly Detection in Link Streams](https://doi.org/10.1145/3616855.3635834)|Esteban Bautista, Laurent Brisson, Cécile Bothorel, Grégory Smits|IMT Atlantique, Comp Sci Dept, Lab STICC, UMR CNRS 6285, Brest, France; IMT Atlantique, LUSSI Dept, Lab STICC UMR CNRS 6285, Brest, France|Given an arbitrary group of computers, how to identify abnormal changes in their communication pattern? How to assess if the absence of some communications is normal or due to a failure? How to distinguish local from global events when communication data are extremely sparse and volatile? Existing approaches for anomaly detection in interaction streams, focusing on edge, nodes or graphs, lack flexibility to monitor arbitrary communication topologies. Moreover, they rely on structural features that are not adapted to highly sparse settings. In this work, we introduce MAD, a novel Multi-scale Anomaly Detection algorithm that (i) allows to query for the normality/abnormality state of an arbitrary group of observed/non-observed communications at a given time; and (ii) handles the highly sparse and uncertain nature of interaction data through a scoring method that is based on a novel probabilistic and multi-scale analysis of sub-graphs. In particular, MAD is (a) flexible: it can assess if any time-stamped subgraph is anomalous, making edge, node and graph anomalies particular instances; (b) interpretable: its multi-scale analysis allows to characterize the scope and nature of the anomalies; (c) efficient: given historical data of length.. and.. observed/non-observed communications to analyze, MAD produces an anomaly score in O(NM); and (d) effective: it significantly outperforms state-of-the-art alternatives tailored for edge, node or graph anomalies.|给定一组任意的计算机，如何识别其通信模式中的异常变化？如何评估缺少某些通信是正常的还是由于故障造成的？当通信数据非常稀疏和不稳定时，如何区分本地事件和全局事件？现有的交互流异常检测方法，主要集中在边缘、节点或图形上，缺乏监控任意通信拓扑的灵活性。此外，它们依赖于不适应高度稀疏环境的结构特征。在这项工作中，我们介绍了 MAD，一种新的多尺度异常检测算法，它(i)允许在给定的时间查询任意组观察/未观察通信的正常/异常状态，(ii)通过基于新的概率和子图的多尺度分析的评分方法处理交互数据的高度稀疏和不确定性。具体来说，MAD 是(a)灵活的: 它可以评估是否有任何时间戳子图是异常的，使边缘，节点和图异常的特殊实例; (b)可解释的: 它的多尺度分析允许描述异常的范围和性质; (c)有效的: 给定长度的历史数据。.还有。.观察/未观察到的通信进行分析，MAD 在 O (NM)中产生异常评分; (d)有效: 它显著优于为边缘、节点或图形异常量身定制的最先进替代方案。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=MAD:+Multi-Scale+Anomaly+Detection+in+Link+Streams)|0| |[Customized and Robust Deep Neural Network Watermarking](https://doi.org/10.1145/3616855.3635812)|TzuYun Chien, ChihYa Shen|Natl Tsing Hua Univ, Dept Comp Sci, Hsinchu, Taiwan|As the excellent performance of deep neural networks (DNNs) enhances a wide spectrum of applications, the protection of intellectual property (IP) of DNNs receives increasing attention recently, and DNN watermarking approaches are thus proposed for ownership verification to avoid potential misuses or thefts of DNN models. However, we observe that existing DNN watermark methods suffer from two major weaknesses: i) Incomplete protection to advanced watermark removal attacks, such as fine-tune attack with large learning rates, re-train after pruning, and most importantly, the distillation attack; ii) Limited customization ability, where multiple watermarked models cannot be uniquely identified, especially after removal attacks. To address these critical issues, we propose two new DNN watermarking approaches, Unified Soft-label Perturbation (USP), which provides robust watermark to detect model thefts, and Customized Soft-label Perturbation (CSP), which is able to embed a different watermark in each copy of the DNN model to enable customized watermarking. Experimental results show that our proposed USP and CSP resist all the watermark removal attacks, especially for the distillation attack, and the proposed CSP achieves very promising watermark customization ability, significantly outperforming the other state-of-the-art baselines.|由于深度神经网络(DNN)的优异性能使其广泛应用，DNN 的知识产权保护近年来受到越来越多的关注，为了避免 DNN 模型的潜在误用或盗窃，提出了 DNN 水印技术用于所有权验证。然而，我们观察到现有的 DNN 水印方法存在两个主要弱点: 1)对高级水印去除攻击的不完全保护，例如具有较高学习率的微调攻击，修剪后重新训练，最重要的是蒸馏攻击; 2)定制能力有限，其中多个水印模型不能唯一识别，特别是在去除攻击之后。为了解决这些关键问题，我们提出了两种新的 DNN 水印方法，统一软标签扰动(USP)和定制软标签扰动(CSP) ，前者提供了检测模型盗窃的稳健水印，后者能够在 DNN 模型的每个副本中嵌入不同的水印，从而实现定制水印。实验结果表明，我们提出的 USP 和 CSP 能够抵抗所有的水印去除攻击，特别是对于蒸馏攻击，并且该 CSP 能够实现非常有前景的水印定制能力，明显优于其他最先进的基线。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Customized+and+Robust+Deep+Neural+Network+Watermarking)|0| -|[Incomplete Graph Learning via Attribute-Structure Decoupled Variational Auto-Encoder](https://doi.org/10.1145/3616855.3635769)|Xinke Jiang, Zidi Qin, Jiarong Xu, Xiang Ao|Chinese Acad Sci, Univ Chinese Acad Sci, Inst Comp Technol, Beijing, Peoples R China; Inst Intelligent Comp Technol, Suzhou, Peoples R China; Fudan Univ, Shanghai, Peoples R China|Graph Neural Networks (GNNs) conventionally operate under the assumption that node attributes are entirely observable. Their performance notably deteriorates when confronted with incomplete graphs due to the inherent message-passing mechanisms. Current solutions either employ classic imputation techniques or adapt GNNs to tolerate missed attributes. However, their ability to generalize is impeded especially when dealing with high rates of missing attributes. To address this, we harness the representations of the essential views on graphs, attributes and structures, into a common shared latent space, ensuring robust tolerance even at high missing rates. Our proposed neural model, named ASD-VAE, parameterizes such space via a coupled-and-decoupled learning procedure, reminiscent of brain cognitive processes and multimodal fusion. Initially, ASD-VAE separately encodes attributes and structures, generating representations for each view. A shared latent space is then learned by maximizing the likelihood of the joint distribution of different view representations through coupling. Then, the shared latent space is decoupled into separate views, and the reconstruction loss of each view is calculated. Finally, the missing values of attributes are imputed from this learned latent space. In this way, the model offers enhanced resilience against skewed and biased distributions typified by missing information and subsequently brings benefits to downstream graph machine-learning tasks. Extensive experiments conducted on four typical real-world incomplete graph datasets demonstrate the superior performance of ASD-VAE against the state-of-the-art.|图神经网络(GNN)通常在假设节点属性完全可观察的情况下运行。由于固有的消息传递机制，当遇到不完整的图时，它们的性能明显下降。当前的解决方案要么使用经典的插补技术，要么使 GNN 能够容忍缺失的属性。然而，他们的概括能力受到阻碍，特别是当处理高比率的缺失属性。为了解决这个问题，我们利用关于图、属性和结构的基本视图的表示进入一个共享的潜在空间，确保即使在高丢失率下也能保持强大的容忍度。我们提出的神经模型，命名为 ASD-VAE，通过耦合和解耦的学习过程参数化这样的空间，让人想起大脑认知过程和多模态融合。最初，ASD-VAE 分别对属性和结构进行编码，为每个视图生成表示。然后通过耦合最大化不同视图表示的联合分布的可能性来学习共享潜空间。然后，将共享潜空间解耦为独立的视图，并计算每个视图的重构损失。最后，从这个学习的潜在空间推算出缺失的属性值。通过这种方式，该模型提供了增强的弹性对倾斜和有偏见的分布典型缺少信息，并随后带来的好处，下游图形机器学习任务。在四个典型的真实世界不完整图形数据集上进行的大量实验表明，ASD-VAE 具有比最先进的图形数据集更好的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Incomplete+Graph+Learning+via+Attribute-Structure+Decoupled+Variational+Auto-Encoder)|0| +|[Incomplete Graph Learning via Attribute-Structure Decoupled Variational Auto-Encoder](https://doi.org/10.1145/3616855.3635769)|Xinke Jiang, Zidi Qin, Jiarong Xu, Xiang Ao|Inst Intelligent Comp Technol, Suzhou, Peoples R China; Fudan Univ, Shanghai, Peoples R China; Chinese Acad Sci, Univ Chinese Acad Sci, Inst Comp Technol, Beijing, Peoples R China|Graph Neural Networks (GNNs) conventionally operate under the assumption that node attributes are entirely observable. Their performance notably deteriorates when confronted with incomplete graphs due to the inherent message-passing mechanisms. Current solutions either employ classic imputation techniques or adapt GNNs to tolerate missed attributes. However, their ability to generalize is impeded especially when dealing with high rates of missing attributes. To address this, we harness the representations of the essential views on graphs, attributes and structures, into a common shared latent space, ensuring robust tolerance even at high missing rates. Our proposed neural model, named ASD-VAE, parameterizes such space via a coupled-and-decoupled learning procedure, reminiscent of brain cognitive processes and multimodal fusion. Initially, ASD-VAE separately encodes attributes and structures, generating representations for each view. A shared latent space is then learned by maximizing the likelihood of the joint distribution of different view representations through coupling. Then, the shared latent space is decoupled into separate views, and the reconstruction loss of each view is calculated. Finally, the missing values of attributes are imputed from this learned latent space. In this way, the model offers enhanced resilience against skewed and biased distributions typified by missing information and subsequently brings benefits to downstream graph machine-learning tasks. Extensive experiments conducted on four typical real-world incomplete graph datasets demonstrate the superior performance of ASD-VAE against the state-of-the-art.|图神经网络(GNN)通常在假设节点属性完全可观察的情况下运行。由于固有的消息传递机制，当遇到不完整的图时，它们的性能明显下降。当前的解决方案要么使用经典的插补技术，要么使 GNN 能够容忍缺失的属性。然而，他们的概括能力受到阻碍，特别是当处理高比率的缺失属性。为了解决这个问题，我们利用关于图、属性和结构的基本视图的表示进入一个共享的潜在空间，确保即使在高丢失率下也能保持强大的容忍度。我们提出的神经模型，命名为 ASD-VAE，通过耦合和解耦的学习过程参数化这样的空间，让人想起大脑认知过程和多模态融合。最初，ASD-VAE 分别对属性和结构进行编码，为每个视图生成表示。然后通过耦合最大化不同视图表示的联合分布的可能性来学习共享潜空间。然后，将共享潜空间解耦为独立的视图，并计算每个视图的重构损失。最后，从这个学习的潜在空间推算出缺失的属性值。通过这种方式，该模型提供了增强的弹性对倾斜和有偏见的分布典型缺少信息，并随后带来的好处，下游图形机器学习任务。在四个典型的真实世界不完整图形数据集上进行的大量实验表明，ASD-VAE 具有比最先进的图形数据集更好的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Incomplete+Graph+Learning+via+Attribute-Structure+Decoupled+Variational+Auto-Encoder)|0| |[Source Free Graph Unsupervised Domain Adaptation](https://doi.org/10.1145/3616855.3635802)|Haitao Mao, Lun Du, Yujia Zheng, Qiang Fu, Zelin Li, Xu Chen, Shi Han, Dongmei Zhang||Graph Neural Networks (GNNs) have achieved great success on a variety of tasks with graph-structural data, among which node classification is an essential one. Unsupervised Graph Domain Adaptation (UGDA) shows its practical value of reducing the labeling cost for node classification. It leverages knowledge from a labeled graph (i.e., source domain) to tackle the same task on another unlabeled graph (i.e., target domain). Most existing UGDA methods heavily rely on the labeled graph in the source domain. They utilize labels from the source domain as the supervision signal and are jointly trained on both the source graph and the target graph. However, in some real-world scenarios, the source graph is inaccessible because of either unavailability or privacy issues. Therefore, we propose a novel scenario named Source Free Unsupervised Graph Domain Adaptation (SFUGDA). In this scenario, the only information we can leverage from the source domain is the well-trained source model, without any exposure to the source graph and its labels. As a result, existing UGDA methods are not feasible anymore. To address the non-trivial adaptation challenges in this practical scenario, we propose a model-agnostic algorithm for domain adaptation to fully exploit the discriminative ability of the source model while preserving the consistency of structural proximity on the target graph. We prove the effectiveness of the proposed algorithm both theoretically and empirically. The experimental results on four cross-domain tasks show consistent improvements of the Macro-F1 score up to 0.17.|图形神经网络(GNN)在处理图形结构数据的各种任务上取得了巨大的成功，其中节点分类是其中的一个重要内容。无监督图域自适应算法(UGDA)在降低节点分类的标记代价方面具有一定的实用价值。它利用来自标记图(即源域)的知识来处理另一个未标记图(即目标域)上的相同任务。大多数现有的 UGDA 方法严重依赖于源域中的标记图。它们利用来自源域的标签作为监督信号，并在源图和目标图上联合训练。但是，在一些真实场景中，源图是不可访问的，这是由于不可用性或隐私问题。因此，我们提出了一种新的场景称为源自由无监督图域适应(SFUGDA)。在这个场景中，我们可以从源域中利用的唯一信息是经过良好训练的源模型，而不需要暴露于源图及其标签。因此，现有的 UGDA 方法不再可行。为了解决这一实际场景中的非平凡自适应问题，我们提出了一种领域自适应的模型无关算法，以充分利用源模型的鉴别能力，同时保持目标图上结构接近度的一致性。从理论和实验两方面证明了该算法的有效性。在四个跨领域任务上的实验结果表明，宏观 F1得分一致提高到0.17。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Source+Free+Graph+Unsupervised+Domain+Adaptation)|0| |[PhoGAD: Graph-based Anomaly Behavior Detection with Persistent Homology Optimization](https://doi.org/10.1145/3616855.3635783)|Ziqi Yuan, Haoyi Zhou, Tianyu Chen, Jianxin Li||A multitude of toxic online behaviors, ranging from network attacks to anonymous traffic and spam, have severely disrupted the smooth operation of networks. Due to the inherent sender-receiver nature of network behaviors, graph-based frameworks are commonly used for detecting anomalous behaviors. However, in real-world scenarios, the boundary between normal and anomalous behaviors tends to be ambiguous. The local heterophily of graphs interferes with the detection, and existing methods based on nodes or edges introduce unwanted noise into representation results, thereby impacting the effectiveness of detection. To address these issues, we propose PhoGAD, a graph-based anomaly detection framework. PhoGAD leverages persistent homology optimization to clarify behavioral boundaries. Building upon this, the weights of adjacent edges are designed to mitigate the effects of local heterophily. Subsequently, to tackle the noise problem, we conduct a formal analysis and propose a disentangled representation-based explicit embedding method, ultimately achieving anomaly behavior detection. Experiments on intrusion, traffic, and spam datasets verify that PhoGAD has surpassed the performance of state-of-the-art (SOTA) frameworks in detection efficacy. Notably, PhoGAD demonstrates robust detection even with diminished anomaly proportions, highlighting its applicability to real-world scenarios. The analysis of persistent homology demonstrates its effectiveness in capturing the topological structure formed by normal edge features. Additionally, ablation experiments validate the effectiveness of the innovative mechanisms integrated within PhoGAD.|从网络攻击到匿名流量和垃圾邮件，大量有毒的网络行为严重扰乱了网络的正常运行。由于网络行为固有的发送方-接收方特性，基于图的框架通常用于检测异常行为。然而，在真实的场景中，正常行为和异常行为之间的界限往往是模糊的。图的局部异质性干扰检测，现有的基于节点或边的检测方法在表示结果中引入了不必要的噪声，影响了检测的有效性。为了解决这些问题，我们提出了 PhogAD，一个基于图形的异常检测框架。PhoGAD 利用持久同源优化来澄清行为边界。在此基础上，设计相邻边的权值以减轻局部异质性的影响。随后，针对噪声问题，进行了形式化分析，提出了一种基于分离表示的显式嵌入方法，最终实现了异常行为检测。在入侵、流量和垃圾邮件数据集上的实验证明，PhoGAD 在检测效率方面已经超过了最先进(State-of-art，SOTA)框架的性能。值得注意的是，PhoGAD 即使在异常比例减少的情况下仍然表现出强大的检测能力，突出了它对真实场景的适用性。对持久同调性的分析表明，该方法能够有效地捕获由正常边缘特征构成的拓扑结构。此外，烧蚀实验验证了集成在 PhoGAD 中的创新机制的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=PhoGAD:+Graph-based+Anomaly+Behavior+Detection+with+Persistent+Homology+Optimization)|0| |[The Devil is in the Data: Learning Fair Graph Neural Networks via Partial Knowledge Distillation](https://doi.org/10.1145/3616855.3635768)|Yuchang Zhu, Jintang Li, Liang Chen, Zibin Zheng||Graph neural networks (GNNs) are being increasingly used in many high-stakes tasks, and as a result, there is growing attention on their fairness recently. GNNs have been shown to be unfair as they tend to make discriminatory decisions toward certain demographic groups, divided by sensitive attributes such as gender and race. While recent works have been devoted to improving their fairness performance, they often require accessible demographic information. This greatly limits their applicability in real-world scenarios due to legal restrictions. To address this problem, we present a demographic-agnostic method to learn fair GNNs via knowledge distillation, namely FairGKD. Our work is motivated by the empirical observation that training GNNs on partial data (i.e., only node attributes or topology data) can improve their fairness, albeit at the cost of utility. To make a balanced trade-off between fairness and utility performance, we employ a set of fairness experts (i.e., GNNs trained on different partial data) to construct the synthetic teacher, which distills fairer and informative knowledge to guide the learning of the GNN student. Experiments on several benchmark datasets demonstrate that FairGKD, which does not require access to demographic information, significantly improves the fairness of GNNs by a large margin while maintaining their utility.|图形神经网络(GNN)在许多高风险任务中的应用越来越广泛，其公平性也越来越受到人们的关注。GNN 被证明是不公平的，因为他们倾向于对某些人口群体作出歧视性的决定，除以性别和种族等敏感特征。虽然最近的工作致力于改善他们的公平性表现，他们往往需要可访问的人口统计信息。由于法律限制，这极大地限制了它们在现实世界场景中的适用性。为了解决这个问题，我们提出了一种通过知识提取来学习公平 GNN 的人口不可知方法，即 FairGKD。我们的工作是基于实证观察，即部分数据(即，只有节点属性或拓扑数据)训练 GNN 可以提高它们的公平性，尽管代价是效用。为了在公平和效用绩效之间做出平衡的权衡，我们雇佣了一组公平专家(即，接受不同部分数据训练的 GNN)来构建合成教师，它提取更公平和信息丰富的知识来指导 GNN 学生的学习。在几个基准数据集上的实验表明，不需要获取人口统计信息的 FairGKD 在保持效用的同时，显著提高了 GNN 的公平性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=The+Devil+is+in+the+Data:+Learning+Fair+Graph+Neural+Networks+via+Partial+Knowledge+Distillation)|0| @@ -111,14 +111,14 @@ |[Overlapping and Robust Edge-Colored Clustering in Hypergraphs](https://doi.org/10.1145/3616855.3635792)|Alex Crane, Brian Lavallee, Blair D. Sullivan, Nate Veldt||A recent trend in data mining has explored (hyper)graph clustering algorithms for data with categorical relationship types. Such algorithms have applications in the analysis of social, co-authorship, and protein interaction networks, to name a few. Many such applications naturally have some overlap between clusters, a nuance which is missing from current combinatorial models. Additionally, existing models lack a mechanism for handling noise in datasets. We address these concerns by generalizing Edge-Colored Clustering, a recent framework for categorical clustering of hypergraphs. Our generalizations allow for a budgeted number of either (a) overlapping cluster assignments or (b) node deletions. For each new model we present a greedy algorithm which approximately minimizes an edge mistake objective, as well as bicriteria approximations where the second approximation factor is on the budget. Additionally, we address the parameterized complexity of each problem, providing FPT algorithms and hardness results.|数据挖掘最近的一个趋势是探索具有分类关系类型的数据的(超)图聚类算法。这样的算法在分析社会、合著者和蛋白质相互作用网络等方面都有应用。许多这样的应用程序在集群之间自然有一些重叠，这是当前组合模型所缺少的细微差别。此外，现有的模型缺乏处理数据集中噪声的机制。我们通过推广边彩色聚类来解决这些问题，边彩色聚类是一个最新的超图分类聚类框架。我们的一般化允许预算中的数量(a)重叠的集群分配或(b)节点删除。对于每一个新的模型，我们提出了一个贪婪算法，近似最小化边缘错误的目标，以及双准则近似，其中第二近似因子的预算。此外，我们解决每个问题的参数化复杂度，提供 FPT 算法和硬度结果。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Overlapping+and+Robust+Edge-Colored+Clustering+in+Hypergraphs)|0| |[TemporalMed: Advancing Medical Dialogues with Time-Aware Responses in Large Language Models](https://doi.org/10.1145/3616855.3635860)|Yuyan Chen, Jin Zhao, Zhihao Wen, Zhixu Li, Yanghua Xiao|Fudan Univ, Sch Comp Sci, Shanghai Key Lab Data Sci, Shanghai, Peoples R China; Singapore Management Univ, Singapore, Singapore|Medical dialogue models predominantly emphasize generating coherent and clinically accurate responses. However, in many clinical scenarios, time plays a pivotal role, often dictating subsequent patient management and interventions. Recognizing the latent importance of temporal dynamics, this paper introduces a novel dimension to medical dialogues: timestamps. We advocate that the integration of time-sensitive directives can profoundly impact medical advice, using an illustrative example of post-surgery care with and without timestamps. Our contributions are three-fold: Firstly, we highlight the intrinsic significance of timestamps in medical conversations, marking a paradigm shift in dialogue modeling. Secondly, we present an innovative dataset and framework explicitly tailored for time-stamped medical dialogues, facilitating the model to not only provide medical counsel but also chronologically outline care regimens. Lastly, empirical evaluations indicate our method's proficiency in time-stamped tasks and reveal an uptick in performance in broader medical Q&A domains. Through our endeavors, we aspire to set new benchmarks in patient-centric and time-sensitive medical dialogue systems.|医学对话模型主要强调产生连贯和临床准确的反应。然而，在许多临床情况下，时间起着关键作用，往往决定随后的病人管理和干预。认识到时间动力学的潜在重要性，本文介绍了医学对话的一个新的维度: 时间戳。我们主张，时间敏感指示的整合可以深刻影响医疗建议，使用一个例子说明手术后护理有和没有时间戳。我们的贡献有三个方面: 首先，我们强调了时间戳在医学对话中的内在意义，标志着对话建模的范式转变。其次，我们提出了一个创新的数据集和框架，明确定制的时间戳医疗对话，促进模型不仅提供医疗咨询，而且按时间顺序概述护理方案。最后，实证评估表明，我们的方法在时间戳任务的熟练程度，并揭示了在更广泛的医疗问答领域的表现上升。通过我们的努力，我们渴望在以病人为中心和时间敏感的医疗对话系统中建立新的基准。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=TemporalMed:+Advancing+Medical+Dialogues+with+Time-Aware+Responses+in+Large+Language+Models)|0| |[CroSSL: Cross-modal Self-Supervised Learning for Time-series through Latent Masking](https://doi.org/10.1145/3616855.3635795)|Shohreh Deldari, Dimitris Spathis, Mohammad Malekzadeh, Fahim Kawsar, Flora D. Salim, Akhil Mathur||Limited availability of labeled data for machine learning on multimodal time-series extensively hampers progress in the field. Self-supervised learning (SSL) is a promising approach to learning data representations without relying on labels. However, existing SSL methods require expensive computations of negative pairs and are typically designed for single modalities, which limits their versatility. We introduce CroSSL (Cross-modal SSL), which puts forward two novel concepts: masking intermediate embeddings produced by modality-specific encoders, and their aggregation into a global embedding through a cross-modal aggregator that can be fed to down-stream classifiers. CroSSL allows for handling missing modalities and end-to-end cross-modal learning without requiring prior data preprocessing for handling missing inputs or negative-pair sampling for contrastive learning. We evaluate our method on a wide range of data, including motion sensors such as accelerometers or gyroscopes and biosignals (heart rate, electroencephalograms, electromyograms, electrooculograms, and electrodermal) to investigate the impact of masking ratios and masking strategies for various data types and the robustness of the learned representations to missing data. Overall, CroSSL outperforms previous SSL and supervised benchmarks using minimal labeled data, and also sheds light on how latent masking can improve cross-modal learning. Our code is open-sourced a https://github.com/dr-bell/CroSSL|多模态时间序列的机器学习标记数据的有限可用性广泛地阻碍了该领域的进展。自监督学习(SSL)是一种很有前途的不依赖于标签的数据表示学习方法。然而，现有的 SSL 方法需要对负数对进行昂贵的计算，并且通常针对单模式设计，这限制了它们的通用性。提出了两个新概念: 掩盖特定模态编码器产生的中间嵌入，以及通过一个可以提供给下游分类器的跨模态聚合器将其聚合为一个全局嵌入。CroSSL 允许处理缺失模式和端到端跨模式学习，而不需要事先数据预处理来处理缺失输入或负对抽样来进行对比学习。我们在广泛的数据上评估我们的方法，包括运动传感器如加速度计或陀螺仪和生物信号(心率，脑电图，肌电图，眼电图和皮肤电图) ，以研究掩蔽比率和掩蔽策略对各种数据类型的影响以及学习表示对缺失数据的鲁棒性。总的来说，使用最少的标记数据，CroSSL 的性能优于以前的 SSL 和监督基准测试，并且还揭示了潜在掩蔽如何改善跨模态学习。我们的代码是开源的 https://github.com/dr-bell/crossl|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=CroSSL:+Cross-modal+Self-Supervised+Learning+for+Time-series+through+Latent+Masking)|0| -|[Guardian: Guarding against Gradient Leakage with Provable Defense for Federated Learning](https://doi.org/10.1145/3616855.3635758)|Mingyuan Fan, Yang Liu, Cen Chen, Chengyu Wang, Minghui Qiu, Wenmeng Zhou|Alibaba Grp, Hangzhou, Peoples R China; Xidian Univ, Xian, Shaanxi, Peoples R China; ByteDance, Shanghai, Peoples R China; East China Normal Univ, Sch Data Sci & Engn, Shanghai, Peoples R China|Federated learning is a privacy-focused learning paradigm, which trains a global model with gradients uploaded from multiple participants, circumventing explicit exposure of private data. However, previous research of gradient leakage attacks suggests that gradients alone are sufficient to reconstruct private data, rendering the privacy protection mechanism of federated learning unreliable. Existing defenses commonly craft transformed gradients based on ground-truth gradients to obfuscate the attacks, but often are less capable of maintaining good model performance together with satisfactory privacy protection. In this paper, we propose a novel yet effective defense framework named guarding against gradient leakage (Guardian) that produces transformed gradients by jointly optimizing two theoretically-derived metrics associated with gradients for performance maintenance and privacy protection. In this way, the transformed gradients produced via Guardian can achieve minimal privacy leakage in theory with the given performance maintenance level. Moreover, we design an ingenious initialization strategy for faster generation of transformed gradients to enhance the practicality of Guardian in real-world applications, while demonstrating theoretical convergence of Guardian to the performance of the global model. Extensive experiments on various tasks show that, without sacrificing much accuracy, Guardian can effectively defend state-of-the-art gradient leakage attacks, compared with the slight effects of baseline defense approaches.|联合学习是一种以隐私为中心的学习范式，它通过从多个参与者上传的梯度来训练一个全球模型，从而避免显性暴露私人数据。然而，以往对梯度泄漏攻击的研究表明，梯度本身就足以重建私有数据，使得联邦学习的隐私保护机制变得不可靠。现有的防御系统通常基于地面真实度梯度进行变换，以模糊攻击，但往往不能保持良好的模型性能和令人满意的隐私保护。在本文中，我们提出了一个新颖而有效的防御框架，即防止梯度泄漏(Guardian) ，该框架通过联合优化两个理论导出的与梯度相关的指标，从而产生转换的梯度，用于性能维护和隐私保护。这样，在给定的性能维护水平下，通过 Guardian 产生的变换梯度可以在理论上实现最小的隐私泄漏。此外，我们设计了一个巧妙的初始化策略，可以更快地生成转换后的梯度，以提高 Guardian 在实际应用中的实用性，同时证明了 Guardian 的理论收敛性与全局模型的性能。在各种任务上的大量实验表明，与基线防御方法的轻微效果相比，Guardian 可以在不牺牲太多精确性的情况下有效防御最先进的梯度泄漏攻击。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Guardian:+Guarding+against+Gradient+Leakage+with+Provable+Defense+for+Federated+Learning)|0| -|[TTC-QuAli: A Text-Table-Chart Dataset for Multimodal Quantity Alignment](https://doi.org/10.1145/3616855.3635777)|Haoyu Dong, Haochen Wang, Anda Zhou, Yue Hu|Peking Univ, Beijing, Peoples R China; Univ Edinburgh, Beijing, Peoples R China; Chinese Acad Sci, Inst Informat Engn, Beijing, Peoples R China|In modern documents, numerical information is often presented using multimodal formats such as text, tables, and charts. However, the heterogeneity of these sources poses a challenge for machines attempting to jointly read and understand the numerical semantics conveyed through text, tables, and charts. In this paper, we introduce a multimodal dataset called Text-Table-Chart Quantity Alignment (TTC-QuAli). This dataset is designed to facilitate a new task that involves linking related quantities across text, tables, and charts. TTC-QuAli is a comprehensive dataset that contains 4,498 quantities in text, aligned with 1,086 chart images and 1,503 tables from real-world statistical reports. It is the first dataset to provide high-quality annotations for linking quantities across multiple modalities, and it includes challenging composite (aggregated/calculated) quantity linking. To address the challenge of bridging representation gaps between different modalities and capturing their shared contextual semantic meaning, we introduce ConTTC, a novel transformer-based cross-modal contrastive learning architecture. This is the first architecture to jointly model text, tables, and charts, and contrastive learning is employed for multimodal quantity linking towards unified representation learning. Our experiments demonstrate that TTC-QuAli presents a significant challenge for existing baselines and serves as a valuable benchmark for future research. Experiment results show that ConTTC significantly outperforms all baseline methods.|在现代文档中，数值信息通常使用文本、表格和图表等多模态格式表示。然而，这些来源的异质性给试图共同阅读和理解通过文本、表格和图表传达的数字语义的机器带来了挑战。本文介绍了一个多模态数据集 TTC-QuAli。这个数据集旨在促进一项新任务，该任务涉及跨文本、表格和图表链接相关数量。TTC-QuAli 是一个全面的数据集，包含4,498个数量的文本，与1,086个图表图像和1,503个来自真实世界统计报告的表格对齐。它是第一个提供高质量注释的数据集，用于跨多种模式的连接数量，它包括具有挑战性的组合(聚合/计算)数量连接。为了解决不同模式之间的表征差异以及获取它们共享的语境语义的问题，我们引入了一种新的基于转换器的跨模式对比学习架构 ConTTC。这是第一个联合建模文本、表格和图表的体系结构，对比学习被用于多模态数量连接到统一的表示学习。我们的实验表明，TTC-QuAli 对现有的基线提出了重大的挑战，并为未来的研究提供了有价值的基准。实验结果表明，ConTTC 方法的性能明显优于所有基线方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=TTC-QuAli:+A+Text-Table-Chart+Dataset+for+Multimodal+Quantity+Alignment)|0| +|[Guardian: Guarding against Gradient Leakage with Provable Defense for Federated Learning](https://doi.org/10.1145/3616855.3635758)|Mingyuan Fan, Yang Liu, Cen Chen, Chengyu Wang, Minghui Qiu, Wenmeng Zhou|ByteDance, Shanghai, Peoples R China; Alibaba Grp, Hangzhou, Peoples R China; East China Normal Univ, Sch Data Sci & Engn, Shanghai, Peoples R China; Xidian Univ, Xian, Shaanxi, Peoples R China|Federated learning is a privacy-focused learning paradigm, which trains a global model with gradients uploaded from multiple participants, circumventing explicit exposure of private data. However, previous research of gradient leakage attacks suggests that gradients alone are sufficient to reconstruct private data, rendering the privacy protection mechanism of federated learning unreliable. Existing defenses commonly craft transformed gradients based on ground-truth gradients to obfuscate the attacks, but often are less capable of maintaining good model performance together with satisfactory privacy protection. In this paper, we propose a novel yet effective defense framework named guarding against gradient leakage (Guardian) that produces transformed gradients by jointly optimizing two theoretically-derived metrics associated with gradients for performance maintenance and privacy protection. In this way, the transformed gradients produced via Guardian can achieve minimal privacy leakage in theory with the given performance maintenance level. Moreover, we design an ingenious initialization strategy for faster generation of transformed gradients to enhance the practicality of Guardian in real-world applications, while demonstrating theoretical convergence of Guardian to the performance of the global model. Extensive experiments on various tasks show that, without sacrificing much accuracy, Guardian can effectively defend state-of-the-art gradient leakage attacks, compared with the slight effects of baseline defense approaches.|联合学习是一种以隐私为中心的学习范式，它通过从多个参与者上传的梯度来训练一个全球模型，从而避免显性暴露私人数据。然而，以往对梯度泄漏攻击的研究表明，梯度本身就足以重建私有数据，使得联邦学习的隐私保护机制变得不可靠。现有的防御系统通常基于地面真实度梯度进行变换，以模糊攻击，但往往不能保持良好的模型性能和令人满意的隐私保护。在本文中，我们提出了一个新颖而有效的防御框架，即防止梯度泄漏(Guardian) ，该框架通过联合优化两个理论导出的与梯度相关的指标，从而产生转换的梯度，用于性能维护和隐私保护。这样，在给定的性能维护水平下，通过 Guardian 产生的变换梯度可以在理论上实现最小的隐私泄漏。此外，我们设计了一个巧妙的初始化策略，可以更快地生成转换后的梯度，以提高 Guardian 在实际应用中的实用性，同时证明了 Guardian 的理论收敛性与全局模型的性能。在各种任务上的大量实验表明，与基线防御方法的轻微效果相比，Guardian 可以在不牺牲太多精确性的情况下有效防御最先进的梯度泄漏攻击。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Guardian:+Guarding+against+Gradient+Leakage+with+Provable+Defense+for+Federated+Learning)|0| +|[TTC-QuAli: A Text-Table-Chart Dataset for Multimodal Quantity Alignment](https://doi.org/10.1145/3616855.3635777)|Haoyu Dong, Haochen Wang, Anda Zhou, Yue Hu|Chinese Acad Sci, Inst Informat Engn, Beijing, Peoples R China; Univ Edinburgh, Beijing, Peoples R China; Peking Univ, Beijing, Peoples R China|In modern documents, numerical information is often presented using multimodal formats such as text, tables, and charts. However, the heterogeneity of these sources poses a challenge for machines attempting to jointly read and understand the numerical semantics conveyed through text, tables, and charts. In this paper, we introduce a multimodal dataset called Text-Table-Chart Quantity Alignment (TTC-QuAli). This dataset is designed to facilitate a new task that involves linking related quantities across text, tables, and charts. TTC-QuAli is a comprehensive dataset that contains 4,498 quantities in text, aligned with 1,086 chart images and 1,503 tables from real-world statistical reports. It is the first dataset to provide high-quality annotations for linking quantities across multiple modalities, and it includes challenging composite (aggregated/calculated) quantity linking. To address the challenge of bridging representation gaps between different modalities and capturing their shared contextual semantic meaning, we introduce ConTTC, a novel transformer-based cross-modal contrastive learning architecture. This is the first architecture to jointly model text, tables, and charts, and contrastive learning is employed for multimodal quantity linking towards unified representation learning. Our experiments demonstrate that TTC-QuAli presents a significant challenge for existing baselines and serves as a valuable benchmark for future research. Experiment results show that ConTTC significantly outperforms all baseline methods.|在现代文档中，数值信息通常使用文本、表格和图表等多模态格式表示。然而，这些来源的异质性给试图共同阅读和理解通过文本、表格和图表传达的数字语义的机器带来了挑战。本文介绍了一个多模态数据集 TTC-QuAli。这个数据集旨在促进一项新任务，该任务涉及跨文本、表格和图表链接相关数量。TTC-QuAli 是一个全面的数据集，包含4,498个数量的文本，与1,086个图表图像和1,503个来自真实世界统计报告的表格对齐。它是第一个提供高质量注释的数据集，用于跨多种模式的连接数量，它包括具有挑战性的组合(聚合/计算)数量连接。为了解决不同模式之间的表征差异以及获取它们共享的语境语义的问题，我们引入了一种新的基于转换器的跨模式对比学习架构 ConTTC。这是第一个联合建模文本、表格和图表的体系结构，对比学习被用于多模态数量连接到统一的表示学习。我们的实验表明，TTC-QuAli 对现有的基线提出了重大的挑战，并为未来的研究提供了有价值的基准。实验结果表明，ConTTC 方法的性能明显优于所有基线方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=TTC-QuAli:+A+Text-Table-Chart+Dataset+for+Multimodal+Quantity+Alignment)|0| |[DeSCo: Towards Generalizable and Scalable Deep Subgraph Counting](https://doi.org/10.1145/3616855.3635788)|Tianyu Fu, Chiyue Wei, Yu Wang, Rex Ying||Subgraph counting is the problem of counting the occurrences of a given query graph in a large target graph. Large-scale subgraph counting is useful in various domains, such as motif counting for social network analysis and loop counting for money laundering detection on transaction networks. Recently, to address the exponential runtime complexity of scalable subgraph counting, neural methods are proposed. However, existing neural counting approaches fall short in three aspects. Firstly, the counts of the same query can vary from zero to millions on different target graphs, posing a much larger challenge than most graph regression tasks. Secondly, current scalable graph neural networks have limited expressive power and fail to efficiently distinguish graphs in count prediction. Furthermore, existing neural approaches cannot predict the occurrence position of queries in the target graph. Here we design DeSCo, a scalable neural deep subgraph counting pipeline, which aims to accurately predict the query count and occurrence position on any target graph after one-time training. Firstly, DeSCo uses a novel canonical partition and divides the large target graph into small neighborhood graphs. The technique greatly reduces the count variation while guaranteeing no missing or double-counting. Secondly, neighborhood counting uses an expressive subgraph-based heterogeneous graph neural network to accurately perform counting in each neighborhood. Finally, gossip propagation propagates neighborhood counts with learnable gates to harness the inductive biases of motif counts. DeSCo is evaluated on eight real-world datasets from various domains. It outperforms state-of-the-art neural methods with 137x improvement in the mean squared error of count prediction, while maintaining the polynomial runtime complexity.|子图计数是在大目标图中计算给定查询图的出现次数的问题。大规模子图计数在各个领域都很有用，例如社交网络分析中的主题计数和交易网络中的洗钱检测中的循环计数。近年来，为了解决可伸缩子图计数的指数运行时复杂性问题，提出了神经网络方法。然而，现有的神经计数方法在三个方面存在不足。首先，在不同的目标图上，同一个查询的计数可能从零到数百万不等，这比大多数图形回归任务带来了更大的挑战。其次，现有的可扩展图形神经网络表达能力有限，无法有效地区分计数预测中的图形。此外，现有的神经网络方法不能预测目标图中查询的出现位置。本文设计了一个可扩展的神经深子图计数流水线 DeSCo，其目的是在一次性训练后准确预测任意目标图上的查询计数和出现位置。首先，DeSCo 使用一种新的规范划分方法，将大目标图划分为小邻域图;。该技术大大减少了计数的变化，同时保证没有丢失或重复计数。其次，邻域计数采用基于表达子图的异构图神经网络对每个邻域进行精确计数。最后，八卦传播利用可学的门来传播邻域计数，以利用主题计数的归纳偏差。DeSCo 在来自不同领域的八个真实世界数据集上进行评估。它比最先进的神经学方法在计数预测均方差上提高了137倍，同时保持了多项式运行时的复杂性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=DeSCo:+Towards+Generalizable+and+Scalable+Deep+Subgraph+Counting)|0| |[CausalMMM: Learning Causal Structure for Marketing Mix Modeling](https://doi.org/10.1145/3616855.3635766)|Chang Gong, Di Yao, Lei Zhang, Sheng Chen, Wenbin Li, Yueyang Su, Jingping Bi|; Chinese Acad Sci, Inst Comp Technol, Beijing, Peoples R China|In online advertising, marketing mix modeling (MMM) is employed to predict the gross merchandise volume (GMV) of brand shops and help decision-makers to adjust the budget allocation of various advertising channels. Traditional MMM methods leveraging regression techniques can fail in handling the complexity of marketing. Although some efforts try to encode the causal structures for better prediction, they have the strict restriction that causal structures are prior-known and unchangeable. In this paper, we define a new causal MMM problem that automatically discovers the interpretable causal structures from data and yields better GMV predictions. To achieve causal MMM, two essential challenges should be addressed: (1) Causal Heterogeneity. The causal structures of different kinds of shops vary a lot. (2) Marketing Response Patterns. Various marketing response patterns i.e., carryover effect and shape effect, have been validated in practice. We argue that causal MMM needs dynamically discover specific causal structures for different shops and the predictions should comply with the prior known marketing response patterns. Thus, we propose CausalMMM that integrates Granger causality in a variational inference framework to measure the causal relationships between different channels and predict the GMV with the regularization of both temporal and saturation marketing response patterns. Extensive experiments show that CausalMMM can not only achieve superior performance of causal structure learning on synthetic datasets with improvements of 5.7% similar to 7.1%, but also enhance the GMV prediction results on a representative E-commerce platform.|在网络广告中，运用营销组合模型(MMM)预测品牌商店的总商品量(GMV) ，帮助决策者调整各种广告渠道的预算分配。利用回归技术的传统 MMM 方法可能无法处理营销的复杂性。尽管有些人试图对因果结构进行编码以便更好地进行预测，但他们受到因果结构是先验已知且不可变的这一严格限制。在本文中，我们定义了一个新的因果 MMM 问题，它可以自动地从数据中发现可解释的因果结构，并且产生更好的 GMV 预测。要实现因果 MMM，必须解决两个基本问题: (1)因果异质性。不同类型商店的因果关系结构差异很大。(2)市场反应模式。各种营销反应模式，即结转效应和形态效应，已在实践中得到验证。我们认为因果 MMM 需要动态地发现不同商店的特定因果结构，并且预测应该符合先前已知的营销反应模式。因此，我们提出了“因果 MMM”，将格兰杰因果关系整合到一个变化推理框架中，以衡量不同渠道之间的因果关系，并通过调整时间和饱和营销反应模式来预测 GMV。大量的实验表明，在一个具有代表性的电子商务平台上，CausalMMM 不仅在合成数据集上取得了与7.1% 相似的5.7% 的因果结构学习优异性能，而且提高了 GMV 预测结果。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=CausalMMM:+Learning+Causal+Structure+for+Marketing+Mix+Modeling)|0| -|[SCAD: Subspace Clustering based Adversarial Detector](https://doi.org/10.1145/3616855.3635835)|Xinrong Hu, Wushuan Chen, Jie Yang, Yi Guo, Xun Yao, Bangchao Wang, Junping Liu, Ce Xu|Wuhan Text Univ, Sch Comp Sci & Artificial Intelligence, Wuhan, Hubei, Peoples R China; Western Sydney Univ, Sch Comp Data & Math Sci, Parramatta, NSW, Australia; Univ Wollongong, Sch Comp & Informat Technol, Wollongong, NSW, Australia|Adversarial examples pose significant challenges for Natural Language Processing (NLP) model robustness, often causing notable performance degradation. While various detection methods have been proposed with the aim of differentiating clean and adversarial inputs, they often require fine-tuning with ample data, which is problematic for low-resource scenarios. To alleviate this issue, a Subspace Clustering based Adversarial Detector (termed SCAD) is proposed in this paper, leveraging a union of subspaces to model the clean data distribution. Specifically, SCAD estimates feature distribution across semantic subspaces, assigning unseen examples to the nearest one for effective discrimination. The construction of semantic subspaces does not require many observations and hence ideal for the low-resource setting. The proposed algorithm achieves detection results better than or competitive with previous state-of-the-arts on a combination of three well-known text classification benchmarks and four attacking methods. Further empirical analysis suggests that SCAD effectively mitigates the low-resource setting where clean training data is limit.|对抗性示例对自然语言处理(NLP)模型的健壮性提出了重大挑战，常常导致显著的性能下降。虽然提出了各种检测方法，目的是区分清洁投入和对抗性投入，但这些方法往往需要利用充足的数据进行微调，这对于资源匮乏的情况是有问题的。为了解决这一问题，本文提出了一种基于子空间聚类的对抗检测器(SCAD) ，利用子空间的联合来建立干净的数据分布模型。具体来说，SCAD 估计特征在语义子空间中的分布，将看不见的例子分配给最近的一个以进行有效的区分。语义子空间的构造不需要很多观察，因此对于低资源设置是理想的。该算法结合了三种常用的文本分类基准和四种攻击方法，取得了比以往更好的检测效果。进一步的实证分析表明，SCAD 有效地缓解了清洁培训数据有限的低资源环境。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=SCAD:+Subspace+Clustering+based+Adversarial+Detector)|0| -|[Capturing Temporal Node Evolution via Self-supervised Learning: A New Perspective on Dynamic Graph Learning](https://doi.org/10.1145/3616855.3635765)|Lingwen Liu, Guangqi Wen, Peng Cao, Jinzhu Yang, Weiping Li, Osmar R. Zaïane|Peking Univ, Sch Software & Microelect, Beijing, Peoples R China; Univ Alberta, Alberta Machine Intelligence Inst, Edmonton, AB, Canada; Northeastern Univ, Sch Comp Sci & Engn, Shenyang, Liaoning, Peoples R China|Dynamic graphs play an important role in many fields like social relationship analysis, recommender systems and medical science, as graphs evolve over time. It is fundamental to capture the evolution patterns for dynamic graphs. Existing works mostly focus on constraining the temporal smoothness between neighbor snapshots, however, fail to capture sharp shifts, which can be beneficial for graph dynamics embedding. To solve it, we assume the evolution of dynamic graph nodes can be split into temporal shift embedding and temporal consistency embedding. Thus, we propose the Self-supervised Temporal-aware Dynamic Graph representation Learning framework (STDGL) for disentangling the temporal shift embedding from temporal consistency embedding via a welldesigned auxiliary task from the perspectives of both node local and global connectivity modeling in a self-supervised manner, further enhancing the learning of interpretable graph representations and improving the performance of various downstream tasks. Extensive experiments on link prediction, edge classification and node classification tasks demonstrate STDGL successfully learns the disentangled temporal shift and consistency representations. Furthermore, the results indicate significant improvements in our STDGL over the state-of-the-art methods, and appealing interpretability and transferability owing to the disentangled node representations.|随着时间的推移，动态图在社会关系分析、推荐系统和医学科学等领域发挥着重要作用。捕捉动态图的演化模式是基础。现有的工作主要集中在限制相邻快照之间的时间平滑，但是未能捕捉到快速的移动，这有利于图动态嵌入。为了解决这个问题，我们假设动态图节点的演化可以分为时间移位嵌入和时间一致性嵌入。因此，我们提出了自监督时间感知动态图表示学习框架(STDGL) ，以自监督的方式从节点局部和全局连通性建模的角度，通过一个设计良好的辅助任务将时间移位嵌入与时间一致性嵌入分离，进一步提高可解释图表示的学习能力，改善各种下游任务的性能。在链路预测、边缘分类和节点分类任务上的大量实验表明，STDGL 成功地学习了分离时间漂移和一致性表示。此外，研究结果显示我们的 STDGL 比最先进的方法有显著的改进，并且由于分离的节点表示而具有吸引力的可解释性和可转移性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Capturing+Temporal+Node+Evolution+via+Self-supervised+Learning:+A+New+Perspective+on+Dynamic+Graph+Learning)|0| +|[SCAD: Subspace Clustering based Adversarial Detector](https://doi.org/10.1145/3616855.3635835)|Xinrong Hu, Wushuan Chen, Jie Yang, Yi Guo, Xun Yao, Bangchao Wang, Junping Liu, Ce Xu|Western Sydney Univ, Sch Comp Data & Math Sci, Parramatta, NSW, Australia; Wuhan Text Univ, Sch Comp Sci & Artificial Intelligence, Wuhan, Hubei, Peoples R China; Univ Wollongong, Sch Comp & Informat Technol, Wollongong, NSW, Australia|Adversarial examples pose significant challenges for Natural Language Processing (NLP) model robustness, often causing notable performance degradation. While various detection methods have been proposed with the aim of differentiating clean and adversarial inputs, they often require fine-tuning with ample data, which is problematic for low-resource scenarios. To alleviate this issue, a Subspace Clustering based Adversarial Detector (termed SCAD) is proposed in this paper, leveraging a union of subspaces to model the clean data distribution. Specifically, SCAD estimates feature distribution across semantic subspaces, assigning unseen examples to the nearest one for effective discrimination. The construction of semantic subspaces does not require many observations and hence ideal for the low-resource setting. The proposed algorithm achieves detection results better than or competitive with previous state-of-the-arts on a combination of three well-known text classification benchmarks and four attacking methods. Further empirical analysis suggests that SCAD effectively mitigates the low-resource setting where clean training data is limit.|对抗性示例对自然语言处理(NLP)模型的健壮性提出了重大挑战，常常导致显著的性能下降。虽然提出了各种检测方法，目的是区分清洁投入和对抗性投入，但这些方法往往需要利用充足的数据进行微调，这对于资源匮乏的情况是有问题的。为了解决这一问题，本文提出了一种基于子空间聚类的对抗检测器(SCAD) ，利用子空间的联合来建立干净的数据分布模型。具体来说，SCAD 估计特征在语义子空间中的分布，将看不见的例子分配给最近的一个以进行有效的区分。语义子空间的构造不需要很多观察，因此对于低资源设置是理想的。该算法结合了三种常用的文本分类基准和四种攻击方法，取得了比以往更好的检测效果。进一步的实证分析表明，SCAD 有效地缓解了清洁培训数据有限的低资源环境。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=SCAD:+Subspace+Clustering+based+Adversarial+Detector)|0| +|[Capturing Temporal Node Evolution via Self-supervised Learning: A New Perspective on Dynamic Graph Learning](https://doi.org/10.1145/3616855.3635765)|Lingwen Liu, Guangqi Wen, Peng Cao, Jinzhu Yang, Weiping Li, Osmar R. Zaïane|Univ Alberta, Alberta Machine Intelligence Inst, Edmonton, AB, Canada; Northeastern Univ, Sch Comp Sci & Engn, Shenyang, Liaoning, Peoples R China; Peking Univ, Sch Software & Microelect, Beijing, Peoples R China|Dynamic graphs play an important role in many fields like social relationship analysis, recommender systems and medical science, as graphs evolve over time. It is fundamental to capture the evolution patterns for dynamic graphs. Existing works mostly focus on constraining the temporal smoothness between neighbor snapshots, however, fail to capture sharp shifts, which can be beneficial for graph dynamics embedding. To solve it, we assume the evolution of dynamic graph nodes can be split into temporal shift embedding and temporal consistency embedding. Thus, we propose the Self-supervised Temporal-aware Dynamic Graph representation Learning framework (STDGL) for disentangling the temporal shift embedding from temporal consistency embedding via a welldesigned auxiliary task from the perspectives of both node local and global connectivity modeling in a self-supervised manner, further enhancing the learning of interpretable graph representations and improving the performance of various downstream tasks. Extensive experiments on link prediction, edge classification and node classification tasks demonstrate STDGL successfully learns the disentangled temporal shift and consistency representations. Furthermore, the results indicate significant improvements in our STDGL over the state-of-the-art methods, and appealing interpretability and transferability owing to the disentangled node representations.|随着时间的推移，动态图在社会关系分析、推荐系统和医学科学等领域发挥着重要作用。捕捉动态图的演化模式是基础。现有的工作主要集中在限制相邻快照之间的时间平滑，但是未能捕捉到快速的移动，这有利于图动态嵌入。为了解决这个问题，我们假设动态图节点的演化可以分为时间移位嵌入和时间一致性嵌入。因此，我们提出了自监督时间感知动态图表示学习框架(STDGL) ，以自监督的方式从节点局部和全局连通性建模的角度，通过一个设计良好的辅助任务将时间移位嵌入与时间一致性嵌入分离，进一步提高可解释图表示的学习能力，改善各种下游任务的性能。在链路预测、边缘分类和节点分类任务上的大量实验表明，STDGL 成功地学习了分离时间漂移和一致性表示。此外，研究结果显示我们的 STDGL 比最先进的方法有显著的改进，并且由于分离的节点表示而具有吸引力的可解释性和可转移性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Capturing+Temporal+Node+Evolution+via+Self-supervised+Learning:+A+New+Perspective+on+Dynamic+Graph+Learning)|0| |[Generative Models for Complex Logical Reasoning over Knowledge Graphs](https://doi.org/10.1145/3616855.3635804)|Yu Liu, Yanan Cao, Shi Wang, Qingyue Wang, Guanqun Bi|Chinese Acad Sci, Inst Informat Engn, Beijing, Peoples R China|Answering complex logical queries over knowledge graphs (KGs) is a fundamental yet challenging task. Recently, query representation has been a mainstream approach to complex logical reasoning, making the target answer and query closer in the embedding space. However, there are still two limitations. First, prior methods model the query as a fixed vector, but ignore the uncertainty of relations on KGs. In fact, different relations may contain different semantic distributions. Second, traditional representation frameworks fail to capture the joint distribution of queries and answers, which can be learned by generative models that have the potential to produce more coherent answers. To alleviate these limitations, we propose a novel generative model, named DiffCLR, which exploits the diffusion model for complex logical reasoning to approximate query distributions. Specifically, we first devise a query transformation to convert logical queries into input sequences by dynamically constructing contextual subgraphs. Then, we integrate them into the diffusion model to execute a multi-step generative process, and a structure-enhanced self-attention is further designed for incorporating the structural features embodied in KGs. Experimental results on two benchmark datasets show our model effectively outperforms state-of-the-art methods, particularly in multi-hop chain queries with significant improvement.|回答知识图表(KGs)上的复杂逻辑查询是一个基本但具有挑战性的任务。最近，查询表示已经成为复杂逻辑推理的主流方法，使得目标回答和查询在嵌入空间中更加接近。但是，仍然存在两个限制。首先，先验方法将查询建模为一个固定的向量，但忽略了 KG 上关系的不确定性。事实上，不同的关系可能包含不同的语义分布。其次，传统的表示框架未能捕捉到查询和答案的联合分布，这可以通过生成模型学习，这些模型有可能产生更一致的答案。为了减轻这些局限性，我们提出了一种新的生成模型，名为 ddCLR，它利用复杂逻辑推理的扩散模型来近似查询分布。具体来说，我们首先设计一个查询转换，通过动态构造上下文子图将逻辑查询转换为输入序列。然后，我们将它们整合到扩散模型中，执行一个多步骤的生成过程，并进一步设计一个结构增强的自我注意，以整合幼稚园所体现的结构特征。在两个基准数据集上的实验结果表明，该模型的性能优于目前最先进的方法，尤其是在多跳链查询方面有明显的改进。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Generative+Models+for+Complex+Logical+Reasoning+over+Knowledge+Graphs)|0| -|[A Linguistic Grounding-Infused Contrastive Learning Approach for Health Mention Classification on Social Media](https://doi.org/10.1145/3616855.3635763)|Usman Naseem, Jinman Kim, Matloob Khushi, Adam G. Dunn|Univ Sydney, Sch Med Sci, Sydney, Australia; Univ Sydney, Sch Comp Sci, Sydney, Australia; Brunel Univ, Dept Comp Sci, London, England|Social media users use disease and symptoms words in different ways, including describing their personal health experiences figuratively or in other general discussions. The health mention classification (HMC) task aims to separate how people use terms, which is important in public health applications. Existing HMC studies address this problem using pretrained language models (PLMs). However, the remaining gaps in the area include the need for linguistic grounding, the requirement for large volumes of labelled data, and that solutions are often only tested on Twitter or Reddit, which provides limited evidence of the transportability of models. To address these gaps, we propose a novel method that uses a transformer-based PLM to obtain a contextual representation of target (disease or symptom) terms coupled with a contrastive loss to establish a larger gap between target terms' literal and figurative uses using linguistic theories. We introduce the use of a simple and effective approach for harvesting candidate instances from the broad corpus and generalising the proposed method using selftraining to address the label scarcity challenge. Our experiments on publicly available health-mention datasets from Twitter (HMC2019) and Reddit (RHMD) demonstrate that our method outperforms the state-of-the-art HMC methods on both datasets for the HMC task. We further analyse the transferability and generalisability of our method and conclude with a discussion on the empirical and ethical considerations of our study.|社交媒体用户使用疾病和症状词的方式各不相同，包括比喻性地描述他们的个人健康经历或在其他一般性讨论中使用。健康提及分类(HMC)任务旨在区分人们如何使用术语，这在公共卫生应用中非常重要。现有的 HMC 研究使用预训练语言模型(PLM)来解决这个问题。然而，这一领域仍然存在的差距包括需要语言基础、需要大量有标签的数据，以及解决方案往往只能在 Twitter 或 Reddit 上测试，因为它们提供的关于模型可移植性的证据有限。为了解决这些差距，我们提出了一种新的方法，使用基于变换器的 PLM 来获得目标(疾病或症状)术语的上下文表示，加上对比损失，使用语言学理论在目标术语的字面和比喻用法之间建立更大的差距。我们介绍了一种简单而有效的方法，用于从广泛的语料库中收集候选实例，并将提出的方法推广使用自我训练来解决标签稀缺性的挑战。我们在来自 Twitter (HMC2019)和 Reddit (RHMD)的公开可用的健康提及数据集上的实验表明，我们的方法在 HMC 任务的两个数据集上优于最先进的 HMC 方法。我们进一步分析了我们的方法的可转换性和普遍性，最后讨论了我们的研究的经验和伦理考虑。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Linguistic+Grounding-Infused+Contrastive+Learning+Approach+for+Health+Mention+Classification+on+Social+Media)|0| +|[A Linguistic Grounding-Infused Contrastive Learning Approach for Health Mention Classification on Social Media](https://doi.org/10.1145/3616855.3635763)|Usman Naseem, Jinman Kim, Matloob Khushi, Adam G. Dunn|Brunel Univ, Dept Comp Sci, London, England; Univ Sydney, Sch Comp Sci, Sydney, Australia; Univ Sydney, Sch Med Sci, Sydney, Australia|Social media users use disease and symptoms words in different ways, including describing their personal health experiences figuratively or in other general discussions. The health mention classification (HMC) task aims to separate how people use terms, which is important in public health applications. Existing HMC studies address this problem using pretrained language models (PLMs). However, the remaining gaps in the area include the need for linguistic grounding, the requirement for large volumes of labelled data, and that solutions are often only tested on Twitter or Reddit, which provides limited evidence of the transportability of models. To address these gaps, we propose a novel method that uses a transformer-based PLM to obtain a contextual representation of target (disease or symptom) terms coupled with a contrastive loss to establish a larger gap between target terms' literal and figurative uses using linguistic theories. We introduce the use of a simple and effective approach for harvesting candidate instances from the broad corpus and generalising the proposed method using selftraining to address the label scarcity challenge. Our experiments on publicly available health-mention datasets from Twitter (HMC2019) and Reddit (RHMD) demonstrate that our method outperforms the state-of-the-art HMC methods on both datasets for the HMC task. We further analyse the transferability and generalisability of our method and conclude with a discussion on the empirical and ethical considerations of our study.|社交媒体用户使用疾病和症状词的方式各不相同，包括比喻性地描述他们的个人健康经历或在其他一般性讨论中使用。健康提及分类(HMC)任务旨在区分人们如何使用术语，这在公共卫生应用中非常重要。现有的 HMC 研究使用预训练语言模型(PLM)来解决这个问题。然而，这一领域仍然存在的差距包括需要语言基础、需要大量有标签的数据，以及解决方案往往只能在 Twitter 或 Reddit 上测试，因为它们提供的关于模型可移植性的证据有限。为了解决这些差距，我们提出了一种新的方法，使用基于变换器的 PLM 来获得目标(疾病或症状)术语的上下文表示，加上对比损失，使用语言学理论在目标术语的字面和比喻用法之间建立更大的差距。我们介绍了一种简单而有效的方法，用于从广泛的语料库中收集候选实例，并将提出的方法推广使用自我训练来解决标签稀缺性的挑战。我们在来自 Twitter (HMC2019)和 Reddit (RHMD)的公开可用的健康提及数据集上的实验表明，我们的方法在 HMC 任务的两个数据集上优于最先进的 HMC 方法。我们进一步分析了我们的方法的可转换性和普遍性，最后讨论了我们的研究的经验和伦理考虑。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Linguistic+Grounding-Infused+Contrastive+Learning+Approach+for+Health+Mention+Classification+on+Social+Media)|0| |[GAD-NR: Graph Anomaly Detection via Neighborhood Reconstruction](https://doi.org/10.1145/3616855.3635767)|Amit Roy, Juan Shu, Jia Li, Carl Yang, Olivier Elshocht, Jeroen Smeets, Pan Li||Graph Anomaly Detection (GAD) is a technique used to identify abnormal nodes within graphs, finding applications in network security, fraud detection, social media spam detection, and various other domains. A common method for GAD is Graph Auto-Encoders (GAEs), which encode graph data into node representations and identify anomalies by assessing the reconstruction quality of the graphs based on these representations. However, existing GAE models are primarily optimized for direct link reconstruction, resulting in nodes connected in the graph being clustered in the latent space. As a result, they excel at detecting cluster-type structural anomalies but struggle with more complex structural anomalies that do not conform to clusters. To address this limitation, we propose a novel solution called GAD-NR, a new variant of GAE that incorporates neighborhood reconstruction for graph anomaly detection. GAD-NR aims to reconstruct the entire neighborhood of a node, encompassing the local structure, self-attributes, and neighbor attributes, based on the corresponding node representation. By comparing the neighborhood reconstruction loss between anomalous nodes and normal nodes, GAD-NR can effectively detect any anomalies. Extensive experimentation conducted on six real-world datasets validates the effectiveness of GAD-NR, showcasing significant improvements (by up to 30% in AUC) over state-of-the-art competitors. The source code for GAD-NR is openly available. Importantly, the comparative analysis reveals that the existing methods perform well only in detecting one or two types of anomalies out of the three types studied. In contrast, GAD-NR excels at detecting all three types of anomalies across the datasets, demonstrating its comprehensive anomaly detection capabilities.|图形异常检测(GAD)是一种识别图形中异常节点的技术，用于寻找网络安全、欺诈检测、社交媒体垃圾邮件检测以及其他领域的应用程序。GAD 的一种常用方法是图形自动编码器(GAE) ，它将图形数据编码成节点表示形式，并通过评估基于这些表示形式的图形的重构质量来识别异常。然而，现有的 GAE 模型主要针对直接链路重构进行了优化，导致图中连接的节点在潜在空间中聚集。因此，他们擅长探测集群式结构异常，但与更复杂的结构异常斗争，不符合集群。为了解决这个问题，我们提出了一种新的解决方案，称为 GAD-NR，这是一种新的 GAE 变体，它结合了图形异常检测的邻域重建。GAD-NR 的目标是在相应节点表示的基础上重构节点的整个邻域，包括节点的局部结构、自身属性和邻居属性。通过比较异常节点和正常节点的邻域重构损失，GAD-NR 可以有效地检测任何异常。在六个真实世界的数据集上进行的广泛实验验证了 GAD-NR 的有效性，显示了与最先进的竞争对手相比的显著改进(AUC 的改进幅度高达30%)。GAD-NR 的源代码是公开的。重要的是，比较分析表明，现有的方法只有在检测一个或两个类型的异常研究的三种类型中执行良好。相比之下，GAD-NR 在检测数据集中所有三种类型的异常方面表现出色，展示了其全面的异常检测能力。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=GAD-NR:+Graph+Anomaly+Detection+via+Neighborhood+Reconstruction)|0| |[Ad-load Balancing via Off-policy Learning in a Content Marketplace](https://doi.org/10.1145/3616855.3635846)|Hitesh Sagtani, Madan Gopal Jhawar, Rishabh Mehrotra, Olivier Jeunen||Ad-load balancing is a critical challenge in online advertising systems, particularly in the context of social media platforms, where the goal is to maximize user engagement and revenue while maintaining a satisfactory user experience. This requires the optimization of conflicting objectives, such as user satisfaction and ads revenue. Traditional approaches to ad-load balancing rely on static allocation policies, which fail to adapt to changing user preferences and contextual factors. In this paper, we present an approach that leverages off-policy learning and evaluation from logged bandit feedback. We start by presenting a motivating analysis of the ad-load balancing problem, highlighting the conflicting objectives between user satisfaction and ads revenue. We emphasize the nuances that arise due to user heterogeneity and the dependence on the user's position within a session. Based on this analysis, we define the problem as determining the optimal ad-load for a particular feed fetch. To tackle this problem, we propose an off-policy learning framework that leverages unbiased estimators such as Inverse Propensity Scoring (IPS) and Doubly Robust (DR) to learn and estimate the policy values using offline collected stochastic data. We present insights from online A/B experiments deployed at scale across over 80 million users generating over 200 million sessions, where we find statistically significant improvements in both user satisfaction metrics and ads revenue for the platform.|广告负载平衡是在线广告系统中的一个关键挑战，特别是在社交媒体平台的背景下，其目标是最大限度地提高用户参与度和收入，同时保持令人满意的用户体验。这需要优化相互冲突的目标，如用户满意度和广告收入。传统的广告负载平衡方法依赖于静态分配策略，不能适应用户偏好和上下文因素的变化。在本文中，我们提出了一种方法，利用非政策学习和评估的日志土匪反馈。我们首先对广告负载平衡问题进行了激励性分析，强调了用户满意度和广告收入之间的矛盾目标。我们强调由于用户异质性和对用户在会话中位置的依赖性而产生的细微差别。在此基础上，我们将该问题定义为确定特定提要的最佳广告负载。为了解决这个问题，我们提出了一个非策略学习框架，利用无偏估计量，如逆倾向评分(IPS)和双稳健(DR)来学习和估计使用离线收集的随机数据的策略值。我们展示了在线 A/B 实验的深刻见解，这些实验在超过8千万用户中进行，产生了超过2亿次的会话，我们发现在用户满意度指标和平台广告收入方面都有统计学意义上的显著改善。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Ad-load+Balancing+via+Off-policy+Learning+in+a+Content+Marketplace)|0| |[ProGAP: Progressive Graph Neural Networks with Differential Privacy Guarantees](https://doi.org/10.1145/3616855.3635761)|Sina Sajadmanesh, Daniel GaticaPerez||Graph Neural Networks (GNNs) have become a popular tool for learning on graphs, but their widespread use raises privacy concerns as graph data can contain personal or sensitive information. Differentially private GNN models have been recently proposed to preserve privacy while still allowing for effective learning over graph-structured datasets. However, achieving an ideal balance between accuracy and privacy in GNNs remains challenging due to the intrinsic structural connectivity of graphs. In this paper, we propose a new differentially private GNN called ProGAP that uses a progressive training scheme to improve such accuracy-privacy trade-offs. Combined with the aggregation perturbation technique to ensure differential privacy, ProGAP splits a GNN into a sequence of overlapping submodels that are trained progressively, expanding from the first submodel to the complete model. Specifically, each submodel is trained over the privately aggregated node embeddings learned and cached by the previous submodels, leading to an increased expressive power compared to previous approaches while limiting the incurred privacy costs. We formally prove that ProGAP ensures edge-level and node-level privacy guarantees for both training and inference stages, and evaluate its performance on benchmark graph datasets. Experimental results demonstrate that ProGAP can achieve up to 5%-10% higher accuracy than existing state-of-the-art differentially private GNNs.|图形神经网络(GNN)已经成为一种流行的图形学习工具，但它的广泛使用引起了人们对隐私的关注，因为图形数据可以包含个人或敏感信息。最近提出了差异私有 GNN 模型，以保护隐私，同时仍然允许对图形结构的数据集进行有效的学习。然而，由于图的内在结构连通性，在 GNN 中实现准确性和隐私性之间的理想平衡仍然具有挑战性。在本文中，我们提出了一个新的差分私有 GNN 称为 ProGAP，它使用一个渐进的训练方案来提高这种准确性-隐私权衡。结合聚合扰动技术以确保差分隐私，ProGAP 将一个 GNN 分割成一系列重叠的子模型，这些子模型被逐步训练，从第一个子模型扩展到完整的模型。具体来说，每个子模型都是在以前的子模型学习和缓存的私有聚合节点嵌入上进行训练的，与以前的方法相比，导致表达能力增加，同时限制了产生的隐私成本。我们正式证明了 ProGAP 在训练阶段和推理阶段都保证了边界层和节点层的隐私保护，并对其在基准图数据集上的性能进行了评估。实验结果表明，ProGAP 可以达到高达5% -10% 的准确率比现有的国家最先进的差分私有 GNN。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=ProGAP:+Progressive+Graph+Neural+Networks+with+Differential+Privacy+Guarantees)|0| @@ -127,31 +127,31 @@ |[Table Meets LLM: Can Large Language Models Understand Structured Table Data? A Benchmark and Empirical Study](https://doi.org/10.1145/3616855.3635752)|Yuan Sui, Mengyu Zhou, Mingjie Zhou, Shi Han, Dongmei Zhang||Large language models (LLMs) are becoming attractive as few-shot reasoners to solve Natural Language (NL)-related tasks. However, there is still much to learn about how well LLMs understand structured data, such as tables. Although tables can be used as input to LLMs with serialization, there is a lack of comprehensive studies that examine whether LLMs can truly comprehend such data. In this paper, we try to understand this by designing a benchmark to evaluate the structural understanding capabilities (SUC) of LLMs. The benchmark we create includes seven tasks, each with its own unique challenges, e.g., cell lookup, row retrieval, and size detection. We perform a series of evaluations on GPT-3.5 and GPT-4. We find that performance varied depending on several input choices, including table input format, content order, role prompting, and partition marks. Drawing from the insights gained through the benchmark evaluations, we propose self-augmentation for effective structural prompting, such as critical value / range identification using internal knowledge of LLMs. When combined with carefully chosen input choices, these structural prompting methods lead to promising improvements in LLM performance on a variety of tabular tasks, e.g., TabFact(↑2.31%), HybridQA(↑2.13%), SQA(↑2.72%), Feverous(↑0.84%), and ToTTo(↑5.68%). We believe that our open source benchmark and proposed prompting methods can serve as a simple yet generic selection for future research.|大型语言模型(LLM)作为解决自然语言(NL)相关任务的少量推理工具正变得越来越有吸引力。然而，关于 LLM 对结构化数据(如表)的理解程度，还有很多需要了解的地方。虽然表格可以作为序列化 LLM 的输入，但是缺乏全面的研究来检查 LLM 是否能够真正理解这些数据。在本文中，我们试图通过设计一个基准来评估 LLM 的结构理解能力(SUC)来理解这一点。我们创建的基准测试包括七个任务，每个任务都有其独特的挑战，例如单元格查找、行检索和大小检测。我们对 GPT-3.5和 GPT-4进行了一系列的评估。我们发现，性能取决于几种输入选择，包括表输入格式、内容顺序、角色提示和分区标记。基于基准评估所获得的见解，我们提出了有效结构激励的自我增强方法，例如利用 LLM 的内部知识进行临界值/范围识别。当结合精心选择的输入选项时，这些结构化的提示方法可以在各种表格任务中提高 LLM 的性能，例如 TabFact (惊2.31%) ，HybridQA (惊2.13%) ，SQA (惊2.72%) ，Feverous (惊0.84%)和 ToTTo (惊5.68%)。我们相信，我们的开源基准和提议的激励方法可以作为一个简单而通用的选择，为未来的研究。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Table+Meets+LLM:+Can+Large+Language+Models+Understand+Structured+Table+Data?+A+Benchmark+and+Empirical+Study)|0| |[Rethinking and Simplifying Bootstrapped Graph Latents](https://doi.org/10.1145/3616855.3635842)|Wangbin Sun, Jintang Li, Liang Chen, Bingzhe Wu, Yatao Bian, Zibin Zheng||Graph contrastive learning (GCL) has emerged as a representative paradigm in graph self-supervised learning, where negative samples are commonly regarded as the key to preventing model collapse and producing distinguishable representations. Recent studies have shown that GCL without negative samples can achieve state-of-the-art performance as well as scalability improvement, with bootstrapped graph latent (BGRL) as a prominent step forward. However, BGRL relies on a complex architecture to maintain the ability to scatter representations, and the underlying mechanisms enabling the success remain largely unexplored. In this paper, we introduce an instance-level decorrelation perspective to tackle the aforementioned issue and leverage it as a springboard to reveal the potential unnecessary model complexity within BGRL. Based on our findings, we present SGCL, a simple yet effective GCL framework that utilizes the outputs from two consecutive iterations as positive pairs, eliminating the negative samples. SGCL only requires a single graph augmentation and a single graph encoder without additional parameters. Extensive experiments conducted on various graph benchmarks demonstrate that SGCL can achieve competitive performance with fewer parameters, lower time and space costs, and significant convergence speedup.|图形对比学习(GCL)已经成为图形自监督学习的一种典型范式，负样本通常被认为是防止模型崩溃和产生可区分表示的关键。最近的研究表明，无负样本的 GCL 可以实现最先进的性能和可扩展性的提高，引导图潜伏(BGRL)是一个突出的进步。然而，BGRL 依赖于一个复杂的体系结构来维护分散表示的能力，而使其成功的底层机制在很大程度上仍然是未知的。在本文中，我们引入了一个实例级的去相关视角来解决上述问题，并利用它作为一个跳板来揭示 BGRL 中潜在的不必要的模型复杂性。基于我们的发现，我们提出了 SGCL，一个简单而有效的 GCL 框架，利用两个连续迭代的输出作为正对，消除了负样本。SGCL 只需要一个图形扩展和一个没有附加参数的图形编码器。在各种图形基准上进行的大量实验表明，SGCL 能以较少的参数、较低的时间和空间开销以及显著的收敛速度获得具有竞争力的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Rethinking+and+Simplifying+Bootstrapped+Graph+Latents)|0| |[Temporal Blind Spots in Large Language Models](https://doi.org/10.1145/3616855.3635818)|Jonas Wallat, Adam Jatowt, Avishek Anand|Delft Univ Technol, Dept Software Technol, Delft, Netherlands; L3S Res Ctr, Hannover, Germany; Univ Innsbruck, Dept Comp Sci, Innsbruck, Austria|Large language models (LLMs) have recently gained significant attention due to their unparalleled zero-shot performance on various natural language processing tasks. However, the pre-training data utilized in LLMs is often confined to a specific corpus, resulting in inherent freshness and temporal scope limitations. Consequently, this raises concerns regarding the effectiveness of LLMs for tasks involving temporal intents. In this study, we aim to investigate the underlying limitations of general-purpose LLMs when deployed for tasks that require a temporal understanding. We pay particular attention to handling factual temporal knowledge through three popular temporal QA datasets. Specifically, we observe low performance on detailed questions about the past and, surprisingly, for rather new information. In manual and automatic testing, we find multiple temporal errors and characterize the conditions under which QA performance deteriorates. Our analysis contributes to understanding LLM limitations and offers valuable insights into developing future models that can better cater to the demands of temporally-oriented tasks. The code is available1.|大型语言模型(LLM)由于其在各种自然语言处理任务中无与伦比的“零打击”性能，近年来受到了广泛的关注。然而，在 LLM 中使用的预训练数据往往局限于特定的语料库，导致固有的新鲜度和时间范围的限制。因此，这引起了关于 LLM 对于涉及时间意图的任务的有效性的关注。在这项研究中，我们的目标是调查通用 LLM 的潜在局限性时，部署的任务，需要一个时间的理解。我们特别关注通过三个流行的时态 QA 数据集处理实际的时态知识。具体来说，我们观察到在关于过去的详细问题上表现不佳，令人惊讶的是，对于相当新的信息。在手动和自动测试中，我们发现了多个时间错误，并描述了 QA 性能恶化的条件。我们的分析有助于理解 LLM 的局限性，并为开发能够更好地满足面向时间的任务需求的未来模型提供了有价值的见解。密码是可用的。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Temporal+Blind+Spots+in+Large+Language+Models)|0| -|[Efficient, Direct, and Restricted Black-Box Graph Evasion Attacks to Any-Layer Graph Neural Networks via Influence Function](https://doi.org/10.1145/3616855.3635826)|Binghui Wang, Minhua Lin, Tianxiang Zhou, Pan Zhou, Ang Li, Meng Pang, Hai Helen Li, Yiran Chen|Univ Maryland, College Pk, MD 20742 USA; Penn State Univ, State Coll, PA USA; IIT, Chicago, IL 60616 USA; Duke Univ, Durham, NC USA; Nanchang Univ, Nanchang, Peoples R China; Huazhong Univ Sci & Technol, Wuhan, Peoples R China|Graph neural network (GNN), the mainstream method to learn on graph data, is vulnerable to graph evasion attacks, where an attacker slightly perturbing the graph structure can fool trainedGNNmodels. Existing work has at least one of the following drawbacks: 1) limited to directly attack two-layer GNNs; 2) inefficient; and 3) impractical, as they need to know full or part of GNN model parameters. We address the above drawbacks and propose an influence-based efficient, direct, and restricted black-box evasion attack to any-layer GNNs. Specifically, we first introduce two influence functions, i.e., feature-label influence and label influence, that are defined on GNNs and label propagation (LP), respectively. Then we observe that GNNs and LP are strongly connected in terms of our defined influences. Based on this, we can then reformulate the evasion attack to GNNs as calculating label influence on LP, which is inherently applicable to any-layer GNNs, while no need to know information about the internal GNN model. Finally, we propose an efficient algorithm to calculate label influence. Experimental results on various graph datasets show that, compared to state-of-the-art white-box attacks, our attack can achieve comparable attack performance, but has a 5-50x speedup when attacking two-layer GNNs. Moreover, our attack is effective to attack multi-layer GNNs1.|图形神经网络(GNN)是对图形数据进行学习的主流方法，容易受到图形规避攻击，攻击者稍微扰动图形结构就可以欺骗训练有素的 GNN 模型。现有的工作至少有以下一个缺点: 1)仅限于直接攻击两层 GNN; 2)效率低下; 3)不切实际，因为他们需要知道全部或部分 GNN 模型参数。针对上述缺点，本文提出了一种基于影响力的高效、直接、有限制的黑盒规避攻击方法。具体来说，我们首先介绍了两个影响函数，即特征标签影响和标签影响，它们分别定义在 GNN 和标签传播(LP)上。然后我们观察到 GNN 和 LP 在我们所定义的影响方面是强相关的。在此基础上，我们可以将对 GNN 的规避攻击重新表述为计算标签对 LP 的影响，这本质上适用于任意层的 GNN，而不需要知道内部 GNN 模型的信息。最后，提出了一种计算标签影响的有效算法。在各种图形数据集上的实验结果表明，与最先进的白盒攻击相比，我们的攻击可以达到相当的攻击性能，但是在攻击两层 GNN 时有5-50倍的加速效果。此外，我们的攻击是有效的攻击多层 GNNs1。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Efficient,+Direct,+and+Restricted+Black-Box+Graph+Evasion+Attacks+to+Any-Layer+Graph+Neural+Networks+via+Influence+Function)|0| +|[Efficient, Direct, and Restricted Black-Box Graph Evasion Attacks to Any-Layer Graph Neural Networks via Influence Function](https://doi.org/10.1145/3616855.3635826)|Binghui Wang, Minhua Lin, Tianxiang Zhou, Pan Zhou, Ang Li, Meng Pang, Hai Helen Li, Yiran Chen|Penn State Univ, State Coll, PA USA; Huazhong Univ Sci & Technol, Wuhan, Peoples R China; Duke Univ, Durham, NC USA; IIT, Chicago, IL 60616 USA; Univ Maryland, College Pk, MD 20742 USA; Nanchang Univ, Nanchang, Peoples R China|Graph neural network (GNN), the mainstream method to learn on graph data, is vulnerable to graph evasion attacks, where an attacker slightly perturbing the graph structure can fool trainedGNNmodels. Existing work has at least one of the following drawbacks: 1) limited to directly attack two-layer GNNs; 2) inefficient; and 3) impractical, as they need to know full or part of GNN model parameters. We address the above drawbacks and propose an influence-based efficient, direct, and restricted black-box evasion attack to any-layer GNNs. Specifically, we first introduce two influence functions, i.e., feature-label influence and label influence, that are defined on GNNs and label propagation (LP), respectively. Then we observe that GNNs and LP are strongly connected in terms of our defined influences. Based on this, we can then reformulate the evasion attack to GNNs as calculating label influence on LP, which is inherently applicable to any-layer GNNs, while no need to know information about the internal GNN model. Finally, we propose an efficient algorithm to calculate label influence. Experimental results on various graph datasets show that, compared to state-of-the-art white-box attacks, our attack can achieve comparable attack performance, but has a 5-50x speedup when attacking two-layer GNNs. Moreover, our attack is effective to attack multi-layer GNNs1.|图形神经网络(GNN)是对图形数据进行学习的主流方法，容易受到图形规避攻击，攻击者稍微扰动图形结构就可以欺骗训练有素的 GNN 模型。现有的工作至少有以下一个缺点: 1)仅限于直接攻击两层 GNN; 2)效率低下; 3)不切实际，因为他们需要知道全部或部分 GNN 模型参数。针对上述缺点，本文提出了一种基于影响力的高效、直接、有限制的黑盒规避攻击方法。具体来说，我们首先介绍了两个影响函数，即特征标签影响和标签影响，它们分别定义在 GNN 和标签传播(LP)上。然后我们观察到 GNN 和 LP 在我们所定义的影响方面是强相关的。在此基础上，我们可以将对 GNN 的规避攻击重新表述为计算标签对 LP 的影响，这本质上适用于任意层的 GNN，而不需要知道内部 GNN 模型的信息。最后，提出了一种计算标签影响的有效算法。在各种图形数据集上的实验结果表明，与最先进的白盒攻击相比，我们的攻击可以达到相当的攻击性能，但是在攻击两层 GNN 时有5-50倍的加速效果。此外，我们的攻击是有效的攻击多层 GNNs1。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Efficient,+Direct,+and+Restricted+Black-Box+Graph+Evasion+Attacks+to+Any-Layer+Graph+Neural+Networks+via+Influence+Function)|0| |[CityCAN: Causal Attention Network for Citywide Spatio-Temporal Forecasting](https://doi.org/10.1145/3616855.3635764)|Chengxin Wang, Yuxuan Liang, Gary Tan||||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=CityCAN:+Causal+Attention+Network+for+Citywide+Spatio-Temporal+Forecasting)|0| |[Distribution Consistency based Self-Training for Graph Neural Networks with Sparse Labels](https://doi.org/10.1145/3616855.3635793)|Fali Wang, Tianxiang Zhao, Suhang Wang||Few-shot node classification poses a significant challenge for Graph Neural Networks (GNNs) due to insufficient supervision and potential distribution shifts between labeled and unlabeled nodes. Self-training has emerged as a widely popular framework to leverage the abundance of unlabeled data, which expands the training set by assigning pseudo-labels to selected unlabeled nodes. Efforts have been made to develop various selection strategies based on confidence, information gain, etc. However, none of these methods takes into account the distribution shift between the training and testing node sets. The pseudo-labeling step may amplify this shift and even introduce new ones, hindering the effectiveness of self-training. Therefore, in this work, we explore the potential of explicitly bridging the distribution shift between the expanded training set and test set during self-training. To this end, we propose a novel Distribution-Consistent Graph Self-Training (DC-GST) framework to identify pseudo-labeled nodes that are both informative and capable of redeeming the distribution discrepancy and formulate it as a differentiable optimization task. A distribution-shift-aware edge predictor is further adopted to augment the graph and increase the model's generalizability in assigning pseudo labels. We evaluate our proposed method on four publicly available benchmark datasets and extensive experiments demonstrate that our framework consistently outperforms state-of-the-art baselines.|由于标记节点和未标记节点之间的监督不足和潜在的分布转移，少镜头节点分类对图神经网络(GNN)提出了严峻的挑战。自我训练已经成为一种广泛流行的利用大量未标记数据的框架，它通过为选定的未标记节点分配伪标记来扩展训练集。人们努力发展各种基于信心、获取信息等的选择策略。然而，这些方法都没有考虑训练和测试节点集之间的分布转移。伪标记步骤可能会放大这种转变，甚至引入新的转变，从而阻碍自我训练的有效性。因此，在这项工作中，我们探索了在自我训练过程中明确地桥接扩展训练集和测试集之间的分布转移的潜力。为此，我们提出了一种新的分布一致图自训练(DC-GST)框架来识别信息量大且能够弥补分布差异的伪标记节点，并将其表述为一个可微优化任务。进一步采用分布移位感知的边缘预测器来增强图形，提高模型在分配伪标签时的泛化能力。我们在四个公开可用的基准数据集上评估了我们提出的方法，大量的实验表明，我们的框架始终优于最先进的基准。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Distribution+Consistency+based+Self-Training+for+Graph+Neural+Networks+with+Sparse+Labels)|0| -|[FairIF: Boosting Fairness in Deep Learning via Influence Functions with Validation Set Sensitive Attributes](https://doi.org/10.1145/3616855.3635844)|Haonan Wang, Ziwei Wu, Jingrui He|Univ Illinois, Sch Informat Sci, Champaign, IL USA; Natl Univ Singapore, Sch Comp, Singapore, Singapore|Empirical loss minimization during machine learning training can inadvertently introduce bias, stemming from discrimination and societal prejudices present in the data. To address the shortcomings of traditional fair machine learning methods-which often rely on sensitive information of training data or mandate significant model alterations-we present FairIF, a unique two-stage training framework. Distinctly, FairIF enhances fairness by recalibrating training sample weights using the influence function. Notably, it employs sensitive information from a validation set, rather than the training set, to determine these weights. This approach accommodates situations with missing or inaccessible sensitive training data. Our FairIF ensures fairness across demographic groups by retraining models on the reweighted data. It stands out by offering a plug-and-play solution, obviating the need for changes in model architecture or the loss function. We demonstrate that the fairness performance of FairIF is guaranteed during testing with only a minimal impact on classification performance. Additionally, we analyze that our framework adeptly addresses issues like group size disparities, distribution shifts, and class size discrepancies. Empirical evaluations on three synthetic and five real-world datasets across six model architectures confirm FairIF's efficiency and scalability. The experimental results indicate superior fairness-utility trade-offs compared to other methods, regardless of bias types or architectural variations. Moreover, the adaptability of FairIF to utilize pretrained models for subsequent tasks and its capability to rectify unfairness originating during the pretraining phase are further validated through our experiments.|在机器学习训练期间，经验性损失最小化可能无意中引入偏见，这些偏见源于数据中存在的歧视和社会偏见。为了解决传统的公平机器学习方法的缺陷——这些方法通常依赖于敏感的训练数据信息或者要求重要的模型改变——我们提出了一个独特的两阶段训练框架 FairIF。显然，FairIF 通过使用影响函数重新校准训练样本权重来提高公平性。值得注意的是，它使用来自验证集而不是训练集的敏感信息来确定这些权重。这种方法适用于缺少或无法访问敏感训练数据的情况。我们的 FairIF 通过对重新加权数据的再培训模型来确保不同人口群体之间的公平性。它通过提供即插即用的解决方案而脱颖而出，避免了对模型架构或损失函数进行更改的需要。我们证明了在测试过程中，FairIF 的公平性能得到了保证，对分类性能的影响最小。此外，我们还分析了我们的框架能够很好地解决诸如团队规模差异、分布变化和班级规模差异等问题。通过对三个合成数据集和五个实际数据集在六个模型架构上的实验评估，证实了 FairIF 的有效性和可扩展性。实验结果表明，与其他方法相比，无论偏差类型或架构变化如何，公平-效用权衡优于其他方法。此外，通过实验进一步验证了 FairIF 对预训练模型用于后续任务的适应性以及对预训练阶段产生的不公平现象的纠正能力。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=FairIF:+Boosting+Fairness+in+Deep+Learning+via+Influence+Functions+with+Validation+Set+Sensitive+Attributes)|0| +|[FairIF: Boosting Fairness in Deep Learning via Influence Functions with Validation Set Sensitive Attributes](https://doi.org/10.1145/3616855.3635844)|Haonan Wang, Ziwei Wu, Jingrui He|Natl Univ Singapore, Sch Comp, Singapore, Singapore; Univ Illinois, Sch Informat Sci, Champaign, IL USA|Empirical loss minimization during machine learning training can inadvertently introduce bias, stemming from discrimination and societal prejudices present in the data. To address the shortcomings of traditional fair machine learning methods-which often rely on sensitive information of training data or mandate significant model alterations-we present FairIF, a unique two-stage training framework. Distinctly, FairIF enhances fairness by recalibrating training sample weights using the influence function. Notably, it employs sensitive information from a validation set, rather than the training set, to determine these weights. This approach accommodates situations with missing or inaccessible sensitive training data. Our FairIF ensures fairness across demographic groups by retraining models on the reweighted data. It stands out by offering a plug-and-play solution, obviating the need for changes in model architecture or the loss function. We demonstrate that the fairness performance of FairIF is guaranteed during testing with only a minimal impact on classification performance. Additionally, we analyze that our framework adeptly addresses issues like group size disparities, distribution shifts, and class size discrepancies. Empirical evaluations on three synthetic and five real-world datasets across six model architectures confirm FairIF's efficiency and scalability. The experimental results indicate superior fairness-utility trade-offs compared to other methods, regardless of bias types or architectural variations. Moreover, the adaptability of FairIF to utilize pretrained models for subsequent tasks and its capability to rectify unfairness originating during the pretraining phase are further validated through our experiments.|在机器学习训练期间，经验性损失最小化可能无意中引入偏见，这些偏见源于数据中存在的歧视和社会偏见。为了解决传统的公平机器学习方法的缺陷——这些方法通常依赖于敏感的训练数据信息或者要求重要的模型改变——我们提出了一个独特的两阶段训练框架 FairIF。显然，FairIF 通过使用影响函数重新校准训练样本权重来提高公平性。值得注意的是，它使用来自验证集而不是训练集的敏感信息来确定这些权重。这种方法适用于缺少或无法访问敏感训练数据的情况。我们的 FairIF 通过对重新加权数据的再培训模型来确保不同人口群体之间的公平性。它通过提供即插即用的解决方案而脱颖而出，避免了对模型架构或损失函数进行更改的需要。我们证明了在测试过程中，FairIF 的公平性能得到了保证，对分类性能的影响最小。此外，我们还分析了我们的框架能够很好地解决诸如团队规模差异、分布变化和班级规模差异等问题。通过对三个合成数据集和五个实际数据集在六个模型架构上的实验评估，证实了 FairIF 的有效性和可扩展性。实验结果表明，与其他方法相比，无论偏差类型或架构变化如何，公平-效用权衡优于其他方法。此外，通过实验进一步验证了 FairIF 对预训练模型用于后续任务的适应性以及对预训练阶段产生的不公平现象的纠正能力。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=FairIF:+Boosting+Fairness+in+Deep+Learning+via+Influence+Functions+with+Validation+Set+Sensitive+Attributes)|0| |[NeuralReconciler for Hierarchical Time Series Forecasting](https://doi.org/10.1145/3616855.3635806)|Shiyu Wang|Ant Grp, Hangzhou, Zhejiang, Peoples R China|Time series forecasting has wide-ranging applications in business intelligence, including predicting logistics demand and estimating power consumption in a smart grid, which subsequently facilitates decision-making processes. In many real-world scenarios, such as department sales of multiple Walmart stores across different locations, time series data possess hierarchical structures with non-linear and non-Gaussian properties. Thus, the task of leveraging structural information among hierarchical time series while learning from non-linear correlations and non-Gaussian data distributions becomes crucial to enhance prediction accuracy. This paper proposes a novel approach named NeuralReconciler for Hierarchical Time Series (HTS) prediction through trainable attention-based reconciliation and Normalizing Flow (NF). The latter is used to approximate the complex (usually non-Gaussian) data distribution for multivariate time series forecasting. To reconcile the HTS data, a new flexible reconciliation strategy via the attention-based encoder decoder neural network is proposed, which is distinct from current methods that rely on strong assumptions (e.g., all forecasts being unbiased estimates and the noise distribution being Gaussian). Furthermore, using the reparameterization trick, each independent component (i.e., forecasts via NF and attention-based reconciliation) is integrated into a trainable end-to-end model. Our proposed NeuralReconciler has been extensively experimented on real-world datasets and achieved consistent state-of-the-art performance compared to well-acknowledged and advanced baselines, with a 20% relative improvement on five benchmarks.|时间序列预测在商业智能中有着广泛的应用，包括预测物流需求和估计智能电网的功耗，从而促进决策过程。在许多实际场景中，例如不同地点的多个沃尔玛商店的部门销售，时间序列数据具有具有非线性和非高斯属性的层次结构。因此，利用层次时间序列之间的结构信息，同时学习非线性相关和非高斯数据分布的任务成为提高预测精度的关键。提出了一种基于注意力协调和归一化流(NF)的层次时间序列(HTS)预测神经协调器方法。后者用于逼近多变量时间序列预测的复杂(通常是非高斯)数据分布。为了协调高温超导数据，提出了一种新的基于注意的编码器解码器神经网络的柔性协调策略，该策略不同于目前依赖于强假设的方法(例如，所有的预测都是无偏估计，噪声分布是高斯分布)。此外，使用重新参数化技巧，每个独立的组成部分(即，通过 NF 和基于注意力的协调预测)被集成到一个可训练的端到端模型中。我们提出的 NeuralReconciler 已经在真实世界的数据集上进行了广泛的实验，与公认的和先进的基线相比，取得了一致的最先进的性能，相对于5个基准有20% 的改善。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=NeuralReconciler+for+Hierarchical+Time+Series+Forecasting)|0| -|[Follow the LIBRA: Guiding Fair Policy for Unified Impression Allocation via Adversarial Rewarding](https://doi.org/10.1145/3616855.3635756)|Xiaoyu Wang, Yonghui Guo, Bin Tan, Tao Yang, Dongbo Huang, Lan Xu, Hao Zhou, Xiangyang Li|Tencent, Shenzhen, Peoples R China; Tencent Co, Shenzhen, Peoples R China; Univ Sci & Technol China, Hefei, Peoples R China|The diverse advertiser demands (brand effects or immediate outcomes) lead to distinct selling (pre-agreed volumes with an underdelivery penalty or compete per auction) and pricing (fixed prices or varying bids) patterns in Guaranteed delivery (GD) and realtime bidding (RTB) advertising. This necessitates fair impression allocation to unify the two markets for promoting ad content diversity and overall revenue. Existing approaches often deprive RTB ads of equal exposure opportunities by prioritizing GD ads, and coarse-grained methods are inferior to 1) Ambiguous reward due to varied objectives and constraints of GD fulfillment and RTB utility, hindering measurement of each allocation's contribution to the global interests; 2) Intensified competition by the coexistence of GD and RTB ads, complicating their mutual relationships; 3) Policy degradation caused by evolving user traffic and bid landscape, requiring adaptivity to distribution shifts. We propose LIBRA, a generative-adversarial framework that unifies GD and RTB ads through request-level modeling. To guide the generative allocator, we solve convex optimization on historical data to derive hindsight optimal allocations that balance fairness and utility. We then train a discriminator to distinguish the generated actions from these solved latent expert policy's demonstrations, providing an integrated reward to align LIBRA with the optimal fair policy. LIBRA employs a self-attention encoder to capture the competitive relations among varying amounts of candidate ads per allocation. Further, it enhances the discriminator with information bottlenecks-based summarizer against overfitting to irrelevant distractors in the ad environment. LIBRA adopts a decoupled structure, where the offline discriminator continuously finetunes with newly-coming allocations and periodically guides the online allocation policy's updates to accommodate online dynamics. LIBRA has been deployed on the Tencent advertising system for over four months, with extensive experiments conducted. Online A/B tests demonstrate significant lifts in ad income (3.17%), overall click-through rate (1.56%), and cost-per-mille (3.20%), contributing a daily revenue increase of hundreds of thousands of RMB.|不同的广告客户需求(品牌效应或直接结果)导致不同的销售(预先商定的数量与交付不足的惩罚或竞争每拍卖)和定价(固定价格或不同出价)模式的保证交付(GD)和实时投标(RTB)广告。这需要公平的印象分配，以统一两个市场，促进广告内容的多样性和总体收入。现有的方法通常通过优先考虑 GD 广告来剥夺 RTB 广告的平等曝光机会，粗粒度的方法不如1)由于 GD 实现和 RTB 效用的不同目标和限制而产生的模糊奖励，阻碍了衡量每个分配对全球利益的贡献; 2)由于 GD 和 RTB 广告共存而加剧的竞争，使它们的相互关系复杂化; 3)由于用户流量和投标环境的变化而导致的政策退化，需要适应分配变化。我们提出了 LIBRA，一个通过请求级建模统一 GD 和 RTB 广告的生成对抗框架。为了指导生成分配器，我们解决历史数据的凸优化，得出事后的最优分配，平衡公平和效用。然后我们训练一个鉴别器来区分这些已解决的潜在专家政策的演示所产生的行为，提供一个综合的奖励来使 LIBRA 与最优公平政策保持一致。LIBRA 使用自我关注编码器来捕捉每次分配中不同数量的候选广告之间的竞争关系。进一步提高了基于信息瓶颈汇总器的识别能力，避免了广告环境中对不相关干扰物的过度拟合。LIBRA 采用解耦结构，离线鉴别器不断调整新的分配，并定期引导在线分配策略的更新以适应在线动态。LIBRA 已经在腾讯广告系统上部署了4个多月，并进行了广泛的实验。在线 a/b 测试显示，广告收入(3.17%)、总点进率(1.56%)和每公里成本(3.20%)都有显著提升，每天的收入增长达到数十万元人民币。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Follow+the+LIBRA:+Guiding+Fair+Policy+for+Unified+Impression+Allocation+via+Adversarial+Rewarding)|0| +|[Follow the LIBRA: Guiding Fair Policy for Unified Impression Allocation via Adversarial Rewarding](https://doi.org/10.1145/3616855.3635756)|Xiaoyu Wang, Yonghui Guo, Bin Tan, Tao Yang, Dongbo Huang, Lan Xu, Hao Zhou, Xiangyang Li|Tencent, Shenzhen, Peoples R China; Univ Sci & Technol China, Hefei, Peoples R China; Tencent Co, Shenzhen, Peoples R China|The diverse advertiser demands (brand effects or immediate outcomes) lead to distinct selling (pre-agreed volumes with an underdelivery penalty or compete per auction) and pricing (fixed prices or varying bids) patterns in Guaranteed delivery (GD) and realtime bidding (RTB) advertising. This necessitates fair impression allocation to unify the two markets for promoting ad content diversity and overall revenue. Existing approaches often deprive RTB ads of equal exposure opportunities by prioritizing GD ads, and coarse-grained methods are inferior to 1) Ambiguous reward due to varied objectives and constraints of GD fulfillment and RTB utility, hindering measurement of each allocation's contribution to the global interests; 2) Intensified competition by the coexistence of GD and RTB ads, complicating their mutual relationships; 3) Policy degradation caused by evolving user traffic and bid landscape, requiring adaptivity to distribution shifts. We propose LIBRA, a generative-adversarial framework that unifies GD and RTB ads through request-level modeling. To guide the generative allocator, we solve convex optimization on historical data to derive hindsight optimal allocations that balance fairness and utility. We then train a discriminator to distinguish the generated actions from these solved latent expert policy's demonstrations, providing an integrated reward to align LIBRA with the optimal fair policy. LIBRA employs a self-attention encoder to capture the competitive relations among varying amounts of candidate ads per allocation. Further, it enhances the discriminator with information bottlenecks-based summarizer against overfitting to irrelevant distractors in the ad environment. LIBRA adopts a decoupled structure, where the offline discriminator continuously finetunes with newly-coming allocations and periodically guides the online allocation policy's updates to accommodate online dynamics. LIBRA has been deployed on the Tencent advertising system for over four months, with extensive experiments conducted. Online A/B tests demonstrate significant lifts in ad income (3.17%), overall click-through rate (1.56%), and cost-per-mille (3.20%), contributing a daily revenue increase of hundreds of thousands of RMB.|不同的广告客户需求(品牌效应或直接结果)导致不同的销售(预先商定的数量与交付不足的惩罚或竞争每拍卖)和定价(固定价格或不同出价)模式的保证交付(GD)和实时投标(RTB)广告。这需要公平的印象分配，以统一两个市场，促进广告内容的多样性和总体收入。现有的方法通常通过优先考虑 GD 广告来剥夺 RTB 广告的平等曝光机会，粗粒度的方法不如1)由于 GD 实现和 RTB 效用的不同目标和限制而产生的模糊奖励，阻碍了衡量每个分配对全球利益的贡献; 2)由于 GD 和 RTB 广告共存而加剧的竞争，使它们的相互关系复杂化; 3)由于用户流量和投标环境的变化而导致的政策退化，需要适应分配变化。我们提出了 LIBRA，一个通过请求级建模统一 GD 和 RTB 广告的生成对抗框架。为了指导生成分配器，我们解决历史数据的凸优化，得出事后的最优分配，平衡公平和效用。然后我们训练一个鉴别器来区分这些已解决的潜在专家政策的演示所产生的行为，提供一个综合的奖励来使 LIBRA 与最优公平政策保持一致。LIBRA 使用自我关注编码器来捕捉每次分配中不同数量的候选广告之间的竞争关系。进一步提高了基于信息瓶颈汇总器的识别能力，避免了广告环境中对不相关干扰物的过度拟合。LIBRA 采用解耦结构，离线鉴别器不断调整新的分配，并定期引导在线分配策略的更新以适应在线动态。LIBRA 已经在腾讯广告系统上部署了4个多月，并进行了广泛的实验。在线 a/b 测试显示，广告收入(3.17%)、总点进率(1.56%)和每公里成本(3.20%)都有显著提升，每天的收入增长达到数十万元人民币。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Follow+the+LIBRA:+Guiding+Fair+Policy+for+Unified+Impression+Allocation+via+Adversarial+Rewarding)|0| |[Continuous-time Autoencoders for Regular and Irregular Time Series Imputation](https://doi.org/10.1145/3616855.3635831)|Hyowon Wi, Yehjin Shin, Noseong Park||Time series imputation is one of the most fundamental tasks for time series. Real-world time series datasets are frequently incomplete (or irregular with missing observations), in which case imputation is strongly required. Many different time series imputation methods have been proposed. Recent self-attention-based methods show the state-of-the-art imputation performance. However, it has been overlooked for a long time to design an imputation method based on continuous-time recurrent neural networks (RNNs), i.e., neural controlled differential equations (NCDEs). To this end, we redesign time series (variational) autoencoders based on NCDEs. Our method, called continuous-time autoencoder (CTA), encodes an input time series sample into a continuous hidden path (rather than a hidden vector) and decodes it to reconstruct and impute the input. In our experiments with 4 datasets and 19 baselines, our method shows the best imputation performance in almost all cases.|时间序列插补是时间序列最基本的任务之一。真实世界的时间序列数据集经常是不完整的(或不规则的，缺少观察值) ，在这种情况下强烈需要插补。人们提出了许多不同的时间序列插补方法。最近的基于自我注意的方法显示了最先进的插补性能。然而，基于连续时间递归神经网络(RNN)的插补方法，即神经控制微分方程(NCDE) ，长期以来一直被忽视。为此，我们重新设计了基于 NCDE 的时间序列(变分)自动编码器。我们的方法称为连续时间自动编码器(CTA) ，将输入的时间序列样本编码成一个连续的隐藏路径(而不是一个隐藏向量) ，并对其进行解码以重建和计算输入。在我们的4个数据集和19个基线的实验中，我们的方法在几乎所有情况下都显示了最佳的插补性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Continuous-time+Autoencoders+for+Regular+and+Irregular+Time+Series+Imputation)|0| |[Hierarchical Multimodal Pre-training for Visually Rich Webpage Understanding](https://doi.org/10.1145/3616855.3635753)|Hongshen Xu, Lu Chen, Zihan Zhao, Da Ma, Ruisheng Cao, Zichen Zhu, Kai Yu||The growing prevalence of visually rich documents, such as webpages and scanned/digital-born documents (images, PDFs, etc.), has led to increased interest in automatic document understanding and information extraction across academia and industry. Although various document modalities, including image, text, layout, and structure, facilitate human information retrieval, the interconnected nature of these modalities presents challenges for neural networks. In this paper, we introduce WebLM, a multimodal pre-training network designed to address the limitations of solely modeling text and structure modalities of HTML in webpages. Instead of processing document images as unified natural images, WebLM integrates the hierarchical structure of document images to enhance the understanding of markup-language-based documents. Additionally, we propose several pre-training tasks to model the interaction among text, structure, and image modalities effectively. Empirical results demonstrate that the pre-trained WebLM significantly surpasses previous state-of-the-art pre-trained models across several webpage understanding tasks. The pre-trained models and code are available at https://github.com/X-LANCE/weblm.|视觉丰富的文档(如网页和扫描/数字文档(图像、 PDF 等))日益流行，导致学术界和行业对自动文档理解和信息抽取的兴趣增加。尽管包括图像、文本、布局和结构在内的各种文档模式有助于人类信息检索，但这些模式的相互关联性对神经网络提出了挑战。本文介绍了 WebLM，这是一个多模式预训练网络，旨在解决网页中纯文本建模和 HTML 结构模式的局限性。WebLM 不再将文档图像作为统一的自然图像处理，而是整合了文档图像的层次结构，以增强对基于标记语言的文档的理解。此外，我们提出了几个预训练任务，以建模文本，结构和图像模式之间的交互作用有效。实证结果表明，预先训练的 WebLM 在几个网页理解任务中显著超过了先前最先进的预先训练的模型。预先训练的模型和代码可在 https://github.com/x-lance/weblm 获得。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Hierarchical+Multimodal+Pre-training+for+Visually+Rich+Webpage+Understanding)|0| -|[Towards Alignment-Uniformity Aware Representation in Graph Contrastive Learning](https://doi.org/10.1145/3616855.3635789)|Rong Yan, Peng Bao, Xiao Zhang, Zhongyi Liu, Hui Liu|Beijing Jiaotong Univ, Beijing, Peoples R China; TravelSky Technol Ltd, Key Lab Intelligent Passenger, Serv Civil Aviat, Beijing, Peoples R China|Graph Contrastive Learning (GCL) methods benefit from two key properties: alignment and uniformity, which encourage the representation of related objects together while pushing apart different objects. Most GCL methods aim to preserve alignment and uniformity through random graph augmentation strategies and indiscriminately negative sampling. However, their performance is highly sensitive to graph augmentation, which requires cumbersome trialand-error and expensive domain-specific knowledge as guidance. Besides, these methods perform negative sampling indiscriminately, which inevitably suffers from sampling bias, i.e., negative samples from the same class as the anchor. To remedy these issues, we propose a unified GCL framework towards Alignment-Uniformity Aware Representation learning (AUAR), which can achieve better alignment while improving uniformity without graph augmentation and negative sampling. Specifically, we propose intra- and inter-alignment loss to align the representations of the node with itself and its cluster centroid to maintain label-invariant. Furthermore, we introduce a uniformity loss with theoretical analysis, which pushes the representations of unrelated nodes from different classes apart and tends to provide informative variance from different classes. Extensive experiments demonstrate that our method gains better performance than existing GCL methods in node classification and clustering tasks across three widely-used datasets.|图形对比学习(GCL)方法受益于两个关键属性: 对齐和一致性，这两个属性鼓励相关对象一起表示，同时推开不同的对象。大多数 GCL 方法的目的是通过随机图增强策略和不加区分的负采样来保持对齐和一致性。然而，它们的性能对图增强非常敏感，这需要繁琐的试验和昂贵的特定领域的知识作为指导。此外，这些方法不加区分地进行负抽样，不可避免地会受到抽样偏差的影响，即来自与锚点同一类别的负抽样。为了解决这些问题，我们提出了一个统一的 GCL 框架来实现对齐-一致性感知表示学习(AUAR) ，该框架可以在不增加图增强和负抽样的情况下实现更好的对齐，同时提高一致性。具体来说，我们提出了内部和内部对齐丢失来对齐节点的表示和它自己以及它的聚类中心来保持标签不变性。此外，我们还引入了理论分析中的一致性损失，它将不同类别的不相关节点的表示分开，并倾向于提供来自不同类别的信息方差。大量的实验表明，在三个广泛使用的数据集上，我们的方法在节点分类和聚类任务方面比现有的 GCL 方法获得了更好的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Towards+Alignment-Uniformity+Aware+Representation+in+Graph+Contrastive+Learning)|0| +|[Towards Alignment-Uniformity Aware Representation in Graph Contrastive Learning](https://doi.org/10.1145/3616855.3635789)|Rong Yan, Peng Bao, Xiao Zhang, Zhongyi Liu, Hui Liu|TravelSky Technol Ltd, Key Lab Intelligent Passenger, Serv Civil Aviat, Beijing, Peoples R China; Beijing Jiaotong Univ, Beijing, Peoples R China|Graph Contrastive Learning (GCL) methods benefit from two key properties: alignment and uniformity, which encourage the representation of related objects together while pushing apart different objects. Most GCL methods aim to preserve alignment and uniformity through random graph augmentation strategies and indiscriminately negative sampling. However, their performance is highly sensitive to graph augmentation, which requires cumbersome trialand-error and expensive domain-specific knowledge as guidance. Besides, these methods perform negative sampling indiscriminately, which inevitably suffers from sampling bias, i.e., negative samples from the same class as the anchor. To remedy these issues, we propose a unified GCL framework towards Alignment-Uniformity Aware Representation learning (AUAR), which can achieve better alignment while improving uniformity without graph augmentation and negative sampling. Specifically, we propose intra- and inter-alignment loss to align the representations of the node with itself and its cluster centroid to maintain label-invariant. Furthermore, we introduce a uniformity loss with theoretical analysis, which pushes the representations of unrelated nodes from different classes apart and tends to provide informative variance from different classes. Extensive experiments demonstrate that our method gains better performance than existing GCL methods in node classification and clustering tasks across three widely-used datasets.|图形对比学习(GCL)方法受益于两个关键属性: 对齐和一致性，这两个属性鼓励相关对象一起表示，同时推开不同的对象。大多数 GCL 方法的目的是通过随机图增强策略和不加区分的负采样来保持对齐和一致性。然而，它们的性能对图增强非常敏感，这需要繁琐的试验和昂贵的特定领域的知识作为指导。此外，这些方法不加区分地进行负抽样，不可避免地会受到抽样偏差的影响，即来自与锚点同一类别的负抽样。为了解决这些问题，我们提出了一个统一的 GCL 框架来实现对齐-一致性感知表示学习(AUAR) ，该框架可以在不增加图增强和负抽样的情况下实现更好的对齐，同时提高一致性。具体来说，我们提出了内部和内部对齐丢失来对齐节点的表示和它自己以及它的聚类中心来保持标签不变性。此外，我们还引入了理论分析中的一致性损失，它将不同类别的不相关节点的表示分开，并倾向于提供来自不同类别的信息方差。大量的实验表明，在三个广泛使用的数据集上，我们的方法在节点分类和聚类任务方面比现有的 GCL 方法获得了更好的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Towards+Alignment-Uniformity+Aware+Representation+in+Graph+Contrastive+Learning)|0| |[GAP: A Grammar and Position-Aware Framework for Efficient Recognition of Multi-Line Mathematical Formulas](https://doi.org/10.1145/3616855.3635776)|Zhe Yang, Qi Liu, Kai Zhang, Shiwei Tong, Enhong Chen|Univ Sci & Technol China, Sch Comp Sci & Technol, Hefei, Anhui, Peoples R China|Formula recognition endeavors to automatically identify mathematical formulas from images. Currently, the Encoder-Decoder model has significantly advanced the translation from image to corresponding formula markups. Nonetheless, previous research primarily concentrated on single-line formula recognition, ignoring the recognition of multi-line formulas, which presents additional challenges such as more stringent grammatical restrictions and twodimensional positions. In this work, we present GAP (Grammar And Position-Aware formula recognition), a comprehensive framework designed to tackle the challenges in multi-line mathematical formula recognition. First, to overcome the limitations imposed by grammar, we design a novel Grammar Aware Contrastive Learning (GACL) module, integrating complex grammar rules into the transcription model through a contrastive learning mechanism. Furthermore, primitive contrastive learning lacks clear directions for comprehending grammar rules and can lead to unstable convergence or prolonged training cycles. To enhance training efficiency, we propose Rank-Based Sampling (RBS) specialized for multi-line formulas, which guides the learning process by the importance ranking of different grammar errors. Finally, spatial location information is critical considering the two-dimensional nature of multiline formulas. To aid the model in keeping track of that global information, we introduced a Visual Coverage (VC) mechanism that incorporates historical attention information into the image features via a parameter-free way. To validate the effectiveness of our GAP framework, we construct a new dataset Multi-Line containing 12,002 multi-line formulas and conduct extensive experiments to show the efficacy of our GAP framework in capturing grammatical rules, enhancing recognition accuracy, and enhancing training efficiency. Codes and datasets are available at https://github.com/Sinon02/GAP.|公式识别致力于从图像中自动识别数学公式。目前，编解码器模型已经大大提高了从图像到相应公式标记的转换。然而，以往的研究主要集中在单行公式的识别上，忽视了对多行公式的识别，这带来了更严格的语法限制和二维位置等额外的挑战。在这项工作中，我们提出了 GAP (语法和位置感知公式识别) ，一个全面的框架，旨在解决多线数学公式识别的挑战。首先，为了克服语法的局限性，我们设计了一个新的语法感知对比学习(GACL)模块，通过对比学习机制将复杂的语法规则集成到转录模型中。此外，原始对比学习缺乏理解语法规则的清晰方向，可能导致收敛不稳定或训练周期延长。为了提高训练效率，提出了一种基于排序的多行公式抽样方法(RBS) ，该方法通过对不同语法错误的重要性排序来指导学习过程。最后，考虑到多线性公式的二维性质，空间位置信息是至关重要的。为了帮助模型跟踪全局信息，我们引入了一种可视化覆盖(VC)机制，通过一种无参数的方式将历史注意力信息合并到图像特征中。为了验证我们的 GAP 框架的有效性，我们构建了一个包含12,002个多行公式的新数据集 Multi-Line，并进行了广泛的实验，以显示我们的 GAP 框架在捕获语法规则、提高识别准确性和提高训练效率方面的有效性。代码和数据集可在 https://github.com/sinon02/gap 获得。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=GAP:+A+Grammar+and+Position-Aware+Framework+for+Efficient+Recognition+of+Multi-Line+Mathematical+Formulas)|0| -|[Maximizing Malicious Influence in Node Injection Attack](https://doi.org/10.1145/3616855.3635790)|Xiao Zhang, Peng Bao, Shirui Pan|Beijing Jiaotong Univ, Beijing, Peoples R China; Griffith Univ, Brisbane, Australia|Graph neural networks (GNNs) have achieved impressive performance in various graph-related tasks. However, recent studies have found that GNNs are vulnerable to adversarial attacks. Node injection attacks (NIA) become an emerging scenario of graph adversarial attacks, where the attacks are performed by injecting malicious nodes into the original graph instead of directly modifying it. In this paper, we focus on a more realistic scenario of NIA, where the attacker is only allowed to inject a small number of nodes to degrade the performance of GNNs with very limited information. We analyze the susceptibility of nodes, and based on this we propose a global node injection attack framework, MaxiMal, to maximize malicious information under a strict black-box setting. MaxiMal first introduces a susceptible-reverse influence sampling strategy to select neighbor nodes that are able to spread malicious information widely. Then contrastive loss is introduced to optimize the objective by updating the edges and features of the injected nodes. Extensive experiments on three benchmark datasets demonstrate the superiority of our proposed MaxiMal over the state-of-the-art approaches.|图神经网络(GNN)在各种与图有关的任务中取得了令人印象深刻的性能。然而，最近的研究发现 GNN 很容易受到敌对攻击。节点注入攻击(NIA)是一种新兴的图形对抗性攻击方案，通过将恶意节点注入原始图形而不是直接修改原始图形来实施攻击。在本文中，我们着重于一个更加真实的 NIA 场景，在这个场景中，攻击者只允许注入少量的节点，以非常有限的信息降低 GNN 的性能。分析了节点的易感性，并在此基础上提出了一种全局节点注入攻击框架 MaxMal，该框架可以在严格的黑盒设置下最大化恶意信息。MaxMal 首先引入敏感-反向影响采样策略来选择能够广泛传播恶意信息的邻居节点。然后引入对比损失，通过更新注入节点的边缘和特征来优化目标。在三个基准数据集上的大量实验证明了我们提出的 MaxMal 相对于最先进的方法的优越性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Maximizing+Malicious+Influence+in+Node+Injection+Attack)|0| +|[Maximizing Malicious Influence in Node Injection Attack](https://doi.org/10.1145/3616855.3635790)|Xiao Zhang, Peng Bao, Shirui Pan|Griffith Univ, Brisbane, Australia; Beijing Jiaotong Univ, Beijing, Peoples R China|Graph neural networks (GNNs) have achieved impressive performance in various graph-related tasks. However, recent studies have found that GNNs are vulnerable to adversarial attacks. Node injection attacks (NIA) become an emerging scenario of graph adversarial attacks, where the attacks are performed by injecting malicious nodes into the original graph instead of directly modifying it. In this paper, we focus on a more realistic scenario of NIA, where the attacker is only allowed to inject a small number of nodes to degrade the performance of GNNs with very limited information. We analyze the susceptibility of nodes, and based on this we propose a global node injection attack framework, MaxiMal, to maximize malicious information under a strict black-box setting. MaxiMal first introduces a susceptible-reverse influence sampling strategy to select neighbor nodes that are able to spread malicious information widely. Then contrastive loss is introduced to optimize the objective by updating the edges and features of the injected nodes. Extensive experiments on three benchmark datasets demonstrate the superiority of our proposed MaxiMal over the state-of-the-art approaches.|图神经网络(GNN)在各种与图有关的任务中取得了令人印象深刻的性能。然而，最近的研究发现 GNN 很容易受到敌对攻击。节点注入攻击(NIA)是一种新兴的图形对抗性攻击方案，通过将恶意节点注入原始图形而不是直接修改原始图形来实施攻击。在本文中，我们着重于一个更加真实的 NIA 场景，在这个场景中，攻击者只允许注入少量的节点，以非常有限的信息降低 GNN 的性能。分析了节点的易感性，并在此基础上提出了一种全局节点注入攻击框架 MaxMal，该框架可以在严格的黑盒设置下最大化恶意信息。MaxMal 首先引入敏感-反向影响采样策略来选择能够广泛传播恶意信息的邻居节点。然后引入对比损失，通过更新注入节点的边缘和特征来优化目标。在三个基准数据集上的大量实验证明了我们提出的 MaxMal 相对于最先进的方法的优越性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Maximizing+Malicious+Influence+in+Node+Injection+Attack)|0| |[Interpretable Imitation Learning with Dynamic Causal Relations](https://doi.org/10.1145/3616855.3635827)|Tianxiang Zhao, Wenchao Yu, Suhang Wang, Lu Wang, Xiang Zhang, Yuncong Chen, Yanchi Liu, Wei Cheng, Haifeng Chen||Imitation learning, which learns agent policy by mimicking expert demonstration, has shown promising results in many applications such as medical treatment regimes and self-driving vehicles. However, it remains a difficult task to interpret control policies learned by the agent. Difficulties mainly come from two aspects: 1) agents in imitation learning are usually implemented as deep neural networks, which are black-box models and lack interpretability; 2) the latent causal mechanism behind agents' decisions may vary along the trajectory, rather than staying static throughout time steps. To increase transparency and offer better interpretability of the neural agent, we propose to expose its captured knowledge in the form of a directed acyclic causal graph, with nodes being action and state variables and edges denoting the causal relations behind predictions. Furthermore, we design this causal discovery process to be state-dependent, enabling it to model the dynamics in latent causal graphs. Concretely, we conduct causal discovery from the perspective of Granger causality and propose a self-explainable imitation learning framework, . The proposed framework is composed of three parts: a dynamic causal discovery module, a causality encoding module, and a prediction module, and is trained in an end-to-end manner. After the model is learned, we can obtain causal relations among states and action variables behind its decisions, exposing policies learned by it. Experimental results on both synthetic and real-world datasets demonstrate the effectiveness of the proposed in learning the dynamic causal graphs for understanding the decision-making of imitation learning meanwhile maintaining high prediction accuracy.|模仿学习通过模仿专家演示来学习智能体策略，在医疗体制和自动驾驶车辆等许多应用领域取得了良好的效果。但是，要解释代理所学到的控制策略仍然是一项困难的任务。难点主要来自两个方面: 1)模拟学习中的智能体通常被实现为深层神经网络，这是黑箱模型，缺乏可解释性; 2)智能体决策背后的潜在因果机制可能会随着轨迹而变化，而不是在整个时间步骤中保持静态。为了增加透明度并提供更好的神经代理的可解释性，我们建议以有向无环因果图的形式揭示其捕获的知识，其中节点是作用，状态变量和边表示预测背后的因果关系。此外，我们设计这个因果发现过程是依赖于状态的，使它能够模拟潜在因果图中的动态。具体来说，我们从格兰杰因果关系的角度进行因果发现，并提出了一个自我解释的模仿学习框架。该框架由动态因果发现模块、因果编码模块和预测模块三部分组成，并以端到端的方式进行训练。在学习模型之后，我们可以获得其决策背后的状态和行动变量之间的因果关系，揭示它所学习的政策。实验结果表明，该方法能够有效地学习动态因果图，从而理解模拟学习的决策过程，同时保持较高的预测精度。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Interpretable+Imitation+Learning+with+Dynamic+Causal+Relations)|0| |[RDGCN: Reinforced Dependency Graph Convolutional Network for Aspect-based Sentiment Analysis](https://doi.org/10.1145/3616855.3635775)|Xusheng Zhao, Hao Peng, Qiong Dai, Xu Bai, Huailiang Peng, Yanbing Liu, Qinglang Guo, Philip S. Yu||Aspect-based sentiment analysis (ABSA) is dedicated to forecasting the sentiment polarity of aspect terms within sentences. Employing graph neural networks to capture structural patterns from syntactic dependency parsing has been confirmed as an effective approach for boosting ABSA. In most works, the topology of dependency trees or dependency-based attention coefficients is often loosely regarded as edges between aspects and opinions, which can result in insufficient and ambiguous syntactic utilization. To address these problems, we propose a new reinforced dependency graph convolutional network (RDGCN) that improves the importance calculation of dependencies in both distance and type views. Initially, we propose an importance calculation criterion for the minimum distances over dependency trees. Under the criterion, we design a distance-importance function that leverages reinforcement learning for weight distribution search and dissimilarity control. Since dependency types often do not have explicit syntax like tree distances, we use global attention and mask mechanisms to design type-importance functions. Finally, we merge these weights and implement feature aggregation and classification. Comprehensive experiments on three popular datasets demonstrate the effectiveness of the criterion and importance functions. RDGCN outperforms state-of-the-art GNN-based baselines in all validations.|基于体的情感分析(ABSA)致力于预测句子中体词的情感极性。利用图形神经网络从句法依赖分析中获取结构模式已被证实是提高 ABSA 的有效途径。在大多数作品中，依赖树或基于依赖的注意系数的拓扑结构往往被松散地视为方面和观点之间的边缘，这可能导致句法利用的不足和歧义。为了解决这些问题，我们提出了一种新的增强依赖图卷积网络(RDGCN) ，它改进了距离视图和类型视图中依赖关系的重要性计算。首先，我们提出了一个依赖树上最小距离的重要计算准则。根据该准则，我们设计了一个距离重要性函数，利用强化学习进行权重分布搜索和差异控制。由于依赖类型通常没有像树距离这样明确的语法，所以我们使用全局注意力和掩码机制来设计类型重要性函数。最后，合并这些权重，实现特征的聚合和分类。通过对三个常用数据集的综合实验，验证了该判据和重要性函数的有效性。在所有验证中，RDGCN 的性能优于最先进的基于 GNN 的基线。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=RDGCN:+Reinforced+Dependency+Graph+Convolutional+Network+for+Aspect-based+Sentiment+Analysis)|0| -|[CreST: A Credible Spatiotemporal Learning Framework for Uncertainty-aware Traffic Forecasting](https://doi.org/10.1145/3616855.3635759)|Zhengyang Zhou, Jiahao Shi, Hongbo Zhang, Qiongyu Chen, Xu Wang, Hongyang Chen, Yang Wang|Zhejiang Lab, Hangzhou, Peoples R China; Univ Sci & Technol China USTC, Hefei, Peoples R China; Univ Sci & Technol China, Hefei, Peoples R China|Spatiotemporal traffic forecasting plays a critical role in intelligent transportation systems, which empowers diverse urban services. Existing traffic forecasting frameworks usually devise various learning strategies to capture spatiotemporal correlations from the perspective of volume itself. However, we argue that previous traffic predictions are still unreliable due to two aspects. First, the influences of context factor-wise interactions on dynamic region-wise correlations are under exploitation. Second, the dynamics induce the credibility issue of forecasting that has not been well-explored. In this paper, we exploit the informative traffic-related context factors to jointly tackle the dynamic regional heterogeneity and explain the stochasticity, towards a credible uncertainty-aware traffic forecasting. Specifically, to internalize the dynamic contextual influences into learning process, we design a context-cross relational embedding to capture interactions between each context, and generate virtual graph topology to dynamically relate pairwise regions with context embedding. To quantify the prediction credibility, we attribute data-side aleatoric uncertainty to contexts and re-utilize them for aleatoric uncertainty quantification. Then we couple a dual-pipeline learning with the same objective to produce the discrepancy of model outputs and quantify model-side epistemic uncertainty. These two uncertainties are fed through a spatiotemporal network for extracting uncertainty evolution patterns. Finally, comprehensive experiments and model deployments have corroborated the credibility of our framework.|时空交通预测在智能交通系统中起着至关重要的作用，为城市提供多样化的服务。现有的交通流量预测框架通常设计各种学习策略，从体积本身的角度来捕捉时空相关性。然而，由于两个方面的原因，我们认为以前的流量预测仍然是不可靠的。首先，上下文因素相互作用对动态区域相关性的影响正在研究之中。其次，这种动态导致预测的可信度问题尚未得到很好的探讨。本文利用信息化交通相关背景因素，共同解决动态区域异质性和随机性问题，从而实现可靠的不确定性交通预测。为了将动态语境的影响内在化到学习过程中，我们设计了一个跨语境的关系嵌入来捕获每个语境之间的交互，并生成虚拟图拓扑来动态关联成对区域与语境嵌入。为了量化预测的可信度，我们将数据边的风险不确定性归因于上下文，并重新利用上下文对风险不确定性进行量化。然后，我们耦合具有相同目标的双流水线学习来产生模型输出的差异和量化模型边的认知不确定性。这两个不确定性通过一个时空网络提取不确定性演化模式。最后，全面的实验和模型部署验证了我们的框架的可信性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=CreST:+A+Credible+Spatiotemporal+Learning+Framework+for+Uncertainty-aware+Traffic+Forecasting)|0| +|[CreST: A Credible Spatiotemporal Learning Framework for Uncertainty-aware Traffic Forecasting](https://doi.org/10.1145/3616855.3635759)|Zhengyang Zhou, Jiahao Shi, Hongbo Zhang, Qiongyu Chen, Xu Wang, Hongyang Chen, Yang Wang|Univ Sci & Technol China USTC, Hefei, Peoples R China; Univ Sci & Technol China, Hefei, Peoples R China; Zhejiang Lab, Hangzhou, Peoples R China|Spatiotemporal traffic forecasting plays a critical role in intelligent transportation systems, which empowers diverse urban services. Existing traffic forecasting frameworks usually devise various learning strategies to capture spatiotemporal correlations from the perspective of volume itself. However, we argue that previous traffic predictions are still unreliable due to two aspects. First, the influences of context factor-wise interactions on dynamic region-wise correlations are under exploitation. Second, the dynamics induce the credibility issue of forecasting that has not been well-explored. In this paper, we exploit the informative traffic-related context factors to jointly tackle the dynamic regional heterogeneity and explain the stochasticity, towards a credible uncertainty-aware traffic forecasting. Specifically, to internalize the dynamic contextual influences into learning process, we design a context-cross relational embedding to capture interactions between each context, and generate virtual graph topology to dynamically relate pairwise regions with context embedding. To quantify the prediction credibility, we attribute data-side aleatoric uncertainty to contexts and re-utilize them for aleatoric uncertainty quantification. Then we couple a dual-pipeline learning with the same objective to produce the discrepancy of model outputs and quantify model-side epistemic uncertainty. These two uncertainties are fed through a spatiotemporal network for extracting uncertainty evolution patterns. Finally, comprehensive experiments and model deployments have corroborated the credibility of our framework.|时空交通预测在智能交通系统中起着至关重要的作用，为城市提供多样化的服务。现有的交通流量预测框架通常设计各种学习策略，从体积本身的角度来捕捉时空相关性。然而，由于两个方面的原因，我们认为以前的流量预测仍然是不可靠的。首先，上下文因素相互作用对动态区域相关性的影响正在研究之中。其次，这种动态导致预测的可信度问题尚未得到很好的探讨。本文利用信息化交通相关背景因素，共同解决动态区域异质性和随机性问题，从而实现可靠的不确定性交通预测。为了将动态语境的影响内在化到学习过程中，我们设计了一个跨语境的关系嵌入来捕获每个语境之间的交互，并生成虚拟图拓扑来动态关联成对区域与语境嵌入。为了量化预测的可信度，我们将数据边的风险不确定性归因于上下文，并重新利用上下文对风险不确定性进行量化。然后，我们耦合具有相同目标的双流水线学习来产生模型输出的差异和量化模型边的认知不确定性。这两个不确定性通过一个时空网络提取不确定性演化模式。最后，全面的实验和模型部署验证了我们的框架的可信性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=CreST:+A+Credible+Spatiotemporal+Learning+Framework+for+Uncertainty-aware+Traffic+Forecasting)|0| |[Pitfalls in Link Prediction with Graph Neural Networks: Understanding the Impact of Target-link Inclusion & Better Practices](https://doi.org/10.1145/3616855.3635786)|Jing Zhu, Yuhang Zhou, Vassilis N. Ioannidis, Shengyi Qian, Wei Ai, Xiang Song, Danai Koutra||Gait recognition is one of the most critical long-distance identification technologies and increasingly gains popularity in both research and industry communities. Despite the significant progress made in indoor datasets, much evidence shows that gait recognition techniques perform poorly in the wild. More importantly, we also find that some conclusions drawn from indoor datasets cannot be generalized to real applications. Therefore, the primary goal of this paper is to present a comprehensive benchmark study for better practicality rather than only a particular model for better performance. To this end, we first develop a flexible and efficient gait recognition codebase named OpenGait. Based on OpenGait, we deeply revisit the recent development of gait recognition by re-conducting the ablative experiments. Encouragingly,we detect some unperfect parts of certain prior woks, as well as new insights. Inspired by these discoveries, we develop a structurally simple, empirically powerful, and practically robust baseline model, GaitBase. Experimentally, we comprehensively compare GaitBase with many current gait recognition methods on multiple public datasets, and the results reflect that GaitBase achieves significantly strong performance in most cases regardless of indoor or outdoor situations. Code is available at https://github.com/ShiqiYu/OpenGait.|步态识别是最关键的远程识别技术之一，在研究和工业界越来越受欢迎。尽管在室内数据集方面取得了重大进展，但大量证据表明，步态识别技术在野外的表现较差。更重要的是，我们还发现，从室内数据集中得出的一些结论不能推广到实际应用中。因此，本文的主要目标是提出一个更好的实用性的全面的基准研究，而不仅仅是一个特定的模型，以获得更好的性能。为此，我们首先开发了一个灵活高效的步态识别代码库 OpenGait。基于 OpenGait，我们重新进行烧蚀实验，深入回顾了近年来步态识别的发展。令人鼓舞的是，我们发现一些不完美的部分，某些以前的工作，以及新的见解。受到这些发现的启发，我们开发了一个结构简单、经验强大、实际可靠的基线模型 GaitBase。在实验上，我们全面比较了 GaitBase 和现有的多个公共数据集上的步态识别方法，结果表明 GaitBase 在大多数情况下无论室内还是室外都取得了显著的性能。密码可于 https://github.com/shiqiyu/opengait 索取。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Pitfalls+in+Link+Prediction+with+Graph+Neural+Networks:+Understanding+the+Impact+of+Target-link+Inclusion+&+Better+Practices)|0| |[MultiSPANS: A Multi-range Spatial-Temporal Transformer Network for Traffic Forecast via Structural Entropy Optimization](https://doi.org/10.1145/3616855.3635820)|Dongcheng Zou, Senzhang Wang, Xuefeng Li, Hao Peng, Yuandong Wang, Chunyang Liu, Kehua Sheng, Bo Zhang||Traffic forecasting is a complex multivariate time-series regression task of paramount importance for traffic management and planning. However, existing approaches often struggle to model complex multi-range dependencies using local spatiotemporal features and road network hierarchical knowledge. To address this, we propose MultiSPANS. First, considering that an individual recording point cannot reflect critical spatiotemporal local patterns, we design multi-filter convolution modules for generating informative ST-token embeddings to facilitate attention computation. Then, based on ST-token and spatial-temporal position encoding, we employ the Transformers to capture long-range temporal and spatial dependencies. Furthermore, we introduce structural entropy theory to optimize the spatial attention mechanism. Specifically, The structural entropy minimization algorithm is used to generate optimal road network hierarchies, i.e., encoding trees. Based on this, we propose a relative structural entropy-based position encoding and a multi-head attention masking scheme based on multi-layer encoding trees. Extensive experiments demonstrate the superiority of the presented framework over several state-of-the-art methods in real-world traffic datasets, and the longer historical windows are effectively utilized. The code is available at https://github.com/SELGroup/MultiSPANS.|交通量预测是一个复杂的多元时间序列回归任务，对交通管理和规划至关重要。然而，现有的方法往往难以利用局部时空特征和道路网层次知识建模复杂的多范围依赖关系。为了解决这个问题，我们提出了 MultiSPANS。首先，考虑到单个记录点不能反映关键的时空局部模式，我们设计了多滤波卷积模块来生成信息性 ST 令牌嵌入，以方便注意力计算。然后，基于 ST 标记和空间-时间位置编码，我们使用变形金刚来捕获长距离的时间和空间依赖。在此基础上，引入结构熵理论对空间注意机制进行优化。具体而言，采用结构熵最小化算法生成最优路网层次结构，即编码树。在此基础上，提出了一种基于相对结构熵的位置编码和基于多层编码树的多头注意掩蔽方案。通过大量的实验证明了该框架相对于现实世界交通数据集中的几种最新方法的优越性，并且有效地利用了较长的历史窗口。密码可在 https://github.com/selgroup/multispans 查阅。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=MultiSPANS:+A+Multi-range+Spatial-Temporal+Transformer+Network+for+Traffic+Forecast+via+Structural+Entropy+Optimization)|0| -|[WordGraph: A Python Package for Reconstructing Interactive Causal Graphical Models from Text Data](https://doi.org/10.1145/3616855.3635698)|Amine Ferdjaoui, Séverine Affeldt, Mohamed Nadif|Univ Paris Cite, SogetiLabs, Paris, France; Univ Paris Cite, Ctr Borelli UMR 9010, Paris, France|We present WordGraph, a Python package for exploring the topics of documents corpora. WordGraph provides causal graphical models from text data vocabulary and proposes interactive visualizations of terms networks. Our ease-to-use package is provided with a prebuilt pipeline to access the main modules through jupyter widgets. It results in the encapsulation of a whole vocabulary exploration process within a single jupyter notebook cell, with straightforward parameters settings and interactive plots. WordGraph pipeline is fully customizable by adding/removing widgets or changing default parameters. To assist users with no background in Python nor jupyter notebook, but willing to explore large corpora topics, we also propose an automatic dashboard generation from the customizable jupyter notebook pipeline in a web application style. WordGraph is available through a GitHub repository.|我们介绍 WordGraph，这是一个用于探索文档语料库主题的 Python 包。WordGraph 从文本数据词汇中提供因果图形模型，并提出术语网络的交互式可视化。我们的易于使用的软件包提供了一个预构建的管道，可以通过 jupyter 小部件访问主要模块。它将整个词汇探索过程封装在一个单独的木星笔记本电脑单元中，具有简单的参数设置和交互式图形。通过添加/删除小部件或更改默认参数，WordGraph 管道是完全可定制的。为了帮助那些既没有 Python 背景，也没有 Jupyter 笔记本背景，但又愿意探索大型语料库主题的用户，我们还提议以 Web 应用程序风格从可定制的 Jupyter 笔记本管道自动生成仪表板。WordGraph 可以通过 GitHub 存储库获得。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=WordGraph:+A+Python+Package+for+Reconstructing+Interactive+Causal+Graphical+Models+from+Text+Data)|0| -|[EvidenceQuest: An Interactive Evidence Discovery System for Explainable Artificial Intelligence](https://doi.org/10.1145/3616855.3635697)|Ambreen Hanif, Amin Beheshti, Xuyun Zhang, Steven Wood, Boualem Benatallah, EuJin Foo|Dublin City Univ, Dublin, Ireland; Prospa, Sydney, NSW, Australia; Macquarie Univ, Sydney, NSW, Australia|Explainable Artificial Intelligence (XAI) aims to make artificial intelligence (AI) systems transparent and understandable to humans, providing clear explanations for the decisions made by AI models. This paper presents a novel pipeline and a digital dashboard that provides a user-friendly platform for interpreting the results of machine learning algorithms using XAI technology. The dashboard utilizes evidence-based design principles to deliver information clearly and concisely, enabling users to better understand the decisions made by their algorithms. We integrate XAI services into the dashboard to explain the algorithm's predictions, allowing users to understand howtheir models function and make informed decisions. We demonstrate a motivating scenario in banking and present how the proposed system enhances transparency and accountability and improves trust in the technology.|可解释人工智能(XAI)旨在使人工智能(AI)系统对人类透明、易懂，为 AI 模型的决策提供清晰的解释。本文提出了一种新的流水线和数字仪表板，为使用 XAI 技术解释机器学习算法的结果提供了一个用户友好的平台。仪表板利用基于证据的设计原则来清晰、简洁地传递信息，使用户能够更好地理解他们的算法所做的决策。我们将 XAI 服务集成到仪表板中，以解释算法的预测，使用户能够理解他们的模型是如何工作的，并做出明智的决策。我们展示了银行业的激励情景，并介绍了拟议的系统如何增强透明度和问责制，并提高对技术的信任。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=EvidenceQuest:+An+Interactive+Evidence+Discovery+System+for+Explainable+Artificial+Intelligence)|0| +|[WordGraph: A Python Package for Reconstructing Interactive Causal Graphical Models from Text Data](https://doi.org/10.1145/3616855.3635698)|Amine Ferdjaoui, Séverine Affeldt, Mohamed Nadif|Univ Paris Cite, Ctr Borelli UMR 9010, Paris, France; Univ Paris Cite, SogetiLabs, Paris, France|We present WordGraph, a Python package for exploring the topics of documents corpora. WordGraph provides causal graphical models from text data vocabulary and proposes interactive visualizations of terms networks. Our ease-to-use package is provided with a prebuilt pipeline to access the main modules through jupyter widgets. It results in the encapsulation of a whole vocabulary exploration process within a single jupyter notebook cell, with straightforward parameters settings and interactive plots. WordGraph pipeline is fully customizable by adding/removing widgets or changing default parameters. To assist users with no background in Python nor jupyter notebook, but willing to explore large corpora topics, we also propose an automatic dashboard generation from the customizable jupyter notebook pipeline in a web application style. WordGraph is available through a GitHub repository.|我们介绍 WordGraph，这是一个用于探索文档语料库主题的 Python 包。WordGraph 从文本数据词汇中提供因果图形模型，并提出术语网络的交互式可视化。我们的易于使用的软件包提供了一个预构建的管道，可以通过 jupyter 小部件访问主要模块。它将整个词汇探索过程封装在一个单独的木星笔记本电脑单元中，具有简单的参数设置和交互式图形。通过添加/删除小部件或更改默认参数，WordGraph 管道是完全可定制的。为了帮助那些既没有 Python 背景，也没有 Jupyter 笔记本背景，但又愿意探索大型语料库主题的用户，我们还提议以 Web 应用程序风格从可定制的 Jupyter 笔记本管道自动生成仪表板。WordGraph 可以通过 GitHub 存储库获得。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=WordGraph:+A+Python+Package+for+Reconstructing+Interactive+Causal+Graphical+Models+from+Text+Data)|0| +|[EvidenceQuest: An Interactive Evidence Discovery System for Explainable Artificial Intelligence](https://doi.org/10.1145/3616855.3635697)|Ambreen Hanif, Amin Beheshti, Xuyun Zhang, Steven Wood, Boualem Benatallah, EuJin Foo|Macquarie Univ, Sydney, NSW, Australia; Prospa, Sydney, NSW, Australia; Dublin City Univ, Dublin, Ireland|Explainable Artificial Intelligence (XAI) aims to make artificial intelligence (AI) systems transparent and understandable to humans, providing clear explanations for the decisions made by AI models. This paper presents a novel pipeline and a digital dashboard that provides a user-friendly platform for interpreting the results of machine learning algorithms using XAI technology. The dashboard utilizes evidence-based design principles to deliver information clearly and concisely, enabling users to better understand the decisions made by their algorithms. We integrate XAI services into the dashboard to explain the algorithm's predictions, allowing users to understand howtheir models function and make informed decisions. We demonstrate a motivating scenario in banking and present how the proposed system enhances transparency and accountability and improves trust in the technology.|可解释人工智能(XAI)旨在使人工智能(AI)系统对人类透明、易懂，为 AI 模型的决策提供清晰的解释。本文提出了一种新的流水线和数字仪表板，为使用 XAI 技术解释机器学习算法的结果提供了一个用户友好的平台。仪表板利用基于证据的设计原则来清晰、简洁地传递信息，使用户能够更好地理解他们的算法所做的决策。我们将 XAI 服务集成到仪表板中，以解释算法的预测，使用户能够理解他们的模型是如何工作的，并做出明智的决策。我们展示了银行业的激励情景，并介绍了拟议的系统如何增强透明度和问责制，并提高对技术的信任。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=EvidenceQuest:+An+Interactive+Evidence+Discovery+System+for+Explainable+Artificial+Intelligence)|0| |[Ginkgo-P: General Illustrations of Knowledge Graphs for Openness as a Platform](https://doi.org/10.1145/3616855.3635701)|Blaine Hill, Lihui Liu, Hanghang Tong|Univ Illinois, Dept Comp Sci, Urbana, IL 61801 USA|Accessibility and openness are two of the most important factors in motivating AI and Web research. One example is as costs to train and deploy large Knowledge Graph (KG) systems increases, valuable auxiliary features such as visualization, explainability, and automation are often overlooked, diminishing impact and popularity. Furthermore, current KG research has undergone a vicissitude to become convoluted and abstract, dissuading collaboration. To this end, we present Ginkgo-P, a platform to automatically illustrate any KG algorithm with nothing but a script and a data file. Additionally, Ginkgo-P elucidates modern KG research on the UMLS dataset with interactive demonstrations on four categories: KG Node Recommendation, KG Completion, KG Question Answering, and KG Reinforcement Learning. These categories and their many applications are increasingly ubiquitous yet lack both introductory and advanced resources to accelerate interest and contributions: with just a few clicks, our demonstration addresses this by providing an open platform for users to integrate individual KG algorithms. The source code for Ginkgo-P is available: we hope that it will propel future KG systems to become more accessible as an open source project.|可访问性和开放性是激发人工智能和网络研究的两个最重要的因素。其中一个例子是，随着培训和部署大型知识图(KG)系统的成本增加，可视化、可解释性和自动化等有价值的辅助特性常常被忽视，影响力和受欢迎程度降低。此外，当前幼儿园的研究也经历了一个变迁，变得复杂而抽象，阻碍了合作。为此，我们提出了银杏 -P，一个平台，自动说明任何 KG 算法，只有一个脚本和数据文件。此外，银杏 -P 阐述了现代幼稚园对 UMLS 数据集的研究与交互式示范的四个类别: 幼稚园节点推荐，幼稚园完成，幼稚园问题回答和幼稚园强化学习。这些类别和它们的许多应用程序越来越普遍，但缺乏引入性和先进的资源来加速兴趣和贡献: 只需几次点击，我们的演示通过为用户提供一个开放平台来集成各个 KG 算法来解决这个问题。Ginkgo-P 的源代码是可用的: 我们希望它将推动未来的 KG 系统作为一个开源项目变得更容易访问。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Ginkgo-P:+General+Illustrations+of+Knowledge+Graphs+for+Openness+as+a+Platform)|0| |[Real-time E-bike Route Planning with Battery Range Prediction](https://doi.org/10.1145/3616855.3635696)|Zhao Li, Guoqi Ren, Yongchun Gu, Siwei Zhou, Xuanwu Liu, Jiaming Huang, Ming Li||||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Real-time+E-bike+Route+Planning+with+Battery+Range+Prediction)|0| |[An Interpretable Brain Graph Contrastive Learning Framework for Brain Disorder Analysis](https://doi.org/10.1145/3616855.3635695)|Xuexiong Luo, Guangwei Dong, Jia Wu, Amin Beheshti, Jian Yang, Shan Xue||||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=An+Interpretable+Brain+Graph+Contrastive+Learning+Framework+for+Brain+Disorder+Analysis)|0| |[Future Timelines: Extraction and Visualization of Future-related Content From News Articles](https://doi.org/10.1145/3616855.3635693)|Juwal Regev, Adam Jatowt, Michael Färber||||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Future+Timelines:+Extraction+and+Visualization+of+Future-related+Content+From+News+Articles)|0| |[Temporal Graph Analysis with TGX](https://doi.org/10.1145/3616855.3635694)|Razieh Shirzadkhani, Shenyang Huang, Elahe Kooshafar, Reihaneh Rabbany, Farimah Poursafaei||||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Temporal+Graph+Analysis+with+TGX)|0| |[A Scalable Open-Source System for Segmenting Urban Areas with Road Networks](https://doi.org/10.1145/3616855.3635703)|Ming Zhang, Yanyan Li, Jianguo Duan, Jizhou Huang, Jingbo Zhou||||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Scalable+Open-Source+System+for+Segmenting+Urban+Areas+with+Road+Networks)|0| -|[Some Useful Things to Know When Combining IR and NLP: The Easy, the Hard and the Ugly](https://doi.org/10.1145/3616855.3636452)|Omar Alonso, Kenneth Church|Amazon, Santa Clara, CA 95054 USA; Northeastern Univ, San Jose, CA USA|Deep nets such as GPT are at the core of the current advances in many systems and applications. Things are moving very fast, and it appears that techniques are out of date within weeks. How can we take advantage of new discoveries and incorporate them into our existing work? Are these radical new developments, repetitions of older concepts, or both? In this tutorial, we aim to bring interested researchers and practitioners up to speed on the recent and ongoing techniques around ML and Deep learning in the context of IR and NLP. Additionally, our goal is to clarify terminology, emphasize fundamentals, and outline new research opportunities.|像 GPT 这样的深网是当前许多系统和应用进展的核心。事情发展得非常快，而且技术似乎在几周内就过时了。我们如何利用新的发现，并将其纳入我们现有的工作？这些是激进的新发展、旧观念的重复，还是两者兼而有之？在本教程中，我们的目的是让感兴趣的研究人员和从业人员加快最近和正在进行的技术在机器学习和深度学习的背景下的 IR 和 NLP。此外，我们的目标是澄清术语，强调基本原理，并概述新的研究机会。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Some+Useful+Things+to+Know+When+Combining+IR+and+NLP:+The+Easy,+the+Hard+and+the+Ugly)|0| +|[Some Useful Things to Know When Combining IR and NLP: The Easy, the Hard and the Ugly](https://doi.org/10.1145/3616855.3636452)|Omar Alonso, Kenneth Church|Northeastern Univ, San Jose, CA USA; Amazon, Santa Clara, CA 95054 USA|Deep nets such as GPT are at the core of the current advances in many systems and applications. Things are moving very fast, and it appears that techniques are out of date within weeks. How can we take advantage of new discoveries and incorporate them into our existing work? Are these radical new developments, repetitions of older concepts, or both? In this tutorial, we aim to bring interested researchers and practitioners up to speed on the recent and ongoing techniques around ML and Deep learning in the context of IR and NLP. Additionally, our goal is to clarify terminology, emphasize fundamentals, and outline new research opportunities.|像 GPT 这样的深网是当前许多系统和应用进展的核心。事情发展得非常快，而且技术似乎在几周内就过时了。我们如何利用新的发现，并将其纳入我们现有的工作？这些是激进的新发展、旧观念的重复，还是两者兼而有之？在本教程中，我们的目的是让感兴趣的研究人员和从业人员加快最近和正在进行的技术在机器学习和深度学习的背景下的 IR 和 NLP。此外，我们的目标是澄清术语，强调基本原理，并概述新的研究机会。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Some+Useful+Things+to+Know+When+Combining+IR+and+NLP:+The+Easy,+the+Hard+and+the+Ugly)|0| |[Introduction to Responsible AI](https://doi.org/10.1145/3616855.3636455)|Ricardo BaezaYates||||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Introduction+to+Responsible+AI)|0| |[Towards Trustworthy Large Language Models](https://doi.org/10.1145/3616855.3636454)|Sanmi Koyejo, Bo Li||||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Towards+Trustworthy+Large+Language+Models)|0| |[Strategic ML: How to Learn With Data That 'Behaves'](https://doi.org/10.1145/3616855.3636453)|Nir Rosenfeld||||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Strategic+ML:+How+to+Learn+With+Data+That+'Behaves')|0| diff --git a/papers/www/www2023.md b/papers/www/www2023.md index f1e7a432..cd17aec9 100644 --- a/papers/www/www2023.md +++ b/papers/www/www2023.md @@ -4,410 +4,410 @@ |---|---|---|---|---|---|---| |[Automated Ontology Evaluation: Evaluating Coverage and Correctness using a Domain Corpus](https://doi.org/10.1145/3543873.3587617)|Antonio Zaitoun, Tomer Sagi, Katja Hose|University of Haifa, Israel; Aalborg University, Denmark; Aalborg University, Denmark and TU Wien, Austria|Ontologies conceptualize domains and are a crucial part of web semantics and information systems. However, re-using an existing ontology for a new task requires a detailed evaluation of the candidate ontology as it may cover only a subset of the domain concepts, contain information that is redundant or misleading, and have inaccurate relations and hierarchies between concepts. Manual evaluation of large and complex ontologies is a tedious task. Thus, a few approaches have been proposed for automated evaluation, ranging from concept coverage to ontology generation from a corpus. Existing approaches, however, are limited by their dependence on external structured knowledge sources, such as a thesaurus, as well as by their inability to evaluate semantic relationships. In this paper, we propose a novel framework to automatically evaluate the domain coverage and semantic correctness of existing ontologies based on domain information derived from text. The approach uses a domain-tuned named-entity-recognition model to extract phrasal concepts. The extracted concepts are then used as a representation of the domain against which we evaluate the candidate ontology’s concepts. We further employ a domain-tuned language model to determine the semantic correctness of the candidate ontology’s relations. We demonstrate our automated approach on several large ontologies from the oceanographic domain and show its agreement with a manual evaluation by domain experts and its superiority over the state-of-the-art.|本体概念化领域，是网络语义和信息系统的重要组成部分。然而，在新任务中重新使用现有的本体需要对候选本体进行详细的评估，因为它可能只涉及领域概念的一个子集，包含冗余或误导的信息，并且概念之间的关系和层次结构不准确。手工评估大型和复杂的本体是一项繁琐的任务。因此，提出了一些自动评估的方法，从概念覆盖到从语料库中生成本体。然而，现有的方法受到依赖于外部结构化知识源(如同义词表)以及无法评估语义关系的限制。本文提出了一种基于文本的领域信息自动评估现有本体的领域覆盖率和语义正确性的框架。该方法使用一个域调整的命名实体识别模型来提取短语概念。然后将提取的概念用作领域的表示，我们根据这个表示来评估候选本体的概念。我们进一步使用领域调优的语言模型来确定候选本体关系的语义正确性。我们展示了我们的自动化方法从海洋学领域的几个大的本体论，并表明其与领域专家的手工评估的一致性及其优越性的最新水平。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Automated+Ontology+Evaluation:+Evaluating+Coverage+and+Correctness+using+a+Domain+Corpus)|2| |[Reinforcement Learning-based Counter-Misinformation Response Generation: A Case Study of COVID-19 Vaccine Misinformation](https://doi.org/10.1145/3543507.3583388)|Bing He, Mustaque Ahamad, Srijan Kumar|Georgia Institute of Technology, USA|The spread of online misinformation threatens public health, democracy, and the broader society. While professional fact-checkers form the first line of defense by fact-checking popular false claims, they do not engage directly in conversations with misinformation spreaders. On the other hand, non-expert ordinary users act as eyes-on-the-ground who proactively counter misinformation -- recent research has shown that 96% counter-misinformation responses are made by ordinary users. However, research also found that 2/3 times, these responses are rude and lack evidence. This work seeks to create a counter-misinformation response generation model to empower users to effectively correct misinformation. This objective is challenging due to the absence of datasets containing ground-truth of ideal counter-misinformation responses, and the lack of models that can generate responses backed by communication theories. In this work, we create two novel datasets of misinformation and counter-misinformation response pairs from in-the-wild social media and crowdsourcing from college-educated students. We annotate the collected data to distinguish poor from ideal responses that are factual, polite, and refute misinformation. We propose MisinfoCorrect, a reinforcement learning-based framework that learns to generate counter-misinformation responses for an input misinformation post. The model rewards the generator to increase the politeness, factuality, and refutation attitude while retaining text fluency and relevancy. Quantitative and qualitative evaluation shows that our model outperforms several baselines by generating high-quality counter-responses. This work illustrates the promise of generative text models for social good -- here, to help create a safe and reliable information ecosystem. The code and data is accessible on https://github.com/claws-lab/MisinfoCorrect.|网上虚假信息的传播威胁着公众健康、民主政体以及更广泛的社会。虽然专业的事实核查人员通过事实核查流行的虚假说法构成了第一道防线，但他们并不直接参与与错误信息传播者的对话。另一方面，非专家的普通用户充当了主动反错误信息的实地眼睛——最近的研究表明，96% 的反错误信息反应是由普通用户做出的。然而，研究还发现，有2/3的情况下，这些回答是粗鲁的，缺乏证据。这项工作旨在创建一个反错误信息响应生成模型，使用户能够有效地纠正错误信息。这一目标具有挑战性，因为缺乏包含理想的反错误信息反应的地面真相的数据集，以及缺乏能够产生由传播理论支持的反应的模型。在这项工作中，我们创建了两个新的数据集的错误信息和反错误信息的反应对野生社会媒体和大学生众包。我们对收集到的数据进行注释，以区分差异和理想的反应，这些反应是事实的、礼貌的和反驳错误信息的。我们提出一个基于强化学习的错误信息纠正框架，学习为输入错误信息的帖子产生反错误信息响应。该模型在保持文本流畅性和相关性的同时，奖励生成者增加礼貌、事实和反驳态度。定量和定性评估表明，我们的模型通过产生高质量的反响优于几个基线。这项工作说明了生成文本模型对社会公益的承诺——在这里，帮助创建一个安全可靠的信息生态系统。代码和数据可在 https://github.com/claws-lab/misinfocorrect 上查阅。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Reinforcement+Learning-based+Counter-Misinformation+Response+Generation:+A+Case+Study+of+COVID-19+Vaccine+Misinformation)|2| -|[A Concept Knowledge Graph for User Next Intent Prediction at Alipay](https://doi.org/10.1145/3543873.3587308)|Yacheng He, Qianghuai Jia, Lin Yuan, Ruopeng Li, Yixin Ou, Ningyu Zhang|Zhejiang University, China; Ant Group, China|This paper illustrates the technologies of user next intent prediction with a concept knowledge graph. The system has been deployed on the Web at Alipay, serving more than 100 million daily active users. To explicitly characterize user intent, we propose AlipayKG, which is an offline concept knowledge graph in the Life-Service domain modeling the historical behaviors of users, the rich content interacted by users and the relations between them. We further introduce a Transformer-based model which integrates expert rules from the knowledge graph to infer the online user's next intent. Experimental results demonstrate that the proposed system can effectively enhance the performance of the downstream tasks while retaining explainability.|本文用概念知识图说明了用户下一意图预测技术。该系统已经部署在支付宝的网站上，为超过1亿日活跃用户提供服务。为了明确表征用户意图，本文提出了 AlipayKG，它是生活服务领域中的一个离线概念知识图，对用户的历史行为、用户交互的丰富内容以及用户之间的关系进行建模。我们进一步介绍了一个基于 Transformer 的模型，该模型集成了来自知识图的专家规则，以推断在线用户的下一个意图。实验结果表明，该系统能够有效地提高下游任务的性能，同时保持可解释性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Concept+Knowledge+Graph+for+User+Next+Intent+Prediction+at+Alipay)|1| -|[Interaction-level Membership Inference Attack Against Federated Recommender Systems](https://doi.org/10.1145/3543507.3583359)|Wei Yuan, Chaoqun Yang, Quoc Viet Hung Nguyen, Lizhen Cui, Tieke He, Hongzhi Yin|Griffith University, Australia; Shandong University, China; Nanjing University, China; The University of Queensland, Australia|The marriage of federated learning and recommender system (FedRec) has been widely used to address the growing data privacy concerns in personalized recommendation services. In FedRecs, users' attribute information and behavior data (i.e., user-item interaction data) are kept locally on their personal devices, therefore, it is considered a fairly secure approach to protect user privacy. As a result, the privacy issue of FedRecs is rarely explored. Unfortunately, several recent studies reveal that FedRecs are vulnerable to user attribute inference attacks, highlighting the privacy concerns of FedRecs. In this paper, we further investigate the privacy problem of user behavior data (i.e., user-item interactions) in FedRecs. Specifically, we perform the first systematic study on interaction-level membership inference attacks on FedRecs. An interaction-level membership inference attacker is first designed, and then the classical privacy protection mechanism, Local Differential Privacy (LDP), is adopted to defend against the membership inference attack. Unfortunately, the empirical analysis shows that LDP is not effective against such new attacks unless the recommendation performance is largely compromised. To mitigate the interaction-level membership attack threats, we design a simple yet effective defense method to significantly reduce the attacker's inference accuracy without losing recommendation performance. Extensive experiments are conducted with two widely used FedRecs (Fed-NCF and Fed-LightGCN) on three real-world recommendation datasets (MovieLens-100K, Steam-200K, and Amazon Cell Phone), and the experimental results show the effectiveness of our solutions.|联邦学习与推荐系统的结合(FedRec)已被广泛用于解决个性化推荐服务中日益增长的数据隐私问题。在 FedRecs 中，用户的属性信息和行为数据(即用户项交互数据)保存在他们的个人设备上，因此，它被认为是保护用户隐私的一种相当安全的方法。因此，FedRecs 的隐私问题很少被探讨。不幸的是，最近的一些研究表明，联邦医疗记录系统容易受到用户属性推理攻击，突出了联邦医疗记录系统的隐私问题。在本文中，我们进一步研究了 FedRecs 中用户行为数据(即用户项交互)的隐私问题。具体来说，我们对 FedRecs 的交互层次成员推理攻击进行了第一次系统研究。首先设计了一个交互级别的成员推断攻击，然后采用经典的隐私保护机制，即本地差分隐私(lDP)来抵御成员推断攻击。遗憾的是，实证分析表明，除非推荐性能受到很大影响，否则 LDP 无法有效地抵抗这种新的攻击。为了减轻交互级别的成员攻击威胁，我们设计了一种简单而有效的防御方法，在不损失推荐性能的前提下显著降低攻击者的推断精度。在三个真实世界的推荐数据集(MovieLens-100K，Stream-200K 和 Amazon Cell Phone)上，用两个广泛使用的 FedRecs (Fed-NCF 和 Fed-LightGCN)进行了广泛的实验，实验结果显示了我们的解决方案的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Interaction-level+Membership+Inference+Attack+Against+Federated+Recommender+Systems)|1| +|[A Concept Knowledge Graph for User Next Intent Prediction at Alipay](https://doi.org/10.1145/3543873.3587308)|Yacheng He, Qianghuai Jia, Lin Yuan, Ruopeng Li, Yixin Ou, Ningyu Zhang|Ant Group, China; Zhejiang University, China|This paper illustrates the technologies of user next intent prediction with a concept knowledge graph. The system has been deployed on the Web at Alipay, serving more than 100 million daily active users. To explicitly characterize user intent, we propose AlipayKG, which is an offline concept knowledge graph in the Life-Service domain modeling the historical behaviors of users, the rich content interacted by users and the relations between them. We further introduce a Transformer-based model which integrates expert rules from the knowledge graph to infer the online user's next intent. Experimental results demonstrate that the proposed system can effectively enhance the performance of the downstream tasks while retaining explainability.|本文用概念知识图说明了用户下一意图预测技术。该系统已经部署在支付宝的网站上，为超过1亿日活跃用户提供服务。为了明确表征用户意图，本文提出了 AlipayKG，它是生活服务领域中的一个离线概念知识图，对用户的历史行为、用户交互的丰富内容以及用户之间的关系进行建模。我们进一步介绍了一个基于 Transformer 的模型，该模型集成了来自知识图的专家规则，以推断在线用户的下一个意图。实验结果表明，该系统能够有效地提高下游任务的性能，同时保持可解释性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Concept+Knowledge+Graph+for+User+Next+Intent+Prediction+at+Alipay)|1| +|[Interaction-level Membership Inference Attack Against Federated Recommender Systems](https://doi.org/10.1145/3543507.3583359)|Wei Yuan, Chaoqun Yang, Quoc Viet Hung Nguyen, Lizhen Cui, Tieke He, Hongzhi Yin|Nanjing University, China; The University of Queensland, Australia; Griffith University, Australia; Shandong University, China|The marriage of federated learning and recommender system (FedRec) has been widely used to address the growing data privacy concerns in personalized recommendation services. In FedRecs, users' attribute information and behavior data (i.e., user-item interaction data) are kept locally on their personal devices, therefore, it is considered a fairly secure approach to protect user privacy. As a result, the privacy issue of FedRecs is rarely explored. Unfortunately, several recent studies reveal that FedRecs are vulnerable to user attribute inference attacks, highlighting the privacy concerns of FedRecs. In this paper, we further investigate the privacy problem of user behavior data (i.e., user-item interactions) in FedRecs. Specifically, we perform the first systematic study on interaction-level membership inference attacks on FedRecs. An interaction-level membership inference attacker is first designed, and then the classical privacy protection mechanism, Local Differential Privacy (LDP), is adopted to defend against the membership inference attack. Unfortunately, the empirical analysis shows that LDP is not effective against such new attacks unless the recommendation performance is largely compromised. To mitigate the interaction-level membership attack threats, we design a simple yet effective defense method to significantly reduce the attacker's inference accuracy without losing recommendation performance. Extensive experiments are conducted with two widely used FedRecs (Fed-NCF and Fed-LightGCN) on three real-world recommendation datasets (MovieLens-100K, Steam-200K, and Amazon Cell Phone), and the experimental results show the effectiveness of our solutions.|联邦学习与推荐系统的结合(FedRec)已被广泛用于解决个性化推荐服务中日益增长的数据隐私问题。在 FedRecs 中，用户的属性信息和行为数据(即用户项交互数据)保存在他们的个人设备上，因此，它被认为是保护用户隐私的一种相当安全的方法。因此，FedRecs 的隐私问题很少被探讨。不幸的是，最近的一些研究表明，联邦医疗记录系统容易受到用户属性推理攻击，突出了联邦医疗记录系统的隐私问题。在本文中，我们进一步研究了 FedRecs 中用户行为数据(即用户项交互)的隐私问题。具体来说，我们对 FedRecs 的交互层次成员推理攻击进行了第一次系统研究。首先设计了一个交互级别的成员推断攻击，然后采用经典的隐私保护机制，即本地差分隐私(lDP)来抵御成员推断攻击。遗憾的是，实证分析表明，除非推荐性能受到很大影响，否则 LDP 无法有效地抵抗这种新的攻击。为了减轻交互级别的成员攻击威胁，我们设计了一种简单而有效的防御方法，在不损失推荐性能的前提下显著降低攻击者的推断精度。在三个真实世界的推荐数据集(MovieLens-100K，Stream-200K 和 Amazon Cell Phone)上，用两个广泛使用的 FedRecs (Fed-NCF 和 Fed-LightGCN)进行了广泛的实验，实验结果显示了我们的解决方案的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Interaction-level+Membership+Inference+Attack+Against+Federated+Recommender+Systems)|1| |[Learning with Exposure Constraints in Recommendation Systems](https://doi.org/10.1145/3543507.3583320)|Omer BenPorat, Rotem Torkan|Faculty of Data and Decision Sciences, Technion - Israel Institute of Technology, Israel|Recommendation systems are dynamic economic systems that balance the needs of multiple stakeholders. A recent line of work studies incentives from the content providers' point of view. Content providers, e.g., vloggers and bloggers, contribute fresh content and rely on user engagement to create revenue and finance their operations. In this work, we propose a contextual multi-armed bandit setting to model the dependency of content providers on exposure. In our model, the system receives a user context in every round and has to select one of the arms. Every arm is a content provider who must receive a minimum number of pulls every fixed time period (e.g., a month) to remain viable in later rounds; otherwise, the arm departs and is no longer available. The system aims to maximize the users' (content consumers) welfare. To that end, it should learn which arms are vital and ensure they remain viable by subsidizing arm pulls if needed. We develop algorithms with sub-linear regret, as well as a lower bound that demonstrates that our algorithms are optimal up to logarithmic factors.|推荐系统是平衡多个利益相关者需求的动态经济系统。最近的一项工作是从内容提供商的角度研究激励机制。内容提供商，例如，视频博客和博客，贡献新的内容，并依靠用户参与来创造收入和资助他们的业务。在这项工作中，我们提出了一个上下文多臂老虎机设置来模拟内容提供者对曝光的依赖。在我们的模型中，系统在每一轮中接收一个用户上下文，并且必须选择一个武器。每只手臂都是一个内容提供者，它必须在每个固定的时间段(例如，一个月)接受最少数量的拉动，以便在以后的回合中保持活力; 否则，这只手臂就会离开，不再可用。该系统旨在最大化用户(内容消费者)的福利。为此，它应该了解哪些武器是至关重要的，并确保他们保持可行的补贴，如果需要的手臂拉。我们开发的算法与次线性遗憾，以及一个下限，表明我们的算法是最优的对数因素。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Learning+with+Exposure+Constraints+in+Recommendation+Systems)|1| -|[On How Zero-Knowledge Proof Blockchain Mixers Improve, and Worsen User Privacy](https://doi.org/10.1145/3543507.3583217)|Zhipeng Wang, Stefanos Chaliasos, Kaihua Qin, Liyi Zhou, Lifeng Gao, Pascal Berrang, Benjamin Livshits, Arthur Gervais|Imperial College London, United Kingdom; University of Birmingham, United Kingdom; UCL, United Kingdom and UC Berkeley, USA|Zero-knowledge proof (ZKP) mixers are one of the most widely-used blockchain privacy solutions, operating on top of smart contract-enabled blockchains. We find that ZKP mixers are tightly intertwined with the growing number of Decentralized Finance (DeFi) attacks and Blockchain Extractable Value (BEV) extractions. Through coin flow tracing, we discover that 205 blockchain attackers and 2,595 BEV extractors leverage mixers as their source of funds, while depositing a total attack revenue of 412.87M USD. Moreover, the US OFAC sanctions against the largest ZKP mixer, Tornado.Cash, have reduced the mixer's daily deposits by more than 80%. Further, ZKP mixers advertise their level of privacy through a so-called anonymity set size, which similarly to k-anonymity allows a user to hide among a set of k other users. Through empirical measurements, we, however, find that these anonymity set claims are mostly inaccurate. For the most popular mixers on Ethereum (ETH) and Binance Smart Chain (BSC), we show how to reduce the anonymity set size on average by 27.34% and 46.02% respectively. Our empirical evidence is also the first to suggest a differing privacy-predilection of users on ETH and BSC. State-of-the-art ZKP mixers are moreover interwoven with the DeFi ecosystem by offering anonymity mining (AM) incentives, i.e., users receive monetary rewards for mixing coins. However, contrary to the claims of related work, we find that AM does not necessarily improve the quality of a mixer's anonymity set. Our findings indicate that AM attracts privacy-ignorant users, who then do not contribute to improving the privacy of other mixer users.|零知识证明(ZKP)混频器是最广泛使用的区块链隐私解决方案之一，运行在智能合同启用的区块链之上。我们发现 ZKP 混频器与不断增加的分散金融(DeFi)攻击和区块链可提取值(BEV)提取紧密相关。通过硬币流追踪，我们发现205个区块链攻击者和2595个 BEV 提取者利用混合器作为他们的资金来源，同时存储总攻击收入为412.87万美元。此外，美国海外资产管制办公室制裁了 ZKP 最大的搅拌机龙卷风。现金，减少了搅拌机的每日存款超过80% 。此外，ZKP 混频器通过所谓的匿名集大小来宣传他们的隐私级别，这与 k 匿名类似，允许用户隐藏在 k 其他用户集中。然而，通过实证测量，我们发现这些匿名集索赔大多是不准确的。针对 Etherum (ETH)和 Binance 智能链(BSC)上最常用的混频器，我们分别给出了如何平均减少匿名集大小27.34% 和46.02% 的方法。我们的经验证明也是第一个提出不同隐私偏好的用户。最先进的 ZKP 混合器还通过提供匿名挖掘(AM)奖励与 DeFi 生态系统交织在一起，也就是说，用户通过混合硬币获得金钱奖励。然而，与相关工作的主张相反，我们发现 AM 并不一定改善混频器匿名集的质量。我们的研究结果表明，AM 吸引了那些对隐私无知的用户，而这些用户并没有帮助改善其他混频器用户的隐私。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=On+How+Zero-Knowledge+Proof+Blockchain+Mixers+Improve,+and+Worsen+User+Privacy)|1| +|[On How Zero-Knowledge Proof Blockchain Mixers Improve, and Worsen User Privacy](https://doi.org/10.1145/3543507.3583217)|Zhipeng Wang, Stefanos Chaliasos, Kaihua Qin, Liyi Zhou, Lifeng Gao, Pascal Berrang, Benjamin Livshits, Arthur Gervais|UCL, United Kingdom and UC Berkeley, USA; University of Birmingham, United Kingdom; Imperial College London, United Kingdom|Zero-knowledge proof (ZKP) mixers are one of the most widely-used blockchain privacy solutions, operating on top of smart contract-enabled blockchains. We find that ZKP mixers are tightly intertwined with the growing number of Decentralized Finance (DeFi) attacks and Blockchain Extractable Value (BEV) extractions. Through coin flow tracing, we discover that 205 blockchain attackers and 2,595 BEV extractors leverage mixers as their source of funds, while depositing a total attack revenue of 412.87M USD. Moreover, the US OFAC sanctions against the largest ZKP mixer, Tornado.Cash, have reduced the mixer's daily deposits by more than 80%. Further, ZKP mixers advertise their level of privacy through a so-called anonymity set size, which similarly to k-anonymity allows a user to hide among a set of k other users. Through empirical measurements, we, however, find that these anonymity set claims are mostly inaccurate. For the most popular mixers on Ethereum (ETH) and Binance Smart Chain (BSC), we show how to reduce the anonymity set size on average by 27.34% and 46.02% respectively. Our empirical evidence is also the first to suggest a differing privacy-predilection of users on ETH and BSC. State-of-the-art ZKP mixers are moreover interwoven with the DeFi ecosystem by offering anonymity mining (AM) incentives, i.e., users receive monetary rewards for mixing coins. However, contrary to the claims of related work, we find that AM does not necessarily improve the quality of a mixer's anonymity set. Our findings indicate that AM attracts privacy-ignorant users, who then do not contribute to improving the privacy of other mixer users.|零知识证明(ZKP)混频器是最广泛使用的区块链隐私解决方案之一，运行在智能合同启用的区块链之上。我们发现 ZKP 混频器与不断增加的分散金融(DeFi)攻击和区块链可提取值(BEV)提取紧密相关。通过硬币流追踪，我们发现205个区块链攻击者和2595个 BEV 提取者利用混合器作为他们的资金来源，同时存储总攻击收入为412.87万美元。此外，美国海外资产管制办公室制裁了 ZKP 最大的搅拌机龙卷风。现金，减少了搅拌机的每日存款超过80% 。此外，ZKP 混频器通过所谓的匿名集大小来宣传他们的隐私级别，这与 k 匿名类似，允许用户隐藏在 k 其他用户集中。然而，通过实证测量，我们发现这些匿名集索赔大多是不准确的。针对 Etherum (ETH)和 Binance 智能链(BSC)上最常用的混频器，我们分别给出了如何平均减少匿名集大小27.34% 和46.02% 的方法。我们的经验证明也是第一个提出不同隐私偏好的用户。最先进的 ZKP 混合器还通过提供匿名挖掘(AM)奖励与 DeFi 生态系统交织在一起，也就是说，用户通过混合硬币获得金钱奖励。然而，与相关工作的主张相反，我们发现 AM 并不一定改善混频器匿名集的质量。我们的研究结果表明，AM 吸引了那些对隐私无知的用户，而这些用户并没有帮助改善其他混频器用户的隐私。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=On+How+Zero-Knowledge+Proof+Blockchain+Mixers+Improve,+and+Worsen+User+Privacy)|1| |[To Store or Not? Online Data Selection for Federated Learning with Limited Storage](https://doi.org/10.1145/3543507.3583426)|Chen Gong, Zhenzhe Zheng, Fan Wu, Yunfeng Shao, Bingshuai Li, Guihai Chen|; Department of Computer Science and Engineering, Shanghai Jiao Tong University, China|Machine learning models have been deployed in mobile networks to deal with massive data from different layers to enable automated network management and intelligence on devices. To overcome high communication cost and severe privacy concerns of centralized machine learning, federated learning (FL) has been proposed to achieve distributed machine learning among networked devices. While the computation and communication limitation has been widely studied, the impact of on-device storage on the performance of FL is still not explored. Without an effective data selection policy to filter the massive streaming data on devices, classical FL can suffer from much longer model training time ($4\times$) and significant inference accuracy reduction ($7\%$), observed in our experiments. In this work, we take the first step to consider the online data selection for FL with limited on-device storage. We first define a new data valuation metric for data evaluation and selection in FL with theoretical guarantees for speeding up model convergence and enhancing final model accuracy, simultaneously. We further design {\ttfamily ODE}, a framework of \textbf{O}nline \textbf{D}ata s\textbf{E}lection for FL, to coordinate networked devices to store valuable data samples. Experimental results on one industrial dataset and three public datasets show the remarkable advantages of {\ttfamily ODE} over the state-of-the-art approaches. Particularly, on the industrial dataset, {\ttfamily ODE} achieves as high as $2.5\times$ speedup of training time and $6\%$ increase in inference accuracy, and is robust to various factors in practical environments.|机器学习模型已经部署在移动网络中，用于处理来自不同层次的大量数据，以实现设备上的自动网络管理和智能化。为了克服集中式机器学习的高通信成本和严重的隐私问题，提出了联邦学习(FL)来实现网络设备之间的分布式机器学习。虽然计算和通信的局限性已经被广泛研究，但是在设备上存储对 FL 性能的影响还没有被探讨。在我们的实验中观察到，如果没有有效的数据选择策略来过滤设备上的大量流数据，经典的 FL 可能会遭受更长的模型训练时间(4倍 $)和显着的推断准确性降低(7% $)。在这项工作中，我们采取的第一步考虑在线数据选择的 FL 与有限的设备上的存储。我们首先定义了一个新的用于 FL 中数据评估和选择的数据估值度量，同时为加快模型收敛速度和提高最终模型精度提供了理论保证。进一步设计了一个基于 textbf { O } nline textbf { D } ata’s textbf { E }选项的框架{ ttfamily ODE } ，用于协调网络设备以存储有价值的数据样本。在一个工业数据集和三个公共数据集上的实验结果表明，{ ttfamily ODE }方法比现有的方法具有显著的优势。特别是在工业数据集上，{ ttfamily ODE }达到了2.5倍的训练时间加速和6% 的推理精度提高，并且对实际环境中的各种因素具有鲁棒性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=To+Store+or+Not?+Online+Data+Selection+for+Federated+Learning+with+Limited+Storage)|1| |[Chain of Explanation: New Prompting Method to Generate Quality Natural Language Explanation for Implicit Hate Speech](https://doi.org/10.1145/3543873.3587320)|Fan Huang, Haewoon Kwak, Jisun An|Luddy School of Informatics, Computing, and Engineering, Indiana University Bloomington, USA|Recent studies have exploited advanced generative language models to generate Natural Language Explanations (NLE) for why a certain text could be hateful. We propose the Chain of Explanation (CoE) Prompting method, using the heuristic words and target group, to generate high-quality NLE for implicit hate speech. We improved the BLUE score from 44.0 to 62.3 for NLE generation by providing accurate target information. We then evaluate the quality of generated NLE using various automatic metrics and human annotations of informativeness and clarity scores.|最近的研究已经开发了先进的生成语言模型来产生自然语言解释(NLE)为什么某个文本可能是可恨的。提出了一种基于启发式词语和目标群的解释链提示方法，用于生成高质量的非线性语言环境。通过提供准确的目标信息，我们将 NLE 生成的 BLUE 评分从44.0提高到62.3。然后，我们使用各种自动指标和信息性和清晰度评分的人工注释来评估生成的 NLE 的质量。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Chain+of+Explanation:+New+Prompting+Method+to+Generate+Quality+Natural+Language+Explanation+for+Implicit+Hate+Speech)|1| -|[NeuKron: Constant-Size Lossy Compression of Sparse Reorderable Matrices and Tensors](https://doi.org/10.1145/3543507.3583226)|Taehyung Kwon, Jihoon Ko, Jinhong Jung, Kijung Shin|Department of Computer Science & Engineering, Jeonbuk National University, Republic of Korea; Kim Jaechul Graduate School of AI, Korea Advanced Institute of Science and Technology, Republic of Korea|Many real-world data are naturally represented as a sparse reorderable matrix, whose rows and columns can be arbitrarily ordered (e.g., the adjacency matrix of a bipartite graph). Storing a sparse matrix in conventional ways requires an amount of space linear in the number of non-zeros, and lossy compression of sparse matrices (e.g., Truncated SVD) typically requires an amount of space linear in the number of rows and columns. In this work, we propose NeuKron for compressing a sparse reorderable matrix into a constant-size space. NeuKron generalizes Kronecker products using a recurrent neural network with a constant number of parameters. NeuKron updates the parameters so that a given matrix is approximated by the product and reorders the rows and columns of the matrix to facilitate the approximation. The updates take time linear in the number of non-zeros in the input matrix, and the approximation of each entry can be retrieved in logarithmic time. We also extend NeuKron to compress sparse reorderable tensors (e.g. multi-layer graphs), which generalize matrices. Through experiments on ten real-world datasets, we show that NeuKron is (a) Compact: requiring up to five orders of magnitude less space than its best competitor with similar approximation errors, (b) Accurate: giving up to 10x smaller approximation error than its best competitors with similar size outputs, and (c) Scalable: successfully compressing a matrix with over 230 million non-zero entries.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=NeuKron:+Constant-Size+Lossy+Compression+of+Sparse+Reorderable+Matrices+and+Tensors)|1| +|[NeuKron: Constant-Size Lossy Compression of Sparse Reorderable Matrices and Tensors](https://doi.org/10.1145/3543507.3583226)|Taehyung Kwon, Jihoon Ko, Jinhong Jung, Kijung Shin|Kim Jaechul Graduate School of AI, Korea Advanced Institute of Science and Technology, Republic of Korea; Department of Computer Science & Engineering, Jeonbuk National University, Republic of Korea|Many real-world data are naturally represented as a sparse reorderable matrix, whose rows and columns can be arbitrarily ordered (e.g., the adjacency matrix of a bipartite graph). Storing a sparse matrix in conventional ways requires an amount of space linear in the number of non-zeros, and lossy compression of sparse matrices (e.g., Truncated SVD) typically requires an amount of space linear in the number of rows and columns. In this work, we propose NeuKron for compressing a sparse reorderable matrix into a constant-size space. NeuKron generalizes Kronecker products using a recurrent neural network with a constant number of parameters. NeuKron updates the parameters so that a given matrix is approximated by the product and reorders the rows and columns of the matrix to facilitate the approximation. The updates take time linear in the number of non-zeros in the input matrix, and the approximation of each entry can be retrieved in logarithmic time. We also extend NeuKron to compress sparse reorderable tensors (e.g. multi-layer graphs), which generalize matrices. Through experiments on ten real-world datasets, we show that NeuKron is (a) Compact: requiring up to five orders of magnitude less space than its best competitor with similar approximation errors, (b) Accurate: giving up to 10x smaller approximation error than its best competitors with similar size outputs, and (c) Scalable: successfully compressing a matrix with over 230 million non-zero entries.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=NeuKron:+Constant-Size+Lossy+Compression+of+Sparse+Reorderable+Matrices+and+Tensors)|1| |[Hierarchical Knowledge Graph Learning Enabled Socioeconomic Indicator Prediction in Location-Based Social Network](https://doi.org/10.1145/3543507.3583239)|Zhilun Zhou, Yu Liu, Jingtao Ding, Depeng Jin, Yong Li|Tsinghua University, China|Socioeconomic indicators reflect location status from various aspects such as demographics, economy, crime and land usage, which play an important role in the understanding of location-based social networks (LBSNs). Especially, several existing works leverage multi-source data for socioeconomic indicator prediction in LBSNs, which however fail to capture semantic information as well as distil comprehensive knowledge therein. On the other hand, knowledge graph (KG), which distils semantic knowledge from multi-source data, has been popular in recent LBSN research, which inspires us to introduce KG for socioeconomic indicator prediction in LBSNs. Specifically, we first construct a location-based KG (LBKG) to integrate various kinds of knowledge from heterogeneous LBSN data, including locations and other related elements like point of interests (POIs), business areas as well as various relationships between them, such as spatial proximity and functional similarity. Then we propose a hierarchical KG learning model to capture both global knowledge from LBKG and domain knowledge from several sub-KGs. Extensive experiments on three datasets demonstrate our model’s superiority over state-of-the-art methods in socioeconomic indicators prediction. Our code is released at: https://github.com/tsinghua-fib-lab/KG-socioeconomic-indicator-prediction.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Hierarchical+Knowledge+Graph+Learning+Enabled+Socioeconomic+Indicator+Prediction+in+Location-Based+Social+Network)|1| -|[Characterization of Simplicial Complexes by Counting Simplets Beyond Four Nodes](https://doi.org/10.1145/3543507.3583332)|Hyunju Kim, Jihoon Ko, Fanchen Bu, Kijung Shin|Kim Jaechul Graduate School of AI, Korea Advanced Institute of Science and Technology, Republic of Korea; School of Electrical Engineering, Korea Advanced Institute of Science and Technology, Republic of Korea|Simplicial complexes are higher-order combinatorial structures which have been used to represent real-world complex systems. In this paper, we concentrate on the local patterns in simplicial complexes called simplets, a generalization of graphlets. We formulate the problem of counting simplets of a given size in a given simplicial complex. For this problem, we extend a sampling algorithm based on color coding from graphs to simplicial complexes, with essential technical novelty. We theoretically analyze our proposed algorithm named SC3, showing its correctness, unbiasedness, convergence, and time/space complexity. Through the extensive experiments on sixteen real-world datasets, we show the superiority of SC3 in terms of accuracy, speed, and scalability, compared to the baseline methods. Finally, we use the counts given by SC3 for simplicial complex analysis, especially for characterization, which is further used for simplicial complex clustering, where SC3 shows a strong ability of characterization with domain-based similarity.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Characterization+of+Simplicial+Complexes+by+Counting+Simplets+Beyond+Four+Nodes)|1| -|[KRACL: Contrastive Learning with Graph Context Modeling for Sparse Knowledge Graph Completion](https://doi.org/10.1145/3543507.3583412)|Zhaoxuan Tan, Zilong Chen, Shangbin Feng, Qingyue Zhang, Qinghua Zheng, Jundong Li, Minnan Luo|University of Virginia, USA; Tsinghua University, China; University of Washington, USA; Xi'an Jiaotong University, China|Knowledge Graph Embeddings (KGE) aim to map entities and relations to low dimensional spaces and have become the \textit{de-facto} standard for knowledge graph completion. Most existing KGE methods suffer from the sparsity challenge, where it is harder to predict entities that appear less frequently in knowledge graphs. In this work, we propose a novel framework KRACL to alleviate the widespread sparsity in KGs with graph context and contrastive learning. Firstly, we propose the Knowledge Relational Attention Network (KRAT) to leverage the graph context by simultaneously projecting neighboring triples to different latent spaces and jointly aggregating messages with the attention mechanism. KRAT is capable of capturing the subtle semantic information and importance of different context triples as well as leveraging multi-hop information in knowledge graphs. Secondly, we propose the knowledge contrastive loss by combining the contrastive loss with cross entropy loss, which introduces more negative samples and thus enriches the feedback to sparse entities. Our experiments demonstrate that KRACL achieves superior results across various standard knowledge graph benchmarks, especially on WN18RR and NELL-995 which have large numbers of low in-degree entities. Extensive experiments also bear out KRACL's effectiveness in handling sparse knowledge graphs and robustness against noisy triples.|知识图嵌入技术(KGE)旨在将实体和关系映射到低维空间，已经成为知识图形完成的文本标准。大多数现有的 KGE 方法都受到稀疏性挑战的影响，在稀疏性挑战中，很难预测知识图中出现频率较低的实体。在这项工作中，我们提出了一个新的框架 KRACL，以缓解广泛的稀疏性幼儿园图形上下文和对比学习。首先，我们提出了知识关系注意网络(KRAT) ，通过将相邻的三元组同时投影到不同的潜在空间，并利用注意机制联合聚集信息，从而利用图的上下文。KRAT 能够捕捉不同上下文三元组的微妙语义信息和重要性，并利用知识图表中的多跳信息。其次，将对比损失和交叉熵损失相结合，提出了知识对比损失，引入了更多的负样本，丰富了对稀疏实体的反馈。我们的实验表明，KRACL 在不同的标准知识图基准测试中，特别是在 WN18RR 和 NELL-995上，获得了优越的结果，这两个基准测试都有大量的低度实体。广泛的实验也证明了 KRACL 在处理稀疏知识图和抗噪声三元组鲁棒性方面的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=KRACL:+Contrastive+Learning+with+Graph+Context+Modeling+for+Sparse+Knowledge+Graph+Completion)|1| +|[Characterization of Simplicial Complexes by Counting Simplets Beyond Four Nodes](https://doi.org/10.1145/3543507.3583332)|Hyunju Kim, Jihoon Ko, Fanchen Bu, Kijung Shin|School of Electrical Engineering, Korea Advanced Institute of Science and Technology, Republic of Korea; Kim Jaechul Graduate School of AI, Korea Advanced Institute of Science and Technology, Republic of Korea|Simplicial complexes are higher-order combinatorial structures which have been used to represent real-world complex systems. In this paper, we concentrate on the local patterns in simplicial complexes called simplets, a generalization of graphlets. We formulate the problem of counting simplets of a given size in a given simplicial complex. For this problem, we extend a sampling algorithm based on color coding from graphs to simplicial complexes, with essential technical novelty. We theoretically analyze our proposed algorithm named SC3, showing its correctness, unbiasedness, convergence, and time/space complexity. Through the extensive experiments on sixteen real-world datasets, we show the superiority of SC3 in terms of accuracy, speed, and scalability, compared to the baseline methods. Finally, we use the counts given by SC3 for simplicial complex analysis, especially for characterization, which is further used for simplicial complex clustering, where SC3 shows a strong ability of characterization with domain-based similarity.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Characterization+of+Simplicial+Complexes+by+Counting+Simplets+Beyond+Four+Nodes)|1| +|[KRACL: Contrastive Learning with Graph Context Modeling for Sparse Knowledge Graph Completion](https://doi.org/10.1145/3543507.3583412)|Zhaoxuan Tan, Zilong Chen, Shangbin Feng, Qingyue Zhang, Qinghua Zheng, Jundong Li, Minnan Luo|University of Virginia, USA; Tsinghua University, China; Xi'an Jiaotong University, China; University of Washington, USA|Knowledge Graph Embeddings (KGE) aim to map entities and relations to low dimensional spaces and have become the \textit{de-facto} standard for knowledge graph completion. Most existing KGE methods suffer from the sparsity challenge, where it is harder to predict entities that appear less frequently in knowledge graphs. In this work, we propose a novel framework KRACL to alleviate the widespread sparsity in KGs with graph context and contrastive learning. Firstly, we propose the Knowledge Relational Attention Network (KRAT) to leverage the graph context by simultaneously projecting neighboring triples to different latent spaces and jointly aggregating messages with the attention mechanism. KRAT is capable of capturing the subtle semantic information and importance of different context triples as well as leveraging multi-hop information in knowledge graphs. Secondly, we propose the knowledge contrastive loss by combining the contrastive loss with cross entropy loss, which introduces more negative samples and thus enriches the feedback to sparse entities. Our experiments demonstrate that KRACL achieves superior results across various standard knowledge graph benchmarks, especially on WN18RR and NELL-995 which have large numbers of low in-degree entities. Extensive experiments also bear out KRACL's effectiveness in handling sparse knowledge graphs and robustness against noisy triples.|知识图嵌入技术(KGE)旨在将实体和关系映射到低维空间，已经成为知识图形完成的文本标准。大多数现有的 KGE 方法都受到稀疏性挑战的影响，在稀疏性挑战中，很难预测知识图中出现频率较低的实体。在这项工作中，我们提出了一个新的框架 KRACL，以缓解广泛的稀疏性幼儿园图形上下文和对比学习。首先，我们提出了知识关系注意网络(KRAT) ，通过将相邻的三元组同时投影到不同的潜在空间，并利用注意机制联合聚集信息，从而利用图的上下文。KRAT 能够捕捉不同上下文三元组的微妙语义信息和重要性，并利用知识图表中的多跳信息。其次，将对比损失和交叉熵损失相结合，提出了知识对比损失，引入了更多的负样本，丰富了对稀疏实体的反馈。我们的实验表明，KRACL 在不同的标准知识图基准测试中，特别是在 WN18RR 和 NELL-995上，获得了优越的结果，这两个基准测试都有大量的低度实体。广泛的实验也证明了 KRACL 在处理稀疏知识图和抗噪声三元组鲁棒性方面的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=KRACL:+Contrastive+Learning+with+Graph+Context+Modeling+for+Sparse+Knowledge+Graph+Completion)|1| |[Migration Reframed? A multilingual analysis on the stance shift in Europe during the Ukrainian crisis](https://doi.org/10.1145/3543507.3583442)|Sergej Wildemann, Claudia Niederée, Erick Elejalde|L3S Research Center, Leibniz Universität Hannover, Germany|The war in Ukraine seems to have positively changed the attitude toward the critical societal topic of migration in Europe -- at least towards refugees from Ukraine. We investigate whether this impression is substantiated by how the topic is reflected in online news and social media, thus linking the representation of the issue on the Web to its perception in society. For this purpose, we combine and adapt leading-edge automatic text processing for a novel multilingual stance detection approach. Starting from 5.5M Twitter posts published by 565 European news outlets in one year, beginning September 2021, plus replies, we perform a multilingual analysis of migration-related media coverage and associated social media interaction for Europe and selected European countries. The results of our analysis show that there is actually a reframing of the discussion illustrated by the terminology change, e.g., from "migrant" to "refugee", often even accentuated with phrases such as "real refugees". However, concerning a stance shift in public perception, the picture is more diverse than expected. All analyzed cases show a noticeable temporal stance shift around the start of the war in Ukraine. Still, there are apparent national differences in the size and stability of this shift.|乌克兰的战争似乎积极地改变了人们对欧洲移民这一关键社会话题的态度——至少是对乌克兰难民的态度。我们调查这种印象是否被在线新闻和社交媒体所反映的主题所证实，从而将问题在网络上的表现与其在社会中的感知联系起来。为此，我们将前沿自动文本处理技术结合起来，提出了一种新的多语言姿态检测方法。从2021年9月开始，565家欧洲新闻机构在一年内发布了550万条 Twitter 帖子，再加上回复，我们对欧洲和选定的欧洲国家的移民相关媒体报道和相关社交媒体互动进行了多语言分析。我们的分析结果表明，术语的变化，例如从“移民”到“难民”，甚至常常用“真正的难民”这样的短语来强调，实际上是对讨论的重新构建。然而，关于公众看法的立场转变，情况比预期的更加多样化。所有分析的案例都表明，在乌克兰战争开始前后，人们的立场发生了明显的时间转变。尽管如此，各国在这种转变的规模和稳定性方面仍存在明显差异。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Migration+Reframed?+A+multilingual+analysis+on+the+stance+shift+in+Europe+during+the+Ukrainian+crisis)|1| |[Multitask Peer Prediction With Task-dependent Strategies](https://doi.org/10.1145/3543507.3583292)|Yichi Zhang, Grant Schoenebeck|University of Michigan, USA|Peer prediction aims to incentivize truthful reports from agents whose reports cannot be assessed with any objective ground truthful information. In the multi-task setting where each agent is asked multiple questions, a sequence of mechanisms have been proposed which are truthful — truth-telling is guaranteed to be an equilibrium, or even better, informed truthful — truth-telling is guaranteed to be one of the best-paid equilibria. However, these guarantees assume agents’ strategies are restricted to be task-independent: an agent’s report on a task is not affected by her information about other tasks. We provide the first discussion on how to design (informed) truthful mechanisms for task-dependent strategies, which allows the agents to report based on all her information on the assigned tasks. We call such stronger mechanisms (informed) omni-truthful. In particular, we propose the joint-disjoint task framework, a new paradigm which builds upon the previous penalty-bonus task framework. First, we show a natural reduction from mechanisms in the penalty-bonus task framework to mechanisms in the joint-disjoint task framework that maps every truthful mechanism to an omni-truthful mechanism. Such a reduction is non-trivial as we show that current penalty-bonus task mechanisms are not, in general, omni-truthful. Second, for a stronger truthful guarantee, we design the matching agreement (MA) mechanism which is informed omni-truthful. Finally, for the MA mechanism in the detail-free setting where no prior knowledge is assumed, we show how many tasks are required to (approximately) retain the truthful guarantees.|同伴预测的目的是激励那些报告不能用任何客观的真实信息进行评估的代理人的真实报告。在多任务环境中，每个主体被问到多个问题，一系列的机制被提出，这些机制是真实的ーー讲真话被保证是一种均衡，甚至更好，知情的真话ーー讲真话被保证是一种收入最高的均衡。然而，这些保证假设代理的策略被限制为独立于任务: 代理关于任务的报告不受她关于其他任务的信息的影响。我们首先讨论了如何为任务依赖策略设计(知情的)真实机制，使得代理人能够根据她对指定任务的所有信息进行报告。我们称这种更强大的机制(知情的)为全面真实。特别地，我们提出了联合-分离任务框架，这是一个建立在以前惩罚-奖励任务框架基础上的新范式。首先，我们展示了从惩罚-奖励任务框架中的机制到联合-不相交任务框架中的机制的自然还原，该机制映射每个真实机制到一个全真机制。这种减少是不平凡的，因为我们表明，目前的惩罚奖励任务机制，一般来说，不是全面真实的。其次，为了获得更强的真实性保证，我们设计了全真信息匹配协议(MA)机制。最后，对于无细节设置且不假设先验知识的 MA 机制，我们展示了需要多少任务才能(近似地)保持真实性保证。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Multitask+Peer+Prediction+With+Task-dependent+Strategies)|1| |[High-Effort Crowds: Limited Liability via Tournaments](https://doi.org/10.1145/3543507.3583334)|Yichi Zhang, Grant Schoenebeck|School of Information, University of Michigan, USA|We consider the crowdsourcing setting where, in response to the assigned tasks, agents strategically decide both how much effort to exert (from a continuum) and whether to manipulate their reports. The goal is to design payment mechanisms that (1) satisfy limited liability (all payments are non-negative), (2) reduce the principal’s cost of budget, (3) incentivize effort and (4) incentivize truthful responses. In our framework, the payment mechanism composes a performance measurement, which noisily evaluates agents’ effort based on their reports, and a payment function, which converts the scores output by the performance measurement to payments. Previous literature suggests applying a peer prediction mechanism combined with a linear payment function. This method can achieve either (1), (3) and (4), or (2), (3) and (4) in the binary effort setting. In this paper, we suggest using a rank-order payment function (tournament). Assuming Gaussian noise, we analytically optimize the rank-order payment function, and identify a sufficient statistic, sensitivity, which serves as a metric for optimizing the performance measurements. This helps us obtain (1), (2) and (3) simultaneously. Additionally, we show that adding noise to agents’ scores can preserve the truthfulness of the performance measurements under the non-linear tournament, which gives us all four objectives. Our real-data estimated agent-based model experiments show that our method can greatly reduce the payment of effort elicitation while preserving the truthfulness of the performance measurement. In addition, we empirically evaluate several commonly used performance measurements in terms of their sensitivities and strategic robustness.|我们考虑众包的设置，在这个设置中，为了响应分配的任务，代理人战略性地决定要付出多少努力(从一个连续体)和是否操纵他们的报告。我们的目标是设计支付机制: (1)满足有限责任(所有支付都是非负数的) ，(2)降低本金的预算成本，(3)激励努力，(4)激励诚实的回应。在我们的框架中，支付机制包括一个绩效度量，它基于代理人的报告对代理人的努力进行嘈杂的评估，以及一个支付函数，它将绩效度量的得分输出转换为支付。以前的文献建议应用同行预测机制结合线性支付函数。该方法可以在二进制努力设置中实现(1)、(3)和(4)或(2)、(3)和(4)。在本文中，我们建议使用等级顺序支付函数(锦标赛)。假设高斯噪声，我们分析优化秩序支付函数，并确定了一个充分的统计，灵敏度，作为一个度量优化性能测量。这有助于我们同时获得(1)、(2)和(3)。此外，我们还发现，在非线性竞争下，给代理人的分数增加噪声可以保持性能测量的真实性，这给了我们四个目标。我们的实际数据估计个体为本模型实验表明，我们的方法可以大大减少努力诱发的支付，同时保持绩效测量的真实性。此外，我们根据灵敏度和战略稳健性对几个常用的性能测量进行了实证评估。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=High-Effort+Crowds:+Limited+Liability+via+Tournaments)|1| |[Knowledge-infused Contrastive Learning for Urban Imagery-based Socioeconomic Prediction](https://doi.org/10.1145/3543507.3583876)|Yu Liu, Xin Zhang, Jingtao Ding, Yanxin Xi, Yong Li|Tsinghua University, China; University of Helsinki, Finland|Monitoring sustainable development goals requires accurate and timely socioeconomic statistics, while ubiquitous and frequently-updated urban imagery in web like satellite/street view images has emerged as an important source for socioeconomic prediction. Especially, recent studies turn to self-supervised contrastive learning with manually designed similarity metrics for urban imagery representation learning and further socioeconomic prediction, which however suffers from effectiveness and robustness issues. To address such issues, in this paper, we propose a Knowledge-infused Contrastive Learning (KnowCL) model for urban imagery-based socioeconomic prediction. Specifically, we firstly introduce knowledge graph (KG) to effectively model the urban knowledge in spatiality, mobility, etc., and then build neural network based encoders to learn representations of an urban image in associated semantic and visual spaces, respectively. Finally, we design a cross-modality based contrastive learning framework with a novel image-KG contrastive loss, which maximizes the mutual information between semantic and visual representations for knowledge infusion. Extensive experiments of applying the learnt visual representations for socioeconomic prediction on three datasets demonstrate the superior performance of KnowCL with over 30\% improvements on $R^2$ compared with baselines. Especially, our proposed KnowCL model can apply to both satellite and street imagery with both effectiveness and transferability achieved, which provides insights into urban imagery-based socioeconomic prediction.|监测可持续发展目标需要准确和及时的社会经济统计数据，而无处不在和经常更新的城市图像，如卫星/街景图像，已成为社会经济预测的一个重要来源。特别是近年来，城市图像表征学习和进一步的社会经济预测的研究主要集中在自监督对比学习和人工设计的相似度量上，但这种方法存在有效性和鲁棒性的问题。为了解决这些问题，本文提出了一个基于知识注入的对比学习(KnowCL)模型，用于基于图像的城市社会经济预测。首先引入知识图(KG)对城市知识进行空间、流动性等方面的有效建模，然后构建基于神经网络的编码器，分别学习相关语义空间和视觉空间中城市图像的表示。最后，我们设计了一个基于交叉模态的对比学习框架，该框架具有一种新的图像对比度损失—— KG 对比度损失，最大化了语义表征和视觉表征之间的相互信息，实现了知识的输入。在三个数据集上应用学习可视化表示进行社会经济预测的广泛实验表明，与基线相比，KnowCL 的性能优越，在 $R ^ 2 $上有超过30% 的改善。特别是，我们提出的 KnowCL 模型可以同时应用于卫星和街道图像，实现了有效性和可转移性，为基于图像的城市社会经济预测提供了见解。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Knowledge-infused+Contrastive+Learning+for+Urban+Imagery-based+Socioeconomic+Prediction)|1| |[Dynamic Embedding-based Retrieval for Personalized Item Recommendations at Instacart](https://doi.org/10.1145/3543873.3587668)|Chuanwei Ruan, Allan Stewart, Han Li, Ryan Ye, David Vengerov, Haixun Wang|Instacart, USA|Personalization is essential in e-commerce, with item recommendation as a critical task. In this paper, we describe a hybrid embedding-based retrieval system for real-time personalized item recommendations on Instacart. Our system addresses unique challenges in the multi-source retrieval system, and includes several key components to make it highly personalized and dynamic. Specifically, our system features a hybrid embedding model that includes a long-term user interests embedding model and a real-time session-based model, which are combined to capture users’ immediate intents and historical interactions. Additionally, we have developed a contextual bandit solution to dynamically adjust the number of candidates from each source and optimally allocate retrieval slots given a limited computational budget. Our modeling and system optimization efforts have enabled us to provide highly personalized item recommendations in real-time at scale to all our customers, including new and long-standing users.|个性化在电子商务中是必不可少的，项目推荐是一项关键任务。本文描述了一个基于嵌入的混合检索系统，用于 Instacart 上的实时个性化项目推荐。我们的系统解决了多源检索系统中的独特挑战，并包含了几个关键组件，使其具有高度的个性化和动态性。具体来说，我们的系统采用混合嵌入模型，包括长期用户兴趣嵌入模型和基于实时会话的嵌入模型，它们结合起来捕获用户的直接意图和历史交互。此外，我们已经开发了一个上下文盗贼解决方案来动态调整每个来源的候选人数量，并在有限的计算预算下优化分配检索时隙。我们的建模和系统优化工作，使我们能够提供高度个性化的项目推荐的实时规模，我们的所有客户，包括新的和长期的用户。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Dynamic+Embedding-based+Retrieval+for+Personalized+Item+Recommendations+at+Instacart)|0| |[A Multi-Granularity Matching Attention Network for Query Intent Classification in E-commerce Retrieval](https://doi.org/10.1145/3543873.3584639)|Chunyuan Yuan, Yiming Qiu, Mingming Li, Haiqing Hu, Songlin Wang, Sulong Xu|JD.com, Beijing, China, China|Query intent classification, which aims at assisting customers to find desired products, has become an essential component of the e-commerce search. Existing query intent classification models either design more exquisite models to enhance the representation learning of queries or explore label-graph and multi-task to facilitate models to learn external information. However, these models cannot capture multi-granularity matching features from queries and categories, which makes them hard to mitigate the gap in the expression between informal queries and categories. This paper proposes a Multi-granularity Matching Attention Network (MMAN), which contains three modules: a self-matching module, a char-level matching module, and a semantic-level matching module to comprehensively extract features from the query and a query-category interaction matrix. In this way, the model can eliminate the difference in expression between queries and categories for query intent classification. We conduct extensive offline and online A/B experiments, and the results show that the MMAN significantly outperforms the strong baselines, which shows the superiority and effectiveness of MMAN. MMAN has been deployed in production and brings great commercial value for our company.|查询意图分类已经成为电子商务搜索的一个重要组成部分，其目的是帮助客户找到期望的产品。现有的查询意图分类模型要么设计更精细的模型来增强查询的表示学习，要么探索标签图和多任务来促进模型学习外部信息。然而，这些模型不能从查询和类别中捕获多粒度匹配特性，这使得它们很难缩小非正式查询和类别之间的表达差距。提出了一种多粒度匹配注意网络(MMAN)模型，该模型包括三个模块: 自匹配模块、字符级匹配模块和语义级匹配模块。通过这种方式，该模型可以消除查询和类别之间在查询意图分类方面的表达差异。我们进行了大量的离线和在线 A/B 实验，结果表明，MMAN 的性能明显优于强基线，显示了 MMAN 的优越性和有效性。MMAN 已投入生产，为公司带来了巨大的商业价值。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Multi-Granularity+Matching+Attention+Network+for+Query+Intent+Classification+in+E-commerce+Retrieval)|0| -|[Divide and Conquer: Towards Better Embedding-based Retrieval for Recommender Systems from a Multi-task Perspective](https://doi.org/10.1145/3543873.3584629)|Yuan Zhang, Xue Dong, Weijie Ding, Biao Li, Peng Jiang, Kun Gai|Shandong University, China; Kuaishou Technology, China; Unaffiliated, China|Embedding-based retrieval (EBR) methods are widely used in modern recommender systems thanks to its simplicity and effectiveness. However, along the journey of deploying and iterating on EBR in production, we still identify some fundamental issues in existing methods. First, when dealing with large corpus of candidate items, EBR models often have difficulties in balancing the performance on distinguishing highly relevant items (positives) from both irrelevant ones (easy negatives) and from somewhat related yet not competitive ones (hard negatives). Also, we have little control in the diversity and fairness of the retrieval results because of the ``greedy'' nature of nearest vector search. These issues compromise the performance of EBR methods in large-scale industrial scenarios. This paper introduces a simple and proven-in-production solution to overcome these issues. The proposed solution takes a divide-and-conquer approach: the whole set of candidate items are divided into multiple clusters and we run EBR to retrieve relevant candidates from each cluster in parallel; top candidates from each cluster are then combined by some controllable merging strategies. This approach allows our EBR models to only concentrate on discriminating positives from mostly hard negatives. It also enables further improvement from a multi-tasking learning (MTL) perspective: retrieval problems within each cluster can be regarded as individual tasks; inspired by recent successes in prompting and prefix-tuning, we propose an efficient task adaption technique further boosting the retrieval performance within each cluster with negligible overheads.|嵌入式检索方法以其简单有效的特点在现代推荐系统中得到了广泛的应用。然而，在生产中部署和迭代 EBR 的过程中，我们仍然发现了现有方法中的一些基本问题。首先，在处理大量候选项目时，EBR 模型往往难以平衡区分高度相关项目(正面)和无关项目(简单负面)以及有些相关但没有竞争性的项目(硬负面)。此外，由于最近向量搜索的“贪婪”特性，我们对检索结果的多样性和公平性几乎没有控制。这些问题影响了 EBR 方法在大规模工业场景中的性能。本文介绍了一个简单且已经在生产中得到验证的解决方案来克服这些问题。该解决方案采用分而治之的方法: 将整个候选项集划分为多个集群，并运行 EBR 并行地从每个集群中检索相关候选项; 然后通过一些可控的合并策略将每个集群中的最优候选项集合起来。这种方法允许我们的 EBR 模型只集中于区分正面和大多数硬负面。它还能从多任务学习(MTL)的角度进一步改进: 每个集群中的检索问题可以被视为单个任务; 受最近在提示和前缀调优方面的成功启发，我们提出了一种有效的任务适应技术，进一步提高了每个集群中的检索性能，开销可以忽略不计。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Divide+and+Conquer:+Towards+Better+Embedding-based+Retrieval+for+Recommender+Systems+from+a+Multi-task+Perspective)|0| -|[Expressive user embedding from churn and recommendation multi-task learning](https://doi.org/10.1145/3543873.3587306)|Huajun Bai, Davide Liu, Thomas Hirtz, Alexandre Boulenger|Tsinghua University, China; Genify, United Arab Emirates; Genify, China|In this paper, we present a Multi-Task model for Recommendation and Churn prediction (MT) in the retail banking industry. The model leverages a hard parameter-sharing framework and consists of a shared multi-stack encoder with multi-head self-attention and two fully connected task heads. It is trained to achieve two multi-class classification tasks: predicting product churn and identifying the next-best products (NBP) for users, individually. Our experiments demonstrate the superiority of the multi-task model compared to its single-task versions, reaching top-1 precision at 78.1% and 77.6%, for churn and NBP prediction respectively. Moreover, we find that the model learns a coherent and expressive high-level representation reflecting user intentions related to both tasks. There is a clear separation between users with acquisitions and users with churn. In addition, acquirers are more tightly clustered compared to the churners. The gradual separability of churning and acquiring users, who diverge in intent, is a desirable property. It provides a basis for model explainability, critical to industry adoption, and also enables other downstream applications. These potential additional benefits, beyond reducing customer attrition and increasing product use–two primary concerns of businesses, make such a model even more valuable.|本文提出了一个零售银行业推荐和流失预测的多任务模型。该模型利用一个硬参数共享框架，由一个具有多头自注意的共享多栈编码器和两个完全连接的任务头组成。它被训练以完成两个多类别的分类任务: 预测产品流失和为用户分别识别次优产品(NBP)。我们的实验证明了多任务模型相对于单任务模型的优越性，在流失预测和 NBP 预测方面分别达到了78.1% 和77.6% 的 Top-1精度。此外，我们发现该模型学习了一个连贯的和表达的高层次表示，反映了与两个任务相关的用户意图。并购用户和流失用户之间有明显的区别。此外，与搅拌器相比，收购者更紧密地聚集在一起。搅动用户和获取用户的逐渐可分性，这是一个可取的特性，因为用户的意图不同。它为模型的可解释性提供了基础，对于工业的采用至关重要，并且还支持其他下游应用程序。这些潜在的额外好处，除了减少客户流失和增加产品使用(企业的两个主要关注点)之外，使得这种模式更加有价值。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Expressive+user+embedding+from+churn+and+recommendation+multi-task+learning)|0| +|[Divide and Conquer: Towards Better Embedding-based Retrieval for Recommender Systems from a Multi-task Perspective](https://doi.org/10.1145/3543873.3584629)|Yuan Zhang, Xue Dong, Weijie Ding, Biao Li, Peng Jiang, Kun Gai|Unaffiliated, China; Kuaishou Technology, China; Shandong University, China|Embedding-based retrieval (EBR) methods are widely used in modern recommender systems thanks to its simplicity and effectiveness. However, along the journey of deploying and iterating on EBR in production, we still identify some fundamental issues in existing methods. First, when dealing with large corpus of candidate items, EBR models often have difficulties in balancing the performance on distinguishing highly relevant items (positives) from both irrelevant ones (easy negatives) and from somewhat related yet not competitive ones (hard negatives). Also, we have little control in the diversity and fairness of the retrieval results because of the ``greedy'' nature of nearest vector search. These issues compromise the performance of EBR methods in large-scale industrial scenarios. This paper introduces a simple and proven-in-production solution to overcome these issues. The proposed solution takes a divide-and-conquer approach: the whole set of candidate items are divided into multiple clusters and we run EBR to retrieve relevant candidates from each cluster in parallel; top candidates from each cluster are then combined by some controllable merging strategies. This approach allows our EBR models to only concentrate on discriminating positives from mostly hard negatives. It also enables further improvement from a multi-tasking learning (MTL) perspective: retrieval problems within each cluster can be regarded as individual tasks; inspired by recent successes in prompting and prefix-tuning, we propose an efficient task adaption technique further boosting the retrieval performance within each cluster with negligible overheads.|嵌入式检索方法以其简单有效的特点在现代推荐系统中得到了广泛的应用。然而，在生产中部署和迭代 EBR 的过程中，我们仍然发现了现有方法中的一些基本问题。首先，在处理大量候选项目时，EBR 模型往往难以平衡区分高度相关项目(正面)和无关项目(简单负面)以及有些相关但没有竞争性的项目(硬负面)。此外，由于最近向量搜索的“贪婪”特性，我们对检索结果的多样性和公平性几乎没有控制。这些问题影响了 EBR 方法在大规模工业场景中的性能。本文介绍了一个简单且已经在生产中得到验证的解决方案来克服这些问题。该解决方案采用分而治之的方法: 将整个候选项集划分为多个集群，并运行 EBR 并行地从每个集群中检索相关候选项; 然后通过一些可控的合并策略将每个集群中的最优候选项集合起来。这种方法允许我们的 EBR 模型只集中于区分正面和大多数硬负面。它还能从多任务学习(MTL)的角度进一步改进: 每个集群中的检索问题可以被视为单个任务; 受最近在提示和前缀调优方面的成功启发，我们提出了一种有效的任务适应技术，进一步提高了每个集群中的检索性能，开销可以忽略不计。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Divide+and+Conquer:+Towards+Better+Embedding-based+Retrieval+for+Recommender+Systems+from+a+Multi-task+Perspective)|0| +|[Expressive user embedding from churn and recommendation multi-task learning](https://doi.org/10.1145/3543873.3587306)|Huajun Bai, Davide Liu, Thomas Hirtz, Alexandre Boulenger|Genify, United Arab Emirates; Tsinghua University, China; Genify, China|In this paper, we present a Multi-Task model for Recommendation and Churn prediction (MT) in the retail banking industry. The model leverages a hard parameter-sharing framework and consists of a shared multi-stack encoder with multi-head self-attention and two fully connected task heads. It is trained to achieve two multi-class classification tasks: predicting product churn and identifying the next-best products (NBP) for users, individually. Our experiments demonstrate the superiority of the multi-task model compared to its single-task versions, reaching top-1 precision at 78.1% and 77.6%, for churn and NBP prediction respectively. Moreover, we find that the model learns a coherent and expressive high-level representation reflecting user intentions related to both tasks. There is a clear separation between users with acquisitions and users with churn. In addition, acquirers are more tightly clustered compared to the churners. The gradual separability of churning and acquiring users, who diverge in intent, is a desirable property. It provides a basis for model explainability, critical to industry adoption, and also enables other downstream applications. These potential additional benefits, beyond reducing customer attrition and increasing product use–two primary concerns of businesses, make such a model even more valuable.|本文提出了一个零售银行业推荐和流失预测的多任务模型。该模型利用一个硬参数共享框架，由一个具有多头自注意的共享多栈编码器和两个完全连接的任务头组成。它被训练以完成两个多类别的分类任务: 预测产品流失和为用户分别识别次优产品(NBP)。我们的实验证明了多任务模型相对于单任务模型的优越性，在流失预测和 NBP 预测方面分别达到了78.1% 和77.6% 的 Top-1精度。此外，我们发现该模型学习了一个连贯的和表达的高层次表示，反映了与两个任务相关的用户意图。并购用户和流失用户之间有明显的区别。此外，与搅拌器相比，收购者更紧密地聚集在一起。搅动用户和获取用户的逐渐可分性，这是一个可取的特性，因为用户的意图不同。它为模型的可解释性提供了基础，对于工业的采用至关重要，并且还支持其他下游应用程序。这些潜在的额外好处，除了减少客户流失和增加产品使用(企业的两个主要关注点)之外，使得这种模式更加有价值。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Expressive+user+embedding+from+churn+and+recommendation+multi-task+learning)|0| |[Continual Transfer Learning for Cross-Domain Click-Through Rate Prediction at Taobao](https://doi.org/10.1145/3543873.3584625)|Lixin Liu, Yanling Wang, Tianming Wang, Dong Guan, Jiawei Wu, Jingxu Chen, Rong Xiao, Wenxiang Zhu, Fei Fang|Alibaba group, China; Renmin University of China, China; Alibaba Group, China|As one of the largest e-commerce platforms in the world, Taobao's recommendation systems (RSs) serve the demands of shopping for hundreds of millions of customers. Click-Through Rate (CTR) prediction is a core component of the RS. One of the biggest characteristics in CTR prediction at Taobao is that there exist multiple recommendation domains where the scales of different domains vary significantly. Therefore, it is crucial to perform cross-domain CTR prediction to transfer knowledge from large domains to small domains to alleviate the data sparsity issue. However, existing cross-domain CTR prediction methods are proposed for static knowledge transfer, ignoring that all domains in real-world RSs are continually time-evolving. In light of this, we present a necessary but novel task named Continual Transfer Learning (CTL), which transfers knowledge from a time-evolving source domain to a time-evolving target domain. In this work, we propose a simple and effective CTL model called CTNet to solve the problem of continual cross-domain CTR prediction at Taobao, and CTNet can be trained efficiently. Particularly, CTNet considers an important characteristic in the industry that models has been continually well-trained for a very long time. So CTNet aims to fully utilize all the well-trained model parameters in both source domain and target domain to avoid losing historically acquired knowledge, and only needs incremental target domain data for training to guarantee efficiency. Extensive offline experiments and online A/B testing at Taobao demonstrate the efficiency and effectiveness of CTNet. CTNet is now deployed online in the recommender systems of Taobao, serving the main traffic of hundreds of millions of active users.|作为世界上最大的电子商务平台之一，淘宝的推荐系统(RS)为数以亿计的顾客提供购物服务。点进率预测是遥感的核心组成部分。淘宝网点击率预测的最大特点之一是存在多个推荐域，不同域的规模差异很大。因此，进行跨域 CTR 预测，将知识从大域转移到小域，以缓解数据稀疏性问题至关重要。然而，现有的跨域 CTR 预测方法都是针对静态知识转移而提出的，忽略了现实 RSS 中的所有域都是不断时间演化的。鉴于此，我们提出了一个必要的，但新颖的任务称为连续转移学习(CTL) ，它将知识从一个时间演化的源领域转移到一个时间演化的目标领域。本文提出了一种简单有效的 CTL 模型 CTNet 来解决淘宝网连续跨域 CTR 预测问题，可以有效地训练 CTNet。特别是，CTNet 认为模特行业的一个重要特征是模特长期以来一直受到良好的培训。因此，CTNet 的目标是充分利用源域和目标域中所有训练有素的模型参数，避免丢失历史获得的知识，只需要增量的目标域数据进行训练，以保证训练效率。在淘宝上的大量离线实验和在线 A/B 测试证明了 CTNet 的效率和有效性。CTNet 现已部署在淘宝网的推荐系统中，为数亿活跃用户的主要流量提供服务。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Continual+Transfer+Learning+for+Cross-Domain+Click-Through+Rate+Prediction+at+Taobao)|0| |[MAKE: Vision-Language Pre-training based Product Retrieval in Taobao Search](https://doi.org/10.1145/3543873.3584627)|Xiaoyang Zheng, Zilong Wang, Sen Li, Ke Xu, Tao Zhuang, Qingwen Liu, Xiaoyi Zeng|; Alibaba Group, China|Taobao Search consists of two phases: the retrieval phase and the ranking phase. Given a user query, the retrieval phase returns a subset of candidate products for the following ranking phase. Recently, the paradigm of pre-training and fine-tuning has shown its potential in incorporating visual clues into retrieval tasks. In this paper, we focus on solving the problem of text-to-multimodal retrieval in Taobao Search. We consider that users' attention on titles or images varies on products. Hence, we propose a novel Modal Adaptation module for cross-modal fusion, which helps assigns appropriate weights on texts and images across products. Furthermore, in e-commerce search, user queries tend to be brief and thus lead to significant semantic imbalance between user queries and product titles. Therefore, we design a separate text encoder and a Keyword Enhancement mechanism to enrich the query representations and improve text-to-multimodal matching. To this end, we present a novel vision-language (V+L) pre-training methods to exploit the multimodal information of (user query, product title, product image). Extensive experiments demonstrate that our retrieval-specific pre-training model (referred to as MAKE) outperforms existing V+L pre-training methods on the text-to-multimodal retrieval task. MAKE has been deployed online and brings major improvements on the retrieval system of Taobao Search.|淘宝搜索包括两个阶段: 检索阶段和排名阶段。给定一个用户查询，检索阶段返回下一个排序阶段的候选产品的子集。最近，预先训练和微调的范式已经显示了其在将视觉线索纳入检索任务方面的潜力。本文主要研究淘宝搜索中文本到多模式检索的问题。我们认为用户对标题或图片的关注因产品而异。因此，我们提出了一个新的模态适应模块的跨模态融合，这有助于分配适当的权重的文本和图像跨产品。此外，在电子商务搜索中，用户查询往往是简短的，从而导致用户查询和产品标题之间的语义严重失衡。因此，我们设计了一个单独的文本编码器和一个关键字增强机制，以丰富查询表示和改善文本到多模式匹配。为此，我们提出了一种新的视觉语言(V + L)预训练方法来利用多模态信息(用户查询、产品标题、产品图像)。大量的实验表明，我们的检索特定的预训练模型(简称 MAKE)在文本到多模态检索任务上优于现有的 V + L 预训练方法。MAKE 已经在线部署，并对淘宝搜索的检索系统进行了重大改进。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=MAKE:+Vision-Language+Pre-training+based+Product+Retrieval+in+Taobao+Search)|0| -|[HAPENS: Hardness-Personalized Negative Sampling for Implicit Collaborative Filtering](https://doi.org/10.1145/3543873.3584631)|Haoxin Liu, Pu Zhao, Si Qin, Yong Shi, Mirror Xu, Qingwei Lin, Dongmei Zhang|Microsoft Bing, China; Microsoft Research, China|For training implicit collaborative filtering (ICF) models, hard negative sampling (HNS) has become a state-of-the-art solution for obtaining negative signals from massive uninteracted items. However, selecting appropriate hardness levels for personalized recommendations remains a fundamental, yet underexplored, problem. Previous HNS works have primarily adjusted the hardness level by tuning a single hyperparameter. However, applying the same hardness level to each user is unsuitable due to varying user behavioral characteristics, the quantity and quality of user records, and different consistencies of models’ inductive biases. Moreover, increasing the number of hyperparameters is not practical due to the massive number of users. To address this important and challenging problem, we propose a model-agnostic and practical approach called hardness-personalized negative sampling (HAPENS). HAPENS uses a two-stage approach: in stage one, it trains the ICF model with a customized objective function that optimizes its worst performance on each user’s interacted item set. In stage two, it utilizes these worst performances as personalized hardness levels with a well-designed sampling distribution, and trains the final model with the same architecture. We evaluated HAPENS on the collected Bing advertising dataset and one public dataset, and the comprehensive experimental results demonstrate its robustness and superiority. Moreover, HAPENS has delivered significant benefits to the Bing advertising system. To the best of our knowledge, we are the first to study this important and challenging problem.|对于训练内隐协同过滤模型(ICF) ，硬负采样(hNS)已成为从大量未交互项目中获取负信号的最新解决方案。然而，为个性化推荐选择合适的硬度水平仍然是一个基本的、尚未得到充分探索的问题。以往的 HNS 工作主要是通过调整单个超参数来调整硬度水平。然而，由于不同的用户行为特征、用户记录的数量和质量以及模型归纳偏差的不同一致性，对每个用户应用相同的硬度水平是不合适的。此外，由于用户数量庞大，增加超参数的数量是不切实际的。为了解决这一重要而具有挑战性的问题，我们提出了一种模型不可知的实用方法，称为硬度个性化阴性采样(HAPENS)。HAPENS 使用两阶段的方法: 在第一阶段，它使用一个定制的目标函数来训练 ICF 模型，该目标函数在每个用户的交互项集上优化其最差的性能。在第二阶段，它利用这些最差的性能作为个性化的硬度水平，具有设计良好的采样分布，并训练最终模型具有相同的架构。我们对搜集到的 Bing 广告数据集和一个公共数据集进行了 HAPENS 评估，综合实验结果表明了 HAPENS 的鲁棒性和优越性。此外，HAPENS 为必应广告系统带来了巨大的好处。据我们所知，我们是第一个研究这个重要而富有挑战性的问题的。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=HAPENS:+Hardness-Personalized+Negative+Sampling+for+Implicit+Collaborative+Filtering)|0| -|[Que2Engage: Embedding-based Retrieval for Relevant and Engaging Products at Facebook Marketplace](https://doi.org/10.1145/3543873.3584633)|Yunzhong He, Yuxin Tian, Mengjiao Wang, Feier Chen, Licheng Yu, Maolong Tang, Congcong Chen, Ning Zhang, Bin Kuang, Arul Prakash|University of California, Merced, USA; Meta, USA|Embedding-based Retrieval (EBR) in e-commerce search is a powerful search retrieval technique to address semantic matches between search queries and products. However, commercial search engines like Facebook Marketplace Search are complex multi-stage systems optimized for multiple business objectives. At Facebook Marketplace, search retrieval focuses on matching search queries with relevant products, while search ranking puts more emphasis on contextual signals to up-rank the more engaging products. As a result, the end-to-end searcher experience is a function of both relevance and engagement, and the interaction between different stages of the system. This presents challenges to EBR systems in order to optimize for better searcher experiences. In this paper we presents Que2Engage, a search EBR system built towards bridging the gap between retrieval and ranking for end-to-end optimizations. Que2Engage takes a multimodal & multitask approach to infuse contextual information into the retrieval stage and to balance different business objectives. We show the effectiveness of our approach via a multitask evaluation framework and thorough baseline comparisons and ablation studies. Que2Engage is deployed on Facebook Marketplace Search and shows significant improvements in searcher engagement in two weeks of A/B testing.|电子商务搜索中的嵌入式检索(EBR)是解决搜索查询与产品之间语义匹配的一种强有力的检索技术。然而，像 Facebook Marketplace Search 这样的商业搜索引擎是为多个业务目标而优化的复杂的多阶段系统。在 Facebook Marketplace，搜索检索侧重于将搜索查询与相关产品进行匹配，而搜索排名更侧重于上下文信号，以提升更具吸引力的产品的排名。因此，端到端的搜索体验是相关性和参与度的函数，以及系统不同阶段之间的相互作用。这对 EBR 系统提出了挑战，以便优化更好的搜索体验。本文介绍了 Que2Engage，这是一个搜索 EBR 系统，旨在弥合检索和排序之间的差距，以实现端到端的优化。Que2Engage 采用多模态和多任务的方法将上下文信息注入检索阶段，并平衡不同的业务目标。我们通过一个多任务评估框架和彻底的基线比较和消融研究来展示我们的方法的有效性。Que2Engage 部署在 Facebook Marketplace Search 上，并在两周的 A/B 测试中显示出搜索者参与度的显著改善。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Que2Engage:+Embedding-based+Retrieval+for+Relevant+and+Engaging+Products+at+Facebook+Marketplace)|0| +|[HAPENS: Hardness-Personalized Negative Sampling for Implicit Collaborative Filtering](https://doi.org/10.1145/3543873.3584631)|Haoxin Liu, Pu Zhao, Si Qin, Yong Shi, Mirror Xu, Qingwei Lin, Dongmei Zhang|Microsoft Research, China; Microsoft Bing, China|For training implicit collaborative filtering (ICF) models, hard negative sampling (HNS) has become a state-of-the-art solution for obtaining negative signals from massive uninteracted items. However, selecting appropriate hardness levels for personalized recommendations remains a fundamental, yet underexplored, problem. Previous HNS works have primarily adjusted the hardness level by tuning a single hyperparameter. However, applying the same hardness level to each user is unsuitable due to varying user behavioral characteristics, the quantity and quality of user records, and different consistencies of models’ inductive biases. Moreover, increasing the number of hyperparameters is not practical due to the massive number of users. To address this important and challenging problem, we propose a model-agnostic and practical approach called hardness-personalized negative sampling (HAPENS). HAPENS uses a two-stage approach: in stage one, it trains the ICF model with a customized objective function that optimizes its worst performance on each user’s interacted item set. In stage two, it utilizes these worst performances as personalized hardness levels with a well-designed sampling distribution, and trains the final model with the same architecture. We evaluated HAPENS on the collected Bing advertising dataset and one public dataset, and the comprehensive experimental results demonstrate its robustness and superiority. Moreover, HAPENS has delivered significant benefits to the Bing advertising system. To the best of our knowledge, we are the first to study this important and challenging problem.|对于训练内隐协同过滤模型(ICF) ，硬负采样(hNS)已成为从大量未交互项目中获取负信号的最新解决方案。然而，为个性化推荐选择合适的硬度水平仍然是一个基本的、尚未得到充分探索的问题。以往的 HNS 工作主要是通过调整单个超参数来调整硬度水平。然而，由于不同的用户行为特征、用户记录的数量和质量以及模型归纳偏差的不同一致性，对每个用户应用相同的硬度水平是不合适的。此外，由于用户数量庞大，增加超参数的数量是不切实际的。为了解决这一重要而具有挑战性的问题，我们提出了一种模型不可知的实用方法，称为硬度个性化阴性采样(HAPENS)。HAPENS 使用两阶段的方法: 在第一阶段，它使用一个定制的目标函数来训练 ICF 模型，该目标函数在每个用户的交互项集上优化其最差的性能。在第二阶段，它利用这些最差的性能作为个性化的硬度水平，具有设计良好的采样分布，并训练最终模型具有相同的架构。我们对搜集到的 Bing 广告数据集和一个公共数据集进行了 HAPENS 评估，综合实验结果表明了 HAPENS 的鲁棒性和优越性。此外，HAPENS 为必应广告系统带来了巨大的好处。据我们所知，我们是第一个研究这个重要而富有挑战性的问题的。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=HAPENS:+Hardness-Personalized+Negative+Sampling+for+Implicit+Collaborative+Filtering)|0| +|[Que2Engage: Embedding-based Retrieval for Relevant and Engaging Products at Facebook Marketplace](https://doi.org/10.1145/3543873.3584633)|Yunzhong He, Yuxin Tian, Mengjiao Wang, Feier Chen, Licheng Yu, Maolong Tang, Congcong Chen, Ning Zhang, Bin Kuang, Arul Prakash|Meta, USA; University of California, Merced, USA|Embedding-based Retrieval (EBR) in e-commerce search is a powerful search retrieval technique to address semantic matches between search queries and products. However, commercial search engines like Facebook Marketplace Search are complex multi-stage systems optimized for multiple business objectives. At Facebook Marketplace, search retrieval focuses on matching search queries with relevant products, while search ranking puts more emphasis on contextual signals to up-rank the more engaging products. As a result, the end-to-end searcher experience is a function of both relevance and engagement, and the interaction between different stages of the system. This presents challenges to EBR systems in order to optimize for better searcher experiences. In this paper we presents Que2Engage, a search EBR system built towards bridging the gap between retrieval and ranking for end-to-end optimizations. Que2Engage takes a multimodal & multitask approach to infuse contextual information into the retrieval stage and to balance different business objectives. We show the effectiveness of our approach via a multitask evaluation framework and thorough baseline comparisons and ablation studies. Que2Engage is deployed on Facebook Marketplace Search and shows significant improvements in searcher engagement in two weeks of A/B testing.|电子商务搜索中的嵌入式检索(EBR)是解决搜索查询与产品之间语义匹配的一种强有力的检索技术。然而，像 Facebook Marketplace Search 这样的商业搜索引擎是为多个业务目标而优化的复杂的多阶段系统。在 Facebook Marketplace，搜索检索侧重于将搜索查询与相关产品进行匹配，而搜索排名更侧重于上下文信号，以提升更具吸引力的产品的排名。因此，端到端的搜索体验是相关性和参与度的函数，以及系统不同阶段之间的相互作用。这对 EBR 系统提出了挑战，以便优化更好的搜索体验。本文介绍了 Que2Engage，这是一个搜索 EBR 系统，旨在弥合检索和排序之间的差距，以实现端到端的优化。Que2Engage 采用多模态和多任务的方法将上下文信息注入检索阶段，并平衡不同的业务目标。我们通过一个多任务评估框架和彻底的基线比较和消融研究来展示我们的方法的有效性。Que2Engage 部署在 Facebook Marketplace Search 上，并在两周的 A/B 测试中显示出搜索者参与度的显著改善。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Que2Engage:+Embedding-based+Retrieval+for+Relevant+and+Engaging+Products+at+Facebook+Marketplace)|0| |[Learning Multi-Stage Multi-Grained Semantic Embeddings for E-Commerce Search](https://doi.org/10.1145/3543873.3584638)|Binbin Wang, Mingming Li, Zhixiong Zeng, Jingwei Zhuo, Songlin Wang, Sulong Xu, Bo Long, Weipeng Yan|JD.com, China|Retrieving relevant items that match users' queries from billion-scale corpus forms the core of industrial e-commerce search systems, in which embedding-based retrieval (EBR) methods are prevailing. These methods adopt a two-tower framework to learn embedding vectors for query and item separately and thus leverage efficient approximate nearest neighbor (ANN) search to retrieve relevant items. However, existing EBR methods usually ignore inconsistent user behaviors in industrial multi-stage search systems, resulting in insufficient retrieval efficiency with a low commercial return. To tackle this challenge, we propose to improve EBR methods by learning Multi-level Multi-Grained Semantic Embeddings(MMSE). We propose the multi-stage information mining to exploit the ordered, clicked, unclicked and random sampled items in practical user behavior data, and then capture query-item similarity via a post-fusion strategy. We then propose multi-grained learning objectives that integrate the retrieval loss with global comparison ability and the ranking loss with local comparison ability to generate semantic embeddings. Both experiments on a real-world billion-scale dataset and online A/B tests verify the effectiveness of MMSE in achieving significant performance improvements on metrics such as offline recall and online conversion rate (CVR).|基于嵌入式检索(EBR)方法是工业电子商务搜索系统的核心，它可以从数十亿规模的语料库中检索出与用户查询相匹配的相关项目。这些方法采用双塔架构，分别学习查询和项目的嵌入向量，从而利用有效的近似最近邻(ANN)搜索来检索相关项目。然而，现有的 EBR 方法往往忽略了工业多阶段搜索系统中不一致的用户行为，导致检索效率不足，商业收益较低。为了解决这一问题，我们提出通过学习多级多粒度语义嵌入(MMSE)来改进 EBR 方法。提出了一种基于多阶段信息挖掘的方法，利用实际用户行为数据中的有序、点击、未点击和随机抽样条目，通过后融合策略获取查询条目的相似性。然后提出多粒度学习目标，将检索损失与全局比较能力、排序损失与局部比较能力相结合，生成语义嵌入。在真实世界的十亿级数据集上的实验和在线 A/B 测试都验证了 MMSE 在离线召回率和在线转换率(CVR)等指标上实现显著性能改进的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Learning+Multi-Stage+Multi-Grained+Semantic+Embeddings+for+E-Commerce+Search)|0| |[CAM2: Conformity-Aware Multi-Task Ranking Model for Large-Scale Recommender Systems](https://doi.org/10.1145/3543873.3584657)|Ameya Raul, Amey Porobo Dharwadker, Brad Schumitsch|Meta Inc., USA|Learning large-scale industrial recommender system models by fitting them to historical user interaction data makes them vulnerable to conformity bias. This may be due to a number of factors, including the fact that user interests may be difficult to determine and that many items are often interacted with based on ecosystem factors other than their relevance to the individual user. In this work, we introduce CAM2, a conformity-aware multi-task ranking model to serve relevant items to users on one of the largest industrial recommendation platforms. CAM2 addresses these challenges systematically by leveraging causal modeling to disentangle users' conformity to popular items from their true interests. This framework is generalizable and can be scaled to support multiple representations of conformity and user relevance in any large-scale recommender system. We provide deeper practical insights and demonstrate the effectiveness of the proposed model through improvements in offline evaluation metrics compared to our production multi-task ranking model. We also show through online experiments that the CAM2 model results in a significant 0.50% increase in aggregated user engagement, coupled with a 0.21% increase in daily active users on Facebook Watch, a popular video discovery and sharing platform serving billions of users.|通过将大规模工业推荐系统模型与历史用户交互数据进行拟合，使其容易受到一致性偏差的影响。这可能是由于若干因素，包括用户的兴趣可能难以确定，而且许多项目往往基于生态系统因素而不是它们与个别用户的相关性进行交互。在这项工作中，我们介绍了 CAM2，一个整合意识的多任务排序模型，以服务于相关项目的用户在一个最大的行业推荐平台。CAM2通过利用因果建模系统地解决这些挑战，从用户的真实兴趣中分离出用户对流行项目的一致性。这个框架是可以推广的，可以扩展到支持任何大规模推荐系统中的多种一致性和用户相关性的表示。我们提供了更深入的实践见解，并证明了该模型的有效性，通过改进离线评估指标相比，我们的生产多任务排序模型。我们还通过在线实验表明，CAM2模型显著增加了0.50% 的聚合用户参与度，同时 Facebook Watch 的日常活跃用户增加了0.21% 。 Facebook Watch 是一个流行的视频发现和分享平台，服务于数十亿用户。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=CAM2:+Conformity-Aware+Multi-Task+Ranking+Model+for+Large-Scale+Recommender+Systems)|0| |[A Deep Behavior Path Matching Network for Click-Through Rate Prediction](https://doi.org/10.1145/3543873.3584662)|Jian Dong, Yisong Yu, Yapeng Zhang, Yimin Lv, Shuli Wang, Beihong Jin, Yongkang Wang, Xingxing Wang, Dong Wang|Institute of Software, Chinese Academy of Sciences, China and University of Chinese Academy of Sciences, China; Meituan Ltd., China; Meituan Ltd, China|User behaviors on an e-commerce app not only contain different kinds of feedback on items but also sometimes imply the cognitive clue of the user's decision-making. For understanding the psychological procedure behind user decisions, we present the behavior path and propose to match the user's current behavior path with historical behavior paths to predict user behaviors on the app. Further, we design a deep neural network for behavior path matching and solve three difficulties in modeling behavior paths: sparsity, noise interference, and accurate matching of behavior paths. In particular, we leverage contrastive learning to augment user behavior paths, provide behavior path self-activation to alleviate the effect of noise, and adopt a two-level matching mechanism to identify the most appropriate candidate. Our model shows excellent performance on two real-world datasets, outperforming the state-of-the-art CTR model. Moreover, our model has been deployed on the Meituan food delivery platform and has accumulated 1.6% improvement in CTR and 1.8% improvement in advertising revenue.|用户在电子商务应用程序上的行为不仅包含对项目的不同类型的反馈，而且有时还意味着用户决策的认知线索。为了理解用户决策背后的心理过程，我们提出了行为路径，并建议匹配用户的当前行为路径和历史行为路径，以预测用户在应用程序上的行为。进一步，我们设计了一个用于行为路径匹配的深层神经网络，解决了行为路径建模中的三个难点: 稀疏性、噪声干扰和行为路径的精确匹配。特别地，我们利用对比学习来增强用户的行为路径，提供行为路径自激活来减轻噪声的影响，并采用两级匹配机制来确定最合适的候选者。我们的模型在两个真实世界的数据集上显示了出色的性能，优于最先进的 CTR 模型。此外，我们的模型已经部署在美团食品配送平台上，点击率累计提高了1.6% ，广告收入累计提高了1.8% 。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Deep+Behavior+Path+Matching+Network+for+Click-Through+Rate+Prediction)|0| |[Cross-lingual Search for e-Commerce based on Query Translatability and Mixed-Domain Fine-Tuning](https://doi.org/10.1145/3543873.3587660)|Jesus PerezMartin, Jorge GomezRobles, Asier GutiérrezFandiño, Pankaj Adsul, Sravanthi Rajanala, Leonardo Lezcano|Walmart Global Tech, USA|Online stores in the US offer a unique scenario for Cross-Lingual Information Retrieval (CLIR) due to the mix of Spanish and English in user queries. Machine Translation (MT) provides an opportunity to lift relevance by translating the Spanish queries to English before delivering them to the search engine. However, polysemy-derived problems, high latency and context scarcity in product search, make generic MT an impractical solution. The wide diversity of products in marketplaces injects non-translatable entities, loanwords, ambiguous morphemes, cross-language ambiguity and a variety of Spanish dialects in the communication between buyers and sellers, posing a thread to the accuracy of MT. In this work, we leverage domain adaptation on a simplified architecture of Neural Machine Translation (NMT) to make both latency and accuracy suitable for e-commerce search. Our NMT model is fine-tuned on a mixed-domain corpus based on engagement data expanded with catalog back-translation techniques. Beyond accuracy, and given that translation is not the goal but the means to relevant results, the problem of Query Translatability is addressed by a classifier on whether the translation should be automatic or explicitly requested. We assembled these models into a query translation system that we tested and launched at Walmart.com , with a statistically significant lift in Spanish GMV and an nDCG gain for Spanish queries of +70%.|在美国的网上商店为跨语言信息检索(CLIR)提供了一个独特的场景，因为在用户查询中混合了西班牙语和英语。机器翻译(MT)提供了一个机会，通过将西班牙语查询翻译成英语，然后再将其传递给搜索引擎，从而提高相关性。然而，产品搜索中的多义性问题、高延迟性和上下文稀缺性使得通用机器翻译成为一种不切实际的解决方案。市场中产品的广泛多样性在买卖双方的交流中注入了不可翻译的实体、外来词、模棱两可的语素、跨语言模糊性和各种西班牙方言，这为神经机器翻译(NMT)的准确性提供了一条线索。在这项工作中，我们利用神经机器翻译(NMT)的简化架构的领域适应性，使延迟和准确性都适用于电子商务搜索。我们的 NMT 模型是在基于使用目录反向翻译技术扩展的参与数据的混合域语料库上进行微调的。除了准确性之外，考虑到翻译不是目标，而是获得相关结果的手段，查询可翻译性问题由分类器处理，分类器决定翻译应该是自动的还是显式的。我们将这些模型组合成一个查询翻译系统，并在 Walmart.com 上进行了测试和推出，西班牙语 GMV 的统计学显著提升，西班牙语查询的 nDCG 增幅为 + 70% 。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Cross-lingual+Search+for+e-Commerce+based+on+Query+Translatability+and+Mixed-Domain+Fine-Tuning)|0| -|[Enhancing User Personalization in Conversational Recommenders](https://doi.org/10.1145/3543507.3583192)|Allen Lin, Ziwei Zhu, Jianling Wang, James Caverlee|Texas A&M University, USA; George Mason University, USA|Conversational recommenders are emerging as a powerful tool to personalize a user's recommendation experience. Through a back-and-forth dialogue, users can quickly hone in on just the right items. Many approaches to conversational recommendation, however, only partially explore the user preference space and make limiting assumptions about how user feedback can be best incorporated, resulting in long dialogues and poor recommendation performance. In this paper, we propose a novel conversational recommendation framework with two unique features: (i) a greedy NDCG attribute selector, to enhance user personalization in the interactive preference elicitation process by prioritizing attributes that most effectively represent the actual preference space of the user; and (ii) a user representation refiner, to effectively fuse together the user preferences collected from the interactive elicitation process to obtain a more personalized understanding of the user. Through extensive experiments on four frequently used datasets, we find the proposed framework not only outperforms all the state-of-the-art conversational recommenders (in terms of both recommendation performance and conversation efficiency), but also provides a more personalized experience for the user under the proposed multi-groundtruth multi-round conversational recommendation setting.|对话式推荐正在成为个性化用户推荐体验的强大工具。通过反复的对话，用户可以快速找到正确的项目。然而，许多会话推荐方法只是部分地探索了用户偏好空间，并对如何最好地整合用户反馈进行了有限的假设，导致了冗长的对话和糟糕的推荐性能。本文提出了一种新的会话推荐框架，该框架具有两个独特的特征: (1)贪婪的 NDCG 属性选择器，通过对最有效地表示用户实际偏好空间的属性进行优先排序，增强交互式偏好启发过程中的用户个性化; (2)用户表示细化器，有效地融合交互式启发过程中收集到的用户偏好，以获得对用户更个性化的理解。通过对四个常用数据集的大量实验，我们发现该框架不仅在推荐性能和会话效率方面优于所有最先进的会话推荐器，而且在提出的多地面真相多轮会话推荐设置下为用户提供了更加个性化的体验。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Enhancing+User+Personalization+in+Conversational+Recommenders)|0| -|[Dual-interest Factorization-heads Attention for Sequential Recommendation](https://doi.org/10.1145/3543507.3583278)|Guanyu Lin, Chen Gao, Yu Zheng, Jianxin Chang, Yanan Niu, Yang Song, Zhiheng Li, Depeng Jin, Yong Li|Tsinghua University, China; Department of Electronic Engineering, Tsinghua University, China; kuaishou, China|Accurate user interest modeling is vital for recommendation scenarios. One of the effective solutions is the sequential recommendation that relies on click behaviors, but this is not elegant in the video feed recommendation where users are passive in receiving the streaming contents and return skip or no-skip behaviors. Here skip and no-skip behaviors can be treated as negative and positive feedback, respectively. With the mixture of positive and negative feedback, it is challenging to capture the transition pattern of behavioral sequence. To do so, FeedRec has exploited a shared vanilla Transformer, which may be inelegant because head interaction of multi-heads attention does not consider different types of feedback. In this paper, we propose Dual-interest Factorization-heads Attention for Sequential Recommendation (short for DFAR) consisting of feedback-aware encoding layer, dual-interest disentangling layer and prediction layer. In the feedback-aware encoding layer, we first suppose each head of multi-heads attention can capture specific feedback relations. Then we further propose factorization-heads attention which can mask specific head interaction and inject feedback information so as to factorize the relation between different types of feedback. Additionally, we propose a dual-interest disentangling layer to decouple positive and negative interests before performing disentanglement on their representations. Finally, we evolve the positive and negative interests by corresponding towers whose outputs are contrastive by BPR loss. Experiments on two real-world datasets show the superiority of our proposed method against state-of-the-art baselines. Further ablation study and visualization also sustain its effectiveness. We release the source code here: https://github.com/tsinghua-fib-lab/WWW2023-DFAR.|准确的用户兴趣建模对于推荐场景至关重要。其中一个有效的解决方案是依赖于点击行为的顺序推荐，但是在视频提要推荐中这并不优雅，因为用户在接收流内容和返回跳过或不跳过行为时是被动的。在这里，跳过和不跳过行为可以分别视为负反馈和正反馈。由于正反馈和负反馈的混合，捕捉行为序列的转换模式具有挑战性。为此，FeedRec 利用了一个共享的香草变压器，这可能是不雅的，因为多头注意的头部交互没有考虑不同类型的反馈。本文提出了由反馈感知编码层、双兴趣分解层和预测层组成的双兴趣分解顺序推荐系统。在反馈感知编码层，我们首先假设多头注意的每个头都能捕获特定的反馈关系。然后进一步提出因子分解-头注意，它可以掩盖特定的头交互，并注入反馈信息，从而对不同类型的反馈之间的关系进行因子分解。此外，我们提出了一个双利益解缠层，以解耦正面和负面的利益之前，执行解缠的表示。最后，我们通过相应的塔进行正负利益演化，其输出由于业务流程重组损失而具有对比性。在两个实际数据集上的实验表明了我们提出的方法对最先进的基线的优越性。进一步的消融研究和可视化也支持其有效性。我们在这里发布源代码: https://github.com/tsinghua-fib-lab/www2023-dfar。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Dual-interest+Factorization-heads+Attention+for+Sequential+Recommendation)|0| +|[Enhancing User Personalization in Conversational Recommenders](https://doi.org/10.1145/3543507.3583192)|Allen Lin, Ziwei Zhu, Jianling Wang, James Caverlee|George Mason University, USA; Texas A&M University, USA|Conversational recommenders are emerging as a powerful tool to personalize a user's recommendation experience. Through a back-and-forth dialogue, users can quickly hone in on just the right items. Many approaches to conversational recommendation, however, only partially explore the user preference space and make limiting assumptions about how user feedback can be best incorporated, resulting in long dialogues and poor recommendation performance. In this paper, we propose a novel conversational recommendation framework with two unique features: (i) a greedy NDCG attribute selector, to enhance user personalization in the interactive preference elicitation process by prioritizing attributes that most effectively represent the actual preference space of the user; and (ii) a user representation refiner, to effectively fuse together the user preferences collected from the interactive elicitation process to obtain a more personalized understanding of the user. Through extensive experiments on four frequently used datasets, we find the proposed framework not only outperforms all the state-of-the-art conversational recommenders (in terms of both recommendation performance and conversation efficiency), but also provides a more personalized experience for the user under the proposed multi-groundtruth multi-round conversational recommendation setting.|对话式推荐正在成为个性化用户推荐体验的强大工具。通过反复的对话，用户可以快速找到正确的项目。然而，许多会话推荐方法只是部分地探索了用户偏好空间，并对如何最好地整合用户反馈进行了有限的假设，导致了冗长的对话和糟糕的推荐性能。本文提出了一种新的会话推荐框架，该框架具有两个独特的特征: (1)贪婪的 NDCG 属性选择器，通过对最有效地表示用户实际偏好空间的属性进行优先排序，增强交互式偏好启发过程中的用户个性化; (2)用户表示细化器，有效地融合交互式启发过程中收集到的用户偏好，以获得对用户更个性化的理解。通过对四个常用数据集的大量实验，我们发现该框架不仅在推荐性能和会话效率方面优于所有最先进的会话推荐器，而且在提出的多地面真相多轮会话推荐设置下为用户提供了更加个性化的体验。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Enhancing+User+Personalization+in+Conversational+Recommenders)|0| +|[Dual-interest Factorization-heads Attention for Sequential Recommendation](https://doi.org/10.1145/3543507.3583278)|Guanyu Lin, Chen Gao, Yu Zheng, Jianxin Chang, Yanan Niu, Yang Song, Zhiheng Li, Depeng Jin, Yong Li|Tsinghua University, China; kuaishou, China; Department of Electronic Engineering, Tsinghua University, China|Accurate user interest modeling is vital for recommendation scenarios. One of the effective solutions is the sequential recommendation that relies on click behaviors, but this is not elegant in the video feed recommendation where users are passive in receiving the streaming contents and return skip or no-skip behaviors. Here skip and no-skip behaviors can be treated as negative and positive feedback, respectively. With the mixture of positive and negative feedback, it is challenging to capture the transition pattern of behavioral sequence. To do so, FeedRec has exploited a shared vanilla Transformer, which may be inelegant because head interaction of multi-heads attention does not consider different types of feedback. In this paper, we propose Dual-interest Factorization-heads Attention for Sequential Recommendation (short for DFAR) consisting of feedback-aware encoding layer, dual-interest disentangling layer and prediction layer. In the feedback-aware encoding layer, we first suppose each head of multi-heads attention can capture specific feedback relations. Then we further propose factorization-heads attention which can mask specific head interaction and inject feedback information so as to factorize the relation between different types of feedback. Additionally, we propose a dual-interest disentangling layer to decouple positive and negative interests before performing disentanglement on their representations. Finally, we evolve the positive and negative interests by corresponding towers whose outputs are contrastive by BPR loss. Experiments on two real-world datasets show the superiority of our proposed method against state-of-the-art baselines. Further ablation study and visualization also sustain its effectiveness. We release the source code here: https://github.com/tsinghua-fib-lab/WWW2023-DFAR.|准确的用户兴趣建模对于推荐场景至关重要。其中一个有效的解决方案是依赖于点击行为的顺序推荐，但是在视频提要推荐中这并不优雅，因为用户在接收流内容和返回跳过或不跳过行为时是被动的。在这里，跳过和不跳过行为可以分别视为负反馈和正反馈。由于正反馈和负反馈的混合，捕捉行为序列的转换模式具有挑战性。为此，FeedRec 利用了一个共享的香草变压器，这可能是不雅的，因为多头注意的头部交互没有考虑不同类型的反馈。本文提出了由反馈感知编码层、双兴趣分解层和预测层组成的双兴趣分解顺序推荐系统。在反馈感知编码层，我们首先假设多头注意的每个头都能捕获特定的反馈关系。然后进一步提出因子分解-头注意，它可以掩盖特定的头交互，并注入反馈信息，从而对不同类型的反馈之间的关系进行因子分解。此外，我们提出了一个双利益解缠层，以解耦正面和负面的利益之前，执行解缠的表示。最后，我们通过相应的塔进行正负利益演化，其输出由于业务流程重组损失而具有对比性。在两个实际数据集上的实验表明了我们提出的方法对最先进的基线的优越性。进一步的消融研究和可视化也支持其有效性。我们在这里发布源代码: https://github.com/tsinghua-fib-lab/www2023-dfar。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Dual-interest+Factorization-heads+Attention+for+Sequential+Recommendation)|0| |[A Cross-Media Retrieval System for Web-SNS-Map Using Suggested Keywords Generating and Ranking Method Based on Search Characteristics](https://doi.org/10.1145/3543873.3587344)|Da Li, Masaki Sugihashi, Tadahiko Kumamoto, Yukiko Kawai|Kyoto Sangyo University, Japan; Chiba Institute of Technology, Japan; Fukuoka University, Japan|The research on multimedia retrieval has lasted for several decades. However, past efforts generally focused on single-media retrieval, where the queries and retrieval results belong to the same media (platform) type, such as social media platforms or search engines. In single-media retrieval, users have to select search media or options based on search characteristics such as contents, time, or spatial distance, they might be unable to retrieve correct results mixed in other media if they carelessly forget to select. In this study, we propose a cross-media retrieval system using suggestion generation methods to integrate three search characteristics of the Web (textual content-based retrieval), SNS (timeliness), and map (spatial distance-aware retrieval). In our previous research, we attempted to improve search efficiency using clustering methods to provide search results to users through related terms, etc. In this paper, we focus on the search efficiency of multiple search media. We utilize Google search engine to obtain the retrieval content from the Web, Twitter to obtain timely information from SNSs, and Google Maps to get geographical information from maps. We apply the obtained retrieval results to analyze the similarities between them by clustering. Then, we generate relevant suggestions and provide them to users. Moreover, we validate the effectiveness of the search results generated by our proposed system.|多媒体检索的研究已经持续了几十年。然而，过去的努力通常集中在单媒体检索，其中查询和检索结果属于相同的媒体(平台)类型，如社会媒体平台或搜索引擎。在单媒体检索中，用户必须根据内容、时间或空间距离等搜索特征选择搜索媒体或选项，如果不小心忘记选择，可能无法检索混合在其他媒体中的正确结果。在本研究中，我们提出一个跨媒体检索系统，利用建议产生的方法来整合网页(文本内容检索)、 SNS (及时性)和地图(空间距离感知检索)的三个搜索特性。在我们以前的研究中，我们尝试使用聚类方法来提高搜索效率，通过相关词汇等为用户提供搜索结果。本文主要研究多种搜索媒体的搜索效率。我们利用谷歌搜索引擎从网络中获取检索内容，利用 Twitter 从 SNS 中获取及时信息，利用谷歌地图从地图中获取地理信息。我们应用所得到的检索结果，通过聚类分析它们之间的相似性。然后，我们生成相关的建议并提供给用户。此外，我们还验证了该系统所产生的搜索结果的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Cross-Media+Retrieval+System+for+Web-SNS-Map+Using+Suggested+Keywords+Generating+and+Ranking+Method+Based+on+Search+Characteristics)|0| -|[A Knowledge Enhanced Hierarchical Fusion Network for CTR Prediction under Account Search Scenario in WeChat](https://doi.org/10.1145/3543873.3584650)|Yuanzhou Yao, Zhao Zhang, Kaijia Yang, Huasheng Liang, Qiang Yan, Fuzheng Zhuang, Yongjun Xu, Boyu Diao, Chao Li|Zhejiang Lab, China; Institute of Computing Technology, Chinese Academy of Sciences, China; WeChat, Tencent, China; Institute of Artificial Intelligence, Beihang University, China|Click-through rate (CTR) estimation plays as a pivotal function module in various online services. Previous studies mainly apply CTR models to the field of recommendation or online advertisement. Indeed, CTR is also critical in information retrieval, since the CTR probability can serve as a valuable feature for a query-document pair. In this paper, we study the CTR task under account search scenario in WeChat, where users search official accounts or mini programs corresponding to an organization. Despite the large number of CTR models, directly applying them to our task is inappropriate since the account retrieval task has a number of specific characteristics. E.g., different from traditional user-centric CTR models, in our task, CTR prediction is query-centric and does not model user information. In addition, queries and accounts are short texts, and heavily rely on prior knowledge and semantic understanding. These characteristics require us to specially design a CTR model for the task. To this end, we propose a novel CTR prediction model named Knowledge eNhanced hIerarchical Fusion nEtwork (KNIFE). Specifically, to tackle the prior information problem, we mine the knowledge graph of accounts as side information; to enhance the representations of queries, we construct a bipartite graph for queries and accounts. In addition, a hierarchical network structure is proposed to fuse the representations of different information in a fine-grained manner. Finally, the representations of queries and accounts are obtained from this hierarchical network and fed into the CTR model together with other features for prediction. We conduct extensive experiments against 12 existing models across two industrial datasets. Both offline and online A/B test results indicate the effectiveness of KNIFE.|在各种网上服务中，点进率评估是一个关键的功能模块。以往的研究主要将点击率模型应用于推荐或在线广告领域。实际上，点击率在信息检索中也很关键，因为点击率可以作为查询-文档对的一个有价值的特性。本文研究了微信中用户搜索官方账号或与组织对应的小程序的帐号搜索情景下的点击率任务。尽管有大量的点击率检索模型，但由于账户检索任务具有许多特殊性，直接将其应用于我们的任务是不合适的。例如，与传统的以用户为中心的 CTR 模型不同，在我们的任务中，CTR 预测是以查询为中心的，不对用户信息建模。此外，查询和帐户是简短的文本，并且严重依赖于先前的知识和语义理解。这些特性要求我们为任务专门设计一个 CTR 模型。为此，我们提出了一种新的 CTR 预测模型——知识增强分层融合网络(KNIFE)。具体来说，为了解决先验信息问题，我们挖掘帐户的知识图作为边信息; 为了增强查询的表示，我们为查询和帐户构造一个二分图。此外，提出了一种分层网络结构，以细粒度的方式融合不同信息的表示。最后，从这个层次网络中获得查询和帐户的表示，并将其与其他用于预测的特征一起反馈到 CTR 模型中。我们对两个工业数据集中的12个现有模型进行了广泛的实验。离线和在线 A/B 测试结果均表明了 KNIFE 的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Knowledge+Enhanced+Hierarchical+Fusion+Network+for+CTR+Prediction+under+Account+Search+Scenario+in+WeChat)|0| +|[A Knowledge Enhanced Hierarchical Fusion Network for CTR Prediction under Account Search Scenario in WeChat](https://doi.org/10.1145/3543873.3584650)|Yuanzhou Yao, Zhao Zhang, Kaijia Yang, Huasheng Liang, Qiang Yan, Fuzheng Zhuang, Yongjun Xu, Boyu Diao, Chao Li|Institute of Computing Technology, Chinese Academy of Sciences, China; Institute of Artificial Intelligence, Beihang University, China; WeChat, Tencent, China; Zhejiang Lab, China|Click-through rate (CTR) estimation plays as a pivotal function module in various online services. Previous studies mainly apply CTR models to the field of recommendation or online advertisement. Indeed, CTR is also critical in information retrieval, since the CTR probability can serve as a valuable feature for a query-document pair. In this paper, we study the CTR task under account search scenario in WeChat, where users search official accounts or mini programs corresponding to an organization. Despite the large number of CTR models, directly applying them to our task is inappropriate since the account retrieval task has a number of specific characteristics. E.g., different from traditional user-centric CTR models, in our task, CTR prediction is query-centric and does not model user information. In addition, queries and accounts are short texts, and heavily rely on prior knowledge and semantic understanding. These characteristics require us to specially design a CTR model for the task. To this end, we propose a novel CTR prediction model named Knowledge eNhanced hIerarchical Fusion nEtwork (KNIFE). Specifically, to tackle the prior information problem, we mine the knowledge graph of accounts as side information; to enhance the representations of queries, we construct a bipartite graph for queries and accounts. In addition, a hierarchical network structure is proposed to fuse the representations of different information in a fine-grained manner. Finally, the representations of queries and accounts are obtained from this hierarchical network and fed into the CTR model together with other features for prediction. We conduct extensive experiments against 12 existing models across two industrial datasets. Both offline and online A/B test results indicate the effectiveness of KNIFE.|在各种网上服务中，点进率评估是一个关键的功能模块。以往的研究主要将点击率模型应用于推荐或在线广告领域。实际上，点击率在信息检索中也很关键，因为点击率可以作为查询-文档对的一个有价值的特性。本文研究了微信中用户搜索官方账号或与组织对应的小程序的帐号搜索情景下的点击率任务。尽管有大量的点击率检索模型，但由于账户检索任务具有许多特殊性，直接将其应用于我们的任务是不合适的。例如，与传统的以用户为中心的 CTR 模型不同，在我们的任务中，CTR 预测是以查询为中心的，不对用户信息建模。此外，查询和帐户是简短的文本，并且严重依赖于先前的知识和语义理解。这些特性要求我们为任务专门设计一个 CTR 模型。为此，我们提出了一种新的 CTR 预测模型——知识增强分层融合网络(KNIFE)。具体来说，为了解决先验信息问题，我们挖掘帐户的知识图作为边信息; 为了增强查询的表示，我们为查询和帐户构造一个二分图。此外，提出了一种分层网络结构，以细粒度的方式融合不同信息的表示。最后，从这个层次网络中获得查询和帐户的表示，并将其与其他用于预测的特征一起反馈到 CTR 模型中。我们对两个工业数据集中的12个现有模型进行了广泛的实验。离线和在线 A/B 测试结果均表明了 KNIFE 的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Knowledge+Enhanced+Hierarchical+Fusion+Network+for+CTR+Prediction+under+Account+Search+Scenario+in+WeChat)|0| |[Multi-Objective Ranking to Boost Navigational Suggestions in eCommerce AutoComplete](https://doi.org/10.1145/3543873.3584649)|Sonali Singh, Sachin Farfade, Prakash Mandayam Comar|Amazon, India|Query AutoComplete (QAC) helps customers complete their search queries quickly by suggesting completed queries. QAC on eCommerce sites usually employ Learning to Rank (LTR) approaches based on customer behaviour signals such as clicks and conversion rates to optimize business metrics. However, they do not exclusively optimize for the quality of suggested queries which results in lack of navigational suggestions like product categories and attributes, e.g., "sports shoes" and "white shoes" for query "shoes". We propose to improve the quality of query suggestions by introducing navigational suggestions without impacting the business metrics. For this purpose, we augment the customer behaviour (CB) based objective with Query-Quality (QQ) objective and assemble them with trainable mixture weights to define multi-objective optimization function. We propose to optimize this multi-objective function by implementing ALMO algorithm to obtain a model robust against any mixture weight. We show that this formulation improves query relevance on an eCommerce QAC dataset by at least 13% over the baseline Deep Pairwise LTR (DeepPLTR) with minimal impact on MRR and results in a lift of 0.26% in GMV in an online A/B test. We also evaluated our approach on public search logs datasets and got improvement in query relevance by using query coherence as QQ objective.|QueryAutoComplete (QAC)通过建议已完成的查询，帮助客户快速完成搜索查询。电子商务网站上的 QAC 通常采用基于客户行为信号(如点击率和转换率)的学习排名(LTR)方法来优化业务指标。然而，它们并不专门针对建议查询的质量进行优化，这会导致缺乏像产品类别和属性这样的导航建议，例如，“运动鞋”和查询“鞋子”的“白鞋子”。我们建议通过引入导航建议而不影响业务度量来提高查询建议的质量。为此，我们将基于顾客行为(CB)的目标与查询质量(QQ)目标相结合，并用可训练的混合权重组合它们来定义多目标优化函数。我们提出通过实现 ALMO 算法来优化这个多目标函数，以获得对任意混合权重的鲁棒模型。我们表明，这种制定方法使电子商务 QAC 数据集的查询相关性比基线 Deep Pairwise LTR (DeepPLTR)至少提高了13% ，对 MRR 的影响最小，并且在线 A/B 测试中导致 GMV 升高0.26% 。对公共检索日志数据集的检索方法进行了评估，并以查询一致性为 QQ 目标，提高了查询相关性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Multi-Objective+Ranking+to+Boost+Navigational+Suggestions+in+eCommerce+AutoComplete)|0| -|[Personalization and Recommendations in Search](https://doi.org/10.1145/3543873.3589749)|Sudarshan Lamkhede, Anlei Dong, Moumita Bhattacharya, Hongning Wang|Microsoft Bing, USA; Dept. of Computer Science, University of Virginia, USA; Netflix Research, USA|The utility of a search system for its users can be further enhanced by providing personalized results and recommendations within the search context. However, the research discussions around these aspects of search remain fragmented across different conferences and workshops. Hence, this workshop aims to bring together researchers and practitioners from industry and academia to engage in the discussions of algorithmic and system challenges in search personalization and effectively recommending within search context.|通过在搜索上下文中提供个性化的结果和建议，可以进一步加强搜索系统对用户的效用。然而，围绕搜索这些方面的研究讨论在不同的会议和研讨会上仍然支离破碎。因此，这个研讨会的目的是聚集业界和学术界的研究人员和从业人员，参与讨论在搜索个性化和有效推荐搜索背景下的算法和系统挑战。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Personalization+and+Recommendations+in+Search)|0| -|[Cooperative Retriever and Ranker in Deep Recommenders](https://doi.org/10.1145/3543507.3583422)|Xu Huang, Defu Lian, Jin Chen, Liu Zheng, Xing Xie, Enhong Chen|University of Science and Technology of China, China; ; Microsoft Research Asia, China; University of Electronic Science and Technology of China, China|Deep recommender systems (DRS) are intensively applied in modern web services. To deal with the massive web contents, DRS employs a two-stage workflow: retrieval and ranking, to generate its recommendation results. The retriever aims to select a small set of relevant candidates from the entire items with high efficiency; while the ranker, usually more precise but time-consuming, is supposed to further refine the best items from the retrieved candidates. Traditionally, the two components are trained either independently or within a simple cascading pipeline, which is prone to poor collaboration effect. Though some latest works suggested to train retriever and ranker jointly, there still exist many severe limitations: item distribution shift between training and inference, false negative, and misalignment of ranking order. As such, it remains to explore effective collaborations between retriever and ranker.|深度推荐系统(DRS)在现代 Web 服务中得到了广泛的应用。为了处理海量的网络内容，DRS 采用了两个阶段的工作流程: 检索和排名，以生成其推荐结果。检索器的目标是从整个项目中高效地选择一小部分相关候选项; 而排名器通常更精确但更耗时，应该从检索到的候选项中进一步提炼出最好的项目。传统上，这两个组件要么单独训练，要么在一个简单的级联管道中训练，这样容易产生较差的协作效果。虽然最新的一些研究提出了联合训练检索器和排序器，但仍然存在很多严重的局限性: 训练和推理之间的项目分布转移、错误否定和排序顺序不一致。因此，仍然需要探索检索器和排名器之间的有效协作。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Cooperative+Retriever+and+Ranker+in+Deep+Recommenders)|0| -|[Modeling Temporal Positive and Negative Excitation for Sequential Recommendation](https://doi.org/10.1145/3543507.3583463)|Chengkai Huang, Shoujin Wang, Xianzhi Wang, Lina Yao|The University of New South Wales, Australia; CSIRO's Data 61, Australia and The University of New South Wales, Australia; University of Technology Sydney, Australia|Sequential recommendation aims to predict the next item which interests users via modeling their interest in items over time. Most of the existing works on sequential recommendation model users’ dynamic interest in specific items while overlooking users’ static interest revealed by some static attribute information of items, e.g., category, brand. Moreover, existing works often only consider the positive excitation of a user’s historical interactions on his/her next choice on candidate items while ignoring the commonly existing negative excitation, resulting in insufficiently modeling dynamic interest. The overlook of static interest and negative excitation will lead to incomplete interest modeling and thus impedes the recommendation performance. To this end, in this paper, we propose modeling both static interest and negative excitation for dynamic interest to further improve the recommendation performance. Accordingly, we design a novel Static-Dynamic Interest Learning (SDIL) framework featured with a novel Temporal Positive and Negative Excitation Modeling (TPNE) module for accurate sequential recommendation. TPNE is specially designed for comprehensively modeling dynamic interest based on temporal positive and negative excitation learning. Extensive experiments on three real-world datasets show that SDIL can effectively capture both static and dynamic interest and outperforms state-of-the-art baselines.|序贯推荐旨在通过建立用户对项目的兴趣模型来预测下一个用户感兴趣的项目。现有的序贯推荐模型大多是建立在用户对特定项目的动态兴趣的基础上，忽略了项目的静态属性信息(如类别、品牌等)所揭示的用户的静态兴趣。此外，现有的作品往往只考虑用户的历史交互作用对他/她的下一个选择的候选项的正激励，而忽略了普遍存在的负激励，导致不足的建模动态兴趣。忽视静态兴趣和负激励会导致兴趣建模的不完整，从而影响推荐性能。为此，本文提出了静态兴趣模型和动态兴趣的负激励模型，以进一步提高推荐性能。因此，我们设计了一个新颖的静态-动态兴趣学习(SDIL)框架，该框架具有一个新颖的时态正负激励建模(TPNE)模块，用于准确的顺序推荐。TPNE 是一种基于时间正负激励学习的动态兴趣综合建模方法。在三个实际数据集上的大量实验表明，SDIL 能够有效地捕获静态和动态兴趣，并且性能优于最先进的基线。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Modeling+Temporal+Positive+and+Negative+Excitation+for+Sequential+Recommendation)|0| +|[Personalization and Recommendations in Search](https://doi.org/10.1145/3543873.3589749)|Sudarshan Lamkhede, Anlei Dong, Moumita Bhattacharya, Hongning Wang|Dept. of Computer Science, University of Virginia, USA; Netflix Research, USA; Microsoft Bing, USA|The utility of a search system for its users can be further enhanced by providing personalized results and recommendations within the search context. However, the research discussions around these aspects of search remain fragmented across different conferences and workshops. Hence, this workshop aims to bring together researchers and practitioners from industry and academia to engage in the discussions of algorithmic and system challenges in search personalization and effectively recommending within search context.|通过在搜索上下文中提供个性化的结果和建议，可以进一步加强搜索系统对用户的效用。然而，围绕搜索这些方面的研究讨论在不同的会议和研讨会上仍然支离破碎。因此，这个研讨会的目的是聚集业界和学术界的研究人员和从业人员，参与讨论在搜索个性化和有效推荐搜索背景下的算法和系统挑战。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Personalization+and+Recommendations+in+Search)|0| +|[Cooperative Retriever and Ranker in Deep Recommenders](https://doi.org/10.1145/3543507.3583422)|Xu Huang, Defu Lian, Jin Chen, Liu Zheng, Xing Xie, Enhong Chen|University of Electronic Science and Technology of China, China; ; Microsoft Research Asia, China; University of Science and Technology of China, China|Deep recommender systems (DRS) are intensively applied in modern web services. To deal with the massive web contents, DRS employs a two-stage workflow: retrieval and ranking, to generate its recommendation results. The retriever aims to select a small set of relevant candidates from the entire items with high efficiency; while the ranker, usually more precise but time-consuming, is supposed to further refine the best items from the retrieved candidates. Traditionally, the two components are trained either independently or within a simple cascading pipeline, which is prone to poor collaboration effect. Though some latest works suggested to train retriever and ranker jointly, there still exist many severe limitations: item distribution shift between training and inference, false negative, and misalignment of ranking order. As such, it remains to explore effective collaborations between retriever and ranker.|深度推荐系统(DRS)在现代 Web 服务中得到了广泛的应用。为了处理海量的网络内容，DRS 采用了两个阶段的工作流程: 检索和排名，以生成其推荐结果。检索器的目标是从整个项目中高效地选择一小部分相关候选项; 而排名器通常更精确但更耗时，应该从检索到的候选项中进一步提炼出最好的项目。传统上，这两个组件要么单独训练，要么在一个简单的级联管道中训练，这样容易产生较差的协作效果。虽然最新的一些研究提出了联合训练检索器和排序器，但仍然存在很多严重的局限性: 训练和推理之间的项目分布转移、错误否定和排序顺序不一致。因此，仍然需要探索检索器和排名器之间的有效协作。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Cooperative+Retriever+and+Ranker+in+Deep+Recommenders)|0| +|[Modeling Temporal Positive and Negative Excitation for Sequential Recommendation](https://doi.org/10.1145/3543507.3583463)|Chengkai Huang, Shoujin Wang, Xianzhi Wang, Lina Yao|CSIRO's Data 61, Australia and The University of New South Wales, Australia; University of Technology Sydney, Australia; The University of New South Wales, Australia|Sequential recommendation aims to predict the next item which interests users via modeling their interest in items over time. Most of the existing works on sequential recommendation model users’ dynamic interest in specific items while overlooking users’ static interest revealed by some static attribute information of items, e.g., category, brand. Moreover, existing works often only consider the positive excitation of a user’s historical interactions on his/her next choice on candidate items while ignoring the commonly existing negative excitation, resulting in insufficiently modeling dynamic interest. The overlook of static interest and negative excitation will lead to incomplete interest modeling and thus impedes the recommendation performance. To this end, in this paper, we propose modeling both static interest and negative excitation for dynamic interest to further improve the recommendation performance. Accordingly, we design a novel Static-Dynamic Interest Learning (SDIL) framework featured with a novel Temporal Positive and Negative Excitation Modeling (TPNE) module for accurate sequential recommendation. TPNE is specially designed for comprehensively modeling dynamic interest based on temporal positive and negative excitation learning. Extensive experiments on three real-world datasets show that SDIL can effectively capture both static and dynamic interest and outperforms state-of-the-art baselines.|序贯推荐旨在通过建立用户对项目的兴趣模型来预测下一个用户感兴趣的项目。现有的序贯推荐模型大多是建立在用户对特定项目的动态兴趣的基础上，忽略了项目的静态属性信息(如类别、品牌等)所揭示的用户的静态兴趣。此外，现有的作品往往只考虑用户的历史交互作用对他/她的下一个选择的候选项的正激励，而忽略了普遍存在的负激励，导致不足的建模动态兴趣。忽视静态兴趣和负激励会导致兴趣建模的不完整，从而影响推荐性能。为此，本文提出了静态兴趣模型和动态兴趣的负激励模型，以进一步提高推荐性能。因此，我们设计了一个新颖的静态-动态兴趣学习(SDIL)框架，该框架具有一个新颖的时态正负激励建模(TPNE)模块，用于准确的顺序推荐。TPNE 是一种基于时间正负激励学习的动态兴趣综合建模方法。在三个实际数据集上的大量实验表明，SDIL 能够有效地捕获静态和动态兴趣，并且性能优于最先进的基线。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Modeling+Temporal+Positive+and+Negative+Excitation+for+Sequential+Recommendation)|0| |[Beyond Two-Tower: Attribute Guided Representation Learning for Candidate Retrieval](https://doi.org/10.1145/3543507.3583254)|Hongyu Shan, Qishen Zhang, Zhongyi Liu, Guannan Zhang, Chenliang Li|Wuhan University, China; antgroup, China|Candidate retrieval is a key part of the modern search engines whose goal is to find candidate items that are semantically related to the query from a large item pool. The core difference against the later ranking stage is the requirement of low latency. Hence, two-tower structure with two parallel yet independent encoder for both query and item is prevalent in many systems. In these efforts, the semantic information of a query and a candidate item is fed into the corresponding encoder and then use their representations for retrieval. With the popularity of pre-trained semantic models, the state-of-the-art for semantic retrieval tasks has achieved the significant performance gain. However, the capacity of learning relevance signals is still limited by the isolation between the query and the item. The interaction-based modeling between the query and the item has been widely validated to be useful for the ranking stage, where more computation cost is affordable. Here, we are quite initerested in an demanding question: how to exploiting query-item interaction-based learning to enhance candidate retrieval and still maintain the low computation cost. Note that an item usually contain various heteorgeneous attributes which could help us understand the item characteristics more precisely. To this end, we propose a novel attribute guided representation learning framework (named AGREE) to enhance the candidate retrieval by exploiting query-attribute relevance. The key idea is to couple the query and item representation learning together during the training phase, but also enable easy decoupling for efficient inference. Specifically, we introduce an attribute fusion layer in the item side to identify most relevant item features for item representation. On the query side, an attribute-aware learning process is introduced to better infer the search intent also from these attributes. After model training, we then decouple the attribute information away from the query encoder, which guarantees the low latency for the inference phase. Extensive experiments over two real-world large-scale datasets demonstrate the superiority of the proposed AGREE against several state-of-the-art technical alternatives. Further online A/B test from AliPay search servise also show that AGREE achieves substantial performance gain over four business metrics. Currently, the proposed AGREE has been deployed online in AliPay for serving major traffic.|候选检索是现代搜索引擎的一个关键部分，其目标是从一个大的项目池中查找与查询语义相关的候选项。与后期排名阶段的核心区别在于对低延迟的要求。因此，双塔结构的两个并行但独立的编码器的查询和项目是普遍存在的许多系统。在这些工作中，查询和候选项的语义信息被输入到相应的编码器中，然后使用它们的表示进行检索。随着预训练语义模型的普及，语义检索任务的性能得到了显著提高。然而，相关信号的学习能力仍然受到查询与项目之间隔离的限制。基于交互的查询和项目之间的建模已被广泛验证是有用的排名阶段，其中更多的计算成本是负担得起的。如何利用基于查询项交互的学习来提高候选检索的效率，同时保持较低的计算成本，是本文研究的热点问题。注意，项目通常包含各种异构属性，这些属性可以帮助我们更精确地理解项目特征。为此，我们提出了一种新的属性引导表示学习框架(AGREE) ，利用查询-属性相关性来增强候选检索。其核心思想是在训练阶段将查询和项目表示学习耦合在一起，同时也为有效的推理提供了简单的解耦。具体来说，我们在项目端引入一个属性融合层来识别项目表示中最相关的项目特征。在查询方面，引入了一个感知属性的学习过程，以更好地从这些属性中推断出搜索意图。经过模型训练后，将属性信息与查询编码器解耦，保证了推理阶段的低延迟。通过两个现实世界大规模数据集的大量实验证明了所提议的 AGREE 相对于几种最先进的技术选择的优越性。支付宝搜索服务的进一步在线 A/B 测试也表明，AGREE 在四个业务指标上取得了显著的性能提升。目前，拟议的《支付宝协议》已在支付宝网上部署，以服务主要流量。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Beyond+Two-Tower:+Attribute+Guided+Representation+Learning+for+Candidate+Retrieval)|0| -|[Improving Content Retrievability in Search with Controllable Query Generation](https://doi.org/10.1145/3543507.3583261)|Gustavo Penha, Enrico Palumbo, Maryam Aziz, Alice Wang, Hugues Bouchard|Spotify, Spain; Spotify, USA; Spotify, Netherlands; Spotify, Italy|An important goal of online platforms is to enable content discovery, i.e. allow users to find a catalog entity they were not familiar with. A pre-requisite to discover an entity, e.g. a book, with a search engine is that the entity is retrievable, i.e. there are queries for which the system will surface such entity in the top results. However, machine-learned search engines have a high retrievability bias, where the majority of the queries return the same entities. This happens partly due to the predominance of narrow intent queries, where users create queries using the title of an already known entity, e.g. in book search 'harry potter'. The amount of broad queries where users want to discover new entities, e.g. in music search 'chill lyrical electronica with an atmospheric feeling to it', and have a higher tolerance to what they might find, is small in comparison. We focus here on two factors that have a negative impact on the retrievability of the entities (I) the training data used for dense retrieval models and (II) the distribution of narrow and broad intent queries issued in the system. We propose CtrlQGen, a method that generates queries for a chosen underlying intent-narrow or broad. We can use CtrlQGen to improve factor (I) by generating training data for dense retrieval models comprised of diverse synthetic queries. CtrlQGen can also be used to deal with factor (II) by suggesting queries with broader intents to users. Our results on datasets from the domains of music, podcasts, and books reveal that we can significantly decrease the retrievability bias of a dense retrieval model when using CtrlQGen. First, by using the generated queries as training data for dense models we make 9% of the entities retrievable (go from zero to non-zero retrievability). Second, by suggesting broader queries to users, we can make 12% of the entities retrievable in the best case.|在线平台的一个重要目标是支持内容发现，即允许用户找到他们不熟悉的目录实体。使用搜索引擎发现一个实体(例如一本书)的先决条件是该实体是可检索的，也就是说，有一些查询系统将在顶部结果中显示该实体。然而，机器学习搜索引擎有很高的可检索性偏差，其中大多数查询返回相同的实体。这部分是由于狭义意图查询的优势，用户使用已知实体的标题创建查询，例如在图书搜索“哈利波特”。用户希望发现新实体的广泛查询的数量，例如在音乐搜索“寒冷的抒情电子乐与大气的感觉”，并有一个更高的容忍度，他们可能会发现，相比之下是小的。这里我们重点讨论对实体的可检索性有负面影响的两个因素(I)用于密集检索模型的训练数据和(II)系统中发出的狭义和广义意图查询的分布。我们提出了 CtrlQGen，一种为选定的底层意图生成查询的方法——狭义的或者广义的。我们可以使用 CtrlQGen 通过为由不同合成查询组成的密集检索模型生成训练数据来改进 factor (I)。CtrlQGen 还可以通过向用户建议具有更广泛意图的查询来处理 factor (II)。我们对音乐、播客和书籍领域的数据集的研究结果表明，当使用 CtrlQGen 时，我们可以显著降低密集检索模型的可检索性偏差。首先，通过使用生成的查询作为密集模型的训练数据，我们使9% 的实体可检索(从零到非零可检索性)。其次，通过向用户建议更广泛的查询，我们可以使12% 的实体在最佳情况下可检索。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Improving+Content+Retrievability+in+Search+with+Controllable+Query+Generation)|0| +|[Improving Content Retrievability in Search with Controllable Query Generation](https://doi.org/10.1145/3543507.3583261)|Gustavo Penha, Enrico Palumbo, Maryam Aziz, Alice Wang, Hugues Bouchard|Spotify, USA; Spotify, Italy; Spotify, Spain; Spotify, Netherlands|An important goal of online platforms is to enable content discovery, i.e. allow users to find a catalog entity they were not familiar with. A pre-requisite to discover an entity, e.g. a book, with a search engine is that the entity is retrievable, i.e. there are queries for which the system will surface such entity in the top results. However, machine-learned search engines have a high retrievability bias, where the majority of the queries return the same entities. This happens partly due to the predominance of narrow intent queries, where users create queries using the title of an already known entity, e.g. in book search 'harry potter'. The amount of broad queries where users want to discover new entities, e.g. in music search 'chill lyrical electronica with an atmospheric feeling to it', and have a higher tolerance to what they might find, is small in comparison. We focus here on two factors that have a negative impact on the retrievability of the entities (I) the training data used for dense retrieval models and (II) the distribution of narrow and broad intent queries issued in the system. We propose CtrlQGen, a method that generates queries for a chosen underlying intent-narrow or broad. We can use CtrlQGen to improve factor (I) by generating training data for dense retrieval models comprised of diverse synthetic queries. CtrlQGen can also be used to deal with factor (II) by suggesting queries with broader intents to users. Our results on datasets from the domains of music, podcasts, and books reveal that we can significantly decrease the retrievability bias of a dense retrieval model when using CtrlQGen. First, by using the generated queries as training data for dense models we make 9% of the entities retrievable (go from zero to non-zero retrievability). Second, by suggesting broader queries to users, we can make 12% of the entities retrievable in the best case.|在线平台的一个重要目标是支持内容发现，即允许用户找到他们不熟悉的目录实体。使用搜索引擎发现一个实体(例如一本书)的先决条件是该实体是可检索的，也就是说，有一些查询系统将在顶部结果中显示该实体。然而，机器学习搜索引擎有很高的可检索性偏差，其中大多数查询返回相同的实体。这部分是由于狭义意图查询的优势，用户使用已知实体的标题创建查询，例如在图书搜索“哈利波特”。用户希望发现新实体的广泛查询的数量，例如在音乐搜索“寒冷的抒情电子乐与大气的感觉”，并有一个更高的容忍度，他们可能会发现，相比之下是小的。这里我们重点讨论对实体的可检索性有负面影响的两个因素(I)用于密集检索模型的训练数据和(II)系统中发出的狭义和广义意图查询的分布。我们提出了 CtrlQGen，一种为选定的底层意图生成查询的方法——狭义的或者广义的。我们可以使用 CtrlQGen 通过为由不同合成查询组成的密集检索模型生成训练数据来改进 factor (I)。CtrlQGen 还可以通过向用户建议具有更广泛意图的查询来处理 factor (II)。我们对音乐、播客和书籍领域的数据集的研究结果表明，当使用 CtrlQGen 时，我们可以显著降低密集检索模型的可检索性偏差。首先，通过使用生成的查询作为密集模型的训练数据，我们使9% 的实体可检索(从零到非零可检索性)。其次，通过向用户建议更广泛的查询，我们可以使12% 的实体在最佳情况下可检索。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Improving+Content+Retrievability+in+Search+with+Controllable+Query+Generation)|0| |[PIE: Personalized Interest Exploration for Large-Scale Recommender Systems](https://doi.org/10.1145/3543873.3584656)|Khushhall Chandra Mahajan, Amey Porobo Dharwadker, Romil Shah, Simeng Qu, Gaurav Bang, Brad Schumitsch|Meta Inc., USA|Recommender systems are increasingly successful in recommending personalized content to users. However, these systems often capitalize on popular content. There is also a continuous evolution of user interests that need to be captured, but there is no direct way to systematically explore users' interests. This also tends to affect the overall quality of the recommendation pipeline as training data is generated from the candidates presented to the user. In this paper, we present a framework for exploration in large-scale recommender systems to address these challenges. It consists of three parts, first the user-creator exploration which focuses on identifying the best creators that users are interested in, second the online exploration framework and third a feed composition mechanism that balances explore and exploit to ensure optimal prevalence of exploratory videos. Our methodology can be easily integrated into an existing large-scale recommender system with minimal modifications. We also analyze the value of exploration by defining relevant metrics around user-creator connections and understanding how this helps the overall recommendation pipeline with strong online gains in creator and ecosystem value. In contrast to the regression on user engagement metrics generally seen while exploring, our method is able to achieve significant improvements of 3.50% in strong creator connections and 0.85% increase in novel creator connections. Moreover, our work has been deployed in production on Facebook Watch, a popular video discovery and sharing platform serving billions of users.|推荐系统在向用户推荐个性化内容方面越来越成功。然而，这些系统往往利用流行的内容。用户兴趣的不断演变也需要被捕捉，但是没有直接的方法来系统地探索用户的兴趣。这也往往影响推荐管道的总体质量，因为培训数据是从向用户提供的候选人中产生的。在本文中，我们提出了一个大规模推荐系统的探索框架，以解决这些挑战。它由三部分组成，第一部分是用户创建者探索，侧重于确定用户感兴趣的最佳创建者，第二部分是在线探索框架，第三部分是平衡探索和利用的馈送组合机制，以确保探索性视频的最佳流行。我们的方法可以很容易地集成到一个现有的大规模推荐系统中，只需要做很少的修改。我们还通过定义用户-创建者连接的相关度量来分析探索的价值，并了解这如何帮助整个推荐流水线在创建者和生态系统价值方面获得强大的在线收益。与探索过程中常见的用户参与度指标的回归相比，我们的方法能够在强创作者关系中获得3.50% 的显著提高，在新创作者关系中获得0.85% 的显著提高。此外，我们的工作已经部署在 Facebook Watch 上，这是一个流行的视频发现和分享平台，为数十亿用户提供服务。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=PIE:+Personalized+Interest+Exploration+for+Large-Scale+Recommender+Systems)|0| |[Improving Product Search with Season-Aware Query-Product Semantic Similarity](https://doi.org/10.1145/3543873.3587625)|Haoming Chen, Yetian Chen, Jingjing Meng, Yang Jiao, Yikai Ni, Yan Gao, Michinari Momma, Yi Sun|Harvard University, USA; Amazon.com, USA|Product search for online shopping should be season-aware, i.e., presenting seasonally relevant products to customers. In this paper, we propose a simple yet effective solution to improve seasonal relevance in product search by incorporating seasonality into language models for semantic matching. We first identify seasonal queries and products by analyzing implicit seasonal contexts through time-series analysis over the past year. Then we introduce explicit seasonal contexts by enhancing the query representation with a season token according to when the query is issued. A new season-enhanced BERT model (SE-BERT) is also proposed to learn the semantic similarity between the resulting seasonal queries and products. SE-BERT utilizes Multi-modal Adaption Gate (MAG) to augment the season-enhanced semantic embedding with other contextual information such as product price and review counts for robust relevance prediction. To better align with the ranking objective, a listwise loss function (neural NDCG) is used to regularize learning. Experimental results validate the effectiveness of the proposed method, which outperforms existing solutions for query-product relevance prediction in terms of NDCG and Price Weighted Purchases (PWP).|网上购物的产品搜寻应具有季节性，即向顾客展示季节性相关的产品。在本文中，我们提出了一个简单而有效的解决方案，以改善产品搜索的季节性相关性，将季节性纳入语义匹配的语言模型。我们首先通过对过去一年的时间序列分析，分析隐含的季节性背景，识别出季节性查询和产品。然后根据查询发出的时间，使用季节标记增强查询表示，从而引入明确的季节上下文。提出了一种新的季节增强 BERT 模型(SE-BERT) ，用于学习产生的季节查询与产品之间的语义相似性。该算法利用多模态自适应门(MAG)增强季节增强语义嵌入，并结合产品价格和评论计数等上下文信息进行鲁棒相关性预测。为了更好地与排名目标保持一致，一个列表损失函数(神经 NDCG)被用来规范学习。实验结果验证了该方法的有效性，在 NDCG 和价格加权购买(PWP)方面优于现有的查询产品相关性预测方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Improving+Product+Search+with+Season-Aware+Query-Product+Semantic+Similarity)|0| |[Blend and Match: Distilling Semantic Search Models with Different Inductive Biases and Model Architectures](https://doi.org/10.1145/3543873.3587629)|Hamed Bonab, Ashutosh Joshi, Ravi Bhatia, Ankit Gandhi, Vijay Huddar, Juhi Naik, Mutasem AlDarabsah, Choon Hui Teo, Jonathan May, Tarun Agarwal, Vaclav Petricek|Amazon, India; Amazon, USA and USC Information Sciences Institute, USA; Amazon, USA|Commercial search engines use different semantic models to augment lexical matches. These models provide candidate items for a user’s query from a target space of millions to billions of items. Models with different inductive biases provide relatively different predictions, making it desirable to launch multiple semantic models in production. However, latency and resource constraints make simultaneously deploying multiple models impractical. In this paper, we introduce a distillation approach, called Blend and Match (BM), to unify two different semantic search models into a single model. We use a Bi-encoder semantic matching model as our primary model and propose a novel loss function to incorporate eXtreme Multi-label Classification (XMC) predictions as the secondary model. Our experiments conducted on two large-scale datasets, collected from a popular e-commerce store, show that our proposed approach significantly improves the recall of the primary Bi-encoder model by 11% to 17% with a minimal loss in precision. We show that traditional knowledge distillation approaches result in a sub-optimal performance for our problem setting, and our BM approach yields comparable rankings with strong Rank Fusion (RF) methods used only if one could deploy multiple models.|商业搜索引擎使用不同的语义模型来增加词汇匹配。这些模型为用户的查询提供从数百万到数十亿的候选项。具有不同归纳偏差的模型提供了相对不同的预测，因此在生产环境中启动多个语义模型是可取的。然而，延迟和资源限制使得同时部署多个模型不切实际。在本文中，我们引入了一种称为“混合与匹配”(Blend and Match，BM)的提取方法，将两个不同的语义搜索模型统一到一个单一的模型中。我们使用一个双编码器语义匹配模型作为我们的主要模型，并提出了一个新的损失函数合并 eXtreme 多标签分类(XMC)预测作为次要模型。我们在两个大规模数据集上进行的实验，从一个流行的电子商务商店收集，表明我们提出的方法显着提高了11% 至17% 的主要双编码器模型的召回率，最小的精度损失。我们表明，传统的知识提取方法导致次优的性能为我们的问题设置，和我们的 BM 方法产生可比的排名与强秩融合(RF)方法只有当一个人可以部署多个模型使用。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Blend+and+Match:+Distilling+Semantic+Search+Models+with+Different+Inductive+Biases+and+Model+Architectures)|0| |[Joint Internal Multi-Interest Exploration and External Domain Alignment for Cross Domain Sequential Recommendation](https://doi.org/10.1145/3543507.3583366)|Weiming Liu, Xiaolin Zheng, Chaochao Chen, Jiajie Su, Xinting Liao, Mengling Hu, Yanchao Tan|Fuzhou Univerisity, China; Zhejiang University, China|Sequential Cross-Domain Recommendation (CDR) has been popularly studied to utilize different domain knowledge and users’ historical behaviors for the next-item prediction. In this paper, we focus on the cross-domain sequential recommendation problem. This commonly exist problem is rather challenging from two perspectives, i.e., the implicit user historical rating sequences are difficult in modeling and the users/items on different domains are mostly non-overlapped. Most previous sequential CDR approaches cannot solve the cross-domain sequential recommendation problem well, since (1) they cannot sufficiently depict the users’ actual preferences, (2) they cannot leverage and transfer useful knowledge across domains. To tackle the above issues, we propose joint Internal multi-interest exploration and External domain alignment for cross domain Sequential Recommendation model (IESRec). IESRec includes two main modules, i.e., internal multi-interest exploration module and external domain alignment module. To reflect the users’ diverse characteristics with multi-interests evolution, we first propose internal temporal optimal transport method in the internal multi-interest exploration module. We further propose external alignment optimal transport method in the external domain alignment module to reduce domain discrepancy for the item embeddings. Our empirical studies on Amazon datasets demonstrate that IESRec significantly outperforms the state-of-the-art models.|序贯跨域推荐(CDR)是一种利用不同领域知识和用户历史行为进行下一个项目预测的方法。本文主要研究跨域序列推荐问题。这个常见的问题从两个方面来看都是相当具有挑战性的，即隐式用户历史评分序列难以建模，而且不同领域的用户/项目大多是非重叠的。以往的顺序 CDR 方法不能很好地解决跨域顺序推荐问题，因为(1)它们不能充分描述用户的实际偏好，(2)它们不能利用和跨域传递有用的知识。为了解决上述问题，我们提出了跨域序列推荐模型(IESRec)的内部多利益探索和外部域对齐的联合方法。IESRec 主要包括两个模块，即内部多兴趣探索模块和外部域对齐模块。为了反映用户的多样性特征和多种利益的演化，我们首先在内部多种利益探索模块中提出了内部时间最优传输方法。进一步提出了外域对齐模块中的外域对齐最优传输方法，以减少项目嵌入时的域差异。我们对亚马逊数据集的实证研究表明，IESRec 明显优于最先进的模型。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Joint+Internal+Multi-Interest+Exploration+and+External+Domain+Alignment+for+Cross+Domain+Sequential+Recommendation)|0| |[Latent User Intent Modeling for Sequential Recommenders](https://doi.org/10.1145/3543873.3584641)|Bo Chang, Alexandros Karatzoglou, Yuyan Wang, Can Xu, Ed H. Chi, Minmin Chen|Google, USA|Sequential recommender models are essential components of modern industrial recommender systems. These models learn to predict the next items a user is likely to interact with based on his/her interaction history on the platform. Most sequential recommenders however lack a higher-level understanding of user intents, which often drive user behaviors online. Intent modeling is thus critical for understanding users and optimizing long-term user experience. We propose a probabilistic modeling approach and formulate user intent as latent variables, which are inferred based on user behavior signals using variational autoencoders (VAE). The recommendation policy is then adjusted accordingly given the inferred user intent. We demonstrate the effectiveness of the latent user intent modeling via offline analyses as well as live experiments on a large-scale industrial recommendation platform.|序贯推荐模型是现代工业推荐系统的重要组成部分。这些模型学习根据用户在平台上的交互历史来预测用户可能与之交互的下一个项目。然而，大多数顺序推荐系统缺乏对用户意图的更高层次的理解，这往往会驱动用户在线行为。因此，意图建模对于理解用户和优化长期用户体验至关重要。提出了一种基于变分自动编码器(VAE)的基于用户行为信号的概率建模方法，并将用户意图表示为潜变量。然后根据推断出的用户意图相应地调整推荐策略。通过离线分析以及在大规模工业推荐平台上的实验，验证了潜在用户意图建模的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Latent+User+Intent+Modeling+for+Sequential+Recommenders)|0| -|[Deep Neural Network with LinUCB: A Contextual Bandit Approach for Personalized Recommendation](https://doi.org/10.1145/3543873.3587684)|Qicai Shi, Feng Xiao, Douglas Pickard, Inga Chen, Liang Chen|Disneystreaming, USA; Disneystreaming, China|Recommender systems are widely used in many Web applications to recommend items which are relevant to a user’s preferences. However, focusing on exploiting user preferences while ignoring exploration will lead to biased feedback and hurt the user’s experience in the long term. The Mutli-Armed Bandit (MAB) is introduced to balance the tradeoff between exploitation and exploration. By utilizing context information in the reward function, contextual bandit algorithms lead to better performance compared to context-free bandit algorithms. However, existing contextual bandit algorithms either assume a linear relation between the expected reward and context features, whose representation power gets limited, or use a deep neural network in the reward function which is impractical in implementation. In this paper, we propose a new contextual bandit algorithm, DeepLinUCB, which leverages the representation power of deep neural network to transform the raw context features in the reward function. Specifically, this deep neural network is dedicated to the recommender system, which is efficient and practical in real-world applications. Furthermore, we conduct extensive experiments in our online recommender system using requests from real-world scenarios and show that DeepLinUCB is efficient and outperforms other bandit algorithms.|在许多 Web 应用程序中，推荐系统被广泛用于推荐与用户首选项相关的项目。然而，只关注用户偏好而忽视探索将导致偏见的反馈，从长远来看会损害用户的体验。为了平衡开发与勘探之间的权衡，引进了多臂匪。通过在奖励函数中利用上下文信息，上下文盗贼算法比无上下文盗贼算法具有更好的性能。然而，现有的上下文盗贼算法要么假定期望奖励与上下文特征之间存在线性关系，其表示能力受到限制，要么在奖励函数中使用深度神经网络，这在实现上是不切实际的。本文提出了一种新的上下文盗贼算法 DeepLinUCB，该算法利用深层神经网络的表示能力来转换奖励函数中的原始上下文特征。具体来说，这种深层神经网络专门用于推荐系统，在实际应用中非常有效和实用。此外，我们使用来自现实场景的请求，在我们的在线推荐系统中进行了大量的实验，结果表明 DeepLinUCB 是高效的，并且优于其他盗贼算法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Deep+Neural+Network+with+LinUCB:+A+Contextual+Bandit+Approach+for+Personalized+Recommendation)|0| +|[Deep Neural Network with LinUCB: A Contextual Bandit Approach for Personalized Recommendation](https://doi.org/10.1145/3543873.3587684)|Qicai Shi, Feng Xiao, Douglas Pickard, Inga Chen, Liang Chen|Disneystreaming, China; Disneystreaming, USA|Recommender systems are widely used in many Web applications to recommend items which are relevant to a user’s preferences. However, focusing on exploiting user preferences while ignoring exploration will lead to biased feedback and hurt the user’s experience in the long term. The Mutli-Armed Bandit (MAB) is introduced to balance the tradeoff between exploitation and exploration. By utilizing context information in the reward function, contextual bandit algorithms lead to better performance compared to context-free bandit algorithms. However, existing contextual bandit algorithms either assume a linear relation between the expected reward and context features, whose representation power gets limited, or use a deep neural network in the reward function which is impractical in implementation. In this paper, we propose a new contextual bandit algorithm, DeepLinUCB, which leverages the representation power of deep neural network to transform the raw context features in the reward function. Specifically, this deep neural network is dedicated to the recommender system, which is efficient and practical in real-world applications. Furthermore, we conduct extensive experiments in our online recommender system using requests from real-world scenarios and show that DeepLinUCB is efficient and outperforms other bandit algorithms.|在许多 Web 应用程序中，推荐系统被广泛用于推荐与用户首选项相关的项目。然而，只关注用户偏好而忽视探索将导致偏见的反馈，从长远来看会损害用户的体验。为了平衡开发与勘探之间的权衡，引进了多臂匪。通过在奖励函数中利用上下文信息，上下文盗贼算法比无上下文盗贼算法具有更好的性能。然而，现有的上下文盗贼算法要么假定期望奖励与上下文特征之间存在线性关系，其表示能力受到限制，要么在奖励函数中使用深度神经网络，这在实现上是不切实际的。本文提出了一种新的上下文盗贼算法 DeepLinUCB，该算法利用深层神经网络的表示能力来转换奖励函数中的原始上下文特征。具体来说，这种深层神经网络专门用于推荐系统，在实际应用中非常有效和实用。此外，我们使用来自现实场景的请求，在我们的在线推荐系统中进行了大量的实验，结果表明 DeepLinUCB 是高效的，并且优于其他盗贼算法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Deep+Neural+Network+with+LinUCB:+A+Contextual+Bandit+Approach+for+Personalized+Recommendation)|0| |[Contrastive Collaborative Filtering for Cold-Start Item Recommendation](https://doi.org/10.1145/3543507.3583286)|Zhihui Zhou, Lilin Zhang, Ning Yang|Sichuan University, China|The cold-start problem is a long-standing challenge in recommender systems. As a promising solution, content-based generative models usually project a cold-start item's content onto a warm-start item embedding to capture collaborative signals from item content so that collaborative filtering can be applied. However, since the training of the cold-start recommendation models is conducted on warm datasets, the existent methods face the issue that the collaborative embeddings of items will be blurred, which significantly degenerates the performance of cold-start item recommendation. To address this issue, we propose a novel model called Contrastive Collaborative Filtering for Cold-start item Recommendation (CCFCRec), which capitalizes on the co-occurrence collaborative signals in warm training data to alleviate the issue of blurry collaborative embeddings for cold-start item recommendation. In particular, we devise a contrastive collaborative filtering (CF) framework, consisting of a content CF module and a co-occurrence CF module to generate the content-based collaborative embedding and the co-occurrence collaborative embedding for a training item, respectively. During the joint training of the two CF modules, we apply a contrastive learning between the two collaborative embeddings, by which the knowledge about the co-occurrence signals can be indirectly transferred to the content CF module, so that the blurry collaborative embeddings can be rectified implicitly by the memorized co-occurrence collaborative signals during the applying phase. Together with the sound theoretical analysis, the extensive experiments conducted on real datasets demonstrate the superiority of the proposed model. The codes and datasets are available on https://github.com/zzhin/CCFCRec.|在推荐系统中，冷启动问题是一个长期存在的挑战。作为一种有前途的解决方案，基于内容的生成模型通常将一个冷启动项目的内容投射到一个嵌入的热启动项目上，以从项目内容中捕获协作信号，从而可以应用协同过滤。然而，由于冷启动推荐模型的训练是在暖数据集上进行的，现有的方法面临着项目协同嵌入模糊的问题，这严重影响了冷启动项目推荐的性能。为了解决这个问题，我们提出了一个新的模型，称为冷启动项目推荐对比协同过滤(CCFCrec) ，它利用共现协作信号在暖培训数据，以减轻问题模糊的协作嵌入冷启动项目推荐。特别地，我们设计了一个对比协同过滤(CF)框架，由一个内容 CF 模块和一个共现 CF 模块组成，分别为一个培训项目生成基于内容的协同嵌入和共现协同嵌入。在两个 CF 模块的联合训练中，我们对两个协同嵌入进行了对比学习，通过对比学习可以将关于共现信号的知识间接转移到内容 CF 模块中，从而在应用阶段可以通过记忆共现协同信号来隐式纠正模糊的协同嵌入。通过在实际数据集上的大量实验，结合理论分析，证明了该模型的优越性。代码和数据集可在 https://github.com/zzhin/ccfcrec 上获得。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Contrastive+Collaborative+Filtering+for+Cold-Start+Item+Recommendation)|0| |[ColdNAS: Search to Modulate for User Cold-Start Recommendation](https://doi.org/10.1145/3543507.3583344)|Shiguang Wu, Yaqing Wang, Qinghe Jing, Daxiang Dong, Dejing Dou, Quanming Yao|Electronic Engineering, Tsinghua University, China; Baidu Inc., China|Making personalized recommendation for cold-start users, who only have a few interaction histories, is a challenging problem in recommendation systems. Recent works leverage hypernetworks to directly map user interaction histories to user-specific parameters, which are then used to modulate predictor by feature-wise linear modulation function. These works obtain the state-of-the-art performance. However, the physical meaning of scaling and shifting in recommendation data is unclear. Instead of using a fixed modulation function and deciding modulation position by expertise, we propose a modulation framework called ColdNAS for user cold-start problem, where we look for proper modulation structure, including function and position, via neural architecture search. We design a search space which covers broad models and theoretically prove that this search space can be transformed to a much smaller space, enabling an efficient and robust one-shot search algorithm. Extensive experimental results on benchmark datasets show that ColdNAS consistently performs the best. We observe that different modulation functions lead to the best performance on different datasets, which validates the necessity of designing a searching-based method. Codes are available at https://github.com/LARS-research/ColdNAS.|在推荐系统中，为只有少量交互历史的冷启动用户进行个性化推荐是一个具有挑战性的问题。最近的研究利用超网络将用户交互历史直接映射到用户特定的参数，然后用特征线性调制函数对预测器进行调制。这些作品获得了最先进的表演水平。然而，推荐数据的缩放和转移的物理意义尚不清楚。为了解决用户冷启动问题，我们提出了一种称为 ColdNAS 的调制框架，该框架通过神经结构搜索寻找合适的调制结构，包括功能和位置，而不是使用固定的调制函数来确定调制位置。我们设计了一个覆盖广泛模型的搜索空间，并从理论上证明了这个搜索空间可以转换成更小的空间，从而实现了一种高效、鲁棒的一次性搜索算法。在基准数据集上的大量实验结果表明，ColdNAS 始终表现最好。我们观察到不同的调制函数对不同的数据集产生最佳的性能，这验证了设计一种基于搜索的方法的必要性。密码可在 https://github.com/lars-research/coldnas 索取。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=ColdNAS:+Search+to+Modulate+for+User+Cold-Start+Recommendation)|0| |[Improving the Relevance of Product Search for Queries with Negations](https://doi.org/10.1145/3543873.3587319)|Felice Antonio Merra, Omar Zaidan, Fabricio de Sousa Nascimento|Amazon, Japan; Amazon, Germany|Product search engines (PSEs) play an essential role in retail websites as they make it easier for users to retrieve relevant products within large catalogs. Despite the continuous progress that has led to increasingly accurate search engines, a limited focus has been given to their performance on queries with negations. Indeed, while we would expect to retrieve different products for the queries “iPhone 13 cover with ring” and “iPhone 13 cover without ring”, this does not happen in popular PSEs with the latter query containing results with the unwanted ring component. The limitation of modern PSEs in understanding negations motivates the need for further investigation. In this work, we start by defining the negation intent in users queries. Then, we design a transformer-based model, named Negation Detector for Queries (ND4Q), that reaches optimal performance in negation detection (+95% on accuracy metrics). Finally, having built the first negation detector for product search queries, we propose a negation-aware filtering strategy, named Filtering Irrelevant Products (FIP). The promising experimental results in improve the PSE relevance performance using FIP (+9.41% on [email protected] for queries where the negation starts with "without") pave the way to additional research effort towards negation-aware PSEs.|产品搜索引擎(PSE)在零售网站中发挥着重要作用，因为它们使用户更容易在大型目录中检索相关产品。尽管不断取得进展，导致搜索引擎越来越准确，但对否定查询的性能关注有限。事实上，虽然我们期望检索不同的产品的查询“ iPhone13盖有戒指”和“ iPhone13盖无戒指”，这不会发生在流行的 PSE 与后者的查询包含不想要的戒指组件的结果。现代 PSE 在理解否定方面的局限性促使了进一步研究的必要性。在这项工作中，我们首先定义用户查询中的否定意图。然后，我们设计了一个基于变压器的模型，称为查询否定检测器(ND4Q) ，它在否定检测中达到了最佳的性能(在准确性指标上 + 95%)。最后，在构建了产品搜索查询的第一个否定检测器的基础上，提出了一种基于否定感知的过滤策略——过滤不相关产品(FIP)。使用 FIP (对于否定以“无”开头的查询，[ email protected ]增加9.41%)改善 PSE 相关性的有希望的实验结果为针对具有否定意识的 PSE 的额外研究努力铺平了道路。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Improving+the+Relevance+of+Product+Search+for+Queries+with+Negations)|0| -|[Movie Ticket, Popcorn, and Another Movie Next Weekend: Time-Aware Service Sequential Recommendation for User Retention](https://doi.org/10.1145/3543873.3584628)|Xiaoyan Yang, Dong Wang, Binbin Hu, Dan Yang, Yue Shen, Jinjie Gu, Zhiqiang Zhang, Shiwei Lyu, Haipeng Zhang, Guannan Zhang|ShanghaiTech University, China; Ant Group, China|When a customer sees a movie recommendation, she may buy the ticket right away, which is the immediate feedback that helps improve the recommender system. Alternatively, she may choose to come back later and this long-term feedback is also modeled to promote user retention. However, the long-term feedback comes with non-trivial challenges in understanding user retention: the complicated correlation between current demands and follow-up demands, coupled with the periodicity of services. For instance, before the movie, the customer buys popcorn through the App, which temporally correlates with the initial movie recommendation. Days later, she checks the App for new movies, as a weekly routine. To address this complexity in a more fine-grained revisit modeling, we propose Time Aware Service Sequential Recommendation (TASSR) for user retention, which is equipped with a multi-task design and an In-category TimeSeqBlock module. Large-scale online and offline experiments demonstrate its significant advantages over competitive baselines.|当顾客看到一部电影的推荐信时，她可能会马上买票，这是一种即时的反馈，有助于提高推荐系统。或者，她可以选择以后再来，这种长期的反馈也被建模以促进用户保留。然而，长期的反馈在理解用户保留方面带来了重大挑战: 当前需求和后续需求之间的复杂关系，以及服务的周期性。例如，在看电影之前，客户通过 App 购买爆米花，这在时间上与最初的电影推荐相关。几天后，她每周例行检查应用程序是否有新电影。为了在更细粒度的再访问建模中解决这一复杂性，我们提出了用于用户保持的时间感知服务序列推荐(TASSR) ，该推荐配备了多任务设计和同类 TimeSeqBlock 模块。大规模的在线和离线实验证明了它相对于竞争基线的显著优势。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Movie+Ticket,+Popcorn,+and+Another+Movie+Next+Weekend:+Time-Aware+Service+Sequential+Recommendation+for+User+Retention)|0| -|[Unified Vision-Language Representation Modeling for E-Commerce Same-style Products Retrieval](https://doi.org/10.1145/3543873.3584632)|Ben Chen, Linbo Jin, Xinxin Wang, Dehong Gao, Wen Jiang, Wei Ning|Alibaba Group, China; Aliaba Group, China|Same-style products retrieval plays an important role in e-commerce platforms, aiming to identify the same products which may have different text descriptions or images. It can be used for similar products retrieval from different suppliers or duplicate products detection of one supplier. Common methods use the image as the detected object, but they only consider the visual features and overlook the attribute information contained in the textual descriptions, and perform weakly for products in image less important industries like machinery, hardware tools and electronic component, even if an additional text matching module is added. In this paper, we propose a unified vision-language modeling method for e-commerce same-style products retrieval, which is designed to represent one product with its textual descriptions and visual contents. It contains one sampling skill to collect positive pairs from user click log with category and relevance constrained, and a novel contrastive loss unit to model the image, text, and image+text representations into one joint embedding space. It is capable of cross-modal product-to-product retrieval, as well as style transfer and user-interactive search. Offline evaluations on annotated data demonstrate its superior retrieval performance, and online testings show it can attract more clicks and conversions. Moreover, this model has already been deployed online for similar products retrieval in alibaba.com, the largest B2B e-commerce platform in the world.|同类产品检索在电子商务平台中起着重要作用，其目的是识别具有不同文本描述或图像的同类产品。它可用于从不同供应商检索相似产品或检测一个供应商的重复产品。一般的检测方法都是以图像作为检测对象，但它们只考虑视觉特征，忽略了文本描述中的属性信息，对于机械、硬件工具和电子元件等图像不太重要的行业的产品，即使增加了额外的文本匹配模块，检测效果也很差。本文提出了一种统一的电子商务同类产品检索的视觉语言建模方法。它包含一种采样技巧，用于从类别和相关性受限的用户点击日志中收集正对，以及一种新的对比度损失单元，用于将图像、文本和图像 + 文本表示建模为一个联合嵌入空间。它能够进行跨模式的产品对产品检索，以及样式转移和用户交互式搜索。对注释数据的离线评估表明它具有优越的检索性能，在线测试表明它可以吸引更多的点击和转换。此外，该模型已经在全球最大的 B2B 电子商务平台阿里巴巴网站(alibaba.com)的类似产品检索中得到应用。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Unified+Vision-Language+Representation+Modeling+for+E-Commerce+Same-style+Products+Retrieval)|0| -|[Task Adaptive Multi-learner Network for Joint CTR and CVR Estimation](https://doi.org/10.1145/3543873.3584653)|Xiaofan Liu, Qinglin Jia, Chuhan Wu, Jingjie Li, Quanyu Dai, Lin Bo, Rui Zhang, Ruiming Tang|Beijing University of Posts and Telecommunications, China; Renmin University of China, China; Huawei Noah's Ark Lab, China; ruizhang.info, China|CTR and CVR are critical factors in personalized applications, and many methods jointly estimate them via multi-task learning to alleviate the ultra-sparsity of conversion behaviors. However, it is still difficult to predict CVR accurately and robustly due to the limited and even biased knowledge extracted by the single model tower optimized on insufficient conversion samples. In this paper, we propose a task adaptive multi-learner (TAML) framework for joint CTR and CVR prediction. We design a hierarchical task adaptive knowledge representation module with different experts to capture knowledge in different granularities, which can effectively exploit the commonalities between CTR and CVR estimation tasks meanwhile keeping their unique characteristics. We apply multiple learners to extract data knowledge from various views and fuse their predictions to obtain accurate and robust scores. To facilitate knowledge sharing across learners, we further perform self-distillation that uses the fused scores to teach different learners. Thorough offline and online experiments show the superiority of TAML in different Ad ranking tasks, and we have deployed it in Huawei’s online advertising platform to serve the main traffic.|CTR 和 CVR 是个性化应用中的关键因素，多种方法通过多任务学习来联合估计它们，以减轻转换行为的超稀疏性。然而，由于单模型塔在转换样本不足的情况下进行了优化，提取的知识有限，甚至有偏差，因此仍然难以准确、稳健地预测 CVR。本文提出了一个任务自适应多学习器(TAML)框架，用于联合 CTR 和 CVR 预测。设计了一个分层任务自适应知识表示模块，采用不同的专家来获取不同粒度的知识，有效地利用了 CTR 和 CVR 估计任务的共性，同时保持了它们的独特性。我们应用多个学习者从不同的角度提取数据知识，并融合他们的预测，以获得准确和稳健的分数。为了促进学习者之间的知识共享，我们进一步使用融合分数来教授不同的学习者。通过线下和线上的实验，我们发现了 TAML 在不同广告排名任务中的优势，并将其应用于华为的在线广告平台，为主要流量提供服务。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Task+Adaptive+Multi-learner+Network+for+Joint+CTR+and+CVR+Estimation)|0| -|[Deep Intention-Aware Network for Click-Through Rate Prediction](https://doi.org/10.1145/3543873.3584661)|Yaxian Xia, Yi Cao, Sihao Hu, Tong Liu, Lingling Lu|Georgia Institute of Technology, USA; Alibaba Group, China; Zhejiang University, China|E-commerce platforms provide entrances for customers to enter mini-apps that can meet their specific shopping requirements. Trigger items displayed on entrance icons can attract more entering. However, conventional Click-Through-Rate (CTR) prediction models, which ignore user instant interest in trigger item, fail to be applied to the new recommendation scenario dubbed Trigger-Induced Recommendation in Mini-Apps (TIRA). Moreover, due to the high stickiness of customers to mini-apps, we argue that existing trigger-based methods that over-emphasize the importance of trigger items, are undesired for TIRA, since a large portion of customer entries are because of their routine shopping habits instead of triggers. We identify that the key to TIRA is to extract customers' personalized entering intention and weigh the impact of triggers based on this intention. To achieve this goal, we convert CTR prediction for TIRA into a separate estimation form, and present Deep Intention-Aware Network (DIAN) with three key elements: 1) Intent Net that estimates user's entering intention, i.e., whether he/she is affected by the trigger or by the habits; 2) Trigger-Aware Net and 3) Trigger-Free Net that estimate CTRs given user's intention is to the trigger-item and the mini-app respectively. Following a joint learning way, DIAN can both accurately predict user intention and dynamically balance the results of trigger-free and trigger-based recommendations based on the estimated intention. Experiments show that DIAN advances state-of-the-art performance in a large real-world dataset, and brings a 9.39% lift of online Item Page View and 4.74% CTR for Juhuasuan, a famous mini-app of Taobao.|电子商务平台为客户提供了进入迷你应用程序，可以满足他们的具体购物需求。触发项目显示在入口图标可以吸引更多的进入。然而，传统的点击率(Click-Through-Rate，CTR)预测模型忽略了用户对触发条目的即时兴趣，无法应用于被称为微型应用程序中的触发诱导推荐(Trigger-)的新推荐场景。此外，由于客户对迷你应用程序的高粘性，我们认为，现有的基于触发器的方法，过分强调触发项目的重要性，是不希望 TIRA，因为大部分客户进入是因为他们的日常购物习惯，而不是触发器。我们认为，TIRA 的关键是提取顾客的个性化进入意图，并根据这一意图权衡触发因素的影响。为了实现这一目标，我们将 TIRA 的 CTR 预测转化为一个单独的估计形式，并提出深度意图感知网络(DIAN)的三个关键要素: 1)意图网络，估计用户的进入意图，即他/她是否受到触发器或习惯的影响; 2)触发感知网络和3)无触发网络，估计给定用户意图的 CTR 分别是触发项目和迷你应用程序。DIAN 采用联合学习的方法，既能准确预测用户意图，又能根据预测意图动态平衡无触发和基于触发的推荐结果。实验表明，DIAN 在一个大型现实数据集中提升了最先进的性能，使在线项目页面查看率提高了9.39% ，淘宝著名小应用聚花酸的点击率提高了4.74% 。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Deep+Intention-Aware+Network+for+Click-Through+Rate+Prediction)|0| +|[Movie Ticket, Popcorn, and Another Movie Next Weekend: Time-Aware Service Sequential Recommendation for User Retention](https://doi.org/10.1145/3543873.3584628)|Xiaoyan Yang, Dong Wang, Binbin Hu, Dan Yang, Yue Shen, Jinjie Gu, Zhiqiang Zhang, Shiwei Lyu, Haipeng Zhang, Guannan Zhang|Ant Group, China; ShanghaiTech University, China|When a customer sees a movie recommendation, she may buy the ticket right away, which is the immediate feedback that helps improve the recommender system. Alternatively, she may choose to come back later and this long-term feedback is also modeled to promote user retention. However, the long-term feedback comes with non-trivial challenges in understanding user retention: the complicated correlation between current demands and follow-up demands, coupled with the periodicity of services. For instance, before the movie, the customer buys popcorn through the App, which temporally correlates with the initial movie recommendation. Days later, she checks the App for new movies, as a weekly routine. To address this complexity in a more fine-grained revisit modeling, we propose Time Aware Service Sequential Recommendation (TASSR) for user retention, which is equipped with a multi-task design and an In-category TimeSeqBlock module. Large-scale online and offline experiments demonstrate its significant advantages over competitive baselines.|当顾客看到一部电影的推荐信时，她可能会马上买票，这是一种即时的反馈，有助于提高推荐系统。或者，她可以选择以后再来，这种长期的反馈也被建模以促进用户保留。然而，长期的反馈在理解用户保留方面带来了重大挑战: 当前需求和后续需求之间的复杂关系，以及服务的周期性。例如，在看电影之前，客户通过 App 购买爆米花，这在时间上与最初的电影推荐相关。几天后，她每周例行检查应用程序是否有新电影。为了在更细粒度的再访问建模中解决这一复杂性，我们提出了用于用户保持的时间感知服务序列推荐(TASSR) ，该推荐配备了多任务设计和同类 TimeSeqBlock 模块。大规模的在线和离线实验证明了它相对于竞争基线的显著优势。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Movie+Ticket,+Popcorn,+and+Another+Movie+Next+Weekend:+Time-Aware+Service+Sequential+Recommendation+for+User+Retention)|0| +|[Unified Vision-Language Representation Modeling for E-Commerce Same-style Products Retrieval](https://doi.org/10.1145/3543873.3584632)|Ben Chen, Linbo Jin, Xinxin Wang, Dehong Gao, Wen Jiang, Wei Ning|Aliaba Group, China; Alibaba Group, China|Same-style products retrieval plays an important role in e-commerce platforms, aiming to identify the same products which may have different text descriptions or images. It can be used for similar products retrieval from different suppliers or duplicate products detection of one supplier. Common methods use the image as the detected object, but they only consider the visual features and overlook the attribute information contained in the textual descriptions, and perform weakly for products in image less important industries like machinery, hardware tools and electronic component, even if an additional text matching module is added. In this paper, we propose a unified vision-language modeling method for e-commerce same-style products retrieval, which is designed to represent one product with its textual descriptions and visual contents. It contains one sampling skill to collect positive pairs from user click log with category and relevance constrained, and a novel contrastive loss unit to model the image, text, and image+text representations into one joint embedding space. It is capable of cross-modal product-to-product retrieval, as well as style transfer and user-interactive search. Offline evaluations on annotated data demonstrate its superior retrieval performance, and online testings show it can attract more clicks and conversions. Moreover, this model has already been deployed online for similar products retrieval in alibaba.com, the largest B2B e-commerce platform in the world.|同类产品检索在电子商务平台中起着重要作用，其目的是识别具有不同文本描述或图像的同类产品。它可用于从不同供应商检索相似产品或检测一个供应商的重复产品。一般的检测方法都是以图像作为检测对象，但它们只考虑视觉特征，忽略了文本描述中的属性信息，对于机械、硬件工具和电子元件等图像不太重要的行业的产品，即使增加了额外的文本匹配模块，检测效果也很差。本文提出了一种统一的电子商务同类产品检索的视觉语言建模方法。它包含一种采样技巧，用于从类别和相关性受限的用户点击日志中收集正对，以及一种新的对比度损失单元，用于将图像、文本和图像 + 文本表示建模为一个联合嵌入空间。它能够进行跨模式的产品对产品检索，以及样式转移和用户交互式搜索。对注释数据的离线评估表明它具有优越的检索性能，在线测试表明它可以吸引更多的点击和转换。此外，该模型已经在全球最大的 B2B 电子商务平台阿里巴巴网站(alibaba.com)的类似产品检索中得到应用。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Unified+Vision-Language+Representation+Modeling+for+E-Commerce+Same-style+Products+Retrieval)|0| +|[Task Adaptive Multi-learner Network for Joint CTR and CVR Estimation](https://doi.org/10.1145/3543873.3584653)|Xiaofan Liu, Qinglin Jia, Chuhan Wu, Jingjie Li, Quanyu Dai, Lin Bo, Rui Zhang, Ruiming Tang|Beijing University of Posts and Telecommunications, China; Renmin University of China, China; ruizhang.info, China; Huawei Noah's Ark Lab, China|CTR and CVR are critical factors in personalized applications, and many methods jointly estimate them via multi-task learning to alleviate the ultra-sparsity of conversion behaviors. However, it is still difficult to predict CVR accurately and robustly due to the limited and even biased knowledge extracted by the single model tower optimized on insufficient conversion samples. In this paper, we propose a task adaptive multi-learner (TAML) framework for joint CTR and CVR prediction. We design a hierarchical task adaptive knowledge representation module with different experts to capture knowledge in different granularities, which can effectively exploit the commonalities between CTR and CVR estimation tasks meanwhile keeping their unique characteristics. We apply multiple learners to extract data knowledge from various views and fuse their predictions to obtain accurate and robust scores. To facilitate knowledge sharing across learners, we further perform self-distillation that uses the fused scores to teach different learners. Thorough offline and online experiments show the superiority of TAML in different Ad ranking tasks, and we have deployed it in Huawei’s online advertising platform to serve the main traffic.|CTR 和 CVR 是个性化应用中的关键因素，多种方法通过多任务学习来联合估计它们，以减轻转换行为的超稀疏性。然而，由于单模型塔在转换样本不足的情况下进行了优化，提取的知识有限，甚至有偏差，因此仍然难以准确、稳健地预测 CVR。本文提出了一个任务自适应多学习器(TAML)框架，用于联合 CTR 和 CVR 预测。设计了一个分层任务自适应知识表示模块，采用不同的专家来获取不同粒度的知识，有效地利用了 CTR 和 CVR 估计任务的共性，同时保持了它们的独特性。我们应用多个学习者从不同的角度提取数据知识，并融合他们的预测，以获得准确和稳健的分数。为了促进学习者之间的知识共享，我们进一步使用融合分数来教授不同的学习者。通过线下和线上的实验，我们发现了 TAML 在不同广告排名任务中的优势，并将其应用于华为的在线广告平台，为主要流量提供服务。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Task+Adaptive+Multi-learner+Network+for+Joint+CTR+and+CVR+Estimation)|0| +|[Deep Intention-Aware Network for Click-Through Rate Prediction](https://doi.org/10.1145/3543873.3584661)|Yaxian Xia, Yi Cao, Sihao Hu, Tong Liu, Lingling Lu|Alibaba Group, China; Zhejiang University, China; Georgia Institute of Technology, USA|E-commerce platforms provide entrances for customers to enter mini-apps that can meet their specific shopping requirements. Trigger items displayed on entrance icons can attract more entering. However, conventional Click-Through-Rate (CTR) prediction models, which ignore user instant interest in trigger item, fail to be applied to the new recommendation scenario dubbed Trigger-Induced Recommendation in Mini-Apps (TIRA). Moreover, due to the high stickiness of customers to mini-apps, we argue that existing trigger-based methods that over-emphasize the importance of trigger items, are undesired for TIRA, since a large portion of customer entries are because of their routine shopping habits instead of triggers. We identify that the key to TIRA is to extract customers' personalized entering intention and weigh the impact of triggers based on this intention. To achieve this goal, we convert CTR prediction for TIRA into a separate estimation form, and present Deep Intention-Aware Network (DIAN) with three key elements: 1) Intent Net that estimates user's entering intention, i.e., whether he/she is affected by the trigger or by the habits; 2) Trigger-Aware Net and 3) Trigger-Free Net that estimate CTRs given user's intention is to the trigger-item and the mini-app respectively. Following a joint learning way, DIAN can both accurately predict user intention and dynamically balance the results of trigger-free and trigger-based recommendations based on the estimated intention. Experiments show that DIAN advances state-of-the-art performance in a large real-world dataset, and brings a 9.39% lift of online Item Page View and 4.74% CTR for Juhuasuan, a famous mini-app of Taobao.|电子商务平台为客户提供了进入迷你应用程序，可以满足他们的具体购物需求。触发项目显示在入口图标可以吸引更多的进入。然而，传统的点击率(Click-Through-Rate，CTR)预测模型忽略了用户对触发条目的即时兴趣，无法应用于被称为微型应用程序中的触发诱导推荐(Trigger-)的新推荐场景。此外，由于客户对迷你应用程序的高粘性，我们认为，现有的基于触发器的方法，过分强调触发项目的重要性，是不希望 TIRA，因为大部分客户进入是因为他们的日常购物习惯，而不是触发器。我们认为，TIRA 的关键是提取顾客的个性化进入意图，并根据这一意图权衡触发因素的影响。为了实现这一目标，我们将 TIRA 的 CTR 预测转化为一个单独的估计形式，并提出深度意图感知网络(DIAN)的三个关键要素: 1)意图网络，估计用户的进入意图，即他/她是否受到触发器或习惯的影响; 2)触发感知网络和3)无触发网络，估计给定用户意图的 CTR 分别是触发项目和迷你应用程序。DIAN 采用联合学习的方法，既能准确预测用户意图，又能根据预测意图动态平衡无触发和基于触发的推荐结果。实验表明，DIAN 在一个大型现实数据集中提升了最先进的性能，使在线项目页面查看率提高了9.39% ，淘宝著名小应用聚花酸的点击率提高了4.74% 。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Deep+Intention-Aware+Network+for+Click-Through+Rate+Prediction)|0| |[Search Personalization at Netflix](https://doi.org/10.1145/3543873.3587675)|Vito Ostuni, Christoph Kofler, Manjesh Nilange, Sudarshan Lamkhede, Dan Zylberglejd|Netflix Inc., USA|At Netflix, personalization plays a key role in several aspects of our user experience, from ranking titles to constructing an optimal Homepage. Although personalization is a well established research field, its application to search presents unique problems and opportunities. In this paper, we describe the evolution of Search personalization at Netflix, its unique challenges, and provide a high level overview of relevant solutions.|在 Netflix，个性化在我们的用户体验的几个方面起着关键作用，从排名标题到建立一个最佳的主页。虽然个性化是一个成熟的研究领域，但是它在搜索中的应用却带来了独特的问题和机遇。在本文中，我们描述了在 Netflix 搜索个性化的演变，其独特的挑战，并提供了相关解决方案的高层次概述。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Search+Personalization+at+Netflix)|0| -|[Pretrained Embeddings for E-commerce Machine Learning: When it Fails and Why?](https://doi.org/10.1145/3543873.3587669)|Da Xu, Bo Yang|LinkedIn, USA; Amazon, USA|The use of pretrained embeddings has become widespread in modern e-commerce machine learning (ML) systems. In practice, however, we have encountered several key issues when using pretrained embedding in a real-world production system, many of which cannot be fully explained by current knowledge. Unfortunately, we find that there is a lack of a thorough understanding of how pre-trained embeddings work, especially their intrinsic properties and interactions with downstream tasks. Consequently, it becomes challenging to make interactive and scalable decisions regarding the use of pre-trained embeddings in practice. Our investigation leads to two significant discoveries about using pretrained embeddings in e-commerce applications. Firstly, we find that the design of the pretraining and downstream models, particularly how they encode and decode information via embedding vectors, can have a profound impact. Secondly, we establish a principled perspective of pre-trained embeddings via the lens of kernel analysis, which can be used to evaluate their predictability, interactively and scalably. These findings help to address the practical challenges we faced and offer valuable guidance for successful adoption of pretrained embeddings in real-world production. Our conclusions are backed by solid theoretical reasoning, benchmark experiments, as well as online testings.|在现代电子商务机器学习(ML)系统中，预训练嵌入技术已经得到了广泛的应用。然而，在实践中，我们遇到了几个关键问题，当使用预训练嵌入在一个真实的生产系统，其中许多不能完全解释现有的知识。不幸的是，我们发现缺乏对预先训练的嵌入如何工作的透彻理解，特别是它们的内在属性和与下游任务的交互。因此，在实践中使用预先训练的嵌入方法时，做出交互式和可扩展的决策变得具有挑战性。我们的调查导致两个重要的发现，使用预训练嵌入在电子商务应用程序。首先，我们发现预训练和下游模型的设计，特别是它们如何通过嵌入向量对信息进行编码和解码，会产生深远的影响。其次，通过核分析的视角，建立了预训练嵌入的原则性视角，可以用来评估预训练嵌入的可预测性、交互性和可扩展性。这些发现有助于解决我们面临的实际挑战，并为在现实生产中成功采用预先培训的嵌入提供了宝贵的指导。我们的结论得到了可靠的理论推理、基准实验以及在线测试的支持。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Pretrained+Embeddings+for+E-commerce+Machine+Learning:+When+it+Fails+and+Why?)|0| +|[Pretrained Embeddings for E-commerce Machine Learning: When it Fails and Why?](https://doi.org/10.1145/3543873.3587669)|Da Xu, Bo Yang|Amazon, USA; LinkedIn, USA|The use of pretrained embeddings has become widespread in modern e-commerce machine learning (ML) systems. In practice, however, we have encountered several key issues when using pretrained embedding in a real-world production system, many of which cannot be fully explained by current knowledge. Unfortunately, we find that there is a lack of a thorough understanding of how pre-trained embeddings work, especially their intrinsic properties and interactions with downstream tasks. Consequently, it becomes challenging to make interactive and scalable decisions regarding the use of pre-trained embeddings in practice. Our investigation leads to two significant discoveries about using pretrained embeddings in e-commerce applications. Firstly, we find that the design of the pretraining and downstream models, particularly how they encode and decode information via embedding vectors, can have a profound impact. Secondly, we establish a principled perspective of pre-trained embeddings via the lens of kernel analysis, which can be used to evaluate their predictability, interactively and scalably. These findings help to address the practical challenges we faced and offer valuable guidance for successful adoption of pretrained embeddings in real-world production. Our conclusions are backed by solid theoretical reasoning, benchmark experiments, as well as online testings.|在现代电子商务机器学习(ML)系统中，预训练嵌入技术已经得到了广泛的应用。然而，在实践中，我们遇到了几个关键问题，当使用预训练嵌入在一个真实的生产系统，其中许多不能完全解释现有的知识。不幸的是，我们发现缺乏对预先训练的嵌入如何工作的透彻理解，特别是它们的内在属性和与下游任务的交互。因此，在实践中使用预先训练的嵌入方法时，做出交互式和可扩展的决策变得具有挑战性。我们的调查导致两个重要的发现，使用预训练嵌入在电子商务应用程序。首先，我们发现预训练和下游模型的设计，特别是它们如何通过嵌入向量对信息进行编码和解码，会产生深远的影响。其次，通过核分析的视角，建立了预训练嵌入的原则性视角，可以用来评估预训练嵌入的可预测性、交互性和可扩展性。这些发现有助于解决我们面临的实际挑战，并为在现实生产中成功采用预先培训的嵌入提供了宝贵的指导。我们的结论得到了可靠的理论推理、基准实验以及在线测试的支持。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Pretrained+Embeddings+for+E-commerce+Machine+Learning:+When+it+Fails+and+Why?)|0| |[GELTOR: A Graph Embedding Method based on Listwise Learning to Rank](https://doi.org/10.1145/3543507.3583193)|Masoud Reyhani Hamedani, JinSu Ryu, SangWook Kim|Hanyang University, Republic of Korea|Similarity-based embedding methods have introduced a new perspective on graph embedding by conforming the similarity distribution of latent vectors in the embedding space to that of nodes in the graph; they show significant effectiveness over conventional embedding methods in various machine learning tasks. In this paper, we first point out the three drawbacks of existing similarity-based embedding methods: inaccurate similarity computation, conflicting optimization goal, and impairing in/out-degree distributions. Then, motivated by these drawbacks, we propose AdaSim*, a novel similarity measure for graphs that is conducive to the similarity-based graph embedding. We finally propose GELTOR, an effective embedding method that employs AdaSim* as a node similarity measure and the concept of learning-to-rank in the embedding process. Contrary to existing methods, GELTOR does not learn the similarity scores distribution; instead, for any target node, GELTOR conforms the ranks of its top-t similar nodes in the embedding space to their original ranks based on AdaSim* scores. We conduct extensive experiments with six real-world datasets to evaluate the effectiveness of GELTOR in graph reconstruction, link prediction, and node classification tasks. Our experimental results show that (1) AdaSim* outperforms AdaSim, RWR, and MCT in computing nodes similarity in graphs, (2) our GETLOR outperforms existing state-of-the-arts and conventional embedding methods in most cases of the above machine learning tasks, thereby implying that learning-to-rank is beneficial to graph embedding.|基于相似性的嵌入方法通过调整嵌入空间中潜在向量与图中节点的相似性分布，为图的嵌入提供了一个新的视角，它们在各种机器学习任务中显示出比传统的嵌入方法更为有效的效果。本文首先指出了现有的基于相似度的嵌入方法存在的三个缺点: 相似度计算不准确、优化目标冲突和损伤内外度分布。然后，基于这些缺点，我们提出了 AdaSim * ，这是一种新的图的相似性度量，有利于基于相似性的图嵌入。最后提出了一种有效的嵌入方法 GELTOR，该方法采用 AdaSim * 作为节点相似性度量，并在嵌入过程中引入了学习排序的概念。与现有的方法相反，GELTOR 不学习相似度分数分布; 相反，对于任何目标节点，GELTOR 根据 AdaSim * 分数将其嵌入空间中的顶部 -t 相似节点的排名与其原始排名保持一致。我们使用六个真实世界的数据集进行了广泛的实验，以评估 GELTOR 在图重建、链路预测和节点分类任务中的有效性。我们的实验结果表明: (1) AdaSim * 在计算图中节点相似度方面优于 AdaSim，RWR 和 MCT; (2)在上述机器学习任务的大多数情况下，我们的 GETLOR 优于现有的最先进的和传统的嵌入方法，从而意味着学习排序有利于图嵌入。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=GELTOR:+A+Graph+Embedding+Method+based+on+Listwise+Learning+to+Rank)|0| -|[On the Theories Behind Hard Negative Sampling for Recommendation](https://doi.org/10.1145/3543507.3583223)|Wentao Shi, Jiawei Chen, Fuli Feng, Jizhi Zhang, Junkang Wu, Chongming Gao, Xiangnan He|University of Science and Technology of China, China; Zhejiang University, China|Negative sampling has been heavily used to train recommender models on large-scale data, wherein sampling hard examples usually not only accelerates the convergence but also improves the model accuracy. Nevertheless, the reasons for the effectiveness of Hard Negative Sampling (HNS) have not been revealed yet. In this work, we fill the research gap by conducting thorough theoretical analyses on HNS. Firstly, we prove that employing HNS on the Bayesian Personalized Ranking (BPR) learner is equivalent to optimizing One-way Partial AUC (OPAUC). Concretely, the BPR equipped with Dynamic Negative Sampling (DNS) is an exact estimator, while with softmax-based sampling is a soft estimator. Secondly, we prove that OPAUC has a stronger connection with Top-K evaluation metrics than AUC and verify it with simulation experiments. These analyses establish the theoretical foundation of HNS in optimizing Top-K recommendation performance for the first time. On these bases, we offer two insightful guidelines for effective usage of HNS: 1) the sampling hardness should be controllable, e.g., via pre-defined hyper-parameters, to adapt to different Top-K metrics and datasets; 2) the smaller the $K$ we emphasize in Top-K evaluation metrics, the harder the negative samples we should draw. Extensive experiments on three real-world benchmarks verify the two guidelines.|负抽样已经被广泛用于大规模数据的推荐模型训练，而硬实例抽样不仅可以加快模型的收敛速度，而且可以提高模型的精度。然而，硬性负样本(HNS)有效性的原因尚未被揭示。本文通过对 HNS 进行深入的理论分析，填补了研究空白。首先，我们证明了对贝叶斯个性化排序(BPR)学习者使用 HNS 等价于优化单向部分 AUC (OPAUC)。具体来说，装有动态负抽样(DNS)的 BPR 是一个精确估计量，而基于软最大抽样的 BPR 是一个软估计量。其次，我们证明了 OPAUC 与 Top-K 评价指标之间的联系比 AUC 更强，并通过仿真实验进行了验证。这些分析首次为 HNS 优化 Top-K 推荐性能奠定了理论基础。在此基础上，我们为有效使用 HNS 提供了两个有见地的指导方针: 1)抽样硬度应该是可控的，例如，通过预定义的超参数，以适应不同的 Top-K 指标和数据集; 2)我们在 Top-K 评估指标中强调的 $K $越小，我们应该抽取的负面样本就越难。在三个真实世界的基准上进行的大量实验验证了这两条准则。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=On+the+Theories+Behind+Hard+Negative+Sampling+for+Recommendation)|0| -|[A Counterfactual Collaborative Session-based Recommender System](https://doi.org/10.1145/3543507.3583321)|Wenzhuo Song, Shoujin Wang, Yan Wang, Kunpeng Liu, Xueyan Liu, Minghao Yin|Macquarie University, Australia; Jilin University, China; Portland State University, USA; Northeast Normal University, China; University of Technology Sydney, Australia|Most session-based recommender systems (SBRSs) focus on extracting information from the observed items in the current session of a user to predict a next item, ignoring the causes outside the session (called outer-session causes, OSCs) that influence the user's selection of items. However, these causes widely exist in the real world, and few studies have investigated their role in SBRSs. In this work, we analyze the causalities and correlations of the OSCs in SBRSs from the perspective of causal inference. We find that the OSCs are essentially the confounders in SBRSs, which leads to spurious correlations in the data used to train SBRS models. To address this problem, we propose a novel SBRS framework named COCO-SBRS (COunterfactual COllaborative Session-Based Recommender Systems) to learn the causality between OSCs and user-item interactions in SBRSs. COCO-SBRS first adopts a self-supervised approach to pre-train a recommendation model by designing pseudo-labels of causes for each user's selection of the item in data to guide the training process. Next, COCO-SBRS adopts counterfactual inference to recommend items based on the outputs of the pre-trained recommendation model considering the causalities to alleviate the data sparsity problem. As a result, COCO-SBRS can learn the causalities in data, preventing the model from learning spurious correlations. The experimental results of our extensive experiments conducted on three real-world datasets demonstrate the superiority of our proposed framework over ten representative SBRSs.|大多数基于会话的推荐系统(SBS)专注于从用户当前会话中观察到的项目中提取信息来预测下一个项目，而忽略了会话之外影响用户选择项目的原因(称为外部会话原因，OSC)。然而，这些原因在现实世界中普遍存在，很少有研究探讨它们在 SBRS 中的作用。本文从因果推理的角度分析了 SBRS 中 OSCs 的因果关系及其相关性。我们发现 OSC 本质上是 SBRS 中的混杂因素，这导致了用于训练 SBRS 模型的数据中存在虚假的相关性。为了解决这一问题，我们提出了一种新的 SBRS 框架 COCO-SBRS (COCO-SBRS，非事实协作的基于会话的推荐系统)来了解在 SBRS 中 OSC 和用户项目交互之间的因果关系。COCO-SBRS 首先采用自我监督的方法对推荐模型进行预训练，为每个用户选择数据中项目的原因设计伪标签，以指导训练过程。其次，COCO-SBRS 采用反事实推理方法，根据预训练推荐模型的输出结果进行推荐，考虑因果关系，以缓解数据稀疏问题。因此，COCO-SBRS 模型可以学习数据中的因果关系，防止模型学习虚假的相关性。我们在三个实际数据集上进行的大量实验结果表明，我们提出的框架优于十个具有代表性的 SBRS。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Counterfactual+Collaborative+Session-based+Recommender+System)|0| +|[On the Theories Behind Hard Negative Sampling for Recommendation](https://doi.org/10.1145/3543507.3583223)|Wentao Shi, Jiawei Chen, Fuli Feng, Jizhi Zhang, Junkang Wu, Chongming Gao, Xiangnan He|Zhejiang University, China; University of Science and Technology of China, China|Negative sampling has been heavily used to train recommender models on large-scale data, wherein sampling hard examples usually not only accelerates the convergence but also improves the model accuracy. Nevertheless, the reasons for the effectiveness of Hard Negative Sampling (HNS) have not been revealed yet. In this work, we fill the research gap by conducting thorough theoretical analyses on HNS. Firstly, we prove that employing HNS on the Bayesian Personalized Ranking (BPR) learner is equivalent to optimizing One-way Partial AUC (OPAUC). Concretely, the BPR equipped with Dynamic Negative Sampling (DNS) is an exact estimator, while with softmax-based sampling is a soft estimator. Secondly, we prove that OPAUC has a stronger connection with Top-K evaluation metrics than AUC and verify it with simulation experiments. These analyses establish the theoretical foundation of HNS in optimizing Top-K recommendation performance for the first time. On these bases, we offer two insightful guidelines for effective usage of HNS: 1) the sampling hardness should be controllable, e.g., via pre-defined hyper-parameters, to adapt to different Top-K metrics and datasets; 2) the smaller the $K$ we emphasize in Top-K evaluation metrics, the harder the negative samples we should draw. Extensive experiments on three real-world benchmarks verify the two guidelines.|负抽样已经被广泛用于大规模数据的推荐模型训练，而硬实例抽样不仅可以加快模型的收敛速度，而且可以提高模型的精度。然而，硬性负样本(HNS)有效性的原因尚未被揭示。本文通过对 HNS 进行深入的理论分析，填补了研究空白。首先，我们证明了对贝叶斯个性化排序(BPR)学习者使用 HNS 等价于优化单向部分 AUC (OPAUC)。具体来说，装有动态负抽样(DNS)的 BPR 是一个精确估计量，而基于软最大抽样的 BPR 是一个软估计量。其次，我们证明了 OPAUC 与 Top-K 评价指标之间的联系比 AUC 更强，并通过仿真实验进行了验证。这些分析首次为 HNS 优化 Top-K 推荐性能奠定了理论基础。在此基础上，我们为有效使用 HNS 提供了两个有见地的指导方针: 1)抽样硬度应该是可控的，例如，通过预定义的超参数，以适应不同的 Top-K 指标和数据集; 2)我们在 Top-K 评估指标中强调的 $K $越小，我们应该抽取的负面样本就越难。在三个真实世界的基准上进行的大量实验验证了这两条准则。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=On+the+Theories+Behind+Hard+Negative+Sampling+for+Recommendation)|0| +|[A Counterfactual Collaborative Session-based Recommender System](https://doi.org/10.1145/3543507.3583321)|Wenzhuo Song, Shoujin Wang, Yan Wang, Kunpeng Liu, Xueyan Liu, Minghao Yin|Jilin University, China; Portland State University, USA; University of Technology Sydney, Australia; Macquarie University, Australia; Northeast Normal University, China|Most session-based recommender systems (SBRSs) focus on extracting information from the observed items in the current session of a user to predict a next item, ignoring the causes outside the session (called outer-session causes, OSCs) that influence the user's selection of items. However, these causes widely exist in the real world, and few studies have investigated their role in SBRSs. In this work, we analyze the causalities and correlations of the OSCs in SBRSs from the perspective of causal inference. We find that the OSCs are essentially the confounders in SBRSs, which leads to spurious correlations in the data used to train SBRS models. To address this problem, we propose a novel SBRS framework named COCO-SBRS (COunterfactual COllaborative Session-Based Recommender Systems) to learn the causality between OSCs and user-item interactions in SBRSs. COCO-SBRS first adopts a self-supervised approach to pre-train a recommendation model by designing pseudo-labels of causes for each user's selection of the item in data to guide the training process. Next, COCO-SBRS adopts counterfactual inference to recommend items based on the outputs of the pre-trained recommendation model considering the causalities to alleviate the data sparsity problem. As a result, COCO-SBRS can learn the causalities in data, preventing the model from learning spurious correlations. The experimental results of our extensive experiments conducted on three real-world datasets demonstrate the superiority of our proposed framework over ten representative SBRSs.|大多数基于会话的推荐系统(SBS)专注于从用户当前会话中观察到的项目中提取信息来预测下一个项目，而忽略了会话之外影响用户选择项目的原因(称为外部会话原因，OSC)。然而，这些原因在现实世界中普遍存在，很少有研究探讨它们在 SBRS 中的作用。本文从因果推理的角度分析了 SBRS 中 OSCs 的因果关系及其相关性。我们发现 OSC 本质上是 SBRS 中的混杂因素，这导致了用于训练 SBRS 模型的数据中存在虚假的相关性。为了解决这一问题，我们提出了一种新的 SBRS 框架 COCO-SBRS (COCO-SBRS，非事实协作的基于会话的推荐系统)来了解在 SBRS 中 OSC 和用户项目交互之间的因果关系。COCO-SBRS 首先采用自我监督的方法对推荐模型进行预训练，为每个用户选择数据中项目的原因设计伪标签，以指导训练过程。其次，COCO-SBRS 采用反事实推理方法，根据预训练推荐模型的输出结果进行推荐，考虑因果关系，以缓解数据稀疏问题。因此，COCO-SBRS 模型可以学习数据中的因果关系，防止模型学习虚假的相关性。我们在三个实际数据集上进行的大量实验结果表明，我们提出的框架优于十个具有代表性的 SBRS。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Counterfactual+Collaborative+Session-based+Recommender+System)|0| |[Debiased Contrastive Learning for Sequential Recommendation](https://doi.org/10.1145/3543507.3583361)|Yuhao Yang, Chao Huang, Lianghao Xia, Chunzhen Huang, Da Luo, Kangyi Lin||Current sequential recommender systems are proposed to tackle the dynamic user preference learning with various neural techniques, such as Transformer and Graph Neural Networks (GNNs). However, inference from the highly sparse user behavior data may hinder the representation ability of sequential pattern encoding. To address the label shortage issue, contrastive learning (CL) methods are proposed recently to perform data augmentation in two fashions: (i) randomly corrupting the sequence data (e.g. stochastic masking, reordering); (ii) aligning representations across pre-defined contrastive views. Although effective, we argue that current CL-based methods have limitations in addressing popularity bias and disentangling of user conformity and real interest. In this paper, we propose a new Debiased Contrastive learning paradigm for Recommendation (DCRec) that unifies sequential pattern encoding with global collaborative relation modeling through adaptive conformity-aware augmentation. This solution is designed to tackle the popularity bias issue in recommendation systems. Our debiased contrastive learning framework effectively captures both the patterns of item transitions within sequences and the dependencies between users across sequences. Our experiments on various real-world datasets have demonstrated that DCRec significantly outperforms state-of-the-art baselines, indicating its efficacy for recommendation. To facilitate reproducibility of our results, we make our implementation of DCRec publicly available at: https://github.com/HKUDS/DCRec.|目前的顺序推荐系统主要采用变压器和图形神经网络(GNN)等多种神经网络技术来解决动态用户偏好学习问题。然而，从高度稀疏的用户行为数据中进行推断可能会阻碍序列模式编码的表示能力。为了解决标签短缺问题，最近提出了对比学习(CL)方法，以两种方式进行数据增强: (i)随机破坏序列数据(例如随机掩蔽，重新排序) ; (ii)跨预定义的对比视图对齐表示。虽然有效，但我们认为目前基于 CL 的方法在解决流行偏差和用户一致性与真实兴趣的分离方面存在局限性。本文提出了一种新的无偏对比推荐学习范式，它通过自适应整合意识增强将序列模式编码与全局协作关系建模相结合。该解决方案旨在解决推荐系统中的流行偏差问题。我们的去偏差对比学习框架有效地捕获了序列中的项目转换模式和用户之间的依赖关系。我们在各种真实世界数据集上的实验表明，DCREc 显著优于最先进的基线，表明其推荐功效。为了便于重复我们的结果，我们将我们的 DCRec 的实现公布于以下 https://github.com/hkuds/DCRec。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Debiased+Contrastive+Learning+for+Sequential+Recommendation)|0| -|[Learning Vector-Quantized Item Representation for Transferable Sequential Recommenders](https://doi.org/10.1145/3543507.3583434)|Yupeng Hou, Zhankui He, Julian J. McAuley, Wayne Xin Zhao|Beijing Key Laboratory of Big Data Management and Analysis Methods, Renmin University of China, China; Renmin University of China, China; UC San Diego, USA|Recently, the generality of natural language text has been leveraged to develop transferable recommender systems. The basic idea is to employ pre-trained language models~(PLM) to encode item text into item representations. Despite the promising transferability, the binding between item text and item representations might be too tight, leading to potential problems such as over-emphasizing the effect of text features and exaggerating the negative impact of domain gap. To address this issue, this paper proposes VQ-Rec, a novel approach to learning Vector-Quantized item representations for transferable sequential Recommenders. The main novelty of our approach lies in the new item representation scheme: it first maps item text into a vector of discrete indices (called item code), and then employs these indices to lookup the code embedding table for deriving item representations. Such a scheme can be denoted as "text $\Longrightarrow$ code $\Longrightarrow$ representation". Based on this representation scheme, we further propose an enhanced contrastive pre-training approach, using semi-synthetic and mixed-domain code representations as hard negatives. Furthermore, we design a new cross-domain fine-tuning method based on a differentiable permutation-based network. Extensive experiments conducted on six public benchmarks demonstrate the effectiveness of the proposed approach, in both cross-domain and cross-platform settings. Code and pre-trained model are available at: https://github.com/RUCAIBox/VQ-Rec.|近年来，人们利用自然语言文本的通用性来开发可转移的推荐系统。其基本思想是使用预先训练好的语言模型 ~ (PLM)将项目文本编码成项目表示。项目文本与项目表征之间的联系过于紧密，可能导致过分强调文本特征的作用，夸大领域差距的负面影响等问题。为了解决这一问题，本文提出了一种新的学习矢量量化项目表示的方法 VQ-Rec。该方法的主要创新点在于新的项目表示方案: 它首先将项目文本映射到一个离散索引的向量(称为项目代码) ，然后使用这些索引查找代码嵌入表以获得项目表示。这样的方案可以表示为“ text $Longrightarrow $code $Longrightarrow $代表”。基于这种表示方案，我们进一步提出了一种增强的对比预训练方法，使用半合成和混合域代码表示作为硬负数。在此基础上，设计了一种新的基于可微置换网络的跨域微调方法。在六个公共基准上进行的大量实验证明了该方法在跨领域和跨平台环境中的有效性。代码和预先训练的模型可在以下 https://github.com/rucaibox/vq-rec 找到:。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Learning+Vector-Quantized+Item+Representation+for+Transferable+Sequential+Recommenders)|0| -|[KAE-Informer: A Knowledge Auto-Embedding Informer for Forecasting Long-Term Workloads of Microservices](https://doi.org/10.1145/3543507.3583288)|Qin Hua, Dingyu Yang, Shiyou Qian, Hanwen Hu, Jian Cao, Guangtao Xue|Shanghai Jiao Tong University, China; Alibaba Group, China|Accurately forecasting workloads in terms of throughput that is quantified as queries per second (QPS) is essential for microservices to elastically adjust their resource allocations. However, long-term QPS prediction is challenging in two aspects: 1) generality across various services with different temporal patterns, 2) characterization of intricate QPS sequences which are entangled by multiple components. In this paper, we propose a knowledge auto-embedding Informer network (KAE-Informer) for forecasting the long-term QPS sequences of microservices. By analyzing a large number of microservice traces, we discover that there are two main decomposable and predictable components in QPS sequences, namely global trend & dominant periodicity (TP) and low-frequency residual patterns with long-range dependencies. These two components are important for accurately forecasting long-term QPS. First, KAE-Informer embeds the knowledge of TP components through mathematical modeling. Second, KAE-Informer designs a convolution ProbSparse self-attention mechanism and a multi-layer event discrimination scheme to extract and embed the knowledge of local context awareness and event regression effect implied in residual components, respectively. We conduct experiments based on three real datasets including a QPS dataset collected from 40 microservices. The experiment results show that KAE-Informer achieves a reduction of MAPE, MAE and RMSE by about 16.6%, 17.6% and 23.1% respectively, compared to the state-of-the-art models.|根据每秒查询(QPS)量化的吞吐量准确预测工作负载对于微服务弹性调整其资源分配至关重要。然而，长期的 QPS 预测在两个方面具有挑战性: 1)不同时间模式的服务之间的一般性，2)被多个组件纠缠在一起的复杂的 QPS 序列的角色塑造。本文提出了一种基于知识自动嵌入的信息网络(KAE-Informer)来预测微服务的长期 QPS 序列。通过对大量微服务跟踪的分析，发现 QPS 序列中存在两个主要的可分解和可预测成分，即全局趋势和主周期(TP)和具有长程依赖性的低频残差模式。这两个组成部分是准确预测长期 QPS 的重要组成部分。首先，KAE-Informer 通过数学建模嵌入 TP 元件的知识。其次，KAE-Informer 分别设计了一种卷积 Probse 自注意机制和一种多层次事件识别方案来提取和嵌入残差分量中隐含的局部上下文感知和事件回归效应的知识。我们基于三个实际数据集进行实验，其中包括从40个微服务中收集的 QPS 数据集。实验结果表明，与现有的模型相比，KAE-Informer 的 MAPE、 MAE 和 RMSE 分别降低了约16.6% 、17.6% 和23.1% 。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=KAE-Informer:+A+Knowledge+Auto-Embedding+Informer+for+Forecasting+Long-Term+Workloads+of+Microservices)|0| +|[Learning Vector-Quantized Item Representation for Transferable Sequential Recommenders](https://doi.org/10.1145/3543507.3583434)|Yupeng Hou, Zhankui He, Julian J. McAuley, Wayne Xin Zhao|Renmin University of China, China; UC San Diego, USA; Beijing Key Laboratory of Big Data Management and Analysis Methods, Renmin University of China, China|Recently, the generality of natural language text has been leveraged to develop transferable recommender systems. The basic idea is to employ pre-trained language models~(PLM) to encode item text into item representations. Despite the promising transferability, the binding between item text and item representations might be too tight, leading to potential problems such as over-emphasizing the effect of text features and exaggerating the negative impact of domain gap. To address this issue, this paper proposes VQ-Rec, a novel approach to learning Vector-Quantized item representations for transferable sequential Recommenders. The main novelty of our approach lies in the new item representation scheme: it first maps item text into a vector of discrete indices (called item code), and then employs these indices to lookup the code embedding table for deriving item representations. Such a scheme can be denoted as "text $\Longrightarrow$ code $\Longrightarrow$ representation". Based on this representation scheme, we further propose an enhanced contrastive pre-training approach, using semi-synthetic and mixed-domain code representations as hard negatives. Furthermore, we design a new cross-domain fine-tuning method based on a differentiable permutation-based network. Extensive experiments conducted on six public benchmarks demonstrate the effectiveness of the proposed approach, in both cross-domain and cross-platform settings. Code and pre-trained model are available at: https://github.com/RUCAIBox/VQ-Rec.|近年来，人们利用自然语言文本的通用性来开发可转移的推荐系统。其基本思想是使用预先训练好的语言模型 ~ (PLM)将项目文本编码成项目表示。项目文本与项目表征之间的联系过于紧密，可能导致过分强调文本特征的作用，夸大领域差距的负面影响等问题。为了解决这一问题，本文提出了一种新的学习矢量量化项目表示的方法 VQ-Rec。该方法的主要创新点在于新的项目表示方案: 它首先将项目文本映射到一个离散索引的向量(称为项目代码) ，然后使用这些索引查找代码嵌入表以获得项目表示。这样的方案可以表示为“ text $Longrightarrow $code $Longrightarrow $代表”。基于这种表示方案，我们进一步提出了一种增强的对比预训练方法，使用半合成和混合域代码表示作为硬负数。在此基础上，设计了一种新的基于可微置换网络的跨域微调方法。在六个公共基准上进行的大量实验证明了该方法在跨领域和跨平台环境中的有效性。代码和预先训练的模型可在以下 https://github.com/rucaibox/vq-rec 找到:。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Learning+Vector-Quantized+Item+Representation+for+Transferable+Sequential+Recommenders)|0| +|[KAE-Informer: A Knowledge Auto-Embedding Informer for Forecasting Long-Term Workloads of Microservices](https://doi.org/10.1145/3543507.3583288)|Qin Hua, Dingyu Yang, Shiyou Qian, Hanwen Hu, Jian Cao, Guangtao Xue|Alibaba Group, China; Shanghai Jiao Tong University, China|Accurately forecasting workloads in terms of throughput that is quantified as queries per second (QPS) is essential for microservices to elastically adjust their resource allocations. However, long-term QPS prediction is challenging in two aspects: 1) generality across various services with different temporal patterns, 2) characterization of intricate QPS sequences which are entangled by multiple components. In this paper, we propose a knowledge auto-embedding Informer network (KAE-Informer) for forecasting the long-term QPS sequences of microservices. By analyzing a large number of microservice traces, we discover that there are two main decomposable and predictable components in QPS sequences, namely global trend & dominant periodicity (TP) and low-frequency residual patterns with long-range dependencies. These two components are important for accurately forecasting long-term QPS. First, KAE-Informer embeds the knowledge of TP components through mathematical modeling. Second, KAE-Informer designs a convolution ProbSparse self-attention mechanism and a multi-layer event discrimination scheme to extract and embed the knowledge of local context awareness and event regression effect implied in residual components, respectively. We conduct experiments based on three real datasets including a QPS dataset collected from 40 microservices. The experiment results show that KAE-Informer achieves a reduction of MAPE, MAE and RMSE by about 16.6%, 17.6% and 23.1% respectively, compared to the state-of-the-art models.|根据每秒查询(QPS)量化的吞吐量准确预测工作负载对于微服务弹性调整其资源分配至关重要。然而，长期的 QPS 预测在两个方面具有挑战性: 1)不同时间模式的服务之间的一般性，2)被多个组件纠缠在一起的复杂的 QPS 序列的角色塑造。本文提出了一种基于知识自动嵌入的信息网络(KAE-Informer)来预测微服务的长期 QPS 序列。通过对大量微服务跟踪的分析，发现 QPS 序列中存在两个主要的可分解和可预测成分，即全局趋势和主周期(TP)和具有长程依赖性的低频残差模式。这两个组成部分是准确预测长期 QPS 的重要组成部分。首先，KAE-Informer 通过数学建模嵌入 TP 元件的知识。其次，KAE-Informer 分别设计了一种卷积 Probse 自注意机制和一种多层次事件识别方案来提取和嵌入残差分量中隐含的局部上下文感知和事件回归效应的知识。我们基于三个实际数据集进行实验，其中包括从40个微服务中收集的 QPS 数据集。实验结果表明，与现有的模型相比，KAE-Informer 的 MAPE、 MAE 和 RMSE 分别降低了约16.6% 、17.6% 和23.1% 。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=KAE-Informer:+A+Knowledge+Auto-Embedding+Informer+for+Forecasting+Long-Term+Workloads+of+Microservices)|0| |[Propaganda Política Pagada: Exploring U.S. Political Facebook Ads en Español](https://doi.org/10.1145/3543507.3583425)|Bruno Coelho, Tobias Lauinger, Laura Edelson, Ian Goldstein, Damon McCoy|New York University, USA|In 2021, the U.S. Hispanic population totaled 62.5 million people, 68% of whom spoke Spanish in their homes. To date, it is unclear which political advertisers address this audience in their preferred language, and whether they do so differently than for English-speaking audiences. In this work, we study differences between political Facebook ads in English and Spanish during 2020, the latest U.S. presidential election. Political advertisers spent $ 1.48 B in English, but only $ 28.8 M in Spanish, disproportionately little compared to the share of Spanish speakers in the population. We further find a lower proportion of election-related advertisers (which additionally are more liberal-leaning than in the English set), and a higher proportion of government agencies in the set of Spanish ads. We perform multilingual topic classification, finding that the most common ad topics in English were also present in Spanish, but to a different extent, and with a different composition of advertisers. Thus, Spanish speakers are served different types of ads from different types of advertisers than English speakers, and in lower amounts; these results raise the question of whether political communication through Facebook ads may be inequitable and effectively disadvantaging the sizeable minority of Spanish speakers in the U.S. population.|2021年，美国西班牙裔人口总数为6250万，其中68% 的人在家里说西班牙语。到目前为止，还不清楚哪些政治广告主用自己喜欢的语言向这些受众发表演讲，以及他们的做法是否与英语受众不同。在这项工作中，我们研究了2020年美国总统大选期间 Facebook 上英语和西班牙语的政治广告之间的差异。政治广告客户在英语广告上花费了14.8亿美元，但在西班牙语广告上只花费了2880万美元，与说西班牙语的人口比例相比，这个数字不成比例。我们进一步发现，与选举有关的广告客户比例较低(此外，这些广告客户比英语广告客户更倾向于自由派) ，而在西班牙语广告客户中，政府机构的比例较高。我们进行了多语言话题分类，发现英语中最常见的广告话题也出现在西班牙语中，但程度不同，广告主的构成也不同。因此，说西班牙语的人比说英语的人得到了不同类型的广告，而且数量较少; 这些结果提出了一个问题: 通过 Facebook 广告进行的政治交流是否不公平，是否有效地损害了美国人口中说西班牙语的少数人的利益。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Propaganda+Política+Pagada:+Exploring+U.S.+Political+Facebook+Ads+en+Español)|0| -|[Learning Denoised and Interpretable Session Representation for Conversational Search](https://doi.org/10.1145/3543507.3583265)|Kelong Mao, Hongjin Qian, Fengran Mo, Zhicheng Dou, Bang Liu, Xiaohua Cheng, Zhao Cao|Renmin University of China, China; Université de Montréal, Canada; RALI & Mila, Université de Montréal, Canada; Huawei Poisson Lab, China|Conversational search supports multi-turn user-system interactions to solve complex information needs. Compared with the traditional single-turn ad-hoc search, conversational search faces a more complex search intent understanding problem because a conversational search session is much longer and contains many noisy tokens. However, existing conversational dense retrieval solutions simply fine-tune the pre-trained ad-hoc query encoder on limited conversational search data, which are hard to achieve satisfactory performance in such a complex conversational search scenario. Meanwhile, the learned latent representation also lacks interpretability that people cannot perceive how the model understands the session. To tackle the above drawbacks, we propose a sparse Lexical-based Conversational REtriever (LeCoRE), which extends the SPLADE model with two well-matched multi-level denoising methods uniformly based on knowledge distillation and external query rewrites to generate denoised and interpretable lexical session representation. Extensive experiments on four public conversational search datasets in both normal and zero-shot evaluation settings demonstrate the strong performance of LeCoRE towards more effective and interpretable conversational search.|会话搜索支持多回合的用户-系统交互，以解决复杂的信息需求。与传统的单向自组织搜索相比，会话搜索面临着更复杂的搜索意图理解问题，因为会话搜索会话更长，且包含大量噪声标记。然而，现有的会话密集检索解决方案只是在有限的会话搜索数据上对预先训练好的自组织查询编码器进行微调，难以在如此复杂的会话搜索场景中获得令人满意的性能。同时，习得的潜在表征也缺乏可解释性，人们无法感知模型是如何理解会话的。针对上述缺点，本文提出了一种基于稀疏词汇的会话检索(Conversational REtriever，LeCoRE)算法，该算法扩展了 SPLADE 模型，采用基于知识提取和外部查询重写的两种匹配性较好的多级去噪方法，均匀地生成去噪和可解释的词汇会话表示。对四个公共会话搜索数据集在正常和零拍评估环境下的大量实验表明，LeCoRE 在更有效和可解释的会话搜索方面具有很强的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Learning+Denoised+and+Interpretable+Session+Representation+for+Conversational+Search)|0| +|[Learning Denoised and Interpretable Session Representation for Conversational Search](https://doi.org/10.1145/3543507.3583265)|Kelong Mao, Hongjin Qian, Fengran Mo, Zhicheng Dou, Bang Liu, Xiaohua Cheng, Zhao Cao|RALI & Mila, Université de Montréal, Canada; Renmin University of China, China; Huawei Poisson Lab, China; Université de Montréal, Canada|Conversational search supports multi-turn user-system interactions to solve complex information needs. Compared with the traditional single-turn ad-hoc search, conversational search faces a more complex search intent understanding problem because a conversational search session is much longer and contains many noisy tokens. However, existing conversational dense retrieval solutions simply fine-tune the pre-trained ad-hoc query encoder on limited conversational search data, which are hard to achieve satisfactory performance in such a complex conversational search scenario. Meanwhile, the learned latent representation also lacks interpretability that people cannot perceive how the model understands the session. To tackle the above drawbacks, we propose a sparse Lexical-based Conversational REtriever (LeCoRE), which extends the SPLADE model with two well-matched multi-level denoising methods uniformly based on knowledge distillation and external query rewrites to generate denoised and interpretable lexical session representation. Extensive experiments on four public conversational search datasets in both normal and zero-shot evaluation settings demonstrate the strong performance of LeCoRE towards more effective and interpretable conversational search.|会话搜索支持多回合的用户-系统交互，以解决复杂的信息需求。与传统的单向自组织搜索相比，会话搜索面临着更复杂的搜索意图理解问题，因为会话搜索会话更长，且包含大量噪声标记。然而，现有的会话密集检索解决方案只是在有限的会话搜索数据上对预先训练好的自组织查询编码器进行微调，难以在如此复杂的会话搜索场景中获得令人满意的性能。同时，习得的潜在表征也缺乏可解释性，人们无法感知模型是如何理解会话的。针对上述缺点，本文提出了一种基于稀疏词汇的会话检索(Conversational REtriever，LeCoRE)算法，该算法扩展了 SPLADE 模型，采用基于知识提取和外部查询重写的两种匹配性较好的多级去噪方法，均匀地生成去噪和可解释的词汇会话表示。对四个公共会话搜索数据集在正常和零拍评估环境下的大量实验表明，LeCoRE 在更有效和可解释的会话搜索方面具有很强的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Learning+Denoised+and+Interpretable+Session+Representation+for+Conversational+Search)|0| |[Fairly Adaptive Negative Sampling for Recommendations](https://doi.org/10.1145/3543507.3583355)|Xiao Chen, Wenqi Fan, Jingfan Chen, Haochen Liu, Zitao Liu, Zhaoxiang Zhang, Qing Li||Pairwise learning strategies are prevalent for optimizing recommendation models on implicit feedback data, which usually learns user preference by discriminating between positive (i.e., clicked by a user) and negative items (i.e., obtained by negative sampling). However, the size of different item groups (specified by item attribute) is usually unevenly distributed. We empirically find that the commonly used uniform negative sampling strategy for pairwise algorithms (e.g., BPR) can inherit such data bias and oversample the majority item group as negative instances, severely countering group fairness on the item side. In this paper, we propose a Fairly adaptive Negative sampling approach (FairNeg), which improves item group fairness via adaptively adjusting the group-level negative sampling distribution in the training process. In particular, it first perceives the model's unfairness status at each step and then adjusts the group-wise sampling distribution with an adaptive momentum update strategy for better facilitating fairness optimization. Moreover, a negative sampling distribution Mixup mechanism is proposed, which gracefully incorporates existing importance-aware sampling techniques intended for mining informative negative samples, thus allowing for achieving multiple optimization purposes. Extensive experiments on four public datasets show our proposed method's superiority in group fairness enhancement and fairness-utility tradeoff.|成对学习策略普遍用于优化隐性反馈数据的推荐模型，它通常通过区分正面(即用户点击)和负面(即通过负面抽样获得)来学习用户偏好。但是，不同项目组(由项目属性指定)的大小通常是不均匀分布的。实证结果表明，成对算法中常用的一致负抽样策略(如 BPR)会继承这种数据偏差，并将多数项目组作为负实例过度抽样，严重影响项目方的群体公平性。本文提出了一种公平自适应负抽样方法(FairNeg) ，该方法通过在训练过程中自适应调整组级负抽样分布来提高项目组的公平性。特别地，它首先在每个步骤中感知模型的不公平状态，然后利用自适应动量更新策略调整分组抽样分布，以更好地促进公平性优化。此外，提出了负抽样分布混合机制，它优雅地结合了现有的重要性感知抽样技术，旨在挖掘信息负样本，从而实现多种优化目的。在四个公共数据集上的大量实验表明，该方法在增强群体公平性和公平-效用权衡方面具有优越性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Fairly+Adaptive+Negative+Sampling+for+Recommendations)|0| |[CNSVRE: A Query Reformulated Search System with Explainable Summarization for Virtual Research Environment](https://doi.org/10.1145/3543873.3587360)|Na Li, Yangjun Zhang, Zhiming Zhao|University of Amsterdam, Netherlands|Computational notebook environments have drawn broad attention in data-centric research applications, e.g., virtual research environment, for exploratory data analysis and algorithm prototyping. Vanilla computational notebook search solutions have been proposed but they do not pay much attention to the information needs of scientific researchers. Previous studies either treat computational notebook search as a code search problem or focus on content-based computational notebook search. The queries being considered are neither research-concerning nor diversified whereas researchers’ information needs are highly specialized and complex. Moreover, relevance evaluation for computational notebooks is tricky and unreliable since computational notebooks contain fragments of text and code and are usually poorly organized. To solve the above challenges, we propose a computational notebook search system for virtual research environment (VRE), i.e., CNSVRE, with scientific query reformulation and computational notebook summarization. We conduct a user study to demonstrate the effectiveness, efficiency, and satisfaction with the system.|计算机笔记本环境在以数据为中心的研究应用中引起了广泛的关注，例如用于探索性数据分析和算法原型的虚拟研究环境。香草计算笔记本搜索解决方案已经提出，但他们没有太多的关注科学研究人员的信息需求。以往的研究要么将计算笔记本搜索视为一个代码搜索问题，要么将重点放在基于内容的计算笔记本搜索上。被考虑的查询既不涉及研究，也不多样化，而研究人员的信息需求是高度专业化和复杂化的。此外，计算笔记本的相关性评估是棘手和不可靠的，因为计算笔记本包含文本和代码片段，通常组织不良。为了解决上述挑战，我们提出了一个虚拟研究环境(即 CNSVRE)的计算笔记本搜索系统，该系统具有科学的查询重构和计算笔记本摘要。我们进行了用户研究，以证明系统的有效性、效率和满意度。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=CNSVRE:+A+Query+Reformulated+Search+System+with+Explainable+Summarization+for+Virtual+Research+Environment)|0| |[Personalized style recommendation via reinforcement learning](https://doi.org/10.1145/3543873.3587367)|Jiyun Luo, Kurchi Subhra Hazra, Wenyu Huo, Rui Li, Abhijit Mahabal|Pinterest Inc., USA|Pinterest fashion and home decor searchers often have different style tastes. Some existing work adopts users’ past engagement to infer style preference. These methods cannot help users discover new styles. Other work requires users to provide text or visual signals to describe their style preference, but users often are not familiar with style terms and do not have the right image to start with. In this paper, we propose a reinforcement learning (RL) method to help users explore and exploit style space without requiring extra user input. Experimental results show that our method improves the success rate of Pinterest fashion and home decor searches by 34.8%.|Pinterest 时尚和家居装饰搜索往往有不同的风格品味。现有的一些工作采用用户过去的接触来推断风格偏好。这些方法不能帮助用户发现新样式。其他工作需要用户提供文本或视觉信号来描述他们的风格偏好，但用户往往不熟悉风格术语，并没有正确的图像开始。在这篇文章中，我们提出了一个强化学习(RL)方法来帮助用户探索和开发样式空间，而不需要额外的用户输入。实验结果表明，该方法提高了 Pinterest 时装和家居装饰搜索的成功率34.8% 。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Personalized+style+recommendation+via+reinforcement+learning)|0| |[HierCat: Hierarchical Query Categorization from Weakly Supervised Data at Facebook Marketplace](https://doi.org/10.1145/3543873.3584622)|Yunzhong He, Cong Zhang, Ruoyan Kong, Chaitanya Kulkarni, Qing Liu, Ashish Gandhe, Amit Nithianandan, Arul Prakash|University of Minnesota Twin Cities, USA; Meta, USA|Query categorization at customer-to-customer e-commerce platforms like Facebook Marketplace is challenging due to the vagueness of search intent, noise in real-world data, and imbalanced training data across languages. Its deployment also needs to consider challenges in scalability and downstream integration in order to translate modeling advances into better search result relevance. In this paper we present HierCat, the query categorization system at Facebook Marketplace. HierCat addresses these challenges by leveraging multi-task pre-training of dual-encoder architectures with a hierarchical inference step to effectively learn from weakly supervised training data mined from searcher engagement. We show that HierCat not only outperforms popular methods in offline experiments, but also leads to 1.4% improvement in NDCG and 4.3% increase in searcher engagement at Facebook Marketplace Search in online A/B testing.|像 Facebook Marketplace 这样的客户对客户的电子商务平台，由于搜索意图的模糊性、现实世界数据中的噪音以及跨语言的不平衡训练数据，查询分类是一个挑战。它的部署还需要考虑可伸缩性和下游集成方面的挑战，以便将建模进展转化为更好的搜索结果相关性。本文介绍了 Facebook Marketplace 的查询分类系统 HierCat。HierCat 通过利用双重编码器架构的多任务预训练和分层推理步骤来解决这些挑战，以有效地学习从搜索引擎参与中挖掘的弱监督训练数据。我们发现 HierCat 不仅在离线实验中表现优于流行的方法，而且在线 A/B 测试中导致 NDCG 改善1.4% ，Facebook Marketplace Search 的搜索者参与度提高4.3% 。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=HierCat:+Hierarchical+Query+Categorization+from+Weakly+Supervised+Data+at+Facebook+Marketplace)|0| -|[Search-based Recommendation: the Case for Difficult Predictions](https://doi.org/10.1145/3543873.3587374)|Ghazaleh Haratinezhad Torbati, Gerhard Weikum, Andrew Yates|University of Amsterdam, Netherlands; Max Planck Institute for Informatics, Germany|Recommender systems have achieved impressive results on benchmark datasets. However, the numbers are often influenced by assumptions made on the data and evaluation mode. This work questions and revises these assumptions, to study and improve the quality, particularly for the difficult case of search-based recommendations. Users start with a personally liked item as a query and look for similar items that match their tastes. User satisfaction requires discovering truly unknown items: new authors of books rather than merely more books of known writers. We propose a unified system architecture that combines interaction-based and content-based signals and leverages language models for Transformer-powered predictions. We present new techniques for selecting negative training samples, and investigate their performance in the underexplored search-based evaluation mode.|推荐系统在基准数据集上取得了令人印象深刻的成果。然而，这些数字往往受到对数据和评估模式的假设的影响。本文对这些假设进行了质疑和修正，以研究和提高质量，特别是针对困难案例的基于搜索的推荐。用户从一个个人喜欢的项目开始查询，然后寻找与他们口味相符的类似项目。用户满意度要求发现真正未知的项目: 书籍的新作者，而不仅仅是知名作家的书籍。我们提出了一个统一的系统体系结构，它结合了基于交互和基于内容的信号，并利用语言模型进行基于 Transformer 的预测。我们提出了选择负训练样本的新技术，并研究了它们在基于搜索的评估模式中的表现。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Search-based+Recommendation:+the+Case+for+Difficult+Predictions)|0| +|[Search-based Recommendation: the Case for Difficult Predictions](https://doi.org/10.1145/3543873.3587374)|Ghazaleh Haratinezhad Torbati, Gerhard Weikum, Andrew Yates|Max Planck Institute for Informatics, Germany; University of Amsterdam, Netherlands|Recommender systems have achieved impressive results on benchmark datasets. However, the numbers are often influenced by assumptions made on the data and evaluation mode. This work questions and revises these assumptions, to study and improve the quality, particularly for the difficult case of search-based recommendations. Users start with a personally liked item as a query and look for similar items that match their tastes. User satisfaction requires discovering truly unknown items: new authors of books rather than merely more books of known writers. We propose a unified system architecture that combines interaction-based and content-based signals and leverages language models for Transformer-powered predictions. We present new techniques for selecting negative training samples, and investigate their performance in the underexplored search-based evaluation mode.|推荐系统在基准数据集上取得了令人印象深刻的成果。然而，这些数字往往受到对数据和评估模式的假设的影响。本文对这些假设进行了质疑和修正，以研究和提高质量，特别是针对困难案例的基于搜索的推荐。用户从一个个人喜欢的项目开始查询，然后寻找与他们口味相符的类似项目。用户满意度要求发现真正未知的项目: 书籍的新作者，而不仅仅是知名作家的书籍。我们提出了一个统一的系统体系结构，它结合了基于交互和基于内容的信号，并利用语言模型进行基于 Transformer 的预测。我们提出了选择负训练样本的新技术，并研究了它们在基于搜索的评估模式中的表现。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Search-based+Recommendation:+the+Case+for+Difficult+Predictions)|0| |[Reweighting Clicks with Dwell Time in Recommendation](https://doi.org/10.1145/3543873.3584624)|Ruobing Xie, Lin Ma, Shaoliang Zhang, Feng Xia, Leyu Lin|WeChat, Tencent, China|The click behavior is the most widely-used user positive feedback in recommendation. However, simply considering each click equally in training may suffer from clickbaits and title-content mismatching, and thus fail to precisely capture users' real satisfaction on items. Dwell time could be viewed as a high-quality quantitative indicator of user preferences on each click, while existing recommendation models do not fully explore the modeling of dwell time. In this work, we focus on reweighting clicks with dwell time in recommendation. Precisely, we first define a new behavior named valid read, which helps to select high-quality click instances for different users and items via dwell time. Next, we propose a normalized dwell time function to reweight click signals in training for recommendation. The Click reweighting model achieves significant improvements on both offline and online evaluations in real-world systems.|点击行为是推荐中使用最广泛的用户正面反馈。然而，在培训中仅仅考虑每一次点击的平等性可能会遭受点击诱惑和标题内容不匹配的问题，因此不能准确地捕捉用户对项目的真正满意度。停留时间可以被视为每次点击时用户偏好的高质量定量指标，而现有的推荐模型并没有充分探索停留时间的建模。在这项工作中，我们将重点放在用推荐中的停留时间重新加权点击。确切地说，我们首先定义一个名为有效读的新行为，它有助于通过停留时间为不同的用户和项目选择高质量的单击实例。接下来，我们提出了一个规范化的停留时间函数来重新加权点击信号的训练推荐。Click 重新加权模型在现实世界系统的离线和在线评估方面都取得了显著的改进。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Reweighting+Clicks+with+Dwell+Time+in+Recommendation)|0| -|[Disentangled Causal Embedding With Contrastive Learning For Recommender System](https://doi.org/10.1145/3543873.3584637)|Weiqi Zhao, Dian Tang, Xin Chen, Dawei Lv, Daoli Ou, Biao Li, Peng Jiang, Kun Gai|Kuaishou Technology, China; Unaffiliated, China|Recommender systems usually rely on observed user interaction data to build personalized recommendation models, assuming that the observed data reflect user interest. However, user interacting with an item may also due to conformity, the need to follow popular items. Most previous studies neglect user's conformity and entangle interest with it, which may cause the recommender systems fail to provide satisfying results. Therefore, from the cause-effect view, disentangling these interaction causes is a crucial issue. It also contributes to OOD problems, where training and test data are out-of-distribution. Nevertheless, it is quite challenging as we lack the signal to differentiate interest and conformity. The data sparsity of pure cause and the items' long-tail problem hinder disentangled causal embedding. In this paper, we propose DCCL, a framework that adopts contrastive learning to disentangle these two causes by sample augmentation for interest and conformity respectively. Futhermore, DCCL is model-agnostic, which can be easily deployed in any industrial online system. Extensive experiments are conducted over two real-world datasets and DCCL outperforms state-of-the-art baselines on top of various backbone models in various OOD environments. We also demonstrate the performance improvements by online A/B testing on Kuaishou, a billion-user scale short-video recommender system.|推荐系统通常依赖于观察到的用户交互数据来建立个性化的推荐模型，假设观察到的数据反映了用户的兴趣。然而，用户与一个项目的互动也可能是由于一致性，需要遵循流行的项目。以往的大多数研究忽视了用户的一致性，并与之产生利益纠葛，这可能导致推荐系统不能提供令人满意的结果。因此，从因果观点来看，解开这些相互作用的原因是一个至关重要的问题。它还会导致面向对象设计(OOD)问题，即培训和测试数据分布不均。然而，这是相当具有挑战性的，因为我们缺乏区分兴趣和一致性的信号。纯因果关系的数据稀疏性和项目的长尾问题阻碍了因果关系的解纠缠嵌入。在本文中，我们提出了 DCCL，一个采用对比学习的框架，分别通过兴趣和从众的样本增加来解决这两个原因。此外，DCCL 是模型无关的，可以很容易地部署在任何工业在线系统。在两个真实世界的数据集上进行了广泛的实验，DCCL 在各种面向对象设计(OOD)环境中的各种骨干模型上的表现优于最先进的基线。我们还通过在 Kuaishou 的在线 A/B 测试展示了性能改进，这是一个拥有10亿用户规模的短视频推荐系统。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Disentangled+Causal+Embedding+With+Contrastive+Learning+For+Recommender+System)|0| +|[Disentangled Causal Embedding With Contrastive Learning For Recommender System](https://doi.org/10.1145/3543873.3584637)|Weiqi Zhao, Dian Tang, Xin Chen, Dawei Lv, Daoli Ou, Biao Li, Peng Jiang, Kun Gai|Unaffiliated, China; Kuaishou Technology, China|Recommender systems usually rely on observed user interaction data to build personalized recommendation models, assuming that the observed data reflect user interest. However, user interacting with an item may also due to conformity, the need to follow popular items. Most previous studies neglect user's conformity and entangle interest with it, which may cause the recommender systems fail to provide satisfying results. Therefore, from the cause-effect view, disentangling these interaction causes is a crucial issue. It also contributes to OOD problems, where training and test data are out-of-distribution. Nevertheless, it is quite challenging as we lack the signal to differentiate interest and conformity. The data sparsity of pure cause and the items' long-tail problem hinder disentangled causal embedding. In this paper, we propose DCCL, a framework that adopts contrastive learning to disentangle these two causes by sample augmentation for interest and conformity respectively. Futhermore, DCCL is model-agnostic, which can be easily deployed in any industrial online system. Extensive experiments are conducted over two real-world datasets and DCCL outperforms state-of-the-art baselines on top of various backbone models in various OOD environments. We also demonstrate the performance improvements by online A/B testing on Kuaishou, a billion-user scale short-video recommender system.|推荐系统通常依赖于观察到的用户交互数据来建立个性化的推荐模型，假设观察到的数据反映了用户的兴趣。然而，用户与一个项目的互动也可能是由于一致性，需要遵循流行的项目。以往的大多数研究忽视了用户的一致性，并与之产生利益纠葛，这可能导致推荐系统不能提供令人满意的结果。因此，从因果观点来看，解开这些相互作用的原因是一个至关重要的问题。它还会导致面向对象设计(OOD)问题，即培训和测试数据分布不均。然而，这是相当具有挑战性的，因为我们缺乏区分兴趣和一致性的信号。纯因果关系的数据稀疏性和项目的长尾问题阻碍了因果关系的解纠缠嵌入。在本文中，我们提出了 DCCL，一个采用对比学习的框架，分别通过兴趣和从众的样本增加来解决这两个原因。此外，DCCL 是模型无关的，可以很容易地部署在任何工业在线系统。在两个真实世界的数据集上进行了广泛的实验，DCCL 在各种面向对象设计(OOD)环境中的各种骨干模型上的表现优于最先进的基线。我们还通过在 Kuaishou 的在线 A/B 测试展示了性能改进，这是一个拥有10亿用户规模的短视频推荐系统。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Disentangled+Causal+Embedding+With+Contrastive+Learning+For+Recommender+System)|0| |[Confidence Ranking for CTR Prediction](https://doi.org/10.1145/3543873.3584643)|Jian Zhu, Congcong Liu, Pei Wang, Xiwei Zhao, Zhangang Lin, Jingping Shao|JD.com, China|Model evolution and data updating are two common phenomena in large-scale real-world machine learning applications, e.g. ads and recommendation systems. To adapt, the real-world system typically retrain with all available data and online learn with recently available data to update the models periodically with the goal of better serving performance. In this paper, we propose a novel framework, named Confidence Ranking, which designs the optimization objective as a ranking function with two different models. Our confidence ranking loss allows direct optimization of the logits output for different convex surrogate functions of metrics, e.g. AUC and Accuracy depending on the target task and dataset. Armed with our proposed methods, our experiments show that the introduction of confidence ranking loss can outperform all baselines on the CTR prediction tasks of public and industrial datasets. This framework has been deployed in the advertisement system of JD.com to serve the main traffic in the fine-rank stage.|模型演化和数据更新是广告和推荐系统等大规模真实世界机器学习应用中的两种常见现象。为了适应这种情况，现实世界中的系统通常使用所有可用的数据进行再培训，并使用最近可用的数据进行在线学习，以便定期更新模型，从而更好地服务于性能。在本文中，我们提出了一个新的框架，称为置信排序，设计的优化目标为一个排序函数与两个不同的模型。我们的置信度排序损失允许直接优化不同凸性度量替代函数的 logit 输出，例如 AUC 和精度取决于目标任务和数据集。实验结果表明，在公共数据集和工业数据集的 CTR 预测任务中，置信度排序损失的引入能够优于所有基线。该框架已经部署在京东的广告系统中，服务于精品阶段的主流流量。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Confidence+Ranking+for+CTR+Prediction)|0| |[Personalised Search in E-Comm Groceries](https://doi.org/10.1145/3543873.3587588)|Ramprabhu Murugesan, Anuja Sharan|Walmart Labs, India; Walmart labs, India|Personalized Search(henceforth called P10d Search) focuses to deliver user-specific search results based on the previous purchases. Search engine retrieves the result based on the defined relevancy algorithm. When a user searches a keyword, search engine constructs the search query based on the defined searchable fields/attributes along with configured relevancy algorithm. Position of the item retrieved in search results is determined by the search algorithm based on the search term. The results are further refined or ranked based on different click stream signals, product features, market data to provide much relevant results. Personalisation provides the ranked the list of items for a given user based on past purchases. Personalisation is agnostic of search query and takes user id, cart additions, site taxonomy and user’s shopping history as input signals. In summary, search engine queries data based on relevancy and personalisation engine retrieves based purely on purchases. Goal of personalised search is to enhance the search results by adding personalised results without affecting the search relevance.|个性化检索(以下简称 P10d 搜索)的重点是提供基于以前购买的特定用户的搜索结果。搜索引擎根据定义的相关算法检索结果。当用户搜索关键字时，搜索引擎根据定义的可搜索字段/属性以及配置的相关性算法构造搜索查询。在搜索结果中检索到的项的位置由基于搜索项的搜索算法确定。根据不同的点击流信号、产品特点、市场数据对结果进行进一步细化或排序，以提供更多相关的结果。个性化为给定用户提供了基于过去购买的商品的排名列表。个性化是不可知的搜索查询，并采取用户 ID，购物车添加，网站分类和用户的购物历史作为输入信号。总之，搜索引擎查询数据的相关性和个性化引擎检索纯粹基于购买。个性化搜索的目标是在不影响搜索相关性的情况下，通过添加个性化搜索结果来提高搜索结果。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Personalised+Search+in+E-Comm+Groceries)|0| |[Graph Embedding for Mapping Interdisciplinary Research Networks](https://doi.org/10.1145/3543873.3587570)|Eoghan Cunningham, Derek Greene|University College Dublin, Ireland|Representation learning is the first step in automating tasks such as research paper recommendation, classification, and retrieval. Due to the accelerating rate of research publication, together with the recognised benefits of interdisciplinary research, systems that facilitate researchers in discovering and understanding relevant works from beyond their immediate school of knowledge are vital. This work explores different methods of research paper representation (or document embedding), to identify those methods that are capable of preserving the interdisciplinary implications of research papers in their embeddings. In addition to evaluating state of the art methods of document embedding in a interdisciplinary citation prediction task, we propose a novel Graph Neural Network architecture designed to preserve the key interdisciplinary implications of research articles in citation network node embeddings. Our proposed method outperforms other GNN-based methods in interdisciplinary citation prediction, without compromising overall citation prediction performance.|表示学习是研究论文推荐、分类和检索等任务自动化的第一步。由于研究发表的速度加快，再加上科际整合的公认好处，有助研究人员发现和理解其直接学校知识以外的相关著作的系统是至关重要的。本文探讨了研究论文表示(或文档嵌入)的不同方法，以确定哪些方法能够在其嵌入过程中保持研究论文的跨学科含义。除了评估在跨学科引文预测任务中嵌入文档的最新方法之外，我们还提出了一种新的图形神经网络体系结构，旨在保存引文网络节点嵌入中研究论文的关键跨学科含义。我们提出的方法在跨学科引文预测方面优于其他基于 GNN 的方法，而不影响整体的引文预测性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Graph+Embedding+for+Mapping+Interdisciplinary+Research+Networks)|0| |[Deep Passage Retrieval in E-Commerce](https://doi.org/10.1145/3543873.3587624)|Vinay Rao Dandin, Ozan Ersoy, Kyung Hyuk Kim|Flipkart US R&D Center, USA|We have developed a conversational assistant called the Decision Assistant (DA) to help customers make purchase decisions. To answer customer queries successfully, we use a question and answering (QnA) system that retrieves data on product pages and extracts answers. With various data sources available on the product pages, we deal with unique challenges such as different terminologies and data formats for successful answer retrieval. In this paper, we propose two different bi-encoder architectures for retrieving data from each of the two data sources considered – product descriptions and specifications. The proposed architectures beat the baseline approaches while maintaining a high recall and low latency in production. We envision that the proposed approaches can be widely applicable to other e-commerce QnA systems.|我们已经开发了一个称为决策助理(DA)的会话助理来帮助客户做出购买决策。为了成功地回答客户的查询，我们使用一个问答(QnA)系统来检索产品页面上的数据并提取答案。随着各种数据源可在产品页面，我们处理独特的挑战，如不同的术语和数据格式，以成功的答案检索。在本文中，我们提出了两种不同的双编码器体系结构来检索数据从每个两个数据源考虑-产品描述和规格。所提出的体系结构打破了基线方法，同时在生产中保持了较高的召回率和较低的延迟。我们设想所提出的方法可以广泛应用于其他电子商务 QnA 系统。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Deep+Passage+Retrieval+in+E-Commerce)|0| |[Quantize Sequential Recommenders Without Private Data](https://doi.org/10.1145/3543507.3583351)|Lingfeng Shi, Yuang Liu, Jun Wang, Wei Zhang|East China Normal University, China|Deep neural networks have achieved great success in sequential recommendation systems. While maintaining high competence in user modeling and next-item recommendation, these models have long been plagued by the numerous parameters and computation, which inhibit them to be deployed on resource-constrained mobile devices. Model quantization, as one of the main paradigms for compression techniques, converts float parameters to low-bit values to reduce parameter redundancy and accelerate inference. To avoid drastic performance degradation, it usually requests a fine-tuning phase with an original dataset. However, the training set of user-item interactions is not always available due to transmission limits or privacy concerns. In this paper, we propose a novel framework to quantize sequential recommenders without access to any real private data. A generator is employed in the framework to synthesize fake sequence samples to feed the quantized sequential recommendation model and minimize the gap with a full-precision sequential recommendation model. The generator and the quantized model are optimized with a min-max game — alternating discrepancy estimation and knowledge transfer. Moreover, we devise a two-level discrepancy modeling strategy to transfer information between the quantized model and the full-precision model. The extensive experiments of various recommendation networks on three public datasets demonstrate the effectiveness of the proposed framework.|深层神经网络在序贯推荐系统中取得了巨大的成功。尽管这些模型在用户建模和下一个项目推荐方面保持了很高的能力，但长期以来，这些模型一直受到众多参数和计算的困扰，这些参数和计算阻碍了它们被部署到资源受限的移动设备上。模型量化作为压缩技术的主要范式之一，将浮点参数转换为低位值，以减少参数冗余，加速推理。为了避免严重的性能下降，它通常要求对原始数据集进行微调。然而，由于传输限制或隐私问题，用户项交互的训练集并不总是可用的。在本文中，我们提出了一个新的框架，量化顺序推荐没有访问任何真正的私有数据。该框架采用生成器对伪序列样本进行合成，以满足量化序列推荐模型的要求，同时采用全精度序列推荐模型使推荐间隔最小化。利用最小-最大对策-交替差异估计和知识转移对生成器和量化模型进行优化。此外，我们还设计了一个两层差异建模策略来传递量化模型和全精度模型之间的信息。在三个公共数据集上对各种推荐网络进行了广泛的实验，证明了该框架的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Quantize+Sequential+Recommenders+Without+Private+Data)|0| |[Adap-τ : Adaptively Modulating Embedding Magnitude for Recommendation](https://doi.org/10.1145/3543507.3583363)|Jiawei Chen, Junkang Wu, Jiancan Wu, Xuezhi Cao, Sheng Zhou, Xiangnan He||||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Adap-τ+:+Adaptively+Modulating+Embedding+Magnitude+for+Recommendation)|0| -|[Clustered Embedding Learning for Recommender Systems](https://doi.org/10.1145/3543507.3583362)|Yizhou Chen, Guangda Huzhang, Anxiang Zeng, Qingtao Yu, Hui Sun, HengYi Li, Jingyi Li, Yabo Ni, Han Yu, Zhiming Zhou|SCSE, Nanyang Technological University, Singapore; Shanghai University of Finance and Economics, China; Shopee Pte Ltd., Singapore|In recent years, recommender systems have advanced rapidly, where embedding learning for users and items plays a critical role. A standard method learns a unique embedding vector for each user and item. However, such a method has two important limitations in real-world applications: 1) it is hard to learn embeddings that generalize well for users and items with rare interactions on their own; and 2) it may incur unbearably high memory costs when the number of users and items scales up. Existing approaches either can only address one of the limitations or have flawed overall performances. In this paper, we propose Clustered Embedding Learning (CEL) as an integrated solution to these two problems. CEL is a plug-and-play embedding learning framework that can be combined with any differentiable feature interaction model. It is capable of achieving improved performance, especially for cold users and items, with reduced memory cost. CEL enables automatic and dynamic clustering of users and items in a top-down fashion, where clustered entities jointly learn a shared embedding. The accelerated version of CEL has an optimal time complexity, which supports efficient online updates. Theoretically, we prove the identifiability and the existence of a unique optimal number of clusters for CEL in the context of nonnegative matrix factorization. Empirically, we validate the effectiveness of CEL on three public datasets and one business dataset, showing its consistently superior performance against current state-of-the-art methods. In particular, when incorporating CEL into the business model, it brings an improvement of $+0.6\%$ in AUC, which translates into a significant revenue gain; meanwhile, the size of the embedding table gets $2650$ times smaller.|近年来，推荐系统发展迅速，其中用户和项目的嵌入式学习起着至关重要的作用。标准方法为每个用户和项学习唯一的嵌入向量。然而，这种方法在实际应用中有两个重要的局限性: 1)很难学习嵌入式技术，这种技术可以很好地适用于用户和具有罕见交互的项目; 2)当用户和项目的数量增加时，它可能会产生难以忍受的高内存成本。现有的方法要么只能解决其中的一个限制，要么具有有缺陷的整体性能。在本文中，我们提出了集群嵌入式学习(CEL)作为这两个问题的综合解决方案。CEL 是一个即插即用的嵌入式学习框架，可以与任何可微的特征交互模型相结合。它能够提高性能，特别是对于冷用户和项目，同时降低内存成本。CEL 以自顶向下的方式支持用户和项目的自动和动态集群，集群实体可以在这种方式下联合学习共享嵌入。CEL 的加速版本具有最佳的时间复杂度，支持高效的在线更新。理论上，我们证明了在非负矩阵分解的情况下，CEL 的可识别性和唯一最优簇数的存在性。通过实验，我们验证了 CEL 在三个公共数据集和一个业务数据集上的有效性，显示了与当前最先进的方法相比，CEL 始终具有优越的性能。特别是，当将 CEL 融入到商业模式中时，它在 AUC 中带来了 $+ 0.6% 的改进，这意味着显著的收入增长; 与此同时，嵌入表的大小变小了2650美元。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Clustered+Embedding+Learning+for+Recommender+Systems)|0| -|[MMMLP: Multi-modal Multilayer Perceptron for Sequential Recommendations](https://doi.org/10.1145/3543507.3583378)|Jiahao Liang, Xiangyu Zhao, Muyang Li, Zijian Zhang, Wanyu Wang, Haochen Liu, Zitao Liu|Guangdong Institute of Smart Education, Jinan University, China; Jilin University, China and City University of Hong Kong, Hong Kong; University of Sydney, Australia; City University of Hong Kong, Hong Kong; Michigan State University, USA|Sequential recommendation aims to offer potentially interesting products to users by capturing their historical sequence of interacted items. Although it has facilitated extensive physical scenarios, sequential recommendation for multi-modal sequences has long been neglected. Multi-modal data that depicts a user’s historical interactions exists ubiquitously, such as product pictures, textual descriptions, and interacted item sequences, providing semantic information from multiple perspectives that comprehensively describe a user’s preferences. However, existing sequential recommendation methods either fail to directly handle multi-modality or suffer from high computational complexity. To address this, we propose a novel Multi-Modal Multi-Layer Perceptron (MMMLP) for maintaining multi-modal sequences for sequential recommendation. MMMLP is a purely MLP-based architecture that consists of three modules - the Feature Mixer Layer, Fusion Mixer Layer, and Prediction Layer - and has an edge on both efficacy and efficiency. Extensive experiments show that MMMLP achieves state-of-the-art performance with linear complexity. We also conduct ablating analysis to verify the contribution of each component. Furthermore, compatible experiments are devised, and the results show that the multi-modal representation learned by our proposed model generally benefits other recommendation models, emphasizing our model’s ability to handle multi-modal information. We have made our code available online to ease reproducibility1.|顺序推荐旨在通过获取用户交互项的历史顺序，为用户提供潜在有趣的产品。虽然它促进了广泛的物理场景，多模态序列的顺序推荐长期以来被忽视。描述用户历史交互的多模态数据无处不在，比如产品图片、文本描述和交互式项目序列，从多个角度提供语义信息，全面描述用户的偏好。然而，现有的顺序推荐方法要么不能直接处理多模态问题，要么计算复杂度较高。为了解决这个问题，我们提出了一种新的多模态多层感知器(MMMLP)来维护多模态序列的顺序推荐。MMLP 是一个纯粹基于 MLP 的架构，它由三个模块组成——特征混合层、融合混合层和预测层——并且在功效和效率方面都有优势。大量的实验表明，MMLP 在线性复杂度方面达到了最先进的性能。我们还进行了烧蚀分析，以验证每个组分的贡献。此外，设计了相容性实验，结果表明，我们提出的模型学习的多模态表示一般有利于其他推荐模型，强调我们的模型的能力，处理多模态信息。我们已经在网上提供了我们的代码，以便于重现。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=MMMLP:+Multi-modal+Multilayer+Perceptron+for+Sequential+Recommendations)|0| -|[AutoMLP: Automated MLP for Sequential Recommendations](https://doi.org/10.1145/3543507.3583440)|Muyang Li, Zijian Zhang, Xiangyu Zhao, Wanyu Wang, Minghao Zhao, Runze Wu, Ruocheng Guo|City University of Hong Kong, Hong Kong and University of Sydney, Australia; Bytedance AI Lab UK, United Kingdom; City University of Hong Kong, Hong Kong; City University of Hong Kong, Hong Kong and Jilin University, China; Fuxi AI Lab, NetEase, China|Sequential recommender systems aim to predict users' next interested item given their historical interactions. However, a long-standing issue is how to distinguish between users' long/short-term interests, which may be heterogeneous and contribute differently to the next recommendation. Existing approaches usually set pre-defined short-term interest length by exhaustive search or empirical experience, which is either highly inefficient or yields subpar results. The recent advanced transformer-based models can achieve state-of-the-art performances despite the aforementioned issue, but they have a quadratic computational complexity to the length of the input sequence. To this end, this paper proposes a novel sequential recommender system, AutoMLP, aiming for better modeling users' long/short-term interests from their historical interactions. In addition, we design an automated and adaptive search algorithm for preferable short-term interest length via end-to-end optimization. Through extensive experiments, we show that AutoMLP has competitive performance against state-of-the-art methods, while maintaining linear computational complexity.|顺序推荐系统的目的是预测用户的下一个感兴趣的项目给予他们的历史交互。然而，一个长期存在的问题是如何区分用户的长期和短期利益，这可能是不同的，并作出不同的贡献下一个建议。现有方法通常通过穷举搜索或实证经验来设定预先确定的短期利率长度，这种方法要么效率极低，要么效果不佳。尽管存在上述问题，最近的先进的基于变压器的模型能够实现最先进的性能，但是它们对于输入序列的长度具有二次计算复杂度。为此，本文提出了一种新的顺序推荐系统—— AutoMLP，旨在从用户的历史交互中更好地建立用户的长期/短期兴趣模型。此外，我们设计了一个自动化和自适应的搜索算法，通过端到端优化较好的短期兴趣长度。通过大量的实验，我们发现 AutoMLP 在保持线性计算复杂度的同时，具有与最先进的方法相竞争的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=AutoMLP:+Automated+MLP+for+Sequential+Recommendations)|0| -|[NASRec: Weight Sharing Neural Architecture Search for Recommender Systems](https://doi.org/10.1145/3543507.3583446)|Tunhou Zhang, Dehua Cheng, Yuchen He, Zhengxing Chen, Xiaoliang Dai, Liang Xiong, Feng Yan, Hai Li, Yiran Chen, Wei Wen|University of Houston, USA; Meta AI, USA; Duke University, USA|The rise of deep neural networks offers new opportunities in optimizing recommender systems. However, optimizing recommender systems using deep neural networks requires delicate architecture fabrication. We propose NASRec, a paradigm that trains a single supernet and efficiently produces abundant models/sub-architectures by weight sharing. To overcome the data multi-modality and architecture heterogeneity challenges in the recommendation domain, NASRec establishes a large supernet (i.e., search space) to search the full architectures. The supernet incorporates versatile choice of operators and dense connectivity to minimize human efforts for finding priors. The scale and heterogeneity in NASRec impose several challenges, such as training inefficiency, operator-imbalance, and degraded rank correlation. We tackle these challenges by proposing single-operator any-connection sampling, operator-balancing interaction modules, and post-training fine-tuning. Our crafted models, NASRecNet, show promising results on three Click-Through Rates (CTR) prediction benchmarks, indicating that NASRec outperforms both manually designed models and existing NAS methods with state-of-the-art performance. Our work is publicly available at https://github.com/facebookresearch/NasRec.|深层神经网络的兴起为优化推荐系统提供了新的机会。然而，使用深层神经网络优化推荐系统需要精细的架构制作。我们提出 NASRec，一个训练单个超级网络并通过权重分享有效地产生丰富的模型/子架构的范例。为了克服推荐域中的数据多态性和体系结构异构性挑战，NASRec 建立了一个大型超网(即搜索空间)来搜索完整的体系结构。超级网结合了多种操作员的选择和密集的连接，以最大限度地减少人的努力，找到前科。NASRec 的规模和异质性带来了一些挑战，如培训效率低下、操作员失衡和等级相关性降低。我们通过提出单操作者任意连接采样、操作者平衡交互模块和训练后微调来应对这些挑战。我们精心设计的模型 NASRecNet 在三个点击率(Click-Through Rate，CTR)预测基准上显示出有希望的结果，表明 NASRecc 的性能优于手工设计的模型和现有的 NAS 方法，具有最先进的性能。我们的工作 https://github.com/facebookresearch/nasrec 公开。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=NASRec:+Weight+Sharing+Neural+Architecture+Search+for+Recommender+Systems)|0| +|[Clustered Embedding Learning for Recommender Systems](https://doi.org/10.1145/3543507.3583362)|Yizhou Chen, Guangda Huzhang, Anxiang Zeng, Qingtao Yu, Hui Sun, HengYi Li, Jingyi Li, Yabo Ni, Han Yu, Zhiming Zhou|Shopee Pte Ltd., Singapore; Shanghai University of Finance and Economics, China; SCSE, Nanyang Technological University, Singapore|In recent years, recommender systems have advanced rapidly, where embedding learning for users and items plays a critical role. A standard method learns a unique embedding vector for each user and item. However, such a method has two important limitations in real-world applications: 1) it is hard to learn embeddings that generalize well for users and items with rare interactions on their own; and 2) it may incur unbearably high memory costs when the number of users and items scales up. Existing approaches either can only address one of the limitations or have flawed overall performances. In this paper, we propose Clustered Embedding Learning (CEL) as an integrated solution to these two problems. CEL is a plug-and-play embedding learning framework that can be combined with any differentiable feature interaction model. It is capable of achieving improved performance, especially for cold users and items, with reduced memory cost. CEL enables automatic and dynamic clustering of users and items in a top-down fashion, where clustered entities jointly learn a shared embedding. The accelerated version of CEL has an optimal time complexity, which supports efficient online updates. Theoretically, we prove the identifiability and the existence of a unique optimal number of clusters for CEL in the context of nonnegative matrix factorization. Empirically, we validate the effectiveness of CEL on three public datasets and one business dataset, showing its consistently superior performance against current state-of-the-art methods. In particular, when incorporating CEL into the business model, it brings an improvement of $+0.6\%$ in AUC, which translates into a significant revenue gain; meanwhile, the size of the embedding table gets $2650$ times smaller.|近年来，推荐系统发展迅速，其中用户和项目的嵌入式学习起着至关重要的作用。标准方法为每个用户和项学习唯一的嵌入向量。然而，这种方法在实际应用中有两个重要的局限性: 1)很难学习嵌入式技术，这种技术可以很好地适用于用户和具有罕见交互的项目; 2)当用户和项目的数量增加时，它可能会产生难以忍受的高内存成本。现有的方法要么只能解决其中的一个限制，要么具有有缺陷的整体性能。在本文中，我们提出了集群嵌入式学习(CEL)作为这两个问题的综合解决方案。CEL 是一个即插即用的嵌入式学习框架，可以与任何可微的特征交互模型相结合。它能够提高性能，特别是对于冷用户和项目，同时降低内存成本。CEL 以自顶向下的方式支持用户和项目的自动和动态集群，集群实体可以在这种方式下联合学习共享嵌入。CEL 的加速版本具有最佳的时间复杂度，支持高效的在线更新。理论上，我们证明了在非负矩阵分解的情况下，CEL 的可识别性和唯一最优簇数的存在性。通过实验，我们验证了 CEL 在三个公共数据集和一个业务数据集上的有效性，显示了与当前最先进的方法相比，CEL 始终具有优越的性能。特别是，当将 CEL 融入到商业模式中时，它在 AUC 中带来了 $+ 0.6% 的改进，这意味着显著的收入增长; 与此同时，嵌入表的大小变小了2650美元。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Clustered+Embedding+Learning+for+Recommender+Systems)|0| +|[MMMLP: Multi-modal Multilayer Perceptron for Sequential Recommendations](https://doi.org/10.1145/3543507.3583378)|Jiahao Liang, Xiangyu Zhao, Muyang Li, Zijian Zhang, Wanyu Wang, Haochen Liu, Zitao Liu|University of Sydney, Australia; City University of Hong Kong, Hong Kong; Jilin University, China and City University of Hong Kong, Hong Kong; Guangdong Institute of Smart Education, Jinan University, China; Michigan State University, USA|Sequential recommendation aims to offer potentially interesting products to users by capturing their historical sequence of interacted items. Although it has facilitated extensive physical scenarios, sequential recommendation for multi-modal sequences has long been neglected. Multi-modal data that depicts a user’s historical interactions exists ubiquitously, such as product pictures, textual descriptions, and interacted item sequences, providing semantic information from multiple perspectives that comprehensively describe a user’s preferences. However, existing sequential recommendation methods either fail to directly handle multi-modality or suffer from high computational complexity. To address this, we propose a novel Multi-Modal Multi-Layer Perceptron (MMMLP) for maintaining multi-modal sequences for sequential recommendation. MMMLP is a purely MLP-based architecture that consists of three modules - the Feature Mixer Layer, Fusion Mixer Layer, and Prediction Layer - and has an edge on both efficacy and efficiency. Extensive experiments show that MMMLP achieves state-of-the-art performance with linear complexity. We also conduct ablating analysis to verify the contribution of each component. Furthermore, compatible experiments are devised, and the results show that the multi-modal representation learned by our proposed model generally benefits other recommendation models, emphasizing our model’s ability to handle multi-modal information. We have made our code available online to ease reproducibility1.|顺序推荐旨在通过获取用户交互项的历史顺序，为用户提供潜在有趣的产品。虽然它促进了广泛的物理场景，多模态序列的顺序推荐长期以来被忽视。描述用户历史交互的多模态数据无处不在，比如产品图片、文本描述和交互式项目序列，从多个角度提供语义信息，全面描述用户的偏好。然而，现有的顺序推荐方法要么不能直接处理多模态问题，要么计算复杂度较高。为了解决这个问题，我们提出了一种新的多模态多层感知器(MMMLP)来维护多模态序列的顺序推荐。MMLP 是一个纯粹基于 MLP 的架构，它由三个模块组成——特征混合层、融合混合层和预测层——并且在功效和效率方面都有优势。大量的实验表明，MMLP 在线性复杂度方面达到了最先进的性能。我们还进行了烧蚀分析，以验证每个组分的贡献。此外，设计了相容性实验，结果表明，我们提出的模型学习的多模态表示一般有利于其他推荐模型，强调我们的模型的能力，处理多模态信息。我们已经在网上提供了我们的代码，以便于重现。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=MMMLP:+Multi-modal+Multilayer+Perceptron+for+Sequential+Recommendations)|0| +|[AutoMLP: Automated MLP for Sequential Recommendations](https://doi.org/10.1145/3543507.3583440)|Muyang Li, Zijian Zhang, Xiangyu Zhao, Wanyu Wang, Minghao Zhao, Runze Wu, Ruocheng Guo|City University of Hong Kong, Hong Kong and Jilin University, China; City University of Hong Kong, Hong Kong and University of Sydney, Australia; Fuxi AI Lab, NetEase, China; City University of Hong Kong, Hong Kong; Bytedance AI Lab UK, United Kingdom|Sequential recommender systems aim to predict users' next interested item given their historical interactions. However, a long-standing issue is how to distinguish between users' long/short-term interests, which may be heterogeneous and contribute differently to the next recommendation. Existing approaches usually set pre-defined short-term interest length by exhaustive search or empirical experience, which is either highly inefficient or yields subpar results. The recent advanced transformer-based models can achieve state-of-the-art performances despite the aforementioned issue, but they have a quadratic computational complexity to the length of the input sequence. To this end, this paper proposes a novel sequential recommender system, AutoMLP, aiming for better modeling users' long/short-term interests from their historical interactions. In addition, we design an automated and adaptive search algorithm for preferable short-term interest length via end-to-end optimization. Through extensive experiments, we show that AutoMLP has competitive performance against state-of-the-art methods, while maintaining linear computational complexity.|顺序推荐系统的目的是预测用户的下一个感兴趣的项目给予他们的历史交互。然而，一个长期存在的问题是如何区分用户的长期和短期利益，这可能是不同的，并作出不同的贡献下一个建议。现有方法通常通过穷举搜索或实证经验来设定预先确定的短期利率长度，这种方法要么效率极低，要么效果不佳。尽管存在上述问题，最近的先进的基于变压器的模型能够实现最先进的性能，但是它们对于输入序列的长度具有二次计算复杂度。为此，本文提出了一种新的顺序推荐系统—— AutoMLP，旨在从用户的历史交互中更好地建立用户的长期/短期兴趣模型。此外，我们设计了一个自动化和自适应的搜索算法，通过端到端优化较好的短期兴趣长度。通过大量的实验，我们发现 AutoMLP 在保持线性计算复杂度的同时，具有与最先进的方法相竞争的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=AutoMLP:+Automated+MLP+for+Sequential+Recommendations)|0| +|[NASRec: Weight Sharing Neural Architecture Search for Recommender Systems](https://doi.org/10.1145/3543507.3583446)|Tunhou Zhang, Dehua Cheng, Yuchen He, Zhengxing Chen, Xiaoliang Dai, Liang Xiong, Feng Yan, Hai Li, Yiran Chen, Wei Wen|Meta AI, USA; Duke University, USA; University of Houston, USA|The rise of deep neural networks offers new opportunities in optimizing recommender systems. However, optimizing recommender systems using deep neural networks requires delicate architecture fabrication. We propose NASRec, a paradigm that trains a single supernet and efficiently produces abundant models/sub-architectures by weight sharing. To overcome the data multi-modality and architecture heterogeneity challenges in the recommendation domain, NASRec establishes a large supernet (i.e., search space) to search the full architectures. The supernet incorporates versatile choice of operators and dense connectivity to minimize human efforts for finding priors. The scale and heterogeneity in NASRec impose several challenges, such as training inefficiency, operator-imbalance, and degraded rank correlation. We tackle these challenges by proposing single-operator any-connection sampling, operator-balancing interaction modules, and post-training fine-tuning. Our crafted models, NASRecNet, show promising results on three Click-Through Rates (CTR) prediction benchmarks, indicating that NASRec outperforms both manually designed models and existing NAS methods with state-of-the-art performance. Our work is publicly available at https://github.com/facebookresearch/NasRec.|深层神经网络的兴起为优化推荐系统提供了新的机会。然而，使用深层神经网络优化推荐系统需要精细的架构制作。我们提出 NASRec，一个训练单个超级网络并通过权重分享有效地产生丰富的模型/子架构的范例。为了克服推荐域中的数据多态性和体系结构异构性挑战，NASRec 建立了一个大型超网(即搜索空间)来搜索完整的体系结构。超级网结合了多种操作员的选择和密集的连接，以最大限度地减少人的努力，找到前科。NASRec 的规模和异质性带来了一些挑战，如培训效率低下、操作员失衡和等级相关性降低。我们通过提出单操作者任意连接采样、操作者平衡交互模块和训练后微调来应对这些挑战。我们精心设计的模型 NASRecNet 在三个点击率(Click-Through Rate，CTR)预测基准上显示出有希望的结果，表明 NASRecc 的性能优于手工设计的模型和现有的 NAS 方法，具有最先进的性能。我们的工作 https://github.com/facebookresearch/nasrec 公开。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=NASRec:+Weight+Sharing+Neural+Architecture+Search+for+Recommender+Systems)|0| |[Membership Inference Attacks Against Sequential Recommender Systems](https://doi.org/10.1145/3543507.3583447)|Zhihao Zhu, Chenwang Wu, Rui Fan, Defu Lian, Enhong Chen|University of Science and Technology of China, China|Recent studies have demonstrated the vulnerability of recommender systems to membership inference attacks, which determine whether a user’s historical data was utilized for model training, posing serious privacy leakage issues. Existing works assumed that member and non-member users follow different recommendation modes, and then infer membership based on the difference vector between the user’s historical behaviors and the recommendation list. The previous frameworks are invalid against inductive recommendations, such as sequential recommendations, since the disparities of difference vectors constructed by the recommendations between members and non-members become imperceptible. This motivates us to dig deeper into the target model. In addition, most MIA frameworks assume that they can obtain some in-distribution data from the same distribution of the target data, which is hard to gain in recommender system. To address these difficulties, we propose a Membership Inference Attack framework against sequential recommenders based on Model Extraction(ME-MIA). Specifically, we train a surrogate model to simulate the target model based on two universal loss functions. For a given behavior sequence, the loss functions ensure the recommended items and corresponding rank of the surrogate model are consistent with the target model’s recommendation. Due to the special training mode of the surrogate model, it is hard to judge which user is its member(non-member). Therefore, we establish a shadow model and use shadow model’s members(non-members) to train the attack model later. Next, we build a user feature generator to construct representative feature vectors from the shadow(surrogate) model. The crafting feature vectors are finally input into the attack model to identify users’ membership. Furthermore, to tackle the high cost of obtaining in-distribution data, we develop two variants of ME-MIA, realizing data-efficient and even data-free MIA by fabricating authentic in-distribution data. Notably, the latter is impossible in the previous works. Finally, we evaluate ME-MIA against multiple sequential recommendation models on three real-world datasets. Experimental results show that ME-MIA and its variants can achieve efficient extraction and outperform state-of-the-art algorithms in terms of attack performance.|最近的研究表明，推荐系统容易受到成员推断攻击，这决定了用户的历史数据是否被用于模型训练，造成严重的隐私泄露问题。现有的研究假设成员用户和非成员用户遵循不同的推荐模式，然后根据用户历史行为和推荐列表之间的差异向量推断成员关系。以前的框架对于归纳推荐(如顺序推荐)是无效的，因为成员和非成员之间由推荐构造的差异向量的差异变得不可察觉。这促使我们更深入地研究目标模型。此外，大多数 MIA 框架都假定它们可以从目标数据的同一分布中获得一些分布内数据，而这在推荐系统中是很难获得的。为了解决这些问题，我们提出了一个基于模型提取(ME-MIA)的针对顺序推荐的成员推理攻击框架。具体来说，我们训练了一个代理模型来模拟目标模型基于两个通用的损失函数。对于给定的行为序列，损失函数保证代理模型的推荐项和相应的等级与目标模型的推荐一致。由于代理模型的特殊训练模式，很难判断哪个用户是它的成员(非成员)。因此，我们建立了一个阴影模型，然后利用阴影模型的成员(非成员)来训练攻击模型。接下来，我们构建一个用户特征生成器来从阴影(代理)模型中构造具有代表性的特征向量。最后将特征向量输入到攻击模型中，识别用户的隶属关系。此外，为了解决获取内部分发数据的高成本问题，我们开发了两种不同的 ME-MIA，通过制作真实的内部分发数据来实现数据高效甚至无数据的 MIA。值得注意的是，后者在前面的作品中是不可能的。最后，我们在三个实际数据集上对多个顺序推荐模型进行 ME-MIA 评估。实验结果表明，ME-MIA 算法及其变体能够实现有效的提取，并且在攻击性能方面优于目前最先进的算法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Membership+Inference+Attacks+Against+Sequential+Recommender+Systems)|0| |[Communicative MARL-based Relevance Discerning Network for Repetition-Aware Recommendation](https://doi.org/10.1145/3543507.3583459)|Kaiyuan Li, Pengfei Wang, Haitao Wang, Qiang Liu, Xingxing Wang, Dong Wang, Shangguang Wang|Beijing University of Posts and Telecommunications, China; Meituan, China|The repeated user-item interaction now is becoming a common phenomenon in the e-commerce scenario. Due to its potential economic profit, various models are emerging to predict which item will be re-interacted based on the user-item interactions. In this specific scenario, item relevance is a critical factor that needs to be concerned, which tends to have different effects on the succeeding re-interacted one (i.e., stimulating or delaying its emergence). It is necessary to make a detailed discernment of item relevance for a better repetition-aware recommendation. Unfortunately, existing works usually mixed all these types, which may disturb the learning process and result in poor performance. In this paper, we introduce a novel Communicative MARL-based Relevance Discerning Network (CARDfor short) to automatically discern the item relevance for a better repetition-aware recommendation. Specifically, CARDformalizes the item relevance discerning problem into a communication selection process in MARL. CARDtreats each unique interacted item as an agent and defines three different communication types over agents, which are stimulative, inhibitive, and noisy respectively. After this, CARDutilizes a Gumbel-enhanced classifier to distinguish the communication types among agents, and an attention-based Reactive Point Process is further designed to transmit the well-discerned stimulative and inhibitive incentives separately among all agents to make an effective collaboration for repetition decisions. Experimental results on two real-world e-commerce datasets show that our proposed method outperforms the state-of-the-art recommendation methods in terms of both sequential and repetition-aware recommenders. Furthermore, CARDis also deployed in the online sponsored search advertising system in Meituan, obtaining a performance improvement of over 1.5% and 1.2% in CTR and effective Cost Per Mille (eCPM) respectively, which is significant to the business.|重复的用户-项目交互现在正在成为电子商务场景中的一个普遍现象。由于其潜在的经济利益，各种模型正在出现，以预测哪些项目将重新交互的基础上，用户项目的交互。在这个特定的场景中，项目相关性是一个需要关注的关键因素，它往往对后续的重新相互作用有不同的影响(即，刺激或延迟其出现)。有必要对项目的相关性进行详细的识别，以便更好地提出有重复意识的建议。不幸的是，现有的作品往往混合了所有这些类型，这可能会干扰学习过程，导致较差的表现。本文介绍了一种新的基于交际 MARL 的关联识别网络(CARD) ，该网络可以自动识别项目的相关性，从而获得更好的重复感知推荐。特别地，CARD 将项目相关性识别问题形式化为 MARL 中的通信选择过程。CARD 将每个独特的交互项目视为一个代理，并定义了代理上的三种不同的通信类型，分别是刺激性、抑制性和噪声性。此后，CARD 利用 Gumbel 增强的分类器来区分代理人之间的通信类型，并进一步设计基于注意力的反应点过程，以在所有代理人之间分别传递明确的刺激和抑制激励，以便为重复决策进行有效的协作。在两个实际电子商务数据集上的实验结果表明，该方法在顺序推荐和重复推荐方面都优于目前最先进的推荐方法。此外，CARD 还部署在在线赞助的搜索广告系统中，美团点击率和有效每公里成本(eCPM)分别提高了1.5% 和1.2% ，这对业务具有重要意义。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Communicative+MARL-based+Relevance+Discerning+Network+for+Repetition-Aware+Recommendation)|0| -|[Personalized Graph Signal Processing for Collaborative Filtering](https://doi.org/10.1145/3543507.3583466)|Jiahao Liu, Dongsheng Li, Hansu Gu, Tun Lu, Peng Zhang, Li Shang, Ning Gu|Amazon, USA; Microsoft Research Asia, China; School of Computer Science, Fudan University, China and Shanghai Key Laboratory of Data Science, Fudan University, China|The collaborative filtering (CF) problem with only user-item interaction information can be solved by graph signal processing (GSP), which uses low-pass filters to smooth the observed interaction signals on the similarity graph to obtain the prediction signals. However, the interaction signal may not be sufficient to accurately characterize user interests and the low-pass filters may ignore the useful information contained in the high-frequency component of the observed signals, resulting in suboptimal accuracy. To this end, we propose a personalized graph signal processing (PGSP) method for collaborative filtering. Firstly, we design the personalized graph signal containing richer user information and construct an augmented similarity graph containing more graph topology information, to more effectively characterize user interests. Secondly, we devise a mixed-frequency graph filter to introduce useful information in the high-frequency components of the observed signals by combining an ideal low-pass filter that smooths signals globally and a linear low-pass filter that smooths signals locally. Finally, we combine the personalized graph signal, the augmented similarity graph and the mixed-frequency graph filter by proposing a pipeline consisting of three key steps: pre-processing, graph convolution and post-processing. Extensive experiments show that PGSP can achieve superior accuracy compared with state-of-the-art CF methods and, as a nonparametric method, PGSP has very high training efficiency.|图形信号处理(gSP)可以解决只有用户-项目交互信息的协同过滤(CF)问题，它使用低通滤波器平滑相似图上观察到的交互信号，以获得预测信号。然而，交互信号可能不足以准确地表征用户的兴趣，而且低通滤波器可能会忽略观测信号的高频分量中包含的有用信息，从而导致次优精度。为此，我们提出了一个个性化的图形信号处理(PgSP)方法来处理协同过滤。首先，设计了包含更丰富用户信息的个性化图形信号，构造了包含更多图形拓扑信息的增广相似度图，以更有效地刻画用户兴趣。其次，我们设计了一个混合频率图形滤波器，通过结合理想的低通滤波器对信号进行全局平滑和线性低通滤波器对信号进行局部平滑，从而在观测信号的高频成分中引入有用的信息。最后，结合个性化图形信号、增强相似图和混合频率图滤波，提出了一种由预处理、图卷积和后处理三个关键步骤组成的流水线。大量的实验表明，与现有的 CF 方法相比，PGSP 具有更高的精度，并且作为一种非参数方法，PGSP 具有很高的训练效率。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Personalized+Graph+Signal+Processing+for+Collaborative+Filtering)|0| -|[Multi-Task Recommendations with Reinforcement Learning](https://doi.org/10.1145/3543507.3583467)|Ziru Liu, Jiejie Tian, Qingpeng Cai, Xiangyu Zhao, Jingtong Gao, Shuchang Liu, Dayou Chen, Tonghao He, Dong Zheng, Peng Jiang, Kun Gai|City University of Hong Kong, China; Unaffiliated, China; Kuaishou, China|In recent years, Multi-task Learning (MTL) has yielded immense success in Recommender System (RS) applications. However, current MTL-based recommendation models tend to disregard the session-wise patterns of user-item interactions because they are predominantly constructed based on item-wise datasets. Moreover, balancing multiple objectives has always been a challenge in this field, which is typically avoided via linear estimations in existing works. To address these issues, in this paper, we propose a Reinforcement Learning (RL) enhanced MTL framework, namely RMTL, to combine the losses of different recommendation tasks using dynamic weights. To be specific, the RMTL structure can address the two aforementioned issues by (i) constructing an MTL environment from session-wise interactions and (ii) training multi-task actor-critic network structure, which is compatible with most existing MTL-based recommendation models, and (iii) optimizing and fine-tuning the MTL loss function using the weights generated by critic networks. Experiments on two real-world public datasets demonstrate the effectiveness of RMTL with a higher AUC against state-of-the-art MTL-based recommendation models. Additionally, we evaluate and validate RMTL's compatibility and transferability across various MTL models.|近年来，多任务学习在推荐系统应用方面取得了巨大的成功。然而，目前基于 MTL 的推荐模型倾向于忽略用户项目交互的会话模式，因为它们主要是基于项目数据集构建的。此外，平衡多个目标一直是这个领域的一个挑战，这通常是通过现有工作中的线性估计来避免的。为了解决这些问题，在本文中，我们提出了一个强化学习增强的 MTL 框架，即 RMTL，它使用动态权重来组合不同推荐任务的丢失。具体来说，RMTL 结构可以解决上述两个问题: (1)通过会话交互构建 MTL 环境; (2)训练与大多数基于 MTL 的推荐模型兼容的多任务参与者-评论者网络结构; (3)利用评论者网络生成的权重优化和微调 MTL 损失函数。在两个真实世界的公共数据集上的实验证明了具有较高 AUC 的 RMTL 对基于最新 MTL 的推荐模型的有效性。此外，我们评估和验证 RMTL 的兼容性和跨各种 MTL 模型的可转移性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Multi-Task+Recommendations+with+Reinforcement+Learning)|0| -|[A Self-Correcting Sequential Recommender](https://doi.org/10.1145/3543507.3583479)|Yujie Lin, Chenyang Wang, Zhumin Chen, Zhaochun Ren, Xin Xin, Qiang Yan, Maarten de Rijke, Xiuzhen Cheng, Pengjie Ren|University of Amsterdam, Netherlands; Shandong University, China; WeChat, Tencent, China|Sequential recommendations aim to capture users' preferences from their historical interactions so as to predict the next item that they will interact with. Sequential recommendation methods usually assume that all items in a user's historical interactions reflect her/his preferences and transition patterns between items. However, real-world interaction data is imperfect in that (i) users might erroneously click on items, i.e., so-called misclicks on irrelevant items, and (ii) users might miss items, i.e., unexposed relevant items due to inaccurate recommendations. To tackle the two issues listed above, we propose STEAM, a Self-correcTing sEquentiAl recoMmender. STEAM first corrects an input item sequence by adjusting the misclicked and/or missed items. It then uses the corrected item sequence to train a recommender and make the next item prediction.We design an item-wise corrector that can adaptively select one type of operation for each item in the sequence. The operation types are 'keep', 'delete' and 'insert.' In order to train the item-wise corrector without requiring additional labeling, we design two self-supervised learning mechanisms: (i) deletion correction (i.e., deleting randomly inserted items), and (ii) insertion correction (i.e., predicting randomly deleted items). We integrate the corrector with the recommender by sharing the encoder and by training them jointly. We conduct extensive experiments on three real-world datasets and the experimental results demonstrate that STEAM outperforms state-of-the-art sequential recommendation baselines. Our in-depth analyses confirm that STEAM benefits from learning to correct the raw item sequences.|序贯推荐旨在从用户的历史交互中获取他们的偏好，从而预测他们将要交互的下一个项目。顺序推荐方法通常假设用户历史交互中的所有项目都反映了用户的偏好和项目之间的转换模式。然而，真实世界的交互数据是不完美的，因为(i)用户可能会错误地点击项目，即所谓的不相关项目的错误点击，以及(ii)用户可能会错过项目，即由于不准确的推荐而未公开的相关项目。为了解决上面列出的两个问题，我们提出 STEAM，一个自我修正的 sEquentiAl 推荐器。STEAM 首先通过调整错误点击和/或错过的项目来更正输入项目序列。然后使用校正后的项目序列来训练推荐者并对下一个项目进行预测。我们设计了一个项目校正器，它可以自适应地为序列中的每个项目选择一种操作类型。操作类型为“ keep”、“ delete”和“ insert”。为了训练项目校正器而不需要额外的标签，我们设计了两个自我监督学习机制: (i)删除校正(即删除随机插入的项目)和(ii)插入校正(即预测随机删除的项目)。我们通过共享编码器和共同训练，将校正器和推荐器结合起来。我们在三个真实世界的数据集上进行了广泛的实验，实验结果表明 STEAM 的性能优于最先进的顺序推荐基线。我们的深入分析证实，STEAM 受益于学习纠正原始项目序列。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Self-Correcting+Sequential+Recommender)|0| +|[Personalized Graph Signal Processing for Collaborative Filtering](https://doi.org/10.1145/3543507.3583466)|Jiahao Liu, Dongsheng Li, Hansu Gu, Tun Lu, Peng Zhang, Li Shang, Ning Gu|Microsoft Research Asia, China; School of Computer Science, Fudan University, China and Shanghai Key Laboratory of Data Science, Fudan University, China; Amazon, USA|The collaborative filtering (CF) problem with only user-item interaction information can be solved by graph signal processing (GSP), which uses low-pass filters to smooth the observed interaction signals on the similarity graph to obtain the prediction signals. However, the interaction signal may not be sufficient to accurately characterize user interests and the low-pass filters may ignore the useful information contained in the high-frequency component of the observed signals, resulting in suboptimal accuracy. To this end, we propose a personalized graph signal processing (PGSP) method for collaborative filtering. Firstly, we design the personalized graph signal containing richer user information and construct an augmented similarity graph containing more graph topology information, to more effectively characterize user interests. Secondly, we devise a mixed-frequency graph filter to introduce useful information in the high-frequency components of the observed signals by combining an ideal low-pass filter that smooths signals globally and a linear low-pass filter that smooths signals locally. Finally, we combine the personalized graph signal, the augmented similarity graph and the mixed-frequency graph filter by proposing a pipeline consisting of three key steps: pre-processing, graph convolution and post-processing. Extensive experiments show that PGSP can achieve superior accuracy compared with state-of-the-art CF methods and, as a nonparametric method, PGSP has very high training efficiency.|图形信号处理(gSP)可以解决只有用户-项目交互信息的协同过滤(CF)问题，它使用低通滤波器平滑相似图上观察到的交互信号，以获得预测信号。然而，交互信号可能不足以准确地表征用户的兴趣，而且低通滤波器可能会忽略观测信号的高频分量中包含的有用信息，从而导致次优精度。为此，我们提出了一个个性化的图形信号处理(PgSP)方法来处理协同过滤。首先，设计了包含更丰富用户信息的个性化图形信号，构造了包含更多图形拓扑信息的增广相似度图，以更有效地刻画用户兴趣。其次，我们设计了一个混合频率图形滤波器，通过结合理想的低通滤波器对信号进行全局平滑和线性低通滤波器对信号进行局部平滑，从而在观测信号的高频成分中引入有用的信息。最后，结合个性化图形信号、增强相似图和混合频率图滤波，提出了一种由预处理、图卷积和后处理三个关键步骤组成的流水线。大量的实验表明，与现有的 CF 方法相比，PGSP 具有更高的精度，并且作为一种非参数方法，PGSP 具有很高的训练效率。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Personalized+Graph+Signal+Processing+for+Collaborative+Filtering)|0| +|[Multi-Task Recommendations with Reinforcement Learning](https://doi.org/10.1145/3543507.3583467)|Ziru Liu, Jiejie Tian, Qingpeng Cai, Xiangyu Zhao, Jingtong Gao, Shuchang Liu, Dayou Chen, Tonghao He, Dong Zheng, Peng Jiang, Kun Gai|Unaffiliated, China; City University of Hong Kong, China; Kuaishou, China|In recent years, Multi-task Learning (MTL) has yielded immense success in Recommender System (RS) applications. However, current MTL-based recommendation models tend to disregard the session-wise patterns of user-item interactions because they are predominantly constructed based on item-wise datasets. Moreover, balancing multiple objectives has always been a challenge in this field, which is typically avoided via linear estimations in existing works. To address these issues, in this paper, we propose a Reinforcement Learning (RL) enhanced MTL framework, namely RMTL, to combine the losses of different recommendation tasks using dynamic weights. To be specific, the RMTL structure can address the two aforementioned issues by (i) constructing an MTL environment from session-wise interactions and (ii) training multi-task actor-critic network structure, which is compatible with most existing MTL-based recommendation models, and (iii) optimizing and fine-tuning the MTL loss function using the weights generated by critic networks. Experiments on two real-world public datasets demonstrate the effectiveness of RMTL with a higher AUC against state-of-the-art MTL-based recommendation models. Additionally, we evaluate and validate RMTL's compatibility and transferability across various MTL models.|近年来，多任务学习在推荐系统应用方面取得了巨大的成功。然而，目前基于 MTL 的推荐模型倾向于忽略用户项目交互的会话模式，因为它们主要是基于项目数据集构建的。此外，平衡多个目标一直是这个领域的一个挑战，这通常是通过现有工作中的线性估计来避免的。为了解决这些问题，在本文中，我们提出了一个强化学习增强的 MTL 框架，即 RMTL，它使用动态权重来组合不同推荐任务的丢失。具体来说，RMTL 结构可以解决上述两个问题: (1)通过会话交互构建 MTL 环境; (2)训练与大多数基于 MTL 的推荐模型兼容的多任务参与者-评论者网络结构; (3)利用评论者网络生成的权重优化和微调 MTL 损失函数。在两个真实世界的公共数据集上的实验证明了具有较高 AUC 的 RMTL 对基于最新 MTL 的推荐模型的有效性。此外，我们评估和验证 RMTL 的兼容性和跨各种 MTL 模型的可转移性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Multi-Task+Recommendations+with+Reinforcement+Learning)|0| +|[A Self-Correcting Sequential Recommender](https://doi.org/10.1145/3543507.3583479)|Yujie Lin, Chenyang Wang, Zhumin Chen, Zhaochun Ren, Xin Xin, Qiang Yan, Maarten de Rijke, Xiuzhen Cheng, Pengjie Ren|WeChat, Tencent, China; University of Amsterdam, Netherlands; Shandong University, China|Sequential recommendations aim to capture users' preferences from their historical interactions so as to predict the next item that they will interact with. Sequential recommendation methods usually assume that all items in a user's historical interactions reflect her/his preferences and transition patterns between items. However, real-world interaction data is imperfect in that (i) users might erroneously click on items, i.e., so-called misclicks on irrelevant items, and (ii) users might miss items, i.e., unexposed relevant items due to inaccurate recommendations. To tackle the two issues listed above, we propose STEAM, a Self-correcTing sEquentiAl recoMmender. STEAM first corrects an input item sequence by adjusting the misclicked and/or missed items. It then uses the corrected item sequence to train a recommender and make the next item prediction.We design an item-wise corrector that can adaptively select one type of operation for each item in the sequence. The operation types are 'keep', 'delete' and 'insert.' In order to train the item-wise corrector without requiring additional labeling, we design two self-supervised learning mechanisms: (i) deletion correction (i.e., deleting randomly inserted items), and (ii) insertion correction (i.e., predicting randomly deleted items). We integrate the corrector with the recommender by sharing the encoder and by training them jointly. We conduct extensive experiments on three real-world datasets and the experimental results demonstrate that STEAM outperforms state-of-the-art sequential recommendation baselines. Our in-depth analyses confirm that STEAM benefits from learning to correct the raw item sequences.|序贯推荐旨在从用户的历史交互中获取他们的偏好，从而预测他们将要交互的下一个项目。顺序推荐方法通常假设用户历史交互中的所有项目都反映了用户的偏好和项目之间的转换模式。然而，真实世界的交互数据是不完美的，因为(i)用户可能会错误地点击项目，即所谓的不相关项目的错误点击，以及(ii)用户可能会错过项目，即由于不准确的推荐而未公开的相关项目。为了解决上面列出的两个问题，我们提出 STEAM，一个自我修正的 sEquentiAl 推荐器。STEAM 首先通过调整错误点击和/或错过的项目来更正输入项目序列。然后使用校正后的项目序列来训练推荐者并对下一个项目进行预测。我们设计了一个项目校正器，它可以自适应地为序列中的每个项目选择一种操作类型。操作类型为“ keep”、“ delete”和“ insert”。为了训练项目校正器而不需要额外的标签，我们设计了两个自我监督学习机制: (i)删除校正(即删除随机插入的项目)和(ii)插入校正(即预测随机删除的项目)。我们通过共享编码器和共同训练，将校正器和推荐器结合起来。我们在三个真实世界的数据集上进行了广泛的实验，实验结果表明 STEAM 的性能优于最先进的顺序推荐基线。我们的深入分析证实，STEAM 受益于学习纠正原始项目序列。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Self-Correcting+Sequential+Recommender)|0| |[Confident Action Decision via Hierarchical Policy Learning for Conversational Recommendation](https://doi.org/10.1145/3543507.3583536)|Heeseon Kim, Hyeongjun Yang, KyongHo Lee|Department of Computer Science, Yonsei University, Republic of Korea|Conversational recommender systems (CRS) aim to acquire a user’s dynamic interests for a successful recommendation. By asking about his/her preferences, CRS explore current needs of a user and recommend items of interest. However, previous works may not determine a proper action in a timely manner which leads to the insufficient information gathering and the waste of conversation turns. Since they learn a single decision policy, it is difficult for them to address the general decision problems in CRS. Besides, existing methods do not distinguish whether the past behaviors inferred from the historical interactions are closely related to the user’s current preference. To address these issues, we propose a novel Hierarchical policy learning based Conversational Recommendation framework (HiCR). HiCR formulates the multi-round decision making process as a hierarchical policy learning scheme, which consists of both a high-level policy and a low-level policy. In detail, the high-level policy aims to determine what type of action to take, such as a recommendation or a query, by observing the comprehensive conversation information. According to the decided action type, the low-level policy selects a specific action, such as which attribute to ask or which item to recommend. The hierarchical conversation policy enables CRS to decide an optimal action, resulting in reducing the unnecessary consumption of conversation turns and the continuous failure of recommendations. Furthermore, in order to filter out the unnecessary historical information when enriching the current user preference, we extract and utilize the informative past behaviors that are attentive to the current needs. Empirical experiments on four real-world datasets show the superiority of our approach against the current state-of-the-art methods.|会话推荐系统(CRS)的目标是获取用户的动态兴趣，从而实现成功的推荐。通过询问用户的偏好，CRS 探索用户当前的需求并推荐感兴趣的项目。然而，以往的作品不能及时确定适当的行动，导致信息收集不足和谈话的浪费。由于他们只学习单一的决策策略，因此很难解决 CRS 中的一般决策问题。此外，现有的方法不能区分从历史交互中推断出的过去行为是否与用户当前的偏好密切相关。为了解决这些问题，我们提出了一种新的基于层次策略学习的会话推荐框架(HiCR)。HiCR 将多轮决策过程描述为一个分层的决策学习方案，该方案由高层决策和低层决策两部分组成。具体来说，高级策略旨在通过观察全面的会话信息来确定采取何种类型的操作，比如推荐或查询。根据确定的操作类型，底层策略选择一个特定的操作，比如询问哪个属性或推荐哪个项目。分层对话策略使 CRS 能够决定一个最优的操作，从而减少不必要的话轮消耗和建议的持续失败。此外，为了在丰富当前用户偏好时过滤掉不必要的历史信息，我们提取并利用了关注当前需求的信息性过去行为。在四个真实世界数据集上的实验表明了我们的方法相对于当前最先进的方法的优越性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Confident+Action+Decision+via+Hierarchical+Policy+Learning+for+Conversational+Recommendation)|0| -|[Mutual Wasserstein Discrepancy Minimization for Sequential Recommendation](https://doi.org/10.1145/3543507.3583529)|Ziwei Fan, Zhiwei Liu, Hao Peng, Philip S. Yu|University of Illinois Chicago, USA; Salesforce AI Research, USA; Beihang University, USA|Self-supervised sequential recommendation significantly improves recommendation performance by maximizing mutual information with well-designed data augmentations. However, the mutual information estimation is based on the calculation of Kullback Leibler divergence with several limitations, including asymmetrical estimation, the exponential need of the sample size, and training instability. Also, existing data augmentations are mostly stochastic and can potentially break sequential correlations with random modifications. These two issues motivate us to investigate an alternative robust mutual information measurement capable of modeling uncertainty and alleviating KL divergence limitations. To this end, we propose a novel self-supervised learning framework based on Mutual WasserStein discrepancy minimization MStein for the sequential recommendation. We propose the Wasserstein Discrepancy Measurement to measure the mutual information between augmented sequences. Wasserstein Discrepancy Measurement builds upon the 2-Wasserstein distance, which is more robust, more efficient in small batch sizes, and able to model the uncertainty of stochastic augmentation processes. We also propose a novel contrastive learning loss based on Wasserstein Discrepancy Measurement. Extensive experiments on four benchmark datasets demonstrate the effectiveness of MStein over baselines. More quantitative analyses show the robustness against perturbations and training efficiency in batch size. Finally, improvements analysis indicates better representations of popular users or items with significant uncertainty. The source code is at https://github.com/zfan20/MStein.|自监督顺序推荐通过设计良好的数据增强最大化互信息，显著提高了推荐性能。然而，互信息估计是基于 Kullback Leibler 散度的计算，具有不对称估计、样本量的指数需求和训练不稳定性等局限性。此外，现有的数据扩充大多是随机的，并可能打破随机修改顺序相关性。这两个问题促使我们研究一种可替代的鲁棒互信息测量方法，该方法能够对不确定性进行建模并减轻 KL 发散的限制。为此，我们提出了一种新的基于 Mutual WasserStein 差异最小化 MStein 的自监督学习框架，用于顺序推荐。我们提出了 Wasserstein 差异度量来度量增广序列之间的互信息。Wasserstein 误差测度建立在2-Wasserstein 距离的基础上，该距离在小批量情况下具有更强的鲁棒性和更高的效率，能够对随机增量过程的不确定性进行建模。我们还提出了一种新的基于 Wasserstein 差异度量的对比学习损失。在四个基准数据集上的大量实验证明了 MStein 在基线上的有效性。进一步的定量分析表明，该算法具有较强的抗干扰能力，并且在批量情况下具有较高的训练效率。最后，改进分析表明，流行用户或具有显著不确定性的项目的表示更好。源代码在 https://github.com/zfan20/mstein。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Mutual+Wasserstein+Discrepancy+Minimization+for+Sequential+Recommendation)|0| +|[Mutual Wasserstein Discrepancy Minimization for Sequential Recommendation](https://doi.org/10.1145/3543507.3583529)|Ziwei Fan, Zhiwei Liu, Hao Peng, Philip S. Yu|Salesforce AI Research, USA; Beihang University, USA; University of Illinois Chicago, USA|Self-supervised sequential recommendation significantly improves recommendation performance by maximizing mutual information with well-designed data augmentations. However, the mutual information estimation is based on the calculation of Kullback Leibler divergence with several limitations, including asymmetrical estimation, the exponential need of the sample size, and training instability. Also, existing data augmentations are mostly stochastic and can potentially break sequential correlations with random modifications. These two issues motivate us to investigate an alternative robust mutual information measurement capable of modeling uncertainty and alleviating KL divergence limitations. To this end, we propose a novel self-supervised learning framework based on Mutual WasserStein discrepancy minimization MStein for the sequential recommendation. We propose the Wasserstein Discrepancy Measurement to measure the mutual information between augmented sequences. Wasserstein Discrepancy Measurement builds upon the 2-Wasserstein distance, which is more robust, more efficient in small batch sizes, and able to model the uncertainty of stochastic augmentation processes. We also propose a novel contrastive learning loss based on Wasserstein Discrepancy Measurement. Extensive experiments on four benchmark datasets demonstrate the effectiveness of MStein over baselines. More quantitative analyses show the robustness against perturbations and training efficiency in batch size. Finally, improvements analysis indicates better representations of popular users or items with significant uncertainty. The source code is at https://github.com/zfan20/MStein.|自监督顺序推荐通过设计良好的数据增强最大化互信息，显著提高了推荐性能。然而，互信息估计是基于 Kullback Leibler 散度的计算，具有不对称估计、样本量的指数需求和训练不稳定性等局限性。此外，现有的数据扩充大多是随机的，并可能打破随机修改顺序相关性。这两个问题促使我们研究一种可替代的鲁棒互信息测量方法，该方法能够对不确定性进行建模并减轻 KL 发散的限制。为此，我们提出了一种新的基于 Mutual WasserStein 差异最小化 MStein 的自监督学习框架，用于顺序推荐。我们提出了 Wasserstein 差异度量来度量增广序列之间的互信息。Wasserstein 误差测度建立在2-Wasserstein 距离的基础上，该距离在小批量情况下具有更强的鲁棒性和更高的效率，能够对随机增量过程的不确定性进行建模。我们还提出了一种新的基于 Wasserstein 差异度量的对比学习损失。在四个基准数据集上的大量实验证明了 MStein 在基线上的有效性。进一步的定量分析表明，该算法具有较强的抗干扰能力，并且在批量情况下具有较高的训练效率。最后，改进分析表明，流行用户或具有显著不确定性的项目的表示更好。源代码在 https://github.com/zfan20/mstein。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Mutual+Wasserstein+Discrepancy+Minimization+for+Sequential+Recommendation)|0| |[Automatic Feature Selection By One-Shot Neural Architecture Search In Recommendation Systems](https://doi.org/10.1145/3543507.3583444)|He Wei, Yuekui Yang, Haiyang Wu, Yangyang Tang, Meixi Liu, Jianfeng Li|Machine learning platform department, TEG, Tencent, China; Machine learning platform department, TEG, Tencent, China and Department of Computer Science and Technology, Beijing National Research Center for Information Science and Technology, Tsinghua University, China|Feature selection is crucial in large-scale recommendation system, which can not only reduce the computational cost, but also improve the recommendation efficiency. Most existing works rank the features and then select the top-k ones as the final feature subset. However, they assess feature importance individually and ignore the interrelationship between features. Consequently, multiple features with high relevance may be selected simultaneously, resulting in sub-optimal result. In this work, we solve this problem by proposing an AutoML-based feature selection framework that can automatically search the optimal feature subset. Specifically, we first embed the search space into a weight-sharing Supernet. Then, a two-stage neural architecture search method is employed to evaluate the feature quality. In the first stage, a well-designed sampling method considering feature convergence fairness is applied to train the Supernet. In the second stage, a reinforcement learning method is used to search for the optimal feature subset efficiently. The Experimental results on two real datasets demonstrate the superior performance of new framework over other solutions. Our proposed method obtain significant improvement with a 20% reduction in the amount of features on the Criteo. More validation experiments demonstrate the ability and robustness of the framework.|特征选择是大规模推荐系统的关键，它不仅可以降低计算量，而且可以提高推荐效率。大多数已有的作品对特征进行排序，然后选择最上面的 k 个特征作为最终的特征子集。然而，他们单独评估特征的重要性，而忽略了特征之间的相互关系。因此，可以同时选择多个高相关性的特征，从而导致次优结果。针对这一问题，本文提出了一种基于 AutoML 的特征选择框架，该框架可以自动搜索最优特征子集。具体来说，我们首先将搜索空间嵌入到一个权重共享的超级网络中。然后，采用两阶段神经网络结构搜索方法对特征质量进行评价。在第一阶段，采用一种设计良好的考虑特征收敛公平性的抽样方法对超网进行训练。在第二阶段，使用强化学习方法有效地搜索最优特征子集。在两个实际数据集上的实验结果表明，新框架的性能优于其他解决方案。我们提出的方法获得了显着的改进，在标准的数量减少了20% 的特征。更多的验证实验证明了该框架的能力和鲁棒性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Automatic+Feature+Selection+By+One-Shot+Neural+Architecture+Search+In+Recommendation+Systems)|0| -|[Catch: Collaborative Feature Set Search for Automated Feature Engineering](https://doi.org/10.1145/3543507.3583527)|Guoshan Lu, Haobo Wang, Saisai Yang, Jing Yuan, Guozheng Yang, Cheng Zang, Gang Chen, Junbo Zhao|Zheshang Bank Co., Ltd., China; Zhejiang University, China; Institute of Computing Innovation, Zhejiang University, China|Feature engineering often plays a crucial role in building mining systems for tabular data, which traditionally requires experienced human experts to perform. Thanks to the rapid advances in reinforcement learning, it has offered an automated alternative, i.e. automated feature engineering (AutoFE). In this work, through scrutiny of the prior AutoFE methods, we characterize several research challenges that remained in this regime, concerning system-wide efficiency, efficacy, and practicality toward production. We then propose Catch, a full-fledged new AutoFE framework that comprehensively addresses the aforementioned challenges. The core to Catch composes a hierarchical-policy reinforcement learning scheme that manifests a collaborative feature engineering exploration and exploitation grounded on the granularity of the whole feature set. At a higher level of the hierarchy, a decision-making module controls the post-processing of the attained feature engineering transformation. We extensively experiment with Catch on 26 academic standardized tabular datasets and 9 industrialized real-world datasets. Measured by numerous metrics and analyses, Catch establishes a new state-of-the-art, from perspectives performance, latency as well as its practicality towards production. Source code1 can be found at https://github.com/1171000709/Catch.|在构建表格数据挖掘系统时，特征工程往往起着至关重要的作用，表格数据挖掘传统上需要有经验的人类专家来完成。由于强化学习的快速发展，它提供了一种自动化的替代方案，即自动化特征工程(AutoFE)。在这项工作中，通过审查以前的自动有限元方法，我们描述了几个研究挑战，仍然在这个制度，关于系统的效率，效率和实用性的生产。然后，我们建议使用 Catch，这是一个成熟的新的 AutoFE 框架，可以全面解决上述挑战。Core to Catch 组成了一个层次化的策略强化学习方案，体现了基于整个特性集粒度的协同特性工程探索和开发。在层次结构的更高层次上，决策模块控制所获得的特征工程变换的后处理。我们对26个学术标准化表格数据集和9个工业化真实世界数据集进行了广泛的实验。通过大量的度量和分析，Catch 从性能、延迟以及对生产的实用性的角度建立了一个新的最先进的状态。源代码1可在 https://github.com/1171000709/catch 找到。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Catch:+Collaborative+Feature+Set+Search+for+Automated+Feature+Engineering)|0| +|[Catch: Collaborative Feature Set Search for Automated Feature Engineering](https://doi.org/10.1145/3543507.3583527)|Guoshan Lu, Haobo Wang, Saisai Yang, Jing Yuan, Guozheng Yang, Cheng Zang, Gang Chen, Junbo Zhao|Zheshang Bank Co., Ltd., China; Institute of Computing Innovation, Zhejiang University, China; Zhejiang University, China|Feature engineering often plays a crucial role in building mining systems for tabular data, which traditionally requires experienced human experts to perform. Thanks to the rapid advances in reinforcement learning, it has offered an automated alternative, i.e. automated feature engineering (AutoFE). In this work, through scrutiny of the prior AutoFE methods, we characterize several research challenges that remained in this regime, concerning system-wide efficiency, efficacy, and practicality toward production. We then propose Catch, a full-fledged new AutoFE framework that comprehensively addresses the aforementioned challenges. The core to Catch composes a hierarchical-policy reinforcement learning scheme that manifests a collaborative feature engineering exploration and exploitation grounded on the granularity of the whole feature set. At a higher level of the hierarchy, a decision-making module controls the post-processing of the attained feature engineering transformation. We extensively experiment with Catch on 26 academic standardized tabular datasets and 9 industrialized real-world datasets. Measured by numerous metrics and analyses, Catch establishes a new state-of-the-art, from perspectives performance, latency as well as its practicality towards production. Source code1 can be found at https://github.com/1171000709/Catch.|在构建表格数据挖掘系统时，特征工程往往起着至关重要的作用，表格数据挖掘传统上需要有经验的人类专家来完成。由于强化学习的快速发展，它提供了一种自动化的替代方案，即自动化特征工程(AutoFE)。在这项工作中，通过审查以前的自动有限元方法，我们描述了几个研究挑战，仍然在这个制度，关于系统的效率，效率和实用性的生产。然后，我们建议使用 Catch，这是一个成熟的新的 AutoFE 框架，可以全面解决上述挑战。Core to Catch 组成了一个层次化的策略强化学习方案，体现了基于整个特性集粒度的协同特性工程探索和开发。在层次结构的更高层次上，决策模块控制所获得的特征工程变换的后处理。我们对26个学术标准化表格数据集和9个工业化真实世界数据集进行了广泛的实验。通过大量的度量和分析，Catch 从性能、延迟以及对生产的实用性的角度建立了一个新的最先进的状态。源代码1可在 https://github.com/1171000709/catch 找到。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Catch:+Collaborative+Feature+Set+Search+for+Automated+Feature+Engineering)|0| |[The Hitchhiker's Guide to Facebook Web Tracking with Invisible Pixels and Click IDs](https://doi.org/10.1145/3543507.3583311)|Paschalis Bekos, Panagiotis Papadopoulos, Evangelos P. Markatos, Nicolas Kourtellis|FORTH, Greece; University of Crete/FORTH, Greece; Telefonica Research, Spain|Over the past years, advertisement companies have used various tracking methods to persistently track users across the web. Such tracking methods usually include first and third-party cookies, cookie synchronization, as well as a variety of fingerprinting mechanisms. Facebook (FB) recently introduced a new tagging mechanism that attaches a one-time tag as a URL parameter (FBCLID) on outgoing links to other websites. Although such a tag does not seem to have enough information to persistently track users, we demonstrate that despite its ephemeral nature, when combined with FB Pixel, it can aid in persistently monitoring user browsing behavior across i) different websites, ii) different actions on each website, iii) time, i.e., both in the past as well as in the future. We refer to this online monitoring of users as FB web tracking. We find that FB Pixel tracks a wide range of user activities on websites with alarming detail, especially on websites classified as sensitive categories under GDPR. Also, we show how the FBCLID tag can be used to match, and thus de-anonymize, activities of online users performed in the distant past (even before those users had a FB account) tracked by FB Pixel. In fact, by combining this tag with cookies that have rolling expiration dates, FB can also keep track of users' browsing activities in the future as well. Our experimental results suggest that 23% of the 10k most popular websites have adopted this technology, and can contribute to this activity tracking on the web. Furthermore, our longitudinal study shows that this type of user activity tracking can go as far back as 2015. Simply said, if a user creates for the first time a FB account today, FB could, under some conditions, match their anonymously collected past web browsing activity to their newly created FB profile, from as far back as 2015 and continue tracking their activity in the future.|在过去的几年里，广告公司使用了各种各样的跟踪方法来持续跟踪网络上的用户。这种跟踪方法通常包括第一方和第三方 cookie、 cookie 同步以及各种指纹识别机制。Facebook (FB)最近推出了一种新的标签机制，它将一次性标签作为 URL 参数(FBCLID)附加到其他网站的外向链接上。虽然这样的标签似乎没有足够的信息来持续跟踪用户，我们证明，尽管它的短暂性质，当结合 FB 像素，它可以帮助持续监测用户浏览行为在 i)不同的网站，ii)不同的行动在每个网站，iii)时间，即在过去和未来。我们把这种对用户的在线监控称为 FB 网络跟踪。我们发现，FB 像素跟踪广泛的用户活动的网站具有惊人的细节，特别是在网站归类为敏感类别下的 GDPR。此外，我们展示了如何使用 FBCLID 标签来匹配，从而去匿名，在线用户的活动执行在遥远的过去(甚至在那些用户有一个 FB 帐户之前)由 FB 像素跟踪。事实上，通过将这个标签与具有滚动过期日期的 cookie 相结合，FB 还可以跟踪用户未来的浏览活动。我们的实验结果表明，23% 的10k 最受欢迎的网站已经采用了这项技术，并可以有助于在网上跟踪这项活动。此外，我们的追踪研究显示，这种类型的用户活动跟踪可以追溯到2015年。简单地说，如果一个用户今天第一次创建一个 FB 帐户，FB 可以，在某些条件下，匹配他们的匿名收集过去的网页浏览活动和他们新创建的 FB 配置文件，从2015年开始，并在未来继续跟踪他们的活动。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=The+Hitchhiker's+Guide+to+Facebook+Web+Tracking+with+Invisible+Pixels+and+Click+IDs)|0| -|[Atrapos: Real-time Evaluation of Metapath Query Workloads](https://doi.org/10.1145/3543507.3583322)|Serafeim Chatzopoulos, Thanasis Vergoulis, Dimitrios Skoutas, Theodore Dalamagas, Christos Tryfonopoulos, Panagiotis Karras|University of the Peloponnese, Greece; IMSI, Athena RC, Greece; Aarhus University, Denmark; University of the Peloponnese, Greece and IMSI, Athena RC, Greece|Heterogeneous information networks (HINs) represent different types of entities and relationships between them. Exploring and mining HINs relies on metapath queries that identify pairs of entities connected by relationships of diverse semantics. While the real-time evaluation of metapath query workloads on large, web-scale HINs is highly demanding in computational cost, current approaches do not exploit interrelationships among the queries. In this paper, we present Atrapos, a new approach for the real-time evaluation of metapath query workloads that leverages a combination of efficient sparse matrix multiplication and intermediate result caching. Atrapos selects intermediate results to cache and reuse by detecting frequent sub-metapaths among workload queries in real time, using a tailor-made data structure, the Overlap Tree, and an associated caching policy. Our experimental study on real data shows that Atrapos accelerates exploratory data analysis and mining on HINs, outperforming off-the-shelf caching approaches and state-of-the-art research prototypes in all examined scenarios.|异构信息网络(HIN)表示不同类型的实体以及它们之间的关系。探索和挖掘 HIN 依赖于元路径查询，这些查询标识由不同语义关系连接的实体对。尽管对大型 Web 规模 HIN 上的元路径查询工作负载进行实时评估对计算成本要求很高，但目前的方法没有利用查询之间的相互关系。在这篇文章中，我们介绍了一种新的实时评估元路径查询工作负载的方法—— Arapos，它结合了高效的稀疏矩阵乘法和中间结果缓存。Apapos 通过实时检测工作负载查询之间频繁的子元路径，使用量身定制的数据结构、重叠树和相关的缓存策略来选择缓存和重用的中间结果。我们对真实数据的实验研究表明，在所有经过检验的场景中，阿特波斯加速了 HIN 的探索性数据分析和挖掘，表现优于现成的缓存方法和最先进的研究原型。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Atrapos:+Real-time+Evaluation+of+Metapath+Query+Workloads)|0| -|[TRAVERS: A Diversity-Based Dynamic Approach to Iterative Relevance Search over Knowledge Graphs](https://doi.org/10.1145/3543507.3583429)|Ziyang Li, Yu Gu, Yulin Shen, Wei Hu, Gong Cheng|Ohio State University, USA; State Key Laboratory for Novel Software Technology, Nanjing University, China|Relevance search over knowledge graphs seeks top-ranked answer entities that are most relevant to a query entity. Since the semantics of relevance varies with the user need and its formalization is difficult for non-experts, existing methods infer semantics from user-provided example answer entities. However, a user may provide very few examples, even none at the beginning of interaction, thereby limiting the effectiveness of such methods. In this paper, we vision a more practical scenario called labeling-based iterative relevance search: instead of effortfully inputting example answer entities, the user effortlessly (e.g., implicitly) labels current answer entities, and is rewarded with improved answer entities in the next iteration. To realize the scenario, our approach TRAVERS incorporates two rankers: a diversity-oriented ranker for supporting cold start and avoiding converging to sub-optimum caused by noisy labels, and a relevance-oriented ranker capable of handling unbalanced labels. Moreover, the two rankers and their combination dynamically evolve over iterations. TRAVERS outperformed a variety of baselines in experiments with simulated and real user behavior.|基于知识图的相关性搜索寻找与查询实体最相关的排名最高的答案实体。由于相关性的语义随用户需求而变化，而且对于非专家来说，相关性的形式化很困难，现有的方法都是从用户提供的示例答案实体中推断语义。然而，用户可能只提供很少的例子，甚至在交互开始时没有例子，从而限制了这些方法的有效性。在本文中，我们设想了一个更实际的场景，叫做基于标签的迭代相关性搜索: 用户不必费力地输入示例答案实体，而是毫不费力地(例如，隐式地)标记当前答案实体，并在下一次迭代中得到改进的答案实体。为了实现该方案，我们的方法 TRAVERS 包含两个排序器: 一个面向多样性的排序器支持冷启动，避免收敛到次优由于噪声标签，和一个相关性导向的排序器能够处理不平衡的标签。此外，这两个排名及其组合在迭代中动态演化。在模拟和真实用户行为的实验中，TRAVERS 的表现优于各种基线。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=TRAVERS:+A+Diversity-Based+Dynamic+Approach+to+Iterative+Relevance+Search+over+Knowledge+Graphs)|0| +|[Atrapos: Real-time Evaluation of Metapath Query Workloads](https://doi.org/10.1145/3543507.3583322)|Serafeim Chatzopoulos, Thanasis Vergoulis, Dimitrios Skoutas, Theodore Dalamagas, Christos Tryfonopoulos, Panagiotis Karras|Aarhus University, Denmark; University of the Peloponnese, Greece and IMSI, Athena RC, Greece; University of the Peloponnese, Greece; IMSI, Athena RC, Greece|Heterogeneous information networks (HINs) represent different types of entities and relationships between them. Exploring and mining HINs relies on metapath queries that identify pairs of entities connected by relationships of diverse semantics. While the real-time evaluation of metapath query workloads on large, web-scale HINs is highly demanding in computational cost, current approaches do not exploit interrelationships among the queries. In this paper, we present Atrapos, a new approach for the real-time evaluation of metapath query workloads that leverages a combination of efficient sparse matrix multiplication and intermediate result caching. Atrapos selects intermediate results to cache and reuse by detecting frequent sub-metapaths among workload queries in real time, using a tailor-made data structure, the Overlap Tree, and an associated caching policy. Our experimental study on real data shows that Atrapos accelerates exploratory data analysis and mining on HINs, outperforming off-the-shelf caching approaches and state-of-the-art research prototypes in all examined scenarios.|异构信息网络(HIN)表示不同类型的实体以及它们之间的关系。探索和挖掘 HIN 依赖于元路径查询，这些查询标识由不同语义关系连接的实体对。尽管对大型 Web 规模 HIN 上的元路径查询工作负载进行实时评估对计算成本要求很高，但目前的方法没有利用查询之间的相互关系。在这篇文章中，我们介绍了一种新的实时评估元路径查询工作负载的方法—— Arapos，它结合了高效的稀疏矩阵乘法和中间结果缓存。Apapos 通过实时检测工作负载查询之间频繁的子元路径，使用量身定制的数据结构、重叠树和相关的缓存策略来选择缓存和重用的中间结果。我们对真实数据的实验研究表明，在所有经过检验的场景中，阿特波斯加速了 HIN 的探索性数据分析和挖掘，表现优于现成的缓存方法和最先进的研究原型。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Atrapos:+Real-time+Evaluation+of+Metapath+Query+Workloads)|0| +|[TRAVERS: A Diversity-Based Dynamic Approach to Iterative Relevance Search over Knowledge Graphs](https://doi.org/10.1145/3543507.3583429)|Ziyang Li, Yu Gu, Yulin Shen, Wei Hu, Gong Cheng|State Key Laboratory for Novel Software Technology, Nanjing University, China; Ohio State University, USA|Relevance search over knowledge graphs seeks top-ranked answer entities that are most relevant to a query entity. Since the semantics of relevance varies with the user need and its formalization is difficult for non-experts, existing methods infer semantics from user-provided example answer entities. However, a user may provide very few examples, even none at the beginning of interaction, thereby limiting the effectiveness of such methods. In this paper, we vision a more practical scenario called labeling-based iterative relevance search: instead of effortfully inputting example answer entities, the user effortlessly (e.g., implicitly) labels current answer entities, and is rewarded with improved answer entities in the next iteration. To realize the scenario, our approach TRAVERS incorporates two rankers: a diversity-oriented ranker for supporting cold start and avoiding converging to sub-optimum caused by noisy labels, and a relevance-oriented ranker capable of handling unbalanced labels. Moreover, the two rankers and their combination dynamically evolve over iterations. TRAVERS outperformed a variety of baselines in experiments with simulated and real user behavior.|基于知识图的相关性搜索寻找与查询实体最相关的排名最高的答案实体。由于相关性的语义随用户需求而变化，而且对于非专家来说，相关性的形式化很困难，现有的方法都是从用户提供的示例答案实体中推断语义。然而，用户可能只提供很少的例子，甚至在交互开始时没有例子，从而限制了这些方法的有效性。在本文中，我们设想了一个更实际的场景，叫做基于标签的迭代相关性搜索: 用户不必费力地输入示例答案实体，而是毫不费力地(例如，隐式地)标记当前答案实体，并在下一次迭代中得到改进的答案实体。为了实现该方案，我们的方法 TRAVERS 包含两个排序器: 一个面向多样性的排序器支持冷启动，避免收敛到次优由于噪声标签，和一个相关性导向的排序器能够处理不平衡的标签。此外，这两个排名及其组合在迭代中动态演化。在模拟和真实用户行为的实验中，TRAVERS 的表现优于各种基线。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=TRAVERS:+A+Diversity-Based+Dynamic+Approach+to+Iterative+Relevance+Search+over+Knowledge+Graphs)|0| |[Message Function Search for Knowledge Graph Embedding](https://doi.org/10.1145/3543507.3583546)|Shimin Di, Lei Chen|The Hong Kong University of Science and Technology (Guangzhou), China; The Hong Kong University of Secience and Technology, China|Recently, many promising embedding models have been proposed to embed knowledge graphs (KGs) and their more general forms, such as n-ary relational data (NRD) and hyper-relational KG (HKG). To promote the data adaptability and performance of embedding models, KG searching methods propose to search for suitable models for a given KG data set. But they are restricted to a single KG form, and the searched models are restricted to a single type of embedding model. To tackle such issues, we propose to build a search space for the message function in graph neural networks (GNNs). However, it is a non-trivial task. Existing message function designs fix the structures and operators, which makes them difficult to handle different KG forms and data sets. Therefore, we first design a novel message function space, which enables both structures and operators to be searched for the given KG form (including KG, NRD, and HKG) and data. The proposed space can flexibly take different KG forms as inputs and is expressive to search for different types of embedding models. Especially, some existing message function designs and some classic KG embedding models can be instantiated as special cases of our space. We empirically show that the searched message functions are data-dependent, and can achieve leading performance on benchmark KGs, NRD, and HKGs.|近年来，人们提出了许多有前途的嵌入模型来嵌入知识图(KG)及其更一般的形式，如 n 元关系数据(NRD)和超关系 KG (HKG)。为了提高嵌入模型的数据适应性和性能，KG 搜索方法提出为给定的 KG 数据集寻找合适的模型。但是它们仅限于单一的 KG 形式，所搜索的模型仅限于单一类型的嵌入模型。为了解决这些问题，我们提出在图神经网络(GNN)中建立一个消息函数的搜索空间。然而，这是一项非常重要的任务。现有的消息功能设计固定了结构和操作符，使得它们难以处理不同的 KG 表单和数据集。因此，我们首先设计一个新的消息函数空间，它允许搜索给定的 KG 表单(包括 KG、 NRD 和 HKG)和数据的结构和操作符。该空间可以灵活地采用不同的 KG 形式作为输入，具有表达性，可以搜索不同类型的嵌入模型。特别是，现有的一些消息函数设计和一些经典的 KG 嵌入模型可以作为我们空间的特例进行实例化。实验结果表明，搜索消息函数具有数据依赖性，可以在基准幼儿园、 NRD 幼儿园和 HKG 幼儿园中取得领先的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Message+Function+Search+for+Knowledge+Graph+Embedding)|0| |[FINGER: Fast Inference for Graph-based Approximate Nearest Neighbor Search](https://doi.org/10.1145/3543507.3583318)|Patrick H. Chen, WeiCheng Chang, JyunYu Jiang, HsiangFu Yu, Inderjit S. Dhillon, ChoJui Hsieh||Approximate K-Nearest Neighbor Search (AKNNS) has now become ubiquitous in modern applications, for example, as a fast search procedure with two tower deep learning models. Graph-based methods for AKNNS in particular have received great attention due to their superior performance. These methods rely on greedy graph search to traverse the data points as embedding vectors in a database. Under this greedy search scheme, we make a key observation: many distance computations do not influence search updates so these computations can be approximated without hurting performance. As a result, we propose FINGER, a fast inference method to achieve efficient graph search. FINGER approximates the distance function by estimating angles between neighboring residual vectors with low-rank bases and distribution matching. The approximated distance can be used to bypass unnecessary computations, which leads to faster searches. Empirically, accelerating a popular graph-based method named HNSW by FINGER is shown to outperform existing graph-based methods by 20%-60% across different benchmark datasets.|近似 K 最近邻搜索(AKNNS)已经成为现代应用中普遍存在的问题，例如，作为一种具有两个塔式深度学习模型的快速搜索过程。基于图的 AKNNS 方法由于其优越的性能而受到了广泛的关注。这些方法依赖于贪婪图搜索，以嵌入向量的形式遍历数据库中的数据点。在这种贪婪的搜索方案下，我们做了一个关键的观察: 许多距离计算不影响搜索更新，所以这些计算可以近似而不损害性能。因此，我们提出了 FINGER，一种快速的推理方法来实现有效的图搜索。FINGER 通过估计低秩基相邻残差向量之间的夹角和分布匹配来逼近距离函数。近似距离可以用来绕过不必要的计算，从而导致更快的搜索。经验表明，通过 FINGER 加速一种流行的基于图的方法 HNSW，在不同的基准数据集上比现有的基于图的方法的性能提高了20% -60% 。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=FINGER:+Fast+Inference+for+Graph-based+Approximate+Nearest+Neighbor+Search)|0| -|[Match4Match: Enhancing Text-Video Retrieval by Maximum Flow with Minimum Cost](https://doi.org/10.1145/3543507.3583365)|Zhongjie Duan, Chengyu Wang, Cen Chen, Wenmeng Zhou, Jun Huang, Weining Qian|Alibaba Group, China; East China Normal University, China|With the explosive growth of video and text data on the web, text-video retrieval has become a vital task for online video platforms. Recently, text-video retrieval methods based on pre-trained models have attracted a lot of attention. However, existing methods cannot effectively capture the fine-grained information in videos, and typically suffer from the hubness problem where a collection of similar videos are retrieved by a large number of different queries. In this paper, we propose Match4Match, a new text-video retrieval method based on CLIP (Contrastive Language-Image Pretraining) and graph optimization theories. To balance calculation efficiency and model accuracy, Match4Match seamlessly supports three inference modes for different application scenarios. In fast vector retrieval mode, we embed texts and videos in the same space and employ a vector retrieval engine to obtain the top K videos. In fine-grained alignment mode, our method fully utilizes the pre-trained knowledge of the CLIP model to align words with corresponding video frames, and uses the fine-grained information to compute text-video similarity more accurately. In flow-style matching mode, to alleviate the detrimental impact of the hubness problem, we model the retrieval problem as a combinatorial optimization problem and solve it using maximum flow with minimum cost algorithm. To demonstrate the effectiveness of our method, we conduct experiments on five public text-video datasets. The overall performance of our proposed method outperforms state-of-the-art methods. Additionally, we evaluate the computational efficiency of Match4Match. Benefiting from the three flexible inference modes, Match4Match can respond to a large number of query requests with low latency or achieve high recall with acceptable time consumption.|随着网络视频和文本数据的爆炸式增长，文本视频检索已经成为在线视频平台的一项重要任务。近年来，基于预训练模型的文本视频检索方法引起了人们的广泛关注。然而，现有的方法不能有效地捕获视频中的细粒度信息，通常会遇到集线器问题，即大量不同的查询检索相似的视频集合。本文提出了一种基于对比语言-图像预训练(CLIP)和图形优化理论的文本-视频检索方法 Match4Match。为了平衡计算效率和模型精度，Match4Match 无缝支持针对不同应用场景的三种推理模式。在快速矢量检索模式下，我们将文本和视频嵌入到同一空间中，并使用矢量检索引擎获取最高 K 视频。在细粒度对齐模式下，该方法充分利用 CLIP 模型的预训练知识对相应的视频帧进行单词对齐，并利用细粒度信息更准确地计算文本-视频的相似度。在流式匹配模式下，为了减轻中继问题的不利影响，我们将检索问题建模为一个组合优化问题，并使用最大流和最小成本算法解决该问题。为了验证该方法的有效性，我们在五个公共文本视频数据集上进行了实验。我们提出的方法的总体性能优于最先进的方法。此外，我们还评估了 Match4Match 的计算效率。Match4Match 得益于这三种灵活的推理模式，可以以较低的延迟响应大量查询请求，或者以可接受的时间消耗实现高召回率。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Match4Match:+Enhancing+Text-Video+Retrieval+by+Maximum+Flow+with+Minimum+Cost)|0| -|[Zero-shot Clarifying Question Generation for Conversational Search](https://doi.org/10.1145/3543507.3583420)|Zhenduo Wang, Yuancheng Tu, Corby Rosset, Nick Craswell, Ming Wu, Qingyao Ai|GitHub Inc, USA; University of Utah, USA; Microsoft Corp, USA; Tsinghua University, China|A long-standing challenge for search and conversational assistants is query intention detection in ambiguous queries. Asking clarifying questions in conversational search has been widely studied and considered an effective solution to resolve query ambiguity. Existing work have explored various approaches for clarifying question ranking and generation. However, due to the lack of real conversational search data, they have to use artificial datasets for training, which limits their generalizability to real-world search scenarios. As a result, the industry has shown reluctance to implement them in reality, further suspending the availability of real conversational search interaction data. The above dilemma can be formulated as a cold start problem of clarifying question generation and conversational search in general. Furthermore, even if we do have large-scale conversational logs, it is not realistic to gather training data that can comprehensively cover all possible queries and topics in open-domain search scenarios. The risk of fitting bias when training a clarifying question retrieval/generation model on incomprehensive dataset is thus another important challenge. In this work, we innovatively explore generating clarifying questions in a zero-shot setting to overcome the cold start problem and we propose a constrained clarifying question generation system which uses both question templates and query facets to guide the effective and precise question generation. The experiment results show that our method outperforms existing state-of-the-art zero-shot baselines by a large margin. Human annotations to our model outputs also indicate our method generates 25.2\% more natural questions, 18.1\% more useful questions, 6.1\% less unnatural and 4\% less useless questions.|模糊查询中的查询意图检测一直是搜索和会话助手面临的挑战。在会话搜索中提出澄清问题被广泛研究，被认为是解决查询歧义的有效方法。现有的工作已经探索了各种方法来澄清问题的排序和生成。然而，由于缺乏真实的会话搜索数据，他们不得不使用人工数据集进行训练，这限制了他们对真实世界搜索场景的普遍性。结果，业界表现出不愿意在现实中实现它们，进一步暂停了真正的会话搜索交互数据的可用性。上述困境可以概括为澄清问题生成和一般会话搜索的冷启动问题。此外，即使我们有大规模的会话日志，收集能够全面涵盖开放域搜索场景中所有可能的查询和主题的培训数据也是不现实的。因此，在不全面的数据集上训练澄清问题检索/生成模型时，拟合偏差的风险是另一个重要的挑战。在这项工作中，我们创新性地探索了在零拍环境下生成澄清问题来克服冷启动问题，并提出了一个有约束的澄清问题生成系统，该系统使用问题模板和查询面来指导有效和准确的问题生成。实验结果表明，我们的方法比现有的最先进的零拍摄基线有很大的优势。我们的模型输出的人工注释也表明我们的方法产生了25.2% 更自然的问题，18.1% 更有用的问题，6.1% 更少的非自然的和4% 更少的无用的问题。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Zero-shot+Clarifying+Question+Generation+for+Conversational+Search)|0| +|[Match4Match: Enhancing Text-Video Retrieval by Maximum Flow with Minimum Cost](https://doi.org/10.1145/3543507.3583365)|Zhongjie Duan, Chengyu Wang, Cen Chen, Wenmeng Zhou, Jun Huang, Weining Qian|East China Normal University, China; Alibaba Group, China|With the explosive growth of video and text data on the web, text-video retrieval has become a vital task for online video platforms. Recently, text-video retrieval methods based on pre-trained models have attracted a lot of attention. However, existing methods cannot effectively capture the fine-grained information in videos, and typically suffer from the hubness problem where a collection of similar videos are retrieved by a large number of different queries. In this paper, we propose Match4Match, a new text-video retrieval method based on CLIP (Contrastive Language-Image Pretraining) and graph optimization theories. To balance calculation efficiency and model accuracy, Match4Match seamlessly supports three inference modes for different application scenarios. In fast vector retrieval mode, we embed texts and videos in the same space and employ a vector retrieval engine to obtain the top K videos. In fine-grained alignment mode, our method fully utilizes the pre-trained knowledge of the CLIP model to align words with corresponding video frames, and uses the fine-grained information to compute text-video similarity more accurately. In flow-style matching mode, to alleviate the detrimental impact of the hubness problem, we model the retrieval problem as a combinatorial optimization problem and solve it using maximum flow with minimum cost algorithm. To demonstrate the effectiveness of our method, we conduct experiments on five public text-video datasets. The overall performance of our proposed method outperforms state-of-the-art methods. Additionally, we evaluate the computational efficiency of Match4Match. Benefiting from the three flexible inference modes, Match4Match can respond to a large number of query requests with low latency or achieve high recall with acceptable time consumption.|随着网络视频和文本数据的爆炸式增长，文本视频检索已经成为在线视频平台的一项重要任务。近年来，基于预训练模型的文本视频检索方法引起了人们的广泛关注。然而，现有的方法不能有效地捕获视频中的细粒度信息，通常会遇到集线器问题，即大量不同的查询检索相似的视频集合。本文提出了一种基于对比语言-图像预训练(CLIP)和图形优化理论的文本-视频检索方法 Match4Match。为了平衡计算效率和模型精度，Match4Match 无缝支持针对不同应用场景的三种推理模式。在快速矢量检索模式下，我们将文本和视频嵌入到同一空间中，并使用矢量检索引擎获取最高 K 视频。在细粒度对齐模式下，该方法充分利用 CLIP 模型的预训练知识对相应的视频帧进行单词对齐，并利用细粒度信息更准确地计算文本-视频的相似度。在流式匹配模式下，为了减轻中继问题的不利影响，我们将检索问题建模为一个组合优化问题，并使用最大流和最小成本算法解决该问题。为了验证该方法的有效性，我们在五个公共文本视频数据集上进行了实验。我们提出的方法的总体性能优于最先进的方法。此外，我们还评估了 Match4Match 的计算效率。Match4Match 得益于这三种灵活的推理模式，可以以较低的延迟响应大量查询请求，或者以可接受的时间消耗实现高召回率。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Match4Match:+Enhancing+Text-Video+Retrieval+by+Maximum+Flow+with+Minimum+Cost)|0| +|[Zero-shot Clarifying Question Generation for Conversational Search](https://doi.org/10.1145/3543507.3583420)|Zhenduo Wang, Yuancheng Tu, Corby Rosset, Nick Craswell, Ming Wu, Qingyao Ai|GitHub Inc, USA; Tsinghua University, China; University of Utah, USA; Microsoft Corp, USA|A long-standing challenge for search and conversational assistants is query intention detection in ambiguous queries. Asking clarifying questions in conversational search has been widely studied and considered an effective solution to resolve query ambiguity. Existing work have explored various approaches for clarifying question ranking and generation. However, due to the lack of real conversational search data, they have to use artificial datasets for training, which limits their generalizability to real-world search scenarios. As a result, the industry has shown reluctance to implement them in reality, further suspending the availability of real conversational search interaction data. The above dilemma can be formulated as a cold start problem of clarifying question generation and conversational search in general. Furthermore, even if we do have large-scale conversational logs, it is not realistic to gather training data that can comprehensively cover all possible queries and topics in open-domain search scenarios. The risk of fitting bias when training a clarifying question retrieval/generation model on incomprehensive dataset is thus another important challenge. In this work, we innovatively explore generating clarifying questions in a zero-shot setting to overcome the cold start problem and we propose a constrained clarifying question generation system which uses both question templates and query facets to guide the effective and precise question generation. The experiment results show that our method outperforms existing state-of-the-art zero-shot baselines by a large margin. Human annotations to our model outputs also indicate our method generates 25.2\% more natural questions, 18.1\% more useful questions, 6.1\% less unnatural and 4\% less useless questions.|模糊查询中的查询意图检测一直是搜索和会话助手面临的挑战。在会话搜索中提出澄清问题被广泛研究，被认为是解决查询歧义的有效方法。现有的工作已经探索了各种方法来澄清问题的排序和生成。然而，由于缺乏真实的会话搜索数据，他们不得不使用人工数据集进行训练，这限制了他们对真实世界搜索场景的普遍性。结果，业界表现出不愿意在现实中实现它们，进一步暂停了真正的会话搜索交互数据的可用性。上述困境可以概括为澄清问题生成和一般会话搜索的冷启动问题。此外，即使我们有大规模的会话日志，收集能够全面涵盖开放域搜索场景中所有可能的查询和主题的培训数据也是不现实的。因此，在不全面的数据集上训练澄清问题检索/生成模型时，拟合偏差的风险是另一个重要的挑战。在这项工作中，我们创新性地探索了在零拍环境下生成澄清问题来克服冷启动问题，并提出了一个有约束的澄清问题生成系统，该系统使用问题模板和查询面来指导有效和准确的问题生成。实验结果表明，我们的方法比现有的最先进的零拍摄基线有很大的优势。我们的模型输出的人工注释也表明我们的方法产生了25.2% 更自然的问题，18.1% 更有用的问题，6.1% 更少的非自然的和4% 更少的无用的问题。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Zero-shot+Clarifying+Question+Generation+for+Conversational+Search)|0| |[Everything Evolves in Personalized PageRank](https://doi.org/10.1145/3543507.3583474)|Zihao Li, Dongqi Fu, Jingrui He|University of Illinois at Urbana-Champaign, USA|Personalized PageRank, as a graphical model, has been proven as an effective solution in many applications such as web page search, recommendation, etc. However, in the real world, the setting of personalized PageRank is usually dynamic like the evolving World Wide Web. On the one hand, the outdated PageRank solution can be sub-optimal for ignoring the evolution pattern. On the other hand, solving the solution from the scratch at each timestamp causes costly computation complexity. Hence, in this paper, we aim to solve the Personalized PageRank effectively and efficiently in a fully dynamic setting, i.e., every component in the Personalized PageRank formula is dependent on time. To this end, we propose the EvePPR method that can track the exact personalized PageRank solution at each timestamp in the fully dynamic setting, and we theoretically and empirically prove the accuracy and time complexity of EvePPR. Moreover, we apply EvePPR to solve the dynamic knowledge graph alignment task, where a fully dynamic setting is necessary but complex. The experiments show that EvePPR outperforms the state-of-the-art baselines for similar nodes retrieval across graphs.|个性化 PageRank 作为一种图形化模型，已被证明是网页搜索、推荐等应用中的一种有效解决方案。然而，在现实世界中，个性化 PageRank 的设置通常是动态的，就像不断发展的万维网一样。一方面，过时的 PageRank 解决方案可能是次优的，因为它忽略了进化模式。另一方面，在每个时间戳从零开始求解解决方案会导致昂贵的计算复杂度。因此，本文的目标是在一个完全动态的环境下有效地解决个性化 PageRank 问题，也就是说，个性化 PageRank 公式中的每个组成部分都依赖于时间。为此，我们提出了 EvePPR 方法，该方法可以在完全动态的环境下精确跟踪每个时间戳的个性化 PageRank 解，并从理论和实验上证明了 EvePPR 方法的准确性和时间复杂度。此外，我们应用 EvePPR 来解决动态知识图对齐任务，其中一个完全动态的设置是必要的，但是复杂的。实验表明，EvePPR 在跨图检索相似节点时优于最新的基线。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Everything+Evolves+in+Personalized+PageRank)|0| -|[Incorporating Explicit Subtopics in Personalized Search](https://doi.org/10.1145/3543507.3583488)|Shuting Wang, Zhicheng Dou, Jing Yao, Yujia Zhou, JiRong Wen|Renmin University of China, China; Social Computing Group, Microsoft Research Asia, China; Renmin University of China, China and Engineering Research Center of Next-Generation Intelligent Search and Recommendation, Ministry of Educationf Education, China|The key to personalized search is modeling user intents to tailor returned results for different users. Existing personalized methods mainly focus on learning implicit user interest vectors. In this paper, we propose ExpliPS, a personalized search model that explicitly incorporates query subtopics into personalization. It models the user’s current intent by estimating the user’s preference over the subtopics of the current query and personalizes the results over the weighted subtopics. We think that in such a way, personalized search could be more explainable and stable. Specifically, we first employ a semantic encoder to learn the representations of the user’s historical behaviours. Then with the historical behaviour representations, a subtopic preference encoder is devised to predict the user’s subtopic preferences on the current query. Finally, we rerank the candidates via a subtopic-aware ranker that prioritizes the documents relevant to the user-preferred subtopics. Experimental results show our model ExpliPS outperforms the state-of-the-art personalized web search models with explainable and stable results.|个性化检索的关键是建立用户意图模型，为不同的用户定制返回的结果。现有的个性化方法主要侧重于学习隐式用户兴趣向量。在这篇文章中，我们提出了 expliPS，一个明确地将查询子主题合并到个性化中的个性化检索模型。它通过估计用户对当前查询的子主题的偏好来建模用户的当前意图，并对加权子主题的结果进行个性化处理。我们认为，通过这种方式，个性化检索可以更容易解释，也更稳定。具体来说，我们首先使用一个语义编码器来学习用户历史行为的表示。然后结合历史行为表示，设计了一种子主题偏好编码器来预测用户对当前查询的子主题偏好。最后，我们通过一个子主题感知排名器对候选人进行重新排名，该排名器对与用户首选子主题相关的文档进行优先排序。实验结果表明，该模型的性能优于目前最先进的个性化网络搜索模型，结果具有可解释性和稳定性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Incorporating+Explicit+Subtopics+in+Personalized+Search)|0| +|[Incorporating Explicit Subtopics in Personalized Search](https://doi.org/10.1145/3543507.3583488)|Shuting Wang, Zhicheng Dou, Jing Yao, Yujia Zhou, JiRong Wen|Renmin University of China, China and Engineering Research Center of Next-Generation Intelligent Search and Recommendation, Ministry of Educationf Education, China; Renmin University of China, China; Social Computing Group, Microsoft Research Asia, China|The key to personalized search is modeling user intents to tailor returned results for different users. Existing personalized methods mainly focus on learning implicit user interest vectors. In this paper, we propose ExpliPS, a personalized search model that explicitly incorporates query subtopics into personalization. It models the user’s current intent by estimating the user’s preference over the subtopics of the current query and personalizes the results over the weighted subtopics. We think that in such a way, personalized search could be more explainable and stable. Specifically, we first employ a semantic encoder to learn the representations of the user’s historical behaviours. Then with the historical behaviour representations, a subtopic preference encoder is devised to predict the user’s subtopic preferences on the current query. Finally, we rerank the candidates via a subtopic-aware ranker that prioritizes the documents relevant to the user-preferred subtopics. Experimental results show our model ExpliPS outperforms the state-of-the-art personalized web search models with explainable and stable results.|个性化检索的关键是建立用户意图模型，为不同的用户定制返回的结果。现有的个性化方法主要侧重于学习隐式用户兴趣向量。在这篇文章中，我们提出了 expliPS，一个明确地将查询子主题合并到个性化中的个性化检索模型。它通过估计用户对当前查询的子主题的偏好来建模用户的当前意图，并对加权子主题的结果进行个性化处理。我们认为，通过这种方式，个性化检索可以更容易解释，也更稳定。具体来说，我们首先使用一个语义编码器来学习用户历史行为的表示。然后结合历史行为表示，设计了一种子主题偏好编码器来预测用户对当前查询的子主题偏好。最后，我们通过一个子主题感知排名器对候选人进行重新排名，该排名器对与用户首选子主题相关的文档进行优先排序。实验结果表明，该模型的性能优于目前最先进的个性化网络搜索模型，结果具有可解释性和稳定性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Incorporating+Explicit+Subtopics+in+Personalized+Search)|0| |[Optimizing Feature Set for Click-Through Rate Prediction](https://doi.org/10.1145/3543507.3583545)|Fuyuan Lyu, Xing Tang, Dugang Liu, Liang Chen, Xiuqiang He, Xue Liu|FiT, Tencent, China; McGill University, Canada; Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ), China|Click-through prediction (CTR) models transform features into latent vectors and enumerate possible feature interactions to improve performance based on the input feature set. Therefore, when selecting an optimal feature set, we should consider the influence of both feature and its interaction. However, most previous works focus on either feature field selection or only select feature interaction based on the fixed feature set to produce the feature set. The former restricts search space to the feature field, which is too coarse to determine subtle features. They also do not filter useless feature interactions, leading to higher computation costs and degraded model performance. The latter identifies useful feature interaction from all available features, resulting in many redundant features in the feature set. In this paper, we propose a novel method named OptFS to address these problems. To unify the selection of feature and its interaction, we decompose the selection of each feature interaction into the selection of two correlated features. Such a decomposition makes the model end-to-end trainable given various feature interaction operations. By adopting feature-level search space, we set a learnable gate to determine whether each feature should be within the feature set. Because of the large-scale search space, we develop a learning-by-continuation training scheme to learn such gates. Hence, OptFS generates the feature set only containing features which improve the final prediction results. Experimentally, we evaluate OptFS on three public datasets, demonstrating OptFS can optimize feature sets which enhance the model performance and further reduce both the storage and computational cost.|点击预测(CTR)模型将特征转换为潜在向量，并列举可能的特征交互，以提高基于输入特征集的性能。因此，在选择最优特征集时，应同时考虑特征及其相互作用的影响。然而，以往的工作主要集中在特征字段的选择或者仅仅基于固定特征集选择特征交互来产生特征集。前者将搜索空间限制在特征域内，特征域太粗，无法确定细微的特征。它们也不过滤无用的特征交互，导致更高的计算成本和降低模型性能。后者从所有可用的特征中识别出有用的特征交互，从而导致特征集中的许多冗余特征。在本文中，我们提出了一种新的方法称为 OptFS 来解决这些问题。为了统一特征选择和特征交互，将每个特征交互的选择分解为两个相关特征的选择。这样的分解使得模型在给定各种特征交互操作的情况下可以进行端到端的训练。通过采用特征级搜索空间，我们设置了一个可学习的门来确定每个特征是否应该在特征集中。由于搜索空间较大，我们提出了一种基于连续学习的训练方案来学习这类门。因此，OptFS 生成的特征集仅包含改善最终预测结果的特征。在实验上，我们对三个公共数据集上的 OptFS 进行了评估，结果表明 OptFS 可以优化特征集，从而提高模型的性能，进一步降低存储和计算成本。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Optimizing+Feature+Set+for+Click-Through+Rate+Prediction)|0| -|[Filtered-DiskANN: Graph Algorithms for Approximate Nearest Neighbor Search with Filters](https://doi.org/10.1145/3543507.3583552)|Siddharth Gollapudi, Neel Karia, Varun Sivashankar, Ravishankar Krishnaswamy, Nikit Begwani, Swapnil Raz, Yiyong Lin, Yin Zhang, Neelam Mahapatro, Premkumar Srinivasan, Amit Singh, Harsha Vardhan Simhadri|Columbia University, USA; Microsoft, USA; Microsoft Research, USA; Microsoft, India; Microsoft Research, India|As Approximate Nearest Neighbor Search (ANNS)-based dense retrieval becomes ubiquitous for search and recommendation scenarios, efficiently answering filtered ANNS queries has become a critical requirement. Filtered ANNS queries ask for the nearest neighbors of a query’s embedding from the points in the index that match the query’s labels such as date, price range, language. There has been little prior work on algorithms that use label metadata associated with vector data to build efficient indices for filtered ANNS queries. Consequently, current indices have high search latency or low recall which is not practical in interactive web-scenarios. We present two algorithms with native support for faster and more accurate filtered ANNS queries: one with streaming support, and another based on batch construction. Central to our algorithms is the construction of a graph-structured index which forms connections not only based on the geometry of the vector data, but also the associated label set. On real-world data with natural labels, both algorithms are an order of magnitude or more efficient for filtered queries than the current state of the art algorithms. The generated indices also be queried from an SSD and support thousands of queries per second at over [email protected]|随着基于近似最近邻搜索(ANNS)的密集检索在搜索和推荐场景中的普及，有效地回答经过滤的 ANNS 查询已成为一个关键要求。经过过滤的 ANNS 查询要求查询嵌入的最近邻居从索引中匹配查询标签的点，如日期，价格范围，语言。使用与矢量数据相关联的标签元数据为经过过滤的 ANNS 查询构建高效索引的算法之前几乎没有研究。因此，当前的索引具有较高的搜索延迟或较低的召回率，这在交互式网络场景中是不实用的。我们提出了两个算法与本地支持更快，更准确的过滤 ANNS 查询: 一个流支持，另一个基于批量构造。我们算法的核心是构造一个图结构索引，它不仅根据矢量数据的几何形状，而且根据相关的标签集形成连接。对于带有自然标签的真实世界数据，这两种算法对于过滤查询来说都是一种数量级，或者比目前最先进的算法效率更高。生成的索引也可以从 SSD 查询，并支持每秒在 over [ email protected ]处的数千个查询|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Filtered-DiskANN:+Graph+Algorithms+for+Approximate+Nearest+Neighbor+Search+with+Filters)|0| +|[Filtered-DiskANN: Graph Algorithms for Approximate Nearest Neighbor Search with Filters](https://doi.org/10.1145/3543507.3583552)|Siddharth Gollapudi, Neel Karia, Varun Sivashankar, Ravishankar Krishnaswamy, Nikit Begwani, Swapnil Raz, Yiyong Lin, Yin Zhang, Neelam Mahapatro, Premkumar Srinivasan, Amit Singh, Harsha Vardhan Simhadri|Microsoft, India; Columbia University, USA; Microsoft Research, India; Microsoft Research, USA; Microsoft, USA|As Approximate Nearest Neighbor Search (ANNS)-based dense retrieval becomes ubiquitous for search and recommendation scenarios, efficiently answering filtered ANNS queries has become a critical requirement. Filtered ANNS queries ask for the nearest neighbors of a query’s embedding from the points in the index that match the query’s labels such as date, price range, language. There has been little prior work on algorithms that use label metadata associated with vector data to build efficient indices for filtered ANNS queries. Consequently, current indices have high search latency or low recall which is not practical in interactive web-scenarios. We present two algorithms with native support for faster and more accurate filtered ANNS queries: one with streaming support, and another based on batch construction. Central to our algorithms is the construction of a graph-structured index which forms connections not only based on the geometry of the vector data, but also the associated label set. On real-world data with natural labels, both algorithms are an order of magnitude or more efficient for filtered queries than the current state of the art algorithms. The generated indices also be queried from an SSD and support thousands of queries per second at over [email protected]|随着基于近似最近邻搜索(ANNS)的密集检索在搜索和推荐场景中的普及，有效地回答经过滤的 ANNS 查询已成为一个关键要求。经过过滤的 ANNS 查询要求查询嵌入的最近邻居从索引中匹配查询标签的点，如日期，价格范围，语言。使用与矢量数据相关联的标签元数据为经过过滤的 ANNS 查询构建高效索引的算法之前几乎没有研究。因此，当前的索引具有较高的搜索延迟或较低的召回率，这在交互式网络场景中是不实用的。我们提出了两个算法与本地支持更快，更准确的过滤 ANNS 查询: 一个流支持，另一个基于批量构造。我们算法的核心是构造一个图结构索引，它不仅根据矢量数据的几何形状，而且根据相关的标签集形成连接。对于带有自然标签的真实世界数据，这两种算法对于过滤查询来说都是一种数量级，或者比目前最先进的算法效率更高。生成的索引也可以从 SSD 查询，并支持每秒在 over [ email protected ]处的数千个查询|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Filtered-DiskANN:+Graph+Algorithms+for+Approximate+Nearest+Neighbor+Search+with+Filters)|0| |[P-MMF: Provider Max-min Fairness Re-ranking in Recommender System](https://doi.org/10.1145/3543507.3583296)|Chen Xu, Sirui Chen, Jun Xu, Weiran Shen, Xiao Zhang, Gang Wang, Zhenhua Dong||In this paper, we address the issue of recommending fairly from the aspect of providers, which has become increasingly essential in multistakeholder recommender systems. Existing studies on provider fairness usually focused on designing proportion fairness (PF) metrics that first consider systematic fairness. However, sociological researches show that to make the market more stable, max-min fairness (MMF) is a better metric. The main reason is that MMF aims to improve the utility of the worst ones preferentially, guiding the system to support the providers in weak market positions. When applying MMF to recommender systems, how to balance user preferences and provider fairness in an online recommendation scenario is still a challenging problem. In this paper, we proposed an online re-ranking model named Provider Max-min Fairness Re-ranking (P-MMF) to tackle the problem. Specifically, P-MMF formulates provider fair recommendation as a resource allocation problem, where the exposure slots are considered the resources to be allocated to providers and the max-min fairness is used as the regularizer during the process. We show that the problem can be further represented as a regularized online optimizing problem and solved efficiently in its dual space. During the online re-ranking phase, a momentum gradient descent method is designed to conduct the dynamic re-ranking. Theoretical analysis showed that the regret of P-MMF can be bounded. Experimental results on four public recommender datasets demonstrated that P-MMF can outperformed the state-of-the-art baselines. Experimental results also show that P-MMF can retain small computationally costs on a corpus with the large number of items.|在本文中，我们从提供者的角度讨论了公平推荐的问题，这在多利益相关者推荐系统中已经变得越来越重要。现有的关于提供者公平性的研究通常集中在设计比例公平性(PF)指标时首先考虑系统公平性。然而，社会学研究表明，为了使市场更加稳定，极大极小公平(MMF)是一个更好的衡量标准。主要原因在于，货币市场基金旨在优先提高最差的基金的效用，引导金融体系支持处于弱势市场地位的基金提供者。在将 MMF 应用于推荐系统时，如何在在线推荐场景中平衡用户偏好和提供者公平性仍然是一个具有挑战性的问题。在这篇文章中，我们提出了一个在线重新排序模型——提供者极大极小公平重新排序(P-MMF)来解决这个问题。具体来说，P-MMF 将提供商公平推荐制定为一个资源分配问题，其中风险承担时段被视为将分配给提供商的资源，而极大极小公平则被用作过程中的规范者。证明了该问题可以进一步表示为正则化在线优化问题，并在其对偶空间中有效地求解。在在线重新排名阶段，动量梯度下降法方法被设计用于进行动态重新排名。理论分析表明，P-MMF 的遗憾是有限的。对四个公共推荐数据集的实验结果表明，P-MMF 能够优于最先进的基线。实验结果还表明，P-MMF 能够在项目数量较多的语料库上保持较小的计算开销。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=P-MMF:+Provider+Max-min+Fairness+Re-ranking+in+Recommender+System)|0| -|[Dual Intent Enhanced Graph Neural Network for Session-based New Item Recommendation](https://doi.org/10.1145/3543507.3583526)|Di Jin, Luzhi Wang, Yizhen Zheng, Guojie Song, Fei Jiang, Xiang Li, Wei Lin, Shirui Pan|Department of Data Science and AI, Faculty of IT, Monash University, Australia; College of Intelligence and Computing, Tianjin University, China; Professional, China; School of Information and Communication Technology, Griffith University, Australia; School of Intelligence Science and Technology, Peking University, China; Meituan, China|Recommender systems are essential to various fields, e.g., e-commerce, e-learning, and streaming media. At present, graph neural networks (GNNs) for session-based recommendations normally can only recommend items existing in users' historical sessions. As a result, these GNNs have difficulty recommending items that users have never interacted with (new items), which leads to a phenomenon of information cocoon. Therefore, it is necessary to recommend new items to users. As there is no interaction between new items and users, we cannot include new items when building session graphs for GNN session-based recommender systems. Thus, it is challenging to recommend new items for users when using GNN-based methods. We regard this challenge as '\textbf{G}NN \textbf{S}ession-based \textbf{N}ew \textbf{I}tem \textbf{R}ecommendation (GSNIR)'. To solve this problem, we propose a dual-intent enhanced graph neural network for it. Due to the fact that new items are not tied to historical sessions, the users' intent is difficult to predict. We design a dual-intent network to learn user intent from an attention mechanism and the distribution of historical data respectively, which can simulate users' decision-making process in interacting with a new item. To solve the challenge that new items cannot be learned by GNNs, inspired by zero-shot learning (ZSL), we infer the new item representation in GNN space by using their attributes. By outputting new item probabilities, which contain recommendation scores of the corresponding items, the new items with higher scores are recommended to users. Experiments on two representative real-world datasets show the superiority of our proposed method. The case study from the real-world verifies interpretability benefits brought by the dual-intent module and the new item reasoning module. The code is available at Github: https://github.com/Ee1s/NirGNN|推荐系统对于电子商务、电子学习和流媒体等各个领域都是必不可少的。目前，基于会话推荐的图神经网络(GNN)通常只能推荐用户历史会话中存在的项目。因此，这些 GNN 很难推荐用户从未接触过的项目(新项目) ，从而导致信息茧现象。因此，有必要向用户推荐新项目。由于新项目和用户之间没有交互，所以在为基于 GNN 会话的推荐系统构建会话图时，我们不能包含新项目。因此，在使用基于 GNN 的方法时，向用户推荐新项目是一个挑战。我们将此挑战视为“ textbf { G } NN textbf { S } session-based textbf { N } ew textbf { I } tem textbf { R }推荐(GSNIR)”。为了解决这一问题，我们提出了一种双意图增强的图神经网络。由于新条目不与历史会话相关联，因此很难预测用户的意图。设计了一个双意图网络，分别从注意机制和历史数据分布中学习用户意图，模拟用户在与新项目交互时的决策过程。为了解决 GNN 无法学习新项目的问题，受零点学习(ZSL)的启发，我们利用 GNN 空间中新项目的属性来推断新项目的表示。通过输出新项目概率，其中包含相应项目的推荐分数，新项目的分数越高，推荐给用户。在两个具有代表性的实际数据集上的实验表明了该方法的优越性。通过实际案例分析，验证了双意图模块和新的项目推理模块所带来的可解释性优势。代码可以在 Github: https://Github.com/ee1s/nirgnn 上找到|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Dual+Intent+Enhanced+Graph+Neural+Network+for+Session-based+New+Item+Recommendation)|0| -|[Cross-domain recommendation via user interest alignment](https://doi.org/10.1145/3543507.3583263)|Chuang Zhao, Hongke Zhao, Ming HE, Jian Zhang, Jianping Fan|College of Management and Economics, Tianjin University, China; College of Management and Economics, Tianjin University, China and AI Lab at Lenovo Research, China; School of Cyberspace Security, Hangzhou Dianzi University, China; AI Lab at Lenovo Research, China|Cross-domain recommendation aims to leverage knowledge from multiple domains to alleviate the data sparsity and cold-start problems in traditional recommender systems. One popular paradigm is to employ overlapping user representations to establish domain connections, thereby improving recommendation performance in all scenarios. Nevertheless, the general practice of this approach is to train user embeddings in each domain separately and then aggregate them in a plain manner, often ignoring potential cross-domain similarities between users and items. Furthermore, considering that their training objective is recommendation task-oriented without specific regularizations, the optimized embeddings disregard the interest alignment among user's views, and even violate the user's original interest distribution. To address these challenges, we propose a novel cross-domain recommendation framework, namely COAST, to improve recommendation performance on dual domains by perceiving the cross-domain similarity between entities and aligning user interests. Specifically, we first construct a unified cross-domain heterogeneous graph and redefine the message passing mechanism of graph convolutional networks to capture high-order similarity of users and items across domains. Targeted at user interest alignment, we develop deep insights from two more fine-grained perspectives of user-user and user-item interest invariance across domains by virtue of affluent unsupervised and semantic signals. We conduct intensive experiments on multiple tasks, constructed from two large recommendation data sets. Extensive results show COAST consistently and significantly outperforms state-of-the-art cross-domain recommendation algorithms as well as classic single-domain recommendation methods.|跨域推荐的目的是利用来自多个域的知识来缓解传统推荐系统中的数据稀疏和冷启动问题。一个流行的范例是使用重叠的用户表示来建立域连接，从而在所有场景中提高推荐性能。然而，这种方法的一般实践是分别训练每个域中的用户嵌入，然后以简单的方式聚合它们，通常忽略用户和项目之间潜在的跨域相似性。此外，考虑到其训练目标是面向推荐任务的，没有具体的规范化，优化嵌入无视用户视图之间的兴趣一致性，甚至违背了用户原有的兴趣分布。为了应对这些挑战，我们提出了一种新的跨域推荐框架，即 COAST，通过感知实体之间的跨域相似性和调整用户兴趣来提高双域推荐的性能。具体来说，我们首先构建一个统一的跨域异构图，重新定义图卷积网络的消息传递机制，以获取跨域用户和项目的高阶相似性。针对用户兴趣对齐，我们从用户-用户和用户项目兴趣不变性的两个更细粒度的角度，通过丰富的无监督和语义信号，开发深刻的见解。我们对两个大型推荐数据集构建的多个任务进行了深入的实验。广泛的结果表明，COAST 始终如一地显著优于最先进的跨域推荐算法和经典的单域推荐方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Cross-domain+recommendation+via+user+interest+alignment)|0| +|[Dual Intent Enhanced Graph Neural Network for Session-based New Item Recommendation](https://doi.org/10.1145/3543507.3583526)|Di Jin, Luzhi Wang, Yizhen Zheng, Guojie Song, Fei Jiang, Xiang Li, Wei Lin, Shirui Pan|School of Information and Communication Technology, Griffith University, Australia; Professional, China; College of Intelligence and Computing, Tianjin University, China; School of Intelligence Science and Technology, Peking University, China; Meituan, China; Department of Data Science and AI, Faculty of IT, Monash University, Australia|Recommender systems are essential to various fields, e.g., e-commerce, e-learning, and streaming media. At present, graph neural networks (GNNs) for session-based recommendations normally can only recommend items existing in users' historical sessions. As a result, these GNNs have difficulty recommending items that users have never interacted with (new items), which leads to a phenomenon of information cocoon. Therefore, it is necessary to recommend new items to users. As there is no interaction between new items and users, we cannot include new items when building session graphs for GNN session-based recommender systems. Thus, it is challenging to recommend new items for users when using GNN-based methods. We regard this challenge as '\textbf{G}NN \textbf{S}ession-based \textbf{N}ew \textbf{I}tem \textbf{R}ecommendation (GSNIR)'. To solve this problem, we propose a dual-intent enhanced graph neural network for it. Due to the fact that new items are not tied to historical sessions, the users' intent is difficult to predict. We design a dual-intent network to learn user intent from an attention mechanism and the distribution of historical data respectively, which can simulate users' decision-making process in interacting with a new item. To solve the challenge that new items cannot be learned by GNNs, inspired by zero-shot learning (ZSL), we infer the new item representation in GNN space by using their attributes. By outputting new item probabilities, which contain recommendation scores of the corresponding items, the new items with higher scores are recommended to users. Experiments on two representative real-world datasets show the superiority of our proposed method. The case study from the real-world verifies interpretability benefits brought by the dual-intent module and the new item reasoning module. The code is available at Github: https://github.com/Ee1s/NirGNN|推荐系统对于电子商务、电子学习和流媒体等各个领域都是必不可少的。目前，基于会话推荐的图神经网络(GNN)通常只能推荐用户历史会话中存在的项目。因此，这些 GNN 很难推荐用户从未接触过的项目(新项目) ，从而导致信息茧现象。因此，有必要向用户推荐新项目。由于新项目和用户之间没有交互，所以在为基于 GNN 会话的推荐系统构建会话图时，我们不能包含新项目。因此，在使用基于 GNN 的方法时，向用户推荐新项目是一个挑战。我们将此挑战视为“ textbf { G } NN textbf { S } session-based textbf { N } ew textbf { I } tem textbf { R }推荐(GSNIR)”。为了解决这一问题，我们提出了一种双意图增强的图神经网络。由于新条目不与历史会话相关联，因此很难预测用户的意图。设计了一个双意图网络，分别从注意机制和历史数据分布中学习用户意图，模拟用户在与新项目交互时的决策过程。为了解决 GNN 无法学习新项目的问题，受零点学习(ZSL)的启发，我们利用 GNN 空间中新项目的属性来推断新项目的表示。通过输出新项目概率，其中包含相应项目的推荐分数，新项目的分数越高，推荐给用户。在两个具有代表性的实际数据集上的实验表明了该方法的优越性。通过实际案例分析，验证了双意图模块和新的项目推理模块所带来的可解释性优势。代码可以在 Github: https://Github.com/ee1s/nirgnn 上找到|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Dual+Intent+Enhanced+Graph+Neural+Network+for+Session-based+New+Item+Recommendation)|0| +|[Cross-domain recommendation via user interest alignment](https://doi.org/10.1145/3543507.3583263)|Chuang Zhao, Hongke Zhao, Ming HE, Jian Zhang, Jianping Fan|College of Management and Economics, Tianjin University, China and AI Lab at Lenovo Research, China; School of Cyberspace Security, Hangzhou Dianzi University, China; College of Management and Economics, Tianjin University, China; AI Lab at Lenovo Research, China|Cross-domain recommendation aims to leverage knowledge from multiple domains to alleviate the data sparsity and cold-start problems in traditional recommender systems. One popular paradigm is to employ overlapping user representations to establish domain connections, thereby improving recommendation performance in all scenarios. Nevertheless, the general practice of this approach is to train user embeddings in each domain separately and then aggregate them in a plain manner, often ignoring potential cross-domain similarities between users and items. Furthermore, considering that their training objective is recommendation task-oriented without specific regularizations, the optimized embeddings disregard the interest alignment among user's views, and even violate the user's original interest distribution. To address these challenges, we propose a novel cross-domain recommendation framework, namely COAST, to improve recommendation performance on dual domains by perceiving the cross-domain similarity between entities and aligning user interests. Specifically, we first construct a unified cross-domain heterogeneous graph and redefine the message passing mechanism of graph convolutional networks to capture high-order similarity of users and items across domains. Targeted at user interest alignment, we develop deep insights from two more fine-grained perspectives of user-user and user-item interest invariance across domains by virtue of affluent unsupervised and semantic signals. We conduct intensive experiments on multiple tasks, constructed from two large recommendation data sets. Extensive results show COAST consistently and significantly outperforms state-of-the-art cross-domain recommendation algorithms as well as classic single-domain recommendation methods.|跨域推荐的目的是利用来自多个域的知识来缓解传统推荐系统中的数据稀疏和冷启动问题。一个流行的范例是使用重叠的用户表示来建立域连接，从而在所有场景中提高推荐性能。然而，这种方法的一般实践是分别训练每个域中的用户嵌入，然后以简单的方式聚合它们，通常忽略用户和项目之间潜在的跨域相似性。此外，考虑到其训练目标是面向推荐任务的，没有具体的规范化，优化嵌入无视用户视图之间的兴趣一致性，甚至违背了用户原有的兴趣分布。为了应对这些挑战，我们提出了一种新的跨域推荐框架，即 COAST，通过感知实体之间的跨域相似性和调整用户兴趣来提高双域推荐的性能。具体来说，我们首先构建一个统一的跨域异构图，重新定义图卷积网络的消息传递机制，以获取跨域用户和项目的高阶相似性。针对用户兴趣对齐，我们从用户-用户和用户项目兴趣不变性的两个更细粒度的角度，通过丰富的无监督和语义信号，开发深刻的见解。我们对两个大型推荐数据集构建的多个任务进行了深入的实验。广泛的结果表明，COAST 始终如一地显著优于最先进的跨域推荐算法和经典的单域推荐方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Cross-domain+recommendation+via+user+interest+alignment)|0| |[A Semantic Partitioning Method for Large-Scale Training of Knowledge Graph Embeddings](https://doi.org/10.1145/3543873.3587537)|Yuhe Bai|Sorbonne University, France|In recent years, knowledge graph embeddings have achieved great success. Many methods have been proposed and achieved state-of-the-art results in various tasks. However, most of the current methods present one or more of the following problems: (i) They only consider fact triplets, while ignoring the ontology information of knowledge graphs. (ii) The obtained embeddings do not contain much semantic information. Therefore, using these embeddings for semantic tasks is problematic. (iii) They do not enable large-scale training. In this paper, we propose a new algorithm that incorporates the ontology of knowledge graphs and partitions the knowledge graph based on classes to include more semantic information for parallel training of large-scale knowledge graph embeddings. Our preliminary results show that our algorithm performs well on several popular benchmarks.|近年来，知识图嵌入技术取得了很大的成功。已经提出了许多方法，并在各种任务中取得了最新的成果。然而，目前的大多数方法都存在以下一个或多个问题: (i)它们只考虑事实三元组，而忽略了知识图的本体信息。(ii)所得的嵌入资料并无太多语义信息。因此，将这些嵌入用于语义任务是有问题的。(iii)不能进行大规模培训。在本文中，我们提出了一个新的算法，它结合了知识图的本体和基于类的知识图划分，以包括更多的语义信息并行训练大规模的知识图嵌入。我们的初步结果表明，我们的算法在几个流行的基准测试中表现良好。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Semantic+Partitioning+Method+for+Large-Scale+Training+of+Knowledge+Graph+Embeddings)|0| |[Intent-Aware Propensity Estimation via Click Pattern Stratification](https://doi.org/10.1145/3543873.3587610)|Ehsan Ebrahimzadeh, Alex Cozzi, Abraham Bagherjeiran|Search Ranking and Monetization, eBay, USA|Counterfactual learning to rank via inverse propensity weighting is the most popular approach to train ranking models using biased implicit user feedback from logged search data. Standard click propensity estimation techniques rely on simple models of user browsing behavior that primarily account for the attributes of the presentation context that affect whether the relevance of an item to the search context is observed. Most notably, the inherent effect of the listwise presentation of the items on users’ propensity for engagement is captured in the position of the presented items on the search result page. In this work, we enrich this position bias based click propensity model by proposing an observation model that further incorporates the underlying search intent, as reflected in the user’s click pattern in the search context. Our approach does not require an intent prediction model based on the content of the search context. Instead, we rely on a simple, yet effective, non-causal estimate of the user’s browsing intent from the number of click events in the search context. We empirically characterize the distinct rank decay patterns of the estimated click propensities in the characterized intent classes. In particular, we demonstrate a sharper decay of click propensities in top ranks for the intent class identified by sparse user clicks and the higher likelihood of observing clicks in lower ranks for the intent class identified by higher number of user clicks. We show that the proposed intent-aware propensity estimation technique helps with training ranking models with more effective personalization and generalization power through empirical results for a ranking task in a major e-commerce platform.|通过逆倾向加权反事实学习排序是最流行的方法来训练排序模型使用有偏见的隐式用户反馈的日志搜索数据。标准的点击倾向评估技术依赖于用户浏览行为的简单模型，这些模型主要解释了表示上下文的属性，这些属性影响了项目与搜索上下文的相关性是否被观察到。最值得注意的是，在搜索结果页面上呈现的项目的位置捕捉到了项目列表方式对用户参与倾向的内在影响。在这项工作中，我们丰富了这个基于位置偏差的点击倾向模型，通过提出一个观察模型，进一步结合潜在的搜索意图，如反映在用户的点击模式在搜索上下文。我们的方法不需要基于搜索上下文内容的意图预测模型。相反，我们依赖于从搜索上下文中的点击事件数量对用户的浏览意图进行简单而有效的非因果估计。我们经验性地描述了特征意图类中估计的点击倾向的不同秩衰减模式。特别是，我们证明了通过稀疏的用户点击识别的意图类别的顶级点击倾向的更强烈的衰减，以及通过更高数量的用户点击识别的意图类别在较低级别中观察到点击的可能性更高。通过对一个大型电子商务平台的排序任务进行实证分析，我们发现提出的意图感知倾向估计技术有助于训练排序模型，使其具有更有效的个性化和泛化能力。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Intent-Aware+Propensity+Estimation+via+Click+Pattern+Stratification)|0| -|[Disentangling Degree-related Biases and Interest for Out-of-Distribution Generalized Directed Network Embedding](https://doi.org/10.1145/3543507.3583271)|Hyunsik Yoo, YeonChang Lee, Kijung Shin, SangWook Kim|Georgia Institute of Technology, USA; Korea Advanced Institute of Science and Technology, Republic of Korea; Hanyang University, Republic of Korea|The goal of directed network embedding is to represent the nodes in a given directed network as embeddings that preserve the asymmetric relationships between nodes. While a number of directed network embedding methods have been proposed, we empirically show that the existing methods lack out-of-distribution generalization abilities against degree-related distributional shifts. To mitigate this problem, we propose ODIN (Out-of-Distribution Generalized Directed Network Embedding), a new directed NE method where we model multiple factors in the formation of directed edges. Then, for each node, ODIN learns multiple embeddings, each of which preserves its corresponding factor, by disentangling interest factors and biases related to in- and out-degrees of nodes. Our experiments on four real-world directed networks demonstrate that disentangling multiple factors enables ODIN to yield out-of-distribution generalized embeddings that are consistently effective under various degrees of shifts in degree distributions. Specifically, ODIN universally outperforms 9 state-of-the-art competitors in 2 LP tasks on 4 real-world datasets under both identical distribution (ID) and non-ID settings. The code is available at https://github.com/hsyoo32/odin.|有向网络嵌入的目的是将给定有向网络中的节点表示为保持节点间不对称关系的嵌入。虽然已经提出了一些有向网络嵌入方法，但是实验表明，现有的方法缺乏对度相关分布偏移的分布外泛化能力。为了解决这一问题，我们提出了一种新的有向网络嵌入方法 ODIN (Out-of-Distribution Generalization Directed Network Embedding) ，该方法对有向边的形成过程中的多个因素进行建模。然后，对于每个节点，ODIN 通过分离与节点内外度相关的兴趣因子和偏差来学习多个嵌入，每个嵌入保留相应的因子。我们在四个真实世界的定向网络上的实验表明，解开多个因素使 ODIN 能够产生分布外的广义嵌入，在度分布的不同程度的转移下一致有效。具体而言，ODIN 在4个真实世界数据集的2个 LP 任务中，在相同的分布(ID)和非 ID 设置下，普遍优于9个最先进的竞争对手。密码可在 https://github.com/hsyoo32/odin 查阅。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Disentangling+Degree-related+Biases+and+Interest+for+Out-of-Distribution+Generalized+Directed+Network+Embedding)|0| -|[Fine-tuning Partition-aware Item Similarities for Efficient and Scalable Recommendation](https://doi.org/10.1145/3543507.3583240)|Tianjun Wei, Jianghong Ma, Tommy W. S. Chow|Harbin Institute of Technology, China; City University of Hong Kong, Hong Kong|Collaborative filtering (CF) is widely searched in recommendation with various types of solutions. Recent success of Graph Convolution Networks (GCN) in CF demonstrates the effectiveness of modeling high-order relationships through graphs, while repetitive graph convolution and iterative batch optimization limit their efficiency. Instead, item similarity models attempt to construct direct relationships through efficient interaction encoding. Despite their great performance, the growing item numbers result in quadratic growth in similarity modeling process, posing critical scalability problems. In this paper, we investigate the graph sampling strategy adopted in latest GCN model for efficiency improving, and identify the potential item group structure in the sampled graph. Based on this, we propose a novel item similarity model which introduces graph partitioning to restrict the item similarity modeling within each partition. Specifically, we show that the spectral information of the original graph is well in preserving global-level information. Then, it is added to fine-tune local item similarities with a new data augmentation strategy acted as partition-aware prior knowledge, jointly to cope with the information loss brought by partitioning. Experiments carried out on 4 datasets show that the proposed model outperforms state-of-the-art GCN models with 10x speed-up and item similarity models with 95\% parameter storage savings.|协同过滤(CF)在推荐中被广泛搜索，提供了各种类型的解决方案。最近，图卷积网络(GCN)在 CF 中的成功证明了通过图建立高阶关系的有效性，而重复图卷积和迭代批处理优化限制了它们的效率。相反，项目相似性模型试图通过有效的交互编码来构建直接关系。尽管它们具有很好的性能，但是在相似性建模过程中，项目数量的增长会导致二次增长，从而产生关键的可扩展性问题。本文研究了最新 GCN 模型中为提高效率而采用的图抽样策略，并识别了抽样图中潜在的项目组结构。在此基础上，提出了一种新的项目相似度模型，该模型引入图划分来约束项目相似度建模。具体地说，我们证明了原始图的光谱信息在保持全局水平信息方面是很好的。然后加入一种新的数据增强策略作为分区感知的先验知识，对局部项相似性进行微调，共同应对分区带来的信息丢失。在4个数据集上进行的实验表明，该模型比最新的 GCN 模型具有10倍的加速度和项目相似度，节省了95% 的参数存储空间。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Fine-tuning+Partition-aware+Item+Similarities+for+Efficient+and+Scalable+Recommendation)|0| -|[Multi-Behavior Recommendation with Cascading Graph Convolution Networks](https://doi.org/10.1145/3543507.3583439)|Zhiyong Cheng, Sai Han, Fan Liu, Lei Zhu, Zan Gao, Yuxin Peng|School of Information Science and Engineering, Shandong Normal University, China; School of Computing, National University of Singapore, Singapore; Shandong Artificial Intelligence Institute, Qilu University of Technology (Shandong Academy of Sciences), China; Wangxuan Institute of Computer Technology, Peking University, China and Peng Cheng Laboratory, China|Multi-behavior recommendation, which exploits auxiliary behaviors (e.g., click and cart) to help predict users' potential interactions on the target behavior (e.g., buy), is regarded as an effective way to alleviate the data sparsity or cold-start issues in recommendation. Multi-behaviors are often taken in certain orders in real-world applications (e.g., click>cart>buy). In a behavior chain, a latter behavior usually exhibits a stronger signal of user preference than the former one does. Most existing multi-behavior models fail to capture such dependencies in a behavior chain for embedding learning. In this work, we propose a novel multi-behavior recommendation model with cascading graph convolution networks (named MB-CGCN). In MB-CGCN, the embeddings learned from one behavior are used as the input features for the next behavior's embedding learning after a feature transformation operation. In this way, our model explicitly utilizes the behavior dependencies in embedding learning. Experiments on two benchmark datasets demonstrate the effectiveness of our model on exploiting multi-behavior data. It outperforms the best baseline by 33.7% and 35.9% on average over the two datasets in terms of Recall@10 and NDCG@10, respectively.|多行为推荐利用辅助行为(如点击和购物车)来帮助预测用户在目标行为(如购买)上的潜在交互，被认为是缓解推荐中数据稀疏或冷启动问题的有效方法。在实际应用程序中，多行为通常按照特定的顺序执行(例如，单击 > 购物车 > 购买)。在行为链中，后一种行为通常比前一种行为表现出更强的用户偏好信号。大多数现有的多行为模型无法在嵌入式学习的行为链中捕获这种依赖关系。提出了一种新的具有级联图卷积网络的多行为推荐模型(MB-CGCN)。在 MB-CGCN 中，从一个行为中学习的嵌入作为特征转换操作后下一个行为的嵌入学习的输入特征。通过这种方式，我们的模型明确地利用了嵌入式学习中的行为依赖。在两个基准数据集上的实验证明了该模型对多行为数据的有效性。以 Recall@10和 NDCG@10计算，该方法比最佳基线的平均值分别高出33.7% 和35.9% 。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Multi-Behavior+Recommendation+with+Cascading+Graph+Convolution+Networks)|0| +|[Disentangling Degree-related Biases and Interest for Out-of-Distribution Generalized Directed Network Embedding](https://doi.org/10.1145/3543507.3583271)|Hyunsik Yoo, YeonChang Lee, Kijung Shin, SangWook Kim|Hanyang University, Republic of Korea; Korea Advanced Institute of Science and Technology, Republic of Korea; Georgia Institute of Technology, USA|The goal of directed network embedding is to represent the nodes in a given directed network as embeddings that preserve the asymmetric relationships between nodes. While a number of directed network embedding methods have been proposed, we empirically show that the existing methods lack out-of-distribution generalization abilities against degree-related distributional shifts. To mitigate this problem, we propose ODIN (Out-of-Distribution Generalized Directed Network Embedding), a new directed NE method where we model multiple factors in the formation of directed edges. Then, for each node, ODIN learns multiple embeddings, each of which preserves its corresponding factor, by disentangling interest factors and biases related to in- and out-degrees of nodes. Our experiments on four real-world directed networks demonstrate that disentangling multiple factors enables ODIN to yield out-of-distribution generalized embeddings that are consistently effective under various degrees of shifts in degree distributions. Specifically, ODIN universally outperforms 9 state-of-the-art competitors in 2 LP tasks on 4 real-world datasets under both identical distribution (ID) and non-ID settings. The code is available at https://github.com/hsyoo32/odin.|有向网络嵌入的目的是将给定有向网络中的节点表示为保持节点间不对称关系的嵌入。虽然已经提出了一些有向网络嵌入方法，但是实验表明，现有的方法缺乏对度相关分布偏移的分布外泛化能力。为了解决这一问题，我们提出了一种新的有向网络嵌入方法 ODIN (Out-of-Distribution Generalization Directed Network Embedding) ，该方法对有向边的形成过程中的多个因素进行建模。然后，对于每个节点，ODIN 通过分离与节点内外度相关的兴趣因子和偏差来学习多个嵌入，每个嵌入保留相应的因子。我们在四个真实世界的定向网络上的实验表明，解开多个因素使 ODIN 能够产生分布外的广义嵌入，在度分布的不同程度的转移下一致有效。具体而言，ODIN 在4个真实世界数据集的2个 LP 任务中，在相同的分布(ID)和非 ID 设置下，普遍优于9个最先进的竞争对手。密码可在 https://github.com/hsyoo32/odin 查阅。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Disentangling+Degree-related+Biases+and+Interest+for+Out-of-Distribution+Generalized+Directed+Network+Embedding)|0| +|[Fine-tuning Partition-aware Item Similarities for Efficient and Scalable Recommendation](https://doi.org/10.1145/3543507.3583240)|Tianjun Wei, Jianghong Ma, Tommy W. S. Chow|City University of Hong Kong, Hong Kong; Harbin Institute of Technology, China|Collaborative filtering (CF) is widely searched in recommendation with various types of solutions. Recent success of Graph Convolution Networks (GCN) in CF demonstrates the effectiveness of modeling high-order relationships through graphs, while repetitive graph convolution and iterative batch optimization limit their efficiency. Instead, item similarity models attempt to construct direct relationships through efficient interaction encoding. Despite their great performance, the growing item numbers result in quadratic growth in similarity modeling process, posing critical scalability problems. In this paper, we investigate the graph sampling strategy adopted in latest GCN model for efficiency improving, and identify the potential item group structure in the sampled graph. Based on this, we propose a novel item similarity model which introduces graph partitioning to restrict the item similarity modeling within each partition. Specifically, we show that the spectral information of the original graph is well in preserving global-level information. Then, it is added to fine-tune local item similarities with a new data augmentation strategy acted as partition-aware prior knowledge, jointly to cope with the information loss brought by partitioning. Experiments carried out on 4 datasets show that the proposed model outperforms state-of-the-art GCN models with 10x speed-up and item similarity models with 95\% parameter storage savings.|协同过滤(CF)在推荐中被广泛搜索，提供了各种类型的解决方案。最近，图卷积网络(GCN)在 CF 中的成功证明了通过图建立高阶关系的有效性，而重复图卷积和迭代批处理优化限制了它们的效率。相反，项目相似性模型试图通过有效的交互编码来构建直接关系。尽管它们具有很好的性能，但是在相似性建模过程中，项目数量的增长会导致二次增长，从而产生关键的可扩展性问题。本文研究了最新 GCN 模型中为提高效率而采用的图抽样策略，并识别了抽样图中潜在的项目组结构。在此基础上，提出了一种新的项目相似度模型，该模型引入图划分来约束项目相似度建模。具体地说，我们证明了原始图的光谱信息在保持全局水平信息方面是很好的。然后加入一种新的数据增强策略作为分区感知的先验知识，对局部项相似性进行微调，共同应对分区带来的信息丢失。在4个数据集上进行的实验表明，该模型比最新的 GCN 模型具有10倍的加速度和项目相似度，节省了95% 的参数存储空间。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Fine-tuning+Partition-aware+Item+Similarities+for+Efficient+and+Scalable+Recommendation)|0| +|[Multi-Behavior Recommendation with Cascading Graph Convolution Networks](https://doi.org/10.1145/3543507.3583439)|Zhiyong Cheng, Sai Han, Fan Liu, Lei Zhu, Zan Gao, Yuxin Peng|Wangxuan Institute of Computer Technology, Peking University, China and Peng Cheng Laboratory, China; Shandong Artificial Intelligence Institute, Qilu University of Technology (Shandong Academy of Sciences), China; School of Computing, National University of Singapore, Singapore; School of Information Science and Engineering, Shandong Normal University, China|Multi-behavior recommendation, which exploits auxiliary behaviors (e.g., click and cart) to help predict users' potential interactions on the target behavior (e.g., buy), is regarded as an effective way to alleviate the data sparsity or cold-start issues in recommendation. Multi-behaviors are often taken in certain orders in real-world applications (e.g., click>cart>buy). In a behavior chain, a latter behavior usually exhibits a stronger signal of user preference than the former one does. Most existing multi-behavior models fail to capture such dependencies in a behavior chain for embedding learning. In this work, we propose a novel multi-behavior recommendation model with cascading graph convolution networks (named MB-CGCN). In MB-CGCN, the embeddings learned from one behavior are used as the input features for the next behavior's embedding learning after a feature transformation operation. In this way, our model explicitly utilizes the behavior dependencies in embedding learning. Experiments on two benchmark datasets demonstrate the effectiveness of our model on exploiting multi-behavior data. It outperforms the best baseline by 33.7% and 35.9% on average over the two datasets in terms of Recall@10 and NDCG@10, respectively.|多行为推荐利用辅助行为(如点击和购物车)来帮助预测用户在目标行为(如购买)上的潜在交互，被认为是缓解推荐中数据稀疏或冷启动问题的有效方法。在实际应用程序中，多行为通常按照特定的顺序执行(例如，单击 > 购物车 > 购买)。在行为链中，后一种行为通常比前一种行为表现出更强的用户偏好信号。大多数现有的多行为模型无法在嵌入式学习的行为链中捕获这种依赖关系。提出了一种新的具有级联图卷积网络的多行为推荐模型(MB-CGCN)。在 MB-CGCN 中，从一个行为中学习的嵌入作为特征转换操作后下一个行为的嵌入学习的输入特征。通过这种方式，我们的模型明确地利用了嵌入式学习中的行为依赖。在两个基准数据集上的实验证明了该模型对多行为数据的有效性。以 Recall@10和 NDCG@10计算，该方法比最佳基线的平均值分别高出33.7% 和35.9% 。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Multi-Behavior+Recommendation+with+Cascading+Graph+Convolution+Networks)|0| |[Cross-domain Recommendation with Behavioral Importance Perception](https://doi.org/10.1145/3543507.3583494)|Hong Chen, Xin Wang, Ruobing Xie, Yuwei Zhou, Wenwu Zhu|WeChat Search Application Department, Tencent, China; Department of Computer Science and Technology, Tsinghua University, China|Cross-domain recommendation (CDR) aims to leverage the source domain information to provide better recommendation for the target domain, which is widely adopted in recommender systems to alleviate the data sparsity and cold-start problems. However, existing CDR methods mostly focus on designing effective model architectures to transfer the source domain knowledge, ignoring the behavior-level effect during the loss optimization process, where behaviors regarding different aspects in the source domain may have different importance for the CDR model optimization. The ignorance of the behavior-level effect will cause the carefully designed model architectures ending up with sub-optimal parameters, which limits the recommendation performance. To tackle the problem, we propose a generic behavioral importance-aware optimization framework for cross-domain recommendation (BIAO). Specifically, we propose a behavioral perceptron which predicts the importance of each source behavior according to the corresponding item’s global impact and local user-specific impact. The joint optimization process of the CDR model and the behavioral perceptron is formulated as a bi-level optimization problem. In the lower optimization, only the CDR model is updated with weighted source behavior loss and the target domain loss, while in the upper optimization, the behavioral perceptron is updated with implicit gradient from a developing dataset obtained through the proposed reorder-and-reuse strategy. Extensive experiments show that our proposed optimization framework consistently improves the performance of different cross-domain recommendation models in 7 cross-domain scenarios, demonstrating that our method can serve as a generic and powerful tool for cross-domain recommendation1.|跨域推荐(CDR)是利用源域信息为目标域提供更好的推荐，在推荐系统中被广泛采用以缓解数据稀疏和冷启动问题。然而，现有的 CDR 方法大多侧重于设计有效的模型结构来传递源域知识，忽略了损失优化过程中的行为级效应，其中源域中不同方面的行为对 CDR 模型优化的重要性不同。对行为级效应的忽视将导致精心设计的模型结构最终得到次优参数，从而限制了推荐性能。为了解决这个问题，我们提出了一个通用的跨域推荐的行为重要性感知优化框架(BIAO)。具体来说，我们提出了一种行为感知器，它根据相应项目的全局影响和局部用户特定影响来预测每个源行为的重要性。CDR 模型和行为感知器的联合优化过程是一个双层次的最佳化问题。在下层优化中，只对 CDR 模型进行加权源行为丢失和目标域丢失的更新，而在上层优化中，行为感知器通过提出的重排序和重用策略从一个正在发展的数据集中获得隐式梯度更新。大量的实验表明，我们提出的优化框架在7个跨领域场景中始终如一地提高了不同跨领域推荐模型的性能，表明我们的方法可以作为跨领域推荐的一个通用和强大的工具1。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Cross-domain+Recommendation+with+Behavioral+Importance+Perception)|0| |[Multi-Lingual Multi-Partite Product Title Matching](https://doi.org/10.1145/3543873.3587322)|HuanLin Tay, WeiJie Tay, Hady W. Lauw|Singapore Management University, Singapore|In a globalized marketplace, one could access products or services from almost anywhere. However, resolving which product in one language corresponds to another product in a different language remains an under-explored problem. We explore this from two perspectives. First, given two products of different languages, how to assess their similarity that could signal a potential match. Second, given products from various languages, how to arrive at a multi-partite clustering that respects cardinality constraints efficiently. We describe algorithms for each perspective and integrate them into a promising solution validated on real-world datasets.|在一个全球化的市场中，人们几乎可以从任何地方获得产品或服务。然而，解决一种语言中的哪种产品对应于另一种语言中的另一种产品仍然是一个尚未得到充分探讨的问题。我们从两个角度来探讨这个问题。首先，给定两种不同语言的产品，如何评估它们的相似性，这可能标志着潜在的匹配。其次，给定来自不同语言的产品，如何有效地得到一个尊重基数约束的多部分聚类。我们描述每个视角的算法，并将它们集成到一个在真实世界数据集上验证的有希望的解决方案中。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Multi-Lingual+Multi-Partite+Product+Title+Matching)|0| |[Multi-interest Recommendation on Shopping for Others](https://doi.org/10.1145/3543873.3587341)|Shuang Li, Yaokun Liu, Xiaowang Zhang, Yuexian Hou, Zhiyong Feng|Tianjin University, China, China; Tianjin University, China|Existing recommendation methods based on multi-interest frameworks effectively model users from multiple aspects to represent complex user interests. However, more research still needs to be done on the behavior of users shopping for others. We propose a Multi-Demander Recommendation (MDR) model to learn different people’s interests from a sequence of actions. We first decouple the feature embeddings of items to learn the static preferences of different demanders. Next, a weighted directed global graph is constructed to model the associations among item categories. We partition short sequences by time intervals and look up category embeddings from the graph to capture dynamic intents. Finally, preferences and intentions are combined with learning the interests of different demanders. The conducted experiments demonstrate that our model improves the accuracy of recommendations.|现有的基于多兴趣框架的推荐方法能够有效地从多个方面对用户进行建模，以表达复杂的用户兴趣。然而，还需要对用户为他人购物的行为进行更多的研究。我们提出了一个多需求推荐(MDR)模型，从一系列的行动中了解不同人的兴趣。首先解耦项目的特征嵌入，学习不同需求者的静态偏好。然后，构造一个加权有向全局图来模拟项目类别之间的关联。我们根据时间间隔对短序列进行划分，并从图中查找类别嵌入以捕获动态意图。最后，将偏好和意图与学习不同需求者的兴趣结合起来。实验表明，该模型提高了推荐的准确性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Multi-interest+Recommendation+on+Shopping+for+Others)|0| -|[Explicit and Implicit Semantic Ranking Framework](https://doi.org/10.1145/3543873.3584621)|Xiaofeng Zhu, Thomas Lin, Vishal Anand, Matthew Calderwood, Eric ClausenBrown, Gord Lueck, Wenwai Yim, Cheng Wu|Microsoft Corporation, USA; Nuance Communications, USA|The core challenge in numerous real-world applications is to match an inquiry to the best document from a mutable and finite set of candidates. Existing industry solutions, especially latency-constrained services, often rely on similarity algorithms that sacrifice quality for speed. In this paper we introduce a generic semantic learning-to-rank framework, Self-training Semantic Cross-attention Ranking (sRank). This transformer-based framework uses linear pairwise loss with mutable training batch sizes and achieves quality gains and high efficiency, and has been applied effectively to show gains on two industry tasks at Microsoft over real-world large-scale data sets: Smart Reply (SR) and Ambient Clinical Intelligence (ACI). In Smart Reply, $sRank$ assists live customers with technical support by selecting the best reply from predefined solutions based on consumer and support agent messages. It achieves 11.7% gain in offline top-one accuracy on the SR task over the previous system, and has enabled 38.7% time reduction in composing messages in telemetry recorded since its general release in January 2021. In the ACI task, sRank selects relevant historical physician templates that serve as guidance for a text summarization model to generate higher quality medical notes. It achieves 35.5% top-one accuracy gain, along with 46% relative ROUGE-L gain in generated medical notes.|在许多实际应用程序中的核心挑战是将查询与来自一组可变且有限的候选文档的最佳文档匹配。现有的行业解决方案，尤其是延迟受限的服务，通常依赖于牺牲质量以提高速度的相似性算法。本文介绍了一个通用的语义学习排序框架——自训练语义交叉注意排序(sRank)。这种基于变压器的框架使用线性成对损失和可变的训练批量大小，实现了质量增益和高效率，并已有效地应用于显示在两个行业任务中的收益在微软超过现实世界的大规模数据集: 智能应答(SR)和环境临床智能(ACI)。在智能答复中，$sRank $通过从基于消费者和支持代理消息的预定义解决方案中选择最佳答复，为现场客户提供技术支持。与之前的系统相比，它在离线状态下获得了11.7% 的最高准确率，并且自2021年1月发布以来，在遥测信息的合成方面减少了38.7% 的时间。在 ACI 任务中，sRank 选择相关的历史医生模板作为文本摘要模型的指导，以生成更高质量的医疗笔记。它实现了35.5% 的最高一级准确性增益，以及46% 的相对 ROUGE-L 增益在生成的医疗记录。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Explicit+and+Implicit+Semantic+Ranking+Framework)|0| +|[Explicit and Implicit Semantic Ranking Framework](https://doi.org/10.1145/3543873.3584621)|Xiaofeng Zhu, Thomas Lin, Vishal Anand, Matthew Calderwood, Eric ClausenBrown, Gord Lueck, Wenwai Yim, Cheng Wu|Nuance Communications, USA; Microsoft Corporation, USA|The core challenge in numerous real-world applications is to match an inquiry to the best document from a mutable and finite set of candidates. Existing industry solutions, especially latency-constrained services, often rely on similarity algorithms that sacrifice quality for speed. In this paper we introduce a generic semantic learning-to-rank framework, Self-training Semantic Cross-attention Ranking (sRank). This transformer-based framework uses linear pairwise loss with mutable training batch sizes and achieves quality gains and high efficiency, and has been applied effectively to show gains on two industry tasks at Microsoft over real-world large-scale data sets: Smart Reply (SR) and Ambient Clinical Intelligence (ACI). In Smart Reply, $sRank$ assists live customers with technical support by selecting the best reply from predefined solutions based on consumer and support agent messages. It achieves 11.7% gain in offline top-one accuracy on the SR task over the previous system, and has enabled 38.7% time reduction in composing messages in telemetry recorded since its general release in January 2021. In the ACI task, sRank selects relevant historical physician templates that serve as guidance for a text summarization model to generate higher quality medical notes. It achieves 35.5% top-one accuracy gain, along with 46% relative ROUGE-L gain in generated medical notes.|在许多实际应用程序中的核心挑战是将查询与来自一组可变且有限的候选文档的最佳文档匹配。现有的行业解决方案，尤其是延迟受限的服务，通常依赖于牺牲质量以提高速度的相似性算法。本文介绍了一个通用的语义学习排序框架——自训练语义交叉注意排序(sRank)。这种基于变压器的框架使用线性成对损失和可变的训练批量大小，实现了质量增益和高效率，并已有效地应用于显示在两个行业任务中的收益在微软超过现实世界的大规模数据集: 智能应答(SR)和环境临床智能(ACI)。在智能答复中，$sRank $通过从基于消费者和支持代理消息的预定义解决方案中选择最佳答复，为现场客户提供技术支持。与之前的系统相比，它在离线状态下获得了11.7% 的最高准确率，并且自2021年1月发布以来，在遥测信息的合成方面减少了38.7% 的时间。在 ACI 任务中，sRank 选择相关的历史医生模板作为文本摘要模型的指导，以生成更高质量的医疗笔记。它实现了35.5% 的最高一级准确性增益，以及46% 的相对 ROUGE-L 增益在生成的医疗记录。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Explicit+and+Implicit+Semantic+Ranking+Framework)|0| |[MPKGAC: Multimodal Product Attribute Completion in E-commerce](https://doi.org/10.1145/3543873.3584623)|Kai Wang, Jianzhi Shao, Tao Zhang, Qijin Chen, Chengfu Huo|Alibaba Group, China|Product attributes can display the selling points of products, helping users find their desired products in search results. However, product attributes are typically incomplete. In e-commerce, products have multimodal features, including original attributes, images, and texts. How to make full use of the multimodal data to complete the missing attributes is the key challenge. To this end, we propose MPKGAC, a powerful three-stream framework that handles multimodal product data for attribute completion. We build a multimodal product knowledge graph (KG) from the multimodal features, and then convert the attribute completion problem into a multimodal KG completion task. MPKGAC encodes each modality separately, fuses them adaptively, and integrates multimodal decoders for prediction. Experiments show that MPKGAC outperforms the best baseline by 6.2% in [email protected] MPKGAC is employed to enrich selling points of the women’s clothing industry at Alibaba.com.cn and improves the click-through rate (CTR) by a relative 2.14%.|产品属性可以显示产品的销售点，帮助用户在搜索结果中找到他们想要的产品。但是，产品属性通常是不完整的。在电子商务中，产品具有多模态特征，包括原始属性、图像和文本。如何充分利用多模态数据来完成缺失的属性是一个关键的挑战。为此，我们提出了 MPKGAC，一个强大的三流框架，处理多通道产品数据的属性完成。首先根据多模态特征构造多模态产品知识图，然后将属性完成问题转化为多模态产品完成任务。MPKGAC 对每种模式分别进行编码，自适应地进行融合，并集成多模式解码器进行预测。实验表明，在阿里巴巴网站上，MPKGAC 的表现优于最佳基线6.2% ，提高了女装行业的销售点，提高了相对2.14% 的点进率。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=MPKGAC:+Multimodal+Product+Attribute+Completion+in+E-commerce)|0| -|[Bootstrapping Contrastive Learning Enhanced Music Cold-Start Matching](https://doi.org/10.1145/3543873.3584626)|Xinping Zhao, Ying Zhang, Qiang Xiao, Yuming Ren, Yingchun Yang|NetEase Cloud Music, NetEase Inc., China; Zhejiang University, China; Zhejiang University, China and NetEase Cloud Music, NetEase Inc., China|We study a particular matching task we call Music Cold-Start Matching. In short, given a cold-start song request, we expect to retrieve songs with similar audiences and then fastly push the cold-start song to the audiences of the retrieved songs to warm up it. However, there are hardly any studies done on this task. Therefore, in this paper, we will formalize the problem of Music Cold-Start Matching detailedly and give a scheme. During the offline training, we attempt to learn high-quality song representations based on song content features. But, we find supervision signals typically follow power-law distribution causing skewed representation learning. To address this issue, we propose a novel contrastive learning paradigm named Bootstrapping Contrastive Learning (BCL) to enhance the quality of learned representations by exerting contrastive regularization. During the online serving, to locate the target audiences more accurately, we propose Clustering-based Audience Targeting (CAT) that clusters audience representations to acquire a few cluster centroids and then locate the target audiences by measuring the relevance between the audience representations and the cluster centroids. Extensive experiments on the offline dataset and online system demonstrate the effectiveness and efficiency of our method. Currently, we have deployed it on NetEase Cloud Music, affecting millions of users.|我们研究一个特殊的匹配任务，我们称之为音乐冷启动匹配。简而言之，给定一个冷启动歌曲请求，我们期望检索具有相似受众的歌曲，然后快速将冷启动歌曲推送给被检索歌曲的受众进行预热。然而，几乎没有任何关于这项任务的研究。因此，本文将音乐冷启动匹配问题进行了详细的形式化描述，并给出了一个解决方案。在离线训练中，我们尝试根据歌曲的内容特征来学习高质量的歌曲表现。但是，我们发现监督信号具有典型的幂律分布特征，从而导致了偏态表征学习。为了解决这一问题，我们提出了一种新的对比学习范式——自举对比学习(BCL) ，通过运用对比正则化来提高学习表征的质量。在在线服务过程中，为了更准确地定位目标受众，我们提出了基于聚类的受众定位(CAT)方法，即通过聚类获取受众表征的几个聚类中心，然后通过测量受众表征与聚类中心之间的相关性来定位目标受众。在离线数据集和在线系统上的大量实验证明了该方法的有效性和高效性。目前，我们已经在网易云音乐上部署了它，影响了数百万用户。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Bootstrapping+Contrastive+Learning+Enhanced+Music+Cold-Start+Matching)|0| -|[Reinforcing User Retention in a Billion Scale Short Video Recommender System](https://doi.org/10.1145/3543873.3584640)|Qingpeng Cai, Shuchang Liu, Xueliang Wang, Tianyou Zuo, Wentao Xie, Bin Yang, Dong Zheng, Peng Jiang, Kun Gai|Kuaishou Technology, China; Unaffiliated, China|Recently, short video platforms have achieved rapid user growth by recommending interesting content to users. The objective of the recommendation is to optimize user retention, thereby driving the growth of DAU (Daily Active Users). Retention is a long-term feedback after multiple interactions of users and the system, and it is hard to decompose retention reward to each item or a list of items. Thus traditional point-wise and list-wise models are not able to optimize retention. In this paper, we choose reinforcement learning methods to optimize the retention as they are designed to maximize the long-term performance. We formulate the problem as an infinite-horizon request-based Markov Decision Process, and our objective is to minimize the accumulated time interval of multiple sessions, which is equal to improving the app open frequency and user retention. However, current reinforcement learning algorithms can not be directly applied in this setting due to uncertainty, bias, and long delay time incurred by the properties of user retention. We propose a novel method, dubbed RLUR, to address the aforementioned challenges. Both offline and live experiments show that RLUR can significantly improve user retention. RLUR has been fully launched in Kuaishou app for a long time, and achieves consistent performance improvement on user retention and DAU.|最近，短视频平台通过向用户推荐有趣的内容实现了用户的快速增长。推荐的目的是优化用户保持率，从而推动 DAU (每日活跃用户)的增长。保留是用户和系统进行多次交互后的长期反馈，很难将保留奖励分解为每个项目或一个项目列表。因此，传统的点模型和列表模型不能优化保留。在本文中，我们选择强化学习方法来优化保留，因为它们旨在最大限度地提高长期绩效。我们把这个问题表述为一个基于无限期请求的马可夫决策过程，我们的目标是最小化多个会话的累积时间间隔，这相当于提高应用程序的开放频率和用户保持率。然而，由于用户保持特性的不确定性、偏差和长时间延迟，现有的强化学习算法不能直接应用于这种设置。我们提出了一种新的方法，称为 RLUR，以解决上述挑战。离线和现场实验都表明，RLUR 可以显著提高用户保持率。RLUR 已经在 Kuaishou 应用程序中全面推出很长时间了，并且在用户保留和 DAU 方面取得了持续的性能改进。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Reinforcing+User+Retention+in+a+Billion+Scale+Short+Video+Recommender+System)|0| +|[Bootstrapping Contrastive Learning Enhanced Music Cold-Start Matching](https://doi.org/10.1145/3543873.3584626)|Xinping Zhao, Ying Zhang, Qiang Xiao, Yuming Ren, Yingchun Yang|Zhejiang University, China and NetEase Cloud Music, NetEase Inc., China; NetEase Cloud Music, NetEase Inc., China; Zhejiang University, China|We study a particular matching task we call Music Cold-Start Matching. In short, given a cold-start song request, we expect to retrieve songs with similar audiences and then fastly push the cold-start song to the audiences of the retrieved songs to warm up it. However, there are hardly any studies done on this task. Therefore, in this paper, we will formalize the problem of Music Cold-Start Matching detailedly and give a scheme. During the offline training, we attempt to learn high-quality song representations based on song content features. But, we find supervision signals typically follow power-law distribution causing skewed representation learning. To address this issue, we propose a novel contrastive learning paradigm named Bootstrapping Contrastive Learning (BCL) to enhance the quality of learned representations by exerting contrastive regularization. During the online serving, to locate the target audiences more accurately, we propose Clustering-based Audience Targeting (CAT) that clusters audience representations to acquire a few cluster centroids and then locate the target audiences by measuring the relevance between the audience representations and the cluster centroids. Extensive experiments on the offline dataset and online system demonstrate the effectiveness and efficiency of our method. Currently, we have deployed it on NetEase Cloud Music, affecting millions of users.|我们研究一个特殊的匹配任务，我们称之为音乐冷启动匹配。简而言之，给定一个冷启动歌曲请求，我们期望检索具有相似受众的歌曲，然后快速将冷启动歌曲推送给被检索歌曲的受众进行预热。然而，几乎没有任何关于这项任务的研究。因此，本文将音乐冷启动匹配问题进行了详细的形式化描述，并给出了一个解决方案。在离线训练中，我们尝试根据歌曲的内容特征来学习高质量的歌曲表现。但是，我们发现监督信号具有典型的幂律分布特征，从而导致了偏态表征学习。为了解决这一问题，我们提出了一种新的对比学习范式——自举对比学习(BCL) ，通过运用对比正则化来提高学习表征的质量。在在线服务过程中，为了更准确地定位目标受众，我们提出了基于聚类的受众定位(CAT)方法，即通过聚类获取受众表征的几个聚类中心，然后通过测量受众表征与聚类中心之间的相关性来定位目标受众。在离线数据集和在线系统上的大量实验证明了该方法的有效性和高效性。目前，我们已经在网易云音乐上部署了它，影响了数百万用户。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Bootstrapping+Contrastive+Learning+Enhanced+Music+Cold-Start+Matching)|0| +|[Reinforcing User Retention in a Billion Scale Short Video Recommender System](https://doi.org/10.1145/3543873.3584640)|Qingpeng Cai, Shuchang Liu, Xueliang Wang, Tianyou Zuo, Wentao Xie, Bin Yang, Dong Zheng, Peng Jiang, Kun Gai|Unaffiliated, China; Kuaishou Technology, China|Recently, short video platforms have achieved rapid user growth by recommending interesting content to users. The objective of the recommendation is to optimize user retention, thereby driving the growth of DAU (Daily Active Users). Retention is a long-term feedback after multiple interactions of users and the system, and it is hard to decompose retention reward to each item or a list of items. Thus traditional point-wise and list-wise models are not able to optimize retention. In this paper, we choose reinforcement learning methods to optimize the retention as they are designed to maximize the long-term performance. We formulate the problem as an infinite-horizon request-based Markov Decision Process, and our objective is to minimize the accumulated time interval of multiple sessions, which is equal to improving the app open frequency and user retention. However, current reinforcement learning algorithms can not be directly applied in this setting due to uncertainty, bias, and long delay time incurred by the properties of user retention. We propose a novel method, dubbed RLUR, to address the aforementioned challenges. Both offline and live experiments show that RLUR can significantly improve user retention. RLUR has been fully launched in Kuaishou app for a long time, and achieves consistent performance improvement on user retention and DAU.|最近，短视频平台通过向用户推荐有趣的内容实现了用户的快速增长。推荐的目的是优化用户保持率，从而推动 DAU (每日活跃用户)的增长。保留是用户和系统进行多次交互后的长期反馈，很难将保留奖励分解为每个项目或一个项目列表。因此，传统的点模型和列表模型不能优化保留。在本文中，我们选择强化学习方法来优化保留，因为它们旨在最大限度地提高长期绩效。我们把这个问题表述为一个基于无限期请求的马可夫决策过程，我们的目标是最小化多个会话的累积时间间隔，这相当于提高应用程序的开放频率和用户保持率。然而，由于用户保持特性的不确定性、偏差和长时间延迟，现有的强化学习算法不能直接应用于这种设置。我们提出了一种新的方法，称为 RLUR，以解决上述挑战。离线和现场实验都表明，RLUR 可以显著提高用户保持率。RLUR 已经在 Kuaishou 应用程序中全面推出很长时间了，并且在用户保留和 DAU 方面取得了持续的性能改进。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Reinforcing+User+Retention+in+a+Billion+Scale+Short+Video+Recommender+System)|0| |[Jointly modeling products and resource pages for task-oriented recommendation](https://doi.org/10.1145/3543873.3584642)|Brendan Duncan, Surya Kallumadi, Taylor BergKirkpatrick, Julian J. McAuley|Lowe's Companies, Inc., USA; UC San Diego, USA|Modeling high-level user intent in recommender systems can improve performance, although it is often difficult to obtain a ground truth measure of this intent. In this paper, we investigate a novel way to obtain such an intent signal by leveraging resource pages associated with a particular task. We jointly model product interactions and resource page interactions to create a system which can recommend both products and resource pages to users. Our experiments consider the domain of home improvement product recommendation, where resource pages are DIY (do-it-yourself) project pages from Lowes.com. Each DIY page provides a list of tools, materials, and step-by-step instructions to complete a DIY project, such as building a deck, installing cabinets, and fixing a leaking pipe. We use this data as an indicator of the intended project, which is a natural high-level intent signal for home improvement shoppers. We then extend a state-of-the-art system to incorporate this new intent data, and show a significant improvement in the ability of the system to recommend products. We further demonstrate that our system can be used to successfully recommend DIY project pages to users. We have taken initial steps towards deploying our method for project recommendation in production on the Lowe’s website and for recommendations through marketing emails.|在推荐系统中建模高级用户意图可以提高性能，尽管通常很难获得这种意图的地面真实度量。在本文中，我们研究了一种通过利用与特定任务相关联的资源页来获得这种意图信号的新方法。我们联合对产品交互和资源页交互进行建模，以创建一个可以向用户推荐产品和资源页的系统。我们的实验考虑了家装产品推荐领域，其中的资源页面是来自 Lowes.com 的 DIY (DIY-it-yourself)项目页面。每一个 DIY 页面都提供了一系列的工具、材料和一步一步的指导来完成一个 DIY 项目，例如建造一个甲板、安装橱柜和修复一个泄漏的管道。我们使用这些数据作为预期项目的指标，这对于家装购物者来说是一个自然的高层次的意图信号。然后，我们扩展了一个最先进的系统来合并这些新的意图数据，并显示系统推荐产品的能力有了显著的提高。我们进一步演示了我们的系统可以用来成功地向用户推荐 DIY 项目页面。我们已经采取了初步步骤，部署我们的方法，项目推荐生产在劳的网站上，并通过营销电子邮件的建议。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Jointly+modeling+products+and+resource+pages+for+task-oriented+recommendation)|0| -|[Meta-Generator Enhanced Multi-Domain Recommendation](https://doi.org/10.1145/3543873.3584652)|Yingyi Zhang, Xianneng Li, Yahe Yu, Jian Tang, Huanfang Deng, Junya Lu, Yeyin Zhang, Qiancheng Jiang, Yunsen Xian, Liqian Yu, Han Liu|Dalian University of Technology, China; Meituan-Dianping Group, China; Meituan, China|Large-scale e-commercial platforms usually contain multiple business fields, which require industrial algorithms to characterize user intents across multiple domains. Numerous efforts have been made in user multi-domain intent modeling to achieve state-of-the-art performance. However, existing methods mainly focus on the domains having rich user information, which makes implementation to domains with sparse or rare user behavior meet with mixed success. Hence, in this paper, we propose a novel method named Meta-generator enhanced multi-Domain model (MetaDomain) to address the above issue. MetaDomain mainly includes two steps, 1) users’ multi-domain intent representation and 2) users’ multi-domain intent fusion. Specifically, in users’ multi-domain intent representation, we use the gradient information from a domain intent extractor to train the domain intent meta-generator, where the domain intent extractor has the input of users’ sequence feature and domain meta-generator has the input of users’ basic feature, hence the capability of generating users’ intent with sparse behavior. Afterward, in users’ multi-domain intent fusion, a domain graph is used to represent the high-order multi-domain connectivity. Extensive experiments have been carried out under a real-world industrial platform named Meituan. Both offline and rigorous online A/B tests under the billion-level data scale demonstrate the superiority of the proposed MetaDomain method over the state-of-the-art baselines. Furthermore comparing with the method using multi-domain sequence features, MetaDomain can reduce the serving latency by 20%. Currently, MetaDomain has been deployed in Meituan one of the largest worldwide Online-to-Offline(O2O) platforms.|大型电子商务平台通常包含多个业务字段，这需要工业算法来描述跨多个域的用户意图。在用户多领域意图建模方面做了大量工作，以实现最先进的性能。然而，现有的方法主要集中在具有丰富用户信息的域上，这使得对于用户行为稀疏或罕见的域的实现成败参半。为此，本文提出了一种新的元生成器增强型多域模型(MetaDomain)来解决上述问题。元域主要包括两个步骤: 1)用户的多域意图表示和2)用户的多域意图融合。具体来说，在用户的多领域意图表示中，我们利用领域意图提取器的梯度信息来训练领域意图元生成器，其中领域意图提取器输入用户的序列特征，领域元生成器输入用户的基本特征，从而具有生成稀疏行为的用户意图的能力。然后，在用户的多域意图融合中，使用一个域图来表示高阶多域连通性。在一个名为“美团”的真实工业平台下，人们进行了广泛的实验。在十亿级数据规模下的离线和严格的在线 A/B 测试都证明了提出的 MetaDomain 方法相对于最先进的基线的优越性。此外，与采用多域序列特征的方法相比，元域可以减少20% 的服务延迟。目前，MetaDomain 已经部署在全球最大的在线到离线(o2O)平台之一的美团上。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Meta-Generator+Enhanced+Multi-Domain+Recommendation)|0| +|[Meta-Generator Enhanced Multi-Domain Recommendation](https://doi.org/10.1145/3543873.3584652)|Yingyi Zhang, Xianneng Li, Yahe Yu, Jian Tang, Huanfang Deng, Junya Lu, Yeyin Zhang, Qiancheng Jiang, Yunsen Xian, Liqian Yu, Han Liu|Meituan, China; Meituan-Dianping Group, China; Dalian University of Technology, China|Large-scale e-commercial platforms usually contain multiple business fields, which require industrial algorithms to characterize user intents across multiple domains. Numerous efforts have been made in user multi-domain intent modeling to achieve state-of-the-art performance. However, existing methods mainly focus on the domains having rich user information, which makes implementation to domains with sparse or rare user behavior meet with mixed success. Hence, in this paper, we propose a novel method named Meta-generator enhanced multi-Domain model (MetaDomain) to address the above issue. MetaDomain mainly includes two steps, 1) users’ multi-domain intent representation and 2) users’ multi-domain intent fusion. Specifically, in users’ multi-domain intent representation, we use the gradient information from a domain intent extractor to train the domain intent meta-generator, where the domain intent extractor has the input of users’ sequence feature and domain meta-generator has the input of users’ basic feature, hence the capability of generating users’ intent with sparse behavior. Afterward, in users’ multi-domain intent fusion, a domain graph is used to represent the high-order multi-domain connectivity. Extensive experiments have been carried out under a real-world industrial platform named Meituan. Both offline and rigorous online A/B tests under the billion-level data scale demonstrate the superiority of the proposed MetaDomain method over the state-of-the-art baselines. Furthermore comparing with the method using multi-domain sequence features, MetaDomain can reduce the serving latency by 20%. Currently, MetaDomain has been deployed in Meituan one of the largest worldwide Online-to-Offline(O2O) platforms.|大型电子商务平台通常包含多个业务字段，这需要工业算法来描述跨多个域的用户意图。在用户多领域意图建模方面做了大量工作，以实现最先进的性能。然而，现有的方法主要集中在具有丰富用户信息的域上，这使得对于用户行为稀疏或罕见的域的实现成败参半。为此，本文提出了一种新的元生成器增强型多域模型(MetaDomain)来解决上述问题。元域主要包括两个步骤: 1)用户的多域意图表示和2)用户的多域意图融合。具体来说，在用户的多领域意图表示中，我们利用领域意图提取器的梯度信息来训练领域意图元生成器，其中领域意图提取器输入用户的序列特征，领域元生成器输入用户的基本特征，从而具有生成稀疏行为的用户意图的能力。然后，在用户的多域意图融合中，使用一个域图来表示高阶多域连通性。在一个名为“美团”的真实工业平台下，人们进行了广泛的实验。在十亿级数据规模下的离线和严格的在线 A/B 测试都证明了提出的 MetaDomain 方法相对于最先进的基线的优越性。此外，与采用多域序列特征的方法相比，元域可以减少20% 的服务延迟。目前，MetaDomain 已经部署在全球最大的在线到离线(o2O)平台之一的美团上。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Meta-Generator+Enhanced+Multi-Domain+Recommendation)|0| |[Integrated Ranking for News Feed with Reinforcement Learning](https://doi.org/10.1145/3543873.3584651)|Menghui Zhu, Wei Xia, Weiwen Liu, Yifan Liu, Ruiming Tang, Weinan Zhang|Shanghai Jiao Tong University, China; Huawei Noah?s Ark Lab, China|With the development of recommender systems, it becomes an increasingly common need to mix multiple item sequences from different sources. Therefore, the integrated ranking stage is proposed to be responsible for this task with re-ranking models. However, existing methods ignore the relation between the sequences, thus resulting in local optimum over the interaction session. To resolve this challenge, in this paper, we propose a new model named NFIRank (News Feed Integrated Ranking with reinforcement learning) and formulate the whole interaction session as a MDP (Markov Decision Process). Sufficient offline experiments are provided to verify the effectiveness of our model. In addition, we deployed our model on Huawei Browser and gained 1.58% improvements in CTR compared with the baseline in online A/B test. Code will be available at https://gitee.com/mindspore/models/tree/master/research/recommend/NFIRank.|随着推荐系统的发展，混合来自不同来源的多个项目序列的需求变得越来越普遍。因此，提出综合排序阶段负责这一任务的重新排序模型。然而，现有的方法忽略了序列之间的关系，从而导致局部最优的交互会话。为了解决这个问题，在本文中，我们提出了一个新的模型 NFIRank (带强化学习的新闻源综合排名) ，并将整个交互会话表示为一个 MDP (马可夫决策过程)。通过离线实验验证了模型的有效性。此外，我们在华为浏览器上部署了我们的模型，与在线 A/B 测试的基线相比，点击率提高了1.58% 。密码将在 https://gitee.com/mindspore/models/tree/master/research/recommend/nfirank 公布。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Integrated+Ranking+for+News+Feed+with+Reinforcement+Learning)|0| |[Measuring e-Commerce Metric Changes in Online Experiments](https://doi.org/10.1145/3543873.3584654)|C. H. Bryan Liu, Emma J. McCoy|ASOS.com, United Kingdom and Imperial College London, United Kingdom; London School of Economics and Political Science, United Kingdom|Digital technology organizations routinely use online experiments (e.g. A/B tests) to guide their product and business decisions. In e-commerce, we often measure changes to transaction- or item-based business metrics such as Average Basket Value (ABV), Average Basket Size (ABS), and Average Selling Price (ASP); yet it remains a common pitfall to ignore the dependency between the value/size of transactions/items during experiment design and analysis. We present empirical evidence on such dependency, its impact on measurement uncertainty, and practical implications on A/B test outcomes if left unmitigated. By making the evidence available, we hope to drive awareness of the pitfall among experimenters in e-commerce and hence encourage the adoption of established mitigation approaches. We also share lessons learned when incorporating selected mitigation approaches into our experimentation analysis platform currently in production.|数字技术组织经常使用在线实验(例如 A/B 测试)来指导他们的产品和商业决策。在电子商务中，我们经常衡量基于交易或项目的业务指标的变化，如平均篮子价值(ABV)、平均篮子大小(ABS)和平均销售价格(ASP) ; 然而，在实验设计和分析过程中忽视交易/项目的价值/大小之间的依赖性仍然是一个常见的陷阱。我们介绍了这种依赖性的经验证明，它对测量不确定性的影响，以及如果不加以缓解的话对 A/B 测试结果的实际影响。通过提供证据，我们希望提高电子商务实验者对这一陷阱的认识，从而鼓励采用既定的缓解办法。我们还分享了将选定的缓解方法纳入我们目前正在生产的实验分析平台的经验教训。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Measuring+e-Commerce+Metric+Changes+in+Online+Experiments)|0| |[Improve retrieval of regional titles in streaming services with dense retrieval](https://doi.org/10.1145/3543873.3587619)|Bhargav Upadhyay, Tejas Khairnar, Anup Kotalwar|Amazon, India|Customers search for movie and series titles released across the world on streaming services like primevideo.com (PV), netflix.com (Netflix). In non-English speaking countries like India, Nepal and many others, the regional titles are transliterated from native language to English and are being searched in English. Given that there can be multiple transliterations possible for almost all the titles, searching for a regional title can be a very frustrating customer experience if these nuances are not handled correctly by the search system. Typing errors make the problem even more challenging. Streaming services uses spell correction and auto-suggestions/auto-complete features to address this issue up to certain extent. Auto-suggest fails when user searches keywords not in scope of the auto-suggest. Spell correction is effective at correcting common typing errors but as these titles doesn’t follow strict grammar rules and new titles constantly added to the catalog, spell correction have limited success. With recent progress in deep learning (DL), embedding vectors based dense retrieval is being used extensively to retrieve semantically relevant documents for a given query. In this work, we have used dense retrieval to address the noise introduced by transliteration variations and typing errors to improve retrieval of regional media titles. In the absent of any relevant dataset to test our hypothesis, we created a new dataset of 40K query title pairs from PV search logs. We also created a baseline by bench-marking PV’s performance on test data. We present an extensive study on the impact of 1. pre-training, 2. data augmentation, 3. positive to negative sample ratio, and 4. choice of loss function on retrieval performance. Our best model has shown 51.24% improvement in [email protected] over PV baseline.|客户可以通过流媒体服务搜索世界各地发行的电影和剧集，比如 primeideo.com (PV)、 Netflix.com (Netflix)。在非英语国家，如印度、尼泊尔和许多其他国家，地区标题被从母语音译成英语，并用英语进行搜索。鉴于几乎所有标题都可能有多种音译，如果搜索系统不能正确处理这些细微差别，那么搜索地区标题可能是一种非常令人沮丧的客户体验。键入错误使问题更具挑战性。流媒体服务使用拼写修正和自动建议/自动完成功能在一定程度上解决了这个问题。当用户搜索不在自动建议范围内的关键字时，自动建议失败。拼写纠正在纠正常见的打字错误方面是有效的，但是由于这些标题没有遵循严格的语法规则，而且新标题不断地添加到目录中，拼写纠正的成功有限。随着深度学习(DL)技术的发展，基于嵌入向量的密集检索技术被广泛应用于给定查询的语义相关文档检索。在本研究中，我们利用密集检索来解决音译变异和打字错误所引起的噪音问题，以提高地区性媒体标题的检索效率。在缺乏相关数据集来检验我们的假设的情况下，我们从 PV 搜索日志中创建了一个40K 查询标题对的新数据集。我们还通过在测试数据上标记 PV 的性能来创建基线。我们提出了一个广泛的研究影响1。训练前2分钟。数据增强，3。正负样本比率，以及4。损失函数对检索性能的选择。我们最好的模型已经显示了51.24% 的改善[电子邮件保护]超过 PV 基线。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Improve+retrieval+of+regional+titles+in+streaming+services+with+dense+retrieval)|0| |[hp-frac: An index to determine Awarded Researchers](https://doi.org/10.1145/3543873.3587597)|Aashay Singhal, Kamalakar Karlapalem|International Institute of Information Technology, Hyderabad, India|In order to advance academic research, it is important to assess and evaluate the academic influence of researchers and the findings they produce. Citation metrics are universally used methods to evaluate researchers. Amongst the several variations of citation metrics, the h-index proposed by Hirsch has become the leading measure. Recent work shows that h-index is not an effective measure to determine scientific impact - due to changing authorship patterns. This can be mitigated by using h-index of a paper to compute h-index of an author. We show that using fractional allocation of h-index gives better results. In this work, we reapply two indices based on the h-index of a single paper. The indices are referred to as: hp-index and hp-frac-index. We run large-scale experiments in three different fields with about a million publications and 3,000 authors. Our experiments show that hp-frac-index provides a unique ranking when compared to h-index. It also performs better than h-index in providing higher ranks to the awarded researcher.|为了推进学术研究，评估和评估研究人员的学术影响力和他们的发现是非常重要的。引文指标是评价科研人员的普遍方法。在引文指标的众多变化中，赫希提出的 h 指标已经成为引文指标的主导指标。最近的研究表明，由于作者模式的改变，h 指数不是确定科学影响的有效方法。这可以通过使用论文的 h- 索引来计算作者的 h- 索引来减轻。我们表明，使用分数分配的 h 指标给出了更好的结果。在这项工作中，我们重新应用两个指标的基础上的单一文件的 h-索引。这些指数被称为: hp-index 和 hp-frac-index。我们在三个不同的领域进行了大规模的实验，发表了大约一百万篇论文，有3000名作者。我们的实验表明，与 h 指数相比，hp-frac 指数提供了一个唯一的排名。在为获奖研究人员提供更高的排名方面，它也比 h-index 表现得更好。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=hp-frac:+An+index+to+determine+Awarded+Researchers)|0| |[Application of an ontology for model cards to generate computable artifacts for linking machine learning information from biomedical research](https://doi.org/10.1145/3543873.3587601)|Muhammad Amith, Licong Cui, Kirk Roberts, Cui Tao|Department of Information Science, University of North Texas, USA; School of Biomedical Informatics, The University of Texas Health Science Center at Houston, USA; School of Biomedical Informatics, University of Texas Health Science Center at Houston, USA|Model card reports provide a transparent description of machine learning models which includes information about their evaluation, limitations, intended use, etc. Federal health agencies have expressed an interest in model cards report for research studies using machine-learning based AI. Previously, we have developed an ontology model for model card reports to structure and formalize these reports. In this paper, we demonstrate a Java-based library (OWL API, FaCT++) that leverages our ontology to publish computable model card reports. We discuss future directions and other use cases that highlight applicability and feasibility of ontology-driven systems to support FAIR challenges.|模型卡片报告提供了机器学习模型的透明描述，包括它们的评估、限制、预期用途等信息。联邦卫生机构对使用基于机器学习的人工智能的研究报告模型卡表示了兴趣。在此之前，我们已经开发了一个模型卡片报告的本体模型来构造和形式化这些报告。在本文中，我们演示了一个基于 Java 的库(OWL API，FaCT + +) ，它利用我们的本体来发布可计算模型卡报告。我们讨论未来的方向和其他用例，突出本体驱动的系统的适用性和可行性，以支持 FAIR 的挑战。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Application+of+an+ontology+for+model+cards+to+generate+computable+artifacts+for+linking+machine+learning+information+from+biomedical+research)|0| |[Stance Inference in Twitter through Graph Convolutional Collaborative Filtering Networks with Minimal Supervision](https://doi.org/10.1145/3543873.3587640)|Zhiwei Zhou, Erick Elejalde|Leibniz Universität Hannover, L3S Research Center, Germany|Social Media (SM) has become a stage for people to share thoughts, emotions, opinions, and almost every other aspect of their daily lives. This abundance of human interaction makes SM particularly attractive for social sensing. Especially during polarizing events such as political elections or referendums, users post information and encourage others to support their side, using symbols such as hashtags to represent their attitudes. However, many users choose not to attach hashtags to their messages, use a different language, or show their position only indirectly. Thus, automatically identifying their opinions becomes a more challenging task. To uncover these implicit perspectives, we propose a collaborative filtering model based on Graph Convolutional Networks that exploits the textual content in messages and the rich connections between users and topics. Moreover, our approach only requires a small annotation effort compared to state-of-the-art solutions. Nevertheless, the proposed model achieves competitive performance in predicting individuals' stances. We analyze users' attitudes ahead of two constitutional referendums in Chile in 2020 and 2022. Using two large Twitter datasets, our model achieves improvements of 3.4% in recall and 3.6% in accuracy over the baselines.|社交媒体(SM)已经成为人们分享思想、情感、观点以及日常生活中几乎所有其他方面的一个舞台。这种丰富的人际互动使 SM 对社会感知特别有吸引力。特别是在政治选举或公民投票等两极分化的活动中，用户发布信息并鼓励其他人支持他们的立场，使用标签等符号来表达他们的态度。但是，许多用户选择不在消息中附加标签，不使用其他语言，或者只间接显示自己的位置。因此，自动识别他们的观点成为一项更具挑战性的任务。为了揭示这些隐含的观点，我们提出了一个基于图形卷积网络的协同过滤模型，该模型利用消息中的文本内容以及用户和主题之间的丰富联系。此外，与最先进的解决方案相比，我们的方法只需要很少的注释工作。然而，所提出的模型在预测个体的立场方面达到了竞争性的表现。我们分析了2020年和2022年智利两次宪法公投前用户的态度。使用两个大型的 Twitter 数据集，我们的模型比基线数据集提高了3.4% 的召回率和3.6% 的准确率。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Stance+Inference+in+Twitter+through+Graph+Convolutional+Collaborative+Filtering+Networks+with+Minimal+Supervision)|0| -|[Retrieving false claims on Twitter during the Russia-Ukraine conflict](https://doi.org/10.1145/3543873.3587571)|Valerio La Gatta, Chiyu Wei, Luca Luceri, Francesco Pierri, Emilio Ferrara|Information Sciences Institute, University of Southern California, USA; Information Sciences Institute, University of Southern California, USA and University of Naples Federico II, Italy; Politecnico di Milano, Italy and Information Sciences Institute, University of Southern California, USA|Nowadays, false and unverified information on social media sway individuals' perceptions during major geo-political events and threaten the quality of the whole digital information ecosystem. Since the Russian invasion of Ukraine, several fact-checking organizations have been actively involved in verifying stories related to the conflict that circulated online. In this paper, we leverage a public repository of fact-checked claims to build a methodological framework for automatically identifying false and unsubstantiated claims spreading on Twitter in February 2022. Our framework consists of two sequential models: First, the claim detection model identifies whether tweets incorporate a (false) claim among those considered in our collection. Then, the claim retrieval model matches the tweets with fact-checked information by ranking verified claims according to their relevance with the input tweet. Both models are based on pre-trained language models and fine-tuned to perform a text classification task and an information retrieval task, respectively. In particular, to validate the effectiveness of our methodology, we consider 83 verified false claims that spread on Twitter during the first week of the invasion, and manually annotate 5,872 tweets according to the claim(s) they report. Our experiments show that our proposed methodology outperforms standard baselines for both claim detection and claim retrieval. Overall, our results highlight how social media providers could effectively leverage semi-automated approaches to identify, track, and eventually moderate false information that spreads on their platforms.|如今，社交媒体上的虚假和未经证实的信息在重大地缘政治事件中左右着人们的看法，威胁着整个数字信息生态系统的质量。自从俄罗斯入侵乌克兰以来，几个事实核查组织一直积极参与核实在网上流传的与冲突有关的故事。在本文中，我们利用一个事实核查索赔的公共数据库，建立一个方法框架，自动识别2022年2月在 Twitter 上传播的虚假和未经证实的索赔。我们的框架由两个顺序模型组成: 首先，索赔检测模型确定 tweet 是否在我们的集合中考虑的索赔中包含(虚假)索赔。然后，索赔检索模型根据索赔与输入索赔的相关性对索赔进行排序，从而将索赔与事实核查信息进行匹配。这两种模型都是基于预先训练好的语言模型，经过微调后分别执行文本分类任务和信息检索分类任务。特别是，为了验证我们的方法的有效性，我们考虑了在入侵的第一周在 Twitter 上传播的83个经过验证的虚假声明，并根据他们报告的声明手动注释了5,872条推文。我们的实验表明，我们提出的方法在索赔检测和索赔检索方面都优于标准基线。总的来说，我们的研究结果强调了社交媒体提供商如何有效地利用半自动化方法来识别、跟踪并最终控制在他们的平台上传播的虚假信息。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Retrieving+false+claims+on+Twitter+during+the+Russia-Ukraine+conflict)|0| +|[Retrieving false claims on Twitter during the Russia-Ukraine conflict](https://doi.org/10.1145/3543873.3587571)|Valerio La Gatta, Chiyu Wei, Luca Luceri, Francesco Pierri, Emilio Ferrara|Politecnico di Milano, Italy and Information Sciences Institute, University of Southern California, USA; Information Sciences Institute, University of Southern California, USA and University of Naples Federico II, Italy; Information Sciences Institute, University of Southern California, USA|Nowadays, false and unverified information on social media sway individuals' perceptions during major geo-political events and threaten the quality of the whole digital information ecosystem. Since the Russian invasion of Ukraine, several fact-checking organizations have been actively involved in verifying stories related to the conflict that circulated online. In this paper, we leverage a public repository of fact-checked claims to build a methodological framework for automatically identifying false and unsubstantiated claims spreading on Twitter in February 2022. Our framework consists of two sequential models: First, the claim detection model identifies whether tweets incorporate a (false) claim among those considered in our collection. Then, the claim retrieval model matches the tweets with fact-checked information by ranking verified claims according to their relevance with the input tweet. Both models are based on pre-trained language models and fine-tuned to perform a text classification task and an information retrieval task, respectively. In particular, to validate the effectiveness of our methodology, we consider 83 verified false claims that spread on Twitter during the first week of the invasion, and manually annotate 5,872 tweets according to the claim(s) they report. Our experiments show that our proposed methodology outperforms standard baselines for both claim detection and claim retrieval. Overall, our results highlight how social media providers could effectively leverage semi-automated approaches to identify, track, and eventually moderate false information that spreads on their platforms.|如今，社交媒体上的虚假和未经证实的信息在重大地缘政治事件中左右着人们的看法，威胁着整个数字信息生态系统的质量。自从俄罗斯入侵乌克兰以来，几个事实核查组织一直积极参与核实在网上流传的与冲突有关的故事。在本文中，我们利用一个事实核查索赔的公共数据库，建立一个方法框架，自动识别2022年2月在 Twitter 上传播的虚假和未经证实的索赔。我们的框架由两个顺序模型组成: 首先，索赔检测模型确定 tweet 是否在我们的集合中考虑的索赔中包含(虚假)索赔。然后，索赔检索模型根据索赔与输入索赔的相关性对索赔进行排序，从而将索赔与事实核查信息进行匹配。这两种模型都是基于预先训练好的语言模型，经过微调后分别执行文本分类任务和信息检索分类任务。特别是，为了验证我们的方法的有效性，我们考虑了在入侵的第一周在 Twitter 上传播的83个经过验证的虚假声明，并根据他们报告的声明手动注释了5,872条推文。我们的实验表明，我们提出的方法在索赔检测和索赔检索方面都优于标准基线。总的来说，我们的研究结果强调了社交媒体提供商如何有效地利用半自动化方法来识别、跟踪并最终控制在他们的平台上传播的虚假信息。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Retrieving+false+claims+on+Twitter+during+the+Russia-Ukraine+conflict)|0| |[Enhancing Hierarchy-Aware Graph Networks with Deep Dual Clustering for Session-based Recommendation](https://doi.org/10.1145/3543507.3583247)|Jiajie Su, Chaochao Chen, Weiming Liu, Fei Wu, Xiaolin Zheng, Haoming Lyu|Zhejiang University, China|Session-based Recommendation aims at predicting the next interacted item based on short anonymous behavior sessions. However, existing solutions neglect to model two inherent properties of sequential representing distributions, i.e., hierarchy structures resulted from item popularity and collaborations existing in both intra- and inter-session. Tackling with these two factors at the same time is challenging. On the one hand, traditional Euclidean space utilized in previous studies fails to capture hierarchy structures due to a restricted representation ability. On the other hand, the intuitive apply of hyperbolic geometry could extract hierarchical patterns but more emphasis on degree distribution weakens intra- and inter-session collaborations. To address the challenges, we propose a Hierarchy-Aware Dual Clustering Graph Network (HADCG) model for session-based recommendation. Towards the first challenge, we design the hierarchy-aware graph modeling module which converts sessions into hyperbolic session graphs, adopting hyperbolic geometry in propagation and attention mechanism so as to integrate chronological and hierarchical information. As for the second challenge, we introduce the deep dual clustering module which develops a two-level clustering strategy, i.e., information regularizer for intra-session clustering and contrastive learner for inter-session clustering, to enhance hyperbolic representation learning from collaborative perspectives and further promote recommendation performance. Extensive experiments on three real-world datasets demonstrate the effectiveness of the proposed HADCG.|基于会话的推荐是基于短的匿名行为会话来预测下一个交互项。然而，现有的解决方案忽视了顺序表示分布的两个固有属性，即由于项目流行和会话内和会话间存在的协作而产生的层次结构。同时处理这两个因素是具有挑战性的。一方面，传统的欧氏空间由于表示能力的限制，无法捕捉层次结构;。另一方面，双曲几何的直观应用可以提取等级模式，但更强调学位分配会削弱会话内部和会话间的协作。针对这一挑战，我们提出了一种基于会话的层次感知双聚类图网络(HADCG)模型。针对第一个挑战，我们设计了层次感知的图形建模模块，它将会话转换为双曲会话图，在传播和注意机制中采用双曲几何，以便整合时间和层次信息。针对第二个挑战，我们引入了深度双聚类模型，提出了一种两级聚类策略，即会话内聚类的信息调整器和会话间聚类的对比学习器，从协作的角度提高双曲表示学习，进一步提高推荐性能。在三个实际数据集上的大量实验证明了所提出的 HADCG 算法的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Enhancing+Hierarchy-Aware+Graph+Networks+with+Deep+Dual+Clustering+for+Session-based+Recommendation)|0| |[Intra and Inter Domain HyperGraph Convolutional Network for Cross-Domain Recommendation](https://doi.org/10.1145/3543507.3583402)|Zhongxuan Han, Xiaolin Zheng, Chaochao Chen, Wenjie Cheng, Yang Yao|Zhejiang Lab, China; Zhejiang University, China|Cross-Domain Recommendation (CDR) aims to solve the data sparsity problem by integrating the strengths of different domains. Though researchers have proposed various CDR methods to effectively transfer knowledge across domains, they fail to address the following key issues, i.e., (1) they cannot model high-order correlations among users and items in every single domain to obtain more accurate representations; (2) they cannot model the correlations among items across different domains. To tackle the above issues, we propose a novel Intra and Inter Domain HyperGraph Convolutional Network (II-HGCN) framework, which includes two main layers in the modeling process, i.e., the intra-domain layer and the inter-domain layer. In the intra-domain layer, we design a user hypergraph and an item hypergraph to model high-order correlations inside every single domain. Thus we can address the data sparsity problem better and learn high-quality representations of users and items. In the inter-domain layer, we propose an inter-domain hypergraph structure to explore correlations among items from different domains based on their interactions with common users. Therefore we can not only transfer the knowledge of users but also combine embeddings of items across domains. Comprehensive experiments on three widely used benchmark datasets demonstrate that II-HGCN outperforms other state-of-the-art methods, especially when datasets are extremely sparse.|跨域推荐(CDR)旨在通过整合不同域的优势来解决数据稀疏性问题。尽管研究人员已经提出了各种 CDR 方法来有效地跨领域传递知识，但他们未能解决以下关键问题，即: (1)他们不能模拟每个领域中用户和项目之间的高阶相关性以获得更准确的表示; (2)他们不能模拟不同领域中项目之间的相关性。为了解决上述问题，我们提出了一种新的域内和域间超图卷积网络(II-HGCN)框架，它包括建模过程中的两个主要层次，即域内层和域间层。在域内层，我们设计了一个用户超图和一个项目超图来模拟每个域内的高阶相关性。因此，我们可以更好地解决数据稀疏问题，并学习用户和项目的高质量表示。在域间层，我们提出了一个域间超图结构来探索来自不同领域的项目之间的相关性，基于它们与公共用户的交互。因此，不仅可以实现用户知识的传递，还可以实现跨域嵌入的组合。在三个广泛使用的基准数据集上的综合实验表明，II-HGCN 优于其他最先进的方法，特别是在数据集极其稀疏的情况下。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Intra+and+Inter+Domain+HyperGraph+Convolutional+Network+for+Cross-Domain+Recommendation)|0| -|[Generating Counterfactual Hard Negative Samples for Graph Contrastive Learning](https://doi.org/10.1145/3543507.3583499)|Haoran Yang, Hongxu Chen, Sixiao Zhang, Xiangguo Sun, Qian Li, Xiangyu Zhao, Guandong Xu|The Chinese University of Hong Kong, Hong Kong; Curtin University, Australia; University of Technology Sydney, Australia; City University of Hong Kong, Hong Kong|Graph contrastive learning has emerged as a powerful tool for unsupervised graph representation learning. The key to the success of graph contrastive learning is to acquire high-quality positive and negative samples as contrasting pairs for the purpose of learning underlying structural semantics of the input graph. Recent works usually sample negative samples from the same training batch with the positive samples, or from an external irrelevant graph. However, a significant limitation lies in such strategies, which is the unavoidable problem of sampling false negative samples. In this paper, we propose a novel method to utilize \textbf{C}ounterfactual mechanism to generate artificial hard negative samples for \textbf{G}raph \textbf{C}ontrastive learning, namely \textbf{CGC}, which has a different perspective compared to those sampling-based strategies. We utilize counterfactual mechanism to produce hard negative samples, which ensures that the generated samples are similar to, but have labels that different from the positive sample. The proposed method achieves satisfying results on several datasets compared to some traditional unsupervised graph learning methods and some SOTA graph contrastive learning methods. We also conduct some supplementary experiments to give an extensive illustration of the proposed method, including the performances of CGC with different hard negative samples and evaluations for hard negative samples generated with different similarity measurements.|图形对比学习已经成为无监督图形表示学习的有力工具。图形对比学习成功的关键是获取高质量的正负样本作为对比对，从而学习输入图的结构语义。最近的作品通常从同一训练批次的正样本中抽取负样本，或者从一个外部不相关的图中抽取负样本。然而，这种策略存在一个显著的局限性，这就是不可避免的采样假阴性样本的问题。本文提出了一种新的利用 textbf { C }反事实机制生成 textbf { G } raph textbf { C }对比学习人工硬负样本的方法，即 textbf { CGC }。我们利用反事实机制生成硬负样本，确保所生成的样本与正样本相似，但有不同于正样本的标签。与传统的无监督图形学习方法和 SOTA 图形对比学习方法相比，该方法在多个数据集上取得了令人满意的效果。我们还进行了一些补充实验，对所提出的方法进行了广泛的说明，包括对不同硬负样本的 CGC 性能和对不同相似度测量产生的硬负样本的评价。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Generating+Counterfactual+Hard+Negative+Samples+for+Graph+Contrastive+Learning)|0| +|[Generating Counterfactual Hard Negative Samples for Graph Contrastive Learning](https://doi.org/10.1145/3543507.3583499)|Haoran Yang, Hongxu Chen, Sixiao Zhang, Xiangguo Sun, Qian Li, Xiangyu Zhao, Guandong Xu|The Chinese University of Hong Kong, Hong Kong; City University of Hong Kong, Hong Kong; University of Technology Sydney, Australia; Curtin University, Australia|Graph contrastive learning has emerged as a powerful tool for unsupervised graph representation learning. The key to the success of graph contrastive learning is to acquire high-quality positive and negative samples as contrasting pairs for the purpose of learning underlying structural semantics of the input graph. Recent works usually sample negative samples from the same training batch with the positive samples, or from an external irrelevant graph. However, a significant limitation lies in such strategies, which is the unavoidable problem of sampling false negative samples. In this paper, we propose a novel method to utilize \textbf{C}ounterfactual mechanism to generate artificial hard negative samples for \textbf{G}raph \textbf{C}ontrastive learning, namely \textbf{CGC}, which has a different perspective compared to those sampling-based strategies. We utilize counterfactual mechanism to produce hard negative samples, which ensures that the generated samples are similar to, but have labels that different from the positive sample. The proposed method achieves satisfying results on several datasets compared to some traditional unsupervised graph learning methods and some SOTA graph contrastive learning methods. We also conduct some supplementary experiments to give an extensive illustration of the proposed method, including the performances of CGC with different hard negative samples and evaluations for hard negative samples generated with different similarity measurements.|图形对比学习已经成为无监督图形表示学习的有力工具。图形对比学习成功的关键是获取高质量的正负样本作为对比对，从而学习输入图的结构语义。最近的作品通常从同一训练批次的正样本中抽取负样本，或者从一个外部不相关的图中抽取负样本。然而，这种策略存在一个显著的局限性，这就是不可避免的采样假阴性样本的问题。本文提出了一种新的利用 textbf { C }反事实机制生成 textbf { G } raph textbf { C }对比学习人工硬负样本的方法，即 textbf { CGC }。我们利用反事实机制生成硬负样本，确保所生成的样本与正样本相似，但有不同于正样本的标签。与传统的无监督图形学习方法和 SOTA 图形对比学习方法相比，该方法在多个数据集上取得了令人满意的效果。我们还进行了一些补充实验，对所提出的方法进行了广泛的说明，包括对不同硬负样本的 CGC 性能和对不同相似度测量产生的硬负样本的评价。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Generating+Counterfactual+Hard+Negative+Samples+for+Graph+Contrastive+Learning)|0| |[Toward Degree Bias in Embedding-Based Knowledge Graph Completion](https://doi.org/10.1145/3543507.3583544)|Harry Shomer, Wei Jin, Wentao Wang, Jiliang Tang|Computer Science, Michigan State University, USA|A fundamental task for knowledge graphs (KGs) is knowledge graph completion (KGC). It aims to predict unseen edges by learning representations for all the entities and relations in a KG. A common concern when learning representations on traditional graphs is degree bias. It can affect graph algorithms by learning poor representations for lower-degree nodes, often leading to low performance on such nodes. However, there has been limited research on whether there exists degree bias for embedding-based KGC and how such bias affects the performance of KGC. In this paper, we validate the existence of degree bias in embedding-based KGC and identify the key factor to degree bias. We then introduce a novel data augmentation method, KG-Mixup, to generate synthetic triples to mitigate such bias. Extensive experiments have demonstrated that our method can improve various embedding-based KGC methods and outperform other methods tackling the bias problem on multiple benchmark datasets.|知识图的一个基本任务是知识图的完成。它的目的是通过学习 KG 中所有实体和关系的表示来预测看不见的边。学习传统图表示的一个常见问题是度偏差。它通过学习低度节点的差表示来影响图算法，经常导致低度节点的性能下降。然而，关于嵌入式 KGC 是否存在程度偏差以及这种偏差如何影响 KGC 性能的研究还很有限。在本文中，我们验证了基于嵌入的 KGC 中存在程度偏差，并找出了影响程度偏差的关键因素。然后，我们引入一种新的数据增强方法，KG 混合，产生合成三元组，以减轻这种偏差。大量的实验表明，该方法可以改进各种基于嵌入的 KGC 方法，并优于其他处理多基准数据集偏差问题的方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Toward+Degree+Bias+in+Embedding-Based+Knowledge+Graph+Completion)|0| -|[LINet: A Location and Intention-Aware Neural Network for Hotel Group Recommendation](https://doi.org/10.1145/3543507.3583202)|Ruitao Zhu, Detao Lv, Yao Yu, Ruihao Zhu, Zhenzhe Zheng, Ke Bu, Quan Lu, Fan Wu|Shanghai Jiao Tong University, China; Alibaba Group, China; Cornell University, USA|Motivated by the collaboration with Fliggy1, a leading Online Travel Platform (OTP), we investigate an important but less explored research topic about optimizing the quality of hotel supply, namely selecting potential profitable hotels in advance to build up adequate room inventory. We formulate a WWW problem, i.e., within a specific time period (When) and potential travel area (Where), which hotels should be recommended to a certain group of users with similar travel intentions (Why). We identify three critical challenges in solving the WWW problem: user groups generation, travel data sparsity and utilization of hotel recommendation information (e.g., period, location and intention). To this end, we propose LINet, a Location and Intention-aware neural Network for hotel group recommendation. Specifically, LINet first identifies user travel intentions for user groups generalization, and then characterizes the group preferences by jointly considering historical user-hotel interaction and spatio-temporal features of hotels. For data sparsity, we develop a graph neural network, which employs long-term data, and further design an auxiliary loss function of location that efficiently exploits data within the same and across different locations. Both offline and online experiments demonstrate the effectiveness of LINet when compared with state-of-the-art methods. LINet has been successfully deployed on Fliggy to retrieve high quality hotels for business development, serving hundreds of hotel operation scenarios and thousands of hotel operators.|受到与领先的在线旅游平台(OTP) Fliggy1合作的启发，我们研究了一个重要但探索较少的优化酒店供应质量的研究课题，即提前选择潜在的盈利酒店，以建立足够的客房库存。我们制定了一个 WWW 问题，即在一个特定的时间段(何时)和潜在的旅游区域(何地) ，哪些酒店应该被推荐给具有相似旅游意图的特定用户群体(为什么)。我们确定了解决 WWW 问题的三个关键挑战: 用户群生成、旅游数据稀疏和酒店推荐信息的利用(例如，时间、地点和意图)。为此，我们提出了 LINet，一个位置和意图感知的神经网络，用于酒店集团推荐。具体来说，LINet 首先通过用户群的概括来识别用户的旅游意图，然后联合考虑历史上用户与酒店的交互和酒店的时空特征来表征用户的群体偏好。针对数据稀疏性问题，提出了一种基于长期数据的图形神经网络，并进一步设计了位置辅助损失函数，有效地利用同一位置和不同位置的数据。离线和在线实验都证明了与最先进的方法相比，LINet 的有效性。LINet 已经成功地部署在 Fliggy 上，为业务发展检索高质量的酒店，为数百家酒店运营方案和数千家酒店运营商提供服务。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=LINet:+A+Location+and+Intention-Aware+Neural+Network+for+Hotel+Group+Recommendation)|0| -|[Distillation from Heterogeneous Models for Top-K Recommendation](https://doi.org/10.1145/3543507.3583209)|SeongKu Kang, Wonbin Kweon, Dongha Lee, Jianxun Lian, Xing Xie, Hwanjo Yu|Yonsei University, Republic of Korea; Pohang University of Science and Technology, Republic of Korea; Microsoft Research Asia, China|Recent recommender systems have shown remarkable performance by using an ensemble of heterogeneous models. However, it is exceedingly costly because it requires resources and inference latency proportional to the number of models, which remains the bottleneck for production. Our work aims to transfer the ensemble knowledge of heterogeneous teachers to a lightweight student model using knowledge distillation (KD), to reduce the huge inference costs while retaining high accuracy. Through an empirical study, we find that the efficacy of distillation severely drops when transferring knowledge from heterogeneous teachers. Nevertheless, we show that an important signal to ease the difficulty can be obtained from the teacher's training trajectory. This paper proposes a new KD framework, named HetComp, that guides the student model by transferring easy-to-hard sequences of knowledge generated from the teachers' trajectories. To provide guidance according to the student's learning state, HetComp uses dynamic knowledge construction to provide progressively difficult ranking knowledge and adaptive knowledge transfer to gradually transfer finer-grained ranking information. Our comprehensive experiments show that HetComp significantly improves the distillation quality and the generalization of the student model.|最近的推荐系统通过使用一系列异构模型显示了显著的性能。然而，它的成本非常高，因为它需要的资源和推理延迟与模型的数量成正比，这仍然是生产的瓶颈。我们的工作旨在利用知识精馏(KD)将异构教师的集成知识转移到一个轻量级的学生模型，以减少庞大的推理成本，同时保持较高的推理精度。通过实证研究发现，异质型教师传授知识时，蒸馏效果严重下降。然而，我们表明，一个重要的信号，缓解困难可以从教师的培训轨迹。提出了一种新的知识发现框架 HetComp，该框架通过传递由教师轨迹生成的易于生成的知识序列来指导学生模型。为了根据学生的学习状态提供指导，HetComp 使用动态知识结构来提供逐步难以排序的知识，并使用自适应知识转移来逐步传递更细粒度的排序信息。我们的综合实验表明，HetComp 显著提高了蒸馏质量和学生模型的推广。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Distillation+from+Heterogeneous+Models+for+Top-K+Recommendation)|0| -|[Exploration and Regularization of the Latent Action Space in Recommendation](https://doi.org/10.1145/3543507.3583244)|Shuchang Liu, Qingpeng Cai, Bowen Sun, Yuhao Wang, Ji Jiang, Dong Zheng, Peng Jiang, Kun Gai, Xiangyu Zhao, Yongfeng Zhang|; Peking University, China; Kuaishou Technology, China; Rutgers University, USA; City University of Hong Kong, China|In recommender systems, reinforcement learning solutions have effectively boosted recommendation performance because of their ability to capture long-term user-system interaction. However, the action space of the recommendation policy is a list of items, which could be extremely large with a dynamic candidate item pool. To overcome this challenge, we propose a hyper-actor and critic learning framework where the policy decomposes the item list generation process into a hyper-action inference step and an effect-action selection step. The first step maps the given state space into a vectorized hyper-action space, and the second step selects the item list based on the hyper-action. In order to regulate the discrepancy between the two action spaces, we design an alignment module along with a kernel mapping function for items to ensure inference accuracy and include a supervision module to stabilize the learning process. We build simulated environments on public datasets and empirically show that our framework is superior in recommendation compared to standard RL baselines.|在推荐系统中，强化学习解决方案有效地提高了推荐性能，因为它们能够捕捉长期的用户系统交互。但是，推荐策略的操作空间是一个项目列表，对于动态候选项目池，这个列表可能非常大。为了克服这一挑战，我们提出了一个超行为者和批评者学习框架，其中策略将项目表生成过程分解为一个超行为推理步骤和一个效果-行为选择步骤。第一步将给定的状态空间映射到向量化的超动作空间，第二步根据超动作选择项目列表。为了调节两个动作空间之间的差异，我们设计了一个对齐模块和一个项目的核映射函数来保证推理的准确性，并包括一个监督模块来稳定学习过程。我们在公共数据集上建立了模拟环境，并且经验表明我们的框架在推荐方面优于标准 RL 基线。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Exploration+and+Regularization+of+the+Latent+Action+Space+in+Recommendation)|0| -|[Compressed Interaction Graph based Framework for Multi-behavior Recommendation](https://doi.org/10.1145/3543507.3583312)|Wei Guo, Chang Meng, Enming Yuan, Zhicheng He, Huifeng Guo, Yingxue Zhang, Bo Chen, Yaochen Hu, Ruiming Tang, Xiu Li, Rui Zhang|Huawei Technologies, Canada; Huawei Noah's Ark Lab, China; Shenzhen International Graduate School, Tsinghua University, China; ruizhang.info, China; Institute for Interdisciplinary Information Sciences, Tsinghua University, China|Multi-types of user behavior data (e.g., clicking, adding to cart, and purchasing) are recorded in most real-world recommendation scenarios, which can help to learn users' multi-faceted preferences. However, it is challenging to explore multi-behavior data due to the unbalanced data distribution and sparse target behavior, which lead to the inadequate modeling of high-order relations when treating multi-behavior data ''as features'' and gradient conflict in multitask learning when treating multi-behavior data ''as labels''. In this paper, we propose CIGF, a Compressed Interaction Graph based Framework, to overcome the above limitations. Specifically, we design a novel Compressed Interaction Graph Convolution Network (CIGCN) to model instance-level high-order relations explicitly. To alleviate the potential gradient conflict when treating multi-behavior data ''as labels'', we propose a Multi-Expert with Separate Input (MESI) network with separate input on the top of CIGCN for multi-task learning. Comprehensive experiments on three large-scale real-world datasets demonstrate the superiority of CIGF. Ablation studies and in-depth analysis further validate the effectiveness of our proposed model in capturing high-order relations and alleviating gradient conflict. The source code and datasets are available at https://github.com/MC-CV/CIGF.|多种类型的用户行为数据(例如，点击、添加到购物车和购买)记录在大多数真实世界的推荐场景中，这有助于了解用户的多方面偏好。然而，由于多行为数据分布不均衡，目标行为稀疏，导致多任务学习中将多行为数据“作为特征”的高阶关系建模不足，将多行为数据“作为标签”的多任务学习中存在梯度冲突。本文提出了一种基于压缩交互图的 CIGF 框架，以克服上述局限性。具体来说，我们设计了一个新的压缩交互图卷积网络(CIGCN)来显式地建模实例级的高阶关系。为了缓解多行为数据“作为标签”时潜在的梯度冲突，本文提出了一种在 CIGCN 顶部具有独立输入的多专家网络，用于多任务学习。通过对三个大规模实际数据集的综合实验，验证了 CIGF 算法的优越性。烧蚀研究和深入分析进一步验证了我们提出的模型在捕获高阶关系和缓解梯度冲突方面的有效性。源代码和数据集可在 https://github.com/mc-cv/cigf 下载。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Compressed+Interaction+Graph+based+Framework+for+Multi-behavior+Recommendation)|0| +|[LINet: A Location and Intention-Aware Neural Network for Hotel Group Recommendation](https://doi.org/10.1145/3543507.3583202)|Ruitao Zhu, Detao Lv, Yao Yu, Ruihao Zhu, Zhenzhe Zheng, Ke Bu, Quan Lu, Fan Wu|Alibaba Group, China; Shanghai Jiao Tong University, China; Cornell University, USA|Motivated by the collaboration with Fliggy1, a leading Online Travel Platform (OTP), we investigate an important but less explored research topic about optimizing the quality of hotel supply, namely selecting potential profitable hotels in advance to build up adequate room inventory. We formulate a WWW problem, i.e., within a specific time period (When) and potential travel area (Where), which hotels should be recommended to a certain group of users with similar travel intentions (Why). We identify three critical challenges in solving the WWW problem: user groups generation, travel data sparsity and utilization of hotel recommendation information (e.g., period, location and intention). To this end, we propose LINet, a Location and Intention-aware neural Network for hotel group recommendation. Specifically, LINet first identifies user travel intentions for user groups generalization, and then characterizes the group preferences by jointly considering historical user-hotel interaction and spatio-temporal features of hotels. For data sparsity, we develop a graph neural network, which employs long-term data, and further design an auxiliary loss function of location that efficiently exploits data within the same and across different locations. Both offline and online experiments demonstrate the effectiveness of LINet when compared with state-of-the-art methods. LINet has been successfully deployed on Fliggy to retrieve high quality hotels for business development, serving hundreds of hotel operation scenarios and thousands of hotel operators.|受到与领先的在线旅游平台(OTP) Fliggy1合作的启发，我们研究了一个重要但探索较少的优化酒店供应质量的研究课题，即提前选择潜在的盈利酒店，以建立足够的客房库存。我们制定了一个 WWW 问题，即在一个特定的时间段(何时)和潜在的旅游区域(何地) ，哪些酒店应该被推荐给具有相似旅游意图的特定用户群体(为什么)。我们确定了解决 WWW 问题的三个关键挑战: 用户群生成、旅游数据稀疏和酒店推荐信息的利用(例如，时间、地点和意图)。为此，我们提出了 LINet，一个位置和意图感知的神经网络，用于酒店集团推荐。具体来说，LINet 首先通过用户群的概括来识别用户的旅游意图，然后联合考虑历史上用户与酒店的交互和酒店的时空特征来表征用户的群体偏好。针对数据稀疏性问题，提出了一种基于长期数据的图形神经网络，并进一步设计了位置辅助损失函数，有效地利用同一位置和不同位置的数据。离线和在线实验都证明了与最先进的方法相比，LINet 的有效性。LINet 已经成功地部署在 Fliggy 上，为业务发展检索高质量的酒店，为数百家酒店运营方案和数千家酒店运营商提供服务。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=LINet:+A+Location+and+Intention-Aware+Neural+Network+for+Hotel+Group+Recommendation)|0| +|[Distillation from Heterogeneous Models for Top-K Recommendation](https://doi.org/10.1145/3543507.3583209)|SeongKu Kang, Wonbin Kweon, Dongha Lee, Jianxun Lian, Xing Xie, Hwanjo Yu|Pohang University of Science and Technology, Republic of Korea; Microsoft Research Asia, China; Yonsei University, Republic of Korea|Recent recommender systems have shown remarkable performance by using an ensemble of heterogeneous models. However, it is exceedingly costly because it requires resources and inference latency proportional to the number of models, which remains the bottleneck for production. Our work aims to transfer the ensemble knowledge of heterogeneous teachers to a lightweight student model using knowledge distillation (KD), to reduce the huge inference costs while retaining high accuracy. Through an empirical study, we find that the efficacy of distillation severely drops when transferring knowledge from heterogeneous teachers. Nevertheless, we show that an important signal to ease the difficulty can be obtained from the teacher's training trajectory. This paper proposes a new KD framework, named HetComp, that guides the student model by transferring easy-to-hard sequences of knowledge generated from the teachers' trajectories. To provide guidance according to the student's learning state, HetComp uses dynamic knowledge construction to provide progressively difficult ranking knowledge and adaptive knowledge transfer to gradually transfer finer-grained ranking information. Our comprehensive experiments show that HetComp significantly improves the distillation quality and the generalization of the student model.|最近的推荐系统通过使用一系列异构模型显示了显著的性能。然而，它的成本非常高，因为它需要的资源和推理延迟与模型的数量成正比，这仍然是生产的瓶颈。我们的工作旨在利用知识精馏(KD)将异构教师的集成知识转移到一个轻量级的学生模型，以减少庞大的推理成本，同时保持较高的推理精度。通过实证研究发现，异质型教师传授知识时，蒸馏效果严重下降。然而，我们表明，一个重要的信号，缓解困难可以从教师的培训轨迹。提出了一种新的知识发现框架 HetComp，该框架通过传递由教师轨迹生成的易于生成的知识序列来指导学生模型。为了根据学生的学习状态提供指导，HetComp 使用动态知识结构来提供逐步难以排序的知识，并使用自适应知识转移来逐步传递更细粒度的排序信息。我们的综合实验表明，HetComp 显著提高了蒸馏质量和学生模型的推广。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Distillation+from+Heterogeneous+Models+for+Top-K+Recommendation)|0| +|[Exploration and Regularization of the Latent Action Space in Recommendation](https://doi.org/10.1145/3543507.3583244)|Shuchang Liu, Qingpeng Cai, Bowen Sun, Yuhao Wang, Ji Jiang, Dong Zheng, Peng Jiang, Kun Gai, Xiangyu Zhao, Yongfeng Zhang|; Kuaishou Technology, China; City University of Hong Kong, China; Rutgers University, USA; Peking University, China|In recommender systems, reinforcement learning solutions have effectively boosted recommendation performance because of their ability to capture long-term user-system interaction. However, the action space of the recommendation policy is a list of items, which could be extremely large with a dynamic candidate item pool. To overcome this challenge, we propose a hyper-actor and critic learning framework where the policy decomposes the item list generation process into a hyper-action inference step and an effect-action selection step. The first step maps the given state space into a vectorized hyper-action space, and the second step selects the item list based on the hyper-action. In order to regulate the discrepancy between the two action spaces, we design an alignment module along with a kernel mapping function for items to ensure inference accuracy and include a supervision module to stabilize the learning process. We build simulated environments on public datasets and empirically show that our framework is superior in recommendation compared to standard RL baselines.|在推荐系统中，强化学习解决方案有效地提高了推荐性能，因为它们能够捕捉长期的用户系统交互。但是，推荐策略的操作空间是一个项目列表，对于动态候选项目池，这个列表可能非常大。为了克服这一挑战，我们提出了一个超行为者和批评者学习框架，其中策略将项目表生成过程分解为一个超行为推理步骤和一个效果-行为选择步骤。第一步将给定的状态空间映射到向量化的超动作空间，第二步根据超动作选择项目列表。为了调节两个动作空间之间的差异，我们设计了一个对齐模块和一个项目的核映射函数来保证推理的准确性，并包括一个监督模块来稳定学习过程。我们在公共数据集上建立了模拟环境，并且经验表明我们的框架在推荐方面优于标准 RL 基线。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Exploration+and+Regularization+of+the+Latent+Action+Space+in+Recommendation)|0| +|[Compressed Interaction Graph based Framework for Multi-behavior Recommendation](https://doi.org/10.1145/3543507.3583312)|Wei Guo, Chang Meng, Enming Yuan, Zhicheng He, Huifeng Guo, Yingxue Zhang, Bo Chen, Yaochen Hu, Ruiming Tang, Xiu Li, Rui Zhang|Huawei Noah's Ark Lab, China; Huawei Technologies, Canada; ruizhang.info, China; Institute for Interdisciplinary Information Sciences, Tsinghua University, China; Shenzhen International Graduate School, Tsinghua University, China|Multi-types of user behavior data (e.g., clicking, adding to cart, and purchasing) are recorded in most real-world recommendation scenarios, which can help to learn users' multi-faceted preferences. However, it is challenging to explore multi-behavior data due to the unbalanced data distribution and sparse target behavior, which lead to the inadequate modeling of high-order relations when treating multi-behavior data ''as features'' and gradient conflict in multitask learning when treating multi-behavior data ''as labels''. In this paper, we propose CIGF, a Compressed Interaction Graph based Framework, to overcome the above limitations. Specifically, we design a novel Compressed Interaction Graph Convolution Network (CIGCN) to model instance-level high-order relations explicitly. To alleviate the potential gradient conflict when treating multi-behavior data ''as labels'', we propose a Multi-Expert with Separate Input (MESI) network with separate input on the top of CIGCN for multi-task learning. Comprehensive experiments on three large-scale real-world datasets demonstrate the superiority of CIGF. Ablation studies and in-depth analysis further validate the effectiveness of our proposed model in capturing high-order relations and alleviating gradient conflict. The source code and datasets are available at https://github.com/MC-CV/CIGF.|多种类型的用户行为数据(例如，点击、添加到购物车和购买)记录在大多数真实世界的推荐场景中，这有助于了解用户的多方面偏好。然而，由于多行为数据分布不均衡，目标行为稀疏，导致多任务学习中将多行为数据“作为特征”的高阶关系建模不足，将多行为数据“作为标签”的多任务学习中存在梯度冲突。本文提出了一种基于压缩交互图的 CIGF 框架，以克服上述局限性。具体来说，我们设计了一个新的压缩交互图卷积网络(CIGCN)来显式地建模实例级的高阶关系。为了缓解多行为数据“作为标签”时潜在的梯度冲突，本文提出了一种在 CIGCN 顶部具有独立输入的多专家网络，用于多任务学习。通过对三个大规模实际数据集的综合实验，验证了 CIGF 算法的优越性。烧蚀研究和深入分析进一步验证了我们提出的模型在捕获高阶关系和缓解梯度冲突方面的有效性。源代码和数据集可在 https://github.com/mc-cv/cigf 下载。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Compressed+Interaction+Graph+based+Framework+for+Multi-behavior+Recommendation)|0| |[Correlative Preference Transfer with Hierarchical Hypergraph Network for Multi-Domain Recommendation](https://doi.org/10.1145/3543507.3583331)|Zixuan Xu, Penghui Wei, Shaoguo Liu, Weimin Zhang, Liang Wang, Bo Zheng|Alibaba Group, China|Advanced recommender systems usually involve multiple domains (such as scenarios or categories) for various marketing strategies, and users interact with them to satisfy diverse demands. The goal of multi-domain recommendation (MDR) is to improve the recommendation performance of all domains simultaneously. Conventional graph neural network based methods usually deal with each domain separately, or train a shared model to serve all domains. The former fails to leverage users' cross-domain behaviors, making the behavior sparseness issue a great obstacle. The latter learns shared user representation with respect to all domains, which neglects users' domain-specific preferences. In this paper we propose $\mathsf{H^3Trans}$, a hierarchical hypergraph network based correlative preference transfer framework for MDR, which represents multi-domain user-item interactions into a unified graph to help preference transfer. $\mathsf{H^3Trans}$ incorporates two hyperedge-based modules, namely dynamic item transfer (Hyper-I) and adaptive user aggregation (Hyper-U). Hyper-I extracts correlative information from multi-domain user-item feedbacks for eliminating domain discrepancy of item representations. Hyper-U aggregates users' scattered preferences in multiple domains and further exploits the high-order (not only pair-wise) connections to improve user representations. Experiments on both public and production datasets verify the superiority of $\mathsf{H^3Trans}$ for MDR.|高级推荐系统通常涉及多个领域(如场景或类别)的不同营销策略，用户与他们互动，以满足不同的需求。多域推荐(MDR)的目标是同时提高所有域的推荐性能。传统的基于图神经网络的方法通常分别处理各个领域，或者训练一个共享模型来服务于所有领域。前者未能充分利用用户的跨域行为，使得行为稀疏性问题成为一大障碍。后者学习所有域的共享用户表示，这忽略了用户特定域的首选项。本文提出了一种基于层次超图网络的 MDR 相关偏好传递框架 $mathsf { H ^ 3Trans } $，它将多领域用户-项目交互表示为一个统一的图，以帮助偏好传递。$mathsf { H ^ 3Trans } $包含两个基于超边界的模块，即动态项传输(Hyper-I)和自适应用户聚合(Hyper-U)。Hyper-I 从多领域用户项目反馈中提取相关信息，消除项目表示的领域差异。Hyper-U 聚合用户在多个域中的分散偏好，并进一步利用高阶(不仅仅是成对)连接来改善用户表示。在公共数据集和生产数据集上的实验验证了 $mathsf { H ^ 3Trans } $用于 MDR 的优越性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Correlative+Preference+Transfer+with+Hierarchical+Hypergraph+Network+for+Multi-Domain+Recommendation)|0| -|[User Retention-oriented Recommendation with Decision Transformer](https://doi.org/10.1145/3543507.3583418)|Kesen Zhao, Lixin Zou, Xiangyu Zhao, Maolin Wang, Dawei Yin|Wuhan University, China; Baidu Inc., China; City University of Hong Kong, Hong Kong|Improving user retention with reinforcement learning~(RL) has attracted increasing attention due to its significant importance in boosting user engagement. However, training the RL policy from scratch without hurting users' experience is unavoidable due to the requirement of trial-and-error searches. Furthermore, the offline methods, which aim to optimize the policy without online interactions, suffer from the notorious stability problem in value estimation or unbounded variance in counterfactual policy evaluation. To this end, we propose optimizing user retention with Decision Transformer~(DT), which avoids the offline difficulty by translating the RL as an autoregressive problem. However, deploying the DT in recommendation is a non-trivial problem because of the following challenges: (1) deficiency in modeling the numerical reward value; (2) data discrepancy between the policy learning and recommendation generation; (3) unreliable offline performance evaluation. In this work, we, therefore, contribute a series of strategies for tackling the exposed issues. We first articulate an efficient reward prompt by weighted aggregation of meta embeddings for informative reward embedding. Then, we endow a weighted contrastive learning method to solve the discrepancy between training and inference. Furthermore, we design two robust offline metrics to measure user retention. Finally, the significant improvement in the benchmark datasets demonstrates the superiority of the proposed method.|使用强化学习 ~ (RL)提高用户保持率已经引起了越来越多的关注，因为它在提高用户参与度方面具有重要意义。然而，由于试错检索的要求，在不损害用户体验的情况下从头开始训练 RL 策略是不可避免的。此外，离线方法的目的是优化政策没有在线交互，受到臭名昭著的稳定性问题的价值估计或无界方差的反事实政策评估。为此，我们提出了利用决策转换器 ~ (DT)来优化用户保留，通过将 RL 转换为一个自回归问题来避免离线困难。然而，在推荐中部署 DT 是一个非常重要的问题，因为它面临以下挑战: (1)数值奖励值建模不足; (2)策略学习和推荐生成之间的数据差异; (3)不可靠的离线性能评估。在这项工作中，我们，因此，贡献了一系列的战略，以解决暴露的问题。我们首先通过元嵌入的加权聚合提出了一个有效的信息嵌入奖励提示。然后，我们提出了一种加权对比学习方法来解决训练和推理之间的差异。此外，我们还设计了两个健壮的离线度量来衡量用户保持率。最后，基准数据集的显著改进证明了该方法的优越性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=User+Retention-oriented+Recommendation+with+Decision+Transformer)|0| -|[Balancing Unobserved Confounding with a Few Unbiased Ratings in Debiased Recommendations](https://doi.org/10.1145/3543507.3583495)|Haoxuan Li, Yanghao Xiao, Chunyuan Zheng, Peng Wu|Beijing Technology and Business University, China; University of Chinese Academy of Sciences, China; University of California, San Diego, USA; Peking University, China|Recommender systems are seen as an effective tool to address information overload, but it is widely known that the presence of various biases makes direct training on large-scale observational data result in sub-optimal prediction performance. In contrast, unbiased ratings obtained from randomized controlled trials or A/B tests are considered to be the golden standard, but are costly and small in scale in reality. To exploit both types of data, recent works proposed to use unbiased ratings to correct the parameters of the propensity or imputation models trained on the biased dataset. However, the existing methods fail to obtain accurate predictions in the presence of unobserved confounding or model misspecification. In this paper, we propose a theoretically guaranteed model-agnostic balancing approach that can be applied to any existing debiasing method with the aim of combating unobserved confounding and model misspecification. The proposed approach makes full use of unbiased data by alternatively correcting model parameters learned with biased data, and adaptively learning balance coefficients of biased samples for further debiasing. Extensive real-world experiments are conducted along with the deployment of our proposal on four representative debiasing methods to demonstrate the effectiveness.|推荐系统被视为解决信息超载问题的有效工具，但众所周知，各种偏差的存在使得对大规模观测数据的直接训练导致次优预测性能。相比之下，通过随机对照试验或 A/B 测试获得的无偏评分被认为是黄金标准，但实际上成本高，规模小。为了利用这两种类型的数据，最近的工作建议使用无偏评级来修正倾向或插补模型的参数训练偏向的数据集。然而，现有的方法不能获得准确的预测存在未观察到的混杂或模型错误说明。本文提出了一种理论保证的模型无关平衡方法，该方法可以应用于任何现有的去偏方法，以消除未观察到的混淆和模型不确定性。该方法充分利用无偏数据，通过交替校正有偏数据学习的模型参数，以及有偏样本的自适应学习平衡系数进一步消除偏差。随着我们的建议在四个有代表性的去偏方法上的部署，广泛的现实世界的实验被进行，以证明有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Balancing+Unobserved+Confounding+with+a+Few+Unbiased+Ratings+in+Debiased+Recommendations)|0| -|[Denoising and Prompt-Tuning for Multi-Behavior Recommendation](https://doi.org/10.1145/3543507.3583513)|Chi Zhang, Rui Chen, Xiangyu Zhao, Qilong Han, Li Li|University of Delaware, USA; Harbin Engineering University, China; City University of Hong Kong, Hong Kong|In practical recommendation scenarios, users often interact with items under multi-typed behaviors (e.g., click, add-to-cart, and purchase). Traditional collaborative filtering techniques typically assume that users only have a single type of behavior with items, making it insufficient to utilize complex collaborative signals to learn informative representations and infer actual user preferences. Consequently, some pioneer studies explore modeling multi-behavior heterogeneity to learn better representations and boost the performance of recommendations for a target behavior. However, a large number of auxiliary behaviors (i.e., click and add-to-cart) could introduce irrelevant information to recommenders, which could mislead the target behavior (i.e., purchase) recommendation, rendering two critical challenges: (i) denoising auxiliary behaviors and (ii) bridging the semantic gap between auxiliary and target behaviors. Motivated by the above observation, we propose a novel framework-Denoising and Prompt-Tuning (DPT) with a three-stage learning paradigm to solve the aforementioned challenges. In particular, DPT is equipped with a pattern-enhanced graph encoder in the first stage to learn complex patterns as prior knowledge in a data-driven manner to guide learning informative representation and pinpointing reliable noise for subsequent stages. Accordingly, we adopt different lightweight tuning approaches with effectiveness and efficiency in the following stages to further attenuate the influence of noise and alleviate the semantic gap among multi-typed behaviors. Extensive experiments on two real-world datasets demonstrate the superiority of DPT over a wide range of state-of-the-art methods. The implementation code is available online at https://github.com/zc-97/DPT.|在实际的推荐场景中，用户经常在多类型行为下与项目交互(例如，单击、添加到购物车和购买)。传统的协同过滤技术通常假设用户只有单一类型的行为与项目，使其不足以利用复杂的协作信号来学习信息表示和推断实际的用户偏好。因此，一些先驱研究探索建立多行为异质性模型，以学习更好的表示方法，并提高针对目标行为的建议的性能。然而，大量的辅助行为(即点击和添加到购物车)可能会向推荐者引入不相关的信息，这可能会误导目标行为(即购买)推荐，造成两个关键的挑战: (i)去噪辅助行为和(ii)桥接辅助行为和目标行为之间的语义差距。基于上述观察，我们提出了一个新的框架-去噪和及时调整(DPT)与三个阶段的学习范式，以解决上述挑战。特别是，DPT 在第一阶段配备了模式增强图形编码器，以数据驱动的方式学习复杂的模式作为先验知识，以指导学习信息表示和确定后续阶段的可靠噪声。相应地，我们在接下来的阶段采用了不同的轻量化方法，以进一步减小噪声的影响，缓解多类型行为之间的语义鸿沟。在两个实际数据集上的大量实验证明了 DPT 相对于一系列最先进的方法的优越性。实施守则可于网上 https://github.com/zc-97/dpt 下载。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Denoising+and+Prompt-Tuning+for+Multi-Behavior+Recommendation)|0| -|[CAMUS: Attribute-Aware Counterfactual Augmentation for Minority Users in Recommendation](https://doi.org/10.1145/3543507.3583538)|Yuxin Ying, Fuzhen Zhuang, Yongchun Zhu, Deqing Wang, Hongwei Zheng|Institute of Computing Technology, Chinese Academy of Sciences, China; School of Computer Science and Engineering, Beihang University, China; Beijing Academy of Blockchain and Edge Computing, China; Institute of Artificial Intelligence, Beihang University, China|Embedding-based methods currently achieved impressive success in recommender systems. However, such methods are more likely to suffer from bias in data distribution, especially the attribute bias problem. For example, when a certain type of user, like the elderly, occupies the mainstream, the recommendation results of minority users would be seriously affected by the mainstream users’ attributes. To address this problem, most existing methods are proposed from the perspective of fairness, which focuses on eliminating unfairness but deteriorates the recommendation performance. Unlike these methods, in this paper, we focus on improving the recommendation performance for minority users of biased attributes. Along this line, we propose a novel attribute-aware Counterfactual Augmentation framework for Minority Users(CAMUS). Specifically, the CAMUS consists of a counterfactual augmenter, a confidence estimator, and a recommender. The counterfactual augmenter conducts data augmentation for the minority group by utilizing the interactions of mainstream users based on a universal counterfactual assumption. Besides, a tri-training-based confidence estimator is applied to ensure the effectiveness of augmentation. Extensive experiments on three real-world datasets have demonstrated the superior performance of the proposed methods. Further case studies verify the universality of the proposed CAMUS framework on different data sparsity, attributes, and models.|基于嵌入的方法目前在推荐系统中取得了令人印象深刻的成功。然而，这些方法更容易受到数据分布偏差的影响，特别是属性偏差问题。例如，当某种类型的用户，如老年人，占据主流时，少数用户的推荐结果会受到主流用户属性的严重影响。为了解决这个问题，现有的方法大多是从公平的角度出发，着重于消除不公平性，但是会降低推荐性能。与这些方法不同的是，本文主要研究如何提高偏向属性的少数用户的推荐性能。在此基础上，我们提出了一种新的面向少数用户的属性感知反事实增强框架(CAMUS)。具体来说，CAMUS 由一个反事实增强器、一个置信度估计器和一个推荐器组成。反事实增强器利用主流用户基于普遍反事实假设的交互作用对少数群体进行数据增强。此外，采用基于三训练的置信估计来保证增广的有效性。在三个实际数据集上的大量实验证明了该方法的优越性能。进一步的案例研究验证了所提出的 CAMUS 框架在不同的数据稀疏性、属性和模型上的通用性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=CAMUS:+Attribute-Aware+Counterfactual+Augmentation+for+Minority+Users+in+Recommendation)|0| -|[Dynamically Expandable Graph Convolution for Streaming Recommendation](https://doi.org/10.1145/3543507.3583237)|Bowei He, Xu He, Yingxue Zhang, Ruiming Tang, Chen Ma|Huawei Noah's Ark Lab, China; Department of Computer Science, City University of Hong Kong, Hong Kong; Huawei Noah's Ark Lab Montreal, Canada; City University of Hong Kong, Hong Kong|Personalized recommender systems have been widely studied and deployed to reduce information overload and satisfy users' diverse needs. However, conventional recommendation models solely conduct a one-time training-test fashion and can hardly adapt to evolving demands, considering user preference shifts and ever-increasing users and items in the real world. To tackle such challenges, the streaming recommendation is proposed and has attracted great attention recently. Among these, continual graph learning is widely regarded as a promising approach for the streaming recommendation by academia and industry. However, existing methods either rely on the historical data replay which is often not practical under increasingly strict data regulations, or can seldom solve the \textit{over-stability} issue. To overcome these difficulties, we propose a novel \textbf{D}ynamically \textbf{E}xpandable \textbf{G}raph \textbf{C}onvolution (DEGC) algorithm from a \textit{model isolation} perspective for the streaming recommendation which is orthogonal to previous methods. Based on the motivation of disentangling outdated short-term preferences from useful long-term preferences, we design a sequence of operations including graph convolution pruning, refining, and expanding to only preserve beneficial long-term preference-related parameters and extract fresh short-term preferences. Moreover, we model the temporal user preference, which is utilized as user embedding initialization, for better capturing the individual-level preference shifts. Extensive experiments on the three most representative GCN-based recommendation models and four industrial datasets demonstrate the effectiveness and robustness of our method.|个性化推荐系统已被广泛研究和部署，以减少信息超载和满足用户的不同需求。然而，考虑到用户偏好的变化以及现实世界中不断增加的用户和项目，传统的推荐模型只能进行一次性的训练测试，很难适应不断变化的需求。为了应对这些挑战，流媒体推荐被提出并引起了人们的广泛关注。其中，连续图学习被学术界和工业界广泛认为是一种很有前途的流推荐方法。然而，现有的方法要么依赖于历史数据的重放，这在日益严格的数据规则下往往是不切实际的，要么很少能解决文本的过稳定性问题。为了克服这些困难，我们从文本{模型隔离}的角度提出了一种新的动态 textbf { D }可扩展 textbf { E } Raph textbf { C }内卷(DEGC)算法用于流式推荐，该算法与以前的方法是正交的。基于将过时的短期偏好与有用的长期偏好分离的动机，我们设计了一系列操作，包括图卷积修剪，细化和扩展，以仅保留有益的长期偏好相关参数并提取新的短期偏好。此外，为了更好地捕获个体层次的偏好变化，我们建立了时态用户偏好模型，并将其用于用户嵌入初始化。在三个最具代表性的基于 GCN 的推荐模型和四个工业数据集上的大量实验证明了该方法的有效性和鲁棒性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Dynamically+Expandable+Graph+Convolution+for+Streaming+Recommendation)|0| -|[CTRLStruct: Dialogue Structure Learning for Open-Domain Response Generation](https://doi.org/10.1145/3543507.3583285)|Congchi Yin, Piji Li, Zhaochun Ren|Shandong University, China; Nanjing University of Aeronautics and Astronautics, China|Dialogue structure discovery is essential in dialogue generation. Well-structured topic flow can leverage background information and predict future topics to help generate controllable and explainable responses. However, most previous work focused on dialogue structure learning in task-oriented dialogue other than open-domain dialogue which is more complicated and challenging. In this paper, we present a new framework CTRLStruct for dialogue structure learning to effectively explore topic-level dialogue clusters as well as their transitions with unlabelled information. Precisely, dialogue utterances encoded by bi-directional Transformer are further trained through a special designed contrastive learning task to improve representation. Then we perform clustering to utterance-level representations and form topic-level clusters that can be considered as vertices in dialogue structure graph. The edges in the graph indicating transition probability between vertices are calculated by mimicking expert behavior in datasets. Finally, dialogue structure graph is integrated into dialogue model to perform controlled response generation. Experiments on two popular open-domain dialogue datasets show our model can generate more coherent responses compared to some excellent dialogue models, as well as outperform some typical sentence embedding methods in dialogue utterance representation. Code is available in GitHub.|对话结构的发现是对话生成的基础。结构良好的主题流可以利用背景信息并预测未来的主题，从而帮助产生可控的和可解释的反应。然而，以往的研究大多侧重于面向任务的对话中的对话结构学习，而开放领域的对话更为复杂和具有挑战性。本文提出了一种新的对话结构学习框架 CTRLstruct，以有效地探索话题层次的对话群及其与未标记信息的过渡。准确地说，双向变压器编码的对话话语通过特别设计的对比学习任务进一步训练，以提高表征能力。然后对话语层面的表征进行聚类，形成话题层面的聚类，这些聚类可以看作是对话结构图中的顶点。通过模拟数据集中的专家行为，计算图中表示顶点间转移概率的边。最后，将对话结构图集成到对话模型中进行受控响应的生成。实验结果表明，与一些优秀的对话模型相比，该模型能够产生更加连贯的对话响应，并且在对话话语表征中优于一些典型的句子嵌入方法。代码可在 GitHub 中获得。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=CTRLStruct:+Dialogue+Structure+Learning+for+Open-Domain+Response+Generation)|0| -|[BlinkViz: Fast and Scalable Approximate Visualization on Very Large Datasets using Neural-Enhanced Mixed Sum-Product Networks](https://doi.org/10.1145/3543507.3583411)|Yimeng Qiao, Yinan Jing, Hanbing Zhang, Zhenying He, Kai Zhang, X. Sean Wang|Shanghai Key Laboratory of Data Science, School of Computer Science, Fudan University, China; Shanghai Key Laboratory of Data Science, School of Software, Fudan University, China|Web-based online interactive visual analytics enjoys popularity in recent years. Traditionally, visualizations are produced directly from querying the underlying data. However, for a very large dataset, this way is so time-consuming that it cannot meet the low-latency requirements of interactive visual analytics. In this paper, we propose a learning-based visualization approach called BlinkViz, which uses a learned model to produce approximate visualizations by leveraging mixed sum-product networks to learn the distribution of the original data. In such a way, it makes visualization faster and more scalable by decoupling visualization and data. In addition, to improve the accuracy of approximate visualizations, we propose an enhanced model by incorporating a neural network with residual structures, which can refine prediction results, especially for visual requests with low selectivity. Extensive experiments show that BlinkViz is extremely fast even on a large dataset with hundreds of millions of data records (over 30GB), responding in sub-seconds (from 2ms to less than 500ms for different requests) while keeping a low error rate. Furthermore, our approach remains scalable on latency and memory footprint size regardless of data size.|基于 Web 的在线交互式可视化分析近年来很受欢迎。传统上，可视化是通过查询底层数据直接生成的。然而，对于一个非常大的数据集，这种方法非常耗时，不能满足交互式可视化分析的低延迟要求。本文提出了一种基于学习的可视化方法 BlinkViz，该方法利用学习模型，通过混合和积网络来学习原始数据的分布情况，从而产生近似可视化效果。通过这种方式，可视化和数据解耦，使得可视化更快、更具可伸缩性。此外，为了提高近似可视化的准确性，我们提出了一个增强的模型，通过结合残差结构的神经网络，可以细化预测结果，特别是对低选择性的可视化请求。大量的实验表明，BlinkViz 即使在拥有数亿条数据记录(超过30GB)的大型数据集上也是极其快速的，响应时间在亚秒(对于不同的请求，响应时间从2毫秒到不到500毫秒) ，同时保持较低的错误率。此外，无论数据大小如何，我们的方法在延迟和内存占用大小上都是可伸缩的。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=BlinkViz:+Fast+and+Scalable+Approximate+Visualization+on+Very+Large+Datasets+using+Neural-Enhanced+Mixed+Sum-Product+Networks)|0| -|[Semi-supervised Adversarial Learning for Complementary Item Recommendation](https://doi.org/10.1145/3543507.3583462)|Koby Bibas, Oren Sar Shalom, Dietmar Jannach|Meta, Israel; Amazon, Israel; University of Klagenfurt, Austria|Complementary item recommendations are a ubiquitous feature of modern e-commerce sites. Such recommendations are highly effective when they are based on collaborative signals like co-purchase statistics. In certain online marketplaces, however, e.g., on online auction sites, constantly new items are added to the catalog. In such cases, complementary item recommendations are often based on item side-information due to a lack of interaction data. In this work, we propose a novel approach that can leverage both item side-information and labeled complementary item pairs to generate effective complementary recommendations for cold items, i.e., for items for which no co-purchase statistics yet exist. Given that complementary items typically have to be of a different category than the seed item, we technically maintain a latent space for each item category. Simultaneously, we learn to project distributed item representations into these category spaces to determine suitable recommendations. The main learning process in our architecture utilizes labeled pairs of complementary items. In addition, we adopt ideas from Cycle Generative Adversarial Networks (CycleGAN) to leverage available item information even in case no labeled data exists for a given item and category. Experiments on three e-commerce datasets show that our method is highly effective.|互补商品推荐是现代电子商务网站的一个普遍特征。当这些建议基于合作信号(如共同购买统计数据)时，它们是非常有效的。然而，在某些在线市场，例如在线拍卖网站，不断有新物品被添加到目录中。在这种情况下，由于缺乏交互数据，补充项目推荐通常基于项目侧信息。在这项工作中，我们提出了一个新颖的方法，可以利用项目侧信息和标记的互补项目对，以产生有效的补充建议，冷的项目，即项目，共同购买统计数据尚不存在。鉴于补充项目通常必须是一个与种子项目不同的类别，我们在技术上为每个项目类别保持一个潜在的空间。同时，我们学习将分布式项表示投射到这些类别空间中，以确定合适的建议。在我们的建筑中，主要的学习过程是使用标记成对的互补项目。此外，我们采用循环生成对抗网络(CycleGAN)的思想来利用可用的项目信息，即使在给定的项目和类别没有标记数据存在的情况下。在三个电子商务数据集上的实验表明，该方法是高效的。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Semi-supervised+Adversarial+Learning+for+Complementary+Item+Recommendation)|0| -|[MaSS: Model-agnostic, Semantic and Stealthy Data Poisoning Attack on Knowledge Graph Embedding](https://doi.org/10.1145/3543507.3583203)|Xiaoyu You, Beina Sheng, Daizong Ding, Mi Zhang, Xudong Pan, Min Yang, Fuli Feng|University of Science and Technology of China, CCCD Key Lab of Ministry of Culture and Tourism, China; Fudan University, School of Computer Science, China|Open-source knowledge graphs are attracting increasing attention. Nevertheless, the openness also raises the concern of data poisoning attacks, that is, the attacker could submit malicious facts to bias the prediction of knowledge graph embedding (KGE) models. Existing studies on such attacks adopt a clear-box setting and neglect the semantic information of the generated facts, making them fail to attack in real-world scenarios. In this work, we consider a more rigorous setting and propose a model-agnostic, semantic, and stealthy data poisoning attack on KGE models from a practical perspective. The main design of our work is to inject indicative paths to make the infected model predict certain malicious facts. With the aid of the proposed opaque-box path injection theory, we theoretically reveal that the attack success rate under the opaque-box setting is determined by the plausibility of triplets on the indicative path. Based on this, we develop a novel and efficient algorithm to search paths that maximize the attack goal, satisfy certain semantic constraints, and preserve certain stealthiness, i.e., the normal functionality of the target KGE will not be influenced although it predicts wrong facts given certain queries. Through extensive evaluation of benchmark datasets and 6 typical knowledge graph embedding models as the victims, we validate the effectiveness in terms of attack success rate (ASR) under opaque-box setting and stealthiness. For example, on FB15k-237, our attack achieves a ASR on DeepPath, with an average ASR over when attacking various KGE models under the opaque-box setting.|开源知识图表越来越受到人们的关注。然而，这种开放性也引起了人们对数据中毒攻击的担忧，即攻击者可能会提交恶意事实来偏向知识图嵌入(KGE)模型的预测。现有的关于此类攻击的研究采用了清晰框设置，忽视了所生成事实的语义信息，使得它们无法在现实世界中进行攻击。在这项工作中，我们考虑了一个更严格的设置，并提出了一个模型无关，语义，隐秘的数据中毒攻击的 KGE 模型从实用的角度。我们工作的主要设计是注入指示性路径，使被感染的模型能够预测某些恶意事实。借助所提出的不透明盒路径注入理论，我们从理论上揭示了在不透明盒设置下的攻击成功率取决于指示路径上三联体的合理性。在此基础上，提出了一种新的高效的路径搜索算法，该算法能够使攻击目标最大化，满足一定的语义约束，并保持一定的隐蔽性。通过对基准数据集和6种典型的知识图嵌入模型作为受害者的广泛评估，验证了在不透明框设置和隐蔽性条件下的攻击成功率(ASR)的有效性。例如，在 FB15k-237上，我们的攻击在 DeepPath 上达到 ASR，在不透明盒子设置下攻击各种 KGE 模型时达到平均 ASR。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=MaSS:+Model-agnostic,+Semantic+and+Stealthy+Data+Poisoning+Attack+on+Knowledge+Graph+Embedding)|0| -|[TaxoComplete: Self-Supervised Taxonomy Completion Leveraging Position-Enhanced Semantic Matching](https://doi.org/10.1145/3543507.3583342)|Ines Arous, Ljiljana Dolamic, Philippe CudréMauroux|armasuisse, Switzerland; University of Fribourg, Switzerland|Taxonomies are used to organize knowledge in many applications, including recommender systems, content browsing, or web search. With the emergence of new concepts, static taxonomies become obsolete as they fail to capture up-to-date knowledge. Several approaches have been proposed to address the problem of maintaining taxonomies automatically. These approaches typically rely on a limited set of neighbors to represent a given node in the taxonomy. However, considering distant nodes could improve the representation of some portions of the taxonomy, especially for those nodes situated in the periphery or in sparse regions of the taxonomy. In this work, we propose TaxoComplete, a self-supervised taxonomy completion framework that learns the representation of nodes leveraging their position in the taxonomy. TaxoComplete uses a self-supervision generation process that selects some nodes and associates each of them with an anchor set, which is a set composed of nodes in the close and distant neighborhood of the selected node. Using self-supervision data, TaxoComplete learns a position-enhanced node representation using two components: (1) a query-anchor semantic matching mechanism, which encodes pairs of nodes and matches their semantic distance to their graph distance, such that nodes that are close in the taxonomy are placed closely in the shared embedding space while distant nodes are placed further apart; (2) a direction-aware propagation module, which embeds the direction of edges in node representation, such that we discriminate relation from other taxonomic relations. Our approach allows the representation of nodes to encapsulate information from a large neighborhood while being aware of the distance separating pairs of nodes in the taxonomy. Extensive experiments on four real-world and large-scale datasets show that TaxoComplete is substantially more effective than state-of-the-art methods (2x more effective in terms of [email protected] ).|分类法用于在许多应用程序中组织知识，包括推荐系统、内容浏览或网络搜索。随着新概念的出现，静态分类法变得过时，因为它们无法捕获最新的知识。已经提出了几种方法来解决自动维护分类法的问题。这些方法通常依赖于一组有限的邻居来表示分类法中的给定节点。然而，考虑远处的节点可以改善分类学的某些部分的表示，特别是对于那些位于分类学的边缘或稀疏区域的节点。在这项工作中，我们提出了 TaxoComplete，一个自我监督的分类完成框架，它学习利用节点在分类中的位置来表示节点。TaxoComplete 使用一个自我监督的生成过程，它选择一些节点，并将它们与锚集相关联，锚集是由所选节点的近邻和远邻的节点组成的集合。TaxoComplete 利用自我监督数据学习位置增强的节点表示，它使用两个组件: (1)查询锚语义匹配机制，对节点对进行编码，并将它们的语义距离匹配到它们的图形距离上，这样在分类学中相近的节点被紧密地放置在共享嵌入空间中，而远处的节点被放置得更远; (2)方向感知传播模块，这种模块在节点表示中嵌入边的方向，这样我们可以区分 < 节点，父节点 > 关系和其他分类关系。我们的方法允许节点的表示来封装来自大邻居的信息，同时知道分类法中节点对之间的距离。在四个真实世界和大规模数据集上的大量实验表明，TaxoComplete 实质上比最先进的方法更有效(在[ email protected ]方面效率高出2倍)。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=TaxoComplete:+Self-Supervised+Taxonomy+Completion+Leveraging+Position-Enhanced+Semantic+Matching)|0| +|[User Retention-oriented Recommendation with Decision Transformer](https://doi.org/10.1145/3543507.3583418)|Kesen Zhao, Lixin Zou, Xiangyu Zhao, Maolin Wang, Dawei Yin|City University of Hong Kong, Hong Kong; Baidu Inc., China; Wuhan University, China|Improving user retention with reinforcement learning~(RL) has attracted increasing attention due to its significant importance in boosting user engagement. However, training the RL policy from scratch without hurting users' experience is unavoidable due to the requirement of trial-and-error searches. Furthermore, the offline methods, which aim to optimize the policy without online interactions, suffer from the notorious stability problem in value estimation or unbounded variance in counterfactual policy evaluation. To this end, we propose optimizing user retention with Decision Transformer~(DT), which avoids the offline difficulty by translating the RL as an autoregressive problem. However, deploying the DT in recommendation is a non-trivial problem because of the following challenges: (1) deficiency in modeling the numerical reward value; (2) data discrepancy between the policy learning and recommendation generation; (3) unreliable offline performance evaluation. In this work, we, therefore, contribute a series of strategies for tackling the exposed issues. We first articulate an efficient reward prompt by weighted aggregation of meta embeddings for informative reward embedding. Then, we endow a weighted contrastive learning method to solve the discrepancy between training and inference. Furthermore, we design two robust offline metrics to measure user retention. Finally, the significant improvement in the benchmark datasets demonstrates the superiority of the proposed method.|使用强化学习 ~ (RL)提高用户保持率已经引起了越来越多的关注，因为它在提高用户参与度方面具有重要意义。然而，由于试错检索的要求，在不损害用户体验的情况下从头开始训练 RL 策略是不可避免的。此外，离线方法的目的是优化政策没有在线交互，受到臭名昭著的稳定性问题的价值估计或无界方差的反事实政策评估。为此，我们提出了利用决策转换器 ~ (DT)来优化用户保留，通过将 RL 转换为一个自回归问题来避免离线困难。然而，在推荐中部署 DT 是一个非常重要的问题，因为它面临以下挑战: (1)数值奖励值建模不足; (2)策略学习和推荐生成之间的数据差异; (3)不可靠的离线性能评估。在这项工作中，我们，因此，贡献了一系列的战略，以解决暴露的问题。我们首先通过元嵌入的加权聚合提出了一个有效的信息嵌入奖励提示。然后，我们提出了一种加权对比学习方法来解决训练和推理之间的差异。此外，我们还设计了两个健壮的离线度量来衡量用户保持率。最后，基准数据集的显著改进证明了该方法的优越性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=User+Retention-oriented+Recommendation+with+Decision+Transformer)|0| +|[Balancing Unobserved Confounding with a Few Unbiased Ratings in Debiased Recommendations](https://doi.org/10.1145/3543507.3583495)|Haoxuan Li, Yanghao Xiao, Chunyuan Zheng, Peng Wu|University of California, San Diego, USA; Beijing Technology and Business University, China; University of Chinese Academy of Sciences, China; Peking University, China|Recommender systems are seen as an effective tool to address information overload, but it is widely known that the presence of various biases makes direct training on large-scale observational data result in sub-optimal prediction performance. In contrast, unbiased ratings obtained from randomized controlled trials or A/B tests are considered to be the golden standard, but are costly and small in scale in reality. To exploit both types of data, recent works proposed to use unbiased ratings to correct the parameters of the propensity or imputation models trained on the biased dataset. However, the existing methods fail to obtain accurate predictions in the presence of unobserved confounding or model misspecification. In this paper, we propose a theoretically guaranteed model-agnostic balancing approach that can be applied to any existing debiasing method with the aim of combating unobserved confounding and model misspecification. The proposed approach makes full use of unbiased data by alternatively correcting model parameters learned with biased data, and adaptively learning balance coefficients of biased samples for further debiasing. Extensive real-world experiments are conducted along with the deployment of our proposal on four representative debiasing methods to demonstrate the effectiveness.|推荐系统被视为解决信息超载问题的有效工具，但众所周知，各种偏差的存在使得对大规模观测数据的直接训练导致次优预测性能。相比之下，通过随机对照试验或 A/B 测试获得的无偏评分被认为是黄金标准，但实际上成本高，规模小。为了利用这两种类型的数据，最近的工作建议使用无偏评级来修正倾向或插补模型的参数训练偏向的数据集。然而，现有的方法不能获得准确的预测存在未观察到的混杂或模型错误说明。本文提出了一种理论保证的模型无关平衡方法，该方法可以应用于任何现有的去偏方法，以消除未观察到的混淆和模型不确定性。该方法充分利用无偏数据，通过交替校正有偏数据学习的模型参数，以及有偏样本的自适应学习平衡系数进一步消除偏差。随着我们的建议在四个有代表性的去偏方法上的部署，广泛的现实世界的实验被进行，以证明有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Balancing+Unobserved+Confounding+with+a+Few+Unbiased+Ratings+in+Debiased+Recommendations)|0| +|[Denoising and Prompt-Tuning for Multi-Behavior Recommendation](https://doi.org/10.1145/3543507.3583513)|Chi Zhang, Rui Chen, Xiangyu Zhao, Qilong Han, Li Li|University of Delaware, USA; City University of Hong Kong, Hong Kong; Harbin Engineering University, China|In practical recommendation scenarios, users often interact with items under multi-typed behaviors (e.g., click, add-to-cart, and purchase). Traditional collaborative filtering techniques typically assume that users only have a single type of behavior with items, making it insufficient to utilize complex collaborative signals to learn informative representations and infer actual user preferences. Consequently, some pioneer studies explore modeling multi-behavior heterogeneity to learn better representations and boost the performance of recommendations for a target behavior. However, a large number of auxiliary behaviors (i.e., click and add-to-cart) could introduce irrelevant information to recommenders, which could mislead the target behavior (i.e., purchase) recommendation, rendering two critical challenges: (i) denoising auxiliary behaviors and (ii) bridging the semantic gap between auxiliary and target behaviors. Motivated by the above observation, we propose a novel framework-Denoising and Prompt-Tuning (DPT) with a three-stage learning paradigm to solve the aforementioned challenges. In particular, DPT is equipped with a pattern-enhanced graph encoder in the first stage to learn complex patterns as prior knowledge in a data-driven manner to guide learning informative representation and pinpointing reliable noise for subsequent stages. Accordingly, we adopt different lightweight tuning approaches with effectiveness and efficiency in the following stages to further attenuate the influence of noise and alleviate the semantic gap among multi-typed behaviors. Extensive experiments on two real-world datasets demonstrate the superiority of DPT over a wide range of state-of-the-art methods. The implementation code is available online at https://github.com/zc-97/DPT.|在实际的推荐场景中，用户经常在多类型行为下与项目交互(例如，单击、添加到购物车和购买)。传统的协同过滤技术通常假设用户只有单一类型的行为与项目，使其不足以利用复杂的协作信号来学习信息表示和推断实际的用户偏好。因此，一些先驱研究探索建立多行为异质性模型，以学习更好的表示方法，并提高针对目标行为的建议的性能。然而，大量的辅助行为(即点击和添加到购物车)可能会向推荐者引入不相关的信息，这可能会误导目标行为(即购买)推荐，造成两个关键的挑战: (i)去噪辅助行为和(ii)桥接辅助行为和目标行为之间的语义差距。基于上述观察，我们提出了一个新的框架-去噪和及时调整(DPT)与三个阶段的学习范式，以解决上述挑战。特别是，DPT 在第一阶段配备了模式增强图形编码器，以数据驱动的方式学习复杂的模式作为先验知识，以指导学习信息表示和确定后续阶段的可靠噪声。相应地，我们在接下来的阶段采用了不同的轻量化方法，以进一步减小噪声的影响，缓解多类型行为之间的语义鸿沟。在两个实际数据集上的大量实验证明了 DPT 相对于一系列最先进的方法的优越性。实施守则可于网上 https://github.com/zc-97/dpt 下载。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Denoising+and+Prompt-Tuning+for+Multi-Behavior+Recommendation)|0| +|[CAMUS: Attribute-Aware Counterfactual Augmentation for Minority Users in Recommendation](https://doi.org/10.1145/3543507.3583538)|Yuxin Ying, Fuzhen Zhuang, Yongchun Zhu, Deqing Wang, Hongwei Zheng|Institute of Artificial Intelligence, Beihang University, China; School of Computer Science and Engineering, Beihang University, China; Institute of Computing Technology, Chinese Academy of Sciences, China; Beijing Academy of Blockchain and Edge Computing, China|Embedding-based methods currently achieved impressive success in recommender systems. However, such methods are more likely to suffer from bias in data distribution, especially the attribute bias problem. For example, when a certain type of user, like the elderly, occupies the mainstream, the recommendation results of minority users would be seriously affected by the mainstream users’ attributes. To address this problem, most existing methods are proposed from the perspective of fairness, which focuses on eliminating unfairness but deteriorates the recommendation performance. Unlike these methods, in this paper, we focus on improving the recommendation performance for minority users of biased attributes. Along this line, we propose a novel attribute-aware Counterfactual Augmentation framework for Minority Users(CAMUS). Specifically, the CAMUS consists of a counterfactual augmenter, a confidence estimator, and a recommender. The counterfactual augmenter conducts data augmentation for the minority group by utilizing the interactions of mainstream users based on a universal counterfactual assumption. Besides, a tri-training-based confidence estimator is applied to ensure the effectiveness of augmentation. Extensive experiments on three real-world datasets have demonstrated the superior performance of the proposed methods. Further case studies verify the universality of the proposed CAMUS framework on different data sparsity, attributes, and models.|基于嵌入的方法目前在推荐系统中取得了令人印象深刻的成功。然而，这些方法更容易受到数据分布偏差的影响，特别是属性偏差问题。例如，当某种类型的用户，如老年人，占据主流时，少数用户的推荐结果会受到主流用户属性的严重影响。为了解决这个问题，现有的方法大多是从公平的角度出发，着重于消除不公平性，但是会降低推荐性能。与这些方法不同的是，本文主要研究如何提高偏向属性的少数用户的推荐性能。在此基础上，我们提出了一种新的面向少数用户的属性感知反事实增强框架(CAMUS)。具体来说，CAMUS 由一个反事实增强器、一个置信度估计器和一个推荐器组成。反事实增强器利用主流用户基于普遍反事实假设的交互作用对少数群体进行数据增强。此外，采用基于三训练的置信估计来保证增广的有效性。在三个实际数据集上的大量实验证明了该方法的优越性能。进一步的案例研究验证了所提出的 CAMUS 框架在不同的数据稀疏性、属性和模型上的通用性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=CAMUS:+Attribute-Aware+Counterfactual+Augmentation+for+Minority+Users+in+Recommendation)|0| +|[Dynamically Expandable Graph Convolution for Streaming Recommendation](https://doi.org/10.1145/3543507.3583237)|Bowei He, Xu He, Yingxue Zhang, Ruiming Tang, Chen Ma|City University of Hong Kong, Hong Kong; Huawei Noah's Ark Lab Montreal, Canada; Department of Computer Science, City University of Hong Kong, Hong Kong; Huawei Noah's Ark Lab, China|Personalized recommender systems have been widely studied and deployed to reduce information overload and satisfy users' diverse needs. However, conventional recommendation models solely conduct a one-time training-test fashion and can hardly adapt to evolving demands, considering user preference shifts and ever-increasing users and items in the real world. To tackle such challenges, the streaming recommendation is proposed and has attracted great attention recently. Among these, continual graph learning is widely regarded as a promising approach for the streaming recommendation by academia and industry. However, existing methods either rely on the historical data replay which is often not practical under increasingly strict data regulations, or can seldom solve the \textit{over-stability} issue. To overcome these difficulties, we propose a novel \textbf{D}ynamically \textbf{E}xpandable \textbf{G}raph \textbf{C}onvolution (DEGC) algorithm from a \textit{model isolation} perspective for the streaming recommendation which is orthogonal to previous methods. Based on the motivation of disentangling outdated short-term preferences from useful long-term preferences, we design a sequence of operations including graph convolution pruning, refining, and expanding to only preserve beneficial long-term preference-related parameters and extract fresh short-term preferences. Moreover, we model the temporal user preference, which is utilized as user embedding initialization, for better capturing the individual-level preference shifts. Extensive experiments on the three most representative GCN-based recommendation models and four industrial datasets demonstrate the effectiveness and robustness of our method.|个性化推荐系统已被广泛研究和部署，以减少信息超载和满足用户的不同需求。然而，考虑到用户偏好的变化以及现实世界中不断增加的用户和项目，传统的推荐模型只能进行一次性的训练测试，很难适应不断变化的需求。为了应对这些挑战，流媒体推荐被提出并引起了人们的广泛关注。其中，连续图学习被学术界和工业界广泛认为是一种很有前途的流推荐方法。然而，现有的方法要么依赖于历史数据的重放，这在日益严格的数据规则下往往是不切实际的，要么很少能解决文本的过稳定性问题。为了克服这些困难，我们从文本{模型隔离}的角度提出了一种新的动态 textbf { D }可扩展 textbf { E } Raph textbf { C }内卷(DEGC)算法用于流式推荐，该算法与以前的方法是正交的。基于将过时的短期偏好与有用的长期偏好分离的动机，我们设计了一系列操作，包括图卷积修剪，细化和扩展，以仅保留有益的长期偏好相关参数并提取新的短期偏好。此外，为了更好地捕获个体层次的偏好变化，我们建立了时态用户偏好模型，并将其用于用户嵌入初始化。在三个最具代表性的基于 GCN 的推荐模型和四个工业数据集上的大量实验证明了该方法的有效性和鲁棒性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Dynamically+Expandable+Graph+Convolution+for+Streaming+Recommendation)|0| +|[CTRLStruct: Dialogue Structure Learning for Open-Domain Response Generation](https://doi.org/10.1145/3543507.3583285)|Congchi Yin, Piji Li, Zhaochun Ren|Nanjing University of Aeronautics and Astronautics, China; Shandong University, China|Dialogue structure discovery is essential in dialogue generation. Well-structured topic flow can leverage background information and predict future topics to help generate controllable and explainable responses. However, most previous work focused on dialogue structure learning in task-oriented dialogue other than open-domain dialogue which is more complicated and challenging. In this paper, we present a new framework CTRLStruct for dialogue structure learning to effectively explore topic-level dialogue clusters as well as their transitions with unlabelled information. Precisely, dialogue utterances encoded by bi-directional Transformer are further trained through a special designed contrastive learning task to improve representation. Then we perform clustering to utterance-level representations and form topic-level clusters that can be considered as vertices in dialogue structure graph. The edges in the graph indicating transition probability between vertices are calculated by mimicking expert behavior in datasets. Finally, dialogue structure graph is integrated into dialogue model to perform controlled response generation. Experiments on two popular open-domain dialogue datasets show our model can generate more coherent responses compared to some excellent dialogue models, as well as outperform some typical sentence embedding methods in dialogue utterance representation. Code is available in GitHub.|对话结构的发现是对话生成的基础。结构良好的主题流可以利用背景信息并预测未来的主题，从而帮助产生可控的和可解释的反应。然而，以往的研究大多侧重于面向任务的对话中的对话结构学习，而开放领域的对话更为复杂和具有挑战性。本文提出了一种新的对话结构学习框架 CTRLstruct，以有效地探索话题层次的对话群及其与未标记信息的过渡。准确地说，双向变压器编码的对话话语通过特别设计的对比学习任务进一步训练，以提高表征能力。然后对话语层面的表征进行聚类，形成话题层面的聚类，这些聚类可以看作是对话结构图中的顶点。通过模拟数据集中的专家行为，计算图中表示顶点间转移概率的边。最后，将对话结构图集成到对话模型中进行受控响应的生成。实验结果表明，与一些优秀的对话模型相比，该模型能够产生更加连贯的对话响应，并且在对话话语表征中优于一些典型的句子嵌入方法。代码可在 GitHub 中获得。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=CTRLStruct:+Dialogue+Structure+Learning+for+Open-Domain+Response+Generation)|0| +|[BlinkViz: Fast and Scalable Approximate Visualization on Very Large Datasets using Neural-Enhanced Mixed Sum-Product Networks](https://doi.org/10.1145/3543507.3583411)|Yimeng Qiao, Yinan Jing, Hanbing Zhang, Zhenying He, Kai Zhang, X. Sean Wang|Shanghai Key Laboratory of Data Science, School of Software, Fudan University, China; Shanghai Key Laboratory of Data Science, School of Computer Science, Fudan University, China|Web-based online interactive visual analytics enjoys popularity in recent years. Traditionally, visualizations are produced directly from querying the underlying data. However, for a very large dataset, this way is so time-consuming that it cannot meet the low-latency requirements of interactive visual analytics. In this paper, we propose a learning-based visualization approach called BlinkViz, which uses a learned model to produce approximate visualizations by leveraging mixed sum-product networks to learn the distribution of the original data. In such a way, it makes visualization faster and more scalable by decoupling visualization and data. In addition, to improve the accuracy of approximate visualizations, we propose an enhanced model by incorporating a neural network with residual structures, which can refine prediction results, especially for visual requests with low selectivity. Extensive experiments show that BlinkViz is extremely fast even on a large dataset with hundreds of millions of data records (over 30GB), responding in sub-seconds (from 2ms to less than 500ms for different requests) while keeping a low error rate. Furthermore, our approach remains scalable on latency and memory footprint size regardless of data size.|基于 Web 的在线交互式可视化分析近年来很受欢迎。传统上，可视化是通过查询底层数据直接生成的。然而，对于一个非常大的数据集，这种方法非常耗时，不能满足交互式可视化分析的低延迟要求。本文提出了一种基于学习的可视化方法 BlinkViz，该方法利用学习模型，通过混合和积网络来学习原始数据的分布情况，从而产生近似可视化效果。通过这种方式，可视化和数据解耦，使得可视化更快、更具可伸缩性。此外，为了提高近似可视化的准确性，我们提出了一个增强的模型，通过结合残差结构的神经网络，可以细化预测结果，特别是对低选择性的可视化请求。大量的实验表明，BlinkViz 即使在拥有数亿条数据记录(超过30GB)的大型数据集上也是极其快速的，响应时间在亚秒(对于不同的请求，响应时间从2毫秒到不到500毫秒) ，同时保持较低的错误率。此外，无论数据大小如何，我们的方法在延迟和内存占用大小上都是可伸缩的。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=BlinkViz:+Fast+and+Scalable+Approximate+Visualization+on+Very+Large+Datasets+using+Neural-Enhanced+Mixed+Sum-Product+Networks)|0| +|[Semi-supervised Adversarial Learning for Complementary Item Recommendation](https://doi.org/10.1145/3543507.3583462)|Koby Bibas, Oren Sar Shalom, Dietmar Jannach|Amazon, Israel; University of Klagenfurt, Austria; Meta, Israel|Complementary item recommendations are a ubiquitous feature of modern e-commerce sites. Such recommendations are highly effective when they are based on collaborative signals like co-purchase statistics. In certain online marketplaces, however, e.g., on online auction sites, constantly new items are added to the catalog. In such cases, complementary item recommendations are often based on item side-information due to a lack of interaction data. In this work, we propose a novel approach that can leverage both item side-information and labeled complementary item pairs to generate effective complementary recommendations for cold items, i.e., for items for which no co-purchase statistics yet exist. Given that complementary items typically have to be of a different category than the seed item, we technically maintain a latent space for each item category. Simultaneously, we learn to project distributed item representations into these category spaces to determine suitable recommendations. The main learning process in our architecture utilizes labeled pairs of complementary items. In addition, we adopt ideas from Cycle Generative Adversarial Networks (CycleGAN) to leverage available item information even in case no labeled data exists for a given item and category. Experiments on three e-commerce datasets show that our method is highly effective.|互补商品推荐是现代电子商务网站的一个普遍特征。当这些建议基于合作信号(如共同购买统计数据)时，它们是非常有效的。然而，在某些在线市场，例如在线拍卖网站，不断有新物品被添加到目录中。在这种情况下，由于缺乏交互数据，补充项目推荐通常基于项目侧信息。在这项工作中，我们提出了一个新颖的方法，可以利用项目侧信息和标记的互补项目对，以产生有效的补充建议，冷的项目，即项目，共同购买统计数据尚不存在。鉴于补充项目通常必须是一个与种子项目不同的类别，我们在技术上为每个项目类别保持一个潜在的空间。同时，我们学习将分布式项表示投射到这些类别空间中，以确定合适的建议。在我们的建筑中，主要的学习过程是使用标记成对的互补项目。此外，我们采用循环生成对抗网络(CycleGAN)的思想来利用可用的项目信息，即使在给定的项目和类别没有标记数据存在的情况下。在三个电子商务数据集上的实验表明，该方法是高效的。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Semi-supervised+Adversarial+Learning+for+Complementary+Item+Recommendation)|0| +|[MaSS: Model-agnostic, Semantic and Stealthy Data Poisoning Attack on Knowledge Graph Embedding](https://doi.org/10.1145/3543507.3583203)|Xiaoyu You, Beina Sheng, Daizong Ding, Mi Zhang, Xudong Pan, Min Yang, Fuli Feng|Fudan University, School of Computer Science, China; University of Science and Technology of China, CCCD Key Lab of Ministry of Culture and Tourism, China|Open-source knowledge graphs are attracting increasing attention. Nevertheless, the openness also raises the concern of data poisoning attacks, that is, the attacker could submit malicious facts to bias the prediction of knowledge graph embedding (KGE) models. Existing studies on such attacks adopt a clear-box setting and neglect the semantic information of the generated facts, making them fail to attack in real-world scenarios. In this work, we consider a more rigorous setting and propose a model-agnostic, semantic, and stealthy data poisoning attack on KGE models from a practical perspective. The main design of our work is to inject indicative paths to make the infected model predict certain malicious facts. With the aid of the proposed opaque-box path injection theory, we theoretically reveal that the attack success rate under the opaque-box setting is determined by the plausibility of triplets on the indicative path. Based on this, we develop a novel and efficient algorithm to search paths that maximize the attack goal, satisfy certain semantic constraints, and preserve certain stealthiness, i.e., the normal functionality of the target KGE will not be influenced although it predicts wrong facts given certain queries. Through extensive evaluation of benchmark datasets and 6 typical knowledge graph embedding models as the victims, we validate the effectiveness in terms of attack success rate (ASR) under opaque-box setting and stealthiness. For example, on FB15k-237, our attack achieves a ASR on DeepPath, with an average ASR over when attacking various KGE models under the opaque-box setting.|开源知识图表越来越受到人们的关注。然而，这种开放性也引起了人们对数据中毒攻击的担忧，即攻击者可能会提交恶意事实来偏向知识图嵌入(KGE)模型的预测。现有的关于此类攻击的研究采用了清晰框设置，忽视了所生成事实的语义信息，使得它们无法在现实世界中进行攻击。在这项工作中，我们考虑了一个更严格的设置，并提出了一个模型无关，语义，隐秘的数据中毒攻击的 KGE 模型从实用的角度。我们工作的主要设计是注入指示性路径，使被感染的模型能够预测某些恶意事实。借助所提出的不透明盒路径注入理论，我们从理论上揭示了在不透明盒设置下的攻击成功率取决于指示路径上三联体的合理性。在此基础上，提出了一种新的高效的路径搜索算法，该算法能够使攻击目标最大化，满足一定的语义约束，并保持一定的隐蔽性。通过对基准数据集和6种典型的知识图嵌入模型作为受害者的广泛评估，验证了在不透明框设置和隐蔽性条件下的攻击成功率(ASR)的有效性。例如，在 FB15k-237上，我们的攻击在 DeepPath 上达到 ASR，在不透明盒子设置下攻击各种 KGE 模型时达到平均 ASR。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=MaSS:+Model-agnostic,+Semantic+and+Stealthy+Data+Poisoning+Attack+on+Knowledge+Graph+Embedding)|0| +|[TaxoComplete: Self-Supervised Taxonomy Completion Leveraging Position-Enhanced Semantic Matching](https://doi.org/10.1145/3543507.3583342)|Ines Arous, Ljiljana Dolamic, Philippe CudréMauroux|University of Fribourg, Switzerland; armasuisse, Switzerland|Taxonomies are used to organize knowledge in many applications, including recommender systems, content browsing, or web search. With the emergence of new concepts, static taxonomies become obsolete as they fail to capture up-to-date knowledge. Several approaches have been proposed to address the problem of maintaining taxonomies automatically. These approaches typically rely on a limited set of neighbors to represent a given node in the taxonomy. However, considering distant nodes could improve the representation of some portions of the taxonomy, especially for those nodes situated in the periphery or in sparse regions of the taxonomy. In this work, we propose TaxoComplete, a self-supervised taxonomy completion framework that learns the representation of nodes leveraging their position in the taxonomy. TaxoComplete uses a self-supervision generation process that selects some nodes and associates each of them with an anchor set, which is a set composed of nodes in the close and distant neighborhood of the selected node. Using self-supervision data, TaxoComplete learns a position-enhanced node representation using two components: (1) a query-anchor semantic matching mechanism, which encodes pairs of nodes and matches their semantic distance to their graph distance, such that nodes that are close in the taxonomy are placed closely in the shared embedding space while distant nodes are placed further apart; (2) a direction-aware propagation module, which embeds the direction of edges in node representation, such that we discriminate relation from other taxonomic relations. Our approach allows the representation of nodes to encapsulate information from a large neighborhood while being aware of the distance separating pairs of nodes in the taxonomy. Extensive experiments on four real-world and large-scale datasets show that TaxoComplete is substantially more effective than state-of-the-art methods (2x more effective in terms of [email protected] ).|分类法用于在许多应用程序中组织知识，包括推荐系统、内容浏览或网络搜索。随着新概念的出现，静态分类法变得过时，因为它们无法捕获最新的知识。已经提出了几种方法来解决自动维护分类法的问题。这些方法通常依赖于一组有限的邻居来表示分类法中的给定节点。然而，考虑远处的节点可以改善分类学的某些部分的表示，特别是对于那些位于分类学的边缘或稀疏区域的节点。在这项工作中，我们提出了 TaxoComplete，一个自我监督的分类完成框架，它学习利用节点在分类中的位置来表示节点。TaxoComplete 使用一个自我监督的生成过程，它选择一些节点，并将它们与锚集相关联，锚集是由所选节点的近邻和远邻的节点组成的集合。TaxoComplete 利用自我监督数据学习位置增强的节点表示，它使用两个组件: (1)查询锚语义匹配机制，对节点对进行编码，并将它们的语义距离匹配到它们的图形距离上，这样在分类学中相近的节点被紧密地放置在共享嵌入空间中，而远处的节点被放置得更远; (2)方向感知传播模块，这种模块在节点表示中嵌入边的方向，这样我们可以区分 < 节点，父节点 > 关系和其他分类关系。我们的方法允许节点的表示来封装来自大邻居的信息，同时知道分类法中节点对之间的距离。在四个真实世界和大规模数据集上的大量实验表明，TaxoComplete 实质上比最先进的方法更有效(在[ email protected ]方面效率高出2倍)。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=TaxoComplete:+Self-Supervised+Taxonomy+Completion+Leveraging+Position-Enhanced+Semantic+Matching)|0| |[Bipartite Graph Convolutional Hashing for Effective and Efficient Top-N Search in Hamming Space](https://doi.org/10.1145/3543507.3583219)|Yankai Chen, Yixiang Fang, Yifei Zhang, Irwin King|The Chinese University of Hong Kong, Hong Kong; The Chinese University of Hong Kong, Shenzhen, China|Searching on bipartite graphs is basal and versatile to many real-world Web applications, e.g., online recommendation, database retrieval, and query-document searching. Given a query node, the conventional approaches rely on the similarity matching with the vectorized node embeddings in the continuous Euclidean space. To efficiently manage intensive similarity computation, developing hashing techniques for graph structured data has recently become an emerging research direction. Despite the retrieval efficiency in Hamming space, prior work is however confronted with catastrophic performance decay. In this work, we investigate the problem of hashing with Graph Convolutional Network on bipartite graphs for effective Top-N search. We propose an end-to-end Bipartite Graph Convolutional Hashing approach, namely BGCH, which consists of three novel and effective modules: (1) adaptive graph convolutional hashing, (2) latent feature dispersion, and (3) Fourier serialized gradient estimation. Specifically, the former two modules achieve the substantial retention of the structural information against the inevitable information loss in hash encoding; the last module develops Fourier Series decomposition to the hashing function in the frequency domain mainly for more accurate gradient estimation. The extensive experiments on six real-world datasets not only show the performance superiority over the competing hashing-based counterparts, but also demonstrate the effectiveness of all proposed model components contained therein.|对于许多实际的 Web 应用程序，如在线推荐、数据库检索和查询文档搜索，二部图搜索是基础的和通用的。给定一个查询节点，传统的方法依赖于与连续欧氏空间中向量化节点嵌入的相似性匹配。为了有效地管理密集型相似度计算，开发图结构化数据的哈希技术已成为一个新兴的研究方向。尽管在汉明空间中反演效率很高，但是先前的工作却面临着灾难性的性能衰减。在本文中，我们研究了用图卷积网络对二部图进行散列以获得有效的 Top-N 搜索的问题。提出了一种端到端的二部图卷积哈希方法，即 BGCH，它由三个新颖有效的模块组成: (1)自适应图卷积哈希，(2)潜在特征分散，(3)傅里叶序列化梯度估计。具体来说，前两个模块针对哈希编码中不可避免的信息损失实现了结构信息的实质性保留，最后一个模块对频域中的哈希函数进行了傅里叶级数分解，以便更准确地进行梯度估计。在六个实际数据集上进行的大量实验不仅表明了该模型的性能优于竞争对手的散列模型，而且证明了其中所有模型组件的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Bipartite+Graph+Convolutional+Hashing+for+Effective+and+Efficient+Top-N+Search+in+Hamming+Space)|0| -|[LED: Lexicon-Enlightened Dense Retriever for Large-Scale Retrieval](https://doi.org/10.1145/3543507.3583294)|Kai Zhang, Chongyang Tao, Tao Shen, Can Xu, Xiubo Geng, Binxing Jiao, Daxin Jiang|University of Technology Sydney, Australia; Microsoft, China; The Ohio State University, USA|Retrieval models based on dense representations in semantic space have become an indispensable branch for first-stage retrieval. These retrievers benefit from surging advances in representation learning towards compressive global sequence-level embeddings. However, they are prone to overlook local salient phrases and entity mentions in texts, which usually play pivot roles in first-stage retrieval. To mitigate this weakness, we propose to make a dense retriever align a well-performing lexicon-aware representation model. The alignment is achieved by weakened knowledge distillations to enlighten the retriever via two aspects -- 1) a lexicon-augmented contrastive objective to challenge the dense encoder and 2) a pair-wise rank-consistent regularization to make dense model's behavior incline to the other. We evaluate our model on three public benchmarks, which shows that with a comparable lexicon-aware retriever as the teacher, our proposed dense one can bring consistent and significant improvements, and even outdo its teacher. In addition, we found our improvement on the dense retriever is complementary to the standard ranker distillation, which can further lift state-of-the-art performance.|基于语义空间密集表示的检索模型已经成为第一阶段检索不可或缺的分支。这些检索器受益于表示学习向压缩全局序列级嵌入方向的飞速发展。然而，它们往往忽视文本中的局部显著短语和实体提及，而这些短语和实体提及在第一阶段的检索中起着关键作用。为了减轻这个弱点，我们建议使一个密集检索对齐一个良好的表现词典感知表示模型。该方法通过弱化知识提取，从两个方面启发检索者: 1)词典增强对比目标，挑战密集编码器; 2)成对秩一致正则化，使密集模型的行为倾向于另一方。我们在三个公共基准上评估了我们的模型，结果表明，与一个具有可比性的词汇感知检索器作为教师，我们提出的密集型可以带来一致和重大的改进，甚至超过它的老师。此外，我们发现我们对稠密检索器的改进是对标准等级精馏的补充，它可以进一步提高最先进的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=LED:+Lexicon-Enlightened+Dense+Retriever+for+Large-Scale+Retrieval)|0| -|[A Passage-Level Reading Behavior Model for Mobile Search](https://doi.org/10.1145/3543507.3583343)|Zhijing Wu, Jiaxin Mao, Kedi Xu, Dandan Song, Heyan Huang|School of Computer Science and Technology, Beijing Institute of Technology, China; School of Computer Science, Carnegie Mellon University, USA; Gaoling School of Artificial Intelligence, Renmin University of China, China|Reading is a vital and complex cognitive activity during users’ information-seeking process. Several studies have focused on understanding users’ reading behavior in desktop search. Their findings greatly contribute to the design of information retrieval models. However, little is known about how users read a result in mobile search, although search currently happens more frequently in mobile scenarios. In this paper, we conduct a lab-based user study to investigate users’ fine-grained reading behavior patterns in mobile search. We find that users’ reading attention allocation is strongly affected by several behavior biases, such as position and selection biases. Inspired by these findings, we propose a probabilistic generative model, the Passage-level Reading behavior Model (PRM), to model users’ reading behavior in mobile search. The PRM utilizes observable passage-level exposure and viewport duration events to infer users’ unobserved skimming event, reading event, and satisfaction perception during the reading process. Besides fitting the passage-level reading behavior, we utilize the fitted parameters of PRM to estimate the passage-level and document-level relevance. Experimental results show that PRM outperforms existing unsupervised relevance estimation models. PRM has strong interpretability and provides valuable insights into the understanding of how users seek and perceive useful information in mobile search.|阅读是用户信息搜索过程中一种重要而复杂的认知活动。一些研究集中在了解用户在桌面搜索中的阅读行为。他们的发现极大地促进了信息检索模型的设计。然而，尽管目前搜索在移动场景中出现的频率更高，但人们对用户在移动搜索中如何阅读结果知之甚少。本文以实验室为基础，对移动搜索中用户的细粒度阅读行为模式进行了研究。研究发现，位置偏差、选择偏差等行为偏差对用户的阅读注意分配有显著影响。受这些发现的启发，我们提出了一个概率生成模型——短文水平阅读行为模型(PRM) ，来模拟用户在移动搜索中的阅读行为。PRM 利用可观察到的文章水平暴露和视窗持续时间事件来推断用户在阅读过程中未观察到的略读事件、阅读事件和满意度感知。除了拟合文章阅读行为外，我们还利用 PRM 的拟合参数来估计文章阅读水平和文档阅读水平的相关性。实验结果表明，PRM 算法的性能优于现有的无监督相关估计模型。PRM 具有很强的可解释性，为理解用户在移动搜索中如何寻找和感知有用信息提供了有价值的见解。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Passage-Level+Reading+Behavior+Model+for+Mobile+Search)|0| -|[PROD: Progressive Distillation for Dense Retrieval](https://doi.org/10.1145/3543507.3583421)|Zhenghao Lin, Yeyun Gong, Xiao Liu, Hang Zhang, Chen Lin, Anlei Dong, Jian Jiao, Jingwen Lu, Daxin Jiang, Rangan Majumder, Nan Duan|Microsoft, China; Microsoft Research Asia, China; Microsoft, USA; School of Informatics, Xiamen University, China|Knowledge distillation is an effective way to transfer knowledge from a strong teacher to an efficient student model. Ideally, we expect the better the teacher is, the better the student. However, this expectation does not always come true. It is common that a better teacher model results in a bad student via distillation due to the nonnegligible gap between teacher and student. To bridge the gap, we propose PROD, a PROgressive Distillation method, for dense retrieval. PROD consists of a teacher progressive distillation and a data progressive distillation to gradually improve the student. We conduct extensive experiments on five widely-used benchmarks, MS MARCO Passage, TREC Passage 19, TREC Document 19, MS MARCO Document and Natural Questions, where PROD achieves the state-of-the-art within the distillation methods for dense retrieval. The code and models will be released.|知识提取是将知识从一个强有力的教师转化为一个有效的学生模型的有效途径。理想情况下，我们期望老师越好，学生越好。然而，这种期望并不总是能够实现。由于教师与学生之间存在着不可忽视的差距，一个较好的教师模型常常通过精馏的方法导致不好的学生。为了填补这一空白，我们提出了一种逐步精馏方法 PROD，用于密集提取。PROD 由一个教师的逐步升华和一个数据的逐步升华组成，以逐步提高学生的素质。我们对五个广泛使用的基准进行了广泛的实验，即 MS MARCO Passage，TREC Passage 19，TREC Document 19，MS MARCO Document and Natural Questions，PROD 在这些基准上实现了用于密集检索的蒸馏方法的最先进水平。代码和模型将被发布。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=PROD:+Progressive+Distillation+for+Dense+Retrieval)|0| -|[Ad Auction Design with Coupon-Dependent Conversion Rate in the Auto-bidding World](https://doi.org/10.1145/3543507.3583230)|Bonan Ni, Xun Wang, Qi Zhang, Pingzhong Tang, Zhourong Chen, Tianjiu Yin, Liangni Lu, Xiaobing Liu, Kewu Sun, Zhe Ma|Institute for Interdisciplinary Information Sciences, Tsinghua University, China; Intelligent Science & Technology Academy of CASIC, China and Scientific Research Key Laboratory of Aerospace Defence Intelligent Systems and Technology, China; TuringSense, China and Institute for Interdisciplinary Information Sciences, Tsinghua University, China; ByteDance, China|Online advertising has become a dominant source of revenue of the Internet. In classic auction theory, only the auctioneer (i.e., the platform) and buyers (i.e., the advertisers) are involved, while the advertising audiences are ignored. For ecommerce advertising, however, the platform can provide coupons for the advertising audiences and nudge them into purchasing more products at lower prices (e.g., 2 dollars off the regular price). Such promotions can lead to an increase in amount and value of purchases. In this paper, we jointly design the coupon value computation, slot allocation, and payment of online advertising in an auto-bidding world. Firstly, we propose the auction mechanism, named CFA-auction (i.e., Coupon-For-the-Audiences-auction), which takes advertising audiences into account in the auction design. We prove the existence of pacing equilibrium, and show that CFA-auction satisfies the IC (incentive compatibility), IR (individual rationality) constraints. Then, we study the optimality of CFA-auction, and prove it can maintain an approximation of the optimal. Finally, experimental evaluation results on both offline dataset as well as online A/B test demonstrate the effectiveness of CFA-auction.|在线广告已成为互联网收入的主要来源。在传统的拍卖理论中，只有拍卖商(即平台)和买家(即广告商)参与，而广告受众被忽略。然而，对于电子商务广告来说，这个平台可以为广告受众提供优惠券，推动他们以更低的价格购买更多的产品(例如，比正常价格低2美元)。此类促销活动可能导致购买数量和价值的增加。在这篇论文中，我们共同设计了自动竞价世界中的优惠券价值计算、时段分配和在线广告支付。首先，我们提出了一种拍卖机制，即 CFA 拍卖(即为受众提供优惠券的拍卖) ，该机制在拍卖设计中考虑了广告受众。我们证明了节奏均衡的存在，并且证明了 CFA 拍卖满足集成激励相容(IC)、个人理性(IR)约束。然后，我们研究了 CFA 拍卖的最优性，并证明了它可以保持最优的近似。最后，通过对离线数据集和在线 A/B 测试的实验结果验证了 CFA 拍卖的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Ad+Auction+Design+with+Coupon-Dependent+Conversion+Rate+in+the+Auto-bidding+World)|0| -|[A Reference-Dependent Model for Web Search Evaluation: Understanding and Measuring the Experience of Boundedly Rational Users](https://doi.org/10.1145/3543507.3583551)|Nuo Chen, Jiqun Liu, Tetsuya Sakai|Waseda University, Japan; The University of Oklahoma, USA|Previous researches demonstrate that users’ actions in search interaction are associated with relative gains and losses to reference points, known as the reference dependence effect. However, this widely confirmed effect is not represented in most user models underpinning existing search evaluation metrics. In this study, we propose a new evaluation metric framework, namely Reference Dependent Metric (ReDeM), for assessing query-level search by incorporating the effect of reference dependence into the modelling of user search behavior. To test the overall effectiveness of the proposed framework, (1) we evaluate the performance, in terms of correlation with user satisfaction, of ReDeMs built upon different reference points against that of the widely-used metrics on three search datasets; (2) we examine the performance of ReDeMs under different task states, like task difficulty and task urgency; and (3) we analyze the statistical reliability of ReDeMs in terms of discriminative power. Experimental results indicate that: (1) ReDeMs integrated with a proper reference point achieve better correlations with user satisfaction than most of the existing metrics, like Discounted Cumulative Gain (DCG) and Rank-Biased Precision (RBP), even though their parameters have already been well-tuned; (2) ReDeMs reach relatively better performance compared to existing metrics when the task triggers a high-level cognitive load; (3) the discriminative power of ReDeMs is far stronger than Expected Reciprocal Rank (ERR), slightly stronger than Precision and similar to DCG, RBP and INST. To our knowledge, this study is the first to explicitly incorporate the reference dependence effect into the user browsing model and offline evaluation metrics. Our work illustrates a promising approach to leveraging the insights about user biases from cognitive psychology in better evaluating user search experience and enhancing user models.|以往的研究表明，用户在搜索交互中的行为与参考点的相对收益和相对损失有关，称为参考依赖效应。然而，这种被广泛证实的效果在大多数支持现有搜索评估指标的用户模型中并没有体现出来。在这项研究中，我们提出了一个新的评估度量框架，即参考依赖度量(ReDeM) ，通过将参考依赖的影响纳入用户搜索行为的建模来评估查询级搜索。为了测试提出的框架的整体有效性，(1)我们评估建立在不同参考点上的 ReDeM 与用户满意度的相关性，与三个搜索数据集上广泛使用的指标的相关性; (2)我们检查 ReDeM 在不同任务状态下的表现，如任务难度和任务紧迫性; (3)我们分析 ReDeM 在区分能力方面的统计可靠性。实验结果表明: (1)与合适的参考点相结合的 ReDeMs 与用户满意度的相关性优于大多数现有指标，如 DCG 和 RBP，尽管它们的参数已经得到了很好的调整; (2)当任务触发高水平的认知负荷时，与现有指标相比，ReDeMs 获得了相对更好的性能; (3) ReDeMs 的区分能力远远强于期望互惠秩序(ERR) ，略强于精度，类似于 DCG、 RBP 和 INST。据我们所知，这项研究是第一个明确地将参考依赖效应纳入用户浏览模型和离线评价指标。我们的工作说明了一种有前途的方法，利用认知心理学对用户偏见的洞察力，更好地评估用户搜索体验和增强用户模型。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Reference-Dependent+Model+for+Web+Search+Evaluation:+Understanding+and+Measuring+the+Experience+of+Boundedly+Rational+Users)|0| +|[LED: Lexicon-Enlightened Dense Retriever for Large-Scale Retrieval](https://doi.org/10.1145/3543507.3583294)|Kai Zhang, Chongyang Tao, Tao Shen, Can Xu, Xiubo Geng, Binxing Jiao, Daxin Jiang|Microsoft, China; University of Technology Sydney, Australia; The Ohio State University, USA|Retrieval models based on dense representations in semantic space have become an indispensable branch for first-stage retrieval. These retrievers benefit from surging advances in representation learning towards compressive global sequence-level embeddings. However, they are prone to overlook local salient phrases and entity mentions in texts, which usually play pivot roles in first-stage retrieval. To mitigate this weakness, we propose to make a dense retriever align a well-performing lexicon-aware representation model. The alignment is achieved by weakened knowledge distillations to enlighten the retriever via two aspects -- 1) a lexicon-augmented contrastive objective to challenge the dense encoder and 2) a pair-wise rank-consistent regularization to make dense model's behavior incline to the other. We evaluate our model on three public benchmarks, which shows that with a comparable lexicon-aware retriever as the teacher, our proposed dense one can bring consistent and significant improvements, and even outdo its teacher. In addition, we found our improvement on the dense retriever is complementary to the standard ranker distillation, which can further lift state-of-the-art performance.|基于语义空间密集表示的检索模型已经成为第一阶段检索不可或缺的分支。这些检索器受益于表示学习向压缩全局序列级嵌入方向的飞速发展。然而，它们往往忽视文本中的局部显著短语和实体提及，而这些短语和实体提及在第一阶段的检索中起着关键作用。为了减轻这个弱点，我们建议使一个密集检索对齐一个良好的表现词典感知表示模型。该方法通过弱化知识提取，从两个方面启发检索者: 1)词典增强对比目标，挑战密集编码器; 2)成对秩一致正则化，使密集模型的行为倾向于另一方。我们在三个公共基准上评估了我们的模型，结果表明，与一个具有可比性的词汇感知检索器作为教师，我们提出的密集型可以带来一致和重大的改进，甚至超过它的老师。此外，我们发现我们对稠密检索器的改进是对标准等级精馏的补充，它可以进一步提高最先进的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=LED:+Lexicon-Enlightened+Dense+Retriever+for+Large-Scale+Retrieval)|0| +|[A Passage-Level Reading Behavior Model for Mobile Search](https://doi.org/10.1145/3543507.3583343)|Zhijing Wu, Jiaxin Mao, Kedi Xu, Dandan Song, Heyan Huang|School of Computer Science, Carnegie Mellon University, USA; Gaoling School of Artificial Intelligence, Renmin University of China, China; School of Computer Science and Technology, Beijing Institute of Technology, China|Reading is a vital and complex cognitive activity during users’ information-seeking process. Several studies have focused on understanding users’ reading behavior in desktop search. Their findings greatly contribute to the design of information retrieval models. However, little is known about how users read a result in mobile search, although search currently happens more frequently in mobile scenarios. In this paper, we conduct a lab-based user study to investigate users’ fine-grained reading behavior patterns in mobile search. We find that users’ reading attention allocation is strongly affected by several behavior biases, such as position and selection biases. Inspired by these findings, we propose a probabilistic generative model, the Passage-level Reading behavior Model (PRM), to model users’ reading behavior in mobile search. The PRM utilizes observable passage-level exposure and viewport duration events to infer users’ unobserved skimming event, reading event, and satisfaction perception during the reading process. Besides fitting the passage-level reading behavior, we utilize the fitted parameters of PRM to estimate the passage-level and document-level relevance. Experimental results show that PRM outperforms existing unsupervised relevance estimation models. PRM has strong interpretability and provides valuable insights into the understanding of how users seek and perceive useful information in mobile search.|阅读是用户信息搜索过程中一种重要而复杂的认知活动。一些研究集中在了解用户在桌面搜索中的阅读行为。他们的发现极大地促进了信息检索模型的设计。然而，尽管目前搜索在移动场景中出现的频率更高，但人们对用户在移动搜索中如何阅读结果知之甚少。本文以实验室为基础，对移动搜索中用户的细粒度阅读行为模式进行了研究。研究发现，位置偏差、选择偏差等行为偏差对用户的阅读注意分配有显著影响。受这些发现的启发，我们提出了一个概率生成模型——短文水平阅读行为模型(PRM) ，来模拟用户在移动搜索中的阅读行为。PRM 利用可观察到的文章水平暴露和视窗持续时间事件来推断用户在阅读过程中未观察到的略读事件、阅读事件和满意度感知。除了拟合文章阅读行为外，我们还利用 PRM 的拟合参数来估计文章阅读水平和文档阅读水平的相关性。实验结果表明，PRM 算法的性能优于现有的无监督相关估计模型。PRM 具有很强的可解释性，为理解用户在移动搜索中如何寻找和感知有用信息提供了有价值的见解。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Passage-Level+Reading+Behavior+Model+for+Mobile+Search)|0| +|[PROD: Progressive Distillation for Dense Retrieval](https://doi.org/10.1145/3543507.3583421)|Zhenghao Lin, Yeyun Gong, Xiao Liu, Hang Zhang, Chen Lin, Anlei Dong, Jian Jiao, Jingwen Lu, Daxin Jiang, Rangan Majumder, Nan Duan|Microsoft, USA; Microsoft Research Asia, China; School of Informatics, Xiamen University, China; Microsoft, China|Knowledge distillation is an effective way to transfer knowledge from a strong teacher to an efficient student model. Ideally, we expect the better the teacher is, the better the student. However, this expectation does not always come true. It is common that a better teacher model results in a bad student via distillation due to the nonnegligible gap between teacher and student. To bridge the gap, we propose PROD, a PROgressive Distillation method, for dense retrieval. PROD consists of a teacher progressive distillation and a data progressive distillation to gradually improve the student. We conduct extensive experiments on five widely-used benchmarks, MS MARCO Passage, TREC Passage 19, TREC Document 19, MS MARCO Document and Natural Questions, where PROD achieves the state-of-the-art within the distillation methods for dense retrieval. The code and models will be released.|知识提取是将知识从一个强有力的教师转化为一个有效的学生模型的有效途径。理想情况下，我们期望老师越好，学生越好。然而，这种期望并不总是能够实现。由于教师与学生之间存在着不可忽视的差距，一个较好的教师模型常常通过精馏的方法导致不好的学生。为了填补这一空白，我们提出了一种逐步精馏方法 PROD，用于密集提取。PROD 由一个教师的逐步升华和一个数据的逐步升华组成，以逐步提高学生的素质。我们对五个广泛使用的基准进行了广泛的实验，即 MS MARCO Passage，TREC Passage 19，TREC Document 19，MS MARCO Document and Natural Questions，PROD 在这些基准上实现了用于密集检索的蒸馏方法的最先进水平。代码和模型将被发布。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=PROD:+Progressive+Distillation+for+Dense+Retrieval)|0| +|[Ad Auction Design with Coupon-Dependent Conversion Rate in the Auto-bidding World](https://doi.org/10.1145/3543507.3583230)|Bonan Ni, Xun Wang, Qi Zhang, Pingzhong Tang, Zhourong Chen, Tianjiu Yin, Liangni Lu, Xiaobing Liu, Kewu Sun, Zhe Ma|Institute for Interdisciplinary Information Sciences, Tsinghua University, China; TuringSense, China and Institute for Interdisciplinary Information Sciences, Tsinghua University, China; ByteDance, China; Intelligent Science & Technology Academy of CASIC, China and Scientific Research Key Laboratory of Aerospace Defence Intelligent Systems and Technology, China|Online advertising has become a dominant source of revenue of the Internet. In classic auction theory, only the auctioneer (i.e., the platform) and buyers (i.e., the advertisers) are involved, while the advertising audiences are ignored. For ecommerce advertising, however, the platform can provide coupons for the advertising audiences and nudge them into purchasing more products at lower prices (e.g., 2 dollars off the regular price). Such promotions can lead to an increase in amount and value of purchases. In this paper, we jointly design the coupon value computation, slot allocation, and payment of online advertising in an auto-bidding world. Firstly, we propose the auction mechanism, named CFA-auction (i.e., Coupon-For-the-Audiences-auction), which takes advertising audiences into account in the auction design. We prove the existence of pacing equilibrium, and show that CFA-auction satisfies the IC (incentive compatibility), IR (individual rationality) constraints. Then, we study the optimality of CFA-auction, and prove it can maintain an approximation of the optimal. Finally, experimental evaluation results on both offline dataset as well as online A/B test demonstrate the effectiveness of CFA-auction.|在线广告已成为互联网收入的主要来源。在传统的拍卖理论中，只有拍卖商(即平台)和买家(即广告商)参与，而广告受众被忽略。然而，对于电子商务广告来说，这个平台可以为广告受众提供优惠券，推动他们以更低的价格购买更多的产品(例如，比正常价格低2美元)。此类促销活动可能导致购买数量和价值的增加。在这篇论文中，我们共同设计了自动竞价世界中的优惠券价值计算、时段分配和在线广告支付。首先，我们提出了一种拍卖机制，即 CFA 拍卖(即为受众提供优惠券的拍卖) ，该机制在拍卖设计中考虑了广告受众。我们证明了节奏均衡的存在，并且证明了 CFA 拍卖满足集成激励相容(IC)、个人理性(IR)约束。然后，我们研究了 CFA 拍卖的最优性，并证明了它可以保持最优的近似。最后，通过对离线数据集和在线 A/B 测试的实验结果验证了 CFA 拍卖的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Ad+Auction+Design+with+Coupon-Dependent+Conversion+Rate+in+the+Auto-bidding+World)|0| +|[A Reference-Dependent Model for Web Search Evaluation: Understanding and Measuring the Experience of Boundedly Rational Users](https://doi.org/10.1145/3543507.3583551)|Nuo Chen, Jiqun Liu, Tetsuya Sakai|The University of Oklahoma, USA; Waseda University, Japan|Previous researches demonstrate that users’ actions in search interaction are associated with relative gains and losses to reference points, known as the reference dependence effect. However, this widely confirmed effect is not represented in most user models underpinning existing search evaluation metrics. In this study, we propose a new evaluation metric framework, namely Reference Dependent Metric (ReDeM), for assessing query-level search by incorporating the effect of reference dependence into the modelling of user search behavior. To test the overall effectiveness of the proposed framework, (1) we evaluate the performance, in terms of correlation with user satisfaction, of ReDeMs built upon different reference points against that of the widely-used metrics on three search datasets; (2) we examine the performance of ReDeMs under different task states, like task difficulty and task urgency; and (3) we analyze the statistical reliability of ReDeMs in terms of discriminative power. Experimental results indicate that: (1) ReDeMs integrated with a proper reference point achieve better correlations with user satisfaction than most of the existing metrics, like Discounted Cumulative Gain (DCG) and Rank-Biased Precision (RBP), even though their parameters have already been well-tuned; (2) ReDeMs reach relatively better performance compared to existing metrics when the task triggers a high-level cognitive load; (3) the discriminative power of ReDeMs is far stronger than Expected Reciprocal Rank (ERR), slightly stronger than Precision and similar to DCG, RBP and INST. To our knowledge, this study is the first to explicitly incorporate the reference dependence effect into the user browsing model and offline evaluation metrics. Our work illustrates a promising approach to leveraging the insights about user biases from cognitive psychology in better evaluating user search experience and enhancing user models.|以往的研究表明，用户在搜索交互中的行为与参考点的相对收益和相对损失有关，称为参考依赖效应。然而，这种被广泛证实的效果在大多数支持现有搜索评估指标的用户模型中并没有体现出来。在这项研究中，我们提出了一个新的评估度量框架，即参考依赖度量(ReDeM) ，通过将参考依赖的影响纳入用户搜索行为的建模来评估查询级搜索。为了测试提出的框架的整体有效性，(1)我们评估建立在不同参考点上的 ReDeM 与用户满意度的相关性，与三个搜索数据集上广泛使用的指标的相关性; (2)我们检查 ReDeM 在不同任务状态下的表现，如任务难度和任务紧迫性; (3)我们分析 ReDeM 在区分能力方面的统计可靠性。实验结果表明: (1)与合适的参考点相结合的 ReDeMs 与用户满意度的相关性优于大多数现有指标，如 DCG 和 RBP，尽管它们的参数已经得到了很好的调整; (2)当任务触发高水平的认知负荷时，与现有指标相比，ReDeMs 获得了相对更好的性能; (3) ReDeMs 的区分能力远远强于期望互惠秩序(ERR) ，略强于精度，类似于 DCG、 RBP 和 INST。据我们所知，这项研究是第一个明确地将参考依赖效应纳入用户浏览模型和离线评价指标。我们的工作说明了一种有前途的方法，利用认知心理学对用户偏见的洞察力，更好地评估用户搜索体验和增强用户模型。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Reference-Dependent+Model+for+Web+Search+Evaluation:+Understanding+and+Measuring+the+Experience+of+Boundedly+Rational+Users)|0| |[Maximizing Submodular Functions for Recommendation in the Presence of Biases](https://doi.org/10.1145/3543507.3583195)|Anay Mehrotra, Nisheeth K. Vishnoi||Subset selection tasks, arise in recommendation systems and search engines and ask to select a subset of items that maximize the value for the user. The values of subsets often display diminishing returns, and hence, submodular functions have been used to model them. If the inputs defining the submodular function are known, then existing algorithms can be used. In many applications, however, inputs have been observed to have social biases that reduce the utility of the output subset. Hence, interventions to improve the utility are desired. Prior works focus on maximizing linear functions -- a special case of submodular functions -- and show that fairness constraint-based interventions can not only ensure proportional representation but also achieve near-optimal utility in the presence of biases. We study the maximization of a family of submodular functions that capture functions arising in the aforementioned applications. Our first result is that, unlike linear functions, constraint-based interventions cannot guarantee any constant fraction of the optimal utility for this family of submodular functions. Our second result is an algorithm for submodular maximization. The algorithm provably outputs subsets that have near-optimal utility for this family under mild assumptions and that proportionally represent items from each group. In empirical evaluation, with both synthetic and real-world data, we observe that this algorithm improves the utility of the output subset for this family of submodular functions over baselines.|子集选择任务，出现在推荐系统和搜索引擎，并要求选择一个子集的项目，最大限度地为用户的价值。子集的值经常显示报酬递减，因此，子模块函数被用来建模它们。如果定义子模函数的输入是已知的，那么可以使用现有的算法。然而，在许多应用中，输入已被观察到具有社会偏差，从而降低了输出子集的效用。因此，需要采取干预措施来改善效用。先前的研究集中在最大化线性函数(次模函数的一个特例) ，并且表明基于公平约束的干预不仅可以确保比例代表制，而且在存在偏差的情况下还可以实现接近最优的效用。我们研究了一类子模函数的最大化问题，这类子模函数捕获上述应用中出现的函数。我们的第一个结果是，与线性函数不同，基于约束的干预不能保证这个子模函数族的最优效用的任何常数部分。我们的第二个结果是一个次模最大化算法。该算法可证明输出的子集具有接近最优的效用为这个家庭在温和的假设和比例代表项目从每组。在实证评价中，我们观察到，无论是合成的还是真实的数据，这个算法都提高了这个子模函数族的输出子集在基线上的效用。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Maximizing+Submodular+Functions+for+Recommendation+in+the+Presence+of+Biases)|0| |[Facility Relocation Search For Good: When Facility Exposure Meets User Convenience](https://doi.org/10.1145/3543507.3583859)|Hui Luo, Zhifeng Bao, J. Shane Culpepper, Mingzhao Li, Yanchang Zhao|RMIT University, Australia; CSIRO, Australia|In this paper, we propose a novel facility relocation problem where facilities (and their services) are portable, which is a combinatorial search problem with many practical applications. Given a set of users, a set of existing facilities, and a set of potential sites, we decide which of the existing facilities to relocate to potential sites, such that two factors are satisfied: (1) facility exposure: facilities after relocation have balanced exposure, namely serving equivalent numbers of users; (2) user convenience: it is convenient for users to access the nearest facility, which provides services with shorter travel distance. This problem is motivated by applications such as dynamically redistributing vaccine resources to align supply with demand for different vaccination centers, and relocating the bike sharing sites daily to improve the transportation efficiency. We first prove that this problem is NP-hard, and then we propose two algorithms: a non-learning best response algorithm () and a reinforcement learning algorithm (). In particular, the best response algorithm finds a Nash equilibrium to balance the facility-related and the user-related goals. To avoid being confined to only one Nash equilibrium, as found in the method, we also propose the reinforcement learning algorithm for long-term benefits, where each facility is an agent and we determine whether a facility needs to be relocated or not. To verify the effectiveness of our methods, we adopt multiple metrics to evaluate not only our objective, but also several other facility exposure equity and user convenience metrics to understand the benefits after facility relocation. Finally, comprehensive experiments using real-world datasets provide insights into the effectiveness of the two algorithms in practice.|在本文中，我们提出了一个新的设施搬迁问题，其中设施(及其服务)是可移植的，这是一个组合搜索问题与许多实际应用。根据一组使用者、一组现有设施及一组可供选择的用地，我们会决定哪些现有设施须迁往可供选择的用地，以满足以下两个因素: (1)设施接触量: 迁移后的设施接触量均衡，即服务相等数目的使用者; (2)使用者方便: 使用者可方便地前往最近的设施，而该设施提供的服务距离较短。这个问题的动机是应用程序，如动态重新分配疫苗资源，以调整供应与不同的疫苗接种中心的需求，并重新安置自行车共享站点，以提高运输效率每天。我们首先证明了这个问题是 NP 难的，然后我们提出了两个算法: 非学习最佳响应算法()和强化学习算法()。特别是，最佳响应算法会找到一个平衡设施相关目标和用户相关目标的纳什均衡点。为了避免只局限于一个纳什均衡点，正如方法中所发现的那样，我们还提出了长期利益的强化学习算法，即每个设施都是一个代理人，我们决定是否需要重新安置一个设施。为了验证我们的方法的有效性，我们不仅采用了多个指标来评估我们的目标，而且还采用了其他一些设施暴露公平性和用户便利性指标来了解设施搬迁后的好处。最后，利用真实世界数据集进行综合实验，验证了这两种算法在实际应用中的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Facility+Relocation+Search+For+Good:+When+Facility+Exposure+Meets+User+Convenience)|0| |[Detecting and Limiting Negative User Experiences in Social Media Platforms](https://doi.org/10.1145/3543507.3583883)|Lluís Garcia Pueyo, Vinodh Kumar Sunkara, Prathyusha Senthil Kumar, Mohit Diwan, Qian Ge, Behrang Javaherian, Vasilis Verroios|Meta Platforms, Inc., USA|Item ranking is important to a social media platform’s success. The order in which posts, videos, messages, comments, ads, used products, notifications are presented to a user greatly affects the time spent on the platform, how often they visit it, how much they interact with each other, and the quantity and quality of the content they post. To this end, item ranking algorithms use models that predict the likelihood of different events, e.g., the user liking, sharing, commenting on a video, clicking/converting on an ad, or opening the platform’s app from a notification. Unfortunately, by solely relying on such event-prediction models, social media platforms tend to over optimize for short-term objectives and ignore the long-term effects. In this paper, we propose an approach that aims at improving item ranking long-term impact. The approach primarily relies on an ML model that predicts negative user experiences. The model utilizes all available UI events: the details of an action can reveal how positive or negative the user experience has been; for example, a user writing a lengthy report asking for a given video to be taken down, likely had a very negative experience. Furthermore, the model takes into account detected integrity (e.g., hostile speech or graphic violence) and quality (e.g., click or engagement bait) issues with the content. Note that those issues can be perceived very differently from different users. Therefore, developing a personalized model, where a prediction refers to a specific user for a specific piece of content at a specific point in time, is a fundamental design choice in our approach. Besides the personalized ML model, our approach consists of two more pieces: (a) the way the personalized model is integrated with an item ranking algorithm and (b) the metrics, methodology, and success criteria for the long term impact of detecting and limiting negative user experiences. Our evaluation process uses extensive A/B testing on the Facebook platform: we compare the impact of our approach in treatment groups against production control groups. The AB test results indicate a 5% to 50% reduction in hides, reports, and submitted feedback. Furthermore, we compare against a baseline that does not include some of the crucial elements of our approach: the comparison shows our approach has a 100x to 30x lower False Positive Ratio than a baseline. Lastly, we present the results from a large scale survey, where we observe a statistically significant improvement of 3 to 6 percent in users’ sentiment regarding content suffering from nudity, clickbait, false / misleading, witnessing-hate, and violence issues.|项目排名对社交媒体平台的成功至关重要。发布、视频、信息、评论、广告、二手产品、通知的顺序对用户在平台上花费的时间、访问频率、互动程度以及发布内容的数量和质量有很大影响。为此，项目排名算法使用模型来预测不同事件的可能性，例如，用户喜欢，分享，评论视频，点击/转换广告，或从通知中打开平台的应用程序。不幸的是，仅仅依靠这种事件预测模型，社交媒体平台往往会过度优化短期目标而忽视长期影响。在本文中，我们提出了一种方法，旨在提高项目排名的长期影响。这种方法主要依靠机器学习模型来预测负面的用户体验。该模型利用了所有可用的 UI 事件: 一个动作的细节可以揭示用户体验的积极或消极程度; 例如，一个用户写了一份长篇报告，要求删除一个给定的视频，很可能有一个非常消极的体验。此外，该模型还考虑了检测到的完整性(例如，敌意言论或暴力画面)和质量(例如，点击或参与诱饵)问题。请注意，这些问题可以从不同的用户看到非常不同。因此，开发个性化的模型，其中预测指的是在特定时间点的特定内容的特定用户，是我们方法中的一个基本设计选择。除了个性化机器学习模型，我们的方法还包括两个部分: (a)个性化模型与项目排序算法的整合方式; (b)检测和限制负面用户体验的长期影响的指标、方法和成功标准。我们的评估过程在 Facebook 平台上使用了广泛的 A/B 测试: 我们比较我们在治疗组和生产控制组中的方法的影响。AB 测试结果表明，皮革、报告和提交的反馈减少了5% 到50% 。此外，我们比较了不包括我们方法的一些关键要素的基线: 比较显示我们的方法比基线低100到30倍的错误阳性率。最后，我们展示了一项大规模调查的结果，我们观察到用户对于遭受裸露、点击诱饵、虚假/误导、目击仇恨和暴力问题的内容的情绪有3% 到6% 的统计显著改善。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Detecting+and+Limiting+Negative+User+Experiences+in+Social+Media+Platforms)|0| |[On Detecting Policy-Related Political Ads: An Exploratory Analysis of Meta Ads in 2022 French Election](https://doi.org/10.1145/3543507.3583875)|Vera Sosnovik, Romaissa Kessi, Maximin Coavoux, Oana Goga|CNRS, France and LIX, Inria, Ecole Polytechnique, Institut Polytechnique de Paris, France; CNRS, France and LIG, Université Grenoble Alpes, Grenoble INP, France|Online political advertising has become the cornerstone of political campaigns. The budget spent solely on political advertising in the U.S. has increased by more than 100% from \$700 million during the 2017-2018 U.S. election cycle to \$1.6 billion during the 2020 U.S. presidential elections. Naturally, the capacity offered by online platforms to micro-target ads with political content has been worrying lawmakers, journalists, and online platforms, especially after the 2016 U.S. presidential election, where Cambridge Analytica has targeted voters with political ads congruent with their personality To curb such risks, both online platforms and regulators (through the DSA act proposed by the European Commission) have agreed that researchers, journalists, and civil society need to be able to scrutinize the political ads running on large online platforms. Consequently, online platforms such as Meta and Google have implemented Ad Libraries that contain information about all political ads running on their platforms. This is the first step on a long path. Due to the volume of available data, it is impossible to go through these ads manually, and we now need automated methods and tools to assist in the scrutiny of political ads. In this paper, we focus on political ads that are related to policy. Understanding which policies politicians or organizations promote and to whom is essential in determining dishonest representations. This paper proposes automated methods based on pre-trained models to classify ads in 14 main policy groups identified by the Comparative Agenda Project (CAP). We discuss several inherent challenges that arise. Finally, we analyze policy-related ads featured on Meta platforms during the 2022 French presidential elections period.|在线政治广告已经成为政治运动的基石。美国政治广告预算从2017-2018年美国大选期间的7亿美元增加到2020年美国总统大选期间的16亿美元，增幅超过100% 。自然，在线平台提供的针对政治内容的微观广告的能力一直令立法者、记者和在线平台感到担忧，尤其是在2016年美国总统大选之后，剑桥分析公司(Cambridge Analytica)针对选民的政治广告符合他们的个性。为了遏制这种风险，在线平台和监管机构(通过欧盟委员会提出的 DSA 法案)已经同意，研究人员、记者和公民社会需要能够审查在大型在线平台上运行的政治广告。因此，像 Meta 和 Google 这样的在线平台已经实现了广告库，其中包含了在其平台上运行的所有政治广告的信息。这是漫长道路上的第一步。由于可获得的数据量很大，手动浏览这些广告是不可能的，我们现在需要自动化的方法和工具来协助审查政治广告。本文主要研究与政策相关的政治广告。了解哪些政治家或组织提倡哪些政策，以及向谁提出这些政策，对于确定不诚实陈述至关重要。本文提出了一种基于预训练模型的广告自动分类方法，用于比较议程项目(CAP)确定的14个主要政策组的广告分类。我们将讨论出现的几个内在挑战。最后，我们分析了2022年法国总统大选期间 Meta 平台上的政策相关广告。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=On+Detecting+Policy-Related+Political+Ads:+An+Exploratory+Analysis+of+Meta+Ads+in+2022+French+Election)|0| -|[A ML-based Approach for HTML-based Style Recommendation](https://doi.org/10.1145/3543873.3587300)|Ryan Aponte, Ryan A. Rossi, Shunan Guo, Jane Hoffswell, Nedim Lipka, Chang Xiao, Gromit YeukYin Chan, Eunyee Koh, Nesreen K. Ahmed|Intel Labs, USA; Adobe Research, USA; Adobe, USA; CMU, USA|Given a large corpus of HTML-based emails (or websites, posters, documents) collected from the web, how can we train a model capable of learning from such rich heterogeneous data for HTML-based style recommendation tasks such as recommending useful design styles or suggesting alternative HTML designs? To address this new learning task, we first decompose each HTML document in the corpus into a sequence of smaller HTML fragments where each fragment may consist of a set of HTML entities such as buttons, images, textual content (titles, paragraphs) and stylistic entities such as background-style, font-style, button-style, among others. From these HTML fragments, we then derive a single large heterogeneous hypergraph that captures the higher-order dependencies between HTML fragments and entities in such fragments, both within the same HTML document as well as across the HTML documents in the corpus. We then formulate this new HTML style recommendation task as a hypergraph representation learning problem and propose an approach to solve it. Our approach is able to learn effective low-dimensional representations of the higher-order fragments that consist of sets of heterogeneous entities as well as low-dimensional representations of the individual entities themselves. We demonstrate the effectiveness of the approach across several design style recommendation tasks. To the best of our knowledge, this work is the first to develop an ML-based model for the task of HTML-based email style recommendation.|鉴于从网上收集的大量基于 HTML 的电子邮件(或网站、海报、文档) ，我们如何才能培养一个模型，使其能够从这些丰富的异构数据中学习基于 HTML 的风格推荐任务，如推荐有用的设计风格或建议替代 HTML 设计？为了解决这个新的学习任务，我们首先将语料库中的每个 HTML 文档分解为一系列较小的 HTML 片段，其中每个片段可能包含一组 HTML 实体，如按钮、图像、文本内容(标题、段落)和风格实体，如背景风格、字体风格、按钮风格等。然后，从这些 HTML 片段中，我们得到一个单一的大型异构超图，它捕获这些片段中 HTML 片段和实体之间的高阶依赖关系，这些依赖关系既存在于同一 HTML 文档中，也存在于语料库中的 HTML 文档之间。然后将这个新的 HTML 风格推荐任务表示为一个超图表示学习问题，并提出了一种解决方法。我们的方法能够学习高阶片段的有效低维表示，这些片段由异构实体集合以及单个实体本身的低维表示组成。我们在几个设计风格的推荐任务中演示了该方法的有效性。据我们所知，这项工作是第一个开发基于机器学习的任务的基于 HTML 的电子邮件样式推荐模型。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+ML-based+Approach+for+HTML-based+Style+Recommendation)|0| -|[Graph-Level Embedding for Time-Evolving Graphs](https://doi.org/10.1145/3543873.3587299)|Lili Wang, Chenghan Huang, Xinyuan Cao, Weicheng Ma, Soroush Vosoughi|Dartmouth College, USA; Georgia Institute of Technology, USA; Jefferies Financial Group LLC, USA|Graph representation learning (also known as network embedding) has been extensively researched with varying levels of granularity, ranging from nodes to graphs. While most prior work in this area focuses on node-level representation, limited research has been conducted on graph-level embedding, particularly for dynamic or temporal networks. However, learning low-dimensional graph-level representations for dynamic networks is critical for various downstream graph retrieval tasks such as temporal graph similarity ranking, temporal graph isomorphism, and anomaly detection. In this paper, we present a novel method for temporal graph-level embedding that addresses this gap. Our approach involves constructing a multilayer graph and using a modified random walk with temporal backtracking to generate temporal contexts for the graph’s nodes. We then train a “document-level’’ language model on these contexts to generate graph-level embeddings. We evaluate our proposed model on five publicly available datasets for the task of temporal graph similarity ranking, and our model outperforms baseline methods. Our experimental results demonstrate the effectiveness of our method in generating graph-level embeddings for dynamic networks.|图表示学习(也称为网络嵌入)已经被广泛研究与不同的粒度级别，从节点到图。虽然该领域的大多数工作集中在节点级表示，但是对图级嵌入的研究很有限，特别是对于动态或时态网络。然而，学习动态网络的低维图级表示对于各种下游图检索任务(如时间图相似性排序、时间图同构和异常检测)至关重要。在本文中，我们提出了一种新的时间图级嵌入方法来解决这一问题。我们的方法包括构造一个多层图，并使用一个修改过的随机游走和时间回溯来为图的节点生成时间上下文。然后，我们在这些上下文上训练一个“文档级”语言模型来生成图级嵌入。我们评估了我们提出的模型在五个公开可用的数据集的时间图相似性排序的任务，我们的模型优于基线方法。实验结果表明了该方法在动态网络图级嵌入生成中的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Graph-Level+Embedding+for+Time-Evolving+Graphs)|0| -|[SpotLight: Visual Insight Recommendation](https://doi.org/10.1145/3543873.3587302)|Camille Harris, Ryan A. Rossi, Sana Malik, Jane Hoffswell, Fan Du, Tak Yeon Lee, Eunyee Koh, Handong Zhao|Adobe Research, USA; Georgia Tech, USA; KAIST, Republic of Korea|Visualization recommendation systems make understanding data more accessible to users of all skill levels by automatically generating visualizations for users to explore. However, most existing visualization recommendation systems focus on ranking all possible visualizations based on the attributes or encodings, which makes it difficult to find the most relevant insights. We therefore introduce a novel class of insight-based visualization recommendation systems that automatically rank and recommend groups of related insights as well as the most important insights within each group. Our approach combines results from different learning-based methods to discover insights automatically and generalizes to a variety of attribute types (e.g., categorical, numerical, and temporal), including non-trivial combinations of these attribute types. To demonstrate the utility of this approach, we implemented a insight-centric visualization recommendation system, SpotLight, and conducted a user study with twelve participants, which showed that users are able to quickly find and understand relevant insights in unfamiliar data.|可视化推荐系统通过自动生成供用户探索的可视化，使所有技能水平的用户更容易理解数据。然而，现有的可视化推荐系统大多侧重于基于属性或编码对所有可能的可视化进行排序，这使得很难找到最相关的见解。因此，我们引入了一类新颖的基于洞察力的可视化推荐系统，该系统可以自动对相关洞察力以及每个组内最重要的洞察力进行排名和推荐。我们的方法结合了来自不同的基于学习的方法的结果，自动发现见解，并推广到各种属性类型(例如，分类，数字和时间) ，包括这些属性类型的非平凡组合。为了证明这种方法的实用性，我们实施了一个以洞察力为中心的可视化推荐系统 SpotLight，并对12名参与者进行了用户研究，结果显示用户能够快速找到并理解不熟悉数据中的相关见解。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=SpotLight:+Visual+Insight+Recommendation)|0| -|[DataExpo: A One-Stop Dataset Service for Open Science Research](https://doi.org/10.1145/3543873.3587305)|Bin Lu, Lyuwen Wu, Lina Yang, Chenxing Sun, Wei Liu, Xiaoying Gan, Shiyu Liang, Luoyi Fu, Xinbing Wang, Chenghu Zhou|Shanghai Jiao Tong University, China; Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, China|The large volumes of data on the Internet provides new opportunities for scientific discovery, especially promoting data-driven open science research. However, due to lack of accurate semantic markups, finding relevant data is still difficult. To address this problem, we develop a one-stop dataset service called DataExpo and propose a deep learning method for automatic metadata ingestion. In this demo paper, we describe the system architecture, and how DataExpo facilitates dataset discovery, search and recommendation. Up till now, DataExpo has indexed over 960,000 datasets from more than 27,000 repositories in the context of Deep-time Digital Earth Program. Demo visitors can explore our service via https://dataexpo.acemap.info.|互联网上的大量数据为科学发现提供了新的机会，特别是促进了数据驱动的开放科学研究。然而，由于缺乏准确的语义标记，找到相关数据仍然很困难。为了解决这个问题，我们开发了一个名为 DataExpo 的一站式数据集服务，并提出了一种自动元数据摄取的深度学习方法。在本演示文章中，我们描述了系统的体系结构，以及 DataExpo 如何促进数据集的发现、搜索和推荐。到目前为止，数据博览会已经从超过27,000个数据库中索引了超过960,000个数据集。示范观众可透过 https://dataexpo.acemap.info 探索我们的服务。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=DataExpo:+A+One-Stop+Dataset+Service+for+Open+Science+Research)|0| +|[A ML-based Approach for HTML-based Style Recommendation](https://doi.org/10.1145/3543873.3587300)|Ryan Aponte, Ryan A. Rossi, Shunan Guo, Jane Hoffswell, Nedim Lipka, Chang Xiao, Gromit YeukYin Chan, Eunyee Koh, Nesreen K. Ahmed|Intel Labs, USA; Adobe, USA; Adobe Research, USA; CMU, USA|Given a large corpus of HTML-based emails (or websites, posters, documents) collected from the web, how can we train a model capable of learning from such rich heterogeneous data for HTML-based style recommendation tasks such as recommending useful design styles or suggesting alternative HTML designs? To address this new learning task, we first decompose each HTML document in the corpus into a sequence of smaller HTML fragments where each fragment may consist of a set of HTML entities such as buttons, images, textual content (titles, paragraphs) and stylistic entities such as background-style, font-style, button-style, among others. From these HTML fragments, we then derive a single large heterogeneous hypergraph that captures the higher-order dependencies between HTML fragments and entities in such fragments, both within the same HTML document as well as across the HTML documents in the corpus. We then formulate this new HTML style recommendation task as a hypergraph representation learning problem and propose an approach to solve it. Our approach is able to learn effective low-dimensional representations of the higher-order fragments that consist of sets of heterogeneous entities as well as low-dimensional representations of the individual entities themselves. We demonstrate the effectiveness of the approach across several design style recommendation tasks. To the best of our knowledge, this work is the first to develop an ML-based model for the task of HTML-based email style recommendation.|鉴于从网上收集的大量基于 HTML 的电子邮件(或网站、海报、文档) ，我们如何才能培养一个模型，使其能够从这些丰富的异构数据中学习基于 HTML 的风格推荐任务，如推荐有用的设计风格或建议替代 HTML 设计？为了解决这个新的学习任务，我们首先将语料库中的每个 HTML 文档分解为一系列较小的 HTML 片段，其中每个片段可能包含一组 HTML 实体，如按钮、图像、文本内容(标题、段落)和风格实体，如背景风格、字体风格、按钮风格等。然后，从这些 HTML 片段中，我们得到一个单一的大型异构超图，它捕获这些片段中 HTML 片段和实体之间的高阶依赖关系，这些依赖关系既存在于同一 HTML 文档中，也存在于语料库中的 HTML 文档之间。然后将这个新的 HTML 风格推荐任务表示为一个超图表示学习问题，并提出了一种解决方法。我们的方法能够学习高阶片段的有效低维表示，这些片段由异构实体集合以及单个实体本身的低维表示组成。我们在几个设计风格的推荐任务中演示了该方法的有效性。据我们所知，这项工作是第一个开发基于机器学习的任务的基于 HTML 的电子邮件样式推荐模型。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+ML-based+Approach+for+HTML-based+Style+Recommendation)|0| +|[Graph-Level Embedding for Time-Evolving Graphs](https://doi.org/10.1145/3543873.3587299)|Lili Wang, Chenghan Huang, Xinyuan Cao, Weicheng Ma, Soroush Vosoughi|Dartmouth College, USA; Jefferies Financial Group LLC, USA; Georgia Institute of Technology, USA|Graph representation learning (also known as network embedding) has been extensively researched with varying levels of granularity, ranging from nodes to graphs. While most prior work in this area focuses on node-level representation, limited research has been conducted on graph-level embedding, particularly for dynamic or temporal networks. However, learning low-dimensional graph-level representations for dynamic networks is critical for various downstream graph retrieval tasks such as temporal graph similarity ranking, temporal graph isomorphism, and anomaly detection. In this paper, we present a novel method for temporal graph-level embedding that addresses this gap. Our approach involves constructing a multilayer graph and using a modified random walk with temporal backtracking to generate temporal contexts for the graph’s nodes. We then train a “document-level’’ language model on these contexts to generate graph-level embeddings. We evaluate our proposed model on five publicly available datasets for the task of temporal graph similarity ranking, and our model outperforms baseline methods. Our experimental results demonstrate the effectiveness of our method in generating graph-level embeddings for dynamic networks.|图表示学习(也称为网络嵌入)已经被广泛研究与不同的粒度级别，从节点到图。虽然该领域的大多数工作集中在节点级表示，但是对图级嵌入的研究很有限，特别是对于动态或时态网络。然而，学习动态网络的低维图级表示对于各种下游图检索任务(如时间图相似性排序、时间图同构和异常检测)至关重要。在本文中，我们提出了一种新的时间图级嵌入方法来解决这一问题。我们的方法包括构造一个多层图，并使用一个修改过的随机游走和时间回溯来为图的节点生成时间上下文。然后，我们在这些上下文上训练一个“文档级”语言模型来生成图级嵌入。我们评估了我们提出的模型在五个公开可用的数据集的时间图相似性排序的任务，我们的模型优于基线方法。实验结果表明了该方法在动态网络图级嵌入生成中的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Graph-Level+Embedding+for+Time-Evolving+Graphs)|0| +|[SpotLight: Visual Insight Recommendation](https://doi.org/10.1145/3543873.3587302)|Camille Harris, Ryan A. Rossi, Sana Malik, Jane Hoffswell, Fan Du, Tak Yeon Lee, Eunyee Koh, Handong Zhao|KAIST, Republic of Korea; Georgia Tech, USA; Adobe Research, USA|Visualization recommendation systems make understanding data more accessible to users of all skill levels by automatically generating visualizations for users to explore. However, most existing visualization recommendation systems focus on ranking all possible visualizations based on the attributes or encodings, which makes it difficult to find the most relevant insights. We therefore introduce a novel class of insight-based visualization recommendation systems that automatically rank and recommend groups of related insights as well as the most important insights within each group. Our approach combines results from different learning-based methods to discover insights automatically and generalizes to a variety of attribute types (e.g., categorical, numerical, and temporal), including non-trivial combinations of these attribute types. To demonstrate the utility of this approach, we implemented a insight-centric visualization recommendation system, SpotLight, and conducted a user study with twelve participants, which showed that users are able to quickly find and understand relevant insights in unfamiliar data.|可视化推荐系统通过自动生成供用户探索的可视化，使所有技能水平的用户更容易理解数据。然而，现有的可视化推荐系统大多侧重于基于属性或编码对所有可能的可视化进行排序，这使得很难找到最相关的见解。因此，我们引入了一类新颖的基于洞察力的可视化推荐系统，该系统可以自动对相关洞察力以及每个组内最重要的洞察力进行排名和推荐。我们的方法结合了来自不同的基于学习的方法的结果，自动发现见解，并推广到各种属性类型(例如，分类，数字和时间) ，包括这些属性类型的非平凡组合。为了证明这种方法的实用性，我们实施了一个以洞察力为中心的可视化推荐系统 SpotLight，并对12名参与者进行了用户研究，结果显示用户能够快速找到并理解不熟悉数据中的相关见解。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=SpotLight:+Visual+Insight+Recommendation)|0| +|[DataExpo: A One-Stop Dataset Service for Open Science Research](https://doi.org/10.1145/3543873.3587305)|Bin Lu, Lyuwen Wu, Lina Yang, Chenxing Sun, Wei Liu, Xiaoying Gan, Shiyu Liang, Luoyi Fu, Xinbing Wang, Chenghu Zhou|Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, China; Shanghai Jiao Tong University, China|The large volumes of data on the Internet provides new opportunities for scientific discovery, especially promoting data-driven open science research. However, due to lack of accurate semantic markups, finding relevant data is still difficult. To address this problem, we develop a one-stop dataset service called DataExpo and propose a deep learning method for automatic metadata ingestion. In this demo paper, we describe the system architecture, and how DataExpo facilitates dataset discovery, search and recommendation. Up till now, DataExpo has indexed over 960,000 datasets from more than 27,000 repositories in the context of Deep-time Digital Earth Program. Demo visitors can explore our service via https://dataexpo.acemap.info.|互联网上的大量数据为科学发现提供了新的机会，特别是促进了数据驱动的开放科学研究。然而，由于缺乏准确的语义标记，找到相关数据仍然很困难。为了解决这个问题，我们开发了一个名为 DataExpo 的一站式数据集服务，并提出了一种自动元数据摄取的深度学习方法。在本演示文章中，我们描述了系统的体系结构，以及 DataExpo 如何促进数据集的发现、搜索和推荐。到目前为止，数据博览会已经从超过27,000个数据库中索引了超过960,000个数据集。示范观众可透过 https://dataexpo.acemap.info 探索我们的服务。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=DataExpo:+A+One-Stop+Dataset+Service+for+Open+Science+Research)|0| |[Mirror: A Natural Language Interface for Data Querying, Summarization, and Visualization](https://doi.org/10.1145/3543873.3587309)|Canwen Xu, Julian J. McAuley, Penghan Wang|Cisco, USA; UC San Diego, USA|We present Mirror, an open-source platform for data exploration and analysis powered by large language models. Mirror offers an intuitive natural language interface for querying databases, and automatically generates executable SQL commands to retrieve relevant data and summarize it in natural language. In addition, users can preview and manually edit the generated SQL commands to ensure the accuracy of their queries. Mirror also generates visualizations to facilitate understanding of the data. Designed with flexibility and human input in mind, Mirror is suitable for both experienced data analysts and non-technical professionals looking to gain insights from their data.|我们介绍了一个基于大型语言模型的开源数据探索和分析平台—— Mirror。Mirror 为查询数据库提供了一个直观的自然语言界面，并自动生成可执行的 SQL 命令来检索相关数据并用自然语言对其进行汇总。此外，用户还可以预览和手动编辑生成的 SQL 命令，以确保查询的准确性。Mirror 还生成可视化，以便于理解数据。设计具有灵活性和人工输入的头脑，镜子是适合于有经验的数据分析师和非技术专业人士寻求获得洞察力从他们的数据。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Mirror:+A+Natural+Language+Interface+for+Data+Querying,+Summarization,+and+Visualization)|0| |[Is the Impression Log Beneficial to Effective Model Training in News Recommender Systems? No, It's NOT](https://doi.org/10.1145/3543873.3587312)|Jeewon Ahn, HongKyun Bae, SangWook Kim||||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Is+the+Impression+Log+Beneficial+to+Effective+Model+Training+in+News+Recommender+Systems?+No,+It's+NOT)|0| -|[Incorporating Embedding to Topic Modeling for More Effective Short Text Analysis](https://doi.org/10.1145/3543873.3587316)|Junaid Rashid, Jungeun Kim, Usman Naseem|School of Computer Science, The University of Sydney, Sydney, Australia, Australia; Department of Data Science, Sejong University, Seoul, Republic of Korea, Republic of Korea; Department of Software, Kongju National University, Cheonan, Republic of Korea, Republic of Korea|With the growing abundance of short text content on websites, analyzing and comprehending these short texts has become a crucial task. Topic modeling is a widely used technique for analyzing short text documents and uncovering the underlying topics. However, traditional topic models face difficulties in accurately extracting topics from short texts due to limited content and their sparse nature. To address these issues, we propose an Embedding-based topic modeling (EmTM) approach that incorporates word embedding and hierarchical clustering to identify significant topics. Experimental results demonstrate the effectiveness of EmTM on two datasets comprising web short texts, Snippet and News. The results indicate a superiority of EmTM over baseline topic models by its exceptional performance in both classification accuracy and topic coherence metrics.|随着网站上短文内容的日益丰富，分析和理解这些短文已经成为一项重要的任务。主题建模是一种广泛使用的分析短文本文档和揭示潜在主题的技术。然而，传统的话题模型由于内容有限和稀疏的特点，很难准确地从短文中提取话题。为了解决这些问题，我们提出了一种基于嵌入的主题建模(EmTM)方法，该方法结合了单词嵌入和层次聚类来识别重要的主题。实验结果表明，该方法能够有效地处理包括网络短文本、片段和新闻在内的两个数据集。结果表明，与基线主题模型相比，EmTM 在分类精度和主题一致性度量方面具有优越性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Incorporating+Embedding+to+Topic+Modeling+for+More+Effective+Short+Text+Analysis)|0| +|[Incorporating Embedding to Topic Modeling for More Effective Short Text Analysis](https://doi.org/10.1145/3543873.3587316)|Junaid Rashid, Jungeun Kim, Usman Naseem|Department of Software, Kongju National University, Cheonan, Republic of Korea, Republic of Korea; School of Computer Science, The University of Sydney, Sydney, Australia, Australia; Department of Data Science, Sejong University, Seoul, Republic of Korea, Republic of Korea|With the growing abundance of short text content on websites, analyzing and comprehending these short texts has become a crucial task. Topic modeling is a widely used technique for analyzing short text documents and uncovering the underlying topics. However, traditional topic models face difficulties in accurately extracting topics from short texts due to limited content and their sparse nature. To address these issues, we propose an Embedding-based topic modeling (EmTM) approach that incorporates word embedding and hierarchical clustering to identify significant topics. Experimental results demonstrate the effectiveness of EmTM on two datasets comprising web short texts, Snippet and News. The results indicate a superiority of EmTM over baseline topic models by its exceptional performance in both classification accuracy and topic coherence metrics.|随着网站上短文内容的日益丰富，分析和理解这些短文已经成为一项重要的任务。主题建模是一种广泛使用的分析短文本文档和揭示潜在主题的技术。然而，传统的话题模型由于内容有限和稀疏的特点，很难准确地从短文中提取话题。为了解决这些问题，我们提出了一种基于嵌入的主题建模(EmTM)方法，该方法结合了单词嵌入和层次聚类来识别重要的主题。实验结果表明，该方法能够有效地处理包括网络短文本、片段和新闻在内的两个数据集。结果表明，与基线主题模型相比，EmTM 在分类精度和主题一致性度量方面具有优越性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Incorporating+Embedding+to+Topic+Modeling+for+More+Effective+Short+Text+Analysis)|0| |[EnhancE: Enhanced Entity and Relation Embedding for Knowledge Hypergraph Link Prediction](https://doi.org/10.1145/3543873.3587326)|Chenxu Wang, Zhao Li, Xin Wang, Zirui Chen|Tianjin University, China|Knowledge Hypergraphs, as the generalization of knowledge graphs, have attracted increasingly widespread attention due to their friendly compatibility with real-world facts. However, link prediction in knowledge hypergraph is still an underexplored field despite the ubiquity of n-ary facts in the real world. Several recent representative embedding-based knowledge hypergraph link prediction methods have proven to be effective in a series of benchmarks, however, they only consider the position (or role) information, ignoring the neighborhood structure among entities and rich semantic information within each fact. To this end, we propose a model named EnhancE for effective link prediction in knowledge hypergraphs. On the one hand, a more expressive entity representation is obtained with both position and neighborhood information added to the initial embedding. On the other hand, rich semantic information of the involved entities within each tuple is incorporated into relation embedding for enhanced representation. Extensive experimental results over real datasets of both knowledge hypergraph and knowledge graph demonstrate the excellent performance of EnhancE compared with a variety of state-of-the-art baselines.|知识超图作为知识图的一种推广，由于其与现实世界事实的友好兼容性而引起了人们越来越广泛的关注。然而，尽管在现实世界中 n 元事实的普遍存在，知识超图中的链接预测仍然是一个未被充分探索的领域。最近几种有代表性的嵌入式知识超图链接预测方法已经被证明在一系列的基准测试中是有效的，但是它们只考虑位置(或角色)信息，忽略了实体间的邻域结构和每个事实中丰富的语义信息。为此，我们提出了一种基于知识超图的有效链接预测模型——增强 E。一方面，在初始嵌入的基础上加入位置信息和邻域信息，得到更具表现力的实体表示;。另一方面，每个元组中所涉及的实体的丰富语义信息被合并到关系嵌入中以增强表示。在知识超图和知识图的实际数据集上进行的大量实验结果表明，与各种最先进的基线相比，增强 E 具有优异的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=EnhancE:+Enhanced+Entity+and+Relation+Embedding+for+Knowledge+Hypergraph+Link+Prediction)|0| |[An Analogical Reasoning Method Based on Multi-task Learning with Relational Clustering](https://doi.org/10.1145/3543873.3587333)|Shuyi Li, Shaojuan Wu, Xiaowang Zhang, Zhiyong Feng|College of Intelligence and Computing, Tianjin University, China|Analogical QA task is a challenging natural language processing problem. When two word pairs are similar in their relationships, we refer to their relations as analogous. Although the analogy method based on word embedding is well developed, the analogy reasoning is far beyond this scope. At present, the methods based on pre-trained language models have explored only the tip of the iceberg. In this paper, we proposed a multi-task learning method for analogical QA task. First, we obtain word-pair representations by leveraging the output embeddings of the [MASK] token in the pre-trained language model. The representations are prepared for two tasks. The first task aims to train an analogical classifier by supervised learning. The second task is an auxiliary task based on relation clustering to generate relation pseudo-labels for word pairs and train relation classifier. Our method guides the model to analyze the relation similarity in analogical reasoning without relation labels. The experiments show that our method achieve excellent performance on four analogical reasoning datasets without the help of external corpus and knowledge. In the most difficult data set E-KAR, it has increased by at least 4%.|类比 QA 任务是一个具有挑战性的自然语言处理问题。当两个词对在关系上相似时，我们把它们的关系称为相似。基于嵌入词的类比推理方法虽然已经得到了很好的发展，但是类比推理远远超出了这个范围。目前，基于预训练语言模型的方法仅仅探索了冰山一角。本文提出了一种类比 QA 任务的多任务学习方法。首先，我们利用预训练语言模型中[ MASK ]令牌的输出嵌入获得词对表示。这些表示准备用于两个任务。第一个任务是通过监督式学习训练一个类比分类器。第二个任务是一个基于关系聚类的辅助任务，用于生成词对的关系伪标签和训练关系分类器。该方法引导模型分析无关联标签的类比推理中的关联相似性。实验结果表明，该方法在不借助外部语料库和知识的情况下，对四个类比推理数据集取得了良好的性能。在最困难的数据集 E-KAR 中，它至少增加了4% 。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=An+Analogical+Reasoning+Method+Based+on+Multi-task+Learning+with+Relational+Clustering)|0| -|[Templet: A Collaborative System for Knowledge Graph Question Answering over Wikidata](https://doi.org/10.1145/3543873.3587335)|Francisca Suárez, Aidan Hogan|DCC, Universidad de Chile, Chile; DCC, Universidad de Chile, Chile and Instituto Milenio Fundamentos de los Datos (IMFD), Chile|We present Templet: an online question answering (QA) system for Wikidata. Templet is based on the collaboratively-edited repository QAWiki, which collects questions in multiple natural languages along with their corresponding structured queries. Templet generates templates from question–query pairs on QAWiki by replacing key entities with identifiers. Using autocompletion, the user can type a question in natural language, select a template, and again using autocompletion, select the entities they wish to insert into the template’s placeholders, generating a concrete question, query and results. The main objectives of Templet are: (i) to enable users to answer potentially complex questions over Wikidata using natural language templates and autocompletion; (ii) to encourage users to collaboratively create new templates via QAWiki, which in turn can benefit not only Templet, but other QA systems.|我们提出的模板: 一个在线问题回答(QA)系统的 Wikidata。Templet 基于协作编辑的存储库 QAWiki，该存储库用多种自然语言收集问题以及相应的结构化查询。Templet 通过使用标识符替换关键实体，从 QAWiki 上的问题-查询对生成模板。使用自动补全，用户可以用自然语言键入一个问题，选择一个模板，然后再次使用自动补全，选择他们希望插入到模板占位符中的实体，生成一个具体的问题、查询和结果。Templet 的主要目标是: (i)使用户能够通过使用自然语言模板和自动完成来回答 Wikidata 上潜在的复杂问题; (ii)鼓励用户通过 QAWiki 合作创建新的模板，这反过来不仅可以使 Templet 受益，还可以使其他 QA 系统受益。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Templet:+A+Collaborative+System+for+Knowledge+Graph+Question+Answering+over+Wikidata)|0| +|[Templet: A Collaborative System for Knowledge Graph Question Answering over Wikidata](https://doi.org/10.1145/3543873.3587335)|Francisca Suárez, Aidan Hogan|DCC, Universidad de Chile, Chile and Instituto Milenio Fundamentos de los Datos (IMFD), Chile; DCC, Universidad de Chile, Chile|We present Templet: an online question answering (QA) system for Wikidata. Templet is based on the collaboratively-edited repository QAWiki, which collects questions in multiple natural languages along with their corresponding structured queries. Templet generates templates from question–query pairs on QAWiki by replacing key entities with identifiers. Using autocompletion, the user can type a question in natural language, select a template, and again using autocompletion, select the entities they wish to insert into the template’s placeholders, generating a concrete question, query and results. The main objectives of Templet are: (i) to enable users to answer potentially complex questions over Wikidata using natural language templates and autocompletion; (ii) to encourage users to collaboratively create new templates via QAWiki, which in turn can benefit not only Templet, but other QA systems.|我们提出的模板: 一个在线问题回答(QA)系统的 Wikidata。Templet 基于协作编辑的存储库 QAWiki，该存储库用多种自然语言收集问题以及相应的结构化查询。Templet 通过使用标识符替换关键实体，从 QAWiki 上的问题-查询对生成模板。使用自动补全，用户可以用自然语言键入一个问题，选择一个模板，然后再次使用自动补全，选择他们希望插入到模板占位符中的实体，生成一个具体的问题、查询和结果。Templet 的主要目标是: (i)使用户能够通过使用自然语言模板和自动完成来回答 Wikidata 上潜在的复杂问题; (ii)鼓励用户通过 QAWiki 合作创建新的模板，这反过来不仅可以使 Templet 受益，还可以使其他 QA 系统受益。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Templet:+A+Collaborative+System+for+Knowledge+Graph+Question+Answering+over+Wikidata)|0| |[OptiRef: Query Optimization for Knowledge Bases](https://doi.org/10.1145/3543873.3587342)|Wafaa El Husseini, Cheikh Brahim El Vaigh, François Goasdoué, Hélène Jaudoin|Univ. Rennes, France; Univ. Bourgogne, France|Ontology-mediated query answering (OMQA) consists in asking database queries on a knowledge base (KB); a KB is a set of facts, the KB’s database, described by domain knowledge, the KB’s ontology. FOL-rewritability is the main OMQA technique: it reformulates a query w.r.t. the KB’s ontology so that the evaluation of the reformulated query on the KB’s database computes the correct answers. However, because this technique embeds the domain knowledge relevant to the query into the reformulated query, a reformulated query may be complex and its optimization is the crux of efficiency. We showcase OptiRef that implements a novel, general optimization framework for efficient query answering on datalog ±, description logic, existential rules, OWL and RDF/S KBs. OptiRef optimizes reformulated queries by rapidly computing, based on a KB’s database summary, simpler (contained) queries with the same answers. We demonstrate OptiRef’s effectiveness on well-established benchmarks: performance is significantly improved in general, up to several orders of magnitude in the best cases!|本体介导的查询回答(OMQA)包括在知识库(KB)上询问数据库查询; 知识库是一组事实，知识库的数据库，由领域知识描述，知识库的本体。FOL 可重写性是主要的 OMQA 技术: 它重新规范查询 w.r.t. 知识库的本体，以便在知识库的数据库上计算重新规范查询的正确答案。然而，由于这种技术将与查询相关的领域知识嵌入到重构查询中，因此重构查询可能比较复杂，其优化是提高查询效率的关键。我们展示了 OptiRef，它实现了一个新颖的、通用的优化框架，用于在数据目录 ± 、描述逻辑、存在规则、 OWL 和 RDF/S 知识库上进行有效的查询应答。OptiRef 通过快速计算优化重新配置的查询，基于知识库的数据库摘要，使用具有相同答案的更简单(包含)的查询。我们展示了 OptiRef 在完善的基准测试上的有效性: 性能总体上得到了显著的改善，在最好的情况下达到了几个数量级！|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=OptiRef:+Query+Optimization+for+Knowledge+Bases)|0| -|[Learning Topical Structured Interfaces from Medical Research Literature](https://doi.org/10.1145/3543873.3587353)|Maitry Chauhan, Anna Pyayt, Michael N. Gubanov|University of South Florida, USA; Florida State University, USA|Accessing large-scale structured datasets such as WDC or CORD-191 is very challenging. Even if one topic (e.g. COVID-19 vaccine efficacy) is of interest, all topical tables in different sources/papers have hundreds of different schemas, depending on the authors, which significantly complicates both finding and querying them. Here we demonstrate a scalable Meta-profiler system, capable of constructing a structured standardized interface to a topic of interest in large-scale (semi-)structured datasets. This interface, that we call Meta-profile represents a multi-dimensional meta-data summary for a selected topic of interest, accumulating all differently structured representations of the topical tables in the dataset. Such Meta-profiles can be used as a rich visualization as well as a robust structural query interface simplifying access to large-scale (semi-)structured data for different user segments, such as data scientists and end users.|访问大规模的结构化数据集，如 WDC 或 CORD-191是非常具有挑战性的。即使一个主题(例如2019冠状病毒疾病疫苗效力)是有趣的，不同来源/论文中的所有主题表格都有数百种不同的模式，这取决于作者，这使得查找和查询这些模式变得非常复杂。在这里，我们演示了一个可伸缩的元分析器系统，它能够为大规模(半)结构化数据集中感兴趣的主题构建一个结构化的标准化接口。我们称之为 Meta-profile 的这个接口表示一个选定主题的多维元数据摘要，它积累了数据集中主题表的所有不同结构的表示。这样的元概要文件可以用作丰富的可视化以及健壮的结构化查询界面，简化不同用户段(如数据科学家和最终用户)对大规模(半)结构化数据的访问。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Learning+Topical+Structured+Interfaces+from+Medical+Research+Literature)|0| -|[DGBCT: A Scalable Distributed Gradient Boosting Causal Tree at Alipay](https://doi.org/10.1145/3543873.3584645)|Jun Zhou, Caizhi Tang, Qing Cui, Yi Ding, Longfei Li, Fei Wu|College of Computer Science and Technology, Zhejiang University, China and Ant Group, China; College of Computer Science and Technology, Zhejiang University, China; Ant Group, China|Causal effect estimation has been increasingly emphasized in the past few years. To handle this problem, tree-based causal methods have been widely used due to their robustness and explainability. However, most of the existing methods are limited to running on a single machine, making it difficult to scale up to hundreds of millions of data in typical industrial scenarios. This paper proposes DGBCT, a Distributed Gradient Boosting Causal Tree to tackle such problem, and the contribution of this paper is three folds. First, we extend the original GBCT method to a multi-treatment setting and take the monotonic constraints into consideration, so that more typical industrial necessities can be resolved with our framework. Moreover, we implement DGBCT based on the ‘Controller-Coordinator-Worker’ framework, in which dual failover mechanism is achieved, and commendable flexibility is ensured. In addition, empirical results show that DGBCT significantly outperforms the state-of-the-art causal trees, and has a near-linear speedup as the number of workers grows. The system is currently deployed in Alipay1 to support the daily business tasks that involve hundreds of millions of users.|因果效应估计在过去的几年中越来越受到重视。为了解决这个问题，基于树的因果关系方法由于其鲁棒性和可解释性而得到了广泛的应用。然而，现有的大多数方法仅限于在单台机器上运行，因此在典型的工业场景中很难扩展到数亿个数据。本文提出了分布式梯度提升因果树 DGBCT 来解决这个问题，本文的贡献有三个方面。首先，我们将原来的 GBCT 方法扩展到一个多处理环境，并且考虑了单调约束，使得我们的框架能够解决更多的典型工业需求。此外，我们在“控制器-协调器-工作者”框架的基础上实现了 DGBCT，实现了双重故障转移机制，并保证了值得称赞的灵活性。此外，实证结果显示，DGBCT 的表现明显优于最先进的因果树，并且随着工作人员数量的增加有近线性的加速效应。该系统目前部署在支付宝1中，以支持涉及数亿用户的日常业务任务。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=DGBCT:+A+Scalable+Distributed+Gradient+Boosting+Causal+Tree+at+Alipay)|0| -|[What Image do You Need? A Two-stage Framework for Image Selection in E-commerce](https://doi.org/10.1145/3543873.3584646)|Sheng You, Chao Wang, Baohua Wu, Jingping Liu, Quan Lu, Guanzhou Han, Yanghua Xiao|Shanghai University, China; Alibaba Group, China; Fudan University, China; East China University of Science and Technology, China|In e-commerce, images are widely used to display more intuitive information about items. Image selection significantly affects the user’s click-through rate (CTR). Most existing work considers the CTR as the target to find an appropriate image. However, these methods are challenging to deploy online efficiently. Also, the selected images may not relate to the item but are profitable to CTR, resulting in the undesirable phenomenon of enticing users to click on the item. To address these issues, we propose a novel two-stage pipeline method with content-based recall model and CTR-based ranking model. The first is realized as a joint method based on the title-image matching model and multi-modal knowledge graph embedding learning model. The second is a CTR-based visually aware scoring model, incorporating the entity textual information and entity images. Experimental results show the effectiveness and efficiency of our method in offline evaluations. After a month of online A/B testing on a travel platform Fliggy, the relative improvement of our method is 5% with respect to seller selection on CTCVR in the searching scenario, and our method further improves pCTR from 3.48% of human pick to 3.53% in the recommendation scenario.|在电子商务中，图像被广泛用于显示更直观的商品信息。图像选择会显著影响用户的点进率。大多数现有的工作都将 CTR 作为寻找合适图像的目标。然而，这些方法在线有效部署是具有挑战性的。此外，所选图像可能与该项目无关，但有利于点击率，导致不良现象的诱惑用户点击该项目。为了解决这些问题，我们提出了一种新的基于内容的召回模型和基于点击率的排序模型的两阶段流水线方法。第一种是基于标题-图像匹配模型和多模态知识图嵌入学习模型的联合方法。第二种是基于 CTR 的视觉感知评分模型，结合了实体文本信息和实体图像。实验结果表明了该方法在离线评估中的有效性和有效性。在旅游平台 Fliggy 上进行了一个月的在线 A/B 测试后，在搜索场景中，我们的方法相对于 CTCVR 上的卖方选择的相对改善为5% ，并且我们的方法进一步将 pCTR 从人类选择的3.48% 提高到推荐场景中的3.53% 。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=What+Image+do+You+Need?+A+Two-stage+Framework+for+Image+Selection+in+E-commerce)|0| +|[Learning Topical Structured Interfaces from Medical Research Literature](https://doi.org/10.1145/3543873.3587353)|Maitry Chauhan, Anna Pyayt, Michael N. Gubanov|Florida State University, USA; University of South Florida, USA|Accessing large-scale structured datasets such as WDC or CORD-191 is very challenging. Even if one topic (e.g. COVID-19 vaccine efficacy) is of interest, all topical tables in different sources/papers have hundreds of different schemas, depending on the authors, which significantly complicates both finding and querying them. Here we demonstrate a scalable Meta-profiler system, capable of constructing a structured standardized interface to a topic of interest in large-scale (semi-)structured datasets. This interface, that we call Meta-profile represents a multi-dimensional meta-data summary for a selected topic of interest, accumulating all differently structured representations of the topical tables in the dataset. Such Meta-profiles can be used as a rich visualization as well as a robust structural query interface simplifying access to large-scale (semi-)structured data for different user segments, such as data scientists and end users.|访问大规模的结构化数据集，如 WDC 或 CORD-191是非常具有挑战性的。即使一个主题(例如2019冠状病毒疾病疫苗效力)是有趣的，不同来源/论文中的所有主题表格都有数百种不同的模式，这取决于作者，这使得查找和查询这些模式变得非常复杂。在这里，我们演示了一个可伸缩的元分析器系统，它能够为大规模(半)结构化数据集中感兴趣的主题构建一个结构化的标准化接口。我们称之为 Meta-profile 的这个接口表示一个选定主题的多维元数据摘要，它积累了数据集中主题表的所有不同结构的表示。这样的元概要文件可以用作丰富的可视化以及健壮的结构化查询界面，简化不同用户段(如数据科学家和最终用户)对大规模(半)结构化数据的访问。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Learning+Topical+Structured+Interfaces+from+Medical+Research+Literature)|0| +|[DGBCT: A Scalable Distributed Gradient Boosting Causal Tree at Alipay](https://doi.org/10.1145/3543873.3584645)|Jun Zhou, Caizhi Tang, Qing Cui, Yi Ding, Longfei Li, Fei Wu|Ant Group, China; College of Computer Science and Technology, Zhejiang University, China; College of Computer Science and Technology, Zhejiang University, China and Ant Group, China|Causal effect estimation has been increasingly emphasized in the past few years. To handle this problem, tree-based causal methods have been widely used due to their robustness and explainability. However, most of the existing methods are limited to running on a single machine, making it difficult to scale up to hundreds of millions of data in typical industrial scenarios. This paper proposes DGBCT, a Distributed Gradient Boosting Causal Tree to tackle such problem, and the contribution of this paper is three folds. First, we extend the original GBCT method to a multi-treatment setting and take the monotonic constraints into consideration, so that more typical industrial necessities can be resolved with our framework. Moreover, we implement DGBCT based on the ‘Controller-Coordinator-Worker’ framework, in which dual failover mechanism is achieved, and commendable flexibility is ensured. In addition, empirical results show that DGBCT significantly outperforms the state-of-the-art causal trees, and has a near-linear speedup as the number of workers grows. The system is currently deployed in Alipay1 to support the daily business tasks that involve hundreds of millions of users.|因果效应估计在过去的几年中越来越受到重视。为了解决这个问题，基于树的因果关系方法由于其鲁棒性和可解释性而得到了广泛的应用。然而，现有的大多数方法仅限于在单台机器上运行，因此在典型的工业场景中很难扩展到数亿个数据。本文提出了分布式梯度提升因果树 DGBCT 来解决这个问题，本文的贡献有三个方面。首先，我们将原来的 GBCT 方法扩展到一个多处理环境，并且考虑了单调约束，使得我们的框架能够解决更多的典型工业需求。此外，我们在“控制器-协调器-工作者”框架的基础上实现了 DGBCT，实现了双重故障转移机制，并保证了值得称赞的灵活性。此外，实证结果显示，DGBCT 的表现明显优于最先进的因果树，并且随着工作人员数量的增加有近线性的加速效应。该系统目前部署在支付宝1中，以支持涉及数亿用户的日常业务任务。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=DGBCT:+A+Scalable+Distributed+Gradient+Boosting+Causal+Tree+at+Alipay)|0| +|[What Image do You Need? A Two-stage Framework for Image Selection in E-commerce](https://doi.org/10.1145/3543873.3584646)|Sheng You, Chao Wang, Baohua Wu, Jingping Liu, Quan Lu, Guanzhou Han, Yanghua Xiao|Fudan University, China; Shanghai University, China; East China University of Science and Technology, China; Alibaba Group, China|In e-commerce, images are widely used to display more intuitive information about items. Image selection significantly affects the user’s click-through rate (CTR). Most existing work considers the CTR as the target to find an appropriate image. However, these methods are challenging to deploy online efficiently. Also, the selected images may not relate to the item but are profitable to CTR, resulting in the undesirable phenomenon of enticing users to click on the item. To address these issues, we propose a novel two-stage pipeline method with content-based recall model and CTR-based ranking model. The first is realized as a joint method based on the title-image matching model and multi-modal knowledge graph embedding learning model. The second is a CTR-based visually aware scoring model, incorporating the entity textual information and entity images. Experimental results show the effectiveness and efficiency of our method in offline evaluations. After a month of online A/B testing on a travel platform Fliggy, the relative improvement of our method is 5% with respect to seller selection on CTCVR in the searching scenario, and our method further improves pCTR from 3.48% of human pick to 3.53% in the recommendation scenario.|在电子商务中，图像被广泛用于显示更直观的商品信息。图像选择会显著影响用户的点进率。大多数现有的工作都将 CTR 作为寻找合适图像的目标。然而，这些方法在线有效部署是具有挑战性的。此外，所选图像可能与该项目无关，但有利于点击率，导致不良现象的诱惑用户点击该项目。为了解决这些问题，我们提出了一种新的基于内容的召回模型和基于点击率的排序模型的两阶段流水线方法。第一种是基于标题-图像匹配模型和多模态知识图嵌入学习模型的联合方法。第二种是基于 CTR 的视觉感知评分模型，结合了实体文本信息和实体图像。实验结果表明了该方法在离线评估中的有效性和有效性。在旅游平台 Fliggy 上进行了一个月的在线 A/B 测试后，在搜索场景中，我们的方法相对于 CTCVR 上的卖方选择的相对改善为5% ，并且我们的方法进一步将 pCTR 从人类选择的3.48% 提高到推荐场景中的3.53% 。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=What+Image+do+You+Need?+A+Two-stage+Framework+for+Image+Selection+in+E-commerce)|0| |[Learning Geolocation by Accurately Matching Customer Addresses via Graph based Active Learning](https://doi.org/10.1145/3543873.3584647)|Saket Maheshwary, Saurabh Sohoney|Amazon, India|We propose a novel adaptation of graph-based active learning for customer address resolution or de-duplication, with the aim to determine if two addresses represent the same physical building or not. For delivery systems, improving address resolution positively impacts multiple downstream systems such as geocoding, route planning and delivery time estimations, leading to an efficient and reliable delivery experience, both for customers as well as delivery agents. Our proposed approach jointly leverages address text, past delivery information and concepts from graph theory to retrieve informative and diverse record pairs to label. We empirically show the effectiveness of our approach on manually curated dataset across addresses from India (IN) and United Arab Emirates (UAE). We achieved absolute improvement in recall on average across IN and UAE while preserving precision over the existing production system. We also introduce delivery point (DP) geocode learning for cold-start addresses as a downstream application of address resolution. In addition to offline evaluation, we also performed online A/B experiments which show that when the production model is augmented with active learnt record pairs, the delivery precision improved by and delivery defects reduced by on an average across shipments from IN and UAE.|我们提出了一种新的基于图的主动学习的客户地址解析或去重复，目的是确定是否两个地址代表相同的物理建筑物。对于送货系统，提高地址分辨率会对多个下游系统产生积极影响，例如地理编码、路径规划和送货时间估计，从而为客户和送货代理带来高效和可靠的送货体验。我们提出的方法共同利用地址文本，过去的传递信息和概念从图论检索信息和不同的记录对标签。我们通过实验证明了我们的方法在人工管理来自印度(IN)和阿拉伯联合酋长国(UAE)的数据集方面的有效性。在保持现有生产系统精度的同时，我们在 IN 和阿联酋的平均召回率上取得了绝对的提高。我们还介绍了用于冷启动地址的交付点(DP)地理编码学习作为地址解析的下游应用。除了离线评估之外，我们还进行了在线 A/B 实验，结果表明，当生产模型增加了主动学习记录对时，从 IN 和阿联酋发货的交付精度提高了，交付缺陷平均减少了。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Learning+Geolocation+by+Accurately+Matching+Customer+Addresses+via+Graph+based+Active+Learning)|0| |[CAViaR: Context Aware Video Recommendations](https://doi.org/10.1145/3543873.3584658)|Khushhall Chandra Mahajan, Aditya Palnitkar, Ameya Raul, Brad Schumitsch|Meta Inc., USA|Many recommendation systems rely on point-wise models, which score items individually. However, point-wise models generating scores for a video are unable to account for other videos being recommended in a query. Due to this, diversity has to be introduced through the application of heuristic-based rules, which are not able to capture user preferences, or make balanced trade-offs in terms of diversity and item relevance. In this paper, we propose a novel method which introduces diversity by modeling the impact of low diversity on user's engagement on individual items, thus being able to account for both diversity and relevance to adjust item scores. The proposed method is designed to be easily pluggable into existing large-scale recommender systems, while introducing minimal changes in the recommendations stack. Our models show significant improvements in offline metrics based on the normalized cross entropy loss compared to production point-wise models. Our approach also shows a substantial increase of 1.7% in topline engagements coupled with a 1.5% increase in daily active users in an A/B test with live traffic on Facebook Watch, which translates into an increase of millions in the number of daily active users for the product.|许多推荐系统依赖于逐点模型，这种模型对项目进行单独评分。然而，为视频生成分数的点模型无法解释在查询中推荐的其他视频。因此，必须通过应用启发式规则来引入多样性，这些规则不能捕捉用户的偏好，也不能在多样性和项目相关性方面做出平衡的权衡。本文提出了一种引入多样性的新方法，该方法通过建立低多样性对用户参与个别项目的影响模型，从而能够同时考虑多样性和相关性来调整项目得分。所提出的方法被设计成可以很容易地插入到现有的大规模推荐系统中，同时在推荐堆栈中引入最小的变化。与生产点模型相比，我们的模型显示了基于归一化交叉熵损失的离线指标的显著改进。我们的方法还显示，顶线参与度大幅增加了1.7% ，在 Facebook Watch 上的实时流量的 A/B 测试中，每日活跃用户增加了1.5% ，这意味着产品的每日活跃用户数量增加了数百万。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=CAViaR:+Context+Aware+Video+Recommendations)|0| |[Towards Building a Mobile App for People on the Spectrum](https://doi.org/10.1145/3543873.3587533)|Victoria Firsanova|Department of Mathematical Linguistics, Saint Petersburg State University, Russian Federation|The inclusion of autistic people can be augmented by a mobile app that provides information without a human mediator making information perception more liberating for people in the spectrum. This paper is an overview of a doctoral work dedicated to the development of a web-based mobile tool for supporting the inclusion of people on the autism spectrum. The work includes UX/UI research conducted with psychiatry experts, web information retrieval study and neural question-answering research. Currently, the study results comprise several mobile app layouts, a retriever-reader model design and fine-tuned neural network for extractive question-answering. Source code and other resources are available at https://github.com/vifirsanova/empi.|自闭症患者的包容性可以通过移动应用程序得到加强，这个程序可以在没有人类中介的情况下提供信息，从而使自闭症患者的信息感知更加自由。这篇文章是一篇博士论文的概述，该论文致力于开发一种基于网络的移动工具，以支持孤独症患者的融入。这项工作包括由精神病学专家进行的用户体验/用户界面研究、网络信息检索研究和神经问答研究。目前，研究结果包括几个移动应用程序的布局，一个检索-阅读器模型设计和微调神经网络提取问题回答。源代码和其他资源可在 https://github.com/vifirsanova/empi 获得。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Towards+Building+a+Mobile+App+for+People+on+the+Spectrum)|0| |[Multi-turn mediated solutions for Conversational Artificial Intelligent systems leveraging graph-based techniques](https://doi.org/10.1145/3543873.3587540)|Riya Naik|Computer Science & Information Systems, Birla Institute Of Technology And Science, Pilani, India|The current era is dominated by intelligent Question Answering (QA) systems that can instantly answer almost all their questions, saving users search time and increasing the throughput and precision in the applied domain. A vast amount of work is being carried out in QA systems to deliver better content satisfying users’ information needs [2]. Since QA systems are ascending the cycle of emerging technologies, there are potential research gaps that can be explored. QA systems form a significant part of Conversational Artificial Intelligent systems giving rise to a new research pathway, i.e., Conversational Question Answering (CQA) systems [32]. We propose to design and develop a CQA system leveraging Hypergraph-based techniques. The approach focuses on the multi-turn conversation and multi-context to gauge users’ exact information needs and deliver better answers. We further aim to address "supporting evidence-based retrieval" for fact-based responsible answer generation. Since the QA system requires a large amount of data and processing, we also intend to investigate hardware performance for effective system utilization.|当今时代的主流是智能问答(QA)系统，它可以即时回答几乎所有的问题，节省用户搜索时间，提高应用领域的吞吐量和精度。为了提供更好的内容以满足用户的信息需求，QA 系统正在进行大量的工作[2]。由于 QA 系统正在提升新兴技术的周期，因此存在可以探索的潜在研究差距。问答系统构成了会话人工智能系统的重要组成部分，从而产生了一种新的研究途径，即会话问答(CQA)系统[32]。我们建议利用基于 Hypergraph 的技术设计和开发一个 CQA 系统。该方法侧重于多回合会话和多上下文，以衡量用户的确切信息需求和提供更好的答案。我们进一步的目标是解决“支持基于证据的检索”的事实为基础的负责任的答案生成。由于 QA 系统需要大量的数据和处理，因此我们还打算研究硬件性能，以便有效地利用系统。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Multi-turn+mediated+solutions+for+Conversational+Artificial+Intelligent+systems+leveraging+graph-based+techniques)|0| |[Graph and Embedding based Approach for Text Clustering: Topic Detection in a Large Multilingual Public Consultation](https://doi.org/10.1145/3543873.3587627)|Nicolas Stefanovitch, Guillaume Jacquet, Bertrand De Longueville|European Commission - Joint Research Centre, Italy|We present a novel algorithm for multilingual text clustering built upon two well studied techniques: multilingual aligned embedding and community detection in graphs. The aim of our algorithm is to discover underlying topics in a multilingual dataset using clustering. We present both a numerical evaluation using silhouette and V-measure metrics, and a qualitative evaluation for which we propose a new systematic approach. Our algorithm presents robust overall performance and its results were empirically evaluated by an analyst. The work we present was done in the context of a large multilingual public consultation, for which our new algorithm was deployed and used on a daily basis.|本文提出了一种新的多语言文本聚类算法，该算法基于两种已经得到广泛研究的技术: 多语言对齐嵌入和图中的社区检测。我们的算法的目的是使用聚类来发现多语言数据集中的基本主题。我们提出了一个数值评估使用轮廓和 V 测量度量，和一个定性的评估，我们提出了一个新的系统方法。我们的算法提出了稳健的整体性能，其结果是经验性的分析评价。我们介绍的工作是在大规模多语种公众协商的背景下完成的，我们的新算法每天都得到部署和使用。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Graph+and+Embedding+based+Approach+for+Text+Clustering:+Topic+Detection+in+a+Large+Multilingual+Public+Consultation)|0| -|[Dual-grained Text-Image Olfactory Matching Model with Mutual Promotion Stages](https://doi.org/10.1145/3543873.3587649)|Yi Shao, Jiande Sun, Ye Jiang, Jing Li|Qingdao University of Science and Technology, China; Shandong Management University, China; Shandong Normal University, China|Olfactory experience has great advantages in awakening human memories and emotions, which may even surpass vision in some cases. Studies have proved that olfactory scene descriptions in images and text content can also arouse human olfactory imagination, but there are still few studies on solving related problems from the perspective of computer vision and NLP. This paper proposes a multimodal model that can detect similar olfactory experience in paired text-image samples. The model builds two stages, coarse-grained and fine-grained. The model adopts the feature fusion method based on pre-trained CLIP for coarse-grained matching training to obtain a preliminary feature extractor to promote fine-grained matching training, and then uses the similarity calculation method based on stacked cross attention for fine-grained matching training to obtain the final feature extractor which in turn promotes coarse-grained matching training. Finally, we manually build an approximate olfactory nouns list during fine-grained matching training, which not only yields significantly better performance when fed back to the fine-grained matching process, but this noun list can be used for future research. Experiments on the MUSTI task dataset of MediaEval2022 prove that the coarse-grained and fine-grained matching stages in proposed model both perform well, and both F1 measures exceed the existing baseline models.|嗅觉经验在唤醒人类记忆和情感方面有很大的优势，在某些情况下甚至可能超越视觉。研究表明，图像和文本内容中的嗅觉场景描述也能激发人类的嗅觉想象，但从计算机视觉和自然语言处理的角度解决相关问题的研究还很少。本文提出了一个多模态模型，可以检测相似的嗅觉经验配对文本图像样本。该模型分为粗粒度和细粒度两个阶段。该模型采用基于预训练 CLIP 的特征融合方法进行粗粒度匹配训练，得到初步的特征提取器以促进细粒度匹配训练，然后采用基于叠加交叉注意的相似度计算方法进行细粒度匹配训练，得到最终的特征提取器以促进粗粒度匹配训练。最后，我们在细粒度匹配训练过程中手动构建了一个近似的嗅觉名词列表，这不仅在反馈到细粒度匹配过程时产生了明显更好的性能，而且这个名词列表可以用于未来的研究。在 MediaEval2022的 MUSTI 任务数据集上的实验证明，所提出的模型中的粗粒度和细粒度匹配阶段都表现良好，而且两种 F1测度都超过了现有的基线模型。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Dual-grained+Text-Image+Olfactory+Matching+Model+with+Mutual+Promotion+Stages)|0| +|[Dual-grained Text-Image Olfactory Matching Model with Mutual Promotion Stages](https://doi.org/10.1145/3543873.3587649)|Yi Shao, Jiande Sun, Ye Jiang, Jing Li|Shandong Management University, China; Qingdao University of Science and Technology, China; Shandong Normal University, China|Olfactory experience has great advantages in awakening human memories and emotions, which may even surpass vision in some cases. Studies have proved that olfactory scene descriptions in images and text content can also arouse human olfactory imagination, but there are still few studies on solving related problems from the perspective of computer vision and NLP. This paper proposes a multimodal model that can detect similar olfactory experience in paired text-image samples. The model builds two stages, coarse-grained and fine-grained. The model adopts the feature fusion method based on pre-trained CLIP for coarse-grained matching training to obtain a preliminary feature extractor to promote fine-grained matching training, and then uses the similarity calculation method based on stacked cross attention for fine-grained matching training to obtain the final feature extractor which in turn promotes coarse-grained matching training. Finally, we manually build an approximate olfactory nouns list during fine-grained matching training, which not only yields significantly better performance when fed back to the fine-grained matching process, but this noun list can be used for future research. Experiments on the MUSTI task dataset of MediaEval2022 prove that the coarse-grained and fine-grained matching stages in proposed model both perform well, and both F1 measures exceed the existing baseline models.|嗅觉经验在唤醒人类记忆和情感方面有很大的优势，在某些情况下甚至可能超越视觉。研究表明，图像和文本内容中的嗅觉场景描述也能激发人类的嗅觉想象，但从计算机视觉和自然语言处理的角度解决相关问题的研究还很少。本文提出了一个多模态模型，可以检测相似的嗅觉经验配对文本图像样本。该模型分为粗粒度和细粒度两个阶段。该模型采用基于预训练 CLIP 的特征融合方法进行粗粒度匹配训练，得到初步的特征提取器以促进细粒度匹配训练，然后采用基于叠加交叉注意的相似度计算方法进行细粒度匹配训练，得到最终的特征提取器以促进粗粒度匹配训练。最后，我们在细粒度匹配训练过程中手动构建了一个近似的嗅觉名词列表，这不仅在反馈到细粒度匹配过程时产生了明显更好的性能，而且这个名词列表可以用于未来的研究。在 MediaEval2022的 MUSTI 任务数据集上的实验证明，所提出的模型中的粗粒度和细粒度匹配阶段都表现良好，而且两种 F1测度都超过了现有的基线模型。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Dual-grained+Text-Image+Olfactory+Matching+Model+with+Mutual+Promotion+Stages)|0| |[MEMER - Multimodal Encoder for Multi-signal Early-stage Recommendations](https://doi.org/10.1145/3543873.3587679)|Mohit Agarwal, Srijan Saket, Rishabh Mehrotra|ShareChat, India|Millions of content gets created daily on platforms like YouTube, Facebook, TikTok etc. Most of such large scale recommender systems are data demanding, thus taking substantial time for content embedding to mature. This problem is aggravated when there is no behavioral data available for new content. Poor quality recommendation for these items lead to user dissatisfaction and short content shelf-life. In this paper we propose a solution MEMER (Multimodal Encoder for Multi-signal Early-stage Recommendations), that utilises the multimodal semantic information of content and uses it to generate better quality embeddings for early-stage items. We demonstrate the flexibility of the framework by extending it to various explicit and implicit user actions. Using these learnt embeddings, we conduct offline and online experiments to verify its effectiveness. The predicted embeddings show significant gains in online early-stage experiments for both videos and images (videos: 44% relative gain in click through rate, 46% relative gain in explicit engagements, 9% relative gain in successful video play, 20% relative reduction in skips, images: 56% relative gain in explicit engagements). This also compares well against the performance of mature embeddings (83.3% RelaImpr (RI) [18] in Successful Video Play, 97.8% RelaImpr in Clicks).|每天都有数以百万计的内容在 YouTube、 Facebook、 TikTok 等平台上被创造出来。大多数这样的大规模推荐系统都需要大量的数据，因此内容嵌入需要大量的时间才能成熟。当没有可用于新内容的行为数据时，这个问题就更加严重了。这些项目的低质量推荐导致用户不满意和内容保质期短。在本文中，我们提出了一个解决方案 MEMER (多信号早期推荐的多模式编码器) ，利用多模式内容语义信息，并使用它为早期项目产生更好的质量嵌入。我们通过将框架扩展到各种显式和隐式用户操作来演示框架的灵活性。使用这些学习嵌入，我们进行离线和在线实验，以验证其有效性。预测的嵌入在视频和图像的在线早期实验中都显示出显著的增益(视频: 点击率相对增益44% ，显性参与相对增益46% ，成功视频播放相对增益9% ，跳过相对减少20% ，图像: 显性参与相对增益56%)。这也与成熟嵌入的性能相当(83.3% RelaImpr (RI)[18]在成功的视频播放，97.8% RelaImpr 在点击)。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=MEMER+-+Multimodal+Encoder+for+Multi-signal+Early-stage+Recommendations)|0| -|[Social Re-Identification Assisted RTO Detection for E-Commerce](https://doi.org/10.1145/3543873.3587620)|Hitkul Jangra, Abinaya K, Soham Saha, Satyajit Banerjee, Muthusamy Chelliah, Ponnurangam Kumaraguru|Flipkart, India; IIIT Delhi, India; IIIT Hyderabad, India|E-commerce features like easy cancellations, returns, and refunds can be exploited by bad actors or uninformed customers, leading to revenue loss for organization. One such problem faced by e-commerce platforms is Return To Origin (RTO), where the user cancels an order while it is in transit for delivery. In such a scenario platform faces logistics and opportunity costs. Traditionally, models trained on historical trends are used to predict the propensity of an order becoming RTO. Sociology literature has highlighted clear correlations between socio-economic indicators and users’ tendency to exploit systems to gain financial advantage. Social media profiles have information about location, education, and profession which have been shown to be an estimator of socio-economic condition. We believe combining social media data with e-commerce information can lead to improvements in a variety of tasks like RTO, recommendation, fraud detection, and credit modeling. In our proposed system, we find the public social profile of an e-commerce user and extract socio-economic features. Internal data fused with extracted social features are used to train a RTO order detection model. Our system demonstrates a performance improvement in RTO detection of 3.1% and 19.9% on precision and recall, respectively. Our system directly impacts the bottom line revenue and shows the applicability of social re-identification in e-commerce.|电子商务的特点，如容易取消，退货和退款可以利用不良行为者或不知情的客户，导致收入损失的组织。电子商务平台面临的一个这样的问题是返还原产地(RTO) ，即用户在运输途中取消订单。在这种情况下，平台面临物流和机会成本。传统上，根据历史趋势训练的模型被用来预测订单成为 RTO 的倾向。社会学文献强调了社会经济指标与用户利用系统获取金融优势的倾向之间的明确相关性。社交媒体档案包含有关地理位置、教育和职业的信息，这些信息已被证明是社会经济状况的估计值。我们相信，将社交媒体数据与电子商务信息结合起来，可以改进诸如 RTO、推荐、欺诈检测和信用建模等多种任务。在我们提出的系统中，我们找到电子商务用户的公共社会轮廓，并提取社会经济特征。利用内部数据与提取的社会特征融合，建立了 RTO 订单检测模型。我们的系统在检测准确率召回率方面的性能改善分别为3.1% 和19.9% 。我们的系统直接影响到底线收入，显示了社会再认同在电子商务中的适用性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Social+Re-Identification+Assisted+RTO+Detection+for+E-Commerce)|0| -|[Contextual Response Interpretation for Automated Structured Interviews: A Case Study in Market Research](https://doi.org/10.1145/3543873.3587657)|Harshita Sahijwani, Kaustubh D. Dhole, Ankur P. Purwar, Venugopal Vasudevan, Eugene Agichtein|Procter & Gamble, USA; Emory University, USA; Procter & Gamble, Singapore|Structured interviews are used in many settings, importantly in market research on topics such as brand perception, customer habits, or preferences, which are critical to product development, marketing, and e-commerce at large. Such interviews generally consist of a series of questions that are asked to a participant. These interviews are typically conducted by skilled interviewers, who interpret the responses from the participants and can adapt the interview accordingly. Using automated conversational agents to conduct such interviews would enable reaching a much larger and potentially more diverse group of participants than currently possible. However, the technical challenges involved in building such a conversational system are relatively unexplored. To learn more about these challenges, we convert a market research multiple-choice questionnaire to a conversational format and conduct a user study. We address the key task of conducting structured interviews, namely interpreting the participant's response, for example, by matching it to one or more predefined options. Our findings can be applied to improve response interpretation for the information elicitation phase of conversational recommender systems.|结构化访谈用于许多场合，重要的是用于市场调查，如品牌认知、客户习惯或偏好，这些对产品开发、市场营销和电子商务至关重要。这种面试通常包括向参与者提出的一系列问题。这些面试通常由技术熟练的面试官进行，他们解释参与者的回答，并能相应地调整面试。使用自动对话代理进行这种访谈将能够接触到比目前可能的更多、可能更多样化的参与者群体。然而，构建这样一个会话系统所涉及的技术挑战相对而言还没有得到探索。为了更多地了解这些挑战，我们将市场调查多项选择问卷转换为会话形式，并进行用户研究。我们解决的关键任务是进行结构化访谈，即解释参与者的反应，例如，通过匹配一个或多个预先定义的选项。我们的研究结果可以用于改善会话推荐系统中信息激发阶段的响应解释。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Contextual+Response+Interpretation+for+Automated+Structured+Interviews:+A+Case+Study+in+Market+Research)|0| -|[Knowledge Graph-Enhanced Neural Query Rewriting](https://doi.org/10.1145/3543873.3587678)|Shahla Farzana, Qunzhi Zhou, Petar Ristoski|University of Illinois Chicago, USA; eBay Inc, USA; eBay Inc., USA|The main task of an e-commerce search engine is to semantically match the user query to the product inventory and retrieve the most relevant items that match the user’s intent. This task is not trivial as often there can be a mismatch between the user’s intent and the product inventory for various reasons, the most prevalent being: (i) the buyers and sellers use different vocabularies, which leads to a mismatch; (ii) the inventory doesn’t contain products that match the user’s intent. To build a successful e-commerce platform it is of paramount importance to be able to address both of these challenges. To do so, query rewriting approaches are used, which try to bridge the semantic gap between the user’s intent and the available product inventory. Such approaches use a combination of query token dropping, replacement and expansion. In this work we introduce a novel Knowledge Graph-enhanced neural query rewriting in the e-commerce domain. We use a relationship-rich product Knowledge Graph to infuse auxiliary knowledge in a transformer-based query rewriting deep neural network. Experiments on two tasks, query pruning and complete query rewriting, show that our proposed approach significantly outperforms a baseline BERT-based query rewriting solution.|电子商务搜索引擎的主要任务是在语义上将用户查询与产品库存相匹配，并检索与用户意图相匹配的最相关项目。这个任务并不是微不足道的，因为在用户的意图和产品库存之间经常会因为各种原因而产生不匹配，最普遍的原因是: (i)买家和卖家使用不同的词汇，这会导致不匹配; (ii)库存不包含符合用户意图的产品。要建立一个成功的电子商务平台，最重要的是能够应对这两个挑战。为此，使用了查询重写方法，这些方法试图弥合用户意图和可用产品目录之间的语义差距。这种方法结合使用查询标记删除、替换和扩展。在这项工作中，我们介绍了一种新的知识图增强神经查询重写在电子商务领域。在基于变压器的查询重写深度神经网络中，我们使用一个关系丰富的产品知识图来注入辅助知识。通过对查询裁剪和完全查询重写两个任务的实验表明，该方法的性能明显优于基于 BERT 的基线查询重写方案。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Knowledge+Graph-Enhanced+Neural+Query+Rewriting)|0| -|[Fairness-aware Differentially Private Collaborative Filtering](https://doi.org/10.1145/3543873.3587577)|Zhenhuan Yang, Yingqiang Ge, Congzhe Su, Dingxian Wang, Xiaoting Zhao, Yiming Ying|University at Albany, SUNY, USA; Etsy, USA; Rutgers University, USA|Recently, there has been an increasing adoption of differential privacy guided algorithms for privacy-preserving machine learning tasks. However, the use of such algorithms comes with trade-offs in terms of algorithmic fairness, which has been widely acknowledged. Specifically, we have empirically observed that the classical collaborative filtering method, trained by differentially private stochastic gradient descent (DP-SGD), results in a disparate impact on user groups with respect to different user engagement levels. This, in turn, causes the original unfair model to become even more biased against inactive users. To address the above issues, we propose \textbf{DP-Fair}, a two-stage framework for collaborative filtering based algorithms. Specifically, it combines differential privacy mechanisms with fairness constraints to protect user privacy while ensuring fair recommendations. The experimental results, based on Amazon datasets, and user history logs collected from Etsy, one of the largest e-commerce platforms, demonstrate that our proposed method exhibits superior performance in terms of both overall accuracy and user group fairness on both shallow and deep recommendation models compared to vanilla DP-SGD.|最近，在保护隐私的机器学习任务中越来越多地采用差分隐私指导算法。然而，这种算法的使用伴随着算法公平性方面的权衡，这已经得到了广泛的认可。具体来说，我们已经经验性地观察到，由差异私人协同过滤(DP-sgd)训练的经典随机梯度下降方法，在不同的用户参与水平方面对用户组产生了不同的影响。这反过来又导致原来的不公平模型对非活动用户变得更加有偏见。为了解决上述问题，我们提出 textbf { DP-fair } ，一个基于协同过滤的算法的两阶段框架。具体来说，它结合了差分隐私机制和公平约束，以保护用户隐私，同时确保公平推荐。基于 Amazon 数据集的实验结果，以及从最大的电子商务平台之一 Etsy 收集的用户历史记录表明，与普通的 DP-SGD 相比，我们提出的方法在浅层和深层推荐模型的总体准确性和用户组公平性方面表现出更好的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Fairness-aware+Differentially+Private+Collaborative+Filtering)|0| +|[Social Re-Identification Assisted RTO Detection for E-Commerce](https://doi.org/10.1145/3543873.3587620)|Hitkul Jangra, Abinaya K, Soham Saha, Satyajit Banerjee, Muthusamy Chelliah, Ponnurangam Kumaraguru|IIIT Hyderabad, India; Flipkart, India; IIIT Delhi, India|E-commerce features like easy cancellations, returns, and refunds can be exploited by bad actors or uninformed customers, leading to revenue loss for organization. One such problem faced by e-commerce platforms is Return To Origin (RTO), where the user cancels an order while it is in transit for delivery. In such a scenario platform faces logistics and opportunity costs. Traditionally, models trained on historical trends are used to predict the propensity of an order becoming RTO. Sociology literature has highlighted clear correlations between socio-economic indicators and users’ tendency to exploit systems to gain financial advantage. Social media profiles have information about location, education, and profession which have been shown to be an estimator of socio-economic condition. We believe combining social media data with e-commerce information can lead to improvements in a variety of tasks like RTO, recommendation, fraud detection, and credit modeling. In our proposed system, we find the public social profile of an e-commerce user and extract socio-economic features. Internal data fused with extracted social features are used to train a RTO order detection model. Our system demonstrates a performance improvement in RTO detection of 3.1% and 19.9% on precision and recall, respectively. Our system directly impacts the bottom line revenue and shows the applicability of social re-identification in e-commerce.|电子商务的特点，如容易取消，退货和退款可以利用不良行为者或不知情的客户，导致收入损失的组织。电子商务平台面临的一个这样的问题是返还原产地(RTO) ，即用户在运输途中取消订单。在这种情况下，平台面临物流和机会成本。传统上，根据历史趋势训练的模型被用来预测订单成为 RTO 的倾向。社会学文献强调了社会经济指标与用户利用系统获取金融优势的倾向之间的明确相关性。社交媒体档案包含有关地理位置、教育和职业的信息，这些信息已被证明是社会经济状况的估计值。我们相信，将社交媒体数据与电子商务信息结合起来，可以改进诸如 RTO、推荐、欺诈检测和信用建模等多种任务。在我们提出的系统中，我们找到电子商务用户的公共社会轮廓，并提取社会经济特征。利用内部数据与提取的社会特征融合，建立了 RTO 订单检测模型。我们的系统在检测准确率召回率方面的性能改善分别为3.1% 和19.9% 。我们的系统直接影响到底线收入，显示了社会再认同在电子商务中的适用性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Social+Re-Identification+Assisted+RTO+Detection+for+E-Commerce)|0| +|[Contextual Response Interpretation for Automated Structured Interviews: A Case Study in Market Research](https://doi.org/10.1145/3543873.3587657)|Harshita Sahijwani, Kaustubh D. Dhole, Ankur P. Purwar, Venugopal Vasudevan, Eugene Agichtein|Procter & Gamble, Singapore; Procter & Gamble, USA; Emory University, USA|Structured interviews are used in many settings, importantly in market research on topics such as brand perception, customer habits, or preferences, which are critical to product development, marketing, and e-commerce at large. Such interviews generally consist of a series of questions that are asked to a participant. These interviews are typically conducted by skilled interviewers, who interpret the responses from the participants and can adapt the interview accordingly. Using automated conversational agents to conduct such interviews would enable reaching a much larger and potentially more diverse group of participants than currently possible. However, the technical challenges involved in building such a conversational system are relatively unexplored. To learn more about these challenges, we convert a market research multiple-choice questionnaire to a conversational format and conduct a user study. We address the key task of conducting structured interviews, namely interpreting the participant's response, for example, by matching it to one or more predefined options. Our findings can be applied to improve response interpretation for the information elicitation phase of conversational recommender systems.|结构化访谈用于许多场合，重要的是用于市场调查，如品牌认知、客户习惯或偏好，这些对产品开发、市场营销和电子商务至关重要。这种面试通常包括向参与者提出的一系列问题。这些面试通常由技术熟练的面试官进行，他们解释参与者的回答，并能相应地调整面试。使用自动对话代理进行这种访谈将能够接触到比目前可能的更多、可能更多样化的参与者群体。然而，构建这样一个会话系统所涉及的技术挑战相对而言还没有得到探索。为了更多地了解这些挑战，我们将市场调查多项选择问卷转换为会话形式，并进行用户研究。我们解决的关键任务是进行结构化访谈，即解释参与者的反应，例如，通过匹配一个或多个预先定义的选项。我们的研究结果可以用于改善会话推荐系统中信息激发阶段的响应解释。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Contextual+Response+Interpretation+for+Automated+Structured+Interviews:+A+Case+Study+in+Market+Research)|0| +|[Knowledge Graph-Enhanced Neural Query Rewriting](https://doi.org/10.1145/3543873.3587678)|Shahla Farzana, Qunzhi Zhou, Petar Ristoski|eBay Inc, USA; eBay Inc., USA; University of Illinois Chicago, USA|The main task of an e-commerce search engine is to semantically match the user query to the product inventory and retrieve the most relevant items that match the user’s intent. This task is not trivial as often there can be a mismatch between the user’s intent and the product inventory for various reasons, the most prevalent being: (i) the buyers and sellers use different vocabularies, which leads to a mismatch; (ii) the inventory doesn’t contain products that match the user’s intent. To build a successful e-commerce platform it is of paramount importance to be able to address both of these challenges. To do so, query rewriting approaches are used, which try to bridge the semantic gap between the user’s intent and the available product inventory. Such approaches use a combination of query token dropping, replacement and expansion. In this work we introduce a novel Knowledge Graph-enhanced neural query rewriting in the e-commerce domain. We use a relationship-rich product Knowledge Graph to infuse auxiliary knowledge in a transformer-based query rewriting deep neural network. Experiments on two tasks, query pruning and complete query rewriting, show that our proposed approach significantly outperforms a baseline BERT-based query rewriting solution.|电子商务搜索引擎的主要任务是在语义上将用户查询与产品库存相匹配，并检索与用户意图相匹配的最相关项目。这个任务并不是微不足道的，因为在用户的意图和产品库存之间经常会因为各种原因而产生不匹配，最普遍的原因是: (i)买家和卖家使用不同的词汇，这会导致不匹配; (ii)库存不包含符合用户意图的产品。要建立一个成功的电子商务平台，最重要的是能够应对这两个挑战。为此，使用了查询重写方法，这些方法试图弥合用户意图和可用产品目录之间的语义差距。这种方法结合使用查询标记删除、替换和扩展。在这项工作中，我们介绍了一种新的知识图增强神经查询重写在电子商务领域。在基于变压器的查询重写深度神经网络中，我们使用一个关系丰富的产品知识图来注入辅助知识。通过对查询裁剪和完全查询重写两个任务的实验表明，该方法的性能明显优于基于 BERT 的基线查询重写方案。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Knowledge+Graph-Enhanced+Neural+Query+Rewriting)|0| +|[Fairness-aware Differentially Private Collaborative Filtering](https://doi.org/10.1145/3543873.3587577)|Zhenhuan Yang, Yingqiang Ge, Congzhe Su, Dingxian Wang, Xiaoting Zhao, Yiming Ying|Etsy, USA; University at Albany, SUNY, USA; Rutgers University, USA|Recently, there has been an increasing adoption of differential privacy guided algorithms for privacy-preserving machine learning tasks. However, the use of such algorithms comes with trade-offs in terms of algorithmic fairness, which has been widely acknowledged. Specifically, we have empirically observed that the classical collaborative filtering method, trained by differentially private stochastic gradient descent (DP-SGD), results in a disparate impact on user groups with respect to different user engagement levels. This, in turn, causes the original unfair model to become even more biased against inactive users. To address the above issues, we propose \textbf{DP-Fair}, a two-stage framework for collaborative filtering based algorithms. Specifically, it combines differential privacy mechanisms with fairness constraints to protect user privacy while ensuring fair recommendations. The experimental results, based on Amazon datasets, and user history logs collected from Etsy, one of the largest e-commerce platforms, demonstrate that our proposed method exhibits superior performance in terms of both overall accuracy and user group fairness on both shallow and deep recommendation models compared to vanilla DP-SGD.|最近，在保护隐私的机器学习任务中越来越多地采用差分隐私指导算法。然而，这种算法的使用伴随着算法公平性方面的权衡，这已经得到了广泛的认可。具体来说，我们已经经验性地观察到，由差异私人协同过滤(DP-sgd)训练的经典随机梯度下降方法，在不同的用户参与水平方面对用户组产生了不同的影响。这反过来又导致原来的不公平模型对非活动用户变得更加有偏见。为了解决上述问题，我们提出 textbf { DP-fair } ，一个基于协同过滤的算法的两阶段框架。具体来说，它结合了差分隐私机制和公平约束，以保护用户隐私，同时确保公平推荐。基于 Amazon 数据集的实验结果，以及从最大的电子商务平台之一 Etsy 收集的用户历史记录表明，与普通的 DP-SGD 相比，我们提出的方法在浅层和深层推荐模型的总体准确性和用户组公平性方面表现出更好的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Fairness-aware+Differentially+Private+Collaborative+Filtering)|0| |[Psychotherapy AI Companion with Reinforcement Learning Recommendations and Interpretable Policy Dynamics](https://doi.org/10.1145/3543873.3587623)|Baihan Lin, Guillermo A. Cecchi, Djallel Bouneffouf|Columbia University, USA; IBM TJ Watson Research Center, USA|We introduce a Reinforcement Learning Psychotherapy AI Companion that generates topic recommendations for therapists based on patient responses. The system uses Deep Reinforcement Learning (DRL) to generate multi-objective policies for four different psychiatric conditions: anxiety, depression, schizophrenia, and suicidal cases. We present our experimental results on the accuracy of recommended topics using three different scales of working alliance ratings: task, bond, and goal. We show that the system is able to capture the real data (historical topics discussed by the therapists) relatively well, and that the best performing models vary by disorder and rating scale. To gain interpretable insights into the learned policies, we visualize policy trajectories in a 2D principal component analysis space and transition matrices. These visualizations reveal distinct patterns in the policies trained with different reward signals and trained on different clinical diagnoses. Our system's success in generating DIsorder-Specific Multi-Objective Policies (DISMOP) and interpretable policy dynamics demonstrates the potential of DRL in providing personalized and efficient therapeutic recommendations.|我们介绍一个强化学习的心理治疗 AI 指南，根据患者的反应为治疗师提供主题建议。该系统使用深度强化学习(DRL)为四种不同的精神疾病(焦虑症、抑郁症、精神分裂症和自杀病例)制定多目标政策。我们使用三种不同的工作联盟评分尺度(任务、联系和目标)对推荐话题的准确性进行了实验研究。我们表明，该系统能够相对较好地捕获真实数据(治疗师讨论的历史主题) ，并且表现最好的模型因障碍和评分尺度而异。为了获得对所学政策的可解释的见解，我们在二维主成分分析空间和转换矩阵中可视化政策轨迹。这些可视化显示了不同奖励信号训练和不同临床诊断训练的政策的不同模式。我们的系统在产生疾病特异性多目标政策(DISMOP)和可解释的政策动态方面的成功表明 DRL 在提供个性化和有效的治疗建议方面的潜力。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Psychotherapy+AI+Companion+with+Reinforcement+Learning+Recommendations+and+Interpretable+Policy+Dynamics)|0| |[Investigating Action-Space Generalization in Reinforcement Learning for Recommendation Systems](https://doi.org/10.1145/3543873.3587661)|Abhishek Naik, Bo Chang, Alexandros Karatzoglou, Martin Mladenov, Ed H. Chi, Minmin Chen|Google Research, USA; University of Alberta, Canada and Alberta Machine Intelligence Institute (Amii), Canada|Recommender systems are used to suggest items to users based on the users’ preferences. Such systems often deal with massive item sets and incredibly sparse user-item interactions, which makes it very challenging to generate high-quality personalized recommendations. Reinforcement learning (RL) is a framework for sequential decision making and naturally formulates recommender-system tasks: recommending items as actions in different user and context states to maximize long-term user experience. We investigate two RL policy parameterizations that generalize sparse user-items interactions by leveraging the relationships between actions: parameterizing the policy over action features as a softmax or Gaussian distribution. Our experiments on synthetic problems suggest that the Gaussian parameterization—which is not commonly used on recommendation tasks—is more robust to the set of action features than the softmax parameterization. Based on these promising results, we propose a more thorough investigation of the theoretical properties and empirical benefits of the Gaussian parameterization for recommender systems.|推荐系统用于根据用户的喜好向用户推荐项目。这样的系统经常处理大量的项目集和难以置信的稀疏的用户-项目交互，这使得生成高质量的个性化推荐非常具有挑战性。推荐强化学习(RL)是一个连续决策的框架，它自然而然地制定了推荐系统的任务: 在不同的用户和上下文状态下，推荐项目作为行动，以最大限度地提高长期用户体验。我们研究了两种 RL 策略参数化，它们通过利用操作之间的关系来推广稀疏的用户-项目交互: 将策略参数化为 softmax 或者正态分布。我们在合成问题上的实验表明，高斯参数化(在推荐任务中并不常用)比 softmax 参量化对动作特征集的鲁棒性更强。基于这些有希望的结果，我们建议对高斯参量化推荐系统的理论特性和经验效益进行更深入的研究。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Investigating+Action-Space+Generalization+in+Reinforcement+Learning+for+Recommendation+Systems)|0| |[Conversion of Legal Agreements into Smart Legal Contracts using NLP](https://doi.org/10.1145/3543873.3587554)|Eason Chen, Niall Roche, YuenHsien Tseng, Walter Hernández, Jiangbo Shangguan, Alastair Moore|HSBC Business School, Peking University, United Kingdom; National Taiwan Normal University, Taiwan; University College London, United Kingdom|A Smart Legal Contract (SLC) is a specialized digital agreement comprising natural language and computable components. The Accord Project provides an open-source SLC framework containing three main modules: Cicero, Concerto, and Ergo. Currently, we need lawyers, programmers, and clients to work together with great effort to create a usable SLC using the Accord Project. This paper proposes a pipeline to automate the SLC creation process with several Natural Language Processing (NLP) models to convert law contracts to the Accord Project's Concerto model. After evaluating the proposed pipeline, we discovered that our NER pipeline accurately detects CiceroMark from Accord Project template text with an accuracy of 0.8. Additionally, our Question Answering method can extract one-third of the Concerto variables from the template text. We also delve into some limitations and possible future research for the proposed pipeline. Finally, we describe a web interface enabling users to build SLCs. This interface leverages the proposed pipeline to convert text documents to Smart Legal Contracts by using NLP models.|智能法律合同(SLC)是一种专门的数字协议，包括自然语言和可计算组件。Accord Project 提供了一个开源的 SLC 框架，其中包含三个主要模块: Cicero、 Concerto 和 Ergo。目前，我们需要律师、程序员和客户共同努力，使用 AccordProject 创建一个可用的 SLC。本文提出了一种使用多种自然语言处理(NLP)模型实现 SLC 创建过程自动化的流水线，将法律合同转换为 Accord Project 的 Concerto 模型。经过评估后，我们发现我们的 NER 流水线能够准确地从雅阁项目模板文本中检测到 CiceroMark，准确率为0.8。此外，我们的问题回答方法可以提取三分之一的协奏曲变量从模板文本。我们还深入探讨了一些局限性和可能的未来研究的建议管道。最后，我们描述了一个允许用户构建 SLC 的 Web 界面。该接口利用拟议的管道，通过使用 NLP 模型将文本文档转换为智能法律合同。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Conversion+of+Legal+Agreements+into+Smart+Legal+Contracts+using+NLP)|0| |[Query-Driven Knowledge Graph Construction using Question Answering and Multimodal Fusion](https://doi.org/10.1145/3543873.3587567)|Yang Peng|University of Florida, USA|Over recent years, large knowledge bases have been constructed to store massive knowledge graphs. However, these knowledge graphs are highly incomplete. To solve this problem, we propose a web-based question answering system with multimodal fusion of unstructured and structured information, to fill in missing information for knowledge bases. To utilize unstructured information from the Web for knowledge graph construction, we design multimodal features and question templates to extract missing facts, which can achieve good quality with very few questions. The question answering system also employs structured information from knowledge bases, such as entity types and entity-to-entity relatedness, to help improve extraction quality. To improve system efficiency, we utilize a few query-driven techniques for web-based question answering to reduce the runtime and provide fast responses to user queries. Extensive experiments have been conducted to demonstrate the effectiveness and efficiency of our system.|近年来，人们建立了大型知识库来存储海量的知识图表。然而，这些知识图是非常不完整的。为了解决这一问题，我们提出了一种基于网络的非结构化和结构化信息多模态融合的问答系统，以填补知识库中缺失的信息。为了利用 Web 中的非结构化信息进行知识图的构造，我们设计了多模态特征和问题模板来提取缺失的事实，这样可以在很少的问题下获得很好的质量。问答系统还利用知识库中的结构化信息，如实体类型和实体间的相关性，以提高抽取质量。为了提高系统的效率，我们采用了一些基于查询驱动的网络问答技术，以减少运行时间，并提供快速响应用户的查询。通过大量的实验验证了该系统的有效性和高效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Query-Driven+Knowledge+Graph+Construction+using+Question+Answering+and+Multimodal+Fusion)|0| -|[Decoding Prompt Syntax: Analysing its Impact on Knowledge Retrieval in Large Language Models](https://doi.org/10.1145/3543873.3587655)|Stephan Linzbach, Tim Tressel, Laura Kallmeyer, Stefan Dietze, Hajira Jabeen|GESIS Leibniz Institut für Sozialwissenschaften, Germany; GESIS Leibniz Institute for Social Sciences, Germany and Heinrich Heine University, Germany; GESIS Leibniz Institute for Social Sciences, Germany; Heinrich Heine University, Germany|Large Language Models (LLMs), with their advanced architectures and training on massive language datasets, contain unexplored knowledge. One method to infer this knowledge is through the use of cloze-style prompts. Typically, these prompts are manually designed because the phrasing of these prompts impacts the knowledge retrieval performance, even if the LLM encodes the desired information. In this paper, we study the impact of prompt syntax on the knowledge retrieval capacity of LLMs. We use a template-based approach to paraphrase simple prompts into prompts with a more complex grammatical structure. We then analyse the LLM performance for these structurally different but semantically equivalent prompts. Our study reveals that simple prompts work better than complex forms of sentences. The performance across the syntactical variations for simple relations (1:1) remains best, with a marginal decrease across different typologies. These results reinforce that simple prompt structures are more effective for knowledge retrieval in LLMs and motivate future research into the impact of prompt syntax on various tasks.|大型语言模型(LLM)具有先进的体系结构和对大量语言数据集的训练，包含了未开发的知识。一种推断这种知识的方法是通过使用完形填空式的提示。通常，这些提示是手动设计的，因为这些提示的措辞会影响知识检索性能，即使 LLM 对所需的信息进行了编码。本文研究了提示语法对 LLM 知识检索能力的影响。我们使用基于模板的方法将简单的提示转述为具有更复杂语法结构的提示。然后，我们分析这些结构不同但语义相等的提示符的 LLM 性能。我们的研究表明，简单的提示语比复杂的句子形式更有效。简单关系(1:1)的句法变化的表现仍然是最好的，不同类型之间的表现略有下降。这些结果强调了简单的提示结构对于 LLM 中的知识检索更有效，并且激发了对提示句法对各种任务的影响的进一步研究。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Decoding+Prompt+Syntax:+Analysing+its+Impact+on+Knowledge+Retrieval+in+Large+Language+Models)|0| +|[Decoding Prompt Syntax: Analysing its Impact on Knowledge Retrieval in Large Language Models](https://doi.org/10.1145/3543873.3587655)|Stephan Linzbach, Tim Tressel, Laura Kallmeyer, Stefan Dietze, Hajira Jabeen|GESIS Leibniz Institut für Sozialwissenschaften, Germany; GESIS Leibniz Institute for Social Sciences, Germany; GESIS Leibniz Institute for Social Sciences, Germany and Heinrich Heine University, Germany; Heinrich Heine University, Germany|Large Language Models (LLMs), with their advanced architectures and training on massive language datasets, contain unexplored knowledge. One method to infer this knowledge is through the use of cloze-style prompts. Typically, these prompts are manually designed because the phrasing of these prompts impacts the knowledge retrieval performance, even if the LLM encodes the desired information. In this paper, we study the impact of prompt syntax on the knowledge retrieval capacity of LLMs. We use a template-based approach to paraphrase simple prompts into prompts with a more complex grammatical structure. We then analyse the LLM performance for these structurally different but semantically equivalent prompts. Our study reveals that simple prompts work better than complex forms of sentences. The performance across the syntactical variations for simple relations (1:1) remains best, with a marginal decrease across different typologies. These results reinforce that simple prompt structures are more effective for knowledge retrieval in LLMs and motivate future research into the impact of prompt syntax on various tasks.|大型语言模型(LLM)具有先进的体系结构和对大量语言数据集的训练，包含了未开发的知识。一种推断这种知识的方法是通过使用完形填空式的提示。通常，这些提示是手动设计的，因为这些提示的措辞会影响知识检索性能，即使 LLM 对所需的信息进行了编码。本文研究了提示语法对 LLM 知识检索能力的影响。我们使用基于模板的方法将简单的提示转述为具有更复杂语法结构的提示。然后，我们分析这些结构不同但语义相等的提示符的 LLM 性能。我们的研究表明，简单的提示语比复杂的句子形式更有效。简单关系(1:1)的句法变化的表现仍然是最好的，不同类型之间的表现略有下降。这些结果强调了简单的提示结构对于 LLM 中的知识检索更有效，并且激发了对提示句法对各种任务的影响的进一步研究。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Decoding+Prompt+Syntax:+Analysing+its+Impact+on+Knowledge+Retrieval+in+Large+Language+Models)|0| |[CS-TGN: Community Search via Temporal Graph Neural Networks](https://doi.org/10.1145/3543873.3587654)|Farnoosh Hashemi, Ali Behrouz, Milad Rezaei Hajidehi|University of British Columbia, Canada|Searching for local communities is an important research challenge that allows for personalized community discovery and supports advanced data analysis in various complex networks, such as the World Wide Web, social networks, and brain networks. The evolution of these networks over time has motivated several recent studies to identify local communities in temporal networks. Given any query nodes, Community Search aims to find a densely connected subgraph containing query nodes. However, existing community search approaches in temporal networks have two main limitations: (1) they adopt pre-defined subgraph patterns to model communities, which cannot find communities that do not conform to these patterns in real-world networks, and (2) they only use the aggregation of disjoint structural information to measure quality, missing the dynamic of connections and temporal properties. In this paper, we propose a query-driven Temporal Graph Convolutional Network (CS-TGN) that can capture flexible community structures by learning from the ground-truth communities in a data-driven manner. CS-TGN first combines the local query-dependent structure and the global graph embedding in each snapshot of the network and then uses a GRU cell with contextual attention to learn the dynamics of interactions and update node embeddings over time. We demonstrate how this model can be used for interactive community search in an online setting, allowing users to evaluate the found communities and provide feedback. Experiments on real-world temporal graphs with ground-truth communities validate the superior quality of the solutions obtained and the efficiency of our model in both temporal and interactive static settings.|搜索当地社区是一个重要的研究挑战，它允许个性化的社区发现，并支持各种复杂网络中的先进数据分析，如万维网、社交网络和大脑网络。随着时间的推移，这些网络的演变促使最近几项研究在时间网络中识别局部群落。给定任何查询节点，Community Search 的目标是找到一个包含查询节点的密集连接子图。然而，现有的时态网络中的社区搜索方法存在两个主要的局限性: (1)它们采用预定义的子图模式来模拟社区，在现实网络中不能找到不符合这些模式的社区; (2)它们只使用不相交的结构信息的聚合来度量质量，缺乏连接的动态性和时态性。本文提出了一种基于查询驱动的时态图卷积网络(CS-TGN) ，该网络通过以数据驱动的方式从地面真相社区中学习知识，可以捕获灵活的社区结构。TGN 首先将本地查询依赖结构和全局图嵌入到网络的每个快照中，然后利用一个具有上下文关注的 GRU 单元来学习交互的动态性，并随着时间的推移更新节点嵌入。我们展示了这个模型如何在在线环境中用于交互式社区搜索，允许用户评估找到的社区并提供反馈。在真实时间图上的实验结果验证了该模型在时间静态和交互静态环境下的优越性和有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=CS-TGN:+Community+Search+via+Temporal+Graph+Neural+Networks)|0| -|[Learned Temporal Aggregations for Fraud Classification on E-Commerce Platforms](https://doi.org/10.1145/3543873.3587632)|Xiao Ling, David Yan, Bilal Alsallakh, Ashutosh Pandey, Manan Bakshi, Pamela Bhattacharya|Meta, Canada; Voxel AI, USA; Meta, USA; North Carolina State University, USA|Fraud and other types of adversarial behavior are serious problems on customer-to-customer (C2C) e-commerce platforms, where harmful behaviors by bad actors erode user trust and safety. Many modern e-commerce integrity systems utilize machine learning (ML) to detect fraud and bad actors. We discuss the practical problems faced by integrity systems which utilize data associated with user interactions with the platform. Specifically, we focus on the challenge of representing the user interaction events, and aggregating their features. We compare the performance of two paradigms to handle the feature temporality when training the ML models: hand-engineered temporal aggregation and a learned aggregation using a sequence encoder. We show that a model which learns a time-aggregation using a sequence encoder outperforms models trained on handcrafted aggregations on the fraud classification task with a real-world dataset.|欺诈和其他类型的对抗行为是 C2C 电子商务平台上的严重问题，不良行为者的有害行为侵蚀了用户的信任和安全。许多现代电子商务完整性系统利用机器学习(ML)来检测欺诈和不良行为者。我们讨论完整性系统所面临的实际问题，这些系统利用与平台的用户交互相关的数据。具体来说，我们关注的挑战是表示用户交互事件，并聚合它们的特性。在训练机器学习模型时，我们比较了两种模式处理特征时间的性能: 手工时间聚合和使用序列编码器的学习聚合。我们展示了一个使用序列编码器学习时间聚合的模型优于使用真实世界数据集进行欺诈分类任务的手工聚合训练的模型。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Learned+Temporal+Aggregations+for+Fraud+Classification+on+E-Commerce+Platforms)|0| +|[Learned Temporal Aggregations for Fraud Classification on E-Commerce Platforms](https://doi.org/10.1145/3543873.3587632)|Xiao Ling, David Yan, Bilal Alsallakh, Ashutosh Pandey, Manan Bakshi, Pamela Bhattacharya|Voxel AI, USA; Meta, USA; North Carolina State University, USA; Meta, Canada|Fraud and other types of adversarial behavior are serious problems on customer-to-customer (C2C) e-commerce platforms, where harmful behaviors by bad actors erode user trust and safety. Many modern e-commerce integrity systems utilize machine learning (ML) to detect fraud and bad actors. We discuss the practical problems faced by integrity systems which utilize data associated with user interactions with the platform. Specifically, we focus on the challenge of representing the user interaction events, and aggregating their features. We compare the performance of two paradigms to handle the feature temporality when training the ML models: hand-engineered temporal aggregation and a learned aggregation using a sequence encoder. We show that a model which learns a time-aggregation using a sequence encoder outperforms models trained on handcrafted aggregations on the fraud classification task with a real-world dataset.|欺诈和其他类型的对抗行为是 C2C 电子商务平台上的严重问题，不良行为者的有害行为侵蚀了用户的信任和安全。许多现代电子商务完整性系统利用机器学习(ML)来检测欺诈和不良行为者。我们讨论完整性系统所面临的实际问题，这些系统利用与平台的用户交互相关的数据。具体来说，我们关注的挑战是表示用户交互事件，并聚合它们的特性。在训练机器学习模型时，我们比较了两种模式处理特征时间的性能: 手工时间聚合和使用序列编码器的学习聚合。我们展示了一个使用序列编码器学习时间聚合的模型优于使用真实世界数据集进行欺诈分类任务的手工聚合训练的模型。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Learned+Temporal+Aggregations+for+Fraud+Classification+on+E-Commerce+Platforms)|0| |[Decency and Decentralisation: Verifiable Decentralised Knowledge Graph Querying](https://doi.org/10.1145/3543873.3587635)|Aisling Third, John Domingue|Knowledge Media Institute, The Open University, United Kingdom|Increasing interest in decentralisation for data and processing on the Web brings with it the need to re-examine methods for verifying data and behaviour for scalable multi-party interactions. We consider factors relevant to verification of querying activity on knowledge graphs in a Trusted Decentralised Web, and set out ideas for future research in this area.|随着人们对数据地方分权和网络处理的兴趣日益增长，人们需要重新审视可扩展的多方交互的数据和行为验证方法。我们考虑了与可信分布式网络中知识图表查询活动验证相关的因素，并为这一领域的未来研究提出了一些想法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Decency+and+Decentralisation:+Verifiable+Decentralised+Knowledge+Graph+Querying)|0| -|[Towards a Decentralized Data Hub and Query System for Federated Dynamic Data Spaces](https://doi.org/10.1145/3543873.3587646)|Danh Le Phuoc, Sonja Schimmler, Anh LeTuan, Uwe A. Kuehn, Manfred Hauswirth|Fraunhofer Institute for Open Communication Systems, Berlin, Germany; TU Berlin, Germany|This position paper proposes a hybrid architecture for secure and efficient data sharing and processing across dynamic data spaces. On the one hand, current centralized approaches are plagued by issues such as lack of privacy and control for users, high costs, and bad performance, making these approaches unsuitable for the decentralized data spaces prevalent in Europe and various industries (decentralized on the conceptual and physical levels while centralized in the underlying implementation). On the other hand, decentralized systems face challenges with limited knowledge of/control over the global system, fair resource utilization, and data provenance. Our proposed Semantic Data Ledger (SDL) approach combines the advantages of both architectures to overcome their limitations. SDL allows users to choose the best combination of centralized and decentralized features, providing a decentralized infrastructure for the publication of structured data with machine-readable semantics. It supports expressive structured queries, secure data sharing, and payment mechanisms based on an underlying autonomous ledger, enabling the implementation of economic models and fair-use strategies.|本文提出了一种跨动态数据空间的安全有效的数据共享和处理的混合体系结构。一方面，当前的集中式方法受到诸如用户缺乏隐私和控制、高成本和性能差等问题的困扰，使得这些方法不适合在欧洲和各种行业盛行的分散式数据空间(在概念和物理层面上分散，而在底层实现中集中)。另一方面，分散系统面临的挑战是对全球系统的了解和控制有限，资源利用不公平，数据来源不明确。我们提出的语义数据分类账(SDL)方法结合了两种体系结构的优点，克服了它们的局限性。SDL 允许用户选择集中和分散特性的最佳组合，为具有机器可读语义的结构化数据的发布提供分散的基础设施。它支持表达式结构化查询、安全数据共享和基于底层自治分类账的支付机制，使经济模型和合理使用策略的实施成为可能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Towards+a+Decentralized+Data+Hub+and+Query+System+for+Federated+Dynamic+Data+Spaces)|0| +|[Towards a Decentralized Data Hub and Query System for Federated Dynamic Data Spaces](https://doi.org/10.1145/3543873.3587646)|Danh Le Phuoc, Sonja Schimmler, Anh LeTuan, Uwe A. Kuehn, Manfred Hauswirth|TU Berlin, Germany; Fraunhofer Institute for Open Communication Systems, Berlin, Germany|This position paper proposes a hybrid architecture for secure and efficient data sharing and processing across dynamic data spaces. On the one hand, current centralized approaches are plagued by issues such as lack of privacy and control for users, high costs, and bad performance, making these approaches unsuitable for the decentralized data spaces prevalent in Europe and various industries (decentralized on the conceptual and physical levels while centralized in the underlying implementation). On the other hand, decentralized systems face challenges with limited knowledge of/control over the global system, fair resource utilization, and data provenance. Our proposed Semantic Data Ledger (SDL) approach combines the advantages of both architectures to overcome their limitations. SDL allows users to choose the best combination of centralized and decentralized features, providing a decentralized infrastructure for the publication of structured data with machine-readable semantics. It supports expressive structured queries, secure data sharing, and payment mechanisms based on an underlying autonomous ledger, enabling the implementation of economic models and fair-use strategies.|本文提出了一种跨动态数据空间的安全有效的数据共享和处理的混合体系结构。一方面，当前的集中式方法受到诸如用户缺乏隐私和控制、高成本和性能差等问题的困扰，使得这些方法不适合在欧洲和各种行业盛行的分散式数据空间(在概念和物理层面上分散，而在底层实现中集中)。另一方面，分散系统面临的挑战是对全球系统的了解和控制有限，资源利用不公平，数据来源不明确。我们提出的语义数据分类账(SDL)方法结合了两种体系结构的优点，克服了它们的局限性。SDL 允许用户选择集中和分散特性的最佳组合，为具有机器可读语义的结构化数据的发布提供分散的基础设施。它支持表达式结构化查询、安全数据共享和基于底层自治分类账的支付机制，使经济模型和合理使用策略的实施成为可能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Towards+a+Decentralized+Data+Hub+and+Query+System+for+Federated+Dynamic+Data+Spaces)|0| |[What are "personal data spaces"?](https://doi.org/10.1145/3543873.3587656)|Viivi Lähteenoja|University of Helsinki, Finland and Aalto University, Finland|While the concept of “data spaces” is no longer new, its specific application to individuals and personal data management is still undeveloped. This short paper presents a vision for “personal data spaces” in the shape of a work-in-progress description of them and some of the conceptual and implementation features envisioned. It is offered for discussion, debate, and improvement by professionals, policymakers, and researchers operating in the intersection of data spaces and personal data management.|虽然“数据空间”的概念不再是新的，但它在个人和个人数据管理方面的具体应用仍然没有得到发展。这篇简短的论文提出了一个“个人数据空间”的愿景，其形式是一个在建的数据空间描述，以及所设想的一些概念和实现特征。它提供了讨论，辩论和改进的专业人士，决策者和研究人员在数据空间和个人数据管理的交叉运作。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=What+are+"personal+data+spaces"?)|0| -|[TAPP: Defining standard provenance information for clinical research data and workflows - Obstacles and opportunities](https://doi.org/10.1145/3543873.3587562)|Kerstin Gierend, Judith A. H. Wodke, Sascha Genehr, Robert Gött, Ron Henkel, Frank Krüger, Markus Mandalka, Lea Michaelis, Alexander Scheuerlein, Max Schröder, Atinkut Zeleke, Dagmar Waltemath|Rostock University Library, University of Rostock, Germany; Faculty of Engineering, Wismar University of Applied Sciences, Germany; Department of Biomedical Informatics, Center for Preventive Medicine and Digital Health, Medical Faculty Mannheim, Heidelberg University, Germany; Institute of Communications Engineering, University of Rostock, Germany; Core Unit Data Integration Center, University Medicine Greifswald, Germany; Institute for Data Science, University of Greifswald, Germany; Medical Informatics Laboratory, University Medicine Greifswald, Germany; Medical Informatics Laboratory, MeDaX Group, University Medicine Greifswald, Germany|Data provenance has raised much attention across disciplines lately, as it has been shown that enrichment of data with provenance information leads to better credibility, renders data more FAIR fostering data reuse. Also, the biomedical domain has recognised the potential of provenance capture. However, several obstacles prevent efficient, automated, and machine-interpretable enrichment of biomedical data with provenance information, such as data heterogeneity, complexity, and sensitivity. Here, we explain how in Germany clinical data are transferred from hospital information systems into a data integration centre to enable secondary use of patient data and how it can be reused as research data. Considering the complex data infrastructures in hospitals, we indicate obstacles and opportunities when collecting provenance information along heterogeneous data processing pipelines. To express provenance data, we indicate the usage of the Fast Healthcare Interoperability Resource (FHIR) provenance resource for healthcare data. In addition, we consider already existing approaches from other research fields and standard communities. As a solution towards high-quality standardised clinical research data, we propose to develop a ’MInimal Requirements for Automated Provenance Information Enrichment’ (MIRAPIE) guideline. As a community project, MIRAPIE should generalise provenance information concepts to allow its world-wide applicability, possibly beyond the health care sector.|数据来源最近引起了跨学科的广泛关注，因为已经表明，用来源信息丰富数据可以提高可信度，使数据更加公平，促进数据重用。此外，生物医学领域已经认识到种源捕获的潜力。然而，一些障碍阻碍了生物医学数据的有效、自动化和机器可解释的来源信息的丰富，例如数据异构性、复杂性和敏感性。在这里，我们解释在德国如何将临床数据从医院信息系统转移到数据集成中心，以便能够对患者数据进行二次使用，以及如何将其重用为研究数据。考虑到医院复杂的数据基础设施，我们指出了沿着异构数据处理管道收集起源信息的障碍和机会。为了表示来源数据，我们指出了 Fast Healthcare Interoperability Resource (FHIR)来源资源对医疗数据的使用。此外，我们还考虑了来自其他研究领域和标准社区的已有方法。作为高质量标准化临床研究数据的解决方案，我们建议制定一个“自动起源信息丰富的最低要求”(MIRAPIE)指南。作为一个社区项目，MIRAPIE 应该推广起源信息的概念，使其在世界范围内适用，可能超出卫生保健部门。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=TAPP:+Defining+standard+provenance+information+for+clinical+research+data+and+workflows+-+Obstacles+and+opportunities)|0| +|[TAPP: Defining standard provenance information for clinical research data and workflows - Obstacles and opportunities](https://doi.org/10.1145/3543873.3587562)|Kerstin Gierend, Judith A. H. Wodke, Sascha Genehr, Robert Gött, Ron Henkel, Frank Krüger, Markus Mandalka, Lea Michaelis, Alexander Scheuerlein, Max Schröder, Atinkut Zeleke, Dagmar Waltemath|Institute for Data Science, University of Greifswald, Germany; Department of Biomedical Informatics, Center for Preventive Medicine and Digital Health, Medical Faculty Mannheim, Heidelberg University, Germany; Medical Informatics Laboratory, MeDaX Group, University Medicine Greifswald, Germany; Institute of Communications Engineering, University of Rostock, Germany; Faculty of Engineering, Wismar University of Applied Sciences, Germany; Core Unit Data Integration Center, University Medicine Greifswald, Germany; Medical Informatics Laboratory, University Medicine Greifswald, Germany; Rostock University Library, University of Rostock, Germany|Data provenance has raised much attention across disciplines lately, as it has been shown that enrichment of data with provenance information leads to better credibility, renders data more FAIR fostering data reuse. Also, the biomedical domain has recognised the potential of provenance capture. However, several obstacles prevent efficient, automated, and machine-interpretable enrichment of biomedical data with provenance information, such as data heterogeneity, complexity, and sensitivity. Here, we explain how in Germany clinical data are transferred from hospital information systems into a data integration centre to enable secondary use of patient data and how it can be reused as research data. Considering the complex data infrastructures in hospitals, we indicate obstacles and opportunities when collecting provenance information along heterogeneous data processing pipelines. To express provenance data, we indicate the usage of the Fast Healthcare Interoperability Resource (FHIR) provenance resource for healthcare data. In addition, we consider already existing approaches from other research fields and standard communities. As a solution towards high-quality standardised clinical research data, we propose to develop a ’MInimal Requirements for Automated Provenance Information Enrichment’ (MIRAPIE) guideline. As a community project, MIRAPIE should generalise provenance information concepts to allow its world-wide applicability, possibly beyond the health care sector.|数据来源最近引起了跨学科的广泛关注，因为已经表明，用来源信息丰富数据可以提高可信度，使数据更加公平，促进数据重用。此外，生物医学领域已经认识到种源捕获的潜力。然而，一些障碍阻碍了生物医学数据的有效、自动化和机器可解释的来源信息的丰富，例如数据异构性、复杂性和敏感性。在这里，我们解释在德国如何将临床数据从医院信息系统转移到数据集成中心，以便能够对患者数据进行二次使用，以及如何将其重用为研究数据。考虑到医院复杂的数据基础设施，我们指出了沿着异构数据处理管道收集起源信息的障碍和机会。为了表示来源数据，我们指出了 Fast Healthcare Interoperability Resource (FHIR)来源资源对医疗数据的使用。此外，我们还考虑了来自其他研究领域和标准社区的已有方法。作为高质量标准化临床研究数据的解决方案，我们建议制定一个“自动起源信息丰富的最低要求”(MIRAPIE)指南。作为一个社区项目，MIRAPIE 应该推广起源信息的概念，使其在世界范围内适用，可能超出卫生保健部门。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=TAPP:+Defining+standard+provenance+information+for+clinical+research+data+and+workflows+-+Obstacles+and+opportunities)|0| |[ProSA: A provenance system for reproducing query results](https://doi.org/10.1145/3543873.3587563)|Tanja Auge|Faculty of Informatics and Data Science, University of Regensburg, Germany|Good scientific work requires comprehensible, transparent and reproducible research. One way to ensure this is to include all data relevant to a study or evaluation when publishing an article. This data should be at least aggregated or anonymized, at best compact and complete, but always resilient. In this paper we present ProSA, a system for calculating the minimal necessary data set, called sub-database. For this, we combine the Chase — a set of algorithms for transforming databases — with additional provenance information. We display the implementation of provenance guided by the ProSA pipeline and show its use to generate an optimized sub-database. Furhter, we demonstrate how the ProSA GUI looks like and present some applications and extensions.|好的科学工作需要可理解、透明和可重复的研究。确保这一点的一个方法是在发表文章时包括与研究或评估相关的所有数据。这些数据应该至少是聚合或匿名的，充其量是紧凑和完整的，但总是具有弹性。本文介绍了 ProSA，一个计算最小必要数据集的系统，称为子数据库。为此，我们将 Chase (一组转换数据库的算法)与其他来源信息结合起来。我们展示了 ProSA 流水线引导的起源实现，并展示了它用于生成优化的子数据库。进一步，我们将演示 ProSA GUI 的外观，并展示一些应用程序和扩展。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=ProSA:+A+provenance+system+for+reproducing+query+results)|0| -|[Hybrid Query and Instance Explanations and Repairs](https://doi.org/10.1145/3543873.3587565)|Seokki Lee, Boris Glavic, Adriane Chapman, Bertram Ludäscher|University of Illinois at Urbana-Champaign, USA; University of Southampton, United Kingdom; Illinois Institute of Technology, USA; University of Cincinnati, USA|Prior work on explaining missing (unexpected) query results identifies which parts of the query or data are responsible for the erroneous result or repairs the query or data to fix such errors. The problem of generating repairs is typically expressed as an optimization problem, i.e., a single repair is returned that is optimal wrt. to some criterion such as minimizing the repair’s side effects. However, such an optimization objective may not concretely model a user’s (often hard to formalize) notion of which repair is “correct”. In this paper, we motivate hybrid explanations and repairs, i.e., that fix both the query and the data. Instead of returning one “optimal” repair, we argue for an approach that empowers the user to explore the space of possible repairs effectively. We also present a proof-of-concept implementation and outline open research problems.|先前解释丢失(意外)查询结果的工作确定了查询或数据的哪些部分对错误结果负责，或者修复查询或数据以修复此类错误。产生维修的问题通常表示为一个最佳化问题，也就是说，一个单一的维修被返回，这是最优的书面意见，以某些标准，如最小化维修的副作用。然而，这样的优化目标可能无法具体地模拟用户(通常很难形式化)的哪种修复是“正确的”概念。在本文中，我们激励混合解释和修复，即，修复查询和数据。与返回一个“最佳”修复相反，我们主张采用一种方法，使用户能够有效地探索可能的修复空间。我们还提出了一个概念验证实现，并概述了开放式研究问题。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Hybrid+Query+and+Instance+Explanations+and+Repairs)|0| -|[Querying Container Provenance](https://doi.org/10.1145/3543873.3587568)|Aniket Modi, Moaz Reyad, Tanu Malik, Ashish Gehani|SRI International, USA; College of Computing and Digital Media, DePaul University, USA and Department of Computer Science and Engineering, IIT Delhi, India; College of Computing and Digital Media, DePaul University, USA|Containers are lightweight mechanisms for the isolation of operating system resources. They are realized by activating a set of namespaces. Given the use of containers in scientific computing, tracking and managing provenance within and across containers is becoming essential for debugging and reproducibility. In this work, we examine the properties of container provenance graphs that result from auditing containerized applications. We observe that the generated container provenance graphs are hypergraphs because one resource may belong to one or more namespaces. We examine the hierarchical behavior of PID, mount, and user namespaces, that are more commonly activated and show that even when represented as hypergraphs, the resulting container provenance graphs are acyclic. We experiment with recently published container logs and identify hypergraph properties.|容器是用于隔离操作系统资源的轻量级机制。它们是通过激活一组名称空间来实现的。鉴于容器在科学计算中的使用，跟踪和管理容器内部和跨容器的出处对于调试和再现性变得至关重要。在本文中，我们研究了审计容器化应用程序所产生的容器起源图的属性。我们注意到，生成的容器起源图是超图，因为一个资源可能属于一个或多个名称空间。我们研究了 PID、 mount 和用户名称空间的分层行为，这些名称空间通常被激活，并且表明即使用超图表示，最终的容器起源图也是无环的。我们使用最近发布的容器日志进行实验，并识别超图属性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Querying+Container+Provenance)|0| -|[Graph-less Collaborative Filtering](https://doi.org/10.1145/3543507.3583196)|Lianghao Xia, Chao Huang, Jiao Shi, Yong Xu|South China University of Technology, China; The University of Hong Kong, Hong Kong|Graph neural networks (GNNs) have shown the power in representation learning over graph-structured user-item interaction data for collaborative filtering (CF) task. However, with their inherently recursive message propagation among neighboring nodes, existing GNN-based CF models may generate indistinguishable and inaccurate user (item) representations due to the over-smoothing and noise effect with low-pass Laplacian smoothing operators. In addition, the recursive information propagation with the stacked aggregators in the entire graph structures may result in poor scalability in practical applications. Motivated by these limitations, we propose a simple and effective collaborative filtering model (SimRec) that marries the power of knowledge distillation and contrastive learning. In SimRec, adaptive transferring knowledge is enabled between the teacher GNN model and a lightweight student network, to not only preserve the global collaborative signals, but also address the over-smoothing issue with representation recalibration. Empirical results on public datasets show that SimRec archives better efficiency while maintaining superior recommendation performance compared with various strong baselines. Our implementations are publicly available at: https://github.com/HKUDS/SimRec.|图形神经网络(GNN)已经显示了在表示学习方面的能力，超过了图形结构的用户-项目交互数据的协同过滤(CF)任务。然而，由于现有的基于 GNN 的 CF 模型固有的相邻节点之间的递归消息传播特性，由于低通拉普拉斯平滑算子的过平滑和噪声效应，可能产生难以区分和不准确的用户(项)表示。此外，在整个图结构中，叠加聚合器的递归信息传播可能导致实际应用中的可扩展性较差。基于这些局限性，我们提出了一个简单有效的协同过滤模型(SimRec) ，它将知识提取和对比学习的力量结合在一起。在 SimRec 中，在教师 GNN 模型和轻量级学生网络之间实现了自适应知识传递，不仅保留了全局协作信号，而且通过表示重校正解决了过于平滑的问题。对公共数据集的实证结果表明，与各种强基线相比，SimRec 在保持优异推荐性能的同时提高了存档效率。我们的实施方案可以在以下 https://github.com/hkuds/simrec 公开获得:。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Graph-less+Collaborative+Filtering)|0| +|[Hybrid Query and Instance Explanations and Repairs](https://doi.org/10.1145/3543873.3587565)|Seokki Lee, Boris Glavic, Adriane Chapman, Bertram Ludäscher|University of Illinois at Urbana-Champaign, USA; Illinois Institute of Technology, USA; University of Southampton, United Kingdom; University of Cincinnati, USA|Prior work on explaining missing (unexpected) query results identifies which parts of the query or data are responsible for the erroneous result or repairs the query or data to fix such errors. The problem of generating repairs is typically expressed as an optimization problem, i.e., a single repair is returned that is optimal wrt. to some criterion such as minimizing the repair’s side effects. However, such an optimization objective may not concretely model a user’s (often hard to formalize) notion of which repair is “correct”. In this paper, we motivate hybrid explanations and repairs, i.e., that fix both the query and the data. Instead of returning one “optimal” repair, we argue for an approach that empowers the user to explore the space of possible repairs effectively. We also present a proof-of-concept implementation and outline open research problems.|先前解释丢失(意外)查询结果的工作确定了查询或数据的哪些部分对错误结果负责，或者修复查询或数据以修复此类错误。产生维修的问题通常表示为一个最佳化问题，也就是说，一个单一的维修被返回，这是最优的书面意见，以某些标准，如最小化维修的副作用。然而，这样的优化目标可能无法具体地模拟用户(通常很难形式化)的哪种修复是“正确的”概念。在本文中，我们激励混合解释和修复，即，修复查询和数据。与返回一个“最佳”修复相反，我们主张采用一种方法，使用户能够有效地探索可能的修复空间。我们还提出了一个概念验证实现，并概述了开放式研究问题。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Hybrid+Query+and+Instance+Explanations+and+Repairs)|0| +|[Querying Container Provenance](https://doi.org/10.1145/3543873.3587568)|Aniket Modi, Moaz Reyad, Tanu Malik, Ashish Gehani|SRI International, USA; College of Computing and Digital Media, DePaul University, USA; College of Computing and Digital Media, DePaul University, USA and Department of Computer Science and Engineering, IIT Delhi, India|Containers are lightweight mechanisms for the isolation of operating system resources. They are realized by activating a set of namespaces. Given the use of containers in scientific computing, tracking and managing provenance within and across containers is becoming essential for debugging and reproducibility. In this work, we examine the properties of container provenance graphs that result from auditing containerized applications. We observe that the generated container provenance graphs are hypergraphs because one resource may belong to one or more namespaces. We examine the hierarchical behavior of PID, mount, and user namespaces, that are more commonly activated and show that even when represented as hypergraphs, the resulting container provenance graphs are acyclic. We experiment with recently published container logs and identify hypergraph properties.|容器是用于隔离操作系统资源的轻量级机制。它们是通过激活一组名称空间来实现的。鉴于容器在科学计算中的使用，跟踪和管理容器内部和跨容器的出处对于调试和再现性变得至关重要。在本文中，我们研究了审计容器化应用程序所产生的容器起源图的属性。我们注意到，生成的容器起源图是超图，因为一个资源可能属于一个或多个名称空间。我们研究了 PID、 mount 和用户名称空间的分层行为，这些名称空间通常被激活，并且表明即使用超图表示，最终的容器起源图也是无环的。我们使用最近发布的容器日志进行实验，并识别超图属性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Querying+Container+Provenance)|0| +|[Graph-less Collaborative Filtering](https://doi.org/10.1145/3543507.3583196)|Lianghao Xia, Chao Huang, Jiao Shi, Yong Xu|The University of Hong Kong, Hong Kong; South China University of Technology, China|Graph neural networks (GNNs) have shown the power in representation learning over graph-structured user-item interaction data for collaborative filtering (CF) task. However, with their inherently recursive message propagation among neighboring nodes, existing GNN-based CF models may generate indistinguishable and inaccurate user (item) representations due to the over-smoothing and noise effect with low-pass Laplacian smoothing operators. In addition, the recursive information propagation with the stacked aggregators in the entire graph structures may result in poor scalability in practical applications. Motivated by these limitations, we propose a simple and effective collaborative filtering model (SimRec) that marries the power of knowledge distillation and contrastive learning. In SimRec, adaptive transferring knowledge is enabled between the teacher GNN model and a lightweight student network, to not only preserve the global collaborative signals, but also address the over-smoothing issue with representation recalibration. Empirical results on public datasets show that SimRec archives better efficiency while maintaining superior recommendation performance compared with various strong baselines. Our implementations are publicly available at: https://github.com/HKUDS/SimRec.|图形神经网络(GNN)已经显示了在表示学习方面的能力，超过了图形结构的用户-项目交互数据的协同过滤(CF)任务。然而，由于现有的基于 GNN 的 CF 模型固有的相邻节点之间的递归消息传播特性，由于低通拉普拉斯平滑算子的过平滑和噪声效应，可能产生难以区分和不准确的用户(项)表示。此外，在整个图结构中，叠加聚合器的递归信息传播可能导致实际应用中的可扩展性较差。基于这些局限性，我们提出了一个简单有效的协同过滤模型(SimRec) ，它将知识提取和对比学习的力量结合在一起。在 SimRec 中，在教师 GNN 模型和轻量级学生网络之间实现了自适应知识传递，不仅保留了全局协作信号，而且通过表示重校正解决了过于平滑的问题。对公共数据集的实证结果表明，与各种强基线相比，SimRec 在保持优异推荐性能的同时提高了存档效率。我们的实施方案可以在以下 https://github.com/hkuds/simrec 公开获得:。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Graph-less+Collaborative+Filtering)|0| |[Collaboration-Aware Graph Convolutional Network for Recommender Systems](https://doi.org/10.1145/3543507.3583229)|Yu Wang, Yuying Zhao, Yi Zhang, Tyler Derr|Vanderbilt university, USA|Graph Neural Networks (GNNs) have been successfully adopted in recommender systems by virtue of the message-passing that implicitly captures collaborative effect. Nevertheless, most of the existing message-passing mechanisms for recommendation are directly inherited from GNNs without scrutinizing whether the captured collaborative effect would benefit the prediction of user preferences. In this paper, we first analyze how message-passing captures the collaborative effect and propose a recommendation-oriented topological metric, Common Interacted Ratio (CIR), which measures the level of interaction between a specific neighbor of a node with the rest of its neighbors. After demonstrating the benefits of leveraging collaborations from neighbors with higher CIR, we propose a recommendation-tailored GNN, Collaboration-Aware Graph Convolutional Network (CAGCN), that goes beyond 1-Weisfeiler-Lehman(1-WL) test in distinguishing non-bipartite-subgraph-isomorphic graphs. Experiments on six benchmark datasets show that the best CAGCN variant outperforms the most representative GNN-based recommendation model, LightGCN, by nearly 10% in Recall@20 and also achieves around 80% speedup. Our code is publicly available at https://github.com/YuWVandy/CAGCN.|图形神经网络(GNN)通过隐式捕捉协作效果的消息传递，已成功地应用于推荐系统中。尽管如此，大多数现有的推荐信息传递机制都是直接从 GNN 继承而来的，没有仔细检查所捕获的协作效应是否有利于预测用户的偏好。在本文中，我们首先分析了消息传递是如何捕获协作效果的，并提出了一个面向推荐的拓扑度量，公共交互比(CIR) ，它测量节点的特定邻居与其他邻居之间的交互水平。在证明了利用具有较高 CIR 的邻居的协作的好处之后，我们提出了一种推荐量身定制的 GNN，协作感知图卷积网络(CAGCN) ，其超越了1-Weisfeiler-Lehman (1-WL)检验在区分非二部子图-同构图。在六个基准数据集上的实验表明，在 Recall@20中，最好的 CAGCN 变体比最具代表性的基于 GNN 的推荐模型 LightGCN 的性能提高了近10% ，并且还实现了约80% 的加速比。我们的代码可以在 https://github.com/yuwvandy/cagcn 上公开获取。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Collaboration-Aware+Graph+Convolutional+Network+for+Recommender+Systems)|0| |[HyConvE: A Novel Embedding Model for Knowledge Hypergraph Link Prediction with Convolutional Neural Networks](https://doi.org/10.1145/3543507.3583256)|Chenxu Wang, Xin Wang, Zhao Li, Zirui Chen, Jianxin Li|Deakin University, Australia; Tianjin University, China|Knowledge hypergraph embedding, which projects entities and n-ary relations into a low-dimensional continuous vector space to predict missing links, remains a challenging area to be explored despite the ubiquity of n-ary relational facts in the real world. Currently, knowledge hypergraph link prediction methods are essentially simple extensions of those used in knowledge graphs, where n-ary relational facts are decomposed into different subelements. Convolutional neural networks have been shown to have remarkable information extraction capabilities in previous work on knowledge graph link prediction. In this paper, we propose a novel embedding-based knowledge hypergraph link prediction model named HyConvE, which exploits the powerful learning ability of convolutional neural networks for effective link prediction. Specifically, we employ 3D convolution to capture the deep interactions of entities and relations to efficiently extract explicit and implicit knowledge in each n-ary relational fact without compromising its translation property. In addition, appropriate relation and position-aware filters are utilized sequentially to perform two-dimensional convolution operations to capture the intrinsic patterns and position information in each n-ary relation, respectively. Extensive experimental results on real datasets of knowledge hypergraphs and knowledge graphs demonstrate the superior performance of HyConvE compared with state-of-the-art baselines.|知识超图嵌入将实体和 n 元关系投影到低维连续向量空间中以预测缺失的链接，尽管 n 元关系事实在现实世界中无处不在，但仍然是一个有待探索的挑战领域。目前，知识超图链接预测方法基本上是知识图的简单扩展，其中 n 元关系事实被分解为不同的子元素。卷积神经网络已被证明具有显著的信息抽取能力在以前的工作中的知识图链接预测。本文提出了一种新的基于嵌入的知识超图链接预测模型 HyConve，该模型利用卷积神经网络强大的学习能力进行有效的链接预测。具体来说，我们使用三维卷积来捕捉实体和关系的深层交互作用，以有效地提取每个 n 元关系事实中的显性和隐性知识，而不损害其翻译性质。此外，还利用合适的关系和位置感知滤波器，分别进行二维卷积运算，捕获每个 n 元关系中的内在模式和位置信息。在知识超图和知识图的实际数据集上进行的大量实验结果表明，与最先进的基线相比，HyConve 方法具有更好的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=HyConvE:+A+Novel+Embedding+Model+for+Knowledge+Hypergraph+Link+Prediction+with+Convolutional+Neural+Networks)|0| |[Efficient Approximation Algorithms for the Diameter-Bounded Max-Coverage Group Steiner Tree Problem](https://doi.org/10.1145/3543507.3583257)|Ke Zhang, Xiaoqing Wang, Gong Cheng|State Key Laboratory for Novel Software Technology, Nanjing University, China|The Diameter-bounded max-Coverage Group Steiner Tree (DCGST) problem has recently been proposed as an expressive way of formulating keyword-based search and exploration of knowledge graphs. It aims at finding a diameter-bounded tree which covers the most given groups of vertices and has the minimum weight. In contrast to its specialization—the classic Group Steiner Tree (GST) problem which has been extensively studied, the emerging DCGST problem still lacks an efficient algorithm. In this paper, we propose Cba, the first approximation algorithm for the DCGST problem, and we prove its worst-case approximation ratio. Furthermore, we incorporate a best-first search strategy with two pruning methods into PrunedCBA, an improved approximation algorithm. Our extensive experiments on real and synthetic graphs demonstrate the effectiveness and efficiency of PrunedCBA.|直径有界的最大覆盖群 Steiner 树(DCGST)问题是近年来提出的一种基于关键字搜索和知识图探索的表示方法。它的目标是找到一个直径有界的树，它覆盖了最多给定的顶点群，并具有最小的权重。与经典的群 Steiner 树(GST)问题相比，新出现的 DCGST 问题仍然缺乏一种有效的算法。在这篇文章中，我们提出了 Cba，这是 DCGST 问题的第一个近似演算法，并且证明了它的最坏情况逼近比。此外，我们将最佳优先搜索策略和两种修剪方法结合到一个改进的近似演算法 PrunedCBA 中。我们在实图和合成图上的大量实验证明了 PrunedCBA 的有效性和效率。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Efficient+Approximation+Algorithms+for+the+Diameter-Bounded+Max-Coverage+Group+Steiner+Tree+Problem)|0| -|[ConsRec: Learning Consensus Behind Interactions for Group Recommendation](https://doi.org/10.1145/3543507.3583277)|Xixi Wu, Yun Xiong, Yao Zhang, Yizhu Jiao, Jiawei Zhang, Yangyong Zhu, Philip S. Yu|Shanghai Key Laboratory of Data Science, School of Computer Science, Fudan University, China; IFM Lab, Department of Computer Science, University of California, Davis, USA; University of Illinois at Chicago, USA; University of Illinois at Urbana-Champaign, USA|Since group activities have become very common in daily life, there is an urgent demand for generating recommendations for a group of users, referred to as group recommendation task. Existing group recommendation methods usually infer groups' preferences via aggregating diverse members' interests. Actually, groups' ultimate choice involves compromises between members, and finally, an agreement can be reached. However, existing individual information aggregation lacks a holistic group-level consideration, failing to capture the consensus information. Besides, their specific aggregation strategies either suffer from high computational costs or become too coarse-grained to make precise predictions. To solve the aforementioned limitations, in this paper, we focus on exploring consensus behind group behavior data. To comprehensively capture the group consensus, we innovatively design three distinct views which provide mutually complementary information to enable multi-view learning, including member-level aggregation, item-level tastes, and group-level inherent preferences. To integrate and balance the multi-view information, an adaptive fusion component is further proposed. As to member-level aggregation, different from existing linear or attentive strategies, we design a novel hypergraph neural network that allows for efficient hypergraph convolutional operations to generate expressive member-level aggregation. We evaluate our ConsRec on two real-world datasets and experimental results show that our model outperforms state-of-the-art methods. An extensive case study also verifies the effectiveness of consensus modeling.|由于小组活动在日常生活中已经非常普遍，因此迫切需要为一组用户提供建议，称为小组推荐任务。现有的群体推荐方法通常通过聚合不同成员的兴趣来推断群体的偏好。实际上，团体的最终选择包括成员之间的妥协，最终可以达成协议。然而，现有的个人信息聚合缺乏整体的群体层面的考虑，未能捕获共识信息。此外，他们特定的聚合策略要么计算成本高，要么过于粗粒度，无法做出精确的预测。为了解决上述局限性，本文重点探讨群体行为数据背后的共识。为了全面捕捉群体共识，我们创新性地设计了三种不同的视图，提供相互补充的信息，使多视图学习成为可能，包括成员层面的聚合、项目层面的品味和群体层面的固有偏好。为了对多视点信息进行集成和平衡，进一步提出了一种自适应融合构件。对于成员级聚集，不同于现有的线性或注意策略，我们设计了一种新的超图神经网络，该网络允许有效的超图卷积操作来产生具有表达能力的成员级聚集。我们在两个真实世界的数据集上评估了我们的 ConsRec，实验结果表明我们的模型优于最先进的方法。一个广泛的案例研究也验证了一致性建模的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=ConsRec:+Learning+Consensus+Behind+Interactions+for+Group+Recommendation)|0| -|[Graph Neural Networks with Diverse Spectral Filtering](https://doi.org/10.1145/3543507.3583324)|Jingwei Guo, Kaizhu Huang, Xinping Yi, Rui Zhang|The University of Liverpool, United Kingdom; Duke Kunshan University, China; Xi'an Jiaotong-Liverpool University, China; Xi'an Jiaotong-Liverpool University; The University of Liverpool, China|Spectral Graph Neural Networks (GNNs) have achieved tremendous success in graph machine learning, with polynomial filters applied for graph convolutions, where all nodes share the identical filter weights to mine their local contexts. Despite the success, existing spectral GNNs usually fail to deal with complex networks (e.g., WWW) due to such homogeneous spectral filtering setting that ignores the regional heterogeneity as typically seen in real-world networks. To tackle this issue, we propose a novel diverse spectral filtering (DSF) framework, which automatically learns node-specific filter weights to exploit the varying local structure properly. Particularly, the diverse filter weights consist of two components — A global one shared among all nodes, and a local one that varies along network edges to reflect node difference arising from distinct graph parts — to balance between local and global information. As such, not only can the global graph characteristics be captured, but also the diverse local patterns can be mined with awareness of different node positions. Interestingly, we formulate a novel optimization problem to assist in learning diverse filters, which also enables us to enhance any spectral GNNs with our DSF framework. We showcase the proposed framework on three state-of-the-arts including GPR-GNN, BernNet, and JacobiConv. Extensive experiments over 10 benchmark datasets demonstrate that our framework can consistently boost model performance by up to 4.92% in node classification tasks, producing diverse filters with enhanced interpretability.|谱图神经网络(GNN)在图形机器学习中取得了巨大的成功，其中多项式滤波器应用于图卷积，其中所有节点共享相同的滤波器权重来挖掘它们的局部上下文。尽管已有的光谱 GNN 在处理复杂网络(如 WWW)时取得了一定的成功，但由于均匀的光谱滤波设置忽略了现实网络中典型的区域异质性，导致 GNN 无法处理复杂网络(如 WWW)。针对这一问题，我们提出了一种新的多谱段滤波(DSF)框架，该框架能够自动学习节点特定的滤波器权值，以适当地利用变化的局部结构。特别地，不同的滤波器权重由两部分组成: 一部分是所有节点共享的全局权重，另一部分是沿网络边缘变化的局部权重，以反映不同图部分产生的节点差异，从而平衡局部和全局信息。因此，不仅可以捕获全局图的特征，而且可以挖掘不同节点位置的不同局部模式。有趣的是，我们制定了一个新的最佳化问题，以帮助学习不同的过滤器，这也使我们能够增强任何光谱 GNN 与我们的 dSF 框架。我们在 GPR-GNN、 BernNet 和 JacobiConv 这三种最新技术的基础上展示了提议的框架。通过对10个基准数据集的大量实验表明，我们的框架可以在节点分类任务中始终如一地将模型性能提高高达4.92% ，产生具有增强可解释性的多样化过滤器。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Graph+Neural+Networks+with+Diverse+Spectral+Filtering)|0| -|[Semi-decentralized Federated Ego Graph Learning for Recommendation](https://doi.org/10.1145/3543507.3583337)|Liang Qu, Ningzhi Tang, Ruiqi Zheng, Quoc Viet Hung Nguyen, Zi Huang, Yuhui Shi, Hongzhi Yin|Southern University of Science and Technology, China; Griffith University, Australia; The University of Queensland, Australia|Collaborative filtering (CF) based recommender systems are typically trained based on personal interaction data (e.g., clicks and purchases) that could be naturally represented as ego graphs. However, most existing recommendation methods collect these ego graphs from all users to compose a global graph to obtain high-order collaborative information between users and items, and these centralized CF recommendation methods inevitably lead to a high risk of user privacy leakage. Although recently proposed federated recommendation systems can mitigate the privacy problem, they either restrict the on-device local training to an isolated ego graph or rely on an additional third-party server to access other ego graphs resulting in a cumbersome pipeline, which is hard to work in practice. In addition, existing federated recommendation systems require resource-limited devices to maintain the entire embedding tables resulting in high communication costs. In light of this, we propose a semi-decentralized federated ego graph learning framework for on-device recommendations, named SemiDFEGL, which introduces new device-to-device collaborations to improve scalability and reduce communication costs and innovatively utilizes predicted interacted item nodes to connect isolated ego graphs to augment local subgraphs such that the high-order user-item collaborative information could be used in a privacy-preserving manner. Furthermore, the proposed framework is model-agnostic, meaning that it could be seamlessly integrated with existing graph neural network-based recommendation methods and privacy protection techniques. To validate the effectiveness of the proposed SemiDFEGL, extensive experiments are conducted on three public datasets, and the results demonstrate the superiority of the proposed SemiDFEGL compared to other federated recommendation methods.|基于协同过滤(CF)的推荐系统通常基于个人交互数据(例如点击和购买)进行培训，这些数据可以自然地表示为自我图表。然而，现有的大多数推荐方法都是从所有用户中收集这些自我图来构成一个全局图，以获得用户和项目之间的高阶协同信息，而这些集中式的 CF 推荐方法不可避免地会导致用户隐私泄露的高风险。虽然最近提出的联邦推荐系统可以缓解隐私问题，但是它们要么将设备上的本地培训限制在一个孤立的自我图上，要么依赖于另外一个第三方服务器来访问其他自我图，从而产生一个繁琐的管道，这在实践中很难实现。此外，现有的联邦推荐系统需要资源有限的设备来维护整个嵌入表，从而导致高通信成本。鉴于此，我们提出了一个半分散的联邦自我图学习框架 SemiDFEGL，该框架引入了新的设备间协作以提高可扩展性和降低通信成本，并创新地利用预测的交互项节点连接孤立的自我图以增强局部子图，从而可以以保护隐私的方式使用高阶用户项协作信息。此外，提出的框架是模型无关的，这意味着它可以与现有的基于图神经网络的推荐方法和隐私保护技术无缝集成。为了验证半 DFEGL 的有效性，在三个公共数据集上进行了广泛的实验，实验结果表明了半 DFEGL 相对于其他联邦推荐方法的优越性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Semi-decentralized+Federated+Ego+Graph+Learning+for+Recommendation)|0| +|[ConsRec: Learning Consensus Behind Interactions for Group Recommendation](https://doi.org/10.1145/3543507.3583277)|Xixi Wu, Yun Xiong, Yao Zhang, Yizhu Jiao, Jiawei Zhang, Yangyong Zhu, Philip S. Yu|University of Illinois at Chicago, USA; Shanghai Key Laboratory of Data Science, School of Computer Science, Fudan University, China; University of Illinois at Urbana-Champaign, USA; IFM Lab, Department of Computer Science, University of California, Davis, USA|Since group activities have become very common in daily life, there is an urgent demand for generating recommendations for a group of users, referred to as group recommendation task. Existing group recommendation methods usually infer groups' preferences via aggregating diverse members' interests. Actually, groups' ultimate choice involves compromises between members, and finally, an agreement can be reached. However, existing individual information aggregation lacks a holistic group-level consideration, failing to capture the consensus information. Besides, their specific aggregation strategies either suffer from high computational costs or become too coarse-grained to make precise predictions. To solve the aforementioned limitations, in this paper, we focus on exploring consensus behind group behavior data. To comprehensively capture the group consensus, we innovatively design three distinct views which provide mutually complementary information to enable multi-view learning, including member-level aggregation, item-level tastes, and group-level inherent preferences. To integrate and balance the multi-view information, an adaptive fusion component is further proposed. As to member-level aggregation, different from existing linear or attentive strategies, we design a novel hypergraph neural network that allows for efficient hypergraph convolutional operations to generate expressive member-level aggregation. We evaluate our ConsRec on two real-world datasets and experimental results show that our model outperforms state-of-the-art methods. An extensive case study also verifies the effectiveness of consensus modeling.|由于小组活动在日常生活中已经非常普遍，因此迫切需要为一组用户提供建议，称为小组推荐任务。现有的群体推荐方法通常通过聚合不同成员的兴趣来推断群体的偏好。实际上，团体的最终选择包括成员之间的妥协，最终可以达成协议。然而，现有的个人信息聚合缺乏整体的群体层面的考虑，未能捕获共识信息。此外，他们特定的聚合策略要么计算成本高，要么过于粗粒度，无法做出精确的预测。为了解决上述局限性，本文重点探讨群体行为数据背后的共识。为了全面捕捉群体共识，我们创新性地设计了三种不同的视图，提供相互补充的信息，使多视图学习成为可能，包括成员层面的聚合、项目层面的品味和群体层面的固有偏好。为了对多视点信息进行集成和平衡，进一步提出了一种自适应融合构件。对于成员级聚集，不同于现有的线性或注意策略，我们设计了一种新的超图神经网络，该网络允许有效的超图卷积操作来产生具有表达能力的成员级聚集。我们在两个真实世界的数据集上评估了我们的 ConsRec，实验结果表明我们的模型优于最先进的方法。一个广泛的案例研究也验证了一致性建模的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=ConsRec:+Learning+Consensus+Behind+Interactions+for+Group+Recommendation)|0| +|[Graph Neural Networks with Diverse Spectral Filtering](https://doi.org/10.1145/3543507.3583324)|Jingwei Guo, Kaizhu Huang, Xinping Yi, Rui Zhang|Duke Kunshan University, China; Xi'an Jiaotong-Liverpool University; The University of Liverpool, China; The University of Liverpool, United Kingdom; Xi'an Jiaotong-Liverpool University, China|Spectral Graph Neural Networks (GNNs) have achieved tremendous success in graph machine learning, with polynomial filters applied for graph convolutions, where all nodes share the identical filter weights to mine their local contexts. Despite the success, existing spectral GNNs usually fail to deal with complex networks (e.g., WWW) due to such homogeneous spectral filtering setting that ignores the regional heterogeneity as typically seen in real-world networks. To tackle this issue, we propose a novel diverse spectral filtering (DSF) framework, which automatically learns node-specific filter weights to exploit the varying local structure properly. Particularly, the diverse filter weights consist of two components — A global one shared among all nodes, and a local one that varies along network edges to reflect node difference arising from distinct graph parts — to balance between local and global information. As such, not only can the global graph characteristics be captured, but also the diverse local patterns can be mined with awareness of different node positions. Interestingly, we formulate a novel optimization problem to assist in learning diverse filters, which also enables us to enhance any spectral GNNs with our DSF framework. We showcase the proposed framework on three state-of-the-arts including GPR-GNN, BernNet, and JacobiConv. Extensive experiments over 10 benchmark datasets demonstrate that our framework can consistently boost model performance by up to 4.92% in node classification tasks, producing diverse filters with enhanced interpretability.|谱图神经网络(GNN)在图形机器学习中取得了巨大的成功，其中多项式滤波器应用于图卷积，其中所有节点共享相同的滤波器权重来挖掘它们的局部上下文。尽管已有的光谱 GNN 在处理复杂网络(如 WWW)时取得了一定的成功，但由于均匀的光谱滤波设置忽略了现实网络中典型的区域异质性，导致 GNN 无法处理复杂网络(如 WWW)。针对这一问题，我们提出了一种新的多谱段滤波(DSF)框架，该框架能够自动学习节点特定的滤波器权值，以适当地利用变化的局部结构。特别地，不同的滤波器权重由两部分组成: 一部分是所有节点共享的全局权重，另一部分是沿网络边缘变化的局部权重，以反映不同图部分产生的节点差异，从而平衡局部和全局信息。因此，不仅可以捕获全局图的特征，而且可以挖掘不同节点位置的不同局部模式。有趣的是，我们制定了一个新的最佳化问题，以帮助学习不同的过滤器，这也使我们能够增强任何光谱 GNN 与我们的 dSF 框架。我们在 GPR-GNN、 BernNet 和 JacobiConv 这三种最新技术的基础上展示了提议的框架。通过对10个基准数据集的大量实验表明，我们的框架可以在节点分类任务中始终如一地将模型性能提高高达4.92% ，产生具有增强可解释性的多样化过滤器。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Graph+Neural+Networks+with+Diverse+Spectral+Filtering)|0| +|[Semi-decentralized Federated Ego Graph Learning for Recommendation](https://doi.org/10.1145/3543507.3583337)|Liang Qu, Ningzhi Tang, Ruiqi Zheng, Quoc Viet Hung Nguyen, Zi Huang, Yuhui Shi, Hongzhi Yin|Southern University of Science and Technology, China; The University of Queensland, Australia; Griffith University, Australia|Collaborative filtering (CF) based recommender systems are typically trained based on personal interaction data (e.g., clicks and purchases) that could be naturally represented as ego graphs. However, most existing recommendation methods collect these ego graphs from all users to compose a global graph to obtain high-order collaborative information between users and items, and these centralized CF recommendation methods inevitably lead to a high risk of user privacy leakage. Although recently proposed federated recommendation systems can mitigate the privacy problem, they either restrict the on-device local training to an isolated ego graph or rely on an additional third-party server to access other ego graphs resulting in a cumbersome pipeline, which is hard to work in practice. In addition, existing federated recommendation systems require resource-limited devices to maintain the entire embedding tables resulting in high communication costs. In light of this, we propose a semi-decentralized federated ego graph learning framework for on-device recommendations, named SemiDFEGL, which introduces new device-to-device collaborations to improve scalability and reduce communication costs and innovatively utilizes predicted interacted item nodes to connect isolated ego graphs to augment local subgraphs such that the high-order user-item collaborative information could be used in a privacy-preserving manner. Furthermore, the proposed framework is model-agnostic, meaning that it could be seamlessly integrated with existing graph neural network-based recommendation methods and privacy protection techniques. To validate the effectiveness of the proposed SemiDFEGL, extensive experiments are conducted on three public datasets, and the results demonstrate the superiority of the proposed SemiDFEGL compared to other federated recommendation methods.|基于协同过滤(CF)的推荐系统通常基于个人交互数据(例如点击和购买)进行培训，这些数据可以自然地表示为自我图表。然而，现有的大多数推荐方法都是从所有用户中收集这些自我图来构成一个全局图，以获得用户和项目之间的高阶协同信息，而这些集中式的 CF 推荐方法不可避免地会导致用户隐私泄露的高风险。虽然最近提出的联邦推荐系统可以缓解隐私问题，但是它们要么将设备上的本地培训限制在一个孤立的自我图上，要么依赖于另外一个第三方服务器来访问其他自我图，从而产生一个繁琐的管道，这在实践中很难实现。此外，现有的联邦推荐系统需要资源有限的设备来维护整个嵌入表，从而导致高通信成本。鉴于此，我们提出了一个半分散的联邦自我图学习框架 SemiDFEGL，该框架引入了新的设备间协作以提高可扩展性和降低通信成本，并创新地利用预测的交互项节点连接孤立的自我图以增强局部子图，从而可以以保护隐私的方式使用高阶用户项协作信息。此外，提出的框架是模型无关的，这意味着它可以与现有的基于图神经网络的推荐方法和隐私保护技术无缝集成。为了验证半 DFEGL 的有效性，在三个公共数据集上进行了广泛的实验，实验结果表明了半 DFEGL 相对于其他联邦推荐方法的优越性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Semi-decentralized+Federated+Ego+Graph+Learning+for+Recommendation)|0| |[SINCERE: Sequential Interaction Networks representation learning on Co-Evolving RiEmannian manifolds](https://doi.org/10.1145/3543507.3583353)|Junda Ye, Zhongbao Zhang, Li Sun, Yang Yan, Feiyang Wang, Fuxin Ren|Beijing University of Posts and Telecommunications, China; North China Electric Power University, China|Sequential interaction networks (SIN) have been commonly adopted in many applications such as recommendation systems, search engines and social networks to describe the mutual influence between users and items/products. Efforts on representing SIN are mainly focused on capturing the dynamics of networks in Euclidean space, and recently plenty of work has extended to hyperbolic geometry for implicit hierarchical learning. Previous approaches which learn the embedding trajectories of users and items achieve promising results. However, there are still a range of fundamental issues remaining open. For example, is it appropriate to place user and item nodes in one identical space regardless of their inherent discrepancy? Instead of residing in a single fixed curvature space, how will the representation spaces evolve when new interaction occurs? To explore these issues for sequential interaction networks, we propose SINCERE, a novel method representing Sequential Interaction Networks on Co-Evolving RiEmannian manifolds. SIN- CERE not only takes the user and item embedding trajectories in respective spaces into account, but also emphasizes on the space evolvement that how curvature changes over time. Specifically, we introduce a fresh cross-geometry aggregation which allows us to propagate information across different Riemannian manifolds without breaking conformal invariance, and a curvature estimator which is delicately designed to predict global curvatures effectively according to current local Ricci curvatures. Extensive experiments on several real-world datasets demonstrate the promising performance of SINCERE over the state-of-the-art sequential interaction prediction methods.|在推荐系统、搜索引擎、社交网络等应用中，用户与产品之间的相互影响通常采用序贯交互网络(SIN)来描述。表示 SIN 的努力主要集中在捕捉欧几里得空间中网络的动态，最近大量的工作已经延伸到了隐含双曲几何的深度学习。以往的方法通过学习用户和项目的嵌入轨迹，取得了良好的效果。然而，仍有一系列基本问题悬而未决。例如，是否应该将用户和项目节点放在一个相同的空间中，而不管它们的固有差异？当发生新的相互作用时，表示空间将如何演化，而不是驻留在一个单一的固定曲率空间中？为了研究序贯相互作用网络的这些问题，我们提出了一种新的方法 SINCARE，它在共进化黎曼流形上表示序贯相互作用网络。SIN-CERE 不仅考虑了用户和项目在各自空间中的嵌入轨迹，而且强调了曲率随时间变化的空间演化。具体地说，我们引入了一个新的交叉几何聚合，它允许我们在不破坏共形不变性的情况下在不同的黎曼流形上传播信息，以及一个精心设计的曲率估计器，它可以根据当前的局部 Ricci 曲率有效地预测全局曲率。在几个真实世界数据集上的大量实验表明，SINCARE 相对于最先进的顺序交互预测方法具有很好的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=SINCERE:+Sequential+Interaction+Networks+representation+learning+on+Co-Evolving+RiEmannian+manifolds)|0| -|[TIGER: Temporal Interaction Graph Embedding with Restarts](https://doi.org/10.1145/3543507.3583433)|Yao Zhang, Yun Xiong, Yongxiang Liao, Yiheng Sun, Yucheng Jin, Xuehao Zheng, Yangyong Zhu|Tencent Weixin Group, China; Fudan University, China|Temporal interaction graphs (TIGs), consisting of sequences of timestamped interaction events, are prevalent in fields like e-commerce and social networks. To better learn dynamic node embeddings that vary over time, researchers have proposed a series of temporal graph neural networks for TIGs. However, due to the entangled temporal and structural dependencies, existing methods have to process the sequence of events chronologically and consecutively to ensure node representations are up-to-date. This prevents existing models from parallelization and reduces their flexibility in industrial applications. To tackle the above challenge, in this paper, we propose TIGER, a TIG embedding model that can restart at any timestamp. We introduce a restarter module that generates surrogate representations acting as the warm initialization of node representations. By restarting from multiple timestamps simultaneously, we divide the sequence into multiple chunks and naturally enable the parallelization of the model. Moreover, in contrast to previous models that utilize a single memory unit, we introduce a dual memory module to better exploit neighborhood information and alleviate the staleness problem. Extensive experiments on four public datasets and one industrial dataset are conducted, and the results verify both the effectiveness and the efficiency of our work.|时间交互图(TIGs)由时间戳交互事件序列组成，在电子商务和社交网络等领域非常普遍。为了更好地学习随时间变化的动态节点嵌入，研究人员提出了一系列针对 TIG 的时间图神经网络。然而，由于时间和结构的依赖性，现有的方法必须按时间顺序和连续地处理事件序列，以确保节点表示是最新的。这阻止了现有模型的并行化，并降低了它们在工业应用程序中的灵活性。为了应对上述挑战，本文提出了一种 TIG 嵌入模型 TIGER，它可以在任意时间戳重新启动。我们引入了一个 restarter 模块，它生成代理表示作为节点表示的温初始化。通过同时从多个时间戳重新开始，我们将序列划分为多个块，自然而然地实现了模型的并行化。此外，相对于以往的单一存储器模型，我们引入了双存储器模块，以更好地利用邻域信息和缓解过时问题。对四个公共数据集和一个工业数据集进行了广泛的实验，实验结果验证了本文工作的有效性和高效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=TIGER:+Temporal+Interaction+Graph+Embedding+with+Restarts)|0| -|[Expressive and Efficient Representation Learning for Ranking Links in Temporal Graphs](https://doi.org/10.1145/3543507.3583476)|Susheel Suresh, Mayank Shrivastava, Arko Mukherjee, Jennifer Neville, Pan Li|Microsoft Research, USA; Microsoft, USA; Purdue University, USA|Temporal graph representation learning (T-GRL) aims to learn representations that model how graph edges evolve over time. While recent works on T-GRL have improved link prediction accuracy in temporal settings, their methods optimize a point-wise loss function independently over future links rather than optimize jointly over a candidate set per node. In applications where resources (e.g., attention) are allocated based on ranking links by likelihood, the use of a ranking loss is preferred. However it is not straightforward to develop a T-GRL method to optimize a ranking loss due to a tradeoff between model expressivity and scalability. In this work, we address these issues and propose a Temporal Graph network for Ranking (TGRank), which significantly improves performance for link prediction tasks by (i) optimizing a list-wise loss for improved ranking, and (ii) incorporating a labeling approach designed to allow for efficient inference over the candidate set jointly, while provably boosting expressivity. We extensively evaluate TGRank over six real networks. TGRank outperforms the state-of-the-art baselines on average by 14.21%↑ (transductive) and 16.25% ↑ (inductive) in ranking metrics while being more efficient (up-to 65 × speed-up) to make inference on large networks.|时态图表示学习(T-GRL)的目的是学习模拟图边如何随时间演化的表示。尽管最近在 T-GRL 上的工作在时间设置上提高了链路预测的精度，但是他们的方法在未来链路上独立优化点损失函数，而不是在每个节点的候选集上联合优化。在应用程序中，资源(如注意力)的分配是基于可能性的排名链接，使用排名损失是首选。然而，由于模型表达性和可伸缩性之间的权衡，开发 T-GRL 方法来优化排名损失并不容易。在这项工作中，我们解决了这些问题，并提出了排名时态图网络(TGRank) ，它通过(i)优化改善排名的列表损失，以及(ii)结合标记方法，以便对候选集合进行有效的推理，同时可证明地提高表现力，从而显着改善链接预测任务的性能。我们广泛评估 TGRank 在六个实际网络。TGRank 在排序指标方面平均比最先进的基线表现出14.21% 惊(转导)和16.25% 惊(归纳)的优势，同时在大型网络上进行推理的效率更高(高达65倍加速)。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Expressive+and+Efficient+Representation+Learning+for+Ranking+Links+in+Temporal+Graphs)|0| -|[Semi-Supervised Embedding of Attributed Multiplex Networks](https://doi.org/10.1145/3543507.3583485)|Ylli Sadikaj, Justus Rass, Yllka Velaj, Claudia Plant|Faculty of Computer Science, University of Vienna, Austria and UniVie Doctoral School Computer Science, University of Vienna, Austria; Faculty of Computer Science, University of Vienna, Austria and ds:Univie, University of Vienna, Austria; Faculty of Computer Science, University of Vienna, Austria|Complex information can be represented as networks (graphs) characterized by a large number of nodes, multiple types of nodes, and multiple types of relationships between them, i.e. multiplex networks. Additionally, these networks are enriched with different types of node features. We propose a Semi-supervised Embedding approach for Attributed Multiplex Networks (SSAMN), to jointly embed nodes, node attributes, and node labels of multiplex networks in a low dimensional space. Network embedding techniques have garnered research attention for real-world applications. However, most existing techniques solely focus on learning the node embeddings, and only a few learn class label embeddings. Our method assumes that we have different classes of nodes and that we know the class label of some, very few nodes for every class. Guided by this type of supervision, SSAMN learns a low-dimensional representation incorporating all information in a large labeled multiplex network. SSAMN integrates techniques from Spectral Embedding and Homogeneity Analysis to improve the embedding of nodes, node attributes, and node labels. Our experiments demonstrate that we only need very few labels per class in order to have a final embedding that preservers the information of the graph. To evaluate the performance of SSAMN, we run experiments on four real-world datasets. The results show that our approach outperforms state-of-the-art methods for downstream tasks such as semi-supervised node classification and node clustering.|复杂的信息可以用网络(图形)来表示，拥有属性包括大量的节点、多种类型的节点以及它们之间多种类型的关系，即多路网络。此外，这些网络丰富了不同类型的节点特征。提出了一种基于半监督嵌入的属性化多路网络(SSAMN)方法，在低维空间中联合嵌入多路网络的节点、节点属性和节点标签。网络嵌入技术已经成为现实应用领域的研究热点。然而，大多数现有的技术只关注于学习节点嵌入，只有少数学习类标签嵌入。我们的方法假设我们有不同的节点类，并且我们知道每个类的一些非常少的节点的类标签。在这种类型的监督指导下，SSAMN 学习了一种低维表示，将所有信息整合到一个大的标记多路网络中。SSAMN 集成了谱嵌入和均匀性分析技术，改进了节点、节点属性和节点标签的嵌入。我们的实验表明，我们只需要非常少的标签每个类，以便有一个最终的嵌入，保存图的信息。为了评估 SSAMN 的表现，我们在四个真实世界的数据集上进行了实验。结果表明，该方法在处理半监督节点分类和节点聚类等下游任务时，性能优于现有方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Semi-Supervised+Embedding+of+Attributed+Multiplex+Networks)|0| -|[Search to Capture Long-range Dependency with Stacking GNNs for Graph Classification](https://doi.org/10.1145/3543507.3583486)|Lanning Wei, Zhiqiang He, Huan Zhao, Quanming Yao|Department of Electronic Engineering, Tsinghua University, China; Institute of Computing Technology, Chinese Academy of Science, China and Lenovo, China; 4Paradigm. Inc, China; Institute of Computing Technology, Chinese Academy of Sciences, China and University of Chinese Academy of Sciences, China|In recent years, Graph Neural Networks (GNNs) have been popular in the graph classification task. Currently, shallow GNNs are more common due to the well-known over-smoothing problem facing deeper GNNs. However, they are sub-optimal without utilizing the information from distant nodes, i.e., the long-range dependencies. The mainstream methods in the graph classification task can extract the long-range dependencies either by designing the pooling operations or incorporating the higher-order neighbors, while they have evident drawbacks by modifying the original graph structure, which may result in information loss in graph structure learning. In this paper, by justifying the smaller influence of the over-smoothing problem in the graph classification task, we evoke the importance of stacking-based GNNs and then employ them to capture the long-range dependencies without modifying the original graph structure. To achieve this, two design needs are given for stacking-based GNNs, i.e., sufficient model depth and adaptive skip-connection schemes. By transforming the two design needs into designing data-specific inter-layer connections, we propose a novel approach with the help of neural architecture search (NAS), which is dubbed LRGNN (Long-Range Graph Neural Networks). Extensive experiments on five datasets show that the proposed LRGNN can achieve the best performance, and obtained data-specific GNNs with different depth and skip-connection schemes, which can better capture the long-range dependencies.|近年来，图形神经网络(GNN)在图形分类任务中得到了广泛的应用。目前，浅 GNN 更常见，由于众所周知的过度平滑问题所面临的深 GNN。然而，如果没有利用来自远程节点的信息(即远程依赖) ，它们就是次优的。图分类任务中的主流方法可以通过设计合并操作或合并高阶邻居来提取远程依赖关系，但通过修改原有的图结构存在明显的缺陷，可能导致图结构学习中的信息丢失。本文通过证明过平滑问题在图分类任务中的影响较小，引出了基于叠加的 GNN 的重要性，并利用它们在不改变原始图结构的情况下捕获长程依赖关系。为了实现这一目标，给出了基于叠加的 GNN 的两种设计需求，即充分的模型深度和自适应跳跃连接方案。通过将这两种设计需求转化为设计数据特定的层间连接，我们提出了一种神经结构搜索(NAS)的新方法，称为长程图形神经网络(LRGNN)。通过对5个数据集的大量实验表明，本文提出的 LRGNN 能够获得最好的性能，并且能够获得具有不同深度和跳跃连接方案的数据特定 GNN，能够更好地捕获远程依赖关系。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Search+to+Capture+Long-range+Dependency+with+Stacking+GNNs+for+Graph+Classification)|0| +|[TIGER: Temporal Interaction Graph Embedding with Restarts](https://doi.org/10.1145/3543507.3583433)|Yao Zhang, Yun Xiong, Yongxiang Liao, Yiheng Sun, Yucheng Jin, Xuehao Zheng, Yangyong Zhu|Fudan University, China; Tencent Weixin Group, China|Temporal interaction graphs (TIGs), consisting of sequences of timestamped interaction events, are prevalent in fields like e-commerce and social networks. To better learn dynamic node embeddings that vary over time, researchers have proposed a series of temporal graph neural networks for TIGs. However, due to the entangled temporal and structural dependencies, existing methods have to process the sequence of events chronologically and consecutively to ensure node representations are up-to-date. This prevents existing models from parallelization and reduces their flexibility in industrial applications. To tackle the above challenge, in this paper, we propose TIGER, a TIG embedding model that can restart at any timestamp. We introduce a restarter module that generates surrogate representations acting as the warm initialization of node representations. By restarting from multiple timestamps simultaneously, we divide the sequence into multiple chunks and naturally enable the parallelization of the model. Moreover, in contrast to previous models that utilize a single memory unit, we introduce a dual memory module to better exploit neighborhood information and alleviate the staleness problem. Extensive experiments on four public datasets and one industrial dataset are conducted, and the results verify both the effectiveness and the efficiency of our work.|时间交互图(TIGs)由时间戳交互事件序列组成，在电子商务和社交网络等领域非常普遍。为了更好地学习随时间变化的动态节点嵌入，研究人员提出了一系列针对 TIG 的时间图神经网络。然而，由于时间和结构的依赖性，现有的方法必须按时间顺序和连续地处理事件序列，以确保节点表示是最新的。这阻止了现有模型的并行化，并降低了它们在工业应用程序中的灵活性。为了应对上述挑战，本文提出了一种 TIG 嵌入模型 TIGER，它可以在任意时间戳重新启动。我们引入了一个 restarter 模块，它生成代理表示作为节点表示的温初始化。通过同时从多个时间戳重新开始，我们将序列划分为多个块，自然而然地实现了模型的并行化。此外，相对于以往的单一存储器模型，我们引入了双存储器模块，以更好地利用邻域信息和缓解过时问题。对四个公共数据集和一个工业数据集进行了广泛的实验，实验结果验证了本文工作的有效性和高效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=TIGER:+Temporal+Interaction+Graph+Embedding+with+Restarts)|0| +|[Expressive and Efficient Representation Learning for Ranking Links in Temporal Graphs](https://doi.org/10.1145/3543507.3583476)|Susheel Suresh, Mayank Shrivastava, Arko Mukherjee, Jennifer Neville, Pan Li|Microsoft, USA; Purdue University, USA; Microsoft Research, USA|Temporal graph representation learning (T-GRL) aims to learn representations that model how graph edges evolve over time. While recent works on T-GRL have improved link prediction accuracy in temporal settings, their methods optimize a point-wise loss function independently over future links rather than optimize jointly over a candidate set per node. In applications where resources (e.g., attention) are allocated based on ranking links by likelihood, the use of a ranking loss is preferred. However it is not straightforward to develop a T-GRL method to optimize a ranking loss due to a tradeoff between model expressivity and scalability. In this work, we address these issues and propose a Temporal Graph network for Ranking (TGRank), which significantly improves performance for link prediction tasks by (i) optimizing a list-wise loss for improved ranking, and (ii) incorporating a labeling approach designed to allow for efficient inference over the candidate set jointly, while provably boosting expressivity. We extensively evaluate TGRank over six real networks. TGRank outperforms the state-of-the-art baselines on average by 14.21%↑ (transductive) and 16.25% ↑ (inductive) in ranking metrics while being more efficient (up-to 65 × speed-up) to make inference on large networks.|时态图表示学习(T-GRL)的目的是学习模拟图边如何随时间演化的表示。尽管最近在 T-GRL 上的工作在时间设置上提高了链路预测的精度，但是他们的方法在未来链路上独立优化点损失函数，而不是在每个节点的候选集上联合优化。在应用程序中，资源(如注意力)的分配是基于可能性的排名链接，使用排名损失是首选。然而，由于模型表达性和可伸缩性之间的权衡，开发 T-GRL 方法来优化排名损失并不容易。在这项工作中，我们解决了这些问题，并提出了排名时态图网络(TGRank) ，它通过(i)优化改善排名的列表损失，以及(ii)结合标记方法，以便对候选集合进行有效的推理，同时可证明地提高表现力，从而显着改善链接预测任务的性能。我们广泛评估 TGRank 在六个实际网络。TGRank 在排序指标方面平均比最先进的基线表现出14.21% 惊(转导)和16.25% 惊(归纳)的优势，同时在大型网络上进行推理的效率更高(高达65倍加速)。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Expressive+and+Efficient+Representation+Learning+for+Ranking+Links+in+Temporal+Graphs)|0| +|[Semi-Supervised Embedding of Attributed Multiplex Networks](https://doi.org/10.1145/3543507.3583485)|Ylli Sadikaj, Justus Rass, Yllka Velaj, Claudia Plant|Faculty of Computer Science, University of Vienna, Austria and ds:Univie, University of Vienna, Austria; Faculty of Computer Science, University of Vienna, Austria; Faculty of Computer Science, University of Vienna, Austria and UniVie Doctoral School Computer Science, University of Vienna, Austria|Complex information can be represented as networks (graphs) characterized by a large number of nodes, multiple types of nodes, and multiple types of relationships between them, i.e. multiplex networks. Additionally, these networks are enriched with different types of node features. We propose a Semi-supervised Embedding approach for Attributed Multiplex Networks (SSAMN), to jointly embed nodes, node attributes, and node labels of multiplex networks in a low dimensional space. Network embedding techniques have garnered research attention for real-world applications. However, most existing techniques solely focus on learning the node embeddings, and only a few learn class label embeddings. Our method assumes that we have different classes of nodes and that we know the class label of some, very few nodes for every class. Guided by this type of supervision, SSAMN learns a low-dimensional representation incorporating all information in a large labeled multiplex network. SSAMN integrates techniques from Spectral Embedding and Homogeneity Analysis to improve the embedding of nodes, node attributes, and node labels. Our experiments demonstrate that we only need very few labels per class in order to have a final embedding that preservers the information of the graph. To evaluate the performance of SSAMN, we run experiments on four real-world datasets. The results show that our approach outperforms state-of-the-art methods for downstream tasks such as semi-supervised node classification and node clustering.|复杂的信息可以用网络(图形)来表示，拥有属性包括大量的节点、多种类型的节点以及它们之间多种类型的关系，即多路网络。此外，这些网络丰富了不同类型的节点特征。提出了一种基于半监督嵌入的属性化多路网络(SSAMN)方法，在低维空间中联合嵌入多路网络的节点、节点属性和节点标签。网络嵌入技术已经成为现实应用领域的研究热点。然而，大多数现有的技术只关注于学习节点嵌入，只有少数学习类标签嵌入。我们的方法假设我们有不同的节点类，并且我们知道每个类的一些非常少的节点的类标签。在这种类型的监督指导下，SSAMN 学习了一种低维表示，将所有信息整合到一个大的标记多路网络中。SSAMN 集成了谱嵌入和均匀性分析技术，改进了节点、节点属性和节点标签的嵌入。我们的实验表明，我们只需要非常少的标签每个类，以便有一个最终的嵌入，保存图的信息。为了评估 SSAMN 的表现，我们在四个真实世界的数据集上进行了实验。结果表明，该方法在处理半监督节点分类和节点聚类等下游任务时，性能优于现有方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Semi-Supervised+Embedding+of+Attributed+Multiplex+Networks)|0| +|[Search to Capture Long-range Dependency with Stacking GNNs for Graph Classification](https://doi.org/10.1145/3543507.3583486)|Lanning Wei, Zhiqiang He, Huan Zhao, Quanming Yao|Department of Electronic Engineering, Tsinghua University, China; Institute of Computing Technology, Chinese Academy of Science, China and Lenovo, China; Institute of Computing Technology, Chinese Academy of Sciences, China and University of Chinese Academy of Sciences, China; 4Paradigm. Inc, China|In recent years, Graph Neural Networks (GNNs) have been popular in the graph classification task. Currently, shallow GNNs are more common due to the well-known over-smoothing problem facing deeper GNNs. However, they are sub-optimal without utilizing the information from distant nodes, i.e., the long-range dependencies. The mainstream methods in the graph classification task can extract the long-range dependencies either by designing the pooling operations or incorporating the higher-order neighbors, while they have evident drawbacks by modifying the original graph structure, which may result in information loss in graph structure learning. In this paper, by justifying the smaller influence of the over-smoothing problem in the graph classification task, we evoke the importance of stacking-based GNNs and then employ them to capture the long-range dependencies without modifying the original graph structure. To achieve this, two design needs are given for stacking-based GNNs, i.e., sufficient model depth and adaptive skip-connection schemes. By transforming the two design needs into designing data-specific inter-layer connections, we propose a novel approach with the help of neural architecture search (NAS), which is dubbed LRGNN (Long-Range Graph Neural Networks). Extensive experiments on five datasets show that the proposed LRGNN can achieve the best performance, and obtained data-specific GNNs with different depth and skip-connection schemes, which can better capture the long-range dependencies.|近年来，图形神经网络(GNN)在图形分类任务中得到了广泛的应用。目前，浅 GNN 更常见，由于众所周知的过度平滑问题所面临的深 GNN。然而，如果没有利用来自远程节点的信息(即远程依赖) ，它们就是次优的。图分类任务中的主流方法可以通过设计合并操作或合并高阶邻居来提取远程依赖关系，但通过修改原有的图结构存在明显的缺陷，可能导致图结构学习中的信息丢失。本文通过证明过平滑问题在图分类任务中的影响较小，引出了基于叠加的 GNN 的重要性，并利用它们在不改变原始图结构的情况下捕获长程依赖关系。为了实现这一目标，给出了基于叠加的 GNN 的两种设计需求，即充分的模型深度和自适应跳跃连接方案。通过将这两种设计需求转化为设计数据特定的层间连接，我们提出了一种神经结构搜索(NAS)的新方法，称为长程图形神经网络(LRGNN)。通过对5个数据集的大量实验表明，本文提出的 LRGNN 能够获得最好的性能，并且能够获得具有不同深度和跳跃连接方案的数据特定 GNN，能够更好地捕获远程依赖关系。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Search+to+Capture+Long-range+Dependency+with+Stacking+GNNs+for+Graph+Classification)|0| |[Cut-matching Games for Generalized Hypergraph Ratio Cuts](https://doi.org/10.1145/3543507.3583539)|Nate Veldt|Texas A&M University, USA|Hypergraph clustering is a basic algorithmic primitive for analyzing complex datasets and systems characterized by multiway interactions, such as group email conversations, groups of co-purchased retail products, and co-authorship data. This paper presents a practical $O(\log n)$-approximation algorithm for a broad class of hypergraph ratio cut clustering objectives. This includes objectives involving generalized hypergraph cut functions, which allow a user to penalize cut hyperedges differently depending on the number of nodes in each cluster. Our method is a generalization of the cut-matching framework for graph ratio cuts, and relies only on solving maximum s-t flow problems in a special reduced graph. It is significantly faster than existing hypergraph ratio cut algorithms, while also solving a more general problem. In numerical experiments on various types of hypergraphs, we show that it quickly finds ratio cut solutions within a small factor of optimality.|Hypergraph 聚类是一种基本的算法原理，用于分析复杂的数据集和多拥有属性交互的系统，比如群组电子邮件对话、共同购买的零售产品组和合著者数据。本文提出了一个实用的 $o (log n) $- 近似演算法，用于一类广泛的超图比率削减聚类目标。这包括涉及广义超图割函数的目标，它允许用户根据每个簇中节点的数量对割超边进行不同的惩罚。该方法是图比割的割匹配框架的推广，仅依赖于求解特殊简化图中的最大 s-t 流问题。它明显快于现有的超图比率割算法，同时也解决了更一般的问题。通过对不同类型超图的数值实验，我们发现它能在一个小的最优性因子内快速找到比率割分解。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Cut-matching+Games+for+Generalized+Hypergraph+Ratio+Cuts)|0| -|[ApeGNN: Node-Wise Adaptive Aggregation in GNNs for Recommendation](https://doi.org/10.1145/3543507.3583530)|Dan Zhang, Yifan Zhu, Yuxiao Dong, Yuandong Wang, Wenzheng Feng, Evgeny Kharlamov, Jie Tang|Tsinghua University, China; Bosch Center for Artificial Intelligence, Germany|In recent years, graph neural networks (GNNs) have made great progress in recommendation. The core mechanism of GNNs-based recommender system is to iteratively aggregate neighboring information on the user-item interaction graph. However, existing GNNs treat users and items equally and cannot distinguish diverse local patterns of each node, which makes them suboptimal in the recommendation scenario. To resolve this challenge, we present a node-wise adaptive graph neural network framework ApeGNN. ApeGNN develops a node-wise adaptive diffusion mechanism for information aggregation, in which each node is enabled to adaptively decide its diffusion weights based on the local structure (e.g., degree). We perform experiments on six widely-used recommendation datasets. The experimental results show that the proposed ApeGNN is superior to the most advanced GNN-based recommender methods (up to 48.94%), demonstrating the effectiveness of node-wise adaptive aggregation.|近年来，图神经网络在推荐方面取得了很大的进展。基于 GNN 的推荐系统的核心机制是在用户-项目交互图上迭代地聚合相邻信息。然而，现有的 GNN 对用户和项目一视同仁，不能区分每个节点的不同本地模式，这使得它们在推荐场景中处于次优状态。为了解决这一问题，我们提出了一种节点自适应图神经网络框架 ApeGNN。ApeGNN 提出了一种基于节点的自适应信息聚合扩散机制，该机制允许每个节点根据局部结构(如度)自适应确定其扩散权重。我们在六个广泛使用的推荐数据集上进行实验。实验结果表明，该算法优于目前最先进的基于 GNN 的推荐方法(48.94%) ，证明了节点自适应聚集的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=ApeGNN:+Node-Wise+Adaptive+Aggregation+in+GNNs+for+Recommendation)|0| -|[Multi-Modal Self-Supervised Learning for Recommendation](https://doi.org/10.1145/3543507.3583206)|Wei Wei, Chao Huang, Lianghao Xia, Chuxu Zhang|University of Hong Kong, Hong Kong; The University of Hong Kong, Hong Kong; Brandeis University, USA|The online emergence of multi-modal sharing platforms (eg, TikTok, Youtube) is powering personalized recommender systems to incorporate various modalities (eg, visual, textual and acoustic) into the latent user representations. While existing works on multi-modal recommendation exploit multimedia content features in enhancing item embeddings, their model representation capability is limited by heavy label reliance and weak robustness on sparse user behavior data. Inspired by the recent progress of self-supervised learning in alleviating label scarcity issue, we explore deriving self-supervision signals with effectively learning of modality-aware user preference and cross-modal dependencies. To this end, we propose a new Multi-Modal Self-Supervised Learning (MMSSL) method which tackles two key challenges. Specifically, to characterize the inter-dependency between the user-item collaborative view and item multi-modal semantic view, we design a modality-aware interactive structure learning paradigm via adversarial perturbations for data augmentation. In addition, to capture the effects that user's modality-aware interaction pattern would interweave with each other, a cross-modal contrastive learning approach is introduced to jointly preserve the inter-modal semantic commonality and user preference diversity. Experiments on real-world datasets verify the superiority of our method in offering great potential for multimedia recommendation over various state-of-the-art baselines. The implementation is released at: https://github.com/HKUDS/MMSSL.|多模式共享平台(如 TikTok、 Youtube)的在线出现为个性化推荐系统提供了动力，将各种模式(如视觉、文本和声学)纳入潜在用户表示。现有的多模态推荐方法利用多媒体内容特征增强项目嵌入，但其模型表示能力受到严重的标签依赖和对稀疏用户行为数据鲁棒性较差的限制。受近年来自我监督学习在缓解标签稀缺问题上的进展的启发，我们探讨了如何通过有效地学习模式感知的用户偏好和跨模式依赖来获得自我监督信号。为此，我们提出了一种新的多模态自主学习(MMSSL)方法，解决了两个关键的挑战。为了刻画用户项目协作视图和项目多模态语义视图之间的相互依赖关系，我们设计了一个基于模态感知的交互式结构学习范式。此外，为了捕捉用户感知情态的交互模式相互交织的影响，引入了一种跨情态对比学习方法，以共同保持多情态语义共性和用户偏好多样性。在现实世界数据集上的实验验证了该方法的优越性，在各种最先进的基线上为多媒体推荐提供了巨大的潜力。实施 https://github.com/hkuds/mmssl 如下:。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Multi-Modal+Self-Supervised+Learning+for+Recommendation)|0| +|[ApeGNN: Node-Wise Adaptive Aggregation in GNNs for Recommendation](https://doi.org/10.1145/3543507.3583530)|Dan Zhang, Yifan Zhu, Yuxiao Dong, Yuandong Wang, Wenzheng Feng, Evgeny Kharlamov, Jie Tang|Bosch Center for Artificial Intelligence, Germany; Tsinghua University, China|In recent years, graph neural networks (GNNs) have made great progress in recommendation. The core mechanism of GNNs-based recommender system is to iteratively aggregate neighboring information on the user-item interaction graph. However, existing GNNs treat users and items equally and cannot distinguish diverse local patterns of each node, which makes them suboptimal in the recommendation scenario. To resolve this challenge, we present a node-wise adaptive graph neural network framework ApeGNN. ApeGNN develops a node-wise adaptive diffusion mechanism for information aggregation, in which each node is enabled to adaptively decide its diffusion weights based on the local structure (e.g., degree). We perform experiments on six widely-used recommendation datasets. The experimental results show that the proposed ApeGNN is superior to the most advanced GNN-based recommender methods (up to 48.94%), demonstrating the effectiveness of node-wise adaptive aggregation.|近年来，图神经网络在推荐方面取得了很大的进展。基于 GNN 的推荐系统的核心机制是在用户-项目交互图上迭代地聚合相邻信息。然而，现有的 GNN 对用户和项目一视同仁，不能区分每个节点的不同本地模式，这使得它们在推荐场景中处于次优状态。为了解决这一问题，我们提出了一种节点自适应图神经网络框架 ApeGNN。ApeGNN 提出了一种基于节点的自适应信息聚合扩散机制，该机制允许每个节点根据局部结构(如度)自适应确定其扩散权重。我们在六个广泛使用的推荐数据集上进行实验。实验结果表明，该算法优于目前最先进的基于 GNN 的推荐方法(48.94%) ，证明了节点自适应聚集的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=ApeGNN:+Node-Wise+Adaptive+Aggregation+in+GNNs+for+Recommendation)|0| +|[Multi-Modal Self-Supervised Learning for Recommendation](https://doi.org/10.1145/3543507.3583206)|Wei Wei, Chao Huang, Lianghao Xia, Chuxu Zhang|University of Hong Kong, Hong Kong; Brandeis University, USA; The University of Hong Kong, Hong Kong|The online emergence of multi-modal sharing platforms (eg, TikTok, Youtube) is powering personalized recommender systems to incorporate various modalities (eg, visual, textual and acoustic) into the latent user representations. While existing works on multi-modal recommendation exploit multimedia content features in enhancing item embeddings, their model representation capability is limited by heavy label reliance and weak robustness on sparse user behavior data. Inspired by the recent progress of self-supervised learning in alleviating label scarcity issue, we explore deriving self-supervision signals with effectively learning of modality-aware user preference and cross-modal dependencies. To this end, we propose a new Multi-Modal Self-Supervised Learning (MMSSL) method which tackles two key challenges. Specifically, to characterize the inter-dependency between the user-item collaborative view and item multi-modal semantic view, we design a modality-aware interactive structure learning paradigm via adversarial perturbations for data augmentation. In addition, to capture the effects that user's modality-aware interaction pattern would interweave with each other, a cross-modal contrastive learning approach is introduced to jointly preserve the inter-modal semantic commonality and user preference diversity. Experiments on real-world datasets verify the superiority of our method in offering great potential for multimedia recommendation over various state-of-the-art baselines. The implementation is released at: https://github.com/HKUDS/MMSSL.|多模式共享平台(如 TikTok、 Youtube)的在线出现为个性化推荐系统提供了动力，将各种模式(如视觉、文本和声学)纳入潜在用户表示。现有的多模态推荐方法利用多媒体内容特征增强项目嵌入，但其模型表示能力受到严重的标签依赖和对稀疏用户行为数据鲁棒性较差的限制。受近年来自我监督学习在缓解标签稀缺问题上的进展的启发，我们探讨了如何通过有效地学习模式感知的用户偏好和跨模式依赖来获得自我监督信号。为此，我们提出了一种新的多模态自主学习(MMSSL)方法，解决了两个关键的挑战。为了刻画用户项目协作视图和项目多模态语义视图之间的相互依赖关系，我们设计了一个基于模态感知的交互式结构学习范式。此外，为了捕捉用户感知情态的交互模式相互交织的影响，引入了一种跨情态对比学习方法，以共同保持多情态语义共性和用户偏好多样性。在现实世界数据集上的实验验证了该方法的优越性，在各种最先进的基线上为多媒体推荐提供了巨大的潜力。实施 https://github.com/hkuds/mmssl 如下:。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Multi-Modal+Self-Supervised+Learning+for+Recommendation)|0| |[Bootstrap Latent Representations for Multi-modal Recommendation](https://doi.org/10.1145/3543507.3583251)|Xin Zhou, Hongyu Zhou, Yong Liu, Zhiwei Zeng, Chunyan Miao, Pengwei Wang, Yuan You, Feijun Jiang|Alibaba, China; Nanyang Technological University, Singapore|This paper studies the multi-modal recommendation problem, where the item multi-modality information (e.g., images and textual descriptions) is exploited to improve the recommendation accuracy. Besides the user-item interaction graph, existing state-of-the-art methods usually use auxiliary graphs (e.g., user-user or item-item relation graph) to augment the learned representations of users and/or items. These representations are often propagated and aggregated on auxiliary graphs using graph convolutional networks, which can be prohibitively expensive in computation and memory, especially for large graphs. Moreover, existing multi-modal recommendation methods usually leverage randomly sampled negative examples in Bayesian Personalized Ranking (BPR) loss to guide the learning of user/item representations, which increases the computational cost on large graphs and may also bring noisy supervision signals into the training process. To tackle the above issues, we propose a novel self-supervised multi-modal recommendation model, dubbed BM3, which requires neither augmentations from auxiliary graphs nor negative samples. Specifically, BM3 first bootstraps latent contrastive views from the representations of users and items with a simple dropout augmentation. It then jointly optimizes three multi-modal objectives to learn the representations of users and items by reconstructing the user-item interaction graph and aligning modality features under both inter- and intra-modality perspectives. BM3 alleviates both the need for contrasting with negative examples and the complex graph augmentation from an additional target network for contrastive view generation. We show BM3 outperforms prior recommendation models on three datasets with number of nodes ranging from 20K to 200K, while achieving a 2-9X reduction in training time. Our code is available at https://github.com/enoche/BM3.|本文研究了多模态推荐问题，该问题利用项目的多模态信息(如图像和文本描述)来提高推荐的准确性。除了用户-项目交互图，现有的方法通常使用辅助图(例如，用户-用户或项目-项目关系图)来增强用户和/或项目的学习表示。这些表示通常使用图卷积网络在辅助图上进行传播和聚合，这在计算和存储方面是非常昂贵的，特别是对于大图。此外，现有的多模态推荐方法通常利用贝叶斯个性化排序(BPR)损失中随机抽样的负例子来指导用户/项目表示的学习，这增加了大图上的计算成本，也可能使噪声监督信号进入训练过程。为了解决上述问题，我们提出了一种新的自监督多模态推荐模型，称为 BM3，它不需要辅助图和负样本的增广。具体来说，BM3首先通过简单的辍学增强从用户和项目的表示中引导潜在的对比视图。然后，通过重构用户-项目交互图和在情态间和情态内对齐情态特征，联合优化三个多模态目标来学习用户和项目的表征。BM3减轻了与负面例子对比的需要，也减轻了从一个额外的目标网络生成对比视图的复杂图形增强。我们发现 BM3在三个数据集上的节点数从20K 到200K 不等，优于先前的推荐模型，同时实现了2-9倍的训练时间缩短。我们的代码可以在 https://github.com/enoche/bm3找到。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Bootstrap+Latent+Representations+for+Multi-modal+Recommendation)|0| -|[Recommendation with Causality enhanced Natural Language Explanations](https://doi.org/10.1145/3543507.3583260)|Jingsen Zhang, Xu Chen, Jiakai Tang, Weiqi Shao, Quanyu Dai, Zhenhua Dong, Rui Zhang|Renmin University of China, China; Huawei Noah's Ark Lab, China; www.ruizhang.info, China|Explainable recommendation has recently attracted increasing attention from both academic and industry communities. Among different explainable strategies, generating natural language explanations is an important method, which can deliver more informative, flexible and readable explanations to facilitate better user decisions. Despite the effectiveness, existing models are mostly optimized based on the observed datasets, which can be skewed due to the selection or exposure bias. To alleviate this problem, in this paper, we formulate the task of explainable recommendation with a causal graph, and design a causality enhanced framework to generate unbiased explanations. More specifically, we firstly define an ideal unbiased learning objective, and then derive a tractable loss for the observational data based on the inverse propensity score (IPS), where the key is a sample re-weighting strategy for equalizing the loss and ideal objective in expectation. Considering that the IPS estimated from the sparse and noisy recommendation datasets can be inaccurate, we introduce a fault tolerant mechanism by minimizing the maximum loss induced by the sample weights near the IPS. For more comprehensive modeling, we further analyze and infer the potential latent confounders induced by the complex and diverse user personalities. We conduct extensive experiments by comparing with the state-of-the-art methods based on three real-world datasets to demonstrate the effectiveness of our method.|可解释的建议最近引起了学术界和工业界越来越多的关注。在不同的解释策略中，生成自然语言解释是一种重要的方法，它可以提供更多的信息，灵活和可读的解释，以便于更好的用户决策。尽管有效，现有的模型大多是优化的基础上观察数据集，这可能会由于选择或曝光偏差。为了解决这一问题，本文利用因果图构造了可解释推荐任务，并设计了一个因果增强框架来生成无偏解释。更具体地说，我们首先定义一个理想的无偏学习目标，然后推导出一个基于逆倾向评分(IPS)的观测数据易处理的损失，其中的关键是一个样本重新加权策略来均衡损失和期望的理想目标。针对由稀疏和噪声推荐数据集估计的 IPS 可能不准确的问题，我们引入了一种容错机制，使 IPS 附近的样本权重引起的最大损失最小。为了更全面的建模，我们进一步分析和推断潜在的潜在混杂因素引起的复杂和多样的用户个性。为了验证该方法的有效性，我们在三个实际数据集上进行了广泛的实验，并与现有的方法进行了比较。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Recommendation+with+Causality+enhanced+Natural+Language+Explanations)|0| -|[Two-Stage Constrained Actor-Critic for Short Video Recommendation](https://doi.org/10.1145/3543507.3583259)|Qingpeng Cai, Zhenghai Xue, Chi Zhang, Wanqi Xue, Shuchang Liu, Ruohan Zhan, Xueliang Wang, Tianyou Zuo, Wentao Xie, Dong Zheng, Peng Jiang, Kun Gai|Hong Kong University of Science and Technology, China; Kuaishou Technology, China; Unaffiliated, China|The wide popularity of short videos on social media poses new opportunities and challenges to optimize recommender systems on the video-sharing platforms. Users sequentially interact with the system and provide complex and multi-faceted responses, including watch time and various types of interactions with multiple videos. One the one hand, the platforms aims at optimizing the users' cumulative watch time (main goal) in long term, which can be effectively optimized by Reinforcement Learning. On the other hand, the platforms also needs to satisfy the constraint of accommodating the responses of multiple user interactions (auxiliary goals) such like, follow, share etc. In this paper, we formulate the problem of short video recommendation as a Constrained Markov Decision Process (CMDP). We find that traditional constrained reinforcement learning algorithms can not work well in this setting. We propose a novel two-stage constrained actor-critic method: At stage one, we learn individual policies to optimize each auxiliary signal. At stage two, we learn a policy to (i) optimize the main signal and (ii) stay close to policies learned at the first stage, which effectively guarantees the performance of this main policy on the auxiliaries. Through extensive offline evaluations, we demonstrate effectiveness of our method over alternatives in both optimizing the main goal as well as balancing the others. We further show the advantage of our method in live experiments of short video recommendations, where it significantly outperforms other baselines in terms of both watch time and interactions. Our approach has been fully launched in the production system to optimize user experiences on the platform.|短视频在社交媒体上的广泛流行为优化视频共享平台上的推荐系统带来了新的机遇和挑战。用户按顺序与系统互动，并提供复杂和多方面的反应，包括观看时间和与多个视频的各种类型的互动。一方面，这些平台旨在长期优化用户的累计观看时间(主要目标) ，这可以通过强化学习有效地优化。另一方面，平台还需要满足适应多用户交互(辅助目标)的响应约束，如跟踪、共享等。在这篇文章中，我们将短视频推荐问题描述为一个约束马可夫决策过程(CMDP)。我们发现传统的约束强化学习算法在这种情况下不能很好地工作。我们提出了一种新的两阶段约束行为者-评论方法: 在第一阶段，我们学习个体策略来优化每个辅助信号。在第二阶段，我们学习了一个策略来(i)优化主信号和(ii)紧跟在第一阶段学到的策略，这有效地保证了这个主策略在辅助系统上的性能。通过广泛的离线评估，我们证明了我们的方法在优化主要目标和平衡其他方面的有效性。我们进一步展示了我们的方法在短视频推荐的现场实验中的优势，在观看时间和交互方面显著优于其他基准。我们的方法已经在生产系统中全面推出，以优化平台上的用户体验。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Two-Stage+Constrained+Actor-Critic+for+Short+Video+Recommendation)|0| +|[Recommendation with Causality enhanced Natural Language Explanations](https://doi.org/10.1145/3543507.3583260)|Jingsen Zhang, Xu Chen, Jiakai Tang, Weiqi Shao, Quanyu Dai, Zhenhua Dong, Rui Zhang|Renmin University of China, China; www.ruizhang.info, China; Huawei Noah's Ark Lab, China|Explainable recommendation has recently attracted increasing attention from both academic and industry communities. Among different explainable strategies, generating natural language explanations is an important method, which can deliver more informative, flexible and readable explanations to facilitate better user decisions. Despite the effectiveness, existing models are mostly optimized based on the observed datasets, which can be skewed due to the selection or exposure bias. To alleviate this problem, in this paper, we formulate the task of explainable recommendation with a causal graph, and design a causality enhanced framework to generate unbiased explanations. More specifically, we firstly define an ideal unbiased learning objective, and then derive a tractable loss for the observational data based on the inverse propensity score (IPS), where the key is a sample re-weighting strategy for equalizing the loss and ideal objective in expectation. Considering that the IPS estimated from the sparse and noisy recommendation datasets can be inaccurate, we introduce a fault tolerant mechanism by minimizing the maximum loss induced by the sample weights near the IPS. For more comprehensive modeling, we further analyze and infer the potential latent confounders induced by the complex and diverse user personalities. We conduct extensive experiments by comparing with the state-of-the-art methods based on three real-world datasets to demonstrate the effectiveness of our method.|可解释的建议最近引起了学术界和工业界越来越多的关注。在不同的解释策略中，生成自然语言解释是一种重要的方法，它可以提供更多的信息，灵活和可读的解释，以便于更好的用户决策。尽管有效，现有的模型大多是优化的基础上观察数据集，这可能会由于选择或曝光偏差。为了解决这一问题，本文利用因果图构造了可解释推荐任务，并设计了一个因果增强框架来生成无偏解释。更具体地说，我们首先定义一个理想的无偏学习目标，然后推导出一个基于逆倾向评分(IPS)的观测数据易处理的损失，其中的关键是一个样本重新加权策略来均衡损失和期望的理想目标。针对由稀疏和噪声推荐数据集估计的 IPS 可能不准确的问题，我们引入了一种容错机制，使 IPS 附近的样本权重引起的最大损失最小。为了更全面的建模，我们进一步分析和推断潜在的潜在混杂因素引起的复杂和多样的用户个性。为了验证该方法的有效性，我们在三个实际数据集上进行了广泛的实验，并与现有的方法进行了比较。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Recommendation+with+Causality+enhanced+Natural+Language+Explanations)|0| +|[Two-Stage Constrained Actor-Critic for Short Video Recommendation](https://doi.org/10.1145/3543507.3583259)|Qingpeng Cai, Zhenghai Xue, Chi Zhang, Wanqi Xue, Shuchang Liu, Ruohan Zhan, Xueliang Wang, Tianyou Zuo, Wentao Xie, Dong Zheng, Peng Jiang, Kun Gai|Hong Kong University of Science and Technology, China; Unaffiliated, China; Kuaishou Technology, China|The wide popularity of short videos on social media poses new opportunities and challenges to optimize recommender systems on the video-sharing platforms. Users sequentially interact with the system and provide complex and multi-faceted responses, including watch time and various types of interactions with multiple videos. One the one hand, the platforms aims at optimizing the users' cumulative watch time (main goal) in long term, which can be effectively optimized by Reinforcement Learning. On the other hand, the platforms also needs to satisfy the constraint of accommodating the responses of multiple user interactions (auxiliary goals) such like, follow, share etc. In this paper, we formulate the problem of short video recommendation as a Constrained Markov Decision Process (CMDP). We find that traditional constrained reinforcement learning algorithms can not work well in this setting. We propose a novel two-stage constrained actor-critic method: At stage one, we learn individual policies to optimize each auxiliary signal. At stage two, we learn a policy to (i) optimize the main signal and (ii) stay close to policies learned at the first stage, which effectively guarantees the performance of this main policy on the auxiliaries. Through extensive offline evaluations, we demonstrate effectiveness of our method over alternatives in both optimizing the main goal as well as balancing the others. We further show the advantage of our method in live experiments of short video recommendations, where it significantly outperforms other baselines in terms of both watch time and interactions. Our approach has been fully launched in the production system to optimize user experiences on the platform.|短视频在社交媒体上的广泛流行为优化视频共享平台上的推荐系统带来了新的机遇和挑战。用户按顺序与系统互动，并提供复杂和多方面的反应，包括观看时间和与多个视频的各种类型的互动。一方面，这些平台旨在长期优化用户的累计观看时间(主要目标) ，这可以通过强化学习有效地优化。另一方面，平台还需要满足适应多用户交互(辅助目标)的响应约束，如跟踪、共享等。在这篇文章中，我们将短视频推荐问题描述为一个约束马可夫决策过程(CMDP)。我们发现传统的约束强化学习算法在这种情况下不能很好地工作。我们提出了一种新的两阶段约束行为者-评论方法: 在第一阶段，我们学习个体策略来优化每个辅助信号。在第二阶段，我们学习了一个策略来(i)优化主信号和(ii)紧跟在第一阶段学到的策略，这有效地保证了这个主策略在辅助系统上的性能。通过广泛的离线评估，我们证明了我们的方法在优化主要目标和平衡其他方面的有效性。我们进一步展示了我们的方法在短视频推荐的现场实验中的优势，在观看时间和交互方面显著优于其他基准。我们的方法已经在生产系统中全面推出，以优化平台上的用户体验。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Two-Stage+Constrained+Actor-Critic+for+Short+Video+Recommendation)|0| |[Robust Recommendation with Adversarial Gaussian Data Augmentation](https://doi.org/10.1145/3543507.3583273)|Zhenlei Wang, Xu Chen|Gaoling School of Artificial Intelligence, Renmin university of China, China|Recommender system holds the promise of accurately understanding and estimating the user preferences. However, due to the extremely sparse user-item interactions, the learned recommender models can be less robust and sensitive to the highly dynamic user preferences and easily changed recommendation environments. To alleviate this problem, in this paper, we propose a simple yet effective robust recommender framework by generating additional samples from the Gaussian distributions. In specific, we design two types of data augmentation strategies. For the first one, we directly produce the data based on the original samples, where we simulate the generation process in the latent space. For the second one, we firstly change the original samples towards the direction of maximizing the loss function, and then produce the data based on the altered samples to make more effective explorations. Based on both of the above strategies, we leverage adversarial training to optimize the recommender model with the generated data which can achieve the largest losses. In addition, we theoretically analyze our framework, and find that the above two data augmentation strategies equal to impose a gradient based regularization on the original recommender models. We conduct extensive experiments based on six real-world datasets to demonstrate the effectiveness of our framework.|推荐系统有希望准确理解和估计用户的偏好。然而，由于用户与项目之间的交互非常稀少，所学习的推荐模型可能对高度动态的用户偏好和容易更改的推荐环境不太健壮和敏感。为了解决这个问题，本文提出了一个简单而有效的鲁棒推荐框架，通过从高斯分布生成额外的样本。具体来说，我们设计了两种类型的数据增强策略。对于第一种方法，我们直接在原始样本的基础上生成数据，模拟潜在空间中的生成过程。第二种方法首先将原始样本向损失函数最大化方向改变，然后根据改变后的样本生成数据，进行更有效的探索。基于上述两种策略，我们利用对抗性训练来优化推荐模型，生成的数据可以达到最大的损失。此外，我们还从理论上分析了我们的框架，发现上述两种数据增强策略相当于在原有的推荐模型上加入了基于梯度的正则化。我们基于六个真实世界的数据集进行了广泛的实验，以证明我们的框架的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Robust+Recommendation+with+Adversarial+Gaussian+Data+Augmentation)|0| -|[Anti-FakeU: Defending Shilling Attacks on Graph Neural Network based Recommender Model](https://doi.org/10.1145/3543507.3583289)|Xiaoyu You, Chi Li, Daizong Ding, Mi Zhang, Fuli Feng, Xudong Pan, Min Yang|University of Science and Technology of China, CCCD Key Lab of Ministry of Culture and Tourism, China; Fudan University, School of Computer Science, China|Graph neural network (GNN) based recommendation models are observed to be more vulnerable against carefully-designed malicious records injected into the system, i.e., shilling attacks, which manipulate the recommendation to common users and therefore impair user trust. In this paper, we for the first time conduct a systematic study on the vulnerability of GNN based recommendation model against the shilling attack. With the aid of theoretical analysis, we attribute the root cause of the vulnerability to its neighborhood aggregation mechanism, which could make the negative impact of attacks propagate rapidly in the system. To restore the robustness of GNN based recommendation model, the key factor lies in detecting malicious records in the system and preventing the propagation of misinformation. To this end, we construct a user-user graph to capture the patterns of malicious behaviors and design a novel GNN based detector to identify fake users. Furthermore, we develop a data augmentation strategy and a joint learning paradigm to train the recommender model and the proposed detector. Extensive experiments on benchmark datasets validate the enhanced robustness of the proposed method in resisting various types of shilling attacks and identifying fake users, e.g., our proposed method fully mitigating the impact of popularity attacks on target items up to , and improving the accuracy of detecting fake users on the Gowalla dataset by .|基于图神经网络(GNN)的推荐模型更容易受到注入系统的精心设计的恶意记录(即先令攻击)的攻击，这些恶意记录操纵普通用户的推荐，从而损害用户的信任。本文首次对基于 GNN 的推荐模型在面对先令攻击时的脆弱性进行了系统的研究。在理论分析的基础上，将易受攻击的根本原因归结为其邻域聚合机制，使得攻击的负面影响在系统中迅速传播。要恢复基于 GNN 的推荐模型的鲁棒性，关键在于检测系统中的恶意记录，防止错误信息的传播。为此，我们构造了一个用户-用户图来捕捉恶意行为的模式，并设计了一种新的基于 GNN 的检测器来识别虚假用户。此外，我们发展了一个数据增强策略和一个联合学习范式来训练推荐模型和建议的检测器。基准数据集的大量实验验证了该方法在抵御各种先令攻击和识别假用户方面的增强鲁棒性，例如，我们提出的方法充分减轻了流行攻击对目标项的影响，并通过以下方法提高了 Gowalla 数据集检测假用户的准确性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Anti-FakeU:+Defending+Shilling+Attacks+on+Graph+Neural+Network+based+Recommender+Model)|0| +|[Anti-FakeU: Defending Shilling Attacks on Graph Neural Network based Recommender Model](https://doi.org/10.1145/3543507.3583289)|Xiaoyu You, Chi Li, Daizong Ding, Mi Zhang, Fuli Feng, Xudong Pan, Min Yang|Fudan University, School of Computer Science, China; University of Science and Technology of China, CCCD Key Lab of Ministry of Culture and Tourism, China|Graph neural network (GNN) based recommendation models are observed to be more vulnerable against carefully-designed malicious records injected into the system, i.e., shilling attacks, which manipulate the recommendation to common users and therefore impair user trust. In this paper, we for the first time conduct a systematic study on the vulnerability of GNN based recommendation model against the shilling attack. With the aid of theoretical analysis, we attribute the root cause of the vulnerability to its neighborhood aggregation mechanism, which could make the negative impact of attacks propagate rapidly in the system. To restore the robustness of GNN based recommendation model, the key factor lies in detecting malicious records in the system and preventing the propagation of misinformation. To this end, we construct a user-user graph to capture the patterns of malicious behaviors and design a novel GNN based detector to identify fake users. Furthermore, we develop a data augmentation strategy and a joint learning paradigm to train the recommender model and the proposed detector. Extensive experiments on benchmark datasets validate the enhanced robustness of the proposed method in resisting various types of shilling attacks and identifying fake users, e.g., our proposed method fully mitigating the impact of popularity attacks on target items up to , and improving the accuracy of detecting fake users on the Gowalla dataset by .|基于图神经网络(GNN)的推荐模型更容易受到注入系统的精心设计的恶意记录(即先令攻击)的攻击，这些恶意记录操纵普通用户的推荐，从而损害用户的信任。本文首次对基于 GNN 的推荐模型在面对先令攻击时的脆弱性进行了系统的研究。在理论分析的基础上，将易受攻击的根本原因归结为其邻域聚合机制，使得攻击的负面影响在系统中迅速传播。要恢复基于 GNN 的推荐模型的鲁棒性，关键在于检测系统中的恶意记录，防止错误信息的传播。为此，我们构造了一个用户-用户图来捕捉恶意行为的模式，并设计了一种新的基于 GNN 的检测器来识别虚假用户。此外，我们发展了一个数据增强策略和一个联合学习范式来训练推荐模型和建议的检测器。基准数据集的大量实验验证了该方法在抵御各种先令攻击和识别假用户方面的增强鲁棒性，例如，我们提出的方法充分减轻了流行攻击对目标项的影响，并通过以下方法提高了 Gowalla 数据集检测假用户的准确性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Anti-FakeU:+Defending+Shilling+Attacks+on+Graph+Neural+Network+based+Recommender+Model)|0| |[Automated Self-Supervised Learning for Recommendation](https://doi.org/10.1145/3543507.3583336)|Lianghao Xia, Chao Huang, Chunzhen Huang, Kangyi Lin, Tao Yu, Ben Kao|Tencent, China; The University of Hong Kong, Hong Kong|Graph neural networks (GNNs) have emerged as the state-of-the-art paradigm for collaborative filtering (CF). To improve the representation quality over limited labeled data, contrastive learning has attracted attention in recommendation and benefited graph-based CF model recently. However, the success of most contrastive methods heavily relies on manually generating effective contrastive views for heuristic-based data augmentation. This does not generalize across different datasets and downstream recommendation tasks, which is difficult to be adaptive for data augmentation and robust to noise perturbation. To fill this crucial gap, this work proposes a unified Automated Collaborative Filtering (AutoCF) to automatically perform data augmentation for recommendation. Specifically, we focus on the generative self-supervised learning framework with a learnable augmentation paradigm that benefits the automated distillation of important self-supervised signals. To enhance the representation discrimination ability, our masked graph autoencoder is designed to aggregate global information during the augmentation via reconstructing the masked subgraph structures. Experiments and ablation studies are performed on several public datasets for recommending products, venues, and locations. Results demonstrate the superiority of AutoCF against various baseline methods. We release the model implementation at https://github.com/HKUDS/AutoCF.|图形神经网络(GNN)已经成为最先进的协同过滤(CF)模式。为了提高有限标记数据的表示质量，对比学习近年来受到推荐界的关注，并受益于基于图的 CF 模型。然而，大多数对比方法的成功在很大程度上依赖于手工生成有效的对比视图，用于基于启发式的数据增强。这不能在不同的数据集和下游推荐任务之间推广，这对于数据增强和抗噪声干扰是很难自适应的。为了填补这个关键的空白，这项工作提出了一个统一的自动化协同过滤(AutoCF)来自动执行数据增强的推荐。具体来说，我们重点研究了具有可学习增强范式的生成式自监督学习框架，该框架有利于自动提取重要的自监督信号。为了提高表示识别能力，我们设计了掩码自动编码器，通过重构掩码子图结构来聚集增强过程中的全局信息。实验和烧蚀研究进行了几个公共数据集推荐产品，场所和地点。结果表明，AutoCF 方法与各种基线方法相比具有优越性。我们在 https://github.com/hkuds/autocf 发布模型实现。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Automated+Self-Supervised+Learning+for+Recommendation)|0| |[AutoDenoise: Automatic Data Instance Denoising for Recommendations](https://doi.org/10.1145/3543507.3583339)|Weilin Lin, Xiangyu Zhao, Yejing Wang, Yuanshao Zhu, Wanyu Wang|City University of Hong Kong, Hong Kong; City University of Hong Kong, Hong Kong and Southern University of Science and Technology, China|Historical user-item interaction datasets are essential in training modern recommender systems for predicting user preferences. However, the arbitrary user behaviors in most recommendation scenarios lead to a large volume of noisy data instances being recorded, which cannot fully represent their true interests. While a large number of denoising studies are emerging in the recommender system community, all of them suffer from highly dynamic data distributions. In this paper, we propose a Deep Reinforcement Learning (DRL) based framework, AutoDenoise, with an Instance Denoising Policy Network, for denoising data instances with an instance selection manner in deep recommender systems. To be specific, AutoDenoise serves as an agent in DRL to adaptively select noise-free and predictive data instances, which can then be utilized directly in training representative recommendation models. In addition, we design an alternate two-phase optimization strategy to train and validate the AutoDenoise properly. In the searching phase, we aim to train the policy network with the capacity of instance denoising; in the validation phase, we find out and evaluate the denoised subset of data instances selected by the trained policy network, so as to validate its denoising ability. We conduct extensive experiments to validate the effectiveness of AutoDenoise combined with multiple representative recommender system models.|历史用户项目交互数据集对于培训现代推荐系统来预测用户偏好是必不可少的。然而，在大多数推荐场景中，任意的用户行为会导致大量有噪声的数据实例被记录下来，而这些数据实例并不能完全代表用户的真实兴趣。虽然在推荐系统社区中出现了大量的去噪研究，但所有这些研究都受到高度动态数据分布的影响。在这篇文章中，我们提出了一个基于深度强化学习的框架，AutoDenoise，和一个实例去噪策略网络，用于在深度推荐系统中用实例选择的方式去除数据实例。具体来说，自动去噪作为 DRL 中的一个代理，自适应地选择无噪声和预测数据实例，然后可以直接用于训练代表性的推荐模型。此外，我们还设计了一个交替的两阶段优化策略来训练和验证自动去噪的正确性。在搜索阶段，我们的目标是训练具有实例去噪能力的策略网络，在验证阶段，我们找出并评估训练后的策略网络选择的数据实例的去噪子集，以验证其去噪能力。我们进行了广泛的实验，以验证自动去噪与多个具有代表性的推荐系统模型相结合的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=AutoDenoise:+Automatic+Data+Instance+Denoising+for+Recommendations)|0| -|[AutoS2AE: Automate to Regularize Sparse Shallow Autoencoders for Recommendation](https://doi.org/10.1145/3543507.3583349)|Rui Fan, Yuanhao Pu, Jin Chen, Zhihao Zhu, Defu Lian, Enhong Chen|School of Computer Science, University of Science and Technology of China, China; School of Data Science, University of Science and Technology of China, China; School of Computer Science, School of Data Science, University of Science and Technology of China, China and State Key Laboratory of Cognitive Intelligence, China; University of Electronic Science and Technology of China, China|The Embarrassingly Shallow Autoencoders (EASE and SLIM) are strong recommendation methods based on implicit feedback, compared to competing methods like iALS and VAE-CF. However, EASE suffers from several major shortcomings. First, the training and inference of EASE can not scale with the increasing number of items since it requires storing and inverting a large dense matrix; Second, though its optimization objective – the square loss– can yield a closed-form solution, it is not consistent with recommendation goal – predicting a personalized ranking on a set of items, so that its performance is far from optimal w.r.t ranking-oriented recommendation metrics. Finally, the regularization coefficients are sensitive w.r.t recommendation accuracy and vary a lot across different datasets, so the fine-tuning of these parameters is important yet time-consuming. To improve training and inference efficiency, we propose a Similarity-Structure Aware Shallow Autoencoder on top of three similarity structures, including Co-Occurrence, KNN and NSW. We then optimize the model with a weighted square loss, which is proven effective for ranking-based recommendation but still capable of deriving closed-form solutions. However, the weight in the loss can not be learned in the training set and is similarly sensitive w.r.t the accuracy to regularization coefficients. To automatically tune the hyperparameters, we design two validation losses on the validation set for guidance, and update the hyperparameters with the gradient of the validation losses. We finally evaluate the proposed method on multiple real-world datasets and show that it outperforms seven competing baselines remarkably, and verify the effectiveness of each part in the proposed method.|令人尴尬的浅层自动编码器(EASE 和 SLIM)是基于隐式反馈的强有力的推荐方法，与 iALS 和 VAE-CF 等竞争方法相比。然而，EASE 有几个主要的缺点。首先，EASE 的训练和推理不能随着项目数量的增加而扩展，因为它需要存储和反演一个大的密集矩阵; 其次，虽然它的优化目标-平方损失-可以产生一个封闭形式的解决方案，但它不符合推荐目标-预测一组项目的个性化排名，因此它的性能远远不是最优的面向网络排名的推荐指标。最后，正则化系数对推荐精度非常敏感，并且在不同的数据集上有很大的差异，因此对这些参数进行微调非常重要，但也非常耗时。为了提高训练和推理效率，本文提出了一种基于共现、 KNN 和 NSW 三种相似结构的相似结构感知浅层自动编码器。然后，我们用加权平方损失优化模型，这被证明是有效的排名为基础的推荐，但仍然能够导出闭合形式的解决方案。然而，损失中的权重不能在训练集中学习，并且对正则化系数的精度同样敏感。为了自动调整超参数，我们在验证集上设计了两个验证损失作为指导，并用验证损失的梯度来更新超参数。最后，我们对该方法在多个实际数据集上的性能进行了评估，结果表明该方法明显优于7个竞争基线，并验证了该方法各部分的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=AutoS2AE:+Automate+to+Regularize+Sparse+Shallow+Autoencoders+for+Recommendation)|0| -|[Improving Recommendation Fairness via Data Augmentation](https://doi.org/10.1145/3543507.3583341)|Lei Chen, Le Wu, Kun Zhang, Richang Hong, Defu Lian, Zhiqiang Zhang, Jun Zhou, Meng Wang|University of Science and Technology of China, China; Hefei University of Technology, China; Hefei University of Technology, China and Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, China; Ant Group, China|Collaborative filtering based recommendation learns users' preferences from all users' historical behavior data, and has been popular to facilitate decision making. R Recently, the fairness issue of recommendation has become more and more essential. A recommender system is considered unfair when it does not perform equally well for different user groups according to users' sensitive attributes~(e.g., gender, race). Plenty of methods have been proposed to alleviate unfairness by optimizing a predefined fairness goal or changing the distribution of unbalanced training data. However, they either suffered from the specific fairness optimization metrics or relied on redesigning the current recommendation architecture. In this paper, we study how to improve recommendation fairness from the data augmentation perspective. The recommendation model amplifies the inherent unfairness of imbalanced training data. We augment imbalanced training data towards balanced data distribution to improve fairness. The proposed framework is generally applicable to any embedding-based recommendation, and does not need to pre-define a fairness metric. Extensive experiments on two real-world datasets clearly demonstrate the superiority of our proposed framework. We publish the source code at https://github.com/newlei/FDA.|基于协同过滤的推荐从所有用户的历史行为数据中了解用户的偏好，并且已经流行起来以促进决策制定。近年来，推荐的公平性问题变得越来越重要。根据用户的敏感属性 ~ (如性别、种族) ，一个推荐系统在不同用户组中的表现不尽相同，这被认为是不公平的。通过优化预定义的公平目标或改变不平衡训练数据的分布，已经提出了许多缓解不公平现象的方法。然而，它们要么受到特定公平性优化指标的影响，要么依赖于重新设计当前的推荐体系结构。本文从数据增强的角度研究如何提高推荐公平性。推荐模型放大了不平衡训练数据固有的不公平性。为了提高训练数据的公平性，我们对不平衡的训练数据进行扩充以达到平衡的数据分布。提出的框架通常适用于任何基于嵌入的建议，并且不需要预先定义公平性度量。在两个实际数据集上的大量实验清楚地表明了我们提出的框架的优越性。我们在 https://github.com/newlei/fda 公布源代码。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Improving+Recommendation+Fairness+via+Data+Augmentation)|0| +|[AutoS2AE: Automate to Regularize Sparse Shallow Autoencoders for Recommendation](https://doi.org/10.1145/3543507.3583349)|Rui Fan, Yuanhao Pu, Jin Chen, Zhihao Zhu, Defu Lian, Enhong Chen|School of Computer Science, University of Science and Technology of China, China; University of Electronic Science and Technology of China, China; School of Computer Science, School of Data Science, University of Science and Technology of China, China and State Key Laboratory of Cognitive Intelligence, China; School of Data Science, University of Science and Technology of China, China|The Embarrassingly Shallow Autoencoders (EASE and SLIM) are strong recommendation methods based on implicit feedback, compared to competing methods like iALS and VAE-CF. However, EASE suffers from several major shortcomings. First, the training and inference of EASE can not scale with the increasing number of items since it requires storing and inverting a large dense matrix; Second, though its optimization objective – the square loss– can yield a closed-form solution, it is not consistent with recommendation goal – predicting a personalized ranking on a set of items, so that its performance is far from optimal w.r.t ranking-oriented recommendation metrics. Finally, the regularization coefficients are sensitive w.r.t recommendation accuracy and vary a lot across different datasets, so the fine-tuning of these parameters is important yet time-consuming. To improve training and inference efficiency, we propose a Similarity-Structure Aware Shallow Autoencoder on top of three similarity structures, including Co-Occurrence, KNN and NSW. We then optimize the model with a weighted square loss, which is proven effective for ranking-based recommendation but still capable of deriving closed-form solutions. However, the weight in the loss can not be learned in the training set and is similarly sensitive w.r.t the accuracy to regularization coefficients. To automatically tune the hyperparameters, we design two validation losses on the validation set for guidance, and update the hyperparameters with the gradient of the validation losses. We finally evaluate the proposed method on multiple real-world datasets and show that it outperforms seven competing baselines remarkably, and verify the effectiveness of each part in the proposed method.|令人尴尬的浅层自动编码器(EASE 和 SLIM)是基于隐式反馈的强有力的推荐方法，与 iALS 和 VAE-CF 等竞争方法相比。然而，EASE 有几个主要的缺点。首先，EASE 的训练和推理不能随着项目数量的增加而扩展，因为它需要存储和反演一个大的密集矩阵; 其次，虽然它的优化目标-平方损失-可以产生一个封闭形式的解决方案，但它不符合推荐目标-预测一组项目的个性化排名，因此它的性能远远不是最优的面向网络排名的推荐指标。最后，正则化系数对推荐精度非常敏感，并且在不同的数据集上有很大的差异，因此对这些参数进行微调非常重要，但也非常耗时。为了提高训练和推理效率，本文提出了一种基于共现、 KNN 和 NSW 三种相似结构的相似结构感知浅层自动编码器。然后，我们用加权平方损失优化模型，这被证明是有效的排名为基础的推荐，但仍然能够导出闭合形式的解决方案。然而，损失中的权重不能在训练集中学习，并且对正则化系数的精度同样敏感。为了自动调整超参数，我们在验证集上设计了两个验证损失作为指导，并用验证损失的梯度来更新超参数。最后，我们对该方法在多个实际数据集上的性能进行了评估，结果表明该方法明显优于7个竞争基线，并验证了该方法各部分的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=AutoS2AE:+Automate+to+Regularize+Sparse+Shallow+Autoencoders+for+Recommendation)|0| +|[Improving Recommendation Fairness via Data Augmentation](https://doi.org/10.1145/3543507.3583341)|Lei Chen, Le Wu, Kun Zhang, Richang Hong, Defu Lian, Zhiqiang Zhang, Jun Zhou, Meng Wang|Ant Group, China; Hefei University of Technology, China; Hefei University of Technology, China and Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, China; University of Science and Technology of China, China|Collaborative filtering based recommendation learns users' preferences from all users' historical behavior data, and has been popular to facilitate decision making. R Recently, the fairness issue of recommendation has become more and more essential. A recommender system is considered unfair when it does not perform equally well for different user groups according to users' sensitive attributes~(e.g., gender, race). Plenty of methods have been proposed to alleviate unfairness by optimizing a predefined fairness goal or changing the distribution of unbalanced training data. However, they either suffered from the specific fairness optimization metrics or relied on redesigning the current recommendation architecture. In this paper, we study how to improve recommendation fairness from the data augmentation perspective. The recommendation model amplifies the inherent unfairness of imbalanced training data. We augment imbalanced training data towards balanced data distribution to improve fairness. The proposed framework is generally applicable to any embedding-based recommendation, and does not need to pre-define a fairness metric. Extensive experiments on two real-world datasets clearly demonstrate the superiority of our proposed framework. We publish the source code at https://github.com/newlei/FDA.|基于协同过滤的推荐从所有用户的历史行为数据中了解用户的偏好，并且已经流行起来以促进决策制定。近年来，推荐的公平性问题变得越来越重要。根据用户的敏感属性 ~ (如性别、种族) ，一个推荐系统在不同用户组中的表现不尽相同，这被认为是不公平的。通过优化预定义的公平目标或改变不平衡训练数据的分布，已经提出了许多缓解不公平现象的方法。然而，它们要么受到特定公平性优化指标的影响，要么依赖于重新设计当前的推荐体系结构。本文从数据增强的角度研究如何提高推荐公平性。推荐模型放大了不平衡训练数据固有的不公平性。为了提高训练数据的公平性，我们对不平衡的训练数据进行扩充以达到平衡的数据分布。提出的框架通常适用于任何基于嵌入的建议，并且不需要预先定义公平性度量。在两个实际数据集上的大量实验清楚地表明了我们提出的框架的优越性。我们在 https://github.com/newlei/fda 公布源代码。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Improving+Recommendation+Fairness+via+Data+Augmentation)|0| |[Robust Preference-Guided Denoising for Graph based Social Recommendation](https://doi.org/10.1145/3543507.3583374)|Yuhan Quan, Jingtao Ding, Chen Gao, Lingling Yi, Depeng Jin, Yong Li|Tsinghua University, China; Tencent, China|Graph Neural Network(GNN) based social recommendation models improve the prediction accuracy of user preference by leveraging GNN in exploiting preference similarity contained in social relations. However, in terms of both effectiveness and efficiency of recommendation, a large portion of social relations can be redundant or even noisy, e.g., it is quite normal that friends share no preference in a certain domain. Existing models do not fully solve this problem of relation redundancy and noise, as they directly characterize social influence over the full social network. In this paper, we instead propose to improve graph based social recommendation by only retaining the informative social relations to ensure an efficient and effective influence diffusion, i.e., graph denoising. Our designed denoising method is preference-guided to model social relation confidence and benefits user preference learning in return by providing a denoised but more informative social graph for recommendation models. Moreover, to avoid interference of noisy social relations, it designs a self-correcting curriculum learning module and an adaptive denoising strategy, both favoring highly-confident samples. Experimental results on three public datasets demonstrate its consistent capability of improving two state-of-the-art social recommendation models by robustly removing 10-40% of original relations. We release the source code at https://github.com/tsinghua-fib-lab/Graph-Denoising-SocialRec.|基于图神经网络(GNN)的社会推荐模型通过利用社会关系中包含的偏好相似性来提高用户偏好的预测精度。然而，就推荐的有效性和效率而言，很大一部分社会关系可能是冗余的，甚至是嘈杂的，例如，朋友在某个领域没有共同的偏好是很正常的。现有的模型并没有完全解决关系冗余和噪声的问题，因为它们直接表征了社会对整个社会网络的影响。在本文中，我们提出改进基于图的社会推荐，只保留信息性的社会关系，以确保有效和有效的影响扩散，即图去噪。我们设计的去噪方法是偏好引导的社会关系模型的信心和有益的用户偏好学习的回报，提供了一个去噪，但更多的信息社会图的推荐模型。同时，为了避免社会关系噪声的干扰，设计了自校正课程学习模块和自适应去噪策略，两者都有利于高自信样本。在三个公共数据集上的实验结果表明，该算法能够通过鲁棒地去除10-40% 的原始关系来改进两个最新的社会推荐模型。我们在 https://github.com/tsinghua-fib-lab/graph-denoising-socialrec 公布源代码。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Robust+Preference-Guided+Denoising+for+Graph+based+Social+Recommendation)|0| |[Few-shot News Recommendation via Cross-lingual Transfer](https://doi.org/10.1145/3543507.3583383)|Taicheng Guo, Lu Yu, Basem Shihada, Xiangliang Zhang||The cold-start problem has been commonly recognized in recommendation systems and studied by following a general idea to leverage the abundant interaction records of warm users to infer the preference of cold users. However, the performance of these solutions is limited by the amount of records available from warm users to use. Thus, building a recommendation system based on few interaction records from a few users still remains a challenging problem for unpopular or early-stage recommendation platforms. This paper focuses on solving the few-shot recommendation problem for news recommendation based on two observations. First, news at diferent platforms (even in diferent languages) may share similar topics.Second, the user preference over these topics is transferable across diferent platforms. Therefore, we propose to solve the few-shot news recommendation problem by transferring the user-news preference from a many-shot source domain to a few-shot target domain. To bridge two domainsthat are even in diferent languages and without any overlapping users and news, we propose a novel unsupervised cross-lingual transfer model as the news encoder that aligns semantically similar news in two domains. A user encoder is constructed on top of the aligned news encoding and transfers the user preference from the source to target domain. Experimental results on two real-world news recommendation datasets show the superior performance of our proposed method on addressing few-shot news recommendation, comparing to the baselines. The source code can be found at https://github.com/taichengguo/Few-shot-NewsRec .|冷启动问题在推荐系统中已经得到了广泛的认可，并且通过利用热用户丰富的交互记录来推断冷用户的偏好这一基本思想进行了研究。但是，这些解决方案的性能受限于可供暖用户使用的记录数量。因此，对于不受欢迎或处于早期阶段的推荐平台来说，建立一个基于少量用户交互记录的推荐系统仍然是一个具有挑战性的问题。本文主要研究基于两个观察值的新闻推荐中的少镜头推荐问题。首先，不同平台的新闻(甚至是不同语言的新闻)可能会有相似的话题。其次，用户对这些主题的偏好可以跨不同的平台传递。因此，我们提出通过将用户新闻偏好从多镜头源域转移到少镜头目标域来解决少镜头新闻推荐问题。为了在两个不同语言的领域之间架起一座桥梁，并且没有任何重叠的用户和新闻，我们提出了一种新的无监督跨语言传输模型作为新闻编码器，它将两个领域中语义相似的新闻进行对齐。用户编码器构造在对齐的新闻编码之上，并将用户首选项从源传输到目标域。在两个实际新闻推荐数据集上的实验结果表明，与基线相比，本文提出的方法在处理少镜头新闻推荐方面具有更好的性能。源代码可以在 https://github.com/taichengguo/few-shot-newsrec 找到。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Few-shot+News+Recommendation+via+Cross-lingual+Transfer)|0| -|[Show Me The Best Outfit for A Certain Scene: A Scene-aware Fashion Recommender System](https://doi.org/10.1145/3543507.3583435)|Tangwei Ye, Liang Hu, Qi Zhang, Zhong Yuan Lai, Usman Naseem, Dora D. Liu|Tongji University, China and DeepBlue Academy of Sciences, China; DeepBlue Academy of Sciences, China and BirenTech Research, China; DeepBlue Academy of Sciences, China; University of Technology Sydney, Australia and DeepBlue Academy of Sciences, China; University of Sydney, Australia|Fashion recommendation (FR) has received increasing attention in the research of new types of recommender systems. Existing fashion recommender systems (FRSs) typically focus on clothing item suggestions for users in three scenarios: 1) how to best recommend fashion items preferred by users; 2) how to best compose a complete outfit, and 3) how to best complete a clothing ensemble. However, current FRSs often overlook an important aspect when making FR, that is, the compatibility of the clothing item or outfit recommendations is highly dependent on the scene context. To this end, we propose the scene-aware fashion recommender system (SAFRS), which uncovers a hitherto unexplored avenue where scene information is taken into account when constructing the FR model. More specifically, our SAFRS addresses this problem by encoding scene and outfit information in separation attention encoders and then fusing the resulting feature embeddings via a novel scene-aware compatibility score function. Extensive qualitative and quantitative experiments are conducted to show that our SAFRS model outperforms all baselines for every evaluated metric.|时尚推荐(FR)在新型推荐系统的研究中受到越来越多的关注。现有的时尚推荐系统(FRSs)主要集中在三个场景中为用户提供服装项目建议: 1)如何最好地推荐用户喜欢的时尚项目; 2)如何最好地组合一套完整的服装; 3)如何最好地完成一套服装。然而，目前的 FRS 在制作 FR 时往往忽略了一个重要方面，即服装项目或服装的兼容性建议高度依赖于场景上下文。为此，我们提出了场景感知时尚推荐系统(SAFRS) ，它揭示了一个迄今为止尚未探索的途径，在构建 FR 模型时，场景信息被考虑在内。更具体地说，我们的 SAFRS 通过在分离注意力编码器中编码场景和装备信息，然后通过一种新颖的场景感知兼容性评分函数融合所得到的特征嵌入来解决这个问题。广泛的定性和定量实验表明，我们的 SAFRS 模型优于所有基线的每一个评估指标。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Show+Me+The+Best+Outfit+for+A+Certain+Scene:+A+Scene-aware+Fashion+Recommender+System)|0| +|[Show Me The Best Outfit for A Certain Scene: A Scene-aware Fashion Recommender System](https://doi.org/10.1145/3543507.3583435)|Tangwei Ye, Liang Hu, Qi Zhang, Zhong Yuan Lai, Usman Naseem, Dora D. Liu|University of Sydney, Australia; Tongji University, China and DeepBlue Academy of Sciences, China; DeepBlue Academy of Sciences, China; DeepBlue Academy of Sciences, China and BirenTech Research, China; University of Technology Sydney, Australia and DeepBlue Academy of Sciences, China|Fashion recommendation (FR) has received increasing attention in the research of new types of recommender systems. Existing fashion recommender systems (FRSs) typically focus on clothing item suggestions for users in three scenarios: 1) how to best recommend fashion items preferred by users; 2) how to best compose a complete outfit, and 3) how to best complete a clothing ensemble. However, current FRSs often overlook an important aspect when making FR, that is, the compatibility of the clothing item or outfit recommendations is highly dependent on the scene context. To this end, we propose the scene-aware fashion recommender system (SAFRS), which uncovers a hitherto unexplored avenue where scene information is taken into account when constructing the FR model. More specifically, our SAFRS addresses this problem by encoding scene and outfit information in separation attention encoders and then fusing the resulting feature embeddings via a novel scene-aware compatibility score function. Extensive qualitative and quantitative experiments are conducted to show that our SAFRS model outperforms all baselines for every evaluated metric.|时尚推荐(FR)在新型推荐系统的研究中受到越来越多的关注。现有的时尚推荐系统(FRSs)主要集中在三个场景中为用户提供服装项目建议: 1)如何最好地推荐用户喜欢的时尚项目; 2)如何最好地组合一套完整的服装; 3)如何最好地完成一套服装。然而，目前的 FRS 在制作 FR 时往往忽略了一个重要方面，即服装项目或服装的兼容性建议高度依赖于场景上下文。为此，我们提出了场景感知时尚推荐系统(SAFRS) ，它揭示了一个迄今为止尚未探索的途径，在构建 FR 模型时，场景信息被考虑在内。更具体地说，我们的 SAFRS 通过在分离注意力编码器中编码场景和装备信息，然后通过一种新颖的场景感知兼容性评分函数融合所得到的特征嵌入来解决这个问题。广泛的定性和定量实验表明，我们的 SAFRS 模型优于所有基线的每一个评估指标。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Show+Me+The+Best+Outfit+for+A+Certain+Scene:+A+Scene-aware+Fashion+Recommender+System)|0| |[Invariant Collaborative Filtering to Popularity Distribution Shift](https://doi.org/10.1145/3543507.3583461)|An Zhang, Jingnan Zheng, Xiang Wang, Yancheng Yuan, TatSeng Chua|Sea-NExT Joint Lab, National University of Singapore, Singapore; National University of Singapore, Singapore; The Hong Kong Polytechnic University, Hong Kong; University of Science and Technology of China, China and Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, China|Collaborative Filtering (CF) models, despite their great success, suffer from severe performance drops due to popularity distribution shifts, where these changes are ubiquitous and inevitable in real-world scenarios. Unfortunately, most leading popularity debiasing strategies, rather than tackling the vulnerability of CF models to varying popularity distributions, require prior knowledge of the test distribution to identify the degree of bias and further learn the popularity-entangled representations to mitigate the bias. Consequently, these models result in significant performance benefits in the target test set, while dramatically deviating the recommendation from users' true interests without knowing the popularity distribution in advance. In this work, we propose a novel learning framework, Invariant Collaborative Filtering (InvCF), to discover disentangled representations that faithfully reveal the latent preference and popularity semantics without making any assumption about the popularity distribution. At its core is the distillation of unbiased preference representations (i.e., user preference on item property), which are invariant to the change of popularity semantics, while filtering out the popularity feature that is unstable or outdated. Extensive experiments on five benchmark datasets and four evaluation settings (i.e., synthetic long-tail, unbiased, temporal split, and out-of-distribution evaluations) demonstrate that InvCF outperforms the state-of-the-art baselines in terms of popularity generalization ability on real recommendations. Visualization studies shed light on the advantages of InvCF for disentangled representation learning. Our codes are available at https://github.com/anzhang314/InvCF.|协同过滤(CF)模型尽管取得了巨大的成功，但由于受欢迎程度的分布变化，性能严重下降，这些变化在现实世界中无处不在，也是不可避免的。不幸的是，大多数领先的流行去偏策略，而不是解决 CF 模型对不同流行分布的脆弱性，需要事先了解测试分布以确定偏倚程度，并进一步学习流行纠缠表示以减轻偏倚。因此，这些模型在目标测试集中产生了显著的性能效益，同时在不事先知道用户流行度分布的情况下，大大偏离了用户的真实兴趣。在这项工作中，我们提出了一个新的学习框架，不变协同过滤(InvCF) ，发现分离的表征，忠实地揭示潜在的偏好和流行语义，而不作任何假设的流行分布。其核心是无偏好的偏好表示(即，用户对项目属性的偏好)的精华，这些偏好对流行语义的变化是不变的，同时过滤掉不稳定或过时的流行特征。对五个基准数据集和四个评估设置(即合成长尾，无偏见，时间分割和分布外评估)的广泛实验表明，InvCF 在真实推荐的普及概括能力方面优于最先进的基线。可视化研究揭示了 InvCF 在分离表征学习中的优势。我们的密码可以在 https://github.com/anzhang314/invcf 找到。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Invariant+Collaborative+Filtering+to+Popularity+Distribution+Shift)|0| |[Code Recommendation for Open Source Software Developers](https://doi.org/10.1145/3543507.3583503)|Yiqiao Jin, Yunsheng Bai, Yanqiao Zhu, Yizhou Sun, Wei Wang|University of California, Los Angeles, USA; Georgia Institute of Technology, USA|Open Source Software (OSS) is forming the spines of technology infrastructures, attracting millions of talents to contribute. Notably, it is challenging and critical to consider both the developers' interests and the semantic features of the project code to recommend appropriate development tasks to OSS developers. In this paper, we formulate the novel problem of code recommendation, whose purpose is to predict the future contribution behaviors of developers given their interaction history, the semantic features of source code, and the hierarchical file structures of projects. Considering the complex interactions among multiple parties within the system, we propose CODER, a novel graph-based code recommendation framework for open source software developers. CODER jointly models microscopic user-code interactions and macroscopic user-project interactions via a heterogeneous graph and further bridges the two levels of information through aggregation on file-structure graphs that reflect the project hierarchy. Moreover, due to the lack of reliable benchmarks, we construct three large-scale datasets to facilitate future research in this direction. Extensive experiments show that our CODER framework achieves superior performance under various experimental settings, including intra-project, cross-project, and cold-start recommendation. We will release all the datasets, code, and utilities for data retrieval upon the acceptance of this work.|开源软件(OSS)正在形成技术基础设施的脊梁，吸引了数以百万计的人才贡献。值得注意的是，同时考虑开发人员的兴趣和项目代码的语义特性，以便向 OSS 开发人员推荐适当的开发任务，这是一项具有挑战性和关键性的工作。本文提出了一个新的代码推荐问题，其目的是根据开发人员的交互历史、源代码的语义特征以及项目的层次化文件结构来预测开发人员未来的贡献行为。考虑到系统中多方之间的复杂交互，我们提出了一种新的基于图的开源软件开发者代码推荐框架 CODER。CODER 通过异构图联合建模微观用户-代码交互和宏观用户-项目交互，并通过聚合反映项目层次结构的文件结构图进一步桥接两个层次的信息。此外，由于缺乏可靠的基准，我们建立了三个大规模的数据集，以方便未来在这方面的研究。大量的实验表明，我们的 CODER 框架在不同的实验环境下，包括项目内、项目间和冷启动推荐，都取得了较好的性能。我们将发布所有的数据集，代码和实用程序的数据检索后，接受这项工作。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Code+Recommendation+for+Open+Source+Software+Developers)|0| |[pFedPrompt: Learning Personalized Prompt for Vision-Language Models in Federated Learning](https://doi.org/10.1145/3543507.3583518)|Tao Guo, Song Guo, Junxiao Wang|The Hong Kong Polytechnic University, Hong Kong|Pre-trained vision-language models like CLIP show great potential in learning representations that capture latent characteristics of users. A recently proposed method called Contextual Optimization (CoOp) introduces the concept of training prompt for adapting pre-trained vision-language models. Given the lightweight nature of this method, researchers have migrated the paradigm from centralized to decentralized system to innovate the collaborative training framework of Federated Learning (FL). However, current prompt training in FL mainly focuses on modeling user consensus and lacks the adaptation to user characteristics, leaving the personalization of prompt largely under-explored. Researches over the past few years have applied personalized FL (pFL) approaches to customizing models for heterogeneous users. Unfortunately, we find that with the variation of modality and training behavior, directly applying the pFL methods to prompt training leads to insufficient personalization and performance. To bridge the gap, we present pFedPrompt, which leverages the unique advantage of multimodality in vision-language models by learning user consensus from linguistic space and adapting to user characteristics in visual space in a non-parametric manner. Through this dual collaboration, the learned prompt will be fully personalized and aligned to the user’s local characteristics. We conduct extensive experiments across various datasets under the FL setting with statistical heterogeneity. The results demonstrate the superiority of our pFedPrompt against the alternative approaches with robust performance.|像 CLIP 这样预先训练好的视觉语言模型在捕捉用户潜在特征的学习表示方面显示出巨大的潜力。最近提出的上下文优化(CoOp)方法引入了训练提示符的概念来适应预先训练好的视觉语言模型。考虑到该方法的轻量级特性，研究人员将该模式从集中式系统迁移到分散式系统，以创新联邦学习(FL)的协同训练框架。然而，目前外语快速教学主要侧重于建立用户共识模型，缺乏对用户特征的适应性，使得个性化快速教学在很大程度上缺乏探索。过去几年的研究已经将个性化 FL (pFL)方法应用于异构用户的定制模型。不幸的是，我们发现，随着模式和训练行为的变化，直接应用 pFL 方法促进训练导致个性化和绩效的不足。为了弥合这一差距，我们提出了 pFedPrompt，它通过从语言空间学习用户共识并以非参数方式适应视觉空间中的用户特征，从而利用了视觉语言模型中多模态的独特优势。通过这种双重协作，学到的提示符将完全个性化，并与用户的本地特征保持一致。我们在统计异质性的 FL 环境下对不同的数据集进行了广泛的实验。实验结果表明，本文提出的 pFedPrompt 算法与其他具有鲁棒性能的方法相比具有优越性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=pFedPrompt:+Learning+Personalized+Prompt+for+Vision-Language+Models+in+Federated+Learning)|0| -|[Word Sense Disambiguation by Refining Target Word Embedding](https://doi.org/10.1145/3543507.3583191)|Xuefeng Zhang, Richong Zhang, Xiaoyang Li, Fanshuang Kong, Junfan Chen, Samuel Mensah, Yongyi Mao|SKLSDE, School of Computer Science and Engineering, Beihang University, China; The University of Sheffield, United Kingdom; University of Ottawa, Canada|Word Sense Disambiguation (WSD) which aims to identify the correct sense of a target word appearing in a specific context is essential for web text analysis. The use of glosses has been explored as a means for WSD. However, only a few works model the correlation between the target context and gloss. We add to the body of literature by presenting a model that employs a multi-head attention mechanism on deep contextual features of the target word and candidate glosses to refine the target word embedding. Furthermore, to encourage the model to learn the relevant part of target features that align with the correct gloss, we recursively alternate attention on target word features and that of candidate glosses to gradually extract the relevant contextual features of the target word, refining its representation and strengthening the final disambiguation results. Empirical studies on the five most commonly used benchmark datasets show that our proposed model is effective and achieves state-of-the-art results.|词义消歧(WSD)是识别特定语境中目标词的正确意义，是网络文本分析的基础。水务署已研究使用注释作为一种方法。然而，只有少数作品模拟了目标语境和注释之间的相关性。本文提出了一种基于目标词深层语境特征和候选修饰语的多目标注意机制来完善目标词嵌入的模型，并对文献进行了补充。此外，为了鼓励模型学习与正确的注释相一致的目标特征的相关部分，我们递归地交替关注目标词特征和候选注释的特征，以逐渐提取目标词的相关上下文特征，完善其表示并加强最终的消歧结果。对五个最常用的基准数据集的实证研究表明，我们提出的模型是有效的，并取得了最先进的结果。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Word+Sense+Disambiguation+by+Refining+Target+Word+Embedding)|0| -|[Dual Policy Learning for Aggregation Optimization in Graph Neural Network-based Recommender Systems](https://doi.org/10.1145/3543507.3583241)|Heesoo Jung, Sangpil Kim, Hogun Park|Dept. of Artificial Intelligence, Sungkyunkwan University, Republic of Korea; Dept. of Artificial Intelligence, Korea University, Republic of Korea; Dept. of Electrical and Computer Engineering, Sungkyunkwan University, Republic of Korea and Dept. of Artificial Intelligence, Sungkyunkwan University, Republic of Korea|Graph Neural Networks (GNNs) provide powerful representations for recommendation tasks. GNN-based recommendation systems capture the complex high-order connectivity between users and items by aggregating information from distant neighbors and can improve the performance of recommender systems. Recently, Knowledge Graphs (KGs) have also been incorporated into the user-item interaction graph to provide more abundant contextual information; they are exploited to address cold-start problems and enable more explainable aggregation in GNN-based recommender systems (GNN-Rs). However, due to the heterogeneous nature of users and items, developing an effective aggregation strategy that works across multiple GNN-Rs, such as LightGCN and KGAT, remains a challenge. In this paper, we propose a novel reinforcement learning-based message passing framework for recommender systems, which we call DPAO (Dual Policy framework for Aggregation Optimization). This framework adaptively determines high-order connectivity to aggregate users and items using dual policy learning. Dual policy learning leverages two Deep-Q-Network models to exploit the user- and item-aware feedback from a GNN-R and boost the performance of the target GNN-R. Our proposed framework was evaluated with both non-KG-based and KG-based GNN-R models on six real-world datasets, and their results show that our proposed framework significantly enhances the recent base model, improving nDCG and Recall by up to 63.7% and 42.9%, respectively. Our implementation code is available at https://github.com/steve30572/DPAO/.|图形神经网络(GNN)为推荐任务提供了强有力的表示。基于 GNN 的推荐系统通过聚合来自远邻的信息来捕获用户和项目之间复杂的高阶连通性，从而提高推荐系统的性能。最近，知识图(KGs)也被纳入到用户项目交互图中，以提供更丰富的上下文信息; 它们被用来解决冷启动问题，并能够在基于 GNN 的推荐系统(GNN-Rs)中实现更可解释的聚合。然而，由于用户和项目的异构性，开发一个跨多个 GNN-R (如 LightGCN 和 KGAT)的有效聚合策略仍然是一个挑战。本文提出了一种新的基于强化学习的推荐系统消息传递框架，称之为聚合优化的双策略框架(DPAO)。该框架使用双策略学习自适应地确定与聚合用户和项目的高阶连通性。双策略学习利用两个 Deep-Q 网络模型来利用来自 GNN-R 的用户和项目感知反馈，提高目标 GNN-R 的性能。我们提出的框架在六个实际数据集上用非 KG 和基于 KG 的 GNN-R 模型进行了评估，结果表明，我们提出的框架显着增强了最近的基础模型，使 nDCG 和 Recall 分别提高了63.7% 和42.9% 。我们的实施守则可于 https://github.com/steve30572/dpao/索取。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Dual+Policy+Learning+for+Aggregation+Optimization+in+Graph+Neural+Network-based+Recommender+Systems)|0| -|[Addressing Heterophily in Graph Anomaly Detection: A Perspective of Graph Spectrum](https://doi.org/10.1145/3543507.3583268)|Yuan Gao, Xiang Wang, Xiangnan He, Zhenguang Liu, Huamin Feng, Yongdong Zhang|University of Science and Technology of China, China; Zhejiang University, China; Beijing Electronic Science And Technology Institute, China|Graph anomaly detection (GAD) suffers from heterophily — abnormal nodes are sparse so that they are connected to vast normal nodes. The current solutions upon Graph Neural Networks (GNNs) blindly smooth the representation of neiboring nodes, thus undermining the discriminative information of the anomalies. To alleviate the issue, recent studies identify and discard inter-class edges through estimating and comparing the node-level representation similarity. However, the representation of a single node can be misleading when the prediction error is high, thus hindering the performance of the edge indicator. In graph signal processing, the smoothness index is a widely adopted metric which plays the role of frequency in classical spectral analysis. Considering the ground truth Y to be a signal on graph, the smoothness index is equivalent to the value of the heterophily ratio. From this perspective, we aim to address the heterophily problem in the spectral domain. First, we point out that heterophily is positively associated with the frequency of a graph. Towards this end, we could prune inter-class edges by simply emphasizing and delineating the high-frequency components of the graph. Recall that graph Laplacian is a high-pass filter, we adopt it to measure the extent of 1-hop label changing of the center node and indicate high-frequency components. As GAD can be formulated as a semi-supervised binary classification problem, only part of the nodes are labeled. As an alternative, we use the prediction of the nodes to estimate it. Through our analysis, we show that prediction errors are less likely to affect the identification process. Extensive empirical evaluations on four benchmarks demonstrate the effectiveness of the indicator over popular homophilic, heterophilic, and tailored fraud detection methods. Our proposed indicator can effectively reduce the heterophily degree of the graph, thus boosting the overall GAD performance. Codes are open-sourced in https://github.com/blacksingular/GHRN.|图形异常检测(GAD)存在异质性ーー异常节点稀疏，因此它们连接到巨大的正常节点。现有的基于图神经网络(GNN)的解决方案盲目地平滑邻近节点的表示，从而破坏了异常的判别信息。为了缓解这一问题，最近的研究通过估计和比较节点级表示相似度来识别和丢弃类间边缘。然而，当预测误差较大时，单个节点的表示可能会产生误导，从而影响边缘指示器的性能。在图形信号处理中，平滑度指数是一个被广泛采用的度量指标，在经典的谱分析中起着频率的作用。考虑到地面真值 Y 是图上的一个信号，光滑度指标等价于异质比的值。从这个角度出发，我们的目标是解决谱域中的异质性问题。首先，我们指出异质性与图的频率成正相关。为了达到这个目的，我们可以通过简单地强调和描述图的高频成分来修剪类间边缘。回想一下，图拉普拉斯是一个高通滤波器，我们采用它来测量中心节点的1跳标签变化的程度，并指示高频分量。由于 GAD 可以表述为一个半监督的二进制分类问题，所以只对部分节点进行标记。作为一种替代方法，我们使用节点的预测来估计它。通过我们的分析，我们表明，预测错误不太可能影响识别过程。对四个基准的广泛的实证评估证明了指标的有效性超过流行的同质性，异质性和量身定制的欺诈检测方法。我们提出的指标可以有效地降低图的异构度，从而提高整体的 GAD 性能。代码在 https://github.com/blacksingular/ghrn 中是开源的。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Addressing+Heterophily+in+Graph+Anomaly+Detection:+A+Perspective+of+Graph+Spectrum)|0| +|[Word Sense Disambiguation by Refining Target Word Embedding](https://doi.org/10.1145/3543507.3583191)|Xuefeng Zhang, Richong Zhang, Xiaoyang Li, Fanshuang Kong, Junfan Chen, Samuel Mensah, Yongyi Mao|University of Ottawa, Canada; SKLSDE, School of Computer Science and Engineering, Beihang University, China; The University of Sheffield, United Kingdom|Word Sense Disambiguation (WSD) which aims to identify the correct sense of a target word appearing in a specific context is essential for web text analysis. The use of glosses has been explored as a means for WSD. However, only a few works model the correlation between the target context and gloss. We add to the body of literature by presenting a model that employs a multi-head attention mechanism on deep contextual features of the target word and candidate glosses to refine the target word embedding. Furthermore, to encourage the model to learn the relevant part of target features that align with the correct gloss, we recursively alternate attention on target word features and that of candidate glosses to gradually extract the relevant contextual features of the target word, refining its representation and strengthening the final disambiguation results. Empirical studies on the five most commonly used benchmark datasets show that our proposed model is effective and achieves state-of-the-art results.|词义消歧(WSD)是识别特定语境中目标词的正确意义，是网络文本分析的基础。水务署已研究使用注释作为一种方法。然而，只有少数作品模拟了目标语境和注释之间的相关性。本文提出了一种基于目标词深层语境特征和候选修饰语的多目标注意机制来完善目标词嵌入的模型，并对文献进行了补充。此外，为了鼓励模型学习与正确的注释相一致的目标特征的相关部分，我们递归地交替关注目标词特征和候选注释的特征，以逐渐提取目标词的相关上下文特征，完善其表示并加强最终的消歧结果。对五个最常用的基准数据集的实证研究表明，我们提出的模型是有效的，并取得了最先进的结果。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Word+Sense+Disambiguation+by+Refining+Target+Word+Embedding)|0| +|[Dual Policy Learning for Aggregation Optimization in Graph Neural Network-based Recommender Systems](https://doi.org/10.1145/3543507.3583241)|Heesoo Jung, Sangpil Kim, Hogun Park|Dept. of Electrical and Computer Engineering, Sungkyunkwan University, Republic of Korea and Dept. of Artificial Intelligence, Sungkyunkwan University, Republic of Korea; Dept. of Artificial Intelligence, Korea University, Republic of Korea; Dept. of Artificial Intelligence, Sungkyunkwan University, Republic of Korea|Graph Neural Networks (GNNs) provide powerful representations for recommendation tasks. GNN-based recommendation systems capture the complex high-order connectivity between users and items by aggregating information from distant neighbors and can improve the performance of recommender systems. Recently, Knowledge Graphs (KGs) have also been incorporated into the user-item interaction graph to provide more abundant contextual information; they are exploited to address cold-start problems and enable more explainable aggregation in GNN-based recommender systems (GNN-Rs). However, due to the heterogeneous nature of users and items, developing an effective aggregation strategy that works across multiple GNN-Rs, such as LightGCN and KGAT, remains a challenge. In this paper, we propose a novel reinforcement learning-based message passing framework for recommender systems, which we call DPAO (Dual Policy framework for Aggregation Optimization). This framework adaptively determines high-order connectivity to aggregate users and items using dual policy learning. Dual policy learning leverages two Deep-Q-Network models to exploit the user- and item-aware feedback from a GNN-R and boost the performance of the target GNN-R. Our proposed framework was evaluated with both non-KG-based and KG-based GNN-R models on six real-world datasets, and their results show that our proposed framework significantly enhances the recent base model, improving nDCG and Recall by up to 63.7% and 42.9%, respectively. Our implementation code is available at https://github.com/steve30572/DPAO/.|图形神经网络(GNN)为推荐任务提供了强有力的表示。基于 GNN 的推荐系统通过聚合来自远邻的信息来捕获用户和项目之间复杂的高阶连通性，从而提高推荐系统的性能。最近，知识图(KGs)也被纳入到用户项目交互图中，以提供更丰富的上下文信息; 它们被用来解决冷启动问题，并能够在基于 GNN 的推荐系统(GNN-Rs)中实现更可解释的聚合。然而，由于用户和项目的异构性，开发一个跨多个 GNN-R (如 LightGCN 和 KGAT)的有效聚合策略仍然是一个挑战。本文提出了一种新的基于强化学习的推荐系统消息传递框架，称之为聚合优化的双策略框架(DPAO)。该框架使用双策略学习自适应地确定与聚合用户和项目的高阶连通性。双策略学习利用两个 Deep-Q 网络模型来利用来自 GNN-R 的用户和项目感知反馈，提高目标 GNN-R 的性能。我们提出的框架在六个实际数据集上用非 KG 和基于 KG 的 GNN-R 模型进行了评估，结果表明，我们提出的框架显着增强了最近的基础模型，使 nDCG 和 Recall 分别提高了63.7% 和42.9% 。我们的实施守则可于 https://github.com/steve30572/dpao/索取。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Dual+Policy+Learning+for+Aggregation+Optimization+in+Graph+Neural+Network-based+Recommender+Systems)|0| +|[Addressing Heterophily in Graph Anomaly Detection: A Perspective of Graph Spectrum](https://doi.org/10.1145/3543507.3583268)|Yuan Gao, Xiang Wang, Xiangnan He, Zhenguang Liu, Huamin Feng, Yongdong Zhang|Beijing Electronic Science And Technology Institute, China; Zhejiang University, China; University of Science and Technology of China, China|Graph anomaly detection (GAD) suffers from heterophily — abnormal nodes are sparse so that they are connected to vast normal nodes. The current solutions upon Graph Neural Networks (GNNs) blindly smooth the representation of neiboring nodes, thus undermining the discriminative information of the anomalies. To alleviate the issue, recent studies identify and discard inter-class edges through estimating and comparing the node-level representation similarity. However, the representation of a single node can be misleading when the prediction error is high, thus hindering the performance of the edge indicator. In graph signal processing, the smoothness index is a widely adopted metric which plays the role of frequency in classical spectral analysis. Considering the ground truth Y to be a signal on graph, the smoothness index is equivalent to the value of the heterophily ratio. From this perspective, we aim to address the heterophily problem in the spectral domain. First, we point out that heterophily is positively associated with the frequency of a graph. Towards this end, we could prune inter-class edges by simply emphasizing and delineating the high-frequency components of the graph. Recall that graph Laplacian is a high-pass filter, we adopt it to measure the extent of 1-hop label changing of the center node and indicate high-frequency components. As GAD can be formulated as a semi-supervised binary classification problem, only part of the nodes are labeled. As an alternative, we use the prediction of the nodes to estimate it. Through our analysis, we show that prediction errors are less likely to affect the identification process. Extensive empirical evaluations on four benchmarks demonstrate the effectiveness of the indicator over popular homophilic, heterophilic, and tailored fraud detection methods. Our proposed indicator can effectively reduce the heterophily degree of the graph, thus boosting the overall GAD performance. Codes are open-sourced in https://github.com/blacksingular/GHRN.|图形异常检测(GAD)存在异质性ーー异常节点稀疏，因此它们连接到巨大的正常节点。现有的基于图神经网络(GNN)的解决方案盲目地平滑邻近节点的表示，从而破坏了异常的判别信息。为了缓解这一问题，最近的研究通过估计和比较节点级表示相似度来识别和丢弃类间边缘。然而，当预测误差较大时，单个节点的表示可能会产生误导，从而影响边缘指示器的性能。在图形信号处理中，平滑度指数是一个被广泛采用的度量指标，在经典的谱分析中起着频率的作用。考虑到地面真值 Y 是图上的一个信号，光滑度指标等价于异质比的值。从这个角度出发，我们的目标是解决谱域中的异质性问题。首先，我们指出异质性与图的频率成正相关。为了达到这个目的，我们可以通过简单地强调和描述图的高频成分来修剪类间边缘。回想一下，图拉普拉斯是一个高通滤波器，我们采用它来测量中心节点的1跳标签变化的程度，并指示高频分量。由于 GAD 可以表述为一个半监督的二进制分类问题，所以只对部分节点进行标记。作为一种替代方法，我们使用节点的预测来估计它。通过我们的分析，我们表明，预测错误不太可能影响识别过程。对四个基准的广泛的实证评估证明了指标的有效性超过流行的同质性，异质性和量身定制的欺诈检测方法。我们提出的指标可以有效地降低图的异构度，从而提高整体的 GAD 性能。代码在 https://github.com/blacksingular/ghrn 中是开源的。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Addressing+Heterophily+in+Graph+Anomaly+Detection:+A+Perspective+of+Graph+Spectrum)|0| |[Ginver: Generative Model Inversion Attacks Against Collaborative Inference](https://doi.org/10.1145/3543507.3583306)|Yupeng Yin, Xianglong Zhang, Huanle Zhang, Feng Li, Yue Yu, Xiuzhen Cheng, Pengfei Hu|School of Computer Science and Technology, Shandong University, China; National University of Defense Technology, China and Peng Cheng Laboratory, China|Deep Learning (DL) has been widely adopted in almost all domains, from threat recognition to medical diagnosis. Albeit its supreme model accuracy, DL imposes a heavy burden on devices as it incurs overwhelming system overhead to execute DL models, especially on Internet-of-Things (IoT) and edge devices. Collaborative inference is a promising approach to supporting DL models, by which the data owner (the victim) runs the first layers of the model on her local device and then a cloud provider (the adversary) runs the remaining layers of the model. Compared to offloading the entire model to the cloud, the collaborative inference approach is more data privacy-preserving as the owner’s model input is not exposed to outsiders. However, we show in this paper that the adversary can restore the victim’s model input by exploiting the output of the victim’s local model. Our attack is dubbed Ginver 1: Generative model inversion attacks against collaborative inference. Once trained, Ginver can infer the victim’s unseen model inputs without remaking the inversion attack model and thus has the generative capability. We extensively evaluate Ginver under different settings (e.g., white-box and black-box of the victim’s local model) and applications (e.g., CIFAR10 and FaceScrub datasets). The experimental results show that Ginver recovers high-quality images from the victims.|从威胁识别到医学诊断，深度学习已被广泛应用于几乎所有的领域。尽管 DL 的模型精确度最高，但它对设备造成了沉重的负担，因为它在执行 DL 模型时会产生巨大的系统开销，特别是在物联网(IoT)和边缘设备上。协作推理是支持 DL 模型的一种有前途的方法，通过这种方法，数据所有者(受害者)在其本地设备上运行模型的第一层，然后云提供者(对手)运行模型的其余层。与将整个模型卸载到云中相比，协作推理方法更能保护数据隐私，因为所有者的模型输入不会暴露给外部人员。然而，本文证明了对手可以通过利用被害人局部模型的输出来恢复被害人的模型输入。我们的攻击被称为 Ginver 1: 针对协作推理的生成模型反转攻击。一旦被训练，Ginver 可以推断出受害者看不见的模型输入，而无需重建反转攻击模型，因此具有生成能力。我们在不同的设置(例如，受害者本地模型的白盒和黑盒)和应用程序(例如，CIFAR10和 FaceScrub 数据集)下广泛评估 Ginver。实验结果表明，Ginver 可以从受害者身上恢复出高质量的图像。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Ginver:+Generative+Model+Inversion+Attacks+Against+Collaborative+Inference)|0| |[All Your Shops Are Belong to Us: Security Weaknesses in E-commerce Platforms](https://doi.org/10.1145/3543507.3583319)|Rohan Pagey, Mohammad Mannan, Amr M. Youssef|Concordia Institute for Information Systems Engineering, Concordia University, Canada|Software as a Service (SaaS) e-commerce platforms for merchants allow individual business owners to set up their online stores almost instantly. Prior work has shown that the checkout flows and payment integration of some e-commerce applications are vulnerable to logic bugs with serious financial consequences, e.g., allowing “shopping for free”. Apart from checkout and payment integration, vulnerabilities in other e-commerce operations have remained largely unexplored, even though they can have far more serious consequences, e.g., enabling “store takeover”. In this work, we design and implement a security evaluation framework to uncover security vulnerabilities in e-commerce operations beyond checkout/payment integration. We use this framework to analyze 32 representative e-commerce platforms, including web services of 24 commercial SaaS platforms and 15 associated Android apps, and 8 open source platforms; these platforms host over 10 million stores as approximated through Google dorks. We uncover several new vulnerabilities with serious consequences, e.g., allowing an attacker to take over all stores under a platform, and listing illegal products at a victim’s store—in addition to “shopping for free” bugs, without exploiting the checkout/payment process. We found 12 platforms vulnerable to store takeover (affecting 41000+ stores) and 6 platforms vulnerable to shopping for free (affecting 19000+ stores, approximated via Google dorks on Oct. 8, 2022). We have responsibly disclosed the vulnerabilities to all affected parties, and requested four CVEs (three assigned, and one is pending review).|软件即服务(SaaS)商家电子商务平台允许个体企业主几乎立即建立他们的在线商店。先前的研究已经表明，一些电子商务应用程序的结帐流程和支付集成容易受到逻辑错误的影响，这些逻辑错误会带来严重的财务后果，例如，允许“免费购物”。除了结账和支付集成，其他电子商务操作中的漏洞基本上还没有得到探索，尽管它们可能产生更为严重的后果，例如“商店接管”。在这项工作中，我们设计和实现了一个安全评估框架，以揭示电子商务运作中的安全漏洞超越结帐/支付集成。我们使用这个框架来分析32个具有代表性的电子商务平台，包括24个商业 SaaS 平台和15个相关的 Android 应用程序的网络服务，以及8个开源平台; 这些平台拥有超过1000万个商店，大致相当于 Google 呆子的数量。我们发现了几个具有严重后果的新漏洞，例如，允许攻击者在一个平台下接管所有商店，以及在受害者的商店中列出非法产品ーー除了“免费购物”的漏洞之外，还没有利用结帐/付款过程。我们发现有12个平台容易被商店接管(影响到41000多家商店) ，6个平台容易被免费购物(影响到19000多家商店，大约在2022年10月8日通过谷歌书呆子)。我们已经负责任地向所有受影响的方面披露了漏洞，并要求四个 CVE (三个分配，一个正在等待审查)。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=All+Your+Shops+Are+Belong+to+Us:+Security+Weaknesses+in+E-commerce+Platforms)|0| -|[An Empirical Study of the Usage of Checksums for Web Downloads](https://doi.org/10.1145/3543507.3583326)|Gaël Bernard, Rémi Coudert, Bertil Chapuis, Kévin Huguenin|Department of Information Systems, University of Lausanne, Switzerland; EPFL, Switzerland; University of Applied Sciences Western Switzerland, Switzerland|Checksums, typically provided on webpages and generated from cryptographic hash functions (e.g., MD5, SHA256) or signature schemes (e.g., PGP), are commonly used on websites to enable users to verify that the files they download have not been tampered with when stored on possibly untrusted servers. In this paper, we elucidate the current practices regarding the usage of checksums for web downloads (hash functions used, visibility and validity of checksums, type of websites and files, etc.), as this has been mostly overlooked so far. Using a snowball-sampling strategy for the 200000 most popular domains of the Web, we first crawled a dataset of 8.5M webpages, from which we built, through an active-learning approach, a unique dataset of 277 diverse webpages that contain checksums. Our analysis of these webpages reveals interesting findings about the usage of checksums. For instance, it shows that checksums are used mostly to verify program files, that weak hash functions are frequently used, and that a non-negligible proportion of the checksums provided on webpages do not match that of their associated files. Finally, we complement our analysis with a survey of the webmasters of the considered webpages (N = 26), thus shedding light on the reasons behind the checksum-related choices they make.|校验和通常在网页上提供，由加密散列函数(例如 MD5、 SHA256)或签名方案(例如 PGP)产生，通常在网站上使用，使用户能够验证他们下载的文件在存储在可能不受信任的服务器上时没有被篡改。在这篇文章中，我们阐述了目前使用校验和进行网页下载的做法(使用的散列函数，校验和的可见性和有效性，网站和文件的类型等) ，因为这是迄今为止大多数被忽视的。使用滚雪球抽样策略对200000个最流行的网络领域，我们首先抓取了850万个网页的数据集，从中，我们通过一个主动学习的方法，建立了一个包含校验和的277个不同网页的独特数据集。我们对这些网页的分析揭示了校验和使用的有趣发现。例如，它表明校验和主要用于验证程序文件，经常使用弱散列函数，网页上提供的校验和中有不可忽视的比例与相关文件的校验和不匹配。最后，我们通过对所考虑的网页(N = 26)的网站管理员进行调查来补充我们的分析，从而阐明他们做出校验和相关选择背后的原因。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=An+Empirical+Study+of+the+Usage+of+Checksums+for+Web+Downloads)|0| -|[Quantifying and Defending against Privacy Threats on Federated Knowledge Graph Embedding](https://doi.org/10.1145/3543507.3583450)|Yuke Hu, Wei Liang, Ruofan Wu, Kai Xiao, Weiqiang Wang, Xiaochen Li, Jinfei Liu, Zhan Qin|Zhejiang University, China and HIC-ZJU, China; Ant Group, China|Knowledge Graph Embedding (KGE) is a fundamental technique that extracts expressive representation from knowledge graph (KG) to facilitate diverse downstream tasks. The emerging federated KGE (FKGE) collaboratively trains from distributed KGs held among clients while avoiding exchanging clients' sensitive raw KGs, which can still suffer from privacy threats as evidenced in other federated model trainings (e.g., neural networks). However, quantifying and defending against such privacy threats remain unexplored for FKGE which possesses unique properties not shared by previously studied models. In this paper, we conduct the first holistic study of the privacy threat on FKGE from both attack and defense perspectives. For the attack, we quantify the privacy threat by proposing three new inference attacks, which reveal substantial privacy risk by successfully inferring the existence of the KG triple from victim clients. For the defense, we propose DP-Flames, a novel differentially private FKGE with private selection, which offers a better privacy-utility tradeoff by exploiting the entity-binding sparse gradient property of FKGE and comes with a tight privacy accountant by incorporating the state-of-the-art private selection technique. We further propose an adaptive privacy budget allocation policy to dynamically adjust defense magnitude across the training procedure. Comprehensive evaluations demonstrate that the proposed defense can successfully mitigate the privacy threat by effectively reducing the success rate of inference attacks from $83.1\%$ to $59.4\%$ on average with only a modest utility decrease.|知识图嵌入(KGE)是一种从知识图中提取表达式的基本技术，可以方便地完成不同的下游任务。新兴的联邦 KGE (FKGE)协同培训客户之间的分布式幼儿园，同时避免交换客户敏感的原始幼儿园，这些幼儿园仍然可能受到隐私威胁，这在其他联邦模型培训(例如，神经网络)中得到了证明。然而，量化和防御这种隐私威胁仍然没有探索的 FKGE，具有独特的性质没有共享以前的研究模型。本文首次从攻击和防御两个角度对 FKGE 隐私威胁进行了全面的研究。对于这种攻击，我们提出了三种新的推理攻击来量化隐私威胁，通过成功地从受害客户端推断出 KG 三元组的存在，揭示了巨大的隐私风险。对于辩方，我们提出 DP-Flames，一种具有私有选择的新型差异私有 FKGE，它通过利用 FKGE 的实体绑定稀疏梯度特性提供了更好的隐私-效用权衡，并通过结合最先进的私有选择技术提供了一个严密的隐私会计。我们进一步提出了一个自适应的隐私预算分配策略，以动态调整整个训练过程中的防御大小。综合评估表明，提出的防御能够成功地减轻隐私威胁，有效地减少推理攻击的成功率从83.1% $至59.4% $平均只有适度的效用减少。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Quantifying+and+Defending+against+Privacy+Threats+on+Federated+Knowledge+Graph+Embedding)|0| +|[An Empirical Study of the Usage of Checksums for Web Downloads](https://doi.org/10.1145/3543507.3583326)|Gaël Bernard, Rémi Coudert, Bertil Chapuis, Kévin Huguenin|University of Applied Sciences Western Switzerland, Switzerland; Department of Information Systems, University of Lausanne, Switzerland; EPFL, Switzerland|Checksums, typically provided on webpages and generated from cryptographic hash functions (e.g., MD5, SHA256) or signature schemes (e.g., PGP), are commonly used on websites to enable users to verify that the files they download have not been tampered with when stored on possibly untrusted servers. In this paper, we elucidate the current practices regarding the usage of checksums for web downloads (hash functions used, visibility and validity of checksums, type of websites and files, etc.), as this has been mostly overlooked so far. Using a snowball-sampling strategy for the 200000 most popular domains of the Web, we first crawled a dataset of 8.5M webpages, from which we built, through an active-learning approach, a unique dataset of 277 diverse webpages that contain checksums. Our analysis of these webpages reveals interesting findings about the usage of checksums. For instance, it shows that checksums are used mostly to verify program files, that weak hash functions are frequently used, and that a non-negligible proportion of the checksums provided on webpages do not match that of their associated files. Finally, we complement our analysis with a survey of the webmasters of the considered webpages (N = 26), thus shedding light on the reasons behind the checksum-related choices they make.|校验和通常在网页上提供，由加密散列函数(例如 MD5、 SHA256)或签名方案(例如 PGP)产生，通常在网站上使用，使用户能够验证他们下载的文件在存储在可能不受信任的服务器上时没有被篡改。在这篇文章中，我们阐述了目前使用校验和进行网页下载的做法(使用的散列函数，校验和的可见性和有效性，网站和文件的类型等) ，因为这是迄今为止大多数被忽视的。使用滚雪球抽样策略对200000个最流行的网络领域，我们首先抓取了850万个网页的数据集，从中，我们通过一个主动学习的方法，建立了一个包含校验和的277个不同网页的独特数据集。我们对这些网页的分析揭示了校验和使用的有趣发现。例如，它表明校验和主要用于验证程序文件，经常使用弱散列函数，网页上提供的校验和中有不可忽视的比例与相关文件的校验和不匹配。最后，我们通过对所考虑的网页(N = 26)的网站管理员进行调查来补充我们的分析，从而阐明他们做出校验和相关选择背后的原因。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=An+Empirical+Study+of+the+Usage+of+Checksums+for+Web+Downloads)|0| +|[Quantifying and Defending against Privacy Threats on Federated Knowledge Graph Embedding](https://doi.org/10.1145/3543507.3583450)|Yuke Hu, Wei Liang, Ruofan Wu, Kai Xiao, Weiqiang Wang, Xiaochen Li, Jinfei Liu, Zhan Qin|Ant Group, China; Zhejiang University, China and HIC-ZJU, China|Knowledge Graph Embedding (KGE) is a fundamental technique that extracts expressive representation from knowledge graph (KG) to facilitate diverse downstream tasks. The emerging federated KGE (FKGE) collaboratively trains from distributed KGs held among clients while avoiding exchanging clients' sensitive raw KGs, which can still suffer from privacy threats as evidenced in other federated model trainings (e.g., neural networks). However, quantifying and defending against such privacy threats remain unexplored for FKGE which possesses unique properties not shared by previously studied models. In this paper, we conduct the first holistic study of the privacy threat on FKGE from both attack and defense perspectives. For the attack, we quantify the privacy threat by proposing three new inference attacks, which reveal substantial privacy risk by successfully inferring the existence of the KG triple from victim clients. For the defense, we propose DP-Flames, a novel differentially private FKGE with private selection, which offers a better privacy-utility tradeoff by exploiting the entity-binding sparse gradient property of FKGE and comes with a tight privacy accountant by incorporating the state-of-the-art private selection technique. We further propose an adaptive privacy budget allocation policy to dynamically adjust defense magnitude across the training procedure. Comprehensive evaluations demonstrate that the proposed defense can successfully mitigate the privacy threat by effectively reducing the success rate of inference attacks from $83.1\%$ to $59.4\%$ on average with only a modest utility decrease.|知识图嵌入(KGE)是一种从知识图中提取表达式的基本技术，可以方便地完成不同的下游任务。新兴的联邦 KGE (FKGE)协同培训客户之间的分布式幼儿园，同时避免交换客户敏感的原始幼儿园，这些幼儿园仍然可能受到隐私威胁，这在其他联邦模型培训(例如，神经网络)中得到了证明。然而，量化和防御这种隐私威胁仍然没有探索的 FKGE，具有独特的性质没有共享以前的研究模型。本文首次从攻击和防御两个角度对 FKGE 隐私威胁进行了全面的研究。对于这种攻击，我们提出了三种新的推理攻击来量化隐私威胁，通过成功地从受害客户端推断出 KG 三元组的存在，揭示了巨大的隐私风险。对于辩方，我们提出 DP-Flames，一种具有私有选择的新型差异私有 FKGE，它通过利用 FKGE 的实体绑定稀疏梯度特性提供了更好的隐私-效用权衡，并通过结合最先进的私有选择技术提供了一个严密的隐私会计。我们进一步提出了一个自适应的隐私预算分配策略，以动态调整整个训练过程中的防御大小。综合评估表明，提出的防御能够成功地减轻隐私威胁，有效地减少推理攻击的成功率从83.1% $至59.4% $平均只有适度的效用减少。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Quantifying+and+Defending+against+Privacy+Threats+on+Federated+Knowledge+Graph+Embedding)|0| |[Sanitizing Sentence Embeddings (and Labels) for Local Differential Privacy](https://doi.org/10.1145/3543507.3583512)|Minxin Du, Xiang Yue, Sherman S. M. Chow, Huan Sun|Department of Computer Science and Engineering, The Ohio State University, USA; Department of Information Engineering, The Chinese University of Hong Kong, Hong Kong|Differentially private (DP) learning, notably DP stochastic gradient descent (DP-SGD), has limited applicability in fine-tuning gigantic pre-trained language models (LMs) for natural language processing tasks. The culprit is the perturbation of gradients (as gigantic as entire models), leading to significant efficiency and accuracy drops. We show how to achieve metric-based local DP (LDP) by sanitizing (high-dimensional) sentence embedding, extracted by LMs and much smaller than gradients. For potential utility improvement, we impose a consistency constraint on the sanitization. We explore two approaches: One is brand new and can directly output consistent noisy embeddings; the other is an upgradation with post-processing. To further mitigate “the curse of dimensionality,” we introduce two trainable linear maps for mediating dimensions without hurting privacy or utility. Our protection can effectively defend against privacy threats on embeddings. It also naturally extends to inference. Our experiments1 show that we reach the non-private accuracy under properly configured parameters, e.g., 0.92 for SST-2 with a privacy budget ϵ = 10 and the reduced dimension as 16. We also sanitize the label for LDP (with another small privacy budget) with limited accuracy losses to fully protect every sequence-label pair.|差异私有(DP)学习，特别是 DP 随机梯度下降(DP-sgd) ，在为自然语言处理任务微调庞大的预训练语言模型(LMs)方面的适用性有限。罪魁祸首是梯度的扰动(像整个模型一样巨大) ，导致了显著的效率和精度下降。我们展示了如何通过消毒(高维)句子嵌入、利用 LMs 提取和比梯度小得多的方法来实现基于度量的局部 DP (LDP)。对于潜在的效用改进，我们对消毒施加一致性约束。我们探索了两种方法: 一种是全新的，可以直接输出一致的噪声嵌入; 另一种是后处理的升级。为了进一步减轻“维数灾难”，我们引入了两个可训练的线性映射，用于在不损害隐私或效用的情况下调节维度。我们的保护可以有效地防御嵌入式系统的隐私威胁。它也自然地延伸到推理。我们的实验表明，在适当的参数配置下，我们达到了非私有精度，例如，0.92的 SST-2与私有预算 ε = 10和降维为16。我们还消毒的标签为 LDP (与另一个小的隐私预算)与有限的准确性损失，以充分保护每个序列标签对。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Sanitizing+Sentence+Embeddings+(and+Labels)+for+Local+Differential+Privacy)|0| |[Heterogeneous Federated Knowledge Graph Embedding Learning and Unlearning](https://doi.org/10.1145/3543507.3583305)|Xiangrong Zhu, Guangyao Li, Wei Hu|State Key Laboratory for Novel Software Technology, Nanjing University, China and National Institute of Healthcare Data Science, Nanjing University, China; State Key Laboratory for Novel Software Technology, Nanjing University, China|Federated Learning (FL) recently emerges as a paradigm to train a global machine learning model across distributed clients without sharing raw data. Knowledge Graph (KG) embedding represents KGs in a continuous vector space, serving as the backbone of many knowledge-driven applications. As a promising combination, federated KG embedding can fully take advantage of knowledge learned from different clients while preserving the privacy of local data. However, realistic problems such as data heterogeneity and knowledge forgetting still remain to be concerned. In this paper, we propose FedLU, a novel FL framework for heterogeneous KG embedding learning and unlearning. To cope with the drift between local optimization and global convergence caused by data heterogeneity, we propose mutual knowledge distillation to transfer local knowledge to global, and absorb global knowledge back. Moreover, we present an unlearning method based on cognitive neuroscience, which combines retroactive interference and passive decay to erase specific knowledge from local clients and propagate to the global model by reusing knowledge distillation. We construct new datasets for assessing realistic performance of the state-of-the-arts. Extensive experiments show that FedLU achieves superior results in both link prediction and knowledge forgetting.|联邦学习(Federated Learning，FL)最近作为一种模式出现，它可以在不共享原始数据的情况下跨分布式客户机训练全局机器学习模型。知识图(KG)嵌入表示连续向量空间中的 KG，作为许多知识驱动应用程序的骨干。作为一种有前途的组合，联邦 KG 嵌入可以充分利用从不同客户端获得的知识，同时保护本地数据的隐私。然而，数据异构性和知识遗忘等现实问题仍然值得关注。本文提出了一种适用于异构 KG 嵌入学习和非学习的 FL 框架 FedLU。针对数据异构导致的局部优化和全局收敛之间的漂移问题，提出了互知识提取方法，将局部知识转化为全局知识，并将全局知识吸收回来。此外，我们还提出了一种基于认知神经科学的去学习方法，它结合了追溯干扰和被动衰减，从本地客户删除特定的知识，并通过重复使用知识提取传播到全球模型。我们建立了新的数据集来评估现实性能的最新技术。大量实验表明，FedLU 在链路预测和知识遗忘方面都取得了较好的效果。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Heterogeneous+Federated+Knowledge+Graph+Embedding+Learning+and+Unlearning)|0| -|[A Single Vector Is Not Enough: Taxonomy Expansion via Box Embeddings](https://doi.org/10.1145/3543507.3583310)|Song Jiang, Qiyue Yao, Qifan Wang, Yizhou Sun|University of California, Los Angeles, USA; Meta AI, USA|Taxonomies, which organize knowledge hierarchically, support various practical web applications such as product navigation in online shopping and user profile tagging on social platforms. Given the continued and rapid emergence of new entities, maintaining a comprehensive taxonomy in a timely manner through human annotation is prohibitively expensive. Therefore, expanding a taxonomy automatically with new entities is essential. Most existing methods for expanding taxonomies encode entities into vector embeddings (i.e., single points). However, we argue that vectors are insufficient to model the “is-a” hierarchy in taxonomy (asymmetrical relation), because two points can only represent pairwise similarity (symmetrical relation). To this end, we propose to project taxonomy entities into boxes (i.e., hyperrectangles). Two boxes can be "contained", "disjoint" and "intersecting", thus naturally representing an asymmetrical taxonomic hierarchy. Upon box embeddings, we propose a novel model BoxTaxo for taxonomy expansion. The core of BoxTaxo is to learn boxes for entities to capture their child-parent hierarchies. To achieve this, BoxTaxo optimizes the box embeddings from a joint view of geometry and probability. BoxTaxo also offers an easy and natural way for inference: examine whether the box of a given new entity is fully enclosed inside the box of a candidate parent from the existing taxonomy. Extensive experiments on two benchmarks demonstrate the effectiveness of BoxTaxo compared to vector based models.|分类法按层次组织知识，支持各种实际的网络应用程序，如在线购物中的产品导航和社交平台上的用户配置文件标签。鉴于新实体的持续和快速出现，通过人工注释及时维护全面的分类是非常昂贵的。因此，使用新实体自动扩展分类法是必不可少的。大多数现有的分类扩展方法都将实体编码为向量嵌入(即单点)。然而，我们认为向量不足以模拟分类学中的“ is-a”层次结构(非对称关系) ，因为两点只能表示成对的相似性(对称关系)。为此，我们建议将分类实体投影到盒子(即超矩形)中。两个盒子可以“包含”、“不相交”和“交叉”，因此自然地代表了一个不对称的分类层次。在盒子嵌入的基础上，提出了一种新的分类扩展模型 BoxTaxo。BoxTaxo 的核心是为实体学习用于捕获其子-父层次结构的框。为了实现这一点，BoxTaxo 从几何和概率的联合视图优化了盒子嵌入。BoxTaxo 还提供了一种简单而自然的推理方法: 检查给定新实体的框是否完全封闭在现有分类法的候选父类的框中。在两个基准上的大量实验证明了 BoxTaxo 与基于向量的模型相比的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Single+Vector+Is+Not+Enough:+Taxonomy+Expansion+via+Box+Embeddings)|0| -|[Knowledge Graph Question Answering with Ambiguous Query](https://doi.org/10.1145/3543507.3583316)|Lihui Liu, Yuzhong Chen, Mahashweta Das, Hao Yang, Hanghang Tong|Department of Computer Science, University of Illinois at Urbana Champaign, USA; visa research, USA|Knowledge graph question answering aims to identify answers of the query according to the facts in the knowledge graph. In the vast majority of the existing works, the input queries are considered perfect and can precisely express the user’s query intention. However, in reality, input queries might be ambiguous and elusive which only contain a limited amount of information. Directly answering these ambiguous queries may yield unwanted answers and deteriorate user experience. In this paper, we propose PReFNet which focuses on answering ambiguous queries with pseudo relevance feedback on knowledge graphs. In order to leverage the hidden (pseudo) relevance information existed in the results that are initially returned from a given query, PReFNet treats the top-k returned candidate answers as a set of most relevant answers, and uses variational Bayesian inference to infer user’s query intention. To boost the quality of the inferred queries, a neighborhood embedding based VGAE model is used to prune inferior inferred queries. The inferred high quality queries will be returned to the users to help them search with ease. Moreover, all the high-quality candidate nodes will be re-ranked according to the inferred queries. The experiment results show that our proposed method can recommend high-quality query graphs to users and improve the question answering accuracy.|知识图问答的目的是根据知识图中的事实来识别问题的答案。在现有的大多数工作中，输入查询被认为是完美的，可以准确地表达用户的查询意图。然而，在现实中，输入查询可能是模糊和难以捉摸的，只包含有限数量的信息。直接回答这些模棱两可的问题可能会得到不想要的答案，并损害用户体验。在这篇文章中，我们提出了一种基于知识图的伪关联反馈回答模糊查询的方法。为了利用最初从给定查询返回的结果中存在的隐藏(伪)相关信息，prefNet 将返回的候选答案视为一组最相关的答案，并使用变异贝叶斯推断来推断用户的查询意图。为了提高推理查询的质量，提出了一种基于邻域嵌入的 VGAE 模型来裁剪劣质推理查询。推断出的高质量查询将返回给用户，以帮助他们轻松搜索。此外，所有高质量的候选节点将根据推断的查询进行重新排序。实验结果表明，该方法可以向用户推荐高质量的查询图，提高问答的准确率。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Knowledge+Graph+Question+Answering+with+Ambiguous+Query)|0| +|[A Single Vector Is Not Enough: Taxonomy Expansion via Box Embeddings](https://doi.org/10.1145/3543507.3583310)|Song Jiang, Qiyue Yao, Qifan Wang, Yizhou Sun|Meta AI, USA; University of California, Los Angeles, USA|Taxonomies, which organize knowledge hierarchically, support various practical web applications such as product navigation in online shopping and user profile tagging on social platforms. Given the continued and rapid emergence of new entities, maintaining a comprehensive taxonomy in a timely manner through human annotation is prohibitively expensive. Therefore, expanding a taxonomy automatically with new entities is essential. Most existing methods for expanding taxonomies encode entities into vector embeddings (i.e., single points). However, we argue that vectors are insufficient to model the “is-a” hierarchy in taxonomy (asymmetrical relation), because two points can only represent pairwise similarity (symmetrical relation). To this end, we propose to project taxonomy entities into boxes (i.e., hyperrectangles). Two boxes can be "contained", "disjoint" and "intersecting", thus naturally representing an asymmetrical taxonomic hierarchy. Upon box embeddings, we propose a novel model BoxTaxo for taxonomy expansion. The core of BoxTaxo is to learn boxes for entities to capture their child-parent hierarchies. To achieve this, BoxTaxo optimizes the box embeddings from a joint view of geometry and probability. BoxTaxo also offers an easy and natural way for inference: examine whether the box of a given new entity is fully enclosed inside the box of a candidate parent from the existing taxonomy. Extensive experiments on two benchmarks demonstrate the effectiveness of BoxTaxo compared to vector based models.|分类法按层次组织知识，支持各种实际的网络应用程序，如在线购物中的产品导航和社交平台上的用户配置文件标签。鉴于新实体的持续和快速出现，通过人工注释及时维护全面的分类是非常昂贵的。因此，使用新实体自动扩展分类法是必不可少的。大多数现有的分类扩展方法都将实体编码为向量嵌入(即单点)。然而，我们认为向量不足以模拟分类学中的“ is-a”层次结构(非对称关系) ，因为两点只能表示成对的相似性(对称关系)。为此，我们建议将分类实体投影到盒子(即超矩形)中。两个盒子可以“包含”、“不相交”和“交叉”，因此自然地代表了一个不对称的分类层次。在盒子嵌入的基础上，提出了一种新的分类扩展模型 BoxTaxo。BoxTaxo 的核心是为实体学习用于捕获其子-父层次结构的框。为了实现这一点，BoxTaxo 从几何和概率的联合视图优化了盒子嵌入。BoxTaxo 还提供了一种简单而自然的推理方法: 检查给定新实体的框是否完全封闭在现有分类法的候选父类的框中。在两个基准上的大量实验证明了 BoxTaxo 与基于向量的模型相比的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Single+Vector+Is+Not+Enough:+Taxonomy+Expansion+via+Box+Embeddings)|0| +|[Knowledge Graph Question Answering with Ambiguous Query](https://doi.org/10.1145/3543507.3583316)|Lihui Liu, Yuzhong Chen, Mahashweta Das, Hao Yang, Hanghang Tong|visa research, USA; Department of Computer Science, University of Illinois at Urbana Champaign, USA|Knowledge graph question answering aims to identify answers of the query according to the facts in the knowledge graph. In the vast majority of the existing works, the input queries are considered perfect and can precisely express the user’s query intention. However, in reality, input queries might be ambiguous and elusive which only contain a limited amount of information. Directly answering these ambiguous queries may yield unwanted answers and deteriorate user experience. In this paper, we propose PReFNet which focuses on answering ambiguous queries with pseudo relevance feedback on knowledge graphs. In order to leverage the hidden (pseudo) relevance information existed in the results that are initially returned from a given query, PReFNet treats the top-k returned candidate answers as a set of most relevant answers, and uses variational Bayesian inference to infer user’s query intention. To boost the quality of the inferred queries, a neighborhood embedding based VGAE model is used to prune inferior inferred queries. The inferred high quality queries will be returned to the users to help them search with ease. Moreover, all the high-quality candidate nodes will be re-ranked according to the inferred queries. The experiment results show that our proposed method can recommend high-quality query graphs to users and improve the question answering accuracy.|知识图问答的目的是根据知识图中的事实来识别问题的答案。在现有的大多数工作中，输入查询被认为是完美的，可以准确地表达用户的查询意图。然而，在现实中，输入查询可能是模糊和难以捉摸的，只包含有限数量的信息。直接回答这些模棱两可的问题可能会得到不想要的答案，并损害用户体验。在这篇文章中，我们提出了一种基于知识图的伪关联反馈回答模糊查询的方法。为了利用最初从给定查询返回的结果中存在的隐藏(伪)相关信息，prefNet 将返回的候选答案视为一组最相关的答案，并使用变异贝叶斯推断来推断用户的查询意图。为了提高推理查询的质量，提出了一种基于邻域嵌入的 VGAE 模型来裁剪劣质推理查询。推断出的高质量查询将返回给用户，以帮助他们轻松搜索。此外，所有高质量的候选节点将根据推断的查询进行重新排序。实验结果表明，该方法可以向用户推荐高质量的查询图，提高问答的准确率。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Knowledge+Graph+Question+Answering+with+Ambiguous+Query)|0| |[Hierarchical Self-Attention Embedding for Temporal Knowledge Graph Completion](https://doi.org/10.1145/3543507.3583397)|Xin Ren, Luyi Bai, Qianwen Xiao, Xiangxi Meng|Northeastern University, China|Temporal Knowledge Graph (TKG) is composed of a series of facts related to timestamps in the real world and has become the basis of many artificial intelligence applications. However, the existing TKG is usually incomplete. It has become a hot research task to infer missing facts based on existing facts in a TKG; namely, Temporal Knowledge Graph Completion (TKGC). The current mainstream TKGC models are embedded models that predict missing facts by representing entities, relations and timestamps as low-dimensional vectors. In order to deal with the TKG structure information, there are some models that try to introduce attention mechanism into the embedding process. But they only consider the structure information of entities or relations, and ignore the structure information of the whole TKG. Moreover, most of them usually treat timestamps as a general feature and cannot take advantage of the potential time series information of the timestamp. To solve these problems, wo propose a new Hierarchical Self-Attention Embedding (HSAE) model which inspired by self-attention mechanism and diachronic embedding technique. For structure information of the whole TKG, we divide the TKG into two layers: entity layer and relation layer, and then apply the self-attention mechanism to the entity layer and relation layer respectively to capture the structure information. For time series information of the timestamp, we capture them by combining positional encoding and diachronic embedding technique into the above two self-attention layers. Finally, we can get the embedded representation vectors of entities, relations and timestamps, which can be combined with other models for better results. We evaluate our model on three TKG datasets: ICEWS14, ICEWS05-15 and GDELT. Experimental results on the TKGC (interpolation) task demonstrate that our model achieves state-of-the-art results.|时间知识图(TKG)由现实世界中与时间戳相关的一系列事实组成，已成为许多人工智能应用的基础。然而，现有的 TKG 通常是不完整的。基于 TKG 中已有事实推断缺失事实，即时态知识图完成(TKGC) ，已成为一个热门的研究课题。目前主流的 TKGC 模型是嵌入式模型，通过将实体、关系和时间戳表示为低维向量来预测缺失事实。为了处理 TKG 的结构信息，有一些模型尝试在嵌入过程中引入注意机制。但他们只考虑实体或关系的结构信息，而忽略了整个 TKG 的结构信息。此外，它们中的大多数通常将时间戳视为一个通用特性，不能利用时间戳的潜在时间序列信息。为了解决这些问题，我们提出了一种新的分层自我注意嵌入(HSAE)模型，该模型受自我注意机制和历时嵌入技术的启发。对于整个 TKG 的结构信息，我们将 TKG 分为实体层和关系层两个层次，然后将自注意机制分别应用于实体层和关系层，以获取 TKG 的结构信息。对于时间戳的时间序列信息，我们将位置编码和历时嵌入技术结合到上述两个自我注意层中来获取它们。最后，我们可以得到实体、关系和时间戳的嵌入式表示向量，这些表示向量可以与其他模型相结合以获得更好的结果。我们在三个 TKG 数据集上评估我们的模型: ICEWS14，ICEWS05-15和 GDELT。在 TKGC (插值)任务上的实验结果表明，我们的模型达到了最先进的效果。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Hierarchical+Self-Attention+Embedding+for+Temporal+Knowledge+Graph+Completion)|0| |[Link Prediction with Attention Applied on Multiple Knowledge Graph Embedding Models](https://doi.org/10.1145/3543507.3583358)|Cosimo Gregucci, Mojtaba Nayyeri, Daniel Hernández, Steffen Staab|University of Stuttgart, Germany; University of Stuttgart, Germany and University of Southampton, United Kingdom|Predicting missing links between entities in a knowledge graph is a fundamental task to deal with the incompleteness of data on the Web. Knowledge graph embeddings map nodes into a vector space to predict new links, scoring them according to geometric criteria. Relations in the graph may follow patterns that can be learned, e.g., some relations might be symmetric and others might be hierarchical. However, the learning capability of different embedding models varies for each pattern and, so far, no single model can learn all patterns equally well. In this paper, we combine the query representations from several models in a unified one to incorporate patterns that are independently captured by each model. Our combination uses attention to select the most suitable model to answer each query. The models are also mapped onto a non-Euclidean manifold, the Poincar\'e ball, to capture structural patterns, such as hierarchies, besides relational patterns, such as symmetry. We prove that our combination provides a higher expressiveness and inference power than each model on its own. As a result, the combined model can learn relational and structural patterns. We conduct extensive experimental analysis with various link prediction benchmarks showing that the combined model outperforms individual models, including state-of-the-art approaches.|预测知识图中实体之间的缺失链接是处理网络数据不完整性的基本任务。知识图嵌入将节点映射到向量空间中，预测新的链接，并根据几何标准对其进行评分。图中的关系可能遵循可以学习的模式，例如，一些关系可能是对称的，另一些可能是等级的。然而，不同嵌入模型的学习能力因模式的不同而异，到目前为止，还没有一个单独的模型能够同样好地学习所有的模式。在本文中，我们将来自多个模型的查询表示结合在一个统一的模型中，以合并由每个模型独立捕获的模式。我们的组合使用注意力来选择最合适的模型来回答每个查询。这些模型还被映射到一个非欧几里德流形，即 Poincar‘ e 球，以捕获结构模式，如层次结构，以及关系模式，如对称性。我们证明，我们的组合提供了更高的表达能力和推理能力比每个模型本身。因此，组合模型可以学习关系模式和结构模式。我们进行了广泛的实验分析与各种链接预测基准表明，组合模型优于个别模型，包括最先进的方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Link+Prediction+with+Attention+Applied+on+Multiple+Knowledge+Graph+Embedding+Models)|0| -|[SeqCare: Sequential Training with External Medical Knowledge Graph for Diagnosis Prediction in Healthcare Data](https://doi.org/10.1145/3543507.3583543)|Yongxin Xu, Xu Chu, Kai Yang, Zhiyuan Wang, Peinie Zou, Hongxin Ding, Junfeng Zhao, Yasha Wang, Bing Xie|Tsinghua University, China; Zhongguancun Laboratory, China; Key Laboratory of High Confidence Software Technologies, Ministry of Education, China and Peking University, China|Deep learning techniques are capable of capturing complex input-output relationships, and have been widely applied to the diagnosis prediction task based on web-based patient electronic health records (EHR) data. To improve the prediction and interpretability of pure data-driven deep learning with only a limited amount of labeled data, a pervasive trend is to assist the model training with knowledge priors from online medical knowledge graphs. However, they marginally investigated the label imbalance and the task-irrelevant noise in the external knowledge graph. The imbalanced label distribution would bias the learning and knowledge extraction towards the majority categories. The task-irrelevant noise introduces extra uncertainty to the model performance. To this end, aiming at by-passing the bias-variance trade-off dilemma, we introduce a new sequential learning framework, dubbed SeqCare, for diagnosis prediction with online medical knowledge graphs. Concretely, in the first step, SeqCare learns a bias-reduced space through a self-supervised graph contrastive learning task. Secondly, SeqCare reduces the learning uncertainty by refining the supervision signal and the graph structure of the knowledge graph simultaneously. Lastly, SeqCare trains the model in the bias-variance reduced space with a self-distillation to further filter out irrelevant information in the data. Experimental evaluations on two real-world datasets show that SeqCare outperforms state-of-the-art approaches. Case studies exemplify the interpretability of SeqCare. Moreover, the medical findings discovered by SeqCare are consistent with experts and medical literature.|深度学习技术能够捕捉复杂的输入输出关系，已广泛应用于基于网络病人电子健康记录(EHR)数据的诊断预测任务中。为了提高纯数据驱动的深度学习的预测性和可解释性，通过在线医学知识图的知识先验来辅助模型训练是一个普遍的趋势。然而，他们对外部知识图中的标签不平衡和任务不相关噪声的研究很少。不均衡的标签分布会使学习和知识抽取偏向于大多数类别。任务无关噪声给模型性能带来额外的不确定性。为此，针对偏差-方差权衡困境，我们引入了一个新的序列学习框架，称为 SeqCare，用于在线医学知识图的诊断预测。具体地说，在第一步中，SeqCare 通过一个自监督的图形对比学习任务学习一个减少偏差的空间。其次，SeqCare 通过同时细化监督信号和知识图的图形结构来降低学习的不确定性。最后，SeqCare 在偏差-方差缩减空间中用自精馏的方法对模型进行训练，以进一步滤除数据中的不相关信息。对两个真实世界数据集的实验评估表明，SeqCare 的性能优于最先进的方法。案例研究例证了 SeqCare 的可解释性。此外，SeqCare 发现的医学发现与专家和医学文献一致。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=SeqCare:+Sequential+Training+with+External+Medical+Knowledge+Graph+for+Diagnosis+Prediction+in+Healthcare+Data)|0| -|[The Thin Ideology of Populist Advertising on Facebook during the 2019 EU Elections](https://doi.org/10.1145/3543507.3583267)|Arthur Capozzi, Gianmarco De Francisci Morales, Yelena Mejova, Corrado Monti, André Panisson|ISI Foundation, Italy; Centai, Italy; Computer Science, Universita' di Torino, Italy|Social media has been an important tool in the expansion of the populist message, and it is thought to have contributed to the electoral success of populist parties in the past decade. This study compares how populist parties advertised on Facebook during the 2019 European Parliamentary election. In particular, we examine commonalities and differences in which audiences they reach and on which issues they focus. By using data from Meta (previously Facebook) Ad Library, we analyze 45k ad campaigns by 39 parties, both populist and mainstream, in Germany, United Kingdom, Italy, Spain, and Poland. While populist parties represent just over 20% of the total expenditure on political ads, they account for 40% of the total impressions$\unicode{x2013}$most of which from Eurosceptic and far-right parties$\unicode{x2013}$thus hinting at a competitive advantage for populist parties on Facebook. We further find that ads posted by populist parties are more likely to reach male audiences, and sometimes much older ones. In terms of issues, populist politicians focus on monetary policy, state bureaucracy and reforms, and security, while the focus on EU and Brexit is on par with non-populist, mainstream parties. However, issue preferences are largely country-specific, thus supporting the view in political science that populism is a "thin ideology", that does not have a universal, coherent policy agenda. This study illustrates the usefulness of publicly available advertising data for monitoring the populist outreach to, and engagement with, millions of potential voters, while outlining the limitations of currently available data.|社交媒体一直是传播民粹主义信息的重要工具，据认为它在过去十年中为民粹主义政党在选举中取得成功做出了贡献。这项研究比较了2019年欧洲议会选举期间民粹主义政党在 Facebook 上的广告。特别是，我们研究了它们所接触到的受众以及它们所关注的问题的共性和差异。通过使用来自 Meta (以前的 Facebook)广告库的数据，我们分析了来自德国、英国、意大利、西班牙和波兰的39个民粹主义和主流政党的45000个广告活动。尽管民粹主义政党仅占政治广告总支出的20% 多一点，但他们占了总印象的40% ，其中大部分来自欧洲怀疑论者和极右翼政党，因此暗示了民粹主义政党在 Facebook 上的竞争优势。我们进一步发现，民粹主义政党发布的广告更有可能触及男性受众，有时甚至是年龄更大的受众。就问题而言，民粹主义政客关注的是货币政策、国家官僚机构和改革以及安全，而关注欧盟和 Brexit 的政党，与非民粹主义的主流政党不相上下。然而，问题的偏好在很大程度上是针对具体国家的，因此支持了政治科学中的观点，即民粹主义是一种“薄弱的意识形态”，没有一个普遍的、连贯的政策议程。这项研究说明了公开可用的广告数据在监测民粹主义者与数百万潜在选民的接触和接触方面的有用性，同时概述了目前可用数据的局限性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=The+Thin+Ideology+of+Populist+Advertising+on+Facebook+during+the+2019+EU+Elections)|0| -|[FlexiFed: Personalized Federated Learning for Edge Clients with Heterogeneous Model Architectures](https://doi.org/10.1145/3543507.3583347)|Kaibin Wang, Qiang He, Feifei Chen, Chunyang Chen, Faliang Huang, Hai Jin, Yun Yang|Huazhong University of Science and Technology, China; Nanning Normal University, China; Huazhong University of Science and Technology, China and Swinburne University of Technology, Australia; Swinburne University of Technology, Australia; Monash University, Australia; Deakin University, Australia|Mobile and Web-of-Things (WoT) devices at the network edge account for more than half of the world’s web traffic, making a great data source for various machine learning (ML) applications, particularly federated learning (FL) which offers a promising solution to privacy-preserving ML feeding on these data. FL allows edge mobile and WoT devices to train a shared global ML model under the orchestration of a central parameter server. In the real world, due to resource heterogeneity, these edge devices often train different versions of models (e.g., VGG-16 and VGG-19) or different ML models (e.g., VGG and ResNet) for the same ML task (e.g., computer vision and speech recognition). Existing FL schemes have assumed that participating edge devices share a common model architecture, and thus cannot facilitate FL across edge devices with heterogeneous ML model architectures. We explored this architecture heterogeneity challenge and found that FL can and should accommodate these edge devices to improve model accuracy and accelerate model training. This paper presents our findings and FlexiFed, a novel scheme for FL across edge devices with heterogeneous model architectures, and three model aggregation strategies for accommodating architecture heterogeneity under FlexiFed. Experiments with four widely-used ML models on four public datasets demonstrate 1) the usefulness of FlexiFed; and 2) that compared with the state-of-the-art FL scheme, FlexiFed improves model accuracy by 2.6%-9.7% and accelerates model convergence by 1.24 × -4.04 ×.|处于网络边缘的移动和物联网(WoT)设备占据了世界网络流量的一半以上，为各种机器学习(ML)应用提供了一个巨大的数据源，特别是联邦学习(FL) ，它为依靠这些数据保护隐私的机器学习提供了一个有前途的解决方案。FL 允许边缘移动和 WoT 设备在一个中心参数服务器的协调下训练一个共享的全球 ML 模型。在现实世界中，由于资源的异质性，这些边缘设备经常为相同的机器学习任务训练不同版本的模型(例如，VGG-16和 VGG-19)或不同的机器学习模型(例如，VGG 和 ResNet)(例如，计算机视觉和语音识别)。现有的 FL 方案假定参与的边缘设备共享一个共同的模型架构，因此不能促进具有异构机器学习模型架构的边缘设备之间的 FL。我们探讨了这种体系结构异构性的挑战，发现 FL 可以而且应该适应这些边缘设备，以提高模型精度和加速模型训练。本文介绍了我们的研究结果和 FlexiFed，这是一个跨具有异构模型结构的边缘设备的 FL 的新方案，以及在 FlexiFed 下适应结构异构性的三种模型聚合策略。在四个公共数据集上对四个广泛使用的机器学习模型进行的实验表明: 1) FlexiFed 的有用性; 2)与最先进的 FL 方案相比，FlexiFed 将模型精度提高了2.6% -9.7% ，并加速了模型收敛1.24 × -4.04 × 。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=FlexiFed:+Personalized+Federated+Learning+for+Edge+Clients+with+Heterogeneous+Model+Architectures)|0| -|[PipeEdge: A Trusted Pipelining Collaborative Edge Training based on Blockchain](https://doi.org/10.1145/3543507.3583413)|Liang Yuan, Qiang He, Feifei Chen, Ruihan Dou, Hai Jin, Yun Yang|Huazhong University of Science and Technology, China; University of Waterloo, Canada; Huazhong University of Science and Technology, China and Swinburne University of Technology, Australia; Swinburne University of Technology, Australia; Deakin University, Australia|Powered by the massive data generated by the blossom of mobile and Web-of-Things (WoT) devices, Deep Neural Networks (DNNs) have developed both in accuracy and size in recent years. Conventional cloud-based DNN training incurs rapidly-increasing data and model transmission overheads as well as privacy issues. Mobile edge computing (MEC) provides a promising solution by facilitating DNN model training on edge servers at the network edge. However, edge servers often suffer from constrained resources and need to collaborate on DNN training. Unfortunately, managed by different telecoms, edge servers cannot properly collaborate with each other without incentives and trust. In this paper, we introduce PipeEdge, a scheme that promotes collaborative edge training between edge servers by introducing incentives and trust based on blockchain. Under the PipeEdge scheme, edge servers can hire trustworthy workers for pipelined DNN training tasks based on model parallelism. We implement PipeEdge and evaluate it comprehensively with four different DNN models. The results show that it outperforms state-of-the-art schemes by up to 173.98% with negligible overheads.|深度神经网络(DNN)由移动设备和物联网(WoT)设备的蓬勃发展所产生的大量数据所驱动，近年来在精度和规模上都有所发展。传统的基于云的 DNN 培训会带来快速增长的数据和模型传输开销以及隐私问题。移动边缘计算(MEC)为网络边缘服务器上的 DNN 模型训练提供了一种有前途的解决方案。然而，边缘服务器经常受到资源的限制，需要协作进行 DNN 培训。不幸的是，由不同电信公司管理的边缘服务器如果没有激励和信任，就无法正确地相互协作。本文介绍了 PipeEdge 方案，该方案通过引入基于区块链的激励和信任来促进边缘服务器之间的协同边缘训练。在 PipeEdge 方案下，边缘服务器可以雇佣值得信赖的工人，根据模型并行性执行流水线 DNN 培训任务。我们实现了 PipeEdge，并使用四种不同的 DNN 模型对其进行了综合评估。结果表明，该方案的性能优于最先进的方案达173.98% ，开销可以忽略不计。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=PipeEdge:+A+Trusted+Pipelining+Collaborative+Edge+Training+based+on+Blockchain)|0| -|[ELASTIC: Edge Workload Forecasting based on Collaborative Cloud-Edge Deep Learning](https://doi.org/10.1145/3543507.3583436)|Yanan Li, Haitao Yuan, Zhe Fu, Xiao Ma, Mengwei Xu, Shangguang Wang|Beijing University of Posts and Telecommunications, China; Tsinghua University, China; Nanyang Technological University, Singapore|With the rapid development of edge computing in the post-COVID19 pandemic period, precise workload forecasting is considered the basis for making full use of the edge limited resources, and both edge service providers (ESPs) and edge service consumers (ESCs) can benefit significantly from it. Existing paradigms of workload forecasting (i.e., edge-only or cloud-only) are improper, due to failing to consider the inter-site correlations and might suffer from significant data transmission delays. With the increasing adoption of edge platforms by web services, it is critical to balance both accuracy and efficiency in workload forecasting. In this paper, we propose ELASTIC, which is the first study that leverages a cloud-edge collaborative paradigm for edge workload forecasting with multi-view graphs. Specifically, at the global stage, we design a learnable aggregation layer on each edge site to reduce the time consumption while capturing the inter-site correlation. Additionally, at the local stage, we design a disaggregation layer combining both the intra-site correlation and inter-site correlation to improve the prediction accuracy. Extensive experiments on realistic edge workload datasets collected from China’s largest edge service provider show that ELASTIC outperforms state-of-the-art methods, decreases time consumption, and reduces communication cost.|随着后 COVID19大流行时期边缘计算的快速发展，精确的工作量预测被认为是充分利用边缘有限资源的基础，边缘服务提供商(ESP)和边缘服务消费者(ESCs)都可以从中受益匪浅。现有的工作量预测模式(即仅边缘预测或仅云预测)是不适当的，因为没有考虑到站点间的相关性，并可能遭受显著的数据传输延迟。随着 Web 服务越来越多地采用边缘平台，在工作负载预测中平衡准确性和效率至关重要。在本文中，我们提出了 ELASTIC，这是第一个利用云端协作范式进行多视图边缘工作负荷预测的研究。具体来说，在全局阶段，我们在每个边缘站点上设计一个可学习的聚合层，以减少时间消耗，同时捕获站点间的相关性。此外，在局部阶段，我们设计了一个解体层，将场内相关性和场间相关性结合起来，以提高预测的准确性。对中国最大的边缘服务提供商收集的真实边缘工作负载数据集进行的大量实验表明，ELASTIC 优于最先进的方法，减少了时间消耗，降低了通信成本。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=ELASTIC:+Edge+Workload+Forecasting+based+on+Collaborative+Cloud-Edge+Deep+Learning)|0| +|[SeqCare: Sequential Training with External Medical Knowledge Graph for Diagnosis Prediction in Healthcare Data](https://doi.org/10.1145/3543507.3583543)|Yongxin Xu, Xu Chu, Kai Yang, Zhiyuan Wang, Peinie Zou, Hongxin Ding, Junfeng Zhao, Yasha Wang, Bing Xie|Tsinghua University, China; Key Laboratory of High Confidence Software Technologies, Ministry of Education, China and Peking University, China; Zhongguancun Laboratory, China|Deep learning techniques are capable of capturing complex input-output relationships, and have been widely applied to the diagnosis prediction task based on web-based patient electronic health records (EHR) data. To improve the prediction and interpretability of pure data-driven deep learning with only a limited amount of labeled data, a pervasive trend is to assist the model training with knowledge priors from online medical knowledge graphs. However, they marginally investigated the label imbalance and the task-irrelevant noise in the external knowledge graph. The imbalanced label distribution would bias the learning and knowledge extraction towards the majority categories. The task-irrelevant noise introduces extra uncertainty to the model performance. To this end, aiming at by-passing the bias-variance trade-off dilemma, we introduce a new sequential learning framework, dubbed SeqCare, for diagnosis prediction with online medical knowledge graphs. Concretely, in the first step, SeqCare learns a bias-reduced space through a self-supervised graph contrastive learning task. Secondly, SeqCare reduces the learning uncertainty by refining the supervision signal and the graph structure of the knowledge graph simultaneously. Lastly, SeqCare trains the model in the bias-variance reduced space with a self-distillation to further filter out irrelevant information in the data. Experimental evaluations on two real-world datasets show that SeqCare outperforms state-of-the-art approaches. Case studies exemplify the interpretability of SeqCare. Moreover, the medical findings discovered by SeqCare are consistent with experts and medical literature.|深度学习技术能够捕捉复杂的输入输出关系，已广泛应用于基于网络病人电子健康记录(EHR)数据的诊断预测任务中。为了提高纯数据驱动的深度学习的预测性和可解释性，通过在线医学知识图的知识先验来辅助模型训练是一个普遍的趋势。然而，他们对外部知识图中的标签不平衡和任务不相关噪声的研究很少。不均衡的标签分布会使学习和知识抽取偏向于大多数类别。任务无关噪声给模型性能带来额外的不确定性。为此，针对偏差-方差权衡困境，我们引入了一个新的序列学习框架，称为 SeqCare，用于在线医学知识图的诊断预测。具体地说，在第一步中，SeqCare 通过一个自监督的图形对比学习任务学习一个减少偏差的空间。其次，SeqCare 通过同时细化监督信号和知识图的图形结构来降低学习的不确定性。最后，SeqCare 在偏差-方差缩减空间中用自精馏的方法对模型进行训练，以进一步滤除数据中的不相关信息。对两个真实世界数据集的实验评估表明，SeqCare 的性能优于最先进的方法。案例研究例证了 SeqCare 的可解释性。此外，SeqCare 发现的医学发现与专家和医学文献一致。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=SeqCare:+Sequential+Training+with+External+Medical+Knowledge+Graph+for+Diagnosis+Prediction+in+Healthcare+Data)|0| +|[The Thin Ideology of Populist Advertising on Facebook during the 2019 EU Elections](https://doi.org/10.1145/3543507.3583267)|Arthur Capozzi, Gianmarco De Francisci Morales, Yelena Mejova, Corrado Monti, André Panisson|ISI Foundation, Italy; Computer Science, Universita' di Torino, Italy; Centai, Italy|Social media has been an important tool in the expansion of the populist message, and it is thought to have contributed to the electoral success of populist parties in the past decade. This study compares how populist parties advertised on Facebook during the 2019 European Parliamentary election. In particular, we examine commonalities and differences in which audiences they reach and on which issues they focus. By using data from Meta (previously Facebook) Ad Library, we analyze 45k ad campaigns by 39 parties, both populist and mainstream, in Germany, United Kingdom, Italy, Spain, and Poland. While populist parties represent just over 20% of the total expenditure on political ads, they account for 40% of the total impressions$\unicode{x2013}$most of which from Eurosceptic and far-right parties$\unicode{x2013}$thus hinting at a competitive advantage for populist parties on Facebook. We further find that ads posted by populist parties are more likely to reach male audiences, and sometimes much older ones. In terms of issues, populist politicians focus on monetary policy, state bureaucracy and reforms, and security, while the focus on EU and Brexit is on par with non-populist, mainstream parties. However, issue preferences are largely country-specific, thus supporting the view in political science that populism is a "thin ideology", that does not have a universal, coherent policy agenda. This study illustrates the usefulness of publicly available advertising data for monitoring the populist outreach to, and engagement with, millions of potential voters, while outlining the limitations of currently available data.|社交媒体一直是传播民粹主义信息的重要工具，据认为它在过去十年中为民粹主义政党在选举中取得成功做出了贡献。这项研究比较了2019年欧洲议会选举期间民粹主义政党在 Facebook 上的广告。特别是，我们研究了它们所接触到的受众以及它们所关注的问题的共性和差异。通过使用来自 Meta (以前的 Facebook)广告库的数据，我们分析了来自德国、英国、意大利、西班牙和波兰的39个民粹主义和主流政党的45000个广告活动。尽管民粹主义政党仅占政治广告总支出的20% 多一点，但他们占了总印象的40% ，其中大部分来自欧洲怀疑论者和极右翼政党，因此暗示了民粹主义政党在 Facebook 上的竞争优势。我们进一步发现，民粹主义政党发布的广告更有可能触及男性受众，有时甚至是年龄更大的受众。就问题而言，民粹主义政客关注的是货币政策、国家官僚机构和改革以及安全，而关注欧盟和 Brexit 的政党，与非民粹主义的主流政党不相上下。然而，问题的偏好在很大程度上是针对具体国家的，因此支持了政治科学中的观点，即民粹主义是一种“薄弱的意识形态”，没有一个普遍的、连贯的政策议程。这项研究说明了公开可用的广告数据在监测民粹主义者与数百万潜在选民的接触和接触方面的有用性，同时概述了目前可用数据的局限性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=The+Thin+Ideology+of+Populist+Advertising+on+Facebook+during+the+2019+EU+Elections)|0| +|[FlexiFed: Personalized Federated Learning for Edge Clients with Heterogeneous Model Architectures](https://doi.org/10.1145/3543507.3583347)|Kaibin Wang, Qiang He, Feifei Chen, Chunyang Chen, Faliang Huang, Hai Jin, Yun Yang|Huazhong University of Science and Technology, China; Monash University, Australia; Swinburne University of Technology, Australia; Deakin University, Australia; Huazhong University of Science and Technology, China and Swinburne University of Technology, Australia; Nanning Normal University, China|Mobile and Web-of-Things (WoT) devices at the network edge account for more than half of the world’s web traffic, making a great data source for various machine learning (ML) applications, particularly federated learning (FL) which offers a promising solution to privacy-preserving ML feeding on these data. FL allows edge mobile and WoT devices to train a shared global ML model under the orchestration of a central parameter server. In the real world, due to resource heterogeneity, these edge devices often train different versions of models (e.g., VGG-16 and VGG-19) or different ML models (e.g., VGG and ResNet) for the same ML task (e.g., computer vision and speech recognition). Existing FL schemes have assumed that participating edge devices share a common model architecture, and thus cannot facilitate FL across edge devices with heterogeneous ML model architectures. We explored this architecture heterogeneity challenge and found that FL can and should accommodate these edge devices to improve model accuracy and accelerate model training. This paper presents our findings and FlexiFed, a novel scheme for FL across edge devices with heterogeneous model architectures, and three model aggregation strategies for accommodating architecture heterogeneity under FlexiFed. Experiments with four widely-used ML models on four public datasets demonstrate 1) the usefulness of FlexiFed; and 2) that compared with the state-of-the-art FL scheme, FlexiFed improves model accuracy by 2.6%-9.7% and accelerates model convergence by 1.24 × -4.04 ×.|处于网络边缘的移动和物联网(WoT)设备占据了世界网络流量的一半以上，为各种机器学习(ML)应用提供了一个巨大的数据源，特别是联邦学习(FL) ，它为依靠这些数据保护隐私的机器学习提供了一个有前途的解决方案。FL 允许边缘移动和 WoT 设备在一个中心参数服务器的协调下训练一个共享的全球 ML 模型。在现实世界中，由于资源的异质性，这些边缘设备经常为相同的机器学习任务训练不同版本的模型(例如，VGG-16和 VGG-19)或不同的机器学习模型(例如，VGG 和 ResNet)(例如，计算机视觉和语音识别)。现有的 FL 方案假定参与的边缘设备共享一个共同的模型架构，因此不能促进具有异构机器学习模型架构的边缘设备之间的 FL。我们探讨了这种体系结构异构性的挑战，发现 FL 可以而且应该适应这些边缘设备，以提高模型精度和加速模型训练。本文介绍了我们的研究结果和 FlexiFed，这是一个跨具有异构模型结构的边缘设备的 FL 的新方案，以及在 FlexiFed 下适应结构异构性的三种模型聚合策略。在四个公共数据集上对四个广泛使用的机器学习模型进行的实验表明: 1) FlexiFed 的有用性; 2)与最先进的 FL 方案相比，FlexiFed 将模型精度提高了2.6% -9.7% ，并加速了模型收敛1.24 × -4.04 × 。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=FlexiFed:+Personalized+Federated+Learning+for+Edge+Clients+with+Heterogeneous+Model+Architectures)|0| +|[PipeEdge: A Trusted Pipelining Collaborative Edge Training based on Blockchain](https://doi.org/10.1145/3543507.3583413)|Liang Yuan, Qiang He, Feifei Chen, Ruihan Dou, Hai Jin, Yun Yang|Huazhong University of Science and Technology, China; Swinburne University of Technology, Australia; Deakin University, Australia; University of Waterloo, Canada; Huazhong University of Science and Technology, China and Swinburne University of Technology, Australia|Powered by the massive data generated by the blossom of mobile and Web-of-Things (WoT) devices, Deep Neural Networks (DNNs) have developed both in accuracy and size in recent years. Conventional cloud-based DNN training incurs rapidly-increasing data and model transmission overheads as well as privacy issues. Mobile edge computing (MEC) provides a promising solution by facilitating DNN model training on edge servers at the network edge. However, edge servers often suffer from constrained resources and need to collaborate on DNN training. Unfortunately, managed by different telecoms, edge servers cannot properly collaborate with each other without incentives and trust. In this paper, we introduce PipeEdge, a scheme that promotes collaborative edge training between edge servers by introducing incentives and trust based on blockchain. Under the PipeEdge scheme, edge servers can hire trustworthy workers for pipelined DNN training tasks based on model parallelism. We implement PipeEdge and evaluate it comprehensively with four different DNN models. The results show that it outperforms state-of-the-art schemes by up to 173.98% with negligible overheads.|深度神经网络(DNN)由移动设备和物联网(WoT)设备的蓬勃发展所产生的大量数据所驱动，近年来在精度和规模上都有所发展。传统的基于云的 DNN 培训会带来快速增长的数据和模型传输开销以及隐私问题。移动边缘计算(MEC)为网络边缘服务器上的 DNN 模型训练提供了一种有前途的解决方案。然而，边缘服务器经常受到资源的限制，需要协作进行 DNN 培训。不幸的是，由不同电信公司管理的边缘服务器如果没有激励和信任，就无法正确地相互协作。本文介绍了 PipeEdge 方案，该方案通过引入基于区块链的激励和信任来促进边缘服务器之间的协同边缘训练。在 PipeEdge 方案下，边缘服务器可以雇佣值得信赖的工人，根据模型并行性执行流水线 DNN 培训任务。我们实现了 PipeEdge，并使用四种不同的 DNN 模型对其进行了综合评估。结果表明，该方案的性能优于最先进的方案达173.98% ，开销可以忽略不计。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=PipeEdge:+A+Trusted+Pipelining+Collaborative+Edge+Training+based+on+Blockchain)|0| +|[ELASTIC: Edge Workload Forecasting based on Collaborative Cloud-Edge Deep Learning](https://doi.org/10.1145/3543507.3583436)|Yanan Li, Haitao Yuan, Zhe Fu, Xiao Ma, Mengwei Xu, Shangguang Wang|Beijing University of Posts and Telecommunications, China; Nanyang Technological University, Singapore; Tsinghua University, China|With the rapid development of edge computing in the post-COVID19 pandemic period, precise workload forecasting is considered the basis for making full use of the edge limited resources, and both edge service providers (ESPs) and edge service consumers (ESCs) can benefit significantly from it. Existing paradigms of workload forecasting (i.e., edge-only or cloud-only) are improper, due to failing to consider the inter-site correlations and might suffer from significant data transmission delays. With the increasing adoption of edge platforms by web services, it is critical to balance both accuracy and efficiency in workload forecasting. In this paper, we propose ELASTIC, which is the first study that leverages a cloud-edge collaborative paradigm for edge workload forecasting with multi-view graphs. Specifically, at the global stage, we design a learnable aggregation layer on each edge site to reduce the time consumption while capturing the inter-site correlation. Additionally, at the local stage, we design a disaggregation layer combining both the intra-site correlation and inter-site correlation to improve the prediction accuracy. Extensive experiments on realistic edge workload datasets collected from China’s largest edge service provider show that ELASTIC outperforms state-of-the-art methods, decreases time consumption, and reduces communication cost.|随着后 COVID19大流行时期边缘计算的快速发展，精确的工作量预测被认为是充分利用边缘有限资源的基础，边缘服务提供商(ESP)和边缘服务消费者(ESCs)都可以从中受益匪浅。现有的工作量预测模式(即仅边缘预测或仅云预测)是不适当的，因为没有考虑到站点间的相关性，并可能遭受显著的数据传输延迟。随着 Web 服务越来越多地采用边缘平台，在工作负载预测中平衡准确性和效率至关重要。在本文中，我们提出了 ELASTIC，这是第一个利用云端协作范式进行多视图边缘工作负荷预测的研究。具体来说，在全局阶段，我们在每个边缘站点上设计一个可学习的聚合层，以减少时间消耗，同时捕获站点间的相关性。此外，在局部阶段，我们设计了一个解体层，将场内相关性和场间相关性结合起来，以提高预测的准确性。对中国最大的边缘服务提供商收集的真实边缘工作负载数据集进行的大量实验表明，ELASTIC 优于最先进的方法，减少了时间消耗，降低了通信成本。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=ELASTIC:+Edge+Workload+Forecasting+based+on+Collaborative+Cloud-Edge+Deep+Learning)|0| |[DUET: A Tuning-Free Device-Cloud Collaborative Parameters Generation Framework for Efficient Device Model Generalization](https://doi.org/10.1145/3543507.3583451)|Zheqi Lv, Wenqiao Zhang, Shengyu Zhang, Kun Kuang, Feng Wang, Yongwei Wang, Zhengyu Chen, Tao Shen, Hongxia Yang, Beng Chin Ooi, Fei Wu|National University of Singapore, Singapore; Alibaba Group, China; Zhejiang University, China|Device Model Generalization (DMG) is a practical yet under-investigated research topic for on-device machine learning applications. It aims to improve the generalization ability of pre-trained models when deployed on resource-constrained devices, such as improving the performance of pre-trained cloud models on smart mobiles. While quite a lot of works have investigated the data distribution shift across clouds and devices, most of them focus on model fine-tuning on personalized data for individual devices to facilitate DMG. Despite their promising, these approaches require on-device re-training, which is practically infeasible due to the overfitting problem and high time delay when performing gradient calculation on real-time data. In this paper, we argue that the computational cost brought by fine-tuning can be rather unnecessary. We consequently present a novel perspective to improving DMG without increasing computational cost, i.e., device-specific parameter generation which directly maps data distribution to parameters. Specifically, we propose an efficient Device-cloUd collaborative parametErs generaTion framework DUET. DUET is deployed on a powerful cloud server that only requires the low cost of forwarding propagation and low time delay of data transmission between the device and the cloud. By doing so, DUET can rehearse the device-specific model weight realizations conditioned on the personalized real-time data for an individual device. Importantly, our DUET elegantly connects the cloud and device as a 'duet' collaboration, frees the DMG from fine-tuning, and enables a faster and more accurate DMG paradigm. We conduct an extensive experimental study of DUET on three public datasets, and the experimental results confirm our framework's effectiveness and generalisability for different DMG tasks.|设备模型综合(DMG)是设备上机器学习应用中一个实用但尚未得到充分研究的课题。它旨在提高预先训练的模型在资源有限的设备上部署时的泛化能力，例如提高预先训练的云模型在智能手机上的性能。虽然许多工作已经研究了跨云和设备的数据分布转移，但大多数工作集中在针对个人设备的个性化数据的模型微调上，以促进 DMG。尽管这些方法很有前景，但是需要在设备上进行再训练，这在实际中是不可行的，因为在对实时数据进行梯度计算时，存在过拟合问题和高时延。在本文中，我们认为微调带来的计算成本可能是相当不必要的。因此，我们提出了一个新的视角来改善 DMG 而不增加计算成本，即，设备特定的参数生成，直接映射数据分布到参数。具体地说，我们提出了一种高效的设备云协同参数生成框架 DUET。DUET 部署在一个强大的云服务器上，只需要较低的转发传播成本和较低的设备与云之间的数据传输延迟。通过这样做，DUET 可以预演设备特定的模型权重实现条件下的个性化实时数据为单个设备。重要的是，我们的 DUET 作为一个“二重奏”协作优雅地连接了云和设备，从微调中解放了 DMG，并支持更快、更准确的 DMG 范例。我们对三个公共数据集上的 DUET 进行了广泛的实验研究，实验结果证实了我们的框架对不同 DMG 任务的有效性和通用性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=DUET:+A+Tuning-Free+Device-Cloud+Collaborative+Parameters+Generation+Framework+for+Efficient+Device+Model+Generalization)|0| |[RL-MPCA: A Reinforcement Learning Based Multi-Phase Computation Allocation Approach for Recommender Systems](https://doi.org/10.1145/3543507.3583313)|Jiahong Zhou, Shunhui Mao, Guoliang Yang, Bo Tang, Qianlong Xie, Lebin Lin, Xingxing Wang, Dong Wang|Meituan, China|Recommender systems aim to recommend the most suitable items to users from a large number of candidates. Their computation cost grows as the number of user requests and the complexity of services (or models) increases. Under the limitation of computation resources (CRs), how to make a trade-off between computation cost and business revenue becomes an essential question. The existing studies focus on dynamically allocating CRs in queue truncation scenarios (i.e., allocating the size of candidates), and formulate the CR allocation problem as an optimization problem with constraints. Some of them focus on single-phase CR allocation, and others focus on multi-phase CR allocation but introduce some assumptions about queue truncation scenarios. However, these assumptions do not hold in other scenarios, such as retrieval channel selection and prediction model selection. Moreover, existing studies ignore the state transition process of requests between different phases, limiting the effectiveness of their approaches. This paper proposes a Reinforcement Learning (RL) based Multi-Phase Computation Allocation approach (RL-MPCA), which aims to maximize the total business revenue under the limitation of CRs. RL-MPCA formulates the CR allocation problem as a Weakly Coupled MDP problem and solves it with an RL-based approach. Specifically, RL-MPCA designs a novel deep Q-network to adapt to various CR allocation scenarios, and calibrates the Q-value by introducing multiple adaptive Lagrange multipliers (adaptive-λ) to avoid violating the global CR constraints. Finally, experiments on the offline simulation environment and online real-world recommender system validate the effectiveness of our approach.|推荐系统的目的是从大量的候选人中向用户推荐最合适的项目。它们的计算成本随着用户请求的数量和服务(或模型)的复杂性的增加而增加。在计算资源有限的情况下，如何在计算成本和业务收益之间取得平衡成为一个必须解决的问题。现有的研究集中于在队列截断情况下(即分配候选人的人数)动态分配登记册编号，并将登记册编号分配问题制订为一个有约束的最佳化问题。其中一些关注于单相 CR 分配，另一些关注于多相 CR 分配，但是引入了一些关于队列截断场景的假设。但是，这些假设在其他场景中不成立，例如检索通道选择和预测模型选择。此外，现有的研究忽视了请求在不同阶段之间的状态转换过程，限制了这些方法的有效性。本文提出了一种基于强化学习的多阶段计算分配方法(RL-MPCA) ，其目标是在客户关系的限制下实现企业总收入的最大化。RL-MPCA 将 CR 分配问题表示为弱耦合 MDP 问题，并用基于 RL 的方法求解。具体来说，RL-MPCA 设计了一种新的深层 Q 网络来适应各种 CR 分配场景，并通过引入多个自适应拉格朗日乘子(Adaptive-λ)来校准 Q 值，以避免违反全局 CR 约束。最后，离线仿真环境和在线真实世界推荐系统的实验验证了我们方法的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=RL-MPCA:+A+Reinforcement+Learning+Based+Multi-Phase+Computation+Allocation+Approach+for+Recommender+Systems)|0| -|[Learning To Rank Resources with GNN](https://doi.org/10.1145/3543507.3583360)|Ulugbek Ergashev, Eduard C. Dragut, Weiyi Meng|Computer and Information Sciences, Temple University, USA; Computer Science, Binghamton University, USA|As the content on the Internet continues to grow, many new dynamically changing and heterogeneous sources of data constantly emerge. A conventional search engine cannot crawl and index at the same pace as the expansion of the Internet. Moreover, a large portion of the data on the Internet is not accessible to traditional search engines. Distributed Information Retrieval (DIR) is a viable solution to this as it integrates multiple shards (resources) and provides a unified access to them. Resource selection is a key component of DIR systems. There is a rich body of literature on resource selection approaches for DIR. A key limitation of the existing approaches is that they primarily use term-based statistical features and do not generally model resource-query and resource-resource relationships. In this paper, we propose a graph neural network (GNN) based approach to learning-to-rank that is capable of modeling resource-query and resource-resource relationships. Specifically, we utilize a pre-trained language model (PTLM) to obtain semantic information from queries and resources. Then, we explicitly build a heterogeneous graph to preserve structural information of query-resource relationships and employ GNN to extract structural information. In addition, the heterogeneous graph is enriched with resource-resource type of edges to further enhance the ranking accuracy. Extensive experiments on benchmark datasets show that our proposed approach is highly effective in resource selection. Our method outperforms the state-of-the-art by 6.4% to 42% on various performance metrics.|随着 Internet 上的内容不断增长，许多新的动态变化和异构的数据源不断出现。传统的搜索引擎无法以与互联网扩展同样的速度爬行和索引。此外，互联网上的大部分数据不能被传统的搜索引擎访问。分布式信息检索(DIR)是一个可行的解决方案，因为它集成了多个碎片(资源) ，并提供了对它们的统一访问。资源选择是 DIR 系统的关键组成部分。关于 DIR 的资源选择方法有大量的文献。现有方法的一个主要局限性在于，它们主要使用基于术语的统计特征，并且一般不对资源-查询和资源-资源关系建模。本文提出了一种基于图神经网络(GNN)的排序学习方法，该方法能够对资源-查询和资源-资源关系进行建模。具体来说，我们利用预先训练的语言模型(PTLM)从查询和资源中获取语义信息。然后，我们显式地构建一个异构图来保存查询-资源关系的结构信息，并使用 GNN 来提取结构信息。此外，对异构图进行了资源-资源类型边的丰富，进一步提高了排序的准确性。对基准数据集的大量实验表明，该方法在资源选择方面是非常有效的。在各种性能指标上，我们的方法比最先进的方法表现好6.4% 到42% 。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Learning+To+Rank+Resources+with+GNN)|0| +|[Learning To Rank Resources with GNN](https://doi.org/10.1145/3543507.3583360)|Ulugbek Ergashev, Eduard C. Dragut, Weiyi Meng|Computer Science, Binghamton University, USA; Computer and Information Sciences, Temple University, USA|As the content on the Internet continues to grow, many new dynamically changing and heterogeneous sources of data constantly emerge. A conventional search engine cannot crawl and index at the same pace as the expansion of the Internet. Moreover, a large portion of the data on the Internet is not accessible to traditional search engines. Distributed Information Retrieval (DIR) is a viable solution to this as it integrates multiple shards (resources) and provides a unified access to them. Resource selection is a key component of DIR systems. There is a rich body of literature on resource selection approaches for DIR. A key limitation of the existing approaches is that they primarily use term-based statistical features and do not generally model resource-query and resource-resource relationships. In this paper, we propose a graph neural network (GNN) based approach to learning-to-rank that is capable of modeling resource-query and resource-resource relationships. Specifically, we utilize a pre-trained language model (PTLM) to obtain semantic information from queries and resources. Then, we explicitly build a heterogeneous graph to preserve structural information of query-resource relationships and employ GNN to extract structural information. In addition, the heterogeneous graph is enriched with resource-resource type of edges to further enhance the ranking accuracy. Extensive experiments on benchmark datasets show that our proposed approach is highly effective in resource selection. Our method outperforms the state-of-the-art by 6.4% to 42% on various performance metrics.|随着 Internet 上的内容不断增长，许多新的动态变化和异构的数据源不断出现。传统的搜索引擎无法以与互联网扩展同样的速度爬行和索引。此外，互联网上的大部分数据不能被传统的搜索引擎访问。分布式信息检索(DIR)是一个可行的解决方案，因为它集成了多个碎片(资源) ，并提供了对它们的统一访问。资源选择是 DIR 系统的关键组成部分。关于 DIR 的资源选择方法有大量的文献。现有方法的一个主要局限性在于，它们主要使用基于术语的统计特征，并且一般不对资源-查询和资源-资源关系建模。本文提出了一种基于图神经网络(GNN)的排序学习方法，该方法能够对资源-查询和资源-资源关系进行建模。具体来说，我们利用预先训练的语言模型(PTLM)从查询和资源中获取语义信息。然后，我们显式地构建一个异构图来保存查询-资源关系的结构信息，并使用 GNN 来提取结构信息。此外，对异构图进行了资源-资源类型边的丰富，进一步提高了排序的准确性。对基准数据集的大量实验表明，该方法在资源选择方面是非常有效的。在各种性能指标上，我们的方法比最先进的方法表现好6.4% 到42% 。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Learning+To+Rank+Resources+with+GNN)|0| |[CgAT: Center-Guided Adversarial Training for Deep Hashing-Based Retrieval](https://doi.org/10.1145/3543507.3583369)|Xunguang Wang, Yiqun Lin, Xiaomeng Li|The Hong Kong University of Science and Technology, China; The Hong Kong University of Science and Technology, China and The Hong Kong University of Science and Technology Shenzhen Research Institute, China|Deep hashing has been extensively utilized in massive image retrieval because of its efficiency and effectiveness. However, deep hashing models are vulnerable to adversarial examples, making it essential to develop adversarial defense methods for image retrieval. Existing solutions achieved limited defense performance because of using weak adversarial samples for training and lacking discriminative optimization objectives to learn robust features. In this paper, we present a min-max based Center-guided Adversarial Training, namely CgAT, to improve the robustness of deep hashing networks through worst adversarial examples. Specifically, we first formulate the center code as a semantically-discriminative representative of the input image content, which preserves the semantic similarity with positive samples and dissimilarity with negative examples. We prove that a mathematical formula can calculate the center code immediately. After obtaining the center codes in each optimization iteration of the deep hashing network, they are adopted to guide the adversarial training process. On the one hand, CgAT generates the worst adversarial examples as augmented data by maximizing the Hamming distance between the hash codes of the adversarial examples and the center codes. On the other hand, CgAT learns to mitigate the effects of adversarial samples by minimizing the Hamming distance to the center codes. Extensive experiments on the benchmark datasets demonstrate the effectiveness of our adversarial training algorithm in defending against adversarial attacks for deep hashing-based retrieval. Compared with the current state-of-the-art defense method, we significantly improve the defense performance by an average of 18.61\%, 12.35\%, and 11.56\% on FLICKR-25K, NUS-WIDE, and MS-COCO, respectively. The code is available at https://github.com/xunguangwang/CgAT.|深度散列由于其高效性和有效性，在海量图像检索中得到了广泛的应用。然而，深度散列模型很容易受到敌对实例的影响，因此开发图像检索的敌对防御方法是非常必要的。现有的解决方案由于使用弱对手样本进行训练，缺乏区分性优化目标来学习鲁棒特征，因此防御性能有限。本文提出了一种基于最小-最大中心引导的对抗训练方法，即 CgAT，通过最坏的对抗实例来提高深度哈希网络的鲁棒性。具体来说，我们首先将中心代码表示为输入图像内容的语义识别代表，保留了正样本的语义相似性和负样本的语义不相似性。我们证明了一个数学公式可以立即计算中心码。在深度哈希网络的每次优化迭代中获得中心码后，采用中心码来指导对抗训练过程。一方面，通过最大化对手的散列码和中心代码之间的汉明距离，CgAT 生成最坏的对手的例子作为增强数据。另一方面，CgAT 学会了通过最小化对中心代码的汉明距离来减轻对抗性样本的影响。在基准数据集上的大量实验证明了我们的对抗性训练算法在防御基于深度散列检索的对抗性攻击方面的有效性。与目前最先进的防御方法相比，FLICKR-25K、 NUS-WIDE 和 MS-COCO 的防御性能分别平均提高了18.61% 、12.35% 和11.56% 。密码可在 https://github.com/xunguangwang/cgat 查阅。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=CgAT:+Center-Guided+Adversarial+Training+for+Deep+Hashing-Based+Retrieval)|0| |[Algorithmic Vibe in Information Retrieval](https://doi.org/10.1145/3543507.3583384)|Ali Montazeralghaem, Nick Craswell, Ryen W. White, Ahmed Hassan Awadallah, Byungki Byun|University of Massachusetts Amherst, USA; Microsoft, USA|When information retrieval systems return a ranked list of results in response to a query, they may be choosing from a large set of candidate results that are equally useful and relevant. This means we might be able to identify a difference between rankers A and B, where ranker A systematically prefers a certain type of relevant results. Ranker A may have this systematic difference (different “vibe”) without having systematically better or worse results according to standard information retrieval metrics. We first show that a vibe difference can exist, comparing two publicly available rankers, where the one that is trained on health-related queries will systematically prefer health-related results, even for non-health queries. We define a vibe metric that lets us see the words that a ranker prefers. We investigate the vibe of search engine clicks vs. human labels. We perform an initial study into correcting for vibe differences to make ranker A more like ranker B via changes in negative sampling during training.|当信息检索系统对一个查询返回一个排名结果列表时，它们可能会从一大堆同样有用和相关的候选结果中进行选择。这意味着我们可能能够识别排名 A 和 B 之间的差异，其中排名 A 系统地偏好某种类型的相关结果。排名 a 可能有这种系统性差异(不同的“内心感应”) ，而没有根据标准的信息检索指标得出系统性更好或更差的结果。我们首先展示了内心感应差异的存在，比较两个公开可用的排名，其中受过健康相关查询培训的人会系统地偏好与健康相关的结果，即使对于非健康查询也是如此。我们定义一个内心感应度量，让我们看到一个排名喜欢的词。我们调查了搜索引擎点击与人类标签之间的关系。我们进行了一个初步的研究，以纠正内心感应的差异，使排名 A 更像排名 B 通过改变负面抽样在训练期间。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Algorithmic+Vibe+in+Information+Retrieval)|0| |[Geographic Information Retrieval Using Wikipedia Articles](https://doi.org/10.1145/3543507.3583469)|Amir Krause, Sara Cohen|The Rachel and Selim Benin School of Computer Science and Engineering, The Hebrew University, Israel|Assigning semantically relevant, real-world locations to documents opens new possibilities to perform geographic information retrieval. We propose a novel approach to automatically determine the latitude-longitude coordinates of appropriate Wikipedia articles with high accuracy, leveraging both text and metadata in the corpus. By examining articles whose base-truth coordinates are known, we show that our method attains a substantial improvement over state of the art works. We subsequently demonstrate how our approach could yield two benefits: (1) detecting significant geolocation errors in Wikipedia; and (2) proposing approximated coordinates for hundreds of thousands of articles which are not traditionally considered to be locations (such as events, ideas or people), opening new possibilities for conceptual geographic retrievals over Wikipedia.|为文档分配语义相关的、现实世界中的位置，为执行地理信息检索开辟了新的可能性。我们提出了一种新的方法，利用语料库中的文本和元数据，高精度地自动确定适当 Wikipedia 文章的经纬度坐标。通过检查文章的基础-真理坐标已知，我们表明，我们的方法取得了实质性的改善状态的艺术作品。我们随后展示了我们的方法如何产生两个好处: (1)检测维基百科中的重大地理定位错误; (2)提出几十万个传统上不被认为是地点的文章(如事件、想法或人)的大致坐标，为维基百科的概念地理检索开辟了新的可能性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Geographic+Information+Retrieval+Using+Wikipedia+Articles)|0| -|[Optimizing Guided Traversal for Fast Learned Sparse Retrieval](https://doi.org/10.1145/3543507.3583497)|Yifan Qiao, Yingrui Yang, Haixin Lin, Tao Yang|Department of Computer Science, University of California, Santa Barbara, USA; Department of Computer Science, University of California at Santa Barbara, USA|Recent studies show that BM25-driven dynamic index skipping can greatly accelerate MaxScore-based document retrieval based on the learned sparse representation derived by DeepImpact. This paper investigates the effectiveness of such a traversal guidance strategy during top k retrieval when using other models such as SPLADE and uniCOIL, and finds that unconstrained BM25-driven skipping could have a visible relevance degradation when the BM25 model is not well aligned with a learned weight model or when retrieval depth k is small. This paper generalizes the previous work and optimizes the BM25 guided index traversal with a two-level pruning control scheme and model alignment for fast retrieval using a sparse representation. Although there can be a cost of increased latency, the proposed scheme is much faster than the original MaxScore method without BM25 guidance while retaining the relevance effectiveness. This paper analyzes the competitiveness of this two-level pruning scheme, and evaluates its tradeoff in ranking relevance and time efficiency when searching several test datasets.|最近的研究表明，BM25驱动的动态指数跳跃可以大大加快基于深度影响学习稀疏表示的基于 MaxScore 的文献检索。本文研究了使用 SPLADE 和 uniCOIL 等其他模型进行 top k 检索时，这种遍历指导策略的有效性，发现当 BM25模型与学习权重模型不匹配或检索深度 k 较小时，无约束 BM25驱动的跳跃可能具有明显的相关性退化。本文在总结前人工作的基础上，采用两级剪枝控制策略和稀疏表示模型对齐方法对 BM25引导的索引遍历进行了优化，实现了快速检索。虽然可能会增加延迟的代价，提出的方案比原来的 MaxScore 方法快得多没有 BM25的指导，同时保留了相关性的有效性。本文分析了这种两级剪枝方案的竞争力，并在搜索多个测试数据集时，对其在排序相关性和时间效率方面的权衡进行了评估。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Optimizing+Guided+Traversal+for+Fast+Learned+Sparse+Retrieval)|0| +|[Optimizing Guided Traversal for Fast Learned Sparse Retrieval](https://doi.org/10.1145/3543507.3583497)|Yifan Qiao, Yingrui Yang, Haixin Lin, Tao Yang|Department of Computer Science, University of California at Santa Barbara, USA; Department of Computer Science, University of California, Santa Barbara, USA|Recent studies show that BM25-driven dynamic index skipping can greatly accelerate MaxScore-based document retrieval based on the learned sparse representation derived by DeepImpact. This paper investigates the effectiveness of such a traversal guidance strategy during top k retrieval when using other models such as SPLADE and uniCOIL, and finds that unconstrained BM25-driven skipping could have a visible relevance degradation when the BM25 model is not well aligned with a learned weight model or when retrieval depth k is small. This paper generalizes the previous work and optimizes the BM25 guided index traversal with a two-level pruning control scheme and model alignment for fast retrieval using a sparse representation. Although there can be a cost of increased latency, the proposed scheme is much faster than the original MaxScore method without BM25 guidance while retaining the relevance effectiveness. This paper analyzes the competitiveness of this two-level pruning scheme, and evaluates its tradeoff in ranking relevance and time efficiency when searching several test datasets.|最近的研究表明，BM25驱动的动态指数跳跃可以大大加快基于深度影响学习稀疏表示的基于 MaxScore 的文献检索。本文研究了使用 SPLADE 和 uniCOIL 等其他模型进行 top k 检索时，这种遍历指导策略的有效性，发现当 BM25模型与学习权重模型不匹配或检索深度 k 较小时，无约束 BM25驱动的跳跃可能具有明显的相关性退化。本文在总结前人工作的基础上，采用两级剪枝控制策略和稀疏表示模型对齐方法对 BM25引导的索引遍历进行了优化，实现了快速检索。虽然可能会增加延迟的代价，提出的方案比原来的 MaxScore 方法快得多没有 BM25的指导，同时保留了相关性的有效性。本文分析了这种两级剪枝方案的竞争力，并在搜索多个测试数据集时，对其在排序相关性和时间效率方面的权衡进行了评估。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Optimizing+Guided+Traversal+for+Fast+Learned+Sparse+Retrieval)|0| |[Stability and Efficiency of Personalised Cultural Markets](https://doi.org/10.1145/3543507.3583315)|Haiqing Zhu, Yun Kuen Cheung, Lexing Xie|Australian National University, Australia; Royal Holloway University of London, United Kingdom|This work is concerned with the dynamics of online cultural markets, namely, attention allocation of many users on a set of digital goods with infinite supply. Such dynamic is important in shaping processes and outcomes in society, from trending items in entertainment, collective knowledge creation, to election outcomes. The outcomes of online cultural markets are susceptible to intricate social influence dynamics, particularly so when the community comprises consumers with heterogeneous interests. This has made formal analysis of these markets improbable. In this paper, we remedy this by establishing robust connections between influence dynamics and optimization processes, in trial-offer markets where the consumer preferences are modelled by multinomial logit. Among other results, we show that the proportional-response-esque influence dynamic is equivalent to stochastic mirror descent on a convex objective function, thus leading to a stable and predictable outcome. When all consumers are homogeneous, the objective function has a natural interpretation as a weighted sum of efficiency and diversity of the culture market. In simulations driven by real-world preferences collected from a large-scale recommender system, we observe that ranking strategies aligned with the underlying heterogeneous preferences are more stable, and achieves higher efficiency and diversity. In simulations driven by real-world preferences collected from a large-scale recommender system, we observe that ranking strategies aligned with the underlying heterogeneous preferences are more stable, and achieves higher efficiency and diversity.|本文研究的是在线文化市场的动态变化，即在无限供应的数字商品上，许多用户的注意力分配。这种动态对于塑造社会的进程和结果至关重要，从娱乐的趋势项目、集体知识创造到选举结果。在线文化市场的结果容易受到错综复杂的社会影响力动态的影响，特别是当社区由具有不同兴趣的消费者组成时。这使得对这些市场的正式分析成为不可能。在本文中，我们通过建立影响力动态和最优化过程之间的强有力的联系来纠正这个问题，在试销市场中，消费者的偏好是由多项式 logit 建模的。在其他结果中，我们表明，比例响应方式的影响动态相当于随机镜下降的凸目标函数，从而导致一个稳定和可预测的结果。当所有消费者都是同质的时候，目标函数就自然地被解释为文化市场效率和多样性的加权和。在从大规模推荐系统中收集的现实世界偏好驱动的模拟中，我们观察到与潜在的异质偏好相一致的排序策略更加稳定，并且实现更高的效率和多样性。在从大规模推荐系统中收集的现实世界偏好驱动的模拟中，我们观察到与潜在的异质偏好相一致的排序策略更加稳定，并且实现更高的效率和多样性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Stability+and+Efficiency+of+Personalised+Cultural+Markets)|0| |[Eligibility Mechanisms: Auctions Meet Information Retrieval](https://doi.org/10.1145/3543507.3583478)|Gagan Goel, Renato Paes Leme, Jon Schneider, David Thompson, Hanrui Zhang|Carnegie Mellon University, USA; Google, USA|The design of internet advertisement systems is both an auction design problem and an information retrieval (IR) problem. As an auction, the designer needs to take the participants incentives into account. As an information retrieval problem, it needs to identify the ad that it is the most relevant to a user out of an enormous set of ad candidates. Those aspects are combined by first having an IR system narrow down the initial set of ad candidates to a manageable size followed by an auction that ranks and prices those candidates. If the IR system uses information about bids, agents could in principle manipulate the system by manipulating the IR stage even when the subsequent auction is truthful. In this paper we investigate the design of truthful IR mechanisms, which we term eligibility mechanisms. We model it as a truthful version of the stochastic probing problem. We show that there is a constant gap between the truthful and non-truthful versions of the stochastic probing problem and exhibit a constant approximation algorithm. En route, we also characterize the set of eligibility mechanisms, which provides necessary and sufficient conditions for an IR system to be truthful.|互联网广告系统的设计既是一个拍卖设计问题，也是一个信息检索(IR)问题。作为一种拍卖，设计师需要考虑参与者的激励因素。作为一个信息检索问题，它需要从大量的候选广告中找出与用户最相关的广告。将这些方面结合起来，首先通过投资者关系系统将最初的广告候选人缩小到一个可管理的规模，然后通过拍卖对这些候选人进行排名和定价。如果 IR 系统使用关于出价的信息，即使随后的拍卖是真实的，代理人原则上也可以通过操纵 IR 阶段来操纵系统。本文研究了真实信息检索机制的设计，我们称之为资格机制。我们将其建模为随机探测问题的真实版本。我们证明了随机探测问题的真实版本和非真实版本之间存在一个恒定的差距，并呈现出一个恒定的近似演算法。在此过程中，我们还刻画了一组资格机制，它为 IR 系统的真实性提供了充分的必要条件。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Eligibility+Mechanisms:+Auctions+Meet+Information+Retrieval)|0| |[Scoping Fairness Objectives and Identifying Fairness Metrics for Recommender Systems: The Practitioners' Perspective](https://doi.org/10.1145/3543507.3583204)|Jessie J. Smith, Lex Beattie, Henriette Cramer|Spotify, USA; University of Colorado, Boulder, USA|Measuring and assessing the impact and “fairness’’ of recommendation algorithms is central to responsible recommendation efforts. However, the complexity of fairness definitions and the proliferation of fairness metrics in research literature have led to a complex decision-making space. This environment makes it challenging for practitioners to operationalize and pick metrics that work within their unique context. This suggests that practitioners require more decision-making support, but it is not clear what type of support would be beneficial. We conducted a literature review of 24 papers to gather metrics introduced by the research community for measuring fairness in recommendation and ranking systems. We organized these metrics into a ‘decision-tree style’ support framework designed to help practitioners scope fairness objectives and identify fairness metrics relevant to their recommendation domain and application context. To explore the feasibility of this approach, we conducted 15 semi-structured interviews using this framework to assess which challenges practitioners may face when scoping fairness objectives and metrics for their system, and which further support may be needed beyond such tools.|衡量和评估推荐算法的影响和“公平性”是负责任的推荐工作的核心。然而，公平定义的复杂性和研究文献中公平度量标准的泛滥导致了决策空间的复杂性。这种环境使得从业人员很难操作和挑选在其独特上下文中工作的度量。这表明从业人员需要更多的决策支持，但不清楚哪种类型的支持将是有益的。我们对24篇论文进行了文献回顾，收集了研究团体引入的衡量推荐和排名系统公平性的指标。我们将这些指标组织成一个“决策树风格”的支持框架，旨在帮助从业人员确定公平目标，并确定与其推荐领域和应用程序上下文相关的公平指标。为了探索这种方法的可行性，我们使用这个框架进行了15次半结构化访谈，以评估从业者在确定公平目标和系统指标时可能面临的挑战，以及这些工具之外可能还需要哪些进一步的支持。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Scoping+Fairness+Objectives+and+Identifying+Fairness+Metrics+for+Recommender+Systems:+The+Practitioners'+Perspective)|0| |[Same Same, But Different: Conditional Multi-Task Learning for Demographic-Specific Toxicity Detection](https://doi.org/10.1145/3543507.3583290)|Soumyajit Gupta, Sooyong Lee, Maria DeArteaga, Matthew Lease||Algorithmic bias often arises as a result of differential subgroup validity, in which predictive relationships vary across groups. For example, in toxic language detection, comments targeting different demographic groups can vary markedly across groups. In such settings, trained models can be dominated by the relationships that best fit the majority group, leading to disparate performance. We propose framing toxicity detection as multi-task learning (MTL), allowing a model to specialize on the relationships that are relevant to each demographic group while also leveraging shared properties across groups. With toxicity detection, each task corresponds to identifying toxicity against a particular demographic group. However, traditional MTL requires labels for all tasks to be present for every data point. To address this, we propose Conditional MTL (CondMTL), wherein only training examples relevant to the given demographic group are considered by the loss function. This lets us learn group specific representations in each branch which are not cross contaminated by irrelevant labels. Results on synthetic and real data show that using CondMTL improves predictive recall over various baselines in general and for the minority demographic group in particular, while having similar overall accuracy.|算法偏差通常由于不同的子群效度而产生，其中预测关系因组而异。例如，在毒性语言检测中，针对不同人口群体的评论可能因群体而有显著差异。在这种情况下，训练有素的模型可以被最适合大多数群体的关系所主导，从而导致不同的表现。我们建议将毒性检测框架为多任务学习(MTL) ，允许模型专门处理与每个人口组相关的关系，同时利用跨组的共享属性。通过毒性检测，每项任务都对应于确定针对特定人口群体的毒性。但是，传统的 MTL 要求为每个数据点显示所有任务的标签。为了解决这个问题，我们提出了条件 MTL (CondMTL) ，其中只有与给定人口组相关的训练实例被损失函数考虑。这使我们能够了解每个分支中不受不相关标签交叉污染的特定分组表示。对合成和真实数据的研究结果表明，使用 CondMTL 提高了对各种基线的预测性回忆，特别是对少数人口群体，同时具有相似的整体准确性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Same+Same,+But+Different:+Conditional+Multi-Task+Learning+for+Demographic-Specific+Toxicity+Detection)|0| |[Towards Explainable Collaborative Filtering with Taste Clusters Learning](https://doi.org/10.1145/3543507.3583303)|Yuntao Du, Jianxun Lian, Jing Yao, Xiting Wang, Mingqi Wu, Lu Chen, Yunjun Gao, Xing Xie||Collaborative Filtering (CF) is a widely used and effective technique for recommender systems. In recent decades, there have been significant advancements in latent embedding-based CF methods for improved accuracy, such as matrix factorization, neural collaborative filtering, and LightGCN. However, the explainability of these models has not been fully explored. Adding explainability to recommendation models can not only increase trust in the decisionmaking process, but also have multiple benefits such as providing persuasive explanations for item recommendations, creating explicit profiles for users and items, and assisting item producers in design improvements. In this paper, we propose a neat and effective Explainable Collaborative Filtering (ECF) model that leverages interpretable cluster learning to achieve the two most demanding objectives: (1) Precise - the model should not compromise accuracy in the pursuit of explainability; and (2) Self-explainable - the model's explanations should truly reflect its decision-making process, not generated from post-hoc methods. The core of ECF is mining taste clusters from user-item interactions and item profiles.We map each user and item to a sparse set of taste clusters, and taste clusters are distinguished by a few representative tags. The user-item preference, users/items' cluster affiliations, and the generation of taste clusters are jointly optimized in an end-to-end manner. Additionally, we introduce a forest mechanism to ensure the model's accuracy, explainability, and diversity. To comprehensively evaluate the explainability quality of taste clusters, we design several quantitative metrics, including in-cluster item coverage, tag utilization, silhouette, and informativeness. Our model's effectiveness is demonstrated through extensive experiments on three real-world datasets.|协同过滤(CF)是推荐系统中广泛使用的有效技术。近几十年来，基于潜在嵌入的 CF 方法在提高准确性方面取得了重大进展，例如矩阵分解、神经协同过滤和 LightGCN。然而，这些模型的可解释性还没有得到充分的探索。为推荐模型增加可解释性不仅可以增加对决策过程的信任，而且还有多种好处，例如为项目推荐提供有说服力的解释，为用户和项目创建明确的配置文件，以及帮助项目生产者改进设计。在本文中，我们提出了一个简洁而有效的可解释协同过滤(ECF)模型，它利用可解释的聚类学习来实现两个最严格的目标: (1)精确——模型在追求可解释性的过程中不应该损害准确性; (2)自我解释——模型的解释应该真实地反映其决策过程，而不是由事后方法产生。ECF 的核心是从用户-项目交互和项目配置文件中挖掘味觉集群。我们将每个用户和项目映射到一个稀疏的味觉集群，味觉集群通过几个代表性的标签来区分。用户项偏好、用户/项目的集群附属关系以及味道集群的生成都以端到端的方式进行了联合优化。此外，我们还引入了森林机制来保证模型的准确性、可解释性和多样性。为了全面评价味觉集群的可解释性质量，我们设计了几个量化指标，包括集群内项目覆盖率、标签利用率、轮廓和信息量。通过对三个实际数据集的大量实验，验证了该模型的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Towards+Explainable+Collaborative+Filtering+with+Taste+Clusters+Learning)|0| -|[Towards Fair Allocation in Social Commerce Platforms](https://doi.org/10.1145/3543507.3583398)|Anjali Gupta, Shreyans J. Nagori, Abhijnan Chakraborty, Rohit Vaish, Sayan Ranu, Prajit Prashant Sinai Nadkarni, Narendra Varma Dasararaju, Muthusamy Chelliah|Flipkart Internet Pvt. Ltd., India; Indian Institute of Technology Delhi, India|Social commerce platforms are emerging businesses where producers sell products through re-sellers who advertise the products to other customers in their social network. Due to the increasing popularity of this business model, thousands of small producers and re-sellers are starting to depend on these platforms for their livelihood; thus, it is important to provide fair earning opportunities to them. The enormous product space in such platforms prohibits manual search, and motivates the need for recommendation algorithms to effectively allocate product exposure and, consequently, earning opportunities. In this work, we focus on the fairness of such allocations in social commerce platforms and formulate the problem of assigning products to re-sellers as a fair division problem with indivisible items under two-sided cardinality constraints, wherein each product must be given to at least a certain number of re-sellers and each re-seller must get a certain number of products. Our work systematically explores various well-studied benchmarks of fairness—including Nash social welfare, envy-freeness up to one item (EF1), and equitability up to one item (EQ1)—from both theoretical and experimental perspectives. We find that the existential and computational guarantees of these concepts known from the unconstrained setting do not extend to our constrained model. To address this limitation, we develop a mixed-integer linear program and other scalable heuristics that provide near-optimal approximation of Nash social welfare in simulated and real social commerce datasets. Overall, our work takes the first step towards achieving provable fairness alongside reasonable revenue guarantees on social commerce platforms.|社交商务平台是新兴企业，生产商通过转销商向社交网络中的其他客户推销产品。由于这种商业模式日益流行，成千上万的小生产商和转售商开始依赖这些平台谋生; 因此，必须为他们提供公平的赚钱机会。这些平台中巨大的产品空间禁止手工搜索，并激发了对推荐算法的需求，以有效地分配产品曝光率，从而获得机会。本文研究了社会商务平台中这种分配的公平性问题，将产品分配给转销商的问题转化为具有双侧基数约束的不可分项的公平分配问题，其中每个产品必须分配给至少一定数量的转销商，每个转销商必须得到一定数量的产品。我们的研究从理论和实验两个角度系统地探讨了各种已经得到充分研究的公平标准，包括纳什社会福利标准、一个项目下的嫉妒自由标准(EF1)和一个项目下的公平标准(EQ1)。我们发现，这些概念的存在性和计算保证已知的无约束设置不扩展到我们的约束模型。为了解决这一局限性，我们开发了一个混合整数线性规划和其他可扩展的启发式算法，在模拟和真实的社会商务数据集中提供纳什社会福利的近似最优近似值。总体而言，我们的工作朝着实现可证明的公平迈出了第一步，同时在社交商务平台上实现了合理的收入保障。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Towards+Fair+Allocation+in+Social+Commerce+Platforms)|0| -|[Fairness-Aware Clique-Preserving Spectral Clustering of Temporal Graphs](https://doi.org/10.1145/3543507.3583423)|Dongqi Fu, Dawei Zhou, Ross Maciejewski, Arie Croitoru, Marcus Boyd, Jingrui He|University of Illinois at Urbana-Champaign, USA; University of Maryland, College Park, USA; Virginia Tech, USA; Arizona State University, USA; George Mason University, USA|With the widespread development of algorithmic fairness, there has been a surge of research interest that aims to generalize the fairness notions from the attributed data to the relational data (graphs). The vast majority of existing work considers the fairness measure in terms of the low-order connectivity patterns (e.g., edges), while overlooking the higher-order patterns (e.g., k-cliques) and the dynamic nature of real-world graphs. For example, preserving triangles from graph cuts during clustering is the key to detecting compact communities; however, if the clustering algorithm only pays attention to triangle-based compactness, then the returned communities lose the fairness guarantee for each group in the graph. Furthermore, in practice, when the graph (e.g., social networks) topology constantly changes over time, one natural question is how can we ensure the compactness and demographic parity at each timestamp efficiently. To address these problems, we start from the static setting and propose a spectral method that preserves clique connections and incorporates demographic fairness constraints in returned clusters at the same time. To make this static method fit for the dynamic setting, we propose two core techniques, Laplacian Update via Edge Filtering and Searching and Eigen-Pairs Update with Singularity Avoided. Finally, all proposed components are combined into an end-to-end clustering framework named F-SEGA, and we conduct extensive experiments to demonstrate the effectiveness, efficiency, and robustness of F-SEGA.|随着算法公平性的广泛发展，将属性数据的公平性概念推广到关系数据(图)的研究兴趣日益高涨。绝大多数现有的工作考虑了低阶连通性模式(例如边)的公平性度量，而忽略了高阶模式(例如 k-cliques)和真实世界图的动态性质。例如，在聚类过程中保留图切割中的三角形是检测紧密群体的关键，但是如果聚类算法只关注基于三角形的紧密性，那么返回的群体就失去了对图中每个群体的公平性保证。此外，在实践中，当图形(例如，社交网络)拓扑随着时间不断变化时，一个自然的问题是我们如何有效地确保每个时间戳的紧凑性和人口平等性。为了解决这些问题，我们从静态设置开始，并提出了一个谱方法，保留了派系连接，同时在返回的集群中引入了人口公平约束。为了使这种静态方法适合于动态设置，我们提出了两个核心技术: 通过边缘滤波和搜索的拉普拉斯更新和避免奇异性的特征对更新。最后，将所有提出的构件组合成一个端到端的聚类框架 F-SEGA，并进行了广泛的实验来验证 F-SEGA 的有效性、高效性和鲁棒性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Fairness-Aware+Clique-Preserving+Spectral+Clustering+of+Temporal+Graphs)|0| -|[HybridEval: A Human-AI Collaborative Approach for Evaluating Design Ideas at Scale](https://doi.org/10.1145/3543507.3583496)|Sepideh Mesbah, Ines Arous, Jie Yang, Alessandro Bozzon|Delft University of Technology, Netherlands; Booking, Netherlands; University of Fribourg, Switzerland|Evaluating design ideas is necessary to predict their success and assess their impact early on in the process. Existing methods rely either on metrics computed by systems that are effective but subject to errors and bias, or experts’ ratings, which are accurate but expensive and long to collect. Crowdsourcing offers a compelling way to evaluate a large number of design ideas in a short amount of time while being cost-effective. Workers’ evaluation is, however, less reliable and might substantially differ from experts’ evaluation. In this work, we investigate workers’ rating behavior and compare it with experts. First, we instrument a crowdsourcing study where we asked workers to evaluate design ideas from three innovation challenges. We show that workers share similar insights with experts but tend to rate more generously and weigh certain criteria more importantly. Next, we develop a hybrid human-AI approach that combines a machine learning model with crowdsourcing to evaluate ideas. Our approach models workers’ reliability and bias while leveraging ideas’ textual content to train a machine learning model. It is able to incorporate experts’ ratings whenever available, to supervise the model training and infer worker performance. Results show that our framework outperforms baseline methods and requires significantly less training data from experts, thus providing a viable solution for evaluating ideas at scale.|评估设计想法是必要的，以预测他们的成功和评估他们的影响早在过程中。现有的方法要么依赖于有效但容易出错和偏差的系统计算出的指标，要么依赖于专家的评分，这些评分准确但昂贵，而且需要很长时间才能收集到。众包提供了一个引人注目的方式来评估大量的设计想法在短时间内，同时具有成本效益。然而，工人的评估不太可靠，可能与专家的评估大不相同。在这项工作中，我们调查工人的评分行为，并与专家进行比较。首先，我们进行了一项众包研究，要求工人评估来自三个创新挑战的设计理念。我们的研究表明，员工与专家有着相似的见解，但他们倾向于更慷慨地给出评价，更重视某些标准。接下来，我们开发了一种混合的人工智能方法，它结合了机器学习模型和众包来评估想法。我们的方法模拟工人的可靠性和偏见，同时利用想法的文本内容来训练机器学习模型。它能够在任何时候合并专家的评分，以监督模型培训和推断工人的表现。结果表明，我们的框架优于基线方法，需要的专家培训数据明显减少，从而为大规模评估想法提供了一个可行的解决方案。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=HybridEval:+A+Human-AI+Collaborative+Approach+for+Evaluating+Design+Ideas+at+Scale)|0| +|[Towards Fair Allocation in Social Commerce Platforms](https://doi.org/10.1145/3543507.3583398)|Anjali Gupta, Shreyans J. Nagori, Abhijnan Chakraborty, Rohit Vaish, Sayan Ranu, Prajit Prashant Sinai Nadkarni, Narendra Varma Dasararaju, Muthusamy Chelliah|Indian Institute of Technology Delhi, India; Flipkart Internet Pvt. Ltd., India|Social commerce platforms are emerging businesses where producers sell products through re-sellers who advertise the products to other customers in their social network. Due to the increasing popularity of this business model, thousands of small producers and re-sellers are starting to depend on these platforms for their livelihood; thus, it is important to provide fair earning opportunities to them. The enormous product space in such platforms prohibits manual search, and motivates the need for recommendation algorithms to effectively allocate product exposure and, consequently, earning opportunities. In this work, we focus on the fairness of such allocations in social commerce platforms and formulate the problem of assigning products to re-sellers as a fair division problem with indivisible items under two-sided cardinality constraints, wherein each product must be given to at least a certain number of re-sellers and each re-seller must get a certain number of products. Our work systematically explores various well-studied benchmarks of fairness—including Nash social welfare, envy-freeness up to one item (EF1), and equitability up to one item (EQ1)—from both theoretical and experimental perspectives. We find that the existential and computational guarantees of these concepts known from the unconstrained setting do not extend to our constrained model. To address this limitation, we develop a mixed-integer linear program and other scalable heuristics that provide near-optimal approximation of Nash social welfare in simulated and real social commerce datasets. Overall, our work takes the first step towards achieving provable fairness alongside reasonable revenue guarantees on social commerce platforms.|社交商务平台是新兴企业，生产商通过转销商向社交网络中的其他客户推销产品。由于这种商业模式日益流行，成千上万的小生产商和转售商开始依赖这些平台谋生; 因此，必须为他们提供公平的赚钱机会。这些平台中巨大的产品空间禁止手工搜索，并激发了对推荐算法的需求，以有效地分配产品曝光率，从而获得机会。本文研究了社会商务平台中这种分配的公平性问题，将产品分配给转销商的问题转化为具有双侧基数约束的不可分项的公平分配问题，其中每个产品必须分配给至少一定数量的转销商，每个转销商必须得到一定数量的产品。我们的研究从理论和实验两个角度系统地探讨了各种已经得到充分研究的公平标准，包括纳什社会福利标准、一个项目下的嫉妒自由标准(EF1)和一个项目下的公平标准(EQ1)。我们发现，这些概念的存在性和计算保证已知的无约束设置不扩展到我们的约束模型。为了解决这一局限性，我们开发了一个混合整数线性规划和其他可扩展的启发式算法，在模拟和真实的社会商务数据集中提供纳什社会福利的近似最优近似值。总体而言，我们的工作朝着实现可证明的公平迈出了第一步，同时在社交商务平台上实现了合理的收入保障。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Towards+Fair+Allocation+in+Social+Commerce+Platforms)|0| +|[Fairness-Aware Clique-Preserving Spectral Clustering of Temporal Graphs](https://doi.org/10.1145/3543507.3583423)|Dongqi Fu, Dawei Zhou, Ross Maciejewski, Arie Croitoru, Marcus Boyd, Jingrui He|Arizona State University, USA; University of Illinois at Urbana-Champaign, USA; Virginia Tech, USA; University of Maryland, College Park, USA; George Mason University, USA|With the widespread development of algorithmic fairness, there has been a surge of research interest that aims to generalize the fairness notions from the attributed data to the relational data (graphs). The vast majority of existing work considers the fairness measure in terms of the low-order connectivity patterns (e.g., edges), while overlooking the higher-order patterns (e.g., k-cliques) and the dynamic nature of real-world graphs. For example, preserving triangles from graph cuts during clustering is the key to detecting compact communities; however, if the clustering algorithm only pays attention to triangle-based compactness, then the returned communities lose the fairness guarantee for each group in the graph. Furthermore, in practice, when the graph (e.g., social networks) topology constantly changes over time, one natural question is how can we ensure the compactness and demographic parity at each timestamp efficiently. To address these problems, we start from the static setting and propose a spectral method that preserves clique connections and incorporates demographic fairness constraints in returned clusters at the same time. To make this static method fit for the dynamic setting, we propose two core techniques, Laplacian Update via Edge Filtering and Searching and Eigen-Pairs Update with Singularity Avoided. Finally, all proposed components are combined into an end-to-end clustering framework named F-SEGA, and we conduct extensive experiments to demonstrate the effectiveness, efficiency, and robustness of F-SEGA.|随着算法公平性的广泛发展，将属性数据的公平性概念推广到关系数据(图)的研究兴趣日益高涨。绝大多数现有的工作考虑了低阶连通性模式(例如边)的公平性度量，而忽略了高阶模式(例如 k-cliques)和真实世界图的动态性质。例如，在聚类过程中保留图切割中的三角形是检测紧密群体的关键，但是如果聚类算法只关注基于三角形的紧密性，那么返回的群体就失去了对图中每个群体的公平性保证。此外，在实践中，当图形(例如，社交网络)拓扑随着时间不断变化时，一个自然的问题是我们如何有效地确保每个时间戳的紧凑性和人口平等性。为了解决这些问题，我们从静态设置开始，并提出了一个谱方法，保留了派系连接，同时在返回的集群中引入了人口公平约束。为了使这种静态方法适合于动态设置，我们提出了两个核心技术: 通过边缘滤波和搜索的拉普拉斯更新和避免奇异性的特征对更新。最后，将所有提出的构件组合成一个端到端的聚类框架 F-SEGA，并进行了广泛的实验来验证 F-SEGA 的有效性、高效性和鲁棒性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Fairness-Aware+Clique-Preserving+Spectral+Clustering+of+Temporal+Graphs)|0| +|[HybridEval: A Human-AI Collaborative Approach for Evaluating Design Ideas at Scale](https://doi.org/10.1145/3543507.3583496)|Sepideh Mesbah, Ines Arous, Jie Yang, Alessandro Bozzon|University of Fribourg, Switzerland; Booking, Netherlands; Delft University of Technology, Netherlands|Evaluating design ideas is necessary to predict their success and assess their impact early on in the process. Existing methods rely either on metrics computed by systems that are effective but subject to errors and bias, or experts’ ratings, which are accurate but expensive and long to collect. Crowdsourcing offers a compelling way to evaluate a large number of design ideas in a short amount of time while being cost-effective. Workers’ evaluation is, however, less reliable and might substantially differ from experts’ evaluation. In this work, we investigate workers’ rating behavior and compare it with experts. First, we instrument a crowdsourcing study where we asked workers to evaluate design ideas from three innovation challenges. We show that workers share similar insights with experts but tend to rate more generously and weigh certain criteria more importantly. Next, we develop a hybrid human-AI approach that combines a machine learning model with crowdsourcing to evaluate ideas. Our approach models workers’ reliability and bias while leveraging ideas’ textual content to train a machine learning model. It is able to incorporate experts’ ratings whenever available, to supervise the model training and infer worker performance. Results show that our framework outperforms baseline methods and requires significantly less training data from experts, thus providing a viable solution for evaluating ideas at scale.|评估设计想法是必要的，以预测他们的成功和评估他们的影响早在过程中。现有的方法要么依赖于有效但容易出错和偏差的系统计算出的指标，要么依赖于专家的评分，这些评分准确但昂贵，而且需要很长时间才能收集到。众包提供了一个引人注目的方式来评估大量的设计想法在短时间内，同时具有成本效益。然而，工人的评估不太可靠，可能与专家的评估大不相同。在这项工作中，我们调查工人的评分行为，并与专家进行比较。首先，我们进行了一项众包研究，要求工人评估来自三个创新挑战的设计理念。我们的研究表明，员工与专家有着相似的见解，但他们倾向于更慷慨地给出评价，更重视某些标准。接下来，我们开发了一种混合的人工智能方法，它结合了机器学习模型和众包来评估想法。我们的方法模拟工人的可靠性和偏见，同时利用想法的文本内容来训练机器学习模型。它能够在任何时候合并专家的评分，以监督模型培训和推断工人的表现。结果表明，我们的框架优于基线方法，需要的专家培训数据明显减少，从而为大规模评估想法提供了一个可行的解决方案。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=HybridEval:+A+Human-AI+Collaborative+Approach+for+Evaluating+Design+Ideas+at+Scale)|0| |[A Multi-task Model for Emotion and Offensive Aided Stance Detection of Climate Change Tweets](https://doi.org/10.1145/3543507.3583860)|Apoorva Upadhyaya, Marco Fisichella, Wolfgang Nejdl|L3S Research Center, Leibniz University Hannover, Germany|In this work, we address the United Nations Sustainable Development Goal 13: Climate Action by focusing on identifying public attitudes toward climate change on social media platforms such as Twitter. Climate change is threatening the health of the planet and humanity. Public engagement is critical to address climate change. However, climate change conversations on Twitter tend to polarize beliefs, leading to misinformation and fake news that influence public attitudes, often dividing them into climate change believers and deniers. Our paper proposes an approach to classify the attitude of climate change tweets (believe/deny/ambiguous) to identify denier statements on Twitter. Most existing approaches for detecting stances and classifying climate change tweets either overlook deniers’ tweets or do not have a suitable architecture. The relevant literature suggests that emotions and higher levels of toxicity are prevalent in climate change Twitter conversations, leading to a delay in appropriate climate action. Therefore, our work focuses on learning stance detection (main task) while exploiting the auxiliary tasks of recognizing emotions and offensive utterances. We propose a multimodal multitasking framework MEMOCLiC that captures the input data using different embedding techniques and attention frameworks, and then incorporates the learned emotional and offensive expressions to obtain an overall representation of the features relevant to the stance of the input tweet. Extensive experiments conducted on a novel curated climate change dataset and two benchmark stance detection datasets (SemEval-2016 and ClimateStance-2022) demonstrate the effectiveness of our approach.|在这项工作中，我们致力于实现联合国可持续发展目标13: 气候行动，重点是在 Twitter 等社交媒体平台上确定公众对气候变化的态度。气候变化正威胁着地球和人类的健康。公众参与对应对气候变化至关重要。然而，Twitter 上关于气候变化的讨论往往会导致信仰的两极分化，导致错误信息和假新闻，从而影响公众态度，往往将公众分为气候变化信徒和否认者。我们的论文提出了一种方法来分类气候变化推文的态度(相信/否认/模棱两可) ，以确定否认者的声明在 Twitter 上。大多数现有的检测立场和分类气候变化推文的方法要么忽略否认者的推文，要么没有一个合适的架构。相关文献表明，在气候变化的 Twitter 对话中，情绪和较高的毒性水平普遍存在，导致适当的气候行动出现延误。因此，我们的工作集中在学习姿势检测(主要任务) ，同时利用辅助任务的识别情绪和攻击性话语。我们提出了一个多模式多任务框架 MEMOCLiC，它使用不同的嵌入技术和注意力框架捕获输入数据，然后结合所学到的情绪和攻击性表达来获得与输入 tweet 立场相关的特征的整体表示。在一个新的策划气候变化数据集和两个基准姿态检测数据集(SemEval-2016和 ClimateStance-2022)上进行的大量实验证明了我们方法的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Multi-task+Model+for+Emotion+and+Offensive+Aided+Stance+Detection+of+Climate+Change+Tweets)|0| |[Cross-center Early Sepsis Recognition by Medical Knowledge Guided Collaborative Learning for Data-scarce Hospitals](https://doi.org/10.1145/3543507.3583989)|Ruiqing Ding, Fangjie Rong, Xiao Han, Leye Wang|Shanghai University of Finance and Economics, China; Peking University, China|There are significant regional inequities in health resources around the world. It has become one of the most focused topics to improve health services for data-scarce hospitals and promote health equity through knowledge sharing among medical institutions. Because electronic medical records (EMRs) contain sensitive personal information, privacy protection is unavoidable and essential for multi-hospital collaboration. In this paper, for a common disease in ICU patients, sepsis, we propose a novel cross-center collaborative learning framework guided by medical knowledge, SofaNet, to achieve early recognition of this disease. The Sepsis-3 guideline, published in 2016, defines that sepsis can be diagnosed by satisfying both suspicion of infection and Sequential Organ Failure Assessment (SOFA) greater than or equal to 2. Based on this knowledge, SofaNet adopts a multi-channel GRU structure to predict SOFA values of different systems, which can be seen as an auxiliary task to generate better health status representations for sepsis recognition. Moreover, we only achieve feature distribution alignment in the hidden space during cross-center collaborative learning, which ensures secure and compliant knowledge transfer without raw data exchange. Extensive experiments on two open clinical datasets, MIMIC-III and Challenge, demonstrate that SofaNet can benefit early sepsis recognition when hospitals only have limited EMRs.|世界各地在卫生资源方面存在着严重的区域不平等。通过医疗机构之间的知识共享，改善数据稀缺的医院的卫生服务，促进卫生公平，已成为最重点的议题之一。由于电子病历(EMR)包含敏感的个人信息，隐私保护是不可避免的，也是多医院合作的必要条件。在这篇文章中，我们针对 ICU 患者常见的一种疾病，败血症，提出了一种新的跨中心合作学习框架，以医学知识为指导，SofaNet，以实现对这种疾病的早期识别。2016年发布的脓毒症3指南规定，脓毒症可以通过满足感染的怀疑和大于或等于2的序贯性器官衰竭评估(SOFA)来诊断。在此基础上，SofaNet 采用多通道 GRU 结构来预测不同系统的 SOFA 值，这可以看作是一个辅助任务，以产生更好的脓毒症识别健康状态表示。此外，我们只有在跨中心合作学习时才能在隐藏空间中实现特征分布对齐，从而确保在没有原始数据交换的情况下安全和兼容的知识传输。在两个开放的临床数据集 MIMIC-III 和 Challenge 上的大量实验表明，当医院只有有限的 EMR 时，SofaNet 可以有利于早期脓毒症识别。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Cross-center+Early+Sepsis+Recognition+by+Medical+Knowledge+Guided+Collaborative+Learning+for+Data-scarce+Hospitals)|0| -|[Breaking Filter Bubble: A Reinforcement Learning Framework of Controllable Recommender System](https://doi.org/10.1145/3543507.3583856)|Zhenyang Li, Yancheng Dong, Chen Gao, Yizhou Zhao, Dong Li, Jianye Hao, Kai Zhang, Yong Li, Zhi Wang|Carnegie Mellon University, USA; Tsinghua Shenzhen International Graduate School, Tsinghua University, China and Research Institute of Tsinghua, Pearl River Delta, China; Huawei Noah's Ark Lab, China; Tsinghua University, China and Huawei Noah's Ark Lab, China; Tsinghua University, China; Tsinghua-Berkeley Shenzhen Institute, Tsinghua Shenzhen International Graduate School, China and Peng Cheng Laboratory, China|In the information-overloaded era of the Web, recommender systems that provide personalized content filtering are now the mainstream portal for users to access Web information. Recommender systems deploy machine learning models to learn users’ preferences from collected historical data, leading to more centralized recommendation results due to the feedback loop. As a result, it will harm the ranking of content outside the narrowed scope and limit the options seen by users. In this work, we first conduct data analysis from a graph view to observe that the users’ feedback is restricted to limited items, verifying the phenomenon of centralized recommendation. We further develop a general simulation framework to derive the procedure of the recommender system, including data collection, model learning, and item exposure, which forms a loop. To address the filter bubble issue under the feedback loop, we then propose a general and easy-to-use reinforcement learning-based method, which can adaptively select few but effective connections between nodes from different communities as the exposure list. We conduct extensive experiments in the simulation framework based on large-scale real-world datasets. The results demonstrate that our proposed reinforcement learning-based control method can serve as an effective solution to alleviate the filter bubble and the separated communities induced by it. We believe the proposed framework of controllable recommendation in this work can inspire not only the researchers of recommender systems, but also a broader community concerned with artificial intelligence algorithms’ impact on humanity, especially for those vulnerable populations on the Web.|在信息过载的 Web 时代，提供个性化内容过滤的推荐系统现在已经成为用户访问 Web 信息的主流门户。推荐系统部署机器学习模型，从收集的历史数据中了解用户的偏好，由于反馈回路的存在，推荐结果更加集中。因此，它将损害内容在狭窄范围之外的排名，并限制用户看到的选项。本文首先从图的角度进行数据分析，发现用户的反馈仅限于有限的项目，验证了集中推荐的现象。我们进一步开发了一个通用的模拟框架来推导推荐系统的过程，包括数据收集、模型学习和项目曝光，形成一个循环。为了解决反馈回路下的过滤泡问题，提出了一种通用的、易于使用的强化学习方法，该方法可以自适应地选择不同社区节点之间少量但有效的连接作为暴露列表。我们在基于大规模真实世界数据集的仿真框架中进行了广泛的实验。结果表明，本文提出的基于强化学习的控制方法可以作为一种有效的解决方案，以减轻过滤气泡及其引起的社区分离。我们相信，这项工作中提出的可控推荐框架不仅可以激励推荐系统的研究人员，而且可以激励更广泛的社区关注人工智能算法对人类的影响，特别是对那些网络上的弱势群体。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Breaking+Filter+Bubble:+A+Reinforcement+Learning+Framework+of+Controllable+Recommender+System)|0| +|[Breaking Filter Bubble: A Reinforcement Learning Framework of Controllable Recommender System](https://doi.org/10.1145/3543507.3583856)|Zhenyang Li, Yancheng Dong, Chen Gao, Yizhou Zhao, Dong Li, Jianye Hao, Kai Zhang, Yong Li, Zhi Wang|Huawei Noah's Ark Lab, China; Tsinghua University, China and Huawei Noah's Ark Lab, China; Tsinghua-Berkeley Shenzhen Institute, Tsinghua Shenzhen International Graduate School, China and Peng Cheng Laboratory, China; Carnegie Mellon University, USA; Tsinghua Shenzhen International Graduate School, Tsinghua University, China and Research Institute of Tsinghua, Pearl River Delta, China; Tsinghua University, China|In the information-overloaded era of the Web, recommender systems that provide personalized content filtering are now the mainstream portal for users to access Web information. Recommender systems deploy machine learning models to learn users’ preferences from collected historical data, leading to more centralized recommendation results due to the feedback loop. As a result, it will harm the ranking of content outside the narrowed scope and limit the options seen by users. In this work, we first conduct data analysis from a graph view to observe that the users’ feedback is restricted to limited items, verifying the phenomenon of centralized recommendation. We further develop a general simulation framework to derive the procedure of the recommender system, including data collection, model learning, and item exposure, which forms a loop. To address the filter bubble issue under the feedback loop, we then propose a general and easy-to-use reinforcement learning-based method, which can adaptively select few but effective connections between nodes from different communities as the exposure list. We conduct extensive experiments in the simulation framework based on large-scale real-world datasets. The results demonstrate that our proposed reinforcement learning-based control method can serve as an effective solution to alleviate the filter bubble and the separated communities induced by it. We believe the proposed framework of controllable recommendation in this work can inspire not only the researchers of recommender systems, but also a broader community concerned with artificial intelligence algorithms’ impact on humanity, especially for those vulnerable populations on the Web.|在信息过载的 Web 时代，提供个性化内容过滤的推荐系统现在已经成为用户访问 Web 信息的主流门户。推荐系统部署机器学习模型，从收集的历史数据中了解用户的偏好，由于反馈回路的存在，推荐结果更加集中。因此，它将损害内容在狭窄范围之外的排名，并限制用户看到的选项。本文首先从图的角度进行数据分析，发现用户的反馈仅限于有限的项目，验证了集中推荐的现象。我们进一步开发了一个通用的模拟框架来推导推荐系统的过程，包括数据收集、模型学习和项目曝光，形成一个循环。为了解决反馈回路下的过滤泡问题，提出了一种通用的、易于使用的强化学习方法，该方法可以自适应地选择不同社区节点之间少量但有效的连接作为暴露列表。我们在基于大规模真实世界数据集的仿真框架中进行了广泛的实验。结果表明，本文提出的基于强化学习的控制方法可以作为一种有效的解决方案，以减轻过滤气泡及其引起的社区分离。我们相信，这项工作中提出的可控推荐框架不仅可以激励推荐系统的研究人员，而且可以激励更广泛的社区关注人工智能算法对人类的影响，特别是对那些网络上的弱势群体。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Breaking+Filter+Bubble:+A+Reinforcement+Learning+Framework+of+Controllable+Recommender+System)|0| |[CollabEquality: A Crowd-AI Collaborative Learning Framework to Address Class-wise Inequality in Web-based Disaster Response](https://doi.org/10.1145/3543507.3583871)|Yang Zhang, Lanyu Shang, Ruohan Zong, Huimin Zeng, Zhenrui Yue, Dong Wang|School of Information Sciences, University of Illinois Urbana-Champaign, USA|Web-based disaster response (WebDR) is emerging as a pervasive approach to acquire real-time situation awareness of disaster events by collecting timely observations from the Web (e.g., social media). This paper studies a class-wise inequality problem in WebDR applications where the objective is to address the limitation of current WebDR solutions that often have imbalanced classification performance across different classes. To address such a limitation, this paper explores the collaborative strengths of the diversified yet complementary biases of AI and crowdsourced human intelligence to ensure a more balanced and accurate performance for WebDR applications. However, two critical challenges exist: 1) it is difficult to identify the imbalanced AI results without knowing the ground-truth WebDR labels a priori; ii) it is non-trivial to address the class-wise inequality problem using potentially imperfect crowd labels. To address the above challenges, we develop CollabEquality, an inequality-aware crowd-AI collaborative learning framework that carefully models the inequality bias of both AI and human intelligence from crowdsourcing systems into a principled learning framework. Extensive experiments on two real-world WebDR applications demonstrate that CollabEquality consistently outperforms the state-of-the-art baselines by significantly reducing class-wise inequality while improving the WebDR classification accuracy.|基于 Web 的灾难响应(WebDR)正在成为一种普遍的方法，通过从 Web (例如，社交媒体)收集及时的观察结果来获得对灾难事件的实时情况感知。本文研究了 WebDR 应用程序中的类别不等式问题，其目的是解决目前 WebDR 解决方案的局限性，这些解决方案通常在不同类别之间具有不平衡的分类性能。为了解决这一局限性，本文探讨了人工智能和众包人类智能的多样化但互补的偏见的协作优势，以确保更平衡和准确的 WebDR 应用程序的性能。然而，存在两个关键的挑战: 1)在不知道基本事实 WebDR 标签的情况下很难识别不平衡的 AI 结果; 2)使用潜在不完美的群体标签来解决类别不平等问题是不平凡的。为了应对上述挑战，我们开发了 Collabequity，这是一个意识到不平等的群体人工智能合作学习框架，它仔细地将人工智能和人类智能的不平等偏见从众包系统建模成一个有原则的学习框架。在两个真实世界的 WebDR 应用程序上进行的大量实验表明，CollabEquity 通过显著减少类别不平等，同时提高 WebDR 分类准确性，始终优于最先进的基线。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=CollabEquality:+A+Crowd-AI+Collaborative+Learning+Framework+to+Address+Class-wise+Inequality+in+Web-based+Disaster+Response)|0| |[MoleRec: Combinatorial Drug Recommendation with Substructure-Aware Molecular Representation Learning](https://doi.org/10.1145/3543507.3583872)|Nianzu Yang, Kaipeng Zeng, Qitian Wu, Junchi Yan|Shanghai Jiao Tong University, China|Combinatorial drug recommendation involves recommending a personalized combination of medication (drugs) to a patient over his/her longitudinal history, which essentially aims at solving a combinatorial optimization problem that pursues high accuracy under the safety constraint. Among existing learning-based approaches, the association between drug substructures (i.e., a sub-graph of the molecule that contributes to certain chemical effect) and the target disease is largely overlooked, though the function of drugs in fact exhibits strong relevance with particular substructures. To address this issue, we propose a molecular substructure-aware encoding method entitled MoleRec that entails a hierarchical architecture aimed at modeling inter-substructure interactions and individual substructures’ impact on patient’s health condition, in order to identify those substructures that really contribute to healing patients. Specifically, MoleRec learns to attentively pooling over substructure representations which will be element-wisely re-scaled by the model’s inferred relevancy with a patient’s health condition to obtain a prior-knowledge-informed drug representation. We further design a weight annealing strategy for drug-drug-interaction (DDI) objective to adaptively control the balance between accuracy and safety criteria throughout training. Experiments on the MIMIC-III dataset demonstrate that our approach achieves new state-of-the-art performance w.r.t. four accuracy and safety metrics. Our source code is publicly available at https://github.com/yangnianzu0515/MoleRec.|组合药物推荐包括在病人的纵向病史中向病人推荐个性化的药物组合(药物) ，其主要目的是解决在安全约束下追求高准确性的组合优化问题。在现有的基于学习的方法中，药物子结构(即有助于某些化学效应的分子的子图)与目标疾病之间的关联在很大程度上被忽视，尽管药物的功能实际上与特定的子结构显示出强烈的相关性。为了解决这个问题，我们提出了一个名为 MoleRec 的分子子结构感知编码方法，它需要一个层次结构，旨在建模子结构间的相互作用和个体子结构对患者健康状况的影响，以确定那些真正有助于治愈患者的子结构。具体而言，MoleRec 学习仔细汇集子结构表示，这些子结构表示将通过模型与患者健康状况的推断相关性进行元素智能重新缩放，以获得事先知情的药物表示。我们进一步设计了药物-药物相互作用(DDI)目标的权重退火策略，以自适应地控制整个训练过程中准确性和安全性标准之间的平衡。在 MIMIC-III 数据集上的实验表明，我们的方法实现了新的最先进的性能和四个准确性和安全性指标。我们的源代码可以在 https://github.com/yangnianzu0515/molerec 上公开。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=MoleRec:+Combinatorial+Drug+Recommendation+with+Substructure-Aware+Molecular+Representation+Learning)|0| -|[Moral Narratives Around the Vaccination Debate on Facebook](https://doi.org/10.1145/3543507.3583865)|Mariano Gastón Beiró, Jacopo D'Ignazi, Victoria Perez Bustos, Maria Florencia Prado, Kyriaki Kalimeri|; Universidad de Buenos Aires. Facultad de Ingeniería, Paseo Colón 850, C1063ACV, Argentina and CONICET, Universidad de Buenos Aires, INTECIN, Paseo Colón 850, C1063ACV, Argentina; ISI Foundation, Italy|Vaccine hesitancy is a complex issue with psychological, cultural, and even societal factors entangled in the decision-making process. The narrative around this process is captured in our everyday interactions; social media data offer a direct and spontaneous view of peoples' argumentation. Here, we analysed more than 500,000 public posts and comments from Facebook Pages dedicated to the topic of vaccination to study the role of moral values and, in particular, the understudied role of the Liberty moral foundation from the actual user-generated text. We operationalise morality by employing the Moral Foundations Theory, while our proposed framework is based on recurrent neural network classifiers with a short memory and entity linking information. Our findings show that the principal moral narratives around the vaccination debate focus on the values of Liberty, Care, and Authority. Vaccine advocates urge compliance with the authorities as prosocial behaviour to protect society. On the other hand, vaccine sceptics mainly build their narrative around the value of Liberty, advocating for the right to choose freely whether to adhere or not to the vaccination. We contribute to the automatic understanding of vaccine hesitancy drivers emerging from user-generated text, providing concrete insights into the moral framing around vaccination decision-making. Especially in emergencies such as the Covid-19 pandemic, contrary to traditional surveys, these insights can be provided contemporary to the event, helping policymakers craft communication campaigns that adequately address the concerns of the hesitant population.|疫苗犹豫不决是一个复杂的问题，心理，文化，甚至社会因素纠缠在决策过程中。围绕这一过程的叙述被捕捉在我们日常的互动中; 社交媒体数据提供了人们争论的直接和自发的视角。在这里，我们分析了超过500,000个来自 Facebook 页面的公开帖子和评论，这些帖子和评论专门针对疫苗接种这一主题，研究道德价值观的作用，特别是从实际的用户生成的文本中研究自由道德基础的被忽视的作用。我们运用道德基础理论来操作道德，而我们提出的框架是基于短记忆的递归神经网络分类器和连接信息的实体。我们的研究结果表明，围绕疫苗接种辩论的主要道德叙事集中在自由、关怀和权威的价值观上。疫苗倡导者敦促当局遵守规定，以此作为保护社会的亲社会行为。另一方面，疫苗怀疑论者主要围绕自由的价值建立他们的叙述，倡导自由选择是否坚持接种疫苗的权利。我们有助于自动理解疫苗犹豫驱动程序出现从用户生成的文本，提供具体的见解围绕疫苗接种决策的道德框架。特别是在2019冠状病毒疾病大流行这样的紧急情况下，与传统的调查不同，这些见解可以在事件发生时提供，帮助政策制定者制定沟通运动，充分解决犹豫不决的民众的关切。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Moral+Narratives+Around+the+Vaccination+Debate+on+Facebook)|0| +|[Moral Narratives Around the Vaccination Debate on Facebook](https://doi.org/10.1145/3543507.3583865)|Mariano Gastón Beiró, Jacopo D'Ignazi, Victoria Perez Bustos, Maria Florencia Prado, Kyriaki Kalimeri|ISI Foundation, Italy; ; Universidad de Buenos Aires. Facultad de Ingeniería, Paseo Colón 850, C1063ACV, Argentina and CONICET, Universidad de Buenos Aires, INTECIN, Paseo Colón 850, C1063ACV, Argentina|Vaccine hesitancy is a complex issue with psychological, cultural, and even societal factors entangled in the decision-making process. The narrative around this process is captured in our everyday interactions; social media data offer a direct and spontaneous view of peoples' argumentation. Here, we analysed more than 500,000 public posts and comments from Facebook Pages dedicated to the topic of vaccination to study the role of moral values and, in particular, the understudied role of the Liberty moral foundation from the actual user-generated text. We operationalise morality by employing the Moral Foundations Theory, while our proposed framework is based on recurrent neural network classifiers with a short memory and entity linking information. Our findings show that the principal moral narratives around the vaccination debate focus on the values of Liberty, Care, and Authority. Vaccine advocates urge compliance with the authorities as prosocial behaviour to protect society. On the other hand, vaccine sceptics mainly build their narrative around the value of Liberty, advocating for the right to choose freely whether to adhere or not to the vaccination. We contribute to the automatic understanding of vaccine hesitancy drivers emerging from user-generated text, providing concrete insights into the moral framing around vaccination decision-making. Especially in emergencies such as the Covid-19 pandemic, contrary to traditional surveys, these insights can be provided contemporary to the event, helping policymakers craft communication campaigns that adequately address the concerns of the hesitant population.|疫苗犹豫不决是一个复杂的问题，心理，文化，甚至社会因素纠缠在决策过程中。围绕这一过程的叙述被捕捉在我们日常的互动中; 社交媒体数据提供了人们争论的直接和自发的视角。在这里，我们分析了超过500,000个来自 Facebook 页面的公开帖子和评论，这些帖子和评论专门针对疫苗接种这一主题，研究道德价值观的作用，特别是从实际的用户生成的文本中研究自由道德基础的被忽视的作用。我们运用道德基础理论来操作道德，而我们提出的框架是基于短记忆的递归神经网络分类器和连接信息的实体。我们的研究结果表明，围绕疫苗接种辩论的主要道德叙事集中在自由、关怀和权威的价值观上。疫苗倡导者敦促当局遵守规定，以此作为保护社会的亲社会行为。另一方面，疫苗怀疑论者主要围绕自由的价值建立他们的叙述，倡导自由选择是否坚持接种疫苗的权利。我们有助于自动理解疫苗犹豫驱动程序出现从用户生成的文本，提供具体的见解围绕疫苗接种决策的道德框架。特别是在2019冠状病毒疾病大流行这样的紧急情况下，与传统的调查不同，这些见解可以在事件发生时提供，帮助政策制定者制定沟通运动，充分解决犹豫不决的民众的关切。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Moral+Narratives+Around+the+Vaccination+Debate+on+Facebook)|0| |[Exploration of Framing Biases in Polarized Online Content Consumption](https://doi.org/10.1145/3543873.3587534)|Markus ReiterHaas|Institute of Interactive Systems and Data Science, Graz University of Technology, Austria|The study of framing bias on the Web is crucial in our digital age, as the framing of information can influence human behavior and decision on critical issues such as health or politics. Traditional frame analysis requires a curated set of frames derived from manual content analysis by domain experts. In this work, we introduce a frame analysis approach based on pretrained Transformer models that let us capture frames in an exploratory manner beyond predefined frames. In our experiments on two public online news and social media datasets, we show that our approach lets us identify underexplored conceptualizations, such as that health-related content is framed in terms of beliefs for conspiracy media, while mainstream media is instead concerned with science. We anticipate our work to be a starting point for further research on exploratory computational framing analysis using pretrained Transformers.|在我们的数字时代，研究网络上的框架偏见是至关重要的，因为信息的框架可以影响人类的行为和决策，如健康或政治等关键问题。传统的框架分析需要一组精心策划的框架，这些框架来自于领域专家的手工内容分析。在这项工作中，我们介绍了一种基于预先训练的变压器模型的帧分析方法，使我们能够以一种探索性的方式捕获超越预先定义的帧。在我们对两个公共在线新闻和社交媒体数据集的实验中，我们表明，我们的方法让我们确定了未被充分探索的概念化，例如，健康相关的内容被框定在阴谋媒体的信念方面，而主流媒体关注的是科学。我们期望我们的工作是一个开始点，进一步研究探索性计算框架分析使用预先训练的变压器。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Exploration+of+Framing+Biases+in+Polarized+Online+Content+Consumption)|0| -|[On Modeling Long-Term User Engagement from Stochastic Feedback](https://doi.org/10.1145/3543873.3587626)|Guoxi Zhang, Xing Yao, Xuanji Xiao|Graduate School of Informatics, Kyoto University, Japan; China Central Depository & Clearing Co., Ltd., China; Shopee Inc., China|An ultimate goal of recommender systems (RS) is to improve user engagement. Reinforcement learning (RL) is a promising paradigm for this goal, as it directly optimizes overall performance of sequential recommendation. However, many existing RL-based approaches induce huge computational overhead, because they require not only the recommended items but also all other candidate items to be stored. This paper proposes an efficient alternative that does not require the candidate items. The idea is to model the correlation between user engagement and items directly from data. Moreover, the proposed approach consider randomness in user feedback and termination behavior, which are ubiquitous for RS but rarely discussed in RL-based prior work. With online A/B experiments on real-world RS, we confirm the efficacy of the proposed approach and the importance of modeling the two types of randomness.|推荐系统(RS)的最终目标是提高用户参与度。强化学习(RL)是这个目标的一个很有前途的范例，因为它直接优化了顺序推荐的整体性能。然而，许多现有的基于 RL 的方法会产生巨大的计算开销，因为它们不仅需要存储推荐的项，而且还需要存储所有其他候选项。本文提出了一种不需要候选项的有效方案。其思想是直接从数据中建模用户参与度和项目之间的相关性。此外，提出的方法考虑了用户反馈和终止行为的随机性，这在 RS 中是普遍存在的，但在基于 RL 的先前工作中很少讨论。通过在现实世界中的在线 A/B 实验，我们证实了该方法的有效性和建模两种类型的随机性的重要性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=On+Modeling+Long-Term+User+Engagement+from+Stochastic+Feedback)|0| +|[On Modeling Long-Term User Engagement from Stochastic Feedback](https://doi.org/10.1145/3543873.3587626)|Guoxi Zhang, Xing Yao, Xuanji Xiao|Shopee Inc., China; China Central Depository & Clearing Co., Ltd., China; Graduate School of Informatics, Kyoto University, Japan|An ultimate goal of recommender systems (RS) is to improve user engagement. Reinforcement learning (RL) is a promising paradigm for this goal, as it directly optimizes overall performance of sequential recommendation. However, many existing RL-based approaches induce huge computational overhead, because they require not only the recommended items but also all other candidate items to be stored. This paper proposes an efficient alternative that does not require the candidate items. The idea is to model the correlation between user engagement and items directly from data. Moreover, the proposed approach consider randomness in user feedback and termination behavior, which are ubiquitous for RS but rarely discussed in RL-based prior work. With online A/B experiments on real-world RS, we confirm the efficacy of the proposed approach and the importance of modeling the two types of randomness.|推荐系统(RS)的最终目标是提高用户参与度。强化学习(RL)是这个目标的一个很有前途的范例，因为它直接优化了顺序推荐的整体性能。然而，许多现有的基于 RL 的方法会产生巨大的计算开销，因为它们不仅需要存储推荐的项，而且还需要存储所有其他候选项。本文提出了一种不需要候选项的有效方案。其思想是直接从数据中建模用户参与度和项目之间的相关性。此外，提出的方法考虑了用户反馈和终止行为的随机性，这在 RS 中是普遍存在的，但在基于 RL 的先前工作中很少讨论。通过在现实世界中的在线 A/B 实验，我们证实了该方法的有效性和建模两种类型的随机性的重要性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=On+Modeling+Long-Term+User+Engagement+from+Stochastic+Feedback)|0| |[CaML: Carbon Footprinting of Household Products with Zero-Shot Semantic Text Similarity](https://doi.org/10.1145/3543507.3583882)|Bharathan Balaji, Venkata Sai Gargeya Vunnava, Geoffrey Guest, Jared Kramer|Amazon, USA|Products contribute to carbon emissions in each phase of their life cycle, from manufacturing to disposal. Estimating the embodied carbon in products is a key step towards understanding their impact, and undertaking mitigation actions. Precise carbon attribution is challenging at scale, requiring both domain expertise and granular supply chain data. As a first-order approximation, standard reports use Economic Input-Output based Life Cycle Assessment (EIO-LCA) which estimates carbon emissions per dollar at an industry sector level using transactions between different parts of the economy. EIO-LCA models map products to an industry sector, and uses the corresponding carbon per dollar estimates to calculate the embodied carbon footprint of a product. An LCA expert needs to map each product to one of upwards of 1000 potential industry sectors. To reduce the annotation burden, the standard practice is to group products by categories, and map categories to their corresponding industry sector. We present CaML, an algorithm to automate EIO-LCA using semantic text similarity matching by leveraging the text descriptions of the product and the industry sector. CaML uses a pre-trained sentence transformer model to rank the top-5 matches, and asks a human to check if any of them are a good match. We annotated 40K products with non-experts. Our results reveal that pre-defined product categories are heterogeneous with respect to EIO-LCA industry sectors, and lead to a large mean absolute percentage error (MAPE) of 51% in kgCO2e/$. CaML outperforms the previous manually intensive method, yielding a MAPE of 22% with no domain labels (zero-shot). We compared annotations of a small sample of 210 products with LCA experts, and find that CaML accuracy is comparable to that of annotations by non-experts.|产品在其生命周期的每个阶段(从生产到处理)都会造成碳排放。评估产品中的含碳量是了解其影响并采取缓解行动的关键一步。精确的碳归属在规模上具有挑战性，需要领域专业知识和细粒度供应链数据。作为一阶近似，标准报告使用基于经济投入产出的生命周期评估(EIO-LCA) ，该评估利用不同经济部门之间的交易，在行业部门水平上估计每美元的碳排放量。生命周期评估模型将产品映射到一个行业部门，并使用相应的每美元碳排放估计值来计算产品的碳足印。LCA 专家需要将每个产品映射到1000个以上的潜在行业部门之一。为了减少注释负担，标准实践是按类别对产品进行分组，并将类别映射到相应的行业部门。我们提出了 CaML，一种利用产品和工业部门的文本描述，使用语义文本相似性匹配实现 EIO-LCA 自动化的算法。CaML 使用一个预先训练好的句子转换模型对前5个匹配项进行排序，并要求人类检查它们中是否有一个是良好匹配的。我们用非专家注释40K 产品。我们的研究结果表明，预定义的产品类别相对于 EIO-LCA 行业部门是异构的，并导致 kgCO2e/$的大平均绝对百分比误差(MAPE)为51% 。CaML 优于以前的手动密集型方法，产生的 MAPE 为22% ，没有域标签(0-shot)。我们将210个产品的小样本注释与 LCA 专家进行了比较，发现 CaML 的准确性与非专家的注释相当。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=CaML:+Carbon+Footprinting+of+Household+Products+with+Zero-Shot+Semantic+Text+Similarity)|0| -|[RDF Playground: An Online Tool for Learning about the Semantic Web](https://doi.org/10.1145/3543873.3587325)|Bastián Inostroza, Raúl Cid, Aidan Hogan|DCC, Universidad de Chile, Chile; DCC, Universidad de Chile, Chile and Instituto Milenio Fundamentos de los Datos (IMFD), Chile|We present RDF Playground: a web-based tool to assist those who wish to learn or teach about the Semantic Web. The tool integrates functionalities relating to the key features of RDF, allowing users to specify an RDF graph in Turtle syntax, visualise it as an interactive graph, query it using SPARQL, reason over it using OWL 2 RL, and to validate it using SHACL or ShEx. The tool further provides the ability to import and explore data from the Web through a graph-based Linked Data browser. We discuss the design and functionality of the tool, its implementation, and the results of a usability study considering students from a Web of Data course that used it for lab assignments. We conclude with a discussion of these results, as well as future directions that we envisage for improving the tool.|我们介绍 RDF Playground: 一个基于 Web 的工具，用于帮助那些希望学习或教授语义 Web 的人。该工具集成了与 RDF 关键特性相关的功能，允许用户用 Turtle 语法指定一个 RDF 图形，将其可视化为一个交互式图形，使用 SPARQL 查询它，使用 OWL 2 RL 推理它，并使用 SHACL 或 ShEx 验证它。该工具还提供了通过基于图形的关联数据浏览器从 Web 导入和探索数据的能力。我们讨论了该工具的设计和功能，它的实现，以及一个可用性研究的结果，该研究考虑了使用它完成实验作业的数据网络课程的学生。最后，我们讨论了这些结果以及我们设想的改进该工具的未来方向。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=RDF+Playground:+An+Online+Tool+for+Learning+about+the+Semantic+Web)|0| -|[Locating Faulty Applications via Semantic and Topology Estimation](https://doi.org/10.1145/3543873.3584660)|Shuyi Niu, Jiawei Jin, Xiutian Huang, Yonggeng Wang, Wenhao Xu, Youyong Kong|Southeast University, China; Ant Group, China|With the explosion of Internet product users, how to locate the faulty ones from numerous back-end applications after a customer complaint has become an essential issue in improving user experience. However, existing solutions mostly rely on manual testing to infer the fault, severely limiting their efficiency. In this paper, we transform the problem of locating faulty applications into two subproblems and propose a fully automated framework. We design a scorecard model in one stage to evaluate the semantic relevance between applications and customer complaints. Then in the other stage, topology graphs that reflect the actual calling relationship and engineering connection relationship between applications are utilized to evaluate the topology relevance between applications. Specifically, we employ a multi-graph co-learning framework constrained by consistency-independence loss and an engineering-theory-driven clustering strategy for the unsupervised learning of graphs. With semantic and topology relevance, we can comprehensively locate relevant faulty applications. Experiments on the Alipay dataset show that our method gains significant improvements in both model performance and efficiency.|随着互联网产品用户的爆炸式增长，如何在用户投诉之后从众多的后端应用程序中定位出故障用户已成为提高用户体验的关键问题。然而，现有的解决方案大多依赖于人工测试来推断故障，这严重限制了它们的效率。本文将故障应用定位问题转化为两个子问题，并提出了一个全自动化的框架。我们在一个阶段中设计了一个记分卡模型来评估应用程序和客户投诉之间的语义相关性。然后在另一个阶段，利用反映实际调用关系和应用间工程连接关系的拓扑图来评估应用间的拓扑相关性。具体来说，我们采用了一个受一致性独立性损失约束的多图协同学习框架和一个工程理论驱动的聚类策略来处理图的非监督式学习。通过语义和拓扑相关性，我们可以全面定位相关的故障应用。在支付宝数据集上的实验表明，该方法在模型性能和效率方面都得到了显著的改善。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Locating+Faulty+Applications+via+Semantic+and+Topology+Estimation)|0| +|[RDF Playground: An Online Tool for Learning about the Semantic Web](https://doi.org/10.1145/3543873.3587325)|Bastián Inostroza, Raúl Cid, Aidan Hogan|DCC, Universidad de Chile, Chile and Instituto Milenio Fundamentos de los Datos (IMFD), Chile; DCC, Universidad de Chile, Chile|We present RDF Playground: a web-based tool to assist those who wish to learn or teach about the Semantic Web. The tool integrates functionalities relating to the key features of RDF, allowing users to specify an RDF graph in Turtle syntax, visualise it as an interactive graph, query it using SPARQL, reason over it using OWL 2 RL, and to validate it using SHACL or ShEx. The tool further provides the ability to import and explore data from the Web through a graph-based Linked Data browser. We discuss the design and functionality of the tool, its implementation, and the results of a usability study considering students from a Web of Data course that used it for lab assignments. We conclude with a discussion of these results, as well as future directions that we envisage for improving the tool.|我们介绍 RDF Playground: 一个基于 Web 的工具，用于帮助那些希望学习或教授语义 Web 的人。该工具集成了与 RDF 关键特性相关的功能，允许用户用 Turtle 语法指定一个 RDF 图形，将其可视化为一个交互式图形，使用 SPARQL 查询它，使用 OWL 2 RL 推理它，并使用 SHACL 或 ShEx 验证它。该工具还提供了通过基于图形的关联数据浏览器从 Web 导入和探索数据的能力。我们讨论了该工具的设计和功能，它的实现，以及一个可用性研究的结果，该研究考虑了使用它完成实验作业的数据网络课程的学生。最后，我们讨论了这些结果以及我们设想的改进该工具的未来方向。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=RDF+Playground:+An+Online+Tool+for+Learning+about+the+Semantic+Web)|0| +|[Locating Faulty Applications via Semantic and Topology Estimation](https://doi.org/10.1145/3543873.3584660)|Shuyi Niu, Jiawei Jin, Xiutian Huang, Yonggeng Wang, Wenhao Xu, Youyong Kong|Ant Group, China; Southeast University, China|With the explosion of Internet product users, how to locate the faulty ones from numerous back-end applications after a customer complaint has become an essential issue in improving user experience. However, existing solutions mostly rely on manual testing to infer the fault, severely limiting their efficiency. In this paper, we transform the problem of locating faulty applications into two subproblems and propose a fully automated framework. We design a scorecard model in one stage to evaluate the semantic relevance between applications and customer complaints. Then in the other stage, topology graphs that reflect the actual calling relationship and engineering connection relationship between applications are utilized to evaluate the topology relevance between applications. Specifically, we employ a multi-graph co-learning framework constrained by consistency-independence loss and an engineering-theory-driven clustering strategy for the unsupervised learning of graphs. With semantic and topology relevance, we can comprehensively locate relevant faulty applications. Experiments on the Alipay dataset show that our method gains significant improvements in both model performance and efficiency.|随着互联网产品用户的爆炸式增长，如何在用户投诉之后从众多的后端应用程序中定位出故障用户已成为提高用户体验的关键问题。然而，现有的解决方案大多依赖于人工测试来推断故障，这严重限制了它们的效率。本文将故障应用定位问题转化为两个子问题，并提出了一个全自动化的框架。我们在一个阶段中设计了一个记分卡模型来评估应用程序和客户投诉之间的语义相关性。然后在另一个阶段，利用反映实际调用关系和应用间工程连接关系的拓扑图来评估应用间的拓扑相关性。具体来说，我们采用了一个受一致性独立性损失约束的多图协同学习框架和一个工程理论驱动的聚类策略来处理图的非监督式学习。通过语义和拓扑相关性，我们可以全面定位相关的故障应用。在支付宝数据集上的实验表明，该方法在模型性能和效率方面都得到了显著的改善。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Locating+Faulty+Applications+via+Semantic+and+Topology+Estimation)|0| |[Analyzing COVID-Related Social Discourse on Twitter using Emotion, Sentiment, Political Bias, Stance, Veracity and Conspiracy Theories](https://doi.org/10.1145/3543873.3587622)|Youri Peskine, Raphaël Troncy, Paolo Papotti|EURECOM, France|Online misinformation has become a major concern in recent years, and it has been further emphasized during the COVID-19 pandemic. Social media platforms, such as Twitter, can be serious vectors of misinformation online. In order to better understand the spread of these fake-news, lies, deceptions, and rumours, we analyze the correlations between the following textual features in tweets: emotion, sentiment, political bias, stance, veracity and conspiracy theories. We train several transformer-based classifiers from multiple datasets to detect these textual features and identify potential correlations using conditional distributions of the labels. Our results show that the online discourse regarding some topics, such as COVID-19 regulations or conspiracy theories, is highly controversial and reflects the actual U.S. political landscape.|近年来，网上虚假信息已成为一个主要问题，在2019冠状病毒疾病大流行期间，这一问题得到了进一步强调。像 Twitter 这样的社交媒体平台可能是网络上错误信息的严重载体。为了更好地理解这些假新闻、谎言、欺骗和谣言的传播，我们分析了以下推文文本特征之间的相关性: 情绪、情绪、政治偏见、立场、真实性和阴谋论。我们训练了多个来自多个数据集的基于转换器的分类器来检测这些文本特征，并使用标签的条件分布来识别潜在的相关性。我们的研究结果表明，网上关于某些话题的讨论，比如2019冠状病毒疾病监管或阴谋论，具有高度的争议性，反映了美国实际的政治环境。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Analyzing+COVID-Related+Social+Discourse+on+Twitter+using+Emotion,+Sentiment,+Political+Bias,+Stance,+Veracity+and+Conspiracy+Theories)|0| -|[Machine Learning for Streaming Media](https://doi.org/10.1145/3543873.3589751)|Sudarshan Lamkhede, Praveen Chandar, Vladan Radosavljevic, Amit Goyal, Lan Luo|University of Southern California, USA; Spotify, USA; Amazon Music, USA; Netflix Research, USA|Streaming media has become a popular medium for consumers of all ages, with people spending several hours a day streaming videos, games, music, or podcasts across devices. Most global streaming services have introduced Machine Learning (ML) into their operations to personalize consumer experience, improve content, and further enhance the value proposition of streaming services. Despite the rapid growth, there is a need to bridge the gap between academic research and industry requirements and build connections between researchers and practitioners in the field. This workshop aims to provide a unique forum for practitioners and researchers interested in Machine Learning to get together, exchange ideas and get a pulse for the state of the art in research and burning issues in the industry.|流媒体已经成为所有年龄段消费者的流行媒体，人们每天花几个小时在各种设备上观看视频、游戏、音乐或播客。大多数全球流媒体服务已经将机器学习(ML)引入到它们的运营中，以个性化消费者体验、改善内容并进一步提高流媒体服务的价值主张。尽管增长迅速，但仍需要弥合学术研究和行业需求之间的差距，并在该领域的研究人员和从业人员之间建立联系。本研讨会旨在为对机器学习感兴趣的从业人员和研究人员提供一个独特的论坛，让他们聚集在一起，交流思想，了解行业研究的最新进展和热点问题。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Machine+Learning+for+Streaming+Media)|0| +|[Machine Learning for Streaming Media](https://doi.org/10.1145/3543873.3589751)|Sudarshan Lamkhede, Praveen Chandar, Vladan Radosavljevic, Amit Goyal, Lan Luo|Netflix Research, USA; University of Southern California, USA; Spotify, USA; Amazon Music, USA|Streaming media has become a popular medium for consumers of all ages, with people spending several hours a day streaming videos, games, music, or podcasts across devices. Most global streaming services have introduced Machine Learning (ML) into their operations to personalize consumer experience, improve content, and further enhance the value proposition of streaming services. Despite the rapid growth, there is a need to bridge the gap between academic research and industry requirements and build connections between researchers and practitioners in the field. This workshop aims to provide a unique forum for practitioners and researchers interested in Machine Learning to get together, exchange ideas and get a pulse for the state of the art in research and burning issues in the industry.|流媒体已经成为所有年龄段消费者的流行媒体，人们每天花几个小时在各种设备上观看视频、游戏、音乐或播客。大多数全球流媒体服务已经将机器学习(ML)引入到它们的运营中，以个性化消费者体验、改善内容并进一步提高流媒体服务的价值主张。尽管增长迅速，但仍需要弥合学术研究和行业需求之间的差距，并在该领域的研究人员和从业人员之间建立联系。本研讨会旨在为对机器学习感兴趣的从业人员和研究人员提供一个独特的论坛，让他们聚集在一起，交流思想，了解行业研究的最新进展和热点问题。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Machine+Learning+for+Streaming+Media)|0| |[Interleaved Online Testing in Large-Scale Systems](https://doi.org/10.1145/3543873.3587572)|Nan Bi, Bai Li, Ruoyuan Gao, Graham Edge, Sachin Ahuja|Amazon, USA|Online testing is indispensable in decision making for information retrieval systems. Interleaving emerges as an online testing method with orders of magnitude higher sensitivity than the pervading A/B testing. It merges the compared results into a single interleaved result to show to users, and attributes user actions back to the systems being tested. However, its pairwise design also brings practical challenges to real-world systems, in terms of effectively comparing multiple (more than two) systems and interpreting the magnitude of raw interleaving measurement. We present two novel methods to address these challenges that make interleaving practically applicable. The first method infers the ordering of multiple systems based on interleaving pairwise results with false discovery control. The second method estimates A/B effect size based on interleaving results using a weighted linear model that adjust for uncertainties of different measurements. We showcase the effectiveness of our methods in large-scale e-commerce experiments, reporting as many as 75 interleaving results, and provide extensive evaluations of their underlying assumptions.|在线测试对于信息检索系统的决策是不可或缺的。交错测试作为一种在线测试方法出现，其灵敏度数量级高于普遍采用的 A/B 测试。它将比较的结果合并到一个交错的结果中以显示给用户，并将用户操作归结到正在测试的系统。然而，它的成对设计也给现实世界的系统带来了实际的挑战，就有效地比较多个(两个以上)系统和解释原始交错测量的大小而言。我们提出了两种新的方法来解决这些挑战，使交织实际上适用。第一种方法是基于错误发现控制交织成对结果来推断多系统的排序。第二种方法基于交错结果估计 A/B 效应大小，使用加权线性模型来调整不同测量的不确定性。我们展示了我们的方法在大规模电子商务实验中的有效性，报告了多达75个交错的结果，并提供了对其基本假设的广泛评估。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Interleaved+Online+Testing+in+Large-Scale+Systems)|0| -|[Impact of COVID-19 Pandemic on Cultural Products Interests](https://doi.org/10.1145/3543873.3587594)|Ke Li, Zhiwen Yu, Ying Zhang, Bin Guo|School of Computer Science, Northwestern Polytechnical University, China and Harbin Engineering University, China; School of Computer Science, Northwestern Polytechnical University, China|The COVID-19 pandemic has had a significant impact on human behaviors and how it influenced peoples’ interests in cultural products is an unsolved problem. While prior studies mostly adopt subjective surveys to find an answer, these methods are always suffering from high cost, limited size, and subjective bias. Inspired by the rich user-oriented data over the Internet, this work explores the possibility to leverage users’ search logs to reflect humans’ underlying cultural product interests. To further examine how the COVID-19 mobility policy might influence cultural interest changes, we propose a new regression discontinuity design that has the additional potential to predict the recovery phase of peoples’ cultural product interests. By analyzing the 1592 search interest time series in 6 countries, we found different patterns of change in interest in movies, music, and art during the COVID-19 pandemic, but a clear overall incremental increase. Across the six countries we studied, we found that changes in interest in cultural products were found to be strongly correlated with mobility and that as mobility declined, interest in movies, music, and art increased by an average of 35, 27 and 20, respectively, with these changes lasting at least eight weeks.|2019冠状病毒疾病疫情对人类行为产生了重大影响，它如何影响人们对文化产品的兴趣是一个尚未解决的问题。以往的研究大多采用主观调查的方法来寻找答案，但这些方法往往成本高、规模有限、存在主观偏差。受互联网上丰富的以用户为导向的数据的启发，这项工作探索了利用用户的搜索日志来反映人类潜在的文化产品兴趣的可能性。为了进一步研究2019冠状病毒疾病流动政策可能如何影响文化兴趣的变化，我们提出了一种新的回归不连续性设计，它具有额外的潜力来预测人们的文化产品兴趣的恢复阶段。通过分析6个国家1592年的搜索兴趣时间序列，我们发现在2019冠状病毒疾病大流行期间，人们对电影、音乐和艺术的兴趣有不同的变化模式，但总体上有明显的增长。在我们研究的六个国家中，我们发现对文化产品兴趣的变化与流动性密切相关，随着流动性的下降，对电影、音乐和艺术的兴趣分别平均增加了35、27和20，这些变化至少持续了八周。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Impact+of+COVID-19+Pandemic+on+Cultural+Products+Interests)|0| -|[Enhancing Data Space Semantic Interoperability through Machine Learning: a Visionary Perspective](https://doi.org/10.1145/3543873.3587658)|Zeyd Boukhers, Christoph Lange, Oya Beyan|Fraunhofer Institute for Applied Information Technology, Germany and Faculty of Medicine and University Hospital Cologne, University of Cologne, Germany; Faculty of Medicine and University Hospital Cologne, University of Cologne, Germany and Fraunhofer Institute for Applied Information Technology, Germany; Fraunhofer Institute for Applied Information Technology, Germany and RWTH Aachen University, Germany|Our vision paper outlines a plan to improve the future of semantic interoperability in data spaces through the application of machine learning. The use of data spaces, where data is exchanged among members in a self-regulated environment, is becoming increasingly popular. However, the current manual practices of managing metadata and vocabularies in these spaces are time-consuming, prone to errors, and may not meet the needs of all stakeholders. By leveraging the power of machine learning, we believe that semantic interoperability in data spaces can be significantly improved. This involves automatically generating and updating metadata, which results in a more flexible vocabulary that can accommodate the diverse terminologies used by different sub-communities. Our vision for the future of data spaces addresses the limitations of conventional data exchange and makes data more accessible and valuable for all members of the community.|我们的愿景文件概述了一个通过机器学习应用改善数据空间语义互操作性未来的计划。数据空间的使用正变得越来越流行，数据空间是在一个自我管理的环境中在成员之间交换数据的地方。然而，在这些空间中管理元数据和词汇表的当前手工实践非常耗时，容易出错，并且可能不能满足所有涉众的需求。通过利用机器学习的力量，我们相信数据空间的语义互操作性可以得到显著改善。这涉及到自动生成和更新元数据，从而产生更灵活的词汇表，可以适应不同子社区使用的不同术语。我们对数据空间未来的展望解决了传统数据交换的局限性，并使数据对社区的所有成员更容易获得和更有价值。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Enhancing+Data+Space+Semantic+Interoperability+through+Machine+Learning:+a+Visionary+Perspective)|0| +|[Impact of COVID-19 Pandemic on Cultural Products Interests](https://doi.org/10.1145/3543873.3587594)|Ke Li, Zhiwen Yu, Ying Zhang, Bin Guo|School of Computer Science, Northwestern Polytechnical University, China; School of Computer Science, Northwestern Polytechnical University, China and Harbin Engineering University, China|The COVID-19 pandemic has had a significant impact on human behaviors and how it influenced peoples’ interests in cultural products is an unsolved problem. While prior studies mostly adopt subjective surveys to find an answer, these methods are always suffering from high cost, limited size, and subjective bias. Inspired by the rich user-oriented data over the Internet, this work explores the possibility to leverage users’ search logs to reflect humans’ underlying cultural product interests. To further examine how the COVID-19 mobility policy might influence cultural interest changes, we propose a new regression discontinuity design that has the additional potential to predict the recovery phase of peoples’ cultural product interests. By analyzing the 1592 search interest time series in 6 countries, we found different patterns of change in interest in movies, music, and art during the COVID-19 pandemic, but a clear overall incremental increase. Across the six countries we studied, we found that changes in interest in cultural products were found to be strongly correlated with mobility and that as mobility declined, interest in movies, music, and art increased by an average of 35, 27 and 20, respectively, with these changes lasting at least eight weeks.|2019冠状病毒疾病疫情对人类行为产生了重大影响，它如何影响人们对文化产品的兴趣是一个尚未解决的问题。以往的研究大多采用主观调查的方法来寻找答案，但这些方法往往成本高、规模有限、存在主观偏差。受互联网上丰富的以用户为导向的数据的启发，这项工作探索了利用用户的搜索日志来反映人类潜在的文化产品兴趣的可能性。为了进一步研究2019冠状病毒疾病流动政策可能如何影响文化兴趣的变化，我们提出了一种新的回归不连续性设计，它具有额外的潜力来预测人们的文化产品兴趣的恢复阶段。通过分析6个国家1592年的搜索兴趣时间序列，我们发现在2019冠状病毒疾病大流行期间，人们对电影、音乐和艺术的兴趣有不同的变化模式，但总体上有明显的增长。在我们研究的六个国家中，我们发现对文化产品兴趣的变化与流动性密切相关，随着流动性的下降，对电影、音乐和艺术的兴趣分别平均增加了35、27和20，这些变化至少持续了八周。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Impact+of+COVID-19+Pandemic+on+Cultural+Products+Interests)|0| +|[Enhancing Data Space Semantic Interoperability through Machine Learning: a Visionary Perspective](https://doi.org/10.1145/3543873.3587658)|Zeyd Boukhers, Christoph Lange, Oya Beyan|Faculty of Medicine and University Hospital Cologne, University of Cologne, Germany and Fraunhofer Institute for Applied Information Technology, Germany; Fraunhofer Institute for Applied Information Technology, Germany and RWTH Aachen University, Germany; Fraunhofer Institute for Applied Information Technology, Germany and Faculty of Medicine and University Hospital Cologne, University of Cologne, Germany|Our vision paper outlines a plan to improve the future of semantic interoperability in data spaces through the application of machine learning. The use of data spaces, where data is exchanged among members in a self-regulated environment, is becoming increasingly popular. However, the current manual practices of managing metadata and vocabularies in these spaces are time-consuming, prone to errors, and may not meet the needs of all stakeholders. By leveraging the power of machine learning, we believe that semantic interoperability in data spaces can be significantly improved. This involves automatically generating and updating metadata, which results in a more flexible vocabulary that can accommodate the diverse terminologies used by different sub-communities. Our vision for the future of data spaces addresses the limitations of conventional data exchange and makes data more accessible and valuable for all members of the community.|我们的愿景文件概述了一个通过机器学习应用改善数据空间语义互操作性未来的计划。数据空间的使用正变得越来越流行，数据空间是在一个自我管理的环境中在成员之间交换数据的地方。然而，在这些空间中管理元数据和词汇表的当前手工实践非常耗时，容易出错，并且可能不能满足所有涉众的需求。通过利用机器学习的力量，我们相信数据空间的语义互操作性可以得到显著改善。这涉及到自动生成和更新元数据，从而产生更灵活的词汇表，可以适应不同子社区使用的不同术语。我们对数据空间未来的展望解决了传统数据交换的局限性，并使数据对社区的所有成员更容易获得和更有价值。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Enhancing+Data+Space+Semantic+Interoperability+through+Machine+Learning:+a+Visionary+Perspective)|0| |[The PLASMA Framework: Laying the Path to Domain-Specific Semantics in Dataspaces](https://doi.org/10.1145/3543873.3587662)|Alexander Paulus, André Pomp, Tobias Meisen|Institute for Technologies and Management of Digital Transformation, University of Wuppertal, Germany|Modern data management is evolving from centralized integration-based solutions to a non-integration-based process of finding, accessing and processing data, as observed within dataspaces. Common reference dataspace architectures assume that sources publish their own domain-specific schema. These schemas, also known as semantic models, can only be partially created automatically and require oversight and refinement by human modellers. Non-expert users, such as mechanical engineers or municipal workers, often have difficulty building models because they are faced with multiple ontologies, classes, and relations, and existing tools are not designed for non-expert users. The PLASMA framework consists of a platform and auxiliary services that focus on providing non-expert users with an accessible way to create and edit semantic models, combining automation approaches and support systems such as a recommendation engine. It also provides data conversion from raw data to RDF. In this paper we highlight the main features, like the modeling interface and the data conversion engine. We discuss how PLASMA as a tool is suitable for building semantic models by non-expert users in the context of dataspaces and show some applications where PLASMA has already been used in data management projects.|现代数据管理正在从基于集中式集成的解决方案演变为基于非集成的查找、访问和处理数据的过程，正如在数据空间中观察到的那样。通用参考数据空间体系结构假设源发布自己的特定于域的模式。这些模式也称为语义模型，只能部分自动创建，需要人类建模者进行监督和细化。非专家用户，如机械工程师或市政工人，往往难以建立模型，因为他们面临着多个本体、类和关系，现有的工具不是为非专家用户设计的。PLASMA 框架由一个平台和辅助服务组成，侧重于为非专家用户提供创建和编辑语义模型的便捷方式，将自动化方法和推荐引擎等支持系统结合起来。它还提供从原始数据到 RDF 的数据转换。本文着重介绍了建模接口和数据转换引擎等主要特性。我们讨论了 PLASMA 作为一种工具是如何适用于非专家用户在数据空间上下文中建立语义模型的，并展示了 PLASMA 已经在数据管理项目中使用的一些应用。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=The+PLASMA+Framework:+Laying+the+Path+to+Domain-Specific+Semantics+in+Dataspaces)|0| |[Pairwise-interactions-based Bayesian Inference of Network Structure from Information Cascades](https://doi.org/10.1145/3543507.3583231)|Chao Gao, Yuchen Wang, Zhen Wang, Xianghua Li, Xuelong Li|Northwestern Polytechnical University, China; School of Artificial Intelligence, Optics and Electronics (iOPEN), Northwestern Polytechnical University, China|An explicit network structure plays an important role when analyzing and understanding diffusion processes. In many scenarios, however, the interactions between nodes in an underlying network are unavailable. Although many methods for inferring a network structure from observed cascades have been proposed, they did not perceive the relationship between pairwise interactions in a cascade. Therefore, this paper proposes a Pairwise-interactions-based Bayesian Inference method (named PBI) to infer the underlying diffusion network structure. More specifically, to get more accurate inference results, we measure the weights of each candidate pairwise interaction in different cascades and add them to the likelihood of a contagion process. In addition, a pre-pruning work is introduced for candidate edges to further improve the inference efficiency. Experiments on synthetic and real-world networks show that PBI achieves significantly better results.|显式的网络结构在分析和理解扩散过程中起着重要作用。然而，在许多场景中，底层网络中的节点之间的交互是不可用的。虽然已经提出了许多从观察到的级联推断网络结构的方法，但它们没有认识到级联中成对相互作用之间的关系。因此，本文提出了一种基于成对互动的贝叶斯推断方法(称为 PBI)来推断基础扩散网络的结构。更具体地说，为了得到更准确的推断结果，我们测量了不同级联中每个候选者成对相互作用的权重，并将它们加到传染过程的可能性上。此外，为了进一步提高推理效率，引入了候选边缘的预剪枝方法。在合成网络和真实网络上的实验表明，PBI 取得了明显的改善效果。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Pairwise-interactions-based+Bayesian+Inference+of+Network+Structure+from+Information+Cascades)|0| -|[Graph Neural Network with Two Uplift Estimators for Label-Scarcity Individual Uplift Modeling](https://doi.org/10.1145/3543507.3583368)|Dingyuan Zhu, Daixin Wang, Zhiqiang Zhang, Kun Kuang, Yan Zhang, Yulin Kang, Jun Zhou|Zhejiang university, China; Ant Group, China|Uplift modeling aims to measure the incremental effect, which we call uplift, of a strategy or action on the users from randomized experiments or observational data. Most existing uplift methods only use individual data, which are usually not informative enough to capture the unobserved and complex hidden factors regarding the uplift. Furthermore, uplift modeling scenario usually has scarce labeled data, especially for the treatment group, which also poses a great challenge for model training. Considering that the neighbors’ features and the social relationships are very informative to characterize a user’s uplift, we propose a graph neural network-based framework with two uplift estimators, called GNUM, to learn from the social graph for uplift estimation. Specifically, we design the first estimator based on a class-transformed target. The estimator is general for all types of outcomes, and is able to comprehensively model the treatment and control group data together to approach the uplift. When the outcome is discrete, we further design the other uplift estimator based on our defined partial labels, which is able to utilize more labeled data from both the treatment and control groups, to further alleviate the label scarcity problem. Comprehensive experiments on a public dataset and two industrial datasets show a superior performance of our proposed framework over state-of-the-art methods under various evaluation metrics. The proposed algorithms have been deployed online to serve real-world uplift estimation scenarios.|提升模型的目的是通过随机实验或观察数据来衡量策略或行动对用户的增量效应，我们称之为提升。大多数现有的抬升方法只使用单独的数据，这些数据通常不足以获取关于抬升的未观测到的复杂的隐藏因素。此外，抬升模型场景通常缺乏标记数据，特别是对于治疗组，这也对模型训练提出了很大的挑战。考虑到邻居的特征和社会关系对于表征用户的提升是非常有用的，我们提出了一种基于图神经网络的提升估计框架，称为 GNUM，以学习社会图的提升估计。具体地说，我们设计了基于类转换目标的第一个估计器。该估计值对于所有类型的结果都是通用的，并且能够将治疗组和对照组的数据综合建模以接近隆起。当结果是离散的，我们进一步设计其他提升估计的基础上我们定义的部分标签，它能够利用更多的标记数据从治疗组和对照组，以进一步减轻标签稀缺问题。对一个公共数据集和两个工业数据集的综合实验表明，在各种评估指标下，我们提出的框架比最先进的方法具有更好的性能。提出的算法已经在线部署，以服务于真实世界的抬升估计场景。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Graph+Neural+Network+with+Two+Uplift+Estimators+for+Label-Scarcity+Individual+Uplift+Modeling)|0| +|[Graph Neural Network with Two Uplift Estimators for Label-Scarcity Individual Uplift Modeling](https://doi.org/10.1145/3543507.3583368)|Dingyuan Zhu, Daixin Wang, Zhiqiang Zhang, Kun Kuang, Yan Zhang, Yulin Kang, Jun Zhou|Ant Group, China; Zhejiang university, China|Uplift modeling aims to measure the incremental effect, which we call uplift, of a strategy or action on the users from randomized experiments or observational data. Most existing uplift methods only use individual data, which are usually not informative enough to capture the unobserved and complex hidden factors regarding the uplift. Furthermore, uplift modeling scenario usually has scarce labeled data, especially for the treatment group, which also poses a great challenge for model training. Considering that the neighbors’ features and the social relationships are very informative to characterize a user’s uplift, we propose a graph neural network-based framework with two uplift estimators, called GNUM, to learn from the social graph for uplift estimation. Specifically, we design the first estimator based on a class-transformed target. The estimator is general for all types of outcomes, and is able to comprehensively model the treatment and control group data together to approach the uplift. When the outcome is discrete, we further design the other uplift estimator based on our defined partial labels, which is able to utilize more labeled data from both the treatment and control groups, to further alleviate the label scarcity problem. Comprehensive experiments on a public dataset and two industrial datasets show a superior performance of our proposed framework over state-of-the-art methods under various evaluation metrics. The proposed algorithms have been deployed online to serve real-world uplift estimation scenarios.|提升模型的目的是通过随机实验或观察数据来衡量策略或行动对用户的增量效应，我们称之为提升。大多数现有的抬升方法只使用单独的数据，这些数据通常不足以获取关于抬升的未观测到的复杂的隐藏因素。此外，抬升模型场景通常缺乏标记数据，特别是对于治疗组，这也对模型训练提出了很大的挑战。考虑到邻居的特征和社会关系对于表征用户的提升是非常有用的，我们提出了一种基于图神经网络的提升估计框架，称为 GNUM，以学习社会图的提升估计。具体地说，我们设计了基于类转换目标的第一个估计器。该估计值对于所有类型的结果都是通用的，并且能够将治疗组和对照组的数据综合建模以接近隆起。当结果是离散的，我们进一步设计其他提升估计的基础上我们定义的部分标签，它能够利用更多的标记数据从治疗组和对照组，以进一步减轻标签稀缺问题。对一个公共数据集和两个工业数据集的综合实验表明，在各种评估指标下，我们提出的框架比最先进的方法具有更好的性能。提出的算法已经在线部署，以服务于真实世界的抬升估计场景。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Graph+Neural+Network+with+Two+Uplift+Estimators+for+Label-Scarcity+Individual+Uplift+Modeling)|0| |[Multi-head Variational Graph Autoencoder Constrained by Sum-product Networks](https://doi.org/10.1145/3543507.3583517)|Riting Xia, Yan Zhang, Chunxu Zhang, Xueyan Liu, Bo Yang|Jilin University, China|Variational graph autoencoder (VGAE) is a promising deep probabilistic model in graph representation learning. However, most existing VGAEs adopt the mean-field assumption, and cannot characterize the graphs with noise well. In this paper, we propose a novel deep probabilistic model for graph analysis, termed Multi-head Variational Graph Autoencoder Constrained by Sum-product Networks (named SPN-MVGAE), which helps to relax the mean-field assumption and learns better latent representation with fault tolerance. Our proposed model SPN-MVGAE uses conditional sum-product networks as constraints to learn the dependencies between latent factors in an end-to-end manner. Furthermore, we introduce the superposition of the latent representations learned by multiple variational networks to represent the final latent representations of nodes. Our model is the first use sum-product networks for graph representation learning, extending the scope of sum-product networks applications. Experimental results show that compared with other baseline methods, our model has competitive advantages in link prediction, fault tolerance, node classification, and graph visualization on real datasets.|变分图自动编码器(VGAE)是图表示学习中一种很有前途的深度概率模型。然而，现有的 VGAE 大多采用平均场假设，不能很好地刻画有噪声的图。本文提出了一种新的图分析的深度概率模型，称为和积网络约束下的多头变分图自动编码器(SPN-MVGAE)。我们提出的模型 SPN-MVGAE 使用条件和积网络作为约束，以端到端的方式学习潜在因素之间的相关性。此外，我们还引入了多变分网络学习的潜在表征的叠加来表示节点的最终潜在表征。该模型首次将和积网络用于图表示学习，扩展了和积网络的应用范围。实验结果表明，与其他基线方法相比，该模型在实际数据集的链接预测、容错、节点分类和图形可视化等方面具有优势。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Multi-head+Variational+Graph+Autoencoder+Constrained+by+Sum-product+Networks)|0| -|[Interactive Log Parsing via Light-weight User Feedback](https://doi.org/10.1145/3543507.3583456)|Liming Wang, Hong Xie, Ye Li, Jian Tan, John C. S. Lui|Alibaba, Hong Kong; Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong; Alibaba, China; College of Computer Science, Chongqing University, China|Template mining is one of the foundational tasks to support log analysis, which supports the diagnosis and troubleshooting of large scale Web applications. This paper develops a human-in-the-loop template mining framework to support interactive log analysis, which is highly desirable in real-world diagnosis or troubleshooting of Web applications but yet previous template mining algorithms fails to support it. We formulate three types of light-weight user feedbacks and based on them we design three atomic human-in-the-loop template mining algorithms. We derive mild conditions under which the outputs of our proposed algorithms are provably correct. We also derive upper bounds on the computational complexity and query complexity of each algorithm. We demonstrate the versatility of our proposed algorithms by combining them to improve the template mining accuracy of five representative algorithms over sixteen widely used benchmark datasets.|模板挖掘是支持日志分析的基本任务之一，它支持大规模 Web 应用程序的诊断和故障排除。本文提出了一种支持交互式日志分析的半人工模板挖掘框架，该框架在 Web 应用程序的实际诊断和故障排除中非常有用，但以往的模板挖掘算法都不支持。我们提出了三种轻量级用户反馈，并在此基础上设计了三种原子人在环模板挖掘算法。我们推导出我们提出的算法输出可证明正确的温和条件。我们还推导了每种算法的计算复杂度和查询复杂度的上界。我们证明了我们提出的算法的通用性，通过结合他们来提高模板挖掘准确性的五个代表性算法超过16个广泛使用的基准数据集。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Interactive+Log+Parsing+via+Light-weight+User+Feedback)|0| +|[Interactive Log Parsing via Light-weight User Feedback](https://doi.org/10.1145/3543507.3583456)|Liming Wang, Hong Xie, Ye Li, Jian Tan, John C. S. Lui|Alibaba, China; Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong; College of Computer Science, Chongqing University, China; Alibaba, Hong Kong|Template mining is one of the foundational tasks to support log analysis, which supports the diagnosis and troubleshooting of large scale Web applications. This paper develops a human-in-the-loop template mining framework to support interactive log analysis, which is highly desirable in real-world diagnosis or troubleshooting of Web applications but yet previous template mining algorithms fails to support it. We formulate three types of light-weight user feedbacks and based on them we design three atomic human-in-the-loop template mining algorithms. We derive mild conditions under which the outputs of our proposed algorithms are provably correct. We also derive upper bounds on the computational complexity and query complexity of each algorithm. We demonstrate the versatility of our proposed algorithms by combining them to improve the template mining accuracy of five representative algorithms over sixteen widely used benchmark datasets.|模板挖掘是支持日志分析的基本任务之一，它支持大规模 Web 应用程序的诊断和故障排除。本文提出了一种支持交互式日志分析的半人工模板挖掘框架，该框架在 Web 应用程序的实际诊断和故障排除中非常有用，但以往的模板挖掘算法都不支持。我们提出了三种轻量级用户反馈，并在此基础上设计了三种原子人在环模板挖掘算法。我们推导出我们提出的算法输出可证明正确的温和条件。我们还推导了每种算法的计算复杂度和查询复杂度的上界。我们证明了我们提出的算法的通用性，通过结合他们来提高模板挖掘准确性的五个代表性算法超过16个广泛使用的基准数据集。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Interactive+Log+Parsing+via+Light-weight+User+Feedback)|0| |[Misbehavior and Account Suspension in an Online Financial Communication Platform](https://doi.org/10.1145/3543507.3583385)|Taro Tsuchiya, Alejandro Cuevas, Thomas Magelinski, Nicolas Christin|Carnegie Mellon University, USA|The expanding accessibility and appeal of investing have attracted millions of new retail investors. As such, investment discussion boards became the de facto communities where traders create, disseminate, and discuss investing ideas. These communities, which can provide useful information to support investors, have anecdotally also attracted a wide range of misbehavior – toxicity, spam/fraud, and reputation manipulation. This paper is the first comprehensive analysis of online misbehavior in the context of investment communities. We study TradingView, the largest online communication platform for financial trading. We collect 2.76M user profiles with their corresponding social graphs, 4.2M historical article posts, and 5.3M comments, including information on nearly 4 000 suspended accounts and 17 000 removed comments. Price fluctuations seem to drive abuse across the platform and certain types of assets, such as “meme” stocks, attract disproportionate misbehavior. Suspended user accounts tend to form more closely-knit communities than those formed by non-suspended accounts; and paying accounts are less likely to be suspended than free accounts even when posting similar levels of content violating platform policies. We conclude by offering guidelines on how to adapt content moderation efforts to fit the particularities of online investment communities.|投资的可及性和吸引力不断扩大，吸引了数以百万计的新散户投资者。因此，投资讨论委员会事实上成为了交易员创建、传播和讨论投资理念的社区。这些社区可以提供有用的信息来支持投资者，据说也吸引了大量的不良行为——毒性、垃圾邮件/欺诈和声誉操纵。本文首次全面分析了投资社区背景下的网络不良行为。我们研究了 TradingView，这是最大的金融交易在线交流平台。我们收集了276万用户的个人资料及其相应的社交图表，420万篇历史文章和530万条评论，包括近4000个被暂停的账户和17000条被删除的评论。价格波动似乎推动了整个平台的滥用，某些类型的资产，如“模因”股票，吸引了不成比例的不当行为。与非暂停用户账户相比，暂停用户账户往往形成更为紧密的社区; 即使发布了违反平台政策的类似水平的内容，付费账户被暂停的可能性也低于免费账户。最后，我们提供了关于如何调整内容审核工作以适应在线投资社区的特殊性的指导方针。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Misbehavior+and+Account+Suspension+in+an+Online+Financial+Communication+Platform)|0| -|[BiSR: Bidirectionally Optimized Super-Resolution for Mobile Video Streaming](https://doi.org/10.1145/3543507.3583519)|Qian Yu, Qing Li, Rui He, Gareth Tyson, Wanxin Shi, Jianhui Lv, Zhenhui Yuan, Peng Zhang, Yulong Lan, Zhicheng Li|SUSTech, China and Peng Cheng Laboratory, China; International Graduate School, Tsinghua University, China; Tencent, China; Hong Kong University of Science and Technology(GZ), China; Peng Cheng Laboratory, China; Northumbria University, United Kingdom|The user experience of mobile web video streaming is often impacted by insufficient and dynamic network bandwidth. In this paper, we design Bidirectionally Optimized Super-Resolution (BiSR) to improve the quality of experience (QoE) for mobile web users under limited bandwidth. BiSR exploits a deep neural network (DNN)-based model to super-resolve key frames efficiently without changing the inter-frame spatial-temporal information. We then propose a downscaling DNN and a mobile-specific optimized lightweight super-resolution DNN to enhance the performance. Finally, a novel reinforcement learning-based adaptive bitrate (ABR) algorithm is proposed to verify the performance of BiSR on real network traces. Our evaluation, using a full system implementation, shows that BiSR saves 26% of bitrate compared to the traditional H.264 codec and improves the SSIM of video by 3.7% compared to the prior state-of-the-art. Overall, BiSR enhances the user-perceived quality of experience by up to 30.6%.|移动网络视频流的用户体验往往受到网络带宽不足和动态性的影响。本文设计了双向优化超分辨率(BiSR)算法，以提高有限带宽下移动网络用户的体验质量(QoE)。BiSR 利用基于深度神经网络(DNN)的模型，在不改变帧间时空信息的情况下，有效地对关键帧进行超分辨。然后，我们提出了一个缩放 DNN 和一个移动专用的优化轻量级超分辨率 DNN，以提高性能。最后，提出了一种新的基于强化学习的自适应比特率(ABR)算法来验证 BiSR 在实际网络跟踪中的性能。我们的评估，使用一个完整的系统实现，表明 BiSR 节省26% 的比特率相比，传统的 H.264编解码器和提高了3.7% 的 SSIM 的视频相比，以前的最先进的国家。总的来说，BiSR 提高了30.6% 的用户感知体验质量。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=BiSR:+Bidirectionally+Optimized+Super-Resolution+for+Mobile+Video+Streaming)|0| -|[Autobidding Auctions in the Presence of User Costs](https://doi.org/10.1145/3543507.3583234)|Yuan Deng, Jieming Mao, Vahab Mirrokni, Hanrui Zhang, Song Zuo|Carnegie Mellon University, USA; Google Research, USA|We study autobidding ad auctions with user costs, where each bidder is value-maximizing subject to a return-over-investment (ROI) constraint, and the seller aims to maximize the social welfare taking into consideration the user's cost of viewing an ad. We show that in the worst case, the approximation ratio of social welfare by running the vanilla VCG auctions with user costs could as bad as 0. To improve the performance of VCG, We propose a new variant of VCG based on properly chosen cost multipliers, and prove that there exist auction-dependent and bidder-dependent cost multipliers that guarantee approximation ratios of 1/2 and 1/4 respectively in terms of the social welfare.|本文研究了具有用户成本的自动竞价广告拍卖，其中每个竞价者的价值最大化受到投资回报率(ROI)的约束，卖方的目标是最大化社会福利，同时考虑用户观看广告的成本。我们指出，在最坏的情况下，通过运行普通的 VCG 拍卖与用户成本的社会福利的近似比率可能为0。为了提高 VCG 的性能，我们提出了一种新的基于合理选择成本乘数的 VCG 变体，并证明了存在拍卖相关成本乘数和投标人相关成本乘数，它们分别保证在社会福利方面的近似比为1/2和1/4。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Autobidding+Auctions+in+the+Presence+of+User+Costs)|0| +|[BiSR: Bidirectionally Optimized Super-Resolution for Mobile Video Streaming](https://doi.org/10.1145/3543507.3583519)|Qian Yu, Qing Li, Rui He, Gareth Tyson, Wanxin Shi, Jianhui Lv, Zhenhui Yuan, Peng Zhang, Yulong Lan, Zhicheng Li|Hong Kong University of Science and Technology(GZ), China; Tencent, China; SUSTech, China and Peng Cheng Laboratory, China; Peng Cheng Laboratory, China; Northumbria University, United Kingdom; International Graduate School, Tsinghua University, China|The user experience of mobile web video streaming is often impacted by insufficient and dynamic network bandwidth. In this paper, we design Bidirectionally Optimized Super-Resolution (BiSR) to improve the quality of experience (QoE) for mobile web users under limited bandwidth. BiSR exploits a deep neural network (DNN)-based model to super-resolve key frames efficiently without changing the inter-frame spatial-temporal information. We then propose a downscaling DNN and a mobile-specific optimized lightweight super-resolution DNN to enhance the performance. Finally, a novel reinforcement learning-based adaptive bitrate (ABR) algorithm is proposed to verify the performance of BiSR on real network traces. Our evaluation, using a full system implementation, shows that BiSR saves 26% of bitrate compared to the traditional H.264 codec and improves the SSIM of video by 3.7% compared to the prior state-of-the-art. Overall, BiSR enhances the user-perceived quality of experience by up to 30.6%.|移动网络视频流的用户体验往往受到网络带宽不足和动态性的影响。本文设计了双向优化超分辨率(BiSR)算法，以提高有限带宽下移动网络用户的体验质量(QoE)。BiSR 利用基于深度神经网络(DNN)的模型，在不改变帧间时空信息的情况下，有效地对关键帧进行超分辨。然后，我们提出了一个缩放 DNN 和一个移动专用的优化轻量级超分辨率 DNN，以提高性能。最后，提出了一种新的基于强化学习的自适应比特率(ABR)算法来验证 BiSR 在实际网络跟踪中的性能。我们的评估，使用一个完整的系统实现，表明 BiSR 节省26% 的比特率相比，传统的 H.264编解码器和提高了3.7% 的 SSIM 的视频相比，以前的最先进的国家。总的来说，BiSR 提高了30.6% 的用户感知体验质量。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=BiSR:+Bidirectionally+Optimized+Super-Resolution+for+Mobile+Video+Streaming)|0| +|[Autobidding Auctions in the Presence of User Costs](https://doi.org/10.1145/3543507.3583234)|Yuan Deng, Jieming Mao, Vahab Mirrokni, Hanrui Zhang, Song Zuo|Google Research, USA; Carnegie Mellon University, USA|We study autobidding ad auctions with user costs, where each bidder is value-maximizing subject to a return-over-investment (ROI) constraint, and the seller aims to maximize the social welfare taking into consideration the user's cost of viewing an ad. We show that in the worst case, the approximation ratio of social welfare by running the vanilla VCG auctions with user costs could as bad as 0. To improve the performance of VCG, We propose a new variant of VCG based on properly chosen cost multipliers, and prove that there exist auction-dependent and bidder-dependent cost multipliers that guarantee approximation ratios of 1/2 and 1/4 respectively in terms of the social welfare.|本文研究了具有用户成本的自动竞价广告拍卖，其中每个竞价者的价值最大化受到投资回报率(ROI)的约束，卖方的目标是最大化社会福利，同时考虑用户观看广告的成本。我们指出，在最坏的情况下，通过运行普通的 VCG 拍卖与用户成本的社会福利的近似比率可能为0。为了提高 VCG 的性能，我们提出了一种新的基于合理选择成本乘数的 VCG 变体，并证明了存在拍卖相关成本乘数和投标人相关成本乘数，它们分别保证在社会福利方面的近似比为1/2和1/4。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Autobidding+Auctions+in+the+Presence+of+User+Costs)|0| |[Online Bidding Algorithms for Return-on-Spend Constrained Advertisers✱](https://doi.org/10.1145/3543507.3583491)|Zhe Feng, Swati Padmanabhan, Di Wang|Google Research, USA; University of Washington, Seattle, USA|Online advertising has recently grown into a highly competitive and complex multi-billion-dollar industry, with advertisers bidding for ad slots at large scales and high frequencies. This has resulted in a growing need for efficient "auto-bidding" algorithms that determine the bids for incoming queries to maximize advertisers' targets subject to their specified constraints. This work explores efficient online algorithms for a single value-maximizing advertiser under an increasingly popular constraint: Return-on-Spend (RoS). We quantify efficiency in terms of regret relative to the optimal algorithm, which knows all queries a priori. We contribute a simple online algorithm that achieves near-optimal regret in expectation while always respecting the specified RoS constraint when the input sequence of queries are i.i.d. samples from some distribution. We also integrate our results with the previous work of Balseiro, Lu, and Mirrokni [BLM20] to achieve near-optimal regret while respecting both RoS and fixed budget constraints. Our algorithm follows the primal-dual framework and uses online mirror descent (OMD) for the dual updates. However, we need to use a non-canonical setup of OMD, and therefore the classic low-regret guarantee of OMD, which is for the adversarial setting in online learning, no longer holds. Nonetheless, in our case and more generally where low-regret dynamics are applied in algorithm design, the gradients encountered by OMD can be far from adversarial but influenced by our algorithmic choices. We exploit this key insight to show our OMD setup achieves low regret in the realm of our algorithm.|最近，在线广告业已发展成为一个竞争激烈、规模高达数十亿美元的复杂行业，广告客户大规模、高频率地竞标广告位置。这就导致了对有效的“自动竞价”算法的需求日益增长，这种算法可以确定收到的查询的出价，从而最大限度地提高广告商的目标，使其受到特定的约束。这项工作探讨了一个单一的价值最大化的广告客户在一个日益流行的约束下有效的在线算法: 支出回报(RoS)。我们量化效率的遗憾相对于优化算法，它知道所有查询的先验。我们提出了一个简单的在线算法，当查询的输入序列是来自某个分布的标识样本时，该算法在期望中达到接近最优的遗憾，同时始终遵守指定的 RoS 约束。我们还将我们的研究结果与 Balseiro、 Lu 和 Mirrokni [ BLM20]的前期工作结合起来，以在尊重 RoS 和固定预算约束的情况下实现近乎最佳的遗憾。我们的算法遵循原始-对偶框架，并使用在线镜像下降(OMD)的双重更新。然而，我们需要使用一个非规范的 OMD 设置，因此 OMD 的经典的低后悔保证，这是在线学习的对抗设置，不再成立。尽管如此，在我们的案例中，以及更一般的低后悔动力学应用于算法设计的情况下，OMD 遇到的梯度可能远不是对手，而是受到我们的算法选择的影响。我们利用这个关键的洞察力来展示我们的 OMD 设置在我们的算法领域实现了低遗憾。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Online+Bidding+Algorithms+for+Return-on-Spend+Constrained+Advertisers✱)|0| |[EDNet: Attention-Based Multimodal Representation for Classification of Twitter Users Related to Eating Disorders](https://doi.org/10.1145/3543507.3583863)|Mohammad Abuhassan, Tarique Anwar, Chengfei Liu, Hannah K. Jarman, Matthew FullerTyszkiewicz|Deakin University, Australia; University of York, United Kingdom; Swinburne University of Technology, Australia|Social media platforms provide rich data sources in several domains. In mental health, individuals experiencing an Eating Disorder (ED) are often hesitant to seek help through conventional healthcare services. However, many people seek help with diet and body image issues on social media. To better distinguish at-risk users who may need help for an ED from those who are simply commenting on ED in social environments, highly sophisticated approaches are required. Assessment of ED risks in such a situation can be done in various ways, and each has its own strengths and weaknesses. Hence, there is a need for and potential benefit of a more complex multimodal approach. To this end, we collect historical tweets, user biographies, and online behaviours of relevant users from Twitter, and generate a reasonably large labelled benchmark dataset. Thereafter, we develop an advanced multimodal deep learning model called EDNet using these data to identify the different types of users with ED engagement (e.g., potential ED sufferers, healthcare professionals, or communicators) and distinguish them from those not experiencing EDs on Twitter. EDNet consists of five deep neural network layers. With the help of its embedding, representation and behaviour modeling layers, it effectively learns the multimodalities of social media. In our experiments, EDNet consistently outperforms all the baseline techniques by significant margins. It achieves an accuracy of up to 94.32% and F1 score of up to 93.91% F1 score. To the best of our knowledge, this is the first such study to propose a multimodal approach for user-level classification according to their engagement with ED content on social media.|社交媒体平台在多个领域提供丰富的数据源。在心理健康方面，患有饮食失调(ED)的个体往往不愿意通过传统的医疗服务寻求帮助。然而，许多人在社交媒体上寻求关于饮食和身体形象问题的帮助。为了更好地区分那些可能需要急诊帮助的高危患者和那些只是在社会环境中评论急诊的患者，需要高度复杂的方法。在这种情况下，评估教育署的风险有多种方法，每种方法各有优缺点。因此，需要一种更复杂的多模式方法，而且这种方法还有潜在的好处。为此，我们从 Twitter 收集历史推文、用户简历和相关用户的在线行为，并生成一个相当大的带标签的基准数据集。此后，我们开发了一种称为 EDNet 的高级多模式深度学习模型，使用这些数据来确定不同类型的 ED 参与用户(例如，潜在的 ED 患者，医疗保健专业人员或沟通者) ，并将他们与 Twitter 上没有经历 ED 的人区分开来。EDNet 由五个深层神经网络层组成。借助其嵌入层、表征层和行为建模层，它有效地学习了社会媒体的多重形态。在我们的实验中，EDNet 始终以显著的优势优于所有的基线技术。该算法的正确率达到94.32% ，F1得分达到93.91% 。据我们所知，这是第一个这样的研究，提出了一个多模式的方法，用户级别的分类，根据他们的参与，教育署的内容在社交媒体上。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=EDNet:+Attention-Based+Multimodal+Representation+for+Classification+of+Twitter+Users+Related+to+Eating+Disorders)|0| |[C-Affinity: A Novel Similarity Measure for Effective Data Clustering](https://doi.org/10.1145/3543873.3587307)|Jiwon Hong, SangWook Kim|Hanyang University, Republic of Korea|Clustering is widely employed in various applications as it is one of the most useful data mining techniques. In performing clustering, a similarity measure, which defines how similar a pair of data objects are, plays an important role. A similarity measure is employed by considering a target dataset’s characteristics. Current similarity measures (or distances) do not reflect the distribution of data objects in a dataset at all. From the clustering point of view, this fact may limit the clustering accuracy. In this paper, we propose c-affinity, a new notion of a similarity measure that reflects the distribution of objects in the given dataset from a clustering point of view. We design c-affinity between any two objects to have a higher value as they are more likely to belong to the same cluster by learning the data distribution. We use random walk with restart (RWR) on the k-nearest neighbor graph of the given dataset to measure (1) how similar a pair of objects are and (2) how densely other objects are distributed between them. Via extensive experiments on sixteen synthetic and real-world datasets, we verify that replacing the existing similarity measure with our c-affinity improves the clustering accuracy significantly.|聚类作为最有用的数据挖掘技术之一，被广泛应用于各种应用程序中。在进行聚类时，一个相似性度量定义了一对数据对象的相似程度，它扮演着重要的角色。通过考虑目标数据集的特征，采用相似性度量。当前的相似性度量(或距离)根本不反映数据集中数据对象的分布。从聚类的角度来看，这一事实可能会限制聚类的准确性。在这篇文章中，我们提出了一个新的概念，即从聚类的角度来反映给定数据集中对象的分布的相似性度量。通过学习数据分布，我们设计任意两个对象之间的 c 亲和关系，使其具有更高的值，因为它们更可能属于同一个集群。我们在给定数据集的 k 最近邻图上使用重启随机游动(RWR)来测量(1)一对对象有多相似，(2)其他对象在它们之间的分布有多密集。通过在16个合成和真实数据集上的大量实验，我们验证了用 c 亲和度取代现有的相似度度量可以显著提高聚类的准确性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=C-Affinity:+A+Novel+Similarity+Measure+for+Effective+Data+Clustering)|0| -|[Knowledge Distillation on Cross-Modal Adversarial Reprogramming for Data-Limited Attribute Inference](https://doi.org/10.1145/3543873.3587313)|Quan Li, Lingwei Chen, Shixiong Jing, Dinghao Wu|Wright State University, USA; Pennsylvania State University, USA|Social media generates a rich source of text data with intrinsic user attributes (e.g., age, gender), where different parties benefit from disclosing them. Attribute inference can be cast as a text classification problem, which, however, suffers from labeled data scarcity. To address this challenge, we propose a data-limited learning model to distill knowledge on adversarial reprogramming of a visual transformer (ViT) for attribute inferences. Not only does this novel cross-modal model transfers the powerful learning capability from ViT, but also leverages unlabeled texts to reduce the demand on labeled data. Experiments on social media datasets demonstrate the state-of-the-art performance of our model on data-limited attribute inferences.|社交媒体产生了丰富的具有内在用户属性(例如，年龄，性别)的文本数据来源，不同的方面从披露这些数据中获益。属性推理可以被看作是一个文本分类问题，但是，这个问题存在标记数据稀缺性。为了解决这个问题，我们提出了一个数据有限的学习模型，以提取知识的对抗性重编程的可视化转换器(ViT)的属性推理。这种新颖的跨模式模型不仅转移了 ViT 强大的学习能力，而且利用未标记的文本来减少对标记数据的需求。在社会媒体数据集上的实验证明了我们的模型在数据有限属性推理上的最新性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Knowledge+Distillation+on+Cross-Modal+Adversarial+Reprogramming+for+Data-Limited+Attribute+Inference)|0| +|[Knowledge Distillation on Cross-Modal Adversarial Reprogramming for Data-Limited Attribute Inference](https://doi.org/10.1145/3543873.3587313)|Quan Li, Lingwei Chen, Shixiong Jing, Dinghao Wu|Pennsylvania State University, USA; Wright State University, USA|Social media generates a rich source of text data with intrinsic user attributes (e.g., age, gender), where different parties benefit from disclosing them. Attribute inference can be cast as a text classification problem, which, however, suffers from labeled data scarcity. To address this challenge, we propose a data-limited learning model to distill knowledge on adversarial reprogramming of a visual transformer (ViT) for attribute inferences. Not only does this novel cross-modal model transfers the powerful learning capability from ViT, but also leverages unlabeled texts to reduce the demand on labeled data. Experiments on social media datasets demonstrate the state-of-the-art performance of our model on data-limited attribute inferences.|社交媒体产生了丰富的具有内在用户属性(例如，年龄，性别)的文本数据来源，不同的方面从披露这些数据中获益。属性推理可以被看作是一个文本分类问题，但是，这个问题存在标记数据稀缺性。为了解决这个问题，我们提出了一个数据有限的学习模型，以提取知识的对抗性重编程的可视化转换器(ViT)的属性推理。这种新颖的跨模式模型不仅转移了 ViT 强大的学习能力，而且利用未标记的文本来减少对标记数据的需求。在社会媒体数据集上的实验证明了我们的模型在数据有限属性推理上的最新性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Knowledge+Distillation+on+Cross-Modal+Adversarial+Reprogramming+for+Data-Limited+Attribute+Inference)|0| |[Copyright Protection and Accountability of Generative AI: Attack, Watermarking and Attribution](https://doi.org/10.1145/3543873.3587321)|Haonan Zhong, Jiamin Chang, Ziyue Yang, Tingmin Wu, Pathum Chamikara Mahawaga Arachchige, Chehara Pathmabandu, Minhui Xue||||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Copyright+Protection+and+Accountability+of+Generative+AI:+Attack,+Watermarking+and+Attribution)|0| |[How Streaming Can Improve the World (Wide Web)](https://doi.org/10.1145/3543873.3587332)|Lucas Vogel, Thomas Springer|TU Dresden, Germany|Since its beginnings, web pages have been based on files. This means that HTML, CSS, and JavaScript are transferred from server to client as files, which by default need to be fully loaded before the web page is displayed. This render-blocking procedure increases loading times significantly, leading to reduced user satisfaction and revenue loss due to lower conversion rates. We present a full implementation of a new approach for loading web pages by splitting up every component and loading the page via a text-based stream. Such a modification aligns with current trends of the HTTP protocol, which has been using streams internally since HTTP/2. It significantly improves loading times, independent of the total page size.|从一开始，网页就是基于文件的。这意味着 HTML、 CSS 和 JavaScript 作为文件从服务器传输到客户端，默认情况下需要在网页显示之前完全加载这些文件。这种渲染阻塞过程大大增加了加载时间，导致用户满意度降低和收入损失，由于较低的转换率。我们提出了一个全新的加载网页的方法，通过拆分每个组件，并通过一个基于文本的流加载网页的完整实现。这种修改符合 HTTP 协议的当前趋势，自 HTTP/2以来，HTTP 协议一直在内部使用流。它显著提高了加载时间，与总页面大小无关。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=How+Streaming+Can+Improve+the+World+(Wide+Web))|0| |[PyPoll: A python library automating mining of networks, discussions and polarization on Twitter](https://doi.org/10.1145/3543873.3587349)|Dimitrios Panteleimon Giakatos, Pavlos Sermpezis, Athena Vakali|Aristotle University of Thessaloniki, Greece|Today online social networks have a high impact in our society as more and more people use them for communicating with each other, express their opinions, participating in public discussions, etc. In particular, Twitter is one of the most popular social network platforms people mainly use for political discussions. This attracted the interest of many research studies that analyzed social phenomena on Twitter, by collecting data, analysing communication patterns, and exploring the structure of user networks. While previous works share many common methodologies for data collection and analysis, these are mainly re-implemented every time by researchers in a custom way. In this paper, we introduce PyPoll an open-source Python library that operationalizes common analysis tasks for Twitter discussions. With PyPoll users can perform Twitter graph mining, calculate the polarization index and generate interactive visualizations without needing third-party tools. We believe that PyPoll can help researchers automate their tasks by giving them methods that are easy to use. Also, we demonstrate the use of the library by presenting two use cases; the PyPoll visualization app, an online application for graph visualizing and sharing, and the Political Lighthouse, a Web portal for displaying the polarization in various political topics on Twitter.|今天，在线社交网络对我们的社会产生了很大的影响，因为越来越多的人使用它们进行交流、表达意见、参与公共讨论等等。特别值得一提的是，Twitter 是人们主要用于政治讨论的最流行的社交网络平台之一。这引起了许多研究的兴趣，这些研究通过收集数据、分析交流模式和探索用户网络的结构来分析 Twitter 上的社会现象。虽然以前的工作共享许多共同的方法收集和分析数据，这些主要是重新实现每次由研究人员在一个自定义的方式。在本文中，我们介绍了 PyPoll，它是一个开放源码的 Python 库，可以为 Twitter 讨论操作常见的分析任务。使用 PyPoll，用户可以执行 Twitter 图形挖掘、计算极化指数和生成交互式可视化，而不需要第三方工具。我们相信 PyPoll 可以帮助研究人员通过提供易于使用的方法来自动化他们的任务。此外，我们通过展示两个用例来演示该库的使用: PyPoll 可视化应用程序，一个用于图形可视化和共享的在线应用程序，以及 Political Lighthouse，一个用于在 Twitter 上显示各种政治主题的两极分化的门户网站。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=PyPoll:+A+python+library+automating+mining+of+networks,+discussions+and+polarization+on+Twitter)|0| |[WebSHAP: Towards Explaining Any Machine Learning Models Anywhere](https://doi.org/10.1145/3543873.3587362)|Zijie J. Wang, Duen Horng Chau|Georgia Institute of Technology, USA|As machine learning (ML) is increasingly integrated into our everyday Web experience, there is a call for transparent and explainable web-based ML. However, existing explainability techniques often require dedicated backend servers, which limit their usefulness as the Web community moves toward in-browser ML for lower latency and greater privacy. To address the pressing need for a client-side explainability solution, we present WebSHAP, the first in-browser tool that adapts the state-of-the-art model-agnostic explainability technique SHAP to the Web environment. Our open-source tool is developed with modern Web technologies such as WebGL that leverage client-side hardware capabilities and make it easy to integrate into existing Web ML applications. We demonstrate WebSHAP in a usage scenario of explaining ML-based loan approval decisions to loan applicants. Reflecting on our work, we discuss the opportunities and challenges for future research on transparent Web ML. WebSHAP is available at https://github.com/poloclub/webshap.|随着机器学习(ML)越来越多地融入我们的日常网络经验，有一个透明的和可解释的基于网络的 ML 的呼吁。然而，现有的可解释性技术通常需要专用的后端服务器，这限制了它们的有用性，因为 Web 社区正在朝着浏览器内机器学习的方向发展，以获得更低的延迟和更大的隐私。为了满足对客户端可解释性解决方案的迫切需求，我们提出了 WebSHAP，这是第一个在浏览器中使最先进的模型无关可解释性技术 SHAP 适用于 Web 环境的工具。我们的开源工具是使用现代 Web 技术(如 WebGL)开发的，这些技术利用了客户端硬件功能，并使其易于集成到现有的 Web ML 应用程序中。我们在向贷款申请者解释基于 ML 的贷款批准决策的使用场景中演示了 WebSHAP。回顾我们的工作，我们讨论了未来研究透明 Web 机器学习的机遇和挑战。「网上民政事务及安全资讯 https://github.com/poloclub/WebSHAP 」已上载至。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=WebSHAP:+Towards+Explaining+Any+Machine+Learning+Models+Anywhere)|0| |[Privacy-Preserving Online Content Moderation: A Federated Learning Use Case](https://doi.org/10.1145/3543873.3587604)|Pantelitsa Leonidou, Nicolas Kourtellis, Nikos Salamanos, Michael Sirivianos|Cyprus University of Technology, Cyprus; Telefonica Research, Spain|Users are daily exposed to a large volume of harmful content on various social network platforms. One solution is developing online moderation tools using Machine Learning techniques. However, the processing of user data by online platforms requires compliance with privacy policies. Federated Learning (FL) is an ML paradigm where the training is performed locally on the users' devices. Although the FL framework complies, in theory, with the GDPR policies, privacy leaks can still occur. For instance, an attacker accessing the final trained model can successfully perform unwanted inference of the data belonging to the users who participated in the training process. In this paper, we propose a privacy-preserving FL framework for online content moderation that incorporates Differential Privacy (DP). To demonstrate the feasibility of our approach, we focus on detecting harmful content on Twitter - but the overall concept can be generalized to other types of misbehavior. We simulate a text classifier - in FL fashion - which can detect tweets with harmful content. We show that the performance of the proposed FL framework can be close to the centralized approach - for both the DP and non-DP FL versions. Moreover, it has a high performance even if a small number of clients (each with a small number of data points) are available for the FL training. When reducing the number of clients (from 50 to 10) or the data points per client (from 1K to 0.1K), the classifier can still achieve ~81% AUC. Furthermore, we extend the evaluation to four other Twitter datasets that capture different types of user misbehavior and still obtain a promising performance (61% - 80% AUC). Finally, we explore the overhead on the users' devices during the FL training phase and show that the local training does not introduce excessive CPU utilization and memory consumption overhead.|用户每天都会在各种社交网络平台上接触到大量的有害内容。一个解决方案是使用机器学习技术开发在线审核工具。然而，通过在线平台处理用户数据需要遵守隐私策略。联邦学习(FL)是一种机器学习范式，其中的培训是在用户的设备上本地执行的。尽管 FL 框架在理论上符合 GDPR 策略，但仍然可能发生隐私泄露。例如，访问最终训练模型的攻击者可以成功地对属于参与训练过程的用户的数据进行不必要的推断。在这篇文章中，我们提出了一个保护隐私的在线内容审查框架，该框架结合了差分隐私(DP)。为了证明我们方法的可行性，我们将重点放在检测 Twitter 上的有害内容上——但总体概念可以推广到其他类型的不当行为。我们模拟了一个文本分类器——以 FL 的方式——它可以检测带有有害内容的 tweet。我们展示了所提出的 FL 框架的性能可以接近于集中式方法-对于 DP 和非 DP FL 版本都是如此。此外，即使只有少量的客户端(每个客户端都有少量的数据点)可用于 FL 培训，它也具有很高的性能。当减少客户端数量(从50到10)或每个客户端的数据点数量(从1K 到0.1 K)时，分类器仍然可以达到约81% 的 AUC。此外，我们将评估扩展到其他四个 Twitter 数据集，这些数据集捕获了不同类型的用户不当行为，并且仍然获得了有希望的性能(61% -80% AUC)。最后，我们研究了 FL 训练阶段用户设备上的开销，结果表明本地训练不会引入过多的 CPU 利用率和内存消耗开销。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Privacy-Preserving+Online+Content+Moderation:+A+Federated+Learning+Use+Case)|0| -|[Intent-based Web Page Summarization with Structure-Aware Chunking and Generative Language Models](https://doi.org/10.1145/3543873.3587372)|HuanYuan Chen, Hong Yu|School of Computer and Information Sciences, University of Massachusetts Lowell, USA; College of Information and Computer Sciences, University of Massachusetts Amherst, USA|This paper introduces a structure-aware method to segment web pages into chunks based on their web structures. We utilize large language models to select chunks correspond to a given intent and generate the abstractive summary. Experiments on a food pantry dataset developed for mitigating food insecurity show that the proposed framework is promising.|介绍了一种基于结构感知的网页分块方法。我们利用大型语言模型来选择与给定意图相对应的块，并生成抽象的摘要。为减轻粮食不安全而开发的食品储藏室数据集的实验表明，所提出的框架是有希望的。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Intent-based+Web+Page+Summarization+with+Structure-Aware+Chunking+and+Generative+Language+Models)|0| -|[Measuring and Detecting Virality on Social Media: The Case of Twitter's Viral Tweets Topic](https://doi.org/10.1145/3543873.3587373)|Tugrulcan Elmas, Stephane Selim, Célia Houssiaux|Indiana University Bloomington, USA; EPFL, Switzerland|Social media posts may go viral and reach large numbers of people within a short period of time. Such posts may threaten the public dialogue if they contain misleading content, making their early detection highly crucial. Previous works proposed their own metrics to annotate if a tweet is viral or not in order to automatically detect them later. However, such metrics may not accurately represent viral tweets or may introduce too many false positives. In this work, we use the ground truth data provided by Twitter's "Viral Tweets" topic to review the current metrics and also propose our own metric. We find that a tweet is more likely to be classified as viral by Twitter if the ratio of retweets to its author's followers exceeds some threshold. We found this threshold to be 2.16 in our experiments. This rule results in less false positives although it favors smaller accounts. We also propose a transformers-based model to early detect viral tweets which reports an F1 score of 0.79. The code and the tweet ids are publicly available at: https://github.com/tugrulz/ViralTweets|社交媒体上的帖子可能会像病毒一样迅速传播，并在短时间内接触到大量人群。如果这些帖子包含误导性内容，就可能威胁到公众对话，因此及早发现非常关键。以前的作品提出了他们自己的标准来注释一条推文是否是病毒性的，以便以后自动检测到它们。然而，这样的指标可能不能准确地代表病毒推文，或者可能会引入太多的假阳性。在这项工作中，我们使用 Twitter 的“ Viral Tweets”主题提供的地面真相数据来回顾当前的指标，并提出我们自己的指标。我们发现，如果一条推文的转发与其作者的关注者的比例超过某个阈值，那么它更有可能被 Twitter 归类为病毒式传播。我们在实验中发现这个阈值是2.16。这个规则导致较少的误报，尽管它有利于较小的帐户。我们还提出了一个基于转换器的模型，以早期检测病毒鸣叫报告的 F1评分为0.79。代码和 tweet id 可以在以下 https://github.com/tugrulz/viraltweets 公开获得:|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Measuring+and+Detecting+Virality+on+Social+Media:+The+Case+of+Twitter's+Viral+Tweets+Topic)|0| -|[Anytime-Valid Confidence Sequences in an Enterprise A/B Testing Platform](https://doi.org/10.1145/3543873.3584635)|Akash Maharaj, Ritwik Sinha, David Arbour, Ian WaudbySmith, Simon Z. Liu, Moumita Sinha, Raghavendra Addanki, Aaditya Ramdas, Manas Garg, Viswanathan Swaminathan|Adobe Research, USA; Carnegie Mellon University, USA; Adobe, USA|A/B tests are the gold standard for evaluating digital experiences on the web. However, traditional "fixed-horizon" statistical methods are often incompatible with the needs of modern industry practitioners as they do not permit continuous monitoring of experiments. Frequent evaluation of fixed-horizon tests ("peeking") leads to inflated type-I error and can result in erroneous conclusions. We have released an experimentation service on the Adobe Experience Platform based on anytime-valid confidence sequences, allowing for continuous monitoring of the A/B test and data-dependent stopping. We demonstrate how we adapted and deployed asymptotic confidence sequences in a full featured A/B testing platform, describe how sample size calculations can be performed, and how alternate test statistics like "lift" can be analyzed. On both simulated data and thousands of real experiments, we show the desirable properties of using anytime-valid methods instead of traditional approaches.|A/B 测试是评估网络数字体验的黄金标准。然而，传统的“固定视野”统计方法往往不符合现代行业从业人员的需要，因为他们不允许连续监测实验。频繁地评估固定水平试验(“窥视”)会导致膨胀的 I 型误差，并可能导致错误的结论。我们已经在 Adobe 体验平台上发布了一个基于随时有效置信序列的实验服务，允许连续监控 A/B 测试和依赖数据的停止。我们展示了我们如何在一个全功能的 A/B 测试平台中调整和部署渐近置信序列，描述了如何执行样本量计算，以及如何分析像“提升”这样的替代测试统计量。在模拟数据和成千上万的实际实验中，我们显示了使用随机有效方法代替传统方法的理想性质。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Anytime-Valid+Confidence+Sequences+in+an+Enterprise+A/B+Testing+Platform)|0| -|[Contrastive Fine-tuning on Few Shot Intent Detection with Topological Intent Tree](https://doi.org/10.1145/3543873.3584648)|Wei Yuan, Martin Dimkovski, Aijun An|Intact Financial Corporation, Canada; Department of Electrical Engineering and Computer Science, York University, Canada|We present a few-shot intent detection model for an enterprise’s conversational dialogue system. The model uses an intent topological tree to guide the search for the user intent using large language models (LLMs). The intents are resolved based on semantic similarities between user utterances and the text descriptions of the internal nodes of the intent tree or the intent examples in the leaf nodes of the tree. Our results show that an off-the-shelf language model can work reasonably well in a large enterprise deployment without fine-tuning, and its performance can be further improved with fine-tuning as more domain-specific data becomes available. We also show that the fine-tuned language model meets and outperforms the state-of-the-art (SOTA) results in resolving conversation intents without training classifiers. With the use of a topological intent tree, our model provides more interpretability to cultivate people’s trust in their decisions.|针对企业会话对话系统，提出了一种少镜头意图检测模型。该模型使用一个意图拓扑树来指导使用大型语言模型(LLM)搜索用户意图。根据用户语句与意图树内部节点的文本描述或树叶节点中的意图示例之间的语义相似性来解析意图。我们的研究结果表明，现成的语言模型可以在不进行微调的情况下在大型企业部署中工作得相当好，而且随着更多特定于领域的数据可用，通过微调可以进一步提高其性能。我们还表明，经过微调的语言模型满足并优于最先进的(SOTA)结果，无需训练分类器就能解析会话意图。通过使用拓扑意图树，我们的模型提供了更多的可解释性来培养人们对其决策的信任。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Contrastive+Fine-tuning+on+Few+Shot+Intent+Detection+with+Topological+Intent+Tree)|0| -|[Visual Item Selection With Voice Assistants: A systems perspective](https://doi.org/10.1145/3543873.3584655)|Prashan Wanigasekara, Rafid AlHumaimidi, Turan Gojayev, Niloofar Gheissari, Achal Dave, Stephen Rawls, Fan Yang, Kechen Qin, Nalin Gupta, Spurthi Sandiri, Chevanthie Dissanayake, Zeynab Raeesy, Emre Barut, Chengwei Su|Amazon, Canada; Amazon, USA; Amazon, Germany|Interacting with voice assistants, such as Amazon Alexa to aid in day-to-day tasks has become a ubiquitous phenomenon in modern-day households. These voice assistants often have screens to provide visual content (e.g., images, videos) to their users. There is an increasing trend of users shopping or searching for products using these devices, yet, these voice assistants do not support commands or queries that contain visual references to the content shown on screen (e.g., “blue one”, “red dress”). We introduce a novel multi-modal visual shopping experience where the voice assistant is aware of the visual content shown on the screen and assists the user in item selection using natural language multi-modal interactions. We detail a practical, lightweight end-to-end system architecture spanning from model fine-tuning, deployment, to skill invocation on an Amazon Echo family device with a screen. We also define a niche “Visual Item Selection” task and evaluate whether we can effectively leverage publicly available multi-modal models, and embeddings produced from these models for the task. We show that open source contrastive embeddings like CLIP [30] and ALBEF [24] have zero-shot accuracy above for the “Visual Item Selection” task on an internally collected visual shopping dataset. By further fine-tuning the embeddings, we obtain further gains of 8.6% to 24.0% in relative accuracy improvement over a baseline. The technology that enables our visual shopping assistant is available as an Alexa Skill in the Alexa Skills store.|与诸如亚马逊 Alexa 这样的语音助手进行交互以帮助完成日常任务，已经成为现代家庭中无处不在的现象。这些语音助手通常有屏幕为用户提供视觉内容(如图像、视频)。用户使用这些设备购物或搜索产品的趋势正在增加，然而，这些语音助手不支持包含对屏幕上显示的内容的可视化参考的命令或查询(例如，“蓝色的”、“红色的裙子”)。我们介绍了一种新颖的多模态视觉购物体验，语音助手可以感知屏幕上显示的视觉内容，并利用自然语言的多模态交互协助用户选择商品。我们详细介绍了一个实用的、轻量级的端到端系统架构，从模型微调、部署到带屏幕的 Amazon Echo 系列设备上的技能调用。我们还定义了一个小型的“可视化项目选择”任务，并评估我们是否能够有效地利用公开可用的多模态模型，以及从这些模型产生的嵌入任务。我们展示了像 CLIP [30]和 ALBEF [24]这样的开源对比嵌入对于内部收集的可视化购物数据集上的“可视化项目选择”任务具有零拍摄精度。通过对嵌入进行进一步的微调，我们获得了比基线相对准确度提高8.6% 到24.0% 的进一步增益。该技术，使我们的视觉购物助理可作为一个 Alexa 技能在 Alexa 技能商店。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Visual+Item+Selection+With+Voice+Assistants:+A+systems+perspective)|0| -|[Multi-Source Domain Adaptation via Latent Domain Reconstruction](https://doi.org/10.1145/3543873.3584659)|Jun Zhou, Chilin Fu, Xiaolu Zhang|College of Computer Science and Technology, Zhejiang University, China and Ant Group, China; Ant Group, China|Multi-Source Domain Adaptation (MSDA) is widely used in various machine learning scenarios for domain shifts between labeled source domains and unlabeled target domains. Conventional MSDA methods are built on a strong hypothesis that data samples from the same source belong to the same domain with the same latent distribution. However, in practice sources and their latent domains are not necessarily one-to-one correspondence. To tackle this problem, a novel Multi-source Reconstructed Domain Adaptation (MRDA) framework for MSDA is proposed. We use an Expectation-Maximization (EM) mechanism that iteratively reconstructs the source domains to recover the latent domains and performs domain adaptation on the reconstructed domains. Specifically, in the E-step, we cluster the samples from multiple sources into different latent domains, and a soft assignment strategy is proposed to avoid cluster imbalance. In the M-step, we freeze the latent domains clustered in the E-step and optimize the objective function for domain adaptation, and a global-specific feature extractor is used to capture both domain-invariant and domain-specific features. Extensive experiments demonstrate that our approach can reconstruct source domains and perform domain adaptation on the reconstructed domains effectively, thus significantly outperforming state-of-the-art (SOTA) baselines (e.g., 1% to 3.1% absolute improvement in AUC).|多源域自适应(MSDA)广泛应用于各种机器学习场景中，用于标记源域和未标记目标域之间的域移位。传统的 MSDA 方法是建立在一个强大的假设，即来自同一来源的数据样本属于同一领域，具有相同的潜在分布。然而，在实践中，来源及其潜在领域并不一定是双射。针对这一问题，提出了一种新的多源重构域自适应(MRDA)框架。我们使用一个期望最大化(EM)机制，迭代地重构源域来恢复潜在域，并对重构域执行域适应。具体来说，在 E 步中，我们将来自多个源的样本聚类到不同的潜在域中，并提出了一种软分配策略来避免聚类不平衡。在 M 步中，我们冻结了聚集在 E 步中的潜在领域，并优化了领域适应的目标函数，并且使用了全局特征提取器来捕获领域不变和领域特定的特征。广泛的实验表明，我们的方法可以重建源域并有效地对重建域进行域适应，从而显着优于最先进的(SOTA)基线(例如，AUC 的1% 至3.1% 的绝对改善)。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Multi-Source+Domain+Adaptation+via+Latent+Domain+Reconstruction)|0| +|[Intent-based Web Page Summarization with Structure-Aware Chunking and Generative Language Models](https://doi.org/10.1145/3543873.3587372)|HuanYuan Chen, Hong Yu|College of Information and Computer Sciences, University of Massachusetts Amherst, USA; School of Computer and Information Sciences, University of Massachusetts Lowell, USA|This paper introduces a structure-aware method to segment web pages into chunks based on their web structures. We utilize large language models to select chunks correspond to a given intent and generate the abstractive summary. Experiments on a food pantry dataset developed for mitigating food insecurity show that the proposed framework is promising.|介绍了一种基于结构感知的网页分块方法。我们利用大型语言模型来选择与给定意图相对应的块，并生成抽象的摘要。为减轻粮食不安全而开发的食品储藏室数据集的实验表明，所提出的框架是有希望的。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Intent-based+Web+Page+Summarization+with+Structure-Aware+Chunking+and+Generative+Language+Models)|0| +|[Measuring and Detecting Virality on Social Media: The Case of Twitter's Viral Tweets Topic](https://doi.org/10.1145/3543873.3587373)|Tugrulcan Elmas, Stephane Selim, Célia Houssiaux|EPFL, Switzerland; Indiana University Bloomington, USA|Social media posts may go viral and reach large numbers of people within a short period of time. Such posts may threaten the public dialogue if they contain misleading content, making their early detection highly crucial. Previous works proposed their own metrics to annotate if a tweet is viral or not in order to automatically detect them later. However, such metrics may not accurately represent viral tweets or may introduce too many false positives. In this work, we use the ground truth data provided by Twitter's "Viral Tweets" topic to review the current metrics and also propose our own metric. We find that a tweet is more likely to be classified as viral by Twitter if the ratio of retweets to its author's followers exceeds some threshold. We found this threshold to be 2.16 in our experiments. This rule results in less false positives although it favors smaller accounts. We also propose a transformers-based model to early detect viral tweets which reports an F1 score of 0.79. The code and the tweet ids are publicly available at: https://github.com/tugrulz/ViralTweets|社交媒体上的帖子可能会像病毒一样迅速传播，并在短时间内接触到大量人群。如果这些帖子包含误导性内容，就可能威胁到公众对话，因此及早发现非常关键。以前的作品提出了他们自己的标准来注释一条推文是否是病毒性的，以便以后自动检测到它们。然而，这样的指标可能不能准确地代表病毒推文，或者可能会引入太多的假阳性。在这项工作中，我们使用 Twitter 的“ Viral Tweets”主题提供的地面真相数据来回顾当前的指标，并提出我们自己的指标。我们发现，如果一条推文的转发与其作者的关注者的比例超过某个阈值，那么它更有可能被 Twitter 归类为病毒式传播。我们在实验中发现这个阈值是2.16。这个规则导致较少的误报，尽管它有利于较小的帐户。我们还提出了一个基于转换器的模型，以早期检测病毒鸣叫报告的 F1评分为0.79。代码和 tweet id 可以在以下 https://github.com/tugrulz/viraltweets 公开获得:|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Measuring+and+Detecting+Virality+on+Social+Media:+The+Case+of+Twitter's+Viral+Tweets+Topic)|0| +|[Anytime-Valid Confidence Sequences in an Enterprise A/B Testing Platform](https://doi.org/10.1145/3543873.3584635)|Akash Maharaj, Ritwik Sinha, David Arbour, Ian WaudbySmith, Simon Z. Liu, Moumita Sinha, Raghavendra Addanki, Aaditya Ramdas, Manas Garg, Viswanathan Swaminathan|Carnegie Mellon University, USA; Adobe, USA; Adobe Research, USA|A/B tests are the gold standard for evaluating digital experiences on the web. However, traditional "fixed-horizon" statistical methods are often incompatible with the needs of modern industry practitioners as they do not permit continuous monitoring of experiments. Frequent evaluation of fixed-horizon tests ("peeking") leads to inflated type-I error and can result in erroneous conclusions. We have released an experimentation service on the Adobe Experience Platform based on anytime-valid confidence sequences, allowing for continuous monitoring of the A/B test and data-dependent stopping. We demonstrate how we adapted and deployed asymptotic confidence sequences in a full featured A/B testing platform, describe how sample size calculations can be performed, and how alternate test statistics like "lift" can be analyzed. On both simulated data and thousands of real experiments, we show the desirable properties of using anytime-valid methods instead of traditional approaches.|A/B 测试是评估网络数字体验的黄金标准。然而，传统的“固定视野”统计方法往往不符合现代行业从业人员的需要，因为他们不允许连续监测实验。频繁地评估固定水平试验(“窥视”)会导致膨胀的 I 型误差，并可能导致错误的结论。我们已经在 Adobe 体验平台上发布了一个基于随时有效置信序列的实验服务，允许连续监控 A/B 测试和依赖数据的停止。我们展示了我们如何在一个全功能的 A/B 测试平台中调整和部署渐近置信序列，描述了如何执行样本量计算，以及如何分析像“提升”这样的替代测试统计量。在模拟数据和成千上万的实际实验中，我们显示了使用随机有效方法代替传统方法的理想性质。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Anytime-Valid+Confidence+Sequences+in+an+Enterprise+A/B+Testing+Platform)|0| +|[Contrastive Fine-tuning on Few Shot Intent Detection with Topological Intent Tree](https://doi.org/10.1145/3543873.3584648)|Wei Yuan, Martin Dimkovski, Aijun An|Department of Electrical Engineering and Computer Science, York University, Canada; Intact Financial Corporation, Canada|We present a few-shot intent detection model for an enterprise’s conversational dialogue system. The model uses an intent topological tree to guide the search for the user intent using large language models (LLMs). The intents are resolved based on semantic similarities between user utterances and the text descriptions of the internal nodes of the intent tree or the intent examples in the leaf nodes of the tree. Our results show that an off-the-shelf language model can work reasonably well in a large enterprise deployment without fine-tuning, and its performance can be further improved with fine-tuning as more domain-specific data becomes available. We also show that the fine-tuned language model meets and outperforms the state-of-the-art (SOTA) results in resolving conversation intents without training classifiers. With the use of a topological intent tree, our model provides more interpretability to cultivate people’s trust in their decisions.|针对企业会话对话系统，提出了一种少镜头意图检测模型。该模型使用一个意图拓扑树来指导使用大型语言模型(LLM)搜索用户意图。根据用户语句与意图树内部节点的文本描述或树叶节点中的意图示例之间的语义相似性来解析意图。我们的研究结果表明，现成的语言模型可以在不进行微调的情况下在大型企业部署中工作得相当好，而且随着更多特定于领域的数据可用，通过微调可以进一步提高其性能。我们还表明，经过微调的语言模型满足并优于最先进的(SOTA)结果，无需训练分类器就能解析会话意图。通过使用拓扑意图树，我们的模型提供了更多的可解释性来培养人们对其决策的信任。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Contrastive+Fine-tuning+on+Few+Shot+Intent+Detection+with+Topological+Intent+Tree)|0| +|[Visual Item Selection With Voice Assistants: A systems perspective](https://doi.org/10.1145/3543873.3584655)|Prashan Wanigasekara, Rafid AlHumaimidi, Turan Gojayev, Niloofar Gheissari, Achal Dave, Stephen Rawls, Fan Yang, Kechen Qin, Nalin Gupta, Spurthi Sandiri, Chevanthie Dissanayake, Zeynab Raeesy, Emre Barut, Chengwei Su|Amazon, Canada; Amazon, Germany; Amazon, USA|Interacting with voice assistants, such as Amazon Alexa to aid in day-to-day tasks has become a ubiquitous phenomenon in modern-day households. These voice assistants often have screens to provide visual content (e.g., images, videos) to their users. There is an increasing trend of users shopping or searching for products using these devices, yet, these voice assistants do not support commands or queries that contain visual references to the content shown on screen (e.g., “blue one”, “red dress”). We introduce a novel multi-modal visual shopping experience where the voice assistant is aware of the visual content shown on the screen and assists the user in item selection using natural language multi-modal interactions. We detail a practical, lightweight end-to-end system architecture spanning from model fine-tuning, deployment, to skill invocation on an Amazon Echo family device with a screen. We also define a niche “Visual Item Selection” task and evaluate whether we can effectively leverage publicly available multi-modal models, and embeddings produced from these models for the task. We show that open source contrastive embeddings like CLIP [30] and ALBEF [24] have zero-shot accuracy above for the “Visual Item Selection” task on an internally collected visual shopping dataset. By further fine-tuning the embeddings, we obtain further gains of 8.6% to 24.0% in relative accuracy improvement over a baseline. The technology that enables our visual shopping assistant is available as an Alexa Skill in the Alexa Skills store.|与诸如亚马逊 Alexa 这样的语音助手进行交互以帮助完成日常任务，已经成为现代家庭中无处不在的现象。这些语音助手通常有屏幕为用户提供视觉内容(如图像、视频)。用户使用这些设备购物或搜索产品的趋势正在增加，然而，这些语音助手不支持包含对屏幕上显示的内容的可视化参考的命令或查询(例如，“蓝色的”、“红色的裙子”)。我们介绍了一种新颖的多模态视觉购物体验，语音助手可以感知屏幕上显示的视觉内容，并利用自然语言的多模态交互协助用户选择商品。我们详细介绍了一个实用的、轻量级的端到端系统架构，从模型微调、部署到带屏幕的 Amazon Echo 系列设备上的技能调用。我们还定义了一个小型的“可视化项目选择”任务，并评估我们是否能够有效地利用公开可用的多模态模型，以及从这些模型产生的嵌入任务。我们展示了像 CLIP [30]和 ALBEF [24]这样的开源对比嵌入对于内部收集的可视化购物数据集上的“可视化项目选择”任务具有零拍摄精度。通过对嵌入进行进一步的微调，我们获得了比基线相对准确度提高8.6% 到24.0% 的进一步增益。该技术，使我们的视觉购物助理可作为一个 Alexa 技能在 Alexa 技能商店。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Visual+Item+Selection+With+Voice+Assistants:+A+systems+perspective)|0| +|[Multi-Source Domain Adaptation via Latent Domain Reconstruction](https://doi.org/10.1145/3543873.3584659)|Jun Zhou, Chilin Fu, Xiaolu Zhang|Ant Group, China; College of Computer Science and Technology, Zhejiang University, China and Ant Group, China|Multi-Source Domain Adaptation (MSDA) is widely used in various machine learning scenarios for domain shifts between labeled source domains and unlabeled target domains. Conventional MSDA methods are built on a strong hypothesis that data samples from the same source belong to the same domain with the same latent distribution. However, in practice sources and their latent domains are not necessarily one-to-one correspondence. To tackle this problem, a novel Multi-source Reconstructed Domain Adaptation (MRDA) framework for MSDA is proposed. We use an Expectation-Maximization (EM) mechanism that iteratively reconstructs the source domains to recover the latent domains and performs domain adaptation on the reconstructed domains. Specifically, in the E-step, we cluster the samples from multiple sources into different latent domains, and a soft assignment strategy is proposed to avoid cluster imbalance. In the M-step, we freeze the latent domains clustered in the E-step and optimize the objective function for domain adaptation, and a global-specific feature extractor is used to capture both domain-invariant and domain-specific features. Extensive experiments demonstrate that our approach can reconstruct source domains and perform domain adaptation on the reconstructed domains effectively, thus significantly outperforming state-of-the-art (SOTA) baselines (e.g., 1% to 3.1% absolute improvement in AUC).|多源域自适应(MSDA)广泛应用于各种机器学习场景中，用于标记源域和未标记目标域之间的域移位。传统的 MSDA 方法是建立在一个强大的假设，即来自同一来源的数据样本属于同一领域，具有相同的潜在分布。然而，在实践中，来源及其潜在领域并不一定是双射。针对这一问题，提出了一种新的多源重构域自适应(MRDA)框架。我们使用一个期望最大化(EM)机制，迭代地重构源域来恢复潜在域，并对重构域执行域适应。具体来说，在 E 步中，我们将来自多个源的样本聚类到不同的潜在域中，并提出了一种软分配策略来避免聚类不平衡。在 M 步中，我们冻结了聚集在 E 步中的潜在领域，并优化了领域适应的目标函数，并且使用了全局特征提取器来捕获领域不变和领域特定的特征。广泛的实验表明，我们的方法可以重建源域并有效地对重建域进行域适应，从而显着优于最先进的(SOTA)基线(例如，AUC 的1% 至3.1% 的绝对改善)。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Multi-Source+Domain+Adaptation+via+Latent+Domain+Reconstruction)|0| |[Human Dimensions of Animal Exploitation: Towards Understanding the International Wildlife Trade and Selfie-Tourism on Twitter](https://doi.org/10.1145/3543873.3587538)|Sean P. Rogers, Jeremiah Onaolapo|University of Vermont, USA|This study investigates statements of participation in an exploitative animal activity on social media website Twitter. The data include social posts (tweets) related to two exploited species - the sloth (N=32,119), and the elephant (N=15,160). Tweets for each of these case studies were examined and labeled. The initial results reveal several features of interaction with exploited species. Namely, there are a high number of tweets indicating that individuals participated in exploited species activities during vacations in destinations that double as native countries for the exploited species. The data also indicate that a large number of exploited species activities take place at fairs, carnivals, and circuses. These initial results shed light on the trends in human participation in activities with exploited species. These findings will offer insight to stakeholders seeking to bolster education programs and quantify the level of animal exploitation.|本研究调查了社交媒体网站 Twitter 上参与剥削动物活动的声明。这些数据包括与两个被开发的物种——树懒(N = 32,119)和大象(N = 15,160)有关的社交帖子(tweet)。这些个案研究的推文都经过了检查和标记。初步结果揭示了与被开发物种相互作用的几个特征。也就是说，有大量推文表明，个人在假期期间参加了被捕捞物种的活动，而目的地是被捕捞物种的本土国家。数据还表明，大量被开发的物种活动发生在集市、嘉年华会和马戏团。这些初步结果说明了人类参与与被捕捞物种有关的活动的趋势。这些发现将为利益相关者提供深刻的见解，以支持教育项目和量化动物剥削的水平。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Human+Dimensions+of+Animal+Exploitation:+Towards+Understanding+the+International+Wildlife+Trade+and+Selfie-Tourism+on+Twitter)|0| |[A Bridge over the Troll: Non-Complementary Activism Online](https://doi.org/10.1145/3543873.3587541)|Emyn Dean|Knowledge Media Institute (KMi), Open University, United Kingdom|Previous research has identified phenomena such as cyberbystander intervention and various other forms of responses to aggressive or hateful behaviours online. In the online media ecosystem, some people from marginalized communities and their allies have attempted to enhance organic engagement by participating in organized activism, which is sometimes characterized as "non-complementary" or "indirect". This paper attempts to identify, recognize, and label this phenomenon, as well as provide suggestions for further research in this area.|以前的研究已经确定了一些现象，例如网络旁观者的干预和对网上攻击性或仇恨行为的各种其他形式的反应。在网络媒体生态系统中，一些来自边缘化社区及其盟友的人试图通过参与有组织的行动主义来加强有机参与，这种行动主义有时被描述为“非互补”或“间接”。本文试图对这一现象进行识别、识别和标记，并为该领域的进一步研究提供建议。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Bridge+over+the+Troll:+Non-Complementary+Activism+Online)|0| |[The DEEP Sensorium: a multidimensional approach to sensory domain labelling](https://doi.org/10.1145/3543873.3587631)|Simona Corciulo, Livio Bioglio, Valerio Basile, Viviana Patti, Rossana Damiano|Dipartimento di Informatica, University of Turin, Italy; Dipartimento di Studi Umanistici, University of Turin, Italy|In this paper, we describe our intuitions about how language technologies can contribute to create new ways to enhance the accessibility of exhibits in cultural contexts by exploiting the knowledge about the history of our senses and the link between perception and language. We evaluate the performance of five multi-class classification models for the task of sensory recognition and introduce the DEEP Sensorium (Deep Engaging Experiences and Practices - Sensorium), a multidimensional dataset that combines cognitive and affective features to inform systematic methodologies for augmenting exhibits with multi-sensory stimuli. For each model, using different feature sets, we show that the features expressing the affective dimension of words combined with sub-lexical features perform better than uni-dimensional training sets.|在本文中，我们描述了我们的直觉，关于语言技术如何能够有助于创造新的方式，通过利用我们的感官历史知识和感知与语言之间的联系，提高展品在文化背景下的可及性。我们评估了五个多类分类模型在感官识别任务中的表现，并介绍了 DEEP Sensorium (DEEP Engaging Experience and Practices-Sensorium) ，这是一个结合认知和情感特征的多维数据集，以通知系统方法用多感官刺激来增强展品。对于每个模型，使用不同的特征集，我们发现结合亚词汇特征表达词的情感维度的特征比一维训练集表现得更好。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=The+DEEP+Sensorium:+a+multidimensional+approach+to+sensory+domain+labelling)|0| -|[A Survey of General Ontologies for the Cross-Industry Domain of Circular Economy](https://doi.org/10.1145/3543873.3587613)|Huanyu Li, Mina Abd Nikooie Pour, Ying Li, Mikael Lindecrantz, Eva Blomqvist, Patrick Lambrix|Linköping University, Sweden and University of Gävle, Sweden; Linköping University, Sweden; Ragn-Sells AB, Sweden|Circular Economy has the goal to reduce value loss and avoid waste by extending the life span of materials and products, including circulating materials or product parts before they become waste. Circular economy models (e.g., circular value networks) are typically complex and networked, involving different cross-industry domains. In the context of a circular value network, multiple actors, such as suppliers, manufacturers, recyclers, and product end-users, may be involved. In addition, there may be various flows of resources, energy, information and value throughout the network. This means that we face the challenge that the data and information from cross-industry domains in a circular economy model are not built on common ground, and as a result are difficult to understand and use for both humans and machines. Using ontologies to represent domain knowledge can enable actors and stakeholders from different industries in the circular economy to communicate using a common language. The knowledge domains involved include circular economy, sustainability, materials, products, manufacturing, and logistics. The objective of this paper is to investigate the landscape of current ontologies for these domains. This will enable us to in the future explore what existing knowledge can be adapted or used to develop ontologies for circular value networks.|循环经济的目标是通过延长材料和产品的寿命来减少价值损失和避免浪费，包括在成为废物之前循环的材料或产品部件。循环经济模型(例如，循环价值网络)通常是复杂和网络化的，涉及不同的跨行业领域。在循环价值网络的背景下，可能涉及多个参与者，如供应商、制造商、回收商和产品最终用户。此外，整个网络可能有各种各样的资源、能源、信息和价值流。这意味着我们面临的挑战是，来自循环经济模型中跨行业领域的数据和信息不是建立在共同的基础之上，因此人类和机器都难以理解和使用。使用本体来表示领域知识可以使循环经济中不同行业的参与者和利益相关者使用一种共同的语言进行交流。所涉及的知识领域包括循环经济、可持续性、材料、产品、制造和物流。本文的目的是研究这些领域当前的本体论景观。这将使我们能够在未来探索什么现有的知识可以被调整或用于开发循环价值网络的本体论。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Survey+of+General+Ontologies+for+the+Cross-Industry+Domain+of+Circular+Economy)|0| +|[A Survey of General Ontologies for the Cross-Industry Domain of Circular Economy](https://doi.org/10.1145/3543873.3587613)|Huanyu Li, Mina Abd Nikooie Pour, Ying Li, Mikael Lindecrantz, Eva Blomqvist, Patrick Lambrix|Linköping University, Sweden; Linköping University, Sweden and University of Gävle, Sweden; Ragn-Sells AB, Sweden|Circular Economy has the goal to reduce value loss and avoid waste by extending the life span of materials and products, including circulating materials or product parts before they become waste. Circular economy models (e.g., circular value networks) are typically complex and networked, involving different cross-industry domains. In the context of a circular value network, multiple actors, such as suppliers, manufacturers, recyclers, and product end-users, may be involved. In addition, there may be various flows of resources, energy, information and value throughout the network. This means that we face the challenge that the data and information from cross-industry domains in a circular economy model are not built on common ground, and as a result are difficult to understand and use for both humans and machines. Using ontologies to represent domain knowledge can enable actors and stakeholders from different industries in the circular economy to communicate using a common language. The knowledge domains involved include circular economy, sustainability, materials, products, manufacturing, and logistics. The objective of this paper is to investigate the landscape of current ontologies for these domains. This will enable us to in the future explore what existing knowledge can be adapted or used to develop ontologies for circular value networks.|循环经济的目标是通过延长材料和产品的寿命来减少价值损失和避免浪费，包括在成为废物之前循环的材料或产品部件。循环经济模型(例如，循环价值网络)通常是复杂和网络化的，涉及不同的跨行业领域。在循环价值网络的背景下，可能涉及多个参与者，如供应商、制造商、回收商和产品最终用户。此外，整个网络可能有各种各样的资源、能源、信息和价值流。这意味着我们面临的挑战是，来自循环经济模型中跨行业领域的数据和信息不是建立在共同的基础之上，因此人类和机器都难以理解和使用。使用本体来表示领域知识可以使循环经济中不同行业的参与者和利益相关者使用一种共同的语言进行交流。所涉及的知识领域包括循环经济、可持续性、材料、产品、制造和物流。本文的目的是研究这些领域当前的本体论景观。这将使我们能够在未来探索什么现有的知识可以被调整或用于开发循环价值网络的本体论。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Survey+of+General+Ontologies+for+the+Cross-Industry+Domain+of+Circular+Economy)|0| |[Improving Netflix Video Quality with Neural Networks](https://doi.org/10.1145/3543873.3587553)|Christos G. Bampis, LiHeng Chen, Zhi Li|Netflix, USA|Video downscaling is an important component of adaptive video streaming, which tailors streaming to screen resolutions of different devices and optimizes picture quality under varying network conditions. With video downscaling, a high-resolution input video is downscaled into multiple lower-resolution videos. This is typically done by a conventional resampling filter like Lanczos. In this talk, we describe how we improved Netflix video quality by developing neural networks for video downscaling and deploying them at scale.|视频缩放是自适应视频流的重要组成部分，它根据不同设备的屏幕分辨率对视频流进行裁剪，并在不同网络条件下优化图像质量。随着视频缩放，高分辨率输入视频被缩放成多个低分辨率视频。这通常是由一个传统的重采样过滤器，如兰科斯。在这个演讲中，我们描述了我们如何通过开发视频缩放的神经网络和大规模部署来提高 Netflix 的视频质量。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Improving+Netflix+Video+Quality+with+Neural+Networks)|0| |[Graph2Feat: Inductive Link Prediction via Knowledge Distillation](https://doi.org/10.1145/3543873.3587596)|Ahmed E. Samy, Zekarias T. Kefato, Sarunas Girdzijauskas|KTH, Royal Institue of Technology, Sweden; KTH, Royal Institute of Technology, Sweden|Link prediction between two nodes is a critical task in graph machine learning. Most approaches are based on variants of graph neural networks (GNNs) that focus on transductive link prediction and have high inference latency. However, many real-world applications require fast inference over new nodes in inductive settings where no information on connectivity is available for these nodes. Thereby, node features provide an inevitable alternative in the latter scenario. To that end, we propose Graph2Feat, which enables inductive link prediction by exploiting knowledge distillation (KD) through the Student-Teacher learning framework. In particular, Graph2Feat learns to match the representations of a lightweight student multi-layer perceptron (MLP) with a more expressive teacher GNN while learning to predict missing links based on the node features, thus attaining both GNN’s expressiveness and MLP’s fast inference. Furthermore, our approach is general; it is suitable for transductive and inductive link predictions on different types of graphs regardless of them being homogeneous or heterogeneous, directed or undirected. We carry out extensive experiments on seven real-world datasets including homogeneous and heterogeneous graphs. Our experiments demonstrate that Graph2Feat significantly outperforms SOTA methods in terms of AUC and average precision in homogeneous and heterogeneous graphs. Finally, Graph2Feat has the minimum inference time compared to the SOTA methods, and 100x acceleration compared to GNNs. The code and datasets are available on GitHub1.|两节点之间的链路预测是图形机器学习中的一个关键问题。大多数方法是基于图神经网络(GNN)的变体，侧重于传导链接预测和具有较高的推理潜伏期。然而，许多实际应用程序需要在归纳设置中对新节点进行快速推理，因为这些节点没有关于连通性的信息。因此，在后一种情况下，节点特性提供了一种不可避免的替代方案。为此，我们提出了 Graph2Feat，它通过学生-教师学习框架利用知识提取(KD)来实现归纳链接预测。特别是，Graph2Feat 学习匹配轻量级学生多层感知器(MLP)和更具表现力的教师 GNN 的表示，同时学习基于节点特征预测缺失链接，从而实现 GNN 的表现力和 MLP 的快速推理。此外，我们的方法是通用的，它适合于对不同类型的图的传导和归纳链接预测，不管它们是同质的或异质的，有向的或无向的。我们在七个真实世界的数据集上进行了广泛的实验，包括同质和异质图。我们的实验表明，Graph2Feat 在 AUC 和均匀和异构图的平均精度方面显著优于 SOTA 方法。最后，Graph2Feat 与 SOTA 方法相比具有最短的推理时间，与 GNN 相比具有100倍的加速度。代码和数据集可以在 GitHub1上获得。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Graph2Feat:+Inductive+Link+Prediction+via+Knowledge+Distillation)|0| |[Universal Model in Online Customer Service](https://doi.org/10.1145/3543873.3587630)|ShuTing Pi, ChengPing Hsieh, Qun Liu, Yuying Zhu|Amazon, USA|Building machine learning models can be a time-consuming process that often takes several months to implement in typical business scenarios. To ensure consistent model performance and account for variations in data distribution, regular retraining is necessary. This paper introduces a solution for improving online customer service in e-commerce by presenting a universal model for predicting labels based on customer questions, without requiring training. Our novel approach involves using machine learning techniques to tag customer questions in transcripts and create a repository of questions and corresponding labels. When a customer requests assistance, an information retrieval model searches the repository for similar questions, and statistical analysis is used to predict the corresponding label. By eliminating the need for individual model training and maintenance, our approach reduces both the model development cycle and costs. The repository only requires periodic updating to maintain accuracy.|构建机器学习模型可能是一个耗时的过程，在典型的业务场景中通常需要几个月才能实现。为了确保模型性能一致，并考虑到数据分布的差异，定期再培训是必要的。本文介绍了一种改进电子商务中在线客户服务的解决方案，提出了一种基于客户问题的通用标签预测模型，该模型不需要培训。我们的新方法包括使用机器学习技术在文本中标记客户的问题，并创建一个问题库和相应的标签。当客户要求协助时，信息检索模型会在储存库中搜索类似的问题，并使用统计分析来预测相应的标签。通过消除对单个模型的培训和维护的需要，我们的方法降低了模型开发周期和成本。存储库只需要定期更新以保持准确性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Universal+Model+in+Online+Customer+Service)|0| -|[Robust Stochastic Multi-Armed Bandits with Historical Data](https://doi.org/10.1145/3543873.3587653)|Sarah Boufelja Yacobi, Djallel Bouneffouf|Imperial College London, United Kingdom; IBM Research, USA|We consider the problem of Stochastic Contextual Multi-Armed Bandits (CMABs) initialised with Historical data. Initialisation with historical data is an example of data-driven regularisation which should, in theory, accelerate the convergence of CMABs. However, in practice, we have little to no control over the underlying generation process of such data, which may exhibit some pathologies, possibly impeding the convergence and the stability of the algorithm. In this paper, we focus on two main challenges: bias selection and data corruption. We propose two new algorithms to solve these specific issues: LinUCB with historical data and offline balancing (OB-HLinUCB) and Robust LinUCB with corrupted historical data (R-HLinUCB). We derive their theoretical regret bounds and discuss their computational performance using real-world datasets.|我们考虑的问题随机上下文多武装匪徒(CMABs)初始化与历史数据。使用历史数据进行初始化，是数据驱动的正规化的一个例子，理论上应能加速 CMAB 的融合。然而，在实践中，我们对这些数据的基本生成过程几乎没有控制，这可能会表现出一些病态，可能会妨碍算法的收敛和稳定性。在本文中，我们主要关注两个主要的挑战: 偏差选择和数据损坏。我们提出了两种新的算法来解决这些具体问题: 具有历史数据和离线平衡的 LinUCB (OB-HLinUCB)和具有损坏历史数据的鲁棒 LinUCB (R-HLinUCB)。我们推导了它们的理论遗憾界限，并利用实际数据集讨论了它们的计算性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Robust+Stochastic+Multi-Armed+Bandits+with+Historical+Data)|0| +|[Robust Stochastic Multi-Armed Bandits with Historical Data](https://doi.org/10.1145/3543873.3587653)|Sarah Boufelja Yacobi, Djallel Bouneffouf|IBM Research, USA; Imperial College London, United Kingdom|We consider the problem of Stochastic Contextual Multi-Armed Bandits (CMABs) initialised with Historical data. Initialisation with historical data is an example of data-driven regularisation which should, in theory, accelerate the convergence of CMABs. However, in practice, we have little to no control over the underlying generation process of such data, which may exhibit some pathologies, possibly impeding the convergence and the stability of the algorithm. In this paper, we focus on two main challenges: bias selection and data corruption. We propose two new algorithms to solve these specific issues: LinUCB with historical data and offline balancing (OB-HLinUCB) and Robust LinUCB with corrupted historical data (R-HLinUCB). We derive their theoretical regret bounds and discuss their computational performance using real-world datasets.|我们考虑的问题随机上下文多武装匪徒(CMABs)初始化与历史数据。使用历史数据进行初始化，是数据驱动的正规化的一个例子，理论上应能加速 CMAB 的融合。然而，在实践中，我们对这些数据的基本生成过程几乎没有控制，这可能会表现出一些病态，可能会妨碍算法的收敛和稳定性。在本文中，我们主要关注两个主要的挑战: 偏差选择和数据损坏。我们提出了两种新的算法来解决这些具体问题: 具有历史数据和离线平衡的 LinUCB (OB-HLinUCB)和具有损坏历史数据的鲁棒 LinUCB (R-HLinUCB)。我们推导了它们的理论遗憾界限，并利用实际数据集讨论了它们的计算性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Robust+Stochastic+Multi-Armed+Bandits+with+Historical+Data)|0| |[Skill Graph Construction From Semantic Understanding](https://doi.org/10.1145/3543873.3587667)|Shiyong Lin, Yiping Yuan, Carol Jin, Yi Pan|LinkedIn, USA|LinkedIn is building a skill graph to power a skill-first talent marketplace. Constructing a skill graph from a flat list is not an trivial task, especially by human curation. In this paper, we leverage the pre-trained large language model BERT to achieve this through semantic understanding on synthetically generated texts as training data. We automatically create positive and negative labels from the seed skill graph. The training data are encoded by pre-trained language models into embeddings and they are consumed by the downstream classification module to classify the relationships between skill pairs.|LinkedIn 正在构建一个技能图表，为技能优先的人才市场提供动力。从一个平面列表中构建一个技能图表并不是一件微不足道的事情，尤其是对于人工管理来说。在本文中，我们利用预训练的大语言模型 BERT，通过对综合生成的文本作为训练数据的语义理解来实现这一点。我们自动从种子技能图中创建正面和负面的标签。训练数据通过预先训练的语言模型进行编码嵌入，然后由下游分类模块使用这些数据对技能对之间的关系进行分类。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Skill+Graph+Construction+From+Semantic+Understanding)|0| -|[Cultural Differences in Signed Ego Networks on Twitter: An Investigatory Analysis](https://doi.org/10.1145/3543873.3587641)|Jack Tacchi, Chiara Boldrini, Andrea Passarella, Marco Conti|Istituto di Informatica e Telematica - Consiglio Nazionale delle Ricerche, Italy and Scuola Normale Superiore, Italy; Istituto di Informatica e Telematica - Consiglio Nazionale delle Ricerche, Italy|Human social behaviour has been observed to adhere to certain structures. One such structure, the Ego Network Model (ENM), has been found almost ubiquitously in human society. Recently, this model has been extended to include signed connections. While the unsigned ENM has been rigorously observed for decades, the signed version is still somewhat novel and lacks the same breadth of observation. Therefore, the main aim of this paper is to examine this signed structure across various categories of individuals from a swathe of culturally distinct regions. Minor differences in the distribution of signs across the SENM can be observed between cultures. However, these can be overwhelmed when the network is centred around a specific topic. Indeed, users who are engaged with specific themes display higher levels of negativity in their networks. This effect is further supported by a significant negative correlation between the number of "general" topics discussed in a network and that network’s percentage of negative connections. These findings suggest that the negativity of communications and relationships on Twitter are very dependent on the topics being discussed and, furthermore, these relationships are more likely to be negative when they are based around a specific topic.|人类的社会行为已经被观察到遵循某些结构。其中一种结构，自我网络模型(ENM) ，在人类社会中几乎无处不在。最近，这个模型已经扩展到包括有符号连接。虽然未签名的 ENM 已经被严格观察了几十年，但是签名版本仍然有些新颖，缺乏同样广度的观察。因此，本文的主要目的是考察来自不同文化区域的不同类别的个体的这种符号结构。不同文化之间可以观察到在 SENM 中符号分布的细微差别。然而，当网络围绕某个特定主题时，这些问题可能会不堪重负。事实上，参与特定主题的用户在他们的网络中表现出更高水平的消极性。网络中讨论的“一般”主题的数量与网络负连接的百分比之间存在显著的负相关性，进一步支持了这种效应。这些发现表明，Twitter 上的交流和关系的消极性很大程度上取决于正在讨论的话题，而且，当这些关系围绕着一个特定的话题时，它们更有可能是消极的。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Cultural+Differences+in+Signed+Ego+Networks+on+Twitter:+An+Investigatory+Analysis)|0| -|[Don't Trust, Verify: The Case of Slashing from a Popular Ethereum Explorer](https://doi.org/10.1145/3543873.3587555)|Zhiguo He, Jiasun Li, Zhengxun Wu|Independent, USA; University of Chicago and NBER, USA; George Mason University, USA|Blockchain explorers are important tools for quick look-ups of on-chain activities. However, as centralized data providers, their reliability remains under-studied. As a case study, we investigate Beaconcha.in , a leading explorer serving Ethereum’s proof-of-stake (PoS) update. According to the explorer, we find that more than 75% of slashable Byzantine actions were not slashed. Since Ethereum relies on the “stake-and-slash" mechanism to align incentives, this finding would at its face value cause concern over Ethereum’s security. However, further investigation reveals that all the apparent unslashed incidents were erroneously recorded due to the explorer’s mishandling of consensus edge cases. Besides the usual message of using caution with centralized information providers, our findings also call for attention to improving the monitoring of blockchain systems that support high-value applications.|区块链探索器是快速查找链上活动的重要工具。然而，作为集中的数据提供者，它们的可靠性仍然没有得到充分的研究。作为一个案例研究，我们调查了 Beaconcha.in，它是一个领先的探索者，服务于以太坊的木桩证明(PoS)更新。根据探险家的说法，我们发现超过75% 的拜占庭式行为没有被砍掉。由于以太坊依靠“利害关系”机制来调整激励机制，这一发现从表面上看会引起人们对以太坊安全性的担忧。然而，进一步的调查表明，所有明显的未删除事件是错误的记录，由于探索者的错误处理共识边缘案件。除了对集中的信息提供者使用谨慎的通常信息之外，我们的研究结果还呼吁注意改进对支持高价值应用的区块链系统的监测。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Don't+Trust,+Verify:+The+Case+of+Slashing+from+a+Popular+Ethereum+Explorer)|0| +|[Cultural Differences in Signed Ego Networks on Twitter: An Investigatory Analysis](https://doi.org/10.1145/3543873.3587641)|Jack Tacchi, Chiara Boldrini, Andrea Passarella, Marco Conti|Istituto di Informatica e Telematica - Consiglio Nazionale delle Ricerche, Italy; Istituto di Informatica e Telematica - Consiglio Nazionale delle Ricerche, Italy and Scuola Normale Superiore, Italy|Human social behaviour has been observed to adhere to certain structures. One such structure, the Ego Network Model (ENM), has been found almost ubiquitously in human society. Recently, this model has been extended to include signed connections. While the unsigned ENM has been rigorously observed for decades, the signed version is still somewhat novel and lacks the same breadth of observation. Therefore, the main aim of this paper is to examine this signed structure across various categories of individuals from a swathe of culturally distinct regions. Minor differences in the distribution of signs across the SENM can be observed between cultures. However, these can be overwhelmed when the network is centred around a specific topic. Indeed, users who are engaged with specific themes display higher levels of negativity in their networks. This effect is further supported by a significant negative correlation between the number of "general" topics discussed in a network and that network’s percentage of negative connections. These findings suggest that the negativity of communications and relationships on Twitter are very dependent on the topics being discussed and, furthermore, these relationships are more likely to be negative when they are based around a specific topic.|人类的社会行为已经被观察到遵循某些结构。其中一种结构，自我网络模型(ENM) ，在人类社会中几乎无处不在。最近，这个模型已经扩展到包括有符号连接。虽然未签名的 ENM 已经被严格观察了几十年，但是签名版本仍然有些新颖，缺乏同样广度的观察。因此，本文的主要目的是考察来自不同文化区域的不同类别的个体的这种符号结构。不同文化之间可以观察到在 SENM 中符号分布的细微差别。然而，当网络围绕某个特定主题时，这些问题可能会不堪重负。事实上，参与特定主题的用户在他们的网络中表现出更高水平的消极性。网络中讨论的“一般”主题的数量与网络负连接的百分比之间存在显著的负相关性，进一步支持了这种效应。这些发现表明，Twitter 上的交流和关系的消极性很大程度上取决于正在讨论的话题，而且，当这些关系围绕着一个特定的话题时，它们更有可能是消极的。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Cultural+Differences+in+Signed+Ego+Networks+on+Twitter:+An+Investigatory+Analysis)|0| +|[Don't Trust, Verify: The Case of Slashing from a Popular Ethereum Explorer](https://doi.org/10.1145/3543873.3587555)|Zhiguo He, Jiasun Li, Zhengxun Wu|Independent, USA; George Mason University, USA; University of Chicago and NBER, USA|Blockchain explorers are important tools for quick look-ups of on-chain activities. However, as centralized data providers, their reliability remains under-studied. As a case study, we investigate Beaconcha.in , a leading explorer serving Ethereum’s proof-of-stake (PoS) update. According to the explorer, we find that more than 75% of slashable Byzantine actions were not slashed. Since Ethereum relies on the “stake-and-slash" mechanism to align incentives, this finding would at its face value cause concern over Ethereum’s security. However, further investigation reveals that all the apparent unslashed incidents were erroneously recorded due to the explorer’s mishandling of consensus edge cases. Besides the usual message of using caution with centralized information providers, our findings also call for attention to improving the monitoring of blockchain systems that support high-value applications.|区块链探索器是快速查找链上活动的重要工具。然而，作为集中的数据提供者，它们的可靠性仍然没有得到充分的研究。作为一个案例研究，我们调查了 Beaconcha.in，它是一个领先的探索者，服务于以太坊的木桩证明(PoS)更新。根据探险家的说法，我们发现超过75% 的拜占庭式行为没有被砍掉。由于以太坊依靠“利害关系”机制来调整激励机制，这一发现从表面上看会引起人们对以太坊安全性的担忧。然而，进一步的调查表明，所有明显的未删除事件是错误的记录，由于探索者的错误处理共识边缘案件。除了对集中的信息提供者使用谨慎的通常信息之外，我们的研究结果还呼吁注意改进对支持高价值应用的区块链系统的监测。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Don't+Trust,+Verify:+The+Case+of+Slashing+from+a+Popular+Ethereum+Explorer)|0| |[An Exploration on Cryptocurrency Corporations' Fiscal Opportunities](https://doi.org/10.1145/3543873.3587603)|Thomas Charest, Masarah PaquetClouston|School of Criminology, University of Montreal, Canada|As the decentralized finance industry gains traction, governments worldwide are creating or modifying legislations to regulate such financial activities. To avoid these new legislations, decentralized finance enterprises may shop for fiscally advantageous jurisdictions. This study explores global tax evasion opportunities for decentralized finance enterprises. Opportunities are identified by considering various jurisdictions’ tax laws on cryptocurrencies along with their corporate income tax rates, corporate capital gains tax rates, level of financial development and level of cryptocurrency adoption. They are visualized with the manifold approximation and projection for dimension reduction (UMAP) technique. The study results show that there exist a substantial number of tax evasion opportunities for decentralized finance enterprises through both traditional offshore jurisdictions and crypto-advantageous jurisdictions. The latter jurisdictions are usually considered high-tax fiscal regimes; but, given that they do not apply tax laws, tax evasion opportunities arise, especially in jurisdictions that have high financial development and high cryptocurrency adoption. Further research should investigate these new opportunities and how they are evolving. Understanding the global landscape surrounding tax evasion opportunities in decentralized finance represents a first step at preventing corporate capital flight of cryptocurrencies.|随着权力下放的金融业获得牵引力，世界各国政府正在制定或修订法律，以管制此类金融活动。为了避免这些新的立法，分散的金融企业可以选择财政上有利的司法管辖区。本研究探讨分散型金融企业的全球逃税机会。透过考虑不同地区有关加密货币的税务法例，以及其公司所得税税率、公司资本增值税税率、金融发展水平和加密货币的采用程度，我们可找出机会。它们是可视化的流形近似和投影维度减化(UMAP)技术。研究结果表明，无论是传统的离岸管辖区还是加密优势管辖区，分散的金融企业都有大量的逃税机会。后者通常被认为是高税收的财政体制; 但是，由于它们不适用税法，逃税机会就会出现，特别是在金融发展程度高、采用加密货币程度高的地区。进一步的研究应该调查这些新的机会以及它们是如何演变的。了解分散金融中逃税机会的全球环境，是防止加密货币企业资本外逃的第一步。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=An+Exploration+on+Cryptocurrency+Corporations'+Fiscal+Opportunities)|0| |[Improving the Exploration/Exploitation Trade-Off in Web Content Discovery](https://doi.org/10.1145/3543873.3587574)|Peter Schulam, Ion Muslea|Amazon Alexa, USA|New web content is published constantly, and although protocols such as RSS can notify subscribers of new pages, they are not always implemented or actively maintained. A more reliable way to discover new content is to periodically re-crawl the target sites. Designing such “content discovery crawlers” has important applications, for example, in web search, digital assistants, business, humanitarian aid, and law enforcement. Existing approaches assume that each site of interest has a relatively small set of unknown “source pages” that, when refreshed, frequently provide hyperlinks to the majority of new content. The state of the art (SOTA) uses ideas from the multi-armed bandit literature to explore candidate sources while simultaneously exploiting known good sources. We observe, however, that the SOTA uses a sub-optimal algorithm for balancing exploration and exploitation. We trace this back to a mismatch between the space of actions that the SOTA algorithm models and the space of actions that the crawler must actually choose from. Our proposed approach, the Thompson crawler (named after the Thompson sampler that drives its refresh decisions), addresses this shortcoming by more faithfully modeling the action space. On a dataset of 4,070 source pages drawn from 53 news domains over a period of 7 weeks, we show that, on average, the Thompson crawler discovers 20% more new pages, finds pages 6 hours earlier, and uses 14 fewer refreshes per 100 pages discovered than the SOTA.|新的 Web 内容不断地发布，尽管 RSS 等协议可以通知订阅者新的页面，但它们并不总是得到实现或积极维护。发现新内容的一种更可靠的方法是定期重新抓取目标站点。设计这样的“内容发现爬虫”有重要的应用，例如，在网络搜索，数字助理，商业，人道主义援助和执法。现有的方法假设每个感兴趣的站点都有一个相对较小的未知“源页面”集合，当刷新时，这些页面经常提供指向大多数新内容的超链接。国家的艺术(SOTA)使用来自多臂老虎机文学的想法来探索候选资源，同时利用已知的好资源。然而，我们观察到 SOTA 使用次优算法来平衡勘探和开发。我们追溯到 SOTA 算法建模的动作空间与爬虫必须实际选择的动作空间之间的不匹配。我们提出的方法是 Thompson 爬行器(以驱动其刷新决策的 Thompson 采样器命名) ，它通过更忠实地建模动作空间来解决这一缺陷。在7周内从53个新闻域抽取的4070个源页面的数据集中，我们发现，与 SOTA 相比，Thompson 爬虫平均多发现20% 的新页面，提前6小时发现页面，并且每发现100个页面少使用14次刷新。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Improving+the+Exploration/Exploitation+Trade-Off+in+Web+Content+Discovery)|0| -|[SoarGraph: Numerical Reasoning over Financial Table-Text Data via Semantic-Oriented Hierarchical Graphs](https://doi.org/10.1145/3543873.3587598)|Fengbin Zhu, Moxin Li, Junbin Xiao, Fuli Feng, Chao Wang, TatSeng Chua|University of Science and Technology of China, China; National University of Singapore, Singapore; 6ESTATES PTE LTD, Singapore|Towards the intelligent understanding of table-text data in the finance domain, previous research explores numerical reasoning over table-text content with Question Answering (QA) tasks. A general framework is to extract supporting evidence from the table and text and then perform numerical reasoning over extracted evidence for inferring the answer. However, existing models are vulnerable to missing supporting evidence, which limits their performance. In this work, we propose a novel Semantic-Oriented Hierarchical Graph (SoarGraph) that models the semantic relationships and dependencies among the different elements (e.g., question, table cells, text paragraphs, quantities, and dates) using hierarchical graphs to facilitate supporting evidence extraction and enhance numerical reasoning capability. We conduct our experiments on two popular benchmarks, FinQA and TAT-QA datasets, and the results show that our SoarGraph significantly outperforms all strong baselines, demonstrating remarkable effectiveness.|针对金融领域中表格文本数据的智能理解问题，以往的研究采用问答(QA)任务对表格文本内容进行数值推理。一个通用的框架是从表格和文本中提取支持证据，然后对提取的证据进行数值推理，从而推断出答案。然而，现有的模型容易失去支持证据，这限制了它们的性能。在这项工作中，我们提出了一种新的面向语义的层次图(SoarGraph) ，它使用层次图来模拟不同元素(如问题、表格单元、文本段落、数量和日期)之间的语义关系和依赖关系，以便于支持证据提取和增强数值推理能力。我们在 FinQA 和 TAT-QA 数据集上进行了实验，结果表明我们的 SoarGraph 显著优于所有强基线，显示出显著的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=SoarGraph:+Numerical+Reasoning+over+Financial+Table-Text+Data+via+Semantic-Oriented+Hierarchical+Graphs)|0| -|[Online to Offline Crossover of White Supremacist Propaganda](https://doi.org/10.1145/3543873.3587569)|Ahmad Diab, BolorErdene Jagdagdorj, Lynnette Hui Xian Ng, YuRu Lin, Michael Miller Yoder|University of Pittsburgh, USA; Carnegie Mellon University, USA|White supremacist extremist groups are a significant domestic terror threat in many Western nations. These groups harness the Internet to spread their ideology via online platforms: blogs, chat rooms, forums, and social media, which can inspire violence offline. In this work, we study the persistence and reach of white supremacist propaganda in both online and offline environments. We also study patterns in narratives that crossover from online to offline environments, or vice versa. From a geospatial analysis, we find that offline propaganda is geographically widespread in the United States, with a slight tendency toward Northeastern states. Propaganda that spreads the farthest and lasts the longest has a patriotic framing and is short, memorable, and repeatable. Through text comparison methods, we illustrate that online propaganda typically leads the appearance of the same propaganda in offline flyers, banners, and graffiti. We hope that this study sheds light on the characteristics of persistent white supremacist narratives both online and offline.|在许多西方国家，白人至上主义极端组织是一个重大的国内恐怖主义威胁。这些团体利用互联网通过在线平台传播他们的意识形态: 博客、聊天室、论坛和社交媒体，这些平台可以在线下煽动暴力。在这项工作中，我们研究了白人至上主义宣传在两个在线和离线环境中的持续性和影响范围。我们也研究从 Online To Offline线上到线下环境中交叉出来的叙述模式，反之亦然。通过地理空间分析，我们发现线下宣传在美国的地理位置上非常普遍，有轻微的美国东北部倾向。传播最广、持续时间最长的宣传具有爱国主义的框架，简短、令人难忘、可重复。通过文本比较的方法，我们说明了在线宣传通常会导致同样的宣传出现在线下传单、横幅和涂鸦中。我们希望这项研究能够揭示白人至上主义叙事的在线和离线。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Online+to+Offline+Crossover+of+White+Supremacist+Propaganda)|0| +|[SoarGraph: Numerical Reasoning over Financial Table-Text Data via Semantic-Oriented Hierarchical Graphs](https://doi.org/10.1145/3543873.3587598)|Fengbin Zhu, Moxin Li, Junbin Xiao, Fuli Feng, Chao Wang, TatSeng Chua|National University of Singapore, Singapore; 6ESTATES PTE LTD, Singapore; University of Science and Technology of China, China|Towards the intelligent understanding of table-text data in the finance domain, previous research explores numerical reasoning over table-text content with Question Answering (QA) tasks. A general framework is to extract supporting evidence from the table and text and then perform numerical reasoning over extracted evidence for inferring the answer. However, existing models are vulnerable to missing supporting evidence, which limits their performance. In this work, we propose a novel Semantic-Oriented Hierarchical Graph (SoarGraph) that models the semantic relationships and dependencies among the different elements (e.g., question, table cells, text paragraphs, quantities, and dates) using hierarchical graphs to facilitate supporting evidence extraction and enhance numerical reasoning capability. We conduct our experiments on two popular benchmarks, FinQA and TAT-QA datasets, and the results show that our SoarGraph significantly outperforms all strong baselines, demonstrating remarkable effectiveness.|针对金融领域中表格文本数据的智能理解问题，以往的研究采用问答(QA)任务对表格文本内容进行数值推理。一个通用的框架是从表格和文本中提取支持证据，然后对提取的证据进行数值推理，从而推断出答案。然而，现有的模型容易失去支持证据，这限制了它们的性能。在这项工作中，我们提出了一种新的面向语义的层次图(SoarGraph) ，它使用层次图来模拟不同元素(如问题、表格单元、文本段落、数量和日期)之间的语义关系和依赖关系，以便于支持证据提取和增强数值推理能力。我们在 FinQA 和 TAT-QA 数据集上进行了实验，结果表明我们的 SoarGraph 显著优于所有强基线，显示出显著的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=SoarGraph:+Numerical+Reasoning+over+Financial+Table-Text+Data+via+Semantic-Oriented+Hierarchical+Graphs)|0| +|[Online to Offline Crossover of White Supremacist Propaganda](https://doi.org/10.1145/3543873.3587569)|Ahmad Diab, BolorErdene Jagdagdorj, Lynnette Hui Xian Ng, YuRu Lin, Michael Miller Yoder|Carnegie Mellon University, USA; University of Pittsburgh, USA|White supremacist extremist groups are a significant domestic terror threat in many Western nations. These groups harness the Internet to spread their ideology via online platforms: blogs, chat rooms, forums, and social media, which can inspire violence offline. In this work, we study the persistence and reach of white supremacist propaganda in both online and offline environments. We also study patterns in narratives that crossover from online to offline environments, or vice versa. From a geospatial analysis, we find that offline propaganda is geographically widespread in the United States, with a slight tendency toward Northeastern states. Propaganda that spreads the farthest and lasts the longest has a patriotic framing and is short, memorable, and repeatable. Through text comparison methods, we illustrate that online propaganda typically leads the appearance of the same propaganda in offline flyers, banners, and graffiti. We hope that this study sheds light on the characteristics of persistent white supremacist narratives both online and offline.|在许多西方国家，白人至上主义极端组织是一个重大的国内恐怖主义威胁。这些团体利用互联网通过在线平台传播他们的意识形态: 博客、聊天室、论坛和社交媒体，这些平台可以在线下煽动暴力。在这项工作中，我们研究了白人至上主义宣传在两个在线和离线环境中的持续性和影响范围。我们也研究从 Online To Offline线上到线下环境中交叉出来的叙述模式，反之亦然。通过地理空间分析，我们发现线下宣传在美国的地理位置上非常普遍，有轻微的美国东北部倾向。传播最广、持续时间最长的宣传具有爱国主义的框架，简短、令人难忘、可重复。通过文本比较的方法，我们说明了在线宣传通常会导致同样的宣传出现在线下传单、横幅和涂鸦中。我们希望这项研究能够揭示白人至上主义叙事的在线和离线。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Online+to+Offline+Crossover+of+White+Supremacist+Propaganda)|0| |[Privacy-Preserving Online Content Moderation with Federated Learning](https://doi.org/10.1145/3543873.3587366)|Pantelitsa Leonidou, Nicolas Kourtellis, Nikos Salamanos, Michael Sirivianos|Cyprus University of Technology, Cyprus; Telefonica Research, Spain|Users are daily exposed to a large volume of harmful content on various social network platforms. One solution is developing online moderation tools using Machine Learning techniques. However, the processing of user data by online platforms requires compliance with privacy policies. Federated Learning (FL) is an ML paradigm where the training is performed locally on the users' devices. Although the FL framework complies, in theory, with the GDPR policies, privacy leaks can still occur. For instance, an attacker accessing the final trained model can successfully perform unwanted inference of the data belonging to the users who participated in the training process. In this paper, we propose a privacy-preserving FL framework for online content moderation that incorporates Differential Privacy (DP). To demonstrate the feasibility of our approach, we focus on detecting harmful content on Twitter - but the overall concept can be generalized to other types of misbehavior. We simulate a text classifier - in FL fashion - which can detect tweets with harmful content. We show that the performance of the proposed FL framework can be close to the centralized approach - for both the DP and non-DP FL versions. Moreover, it has a high performance even if a small number of clients (each with a small number of data points) are available for the FL training. When reducing the number of clients (from 50 to 10) or the data points per client (from 1K to 0.1K), the classifier can still achieve ~81% AUC. Furthermore, we extend the evaluation to four other Twitter datasets that capture different types of user misbehavior and still obtain a promising performance (61% - 80% AUC). Finally, we explore the overhead on the users' devices during the FL training phase and show that the local training does not introduce excessive CPU utilization and memory consumption overhead.|用户每天都会在各种社交网络平台上接触到大量的有害内容。一个解决方案是使用机器学习技术开发在线审核工具。然而，通过在线平台处理用户数据需要遵守隐私策略。联邦学习(FL)是一种机器学习范式，其中的培训是在用户的设备上本地执行的。尽管 FL 框架在理论上符合 GDPR 策略，但仍然可能发生隐私泄露。例如，访问最终训练模型的攻击者可以成功地对属于参与训练过程的用户的数据进行不必要的推断。在这篇文章中，我们提出了一个保护隐私的在线内容审查框架，该框架结合了差分隐私(DP)。为了证明我们方法的可行性，我们将重点放在检测 Twitter 上的有害内容上——但总体概念可以推广到其他类型的不当行为。我们模拟了一个文本分类器——以 FL 的方式——它可以检测带有有害内容的 tweet。我们展示了所提出的 FL 框架的性能可以接近于集中式方法——对于 DP 和非 DP FL 版本都是如此。此外，即使只有少量的客户端(每个客户端都有少量的数据点)可用于 FL 培训，它也具有很高的性能。当减少客户端数量(从50到10)或每个客户端的数据点数量(从1K 到0.1 K)时，分类器仍然可以达到约81% 的 AUC。此外，我们将评估扩展到其他四个 Twitter 数据集，这些数据集捕获不同类型的用户不当行为，并仍然获得有希望的性能(61% -80% AUC)。最后，我们研究了 FL 训练阶段用户设备上的开销，结果表明本地训练不会引入过多的 CPU 利用率和内存消耗开销。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Privacy-Preserving+Online+Content+Moderation+with+Federated+Learning)|0| |[Graph-based Approach for Studying Spread of Radical Online Sentiment](https://doi.org/10.1145/3543873.3587634)|Le Nguyen, Nidhi Rastogi|Golisano College of Computing and Information Science, Department of Software Engineering, Rochester Institute of Technology, USA|The spread of radicalization through the Internet is a growing problem. We are witnessing a rise in online hate groups, inspiring the impressionable and vulnerable population towards extreme actions in the real world. In this paper, we study the spread of hate sentiments in online forums by collecting 1,973 long comment threads (30+ comments per thread) posted on dark-web forums and containing a combination of benign posts and radical comments on the Islamic religion. This framework allows us to leverage network analysis tools to investigate sentiment propagation through a social network. By combining sentiment analysis, social network analysis, and graph theory, we aim to shed light on the propagation of hate speech in online forums and the extent to which such speech can influence individuals. The results of the intra-thread analysis suggests that sentiment tends to cluster within comment threads, with around 75% of connected members sharing similar sentiments. They also indicate that online forums can act as echo chambers where people with similar views reinforce each other’s beliefs and opinions. On the other hand, the inter-thread shows that 64% of connected threads share similar sentiments, suggesting similarities between the ideologies present in different threads and that there likely is a wider network of individuals spreading hate speech across different forums. Finally, we plan to study this work with a larger dataset, which could provide further insights into the spread of hate speech in online forums and how to mitigate it.|通过互联网传播激进主义是一个日益严重的问题。我们正在目睹网上仇恨团体的增加，鼓励易受影响的弱势群体在现实世界中采取极端行动。在这篇论文中，我们通过收集在暗网论坛上发布的1973条长评论(每条评论超过30条) ，以及包含对伊斯兰教的善意帖子和激进评论的组合，来研究在网络论坛上仇恨情绪的传播。这个框架允许我们利用网络分析工具来调查通过社交网络的情感传播。通过结合情绪分析、社会网络分析和图论，我们旨在阐明仇恨言论在网络论坛中的传播以及这种言论对个人的影响程度。内部线程分析的结果表明，情绪倾向于聚集在评论线程中，大约75% 的连接成员共享类似的情绪。他们还指出，在线论坛可以充当回音室，在这里，持有相似观点的人们可以相互加强信念和观点。另一方面，线程之间显示，64% 的连接线程有相似的情感，表明存在于不同线程的意识形态之间的相似性，并且可能有一个更广泛的个人网络在不同的论坛上传播仇恨言论。最后，我们计划利用一个更大的数据集来研究这项工作，它可以为在线论坛中仇恨言论的传播以及如何减轻这种传播提供进一步的见解。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Graph-based+Approach+for+Studying+Spread+of+Radical+Online+Sentiment)|0| -|[Swinging in the States: Does disinformation on Twitter mirror the US presidential election system?](https://doi.org/10.1145/3543873.3587638)|Manuel Pratelli, Marinella Petrocchi, Fabio Saracco, Rocco De Nicola|“Enrico Fermi” Research Center (CREF), Italy and IMT - School for Advanced Studies Lucca, Italy; IMT - School for Advanced Studies Lucca, Italy and Istituto di Informatica e Telematica (IIT) CNR, Italy; Istituto di Informatica e Telematica (IIT) CNR, Italy and IMT - School for Advanced Studies Lucca, Italy; IMT - School for Advanced Studies Lucca, Italy|For more than a decade scholars have been investigating the disinformation flow on social media contextually to societal events, like, e.g., elections. In this paper, we analyze the Twitter traffic related to the US 2020 pre-election debate and ask whether it mirrors the electoral system. The U.S. electoral system provides that, regardless of the actual vote gap, the premier candidate who received more votes in one state `takes' that state. Criticisms of this system have pointed out that election campaigns can be more intense in particular key states to achieve victory, so-called {\it swing states}. Our intuition is that election debate may cause more traffic on Twitter-and probably be more plagued by misinformation-when associated with swing states. The results mostly confirm the intuition. About 88\% of the entire traffic can be associated with swing states, and links to non-trustworthy news are shared far more in swing-related traffic than the same type of news in safe-related traffic. Considering traffic origin instead, non-trustworthy tweets generated by automated accounts, so-called social bots, are mostly associated with swing states. Our work sheds light on the role an electoral system plays in the evolution of online debates, with, in the spotlight, disinformation and social bots.|十多年来，学者们一直在研究社交媒体上的虚假信息与社会事件的关系，比如选举。在本文中，我们分析了与美国2020年大选前辩论有关的 Twitter 流量，并询问它是否反映了选举制度。美国的选举制度规定，不管实际的选票差距如何，在一个州获得更多选票的总理候选人“接受”该州。对这种制度的批评指出，竞选活动可以更加激烈，在特定的关键州取得胜利，所谓的“摇摆州”。我们的直觉是，当选举辩论与摇摆州联系在一起时，可能会在 Twitter 上引起更多流量，而且可能更容易受到错误信息的困扰。结果大多证实了直觉。大约88% 的流量可以与摇摆州联系起来，与安全相关的流量相比，与摇摆州相关的流量中分享的不可信新闻的链接要多得多。相反，考虑到流量来源，由自动账户(即所谓的社交机器人)生成的不可信的 tweet 大多与摇摆州有关。我们的工作揭示了选举系统在网络辩论演变中所扮演的角色，聚光灯下是虚假信息和社交机器人。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Swinging+in+the+States:+Does+disinformation+on+Twitter+mirror+the+US+presidential+election+system?)|0| +|[Swinging in the States: Does disinformation on Twitter mirror the US presidential election system?](https://doi.org/10.1145/3543873.3587638)|Manuel Pratelli, Marinella Petrocchi, Fabio Saracco, Rocco De Nicola|“Enrico Fermi” Research Center (CREF), Italy and IMT - School for Advanced Studies Lucca, Italy; Istituto di Informatica e Telematica (IIT) CNR, Italy and IMT - School for Advanced Studies Lucca, Italy; IMT - School for Advanced Studies Lucca, Italy and Istituto di Informatica e Telematica (IIT) CNR, Italy; IMT - School for Advanced Studies Lucca, Italy|For more than a decade scholars have been investigating the disinformation flow on social media contextually to societal events, like, e.g., elections. In this paper, we analyze the Twitter traffic related to the US 2020 pre-election debate and ask whether it mirrors the electoral system. The U.S. electoral system provides that, regardless of the actual vote gap, the premier candidate who received more votes in one state `takes' that state. Criticisms of this system have pointed out that election campaigns can be more intense in particular key states to achieve victory, so-called {\it swing states}. Our intuition is that election debate may cause more traffic on Twitter-and probably be more plagued by misinformation-when associated with swing states. The results mostly confirm the intuition. About 88\% of the entire traffic can be associated with swing states, and links to non-trustworthy news are shared far more in swing-related traffic than the same type of news in safe-related traffic. Considering traffic origin instead, non-trustworthy tweets generated by automated accounts, so-called social bots, are mostly associated with swing states. Our work sheds light on the role an electoral system plays in the evolution of online debates, with, in the spotlight, disinformation and social bots.|十多年来，学者们一直在研究社交媒体上的虚假信息与社会事件的关系，比如选举。在本文中，我们分析了与美国2020年大选前辩论有关的 Twitter 流量，并询问它是否反映了选举制度。美国的选举制度规定，不管实际的选票差距如何，在一个州获得更多选票的总理候选人“接受”该州。对这种制度的批评指出，竞选活动可以更加激烈，在特定的关键州取得胜利，所谓的“摇摆州”。我们的直觉是，当选举辩论与摇摆州联系在一起时，可能会在 Twitter 上引起更多流量，而且可能更容易受到错误信息的困扰。结果大多证实了直觉。大约88% 的流量可以与摇摆州联系起来，与安全相关的流量相比，与摇摆州相关的流量中分享的不可信新闻的链接要多得多。相反，考虑到流量来源，由自动账户(即所谓的社交机器人)生成的不可信的 tweet 大多与摇摆州有关。我们的工作揭示了选举系统在网络辩论演变中所扮演的角色，聚光灯下是虚假信息和社交机器人。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Swinging+in+the+States:+Does+disinformation+on+Twitter+mirror+the+US+presidential+election+system?)|0| |[Analyzing Activity and Suspension Patterns of Twitter Bots Attacking Turkish Twitter Trends by a Longitudinal Dataset](https://doi.org/10.1145/3543873.3587650)|Tugrulcan Elmas|Indiana University Bloomington, USA|Twitter bots amplify target content in a coordinated manner to make them appear popular, which is an astroturfing attack. Such attacks promote certain keywords to push them to Twitter trends to make them visible to a broader audience. Past work on such fake trends revealed a new astroturfing attack named ephemeral astroturfing that employs a very unique bot behavior in which bots post and delete generated tweets in a coordinated manner. As such, it is easy to mass-annotate such bots reliably, making them a convenient source of ground truth for bot research. In this paper, we detect and disclose over 212,000 such bots targeting Turkish trends, which we name astrobots. We also analyze their activity and suspension patterns. We found that Twitter purged those bots en-masse 6 times since June 2018. However, the adversaries reacted quickly and deployed new bots that were created years ago. We also found that many such bots do not post tweets apart from promoting fake trends, which makes it challenging for bot detection methods to detect them. Our work provides insights into platforms' content moderation practices and bot detection research. The dataset is publicly available at https://github.com/tugrulz/EphemeralAstroturfing.|Twitter 机器人以一种协调的方式放大目标内容，让它们看起来很受欢迎，这是一种草根攻击。这样的攻击促进了某些关键词，将它们推向 Twitter 趋势，使它们对更广泛的受众显而易见。过去对这种虚假趋势的研究揭示了一种名为“短暂星空草坪”的新型星空草坪攻击，该攻击采用了一种非常独特的机器人行为，机器人以协调的方式发布和删除生成的推文。因此，很容易可靠地对这些机器人进行大量注释，使它们成为机器人研究的一个方便的地面真相来源。在这篇论文中，我们发现并揭露了超过212,000个这样的机器人瞄准了土耳其的趋势，我们将其命名为“太空机器人”。我们还分析了它们的活动和悬浮模式。我们发现，自2018年6月以来，Twitter 共清除了这些机器人6次。然而，对手反应迅速，部署了多年前创建的新机器人。我们还发现，许多这样的机器人除了推广虚假趋势之外，不会发布推文，这使得机器人检测方法很难检测到它们。我们的工作为平台的内容审核实践和机器人检测研究提供了见解。该数据集可在 https://github.com/tugrulz/ephemeralastroturfing 公开获取。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Analyzing+Activity+and+Suspension+Patterns+of+Twitter+Bots+Attacking+Turkish+Twitter+Trends+by+a+Longitudinal+Dataset)|0| |[Socio-Emotional Computational Analysis of Propaganda Campaigns on Social Media Users in the Middle East](https://doi.org/10.1145/3543873.3587677)|Zain A. Halloush, Ahmed Aleroud, Craig Albert|Pamplin College of Arts, Humanities, and Social Sciences, Augusta University, USA; School of Computer and Cyber Sciences, Augusta University, Augusta University, USA|Society has been significantly impacted by social media platforms in almost every aspect of their life. This impact has been effectively formulating people’s global mindsets and opinions on political, economic, and social events. Such waves of opinion formation are referred to as propagandas and misinformation. Online propaganda influences the emotional and psychological orientation of people. The remarkable leaps in Machine Learning models and Natural Language Processing have helped in analyzing the emotional and psychological effects of cyber social threats such as propaganda campaigns on different nations, specifically in the Middle East, where rates of disputes have risen after the Arab Spring and the ongoing crises. In this paper, we present an approach to detect propagandas and the associated emotional and psychological aspects from social media news headlines that contain such a contextualized cyber social attack. We created a new dataset of headlines containing propaganda tweets and another dataset of potential emotions that the audience might endure when being exposed to such propaganda headlines. We believe that this is the first research to address the detection of emotional reactions linked to propaganda types on social media in the Middle East.|社会几乎在其生活的各个方面都受到社交媒体平台的显著影响。这种影响有效地形成了人们对政治、经济和社会事件的全球心态和观点。这种形成意见的浪潮被称为宣传和错误信息。网络宣传影响着人们的情感和心理取向。机器学习模型和自然语言处理的显著飞跃有助于分析网络社会威胁的情感和心理影响，例如针对不同国家的宣传活动，特别是在中东，在阿拉伯之春和持续的危机之后，那里的争端比率已经上升。在本文中，我们提出了一种方法来检测宣传和相关的情绪和心理方面的社会媒体新闻标题，其中包含这样一个情境化的网络社会攻击。我们创建了一个新的标题数据集，其中包含宣传推文和另一个潜在情绪数据集，观众在接触这些宣传标题时可能会忍受这些情绪。我们认为，这是第一次研究如何在中东社交媒体上发现与宣传类型相关的情绪反应。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Socio-Emotional+Computational+Analysis+of+Propaganda+Campaigns+on+Social+Media+Users+in+the+Middle+East)|0| |[Towards a Semantic Approach for Linked Dataspace, Model and Data Cards](https://doi.org/10.1145/3543873.3587659)|Andy Donald, Apostolos Galanopoulos, Edward Curry, Emir Muñoz, Ihsan Ullah, M. A. Waskow, Maciej Dabrowski, Manan Kalra|Genesys Cloud Services Inc., Bonham Quay, Galway, Ireland, Ireland; Insight SFI Centre for Data Analytics, Data Science Institute, University of Galway, Galway, Ireland, Ireland|The vast majority of artificial intelligence practitioners overlook the importance of documentation when building and publishing models and datasets. However, due to the recent trend in the explainability and fairness of AI models, several frameworks have been proposed such as Model Cards, and Data Cards, among others, to help in the appropriate re-usage of those models and datasets. In addition, because of the introduction of the dataspace concept for similar datasets in one place, there is potential that similar Model Cards, Data Cards, Service Cards, and Dataspace Cards can be linked to extract helpful information for better decision-making about which model and data can be used for a specific application. This paper reviews the case for considering a Semantic Web approach for exchanging Model/Data Cards as Linked Data or knowledge graphs in a dataspace, making them machine-readable. We discuss the basic concepts and propose a schema for linking Data Cards and Model Cards within a dataspace. In addition, we introduce the concept of a dataspace card which can be a starting point for extracting knowledge about models and datasets in a dataspace. This helps in building trust and reuse of models and data among companies and individuals participating as publishers or consumers of such assets.|绝大多数人工智能从业者在构建和发布模型和数据集时忽视了文档的重要性。然而，由于人工智能模型的可解释性和公平性最近的趋势，已经提出了几个框架，如模型卡和数据卡等，以帮助这些模型和数据集的适当重用。此外，由于在一个地方引入了类似数据集的数据空间概念，可以将类似的模型卡、数据卡、服务卡和数据空间卡联系起来，提取有用的信息，以便更好地决定哪些模型和数据可用于特定应用。本文回顾了在数据空间中考虑语义 Web 方法将模型/数据卡交换为链接数据或知识图的情况，从而使它们具有机器可读性。我们讨论了基本概念，并提出了一个在数据空间中连接数据卡和模型卡的模式。此外，我们还介绍了数据空间卡的概念，它可以作为提取数据空间中模型和数据集知识的起点。这有助于在作为此类资产的发布者或消费者参与的公司和个人之间建立模型和数据的信任和重用。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Towards+a+Semantic+Approach+for+Linked+Dataspace,+Model+and+Data+Cards)|0| -|[Semantics in Dataspaces: Origin and Future Directions](https://doi.org/10.1145/3543873.3587689)|Johannes TheissenLipp, Max Kocher, Christoph Lange, Stefan Decker, Alexander Paulus, André Pomp, Edward Curry|RWTH Aachen University, Germany; University of Galway, Ireland; University of Wuppertal, Germany; RWTH Aachen University, Germany and Fraunhofer Institute for Applied Information Technology FIT, Germany|The term dataspace was coined two decades ago [12] and has evolved since then. Definitions range from (i) an abstraction for data management in an identifiable scope [15] over (iii) a multi-sided data platform connecting participants in an ecosystem [21] to (iii) interlinking data towards loosely connected (global) information [17]. Many implementations and scientific notions follow different interpretations of the term dataspace, but agree on some use of semantic technologies. For example, dataspaces such as the European Open Science Cloud and the German National Research Data Infrastructure are committed to applying the FAIR principles [11, 16]. Dataspaces built on top of Gaia-X are using semantic methods for service Self-Descriptions [13]. This paper investigates ongoing dataspace efforts and aims to provide insights on the definition of the term dataspace, the usage of semantics and FAIR principles, and future directions for the role of semantics in dataspaces.|数据空间这个术语是在20年前创造出来的，并且从那时起一直在进化。定义范围从(i)可识别范围内的数据管理的抽象[15]到(iii)连接生态系统参与者的多边数据平台[21]到(iii)将数据互联到松散连接的(全球)信息[17]。许多实现和科学概念遵循对数据空间这一术语的不同解释，但对语义技术的某些使用达成了一致。例如，欧洲开放科学云和德国国家研究数据基础设施等数据空间致力于应用 FAIR 原则[11,16]。建立在 Gaia-X 之上的数据空间正在使用语义方法进行服务自我描述[13]。本文研究了正在进行的数据空间工作，旨在提供对术语数据空间的定义、语义和 FAIR 原则的使用以及语义在数据空间中的作用的未来方向的见解。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Semantics+in+Dataspaces:+Origin+and+Future+Directions)|0| +|[Semantics in Dataspaces: Origin and Future Directions](https://doi.org/10.1145/3543873.3587689)|Johannes TheissenLipp, Max Kocher, Christoph Lange, Stefan Decker, Alexander Paulus, André Pomp, Edward Curry|University of Galway, Ireland; University of Wuppertal, Germany; RWTH Aachen University, Germany; RWTH Aachen University, Germany and Fraunhofer Institute for Applied Information Technology FIT, Germany|The term dataspace was coined two decades ago [12] and has evolved since then. Definitions range from (i) an abstraction for data management in an identifiable scope [15] over (iii) a multi-sided data platform connecting participants in an ecosystem [21] to (iii) interlinking data towards loosely connected (global) information [17]. Many implementations and scientific notions follow different interpretations of the term dataspace, but agree on some use of semantic technologies. For example, dataspaces such as the European Open Science Cloud and the German National Research Data Infrastructure are committed to applying the FAIR principles [11, 16]. Dataspaces built on top of Gaia-X are using semantic methods for service Self-Descriptions [13]. This paper investigates ongoing dataspace efforts and aims to provide insights on the definition of the term dataspace, the usage of semantics and FAIR principles, and future directions for the role of semantics in dataspaces.|数据空间这个术语是在20年前创造出来的，并且从那时起一直在进化。定义范围从(i)可识别范围内的数据管理的抽象[15]到(iii)连接生态系统参与者的多边数据平台[21]到(iii)将数据互联到松散连接的(全球)信息[17]。许多实现和科学概念遵循对数据空间这一术语的不同解释，但对语义技术的某些使用达成了一致。例如，欧洲开放科学云和德国国家研究数据基础设施等数据空间致力于应用 FAIR 原则[11,16]。建立在 Gaia-X 之上的数据空间正在使用语义方法进行服务自我描述[13]。本文研究了正在进行的数据空间工作，旨在提供对术语数据空间的定义、语义和 FAIR 原则的使用以及语义在数据空间中的作用的未来方向的见解。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Semantics+in+Dataspaces:+Origin+and+Future+Directions)|0| |[Efficient Sampling for Big Provenance](https://doi.org/10.1145/3543873.3587556)|Sara Moshtaghi Largani, Seokki Lee|University of Cincinnati, USA|Provenance has been studied extensively to explain existing and missing results for many applications while focusing on scalability and usability challenges. Recently, techniques that efficiently compute a compact representation of provenance have been introduced. In this work, we introduce a practical solution that computes a sample of provenance for existing results without computing full provenance. Our technique computes a sample of provenance based on the distribution of provenance wrt the query result that is estimated from the distribution of input data while considering the correlation among the data. The preliminary evaluation demonstrates that comparing to the naive approach our method efficiently computes a sample of (large size of) provenance with low errors.|起源已被广泛研究，以解释许多应用程序的现有结果和缺失结果，同时关注可伸缩性和可用性方面的挑战。最近，有效地计算种源的紧凑表示的技术已经被引入。在这项工作中，我们介绍了一个实用的解决方案，计算现有结果的来源样本，而不计算完整的来源。该方法在考虑数据间相关性的基础上，根据来源分布计算出一个来源样本，并根据输入数据的分布估计出查询结果。初步评估表明，与初始方法相比，我们的方法有效地计算了一个样本(大规模)来源低误差。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Efficient+Sampling+for+Big+Provenance)|0| |[Provenance Tracking for End-to-End Machine Learning Pipelines](https://doi.org/10.1145/3543873.3587557)|Stefan Grafberger, Paul Groth, Sebastian Schelter|University of Amsterdam, Netherlands|No abstract available.|没有摘要。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Provenance+Tracking+for+End-to-End+Machine+Learning+Pipelines)|0| -|[SeeGera: Self-supervised Semi-implicit Graph Variational Auto-encoders with Masking](https://doi.org/10.1145/3543507.3583245)|Xiang Li, Tiandi Ye, Caihua Shan, Dongsheng Li, Ming Gao|Microsoft Research Asia, China; East China Normal University, China|Generative graph self-supervised learning (SSL) aims to learn node representations by reconstructing the input graph data. However, most existing methods focus on unsupervised learning tasks only and very few work has shown its superiority over the state-of-the-art graph contrastive learning (GCL) models, especially on the classification task. While a very recent model has been proposed to bridge the gap, its performance on unsupervised learning tasks is still unknown. In this paper, to comprehensively enhance the performance of generative graph SSL against other GCL models on both unsupervised and supervised learning tasks, we propose the SeeGera model, which is based on the family of self-supervised variational graph auto-encoder (VGAE). Specifically, SeeGera adopts the semi-implicit variational inference framework, a hierarchical variational framework, and mainly focuses on feature reconstruction and structure/feature masking. On the one hand, SeeGera co-embeds both nodes and features in the encoder and reconstructs both links and features in the decoder. Since feature embeddings contain rich semantic information on features, they can be combined with node embeddings to provide fine-grained knowledge for feature reconstruction. On the other hand, SeeGera adds an additional layer for structure/feature masking to the hierarchical variational framework, which boosts the model generalizability. We conduct extensive experiments comparing SeeGera with 9 other state-of-the-art competitors. Our results show that SeeGera can compare favorably against other state-of-the-art GCL methods in a variety of unsupervised and supervised learning tasks.|生成图自监督学习(SSL)的目的是通过重构输入图数据来学习节点表示。然而，大多数现有的方法只关注非监督式学习任务，很少有研究显示其优于最先进的图形对比学习(gCL)模型，特别是在分类任务上。虽然最近有人提出了一个模型来弥补这一差距，但它在非监督式学习任务上的表现仍然是未知的。为了全面提高生成图 SSL 在无监督任务和监督式学习任务中相对于其他 GCL 模型的性能，我们提出了基于自监督变分图自动编码器(vgAE)系列的 SeeGera 模型。具体来说，SeeGera 采用了半隐式变分推理框架，即层次变分框架，主要研究特征重构和结构/特征掩蔽。一方面，SeeGera 在编码器中共同嵌入节点和特征，并在解码器中重构链路和特征。由于特征嵌入包含丰富的特征语义信息，因此它们可以与节点嵌入相结合，为特征重构提供细粒度的知识。另一方面，SeeGera 为层次变分框架增加了一个结构/特征屏蔽层，从而提高了模型的通用性。我们进行了广泛的实验比较 SeeGera 与其他9个国家的最先进的竞争对手。我们的研究结果表明，SeeGera 可以在各种无监督和无监督式学习的任务中与其他最先进的 GCL 方法相比较。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=SeeGera:+Self-supervised+Semi-implicit+Graph+Variational+Auto-encoders+with+Masking)|0| +|[SeeGera: Self-supervised Semi-implicit Graph Variational Auto-encoders with Masking](https://doi.org/10.1145/3543507.3583245)|Xiang Li, Tiandi Ye, Caihua Shan, Dongsheng Li, Ming Gao|East China Normal University, China; Microsoft Research Asia, China|Generative graph self-supervised learning (SSL) aims to learn node representations by reconstructing the input graph data. However, most existing methods focus on unsupervised learning tasks only and very few work has shown its superiority over the state-of-the-art graph contrastive learning (GCL) models, especially on the classification task. While a very recent model has been proposed to bridge the gap, its performance on unsupervised learning tasks is still unknown. In this paper, to comprehensively enhance the performance of generative graph SSL against other GCL models on both unsupervised and supervised learning tasks, we propose the SeeGera model, which is based on the family of self-supervised variational graph auto-encoder (VGAE). Specifically, SeeGera adopts the semi-implicit variational inference framework, a hierarchical variational framework, and mainly focuses on feature reconstruction and structure/feature masking. On the one hand, SeeGera co-embeds both nodes and features in the encoder and reconstructs both links and features in the decoder. Since feature embeddings contain rich semantic information on features, they can be combined with node embeddings to provide fine-grained knowledge for feature reconstruction. On the other hand, SeeGera adds an additional layer for structure/feature masking to the hierarchical variational framework, which boosts the model generalizability. We conduct extensive experiments comparing SeeGera with 9 other state-of-the-art competitors. Our results show that SeeGera can compare favorably against other state-of-the-art GCL methods in a variety of unsupervised and supervised learning tasks.|生成图自监督学习(SSL)的目的是通过重构输入图数据来学习节点表示。然而，大多数现有的方法只关注非监督式学习任务，很少有研究显示其优于最先进的图形对比学习(gCL)模型，特别是在分类任务上。虽然最近有人提出了一个模型来弥补这一差距，但它在非监督式学习任务上的表现仍然是未知的。为了全面提高生成图 SSL 在无监督任务和监督式学习任务中相对于其他 GCL 模型的性能，我们提出了基于自监督变分图自动编码器(vgAE)系列的 SeeGera 模型。具体来说，SeeGera 采用了半隐式变分推理框架，即层次变分框架，主要研究特征重构和结构/特征掩蔽。一方面，SeeGera 在编码器中共同嵌入节点和特征，并在解码器中重构链路和特征。由于特征嵌入包含丰富的特征语义信息，因此它们可以与节点嵌入相结合，为特征重构提供细粒度的知识。另一方面，SeeGera 为层次变分框架增加了一个结构/特征屏蔽层，从而提高了模型的通用性。我们进行了广泛的实验比较 SeeGera 与其他9个国家的最先进的竞争对手。我们的研究结果表明，SeeGera 可以在各种无监督和无监督式学习的任务中与其他最先进的 GCL 方法相比较。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=SeeGera:+Self-supervised+Semi-implicit+Graph+Variational+Auto-encoders+with+Masking)|0| |[Lightweight source localization for large-scale social networks](https://doi.org/10.1145/3543507.3583299)|Zhen Wang, Dongpeng Hou, Chao Gao, Xiaoyu Li, Xuelong Li|Northwestern Polytechnical University, China; School of Artificial Intelligence, Optics and Electronics (iOPEN), Northwestern Polytechnical University, China|The rapid diffusion of hazardous information in large-flow-based social media causes great economic losses and potential threats to society. It is crucial to infer the inner information source as early as possible to prevent further losses. However, existing localization methods wait until all deployed sensors obtain propagation information before starting source inference within a network, and hence the best opportunity to control propagation is missed. In this paper, we propose a new localization strategy based on finite deployed sensors, named Greedy-coverage-based Rapid Source Localization (GRSL), to rapidly, flexibly and accurately infer the source in the early propagation stage of large-scale networks. There are two phases in GRSL. In the first phase, the Greedy-based Strategy (GS) greedily deploys sensors to rapidly achieve wide area coverage at a low cost. In the second phase, when a propagation event within a network is observed by a part of the sensors, the Inference Strategy (IS) with an earlier response mechanism begins executing the source inference task in an earlier small infected area. Comprehensive experiments with the SOTA methods demonstrate the superior performance and robustness of GRSL in various application scenarios.|危险信息在基于大流量的社交媒体上的快速传播给社会造成了巨大的经济损失和潜在的威胁。为了防止进一步的损失，尽早推断出内部信息源至关重要。然而，现有的定位方法要等到所有部署的传感器获得传播信息后，才能在网络中开始源推理，因此错过了控制传播的最佳机会。提出了一种基于有限部署传感器的快速信源定位策略，即基于贪婪覆盖的快速信源定位(GRSL)策略，可以在大规模网络传播的早期阶段快速、灵活、准确地推断信源。GRSL 有两个阶段。在第一阶段，基于贪婪策略(GS)贪婪地部署传感器，以低成本快速实现广域覆盖。在第二个阶段，当一部分传感器观察到网络中的传播事件时，具有早期响应机制的推理策略(IS)开始在早期的小受感染区域中执行源推理任务。通过 SOTA 方法的综合实验，证明了 GRSL 在各种应用场景下的优越性能和鲁棒性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Lightweight+source+localization+for+large-scale+social+networks)|0| -|[xGCN: An Extreme Graph Convolutional Network for Large-scale Social Link Prediction](https://doi.org/10.1145/3543507.3583340)|Xiran Song, Jianxun Lian, Hong Huang, Zihan Luo, Wei Zhou, Xue Lin, Mingqi Wu, Chaozhuo Li, Xing Xie, Hai Jin|Microsoft Gaming, USA; National Engineering Research Center for Big Data Technology and System, Services Computing Technology and System Lab, Cluster and Grid Computing Lab, School of Computer Science and Technology, Huazhong University of Science and Technology, China; Microsoft Research Asia, China|Graph neural networks (GNNs) have seen widespread usage across multiple real-world applications, yet in transductive learning, they still face challenges in accuracy, efficiency, and scalability, due to the extensive number of trainable parameters in the embedding table and the paradigm of stacking neighborhood aggregations. This paper presents a novel model called xGCN for large-scale network embedding, which is a practical solution for link predictions. xGCN addresses these issues by encoding graph-structure data in an extreme convolutional manner, and has the potential to push the performance of network embedding-based link predictions to a new record. Specifically, instead of assigning each node with a directly learnable embedding vector, xGCN regards node embeddings as static features. It uses a propagation operation to smooth node embeddings and relies on a Refinement neural Network (RefNet) to transform the coarse embeddings derived from the unsupervised propagation into new ones that optimize a training objective. The output of RefNet, which are well-refined embeddings, will replace the original node embeddings. This process is repeated iteratively until the model converges to a satisfying status. Experiments on three social network datasets with link prediction tasks show that xGCN not only achieves the best accuracy compared with a series of competitive baselines but also is highly efficient and scalable.|图形神经网络(GNN)在多种现实应用中得到了广泛的应用，然而在传导学习中，由于嵌入表中大量的可训练参数和邻域聚合堆叠的范式，它们在精度、效率和可扩展性方面仍然面临挑战。提出了一种新的大规模网络嵌入模型 xGCN，该模型是链路预测的一种实用解决方案。XGCN 通过以极端卷积方式编码图形结构数据来解决这些问题，并且有可能将基于网络嵌入的链接预测的性能提升到一个新的记录。具体来说，xGCN 没有为每个节点分配一个可直接学习的嵌入向量，而是将节点嵌入视为静态特征。该算法利用传播操作来平滑节点嵌入，并依靠细化神经网络(RefNet)将无监督传播产生的粗嵌入转化为优化训练目标的新嵌入。RefNet 的输出是经过良好改进的嵌入，它将取代原始节点嵌入。这个过程反复重复，直到模型收敛到一个令人满意的状态。通过对三个具有链接预测任务的社会网络数据集的实验表明，xGCN 不仅比一系列竞争性基线获得了最佳的预测精度，而且具有高效性和可扩展性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=xGCN:+An+Extreme+Graph+Convolutional+Network+for+Large-scale+Social+Link+Prediction)|0| -|[GraphPrompt: Unifying Pre-Training and Downstream Tasks for Graph Neural Networks](https://doi.org/10.1145/3543507.3583386)|Zemin Liu, Xingtong Yu, Yuan Fang, Xinming Zhang|University of Science and Technology of China, China; National University of Singapore, Singapore; Singapore Management University, Singapore|Graphs can model complex relationships between objects, enabling a myriad of Web applications such as online page/article classification and social recommendation. While graph neural networks(GNNs) have emerged as a powerful tool for graph representation learning, in an end-to-end supervised setting, their performance heavily rely on a large amount of task-specific supervision. To reduce labeling requirement, the "pre-train, fine-tune" and "pre-train, prompt" paradigms have become increasingly common. In particular, prompting is a popular alternative to fine-tuning in natural language processing, which is designed to narrow the gap between pre-training and downstream objectives in a task-specific manner. However, existing study of prompting on graphs is still limited, lacking a universal treatment to appeal to different downstream tasks. In this paper, we propose GraphPrompt, a novel pre-training and prompting framework on graphs. GraphPrompt not only unifies pre-training and downstream tasks into a common task template, but also employs a learnable prompt to assist a downstream task in locating the most relevant knowledge from the pre-train model in a task-specific manner. Finally, we conduct extensive experiments on five public datasets to evaluate and analyze GraphPrompt.|图形可以模拟对象之间的复杂关系，支持无数的 Web 应用程序，例如在线页面/文章分类和社交推荐。尽管图神经网络(GNN)已经成为图表示学习的有力工具，但在端到端的监督环境下，它们的性能很大程度上依赖于大量的任务特定监督。为了减少标签要求，“预训练，微调”和“预训练，及时”的范例已经变得越来越普遍。特别是，提示是自然语言处理中微调的一种流行替代方法，其目的是以特定于任务的方式缩小培训前和下游目标之间的差距。然而，现有的图形激励研究仍然是有限的，缺乏一个普遍的治疗呼吁不同的下游任务。本文提出了一种新的图形预训练和提示框架 GraphPrompt。GraphPrompt 不仅将预训练和下游任务统一到一个共同的任务模板中，而且还使用可学习的提示来帮助下游任务以特定于任务的方式从预训练模型中找到最相关的知识。最后，我们在五个公共数据集上进行了广泛的实验来评估和分析 GraphPrompt。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=GraphPrompt:+Unifying+Pre-Training+and+Downstream+Tasks+for+Graph+Neural+Networks)|0| -|[FedACK: Federated Adversarial Contrastive Knowledge Distillation for Cross-Lingual and Cross-Model Social Bot Detection](https://doi.org/10.1145/3543507.3583500)|Yingguang Yang, Renyu Yang, Hao Peng, Yangyang Li, Tong Li, Yong Liao, Pengyuan Zhou|NERC-RPP, CAEIT, China; University of Science and Technology of China, China; Beihang University, China; Tsinghua University, China; University of Leeds, United Kingdom|Social bot detection is of paramount importance to the resilience and security of online social platforms. The state-of-the-art detection models are siloed and have largely overlooked a variety of data characteristics from multiple cross-lingual platforms. Meanwhile, the heterogeneity of data distribution and model architecture makes it intricate to devise an efficient cross-platform and cross-model detection framework. In this paper, we propose FedACK, a new federated adversarial contrastive knowledge distillation framework for social bot detection. We devise a GAN-based federated knowledge distillation mechanism for efficiently transferring knowledge of data distribution among clients. In particular, a global generator is used to extract the knowledge of global data distribution and distill it into each client's local model. We leverage local discriminator to enable customized model design and use local generator for data enhancement with hard-to-decide samples. Local training is conducted as multi-stage adversarial and contrastive learning to enable consistent feature spaces among clients and to constrain the optimization direction of local models, reducing the divergences between local and global models. Experiments demonstrate that FedACK outperforms the state-of-the-art approaches in terms of accuracy, communication efficiency, and feature space consistency.|社交机器人检测对于在线社交平台的弹性和安全性至关重要。最先进的检测模型是孤立的，在很大程度上忽略了来自多个跨语言平台的各种数据特征。同时，数据分布和模型结构的异构性使得设计一个有效的跨平台、跨模型检测框架变得复杂。在本文中，我们提出了一个新的联邦对抗性对比知识提取框架 FedACK，用于社会机器人检测。设计了一种基于 GAN 的联邦知识提取机制，用于在客户端之间有效地传递数据分布的知识。特别地，全局生成器用于提取全局数据分布的知识，并将其提取到每个客户机的本地模型中。我们利用局部鉴别器来实现定制的模型设计，并使用局部生成器对难以确定的样本进行数据增强。局部训练作为多阶段对抗性和对比性学习进行，以使客户之间的特征空间保持一致，并约束局部模型的优化方向，减少局部模型和全局模型之间的差异。实验结果表明，FedACK 算法在准确性、通信效率和特征空间一致性方面优于目前最先进的算法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=FedACK:+Federated+Adversarial+Contrastive+Knowledge+Distillation+for+Cross-Lingual+and+Cross-Model+Social+Bot+Detection)|0| -|[Self-training through Classifier Disagreement for Cross-Domain Opinion Target Extraction](https://doi.org/10.1145/3543507.3583325)|Kai Sun, Richong Zhang, Samuel Mensah, Nikolaos Aletras, Yongyi Mao, Xudong Liu|; SKLSDE, School of Computer Science and Engineering, Beihang University, China; School of Electrical Engineering and Computer Science, University of Ottawa, Canada; Computer Science Department, University of Sheffield, UK, United Kingdom|Opinion target extraction (OTE) or aspect extraction (AE) is a fundamental task in opinion mining that aims to extract the targets (or aspects) on which opinions have been expressed. Recent work focus on cross-domain OTE, which is typically encountered in real-world scenarios, where the testing and training distributions differ. Most methods use domain adversarial neural networks that aim to reduce the domain gap between the labelled source and unlabelled target domains to improve target domain performance. However, this approach only aligns feature distributions and does not account for class-wise feature alignment, leading to suboptimal results. Semi-supervised learning (SSL) has been explored as a solution, but is limited by the quality of pseudo-labels generated by the model. Inspired by the theoretical foundations in domain adaptation [2], we propose a new SSL approach that opts for selecting target samples whose model output from a domain-specific teacher and student network disagree on the unlabelled target data, in an effort to boost the target domain performance. Extensive experiments on benchmark cross-domain OTE datasets show that this approach is effective and performs consistently well in settings with large domain shifts.|意见目标提取(OTE)或方面提取(AE)是意见挖掘中的一个基本任务，其目的是提取表达意见的目标(或方面)。最近的工作集中在跨域 OTE 上，这是在现实世界的场景中经常遇到的问题，其中测试和训练的分布是不同的。大多数方法使用域对抗神经网络，目的是减少标记源和未标记目标域之间的域差，以提高目标域的性能。然而，这种方法只对齐了特征分布，没有考虑类别特征对齐，导致次优结果。半监督学习(SSL)作为一种解决方案，但受到模型生成的伪标签质量的限制。受领域适应理论基础[2]的启发，我们提出了一种新的 SSL 方法，选择领域特定的教师和学生网络的模型输出与未标记的目标数据不一致的目标样本，以提高目标领域的性能。在基准跨域 OTE 数据集上的大量实验表明，该方法是有效的，并且在具有较大域移位的情况下表现一致。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Self-training+through+Classifier+Disagreement+for+Cross-Domain+Opinion+Target+Extraction)|0| -|[Fast and Multi-aspect Mining of Complex Time-stamped Event Streams](https://doi.org/10.1145/3543507.3583370)|Kota Nakamura, Yasuko Matsubara, Koki Kawabata, Yuhei Umeda, Yuichiro Wada, Yasushi Sakurai|SANKEN, Osaka University, Japan; AI Lab., Fujitsu, Japan; AI Lab., Fujitsu, Japan and AIP, RIKEN, Japan|Given a huge, online stream of time-evolving events with multiple attributes, such as online shopping logs: (item, price, brand, time), and local mobility activities: (pick-up and drop-off locations, time), how can we summarize large, dynamic high-order tensor streams? How can we see any hidden patterns, rules, and anomalies? Our answer is to focus on two types of patterns, i.e., ''regimes'' and ''components'', for which we present CubeScope, an efficient and effective method over high-order tensor streams. Specifically, it identifies any sudden discontinuity and recognizes distinct dynamical patterns, ''regimes'' (e.g., weekday/weekend/holiday patterns). In each regime, it also performs multi-way summarization for all attributes (e.g., item, price, brand, and time) and discovers hidden ''components'' representing latent groups (e.g., item/brand groups) and their relationship. Thanks to its concise but effective summarization, CubeScope can also detect the sudden appearance of anomalies and identify the types of anomalies that occur in practice. Our proposed method has the following properties: (a) Effective: it captures dynamical multi-aspect patterns, i.e., regimes and components, and statistically summarizes all the events; (b) General: it is practical for successful application to data compression, pattern discovery, and anomaly detection on various types of tensor streams; (c) Scalable: our algorithm does not depend on the length of the data stream and its dimensionality. Extensive experiments on real datasets demonstrate that CubeScope finds meaningful patterns and anomalies correctly, and consistently outperforms the state-of-the-art methods as regards accuracy and execution speed.|给定一个巨大的，在线时间演化事件流，具有多种属性，如在线购物日志: (项目，价格，品牌，时间) ，和本地流动性活动: (上下车地点，时间) ，我们如何总结大型，动态高阶张量流？我们怎么才能看到隐藏的模式，规则和异常呢？我们的答案是关注两种类型的模式，即“体制”和“组件”，对于这两种模式，我们提出 CubeScope，一种高阶张量流上的高效和有效的方法。具体来说，它识别任何突然的不连续性，并识别不同的动态模式，“制度”(例如，工作日/周末/假日模式)。在每个体系中，它还对所有属性(例如，商品、价格、品牌和时间)进行多方面的总结，并发现代表潜在群体(例如，商品/品牌群体)及其关系的隐藏“组成部分”。由于其简洁而有效的总结，CubeScope 还可以检测异常的突然出现并识别实际发生的异常类型。我们提出的方法具有以下特点: (a)有效: 它捕获动态的多方面模式，即制度和组成部分，并对所有事件进行统计总结; (b)一般情况: 成功应用于各种类型的张量流的数据压缩、模式发现和异常检测是可行的; (c)可扩展性: 我们的算法不依赖于数据流的长度及其维度。在真实数据集上的大量实验表明，CubeScope 能够正确地发现有意义的模式和异常，并且在准确性和执行速度方面始终优于最先进的方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Fast+and+Multi-aspect+Mining+of+Complex+Time-stamped+Event+Streams)|0| +|[xGCN: An Extreme Graph Convolutional Network for Large-scale Social Link Prediction](https://doi.org/10.1145/3543507.3583340)|Xiran Song, Jianxun Lian, Hong Huang, Zihan Luo, Wei Zhou, Xue Lin, Mingqi Wu, Chaozhuo Li, Xing Xie, Hai Jin|National Engineering Research Center for Big Data Technology and System, Services Computing Technology and System Lab, Cluster and Grid Computing Lab, School of Computer Science and Technology, Huazhong University of Science and Technology, China; Microsoft Research Asia, China; Microsoft Gaming, USA|Graph neural networks (GNNs) have seen widespread usage across multiple real-world applications, yet in transductive learning, they still face challenges in accuracy, efficiency, and scalability, due to the extensive number of trainable parameters in the embedding table and the paradigm of stacking neighborhood aggregations. This paper presents a novel model called xGCN for large-scale network embedding, which is a practical solution for link predictions. xGCN addresses these issues by encoding graph-structure data in an extreme convolutional manner, and has the potential to push the performance of network embedding-based link predictions to a new record. Specifically, instead of assigning each node with a directly learnable embedding vector, xGCN regards node embeddings as static features. It uses a propagation operation to smooth node embeddings and relies on a Refinement neural Network (RefNet) to transform the coarse embeddings derived from the unsupervised propagation into new ones that optimize a training objective. The output of RefNet, which are well-refined embeddings, will replace the original node embeddings. This process is repeated iteratively until the model converges to a satisfying status. Experiments on three social network datasets with link prediction tasks show that xGCN not only achieves the best accuracy compared with a series of competitive baselines but also is highly efficient and scalable.|图形神经网络(GNN)在多种现实应用中得到了广泛的应用，然而在传导学习中，由于嵌入表中大量的可训练参数和邻域聚合堆叠的范式，它们在精度、效率和可扩展性方面仍然面临挑战。提出了一种新的大规模网络嵌入模型 xGCN，该模型是链路预测的一种实用解决方案。XGCN 通过以极端卷积方式编码图形结构数据来解决这些问题，并且有可能将基于网络嵌入的链接预测的性能提升到一个新的记录。具体来说，xGCN 没有为每个节点分配一个可直接学习的嵌入向量，而是将节点嵌入视为静态特征。该算法利用传播操作来平滑节点嵌入，并依靠细化神经网络(RefNet)将无监督传播产生的粗嵌入转化为优化训练目标的新嵌入。RefNet 的输出是经过良好改进的嵌入，它将取代原始节点嵌入。这个过程反复重复，直到模型收敛到一个令人满意的状态。通过对三个具有链接预测任务的社会网络数据集的实验表明，xGCN 不仅比一系列竞争性基线获得了最佳的预测精度，而且具有高效性和可扩展性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=xGCN:+An+Extreme+Graph+Convolutional+Network+for+Large-scale+Social+Link+Prediction)|0| +|[GraphPrompt: Unifying Pre-Training and Downstream Tasks for Graph Neural Networks](https://doi.org/10.1145/3543507.3583386)|Zemin Liu, Xingtong Yu, Yuan Fang, Xinming Zhang|National University of Singapore, Singapore; Singapore Management University, Singapore; University of Science and Technology of China, China|Graphs can model complex relationships between objects, enabling a myriad of Web applications such as online page/article classification and social recommendation. While graph neural networks(GNNs) have emerged as a powerful tool for graph representation learning, in an end-to-end supervised setting, their performance heavily rely on a large amount of task-specific supervision. To reduce labeling requirement, the "pre-train, fine-tune" and "pre-train, prompt" paradigms have become increasingly common. In particular, prompting is a popular alternative to fine-tuning in natural language processing, which is designed to narrow the gap between pre-training and downstream objectives in a task-specific manner. However, existing study of prompting on graphs is still limited, lacking a universal treatment to appeal to different downstream tasks. In this paper, we propose GraphPrompt, a novel pre-training and prompting framework on graphs. GraphPrompt not only unifies pre-training and downstream tasks into a common task template, but also employs a learnable prompt to assist a downstream task in locating the most relevant knowledge from the pre-train model in a task-specific manner. Finally, we conduct extensive experiments on five public datasets to evaluate and analyze GraphPrompt.|图形可以模拟对象之间的复杂关系，支持无数的 Web 应用程序，例如在线页面/文章分类和社交推荐。尽管图神经网络(GNN)已经成为图表示学习的有力工具，但在端到端的监督环境下，它们的性能很大程度上依赖于大量的任务特定监督。为了减少标签要求，“预训练，微调”和“预训练，及时”的范例已经变得越来越普遍。特别是，提示是自然语言处理中微调的一种流行替代方法，其目的是以特定于任务的方式缩小培训前和下游目标之间的差距。然而，现有的图形激励研究仍然是有限的，缺乏一个普遍的治疗呼吁不同的下游任务。本文提出了一种新的图形预训练和提示框架 GraphPrompt。GraphPrompt 不仅将预训练和下游任务统一到一个共同的任务模板中，而且还使用可学习的提示来帮助下游任务以特定于任务的方式从预训练模型中找到最相关的知识。最后，我们在五个公共数据集上进行了广泛的实验来评估和分析 GraphPrompt。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=GraphPrompt:+Unifying+Pre-Training+and+Downstream+Tasks+for+Graph+Neural+Networks)|0| +|[FedACK: Federated Adversarial Contrastive Knowledge Distillation for Cross-Lingual and Cross-Model Social Bot Detection](https://doi.org/10.1145/3543507.3583500)|Yingguang Yang, Renyu Yang, Hao Peng, Yangyang Li, Tong Li, Yong Liao, Pengyuan Zhou|Beihang University, China; NERC-RPP, CAEIT, China; University of Science and Technology of China, China; University of Leeds, United Kingdom; Tsinghua University, China|Social bot detection is of paramount importance to the resilience and security of online social platforms. The state-of-the-art detection models are siloed and have largely overlooked a variety of data characteristics from multiple cross-lingual platforms. Meanwhile, the heterogeneity of data distribution and model architecture makes it intricate to devise an efficient cross-platform and cross-model detection framework. In this paper, we propose FedACK, a new federated adversarial contrastive knowledge distillation framework for social bot detection. We devise a GAN-based federated knowledge distillation mechanism for efficiently transferring knowledge of data distribution among clients. In particular, a global generator is used to extract the knowledge of global data distribution and distill it into each client's local model. We leverage local discriminator to enable customized model design and use local generator for data enhancement with hard-to-decide samples. Local training is conducted as multi-stage adversarial and contrastive learning to enable consistent feature spaces among clients and to constrain the optimization direction of local models, reducing the divergences between local and global models. Experiments demonstrate that FedACK outperforms the state-of-the-art approaches in terms of accuracy, communication efficiency, and feature space consistency.|社交机器人检测对于在线社交平台的弹性和安全性至关重要。最先进的检测模型是孤立的，在很大程度上忽略了来自多个跨语言平台的各种数据特征。同时，数据分布和模型结构的异构性使得设计一个有效的跨平台、跨模型检测框架变得复杂。在本文中，我们提出了一个新的联邦对抗性对比知识提取框架 FedACK，用于社会机器人检测。设计了一种基于 GAN 的联邦知识提取机制，用于在客户端之间有效地传递数据分布的知识。特别地，全局生成器用于提取全局数据分布的知识，并将其提取到每个客户机的本地模型中。我们利用局部鉴别器来实现定制的模型设计，并使用局部生成器对难以确定的样本进行数据增强。局部训练作为多阶段对抗性和对比性学习进行，以使客户之间的特征空间保持一致，并约束局部模型的优化方向，减少局部模型和全局模型之间的差异。实验结果表明，FedACK 算法在准确性、通信效率和特征空间一致性方面优于目前最先进的算法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=FedACK:+Federated+Adversarial+Contrastive+Knowledge+Distillation+for+Cross-Lingual+and+Cross-Model+Social+Bot+Detection)|0| +|[Self-training through Classifier Disagreement for Cross-Domain Opinion Target Extraction](https://doi.org/10.1145/3543507.3583325)|Kai Sun, Richong Zhang, Samuel Mensah, Nikolaos Aletras, Yongyi Mao, Xudong Liu|; School of Electrical Engineering and Computer Science, University of Ottawa, Canada; SKLSDE, School of Computer Science and Engineering, Beihang University, China; Computer Science Department, University of Sheffield, UK, United Kingdom|Opinion target extraction (OTE) or aspect extraction (AE) is a fundamental task in opinion mining that aims to extract the targets (or aspects) on which opinions have been expressed. Recent work focus on cross-domain OTE, which is typically encountered in real-world scenarios, where the testing and training distributions differ. Most methods use domain adversarial neural networks that aim to reduce the domain gap between the labelled source and unlabelled target domains to improve target domain performance. However, this approach only aligns feature distributions and does not account for class-wise feature alignment, leading to suboptimal results. Semi-supervised learning (SSL) has been explored as a solution, but is limited by the quality of pseudo-labels generated by the model. Inspired by the theoretical foundations in domain adaptation [2], we propose a new SSL approach that opts for selecting target samples whose model output from a domain-specific teacher and student network disagree on the unlabelled target data, in an effort to boost the target domain performance. Extensive experiments on benchmark cross-domain OTE datasets show that this approach is effective and performs consistently well in settings with large domain shifts.|意见目标提取(OTE)或方面提取(AE)是意见挖掘中的一个基本任务，其目的是提取表达意见的目标(或方面)。最近的工作集中在跨域 OTE 上，这是在现实世界的场景中经常遇到的问题，其中测试和训练的分布是不同的。大多数方法使用域对抗神经网络，目的是减少标记源和未标记目标域之间的域差，以提高目标域的性能。然而，这种方法只对齐了特征分布，没有考虑类别特征对齐，导致次优结果。半监督学习(SSL)作为一种解决方案，但受到模型生成的伪标签质量的限制。受领域适应理论基础[2]的启发，我们提出了一种新的 SSL 方法，选择领域特定的教师和学生网络的模型输出与未标记的目标数据不一致的目标样本，以提高目标领域的性能。在基准跨域 OTE 数据集上的大量实验表明，该方法是有效的，并且在具有较大域移位的情况下表现一致。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Self-training+through+Classifier+Disagreement+for+Cross-Domain+Opinion+Target+Extraction)|0| +|[Fast and Multi-aspect Mining of Complex Time-stamped Event Streams](https://doi.org/10.1145/3543507.3583370)|Kota Nakamura, Yasuko Matsubara, Koki Kawabata, Yuhei Umeda, Yuichiro Wada, Yasushi Sakurai|AI Lab., Fujitsu, Japan and AIP, RIKEN, Japan; SANKEN, Osaka University, Japan; AI Lab., Fujitsu, Japan|Given a huge, online stream of time-evolving events with multiple attributes, such as online shopping logs: (item, price, brand, time), and local mobility activities: (pick-up and drop-off locations, time), how can we summarize large, dynamic high-order tensor streams? How can we see any hidden patterns, rules, and anomalies? Our answer is to focus on two types of patterns, i.e., ''regimes'' and ''components'', for which we present CubeScope, an efficient and effective method over high-order tensor streams. Specifically, it identifies any sudden discontinuity and recognizes distinct dynamical patterns, ''regimes'' (e.g., weekday/weekend/holiday patterns). In each regime, it also performs multi-way summarization for all attributes (e.g., item, price, brand, and time) and discovers hidden ''components'' representing latent groups (e.g., item/brand groups) and their relationship. Thanks to its concise but effective summarization, CubeScope can also detect the sudden appearance of anomalies and identify the types of anomalies that occur in practice. Our proposed method has the following properties: (a) Effective: it captures dynamical multi-aspect patterns, i.e., regimes and components, and statistically summarizes all the events; (b) General: it is practical for successful application to data compression, pattern discovery, and anomaly detection on various types of tensor streams; (c) Scalable: our algorithm does not depend on the length of the data stream and its dimensionality. Extensive experiments on real datasets demonstrate that CubeScope finds meaningful patterns and anomalies correctly, and consistently outperforms the state-of-the-art methods as regards accuracy and execution speed.|给定一个巨大的，在线时间演化事件流，具有多种属性，如在线购物日志: (项目，价格，品牌，时间) ，和本地流动性活动: (上下车地点，时间) ，我们如何总结大型，动态高阶张量流？我们怎么才能看到隐藏的模式，规则和异常呢？我们的答案是关注两种类型的模式，即“体制”和“组件”，对于这两种模式，我们提出 CubeScope，一种高阶张量流上的高效和有效的方法。具体来说，它识别任何突然的不连续性，并识别不同的动态模式，“制度”(例如，工作日/周末/假日模式)。在每个体系中，它还对所有属性(例如，商品、价格、品牌和时间)进行多方面的总结，并发现代表潜在群体(例如，商品/品牌群体)及其关系的隐藏“组成部分”。由于其简洁而有效的总结，CubeScope 还可以检测异常的突然出现并识别实际发生的异常类型。我们提出的方法具有以下特点: (a)有效: 它捕获动态的多方面模式，即制度和组成部分，并对所有事件进行统计总结; (b)一般情况: 成功应用于各种类型的张量流的数据压缩、模式发现和异常检测是可行的; (c)可扩展性: 我们的算法不依赖于数据流的长度及其维度。在真实数据集上的大量实验表明，CubeScope 能够正确地发现有意义的模式和异常，并且在准确性和执行速度方面始终优于最先进的方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Fast+and+Multi-aspect+Mining+of+Complex+Time-stamped+Event+Streams)|0| |[PDSum: Prototype-driven Continuous Summarization of Evolving Multi-document Sets Stream](https://doi.org/10.1145/3543507.3583371)|Susik Yoon, Hou Pong Chan, Jiawei Han|University of Illinois at Urbana-Champaign, USA; University of Macau, Macao|Summarizing text-rich documents has been long studied in the literature, but most of the existing efforts have been made to summarize a static and predefined multi-document set. With the rapid development of online platforms for generating and distributing text-rich documents, there arises an urgent need for continuously summarizing dynamically evolving multi-document sets where the composition of documents and sets is changing over time. This is especially challenging as the summarization should be not only effective in incorporating relevant, novel, and distinctive information from each concurrent multi-document set, but also efficient in serving online applications. In this work, we propose a new summarization problem, Evolving Multi-Document sets stream Summarization (EMDS), and introduce a novel unsupervised algorithm PDSum with the idea of prototype-driven continuous summarization. PDSum builds a lightweight prototype of each multi-document set and exploits it to adapt to new documents while preserving accumulated knowledge from previous documents. To update new summaries, the most representative sentences for each multi-document set are extracted by measuring their similarities to the prototypes. A thorough evaluation with real multi-document sets streams demonstrates that PDSum outperforms state-of-the-art unsupervised multi-document summarization algorithms in EMDS in terms of relevance, novelty, and distinctiveness and is also robust to various evaluation settings.|总结文本丰富的文档在文献中已经研究了很长时间，但现有的大多数努力都是总结一个静态的和预定义的多文档集。随着生成和分发文本丰富的文件的在线平台的迅速发展，迫切需要不断总结动态演变的多文件集，其中文件和文件集的组成随着时间的推移而变化。这尤其具有挑战性，因为摘要不仅应该有效地合并每个并发多文档集中的相关、新颖和独特信息，而且还应该有效地为在线应用程序提供服务。本文提出了一个新的文摘问题——进化多文档集流文摘(EMDS) ，并引入了一种基于原型驱动的连续文摘思想的无监督算法 PDSum。PDSum 为每个多文档集构建一个轻量级原型，并利用它来适应新文档，同时保留以前文档中积累的知识。为了更新新的摘要，通过测量每个多文档集合与原型的相似性来提取最有代表性的句子。对真实多文档集合流的全面评估表明，PDSum 在相关性、新颖性和独特性方面优于 EMDS 中最先进的无监督多文档摘要算法，并且对各种评估设置也具有鲁棒性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=PDSum:+Prototype-driven+Continuous+Summarization+of+Evolving+Multi-document+Sets+Stream)|0| -|[Learning Disentangled Representation via Domain Adaptation for Dialogue Summarization](https://doi.org/10.1145/3543507.3583389)|Jinpeng Li, Yingce Xia, Xin Cheng, Dongyan Zhao, Rui Yan|Gaoling School of Artificial Intelligence, Renmin University of China, China; Wangxuan Institute of Computer Technology, Peking University, China and National Key Laboratory of General Artificial Intelligence, Beijing Institute for General Artificial Intelligence, China; Microsoft Research, China; Wangxuan Institute of Computer Technology, Peking University, China|Dialogue summarization, which aims to generate a summary for an input dialogue, plays a vital role in intelligent dialogue systems. The end-to-end models have achieved satisfactory performance in summarization, but the success is built upon enough annotated data, which is costly to obtain, especially in the dialogue summarization. To leverage the rich external data, previous works first pre-train the model on the other domain data (e.g., the news domain), and then fine-tune it directly on the dialogue domain. The data from different domains are equally treated during the training process, while the vast differences between dialogues (usually informal, repetitive, and with multiple speakers) and conventional articles (usually formal and concise) are neglected. In this work, we propose to use a disentangled representation method to reduce the deviation between data in different domains, where the input data is disentangled into domain-invariant and domain-specific representations. The domain-invariant representation carries context information that is supposed to be the same across domains (e.g., news, dialogue) and the domain-specific representation indicates the input data belongs to a particular domain. We use adversarial learning and contrastive learning to constrain the disentangled representations to the target space. Furthermore, we propose two novel reconstruction strategies, namely backtracked and cross-track reconstructions, which aim to reduce the domain characteristics of out-of-domain data and mitigate the domain bias of the model. Experimental results on three public datasets show that our model significantly outperforms the strong baselines.|对话摘要是智能对话系统中的一个重要组成部分，其目的是为输入对话生成摘要。端到端模型在摘要方面取得了令人满意的效果，但是这种成功是建立在足够的注释数据的基础上的，而这些注释数据的获取成本很高，尤其是在对话摘要方面。为了利用丰富的外部数据，以前的工作首先在其他领域数据(例如，新闻领域)上预训练模型，然后直接在对话领域进行微调。不同领域的数据在训练过程中被平等对待，而对话(通常是非正式的、重复的、多人使用的)和传统文章(通常是正式的和简洁的)之间的巨大差异被忽略。在这项工作中，我们提出使用一个分离的表示方法，以减少不同领域的数据之间的偏差，其中输入数据被分离成领域不变和领域特定的表示。领域不变表示携带的上下文信息应该是相同的跨领域(例如，新闻，对话)和领域特定的表示表明输入数据属于一个特定的领域。我们使用对抗学习和对比学习来约束离散表征到目标空间。此外，我们提出了两种新的重建策略，即回溯重建和交叉跟踪重建，旨在减少域外数据的域特征和减轻模型的域偏差。在三个公共数据集上的实验结果表明，我们的模型明显优于强基线。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Learning+Disentangled+Representation+via+Domain+Adaptation+for+Dialogue+Summarization)|0| +|[Learning Disentangled Representation via Domain Adaptation for Dialogue Summarization](https://doi.org/10.1145/3543507.3583389)|Jinpeng Li, Yingce Xia, Xin Cheng, Dongyan Zhao, Rui Yan|Microsoft Research, China; Gaoling School of Artificial Intelligence, Renmin University of China, China; Wangxuan Institute of Computer Technology, Peking University, China; Wangxuan Institute of Computer Technology, Peking University, China and National Key Laboratory of General Artificial Intelligence, Beijing Institute for General Artificial Intelligence, China|Dialogue summarization, which aims to generate a summary for an input dialogue, plays a vital role in intelligent dialogue systems. The end-to-end models have achieved satisfactory performance in summarization, but the success is built upon enough annotated data, which is costly to obtain, especially in the dialogue summarization. To leverage the rich external data, previous works first pre-train the model on the other domain data (e.g., the news domain), and then fine-tune it directly on the dialogue domain. The data from different domains are equally treated during the training process, while the vast differences between dialogues (usually informal, repetitive, and with multiple speakers) and conventional articles (usually formal and concise) are neglected. In this work, we propose to use a disentangled representation method to reduce the deviation between data in different domains, where the input data is disentangled into domain-invariant and domain-specific representations. The domain-invariant representation carries context information that is supposed to be the same across domains (e.g., news, dialogue) and the domain-specific representation indicates the input data belongs to a particular domain. We use adversarial learning and contrastive learning to constrain the disentangled representations to the target space. Furthermore, we propose two novel reconstruction strategies, namely backtracked and cross-track reconstructions, which aim to reduce the domain characteristics of out-of-domain data and mitigate the domain bias of the model. Experimental results on three public datasets show that our model significantly outperforms the strong baselines.|对话摘要是智能对话系统中的一个重要组成部分，其目的是为输入对话生成摘要。端到端模型在摘要方面取得了令人满意的效果，但是这种成功是建立在足够的注释数据的基础上的，而这些注释数据的获取成本很高，尤其是在对话摘要方面。为了利用丰富的外部数据，以前的工作首先在其他领域数据(例如，新闻领域)上预训练模型，然后直接在对话领域进行微调。不同领域的数据在训练过程中被平等对待，而对话(通常是非正式的、重复的、多人使用的)和传统文章(通常是正式的和简洁的)之间的巨大差异被忽略。在这项工作中，我们提出使用一个分离的表示方法，以减少不同领域的数据之间的偏差，其中输入数据被分离成领域不变和领域特定的表示。领域不变表示携带的上下文信息应该是相同的跨领域(例如，新闻，对话)和领域特定的表示表明输入数据属于一个特定的领域。我们使用对抗学习和对比学习来约束离散表征到目标空间。此外，我们提出了两种新的重建策略，即回溯重建和交叉跟踪重建，旨在减少域外数据的域特征和减轻模型的域偏差。在三个公共数据集上的实验结果表明，我们的模型明显优于强基线。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Learning+Disentangled+Representation+via+Domain+Adaptation+for+Dialogue+Summarization)|0| |[Towards Understanding Consumer Healthcare Questions on the Web with Semantically Enhanced Contrastive Learning](https://doi.org/10.1145/3543507.3583449)|Shweta Yadav, Stefan Cobeli, Cornelia Caragea|University of Illinois at Chicago, USA|In recent years, seeking health information on the web has become a preferred way for healthcare consumers to support their information needs. Generally, healthcare consumers use long and detailed questions with several peripheral details to express their healthcare concerns, contributing to natural language understanding challenges. One way to address this challenge is by summarizing the questions. However, most of the existing abstractive summarization systems generate impeccably fluent yet factually incorrect summaries. In this paper, we present a semantically-enhanced contrastive learning-based framework for generating abstractive question summaries that are faithful and factually correct. We devised multiple strategies based on question semantics to generate the erroneous (negative) summaries, such that the model has the understanding of plausible and incorrect perturbations of the original summary. Our extensive experimental results on two benchmark consumer health question summarization datasets confirm the effectiveness of our proposed method by achieving state-of-the-art performance and generating factually correct and fluent summaries, as measured by human evaluation.|近年来，在网上寻找健康信息已成为医疗保健消费者支持其信息需求的首选方式。一般来说，医疗保健消费者使用长而详细的问题和几个外围细节来表达他们的医疗保健关注，有助于自然语言理解的挑战。解决这个问题的一个方法是总结问题。然而，大多数现有的抽象摘要系统生成的摘要无可挑剔地流畅，但事实上是不正确的。本文提出了一个基于语义增强的对比学习框架，用于生成抽象的、忠实的、事实正确的问题摘要。我们设计了多种基于问题语义的策略来生成错误的(否定的)总结，使得模型能够理解原始总结的合理和不正确的扰动。我们在两个基准的消费者健康问题摘要数据集上的广泛实验结果证实了我们提出的方法的有效性，通过实现最先进的性能和生成事实上正确和流畅的摘要，由人类评估测量。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Towards+Understanding+Consumer+Healthcare+Questions+on+the+Web+with+Semantically+Enhanced+Contrastive+Learning)|0| |[Modeling Dynamic Interactions over Tensor Streams](https://doi.org/10.1145/3543507.3583458)|Koki Kawabata, Yasuko Matsubara, Yasushi Sakurai|SANKEN, Osaka University, Japan|Many web applications, such as search engines and social network services, are continuously producing a huge number of events with a multi-order tensor form, {count;query, location, …, timestamp}, and so how can we discover important trends to enables us to forecast long-term future events? Can we interpret any relationships between events that determine the trends from multi-aspect perspectives? Real-world online activities can be composed of (1) many time-changing interactions that control trends, for example, competition/cooperation to gain user attention, as well as (2) seasonal patterns that covers trends. To model the shifting trends via interactions, namely dynamic interactions over tensor streams, in this paper, we propose a streaming algorithm, DISMO, that we designed to discover Dynamic Interactions and Seasonality in a Multi-Order tensor. Our approach has the following properties. (a) Interpretable: it incorporates interpretable non-linear differential equations in tensor factorization so that it can reveal latent interactive relationships and thus generate future events effectively; (b) Dynamic: it can be aware of shifting trends by switching multi-aspect factors while summarizing their characteristics incrementally; and (c) Automatic: it finds every factor automatically without losing forecasting accuracy. Extensive experiments on real datasets demonstrate that our algorithm extracts interpretable interactions between data attributes, while simultaneously providing improved forecasting accuracy and a great reduction in computational time.|许多网络应用程序，如搜索引擎和社交网络服务，不断产生大量的事件与多阶张量形式，{ count; query，location，... ，time戳} ，所以我们如何发现重要的趋势，使我们能够预测长期的未来事件？我们能否从多方面的角度解释事件之间确定趋势的任何关系？现实世界的在线活动可以包括(1)许多时间变化的交互，控制趋势，例如，竞争/合作，以获得用户的关注，以及(2)涵盖趋势的季节性模式。为了通过张量流的动态相互作用(即动态相互作用)来模拟变化趋势，在本文中，我们提出了一个流算法，DISMO，我们设计它来发现多阶张量中的动态相互作用和季节性。我们的方法具有以下属性。(a)可解释性: 在张量因子分解中加入可解释的非线性微分方程，以便能够揭示潜在的交互关系，从而有效地产生未来事件; (b)动态性: 通过切换多方面因子，逐步总结其特征，它可以意识到变化的趋势; (c)自动性: 它自动找到每个因子，而不会失去预测的准确性。在实际数据集上的大量实验表明，该算法提取了数据属性之间可解释的交互，同时提高了预测精度，大大减少了计算时间。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Modeling+Dynamic+Interactions+over+Tensor+Streams)|0| |[Constrained Subset Selection from Data Streams for Profit Maximization](https://doi.org/10.1145/3543507.3583490)|Shuang Cui, Kai Han, Jing Tang, He Huang|The Hong Kong University of Science and Technology (Guangzhou), The Hong Kong University of Science and Technology, China; School of Computer Science and Technology, Soochow University, China; School of Computer Science and Technology, University of Science and Technology of China, China|The problem of constrained subset selection from a large data stream for profit maximization has many applications in web data mining and machine learning, such as social advertising, team formation and recommendation systems. Such a problem can be formulated as maximizing a regularized submodular function under certain constraints. In this paper, we consider a generalized k-system constraint, which captures various requirements in real-world applications. For this problem, we propose the first streaming algorithm with provable performance bounds, leveraging a novel multitudinous distorted filter framework. The empirical performance of our algorithm is extensively evaluated in several applications including web data mining and recommendation systems, and the experimental results demonstrate the superiorities of our algorithm in terms of both effectiveness and efficiency.|面向利润最大化的大型数据流的约束子集选择问题在网络数据挖掘和机器学习中有许多应用，例如社交广告、团队组建和推荐系统。这类问题可以表述为在一定约束条件下正则化子模函数的最大化问题。在本文中，我们考虑了一个广义 k 系统约束，它能够满足实际应用中的各种需求。针对这个问题，我们提出了第一个具有可证明性能界限的流式算法，利用了一个新的大量失真过滤器框架。该算法的经验性能在网络数据挖掘和推荐系统等应用中得到了广泛的评价，实验结果表明了该算法在效率和效果方面的优越性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Constrained+Subset+Selection+from+Data+Streams+for+Profit+Maximization)|0| |[SCStory: Self-supervised and Continual Online Story Discovery](https://doi.org/10.1145/3543507.3583507)|Susik Yoon, Yu Meng, Dongha Lee, Jiawei Han|University of Illinois at Urbana-Champaign, USA; Yonsei University, Republic of Korea|We present a framework SCStory for online story discovery, that helps people digest rapidly published news article streams in real-time without human annotations. To organize news article streams into stories, existing approaches directly encode the articles and cluster them based on representation similarity. However, these methods yield noisy and inaccurate story discovery results because the generic article embeddings do not effectively reflect the story-indicative semantics in an article and cannot adapt to the rapidly evolving news article streams. SCStory employs self-supervised and continual learning with a novel idea of story-indicative adaptive modeling of news article streams. With a lightweight hierarchical embedding module that first learns sentence representations and then article representations, SCStory identifies story-relevant information of news articles and uses them to discover stories. The embedding module is continuously updated to adapt to evolving news streams with a contrastive learning objective, backed up by two unique techniques, confidence-aware memory replay and prioritized-augmentation, employed for label absence and data scarcity problems. Thorough experiments on real and the latest news data sets demonstrate that SCStory outperforms existing state-of-the-art algorithms for unsupervised online story discovery.|我们提出了一个用于在线故事发现的框架 SCStory，它可以帮助人们在没有人工注释的情况下实时消化快速发布的新闻文章流。为了将新闻文章流组织成故事，现有的方法直接对文章进行编码，并根据表示相似性对文章进行聚类。然而，这些方法产生的噪音和不准确的故事发现结果，因为通用文章嵌入不能有效地反映故事指示语义在一篇文章，不能适应迅速发展的新闻文章流。SCStory 采用自我监督和持续学习的思想，对新闻文章流进行故事指示性自适应建模。SCStory 使用一个轻量级的层次化嵌入模块，首先学习句子表示，然后学习文章表示，SCStory 识别新闻文章的故事相关信息，并使用它们来发现故事。嵌入模块不断更新，以适应具有对比学习目标的不断发展的新闻流，并辅以两种独特的技术: 可信度感知记忆重放和优先级增强，用于解决标签缺失和数据稀缺问题。对真实新闻和最新新闻数据集的彻底实验表明，SCStory 在无监督的在线故事发现方面优于现有的最先进算法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=SCStory:+Self-supervised+and+Continual+Online+Story+Discovery)|0| -|[Know Your Transactions: Real-time and Generic Transaction Semantic Representation on Blockchain & Web3 Ecosystem](https://doi.org/10.1145/3543507.3583537)|Zhiying Wu, Jieli Liu, Jiajing Wu, Zibin Zheng, Xiapu Luo, Ting Chen|University of Electronic Science and Technologyo of China, China; Sun Yat-sen University, China; Hong Kong Polytechnic University, China|Web3, based on blockchain technology, is the evolving next generation Internet of value. Massive active applications on Web3, e.g. DeFi and NFT, usually rely on blockchain transactions to achieve value transfer as well as complex and diverse custom logic and intentions. Various risky or illegal behaviors such as financial fraud, hacking, money laundering are currently rampant in the blockchain ecosystem, and it is thus important to understand the intent behind the pseudonymous transactions. To reveal the intent of transactions, much effort has been devoted to extracting some particular transaction semantics through specific expert experiences. However, the limitations of existing methods in terms of effectiveness and generalization make it difficult to extract diverse transaction semantics in the rapidly growing and evolving Web3 ecosystem. In this paper, we propose the Motif-based Transaction Semantics representation method (MoTS), which can capture the transaction semantic information in the real-time transaction data workflow. To the best of our knowledge, MoTS is the first general semantic extraction method in Web3 blockchain ecosystem. Experimental results show that MoTS can effectively distinguish different transaction semantics in real-time, and can be used for various downstream tasks, giving new insights to understand the Web3 blockchain ecosystem. Our codes are available at https://github.com/wuzhy1ng/MoTS.|基于区块链技术的 Web3是不断发展的下一代价值互联网。Web3上的大量活动应用程序，例如 DeFi 和 NFT，通常依赖区块链事务来实现价值转移以及复杂多样的定制逻辑和意图。各种风险或非法行为，如金融欺诈，黑客，洗钱目前在区块链生态系统中猖獗，因此了解这些假名交易背后的意图非常重要。为了揭示事务的意图，人们花费了大量的精力通过特定的专家经验提取特定的事务语义。然而，现有方法在有效性和泛化方面的局限性使得在快速增长和发展的 Web3生态系统中很难提取不同的事务语义。在本文中，我们提出了基于主题的事务语义表示方法(moTS) ，它可以捕获实时事务数据流中的事务语义信息。据我们所知，MoTS 是 Web3区块链生态系统中第一个通用的语义提取方法。实验结果表明，MoTS 可以有效地实时区分不同的事务语义，并可用于各种下游任务，为理解 Web3区块链生态系统提供了新的视角。我们的密码可以在 https://github.com/wuzhy1ng/mots 找到。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Know+Your+Transactions:+Real-time+and+Generic+Transaction+Semantic+Representation+on+Blockchain+&+Web3+Ecosystem)|0| +|[Know Your Transactions: Real-time and Generic Transaction Semantic Representation on Blockchain & Web3 Ecosystem](https://doi.org/10.1145/3543507.3583537)|Zhiying Wu, Jieli Liu, Jiajing Wu, Zibin Zheng, Xiapu Luo, Ting Chen|Hong Kong Polytechnic University, China; Sun Yat-sen University, China; University of Electronic Science and Technologyo of China, China|Web3, based on blockchain technology, is the evolving next generation Internet of value. Massive active applications on Web3, e.g. DeFi and NFT, usually rely on blockchain transactions to achieve value transfer as well as complex and diverse custom logic and intentions. Various risky or illegal behaviors such as financial fraud, hacking, money laundering are currently rampant in the blockchain ecosystem, and it is thus important to understand the intent behind the pseudonymous transactions. To reveal the intent of transactions, much effort has been devoted to extracting some particular transaction semantics through specific expert experiences. However, the limitations of existing methods in terms of effectiveness and generalization make it difficult to extract diverse transaction semantics in the rapidly growing and evolving Web3 ecosystem. In this paper, we propose the Motif-based Transaction Semantics representation method (MoTS), which can capture the transaction semantic information in the real-time transaction data workflow. To the best of our knowledge, MoTS is the first general semantic extraction method in Web3 blockchain ecosystem. Experimental results show that MoTS can effectively distinguish different transaction semantics in real-time, and can be used for various downstream tasks, giving new insights to understand the Web3 blockchain ecosystem. Our codes are available at https://github.com/wuzhy1ng/MoTS.|基于区块链技术的 Web3是不断发展的下一代价值互联网。Web3上的大量活动应用程序，例如 DeFi 和 NFT，通常依赖区块链事务来实现价值转移以及复杂多样的定制逻辑和意图。各种风险或非法行为，如金融欺诈，黑客，洗钱目前在区块链生态系统中猖獗，因此了解这些假名交易背后的意图非常重要。为了揭示事务的意图，人们花费了大量的精力通过特定的专家经验提取特定的事务语义。然而，现有方法在有效性和泛化方面的局限性使得在快速增长和发展的 Web3生态系统中很难提取不同的事务语义。在本文中，我们提出了基于主题的事务语义表示方法(moTS) ，它可以捕获实时事务数据流中的事务语义信息。据我们所知，MoTS 是 Web3区块链生态系统中第一个通用的语义提取方法。实验结果表明，MoTS 可以有效地实时区分不同的事务语义，并可用于各种下游任务，为理解 Web3区块链生态系统提供了新的视角。我们的密码可以在 https://github.com/wuzhy1ng/mots 找到。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Know+Your+Transactions:+Real-time+and+Generic+Transaction+Semantic+Representation+on+Blockchain+&+Web3+Ecosystem)|0| |[Toward Open-domain Slot Filling via Self-supervised Co-training](https://doi.org/10.1145/3543507.3583541)|Adib Mosharrof, Moghis Fereidouni, A. B. Siddique|University of Kentucky, USA|Slot filling is one of the critical tasks in modern conversational systems. The majority of existing literature employs supervised learning methods, which require labeled training data for each new domain. Zero-shot learning and weak supervision approaches, among others, have shown promise as alternatives to manual labeling. Nonetheless, these learning paradigms are significantly inferior to supervised learning approaches in terms of performance. To minimize this performance gap and demonstrate the possibility of open-domain slot filling, we propose a Self-supervised Co-training framework, called SCot, that requires zero in-domain manually labeled training examples and works in three phases. Phase one acquires two sets of complementary pseudo labels automatically. Phase two leverages the power of the pre-trained language model BERT, by adapting it for the slot filling task using these sets of pseudo labels. In phase three, we introduce a self-supervised cotraining mechanism, where both models automatically select highconfidence soft labels to further improve the performance of the other in an iterative fashion. Our thorough evaluations show that SCot outperforms state-of-the-art models by 45.57% and 37.56% on SGD and MultiWoZ datasets, respectively. Moreover, our proposed framework SCot achieves comparable performance when compared to state-of-the-art fully supervised models.|时隙填充是现代会话系统的关键任务之一。现有的大多数文献采用监督式学习方法，每个新领域都需要有标签的训练数据。零拍摄学习和薄弱的监督方法，除其他外，已经显示出作为手工标签替代品的前景。尽管如此，这些学习模式在表现方面明显逊色于监督式学习方法。为了尽可能减小这种性能差距，并证明开放域时隙填充的可能性，我们提出了一种称为 SCot 的自监督协同训练框架，它需要零域内手动标记的训练例子，并分三个阶段工作。第一阶段自动获取两套互补的伪标签。第二阶段利用预先训练好的语言模型 BERT 的能力，通过使用这些伪标签集使其适应插槽填充任务。在第三阶段，我们引入一个自我监督的协同训练机制，其中两个模型自动选择高置信软标签，以进一步提高其他在迭代方式的性能。我们的全面评估表明，在 SGD 和 MultiWoZ 数据集上，SCot 的性能分别比最先进的模型高出45.57% 和37.56% 。此外，我们提出的框架 SCot 实现了可比的性能相比，国家的最先进的全监督模型。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Toward+Open-domain+Slot+Filling+via+Self-supervised+Co-training)|0| |[Measuring and Evading Turkmenistan's Internet Censorship: A Case Study in Large-Scale Measurements of a Low-Penetration Country](https://doi.org/10.1145/3543507.3583189)|Sadia Nourin, Van Tran, Xi Jiang, Kevin Bock, Nick Feamster, Nguyen Phong Hoang, Dave Levin|University of Maryland, USA; University of Chicago, USA|Since 2006, Turkmenistan has been listed as one of the few Internet enemies by Reporters without Borders due to its extensively censored Internet and strictly regulated information control policies. Existing reports of filtering in Turkmenistan rely on a small number of vantage points or test a small number of websites. Yet, the country's poor Internet adoption rates and small population can make more comprehensive measurement challenging. With a population of only six million people and an Internet penetration rate of only 38%, it is challenging to either recruit in-country volunteers or obtain vantage points to conduct remote network measurements at scale. We present the largest measurement study to date of Turkmenistan's Web censorship. To do so, we developed TMC, which tests the blocking status of millions of domains across the three foundational protocols of the Web (DNS, HTTP, and HTTPS). Importantly, TMC does not require access to vantage points in the country. We apply TMC to 15.5M domains, our results reveal that Turkmenistan censors more than 122K domains, using different blocklists for each protocol. We also reverse-engineer these censored domains, identifying 6K over-blocking rules causing incidental filtering of more than 5.4M domains. Finally, we use Geneva, an open-source censorship evasion tool, to discover five new censorship evasion strategies that can defeat Turkmenistan's censorship at both transport and application layers. We will publicly release both the data collected by TMC and the code for censorship evasion.|自2006年以来，土库曼斯坦由于其广泛的互联网审查和严格的信息控制政策而被列为少数几个互联网无国界记者之一。土库曼斯坦现有的过滤报告依赖于少数有利位置或测试少数网站。然而，该国糟糕的互联网使用率和较小的人口可以使更全面的测量具有挑战性。由于人口只有600万，互联网普及率只有38% ，因此，要么征聘国内志愿人员，要么获得有利位置，进行大规模的远程网络测量，这是一个挑战。我们提出了迄今为止土库曼斯坦网络审查最大的测量研究。为此，我们开发了 TMC，它测试跨 Web 的三个基本协议(DNS、 HTTP 和 HTTPS)的数百万个域的阻塞状态。重要的是，TMC 不要求进入该国的有利位置。我们将 TMC 应用于1550万个域名，我们的结果显示，土库曼斯坦审查超过122K 域名，使用不同的区块列表为每个协议。我们还反向工程这些删除域，确定6K 过度阻塞规则导致超过5.4 M 域的附带过滤。最后，我们使用日内瓦，一个开源的审查规避工具，发现五个新的审查规避策略，可以击败土库曼斯坦的审查在传输和应用层。我们将公开发布 TMC 收集的数据和规避审查的代码。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Measuring+and+Evading+Turkmenistan's+Internet+Censorship:+A+Case+Study+in+Large-Scale+Measurements+of+a+Low-Penetration+Country)|0| -|[NetGuard: Protecting Commercial Web APIs from Model Inversion Attacks using GAN-generated Fake Samples](https://doi.org/10.1145/3543507.3583224)|Xueluan Gong, Ziyao Wang, Yanjiao Chen, Qian Wang, Cong Wang, Chao Shen|Wuhan University, China; Zhejiang University, China; Xi'an Jiaotong University, China; City University of Hong Kong, China|Recently more and more cloud service providers (e.g., Microsoft, Google, and Amazon) have commercialized their well-trained deep learning models by providing limited access via web API interfaces. However, it is shown that these APIs are susceptible to model inversion attacks, where attackers can recover the training data with high fidelity, which may cause serious privacy leakage.Existing defenses against model inversion attacks, however, hinder the model performance and are ineffective for more advanced attacks, e.g., Mirror [4]. In this paper, we proposed NetGuard, a novel utility-aware defense methodology against model inversion attacks (MIAs). Unlike previous works that perturb prediction outputs of the victim model, we propose to mislead the MIA effort by inserting engineered fake samples during the training process. A generative adversarial network (GAN) is carefully built to construct fake training samples to mislead the attack model without degrading the performance of the victim model. Besides, we adopt continual learning to further improve the utility of the victim model. Extensive experiments on CelebA, VGG-Face, and VGG-Face2 datasets show that NetGuard is superior to existing defenses, including DP [37] and Ad-mi [32] on state-of-the-art model inversion attacks, i.e., DMI [8], Mirror [4], Privacy [12], and Alignment [34].|最近越来越多的云服务提供商(如微软、谷歌和亚马逊)通过提供有限的 Web API 接口访问，将他们训练有素的深度学习模型商业化。然而，这些 API 容易受到模型反转攻击，攻击者可以恢复高保真的训练数据，从而导致严重的隐私泄漏。然而，针对模型反转攻击的现有防御措施阻碍了模型的性能，并且对于更高级的攻击无效，例如，Mirror [4]。在本文中，我们提出了一种新的效用感知的防御模型反转攻击(MIA)方法 NetGuard。不同于以往的工作，扰乱预测输出的受害者模型，我们建议误导 MIA 的努力，插入工程假样本在训练过程中。构造一个生成式对抗网络(GAN)来构造虚假的训练样本，在不降低受害者模型性能的前提下误导攻击模型。此外，我们采用不断学习的方法进一步提高了被害人模型的有效性。在 CelebA，VGG-Face 和 VGG-Face2数据集上的大量实验表明，NetGuard 优于现有的防御系统，包括 DP [37]和 Ad-mi [32]对最先进的模型反转攻击，即 DMI [8] ，Mirror [4] ，Privacy [12]和 Align [34]。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=NetGuard:+Protecting+Commercial+Web+APIs+from+Model+Inversion+Attacks+using+GAN-generated+Fake+Samples)|0| -|[Meteor: Improved Secure 3-Party Neural Network Inference with Reducing Online Communication Costs](https://doi.org/10.1145/3543507.3583272)|Ye Dong, Xiaojun Chen, Weizhan Jing, Kaiyun Li, Weiping Wang|Institute of Information Engineering,Chinese Academy of Sciences, China and School of Cyber Security, University of Chinese Academy of Sciences, China; ; Institute of Information Engineering, Chinese Academy of Sciences, China and School of Cyber Security, University of Chinese Academy of Sciences, China|Secure neural network inference has been a promising solution to private Deep-Learning-as-a-Service, which enables the service provider and user to execute neural network inference without revealing their private inputs. However, the expensive overhead of current schemes is still an obstacle when applied in real applications. In this work, we present Meteor, an online communication-efficient and fast secure 3-party computation neural network inference system aginst semi-honest adversary in honest-majority. The main contributions of Meteor are two-fold: i) We propose a new and improved 3-party secret sharing scheme stemming from the linearity of replicated secret sharing, and design efficient protocols for the basic cryptographic primitives, including linear operations, multiplication, most significant bit extraction, and multiplexer. ii) Furthermore, we build efficient and secure blocks for the widely used neural network operators such as Matrix Multiplication, ReLU, and Maxpool, along with exploiting several specific optimizations for better efficiency. Our total communication with the setup phase is a little larger than SecureNN (PoPETs’19) and Falcon (PoPETs’21), two state-of-the-art solutions, but the gap is not significant when the online phase must be optimized as a priority. Using Meteor, we perform extensive evaluations on various neural networks. Compared to SecureNN and Falcon, we reduce the online communication costs by up to 25.6 × and 1.5 ×, and improve the running-time by at most 9.8 × (resp. 8.1 ×) and 1.5 × (resp. 2.1 ×) in LAN (resp. WAN) for the online inference.|安全神经网络推理已成为私有深度学习即服务(Deep-Learning-as-a-Service)的一种有前途的解决方案，它使服务提供者和用户能够在不暴露其私有输入的情况下执行神经网络推理。然而，在实际应用中，现有方案昂贵的开销仍然是一个障碍。在这项工作中，我们提出了一个在线通信效率和快速安全的三方计算神经网络推理系统对半诚实的对手在诚实的大多数。基于复制密钥共享的线性特性，提出了一种新的改进的三方密钥共享方案，并针对基本密码原语设计了高效的协议，包括线性运算、乘法、最大位提取和多路复用。Ii)此外，我们还为广泛使用的神经网络操作器(如矩阵乘法、 relU 和 Maxpool)构建了高效、安全的模块，同时利用一些特定的优化来提高效率。我们与安装阶段的总体沟通略大于 SecureNN (PoPETs’19)和 Falcon (PoPETs’21)这两种最先进的解决方案，但当在线阶段必须优先优化时，差距并不显着。使用流星，我们对各种神经网络进行广泛的评估。与 SecureNN 和 Falcon 相比，在线通信成本分别降低了25.6 × 和1.5 × ，运行时间最多提高了9.8 × 。8.1 ×)和1.5 × (分辨率)。2.1 ×).WAN)进行在线推理。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Meteor:+Improved+Secure+3-Party+Neural+Network+Inference+with+Reducing+Online+Communication+Costs)|0| +|[NetGuard: Protecting Commercial Web APIs from Model Inversion Attacks using GAN-generated Fake Samples](https://doi.org/10.1145/3543507.3583224)|Xueluan Gong, Ziyao Wang, Yanjiao Chen, Qian Wang, Cong Wang, Chao Shen|Wuhan University, China; City University of Hong Kong, China; Xi'an Jiaotong University, China; Zhejiang University, China|Recently more and more cloud service providers (e.g., Microsoft, Google, and Amazon) have commercialized their well-trained deep learning models by providing limited access via web API interfaces. However, it is shown that these APIs are susceptible to model inversion attacks, where attackers can recover the training data with high fidelity, which may cause serious privacy leakage.Existing defenses against model inversion attacks, however, hinder the model performance and are ineffective for more advanced attacks, e.g., Mirror [4]. In this paper, we proposed NetGuard, a novel utility-aware defense methodology against model inversion attacks (MIAs). Unlike previous works that perturb prediction outputs of the victim model, we propose to mislead the MIA effort by inserting engineered fake samples during the training process. A generative adversarial network (GAN) is carefully built to construct fake training samples to mislead the attack model without degrading the performance of the victim model. Besides, we adopt continual learning to further improve the utility of the victim model. Extensive experiments on CelebA, VGG-Face, and VGG-Face2 datasets show that NetGuard is superior to existing defenses, including DP [37] and Ad-mi [32] on state-of-the-art model inversion attacks, i.e., DMI [8], Mirror [4], Privacy [12], and Alignment [34].|最近越来越多的云服务提供商(如微软、谷歌和亚马逊)通过提供有限的 Web API 接口访问，将他们训练有素的深度学习模型商业化。然而，这些 API 容易受到模型反转攻击，攻击者可以恢复高保真的训练数据，从而导致严重的隐私泄漏。然而，针对模型反转攻击的现有防御措施阻碍了模型的性能，并且对于更高级的攻击无效，例如，Mirror [4]。在本文中，我们提出了一种新的效用感知的防御模型反转攻击(MIA)方法 NetGuard。不同于以往的工作，扰乱预测输出的受害者模型，我们建议误导 MIA 的努力，插入工程假样本在训练过程中。构造一个生成式对抗网络(GAN)来构造虚假的训练样本，在不降低受害者模型性能的前提下误导攻击模型。此外，我们采用不断学习的方法进一步提高了被害人模型的有效性。在 CelebA，VGG-Face 和 VGG-Face2数据集上的大量实验表明，NetGuard 优于现有的防御系统，包括 DP [37]和 Ad-mi [32]对最先进的模型反转攻击，即 DMI [8] ，Mirror [4] ，Privacy [12]和 Align [34]。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=NetGuard:+Protecting+Commercial+Web+APIs+from+Model+Inversion+Attacks+using+GAN-generated+Fake+Samples)|0| +|[Meteor: Improved Secure 3-Party Neural Network Inference with Reducing Online Communication Costs](https://doi.org/10.1145/3543507.3583272)|Ye Dong, Xiaojun Chen, Weizhan Jing, Kaiyun Li, Weiping Wang|; Institute of Information Engineering,Chinese Academy of Sciences, China and School of Cyber Security, University of Chinese Academy of Sciences, China; Institute of Information Engineering, Chinese Academy of Sciences, China and School of Cyber Security, University of Chinese Academy of Sciences, China|Secure neural network inference has been a promising solution to private Deep-Learning-as-a-Service, which enables the service provider and user to execute neural network inference without revealing their private inputs. However, the expensive overhead of current schemes is still an obstacle when applied in real applications. In this work, we present Meteor, an online communication-efficient and fast secure 3-party computation neural network inference system aginst semi-honest adversary in honest-majority. The main contributions of Meteor are two-fold: i) We propose a new and improved 3-party secret sharing scheme stemming from the linearity of replicated secret sharing, and design efficient protocols for the basic cryptographic primitives, including linear operations, multiplication, most significant bit extraction, and multiplexer. ii) Furthermore, we build efficient and secure blocks for the widely used neural network operators such as Matrix Multiplication, ReLU, and Maxpool, along with exploiting several specific optimizations for better efficiency. Our total communication with the setup phase is a little larger than SecureNN (PoPETs’19) and Falcon (PoPETs’21), two state-of-the-art solutions, but the gap is not significant when the online phase must be optimized as a priority. Using Meteor, we perform extensive evaluations on various neural networks. Compared to SecureNN and Falcon, we reduce the online communication costs by up to 25.6 × and 1.5 ×, and improve the running-time by at most 9.8 × (resp. 8.1 ×) and 1.5 × (resp. 2.1 ×) in LAN (resp. WAN) for the online inference.|安全神经网络推理已成为私有深度学习即服务(Deep-Learning-as-a-Service)的一种有前途的解决方案，它使服务提供者和用户能够在不暴露其私有输入的情况下执行神经网络推理。然而，在实际应用中，现有方案昂贵的开销仍然是一个障碍。在这项工作中，我们提出了一个在线通信效率和快速安全的三方计算神经网络推理系统对半诚实的对手在诚实的大多数。基于复制密钥共享的线性特性，提出了一种新的改进的三方密钥共享方案，并针对基本密码原语设计了高效的协议，包括线性运算、乘法、最大位提取和多路复用。Ii)此外，我们还为广泛使用的神经网络操作器(如矩阵乘法、 relU 和 Maxpool)构建了高效、安全的模块，同时利用一些特定的优化来提高效率。我们与安装阶段的总体沟通略大于 SecureNN (PoPETs’19)和 Falcon (PoPETs’21)这两种最先进的解决方案，但当在线阶段必须优先优化时，差距并不显着。使用流星，我们对各种神经网络进行广泛的评估。与 SecureNN 和 Falcon 相比，在线通信成本分别降低了25.6 × 和1.5 × ，运行时间最多提高了9.8 × 。8.1 ×)和1.5 × (分辨率)。2.1 ×).WAN)进行在线推理。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Meteor:+Improved+Secure+3-Party+Neural+Network+Inference+with+Reducing+Online+Communication+Costs)|0| |[IRWArt: Levering Watermarking Performance for Protecting High-quality Artwork Images](https://doi.org/10.1145/3543507.3583489)|Yuanjing Luo, Tongqing Zhou, Fang Liu, Zhiping Cai|Hunan University, China; National University of Defense Technology, China|Increasing artwork plagiarism incidents underscores the urgent need for reliable copyright protection for high-quality artwork images. Although watermarking is helpful to this issue, existing methods are limited in imperceptibility and robustness. To provide high-level protection for valuable artwork images, we propose a novel invisible robust watermarking framework, dubbed as IRWArt. In our architecture, the embedding and recovery of the watermark are treated as a pair of image transformations’ inverse problems, and can be implemented through the forward and backward processes of an invertible neural networks (INN), respectively. For high visual quality, we embed the watermark in high-frequency domains with minimal impact on artwork and supervise image reconstruction using a human visual system(HVS)-consistent deep perceptual loss. For strong plagiarism-resistant, we construct a quality enhancement module for the embedded image against possible distortions caused by plagiarism actions. Moreover, the two-stagecontrastive training strategy enables the simultaneous realization of the above two goals. Experimental results on 4 datasets demonstrate the superiority of our IRWArt over other state-of-the-art watermarking methods. Code: https://github.com/1024yy/IRWArt.|越来越多的艺术品剽窃事件突出表明，迫切需要为高质量的艺术品图像提供可靠的版权保护。虽然水印技术有助于解决这一问题，但现有的水印方法在不可感知性和鲁棒性方面存在局限性。为了对有价值的艺术品图像提供高层次的保护，我们提出了一种新的不可见的鲁棒水印框架，称为 IRWArt。在我们的体系结构中，水印的嵌入和恢复被视为一对图像变换的逆问题，可以分别通过可逆神经网络(INN)的正向和反向过程来实现。为了提高视觉质量，我们将水印嵌入到高频域中，尽量减少对作品的影响，并使用人类视觉系统(HVS)一致的深度感知损失来监督图像重建。为了提高嵌入图像的抗剽窃能力，我们构建了一个质量增强模块，用于对抗剽窃行为可能造成的图像失真。此外，两阶段对比训练策略可以同时实现上述两个目标。在4个数据集上的实验结果表明了我们的 IRWArt 相对于其他最先进的水印方法的优越性。密码: https://github.com/1024yy/irwart。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=IRWArt:+Levering+Watermarking+Performance+for+Protecting+High-quality+Artwork+Images)|0| |[CapEnrich: Enriching Caption Semantics for Web Images via Cross-modal Pre-trained Knowledge](https://doi.org/10.1145/3543507.3583232)|Linli Yao, Weijing Chen, Qin Jin|School of Information, Renmin University of China, China|Automatically generating textual descriptions for massive unlabeled images on the web can greatly benefit realistic web applications, e.g. multimodal retrieval and recommendation. However, existing models suffer from the problem of generating ``over-generic'' descriptions, such as their tendency to generate repetitive sentences with common concepts for different images. These generic descriptions fail to provide sufficient textual semantics for ever-changing web images. Inspired by the recent success of Vision-Language Pre-training (VLP) models that learn diverse image-text concept alignment during pretraining, we explore leveraging their cross-modal pre-trained knowledge to automatically enrich the textual semantics of image descriptions. With no need for additional human annotations, we propose a plug-and-play framework, i.e CapEnrich, to complement the generic image descriptions with more semantic details. Specifically, we first propose an automatic data-building strategy to get desired training sentences, based on which we then adopt prompting strategies, i.e. learnable and template prompts, to incentivize VLP models to generate more textual details. For learnable templates, we fix the whole VLP model and only tune the prompt vectors, which leads to two advantages: 1) the pre-training knowledge of VLP models can be reserved as much as possible to describe diverse visual concepts; 2) only lightweight trainable parameters are required, so it is friendly to low data resources. Extensive experiments show that our method significantly improves the descriptiveness and diversity of generated sentences for web images. The code is available at https://github.com/yaolinli/CapEnrich.|自动生成文本描述的大量未标记的图像在网络上可以大大有益于现实的网络应用程序，例如多模式检索和推荐。然而，现有的模型存在“过度泛型”描述的问题，例如它们倾向于为不同的图像生成具有共同概念的重复句子。这些通用的描述无法为不断变化的网络图像提供足够的文本语义。受最近成功的视觉语言预训练(VLP)模型的启发，我们在预训练过程中学习了不同的图像-文本概念对齐，我们探索利用它们的跨模式预训练知识来自动丰富图像描述的文本语义。由于不需要额外的人工注释，我们提出了一个即插即用的框架，即 CapEnrich，用更多的语义细节来补充通用图像描述。具体来说，我们首先提出一个自动的数据建立策略，以获得所需的训练句子，然后在此基础上，我们采用提示策略，即可学习和模板提示，以激励 VLP 模型生成更多的文本细节。对于可学习的模板，我们修正了整个 VLP 模型，只对提示向量进行调整，这带来了两个好处: 1) VLP 模型的预训练知识可以尽可能地保留，以描述不同的视觉概念; 2)只需要轻量级的可训练参数，因此对低资源量的数据是友好的。大量实验表明，该方法显著提高了网络图像生成句子的描述性和多样性。密码可在 https://github.com/yaolinli/capenrich 查阅。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=CapEnrich:+Enriching+Caption+Semantics+for+Web+Images+via+Cross-modal+Pre-trained+Knowledge)|0| |[MLN4KB: an efficient Markov logic network engine for large-scale knowledge bases and structured logic rules](https://doi.org/10.1145/3543507.3583248)|Huang Fang, Yang Liu, Yunfeng Cai, Mingming Sun|Baidu, China|Markov logic network (MLN) is a powerful statistical modeling framework for probabilistic logic reasoning. Despite the elegancy and effectiveness of MLN, the inference of MLN is known to suffer from an efficiency issue. Even the state-of-the-art MLN engines can not scale to medium-size real-world knowledge bases in the open-world setting, i.e., all unobserved facts in the knowledge base need predictions. In this work, by focusing on a certain class of first-order logic rules that are sufficiently expressive, we develop a highly efficient MLN inference engine called MLN4KB that can leverage the sparsity of knowledge bases. MLN4KB enjoys quite strong theoretical properties; its space and time complexities can be exponentially smaller than existing MLN engines. Experiments on both synthetic and real-world knowledge bases demonstrate the effectiveness of the proposed method. MLN4KB is orders of magnitudes faster (more than 103 times faster on some datasets) than existing MLN engines in the open-world setting. Without any approximation tricks, MLN4KB can scale to real-world knowledge bases including WN-18 and YAGO3-10 and achieve decent prediction accuracy without bells and whistles. We implement MLN4KB as a Julia package called MLN4KB.jl. The package supports both maximum a posteriori (MAP) inference and learning the weights of rules. MLN4KB.jl is public available at https://github.com/baidu-research/MLN4KB .|马尔可夫逻辑网络(MLN)是一个强大的统计建模框架，用于概率逻辑推理。尽管 MLN 具有优雅性和有效性，但 MLN 的推理却存在效率问题。即使是最先进的 MLN 引擎也无法在开放世界环境下扩展到中等规模的现实世界知识库，即知识库中所有未观察到的事实都需要预测。在这项工作中，通过关注一类具有足够表现力的一阶逻辑规则，我们开发了一种高效的 MLN 推理机，称为 MLN4kB，它可以利用稀缺的知识库。MLN4KB 具有很强的理论性能，其空间和时间复杂度可以指数小于现有的 MLN 发动机。在综合知识库和现实知识库上的实验表明了该方法的有效性。MLN4KB 比开放世界中现有的 MLN 引擎快几个数量级(在一些数据集上快103倍以上)。在没有任何近似技巧的情况下，MLN4KB 可以扩展到包括 WN-18和 YAGO3-10在内的真实世界的知识库，并且不需要花哨的功夫就能获得相当高的预测精度。我们将 MLN4KB 实现为一个名为 MLN4KB.jl 的 Julia 包。该软件包支持最大后验(MAP)推理和学习规则的权重。MLn4kB.jl 可于 https://github.com/baidu-research/mln4kb 索取。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=MLN4KB:+an+efficient+Markov+logic+network+engine+for+large-scale+knowledge+bases+and+structured+logic+rules)|0| -|[Learning Long- and Short-term Representations for Temporal Knowledge Graph Reasoning](https://doi.org/10.1145/3543507.3583242)|Mengqi Zhang, Yuwei Xia, Qiang Liu, Shu Wu, Liang Wang|School of Artificial Intelligence, University of Chinese Academy of Sciences, China and CRIPAC, MAIS, Institute of Automation, Chinese Academy of Sciences, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, China and CRIPAC,MAIS, Institute of Automation, Chinese Academy of Sciences, China; School of Cyber Security, University of Chinese Academy of Sciences, China and Institute of Information Engineering, Chinese Academy of Sciences, China|Temporal Knowledge graph (TKG) reasoning aims to predict missing facts based on historical TKG data. Most of the existing methods are incapable of explicitly modeling the long-term time dependencies from history and neglect the adaptive integration of the long- and short-term information. To tackle these problems, we propose a novel method that utilizes a designed Hierarchical Relational Graph Neural Network to learn the Long- and Short-term representations for TKG reasoning, namely HGLS. Specifically, to explicitly associate entities in different timestamps, we first transform the TKG into a global graph. Based on the built graph, we design a Hierarchical Relational Graph Neural Network that executes in two levels: The sub-graph level is to capture the semantic dependencies within concurrent facts of each KG. And the global-graph level aims to model the temporal dependencies between entities. Furthermore, we design a module to extract the long- and short-term information from the output of these two levels. Finally, the long- and short-term representations are fused into a unified one by Gating Integration for entity prediction. Extensive experiments on four datasets demonstrate the effectiveness of HGLS.|时态知识图(TKG)推理的目的是根据历史 TKG 数据预测缺失事实。现有的方法大多不能从历史上明确地建立长期时间依赖关系模型，忽视了长期和短期信息的自适应集成。为了解决这些问题，我们提出了一种利用所设计的层次关系图神经网络来学习 TKG 推理的长期和短期表示的新方法，即 HGLS。具体来说，为了显式地关联不同时间戳中的实体，我们首先将 TKG 转换为一个全局图。基于构建的图，我们设计了一个分层关系图神经网络，该网络在两个层次上执行: 子图层次是在每个 KG 的并发事实中捕获语义依赖。全局图层次的目标是建立实体之间的时间依赖关系模型。此外，我们还设计了一个模块，从这两个级别的输出中提取长期和短期信息。最后，通过门限积分将长期和短期表示融合为一个统一的表示，用于实体预测。在四个数据集上的大量实验证明了 HGLS 的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Learning+Long-+and+Short-term+Representations+for+Temporal+Knowledge+Graph+Reasoning)|0| -|[Mutually-paced Knowledge Distillation for Cross-lingual Temporal Knowledge Graph Reasoning](https://doi.org/10.1145/3543507.3583407)|Ruijie Wang, Zheng Li, Jingfeng Yang, Tianyu Cao, Chao Zhang, Bing Yin, Tarek F. Abdelzaher|School of Computational Science and Engineering, Georgia Institute of Technology, USA; Department of Computer Science, University of Illinois Urbana-Champaign, USA; Amazon.com Inc, USA|This paper investigates cross-lingual temporal knowledge graph reasoning problem, which aims to facilitate reasoning on Temporal Knowledge Graphs (TKGs) in low-resource languages by transfering knowledge from TKGs in high-resource ones. The cross-lingual distillation ability across TKGs becomes increasingly crucial, in light of the unsatisfying performance of existing reasoning methods on those severely incomplete TKGs, especially in low-resource languages. However, it poses tremendous challenges in two aspects. First, the cross-lingual alignments, which serve as bridges for knowledge transfer, are usually too scarce to transfer sufficient knowledge between two TKGs. Second, temporal knowledge discrepancy of the aligned entities, especially when alignments are unreliable, can mislead the knowledge distillation process. We correspondingly propose a mutually-paced knowledge distillation model MP-KD, where a teacher network trained on a source TKG can guide the training of a student network on target TKGs with an alignment module. Concretely, to deal with the scarcity issue, MP-KD generates pseudo alignments between TKGs based on the temporal information extracted by our representation module. To maximize the efficacy of knowledge transfer and control the noise caused by the temporal knowledge discrepancy, we enhance MP-KD with a temporal cross-lingual attention mechanism to dynamically estimate the alignment strength. The two procedures are mutually paced along with model training. Extensive experiments on twelve cross-lingual TKG transfer tasks in the EventKG benchmark demonstrate the effectiveness of the proposed MP-KD method.|本文研究了跨语言时态知识图的推理问题，目的是通过将知识从低资源语言的时态知识图中转移到高资源语言的时态知识图中来实现对低资源语言的时态知识图的推理。由于现有的推理方法对于严重不完备的 TKG，特别是在资源不足的 TKG 中的推理效果不理想，跨 TKG 的跨语言精馏能力变得越来越重要。然而，它在两个方面提出了巨大的挑战。首先，作为知识转移桥梁的跨语言对齐通常过于稀缺，无法在两个 TKG 之间转移足够的知识。其次，排列实体的时间知识差异，特别是排列不可靠时，会误导知识提取过程。相应地，我们提出了一个相互节奏的知识提取模型 MP-KD，其中一个在源 TKG 上训练的教师网络可以用一个对齐模块来指导学生网络在目标 TKG 上的训练。具体来说，MP-KD 基于表示模块提取的时间信息生成 TKG 之间的伪对齐，以解决缺失问题。为了最大限度地提高知识转移的效率，控制时间知识差异带来的噪声，我们采用时间跨语言注意机制增强 MP-KD，动态估计对齐强度。这两个程序与模型训练是相互配合的。通过对 EventKG 基准中12个跨语言 TKG 传递任务的大量实验，证明了所提出的 MP-KD 方法的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Mutually-paced+Knowledge+Distillation+for+Cross-lingual+Temporal+Knowledge+Graph+Reasoning)|0| -|[Large-Scale Analysis of New Employee Network Dynamics](https://doi.org/10.1145/3543507.3583400)|Yulin Yu, Longqi Yang, Siân Lindley, Mengting Wan|School of Information, University of Michigan, USA; Microsoft, USA; Microsoft Research, United Kingdom|The COVID-19 pandemic has accelerated digital transformations across industries, but also introduced new challenges into workplaces, including the difficulties of effectively socializing with colleagues when working remotely. This challenge is exacerbated for new employees who need to develop workplace networks from the outset. In this paper, by analyzing a large-scale telemetry dataset of more than 10,000 Microsoft employees who joined the company in the first three months of 2022, we describe how new employees interact and telecommute with their colleagues during their ``onboarding'' period. Our results reveal that although new hires are gradually expanding networks over time, there still exists significant gaps between their network statistics and those of tenured employees even after the six-month onboarding phase. We also observe that heterogeneity exists among new employees in how their networks change over time, where employees whose job tasks do not necessarily require extensive and diverse connections could be at a disadvantaged position in this onboarding process. By investigating how web-based people recommendations in organizational knowledge base facilitate new employees naturally expand their networks, we also demonstrate the potential of web-based applications for addressing the aforementioned socialization challenges. Altogether, our findings provide insights on new employee network dynamics in remote and hybrid work environments, which may help guide organizational leaders and web application developers on quantifying and improving the socialization experiences of new employees in digital workplaces.|2019冠状病毒疾病疫情加速了各行各业的数字化转型，但也给工作场所带来了新的挑战，包括在远程工作时难以有效地与同事进行社交。对于从一开始就需要发展工作场所网络的新员工来说，这一挑战更加严峻。在这篇论文中，我们通过分析一个大规模的遥测数据集，这个数据集包含了2022年前三个月加入微软的10,000多名员工，我们描述了新员工在“入职”期间是如何与他们的同事进行互动和远程办公的。我们的研究结果表明，尽管新员工的人际网络随着时间的推移逐渐扩大，但即使在六个月的入职阶段之后，他们的人际网络统计数据与终身雇员之间仍然存在显著差距。我们还观察到，新员工的网络随着时间的推移如何变化存在异质性，其工作任务不一定需要广泛和多样化的联系的员工可能在这一入职过程中处于不利地位。通过调查组织知识库中基于网络的人员推荐如何促进新员工自然地扩展他们的网络，我们也展示了基于网络的应用程序在解决上述社会化挑战方面的潜力。总之，我们的研究结果提供了远程和混合工作环境中新员工网络动态的见解，这可能有助于指导组织领导者和网络应用程序开发人员量化和改善新员工在数字工作场所的社会化经验。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Large-Scale+Analysis+of+New+Employee+Network+Dynamics)|0| -|[MassNE: Exploring Higher-Order Interactions with Marginal Effect for Massive Battle Outcome Prediction](https://doi.org/10.1145/3543507.3583390)|Yin Gu, Kai Zhang, Qi Liu, Xin Lin, Zhenya Huang, Enhong Chen|Anhui Province Key Laboratory of Big Data Analysis and Application, University of Science and Technology of China & State Key Laboratory of Cognitive Intelligence, China; Anhui Province Key Laboratory of Big Data Analysis and Application, University of Science and Technology of China & State Key Laboratory of Cognitive Intelligence, China and Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, China|In online games, predicting massive battle outcomes is a fundamental task of many applications, such as team optimization and tactical formulation. Existing works do not pay adequate attention to the massive battle. They either seek to evaluate individuals in isolation or mine simple pair-wise interactions between individuals, neither of which effectively captures the intricate interactions between massive units (e.g., individuals). Furthermore, as the team size increases, the phenomenon of diminishing marginal utility of units emerges. Such a diminishing pattern is rarely noticed in previous work, and how to capture it from data remains a challenge. To this end, we propose a novel Massive battle outcome predictor with margiNal Effect modules, namely MassNE, which comprehensively incorporates individual effects, cooperation effects (i.e., intra-team interactions) and suppression effects (i.e., inter-team interactions) for predicting battle outcomes. Specifically, we design marginal effect modules to learn how units’ marginal utility changing respect to their number, where the monotonicity assumption is applied to ensure rationality. In addition, we evaluate the current classical models and provide mathematical proofs that MassNE is able to generalize several earlier works in massive settings. Massive battle datasets generated by StarCraft II APIs are adopted to evaluate the performances of MassNE. Extensive experiments empirically demonstrate the effectiveness of MassNE, and MassNE can reveal reasonable cooperation effects, suppression effects, and marginal utilities of combat units from the data.|在网络游戏中，预测大规模战斗的结果是许多应用程序的基本任务，如团队优化和战术制定。现有的工程对这场大规模的战斗没有给予足够的重视。他们要么试图孤立地评估个体，要么挖掘个体之间简单的成对互动，这两种方法都没有有效地捕捉到大量单位(例如个体)之间错综复杂的互动。此外，随着团队规模的扩大，单位边际效用递减的现象也随之出现。这种递减模式在以前的工作中很少被注意到，如何从数据中捕获它仍然是一个挑战。为此，我们提出了一个新的具有边际效应模块的大规模战斗结果预测器，即 MassNE，它综合了个体效应，合作效应(即团队内部相互作用)和抑制效应(即团队间相互作用)来预测战斗结果。具体来说，我们设计边际效应模块来研究单位的边际效用是如何随着数量的变化而变化的，其中使用了单调性假设来确保合理性。此外，我们评估了目前的经典模型，并提供数学证明，MassNE 能够在大规模的背景下推广几个早期的作品。采用星际争霸 II API 生成的海量战斗数据集对 MassNE 的性能进行评估。大量的实验结果表明，MassNE 和 MassNE 能够从数据中揭示作战单元的合理协同效应、抑制效应和边际效用。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=MassNE:+Exploring+Higher-Order+Interactions+with+Marginal+Effect+for+Massive+Battle+Outcome+Prediction)|0| +|[Learning Long- and Short-term Representations for Temporal Knowledge Graph Reasoning](https://doi.org/10.1145/3543507.3583242)|Mengqi Zhang, Yuwei Xia, Qiang Liu, Shu Wu, Liang Wang|School of Artificial Intelligence, University of Chinese Academy of Sciences, China and CRIPAC,MAIS, Institute of Automation, Chinese Academy of Sciences, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, China and CRIPAC, MAIS, Institute of Automation, Chinese Academy of Sciences, China; School of Cyber Security, University of Chinese Academy of Sciences, China and Institute of Information Engineering, Chinese Academy of Sciences, China|Temporal Knowledge graph (TKG) reasoning aims to predict missing facts based on historical TKG data. Most of the existing methods are incapable of explicitly modeling the long-term time dependencies from history and neglect the adaptive integration of the long- and short-term information. To tackle these problems, we propose a novel method that utilizes a designed Hierarchical Relational Graph Neural Network to learn the Long- and Short-term representations for TKG reasoning, namely HGLS. Specifically, to explicitly associate entities in different timestamps, we first transform the TKG into a global graph. Based on the built graph, we design a Hierarchical Relational Graph Neural Network that executes in two levels: The sub-graph level is to capture the semantic dependencies within concurrent facts of each KG. And the global-graph level aims to model the temporal dependencies between entities. Furthermore, we design a module to extract the long- and short-term information from the output of these two levels. Finally, the long- and short-term representations are fused into a unified one by Gating Integration for entity prediction. Extensive experiments on four datasets demonstrate the effectiveness of HGLS.|时态知识图(TKG)推理的目的是根据历史 TKG 数据预测缺失事实。现有的方法大多不能从历史上明确地建立长期时间依赖关系模型，忽视了长期和短期信息的自适应集成。为了解决这些问题，我们提出了一种利用所设计的层次关系图神经网络来学习 TKG 推理的长期和短期表示的新方法，即 HGLS。具体来说，为了显式地关联不同时间戳中的实体，我们首先将 TKG 转换为一个全局图。基于构建的图，我们设计了一个分层关系图神经网络，该网络在两个层次上执行: 子图层次是在每个 KG 的并发事实中捕获语义依赖。全局图层次的目标是建立实体之间的时间依赖关系模型。此外，我们还设计了一个模块，从这两个级别的输出中提取长期和短期信息。最后，通过门限积分将长期和短期表示融合为一个统一的表示，用于实体预测。在四个数据集上的大量实验证明了 HGLS 的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Learning+Long-+and+Short-term+Representations+for+Temporal+Knowledge+Graph+Reasoning)|0| +|[Mutually-paced Knowledge Distillation for Cross-lingual Temporal Knowledge Graph Reasoning](https://doi.org/10.1145/3543507.3583407)|Ruijie Wang, Zheng Li, Jingfeng Yang, Tianyu Cao, Chao Zhang, Bing Yin, Tarek F. Abdelzaher|Amazon.com Inc, USA; School of Computational Science and Engineering, Georgia Institute of Technology, USA; Department of Computer Science, University of Illinois Urbana-Champaign, USA|This paper investigates cross-lingual temporal knowledge graph reasoning problem, which aims to facilitate reasoning on Temporal Knowledge Graphs (TKGs) in low-resource languages by transfering knowledge from TKGs in high-resource ones. The cross-lingual distillation ability across TKGs becomes increasingly crucial, in light of the unsatisfying performance of existing reasoning methods on those severely incomplete TKGs, especially in low-resource languages. However, it poses tremendous challenges in two aspects. First, the cross-lingual alignments, which serve as bridges for knowledge transfer, are usually too scarce to transfer sufficient knowledge between two TKGs. Second, temporal knowledge discrepancy of the aligned entities, especially when alignments are unreliable, can mislead the knowledge distillation process. We correspondingly propose a mutually-paced knowledge distillation model MP-KD, where a teacher network trained on a source TKG can guide the training of a student network on target TKGs with an alignment module. Concretely, to deal with the scarcity issue, MP-KD generates pseudo alignments between TKGs based on the temporal information extracted by our representation module. To maximize the efficacy of knowledge transfer and control the noise caused by the temporal knowledge discrepancy, we enhance MP-KD with a temporal cross-lingual attention mechanism to dynamically estimate the alignment strength. The two procedures are mutually paced along with model training. Extensive experiments on twelve cross-lingual TKG transfer tasks in the EventKG benchmark demonstrate the effectiveness of the proposed MP-KD method.|本文研究了跨语言时态知识图的推理问题，目的是通过将知识从低资源语言的时态知识图中转移到高资源语言的时态知识图中来实现对低资源语言的时态知识图的推理。由于现有的推理方法对于严重不完备的 TKG，特别是在资源不足的 TKG 中的推理效果不理想，跨 TKG 的跨语言精馏能力变得越来越重要。然而，它在两个方面提出了巨大的挑战。首先，作为知识转移桥梁的跨语言对齐通常过于稀缺，无法在两个 TKG 之间转移足够的知识。其次，排列实体的时间知识差异，特别是排列不可靠时，会误导知识提取过程。相应地，我们提出了一个相互节奏的知识提取模型 MP-KD，其中一个在源 TKG 上训练的教师网络可以用一个对齐模块来指导学生网络在目标 TKG 上的训练。具体来说，MP-KD 基于表示模块提取的时间信息生成 TKG 之间的伪对齐，以解决缺失问题。为了最大限度地提高知识转移的效率，控制时间知识差异带来的噪声，我们采用时间跨语言注意机制增强 MP-KD，动态估计对齐强度。这两个程序与模型训练是相互配合的。通过对 EventKG 基准中12个跨语言 TKG 传递任务的大量实验，证明了所提出的 MP-KD 方法的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Mutually-paced+Knowledge+Distillation+for+Cross-lingual+Temporal+Knowledge+Graph+Reasoning)|0| +|[Large-Scale Analysis of New Employee Network Dynamics](https://doi.org/10.1145/3543507.3583400)|Yulin Yu, Longqi Yang, Siân Lindley, Mengting Wan|Microsoft, USA; School of Information, University of Michigan, USA; Microsoft Research, United Kingdom|The COVID-19 pandemic has accelerated digital transformations across industries, but also introduced new challenges into workplaces, including the difficulties of effectively socializing with colleagues when working remotely. This challenge is exacerbated for new employees who need to develop workplace networks from the outset. In this paper, by analyzing a large-scale telemetry dataset of more than 10,000 Microsoft employees who joined the company in the first three months of 2022, we describe how new employees interact and telecommute with their colleagues during their ``onboarding'' period. Our results reveal that although new hires are gradually expanding networks over time, there still exists significant gaps between their network statistics and those of tenured employees even after the six-month onboarding phase. We also observe that heterogeneity exists among new employees in how their networks change over time, where employees whose job tasks do not necessarily require extensive and diverse connections could be at a disadvantaged position in this onboarding process. By investigating how web-based people recommendations in organizational knowledge base facilitate new employees naturally expand their networks, we also demonstrate the potential of web-based applications for addressing the aforementioned socialization challenges. Altogether, our findings provide insights on new employee network dynamics in remote and hybrid work environments, which may help guide organizational leaders and web application developers on quantifying and improving the socialization experiences of new employees in digital workplaces.|2019冠状病毒疾病疫情加速了各行各业的数字化转型，但也给工作场所带来了新的挑战，包括在远程工作时难以有效地与同事进行社交。对于从一开始就需要发展工作场所网络的新员工来说，这一挑战更加严峻。在这篇论文中，我们通过分析一个大规模的遥测数据集，这个数据集包含了2022年前三个月加入微软的10,000多名员工，我们描述了新员工在“入职”期间是如何与他们的同事进行互动和远程办公的。我们的研究结果表明，尽管新员工的人际网络随着时间的推移逐渐扩大，但即使在六个月的入职阶段之后，他们的人际网络统计数据与终身雇员之间仍然存在显著差距。我们还观察到，新员工的网络随着时间的推移如何变化存在异质性，其工作任务不一定需要广泛和多样化的联系的员工可能在这一入职过程中处于不利地位。通过调查组织知识库中基于网络的人员推荐如何促进新员工自然地扩展他们的网络，我们也展示了基于网络的应用程序在解决上述社会化挑战方面的潜力。总之，我们的研究结果提供了远程和混合工作环境中新员工网络动态的见解，这可能有助于指导组织领导者和网络应用程序开发人员量化和改善新员工在数字工作场所的社会化经验。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Large-Scale+Analysis+of+New+Employee+Network+Dynamics)|0| +|[MassNE: Exploring Higher-Order Interactions with Marginal Effect for Massive Battle Outcome Prediction](https://doi.org/10.1145/3543507.3583390)|Yin Gu, Kai Zhang, Qi Liu, Xin Lin, Zhenya Huang, Enhong Chen|Anhui Province Key Laboratory of Big Data Analysis and Application, University of Science and Technology of China & State Key Laboratory of Cognitive Intelligence, China and Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, China; Anhui Province Key Laboratory of Big Data Analysis and Application, University of Science and Technology of China & State Key Laboratory of Cognitive Intelligence, China|In online games, predicting massive battle outcomes is a fundamental task of many applications, such as team optimization and tactical formulation. Existing works do not pay adequate attention to the massive battle. They either seek to evaluate individuals in isolation or mine simple pair-wise interactions between individuals, neither of which effectively captures the intricate interactions between massive units (e.g., individuals). Furthermore, as the team size increases, the phenomenon of diminishing marginal utility of units emerges. Such a diminishing pattern is rarely noticed in previous work, and how to capture it from data remains a challenge. To this end, we propose a novel Massive battle outcome predictor with margiNal Effect modules, namely MassNE, which comprehensively incorporates individual effects, cooperation effects (i.e., intra-team interactions) and suppression effects (i.e., inter-team interactions) for predicting battle outcomes. Specifically, we design marginal effect modules to learn how units’ marginal utility changing respect to their number, where the monotonicity assumption is applied to ensure rationality. In addition, we evaluate the current classical models and provide mathematical proofs that MassNE is able to generalize several earlier works in massive settings. Massive battle datasets generated by StarCraft II APIs are adopted to evaluate the performances of MassNE. Extensive experiments empirically demonstrate the effectiveness of MassNE, and MassNE can reveal reasonable cooperation effects, suppression effects, and marginal utilities of combat units from the data.|在网络游戏中，预测大规模战斗的结果是许多应用程序的基本任务，如团队优化和战术制定。现有的工程对这场大规模的战斗没有给予足够的重视。他们要么试图孤立地评估个体，要么挖掘个体之间简单的成对互动，这两种方法都没有有效地捕捉到大量单位(例如个体)之间错综复杂的互动。此外，随着团队规模的扩大，单位边际效用递减的现象也随之出现。这种递减模式在以前的工作中很少被注意到，如何从数据中捕获它仍然是一个挑战。为此，我们提出了一个新的具有边际效应模块的大规模战斗结果预测器，即 MassNE，它综合了个体效应，合作效应(即团队内部相互作用)和抑制效应(即团队间相互作用)来预测战斗结果。具体来说，我们设计边际效应模块来研究单位的边际效用是如何随着数量的变化而变化的，其中使用了单调性假设来确保合理性。此外，我们评估了目前的经典模型，并提供数学证明，MassNE 能够在大规模的背景下推广几个早期的作品。采用星际争霸 II API 生成的海量战斗数据集对 MassNE 的性能进行评估。大量的实验结果表明，MassNE 和 MassNE 能够从数据中揭示作战单元的合理协同效应、抑制效应和边际效用。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=MassNE:+Exploring+Higher-Order+Interactions+with+Marginal+Effect+for+Massive+Battle+Outcome+Prediction)|0| |[Online Advertising in Ukraine and Russia During the 2022 Russian Invasion](https://doi.org/10.1145/3543507.3583484)|Christina Yeung, Umar Iqbal, Yekaterina Tsipenyuk O'Neil, Tadayoshi Kohno, Franziska Roesner|Security and Privacy Research Lab, University of Washington, USA; Micro Focus, USA|Online ads are a major source of information on the web. The mass reach of online advertising is often leveraged for information dissemination, at times with an objective to influence public opinion (e.g., election misinformation). We hypothesized that online advertising, due to its reach and potential, might have been used to spread information around the 2022 Russian invasion of Ukraine. Thus, to understand the online ad ecosystem during this conflict, we conducted a five-month long large-scale measurement study of online advertising in Ukraine, Russia, and the US. We studied advertising trends of ad platforms that delivered ads in Ukraine, Russia, and the US and conducted an in-depth qualitative analysis of the conflict-related ad content. We found that prominent US-based advertisers continued to support Russian websites, and a portion of online ads were used to spread conflict-related information, including protesting the invasion, and spreading awareness, which might have otherwise potentially been censored in Russia.|在线广告是网络信息的主要来源。在线广告的广泛传播经常被用于信息传播，有时是为了影响公众舆论(例如，选举错误信息)。我们假设，由于网络广告的影响力和潜力，它可能被用来传播2022年俄罗斯入侵乌克兰前后的信息。因此，为了了解这场冲突中的在线广告生态系统，我们对乌克兰、俄罗斯和美国的在线广告进行了为期五个月的大规模测量研究。我们研究了在乌克兰、俄罗斯和美国发布广告的广告平台的广告趋势，并对与冲突相关的广告内容进行了深入的定性分析。我们发现，美国知名的广告商继续支持俄罗斯网站，一部分在线广告被用于传播与冲突有关的信息，包括抗议入侵和传播意识，否则这些信息可能会在俄罗斯受到审查。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Online+Advertising+in+Ukraine+and+Russia+During+the+2022+Russian+Invasion)|0| -|[Understanding the Behaviors of Toxic Accounts on Reddit](https://doi.org/10.1145/3543507.3583522)|Deepak Kumar, Jeff T. Hancock, Kurt Thomas, Zakir Durumeric|Stanford University, USA; Google, USA|Toxic comments are the top form of hate and harassment experienced online. While many studies have investigated the types of toxic comments posted online, the effects that such content has on people, and the impact of potential defenses, no study has captured the behaviors of the accounts that post toxic comments or how such attacks are operationalized. In this paper, we present a measurement study of 929K accounts that post toxic comments on Reddit over an 18 month period. Combined, these accounts posted over 14 million toxic comments that encompass insults, identity attacks, threats of violence, and sexual harassment. We explore the impact that these accounts have on Reddit, the targeting strategies that abusive accounts adopt, and the distinct patterns that distinguish classes of abusive accounts. Our analysis informs the nuanced interventions needed to curb unwanted toxic behaviors online.|有害的评论是网络上最常见的仇恨和骚扰。尽管许多研究调查了网上发布的有毒评论的类型、这些内容对人们的影响以及潜在防御的影响，但没有一项研究捕捉到发布有毒评论的账户的行为或者这种攻击是如何操作的。在这篇论文中，我们提出了一个测量研究的929K 帐户后，有毒评论 Reddit 在18个月期间。这些账户总共发布了超过1400万条有毒评论，包括侮辱、身份攻击、暴力威胁和性骚扰。我们探讨了这些账户对 Reddit 的影响，滥用账户采用的定位策略，以及区分滥用账户类别的独特模式。我们的分析为控制网上不良行为提供了细致入微的干预措施。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Understanding+the+Behaviors+of+Toxic+Accounts+on+Reddit)|0| +|[Understanding the Behaviors of Toxic Accounts on Reddit](https://doi.org/10.1145/3543507.3583522)|Deepak Kumar, Jeff T. Hancock, Kurt Thomas, Zakir Durumeric|Google, USA; Stanford University, USA|Toxic comments are the top form of hate and harassment experienced online. While many studies have investigated the types of toxic comments posted online, the effects that such content has on people, and the impact of potential defenses, no study has captured the behaviors of the accounts that post toxic comments or how such attacks are operationalized. In this paper, we present a measurement study of 929K accounts that post toxic comments on Reddit over an 18 month period. Combined, these accounts posted over 14 million toxic comments that encompass insults, identity attacks, threats of violence, and sexual harassment. We explore the impact that these accounts have on Reddit, the targeting strategies that abusive accounts adopt, and the distinct patterns that distinguish classes of abusive accounts. Our analysis informs the nuanced interventions needed to curb unwanted toxic behaviors online.|有害的评论是网络上最常见的仇恨和骚扰。尽管许多研究调查了网上发布的有毒评论的类型、这些内容对人们的影响以及潜在防御的影响，但没有一项研究捕捉到发布有毒评论的账户的行为或者这种攻击是如何操作的。在这篇论文中，我们提出了一个测量研究的929K 帐户后，有毒评论 Reddit 在18个月期间。这些账户总共发布了超过1400万条有毒评论，包括侮辱、身份攻击、暴力威胁和性骚扰。我们探讨了这些账户对 Reddit 的影响，滥用账户采用的定位策略，以及区分滥用账户类别的独特模式。我们的分析为控制网上不良行为提供了细致入微的干预措施。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Understanding+the+Behaviors+of+Toxic+Accounts+on+Reddit)|0| |[Online Reviews Are Leading Indicators of Changes in K-12 School Attributes](https://doi.org/10.1145/3543507.3583531)|Linsen Li, Aron Culotta, Douglas N. Harris, Nicholas Mattei|Department of Economics, Tulane University, USA; Department of Computer Science, Tulane University, USA|School rating websites are increasingly used by parents to assess the quality and fit of U.S. K-12 schools for their children. These online reviews often contain detailed descriptions of a school’s strengths and weaknesses, which both reflect and inform perceptions of a school. Existing work on these text reviews has focused on finding words or themes that underlie these perceptions, but has stopped short of using the textual reviews as leading indicators of school performance. In this paper, we investigate to what extent the language used in online reviews of a school is predictive of changes in the attributes of that school, such as its socio-economic makeup and student test scores. Using over 300K reviews of 70K U.S. schools from a popular ratings website, we apply language processing models to predict whether schools will significantly increase or decrease in an attribute of interest over a future time horizon. We find that using the text improves predictive performance significantly over a baseline model that does not include text but only the historical time-series of the indicators themselves, suggesting that the review text carries predictive power. A qualitative analysis of the most predictive terms and phrases used in the text reviews indicates a number of topics that serve as leading indicators, such as diversity, changes in school leadership, a focus on testing, and school safety.|越来越多的家长使用学校评级网站来评估美国 K-12学校的质量和适合他们孩子的程度。这些在线评论通常包含对一所学校优势和劣势的详细描述，这些优势和劣势既反映了对一所学校的看法，也反映了对这所学校的看法。关于这些文本审查的现有工作集中在寻找构成这些观念的词汇或主题，但没有使用文本审查作为学校表现的主要指标。在这篇论文中，我们调查了在网上评论一所学校时使用的语言在多大程度上可以预测该学校的属性变化，例如其社会经济构成和学生考试成绩。我们利用一个受欢迎的评级网站对美国70000所学校的300K 评论，应用语言处理模型来预测学校在未来的时间范围内是否会显著增加或减少兴趣属性。我们发现使用文本比不包括文本但仅包括指标本身的历史时间序列的基线模型显着提高了预测性能，这表明审查文本具有预测能力。对文本评论中使用的最具预测性的术语和短语进行定性分析，可以发现一些主要指标，如多样性、学校领导层的变化、对测试的关注和学校安全。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Online+Reviews+Are+Leading+Indicators+of+Changes+in+K-12+School+Attributes)|0| |[Beyond Fine-Tuning: Efficient and Effective Fed-Tuning for Mobile/Web Users](https://doi.org/10.1145/3543507.3583212)|Bingyan Liu, Yifeng Cai, Hongzhe Bi, Ziqi Zhang, Ding Li, Yao Guo, Xiangqun Chen|Beijing University of Posts and Telecommunications, China; Peking University, China|Fine-tuning is a typical mechanism to achieve model adaptation for mobile/web users, where a model trained by the cloud is further retrained to fit the target user task. While traditional fine-tuning has been proved effective, it only utilizes local data to achieve adaptation, failing to take advantage of the valuable knowledge from other mobile/web users. In this paper, we attempt to extend the local-user fine-tuning to multi-user fed-tuning with the help of Federated Learning (FL). Following the new paradigm, we propose EEFT, a framework aiming to achieve Efficient and Effective Fed-Tuning for mobile/web users. The key idea is to introduce lightweight but effective adaptation modules to the pre-trained model, such that we can freeze the pre-trained model and just focus on optimizing the modules to achieve cost reduction and selective task cooperation. Extensive experiments on our constructed benchmark demonstrate the effectiveness and efficiency of the proposed framework.|微调是为移动/网络用户实现模型自适应的典型机制，其中云训练的模型将进一步再训练以适应目标用户的任务。虽然传统的微调已被证明是有效的，但它只利用本地数据来实现适应，而没有利用其他移动/网络用户的宝贵知识。本文尝试在联邦学习(FL)的帮助下，将本地用户调优扩展到多用户馈源调优。遵循这一新的范式，我们提出了 EEFT 框架，旨在为移动/网络用户实现高效和有效的馈线调整。其核心思想是在预训练模型中引入轻量级但有效的自适应模块，这样我们就可以冻结预训练模型，集中精力优化模块以达到降低成本和选择性任务协作的目的。在我们构建的基准上的大量实验证明了该框架的有效性和效率。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Beyond+Fine-Tuning:+Efficient+and+Effective+Fed-Tuning+for+Mobile/Web+Users)|0| |[Automated WebAssembly Function Purpose Identification With Semantics-Aware Analysis](https://doi.org/10.1145/3543507.3583235)|Alan Romano, Weihang Wang|University of Southern California, USA|WebAssembly is a recent web standard built for better performance in web applications. The standard defines a binary code format to use as a compilation target for a variety of languages, such as C, C++, and Rust. The standard also defines a text representation for readability, although, WebAssembly modules are difficult to interpret by human readers, regardless of their experience level. This makes it difficult to understand and maintain any existing WebAssembly code. As a result, third-party WebAssembly modules need to be implicitly trusted by developers as verifying the functionality themselves may not be feasible. To this end, we construct WASPur, a tool to automatically identify the purposes of WebAssembly functions. To build this tool, we first construct an extensive collection of WebAssembly samples that represent the state of WebAssembly. Second, we analyze the dataset and identify the diverse use cases of the collected WebAssembly modules. We leverage the dataset of WebAssembly modules to construct semantics-aware intermediate representations (IR) of the functions in the modules. We encode the function IR for use in a machine learning classifier, and we find that this classifier can predict the similarity of a given function against known named functions with an accuracy rate of 88.07%. We hope our tool will enable inspection of optimized and minified WebAssembly modules that remove function names and most other semantic identifiers.|WebAssembly 是最近为了在 Web 应用程序中获得更好的性能而建立的 Web 标准。该标准定义了一种二进制代码格式，用作各种语言(如 C、 C + + 和 Rust)的编译目标。该标准还为可读性定义了一个文本表示，尽管 WebAssembly 模块很难被人类读者解释，不管他们的经验水平如何。这使得理解和维护任何现有的 WebAssembly 代码变得非常困难。因此，开发人员需要隐式地信任第三方 WebAssembly 模块，因为验证功能本身可能是不可行的。为此，我们构建了 WASPur，这是一个自动识别 WebAssembly 函数用途的工具。为了构建这个工具，我们首先构建一个广泛的 WebAssembly 示例集合，这些示例代表 WebAssembly 的状态。其次，我们分析数据集并识别收集的 WebAssembly 模块的不同用例。我们利用 WebAssembly 模块的数据集来构造模块中函数的语义感知中间表示(IR)。将函数 IR 编码后用于机器学习分类器，结果表明，该分类器可以预测给定函数与已知命名函数的相似度，准确率为88.07% 。我们希望我们的工具能够检查优化和缩小的 WebAssembly 模块，这些模块删除了函数名和大多数其他语义标识符。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Automated+WebAssembly+Function+Purpose+Identification+With+Semantics-Aware+Analysis)|0| |[SCTAP: Supporting Scenario-Centric Trigger-Action Programming based on Software-Defined Physical Environments](https://doi.org/10.1145/3543507.3583293)|Bingkun Sun, Liwei Shen, Xin Peng, Ziming Wang|School of Computer Science and Shanghai Key Laboratory of Data Science, Fudan University, China|The physical world we live in is accelerating digitalization with the vigorous development of Internet of Things (IoT). Following this trend, Web of Things (WoT) further enables fast and efficient creation of various applications that perceive and act on the physical world using standard Web technologies. A popular way for creating WoT applications is Trigger-Action Programming (TAP), which allows users to orchestrate the capabilities of IoT devices in the form of “if trigger, then action”. However, existing TAP approaches don’t support scenario-centric WoT applications which involve abstract modeling of physical environments and complex spatio-temporal dependencies between events and actions. In this paper, we propose an approach called SCTAP which supports Scenario-Centric Trigger-Action Programming based on software-defined physical environments. SCTAP defines a structured and conceptual representation for physical environments, which provides the required programming abstractions for WoT applications. Based on the representation, SCTAP defines a grammar for specifying scenario-centric WoT applications with spatio-temporal dependencies. Furthermore, we design a service-based architecture for SCTAP which supports the integration of device access, event perception, environment representation, and rule execution in a loosely-coupled and extensible way. We implement SCTAP as a WoT infrastructure and evaluate it with two case studies including a smart laboratory and a smart coffee house. The results confirm the usability, feasibility and efficiency of SCTAP and its implementation.|随着物联网的蓬勃发展，我们生活的物质世界正在加速数字化进程。遵循这一趋势，物联网(Web of Things，WoT)进一步支持使用标准 Web 技术快速高效地创建各种应用程序，这些应用程序使用物理世界来感知和操作。创建物联网应用程序的一种流行方式是触发行动编程(Trigger-Action Programming，TAP) ，它允许用户以“如果触发，那么行动”的形式编排物联网设备的功能。然而，现有的 TAP 方法不支持以场景为中心的 WoT 应用程序，这些应用程序涉及物理环境的抽象建模以及事件和操作之间复杂的时空依赖关系。在本文中，我们提出了一种支持基于软件定义的物理环境的以场景为中心的触发行为编程的 SCTAP 方法。SCTAP 定义了物理环境的结构化和概念化表示，为 WoT 应用程序提供了所需的编程抽象。基于这种表示，SCTAP 定义了一种语法，用于指定具有时空依赖性的以场景为中心的 WoT 应用程序。此外，我们还为 SCTAP 设计了一个基于服务的体系结构，该体系结构以松耦合和可扩展的方式支持设备访问、事件感知、环境表示和规则执行的集成。我们将 SCTAP 作为一个 WoT 基础设施来实施，并通过两个案例研究对其进行评估，其中包括一个智能实验室和一个智能咖啡屋。实验结果验证了 SCTAP 及其实现的可用性、可行性和有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=SCTAP:+Supporting+Scenario-Centric+Trigger-Action+Programming+based+on+Software-Defined+Physical+Environments)|0| -|[DeeProphet: Improving HTTP Adaptive Streaming for Low Latency Live Video by Meticulous Bandwidth Prediction](https://doi.org/10.1145/3543507.3583364)|Kefan Chen, Bo Wang, Wufan Wang, Xiaoyu Li, Fengyuan Ren|Tsinghua University, China; Beijing Institute of Technology, China; Tsinghua University, China and Zhongguancun Laboratory, China|The performance of HTTP adaptive streaming (HAS) depends heavily on the prediction of end-to-end network bandwidth. The increasingly popular low latency live streaming (LLLS) faces greater challenges since it requires accurate, short-term bandwidth prediction, compared with VOD streaming which needs long-term bandwidth prediction and has good tolerance against prediction error. Part of the challenges comes from the fact that short-term bandwidth experiences both large abrupt changes and uncertain fluctuations. Additionally, it is hard to obtain valid bandwidth measurement samples in LLLS due to its inter-chunk and intra-chunk sending idleness. In this work, we present DeeProphet, a system for accurate bandwidth prediction in LLLS to improve the performance of HAS. DeeProphet overcomes the above challenges by collecting valid measurement samples using fine-grained TCP state information to identify the packet bursting intervals, and by combining the time series model and learning-based model to predict both large change and uncertain fluctuations. Experiment results show that DeeProphet improves the overall QoE by 17.7%-359.2% compared with state-of-the-art LLLS ABR algorithms, and reduces the median bandwidth prediction error to 2.7%.|HTTP 自适应流(HAS)的性能在很大程度上取决于端到端网络带宽的预测。相对于需要长期带宽预测且对预测误差有较好承受能力的 VOD 流，低延迟直播流(LLLS)由于需要准确、短期的带宽预测而面临着更大的挑战。部分挑战来自短期带宽经历巨大的突变和不确定的波动这一事实。此外，在 LLLS 中，由于组间和组内发送空闲，很难获得有效的带宽测量样本。在这项工作中，我们提出了 DeeProphet，一个在 LLLS 中准确的带宽预测系统，以改善 HAS 的性能。DeeProphet 通过使用细粒度的 TCP 状态信息收集有效的测量样本来识别数据包的爆发间隔，并结合时间序列模型和基于学习的模型来预测大的变化和不确定的波动，克服了上述挑战。实验结果表明，与最先进的 LLLS ABR 算法相比，DeeProphet 算法的总体 QoE 提高了17.7% -359.2% ，中值带宽预测误差降低到2.7% 。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=DeeProphet:+Improving+HTTP+Adaptive+Streaming+for+Low+Latency+Live+Video+by+Meticulous+Bandwidth+Prediction)|0| -|[Is IPFS Ready for Decentralized Video Streaming?](https://doi.org/10.1145/3543507.3583404)|Zhengyu Wu, ChengHao Ryan Yang, Santiago Vargas, Aruna Balasubramanian|Computer Science, Northeastern University, USA; Computer Science, Stony Brook University, USA|InterPlanetary File System (IPFS) is a peer-to-peer protocol for decentralized content storage and retrieval. The IPFS platform has the potential to help users evade censorship and avoid a central point of failure. IPFS is seeing increasing adoption for distributing various kinds of files, including video. However, the performance of video streaming on IPFS has not been well-studied. We conduct a measurement study with over 28,000 videos hosted on the IPFS network and find that video streaming experiences high stall rates due to relatively high Round Trip Times (RTT). Further, videos are encoded using a single static quality, because of which streaming cannot adapt to different network conditions. A natural approach is to use adaptive bitrate (ABR) algorithms for streaming, which encode videos in multiple qualities and streams according to the throughput available. However, traditional ABR algorithms perform poorly on IPFS because the throughput cannot be estimated correctly. The main problem is that video segments can be retrieved from multiple sources, making it difficult to estimate the throughput. To overcome this issue, we have designed Telescope, an IPFS-aware ABR system. We conduct experiments on the IPFS network, where IPFS video providers are geographically distributed across the globe. Our results show that Telescope significantly improves the Quality of Experience (QoE) of videos, for a diverse set of network and cache conditions, compared to traditional ABR.|行星文件系统(IPFS)是一种用于分散内容存储和检索的对等协议。IPFS 平台具有帮助用户规避审查和避免中心故障点的潜力。IPFS 越来越多地被用于发布各种文件，包括视频。然而，IPFS 上视频流的性能还没有得到很好的研究。我们对 IPFS 网络上的28,000多个视频进行了测量研究，发现由于往返时间(RTT)相对较高，视频流经历了较高的失速率。而且，视频是使用单一静态质量进行编码的，因为流不能适应不同的网络条件。一种自然的方法是使用自适应比特率(ABR)算法进行视频流编码，它根据可用的吞吐量以多种质量和流的形式对视频进行编码。然而，传统的 ABR 算法在 IPFS 上表现不佳，因为不能正确估计吞吐量。主要问题是视频片段可以从多个源检索，这使得估计吞吐量变得困难。为了克服这个问题，我们设计了望远镜，一个 IPFS 感知 ABR 系统。我们在 IPFS 网络上进行实验，IPFS 视频提供商分布在全球各地。我们的研究结果表明，与传统的 ABR 相比，Telescope 在不同的网络和缓存条件下显著提高了视频的体验质量(QoE)。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Is+IPFS+Ready+for+Decentralized+Video+Streaming?)|0| +|[DeeProphet: Improving HTTP Adaptive Streaming for Low Latency Live Video by Meticulous Bandwidth Prediction](https://doi.org/10.1145/3543507.3583364)|Kefan Chen, Bo Wang, Wufan Wang, Xiaoyu Li, Fengyuan Ren|Tsinghua University, China; Tsinghua University, China and Zhongguancun Laboratory, China; Beijing Institute of Technology, China|The performance of HTTP adaptive streaming (HAS) depends heavily on the prediction of end-to-end network bandwidth. The increasingly popular low latency live streaming (LLLS) faces greater challenges since it requires accurate, short-term bandwidth prediction, compared with VOD streaming which needs long-term bandwidth prediction and has good tolerance against prediction error. Part of the challenges comes from the fact that short-term bandwidth experiences both large abrupt changes and uncertain fluctuations. Additionally, it is hard to obtain valid bandwidth measurement samples in LLLS due to its inter-chunk and intra-chunk sending idleness. In this work, we present DeeProphet, a system for accurate bandwidth prediction in LLLS to improve the performance of HAS. DeeProphet overcomes the above challenges by collecting valid measurement samples using fine-grained TCP state information to identify the packet bursting intervals, and by combining the time series model and learning-based model to predict both large change and uncertain fluctuations. Experiment results show that DeeProphet improves the overall QoE by 17.7%-359.2% compared with state-of-the-art LLLS ABR algorithms, and reduces the median bandwidth prediction error to 2.7%.|HTTP 自适应流(HAS)的性能在很大程度上取决于端到端网络带宽的预测。相对于需要长期带宽预测且对预测误差有较好承受能力的 VOD 流，低延迟直播流(LLLS)由于需要准确、短期的带宽预测而面临着更大的挑战。部分挑战来自短期带宽经历巨大的突变和不确定的波动这一事实。此外，在 LLLS 中，由于组间和组内发送空闲，很难获得有效的带宽测量样本。在这项工作中，我们提出了 DeeProphet，一个在 LLLS 中准确的带宽预测系统，以改善 HAS 的性能。DeeProphet 通过使用细粒度的 TCP 状态信息收集有效的测量样本来识别数据包的爆发间隔，并结合时间序列模型和基于学习的模型来预测大的变化和不确定的波动，克服了上述挑战。实验结果表明，与最先进的 LLLS ABR 算法相比，DeeProphet 算法的总体 QoE 提高了17.7% -359.2% ，中值带宽预测误差降低到2.7% 。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=DeeProphet:+Improving+HTTP+Adaptive+Streaming+for+Low+Latency+Live+Video+by+Meticulous+Bandwidth+Prediction)|0| +|[Is IPFS Ready for Decentralized Video Streaming?](https://doi.org/10.1145/3543507.3583404)|Zhengyu Wu, ChengHao Ryan Yang, Santiago Vargas, Aruna Balasubramanian|Computer Science, Stony Brook University, USA; Computer Science, Northeastern University, USA|InterPlanetary File System (IPFS) is a peer-to-peer protocol for decentralized content storage and retrieval. The IPFS platform has the potential to help users evade censorship and avoid a central point of failure. IPFS is seeing increasing adoption for distributing various kinds of files, including video. However, the performance of video streaming on IPFS has not been well-studied. We conduct a measurement study with over 28,000 videos hosted on the IPFS network and find that video streaming experiences high stall rates due to relatively high Round Trip Times (RTT). Further, videos are encoded using a single static quality, because of which streaming cannot adapt to different network conditions. A natural approach is to use adaptive bitrate (ABR) algorithms for streaming, which encode videos in multiple qualities and streams according to the throughput available. However, traditional ABR algorithms perform poorly on IPFS because the throughput cannot be estimated correctly. The main problem is that video segments can be retrieved from multiple sources, making it difficult to estimate the throughput. To overcome this issue, we have designed Telescope, an IPFS-aware ABR system. We conduct experiments on the IPFS network, where IPFS video providers are geographically distributed across the globe. Our results show that Telescope significantly improves the Quality of Experience (QoE) of videos, for a diverse set of network and cache conditions, compared to traditional ABR.|行星文件系统(IPFS)是一种用于分散内容存储和检索的对等协议。IPFS 平台具有帮助用户规避审查和避免中心故障点的潜力。IPFS 越来越多地被用于发布各种文件，包括视频。然而，IPFS 上视频流的性能还没有得到很好的研究。我们对 IPFS 网络上的28,000多个视频进行了测量研究，发现由于往返时间(RTT)相对较高，视频流经历了较高的失速率。而且，视频是使用单一静态质量进行编码的，因为流不能适应不同的网络条件。一种自然的方法是使用自适应比特率(ABR)算法进行视频流编码，它根据可用的吞吐量以多种质量和流的形式对视频进行编码。然而，传统的 ABR 算法在 IPFS 上表现不佳，因为不能正确估计吞吐量。主要问题是视频片段可以从多个源检索，这使得估计吞吐量变得困难。为了克服这个问题，我们设计了望远镜，一个 IPFS 感知 ABR 系统。我们在 IPFS 网络上进行实验，IPFS 视频提供商分布在全球各地。我们的研究结果表明，与传统的 ABR 相比，Telescope 在不同的网络和缓存条件下显著提高了视频的体验质量(QoE)。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Is+IPFS+Ready+for+Decentralized+Video+Streaming?)|0| |[SISSI: An Architecture for Semantic Interoperable Self-Sovereign Identity-based Access Control on the Web](https://doi.org/10.1145/3543507.3583409)|Christoph H.J. Braun, Vasil Papanchev, Tobias Käfer|Karlsruhe Institute of Technology, Germany|We present an architecture for authentication and authorization on the Web that is based on the Self-Sovereign Identity paradigm. Using our architecture, we aim to achieve semantic interoperability across different approaches to SSI. We build on the underlying RDF data model of the W3C’s recommendation for Verifiable Credentials and specify semantic access control rules using SHACL. Our communication protocol for an authorization process is based on Decentralised Identifiers and extends the Hyperledger Aries Present Proof protocol. We propose a modular architecture that allows for flexible extension, e. g., for supporting more signature schemes or Decentralised Identifier Methods. For evaluation, we implemented a Proof-of-Concept: We show that a Web-based approach to SSI outperfoms a blockchain-based approach to SSI in terms of End-to-End execution time.|我们提出了一种基于自主身份认证范式的 Web 身份验证和授权体系结构。利用我们的架构，我们的目标是在不同的 SSI 方法之间实现语义互操作性。我们基于 W3C 推荐的可验证凭证的底层 RDF 数据模型，并使用 SHACL 指定语义访问控制规则。我们的授权过程的通信协议是基于分散的标识符，并扩展了 Hyperledger 白羊目前的证明协议。我们提出了一个模块化的体系结构，允许灵活的扩展，例如，支持更多的签名方案或分散的标识符方法。对于评估，我们实现了一个概念验证: 我们展示了基于 Web 的 SSI 方法在端到端执行时间方面优于基于区块链的 SSI 方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=SISSI:+An+Architecture+for+Semantic+Interoperable+Self-Sovereign+Identity-based+Access+Control+on+the+Web)|0| |[Detecting Socially Abnormal Highway Driving Behaviors via Recurrent Graph Attention Networks](https://doi.org/10.1145/3543507.3583452)|Yue Hu, Yuhang Zhang, Yanbing Wang, Daniel B. Work|Vanderbilt University, USA|With the rapid development of Internet of Things technologies, the next generation traffic monitoring infrastructures are connected via the web, to aid traffic data collection and intelligent traffic management. One of the most important tasks in traffic is anomaly detection, since abnormal drivers can reduce traffic efficiency and cause safety issues. This work focuses on detecting abnormal driving behaviors from trajectories produced by highway video surveillance systems. Most of the current abnormal driving behavior detection methods focus on a limited category of abnormal behaviors that deal with a single vehicle without considering vehicular interactions. In this work, we consider the problem of detecting a variety of socially abnormal driving behaviors, i.e., behaviors that do not conform to the behavior of other nearby drivers. This task is complicated by the variety of vehicular interactions and the spatial-temporal varying nature of highway traffic. To solve this problem, we propose an autoencoder with a Recurrent Graph Attention Network that can capture the highway driving behaviors contextualized on the surrounding cars, and detect anomalies that deviate from learned patterns. Our model is scalable to large freeways with thousands of cars. Experiments on data generated from traffic simulation software show that our model is the only one that can spot the exact vehicle conducting socially abnormal behaviors, among the state-of-the-art anomaly detection models. We further show the performance on real world HighD traffic dataset, where our model detects vehicles that violate the local driving norms.|随着物联网技术的飞速发展，下一代的交通监控基础设施通过网络连接起来，有助于交通数据的采集和智能交通管理。交通中最重要的任务之一就是异常检测，因为不正常的司机会降低交通效率并引起安全问题。本文主要研究如何从高速公路视频监控系统产生的轨迹中检测出不正常的驾驶行为。目前的异常驾驶行为检测方法大多集中在有限的一类异常驾驶行为上，这类异常驾驶行为只检测一辆车，而不考虑车辆之间的相互作用。在这项工作中，我们考虑的问题，检测各种社会异常驾驶行为，即行为不符合其他附近的驾驶员的行为。由于车辆相互作用的多样性以及高速公路交通的时空变化特性，这项任务变得更加复杂。为了解决这一问题，我们提出了一种基于循环图形注意网络的自动编码器，它可以捕获与周围车辆相关的高速公路驾驶行为，并检测偏离学习模式的异常。我们的模型可扩展到拥有数千辆汽车的大型高速公路。对交通模拟软体数据的实验表明，在最先进的异常检测模型中，我们的模型是唯一能够准确识别出行为异常的车辆的模型。我们进一步显示在真实世界的高速交通数据集，其中我们的模型检测违反当地驾驶规范的车辆的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Detecting+Socially+Abnormal+Highway+Driving+Behaviors+via+Recurrent+Graph+Attention+Networks)|0| |[GROUP: An End-to-end Multi-step-ahead Workload Prediction Approach Focusing on Workload Group Behavior](https://doi.org/10.1145/3543507.3583460)|Binbin Feng, Zhijun Ding|Tongji University, China|Accurately forecasting workloads can enable web service providers to achieve proactive runtime management for applications and ensure service quality and cost efficiency. For cloud-native applications, multiple containers collaborate to handle user requests, making each container’s workload changes influenced by workload group behavior. However, existing approaches mainly analyze the individual changes of each container and do not explicitly model the workload group evolution of containers, resulting in sub-optimal results. Therefore, we propose a workload prediction method, GROUP, which implements the shifts of workload prediction focus from individual to group, workload group behavior representation from data similarity to data correlation, and workload group behavior evolution from implicit modeling to explicit modeling. First, we model the workload group behavior and its evolution from multiple perspectives. Second, we propose a container correlation calculation algorithm that considers static and dynamic container information to represent the workload group behavior. Third, we propose an end-to-end multi-step-ahead prediction method that explicitly portrays the complex relationship between the evolution of workload group behavior and the workload changes of each container. Lastly, enough experiments on public datasets show the advantages of GROUP, which provides an effective solution to achieve workload prediction for cloud-native applications.|准确预测工作负载可以使 Web 服务提供商实现应用程序的主动运行时管理，并确保服务质量和成本效率。对于云本地应用程序，多个容器协作处理用户请求，使每个容器的工作负载变化受到工作负载组行为的影响。然而，现有的方法主要分析每个集装箱的个别变化，并没有明确的模型集装箱的工作负载组演化，导致次优结果。为此，提出了一种工作负载预测方法 GROUP，该方法实现了工作负载预测焦点从个体到群体的转移，实现了工作负载群体行为从数据相似性到数据相关性的表示，实现了工作负载群体行为从隐式建模到显式建模的演化。首先，我们从多个角度对工作负载组行为及其演化进行建模。其次，提出了一种考虑静态和动态容器信息来表示工作负载组行为的容器关联计算算法。第三，提出了一种端到端多步提前预测方法，该方法明确描述了工作负载组行为的演化与每个容器的工作负载变化之间的复杂关系。最后，在公共数据集上进行了大量的实验，验证了 GROUP 的优势，为云本地应用程序实现工作负载预测提供了有效的解决方案。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=GROUP:+An+End-to-end+Multi-step-ahead+Workload+Prediction+Approach+Focusing+on+Workload+Group+Behavior)|0| -|[FANS: Fast Non-Autoregressive Sequence Generation for Item List Continuation](https://doi.org/10.1145/3543507.3583430)|Qijiong Liu, Jieming Zhu, Jiahao Wu, Tiandeng Wu, Zhenhua Dong, XiaoMing Wu|The Hong Kong Polytechnic University, Hong Kong; Huawei Noah's Ark Lab, China; Huawei Technolologies Co., Ltd, China|User-curated item lists, such as video-based playlists on Youtube and book-based lists on Goodreads, have become prevalent for content sharing on online platforms. Item list continuation is proposed to model the overall trend of a list and predict subsequent items. Recently, Transformer-based models have shown promise in comprehending contextual information and capturing item relationships in a list. However, deploying them in real-time industrial applications is challenging, mainly because the autoregressive generation mechanism used in them is time-consuming. In this paper, we propose a novel fast non-autoregressive sequence generation model, namely FANS, to enhance inference efficiency and quality for item list continuation. First, we use a non-autoregressive generation mechanism to decode next $K$ items simultaneously instead of one by one in existing models. Then, we design a two-stage classifier to replace the vanilla classifier used in current transformer-based models to further reduce the decoding time. Moreover, to improve the quality of non-autoregressive generation, we employ a curriculum learning strategy to optimize training. Experimental results on four real-world item list continuation datasets including Zhihu, Spotify, AotM, and Goodreads show that our FANS model can significantly improve inference efficiency (up to 8.7x) while achieving competitive or better generation quality for item list continuation compared with the state-of-the-art autoregressive models. We also validate the efficiency of FANS in an industrial setting. Our source code and data will be available at MindSpore/models and Github.|用户策划的项目列表，如 Youtube 上基于视频的播放列表和 Goodreads 上基于图书的列表，已经成为在线平台上内容共享的流行。提出项目表延续模型来模拟项目表的总体趋势并预测后续项目。最近，基于 Transformer 的模型在理解上下文信息和捕获列表中的项目关系方面显示了前景。然而，将它们部署到实时工业应用程序中是具有挑战性的，主要是因为它们中使用的自回归生成机制非常耗时。本文提出了一种新的快速非自回归序列生成模型 FANS，以提高项目表延拓的推理效率和推理质量。首先，我们使用一个非自回归生成机制来同时解码下一个 $K $条目，而不是在现有模型中逐个解码。然后，我们设计了一个两级分类器来取代基于电流互感器的模型中使用的香草分类器，以进一步减少解码时间。此外，为了提高非自回归生成的质量，我们采用课程学习策略来优化培训。实验结果表明，与最先进的自回归模型相比，我们的 FANS 模型可以显著提高推理效率(高达8.7倍) ，同时实现项目列表延续的竞争性或更好的生成质量。我们还验证了 FANS 在工业环境中的有效性。我们的源代码和数据可以在 MindSpore/model 和 Github 上找到。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=FANS:+Fast+Non-Autoregressive+Sequence+Generation+for+Item+List+Continuation)|0| -|[DANCE: Learning A Domain Adaptive Framework for Deep Hashing](https://doi.org/10.1145/3543507.3583445)|Haixin Wang, Jinan Sun, Xiang Wei, Shikun Zhang, Chong Chen, XianSheng Hua, Xiao Luo|BIGO Inc., Singapore; Department of Computer Science, UCLA, USA; Zhejiang University, China; Peking University, China|This paper studies unsupervised domain adaptive hashing, which aims to transfer a hashing model from a label-rich source domain to a label-scarce target domain. Current state-of-the-art approaches generally resolve the problem by integrating pseudo-labeling and domain adaptation techniques into deep hashing paradigms. Nevertheless, they usually suffer from serious class imbalance in pseudo-labels and suboptimal domain alignment caused by the neglection of the intrinsic structures of two domains. To address this issue, we propose a novel method named unbiaseD duAl hashiNg Contrastive lEarning (DANCE) for domain adaptive image retrieval. The core of our DANCE is to perform contrastive learning on hash codes from both instance level and prototype level. To begin, DANCE utilizes label information to guide instance-level hashing contrastive learning in the source domain. To generate unbiased and reliable pseudo-labels for semantic learning in the target domain, we uniformly select samples around each label embedding in the Hamming space. A momentum-update scheme is also utilized to smooth the optimization process. Additionally, we measure the semantic prototype representations in both source and target domains and incorporate them into a domain-aware prototype-level contrastive learning paradigm, which enhances domain alignment in the Hamming space while maximizing the model capacity. Experimental results on a number of well-known domain adaptive retrieval benchmarks validate the effectiveness of our proposed DANCE compared to a variety of competing baselines in different settings.|本文研究了无监督域自适应哈希算法，目的是将一个哈希模型从一个标签丰富的源域转移到一个标签稀缺的目标域。当前最先进的方法通常通过将伪标记和域适应技术集成到深度散列范例中来解决这个问题。然而，由于忽略了两个域的内在结构，它们常常会出现伪标签的类别不平衡和域对齐不理想的问题。为了解决这个问题，我们提出了一种新的无偏 DduAl hashiNg 对比度学习方法(DANCE)用于领域自适应图像检索。我们的 DANCE 的核心是从实例级和原型级对哈希码进行对比学习。首先，DANCE 利用标签信息来指导源域中的实例级散列对比学习。为了在目标域中生成无偏、可靠的语义学习伪标签，在汉明空间中对每个标签周围的样本进行统一选择。利用动量更新策略使优化过程平滑。此外，我们在源域和目标域都测量了语义原型表示，并将它们整合到领域感知的原型级对比学习范式中，该范式增强了汉明空间中的领域对齐，同时最大化了模型容量。在一些著名的领域自适应检索基准上的实验结果验证了我们提出的 DANCE 相对于不同设置下的各种竞争基准的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=DANCE:+Learning+A+Domain+Adaptive+Framework+for+Deep+Hashing)|0| -|[Differentiable Optimized Product Quantization and Beyond](https://doi.org/10.1145/3543507.3583482)|Zepu Lu, Defu Lian, Jin Zhang, Zaixi Zhang, Chao Feng, Hao Wang, Enhong Chen|University of Science and Technology of China, School of Data Science, China; University of Science and Technology of China, School of Computer Science, School of Data Science, China and State Key Laboratory of Cognitive Intelligence, China; University of Science and Technology of China, School of Computer Science, China|Vector quantization techniques, such as Product Quantization (PQ), play a vital role in approximate nearest neighbor search (ANNs) and maximum inner product search (MIPS) owing to their remarkable search and storage efficiency. However, the indexes in vector quantization cannot be trained together with the inference models since data indexing is not differentiable. To this end, differentiable vector quantization approaches, such as DiffPQ and DeepPQ, have been recently proposed, but existing methods have two drawbacks. First, they do not impose any constraints on codebooks, such that the resultant codebooks lack diversity, leading to limited retrieval performance. Second, since data indexing resorts to operator, differentiability is usually achieved by either relaxation or Straight-Through Estimation (STE), which leads to biased gradient and slow convergence. To address these problems, we propose a Differentiable Optimized Product Quantization method (DOPQ) and beyond in this paper. Particularly, each data is projected into multiple orthogonal spaces, to generate multiple views of data. Thus, each codebook is learned with one view of data, guaranteeing the diversity of codebooks. Moreover, instead of simple differentiable relaxation, DOPQ optimizes the loss based on direct loss minimization, significantly reducing the gradient bias problem. Finally, DOPQ is evaluated with seven datasets of both recommendation and image search tasks. Extensive experimental results show that DOPQ outperforms state-of-the-art baselines by a large margin.|向量量化技术，例如产品量化技术，由于其卓越的搜索和存储效率，在近似最近邻搜索(ANN)和最大内部产品搜索(MIPS)方面发挥了重要作用。然而，由于数据索引是不可微的，因此不能将向量量化中的索引与推断模型一起训练。为了达到这个目的，最近提出了一些可微分的向量量化方法，比如迪普 PQ 和 DeepPQ，但是现有的方法有两个缺点。首先，它们不对代码书施加任何约束，因此得到的代码书缺乏多样性，导致检索性能有限。其次，由于数据索引依赖于算子，可微性通常是通过松弛估计或直接估计(STE)来实现的，这导致了有偏的梯度和收敛速度慢。针对这些问题，本文提出了一种可微优化产品量化方法(DOPQ)。特别是，每个数据被投影到多个正交空间，以生成多个数据视图。因此，每个代码本都是以一种数据视图来学习的，从而保证了代码本的多样性。此外，DOPQ 不再是简单的可微松弛，而是基于直接损耗最小化优化损耗，大大减少了梯度偏差问题。最后，利用推荐任务和图像搜索任务的七个数据集对 DOPQ 进行评估。广泛的实验结果表明，DOPQ 的性能大大优于最先进的基线。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Differentiable+Optimized+Product+Quantization+and+Beyond)|0| +|[FANS: Fast Non-Autoregressive Sequence Generation for Item List Continuation](https://doi.org/10.1145/3543507.3583430)|Qijiong Liu, Jieming Zhu, Jiahao Wu, Tiandeng Wu, Zhenhua Dong, XiaoMing Wu|Huawei Noah's Ark Lab, China; The Hong Kong Polytechnic University, Hong Kong; Huawei Technolologies Co., Ltd, China|User-curated item lists, such as video-based playlists on Youtube and book-based lists on Goodreads, have become prevalent for content sharing on online platforms. Item list continuation is proposed to model the overall trend of a list and predict subsequent items. Recently, Transformer-based models have shown promise in comprehending contextual information and capturing item relationships in a list. However, deploying them in real-time industrial applications is challenging, mainly because the autoregressive generation mechanism used in them is time-consuming. In this paper, we propose a novel fast non-autoregressive sequence generation model, namely FANS, to enhance inference efficiency and quality for item list continuation. First, we use a non-autoregressive generation mechanism to decode next $K$ items simultaneously instead of one by one in existing models. Then, we design a two-stage classifier to replace the vanilla classifier used in current transformer-based models to further reduce the decoding time. Moreover, to improve the quality of non-autoregressive generation, we employ a curriculum learning strategy to optimize training. Experimental results on four real-world item list continuation datasets including Zhihu, Spotify, AotM, and Goodreads show that our FANS model can significantly improve inference efficiency (up to 8.7x) while achieving competitive or better generation quality for item list continuation compared with the state-of-the-art autoregressive models. We also validate the efficiency of FANS in an industrial setting. Our source code and data will be available at MindSpore/models and Github.|用户策划的项目列表，如 Youtube 上基于视频的播放列表和 Goodreads 上基于图书的列表，已经成为在线平台上内容共享的流行。提出项目表延续模型来模拟项目表的总体趋势并预测后续项目。最近，基于 Transformer 的模型在理解上下文信息和捕获列表中的项目关系方面显示了前景。然而，将它们部署到实时工业应用程序中是具有挑战性的，主要是因为它们中使用的自回归生成机制非常耗时。本文提出了一种新的快速非自回归序列生成模型 FANS，以提高项目表延拓的推理效率和推理质量。首先，我们使用一个非自回归生成机制来同时解码下一个 $K $条目，而不是在现有模型中逐个解码。然后，我们设计了一个两级分类器来取代基于电流互感器的模型中使用的香草分类器，以进一步减少解码时间。此外，为了提高非自回归生成的质量，我们采用课程学习策略来优化培训。实验结果表明，与最先进的自回归模型相比，我们的 FANS 模型可以显著提高推理效率(高达8.7倍) ，同时实现项目列表延续的竞争性或更好的生成质量。我们还验证了 FANS 在工业环境中的有效性。我们的源代码和数据可以在 MindSpore/model 和 Github 上找到。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=FANS:+Fast+Non-Autoregressive+Sequence+Generation+for+Item+List+Continuation)|0| +|[DANCE: Learning A Domain Adaptive Framework for Deep Hashing](https://doi.org/10.1145/3543507.3583445)|Haixin Wang, Jinan Sun, Xiang Wei, Shikun Zhang, Chong Chen, XianSheng Hua, Xiao Luo|Department of Computer Science, UCLA, USA; BIGO Inc., Singapore; Zhejiang University, China; Peking University, China|This paper studies unsupervised domain adaptive hashing, which aims to transfer a hashing model from a label-rich source domain to a label-scarce target domain. Current state-of-the-art approaches generally resolve the problem by integrating pseudo-labeling and domain adaptation techniques into deep hashing paradigms. Nevertheless, they usually suffer from serious class imbalance in pseudo-labels and suboptimal domain alignment caused by the neglection of the intrinsic structures of two domains. To address this issue, we propose a novel method named unbiaseD duAl hashiNg Contrastive lEarning (DANCE) for domain adaptive image retrieval. The core of our DANCE is to perform contrastive learning on hash codes from both instance level and prototype level. To begin, DANCE utilizes label information to guide instance-level hashing contrastive learning in the source domain. To generate unbiased and reliable pseudo-labels for semantic learning in the target domain, we uniformly select samples around each label embedding in the Hamming space. A momentum-update scheme is also utilized to smooth the optimization process. Additionally, we measure the semantic prototype representations in both source and target domains and incorporate them into a domain-aware prototype-level contrastive learning paradigm, which enhances domain alignment in the Hamming space while maximizing the model capacity. Experimental results on a number of well-known domain adaptive retrieval benchmarks validate the effectiveness of our proposed DANCE compared to a variety of competing baselines in different settings.|本文研究了无监督域自适应哈希算法，目的是将一个哈希模型从一个标签丰富的源域转移到一个标签稀缺的目标域。当前最先进的方法通常通过将伪标记和域适应技术集成到深度散列范例中来解决这个问题。然而，由于忽略了两个域的内在结构，它们常常会出现伪标签的类别不平衡和域对齐不理想的问题。为了解决这个问题，我们提出了一种新的无偏 DduAl hashiNg 对比度学习方法(DANCE)用于领域自适应图像检索。我们的 DANCE 的核心是从实例级和原型级对哈希码进行对比学习。首先，DANCE 利用标签信息来指导源域中的实例级散列对比学习。为了在目标域中生成无偏、可靠的语义学习伪标签，在汉明空间中对每个标签周围的样本进行统一选择。利用动量更新策略使优化过程平滑。此外，我们在源域和目标域都测量了语义原型表示，并将它们整合到领域感知的原型级对比学习范式中，该范式增强了汉明空间中的领域对齐，同时最大化了模型容量。在一些著名的领域自适应检索基准上的实验结果验证了我们提出的 DANCE 相对于不同设置下的各种竞争基准的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=DANCE:+Learning+A+Domain+Adaptive+Framework+for+Deep+Hashing)|0| +|[Differentiable Optimized Product Quantization and Beyond](https://doi.org/10.1145/3543507.3583482)|Zepu Lu, Defu Lian, Jin Zhang, Zaixi Zhang, Chao Feng, Hao Wang, Enhong Chen|University of Science and Technology of China, School of Computer Science, School of Data Science, China and State Key Laboratory of Cognitive Intelligence, China; University of Science and Technology of China, School of Computer Science, China; University of Science and Technology of China, School of Data Science, China|Vector quantization techniques, such as Product Quantization (PQ), play a vital role in approximate nearest neighbor search (ANNs) and maximum inner product search (MIPS) owing to their remarkable search and storage efficiency. However, the indexes in vector quantization cannot be trained together with the inference models since data indexing is not differentiable. To this end, differentiable vector quantization approaches, such as DiffPQ and DeepPQ, have been recently proposed, but existing methods have two drawbacks. First, they do not impose any constraints on codebooks, such that the resultant codebooks lack diversity, leading to limited retrieval performance. Second, since data indexing resorts to operator, differentiability is usually achieved by either relaxation or Straight-Through Estimation (STE), which leads to biased gradient and slow convergence. To address these problems, we propose a Differentiable Optimized Product Quantization method (DOPQ) and beyond in this paper. Particularly, each data is projected into multiple orthogonal spaces, to generate multiple views of data. Thus, each codebook is learned with one view of data, guaranteeing the diversity of codebooks. Moreover, instead of simple differentiable relaxation, DOPQ optimizes the loss based on direct loss minimization, significantly reducing the gradient bias problem. Finally, DOPQ is evaluated with seven datasets of both recommendation and image search tasks. Extensive experimental results show that DOPQ outperforms state-of-the-art baselines by a large margin.|向量量化技术，例如产品量化技术，由于其卓越的搜索和存储效率，在近似最近邻搜索(ANN)和最大内部产品搜索(MIPS)方面发挥了重要作用。然而，由于数据索引是不可微的，因此不能将向量量化中的索引与推断模型一起训练。为了达到这个目的，最近提出了一些可微分的向量量化方法，比如迪普 PQ 和 DeepPQ，但是现有的方法有两个缺点。首先，它们不对代码书施加任何约束，因此得到的代码书缺乏多样性，导致检索性能有限。其次，由于数据索引依赖于算子，可微性通常是通过松弛估计或直接估计(STE)来实现的，这导致了有偏的梯度和收敛速度慢。针对这些问题，本文提出了一种可微优化产品量化方法(DOPQ)。特别是，每个数据被投影到多个正交空间，以生成多个数据视图。因此，每个代码本都是以一种数据视图来学习的，从而保证了代码本的多样性。此外，DOPQ 不再是简单的可微松弛，而是基于直接损耗最小化优化损耗，大大减少了梯度偏差问题。最后，利用推荐任务和图像搜索任务的七个数据集对 DOPQ 进行评估。广泛的实验结果表明，DOPQ 的性能大大优于最先进的基线。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Differentiable+Optimized+Product+Quantization+and+Beyond)|0| |[Auctions without commitment in the auto-bidding world](https://doi.org/10.1145/3543507.3583416)|Andrés Perlroth, Aranyak Mehta||Advertisers in online ad auctions are increasingly using auto-bidding mechanisms to bid into auctions instead of directly bidding their value manually. One prominent auto-bidding format is the target cost-per-acquisition (tCPA) which maximizes the volume of conversions subject to a return-of-investment constraint. From an auction theoretic perspective however, this trend seems to go against foundational results that postulate that for profit-maximizing bidders, it is optimal to use a classic bidding system like marginal CPA (mCPA) bidding rather than using strategies like tCPA. In this paper we rationalize the adoption of such seemingly sub-optimal bidding within the canonical quasi-linear framework. The crux of the argument lies in the notion of commitment. We consider a multi-stage game where first the auctioneer declares the auction rules; then bidders select either the tCPA or mCPA bidding format and then, if the auctioneer lacks commitment, it can revisit the rules of the auction (e.g., may readjust reserve prices depending on the observed bids). Our main result is that so long as a bidder believes that the auctioneer lacks commitment to follow the rule of the declared auction then the bidder will make a higher profit by choosing the tCPA format over the mCPA format. We then explore the commitment consequences for the auctioneer. In a simplified version of the model where there is only one bidder, we show that the tCPA subgame admits a credible equilibrium while the mCPA format does not. That is, when the bidder chooses the tCPA format the auctioneer can credibly implement the auction rules announced at the beginning of the game. We also show that, under some mild conditions, the auctioneer's revenue is larger when the bidder uses the tCPA format rather than mCPA. We further quantify the value for the auctioneer to be able to commit to the declared auction rules.|在线广告拍卖中，广告商越来越多地使用自动竞价机制进行竞价，而不是直接手动竞价。一个突出的自动投标格式是每次收购的目标成本(tCPA) ，它在投资回报约束下使转换量最大化。然而，从拍卖理论的角度来看，这种趋势似乎违背了基本结论，即对于利润最大化的投标者来说，使用经典的投标系统如边际 CPA (mCPA)投标比使用 tCPA 策略更为理想。本文在正则拟线性框架下，对这种看似次优的投标方式进行了合理化处理。争论的核心在于承诺的概念。我们考虑一个多阶段的博弈，首先拍卖商声明拍卖规则; 然后竞标者选择 tCPA 或 mCPA 的出价格式，然后，如果拍卖商缺乏承诺，它可以重新审视拍卖规则(例如，可以根据观察到的出价调整底价)。我们的主要结果是，只要投标人认为拍卖商缺乏承诺，以遵守宣布的拍卖规则，那么投标人将通过选择 tCPA 格式，而不是 mCPA 格式，获得更高的利润。然后，我们探讨承诺后果的拍卖商。在只有一个投标人的模型的简化版本中，我们证明了 tCPA 子博弈允许一个可信的均衡，而 mCPA 格式不允许。也就是说，当投标人选择 tCPA 格式时，拍卖人可以可信地执行游戏开始时宣布的拍卖规则。我们还表明，在一些温和的条件下，当竞标者使用 tCPA 格式而不是 mCPA 格式时，拍卖商的收入会更大。我们进一步量化拍卖师能够遵守公开的拍卖规则的价值。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Auctions+without+commitment+in+the+auto-bidding+world)|0| -|[Online resource allocation in Markov Chains](https://doi.org/10.1145/3543507.3583428)|Jianhao Jia, Hao Li, Kai Liu, Ziqi Liu, Jun Zhou, Nikolai Gravin, Zhihao Gavin Tang|Shanghai University of Finance and Economics, China; Ant Group, China|A large body of work in Computer Science and Operations Research study online algorithms for stochastic resource allocation problems. The most common assumption is that the online requests have randomly generated i.i.d. types. This assumption is well justified for static markets and/or relatively short time periods. We consider dynamic markets, whose states evolve as a random walk in a market-specific Markov Chain. This is a new model that generalizes previous i.i.d. settings. We identify important parameters of the Markov chain that is crucial for obtaining good approximation guarantees to the expected value of the optimal offline algorithm which knows realizations of all requests in advance. We focus on a stylized single-resource setting and: (i) generalize the well-known Prophet Inequality from the optimal stopping theory (single-unit setting) to Markov Chain setting; (ii) in multi-unit setting, design a simple algorithm that is asymptotically optimal under mild assumptions on the underlying Markov chain.|计算机科学与运筹学中的大量工作研究随机资源分配问题的在线算法。最常见的假设是，联机请求随机生成 i.id 类型。对于静态市场和/或相对较短的时间段，这种假设是合理的。我们考虑动态市场，其状态演化为特定市场马尔可夫链中的随机游动。这是一个新的模型，推广了以前的 ID 设置。我们确定了马尔可夫链的重要参数，这些参数对提前知道所有请求实现的最优离线算法的期望值获得良好的近似保证至关重要。我们重点研究了一个程式化的单资源设置，并且: (i)将著名的先知不等式从最优停止理论(单单元设置)推广到马尔可夫链设置; (ii)在多单元设置中，设计了一个简单的算法，该算法在基础马尔可夫链的温和假设下是渐近最优的。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Online+resource+allocation+in+Markov+Chains)|0| +|[Online resource allocation in Markov Chains](https://doi.org/10.1145/3543507.3583428)|Jianhao Jia, Hao Li, Kai Liu, Ziqi Liu, Jun Zhou, Nikolai Gravin, Zhihao Gavin Tang|Ant Group, China; Shanghai University of Finance and Economics, China|A large body of work in Computer Science and Operations Research study online algorithms for stochastic resource allocation problems. The most common assumption is that the online requests have randomly generated i.i.d. types. This assumption is well justified for static markets and/or relatively short time periods. We consider dynamic markets, whose states evolve as a random walk in a market-specific Markov Chain. This is a new model that generalizes previous i.i.d. settings. We identify important parameters of the Markov chain that is crucial for obtaining good approximation guarantees to the expected value of the optimal offline algorithm which knows realizations of all requests in advance. We focus on a stylized single-resource setting and: (i) generalize the well-known Prophet Inequality from the optimal stopping theory (single-unit setting) to Markov Chain setting; (ii) in multi-unit setting, design a simple algorithm that is asymptotically optimal under mild assumptions on the underlying Markov chain.|计算机科学与运筹学中的大量工作研究随机资源分配问题的在线算法。最常见的假设是，联机请求随机生成 i.id 类型。对于静态市场和/或相对较短的时间段，这种假设是合理的。我们考虑动态市场，其状态演化为特定市场马尔可夫链中的随机游动。这是一个新的模型，推广了以前的 ID 设置。我们确定了马尔可夫链的重要参数，这些参数对提前知道所有请求实现的最优离线算法的期望值获得良好的近似保证至关重要。我们重点研究了一个程式化的单资源设置，并且: (i)将著名的先知不等式从最优停止理论(单单元设置)推广到马尔可夫链设置; (ii)在多单元设置中，设计了一个简单的算法，该算法在基础马尔可夫链的温和假设下是渐近最优的。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Online+resource+allocation+in+Markov+Chains)|0| |[Worst-Case Welfare of Item Pricing in the Tollbooth Problem](https://doi.org/10.1145/3543507.3583432)|Zihan Tan, Yifeng Teng, Mingfei Zhao|Google Research, USA; Center for Discrete Mathematics and Theoretical Computer Science, Rutgers University, USA|We study the worst-case welfare of item pricing in the \emph{tollbooth problem}. The problem was first introduced by Guruswami et al, and is a special case of the combinatorial auction in which (i) each of the $m$ items in the auction is an edge of some underlying graph; and (ii) each of the $n$ buyers is single-minded and only interested in buying all edges of a single path. We consider the competitive ratio between the hindsight optimal welfare and the optimal worst-case welfare among all item-pricing mechanisms, when the order of the arriving buyers is adversarial. We assume that buyers own the \emph{tie-breaking} power, i.e. they can choose whether or not to buy the demand path at 0 utility. We prove a tight competitive ratio of $3/2$ when the underlying graph is a single path (also known as the \emph{highway} problem), whereas item-pricing can achieve the hindsight optimal if the seller is allowed to choose a proper tie-breaking rule to maximize the welfare. Moreover, we prove an $O(1)$ upper bound of competitive ratio when the underlying graph is a tree. For general graphs, we prove an $\Omega(m^{1/8})$ lower bound of the competitive ratio. We show that an $m^{\Omega(1)}$ competitive ratio is unavoidable even if the graph is a grid, or if the capacity of every edge is augmented by a constant factor $c$. The results hold even if the seller has tie-breaking power.|研究了收费站问题中项目定价的最坏情况下的福利问题。这个问题首先由 Guruswami 等人提出，是组合拍卖的一个特例，其中(i)拍卖中的每个 $m $物品都是某个基础图的边缘; (ii)每个 $n $买家都是一心一意的，只对购买单一路径的所有边缘感兴趣。我们考虑了当购买者到达顺序为对抗性时，所有商品定价机制中事后最优福利和最坏情况下最优福利之间的竞争比。我们假设买方拥有方幂{纽带断裂}功率，也就是说，他们可以选择是否以0效用购买需求路径。当基本图是单一路径时，我们证明了 $3/2 $的激烈竞争比率(也称为高速公路问题) ，然而，如果允许卖方选择一个合适的拆分规则来使福利最大化，则项目定价可以实现事后最优。此外，我们证明了当底层图是树时，竞争比率的上界为 $O (1)。对于一般图，我们证明了竞争比率的一个 $Omega (m ^ {1/8}) $下界。我们证明了即使图是一个网格，或者如果每个边的容量增加了一个常数因子 $c $，一个 $m ^ { Omega (1)} $竞争比率也是不可避免的。即使卖方具有打破平局的能力，结果仍然成立。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Worst-Case+Welfare+of+Item+Pricing+in+the+Tollbooth+Problem)|0| |[Learning to Bid in Contextual First Price Auctions✱](https://doi.org/10.1145/3543507.3583427)|Ashwinkumar Badanidiyuru, Zhe Feng, Guru Guruganesh|Google, USA|In this paper, we investigate the problem about how to bid in repeated contextual first price auctions. We consider a single bidder (learner) who repeatedly bids in the first price auctions: at each time $t$, the learner observes a context $x_t\in \mathbb{R}^d$ and decides the bid based on historical information and $x_t$. We assume a structured linear model of the maximum bid of all the others $m_t = \alpha_0\cdot x_t + z_t$, where $\alpha_0\in \mathbb{R}^d$ is unknown to the learner and $z_t$ is randomly sampled from a noise distribution $\mathcal{F}$ with log-concave density function $f$. We consider both \emph{binary feedback} (the learner can only observe whether she wins or not) and \emph{full information feedback} (the learner can observe $m_t$) at the end of each time $t$. For binary feedback, when the noise distribution $\mathcal{F}$ is known, we propose a bidding algorithm, by using maximum likelihood estimation (MLE) method to achieve at most $\widetilde{O}(\sqrt{\log(d) T})$ regret. Moreover, we generalize this algorithm to the setting with binary feedback and the noise distribution is unknown but belongs to a parametrized family of distributions. For the full information feedback with \emph{unknown} noise distribution, we provide an algorithm that achieves regret at most $\widetilde{O}(\sqrt{dT})$. Our approach combines an estimator for log-concave density functions and then MLE method to learn the noise distribution $\mathcal{F}$ and linear weight $\alpha_0$ simultaneously. We also provide a lower bound result such that any bidding policy in a broad class must achieve regret at least $\Omega(\sqrt{T})$, even when the learner receives the full information feedback and $\mathcal{F}$ is known.|本文研究了重复背景下第一价格拍卖中的投标问题。我们考虑一个在第一次价格拍卖中重复出价的单个投标者(学习者) : 在每次 $t $时，学习者观察一个上下文 $x _ t 在 mathbb { R } ^ d $中，并根据历史信息和 $x _ t $决定出价。我们假设一个结构化的线性模型的最大出价的所有其他 $m _ t = alpha _ 0 cdot x _ t + z _ t $，其中 $alpha _ 0在 mathbb { R } ^ d $是未知的学习者和 $z _ t $是随机抽样从噪声分布 $数学{ F } $与对数凹密度函数 $f $。我们考虑两种情况: 每次结束时的情况(学习者只能观察自己是否胜出)和每次结束时的情况(学习者可以观察 $m _ t $)。对于二进制反馈，当噪声分布已知 $数学{ F } $时，我们提出了一种竞价算法，利用最大似然估计(MLE)方法实现最大宽波长{ O }(sqrt { log (d) T }) $后悔。此外，我们将该算法推广到二进制反馈环境下，噪声分布是未知的，但属于参数化分布族。对于具有未知噪声分布的完全信息反馈，我们提出了一种最多可以实现遗憾的算法。该方法将对数凹密度函数的估计量与 MLE 方法相结合，同时学习噪声分布的数学{ F } $和线性加权 $alpha _ 0 $。我们还提供了一个下界结果，即使学习者收到完整的信息反馈并且已知 $数学{ F } $，广义类中的任何投标策略都必须达到至少后悔 $Omega (sqrt { T }) $。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Learning+to+Bid+in+Contextual+First+Price+Auctions✱)|0| |[Efficiency of Non-Truthful Auctions in Auto-bidding: The Power of Randomization](https://doi.org/10.1145/3543507.3583492)|Christopher Liaw, Aranyak Mehta, Andrés Perlroth|Google, USA|Auto-bidding is now widely adopted as an interface between advertisers and internet advertising as it allows advertisers to specify high-level goals, such as maximizing value subject to a value-per-spend constraint. Prior research has mainly focused on auctions that are truthful (such as a second-price auction) because these auctions admit simple (uniform) bidding strategies and are thus simpler to analyze. The main contribution of this paper is to characterize the efficiency across the spectrum of all auctions, including non-truthful auctions for which optimal bidding may be complex. For deterministic auctions, we show a dominance result: any uniform bidding equilibrium of a second-price auction (SPA) can be mapped to an equilibrium of any other auction – for example, first price auction (FPA) – with identical outcomes. In this sense, SPA with uniform bidding is an instance-wise optimal deterministic auction. Consequently, the price of anarchy (PoA) of any deterministic auction is at least the PoA of SPA with uniform bidding, which is known to be 2. We complement this by showing that the PoA of FPA without uniform bidding is 2. Next, we show, surprisingly, that truthful pricing is not dominant in the randomized setting. There is a randomized version of FPA that achieves a strictly smaller price of anarchy than its truthful counterpart when there are two bidders per query. Furthermore, this randomized FPA achieves the best-known PoA for two bidders, thus showing the power of non-truthfulness when combined with randomization. Finally, we show that no prior-free auction (even randomized, non-truthful) can improve on a PoA bound of 2 when there are a large number of advertisers per auction. These results should be interpreted qualitatively as follows. When the auction pressure is low, randomization and non-truthfulness is beneficial. On the other hand, if the auction pressure is intense, the benefits diminishes and it is optimal to implement a second-price auction.|自动竞价现在被广泛采用作为广告商和互联网广告之间的一个界面，因为它允许广告商指定高层次的目标，例如受每次支出价值约束的价值最大化。先前的研究主要集中在真实的拍卖(如二级价格拍卖) ，因为这些拍卖采用简单(统一)的竞价策略，因此更容易分析。本文的主要贡献是描述所有拍卖的效率，包括非真实的拍卖，其中最优投标可能是复杂的。对于确定性拍卖，我们给出了一个优势结果: 第二价格拍卖(SPA)的任何统一竞价均衡可以映射到任何其他拍卖的均衡，例如第一价格拍卖(FPA) ，具有相同的结果。从这个意义上说，具有统一报价的 SPA 是一种实例最优确定性拍卖。因此，任何确定性拍卖的无政府状态价格(PoA)至少是统一竞价下的 SPA 的 PoA，即为2。我们通过显示无统一投标的 FPA 的 PoA 是2来补充这一点。接下来，我们出人意料地表明，真实定价在随机设置中并不占主导地位。有一个随机版本的 FPA，实现了一个严格的较小的无政府状态的价格比其真实的对应物时，每个查询有两个投标人。此外，这种随机 FPA 实现了两个投标人最知名的 PoA，从而显示了非真实性的力量时，结合随机。最后，我们证明了当每次拍卖中有大量的广告商时，任何先验自由拍卖(即使是随机的，非真实的)都不能改善 PoA 的界为2。这些结果应该定性地解释如下。当拍卖压力较小时，随机性和非真实性是有利的。另一方面，如果拍卖压力很大，收益就会减少，实施二级价格拍卖是最佳选择。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Efficiency+of+Non-Truthful+Auctions+in+Auto-bidding:+The+Power+of+Randomization)|0| -|[Platform Behavior under Market Shocks: A Simulation Framework and Reinforcement-Learning Based Study](https://doi.org/10.1145/3543507.3583523)|Xintong Wang, Gary Qiurui Ma, Alon Eden, Clara Li, Alexander Trott, Stephan Zheng, David C. Parkes|Harvard University, USA; Salesforce Research, USA; Hebrew University of Jerusalem, Israel|We study the behavior of an economic platform (e.g., Amazon, Uber Eats, Instacart) under shocks, such as COVID-19 lockdowns, and the effect of different regulation considerations. To this end, we develop a multi-agent simulation environment of a platform economy in a multi-period setting where shocks may occur and disrupt the economy. Buyers and sellers are heterogeneous and modeled as economically-motivated agents, choosing whether or not to pay fees to access the platform. We use deep reinforcement learning to model the fee-setting and matching behavior of the platform, and consider two major types of regulation frameworks: (1) taxation policies and (2) platform fee restrictions. We offer a number of simulated experiments that cover different market settings and shed light on regulatory tradeoffs. Our results show that while many interventions are ineffective with a sophisticated platform actor, we identify a particular kind of regulation—fixing fees to the optimal, no-shock fees while still allowing a platform to choose how to match buyers and sellers—as holding promise for promoting the efficiency and resilience of the economic system.|我们研究经济平台(如亚马逊、 Uber Eats、 Instacart)在2019冠状病毒疾病封锁等冲击下的行为，以及不同监管考虑因素的影响。为此，我们开发了一个平台经济的多主体仿真环境，在多个时期的设置中，可能会发生冲击和破坏经济。买家和卖家是异构的，他们被塑造成具有经济动机的代理人，选择是否支付费用来访问平台。我们使用深度强化学习来模拟平台的收费设定和匹配行为，并考虑两种主要的监管框架: (1)税收政策和(2)平台收费限制。我们提供了一些模拟实验，涵盖了不同的市场设置，并阐明了监管权衡。我们的研究结果表明，尽管许多干预措施对于一个成熟的平台参与者来说是无效的，但我们确定了一种特殊的监管——将费用固定在最优的、不会引起冲击的费用上，同时仍允许平台选择如何匹配买卖双方——有望提高经济体系的效率和弹性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Platform+Behavior+under+Market+Shocks:+A+Simulation+Framework+and+Reinforcement-Learning+Based+Study)|0| -|[Near-Optimal Experimental Design Under the Budget Constraint in Online Platforms](https://doi.org/10.1145/3543507.3583528)|Yongkang Guo, Yuan Yuan, Jinshan Zhang, Yuqing Kong, Zhihua Zhu, Zheng Cai|Tencent Technology (Shenzhen) Co., Ltd., China; Zhejiang University, China; Peking University, China; Purdue University, USA|A/B testing, or controlled experiments, is the gold standard approach to causally compare the performance of algorithms on online platforms. However, conventional Bernoulli randomization in A/B testing faces many challenges such as spillover and carryover effects. Our study focuses on another challenge, especially for A/B testing on two-sided platforms -- budget constraints. Buyers on two-sided platforms often have limited budgets, where the conventional A/B testing may be infeasible to be applied, partly because two variants of allocation algorithms may conflict and lead some buyers to exceed their budgets if they are implemented simultaneously. We develop a model to describe two-sided platforms where buyers have limited budgets. We then provide an optimal experimental design that guarantees small bias and minimum variance. Bias is lower when there is more budget and a higher supply-demand rate. We test our experimental design on both synthetic data and real-world data, which verifies the theoretical results and shows our advantage compared to Bernoulli randomization.|A/B 测试或对照实验是在线平台上对算法性能进行因果比较的黄金标准方法。然而，传统的伯努利随机化在 A/B 测试中面临着许多挑战，如溢出效应和结转效应。我们的研究侧重于另一个挑战，尤其是在双边平台上的 A/B 测试——预算约束。双边平台上的买家往往预算有限，传统的 A/B 测试可能无法应用，部分原因是两种不同的分配算法可能相互冲突，导致一些买家在同时实施时超出预算。我们开发了一个模型来描述买家预算有限的双边平台。然后，我们提供了一个最佳的实验设计，保证小偏差和最小方差。当有更多的预算和更高的供求比率时，偏差就会降低。我们在合成数据和实际数据上对实验设计进行了检验，验证了理论结果，显示了我们比伯努利随机化方法的优越性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Near-Optimal+Experimental+Design+Under+the+Budget+Constraint+in+Online+Platforms)|0| +|[Platform Behavior under Market Shocks: A Simulation Framework and Reinforcement-Learning Based Study](https://doi.org/10.1145/3543507.3583523)|Xintong Wang, Gary Qiurui Ma, Alon Eden, Clara Li, Alexander Trott, Stephan Zheng, David C. Parkes|Salesforce Research, USA; Harvard University, USA; Hebrew University of Jerusalem, Israel|We study the behavior of an economic platform (e.g., Amazon, Uber Eats, Instacart) under shocks, such as COVID-19 lockdowns, and the effect of different regulation considerations. To this end, we develop a multi-agent simulation environment of a platform economy in a multi-period setting where shocks may occur and disrupt the economy. Buyers and sellers are heterogeneous and modeled as economically-motivated agents, choosing whether or not to pay fees to access the platform. We use deep reinforcement learning to model the fee-setting and matching behavior of the platform, and consider two major types of regulation frameworks: (1) taxation policies and (2) platform fee restrictions. We offer a number of simulated experiments that cover different market settings and shed light on regulatory tradeoffs. Our results show that while many interventions are ineffective with a sophisticated platform actor, we identify a particular kind of regulation—fixing fees to the optimal, no-shock fees while still allowing a platform to choose how to match buyers and sellers—as holding promise for promoting the efficiency and resilience of the economic system.|我们研究经济平台(如亚马逊、 Uber Eats、 Instacart)在2019冠状病毒疾病封锁等冲击下的行为，以及不同监管考虑因素的影响。为此，我们开发了一个平台经济的多主体仿真环境，在多个时期的设置中，可能会发生冲击和破坏经济。买家和卖家是异构的，他们被塑造成具有经济动机的代理人，选择是否支付费用来访问平台。我们使用深度强化学习来模拟平台的收费设定和匹配行为，并考虑两种主要的监管框架: (1)税收政策和(2)平台收费限制。我们提供了一些模拟实验，涵盖了不同的市场设置，并阐明了监管权衡。我们的研究结果表明，尽管许多干预措施对于一个成熟的平台参与者来说是无效的，但我们确定了一种特殊的监管——将费用固定在最优的、不会引起冲击的费用上，同时仍允许平台选择如何匹配买卖双方——有望提高经济体系的效率和弹性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Platform+Behavior+under+Market+Shocks:+A+Simulation+Framework+and+Reinforcement-Learning+Based+Study)|0| +|[Near-Optimal Experimental Design Under the Budget Constraint in Online Platforms](https://doi.org/10.1145/3543507.3583528)|Yongkang Guo, Yuan Yuan, Jinshan Zhang, Yuqing Kong, Zhihua Zhu, Zheng Cai|Purdue University, USA; Tencent Technology (Shenzhen) Co., Ltd., China; Zhejiang University, China; Peking University, China|A/B testing, or controlled experiments, is the gold standard approach to causally compare the performance of algorithms on online platforms. However, conventional Bernoulli randomization in A/B testing faces many challenges such as spillover and carryover effects. Our study focuses on another challenge, especially for A/B testing on two-sided platforms -- budget constraints. Buyers on two-sided platforms often have limited budgets, where the conventional A/B testing may be infeasible to be applied, partly because two variants of allocation algorithms may conflict and lead some buyers to exceed their budgets if they are implemented simultaneously. We develop a model to describe two-sided platforms where buyers have limited budgets. We then provide an optimal experimental design that guarantees small bias and minimum variance. Bias is lower when there is more budget and a higher supply-demand rate. We test our experimental design on both synthetic data and real-world data, which verifies the theoretical results and shows our advantage compared to Bernoulli randomization.|A/B 测试或对照实验是在线平台上对算法性能进行因果比较的黄金标准方法。然而，传统的伯努利随机化在 A/B 测试中面临着许多挑战，如溢出效应和结转效应。我们的研究侧重于另一个挑战，尤其是在双边平台上的 A/B 测试——预算约束。双边平台上的买家往往预算有限，传统的 A/B 测试可能无法应用，部分原因是两种不同的分配算法可能相互冲突，导致一些买家在同时实施时超出预算。我们开发了一个模型来描述买家预算有限的双边平台。然后，我们提供了一个最佳的实验设计，保证小偏差和最小方差。当有更多的预算和更高的供求比率时，偏差就会降低。我们在合成数据和实际数据上对实验设计进行了检验，验证了理论结果，显示了我们比伯努利随机化方法的优越性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Near-Optimal+Experimental+Design+Under+the+Budget+Constraint+in+Online+Platforms)|0| |[A Method to Assess and Explain Disparate Impact in Online Retailing](https://doi.org/10.1145/3543507.3583270)|Rafael BecerrilArreola|University of South Carolina, USA|This paper presents a method for assessing whether algorithmic decision making induces disparate impact in online retailing. The proposed method specifies a statistical design, a sampling algorithm, and a technological setup for data collection through web crawling. The statistical design reduces the dimensionality of the problem and ensures that the data collected are representative, variation-rich, and suitable for the investigation of the causes behind any observed disparities. Implementations of the method can collect data on algorithmic decisions, such as price, recommendations, and delivery fees that can be matched to website visitor demographic data from established sources such as censuses and large scale surveys. The combined data can be used to investigate the presence and causes of disparate impact, potentially helping online retailers audit their algorithms without collecting or holding the demographic data of their users. The proposed method is illustrated in the context of the automated pricing decisions of a leading retailer in the United States. A custom-built platform implemented the method to collect data for nearly 20,000 different grocery products at more than 3,000 randomly-selected zip codes. The data collected indicates that prices are higher for locations with high proportions of minority households. Although these price disparities can be partly attributed to algorithmic biases, they are mainly explained by local factors and therefore can be regarded as business necessities.|本文介绍了一种评估算法决策是否会对网络购物产生不同影响的方法。提出了一种统计设计、抽样算法和网络爬行数据采集技术。统计设计降低了问题的维度，并确保收集的数据具有代表性，变异丰富，适合于调查任何观察到的差异背后的原因。该方法的实施可以收集关于算法决策的数据，如价格、推荐和交付费用，这些数据可以与人口普查和大规模调查等现有来源的网站访问者人口统计数据相匹配。合并后的数据可以用来调查不同影响的存在和原因，有可能帮助在线零售商在不收集或持有用户人口统计数据的情况下审计他们的算法。该方法是在美国领先零售商的自动定价决策的背景下进行的。一个定制的平台实现了这种方法，可以在3000多个随机选择的邮政编码中收集近20,000种不同食品杂货的数据。所收集的数据表明，少数民族家庭比例较高的地区价格较高。虽然这些价格差异可部分归因于算法偏差，但它们主要是由当地因素造成的，因此可被视为商业必需品。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Method+to+Assess+and+Explain+Disparate+Impact+in+Online+Retailing)|0| |[Simplistic Collection and Labeling Practices Limit the Utility of Benchmark Datasets for Twitter Bot Detection](https://doi.org/10.1145/3543507.3583214)|Chris Hays, Zachary Schutzman, Manish Raghavan, Erin Walk, Philipp Zimmer|Massachusetts Institute of Technology, USA|Accurate bot detection is necessary for the safety and integrity of online platforms. It is also crucial for research on the influence of bots in elections, the spread of misinformation, and financial market manipulation. Platforms deploy infrastructure to flag or remove automated accounts, but their tools and data are not publicly available. Thus, the public must rely on third-party bot detection. These tools employ machine learning and often achieve near perfect performance for classification on existing datasets, suggesting bot detection is accurate, reliable and fit for use in downstream applications. We provide evidence that this is not the case and show that high performance is attributable to limitations in dataset collection and labeling rather than sophistication of the tools. Specifically, we show that simple decision rules -- shallow decision trees trained on a small number of features -- achieve near-state-of-the-art performance on most available datasets and that bot detection datasets, even when combined together, do not generalize well to out-of-sample datasets. Our findings reveal that predictions are highly dependent on each dataset's collection and labeling procedures rather than fundamental differences between bots and humans. These results have important implications for both transparency in sampling and labeling procedures and potential biases in research using existing bot detection tools for pre-processing.|为了保证在线平台的安全性和完整性，精确的机器人检测是必要的。对于研究机器人对选举、错误信息传播和金融市场操纵的影响也至关重要。平台部署基础设施来标记或删除自动帐户，但是它们的工具和数据不公开。因此，公众必须依靠第三方机器人检测。这些工具采用机器学习，并经常达到近乎完美的性能分类的现有数据集，表明机器人检测是准确的，可靠的，适合在下游应用。我们提供的证据表明情况并非如此，并表明高性能是由于数据集收集和标签的局限性，而不是由于工具的复杂性。具体来说，我们展示了简单的决策规则——基于少量特征训练的浅层决策树——在大多数可用数据集上实现了接近最先进的性能，而且机器人检测数据集，即使组合在一起，也不能很好地推广到样本外的数据集。我们的研究结果表明，预测高度依赖于每个数据集的收集和标记程序，而不是机器人和人类之间的根本差异。这些结果对采样和标记过程的透明度以及使用现有的机器人检测工具进行预处理的研究中的潜在偏差都有重要的意义。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Simplistic+Collection+and+Labeling+Practices+Limit+the+Utility+of+Benchmark+Datasets+for+Twitter+Bot+Detection)|0| |[A Dataset on Malicious Paper Bidding in Peer Review](https://doi.org/10.1145/3543507.3583424)|Steven Jecmen, Minji Yoon, Vincent Conitzer, Nihar B. Shah, Fei Fang|Carnegie Mellon University, USA|In conference peer review, reviewers are often asked to provide "bids" on each submitted paper that express their interest in reviewing that paper. A paper assignment algorithm then uses these bids (along with other data) to compute a high-quality assignment of reviewers to papers. However, this process has been exploited by malicious reviewers who strategically bid in order to unethically manipulate the paper assignment, crucially undermining the peer review process. For example, these reviewers may aim to get assigned to a friend's paper as part of a quid-pro-quo deal. A critical impediment towards creating and evaluating methods to mitigate this issue is the lack of any publicly-available data on malicious paper bidding. In this work, we collect and publicly release a novel dataset to fill this gap, collected from a mock conference activity where participants were instructed to bid either honestly or maliciously. We further provide a descriptive analysis of the bidding behavior, including our categorization of different strategies employed by participants. Finally, we evaluate the ability of each strategy to manipulate the assignment, and also evaluate the performance of some simple algorithms meant to detect malicious bidding. The performance of these detection algorithms can be taken as a baseline for future research on detecting malicious bidding.|在会议同行评审中，评审人员经常被要求对每篇提交的论文提供“出价”，以表达他们对评审该论文的兴趣。然后，论文分配算法使用这些出价(以及其他数据)来计算高质量的论文审稿人分配。然而，这一过程已被恶意审查者利用，他们策略性地投标，以便不道德地操纵论文分配，从而严重破坏同行审查过程。例如，这些评论家可能会把分配到朋友的论文作为交换条件的一部分。创建和评价缓解这一问题的方法的一个关键障碍是缺乏关于恶意纸张投标的任何公开数据。在这项工作中，我们收集并公开发布了一个新的数据集来填补这个空白，这个数据集是从一个模拟会议活动中收集的，在这个活动中，参与者被要求诚实地或恶意地出价。我们进一步提供了一个描述性分析的投标行为，包括我们的分类不同的策略采用的参与者。最后，我们评估了每个策略操纵分配的能力，并且评估了一些简单算法的性能，这些算法旨在检测恶意投标。这些检测算法的性能可以作为今后恶意竞价检测研究的基准。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Dataset+on+Malicious+Paper+Bidding+in+Peer+Review)|0| -|[Exploring Social Media for Early Detection of Depression in COVID-19 Patients](https://doi.org/10.1145/3543507.3583867)|Jiageng Wu, Xian Wu, Yining Hua, Shixu Lin, Yefeng Zheng, Jie Yang|Harvard University, USA; Tencent Jarvis Lab, China; Zhejiang University, China|The COVID-19 pandemic has caused substantial damage to global health. Even though three years have passed, the world continues to struggle with the virus. Concerns are growing about the impact of COVID-19 on the mental health of infected individuals, who are more likely to experience depression, which can have long-lasting consequences for both the affected individuals and the world. Detection and intervention at an early stage can reduce the risk of depression in COVID-19 patients. In this paper, we investigated the relationship between COVID-19 infection and depression through social media analysis. Firstly, we managed a dataset of COVID-19 patients that contains information about their social media activity both before and after infection. Secondly,We conducted an extensive analysis of this dataset to investigate the characteristic of COVID-19 patients with a higher risk of depression. Thirdly, we proposed a deep neural network for early prediction of depression risk. This model considers daily mood swings as a psychiatric signal and incorporates textual and emotional characteristics via knowledge distillation. Experimental results demonstrate that our proposed framework outperforms baselines in detecting depression risk, with an AUROC of 0.9317 and an AUPRC of 0.8116. Our model has the potential to enable public health organizations to initiate prompt intervention with high-risk patients|2019冠状病毒疾病大流行对全球健康造成了严重损害。尽管三年过去了，世界仍在与病毒斗争。人们越来越担心2019冠状病毒疾病对感染者心理健康的影响，他们更有可能经历抑郁症，这可能对受影响的个人和世界产生长期后果。早期发现和干预可以降低2019冠状病毒疾病患者患抑郁症的风险。在这篇论文中，我们通过社会媒体分析调查了2019冠状病毒疾病感染和抑郁症之间的关系。首先，我们管理了一个2019冠状病毒疾病患者的数据集，其中包含了他们在感染前后的社交媒体活动信息。其次，我们对这个数据集进行了广泛的分析，以调查2019冠状病毒疾病抑郁风险较高的患者的特征。第三，我们提出了一个深层神经网络用于抑郁风险的早期预测。该模型将日常情绪波动视为一种精神信号，通过知识提取将文本特征和情绪特征结合起来。实验结果表明，我们提出的框架在检测抑郁风险方面优于基线，AUROC 为0.9317，AUPRC 为0.8116。我们的模型有潜力使公共卫生组织能够对高危患者进行及时干预|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Exploring+Social+Media+for+Early+Detection+of+Depression+in+COVID-19+Patients)|0| +|[Exploring Social Media for Early Detection of Depression in COVID-19 Patients](https://doi.org/10.1145/3543507.3583867)|Jiageng Wu, Xian Wu, Yining Hua, Shixu Lin, Yefeng Zheng, Jie Yang|Tencent Jarvis Lab, China; Harvard University, USA; Zhejiang University, China|The COVID-19 pandemic has caused substantial damage to global health. Even though three years have passed, the world continues to struggle with the virus. Concerns are growing about the impact of COVID-19 on the mental health of infected individuals, who are more likely to experience depression, which can have long-lasting consequences for both the affected individuals and the world. Detection and intervention at an early stage can reduce the risk of depression in COVID-19 patients. In this paper, we investigated the relationship between COVID-19 infection and depression through social media analysis. Firstly, we managed a dataset of COVID-19 patients that contains information about their social media activity both before and after infection. Secondly,We conducted an extensive analysis of this dataset to investigate the characteristic of COVID-19 patients with a higher risk of depression. Thirdly, we proposed a deep neural network for early prediction of depression risk. This model considers daily mood swings as a psychiatric signal and incorporates textual and emotional characteristics via knowledge distillation. Experimental results demonstrate that our proposed framework outperforms baselines in detecting depression risk, with an AUROC of 0.9317 and an AUPRC of 0.8116. Our model has the potential to enable public health organizations to initiate prompt intervention with high-risk patients|2019冠状病毒疾病大流行对全球健康造成了严重损害。尽管三年过去了，世界仍在与病毒斗争。人们越来越担心2019冠状病毒疾病对感染者心理健康的影响，他们更有可能经历抑郁症，这可能对受影响的个人和世界产生长期后果。早期发现和干预可以降低2019冠状病毒疾病患者患抑郁症的风险。在这篇论文中，我们通过社会媒体分析调查了2019冠状病毒疾病感染和抑郁症之间的关系。首先，我们管理了一个2019冠状病毒疾病患者的数据集，其中包含了他们在感染前后的社交媒体活动信息。其次，我们对这个数据集进行了广泛的分析，以调查2019冠状病毒疾病抑郁风险较高的患者的特征。第三，我们提出了一个深层神经网络用于抑郁风险的早期预测。该模型将日常情绪波动视为一种精神信号，通过知识提取将文本特征和情绪特征结合起来。实验结果表明，我们提出的框架在检测抑郁风险方面优于基线，AUROC 为0.9317，AUPRC 为0.8116。我们的模型有潜力使公共卫生组织能够对高危患者进行及时干预|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Exploring+Social+Media+for+Early+Detection+of+Depression+in+COVID-19+Patients)|0| |[Identifying Checkworthy CURE Claims on Twitter](https://doi.org/10.1145/3543507.3583870)|Sujatha Das Gollapalli, Mingzhe Du, SeeKiong Ng|Institute of Data Science, National University of Singapore, Singapore and Nanyang Technological University, Singapore; Institute of Data Science, National University of Singapore, Singapore and Centre for Trusted Internet and Community, National University of Singapore, Singapore|Medical claims on social media, if left unchecked, have the potential to directly affect the well-being of consumers of online health information. However, existing studies on claim detection do not specifically focus on medical cure aspects, neither do they address if a cure claim is “checkworthy", an indicator of whether a claim is potentially beneficial or harmful, if unchecked. In this paper, we address these limitations by compiling CW-CURE, a novel dataset of CURE tweets, namely tweets containing claims on prevention, diagnoses, risks, treatments, and cures of medical conditions. CW-CURE contains tweets on four major health conditions, namely, Alzheimer’s disease, Cancer, Diabetes, and Depression annotated for claims, their “checkworthiness", as well as the different types of claims such as quantitative claim, correlation/causation, personal experience, and future prediction. We describe our processing pipeline for compiling CW-CURE and present classification results on CURE tweets using transformer-based models. In particular, we harness claim-type information obtained with zero-shot learning to show significant improvements in checkworthiness identification. Through CW-CURE, we hope to enable research on models for effective identification and flagging of impactful CURE content, to safeguard the public’s consumption of medical content online.|社交媒体上的医疗声明，如果不加以审查，就有可能直接影响网上健康信息消费者的福祉。然而，现有的索赔检测研究并没有特别关注医疗治疗方面，也没有涉及治疗索赔是否“值得检查”，如果未经检查，这是一个索赔是否具有潜在有益或有害的指标。在本文中，我们通过编译 CW-CURE，一个新的 CURE 推文数据集，即包含关于预防、诊断、风险、治疗和医疗条件治愈的声明的推文，来解决这些局限性。CW-CURE 包含了关于四种主要健康状况的推文，即: 阿尔茨海默氏病、癌症、糖尿病和抑郁症，它们的“检查价值”，以及不同类型的声明，如定量声明、相关性/因果关系、个人经验和未来预测。我们描述了用于编译 CW-CURE 的处理流水线，并使用基于转换器的模型给出了 CURE tweet 的分类结果。特别是，我们利用通过零拍学习获得的索赔类型信息来显示可检查性识别方面的显著改进。通过 CW-CURE，我们希望能够研究有效识别和标记影响 CURE 内容的模型，以保障公众对网上医疗内容的消费。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Identifying+Checkworthy+CURE+Claims+on+Twitter)|0| |[The Impact of Covid-19 on Online Discussions: the Case Study of the Sanctioned Suicide Forum](https://doi.org/10.1145/3543507.3583879)|Elisa Sartori, Luca Pajola, Giovanni Da San Martino, Mauro Conti|University of Padua, Italy|The COVID-19 pandemic has been at the center of the lives of many of us for at least a couple of years, during which periods of isolation and lockdowns were common. How all that affected our mental well-being, especially the ones’ who were already in distress? To investigate the matter we analyse the online discussions on Sanctioned Suicide, a forum where users discuss suicide-related topics freely. We collected discussions starting from March 2018 (before pandemic) up to July 2022, for a total of 53K threads with 700K comments and 16K users. We investigate the impact of COVID-19 on the discussions in the forum. The data show that covid, while being present in the discussions, especially during the first lockdown, has not been the main reason why new users registered to the forum. However, covid appears to be indirectly connected to other causes of distress for the users, i.e. anxiety for the economy.|2019冠状病毒疾病大流行已经成为我们许多人生活的中心至少有几年的时间，在这期间，隔离和封锁是常见的。这一切是如何影响我们的心理健康的，尤其是那些已经处于痛苦之中的人？为了调查这个问题，我们分析了网上关于“制裁性自杀”的讨论，这是一个用户可以自由讨论自杀相关话题的论坛。我们收集了从2018年3月(大流行之前)到2022年7月的讨论，共有53K 线程和700K 评论和16K 用户。我们调查2019冠状病毒疾病对论坛讨论的影响。数据显示，尽管冠状病毒疾病参与了讨论，尤其是在第一次封锁期间，但这并不是新用户注册论坛的主要原因。然而，冠状病毒疾病似乎与使用者的其他忧虑因素(即对经济的忧虑)有间接关系。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=The+Impact+of+Covid-19+on+Online+Discussions:+the+Case+Study+of+the+Sanctioned+Suicide+Forum)|0| |[Learning like human annotators: Cyberbullying detection in lengthy social media sessions](https://doi.org/10.1145/3543507.3583873)|Peiling Yi, Arkaitz Zubiaga|Queen Mary University of London, United Kingdom|The inherent characteristic of cyberbullying of being a recurrent attitude calls for the investigation of the problem by looking at social media sessions as a whole, beyond just isolated social media posts. However, the lengthy nature of social media sessions challenges the applicability and performance of session-based cyberbullying detection models. This is especially true when one aims to use state-of-the-art Transformer-based pre-trained language models, which only take inputs of a limited length. In this paper, we address this limitation of transformer models by proposing a conceptually intuitive framework called LS-CB, which enables cyberbullying detection from lengthy social media sessions. LS-CB relies on the intuition that we can effectively aggregate the predictions made by transformer models on smaller sliding windows extracted from lengthy social media sessions, leading to an overall improved performance. Our extensive experiments with six transformer models on two session-based datasets show that LS-CB consistently outperforms three types of competitive baselines including state-of-the-art cyberbullying detection models. In addition, we conduct a set of qualitative analyses to validate the hypotheses that cyberbullying incidents can be detected through aggregated analysis of smaller chunks derived from lengthy social media sessions (H1), and that cyberbullying incidents can occur at different points of the session (H2), hence positing that frequently used text truncation strategies are suboptimal compared to relying on holistic views of sessions. Our research in turn opens an avenue for fine-grained cyberbullying detection within sessions in future work.|网络欺凌作为一种反复出现的态度，其固有的特征要求我们将社交媒体会议作为一个整体来研究这个问题，而不仅仅是孤立的社交媒体帖子。然而，社交媒体会话的冗长性质对基于会话的网络欺凌检测模型的适用性和性能提出了挑战。当一个人的目标是使用基于最先进变压器的预先训练的语言模型时，这种情况尤其如此，因为这种语言模型只接受有限长度的输入。在本文中，我们通过提出一个称为 LS-CB 的概念直观的框架来解决变压器模型的这种局限性，该框架能够从冗长的社交媒体会话中检测出网络欺凌。LS-CB 依赖于这样一种直觉，即我们可以有效地将变压器模型的预测聚合在从冗长的社交媒体会话中提取的较小的滑动窗口上，从而导致整体性能的提高。我们在两个基于会话的数据集上对六个变压器模型进行了广泛的实验，结果表明 LS-CB 始终优于包括最先进的网络欺凌检测模型在内的三类竞争基线。此外，我们还进行了一系列定性分析，以验证网络欺凌事件可以通过对冗长的社交媒体会话(H1)产生的较小块进行聚合分析来检测的假设，并且网络欺凌事件可以发生在会话的不同点(H2) ，因此认为经常使用的文本截断策略与依赖会话的整体观点相比是次优的。我们的研究反过来又为未来工作中的会话中细粒度的网络欺凌检测开辟了一条道路。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Learning+like+human+annotators:+Cyberbullying+detection+in+lengthy+social+media+sessions)|0| -|[Vertical Federated Knowledge Transfer via Representation Distillation for Healthcare Collaboration Networks](https://doi.org/10.1145/3543507.3583874)|Chungju Huang, Leye Wang, Xiao Han|School of Computer Science, Peking University, China; School of Information Management and Engineering, Shanghai University of Finance and Economics, China|Collaboration between healthcare institutions can significantly lessen the imbalance in medical resources across various geographic areas. However, directly sharing diagnostic information between institutions is typically not permitted due to the protection of patients' highly sensitive privacy. As a novel privacy-preserving machine learning paradigm, federated learning (FL) makes it possible to maximize the data utility among multiple medical institutions. These feature-enrichment FL techniques are referred to as vertical FL (VFL). Traditional VFL can only benefit multi-parties' shared samples, which strongly restricts its application scope. In order to improve the information-sharing capability and innovation of various healthcare-related institutions, and then to establish a next-generation open medical collaboration network, we propose a unified framework for vertical federated knowledge transfer mechanism (VFedTrans) based on a novel cross-hospital representation distillation component. Specifically, our framework includes three steps. First, shared samples' federated representations are extracted by collaboratively modeling multi-parties' joint features with current efficient vertical federated representation learning methods. Second, for each hospital, we learn a local-representation-distilled module, which can transfer the knowledge from shared samples' federated representations to enrich local samples' representations. Finally, each hospital can leverage local samples' representations enriched by the distillation module to boost arbitrary downstream machine learning tasks. The experiments on real-life medical datasets verify the knowledge transfer effectiveness of our framework.|医疗机构之间的合作可以显著减少各地区医疗资源的不平衡。然而，由于对患者高度敏感的隐私的保护，医疗机构之间通常不允许直接共享诊断信息。联邦学习(FL)作为一种新的保护隐私的机器学习范式，使得多个医疗机构之间的数据效用最大化成为可能。这些特征丰富的 FL 技术被称为垂直 FL (VFL)。传统的 VFL 只能有利于多方共享样本，这严重制约了它的应用范围。为了提高各医疗机构的信息共享能力和创新能力，进而建立下一代开放式医疗协作网络，提出了一种基于新型跨医院表示蒸馏组件的垂直联邦知识转移机制(VFedTrans)统一框架。具体来说，我们的框架包括三个步骤。首先，利用现有高效的垂直联邦表示学习方法，对多方联合特征进行协同建模，提取共享样本的联邦表示;。其次，针对每个医院，我们学习了一个局部表示提取模块，该模块可以从共享样本的联邦表示中转移知识以丰富局部样本的表示。最后，每家医院可以利用蒸馏模块丰富的本地样本表示来增强任意下游机器学习任务。在实际医学数据集上的实验验证了该框架的知识转移效果。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Vertical+Federated+Knowledge+Transfer+via+Representation+Distillation+for+Healthcare+Collaboration+Networks)|0| -|[On Graph Time-Series Representations for Temporal Networks](https://doi.org/10.1145/3543873.3587301)|Ryan A. Rossi, Nesreen K. Ahmed, Namyong Park|Adobe Research, USA; Intel Labs, USA; Meta, USA|Representations of temporal networks arising from a stream of edges lie at the heart of models learned on it and its performance on downstream applications. While previous work on dynamic modeling and embedding have focused on representing a stream of timestamped edges using a time-series of graphs based on a specific time-scale τ (e.g., 1 month), we introduce the notion of an ϵ -graph time-series that uses a fixed number of edges for each graph, and show its effectiveness in capturing fundamental structural graph statistics over time. The results indicate that the ϵ -graph time-series representation effectively captures the structural properties of the graphs across time whereas the commonly used τ -graph time-series representation captures the frequency of edges and temporal patterns with respect to their arrival in the application time. These results have many important implications especially on the design of new GNN-based models for temporal networks as well as for understanding existing models and their limitations.|由边流产生的时间网络的表示是在它上面学习的模型及其在下游应用中的性能的核心。以往的动态建模和嵌入工作主要集中在使用基于特定时间尺度 τ (例如，1个月)的时间序列图表示一系列带时间戳的边，我们引入了 ε 图时间序列的概念，每个图使用固定数目的边，并显示了其在获取基本结构图随时间变化的统计信息方面的有效性。结果表明，ε 图时间序列表示方法能够有效地捕捉图的跨时间结构特性，而常用的 τ 图时间序列表示方法能够捕捉边和时间模式在应用时间到达的频率。这些结果对于设计新的基于 GNN 的时态网络模型以及理解现有模型及其局限性具有重要意义。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=On+Graph+Time-Series+Representations+for+Temporal+Networks)|0| +|[Vertical Federated Knowledge Transfer via Representation Distillation for Healthcare Collaboration Networks](https://doi.org/10.1145/3543507.3583874)|Chungju Huang, Leye Wang, Xiao Han|School of Information Management and Engineering, Shanghai University of Finance and Economics, China; School of Computer Science, Peking University, China|Collaboration between healthcare institutions can significantly lessen the imbalance in medical resources across various geographic areas. However, directly sharing diagnostic information between institutions is typically not permitted due to the protection of patients' highly sensitive privacy. As a novel privacy-preserving machine learning paradigm, federated learning (FL) makes it possible to maximize the data utility among multiple medical institutions. These feature-enrichment FL techniques are referred to as vertical FL (VFL). Traditional VFL can only benefit multi-parties' shared samples, which strongly restricts its application scope. In order to improve the information-sharing capability and innovation of various healthcare-related institutions, and then to establish a next-generation open medical collaboration network, we propose a unified framework for vertical federated knowledge transfer mechanism (VFedTrans) based on a novel cross-hospital representation distillation component. Specifically, our framework includes three steps. First, shared samples' federated representations are extracted by collaboratively modeling multi-parties' joint features with current efficient vertical federated representation learning methods. Second, for each hospital, we learn a local-representation-distilled module, which can transfer the knowledge from shared samples' federated representations to enrich local samples' representations. Finally, each hospital can leverage local samples' representations enriched by the distillation module to boost arbitrary downstream machine learning tasks. The experiments on real-life medical datasets verify the knowledge transfer effectiveness of our framework.|医疗机构之间的合作可以显著减少各地区医疗资源的不平衡。然而，由于对患者高度敏感的隐私的保护，医疗机构之间通常不允许直接共享诊断信息。联邦学习(FL)作为一种新的保护隐私的机器学习范式，使得多个医疗机构之间的数据效用最大化成为可能。这些特征丰富的 FL 技术被称为垂直 FL (VFL)。传统的 VFL 只能有利于多方共享样本，这严重制约了它的应用范围。为了提高各医疗机构的信息共享能力和创新能力，进而建立下一代开放式医疗协作网络，提出了一种基于新型跨医院表示蒸馏组件的垂直联邦知识转移机制(VFedTrans)统一框架。具体来说，我们的框架包括三个步骤。首先，利用现有高效的垂直联邦表示学习方法，对多方联合特征进行协同建模，提取共享样本的联邦表示;。其次，针对每个医院，我们学习了一个局部表示提取模块，该模块可以从共享样本的联邦表示中转移知识以丰富局部样本的表示。最后，每家医院可以利用蒸馏模块丰富的本地样本表示来增强任意下游机器学习任务。在实际医学数据集上的实验验证了该框架的知识转移效果。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Vertical+Federated+Knowledge+Transfer+via+Representation+Distillation+for+Healthcare+Collaboration+Networks)|0| +|[On Graph Time-Series Representations for Temporal Networks](https://doi.org/10.1145/3543873.3587301)|Ryan A. Rossi, Nesreen K. Ahmed, Namyong Park|Intel Labs, USA; Meta, USA; Adobe Research, USA|Representations of temporal networks arising from a stream of edges lie at the heart of models learned on it and its performance on downstream applications. While previous work on dynamic modeling and embedding have focused on representing a stream of timestamped edges using a time-series of graphs based on a specific time-scale τ (e.g., 1 month), we introduce the notion of an ϵ -graph time-series that uses a fixed number of edges for each graph, and show its effectiveness in capturing fundamental structural graph statistics over time. The results indicate that the ϵ -graph time-series representation effectively captures the structural properties of the graphs across time whereas the commonly used τ -graph time-series representation captures the frequency of edges and temporal patterns with respect to their arrival in the application time. These results have many important implications especially on the design of new GNN-based models for temporal networks as well as for understanding existing models and their limitations.|由边流产生的时间网络的表示是在它上面学习的模型及其在下游应用中的性能的核心。以往的动态建模和嵌入工作主要集中在使用基于特定时间尺度 τ (例如，1个月)的时间序列图表示一系列带时间戳的边，我们引入了 ε 图时间序列的概念，每个图使用固定数目的边，并显示了其在获取基本结构图随时间变化的统计信息方面的有效性。结果表明，ε 图时间序列表示方法能够有效地捕捉图的跨时间结构特性，而常用的 τ 图时间序列表示方法能够捕捉边和时间模式在应用时间到达的频率。这些结果对于设计新的基于 GNN 的时态网络模型以及理解现有模型及其局限性具有重要意义。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=On+Graph+Time-Series+Representations+for+Temporal+Networks)|0| |[Lower Risks, Better Choices: Stock Correlation Based Portfolio Selection in Stock Markets](https://doi.org/10.1145/3543873.3587298)|Di Luo, Weiheng Liao, Rui Yan|Renmin University of China, China; Made by Data, United Kingdom and Renmin University of China, China|Over the past few years, we’ve seen a huge interest in applying AI techniques to develop investment strategies both in academia and the finance industry. However, we note that generating returns is not always the sole investment objective. Take large pension funds for example, they are considerably more risk-averse as opposed to profit-seeking. With this observation, we propose a Risk-balanced Deep Portfolio Constructor (RDPC) that takes risk into explicit consideration. RDPC is an end-to-end reinforcement learning-based transformer trained to optimize both returns and risk, with a hard attention mechanism that learns the relationship between asset pairs, imitating the powerful pairs trading strategy widely adopted by many investors. Experiments on real-world data show that RDPC achieves state-of-the-art performance not just on risk metrics such as maximum drawdown, but also on risk-adjusted returns metrics including Sharpe ratio and Calmar ratio.|在过去的几年里，我们已经看到了在学术界和金融业应用人工智能技术开发投资策略的巨大兴趣。然而，我们注意到产生回报并不总是唯一的投资目标。以大型养老基金为例，它们的风险厌恶程度远高于追逐利润的程度。根据这一观察，我们提出了一个风险平衡的深度投资组合构造函数(RDPC) ，它将风险考虑在内。RDPC 是一种基于端到端强化学习的变换器，训练用于优化收益和风险，具有硬注意机制，学习资产对之间的关系，模仿许多投资者广泛采用的强大的成对交易策略。对实际数据的实验表明，RDPC 不仅在最大提取率等风险指标上取得了最佳表现，而且在 Sharpe 比率和 Calmar 比率等风险调整后的收益指标上也取得了最佳表现。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Lower+Risks,+Better+Choices:+Stock+Correlation+Based+Portfolio+Selection+in+Stock+Markets)|0| |[Creation and Analysis of a Corpus of Scam Emails Targeting Universities](https://doi.org/10.1145/3543873.3587303)|Grace Ciambrone, Shomir Wilson|Human Language Technologies Lab, Pennsylvania State University, USA|Email-based scams pose a threat to the personally identifiable information and financial safety of all email users. Within a university environment, the risks are potentially greater: traditional students (i.e., within an age range typical of college students) often lack the experience and knowledge of older email users. By understanding the topics, temporal trends, and other patterns of scam emails targeting universities, these institutions can be better equipped to reduce this threat by improving their filtering methods and educating their users. While anecdotal evidence suggests common topics and trends in these scams, the empirical evidence is limited. Observing that large universities are uniquely positioned to gather and share information about email scams, we built a corpus of 5,155 English language scam emails scraped from information security websites of five large universities in the United States. We use Latent Dirichlet Allocation (LDA) topic modelling to assess the landscape and trends of scam emails sent to university addresses. We examine themes chronologically and observe that topics vary over time, indicating changes in scammer strategies. For example, scams targeting students with disabilities have steadily risen in popularity since they first appeared in 2015, while password scams experienced a boom in 2016 but have lessened in recent years. To encourage further research to mitigate the threat of email scams, we release this corpus for others to study.|基于电子邮件的骗案对所有电子邮件用户的个人身份信息和财务安全构成威胁。在大学环境中，风险可能更大: 传统的学生(即在大学生的典型年龄范围内)往往缺乏老年电子邮件用户的经验和知识。通过了解针对大学的电子邮件诈骗的主题、时间趋势和其他模式，这些机构可以通过改进过滤方法和教育用户来更好地减少这种威胁。虽然轶事证据表明了这些骗局的共同主题和趋势，但经验证明有限。考虑到大型大学在收集和分享电子邮件诈骗信息方面的独特地位，我们建立了一个从美国五所大型大学的信息安全网站上搜集到的5,155封英语诈骗电子邮件的语料库。我们使用隐含狄利克雷分布主题模型来评估发送到大学地址的诈骗电子邮件的情况和趋势。我们按时间顺序检查主题，并观察到主题随着时间的推移而变化，表明骗子策略的变化。例如，针对残疾学生的欺诈自2015年首次出现以来，受欢迎程度稳步上升，而密码欺诈在2016年经历了一次繁荣，但近年来有所减少。为了鼓励进一步研究以减轻电子邮件诈骗的威胁，我们发布了这个语料库供其他人研究。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Creation+and+Analysis+of+a+Corpus+of+Scam+Emails+Targeting+Universities)|0| |[STRUM: Extractive Aspect-Based Contrastive Summarization](https://doi.org/10.1145/3543873.3587304)|Beliz Gunel, Sandeep Tata, Marc Najork|Google, USA|Comparative decisions, such as picking between two cars or deciding between two hiking trails, require the users to visit multiple webpages and contrast the choices along relevant aspects. Given the impressive capabilities of pre-trained large language models [4, 11], we ask whether they can help automate such analysis. We refer to this task as extractive aspect-based contrastive summarization which involves constructing a structured summary that compares the choices along relevant aspects. In this paper, we propose a novel method called STRUM for this task that can generalize across domains without requiring any human-written summaries or fixed aspect list as supervision. Given a set of relevant input webpages, STRUM solves this problem using two pre-trained T5-based [11] large language models: first one fine-tuned for aspect and value extraction [14], and second one fine-tuned for natural language inference [13]. We showcase the abilities of our method across different domains, identify shortcomings, and discuss questions that we believe will be critical in this new line of research.|比较决策，例如在两辆汽车之间选择或在两条徒步路线之间选择，需要用户访问多个网页，并沿相关方面对比选择。鉴于预先训练的大型语言模型的令人印象深刻的能力[4,11] ，我们问他们是否能够帮助自动化这样的分析。我们把这个任务称为基于方面的对比总结，它包括构建一个结构化的总结，比较相关方面的选择。在本文中，我们提出了一种新的方法，称为 STRUM 的任务，可以在不需要任何人写摘要或固定的方面列表作为监督跨领域泛化。给定一组相关的输入网页，STRUM 使用两个预先训练的基于 T5的[11]大型语言模型来解决这个问题: 第一个模型针对方面和值提取进行了微调[14] ，第二个模型针对自然语言推理进行了微调[13]。我们展示我们的方法在不同领域的能力，确定缺点，并讨论问题，我们认为将是这个新的研究路线的关键。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=STRUM:+Extractive+Aspect-Based+Contrastive+Summarization)|0| |[gDoc: Automatic Generation of Structured API Documentation](https://doi.org/10.1145/3543873.3587310)|Shujun Wang, Yongqiang Tian, Dengcheng He|Alibaba Group, China|Generating and maintaining API documentation with integrity and consistency can be time-consuming and expensive for evolving APIs. To solve this problem, several approaches have been proposed to automatically generate high-quality API documentation based on a combination of knowledge from different web sources. However, current researches are weak in handling unpopular APIs and cannot generate structured API documentation. Hence, in this poster, we propose a hybrid technique(namely \textit{gDoc}) for the automatic generation of structured API documentation. We first present a fine-grained search-based strategy to generate the description for partial API parameters via computing the relevance between various APIs, ensuring the consistency of API documentation. Then, we employ the cross-modal pretraining Seq2Seq model M6 to generate a structured API document for each API, which treats the document generation problem as a translation problem. Finally, we propose a heuristic algorithm to extract practical parameter examples from API request logs. The experiments evaluated on the online system show that this work's approach significantly improves the effectiveness and efficiency of API document generation.|生成和维护具有完整性和一致性的 API 文档对于不断发展的 API 来说是非常耗时和昂贵的。为了解决这个问题，已经提出了几种方法来自动生成高质量的 API 文档的基础上的知识组合从不同的网络来源。然而，目前的研究在处理不流行的 API 方面还很薄弱，无法生成结构化的 API 文档。因此，在这张海报中，我们提出了一种混合技术(即 texttit { gDoc }) ，用于自动生成结构化 API 文档。我们首先提出了一种基于细粒度搜索的策略，通过计算不同 API 之间的相关性来生成部分 API 参数的描述，从而确保 API 文档的一致性。然后，我们使用跨模式预训练 Seq2Seq 模型 M6为每个 API 生成一个结构化的 API 文档，它将文档生成问题作为一个翻译问题来处理。最后，提出了一种启发式算法，用于从 API 请求日志中提取实际参数示例。在线系统的实验结果表明，该方法显著提高了 API 文档生成的有效性和效率。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=gDoc:+Automatic+Generation+of+Structured+API+Documentation)|0| |[Reduce API Debugging Overhead via Knowledge Prepositioning](https://doi.org/10.1145/3543873.3587311)|Shujun Wang, Yongqiang Tian, Dengcheng He|Alibaba Group, China|OpenAPI indicates a behavior where producers offer Application Programming Interfaces (APIs) to help end-users access their data, resources, and services. Generally, API has many parameters that need to be entered. However, it is challenging for users to understand and document these parameters correctly. This paper develops an API workbench to help users learn and debug APIs. Based on this workbench, much exploratory work has been proposed to reduce the overhead of learning and debugging APIs. We explore the knowledge, such as parameter characteristics (e.g., enumerability) and constraints (e.g., maximum/minimum value), from the massive API call logs to narrow the range of parameter values. Then, we propose a fine-grained approach to enrich the API documentation by extracting dependency knowledge between APIs. Finally, we present a learning-based prediction method to predict API execution results before the API is called, significantly reducing user debugging cycles. The experiments evaluated on the online system show that this work's approach substantially improves the user experience of debugging OpenAPIs.|OpenAPI 指的是生产者提供应用程序编程接口(API)来帮助终端用户访问他们的数据、资源和服务的行为。通常，API 有许多需要输入的参数。然而，对于用户来说，正确理解和记录这些参数是一个挑战。本文开发了一个 API 工作台来帮助用户学习和调试 API。基于这个工作台，已经提出了许多探索性的工作，以减少学习和调试 API 的开销。我们研究了大量 API 调用日志中的知识，如参数特征(例如可枚举性)和约束(例如最大/最小值) ，以缩小参数值的范围。然后，我们提出了一种细粒度的方法，通过提取 API 之间的依赖性知识来丰富 API 文档。最后，我们提出了一种基于学习的预测方法，可以在调用 API 之前预测 API 的执行结果，从而大大缩短用户调试周期。在线系统的实验表明，该方法大大提高了用户调试 OpenAPI 的体验。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Reduce+API+Debugging+Overhead+via+Knowledge+Prepositioning)|0| -|[Augmenting Visualizations with Predictive and Investigative Insights to Facilitate Decision Making](https://doi.org/10.1145/3543873.3587317)|Md Main Uddin Rony, Fan Du, Ryan A. Rossi, Jane Hoffswell, Niyati Chhaya, Iftikhar Ahamath Burhanuddin, Eunyee Koh|Adobe Research, USA; University of Maryland, USA; Adobe Research, India|Many people find it difficult to comprehend basic charts on the web, let alone make effective decisions from them. To address this gap, several ML models aim to automatically detect useful insights from charts and narrate them in a simpler textual format. However, most of these solutions can only detect basic factual insights (a.k.a. descriptive insights) that are already present in the chart, which may help with chart comprehension, but not decision-making. In this work, we study whether more advanced predictive and investigative insights can help users understand what will happen next and what actions they should take. These advanced insights can help decision-makers better understand the reasons behind anomaly events, predict future unfolding trends, and recommend possible actions for optimizing business outcomes. Through a study with 18 participants, we found that predictive and investigative insights lead to more insights recorded by users on average and better effectiveness ratings.|许多人发现很难理解网上的基本图表，更不用说从中做出有效的决策了。为了弥补这一差距，一些机器学习模型的目标是从图表中自动发现有用的见解，并以更简单的文本格式叙述它们。然而，这些解决方案中的大多数只能检测出图表中已经存在的基本事实洞察力(又称描述性洞察力) ，这可能有助于图表理解，但不能帮助决策。在这项工作中，我们研究是否更先进的预测和调查洞察力可以帮助用户了解接下来会发生什么，他们应该采取什么行动。这些先进的见解可以帮助决策者更好地理解异常事件背后的原因，预测未来的发展趋势，并为优化业务结果提出可能的行动建议。通过一项有18名参与者参与的研究，我们发现预测性和调查性的洞察力可以让用户记录下更多的洞察力和更好的效率评级。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Augmenting+Visualizations+with+Predictive+and+Investigative+Insights+to+Facilitate+Decision+Making)|0| +|[Augmenting Visualizations with Predictive and Investigative Insights to Facilitate Decision Making](https://doi.org/10.1145/3543873.3587317)|Md Main Uddin Rony, Fan Du, Ryan A. Rossi, Jane Hoffswell, Niyati Chhaya, Iftikhar Ahamath Burhanuddin, Eunyee Koh|Adobe Research, India; University of Maryland, USA; Adobe Research, USA|Many people find it difficult to comprehend basic charts on the web, let alone make effective decisions from them. To address this gap, several ML models aim to automatically detect useful insights from charts and narrate them in a simpler textual format. However, most of these solutions can only detect basic factual insights (a.k.a. descriptive insights) that are already present in the chart, which may help with chart comprehension, but not decision-making. In this work, we study whether more advanced predictive and investigative insights can help users understand what will happen next and what actions they should take. These advanced insights can help decision-makers better understand the reasons behind anomaly events, predict future unfolding trends, and recommend possible actions for optimizing business outcomes. Through a study with 18 participants, we found that predictive and investigative insights lead to more insights recorded by users on average and better effectiveness ratings.|许多人发现很难理解网上的基本图表，更不用说从中做出有效的决策了。为了弥补这一差距，一些机器学习模型的目标是从图表中自动发现有用的见解，并以更简单的文本格式叙述它们。然而，这些解决方案中的大多数只能检测出图表中已经存在的基本事实洞察力(又称描述性洞察力) ，这可能有助于图表理解，但不能帮助决策。在这项工作中，我们研究是否更先进的预测和调查洞察力可以帮助用户了解接下来会发生什么，他们应该采取什么行动。这些先进的见解可以帮助决策者更好地理解异常事件背后的原因，预测未来的发展趋势，并为优化业务结果提出可能的行动建议。通过一项有18名参与者参与的研究，我们发现预测性和调查性的洞察力可以让用户记录下更多的洞察力和更好的效率评级。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Augmenting+Visualizations+with+Predictive+and+Investigative+Insights+to+Facilitate+Decision+Making)|0| |[OntoEval: an Automated Ontology Evaluation System](https://doi.org/10.1145/3543873.3587318)|Antonio Zaitoun, Tomer Sagi, Katja Hose|University of Haifa, Israel; Aalborg University, Denmark; Aalborg University, Denmark and TU Wien, Austria|Developing semantically-aware web services requires comprehensive and accurate ontologies. Evaluating an existing ontology or adapting it is a labor-intensive and complex task for which no automated tools exist. Nevertheless, in this paper we propose a tool that aims at making this vision come true, i.e., we present a tool for the automated evaluation of ontologies that allows one to rapidly assess an ontology’s coverage of a domain and identify specific problems in the ontology’s structure. The tool evaluates the domain coverage and correctness of parent-child relations of a given ontology based on domain information derived from a text corpus representing the domain. The tool provides both overall statistics and detailed analysis of sub-graphs of the ontology. In the demo, we show how these features can be used for the iterative improvement of an ontology.|开发语义感知的 Web 服务需要全面和准确的本体。评估一个已存在的本体或对其进行适应是一个劳动密集型和复杂的任务，没有自动化的工具可以完成。然而，在本文中，我们提出了一个工具，旨在使这个愿景成真，即，我们提出了一个工具，为本体的自动评估，允许一个人快速评估本体的覆盖范围的领域，并确定具体问题的本体的结构。该工具基于从表示领域的文本语料库中获得的领域信息，评估给定本体的领域覆盖率和父子关系的正确性。该工具提供了本体的整体统计和子图的详细分析。在演示中，我们展示了如何将这些特性用于本体的迭代改进。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=OntoEval:+an+Automated+Ontology+Evaluation+System)|0| |[Public Spot Instance Dataset Archive Service](https://doi.org/10.1145/3543873.3587314)|Kyunghwan Kim, Subin Park, Jaeil Hwang, Hyeonyoung Lee, Seokhyeon Kang, Kyungyong Lee|Distributed Data Processing System Lab, CS Department, Kookmin University, Republic of Korea|Spot instances offered by major cloud vendors allow users to use cloud instances cost-effectively but with the risk of sudden instance interruption. To enable efficient use of spot instances by users, cloud vendors provide various datasets that reflect the current status of spot instance services, such as savings ratio, interrupt ratio, and instant availability. However, this information is scattered, and they require distinct access mechanisms and pose query constraints. Hence, ordinary users find it difficult to use the dataset to optimize spot instance usage. To resolve this issue, we propose a multi-cloud spot instance dataset service that is publicly available. This will help cloud users and system researchers to use spot instances from multiple cloud vendors to build a cost-efficient and reliable environment expediting cloud system research.|主要云供应商提供的现货实例允许用户以具有成本效益的方式使用云实例，但存在实例突然中断的风险。为了使用户能够有效地使用现场实例，云供应商提供了反映现场实例服务当前状态的各种数据集，例如节约率、中断率和即时可用性。但是，这些信息是分散的，它们需要不同的访问机制并构成查询约束。因此，普通用户发现很难使用数据集来优化现场实例的使用。为了解决这个问题，我们提出了一个公开可用的多云点实例数据集服务。这将有助于云用户和系统研究人员使用来自多个云供应商的现场实例来构建一个成本效益高、可靠的环境，从而加快云系统研究。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Public+Spot+Instance+Dataset+Archive+Service)|0| -|[Towards Deeper Graph Neural Networks via Layer-Adaptive](https://doi.org/10.1145/3543873.3587323)|Bingbing Xu, Bin Xie, Huawei Shen|Tiangong University, China; Institute of Computing Technology, Chinese Academy of Sciences, China|Graph neural networks have achieved state-of-the-art performance on graph-related tasks. Previous methods observed that GNNs’ performance degrades as the number of layers increases and attributed this phenomenon to over-smoothing caused by the stacked propagation. However, we proved experimentally and theoretically that it is overfitting rather than propagation that causes performance degradation. We propose a novel framework: layer-adaptive GNN (LAGNN) consisting of two modules: adaptive layer selection and random Droplayer, which can adaptively determine the number of layers and thus alleviate overfitting. We attached this general framework to two representative GNNs and achieved consistency improvements on six representative datasets.|图形神经网络在处理与图形相关的任务时取得了最先进的性能。先前的方法观察到，GNN 的性能随着层数的增加而下降，并将这种现象归因于叠加传播引起的过平滑。然而，我们从实验和理论上证明了过拟合而不是传播会导致性能下降。我们提出了一个新的框架: 层自适应 GNN (LAGNN) ，它由两个模块组成: 自适应层选择和随机 Droplayer，可以自适应地确定层数，从而减少过拟合。我们将这个总体框架附加到两个有代表性的 GNN 上，并在六个有代表性的数据集上实现了一致性改进。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Towards+Deeper+Graph+Neural+Networks+via+Layer-Adaptive)|0| -|[qEndpoint: A Wikidata SPARQL endpoint on commodity hardware](https://doi.org/10.1145/3543873.3587327)|Antoine Willerval, Angela Bonifati, Dennis Diefenbach|LIRIS, University of Lyon 1, France and The QA Company, France; The QA Company, France; LIRIS, University of Lyon 1, France|In this work, we demonstrate how to setup a Wikidata SPARQL endpoint on commodity hardware resources. We achieve this by using a novel triple store called qEndpoint, which uses a read-only partition based on HDT and a write partition based on RDF4J. We show that qEndpoint can index and query the entire Wikidata dump (currently 17 billion triples) on a machine with 600GB SSD, 10 cores and 10GB of RAM, while keeping the query performance comparable with other SPARQL endpoints indexing Wikidata. In a nutshell, we present the first SPARQL endpoint over Wikidata that can run on commodity hardware while preserving the query run time of existing implementations. Our work goes in the direction of democratizing the access to Wikidata as well as to other large-scale Knowledge Graphs published on the Web. The source code of qEndpoint along with the query workloads are publicly available.|在这项工作中，我们将演示如何在商品硬件资源上建立一个维基数据查询服务。我们通过使用一种称为 qEndpoint 的新型三重存储来实现这一点，它使用基于 HDT 的只读分区和基于 RDF4J 的写分区。我们展示了 qEndpoint 可以在600GB SSD、10个核和10GB RAM 的机器上索引和查询整个 Wikidata 转储(目前是170亿个三元组) ，同时保持查询性能与其他 SPARQL 端点索引 Wikidata 相当。简而言之，我们在 Wikidata 上提供了第一个 SPARQL 端点，它可以在普通硬件上运行，同时保留现有实现的查询运行时。我们的工作是朝着民主化的方向进入 Wikidata，以及其他大规模的知识图表出版在网上。QEndpoint 的源代码以及查询工作负载都是公开的。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=qEndpoint:+A+Wikidata+SPARQL+endpoint+on+commodity+hardware)|0| -|[Metadatamatic: A Web application to Create a Dataset Description](https://doi.org/10.1145/3543873.3587328)|Pierre Maillot, Olivier Corby, Catherine Faron, Fabien Gandon, Franck Michel|I3S, Univ. Cote d'Azur, Inria, CNRS, France; I3S, Univ. Cote d'Azur, CNRS, Inria, France|This article introduces Metadatamatic, an open-source, online, user-friendly tool for generating the description of a knowledge base. It supports the description of any RDF dataset via a user-friendly web form that does not require prior knowledge of the vocabularies begin used, and can enrich the description with automatically generated statistics if the dataset is accessible from a public SPARQL endpoint. We discuss the models and methods behind the tool, and present some initial results suggesting that Metadatamatic can help in increasing the visibility of public knowledge bases.|本文介绍了 Metadatamatic，这是一个开源的、在线的、用户友好的工具，用于生成知识库的描述。它支持通过用户友好的 Web 表单描述任何 RDF 数据集，不需要事先了解开始使用的词汇表，并且可以通过自动生成的统计数据来丰富描述，如果数据集可以从公共 SPARQL 端点访问的话。我们讨论了该工具背后的模型和方法，并提出了一些初步结果，表明元数据可以帮助提高公共知识库的可见性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Metadatamatic:+A+Web+application+to+Create+a+Dataset+Description)|0| +|[Towards Deeper Graph Neural Networks via Layer-Adaptive](https://doi.org/10.1145/3543873.3587323)|Bingbing Xu, Bin Xie, Huawei Shen|Institute of Computing Technology, Chinese Academy of Sciences, China; Tiangong University, China|Graph neural networks have achieved state-of-the-art performance on graph-related tasks. Previous methods observed that GNNs’ performance degrades as the number of layers increases and attributed this phenomenon to over-smoothing caused by the stacked propagation. However, we proved experimentally and theoretically that it is overfitting rather than propagation that causes performance degradation. We propose a novel framework: layer-adaptive GNN (LAGNN) consisting of two modules: adaptive layer selection and random Droplayer, which can adaptively determine the number of layers and thus alleviate overfitting. We attached this general framework to two representative GNNs and achieved consistency improvements on six representative datasets.|图形神经网络在处理与图形相关的任务时取得了最先进的性能。先前的方法观察到，GNN 的性能随着层数的增加而下降，并将这种现象归因于叠加传播引起的过平滑。然而，我们从实验和理论上证明了过拟合而不是传播会导致性能下降。我们提出了一个新的框架: 层自适应 GNN (LAGNN) ，它由两个模块组成: 自适应层选择和随机 Droplayer，可以自适应地确定层数，从而减少过拟合。我们将这个总体框架附加到两个有代表性的 GNN 上，并在六个有代表性的数据集上实现了一致性改进。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Towards+Deeper+Graph+Neural+Networks+via+Layer-Adaptive)|0| +|[qEndpoint: A Wikidata SPARQL endpoint on commodity hardware](https://doi.org/10.1145/3543873.3587327)|Antoine Willerval, Angela Bonifati, Dennis Diefenbach|LIRIS, University of Lyon 1, France and The QA Company, France; LIRIS, University of Lyon 1, France; The QA Company, France|In this work, we demonstrate how to setup a Wikidata SPARQL endpoint on commodity hardware resources. We achieve this by using a novel triple store called qEndpoint, which uses a read-only partition based on HDT and a write partition based on RDF4J. We show that qEndpoint can index and query the entire Wikidata dump (currently 17 billion triples) on a machine with 600GB SSD, 10 cores and 10GB of RAM, while keeping the query performance comparable with other SPARQL endpoints indexing Wikidata. In a nutshell, we present the first SPARQL endpoint over Wikidata that can run on commodity hardware while preserving the query run time of existing implementations. Our work goes in the direction of democratizing the access to Wikidata as well as to other large-scale Knowledge Graphs published on the Web. The source code of qEndpoint along with the query workloads are publicly available.|在这项工作中，我们将演示如何在商品硬件资源上建立一个维基数据查询服务。我们通过使用一种称为 qEndpoint 的新型三重存储来实现这一点，它使用基于 HDT 的只读分区和基于 RDF4J 的写分区。我们展示了 qEndpoint 可以在600GB SSD、10个核和10GB RAM 的机器上索引和查询整个 Wikidata 转储(目前是170亿个三元组) ，同时保持查询性能与其他 SPARQL 端点索引 Wikidata 相当。简而言之，我们在 Wikidata 上提供了第一个 SPARQL 端点，它可以在普通硬件上运行，同时保留现有实现的查询运行时。我们的工作是朝着民主化的方向进入 Wikidata，以及其他大规模的知识图表出版在网上。QEndpoint 的源代码以及查询工作负载都是公开的。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=qEndpoint:+A+Wikidata+SPARQL+endpoint+on+commodity+hardware)|0| +|[Metadatamatic: A Web application to Create a Dataset Description](https://doi.org/10.1145/3543873.3587328)|Pierre Maillot, Olivier Corby, Catherine Faron, Fabien Gandon, Franck Michel|I3S, Univ. Cote d'Azur, CNRS, Inria, France; I3S, Univ. Cote d'Azur, Inria, CNRS, France|This article introduces Metadatamatic, an open-source, online, user-friendly tool for generating the description of a knowledge base. It supports the description of any RDF dataset via a user-friendly web form that does not require prior knowledge of the vocabularies begin used, and can enrich the description with automatically generated statistics if the dataset is accessible from a public SPARQL endpoint. We discuss the models and methods behind the tool, and present some initial results suggesting that Metadatamatic can help in increasing the visibility of public knowledge bases.|本文介绍了 Metadatamatic，这是一个开源的、在线的、用户友好的工具，用于生成知识库的描述。它支持通过用户友好的 Web 表单描述任何 RDF 数据集，不需要事先了解开始使用的词汇表，并且可以通过自动生成的统计数据来丰富描述，如果数据集可以从公共 SPARQL 端点访问的话。我们讨论了该工具背后的模型和方法，并提出了一些初步结果，表明元数据可以帮助提高公共知识库的可见性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Metadatamatic:+A+Web+application+to+Create+a+Dataset+Description)|0| |[What do LLMs Know about Financial Markets? A Case Study on Reddit Market Sentiment Analysis](https://doi.org/10.1145/3543873.3587324)|Xiang Deng, Vasilisa Bashlovkina, Feng Han, Simon Baumgartner, Michael Bendersky|Google Research, USA; The Ohio State University, USA|Market sentiment analysis on social media content requires knowledge of both financial markets and social media jargon, which makes it a challenging task for human raters. The resulting lack of high-quality labeled data stands in the way of conventional supervised learning methods. Instead, we approach this problem using semi-supervised learning with a large language model (LLM). Our pipeline generates weak financial sentiment labels for Reddit posts with an LLM and then uses that data to train a small model that can be served in production. We find that prompting the LLM to produce Chain-of-Thought summaries and forcing it through several reasoning paths helps generate more stable and accurate labels, while using a regression loss further improves distillation quality. With only a handful of prompts, the final model performs on par with existing supervised models. Though production applications of our model are limited by ethical considerations, the model's competitive performance points to the great potential of using LLMs for tasks that otherwise require skill-intensive annotation.|对社交媒体内容的市场情绪分析需要金融市场和社交媒体术语的双重知识，这对人类评估者来说是一项具有挑战性的任务。由此导致的高质量标记数据的缺乏阻碍了传统的监督式学习检测方法。相反，我们使用大语言模型(LLM)的半监督学习来解决这个问题。我们的管道为 Reddit 的 LLM 帖子生成了弱的金融情绪标签，然后使用这些数据来训练一个可以在生产中使用的小型模型。我们发现，提示 LLM 生成思想链摘要并强制它通过几个推理路径有助于生成更稳定和准确的标签，同时使用回归损失进一步提高蒸馏质量。由于只有少量的提示，最终模型的表现与现有的监督模型不相上下。尽管我们的模型的生产应用受到伦理考虑的限制，但是模型的竞争性性能指出了使用 LLM 完成需要技能密集型注释的任务的巨大潜力。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=What+do+LLMs+Know+about+Financial+Markets?+A+Case+Study+on+Reddit+Market+Sentiment+Analysis)|0| -|[GAT-DNS: DNS Multivariate Time Series Prediction Model Based on Graph Attention Network](https://doi.org/10.1145/3543873.3587329)|Xiaofeng Lu, Xiaoyu Zhang, Pietro Lió|University of Cambridge, United Kingdom; Beijing University of Posts and Telecommunications, China|As one of the most basic services of the Internet, DNS has suffered a lot of attacks. Existing attack detection methods rely on the learning of malicious samples, so it is difficult to detect new attacks and long-period attacks. This paper transforms the DNS data flow into time series, and proposes a DNS anomaly detection method based on graph attention network and graph embedding (GAT-DNS). GAT-DNS establishes a multivariate time series model to depict the DNS service status. When the actual flow of a feature exceeds the predicted range, it is considered that abnormal DNS behavior is found. In this paper, vertex dependency is proposed to describe the dependency between features. The features with high vertex dependency values are deleted to achieve model compression. This improves the system efficiency. Experiments on open data sets show that compared with the latest AD-Bop and QLAD methods, GAT-DNS method not only improves the precision, recall and F1 value, but also improves the time efficiency of the model.|作为互联网最基本的服务之一，域名解析系统遭受了大量的攻击。现有的攻击检测方法依赖于对恶意样本的学习，因此很难检测到新的攻击和长周期的攻击。本文将 DNS 数据流转换为时间序列，提出了一种基于图注意网络和图嵌入(gat-DNS)的 DNS 异常检测方法。GAT-DNS 建立了描述 DNS 服务状态的多变量时间序列模型。当一个特征的实际流量超过预测范围时，就认为发现了异常的 DNS 行为。本文提出用顶点依赖来描述特征之间的依赖关系。删除顶点依赖度高的特征以实现模型压缩。这提高了系统的效率。对开放数据集的实验表明，与最新的 AD-Bop 和 QLAD 方法相比，GAT-DNS 方法不仅提高了模型的精度、召回率和 F1值，而且提高了模型的时间效率。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=GAT-DNS:+DNS+Multivariate+Time+Series+Prediction+Model+Based+on+Graph+Attention+Network)|0| -|[Weedle: Composable Dashboard for Data-Centric NLP in Computational Notebooks](https://doi.org/10.1145/3543873.3587330)|Nahyun Kwon, Hannah Kim, Sajjadur Rahman, Dan Zhang, Estevam Hruschka|Texas A&M University, USA; Megagon Labs, USA|Data-centric NLP is a highly iterative process requiring careful exploration of text data throughout entire model development lifecycle. Unfortunately, existing data exploration tools are not suitable to support data-centric NLP because of workflow discontinuity and lack of support for unstructured text. In response, we propose Weedle, a seamless and customizable exploratory text analysis system for data-centric NLP. Weedle is equipped with built-in text transformation operations and a suite of visual analysis features. With its widget, users can compose customizable dashboards interactively and programmatically in computational notebooks.|以数据为中心的 NLP 是一个高度迭代的过程，需要在整个模型开发生命周期中仔细探索文本数据。遗憾的是，由于工作流的不连续性和缺乏对非结构化文本的支持，现有的数据探索工具不适合支持以数据为中心的自然语言处理。作为回应，我们提出了 Weedle，一个无缝和可定制的探索性文本分析系统，用于以数据为中心的自然语言处理。Weedle 配备了内置的文本转换操作和一套可视化分析功能。通过它的小部件，用户可以在计算笔记本中交互式和编程式地组合可定制的仪表板。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Weedle:+Composable+Dashboard+for+Data-Centric+NLP+in+Computational+Notebooks)|0| +|[GAT-DNS: DNS Multivariate Time Series Prediction Model Based on Graph Attention Network](https://doi.org/10.1145/3543873.3587329)|Xiaofeng Lu, Xiaoyu Zhang, Pietro Lió|Beijing University of Posts and Telecommunications, China; University of Cambridge, United Kingdom|As one of the most basic services of the Internet, DNS has suffered a lot of attacks. Existing attack detection methods rely on the learning of malicious samples, so it is difficult to detect new attacks and long-period attacks. This paper transforms the DNS data flow into time series, and proposes a DNS anomaly detection method based on graph attention network and graph embedding (GAT-DNS). GAT-DNS establishes a multivariate time series model to depict the DNS service status. When the actual flow of a feature exceeds the predicted range, it is considered that abnormal DNS behavior is found. In this paper, vertex dependency is proposed to describe the dependency between features. The features with high vertex dependency values are deleted to achieve model compression. This improves the system efficiency. Experiments on open data sets show that compared with the latest AD-Bop and QLAD methods, GAT-DNS method not only improves the precision, recall and F1 value, but also improves the time efficiency of the model.|作为互联网最基本的服务之一，域名解析系统遭受了大量的攻击。现有的攻击检测方法依赖于对恶意样本的学习，因此很难检测到新的攻击和长周期的攻击。本文将 DNS 数据流转换为时间序列，提出了一种基于图注意网络和图嵌入(gat-DNS)的 DNS 异常检测方法。GAT-DNS 建立了描述 DNS 服务状态的多变量时间序列模型。当一个特征的实际流量超过预测范围时，就认为发现了异常的 DNS 行为。本文提出用顶点依赖来描述特征之间的依赖关系。删除顶点依赖度高的特征以实现模型压缩。这提高了系统的效率。对开放数据集的实验表明，与最新的 AD-Bop 和 QLAD 方法相比，GAT-DNS 方法不仅提高了模型的精度、召回率和 F1值，而且提高了模型的时间效率。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=GAT-DNS:+DNS+Multivariate+Time+Series+Prediction+Model+Based+on+Graph+Attention+Network)|0| +|[Weedle: Composable Dashboard for Data-Centric NLP in Computational Notebooks](https://doi.org/10.1145/3543873.3587330)|Nahyun Kwon, Hannah Kim, Sajjadur Rahman, Dan Zhang, Estevam Hruschka|Megagon Labs, USA; Texas A&M University, USA|Data-centric NLP is a highly iterative process requiring careful exploration of text data throughout entire model development lifecycle. Unfortunately, existing data exploration tools are not suitable to support data-centric NLP because of workflow discontinuity and lack of support for unstructured text. In response, we propose Weedle, a seamless and customizable exploratory text analysis system for data-centric NLP. Weedle is equipped with built-in text transformation operations and a suite of visual analysis features. With its widget, users can compose customizable dashboards interactively and programmatically in computational notebooks.|以数据为中心的 NLP 是一个高度迭代的过程，需要在整个模型开发生命周期中仔细探索文本数据。遗憾的是，由于工作流的不连续性和缺乏对非结构化文本的支持，现有的数据探索工具不适合支持以数据为中心的自然语言处理。作为回应，我们提出了 Weedle，一个无缝和可定制的探索性文本分析系统，用于以数据为中心的自然语言处理。Weedle 配备了内置的文本转换操作和一套可视化分析功能。通过它的小部件，用户可以在计算笔记本中交互式和编程式地组合可定制的仪表板。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Weedle:+Composable+Dashboard+for+Data-Centric+NLP+in+Computational+Notebooks)|0| |[The Web Data Commons Schema.org Data Set Series](https://doi.org/10.1145/3543873.3587331)|Alexander Brinkmann, Anna Primpeli, Christian Bizer|University of Mannheim, Germany|Millions of websites have started to annotate structured data within their HTML pages using the schema.org vocabulary. Popular entity types annotated with schema.org terms are products, local businesses, events, and job postings. The Web Data Commons project has been extracting schema.org data from the Common Crawl every year since 2013 and offers the extracted data for public download in the form of the schema.org data set series. The latest release in the series consists of 106 billion RDF quads describing 3.1 billion entities. The entity descriptions originate from 12.8 million different websites. From a Web Science perspective, the data set series lays the foundation for analyzing the adoption process of schema.org annotations on the Web over the past decade. From a machine learning perspective, the annotations provide a large pool of training data for tasks such as product matching, product or job categorization, information extraction, or question answering. This poster gives an overview of the content of the Web Data Commons schema.org data set series. It highlights trends in the adoption of schema.org annotations on the Web and discusses how the annotations are being used as training data for machine learning applications.|数以百万计的网站已经开始使用 schema.org 词汇表在 HTML 页面中注释结构化数据。用 schema.org 术语注释的流行实体类型是产品、本地企业、事件和招聘信息。自2013年以来，Web Data Commons 项目每年都从 Common Crawl 中提取 schema.org 数据，并以 schema.org 数据集系列的形式提供提取的数据供公众下载。该系列的最新版本由1060亿个 RDF 四元组组成，描述了31亿个实体。实体描述来自1280万个不同的网站。从 Web 科学的角度来看，数据集系列为分析过去十年中 Schema.org 注释在 Web 上的采用过程奠定了基础。从机器学习的角度来看，注释为产品匹配、产品或工作分类、信息抽取或问题回答等任务提供了大量的培训数据。本海报概述了 Web Data Commons schema.org 数据集系列的内容。它强调了在 Web 上采用 schema.org 注释的趋势，并讨论了如何将这些注释用作机器学习应用程序的培训数据。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=The+Web+Data+Commons+Schema.org+Data+Set+Series)|0| -|[Class Cardinality Comparison as a Fermi Problem](https://doi.org/10.1145/3543873.3587334)|Shrestha Ghosh, Simon Razniewski, Gerhard Weikum|Max Planck Institute for Informatics, Germany and Saarland University, Germany; Max Planck Institute for Informatics, Germany|Questions on class cardinality comparisons are quite tricky to answer and come with its own challenges. They require some kind of reasoning since web documents and knowledge bases, indispensable sources of information, rarely store direct answers to questions, such as, ``Are there more astronauts or Physics Nobel Laureates?'' We tackle questions on class cardinality comparison by tapping into three sources for absolute cardinalities as well as the cardinalities of orthogonal subgroups of the classes. We propose novel techniques for aggregating signals with partial coverage for more reliable estimates and evaluate them on a dataset of 4005 class pairs, achieving an accuracy of 83.7%.|关于类基数比较的问题很难回答，而且有其自身的挑战。它们需要某种推理，因为网络文档和知识库是不可或缺的信息来源，很少存储对诸如“有更多的宇航员或物理学诺贝尔奖获得者吗?”我们利用类的绝对基数和正交子群的基数的三个来源来解决类基数比较的问题。我们提出了一种新的技术，用于聚集具有部分覆盖的信号，以获得更可靠的估计，并在4005个类对的数据集上对它们进行评估，实现了83.7% 的准确率。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Class+Cardinality+Comparison+as+a+Fermi+Problem)|0| +|[Class Cardinality Comparison as a Fermi Problem](https://doi.org/10.1145/3543873.3587334)|Shrestha Ghosh, Simon Razniewski, Gerhard Weikum|Max Planck Institute for Informatics, Germany; Max Planck Institute for Informatics, Germany and Saarland University, Germany|Questions on class cardinality comparisons are quite tricky to answer and come with its own challenges. They require some kind of reasoning since web documents and knowledge bases, indispensable sources of information, rarely store direct answers to questions, such as, ``Are there more astronauts or Physics Nobel Laureates?'' We tackle questions on class cardinality comparison by tapping into three sources for absolute cardinalities as well as the cardinalities of orthogonal subgroups of the classes. We propose novel techniques for aggregating signals with partial coverage for more reliable estimates and evaluate them on a dataset of 4005 class pairs, achieving an accuracy of 83.7%.|关于类基数比较的问题很难回答，而且有其自身的挑战。它们需要某种推理，因为网络文档和知识库是不可或缺的信息来源，很少存储对诸如“有更多的宇航员或物理学诺贝尔奖获得者吗?”我们利用类的绝对基数和正交子群的基数的三个来源来解决类基数比较的问题。我们提出了一种新的技术，用于聚集具有部分覆盖的信号，以获得更可靠的估计，并在4005个类对的数据集上对它们进行评估，实现了83.7% 的准确率。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Class+Cardinality+Comparison+as+a+Fermi+Problem)|0| |[Towards a Critical Open-Source Software Database](https://doi.org/10.1145/3543873.3587336)|Tobias Dam, Lukas Daniel Klausner, Sebastian Neumaier|St. Pölten University of Applied Sciences, Austria|Open-source software (OSS) plays a vital role in the modern software ecosystem. However, the maintenance and sustainability of OSS projects can be challenging. In this paper, we present the CrOSSD project, which aims to build a database of OSS projects and measure their current project "health" status. In the project, we will use both quantitative and qualitative metrics to evaluate the health of OSS projects. The quantitative metrics will be gathered through automated crawling of meta information such as the number of contributors, commits and lines of code. Qualitative metrics will be gathered for selected "critical" projects through manual analysis and automated tools, including aspects such as sustainability, funding, community engagement and adherence to security policies. The results of the analysis will be presented on a user-friendly web platform, which will allow users to view the health of individual OSS projects as well as the overall health of the OSS ecosystem. With this approach, the CrOSSD project provides a comprehensive and up-to-date view of the health of OSS projects, making it easier for developers, maintainers and other stakeholders to understand the health of OSS projects and make informed decisions about their use and maintenance.|开源软件在现代软件生态系统中扮演着重要的角色。然而，开放源码软件项目的维护和可持续性可能是具有挑战性的。本文介绍了 CrOSSD 项目，该项目旨在建立一个开放源码软件项目数据库，并测量其当前项目的“健康”状态。在该项目中，我们将使用定量和定性指标来评估开放源码软件项目的健康状况。定量指标将通过自动抓取元信息(如贡献者的数量、提交和代码行数)来收集。通过手工分析和自动化工具，包括可持续性、资金、社区参与和遵守安全政策等方面，将为选定的“关键”项目收集定性指标。分析结果将在一个方便用户的网络平台上公布，使用户能够查看各个开放源码软件项目的健康状况以及开放源码软件生态系统的总体健康状况。通过这种方法，CrOSSD 项目提供了开放源码软件项目健康状况的全面和最新视图，使开发人员、维护人员和其他利益攸关方更容易了解开放源码软件项目的健康状况，并就其使用和维护作出明智的决定。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Towards+a+Critical+Open-Source+Software+Database)|0| |[Task-Specific Data Augmentation for Zero-shot and Few-shot Stance Detection](https://doi.org/10.1145/3543873.3587337)|Jiarui Zhang, Shaojuan Wu, Xiaowang Zhang, Zhiyong Feng|College of Intelligence and Computing, Tianjin University, China|Various targets keep coming up on social media, and most of them lack labeled data. In this paper, we focus on zero-shot and few-shot stance detection, which aims to identify stances with few or even no training instances. In order to solve the lack of labeled data and implicit stance expression, we propose a self-supervised data augment approach based on coreference resolution. The method is specific for stance detection to generate more stable data and reduce the variance within and between classes to achieve a balance between validity and robustness. Considering the diversity of comments, we propose a novel multi-task stance detection framework of target-related fragment extraction and stance detection, which can enhance attention on target-related fragments and reduce the noise of other fragments. Experiments show that the proposed approach achieves state-of-the-art performance in zero-shot and few-shot stance detection.|社交媒体上不断出现各种各样的目标，其中大多数都缺乏标记数据。本文重点研究了零射击和少射击姿态检测，目的是在很少甚至没有训练实例的情况下进行姿态识别。为了解决缺乏标记数据和隐式姿态表达的问题，提出了一种基于共参考分辨率的自监督数据增强方法。该方法特别适用于姿态检测，以产生更稳定的数据，减少类内和类间的方差，实现有效性和鲁棒性之间的平衡。考虑到评论的多样性，本文提出了一种新的多任务姿态检测框架，该框架可以提高对目标相关碎片的关注度，降低其他碎片的噪声。实验表明，该方法在零射击和少射击姿态检测方面取得了较好的效果。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Task-Specific+Data+Augmentation+for+Zero-shot+and+Few-shot+Stance+Detection)|0| |[Measuring Potential Performance Gains of Python Web Applications with pyUpgradeSim](https://doi.org/10.1145/3543873.3587338)|Anthony Shaw, Amin Beheshti|School of Computing, Macquarie University, Australia|Python is a popular programming language for web development. However, optimizing the performance of Python web applications is a challenging task for developers. This paper presents a new approach to measuring the potential performance gains of upgraded Python web applications. Our approach is based on the provision of an interactive service that assists developers in optimizing their Python code through changes to the underlying system. The service uses profiling and visualization techniques to identify performance bottlenecks. We demonstrate and evaluate the effectiveness of our approach through a series of experiments on real-world Python web applications, measuring performance differences in between versions and the benefits of migrating at a reduced cost. The results show promising improvement in performance without any required code changes.|Python 是一种流行的 Web 开发编程语言。然而，优化 Pythonweb 应用程序的性能对于开发人员来说是一项具有挑战性的任务。本文提出了一种新的方法来衡量升级后的 Python Web 应用程序的潜在性能收益。我们的方法基于交互式服务的提供，该服务通过对底层系统的更改帮助开发人员优化他们的 Python 代码。该服务使用分析和可视化技术来识别性能瓶颈。我们通过在真实的 Python Web 应用程序上的一系列实验来演示和评估我们的方法的有效性，测量不同版本之间的性能差异以及以较低成本迁移的好处。结果显示，在不需要任何代码更改的情况下，性能有了很大的改善。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Measuring+Potential+Performance+Gains+of+Python+Web+Applications+with+pyUpgradeSim)|0| |[mStore: Schema Mining based-RDF Data Storage](https://doi.org/10.1145/3543873.3587339)|Guopeng Zheng, Tenglong Ren, Lulu Yang, Xiaowang Zhang, Zhiyong Feng|College of Intelligence and Computing, Tianjin University, China|The relationship-based approach is an efficient solution strategy for distributed RDF data management. The schema of tables can directly affect the system’s storage efficiency and query performance. Most current approaches are based on fixed schema(e.g., VP, ExtVP). When facing large-scale RDF datasets and complex SPARQL queries requiring many joins, such methods suffer from problems such as long pre-processing time and poor query performance. Schemas with Pareto Optimality between the system’s space consumption and query efficiency are needed but also hard to find. Therefore, we propose mStore, a prototype system with flexible schemas based on schema mining. The intuition behind our approach is that we want to divide the combinations of predicates with higher relevance into the same schema, which can replace costly joins with low-cost selects, improving the query performance. The results show that our system performs better on complex query workloads while reducing the pre-processing time overhead compared to systems with fixed schema partitioning strategies.|基于关系的方法是一种有效的分布式 RDF 数据管理解决方案。表的模式直接影响系统的存储效率和查询性能。大多数当前的方法都基于固定的模式(例如 VP、 ExtVP)。当面对大规模 RDF 数据集和需要许多连接的复杂 SPARQL 查询时，这类方法会遇到预处理时间长和查询性能差等问题。系统的空间消耗和查询效率之间存在帕累托最优的模式是必需的，但也很难找到。因此，我们提出了基于模式挖掘的具有灵活模式的原型系统 mStore。我们的方法背后的直觉是，我们希望将具有更高相关性的谓词组合划分到同一模式中，这样可以用低成本选择代替代价高昂的连接，从而提高查询性能。结果表明，与采用固定模式划分策略的系统相比，该系统在处理复杂查询工作负载的同时，减少了预处理时间开销。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=mStore:+Schema+Mining+based-RDF+Data+Storage)|0| -|[Identifying Topic and Cause for Sarcasm: An Unsupervised Knowledge-enhanced Prompt Method](https://doi.org/10.1145/3543873.3587343)|Minjie Yuan, Qiudan Li, Xue Mao, Daniel Dajun Zeng|State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Science, China; State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Science, China and School of Artificial Intelligence, University of Chinese Academy of Sciences, China|Sarcasm is usually emotional and topical. Mining the characteristics of sarcasm semantics in different emotional tendencies and topic expressions helps gain insight into the sarcasm cause. Most of the existing work detect sarcasm or topic label based on a supervised learning framework, which requires heavy data annotation work. To overcome the above challenges, inspired by the multi-task learning framework, this paper proposes an unsupervised knowledge-enhanced prompt method. This method uses the similarity interaction mechanism to mine the hidden relationship between the sarcasm cause and topic, which integrates external knowledge, such as syntax and emotion, into the prompting and generation process. Additionally, it identifies the sarcasm cause and topic simultaneously. Experimental results on a real-world dataset verify the effectiveness of the proposed model.|讽刺通常是情绪化的，主题化的。挖掘不同情感倾向和话题表达的讽刺语义特征有助于深入了解讽刺的成因。现有的大多数工作都是基于一个监督式学习框架来检测讽刺或主题标签，这需要大量的数据注释工作。为了克服上述挑战，受多任务学习框架的启发，本文提出了一种无监督知识增强的提示方法。该方法利用相似交互机制挖掘话题与讽刺原因之间的隐含关系，将句法、情感等外部知识融入到讽刺的提示和生成过程中。此外，它还能同时识别讽刺的原因和主题。在实际数据集上的实验结果验证了该模型的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Identifying+Topic+and+Cause+for+Sarcasm:+An+Unsupervised+Knowledge-enhanced+Prompt+Method)|0| +|[Identifying Topic and Cause for Sarcasm: An Unsupervised Knowledge-enhanced Prompt Method](https://doi.org/10.1145/3543873.3587343)|Minjie Yuan, Qiudan Li, Xue Mao, Daniel Dajun Zeng|State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Science, China and School of Artificial Intelligence, University of Chinese Academy of Sciences, China; State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Science, China|Sarcasm is usually emotional and topical. Mining the characteristics of sarcasm semantics in different emotional tendencies and topic expressions helps gain insight into the sarcasm cause. Most of the existing work detect sarcasm or topic label based on a supervised learning framework, which requires heavy data annotation work. To overcome the above challenges, inspired by the multi-task learning framework, this paper proposes an unsupervised knowledge-enhanced prompt method. This method uses the similarity interaction mechanism to mine the hidden relationship between the sarcasm cause and topic, which integrates external knowledge, such as syntax and emotion, into the prompting and generation process. Additionally, it identifies the sarcasm cause and topic simultaneously. Experimental results on a real-world dataset verify the effectiveness of the proposed model.|讽刺通常是情绪化的，主题化的。挖掘不同情感倾向和话题表达的讽刺语义特征有助于深入了解讽刺的成因。现有的大多数工作都是基于一个监督式学习框架来检测讽刺或主题标签，这需要大量的数据注释工作。为了克服上述挑战，受多任务学习框架的启发，本文提出了一种无监督知识增强的提示方法。该方法利用相似交互机制挖掘话题与讽刺原因之间的隐含关系，将句法、情感等外部知识融入到讽刺的提示和生成过程中。此外，它还能同时识别讽刺的原因和主题。在实际数据集上的实验结果验证了该模型的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Identifying+Topic+and+Cause+for+Sarcasm:+An+Unsupervised+Knowledge-enhanced+Prompt+Method)|0| |[The Community Notes Observatory: Can Crowdsourced Fact-Checking be Trusted in Practice?](https://doi.org/10.1145/3543873.3587340)|Luca Righes, Mohammed Saeed, Gianluca Demartini, Paolo Papotti|EURECOM, France; The University of Queensland, Australia|Fact-checking is an important tool in fighting online misinformation. However, it requires expert human resources, and thus does not scale well on social media because of the flow of new content. Crowdsourcing has been proposed to tackle this challenge, as it can scale with a smaller cost, but it has always been studied in controlled environments. In this demo, we present the Community Notes Observatory, an online system to evaluate the first large-scale effort of crowdsourced fact-checking deployed in practice. We let demo attendees search and analyze tweets that are fact-checked by Community Notes users and compare the crowd’s activity against professional fact-checkers. The attendees will explore evidence of i) differences in how the crowd and experts select content to be checked, ii) how the crowd and the experts retrieve different resources to fact-check, and iii) the edge the crowd shows in fact-checking scalability and efficiency as compared to expert checkers.|事实核查是打击网络虚假信息的重要工具。然而，它需要专业的人力资源，因此由于新内容的流动，在社交媒体上的扩展性不好。众包已经被提议来解决这一挑战，因为它可以以较低的成本扩大规模，但它一直在受控的环境中进行研究。在这个演示中，我们展示了“社区笔记观察站”，一个在线系统，用于评估在实践中部署的第一个大规模众包事实核查工作。我们让演示的参与者搜索和分析由 Community Notes 用户进行事实核查的 tweet，并将人群的活动与专业的事实核查者进行比较。与会者将探索以下证据: i)群体和专家如何选择要检查的内容，ii)群体和专家如何检索不同的资源以进行事实检查，iii)群体在事实检查的可扩展性和效率方面表现出与专家检查员相比的优势。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=The+Community+Notes+Observatory:+Can+Crowdsourced+Fact-Checking+be+Trusted+in+Practice?)|0| |[EasySpider: A No-Code Visual System for Crawling the Web](https://doi.org/10.1145/3543873.3587345)|Naibo Wang, Wenjie Feng, Jianwei Yin, SeeKiong Ng|National University of Singapore, Singapore; Zhejiang University, China|The web is a treasure trove for data that is increasingly used by computer scientists for building large machine learning models as well as non-computer scientists for social studies or marketing analyses. As such, web-crawling is an essential tool for both computational and non-computational scientists to conduct research. However, most of the existing web crawler frameworks and software products either require professional coding skills without an easy-to-use graphic user interface or are expensive and limited in features. They are thus not friendly to newbies and inconvenient for complicated web-crawling tasks. In this paper, we present an easy-to-use visual web crawler system, EasySpider, for designing and executing web crawling tasks without coding. The workflow of a new web crawling task can be visually programmed by following EasySpider’s visual wizard on the target webpages using an intuitive point-and-click interface. The generated crawler task can then be easily invoked locally or as a web service. Our EasySpider is cross-platform and flexible to adapt to different web-resources. It also supports advanced configuration for complicated tasks and extension. The whole system is open-sourced and transparent for free-access at GitHub 1, which avoids possible privacy leakage.|网络是数据的宝库，计算机科学家越来越多地使用这些数据来建立大型机器学习模型，非计算机科学家也越来越多地使用这些数据来进行社会研究或营销分析。因此，网络爬行是计算和非计算科学家进行研究的重要工具。然而，大多数现有的网络爬虫框架和软件产品要么需要专业的编码技能，而没有易于使用的图形用户界面，要么价格昂贵，功能有限。因此，他们不友好的新手和不方便的复杂的网络爬行任务。本文介绍了一个易于使用的可视化网络爬虫系统 EasySpider，它可以在不需要编码的情况下设计和执行网络爬虫任务。一个新的网页爬行任务的工作流程，可以通过遵循 EasySpider 的视觉向导在目标网页上使用一个直观的点击界面可视化编程。生成的爬虫任务可以很容易地在本地调用或作为 Web 服务调用。我们的 EasySpider 是跨平台和灵活的，以适应不同的网络资源。它还支持复杂任务和扩展的高级配置。GitHub 1的整个系统是开源和透明的，可以免费访问，避免了可能的隐私泄露。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=EasySpider:+A+No-Code+Visual+System+for+Crawling+the+Web)|0| |[Persona Consistent Dialogue Generation via Contrastive Learning](https://doi.org/10.1145/3543873.3587346)|Zhenfeng Han, Sai Zhang, Xiaowang Zhang|College of Intelligence and Computing, Tianjin University, China|The inclusion of explicit personas in generation models has gained significant attention as a means of developing intelligent dialogue agents. However, large pretrained generation models often produce inconsistent responses with persona. We investigate the model generation behavior to identify signs of inconsistency and observe inconsistent behavior patterns. In this work, we introduce contrastive learning into persona consistent dialogue generation, building on the idea that humans learn not just from positive feedback, but also from identifying and correcting undesirable behaviors. According to the inconsistent patterns, we design two strategies to construct high-quality negative samples, which are critical for contrastive learning efficacy. Experimental results demonstrate that our method can effectively improve the consistency of the responses while improving its dialogue quality on both automatic and human evaluation.|作为开发智能对话代理的一种手段，在生成模型中包含明确的人物角色受到了极大的关注。然而，大型预训练生成模型往往产生与人物角色不一致的反应。我们研究模型生成行为，以识别不一致的迹象和观察不一致的行为模式。在这项工作中，我们将对比学习引入到人格一致性对话生成中，建立在这样一个理念上，即人类不仅从积极的反馈中学习，而且从识别和纠正不良行为中学习。根据不一致的学习模式，我们设计了两种策略来构建高质量的负样本，这两种策略对于对比学习效能是至关重要的。实验结果表明，该方法可以有效地提高响应的一致性，同时提高对话质量的自动和人为评价。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Persona+Consistent+Dialogue+Generation+via+Contrastive+Learning)|0| |[TML: A Temporal-aware Multitask Learning Framework for Time-sensitive Question Answering](https://doi.org/10.1145/3543873.3587347)|Ziqiang Chen, Shaojuan Wu, Xiaowang Zhang, Zhiyong Feng|College of Intelligence and Computing, Tianjin University, China|Many facts change over time, Time-sensitive Question Answering(TSQA) answers questions about time evolution facts to test the model’s ability in the dimension of the time. The existing methods obtain the representations of questions and documents and then compute their similarity to find the answer spans. These methods perform well in simple moment questions, but they are difficult to solve hard duration problems that need temporal relations and temporal numeric comparisons. In this paper, we propose Temporal-aware Multitask Learning (TML) with three auxiliary tasks to tackle with them. First, we propose a temporal-aware sequence labeling task to help the model distinguish the temporal expressions by detecting temporal types of tokens in the document. Then a temporal-aware masked language modeling task is used to capture the temporal relation between events based on the context. Furthermore, temporal-aware order learning is proposed to inject the ability of numeric comparison into the model. We carried out comprehensive experiments on the TimeQA benchmark, aiming to evaluate the performance of our proposed methodology in handling temporal question answering. TML significantly outperforms the baselines by a relative 10% on the two splits of the dataset.|随着时间的推移，许多事实发生了变化，时间敏感问题回答(TSQA)回答了关于时间演化事实的问题，以检验模型在时间维度上的能力。现有的方法获得问题和文档的表示，然后计算它们的相似度来找到答案范围。这些方法在解决简单矩问题时表现良好，但在解决需要时间关系和时间数值比较的难度较大的持续时间问题时存在一定的困难。本文提出了一种基于时间感知的多任务学习(TML)方法，该方法包括三个辅助任务。首先，我们提出一个时间感知序列标记任务，通过检测文档中令牌的时间类型来帮助模型区分时间表达式。然后利用一个时间感知的掩蔽语言建模任务，根据上下文捕获事件之间的时间关系。在此基础上，提出了一种基于时间感知的顺序学习方法，将数值比较能力引入到模型中。我们在 TimeQA 基准上进行了全面的实验，旨在评估我们提出的方法在处理时间问题回答中的性能。在数据集的两个部分上，TML 显著地比基线高出10% 。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=TML:+A+Temporal-aware+Multitask+Learning+Framework+for+Time-sensitive+Question+Answering)|0| |[Addressing socially destructive disinformation on the web with advanced AI tools: Russia as a case study](https://doi.org/10.1145/3543873.3587348)|Florian Barbaro, Andy Skumanich|Innov8ai, USA|Today, disinformation (i.e. deliberate misinformation) is omnipresent in all web communication channels. There is a developing explosion in this socially disruptive mode of web-based information. Increasingly, we have seen various countries developing advanced methods to spread their targeted disinformation. To address this flood of disinformation will require refined strategies to capture and evaluate the messages. In this paper, we present both a quantitative and a qualitative analysis of online social and information networks to better evaluate the characteristics of the disinformation campaigns. We focus on the case of Russian-generated disinformation, which has been developed to an elevated level. We demonstrate an effective approach based on a new dataset to study the Russian campaign composed of 14497 cases of dis-information and the corresponding counter-dis-information. Although this case is of high current relevance, there is very limited published evaluation. We provide a novel analysis and present a methodology to characterize this disinformation. We based our investigation on a Spherical k-means algorithm to determine the main topics of the disinformation and to discover the key trends. We employ distilBERT algorithm and achieve a high accuracy F1-score of 98.8 demonstrating good quantitative capabilities. We propose the methodology as a template for further exploration and analysis.|今天，虚假信息(即故意的错误信息)在所有的网络沟通渠道中无处不在。这种具有社会破坏性的基于网络的信息模式正在迅速发展。我们已经看到越来越多的国家开发出先进的方法来传播他们有针对性的虚假信息。为了解决这种虚假信息的泛滥，需要改进策略来捕捉和评估这些信息。在本文中，我们提出了一个定量和定性的分析在线社会和信息网络，以更好地评估特点的虚假信息运动。我们的重点是俄罗斯产生的虚假信息的情况下，这已经发展到一个提高的水平。基于一个新的数据集，我们展示了一个有效的方法来研究由14497个虚假信息和相应的反虚假信息案例组成的俄罗斯战役。尽管这个案例目前具有很高的相关性，但公开发表的评价非常有限。我们提供了一个新颖的分析，并提出了一种方法来描述这种假信息。我们的研究基于球面 k-means 算法，以确定虚假信息的主要课题和发现的关键趋势。我们采用蒸馏 BERT 算法，实现了98.8的高精度 F1评分，显示了良好的定量能力。我们建议将该方法作为进一步探索和分析的模板。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Addressing+socially+destructive+disinformation+on+the+web+with+advanced+AI+tools:+Russia+as+a+case+study)|0| -|[Katti: An Extensive and Scalable Tool for Website Analyses](https://doi.org/10.1145/3543873.3587351)|Florian Nettersheim, Stephan Arlt, Michael Rademacher, Florian Dehling|Federal Office for Information Security, Germany; University of Applied Sciences Bonn-Rhein-Sieg, Germany; Fraunhofer FKIE, Germany|Research on web security and privacy frequently relies on tools that analyze a set of websites. One major obstacle to the judicious analysis is the employment of a rock-solid and feature-rich web crawler. For example, the automated analysis of ad-malware campaigns on websites requests crawling a vast set of domains on multiple real web browsers, while simultaneously mitigating bot detections and applying user interactions on websites. Further, the ability to attach various threat analysis frameworks lacks current tooling efforts in web crawling and analyses. In this paper we introduce Katti, which overcomes several of today’s technical hurdles in web crawling. Our tool employs a distributed task queue that efficiently and reliably handles both large crawling and threat analyses requests. Katti extensively collects all available web data through an integrated person-in-the-middle proxy. Moreover, Katti is not limited to a specific use case, allowing users to easily customize our tool to their individual research intends.|对网络安全和隐私的研究通常依赖于分析一组网站的工具。明智分析的一个主要障碍是使用坚固的特性丰富的 Web 爬虫程序。例如，对网站上的广告恶意软件活动的自动分析要求在多个真实的网络浏览器上爬行大量域名，同时减少机器人检测并在网站上应用用户交互。此外，附加各种威胁分析框架的能力缺乏目前在网络爬行和分析工具的努力。在这篇文章中，我们介绍了 Katti，它克服了当今网络爬行中的一些技术障碍。我们的工具使用了一个分布式任务队列，可以高效可靠地处理大型爬行和威胁分析请求。Katti 通过一个集成的中间人代理广泛地收集所有可用的网络数据。此外，Katti 并不局限于特定的用例，允许用户根据自己的研究意图轻松定制我们的工具。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Katti:+An+Extensive+and+Scalable+Tool+for+Website+Analyses)|0| -|[Graph Induced Transformer Network for Detection of Politeness and Formality in Text](https://doi.org/10.1145/3543873.3587352)|Tirthankar Dasgupta, Manjira Sinha, Chundru Geetha Praveen|IIT Kharagpur, India; Indian Institute of Technology Kharagpur, India; Tata Consultancy Services, India|Formality and politeness are two of the most commonly studied stylistic dimensions of language that convey, authority, amount of shared context, and social distances among the communicators and are known to affect user behavior significantly. Formality in the text refers to the type of language used in situations when the speaker is very careful about the choice of words and sentence structure. In this paper, we propose a graph-induced transformer network (GiTN) to detect formality and politeness in text automatically. The proposed model exploits the latent linguistic features present in the text to identify the aforementioned stylistic factors. The proposed model is evaluated with multiple datasets across domains. We found that the proposed model’s performance surpasses most baseline systems.|正式和礼貌是语言的两个最常被研究的文体维度，它们传达了交际者之间的信息、权威、共享语境的数量和社会距离，并且已知它们会显著地影响使用者的行为。语篇中的正式语言是指说话人在选择词语和句子结构时非常谨慎的语言类型。本文提出了一种图形感应变压器网络(GiTN)来自动检测文本中的礼貌和礼貌。该模型利用文本中潜在的语言特征来识别上述文体因素。该模型使用跨领域的多个数据集进行评估。我们发现该模型的性能优于大多数基线系统。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Graph+Induced+Transformer+Network+for+Detection+of+Politeness+and+Formality+in+Text)|0| -|[Visualizing How-Provenance Explanations for SPARQL Queries](https://doi.org/10.1145/3543873.3587350)|Luis Galárraga, Daniel Hernández, Anas Katim, Katja Hose|INSA Rouen, France; IRISA, Inria, France; University of Stuttgart, Germany; Aalborg University, Denmark and TU Wien, Austria|Knowledge graphs (KGs) are vast collections of machine-readable information, usually modeled in RDF and queried with SPARQL. KGs have opened the door to a plethora of applications such as Web search or smart assistants that query and process the knowledge contained in those KGs. An important, but often disregarded, aspect of querying KGs is query provenance: explanations of the data sources and transformations that made a query result possible. In this article we demonstrate, through a Web application, the capabilities of SPARQLprov, an engine-agnostic method that annotates query results with how-provenance annotations. To this end, SPARQLprov resorts to query rewriting techniques, which make it applicable to already deployed SPARQL endpoints. We describe the principles behind SPARQLprov and discuss perspectives on visualizing how-provenance explanations for SPARQL queries.|知识图(KGs)是机器可读信息的大量集合，通常以 RDF 建模并使用 SPARQL 进行查询。幼儿园已经为大量的应用程序打开了大门，例如 Web 搜索或智能助手，它们可以查询和处理这些幼儿园所包含的知识。查询 KG 的一个重要但经常被忽视的方面是查询来源: 解释使查询结果成为可能的数据源和转换。在本文中，我们通过一个 Web 应用程序演示了 SPARQLprov 的功能，这是一种不依赖引擎的方法，它使用 how-source 注释对查询结果进行注释。为此，SPARQLprov 采用查询重写技术，这使得它适用于已经部署的 SPARQL 端点。我们描述了 SPARQLprov 背后的原理，并讨论了如何可视化 SPARQL 查询的起源解释。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Visualizing+How-Provenance+Explanations+for+SPARQL+Queries)|0| -|[Counterfactual Reasoning for Decision Model Fairness Assessment](https://doi.org/10.1145/3543873.3587354)|Giandomenico Cornacchia, Vito Walter Anelli, Fedelucio Narducci, Azzurra Ragone, Eugenio Di Sciascio|University of Bari, Italy; Politecnico di Bari, Italy|The increasing application of Artificial Intelligence and Machine Learning models poses potential risks of unfair behaviour and, in the light of recent regulations, has attracted the attention of the research community. Several researchers focused on seeking new fairness definitions or developing approaches to identify biased predictions. These approaches focus solely on a discrete and limited space; only a few analyze the minimum variations required in the user characteristics to ensure a positive outcome for the individuals (counterfactuals). In that direction, the methodology proposed in this paper aims to unveil unfair model behaviors using counterfactual reasoning in the case of fairness under unawareness. The method also proposes two new metrics that analyse the (estimated) sensitive information of counterfactual samples with the help of an external oracle. Experimental results on three data sets show the effectiveness of our approach for disclosing unfair behaviour of state-of-the-art Machine Learning and debiasing models. Source code is available at https://github.com/giandos200/WWW-23-Counterfactual-Fair-Opportunity-Poster-.|越来越多的人工智能和机器学习模型的应用带来了不公平行为的潜在风险，并且，根据最近的规定，已经引起了研究界的注意。一些研究人员致力于寻找新的公平定义或发展方法来识别有偏见的预测。这些方法只集中在一个离散和有限的空间; 只有少数几个分析最小的变化需要在用户特征，以确保一个积极的结果为个人(反事实)。在这个方向上，本文提出的方法论旨在揭示在无意识的公平情况下使用反事实推理的不公平模型行为。该方法还提出了两个新的度量分析(估计)敏感信息的反事实样本的帮助下，外部预言。在三个数据集上的实验结果表明了我们的方法在揭示最先进的机器学习的不公平行为和消除模型偏差方面的有效性。源代码可在 https://github.com/giandos200/www-23-counterfactual-fair-opportunity-poster- 下载。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Counterfactual+Reasoning+for+Decision+Model+Fairness+Assessment)|0| -|[Wikidata Atlas: Putting Wikidata on the Map](https://doi.org/10.1145/3543873.3587356)|Benjamín Del Pino, Aidan Hogan|DCC, Universidad de Chile, Chile; DCC, Universidad de Chile, Chile and Instituto Milenio Fundamentos de los Datos (IMFD), Chile|Wikidata Atlas is an online system that allows users to explore Wikidata items on an interactive global map; for example, users can explore the global distribution of all lighthouses described by Wikidata. Designing such a system poses challenges in terms of scalability, where some classes have hundreds of thousands of instances; efficiency, where visualisations are generated live; freshness, where we want changes on Wikidata to be reflected as they happen in the system; and usability, where we aim for the system to be accessible for a broad audience. Herein we describe the design and implementation of the system in light of these challenges.|Wikidata Atlas 是一个在线系统，允许用户在交互式全球地图上探索 Wikidata 项目; 例如，用户可以探索 Wikidata 描述的所有灯塔的全球分布。设计这样一个系统在可扩展性方面提出了挑战，有些类有成千上万的实例; 在效率方面，可视化是生成实时的; 在新鲜方面，我们希望 Wikidata 的变化在系统中反映出来; 在可用性方面，我们的目标是让广大用户能够访问这个系统。在这里，我们描述了针对这些挑战的系统的设计和实现。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Wikidata+Atlas:+Putting+Wikidata+on+the+Map)|0| -|[How Algorithm Awareness Impacts Algospeak Use on TikTok](https://doi.org/10.1145/3543873.3587355)|Daniel Klug, Ella Steen, Kathryn Yurechko|Gordon College, USA; Washington and Lee University, USA; Software and Societal Systems Department, Carnegie Mellon University, USA|Algospeak refers to social media users intentionally altering or substituting words when creating or sharing online content, for example, using ‘le$bean’ for ‘lesbian’. This study discusses the characteristics of algospeak as a computer-mediated language phenomenon on TikTok with regards to users’ algorithmic literacy and their awareness of how the platform’s algorithms work. We then present results from an interview study with TikTok creators on their motivations to utilize algospeak. Our results indicate that algospeak is used to oppose TikTok’s algorithmic moderation system in order to prevent unjust content violations and shadowbanning when posting about benign yet seemingly unwanted subjects on TikTok. In this, we find that although algospeak helps to prevent consequences, it often impedes the creation of quality content. We provide an adapted definition of algospeak and new insights into user-platform interactions in the context of algorithmic systems and algorithm awareness.|Algospeak 指的是社交媒体用户在创建或分享在线内容时故意修改或替换词语，例如，使用“ le $bean”表示“女同性恋”。这项研究讨论了算法语音作为 TikTok 上一种计算机中介语言现象的特点，涉及到用户的算法素养和他们对该平台的算法如何工作的认识。然后，我们展示了 TikTok 创作者关于他们使用算法演讲的动机的访谈研究结果。我们的研究结果表明，algospeak 被用来对抗 TikTok 的算法审核系统，以防止在 TikTok 上发布有关良性但似乎不受欢迎的主题时，出现不公正的内容违规和影子禁令。在这一点上，我们发现，尽管算法演讲有助于防止后果，但它往往阻碍创建高质量的内容。我们提供了一个自适应的定义和算法系统和算法意识背景下的用户-平台交互的新见解。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=How+Algorithm+Awareness+Impacts+Algospeak+Use+on+TikTok)|0| +|[Katti: An Extensive and Scalable Tool for Website Analyses](https://doi.org/10.1145/3543873.3587351)|Florian Nettersheim, Stephan Arlt, Michael Rademacher, Florian Dehling|University of Applied Sciences Bonn-Rhein-Sieg, Germany; Federal Office for Information Security, Germany; Fraunhofer FKIE, Germany|Research on web security and privacy frequently relies on tools that analyze a set of websites. One major obstacle to the judicious analysis is the employment of a rock-solid and feature-rich web crawler. For example, the automated analysis of ad-malware campaigns on websites requests crawling a vast set of domains on multiple real web browsers, while simultaneously mitigating bot detections and applying user interactions on websites. Further, the ability to attach various threat analysis frameworks lacks current tooling efforts in web crawling and analyses. In this paper we introduce Katti, which overcomes several of today’s technical hurdles in web crawling. Our tool employs a distributed task queue that efficiently and reliably handles both large crawling and threat analyses requests. Katti extensively collects all available web data through an integrated person-in-the-middle proxy. Moreover, Katti is not limited to a specific use case, allowing users to easily customize our tool to their individual research intends.|对网络安全和隐私的研究通常依赖于分析一组网站的工具。明智分析的一个主要障碍是使用坚固的特性丰富的 Web 爬虫程序。例如，对网站上的广告恶意软件活动的自动分析要求在多个真实的网络浏览器上爬行大量域名，同时减少机器人检测并在网站上应用用户交互。此外，附加各种威胁分析框架的能力缺乏目前在网络爬行和分析工具的努力。在这篇文章中，我们介绍了 Katti，它克服了当今网络爬行中的一些技术障碍。我们的工具使用了一个分布式任务队列，可以高效可靠地处理大型爬行和威胁分析请求。Katti 通过一个集成的中间人代理广泛地收集所有可用的网络数据。此外，Katti 并不局限于特定的用例，允许用户根据自己的研究意图轻松定制我们的工具。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Katti:+An+Extensive+and+Scalable+Tool+for+Website+Analyses)|0| +|[Graph Induced Transformer Network for Detection of Politeness and Formality in Text](https://doi.org/10.1145/3543873.3587352)|Tirthankar Dasgupta, Manjira Sinha, Chundru Geetha Praveen|Tata Consultancy Services, India; IIT Kharagpur, India; Indian Institute of Technology Kharagpur, India|Formality and politeness are two of the most commonly studied stylistic dimensions of language that convey, authority, amount of shared context, and social distances among the communicators and are known to affect user behavior significantly. Formality in the text refers to the type of language used in situations when the speaker is very careful about the choice of words and sentence structure. In this paper, we propose a graph-induced transformer network (GiTN) to detect formality and politeness in text automatically. The proposed model exploits the latent linguistic features present in the text to identify the aforementioned stylistic factors. The proposed model is evaluated with multiple datasets across domains. We found that the proposed model’s performance surpasses most baseline systems.|正式和礼貌是语言的两个最常被研究的文体维度，它们传达了交际者之间的信息、权威、共享语境的数量和社会距离，并且已知它们会显著地影响使用者的行为。语篇中的正式语言是指说话人在选择词语和句子结构时非常谨慎的语言类型。本文提出了一种图形感应变压器网络(GiTN)来自动检测文本中的礼貌和礼貌。该模型利用文本中潜在的语言特征来识别上述文体因素。该模型使用跨领域的多个数据集进行评估。我们发现该模型的性能优于大多数基线系统。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Graph+Induced+Transformer+Network+for+Detection+of+Politeness+and+Formality+in+Text)|0| +|[Visualizing How-Provenance Explanations for SPARQL Queries](https://doi.org/10.1145/3543873.3587350)|Luis Galárraga, Daniel Hernández, Anas Katim, Katja Hose|University of Stuttgart, Germany; INSA Rouen, France; IRISA, Inria, France; Aalborg University, Denmark and TU Wien, Austria|Knowledge graphs (KGs) are vast collections of machine-readable information, usually modeled in RDF and queried with SPARQL. KGs have opened the door to a plethora of applications such as Web search or smart assistants that query and process the knowledge contained in those KGs. An important, but often disregarded, aspect of querying KGs is query provenance: explanations of the data sources and transformations that made a query result possible. In this article we demonstrate, through a Web application, the capabilities of SPARQLprov, an engine-agnostic method that annotates query results with how-provenance annotations. To this end, SPARQLprov resorts to query rewriting techniques, which make it applicable to already deployed SPARQL endpoints. We describe the principles behind SPARQLprov and discuss perspectives on visualizing how-provenance explanations for SPARQL queries.|知识图(KGs)是机器可读信息的大量集合，通常以 RDF 建模并使用 SPARQL 进行查询。幼儿园已经为大量的应用程序打开了大门，例如 Web 搜索或智能助手，它们可以查询和处理这些幼儿园所包含的知识。查询 KG 的一个重要但经常被忽视的方面是查询来源: 解释使查询结果成为可能的数据源和转换。在本文中，我们通过一个 Web 应用程序演示了 SPARQLprov 的功能，这是一种不依赖引擎的方法，它使用 how-source 注释对查询结果进行注释。为此，SPARQLprov 采用查询重写技术，这使得它适用于已经部署的 SPARQL 端点。我们描述了 SPARQLprov 背后的原理，并讨论了如何可视化 SPARQL 查询的起源解释。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Visualizing+How-Provenance+Explanations+for+SPARQL+Queries)|0| +|[Counterfactual Reasoning for Decision Model Fairness Assessment](https://doi.org/10.1145/3543873.3587354)|Giandomenico Cornacchia, Vito Walter Anelli, Fedelucio Narducci, Azzurra Ragone, Eugenio Di Sciascio|Politecnico di Bari, Italy; University of Bari, Italy|The increasing application of Artificial Intelligence and Machine Learning models poses potential risks of unfair behaviour and, in the light of recent regulations, has attracted the attention of the research community. Several researchers focused on seeking new fairness definitions or developing approaches to identify biased predictions. These approaches focus solely on a discrete and limited space; only a few analyze the minimum variations required in the user characteristics to ensure a positive outcome for the individuals (counterfactuals). In that direction, the methodology proposed in this paper aims to unveil unfair model behaviors using counterfactual reasoning in the case of fairness under unawareness. The method also proposes two new metrics that analyse the (estimated) sensitive information of counterfactual samples with the help of an external oracle. Experimental results on three data sets show the effectiveness of our approach for disclosing unfair behaviour of state-of-the-art Machine Learning and debiasing models. Source code is available at https://github.com/giandos200/WWW-23-Counterfactual-Fair-Opportunity-Poster-.|越来越多的人工智能和机器学习模型的应用带来了不公平行为的潜在风险，并且，根据最近的规定，已经引起了研究界的注意。一些研究人员致力于寻找新的公平定义或发展方法来识别有偏见的预测。这些方法只集中在一个离散和有限的空间; 只有少数几个分析最小的变化需要在用户特征，以确保一个积极的结果为个人(反事实)。在这个方向上，本文提出的方法论旨在揭示在无意识的公平情况下使用反事实推理的不公平模型行为。该方法还提出了两个新的度量分析(估计)敏感信息的反事实样本的帮助下，外部预言。在三个数据集上的实验结果表明了我们的方法在揭示最先进的机器学习的不公平行为和消除模型偏差方面的有效性。源代码可在 https://github.com/giandos200/www-23-counterfactual-fair-opportunity-poster- 下载。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Counterfactual+Reasoning+for+Decision+Model+Fairness+Assessment)|0| +|[Wikidata Atlas: Putting Wikidata on the Map](https://doi.org/10.1145/3543873.3587356)|Benjamín Del Pino, Aidan Hogan|DCC, Universidad de Chile, Chile and Instituto Milenio Fundamentos de los Datos (IMFD), Chile; DCC, Universidad de Chile, Chile|Wikidata Atlas is an online system that allows users to explore Wikidata items on an interactive global map; for example, users can explore the global distribution of all lighthouses described by Wikidata. Designing such a system poses challenges in terms of scalability, where some classes have hundreds of thousands of instances; efficiency, where visualisations are generated live; freshness, where we want changes on Wikidata to be reflected as they happen in the system; and usability, where we aim for the system to be accessible for a broad audience. Herein we describe the design and implementation of the system in light of these challenges.|Wikidata Atlas 是一个在线系统，允许用户在交互式全球地图上探索 Wikidata 项目; 例如，用户可以探索 Wikidata 描述的所有灯塔的全球分布。设计这样一个系统在可扩展性方面提出了挑战，有些类有成千上万的实例; 在效率方面，可视化是生成实时的; 在新鲜方面，我们希望 Wikidata 的变化在系统中反映出来; 在可用性方面，我们的目标是让广大用户能够访问这个系统。在这里，我们描述了针对这些挑战的系统的设计和实现。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Wikidata+Atlas:+Putting+Wikidata+on+the+Map)|0| +|[How Algorithm Awareness Impacts Algospeak Use on TikTok](https://doi.org/10.1145/3543873.3587355)|Daniel Klug, Ella Steen, Kathryn Yurechko|Washington and Lee University, USA; Software and Societal Systems Department, Carnegie Mellon University, USA; Gordon College, USA|Algospeak refers to social media users intentionally altering or substituting words when creating or sharing online content, for example, using ‘le$bean’ for ‘lesbian’. This study discusses the characteristics of algospeak as a computer-mediated language phenomenon on TikTok with regards to users’ algorithmic literacy and their awareness of how the platform’s algorithms work. We then present results from an interview study with TikTok creators on their motivations to utilize algospeak. Our results indicate that algospeak is used to oppose TikTok’s algorithmic moderation system in order to prevent unjust content violations and shadowbanning when posting about benign yet seemingly unwanted subjects on TikTok. In this, we find that although algospeak helps to prevent consequences, it often impedes the creation of quality content. We provide an adapted definition of algospeak and new insights into user-platform interactions in the context of algorithmic systems and algorithm awareness.|Algospeak 指的是社交媒体用户在创建或分享在线内容时故意修改或替换词语，例如，使用“ le $bean”表示“女同性恋”。这项研究讨论了算法语音作为 TikTok 上一种计算机中介语言现象的特点，涉及到用户的算法素养和他们对该平台的算法如何工作的认识。然后，我们展示了 TikTok 创作者关于他们使用算法演讲的动机的访谈研究结果。我们的研究结果表明，algospeak 被用来对抗 TikTok 的算法审核系统，以防止在 TikTok 上发布有关良性但似乎不受欢迎的主题时，出现不公正的内容违规和影子禁令。在这一点上，我们发现，尽管算法演讲有助于防止后果，但它往往阻碍创建高质量的内容。我们提供了一个自适应的定义和算法系统和算法意识背景下的用户-平台交互的新见解。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=How+Algorithm+Awareness+Impacts+Algospeak+Use+on+TikTok)|0| |[Computing and Visualizing Agro-Meteorological Parameters based on an Observational Weather Knowledge Graph](https://doi.org/10.1145/3543873.3587357)|Nadia Yacoubi Ayadi, Catherine Faron, Franck Michel, Fabien Gandon, Olivier Corby|Université Côte d'Azur, CNRS, I3S (UMR 7271),France, France; Université Côte d'Azur, CNRS, I3S (UMR 7271), France, France|Linked-data principles are more and more adopted to integrate and publish semantically described open data using W3C standards resulting in a large amount of available resources [7]. In particular, meteorological sensor data have been uplifted into public RDF graphs, such as WeKG-MF which offers access to a large set of meteorological variables described through spatial and temporal dimensions. Nevertheless, these resources include huge numbers of raw observations that are tedious to be explored and reused by lay users. In this paper, we leverage WeKG-MF to compute important agro-meteorological parameters and views with SPARQL queries. As a result, we deployed a LOD platform as a web application to allow users to navigate, consume and produce linked datasets of agro-meterological parameters calculated on-the-fly.|使用 W3C 标准集成和发布语义描述的开放数据时，越来越多地采用链接数据原则，从而产生了大量的可用资源[7]。特别是，气象传感器数据已被提升为公共 RDF 图，例如 WeKG-MF，它提供了通过空间和时间维度描述的大量气象变量。尽管如此，这些资源包括大量的原始观察资料，这些资料对于非专业用户来说是单调乏味的，需要进行探索和重复使用。本文利用 WeKG-MF 算法，通过 SPARQL 查询来计算重要的农业气象参数和视图。因此，我们部署了一个 LOD 平台作为 Web 应用程序，允许用户导航、使用和生成连接的数据集，这些数据集是动态计算的农业气象参数。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Computing+and+Visualizing+Agro-Meteorological+Parameters+based+on+an+Observational+Weather+Knowledge+Graph)|0| |[Injecting data into ODRL privacy policies dynamically with RDF mappings](https://doi.org/10.1145/3543873.3587358)|Juan CanoBenito, Andrea Cimmino, Raúl GarcíaCastro|Universidad Politécnica de Madrid, Spain|The privacy of the data provided by available sources is one of the major concerns of our era. In order to address this challenge, the W3C has promoted recommendations to allow expressing privacy policies. One of these recommendations is the Open Digital Rights Language (ODRL) vocabulary. Although this standard has wide adoption, it is not suitable in domains such as IoT, Ubiquitous and Mobile Computing, or discovery. The reason behind is the fact that ODRL privacy policies are not able to cope with dynamic information that may come from external sources of data and, therefore, these policies can not define privacy restrictions upon data that is not already written in the policy beforehand. In this demo paper, a solution to this challenge is presented. It is shown how ODRL policies can overcome the aforementioned limitation by being combined with a mapping language for RDF materialisation. The article shows how ODRL policies are able to consider data coming from an external data source when they are solved, in particular, a weather forecast API that provides temperature values. The demonstration defines an ODRL policy that grants access to a resource only when the temperature of the API is above a certain value.|由可用来源提供的数据的隐私性是我们这个时代的主要关注点之一。为了应对这一挑战，W3C 提出了允许表达隐私策略的建议。其中一个建议是开放数字版权语言(ODRL)词汇表。尽管这个标准已经被广泛采用，但它并不适用于物联网、无处不在和移动计算或发现等领域。其背后的原因是 ODRL 隐私策略不能处理可能来自外部数据源的动态信息，因此，这些策略不能定义未事先在策略中编写的数据的隐私限制。在本演示文件中，提出了解决这一挑战的方案。本文展示了 ODRL 策略如何通过与用于 RDF 实现的映射语言相结合来克服上述限制。本文展示了 ODRL 策略如何能够在解决来自外部数据源的数据时考虑这些数据，特别是提供温度值的天气预报 API。该演示定义了一个 ODRL 策略，该策略仅当 API 的温度高于某个值时才授予对资源的访问权。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Injecting+data+into+ODRL+privacy+policies+dynamically+with+RDF+mappings)|0| -|[MediSage: An AI Assistant for Healthcare via Composition of Neural-Symbolic Reasoning Operators](https://doi.org/10.1145/3543873.3587361)|Sutanay Choudhury, Khushbu Agarwal, Colby Ham, Suzanne Tamang|Stanford University, USA; Pacific Northwest National Laboratory, USA|We introduce MediSage, an AI decision support assistant for medical professionals and caregivers that simplifies the way in which they interact with different modalities of electronic health records (EHRs) through a conversational interface. It provides step-by-step reasoning support to an end-user to summarize patient health, predict patient outcomes and provide comprehensive and personalized healthcare recommendations. MediSage provides these reasoning capabilities by using a knowledge graph that combines general purpose clinical knowledge resources with recent-most information from the EHR data. By combining the structured representation of knowledge with the predictive power of neural models trained over both EHR and knowledge graph data, MediSage brings explainability by construction and represents a stepping stone into the future through further integration with biomedical language models.|我们介绍了 MediSage，一个面向医疗专业人员和护理人员的人工智能决策支持助手，通过对话界面简化了他们与不同形式的电子健康记录(EHRs)交互的方式。它为最终用户提供逐步推理支持，以总结患者健康状况，预测患者结果，并提供全面和个性化的医疗保健建议。MediSage 通过使用知识图表提供这些推理能力，该知识图表结合了通用临床知识资源和来自 EHR 数据的最新信息。通过将知识的结构化表示与对 EHR 和知识图数据进行训练的神经模型的预测能力相结合，MediSage 通过构建带来了可解释性，并通过与生物医学语言模型的进一步整合代表了未来的一块垫脚石。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=MediSage:+An+AI+Assistant+for+Healthcare+via+Composition+of+Neural-Symbolic+Reasoning+Operators)|0| +|[MediSage: An AI Assistant for Healthcare via Composition of Neural-Symbolic Reasoning Operators](https://doi.org/10.1145/3543873.3587361)|Sutanay Choudhury, Khushbu Agarwal, Colby Ham, Suzanne Tamang|Pacific Northwest National Laboratory, USA; Stanford University, USA|We introduce MediSage, an AI decision support assistant for medical professionals and caregivers that simplifies the way in which they interact with different modalities of electronic health records (EHRs) through a conversational interface. It provides step-by-step reasoning support to an end-user to summarize patient health, predict patient outcomes and provide comprehensive and personalized healthcare recommendations. MediSage provides these reasoning capabilities by using a knowledge graph that combines general purpose clinical knowledge resources with recent-most information from the EHR data. By combining the structured representation of knowledge with the predictive power of neural models trained over both EHR and knowledge graph data, MediSage brings explainability by construction and represents a stepping stone into the future through further integration with biomedical language models.|我们介绍了 MediSage，一个面向医疗专业人员和护理人员的人工智能决策支持助手，通过对话界面简化了他们与不同形式的电子健康记录(EHRs)交互的方式。它为最终用户提供逐步推理支持，以总结患者健康状况，预测患者结果，并提供全面和个性化的医疗保健建议。MediSage 通过使用知识图表提供这些推理能力，该知识图表结合了通用临床知识资源和来自 EHR 数据的最新信息。通过将知识的结构化表示与对 EHR 和知识图数据进行训练的神经模型的预测能力相结合，MediSage 通过构建带来了可解释性，并通过与生物医学语言模型的进一步整合代表了未来的一块垫脚石。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=MediSage:+An+AI+Assistant+for+Healthcare+via+Composition+of+Neural-Symbolic+Reasoning+Operators)|0| |[Depicting Vocabulary Summaries with Devos](https://doi.org/10.1145/3543873.3587359)|Ahmad Alobaid, Jhon Toledo, Óscar Corcho, María PovedaVillalón|Universidad Politécnica de Madrid, Spain|Communicating ontologies to potential users is still a difficult and time-consuming task. Even for small ones, users need to invest time to determine whether to reuse them. Providing diagrams together with the ontologies facilitates the task of understanding the model from a user perspective. While some tools are available for depicting ontologies, and the code could also be inspected using ontology editors’ graphical interfaces, in many cases, the diagrams are too big or complex. The main objective of this demo is to present Devos, a system to generate ontology diagrams based on different strategies for summarizing the ontology.|与潜在用户交流本体仍然是一项困难和耗时的任务。即使是对于小型项目，用户也需要投入时间来决定是否重用它们。提供图表和本体论有助于从用户角度理解模型。虽然有一些工具可用于描述本体，代码也可以使用本体编辑器的图形界面进行检查，但在许多情况下，图过大或过于复杂。本演示的主要目的是介绍 Devos，一个基于不同策略生成本体图的系统，用于总结本体。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Depicting+Vocabulary+Summaries+with+Devos)|0| |[Child Sexual Abuse Awareness and Support Seeking on Reddit: A thematic Analysis](https://doi.org/10.1145/3543873.3587363)|Siva Sahitya Simhadri, Tatiana Ringenberg|Purdue University, USA; Computer and Information Technology, Purdue University, USA|Child sexual abuse (CSA) is a pervasive issue in both online and physical contexts. Social media has grown in popularity as a platform for offering awareness, support, and community for those seeking help or advice regarding CSA. One popular social media platform in which such communities has formed is Reddit. In this study, we use both LDA and a reflexive thematic analysis to understand the types of engagements users have with subreddits aimed at CSA awareness. Through the reflexive thematic analysis, we identified six themes including strong negative emotions and phrasing, seeking help, personal experiences and their impact, measurement strategies to prevent abuse, provisioning of support, and the problematic nature of the Omegle platform. This research has implications for those creating awareness materials around CSA safety as well as for child advocacy groups.|儿童性虐待(CSA)是一个普遍存在的问题，无论是在网络上还是在物理环境中。社交媒体作为一个平台，为那些寻求关于 CSA 的帮助或建议的人提供意识、支持和社区，已经越来越受欢迎。Reddit 是一个流行的社交媒体平台，这样的社区已经形成。在这项研究中，我们使用 LDA 和一个反思性的主题分析来理解用户对于旨在提高 CSA 意识的子版块的参与类型。通过反思性主题分析，我们确定了六个主题，包括强烈的负面情绪和措辞，寻求帮助，个人经历及其影响，防止滥用的测量策略，提供支持，以及 Omegle 平台的问题性质。这项研究对那些围绕 CSA 安全创建宣传材料的人以及儿童倡导团体具有启示意义。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Child+Sexual+Abuse+Awareness+and+Support+Seeking+on+Reddit:+A+thematic+Analysis)|0| |[Violentometer: measuring violence on the Web in real time](https://doi.org/10.1145/3543873.3587364)|Henrique S. Xavier|Ceweb, NIC.br, Brazil|This paper describes a system for monitoring in real time the level of violence on Web platforms through the use of an artificial intelligence model to classify textual data according to their content. The system was successfully implemented and tested during the electoral campaign period of the Brazilian 2022 elections by using it to monitor the attacks directed to thousands of candidates on Twitter. We show that, despite an accurate and absolute quantification of violence is not feasible, the system yields differential measures of violence levels that can be useful for understanding human behavior online.|本文描述了一个实时监测网络平台上暴力程度的系统，该系统通过使用人工智能模型来根据文本数据的内容进行分类。该系统在巴西2022年选举竞选期间成功实施和测试，用于监测针对 Twitter 上数千名候选人的攻击。我们的研究表明，尽管对暴力行为进行准确和绝对的量化是不可行的，但该系统可以产生对暴力程度的不同衡量标准，这对于理解人类在线行为是有用的。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Violentometer:+measuring+violence+on+the+Web+in+real+time)|0| @@ -417,11 +417,11 @@ |[A Detection System for Comfortable Locations Based on Facial Expression Analysis While Riding Bicycles](https://doi.org/10.1145/3543873.3587371)|Ryuta Yamaguchi, Panote Siriaraya, Tomoki Yoshihisa, Shinji Shimojo, Yukiko Kawai|Kyoto Sangyo University, Japan; Kyoto Institute of Technology, Japan; Osaka University, Japan|In recent years, the use of bicycle as a healthy and economical means of transportation has been promoted worldwide. In addition, with the increase in bicycle commuting due to the COVID-19, the use of bicycles are attracting attention as a last-mile means of transportation in Mobility as a Service(MaaS). To help ensure a safe and comfortable ride using a smartphone mounted on a bicycle, this study focuses on analyzing facial expressions while riding to determine potential comfort along the route with the surrounding environment and to provide a map that users can explicitly feedback(FB) after riding. Combining the emotions of facial expressions while riding and FB, we annotate comfort to different locations. Afterwards, we verify the relationship between locations with high level of comfort based on the acquired data and the surrounding environment of those locations using Google Street View(GSV).|近年来，自行车作为一种健康、经济的交通工具在世界范围内得到了推广。此外，随着自行车上下班2019冠状病毒疾病的增加，自行车作为移动即服务(Mobility as a Service，MaaS)的最后一英里交通工具正在引起人们的注意。为了确保安全和舒适的骑行，使用安装在自行车上的智能手机，这项研究侧重于分析骑行时的面部表情，以确定潜在的舒适性沿着路线与周围环境，并提供一个地图，用户可以明确反馈(FB)后，骑车。结合面部表情的情绪，而骑和 FB，我们注释舒适到不同的位置。然后，我们利用谷歌街景(Google Street View，GSV) ，根据获得的数据和周围环境，验证高舒适度地点之间的关系。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Detection+System+for+Comfortable+Locations+Based+on+Facial+Expression+Analysis+While+Riding+Bicycles)|0| |[Efficient Fair Graph Representation Learning Using a Multi-level Framework](https://doi.org/10.1145/3543873.3587369)|Yuntian He, Saket Gurukar, Srinivasan Parthasarathy|The Ohio State University, USA|Graph representation learning models have demonstrated great capability in many real-world applications. Nevertheless, prior research reveals that these models can learn biased representations leading to unfair outcomes. A few works have been proposed to mitigate the bias in graph representations. However, most existing works require exceptional time and computing resources for training and fine-tuning. In this demonstration, we propose a framework FairMILE for efficient fair graph representation learning. FairMILE allows the user to efficiently learn fair graph representations while preserving utility. In addition, FairMILE can work in conjunction with any unsupervised embedding approach based on the user’s preference and accommodate various fairness constraints. The demonstration will introduce the methodology of FairMILE, showcase how to set up and run this framework, and demonstrate our effectiveness and efficiency to the audience through both quantitative metrics and visualization.|图表示学习模型已经在许多实际应用中显示出了巨大的能力。然而，先前的研究表明，这些模型可以学习有偏见的表征，导致不公平的结果。为了减小图表示中的偏差，人们提出了一些工作。然而，大多数现有的工作需要额外的时间和计算资源进行培训和微调。在这个演示中，我们提出了一个有效的公平图表示学习框架 FairMILE。FairMILE 允许用户在保持实用性的同时有效地学习公平图表示法。此外，FairMILE 可以与任何基于用户偏好的无监督嵌入方法协同工作，并适应各种公平性约束。演示将介绍 FairMILE 的方法，展示如何建立和运行这个框架，并通过定量度量和可视化向观众展示我们的有效性和效率。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Efficient+Fair+Graph+Representation+Learning+Using+a+Multi-level+Framework)|0| |[Simple Multi-view Can Bring Powerful Graph Neural Network](https://doi.org/10.1145/3543873.3587375)|Bingbing Xu, Yang Li, Qi Cao, Huawei Shen|Institute of Computing Technology, Chinese Academy of Sciences, China|Graph neural networks have achieved state-of-the-art performance on graph-related tasks through layer-wise neighborhood aggregation. Previous works aim to achieve powerful capability via designing injective neighborhood aggregation functions in each layer, which is difficult to determine and numerous additional parameters make it difficult to train these models. It is the input space and the aggregation function that achieve powerful capability at the same time. Instead of designing complexity aggregation functions, we propose a simple and effective framework, namely MV-GNN, to improve the model expressive power via constructing the new input space. Precisely, MV-GNN samples multi-view subgraphs for each node, and any GNN model can be applied to these views. The representation of target node is finally obtained via aggregating all views injectively. Two typical GNNs (i.e., GCN and GAT) are adopted as base models in the proposed framework, and we demonstrate the effectiveness of MV-GNN through extensive experiments.|图形神经网络通过分层邻域聚合实现了对图形相关任务的高性能处理。以往的工作都是通过设计各层的内射邻域聚合函数来实现强大的功能，但是这种功能很难确定，而且大量的附加参数使得这些模型的训练变得非常困难。正是输入空间和聚合函数同时实现了强大的功能。为了提高模型的表达能力，我们提出了一个简单有效的 MV-GNN 框架，通过构造新的输入空间来代替设计复杂的聚合函数。准确地说，MV-GNN 为每个节点采样多视图子图，并且任何 GNN 模型都可以应用于这些视图。最后通过内射聚合所有视图得到目标节点的表示。该框架采用两种典型的 GNN (即 GCN 和 GAT)作为基本模型，并通过大量实验验证了 MV-GNN 的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Simple+Multi-view+Can+Bring+Powerful+Graph+Neural+Network)|0| -|[EDITS: An Easy-to-difficult Training Strategy for Cloud Failure Prediction](https://doi.org/10.1145/3543873.3584630)|Qingwei Lin, Tianci Li, Pu Zhao, Yudong Liu, Minghua Ma, Lingling Zheng, Murali Chintalapati, Bo Liu, Paul Wang, Hongyu Zhang, Yingnong Dang, Saravan Rajmohan, Dongmei Zhang|Microsoft 365, China; Microsoft Research, China; Peking University, Microsoft Research, China; Microsoft 365, USA; Chongqing University, The University of Newcastle, China; Microsoft Azure, USA|Cloud failures have been a major threat to the reliability of cloud services. Many failure prediction approaches have been proposed to predict cloud failures before they actually occur, so that proactive actions can be taken to ensure service reliability. In industrial practice, existing failure prediction approaches mainly focus on utilizing state-of-the-art time series models to enhance the performance of failure prediction but neglect the training strategy. However, as curriculum learning points out, models perform better when they are trained with data in an order of easy-to-difficult. In this paper, we propose EDITS, a novel training strategy for cloud failure prediction, which greatly improves the performance of the existing cloud failure prediction models. Our experimental results on industrial and public datasets show that EDITS can obviously enhance the performance of cloud failure prediction model. In addition, EDITS also outperforms other curriculum learning methods. More encouragingly, our proposed EDITS has been successfully applied to Microsoft 365 and Azure online service systems, and has obviously reduced financial losses caused by cloud failures.|云故障一直是云服务可靠性的主要威胁。已经提出了许多故障预测方法，以便在云故障实际发生之前进行预测，从而可以采取主动行动来确保服务的可靠性。在工业实践中，现有的故障预测方法主要是利用最新的时间序列模型来提高故障预测的性能，而忽视了训练策略。然而，正如课程学习所指出的那样，当模型使用数据按“易于难”的顺序进行训练时，它们的表现会更好。本文提出了一种新的云失效预测训练策略 EDITS，大大提高了现有云失效预测模型的性能。我们在工业和公共数据集上的实验结果表明，EDITS 可以明显提高云失效预测模型的性能。此外，EDITS 还优于其他课程学习方法。更令人鼓舞的是，我们提出的 EDITS 已经成功地应用于微软365和 Azure 在线服务系统，显然减少了云故障造成的财务损失。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=EDITS:+An+Easy-to-difficult+Training+Strategy+for+Cloud+Failure+Prediction)|0| -|[Multi-Agent Reinforcement Learning with Shared Policy for Cloud Quota Management Problem](https://doi.org/10.1145/3543873.3584634)|Tong Cheng, Hang Dong, Lu Wang, Bo Qiao, Si Qin, Qingwei Lin, Dongmei Zhang, Saravan Rajmohan, Thomas Moscibroda|Microsoft Azure, USA; Microsoft Research, China; Microsoft 365, USA|Quota is often used in resource allocation and management scenarios to prevent abuse of resource and increase the efficiency of resource utilization. Quota management is usually fulfilled with a set of rules maintained by the system administrator. However, maintaining these rules usually needs deep domain knowledge. Moreover, arbitrary rules usually cannot guarantee both high resource utilization and fairness at the same time. In this paper, we propose a reinforcement learning framework to automatically respond to quota requests in cloud computing platforms with distinctive usage characteristics for users. Extensive experimental results have demonstrated the superior performance of our framework on achieving a great trade-off between efficiency and fairness.|配额经常用于资源分配和管理场景，以防止资源滥用和提高资源利用效率。配额管理通常由系统管理员维持一套规则来执行。然而，维护这些规则通常需要深入的领域知识。此外，任意规则通常不能同时保证高资源利用率和公平性。在本文中，我们建议一个强化学习架构，以自动回应用户在具有不同使用特征的云端运算平台上的配额要求。大量的实验结果表明，我们的框架在实现效率和公平之间的巨大平衡方面具有优越的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Multi-Agent+Reinforcement+Learning+with+Shared+Policy+for+Cloud+Quota+Management+Problem)|0| +|[EDITS: An Easy-to-difficult Training Strategy for Cloud Failure Prediction](https://doi.org/10.1145/3543873.3584630)|Qingwei Lin, Tianci Li, Pu Zhao, Yudong Liu, Minghua Ma, Lingling Zheng, Murali Chintalapati, Bo Liu, Paul Wang, Hongyu Zhang, Yingnong Dang, Saravan Rajmohan, Dongmei Zhang|Chongqing University, The University of Newcastle, China; Peking University, Microsoft Research, China; Microsoft 365, USA; Microsoft Azure, USA; Microsoft Research, China; Microsoft 365, China|Cloud failures have been a major threat to the reliability of cloud services. Many failure prediction approaches have been proposed to predict cloud failures before they actually occur, so that proactive actions can be taken to ensure service reliability. In industrial practice, existing failure prediction approaches mainly focus on utilizing state-of-the-art time series models to enhance the performance of failure prediction but neglect the training strategy. However, as curriculum learning points out, models perform better when they are trained with data in an order of easy-to-difficult. In this paper, we propose EDITS, a novel training strategy for cloud failure prediction, which greatly improves the performance of the existing cloud failure prediction models. Our experimental results on industrial and public datasets show that EDITS can obviously enhance the performance of cloud failure prediction model. In addition, EDITS also outperforms other curriculum learning methods. More encouragingly, our proposed EDITS has been successfully applied to Microsoft 365 and Azure online service systems, and has obviously reduced financial losses caused by cloud failures.|云故障一直是云服务可靠性的主要威胁。已经提出了许多故障预测方法，以便在云故障实际发生之前进行预测，从而可以采取主动行动来确保服务的可靠性。在工业实践中，现有的故障预测方法主要是利用最新的时间序列模型来提高故障预测的性能，而忽视了训练策略。然而，正如课程学习所指出的那样，当模型使用数据按“易于难”的顺序进行训练时，它们的表现会更好。本文提出了一种新的云失效预测训练策略 EDITS，大大提高了现有云失效预测模型的性能。我们在工业和公共数据集上的实验结果表明，EDITS 可以明显提高云失效预测模型的性能。此外，EDITS 还优于其他课程学习方法。更令人鼓舞的是，我们提出的 EDITS 已经成功地应用于微软365和 Azure 在线服务系统，显然减少了云故障造成的财务损失。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=EDITS:+An+Easy-to-difficult+Training+Strategy+for+Cloud+Failure+Prediction)|0| +|[Multi-Agent Reinforcement Learning with Shared Policy for Cloud Quota Management Problem](https://doi.org/10.1145/3543873.3584634)|Tong Cheng, Hang Dong, Lu Wang, Bo Qiao, Si Qin, Qingwei Lin, Dongmei Zhang, Saravan Rajmohan, Thomas Moscibroda|Microsoft Research, China; Microsoft 365, USA; Microsoft Azure, USA|Quota is often used in resource allocation and management scenarios to prevent abuse of resource and increase the efficiency of resource utilization. Quota management is usually fulfilled with a set of rules maintained by the system administrator. However, maintaining these rules usually needs deep domain knowledge. Moreover, arbitrary rules usually cannot guarantee both high resource utilization and fairness at the same time. In this paper, we propose a reinforcement learning framework to automatically respond to quota requests in cloud computing platforms with distinctive usage characteristics for users. Extensive experimental results have demonstrated the superior performance of our framework on achieving a great trade-off between efficiency and fairness.|配额经常用于资源分配和管理场景，以防止资源滥用和提高资源利用效率。配额管理通常由系统管理员维持一套规则来执行。然而，维护这些规则通常需要深入的领域知识。此外，任意规则通常不能同时保证高资源利用率和公平性。在本文中，我们建议一个强化学习架构，以自动回应用户在具有不同使用特征的云端运算平台上的配额要求。大量的实验结果表明，我们的框架在实现效率和公平之间的巨大平衡方面具有优越的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Multi-Agent+Reinforcement+Learning+with+Shared+Policy+for+Cloud+Quota+Management+Problem)|0| |[Job Type Extraction for Service Businesses](https://doi.org/10.1145/3543873.3584636)|Cheng Li, Yaping Qi, Hayk Zakaryan, Mingyang Zhang, Michael Bendersky, Yonghua Wu, Marc Najork|Google, USA|Google My Business (GMB) is a platform that hosts business profiles, which will be displayed when a user issues a relevant query on Google Search or Google Maps. GMB businesses provide a wide variety of services, from home cleaning and repair, to legal consultation. However, the exact details of the service provided (a.k.a. job types), are often missing in business profiles. This places the burden of finding these details on the users. To alleviate this burden, we built a pipeline to automatically extract the job types from business websites. We share the various challenges we faced while developing this pipeline, and how we effectively addressed these challenges by (1) utilizing structured content to tackle the cold start problem for dataset collection; (2) exploiting context information to improve model performance without hurting scalability; and (3) formulating the extraction problem as a retrieval task to improve both generalizability, efficiency, and coverage. The pipeline has been deployed for over a year and is scalable enough to be periodically refreshed. The extracted job types are serving users of Google Search and Google Maps, with significant improvements in both precision and coverage.|Google My Business (GMB)是一个托管业务档案的平台，当用户在 Google 搜索或 Google 地图上发出相关查询时，将显示这些档案。专线小巴业务提供多种服务，从家居清洁和维修，以法律咨询。然而，所提供的服务的确切细节(也就是作业类型)在业务概要文件中经常缺失。这将查找这些详细信息的负担放在用户身上。为了减轻这种负担，我们建立了一个管道，可以自动从商业网站提取工作类型。我们分享了我们在开发这个流水线时面临的各种挑战，以及我们如何有效地解决这些挑战: (1)利用结构化内容来解决数据集收集的冷启动问题; (2)利用上下文信息来提高模型性能而不损害可扩展性; (3)将提取问题作为一个检索任务来提高概括性、效率和覆盖率。该管道已经部署了一年多，可伸缩性足以定期更新。提取出来的工作类型为谷歌搜索和谷歌地图的用户提供服务，在精确度和覆盖率方面都有显著提高。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Job+Type+Extraction+for+Service+Businesses)|0| -|[A Practical Rule Learning Framework for Risk Management](https://doi.org/10.1145/3543873.3584644)|Jun Zhou, Meng Li, Lu Yu, Longfei Li, Fei Wu|College of Computer Science and Technology, Zhejiang University, China and Ant Group, China; College of Computer Science and Technology, Zhejiang University, China; Ant Group, China|Identifying the fraud risk of applications on the web platform is a critical challenge with both requirements of effectiveness and interpretability. In these high-stakes web applications especially in financial scenarios, decision rules have been extensively used due to the rising requirements for explainable artificial intelligence (XAI). In this work, we develop a rule learning framework with rule mining and rule refining modules for addressing the learning efficiency and class imbalance issues while making the decision rules more broadly and simply applicable to risk management scenarios. On four benchmark data-sets and two large-scale data-sets, the classification performance, interpretability, and scalability of the framework have been proved, achieving at least a 26.2% relative improvement over the state-of-the-art (SOTA) models. The system is currently being used by hundreds of millions of users and dealing with an enormous number of transactions in Ant Group, which is one of the largest mobile payment platforms in the world.|鉴于有效性和可解释性的要求，确定网络平台上应用程序的欺诈风险是一个关键的挑战。在这些高风险的网络应用中，尤其是在金融场景中，由于对可解释人工智能(XAI)的需求日益增长，决策规则得到了广泛的应用。在这项工作中，我们开发了一个规则学习框架与规则挖掘和规则精炼模块，以解决学习效率和类不平衡的问题，同时使决策规则更广泛和简单地适用于风险管理场景。在四个基准数据集和两个大规模数据集上，证明了该框架的分类性能、可解释性和可扩展性，比最新的 SOTA 模型至少提高了26.2% 。蚂蚁集团是世界上最大的移动支付平台之一，目前有数亿用户正在使用该系统处理大量交易。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Practical+Rule+Learning+Framework+for+Risk+Management)|0| -|[WAM-studio, a Digital Audio Workstation (DAW) for the Web](https://doi.org/10.1145/3543873.3587987)|Michel Buffa, Antoine VidalMazuy|Université Côte d'Azur / I3S / INRIA, France; University Côte d'Azur / I3S Laboratory / INRIA, France|This paper presents WAM Studio, an open source, online Digital Audio Workstation (DAW) that takes advantages of several W3C Web APIs, such as Web Audio, Web Assembly, Web Components, Web Midi, Media Devices etc. It also uses the Web Audio Modules proposal that has been designed to facilitate the development of inter-operable audio plugins (effects, virtual instruments, virtual piano keyboards as controllers etc.) and host applications. DAWs are feature-rich software and therefore particularly complex to develop in terms of design, implementation, performances and ergonomics. Very few commercial online DAWs exist today and the only open-source examples lack features (no support for inter-operable plugins, for example) and do not take advantage of the recent possibilities offered by modern W3C APIs (e.g. AudioWorklets/Web Assembly). WAM Studio was developed as an open-source technology demonstrator with the aim of showcasing the potential of the web platform, made possible by these APIs. The paper highlights some of the difficulties we encountered (i.e limitations due to the sandboxed and constrained environments that are Web browsers, latency compensation etc.). An online demo, as well as a GitHub repository for the source code are available.|这篇文章介绍了 WAM 工作室，一个开源的在线数位音频工作站，它利用了一些 W3C 网络应用程序接口的优势，例如网络音频、网络组装、网络组件、网络迷笛、媒体设备等。它还使用了网络音频模块的建议，旨在促进互操作的音频插件(效果，虚拟乐器，虚拟钢琴键盘作为控制器等)和主机应用程序的开发。DAW 是功能丰富的软件，因此在设计、实现、性能和人机工程学方面开发特别复杂。现在很少有商业在线 DAW 存在，唯一的开源示例缺乏特性(例如，不支持可互操作的插件) ，也没有利用现代 W3C API (例如 AudioWorklets/Web Assembly)提供的最新可能性。WAM 工作室是作为一个开源样品开发的，目的是展示 web 平台的潜力，这些 API 使其成为可能。本文强调了我们遇到的一些困难(即由于沙箱和 Web 浏览器等受限环境的限制，延迟补偿等)。可以使用在线演示和 GitHub 源代码库。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=WAM-studio,+a+Digital+Audio+Workstation+(DAW)+for+the+Web)|0| +|[A Practical Rule Learning Framework for Risk Management](https://doi.org/10.1145/3543873.3584644)|Jun Zhou, Meng Li, Lu Yu, Longfei Li, Fei Wu|Ant Group, China; College of Computer Science and Technology, Zhejiang University, China; College of Computer Science and Technology, Zhejiang University, China and Ant Group, China|Identifying the fraud risk of applications on the web platform is a critical challenge with both requirements of effectiveness and interpretability. In these high-stakes web applications especially in financial scenarios, decision rules have been extensively used due to the rising requirements for explainable artificial intelligence (XAI). In this work, we develop a rule learning framework with rule mining and rule refining modules for addressing the learning efficiency and class imbalance issues while making the decision rules more broadly and simply applicable to risk management scenarios. On four benchmark data-sets and two large-scale data-sets, the classification performance, interpretability, and scalability of the framework have been proved, achieving at least a 26.2% relative improvement over the state-of-the-art (SOTA) models. The system is currently being used by hundreds of millions of users and dealing with an enormous number of transactions in Ant Group, which is one of the largest mobile payment platforms in the world.|鉴于有效性和可解释性的要求，确定网络平台上应用程序的欺诈风险是一个关键的挑战。在这些高风险的网络应用中，尤其是在金融场景中，由于对可解释人工智能(XAI)的需求日益增长，决策规则得到了广泛的应用。在这项工作中，我们开发了一个规则学习框架与规则挖掘和规则精炼模块，以解决学习效率和类不平衡的问题，同时使决策规则更广泛和简单地适用于风险管理场景。在四个基准数据集和两个大规模数据集上，证明了该框架的分类性能、可解释性和可扩展性，比最新的 SOTA 模型至少提高了26.2% 。蚂蚁集团是世界上最大的移动支付平台之一，目前有数亿用户正在使用该系统处理大量交易。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Practical+Rule+Learning+Framework+for+Risk+Management)|0| +|[WAM-studio, a Digital Audio Workstation (DAW) for the Web](https://doi.org/10.1145/3543873.3587987)|Michel Buffa, Antoine VidalMazuy|University Côte d'Azur / I3S Laboratory / INRIA, France; Université Côte d'Azur / I3S / INRIA, France|This paper presents WAM Studio, an open source, online Digital Audio Workstation (DAW) that takes advantages of several W3C Web APIs, such as Web Audio, Web Assembly, Web Components, Web Midi, Media Devices etc. It also uses the Web Audio Modules proposal that has been designed to facilitate the development of inter-operable audio plugins (effects, virtual instruments, virtual piano keyboards as controllers etc.) and host applications. DAWs are feature-rich software and therefore particularly complex to develop in terms of design, implementation, performances and ergonomics. Very few commercial online DAWs exist today and the only open-source examples lack features (no support for inter-operable plugins, for example) and do not take advantage of the recent possibilities offered by modern W3C APIs (e.g. AudioWorklets/Web Assembly). WAM Studio was developed as an open-source technology demonstrator with the aim of showcasing the potential of the web platform, made possible by these APIs. The paper highlights some of the difficulties we encountered (i.e limitations due to the sandboxed and constrained environments that are Web browsers, latency compensation etc.). An online demo, as well as a GitHub repository for the source code are available.|这篇文章介绍了 WAM 工作室，一个开源的在线数位音频工作站，它利用了一些 W3C 网络应用程序接口的优势，例如网络音频、网络组装、网络组件、网络迷笛、媒体设备等。它还使用了网络音频模块的建议，旨在促进互操作的音频插件(效果，虚拟乐器，虚拟钢琴键盘作为控制器等)和主机应用程序的开发。DAW 是功能丰富的软件，因此在设计、实现、性能和人机工程学方面开发特别复杂。现在很少有商业在线 DAW 存在，唯一的开源示例缺乏特性(例如，不支持可互操作的插件) ，也没有利用现代 W3C API (例如 AudioWorklets/Web Assembly)提供的最新可能性。WAM 工作室是作为一个开源样品开发的，目的是展示 web 平台的潜力，这些 API 使其成为可能。本文强调了我们遇到的一些困难(即由于沙箱和 Web 浏览器等受限环境的限制，延迟补偿等)。可以使用在线演示和 GitHub 源代码库。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=WAM-studio,+a+Digital+Audio+Workstation+(DAW)+for+the+Web)|0| |[The Capable Web](https://doi.org/10.1145/3543873.3587988)|Thomas Steiner|Google Germany GmbH, Germany|In this paper, I discuss arguments in favor and in disfavor of building for the Web. I look at three extraordinary examples of apps built for the Web, and analyze reasons their creators provided for doing so. In continuation, I look at the decline of interest in cross-platform app frameworks with the exception of Flutter, which leads me to the two research questions RQ1 "Why do people not fully bet on PWA" and RQ2 "Why is Flutter so popular". My hypothesis for why developers don’t more frequently set on the Web is that in many cases they (or their non-technical reporting lines) don’t realize how powerful it has become. To counter that, I introduce a Web app and a browser extension that demonstrate the Web’s capabilities.|在本文中，我讨论了支持和反对为 Web 构建的论点。我看了三个为 Web 构建的非凡应用程序的例子，并分析了它们的创建者为此提供的原因。接下来，我看到除了 Flutter 之外，人们对跨平台应用框架的兴趣正在下降，这让我想到了两个研究问题: RQ1“为什么人们没有完全押注于 PWA”和 RQ2“为什么 Flutter 如此受欢迎”。我对开发人员为什么不更频繁地设置在 Web 上的假设是，在许多情况下，他们(或者他们的非技术报告线)没有意识到 Web 已经变得多么强大。为了解决这个问题，我引入了一个 Web 应用程序和一个浏览器扩展来展示 Web 的功能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=The+Capable+Web)|0| |[MetaCrimes: Criminal accountability for conducts in the Metaverse](https://doi.org/10.1145/3543873.3587535)|Gian Marco Bovenzi|Center for Higher Defence Studies, University of Turin, Italy|The research addresses a topic whose precise boundaries are yet to be defined: the criminal accountability for conducts committed in the Metaverse. Following a short introduction motivating the reason why this issue has to be considered as pivotal both for the Web and for the society, the main problem raised by the research will be identified, namely, whether an action taken against a person, that in real-life would be a criminal conduct, is considerable as a crime in the Metaverse as well. A short assessment of the (very little so far) current state of the art, as well as the proposed approach and methodology will be then overviewed; finally, the contribution shows its current results, and concludes stating that countries are highly encouraged to shape respective criminal frameworks when applied to the Metaverse, and that the international community should consider the topic as a priority in its agenda. Nevertheless, further experimental and research work has still to be made.|这项研究涉及一个尚未界定确切界限的主题: 在元宇宙中犯下的行为的刑事责任。在简短介绍了为什么这个问题必须被认为是网络和社会的关键的原因之后，研究提出的主要问题将被确定，即，对一个人采取的行动，在现实生活中将是一种犯罪行为，在元宇宙中是否也是一种犯罪。接下来，我们将对目前的最新技术状况(到目前为止很少)进行简短的评估，并对提出的方法和方法进行概述; 最后，这份报告展示了目前的结果，并得出结论说，当适用于元宇宙时，各国都被大力鼓励形成各自的犯罪框架，国际社会应该将这一主题作为其议程上的优先事项。然而，进一步的实验和研究工作还有待开展。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=MetaCrimes:+Criminal+accountability+for+conducts+in+the+Metaverse)|0| |[SEKA: Seeking Knowledge Graph Anomalies](https://doi.org/10.1145/3543873.3587536)|Asara Senaratne|School of Computing, The Australian National University, Australia|Knowledge Graphs (KGs) form the backbone of many knowledge dependent applications such as search engines and digital personal assistants. KGs are generally constructed either manually or automatically using a variety of extraction techniques applied over multiple data sources. Due to the diverse quality of these data sources, there are likely anomalies introduced into any KG. Hence, it is unrealistic to expect a perfect archive of knowledge. Given how large KGs can be, manual validation is impractical, necessitating an automated approach for anomaly detection in KGs. To improve KG quality, and to identify interesting and abnormal triples (edges) and entities (nodes) that are worth investigating, we introduce SEKA, a novel unsupervised approach to detect anomalous triples and entities in a KG using both the structural characteristics and the content of edges and nodes of the graph. While an anomaly can be an interesting or unusual discovery, such as a fraudulent transaction requiring human intervention, anomaly detection can also identify potential errors. We propose a novel approach named Corroborative Path Algorithm to generate a matrix of semantic features, which we then use to train a one-class Support Vector Machine to identify abnormal triples and entities with no dependency on external sources. We evaluate our approach on four real-world KGs demonstrating the ability of SEKA to detect anomalies, and to outperform comparative baselines.|知识图(KGs)构成了许多依赖于知识的应用程序(如搜索引擎和数字个人助理)的主干。幼稚园一般以人手或自动方式建构，并采用多种抽取技术，应用于多个资料来源。由于这些数据源的质量各不相同，可能会在任何 KG 中引入异常。因此，期望一个完美的知识档案是不现实的。鉴于幼稚园的规模庞大，人手验证并不切实可行，因此必须采用自动化方法处理幼稚园的异常检测。为了提高 KG 质量，并识别有趣和异常的三元组(边)和实体(节点)值得研究，我们引入 SEKA，一种新的无监督方法来检测 KG 中的异常三元组和实体，使用图的结构特征和边和节点的内容。虽然异常可能是一个有趣或不寻常的发现，例如需要人为干预的欺诈性交易，但异常检测也可以识别潜在的错误。我们提出了一种新颖的方法，称为证实路径算法，以生成一个语义特征矩阵，然后我们使用训练一个一类的支持向量机识别不正常的三元组和实体没有依赖于外部来源。我们评估了我们的方法在四个真实世界的幼儿园展示 SEKA 的能力，检测异常，并超过比较基线。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=SEKA:+Seeking+Knowledge+Graph+Anomalies)|0| @@ -429,258 +429,258 @@ |[Caught in the Game: On the History and Evolution of Web Browser Gaming](https://doi.org/10.1145/3543873.3585572)|Naif Mehanna, Walter Rudametkin|Univ Lille, CNRS, Inria, UMR 9189 CRIStAL, France; Univ Rennes, Institut Universitaire de France (IUF), CNRS, Inria, UMR 6074 IRISA, France|Web browsers have come a long way since their inception, evolving from a simple means of displaying text documents over the network to complex software stacks with advanced graphics and network capabilities. As personal computers grew in popularity, developers jumped at the opportunity to deploy cross-platform games with centralized management and a low barrier to entry. Simply going to the right address is now enough to start a game. From text-based to GPU-powered 3D games, browser gaming has evolved to become a strong alternative to traditional console and mobile-based gaming, targeting both casual and advanced gamers. Browser technology has also evolved to accommodate more demanding applications, sometimes even supplanting functions typically left to the operating system. Today, websites display rich, computationally intensive, hardware-accelerated graphics, allowing developers to build ever-more impressive applications and games.In this paper, we present the evolution of browser gaming and the technologies that enabled it, from the release of the first text-based games in the early 1990s to current open-world and game-engine-powered browser games. We discuss the societal impact of browser gaming and how it has allowed a new target audience to accessdigital gaming. Finally, we review the potential future evolution ofthe browser gaming industry.|Web 浏览器自问世以来已经走过了漫长的道路，从通过网络显示文本文档的简单方法演变为具有先进图形和网络功能的复杂软件栈。随着个人电脑越来越受欢迎，开发人员抓住机会部署集中管理和低门槛的跨平台游戏。现在只要找到正确的地址就足以开始一场游戏了。从基于文本的游戏到基于 GPU 的3D 游戏，浏览器游戏已经发展成为传统游戏机和基于移动设备的游戏的强大替代品，目标客户既包括休闲玩家，也包括高级玩家。浏览器技术也在发展，以适应更加苛刻的应用程序，有时甚至取代通常留给操作系统的功能。今天，网站显示丰富的，计算密集的，硬件加速的图形，允许开发人员建立更令人印象深刻的应用程序和游戏。在这篇文章中，我们介绍了浏览器游戏的演变和技术，从20世纪90年代初发布的第一个基于文本的游戏到现在的开放世界和游戏引擎驱动的浏览器游戏。我们讨论了浏览器游戏的社会影响，以及它是如何让一个新的目标受众访问数字游戏的。最后，我们回顾了未来浏览器游戏产业的潜在发展趋势。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Caught+in+the+Game:+On+the+History+and+Evolution+of+Web+Browser+Gaming)|0| |[Identifying Stable States of Large Signed Graphs](https://doi.org/10.1145/3543873.3587544)|Muhieddine Shebaro, Jelena Tesic|Department of Computer Science, Texas State University, USA|Signed network graphs provide a way to model complex relationships and interdependencies between entities: negative edges allow for a deeper study of social dynamics. One approach to achieving balance in a network is to model the sources of conflict through structural balance. Current methods focus on computing the frustration index or finding the largest balanced clique, but these do not account for multiple ways to reach a consensus or scale well for large, sparse networks. In this paper, we propose an expansion of the frustration cloud computation and compare various tree-sampling algorithms that can discover a high number of diverse balanced states. Then, we compute and compare the frequencies of balanced states produced by each. Finally, we investigate these techniques’ impact on the consensus feature space.|签名网络图提供了一种模拟实体之间复杂关系和相互依赖的方法: 负边允许对社会动态进行更深入的研究。在网络中实现平衡的一种方法是通过结构平衡来模拟冲突的根源。目前的方法侧重于计算挫折指数或寻找最大的平衡集团，但这些并没有考虑到多种方式达成共识或规模良好的大型，稀疏的网络。本文提出了一种挫折云计算的扩展方法，并对各种能够发现大量不同平衡状态的树抽样算法进行了比较。然后，我们计算并比较了每种方法产生的平衡态的频率。最后，我们研究了这些技术对一致性特征空间的影响。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Identifying+Stable+States+of+Large+Signed+Graphs)|0| |[Those who are left behind: A chronicle of internet access in Cuba](https://doi.org/10.1145/3543873.3585573)|Brenda Reyes Ayala|University of Alberta, Canada|This paper presents a personal chronicle of internet access in Cuba from the perspective of a visitor to the island. It is told across three time periods: 1997, 2010, and 2021. The story describes how the island first connected to the internet in the 90s, how internet access evolved throughout the 2000s, and ends in the role the internet played in the government protests on July 11, 2021. The article analyzes how internet access in Cuba has changed over the decades and its effects on civil society. It discusses issues such as Cuba’s technological infrastructure, internet censorship, and free expression.|本文从一位访问古巴的游客的角度，介绍了古巴互联网接入的个人编年史。它分为三个时间段: 1997年、2010年和2021年。这个故事描述了这个岛屿在90年代如何首次连接到互联网，互联网接入在整个2000年代如何演变，并以互联网在2021年7月11日政府抗议活动中所扮演的角色告终。文章分析了几十年来古巴互联网接入的变化及其对公民社会的影响。报告讨论了古巴的技术基础设施、互联网审查和言论自由等问题。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Those+who+are+left+behind:+A+chronicle+of+internet+access+in+Cuba)|0| -|[Reflex-in: Generate Music on the Web with Real-time Brain Wave](https://doi.org/10.1145/3543873.3587315)|Shihong Ren, Michel Buffa, Laurent Pottier, Yang Yu, Gerwin Schalk|Laboratoire d'Études du Contemporain en Littératures, Langues, Arts, Université Jean Monnet, France; Laboratoire d'Informatique, Signaux et Systèmes de Sophia Antipolis, Université Côte d'Azur, France; Frontier Lab for Applied Neurotechnology, Tianqiao and Chrissy Chen Institute, China; Shanghai Key Laboratory for Music Acoustic, Shanghai Conservatory of Music, China and Laboratoire d'Études du Contemporain en Littératures, Langues, Arts, Université Jean Monnet, France; Shanghai Key Laboratory for Music Acoustic, Shanghai Conservatory of Music, China|Reflex-in is a sound installation that uses brain-wave streams to create music composition within the Web environment in real time. The work incorporates various state-of-the-art Web technologies, including Web Audio, WebSocket, WebAssembly, and WebGL. The music generated from the algorithm - mapping brain wave signal to musical events - aims to produce a form of furniture music that is relaxing and meditative, possibly therapeutic. This effect can be further enhanced through binaural beats or other forms of auditory stimulation, also known as “digital drugs,” which can be enabled through the user interface. The system represents a potential avenue for the development of closed-loop brain-computer interfaces by using the listener’s own brain waves as the source of musical stimuli, which can be used for therapeutic or medical purposes.|Reflex-in 是一个声音装置，利用脑电波流在网络环境中实时创建音乐创作。这项工作结合了各种最先进的 Web 技术，包括 Web Audio、 WebSocket、 WebAssembly 和 WebGL。由算法产生的音乐——将脑电波信号映射到音乐事件——旨在产生一种家具音乐的形式，这种音乐是放松和冥想的，可能是治疗性的。这种效果可以通过双耳节拍或其他形式的听觉刺激进一步加强，也被称为“数字药物”，这可以通过用户界面启用。该系统利用听众自身的脑电波作为音乐刺激源，为开发闭环脑-计算机接口提供了一条潜在的途径，可用于治疗或医疗目的。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Reflex-in:+Generate+Music+on+the+Web+with+Real-time+Brain+Wave)|0| -|[Wikidata: The Making Of](https://doi.org/10.1145/3543873.3585579)|Denny Vrandecic, Lydia Pintscher, Markus Krötzsch|TU Dresden, Germany; Wikimedia Foundation, USA; Wikimedia Deutschland, Germany|Wikidata, now a decade old, is the largest public knowledge graph, with data on more than 100 million concepts contributed by over 560,000 editors. It is widely used in applications and research. At its launch in late 2012, however, it was little more than a hopeful new Wikimedia project, with no content, almost no community, and a severely restricted platform. Seven years earlier still, in 2005, it was merely a rough idea of a few PhD students, a conceptual nucleus that had yet to pick up many important influences from others to turn into what is now called Wikidata. In this paper, we try to recount this remarkable journey, and we review what has been accomplished, what has been given up on, and what is yet left to do for the future.|维基数据，现在已经有十年的历史，是最大的公共知识图表，由超过56万名编辑贡献了超过1亿个概念的数据。它在应用和研究中得到了广泛的应用。然而，在2012年末推出时，它只不过是一个充满希望的新维基媒体项目，没有内容，几乎没有社区，平台受到严格限制。七年前，也就是2005年，这只是几个博士生的一个粗略想法，一个概念核心，还没有从其他人那里获得许多重要的影响，转变成现在所谓的 Wikidata。在这篇文章中，我们试图回顾这个非凡的旅程，我们回顾什么已经完成，什么已经放弃，还有什么是未来要做的。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Wikidata:+The+Making+Of)|0| +|[Reflex-in: Generate Music on the Web with Real-time Brain Wave](https://doi.org/10.1145/3543873.3587315)|Shihong Ren, Michel Buffa, Laurent Pottier, Yang Yu, Gerwin Schalk|Shanghai Key Laboratory for Music Acoustic, Shanghai Conservatory of Music, China and Laboratoire d'Études du Contemporain en Littératures, Langues, Arts, Université Jean Monnet, France; Shanghai Key Laboratory for Music Acoustic, Shanghai Conservatory of Music, China; Frontier Lab for Applied Neurotechnology, Tianqiao and Chrissy Chen Institute, China; Laboratoire d'Études du Contemporain en Littératures, Langues, Arts, Université Jean Monnet, France; Laboratoire d'Informatique, Signaux et Systèmes de Sophia Antipolis, Université Côte d'Azur, France|Reflex-in is a sound installation that uses brain-wave streams to create music composition within the Web environment in real time. The work incorporates various state-of-the-art Web technologies, including Web Audio, WebSocket, WebAssembly, and WebGL. The music generated from the algorithm - mapping brain wave signal to musical events - aims to produce a form of furniture music that is relaxing and meditative, possibly therapeutic. This effect can be further enhanced through binaural beats or other forms of auditory stimulation, also known as “digital drugs,” which can be enabled through the user interface. The system represents a potential avenue for the development of closed-loop brain-computer interfaces by using the listener’s own brain waves as the source of musical stimuli, which can be used for therapeutic or medical purposes.|Reflex-in 是一个声音装置，利用脑电波流在网络环境中实时创建音乐创作。这项工作结合了各种最先进的 Web 技术，包括 Web Audio、 WebSocket、 WebAssembly 和 WebGL。由算法产生的音乐——将脑电波信号映射到音乐事件——旨在产生一种家具音乐的形式，这种音乐是放松和冥想的，可能是治疗性的。这种效果可以通过双耳节拍或其他形式的听觉刺激进一步加强，也被称为“数字药物”，这可以通过用户界面启用。该系统利用听众自身的脑电波作为音乐刺激源，为开发闭环脑-计算机接口提供了一条潜在的途径，可用于治疗或医疗目的。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Reflex-in:+Generate+Music+on+the+Web+with+Real-time+Brain+Wave)|0| +|[Wikidata: The Making Of](https://doi.org/10.1145/3543873.3585579)|Denny Vrandecic, Lydia Pintscher, Markus Krötzsch|Wikimedia Foundation, USA; Wikimedia Deutschland, Germany; TU Dresden, Germany|Wikidata, now a decade old, is the largest public knowledge graph, with data on more than 100 million concepts contributed by over 560,000 editors. It is widely used in applications and research. At its launch in late 2012, however, it was little more than a hopeful new Wikimedia project, with no content, almost no community, and a severely restricted platform. Seven years earlier still, in 2005, it was merely a rough idea of a few PhD students, a conceptual nucleus that had yet to pick up many important influences from others to turn into what is now called Wikidata. In this paper, we try to recount this remarkable journey, and we review what has been accomplished, what has been given up on, and what is yet left to do for the future.|维基数据，现在已经有十年的历史，是最大的公共知识图表，由超过56万名编辑贡献了超过1亿个概念的数据。它在应用和研究中得到了广泛的应用。然而，在2012年末推出时，它只不过是一个充满希望的新维基媒体项目，没有内容，几乎没有社区，平台受到严格限制。七年前，也就是2005年，这只是几个博士生的一个粗略想法，一个概念核心，还没有从其他人那里获得许多重要的影响，转变成现在所谓的 Wikidata。在这篇文章中，我们试图回顾这个非凡的旅程，我们回顾什么已经完成，什么已经放弃，还有什么是未来要做的。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Wikidata:+The+Making+Of)|0| |[A History of Diversity in The Web (Conference)](https://doi.org/10.1145/3543873.3585576)|Siddharth D. Jaiswal, Animesh Mukherjee|Indian Institute of Technology, Kharagpur, India|The Web has grown considerably since its inception and opened up a multitude of opportunities for people all around the world for work, leisure, and learning. These opportunities were limited to western audiences earlier on, but globalization has now put almost the entire world online. While there is a growing social understanding and acknowledgment of various gender and ethnic groups in society, we still have a long way to go toward achieving equity in gender and ethnic representations, especially in the workplace. In this paper, we attempt to quantify the diversity and evenness in terms of gender and ethnicity of The WebConference participants over its 30 year history. The choice is motivated by the monumental contribution of this conference to the evolution of the web. In particular, we study the gender and ethnicity of program committee members, authors and other speakers at the conference between 1994-2022. We also generate the co-speaker network over the three decades to study how closely the speakers work with each other. Our findings show that we still have a long way to go before achieving fair representation at The WebConference, especially for female participants and individuals from non-White, non-Asian ethnicities.|自从网络诞生以来，它已经有了很大的发展，为世界各地的人们提供了大量的工作、休闲和学习的机会。早些时候，这些机会仅限于西方受众，但现在全球化已经使几乎整个世界上网。尽管社会上对各种性别和族裔群体的理解和认可日益增加，但要实现性别和族裔代表性方面的公平，特别是在工作场所，我们仍有很长的路要走。在本文中，我们试图量化的多样性和均匀性的性别和种族方面的网络会议与会者在其30年的历史。这一选择的动机是这次会议对网络发展的巨大贡献。特别是，我们研究了1994-2022年间项目委员会成员、作者和其他发言者的性别和种族。我们还在过去三十年中建立了共同发言者网络，以研究发言者之间的合作程度。我们的研究结果表明，要在网络会议上实现公平的代表性，我们还有很长的路要走，特别是对于女性与会者和来自非白人、非亚洲种族的个人来说。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+History+of+Diversity+in+The+Web+(Conference))|0| |[Why are Hyperlinks Blue?: A deep dive into browser hyperlink color history](https://doi.org/10.1145/3543873.3587714)|Elise Blanchard|Mozilla, USA|The internet has ingrained itself into every aspect of our lives, but there's one aspect of the digital world that some take for granted. Did you ever notice that many links, specifically hyperlinks, are blue? When a coworker casually asked me why links are blue, I was stumped. As a user experience designer who has created websites since 2001, I've always made my links blue. I have advocated for the specific shade of blue, and for the consistent application of blue, yes, but I've never stopped and wondered, why are links blue? It was just a fact of life. Grass is green and hyperlinks are blue. Culturally, we associate links with the color blue so much that in 2016, when Google changed its links to black, it created quite a disruption [1]. But now, I find myself all consumed by the question, WHY are links blue? WHO decided to make them blue? WHEN was this decision made, and HOW has this decision made such a lasting impact? Mosaic, an early browser released by Marc Andreessen and Eric Bina on January 23, 1993 [2], had blue hyperlinks. To truly understand the origin and evolution of hyperlinks, I took a journey through technology history and interfaces to explore how links were handled before color monitors, and how interfaces and hyperlinks rapidly evolved once color monitors became an option.|互联网已经在我们生活的方方面面根深蒂固，但是在数字世界的某一方面，有些人认为是理所当然的。你有没有注意到许多链接，特别是超链接，是蓝色的？当一位同事不经意地问我为什么链接是蓝色的时候，我被难住了。作为一个从2001年开始创建网站的用户体验设计师，我总是把我的链接设成蓝色。我一直主张特定的蓝色阴影，并为蓝色的一致应用，是的，但我从来没有停止和疑惑，为什么链接是蓝色的？这就是生活。草是绿色的，超链接是蓝色的。从文化上讲，我们把链接和蓝色联系在一起，以至于在2016年，当谷歌把链接改成黑色时，它造成了相当大的破坏[1]。但是现在，我发现自己完全被这个问题所困扰，为什么链接是蓝色的？谁决定把它们变成蓝色的？这个决定是什么时候做出的? 这个决定是如何产生如此持久的影响的？马克 · 安德森(Marc Andreessen)和埃里克 · 比纳(Eric Bina)于1993年1月23日发布的早期浏览器 Mosaic 有蓝色的超链接。为了真正理解超链接的起源和演变，我通过技术历史和界面来探索在彩色显示器出现之前链接是如何处理的，以及一旦彩色显示器成为一种选择，界面和超链接是如何迅速演变的。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Why+are+Hyperlinks+Blue?:+A+deep+dive+into+browser+hyperlink+color+history)|0| |[The One Hundred Year Web](https://doi.org/10.1145/3543873.3585578)|Steven Pemberton|CWI, Netherlands|The year 2023 marks the thirty-second anniversary of the World Wide Web being announced. In the intervening years, the web has become an essential part of the fabric of society. Part of that is that huge amounts of information that used to be available (only) on paper is now available (only) electronically. One of the dangers of this is that owners of information often treat the data as ephemeral, and delete old information once it becomes out of date. As a result society is at risk of losing large parts of its history. So it is time to assess how we use the web, how it has been designed, and what we should do to ensure that in one hundred years time (and beyond) we will still be able to access, and read, what we are now producing. We can still read 100 year-old books; that should not be any different for the web. This paper takes a historical view of the web, and discusses the web from its early days: why it was successful compared with other similar systems emerging at the time, the things it did right, the mistakes that were made, and how it has developed to the web we know today, to what extent it meets the requirements needed for such an essential part of society's infrastructure, and what still needs to be done.|2023年是万维网发布32周年。在这些年里，网络已经成为社会结构的重要组成部分。部分原因是过去只能在纸上获得的大量信息现在只能通过电子方式获得。这样做的危险之一是，信息的所有者常常将数据视为昙花一现，一旦过时就会删除旧信息。因此，社会面临着失去大部分历史的危险。因此，现在是时候评估我们如何使用网络，它是如何被设计的，以及我们应该做些什么来确保在一百年以后我们仍然能够访问和阅读我们现在正在生产的东西。我们仍然可以阅读100年前的书籍，这对于网络来说应该没有什么不同。本文从历史的角度来看待万维网，并讨论了万维网的早期发展: 为什么它比当时出现的其他类似系统更成功，它做对了什么，犯了什么错误，以及它是如何发展到我们今天所知道的万维网的，它在多大程度上满足了社会基础设施这一重要组成部分所需要的要求，以及还需要做些什么。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=The+One+Hundred+Year+Web)|0| |[Followers Tell Who an Influencer Is](https://doi.org/10.1145/3543873.3587576)|Dheeman Saha, Md Rashidul Hasan, Abdullah Mueen|Department of Computer Science, University of New Mexico, USA; Department of Mathematics and Statistics, University of New Mexico, USA|Influencers are followed by a relatively smaller group of people on social media platforms under a common theme. Unlike the global celebrities, it is challenging to categorize influencers into general categories of fame (e.g., Politics, Religion, Entertainment, etc.) because of their overlapping and narrow reach to people interested in these categories. In this paper, we focus on categorizing influencers based on their followers. We exploit the top-1K Twitter celebrities to identify the common interest among the followers of an influencer as his/her category. We annotate the top one thousand celebrities in multiple categories of popularity, language, and locations. Such categorization is essential for targeted marketing, recommending experts, etc. We define a novel FollowerSimilarity between the set of followers of an influencer and a celebrity. We propose an inverted index to calculate similarity values efficiently. We exploit the similarity score in a K-Nearest Neighbor classifier and visualize the top celebrities over a neighborhood-embedded space.|在社交媒体平台上，只有相对较少的一部分人关注影响者，他们的主题是一致的。与全球名人不同的是，将影响者分为一般的名声类别(例如，政治、宗教、娱乐等)是很有挑战性的，因为这些类别对于感兴趣的人来说是重叠和狭窄的。在这篇文章中，我们着重于根据他们的追随者对影响者进行分类。我们利用前1000名的 Twitter 名人来确定一个有影响力的人的追随者的共同兴趣作为他/她的类别。我们根据受欢迎程度、语言和地理位置来评选前一千位名人。这样的分类对于有针对性的营销、推荐专家等是必不可少的。我们定义了一个小说的追随者之间的影响者和名人的追随者集相似性。我们提出了一个反向指数来有效地计算相似度值。我们利用 K- 最近邻分类器中的相似性得分，并在邻里嵌入空间上可视化顶级名人。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Followers+Tell+Who+an+Influencer+Is)|0| -|[Smart Cities as Hubs: a use case from Biotechnology](https://doi.org/10.1145/3543873.3587582)|Tsapadikou Asteria, Leonidas G. Anthopoulos|Department of Biochemistry and Biotechnology, University of Thessaly, Greece; Department of Business Administration, University of Thessaly, Greece|Smart city platforms operate as central points of access for locally collected data. The smart city hub (SCHub) introduces a new concept that aims to homogenize data, service, human and material flows in cities. A proof of concept is based on non-typical data flows, like the ones that are collected by biotechnological activities, like Diet-related non-communicable diseases (NCDs). NCDs are responsible for 1 in 5 deaths globally. Most of the diet related NCDs are related to the gut microbiome, the microbial community that resides in our gastrointestinal tract. The imbalance or loss of microbiome diversity is one of the main factors leading to NCDs by affecting various functions, including energy metabolism, intestinal permeability, and brain function. Gut dysbiosis is reflected in altered concentrations of Short Chain Fatty Acids (SCFAs), produced by the gut microbiota. A microcapsule system can play the role of a sensor that collects data from the local community and transmits it to the SCHub in order for the doctors to receive the appropriate patients information and define the appropriate treatment method; for the city to process anonymized information and measure community's health in diet terms. A prototype with a biosensor that correlates the amount of gut SCFAs with gut microbiome functional capacities is presented in this paper, together with the use-case scenario that engages the SCHub.|智能城市平台作为本地收集数据的中心接入点运作。智能城市中心(SCHub)引入了一个新的概念，旨在统一城市中的数据、服务、人员和物质流。概念的证明是基于非典型的数据流，如通过生物技术活动收集的数据流，如与饮食相关的非传染性疾病(NCD)。全球五分之一的死亡是由非传染性疾病造成的。大部分与饮食有关的非传染性疾病都与肠道微生物有关，肠道微生物群落是我们肠粘膜中的微生物群落。微生物多样性的失衡或丧失是影响能量代谢、肠道通透性和脑功能等多种功能而导致非传染性疾病的主要因素之一。肠道生态失调反映在由肠道微生物群产生的短链脂肪酸(SCFAs)浓度的改变上。微胶囊系统可以发挥传感器的作用，从当地社区收集数据，并将其传送到 SCHub，以便医生接收适当的患者信息，并确定适当的治疗方法; 城市处理匿名信息，并以饮食方式衡量社区的健康。本文介绍了一个具有将肠道 SCFAs 量与肠道微生物组功能能力相关联的生物传感器的原型，以及参与 SCHub 的用例场景。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Smart+Cities+as+Hubs:+a+use+case+from+Biotechnology)|0| +|[Smart Cities as Hubs: a use case from Biotechnology](https://doi.org/10.1145/3543873.3587582)|Tsapadikou Asteria, Leonidas G. Anthopoulos|Department of Business Administration, University of Thessaly, Greece; Department of Biochemistry and Biotechnology, University of Thessaly, Greece|Smart city platforms operate as central points of access for locally collected data. The smart city hub (SCHub) introduces a new concept that aims to homogenize data, service, human and material flows in cities. A proof of concept is based on non-typical data flows, like the ones that are collected by biotechnological activities, like Diet-related non-communicable diseases (NCDs). NCDs are responsible for 1 in 5 deaths globally. Most of the diet related NCDs are related to the gut microbiome, the microbial community that resides in our gastrointestinal tract. The imbalance or loss of microbiome diversity is one of the main factors leading to NCDs by affecting various functions, including energy metabolism, intestinal permeability, and brain function. Gut dysbiosis is reflected in altered concentrations of Short Chain Fatty Acids (SCFAs), produced by the gut microbiota. A microcapsule system can play the role of a sensor that collects data from the local community and transmits it to the SCHub in order for the doctors to receive the appropriate patients information and define the appropriate treatment method; for the city to process anonymized information and measure community's health in diet terms. A prototype with a biosensor that correlates the amount of gut SCFAs with gut microbiome functional capacities is presented in this paper, together with the use-case scenario that engages the SCHub.|智能城市平台作为本地收集数据的中心接入点运作。智能城市中心(SCHub)引入了一个新的概念，旨在统一城市中的数据、服务、人员和物质流。概念的证明是基于非典型的数据流，如通过生物技术活动收集的数据流，如与饮食相关的非传染性疾病(NCD)。全球五分之一的死亡是由非传染性疾病造成的。大部分与饮食有关的非传染性疾病都与肠道微生物有关，肠道微生物群落是我们肠粘膜中的微生物群落。微生物多样性的失衡或丧失是影响能量代谢、肠道通透性和脑功能等多种功能而导致非传染性疾病的主要因素之一。肠道生态失调反映在由肠道微生物群产生的短链脂肪酸(SCFAs)浓度的改变上。微胶囊系统可以发挥传感器的作用，从当地社区收集数据，并将其传送到 SCHub，以便医生接收适当的患者信息，并确定适当的治疗方法; 城市处理匿名信息，并以饮食方式衡量社区的健康。本文介绍了一个具有将肠道 SCFAs 量与肠道微生物组功能能力相关联的生物传感器的原型，以及参与 SCHub 的用例场景。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Smart+Cities+as+Hubs:+a+use+case+from+Biotechnology)|0| |[Do bridges dream of water pollutants? Towards DreamsKG, a knowledge graph to make digital access for sustainable environmental assessment come true](https://doi.org/10.1145/3543873.3587590)|Darío Garigliotti, Johannes Bjerva, Finn Årup Nielsen, Annika Butzbach, Ivar Lyhne, Lone Kørnøv, Katja Hose|Technical University of Denmark, Denmark; Aalborg University, Denmark; Aalborg University, Denmark and TU Wien, Austria|An environmental assessment (EA) report describes and assesses the environmental impact of a series of activities involved in the development of a project. As such, EA is a key tool for sustainability. Improving information access to EA reporting is a billion-euro untapped business opportunity to build an engaging, efficient digital experience for EA. We aim to become a landmark initiative in making this experience come true, by transforming the traditional manual assessment of numerous heterogeneous reports by experts into a computer-assisted approach. Specifically, a knowledge graph that represents and stores facts about EA practice allows for what it is so far only accessible manually to become machine-readable, and by this, to enable downstream information access services. This paper describes the ongoing process of building DreamsKG, a knowledge graph that stores relevant data- and expert-driven EA reporting and practicing in Denmark. Representation of cause-effect relations in EA and integration of Sustainable Developmental Goals (SDGs) are among its prominent features.|环境评估(EA)报告描述并评估了项目开发过程中一系列活动对环境的影响。因此，EA 是实现可持续性的关键工具。改善对 EA 报告的信息访问是一个价值10亿欧元的未开发商业机会，可以为 EA 建立一个吸引人的、高效的数字体验。我们的目标是通过将专家对大量不同报告的传统手工评估转变为计算机辅助办法，成为实现这一经验的一项具有里程碑意义的举措。具体来说，一个表示和存储关于 EA 实践的事实的知识图允许目前为止只能手动访问的东西变得机器可读，并且通过这种方式使下游信息访问服务成为可能。本文描述了正在进行的构建 DreamsKG 的过程，这是一个存储相关数据和专家驱动的 EA 报告和实践的知识图表。因果关系在可持续发展目标中的体现和可持续发展目标的整合是可持续发展目标的显著特征。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Do+bridges+dream+of+water+pollutants?+Towards+DreamsKG,+a+knowledge+graph+to+make+digital+access+for+sustainable+environmental+assessment+come+true)|0| |[Towards High Resolution Urban Heat Analysis: Incorporating Thermal Drones to Enhance Satellite Based Urban Heatmaps](https://doi.org/10.1145/3543873.3587682)|Bryan Rickens, Navid Hashemi Tonekaboni|Department of Computer Science, College of Charleston, USA|As remote-sensing becomes more actively utilized in the environmental sciences, our research continues the efforts in adapting smart cities by using civilian UAVs and drones for land surface temperature (LST) analysis. Given the increased spatial resolution that this technology provides as compared to standard satellite measurements, we sought to further study the urban heat island (UHI) effect – specifically when it comes to heterogeneous and dynamic landscapes such as the Charleston peninsula. Furthermore, we sought to develop a method to enhance the spatial resolution of publicly available LST temperature data (such as those measured from the Landsat satellites) by building a machine learning model utilizing remote-sensed data from drones. While we found a high correlation and an accurate degree of prediction for areas of open water and vegetation (respectively), our model struggled when it came to areas containing highly impervious surfaces. We believe, however, that these findings further illustrate the discrepancy between high and medium spatial resolutions, and demonstrate how urban environments specifically are prone to inaccurate LST measurements and are uniquely in need of an industry pursuit of higher spatial resolution for hyperlocal environmental sciences and urban analysis.|随着遥感技术在环境科学中的应用越来越活跃，我们的研究继续通过使用民用无人机和无人机进行地表温度(LST)分析来适应智能城市。与标准卫星测量相比，这种技术提供了更高的空间分辨率，因此我们试图进一步研究城市热岛效应——特别是当涉及到查尔斯顿半岛等异质和动态景观时。此外，我们试图开发一种方法，通过利用无人机的遥感数据建立一个机器学习模型，来提高公开可用的 LST 温度数据(例如从 Landsat 卫星测量的数据)的空间分辨率。虽然我们发现了开放水域和植被区域(分别)的高度相关性和准确度，但我们的模型在涉及到高度不透水表面的区域时遇到了困难。然而，我们认为，这些发现进一步说明了高和中等空间分辨率之间的差异，并表明城市环境特别容易出现不准确的 LST 测量，并且特别需要为超地方环境科学和城市分析寻求更高的空间分辨率。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Towards+High+Resolution+Urban+Heat+Analysis:+Incorporating+Thermal+Drones+to+Enhance+Satellite+Based+Urban+Heatmaps)|0| |[Towards a Sustainability Index Calculator for Smart Cities](https://doi.org/10.1145/3543873.3587683)|Ramesh Gorantla, Srividya Bansal|Arizona State University, USA|In this era of rapid urbanization and our endeavors to create more smart cities, it's crucial to keep track of how our society and neighborhood are getting impacted. It is important to make conscious decisions to keep harmony in sustainability. There are multiple frameworks to evaluate how sustainability is measured and to understand how sustainable a place is, be it a city or a region, and one such framework is the Circles of Sustainability. Though these frameworks offer good solutions, it is a challenge to collect relevant data to make the framework widely usable. This paper focuses on this specific issue by utilizing the methodology introduced in the framework and applying it practically to better understand how sustainable our cities and society are. We present a unique web-based application which utilizes publicly accessible data to compute sustainability scores and rank for every city and presents the results in an intelligent and easy to comprehend visual interface. The paper also discusses the technical difficulties associated with creating such an application, including data collection, data processing, data integration, and scoring algorithm. The paper concludes by discussing the needs for such practical solutions for promoting sustainable urban development.|在这个快速城市化的时代，我们努力创造更多的智慧城市，跟踪我们的社会和邻里如何受到影响是至关重要的。做出有意识的决定以保持可持续性中的和谐是很重要的。有多个框架可以评估如何衡量可持续性，并了解一个地方(无论是一个城市还是一个地区)的可持续程度，其中一个框架就是可持续发展循环。尽管这些框架提供了很好的解决方案，但是收集相关数据以使框架广泛可用仍然是一个挑战。本文着眼于这一具体问题，利用框架中介绍的方法，并将其实际应用于更好地理解我们的城市和社会的可持续性。我们提出了一个独特的网络应用程序，利用公开数据来计算每个城市的可持续发展得分和排名，并在一个智能和易于理解的视觉界面上呈现结果。本文还讨论了与创建此类应用程序相关的技术难点，包括数据收集、数据处理、数据集成和评分算法。文章最后讨论了促进城市可持续发展的实际解决方案的需要。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Towards+a+Sustainability+Index+Calculator+for+Smart+Cities)|0| -|[Sustainable Grain Transportation in Ukraine Amidst War Utilizing KNARM and KnowWhereGraph](https://doi.org/10.1145/3543873.3587618)|Yinglun Zhang, Antonina Broyaka, Jude Kastens, Allen M. Featherstone, Cogan Shimizu, Pascal Hitzler, Hande KüçükMcGinty|Wright State University, USA; Kansas State Univerisity, USA; Department of Agricultural Economics, Kansas State Univerisity, USA; University of Kansas, USA; Kansas State University, USA|In this work, we propose a sustainable path-finding application for grain transportation during the ongoing Russian military invasion in Ukraine. This application is to build a suite of algorithms to find possible optimal paths for transporting grain that remains in Ukraine. The application uses the KNowledge Acquisition and Representation Methodology(KNARM) and the KnowWhereGraph to achieve this goal. Currently, we are working towards creating an ontology that will allow for a more effective heuristic approach by incorporating the lessons learned from the KnowWhereGraph. The aim is to enhance the path-finding process and provide more accurate and efficient results. In the future, we will continue exploring and implementing new techniques that can further improve the sustainability of the path-finding applications with a knowledge graph backend for grain transportation through hazardous and adversarial environments. The code is available upon reviewer’s request. It can not be made public due to the sensitive nature of the data.|在这项工作中，我们提出了一个可持续的路径寻找应用粮食运输期间正在进行的俄罗斯军事入侵乌克兰。这个应用程序是建立一套算法，以找到可能的最佳路径运输粮食，仍然在乌克兰。应用程序采用知识获取与表示方法(KNARM)和知识图来实现这一目标。目前，我们正在努力创建一个本体论，通过整合从“知识图表”中吸取的经验教训，可以采用更有效的启发式方法。目的是加强路径寻找过程，并提供更准确和有效的结果。今后，我们将继续探索和实施新技术，以进一步提高路径寻找应用程序的可持续性，为通过危险和敌对环境的粮食运输提供知识图表后端。代码可根据审核人员的要求使用。由于数据的敏感性，它不能公开。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Sustainable+Grain+Transportation+in+Ukraine+Amidst+War+Utilizing+KNARM+and+KnowWhereGraph)|0| +|[Sustainable Grain Transportation in Ukraine Amidst War Utilizing KNARM and KnowWhereGraph](https://doi.org/10.1145/3543873.3587618)|Yinglun Zhang, Antonina Broyaka, Jude Kastens, Allen M. Featherstone, Cogan Shimizu, Pascal Hitzler, Hande KüçükMcGinty|Department of Agricultural Economics, Kansas State Univerisity, USA; Kansas State Univerisity, USA; Wright State University, USA; University of Kansas, USA; Kansas State University, USA|In this work, we propose a sustainable path-finding application for grain transportation during the ongoing Russian military invasion in Ukraine. This application is to build a suite of algorithms to find possible optimal paths for transporting grain that remains in Ukraine. The application uses the KNowledge Acquisition and Representation Methodology(KNARM) and the KnowWhereGraph to achieve this goal. Currently, we are working towards creating an ontology that will allow for a more effective heuristic approach by incorporating the lessons learned from the KnowWhereGraph. The aim is to enhance the path-finding process and provide more accurate and efficient results. In the future, we will continue exploring and implementing new techniques that can further improve the sustainability of the path-finding applications with a knowledge graph backend for grain transportation through hazardous and adversarial environments. The code is available upon reviewer’s request. It can not be made public due to the sensitive nature of the data.|在这项工作中，我们提出了一个可持续的路径寻找应用粮食运输期间正在进行的俄罗斯军事入侵乌克兰。这个应用程序是建立一套算法，以找到可能的最佳路径运输粮食，仍然在乌克兰。应用程序采用知识获取与表示方法(KNARM)和知识图来实现这一目标。目前，我们正在努力创建一个本体论，通过整合从“知识图表”中吸取的经验教训，可以采用更有效的启发式方法。目的是加强路径寻找过程，并提供更准确和有效的结果。今后，我们将继续探索和实施新技术，以进一步提高路径寻找应用程序的可持续性，为通过危险和敌对环境的粮食运输提供知识图表后端。代码可根据审核人员的要求使用。由于数据的敏感性，它不能公开。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Sustainable+Grain+Transportation+in+Ukraine+Amidst+War+Utilizing+KNARM+and+KnowWhereGraph)|0| |[Entity and Event Topic Extraction from Podcast Episode Title and Description Using Entity Linking](https://doi.org/10.1145/3543873.3587648)|Christian Siagian, Amina Shabbeer|Amazon, USA|To improve Amazon Music podcast services and customer engagements, we introduce Entity-Linked Topic Extraction (ELTE) to identify well-known entity and event topics from podcast episodes. An entity can be a person, organization, work-of-art, etc., while an event, such as the Opioid epidemic, occurs at specific point(s) in time. ELTE first extracts key-phrases from episode title and description metadata. It then uses entity linking to canonicalize them against Wikipedia knowledge base (KB), ensuring that the topics exist in the real world. ELTE also models NIL-predictions for entity or event topics that are not in the KB, as well as topics that are not of entity or event type. To test the model, we construct a podcast topic database of 1166 episodes from various categories. Each episode comes with a Wiki-link annotated main topic or NIL-prediction. ELTE produces the best overall Exact Match EM score of .84, with by-far the best EM of .89 among the entity or event type episodes, as well as NIL-predictions for episodes without entity or event main topic (EM score of .86).|为了改善亚马逊音乐播客服务和客户参与，我们引入了实体链接主题提取(ELTE) ，以从播客节目中识别出知名的实体和事件主题。一个实体可以是一个人、一个组织、一件艺术品等，而一个事件，如阿片类药物流行病，发生在特定的时间点。ELTE 首先从剧集标题和描述元数据中提取关键词。然后使用实体链接对 Wikipedia 知识库(KB)进行规范化，确保主题存在于现实世界中。ELTE 还为不在 KB 中的实体或事件主题以及不属于实体或事件类型的主题建模 NIL 预测。为了测试这个模型，我们构建了一个来自不同类别的1166集播客主题数据库。每集都有一个带有 Wiki 链接注释的主题或 NIL 预测。ELTE 产生最好的整体精确匹配 EM 评分为.84，在实体或事件类型事件中，迄今为止最好的 EM 为.89，以及对没有实体或事件主题的事件的 NIL 预测(EM 评分为.86)。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Entity+and+Event+Topic+Extraction+from+Podcast+Episode+Title+and+Description+Using+Entity+Linking)|0| -|[NASA Science Mission Directorate Knowledge Graph Discovery](https://doi.org/10.1145/3543873.3587585)|Roelien C. Timmer, Megan Mark, Fech Scen Khoo, Marcella Scoczynski Ribeiro Martins, Anamaria Berea, Gregory Renard, Kaylin M. Bugbee|; Federal University of Technology, Brazil; NASA Marshall Space Flight Center, USA; The University of New South Wales, Australia; The Applied AI Company (AAICO), USA; George Mason University, USA|The size of the National Aeronautics and Space Administration (NASA) Science Mission Directorate (SMD) is growing exponentially, allowing researchers to make discoveries. However, making discoveries is challenging and time-consuming due to the size of the data catalogs, and as many concepts and data are indirectly connected. This paper proposes a pipeline to generate knowledge graphs (KGs) representing different NASA SMD domains. These KGs can be used as the basis for dataset search engines, saving researchers time and supporting them in finding new connections. We collected textual data and used several modern natural language processing (NLP) methods to create the nodes and the edges of the KGs. We explore the cross-domain connections, discuss our challenges, and provide future directions to inspire researchers working on similar challenges.|美国国家航空航天局美国国家航空暨太空总署科学任务理事会的规模正以指数级增长，使得研究人员得以有所发现。然而，由于数据目录的大小以及许多概念和数据之间的间接联系，进行发现是具有挑战性和耗时的。提出了一种流水线生成代表不同 NASA SMD 领域的知识图(KGs)的方法。这些 KG 可以作为数据集搜索引擎的基础，节省研究人员的时间，并支持他们发现新的连接。我们收集文本数据，并使用几种现代自然语言处理(NLP)方法来创建幼儿园的节点和边缘。我们探索跨领域的联系，讨论我们的挑战，并提供未来的方向，以激励研究人员致力于类似的挑战。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=NASA+Science+Mission+Directorate+Knowledge+Graph+Discovery)|0| +|[NASA Science Mission Directorate Knowledge Graph Discovery](https://doi.org/10.1145/3543873.3587585)|Roelien C. Timmer, Megan Mark, Fech Scen Khoo, Marcella Scoczynski Ribeiro Martins, Anamaria Berea, Gregory Renard, Kaylin M. Bugbee|; The Applied AI Company (AAICO), USA; The University of New South Wales, Australia; NASA Marshall Space Flight Center, USA; Federal University of Technology, Brazil; George Mason University, USA|The size of the National Aeronautics and Space Administration (NASA) Science Mission Directorate (SMD) is growing exponentially, allowing researchers to make discoveries. However, making discoveries is challenging and time-consuming due to the size of the data catalogs, and as many concepts and data are indirectly connected. This paper proposes a pipeline to generate knowledge graphs (KGs) representing different NASA SMD domains. These KGs can be used as the basis for dataset search engines, saving researchers time and supporting them in finding new connections. We collected textual data and used several modern natural language processing (NLP) methods to create the nodes and the edges of the KGs. We explore the cross-domain connections, discuss our challenges, and provide future directions to inspire researchers working on similar challenges.|美国国家航空航天局美国国家航空暨太空总署科学任务理事会的规模正以指数级增长，使得研究人员得以有所发现。然而，由于数据目录的大小以及许多概念和数据之间的间接联系，进行发现是具有挑战性和耗时的。提出了一种流水线生成代表不同 NASA SMD 领域的知识图(KGs)的方法。这些 KG 可以作为数据集搜索引擎的基础，节省研究人员的时间，并支持他们发现新的连接。我们收集文本数据，并使用几种现代自然语言处理(NLP)方法来创建幼儿园的节点和边缘。我们探索跨领域的联系，讨论我们的挑战，并提供未来的方向，以激励研究人员致力于类似的挑战。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=NASA+Science+Mission+Directorate+Knowledge+Graph+Discovery)|0| |[Scientific Data Extraction from Oceanographic Papers](https://doi.org/10.1145/3543873.3587595)|Bartal Eyðfinsson Veyhe, Tomer Sagi, Katja Hose|Computer Science, Aalborg Universitet, Denmark; Computer Science, Aalborg University, Denmark; Aalborg University, Denmark and TU Wien, Austria|Scientific data collected in the oceanographic domain is invaluable to researchers when performing meta-analyses and examining changes over time in oceanic environments. However, many of the data samples and subsequent analyses published by researchers are not uploaded to a repository leaving the scientific paper as the only available source. Automated extraction of scientific data is, therefore, a valuable tool for such researchers. Specifically, much of the most valuable data in scientific papers are structured as tables, making these a prime target for information extraction research. Using the data relies on an additional step where the concepts mentioned in the tables, such as names of measures, units, and biological species, are identified within a domain ontology. Unfortunately, state-of-the-art table extraction leaves much to be desired and has not been attempted on a large scale on oceanographic papers. Furthermore, while entity linking in the context of a full paragraph of text has been heavily researched, it is still lacking in this harder task of linking single concepts. In this work, we present an annotated benchmark dataset of data tables from oceanographic papers. We further present the result of an evaluation on the extraction of these tables and the linking of the contained entities to the domain and general-purpose knowledge bases using the current state of the art. We highlight the challenges and quantify the performance of current tools for table extraction and table-concept linking.|在海洋学领域收集的科学数据对于研究人员进行综合分析和研究海洋环境随时间的变化是非常宝贵的。然而，研究人员发表的许多数据样本和随后的分析没有上传到存储库中，使科学论文成为唯一可用的来源。因此，科学数据的自动提取对这些研究人员来说是一个有价值的工具。具体来说，科学论文中许多最有价值的数据都是以表格的形式结构的，这使得这些数据成为信息抽取研究的首要目标。使用数据依赖于一个额外的步骤，在这个步骤中，表中提到的概念，例如度量值、单元和生物物种的名称，在一个领域本体中被识别出来。不幸的是，目前最先进的表格提取技术还有很多不足之处，在海洋学论文中也没有大规模地进行过尝试。此外，虽然对整段文字背景下的实体链接进行了大量研究，但在连接单个概念这一更艰巨的任务方面仍然缺乏研究。在这项工作中，我们提出了一个注释的基准数据集的数据表从海洋学论文。我们进一步介绍了使用当前技术状态对这些表的提取以及所包含的实体与领域和通用知识库的链接进行评估的结果。我们强调了挑战，并量化了当前表提取和表概念链接工具的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Scientific+Data+Extraction+from+Oceanographic+Papers)|0| -|[Cross-Team Collaboration and Diversity in the Bridge2AI Project](https://doi.org/10.1145/3543873.3587579)|Huimin Xu, Chitrank Gupta, Zhandos Sembay, Swathi Thaker, Pamela PayneFoster, Jake Chen, Ying Ding|University of Alabama at Birmingham, USA; University of Alabama, USA; The University of Texas at Austin, USA|The Bridge2AI project, funded by the National Institutes of Health, involves researchers from different disciplines and backgrounds to develop well-curated AI health data and tools. Understanding cross-disciplinary and cross-organizational collaboration at the individual, team, and project levels is critical. In this paper, we matched Bridge2AI team members to the PubMed Knowledge dataset to get their health-related publications. We built the collaboration network for Bridge2AI members and all of their collaborators and sorted out researchers with the largest degree of centrality and betweenness centrality. Our finding suggests that Bridge2AI members need to strengthen internal collaborations and boost mutual understanding in this project. We also applied machine learning methods to cluster all the researchers and labeled publication topics in different clusters. Finally, by identifying the gender/racial diversity of researchers, we found that teams with higher racial diversity receive more citations, and individuals with diverse gender collaborators publish more papers.|由国立卫生研究院资助的 Bridge2AI 项目，让来自不同学科和背景的研究人员参与开发精心策划的人工智能健康数据和工具。理解个人、团队和项目级别的跨学科和跨组织协作是至关重要的。在本文中，我们将 Bridge2AI 团队成员与 PubMed 知识数据集进行匹配，以获得他们与健康相关的出版物。我们为 Bridge2AI 成员及其所有合作者建立了协作网络，并以最大程度的中心性和中间中心性对研究人员进行了分类。我们的研究结果表明，Bridge2AI 成员需要加强内部协作，促进相互了解，在这个项目。我们还应用机器学习方法对所有的研究者进行聚类，并将发表的主题标记在不同的聚类中。最后，通过识别研究人员的性别/种族多样性，我们发现种族多样性较高的团队获得了更多的引用，而性别合作者不同的个人发表了更多的论文。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Cross-Team+Collaboration+and+Diversity+in+the+Bridge2AI+Project)|0| -|[A New Annotation Method and Dataset for Layout Analysis of Long Documents](https://doi.org/10.1145/3543873.3587609)|Aman Ahuja, Kevin Dinh, Brian Dinh, William A. Ingram, Edward A. Fox|Virginia Tech, USA; Department of Computer Science, Virginia Tech, USA|Parsing long documents, such as books, theses, and dissertations, is an important component of information extraction from scholarly documents. Layout analysis methods based on object detection have been developed in recent years to help with PDF document parsing. However, several challenges hinder the adoption of such methods for scholarly documents such as theses and dissertations. These include (a) the manual effort and resources required to annotate training datasets, (b) the scanned nature of many documents and the inherent noise present resulting from the capture process, and (c) the imbalanced distribution of various types of elements in the documents. In this paper, we address some of the challenges related to object detection based layout analysis for scholarly long documents. First, we propose an AI-aided annotation method to help develop training datasets for object detection based layout analysis. This leverages the knowledge of existing trained models to help human annotators, thus reducing the time required for annotation. It also addresses the class imbalance problem, guiding annotators to focus on labeling instances of rare classes. We also introduce ETD-ODv2, a novel dataset for object detection on electronic theses and dissertations (ETDs). In addition to the page images included in ETD-OD [1], our dataset consists of more than 16K manually annotated page images originating from 100 scanned ETDs, along with annotations for 20K page images primarily consisting of rare classes that were labeled using the proposed framework. The new dataset thus covers a diversity of document types, viz., scanned and born-digital, and is better balanced in terms of training samples from different object categories.|解析长篇文献，例如书籍、论文和论文，是学术文献信息抽取的一个重要组成部分。基于目标检测的布局分析方法近年来得到了发展，以帮助解析 PDF 文档。然而，一些挑战阻碍采用这种方法的学术文献，如论文和论文。这些因素包括: (a)对训练数据集进行注释所需的人工努力和资源; (b)许多文档的扫描性质以及捕获过程产生的固有噪音; (c)文档中各类元素的分布不平衡。在本文中，我们将讨论一些与基于目标检测的学术长文档布局分析相关的挑战。首先，我们提出了一个人工智能辅助注释方法，以帮助开发基于目标检测的布局分析的训练数据集。这将利用现有训练有素的模型的知识来帮助人工注释者，从而减少注释所需的时间。它还解决了类不平衡的问题，指导注释器关注稀有类的标记实例。我们还介绍了一个新的电子学位论文目标检测数据集(eTD-ODv2)。除了 ETD-OD [1]中包含的页面图像之外，我们的数据集由来自100个扫描的 ETD 的超过16K 的手动注释页面图像以及主要由使用所提出的框架标记的罕见类组成的20K 页面图像的注释组成。因此，新的数据集涵盖了文档类型的多样性，即扫描和数字化，并且在不同对象类别的训练样本方面更好地平衡。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+New+Annotation+Method+and+Dataset+for+Layout+Analysis+of+Long+Documents)|0| -|[Towards InnoGraph: A Knowledge Graph for AI Innovation](https://doi.org/10.1145/3543873.3587614)|M. Besher Massri, Blerina Spahiu, Marko Grobelnik, Vladimir Alexiev, Matteo Palmonari, Dumitru Roman|University of Milano-Bicocca, Italy; SINTEF AS, Norway; Department of Artificial Intelligence, Jozef Stefan Institute, Slovenia and Jozef Stefan International Postgraduate School, Slovenia; Department of Artificial Intelligence, Jozef Stefan Institute, Slovenia; Ontotext (Sirma AI), Bulgaria|Researchers seeking to comprehend the state-of-the-art innovations in a particular field of study must examine recent patents and scientific articles in that domain. Innovation ecosystems consist of interconnected information about entities such as researchers, institutions, projects, products, and technologies. However, representing such information in a machine-readable format is challenging because concepts like "knowledge" are not easily represented. Nonetheless, even a partial representation of innovation ecosystems provides valuable insights. Therefore, representing innovation ecosystems as knowledge graphs (KGs) would enable advanced data analysis and generate new insights. To this end, we propose InnoGraph, a framework that integrates multiple heterogeneous data sources to build a Knowledge Graph of the worldwide AI innovation ecosystem.|寻求理解某一特定研究领域最先进创新的研究人员必须审查该领域的最新专利和科学论文。创新生态系统由关于研究人员、机构、项目、产品和技术等实体的相互关联的信息组成。然而，用机器可读的格式表示这些信息是具有挑战性的，因为像“知识”这样的概念不容易表示。尽管如此，即使是对创新生态系统的部分描述也提供了有价值的见解。因此，将创新生态系统表示为知识图表(KGs)将能够进行高级数据分析并产生新的见解。为此，我们提出了 InnoGraph 框架，该框架集成了多个异构数据源，构建了全球人工智能创新生态系统的知识图。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Towards+InnoGraph:+A+Knowledge+Graph+for+AI+Innovation)|0| +|[Cross-Team Collaboration and Diversity in the Bridge2AI Project](https://doi.org/10.1145/3543873.3587579)|Huimin Xu, Chitrank Gupta, Zhandos Sembay, Swathi Thaker, Pamela PayneFoster, Jake Chen, Ying Ding|University of Alabama, USA; University of Alabama at Birmingham, USA; The University of Texas at Austin, USA|The Bridge2AI project, funded by the National Institutes of Health, involves researchers from different disciplines and backgrounds to develop well-curated AI health data and tools. Understanding cross-disciplinary and cross-organizational collaboration at the individual, team, and project levels is critical. In this paper, we matched Bridge2AI team members to the PubMed Knowledge dataset to get their health-related publications. We built the collaboration network for Bridge2AI members and all of their collaborators and sorted out researchers with the largest degree of centrality and betweenness centrality. Our finding suggests that Bridge2AI members need to strengthen internal collaborations and boost mutual understanding in this project. We also applied machine learning methods to cluster all the researchers and labeled publication topics in different clusters. Finally, by identifying the gender/racial diversity of researchers, we found that teams with higher racial diversity receive more citations, and individuals with diverse gender collaborators publish more papers.|由国立卫生研究院资助的 Bridge2AI 项目，让来自不同学科和背景的研究人员参与开发精心策划的人工智能健康数据和工具。理解个人、团队和项目级别的跨学科和跨组织协作是至关重要的。在本文中，我们将 Bridge2AI 团队成员与 PubMed 知识数据集进行匹配，以获得他们与健康相关的出版物。我们为 Bridge2AI 成员及其所有合作者建立了协作网络，并以最大程度的中心性和中间中心性对研究人员进行了分类。我们的研究结果表明，Bridge2AI 成员需要加强内部协作，促进相互了解，在这个项目。我们还应用机器学习方法对所有的研究者进行聚类，并将发表的主题标记在不同的聚类中。最后，通过识别研究人员的性别/种族多样性，我们发现种族多样性较高的团队获得了更多的引用，而性别合作者不同的个人发表了更多的论文。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Cross-Team+Collaboration+and+Diversity+in+the+Bridge2AI+Project)|0| +|[A New Annotation Method and Dataset for Layout Analysis of Long Documents](https://doi.org/10.1145/3543873.3587609)|Aman Ahuja, Kevin Dinh, Brian Dinh, William A. Ingram, Edward A. Fox|Department of Computer Science, Virginia Tech, USA; Virginia Tech, USA|Parsing long documents, such as books, theses, and dissertations, is an important component of information extraction from scholarly documents. Layout analysis methods based on object detection have been developed in recent years to help with PDF document parsing. However, several challenges hinder the adoption of such methods for scholarly documents such as theses and dissertations. These include (a) the manual effort and resources required to annotate training datasets, (b) the scanned nature of many documents and the inherent noise present resulting from the capture process, and (c) the imbalanced distribution of various types of elements in the documents. In this paper, we address some of the challenges related to object detection based layout analysis for scholarly long documents. First, we propose an AI-aided annotation method to help develop training datasets for object detection based layout analysis. This leverages the knowledge of existing trained models to help human annotators, thus reducing the time required for annotation. It also addresses the class imbalance problem, guiding annotators to focus on labeling instances of rare classes. We also introduce ETD-ODv2, a novel dataset for object detection on electronic theses and dissertations (ETDs). In addition to the page images included in ETD-OD [1], our dataset consists of more than 16K manually annotated page images originating from 100 scanned ETDs, along with annotations for 20K page images primarily consisting of rare classes that were labeled using the proposed framework. The new dataset thus covers a diversity of document types, viz., scanned and born-digital, and is better balanced in terms of training samples from different object categories.|解析长篇文献，例如书籍、论文和论文，是学术文献信息抽取的一个重要组成部分。基于目标检测的布局分析方法近年来得到了发展，以帮助解析 PDF 文档。然而，一些挑战阻碍采用这种方法的学术文献，如论文和论文。这些因素包括: (a)对训练数据集进行注释所需的人工努力和资源; (b)许多文档的扫描性质以及捕获过程产生的固有噪音; (c)文档中各类元素的分布不平衡。在本文中，我们将讨论一些与基于目标检测的学术长文档布局分析相关的挑战。首先，我们提出了一个人工智能辅助注释方法，以帮助开发基于目标检测的布局分析的训练数据集。这将利用现有训练有素的模型的知识来帮助人工注释者，从而减少注释所需的时间。它还解决了类不平衡的问题，指导注释器关注稀有类的标记实例。我们还介绍了一个新的电子学位论文目标检测数据集(eTD-ODv2)。除了 ETD-OD [1]中包含的页面图像之外，我们的数据集由来自100个扫描的 ETD 的超过16K 的手动注释页面图像以及主要由使用所提出的框架标记的罕见类组成的20K 页面图像的注释组成。因此，新的数据集涵盖了文档类型的多样性，即扫描和数字化，并且在不同对象类别的训练样本方面更好地平衡。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+New+Annotation+Method+and+Dataset+for+Layout+Analysis+of+Long+Documents)|0| +|[Towards InnoGraph: A Knowledge Graph for AI Innovation](https://doi.org/10.1145/3543873.3587614)|M. Besher Massri, Blerina Spahiu, Marko Grobelnik, Vladimir Alexiev, Matteo Palmonari, Dumitru Roman|University of Milano-Bicocca, Italy; Ontotext (Sirma AI), Bulgaria; SINTEF AS, Norway; Department of Artificial Intelligence, Jozef Stefan Institute, Slovenia and Jozef Stefan International Postgraduate School, Slovenia; Department of Artificial Intelligence, Jozef Stefan Institute, Slovenia|Researchers seeking to comprehend the state-of-the-art innovations in a particular field of study must examine recent patents and scientific articles in that domain. Innovation ecosystems consist of interconnected information about entities such as researchers, institutions, projects, products, and technologies. However, representing such information in a machine-readable format is challenging because concepts like "knowledge" are not easily represented. Nonetheless, even a partial representation of innovation ecosystems provides valuable insights. Therefore, representing innovation ecosystems as knowledge graphs (KGs) would enable advanced data analysis and generate new insights. To this end, we propose InnoGraph, a framework that integrates multiple heterogeneous data sources to build a Knowledge Graph of the worldwide AI innovation ecosystem.|寻求理解某一特定研究领域最先进创新的研究人员必须审查该领域的最新专利和科学论文。创新生态系统由关于研究人员、机构、项目、产品和技术等实体的相互关联的信息组成。然而，用机器可读的格式表示这些信息是具有挑战性的，因为像“知识”这样的概念不容易表示。尽管如此，即使是对创新生态系统的部分描述也提供了有价值的见解。因此，将创新生态系统表示为知识图表(KGs)将能够进行高级数据分析并产生新的见解。为此，我们提出了 InnoGraph 框架，该框架集成了多个异构数据源，构建了全球人工智能创新生态系统的知识图。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Towards+InnoGraph:+A+Knowledge+Graph+for+AI+Innovation)|0| |[Assessing Scientific Contributions in Data Sharing Spaces](https://doi.org/10.1145/3543873.3587608)|Kacy Adams, Fernando Spadea, Conor Flynn, Oshani Seneviratne||In the present academic landscape, the process of collecting data is slow, and the lax infrastructures for data collaborations lead to significant delays in coming up with and disseminating conclusive findings. Therefore, there is an increasing need for a secure, scalable, and trustworthy data-sharing ecosystem that promotes and rewards collaborative data-sharing efforts among researchers, and a robust incentive mechanism is required to achieve this objective. Reputation-based incentives, such as the h-index, have historically played a pivotal role in the academic community. However, the h-index suffers from several limitations. This paper introduces the SCIENCE-index, a blockchain-based metric measuring a researcher's scientific contributions. Utilizing the Microsoft Academic Graph and machine learning techniques, the SCIENCE-index predicts the progress made by a researcher over their career and provides a soft incentive for sharing their datasets with peer researchers. To incentivize researchers to share their data, the SCIENCE-index is augmented to include a data-sharing parameter. DataCite, a database of openly available datasets, proxies this parameter, which is further enhanced by including a researcher's data-sharing activity. Our model is evaluated by comparing the distribution of its output for geographically diverse researchers to that of the h-index. We observe that it results in a much more even spread of evaluations. The SCIENCE-index is a crucial component in constructing a decentralized protocol that promotes trust-based data sharing, addressing the current inequity in dataset sharing. The work outlined in this paper provides the foundation for assessing scientific contributions in future data-sharing spaces powered by decentralized applications.|在目前的学术环境中，收集数据的进程缓慢，数据合作基础设施松懈，导致在提出和传播结论性结论方面出现重大延误。因此，越来越需要一个安全、可扩展和可信赖的数据共享生态系统，以促进和奖励研究人员之间的协作数据共享努力，并需要一个强有力的激励机制来实现这一目标。基于声誉的激励机制，如 h 指数，历来在学术界发挥着关键作用。然而，h 索引存在一些局限性。本文介绍了科学指数，一个基于区块链的度量衡量研究人员的科学贡献。利用微软学术图表和机器学习技术，科学指数预测研究人员在其职业生涯中取得的进展，并为与同行研究人员共享数据集提供软激励。为了激励研究人员共享他们的数据，科学索引被扩大到包括一个数据共享参数。DataCite 是一个公开可用数据集的数据库，它代理这个参数，通过包含研究人员的数据共享活动进一步增强了这个参数。我们的模型是通过比较不同地理区域的研究人员的产出分布和 h 指数的分布来评估的。我们注意到，它导致评价的分布更加均匀。科学索引是构建分散协议的重要组成部分，该协议促进了基于信任的数据共享，解决了当前数据集共享中的不公平问题。本文概述的工作为评估未来由分散应用驱动的数据共享空间的科学贡献提供了基础。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Assessing+Scientific+Contributions+in+Data+Sharing+Spaces)|0| -|[Promoting Inactive Members in Edge-Building Marketplace](https://doi.org/10.1145/3543873.3587647)|Ayan Acharya, Siyuan Gao, Borja Ocejo, Kinjal Basu, Ankan Saha, Sathiya Keerthi Selvaraj, Rahul Mazumder, Parag Agrawal, Aman Gupta|LinkedIn Corporation, USA; LinkedIn Corporation, USA and Massachusetts Institute of Technology, USA; linkedin corporation, USA; LinkedIn Inc., USA; LinkedIn, USA|Social networks are platforms where content creators and consumers share and consume content. The edge recommendation system, which determines who a member should connect with, significantly impacts the reach and engagement of the audience on such networks. This paper emphasizes improving the experience of inactive members (IMs) who do not have a large connection network by recommending better connections. To that end, we propose a multi-objective linear optimization framework and solve it using accelerated gradient descent. We report our findings regarding key business metrics related to user engagement on LinkedIn, a professional network with over 850 million members.|社交网络是内容创作者和消费者共享和消费内容的平台。边缘推荐系统决定了一个成员应该与谁联系，它显著地影响了受众在这些网络上的接触范围和参与程度。本文强调通过推荐更好的连接来改善那些没有大型连接网络的非活动成员(IM)的体验。为此，我们提出了一个多目标线性优化框架，并使用加速梯度下降法进行求解。我们报告关于 LinkedIn 用户参与度的关键商业指标的调查结果，LinkedIn 是一个拥有超过8.5亿会员的专业网络。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Promoting+Inactive+Members+in+Edge-Building+Marketplace)|0| -|[CLIME: Completeness-Constrained LIME](https://doi.org/10.1145/3543873.3587652)|Claudia V. Roberts, Ehtsham Elahi, Ashok Chandrashekar|Princeton University, USA; Netflix, Inc., USA; WarnerMedia, USA|We evaluate two popular local explainability techniques, LIME and SHAP, on a movie recommendation task. We discover that the two methods behave very differently depending on the sparsity of the data set, where sparsity is defined by the amount of historical viewing data available to explain a movie recommendation for a particular data instance. We find that LIME does better than SHAP in dense segments of the data set and SHAP does better in sparse segments. We trace this difference to the differing bias-variance characteristics of the underlying estimators of LIME and SHAP. We find that SHAP exhibits lower variance in sparse segments of the data compared to LIME. We attribute this lower variance to the completeness constraint property inherent in SHAP and missing in LIME. This constraint acts as a regularizer and therefore increases the bias of the SHAP estimator but decreases its variance, leading to a favorable bias-variance trade-off especially in high sparsity data settings. With this insight, we introduce the same constraint into LIME and formulate a novel local explainabilty framework called Completeness-Constrained LIME (CLIME) that is superior to LIME and much faster than SHAP.|在一个电影推荐任务中，我们评估了两种流行的局部可解释性技术，LIME 和 SHAP。我们发现，这两种方法的行为非常不同，这取决于数据集的稀疏性，其中稀疏性是由可用于解释特定数据实例的电影推荐的历史观看数据量来定义的。我们发现 LIME 在数据集的密集段中比 SHAP 做得更好，而 SHAP 在稀疏段中做得更好。我们将这种差异追溯到 LIME 和 SHAP 基本估计量的不同偏差-方差特征。我们发现，与 LIME 相比，SHAP 在数据的稀疏片段中表现出较低的方差。我们将这种较低的方差归因于 SHAP 中固有的完备性约束属性和 LIME 中的缺失。这种约束作为一个正则化器，因此增加了 SHAP 估计器的偏差，但减少其方差，导致一个有利的偏差-方差权衡，特别是在高稀疏数据设置。基于这种认识，我们将同样的约束引入到 LIME 中，并且提出了一种新的局部解释框架，称为完整性约束 LIME (完整性约束 LIME) ，它优于 LIME，并且比 SHAP 快得多。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=CLIME:+Completeness-Constrained+LIME)|0| +|[Promoting Inactive Members in Edge-Building Marketplace](https://doi.org/10.1145/3543873.3587647)|Ayan Acharya, Siyuan Gao, Borja Ocejo, Kinjal Basu, Ankan Saha, Sathiya Keerthi Selvaraj, Rahul Mazumder, Parag Agrawal, Aman Gupta|LinkedIn Corporation, USA; LinkedIn, USA; LinkedIn Corporation, USA and Massachusetts Institute of Technology, USA; linkedin corporation, USA; LinkedIn Inc., USA|Social networks are platforms where content creators and consumers share and consume content. The edge recommendation system, which determines who a member should connect with, significantly impacts the reach and engagement of the audience on such networks. This paper emphasizes improving the experience of inactive members (IMs) who do not have a large connection network by recommending better connections. To that end, we propose a multi-objective linear optimization framework and solve it using accelerated gradient descent. We report our findings regarding key business metrics related to user engagement on LinkedIn, a professional network with over 850 million members.|社交网络是内容创作者和消费者共享和消费内容的平台。边缘推荐系统决定了一个成员应该与谁联系，它显著地影响了受众在这些网络上的接触范围和参与程度。本文强调通过推荐更好的连接来改善那些没有大型连接网络的非活动成员(IM)的体验。为此，我们提出了一个多目标线性优化框架，并使用加速梯度下降法进行求解。我们报告关于 LinkedIn 用户参与度的关键商业指标的调查结果，LinkedIn 是一个拥有超过8.5亿会员的专业网络。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Promoting+Inactive+Members+in+Edge-Building+Marketplace)|0| +|[CLIME: Completeness-Constrained LIME](https://doi.org/10.1145/3543873.3587652)|Claudia V. Roberts, Ehtsham Elahi, Ashok Chandrashekar|Princeton University, USA; WarnerMedia, USA; Netflix, Inc., USA|We evaluate two popular local explainability techniques, LIME and SHAP, on a movie recommendation task. We discover that the two methods behave very differently depending on the sparsity of the data set, where sparsity is defined by the amount of historical viewing data available to explain a movie recommendation for a particular data instance. We find that LIME does better than SHAP in dense segments of the data set and SHAP does better in sparse segments. We trace this difference to the differing bias-variance characteristics of the underlying estimators of LIME and SHAP. We find that SHAP exhibits lower variance in sparse segments of the data compared to LIME. We attribute this lower variance to the completeness constraint property inherent in SHAP and missing in LIME. This constraint acts as a regularizer and therefore increases the bias of the SHAP estimator but decreases its variance, leading to a favorable bias-variance trade-off especially in high sparsity data settings. With this insight, we introduce the same constraint into LIME and formulate a novel local explainabilty framework called Completeness-Constrained LIME (CLIME) that is superior to LIME and much faster than SHAP.|在一个电影推荐任务中，我们评估了两种流行的局部可解释性技术，LIME 和 SHAP。我们发现，这两种方法的行为非常不同，这取决于数据集的稀疏性，其中稀疏性是由可用于解释特定数据实例的电影推荐的历史观看数据量来定义的。我们发现 LIME 在数据集的密集段中比 SHAP 做得更好，而 SHAP 在稀疏段中做得更好。我们将这种差异追溯到 LIME 和 SHAP 基本估计量的不同偏差-方差特征。我们发现，与 LIME 相比，SHAP 在数据的稀疏片段中表现出较低的方差。我们将这种较低的方差归因于 SHAP 中固有的完备性约束属性和 LIME 中的缺失。这种约束作为一个正则化器，因此增加了 SHAP 估计器的偏差，但减少其方差，导致一个有利的偏差-方差权衡，特别是在高稀疏数据设置。基于这种认识，我们将同样的约束引入到 LIME 中，并且提出了一种新的局部解释框架，称为完整性约束 LIME (完整性约束 LIME) ，它优于 LIME，并且比 SHAP 快得多。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=CLIME:+Completeness-Constrained+LIME)|0| |[DeepPvisit: A Deep Survival Model for Notification Management](https://doi.org/10.1145/3543873.3587666)|Guangyu Yang, Efrem Ghebreab, Jiaxi Xu, Xianen Qiu, Yiping Yuan, Wensheng Sun|LinkedIn Corporation, USA|Notification is a core feature of mobile applications. They inform users about a variety of events happening in the communities. Users may take immediate action to visit the app or ignore the notifications depending on the timing and the relevance of a notification to the user. In this paper, we present the design, implementation, and evaluation of DeepPvisit, a novel probabilistic deep learning survival method for modeling interactions between a user visit and a mobile notification decision, targeting notification volume and delivery time optimization, and driving long-term user engagements. Offline evaluations and online A/B test experiments show DeepPvisit outperforms the existing survival regression model and the other baseline models and delivers better business metrics online.|通知是移动应用程序的一个核心特性。它们向用户通报社区中发生的各种事件。用户可以立即采取行动访问应用程序或忽略通知，这取决于时间和相关性的通知给用户。在本文中，我们提出了一种新的概率深度学习生存方法 DeepPview 的设计、实现和评估，该方法用于建模用户访问和移动通知决策之间的交互，针对通知量和交付时间的优化，以及驱动长期用户参与。离线评估和在线 A/B 测试实验表明，DeepPview 的表现优于现有的生存回归模型和其他基线模型，并且在线提供了更好的业务指标。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=DeepPvisit:+A+Deep+Survival+Model+for+Notification+Management)|0| |[Digital Twins for Radiation Oncology](https://doi.org/10.1145/3543873.3587688)|James Jensen, Jun Deng|Department of Therapeutic Radiology, Yale University, USA|Digital twin technology has revolutionized the state-of-the-art practice in many industries, and digital twins have a natural application to modeling cancer patients. By simulating patients at a more fundamental level than conventional machine learning models, digital twins can provide unique insights by predicting each patient's outcome trajectory. This has numerous associated benefits, including patient-specific clinical decision-making support and the potential for large-scale virtual clinical trials. Historically, it has not been feasible to use digital twin technology to model cancer patients because of the large number of variables that impact each patient's outcome trajectory, including genotypic, phenotypic, social, and environmental factors. However, the path to digital twins in radiation oncology is becoming possible due to recent progress, such as multiscale modeling techniques that estimate patient-specific cellular, molecular, and histological distributions, and modern cryptographic techniques that enable secure and efficient centralization of patient data across multiple institutions. With these and other future scientific advances, digital twins for radiation oncology will likely become feasible. This work discusses the likely generalized architecture of patient-specific digital twins and digital twin networks, as well as the benefits, existing barriers, and potential gateways to the application of digital twin technology in radiation oncology.|数字双胞胎技术革新了许多行业最先进的实践，数字双胞胎有一个自然的应用模型癌症患者。通过在比传统机器学习模型更基础的水平上模拟患者，数字双胞胎可以通过预测每个患者的结局轨迹提供独特的见解。这有许多相关的好处，包括针对患者的临床决策支持和大规模虚拟临床试验的潜力。从历史上看，使用数字双胞胎技术来模拟癌症患者是不可行的，因为影响每个患者结果轨迹的变量很多，包括基因型，表型，社会和环境因素。然而，由于最近的进展，例如估计患者特异性细胞，分子和组织学分布的多尺度建模技术以及能够在多个机构中安全有效地集中患者数据的现代密码技术，放射肿瘤学中的数字双胞胎正在成为可能。随着这些以及其他未来的科学进步，用于放射肿瘤学的数字双胞胎很可能变得可行。这项工作讨论了患者特异性数字双胞胎和数字双胞胎网络的可能的广义结构，以及数字双胞胎技术在放射肿瘤学中应用的益处，现有的障碍和潜在的门户。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Digital+Twins+for+Radiation+Oncology)|0| -|[Graph-Based Hierarchical Attention Network for Suicide Risk Detection on Social Media](https://doi.org/10.1145/3543873.3587587)|Usman Naseem, Jinman Kim, Matloob Khushi, Adam G. Dunn|Department of Computer Science, Brunel University, United Kingdom; School of Computer Science, The University of Sydney, Australia; School of Medical Sciences, The University of Sydney, Australia|The widespread use of social media for expressing personal thoughts and emotions makes it a valuable resource for identifying individuals at risk of suicide. Existing sequential learning-based methods have shown promising results. However, these methods may fail to capture global features. Due to its inherent ability to learn interconnected data, graph-based methods can address this gap. In this paper, we present a new graph-based hierarchical attention network (GHAN) that uses a graph convolutional neural network with an ordinal loss to improve suicide risk identification on social media. Specifically, GHAN first captures global features by constructing three graphs to capture semantic, syntactic, and sequential contextual information. Then encoded textual features are fed to attentive transformers’ encoder and optimized to factor in the increasing suicide risk levels using an ordinal classification layer hierarchically for suicide risk detection. Experimental results show that the proposed GHAN outperformed state-of-the-art methods on a public Reddit dataset.|社交媒体在表达个人思想和情感方面的广泛使用，使其成为识别有自杀风险的个人的宝贵资源。现有的基于序列学习的方法已经取得了很好的效果。但是，这些方法可能无法捕获全局特征。由于其固有的学习互连数据的能力，基于图的方法可以解决这一差距。在这篇论文中，我们提出了一个新的基于图形的分层注意网络(GHAN) ，它使用一个带有序数损失的图形卷积神经网络来改善社交媒体上的自杀风险识别。具体来说，GHAN 首先通过构造三个图来捕获语义、语法和顺序上下文信息，从而捕获全局特征。然后将编码后的文本特征提供给注意变压器的编码器，并使用一个分层的有序分类层对自杀风险检测进行优化，以考虑自杀风险增加的因素。实验结果表明，所提出的 GHAN 在公共 Reddit 数据集上的性能优于最先进的方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Graph-Based+Hierarchical+Attention+Network+for+Suicide+Risk+Detection+on+Social+Media)|0| -|[I'm out of breath from laughing! I think? A dataset of COVID-19 Humor and its toxic variants](https://doi.org/10.1145/3543873.3587591)|Neha Reddy Bogireddy, Smriti Suresh, Sunny Rai|University of Pennsylvania, USA; Boston University, USA; George Mason University, USA|Humor is a cognitive construct that predominantly evokes the feeling of mirth. During the COVID-19 pandemic, the situations that arouse out of the pandemic were so incongruous to the world we knew that even factual statements often had a humorous reaction. In this paper, we present a dataset of 2510 samples hand-annotated with labels such as humor style, type, theme, target and stereotypes formed or exploited while creating the humor in addition to 909 memes. Our dataset comprises Reddit posts, comments, Onion news headlines, real news headlines, and tweets. We evaluate the task of humor detection and maladaptive humor detection on state-of-the-art models namely RoBERTa and GPT-3. The finetuned models trained on our dataset show significant gains over zero-shot models including GPT-3 when detecting humor. Even though GPT-3 is good at generating meaningful explanations, we observed that it fails to detect maladaptive humor due to the absence of overt targets and profanities. We believe that the presented dataset will be helpful in designing computational methods for topical humor processing as it provides a unique sample set to study the theory of incongruity in a post-pandemic world. The data is available to research community at https://github.com/smritae01/Covid19_Humor.|幽默是一种主要唤起快乐感觉的认知结构。在2019冠状病毒疾病大流行期间，由大流行引发的情况与世界格格不入，我们知道，即使是事实陈述往往也会引发幽默反应。本文收集了2510个幽默样本，除了909个模因外，还对幽默的风格、类型、主题、目标、刻板印象等进行了手工标注。我们的数据集包括 Reddit 帖子、评论、洋葱新闻标题、真实新闻标题和 tweet。我们在最先进的模型 RoBERTa 和 GPT-3上评估了幽默检测和非适应性幽默检测的任务。在我们的数据集上训练的微调模型显示在检测幽默时比包括 GPT-3在内的零拍模型有显著的提高。尽管 GPT-3在产生有意义的解释方面做得很好，但我们观察到，由于缺乏明显的目标和亵渎，它无法检测到适应不良的幽默。我们相信，本文提供的数据集将有助于设计局部幽默处理的计算方法，因为它提供了一个独特的样本集来研究大流行后的世界中的不一致性理论。这些数据可供研究团体 https://github.com/smritae01/covid19_humor 使用。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=I'm+out+of+breath+from+laughing!+I+think?+A+dataset+of+COVID-19+Humor+and+its+toxic+variants)|0| +|[Graph-Based Hierarchical Attention Network for Suicide Risk Detection on Social Media](https://doi.org/10.1145/3543873.3587587)|Usman Naseem, Jinman Kim, Matloob Khushi, Adam G. Dunn|School of Medical Sciences, The University of Sydney, Australia; School of Computer Science, The University of Sydney, Australia; Department of Computer Science, Brunel University, United Kingdom|The widespread use of social media for expressing personal thoughts and emotions makes it a valuable resource for identifying individuals at risk of suicide. Existing sequential learning-based methods have shown promising results. However, these methods may fail to capture global features. Due to its inherent ability to learn interconnected data, graph-based methods can address this gap. In this paper, we present a new graph-based hierarchical attention network (GHAN) that uses a graph convolutional neural network with an ordinal loss to improve suicide risk identification on social media. Specifically, GHAN first captures global features by constructing three graphs to capture semantic, syntactic, and sequential contextual information. Then encoded textual features are fed to attentive transformers’ encoder and optimized to factor in the increasing suicide risk levels using an ordinal classification layer hierarchically for suicide risk detection. Experimental results show that the proposed GHAN outperformed state-of-the-art methods on a public Reddit dataset.|社交媒体在表达个人思想和情感方面的广泛使用，使其成为识别有自杀风险的个人的宝贵资源。现有的基于序列学习的方法已经取得了很好的效果。但是，这些方法可能无法捕获全局特征。由于其固有的学习互连数据的能力，基于图的方法可以解决这一差距。在这篇论文中，我们提出了一个新的基于图形的分层注意网络(GHAN) ，它使用一个带有序数损失的图形卷积神经网络来改善社交媒体上的自杀风险识别。具体来说，GHAN 首先通过构造三个图来捕获语义、语法和顺序上下文信息，从而捕获全局特征。然后将编码后的文本特征提供给注意变压器的编码器，并使用一个分层的有序分类层对自杀风险检测进行优化，以考虑自杀风险增加的因素。实验结果表明，所提出的 GHAN 在公共 Reddit 数据集上的性能优于最先进的方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Graph-Based+Hierarchical+Attention+Network+for+Suicide+Risk+Detection+on+Social+Media)|0| +|[I'm out of breath from laughing! I think? A dataset of COVID-19 Humor and its toxic variants](https://doi.org/10.1145/3543873.3587591)|Neha Reddy Bogireddy, Smriti Suresh, Sunny Rai|University of Pennsylvania, USA; George Mason University, USA; Boston University, USA|Humor is a cognitive construct that predominantly evokes the feeling of mirth. During the COVID-19 pandemic, the situations that arouse out of the pandemic were so incongruous to the world we knew that even factual statements often had a humorous reaction. In this paper, we present a dataset of 2510 samples hand-annotated with labels such as humor style, type, theme, target and stereotypes formed or exploited while creating the humor in addition to 909 memes. Our dataset comprises Reddit posts, comments, Onion news headlines, real news headlines, and tweets. We evaluate the task of humor detection and maladaptive humor detection on state-of-the-art models namely RoBERTa and GPT-3. The finetuned models trained on our dataset show significant gains over zero-shot models including GPT-3 when detecting humor. Even though GPT-3 is good at generating meaningful explanations, we observed that it fails to detect maladaptive humor due to the absence of overt targets and profanities. We believe that the presented dataset will be helpful in designing computational methods for topical humor processing as it provides a unique sample set to study the theory of incongruity in a post-pandemic world. The data is available to research community at https://github.com/smritae01/Covid19_Humor.|幽默是一种主要唤起快乐感觉的认知结构。在2019冠状病毒疾病大流行期间，由大流行引发的情况与世界格格不入，我们知道，即使是事实陈述往往也会引发幽默反应。本文收集了2510个幽默样本，除了909个模因外，还对幽默的风格、类型、主题、目标、刻板印象等进行了手工标注。我们的数据集包括 Reddit 帖子、评论、洋葱新闻标题、真实新闻标题和 tweet。我们在最先进的模型 RoBERTa 和 GPT-3上评估了幽默检测和非适应性幽默检测的任务。在我们的数据集上训练的微调模型显示在检测幽默时比包括 GPT-3在内的零拍模型有显著的提高。尽管 GPT-3在产生有意义的解释方面做得很好，但我们观察到，由于缺乏明显的目标和亵渎，它无法检测到适应不良的幽默。我们相信，本文提供的数据集将有助于设计局部幽默处理的计算方法，因为它提供了一个独特的样本集来研究大流行后的世界中的不一致性理论。这些数据可供研究团体 https://github.com/smritae01/covid19_humor 使用。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=I'm+out+of+breath+from+laughing!+I+think?+A+dataset+of+COVID-19+Humor+and+its+toxic+variants)|0| |[LLMs to the Moon? Reddit Market Sentiment Analysis with Large Language Models](https://doi.org/10.1145/3543873.3587605)|Xiang Deng, Vasilisa Bashlovkina, Feng Han, Simon Baumgartner, Michael Bendersky|Google, USA; The Ohio State University, USA|Market sentiment analysis on social media content requires knowledge of both financial markets and social media jargon, which makes it a challenging task for human raters. The resulting lack of high-quality labeled data stands in the way of conventional supervised learning methods. In this work, we conduct a case study approaching this problem with semi-supervised learning using a large language model (LLM). We select Reddit as the target social media platform due to its broad coverage of topics and content types. Our pipeline first generates weak financial sentiment labels for Reddit posts with an LLM and then uses that data to train a small model that can be served in production. We find that prompting the LLM to produce Chain-of-Thought summaries and forcing it through several reasoning paths helps generate more stable and accurate labels, while training the student model using a regression loss further improves distillation quality. With only a handful of prompts, the final model performs on par with existing supervised models. Though production applications of our model are limited by ethical considerations, the model’s competitive performance points to the great potential of using LLMs for tasks that otherwise require skill-intensive annotation.|对社交媒体内容的市场情绪分析需要金融市场和社交媒体术语的双重知识，这对评估人员来说是一项具有挑战性的任务。由此导致的高质量标记数据的缺乏阻碍了传统的监督式学习分析方法的发展。在这项工作中，我们进行了一个案例研究来解决这个问题的半监督学习使用大型语言模型(LLM)。我们选择 Reddit 作为目标社交媒体平台，是因为它涵盖了广泛的主题和内容类型。我们的流水线首先为 Reddit 上有 LLM 的帖子生成弱金融情绪标签，然后使用这些数据来训练一个小型模型，这个模型可以在生产中使用。我们发现提示 LLM 生成思想链摘要并强制它通过几种推理路径有助于生成更稳定和准确的标签，同时使用回归损失训练学生模型进一步提高蒸馏质量。由于只有少量的提示，最终模型的表现与现有的监督模型不相上下。尽管我们模型的生产应用受到伦理考虑的限制，但是模型的竞争性表现指出了在需要技能密集型注释的任务中使用 LLM 的巨大潜力。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=LLMs+to+the+Moon?+Reddit+Market+Sentiment+Analysis+with+Large+Language+Models)|0| |[Forecasting COVID-19 Vaccination Rates using Social Media Data](https://doi.org/10.1145/3543873.3587639)|Xintian Li, Aron Culotta|Department of Computer Science, Tulane University, USA|The COVID-19 pandemic has had a profound impact on the global community, and vaccination has been recognized as a crucial intervention. To gain insight into public perceptions of COVID-19 vaccines, survey studies and the analysis of social media platforms have been conducted. However, existing methods lack consideration of individual vaccination intentions or status and the relationship between public perceptions and actual vaccine uptake. To address these limitations, this study proposes a text classification approach to identify tweets indicating a user’s intent or status on vaccination. A comparative analysis between the proportions of tweets from different categories and real-world vaccination data reveals notable alignment, suggesting that tweets may serve as a precursor to actual vaccination status. Further, regression analysis and time series forecasting were performed to explore the potential of tweet data, demonstrating the significance of incorporating tweet data in predicting future vaccination status. Finally, clustering was applied to the tweet sets with positive and negative labels to gain insights into underlying focuses of each stance.|2019冠状病毒疾病大流行对全球社会产生了深远影响，疫苗接种已被认为是一种重要的干预措施。为了深入了解公众对2019冠状病毒疾病疫苗的看法，进行了调查研究和对社交媒体平台的分析。然而，现有的方法缺乏对个人接种意图或状况的考虑，以及公众观念和实际接种疫苗之间的关系。为了解决这些局限性，本研究提出了一种文本分类方法来识别表明用户接种疫苗的意图或状态的 tweet。对来自不同类别的推文比例与真实疫苗接种数据的比较分析显示出显著的一致性，表明推文可能作为实际疫苗接种状态的前兆。此外，还进行了回归分析和时间序列预测，以探索 tweet 数据的潜力，表明将 tweet 数据纳入预测未来疫苗接种状况的重要性。最后，将聚类应用于带有正面和负面标签的 tweet 集合，以获得对每个立场的潜在焦点的洞察。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Forecasting+COVID-19+Vaccination+Rates+using+Social+Media+Data)|0| |[A Cross-Modal Study of Pain Across Communities in the United States](https://doi.org/10.1145/3543873.3587642)|Arnav Aggarwal, Sunny Rai, Salvatore Giorgi, Shreya Havaldar, Garrick Sherman, Juhi Mittal, Sharath Chandra Guntuku|University of Pennsylvania, USA|Pain is one of the most prevalent reasons for seeking medical attention in the United States. Understanding how different communities report and express pain can aid in directing medical efforts and in advancing precision pain management. Using a large-scale self-report survey data set on pain from Gallup (2.5 million surveys) and social media posts from Twitter (1.8 million tweets), we investigate a) if Twitter posts could predict community-level pain and b) how expressions of pain differ across communities in the United States. Beyond observing an improvement of over 9% (in Pearson r) when using Twitter language over demographics to predict community-level pain, our study reveals that the discourse on pain varied significantly across communities in the United States. Evangelical Hubs frequently post about God, lessons from struggle, and prayers when expressing pain, whereas Working Class Country posts about regret and extreme endurance. Academic stresses, injuries, painkillers, and surgeries were the most commonly discussed pain themes in College Towns; Graying America discussed therapy, used emotional language around empathy and anger, and posted about chronic pain treatment; the African American South posted about struggles, patience, and faith when talking about pain. Our study demonstrates the efficacy of using Twitter to predict survey-based self-reports of pain across communities and has implications in aiding community-focused pain management interventions.|在美国，疼痛是寻求医疗救助的最普遍的原因之一。了解不同的社区如何报告和表达疼痛可以帮助指导医疗工作和推进精确的疼痛管理。利用盖洛普(Gallup)的大规模疼痛自我报告调查数据集(250万份调查)和 Twitter 的社交媒体帖子(180万条推文) ，我们调查 a) Twitter 帖子是否可以预测社区层面的疼痛，b)在美国社区中疼痛的表达是如何不同的。除了观察到使用 Twitter 语言比人口统计学预测社区级疼痛的改善超过9% (在 Pearson r)之外，我们的研究显示，在美国社区中关于疼痛的话语差异显着。福音中心频繁发布关于上帝，挣扎的教训，祈祷时表达痛苦，而工人阶级国家发布关于遗憾和极端忍耐力。学术压力、伤害、止痛药和手术是大学城最常讨论的疼痛主题; Graying America 讨论治疗，使用同理心和愤怒的情感语言，发布慢性疼痛治疗;。我们的研究证明了使用 Twitter 来预测基于调查的跨社区疼痛自我报告的有效性，并且在帮助以社区为中心的疼痛管理干预方面具有意义。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Cross-Modal+Study+of+Pain+Across+Communities+in+the+United+States)|0| |[Claim Extraction and Dynamic Stance Detection in COVID-19 Tweets](https://doi.org/10.1145/3543873.3587643)|Noushin Salek Faramarzi, Fateme Hashemi Chaleshtori, Hossein Shirazi, Indrakshi Ray, Ritwik Banerjee|San Diego State University, USA; Stony Brook University, USA; Colorado State University, USA|The information ecosystem today is noisy, and rife with messages that contain a mix of objective claims and subjective remarks or reactions. Any automated system that intends to capture the social, cultural, or political zeitgeist, must be able to analyze the claims as well as the remarks. Due to the deluge of such messages on social media, and their tremendous power to shape our perceptions, there has never been a greater need to automate these analyses, which play a pivotal role in fact-checking, opinion mining, understanding opinion trends, and other such downstream tasks of social consequence. In this noisy ecosystem, not all claims are worth checking for veracity. Such a check-worthy claim, moreover, must be accurately distilled from subjective remarks surrounding it. Finally, and especially for understanding opinion trends, it is important to understand the stance of the remarks or reactions towards that specific claim. To this end, we introduce a COVID-19 Twitter dataset, and present a three-stage process to (i) determine whether a given Tweet is indeed check-worthy, and if so, (ii) which portion of the Tweet ought to be checked for veracity, and finally, (iii) determine the author’s stance towards the claim in that Tweet, thus introducing the novel task of topic-agnostic stance detection.|今天的信息生态系统是嘈杂的，充斥着包含客观主张和主观评论或反应的信息。任何旨在捕捉社会、文化或政治时代精神的自动化系统，必须能够分析索赔和评论。由于社交媒体上这类信息的泛滥，以及它们塑造我们认知的巨大力量，从来没有比现在更需要自动化这些分析，它们在事实核查、意见挖掘、理解意见趋势以及其他社会后果的下游任务中发挥着关键作用。在这个嘈杂的生态系统中，并非所有的声明都值得检查其准确性。此外，这种值得核实的说法必须准确地从围绕它的主观评论中提炼出来。最后，尤其是为了理解舆论趋势，理解评论的立场或对具体主张的反应是很重要的。为此，我们引入了一个2019冠状病毒疾病的 Twitter 数据集，并提出了一个三阶段的过程来(i)确定给定的 Twitter 是否确实值得检查，如果值得检查，(ii)应该检查 Twitter 的哪一部分的真实性，最后，(iii)确定作者对该 Twitter 中的声明的立场，从而引入了主题不可知立场检测的新任务。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Claim+Extraction+and+Dynamic+Stance+Detection+in+COVID-19+Tweets)|0| |[Self-supervised Pre-training and Semi-supervised Learning for Extractive Dialog Summarization](https://doi.org/10.1145/3543873.3587680)|Yingying Zhuang, Jiecheng Song, Narayanan Sadagopan, Anurag Beniwal|Amazon, USA|Language model pre-training has led to state-of-the-art performance in text summarization. While a variety of pre-trained transformer models are available nowadays, they are mostly trained on documents. In this study we introduce self-supervised pre-training to enhance the BERT model’s semantic and structural understanding of dialog texts from social media. We also propose a semi-supervised teacher-student learning framework to address the common issue of limited available labels in summarization datasets. We empirically evaluate our approach on extractive summarization task with the TWEETSUMM corpus, a recently introduced dialog summarization dataset from Twitter customer care conversations and demonstrate that our self-supervised pre-training and semi-supervised teacher-student learning are both beneficial in comparison to other pre-trained models. Additionally, we compare pre-training and teacher-student learning in various low data-resource settings, and find that pre-training outperforms teacher-student learning and the differences between the two are more significant when the available labels are scarce.|语言模型的预先训练使文本摘要的表现达到了最高水平。虽然各种预先训练的变压器模型现在可用，他们大多数是在文件的培训。在本研究中，我们引入自我监督预训练来提高 BERT 模型对社交媒体对话文本的语义和结构理解。我们还提出了一个半监督的师生学习框架，以解决有限的可用标签摘要数据集的共同问题。我们使用 TWEETSUMM 语料库(最近引入的来自 Twitter 客户关心对话的对话摘要数据集)对我们的提取摘要任务的方法进行了经验性评估，并证明我们的自我监督预训练和半监督师生学习与其他预训练模型相比都是有益的。此外，我们比较了在各种低数据资源环境下的预先培训和师生学习，发现预先培训优于师生学习，当可用标签稀缺时，两者之间的差异更为显著。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Self-supervised+Pre-training+and+Semi-supervised+Learning+for+Extractive+Dialog+Summarization)|0| |[Ready, Aim, Snipe! Analysis of Sniper Bots and their Impact on the DeFi Ecosystem](https://doi.org/10.1145/3543873.3587612)|Federico Cernera, Massimo La Morgia, Alessandro Mei, Alberto Maria Mongardini, Francesco Sassi|Sapienza University of Rome, Italy|In the world of cryptocurrencies, public listing of a new token often generates significant hype, in many cases causing its price to skyrocket in a few seconds. In this scenario, timing is crucial to determine the success or failure of an investment opportunity. In this work, we present an in-depth analysis of sniper bots, automated tools designed to buy tokens as soon as they are listed on the market. We leverage GitHub open-source repositories of sniper bots to analyze their features and how they are implemented. Then, we build a dataset of Ethereum and BNB Smart Chain (BSC) liquidity pools to identify addresses that serially take advantage of sniper bots. Our findings reveal 14,029 sniping operations on Ethereum and 1,395,042 in BSC that bought tokens for a total of $10,144,808 dollars and $18,720,447, respectively. We find that Ethereum operations have a higher success rate but require a larger investment. Finally, we analyze token smart contracts to identify mechanisms that can hinder sniper bots.|在加密货币的世界里，一种新令牌的公开上市往往会产生巨大的炒作效应，在许多情况下会导致其价格在几秒钟内飙升。在这种情况下，时机对于决定投资机会的成败至关重要。在这项工作中，我们提出了一个狙击手机器人的深入分析，自动化工具设计购买代币，只要他们在市场上市。我们利用 GitHub 开源的狙击机器人仓库来分析它们的特性以及它们是如何实现的。然后，我们建立一个数据集的以太坊和 BNB 智能链(BSC)流动性池，以确定地址，连续利用狙击手机器人的优势。我们的调查结果显示，在以太坊有14,029次狙击行动，在 BSC 有1,395,042次狙击行动，分别以10,144,808美元和18,720,447美元的价格购买代币。我们发现以太坊的操作有较高的成功率，但需要较大的投资。最后，我们分析令牌智能合同，以确定机制，可以阻碍狙击手机器人。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Ready,+Aim,+Snipe!+Analysis+of+Sniper+Bots+and+their+Impact+on+the+DeFi+Ecosystem)|0| -|[Regime-based Implied Stochastic Volatility Model for Crypto Option Pricing](https://doi.org/10.1145/3543873.3587621)|Danial Saef, Yuanrong Wang, Tomaso Aste|Humboldt University Berlin, Germany; University College London, United Kingdom|The increasing adoption of Digital Assets (DAs), such as Bitcoin (BTC), rises the need for accurate option pricing models. Yet, existing methodologies fail to cope with the volatile nature of the emerging DAs. Many models have been proposed to address the unorthodox market dynamics and frequent disruptions in the microstructure caused by the non-stationarity, and peculiar statistics, in DA markets. However, they are either prone to the curse of dimensionality, as additional complexity is required to employ traditional theories, or they overfit historical patterns that may never repeat. Instead, we leverage recent advances in market regime (MR) clustering with the Implied Stochastic Volatility Model (ISVM). Time-regime clustering is a temporal clustering method, that clusters the historic evolution of a market into different volatility periods accounting for non-stationarity. ISVM can incorporate investor expectations in each of the sentiment-driven periods by using implied volatility (IV) data. In this paper, we applied this integrated time-regime clustering and ISVM method (termed MR-ISVM) to high-frequency data on BTC options at the popular trading platform Deribit. We demonstrate that MR-ISVM contributes to overcome the burden of complex adaption to jumps in higher order characteristics of option pricing models. This allows us to price the market based on the expectations of its participants in an adaptive fashion.|随着比特币(BTC)等数字资产(DA)的日益普及，对精确期权定价模型的需求也随之增加。然而，现有的方法无法应对新出现的 DA 的不稳定性。为了解决 DA 市场中由于非平稳性和特殊统计引起的非正统市场动态和微观结构频繁中断的问题，人们提出了许多模型。然而，它们要么容易产生维数灾难，因为采用传统理论需要额外的复杂性，要么过于符合可能永远不会重复的历史模式。相反，我们利用最近在市场机制(MR)集群方面的进展，采用隐含 volatility Model 模型(ISVM)。时域聚类是一种时间聚类方法，它将市场的历史演化过程聚类为考虑非平稳性的不同波动周期。ISVM 可以通过使用隐含波动性(IV)数据，在每个情绪驱动的时期纳入投资者预期。本文将时域聚类与 ISVM 相结合的方法(称为 MR-ISVM)应用于广受欢迎的交易平台 Deribit 上的 BTC 期权的高频数据。我们证明 MR-ISVM 有助于克服期权定价模型高阶特征对跳跃的复杂适应负担。这使我们能够根据市场参与者的期望，以一种适应性的方式为市场定价。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Regime-based+Implied+Stochastic+Volatility+Model+for+Crypto+Option+Pricing)|0| -|[NLP4KGC: Natural Language Processing for Knowledge Graph Construction](https://doi.org/10.1145/3543873.3589746)|Edlira Vakaj, Sanju Tiwari, Nandana Mihindukulasooriya, Fernando OrtizRodríguez, Ryan McGranaghan|Universidad Autónoma de Tamaulipas, Mexico; IBM Research, Ireland; Computing and Data Science/Natural Language Processing Lab, Birmingham City University, United Kingdom; NASA Jet Propulsion Laboratory, USA|No abstract available.|没有摘要。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=NLP4KGC:+Natural+Language+Processing+for+Knowledge+Graph+Construction)|0| +|[Regime-based Implied Stochastic Volatility Model for Crypto Option Pricing](https://doi.org/10.1145/3543873.3587621)|Danial Saef, Yuanrong Wang, Tomaso Aste|University College London, United Kingdom; Humboldt University Berlin, Germany|The increasing adoption of Digital Assets (DAs), such as Bitcoin (BTC), rises the need for accurate option pricing models. Yet, existing methodologies fail to cope with the volatile nature of the emerging DAs. Many models have been proposed to address the unorthodox market dynamics and frequent disruptions in the microstructure caused by the non-stationarity, and peculiar statistics, in DA markets. However, they are either prone to the curse of dimensionality, as additional complexity is required to employ traditional theories, or they overfit historical patterns that may never repeat. Instead, we leverage recent advances in market regime (MR) clustering with the Implied Stochastic Volatility Model (ISVM). Time-regime clustering is a temporal clustering method, that clusters the historic evolution of a market into different volatility periods accounting for non-stationarity. ISVM can incorporate investor expectations in each of the sentiment-driven periods by using implied volatility (IV) data. In this paper, we applied this integrated time-regime clustering and ISVM method (termed MR-ISVM) to high-frequency data on BTC options at the popular trading platform Deribit. We demonstrate that MR-ISVM contributes to overcome the burden of complex adaption to jumps in higher order characteristics of option pricing models. This allows us to price the market based on the expectations of its participants in an adaptive fashion.|随着比特币(BTC)等数字资产(DA)的日益普及，对精确期权定价模型的需求也随之增加。然而，现有的方法无法应对新出现的 DA 的不稳定性。为了解决 DA 市场中由于非平稳性和特殊统计引起的非正统市场动态和微观结构频繁中断的问题，人们提出了许多模型。然而，它们要么容易产生维数灾难，因为采用传统理论需要额外的复杂性，要么过于符合可能永远不会重复的历史模式。相反，我们利用最近在市场机制(MR)集群方面的进展，采用隐含 volatility Model 模型(ISVM)。时域聚类是一种时间聚类方法，它将市场的历史演化过程聚类为考虑非平稳性的不同波动周期。ISVM 可以通过使用隐含波动性(IV)数据，在每个情绪驱动的时期纳入投资者预期。本文将时域聚类与 ISVM 相结合的方法(称为 MR-ISVM)应用于广受欢迎的交易平台 Deribit 上的 BTC 期权的高频数据。我们证明 MR-ISVM 有助于克服期权定价模型高阶特征对跳跃的复杂适应负担。这使我们能够根据市场参与者的期望，以一种适应性的方式为市场定价。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Regime-based+Implied+Stochastic+Volatility+Model+for+Crypto+Option+Pricing)|0| +|[NLP4KGC: Natural Language Processing for Knowledge Graph Construction](https://doi.org/10.1145/3543873.3589746)|Edlira Vakaj, Sanju Tiwari, Nandana Mihindukulasooriya, Fernando OrtizRodríguez, Ryan McGranaghan|NASA Jet Propulsion Laboratory, USA; Computing and Data Science/Natural Language Processing Lab, Birmingham City University, United Kingdom; Universidad Autónoma de Tamaulipas, Mexico; IBM Research, Ireland|No abstract available.|没有摘要。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=NLP4KGC:+Natural+Language+Processing+for+Knowledge+Graph+Construction)|0| |[GrapeQA: GRaph Augmentation and Pruning to Enhance Question-Answering](https://doi.org/10.1145/3543873.3587651)|Dhaval Taunk, Lakshya Khanna, Siri Venkata Pavan Kumar Kandru, Vasudeva Varma, Charu Sharma, Makarand Tapaswi|International Institute of Information Technology, India|Commonsense question-answering (QA) methods combine the power of pre-trained Language Models (LM) with the reasoning provided by Knowledge Graphs (KG). A typical approach collects nodes relevant to the QA pair from a KG to form a Working Graph (WG) followed by reasoning using Graph Neural Networks(GNNs). This faces two major challenges: (i) it is difficult to capture all the information from the QA in the WG, and (ii) the WG contains some irrelevant nodes from the KG. To address these, we propose GrapeQA with two simple improvements on the WG: (i) Prominent Entities for Graph Augmentation identifies relevant text chunks from the QA pair and augments the WG with corresponding latent representations from the LM, and (ii) Context-Aware Node Pruning removes nodes that are less relevant to the QA pair. We evaluate our results on OpenBookQA, CommonsenseQA and MedQA-USMLE and see that GrapeQA shows consistent improvements over its LM + KG predecessor (QA-GNN in particular) and large improvements on OpenBookQA.|常识问答(QA)方法结合了预训练语言模型(LM)和知识图(KG)提供的推理能力。一种典型的方法是从一个 KG 中收集与 QA 对相关的节点，形成一个工作图(WG) ，然后使用图神经网络(GNN)进行推理。这面临着两个主要的挑战: (i)很难从工作组中的 QA 获取所有信息，以及(ii)工作组包含来自 KG 的一些不相关的节点。为了解决这些问题，我们提出了 GrapeQA，对 WG 进行了两个简单的改进: (i)用于图增强的突出实体识别 QA 对中的相关文本块，并用来自 LM 的相应潜在表示增强 WG; (ii)上下文感知节点修剪删除与 QA 对不太相关的节点。我们在 OpenBookQA，CommonsenseQA 和 MedQA-USMLE 上评估了我们的结果，发现 GrapeQA 比它的 LM + KG 前辈(尤其是 QA-GNN)有一致的改进，并且在 OpenBookQA 上有很大的改进。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=GrapeQA:+GRaph+Augmentation+and+Pruning+to+Enhance+Question-Answering)|0| |[Federated Learning for Metaverse: A Survey](https://doi.org/10.1145/3543873.3587584)|Yao Chen, Shan Huang, Wensheng Gan, Gengsen Huang, Yongdong Wu|Jinan University, China|The metaverse, which is at the stage of innovation and exploration, faces the dilemma of data collection and the problem of private data leakage in the process of development. This can seriously hinder the widespread deployment of the metaverse. Fortunately, federated learning (FL) is a solution to the above problems. FL is a distributed machine learning paradigm with privacy-preserving features designed for a large number of edge devices. Federated learning for metaverse (FL4M) will be a powerful tool. Because FL allows edge devices to participate in training tasks locally using their own data, computational power, and model-building capabilities. Applying FL to the metaverse not only protects the data privacy of participants but also reduces the need for high computing power and high memory on servers. Until now, there have been many studies about FL and the metaverse, respectively. In this paper, we review some of the early advances of FL4M, which will be a research direction with unlimited development potential. We first introduce the concepts of metaverse and FL, respectively. Besides, we discuss the convergence of key metaverse technologies and FL in detail, such as big data, communication technology, the Internet of Things, edge computing, blockchain, and extended reality. Finally, we discuss some key challenges and promising directions of FL4M in detail. In summary, we hope that our up-to-date brief survey can help people better understand FL4M and build a fair, open, and secure metaverse.|元宇宙正处于创新和探索阶段，在发展过程中面临着数据采集的困境和私有数据泄漏的问题。这可能会严重阻碍元宇宙的广泛部署。幸运的是，联邦学习(FL)是上述问题的解决方案。FL 是一种为大量边缘设备设计的具有保密特性的分布式机器学习范式。元宇宙的联邦学习(FL4M)将是一个强大的工具。因为 FL 允许边缘设备使用它们自己的数据、计算能力和模型构建能力在本地参与培训任务。将 FL 应用于元宇宙不仅保护了参与者的数据隐私，而且减少了对服务器上高计算能力和高内存的需求。到目前为止，已有许多研究分别对 FL 和元宇宙进行了研究。本文综述了 FL4M 的一些早期进展，认为 FL4M 是一个具有无限发展潜力的研究方向。我们首先分别介绍了元宇宙和 FL 的概念。此外，本文还详细讨论了大数据、通信技术、物联网、边缘计算、区块链以及扩展现实等关键技术与 FL 的融合问题。最后，我们详细讨论了 FL4M 的一些关键挑战和发展方向。总之，我们希望我们最新的简短调查可以帮助人们更好地理解 FL4M，并建立一个公平、开放和安全的元宇宙。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Federated+Learning+for+Metaverse:+A+Survey)|0| |[Understanding the Impact of Label Skewness and Optimization on Federated Learning for Text Classification](https://doi.org/10.1145/3543873.3587599)|Sumam Francis, Kanimozhi Uma, MarieFrancine Moens|LIIR lab, Department of Computer Science, KU Leuven, Belgium|Federated Learning (FL), also known as collaborative learning, is a distributed machine learning approach that collaboratively learns a shared prediction model without explicitly sharing private data. When dealing with sensitive data, privacy measures need to be carefully considered. Optimizers have a massive role in accelerating the learning process given the high dimensionality and non-convexity of the search space. The data partitioning in FL can be assumed to be either IID (independent and identically distributed) or non-IID. In this paper, we experiment with the impact of applying different adaptive optimization methods for FL frameworks in both IID and non-IID setups. We analyze the effects of label and quantity skewness, learning rate, and local client training on the learning process of optimizers as well as the overall performance of the global model. We evaluate the FL hyperparameter settings on biomedical text classification tasks on two datasets ADE V2 (Adverse Drug Effect: 2 classes) and Clinical-Trials (Reasons to stop trials: 17 classes).|联邦学习(FL) ，也称为合作学习学习，是一种分布式机器学习方法，它协作学习共享的预测模型，而不显式地共享私有数据。在处理敏感数据时，需要仔细考虑隐私措施。由于搜索空间的高维性和非凸性，优化器在加速学习过程中起着巨大的作用。FL 中的数据分区可以假定为 IID (独立且同分布)或非 IID。在本文中，我们实验了在 IID 和非 IID 设置中对 FL 框架应用不同的自适应优化方法的影响。我们分析了标签偏度和数量偏度、学习率和局部客户训练对优化器学习过程的影响以及全局模型的整体性能。我们评估两个数据集 ADE V2(不良药物效应: 2类)和临床试验(停止试验的原因: 17类)的生物医学文本分类任务的 FL 超参数设置。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Understanding+the+Impact+of+Label+Skewness+and+Optimization+on+Federated+Learning+for+Text+Classification)|0| -|[A Survey of Trustworthy Federated Learning with Perspectives on Security, Robustness and Privacy](https://doi.org/10.1145/3543873.3587681)|Yifei Zhang, Dun Zeng, Jinglong Luo, Zenglin Xu, Irwin King|Harbin Institute of Technology and Peng Cheng Lab, China; University of Electronic Science and Technology of China and Peng Cheng Lab, China; The Chinese University of Hong Kong, Hong Kong|Trustworthy artificial intelligence (AI) technology has revolutionized daily life and greatly benefited human society. Among various AI technologies, Federated Learning (FL) stands out as a promising solution for diverse real-world scenarios, ranging from risk evaluation systems in finance to cutting-edge technologies like drug discovery in life sciences. However, challenges around data isolation and privacy threaten the trustworthiness of FL systems. Adversarial attacks against data privacy, learning algorithm stability, and system confidentiality are particularly concerning in the context of distributed training in federated learning. Therefore, it is crucial to develop FL in a trustworthy manner, with a focus on security, robustness, and privacy. In this survey, we propose a comprehensive roadmap for developing trustworthy FL systems and summarize existing efforts from three key aspects: security, robustness, and privacy. We outline the threats that pose vulnerabilities to trustworthy federated learning across different stages of development, including data processing, model training, and deployment. To guide the selection of the most appropriate defense methods, we discuss specific technical solutions for realizing each aspect of Trustworthy FL (TFL). Our approach differs from previous work that primarily discusses TFL from a legal perspective or presents FL from a high-level, non-technical viewpoint.|值得信赖的人工智能(AI)技术彻底改变了人们的日常生活，极大地造福了人类社会。在各种人工智能技术中，联邦学习(FL)脱颖而出，作为一种有前途的解决方案，适用于从金融风险评估系统到生命科学药物发现等尖端技术的各种现实情景。然而，围绕数据隔离和隐私的挑战威胁着 FL 系统的可信度。在联邦学习的分布式训练环境中，对数据隐私、学习算法稳定性和系统机密性的对抗性攻击尤其受到关注。因此，以一种值得信赖的方式开发 FL 是至关重要的，重点是安全性、健壮性和隐私性。在本调查中，我们提出了一个开发可信 FL 系统的全面路线图，并从三个关键方面总结了现有的工作: 安全性、健壮性和隐私性。我们概述了在不同的开发阶段(包括数据处理、模型培训和部署)对可信联邦学习构成的威胁。为了指导选择最合适的防御方法，我们讨论了具体的技术解决方案，以实现值得信赖的外交(TFL)的各个方面。我们的方法不同于以前的工作，主要讨论任务型外语从法律的角度或提出从一个高层次的，非技术的观点。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Survey+of+Trustworthy+Federated+Learning+with+Perspectives+on+Security,+Robustness+and+Privacy)|0| +|[A Survey of Trustworthy Federated Learning with Perspectives on Security, Robustness and Privacy](https://doi.org/10.1145/3543873.3587681)|Yifei Zhang, Dun Zeng, Jinglong Luo, Zenglin Xu, Irwin King|The Chinese University of Hong Kong, Hong Kong; Harbin Institute of Technology and Peng Cheng Lab, China; University of Electronic Science and Technology of China and Peng Cheng Lab, China|Trustworthy artificial intelligence (AI) technology has revolutionized daily life and greatly benefited human society. Among various AI technologies, Federated Learning (FL) stands out as a promising solution for diverse real-world scenarios, ranging from risk evaluation systems in finance to cutting-edge technologies like drug discovery in life sciences. However, challenges around data isolation and privacy threaten the trustworthiness of FL systems. Adversarial attacks against data privacy, learning algorithm stability, and system confidentiality are particularly concerning in the context of distributed training in federated learning. Therefore, it is crucial to develop FL in a trustworthy manner, with a focus on security, robustness, and privacy. In this survey, we propose a comprehensive roadmap for developing trustworthy FL systems and summarize existing efforts from three key aspects: security, robustness, and privacy. We outline the threats that pose vulnerabilities to trustworthy federated learning across different stages of development, including data processing, model training, and deployment. To guide the selection of the most appropriate defense methods, we discuss specific technical solutions for realizing each aspect of Trustworthy FL (TFL). Our approach differs from previous work that primarily discusses TFL from a legal perspective or presents FL from a high-level, non-technical viewpoint.|值得信赖的人工智能(AI)技术彻底改变了人们的日常生活，极大地造福了人类社会。在各种人工智能技术中，联邦学习(FL)脱颖而出，作为一种有前途的解决方案，适用于从金融风险评估系统到生命科学药物发现等尖端技术的各种现实情景。然而，围绕数据隔离和隐私的挑战威胁着 FL 系统的可信度。在联邦学习的分布式训练环境中，对数据隐私、学习算法稳定性和系统机密性的对抗性攻击尤其受到关注。因此，以一种值得信赖的方式开发 FL 是至关重要的，重点是安全性、健壮性和隐私性。在本调查中，我们提出了一个开发可信 FL 系统的全面路线图，并从三个关键方面总结了现有的工作: 安全性、健壮性和隐私性。我们概述了在不同的开发阶段(包括数据处理、模型培训和部署)对可信联邦学习构成的威胁。为了指导选择最合适的防御方法，我们讨论了具体的技术解决方案，以实现值得信赖的外交(TFL)的各个方面。我们的方法不同于以前的工作，主要讨论任务型外语从法律的角度或提出从一个高层次的，非技术的观点。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Survey+of+Trustworthy+Federated+Learning+with+Perspectives+on+Security,+Robustness+and+Privacy)|0| |[A Federated Learning Benchmark for Drug-Target Interaction](https://doi.org/10.1145/3543873.3587687)|Gianluca Mittone, Filip Svoboda, Marco Aldinucci, Nicholas D. Lane, Pietro Lió|University of Cambridge, United Kingdom; Department of Computer Science, University of Turin, Italy|Aggregating pharmaceutical data in the drug-target interaction (DTI) domain has the potential to deliver life-saving breakthroughs. It is, however, notoriously difficult due to regulatory constraints and commercial interests. This work proposes the application of federated learning, which we argue to be reconcilable with the industry's constraints, as it does not require sharing of any information that would reveal the entities' data or any other high-level summary of it. When used on a representative GraphDTA model and the KIBA dataset it achieves up to 15% improved performance relative to the best available non-privacy preserving alternative. Our extensive battery of experiments shows that, unlike in other domains, the non-IID data distribution in the DTI datasets does not deteriorate FL performance. Additionally, we identify a material trade-off between the benefits of adding new data, and the cost of adding more clients.|在药物-靶标相互作用(DTI)领域聚合药物数据有可能带来拯救生命的突破。然而，由于监管限制和商业利益，这是出了名的困难。这项工作提出了联邦学习的应用，我们认为这与行业的约束是一致的，因为它不需要共享任何信息来揭示实体的数据或任何其他高层次的摘要。当在一个有代表性的 GraphDTA 模型和 KIBA 数据集上使用时，相对于最好的非隐私保护方案，它实现了高达15% 的性能提高。我们大量的实验表明，与其他领域不同，DTI 数据集中的非 IID 数据分布不会降低 FL 性能。此外，我们还确定了添加新数据的好处与添加更多客户端的成本之间的实质性权衡。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Federated+Learning+Benchmark+for+Drug-Target+Interaction)|0| -|[Towards Timeline Generation with Abstract Meaning Representation](https://doi.org/10.1145/3543873.3587670)|Behrooz Mansouri, Ricardo Campos, Adam Jatowt|University of Innsbruck, Austria; Department of Computer Science, University of Southern Maine, USA; Ci2 - Polytechnic Institute of Tomar; INESC TEC, Portugal|Timeline summarization (TLS) is a challenging research task that requires researchers to distill extensive and intricate temporal data into a concise and easily comprehensible representation. This paper proposes a novel approach to timeline summarization using Abstract Meaning Representations (AMRs), a graphical representation of the text where the nodes are semantic concepts and the edges denote relationships between concepts. With AMR, sentences with different wordings, but similar semantics, have similar representations. To make use of this feature for timeline summarization, a two-step sentence selection method that leverages features extracted from both AMRs and the text is proposed. First, AMRs are generated for each sentence. Sentences are then filtered out by removing those with no named-entities and keeping the ones with the highest number of named-entities. In the next step, sentences to appear in the timeline are selected based on two scores: Inverse Document Frequency (IDF) of AMR nodes combined with the score obtained by applying a keyword extraction method to the text. Our experimental results on the TLS-Covid19 test collection demonstrate the potential of the proposed approach.|时间轴摘要(TLS)是一项具有挑战性的研究任务，它要求研究人员将广泛而复杂的时间数据提取到一个简洁而易于理解的表示中。本文提出了一种基于抽象意义表示(AMR)的时间轴概括方法，AMR 是一种文本的图形表示方法，其中节点是语义概念，边表示概念之间的关系。使用 AMR，具有不同词汇但语义相似的句子具有相似的表示。为了利用这一特征进行时间轴摘要，提出了一种利用 AMR 和文本提取特征的两步句子选择方法。首先，为每个句子生成 AMR。然后，通过删除没有命名实体的句子并保留命名实体数量最多的句子来过滤掉句子。在下一步中，根据两个得分来选择出现在时间轴中的句子: AMR 节点的反向文档频率(IDF)与通过对文本应用关键字提取方法获得的得分相结合。我们在 TLS-Covid19测试集上的实验结果证明了该方法的潜力。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Towards+Timeline+Generation+with+Abstract+Meaning+Representation)|0| +|[Towards Timeline Generation with Abstract Meaning Representation](https://doi.org/10.1145/3543873.3587670)|Behrooz Mansouri, Ricardo Campos, Adam Jatowt|University of Innsbruck, Austria; Ci2 - Polytechnic Institute of Tomar; INESC TEC, Portugal; Department of Computer Science, University of Southern Maine, USA|Timeline summarization (TLS) is a challenging research task that requires researchers to distill extensive and intricate temporal data into a concise and easily comprehensible representation. This paper proposes a novel approach to timeline summarization using Abstract Meaning Representations (AMRs), a graphical representation of the text where the nodes are semantic concepts and the edges denote relationships between concepts. With AMR, sentences with different wordings, but similar semantics, have similar representations. To make use of this feature for timeline summarization, a two-step sentence selection method that leverages features extracted from both AMRs and the text is proposed. First, AMRs are generated for each sentence. Sentences are then filtered out by removing those with no named-entities and keeping the ones with the highest number of named-entities. In the next step, sentences to appear in the timeline are selected based on two scores: Inverse Document Frequency (IDF) of AMR nodes combined with the score obtained by applying a keyword extraction method to the text. Our experimental results on the TLS-Covid19 test collection demonstrate the potential of the proposed approach.|时间轴摘要(TLS)是一项具有挑战性的研究任务，它要求研究人员将广泛而复杂的时间数据提取到一个简洁而易于理解的表示中。本文提出了一种基于抽象意义表示(AMR)的时间轴概括方法，AMR 是一种文本的图形表示方法，其中节点是语义概念，边表示概念之间的关系。使用 AMR，具有不同词汇但语义相似的句子具有相似的表示。为了利用这一特征进行时间轴摘要，提出了一种利用 AMR 和文本提取特征的两步句子选择方法。首先，为每个句子生成 AMR。然后，通过删除没有命名实体的句子并保留命名实体数量最多的句子来过滤掉句子。在下一步中，根据两个得分来选择出现在时间轴中的句子: AMR 节点的反向文档频率(IDF)与通过对文本应用关键字提取方法获得的得分相结合。我们在 TLS-Covid19测试集上的实验结果证明了该方法的潜力。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Towards+Timeline+Generation+with+Abstract+Meaning+Representation)|0| |[Gone, Gone, but Not Really, and Gone, But Not forgotten: A Typology of Website Recoverability](https://doi.org/10.1145/3543873.3587671)|Brenda Reyes Ayala|University of Alberta, Canada|This paper presents a qualitative analysis of the recoverability of various webpages on the live web, using their archived counterparts as a baseline. We used a heterogeneous dataset consisting of four web archive collections, each with varying degrees of content drift. We were able to recover a small number of webpages previously thought to have been lost and analyzed their content and evolution. Our analysis yielded three types of lost webpages: 1) those that are not recoverable (with three subtypes), 2) those that are fully recoverable, and 3) those that are partially recoverable. The analysis presented here attempts to establish clear definitions and boundaries between the different degrees of webpage recoverabilty. By using a few simple methods, web archivists could discover the new locations of web content that was previously deemed lost, and include them in future crawling efforts, and lead to more complete web archives with less content drift.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Gone,+Gone,+but+Not+Really,+and+Gone,+But+Not+forgotten:+A+Typology+of+Website+Recoverability)|0| |[Detecting the Hidden Dynamics of Networked Actors Using Temporal Correlations](https://doi.org/10.1145/3543873.3587672)|Keeley Erhardt, Dina Albassam|King Abdulaziz City for Science and Technology, Saudi Arabia; Massachusetts Institute of Technology, USA|Influence campaigns pose a threat to fact-based reasoning, erode trust in institutions, and tear at the fabric of our society. In the 21st century, influence campaigns have rapidly evolved, taking on new online identities. Many of these propaganda campaigns are persistent and well-resourced, making their identification and removal both hard and expensive. Social media companies have predominantly aimed to counter the threat of online propaganda by prioritizing the moderation of "coordinated inauthentic behavior". This strategy focuses on identifying orchestrated campaigns explicitly intended to deceive, rather than individual social media accounts or posts. In this paper, we study the Twitter footprint of a multi-year influence campaign linked to the Russian government. Drawing from the influence model, a generative model that describes the interactions between networked Markov chains, we demonstrate how temporal correlations in the sequential decision processes of individual social media accounts can reveal coordinated inauthentic activity.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Detecting+the+Hidden+Dynamics+of+Networked+Actors+Using+Temporal+Correlations)|0| -|[The Age of Snippet Programming: Toward Understanding Developer Communities in Stack Overflow and Reddit](https://doi.org/10.1145/3543873.3587673)|Alessia Antelmi, Gennaro Cordasco, Daniele De Vinco, Carmine Spagnuolo|Department of Computer Science, Università degli Studi di Salerno, Italy; Department of Psychology, Università della Campania, Italy|Today, coding skills are among the most required competencies worldwide, often also for non-computer scientists. Because of this trend, community contribution-based, question-and-answer (Q&A) platforms became prominent for finding the proper solution to all programming issues. Stack Overflow has been the most popular platform for technical-related questions for years. Still, recently, some programming-related subreddits of Reddit have become a standing stone for questions and discussions. This work investigates the developers’ behavior and community formation around the twenty most popular programming languages. We examined two consecutive years of programming-related questions from Stack Overflow and Reddit, performing a longitudinal study on users’ posting activity and their high-order interaction patterns abstracted via hypergraphs. Our analysis highlighted crucial differences in how these Q&A platforms are utilized by their users. In line with previous literature, it emphasized the constant decline of Stack Overflow in favor of more community-friendly platforms, such as Reddit, which has been growing rapidly lately.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=The+Age+of+Snippet+Programming:+Toward+Understanding+Developer+Communities+in+Stack+Overflow+and+Reddit)|0| -|[Temporal Ordinance Mining for Event-Driven Social Media Reaction Analytics](https://doi.org/10.1145/3543873.3587674)|Aparna S. Varde, Gerard de Melo, Boxiang Dong|AI & Intelligent Systems, HPI, University of Potsdam, Germany; Dept. of Computer Science, Montclair State University, USA; Dept. of Computer Science; Clean Energy & Sustainability Analytics Center, Montclair State University, USA|As a growing number of policies are adopted to address the substantial rise in urbanization, there is a significant push for smart governance, endowing transparency in decision-making and enabling greater public involvement. The thriving concept of smart governance goes beyond just cities, ultimately aiming at a smart planet. Ordinances (local laws) affect our life with regard to health, business, etc. This is particularly notable during major events such as the recent pandemic, which may lead to rapid changes in ordinances, pertaining for instance to public safety, disaster management, and recovery phases. However, many citizens view ordinances as impervious and complex. This position paper proposes a research agenda enabling novel forms of ordinance content analysis over time and temporal web question answering (QA) for both legislators and the broader public. Along with this, we aim to analyze social media posts so as to track the public opinion before and after the introduction of ordinances. Challenges include addressing concepts changing over time and infusing subtle human reasoning in mining, which we aim to address by harnessing terminology evolution methods and commonsense knowledge sources, respectively. We aim to make the results of the historical ordinance mining and event-driven analysis seamlessly accessible, relying on a robust semantic understanding framework to flexibly support web QA.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Temporal+Ordinance+Mining+for+Event-Driven+Social+Media+Reaction+Analytics)|0| +|[The Age of Snippet Programming: Toward Understanding Developer Communities in Stack Overflow and Reddit](https://doi.org/10.1145/3543873.3587673)|Alessia Antelmi, Gennaro Cordasco, Daniele De Vinco, Carmine Spagnuolo|Department of Psychology, Università della Campania, Italy; Department of Computer Science, Università degli Studi di Salerno, Italy|Today, coding skills are among the most required competencies worldwide, often also for non-computer scientists. Because of this trend, community contribution-based, question-and-answer (Q&A) platforms became prominent for finding the proper solution to all programming issues. Stack Overflow has been the most popular platform for technical-related questions for years. Still, recently, some programming-related subreddits of Reddit have become a standing stone for questions and discussions. This work investigates the developers’ behavior and community formation around the twenty most popular programming languages. We examined two consecutive years of programming-related questions from Stack Overflow and Reddit, performing a longitudinal study on users’ posting activity and their high-order interaction patterns abstracted via hypergraphs. Our analysis highlighted crucial differences in how these Q&A platforms are utilized by their users. In line with previous literature, it emphasized the constant decline of Stack Overflow in favor of more community-friendly platforms, such as Reddit, which has been growing rapidly lately.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=The+Age+of+Snippet+Programming:+Toward+Understanding+Developer+Communities+in+Stack+Overflow+and+Reddit)|0| +|[Temporal Ordinance Mining for Event-Driven Social Media Reaction Analytics](https://doi.org/10.1145/3543873.3587674)|Aparna S. Varde, Gerard de Melo, Boxiang Dong|Dept. of Computer Science; Clean Energy & Sustainability Analytics Center, Montclair State University, USA; AI & Intelligent Systems, HPI, University of Potsdam, Germany; Dept. of Computer Science, Montclair State University, USA|As a growing number of policies are adopted to address the substantial rise in urbanization, there is a significant push for smart governance, endowing transparency in decision-making and enabling greater public involvement. The thriving concept of smart governance goes beyond just cities, ultimately aiming at a smart planet. Ordinances (local laws) affect our life with regard to health, business, etc. This is particularly notable during major events such as the recent pandemic, which may lead to rapid changes in ordinances, pertaining for instance to public safety, disaster management, and recovery phases. However, many citizens view ordinances as impervious and complex. This position paper proposes a research agenda enabling novel forms of ordinance content analysis over time and temporal web question answering (QA) for both legislators and the broader public. Along with this, we aim to analyze social media posts so as to track the public opinion before and after the introduction of ordinances. Challenges include addressing concepts changing over time and infusing subtle human reasoning in mining, which we aim to address by harnessing terminology evolution methods and commonsense knowledge sources, respectively. We aim to make the results of the historical ordinance mining and event-driven analysis seamlessly accessible, relying on a robust semantic understanding framework to flexibly support web QA.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Temporal+Ordinance+Mining+for+Event-Driven+Social+Media+Reaction+Analytics)|0| |[A Chinese Fine-grained Financial Event Extraction Dataset](https://doi.org/10.1145/3543873.3587578)|Mengjie Wu, Maofu Liu, Luyao Wang, Huijun Hu|School of Computer Science and Technology, Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System,Wuhan University of Science and Technology, China|The existing datasets are mostly composed of official documents, statements, news articles, and so forth. So far, only a little attention has been paid to the numerals in financial social comments. Therefore, this paper presents CFinNumAttr, a financial numeral attribute dataset in Chinese via annotating the stock reviews and comments collected from social networking platform. We also conduct several experiments on the CFinNumAttr dataset with state-of-the-art methods to discover the importance of the financial numeral attributes. The experimental results on the CFinNumAttr dataset show that the numeral attributes in social reviews or comments contain rich semantic information, and the numeral clue extraction and attribute classification tasks can make a great improvement in financial text understanding.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Chinese+Fine-grained+Financial+Event+Extraction+Dataset)|0| -|[Financial Technology on the Web](https://doi.org/10.1145/3543873.3589738)|ChungChi Chen, HenHsen Huang, Hiroya Takamura, HsinHsi Chen|Institute of Information Science, Academia Sinica, Taiwan; National Institute of Advanced Industrial Science and Technology, Japan; National Taiwan University, Taiwan|This paper shares our observations based on our three-year experience organizing the FinWeb workshop series. In addition to the widely-discussed topic, content analysis, we notice two tendencies for FinTech applications: customers’ behavior analysis and finance-oriented LegalTech. We also briefly share our idea on the research direction about reliable and trustworthy FinWeb from the investment perspective.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Financial+Technology+on+the+Web)|0| -|[Aspect-based Summarization of Legal Case Files using Sentence Classification](https://doi.org/10.1145/3543873.3587611)|Nikhil E, Anshul Padhi, Pulkit Parikh, Swati Kanwal, Kamalakar Karlapalem, Natraj Raman|JP Morgan AI Research, United Kingdom; IIIT Hyderabad, Canada; IIIT Hyderabad, India|Aspect-based summarization of a legal case file related to regulating bodies allows different stakeholders to consume information of interest therein efficiently. In this paper, we propose a multi-step process to achieve the same. First, we explore the semantic sentence segmentation of SEBI case files via classification. We also propose a dataset of Indian legal adjudicating orders which contain tags from carefully crafted domain-specific sentence categories with the help of legal experts. We experiment with various machine learning and deep learning methods for this multi-class classification. Then, we examine the performance of numerous summarization methods on the segmented document to generate persona-specific summaries. Finally, we develop a pipeline making use of the best methods in both sub-tasks to achieve high recall.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Aspect-based+Summarization+of+Legal+Case+Files+using+Sentence+Classification)|0| -|[Exploiting graph metrics to detect anomalies in cross-country money transfer temporal networks](https://doi.org/10.1145/3543873.3587602)|Salvatore Vilella, Arthur Thomas Edward Capozzi Lupi, Giancarlo Ruffo, Marco Fornasiero, Dario Moncalvo, Valeria Ricci, Silvia Ronchiadin|Anti Financial Crime Digital Hub, Italy; Department of Computer Science, University of Turin, Italy; Università degli Studi del Piemonte Orientale, Italy|During the last decades, Anti-Financial Crime (AFC) entities and Financial Institutions have put a constantly increasing effort to reduce financial crime and detect fraudulent activities, that are changing and developing in extremely complex ways. We propose an anomaly detection approach based on network analysis to help AFC officers navigating through the high load of information that is typical of AFC data-driven scenarios. By experimenting on a large financial dataset of more than 80M cross-country wire transfers, we leverage on the properties of complex networks to develop a tool for explainable anomaly detection, that can help in identifying outliers that could be engaged in potentially malicious activities according to financial regulations. We identify a set of network metrics that provide useful insights on individual nodes; by keeping track of the evolution over time of the metric-based node rankings, we are able to highlight sudden and unexpected changes in the roles of individual nodes that deserve further attention by AFC officers. Such changes can hardly be noticed by means of current AFC practices, that sometimes can lack a higher-level, global vision of the system. This approach represents a preliminary step in the automation of AFC and AML processes, serving the purpose of facilitating the work of AFC officers by providing them with a top-down view of the picture emerging from financial data.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Exploiting+graph+metrics+to+detect+anomalies+in+cross-country+money+transfer+temporal+networks)|0| -|[Multiple-Agent Deep Reinforcement Learning for Avatar Migration in Vehicular Metaverses](https://doi.org/10.1145/3543873.3587573)|Junlong Chen, Jiangtian Nie, Minrui Xu, Lingjuan Lyu, Zehui Xiong, Jiawen Kang, Yongju Tong, Wenchao Jiang|Singapore University of Technology and Design, Singapore; Guangdong University of Technology, China; Nanyang Technological University, Singapore; Sony(Japan), Japan|Vehicular Metaverses are widely considered as the next Internet revolution to build a 3D virtual world with immersive virtual-real interaction for passengers and drivers. In vehicular Metaverse applications, avatars are digital representations of on-board users to obtain and manage immersive vehicular services (i.e., avatar tasks) in Metaverses and the data they generate. However, traditional Internet of Vehicles (IoV) data management solutions have serious data security risks and privacy protection. Fortunately, blockchain-based Web 3.0 enables avatars to have an ownership identity to securely manage the data owned by users in a decentralized and transparent manner. To ensure users’ immersive experiences and securely manage their data, avatar tasks often require significant computing resources. Therefore, it is impractical for the vehicles to process avatar tasks locally, massive computation resources are needed to support the avatar tasks. To this end, offloading avatar tasks to nearby RoadSide Units (RSUs) is a promising solution to avoid computation overload. To ensure real-time and continuous Metaverse services, the avatar tasks should be migrated among the RSUs when the vehicle navigation. It is challenging for the vehicles to independently decide whether migrate or not according to current and future avatar states. Therefore, in this paper, we propose a new avatar task migration framework for vehicular Metaverses. We then formulate the avatar task migration problem as a Partially Observable Markov Decision Process (POMDP), and apply a Multi-Agent Deep Reinforcement Learning (MADRL) algorithm to dynamically make migration decisions for avatar tasks. Numerous results show that our proposed algorithm outperforms existing baselines for avatar task migration and enables immersive vehicular Metaverse services.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Multiple-Agent+Deep+Reinforcement+Learning+for+Avatar+Migration+in+Vehicular+Metaverses)|0| -|[China's First Natural Language-based AI ChatBot Trader](https://doi.org/10.1145/3543873.3587633)|James Y. Zhang, Zhi Li, Hao Fang, Jun Wu, Zhongnan Shen, Jing Zheng, Wei Chu, Weiping Duan, Peng Xu|MyBank, China; Ant Group, USA|Repo (repurchase agreement) trading provides easy access to short-term financing secured by a pledge of collateral and plays an important role in the global financial system. However, repo traders face many tough challenges in their job, from managing complex financial transactions to keeping up with changing market trends and regulations in the complex financial transactions involved. Besides the difficult and tedious processes that take a lot of time and energy, repo traders need to keep up to date with various laws, regulations, and financial trends that may affect their job, worsened by the exposure to a variety of market risks. As the leader of the FinTech industry, Ant Group launched a new initiative to alleviate the affliction of the repo traders at MyBank1. By leveraging many existing platform technologies, such as AI ChatBot and forecasting platforms, and with the collective work of various engineering groups, we are able to create a ChatBot that communicates with other human traders in natural language and create electronic contracts based on the negotiated terms, equipped with proper trading strategies based on forecasting results. The fully automatic workflow not only frees our trader from tedious routines, but also reduces potential human errors. At the same time, it enables refined portfolio and risk management, while opening up the possibility to apply neural network-based trading strategies, and yielding greater returns comparing to traditional workflow reliant on human experiences. Our system has evolved beyond just providing services to our own traders, to now a fully commercialized product, covering other types of interbank trading.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=China's+First+Natural+Language-based+AI+ChatBot+Trader)|0| -|[Web 3.0: The Future of Internet](https://doi.org/10.1145/3543873.3587583)|Wensheng Gan, Zhenqiang Ye, Shicheng Wan, Philip S. Yu|Jinan University, China; University of Illinois at Chicago, USA; Guangdong University of Technology, China|With the rapid growth of the Internet, human daily life has become deeply bound to the Internet. To take advantage of massive amounts of data and information on the internet, the Web architecture is continuously being reinvented and upgraded. From the static informative characteristics of Web 1.0 to the dynamic interactive features of Web 2.0, scholars and engineers have worked hard to make the internet world more open, inclusive, and equal. Indeed, the next generation of Web evolution (i.e., Web 3.0) is already coming and shaping our lives. Web 3.0 is a decentralized Web architecture that is more intelligent and safer than before. The risks and ruin posed by monopolists or criminals will be greatly reduced by a complete reconstruction of the Internet and IT infrastructure. In a word, Web 3.0 is capable of addressing web data ownership according to distributed technology. It will optimize the internet world from the perspectives of economy, culture, and technology. Then it promotes novel content production methods, organizational structures, and economic forms. However, Web 3.0 is not mature and is now being disputed. Herein, this paper presents a comprehensive survey of Web 3.0, with a focus on current technologies, challenges, opportunities, and outlook. This article first introduces a brief overview of the history of World Wide Web as well as several differences among Web 1.0, Web 2.0, Web 3.0, and Web3. Then, some technical implementations of Web 3.0 are illustrated in detail. We discuss the revolution and benefits that Web 3.0 brings. Finally, we explore several challenges and issues in this promising area.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Web+3.0:+The+Future+of+Internet)|0| -|[DSNet: Efficient Lightweight Model for Video Salient Object Detection for IoT and WoT Applications](https://doi.org/10.1145/3543873.3587592)|Hemraj Singh, Mridula Verma, Ramalingaswamy Cheruku|Institute for Development and Research in Banking Technology, India; National Institute of Technology Warangal, India|The most challenging aspects of deploying deep models in IoT and embedded systems are extensive computational complexity and large training and inference time. Although various lightweight versions of state-of-the-art models are also being designed, maintaining the performance of such models is difficult. To overcome these problems, an efficient, lightweight, Deformable Separable Network (DSNet) is proposed for video salient object detection tasks, mainly for mobile and embedded vision applications. DSNet is equipped with a Deformable Convolution Network (DeCNet), Separable Convolution Network (SCNet), and Depth-wise Attention Response Propagation (DARP) module, which makes it maintain the trade-off between accuracy and latency. The proposed model generates saliency maps considering both the background and foreground simultaneously, making it perform better in unconstrained scenarios (such as partial occlusion, deformable background/objects, and illumination effect). The extensive experiments conducted on six benchmark datasets demonstrate that the proposed model outperforms state-of-art approaches in terms of computational complexity, number of parameters, and latency measures.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=DSNet:+Efficient+Lightweight+Model+for+Video+Salient+Object+Detection+for+IoT+and+WoT+Applications)|0| -|[Weighted Statistically Significant Pattern Mining](https://doi.org/10.1145/3543873.3587586)|Tingfu Zhou, Zhenlian Qi, Wensheng Gan, Shicheng Wan, Guoting Chen|Jinan University, China; Harbin Institute of Technology, China; Guangdong University of Technology, China; Guangdong Eco-Engineering Polytechnic, China|Pattern discovery (aka pattern mining) is a fundamental task in the field of data science. Statistically significant pattern mining (SSPM) is the task of finding useful patterns that statistically occur more often from databases for one class than for another. The existing SSPM task does not consider the weight of each item. While in the real world, the significant level of different items/objects is various. Therefore, in this paper, we introduce the Weighted Statistically Significant Patterns Mining (WSSPM) problem and propose a novel WSSpm algorithm to successfully solve it. We present a new framework that effectively mines weighted statistically significant patterns by combining the weighted upper-bound model and the multiple hypotheses test. We also propose a new weighted support threshold that can satisfy the demand of WSSPM and prove its correctness and completeness. Besides, our weighted support threshold and modified weighted upper-bound can effectively shrink the mining range. Finally, experimental results on several real datasets show that the WSSpm algorithm performs well in terms of execution time and memory storage.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Weighted+Statistically+Significant+Pattern+Mining)|0| -|[The Human-Centric Metaverse: A Survey](https://doi.org/10.1145/3543873.3587593)|Riyan Yang, Lin Li, Wensheng Gan, Zefeng Chen, Zhenlian Qi|Jinan University, China; Guangdong Eco-Engineering Polytechnic, China|In the era of the Web of Things, the Metaverse is expected to be the landing site for the next generation of the Internet, resulting in the increased popularity of related technologies and applications in recent years and gradually becoming the focus of Internet research. The Metaverse, as a link between the real and virtual worlds, can provide users with immersive experiences. As the concept of the Metaverse grows in popularity, many scholars and developers begin to focus on the Metaverse's ethics and core. This paper argues that the Metaverse should be centered on humans. That is, humans constitute the majority of the Metaverse. As a result, we begin this paper by introducing the Metaverse's origins, characteristics, related technologies, and the concept of the human-centric Metaverse (HCM). Second, we discuss the manifestation of human-centric in the Metaverse. Finally, we discuss some current issues in the construction of HCM. In this paper, we provide a detailed review of the applications of human-centric technologies in the Metaverse, as well as the relevant HCM application scenarios. We hope that this paper can provide researchers and developers with some directions and ideas for human-centric Metaverse construction.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=The+Human-Centric+Metaverse:+A+Survey)|0| -|[Can Deepfakes be created on a whim?](https://doi.org/10.1145/3543873.3587581)|Pulak Mehta, Gauri Jagatap, Kevin Gallagher, Brian Timmerman, Progga Deb, Siddharth Garg, Rachel Greenstadt, Brendan DolanGavitt|NOVA LINCS & Universidade NOVA de Lisboa, Portugal; New York University, USA|Recent advancements in machine learning and computer vision have led to the proliferation of Deepfakes. As technology democratizes over time, there is an increasing fear that novice users can create Deepfakes, to discredit others and undermine public discourse. In this paper, we conduct user studies to understand whether participants with advanced computer skills and varying level of computer science expertise can create Deepfakes of a person saying a target statement using limited media files. We conduct two studies; in the first study (n = 39) participants try creating a target Deepfake in a constrained time frame using any tool they desire. In the second study (n = 29) participants use pre-specified deep learning based tools to create the same Deepfake. We find that for the first study, of the participants successfully created complete Deepfakes with audio and video, whereas for the second user study, of the participants were successful in stitching target speech to the target video. We further use Deepfake detection software tools as well as human examiner-based analysis, to classify the successfully generated Deepfake outputs as fake, suspicious, or real. The software detector classified of the Deepfakes as fake, whereas the human examiners classified of the videos as fake. We conclude that creating Deepfakes is a simple enough task for a novice user given adequate tools and time; however, the resulting Deepfakes are not sufficiently real-looking and are unable to completely fool detection software as well as human examiners.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Can+Deepfakes+be+created+on+a+whim?)|0| +|[Financial Technology on the Web](https://doi.org/10.1145/3543873.3589738)|ChungChi Chen, HenHsen Huang, Hiroya Takamura, HsinHsi Chen|National Institute of Advanced Industrial Science and Technology, Japan; National Taiwan University, Taiwan; Institute of Information Science, Academia Sinica, Taiwan|This paper shares our observations based on our three-year experience organizing the FinWeb workshop series. In addition to the widely-discussed topic, content analysis, we notice two tendencies for FinTech applications: customers’ behavior analysis and finance-oriented LegalTech. We also briefly share our idea on the research direction about reliable and trustworthy FinWeb from the investment perspective.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Financial+Technology+on+the+Web)|0| +|[Aspect-based Summarization of Legal Case Files using Sentence Classification](https://doi.org/10.1145/3543873.3587611)|Nikhil E, Anshul Padhi, Pulkit Parikh, Swati Kanwal, Kamalakar Karlapalem, Natraj Raman|IIIT Hyderabad, Canada; IIIT Hyderabad, India; JP Morgan AI Research, United Kingdom|Aspect-based summarization of a legal case file related to regulating bodies allows different stakeholders to consume information of interest therein efficiently. In this paper, we propose a multi-step process to achieve the same. First, we explore the semantic sentence segmentation of SEBI case files via classification. We also propose a dataset of Indian legal adjudicating orders which contain tags from carefully crafted domain-specific sentence categories with the help of legal experts. We experiment with various machine learning and deep learning methods for this multi-class classification. Then, we examine the performance of numerous summarization methods on the segmented document to generate persona-specific summaries. Finally, we develop a pipeline making use of the best methods in both sub-tasks to achieve high recall.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Aspect-based+Summarization+of+Legal+Case+Files+using+Sentence+Classification)|0| +|[Exploiting graph metrics to detect anomalies in cross-country money transfer temporal networks](https://doi.org/10.1145/3543873.3587602)|Salvatore Vilella, Arthur Thomas Edward Capozzi Lupi, Giancarlo Ruffo, Marco Fornasiero, Dario Moncalvo, Valeria Ricci, Silvia Ronchiadin|Anti Financial Crime Digital Hub, Italy; Università degli Studi del Piemonte Orientale, Italy; Department of Computer Science, University of Turin, Italy|During the last decades, Anti-Financial Crime (AFC) entities and Financial Institutions have put a constantly increasing effort to reduce financial crime and detect fraudulent activities, that are changing and developing in extremely complex ways. We propose an anomaly detection approach based on network analysis to help AFC officers navigating through the high load of information that is typical of AFC data-driven scenarios. By experimenting on a large financial dataset of more than 80M cross-country wire transfers, we leverage on the properties of complex networks to develop a tool for explainable anomaly detection, that can help in identifying outliers that could be engaged in potentially malicious activities according to financial regulations. We identify a set of network metrics that provide useful insights on individual nodes; by keeping track of the evolution over time of the metric-based node rankings, we are able to highlight sudden and unexpected changes in the roles of individual nodes that deserve further attention by AFC officers. Such changes can hardly be noticed by means of current AFC practices, that sometimes can lack a higher-level, global vision of the system. This approach represents a preliminary step in the automation of AFC and AML processes, serving the purpose of facilitating the work of AFC officers by providing them with a top-down view of the picture emerging from financial data.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Exploiting+graph+metrics+to+detect+anomalies+in+cross-country+money+transfer+temporal+networks)|0| +|[Multiple-Agent Deep Reinforcement Learning for Avatar Migration in Vehicular Metaverses](https://doi.org/10.1145/3543873.3587573)|Junlong Chen, Jiangtian Nie, Minrui Xu, Lingjuan Lyu, Zehui Xiong, Jiawen Kang, Yongju Tong, Wenchao Jiang|Singapore University of Technology and Design, Singapore; Nanyang Technological University, Singapore; Guangdong University of Technology, China; Sony(Japan), Japan|Vehicular Metaverses are widely considered as the next Internet revolution to build a 3D virtual world with immersive virtual-real interaction for passengers and drivers. In vehicular Metaverse applications, avatars are digital representations of on-board users to obtain and manage immersive vehicular services (i.e., avatar tasks) in Metaverses and the data they generate. However, traditional Internet of Vehicles (IoV) data management solutions have serious data security risks and privacy protection. Fortunately, blockchain-based Web 3.0 enables avatars to have an ownership identity to securely manage the data owned by users in a decentralized and transparent manner. To ensure users’ immersive experiences and securely manage their data, avatar tasks often require significant computing resources. Therefore, it is impractical for the vehicles to process avatar tasks locally, massive computation resources are needed to support the avatar tasks. To this end, offloading avatar tasks to nearby RoadSide Units (RSUs) is a promising solution to avoid computation overload. To ensure real-time and continuous Metaverse services, the avatar tasks should be migrated among the RSUs when the vehicle navigation. It is challenging for the vehicles to independently decide whether migrate or not according to current and future avatar states. Therefore, in this paper, we propose a new avatar task migration framework for vehicular Metaverses. We then formulate the avatar task migration problem as a Partially Observable Markov Decision Process (POMDP), and apply a Multi-Agent Deep Reinforcement Learning (MADRL) algorithm to dynamically make migration decisions for avatar tasks. Numerous results show that our proposed algorithm outperforms existing baselines for avatar task migration and enables immersive vehicular Metaverse services.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Multiple-Agent+Deep+Reinforcement+Learning+for+Avatar+Migration+in+Vehicular+Metaverses)|0| +|[China's First Natural Language-based AI ChatBot Trader](https://doi.org/10.1145/3543873.3587633)|James Y. Zhang, Zhi Li, Hao Fang, Jun Wu, Zhongnan Shen, Jing Zheng, Wei Chu, Weiping Duan, Peng Xu|Ant Group, USA; MyBank, China|Repo (repurchase agreement) trading provides easy access to short-term financing secured by a pledge of collateral and plays an important role in the global financial system. However, repo traders face many tough challenges in their job, from managing complex financial transactions to keeping up with changing market trends and regulations in the complex financial transactions involved. Besides the difficult and tedious processes that take a lot of time and energy, repo traders need to keep up to date with various laws, regulations, and financial trends that may affect their job, worsened by the exposure to a variety of market risks. As the leader of the FinTech industry, Ant Group launched a new initiative to alleviate the affliction of the repo traders at MyBank1. By leveraging many existing platform technologies, such as AI ChatBot and forecasting platforms, and with the collective work of various engineering groups, we are able to create a ChatBot that communicates with other human traders in natural language and create electronic contracts based on the negotiated terms, equipped with proper trading strategies based on forecasting results. The fully automatic workflow not only frees our trader from tedious routines, but also reduces potential human errors. At the same time, it enables refined portfolio and risk management, while opening up the possibility to apply neural network-based trading strategies, and yielding greater returns comparing to traditional workflow reliant on human experiences. Our system has evolved beyond just providing services to our own traders, to now a fully commercialized product, covering other types of interbank trading.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=China's+First+Natural+Language-based+AI+ChatBot+Trader)|0| +|[Web 3.0: The Future of Internet](https://doi.org/10.1145/3543873.3587583)|Wensheng Gan, Zhenqiang Ye, Shicheng Wan, Philip S. Yu|Jinan University, China; Guangdong University of Technology, China; University of Illinois at Chicago, USA|With the rapid growth of the Internet, human daily life has become deeply bound to the Internet. To take advantage of massive amounts of data and information on the internet, the Web architecture is continuously being reinvented and upgraded. From the static informative characteristics of Web 1.0 to the dynamic interactive features of Web 2.0, scholars and engineers have worked hard to make the internet world more open, inclusive, and equal. Indeed, the next generation of Web evolution (i.e., Web 3.0) is already coming and shaping our lives. Web 3.0 is a decentralized Web architecture that is more intelligent and safer than before. The risks and ruin posed by monopolists or criminals will be greatly reduced by a complete reconstruction of the Internet and IT infrastructure. In a word, Web 3.0 is capable of addressing web data ownership according to distributed technology. It will optimize the internet world from the perspectives of economy, culture, and technology. Then it promotes novel content production methods, organizational structures, and economic forms. However, Web 3.0 is not mature and is now being disputed. Herein, this paper presents a comprehensive survey of Web 3.0, with a focus on current technologies, challenges, opportunities, and outlook. This article first introduces a brief overview of the history of World Wide Web as well as several differences among Web 1.0, Web 2.0, Web 3.0, and Web3. Then, some technical implementations of Web 3.0 are illustrated in detail. We discuss the revolution and benefits that Web 3.0 brings. Finally, we explore several challenges and issues in this promising area.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Web+3.0:+The+Future+of+Internet)|0| +|[DSNet: Efficient Lightweight Model for Video Salient Object Detection for IoT and WoT Applications](https://doi.org/10.1145/3543873.3587592)|Hemraj Singh, Mridula Verma, Ramalingaswamy Cheruku|National Institute of Technology Warangal, India; Institute for Development and Research in Banking Technology, India|The most challenging aspects of deploying deep models in IoT and embedded systems are extensive computational complexity and large training and inference time. Although various lightweight versions of state-of-the-art models are also being designed, maintaining the performance of such models is difficult. To overcome these problems, an efficient, lightweight, Deformable Separable Network (DSNet) is proposed for video salient object detection tasks, mainly for mobile and embedded vision applications. DSNet is equipped with a Deformable Convolution Network (DeCNet), Separable Convolution Network (SCNet), and Depth-wise Attention Response Propagation (DARP) module, which makes it maintain the trade-off between accuracy and latency. The proposed model generates saliency maps considering both the background and foreground simultaneously, making it perform better in unconstrained scenarios (such as partial occlusion, deformable background/objects, and illumination effect). The extensive experiments conducted on six benchmark datasets demonstrate that the proposed model outperforms state-of-art approaches in terms of computational complexity, number of parameters, and latency measures.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=DSNet:+Efficient+Lightweight+Model+for+Video+Salient+Object+Detection+for+IoT+and+WoT+Applications)|0| +|[Weighted Statistically Significant Pattern Mining](https://doi.org/10.1145/3543873.3587586)|Tingfu Zhou, Zhenlian Qi, Wensheng Gan, Shicheng Wan, Guoting Chen|Guangdong Eco-Engineering Polytechnic, China; Jinan University, China; Guangdong University of Technology, China; Harbin Institute of Technology, China|Pattern discovery (aka pattern mining) is a fundamental task in the field of data science. Statistically significant pattern mining (SSPM) is the task of finding useful patterns that statistically occur more often from databases for one class than for another. The existing SSPM task does not consider the weight of each item. While in the real world, the significant level of different items/objects is various. Therefore, in this paper, we introduce the Weighted Statistically Significant Patterns Mining (WSSPM) problem and propose a novel WSSpm algorithm to successfully solve it. We present a new framework that effectively mines weighted statistically significant patterns by combining the weighted upper-bound model and the multiple hypotheses test. We also propose a new weighted support threshold that can satisfy the demand of WSSPM and prove its correctness and completeness. Besides, our weighted support threshold and modified weighted upper-bound can effectively shrink the mining range. Finally, experimental results on several real datasets show that the WSSpm algorithm performs well in terms of execution time and memory storage.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Weighted+Statistically+Significant+Pattern+Mining)|0| +|[The Human-Centric Metaverse: A Survey](https://doi.org/10.1145/3543873.3587593)|Riyan Yang, Lin Li, Wensheng Gan, Zefeng Chen, Zhenlian Qi|Guangdong Eco-Engineering Polytechnic, China; Jinan University, China|In the era of the Web of Things, the Metaverse is expected to be the landing site for the next generation of the Internet, resulting in the increased popularity of related technologies and applications in recent years and gradually becoming the focus of Internet research. The Metaverse, as a link between the real and virtual worlds, can provide users with immersive experiences. As the concept of the Metaverse grows in popularity, many scholars and developers begin to focus on the Metaverse's ethics and core. This paper argues that the Metaverse should be centered on humans. That is, humans constitute the majority of the Metaverse. As a result, we begin this paper by introducing the Metaverse's origins, characteristics, related technologies, and the concept of the human-centric Metaverse (HCM). Second, we discuss the manifestation of human-centric in the Metaverse. Finally, we discuss some current issues in the construction of HCM. In this paper, we provide a detailed review of the applications of human-centric technologies in the Metaverse, as well as the relevant HCM application scenarios. We hope that this paper can provide researchers and developers with some directions and ideas for human-centric Metaverse construction.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=The+Human-Centric+Metaverse:+A+Survey)|0| +|[Can Deepfakes be created on a whim?](https://doi.org/10.1145/3543873.3587581)|Pulak Mehta, Gauri Jagatap, Kevin Gallagher, Brian Timmerman, Progga Deb, Siddharth Garg, Rachel Greenstadt, Brendan DolanGavitt|New York University, USA; NOVA LINCS & Universidade NOVA de Lisboa, Portugal|Recent advancements in machine learning and computer vision have led to the proliferation of Deepfakes. As technology democratizes over time, there is an increasing fear that novice users can create Deepfakes, to discredit others and undermine public discourse. In this paper, we conduct user studies to understand whether participants with advanced computer skills and varying level of computer science expertise can create Deepfakes of a person saying a target statement using limited media files. We conduct two studies; in the first study (n = 39) participants try creating a target Deepfake in a constrained time frame using any tool they desire. In the second study (n = 29) participants use pre-specified deep learning based tools to create the same Deepfake. We find that for the first study, of the participants successfully created complete Deepfakes with audio and video, whereas for the second user study, of the participants were successful in stitching target speech to the target video. We further use Deepfake detection software tools as well as human examiner-based analysis, to classify the successfully generated Deepfake outputs as fake, suspicious, or real. The software detector classified of the Deepfakes as fake, whereas the human examiners classified of the videos as fake. We conclude that creating Deepfakes is a simple enough task for a novice user given adequate tools and time; however, the resulting Deepfakes are not sufficiently real-looking and are unable to completely fool detection software as well as human examiners.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Can+Deepfakes+be+created+on+a+whim?)|0| |[On Cohesively Polarized Communities in Signed Networks](https://doi.org/10.1145/3543873.3587698)|Jason Niu, Ahmet Erdem Sariyüce|University at Buffalo, USA|Locating and characterizing polarization is one of the most important issues to enable a healthier web ecosystem. Finding groups of nodes that form strongly stable agreements and participate in collective conflicts with other groups is an important problem in this context. Previous works approach this problem by finding balanced subgraphs, in which the polarity measure is optimized, that result in large subgraphs without a clear notion of agreement or conflict. In real-world signed networks, balanced subgraphs are often not polarized as in the case of a subgraph with only positive edges. To remedy this issue, we leverage the notion of cohesion — we find pairs of cohesively polarized communities where each node in a community is positively connected to nodes in the same community and negatively connected to nodes in the other community. To capture the cohesion along with the polarization, we define a new measure, dichotomy. We leverage the balanced triangles, which model the cohesion and polarization at the same time, to design a heuristic that results in good seedbeds for polarized communities in real-world signed networks. Then, we introduce the electron decomposition which finds cohesively polarized communities with high dichotomy score. In an extensive experimental evaluation, we show that our method finds cohesively polarized communities and outperforms the state-of-the-art methods with respect to several measures. Moreover, our algorithm is more efficient than the existing methods and practical for large-scale networks.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=On+Cohesively+Polarized+Communities+in+Signed+Networks)|0| -|[Towards Automated Detection of Risky Images Shared by Youth on Social Media](https://doi.org/10.1145/3543873.3587607)|Jinkyung Park, Joshua Gracie, Ashwaq Alsoubai, Gianluca Stringhini, Vivek K. Singh, Pamela J. Wisniewski|University of Central Florida, USA; Vanderbilt University, USA; Boston University, USA; Rutgers University, USA|With the growing ubiquity of the Internet and access to media-based social media platforms, the risks associated with media content sharing on social media and the need for safety measures against such risks have grown paramount. At the same time, risk is highly contextualized, especially when it comes to media content youth share privately on social media. In this work, we conducted qualitative content analyses on risky media content flagged by youth participants and research assistants of similar ages to explore contextual dimensions of youth online risks. The contextual risk dimensions were then used to inform semi- and self-supervised state-of-the-art vision transformers to automate the process of identifying risky images shared by youth. We found that vision transformers are capable of learning complex image features for use in automated risk detection and classification. The results of our study serve as a foundation for designing contextualized and youth-centered machine-learning methods for automated online risk detection.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Towards+Automated+Detection+of+Risky+Images+Shared+by+Youth+on+Social+Media)|0| +|[Towards Automated Detection of Risky Images Shared by Youth on Social Media](https://doi.org/10.1145/3543873.3587607)|Jinkyung Park, Joshua Gracie, Ashwaq Alsoubai, Gianluca Stringhini, Vivek K. Singh, Pamela J. Wisniewski|Boston University, USA; Rutgers University, USA; Vanderbilt University, USA; University of Central Florida, USA|With the growing ubiquity of the Internet and access to media-based social media platforms, the risks associated with media content sharing on social media and the need for safety measures against such risks have grown paramount. At the same time, risk is highly contextualized, especially when it comes to media content youth share privately on social media. In this work, we conducted qualitative content analyses on risky media content flagged by youth participants and research assistants of similar ages to explore contextual dimensions of youth online risks. The contextual risk dimensions were then used to inform semi- and self-supervised state-of-the-art vision transformers to automate the process of identifying risky images shared by youth. We found that vision transformers are capable of learning complex image features for use in automated risk detection and classification. The results of our study serve as a foundation for designing contextualized and youth-centered machine-learning methods for automated online risk detection.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Towards+Automated+Detection+of+Risky+Images+Shared+by+Youth+on+Social+Media)|0| |[Detecting Social Media Manipulation in Low-Resource Languages](https://doi.org/10.1145/3543873.3587615)|Samar Haider, Luca Luceri, Ashok Deb, Adam Badawy, Nanyun Peng, Emilio Ferrara|University of Southern California, USA|Social media have been deliberately used for malicious purposes, including political manipulation and disinformation. Most research focuses on high-resource languages. However, malicious actors share content across countries and languages, including low-resource ones. Here, we investigate whether and to what extent malicious actors can be detected in low-resource language settings. We discovered that a high number of accounts posting in Tagalog were suspended as part of Twitter's crackdown on interference operations after the 2016 US Presidential election. By combining text embedding and transfer learning, our framework can detect, with promising accuracy, malicious users posting in Tagalog without any prior knowledge or training on malicious content in that language. We first learn an embedding model for each language, namely a high-resource language (English) and a low-resource one (Tagalog), independently. Then, we learn a mapping between the two latent spaces to transfer the detection model. We demonstrate that the proposed approach significantly outperforms state-of-the-art models, including BERT, and yields marked advantages in settings with very limited training data -- the norm when dealing with detecting malicious activity in online platforms.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Detecting+Social+Media+Manipulation+in+Low-Resource+Languages)|0| |[Text Mining-based Social-Psychological Vulnerability Analysis of Potential Victims To Cybergrooming: Insights and Lessons Learned](https://doi.org/10.1145/3543873.3587636)|Zhen Guo, Pei Wang, JinHee Cho, Lifu Huang|Microsoft, USA; Computer Science, Virginia Tech, USA|Cybergrooming is a serious cybercrime that primarily targets youths through online platforms. Although reactive predator detection methods have been studied, proactive victim protection and crime prevention can also be achieved through vulnerability analysis of potential youth victims. Despite its significance, vulnerability analysis has not been thoroughly studied in the data science literature, while several social science studies used survey-based methods. To address this gap, we investigate humans’ social-psychological traits and quantify key vulnerability factors to cybergrooming by analyzing text features in the Linguistic Inquiry and Word Count (LIWC). Through pairwise correlation studies, we demonstrate the degrees of key vulnerability dimensions to cybergrooming from youths’ conversational features. Our findings reveal that victims have negative correlations with family and community traits, contrasting with previous social survey studies that indicated family relationships or social support as key vulnerability factors. We discuss the current limitations of text mining analysis and suggest cross-validation methods to increase the validity of research findings. Overall, this study provides valuable insights into understanding the vulnerability factors to cybergrooming and highlights the importance of adopting multidisciplinary approaches.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Text+Mining-based+Social-Psychological+Vulnerability+Analysis+of+Potential+Victims+To+Cybergrooming:+Insights+and+Lessons+Learned)|0| |[Evaluating the Emergence of Collective Identity using Socio-Computational Techniques](https://doi.org/10.1145/3543873.3587637)|Billy Spann, Nitin Agarwal, David Stafford, Obianuju Okeke|Collaboratorium for Social Media and Online Behavioral Studies (COSMOS) - University of Arkansas at Little Rock, USA|Social media platforms provide fertile ground for investigating the processes of identity creation and communication that shape individual and public opinion. The computational methods used in social network analysis have opened the way for new approaches to be used to understand the psychological and social processes that occur when users take part in online social movements or digital activism. The research in this paper takes an interdisciplinary approach bridging social identity and deindividuation theories to show how shared, individual social identities merge into a collective identity using computational techniques. We demonstrate a novel approach to evaluating the emergence of collective identity by measuring: 1) the statistical similarity of discussion topics within online communities and 2) the strength of these communities by examining network modularity and assortative properties of the network. To accomplish this, we examined the online connective action campaign of the #stopthesteal movement that emerged during the 2020 U.S. Presidential Election. Our dataset consisted of 838,395 tweets posted by 178,296 users collected from January 04, 2020, to January 31, 2021. The results show that the network becomes more cohesive and topic similarity increases within communities leading up to and just after the elections (event 1) and the U.S. Capitol riot (event 2). Taking this multi-method approach of measuring content and network structure over time helps researchers and social scientists understand the emergence of a collective community as it is being constructed. The use of computational methods to study collective identity formation can help researchers identify the behaviors and social dynamics emerging from this type of cyber-collective movement that often serve as catalysts for these types of events. Finally, this research offers a new way to assess the psycho-social drivers of participant behaviors in cyber collective action.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Evaluating+the+Emergence+of+Collective+Identity+using+Socio-Computational+Techniques)|0| -|[Trusting Decentralised Knowledge Graphs and Web Data at the Web Conference](https://doi.org/10.1145/3543873.3589756)|John Domingue, Aisling Third, MariaEsther Vidal, Philipp D. Rohde, Juan Cano, Andrea Cimmino, Ruben Verborgh|Ghent University, Belgium; Leibniz University Hannover and TIB Leibniz Information Centre for Science and Technology, Germany; Universidad Politécnica de Madrid, Spain; The Open University, United Kingdom|Knowledge Graphs have become a foundation for sharing data on the web and building intelligent services across many sectors and also within some of the most successful corporations in the world. The over centralisation of data on the web, however, has been raised as a concern by a number of prominent researchers in the field. For example, at the beginning of 2022 a €2.7B civil lawsuit was launched against Meta on the basis that it has abused its market dominance to impose unfair terms and conditions on UK users in order to exploit their personal data. Data centralisation can lead to a number of problems including: lock-in/siloing effects, lack of user control over their personal data, limited incentives and opportunities for interoperability and openness, and the resulting detrimental effects on privacy and innovation. A number of diverse approaches and technologies exist for decentralising data, such as federated querying and distributed ledgers. The main question is, though, what does decentralisation really mean for web data and Knowledge Graphs? What are the main issues and tradeoffs involved? These questions and others are addressed in this workshop.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Trusting+Decentralised+Knowledge+Graphs+and+Web+Data+at+the+Web+Conference)|0| -|[A Decentralised Persistent Identification Layer for DCAT Datasets](https://doi.org/10.1145/3543873.3587589)|Fabian Kirstein, Anton Altenbernd, Sonja Schimmler, Manfred Hauswirth|Fraunhofer FOKUS, Germany; TU Berlin, Open Distributed Systems, Germany and Weizenbaum Institute, Germany; Fraunhofer FOKUS, Germany and Weizenbaum Institute, Germany|The Data Catalogue Vocabulary (DCAT) standard is a popular RDF vocabulary for publishing metadata about data catalogs and a valuable foundation for creating Knowledge Graphs. It has widespread application in the (Linked) Open Data and scientific communities. However, DCAT does not specify a robust mechanism to create and maintain persistent identifiers for the datasets. It relies on Internationalized Resource Identifiers (IRIs), that are not necessarily unique, resolvable and persistent. This impedes findability, citation abilities, and traceability of derived and aggregated data artifacts. As a remedy, we propose a decentralized identifier registry where persistent identifiers are managed by a set of collaborative distributed nodes. Every node gives full access to all identifiers, since an unambiguous state is shared across all nodes. This facilitates a common view on the identifiers without the need for a (virtually) centralized directory. To support this architecture, we propose a data model and network methodology based on a distributed ledger and the W3C recommendation for Decentralized Identifiers (DID). We implemented our approach as a working prototype on a five-peer test network based on Hyperledger Fabric.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Decentralised+Persistent+Identification+Layer+for+DCAT+Datasets)|0| -|[Practical challenges of ODRL and potential courses of action](https://doi.org/10.1145/3543873.3587628)|Andrea Cimmino, Juan CanoBenito, Raúl GarcíaCastro|UPM, Spain; Universidad Politécnica de Madrid, Spain|The Open Digital Rights Language (ODRL) is a standard widely adopted to express privacy policies. This article presents several challenges identified in the context of the European project AURORAL in which ODRL is used to express privacy policies for Smart Communities and Rural Areas. The article presents that some challenges should be addressed directly by the ODRL standardisation group to achieve the best course of action, although others exists. For others, the authors have presented a potential solution, in particular, for considering dynamic values coming from external data sources into privacy policies. Finally, the last challenge is an open research question, since it revolves around the interoperability of privacy policies that belong to different systems and that are expressed with different privacy languages.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Practical+challenges+of+ODRL+and+potential+courses+of+action)|0| +|[Trusting Decentralised Knowledge Graphs and Web Data at the Web Conference](https://doi.org/10.1145/3543873.3589756)|John Domingue, Aisling Third, MariaEsther Vidal, Philipp D. Rohde, Juan Cano, Andrea Cimmino, Ruben Verborgh|Leibniz University Hannover and TIB Leibniz Information Centre for Science and Technology, Germany; Ghent University, Belgium; Universidad Politécnica de Madrid, Spain; The Open University, United Kingdom|Knowledge Graphs have become a foundation for sharing data on the web and building intelligent services across many sectors and also within some of the most successful corporations in the world. The over centralisation of data on the web, however, has been raised as a concern by a number of prominent researchers in the field. For example, at the beginning of 2022 a €2.7B civil lawsuit was launched against Meta on the basis that it has abused its market dominance to impose unfair terms and conditions on UK users in order to exploit their personal data. Data centralisation can lead to a number of problems including: lock-in/siloing effects, lack of user control over their personal data, limited incentives and opportunities for interoperability and openness, and the resulting detrimental effects on privacy and innovation. A number of diverse approaches and technologies exist for decentralising data, such as federated querying and distributed ledgers. The main question is, though, what does decentralisation really mean for web data and Knowledge Graphs? What are the main issues and tradeoffs involved? These questions and others are addressed in this workshop.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Trusting+Decentralised+Knowledge+Graphs+and+Web+Data+at+the+Web+Conference)|0| +|[A Decentralised Persistent Identification Layer for DCAT Datasets](https://doi.org/10.1145/3543873.3587589)|Fabian Kirstein, Anton Altenbernd, Sonja Schimmler, Manfred Hauswirth|TU Berlin, Open Distributed Systems, Germany and Weizenbaum Institute, Germany; Fraunhofer FOKUS, Germany; Fraunhofer FOKUS, Germany and Weizenbaum Institute, Germany|The Data Catalogue Vocabulary (DCAT) standard is a popular RDF vocabulary for publishing metadata about data catalogs and a valuable foundation for creating Knowledge Graphs. It has widespread application in the (Linked) Open Data and scientific communities. However, DCAT does not specify a robust mechanism to create and maintain persistent identifiers for the datasets. It relies on Internationalized Resource Identifiers (IRIs), that are not necessarily unique, resolvable and persistent. This impedes findability, citation abilities, and traceability of derived and aggregated data artifacts. As a remedy, we propose a decentralized identifier registry where persistent identifiers are managed by a set of collaborative distributed nodes. Every node gives full access to all identifiers, since an unambiguous state is shared across all nodes. This facilitates a common view on the identifiers without the need for a (virtually) centralized directory. To support this architecture, we propose a data model and network methodology based on a distributed ledger and the W3C recommendation for Decentralized Identifiers (DID). We implemented our approach as a working prototype on a five-peer test network based on Hyperledger Fabric.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Decentralised+Persistent+Identification+Layer+for+DCAT+Datasets)|0| +|[Practical challenges of ODRL and potential courses of action](https://doi.org/10.1145/3543873.3587628)|Andrea Cimmino, Juan CanoBenito, Raúl GarcíaCastro|Universidad Politécnica de Madrid, Spain; UPM, Spain|The Open Digital Rights Language (ODRL) is a standard widely adopted to express privacy policies. This article presents several challenges identified in the context of the European project AURORAL in which ODRL is used to express privacy policies for Smart Communities and Rural Areas. The article presents that some challenges should be addressed directly by the ODRL standardisation group to achieve the best course of action, although others exists. For others, the authors have presented a potential solution, in particular, for considering dynamic values coming from external data sources into privacy policies. Finally, the last challenge is an open research question, since it revolves around the interoperability of privacy policies that belong to different systems and that are expressed with different privacy languages.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Practical+challenges+of+ODRL+and+potential+courses+of+action)|0| |[The Web and Linked Data as a Solid Foundation for Dataspaces](https://doi.org/10.1145/3543873.3587616)|Sascha Meckler, Rene Dorsch, Daniel Henselmann, Andreas Harth|Fraunhofer IIS, Fraunhofer Institute for Integrated Circuits IIS, Germany|The concepts for dataspaces range from database management systems to cross-company platforms for data and applications. In this short paper, we present the “Solid Data Space” (SDS), a concept for dataspaces that build on top of the (Semantic) Web and Social Linked Data (Solid). Existing Web technologies and Linked Data principles form the foundation for open, decentralized networks for sovereign data exchange between citizens, organizations and companies. Domain-specific dataspace implementations can extend the agreements for communication and collaboration to enable specific functionality. We compare the SDS with principles and components of the emerging International Data Spaces to identify similarities and point out technological differences.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=The+Web+and+Linked+Data+as+a+Solid+Foundation+for+Dataspaces)|0| |[Extending Actor Models in Data Spaces](https://doi.org/10.1145/3543873.3587645)|Hendrik Meyer zum Felde, Maarten Kollenstart, Thomas Bellebaum, Simon Dalmolen, Gerd Brost|Fraunhofer AISEC, Germany; TNO, Netherlands|In today’s internet almost any party can share sets of data with each other. However, creating frameworks and regulated realms for the sharing of data is very complex when multiple parties are involved and complicated regulation comes into play. As solution data spaces were introduced to enable participating parties to share data among themselves in an organized, regulated and standardized way. However, contract data processors, acting as data space participants, are currently unable to execute data requests on behalf of their contract partners. Here we show that an on-behalf-of actor model can be easily added to existing data spaces. We demonstrate how this extension can be realized using verifiable credentials. We provide a sample use case, a detailed sequence diagram and discuss necessary architectural adaptations and additions to established protocols. Using the extensions explained in this work numerous real life use cases which previously could technically not be realized can now be covered. This enables future data spaces to provide more dynamic and complex real world use cases.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Extending+Actor+Models+in+Data+Spaces)|0| |[Towards Decentralised Learning Analytics (Positioning Paper)](https://doi.org/10.1145/3543873.3587644)|Audrey Ekuban, John Domingue|The Open University, United Kingdom|When students interact with an online course, the routes they take when navigating through the course can be captured. Learning Analytics is the process of measuring, collecting, recording, and analysing this Student Activity Data. Predictive Learning Analytics, a sub-field of Learning Analytics, can help to identify students who are at risk of dropping out or failing, as well as students who are close to a grade boundary. Course tutors can use the insights provided by the analyses to offer timely assistance to these students. Despite its usefulness, there are privacy and ethical issues with the typically centralised approach to Predictive Learning Analytics. In this positioning paper, it is proposed that the issues associated with Predictive Learning Analytics can be alleviated, in a framework called EMPRESS, by combining 1) self-sovereign data, where data owners control who legitimately has access to data pertaining to them, 2) Federated Learning, where the data remains on the data owner’s device and/or the data is processed by the data owners themselves, and 3) Graph Convolutional Networks for Heterogeneous graphs, which are examples of knowledge graphs.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Towards+Decentralised+Learning+Analytics+(Positioning+Paper))|0| -|[Analyzing Distributed Medical Data in FAIR Data Spaces](https://doi.org/10.1145/3543873.3587663)|Mehrshad Jaberansary, Macedo Maia, Yeliz Ucer Yediel, Oya Beyan, Toralf Kirsten|Fraunhofer Institute for Applied Information Technology (FIT), Germany and Information Systems and Database Technology Research Group, RWTH Aachen University, 52062 Aachen, Germany, Germany; Institute for Biomedical Informatics, Faculty of Medicine and University Hospital Cologne, University of Cologne, Germany and Fraunhofer Institute for Applied Information Techniques (FIT), Germany; Institute for Biomedical Informatics, Faculty of Medicine and University Hospital Cologne, University of Cologne, Germany; Institute for Medical Informatics, Statistics and Epidemiology (IMISE), Leipzig University, Germany|The exponential growth in data production has led to increasing demand for high-quality data-driven services. Additionally, the benefits of data-driven analysis are vast and have significantly propelled research in many fields. Data sharing benefits scientific advancement, as it promotes transparency, and collaboration, accelerates research and aids in making informed decisions. The European strategy for data aims to create a single data market that ensures Europe’s global competitiveness and data sovereignty. Common European Data Spaces ensure that data from different sources are available in the economy and society, while data providers (e.g., hospitals and scientists) control data access. The National Research Data Infrastructure for Personal Health Data (NFDI4Health) initiative is a prime example of an effort focused on data from clinical trials and public health studies. Collecting and analyzing this data is essential to developing novel therapies, comprehensive care approaches, and preventive measures in modern healthcare systems. This work describes distributed data analysis services and components that adhere to the FAIR data principles (Findable, Accessible, Interoperable, and Reusable) within the data space environment. We focus on distributed analytics functionality in Gaia-X-based data spaces. Gaia-X offers a trustworthy federation of data infrastructure and service providers for European countries.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Analyzing+Distributed+Medical+Data+in+FAIR+Data+Spaces)|0| +|[Analyzing Distributed Medical Data in FAIR Data Spaces](https://doi.org/10.1145/3543873.3587663)|Mehrshad Jaberansary, Macedo Maia, Yeliz Ucer Yediel, Oya Beyan, Toralf Kirsten|Institute for Biomedical Informatics, Faculty of Medicine and University Hospital Cologne, University of Cologne, Germany and Fraunhofer Institute for Applied Information Techniques (FIT), Germany; Institute for Medical Informatics, Statistics and Epidemiology (IMISE), Leipzig University, Germany; Fraunhofer Institute for Applied Information Technology (FIT), Germany and Information Systems and Database Technology Research Group, RWTH Aachen University, 52062 Aachen, Germany, Germany; Institute for Biomedical Informatics, Faculty of Medicine and University Hospital Cologne, University of Cologne, Germany|The exponential growth in data production has led to increasing demand for high-quality data-driven services. Additionally, the benefits of data-driven analysis are vast and have significantly propelled research in many fields. Data sharing benefits scientific advancement, as it promotes transparency, and collaboration, accelerates research and aids in making informed decisions. The European strategy for data aims to create a single data market that ensures Europe’s global competitiveness and data sovereignty. Common European Data Spaces ensure that data from different sources are available in the economy and society, while data providers (e.g., hospitals and scientists) control data access. The National Research Data Infrastructure for Personal Health Data (NFDI4Health) initiative is a prime example of an effort focused on data from clinical trials and public health studies. Collecting and analyzing this data is essential to developing novel therapies, comprehensive care approaches, and preventive measures in modern healthcare systems. This work describes distributed data analysis services and components that adhere to the FAIR data principles (Findable, Accessible, Interoperable, and Reusable) within the data space environment. We focus on distributed analytics functionality in Gaia-X-based data spaces. Gaia-X offers a trustworthy federation of data infrastructure and service providers for European countries.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Analyzing+Distributed+Medical+Data+in+FAIR+Data+Spaces)|0| |[Requirements and Building Blocks for Manufacturing Dataspaces](https://doi.org/10.1145/3543873.3587664)|Rohit A. Deshmukh, Sisay Adugna Chala, Christoph Lange|Fraunhofer Institute for Applied Information Technology FIT, 53754 Sankt Augustin, Germany and Chair of Databases and Information Systems (i5), RWTH Aachen University, 52074 Aachen, Germany; Fraunhofer Institute for Applied Information Technology FIT, 53754 Sankt Augustin, Germany|With the advent and pervasiveness of the Internet of Things (IoT), big data and cloud computing technologies, digitalization in enterprises and factories has rapidly increased in the last few years. Digital platforms have emerged as an effective mechanism for enabling the management and sharing of data from various companies. To enable sharing of data beyond the platform boundaries, definition of new platform federation approaches is on the rise. This makes enabling the federation of digital platforms a key requirement for large-scale dataspaces. Therefore, the identification of platform federation requirements and building blocks for such dataspaces needs to be systematically addressed. In this paper, we try to systematically explore the high-level requirements for enabling a federation of digital platforms in the manufacturing domain and identify a set of building blocks. We integrate the requirements and building blocks into the notion of dataspaces. The identified requirements and building blocks act as a blueprint for designing and instantiating new dataspaces, thereby speeding up the development process and reducing costs. We present a case study to illustrate how the use of common building blocks can act as common guiding principles and result in more complete and interoperable implementations.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Requirements+and+Building+Blocks+for+Manufacturing+Dataspaces)|0| -|[Towards Multimodal Knowledge Graphs for Data Spaces](https://doi.org/10.1145/3543873.3587665)|Atiya Usmani, Muhammad Jaleed Khan, John G. Breslin, Edward Curry|SFI Centre for Research Training in Artificial Intelligence, Data Science Institute, University of Galway, Ireland; Insight SFI Research Centre for Data Analytics, Data Science Institute, University of Galway, Ireland|Multimodal knowledge graphs have the potential to enhance data spaces by providing a unified and semantically grounded structured representation of multimodal data produced by multiple sources. With the ability to integrate and analyze data in real-time, multimodal knowledge graphs offer a wealth of insights for smart city applications, such as monitoring traffic flow, air quality, public safety, and identifying potential hazards. Knowledge enrichment can enable a more comprehensive representation of multimodal data and intuitive decision-making with improved expressiveness and generalizability. However, challenges remain in effectively modelling the complex relationships between and within different types of modalities in data spaces and infusing common sense knowledge from external sources. This paper reviews the related literature and identifies major challenges and key requirements for effectively developing multimodal knowledge graphs for data spaces, and proposes an ontology for their construction.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Towards+Multimodal+Knowledge+Graphs+for+Data+Spaces)|0| +|[Towards Multimodal Knowledge Graphs for Data Spaces](https://doi.org/10.1145/3543873.3587665)|Atiya Usmani, Muhammad Jaleed Khan, John G. Breslin, Edward Curry|Insight SFI Research Centre for Data Analytics, Data Science Institute, University of Galway, Ireland; SFI Centre for Research Training in Artificial Intelligence, Data Science Institute, University of Galway, Ireland|Multimodal knowledge graphs have the potential to enhance data spaces by providing a unified and semantically grounded structured representation of multimodal data produced by multiple sources. With the ability to integrate and analyze data in real-time, multimodal knowledge graphs offer a wealth of insights for smart city applications, such as monitoring traffic flow, air quality, public safety, and identifying potential hazards. Knowledge enrichment can enable a more comprehensive representation of multimodal data and intuitive decision-making with improved expressiveness and generalizability. However, challenges remain in effectively modelling the complex relationships between and within different types of modalities in data spaces and infusing common sense knowledge from external sources. This paper reviews the related literature and identifies major challenges and key requirements for effectively developing multimodal knowledge graphs for data spaces, and proposes an ontology for their construction.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Towards+Multimodal+Knowledge+Graphs+for+Data+Spaces)|0| |[SPACE_DS: Towards a Circular Economy Data Space](https://doi.org/10.1145/3543873.3587685)|André Pomp, Maike Jansen, Holger Berg, Tobias Meisen|Institute for Technologies and Management of Digital Transformation, University of Wuppertal, Germany; Wuppertal Institute for Climate, Environment and Energy, Germany|The circular economy (CE) is essential to achieving a sustainable future through resource conservation and climate protection. Efficient use of materials and products over time is a critical aspect of CE, helping to reduce CO2 emissions, waste and resource consumption. The Digital Product Passport (DPP) is a CE-specific approach that contains information about components and their origin, and can also provide environmental and social impact assessments. However, creating a DPP requires collecting and analyzing data from many different stakeholders along the supply chain and even throughout the product lifecycle. In this paper, we present a concept for the SPACE_DS, which is a data space for circular economy data. A key point here is that the SPACE_DS enables the creation of DPPs by especially considering privacy and security concerns of data providers.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=SPACE_DS:+Towards+a+Circular+Economy+Data+Space)|0| |[Towards a Data Space for Interoperability of Analytic Provenance](https://doi.org/10.1145/3543873.3587686)|Tristan Langer, André Pomp, Tobias Meisen|Institute for Technologies and Management of Digital Transformation, University of Wuppertal, Germany|Capturing, visualizing and analyzing provenance data to better understand and support analytic reasoning processes is a rapidly growing research field named analytic provenance. Provenance data includes the state of a visualization within a tool as well as the user’s interactions performed while interacting with the tool. Research in this field has produced in many new approaches that generate data for specific tools and use cases. However, since a variety of tools are used and analytic tasks are performed in real analysis use cases there is a problem in building an interoperable baseline data corpus for investigation of the transferability of different approaches. In this paper, we present a visionary data space architecture for integrating and processing analytic provenance data in a unified way using semantic modeling. We discuss emerging challenges and research opportunities to realize such a vision using semantic models in data spaces to enable analytic provenance data interoperability.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Towards+a+Data+Space+for+Interoperability+of+Analytic+Provenance)|0| |[Provenance for Lattice QCD workflows](https://doi.org/10.1145/3543873.3587559)|Tanja Auge, Gunnar Bali, Meike Klettke, Bertram Ludäscher, Wolfgang Söldner, Simon Weishäupl, Tilo Wettig|Department of Physics, University of Regensburg, Germany; School of Information Sciences, University of Illinois at Urbana-Champaign, USA; Faculty of Computer Science and Data Science, University of Regensburg, Germany|We present a provenance model for the generic workflow of numerical Lattice Quantum Chromodynamics (QCD) calculations, which constitute an important component of particle physics research. These calculations are carried out on the largest supercomputers worldwide with data in the multi-PetaByte range being generated and analyzed. In the Lattice QCD community, a custom metadata standard (QCDml) that includes certain provenance information already exists for one part of the workflow, the so-called generation of configurations. In this paper, we follow the W3C PROV standard and formulate a provenance model that includes both the generation part and the so-called measurement part of the Lattice QCD workflow. We demonstrate the applicability of this model and show how the model can be used to answer some provenance-related research questions. However, many important provenance questions in the Lattice QCD community require extensions of this provenance model. To this end, we propose a multi-layered provenance approach that combines prospective and retrospective elements.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Provenance+for+Lattice+QCD+workflows)|0| -|[Implementing an Environmental Management System Using Provenance-By-Design](https://doi.org/10.1145/3543873.3587560)|Luc Moreau, Nicola Hogan, Nick O'Donnell|Estates and Facilities, King's College London, United Kingdom; Department of Informatics, King's College London, United Kingdom|Organisations have to comply with environmental regulations to protect the environment and meet internationally agreed climate change targets. To assist organisations, processes and standards are being defined to manage these compliance obligations. They typically rely on a notion of Environmental Management System (EMS), defined as a reflective framework allowing organisations to set and manage their goals, and demonstrate they follow due processes in order to comply with prevailing regulations. The importance of these obligations can be highlighted by the fact that failing to comply may lead to significant liabilities for organisations. An EMS framework, typically structured as a set of documents and spreadsheets, contains a record of continuously evolving regulations, teams, stakeholders, actions and updates. However, the maintainance of an EMS is often human driven, and therefore is error prone despite the meticulousness of environmental officers, and further requires external human auditing to check their validity. To avoid green washing, but also to contain the burden and cost of compliance, it is desirable for these claims to be checked by trusted automated means. Provenance is ideally suited to track the changes occurring in an EMS, allowing queries to determine precisely which compliance objective is prevailing at any point in time, whether it is being met, and who is responsible for it. Thus, this paper has a dual aim: first, it investigates the benefits of provenance for EMS, second, it presents the application of an emerging approach “Provenance-By-Design”, which automatically converts a specification of an EMS data model and its provenance to a data backend, a service for processing and querying of EMS provenance data, a client-side library to interact with such a service, and a simple user interface allowing developers to navigate the provenance. The application of a Provenance-By-Design approach to EMS applications results in novel opportunities for a provenance-based EMS; we present our preliminary reflection on their potential.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Implementing+an+Environmental+Management+System+Using+Provenance-By-Design)|0| +|[Implementing an Environmental Management System Using Provenance-By-Design](https://doi.org/10.1145/3543873.3587560)|Luc Moreau, Nicola Hogan, Nick O'Donnell|Department of Informatics, King's College London, United Kingdom; Estates and Facilities, King's College London, United Kingdom|Organisations have to comply with environmental regulations to protect the environment and meet internationally agreed climate change targets. To assist organisations, processes and standards are being defined to manage these compliance obligations. They typically rely on a notion of Environmental Management System (EMS), defined as a reflective framework allowing organisations to set and manage their goals, and demonstrate they follow due processes in order to comply with prevailing regulations. The importance of these obligations can be highlighted by the fact that failing to comply may lead to significant liabilities for organisations. An EMS framework, typically structured as a set of documents and spreadsheets, contains a record of continuously evolving regulations, teams, stakeholders, actions and updates. However, the maintainance of an EMS is often human driven, and therefore is error prone despite the meticulousness of environmental officers, and further requires external human auditing to check their validity. To avoid green washing, but also to contain the burden and cost of compliance, it is desirable for these claims to be checked by trusted automated means. Provenance is ideally suited to track the changes occurring in an EMS, allowing queries to determine precisely which compliance objective is prevailing at any point in time, whether it is being met, and who is responsible for it. Thus, this paper has a dual aim: first, it investigates the benefits of provenance for EMS, second, it presents the application of an emerging approach “Provenance-By-Design”, which automatically converts a specification of an EMS data model and its provenance to a data backend, a service for processing and querying of EMS provenance data, a client-side library to interact with such a service, and a simple user interface allowing developers to navigate the provenance. The application of a Provenance-By-Design approach to EMS applications results in novel opportunities for a provenance-based EMS; we present our preliminary reflection on their potential.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Implementing+an+Environmental+Management+System+Using+Provenance-By-Design)|0| |[Trust the Process: Analyzing Prospective Provenance for Data Cleaning](https://doi.org/10.1145/3543873.3587558)|Nikolaus Nova Parulian, Bertram Ludäscher|School of Information Sciences, University of Illinois at Urbana Champaign, USA|In the field of data-driven research and analysis, the quality of results largely depends on the quality of the data used. Data cleaning is a crucial step in improving the quality of data. Still, it is equally important to document the steps made during the data cleaning process to ensure transparency and enable others to assess the quality of the resulting data. While provenance models such as W3C PROV have been introduced to track changes and events related to any entity, their use in documenting the provenance of data-cleaning workflows can be challenging, particularly when mixing different types of documents or entities in the model. To address this, we propose a conceptual model and analysis that breaks down data-cleaning workflows into process abstraction and workflow recipes, refining operations to the column level. This approach provides users with detailed provenance information, enabling transparency, auditing, and support for data cleaning workflow improvements. Our model has several features that allow static analysis, e.g., to determine the minimal input schema and expected output schema for running a recipe, to identify which steps violate the column schema requirement constraint, and to assess the reusability of a recipe on a new dataset. We hope that our model and analysis will contribute to making data processing more transparent, accessible, and reusable.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Trust+the+Process:+Analyzing+Prospective+Provenance+for+Data+Cleaning)|0| -|[Deep Learning Provenance Data Integration: a Practical Approach](https://doi.org/10.1145/3543873.3587561)|Débora B. Pina, Adriane Chapman, Daniel de Oliveira, Marta Mattoso|Fluminense Federal University (UFF), Brazil; University of Southampton, United Kingdom; Federal University of Rio de Janeiro (UFRJ), Brazil|A Deep Learning (DL) life cycle involves several data transformations, such as performing data pre-processing, defining datasets to train and test a deep neural network (DNN), and training and evaluating the DL model. Choosing a final model requires DL model selection, which involves analyzing data from several training configurations (e.g. hyperparameters and DNN architectures). Tracing training data back to pre-processing operations can provide insights into the model selection step. Provenance is a natural solution to represent data derivation of the whole DL life cycle. However, there are challenges in providing an integration of the provenance of these different steps. There are a few approaches to capturing and integrating provenance data from the DL life cycle, but they require that the same provenance capture solution is used along all the steps, which can limit interoperability and flexibility when choosing the DL environment. Therefore, in this work, we present a prototype for provenance data integration using different capture solutions. We show use cases where the integrated provenance from pre-processing and training steps can show how data pre-processing decisions influenced the model selection. Experiments were performed using real-world datasets to train a DNN and provided evidence of the integration between the considered steps, answering queries such as how the data used to train a model that achieved a specific result was processed.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Deep+Learning+Provenance+Data+Integration:+a+Practical+Approach)|0| +|[Deep Learning Provenance Data Integration: a Practical Approach](https://doi.org/10.1145/3543873.3587561)|Débora B. Pina, Adriane Chapman, Daniel de Oliveira, Marta Mattoso|Federal University of Rio de Janeiro (UFRJ), Brazil; University of Southampton, United Kingdom; Fluminense Federal University (UFF), Brazil|A Deep Learning (DL) life cycle involves several data transformations, such as performing data pre-processing, defining datasets to train and test a deep neural network (DNN), and training and evaluating the DL model. Choosing a final model requires DL model selection, which involves analyzing data from several training configurations (e.g. hyperparameters and DNN architectures). Tracing training data back to pre-processing operations can provide insights into the model selection step. Provenance is a natural solution to represent data derivation of the whole DL life cycle. However, there are challenges in providing an integration of the provenance of these different steps. There are a few approaches to capturing and integrating provenance data from the DL life cycle, but they require that the same provenance capture solution is used along all the steps, which can limit interoperability and flexibility when choosing the DL environment. Therefore, in this work, we present a prototype for provenance data integration using different capture solutions. We show use cases where the integrated provenance from pre-processing and training steps can show how data pre-processing decisions influenced the model selection. Experiments were performed using real-world datasets to train a DNN and provided evidence of the integration between the considered steps, answering queries such as how the data used to train a model that achieved a specific result was processed.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Deep+Learning+Provenance+Data+Integration:+a+Practical+Approach)|0| |[Providing Data on Financial Results of Public Companies Enriched with Provenance for OBInvest](https://doi.org/10.1145/3543873.3587566)|Saulo Almeida, Gilberto Passos, Valquire Jesus, Sérgio Manuel Serra da Cruz, Jorge Zavaleta|UFRJ, Brazil|Financial Literacy (FL) initiatives, aimed at young people in formal or informal learning spaces, are defended and implemented in several countries, being encouraged since 2005 by the Organization for Economic Co-operation and Development (OECD). In Brazil, the teaching and learning process in several areas has been stimulated through Academic Competitions generally called Knowledge Olympics, which are essentially student contests that aim to encourage, find talent and awaken interest in the field knowledge presented in the competition. It was precisely for this purpose that the Brazilian Investment Olympics (OBInvest) was born, aiming to democratize access to education and promote reflections on economic and financial issues, through a FL perspective for high school students from all over the country. One of OBInvest’s objectives is to help boosting the development of computational tools, aiming to provide easier access to fundamental data for decision-making in the field of finance. However, from the tools developed by OBInvest, it was noted that the creation of new educational tools would be enhanced through the use of datasets enriched with provenance and aligned with FAIR principles. This work aims to offer a computational strategy based on data science techniques, which is easy to use and also provides curated data series through a reproducible pipeline, using open data on financial reports from publicly listed Brazilian companies, provided by the Brazilian Security and Exchange Commission, called Comissão de Valores Mobiliarios (CMV). During the exploration of related works, we found just a few academic works that use CVM data with little expressive results, which motivated the development of a tool called DRE-CVM, that was supported by computational tools, with a focus on the Python language, Pandas library, the KNIME workflow platform, and Jupyter integrated development environments, running on the Anaconda3 platform over a Docker container. It’s also possible run this experiment in the Google Colabotory cloud environment. This processing it’s capable of executing reproducible pipelines and using curated, fairified, and annotated data with the retrospective source metadata of the financial statements of publicly traded Brazilian companies. The artifact uses pipelines that can be reused by students and other interested parties in finance to study the behaviors of a company’s time series results and thus introduce research on predicting future results. The last executable version of the DRE-CVM experiment can be accessed through Zenodo website at https://doi.org/10.5281/zenodo.7110653 and can be reproduced using a Docker Container available on DockerHub repository. Some improvements can be incorporated into the presented work, the main suggestions for future work are: (i) Perform more substantial analyses on the created dataset, such as predicting results based on the history of demonstration results; (ii) Recover other types of information made available by CVM, to be used during the activities of the Brazilian Investment Olympics; (iii) Adapt the docker image so that it can be executed in the My Binder cloud environment, aiming to improve reproducibility issues.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Providing+Data+on+Financial+Results+of+Public+Companies+Enriched+with+Provenance+for+OBInvest)|0| |[Using diversity as a source of scientific innovation for the Web](https://doi.org/10.1145/3543507.3593046)|Barbara Poblete|Department of Computer Science, University of Chile, Chile|The Web has become a resource that allows us to make sense of social phenomena around the world. This started the moment users became content creators, and has grown with the emergence of social platforms tailored to our need to connect and share with others. Throughout my work, I’ve come to appreciate how social media has democratized access to real-world news and social sentiment, while also witnessing the loss of trust created by fake information. As a computer scientist from Chile in Latin America, I have worked on a range of problems that were driven by local needs. Many times, I have tried to apply state of the art solutions to well-known problems, only to find that these don’t work outside of their initial evaluation dataset. In this talk, I’ll discuss how geographical, language, and social diversity have opened new avenues for innovation and better understanding the social Web. I’ll also show that to truly create useful technological solutions, we must develop inclusive research and resources.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Using+diversity+as+a+source+of+scientific+innovation+for+the+Web)|0| |[Concept Regulation in the Social Sciences](https://doi.org/10.1145/3543507.3593050)|Zachary Elkins|University of Texas at Austin, USA|The sciences, notably biology and medicine, operate with highly regulated taxonomies and ontologies. The Social Sciences, on the other hand, muddle through in a proverbial tower of Babel. There may be some real benefits to an undisciplined set of ideas, but also some real costs. Over the last ten years, political scientists have attempted to get their semantic act by cooperating to formalize their vocabulary. The result has been a dramatic improvement in how scholars diagnose and treat problems of democracy, as well as a set of web applications that have changed the way countries write constitutions. Nevertheless, these methods of semantic cooperation have exposed some persistent challenges of “social engineering,” ones that may have tractable web solutions.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Concept+Regulation+in+the+Social+Sciences)|0| |[GNNs and Graph Generative models for biomedical applications](https://doi.org/10.1145/3543507.3593049)|Michalis Vazirgiannis|Ecole Polytechnique de France, France|Graph generative models are recently gaining significant interest in current application domains. They are commonly used to model social networks, knowledge graphs, and protein-protein interaction networks. In this talk we will present the potential of graph generative models and our recent relevant efforts in the biomedical domain. More specifically we present a novel architecture that generates medical records as graphs with privacy guarantees. We capitalize and modify the graph Variational autoencoders (VAEs) architecture. We train the generative model with the well known MIMIC medical database and achieve generated data that are very similar to the real ones yet provide privacy guarantees. We also develop new GNNs for predicting antibiotic resistance and other protein related downstream tasks such as enzymes classifications and Gene Ontology classification. We achieve there as well promising results with potential for future application in broader biomedical related tasks. Finally we present future research directions for multi modal generative models involving graphs.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=GNNs+and+Graph+Generative+models+for+biomedical+applications)|0| |[Decolonizing Creative Labor in the age of AI](https://doi.org/10.1145/3543507.3593047)|Payal Arora|Erasmus University, Netherlands|Creative AI has got us asking existential questions of what makes us human. To crack the code, you need to crack the culture that makes us who we are. Who and what is creative remains largely disconnected from diverse and global cultural norms, rendering existing technology suboptimal and even unusable to the world’s majority. Creativity has long been dictated by the aesthetic taste, values, needs, concerns, and aspirations of the West. Today, India and China alone account for the majority of the world’s users. The Global South are fast shaping data systems in ways that remain underexamined and siloed as “Rest of World” among industry and government folks. With the rise of the creator economy across sectors, questions abound on creative rights, provenance, fairness, labor, and representation. This talk discusses concerns around digital labor, data materiality, media literacies, creative value, and online expression. In doing so, it sets a pathway towards designing inclusive and intersectional systems that transcend borders.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Decolonizing+Creative+Labor+in+the+age+of+AI)|0| -|[Connectivity](https://doi.org/10.1145/3543507.3593048)|Robert Melancton Metcalfe|Univ Vienna, Dept Pharmaceut Chem, Pharmacoinformat Res Grp, A-1090 Vienna, Austria; Gladstone Inst, Inst Data Sci & Biotechnol, San Francisco, CA 94158 USA; Univ Fed Minas Gerais, Dept Bioquim & Imunol, Inst Ciencias Biol, BR-31270901 Belo Horizonte, MG, Brazil; Maastricht Univ, NUTRIM, Dept Bioinformat BiGCaT, NL-6229 ER Maastricht, Netherlands; Micelio, B-2180 Antwerp, Belgium|WikiPathways (https://www.wikipathways.org) is a biological pathway database known for its collaborative nature and open science approaches. With the core idea of the scientific community developing and curating biological knowledge in pathway models, WikiPathways lowers all barriers for accessing and using its content. Increasingly more content creators, initiatives, projects and tools have started using WikiPathways. Central in this growth and increased use of WikiPathways are the various communities that focus on particular subsets of molecular pathways such as for rare diseases and lipid metabolism. Knowledge from published pathway figures helps prioritize pathway development, using optical character and named entity recognition. We show the growth of WikiPathways over the last three years, highlight the new communities and collaborations of pathway authors and curators, and describe various technologies to connect to external resources and initiatives. The road toward a sustainable, community-driven pathway database goes through integration with other resources such as Wikidata and allowing more use, curation and redistribution of WikiPathways content.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Connectivity)|0| -|[Fair Graph Representation Learning via Diverse Mixture-of-Experts](https://doi.org/10.1145/3543507.3583207)|Zheyuan Liu, Chunhui Zhang, Yijun Tian, Erchi Zhang, Chao Huang, Yanfang Ye, Chuxu Zhang|University of Notre Dame, USA; University of Hong Kong, Hong Kong; Brandeis University, USA|Graph Neural Networks (GNNs) have demonstrated a great representation learning capability on graph data and have been utilized in various downstream applications. However, real-world data in web-based applications (e.g., recommendation and advertising) always contains bias, preventing GNNs from learning fair representations. Although many works were proposed to address the fairness issue, they suffer from the significant problem of insufficient learnable knowledge with limited attributes after debiasing. To address this problem, we develop Graph-Fairness Mixture of Experts (G-Fame), a novel plug-and-play method to assist any GNNs to learn distinguishable representations with unbiased attributes. Furthermore, based on G-Fame, we propose G-Fame++, which introduces three novel strategies to improve the representation fairness from node representations, model layer, and parameter redundancy perspectives. In particular, we first present the embedding diversified method to learn distinguishable node representations. Second, we design the layer diversified strategy to maximize the output difference of distinct model layers. Third, we introduce the expert diversified method to minimize expert parameter similarities to learn diverse and complementary representations. Extensive experiments demonstrate the superiority of G-Fame and G-Fame++ in both accuracy and fairness, compared to state-of-the-art methods across multiple graph datasets.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Fair+Graph+Representation+Learning+via+Diverse+Mixture-of-Experts)|0| -|[Multi-Aspect Heterogeneous Graph Augmentation](https://doi.org/10.1145/3543507.3583208)|Yuchen Zhou, Yanan Cao, Yongchao Liu, Yanmin Shang, Peng Zhang, Zheng Lin, Yun Yue, Baokun Wang, Xing Fu, Weiqiang Wang|Institute of Information Engineering, Chinese Academy of Sciences, China and School of Cyber Security, University of Chinese Academy of Sciences, China; Cyberspace Institute of Advanced Technology, Guangzhou University, China; School of Cyber Security, University of Chinese Academy of Sciences, China and Institute of Information Engineering, Chinese Academy of Sciences, China; Ant Group, China|Data augmentation has been widely studied as it can be used to improve the generalizability of graph representation learning models. However, existing works focus only on the data augmentation on homogeneous graphs. Data augmentation for heterogeneous graphs remains under-explored. Considering that heterogeneous graphs contain different types of nodes and links, ignoring the type information and directly applying the data augmentation methods of homogeneous graphs to heterogeneous graphs will lead to suboptimal results. In this paper, we propose a novel Multi-Aspect Heterogeneous Graph Augmentation framework named MAHGA. Specifically, MAHGA consists of two core augmentation strategies: structure-level augmentation and metapath-level augmentation. Structure-level augmentation pays attention to network schema aspect and designs a relation-aware conditional variational auto-encoder that can generate synthetic features of neighbors to augment the nodes and the node types with scarce links. Metapath-level augmentation concentrates on metapath aspect, which constructs metapath reachable graphs for different metapaths and estimates the graphons of them. By sampling and mixing up based on the graphons, MAHGA yields intra-metapath and inter-metapath augmentation. Finally, we conduct extensive experiments on multiple benchmarks to validate the effectiveness of MAHGA. Experimental results demonstrate that our method improves the performances across a set of heterogeneous graph learning models and datasets.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Multi-Aspect+Heterogeneous+Graph+Augmentation)|0| +|[Connectivity](https://doi.org/10.1145/3543507.3593048)|Robert Melancton Metcalfe|Micelio, B-2180 Antwerp, Belgium; Gladstone Inst, Inst Data Sci & Biotechnol, San Francisco, CA 94158 USA; Univ Vienna, Dept Pharmaceut Chem, Pharmacoinformat Res Grp, A-1090 Vienna, Austria; Univ Fed Minas Gerais, Dept Bioquim & Imunol, Inst Ciencias Biol, BR-31270901 Belo Horizonte, MG, Brazil; Maastricht Univ, NUTRIM, Dept Bioinformat BiGCaT, NL-6229 ER Maastricht, Netherlands|WikiPathways (https://www.wikipathways.org) is a biological pathway database known for its collaborative nature and open science approaches. With the core idea of the scientific community developing and curating biological knowledge in pathway models, WikiPathways lowers all barriers for accessing and using its content. Increasingly more content creators, initiatives, projects and tools have started using WikiPathways. Central in this growth and increased use of WikiPathways are the various communities that focus on particular subsets of molecular pathways such as for rare diseases and lipid metabolism. Knowledge from published pathway figures helps prioritize pathway development, using optical character and named entity recognition. We show the growth of WikiPathways over the last three years, highlight the new communities and collaborations of pathway authors and curators, and describe various technologies to connect to external resources and initiatives. The road toward a sustainable, community-driven pathway database goes through integration with other resources such as Wikidata and allowing more use, curation and redistribution of WikiPathways content.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Connectivity)|0| +|[Fair Graph Representation Learning via Diverse Mixture-of-Experts](https://doi.org/10.1145/3543507.3583207)|Zheyuan Liu, Chunhui Zhang, Yijun Tian, Erchi Zhang, Chao Huang, Yanfang Ye, Chuxu Zhang|University of Notre Dame, USA; Brandeis University, USA; University of Hong Kong, Hong Kong|Graph Neural Networks (GNNs) have demonstrated a great representation learning capability on graph data and have been utilized in various downstream applications. However, real-world data in web-based applications (e.g., recommendation and advertising) always contains bias, preventing GNNs from learning fair representations. Although many works were proposed to address the fairness issue, they suffer from the significant problem of insufficient learnable knowledge with limited attributes after debiasing. To address this problem, we develop Graph-Fairness Mixture of Experts (G-Fame), a novel plug-and-play method to assist any GNNs to learn distinguishable representations with unbiased attributes. Furthermore, based on G-Fame, we propose G-Fame++, which introduces three novel strategies to improve the representation fairness from node representations, model layer, and parameter redundancy perspectives. In particular, we first present the embedding diversified method to learn distinguishable node representations. Second, we design the layer diversified strategy to maximize the output difference of distinct model layers. Third, we introduce the expert diversified method to minimize expert parameter similarities to learn diverse and complementary representations. Extensive experiments demonstrate the superiority of G-Fame and G-Fame++ in both accuracy and fairness, compared to state-of-the-art methods across multiple graph datasets.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Fair+Graph+Representation+Learning+via+Diverse+Mixture-of-Experts)|0| +|[Multi-Aspect Heterogeneous Graph Augmentation](https://doi.org/10.1145/3543507.3583208)|Yuchen Zhou, Yanan Cao, Yongchao Liu, Yanmin Shang, Peng Zhang, Zheng Lin, Yun Yue, Baokun Wang, Xing Fu, Weiqiang Wang|Ant Group, China; School of Cyber Security, University of Chinese Academy of Sciences, China and Institute of Information Engineering, Chinese Academy of Sciences, China; Institute of Information Engineering, Chinese Academy of Sciences, China and School of Cyber Security, University of Chinese Academy of Sciences, China; Cyberspace Institute of Advanced Technology, Guangzhou University, China|Data augmentation has been widely studied as it can be used to improve the generalizability of graph representation learning models. However, existing works focus only on the data augmentation on homogeneous graphs. Data augmentation for heterogeneous graphs remains under-explored. Considering that heterogeneous graphs contain different types of nodes and links, ignoring the type information and directly applying the data augmentation methods of homogeneous graphs to heterogeneous graphs will lead to suboptimal results. In this paper, we propose a novel Multi-Aspect Heterogeneous Graph Augmentation framework named MAHGA. Specifically, MAHGA consists of two core augmentation strategies: structure-level augmentation and metapath-level augmentation. Structure-level augmentation pays attention to network schema aspect and designs a relation-aware conditional variational auto-encoder that can generate synthetic features of neighbors to augment the nodes and the node types with scarce links. Metapath-level augmentation concentrates on metapath aspect, which constructs metapath reachable graphs for different metapaths and estimates the graphons of them. By sampling and mixing up based on the graphons, MAHGA yields intra-metapath and inter-metapath augmentation. Finally, we conduct extensive experiments on multiple benchmarks to validate the effectiveness of MAHGA. Experimental results demonstrate that our method improves the performances across a set of heterogeneous graph learning models and datasets.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Multi-Aspect+Heterogeneous+Graph+Augmentation)|0| |[Testing Cluster Properties of Signed Graphs](https://doi.org/10.1145/3543507.3583213)|Florian Adriaens, Simon Apers|Université de Paris, CNRS, IRIF, France; University of Helsinki, Finland|This work initiates the study of property testing in signed graphs, where every edge has either a positive or a negative sign. We show that there exist sublinear query and time algorithms for testing three key properties of signed graphs: balance (or 2-clusterability), clusterability and signed triangle freeness. We consider both the dense graph model, where one queries the adjacency matrix entries of a signed graph, and the bounded-degree model, where one queries for the neighbors of a node and the sign of the connecting edge. Our algorithms use a variety of tools from unsigned graph property testing, as well as reductions from one setting to the other. Our main technical contribution is a sublinear algorithm for testing clusterability in the bounded-degree model. This contrasts with the property of k-clusterability in unsigned graphs, which is not testable with a sublinear number of queries in the bounded-degree model. We experimentally evaluate the complexity and usefulness of several of our testers on real-life and synthetic datasets.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Testing+Cluster+Properties+of+Signed+Graphs)|0| |[RSGNN: A Model-agnostic Approach for Enhancing the Robustness of Signed Graph Neural Networks](https://doi.org/10.1145/3543507.3583221)|Zeyu Zhang, Jiamou Liu, Xianda Zheng, Yifei Wang, Pengqian Han, Yupan Wang, Kaiqi Zhao, Zijian Zhang|; The University of Auckland, New Zealand; Beijing Institute of Technology, China|Signed graphs model complex relations using both positive and negative edges. Signed graph neural networks (SGNN) are powerful tools to analyze signed graphs. We address the vulnerability of SGNN to potential edge noise in the input graph. Our goal is to strengthen existing SGNN allowing them to withstand edge noises by extracting robust representations for signed graphs. First, we analyze the expressiveness of SGNN using an extended Weisfeiler-Lehman (WL) graph isomorphism test and identify the limitations to SGNN over triangles that are unbalanced. Then, we design some structure-based regularizers to be used in conjunction with an SGNN that highlight intrinsic properties of a signed graph. The tools and insights above allow us to propose a novel framework, Robust Signed Graph Neural Network (RSGNN), which adopts a dual architecture that simultaneously denoises the graph while learning node representations. We validate the performance of our model empirically on four real-world signed graph datasets, i.e., Bitcoin_OTC, Bitcoin_Alpha, Epinion and Slashdot, RSGNN can clearly improve the robustness of popular SGNN models. When the signed graphs are affected by random noise, our method outperforms baselines by up to 9.35% Binary-F1 for link sign prediction. Our implementation is available in PyTorch1.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=RSGNN:+A+Model-agnostic+Approach+for+Enhancing+the+Robustness+of+Signed+Graph+Neural+Networks)|0| |[Multi-aspect Diffusion Network Inference](https://doi.org/10.1145/3543507.3583228)|Hao Huang, Keqi Han, Beicheng Xu, Ting Gan|Wuhan University, China|To learn influence relationships between nodes in a diffusion network, most existing approaches resort to precise timestamps of historical node infections. The target network is customarily assumed as an one-aspect diffusion network, with homogeneous influence relationships. Nonetheless, tracing node infection timestamps is often infeasible due to high cost, and the type of influence relationships may be heterogeneous because of the diversity of propagation media. In this work, we study how to infer a multi-aspect diffusion network with heterogeneous influence relationships, using only node infection statuses that are more readily accessible in practice. Equipped with a probabilistic generative model, we iteratively conduct a posteriori, quantitative analysis on historical diffusion results of the network, and infer the structure and strengths of homogeneous influence relationships in each aspect. Extensive experiments on both synthetic and real-world networks are conducted, and the results verify the effectiveness and efficiency of our approach.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Multi-aspect+Diffusion+Network+Inference)|0| -|[Encoding Node Diffusion Competence and Role Significance for Network Dismantling](https://doi.org/10.1145/3543507.3583233)|Jiazheng Zhang, Bang Wang|School of Electronic Information and Communications, Huazhong University of Science and Technology, China; School of Electronic Information and Communications, Huazhong University of Science and Technology (HUST), Wuhan, China, China|Percolation theory shows that removing a small fraction of critical nodes can lead to the disintegration of a large network into many disconnected tiny subnetworks. The network dismantling task focuses on how to efficiently select the least such critical nodes. Most existing approaches focus on measuring nodes' importance from either functional or topological viewpoint. Different from theirs, we argue that nodes' importance can be measured from both of the two complementary aspects: The functional importance can be based on the nodes' competence in relaying network information; While the topological importance can be measured from nodes' regional structural patterns. In this paper, we propose an unsupervised learning framework for network dismantling, called DCRS, which encodes and fuses both node diffusion competence and role significance. Specifically, we propose a graph diffusion neural network which emulates information diffusion for competence encoding; We divide nodes with similar egonet structural patterns into a few roles, and construct a role graph on which to encode node role significance. The DCRS converts and fuses the two encodings to output a final ranking score for selecting critical nodes. Experiments on both real-world networks and synthetic networks demonstrate that our scheme significantly outperforms the state-of-the-art competitors for its mostly requiring much fewer nodes to dismantle a network.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Encoding+Node+Diffusion+Competence+and+Role+Significance+for+Network+Dismantling)|0| +|[Encoding Node Diffusion Competence and Role Significance for Network Dismantling](https://doi.org/10.1145/3543507.3583233)|Jiazheng Zhang, Bang Wang|School of Electronic Information and Communications, Huazhong University of Science and Technology (HUST), Wuhan, China, China; School of Electronic Information and Communications, Huazhong University of Science and Technology, China|Percolation theory shows that removing a small fraction of critical nodes can lead to the disintegration of a large network into many disconnected tiny subnetworks. The network dismantling task focuses on how to efficiently select the least such critical nodes. Most existing approaches focus on measuring nodes' importance from either functional or topological viewpoint. Different from theirs, we argue that nodes' importance can be measured from both of the two complementary aspects: The functional importance can be based on the nodes' competence in relaying network information; While the topological importance can be measured from nodes' regional structural patterns. In this paper, we propose an unsupervised learning framework for network dismantling, called DCRS, which encodes and fuses both node diffusion competence and role significance. Specifically, we propose a graph diffusion neural network which emulates information diffusion for competence encoding; We divide nodes with similar egonet structural patterns into a few roles, and construct a role graph on which to encode node role significance. The DCRS converts and fuses the two encodings to output a final ranking score for selecting critical nodes. Experiments on both real-world networks and synthetic networks demonstrate that our scheme significantly outperforms the state-of-the-art competitors for its mostly requiring much fewer nodes to dismantle a network.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Encoding+Node+Diffusion+Competence+and+Role+Significance+for+Network+Dismantling)|0| |[Opinion Maximization in Social Networks via Leader Selection](https://doi.org/10.1145/3543507.3583243)|Xiaotian Zhou, Zhongzhi Zhang|Fudan University, China|We study a leader selection problem for the DeGroot model of opinion dynamics in a social network with n nodes and m edges, in the presence of s0 = O(1) leaders with opinion 0. Concretely, we consider the problem of maximizing the average opinion in equilibrium by selecting k = O(1) leaders with opinion 1 from the remaining n − s0 nodes, which was previously proved to be NP-hard. A deterministic greedy algorithm was also proposed to approximately solve the problem, which has an approximation factor (1 − 1/e) and time complexity O(n3), and thus does not apply to large networks. In this paper, we first give an interpretation for the opinion of each node in equilibrium and the disagreement of the model from the perspective of resistor networks. We then develop a fast randomized greedy algorithm to solve the problem. To this end, we express the average opinion in terms of the pseudoinverse and Schur complement of Laplacian matrix for . The key ingredients of our randomized algorithm are Laplacian solvers and node sparsifiers, where the latter can preserve pairwise effective resistance by viewing Schur complement as random walks with average length l. For any error parameter ϵ > 0, at each iteration, the randomized algorithm selects a node that deviates from the local optimum marginal gain at most ϵ. The time complexity of the fast algorithm is O(mkllog nϵ− 2). Extensive experiments on various real networks show that the effectiveness of our randomized algorithm is similar to that of the deterministic algorithm, both of which are better than several baseline algorithms, and that our randomized algorithm is more efficient and scalable to large graphs with more than one million nodes.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Opinion+Maximization+in+Social+Networks+via+Leader+Selection)|0| -|[Graph Self-supervised Learning with Augmentation-aware Contrastive Learning](https://doi.org/10.1145/3543507.3583246)|Dong Chen, Xiang Zhao, Wei Wang, Zhen Tan, Weidong Xiao|Laboratory for Big Data and Decision, National University of Defense Technology, China; College of Systems Engineering, National University of Defense Technology, China; Data Science and Analytics Thrust, Information Hub, The Hong Kong University of Science and Technology (Guangzhou), China|Graph self-supervised learning aims to mine useful information from unlabeled graph data, and has been successfully applied to pre-train graph representations. Many existing approaches use contrastive learning to learn powerful embeddings by learning contrastively from two augmented graph views. However, none of these graph contrastive methods fully exploits the diversity of different augmentations, and hence is prone to overfitting and limited generalization ability of learned representations. In this paper, we propose a novel Graph Self-supervised Learning method with Augmentation-aware Contrastive Learning. Our method is based on the finding that the pre-trained model after adding augmentation diversity can achieve better generalization ability. To make full use of the information from the diverse augmentation method, this paper constructs new augmentation-aware prediction task which complementary with the contrastive learning task. Similar to how pre-training requires fast adaptation to different downstream tasks, we simulate train-test adaptation on the constructed tasks for further enhancing the learning ability; this strategy can be deemed as a form of meta-learning. Experimental results show that our method outperforms previous methods and learns better representations for a variety of downstream tasks.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Graph+Self-supervised+Learning+with+Augmentation-aware+Contrastive+Learning)|0| -|[Unifying and Improving Graph Convolutional Neural Networks with Wavelet Denoising Filters](https://doi.org/10.1145/3543507.3583253)|Liangtian Wan, Xiaona Li, Huijin Han, Xiaoran Yan, Lu Sun, Zhaolong Ning, Feng Xia|Research Center of Big Data Intelligence, Research Institute of Artificial Intelligence, Zhejiang Lab, China; School of Computing Technologies, RMIT University, Australia; School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing University of Posts and Telecommunications, China; Department of Communication Engineering, Institute of Information Science Technology, Dalian Maritime University, China; Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province, School of Software, Dalian University of Technology, China|Graph convolutional neural network (GCN) is a powerful deep learning framework for network data. However, variants of graph neural architectures can lead to drastically different performance on different tasks. Model comparison calls for a unifying framework with interpretability and principled experimental procedures. Based on the theories from graph signal processing (GSP), we show that GCN’s capability is fundamentally limited by the uncertainty principle, and wavelets provide a controllable trade-off between local and global information. We adapt wavelet denoising filters to the graph domain, unifying popular variants of GCN under a common interpretable mathematical framework. Furthermore, we propose WaveThresh and WaveShrink which are novel GCN models based on proven denoising filters from the signal processing literature. Empirically, we evaluate our models and other popular GCNs under a more principled procedure and analyze how trade-offs between local and global graph signals can lead to better performance in different datasets.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Unifying+and+Improving+Graph+Convolutional+Neural+Networks+with+Wavelet+Denoising+Filters)|0| -|[Neighborhood Structure Configuration Models](https://doi.org/10.1145/3543507.3583266)|Felix I. Stamm, Michael Scholkemper, Michael T. Schaub, Markus Strohmaier|RWTH Aachen University, Germany; University of Mannheim & GESIS, Germany|We develop a new method to efficiently sample synthetic networks that preserve the d-hop neighborhood structure of a given network for any given d. The proposed algorithm trades off the diversity in network samples against the depth of the neighborhood structure that is preserved. Our key innovation is to employ a colored Configuration Model with colors derived from iterations of the so-called Color Refinement algorithm. We prove that with increasing iterations the preserved structural information increases: the generated synthetic networks and the original network become more and more similar, and are eventually indistinguishable in terms of centrality measures such as PageRank, HITS, Katz centrality and eigenvector centrality. Our work enables to efficiently generate samples with a precisely controlled similarity to the original network, especially for large networks.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Neighborhood+Structure+Configuration+Models)|0| -|[CurvDrop: A Ricci Curvature Based Approach to Prevent Graph Neural Networks from Over-Smoothing and Over-Squashing](https://doi.org/10.1145/3543507.3583269)|Yang Liu, Chuan Zhou, Shirui Pan, Jia Wu, Zhao Li, Hongyang Chen, Peng Zhang|Academy of Mathematics and Systems Science, Chinese Academy of Sciences, China; Hangzhou link2do Technology, China; AMSS, Chinese Academy of Science, China and School of Cyber Security, University of Chinese Academy of Science, China; School of Information and Communication Technology, Griffith University, Australia; Research Center for Graph Computing, Zhejiang Lab, China; School of Computing, Macquarie University, Australia; Cyberspace Institute of Advanced Technology, Guangzhou University, China|Graph neural networks (GNNs) are powerful models to handle graph data and can achieve state-of-the-art in many critical tasks including node classification and link prediction. However, existing graph neural networks still face both challenges of over-smoothing and over-squashing based on previous literature. To this end, we propose a new Curvature-based topology-aware Dropout sampling technique named CurvDrop, in which we integrate the Discrete Ricci Curvature into graph neural networks to enable more expressive graph models. Also, this work can improve graph neural networks by quantifying connections in graphs and using structural information such as community structures in graphs. As a result, our method can tackle the both challenges of over-smoothing and over-squashing with theoretical justification. Also, numerous experiments on public datasets show the effectiveness and robustness of our proposed method. The code and data are released in https://github.com/liu-yang-maker/Curvature-based-Dropout.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=CurvDrop:+A+Ricci+Curvature+Based+Approach+to+Prevent+Graph+Neural+Networks+from+Over-Smoothing+and+Over-Squashing)|0| +|[Graph Self-supervised Learning with Augmentation-aware Contrastive Learning](https://doi.org/10.1145/3543507.3583246)|Dong Chen, Xiang Zhao, Wei Wang, Zhen Tan, Weidong Xiao|College of Systems Engineering, National University of Defense Technology, China; Data Science and Analytics Thrust, Information Hub, The Hong Kong University of Science and Technology (Guangzhou), China; Laboratory for Big Data and Decision, National University of Defense Technology, China|Graph self-supervised learning aims to mine useful information from unlabeled graph data, and has been successfully applied to pre-train graph representations. Many existing approaches use contrastive learning to learn powerful embeddings by learning contrastively from two augmented graph views. However, none of these graph contrastive methods fully exploits the diversity of different augmentations, and hence is prone to overfitting and limited generalization ability of learned representations. In this paper, we propose a novel Graph Self-supervised Learning method with Augmentation-aware Contrastive Learning. Our method is based on the finding that the pre-trained model after adding augmentation diversity can achieve better generalization ability. To make full use of the information from the diverse augmentation method, this paper constructs new augmentation-aware prediction task which complementary with the contrastive learning task. Similar to how pre-training requires fast adaptation to different downstream tasks, we simulate train-test adaptation on the constructed tasks for further enhancing the learning ability; this strategy can be deemed as a form of meta-learning. Experimental results show that our method outperforms previous methods and learns better representations for a variety of downstream tasks.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Graph+Self-supervised+Learning+with+Augmentation-aware+Contrastive+Learning)|0| +|[Unifying and Improving Graph Convolutional Neural Networks with Wavelet Denoising Filters](https://doi.org/10.1145/3543507.3583253)|Liangtian Wan, Xiaona Li, Huijin Han, Xiaoran Yan, Lu Sun, Zhaolong Ning, Feng Xia|Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province, School of Software, Dalian University of Technology, China; School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing University of Posts and Telecommunications, China; School of Computing Technologies, RMIT University, Australia; Department of Communication Engineering, Institute of Information Science Technology, Dalian Maritime University, China; Research Center of Big Data Intelligence, Research Institute of Artificial Intelligence, Zhejiang Lab, China|Graph convolutional neural network (GCN) is a powerful deep learning framework for network data. However, variants of graph neural architectures can lead to drastically different performance on different tasks. Model comparison calls for a unifying framework with interpretability and principled experimental procedures. Based on the theories from graph signal processing (GSP), we show that GCN’s capability is fundamentally limited by the uncertainty principle, and wavelets provide a controllable trade-off between local and global information. We adapt wavelet denoising filters to the graph domain, unifying popular variants of GCN under a common interpretable mathematical framework. Furthermore, we propose WaveThresh and WaveShrink which are novel GCN models based on proven denoising filters from the signal processing literature. Empirically, we evaluate our models and other popular GCNs under a more principled procedure and analyze how trade-offs between local and global graph signals can lead to better performance in different datasets.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Unifying+and+Improving+Graph+Convolutional+Neural+Networks+with+Wavelet+Denoising+Filters)|0| +|[Neighborhood Structure Configuration Models](https://doi.org/10.1145/3543507.3583266)|Felix I. Stamm, Michael Scholkemper, Michael T. Schaub, Markus Strohmaier|University of Mannheim & GESIS, Germany; RWTH Aachen University, Germany|We develop a new method to efficiently sample synthetic networks that preserve the d-hop neighborhood structure of a given network for any given d. The proposed algorithm trades off the diversity in network samples against the depth of the neighborhood structure that is preserved. Our key innovation is to employ a colored Configuration Model with colors derived from iterations of the so-called Color Refinement algorithm. We prove that with increasing iterations the preserved structural information increases: the generated synthetic networks and the original network become more and more similar, and are eventually indistinguishable in terms of centrality measures such as PageRank, HITS, Katz centrality and eigenvector centrality. Our work enables to efficiently generate samples with a precisely controlled similarity to the original network, especially for large networks.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Neighborhood+Structure+Configuration+Models)|0| +|[CurvDrop: A Ricci Curvature Based Approach to Prevent Graph Neural Networks from Over-Smoothing and Over-Squashing](https://doi.org/10.1145/3543507.3583269)|Yang Liu, Chuan Zhou, Shirui Pan, Jia Wu, Zhao Li, Hongyang Chen, Peng Zhang|School of Information and Communication Technology, Griffith University, Australia; School of Computing, Macquarie University, Australia; AMSS, Chinese Academy of Science, China and School of Cyber Security, University of Chinese Academy of Science, China; Research Center for Graph Computing, Zhejiang Lab, China; Cyberspace Institute of Advanced Technology, Guangzhou University, China; Academy of Mathematics and Systems Science, Chinese Academy of Sciences, China; Hangzhou link2do Technology, China|Graph neural networks (GNNs) are powerful models to handle graph data and can achieve state-of-the-art in many critical tasks including node classification and link prediction. However, existing graph neural networks still face both challenges of over-smoothing and over-squashing based on previous literature. To this end, we propose a new Curvature-based topology-aware Dropout sampling technique named CurvDrop, in which we integrate the Discrete Ricci Curvature into graph neural networks to enable more expressive graph models. Also, this work can improve graph neural networks by quantifying connections in graphs and using structural information such as community structures in graphs. As a result, our method can tackle the both challenges of over-smoothing and over-squashing with theoretical justification. Also, numerous experiments on public datasets show the effectiveness and robustness of our proposed method. The code and data are released in https://github.com/liu-yang-maker/Curvature-based-Dropout.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=CurvDrop:+A+Ricci+Curvature+Based+Approach+to+Prevent+Graph+Neural+Networks+from+Over-Smoothing+and+Over-Squashing)|0| |[A Post-Training Framework for Improving Heterogeneous Graph Neural Networks](https://doi.org/10.1145/3543507.3583282)|Cheng Yang, Xumeng Gong, Chuan Shi, Philip S. Yu|Beijing University of Posts and Telecommunications, China; UNIVERSITY OF ILLINOIS AT CHICAGO, USA|Recent years have witnessed the success of heterogeneous graph neural networks (HGNNs) in modeling heterogeneous information networks (HINs). In this paper, we focus on the benchmark task of HGNNs, i.e., node classification, and empirically find that typical HGNNs are not good at predicting the label of a test node whose receptive field (1) has few training nodes from the same category or (2) has multiple training nodes from different categories. A possible explanation is that their message passing mechanisms may involve noises from different categories, and cannot fully explore task-specific knowledge such as the label dependency between distant nodes. Therefore, instead of introducing a new HGNN model, we propose a general post-training framework that can be applied on any pretrained HGNNs to further inject task-specific knowledge and enhance their prediction performance. Specifically, we first design an auxiliary system that estimates node labels based on (1) a global inference module of multi-channel label propagation and (2) a local inference module of network schema-aware prediction. The mechanism of our auxiliary system can complement the pretrained HGNNs by providing extra task-specific knowledge. During the post-training process, we will strengthen both system-level and module-level consistencies to encourage the cooperation between a pretrained HGNN and our auxiliary system. In this way, both systems can learn from each other for better performance. In experiments, we apply our framework to four typical HGNNs. Experimental results on three benchmark datasets show that compared with pretrained HGNNs, our post-training framework can enhance Micro-F1 by a relative improvement of 3.9% on average. Code, data and appendix are available at https://github.com/GXM1141/HGPF.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Post-Training+Framework+for+Improving+Heterogeneous+Graph+Neural+Networks)|0| |[Link Prediction on Latent Heterogeneous Graphs](https://doi.org/10.1145/3543507.3583284)|TrungKien Nguyen, Zemin Liu, Yuan Fang|School of Computing, National University of Singapore, Singapore; School of Computing & Information Systems, Singapore Management University, Singapore|On graph data, the multitude of node or edge types gives rise to heterogeneous information networks (HINs). To preserve the heterogeneous semantics on HINs, the rich node/edge types become a cornerstone of HIN representation learning. However, in real-world scenarios, type information is often noisy, missing or inaccessible. Assuming no type information is given, we define a so-called latent heterogeneous graph (LHG), which carries latent heterogeneous semantics as the node/edge types cannot be observed. In this paper, we study the challenging and unexplored problem of link prediction on an LHG. As existing approaches depend heavily on type-based information, they are suboptimal or even inapplicable on LHGs. To address the absence of type information, we propose a model named LHGNN, based on the novel idea of semantic embedding at node and path levels, to capture latent semantics on and between nodes. We further design a personalization function to modulate the heterogeneous contexts conditioned on their latent semantics w.r.t. the target node, to enable finer-grained aggregation. Finally, we conduct extensive experiments on four benchmark datasets, and demonstrate the superior performance of LHGNN.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Link+Prediction+on+Latent+Heterogeneous+Graphs)|0| |[Predicting the Silent Majority on Graphs: Knowledge Transferable Graph Neural Network](https://doi.org/10.1145/3543507.3583287)|Wendong Bi, Bingbing Xu, Xiaoqian Sun, Li Xu, Huawei Shen, Xueqi Cheng|Institute of Computing Technology, Chinese Academy of Sciences, China; Institute of Computing Technology, University of Chinese Academy of Sciences, China|Graphs consisting of vocal nodes ("the vocal minority") and silent nodes ("the silent majority"), namely VS-Graph, are ubiquitous in the real world. The vocal nodes tend to have abundant features and labels. In contrast, silent nodes only have incomplete features and rare labels, e.g., the description and political tendency of politicians (vocal) are abundant while not for ordinary people (silent) on the twitter's social network. Predicting the silent majority remains a crucial yet challenging problem. However, most existing message-passing based GNNs assume that all nodes belong to the same domain, without considering the missing features and distribution-shift between domains, leading to poor ability to deal with VS-Graph. To combat the above challenges, we propose Knowledge Transferable Graph Neural Network (KT-GNN), which models distribution shifts during message passing and representation learning by transferring knowledge from vocal nodes to silent nodes. Specifically, we design the domain-adapted "feature completion and message passing mechanism" for node representation learning while preserving domain difference. And a knowledge transferable classifier based on KL-divergence is followed. Comprehensive experiments on real-world scenarios (i.e., company financial risk assessment and political elections) demonstrate the superior performance of our method. Our source code has been open sourced.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Predicting+the+Silent+Majority+on+Graphs:+Knowledge+Transferable+Graph+Neural+Network)|0| -|[Automated Spatio-Temporal Graph Contrastive Learning](https://doi.org/10.1145/3543507.3583304)|Qianru Zhang, Chao Huang, Lianghao Xia, Zheng Wang, Zhonghang Li, SiuMing Yiu|Huawei Singapore Research Center, Singapore; South China University of Technology, China; The University of Hong Kong, Hong Kong|Among various region embedding methods, graph-based region relation learning models stand out, owing to their strong structure representation ability for encoding spatial correlations with graph neural networks. Despite their effectiveness, several key challenges have not been well addressed in existing methods: i) Data noise and missing are ubiquitous in many spatio-temporal scenarios due to a variety of factors. ii) Input spatio-temporal data (e.g., mobility traces) usually exhibits distribution heterogeneity across space and time. In such cases, current methods are vulnerable to the quality of the generated region graphs, which may lead to suboptimal performance. In this paper, we tackle the above challenges by exploring the Automated Spatio-Temporal graph contrastive learning paradigm (AutoST) over the heterogeneous region graph generated from multi-view data sources. Our \model\ framework is built upon a heterogeneous graph neural architecture to capture the multi-view region dependencies with respect to POI semantics, mobility flow patterns and geographical positions. To improve the robustness of our GNN encoder against data noise and distribution issues, we design an automated spatio-temporal augmentation scheme with a parameterized contrastive view generator. AutoST can adapt to the spatio-temporal heterogeneous graph with multi-view semantics well preserved. Extensive experiments for three downstream spatio-temporal mining tasks on several real-world datasets demonstrate the significant performance gain achieved by our \model\ over a variety of baselines. The code is publicly available at https://github.com/HKUDS/AutoST.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Automated+Spatio-Temporal+Graph+Contrastive+Learning)|0| +|[Automated Spatio-Temporal Graph Contrastive Learning](https://doi.org/10.1145/3543507.3583304)|Qianru Zhang, Chao Huang, Lianghao Xia, Zheng Wang, Zhonghang Li, SiuMing Yiu|Huawei Singapore Research Center, Singapore; The University of Hong Kong, Hong Kong; South China University of Technology, China|Among various region embedding methods, graph-based region relation learning models stand out, owing to their strong structure representation ability for encoding spatial correlations with graph neural networks. Despite their effectiveness, several key challenges have not been well addressed in existing methods: i) Data noise and missing are ubiquitous in many spatio-temporal scenarios due to a variety of factors. ii) Input spatio-temporal data (e.g., mobility traces) usually exhibits distribution heterogeneity across space and time. In such cases, current methods are vulnerable to the quality of the generated region graphs, which may lead to suboptimal performance. In this paper, we tackle the above challenges by exploring the Automated Spatio-Temporal graph contrastive learning paradigm (AutoST) over the heterogeneous region graph generated from multi-view data sources. Our \model\ framework is built upon a heterogeneous graph neural architecture to capture the multi-view region dependencies with respect to POI semantics, mobility flow patterns and geographical positions. To improve the robustness of our GNN encoder against data noise and distribution issues, we design an automated spatio-temporal augmentation scheme with a parameterized contrastive view generator. AutoST can adapt to the spatio-temporal heterogeneous graph with multi-view semantics well preserved. Extensive experiments for three downstream spatio-temporal mining tasks on several real-world datasets demonstrate the significant performance gain achieved by our \model\ over a variety of baselines. The code is publicly available at https://github.com/HKUDS/AutoST.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Automated+Spatio-Temporal+Graph+Contrastive+Learning)|0| |[Robust Mid-Pass Filtering Graph Convolutional Networks](https://doi.org/10.1145/3543507.3583335)|Jincheng Huang, Lun Du, Xu Chen, Qiang Fu, Shi Han, Dongmei Zhang|Microsoft Research Asia, China; Microsoft Research Aisa, China; School of Computer Science, Southwest Petroleum University, China|Graph convolutional networks (GCNs) are currently the most promising paradigm for dealing with graph-structure data, while recent studies have also shown that GCNs are vulnerable to adversarial attacks. Thus developing GCN models that are robust to such attacks become a hot research topic. However, the structural purification learning-based or robustness constraints-based defense GCN methods are usually designed for specific data or attacks, and introduce additional objective that is not for classification. Extra training overhead is also required in their design. To address these challenges, we conduct in-depth explorations on mid-frequency signals on graphs and propose a simple yet effective Mid-pass filter GCN (Mid-GCN). Theoretical analyses guarantee the robustness of signals through the mid-pass filter, and we also shed light on the properties of different frequency signals under adversarial attacks. Extensive experiments on six benchmark graph data further verify the effectiveness of our designed Mid-GCN in node classification accuracy compared to state-of-the-art GCNs under various adversarial attack strategies.||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Robust+Mid-Pass+Filtering+Graph+Convolutional+Networks)|0| -|[PARROT: Position-Aware Regularized Optimal Transport for Network Alignment](https://doi.org/10.1145/3543507.3583357)|Zhichen Zeng, Si Zhang, Yinglong Xia, Hanghang Tong|University of Illinois at Urbana-Champaign, USA; Computer Science, University of Illinois at Urbana-Champaign, USA; Meta, USA|Network alignment is a critical steppingstone behind a variety of multi-network mining tasks. Most of the existing methods essentially optimize a Frobenius-like distance or ranking-based loss, ignoring the underlying geometry of graph data. Optimal transport (OT), together with Wasserstein distance, has emerged to be a powerful approach accounting for the underlying geometry explicitly. Promising as it might be, the state-of-the-art OT-based alignment methods suffer from two fundamental limitations, including (1) effectiveness due to the insufficient use of topology and consistency information and (2) scalability due to the non-convex formulation and repeated computationally costly loss calculation. In this paper, we propose a position-aware regularized optimal transport framework for network alignment named PARROT. To tackle the effectiveness issue, the proposed PARROT captures topology information by random walk with restart, with three carefully designed consistency regularization terms. To tackle the scalability issue, the regularized OT problem is decomposed into a series of convex subproblems and can be efficiently solved by the proposed constrained proximal point method with guaranteed convergence. Extensive experiments show that our algorithm achieves significant improvements in both effectiveness and scalability, outperforming the state-of-the-art network alignment methods and speeding up existing OT-based methods by up to 100 times.|网络对齐是多种网络挖掘任务背后的关键步骤。现有的大多数方法基本上都是优化一个 Frobenius 式的距离或基于排序的损失，而忽略了图形数据的基本几何形状。最优运输(OT) ，加上 Wasserstein 距离，已经成为一个强大的方法，明确地解释了基础的几何学。尽管它可能是有希望的，但是最先进的基于 OT 的比对方法受到两个基本限制，包括(1)由于拓扑和一致性信息使用不足而导致的有效性和(2)由于非凸公式和重复计算代价高昂的损失计算而导致的可伸缩性。本文提出了一种基于位置感知的网络对准正则化最优传输框架 PARROT。为了解决有效性问题，提出的 PARROT 通过重新启动的随机游动捕获拓扑信息，并使用三个精心设计的一致性正则化项。为了解决可扩展性问题，将正则 OT 问题分解为一系列凸子问题，并利用所提出的具有保证收敛性的约束近似点方法进行有效求解。大量的实验表明，我们的算法在有效性和可扩展性方面都取得了显著的改进，性能优于最先进的网络对齐方法，并将现有的基于 OT 的方法提高了100倍。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=PARROT:+Position-Aware+Regularized+Optimal+Transport+for+Network+Alignment)|0| +|[PARROT: Position-Aware Regularized Optimal Transport for Network Alignment](https://doi.org/10.1145/3543507.3583357)|Zhichen Zeng, Si Zhang, Yinglong Xia, Hanghang Tong|Computer Science, University of Illinois at Urbana-Champaign, USA; University of Illinois at Urbana-Champaign, USA; Meta, USA|Network alignment is a critical steppingstone behind a variety of multi-network mining tasks. Most of the existing methods essentially optimize a Frobenius-like distance or ranking-based loss, ignoring the underlying geometry of graph data. Optimal transport (OT), together with Wasserstein distance, has emerged to be a powerful approach accounting for the underlying geometry explicitly. Promising as it might be, the state-of-the-art OT-based alignment methods suffer from two fundamental limitations, including (1) effectiveness due to the insufficient use of topology and consistency information and (2) scalability due to the non-convex formulation and repeated computationally costly loss calculation. In this paper, we propose a position-aware regularized optimal transport framework for network alignment named PARROT. To tackle the effectiveness issue, the proposed PARROT captures topology information by random walk with restart, with three carefully designed consistency regularization terms. To tackle the scalability issue, the regularized OT problem is decomposed into a series of convex subproblems and can be efficiently solved by the proposed constrained proximal point method with guaranteed convergence. Extensive experiments show that our algorithm achieves significant improvements in both effectiveness and scalability, outperforming the state-of-the-art network alignment methods and speeding up existing OT-based methods by up to 100 times.|网络对齐是多种网络挖掘任务背后的关键步骤。现有的大多数方法基本上都是优化一个 Frobenius 式的距离或基于排序的损失，而忽略了图形数据的基本几何形状。最优运输(OT) ，加上 Wasserstein 距离，已经成为一个强大的方法，明确地解释了基础的几何学。尽管它可能是有希望的，但是最先进的基于 OT 的比对方法受到两个基本限制，包括(1)由于拓扑和一致性信息使用不足而导致的有效性和(2)由于非凸公式和重复计算代价高昂的损失计算而导致的可伸缩性。本文提出了一种基于位置感知的网络对准正则化最优传输框架 PARROT。为了解决有效性问题，提出的 PARROT 通过重新启动的随机游动捕获拓扑信息，并使用三个精心设计的一致性正则化项。为了解决可扩展性问题，将正则 OT 问题分解为一系列凸子问题，并利用所提出的具有保证收敛性的约束近似点方法进行有效求解。大量的实验表明，我们的算法在有效性和可扩展性方面都取得了显著的改进，性能优于最先进的网络对齐方法，并将现有的基于 OT 的方法提高了100倍。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=PARROT:+Position-Aware+Regularized+Optimal+Transport+for+Network+Alignment)|0| |[Label Information Enhanced Fraud Detection against Low Homophily in Graphs](https://doi.org/10.1145/3543507.3583373)|Yuchen Wang, Jinghui Zhang, Zhengjie Huang, Weibin Li, Shikun Feng, Ziheng Ma, Yu Sun, Dianhai Yu, Fang Dong, Jiahui Jin, Beilun Wang, Junzhou Luo|Southeast University, China; Baidu Inc., China|Node classification is a substantial problem in graph-based fraud detection. Many existing works adopt Graph Neural Networks (GNNs) to enhance fraud detectors. While promising, currently most GNN-based fraud detectors fail to generalize to the low homophily setting. Besides, label utilization has been proved to be significant factor for node classification problem. But we find they are less effective in fraud detection tasks due to the low homophily in graphs. In this work, we propose GAGA, a novel Group AGgregation enhanced TrAnsformer, to tackle the above challenges. Specifically, the group aggregation provides a portable method to cope with the low homophily issue. Such an aggregation explicitly integrates the label information to generate distinguishable neighborhood information. Along with group aggregation, an attempt towards end-to-end trainable group encoding is proposed which augments the original feature space with the class labels. Meanwhile, we devise two additional learnable encodings to recognize the structural and relational context. Then, we combine the group aggregation and the learnable encodings into a Transformer encoder to capture the semantic information. Experimental results clearly show that GAGA outperforms other competitive graph-based fraud detectors by up to 24.39% on two trending public datasets and a real-world industrial dataset from Anonymous. Even more, the group aggregation is demonstrated to outperform other label utilization methods (e.g., C&S, BoT/UniMP) in the low homophily setting.|节点分类是基于图的欺诈检测中的一个重要问题。现有的许多工作都采用图神经网络(GNN)来增强欺诈检测器。虽然有希望，目前大多数基于 GNN 的欺诈检测器不能泛化到低同调设置。此外，标签利用率也被证明是解决节点分类问题的重要因素。但是我们发现，由于图的同调性较低，它们在欺诈检测任务中的效率较低。在这项工作中，我们提出了 GAGA，一个新颖的群集增强的转换器，以解决上述挑战。具体来说，组聚合提供了一种可移植的方法来处理低同质性问题。这种聚合显式地集成标签信息以生成可区分的邻域信息。在分组聚合的基础上，提出了一种端到端的可训练分组编码方法，该方法通过类标签来增加原始特征空间。同时，我们设计了两个额外的可学习编码来识别结构和关系语境。然后，我们将分组聚合和可学习的编码结合到一个跑车编码器中来捕捉语义信息。实验结果清楚地表明，GAGA 在两个趋势公共数据集和一个来自 Anonymous 的真实世界工业数据集上的表现优于其他竞争性的基于图的欺诈检测器高达24.39% 。更重要的是，在低同质性设置中，组聚合被证明优于其他标签利用方法(例如 C & S、 BoT/UniMP)。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Label+Information+Enhanced+Fraud+Detection+against+Low+Homophily+in+Graphs)|0| |[An Attentional Multi-scale Co-evolving Model for Dynamic Link Prediction](https://doi.org/10.1145/3543507.3583396)|Guozhen Zhang, Tian Ye, Depeng Jin, Yong Li|Tsinghua University, China; Department of Electronic Engineering, Tsinghua University, China|Dynamic link prediction is essential for a wide range of domains, including social networks, bioinformatics, knowledge bases, and recommender systems. Existing works have demonstrated that structural information and temporal information are two of the most important information for this problem. However, existing works either focus on modeling them independently or modeling the temporal dynamics of a single structural scale, neglecting the complex correlations among them. This paper proposes to model the inherent correlations among the evolving dynamics of different structural scales for dynamic link prediction. Following this idea, we propose an Attentional Multi-scale Co-evolving Network (AMCNet). Specifically, We model multi-scale structural information by a motif-based graph neural network with multi-scale pooling. Then, we design a hierarchical attention-based sequence-to-sequence model for learning the complex correlations among the evolution dynamics of different structural scales. Extensive experiments on four real-world datasets with different characteristics demonstrate that AMCNet significantly outperforms the state-of-the-art in both single-step and multi-step dynamic link prediction tasks.|动态链接预测对于广泛的领域是必不可少的，包括社会网络、生物信息学、知识库和推荐系统。已有的研究表明，结构信息和时间信息是解决这一问题的两个重要信息。然而，现有的研究要么侧重于对它们进行独立的建模，要么侧重于对单一结构尺度的时间动力学进行建模，而忽视了它们之间复杂的相关性。本文提出建立不同结构尺度演化动力学之间的内在相关性模型，用于动态链接预测。根据这一思想，我们提出了一个注意力多尺度协同演化网络(AMCNet)。具体地说，我们利用基于模序的多尺度混合图神经网络对多尺度结构信息进行建模。然后，我们设计了一个分层的基于注意的序列-序列模型来学习不同结构尺度的进化动力学之间的复杂相关性。在四个不同特征的实际数据集上进行的大量实验表明，AMCNet 在单步和多步动态链接预测任务中的性能都明显优于最新技术。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=An+Attentional+Multi-scale+Co-evolving+Model+for+Dynamic+Link+Prediction)|0| -|[Robust Graph Representation Learning for Local Corruption Recovery](https://doi.org/10.1145/3543507.3583399)|Bingxin Zhou, Yuanhong Jiang, Yuguang Wang, Jingwei Liang, Junbin Gao, Shirui Pan, Xiaoqun Zhang|Shanghai Jiao Tong University, China and The University of Sydney, Australia; Shanghai Jiao Tong University, China; Griffith University, Australia; The University of Sydney, Australia; Shanghai Jiao Tong University, China and Shanghai Artificial Intelligence Laboratory, China|The performance of graph representation learning is affected by the quality of graph input. While existing research usually pursues a globally smoothed graph embedding, we believe the rarely observed anomalies are as well harmful to an accurate prediction. This work establishes a graph learning scheme that automatically detects (locally) corrupted feature attributes and recovers robust embedding for prediction tasks. The detection operation leverages a graph autoencoder, which does not make any assumptions about the distribution of the local corruptions. It pinpoints the positions of the anomalous node attributes in an unbiased mask matrix, where robust estimations are recovered with sparsity promoting regularizer. The optimizer approaches a new embedding that is sparse in the framelet domain and conditionally close to input observations. Extensive experiments are provided to validate our proposed model can recover a robust graph representation from black-box poisoning and achieve excellent performance.|图形表示学习的性能受到图形输入质量的影响。虽然现有的研究通常追求全局光滑图嵌入，我们认为很少观察到的异常是有害的，以及准确的预测。本文建立了一个图形学习方案，该方案能够自动检测(局部)被破坏的特征属性并恢复预测任务的鲁棒嵌入。检测操作利用图形自动编码器，该编码器不对本地腐败的分布做任何假设。该算法在无偏掩码矩阵中精确定位异常节点属性，利用稀疏增强正则化器恢复鲁棒估计。该优化器能够实现一种新的嵌入算法，该算法在帧小波域中是稀疏的，并且有条件地接近输入观测值。通过大量的实验验证了该模型能够从黑盒中毒中恢复出一个鲁棒的图表示，并取得了良好的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Robust+Graph+Representation+Learning+for+Local+Corruption+Recovery)|0| -|[Hyperbolic Geometric Graph Representation Learning for Hierarchy-imbalance Node Classification](https://doi.org/10.1145/3543507.3583403)|Xingcheng Fu, Yuecen Wei, Qingyun Sun, Haonan Yuan, Jia Wu, Hao Peng, Jianxin Li|Guangxi Key Lab of Multi-source Information Mining Security, Guangxi Normal University, China; School of Computer Science and Engineering, Beihang University, China; School of Computer Science and Engineering, Beihang University, China and Zhongguancun Lab, China; School of Cyber Science and Technology, Beihang University, China; School of Computing, Macquarie University, Australia|Learning unbiased node representations for imbalanced samples in the graph has become a more remarkable and important topic. For the graph, a significant challenge is that the topological properties of the nodes (e.g., locations, roles) are unbalanced (topology-imbalance), other than the number of training labeled nodes (quantity-imbalance). Existing studies on topology-imbalance focus on the location or the local neighborhood structure of nodes, ignoring the global underlying hierarchical properties of the graph, i.e., hierarchy. In the real-world scenario, the hierarchical structure of graph data reveals important topological properties of graphs and is relevant to a wide range of applications. We find that training labeled nodes with different hierarchical properties have a significant impact on the node classification tasks and confirm it in our experiments. It is well known that hyperbolic geometry has a unique advantage in representing the hierarchical structure of graphs. Therefore, we attempt to explore the hierarchy-imbalance issue for node classification of graph neural networks with a novelty perspective of hyperbolic geometry, including its characteristics and causes. Then, we propose a novel hyperbolic geometric hierarchy-imbalance learning framework, named HyperIMBA, to alleviate the hierarchy-imbalance issue caused by uneven hierarchy-levels and cross-hierarchy connectivity patterns of labeled nodes.Extensive experimental results demonstrate the superior effectiveness of HyperIMBA for hierarchy-imbalance node classification tasks.|学习图中不平衡样本的无偏节点表示已经成为一个非常引人注目和重要的课题。对于图，一个重大的挑战是，除了训练标记节点的数量(数量不平衡)之外，节点的拓扑属性(例如，位置，角色)是不平衡的(拓扑不平衡)。现有关于拓扑不平衡的研究主要集中在节点的位置或局部邻域结构上，忽略了图的全局潜在层次性质，即层次结构。在真实场景中，图数据的层次结构揭示了图的重要拓扑性质，并且与广泛的应用相关。我们发现训练具有不同层次属性的标记节点对节点分类任务有显著的影响，并在实验中得到了验证。众所周知，双曲几何在表示图形的层次结构方面具有独特的优势。因此，我们尝试从新颖的双曲几何角度探讨图形神经网络节点分类的层次不平衡问题，包括其特点和成因。在此基础上，提出了一种新的双曲型几何层次-不平衡学习框架 HyperIMBA，以解决标记节点层次不均匀和跨层次连通模式引起的层次-不平衡问题。大量的实验结果表明，HyperIMBA 对层次不平衡节点分类任务具有较好的效果。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Hyperbolic+Geometric+Graph+Representation+Learning+for+Hierarchy-imbalance+Node+Classification)|0| -|[Graph Neural Networks without Propagation](https://doi.org/10.1145/3543507.3583419)|Liang Yang, Qiuliang Zhang, Runjie Shi, Wenmiao Zhou, Bingxin Niu, Chuan Wang, Xiaochun Cao, Dongxiao He, Zhen Wang, Yuanfang Guo|Northwestern Polytechnical University, China; Sun Yat-sen University, China; Tianjin University, China; Beihang University, China; Chinese Academy of Sciences, China; Hebei University of Technology, China|Due to the simplicity, intuition and explanation, most Graph Neural Networks (GNNs) are proposed by following the pipeline of message passing. Although they achieve superior performances in many tasks, propagation-based GNNs possess three essential drawbacks. Firstly, the propagation tends to produce smooth effect, which meets the inductive bias of homophily, and causes two serious issues: over-smoothing issue and performance drop on networks with heterophily. Secondly, the propagations to each node are irrelevant, which prevents GNNs from modeling high-order relation, and cause the GNNs fragile to the attributes noises. Thirdly, propagation-based GNNs may be fragile to topology noise, since they heavily relay on propagation over the topology. Therefore, the propagation, as the key component of most GNNs, may be the essence of some serious issues in GNNs. To get to the root of these issue, this paper attempts to replace the propagation with a novel local operation. Quantitative experimental analysis reveals: 1) the existence of low-rank characteristic in the node attributes from ego-networks and 2) the performance improvement by reducing its rank. Motivated by this finding, this paper propose the Low-Rank GNNs, whose key component is the low-rank attribute matrix approximation in ego-network. The graph topology is employed to construct the ego-networks instead of message propagation, which is sensitive to topology noises. The proposed Low-Rank GNNs posses some attractive characteristics, including robust to topology and attribute noises, parameter-free and parallelizable. Experimental evaluations demonstrate the superior performance, robustness to noises and universality of the proposed Low-Rank GNNs.|由于图神经网络的简单性、直观性和解释性，大多数图神经网络都是通过跟踪消息传递流水线来实现的。尽管基于传播的 GNN 在许多任务中取得了优越的性能，但是它有三个基本的缺点。首先，传播趋向于产生平滑效应，这满足了异质网络的归纳偏差，导致了两个严重的问题: 过平滑问题和异质网络的性能下降。其次，对每个节点的传播是不相关的，这阻碍了 GNN 对高阶关系的建模，使得 GNN 对属性噪声非常脆弱。第三，基于传播的 GNN 很容易受到拓扑噪声的影响，因为它们严重依赖于拓扑上的传播。因此，传播作为大多数 GNN 的关键组成部分，可能是 GNN 中一些严重问题的实质。为了找到这些问题的根源，本文试图用一种新的局部操作来代替传播。定量实验分析表明: 1)自我网络节点属性存在低级特征，2)通过降低自我网络节点属性的排名可以提高节点的性能。基于这一发现，本文提出了低秩 GNN，其核心部分是自我网络中的低秩属性矩阵逼近。利用图拓扑结构代替对拓扑噪声敏感的消息传播，构造自我网络。所提出的低阶 GNN 具有对拓扑和属性噪声的鲁棒性、无参数性和可并行性等特点。实验结果表明，所提出的低阶 GNN 具有良好的性能、抗噪声能力和通用性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Graph+Neural+Networks+without+Propagation)|0| -|[Self-Supervised Teaching and Learning of Representations on Graphs](https://doi.org/10.1145/3543507.3583441)|Liangtian Wan, Zhenqiang Fu, Lu Sun, Xianpeng Wang, Gang Xu, Xiaoran Yan, Feng Xia|State Key Laboratory of Marine Resource Utilization in South China Sea, School of Information and Communication Engineering, Hainan University, China; Research Center of Big Data Intelligence, Research Institute of Artificial Intelligence, Zhejiang Lab, China; School of Computing Technologies, RMIT University, Australia; State Key Laboratory of Millimeter Waves, School of Information Science and Engineering, Southeast University, China; Department of Communication Engineering, Institute of Information Science Technology, Dalian Maritime University, China; Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province, School of Software, Dalian University of Technology, China|Recent years have witnessed significant advances in graph contrastive learning (GCL), while most GCL models use graph neural networks as encoders based on supervised learning. In this work, we propose a novel graph learning model called GraphTL, which explores self-supervised teaching and learning of representations on graphs. One critical objective of GCL is to retain original graph information. For this purpose, we design an encoder based on the idea of unsupervised dimensionality reduction of locally linear embedding (LLE). Specifically, we map one iteration of the LLE to one layer of the network. To guide the encoder to better retain the original graph information, we propose an unbalanced contrastive model consisting of two views, which are the learning view and the teaching view, respectively. Furthermore, we consider the nodes that are identical in muti-views as positive node pairs, and design the node similarity scorer so that the model can select positive samples of a target node. Extensive experiments have been conducted over multiple datasets to evaluate the performance of GraphTL in comparison with baseline models. Results demonstrate that GraphTL can reduce distances between similar nodes while preserving network topological and feature information, yielding better performance in node classification.|近年来，图形对比学习已经取得了显著的进展，而大多数图形对比学习模型使用图形神经网络作为基于监督式学习的编码器。在这项工作中，我们提出了一个新的图形学习模型称为 GraphTL，它探索自我监督的教学和图表示的学习。GCL 的一个关键目标是保留原始的图形信息。为此，我们设计了一个基于局部线性嵌入(lLE)无监督降维思想的编码器。具体来说，我们将 LLE 的一个迭代映射到网络的一个层。为了指导编码器更好地保留原始图形信息，本文提出了一种由学习视图和教学视图组成的非平衡对比模型。此外，我们将多视图中相同的节点视为正节点对，并设计了节点相似度计分器，使模型能够选择目标节点的正样本。在多个数据集上进行了广泛的实验，以评估 GraphTL 与基线模型相比的性能。结果表明，GraphTL 在保留网络拓扑和特征信息的同时，可以减少相似节点之间的距离，在节点分类方面取得了较好的效果。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Self-Supervised+Teaching+and+Learning+of+Representations+on+Graphs)|0| -|[SE-GSL: A General and Effective Graph Structure Learning Framework through Structural Entropy Optimization](https://doi.org/10.1145/3543507.3583453)|Dongcheng Zou, Hao Peng, Xiang Huang, Renyu Yang, Jianxin Li, Jia Wu, Chunyang Liu, Philip S. Yu|University of Illinois Chicago, USA; Didi Chuxing, China; Beihang University, China; Macquarie University, Australia|Graph Neural Networks (GNNs) are de facto solutions to structural data learning. However, it is susceptible to low-quality and unreliable structure, which has been a norm rather than an exception in real-world graphs. Existing graph structure learning (GSL) frameworks still lack robustness and interpretability. This paper proposes a general GSL framework, SE-GSL, through structural entropy and the graph hierarchy abstracted in the encoding tree. Particularly, we exploit the one-dimensional structural entropy to maximize embedded information content when auxiliary neighbourhood attributes are fused to enhance the original graph. A new scheme of constructing optimal encoding trees is proposed to minimize the uncertainty and noises in the graph whilst assuring proper community partition in hierarchical abstraction. We present a novel sample-based mechanism for restoring the graph structure via node structural entropy distribution. It increases the connectivity among nodes with larger uncertainty in lower-level communities. SE-GSL is compatible with various GNN models and enhances the robustness towards noisy and heterophily structures. Extensive experiments show significant improvements in the effectiveness and robustness of structure learning and node representation learning.|图神经网络(GNN)是结构数据学习的实际解决方案。但是，它容易受到低质量和不可靠结构的影响，这在现实世界的图形中已经成为一种规范而不是例外。现有的图结构学习(GSL)框架仍然缺乏健壮性和可解释性。本文通过结构熵和编码树中抽象出的图层次结构，提出了一个通用的 GSL 框架 SE-GSL。特别地，当融合辅助邻域属性来增强原始图时，我们利用一维结构熵来最大化嵌入信息的内容。提出了一种新的构造最优编码树的方法，以最小化图中的不确定性和噪声，同时在分层抽象中保证适当的社区划分。提出了一种基于样本的节点结构熵分布恢复图结构的新机制。它增加了低层社区中具有较大不确定性的节点之间的连通性。SE-GSL 与各种 GNN 模型兼容，增强了对噪声和异质结构的鲁棒性。广泛的实验表明，结构学习和节点表示学习的有效性和鲁棒性有了显著的提高。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=SE-GSL:+A+General+and+Effective+Graph+Structure+Learning+Framework+through+Structural+Entropy+Optimization)|0| -|[Homophily-oriented Heterogeneous Graph Rewiring](https://doi.org/10.1145/3543507.3583454)|Jiayan Guo, Lun Du, Wendong Bi, Qiang Fu, Xiaojun Ma, Xu Chen, Shi Han, Dongmei Zhang, Yan Zhang|School of Intelligence Science and Technology, Peking University, China; Institute of Computing Technology, Chinese Academy of Sciences, China; Microsoft Research Asia, China|With the rapid development of the World Wide Web (WWW), heterogeneous graphs (HG) have explosive growth. Recently, heterogeneous graph neural network (HGNN) has shown great potential in learning on HG. Current studies of HGNN mainly focus on some HGs with strong homophily properties (nodes connected by meta-path tend to have the same labels), while few discussions are made in those that are less homophilous. Recently, there have been many works on homogeneous graphs with heterophily. However, due to heterogeneity, it is non-trivial to extend their approach to deal with HGs with heterophily. In this work, based on empirical observations, we propose a meta-path-induced metric to measure the homophily degree of a HG. We also find that current HGNNs may have degenerated performance when handling HGs with less homophilous properties. Thus it is essential to increase the generalization ability of HGNNs on non-homophilous HGs. To this end, we propose HDHGR, a homophily-oriented deep heterogeneous graph rewiring approach that modifies the HG structure to increase the performance of HGNN. We theoretically verify HDHGR. In addition, experiments on real-world HGs demonstrate the effectiveness of HDHGR, which brings at most more than 10% relative gain.|随着万维网(WWW)的迅速发展，异构图形(HG)呈现出爆炸性的增长。近年来，异构图形神经网络(HGNN)在 HG 学习中显示出巨大的潜力。目前对 HGNN 的研究主要集中在一些具有强同质性的 HG (通过元路径连接的节点往往具有相同的标签) ，而对于那些不同质性的 HG 的研究很少。近年来，关于具有异拓的齐图的研究已经有了大量的工作。然而，由于异质性的原因，扩展它们的方法来处理具有异质性的 HGs 是非常重要的。在这项工作中，基于经验观察，我们提出了一个元路径诱导度量测量 HG 的同调度。我们还发现，当处理具有较少同质性的 HG 时，当前的 HGNN 可能具有退化的性能。因此，提高 HGNN 在非同源 HGs 上的泛化能力至关重要。为此，我们提出了一种面向同构的深度异构图重布线方法 HDHGR，该方法通过修改 HG 结构来提高 HGNN 的性能。我们理论上验证了 HDHGR。此外，在现实世界中进行的汞实验证明了 HDHGR 的有效性，它最多带来超过10% 的相对收益。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Homophily-oriented+Heterogeneous+Graph+Rewiring)|0| +|[Robust Graph Representation Learning for Local Corruption Recovery](https://doi.org/10.1145/3543507.3583399)|Bingxin Zhou, Yuanhong Jiang, Yuguang Wang, Jingwei Liang, Junbin Gao, Shirui Pan, Xiaoqun Zhang|Shanghai Jiao Tong University, China; Shanghai Jiao Tong University, China and The University of Sydney, Australia; The University of Sydney, Australia; Shanghai Jiao Tong University, China and Shanghai Artificial Intelligence Laboratory, China; Griffith University, Australia|The performance of graph representation learning is affected by the quality of graph input. While existing research usually pursues a globally smoothed graph embedding, we believe the rarely observed anomalies are as well harmful to an accurate prediction. This work establishes a graph learning scheme that automatically detects (locally) corrupted feature attributes and recovers robust embedding for prediction tasks. The detection operation leverages a graph autoencoder, which does not make any assumptions about the distribution of the local corruptions. It pinpoints the positions of the anomalous node attributes in an unbiased mask matrix, where robust estimations are recovered with sparsity promoting regularizer. The optimizer approaches a new embedding that is sparse in the framelet domain and conditionally close to input observations. Extensive experiments are provided to validate our proposed model can recover a robust graph representation from black-box poisoning and achieve excellent performance.|图形表示学习的性能受到图形输入质量的影响。虽然现有的研究通常追求全局光滑图嵌入，我们认为很少观察到的异常是有害的，以及准确的预测。本文建立了一个图形学习方案，该方案能够自动检测(局部)被破坏的特征属性并恢复预测任务的鲁棒嵌入。检测操作利用图形自动编码器，该编码器不对本地腐败的分布做任何假设。该算法在无偏掩码矩阵中精确定位异常节点属性，利用稀疏增强正则化器恢复鲁棒估计。该优化器能够实现一种新的嵌入算法，该算法在帧小波域中是稀疏的，并且有条件地接近输入观测值。通过大量的实验验证了该模型能够从黑盒中毒中恢复出一个鲁棒的图表示，并取得了良好的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Robust+Graph+Representation+Learning+for+Local+Corruption+Recovery)|0| +|[Hyperbolic Geometric Graph Representation Learning for Hierarchy-imbalance Node Classification](https://doi.org/10.1145/3543507.3583403)|Xingcheng Fu, Yuecen Wei, Qingyun Sun, Haonan Yuan, Jia Wu, Hao Peng, Jianxin Li|School of Computing, Macquarie University, Australia; Guangxi Key Lab of Multi-source Information Mining Security, Guangxi Normal University, China; School of Cyber Science and Technology, Beihang University, China; School of Computer Science and Engineering, Beihang University, China; School of Computer Science and Engineering, Beihang University, China and Zhongguancun Lab, China|Learning unbiased node representations for imbalanced samples in the graph has become a more remarkable and important topic. For the graph, a significant challenge is that the topological properties of the nodes (e.g., locations, roles) are unbalanced (topology-imbalance), other than the number of training labeled nodes (quantity-imbalance). Existing studies on topology-imbalance focus on the location or the local neighborhood structure of nodes, ignoring the global underlying hierarchical properties of the graph, i.e., hierarchy. In the real-world scenario, the hierarchical structure of graph data reveals important topological properties of graphs and is relevant to a wide range of applications. We find that training labeled nodes with different hierarchical properties have a significant impact on the node classification tasks and confirm it in our experiments. It is well known that hyperbolic geometry has a unique advantage in representing the hierarchical structure of graphs. Therefore, we attempt to explore the hierarchy-imbalance issue for node classification of graph neural networks with a novelty perspective of hyperbolic geometry, including its characteristics and causes. Then, we propose a novel hyperbolic geometric hierarchy-imbalance learning framework, named HyperIMBA, to alleviate the hierarchy-imbalance issue caused by uneven hierarchy-levels and cross-hierarchy connectivity patterns of labeled nodes.Extensive experimental results demonstrate the superior effectiveness of HyperIMBA for hierarchy-imbalance node classification tasks.|学习图中不平衡样本的无偏节点表示已经成为一个非常引人注目和重要的课题。对于图，一个重大的挑战是，除了训练标记节点的数量(数量不平衡)之外，节点的拓扑属性(例如，位置，角色)是不平衡的(拓扑不平衡)。现有关于拓扑不平衡的研究主要集中在节点的位置或局部邻域结构上，忽略了图的全局潜在层次性质，即层次结构。在真实场景中，图数据的层次结构揭示了图的重要拓扑性质，并且与广泛的应用相关。我们发现训练具有不同层次属性的标记节点对节点分类任务有显著的影响，并在实验中得到了验证。众所周知，双曲几何在表示图形的层次结构方面具有独特的优势。因此，我们尝试从新颖的双曲几何角度探讨图形神经网络节点分类的层次不平衡问题，包括其特点和成因。在此基础上，提出了一种新的双曲型几何层次-不平衡学习框架 HyperIMBA，以解决标记节点层次不均匀和跨层次连通模式引起的层次-不平衡问题。大量的实验结果表明，HyperIMBA 对层次不平衡节点分类任务具有较好的效果。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Hyperbolic+Geometric+Graph+Representation+Learning+for+Hierarchy-imbalance+Node+Classification)|0| +|[Graph Neural Networks without Propagation](https://doi.org/10.1145/3543507.3583419)|Liang Yang, Qiuliang Zhang, Runjie Shi, Wenmiao Zhou, Bingxin Niu, Chuan Wang, Xiaochun Cao, Dongxiao He, Zhen Wang, Yuanfang Guo|Sun Yat-sen University, China; Northwestern Polytechnical University, China; Beihang University, China; Chinese Academy of Sciences, China; Hebei University of Technology, China; Tianjin University, China|Due to the simplicity, intuition and explanation, most Graph Neural Networks (GNNs) are proposed by following the pipeline of message passing. Although they achieve superior performances in many tasks, propagation-based GNNs possess three essential drawbacks. Firstly, the propagation tends to produce smooth effect, which meets the inductive bias of homophily, and causes two serious issues: over-smoothing issue and performance drop on networks with heterophily. Secondly, the propagations to each node are irrelevant, which prevents GNNs from modeling high-order relation, and cause the GNNs fragile to the attributes noises. Thirdly, propagation-based GNNs may be fragile to topology noise, since they heavily relay on propagation over the topology. Therefore, the propagation, as the key component of most GNNs, may be the essence of some serious issues in GNNs. To get to the root of these issue, this paper attempts to replace the propagation with a novel local operation. Quantitative experimental analysis reveals: 1) the existence of low-rank characteristic in the node attributes from ego-networks and 2) the performance improvement by reducing its rank. Motivated by this finding, this paper propose the Low-Rank GNNs, whose key component is the low-rank attribute matrix approximation in ego-network. The graph topology is employed to construct the ego-networks instead of message propagation, which is sensitive to topology noises. The proposed Low-Rank GNNs posses some attractive characteristics, including robust to topology and attribute noises, parameter-free and parallelizable. Experimental evaluations demonstrate the superior performance, robustness to noises and universality of the proposed Low-Rank GNNs.|由于图神经网络的简单性、直观性和解释性，大多数图神经网络都是通过跟踪消息传递流水线来实现的。尽管基于传播的 GNN 在许多任务中取得了优越的性能，但是它有三个基本的缺点。首先，传播趋向于产生平滑效应，这满足了异质网络的归纳偏差，导致了两个严重的问题: 过平滑问题和异质网络的性能下降。其次，对每个节点的传播是不相关的，这阻碍了 GNN 对高阶关系的建模，使得 GNN 对属性噪声非常脆弱。第三，基于传播的 GNN 很容易受到拓扑噪声的影响，因为它们严重依赖于拓扑上的传播。因此，传播作为大多数 GNN 的关键组成部分，可能是 GNN 中一些严重问题的实质。为了找到这些问题的根源，本文试图用一种新的局部操作来代替传播。定量实验分析表明: 1)自我网络节点属性存在低级特征，2)通过降低自我网络节点属性的排名可以提高节点的性能。基于这一发现，本文提出了低秩 GNN，其核心部分是自我网络中的低秩属性矩阵逼近。利用图拓扑结构代替对拓扑噪声敏感的消息传播，构造自我网络。所提出的低阶 GNN 具有对拓扑和属性噪声的鲁棒性、无参数性和可并行性等特点。实验结果表明，所提出的低阶 GNN 具有良好的性能、抗噪声能力和通用性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Graph+Neural+Networks+without+Propagation)|0| +|[Self-Supervised Teaching and Learning of Representations on Graphs](https://doi.org/10.1145/3543507.3583441)|Liangtian Wan, Zhenqiang Fu, Lu Sun, Xianpeng Wang, Gang Xu, Xiaoran Yan, Feng Xia|Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province, School of Software, Dalian University of Technology, China; State Key Laboratory of Millimeter Waves, School of Information Science and Engineering, Southeast University, China; State Key Laboratory of Marine Resource Utilization in South China Sea, School of Information and Communication Engineering, Hainan University, China; School of Computing Technologies, RMIT University, Australia; Department of Communication Engineering, Institute of Information Science Technology, Dalian Maritime University, China; Research Center of Big Data Intelligence, Research Institute of Artificial Intelligence, Zhejiang Lab, China|Recent years have witnessed significant advances in graph contrastive learning (GCL), while most GCL models use graph neural networks as encoders based on supervised learning. In this work, we propose a novel graph learning model called GraphTL, which explores self-supervised teaching and learning of representations on graphs. One critical objective of GCL is to retain original graph information. For this purpose, we design an encoder based on the idea of unsupervised dimensionality reduction of locally linear embedding (LLE). Specifically, we map one iteration of the LLE to one layer of the network. To guide the encoder to better retain the original graph information, we propose an unbalanced contrastive model consisting of two views, which are the learning view and the teaching view, respectively. Furthermore, we consider the nodes that are identical in muti-views as positive node pairs, and design the node similarity scorer so that the model can select positive samples of a target node. Extensive experiments have been conducted over multiple datasets to evaluate the performance of GraphTL in comparison with baseline models. Results demonstrate that GraphTL can reduce distances between similar nodes while preserving network topological and feature information, yielding better performance in node classification.|近年来，图形对比学习已经取得了显著的进展，而大多数图形对比学习模型使用图形神经网络作为基于监督式学习的编码器。在这项工作中，我们提出了一个新的图形学习模型称为 GraphTL，它探索自我监督的教学和图表示的学习。GCL 的一个关键目标是保留原始的图形信息。为此，我们设计了一个基于局部线性嵌入(lLE)无监督降维思想的编码器。具体来说，我们将 LLE 的一个迭代映射到网络的一个层。为了指导编码器更好地保留原始图形信息，本文提出了一种由学习视图和教学视图组成的非平衡对比模型。此外，我们将多视图中相同的节点视为正节点对，并设计了节点相似度计分器，使模型能够选择目标节点的正样本。在多个数据集上进行了广泛的实验，以评估 GraphTL 与基线模型相比的性能。结果表明，GraphTL 在保留网络拓扑和特征信息的同时，可以减少相似节点之间的距离，在节点分类方面取得了较好的效果。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Self-Supervised+Teaching+and+Learning+of+Representations+on+Graphs)|0| +|[SE-GSL: A General and Effective Graph Structure Learning Framework through Structural Entropy Optimization](https://doi.org/10.1145/3543507.3583453)|Dongcheng Zou, Hao Peng, Xiang Huang, Renyu Yang, Jianxin Li, Jia Wu, Chunyang Liu, Philip S. Yu|Didi Chuxing, China; Macquarie University, Australia; Beihang University, China; University of Illinois Chicago, USA|Graph Neural Networks (GNNs) are de facto solutions to structural data learning. However, it is susceptible to low-quality and unreliable structure, which has been a norm rather than an exception in real-world graphs. Existing graph structure learning (GSL) frameworks still lack robustness and interpretability. This paper proposes a general GSL framework, SE-GSL, through structural entropy and the graph hierarchy abstracted in the encoding tree. Particularly, we exploit the one-dimensional structural entropy to maximize embedded information content when auxiliary neighbourhood attributes are fused to enhance the original graph. A new scheme of constructing optimal encoding trees is proposed to minimize the uncertainty and noises in the graph whilst assuring proper community partition in hierarchical abstraction. We present a novel sample-based mechanism for restoring the graph structure via node structural entropy distribution. It increases the connectivity among nodes with larger uncertainty in lower-level communities. SE-GSL is compatible with various GNN models and enhances the robustness towards noisy and heterophily structures. Extensive experiments show significant improvements in the effectiveness and robustness of structure learning and node representation learning.|图神经网络(GNN)是结构数据学习的实际解决方案。但是，它容易受到低质量和不可靠结构的影响，这在现实世界的图形中已经成为一种规范而不是例外。现有的图结构学习(GSL)框架仍然缺乏健壮性和可解释性。本文通过结构熵和编码树中抽象出的图层次结构，提出了一个通用的 GSL 框架 SE-GSL。特别地，当融合辅助邻域属性来增强原始图时，我们利用一维结构熵来最大化嵌入信息的内容。提出了一种新的构造最优编码树的方法，以最小化图中的不确定性和噪声，同时在分层抽象中保证适当的社区划分。提出了一种基于样本的节点结构熵分布恢复图结构的新机制。它增加了低层社区中具有较大不确定性的节点之间的连通性。SE-GSL 与各种 GNN 模型兼容，增强了对噪声和异质结构的鲁棒性。广泛的实验表明，结构学习和节点表示学习的有效性和鲁棒性有了显著的提高。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=SE-GSL:+A+General+and+Effective+Graph+Structure+Learning+Framework+through+Structural+Entropy+Optimization)|0| +|[Homophily-oriented Heterogeneous Graph Rewiring](https://doi.org/10.1145/3543507.3583454)|Jiayan Guo, Lun Du, Wendong Bi, Qiang Fu, Xiaojun Ma, Xu Chen, Shi Han, Dongmei Zhang, Yan Zhang|Institute of Computing Technology, Chinese Academy of Sciences, China; Microsoft Research Asia, China; School of Intelligence Science and Technology, Peking University, China|With the rapid development of the World Wide Web (WWW), heterogeneous graphs (HG) have explosive growth. Recently, heterogeneous graph neural network (HGNN) has shown great potential in learning on HG. Current studies of HGNN mainly focus on some HGs with strong homophily properties (nodes connected by meta-path tend to have the same labels), while few discussions are made in those that are less homophilous. Recently, there have been many works on homogeneous graphs with heterophily. However, due to heterogeneity, it is non-trivial to extend their approach to deal with HGs with heterophily. In this work, based on empirical observations, we propose a meta-path-induced metric to measure the homophily degree of a HG. We also find that current HGNNs may have degenerated performance when handling HGs with less homophilous properties. Thus it is essential to increase the generalization ability of HGNNs on non-homophilous HGs. To this end, we propose HDHGR, a homophily-oriented deep heterogeneous graph rewiring approach that modifies the HG structure to increase the performance of HGNN. We theoretically verify HDHGR. In addition, experiments on real-world HGs demonstrate the effectiveness of HDHGR, which brings at most more than 10% relative gain.|随着万维网(WWW)的迅速发展，异构图形(HG)呈现出爆炸性的增长。近年来，异构图形神经网络(HGNN)在 HG 学习中显示出巨大的潜力。目前对 HGNN 的研究主要集中在一些具有强同质性的 HG (通过元路径连接的节点往往具有相同的标签) ，而对于那些不同质性的 HG 的研究很少。近年来，关于具有异拓的齐图的研究已经有了大量的工作。然而，由于异质性的原因，扩展它们的方法来处理具有异质性的 HGs 是非常重要的。在这项工作中，基于经验观察，我们提出了一个元路径诱导度量测量 HG 的同调度。我们还发现，当处理具有较少同质性的 HG 时，当前的 HGNN 可能具有退化的性能。因此，提高 HGNN 在非同源 HGs 上的泛化能力至关重要。为此，我们提出了一种面向同构的深度异构图重布线方法 HDHGR，该方法通过修改 HG 结构来提高 HGNN 的性能。我们理论上验证了 HDHGR。此外，在现实世界中进行的汞实验证明了 HDHGR 的有效性，它最多带来超过10% 的相对收益。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Homophily-oriented+Heterogeneous+Graph+Rewiring)|0| |[HGWaveNet: A Hyperbolic Graph Neural Network for Temporal Link Prediction](https://doi.org/10.1145/3543507.3583455)|Qijie Bai, Changli Nie, Haiwei Zhang, Dongming Zhao, Xiaojie Yuan|China Mobile Communication Group Tianjin Co., Ltd, China; College of CS, TJ Key Lab of NDST, Nankai University, China|Temporal link prediction, aiming to predict future edges between paired nodes in a dynamic graph, is of vital importance in diverse applications. However, existing methods are mainly built upon uniform Euclidean space, which has been found to be conflict with the power-law distributions of real-world graphs and unable to represent the hierarchical connections between nodes effectively. With respect to the special data characteristic, hyperbolic geometry offers an ideal alternative due to its exponential expansion property. In this paper, we propose HGWaveNet, a novel hyperbolic graph neural network that fully exploits the fitness between hyperbolic spaces and data distributions for temporal link prediction. Specifically, we design two key modules to learn the spatial topological structures and temporal evolutionary information separately. On the one hand, a hyperbolic diffusion graph convolution (HDGC) module effectively aggregates information from a wider range of neighbors. On the other hand, the internal order of causal correlation between historical states is captured by hyperbolic dilated causal convolution (HDCC) modules. The whole model is built upon the hyperbolic spaces to preserve the hierarchical structural information in the entire data flow. To prove the superiority of HGWaveNet, extensive experiments are conducted on six real-world graph datasets and the results show a relative improvement by up to 6.67% on AUC for temporal link prediction over SOTA methods.|时间链路预测，旨在预测动态图中成对节点之间的未来边缘，在不同的应用中具有重要意义。然而，现有的方法主要建立在统一的欧氏空间上，这与现实世界图的幂律分布相冲突，不能有效地表示节点之间的层次关系。关于特殊的数据特性，双曲几何提供了一个理想的选择，由于其指数膨胀性质。本文提出了一种新的双曲型图形神经网络 HGWave Net，它充分利用双曲型空间和数据分布之间的适应性来进行时间链路预测。具体来说，我们设计了两个关键模块来分别学习空间拓扑结构和时间进化信息。一方面，双曲扩散图卷积(HDGC)模块有效地聚集了来自更广泛的邻居的信息。另一方面，历史状态之间因果关系的内部顺序被双曲扩张因果卷积(HDCC)模块所捕获。整个模型建立在双曲空间的基础上，保留了整个数据流中的层次结构信息。为了证明 HGWaveNet 的优越性，在6个实际图形数据集上进行了广泛的实验，结果表明，与 SOTA 方法相比，时间链路预测的 AUC 相对提高了6.67% 。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=HGWaveNet:+A+Hyperbolic+Graph+Neural+Network+for+Temporal+Link+Prediction)|0| -|[Rethinking Structural Encodings: Adaptive Graph Transformer for Node Classification Task](https://doi.org/10.1145/3543507.3583464)|Xiaojun Ma, Qin Chen, Yi Wu, Guojie Song, Liang Wang, Bo Zheng|Peking University, China; Alibaba Group, China; Microsoft, China|Graph Transformers have proved their advantages in graph data mining with elaborate Positional Encodings, especially in graph-level tasks. However, their application in the node classification task has not been fully exploited yet. In the node classification task, existing Graph Transformers with Positional Encodings are limited by the following issues: (i) PEs describing the node’s positional identities are insufficient for the node classification task on complex graphs, where a full portrayal of the local node property is needed. (ii) PEs for graphs are integrated with Transformers in a constant schema, resulting in the ignorance of local patterns that may vary among different nodes. In this paper, we propose Adaptive Graph Transformer (AGT) to tackle above issues. AGT consists of a Learnable Centrality Encoding and a Kernelized Local Structure Encoding. The two modules extract structural patterns from centrality and subgraph views in a learnable and scalable manner. Further, we design the Adaptive Transformer Block to adaptively integrate the attention scores and Structural Encodings in a node-specific manner. AGT achieves state-of-the-art performances on nine real-world web graphs (up to 1.6 million nodes). Furthermore, AGT shows outstanding results on two series of synthetic graphs with ranges of heterophily and noise ratios.|图形变换器通过精细的位置编码，尤其是在图级任务中，证明了它在图数据挖掘中的优势。但是，它们在节点分类任务中的应用还没有得到充分利用。在节点分类任务中，现有的带有位置编码的图形变换器受到以下问题的限制: (i)描述节点位置标识的 PE 不足以完成复杂图形上的节点分类任务，这需要对本地节点属性进行全面描述。(ii)图形的 PE 与变压器在一个固定的模式下集成，导致对不同节点之间可能变化的局部模式的忽视。本文提出了自适应图形变换器(AGT)来解决上述问题。AGT 由可学习中心性编码和核化局部结构编码两部分组成。这两个模块以一种可学习和可扩展的方式从中心性和子图视图中提取结构模式。进一步，我们设计了自适应变压器块，以节点特定的方式自适应地整合注意分数和结构编码。AGT 在9个真实世界的网络图表(多达160万个节点)上实现了最先进的性能。此外，AGT 在两组具有异质性和噪声比范围的合成图上显示了优异的结果。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Rethinking+Structural+Encodings:+Adaptive+Graph+Transformer+for+Node+Classification+Task)|0| +|[Rethinking Structural Encodings: Adaptive Graph Transformer for Node Classification Task](https://doi.org/10.1145/3543507.3583464)|Xiaojun Ma, Qin Chen, Yi Wu, Guojie Song, Liang Wang, Bo Zheng|Microsoft, China; Alibaba Group, China; Peking University, China|Graph Transformers have proved their advantages in graph data mining with elaborate Positional Encodings, especially in graph-level tasks. However, their application in the node classification task has not been fully exploited yet. In the node classification task, existing Graph Transformers with Positional Encodings are limited by the following issues: (i) PEs describing the node’s positional identities are insufficient for the node classification task on complex graphs, where a full portrayal of the local node property is needed. (ii) PEs for graphs are integrated with Transformers in a constant schema, resulting in the ignorance of local patterns that may vary among different nodes. In this paper, we propose Adaptive Graph Transformer (AGT) to tackle above issues. AGT consists of a Learnable Centrality Encoding and a Kernelized Local Structure Encoding. The two modules extract structural patterns from centrality and subgraph views in a learnable and scalable manner. Further, we design the Adaptive Transformer Block to adaptively integrate the attention scores and Structural Encodings in a node-specific manner. AGT achieves state-of-the-art performances on nine real-world web graphs (up to 1.6 million nodes). Furthermore, AGT shows outstanding results on two series of synthetic graphs with ranges of heterophily and noise ratios.|图形变换器通过精细的位置编码，尤其是在图级任务中，证明了它在图数据挖掘中的优势。但是，它们在节点分类任务中的应用还没有得到充分利用。在节点分类任务中，现有的带有位置编码的图形变换器受到以下问题的限制: (i)描述节点位置标识的 PE 不足以完成复杂图形上的节点分类任务，这需要对本地节点属性进行全面描述。(ii)图形的 PE 与变压器在一个固定的模式下集成，导致对不同节点之间可能变化的局部模式的忽视。本文提出了自适应图形变换器(AGT)来解决上述问题。AGT 由可学习中心性编码和核化局部结构编码两部分组成。这两个模块以一种可学习和可扩展的方式从中心性和子图视图中提取结构模式。进一步，我们设计了自适应变压器块，以节点特定的方式自适应地整合注意分数和结构编码。AGT 在9个真实世界的网络图表(多达160万个节点)上实现了最先进的性能。此外，AGT 在两组具有异质性和噪声比范围的合成图上显示了优异的结果。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Rethinking+Structural+Encodings:+Adaptive+Graph+Transformer+for+Node+Classification+Task)|0| |[Federated Node Classification over Graphs with Latent Link-type Heterogeneity](https://doi.org/10.1145/3543507.3583471)|Han Xie, Li Xiong, Carl Yang|Emory University, USA|Federated learning (FL) aims to train powerful and generalized global models without putting distributed data together, which has been shown effective in various domains of machine learning. The non-IIDness of data across local clients has been a major challenge for FL. In graphs, one specifically important perspective of non-IIDness is manifested in the link-type heterogeneity underlying homogeneous graphs– the seemingly uniform links captured in most real-world networks can carry different levels of homophily or semantics of relations, while the exact sets and distributions of such latent link-types can further differ across local clients. Through our preliminary data analysis, we are motivated to design a new graph FL framework that can simultaneously discover latent link-types and model message-passing w.r.t. the discovered link-types through the collaboration of distributed local clients. Specifically, we propose a framework FedLit that can dynamically detect the latent link-types during FL via an EM-based clustering algorithm and differentiate the message-passing through different types of links via multiple convolution channels. For experiments, we synthesize multiple realistic datasets of graphs with latent heterogeneous link-types from real-world data, and partition them with different levels of link-type heterogeneity. Comprehensive experimental results and in-depth analysis have demonstrated both superior performance and rational behaviors of our proposed techniques.|联邦学习(FL)旨在不将分布式数据放在一起而训练强大的广义全局模型，这已经在机器学习的各个领域中被证明是有效的。跨本地客户端的数据的非 IID 性一直是 FL 面临的主要挑战。在图中，非 IIDness 的一个特别重要的视角表现在同质图的链接类型异质性中——在大多数现实世界网络中捕获的看似统一的链接可以携带不同层次的同质性或关系语义，而这种潜在链接类型的确切集合和分布可以在本地客户端之间进一步不同。通过初步的数据分析，设计了一个新的图形 FL 框架，该框架可以通过分布式本地客户机的协作，同时发现潜在的链接类型和模型消息传递发现的链接类型。具体来说，我们提出了一个框架 FedLit，它可以通过一个基于 EM 的聚类算法动态检测 FL 中的潜在链接类型，并通过多个卷积通道区分不同类型的链接传递信息。对于实验，我们从现实数据中合成了多个具有潜在异构链接类型的图的现实数据集，并将它们按照不同的链接类型异构程度进行划分。综合实验结果和深入分析表明，我们提出的技术具有优越的性能和合理的行为。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Federated+Node+Classification+over+Graphs+with+Latent+Link-type+Heterogeneity)|0| -|[CMINet: a Graph Learning Framework for Content-aware Multi-channel Influence Diffusion](https://doi.org/10.1145/3543507.3583465)|HsiWen Chen, DeNian Yang, WangChien Lee, Philip S. Yu, MingSyan Chen|University of Illinois Chicago, USA; Pennsylvania State University, USA; Academia Sinica, Taiwan; National Taiwan University, Taiwan; National Taiwan University, Taiwan and Academia Sinica, Taiwan|The phenomena of influence diffusion on social networks have received tremendous research interests in the past decade. While most prior works mainly focus on predicting the total influence spread on a single network, a marketing campaign that exploits influence diffusion often involves multiple channels with various information disseminated on different media. In this paper, we introduce a new influence estimation problem, namely Content-aware Multi-channel Influence Diffusion (CMID), and accordingly propose CMINet to predict newly influenced users, given a set of seed users with different multimedia contents. In CMINet, we first introduce DiffGNN to encode the influencing power of users (nodes) and Influence-aware Optimal Transport (IOT) to align the embeddings to address the distribution shift across different diffusion channels. Then, we transform CMID into a node classification problem and propose Social-based Multimedia Feature Extractor (SMFE) and Content-aware Multi-channel Influence Propagation (CMIP) to jointly learn the user preferences on multimedia contents and predict the susceptibility of users. Furthermore, we prove that CMINet preserves monotonicity and submodularity, thus enabling (1 − 1/e)-approximate solutions for influence maximization. Experimental results manifest that CMINet outperforms eleven baselines on three public datasets.|近十年来，社会网络中的影响扩散现象受到了广泛的关注。虽然大多数以前的工作主要集中在预测总的影响传播在一个单一的网络，利用影响传播的营销活动往往涉及多个渠道，在不同的媒体传播各种各样的信息。本文提出了一个新的影响估计问题，即内容感知的多信道影响扩散(CMID) ，并相应地提出了 CMINet 来预测新的影响用户，给出了一组具有不同多媒体内容的种子用户。在 CMINet 中，我们首先引入区分 GNN 编码用户(节点)的影响力，以及感知影响的最优传输(IOT)来调整嵌入以解决不同扩散通道间的分布转移问题。然后，将 CMID 转化为一个节点分类问题，提出了基于社会化的多媒体特征提取器(SMFE)和内容感知的多信道影响传播(CMIP) ，以共同学习用户对多媒体内容的偏好并预测用户的易感性。此外，我们证明了 CMINet 保持了单调性和次模性，从而使(1-1/e)近似解的影响最大化。实验结果表明，CMINet 在三个公共数据集上的表现优于十一个基线。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=CMINet:+a+Graph+Learning+Framework+for+Content-aware+Multi-channel+Influence+Diffusion)|0| -|[Auto-HeG: Automated Graph Neural Network on Heterophilic Graphs](https://doi.org/10.1145/3543507.3583498)|Xin Zheng, Miao Zhang, Chunyang Chen, Qin Zhang, Chuan Zhou, Shirui Pan|Shenzhen University, China; Monash University, Australia; Griffith University, Australia; Harbin Institute of Technology (Shenzhen), China; Chinese Academy of Sciences, China|Graph neural architecture search (NAS) has gained popularity in automatically designing powerful graph neural networks (GNNs) with relieving human efforts. However, existing graph NAS methods mainly work under the homophily assumption and overlook another important graph property, i.e., heterophily, which exists widely in various real-world applications. To date, automated heterophilic graph learning with NAS is still a research blank to be filled in. Due to the complexity and variety of heterophilic graphs, the critical challenge of heterophilic graph NAS mainly lies in developing the heterophily-specific search space and strategy. Therefore, in this paper, we propose a novel automated graph neural network on heterophilic graphs, namely Auto-HeG, to automatically build heterophilic GNN models with expressive learning abilities. Specifically, Auto-HeG incorporates heterophily into all stages of automatic heterophilic graph learning, including search space design, supernet training, and architecture selection. Through the diverse message-passing scheme with joint micro-level and macro-level designs, we first build a comprehensive heterophilic GNN search space, enabling Auto-HeG to integrate complex and various heterophily of graphs. With a progressive supernet training strategy, we dynamically shrink the initial search space according to layer-wise variation of heterophily, resulting in a compact and efficient supernet. Taking a heterophily-aware distance criterion as the guidance, we conduct heterophilic architecture selection in the leave-one-out pattern, so that specialized and expressive heterophilic GNN architectures can be derived. Extensive experiments illustrate the superiority of Auto-HeG in developing excellent heterophilic GNNs to human-designed models and graph NAS models.|图神经结构搜索(NAS)在自动设计功能强大的图神经网络(GNN)方面得到了广泛的应用。然而，现有的图 NAS 方法主要是在同伦假设下工作，忽略了在各种实际应用中广泛存在的另一个重要图性质，即异伦性质。迄今为止，利用 NAS 进行自动异质图学习仍然是一个有待填补的研究空白。由于异构图的复杂性和多样性，异构图 NAS 的关键挑战主要在于开发异构特定的搜索空间和策略。因此，本文提出了一种新的基于异质图的自动图神经网络 Auto-HeG，用于自动构建具有表达学习能力的异质 GNN 模型。具体来说，Auto-HeG 将异质性整合到自动异质性图学习的所有阶段，包括搜索空间设计、超网训练和体系结构选择。通过联合微观和宏观层次设计的多样化消息传递方案，首先构建一个全面的异构 GNN 搜索空间，使 Auto-HeG 能够集成复杂多样的异构图。采用渐进式超网训练策略，根据异质性的分层变化动态缩小初始搜索空间，形成紧凑高效的超网。以异质性感知距离标准为指导，在留一出模式下进行异质性体系结构选择，从而得到专门的、具有表现力的异质性 GNN 体系结构。大量的实验证明了 Auto-HeG 在开发优秀的异质性 GNN 方面优于人工设计的模型和图形 NAS 模型。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Auto-HeG:+Automated+Graph+Neural+Network+on+Heterophilic+Graphs)|0| -|[HINormer: Representation Learning On Heterogeneous Information Networks with Graph Transformer](https://doi.org/10.1145/3543507.3583493)|Qiheng Mao, Zemin Liu, Chenghao Liu, Jianling Sun|National University of Singapore, Singapore; Salesforce Research Asia, Singapore; Zhejiang University, China and Alibaba-Zhejiang University Joint Institute of Frontier Technologies, China|Recent studies have highlighted the limitations of message-passing based graph neural networks (GNNs), e.g., limited model expressiveness, over-smoothing, over-squashing, etc. To alleviate these issues, Graph Transformers (GTs) have been proposed which work in the paradigm that allows message passing to a larger coverage even across the whole graph. Hinging on the global range attention mechanism, GTs have shown a superpower for representation learning on homogeneous graphs. However, the investigation of GTs on heterogeneous information networks (HINs) is still under-exploited. In particular, on account of the existence of heterogeneity, HINs show distinct data characteristics and thus require different treatment. To bridge this gap, in this paper we investigate the representation learning on HINs with Graph Transformer, and propose a novel model named HINormer, which capitalizes on a larger-range aggregation mechanism for node representation learning. In particular, assisted by two major modules, i.e., a local structure encoder and a heterogeneous relation encoder, HINormer can capture both the structural and heterogeneous information of nodes on HINs for comprehensive node representations. We conduct extensive experiments on four HIN benchmark datasets, which demonstrate that our proposed model can outperform the state-of-the-art.|最近的研究强调了基于消息传递的图神经网络(GNN)的局限性，如模型表达能力有限、过度平滑、过度压缩等。为了缓解这些问题，已经提出了图形转换器(Graph Transformers，GTs) ，它的工作范式允许消息传递到更大的覆盖范围，甚至在整个图中。基于全局范围注意机制，GT 在齐次图表示学习中表现出了超强的表示学习能力。然而，在异构信息网络(HIN)上的 GT 研究仍然处于起步阶段。特别是，由于存在异质性，HIN 显示出不同的数据特征，因此需要不同的处理。为了弥补这一差距，本文研究了基于图形变换的 HIN 表示学习，提出了一种新的 HINormer 模型，该模型利用了一种更大范围的聚合机制来进行节点表示学习。HINormer 在局部结构编码器和异构关系编码器这两个主要模块的协助下，可以同时捕获 HIN 上节点的结构和异构信息，从而实现节点的全面表示。我们在四个 HIN 基准数据集上进行了广泛的实验，实验结果表明我们提出的模型可以胜过最先进的水平。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=HINormer:+Representation+Learning+On+Heterogeneous+Information+Networks+with+Graph+Transformer)|0| +|[CMINet: a Graph Learning Framework for Content-aware Multi-channel Influence Diffusion](https://doi.org/10.1145/3543507.3583465)|HsiWen Chen, DeNian Yang, WangChien Lee, Philip S. Yu, MingSyan Chen|Academia Sinica, Taiwan; University of Illinois Chicago, USA; Pennsylvania State University, USA; National Taiwan University, Taiwan and Academia Sinica, Taiwan; National Taiwan University, Taiwan|The phenomena of influence diffusion on social networks have received tremendous research interests in the past decade. While most prior works mainly focus on predicting the total influence spread on a single network, a marketing campaign that exploits influence diffusion often involves multiple channels with various information disseminated on different media. In this paper, we introduce a new influence estimation problem, namely Content-aware Multi-channel Influence Diffusion (CMID), and accordingly propose CMINet to predict newly influenced users, given a set of seed users with different multimedia contents. In CMINet, we first introduce DiffGNN to encode the influencing power of users (nodes) and Influence-aware Optimal Transport (IOT) to align the embeddings to address the distribution shift across different diffusion channels. Then, we transform CMID into a node classification problem and propose Social-based Multimedia Feature Extractor (SMFE) and Content-aware Multi-channel Influence Propagation (CMIP) to jointly learn the user preferences on multimedia contents and predict the susceptibility of users. Furthermore, we prove that CMINet preserves monotonicity and submodularity, thus enabling (1 − 1/e)-approximate solutions for influence maximization. Experimental results manifest that CMINet outperforms eleven baselines on three public datasets.|近十年来，社会网络中的影响扩散现象受到了广泛的关注。虽然大多数以前的工作主要集中在预测总的影响传播在一个单一的网络，利用影响传播的营销活动往往涉及多个渠道，在不同的媒体传播各种各样的信息。本文提出了一个新的影响估计问题，即内容感知的多信道影响扩散(CMID) ，并相应地提出了 CMINet 来预测新的影响用户，给出了一组具有不同多媒体内容的种子用户。在 CMINet 中，我们首先引入区分 GNN 编码用户(节点)的影响力，以及感知影响的最优传输(IOT)来调整嵌入以解决不同扩散通道间的分布转移问题。然后，将 CMID 转化为一个节点分类问题，提出了基于社会化的多媒体特征提取器(SMFE)和内容感知的多信道影响传播(CMIP) ，以共同学习用户对多媒体内容的偏好并预测用户的易感性。此外，我们证明了 CMINet 保持了单调性和次模性，从而使(1-1/e)近似解的影响最大化。实验结果表明，CMINet 在三个公共数据集上的表现优于十一个基线。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=CMINet:+a+Graph+Learning+Framework+for+Content-aware+Multi-channel+Influence+Diffusion)|0| +|[Auto-HeG: Automated Graph Neural Network on Heterophilic Graphs](https://doi.org/10.1145/3543507.3583498)|Xin Zheng, Miao Zhang, Chunyang Chen, Qin Zhang, Chuan Zhou, Shirui Pan|Monash University, Australia; Chinese Academy of Sciences, China; Griffith University, Australia; Shenzhen University, China; Harbin Institute of Technology (Shenzhen), China|Graph neural architecture search (NAS) has gained popularity in automatically designing powerful graph neural networks (GNNs) with relieving human efforts. However, existing graph NAS methods mainly work under the homophily assumption and overlook another important graph property, i.e., heterophily, which exists widely in various real-world applications. To date, automated heterophilic graph learning with NAS is still a research blank to be filled in. Due to the complexity and variety of heterophilic graphs, the critical challenge of heterophilic graph NAS mainly lies in developing the heterophily-specific search space and strategy. Therefore, in this paper, we propose a novel automated graph neural network on heterophilic graphs, namely Auto-HeG, to automatically build heterophilic GNN models with expressive learning abilities. Specifically, Auto-HeG incorporates heterophily into all stages of automatic heterophilic graph learning, including search space design, supernet training, and architecture selection. Through the diverse message-passing scheme with joint micro-level and macro-level designs, we first build a comprehensive heterophilic GNN search space, enabling Auto-HeG to integrate complex and various heterophily of graphs. With a progressive supernet training strategy, we dynamically shrink the initial search space according to layer-wise variation of heterophily, resulting in a compact and efficient supernet. Taking a heterophily-aware distance criterion as the guidance, we conduct heterophilic architecture selection in the leave-one-out pattern, so that specialized and expressive heterophilic GNN architectures can be derived. Extensive experiments illustrate the superiority of Auto-HeG in developing excellent heterophilic GNNs to human-designed models and graph NAS models.|图神经结构搜索(NAS)在自动设计功能强大的图神经网络(GNN)方面得到了广泛的应用。然而，现有的图 NAS 方法主要是在同伦假设下工作，忽略了在各种实际应用中广泛存在的另一个重要图性质，即异伦性质。迄今为止，利用 NAS 进行自动异质图学习仍然是一个有待填补的研究空白。由于异构图的复杂性和多样性，异构图 NAS 的关键挑战主要在于开发异构特定的搜索空间和策略。因此，本文提出了一种新的基于异质图的自动图神经网络 Auto-HeG，用于自动构建具有表达学习能力的异质 GNN 模型。具体来说，Auto-HeG 将异质性整合到自动异质性图学习的所有阶段，包括搜索空间设计、超网训练和体系结构选择。通过联合微观和宏观层次设计的多样化消息传递方案，首先构建一个全面的异构 GNN 搜索空间，使 Auto-HeG 能够集成复杂多样的异构图。采用渐进式超网训练策略，根据异质性的分层变化动态缩小初始搜索空间，形成紧凑高效的超网。以异质性感知距离标准为指导，在留一出模式下进行异质性体系结构选择，从而得到专门的、具有表现力的异质性 GNN 体系结构。大量的实验证明了 Auto-HeG 在开发优秀的异质性 GNN 方面优于人工设计的模型和图形 NAS 模型。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Auto-HeG:+Automated+Graph+Neural+Network+on+Heterophilic+Graphs)|0| +|[HINormer: Representation Learning On Heterogeneous Information Networks with Graph Transformer](https://doi.org/10.1145/3543507.3583493)|Qiheng Mao, Zemin Liu, Chenghao Liu, Jianling Sun|Salesforce Research Asia, Singapore; National University of Singapore, Singapore; Zhejiang University, China and Alibaba-Zhejiang University Joint Institute of Frontier Technologies, China|Recent studies have highlighted the limitations of message-passing based graph neural networks (GNNs), e.g., limited model expressiveness, over-smoothing, over-squashing, etc. To alleviate these issues, Graph Transformers (GTs) have been proposed which work in the paradigm that allows message passing to a larger coverage even across the whole graph. Hinging on the global range attention mechanism, GTs have shown a superpower for representation learning on homogeneous graphs. However, the investigation of GTs on heterogeneous information networks (HINs) is still under-exploited. In particular, on account of the existence of heterogeneity, HINs show distinct data characteristics and thus require different treatment. To bridge this gap, in this paper we investigate the representation learning on HINs with Graph Transformer, and propose a novel model named HINormer, which capitalizes on a larger-range aggregation mechanism for node representation learning. In particular, assisted by two major modules, i.e., a local structure encoder and a heterogeneous relation encoder, HINormer can capture both the structural and heterogeneous information of nodes on HINs for comprehensive node representations. We conduct extensive experiments on four HIN benchmark datasets, which demonstrate that our proposed model can outperform the state-of-the-art.|最近的研究强调了基于消息传递的图神经网络(GNN)的局限性，如模型表达能力有限、过度平滑、过度压缩等。为了缓解这些问题，已经提出了图形转换器(Graph Transformers，GTs) ，它的工作范式允许消息传递到更大的覆盖范围，甚至在整个图中。基于全局范围注意机制，GT 在齐次图表示学习中表现出了超强的表示学习能力。然而，在异构信息网络(HIN)上的 GT 研究仍然处于起步阶段。特别是，由于存在异质性，HIN 显示出不同的数据特征，因此需要不同的处理。为了弥补这一差距，本文研究了基于图形变换的 HIN 表示学习，提出了一种新的 HINormer 模型，该模型利用了一种更大范围的聚合机制来进行节点表示学习。HINormer 在局部结构编码器和异构关系编码器这两个主要模块的协助下，可以同时捕获 HIN 上节点的结构和异构信息，从而实现节点的全面表示。我们在四个 HIN 基准数据集上进行了广泛的实验，实验结果表明我们提出的模型可以胜过最先进的水平。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=HINormer:+Representation+Learning+On+Heterogeneous+Information+Networks+with+Graph+Transformer)|0| |[Minimum Topology Attacks for Graph Neural Networks](https://doi.org/10.1145/3543507.3583509)|Mengmei Zhang, Xiao Wang, Chuan Shi, Lingjuan Lyu, Tianchi Yang, Junping Du|Beijing University of Posts and Telecommunications, China; Sony AI, China|With the great popularity of Graph Neural Networks (GNNs), their robustness to adversarial topology attacks has received significant attention. Although many attack methods have been proposed, they mainly focus on fixed-budget attacks, aiming at finding the most adversarial perturbations within a fixed budget for target node. However, considering the varied robustness of each node, there is an inevitable dilemma caused by the fixed budget, i.e., no successful perturbation is found when the budget is relatively small, while if it is too large, the yielding redundant perturbations will hurt the invisibility. To break this dilemma, we propose a new type of topology attack, named minimum-budget topology attack, aiming to adaptively find the minimum perturbation sufficient for a successful attack on each node. To this end, we propose an attack model, named MiBTack, based on a dynamic projected gradient descent algorithm, which can effectively solve the involving non-convex constraint optimization on discrete topology. Extensive results on three GNNs and four real-world datasets show that MiBTack can successfully lead all target nodes misclassified with the minimum perturbation edges. Moreover, the obtained minimum budget can be used to measure node robustness, so we can explore the relationships of robustness, topology, and uncertainty for nodes, which is beyond what the current fixed-budget topology attacks can offer.|随着图形神经网络(GNN)的普及，其对抗性拓扑攻击的鲁棒性受到了广泛的关注。虽然已经提出了许多攻击方法，但它们主要针对固定预算的攻击，目的是在目标节点的固定预算内寻找最具对抗性的扰动。然而，考虑到每个节点的鲁棒性各不相同，固定预算不可避免地会产生一个两难问题，即当预算相对较小时，不会找到成功的扰动，而当预算过大时，屈服冗余扰动将损害不可见性。为了打破这一困境，我们提出了一种新的拓扑攻击方法——最小预算拓扑攻击，目的是自适应地寻找对每个节点成功进行攻击所需的最小扰动。为此，我们提出了一种基于动态投影梯度下降法算法的攻击模型—— MiBTack，该模型能够有效地解决离散拓扑中涉及非凸约束的优化问题。在三个 GNN 和四个实际数据集上的广泛应用结果表明，MiBTack 能够成功地引导所有目标节点以最小扰动边错误分类。此外，所得到的最小预算可以用来衡量节点的鲁棒性，因此我们可以探索节点的鲁棒性、拓扑结构和不确定性之间的关系，这是目前固定预算拓扑攻击所不能提供的。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Minimum+Topology+Attacks+for+Graph+Neural+Networks)|0| |[Learning Mixtures of Markov Chains with Quality Guarantees](https://doi.org/10.1145/3543507.3583524)|Fabian Spaeh, Charalampos E. Tsourakakis|Department of Computer Science, Boston University, USA|A large number of modern applications ranging from listening songs online and browsing the Web to using a navigation app on a smartphone generate a plethora of user trails. Clustering such trails into groups with a common sequence pattern can reveal significant structure in human behavior that can lead to improving user experience through better recommendations, and even prevent suicides [LMCR14]. One approach to modeling this problem mathematically is as a mixture of Markov chains. Recently, Gupta, Kumar and Vassilvitski [GKV16] introduced an algorithm (GKV-SVD) based on the singular value decomposition (SVD) that under certain conditions can perfectly recover a mixture of L chains on n states, given only the distribution of trails of length 3 (3-trail). In this work we contribute to the problem of unmixing Markov chains by highlighting and addressing two important constraints of the GKV-SVD algorithm [GKV16]: some chains in the mixture may not even be weakly connected, and secondly in practice one does not know beforehand the true number of chains. We resolve these issues in the Gupta et al. paper [GKV16]. Specifically, we propose an algebraic criterion that enables us to choose a value of L efficiently that avoids overfitting. Furthermore, we design a reconstruction algorithm that outputs the true mixture in the presence of disconnected chains and is robust to noise. We complement our theoretical results with experiments on both synthetic and real data, where we observe that our method outperforms the GKV-SVD algorithm. Finally, we empirically observe that combining an EM-algorithm with our method performs best in practice, both in terms of reconstruction error with respect to the distribution of 3-trails and the mixture of Markov Chains.|大量的现代应用程序，从在线听歌、浏览网页到在智能手机上使用导航应用程序，都会产生大量的用户踪迹。将这些痕迹聚类到具有共同序列模式的群体中，可以揭示人类行为的重要结构，通过更好的推荐来改善用户体验，甚至可以防止自杀[ LMCR14]。将这个问题数学化建模的一种方法是作为马尔可夫链的混合。最近，Gupta，Kumar 和 Vassilvitski [ GKV16]引入了一种基于奇异值分解(SVD)的算法(GKV-SVD) ，该算法在一定条件下可以完美地恢复 n 个状态上的 L 链的混合，只需给定长度为3(3-路径)的路径分布。在这项工作中，我们通过突出和解决 GKV-SVD 算法[ GKV16]的两个重要约束来解决马尔可夫链的分解问题: 混合物中的一些链甚至可能不是弱连通的，其次在实践中人们事先不知道链的真实数量。我们在 Gupta 等人的论文[ GKV16]中解决了这些问题。具体来说，我们提出了一个代数准则，使我们能够有效地选择 L 的值，避免过度拟合。此外，我们还设计了一种重建算法，该算法在存在不连通链的情况下输出真混合，并且对噪声具有鲁棒性。我们通过对合成和实际数据的实验来补充我们的理论结果，我们观察到我们的方法优于 GKV-SVD 算法。最后，我们实验性地观察到将 EM 算法与我们的方法结合在一起在实际中表现最好，无论是在重建误差方面的3-轨迹的分布和马尔可夫链的混合。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Learning+Mixtures+of+Markov+Chains+with+Quality+Guarantees)|0| |[GIF: A General Graph Unlearning Strategy via Influence Function](https://doi.org/10.1145/3543507.3583521)|Jiancan Wu, Yi Yang, Yuchun Qian, Yongduo Sui, Xiang Wang, Xiangnan He|University of Science and Technology of China, China|With the greater emphasis on privacy and security in our society, the problem of graph unlearning -- revoking the influence of specific data on the trained GNN model, is drawing increasing attention. However, ranging from machine unlearning to recently emerged graph unlearning methods, existing efforts either resort to retraining paradigm, or perform approximate erasure that fails to consider the inter-dependency between connected neighbors or imposes constraints on GNN structure, therefore hard to achieve satisfying performance-complexity trade-offs. In this work, we explore the influence function tailored for graph unlearning, so as to improve the unlearning efficacy and efficiency for graph unlearning. We first present a unified problem formulation of diverse graph unlearning tasks \wrt node, edge, and feature. Then, we recognize the crux to the inability of traditional influence function for graph unlearning, and devise Graph Influence Function (GIF), a model-agnostic unlearning method that can efficiently and accurately estimate parameter changes in response to a $\epsilon$-mass perturbation in deleted data. The idea is to supplement the objective of the traditional influence function with an additional loss term of the influenced neighbors due to the structural dependency. Further deductions on the closed-form solution of parameter changes provide a better understanding of the unlearning mechanism. We conduct extensive experiments on four representative GNN models and three benchmark datasets to justify the superiority of GIF for diverse graph unlearning tasks in terms of unlearning efficacy, model utility, and unlearning efficiency. Our implementations are available at \url{https://github.com/wujcan/GIF-torch/}.|随着社会对隐私和安全问题的日益重视，图的去学习问题——去除特定数据对训练后的 GNN 模型的影响——日益引起人们的关注。然而，从机器学习到最近出现的图学习方法，现有的研究要么采用再训练范式，要么进行近似擦除，不考虑连通邻居之间的相互依赖性，要么对 GNN 结构施加约束，因此很难实现令人满意的性能-复杂度权衡。本文研究了适用于图形学习的影响函数，以提高图形学习的学习效率和效果。本文首先给出了不同的图去学习任务的节点、边和特征的统一问题表达式。然后，我们认识到了传统影响函数无法进行图形去学习的症结所在，并设计了图形影响函数(GIF) ，这是一种模型无关的去学习方法，可以有效而准确地估计在已删除数据中响应 $ε 质量扰动的参数变化。其思想是在传统影响函数的基础上，增加受影响邻域的结构依赖性损失项，以补充传统影响函数的目标。对参数变化的闭式解的进一步推导可以更好地理解遗忘机制。我们在四个具有代表性的 GNN 模型和三个基准数据集上进行了广泛的实验，以验证 GIF 在不同的图形忘却任务中在忘却效率、模型效用和忘却效率方面的优越性。我们的实现可以在 url { https://github.com/wujcan/gif-torch/}获得。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=GIF:+A+General+Graph+Unlearning+Strategy+via+Influence+Function)|0| -|[INCREASE: Inductive Graph Representation Learning for Spatio-Temporal Kriging](https://doi.org/10.1145/3543507.3583525)|Chuanpan Zheng, Xiaoliang Fan, Cheng Wang, Jianzhong Qi, Chaochao Chen, Longbiao Chen|University of Melbourne, Australia; Zhejiang University, China; School of Informatics, Xiamen University, China|Spatio-temporal kriging is an important problem in web and social applications, such as Web or Internet of Things, where things (e.g., sensors) connected into a web often come with spatial and temporal properties. It aims to infer knowledge for (the things at) unobserved locations using the data from (the things at) observed locations during a given time period of interest. This problem essentially requires \emph{inductive learning}. Once trained, the model should be able to perform kriging for different locations including newly given ones, without retraining. However, it is challenging to perform accurate kriging results because of the heterogeneous spatial relations and diverse temporal patterns. In this paper, we propose a novel inductive graph representation learning model for spatio-temporal kriging. We first encode heterogeneous spatial relations between the unobserved and observed locations by their spatial proximity, functional similarity, and transition probability. Based on each relation, we accurately aggregate the information of most correlated observed locations to produce inductive representations for the unobserved locations, by jointly modeling their similarities and differences. Then, we design relation-aware gated recurrent unit (GRU) networks to adaptively capture the temporal correlations in the generated sequence representations for each relation. Finally, we propose a multi-relation attention mechanism to dynamically fuse the complex spatio-temporal information at different time steps from multiple relations to compute the kriging output. Experimental results on three real-world datasets show that our proposed model outperforms state-of-the-art methods consistently, and the advantage is more significant when there are fewer observed locations. Our code is available at https://github.com/zhengchuanpan/INCREASE.|时空克里金是网络和社会应用中的一个重要问题，如网络或物联网，在这些应用中，连接到网络的东西(例如传感器)通常具有空间和时间属性。它的目的是推断知识(的东西)未被观察的位置使用数据从(的东西)观察位置在给定的时间段的兴趣。这个问题本质上需要方差{归纳学习}。一旦训练，模型应该能够执行不同的位置，包括新给定的克里金，没有再训练。然而，由于空间关系的异质性和时间模式的多样性，使得准确的克里格分析结果具有挑战性。本文提出了一种新的时空克里金归纳图表示学习模型。我们首先编码的异质空间关系之间的未观察和观察位置的空间接近，功能相似性和转移概率。基于每个关系，我们通过联合建模它们的相似性和差异性，准确地聚合大多数相关观测位置的信息，为未观测位置产生归纳表示。然后，我们设计关系感知门控回归单元(GRU)网络来自适应地捕获每个关系生成的序列表示中的时间相关性。最后，提出了一种多关系注意机制，将多关系在不同时间步长上的复杂时空信息动态融合，计算克里金输出。在三个实际数据集上的实验结果表明，我们提出的模型的性能优于最先进的方法一致，并且当观测位置较少时，优势更显著。我们的代码可以在 https://github.com/zhengchuanpan/increase 找到。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=INCREASE:+Inductive+Graph+Representation+Learning+for+Spatio-Temporal+Kriging)|0| +|[INCREASE: Inductive Graph Representation Learning for Spatio-Temporal Kriging](https://doi.org/10.1145/3543507.3583525)|Chuanpan Zheng, Xiaoliang Fan, Cheng Wang, Jianzhong Qi, Chaochao Chen, Longbiao Chen|University of Melbourne, Australia; School of Informatics, Xiamen University, China; Zhejiang University, China|Spatio-temporal kriging is an important problem in web and social applications, such as Web or Internet of Things, where things (e.g., sensors) connected into a web often come with spatial and temporal properties. It aims to infer knowledge for (the things at) unobserved locations using the data from (the things at) observed locations during a given time period of interest. This problem essentially requires \emph{inductive learning}. Once trained, the model should be able to perform kriging for different locations including newly given ones, without retraining. However, it is challenging to perform accurate kriging results because of the heterogeneous spatial relations and diverse temporal patterns. In this paper, we propose a novel inductive graph representation learning model for spatio-temporal kriging. We first encode heterogeneous spatial relations between the unobserved and observed locations by their spatial proximity, functional similarity, and transition probability. Based on each relation, we accurately aggregate the information of most correlated observed locations to produce inductive representations for the unobserved locations, by jointly modeling their similarities and differences. Then, we design relation-aware gated recurrent unit (GRU) networks to adaptively capture the temporal correlations in the generated sequence representations for each relation. Finally, we propose a multi-relation attention mechanism to dynamically fuse the complex spatio-temporal information at different time steps from multiple relations to compute the kriging output. Experimental results on three real-world datasets show that our proposed model outperforms state-of-the-art methods consistently, and the advantage is more significant when there are fewer observed locations. Our code is available at https://github.com/zhengchuanpan/INCREASE.|时空克里金是网络和社会应用中的一个重要问题，如网络或物联网，在这些应用中，连接到网络的东西(例如传感器)通常具有空间和时间属性。它的目的是推断知识(的东西)未被观察的位置使用数据从(的东西)观察位置在给定的时间段的兴趣。这个问题本质上需要方差{归纳学习}。一旦训练，模型应该能够执行不同的位置，包括新给定的克里金，没有再训练。然而，由于空间关系的异质性和时间模式的多样性，使得准确的克里格分析结果具有挑战性。本文提出了一种新的时空克里金归纳图表示学习模型。我们首先编码的异质空间关系之间的未观察和观察位置的空间接近，功能相似性和转移概率。基于每个关系，我们通过联合建模它们的相似性和差异性，准确地聚合大多数相关观测位置的信息，为未观测位置产生归纳表示。然后，我们设计关系感知门控回归单元(GRU)网络来自适应地捕获每个关系生成的序列表示中的时间相关性。最后，提出了一种多关系注意机制，将多关系在不同时间步长上的复杂时空信息动态融合，计算克里金输出。在三个实际数据集上的实验结果表明，我们提出的模型的性能优于最先进的方法一致，并且当观测位置较少时，优势更显著。我们的代码可以在 https://github.com/zhengchuanpan/increase 找到。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=INCREASE:+Inductive+Graph+Representation+Learning+for+Spatio-Temporal+Kriging)|0| |[Unlearning Graph Classifiers with Limited Data Resources](https://doi.org/10.1145/3543507.3583547)|Chao Pan, Eli Chien, Olgica Milenkovic|University of Illinois, Urbana-Champaign, USA|As the demand for user privacy grows, controlled data removal (machine unlearning) is becoming an important feature of machine learning models for data-sensitive Web applications such as social networks and recommender systems. Nevertheless, at this point it is still largely unknown how to perform efficient machine unlearning of graph neural networks (GNNs); this is especially the case when the number of training samples is small, in which case unlearning can seriously compromise the performance of the model. To address this issue, we initiate the study of unlearning the Graph Scattering Transform (GST), a mathematical framework that is efficient, provably stable under feature or graph topology perturbations, and offers graph classification performance comparable to that of GNNs. Our main contribution is the first known nonlinear approximate graph unlearning method based on GSTs. Our second contribution is a theoretical analysis of the computational complexity of the proposed unlearning mechanism, which is hard to replicate for deep neural networks. Our third contribution are extensive simulation results which show that, compared to complete retraining of GNNs after each removal request, the new GST-based approach offers, on average, a 10.38x speed-up and leads to a 2.6% increase in test accuracy during unlearning of 90 out of 100 training graphs from the IMDB dataset (10% training ratio). Our implementation is available online at https://doi.org/10.5281/zenodo.7613150.|随着对用户隐私需求的增长，受控数据删除(机器去除)正在成为数据敏感的 Web 应用程序(如社交网络和推荐系统)机器学习模型的一个重要特征。然而，在这一点上，如何对图神经网络(GNN)进行有效的机器学习仍然是一个很大的未知数; 特别是在训练样本数量很少的情况下，在这种情况下，学习会严重影响模型的性能。为了解决这个问题，我们开始研究去除图散射变换(GST) ，这是一个在特征或图拓扑扰动下高效、可证明稳定的数学框架，并提供了与 GNN 相当的图分类性能。我们的主要贡献是第一个已知的基于 GST 的非线性近似图去学习方法。我们的第二个贡献是对所提出的去学习机制的计算复杂性进行了理论分析，这种机制对于深层神经网络来说是难以复制的。我们的第三个贡献是广泛的模拟结果，这些结果表明，与每次删除请求后完成 GNN 的再训练相比，基于 GST 的新方法平均提供10.38倍的加速，并导致在从 IMDB 数据集中学习100个训练图表中的90个(10% 训练比率)时，测试准确率提高2.6% 。我们的实施方案可于网上 https://doi.org/10.5281/zenodo.7613150下载。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Unlearning+Graph+Classifiers+with+Limited+Data+Resources)|0| -|[GraphMAE2: A Decoding-Enhanced Masked Self-Supervised Graph Learner](https://doi.org/10.1145/3543507.3583379)|Zhenyu Hou, Yufei He, Yukuo Cen, Xiao Liu, Yuxiao Dong, Evgeny Kharlamov, Jie Tang|Tsinghua University, China; Beijing Institute of Technology, China; Bosch Center for Artificial Intelligence, Germany|Graph self-supervised learning (SSL), including contrastive and generative approaches, offers great potential to address the fundamental challenge of label scarcity in real-world graph data. Among both sets of graph SSL techniques, the masked graph autoencoders (e.g., GraphMAE)--one type of generative method--have recently produced promising results. The idea behind this is to reconstruct the node features (or structures)--that are randomly masked from the input--with the autoencoder architecture. However, the performance of masked feature reconstruction naturally relies on the discriminability of the input features and is usually vulnerable to disturbance in the features. In this paper, we present a masked self-supervised learning framework GraphMAE2 with the goal of overcoming this issue. The idea is to impose regularization on feature reconstruction for graph SSL. Specifically, we design the strategies of multi-view random re-mask decoding and latent representation prediction to regularize the feature reconstruction. The multi-view random re-mask decoding is to introduce randomness into reconstruction in the feature space, while the latent representation prediction is to enforce the reconstruction in the embedding space. Extensive experiments show that GraphMAE2 can consistently generate top results on various public datasets, including at least 2.45% improvements over state-of-the-art baselines on ogbn-Papers100M with 111M nodes and 1.6B edges.|图的自监督学习(SSL)包括对比学习和生成学习，为解决现实世界图数据中标签稀缺的基本问题提供了巨大的潜力。在这两种图形 SSL 技术中，掩码图形自动编码器(如 GraphMAE)——一种生成方法——最近已经产生了有希望的结果。其背后的想法是使用自动编码器体系结构重建节点特性(或结构)——这些特性从输入中随机屏蔽。然而，掩蔽特征重建的性能自然依赖于输入特征的可识别性，并且通常容易受到特征的干扰。在本文中，我们提出了一个屏蔽自监督学习框架 GraphMAE2，目的是克服这个问题。其思想是在图 SSL 的特征重构中加入正则化。具体来说，我们设计了多视点随机重掩码解码和潜在表征预测的策略来规范特征重建。多视点随机重掩码译码是在特征空间重构中引入随机性，而潜在表示预测是在嵌入空间中加强重构。大量的实验表明，GraphMAE2可以始终如一地在各种公共数据集上产生最佳结果，包括在具有111M 节点和1.6 B 边的 ogbn-Papers100M 上比最先进的基线至少提高2.45% 。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=GraphMAE2:+A+Decoding-Enhanced+Masked+Self-Supervised+Graph+Learner)|0| -|[KGTrust: Evaluating Trustworthiness of SIoT via Knowledge Enhanced Graph Neural Networks](https://doi.org/10.1145/3543507.3583549)|Zhizhi Yu, Di Jin, Cuiying Huo, Zhiqiang Wang, Xiulong Liu, Heng Qi, Jia Wu, Lingfei Wu|School of Computer Science and Technology, Dalian University of Technology, China; Content and Knowledge Graph, Pinterest, USA; College of Intelligence and Computing, Tianjin University, China; School of Computing, Macquarie University, Australia|Social Internet of Things (SIoT), a promising and emerging paradigm that injects the notion of social networking into smart objects (i.e., things), paving the way for the next generation of Internet of Things. However, due to the risks and uncertainty, a crucial and urgent problem to be settled is establishing reliable relationships within SIoT, that is, trust evaluation. Graph neural networks for trust evaluation typically adopt a straightforward way such as one-hot or node2vec to comprehend node characteristics, which ignores the valuable semantic knowledge attached to nodes. Moreover, the underlying structure of SIoT is usually complex, including both the heterogeneous graph structure and pairwise trust relationships, which renders hard to preserve the properties of SIoT trust during information propagation. To address these aforementioned problems, we propose a novel knowledge-enhanced graph neural network (KGTrust) for better trust evaluation in SIoT. Specifically, we first extract useful knowledge from users' comment behaviors and external structured triples related to object descriptions, in order to gain a deeper insight into the semantics of users and objects. Furthermore, we introduce a discriminative convolutional layer that utilizes heterogeneous graph structure, node semantics, and augmented trust relationships to learn node embeddings from the perspective of a user as a trustor or a trustee, effectively capturing multi-aspect properties of SIoT trust during information propagation. Finally, a trust prediction layer is developed to estimate the trust relationships between pairwise nodes. Extensive experiments on three public datasets illustrate the superior performance of KGTrust over state-of-the-art methods.|社交物联网(SIoT)是一种很有前途的新兴模式，它将社交网络的概念注入到智能对象(即物品)中，为下一代物联网铺平了道路。然而，由于存在风险和不确定性，在 SIoT 中建立可靠的关系，即信任评估，是一个亟待解决的关键问题。用于信任评估的图神经网络通常采用一个热点或节点2vec 等直观的方法来理解节点特征，忽略了节点所附带的有价值的语义知识。此外，SIoT 的底层结构通常是复杂的，包括异构图结构和成对信任关系，这使得在信息传播过程中很难保持 SIoT 信任的性质。为了解决上述问题，我们提出了一种新的知识增强图形神经网络(KGTrust) ，用于 SIoT 中更好的信任评估。具体来说，我们首先从用户的评论行为和与对象描述相关的外部结构化三元组中提取有用的知识，以便更深入地了解用户和对象的语义。此外，我们还引入了一个判别卷积层，该层利用异构图结构、节点语义和增强的信任关系，从用户作为信任者或受信者的角度学习节点嵌入，有效地捕获信息传播过程中 SIoT 信任的多方面特性。最后，开发了一个信任预测层来估计成对节点之间的信任关系。在三个公共数据集上的大量实验表明，KGTrust 的性能优于最先进的方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=KGTrust:+Evaluating+Trustworthiness+of+SIoT+via+Knowledge+Enhanced+Graph+Neural+Networks)|0| +|[GraphMAE2: A Decoding-Enhanced Masked Self-Supervised Graph Learner](https://doi.org/10.1145/3543507.3583379)|Zhenyu Hou, Yufei He, Yukuo Cen, Xiao Liu, Yuxiao Dong, Evgeny Kharlamov, Jie Tang|Bosch Center for Artificial Intelligence, Germany; Tsinghua University, China; Beijing Institute of Technology, China|Graph self-supervised learning (SSL), including contrastive and generative approaches, offers great potential to address the fundamental challenge of label scarcity in real-world graph data. Among both sets of graph SSL techniques, the masked graph autoencoders (e.g., GraphMAE)--one type of generative method--have recently produced promising results. The idea behind this is to reconstruct the node features (or structures)--that are randomly masked from the input--with the autoencoder architecture. However, the performance of masked feature reconstruction naturally relies on the discriminability of the input features and is usually vulnerable to disturbance in the features. In this paper, we present a masked self-supervised learning framework GraphMAE2 with the goal of overcoming this issue. The idea is to impose regularization on feature reconstruction for graph SSL. Specifically, we design the strategies of multi-view random re-mask decoding and latent representation prediction to regularize the feature reconstruction. The multi-view random re-mask decoding is to introduce randomness into reconstruction in the feature space, while the latent representation prediction is to enforce the reconstruction in the embedding space. Extensive experiments show that GraphMAE2 can consistently generate top results on various public datasets, including at least 2.45% improvements over state-of-the-art baselines on ogbn-Papers100M with 111M nodes and 1.6B edges.|图的自监督学习(SSL)包括对比学习和生成学习，为解决现实世界图数据中标签稀缺的基本问题提供了巨大的潜力。在这两种图形 SSL 技术中，掩码图形自动编码器(如 GraphMAE)——一种生成方法——最近已经产生了有希望的结果。其背后的想法是使用自动编码器体系结构重建节点特性(或结构)——这些特性从输入中随机屏蔽。然而，掩蔽特征重建的性能自然依赖于输入特征的可识别性，并且通常容易受到特征的干扰。在本文中，我们提出了一个屏蔽自监督学习框架 GraphMAE2，目的是克服这个问题。其思想是在图 SSL 的特征重构中加入正则化。具体来说，我们设计了多视点随机重掩码解码和潜在表征预测的策略来规范特征重建。多视点随机重掩码译码是在特征空间重构中引入随机性，而潜在表示预测是在嵌入空间中加强重构。大量的实验表明，GraphMAE2可以始终如一地在各种公共数据集上产生最佳结果，包括在具有111M 节点和1.6 B 边的 ogbn-Papers100M 上比最先进的基线至少提高2.45% 。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=GraphMAE2:+A+Decoding-Enhanced+Masked+Self-Supervised+Graph+Learner)|0| +|[KGTrust: Evaluating Trustworthiness of SIoT via Knowledge Enhanced Graph Neural Networks](https://doi.org/10.1145/3543507.3583549)|Zhizhi Yu, Di Jin, Cuiying Huo, Zhiqiang Wang, Xiulong Liu, Heng Qi, Jia Wu, Lingfei Wu|College of Intelligence and Computing, Tianjin University, China; School of Computing, Macquarie University, Australia; Content and Knowledge Graph, Pinterest, USA; School of Computer Science and Technology, Dalian University of Technology, China|Social Internet of Things (SIoT), a promising and emerging paradigm that injects the notion of social networking into smart objects (i.e., things), paving the way for the next generation of Internet of Things. However, due to the risks and uncertainty, a crucial and urgent problem to be settled is establishing reliable relationships within SIoT, that is, trust evaluation. Graph neural networks for trust evaluation typically adopt a straightforward way such as one-hot or node2vec to comprehend node characteristics, which ignores the valuable semantic knowledge attached to nodes. Moreover, the underlying structure of SIoT is usually complex, including both the heterogeneous graph structure and pairwise trust relationships, which renders hard to preserve the properties of SIoT trust during information propagation. To address these aforementioned problems, we propose a novel knowledge-enhanced graph neural network (KGTrust) for better trust evaluation in SIoT. Specifically, we first extract useful knowledge from users' comment behaviors and external structured triples related to object descriptions, in order to gain a deeper insight into the semantics of users and objects. Furthermore, we introduce a discriminative convolutional layer that utilizes heterogeneous graph structure, node semantics, and augmented trust relationships to learn node embeddings from the perspective of a user as a trustor or a trustee, effectively capturing multi-aspect properties of SIoT trust during information propagation. Finally, a trust prediction layer is developed to estimate the trust relationships between pairwise nodes. Extensive experiments on three public datasets illustrate the superior performance of KGTrust over state-of-the-art methods.|社交物联网(SIoT)是一种很有前途的新兴模式，它将社交网络的概念注入到智能对象(即物品)中，为下一代物联网铺平了道路。然而，由于存在风险和不确定性，在 SIoT 中建立可靠的关系，即信任评估，是一个亟待解决的关键问题。用于信任评估的图神经网络通常采用一个热点或节点2vec 等直观的方法来理解节点特征，忽略了节点所附带的有价值的语义知识。此外，SIoT 的底层结构通常是复杂的，包括异构图结构和成对信任关系，这使得在信息传播过程中很难保持 SIoT 信任的性质。为了解决上述问题，我们提出了一种新的知识增强图形神经网络(KGTrust) ，用于 SIoT 中更好的信任评估。具体来说，我们首先从用户的评论行为和与对象描述相关的外部结构化三元组中提取有用的知识，以便更深入地了解用户和对象的语义。此外，我们还引入了一个判别卷积层，该层利用异构图结构、节点语义和增强的信任关系，从用户作为信任者或受信者的角度学习节点嵌入，有效地捕获信息传播过程中 SIoT 信任的多方面特性。最后，开发了一个信任预测层来估计成对节点之间的信任关系。在三个公共数据集上的大量实验表明，KGTrust 的性能优于最先进的方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=KGTrust:+Evaluating+Trustworthiness+of+SIoT+via+Knowledge+Enhanced+Graph+Neural+Networks)|0| |[CogDL: A Comprehensive Library for Graph Deep Learning](https://doi.org/10.1145/3543507.3583472)|Yukuo Cen, Zhenyu Hou, Yan Wang, Qibin Chen, Yizhen Luo, Zhongming Yu, Hengrui Zhang, Xingcheng Yao, Aohan Zeng, Shiguang Guo, Yuxiao Dong, Yang Yang, Peng Zhang, Guohao Dai, Yu Wang, Chang Zhou, Hongxia Yang, Jie Tang|Tsinghua University, China; Zhipu AI, China; Alibaba Group, China; Zhejiang University, China|Graph neural networks (GNNs) have attracted tremendous attention from the graph learning community in recent years. It has been widely adopted in various real-world applications from diverse domains, such as social networks and biological graphs. The research and applications of graph deep learning present new challenges, including the sparse nature of graph data, complicated training of GNNs, and non-standard evaluation of graph tasks. To tackle the issues, we present CogDL, a comprehensive library for graph deep learning that allows researchers and practitioners to conduct experiments, compare methods, and build applications with ease and efficiency. In CogDL, we propose a unified design for the training and evaluation of GNN models for various graph tasks, making it unique among existing graph learning libraries. By utilizing this unified trainer, CogDL can optimize the GNN training loop with several training techniques, such as mixed precision training. Moreover, we develop efficient sparse operators for CogDL, enabling it to become the most competitive graph library for efficiency. Another important CogDL feature is its focus on ease of use with the aim of facilitating open and reproducible research of graph learning. We leverage CogDL to report and maintain benchmark results on fundamental graph tasks, which can be reproduced and directly used by the community.|近年来，图神经网络(GNN)引起了图学习界的广泛关注。它已被广泛应用于各种不同领域的现实世界应用，如社会网络和生物图表。图形深度学习的研究和应用面临着新的挑战，包括图形数据的稀疏性、图形神经网络的复杂训练以及图形任务的非标准评估。为了解决这些问题，我们提出了 CogDL，一个图形深度学习的综合库，允许研究人员和从业人员进行实验，比较方法，并轻松有效地构建应用程序。在 CogDL 中，我们提出了一种针对各种图形任务的 GNN 模型训练和评估的统一设计，使其在现有的图形学习库中独树一帜。利用这种统一的训练器，CogDL 可以通过混合精度训练等多种训练技术对 GNN 训练回路进行优化。此外，我们还为 CogDL 开发了高效的稀疏算子，使其成为最具竞争力的高效图形库。CogDL 的另一个重要特性是其对易用性的关注，旨在促进图形学习的开放性和可重复性研究。我们利用 CogDL 报告和维护基本图任务的基准测试结果，这些结果可以被社区复制和直接使用。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=CogDL:+A+Comprehensive+Library+for+Graph+Deep+Learning)|0| -|[Tracing Knowledge Instead of Patterns: Stable Knowledge Tracing with Diagnostic Transformer](https://doi.org/10.1145/3543507.3583255)|Yu Yin, Le Dai, Zhenya Huang, Shuanghong Shen, Fei Wang, Qi Liu, Enhong Chen, Xin Li|University of Science and Technology of China, China and iFLYTEK Co., Ltd, China; Anhui Province Key Laboratory of Big Data Analysis and Application, School of Data Science, University of Science and Technology of China, China; Anhui Province Key Laboratory of Big Data Analysis and Application, School of Computer Science and Technology & School of Data Science, University of Science and Technology of China, China; Anhui Province Key Laboratory of Big Data Analysis and Application, School of Computer Science and Technology, University of Science and Technology of China, China|Knowledge Tracing (KT) aims at tracing the evolution of the knowledge states along the learning process of a learner. It has become a crucial task for online learning systems to model the learning process of their users, and further provide their users a personalized learning guidance. However, recent developments in KT based on deep neural networks mostly focus on increasing the accuracy of predicting the next performance of students. We argue that current KT modeling, as well as training paradigm, can lead to models tracing patterns of learner’s learning activities, instead of their evolving knowledge states. In this paper, we propose a new architecture, Diagnostic Transformer (DTransformer), along with a new training paradigm, to tackle this challenge. With DTransformer, we build the architecture from question-level to knowledge-level, explicitly diagnosing learner’s knowledge proficiency from each question mastery states. We also propose a novel training algorithm based on contrastive learning that focuses on maintaining the stability of the knowledge state diagnosis. Through extensive experiments, we will show that with its understanding of knowledge state evolution, DTransformer achieves a better performance prediction accuracy and more stable knowledge state tracing results. We will also show that DTransformer is less sensitive to specific patterns with case study. We open-sourced our code and data at https://github.com/yxonic/DTransformer.|知识追踪(KT)旨在追踪学习者在学习过程中知识状态的演化过程。建立在线学习系统用户学习过程的模型，进一步为用户提供个性化的学习指导，已成为在线学习系统的一项重要任务。然而，基于深层神经网络的 KT 的最新发展主要集中在提高预测学生下一步成绩的准确性上。我们认为，目前的 KT 模型和训练范式可以导致模型跟踪学习者的学习活动模式，而不是其演化的知识状态。在本文中，我们提出了一个新的体系结构，诊断变压器(变压器) ，以及一个新的训练范例，以应对这一挑战。使用 Dformer，我们构建了从问题级到知识级的体系结构，从每个问题掌握状态明确诊断学习者的知识熟练程度。提出了一种新的基于对比学习的训练算法，着重于保持知识状态诊断的稳定性。通过大量的实验，我们将表明，由于对知识状态演化的理解，Dformer 获得了更好的性能预测精度和更稳定的知识状态跟踪结果。我们还将通过案例研究说明 Dformer 对特定模式的敏感性较低。我们开源代码和数据的 https://github.com/yxonic/dtransformer。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Tracing+Knowledge+Instead+of+Patterns:+Stable+Knowledge+Tracing+with+Diagnostic+Transformer)|0| +|[Tracing Knowledge Instead of Patterns: Stable Knowledge Tracing with Diagnostic Transformer](https://doi.org/10.1145/3543507.3583255)|Yu Yin, Le Dai, Zhenya Huang, Shuanghong Shen, Fei Wang, Qi Liu, Enhong Chen, Xin Li|Anhui Province Key Laboratory of Big Data Analysis and Application, School of Computer Science and Technology & School of Data Science, University of Science and Technology of China, China; University of Science and Technology of China, China and iFLYTEK Co., Ltd, China; Anhui Province Key Laboratory of Big Data Analysis and Application, School of Data Science, University of Science and Technology of China, China; Anhui Province Key Laboratory of Big Data Analysis and Application, School of Computer Science and Technology, University of Science and Technology of China, China|Knowledge Tracing (KT) aims at tracing the evolution of the knowledge states along the learning process of a learner. It has become a crucial task for online learning systems to model the learning process of their users, and further provide their users a personalized learning guidance. However, recent developments in KT based on deep neural networks mostly focus on increasing the accuracy of predicting the next performance of students. We argue that current KT modeling, as well as training paradigm, can lead to models tracing patterns of learner’s learning activities, instead of their evolving knowledge states. In this paper, we propose a new architecture, Diagnostic Transformer (DTransformer), along with a new training paradigm, to tackle this challenge. With DTransformer, we build the architecture from question-level to knowledge-level, explicitly diagnosing learner’s knowledge proficiency from each question mastery states. We also propose a novel training algorithm based on contrastive learning that focuses on maintaining the stability of the knowledge state diagnosis. Through extensive experiments, we will show that with its understanding of knowledge state evolution, DTransformer achieves a better performance prediction accuracy and more stable knowledge state tracing results. We will also show that DTransformer is less sensitive to specific patterns with case study. We open-sourced our code and data at https://github.com/yxonic/DTransformer.|知识追踪(KT)旨在追踪学习者在学习过程中知识状态的演化过程。建立在线学习系统用户学习过程的模型，进一步为用户提供个性化的学习指导，已成为在线学习系统的一项重要任务。然而，基于深层神经网络的 KT 的最新发展主要集中在提高预测学生下一步成绩的准确性上。我们认为，目前的 KT 模型和训练范式可以导致模型跟踪学习者的学习活动模式，而不是其演化的知识状态。在本文中，我们提出了一个新的体系结构，诊断变压器(变压器) ，以及一个新的训练范例，以应对这一挑战。使用 Dformer，我们构建了从问题级到知识级的体系结构，从每个问题掌握状态明确诊断学习者的知识熟练程度。提出了一种新的基于对比学习的训练算法，着重于保持知识状态诊断的稳定性。通过大量的实验，我们将表明，由于对知识状态演化的理解，Dformer 获得了更好的性能预测精度和更稳定的知识状态跟踪结果。我们还将通过案例研究说明 Dformer 对特定模式的敏感性较低。我们开源代码和数据的 https://github.com/yxonic/dtransformer。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Tracing+Knowledge+Instead+of+Patterns:+Stable+Knowledge+Tracing+with+Diagnostic+Transformer)|0| |[Learning to Simulate Daily Activities via Modeling Dynamic Human Needs](https://doi.org/10.1145/3543507.3583276)|Yuan Yuan, Huandong Wang, Jingtao Ding, Depeng Jin, Yong Li|Department of Electronic Engineering, Tsinghua University, China|Daily activity data that records individuals' various types of activities in daily life are widely used in many applications such as activity scheduling, activity recommendation, and policymaking. Though with high value, its accessibility is limited due to high collection costs and potential privacy issues. Therefore, simulating human activities to produce massive high-quality data is of great importance to benefit practical applications. However, existing solutions, including rule-based methods with simplified assumptions of human behavior and data-driven methods directly fitting real-world data, both cannot fully qualify for matching reality. In this paper, motivated by the classic psychological theory, Maslow's need theory describing human motivation, we propose a knowledge-driven simulation framework based on generative adversarial imitation learning. To enhance the fidelity and utility of the generated activity data, our core idea is to model the evolution of human needs as the underlying mechanism that drives activity generation in the simulation model. Specifically, this is achieved by a hierarchical model structure that disentangles different need levels, and the use of neural stochastic differential equations that successfully captures piecewise-continuous characteristics of need dynamics. Extensive experiments demonstrate that our framework outperforms the state-of-the-art baselines in terms of data fidelity and utility. Besides, we present the insightful interpretability of the need modeling. The code is available at https://github.com/tsinghua-fib-lab/SAND.|记录个人日常生活中各种类型活动的日常活动数据被广泛应用于活动计划、活动推荐和决策等许多应用中。虽然具有很高的价值，但由于收集成本高和潜在的隐私问题，其可访问性受到限制。因此，模拟人类活动产生大量高质量的数据对于实际应用具有重要意义。然而，现有的解决方案，包括基于人类行为简化假设的规则方法和直接拟合真实世界数据的数据驱动方法，都不能完全符合现实。本文以经典心理学理论——马斯洛需要理论为基础，提出了一种基于生成对抗模仿学习的知识驱动模拟框架。为了提高生成的活动数据的保真度和实用性，我们的核心思想是将人类需求的演化建模为驱动仿真模型中活动生成的底层机制。具体来说，这是通过一个层次化的模型结构，解开不同的需求水平，并使用神经随机微分方程，成功地捕获分段连续的需求动态特征。大量的实验表明，我们的框架在数据保真度和实用性方面优于最先进的基线。此外，我们提出了深刻的需求建模的可解释性。密码可在 https://github.com/tsinghua-fib-lab/sand 查阅。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Learning+to+Simulate+Daily+Activities+via+Modeling+Dynamic+Human+Needs)|0| -|[Controllable Universal Fair Representation Learning](https://doi.org/10.1145/3543507.3583307)|Yue Cui, Ma Chen, Kai Zheng, Lei Chen, Xiaofang Zhou|City University of Hong Kong, Hong Kong; The Hong Kong University of Science and Technology, Hong Kong; University of Electronic Science and Technology of China, China|Learning fair and transferable representations of users that can be used for a wide spectrum of downstream tasks (specifically, machine learning models) has great potential in fairness-aware Web services. Existing studies focus on debiasing w.r.t. a small scale of (one or a handful of) fixed pre-defined sensitive attributes. However, in real practice, downstream data users can be interested in various protected groups and these are usually not known as prior. This requires the learned representations to be fair w.r.t. all possible sensitive attributes. We name this task universal fair representation learning, in which an exponential number of sensitive attributes need to be dealt with, bringing the challenges of unreasonable computational cost and un-guaranteed fairness constraints. To address these problems, we propose a controllable universal fair representation learning (CUFRL) method. An effective bound is first derived via the lens of mutual information to guarantee parity of the universal set of sensitive attributes while maintaining the accuracy of downstream tasks. We also theoretically establish that the number of sensitive attributes that need to be processed can be reduced from exponential to linear. Experiments on two public real-world datasets demonstrate CUFRL can achieve significantly better accuracy-fairness trade-off compared with baseline approaches.|学习可用于广泛的下游任务(特别是机器学习模型)的用户的公平和可转移的表示在公平感知的 Web 服务中具有巨大的潜力。现有的研究主要集中在消除一小部分(一个或少数)固定的预先定义的敏感属性的偏差。然而，在实际操作中，下游数据用户可能对各种受保护的组感兴趣，这些组通常不被称为优先级。这要求学习的表示是公平的，所有可能的敏感属性都是公平的。我们将这个任务命名为通用公平表示学习，其中需要处理的敏感属性数目呈指数增长，带来了计算代价不合理和公平性约束不能保证的挑战。为了解决这些问题，我们提出了一种可控的通用公平表示学习(CUFRL)方法。首先通过互信息透镜导出有效界，以保证通用敏感属性集的奇偶性，同时保持下游任务的准确性。我们还从理论上建立了需要处理的敏感属性的数量可以从指数减少到线性。在两个公共实际数据集上的实验表明，与基线方法相比，CUFRL 能够获得更好的精度-公平性权衡。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Controllable+Universal+Fair+Representation+Learning)|0| -|[Response-act Guided Reinforced Dialogue Generation for Mental Health Counseling](https://doi.org/10.1145/3543507.3583380)|Aseem Srivastava, Ishan Pandey, Md. Shad Akhtar, Tanmoy Chakraborty|IIIT Delhi, India; IIT Delhi, India|Virtual Mental Health Assistants (VMHAs) have become a prevalent method for receiving mental health counseling in the digital healthcare space. An assistive counseling conversation commences with natural open-ended topics to familiarize the client with the environment and later converges into more fine-grained domain-specific topics. Unlike other conversational systems, which are categorized as open-domain or task-oriented systems, VMHAs possess a hybrid conversational flow. These counseling bots need to comprehend various aspects of the conversation, such as dialogue-acts, intents, etc., to engage the client in an effective conversation. Although the surge in digital health research highlights applications of many general-purpose response generation systems, they are barely suitable in the mental health domain -- the prime reason is the lack of understanding in mental health counseling. Moreover, in general, dialogue-act guided response generators are either limited to a template-based paradigm or lack appropriate semantics. To this end, we propose READER -- a REsponse-Act guided reinforced Dialogue genERation model for the mental health counseling conversations. READER is built on transformer to jointly predict a potential dialogue-act d(t+1) for the next utterance (aka response-act) and to generate an appropriate response u(t+1). Through the transformer-reinforcement-learning (TRL) with Proximal Policy Optimization (PPO), we guide the response generator to abide by d(t+1) and ensure the semantic richness of the responses via BERTScore in our reward computation. We evaluate READER on HOPE, a benchmark counseling conversation dataset and observe that it outperforms several baselines across several evaluation metrics -- METEOR, ROUGE, and BERTScore. We also furnish extensive qualitative and quantitative analyses on results, including error analysis, human evaluation, etc.|虚拟心理健康助理(VMHA)已成为数字化医疗空间中接受心理健康咨询的普遍方法。辅助性咨询对话以自然的开放式主题开始，以使客户熟悉环境，然后汇聚成更细粒度的特定领域的主题。与其他会话系统不同的是，VMHA 具有混合会话流。这些咨询机器人需要理解对话的各个方面，例如对话行为、意图等，以使客户参与到有效的对话中。尽管数字健康研究的高潮突出了许多通用反应生成系统的应用，但它们几乎不适合于心理健康领域——主要原因是缺乏对心理健康咨询的理解。此外，一般而言，对话行为指导的响应生成器要么局限于基于模板的范例，要么缺乏适当的语义。为此，我们提出了 READER ——一个由响应-行为引导的强化对话生成模型，用于心理健康咨询对话。读取器是建立在变压器联合预测潜在的对话行为 d (t + 1)为下一个话语(又名反应行为) ，并产生一个适当的反应 u (t + 1)。通过最近策略优化(PPO)的变压器强化学习(TRL) ，引导响应生成器遵守 d (t + 1) ，并在奖励计算中通过 BERTScore 保证响应的语义丰富性。我们在 HOPE 上评估 READER，HOPE 是一个基准咨询会话数据集，并观察到它在几个评估指标—— METEOR、 ROUGE 和 BERTScore 上的表现优于几个基准。我们还提供了广泛的定性和定量分析的结果，包括误差分析，人的评价等。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Response-act+Guided+Reinforced+Dialogue+Generation+for+Mental+Health+Counseling)|0| -|[Offline Policy Evaluation in Large Action Spaces via Outcome-Oriented Action Grouping](https://doi.org/10.1145/3543507.3583448)|Jie Peng, Hao Zou, Jiashuo Liu, Shaoming Li, Yibao Jiang, Jian Pei, Peng Cui|Tsinghua University, China; Duke University, USA; Meituan, China|Offline policy evaluation (OPE) aims to accurately estimate the performance of a hypothetical policy using only historical data, which has drawn increasing attention in a wide range of applications including recommender systems and personalized medicine. With the presence of rising granularity of consumer data, many industries started exploring larger action candidate spaces to support more precise personalized action. While inverse propensity score (IPS) is a standard OPE estimator, it suffers from more severe variance issues with increasing action spaces. To address this issue, we theoretically prove that the estimation variance can be reduced by merging actions into groups while the distinction among these action effects on the outcome can induce extra bias. Motivated by these, we propose a novel IPS estimator with outcome-oriented action Grouping (GroupIPS), which leverages a Lipschitz regularized network to measure the distance of action effects in the embedding space and merges nearest action neighbors. This strategy enables more robust estimation by achieving smaller variances while inducing minor additional bias. Empirically, extensive experiments on both synthetic and real world datasets demonstrate the effectiveness of our proposed method.|离线政策评估(OPE)旨在仅使用历史数据来准确地估计假设政策的性能，这已经引起了包括推荐系统和个体化医学在内的广泛应用中的越来越多的关注。随着消费者数据粒度的增加，许多行业开始探索更大的行动候选空间，以支持更精确的个性化行动。虽然逆倾向得分(IPS)是一个标准的 OPE 估计器，但随着行为空间的增加，它会遇到更严重的方差问题。为了解决这个问题，我们从理论上证明了估计方差可以通过合并行动成组而减少，而这些行动效应之间的区别对结果可能会导致额外的偏差。在此基础上，提出了一种新的基于结果导向的动作分组的 IPS 估计器(GroupIPS) ，该估计器利用 Lipschitz 正则化网络测量嵌入空间中动作效应的距离并合并最近的动作邻居。这种策略通过实现较小的方差同时诱导较小的附加偏差来实现更稳健的估计。经验证明，在合成和真实世界数据集上的广泛实验证明了我们提出的方法的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Offline+Policy+Evaluation+in+Large+Action+Spaces+via+Outcome-Oriented+Action+Grouping)|0| -|[Web Table Formatting Affects Readability on Mobile Devices](https://doi.org/10.1145/3543507.3583506)|Christopher Tensmeyer, Zoya Bylinskii, Tianyuan Cai, Dave Miller, Ani Nenkova, Aleena Gertrudes Niklaus, Shaun Wallace|Adobe Research, USA; Adobe, USA; Tufts, USA; Brown University, USA|Reading large tables on small mobile screens presents serious usability challenges that can be addressed, in part, by better table formatting. However, there are few evidenced-based guidelines for formatting mobile tables to improve readability. For this work, we first conducted a survey to investigate how people interact with tables on mobile devices and conducted a study with designers to identify which design considerations are most critical. Based on these findings, we designed and conducted three large scale studies with remote crowdworker participants. Across the studies, we analyze over 14,000 trials from 590 participants who each viewed and answered questions about 28 diverse tables rendered in different formats. We find that smaller cell padding and frozen headers lead to faster task completion, and that while zebra striping and row borders do not speed up tasks, they are still subjectively preferred by participants.|在小型移动屏幕上阅读大型表格提出了严峻的可用性挑战，这些挑战可以通过更好的表格格式来部分解决。然而，很少有基于证据的指导方针来格式化移动表以提高可读性。对于这项工作，我们首先进行了一项调查，以调查人们如何与移动设备上的表格互动，并与设计师进行了一项研究，以确定哪些设计考虑是最关键的。基于这些发现，我们设计并进行了三个大规模的研究与远程众包工作者的参与者。在这些研究中，我们分析了来自590名参与者的超过14,000个试验，每个参与者观看并回答了28个不同格式表格的问题。我们发现较小的单元格填充和冻结的标题可以更快地完成任务，虽然斑马条纹和行边框不能加速任务，但它们仍然是参与者的主观偏好。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Web+Table+Formatting+Affects+Readability+on+Mobile+Devices)|0| +|[Controllable Universal Fair Representation Learning](https://doi.org/10.1145/3543507.3583307)|Yue Cui, Ma Chen, Kai Zheng, Lei Chen, Xiaofang Zhou|The Hong Kong University of Science and Technology, Hong Kong; City University of Hong Kong, Hong Kong; University of Electronic Science and Technology of China, China|Learning fair and transferable representations of users that can be used for a wide spectrum of downstream tasks (specifically, machine learning models) has great potential in fairness-aware Web services. Existing studies focus on debiasing w.r.t. a small scale of (one or a handful of) fixed pre-defined sensitive attributes. However, in real practice, downstream data users can be interested in various protected groups and these are usually not known as prior. This requires the learned representations to be fair w.r.t. all possible sensitive attributes. We name this task universal fair representation learning, in which an exponential number of sensitive attributes need to be dealt with, bringing the challenges of unreasonable computational cost and un-guaranteed fairness constraints. To address these problems, we propose a controllable universal fair representation learning (CUFRL) method. An effective bound is first derived via the lens of mutual information to guarantee parity of the universal set of sensitive attributes while maintaining the accuracy of downstream tasks. We also theoretically establish that the number of sensitive attributes that need to be processed can be reduced from exponential to linear. Experiments on two public real-world datasets demonstrate CUFRL can achieve significantly better accuracy-fairness trade-off compared with baseline approaches.|学习可用于广泛的下游任务(特别是机器学习模型)的用户的公平和可转移的表示在公平感知的 Web 服务中具有巨大的潜力。现有的研究主要集中在消除一小部分(一个或少数)固定的预先定义的敏感属性的偏差。然而，在实际操作中，下游数据用户可能对各种受保护的组感兴趣，这些组通常不被称为优先级。这要求学习的表示是公平的，所有可能的敏感属性都是公平的。我们将这个任务命名为通用公平表示学习，其中需要处理的敏感属性数目呈指数增长，带来了计算代价不合理和公平性约束不能保证的挑战。为了解决这些问题，我们提出了一种可控的通用公平表示学习(CUFRL)方法。首先通过互信息透镜导出有效界，以保证通用敏感属性集的奇偶性，同时保持下游任务的准确性。我们还从理论上建立了需要处理的敏感属性的数量可以从指数减少到线性。在两个公共实际数据集上的实验表明，与基线方法相比，CUFRL 能够获得更好的精度-公平性权衡。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Controllable+Universal+Fair+Representation+Learning)|0| +|[Response-act Guided Reinforced Dialogue Generation for Mental Health Counseling](https://doi.org/10.1145/3543507.3583380)|Aseem Srivastava, Ishan Pandey, Md. Shad Akhtar, Tanmoy Chakraborty|IIT Delhi, India; IIIT Delhi, India|Virtual Mental Health Assistants (VMHAs) have become a prevalent method for receiving mental health counseling in the digital healthcare space. An assistive counseling conversation commences with natural open-ended topics to familiarize the client with the environment and later converges into more fine-grained domain-specific topics. Unlike other conversational systems, which are categorized as open-domain or task-oriented systems, VMHAs possess a hybrid conversational flow. These counseling bots need to comprehend various aspects of the conversation, such as dialogue-acts, intents, etc., to engage the client in an effective conversation. Although the surge in digital health research highlights applications of many general-purpose response generation systems, they are barely suitable in the mental health domain -- the prime reason is the lack of understanding in mental health counseling. Moreover, in general, dialogue-act guided response generators are either limited to a template-based paradigm or lack appropriate semantics. To this end, we propose READER -- a REsponse-Act guided reinforced Dialogue genERation model for the mental health counseling conversations. READER is built on transformer to jointly predict a potential dialogue-act d(t+1) for the next utterance (aka response-act) and to generate an appropriate response u(t+1). Through the transformer-reinforcement-learning (TRL) with Proximal Policy Optimization (PPO), we guide the response generator to abide by d(t+1) and ensure the semantic richness of the responses via BERTScore in our reward computation. We evaluate READER on HOPE, a benchmark counseling conversation dataset and observe that it outperforms several baselines across several evaluation metrics -- METEOR, ROUGE, and BERTScore. We also furnish extensive qualitative and quantitative analyses on results, including error analysis, human evaluation, etc.|虚拟心理健康助理(VMHA)已成为数字化医疗空间中接受心理健康咨询的普遍方法。辅助性咨询对话以自然的开放式主题开始，以使客户熟悉环境，然后汇聚成更细粒度的特定领域的主题。与其他会话系统不同的是，VMHA 具有混合会话流。这些咨询机器人需要理解对话的各个方面，例如对话行为、意图等，以使客户参与到有效的对话中。尽管数字健康研究的高潮突出了许多通用反应生成系统的应用，但它们几乎不适合于心理健康领域——主要原因是缺乏对心理健康咨询的理解。此外，一般而言，对话行为指导的响应生成器要么局限于基于模板的范例，要么缺乏适当的语义。为此，我们提出了 READER ——一个由响应-行为引导的强化对话生成模型，用于心理健康咨询对话。读取器是建立在变压器联合预测潜在的对话行为 d (t + 1)为下一个话语(又名反应行为) ，并产生一个适当的反应 u (t + 1)。通过最近策略优化(PPO)的变压器强化学习(TRL) ，引导响应生成器遵守 d (t + 1) ，并在奖励计算中通过 BERTScore 保证响应的语义丰富性。我们在 HOPE 上评估 READER，HOPE 是一个基准咨询会话数据集，并观察到它在几个评估指标—— METEOR、 ROUGE 和 BERTScore 上的表现优于几个基准。我们还提供了广泛的定性和定量分析的结果，包括误差分析，人的评价等。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Response-act+Guided+Reinforced+Dialogue+Generation+for+Mental+Health+Counseling)|0| +|[Offline Policy Evaluation in Large Action Spaces via Outcome-Oriented Action Grouping](https://doi.org/10.1145/3543507.3583448)|Jie Peng, Hao Zou, Jiashuo Liu, Shaoming Li, Yibao Jiang, Jian Pei, Peng Cui|Tsinghua University, China; Meituan, China; Duke University, USA|Offline policy evaluation (OPE) aims to accurately estimate the performance of a hypothetical policy using only historical data, which has drawn increasing attention in a wide range of applications including recommender systems and personalized medicine. With the presence of rising granularity of consumer data, many industries started exploring larger action candidate spaces to support more precise personalized action. While inverse propensity score (IPS) is a standard OPE estimator, it suffers from more severe variance issues with increasing action spaces. To address this issue, we theoretically prove that the estimation variance can be reduced by merging actions into groups while the distinction among these action effects on the outcome can induce extra bias. Motivated by these, we propose a novel IPS estimator with outcome-oriented action Grouping (GroupIPS), which leverages a Lipschitz regularized network to measure the distance of action effects in the embedding space and merges nearest action neighbors. This strategy enables more robust estimation by achieving smaller variances while inducing minor additional bias. Empirically, extensive experiments on both synthetic and real world datasets demonstrate the effectiveness of our proposed method.|离线政策评估(OPE)旨在仅使用历史数据来准确地估计假设政策的性能，这已经引起了包括推荐系统和个体化医学在内的广泛应用中的越来越多的关注。随着消费者数据粒度的增加，许多行业开始探索更大的行动候选空间，以支持更精确的个性化行动。虽然逆倾向得分(IPS)是一个标准的 OPE 估计器，但随着行为空间的增加，它会遇到更严重的方差问题。为了解决这个问题，我们从理论上证明了估计方差可以通过合并行动成组而减少，而这些行动效应之间的区别对结果可能会导致额外的偏差。在此基础上，提出了一种新的基于结果导向的动作分组的 IPS 估计器(GroupIPS) ，该估计器利用 Lipschitz 正则化网络测量嵌入空间中动作效应的距离并合并最近的动作邻居。这种策略通过实现较小的方差同时诱导较小的附加偏差来实现更稳健的估计。经验证明，在合成和真实世界数据集上的广泛实验证明了我们提出的方法的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Offline+Policy+Evaluation+in+Large+Action+Spaces+via+Outcome-Oriented+Action+Grouping)|0| +|[Web Table Formatting Affects Readability on Mobile Devices](https://doi.org/10.1145/3543507.3583506)|Christopher Tensmeyer, Zoya Bylinskii, Tianyuan Cai, Dave Miller, Ani Nenkova, Aleena Gertrudes Niklaus, Shaun Wallace|Brown University, USA; Tufts, USA; Adobe, USA; Adobe Research, USA|Reading large tables on small mobile screens presents serious usability challenges that can be addressed, in part, by better table formatting. However, there are few evidenced-based guidelines for formatting mobile tables to improve readability. For this work, we first conducted a survey to investigate how people interact with tables on mobile devices and conducted a study with designers to identify which design considerations are most critical. Based on these findings, we designed and conducted three large scale studies with remote crowdworker participants. Across the studies, we analyze over 14,000 trials from 590 participants who each viewed and answered questions about 28 diverse tables rendered in different formats. We find that smaller cell padding and frozen headers lead to faster task completion, and that while zebra striping and row borders do not speed up tasks, they are still subjectively preferred by participants.|在小型移动屏幕上阅读大型表格提出了严峻的可用性挑战，这些挑战可以通过更好的表格格式来部分解决。然而，很少有基于证据的指导方针来格式化移动表以提高可读性。对于这项工作，我们首先进行了一项调查，以调查人们如何与移动设备上的表格互动，并与设计师进行了一项研究，以确定哪些设计考虑是最关键的。基于这些发现，我们设计并进行了三个大规模的研究与远程众包工作者的参与者。在这些研究中，我们分析了来自590名参与者的超过14,000个试验，每个参与者观看并回答了28个不同格式表格的问题。我们发现较小的单元格填充和冻结的标题可以更快地完成任务，虽然斑马条纹和行边框不能加速任务，但它们仍然是参与者的主观偏好。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Web+Table+Formatting+Affects+Readability+on+Mobile+Devices)|0| |[Web Structure Derived Clustering for Optimised Web Accessibility Evaluation](https://doi.org/10.1145/3543507.3583508)|Alexander Hambley, Yeliz Yesilada, Markel Vigo, Simon Harper|University of Manchester, United Kingdom; Middle East Technical University Northern Cyprus Campus, Turkey|Web accessibility evaluation is a costly and complex process due to limited time, resources and ambiguity. To optimise the accessibility evaluation process, we aim to reduce the number of pages auditors must review by employing statistically representative pages, reducing a site of thousands of pages to a manageable review of archetypal pages. Our paper focuses on representativeness, one of six proposed metrics that form our methodology, to address the limitations we have identified with the W3C Website Accessibility Conformance Evaluation Methodology (WCAG-EM). These include the evaluative scope, the non-probabilistic sampling approach, and the potential for bias within the selected sample. Representativeness, in particular, is a metric to assess the quality and coverage of sampling. To measure this, we systematically evaluate five web page representations with a website of 388 pages, including tags, structure, the DOM tree, content, and a mixture of structure and content. Our findings highlight the importance of including structural components in representations. We validate our conclusions using the same methodology for three additional random sites of 500 pages. As an exclusive attribute, we find that features derived from web content are suboptimal and can lead to lower quality and more disparate clustering for optimised accessibility evaluation.|由于时间、资源和含糊不清，网页亲和力评估是一个昂贵而复杂的过程。为了优化易访问性评估过程，我们的目标是通过使用具有统计代表性的页面，减少审计人员必须审查的页面数量，将数千页的网站减少到一个可管理的原型页面审查。我们的论文主要关注代表性，这是构成我们方法论的六个指标之一，以解决我们在 W3C 网站可访问性一致性评估方法(WCAG-EM)中发现的局限性。这些包括评估范围，非概率抽样方法，以及在选定的样本中存在偏差的可能性。具体而言，代表性是评估抽样质量和覆盖面的一个指标。为了衡量这一点，我们系统地评估了拥有388个页面的网站的五种网页表示形式，包括标签、结构、 DOM 树、内容以及结构和内容的混合。我们的发现强调了在表征中包含结构成分的重要性。我们使用相同的方法对另外三个500页的随机站点验证了我们的结论。作为一个独有的属性，我们发现从网页内容衍生出来的特征是次优的，可能导致质量下降和更多不同的聚类来优化易访问性评价。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Web+Structure+Derived+Clustering+for+Optimised+Web+Accessibility+Evaluation)|0| -|[Hashtag-Guided Low-Resource Tweet Classification](https://doi.org/10.1145/3543507.3583194)|Shizhe Diao, Sedrick Scott Keh, Liangming Pan, Zhiliang Tian, Yan Song, Tong Zhang|University of Science and Technology of China, China; University of California, Santa Barbara, USA; Carnegie Mellon University, USA; The Hong Kong University of Science and Technology, Hong Kong|Social media classification tasks (e.g., tweet sentiment analysis, tweet stance detection) are challenging because social media posts are typically short, informal, and ambiguous. Thus, training on tweets is challenging and demands large-scale human-annotated labels, which are time-consuming and costly to obtain. In this paper, we find that providing hashtags to social media tweets can help alleviate this issue because hashtags can enrich short and ambiguous tweets in terms of various information, such as topic, sentiment, and stance. This motivates us to propose a novel Hashtag-guided Tweet Classification model (HashTation), which automatically generates meaningful hashtags for the input tweet to provide useful auxiliary signals for tweet classification. To generate high-quality and insightful hashtags, our hashtag generation model retrieves and encodes the post-level and entity-level information across the whole corpus. Experiments show that HashTation achieves significant improvements on seven low-resource tweet classification tasks, in which only a limited amount of training data is provided, showing that automatically enriching tweets with model-generated hashtags could significantly reduce the demand for large-scale human-labeled data. Further analysis demonstrates that HashTation is able to generate high-quality hashtags that are consistent with the tweets and their labels. The code is available at https://github.com/shizhediao/HashTation.|社交媒体分类任务(例如，推文情绪分析，推文姿态检测)是具有挑战性的，因为社交媒体的帖子通常是短小、非正式和模棱两可的。因此，对 tweet 的培训是具有挑战性的，需要大规模的人工注释标签，这是耗时和昂贵的获得。在本文中，我们发现为社交媒体 tweet 提供 # 标签可以帮助缓解这个问题，因为 # 标签可以丰富各种信息，如主题、情感和立场方面的短小和模糊的 tweet。这促使我们提出一种新的 Hashtag 引导的 Tweet 分类模型(HashTation) ，它自动为输入 Tweet 生成有意义的 hashtag，为 Tweet 分类提供有用的辅助信号。为了生成高质量和有见地的 # 标签，我们的 # 标签生成模型检索并编码整个语料库的后级和实体级信息。实验表明，HashTation 在七个低资源的 tweet 分类任务上取得了显著的改进，其中只提供了有限数量的训练数据，这表明使用模型生成的 # 标签自动丰富 tweet 可以显著降低对大规模人类标记数据的需求。进一步的分析表明，HashTation 能够生成与 tweet 及其标签一致的高质量 # 标签。密码可在 https://github.com/shizhediao/hashtation 查阅。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Hashtag-Guided+Low-Resource+Tweet+Classification)|0| -|[FormerTime: Hierarchical Multi-Scale Representations for Multivariate Time Series Classification](https://doi.org/10.1145/3543507.3583205)|Mingyue Cheng, Qi Liu, Zhiding Liu, Zhi Li, Yucong Luo, Enhong Chen|Tsinghua University, China; University of Science and Technology of China, China and State Key Laboratory of Cognitive Intelligence, China|Deep learning-based algorithms, e.g., convolutional networks, have significantly facilitated multivariate time series classification (MTSC) task. Nevertheless, they suffer from the limitation in modeling long-range dependence due to the nature of convolution operations. Recent advancements have shown the potential of transformers to capture long-range dependence. However, it would incur severe issues, such as fixed scale representations, temporal-invariant and quadratic time complexity, with transformers directly applicable to the MTSC task because of the distinct properties of time series data. To tackle these issues, we propose FormerTime, an hierarchical representation model for improving the classification capacity for the MTSC task. In the proposed FormerTime, we employ a hierarchical network architecture to perform multi-scale feature maps. Besides, a novel transformer encoder is further designed, in which an efficient temporal reduction attention layer and a well-informed contextual positional encoding generating strategy are developed. To sum up, FormerTime exhibits three aspects of merits: (1) learning hierarchical multi-scale representations from time series data, (2) inheriting the strength of both transformers and convolutional networks, and (3) tacking the efficiency challenges incurred by the self-attention mechanism. Extensive experiments performed on $10$ publicly available datasets from UEA archive verify the superiorities of the FormerTime compared to previous competitive baselines.|基于深度学习的算法，如卷积网络，极大地促进了多变量时间序列分类(MTSC)任务。然而，由于卷积运算的性质，它们在建立长程依赖模型方面存在局限性。最近的进展显示了变压器捕获远程依赖的潜力。然而，由于时间序列数据的独特性质，变压器直接适用于 MTSC 任务会产生严重的问题，如固定尺度表示、时间不变性和二次时间复杂性等。为了解决这些问题，我们提出了 FormerTime，一种用于提高 MTSC 任务分类能力的层次化表示模型。在所提出的 FormerTime 中，我们采用了层次化的网络结构来执行多尺度的特征映射。此外，本文还设计了一种新型的变压器编码器，其中提出了一种有效的时间约简注意层和充分知情的上下文位置编码生成策略。综上所述，FormerTime 具有三个方面的优点: (1)从时间序列数据中学习分层多尺度表示; (2)继承变压器和卷积网络的优点; (3)解决自我注意机制带来的效率挑战。对 UEA 档案中10美元可公开获得的数据集进行的广泛实验验证了 FormerTime 相对于以前的竞争基线的优势。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=FormerTime:+Hierarchical+Multi-Scale+Representations+for+Multivariate+Time+Series+Classification)|0| +|[Hashtag-Guided Low-Resource Tweet Classification](https://doi.org/10.1145/3543507.3583194)|Shizhe Diao, Sedrick Scott Keh, Liangming Pan, Zhiliang Tian, Yan Song, Tong Zhang|The Hong Kong University of Science and Technology, Hong Kong; University of California, Santa Barbara, USA; Carnegie Mellon University, USA; University of Science and Technology of China, China|Social media classification tasks (e.g., tweet sentiment analysis, tweet stance detection) are challenging because social media posts are typically short, informal, and ambiguous. Thus, training on tweets is challenging and demands large-scale human-annotated labels, which are time-consuming and costly to obtain. In this paper, we find that providing hashtags to social media tweets can help alleviate this issue because hashtags can enrich short and ambiguous tweets in terms of various information, such as topic, sentiment, and stance. This motivates us to propose a novel Hashtag-guided Tweet Classification model (HashTation), which automatically generates meaningful hashtags for the input tweet to provide useful auxiliary signals for tweet classification. To generate high-quality and insightful hashtags, our hashtag generation model retrieves and encodes the post-level and entity-level information across the whole corpus. Experiments show that HashTation achieves significant improvements on seven low-resource tweet classification tasks, in which only a limited amount of training data is provided, showing that automatically enriching tweets with model-generated hashtags could significantly reduce the demand for large-scale human-labeled data. Further analysis demonstrates that HashTation is able to generate high-quality hashtags that are consistent with the tweets and their labels. The code is available at https://github.com/shizhediao/HashTation.|社交媒体分类任务(例如，推文情绪分析，推文姿态检测)是具有挑战性的，因为社交媒体的帖子通常是短小、非正式和模棱两可的。因此，对 tweet 的培训是具有挑战性的，需要大规模的人工注释标签，这是耗时和昂贵的获得。在本文中，我们发现为社交媒体 tweet 提供 # 标签可以帮助缓解这个问题，因为 # 标签可以丰富各种信息，如主题、情感和立场方面的短小和模糊的 tweet。这促使我们提出一种新的 Hashtag 引导的 Tweet 分类模型(HashTation) ，它自动为输入 Tweet 生成有意义的 hashtag，为 Tweet 分类提供有用的辅助信号。为了生成高质量和有见地的 # 标签，我们的 # 标签生成模型检索并编码整个语料库的后级和实体级信息。实验表明，HashTation 在七个低资源的 tweet 分类任务上取得了显著的改进，其中只提供了有限数量的训练数据，这表明使用模型生成的 # 标签自动丰富 tweet 可以显著降低对大规模人类标记数据的需求。进一步的分析表明，HashTation 能够生成与 tweet 及其标签一致的高质量 # 标签。密码可在 https://github.com/shizhediao/hashtation 查阅。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Hashtag-Guided+Low-Resource+Tweet+Classification)|0| +|[FormerTime: Hierarchical Multi-Scale Representations for Multivariate Time Series Classification](https://doi.org/10.1145/3543507.3583205)|Mingyue Cheng, Qi Liu, Zhiding Liu, Zhi Li, Yucong Luo, Enhong Chen|University of Science and Technology of China, China and State Key Laboratory of Cognitive Intelligence, China; Tsinghua University, China|Deep learning-based algorithms, e.g., convolutional networks, have significantly facilitated multivariate time series classification (MTSC) task. Nevertheless, they suffer from the limitation in modeling long-range dependence due to the nature of convolution operations. Recent advancements have shown the potential of transformers to capture long-range dependence. However, it would incur severe issues, such as fixed scale representations, temporal-invariant and quadratic time complexity, with transformers directly applicable to the MTSC task because of the distinct properties of time series data. To tackle these issues, we propose FormerTime, an hierarchical representation model for improving the classification capacity for the MTSC task. In the proposed FormerTime, we employ a hierarchical network architecture to perform multi-scale feature maps. Besides, a novel transformer encoder is further designed, in which an efficient temporal reduction attention layer and a well-informed contextual positional encoding generating strategy are developed. To sum up, FormerTime exhibits three aspects of merits: (1) learning hierarchical multi-scale representations from time series data, (2) inheriting the strength of both transformers and convolutional networks, and (3) tacking the efficiency challenges incurred by the self-attention mechanism. Extensive experiments performed on $10$ publicly available datasets from UEA archive verify the superiorities of the FormerTime compared to previous competitive baselines.|基于深度学习的算法，如卷积网络，极大地促进了多变量时间序列分类(MTSC)任务。然而，由于卷积运算的性质，它们在建立长程依赖模型方面存在局限性。最近的进展显示了变压器捕获远程依赖的潜力。然而，由于时间序列数据的独特性质，变压器直接适用于 MTSC 任务会产生严重的问题，如固定尺度表示、时间不变性和二次时间复杂性等。为了解决这些问题，我们提出了 FormerTime，一种用于提高 MTSC 任务分类能力的层次化表示模型。在所提出的 FormerTime 中，我们采用了层次化的网络结构来执行多尺度的特征映射。此外，本文还设计了一种新型的变压器编码器，其中提出了一种有效的时间约简注意层和充分知情的上下文位置编码生成策略。综上所述，FormerTime 具有三个方面的优点: (1)从时间序列数据中学习分层多尺度表示; (2)继承变压器和卷积网络的优点; (3)解决自我注意机制带来的效率挑战。对 UEA 档案中10美元可公开获得的数据集进行的广泛实验验证了 FormerTime 相对于以前的竞争基线的优势。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=FormerTime:+Hierarchical+Multi-Scale+Representations+for+Multivariate+Time+Series+Classification)|0| |[HISum: Hyperbolic Interaction Model for Extractive Multi-Document Summarization](https://doi.org/10.1145/3543507.3583197)|Mingyang Song, Yi Feng, Liping Jing|Beijing Jiaotong University, China|Extractive summarization helps provide a short description or a digest of news or other web texts. It enhances the reading experience of users, especially when they are reading on small displays (e.g., mobile phones). Matching-based methods are recently proposed for the extractive summarization task, which extracts a summary from a global view via a document-summary matching framework. However, these methods only calculate similarities between candidate summaries and the entire document embeddings, insufficiently capturing interactions between different contextual information in the document to accurately estimate the importance of candidates. In this paper, we propose a new hyperbolic interaction model for extractive multi-document summarization (HISum). Specifically, HISum first learns document and candidate summary representations in the same hyperbolic space to capture latent hierarchical structures and then estimates the importance scores of candidates by jointly modeling interactions between each candidate and the document from global and local views. Finally, the importance scores are used to rank and extract the best candidate as the extracted summary. Experimental results on several benchmarks show that HISum outperforms the state-of-the-art extractive baselines1.|提取摘要有助于提供新闻或其他网络文本的简短描述或摘要。它增强了用户的阅读体验，特别是当他们在小型显示器(如手机)上阅读时。最近提出了一种基于匹配的提取摘要方法，该方法通过文档摘要匹配框架从全局视图中提取摘要。然而，这些方法只计算候选摘要和整个文档嵌入之间的相似性，不能充分捕获文档中不同上下文信息之间的交互作用，从而无法准确估计候选摘要的重要性。本文提出了一种新的双曲型交互模型，用于提取多文档摘要(HISum)。具体来说，hISum 首先在同一个双曲空间中学习文档和候选人摘要表示，以捕获潜在的层次结构，然后通过联合建模每个候选人和文档之间的互动，从全局和局部视图估计候选人的重要性得分。最后，利用重要性得分对最佳候选人进行排序和提取，作为提取的总结。对几个基准测试的实验结果表明，HISum 的性能优于最先进的提取基准1。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=HISum:+Hyperbolic+Interaction+Model+for+Extractive+Multi-Document+Summarization)|0| |[Descartes: Generating Short Descriptions of Wikipedia Articles](https://doi.org/10.1145/3543507.3583220)|Marija Sakota, Maxime Peyrard, Robert West|EPFL, Switzerland|Wikipedia is one of the richest knowledge sources on the Web today. In order to facilitate navigating, searching, and maintaining its content, Wikipedia's guidelines state that all articles should be annotated with a so-called short description indicating the article's topic (e.g., the short description of beer is "Alcoholic drink made from fermented cereal grains"). Nonetheless, a large fraction of articles (ranging from 10.2% in Dutch to 99.7% in Kazakh) have no short description yet, with detrimental effects for millions of Wikipedia users. Motivated by this problem, we introduce the novel task of automatically generating short descriptions for Wikipedia articles and propose Descartes, a multilingual model for tackling it. Descartes integrates three sources of information to generate an article description in a target language: the text of the article in all its language versions, the already-existing descriptions (if any) of the article in other languages, and semantic type information obtained from a knowledge graph. We evaluate a Descartes model trained for handling 25 languages simultaneously, showing that it beats baselines (including a strong translation-based baseline) and performs on par with monolingual models tailored for specific languages. A human evaluation on three languages further shows that the quality of Descartes's descriptions is largely indistinguishable from that of human-written descriptions; e.g., 91.3% of our English descriptions (vs. 92.1% of human-written descriptions) pass the bar for inclusion in Wikipedia, suggesting that Descartes is ready for production, with the potential to support human editors in filling a major gap in today's Wikipedia across languages.|维基百科是当今网络上最丰富的知识资源之一。为了便于浏览、搜索和维护其内容，维基百科的指导方针规定，所有文章都应该加注所谓的简短描述，指明文章的主题(例如，对啤酒的简短描述是“用发酵的谷物制成的含酒精饮料”)。尽管如此，大部分文章(荷兰语为10.2% ，哈萨克语为99.7%)还没有简短的描述，这对数百万维基百科用户造成了不利影响。基于这个问题，我们引入了自动生成维基百科文章简短描述的新任务，并提出了笛卡尔模型，一个多语言模型来解决这个问题。笛卡尔整合了三种信息来源，以目标语言生成文章描述: 文章文本的所有语言版本，已经存在的文章描述(如果有的话)的其他语言，和语义类型信息获得的知识图。我们评估了笛卡尔模型同时处理25种语言的训练，表明它打破了基线(包括一个强大的基于翻译的基线) ，并表现出与为特定语言量身定制的单语言模型相当的水平。人类对三种语言的评估进一步表明，笛卡尔描述的质量在很大程度上与人类书写的描述没有区别; 例如，91.3% 的英文描述(相对于92.1% 的人类书写的描述)通过了纳入维基百科的门槛，表明笛卡尔已经准备好生产，有可能支持人类编辑填补当今维基百科跨语言的主要空白。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Descartes:+Generating+Short+Descriptions+of+Wikipedia+Articles)|0| -|[A Dual Prompt Learning Framework for Few-Shot Dialogue State Tracking](https://doi.org/10.1145/3543507.3583238)|Yuting Yang, Wenqiang Lei, Pei Huang, Juan Cao, Jintao Li, TatSeng Chua|Sichuan University, China; Institute of Computing Technology, Chinese Academy of Sciences, China; Institute of Computing Technology, Chinese Academy of Sciences; University of Chinese Academy of Sciences, China; National University of Singapore, Singapore; Stanford University, USA|Dialogue state tracking (DST) module is an important component for task-oriented dialog systems to understand users' goals and needs. Collecting dialogue state labels including slots and values can be costly, especially with the wide application of dialogue systems in more and more new-rising domains. In this paper, we focus on how to utilize the language understanding and generation ability of pre-trained language models for DST. We design a dual prompt learning framework for few-shot DST. Specifically, we consider the learning of slot generation and value generation as dual tasks, and two prompts are designed based on such a dual structure to incorporate task-related knowledge of these two tasks respectively. In this way, the DST task can be formulated as a language modeling task efficiently under few-shot settings. Experimental results on two task-oriented dialogue datasets show that the proposed method not only outperforms existing state-of-the-art few-shot methods, but also can generate unseen slots. It indicates that DST-related knowledge can be probed from PLM and utilized to address low-resource DST efficiently with the help of prompt learning.|对话状态跟踪(DST)模块是面向任务的对话系统理解用户目标和需求的重要组成部分。收集包括插槽和价值观在内的对话状态标签可能成本高昂，特别是随着对话系统在越来越多新兴领域的广泛应用。本文主要研究如何利用预训练语言模型的语言理解能力和生成能力来进行 DST。我们设计了一个双提示学习框架的少拍 DST。具体而言，我们将时隙生成和价值生成的学习视为双重任务，并基于这种双重结构设计了两个提示，分别结合这两个任务的任务相关知识。通过这种方式，DST 任务可以有效地表述为一种语言建模任务在少镜头设置下。在两个面向任务的对话数据集上的实验结果表明，该方法不仅优于现有的最先进的少镜头方法，而且能够产生看不见的时隙。结果表明，在快速学习的帮助下，可以从 PLM 中探索与 DST 相关的知识，并利用这些知识有效地解决低资源的 DST 问题。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Dual+Prompt+Learning+Framework+for+Few-Shot+Dialogue+State+Tracking)|0| +|[A Dual Prompt Learning Framework for Few-Shot Dialogue State Tracking](https://doi.org/10.1145/3543507.3583238)|Yuting Yang, Wenqiang Lei, Pei Huang, Juan Cao, Jintao Li, TatSeng Chua|National University of Singapore, Singapore; Institute of Computing Technology, Chinese Academy of Sciences; University of Chinese Academy of Sciences, China; Institute of Computing Technology, Chinese Academy of Sciences, China; Sichuan University, China; Stanford University, USA|Dialogue state tracking (DST) module is an important component for task-oriented dialog systems to understand users' goals and needs. Collecting dialogue state labels including slots and values can be costly, especially with the wide application of dialogue systems in more and more new-rising domains. In this paper, we focus on how to utilize the language understanding and generation ability of pre-trained language models for DST. We design a dual prompt learning framework for few-shot DST. Specifically, we consider the learning of slot generation and value generation as dual tasks, and two prompts are designed based on such a dual structure to incorporate task-related knowledge of these two tasks respectively. In this way, the DST task can be formulated as a language modeling task efficiently under few-shot settings. Experimental results on two task-oriented dialogue datasets show that the proposed method not only outperforms existing state-of-the-art few-shot methods, but also can generate unseen slots. It indicates that DST-related knowledge can be probed from PLM and utilized to address low-resource DST efficiently with the help of prompt learning.|对话状态跟踪(DST)模块是面向任务的对话系统理解用户目标和需求的重要组成部分。收集包括插槽和价值观在内的对话状态标签可能成本高昂，特别是随着对话系统在越来越多新兴领域的广泛应用。本文主要研究如何利用预训练语言模型的语言理解能力和生成能力来进行 DST。我们设计了一个双提示学习框架的少拍 DST。具体而言，我们将时隙生成和价值生成的学习视为双重任务，并基于这种双重结构设计了两个提示，分别结合这两个任务的任务相关知识。通过这种方式，DST 任务可以有效地表述为一种语言建模任务在少镜头设置下。在两个面向任务的对话数据集上的实验结果表明，该方法不仅优于现有的最先进的少镜头方法，而且能够产生看不见的时隙。结果表明，在快速学习的帮助下，可以从 PLM 中探索与 DST 相关的知识，并利用这些知识有效地解决低资源的 DST 问题。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Dual+Prompt+Learning+Framework+for+Few-Shot+Dialogue+State+Tracking)|0| |[TTS: A Target-based Teacher-Student Framework for Zero-Shot Stance Detection](https://doi.org/10.1145/3543507.3583250)|Yingjie Li, Chenye Zhao, Cornelia Caragea|Computer Science, University of Illinois at Chicago, USA|The goal of zero-shot stance detection (ZSSD) is to identify the stance (in favor of, against, or neutral) of a text towards an unseen target in the inference stage. In this paper, we explore this problem from a novel angle by proposing a Target-based Teacher-Student learning (TTS) framework. Specifically, we first augment the training set by extracting diversified targets that are unseen during training with a keyphrase generation model. Then, we develop a teacher-student framework which effectively utilizes the augmented data. Extensive experiments show that our model significantly outperforms state-of-the-art ZSSD baselines on the available benchmark dataset for this task by 8.9% in macro-averaged F1. In addition, previous ZSSD requires human-annotated targets and labels during training, which may not be available in real-world applications. Therefore, we go one step further by proposing a more challenging open-world ZSSD task: identifying the stance of a text towards an unseen target without human-annotated targets and stance labels. We show that our TTS can be easily adapted to the new task. Remarkably, TTS without human-annotated targets and stance labels even significantly outperforms previous state-of-the-art ZSSD baselines trained with human-annotated data. We publicly release our code 1 to facilitate future research.|零拍姿态检测(ZSSD)的目标是在推理阶段识别文本对一个看不见的目标的姿态(支持、反对或中立)。本文从一个新的角度探讨了这个问题，提出了一个基于目标的师生学习(TTS)框架。具体来说，我们首先通过关键词生成模型提取训练过程中看不到的多样化目标来增加训练集。然后，我们开发了一个有效利用增强数据的师生框架。广泛的实验表明，我们的模型显着优于现有的 ZSSD 基准测试数据集的8.9% ，这项任务在宏观平均的 F1。此外，以前的 ZSSD 在培训期间需要人工注释的目标和标签，这在现实世界的应用程序中可能是不可用的。因此，我们更进一步，提出了一个更具挑战性的开放世界 ZSSD 任务: 确定文本对一个看不见的目标的立场，而没有人工注释的目标和立场标签。我们表明，我们的 TTS 可以很容易地适应新的任务。值得注意的是，没有人工注释目标和立场标签的 TTS 甚至明显优于以前用人工注释数据训练的最先进的 ZSSD 基线。我们公开发布代码1以促进未来的研究。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=TTS:+A+Target-based+Teacher-Student+Framework+for+Zero-Shot+Stance+Detection)|0| -|[CL-WSTC: Continual Learning for Weakly Supervised Text Classification on the Internet](https://doi.org/10.1145/3543507.3583249)|Miaomiao Li, Jiaqi Zhu, Xin Yang, Yi Yang, Qiang Gao, Hongan Wang|Institute of Software, Chinese Academy of Sciences, China and University of Chinese Academy of Sciences, China; Institute of Software, Chinese Academy of Sciences, China; Southwestern University of Finance and Economics, China|Continual text classification is an important research direction in Web mining. Existing works are limited to supervised approaches relying on abundant labeled data, but in the open and dynamic environment of Internet, involving constant semantic change of known topics and the appearance of unknown topics, text annotations are hard to access in time for each period. That calls for the technique of weakly supervised text classification (WSTC), which requires just seed words for each category and has succeed in static text classification tasks. However, there are still no studies of applying WSTC methods in a continual learning paradigm to actually accommodate the open and evolving Internet. In this paper, we tackle this problem for the first time and propose a framework, named Continual Learning for Weakly Supervised Text Classification (CL-WSTC), which can take any WSTC method as base model. It consists of two modules, classification decision with delay and seed word updating. In the former, the probability threshold for each category in each period is adaptively learned to determine the acceptance/rejection of texts. In the latter, with candidate words output by the base model, seed words are added and deleted via reinforcement learning with immediate rewards, according to an empirically certified unsupervised measure. Extensive experiments show that our approach has strong universality and can achieve a better trade-off between classification accuracy and decision timeliness compared to non-continual counterparts, with intuitively interpretable updating of seed words.|连续文本分类是 Web 挖掘的一个重要研究方向。现有的著作仅限于依赖于大量标记数据的监督方法，但在互联网的开放动态环境中，涉及已知主题的语义不断变化和未知主题的出现，文本注释难以在每个时期及时获取。这就要求采用弱监督文本分类(WSTC)技术，对每个类别只需要种子词，并成功地完成了静态文本分类任务。然而，仍然没有研究应用 WSTC 方法在一个持续的学习范式，以实际适应开放和不断发展的互联网。本文首次提出了弱监督文本分类的持续学习框架(CL-WSTC) ，该框架可以采用任何 WSTC 方法作为基本模型。它包括延迟分类决策和种子词更新两个模块。在前者中，每个时间段内每个类别的概率阈值是自适应学习的，以确定文本的接受/拒绝。在后一种情况下，根据经验认证的无监督测量方法，在基础模型输出候选词的情况下，种子词通过带有即时奖励的强化学习被添加和删除。大量的实验表明，该方法具有很强的通用性，能够在分类精度和决策及时性之间取得比非连续对应方法更好的平衡，并能直观地解释种子词的更新。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=CL-WSTC:+Continual+Learning+for+Weakly+Supervised+Text+Classification+on+the+Internet)|0| +|[CL-WSTC: Continual Learning for Weakly Supervised Text Classification on the Internet](https://doi.org/10.1145/3543507.3583249)|Miaomiao Li, Jiaqi Zhu, Xin Yang, Yi Yang, Qiang Gao, Hongan Wang|Institute of Software, Chinese Academy of Sciences, China; Institute of Software, Chinese Academy of Sciences, China and University of Chinese Academy of Sciences, China; Southwestern University of Finance and Economics, China|Continual text classification is an important research direction in Web mining. Existing works are limited to supervised approaches relying on abundant labeled data, but in the open and dynamic environment of Internet, involving constant semantic change of known topics and the appearance of unknown topics, text annotations are hard to access in time for each period. That calls for the technique of weakly supervised text classification (WSTC), which requires just seed words for each category and has succeed in static text classification tasks. However, there are still no studies of applying WSTC methods in a continual learning paradigm to actually accommodate the open and evolving Internet. In this paper, we tackle this problem for the first time and propose a framework, named Continual Learning for Weakly Supervised Text Classification (CL-WSTC), which can take any WSTC method as base model. It consists of two modules, classification decision with delay and seed word updating. In the former, the probability threshold for each category in each period is adaptively learned to determine the acceptance/rejection of texts. In the latter, with candidate words output by the base model, seed words are added and deleted via reinforcement learning with immediate rewards, according to an empirically certified unsupervised measure. Extensive experiments show that our approach has strong universality and can achieve a better trade-off between classification accuracy and decision timeliness compared to non-continual counterparts, with intuitively interpretable updating of seed words.|连续文本分类是 Web 挖掘的一个重要研究方向。现有的著作仅限于依赖于大量标记数据的监督方法，但在互联网的开放动态环境中，涉及已知主题的语义不断变化和未知主题的出现，文本注释难以在每个时期及时获取。这就要求采用弱监督文本分类(WSTC)技术，对每个类别只需要种子词，并成功地完成了静态文本分类任务。然而，仍然没有研究应用 WSTC 方法在一个持续的学习范式，以实际适应开放和不断发展的互联网。本文首次提出了弱监督文本分类的持续学习框架(CL-WSTC) ，该框架可以采用任何 WSTC 方法作为基本模型。它包括延迟分类决策和种子词更新两个模块。在前者中，每个时间段内每个类别的概率阈值是自适应学习的，以确定文本的接受/拒绝。在后一种情况下，根据经验认证的无监督测量方法，在基础模型输出候选词的情况下，种子词通过带有即时奖励的强化学习被添加和删除。大量的实验表明，该方法具有很强的通用性，能够在分类精度和决策及时性之间取得比非连续对应方法更好的平衡，并能直观地解释种子词的更新。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=CL-WSTC:+Continual+Learning+for+Weakly+Supervised+Text+Classification+on+the+Internet)|0| |[Learning Robust Multi-Modal Representation for Multi-Label Emotion Recognition via Adversarial Masking and Perturbation](https://doi.org/10.1145/3543507.3583258)|Shiping Ge, Zhiwei Jiang, Zifeng Cheng, Cong Wang, Yafeng Yin, Qing Gu|State Key Laboratory for Novel Software Technology, Nanjing University, China|Recognizing emotions from multi-modal data is an emotion recognition task that requires strong multi-modal representation ability. The general approach to this task is to naturally train the representation model on training data without intervention. However, such natural training scheme is prone to modality bias of representation (i.e., tending to over-encode some informative modalities while neglecting other modalities) and data bias of training (i.e., tending to overfit training data). These biases may lead to instability (e.g., performing poorly when the neglected modality is dominant for recognition) and weak generalization (e.g., performing poorly when unseen data is inconsistent with overfitted data) of the model on unseen data. To address these problems, this paper presents two adversarial training strategies to learn more robust multi-modal representation for multi-label emotion recognition. Firstly, we propose an adversarial temporal masking strategy, which can enhance the encoding of other modalities by masking the most emotion-related temporal units (e.g., words for text or frames for video) of the informative modality. Secondly, we propose an adversarial parameter perturbation strategy, which can enhance the generalization of the model by adding the adversarial perturbation to the parameters of model. Both strategies boost model performance on the benchmark MMER datasets CMU-MOSEI and NEMu. Experimental results demonstrate the effectiveness of the proposed method compared with the previous state-of-the-art method. Code will be released at https://github.com/ShipingGe/MMER.|从多模态数据中识别情绪是一项需要很强的多模态表征能力的情绪识别任务。解决这一问题的一般方法是在不进行干预的情况下自然地对训练数据的表示模型进行训练。然而，这种自然的训练方案倾向于表征的模态偏差(即倾向于过度编码一些信息模式而忽略其他模式)和训练的数据偏差(即倾向于过度拟合训练数据)。这些偏差可能导致模型的不稳定性(例如，当被忽视的模式主要用于识别时表现不佳)和弱泛化(例如，当看不见的数据与过度拟合的数据不一致时表现不佳)。针对这些问题，本文提出了两种对抗性训练策略来学习多标签情绪识别的鲁棒性多模态表示。首先，本文提出了一种对抗性时间掩蔽策略，该策略通过掩蔽信息模式中与情绪最相关的时间单元(如文本中的词或视频中的帧)来增强其他模式的编码。其次，提出了一种对抗性参数摄动策略，通过在模型参数中加入对抗性摄动来提高模型的泛化能力。这两种策略都提高了基准 MMER 数据集 CMU-MOSEI 和 NEMu 的模型性能。实验结果表明，与已有的最新方法相比，本文提出的方法是有效的。密码将在 https://github.com/shipingge/mmer 公布。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Learning+Robust+Multi-Modal+Representation+for+Multi-Label+Emotion+Recognition+via+Adversarial+Masking+and+Perturbation)|0| -|[Continual Few-shot Learning with Transformer Adaptation and Knowledge Regularization](https://doi.org/10.1145/3543507.3583262)|Xin Wang, Yue Liu, Jiapei Fan, Weigao Wen, Hui Xue, Wenwu Zhu|Department of Computer Science and Technology, Tsinghua University, China; Alibaba Group, China|Continual few-shot learning, as a paradigm that simultaneously solves continual learning and few-shot learning, has become a challenging problem in machine learning. An eligible continual few-shot learning model is expected to distinguish all seen classes upon new categories arriving, where each category only includes very few labeled data. However, existing continual few-shot learning methods only consider the visual modality, where the distributions of new categories often indistinguishably overlap with old categories, thus resulting in the severe catastrophic forgetting problem. To tackle this problem, in this paper we study continual few-shot learning with the assistance of semantic knowledge by simultaneously taking both visual modality and semantic concepts of categories into account. We propose a Continual few-shot learning algorithm with Semantic knowledge Regularization (CoSR) for adapting to the distribution changes of visual prototypes through a Transformer-based prototype adaptation mechanism. Specifically, the original visual prototypes from the backbone are fed into the well-designed Transformer with corresponding semantic concepts, where the semantic concepts are extracted from all categories. The semantic-level regularization forces the categories with similar semantics to be closely distributed, while the opposite ones are constrained to be far away from each other. The semantic regularization improves the model’s ability to distinguish between new and old categories, thus significantly mitigating the catastrophic forgetting problem in continual few-shot learning. Extensive experiments on CIFAR100, miniImageNet, CUB200 and an industrial dataset with long-tail distribution demonstrate the advantages of our CoSR model compared with state-of-the-art methods.|连续少镜头学习作为一种同时解决连续学习和少镜头学习的范式，已经成为机器学习中一个具有挑战性的问题。一个符合条件的连续少镜头学习模型被期望在新类别到达时区分所有看到的类，其中每个类别只包含很少的标记数据。然而，现有的连续少镜头学习方法只考虑视觉模态，新类别的分布往往与旧类别重叠不明显，从而导致严重的灾难性遗忘问题。为了解决这一问题，本文同时考虑了视觉情态和范畴的语义概念，研究了在语义知识辅助下的连续少镜头学习。提出了一种基于语义知识正则化(CoSR)的连续少镜头学习算法，该算法通过一种基于变换器的原型自适应机制来适应视觉原型的分布变化。具体来说，来自主干的原始可视化原型被输入到设计良好的 Transformer 中，其中包含相应的语义概念，语义概念从所有类别中提取出来。语义层次的正则化迫使具有相似语义的范畴紧密分布，而相反的范畴被约束得彼此远离。语义正则化提高了模型区分新旧类别的能力，从而显著减轻了连续少镜头学习中的灾难性遗忘问题。在 CIFAR100，miniImageNet，CUB200和具有长尾分布的工业数据集上的广泛实验证明了我们的 CoSR 模型与最先进的方法相比的优势。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Continual+Few-shot+Learning+with+Transformer+Adaptation+and+Knowledge+Regularization)|0| -|[Open-World Social Event Classification](https://doi.org/10.1145/3543507.3583291)|Shengsheng Qian, Hong Chen, Dizhan Xue, Quan Fang, Changsheng Xu|Henan Institute of Advanced Technology, Zhengzhou University, China; Institute of Automation, Chinese Academy of Sciences; University of Chinese Academy of Sciences, China and Peng Cheng Laboratory, China; Institute of Automation, Chinese Academy of Sciences; University of Chinese Academy of Sciences, China|With the rapid development of Internet and the expanding scale of social media, social event classification has attracted increasing attention. The key to social event classification is effectively leveraging the visual and textual semantics for classification. However, most of the existing approaches may suffer from the following limitations: (1) Most of them just simply concatenate the image features and text features to get the multimodal features and ignore the fine-grained semantic relationship between modalities. (2) The majority of them hold the closed-world assumption that all classes in test are already seen in training, while this assumption can be easily broken in real-world applications. In practice, new events on Internet may not belong to any existing/seen class, and therefore cannot be correctly identified by closed-world learning algorithms. To tackle these challenges, we propose an Open-World Social Event Classifier (OWSEC) model in this paper. Firstly, we design a multimodal mask transformer network to capture cross-modal semantic relations and fuse fine-grained multimodal features of social events while masking redundant information. Secondly, we design an open-world classifier and propose a cross-modal event mixture mechanism with a novel open-world classification loss to capture the potential distribution space of the unseen class. Extensive experiments on two public datasets demonstrate the superiority of our proposed OWSEC model for open-world social event classification.|随着互联网的迅速发展和社会媒体规模的不断扩大，社会事件分类越来越受到人们的关注。社会事件分类的关键是有效地利用视觉语义和文本语义进行分类。然而，现有的方法大多存在以下局限性: (1)大多数方法只是简单地将图像特征和文本特征连接起来，得到多模态特征，而忽略了模态之间细粒度的语义关系。(2)他们大多数人持有的封闭世界的假设，所有的类在测试中已经看到在训练，而这一假设可以很容易地打破在现实世界的应用。在实践中，互联网上的新事件可能不属于任何现有的/可见的类，因此不能被封闭世界的学习算法正确识别。为了应对这些挑战，本文提出了一种开放世界社会事件分类器(OWSEC)模型。首先，我们设计了一个多模态掩模变压器网络来捕获跨模态的语义关系，融合社会事件的细粒度多模态特征，同时掩模冗余信息。其次，我们设计了一个开放世界分类器，并提出了一种具有新的开放世界分类损失的跨模态事件混合机制，以捕获看不见类的潜在分布空间。在两个公共数据集上的大量实验证明了我们提出的 OWSEC 模型在开放世界社会事件分类中的优越性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Open-World+Social+Event+Classification)|0| -|[KHAN: Knowledge-Aware Hierarchical Attention Networks for Accurate Political Stance Prediction](https://doi.org/10.1145/3543507.3583300)|YunYong Ko, Seongeun Ryu, Soeun Han, Youngseung Jeon, Jaehoon Kim, Sohyun Park, Kyungsik Han, Hanghang Tong, SangWook Kim|University of Illinois at Urbana-Champaign, USA; University of California, Los Angeles, USA; Ajou University, Republic of Korea; Hanyang University, Republic of Korea|The political stance prediction for news articles has been widely studied to mitigate the echo chamber effect -- people fall into their thoughts and reinforce their pre-existing beliefs. The previous works for the political stance problem focus on (1) identifying political factors that could reflect the political stance of a news article and (2) capturing those factors effectively. Despite their empirical successes, they are not sufficiently justified in terms of how effective their identified factors are in the political stance prediction. Motivated by this, in this work, we conduct a user study to investigate important factors in political stance prediction, and observe that the context and tone of a news article (implicit) and external knowledge for real-world entities appearing in the article (explicit) are important in determining its political stance. Based on this observation, we propose a novel knowledge-aware approach to political stance prediction (KHAN), employing (1) hierarchical attention networks (HAN) to learn the relationships among words and sentences in three different levels and (2) knowledge encoding (KE) to incorporate external knowledge for real-world entities into the process of political stance prediction. Also, to take into account the subtle and important difference between opposite political stances, we build two independent political knowledge graphs (KG) (i.e., KG-lib and KG-con) by ourselves and learn to fuse the different political knowledge. Through extensive evaluations on three real-world datasets, we demonstrate the superiority of DASH in terms of (1) accuracy, (2) efficiency, and (3) effectiveness.|新闻文章的政治立场预测已被广泛研究，以减轻回声室效应——人们陷入他们的思想和加强他们先前存在的信念。以往关于政治立场问题的研究主要集中在: (1)识别能够反映新闻文章政治立场的政治因素; (2)有效地捕捉这些因素。尽管它们在实践中取得了成功，但就其所确定的因素在预测政治立场方面的有效性而言，它们没有得到充分的证明。在此基础上，本文进行了一项用户研究，考察了政治立场预测中的重要因素，发现新闻文章的语境和语气(隐含)以及文章中出现的现实世界实体的外部知识(显性)对于确定其政治立场非常重要。在此基础上，我们提出了一种新的知识感知的政治立场预测方法(KHAN) ，该方法采用(1)分层注意网络(HAN)来学习三个不同层次的词语和句子之间的关系，(2)知识编码(KE)将现实世界实体的外部知识融入政治立场预测过程。同时，为了考虑对立政治立场之间微妙而重要的差异，我们自己构建了两个独立的政治知识图(即 KG-lib 和 KG-con) ，并学会了融合不同的政治知识。通过对三个实际数据集的广泛评估，我们证明了 DASH 在(1)准确性、(2)效率和(3)有效性方面的优越性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=KHAN:+Knowledge-Aware+Hierarchical+Attention+Networks+for+Accurate+Political+Stance+Prediction)|0| -|[Improving (Dis)agreement Detection with Inductive Social Relation Information From Comment-Reply Interactions](https://doi.org/10.1145/3543507.3583314)|Yun Luo, Zihan Liu, Stan Z. Li, Yue Zhang|Westlake university, China; Westlake University, China|(Dis)agreement detection aims to identify the authors' attitudes or positions (\textit{{agree, disagree, neutral}}) towards a specific text. It is limited for existing methods merely using textual information for identifying (dis)agreements, especially for cross-domain settings. Social relation information can play an assistant role in the (dis)agreement task besides textual information. We propose a novel method to extract such relation information from (dis)agreement data into an inductive social relation graph, merely using the comment-reply pairs without any additional platform-specific information. The inductive social relation globally considers the historical discussion and the relation between authors. Textual information based on a pre-trained language model and social relation information encoded by pre-trained RGCN are jointly considered for (dis)agreement detection. Experimental results show that our model achieves state-of-the-art performance for both the in-domain and cross-domain tasks on the benchmark -- DEBAGREEMENT. We find social relations can boost the performance of the (dis)agreement detection model, especially for the long-token comment-reply pairs, demonstrating the effectiveness of the social relation graph. We also explore the effect of the knowledge graph embedding methods, the information fusing method, and the time interval in constructing the social relation graph, which shows the effectiveness of our model.|协议检测的目的是识别作者对特定文本的态度或立场(文本{{同意，不同意，中立}})。对于仅仅使用文本信息来识别(dis)协议的现有方法，尤其是对于跨域设置，它是有限的。除了文本信息外，社会关系信息还可以在异议任务中发挥辅助作用。我们提出了一种新的方法来提取这种关系信息从(的)协议数据到归纳的社会关系图，只使用评论-回复对没有任何额外的平台特定的信息。归纳社会关系从整体上考虑历史讨论和作者之间的关系。将基于预训练语言模型的文本信息和预训练 RGCN 编码的社会关系信息联合起来进行异议检测。实验结果表明，该模型在基准 DEBAGREEMENT 上实现了领域内和跨领域任务的最佳性能。我们发现社会关系可以提高异议检测模型的性能，特别是对于长令牌的评论-回复对，证明了社会关系图的有效性。探讨了知识图嵌入方法、信息融合方法和时间间隔对构建社会关系图的影响，表明了该模型的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Improving+(Dis)agreement+Detection+with+Inductive+Social+Relation+Information+From+Comment-Reply+Interactions)|0| +|[Continual Few-shot Learning with Transformer Adaptation and Knowledge Regularization](https://doi.org/10.1145/3543507.3583262)|Xin Wang, Yue Liu, Jiapei Fan, Weigao Wen, Hui Xue, Wenwu Zhu|Alibaba Group, China; Department of Computer Science and Technology, Tsinghua University, China|Continual few-shot learning, as a paradigm that simultaneously solves continual learning and few-shot learning, has become a challenging problem in machine learning. An eligible continual few-shot learning model is expected to distinguish all seen classes upon new categories arriving, where each category only includes very few labeled data. However, existing continual few-shot learning methods only consider the visual modality, where the distributions of new categories often indistinguishably overlap with old categories, thus resulting in the severe catastrophic forgetting problem. To tackle this problem, in this paper we study continual few-shot learning with the assistance of semantic knowledge by simultaneously taking both visual modality and semantic concepts of categories into account. We propose a Continual few-shot learning algorithm with Semantic knowledge Regularization (CoSR) for adapting to the distribution changes of visual prototypes through a Transformer-based prototype adaptation mechanism. Specifically, the original visual prototypes from the backbone are fed into the well-designed Transformer with corresponding semantic concepts, where the semantic concepts are extracted from all categories. The semantic-level regularization forces the categories with similar semantics to be closely distributed, while the opposite ones are constrained to be far away from each other. The semantic regularization improves the model’s ability to distinguish between new and old categories, thus significantly mitigating the catastrophic forgetting problem in continual few-shot learning. Extensive experiments on CIFAR100, miniImageNet, CUB200 and an industrial dataset with long-tail distribution demonstrate the advantages of our CoSR model compared with state-of-the-art methods.|连续少镜头学习作为一种同时解决连续学习和少镜头学习的范式，已经成为机器学习中一个具有挑战性的问题。一个符合条件的连续少镜头学习模型被期望在新类别到达时区分所有看到的类，其中每个类别只包含很少的标记数据。然而，现有的连续少镜头学习方法只考虑视觉模态，新类别的分布往往与旧类别重叠不明显，从而导致严重的灾难性遗忘问题。为了解决这一问题，本文同时考虑了视觉情态和范畴的语义概念，研究了在语义知识辅助下的连续少镜头学习。提出了一种基于语义知识正则化(CoSR)的连续少镜头学习算法，该算法通过一种基于变换器的原型自适应机制来适应视觉原型的分布变化。具体来说，来自主干的原始可视化原型被输入到设计良好的 Transformer 中，其中包含相应的语义概念，语义概念从所有类别中提取出来。语义层次的正则化迫使具有相似语义的范畴紧密分布，而相反的范畴被约束得彼此远离。语义正则化提高了模型区分新旧类别的能力，从而显著减轻了连续少镜头学习中的灾难性遗忘问题。在 CIFAR100，miniImageNet，CUB200和具有长尾分布的工业数据集上的广泛实验证明了我们的 CoSR 模型与最先进的方法相比的优势。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Continual+Few-shot+Learning+with+Transformer+Adaptation+and+Knowledge+Regularization)|0| +|[Open-World Social Event Classification](https://doi.org/10.1145/3543507.3583291)|Shengsheng Qian, Hong Chen, Dizhan Xue, Quan Fang, Changsheng Xu|Institute of Automation, Chinese Academy of Sciences; University of Chinese Academy of Sciences, China; Henan Institute of Advanced Technology, Zhengzhou University, China; Institute of Automation, Chinese Academy of Sciences; University of Chinese Academy of Sciences, China and Peng Cheng Laboratory, China|With the rapid development of Internet and the expanding scale of social media, social event classification has attracted increasing attention. The key to social event classification is effectively leveraging the visual and textual semantics for classification. However, most of the existing approaches may suffer from the following limitations: (1) Most of them just simply concatenate the image features and text features to get the multimodal features and ignore the fine-grained semantic relationship between modalities. (2) The majority of them hold the closed-world assumption that all classes in test are already seen in training, while this assumption can be easily broken in real-world applications. In practice, new events on Internet may not belong to any existing/seen class, and therefore cannot be correctly identified by closed-world learning algorithms. To tackle these challenges, we propose an Open-World Social Event Classifier (OWSEC) model in this paper. Firstly, we design a multimodal mask transformer network to capture cross-modal semantic relations and fuse fine-grained multimodal features of social events while masking redundant information. Secondly, we design an open-world classifier and propose a cross-modal event mixture mechanism with a novel open-world classification loss to capture the potential distribution space of the unseen class. Extensive experiments on two public datasets demonstrate the superiority of our proposed OWSEC model for open-world social event classification.|随着互联网的迅速发展和社会媒体规模的不断扩大，社会事件分类越来越受到人们的关注。社会事件分类的关键是有效地利用视觉语义和文本语义进行分类。然而，现有的方法大多存在以下局限性: (1)大多数方法只是简单地将图像特征和文本特征连接起来，得到多模态特征，而忽略了模态之间细粒度的语义关系。(2)他们大多数人持有的封闭世界的假设，所有的类在测试中已经看到在训练，而这一假设可以很容易地打破在现实世界的应用。在实践中，互联网上的新事件可能不属于任何现有的/可见的类，因此不能被封闭世界的学习算法正确识别。为了应对这些挑战，本文提出了一种开放世界社会事件分类器(OWSEC)模型。首先，我们设计了一个多模态掩模变压器网络来捕获跨模态的语义关系，融合社会事件的细粒度多模态特征，同时掩模冗余信息。其次，我们设计了一个开放世界分类器，并提出了一种具有新的开放世界分类损失的跨模态事件混合机制，以捕获看不见类的潜在分布空间。在两个公共数据集上的大量实验证明了我们提出的 OWSEC 模型在开放世界社会事件分类中的优越性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Open-World+Social+Event+Classification)|0| +|[KHAN: Knowledge-Aware Hierarchical Attention Networks for Accurate Political Stance Prediction](https://doi.org/10.1145/3543507.3583300)|YunYong Ko, Seongeun Ryu, Soeun Han, Youngseung Jeon, Jaehoon Kim, Sohyun Park, Kyungsik Han, Hanghang Tong, SangWook Kim|Hanyang University, Republic of Korea; University of Illinois at Urbana-Champaign, USA; Ajou University, Republic of Korea; University of California, Los Angeles, USA|The political stance prediction for news articles has been widely studied to mitigate the echo chamber effect -- people fall into their thoughts and reinforce their pre-existing beliefs. The previous works for the political stance problem focus on (1) identifying political factors that could reflect the political stance of a news article and (2) capturing those factors effectively. Despite their empirical successes, they are not sufficiently justified in terms of how effective their identified factors are in the political stance prediction. Motivated by this, in this work, we conduct a user study to investigate important factors in political stance prediction, and observe that the context and tone of a news article (implicit) and external knowledge for real-world entities appearing in the article (explicit) are important in determining its political stance. Based on this observation, we propose a novel knowledge-aware approach to political stance prediction (KHAN), employing (1) hierarchical attention networks (HAN) to learn the relationships among words and sentences in three different levels and (2) knowledge encoding (KE) to incorporate external knowledge for real-world entities into the process of political stance prediction. Also, to take into account the subtle and important difference between opposite political stances, we build two independent political knowledge graphs (KG) (i.e., KG-lib and KG-con) by ourselves and learn to fuse the different political knowledge. Through extensive evaluations on three real-world datasets, we demonstrate the superiority of DASH in terms of (1) accuracy, (2) efficiency, and (3) effectiveness.|新闻文章的政治立场预测已被广泛研究，以减轻回声室效应——人们陷入他们的思想和加强他们先前存在的信念。以往关于政治立场问题的研究主要集中在: (1)识别能够反映新闻文章政治立场的政治因素; (2)有效地捕捉这些因素。尽管它们在实践中取得了成功，但就其所确定的因素在预测政治立场方面的有效性而言，它们没有得到充分的证明。在此基础上，本文进行了一项用户研究，考察了政治立场预测中的重要因素，发现新闻文章的语境和语气(隐含)以及文章中出现的现实世界实体的外部知识(显性)对于确定其政治立场非常重要。在此基础上，我们提出了一种新的知识感知的政治立场预测方法(KHAN) ，该方法采用(1)分层注意网络(HAN)来学习三个不同层次的词语和句子之间的关系，(2)知识编码(KE)将现实世界实体的外部知识融入政治立场预测过程。同时，为了考虑对立政治立场之间微妙而重要的差异，我们自己构建了两个独立的政治知识图(即 KG-lib 和 KG-con) ，并学会了融合不同的政治知识。通过对三个实际数据集的广泛评估，我们证明了 DASH 在(1)准确性、(2)效率和(3)有效性方面的优越性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=KHAN:+Knowledge-Aware+Hierarchical+Attention+Networks+for+Accurate+Political+Stance+Prediction)|0| +|[Improving (Dis)agreement Detection with Inductive Social Relation Information From Comment-Reply Interactions](https://doi.org/10.1145/3543507.3583314)|Yun Luo, Zihan Liu, Stan Z. Li, Yue Zhang|Westlake University, China; Westlake university, China|(Dis)agreement detection aims to identify the authors' attitudes or positions (\textit{{agree, disagree, neutral}}) towards a specific text. It is limited for existing methods merely using textual information for identifying (dis)agreements, especially for cross-domain settings. Social relation information can play an assistant role in the (dis)agreement task besides textual information. We propose a novel method to extract such relation information from (dis)agreement data into an inductive social relation graph, merely using the comment-reply pairs without any additional platform-specific information. The inductive social relation globally considers the historical discussion and the relation between authors. Textual information based on a pre-trained language model and social relation information encoded by pre-trained RGCN are jointly considered for (dis)agreement detection. Experimental results show that our model achieves state-of-the-art performance for both the in-domain and cross-domain tasks on the benchmark -- DEBAGREEMENT. We find social relations can boost the performance of the (dis)agreement detection model, especially for the long-token comment-reply pairs, demonstrating the effectiveness of the social relation graph. We also explore the effect of the knowledge graph embedding methods, the information fusing method, and the time interval in constructing the social relation graph, which shows the effectiveness of our model.|协议检测的目的是识别作者对特定文本的态度或立场(文本{{同意，不同意，中立}})。对于仅仅使用文本信息来识别(dis)协议的现有方法，尤其是对于跨域设置，它是有限的。除了文本信息外，社会关系信息还可以在异议任务中发挥辅助作用。我们提出了一种新的方法来提取这种关系信息从(的)协议数据到归纳的社会关系图，只使用评论-回复对没有任何额外的平台特定的信息。归纳社会关系从整体上考虑历史讨论和作者之间的关系。将基于预训练语言模型的文本信息和预训练 RGCN 编码的社会关系信息联合起来进行异议检测。实验结果表明，该模型在基准 DEBAGREEMENT 上实现了领域内和跨领域任务的最佳性能。我们发现社会关系可以提高异议检测模型的性能，特别是对于长令牌的评论-回复对，证明了社会关系图的有效性。探讨了知识图嵌入方法、信息融合方法和时间间隔对构建社会关系图的影响，表明了该模型的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Improving+(Dis)agreement+Detection+with+Inductive+Social+Relation+Information+From+Comment-Reply+Interactions)|0| |[Dynalogue: A Transformer-Based Dialogue System with Dynamic Attention](https://doi.org/10.1145/3543507.3583330)|Rongjunchen Zhang, Tingmin Wu, Xiao Chen, Sheng Wen, Surya Nepal, Cécile Paris, Yang Xiang|Monash University, Australia; CSIRO's Data61, Australia; Swinburne University of Technology, Australia|Businesses face a range of cyber risks, both external threats and internal vulnerabilities that continue to evolve over time. As cyber attacks continue to increase in complexity and sophistication, more organisations will experience them. For this reason, it is important that organisations seek timely consultancy from cyber professionals so that they can respond to and recover from cyber attacks as quickly as possible. However, huge surges in cyber attacks have long left cyber professionals short of what is required to cover the security needs. This problem is getting worse when an increasing number of people choose to work from home during the pandemic because this situation usually yields extra communication cost. In this paper, we propose to develop a cybersecurity-oriented dialogue system, called Dynalogue1, which can provide consultancy online as a cyber professional. For the first time, Dynalogue provides a promising solution to mitigate the need for cyber professionals via automatically generating problem-targeted conversions to victims of cyber attacks. In spite of many dialogue systems developed in the past, Dynalogue provides a distinct capability of handling long and complicated sentences that are common in cybersecurity-related conversations. It is challenging to have this capability because limited memory in dialogue systems can be hard to accommodate sufficient key information of long sentences. To overcome this challenge, Dynalogue utilises an attention mechanism that dynamically captures key semantics within a sentence instead of using fix window to cut off the sentence. To evaluate Dynalogue, we collect 67K real-world conversations (0.6M utterances) from Bleeping Computer2, which is one of the most popular cybersecurity consultancy websites in the world. The results suggest that Dynalogue outperforms all the existing dialogue systems with 1% ∼ 9% improvements on all different metrics. We further run Dynalogue on the public dataset WikiHow to validate its compatibility in other domains where conversations are also long and complicated. Dynalogue also outperforms all the other methods with at most 2.4% improvement.|企业面临一系列网络风险，包括外部威胁和内部漏洞，这些风险将随着时间的推移不断演变。随着网络攻击的复杂性和复杂程度不断提高，将有更多组织经历这些攻击。基于这个原因，机构必须及时向网络专业人士寻求咨询，以便他们能够尽快应对和恢复网络攻击。然而，长期以来，网络攻击的激增使得网络专业人士无法满足安全需求。当越来越多的人在大流行期间选择在家工作时，这个问题变得更加严重，因为这种情况通常会产生额外的通信费用。在本文中，我们建议开发一个面向网络安全的对话系统，称为 Dynalogue1，它可以作为一个网络专业人员提供在线咨询。Dynalog 首次提供了一个有前途的解决方案，通过自动生成针对问题的转换，减轻对网络专业人员的需求，转换为网络攻击的受害者。尽管过去开发了许多对话系统，Dynalog 提供了一种独特的能力，可以处理与网络安全相关的对话中常见的长而复杂的句子。具备这种能力是具有挑战性的，因为在对话系统中，有限的记忆可能难以容纳足够的长句关键信息。为了克服这个挑战，Dynalog 使用了一种注意机制，动态捕捉句子中的关键语义，而不是使用修复窗口来切断句子。为了评估 Dynalog，我们收集了来自 Bleping Computer2(世界上最受欢迎的网络安全咨询网站之一)的67K 真实世界的对话(0.6 M 的话语)。实验结果表明，Dynalog 的性能优于现有的所有对话系统，在所有不同指标上的性能提高了1% -9% 。我们进一步在公共数据集 WikiHow 上运行 Dynalog，以验证其在其他领域的兼容性，因为这些领域的会话也是冗长而复杂的。动态模拟也优于所有其他方法，最多有2.4% 的改进。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Dynalogue:+A+Transformer-Based+Dialogue+System+with+Dynamic+Attention)|0| |[Active Learning from the Web](https://doi.org/10.1145/3543507.3583346)|Ryoma Sato|Kyoto University, Japan and RIKEN AIP, Japan|Labeling data is one of the most costly processes in machine learning pipelines. Active learning is a standard approach to alleviating this problem. Pool-based active learning first builds a pool of unlabelled data and iteratively selects data to be labeled so that the total number of required labels is minimized, keeping the model performance high. Many effective criteria for choosing data from the pool have been proposed in the literature. However, how to build the pool is less explored. Specifically, most of the methods assume that a task-specific pool is given for free. In this paper, we advocate that such a task-specific pool is not always available and propose the use of a myriad of unlabelled data on the Web for the pool for which active learning is applied. As the pool is extremely large, it is likely that relevant data exist in the pool for many tasks, and we do not need to explicitly design and build the pool for each task. The challenge is that we cannot compute the acquisition scores of all data exhaustively due to the size of the pool. We propose an efficient method, Seafaring, to retrieve informative data in terms of active learning from the Web using a user-side information retrieval algorithm. In the experiments, we use the online Flickr environment as the pool for active learning. This pool contains more than ten billion images and is several orders of magnitude larger than the existing pools in the literature for active learning. We confirm that our method performs better than existing approaches of using a small unlabelled pool.|标记数据是机器学习管道中最昂贵的过程之一。主动学习是缓解这一问题的标准方法。基于池的主动学习首先构建一个未标记数据池，并迭代地选择要标记的数据，以使所需标记的总数最小化，从而保持模型的高性能。文献中已经提出了许多有效的数据选择标准。但是，如何构建池的探索较少。具体来说，大多数方法都假设特定于任务的池是免费提供的。在本文中，我们主张这样一个特定于任务的池并不总是可用的，并建议在 Web 上使用大量未标记的数据作为应用主动学习的池。由于池非常大，因此很可能存在许多任务的相关数据，我们不需要为每个任务显式设计和构建池。挑战在于，由于池的大小，我们无法详尽地计算所有数据的获取分数。我们提出了一种有效的方法，Seafaring，通过使用用户端的信息检索算法从网络中检索主动学习方面的信息数据。在实验中，我们使用在线 Flickr 环境作为主动学习的池。这个数量级包含超过一百亿张图片，比文献中现有的主动学习图片库还要大好几倍。我们确认我们的方法比使用一个小的未标记池的现有方法性能更好。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Active+Learning+from+the+Web)|0| |[The Effect of Metadata on Scientific Literature Tagging: A Cross-Field Cross-Model Study](https://doi.org/10.1145/3543507.3583354)|Yu Zhang, Bowen Jin, Qi Zhu, Yu Meng, Jiawei Han|University of Illinois at Urbana-Champaign, USA|Due to the exponential growth of scientific publications on the Web, there is a pressing need to tag each paper with fine-grained topics so that researchers can track their interested fields of study rather than drowning in the whole literature. Scientific literature tagging is beyond a pure multi-label text classification task because papers on the Web are prevalently accompanied by metadata information such as venues, authors, and references, which may serve as additional signals to infer relevant tags. Although there have been studies making use of metadata in academic paper classification, their focus is often restricted to one or two scientific fields (e.g., computer science and biomedicine) and to one specific model. In this work, we systematically study the effect of metadata on scientific literature tagging across 19 fields. We select three representative multi-label classifiers (i.e., a bag-of-words model, a sequence-based model, and a pre-trained language model) and explore their performance change in scientific literature tagging when metadata are fed to the classifiers as additional features. We observe some ubiquitous patterns of metadata's effects across all fields (e.g., venues are consistently beneficial to paper tagging in almost all cases), as well as some unique patterns in fields other than computer science and biomedicine, which are not explored in previous studies.|由于互联网上科学出版物的指数增长，迫切需要给每篇论文贴上细致的标签，这样研究人员就可以跟踪他们感兴趣的研究领域，而不是淹没在整篇文献中。科学文献标注不仅仅是一个纯粹的多标签文本分类任务，因为网络上的文献通常伴随着元数据信息，如地点、作者和参考文献，这些元数据信息可以作为推断相关标签的附加信号。尽管在学术论文分类中使用元数据的研究已经出现，但是它们的重点往往局限于一个或两个科学领域(例如，计算机科学和生物医学)和一个特定的模型。本文系统地研究了元数据对19个领域的科技文献标注的影响。我们选择了三个有代表性的多标签分类器(即，一个词袋模型，一个基于序列的模型和一个预先训练的语言模型) ，并探讨了当元数据作为额外特征提供给分类器时，它们在科学文献标签中的性能变化。我们观察到在所有领域中元数据影响的一些无处不在的模式(例如，场所在几乎所有情况下都始终有利于纸张标签) ，以及在计算机科学和生物医学以外的领域中的一些独特模式，这在以前的研究中没有探索。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=The+Effect+of+Metadata+on+Scientific+Literature+Tagging:+A+Cross-Field+Cross-Model+Study)|0| |["Why is this misleading?": Detecting News Headline Hallucinations with Explanations](https://doi.org/10.1145/3543507.3583375)|Jiaming Shen, Jialu Liu, Daniel Finnie, Negar Rahmati, Mike Bendersky, Marc Najork|Google, USA|Automatic headline generation enables users to comprehend ongoing news events promptly and has recently become an important task in web mining and natural language processing. With the growing need for news headline generation, we argue that the hallucination issue, namely the generated headlines being not supported by the original news stories, is a critical challenge for the deployment of this feature in web-scale systems Meanwhile, due to the infrequency of hallucination cases and the requirement of careful reading for raters to reach the correct consensus, it is difficult to acquire a large dataset for training a model to detect such hallucinations through human curation. In this work, we present a new framework named ExHalder to address this challenge for headline hallucination detection. ExHalder adapts the knowledge from public natural language inference datasets into the news domain and learns to generate natural language sentences to explain the hallucination detection results. To evaluate the model performance, we carefully collect a dataset with more than six thousand labeled pairs. Extensive experiments on this dataset and another six public ones demonstrate that ExHalder can identify hallucinated headlines accurately and justifies its predictions with human-readable natural language explanations.|自动生成标题使用户能够迅速理解正在进行的新闻事件，近年来已成为网络挖掘和自然语言处理中的一项重要任务。随着对新闻标题生成的需求日益增长，我们认为，幻觉问题，即生成的标题没有得到原始新闻故事的支持，是在网络规模系统中部署这一功能的一个关键挑战。同时，由于幻觉案例的频率很低，而且评估者需要仔细阅读才能达成正确的共识，因此很难获得一个大型数据集来训练模型，通过人类管理来检测这种幻觉。在这项工作中，我们提出了一个新的框架名为 ExHalder，以解决这一挑战的标题幻觉检测。ExHalder 将来自公共自然语言推理数据集的知识应用到新闻领域，并学习生成自然语言句子来解释幻觉检测结果。为了评估模型的性能，我们仔细地收集了超过六千个标有“文章，标题 > 对”的数据集。在这个数据集和另外六个公共数据集上的大量实验表明，ExHalder 可以准确地识别出幻觉标题，并用人类可读的自然语言解释来证明其预测的正确性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q="Why+is+this+misleading?":+Detecting+News+Headline+Hallucinations+with+Explanations)|0| -|[DIWIFT: Discovering Instance-wise Influential Features for Tabular Data](https://doi.org/10.1145/3543507.3583382)|Dugang Liu, Pengxiang Cheng, Hong Zhu, Xing Tang, Yanyu Chen, Xiaoting Wang, Weike Pan, Zhong Ming, Xiuqiang He|College of Computer Science and Software Engineering, Shenzhen University, China; Shenzhen University, China; Tsinghua-Berkeley Shenzhen Institute, China; Huawei Technologies Co Ltd, China; FIT, Tencent, China; Fit, Tencent, China|Tabular data is one of the most common data storage formats behind many real-world web applications such as retail, banking, and e-commerce. The success of these web applications largely depends on the ability of the employed machine learning model to accurately distinguish influential features from all the predetermined features in tabular data. Intuitively, in practical business scenarios, different instances should correspond to different sets of influential features, and the set of influential features of the same instance may vary in different scenarios. However, most existing methods focus on global feature selection assuming that all instances have the same set of influential features, and few methods considering instance-wise feature selection ignore the variability of influential features in different scenarios. In this paper, we first introduce a new perspective based on the influence function for instance-wise feature selection, and give some corresponding theoretical insights, the core of which is to use the influence function as an indicator to measure the importance of an instance-wise feature. We then propose a new solution for discovering instance-wise influential features in tabular data (DIWIFT), where a self-attention network is used as a feature selection model and the value of the corresponding influence function is used as an optimization objective to guide the model. Benefiting from the advantage of the influence function, i.e., its computation does not depend on a specific architecture and can also take into account the data distribution in different scenarios, our DIWIFT has better flexibility and robustness. Finally, we conduct extensive experiments on both synthetic and real-world datasets to validate the effectiveness of our DIWIFT.|表格数据是许多真实网络应用程序(如零售、银行和电子商务)背后最常见的数据存储格式之一。这些 Web 应用程序的成功很大程度上取决于所使用的机器学习模型能够准确地区分表格数据中所有预先确定的特征和有影响的特征。直观地说，在实际的业务场景中，不同的实例应该对应于不同的影响特性集，同一实例的影响特性集在不同的场景中可能会有所不同。然而，现有的方法大多集中于全局特征选择，假设所有实例具有相同的影响特征集，很少有方法考虑实例特征选择忽略了不同场景中影响特征的可变性。本文首先介绍了基于影响函数的实例特征选择的新视角，并给出了相应的理论见解，其核心是利用影响函数作为指标来衡量实例特征的重要性。然后提出了一种新的表格数据实例影响特征发现方法(DIWIFT) ，该方法采用自注意网络作为特征选择模型，并以相应影响函数的值作为优化目标来指导模型。利用影响函数的优点，即它的计算不依赖于特定的体系结构，也可以考虑不同场景中的数据分布，我们的 DIWIFT 具有更好的灵活性和鲁棒性。最后，我们在合成数据集和真实数据集上进行了广泛的实验，以验证我们的 DIWIFT 算法的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=DIWIFT:+Discovering+Instance-wise+Influential+Features+for+Tabular+Data)|0| -|[XWikiGen: Cross-lingual Summarization for Encyclopedic Text Generation in Low Resource Languages](https://doi.org/10.1145/3543507.3583405)|Dhaval Taunk, Shivprasad Sagare, Anupam Patil, Shivansh Subramanian, Manish Gupta, Vasudeva Varma|International Institute of Information Technology, Hyderabad, India and Microsoft India, India; SCTR's Pune Institute of Computer Technology, India; International Institute of Information Technology, Hyderabad, India|Lack of encyclopedic text contributors, especially on Wikipedia, makes automated text generation for low resource (LR) languages a critical problem. Existing work on Wikipedia text generation has focused on English only where English reference articles are summarized to generate English Wikipedia pages. But, for low-resource languages, the scarcity of reference articles makes monolingual summarization ineffective in solving this problem. Hence, in this work, we propose XWikiGen, which is the task of cross-lingual multi-document summarization of text from multiple reference articles, written in various languages, to generate Wikipedia-style text. Accordingly, we contribute a benchmark dataset, XWikiRef, spanning ~69K Wikipedia articles covering five domains and eight languages. We harness this dataset to train a two-stage system where the input is a set of citations and a section title and the output is a section-specific LR summary. The proposed system is based on a novel idea of neural unsupervised extractive summarization to coarsely identify salient information followed by a neural abstractive model to generate the section-specific text. Extensive experiments show that multi-domain training is better than the multi-lingual setup on average.|缺乏百科全书式的文本贡献者，特别是在 Wikipedia 上，使得低资源(LR)语言的自动文本生成成为一个关键问题。现有的维基百科文本生成工作主要集中在英文方面，即将英文参考文章进行总结以生成英文维基百科页面。但是，对于资源匮乏的语言来说，参考文献的稀缺性使得单语文摘无法有效地解决这一问题。因此，在这项工作中，我们提出 XWikiGen，这是一个跨语言的多文档摘要文本的任务，从多个参考文章，写在不同的语言，生成维基百科风格的文本。因此，我们提供了一个基准数据集，XWikiRef，涵盖了5个域和8种语言的69K 维基百科文章。我们利用这个数据集来训练一个两阶段的系统，其中输入是一组引文和一个章节标题，输出是一个章节特定的 LR 摘要。该系统基于神经元无监督提取概括的新思想，对显著信息进行粗略识别，然后利用神经元抽象模型生成特定部分的文本。大量实验表明，多领域训练平均优于多语种训练。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=XWikiGen:+Cross-lingual+Summarization+for+Encyclopedic+Text+Generation+in+Low+Resource+Languages)|0| +|[DIWIFT: Discovering Instance-wise Influential Features for Tabular Data](https://doi.org/10.1145/3543507.3583382)|Dugang Liu, Pengxiang Cheng, Hong Zhu, Xing Tang, Yanyu Chen, Xiaoting Wang, Weike Pan, Zhong Ming, Xiuqiang He|Tsinghua-Berkeley Shenzhen Institute, China; College of Computer Science and Software Engineering, Shenzhen University, China; Huawei Technologies Co Ltd, China; Fit, Tencent, China; FIT, Tencent, China; Shenzhen University, China|Tabular data is one of the most common data storage formats behind many real-world web applications such as retail, banking, and e-commerce. The success of these web applications largely depends on the ability of the employed machine learning model to accurately distinguish influential features from all the predetermined features in tabular data. Intuitively, in practical business scenarios, different instances should correspond to different sets of influential features, and the set of influential features of the same instance may vary in different scenarios. However, most existing methods focus on global feature selection assuming that all instances have the same set of influential features, and few methods considering instance-wise feature selection ignore the variability of influential features in different scenarios. In this paper, we first introduce a new perspective based on the influence function for instance-wise feature selection, and give some corresponding theoretical insights, the core of which is to use the influence function as an indicator to measure the importance of an instance-wise feature. We then propose a new solution for discovering instance-wise influential features in tabular data (DIWIFT), where a self-attention network is used as a feature selection model and the value of the corresponding influence function is used as an optimization objective to guide the model. Benefiting from the advantage of the influence function, i.e., its computation does not depend on a specific architecture and can also take into account the data distribution in different scenarios, our DIWIFT has better flexibility and robustness. Finally, we conduct extensive experiments on both synthetic and real-world datasets to validate the effectiveness of our DIWIFT.|表格数据是许多真实网络应用程序(如零售、银行和电子商务)背后最常见的数据存储格式之一。这些 Web 应用程序的成功很大程度上取决于所使用的机器学习模型能够准确地区分表格数据中所有预先确定的特征和有影响的特征。直观地说，在实际的业务场景中，不同的实例应该对应于不同的影响特性集，同一实例的影响特性集在不同的场景中可能会有所不同。然而，现有的方法大多集中于全局特征选择，假设所有实例具有相同的影响特征集，很少有方法考虑实例特征选择忽略了不同场景中影响特征的可变性。本文首先介绍了基于影响函数的实例特征选择的新视角，并给出了相应的理论见解，其核心是利用影响函数作为指标来衡量实例特征的重要性。然后提出了一种新的表格数据实例影响特征发现方法(DIWIFT) ，该方法采用自注意网络作为特征选择模型，并以相应影响函数的值作为优化目标来指导模型。利用影响函数的优点，即它的计算不依赖于特定的体系结构，也可以考虑不同场景中的数据分布，我们的 DIWIFT 具有更好的灵活性和鲁棒性。最后，我们在合成数据集和真实数据集上进行了广泛的实验，以验证我们的 DIWIFT 算法的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=DIWIFT:+Discovering+Instance-wise+Influential+Features+for+Tabular+Data)|0| +|[XWikiGen: Cross-lingual Summarization for Encyclopedic Text Generation in Low Resource Languages](https://doi.org/10.1145/3543507.3583405)|Dhaval Taunk, Shivprasad Sagare, Anupam Patil, Shivansh Subramanian, Manish Gupta, Vasudeva Varma|SCTR's Pune Institute of Computer Technology, India; International Institute of Information Technology, Hyderabad, India and Microsoft India, India; International Institute of Information Technology, Hyderabad, India|Lack of encyclopedic text contributors, especially on Wikipedia, makes automated text generation for low resource (LR) languages a critical problem. Existing work on Wikipedia text generation has focused on English only where English reference articles are summarized to generate English Wikipedia pages. But, for low-resource languages, the scarcity of reference articles makes monolingual summarization ineffective in solving this problem. Hence, in this work, we propose XWikiGen, which is the task of cross-lingual multi-document summarization of text from multiple reference articles, written in various languages, to generate Wikipedia-style text. Accordingly, we contribute a benchmark dataset, XWikiRef, spanning ~69K Wikipedia articles covering five domains and eight languages. We harness this dataset to train a two-stage system where the input is a set of citations and a section title and the output is a section-specific LR summary. The proposed system is based on a novel idea of neural unsupervised extractive summarization to coarsely identify salient information followed by a neural abstractive model to generate the section-specific text. Extensive experiments show that multi-domain training is better than the multi-lingual setup on average.|缺乏百科全书式的文本贡献者，特别是在 Wikipedia 上，使得低资源(LR)语言的自动文本生成成为一个关键问题。现有的维基百科文本生成工作主要集中在英文方面，即将英文参考文章进行总结以生成英文维基百科页面。但是，对于资源匮乏的语言来说，参考文献的稀缺性使得单语文摘无法有效地解决这一问题。因此，在这项工作中，我们提出 XWikiGen，这是一个跨语言的多文档摘要文本的任务，从多个参考文章，写在不同的语言，生成维基百科风格的文本。因此，我们提供了一个基准数据集，XWikiRef，涵盖了5个域和8种语言的69K 维基百科文章。我们利用这个数据集来训练一个两阶段的系统，其中输入是一组引文和一个章节标题，输出是一个章节特定的 LR 摘要。该系统基于神经元无监督提取概括的新思想，对显著信息进行粗略识别，然后利用神经元抽象模型生成特定部分的文本。大量实验表明，多领域训练平均优于多语种训练。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=XWikiGen:+Cross-lingual+Summarization+for+Encyclopedic+Text+Generation+in+Low+Resource+Languages)|0| |[Learning Structural Co-occurrences for Structured Web Data Extraction in Low-Resource Settings](https://doi.org/10.1145/3543507.3583387)|Zhenyu Zhang, Bowen Yu, Tingwen Liu, Tianyun Liu, Yubin Wang, Li Guo|Institute of Information Engineering, Chinese Academy of Sciences, China and School of Cyber Security, University of Chinese Academy of Sciences, China|Extracting structured information from all manner of webpages is an important problem with the potential to automate many real-world applications. Recent work has shown the effectiveness of leveraging DOM trees and pre-trained language models to describe and encode webpages. However, they typically optimize the model to learn the semantic co-occurrence of elements and labels in the same webpage, thus their effectiveness depends on sufficient labeled data, which is labor-intensive. In this paper, we further observe structural co-occurrences in different webpages of the same website: the same position in the DOM tree usually plays the same semantic role, and the DOM nodes in this position also share similar surface forms. Motivated by this, we propose a novel method, Structor, to effectively incorporate the structural co-occurrences over DOM tree and surface form into pre-trained language models. Such structural co-occurrences help the model learn the task better under low-resource settings, and we study two challenging experimental scenarios: website-level low-resource setting and webpage-level low-resource setting, to evaluate our approach. Extensive experiments on the public SWDE dataset show that Structor significantly outperforms the state-of-the-art models in both settings, and even achieves three times the performance of the strong baseline model in the case of extreme lack of training data.|从各种各样的网页中提取结构化信息是一个重要的问题，它有可能使许多现实世界的应用程序自动化。最近的工作已经显示了利用 DOM 树和预先训练的语言模型来描述和编码网页的有效性。然而，它们通常优化模型来学习同一网页中元素和标签的语义共现，因此它们的有效性依赖于足够的标签数据，这是劳动密集型的。在本文中，我们进一步观察了同一网站的不同网页在结构上的共现: 同一位置的 DOM 树通常扮演着相同的语义角色，而这个位置的 DOM 节点也具有相似的表面形式。受此启发，我们提出了一种新的方法，Structor，有效地将结构共现的 DOM 树和表面形式预先训练的语言模型。这种结构性共现有助于模型在低资源环境下更好地学习任务，我们研究了两个具有挑战性的实验场景: 网站级低资源环境和网页级低资源环境，以评估我们的方法。在公开的 SWDE 数据集上进行的大量实验表明，在这两种情况下，Structor 的性能都明显优于最先进的模型，甚至在极度缺乏训练数据的情况下，它的性能是强基线模型的三倍。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Learning+Structural+Co-occurrences+for+Structured+Web+Data+Extraction+in+Low-Resource+Settings)|0| -|[TMMDA: A New Token Mixup Multimodal Data Augmentation for Multimodal Sentiment Analysis](https://doi.org/10.1145/3543507.3583406)|Xianbing Zhao, Yixin Chen, Sicen Liu, Xuan Zang, Yang Xiang, Buzhou Tang|Peng Cheng Laboratory, Shenzhen, China, China; Harbin Institute of Technology (Shenzhen), China|Existing methods for Multimodal Sentiment Analysis (MSA) mainly focus on integrating multimodal data effectively on limited multimodal data. Learning more informative multimodal representation often relies on large-scale labeled datasets, which are difficult and unrealistic to obtain. To learn informative multimodal representation on limited labeled datasets as more as possible, we proposed TMMDA for MSA, a new Token Mixup Multimodal Data Augmentation, which first generates new virtual modalities from the mixed token-level representation of raw modalities, and then enhances the representation of raw modalities by utilizing the representation of the generated virtual modalities. To preserve semantics during virtual modality generation, we propose a novel cross-modal token mixup strategy based on the generative adversarial network. Extensive experiments on two benchmark datasets, i.e., CMU-MOSI and CMU-MOSEI, verify the superiority of our model compared with several state-of-the-art baselines. The code is available at https://github.com/xiaobaicaihhh/TMMDA.|现有的多模态情绪分析(MSA)方法主要集中在有限的多模态数据上有效地集成多模态数据。学习更多信息的多模态表示通常依赖于大规模的标记数据集，这是困难的和不现实的获得。为了尽可能多地学习有限标记数据集上的信息化多模态表示，我们提出了一种新的令牌混合多模态数据增强算法 MSA-TMMDA，该算法首先利用原始模态的混合令牌级表示生成新的虚拟模态，然后利用生成的虚拟模态的表示来增强原始模态的表示。为了在虚拟形态生成过程中保持语义，提出了一种新的基于生成对抗网络的跨模态令牌混合策略。通过对 CMU-MOSI 和 CMU-MOSEI 两个基准数据集的大量实验，验证了该模型相对于几个最先进的基准线的优越性。密码可在 https://github.com/xiaobaicaihhh/tmmda 查阅。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=TMMDA:+A+New+Token+Mixup+Multimodal+Data+Augmentation+for+Multimodal+Sentiment+Analysis)|0| -|[Node-wise Diffusion for Scalable Graph Learning](https://doi.org/10.1145/3543507.3583408)|Keke Huang, Jing Tang, Juncheng Liu, Renchi Yang, Xiaokui Xiao|National University of Singapore, Singapore; Hong Kong Baptist University, Hong Kong; The Hong Kong University of Science and Technology (Guangzhou), China and The Hong Kong Uni. of Sci. and Tech., Hong Kong; [email protected]|Graph Neural Networks (GNNs) have shown superior performance for semi-supervised learning of numerous web applications, such as classification on web services and pages, analysis of online social networks, and recommendation in e-commerce. The state of the art derives representations for all nodes in graphs following the same diffusion (message passing) model without discriminating their uniqueness. However, (i) labeled nodes involved in model training usually account for a small portion of graphs in the semi-supervised setting, and (ii) different nodes locate at different graph local contexts and it inevitably degrades the representation qualities if treating them undistinguishedly in diffusion. To address the above issues, we develop NDM, a universal node-wise diffusion model, to capture the unique characteristics of each node in diffusion, by which NDM is able to yield high-quality node representations. In what follows, we customize NDM for semi-supervised learning and design the NIGCN model. In particular, NIGCN advances the efficiency significantly since it (i) produces representations for labeled nodes only and (ii) adopts well-designed neighbor sampling techniques tailored for node representation generation. Extensive experimental results on various types of web datasets, including citation, social and co-purchasing graphs, not only verify the state-of-the-art effectiveness of NIGCN but also strongly support the remarkable scalability of NIGCN. In particular, NIGCN completes representation generation and training within 10 seconds on the dataset with hundreds of millions of nodes and billions of edges, up to orders of magnitude speedups over the baselines, while achieving the highest F1-scores on classification.|图形神经网络(GNN)在许多网络应用程序的半监督学习方面表现出卓越的性能，例如对网络服务和页面的分类、对在线社交网络的分析以及电子商务中的推荐。目前的技术状况是在不区分节点唯一性的前提下，推导出图中所有节点遵循相同扩散(消息传递)模型的表示形式。然而，(i)模型训练中的标记节点在半监督环境中通常只占图的一小部分，(ii)不同的节点位于不同的图局部上下文中，如果在扩散中不加区分地处理它们，则不可避免地会降低表示质量。为了解决上述问题，我们开发了 NDM，一个通用的节点扩散模型，以捕捉每个节点在扩散中的独特特征，通过 NDM 能够产生高质量的节点表示。接下来，我们为半监督学习定制了 NDM，并设计了 NIGCN 模型。特别地，NIGCN 显著提高了效率，因为它(i)仅产生标记节点的表示，(ii)采用了为节点表示生成量身定制的精心设计的邻居抽样技术。在各种类型的网络数据集(包括引用、社会和共同购买图表)上的广泛实验结果不仅验证了 NIGCN 的最新有效性，而且强烈支持 NIGCN 的显著可扩展性。特别是，NIGCN 在10秒内完成数据集上数亿个节点和数十亿条边的表示生成和训练，在基线上数量级加速，同时在分类上获得最高的 f 1分数。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Node-wise+Diffusion+for+Scalable+Graph+Learning)|0| +|[TMMDA: A New Token Mixup Multimodal Data Augmentation for Multimodal Sentiment Analysis](https://doi.org/10.1145/3543507.3583406)|Xianbing Zhao, Yixin Chen, Sicen Liu, Xuan Zang, Yang Xiang, Buzhou Tang|Harbin Institute of Technology (Shenzhen), China; Peng Cheng Laboratory, Shenzhen, China, China|Existing methods for Multimodal Sentiment Analysis (MSA) mainly focus on integrating multimodal data effectively on limited multimodal data. Learning more informative multimodal representation often relies on large-scale labeled datasets, which are difficult and unrealistic to obtain. To learn informative multimodal representation on limited labeled datasets as more as possible, we proposed TMMDA for MSA, a new Token Mixup Multimodal Data Augmentation, which first generates new virtual modalities from the mixed token-level representation of raw modalities, and then enhances the representation of raw modalities by utilizing the representation of the generated virtual modalities. To preserve semantics during virtual modality generation, we propose a novel cross-modal token mixup strategy based on the generative adversarial network. Extensive experiments on two benchmark datasets, i.e., CMU-MOSI and CMU-MOSEI, verify the superiority of our model compared with several state-of-the-art baselines. The code is available at https://github.com/xiaobaicaihhh/TMMDA.|现有的多模态情绪分析(MSA)方法主要集中在有限的多模态数据上有效地集成多模态数据。学习更多信息的多模态表示通常依赖于大规模的标记数据集，这是困难的和不现实的获得。为了尽可能多地学习有限标记数据集上的信息化多模态表示，我们提出了一种新的令牌混合多模态数据增强算法 MSA-TMMDA，该算法首先利用原始模态的混合令牌级表示生成新的虚拟模态，然后利用生成的虚拟模态的表示来增强原始模态的表示。为了在虚拟形态生成过程中保持语义，提出了一种新的基于生成对抗网络的跨模态令牌混合策略。通过对 CMU-MOSI 和 CMU-MOSEI 两个基准数据集的大量实验，验证了该模型相对于几个最先进的基准线的优越性。密码可在 https://github.com/xiaobaicaihhh/tmmda 查阅。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=TMMDA:+A+New+Token+Mixup+Multimodal+Data+Augmentation+for+Multimodal+Sentiment+Analysis)|0| +|[Node-wise Diffusion for Scalable Graph Learning](https://doi.org/10.1145/3543507.3583408)|Keke Huang, Jing Tang, Juncheng Liu, Renchi Yang, Xiaokui Xiao|[email protected]; Hong Kong Baptist University, Hong Kong; National University of Singapore, Singapore; The Hong Kong University of Science and Technology (Guangzhou), China and The Hong Kong Uni. of Sci. and Tech., Hong Kong|Graph Neural Networks (GNNs) have shown superior performance for semi-supervised learning of numerous web applications, such as classification on web services and pages, analysis of online social networks, and recommendation in e-commerce. The state of the art derives representations for all nodes in graphs following the same diffusion (message passing) model without discriminating their uniqueness. However, (i) labeled nodes involved in model training usually account for a small portion of graphs in the semi-supervised setting, and (ii) different nodes locate at different graph local contexts and it inevitably degrades the representation qualities if treating them undistinguishedly in diffusion. To address the above issues, we develop NDM, a universal node-wise diffusion model, to capture the unique characteristics of each node in diffusion, by which NDM is able to yield high-quality node representations. In what follows, we customize NDM for semi-supervised learning and design the NIGCN model. In particular, NIGCN advances the efficiency significantly since it (i) produces representations for labeled nodes only and (ii) adopts well-designed neighbor sampling techniques tailored for node representation generation. Extensive experimental results on various types of web datasets, including citation, social and co-purchasing graphs, not only verify the state-of-the-art effectiveness of NIGCN but also strongly support the remarkable scalability of NIGCN. In particular, NIGCN completes representation generation and training within 10 seconds on the dataset with hundreds of millions of nodes and billions of edges, up to orders of magnitude speedups over the baselines, while achieving the highest F1-scores on classification.|图形神经网络(GNN)在许多网络应用程序的半监督学习方面表现出卓越的性能，例如对网络服务和页面的分类、对在线社交网络的分析以及电子商务中的推荐。目前的技术状况是在不区分节点唯一性的前提下，推导出图中所有节点遵循相同扩散(消息传递)模型的表示形式。然而，(i)模型训练中的标记节点在半监督环境中通常只占图的一小部分，(ii)不同的节点位于不同的图局部上下文中，如果在扩散中不加区分地处理它们，则不可避免地会降低表示质量。为了解决上述问题，我们开发了 NDM，一个通用的节点扩散模型，以捕捉每个节点在扩散中的独特特征，通过 NDM 能够产生高质量的节点表示。接下来，我们为半监督学习定制了 NDM，并设计了 NIGCN 模型。特别地，NIGCN 显著提高了效率，因为它(i)仅产生标记节点的表示，(ii)采用了为节点表示生成量身定制的精心设计的邻居抽样技术。在各种类型的网络数据集(包括引用、社会和共同购买图表)上的广泛实验结果不仅验证了 NIGCN 的最新有效性，而且强烈支持 NIGCN 的显著可扩展性。特别是，NIGCN 在10秒内完成数据集上数亿个节点和数十亿条边的表示生成和训练，在基线上数量级加速，同时在分类上获得最高的 f 1分数。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Node-wise+Diffusion+for+Scalable+Graph+Learning)|0| |[MetaTroll: Few-shot Detection of State-Sponsored Trolls with Transformer Adapters](https://doi.org/10.1145/3543507.3583417)|Lin Tian, Xiuzhen Zhang, Jey Han Lau|RMIT University, Australia; The University of Melbourne, Australia|State-sponsored trolls are the main actors of influence campaigns on social media and automatic troll detection is important to combat misinformation at scale. Existing troll detection models are developed based on training data for known campaigns (e.g.\ the influence campaign by Russia's Internet Research Agency on the 2016 US Election), and they fall short when dealing with {\em novel} campaigns with new targets. We propose MetaTroll, a text-based troll detection model based on the meta-learning framework that enables high portability and parameter-efficient adaptation to new campaigns using only a handful of labelled samples for few-shot transfer. We introduce \textit{campaign-specific} transformer adapters to MetaTroll to ``memorise'' campaign-specific knowledge so as to tackle catastrophic forgetting, where a model ``forgets'' how to detect trolls from older campaigns due to continual adaptation. Our experiments demonstrate that MetaTroll substantially outperforms baselines and state-of-the-art few-shot text classification models. Lastly, we explore simple approaches to extend MetaTroll to multilingual and multimodal detection. Source code for MetaTroll is available at: https://github.com/ltian678/metatroll-code.git.|国家赞助的喷子是影响社交媒体运动的主要行为者，自动检测喷子对于大规模打击错误信息非常重要。现有的网络喷子侦测模型是基于已知竞选活动的训练数据(例如俄罗斯互联网研究机构对2016年美国大选的影响力竞选)开发的，在处理有新目标的“新奇”竞选活动时，它们存在不足。我们提出了 MetaTroll，一个基于元学习框架的基于文本的喷子检测模型，该模型能够实现高可移植性和参数高效适应新的广告活动，只需使用少量标记样本进行短镜头传输。我们在 MetaTroll 中引入了文本{战役特定}变压器适配器，以“记忆”战役特定的知识，从而解决灾难性遗忘问题，即由于不断的适应，模型“忘记”如何检测来自旧战役的巨魔。我们的实验表明，MetaTroll 的性能大大优于基线和最先进的少镜头文本分类模型。最后，我们探索了将 MetaTroll 扩展到多语言和多模式检测的简单方法。MetaTroll 的源代码可以在以下 https://github.com/ltian678/MetaTroll-code.git 找到:。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=MetaTroll:+Few-shot+Detection+of+State-Sponsored+Trolls+with+Transformer+Adapters)|0| -|[EmpMFF: A Multi-factor Sequence Fusion Framework for Empathetic Response Generation](https://doi.org/10.1145/3543507.3583438)|Xiaobing Pang, Yequan Wang, Siqi Fan, Lisi Chen, Shuo Shang, Peng Han|Beijing Academy of Artificial Intelligence, China; University Of Electronic Science And Technology Of China, China|Empathy is one of the fundamental abilities of dialog systems. In order to build more intelligent dialogue systems, it’s important to learn how to demonstrate empathy toward others. Existing studies focus on identifying and leveraging the user’s coarse emotion to generate empathetic responses. However, human emotion and dialog act (e.g., intent) evolve as the talk goes along in an empathetic dialogue. This leads to the generated responses with very different intents from the human responses. As a result, empathy failure is ultimately caused. Therefore, using fine-grained emotion and intent sequential data on conversational emotions and dialog act is crucial for empathetic response generation. On the other hand, existing empathy models overvalue the empathy of responses while ignoring contextual relevance, which results in repetitive model-generated responses. To address these issues, we propose a Multi-Factor sequence Fusion framework (EmpMFF) based on conditional variational autoencoder. To generate empathetic responses, the proposed EmpMFF encodes a combination of contextual, emotion, and intent information into a continuous latent variable, which is then fed into the decoder. Experiments on the EmpatheticDialogues benchmark dataset demonstrate that EmpMFF exhibits exceptional performance in both automatic and human evaluations.|移情是对话系统的基本能力之一。为了建立更加智能的对话系统，学习如何对他人表现出同理心是很重要的。现有的研究集中在识别和利用用户的粗糙的情绪，以产生移情反应。然而，人类的情感和对话行为(例如，意图)是随着对话的进行而发展的。这导致所产生的反应与人类的反应有着非常不同的意图。因此，移情失败是最终导致的。因此，利用细粒度的情绪和意图序列数据对会话情绪和对话行为进行研究对于产生移情反应是至关重要的。另一方面，现有的移情模型高估了反应的移情作用，而忽视了语境相关性，从而导致重复的模型产生反应。为了解决这些问题，我们提出了一种基于条件变分自动编码器的多因子序列融合框架(EmpMFF)。为了产生移情反应，所提出的 EmpMFF 将上下文、情感和意图信息的组合编码成一个连续的潜变量，然后将其输入解码器。在 EmpatheticDialog 基准数据集上的实验表明，EmpMFF 在自动评估和人工评估方面都表现出非凡的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=EmpMFF:+A+Multi-factor+Sequence+Fusion+Framework+for+Empathetic+Response+Generation)|0| -|[CEIL: A General Classification-Enhanced Iterative Learning Framework for Text Clustering](https://doi.org/10.1145/3543507.3583457)|Mingjun Zhao, Mengzhen Wang, Yinglong Ma, Di Niu, Haijiang Wu|Electrical & Computer Engineering, University of Alberta, Canada; DiDi Global, China; North China Electric Power University, China|Text clustering, as one of the most fundamental challenges in unsupervised learning, aims at grouping semantically similar text segments without relying on human annotations. With the rapid development of deep learning, deep clustering has achieved significant advantages over traditional clustering methods. Despite the effectiveness, most existing deep text clustering methods rely heavily on representations pre-trained in general domains, which may not be the most suitable solution for clustering in specific target domains. To address this issue, we propose CEIL, a novel Classification-Enhanced Iterative Learning framework for short text clustering, which aims at generally promoting the clustering performance by introducing a classification objective to iteratively improve feature representations. In each iteration, we first adopt a language model to retrieve the initial text representations, from which the clustering results are collected using our proposed Category Disentangled Contrastive Clustering (CDCC) algorithm. After strict data filtering and aggregation processes, samples with clean category labels are retrieved, which serve as supervision information to update the language model with the classification objective via a prompt learning approach. Finally, the updated language model with improved representation ability is used to enhance clustering in the next iteration. Extensive experiments demonstrate that the CEIL framework significantly improves the clustering performance over iterations, and is generally effective on various clustering algorithms. Moreover, by incorporating CEIL on CDCC, we achieve the state-of-the-art clustering performance on a wide range of short text clustering benchmarks outperforming other strong baseline methods.|文本聚类是非监督式学习领域最基本的挑战之一，其目的是在不依赖人工注释的情况下对语义相似的文本片段进行聚类。随着深度学习的快速发展，深度聚类方法已经取得了传统聚类方法所不能比拟的显著优势。目前的深度文本聚类方法虽然有效，但大多依赖于在一般领域中预先训练的表示，这可能不是最适合在特定目标领域中进行聚类的解决方案。为了解决这个问题，我们提出了 CEIL，一个新的分类增强的短文本聚类迭代学习框架，旨在通过引入一个分类目标来迭代地改进特征表示，从而提高聚类性能。在每次迭代中，我们首先采用一种语言模型来检索初始文本表示，然后使用我们提出的类别分离对比聚类(CDCC)算法从中收集聚类结果。经过严格的数据过滤和聚合处理，检索出具有清晰类别标签的样本，作为监督信息，通过快速学习方法更新语言模型，使其符合分类目标。最后，在下一次迭代中使用具有改进表示能力的更新语言模型来增强聚类。大量的实验表明，CEIL 框架在迭代过程中显著提高了聚类性能，并且对各种聚类算法都是有效的。此外，通过在 CDCC 上结合 CEIL，我们在一系列短文本聚类基准上取得了比其他强基准方法更好的聚类性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=CEIL:+A+General+Classification-Enhanced+Iterative+Learning+Framework+for+Text+Clustering)|0| -|[Interval-censored Transformer Hawkes: Detecting Information Operations using the Reaction of Social Systems](https://doi.org/10.1145/3543507.3583481)|Quyu Kong, Pio Calderon, Rohit Ram, Olga Boichak, MarianAndrei Rizoiu|University of Technology, Sydney, Australia; The University of Sydney, Australia; Alibaba Group, China and University of Technology, Sydney, Australia|Social media is being increasingly weaponized by state-backed actors to elicit reactions, push narratives and sway public opinion. These are known as Information Operations (IO). The covert nature of IO makes their detection difficult. This is further amplified by missing data due to the user and content removal and privacy requirements. This work advances the hypothesis that the very reactions that Information Operations seek to elicit within the target social systems can be used to detect them. We propose an Interval-censored Transformer Hawkes (IC-TH) architecture and a novel data encoding scheme to account for both observed and missing data. We derive a novel log-likelihood function that we deploy together with a contrastive learning procedure. We showcase the performance of IC-TH on three real-world Twitter datasets and two learning tasks: future popularity prediction and item category prediction. The latter is particularly significant. Using the retweeting timing and patterns solely, we can predict the category of YouTube videos, guess whether news publishers are reputable or controversial and, most importantly, identify state-backed IO agent accounts. Additional qualitative investigations uncover that the automatically discovered clusters of Russian-backed agents appear to coordinate their behavior, activating simultaneously to push specific narratives.|社交媒体正在越来越多地被政府支持的行为者用作武器，以引发反应、推动叙事和左右公众舆论。这些被称为信息操作(IO)。IO 的隐蔽性使得它们的检测变得困难。由于用户和内容删除以及隐私要求而导致的数据丢失进一步加剧了这种情况。这项工作提出了一个假设，即信息作战部门试图在目标社会系统中引发的反应可以被用来检测它们。提出了一种区间截尾变压器霍克斯(IC-TH)结构和一种新的数据编码方案，以解决观测数据和缺失数据的问题。我们推导了一个新的对数似然函数，我们部署了一个对比学习过程。我们在三个真实的 Twitter 数据集和两个学习任务上展示了 IC-TH 的性能: 未来流行度预测和项目类别预测。后者尤其重要。仅仅使用转发时间和模式，我们就可以预测 YouTube 视频的类别，猜测新闻出版商是否有声誉或有争议，最重要的是，确定国家支持的 IO 代理账户。进一步的定性调查发现，自动发现的俄罗斯支持的代理人集群似乎协调他们的行为，同时激活推动具体的叙述。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Interval-censored+Transformer+Hawkes:+Detecting+Information+Operations+using+the+Reaction+of+Social+Systems)|0| +|[EmpMFF: A Multi-factor Sequence Fusion Framework for Empathetic Response Generation](https://doi.org/10.1145/3543507.3583438)|Xiaobing Pang, Yequan Wang, Siqi Fan, Lisi Chen, Shuo Shang, Peng Han|University Of Electronic Science And Technology Of China, China; Beijing Academy of Artificial Intelligence, China|Empathy is one of the fundamental abilities of dialog systems. In order to build more intelligent dialogue systems, it’s important to learn how to demonstrate empathy toward others. Existing studies focus on identifying and leveraging the user’s coarse emotion to generate empathetic responses. However, human emotion and dialog act (e.g., intent) evolve as the talk goes along in an empathetic dialogue. This leads to the generated responses with very different intents from the human responses. As a result, empathy failure is ultimately caused. Therefore, using fine-grained emotion and intent sequential data on conversational emotions and dialog act is crucial for empathetic response generation. On the other hand, existing empathy models overvalue the empathy of responses while ignoring contextual relevance, which results in repetitive model-generated responses. To address these issues, we propose a Multi-Factor sequence Fusion framework (EmpMFF) based on conditional variational autoencoder. To generate empathetic responses, the proposed EmpMFF encodes a combination of contextual, emotion, and intent information into a continuous latent variable, which is then fed into the decoder. Experiments on the EmpatheticDialogues benchmark dataset demonstrate that EmpMFF exhibits exceptional performance in both automatic and human evaluations.|移情是对话系统的基本能力之一。为了建立更加智能的对话系统，学习如何对他人表现出同理心是很重要的。现有的研究集中在识别和利用用户的粗糙的情绪，以产生移情反应。然而，人类的情感和对话行为(例如，意图)是随着对话的进行而发展的。这导致所产生的反应与人类的反应有着非常不同的意图。因此，移情失败是最终导致的。因此，利用细粒度的情绪和意图序列数据对会话情绪和对话行为进行研究对于产生移情反应是至关重要的。另一方面，现有的移情模型高估了反应的移情作用，而忽视了语境相关性，从而导致重复的模型产生反应。为了解决这些问题，我们提出了一种基于条件变分自动编码器的多因子序列融合框架(EmpMFF)。为了产生移情反应，所提出的 EmpMFF 将上下文、情感和意图信息的组合编码成一个连续的潜变量，然后将其输入解码器。在 EmpatheticDialog 基准数据集上的实验表明，EmpMFF 在自动评估和人工评估方面都表现出非凡的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=EmpMFF:+A+Multi-factor+Sequence+Fusion+Framework+for+Empathetic+Response+Generation)|0| +|[CEIL: A General Classification-Enhanced Iterative Learning Framework for Text Clustering](https://doi.org/10.1145/3543507.3583457)|Mingjun Zhao, Mengzhen Wang, Yinglong Ma, Di Niu, Haijiang Wu|North China Electric Power University, China; DiDi Global, China; Electrical & Computer Engineering, University of Alberta, Canada|Text clustering, as one of the most fundamental challenges in unsupervised learning, aims at grouping semantically similar text segments without relying on human annotations. With the rapid development of deep learning, deep clustering has achieved significant advantages over traditional clustering methods. Despite the effectiveness, most existing deep text clustering methods rely heavily on representations pre-trained in general domains, which may not be the most suitable solution for clustering in specific target domains. To address this issue, we propose CEIL, a novel Classification-Enhanced Iterative Learning framework for short text clustering, which aims at generally promoting the clustering performance by introducing a classification objective to iteratively improve feature representations. In each iteration, we first adopt a language model to retrieve the initial text representations, from which the clustering results are collected using our proposed Category Disentangled Contrastive Clustering (CDCC) algorithm. After strict data filtering and aggregation processes, samples with clean category labels are retrieved, which serve as supervision information to update the language model with the classification objective via a prompt learning approach. Finally, the updated language model with improved representation ability is used to enhance clustering in the next iteration. Extensive experiments demonstrate that the CEIL framework significantly improves the clustering performance over iterations, and is generally effective on various clustering algorithms. Moreover, by incorporating CEIL on CDCC, we achieve the state-of-the-art clustering performance on a wide range of short text clustering benchmarks outperforming other strong baseline methods.|文本聚类是非监督式学习领域最基本的挑战之一，其目的是在不依赖人工注释的情况下对语义相似的文本片段进行聚类。随着深度学习的快速发展，深度聚类方法已经取得了传统聚类方法所不能比拟的显著优势。目前的深度文本聚类方法虽然有效，但大多依赖于在一般领域中预先训练的表示，这可能不是最适合在特定目标领域中进行聚类的解决方案。为了解决这个问题，我们提出了 CEIL，一个新的分类增强的短文本聚类迭代学习框架，旨在通过引入一个分类目标来迭代地改进特征表示，从而提高聚类性能。在每次迭代中，我们首先采用一种语言模型来检索初始文本表示，然后使用我们提出的类别分离对比聚类(CDCC)算法从中收集聚类结果。经过严格的数据过滤和聚合处理，检索出具有清晰类别标签的样本，作为监督信息，通过快速学习方法更新语言模型，使其符合分类目标。最后，在下一次迭代中使用具有改进表示能力的更新语言模型来增强聚类。大量的实验表明，CEIL 框架在迭代过程中显著提高了聚类性能，并且对各种聚类算法都是有效的。此外，通过在 CDCC 上结合 CEIL，我们在一系列短文本聚类基准上取得了比其他强基准方法更好的聚类性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=CEIL:+A+General+Classification-Enhanced+Iterative+Learning+Framework+for+Text+Clustering)|0| +|[Interval-censored Transformer Hawkes: Detecting Information Operations using the Reaction of Social Systems](https://doi.org/10.1145/3543507.3583481)|Quyu Kong, Pio Calderon, Rohit Ram, Olga Boichak, MarianAndrei Rizoiu|University of Technology, Sydney, Australia; Alibaba Group, China and University of Technology, Sydney, Australia; The University of Sydney, Australia|Social media is being increasingly weaponized by state-backed actors to elicit reactions, push narratives and sway public opinion. These are known as Information Operations (IO). The covert nature of IO makes their detection difficult. This is further amplified by missing data due to the user and content removal and privacy requirements. This work advances the hypothesis that the very reactions that Information Operations seek to elicit within the target social systems can be used to detect them. We propose an Interval-censored Transformer Hawkes (IC-TH) architecture and a novel data encoding scheme to account for both observed and missing data. We derive a novel log-likelihood function that we deploy together with a contrastive learning procedure. We showcase the performance of IC-TH on three real-world Twitter datasets and two learning tasks: future popularity prediction and item category prediction. The latter is particularly significant. Using the retweeting timing and patterns solely, we can predict the category of YouTube videos, guess whether news publishers are reputable or controversial and, most importantly, identify state-backed IO agent accounts. Additional qualitative investigations uncover that the automatically discovered clusters of Russian-backed agents appear to coordinate their behavior, activating simultaneously to push specific narratives.|社交媒体正在越来越多地被政府支持的行为者用作武器，以引发反应、推动叙事和左右公众舆论。这些被称为信息操作(IO)。IO 的隐蔽性使得它们的检测变得困难。由于用户和内容删除以及隐私要求而导致的数据丢失进一步加剧了这种情况。这项工作提出了一个假设，即信息作战部门试图在目标社会系统中引发的反应可以被用来检测它们。提出了一种区间截尾变压器霍克斯(IC-TH)结构和一种新的数据编码方案，以解决观测数据和缺失数据的问题。我们推导了一个新的对数似然函数，我们部署了一个对比学习过程。我们在三个真实的 Twitter 数据集和两个学习任务上展示了 IC-TH 的性能: 未来流行度预测和项目类别预测。后者尤其重要。仅仅使用转发时间和模式，我们就可以预测 YouTube 视频的类别，猜测新闻出版商是否有声誉或有争议，最重要的是，确定国家支持的 IO 代理账户。进一步的定性调查发现，自动发现的俄罗斯支持的代理人集群似乎协调他们的行为，同时激活推动具体的叙述。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Interval-censored+Transformer+Hawkes:+Detecting+Information+Operations+using+the+Reaction+of+Social+Systems)|0| |[Towards Model Robustness: Generating Contextual Counterfactuals for Entities in Relation Extraction](https://doi.org/10.1145/3543507.3583504)|Mi Zhang, Tieyun Qian, Ting Zhang, Xin Miao|School of Computer Science, Wuhan University, China|The goal of relation extraction (RE) is to extract the semantic relations between/among entities in the text. As a fundamental task in information systems, it is crucial to ensure the robustness of RE models. Despite the high accuracy current deep neural models have achieved in RE tasks, they are easily affected by spurious correlations. One solution to this problem is to train the model with counterfactually augmented data (CAD) such that it can learn the causation rather than the confounding. However, no attempt has been made on generating counterfactuals for RE tasks. In this paper, we formulate the problem of automatically generating CAD for RE tasks from an entity-centric viewpoint, and develop a novel approach to derive contextual counterfactuals for entities. Specifically, we exploit two elementary topological properties, i.e., the centrality and the shortest path, in syntactic and semantic dependency graphs, to first identify and then intervene on the contextual causal features for entities. We conduct a comprehensive evaluation on four RE datasets by combining our proposed approach with a variety of RE backbones. Results prove that our approach not only improves the performance of the backbones but also makes them more robust in the out-of-domain test 1.|关系抽取的目的是提取文本中实体之间的语义关系。作为信息系统中的一项基础性工作，确保可重构模型的鲁棒性至关重要。尽管目前的深层神经模型在逆向工程任务中已经达到了很高的精度，但是它们很容易受到伪相关的影响。解决这一问题的一种方法是用反事实扩充数据(CAD)训练模型，使其能够学习因果关系而不是混淆。然而，没有尝试为 RE 任务生成反事实。本文从实体中心的观点出发，提出了面向 RE 任务的 CAD 自动生成问题，并提出了一种新的实体上下文反事实推导方法。具体来说，我们利用句法和语义依赖图中的两个基本拓扑性质，即中心性和最短路径，首先识别并干预实体的上下文因果特征。我们对四个 RE 数据集进行了综合评估，结合我们提出的方法与各种 RE 骨干。实验结果表明，该方法不仅提高了骨干网的性能，而且使骨干网在域外测试1中具有更强的鲁棒性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Towards+Model+Robustness:+Generating+Contextual+Counterfactuals+for+Entities+in+Relation+Extraction)|0| |[CitationSum: Citation-aware Graph Contrastive Learning for Scientific Paper Summarization](https://doi.org/10.1145/3543507.3583505)|Zheheng Luo, Qianqian Xie, Sophia Ananiadou|University of Manchester, United Kingdom; The University of Manchester, United Kingdom|Citation graphs can be helpful in generating high-quality summaries of scientific papers, where references of a scientific paper and their correlations can provide additional knowledge for contextualising its background and main contributions. Despite the promising contributions of citation graphs, it is still challenging to incorporate them into summarization tasks. This is due to the difficulty of accurately identifying and leveraging relevant content in references for a source paper, as well as capturing their correlations of different intensities. Existing methods either ignore references or utilize only abstracts indiscriminately from them, failing to tackle the challenge mentioned above. To fill that gap, we propose a novel citation-aware scientific paper summarization framework based on citation graphs, able to accurately locate and incorporate the salient contents from references, as well as capture varying relevance between source papers and their references. Specifically, we first build a domain-specific dataset PubMedCite with about 192K biomedical scientific papers and a large citation graph preserving 917K citation relationships between them. It is characterized by preserving the salient contents extracted from full texts of references, and the weighted correlation between the salient contents of references and the source paper. Based on it, we design a self-supervised citation-aware summarization framework (CitationSum) with graph contrastive learning, which boosts the summarization generation by efficiently fusing the salient information in references with source paper contents under the guidance of their correlations. Experimental results show that our model outperforms the state-of-the-art methods, due to efficiently leveraging the information of references and citation correlations.|引文图可以帮助生成高质量的科学论文摘要，其中科学论文的参考文献及其相关性可以提供额外的知识，以便将其背景和主要贡献联系起来。尽管引文图有很大的贡献，但是将它们整合到摘要任务中仍然具有挑战性。这是因为很难准确地识别和利用源文件参考文献中的相关内容，以及捕捉它们之间不同强度的相关性。现有的方法要么忽略引用，要么只是不加选择地使用它们的摘要，未能解决上面提到的问题。为了填补这一空白，我们提出了一种新的基于引文图的引文感知科技论文摘要框架，该框架能够准确地定位和整合引文中的显著内容，并能够捕获原始论文及其引文之间的不同相关性。具体来说，我们首先构建一个特定领域的数据集 PubMedCite，其中包含大约192K 的生物医学科学论文，以及一个大型引文图，保留它们之间的917K 引文关系。它的拥有属性是保留从参考文献全文中提取的显著内容，以及参考文献的显著内容与原文之间的加权相关性。在此基础上，设计了一个基于图形对比学习的自监督引文感知摘要框架(CitationSum)。实验结果表明，由于有效地利用了参考文献信息和引文相关性，我们的模型优于现有的方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=CitationSum:+Citation-aware+Graph+Contrastive+Learning+for+Scientific+Paper+Summarization)|0| -|[Set in Stone: Analysis of an Immutable Web3 Social Media Platform](https://doi.org/10.1145/3543507.3583510)|Wenrui Zuo, Aravindh Raman, Raul J. Mondragón, Gareth Tyson|Queen Mary University of London, United Kingdom; Telefónica Research, Spain; Hong Kong University of Science and Technology, Hong Kong|There has been growing interest in the so-called “Web3” movement. This loosely refers to a mix of decentralized technologies, often underpinned by blockchain technologies. Among these, Web3 social media platforms have begun to emerge. These store all social interaction data (e.g., posts) on a public ledger, removing the need for centralized data ownership and management. But this comes at a cost, which some argue is prohibitively expensive. As an exemplar within this growing ecosytem, we explore memo.cash, a microblogging service built on the Bitcoin Cash (BCH) blockchain. We gather data for 24K users, 317K posts, 2.57M user actions, which have facilitated $6.75M worth of transactions. A particularly unique feature is that users must pay BCH tokens for each interaction (e.g., posting, following). We study how this may impact the social makeup of the platform. We therefore study memo.cash as both a social network and a transaction platform.|人们对所谓的“ Web3”运动越来越感兴趣。这松散地指的是分散技术的混合，通常由区块链技术支持。其中，Web3社交媒体平台已经开始出现。它们将所有的社会交互数据(例如，文章)存储在一个公共分类账上，从而消除了集中数据所有权和管理的需要。但这是有代价的，有些人认为代价高得令人望而却步。作为这个不断增长的生态系统的一个范例，我们探索了 mem.Cash，一个基于比特币现金(BCH)区块链的微博客服务。我们收集了24K 用户、317K 帖子、257万用户行为的数据，这些数据促成了价值675万美元的交易。一个特别独特的特性是，用户必须为每次交互(例如，发布、跟踪)支付 BCH 令牌。我们研究这可能会如何影响平台的社会构成。因此，我们将 Memo.cash 作为一个社交网络和一个交易平台来研究。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Set+in+Stone:+Analysis+of+an+Immutable+Web3+Social+Media+Platform)|0| +|[Set in Stone: Analysis of an Immutable Web3 Social Media Platform](https://doi.org/10.1145/3543507.3583510)|Wenrui Zuo, Aravindh Raman, Raul J. Mondragón, Gareth Tyson|Telefónica Research, Spain; Queen Mary University of London, United Kingdom; Hong Kong University of Science and Technology, Hong Kong|There has been growing interest in the so-called “Web3” movement. This loosely refers to a mix of decentralized technologies, often underpinned by blockchain technologies. Among these, Web3 social media platforms have begun to emerge. These store all social interaction data (e.g., posts) on a public ledger, removing the need for centralized data ownership and management. But this comes at a cost, which some argue is prohibitively expensive. As an exemplar within this growing ecosytem, we explore memo.cash, a microblogging service built on the Bitcoin Cash (BCH) blockchain. We gather data for 24K users, 317K posts, 2.57M user actions, which have facilitated $6.75M worth of transactions. A particularly unique feature is that users must pay BCH tokens for each interaction (e.g., posting, following). We study how this may impact the social makeup of the platform. We therefore study memo.cash as both a social network and a transaction platform.|人们对所谓的“ Web3”运动越来越感兴趣。这松散地指的是分散技术的混合，通常由区块链技术支持。其中，Web3社交媒体平台已经开始出现。它们将所有的社会交互数据(例如，文章)存储在一个公共分类账上，从而消除了集中数据所有权和管理的需要。但这是有代价的，有些人认为代价高得令人望而却步。作为这个不断增长的生态系统的一个范例，我们探索了 mem.Cash，一个基于比特币现金(BCH)区块链的微博客服务。我们收集了24K 用户、317K 帖子、257万用户行为的数据，这些数据促成了价值675万美元的交易。一个特别独特的特性是，用户必须为每次交互(例如，发布、跟踪)支付 BCH 令牌。我们研究这可能会如何影响平台的社会构成。因此，我们将 Memo.cash 作为一个社交网络和一个交易平台来研究。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Set+in+Stone:+Analysis+of+an+Immutable+Web3+Social+Media+Platform)|0| |[Show me your NFT and I tell you how it will perform: Multimodal representation learning for NFT selling price prediction](https://doi.org/10.1145/3543507.3583520)|Davide Costa, Lucio La Cava, Andrea Tagarelli|DIMES - Dept. Computer Engineering, Modeling, Electronics, and Systems Engineering, University of Calabria, Italy|Non-Fungible Tokens (NFTs) represent deeds of ownership, based on blockchain technologies and smart contracts, of unique crypto assets on digital art forms (e.g., artworks or collectibles). In the spotlight after skyrocketing in 2021, NFTs have attracted the attention of crypto enthusiasts and investors intent on placing promising investments in this profitable market. However, the NFT financial performance prediction has not been widely explored to date. In this work, we address the above problem based on the hypothesis that NFT images and their textual descriptions are essential proxies to predict the NFT selling prices. To this purpose, we propose MERLIN, a novel multimodal deep learning framework designed to train Transformer-based language and visual models, along with graph neural network models, on collections of NFTs' images and texts. A key aspect in MERLIN is its independence on financial features, as it exploits only the primary data a user interested in NFT trading would like to deal with, i.e., NFT images and textual descriptions. By learning dense representations of such data, a price-category classification task is performed by MERLIN models, which can also be tuned according to user preferences in the inference phase to mimic different risk-return investment profiles. Experimental evaluation on a publicly available dataset has shown that MERLIN models achieve significant performances according to several financial assessment criteria, fostering profitable investments, and also beating baseline machine-learning classifiers based on financial features.|非可替换令牌(Non-Fungible Tokens，NFT)代表基于区块链技术和智能合同的所有权契约，在数字艺术形式(例如艺术品或收藏品)上拥有独特的加密资产。自2021年飙升以来，非加密货币基金已经吸引了加密货币爱好者和投资者的注意力，他们打算在这个有利可图的市场进行有前途的投资。然而，到目前为止，NFT 财务业绩预测还没有得到广泛的研究。本文基于 NFT 图像及其文本描述是预测 NFT 销售价格的重要指标这一假设，对上述问题进行了研究。为此，我们提出了 MERLIN，一个新的多模式深度学习框架，旨在训练基于变压器的语言和视觉模型，连同图形神经网络模型，对 NFT 的图像和文本的收集。MERLIN 的一个关键方面是其独立于金融功能，因为它只利用对 NFT 交易感兴趣的用户希望处理的主要数据，即 NFT 图像和文本描述。通过学习这些数据的密集表示，MERLIN 模型执行价格类别分类任务，该模型还可以根据推断阶段的用户偏好进行调整，以模拟不同的风险-收益投资情况。对公开数据集的实验评估表明，MERLIN 模型根据若干财务评估标准取得了显著的性能，促进了有利可图的投资，并且击败了基于财务特征的基线机器学习分类器。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Show+me+your+NFT+and+I+tell+you+how+it+will+perform:+Multimodal+representation+learning+for+NFT+selling+price+prediction)|0| -|[CoTel: Ontology-Neural Co-Enhanced Text Labeling](https://doi.org/10.1145/3543507.3583533)|MiaoHui Song, Lan Zhang, Mu Yuan, Zichong Li, Qi Song, Yijun Liu, Guidong Zheng|University of Science and Technology of China, China; China Merchants Bank, China; University of Science and Technology of China, China and Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, China|The success of many web services relies on the large-scale domain-specific high-quality labeled dataset. Insufficient public datasets motivate us to reduce the cost of data labeling while maintaining high accuracy in support of intelligent web applications. The rule-based method and the learning-based method are common techniques for labeling. In this work, we study how to utilize the rule-based and learning-based methods for resource-effective text labeling. We propose CoTel, the first ontology-neural co-enhanced framework for text labeling. We propose critical ontology extraction in the rule-based module and ontology-enhanced loss prediction in the learning-based module. CoTel can integrate explicit labeling rules and implicit labeling models and make them help each other to improve resource efficiency in text labeling tasks. We evaluate CoTel on both public datasets and real applications with three different tasks. Compared with the baseline, CoTel can reduce the time cost by 64.75% (a 2.84× speedup) and the number of labeling by 62.07%.|许多 Web 服务的成功依赖于特定领域的大规模高质量标记数据集。公共数据集的不足促使我们降低数据标签的成本，同时保持高精度以支持智能网络应用程序。基于规则的方法和基于学习的方法是常用的标记技术。在这项工作中，我们研究了如何利用基于规则和基于学习的方法资源有效的文本标注。我们提出了 CoTel，第一个本体-神经协同增强的文本标注框架。提出了基于规则模块的关键本体抽取和基于学习模块的本体增强损失预测。CoTel 可以集成显式标注规则和隐式标注模型，使两者相辅相成，提高文本标注任务的资源利用效率。我们评估 CoTel 的公共数据集和实际应用程序与三个不同的任务。与基准相比，CoTel 可以减少64.75% 的时间成本(2.84倍加速)和62.07% 的标签数量。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=CoTel:+Ontology-Neural+Co-Enhanced+Text+Labeling)|0| -|[Extracting Cultural Commonsense Knowledge at Scale](https://doi.org/10.1145/3543507.3583535)|TuanPhong Nguyen, Simon Razniewski, Aparna S. Varde, Gerhard Weikum|Montclair State University, USA; Max Planck Institute for Informatics, Germany|Structured knowledge is important for many AI applications. Commonsense knowledge, which is crucial for robust human-centric AI, is covered by a small number of structured knowledge projects. However, they lack knowledge about human traits and behaviors conditioned on socio-cultural contexts, which is crucial for situative AI. This paper presents CANDLE, an end-to-end methodology for extracting high-quality cultural commonsense knowledge (CCSK) at scale. CANDLE extracts CCSK assertions from a huge web corpus and organizes them into coherent clusters, for 3 domains of subjects (geography, religion, occupation) and several cultural facets (food, drinks, clothing, traditions, rituals, behaviors). CANDLE includes judicious techniques for classification-based filtering and scoring of interestingness. Experimental evaluations show the superiority of the CANDLE CCSK collection over prior works, and an extrinsic use case demonstrates the benefits of CCSK for the GPT-3 language model. Code and data can be accessed at https://candle.mpi-inf.mpg.de/.|结构化知识对于许多人工智能应用非常重要。常识知识对于健壮的以人为中心的人工智能是至关重要的，它被少量的结构化知识项目所覆盖。然而，他们缺乏关于社会文化背景下的人类特征和行为的知识，这对于情境人工智能是至关重要的。本文提出了一种端到端的方法 CANDLE，用于在规模上提取高质量的文化常识知识(CCSK)。CANDLE 从一个庞大的网络语料库中提取出 CCSK 断言，并将它们组织成连贯的集群，涉及3个主题领域(地理、宗教、职业)和几个文化方面(食物、饮料、服装、传统、仪式、行为)。CANDLE 包括基于分类的过滤和感兴趣度评分的明智技术。实验结果表明 CANDLE CCSK 集合优于以往的工作，并通过一个外部用例说明 CCSK 集合对 GPT-3语言模型的优越性。代码和数据可在 https://candle.mpi-inf.mpg.de/查阅。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Extracting+Cultural+Commonsense+Knowledge+at+Scale)|0| -|[Unsupervised Event Chain Mining from Multiple Documents](https://doi.org/10.1145/3543507.3583295)|Yizhu Jiao, Ming Zhong, Jiaming Shen, Yunyi Zhang, Chao Zhang, Jiawei Han|Georgia Institute of Technology, USA; Google Research, USA; University of Illinois Urbana-Champaign, USA|Massive and fast-evolving news articles keep emerging on the web. To effectively summarize and provide concise insights into real-world events, we propose a new event knowledge extraction task Event Chain Mining in this paper. Given multiple documents about a super event, it aims to mine a series of salient events in temporal order. For example, the event chain of super event Mexico Earthquake in 2017 is {earthquake hit Mexico, destroy houses, kill people, block roads}. This task can help readers capture the gist of texts quickly, thereby improving reading efficiency and deepening text comprehension. To address this task, we regard an event as a cluster of different mentions of similar meanings. In this way, we can identify the different expressions of events, enrich their semantic knowledge and replenish relation information among them. Taking events as the basic unit, we present a novel unsupervised framework, EMiner. Specifically, we extract event mentions from texts and merge them with similar meanings into a cluster as a single event. By jointly incorporating both content and commonsense, essential events are then selected and arranged chronologically to form an event chain. Meanwhile, we annotate a multi-document benchmark to build a comprehensive testbed for the proposed task. Extensive experiments are conducted to verify the effectiveness of EMiner in terms of both automatic and human evaluations.|大量迅速发展的新闻文章不断出现在网络上。为了有效地总结和提供对现实世界事件的简明见解，本文提出了一种新的事件知识提取任务事件链挖掘。给定关于一个超级事件的多个文档，目的是按时间顺序挖掘一系列突出事件。例如，2017年墨西哥大地震的事件链是{地震袭击墨西哥，摧毁房屋，造成人员死亡，封锁道路}。这项任务可以帮助读者快速掌握文章的要点，从而提高阅读效率，深化文章理解。为了解决这个问题，我们把一个事件看作是一组提到相似含义的不同事件。通过这种方法，我们可以识别事件的不同表达形式，丰富它们的语义知识，补充它们之间的关系信息。以事件为基本单元，提出了一种新的无监督框架 EMiner。具体来说，我们从文本中提取事件提及，并将具有相似含义的事件合并到一个集群中作为单个事件。通过结合内容和常识，重要的事件然后选择和安排的时间顺序，形成一个事件链。同时，我们注释了一个多文档基准，以建立一个全面的测试平台，提出了任务。为了验证 EMiner 在自动评估和人工评估方面的有效性，进行了大量的实验。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Unsupervised+Event+Chain+Mining+from+Multiple+Documents)|0| +|[CoTel: Ontology-Neural Co-Enhanced Text Labeling](https://doi.org/10.1145/3543507.3583533)|MiaoHui Song, Lan Zhang, Mu Yuan, Zichong Li, Qi Song, Yijun Liu, Guidong Zheng|China Merchants Bank, China; University of Science and Technology of China, China and Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, China; University of Science and Technology of China, China|The success of many web services relies on the large-scale domain-specific high-quality labeled dataset. Insufficient public datasets motivate us to reduce the cost of data labeling while maintaining high accuracy in support of intelligent web applications. The rule-based method and the learning-based method are common techniques for labeling. In this work, we study how to utilize the rule-based and learning-based methods for resource-effective text labeling. We propose CoTel, the first ontology-neural co-enhanced framework for text labeling. We propose critical ontology extraction in the rule-based module and ontology-enhanced loss prediction in the learning-based module. CoTel can integrate explicit labeling rules and implicit labeling models and make them help each other to improve resource efficiency in text labeling tasks. We evaluate CoTel on both public datasets and real applications with three different tasks. Compared with the baseline, CoTel can reduce the time cost by 64.75% (a 2.84× speedup) and the number of labeling by 62.07%.|许多 Web 服务的成功依赖于特定领域的大规模高质量标记数据集。公共数据集的不足促使我们降低数据标签的成本，同时保持高精度以支持智能网络应用程序。基于规则的方法和基于学习的方法是常用的标记技术。在这项工作中，我们研究了如何利用基于规则和基于学习的方法资源有效的文本标注。我们提出了 CoTel，第一个本体-神经协同增强的文本标注框架。提出了基于规则模块的关键本体抽取和基于学习模块的本体增强损失预测。CoTel 可以集成显式标注规则和隐式标注模型，使两者相辅相成，提高文本标注任务的资源利用效率。我们评估 CoTel 的公共数据集和实际应用程序与三个不同的任务。与基准相比，CoTel 可以减少64.75% 的时间成本(2.84倍加速)和62.07% 的标签数量。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=CoTel:+Ontology-Neural+Co-Enhanced+Text+Labeling)|0| +|[Extracting Cultural Commonsense Knowledge at Scale](https://doi.org/10.1145/3543507.3583535)|TuanPhong Nguyen, Simon Razniewski, Aparna S. Varde, Gerhard Weikum|Max Planck Institute for Informatics, Germany; Montclair State University, USA|Structured knowledge is important for many AI applications. Commonsense knowledge, which is crucial for robust human-centric AI, is covered by a small number of structured knowledge projects. However, they lack knowledge about human traits and behaviors conditioned on socio-cultural contexts, which is crucial for situative AI. This paper presents CANDLE, an end-to-end methodology for extracting high-quality cultural commonsense knowledge (CCSK) at scale. CANDLE extracts CCSK assertions from a huge web corpus and organizes them into coherent clusters, for 3 domains of subjects (geography, religion, occupation) and several cultural facets (food, drinks, clothing, traditions, rituals, behaviors). CANDLE includes judicious techniques for classification-based filtering and scoring of interestingness. Experimental evaluations show the superiority of the CANDLE CCSK collection over prior works, and an extrinsic use case demonstrates the benefits of CCSK for the GPT-3 language model. Code and data can be accessed at https://candle.mpi-inf.mpg.de/.|结构化知识对于许多人工智能应用非常重要。常识知识对于健壮的以人为中心的人工智能是至关重要的，它被少量的结构化知识项目所覆盖。然而，他们缺乏关于社会文化背景下的人类特征和行为的知识，这对于情境人工智能是至关重要的。本文提出了一种端到端的方法 CANDLE，用于在规模上提取高质量的文化常识知识(CCSK)。CANDLE 从一个庞大的网络语料库中提取出 CCSK 断言，并将它们组织成连贯的集群，涉及3个主题领域(地理、宗教、职业)和几个文化方面(食物、饮料、服装、传统、仪式、行为)。CANDLE 包括基于分类的过滤和感兴趣度评分的明智技术。实验结果表明 CANDLE CCSK 集合优于以往的工作，并通过一个外部用例说明 CCSK 集合对 GPT-3语言模型的优越性。代码和数据可在 https://candle.mpi-inf.mpg.de/查阅。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Extracting+Cultural+Commonsense+Knowledge+at+Scale)|0| +|[Unsupervised Event Chain Mining from Multiple Documents](https://doi.org/10.1145/3543507.3583295)|Yizhu Jiao, Ming Zhong, Jiaming Shen, Yunyi Zhang, Chao Zhang, Jiawei Han|Google Research, USA; University of Illinois Urbana-Champaign, USA; Georgia Institute of Technology, USA|Massive and fast-evolving news articles keep emerging on the web. To effectively summarize and provide concise insights into real-world events, we propose a new event knowledge extraction task Event Chain Mining in this paper. Given multiple documents about a super event, it aims to mine a series of salient events in temporal order. For example, the event chain of super event Mexico Earthquake in 2017 is {earthquake hit Mexico, destroy houses, kill people, block roads}. This task can help readers capture the gist of texts quickly, thereby improving reading efficiency and deepening text comprehension. To address this task, we regard an event as a cluster of different mentions of similar meanings. In this way, we can identify the different expressions of events, enrich their semantic knowledge and replenish relation information among them. Taking events as the basic unit, we present a novel unsupervised framework, EMiner. Specifically, we extract event mentions from texts and merge them with similar meanings into a cluster as a single event. By jointly incorporating both content and commonsense, essential events are then selected and arranged chronologically to form an event chain. Meanwhile, we annotate a multi-document benchmark to build a comprehensive testbed for the proposed task. Extensive experiments are conducted to verify the effectiveness of EMiner in terms of both automatic and human evaluations.|大量迅速发展的新闻文章不断出现在网络上。为了有效地总结和提供对现实世界事件的简明见解，本文提出了一种新的事件知识提取任务事件链挖掘。给定关于一个超级事件的多个文档，目的是按时间顺序挖掘一系列突出事件。例如，2017年墨西哥大地震的事件链是{地震袭击墨西哥，摧毁房屋，造成人员死亡，封锁道路}。这项任务可以帮助读者快速掌握文章的要点，从而提高阅读效率，深化文章理解。为了解决这个问题，我们把一个事件看作是一组提到相似含义的不同事件。通过这种方法，我们可以识别事件的不同表达形式，丰富它们的语义知识，补充它们之间的关系信息。以事件为基本单元，提出了一种新的无监督框架 EMiner。具体来说，我们从文本中提取事件提及，并将具有相似含义的事件合并到一个集群中作为单个事件。通过结合内容和常识，重要的事件然后选择和安排的时间顺序，形成一个事件链。同时，我们注释了一个多文档基准，以建立一个全面的测试平台，提出了任务。为了验证 EMiner 在自动评估和人工评估方面的有效性，进行了大量的实验。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Unsupervised+Event+Chain+Mining+from+Multiple+Documents)|0| |[A Multi-view Meta-learning Approach for Multi-modal Response Generation](https://doi.org/10.1145/3543507.3583548)|Zhiliang Tian, Zheng Xie, Fuqiang Lin, Yiping Song|National University of Defense Technology, China|As massive conversation examples are easily accessible on the Internet, we are now able to organize large-scale conversation corpora to build chatbots in a data-driven manner. Multi-modal social chatbots produce conversational utterances according to both textual utterances and vision signals. Due to the difficulty of bridging different modalities, the dialogue generation model of chatbots falls into local minima that only capture the mapping between textual input and textual output, as a result, it almost ignores the non-textual signals. Further, similar to the dialogue model with plain text as input and output, the generated responses from multi-modal dialogue also lack diversity and informativeness. In this paper, to address the above issues, we propose a Multi-View Meta-Learning (MultiVML) algorithm that groups samples in multiple views and customizes generation models to different groups. We employ a multi-view clustering to group the training samples so as to attend more to the unique information in non-textual modality. Tailoring different sets of model parameters for each group boosts the genereation diversity via meta-learning. We evaluate MultiVML on two variants of the OpenViDial benchmark datasets. The experiments show that our model not only better explore the information from multiple modalities, but also excels baselines in both quality and diversity.|由于大量的会话实例在互联网上很容易访问，我们现在能够组织大规模的会话语料库来以数据驱动的方式构建聊天机器人。多模态社交聊天机器人根据文本话语和视觉信号产生会话话语。由于不同模式之间难以衔接，聊天机器人的对话生成模型陷入局部极小，只能捕捉文本输入和文本输出之间的映射，因此几乎忽略了非文本信号。此外，与以纯文本作为输入和输出的对话模式类似，多模式对话产生的回应也缺乏多样性和信息性。针对上述问题，本文提出了一种多视图元学习(MultiVML)算法，该算法将样本分组到多个视图中，并根据不同的视图组定制生成模型。采用多视角聚类方法对训练样本进行分组，以更多地关注非文本情态下的独特信息。通过元学习为每个群体裁剪不同的模型参数集合，提高了生成的多样性。我们在 OpenViDial 基准数据集的两个变体上评估 MultiVML。实验表明，该模型不仅能够更好地探索来自多种模式的信息，而且在质量和多样性方面都优于基线。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Multi-view+Meta-learning+Approach+for+Multi-modal+Response+Generation)|0| -|[Provenance of Training without Training Data: Towards Privacy-Preserving DNN Model Ownership Verification](https://doi.org/10.1145/3543507.3583198)|Yunpeng Liu, Kexin Li, Zhuotao Liu, Bihan Wen, Ke Xu, Weiqiang Wang, Wenbiao Zhao, Qi Li|University of Toronto, Canada; Ant Group, China; Tsinghua University, China and Zhongguancun Laboratory, China; Nanyang Technological University, Singapore; Tsinghua University, China|In the era of deep learning, it is critical to protect the intellectual property of high-performance deep neural network (DNN) models. Existing proposals, however, are subject to adversarial ownership forgery (e.g., methods based on watermarks or fingerprints) or require full access to the original training dataset for ownership verification (e.g., methods requiring the replay of the learning process). In this paper, we propose a novel Provenance of Training (PoT) scheme, the first empirical study towards verifying DNN model ownership without accessing any original dataset while being robust against existing attacks. At its core, PoT relies on a coherent model chain built from the intermediate checkpoints saved during model training to serve as the ownership certificate. Through an in-depth analysis of model training, we propose six key properties that a legitimate model chain shall naturally hold. In contrast, it is difficult for the adversary to forge a model chain that satisfies these properties simultaneously without performing actual training. We systematically analyze PoT’s robustness against various possible attacks, including the adaptive attacks that are designed given the full knowledge of PoT’s design, and further perform extensive empirical experiments to demonstrate our security analysis.|在深度学习时代，保护高性能深度神经网络(DNN)模型的知识产权至关重要。然而，现有的建议会受到对抗性所有权伪造(例如，基于水印或指纹的方法)或要求完全访问原始培训数据集以进行所有权核实(例如，需要重播学习过程的方法)的影响。在本文中，我们提出了一个新的训练起源(PoT)方案，第一个实证研究是在不访问任何原始数据集的情况下验证 DNN 模型的所有权，同时对现有的攻击具有鲁棒性。在其核心，PoT 依赖于一个连贯的模型链，这个模型链是从模型培训期间保存的中间检查点构建的，作为所有权证书。通过对模型训练的深入分析，我们提出了一个合法的模型链必须具备的六个关键性质。相比之下，如果不进行实际训练，对手很难建立一个同时满足这些特性的模型链。我们系统地分析了 PoT 对各种可能攻击的鲁棒性，包括在充分了解 PoT 设计知识的情况下设计的自适应攻击，并进一步进行了广泛的实证实验来验证我们的安全性分析。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Provenance+of+Training+without+Training+Data:+Towards+Privacy-Preserving+DNN+Model+Ownership+Verification)|0| -|[Efficient and Low Overhead Website Fingerprinting Attacks and Defenses based on TCP/IP Traffic](https://doi.org/10.1145/3543507.3583200)|Guodong Huang, Chuan Ma, Ming Ding, Yuwen Qian, Chunpeng Ge, Liming Fang, Zhe Liu|Shandong Univerisity, China; Zhejiang Lab, China; Nanjing University of Science and Technology, China; Data 61, CSIRO, Sydney, Australia; Nanjing University of Aeronautics and Astronautics, China|Website fingerprinting attack is an extensively studied technique used in a web browser to analyze traffic patterns and thus infer confidential information about users. Several website fingerprinting attacks based on machine learning and deep learning tend to use the most typical features to achieve a satisfactory performance of attacking rate. However, these attacks suffer from several practical implementation factors, such as a skillfully pre-processing step or a clean dataset. To defend against such attacks, random packet defense (RPD) with a high cost of excessive network overhead is usually applied. In this work, we first propose a practical filter-assisted attack against RPD, which can filter out the injected noises using the statistical characteristics of TCP/IP traffic. Then, we propose a list-assisted defensive mechanism to defend the proposed attack method. To achieve a configurable trade-off between the defense and the network overhead, we further improve the list-based defense by a traffic splitting mechanism, which can combat the mentioned attacks as well as save a considerable amount of network overhead. In the experiments, we collect real-life traffic patterns using three mainstream browsers, i.e., Microsoft Edge, Google Chrome, and Mozilla Firefox, and extensive results conducted on the closed and open-world datasets show the effectiveness of the proposed algorithms in terms of defense accuracy and network efficiency.|网站指纹攻击是一种被广泛研究的技术，用于网络浏览器分析流量模式，从而推断出用户的机密信息。几种基于机器学习和深度学习的网站指纹攻击都倾向于使用最典型的特征来获得满意的攻击率。然而，这些攻击受到几个实际实现因素的影响，例如一个熟练的预处理步骤或一个干净的数据集。为了防御这种攻击，通常采用具有高额网络开销的随机分组防御(RPD)技术。本文首先提出了一种实用的针对 RPD 的滤波辅助攻击方法，该方法可以利用 TCP/IP 流量的统计特性来滤除注入的噪声。然后，我们提出了一个列表辅助防御机制来防御所提出的攻击方法。为了在防御和网络开销之间实现可配置的平衡，我们进一步改进了基于列表的防御，采用了流量分割机制，可以抵抗上述攻击，并且节省了大量的网络开销。在实验中，我们使用三种主流浏览器，即 Microsoft Edge、 Google Chrome 和 Mozilla Firefox 收集了真实的流量模式，在封闭和开放世界的数据集上进行的大量实验结果显示了所提出的算法在防御精度和网络效率方面的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Efficient+and+Low+Overhead+Website+Fingerprinting+Attacks+and+Defenses+based+on+TCP/IP+Traffic)|0| -|[Curriculum Graph Poisoning](https://doi.org/10.1145/3543507.3583211)|Hanwen Liu, Peilin Zhao, Tingyang Xu, Yatao Bian, Junzhou Huang, Yuesheng Zhu, Yadong Mu|University of Texas at Arlington, USA; Peking University, China; Tencent AI Lab, China|Despite the success of graph neural networks (GNNs) over the Web in recent years, the typical transductive learning setting for node classification requires GNNs to be retrained frequently, making them vulnerable to poisoning attacks by corrupting the training graph. Poisoning attacks on graphs are, however, non-trivial as the attack space is potentially large, and the discrete graph structure makes the poisoning function non-differentiable. In this paper, we revisit the bi-level optimization problem in graph poisoning and propose a novel graph poisoning method, termed Curriculum Graph Poisoning (CuGPo), inspired by curriculum learning. In contrast to other poisoning attacks that use heuristics or directly optimize the graph, our method learns to generate poisoned graphs from basic adversarial knowledge first and advanced knowledge later. Specifically, for the outer optimization, we utilize the slightly perturbed graphs which represent the easy poisoning task at the beginning, and then enlarge the attack space until the final; for the inner optimization, we firstly exploit the knowledge from the clean graph and then adapt quickly to perturbed graphs to obtain the adversarial knowledge. Extensive experiments demonstrate that CuGPo achieves state-of-the-art performance in graph poisoning attacks.|尽管近年来图形神经网络(GNN)在网络上取得了成功，但是典型的传递式节点分类学习环境要求 GNN 经常被重新训练，这使得它们容易受到损坏训练图的中毒攻击。然而，由于图的攻击空间可能很大，并且离散的图结构使得中毒函数不可微，因此对图的中毒攻击是非平凡的。在这篇文章中，我们重新审视了图表中毒的双层最佳化问题，并提出了一种新的图表中毒方法，称为课程图表中毒(CuGPo) ，它是受课程学习的启发而提出的。与其他使用启发式或直接优化图表的中毒攻击不同，我们的方法首先学习从基本的对抗性知识生成中毒图表，然后再从高级知识生成中毒图表。具体而言，对于外部优化问题，我们首先利用代表容易中毒任务的微扰图，然后扩大攻击空间直到最后; 对于内部优化问题，我们首先利用干净图中的知识，然后对微扰图进行快速适应，以获得对抗性知识。大量的实验表明，CuGPo 在图形中毒攻击中取得了最先进的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Curriculum+Graph+Poisoning)|0| +|[Provenance of Training without Training Data: Towards Privacy-Preserving DNN Model Ownership Verification](https://doi.org/10.1145/3543507.3583198)|Yunpeng Liu, Kexin Li, Zhuotao Liu, Bihan Wen, Ke Xu, Weiqiang Wang, Wenbiao Zhao, Qi Li|Ant Group, China; Nanyang Technological University, Singapore; Tsinghua University, China and Zhongguancun Laboratory, China; University of Toronto, Canada; Tsinghua University, China|In the era of deep learning, it is critical to protect the intellectual property of high-performance deep neural network (DNN) models. Existing proposals, however, are subject to adversarial ownership forgery (e.g., methods based on watermarks or fingerprints) or require full access to the original training dataset for ownership verification (e.g., methods requiring the replay of the learning process). In this paper, we propose a novel Provenance of Training (PoT) scheme, the first empirical study towards verifying DNN model ownership without accessing any original dataset while being robust against existing attacks. At its core, PoT relies on a coherent model chain built from the intermediate checkpoints saved during model training to serve as the ownership certificate. Through an in-depth analysis of model training, we propose six key properties that a legitimate model chain shall naturally hold. In contrast, it is difficult for the adversary to forge a model chain that satisfies these properties simultaneously without performing actual training. We systematically analyze PoT’s robustness against various possible attacks, including the adaptive attacks that are designed given the full knowledge of PoT’s design, and further perform extensive empirical experiments to demonstrate our security analysis.|在深度学习时代，保护高性能深度神经网络(DNN)模型的知识产权至关重要。然而，现有的建议会受到对抗性所有权伪造(例如，基于水印或指纹的方法)或要求完全访问原始培训数据集以进行所有权核实(例如，需要重播学习过程的方法)的影响。在本文中，我们提出了一个新的训练起源(PoT)方案，第一个实证研究是在不访问任何原始数据集的情况下验证 DNN 模型的所有权，同时对现有的攻击具有鲁棒性。在其核心，PoT 依赖于一个连贯的模型链，这个模型链是从模型培训期间保存的中间检查点构建的，作为所有权证书。通过对模型训练的深入分析，我们提出了一个合法的模型链必须具备的六个关键性质。相比之下，如果不进行实际训练，对手很难建立一个同时满足这些特性的模型链。我们系统地分析了 PoT 对各种可能攻击的鲁棒性，包括在充分了解 PoT 设计知识的情况下设计的自适应攻击，并进一步进行了广泛的实证实验来验证我们的安全性分析。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Provenance+of+Training+without+Training+Data:+Towards+Privacy-Preserving+DNN+Model+Ownership+Verification)|0| +|[Efficient and Low Overhead Website Fingerprinting Attacks and Defenses based on TCP/IP Traffic](https://doi.org/10.1145/3543507.3583200)|Guodong Huang, Chuan Ma, Ming Ding, Yuwen Qian, Chunpeng Ge, Liming Fang, Zhe Liu|Zhejiang Lab, China; Shandong Univerisity, China; Data 61, CSIRO, Sydney, Australia; Nanjing University of Science and Technology, China; Nanjing University of Aeronautics and Astronautics, China|Website fingerprinting attack is an extensively studied technique used in a web browser to analyze traffic patterns and thus infer confidential information about users. Several website fingerprinting attacks based on machine learning and deep learning tend to use the most typical features to achieve a satisfactory performance of attacking rate. However, these attacks suffer from several practical implementation factors, such as a skillfully pre-processing step or a clean dataset. To defend against such attacks, random packet defense (RPD) with a high cost of excessive network overhead is usually applied. In this work, we first propose a practical filter-assisted attack against RPD, which can filter out the injected noises using the statistical characteristics of TCP/IP traffic. Then, we propose a list-assisted defensive mechanism to defend the proposed attack method. To achieve a configurable trade-off between the defense and the network overhead, we further improve the list-based defense by a traffic splitting mechanism, which can combat the mentioned attacks as well as save a considerable amount of network overhead. In the experiments, we collect real-life traffic patterns using three mainstream browsers, i.e., Microsoft Edge, Google Chrome, and Mozilla Firefox, and extensive results conducted on the closed and open-world datasets show the effectiveness of the proposed algorithms in terms of defense accuracy and network efficiency.|网站指纹攻击是一种被广泛研究的技术，用于网络浏览器分析流量模式，从而推断出用户的机密信息。几种基于机器学习和深度学习的网站指纹攻击都倾向于使用最典型的特征来获得满意的攻击率。然而，这些攻击受到几个实际实现因素的影响，例如一个熟练的预处理步骤或一个干净的数据集。为了防御这种攻击，通常采用具有高额网络开销的随机分组防御(RPD)技术。本文首先提出了一种实用的针对 RPD 的滤波辅助攻击方法，该方法可以利用 TCP/IP 流量的统计特性来滤除注入的噪声。然后，我们提出了一个列表辅助防御机制来防御所提出的攻击方法。为了在防御和网络开销之间实现可配置的平衡，我们进一步改进了基于列表的防御，采用了流量分割机制，可以抵抗上述攻击，并且节省了大量的网络开销。在实验中，我们使用三种主流浏览器，即 Microsoft Edge、 Google Chrome 和 Mozilla Firefox 收集了真实的流量模式，在封闭和开放世界的数据集上进行的大量实验结果显示了所提出的算法在防御精度和网络效率方面的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Efficient+and+Low+Overhead+Website+Fingerprinting+Attacks+and+Defenses+based+on+TCP/IP+Traffic)|0| +|[Curriculum Graph Poisoning](https://doi.org/10.1145/3543507.3583211)|Hanwen Liu, Peilin Zhao, Tingyang Xu, Yatao Bian, Junzhou Huang, Yuesheng Zhu, Yadong Mu|University of Texas at Arlington, USA; Tencent AI Lab, China; Peking University, China|Despite the success of graph neural networks (GNNs) over the Web in recent years, the typical transductive learning setting for node classification requires GNNs to be retrained frequently, making them vulnerable to poisoning attacks by corrupting the training graph. Poisoning attacks on graphs are, however, non-trivial as the attack space is potentially large, and the discrete graph structure makes the poisoning function non-differentiable. In this paper, we revisit the bi-level optimization problem in graph poisoning and propose a novel graph poisoning method, termed Curriculum Graph Poisoning (CuGPo), inspired by curriculum learning. In contrast to other poisoning attacks that use heuristics or directly optimize the graph, our method learns to generate poisoned graphs from basic adversarial knowledge first and advanced knowledge later. Specifically, for the outer optimization, we utilize the slightly perturbed graphs which represent the easy poisoning task at the beginning, and then enlarge the attack space until the final; for the inner optimization, we firstly exploit the knowledge from the clean graph and then adapt quickly to perturbed graphs to obtain the adversarial knowledge. Extensive experiments demonstrate that CuGPo achieves state-of-the-art performance in graph poisoning attacks.|尽管近年来图形神经网络(GNN)在网络上取得了成功，但是典型的传递式节点分类学习环境要求 GNN 经常被重新训练，这使得它们容易受到损坏训练图的中毒攻击。然而，由于图的攻击空间可能很大，并且离散的图结构使得中毒函数不可微，因此对图的中毒攻击是非平凡的。在这篇文章中，我们重新审视了图表中毒的双层最佳化问题，并提出了一种新的图表中毒方法，称为课程图表中毒(CuGPo) ，它是受课程学习的启发而提出的。与其他使用启发式或直接优化图表的中毒攻击不同，我们的方法首先学习从基本的对抗性知识生成中毒图表，然后再从高级知识生成中毒图表。具体而言，对于外部优化问题，我们首先利用代表容易中毒任务的微扰图，然后扩大攻击空间直到最后; 对于内部优化问题，我们首先利用干净图中的知识，然后对微扰图进行快速适应，以获得对抗性知识。大量的实验表明，CuGPo 在图形中毒攻击中取得了最先进的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Curriculum+Graph+Poisoning)|0| |[Transferring Audio Deepfake Detection Capability across Languages](https://doi.org/10.1145/3543507.3583222)|Zhongjie Ba, Qing Wen, Peng Cheng, Yuwei Wang, Feng Lin, Li Lu, Zhenguang Liu|Zhejiang University, China and ZJU-Hangzhou Global Scientific and Technological Innovation Center, China|The proliferation of deepfake content has motivated a surge of detection studies. However, existing detection methods in the audio area exclusively work in English, and there is a lack of data resources in other languages. Cross-lingual deepfake detection, a critical but rarely explored area, urges more study. This paper conducts the first comprehensive study on the cross-lingual perspective of deepfake detection. We observe that English data enriched in deepfake algorithms can teach a detector the knowledge of various spoofing artifacts, contributing to performing detection across language domains. Based on the observation, we first construct a first-of-its-kind cross-lingual evaluation dataset including heterogeneous spoofed speech uttered in the two most widely spoken languages, then explored domain adaptation (DA) techniques to transfer the artifacts detection capability and propose effective and practical DA strategies fitting the cross-lingual scenario. Our adversarial-based DA paradigm teaches the model to learn real/fake knowledge while losing language dependency. Extensive experiments over 137-hour audio clips validate the adapted models can detect fake audio generated by unseen algorithms in the new domain.|深度伪造内容的泛滥激发了侦查研究的高潮。然而，现有的音频检测方法都是用英语进行的，缺乏其他语言的数据资源。跨语言深度伪造检测是一个关键但很少被探索的领域，促使更多的研究。本文首次对深度伪造检测的跨语言视角进行了全面的研究。我们观察到，在深度伪造算法中丰富的英语数据可以教给检测器各种欺骗伪造的知识，有助于跨语言领域执行检测。在此基础上，我们首先构建了一个包含两种最常用语言的异质欺骗语音的跨语言评价数据集，然后探索了领域适应(DA)技术来传递伪影检测能力，并提出了适合跨语言场景的有效而实用的 DA 策略。我们的基于对抗的 DA 范式教导模型学习真/假知识，同时失去语言依赖性。经过137小时的大量实验，验证了改进后的模型能够检测到新领域中未知算法产生的假音频。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Transferring+Audio+Deepfake+Detection+Capability+across+Languages)|0| |[Web Photo Source Identification based on Neural Enhanced Camera Fingerprint](https://doi.org/10.1145/3543507.3583225)|Feng Qian, Sifeng He, Honghao Huang, Huanyu Ma, Xiaobo Zhang, Lei Yang|Ant Group, China|With the growing popularity of smartphone photography in recent years, web photos play an increasingly important role in all walks of life. Source camera identification of web photos aims to establish a reliable linkage from the captured images to their source cameras, and has a broad range of applications, such as image copyright protection, user authentication, investigated evidence verification, etc. This paper presents an innovative and practical source identification framework that employs neural-network enhanced sensor pattern noise to trace back web photos efficiently while ensuring security. Our proposed framework consists of three main stages: initial device fingerprint registration, fingerprint extraction and cryptographic connection establishment while taking photos, and connection verification between photos and source devices. By incorporating metric learning and frequency consistency into the deep network design, our proposed fingerprint extraction algorithm achieves state-of-the-art performance on modern smartphone photos for reliable source identification. Meanwhile, we also propose several optimization sub-modules to prevent fingerprint leakage and improve accuracy and efficiency. Finally for practical system design, two cryptographic schemes are introduced to reliably identify the correlation between registered fingerprint and verified photo fingerprint, i.e. fuzzy extractor and zero-knowledge proof (ZKP). The codes for fingerprint extraction network and benchmark dataset with modern smartphone cameras photos are all publicly available at https://github.com/PhotoNecf/PhotoNecf.|随着近年来智能手机摄影的日益普及，网络照片在各行各业中扮演着越来越重要的角色。网络照片的源摄像头识别旨在建立从捕获的图像到源摄像头的可靠联系，具有广泛的应用，如图像版权保护、用户认证、调查证据验证等。提出了一种新颖实用的源识别框架，该框架利用神经网络增强的传感器模式噪声，在保证安全的前提下有效地追踪网络照片。我们提出的框架包括三个主要阶段: 初始设备指纹注册、指纹提取和拍摄照片时的密码连接建立，以及照片与源设备之间的连接验证。通过在深度网络设计中引入度量学习和频率一致性，我们提出的指纹提取算法实现了对现代智能手机照片的可靠识别。同时，为了防止指纹泄漏，提高指纹识别的准确性和效率，提出了几个优化子模块。最后针对实际系统设计，提出了两种可靠识别已注册指纹与已验证照片指纹之间相关性的密码体制，即模糊提取器和零知识证明(ZKP)。指纹提取网络的代码和现代智能手机相机照片的基准数据集都已在 https://github.com/photonecf/photonecf 公开发布。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Web+Photo+Source+Identification+based+on+Neural+Enhanced+Camera+Fingerprint)|0| -|[TFE-GNN: A Temporal Fusion Encoder Using Graph Neural Networks for Fine-grained Encrypted Traffic Classification](https://doi.org/10.1145/3543507.3583227)|Haozhen Zhang, Le Yu, Xi Xiao, Qing Li, Francesco Mercaldo, Xiapu Luo, Qixu Liu|Shenzhen International Graduate School, Tsinghua University, China; Department of Computing, The Hong Kong Polytechnic University, Hong Kong; Institute of Information Engineering, Chinese Academy of Sciences, China; University of Molise, Italy and IIT-CNR, Italy; Peng Cheng Laboratory, China|Encrypted traffic classification is receiving widespread attention from researchers and industrial companies. However, the existing methods only extract flow-level features, failing to handle short flows because of unreliable statistical properties, or treat the header and payload equally, failing to mine the potential correlation between bytes. Therefore, in this paper, we propose a byte-level traffic graph construction approach based on point-wise mutual information (PMI), and a model named Temporal Fusion Encoder using Graph Neural Networks (TFE-GNN) for feature extraction. In particular, we design a dual embedding layer, a GNN-based traffic graph encoder as well as a cross-gated feature fusion mechanism, which can first embed the header and payload bytes separately and then fuses them together to obtain a stronger feature representation. The experimental results on two real datasets demonstrate that TFE-GNN outperforms multiple state-of-the-art methods in fine-grained encrypted traffic classification tasks.|加密流量分类受到了研究人员和工业企业的广泛关注。然而，现有的方法只能提取流级特征，由于统计特征不可靠而无法处理短流，或者对报头和有效负载一视同仁，无法挖掘字节之间潜在的相关性。因此，本文提出了一种基于点向互信息(PMI)的字节级流量图构造方法，并提出了一种基于图形神经网络(TFE-GNN)的时态融合编码器(TFE-GNN)模型用于流量图的特征提取。特别地，我们设计了一个双嵌入层、一个基于 GNN 的流量图编码器以及一个跨门限的特征融合机制，该机制首先分别嵌入报头和有效载荷字节，然后将它们融合在一起以获得更强的特征表示。在两个实际数据集上的实验结果表明，在细粒度加密流量分类任务中，TFE-GNN 方法的性能优于多种最新方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=TFE-GNN:+A+Temporal+Fusion+Encoder+Using+Graph+Neural+Networks+for+Fine-grained+Encrypted+Traffic+Classification)|0| -|[Time-manipulation Attack: Breaking Fairness against Proof of Authority Aura](https://doi.org/10.1145/3543507.3583252)|Xinrui Zhang, Rujia Li, Qin Wang, Qi Wang, Sisi Duan|CSIRO Data61, Australia; Research Institute of Trustworthy Autonomous Systems, Department of Computer Science and Engineering, Southern University of Science and Technology, China and Institute for Advanced Study, Tsinghua University, China; Research Institute of Trustworthy Autonomous Systems, Department of Computer Science and Engineering, Southern University of Science and Technology, China; Institute for Advanced Study, Tsinghua University, China; Research Institute of Trustworthy Autonomous Systems, Department of Computer Science and Engineering, Southern University of Science and Technology, China and School of Computer Science, The University of Sydney, Australia|As blockchain-based commercial projects and startups flourish, efficiency becomes one of the critical metrics in designing blockchain systems. Due to its high efficiency, Proof of Authority (PoA) Aura has become one of the most widely adopted consensus solutions for blockchains. Our research finds over 4,000 projects have used Aura and its variants. In this paper, we provide a rigorous analysis of Aura. We propose three types of time-manipulation attacks, where a malicious leader simply needs to modify the timestamp in its proposed block or delay it to extract extra benefits. These attacks can easily break the legal leader election, thus directly harming the fairness of the block proposal. We apply our attacks to a mature Aura project called OpenEthereum. By repeatedly conducting our attacks1 over 15 days, we find that an adversary can gain on average 200% mining rewards of their fair shares. Furthermore, such attacks can even indirectly break the finality of blocks and the safety of the system. Based on the deployment of Aura as of September 2022, the potentially affected market cap is up to 2.13 billion USD. As a by-product, we further discuss solutions to mitigate such issues and report our observations to official teams.|随着基于区块链的商业项目和初创企业的蓬勃发展，效率成为设计区块链系统的关键指标之一。由于其高效率，证明权威(PoA)光环已成为一个最广泛采用的共识解决方案的区块链。我们的研究发现超过4000个项目已经使用了 Aura 及其变体。在本文中，我们提供了一个严格的光环分析。我们提出了三种类型的时间操纵攻击，其中恶意领导者只需修改其提议的块中的时间戳或延迟它，以获取额外的好处。这些攻击很容易破坏法定领导人的选举，从而直接损害阻止提案的公正性。我们将我们的攻击应用到一个称为 OpenEtherum 的成熟的 Aura 项目中。通过在15天内反复进行我们的攻击，我们发现敌人可以平均获得200% 的挖掘奖励。此外，这种攻击甚至可以间接地破坏块的终结性和系统的安全性。根据2022年9月 Aura 的部署，潜在受影响的市值高达21.3亿美元。作为副产品，我们进一步讨论解决方案，以减轻这些问题，并向官方团队报告我们的观察结果。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Time-manipulation+Attack:+Breaking+Fairness+against+Proof+of+Authority+Aura)|0| +|[TFE-GNN: A Temporal Fusion Encoder Using Graph Neural Networks for Fine-grained Encrypted Traffic Classification](https://doi.org/10.1145/3543507.3583227)|Haozhen Zhang, Le Yu, Xi Xiao, Qing Li, Francesco Mercaldo, Xiapu Luo, Qixu Liu|Institute of Information Engineering, Chinese Academy of Sciences, China; Peng Cheng Laboratory, China; University of Molise, Italy and IIT-CNR, Italy; Department of Computing, The Hong Kong Polytechnic University, Hong Kong; Shenzhen International Graduate School, Tsinghua University, China|Encrypted traffic classification is receiving widespread attention from researchers and industrial companies. However, the existing methods only extract flow-level features, failing to handle short flows because of unreliable statistical properties, or treat the header and payload equally, failing to mine the potential correlation between bytes. Therefore, in this paper, we propose a byte-level traffic graph construction approach based on point-wise mutual information (PMI), and a model named Temporal Fusion Encoder using Graph Neural Networks (TFE-GNN) for feature extraction. In particular, we design a dual embedding layer, a GNN-based traffic graph encoder as well as a cross-gated feature fusion mechanism, which can first embed the header and payload bytes separately and then fuses them together to obtain a stronger feature representation. The experimental results on two real datasets demonstrate that TFE-GNN outperforms multiple state-of-the-art methods in fine-grained encrypted traffic classification tasks.|加密流量分类受到了研究人员和工业企业的广泛关注。然而，现有的方法只能提取流级特征，由于统计特征不可靠而无法处理短流，或者对报头和有效负载一视同仁，无法挖掘字节之间潜在的相关性。因此，本文提出了一种基于点向互信息(PMI)的字节级流量图构造方法，并提出了一种基于图形神经网络(TFE-GNN)的时态融合编码器(TFE-GNN)模型用于流量图的特征提取。特别地，我们设计了一个双嵌入层、一个基于 GNN 的流量图编码器以及一个跨门限的特征融合机制，该机制首先分别嵌入报头和有效载荷字节，然后将它们融合在一起以获得更强的特征表示。在两个实际数据集上的实验结果表明，在细粒度加密流量分类任务中，TFE-GNN 方法的性能优于多种最新方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=TFE-GNN:+A+Temporal+Fusion+Encoder+Using+Graph+Neural+Networks+for+Fine-grained+Encrypted+Traffic+Classification)|0| +|[Time-manipulation Attack: Breaking Fairness against Proof of Authority Aura](https://doi.org/10.1145/3543507.3583252)|Xinrui Zhang, Rujia Li, Qin Wang, Qi Wang, Sisi Duan|Research Institute of Trustworthy Autonomous Systems, Department of Computer Science and Engineering, Southern University of Science and Technology, China and School of Computer Science, The University of Sydney, Australia; Institute for Advanced Study, Tsinghua University, China; Research Institute of Trustworthy Autonomous Systems, Department of Computer Science and Engineering, Southern University of Science and Technology, China and Institute for Advanced Study, Tsinghua University, China; CSIRO Data61, Australia; Research Institute of Trustworthy Autonomous Systems, Department of Computer Science and Engineering, Southern University of Science and Technology, China|As blockchain-based commercial projects and startups flourish, efficiency becomes one of the critical metrics in designing blockchain systems. Due to its high efficiency, Proof of Authority (PoA) Aura has become one of the most widely adopted consensus solutions for blockchains. Our research finds over 4,000 projects have used Aura and its variants. In this paper, we provide a rigorous analysis of Aura. We propose three types of time-manipulation attacks, where a malicious leader simply needs to modify the timestamp in its proposed block or delay it to extract extra benefits. These attacks can easily break the legal leader election, thus directly harming the fairness of the block proposal. We apply our attacks to a mature Aura project called OpenEthereum. By repeatedly conducting our attacks1 over 15 days, we find that an adversary can gain on average 200% mining rewards of their fair shares. Furthermore, such attacks can even indirectly break the finality of blocks and the safety of the system. Based on the deployment of Aura as of September 2022, the potentially affected market cap is up to 2.13 billion USD. As a by-product, we further discuss solutions to mitigate such issues and report our observations to official teams.|随着基于区块链的商业项目和初创企业的蓬勃发展，效率成为设计区块链系统的关键指标之一。由于其高效率，证明权威(PoA)光环已成为一个最广泛采用的共识解决方案的区块链。我们的研究发现超过4000个项目已经使用了 Aura 及其变体。在本文中，我们提供了一个严格的光环分析。我们提出了三种类型的时间操纵攻击，其中恶意领导者只需修改其提议的块中的时间戳或延迟它，以获取额外的好处。这些攻击很容易破坏法定领导人的选举，从而直接损害阻止提案的公正性。我们将我们的攻击应用到一个称为 OpenEtherum 的成熟的 Aura 项目中。通过在15天内反复进行我们的攻击，我们发现敌人可以平均获得200% 的挖掘奖励。此外，这种攻击甚至可以间接地破坏块的终结性和系统的安全性。根据2022年9月 Aura 的部署，潜在受影响的市值高达21.3亿美元。作为副产品，我们进一步讨论解决方案，以减轻这些问题，并向官方团队报告我们的观察结果。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Time-manipulation+Attack:+Breaking+Fairness+against+Proof+of+Authority+Aura)|0| |[Do NFTs' Owners Really Possess their Assets? A First Look at the NFT-to-Asset Connection Fragility](https://doi.org/10.1145/3543507.3583281)|Ziwei Wang, Jiashi Gao, Xuetao Wei|Southern University of Science and Technology, China|NFTs (Non-Fungible Tokens) have experienced an explosive growth and their record-breaking prices have been witnessed. Typically, the assets that NFTs represent are stored off-chain with a pointer, e.g., multi-hop URLs, due to the costly on-chain storage. Hence, this paper aims to answer the question: Is the NFT-to-Asset connection fragile? This paper makes a first step towards this end by characterizing NFT-to-Asset connections of 12,353 Ethereum NFT Contracts (6,234,141 NFTs in total) from three perspectives, storage, accessibility and duplication. In order to overcome challenges of affecting the measurement accuracy, e.g., IPFS instability and the changing availability of both IPFS and servers' data, we propose to leverage multiple gateways to enlarge the data coverage and extend a longer measurement period with non-trivial efforts. Results of our extensive study show that such connection is very fragile in practice. The loss, unavailability, or duplication of off-chain assets could render value of NFTs worthless. For instance, we find that assets of 25.24% of Ethereum NFT contracts are not accessible, and 21.48% of Ethereum NFT contracts include duplicated assets. Our work sheds light on the fragility along the NFT-to-Asset connection, which could help the NFT community to better enhance the trust of off-chain assets.|NFT (非可替换令牌)经历了爆炸性的增长，其破纪录的价格已经见证。通常，由于昂贵的上链存储，NFT 表示的资产通过一个指针(例如，多跳 URL)在链外存储。因此，本文旨在回答这样一个问题: NFT 与资产之间的联系是否脆弱？本文从存储、可访问性和复制三个角度对12,353个以太 NFT 合同(总共6,234,141个 NFT)的 NFT-to-Asset 连接进行了描述，从而为实现这一目标迈出了第一步。为了克服影响测量准确性的挑战，例如 IPFS 的不稳定性以及 IPFS 和服务器数据可用性的变化，我们建议利用多个网关来扩大数据覆盖范围，并通过非平凡的努力延长测量周期。我们广泛的研究结果表明，这种联系在实践中是非常脆弱的。脱链资产的损失、不可用性或重复可能使 NFT 的价值变得毫无价值。例如，我们发现25.24% 的以太基金合同中的资产是不可访问的，21.48% 的以太基金合同中包含重复资产。我们的工作揭示了 NFT-to-Asset 连接的脆弱性，这可以帮助 NFT 社区更好地增强脱链资产的信任。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Do+NFTs'+Owners+Really+Possess+their+Assets?+A+First+Look+at+the+NFT-to-Asset+Connection+Fragility)|0| |[Preserving Missing Data Distribution in Synthetic Data](https://doi.org/10.1145/3543507.3583297)|Xinyue Wang, Hafiz Salman Asif, Jaideep Vaidya|Rutgers University, USA|Data from Web artifacts and from the Web is often sensitive and cannot be directly shared for data analysis. Therefore, synthetic data generated from the real data is increasingly used as a privacy-preserving substitute. In many cases, real data from the web has missing values where the missingness itself possesses important informational content, which domain experts leverage to improve their analysis. However, this information content is lost if either imputation or deletion is used before synthetic data generation. In this paper, we propose several methods to generate synthetic data that preserve both the observable and the missing data distributions. An extensive empirical evaluation over a range of carefully fabricated and real world datasets demonstrates the effectiveness of our approach.|来自 Web 工件和 Web 的数据通常是敏感的，不能直接共享用于数据分析。因此，由真实数据生成的合成数据越来越多地被用作保护隐私的替代品。在许多情况下，来自网络的真实数据具有缺失值，而缺失值本身具有重要的信息内容，领域专家利用这些信息来改进他们的分析。但是，如果在合成数据生成之前使用插入或删除，则此信息内容将丢失。在本文中，我们提出了几种生成综合数据的方法，既保留了可观测的数据分布，也保留了缺失的数据分布。对一系列精心制作的真实世界数据集进行了广泛的实证评估，证明了我们方法的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Preserving+Missing+Data+Distribution+in+Synthetic+Data)|0| |[Not Seen, Not Heard in the Digital World! Measuring Privacy Practices in Children's Apps](https://doi.org/10.1145/3543507.3583327)|Ruoxi Sun, Minhui Xue, Gareth Tyson, Shuo Wang, Seyit Camtepe, Surya Nepal|University of Adelaide, Australia and CSIRO's Data61, Australia; CSIRO's Data61, Australia and Cybersecurity CRC, Australia; Hong Kong University of Science and Technology (GZ), China|The digital age has brought a world of opportunity to children. Connectivity can be a game-changer for some of the world's most marginalized children. However, while legislatures around the world have enacted regulations to protect children's online privacy, and app stores have instituted various protections, privacy in mobile apps remains a growing concern for parents and wider society. In this paper, we explore the potential privacy issues and threats that exist in these apps. We investigate 20,195 mobile apps from the Google Play store that are designed particularly for children (Family apps) or include children in their target user groups (Normal apps). Using both static and dynamic analysis, we find that 4.47% of Family apps request location permissions, even though collecting location information from children is forbidden by the Play store, and 81.25% of Family apps use trackers (which are not allowed in children's apps). Even major developers with 40+ kids apps on the Play store use ad trackers. Furthermore, we find that most permission request notifications are not well designed for children, and 19.25% apps have inconsistent content age ratings across the different protection authorities. Our findings suggest that, despite significant attention to children's privacy, a large gap between regulatory provisions, app store policies, and actual development practices exist. Our research sheds light for government policymakers, app stores, and developers.|数字时代给孩子们带来了一个充满机遇的世界。对于世界上一些最边缘化的儿童来说，网络连接可以改变游戏规则。然而，尽管世界各地的立法机构已经制定了保护儿童在线隐私的法规，应用商店也制定了各种各样的保护措施，但移动应用的隐私仍然是父母和更广泛的社会日益关注的问题。在本文中，我们探讨了这些应用程序中潜在的隐私问题和威胁。我们调查了来自 Google Play 商店的20,195个专门为儿童设计的移动应用程序(家庭应用程序) ，或者将儿童包括在他们的目标用户群中(普通应用程序)。通过静态和动态分析，我们发现4.47% 的家庭应用程序请求位置权限，尽管从儿童收集位置信息是被 Play 商店禁止的，81.25% 的家庭应用程序使用追踪器(这在儿童应用程序中是不允许的)。即使是在 Play 商店上拥有40多个儿童应用程序的大型开发商也使用广告跟踪器。此外，我们发现大多数的许可请求通知并没有很好地为儿童设计，19.25% 的应用程序在不同的保护机构中有不一致的内容年龄评级。我们的研究结果表明，尽管对儿童隐私的重视程度很高，但监管规定、应用程序商店政策和实际开发实践之间仍存在很大差距。我们的研究为政府决策者、应用商店和开发者提供了启示。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Not+Seen,+Not+Heard+in+the+Digital+World!+Measuring+Privacy+Practices+in+Children's+Apps)|0| |[Automatic Discovery of Emerging Browser Fingerprinting Techniques](https://doi.org/10.1145/3543507.3583333)|Junhua Su, Alexandros Kapravelos|Department of Computer Science, North Carolina State University, USA|With the progression of modern browsers, online tracking has become the most concerning issue for preserving privacy on the web. As major browser vendors plan to or already ban third-party cookies, trackers have to shift towards browser fingerprinting by incorporating novel browser APIs into their tracking arsenal. Understanding how new browser APIs are abused in browser fingerprinting techniques is a significant step toward ensuring protection from online tracking. In this paper, we propose a novel hybrid system, named BFAD, that automatically identifies previously unknown browser fingerprinting APIs in the wild. The system combines dynamic and static analysis to accurately reveal browser API usage and automatically infer browser fingerprinting behavior. Based on the observation that a browser fingerprint is constructed by pulling information from multiple APIs, we leverage dynamic analysis and a locality-based algorithm to discover all involved APIs and static analysis on the dataflow of fingerprinting information to accurately associate them together. Our system discovers 231 fingerprinting APIs in Alexa top 10K domains, starting with only 35 commonly known fingerprinting APIs and 17 data transmission APIs. Out of 231 APIs, 161 of them are not identified by state-of-the-art detection systems. Since our approach is fully automated, we repeat our experiments 11 months later and discover 18 new fingerprinting APIs that were not discovered in our previous experiment. We present with case studies the fingerprinting ability of a total of 249 detected APIs.|随着现代浏览器的发展，在线跟踪已经成为保护网络隐私最令人关注的问题。随着主要浏览器厂商计划或已经禁止第三方 cookie，跟踪器必须转向使用浏览器指纹识别技术，将新的浏览器 API 纳入他们的跟踪武器库。了解新的浏览器 API 是如何在浏览器指纹识别技术中被滥用的，是确保免受在线跟踪的重要一步。在本文中，我们提出了一个新的混合系统，称为 BFAD，自动识别以前未知的浏览器指纹 API 在野外。该系统结合了动态和静态分析，能够准确地揭示浏览器 API 的使用情况，并自动推断浏览器的指纹识别行为。基于从多个 API 中提取信息来构建一个浏览器指纹的观察，我们利用动态分析和基于位置的算法来发现所有涉及的 API 和指纹信息数据流的静态分析，以准确地将它们关联在一起。我们的系统在 Alexa top 10K 域中发现了231个指纹 API，最初只有35个常见的指纹 API 和17个数据传输 API。在231个 API 中，有161个没有被最先进的检测系统识别。由于我们的方法是完全自动化的，我们在11个月后重复我们的实验，发现了18个新的指纹 API，这些 API 在我们以前的实验中没有发现。我们提出的案例研究的指纹能力，共检测到249个原料药。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Automatic+Discovery+of+Emerging+Browser+Fingerprinting+Techniques)|0| -|[BERT4ETH: A Pre-trained Transformer for Ethereum Fraud Detection](https://doi.org/10.1145/3543507.3583345)|Sihao Hu, Zhen Zhang, Bingqiao Luo, Shengliang Lu, Bingsheng He, Ling Liu|National University of Singapore, Singapore and Georgia Institute of Technology, USA; National University of Singapore, Singapore; Georgia Institute of Technology, USA|As various forms of fraud proliferate on Ethereum, it is imperative to safeguard against these malicious activities to protect susceptible users from being victimized. While current studies solely rely on graph-based fraud detection approaches, it is argued that they may not be well-suited for dealing with highly repetitive, skew-distributed and heterogeneous Ethereum transactions. To address these challenges, we propose BERT4ETH, a universal pre-trained Transformer encoder that serves as an account representation extractor for detecting various fraud behaviors on Ethereum. BERT4ETH features the superior modeling capability of Transformer to capture the dynamic sequential patterns inherent in Ethereum transactions, and addresses the challenges of pre-training a BERT model for Ethereum with three practical and effective strategies, namely repetitiveness reduction, skew alleviation and heterogeneity modeling. Our empirical evaluation demonstrates that BERT4ETH outperforms state-of-the-art methods with significant enhancements in terms of the phishing account detection and de-anonymization tasks. The code for BERT4ETH is available at: https://github.com/git-disl/BERT4ETH.|由于各种形式的欺诈行为在 Ethereum 大行其道，我们必须防范这些恶意活动，以免易受影响的使用者成为受害者。虽然目前的研究仅仅依赖于基于图表的欺诈检测方法，但有人认为，这些方法可能不太适合处理高度重复、倾斜分布和异构的以太坊交易。为了应对这些挑战，我们提出 BERT4ETH，一个通用的预先训练的变压器编码器，作为一个帐户表示提取器检测各种欺诈行为的以太。BERT4eTH 的特点是它具有出色的建模能力，能够捕捉到 Ethereum 交易中固有的动态顺序模式，并通过三种实用而有效的策略，即重复性减少、偏移缓解和异质性建模，解决了预先培训以太坊 BERT 模型的挑战。我们的实证评估表明，BERT4ETH 优于最先进的方法，在钓鱼账户检测和去匿名任务方面有显著的增强。BERT4eTH 的代码可以在以下 https://github.com/git-disl/BERT4ETH 找到。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=BERT4ETH:+A+Pre-trained+Transformer+for+Ethereum+Fraud+Detection)|0| -|[Training-free Lexical Backdoor Attacks on Language Models](https://doi.org/10.1145/3543507.3583348)|Yujin Huang, Terry Yue Zhuo, Qiongkai Xu, Han Hu, Xingliang Yuan, Chunyang Chen|Monash University, Australia; The University of Melbourne, Australia; Monash University, Australia and CSIRO's Data61, Australia|Large-scale language models have achieved tremendous success across various natural language processing (NLP) applications. Nevertheless, language models are vulnerable to backdoor attacks, which inject stealthy triggers into models for steering them to undesirable behaviors. Most existing backdoor attacks, such as data poisoning, require further (re)training or fine-tuning language models to learn the intended backdoor patterns. The additional training process however diminishes the stealthiness of the attacks, as training a language model usually requires long optimization time, a massive amount of data, and considerable modifications to the model parameters. In this work, we propose Training-Free Lexical Backdoor Attack (TFLexAttack) as the first training-free backdoor attack on language models. Our attack is achieved by injecting lexical triggers into the tokenizer of a language model via manipulating its embedding dictionary using carefully designed rules. These rules are explainable to human developers which inspires attacks from a wider range of hackers. The sparse manipulation of the dictionary also habilitates the stealthiness of our attack. We conduct extensive experiments on three dominant NLP tasks based on nine language models to demonstrate the effectiveness and universality of our attack. The code of this work is available at https://github.com/Jinxhy/TFLexAttack.|大规模的语言模型在各种自然语言处理(NLP)应用程序中取得了巨大的成功。然而，语言模型容易受到后门攻击，后门攻击会在模型中注入隐形触发器，引导语言模型产生不良行为。大多数现有的后门攻击，例如数据中毒，需要进一步(重新)训练或微调语言模型来学习预期的后门模式。然而，额外的训练过程降低了攻击的隐蔽性，因为训练一个语言模型通常需要很长的优化时间、大量的数据和对模型参数的相当大的修改。在这项工作中，我们提出的训练自由词汇后门攻击(TFLex颗粒攻击)作为第一个训练自由后门攻击的语言模型。我们的攻击是通过使用精心设计的规则来操纵语言模型的嵌入字典，从而将词法触发器注入到语言模型的标记器中来实现的。这些规则对人类开发人员来说是可以解释的，这些规则激发了更广泛的黑客攻击。字典的稀疏处理也证明了我们攻击的隐蔽性。我们基于九种语言模型对三种主要的自然语言处理任务进行了广泛的实验，以验证我们的攻击的有效性和通用性。这项工作的代码可在 https://github.com/jinxhy/tflexattack 查阅。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Training-free+Lexical+Backdoor+Attacks+on+Language+Models)|0| -|[The Benefits of Vulnerability Discovery and Bug Bounty Programs: Case Studies of Chromium and Firefox](https://doi.org/10.1145/3543507.3583352)|Soodeh Atefi, Amutheezan Sivagnanam, Afiya Ayman, Jens Grossklags, Aron Laszka|Technical University of Munich, Germany; University of Houston, USA; Pennsylvania State University, USA|Recently, bug-bounty programs have gained popularity and become a significant part of the security culture of many organizations. Bug-bounty programs enable organizations to enhance their security posture by harnessing the diverse expertise of crowds of external security experts (i.e., bug hunters). Nonetheless, quantifying the benefits of bug-bounty programs remains elusive, which presents a significant challenge for managing them. Previous studies focused on measuring their benefits in terms of the number of vulnerabilities reported or based on the properties of the reported vulnerabilities, such as severity or exploitability. However, beyond these inherent properties, the value of a report also depends on the probability that the vulnerability would be discovered by a threat actor before an internal expert could discover and patch it. In this paper, we present a data-driven study of the Chromium and Firefox vulnerability-reward programs. First, we estimate the difficulty of discovering a vulnerability using the probability of rediscovery as a novel metric. Our findings show that vulnerability discovery and patching provide clear benefits by making it difficult for threat actors to find vulnerabilities; however, we also identify opportunities for improvement, such as incentivizing bug hunters to focus more on development releases. Second, we compare the types of vulnerabilities that are discovered internally vs. externally and those that are exploited by threat actors. We observe significant differences between vulnerabilities found by external bug hunters, internal security teams, and external threat actors, which indicates that bug-bounty programs provide an important benefit by complementing the expertise of internal teams, but also that external hunters should be incentivized more to focus on the types of vulnerabilities that are likely to be exploited by threat actors.|最近，bug 奖励程序已经变得流行起来，并且成为许多组织安全文化的重要组成部分。漏洞奖励计划使组织能够通过利用外部安全专家(例如，漏洞搜寻者)的多样化专业知识来加强他们的安全态势。尽管如此，量化漏洞奖励计划的好处仍然是难以捉摸的，这对管理它们提出了重大挑战。以前的研究侧重于根据所报告的脆弱性的数量或根据所报告的脆弱性的特性(如严重性或可利用性)来衡量其益处。然而，除了这些固有属性之外，报告的价值还取决于在内部专家发现和修补漏洞之前，威胁行为者发现漏洞的可能性。在本文中，我们提出了一个数据驱动的研究 Chromium 和 Firefox 漏洞奖励程序。首先，我们使用重新发现的概率作为一个新的度量标准来估计发现漏洞的难度。我们的研究结果表明，漏洞发现和补丁通过使威胁行为者难以发现漏洞提供了明显的好处; 然而，我们也确定了改进的机会，例如鼓励 bug 搜索者更多地关注开发版本。其次，我们比较了内部和外部发现的漏洞类型，以及那些被威胁行为者利用的漏洞类型。我们观察到外部漏洞猎人、内部安全团队和外部威胁行为者发现的漏洞之间存在显著差异，这表明漏洞赏金计划通过补充内部团队的专业知识提供了一个重要的好处，但也表明应该更多地激励外部猎人关注可能被威胁行为者利用的漏洞类型。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=The+Benefits+of+Vulnerability+Discovery+and+Bug+Bounty+Programs:+Case+Studies+of+Chromium+and+Firefox)|0| -|[Net-track: Generic Web Tracking Detection Using Packet Metadata](https://doi.org/10.1145/3543507.3583372)|Dongkeun Lee, Minwoo Joo, Wonjun Lee|Korea University, Republic of Korea; Samsung Research, Republic of Korea|While third-party trackers breach users’ privacy by compiling large amounts of personal data through web tracking techniques, combating these trackers is still left at the hand of each user. Although network operators may attempt a network-wide detection of trackers through inspecting all web traffic inside the network, their methods are not only privacy-intrusive but of limited accuracy as these are susceptible to domain changes or ineffective against encrypted traffic. To this end, in this paper, we propose Net-track, a novel approach to managing a secure web environment through platform-independent, encryption-agnostic detection of trackers. Utilizing only side-channel data from network traffic that are still available when encrypted, Net-track accurately detects trackers network-wide, irrespective of user’s browsers or devices without looking into packet payloads or resources fetched from the web server. This prevents user data from leaking to tracking servers in a privacy-preserving manner. By measuring statistics from traffic traces and their similarities, we show distinctions between benign traffic and tracker traffic in their traffic patterns and build Net-track based on the features that fully capture trackers’ distinctive characteristics. Evaluation results show that Net-track is able to detect trackers with 94.02% accuracy and can even discover new trackers yet unrecognized by existing filter lists. Furthermore, Net-track shows its potential for real-time detection, maintaining its performance when using only a portion of each traffic trace.|虽然第三方追踪器通过网络追踪技术编译大量个人数据，侵犯了用户的隐私，但是对抗这些追踪器仍然留在每个用户的手中。尽管网络运营商可以通过检查网络内的所有网络流量来尝试在全网范围内检测跟踪器，但是他们的方法不仅侵犯隐私，而且精确度有限，因为这些方法容易受到域变化的影响，或者对加密流量无效。为此，在本文中，我们提出了网络跟踪，一种新颖的方法来管理一个安全的网络环境，通过平台无关，加密无关的检测跟踪器。网络跟踪仅利用加密后仍然可用的网络流量中的边通道数据，精确检测网络范围内的跟踪器，而不考虑用户的浏览器或设备，无需查看从网络服务器获取的数据包有效载荷或资源。这可以防止用户数据以保护隐私的方式泄露给跟踪服务器。通过测量交通路径的统计信息及其相似性，揭示了良性交通与跟踪交通在交通模式上的区别，并在充分反映跟踪者特征的基础上构建了网络交通路径。评估结果表明，网络跟踪能够以94.02% 的准确率检测出跟踪者，甚至能够发现现有过滤器列表无法识别的新跟踪者。此外，Net-track 显示了其实时检测的潜力，当只使用每个流量跟踪的一部分时，仍能保持其性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Net-track:+Generic+Web+Tracking+Detection+Using+Packet+Metadata)|0| +|[BERT4ETH: A Pre-trained Transformer for Ethereum Fraud Detection](https://doi.org/10.1145/3543507.3583345)|Sihao Hu, Zhen Zhang, Bingqiao Luo, Shengliang Lu, Bingsheng He, Ling Liu|National University of Singapore, Singapore; National University of Singapore, Singapore and Georgia Institute of Technology, USA; Georgia Institute of Technology, USA|As various forms of fraud proliferate on Ethereum, it is imperative to safeguard against these malicious activities to protect susceptible users from being victimized. While current studies solely rely on graph-based fraud detection approaches, it is argued that they may not be well-suited for dealing with highly repetitive, skew-distributed and heterogeneous Ethereum transactions. To address these challenges, we propose BERT4ETH, a universal pre-trained Transformer encoder that serves as an account representation extractor for detecting various fraud behaviors on Ethereum. BERT4ETH features the superior modeling capability of Transformer to capture the dynamic sequential patterns inherent in Ethereum transactions, and addresses the challenges of pre-training a BERT model for Ethereum with three practical and effective strategies, namely repetitiveness reduction, skew alleviation and heterogeneity modeling. Our empirical evaluation demonstrates that BERT4ETH outperforms state-of-the-art methods with significant enhancements in terms of the phishing account detection and de-anonymization tasks. The code for BERT4ETH is available at: https://github.com/git-disl/BERT4ETH.|由于各种形式的欺诈行为在 Ethereum 大行其道，我们必须防范这些恶意活动，以免易受影响的使用者成为受害者。虽然目前的研究仅仅依赖于基于图表的欺诈检测方法，但有人认为，这些方法可能不太适合处理高度重复、倾斜分布和异构的以太坊交易。为了应对这些挑战，我们提出 BERT4ETH，一个通用的预先训练的变压器编码器，作为一个帐户表示提取器检测各种欺诈行为的以太。BERT4eTH 的特点是它具有出色的建模能力，能够捕捉到 Ethereum 交易中固有的动态顺序模式，并通过三种实用而有效的策略，即重复性减少、偏移缓解和异质性建模，解决了预先培训以太坊 BERT 模型的挑战。我们的实证评估表明，BERT4ETH 优于最先进的方法，在钓鱼账户检测和去匿名任务方面有显著的增强。BERT4eTH 的代码可以在以下 https://github.com/git-disl/BERT4ETH 找到。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=BERT4ETH:+A+Pre-trained+Transformer+for+Ethereum+Fraud+Detection)|0| +|[Training-free Lexical Backdoor Attacks on Language Models](https://doi.org/10.1145/3543507.3583348)|Yujin Huang, Terry Yue Zhuo, Qiongkai Xu, Han Hu, Xingliang Yuan, Chunyang Chen|The University of Melbourne, Australia; Monash University, Australia and CSIRO's Data61, Australia; Monash University, Australia|Large-scale language models have achieved tremendous success across various natural language processing (NLP) applications. Nevertheless, language models are vulnerable to backdoor attacks, which inject stealthy triggers into models for steering them to undesirable behaviors. Most existing backdoor attacks, such as data poisoning, require further (re)training or fine-tuning language models to learn the intended backdoor patterns. The additional training process however diminishes the stealthiness of the attacks, as training a language model usually requires long optimization time, a massive amount of data, and considerable modifications to the model parameters. In this work, we propose Training-Free Lexical Backdoor Attack (TFLexAttack) as the first training-free backdoor attack on language models. Our attack is achieved by injecting lexical triggers into the tokenizer of a language model via manipulating its embedding dictionary using carefully designed rules. These rules are explainable to human developers which inspires attacks from a wider range of hackers. The sparse manipulation of the dictionary also habilitates the stealthiness of our attack. We conduct extensive experiments on three dominant NLP tasks based on nine language models to demonstrate the effectiveness and universality of our attack. The code of this work is available at https://github.com/Jinxhy/TFLexAttack.|大规模的语言模型在各种自然语言处理(NLP)应用程序中取得了巨大的成功。然而，语言模型容易受到后门攻击，后门攻击会在模型中注入隐形触发器，引导语言模型产生不良行为。大多数现有的后门攻击，例如数据中毒，需要进一步(重新)训练或微调语言模型来学习预期的后门模式。然而，额外的训练过程降低了攻击的隐蔽性，因为训练一个语言模型通常需要很长的优化时间、大量的数据和对模型参数的相当大的修改。在这项工作中，我们提出的训练自由词汇后门攻击(TFLex颗粒攻击)作为第一个训练自由后门攻击的语言模型。我们的攻击是通过使用精心设计的规则来操纵语言模型的嵌入字典，从而将词法触发器注入到语言模型的标记器中来实现的。这些规则对人类开发人员来说是可以解释的，这些规则激发了更广泛的黑客攻击。字典的稀疏处理也证明了我们攻击的隐蔽性。我们基于九种语言模型对三种主要的自然语言处理任务进行了广泛的实验，以验证我们的攻击的有效性和通用性。这项工作的代码可在 https://github.com/jinxhy/tflexattack 查阅。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Training-free+Lexical+Backdoor+Attacks+on+Language+Models)|0| +|[The Benefits of Vulnerability Discovery and Bug Bounty Programs: Case Studies of Chromium and Firefox](https://doi.org/10.1145/3543507.3583352)|Soodeh Atefi, Amutheezan Sivagnanam, Afiya Ayman, Jens Grossklags, Aron Laszka|Pennsylvania State University, USA; Technical University of Munich, Germany; University of Houston, USA|Recently, bug-bounty programs have gained popularity and become a significant part of the security culture of many organizations. Bug-bounty programs enable organizations to enhance their security posture by harnessing the diverse expertise of crowds of external security experts (i.e., bug hunters). Nonetheless, quantifying the benefits of bug-bounty programs remains elusive, which presents a significant challenge for managing them. Previous studies focused on measuring their benefits in terms of the number of vulnerabilities reported or based on the properties of the reported vulnerabilities, such as severity or exploitability. However, beyond these inherent properties, the value of a report also depends on the probability that the vulnerability would be discovered by a threat actor before an internal expert could discover and patch it. In this paper, we present a data-driven study of the Chromium and Firefox vulnerability-reward programs. First, we estimate the difficulty of discovering a vulnerability using the probability of rediscovery as a novel metric. Our findings show that vulnerability discovery and patching provide clear benefits by making it difficult for threat actors to find vulnerabilities; however, we also identify opportunities for improvement, such as incentivizing bug hunters to focus more on development releases. Second, we compare the types of vulnerabilities that are discovered internally vs. externally and those that are exploited by threat actors. We observe significant differences between vulnerabilities found by external bug hunters, internal security teams, and external threat actors, which indicates that bug-bounty programs provide an important benefit by complementing the expertise of internal teams, but also that external hunters should be incentivized more to focus on the types of vulnerabilities that are likely to be exploited by threat actors.|最近，bug 奖励程序已经变得流行起来，并且成为许多组织安全文化的重要组成部分。漏洞奖励计划使组织能够通过利用外部安全专家(例如，漏洞搜寻者)的多样化专业知识来加强他们的安全态势。尽管如此，量化漏洞奖励计划的好处仍然是难以捉摸的，这对管理它们提出了重大挑战。以前的研究侧重于根据所报告的脆弱性的数量或根据所报告的脆弱性的特性(如严重性或可利用性)来衡量其益处。然而，除了这些固有属性之外，报告的价值还取决于在内部专家发现和修补漏洞之前，威胁行为者发现漏洞的可能性。在本文中，我们提出了一个数据驱动的研究 Chromium 和 Firefox 漏洞奖励程序。首先，我们使用重新发现的概率作为一个新的度量标准来估计发现漏洞的难度。我们的研究结果表明，漏洞发现和补丁通过使威胁行为者难以发现漏洞提供了明显的好处; 然而，我们也确定了改进的机会，例如鼓励 bug 搜索者更多地关注开发版本。其次，我们比较了内部和外部发现的漏洞类型，以及那些被威胁行为者利用的漏洞类型。我们观察到外部漏洞猎人、内部安全团队和外部威胁行为者发现的漏洞之间存在显著差异，这表明漏洞赏金计划通过补充内部团队的专业知识提供了一个重要的好处，但也表明应该更多地激励外部猎人关注可能被威胁行为者利用的漏洞类型。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=The+Benefits+of+Vulnerability+Discovery+and+Bug+Bounty+Programs:+Case+Studies+of+Chromium+and+Firefox)|0| +|[Net-track: Generic Web Tracking Detection Using Packet Metadata](https://doi.org/10.1145/3543507.3583372)|Dongkeun Lee, Minwoo Joo, Wonjun Lee|Samsung Research, Republic of Korea; Korea University, Republic of Korea|While third-party trackers breach users’ privacy by compiling large amounts of personal data through web tracking techniques, combating these trackers is still left at the hand of each user. Although network operators may attempt a network-wide detection of trackers through inspecting all web traffic inside the network, their methods are not only privacy-intrusive but of limited accuracy as these are susceptible to domain changes or ineffective against encrypted traffic. To this end, in this paper, we propose Net-track, a novel approach to managing a secure web environment through platform-independent, encryption-agnostic detection of trackers. Utilizing only side-channel data from network traffic that are still available when encrypted, Net-track accurately detects trackers network-wide, irrespective of user’s browsers or devices without looking into packet payloads or resources fetched from the web server. This prevents user data from leaking to tracking servers in a privacy-preserving manner. By measuring statistics from traffic traces and their similarities, we show distinctions between benign traffic and tracker traffic in their traffic patterns and build Net-track based on the features that fully capture trackers’ distinctive characteristics. Evaluation results show that Net-track is able to detect trackers with 94.02% accuracy and can even discover new trackers yet unrecognized by existing filter lists. Furthermore, Net-track shows its potential for real-time detection, maintaining its performance when using only a portion of each traffic trace.|虽然第三方追踪器通过网络追踪技术编译大量个人数据，侵犯了用户的隐私，但是对抗这些追踪器仍然留在每个用户的手中。尽管网络运营商可以通过检查网络内的所有网络流量来尝试在全网范围内检测跟踪器，但是他们的方法不仅侵犯隐私，而且精确度有限，因为这些方法容易受到域变化的影响，或者对加密流量无效。为此，在本文中，我们提出了网络跟踪，一种新颖的方法来管理一个安全的网络环境，通过平台无关，加密无关的检测跟踪器。网络跟踪仅利用加密后仍然可用的网络流量中的边通道数据，精确检测网络范围内的跟踪器，而不考虑用户的浏览器或设备，无需查看从网络服务器获取的数据包有效载荷或资源。这可以防止用户数据以保护隐私的方式泄露给跟踪服务器。通过测量交通路径的统计信息及其相似性，揭示了良性交通与跟踪交通在交通模式上的区别，并在充分反映跟踪者特征的基础上构建了网络交通路径。评估结果表明，网络跟踪能够以94.02% 的准确率检测出跟踪者，甚至能够发现现有过滤器列表无法识别的新跟踪者。此外，Net-track 显示了其实时检测的潜力，当只使用每个流量跟踪的一部分时，仍能保持其性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Net-track:+Generic+Web+Tracking+Detection+Using+Packet+Metadata)|0| |[Cross-Modality Mutual Learning for Enhancing Smart Contract Vulnerability Detection on Bytecode](https://doi.org/10.1145/3543507.3583367)|Peng Qian, Zhenguang Liu, Yifang Yin, Qinming He|Institute for Infocomm Research, A*STAR, Singapore; Zhejiang University, China|Over the past couple of years, smart contracts have been plagued by multifarious vulnerabilities, which have led to catastrophic financial losses. Their security issues, therefore, have drawn intense attention. As countermeasures, a family of tools has been developed to identify vulnerabilities in smart contracts at the source-code level. Unfortunately, only a small fraction of smart contracts is currently open-sourced. Another spectrum of work is presented to deal with pure bytecode, but most such efforts still suffer from relatively low performance due to the inherent difficulty in restoring abundant semantics in the source code from the bytecode. This paper proposes a novel cross-modality mutual learning framework for enhancing smart contract vulnerability detection on bytecode. Specifically, we engage in two networks, a student network as the primary network and a teacher network as the auxiliary network. takes two modalities, i.e., source code and its corresponding bytecode as inputs, while is fed with only bytecode. By learning from , is trained to infer the missed source code embeddings and combine both modalities to approach precise vulnerability detection. To further facilitate mutual learning between and , we present a cross-modality mutual learning loss and two transfer losses. As a side contribution, we construct and release a labeled smart contract dataset that concerns four types of common vulnerabilities. Experimental results show that our method significantly surpasses state-of-the-art approaches.|在过去的几年里，聪明的合同一直受到各种漏洞的困扰，这些漏洞导致了灾难性的财务损失。因此，他们的安全问题引起了强烈的关注。作为对策，已经开发了一系列工具来在源代码级别识别智能契约中的漏洞。不幸的是，目前只有一小部分智能合同是开源的。提出了另一种处理纯字节码的工作范围，但是由于从字节码恢复源代码中丰富的语义存在固有的困难，大多数此类工作的性能仍然相对较低。提出了一种新的跨模态互学习框架，用于提高字节码智能契约漏洞检测能力。具体来说，我们从事两个网络，一个是以学生网络为主的网络，另一个是以教师网络为辅助的网络。采用两种模式，即源代码及其相应的字节码作为输入，而只提供字节码。通过学习，训练来推断错过的源代码嵌入，并结合两种方式来接近精确的漏洞检测。为了进一步促进相互学习和交叉学习，我们提出了一种交叉学习方式的相互学习损失和两种转移损失。作为附带贡献，我们构建并发布了一个标签化的智能契约数据集，它涉及到四种常见的漏洞。实验结果表明，我们的方法明显优于国家的最先进的方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Cross-Modality+Mutual+Learning+for+Enhancing+Smart+Contract+Vulnerability+Detection+on+Bytecode)|0| |[The Chameleon on the Web: an Empirical Study of the Insidious Proactive Web Defacements](https://doi.org/10.1145/3543507.3583377)|Rui Zhao|University of Nebraska at Omaha, USA|Web defacement is one of the major promotional channels for online underground economies. It regularly compromises benign websites and injects fraudulent content to promote illicit goods and services. It inflicts significant harm to websites’ reputations and revenues and may lead to legal ramifications. In this paper, we uncover proactive web defacements, where the involved web pages (i.e., landing pages) proactively deface themselves within browsers using JavaScript (i.e., control scripts). Proactive web defacements have not yet received attention from research communities, anti-hacking organizations, or law-enforcement officials. To detect proactive web defacements, we designed a practical tool, PACTOR. It runs in the browser and intercepts JavaScript API calls that manipulate web page content. It takes snapshots of the rendered HTML source code immediately before and after the intercepted API calls and detects proactive web defacements by visually comparing every two consecutive snapshots. Our two-month empirical study, using PACTOR, on 2,454 incidents of proactive web defacements shows that they can evade existing URL safety-checking tools and effectively promote the ranking of their landing pages using legitimate content/keywords. We also investigated the vendor network of proactive web defacements and reported all the involved domains to law-enforcement officials and URL-safety checking tools.|网络涂鸦是网络地下经济的主要推广渠道之一。它经常损害良性网站和注入欺诈内容，以促进非法商品和服务。它对网站的声誉和收入造成重大损害，并可能导致法律后果。在本文中，我们揭示了前瞻性的网页涂改，其中涉及的网页(即登陆页)主动涂改自己在浏览器中使用 JavaScript (即控制脚本)。主动的网页涂鸦还没有得到研究团体、反黑客组织或执法官员的关注。为了检测主动的网页破坏，我们设计了一个实用的工具，PACTOR。它在浏览器中运行并拦截操作网页内容的 JavaScriptAPI 调用。它在截获的 API 调用之前和之后立即获取呈现的 HTML 源代码的快照，并通过可视化地比较每两个连续的快照来检测主动的 Web 破坏。我们通过使用 PACTOR 对2,454起主动网页破坏事件进行了为期两个月的实证研究，结果表明他们可以逃避现有的 URL 安全检查工具，并有效地利用合法的内容/关键词来提高其登陆页面的排名。我们还调查了供应商网络的主动网页污损，并报告了所有涉及的领域执法官员和网址安全检查工具。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=The+Chameleon+on+the+Web:+an+Empirical+Study+of+the+Insidious+Proactive+Web+Defacements)|0| |[Shield: Secure Allegation Escrow System with Stronger Guarantees](https://doi.org/10.1145/3543507.3583391)|Nishat Koti, Varsha Bhat Kukkala, Arpita Patra, Bhavish Raj Gopal|Indian Institute of Science, India|The rising issues of harassment, exploitation, corruption and other forms of abuse have led victims to seek comfort by acting in unison against common perpetrators. This is corroborated by the widespread #MeToo movement, which was explicitly against sexual harassment. Installation of escrow systems has allowed victims to report such incidents. The escrows are responsible for identifying the perpetrator and taking the necessary action to bring justice to all its victims. However, users hesitate to participate in these systems due to the fear of such sensitive reports being leaked to perpetrators, who may further misuse them. Thus, to increase trust in the system, cryptographic solutions are being designed to realize web-based secure allegation escrow (SAE) systems. While the work of Arun et al. (NDSS’20) presents the state-of-the-art solution, we identify attacks that can leak sensitive information and compromise victim privacy. We also report issues present in prior works that were left unidentified. Having identified the attacks and issues in all prior works, we put forth an SAE system that overcomes these while retaining all the existing salient features. The cryptographic technique of secure multiparty computation (MPC) serves as the primary underlying tool in designing our system. At the heart of our system lies a new duplicity check protocol and an improved matching protocol. We also provide essential features such as allegation modification and deletion, which were absent in the state of the art. To demonstrate feasibility, we benchmark the proposed system with state-of-the-art MPC protocols and report the cost of processing an allegation. Different settings that affect system performance are analyzed, and the reported values showcase the practicality of our solution.|骚扰、剥削、腐败和其他形式的虐待问题日益严重，导致受害者为了寻求安慰，联合起来对付共同的肇事者。这一点得到了广泛开展的“ # 我也是”(# meToo)运动的证实，该运动明确反对性骚扰。通过安装第三方托管系统，受害者可以报告此类事件。代管人负责查明肇事者，并采取必要行动为所有受害者伸张正义。但是，用户不愿意参与这些系统，因为他们担心这些敏感的报告会泄露给犯罪者，他们可能会进一步滥用这些报告。因此，为了增加对系统的信任，正在设计加密解决方案来实现基于 Web 的安全指控代管(SAE)系统。虽然 Arun 等人(NDSS’20)的工作提出了最先进的解决方案，但我们确定的攻击可以泄露敏感信息和损害受害者的隐私。我们也报告在以前的工程存在的问题，留下了不明。在确定了以前所有工作中的攻击和问题之后，我们提出了一个 SAE 系统，它克服了这些问题，同时保留了所有现有的显著特征。安全多方计算(MPC)加密技术是我们设计系统的主要基础工具。该系统的核心是一个新的双重性检测协议和一个改进的匹配协议。我们还提供了指控修改和删除等基本功能，这些功能在目前的技术水平上是不存在的。为了证明可行性，我们使用最先进的 MPC 协议对提议的系统进行基准测试，并报告处理指控的成本。分析了影响系统性能的不同设置，报告的值展示了我们的解决方案的实用性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Shield:+Secure+Allegation+Escrow+System+with+Stronger+Guarantees)|0| |[Unnoticeable Backdoor Attacks on Graph Neural Networks](https://doi.org/10.1145/3543507.3583392)|Enyan Dai, Minhua Lin, Xiang Zhang, Suhang Wang|Pennsylvania State University, USA|Graph Neural Networks (GNNs) have achieved promising results in various tasks such as node classification and graph classification. Recent studies find that GNNs are vulnerable to adversarial attacks. However, effective backdoor attacks on graphs are still an open problem. In particular, backdoor attack poisons the graph by attaching triggers and the target class label to a set of nodes in the training graph. The backdoored GNNs trained on the poisoned graph will then be misled to predict test nodes to target class once attached with triggers. Though there are some initial efforts in graph backdoor attacks, our empirical analysis shows that they may require a large attack budget for effective backdoor attacks and the injected triggers can be easily detected and pruned. Therefore, in this paper, we study a novel problem of unnoticeable graph backdoor attacks with limited attack budget. To fully utilize the attack budget, we propose to deliberately select the nodes to inject triggers and target class labels in the poisoning phase. An adaptive trigger generator is deployed to obtain effective triggers that are difficult to be noticed. Extensive experiments on real-world datasets against various defense strategies demonstrate the effectiveness of our proposed method in conducting effective unnoticeable backdoor attacks.|图神经网络在节点分类、图分类等方面都取得了很好的效果。最近的研究发现 GNN 很容易受到敌对攻击。然而，对图的有效后门攻击仍然是一个悬而未决的问题。特别是，后门攻击通过将触发器和目标类标签附加到训练图中的一组节点上来毒害图。在中毒图上训练的后门 GNN 将被误导，一旦与触发器连接，就可以预测测试节点到目标类。虽然对于图形后门攻击已经有了一些初步的研究，但是我们的实证分析表明，对于有效的后门攻击来说，它们可能需要大量的攻击预算，并且注入的触发器可以很容易地被检测到和删除。因此，本文研究了一个新的攻击预算有限的不易察觉的图形后门攻击问题。为了充分利用攻击预算，我们建议在中毒阶段刻意选择节点来注入触发器和目标类标签。自适应触发发生器的部署，以获得难以注意到的有效触发器。针对不同防御策略对真实世界数据集进行的大量实验证明了我们提出的方法在进行有效的不易察觉的后门攻击方面的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Unnoticeable+Backdoor+Attacks+on+Graph+Neural+Networks)|0| -|[Bad Apples: Understanding the Centralized Security Risks in Decentralized Ecosystems](https://doi.org/10.1145/3543507.3583393)|Kailun Yan, Jilian Zhang, Xiangyu Liu, Wenrui Diao, Shanqing Guo|Jinan University, China; Shandong University, China; Alibaba Group, China|The blockchain-powered decentralized applications and systems have been widely deployed in recent years. The decentralization feature promises users anonymity, security, and non-censorship, which is especially welcomed in the areas of decentralized finance and digital assets. From the perspective of most common users, a decentralized ecosystem means every service follows the principle of decentralization. However, we find that the services in a decentralized ecosystem still may contain centralized components or scenarios, like third-party SDKs and privileged operations, which violate the promise of decentralization and may cause a series of centralized security risks. In this work, we systematically study the centralized security risks existing in decentralized ecosystems. Specifically, we identify seven centralized security risks in the deployment of two typical decentralized services – crypto wallets and DApps, such as anonymity loss and overpowered owner. Also, to measure these risks in the wild, we designed an automated detection tool called Naga and carried out large-scale experiments. Based on the measurement of 28 Ethereum crypto wallets (Android version) and 110,506 on-chain smart contracts, the result shows that the centralized security risks are widespread. Up to 96.4% of wallets and 83.5% of contracts exist at least one security risk, including 260 well-known tokens with a total market cap of over $98 billion.|区块链驱动的分散式应用和系统近年来得到了广泛的应用。这个地方分权功能允许用户匿名、安全和无审查，在分散金融和数字资产领域尤其受欢迎。从大多数普通用户的角度来看，一个分散的生态系统意味着每个服务都遵循地方分权原则。然而，我们发现，在一个分散的生态系统中，服务仍然可能包含集中的组件或场景，如第三方 SDK 和特权操作，这违反了地方分权的承诺，并可能导致一系列集中的安全风险。本文系统地研究了分散式生态系统中存在的集中式安全风险。具体来说，我们在部署两种典型的分散式服务(加密钱包和 DApps)时确定了七种集中式安全风险，比如匿名性损失和过度拥有者。此外，为了在野外测量这些风险，我们设计了一种叫做娜迦的自动检测工具，并进行了大规模的实验。通过对28个以太加密钱包(Android 版)和110,506个上链智能合同的测量，结果表明，集中式安全风险普遍存在。高达96.4% 的钱包和83.5% 的合同至少存在一种安全风险，其中包括260种知名令牌，总市值超过980亿美元。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Bad+Apples:+Understanding+the+Centralized+Security+Risks+in+Decentralized+Ecosystems)|0| +|[Bad Apples: Understanding the Centralized Security Risks in Decentralized Ecosystems](https://doi.org/10.1145/3543507.3583393)|Kailun Yan, Jilian Zhang, Xiangyu Liu, Wenrui Diao, Shanqing Guo|Jinan University, China; Alibaba Group, China; Shandong University, China|The blockchain-powered decentralized applications and systems have been widely deployed in recent years. The decentralization feature promises users anonymity, security, and non-censorship, which is especially welcomed in the areas of decentralized finance and digital assets. From the perspective of most common users, a decentralized ecosystem means every service follows the principle of decentralization. However, we find that the services in a decentralized ecosystem still may contain centralized components or scenarios, like third-party SDKs and privileged operations, which violate the promise of decentralization and may cause a series of centralized security risks. In this work, we systematically study the centralized security risks existing in decentralized ecosystems. Specifically, we identify seven centralized security risks in the deployment of two typical decentralized services – crypto wallets and DApps, such as anonymity loss and overpowered owner. Also, to measure these risks in the wild, we designed an automated detection tool called Naga and carried out large-scale experiments. Based on the measurement of 28 Ethereum crypto wallets (Android version) and 110,506 on-chain smart contracts, the result shows that the centralized security risks are widespread. Up to 96.4% of wallets and 83.5% of contracts exist at least one security risk, including 260 well-known tokens with a total market cap of over $98 billion.|区块链驱动的分散式应用和系统近年来得到了广泛的应用。这个地方分权功能允许用户匿名、安全和无审查，在分散金融和数字资产领域尤其受欢迎。从大多数普通用户的角度来看，一个分散的生态系统意味着每个服务都遵循地方分权原则。然而，我们发现，在一个分散的生态系统中，服务仍然可能包含集中的组件或场景，如第三方 SDK 和特权操作，这违反了地方分权的承诺，并可能导致一系列集中的安全风险。本文系统地研究了分散式生态系统中存在的集中式安全风险。具体来说，我们在部署两种典型的分散式服务(加密钱包和 DApps)时确定了七种集中式安全风险，比如匿名性损失和过度拥有者。此外，为了在野外测量这些风险，我们设计了一种叫做娜迦的自动检测工具，并进行了大规模的实验。通过对28个以太加密钱包(Android 版)和110,506个上链智能合同的测量，结果表明，集中式安全风险普遍存在。高达96.4% 的钱包和83.5% 的合同至少存在一种安全风险，其中包括260种知名令牌，总市值超过980亿美元。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Bad+Apples:+Understanding+the+Centralized+Security+Risks+in+Decentralized+Ecosystems)|0| |[Scan Me If You Can: Understanding and Detecting Unwanted Vulnerability Scanning](https://doi.org/10.1145/3543507.3583394)|Xigao Li, Babak Amin Azad, Amir Rahmati, Nick Nikiforakis|Computer Science, Stony Brook University, USA|Web vulnerability scanners (WVS) are an indispensable tool for penetration testers and developers of web applications, allowing them to identify and fix low-hanging vulnerabilities before they are discovered by attackers. Unfortunately, malicious actors leverage the very same tools to identify and exploit vulnerabilities in third-party websites. Existing research in the WVS space is largely concerned with how many vulnerabilities these tools can discover, as opposed to trying to identify the tools themselves when they are used illicitly. In this work, we design a testbed to characterize web vulnerability scanners using browser-based and network-based fingerprinting techniques. We conduct a measurement study over 12 web vulnerability scanners as well as 159 users who were recruited to interact with the same web applications that were targeted by the evaluated WVSs. By contrasting the traffic and behavior of these two groups, we discover tool-specific and type-specific behaviors in WVSs that are absent from regular users. Based on these observations, we design and build ScannerScope, a machine-learning-based, web vulnerability scanner detection system. ScannerScope consists of a transparent reverse proxy that injects fingerprinting modules on the fly without the assistance (or knowledge) of the protected web applications. Our evaluation results show that ScannerScope can effectively detect WVSs and protect web applications against unwanted vulnerability scanning, with a detection accuracy of over 99% combined with near-zero false positives on human-visitor traffic. Finally, we show that the asynchronous design of ScannerScope results in a negligible impact on server performance and demonstrate that its classifier can resist adversarial ML attacks launched by sophisticated adversaries.|Web 漏洞扫描器(WVS)是渗透测试人员和 Web 应用程序开发人员不可或缺的工具，它允许他们在发现低悬挂漏洞之前识别和修复这些漏洞。不幸的是，恶意行为者利用同样的工具来识别和利用第三方网站的漏洞。WVS 领域的现有研究主要关注这些工具可以发现多少漏洞，而不是在工具被非法使用时试图确定它们本身。在这项工作中，我们设计了一个基于浏览器和基于网络的指纹识别技术来描述网络漏洞扫描器的测试平台。我们对12个网络漏洞扫描器以及159个用户进行了测量研究，这些用户被招募来与被评估的 WVSs 所针对的相同的网络应用程序进行交互。通过对比这两个组的流量和行为，我们发现了常规用户所不具备的工具特定和类型特定的行为。基于这些观察，我们设计并构建了一个基于机器学习的网络漏洞扫描器检测系统 scannerScope。ScanerScope 由一个透明的反向代理组成，它可以在没有受保护的 Web 应用程序的帮助(或知识)的情况下动态地注入指纹识别模块。我们的评估结果表明，ScanerScope 能够有效地检测 WVSs，保护 Web 应用程序免受不必要的漏洞扫描，检测准确率超过99% ，并且对人类访问者流量的误报几乎为零。最后，我们证明了 ScanerScope 的异步设计对服务器性能的影响可以忽略不计，并且证明了它的分类器可以抵抗复杂对手发起的对抗性 ML 攻击。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Scan+Me+If+You+Can:+Understanding+and+Detecting+Unwanted+Vulnerability+Scanning)|0| |[The More Things Change, the More They Stay the Same: Integrity of Modern JavaScript](https://doi.org/10.1145/3543507.3583395)|Johnny So, Michael Ferdman, Nick Nikiforakis|Stony Brook University, USA|The modern web is a collection of remote resources that are identified by their location and composed of interleaving networks of trust. Supply chain attacks compromise the users of a target domain by leveraging its often large set of trusted third parties who provide resources such as JavaScript. The ubiquity of JavaScript, paired with its ability to execute arbitrary code on client machines, makes this particular web resource an ideal vector for supply chain attacks. Currently, there exists no robust method for users browsing the web to verify that the script content they receive from a third party is the expected content. In this paper, we present key insights to inform the design of robust integrity mechanisms, derived from our large-scale analyses of the 6M scripts we collected while crawling 44K domains every day for 77 days. We find that scripts that frequently change should be considered first-class citizens in the modern web ecosystem, and that the ways in which scripts change remain constant over time. Furthermore, we present analyses on the use of strict integrity verification (e.g., Subresource Integrity) at the granularity of the script providers themselves, offering a more complete perspective and demonstrating that the use of strict integrity alone cannot provide satisfactory security guarantees. We conclude that it is infeasible for a client to distinguish benign changes from malicious ones without additional, external knowledge, motivating the need for a new protocol to provide clients the necessary context to assess the potential ramifications of script changes.|现代网络是远程资源的集合，这些资源通过其位置来识别，并由交错的信任网络组成。供应链攻击通过利用目标域的大量可信第三方(提供诸如 JavaScript 之类的资源)来损害目标域的用户。JavaScript 的无处不在，加上它在客户端机器上执行任意代码的能力，使得这种特殊的 Web 资源成为供应链攻击的理想载体。目前，还没有一种健壮的方法可以让浏览网页的用户验证他们从第三方收到的脚本内容是否是预期的内容。在本文中，我们提出了关键的见解，通知设计健壮的完整性机制，从我们的大规模分析6M 脚本，我们收集了爬行44K 域每天77天。我们发现，在现代网络生态系统中，经常变化的脚本应该被视为一流的公民，而且脚本变化的方式随着时间的推移保持不变。此外，我们在脚本提供程序本身的粒度上分析了严格完整性验证(例如，子资源完整性)的使用，提供了一个更完整的视角，并证明仅仅使用严格完整性不能提供令人满意的安全保证。我们的结论是，在没有额外的外部知识的情况下，客户端将良性变化与恶意变化区分开来是不可行的，因此需要一个新的协议来为客户端提供必要的上下文，以评估脚本变化的潜在影响。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=The+More+Things+Change,+the+More+They+Stay+the+Same:+Integrity+of+Modern+JavaScript)|0| -|[AppSniffer: Towards Robust Mobile App Fingerprinting Against VPN](https://doi.org/10.1145/3543507.3583473)|Sanghak Oh, Minwook Lee, Hyunwoo Lee, Elisa Bertino, Hyoungshick Kim|Korea Institute of Energy Technology, Republic of Korea; Purdue University, USA; Sungkyunkwan University, Republic of Korea|Application fingerprinting is a useful data analysis technique for network administrators, marketing agencies, and security analysts. For example, an administrator can adopt application fingerprinting techniques to determine whether a user’s network access is allowed. Several mobile application fingerprinting techniques (e.g., FlowPrint, AppScanner, and ET-BERT) were recently introduced to identify applications using the characteristics of network traffic. However, we find that the performance of the existing mobile application fingerprinting systems significantly degrades when a virtual private network (VPN) is used. To address such a shortcoming, we propose a framework dubbed AppSniffer that uses a two-stage classification process for mobile app fingerprinting. In the first stage, we distinguish VPN traffic from normal traffic; in the second stage, we use the optimal model for each traffic type. Specifically, we propose a stacked ensemble model using Light Gradient Boosting Machine (LightGBM) and a FastAI library-based neural network model to identify applications’ traffic when a VPN is used. To show the feasibility of AppSniffer, we evaluate the detection accuracy of AppSniffer for 150 popularly used Android apps. Our experimental results show that AppSniffer effectively identifies mobile applications over VPNs with F1-scores between 84.66% and 95.49% across four different VPN protocols. In contrast, the best state-of-the-art method (i.e., AppScanner) demonstrates significantly lower F1-scores between 25.63% and 47.56% in the same settings. Overall, when normal traffic and VPN traffic are mixed, AppSniffer achieves an F1-score of 90.63%, which is significantly better than AppScanner that shows an F1-score of 70.36%.|应用程序指纹分析对于网络管理员、营销机构和安全分析师来说是一种有用的数据分析技术。例如，管理员可以采用应用程序指纹技术来确定是否允许用户的网络访问。最近引入了几种移动应用程序指纹识别技术(例如 FlowPrint、 AppScanner 和 ET-BERT)来利用网络流量的特征识别应用程序。然而，我们发现当使用虚拟专用网络(VPN)时，现有的移动应用指纹识别系统的性能会显著下降。为了解决这个问题，我们提出了一个名为 AppSniffer 的框架，该框架使用两阶段的分类过程进行移动应用指纹识别。在第一阶段，我们将 VPN 流量与正常流量区分开来，在第二阶段，我们使用每种流量类型的最优模型。具体来说，我们提出了一个使用梯度提升机(LightGBM)的堆叠集成模型和一个基于 FastAI 库的神经网络模型来识别使用 VPN 时应用程序的流量。为了证明 AppSniffer 的可行性，我们评估了 AppSniffer 对150个广泛使用的 Android 应用程序的检测准确性。我们的实验结果表明，AppSniffer 能够有效地识别 VPN 上的移动应用，在四种不同的 VPN 协议中，F1得分在84.66% 到95.49% 之间。相比之下，最先进的方法(即 AppScanner)显示，在相同的设置下，F1得分在25.63% 和47.56% 之间显著降低。总的来说，当正常流量和 VPN 流量混合时，AppSniffer 的 F1得分为90.63% ，明显优于 AppScanner 的70.36% 的 F1得分。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=AppSniffer:+Towards+Robust+Mobile+App+Fingerprinting+Against+VPN)|0| +|[AppSniffer: Towards Robust Mobile App Fingerprinting Against VPN](https://doi.org/10.1145/3543507.3583473)|Sanghak Oh, Minwook Lee, Hyunwoo Lee, Elisa Bertino, Hyoungshick Kim|Sungkyunkwan University, Republic of Korea; Korea Institute of Energy Technology, Republic of Korea; Purdue University, USA|Application fingerprinting is a useful data analysis technique for network administrators, marketing agencies, and security analysts. For example, an administrator can adopt application fingerprinting techniques to determine whether a user’s network access is allowed. Several mobile application fingerprinting techniques (e.g., FlowPrint, AppScanner, and ET-BERT) were recently introduced to identify applications using the characteristics of network traffic. However, we find that the performance of the existing mobile application fingerprinting systems significantly degrades when a virtual private network (VPN) is used. To address such a shortcoming, we propose a framework dubbed AppSniffer that uses a two-stage classification process for mobile app fingerprinting. In the first stage, we distinguish VPN traffic from normal traffic; in the second stage, we use the optimal model for each traffic type. Specifically, we propose a stacked ensemble model using Light Gradient Boosting Machine (LightGBM) and a FastAI library-based neural network model to identify applications’ traffic when a VPN is used. To show the feasibility of AppSniffer, we evaluate the detection accuracy of AppSniffer for 150 popularly used Android apps. Our experimental results show that AppSniffer effectively identifies mobile applications over VPNs with F1-scores between 84.66% and 95.49% across four different VPN protocols. In contrast, the best state-of-the-art method (i.e., AppScanner) demonstrates significantly lower F1-scores between 25.63% and 47.56% in the same settings. Overall, when normal traffic and VPN traffic are mixed, AppSniffer achieves an F1-score of 90.63%, which is significantly better than AppScanner that shows an F1-score of 70.36%.|应用程序指纹分析对于网络管理员、营销机构和安全分析师来说是一种有用的数据分析技术。例如，管理员可以采用应用程序指纹技术来确定是否允许用户的网络访问。最近引入了几种移动应用程序指纹识别技术(例如 FlowPrint、 AppScanner 和 ET-BERT)来利用网络流量的特征识别应用程序。然而，我们发现当使用虚拟专用网络(VPN)时，现有的移动应用指纹识别系统的性能会显著下降。为了解决这个问题，我们提出了一个名为 AppSniffer 的框架，该框架使用两阶段的分类过程进行移动应用指纹识别。在第一阶段，我们将 VPN 流量与正常流量区分开来，在第二阶段，我们使用每种流量类型的最优模型。具体来说，我们提出了一个使用梯度提升机(LightGBM)的堆叠集成模型和一个基于 FastAI 库的神经网络模型来识别使用 VPN 时应用程序的流量。为了证明 AppSniffer 的可行性，我们评估了 AppSniffer 对150个广泛使用的 Android 应用程序的检测准确性。我们的实验结果表明，AppSniffer 能够有效地识别 VPN 上的移动应用，在四种不同的 VPN 协议中，F1得分在84.66% 到95.49% 之间。相比之下，最先进的方法(即 AppScanner)显示，在相同的设置下，F1得分在25.63% 和47.56% 之间显著降低。总的来说，当正常流量和 VPN 流量混合时，AppSniffer 的 F1得分为90.63% ，明显优于 AppScanner 的70.36% 的 F1得分。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=AppSniffer:+Towards+Robust+Mobile+App+Fingerprinting+Against+VPN)|0| |[RICC: Robust Collective Classification of Sybil Accounts](https://doi.org/10.1145/3543507.3583475)|Dongwon Shin, Suyoung Lee, Sooel Son|School of Computing, KAIST, Republic of Korea|A Sybil attack is a critical threat that undermines the trust and integrity of web services by creating and exploiting a large number of fake (i.e., Sybil) accounts. To mitigate this threat, previous studies have proposed leveraging collective classification to detect Sybil accounts. Recently, researchers have demonstrated that state-of-the-art adversarial attacks are able to bypass existing collective classification methods, posing a new security threat. To this end, we propose RICC, the first robust collective classification framework, designed to identify adversarial Sybil accounts created by adversarial attacks. RICC leverages the novel observation that these adversarial attacks are highly tailored to a target collective classification model to optimize the attack budget. Owing to this adversarial strategy, the classification results for adversarial Sybil accounts often significantly change when deploying a new training set different from the original training set used for assigning prior reputation scores to user accounts. Leveraging this observation, RICC achieves robustness in collective classification by stabilizing classification results across different training sets randomly sampled in each round. RICC achieves false negative rates of 0.01, 0.11, 0.00, and 0.01 in detecting adversarial Sybil accounts for the Enron, Facebook, Twitter_S, and Twitter_L datasets, respectively. It also attains respective AUCs of 0.99, 1.00, 0.89, and 0.74 for these datasets, achieving high performance on the original task of detecting Sybil accounts. RICC significantly outperforms all existing Sybil detection methods, demonstrating superior robustness and efficacy in the collective classification of Sybil accounts.|Sybil 攻击是一个严重的威胁，它通过创建和利用大量虚假(比如 Sybil)帐户来破坏 Web 服务的信任和完整性。为了减轻这种威胁，以前的研究已经提出利用集体分类来检测 Sybil 帐户。最近，研究人员已经证明，最先进的对手攻击能够绕过现有的集体分类方法，构成新的安全威胁。为此，我们提出了 RICC，第一个健壮的集体分类框架，旨在识别由对手攻击创建的对手 Sybil 帐户。RICC 利用这种新颖的观察，即这些对手攻击高度适合于目标集体分类模型，以优化攻击预算。由于这种对抗性战略，当部署一套不同于原先用于为用户账户分配以前声誉评分的训练集的新训练集时，对抗性 Sybil 账户的分类结果往往会发生重大变化。利用这一观察结果，RICC 通过稳定每轮随机采样的不同训练集的分类结果来实现集体分类的鲁棒性。RICC 在检测 Enron，Facebook，Twitter _ S 和 Twitter _ L 数据集的对手 Sybil 帐户时分别达到0.01,0.11,0.00和0.01的假阴性率。它还为这些数据集分别获得0.99、1.00、0.89和0.74的 AUC，从而在检测 Sybil 帐户的原始任务上实现了高性能。RICC 显著优于所有现有的 Sybil 检测方法，在 Sybil 帐户的集体分类中显示出优越的稳健性和有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=RICC:+Robust+Collective+Classification+of+Sybil+Accounts)|0| |[ZTLS: A DNS-based Approach to Zero Round Trip Delay in TLS handshake](https://doi.org/10.1145/3543507.3583516)|Sangwon Lim, Hyeonmin Lee, Hyunsoo Kim, Hyunwoo Lee, Ted Taekyoung Kwon|Korea Institute of Energy Technology, Republic of Korea; Seoul National University, Republic of Korea|Establishing secure connections fast to end-users is crucial to online services. However, when a client sets up a TLS session with a server, the TLS handshake needs one round trip time (RTT) to negotiate a session key. Additionally, establishing a TLS session also requires a DNS lookup (e.g., the A record lookup to fetch the IP address of the server) and a TCP handshake. In this paper, we propose ZTLS to eliminate the 1-RTT latency for the TLS handshake by leveraging the DNS. In ZTLS, a server distributes TLS handshake-related data (i.e., Diffie-Hellman elements), dubbed Z-data, as DNS records. A ZTLS client can fetch Z-data by DNS lookups and derive a session key. With the session key, the client can send encrypted data along with its ClientHello, achieving 0-RTT. ZTLS supports incremental deployability on the current TLS-based infrastructure. Our prototype-based experiments show that ZTLS is 1-RTT faster than TLS in terms of the first response time.|快速建立与终端用户的安全连接对在线服务至关重要。但是，当客户端设置与服务器的 TLS 会话时，TLS 握手需要一个往返时间(RTT)来协商会话密钥。此外，建立 TLS 会话还需要 DNS 查找(例如，A 记录查找以获取服务器的 IP 地址)和 TCP 握手。在本文中，我们提出利用 DNS 来消除 TLS 握手的1-RTT 延迟。在 ZTLS 中，服务器将与 TLS 握手相关的数据(例如，Diffie-Hellman 元素)(称为 Z-data)作为 DNS 记录分发。ZTLS 客户机可以通过 DNS 查找获取 Z 数据并派生会话密钥。使用会话密钥，客户端可以发送加密的数据及其 ClientHello，从而实现0-RTT。ZTLS 支持当前基于 TLS 的基础设施上的增量式部署。基于原型的实验表明，ZTLS 在第一响应时间方面比 TLS 快1-RTT。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=ZTLS:+A+DNS-based+Approach+to+Zero+Round+Trip+Delay+in+TLS+handshake)|0| -|[AgrEvader: Poisoning Membership Inference against Byzantine-robust Federated Learning](https://doi.org/10.1145/3543507.3583542)|Yanjun Zhang, Guangdong Bai, Mahawaga Arachchige Pathum Chamikara, Mengyao Ma, Liyue Shen, Jingwei Wang, Surya Nepal, Minhui Xue, Long Wang, Joseph K. Liu|CSIRO's Data61, Australia; Monash University, Australia; Intelligent Engine Department, Ant Group, MYBank, China; Deakin University, Australia; The University of Queensland, Australia|The Poisoning Membership Inference Attack (PMIA) is a newly emerging privacy attack that poses a significant threat to federated learning (FL). An adversary conducts data poisoning (i.e., performing adversarial manipulations on training examples) to extract membership information by exploiting the changes in loss resulting from data poisoning. The PMIA significantly exacerbates the traditional poisoning attack that is primarily focused on model corruption. However, there has been a lack of a comprehensive systematic study that thoroughly investigates this topic. In this work, we conduct a benchmark evaluation to assess the performance of PMIA against the Byzantine-robust FL setting that is specifically designed to mitigate poisoning attacks. We find that all existing coordinate-wise averaging mechanisms fail to defend against the PMIA, while the detect-then-drop strategy was proven to be effective in most cases, implying that the poison injection is memorized and the poisonous effect rarely dissipates. Inspired by this observation, we propose AgrEvader, a PMIA that maximizes the adversarial impact on the victim samples while circumventing the detection by Byzantine-robust mechanisms. AgrEvader significantly outperforms existing PMIAs. For instance, AgrEvader achieved a high attack accuracy of between 72.78% (on CIFAR-10) to 97.80% (on Texas100), which is an average accuracy increase of 13.89% compared to the strongest PMIA reported in the literature. We evaluated AgrEvader on five datasets across different domains, against a comprehensive list of threat models, which included black-box, gray-box and white-box models for targeted and non-targeted scenarios. AgrEvader demonstrated consistent high accuracy across all settings tested. The code is available at: https://github.com/PrivSecML/AgrEvader.|中毒成员推理攻击(PMIA)是一种新兴的隐私攻击，对联邦学习(FL)构成了严重的威胁。一个对手进行数据中毒(即，对训练例子进行对抗性操作) ，通过利用数据中毒导致的损失变化来提取会员信息。PMIA 显著加剧了主要关注模型腐败的传统中毒攻击。然而，对这一课题的深入研究一直缺乏全面系统的研究。在这项工作中，我们进行了一个基准评估，以评估性能的 PMIA 对拜占庭稳健的 FL 设置，是专门设计用于减轻中毒攻击。我们发现所有现有的坐标平均机制都不能抵御 PMIA，而检测然后下降策略在大多数情况下被证明是有效的，这意味着毒物注射被记住，毒性效应很少消散。受这个观察的启发，我们提出 AgrEvader，一个 PMIA，它可以最大化对受害者样本的对抗性影响，同时规避拜占庭稳健机制的检测。AgrEvader 的性能明显优于现有的 PMIA。例如，AgrEvader 实现了72.78% (在 CIFAR-10上)至97.80% (在 Texas 100上)的高攻击准确率，与文献中报道的最强 PMIA 相比，平均准确率提高了13.89% 。我们在不同领域的五个数据集上对 AgrEvader 进行了评估，针对的是一个全面的威胁模型列表，其中包括针对目标和非目标场景的黑盒、灰盒和白盒模型。AgrEvader 在所有测试环境中都表现出一致的高精度。密码可于以下 https://github.com/privsecml/agrevader 索取:。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=AgrEvader:+Poisoning+Membership+Inference+against+Byzantine-robust+Federated+Learning)|0| -|[Event Prediction using Case-Based Reasoning over Knowledge Graphs](https://doi.org/10.1145/3543507.3583201)|Sola Shirai, Debarun Bhattacharjya, Oktie Hassanzadeh|IBM Research, USA; Rensselaer Polytechnic Institute, USA|Applying link prediction (LP) methods over knowledge graphs (KG) for tasks such as causal event prediction presents an exciting opportunity. However, typical LP models are ill-suited for this task as they are incapable of performing inductive link prediction for new, unseen event entities and they require retraining as knowledge is added or changed in the underlying KG. We introduce a case-based reasoning model, EvCBR, to predict properties about new consequent events based on similar cause-effect events present in the KG. EvCBR uses statistical measures to identify similar events and performs path-based predictions, requiring no training step. To generalize our methods beyond the domain of event prediction, we frame our task as a 2-hop LP task, where the first hop is a causal relation connecting a cause event to a new effect event and the second hop is a property about the new event which we wish to predict. The effectiveness of our method is demonstrated using a novel dataset of newsworthy events with causal relations curated from Wikidata, where EvCBR outperforms baselines including translational-distance-based, GNN-based, and rule-based LP models.|将知识图上的链接预测(LP)方法应用于因果事件预测等任务，提供了一个令人兴奋的机会。然而，典型的线性规划模型不适合这项任务，因为它们不能对新的、看不见的事件实体执行归纳链接预测，并且当知识在基础 KG 中被添加或改变时，它们需要再培训。我们引入一个案例推论模型，EvCBR，基于 KG 中相似的因果事件来预测新的结果事件的性质。EvCBR 使用统计方法来识别类似的事件，并执行基于路径的预测，不需要训练步骤。为了将我们的方法推广到事件预测领域之外，我们将我们的任务框架为一个2跳 LP 任务，其中第一跳是连接一个因果事件和一个新的结果事件的因果关系，第二跳是我们希望预测的新事件的性质。我们的方法的有效性是通过使用一个新的具有新闻价值的事件数据集和从 Wikidata 策划的因果关系来证明的，在那里，EvCBR 优于包括基于平移距离、基于 GNN 和基于规则的 LP 模型在内的基线。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Event+Prediction+using+Case-Based+Reasoning+over+Knowledge+Graphs)|0| -|[Wikidata as a seed for Web Extraction](https://doi.org/10.1145/3543507.3583236)|Kunpeng Guo, Dennis Diefenbach, Antoine Gourru, Christophe Gravier|The QA Company SAS, France and Laboratoire Hubert Curien UMR 5516, Université Jean Monnet, France; Laboratoire Hubert Curien UMR 5516, Université Jean Monnet, France; The QA Company SAS, France and Laboratoire Hubert Curien, UMR CNRS 5516, Université Jean Monnet, France|Wikidata has grown to a knowledge graph with an impressive size. To date, it contains more than 17 billion triples collecting information about people, places, films, stars, publications, proteins, and many more. On the other side, most of the information on the Web is not published in highly structured data repositories like Wikidata, but rather as unstructured and semi-structured content, more concretely in HTML pages containing text and tables. Finding, monitoring, and organizing this data in a knowledge graph is requiring considerable work from human editors. The volume and complexity of the data make this task difficult and time-consuming. In this work, we present a framework that is able to identify and extract new facts that are published under multiple Web domains so that they can be proposed for validation by Wikidata editors. The framework is relying on question-answering technologies. We take inspiration from ideas that are used to extract facts from textual collections and adapt them to extract facts from Web pages. For achieving this, we demonstrate that language models can be adapted to extract facts not only from textual collections but also from Web pages. By exploiting the information already contained in Wikidata the proposed framework can be trained without the need for any additional learning signals and can extract new facts for a wide range of properties and domains. Following this path, Wikidata can be used as a seed to extract facts on the Web. Our experiments show that we can achieve a mean performance of 84.07 at F1-score. Moreover, our estimations show that we can potentially extract millions of facts that can be proposed for human validation. The goal is to help editors in their daily tasks and contribute to the completion of the Wikidata knowledge graph.|Wikidata 已经发展成为一个知识图表，其规模令人印象深刻。到目前为止，它包含超过170亿三倍收集信息的人，地方，电影，明星，出版物，蛋白质，和更多。另一方面，Web 上的大部分信息并不是像 Wikidata 那样在高度结构化的数据库中发布的，而是作为非结构化和半结构化的内容，更具体地说，是在包含文本和表格的 HTML 页面中发布的。在知识图中查找、监视和组织这些数据需要编辑人员进行大量的工作。数据的数量和复杂性使这项任务变得困难和耗时。在这项工作中，我们提出了一个框架，它能够识别和提取在多个 Web 域下发布的新事实，以便它们可以提交给 Wikidata 编辑器进行验证。该框架依赖于问答技术。我们从用于从文本集合中提取事实的想法中获得灵感，并对这些想法进行调整以从网页中提取事实。为了实现这一点，我们演示了语言模型不仅可以从文本集合中提取事实，还可以从 Web 页面中提取事实。通过利用 Wikidata 已有的信息，建议的框架可以在不需要任何额外学习信号的情况下进行培训，并且可以为广泛的属性和领域提取新的事实。沿着这条路径，Wikidata 可以作为种子在 Web 上提取事实。我们的实验表明，我们可以达到平均性能的84.07在 F1分数。此外，我们的估计表明，我们可以提取数以百万计的事实，可以提出人类验证。目标是帮助编辑完成他们的日常任务，并为完成 Wikidata 知识图谱做出贡献。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Wikidata+as+a+seed+for+Web+Extraction)|0| -|[Meta-Learning Based Knowledge Extrapolation for Temporal Knowledge Graph](https://doi.org/10.1145/3543507.3583279)|Zhongwu Chen, Chengjin Xu, Fenglong Su, Zhen Huang, Yong Dou|National University of Defense Technology, China; International Digital Economy Academy, China|In the last few years, the solution to Knowledge Graph (KG) completion via learning embeddings of entities and relations has attracted a surge of interest. Temporal KGs(TKGs) extend traditional Knowledge Graphs (KGs) by associating static triples with timestamps forming quadruples. Different from KGs and TKGs in the transductive setting, constantly emerging entities and relations in incomplete TKGs create demand to predict missing facts with unseen components, which is the extrapolation setting. Traditional temporal knowledge graph embedding (TKGE) methods are limited in the extrapolation setting since they are trained within a fixed set of components. In this paper, we propose a Meta-Learning based Temporal Knowledge Graph Extrapolation (MTKGE) model, which is trained on link prediction tasks sampled from the existing TKGs and tested in the emerging TKGs with unseen entities and relations. Specifically, we meta-train a GNN framework that captures relative position patterns and temporal sequence patterns between relations. The learned embeddings of patterns can be transferred to embed unseen components. Experimental results on two different TKG extrapolation datasets show that MTKGE consistently outperforms both the existing state-of-the-art models for knowledge graph extrapolation and specifically adapted KGE and TKGE baselines.|近年来，通过实体和关系的学习嵌入解决知识图(KG)完备性问题引起了人们极大的兴趣。时态 KGs (TKGs)通过将静态三元组与时间戳关联形成四元组，从而扩展了传统知识图(KGs)。与传导性背景下的幼稚园和传统幼稚园不同，不完整的传统幼稚园中不断出现的实体和关系创造了用看不见的成分预测缺失事实的需求，这就是外推背景。传统的时态知识图嵌入(TKGE)方法由于是在固定的组件集内进行训练，在外推设置上受到限制。本文提出了一种基于元学习的时态知识图外推(MTKGE)模型，该模型对从现有 TKG 中抽取的链接预测任务进行训练，并在新出现的具有不可见实体和关系的 TKG 中进行测试。具体地说，我们元训练了一个 GNN 框架，它捕获了关系之间的相对位置模式和时间序列模式。模式的学习嵌入可以转换为嵌入不可见的组件。在两个不同的 TKG 外推数据集上的实验结果表明，MTKGE 始终优于现有的最先进的知识图外推模型和特别适应的 KGE 和 TKGE 基线。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Meta-Learning+Based+Knowledge+Extrapolation+for+Temporal+Knowledge+Graph)|0| -|[Can Persistent Homology provide an efficient alternative for Evaluation of Knowledge Graph Completion Methods?](https://doi.org/10.1145/3543507.3583308)|Anson Bastos, Kuldeep Singh, Abhishek Nadgeri, Johannes Hoffart, Manish Singh, Toyotaro Suzumura|; IIT, Hyderabad, India; Cerence GmbH and Zerotha Research, Germany; RWTH Aachen, Germany; SAP, Germany|In this paper we present a novel method, $\textit{Knowledge Persistence}$ ($\mathcal{KP}$), for faster evaluation of Knowledge Graph (KG) completion approaches. Current ranking-based evaluation is quadratic in the size of the KG, leading to long evaluation times and consequently a high carbon footprint. $\mathcal{KP}$ addresses this by representing the topology of the KG completion methods through the lens of topological data analysis, concretely using persistent homology. The characteristics of persistent homology allow $\mathcal{KP}$ to evaluate the quality of the KG completion looking only at a fraction of the data. Experimental results on standard datasets show that the proposed metric is highly correlated with ranking metrics (Hits@N, MR, MRR). Performance evaluation shows that $\mathcal{KP}$ is computationally efficient: In some cases, the evaluation time (validation+test) of a KG completion method has been reduced from 18 hours (using Hits@10) to 27 seconds (using $\mathcal{KP}$), and on average (across methods & data) reduces the evaluation time (validation+test) by $\approx$ $\textbf{99.96}\%$.|本文提出了一种快速评估知识图(KG)完成方法的新方法，即知识持久化(KP)方法。目前以排名为基础的评估是以幼稚园的规模作为二次评估，评估时间较长，因此碳足印较高。$mathcal { KP } $通过拓扑数据分析的透镜来表示 KG 完成方法的拓扑，具体地使用持久同调来解决这个问题。持久同源的特征允许 $mathcal { KP } $评估 KG 完成的质量，只查看一小部分数据。在标准数据集上的实验结果表明，提出的度量与排序度量(Hits@N，MR，MRR)高度相关。性能评估表明 $mathcal { KP } $在计算上是有效的: 在某些情况下，KG 完成方法的评估时间(验证 + 测试)已经从18小时(使用 Hits@10)减少到27秒(使用 $mathcal { KP } $) ，并且平均(跨方法和数据)将评估时间(验证 + 测试)减少约 $$textbf {99.96}% $。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Can+Persistent+Homology+provide+an+efficient+alternative+for+Evaluation+of+Knowledge+Graph+Completion+Methods?)|0| +|[AgrEvader: Poisoning Membership Inference against Byzantine-robust Federated Learning](https://doi.org/10.1145/3543507.3583542)|Yanjun Zhang, Guangdong Bai, Mahawaga Arachchige Pathum Chamikara, Mengyao Ma, Liyue Shen, Jingwei Wang, Surya Nepal, Minhui Xue, Long Wang, Joseph K. Liu|Monash University, Australia; Intelligent Engine Department, Ant Group, MYBank, China; Deakin University, Australia; The University of Queensland, Australia; CSIRO's Data61, Australia|The Poisoning Membership Inference Attack (PMIA) is a newly emerging privacy attack that poses a significant threat to federated learning (FL). An adversary conducts data poisoning (i.e., performing adversarial manipulations on training examples) to extract membership information by exploiting the changes in loss resulting from data poisoning. The PMIA significantly exacerbates the traditional poisoning attack that is primarily focused on model corruption. However, there has been a lack of a comprehensive systematic study that thoroughly investigates this topic. In this work, we conduct a benchmark evaluation to assess the performance of PMIA against the Byzantine-robust FL setting that is specifically designed to mitigate poisoning attacks. We find that all existing coordinate-wise averaging mechanisms fail to defend against the PMIA, while the detect-then-drop strategy was proven to be effective in most cases, implying that the poison injection is memorized and the poisonous effect rarely dissipates. Inspired by this observation, we propose AgrEvader, a PMIA that maximizes the adversarial impact on the victim samples while circumventing the detection by Byzantine-robust mechanisms. AgrEvader significantly outperforms existing PMIAs. For instance, AgrEvader achieved a high attack accuracy of between 72.78% (on CIFAR-10) to 97.80% (on Texas100), which is an average accuracy increase of 13.89% compared to the strongest PMIA reported in the literature. We evaluated AgrEvader on five datasets across different domains, against a comprehensive list of threat models, which included black-box, gray-box and white-box models for targeted and non-targeted scenarios. AgrEvader demonstrated consistent high accuracy across all settings tested. The code is available at: https://github.com/PrivSecML/AgrEvader.|中毒成员推理攻击(PMIA)是一种新兴的隐私攻击，对联邦学习(FL)构成了严重的威胁。一个对手进行数据中毒(即，对训练例子进行对抗性操作) ，通过利用数据中毒导致的损失变化来提取会员信息。PMIA 显著加剧了主要关注模型腐败的传统中毒攻击。然而，对这一课题的深入研究一直缺乏全面系统的研究。在这项工作中，我们进行了一个基准评估，以评估性能的 PMIA 对拜占庭稳健的 FL 设置，是专门设计用于减轻中毒攻击。我们发现所有现有的坐标平均机制都不能抵御 PMIA，而检测然后下降策略在大多数情况下被证明是有效的，这意味着毒物注射被记住，毒性效应很少消散。受这个观察的启发，我们提出 AgrEvader，一个 PMIA，它可以最大化对受害者样本的对抗性影响，同时规避拜占庭稳健机制的检测。AgrEvader 的性能明显优于现有的 PMIA。例如，AgrEvader 实现了72.78% (在 CIFAR-10上)至97.80% (在 Texas 100上)的高攻击准确率，与文献中报道的最强 PMIA 相比，平均准确率提高了13.89% 。我们在不同领域的五个数据集上对 AgrEvader 进行了评估，针对的是一个全面的威胁模型列表，其中包括针对目标和非目标场景的黑盒、灰盒和白盒模型。AgrEvader 在所有测试环境中都表现出一致的高精度。密码可于以下 https://github.com/privsecml/agrevader 索取:。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=AgrEvader:+Poisoning+Membership+Inference+against+Byzantine-robust+Federated+Learning)|0| +|[Event Prediction using Case-Based Reasoning over Knowledge Graphs](https://doi.org/10.1145/3543507.3583201)|Sola Shirai, Debarun Bhattacharjya, Oktie Hassanzadeh|Rensselaer Polytechnic Institute, USA; IBM Research, USA|Applying link prediction (LP) methods over knowledge graphs (KG) for tasks such as causal event prediction presents an exciting opportunity. However, typical LP models are ill-suited for this task as they are incapable of performing inductive link prediction for new, unseen event entities and they require retraining as knowledge is added or changed in the underlying KG. We introduce a case-based reasoning model, EvCBR, to predict properties about new consequent events based on similar cause-effect events present in the KG. EvCBR uses statistical measures to identify similar events and performs path-based predictions, requiring no training step. To generalize our methods beyond the domain of event prediction, we frame our task as a 2-hop LP task, where the first hop is a causal relation connecting a cause event to a new effect event and the second hop is a property about the new event which we wish to predict. The effectiveness of our method is demonstrated using a novel dataset of newsworthy events with causal relations curated from Wikidata, where EvCBR outperforms baselines including translational-distance-based, GNN-based, and rule-based LP models.|将知识图上的链接预测(LP)方法应用于因果事件预测等任务，提供了一个令人兴奋的机会。然而，典型的线性规划模型不适合这项任务，因为它们不能对新的、看不见的事件实体执行归纳链接预测，并且当知识在基础 KG 中被添加或改变时，它们需要再培训。我们引入一个案例推论模型，EvCBR，基于 KG 中相似的因果事件来预测新的结果事件的性质。EvCBR 使用统计方法来识别类似的事件，并执行基于路径的预测，不需要训练步骤。为了将我们的方法推广到事件预测领域之外，我们将我们的任务框架为一个2跳 LP 任务，其中第一跳是连接一个因果事件和一个新的结果事件的因果关系，第二跳是我们希望预测的新事件的性质。我们的方法的有效性是通过使用一个新的具有新闻价值的事件数据集和从 Wikidata 策划的因果关系来证明的，在那里，EvCBR 优于包括基于平移距离、基于 GNN 和基于规则的 LP 模型在内的基线。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Event+Prediction+using+Case-Based+Reasoning+over+Knowledge+Graphs)|0| +|[Wikidata as a seed for Web Extraction](https://doi.org/10.1145/3543507.3583236)|Kunpeng Guo, Dennis Diefenbach, Antoine Gourru, Christophe Gravier|The QA Company SAS, France and Laboratoire Hubert Curien, UMR CNRS 5516, Université Jean Monnet, France; The QA Company SAS, France and Laboratoire Hubert Curien UMR 5516, Université Jean Monnet, France; Laboratoire Hubert Curien UMR 5516, Université Jean Monnet, France|Wikidata has grown to a knowledge graph with an impressive size. To date, it contains more than 17 billion triples collecting information about people, places, films, stars, publications, proteins, and many more. On the other side, most of the information on the Web is not published in highly structured data repositories like Wikidata, but rather as unstructured and semi-structured content, more concretely in HTML pages containing text and tables. Finding, monitoring, and organizing this data in a knowledge graph is requiring considerable work from human editors. The volume and complexity of the data make this task difficult and time-consuming. In this work, we present a framework that is able to identify and extract new facts that are published under multiple Web domains so that they can be proposed for validation by Wikidata editors. The framework is relying on question-answering technologies. We take inspiration from ideas that are used to extract facts from textual collections and adapt them to extract facts from Web pages. For achieving this, we demonstrate that language models can be adapted to extract facts not only from textual collections but also from Web pages. By exploiting the information already contained in Wikidata the proposed framework can be trained without the need for any additional learning signals and can extract new facts for a wide range of properties and domains. Following this path, Wikidata can be used as a seed to extract facts on the Web. Our experiments show that we can achieve a mean performance of 84.07 at F1-score. Moreover, our estimations show that we can potentially extract millions of facts that can be proposed for human validation. The goal is to help editors in their daily tasks and contribute to the completion of the Wikidata knowledge graph.|Wikidata 已经发展成为一个知识图表，其规模令人印象深刻。到目前为止，它包含超过170亿三倍收集信息的人，地方，电影，明星，出版物，蛋白质，和更多。另一方面，Web 上的大部分信息并不是像 Wikidata 那样在高度结构化的数据库中发布的，而是作为非结构化和半结构化的内容，更具体地说，是在包含文本和表格的 HTML 页面中发布的。在知识图中查找、监视和组织这些数据需要编辑人员进行大量的工作。数据的数量和复杂性使这项任务变得困难和耗时。在这项工作中，我们提出了一个框架，它能够识别和提取在多个 Web 域下发布的新事实，以便它们可以提交给 Wikidata 编辑器进行验证。该框架依赖于问答技术。我们从用于从文本集合中提取事实的想法中获得灵感，并对这些想法进行调整以从网页中提取事实。为了实现这一点，我们演示了语言模型不仅可以从文本集合中提取事实，还可以从 Web 页面中提取事实。通过利用 Wikidata 已有的信息，建议的框架可以在不需要任何额外学习信号的情况下进行培训，并且可以为广泛的属性和领域提取新的事实。沿着这条路径，Wikidata 可以作为种子在 Web 上提取事实。我们的实验表明，我们可以达到平均性能的84.07在 F1分数。此外，我们的估计表明，我们可以提取数以百万计的事实，可以提出人类验证。目标是帮助编辑完成他们的日常任务，并为完成 Wikidata 知识图谱做出贡献。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Wikidata+as+a+seed+for+Web+Extraction)|0| +|[Meta-Learning Based Knowledge Extrapolation for Temporal Knowledge Graph](https://doi.org/10.1145/3543507.3583279)|Zhongwu Chen, Chengjin Xu, Fenglong Su, Zhen Huang, Yong Dou|International Digital Economy Academy, China; National University of Defense Technology, China|In the last few years, the solution to Knowledge Graph (KG) completion via learning embeddings of entities and relations has attracted a surge of interest. Temporal KGs(TKGs) extend traditional Knowledge Graphs (KGs) by associating static triples with timestamps forming quadruples. Different from KGs and TKGs in the transductive setting, constantly emerging entities and relations in incomplete TKGs create demand to predict missing facts with unseen components, which is the extrapolation setting. Traditional temporal knowledge graph embedding (TKGE) methods are limited in the extrapolation setting since they are trained within a fixed set of components. In this paper, we propose a Meta-Learning based Temporal Knowledge Graph Extrapolation (MTKGE) model, which is trained on link prediction tasks sampled from the existing TKGs and tested in the emerging TKGs with unseen entities and relations. Specifically, we meta-train a GNN framework that captures relative position patterns and temporal sequence patterns between relations. The learned embeddings of patterns can be transferred to embed unseen components. Experimental results on two different TKG extrapolation datasets show that MTKGE consistently outperforms both the existing state-of-the-art models for knowledge graph extrapolation and specifically adapted KGE and TKGE baselines.|近年来，通过实体和关系的学习嵌入解决知识图(KG)完备性问题引起了人们极大的兴趣。时态 KGs (TKGs)通过将静态三元组与时间戳关联形成四元组，从而扩展了传统知识图(KGs)。与传导性背景下的幼稚园和传统幼稚园不同，不完整的传统幼稚园中不断出现的实体和关系创造了用看不见的成分预测缺失事实的需求，这就是外推背景。传统的时态知识图嵌入(TKGE)方法由于是在固定的组件集内进行训练，在外推设置上受到限制。本文提出了一种基于元学习的时态知识图外推(MTKGE)模型，该模型对从现有 TKG 中抽取的链接预测任务进行训练，并在新出现的具有不可见实体和关系的 TKG 中进行测试。具体地说，我们元训练了一个 GNN 框架，它捕获了关系之间的相对位置模式和时间序列模式。模式的学习嵌入可以转换为嵌入不可见的组件。在两个不同的 TKG 外推数据集上的实验结果表明，MTKGE 始终优于现有的最先进的知识图外推模型和特别适应的 KGE 和 TKGE 基线。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Meta-Learning+Based+Knowledge+Extrapolation+for+Temporal+Knowledge+Graph)|0| +|[Can Persistent Homology provide an efficient alternative for Evaluation of Knowledge Graph Completion Methods?](https://doi.org/10.1145/3543507.3583308)|Anson Bastos, Kuldeep Singh, Abhishek Nadgeri, Johannes Hoffart, Manish Singh, Toyotaro Suzumura|; Cerence GmbH and Zerotha Research, Germany; IIT, Hyderabad, India; RWTH Aachen, Germany; SAP, Germany|In this paper we present a novel method, $\textit{Knowledge Persistence}$ ($\mathcal{KP}$), for faster evaluation of Knowledge Graph (KG) completion approaches. Current ranking-based evaluation is quadratic in the size of the KG, leading to long evaluation times and consequently a high carbon footprint. $\mathcal{KP}$ addresses this by representing the topology of the KG completion methods through the lens of topological data analysis, concretely using persistent homology. The characteristics of persistent homology allow $\mathcal{KP}$ to evaluate the quality of the KG completion looking only at a fraction of the data. Experimental results on standard datasets show that the proposed metric is highly correlated with ranking metrics (Hits@N, MR, MRR). Performance evaluation shows that $\mathcal{KP}$ is computationally efficient: In some cases, the evaluation time (validation+test) of a KG completion method has been reduced from 18 hours (using Hits@10) to 27 seconds (using $\mathcal{KP}$), and on average (across methods & data) reduces the evaluation time (validation+test) by $\approx$ $\textbf{99.96}\%$.|本文提出了一种快速评估知识图(KG)完成方法的新方法，即知识持久化(KP)方法。目前以排名为基础的评估是以幼稚园的规模作为二次评估，评估时间较长，因此碳足印较高。$mathcal { KP } $通过拓扑数据分析的透镜来表示 KG 完成方法的拓扑，具体地使用持久同调来解决这个问题。持久同源的特征允许 $mathcal { KP } $评估 KG 完成的质量，只查看一小部分数据。在标准数据集上的实验结果表明，提出的度量与排序度量(Hits@N，MR，MRR)高度相关。性能评估表明 $mathcal { KP } $在计算上是有效的: 在某些情况下，KG 完成方法的评估时间(验证 + 测试)已经从18小时(使用 Hits@10)减少到27秒(使用 $mathcal { KP } $) ，并且平均(跨方法和数据)将评估时间(验证 + 测试)减少约 $$textbf {99.96}% $。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Can+Persistent+Homology+provide+an+efficient+alternative+for+Evaluation+of+Knowledge+Graph+Completion+Methods?)|0| |[Attribute-Consistent Knowledge Graph Representation Learning for Multi-Modal Entity Alignment](https://doi.org/10.1145/3543507.3583328)|Qian Li, Shu Guo, Yangyifei Luo, Cheng Ji, Lihong Wang, Jiawei Sheng, Jianxin Li|National Computer Network Emergency Response Technical Team/Coordination Center of China, China; Beihang University, China; Institute of Information Engineering, Chinese Academy of Sciences, China|The multi-modal entity alignment (MMEA) aims to find all equivalent entity pairs between multi-modal knowledge graphs (MMKGs). Rich attributes and neighboring entities are valuable for the alignment task, but existing works ignore contextual gap problems that the aligned entities have different numbers of attributes on specific modality when learning entity representations. In this paper, we propose a novel attribute-consistent knowledge graph representation learning framework for MMEA (ACK-MMEA) to compensate the contextual gaps through incorporating consistent alignment knowledge. Attribute-consistent KGs (ACKGs) are first constructed via multi-modal attribute uniformization with merge and generate operators so that each entity has one and only one uniform feature in each modality. The ACKGs are then fed into a relation-aware graph neural network with random dropouts, to obtain aggregated relation representations and robust entity representations. In order to evaluate the ACK-MMEA facilitated for entity alignment, we specially design a joint alignment loss for both entity and attribute evaluation. Extensive experiments conducted on two benchmark datasets show that our approach achieves excellent performance compared to its competitors.|多模态实体对齐(MMEA)的目的是寻找多模态知识图(MMKG)之间的所有等价实体对。丰富的属性和相邻实体对于对齐任务是有价值的，但现有的研究忽略了在学习实体表示时，对齐实体在特定情态下具有不同属性数目的上下文缺口问题。本文提出了一种新的基于属性一致性知识图表示的 MMEA (ACK-MMEA)学习框架，通过引入一致性对齐知识来弥补语境差异。属性一致的 KG (ACKG)首先通过多模态属性一致化构造，并使用合并和生成算子，使每个实体在每个模态中只有一个一致的特征。然后将这些 ACKG 输入到一个具有随机辍学的关系感知图神经网络中，以获得聚合关系表示和鲁棒实体表示。为了方便地评估 ACK-MMEA 算法对实体对齐的效果，我们特别设计了一种用于实体和属性评估的联合对齐损失算法。在两个基准数据集上进行的大量实验表明，与竞争对手相比，我们的方法取得了很好的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Attribute-Consistent+Knowledge+Graph+Representation+Learning+for+Multi-Modal+Entity+Alignment)|0| -|[Hierarchy-Aware Multi-Hop Question Answering over Knowledge Graphs](https://doi.org/10.1145/3543507.3583376)|Junnan Dong, Qinggang Zhang, Xiao Huang, Keyu Duan, Qiaoyu Tan, Zhimeng Jiang|Texas A&M University, USA; The Hong Kong Polytechnic University, Hong Kong; National University of Singapore, Singapore|Knowledge graphs (KGs) have been widely used to enhance complex question answering (QA). To understand complex questions, existing studies employ language models (LMs) to encode contexts. Despite the simplicity, they neglect the latent relational information among question concepts and answers in KGs. While question concepts ubiquitously present hyponymy at the semantic level, e.g., mammals and animals, this feature is identically reflected in the hierarchical relations in KGs, e.g., a_type_of. Therefore, we are motivated to explore comprehensive reasoning by the hierarchical structures in KGs to help understand questions. However, it is non-trivial to reason over tree-like structures compared with chained paths. Moreover, identifying appropriate hierarchies relies on expertise. To this end, we propose HamQA, a novel Hierarchy-aware multi-hop Question Answering framework on knowledge graphs, to effectively align the mutual hierarchical information between question contexts and KGs. The entire learning is conducted in Hyperbolic space, inspired by its advantages of embedding hierarchical structures. Specifically, (i) we design a context-aware graph attentive network to capture context information. (ii) Hierarchical structures are continuously preserved in KGs by minimizing the Hyperbolic geodesic distances. The comprehensive reasoning is conducted to jointly train both components and provide a top-ranked candidate as an optimal answer. We achieve a higher ranking than the state-of-the-art multi-hop baselines on the official OpenBookQA leaderboard with an accuracy of 85%.|知识图已被广泛应用于提高复杂问题回答(QA)能力。为了理解复杂的问题，现有的研究采用语言模型(LM)对语境进行编码。幼儿教育中的问题概念和答案尽管简单，却忽视了潜在的关系信息。问题概念普遍存在于语义层面上的上下义关系，例如哺乳动物和动物，而这一特征在幼儿园的等级关系中得到了完全相同的体现，例如 a _ type _ of。因此，本研究旨在探讨幼稚园层级结构下的综合推理模式，以帮助学生理解问题。然而，与链式路径相比，对树状结构进行推理是非常重要的。此外，识别适当的层次结构依赖于专业知识。为此，我们提出了一种新的基于知识图的层次感知多跳问答框架 HamQA，以有效地调整问题上下文和 KG 之间的相互层次信息。整个学习过程是在双曲空间中进行的，灵感来自于其嵌入层级结构的优势。具体来说，(i)我们设计了一个上下文感知图注意网络来捕获上下文信息。(ii)幼稚园透过尽量减少双曲线测地距离，不断保留层次结构。通过综合推理对两个构件进行联合训练，并提供一个排名靠前的候选者作为最优答案。在 OpenBookQA 的官方排行榜上，我们获得了比最先进的多跳基线更高的排名，准确率达到85% 。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Hierarchy-Aware+Multi-Hop+Question+Answering+over+Knowledge+Graphs)|0| +|[Hierarchy-Aware Multi-Hop Question Answering over Knowledge Graphs](https://doi.org/10.1145/3543507.3583376)|Junnan Dong, Qinggang Zhang, Xiao Huang, Keyu Duan, Qiaoyu Tan, Zhimeng Jiang|National University of Singapore, Singapore; The Hong Kong Polytechnic University, Hong Kong; Texas A&M University, USA|Knowledge graphs (KGs) have been widely used to enhance complex question answering (QA). To understand complex questions, existing studies employ language models (LMs) to encode contexts. Despite the simplicity, they neglect the latent relational information among question concepts and answers in KGs. While question concepts ubiquitously present hyponymy at the semantic level, e.g., mammals and animals, this feature is identically reflected in the hierarchical relations in KGs, e.g., a_type_of. Therefore, we are motivated to explore comprehensive reasoning by the hierarchical structures in KGs to help understand questions. However, it is non-trivial to reason over tree-like structures compared with chained paths. Moreover, identifying appropriate hierarchies relies on expertise. To this end, we propose HamQA, a novel Hierarchy-aware multi-hop Question Answering framework on knowledge graphs, to effectively align the mutual hierarchical information between question contexts and KGs. The entire learning is conducted in Hyperbolic space, inspired by its advantages of embedding hierarchical structures. Specifically, (i) we design a context-aware graph attentive network to capture context information. (ii) Hierarchical structures are continuously preserved in KGs by minimizing the Hyperbolic geodesic distances. The comprehensive reasoning is conducted to jointly train both components and provide a top-ranked candidate as an optimal answer. We achieve a higher ranking than the state-of-the-art multi-hop baselines on the official OpenBookQA leaderboard with an accuracy of 85%.|知识图已被广泛应用于提高复杂问题回答(QA)能力。为了理解复杂的问题，现有的研究采用语言模型(LM)对语境进行编码。幼儿教育中的问题概念和答案尽管简单，却忽视了潜在的关系信息。问题概念普遍存在于语义层面上的上下义关系，例如哺乳动物和动物，而这一特征在幼儿园的等级关系中得到了完全相同的体现，例如 a _ type _ of。因此，本研究旨在探讨幼稚园层级结构下的综合推理模式，以帮助学生理解问题。然而，与链式路径相比，对树状结构进行推理是非常重要的。此外，识别适当的层次结构依赖于专业知识。为此，我们提出了一种新的基于知识图的层次感知多跳问答框架 HamQA，以有效地调整问题上下文和 KG 之间的相互层次信息。整个学习过程是在双曲空间中进行的，灵感来自于其嵌入层级结构的优势。具体来说，(i)我们设计了一个上下文感知图注意网络来捕获上下文信息。(ii)幼稚园透过尽量减少双曲线测地距离，不断保留层次结构。通过综合推理对两个构件进行联合训练，并提供一个排名靠前的候选者作为最优答案。在 OpenBookQA 的官方排行榜上，我们获得了比最先进的多跳基线更高的排名，准确率达到85% 。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Hierarchy-Aware+Multi-Hop+Question+Answering+over+Knowledge+Graphs)|0| |[Unsupervised Entity Alignment for Temporal Knowledge Graphs](https://doi.org/10.1145/3543507.3583381)|Xiaoze Liu, Junyang Wu, Tianyi Li, Lu Chen, Yunjun Gao|Aalborg University, Denmark; Zhejiang University, China|Entity alignment (EA) is a fundamental data integration task that identifies equivalent entities between different knowledge graphs (KGs). Temporal Knowledge graphs (TKGs) extend traditional knowledge graphs by introducing timestamps, which have received increasing attention. State-of-the-art time-aware EA studies have suggested that the temporal information of TKGs facilitates the performance of EA. However, existing studies have not thoroughly exploited the advantages of temporal information in TKGs. Also, they perform EA by pre-aligning entity pairs, which can be labor-intensive and thus inefficient. In this paper, we present DualMatch which effectively fuses the relational and temporal information for EA. DualMatch transfers EA on TKGs into a weighted graph matching problem. More specifically, DualMatch is equipped with an unsupervised method, which achieves EA without necessitating seed alignment. DualMatch has two steps: (i) encoding temporal and relational information into embeddings separately using a novel label-free encoder, Dual-Encoder; and (ii) fusing both information and transforming it into alignment using a novel graph-matching-based decoder, GM-Decoder. DualMatch is able to perform EA on TKGs with or without supervision, due to its capability of effectively capturing temporal information. Extensive experiments on three real-world TKG datasets offer the insight that DualMatch outperforms the state-of-the-art methods in terms of H@1 by 2.4% - 10.7% and MRR by 1.7% - 7.6%, respectively.|实体对齐(EA)是识别不同知识图之间等价实体的基本数据集成任务。时态知识图(TKGs)通过引入时间戳对传统知识图进行扩展，越来越受到人们的关注。最先进的时间意识电位研究表明，TKG 的时间信息有助于电位的执行。然而，现有的研究并没有充分利用时间信息在 TKG 中的优势。此外，它们通过预对齐实体对来执行 EA，这可能是劳动密集型的，因此效率低下。本文提出了一种有效融合关系信息和时间信息的双匹配算法。DualMatch 将 TKG 上的 EA 转化为一个加权图匹配问题。更具体地说，DualMatch 配备了一种无监督的方法，它不需要种子对齐就可以实现 EA。DualMatch 有两个步骤: (i)使用一种新的无标签编码器——双编码器，将时间和关系信息分别编码到嵌入中; (ii)使用一种新的基于图匹配的解码器—— GM-Decder，将两种信息融合并将其转换为对齐。DualMatch 能够在有监督或无监督的情况下对 TKG 进行 EA，因为它能够有效地捕获时间信息。在三个真实世界的 TKG 数据集上进行的广泛实验提供了这样的见解: DualMatch 在 H@1方面分别优于最先进的方法2.4% -10.7% 和 MRR 1.7% -7.6% 。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Unsupervised+Entity+Alignment+for+Temporal+Knowledge+Graphs)|0| |[IMF: Interactive Multimodal Fusion Model for Link Prediction](https://doi.org/10.1145/3543507.3583554)|Xinhang Li, Xiangyu Zhao, Jiaxing Xu, Yong Zhang, Chunxiao Xing|School of Data Science, City University of Hong Kong, Hong Kong; Department of Computer Science and Technology, Tsinghua University, China; School of Computer Science and Engineering, Nanyang Technological University, Singapore|Link prediction aims to identify potential missing triples in knowledge graphs. To get better results, some recent studies have introduced multimodal information to link prediction. However, these methods utilize multimodal information separately and neglect the complicated interaction between different modalities. In this paper, we aim at better modeling the inter-modality information and thus introduce a novel Interactive Multimodal Fusion (IMF) model to integrate knowledge from different modalities. To this end, we propose a two-stage multimodal fusion framework to preserve modality-specific knowledge as well as take advantage of the complementarity between different modalities. Instead of directly projecting different modalities into a unified space, our multimodal fusion module limits the representations of different modalities independent while leverages bilinear pooling for fusion and incorporates contrastive learning as additional constraints. Furthermore, the decision fusion module delivers the learned weighted average over the predictions of all modalities to better incorporate the complementarity of different modalities. Our approach has been demonstrated to be effective through empirical evaluations on several real-world datasets. The implementation code is available online at https://github.com/HestiaSky/IMF-Pytorch.|链接预测旨在识别知识图中潜在的缺失三元组。为了得到更好的结果，最近的一些研究引入了多模态信息来链接预测。然而，这些方法分别利用多模态信息，而忽略了不同模态之间复杂的相互作用。本文旨在更好地建立模态间信息模型，从而引入一种新的交互式多模态融合(IMF)模型来整合来自不同模态的知识。为此，我们提出了一个两阶段的多模式融合框架，以保存模式特定的知识，并利用不同模式之间的互补性。我们的多模态融合模块不是直接将不同的模式投射到一个统一的空间中，而是限制不同模式独立的表示，同时利用双线性融合池进行融合，并将对比学习作为额外的约束。此外，决策融合模块提供所有模式预测的加权平均数，以更好地纳入不同模式的互补性。我们的方法已被证明是有效的，通过几个实际数据集的经验评估。实施守则可于网上 https://github.com/hestiasky/imf-pytorch 下载。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=IMF:+Interactive+Multimodal+Fusion+Model+for+Link+Prediction)|0| -|[Structure Pretraining and Prompt Tuning for Knowledge Graph Transfer](https://doi.org/10.1145/3543507.3583301)|Wen Zhang, Yushan Zhu, Mingyang Chen, Yuxia Geng, Yufeng Huang, Yajing Xu, Wenting Song, Huajun Chen|Zhejiang University, China; Huawei Technologies Co., Ltd, China|Knowledge graphs (KG) are essential background knowledge providers in many tasks. When designing models for KG-related tasks, one of the key tasks is to devise the Knowledge Representation and Fusion (KRF) module that learns the representation of elements from KGs and fuses them with task representations. While due to the difference of KGs and perspectives to be considered during fusion across tasks, duplicate and ad hoc KRF modules design are conducted among tasks. In this paper, we propose a novel knowledge graph pretraining model KGTransformer that could serve as a uniform KRF module in diverse KG-related tasks. We pretrain KGTransformer with three self-supervised tasks with sampled sub-graphs as input. For utilization, we propose a general prompt-tuning mechanism regarding task data as a triple prompt to allow flexible interactions between task KGs and task data. We evaluate pretrained KGTransformer on three tasks, triple classification, zero-shot image classification, and question answering. KGTransformer consistently achieves better results than specifically designed task models. Through experiments, we justify that the pretrained KGTransformer could be used off the shelf as a general and effective KRF module across KG-related tasks. The code and datasets are available at https://github.com/zjukg/KGTransformer.|在许多任务中，知识图是必不可少的背景知识提供者。在设计幼儿园相关任务的模型时，其中一个关键任务是设计知识表示与融合(KRF)模块，该模块学习幼儿园元素的表示并将其与任务表示融合。由于不同任务之间的融合需要考虑幼儿园和视角的差异，在不同任务之间进行了重复和临时的 KRF 模块设计。在本文中，我们提出了一个新的知识图预训练模型 KG 变换器，可以作为一个统一的 KRF 模块在不同的 KG 相关的任务。我们以采样子图作为输入，预训练三个自我监督任务。为了利用起见，我们提出了一种通用的提示调优机制，将任务数据视为三重提示，以允许任务 KG 和任务数据之间进行灵活的交互。我们评估预先训练的 KG 变压器在三个任务，三重分类，零镜头图像分类和问题回答。与专门设计的任务模型相比，KG 变压器始终能够取得更好的结果。通过实验证明，预先训练好的 KG 变压器可以作为一个通用的、有效的 KRF 模块在 KG 相关的任务中使用。代码和数据集可在 https://github.com/zjukg/kgtransformer 下载。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Structure+Pretraining+and+Prompt+Tuning+for+Knowledge+Graph+Transfer)|0| -|[TEA: Time-aware Entity Alignment in Knowledge Graphs](https://doi.org/10.1145/3543507.3583317)|Yu Liu, Wen Hua, Kexuan Xin, Saeid Hosseini, Xiaofang Zhou|Sohar University, Oman; School of Information Technology and Electrical Engineering, The University of Queensland, Australia; Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, China; School of Information Science and Technology, University of International Relations, China; Department of Computing, The Hong Kong Polytechnic University, China|Entity alignment (EA) aims to identify equivalent entities between knowledge graphs (KGs), which is a key technique to improve the coverage of existing KGs. Current EA models largely ignore the importance of time information contained in KGs and treat relational facts or attribute values of entities as time-invariant. However, real-world entities could evolve over time, making the knowledge of the aligned entities very different in multiple KGs. This may cause incorrect matching between KGs if such entity dynamics is ignored. In this paper, we propose a time-aware entity alignment (TEA) model that discovers the entity evolving behaviour by exploring the time contexts in KGs and aggregates various contextual information to make the alignment decision. In particular, we address two main challenges in the TEA model: 1) How to identify highly-correlated temporal facts; 2) How to capture entity dynamics and incorporate it to learn a more informative entity representation for the alignment task. Experiments on real-world datasets1 verify the superiority of our TEA model over state-of-the-art entity aligners.|实体对齐(EA)的目的是识别知识图之间的等价实体，这是提高现有知识图覆盖率的关键技术。现有的 EA 模型大多忽略了幼儿园教师所包含的时间信息的重要性，而将实体的关系事实或属性值视为时不变量。然而，真实世界的实体可以随着时间的推移而发展，使得在多个 KG 中对齐的实体的知识非常不同。如果忽略这种实体动态，则可能导致 KG 之间的不正确匹配。本文提出了一种基于时间感知的实体对齐(TEA)模型，该模型通过对幼儿园时间环境的探索，发现实体的演化行为，并聚合各种环境信息进行对齐决策。特别是，我们解决了 TEA 模型中的两个主要挑战: 1)如何识别高度相关的时间事实; 2)如何捕获实体动态并将其结合起来，以便为对齐任务学习一个更具信息性的实体表示。在实际数据集上的实验验证了我们的 TEA 模型相对于最先进的实体校准器的优越性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=TEA:+Time-aware+Entity+Alignment+in+Knowledge+Graphs)|0| +|[Structure Pretraining and Prompt Tuning for Knowledge Graph Transfer](https://doi.org/10.1145/3543507.3583301)|Wen Zhang, Yushan Zhu, Mingyang Chen, Yuxia Geng, Yufeng Huang, Yajing Xu, Wenting Song, Huajun Chen|Huawei Technologies Co., Ltd, China; Zhejiang University, China|Knowledge graphs (KG) are essential background knowledge providers in many tasks. When designing models for KG-related tasks, one of the key tasks is to devise the Knowledge Representation and Fusion (KRF) module that learns the representation of elements from KGs and fuses them with task representations. While due to the difference of KGs and perspectives to be considered during fusion across tasks, duplicate and ad hoc KRF modules design are conducted among tasks. In this paper, we propose a novel knowledge graph pretraining model KGTransformer that could serve as a uniform KRF module in diverse KG-related tasks. We pretrain KGTransformer with three self-supervised tasks with sampled sub-graphs as input. For utilization, we propose a general prompt-tuning mechanism regarding task data as a triple prompt to allow flexible interactions between task KGs and task data. We evaluate pretrained KGTransformer on three tasks, triple classification, zero-shot image classification, and question answering. KGTransformer consistently achieves better results than specifically designed task models. Through experiments, we justify that the pretrained KGTransformer could be used off the shelf as a general and effective KRF module across KG-related tasks. The code and datasets are available at https://github.com/zjukg/KGTransformer.|在许多任务中，知识图是必不可少的背景知识提供者。在设计幼儿园相关任务的模型时，其中一个关键任务是设计知识表示与融合(KRF)模块，该模块学习幼儿园元素的表示并将其与任务表示融合。由于不同任务之间的融合需要考虑幼儿园和视角的差异，在不同任务之间进行了重复和临时的 KRF 模块设计。在本文中，我们提出了一个新的知识图预训练模型 KG 变换器，可以作为一个统一的 KRF 模块在不同的 KG 相关的任务。我们以采样子图作为输入，预训练三个自我监督任务。为了利用起见，我们提出了一种通用的提示调优机制，将任务数据视为三重提示，以允许任务 KG 和任务数据之间进行灵活的交互。我们评估预先训练的 KG 变压器在三个任务，三重分类，零镜头图像分类和问题回答。与专门设计的任务模型相比，KG 变压器始终能够取得更好的结果。通过实验证明，预先训练好的 KG 变压器可以作为一个通用的、有效的 KRF 模块在 KG 相关的任务中使用。代码和数据集可在 https://github.com/zjukg/kgtransformer 下载。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Structure+Pretraining+and+Prompt+Tuning+for+Knowledge+Graph+Transfer)|0| +|[TEA: Time-aware Entity Alignment in Knowledge Graphs](https://doi.org/10.1145/3543507.3583317)|Yu Liu, Wen Hua, Kexuan Xin, Saeid Hosseini, Xiaofang Zhou|Sohar University, Oman; Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, China; School of Information Science and Technology, University of International Relations, China; Department of Computing, The Hong Kong Polytechnic University, China; School of Information Technology and Electrical Engineering, The University of Queensland, Australia|Entity alignment (EA) aims to identify equivalent entities between knowledge graphs (KGs), which is a key technique to improve the coverage of existing KGs. Current EA models largely ignore the importance of time information contained in KGs and treat relational facts or attribute values of entities as time-invariant. However, real-world entities could evolve over time, making the knowledge of the aligned entities very different in multiple KGs. This may cause incorrect matching between KGs if such entity dynamics is ignored. In this paper, we propose a time-aware entity alignment (TEA) model that discovers the entity evolving behaviour by exploring the time contexts in KGs and aggregates various contextual information to make the alignment decision. In particular, we address two main challenges in the TEA model: 1) How to identify highly-correlated temporal facts; 2) How to capture entity dynamics and incorporate it to learn a more informative entity representation for the alignment task. Experiments on real-world datasets1 verify the superiority of our TEA model over state-of-the-art entity aligners.|实体对齐(EA)的目的是识别知识图之间的等价实体，这是提高现有知识图覆盖率的关键技术。现有的 EA 模型大多忽略了幼儿园教师所包含的时间信息的重要性，而将实体的关系事实或属性值视为时不变量。然而，真实世界的实体可以随着时间的推移而发展，使得在多个 KG 中对齐的实体的知识非常不同。如果忽略这种实体动态，则可能导致 KG 之间的不正确匹配。本文提出了一种基于时间感知的实体对齐(TEA)模型，该模型通过对幼儿园时间环境的探索，发现实体的演化行为，并聚合各种环境信息进行对齐决策。特别是，我们解决了 TEA 模型中的两个主要挑战: 1)如何识别高度相关的时间事实; 2)如何捕获实体动态并将其结合起来，以便为对齐任务学习一个更具信息性的实体表示。在实际数据集上的实验验证了我们的 TEA 模型相对于最先进的实体校准器的优越性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=TEA:+Time-aware+Entity+Alignment+in+Knowledge+Graphs)|0| |[Knowledge Graph Completion with Counterfactual Augmentation](https://doi.org/10.1145/3543507.3583401)|Heng Chang, Jie Cai, Jia Li|Tsinghua University, China; Hong Kong University of Science and Technology (Guangzhou), China|Graph Neural Networks (GNNs) have demonstrated great success in Knowledge Graph Completion (KGC) by modeling how entities and relations interact in recent years. However, most of them are designed to learn from the observed graph structure, which appears to have imbalanced relation distribution during the training stage. Motivated by the causal relationship among the entities on a knowledge graph, we explore this defect through a counterfactual question: "would the relation still exist if the neighborhood of entities became different from observation?". With a carefully designed instantiation of a causal model on the knowledge graph, we generate the counterfactual relations to answer the question by regarding the representations of entity pair given relation as context, structural information of relation-aware neighborhood as treatment, and validity of the composed triplet as the outcome. Furthermore, we incorporate the created counterfactual relations with the GNN-based framework on KGs to augment their learning of entity pair representations from both the observed and counterfactual relations. Experiments on benchmarks show that our proposed method outperforms existing methods on the task of KGC, achieving new state-of-the-art results. Moreover, we demonstrate that the proposed counterfactual relations-based augmentation also enhances the interpretability of the GNN-based framework through the path interpretations of predictions.|近年来，图神经网络(GNN)通过对实体和关系之间的相互作用进行建模，在知识图完成(KGC)方面取得了巨大的成功。然而，它们中的大多数都是为了学习观察到的图形结构，在训练阶段，这些图形结构似乎有不平衡的关系分布。受知识图上实体之间因果关系的启发，我们通过一个反事实的问题来探讨这一缺陷: “如果实体的邻域变得不同于观察，这种关系还会存在吗?”.通过在知识图上精心设计的因果模型实例，我们将给定关系的实体对的表示作为上下文，关系意识邻域的结构信息作为处理，组合三元组的有效性作为结果，生成反事实关系来回答这个问题。此外，我们将所建立的反事实关系与基于 GNN 的幼稚园框架结合起来，以增强幼稚园学生从观察到的关系和反事实关系中学习实体对表征的能力。基准测试实验表明，该方法在 KGC 任务上优于现有方法，取得了新的研究成果。此外，我们还证明了所提出的基于反事实关系的增强也通过对预测的路径解释增强了基于 GNN 的框架的可解释性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Knowledge+Graph+Completion+with+Counterfactual+Augmentation)|0| -|[Learning Social Meta-knowledge for Nowcasting Human Mobility in Disaster](https://doi.org/10.1145/3543507.3583991)|Renhe Jiang, Zhaonan Wang, Yudong Tao, Chuang Yang, Xuan Song, Ryosuke Shibasaki, ShuChing Chen, MeiLing Shyu|The University of Tokyo, Japan; University of Miami, USA; University of Missouri-Kansas City, USA|Human mobility nowcasting is a fundamental research problem for intelligent transportation planning, disaster responses and management, etc. In particular, human mobility under big disasters such as hurricanes and pandemics deviates from its daily routine to a large extent, which makes the task more challenging. Existing works mainly focus on traffic or crowd flow prediction in normal situations. To tackle this problem, in this study, disaster-related Twitter data is incorporated as a covariate to understand the public awareness and attention about the disaster events and thus perceive their impacts on the human mobility. Accordingly, we propose a Meta-knowledge-Memorizable Spatio-Temporal Network (MemeSTN), which leverages memory network and meta-learning to fuse social media and human mobility data. Extensive experiments over three real-world disasters including Japan 2019 typhoon season, Japan 2020 COVID-19 pandemic, and US 2019 hurricane season were conducted to illustrate the effectiveness of our proposed solution. Compared to the state-of-the-art spatio-temporal deep models and multivariate-time-series deep models, our model can achieve superior performance for nowcasting human mobility in disaster situations at both country level and state level.|人员流动临近预测是智能交通规划、灾害响应和管理等领域的基础性研究问题。特别是，在飓风和大流行病等重大灾害下，人员的流动性在很大程度上偏离了日常工作，这使得这项任务更具挑战性。现有的工作主要集中在正常情况下的交通或人流预测。为了解决这一问题，在这项研究中，将与灾害有关的推特数据作为一个协变量纳入，以了解公众对灾害事件的认识和关注，从而认识到它们对人员流动的影响。因此，我们提出了一个元知识记忆时空网络(MemeSTN) ，它利用记忆网络和元学习融合社会媒体和人类流动性数据。通过对日本2019年台风季节、日本2020年2019冠状病毒疾病大流行和美国2019年飓风季节等三种现实灾害的大量实验，说明了我们提出的解决方案的有效性。与现有的时空深度模型和多变量时间序列深度模型相比，该模型能够在国家和国家两级的灾害情况下实现人员流动的临近预测。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Learning+Social+Meta-knowledge+for+Nowcasting+Human+Mobility+in+Disaster)|0| -|[Cashing in on Contacts: Characterizing the OnlyFans Ecosystem](https://doi.org/10.1145/3543507.3583210)|Pelayo Vallina, Ignacio Castro, Gareth Tyson|Queen Mary University of London, United Kingdom; Hong Kong University of Science and Technology, China; IMDEA Networks, Spain and Universidad Carlos III de Madrid, Spain|Adult video-sharing has undergone dramatic shifts. New platforms that directly interconnect (often amateur) producers and consumers now allow content creators to promote material across the web and directly monetize the content they produce. OnlyFans is the most prominent example of this new trend. OnlyFans is a content subscription service where creators earn money from users who subscribe to their material. In contrast to prior adult platforms, OnlyFans emphasizes creator-consumer interaction for audience accumulation and maintenance. This results in a wide cross-platform ecosystem geared towards bringing consumers to creators’ accounts. In this paper, we inspect this emerging ecosystem, focusing on content creators and the third-party platforms they connect to.|成人视频分享经历了戏剧性的转变。新的平台直接连接(通常是业余的)生产者和消费者，现在允许内容创作者通过网络推广材料，并直接将他们制作的内容货币化。“只有粉丝”是这一新趋势最突出的例子。OnlyFans 是一个内容订阅服务，创作者从订阅他们材料的用户那里赚钱。与之前的成人平台不同，OnlyFans 强调创作者与消费者之间的互动，以促进观众的积累和维护。这导致了一个广泛的跨平台生态系统，旨在将消费者带到创造者的帐户中。在本文中，我们考察了这个新兴的生态系统，重点关注内容创建者和他们连接到的第三方平台。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Cashing+in+on+Contacts:+Characterizing+the+OnlyFans+Ecosystem)|0| +|[Learning Social Meta-knowledge for Nowcasting Human Mobility in Disaster](https://doi.org/10.1145/3543507.3583991)|Renhe Jiang, Zhaonan Wang, Yudong Tao, Chuang Yang, Xuan Song, Ryosuke Shibasaki, ShuChing Chen, MeiLing Shyu|University of Missouri-Kansas City, USA; University of Miami, USA; The University of Tokyo, Japan|Human mobility nowcasting is a fundamental research problem for intelligent transportation planning, disaster responses and management, etc. In particular, human mobility under big disasters such as hurricanes and pandemics deviates from its daily routine to a large extent, which makes the task more challenging. Existing works mainly focus on traffic or crowd flow prediction in normal situations. To tackle this problem, in this study, disaster-related Twitter data is incorporated as a covariate to understand the public awareness and attention about the disaster events and thus perceive their impacts on the human mobility. Accordingly, we propose a Meta-knowledge-Memorizable Spatio-Temporal Network (MemeSTN), which leverages memory network and meta-learning to fuse social media and human mobility data. Extensive experiments over three real-world disasters including Japan 2019 typhoon season, Japan 2020 COVID-19 pandemic, and US 2019 hurricane season were conducted to illustrate the effectiveness of our proposed solution. Compared to the state-of-the-art spatio-temporal deep models and multivariate-time-series deep models, our model can achieve superior performance for nowcasting human mobility in disaster situations at both country level and state level.|人员流动临近预测是智能交通规划、灾害响应和管理等领域的基础性研究问题。特别是，在飓风和大流行病等重大灾害下，人员的流动性在很大程度上偏离了日常工作，这使得这项任务更具挑战性。现有的工作主要集中在正常情况下的交通或人流预测。为了解决这一问题，在这项研究中，将与灾害有关的推特数据作为一个协变量纳入，以了解公众对灾害事件的认识和关注，从而认识到它们对人员流动的影响。因此，我们提出了一个元知识记忆时空网络(MemeSTN) ，它利用记忆网络和元学习融合社会媒体和人类流动性数据。通过对日本2019年台风季节、日本2020年2019冠状病毒疾病大流行和美国2019年飓风季节等三种现实灾害的大量实验，说明了我们提出的解决方案的有效性。与现有的时空深度模型和多变量时间序列深度模型相比，该模型能够在国家和国家两级的灾害情况下实现人员流动的临近预测。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Learning+Social+Meta-knowledge+for+Nowcasting+Human+Mobility+in+Disaster)|0| +|[Cashing in on Contacts: Characterizing the OnlyFans Ecosystem](https://doi.org/10.1145/3543507.3583210)|Pelayo Vallina, Ignacio Castro, Gareth Tyson|Hong Kong University of Science and Technology, China; IMDEA Networks, Spain and Universidad Carlos III de Madrid, Spain; Queen Mary University of London, United Kingdom|Adult video-sharing has undergone dramatic shifts. New platforms that directly interconnect (often amateur) producers and consumers now allow content creators to promote material across the web and directly monetize the content they produce. OnlyFans is the most prominent example of this new trend. OnlyFans is a content subscription service where creators earn money from users who subscribe to their material. In contrast to prior adult platforms, OnlyFans emphasizes creator-consumer interaction for audience accumulation and maintenance. This results in a wide cross-platform ecosystem geared towards bringing consumers to creators’ accounts. In this paper, we inspect this emerging ecosystem, focusing on content creators and the third-party platforms they connect to.|成人视频分享经历了戏剧性的转变。新的平台直接连接(通常是业余的)生产者和消费者，现在允许内容创作者通过网络推广材料，并直接将他们制作的内容货币化。“只有粉丝”是这一新趋势最突出的例子。OnlyFans 是一个内容订阅服务，创作者从订阅他们材料的用户那里赚钱。与之前的成人平台不同，OnlyFans 强调创作者与消费者之间的互动，以促进观众的积累和维护。这导致了一个广泛的跨平台生态系统，旨在将消费者带到创造者的帐户中。在本文中，我们考察了这个新兴的生态系统，重点关注内容创建者和他们连接到的第三方平台。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Cashing+in+on+Contacts:+Characterizing+the+OnlyFans+Ecosystem)|0| |[Automated Content Moderation Increases Adherence to Community Guidelines](https://doi.org/10.1145/3543507.3583275)|Manoel Horta Ribeiro, Justin Cheng, Robert West|Facebook, USA; EPFL, Switzerland|Online social media platforms use automated moderation systems to remove or reduce the visibility of rule-breaking content. While previous work has documented the importance of manual content moderation, the effects of automated content moderation remain largely unknown. Here, in a large study of Facebook comments (n=412M), we used a fuzzy regression discontinuity design to measure the impact of automated content moderation on subsequent rule-breaking behavior (number of comments hidden/deleted) and engagement (number of additional comments posted). We found that comment deletion decreased subsequent rule-breaking behavior in shorter threads (20 or fewer comments), even among other participants, suggesting that the intervention prevented conversations from derailing. Further, the effect of deletion on the affected user's subsequent rule-breaking behavior was longer-lived than its effect on reducing commenting in general, suggesting that users were deterred from rule-breaking but not from commenting. In contrast, hiding (rather than deleting) content had small and statistically insignificant effects. Our results suggest that automated content moderation increases adherence to community guidelines.|在线社交媒体平台使用自动审核系统来删除或减少违规内容的可见性。虽然以前的工作已经记录了手动内容审核的重要性，但是自动内容审核的影响仍然很大程度上是未知的。在这里，在一项对 Facebook 评论(n = 412M)的大型研究中，我们使用模糊回归不连续性设计来衡量自动内容审核对后续违规行为(隐藏/删除评论数量)和参与度(发布额外评论数量)的影响。我们发现删除评论可以减少更短的线程(20条或更少的评论)中随后的违规行为，甚至在其他参与者中也是如此，这表明干预可以防止对话脱轨。此外，删除对受影响用户随后的违规行为的影响比它对减少一般评论的影响更为持久，这表明用户被阻止违规但不被阻止评论。相比之下，隐藏(而不是删除)内容的影响很小，而且在统计学上无关紧要。我们的研究结果表明，自动内容审核增加了对社区指导方针的遵守。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Automated+Content+Moderation+Increases+Adherence+to+Community+Guidelines)|0| -|[Mental Health Coping Stories on Social Media: A Causal-Inference Study of Papageno Effect](https://doi.org/10.1145/3543507.3583350)|Yunhao Yuan, Koustuv Saha, Barbara Keller, Erkki Tapio Isometsä, Talayeh Aledavood|Microsoft Research, Canada; Department of Computer Science, Aalto University, Finland; University of Helsinki, Finland|The Papageno effect concerns how media can play a positive role in preventing and mitigating suicidal ideation and behaviors. With the increasing ubiquity and widespread use of social media, individuals often express and share lived experiences and struggles with mental health. However, there is a gap in our understanding about the existence and effectiveness of the Papageno effect in social media, which we study in this paper. In particular, we adopt a causal-inference framework to examine the impact of exposure to mental health coping stories on individuals on Twitter. We obtain a Twitter dataset with $\sim$2M posts by $\sim$10K individuals. We consider engaging with coping stories as the Treatment intervention, and adopt a stratified propensity score approach to find matched cohorts of Treatment and Control individuals. We measure the psychosocial shifts in affective, behavioral, and cognitive outcomes in longitudinal Twitter data before and after engaging with the coping stories. Our findings reveal that, engaging with coping stories leads to decreased stress and depression, and improved expressive writing, diversity, and interactivity. Our work discusses the practical and platform design implications in supporting mental wellbeing.|帕帕盖诺效应关注媒体如何在预防和减轻自杀念头和行为方面发挥积极作用。随着社交媒体的日益普及和广泛使用，个人往往表达和分享生活经历和心理健康的斗争。然而，对于社交媒体中巴巴哥诺效应的存在和有效性，我们的理解还存在差距，本文对此进行了研究。特别是，我们采用了一个因果推理框架来检验在 Twitter 上接触心理健康应对故事对个人的影响。我们获得了一个由 $sim $10K 个人发布的200万美元的 Twitter 数据集。我们考虑将应对故事作为治疗干预措施，并采用分层倾向评分方法来寻找匹配的治疗和控制个体队列。我们在参与应对故事之前和之后的纵向 Twitter 数据中测量情感、行为和认知结果的心理社会变化。我们的研究结果表明，参与应对故事可以减少压力和抑郁，提高表达性写作、多样性和互动性。我们的工作讨论了支持心理健康的实践和平台设计含义。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Mental+Health+Coping+Stories+on+Social+Media:+A+Causal-Inference+Study+of+Papageno+Effect)|0| +|[Mental Health Coping Stories on Social Media: A Causal-Inference Study of Papageno Effect](https://doi.org/10.1145/3543507.3583350)|Yunhao Yuan, Koustuv Saha, Barbara Keller, Erkki Tapio Isometsä, Talayeh Aledavood|Department of Computer Science, Aalto University, Finland; University of Helsinki, Finland; Microsoft Research, Canada|The Papageno effect concerns how media can play a positive role in preventing and mitigating suicidal ideation and behaviors. With the increasing ubiquity and widespread use of social media, individuals often express and share lived experiences and struggles with mental health. However, there is a gap in our understanding about the existence and effectiveness of the Papageno effect in social media, which we study in this paper. In particular, we adopt a causal-inference framework to examine the impact of exposure to mental health coping stories on individuals on Twitter. We obtain a Twitter dataset with $\sim$2M posts by $\sim$10K individuals. We consider engaging with coping stories as the Treatment intervention, and adopt a stratified propensity score approach to find matched cohorts of Treatment and Control individuals. We measure the psychosocial shifts in affective, behavioral, and cognitive outcomes in longitudinal Twitter data before and after engaging with the coping stories. Our findings reveal that, engaging with coping stories leads to decreased stress and depression, and improved expressive writing, diversity, and interactivity. Our work discusses the practical and platform design implications in supporting mental wellbeing.|帕帕盖诺效应关注媒体如何在预防和减轻自杀念头和行为方面发挥积极作用。随着社交媒体的日益普及和广泛使用，个人往往表达和分享生活经历和心理健康的斗争。然而，对于社交媒体中巴巴哥诺效应的存在和有效性，我们的理解还存在差距，本文对此进行了研究。特别是，我们采用了一个因果推理框架来检验在 Twitter 上接触心理健康应对故事对个人的影响。我们获得了一个由 $sim $10K 个人发布的200万美元的 Twitter 数据集。我们考虑将应对故事作为治疗干预措施，并采用分层倾向评分方法来寻找匹配的治疗和控制个体队列。我们在参与应对故事之前和之后的纵向 Twitter 数据中测量情感、行为和认知结果的心理社会变化。我们的研究结果表明，参与应对故事可以减少压力和抑郁，提高表达性写作、多样性和互动性。我们的工作讨论了支持心理健康的实践和平台设计含义。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Mental+Health+Coping+Stories+on+Social+Media:+A+Causal-Inference+Study+of+Papageno+Effect)|0| |[A First Look at Public Service Websites from the Affordability Lens](https://doi.org/10.1145/3543507.3583415)|Rumaisa Habib, Aimen Inam, Ayesha Ali, Ihsan Ayyub Qazi, Zafar Ayyub Qazi|LUMS, Pakistan|Public service websites act as official gateways to services provided by governments. Many of these websites are essential for citizens to receive reliable information and online government services. However, the lack of affordability of mobile broadband services in many developing countries and the rising complexity of websites create barriers for citizens in accessing these government websites. This paper presents the first large-scale analysis of the affordability of public service websites in developing countries. We do this by collecting a corpus of 1900 public service websites, including public websites from nine developing countries and for comparison websites from nine developed countries. Our investigation is driven by website complexity analysis as well as evaluation through a recently proposed affordability index. Our analysis reveals that, in general, public service websites in developing countries do not meet the affordability target set by the UN’s Broadband Commission. However, we show that several countries can be brought within or closer to the affordability target by implementing webpage optimizations to reduce page sizes. We also discuss policy interventions that can help make access to public service website more affordable.|公共服务网站作为政府提供服务的官方门户。其中许多网站对于公民获得可靠信息和在线政府服务至关重要。然而，许多发展中国家缺乏负担得起的移动宽带服务，以及网站日益复杂，给公民访问这些政府网站造成了障碍。本文首次对发展中国家公共服务网站的可负担性进行了大规模分析。为此，我们收集了1900个公共服务网站，包括9个发展中国家的公共网站和9个发达国家的比较网站。我们的调查是由网站的复杂性分析以及评估通过最近提出的负担能力指数。我们的分析表明，总的来说，发展中国家的公共服务网站不能达到联合国宽带委员会设定的负担能力目标。然而，我们表明，若干国家可以通过实施网页优化来减少页面大小，从而达到或接近负担能力目标。我们还讨论了政策干预措施，这些措施可以帮助人们更负担得起访问公共服务网站的费用。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+First+Look+at+Public+Service+Websites+from+the+Affordability+Lens)|0| |[Who Funds Misinformation? A Systematic Analysis of the Ad-related Profit Routines of Fake News Sites](https://doi.org/10.1145/3543507.3583443)|Emmanouil Papadogiannakis, Panagiotis Papadopoulos, Evangelos P. Markatos, Nicolas Kourtellis||Fake news is an age-old phenomenon, widely assumed to be associated with political propaganda published to sway public opinion. Yet, with the growth of social media, it has become a lucrative business for Web publishers. Despite many studies performed and countermeasures proposed, unreliable news sites have increased in the last years their share of engagement among the top performing news sources. Stifling fake news impact depends on our efforts in limiting the (economic) incentives of fake news producers. In this paper, we aim at enhancing the transparency around these exact incentives, and explore: Who supports the existence of fake news websites via paid ads, either as an advertiser or an ad seller? Who owns these websites and what other Web business are they into? We are the first to systematize the auditing process of fake news revenue flows. We identify the companies that advertise in fake news websites and the intermediary companies responsible for facilitating those ad revenues. We study more than 2,400 popular news websites and show that well-known ad networks, such as Google and IndexExchange, have a direct advertising relation with more than 40% of fake news websites. Using a graph clustering approach on 114.5K sites, we show that entities who own fake news sites, also operate other types of websites pointing to the fact that owning a fake news website is part of a broader business operation.|假新闻是一种古老的现象，人们普遍认为它与政治宣传有关，通过发布假新闻来影响公众舆论。然而，随着社交媒体的发展，它已经成为网络出版商的一项有利可图的业务。尽管进行了许多研究并提出了对策，但不可靠的新闻网站在最佳新闻来源中所占的份额在过去几年中有所增加。遏制假新闻的影响取决于我们在限制假新闻制造者的(经济)动机方面的努力。在本文中，我们旨在提高透明度周围这些确切的激励，并探讨: 谁支持存在的虚假新闻网站通过付费广告，无论是作为广告商还是广告销售商？谁拥有这些网站，他们还涉足哪些其他网络业务？我们率先对虚假新闻收入流的审计过程进行了系统化。我们识别那些在假新闻网站上做广告的公司和那些为这些广告收入提供便利的中介公司。我们研究了2400多个热门新闻网站，发现谷歌和 IndexExchange 等知名广告网站与40% 以上的假新闻网站有直接的广告关系。通过对114.5 K 网站的图形聚类分析，我们发现拥有假新闻网站的实体，同时也经营其他类型的网站，这表明拥有假新闻网站是更广泛商业运作的一部分。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Who+Funds+Misinformation?+A+Systematic+Analysis+of+the+Ad-related+Profit+Routines+of+Fake+News+Sites)|0| -|[Evidence of Demographic rather than Ideological Segregation in News Discussion on Reddit](https://doi.org/10.1145/3543507.3583468)|Corrado Monti, Jacopo D'Ignazi, Michele Starnini, Gianmarco De Francisci Morales|CENTAI, Italy; ISI Foundation, Italy|We evaluate homophily and heterophily among ideological and demographic groups in a typical opinion formation context: online discussions of current news. We analyze user interactions across five years in the r/news community on Reddit, one of the most visited websites in the United States. Then, we estimate demographic and ideological attributes of these users. Thanks to a comparison with a carefully-crafted network null model, we establish which pairs of attributes foster interactions and which ones inhibit them. Individuals prefer to engage with the opposite ideological side, which contradicts the echo chamber narrative. Instead, demographic groups are homophilic, as individuals tend to interact within their own group - even in an online setting where such attributes are not directly observable. In particular, we observe age and income segregation consistently across years: users tend to avoid interactions when belonging to different groups. These results persist after controlling for the degree of interest by each demographic group in different news topics. Our findings align with the theory that affective polarization - the difficulty in socializing across political boundaries-is more connected with an increasingly divided society, rather than ideological echo chambers on social media. We publicly release our anonymized data set and all the code to reproduce our results: https://github.com/corradomonti/demographic-homophily|我们在一个典型的意见形成背景下评估意识形态和人口群体之间的同质性和异质性: 当前新闻的在线讨论。我们在美国访问量最大的网站之一 Reddit 的 r/news 社区分析了五年来的用户交互。然后，我们估计这些用户的人口统计学和意识形态属性。通过与精心构建的网络空模型进行比较，我们确定了哪些属性对促进交互，哪些属性对抑制交互。个人倾向于接触相反的意识形态，这与回音室叙事相矛盾。相反，人口统计学群体是同性恋，因为个人往往在他们自己的群体内互动-即使在网上设置，这些属性是不能直接观察到的。特别是，我们观察到年龄和收入隔离持续多年: 当用户属于不同的群体时，他们倾向于避免互动。在控制了每个人口群体对不同新闻主题的兴趣程度之后，这些结果仍然存在。我们的研究结果符合这样一个理论，即情感的两极分化——跨越政治界限进行社交的困难——更多地与日益分裂的社会有关，而不是与社交媒体上的意识形态回音室有关。我们公开发布我们的匿名数据集和所有重现结果的代码: https://github.com/corradomonti/demographic-homophily|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Evidence+of+Demographic+rather+than+Ideological+Segregation+in+News+Discussion+on+Reddit)|0| -|[Longitudinal Assessment of Reference Quality on Wikipedia](https://doi.org/10.1145/3543507.3583218)|Aitolkyn Baigutanova, Jaehyeon Myung, Diego SáezTrumper, AiJou Chou, Miriam Redi, Changwook Jung, Meeyoung Cha|Wikimedia Foundation, United Kingdom; KAIST, Republic of Korea; Wikimedia Foundation, Spain; IBS, Republic of Korea and KAIST, Republic of Korea; School of Computing, KAIST, Republic of Korea|Wikipedia plays a crucial role in the integrity of the Web. This work analyzes the reliability of this global encyclopedia through the lens of its references. We operationalize the notion of reference quality by defining reference need (RN), i.e., the percentage of sentences missing a citation, and reference risk (RR), i.e., the proportion of non-authoritative references. We release Citation Detective, a tool for automatically calculating the RN score, and discover that the RN score has dropped by 20 percent point in the last decade, with more than half of verifiable statements now accompanying references. The RR score has remained below 1% over the years as a result of the efforts of the community to eliminate unreliable references. We propose pairing novice and experienced editors on the same Wikipedia article as a strategy to enhance reference quality. Our quasi-experiment indicates that such a co-editing experience can result in a lasting advantage in identifying unreliable sources in future edits. As Wikipedia is frequently used as the ground truth for numerous Web applications, our findings and suggestions on its reliability can have a far-reaching impact. We discuss the possibility of other Web services adopting Wiki-style user collaboration to eliminate unreliable content.|维基百科在网络的完整性中扮演着至关重要的角色。本文从参考文献的角度分析了这部全球百科全书的可靠性。我们通过定义参考需求(RN) ，即缺少引用的句子的百分比和参考风险(RR) ，即非权威参考文献的比例，来操作参考质量的概念。我们发布了引文侦探，一个自动计算 RN 分数的工具，发现 RN 分数在过去的十年里下降了20% ，超过一半的可验证的陈述现在伴随着参考文献。多年来，由于社区努力消除不可靠的参考文献，RR 评分一直保持在1% 以下。我们建议将新手和经验丰富的编辑配对在同一维基百科文章上，作为提高参考文献质量的策略。我们的准实验表明，这种合作编辑的经验可以导致一个持久的优势，在识别不可靠的来源在未来的编辑。由于维基百科经常被用作众多 Web 应用程序的基本事实，我们对其可靠性的发现和建议可能产生深远的影响。我们讨论了其他 Web 服务采用 Wiki 风格的用户协作来消除不可靠内容的可能性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Longitudinal+Assessment+of+Reference+Quality+on+Wikipedia)|0| -|[Gateway Entities in Problematic Trajectories](https://doi.org/10.1145/3543507.3583283)|Xi Leslie Chen, Abhratanu Dutta, Sindhu Ernala, Stratis Ioannidis, Shankar Kalyanaraman, Israel Nir, Udi Weinsberg|Northwestern, USA; Columbia University, USA; Northeastern, USA; Meta, USA|Social media platforms like Facebook and YouTube connect people with communities that reflect their own values and experiences. People discover new communities either organically or through algorithmic recommendations based on their interests and preferences. We study online journeys users take through these communities, focusing particularly on ones that may lead to problematic outcomes. In particular, we propose and explore the concept of gateways, namely, entities associated with a higher likelihood of subsequent engagement with problematic content. We show, via a real-world application on Facebook groups, that a simple definition of gateway entities can be leveraged to reduce exposure to problematic content by 1% without any adverse impact on user engagement metrics. Motivated by this finding, we propose several formal definitions of gateways, via both frequentist and survival analysis methods, and evaluate their efficacy in predicting user behavior through offline experiments. Frequentist, duration-insensitive methods predict future harmful engagements with an 0.64–0.83 AUC, while survival analysis methods improve this to 0.72–0.90 AUC.|Facebook 和 YouTube 等社交媒体平台将人们与反映他们自身价值观和经历的社区联系起来。人们发现新的社区要么是有机的，要么是通过基于兴趣和偏好的算法推荐。我们研究用户通过这些社区进行的在线旅行，特别关注那些可能导致有问题的结果的在线旅行。特别是，我们提出并探索了网关的概念，即与后续参与有问题内容的可能性较高相关的实体。我们通过 Facebook 群组上的一个现实应用程序展示了，一个简单的网关实体定义可以用来减少1% 的问题内容暴露，而不会对用户参与度指标产生任何不利影响。基于这一发现，我们通过频率分析和生存分析的方法提出了几种网关的形式化定义，并通过离线实验评估了它们在预测用户行为方面的有效性。频繁，持续时间不敏感的方法预测未来的有害参与与0.64 -0.83 AUC，而生存分析方法将其改善为0.72 -0.90 AUC。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Gateway+Entities+in+Problematic+Trajectories)|0| -|[Unsupervised Anomaly Detection on Microservice Traces through Graph VAE](https://doi.org/10.1145/3543507.3583215)|Zhe Xie, Haowen Xu, Wenxiao Chen, Wanxue Li, Huai Jiang, Liangfei Su, Hanzhang Wang, Dan Pei|Department of Computer Science and Technology, Tsinghua University, China; eBay, USA; eBay, China|The microservice architecture is widely employed in large Internet systems. For each user request, a few of the microservices are called, and a trace is formed to record the tree-like call dependencies among microservices and the time consumption at each call node. Traces are useful in diagnosing system failures, but their complex structures make it difficult to model their patterns and detect their anomalies. In this paper, we propose a novel dual-variable graph variational autoencoder (VAE) for unsupervised anomaly detection on microservice traces. To reconstruct the time consumption of nodes, we propose a novel dispatching layer. We find that the inversion of negative log-likelihood (NLL) appears for some anomalous samples, which makes the anomaly score infeasible for anomaly detection. To address this, we point out that the NLL can be decomposed into KL-divergence and data entropy, whereas lower-dimensional anomalies can introduce an entropy gap with normal inputs. We propose three techniques to mitigate this entropy gap for trace anomaly detection: Bernoulli & Categorical Scaling, Node Count Normalization, and Gaussian Std-Limit. On five trace datasets from a top Internet company, our proposed TraceVAE achieves excellent F-scores.|微服务体系结构在大型 Internet 系统中得到了广泛的应用。对于每个用户请求，调用一些微服务，并形成一个跟踪来记录微服务之间的树状调用依赖关系以及每个调用节点的时间消耗。跟踪在诊断系统故障中很有用，但是它们的复杂结构使得建模它们的模式和检测它们的异常变得困难。在这篇文章中，我们提出了一个新的双变量图变分自动编码器(VAE) ，用于微服务跟踪的无监督异常检测。为了重构节点的时间消耗，我们提出了一种新的调度层。我们发现，一些异常样本会出现负对数似然(NLL)反演，这使得异常评分对于异常检测来说是不可行的。为了解决这个问题，我们指出 NLL 可以分解为 KL- 散度和数据熵，而低维异常可以引入正常输入的熵差。我们提出了三种技术来缓解跟踪异常检测的熵差: 伯努利和分类标度、节点计数归一化和高斯标准极限。在来自一家顶级互联网公司的五个跟踪数据集中，我们提出的 TraceVAE 获得了优秀的 F 分数。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Unsupervised+Anomaly+Detection+on+Microservice+Traces+through+Graph+VAE)|0| -|[FedEdge: Accelerating Edge-Assisted Federated Learning](https://doi.org/10.1145/3543507.3583264)|Kaibin Wang, Qiang He, Feifei Chen, Hai Jin, Yun Yang|Huazhong University of Science and Technology, China; Huazhong University of Science and Technology, China and Swinburne University of Technology, Australia; Deakin University, Australia; Swinburne University of Technology, Australia|Federated learning (FL) has been widely acknowledged as a promising solution to training machine learning (ML) model training with privacy preservation. To reduce the traffic overheads incurred by FL systems, edge servers have been included between clients and the parameter server to aggregate clients’ local models. Recent studies on this edge-assisted hierarchical FL scheme have focused on ensuring or accelerating model convergence by coping with various factors, e.g., uncertain network conditions, unreliable clients, heterogeneous compute resources, etc. This paper presents our three new discoveries of the edge-assisted hierarchical FL scheme: 1) it wastes significant time during its two-phase training rounds; 2) it does not recognize or utilize model diversity when producing a global model; and 3) it is vulnerable to model poisoning attacks. To overcome these drawbacks, we propose FedEdge, a novel edge-assisted hierarchical FL scheme that accelerates model training with asynchronous local federated training and adaptive model aggregation. Extensive experiments are conducted on two widely-used public datasets. The results demonstrate that, compared with state-of-the-art FL schemes, FedEdge accelerates model convergence by 1.14 × −3.20 ×, and improves model accuracy by 2.14% - 6.63%.|联邦学习(FL)作为一种具有隐私保护的训练机器学习(ML)模型的解决方案，已经得到了广泛的认可。为了减少 FL 系统带来的流量开销，在客户端和参数服务器之间引入了边缘服务器，以聚合客户端的本地模型。近年来关于边缘辅助层次 FL 方案的研究主要集中在通过处理不确定的网络条件、不可靠的客户端、异构的计算资源等因素来保证或加速模型的收敛。本文介绍了边缘辅助层次 FL 方案的三个新发现: 1)它在两阶段训练中浪费了大量的时间; 2)它在生成全局模型时不能识别或利用模型的多样性; 3)它容易受到模型中毒攻击。为了克服这些缺点，我们提出了一种新的边缘辅助分层 FL 方案 FedEdge，该方案通过异步局部联邦训练和自适应模型聚合来加速模型训练。在两个广泛使用的公共数据集上进行了广泛的实验。结果表明，与现有的 FL 格式相比，FedEdge 格式加速模型收敛速度为1.14 × -3.20 × ，模型精度提高了2.14% -6.63% 。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=FedEdge:+Accelerating+Edge-Assisted+Federated+Learning)|0| +|[Evidence of Demographic rather than Ideological Segregation in News Discussion on Reddit](https://doi.org/10.1145/3543507.3583468)|Corrado Monti, Jacopo D'Ignazi, Michele Starnini, Gianmarco De Francisci Morales|ISI Foundation, Italy; CENTAI, Italy|We evaluate homophily and heterophily among ideological and demographic groups in a typical opinion formation context: online discussions of current news. We analyze user interactions across five years in the r/news community on Reddit, one of the most visited websites in the United States. Then, we estimate demographic and ideological attributes of these users. Thanks to a comparison with a carefully-crafted network null model, we establish which pairs of attributes foster interactions and which ones inhibit them. Individuals prefer to engage with the opposite ideological side, which contradicts the echo chamber narrative. Instead, demographic groups are homophilic, as individuals tend to interact within their own group - even in an online setting where such attributes are not directly observable. In particular, we observe age and income segregation consistently across years: users tend to avoid interactions when belonging to different groups. These results persist after controlling for the degree of interest by each demographic group in different news topics. Our findings align with the theory that affective polarization - the difficulty in socializing across political boundaries-is more connected with an increasingly divided society, rather than ideological echo chambers on social media. We publicly release our anonymized data set and all the code to reproduce our results: https://github.com/corradomonti/demographic-homophily|我们在一个典型的意见形成背景下评估意识形态和人口群体之间的同质性和异质性: 当前新闻的在线讨论。我们在美国访问量最大的网站之一 Reddit 的 r/news 社区分析了五年来的用户交互。然后，我们估计这些用户的人口统计学和意识形态属性。通过与精心构建的网络空模型进行比较，我们确定了哪些属性对促进交互，哪些属性对抑制交互。个人倾向于接触相反的意识形态，这与回音室叙事相矛盾。相反，人口统计学群体是同性恋，因为个人往往在他们自己的群体内互动-即使在网上设置，这些属性是不能直接观察到的。特别是，我们观察到年龄和收入隔离持续多年: 当用户属于不同的群体时，他们倾向于避免互动。在控制了每个人口群体对不同新闻主题的兴趣程度之后，这些结果仍然存在。我们的研究结果符合这样一个理论，即情感的两极分化——跨越政治界限进行社交的困难——更多地与日益分裂的社会有关，而不是与社交媒体上的意识形态回音室有关。我们公开发布我们的匿名数据集和所有重现结果的代码: https://github.com/corradomonti/demographic-homophily|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Evidence+of+Demographic+rather+than+Ideological+Segregation+in+News+Discussion+on+Reddit)|0| +|[Longitudinal Assessment of Reference Quality on Wikipedia](https://doi.org/10.1145/3543507.3583218)|Aitolkyn Baigutanova, Jaehyeon Myung, Diego SáezTrumper, AiJou Chou, Miriam Redi, Changwook Jung, Meeyoung Cha|KAIST, Republic of Korea; School of Computing, KAIST, Republic of Korea; Wikimedia Foundation, Spain; Wikimedia Foundation, United Kingdom; IBS, Republic of Korea and KAIST, Republic of Korea|Wikipedia plays a crucial role in the integrity of the Web. This work analyzes the reliability of this global encyclopedia through the lens of its references. We operationalize the notion of reference quality by defining reference need (RN), i.e., the percentage of sentences missing a citation, and reference risk (RR), i.e., the proportion of non-authoritative references. We release Citation Detective, a tool for automatically calculating the RN score, and discover that the RN score has dropped by 20 percent point in the last decade, with more than half of verifiable statements now accompanying references. The RR score has remained below 1% over the years as a result of the efforts of the community to eliminate unreliable references. We propose pairing novice and experienced editors on the same Wikipedia article as a strategy to enhance reference quality. Our quasi-experiment indicates that such a co-editing experience can result in a lasting advantage in identifying unreliable sources in future edits. As Wikipedia is frequently used as the ground truth for numerous Web applications, our findings and suggestions on its reliability can have a far-reaching impact. We discuss the possibility of other Web services adopting Wiki-style user collaboration to eliminate unreliable content.|维基百科在网络的完整性中扮演着至关重要的角色。本文从参考文献的角度分析了这部全球百科全书的可靠性。我们通过定义参考需求(RN) ，即缺少引用的句子的百分比和参考风险(RR) ，即非权威参考文献的比例，来操作参考质量的概念。我们发布了引文侦探，一个自动计算 RN 分数的工具，发现 RN 分数在过去的十年里下降了20% ，超过一半的可验证的陈述现在伴随着参考文献。多年来，由于社区努力消除不可靠的参考文献，RR 评分一直保持在1% 以下。我们建议将新手和经验丰富的编辑配对在同一维基百科文章上，作为提高参考文献质量的策略。我们的准实验表明，这种合作编辑的经验可以导致一个持久的优势，在识别不可靠的来源在未来的编辑。由于维基百科经常被用作众多 Web 应用程序的基本事实，我们对其可靠性的发现和建议可能产生深远的影响。我们讨论了其他 Web 服务采用 Wiki 风格的用户协作来消除不可靠内容的可能性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Longitudinal+Assessment+of+Reference+Quality+on+Wikipedia)|0| +|[Gateway Entities in Problematic Trajectories](https://doi.org/10.1145/3543507.3583283)|Xi Leslie Chen, Abhratanu Dutta, Sindhu Ernala, Stratis Ioannidis, Shankar Kalyanaraman, Israel Nir, Udi Weinsberg|Meta, USA; Northwestern, USA; Columbia University, USA; Northeastern, USA|Social media platforms like Facebook and YouTube connect people with communities that reflect their own values and experiences. People discover new communities either organically or through algorithmic recommendations based on their interests and preferences. We study online journeys users take through these communities, focusing particularly on ones that may lead to problematic outcomes. In particular, we propose and explore the concept of gateways, namely, entities associated with a higher likelihood of subsequent engagement with problematic content. We show, via a real-world application on Facebook groups, that a simple definition of gateway entities can be leveraged to reduce exposure to problematic content by 1% without any adverse impact on user engagement metrics. Motivated by this finding, we propose several formal definitions of gateways, via both frequentist and survival analysis methods, and evaluate their efficacy in predicting user behavior through offline experiments. Frequentist, duration-insensitive methods predict future harmful engagements with an 0.64–0.83 AUC, while survival analysis methods improve this to 0.72–0.90 AUC.|Facebook 和 YouTube 等社交媒体平台将人们与反映他们自身价值观和经历的社区联系起来。人们发现新的社区要么是有机的，要么是通过基于兴趣和偏好的算法推荐。我们研究用户通过这些社区进行的在线旅行，特别关注那些可能导致有问题的结果的在线旅行。特别是，我们提出并探索了网关的概念，即与后续参与有问题内容的可能性较高相关的实体。我们通过 Facebook 群组上的一个现实应用程序展示了，一个简单的网关实体定义可以用来减少1% 的问题内容暴露，而不会对用户参与度指标产生任何不利影响。基于这一发现，我们通过频率分析和生存分析的方法提出了几种网关的形式化定义，并通过离线实验评估了它们在预测用户行为方面的有效性。频繁，持续时间不敏感的方法预测未来的有害参与与0.64 -0.83 AUC，而生存分析方法将其改善为0.72 -0.90 AUC。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Gateway+Entities+in+Problematic+Trajectories)|0| +|[Unsupervised Anomaly Detection on Microservice Traces through Graph VAE](https://doi.org/10.1145/3543507.3583215)|Zhe Xie, Haowen Xu, Wenxiao Chen, Wanxue Li, Huai Jiang, Liangfei Su, Hanzhang Wang, Dan Pei|eBay, China; eBay, USA; Department of Computer Science and Technology, Tsinghua University, China|The microservice architecture is widely employed in large Internet systems. For each user request, a few of the microservices are called, and a trace is formed to record the tree-like call dependencies among microservices and the time consumption at each call node. Traces are useful in diagnosing system failures, but their complex structures make it difficult to model their patterns and detect their anomalies. In this paper, we propose a novel dual-variable graph variational autoencoder (VAE) for unsupervised anomaly detection on microservice traces. To reconstruct the time consumption of nodes, we propose a novel dispatching layer. We find that the inversion of negative log-likelihood (NLL) appears for some anomalous samples, which makes the anomaly score infeasible for anomaly detection. To address this, we point out that the NLL can be decomposed into KL-divergence and data entropy, whereas lower-dimensional anomalies can introduce an entropy gap with normal inputs. We propose three techniques to mitigate this entropy gap for trace anomaly detection: Bernoulli & Categorical Scaling, Node Count Normalization, and Gaussian Std-Limit. On five trace datasets from a top Internet company, our proposed TraceVAE achieves excellent F-scores.|微服务体系结构在大型 Internet 系统中得到了广泛的应用。对于每个用户请求，调用一些微服务，并形成一个跟踪来记录微服务之间的树状调用依赖关系以及每个调用节点的时间消耗。跟踪在诊断系统故障中很有用，但是它们的复杂结构使得建模它们的模式和检测它们的异常变得困难。在这篇文章中，我们提出了一个新的双变量图变分自动编码器(VAE) ，用于微服务跟踪的无监督异常检测。为了重构节点的时间消耗，我们提出了一种新的调度层。我们发现，一些异常样本会出现负对数似然(NLL)反演，这使得异常评分对于异常检测来说是不可行的。为了解决这个问题，我们指出 NLL 可以分解为 KL- 散度和数据熵，而低维异常可以引入正常输入的熵差。我们提出了三种技术来缓解跟踪异常检测的熵差: 伯努利和分类标度、节点计数归一化和高斯标准极限。在来自一家顶级互联网公司的五个跟踪数据集中，我们提出的 TraceVAE 获得了优秀的 F 分数。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Unsupervised+Anomaly+Detection+on+Microservice+Traces+through+Graph+VAE)|0| +|[FedEdge: Accelerating Edge-Assisted Federated Learning](https://doi.org/10.1145/3543507.3583264)|Kaibin Wang, Qiang He, Feifei Chen, Hai Jin, Yun Yang|Deakin University, Australia; Huazhong University of Science and Technology, China; Huazhong University of Science and Technology, China and Swinburne University of Technology, Australia; Swinburne University of Technology, Australia|Federated learning (FL) has been widely acknowledged as a promising solution to training machine learning (ML) model training with privacy preservation. To reduce the traffic overheads incurred by FL systems, edge servers have been included between clients and the parameter server to aggregate clients’ local models. Recent studies on this edge-assisted hierarchical FL scheme have focused on ensuring or accelerating model convergence by coping with various factors, e.g., uncertain network conditions, unreliable clients, heterogeneous compute resources, etc. This paper presents our three new discoveries of the edge-assisted hierarchical FL scheme: 1) it wastes significant time during its two-phase training rounds; 2) it does not recognize or utilize model diversity when producing a global model; and 3) it is vulnerable to model poisoning attacks. To overcome these drawbacks, we propose FedEdge, a novel edge-assisted hierarchical FL scheme that accelerates model training with asynchronous local federated training and adaptive model aggregation. Extensive experiments are conducted on two widely-used public datasets. The results demonstrate that, compared with state-of-the-art FL schemes, FedEdge accelerates model convergence by 1.14 × −3.20 ×, and improves model accuracy by 2.14% - 6.63%.|联邦学习(FL)作为一种具有隐私保护的训练机器学习(ML)模型的解决方案，已经得到了广泛的认可。为了减少 FL 系统带来的流量开销，在客户端和参数服务器之间引入了边缘服务器，以聚合客户端的本地模型。近年来关于边缘辅助层次 FL 方案的研究主要集中在通过处理不确定的网络条件、不可靠的客户端、异构的计算资源等因素来保证或加速模型的收敛。本文介绍了边缘辅助层次 FL 方案的三个新发现: 1)它在两阶段训练中浪费了大量的时间; 2)它在生成全局模型时不能识别或利用模型的多样性; 3)它容易受到模型中毒攻击。为了克服这些缺点，我们提出了一种新的边缘辅助分层 FL 方案 FedEdge，该方案通过异步局部联邦训练和自适应模型聚合来加速模型训练。在两个广泛使用的公共数据集上进行了广泛的实验。结果表明，与现有的 FL 格式相比，FedEdge 格式加速模型收敛速度为1.14 × -3.20 × ，模型精度提高了2.14% -6.63% 。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=FedEdge:+Accelerating+Edge-Assisted+Federated+Learning)|0| |[CausIL: Causal Graph for Instance Level Microservice Data](https://doi.org/10.1145/3543507.3583274)|Sarthak Chakraborty, Shaddy Garg, Shubham Agarwal, Ayush Chauhan, Shiv Kumar Saini|Adobe Research, India; The University of Texas at Austin, USA; Adobe, India|AI-based monitoring has become crucial for cloud-based services due to its scale. A common approach to AI-based monitoring is to detect causal relationships among service components and build a causal graph. Availability of domain information makes cloud systems even better suited for such causal detection approaches. In modern cloud systems, however, auto-scalers dynamically change the number of microservice instances, and a load-balancer manages the load on each instance. This poses a challenge for off-the-shelf causal structure detection techniques as they neither incorporate the system architectural domain information nor provide a way to model distributed compute across varying numbers of service instances. To address this, we develop CausIL, which detects a causal structure among service metrics by considering compute distributed across dynamic instances and incorporating domain knowledge derived from system architecture. Towards the application in cloud systems, CausIL estimates a causal graph using instance-specific variations in performance metrics, modeling multiple instances of a service as independent, conditional on system assumptions. Simulation study shows the efficacy of CausIL over baselines by improving graph estimation accuracy by ~25% as measured by Structural Hamming Distance whereas the real-world dataset demonstrates CausIL's applicability in deployment settings.|基于人工智能的监控由于其规模已经成为云服务的关键。基于人工智能监控的一种常见方法是检测服务组件之间的因果关系，并建立因果图。领域信息的可用性使得云系统更加适合这种因果检测方法。然而，在现代云系统中，自动伸缩器动态地更改微服务实例的数量，并且负载平衡器管理每个实例上的负载。这对现成的因果结构检测技术提出了挑战，因为它们既没有合并系统架构领域信息，也没有提供跨不同数量的服务实例建模分布式计算的方法。为了解决这个问题，我们开发了 CausIL，它通过考虑跨动态实例分布的计算和合并来自系统架构的领域知识来检测服务度量之间的因果结构。对于云系统中的应用程序，CausIL 使用特定于实例的性能指标变化来估计因果图，将服务的多个实例建模为独立的，并以系统假设为条件。仿真研究显示，通过结构化汉明距离测量，causIL 的图形估计精度比基线提高了约25% ，而现实世界的数据集证明了 causIL 在部署设置中的适用性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=CausIL:+Causal+Graph+for+Instance+Level+Microservice+Data)|0| -|[Learning Cooperative Oversubscription for Cloud by Chance-Constrained Multi-Agent Reinforcement Learning](https://doi.org/10.1145/3543507.3583298)|Junjie Sheng, Lu Wang, Fangkai Yang, Bo Qiao, Hang Dong, Xiangfeng Wang, Bo Jin, Jun Wang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang|Microsoft Research, China; East China Normal University, China; Microsoft 365, USA|Oversubscription is a common practice for improving cloud resource utilization. It allows the cloud service provider to sell more resources than the physical limit, assuming not all users would fully utilize the resources simultaneously. However, how to design an oversubscription policy that improves utilization while satisfying the some safety constraints remains an open problem. Existing methods and industrial practices are over-conservative, ignoring the coordination of diverse resource usage patterns and probabilistic constraints. To address these two limitations, this paper formulates the oversubscription for cloud as a chance-constrained optimization problem and propose an effective Chance Constrained Multi-Agent Reinforcement Learning (C2MARL) method to solve this problem. Specifically, C2MARL reduces the number of constraints by considering their upper bounds and leverages a multi-agent reinforcement learning paradigm to learn a safe and optimal coordination policy. We evaluate our C2MARL on an internal cloud platform and public cloud datasets. Experiments show that our C2MARL outperforms existing methods in improving utilization ($20\%\sim 86\%$) under different levels of safety constraints.|超额订阅是提高云资源利用率的常见做法。它允许云服务提供商出售比物理限制更多的资源，假设并非所有用户都会同时充分利用这些资源。然而，如何设计一个超额订阅策略来提高利用率，同时满足一些安全约束仍然是一个悬而未决的问题。现有的方法和工业实践过于保守，忽视了不同资源使用模式和概率约束的协调。为了解决这两个限制，本文将云计算的超额认购作为一个机会约束的最佳化问题，并提出了一种有效的机会约束多强化学习代理(c2MARL)方法来解决这个问题。具体来说，C2MARL 通过考虑约束的上限来减少约束的数量，并利用多主体强化学习范式来学习安全和最优的协调策略。我们在内部云平台和公共云数据集上评估 C2MARL。实验表明，在不同的安全约束水平下，我们的 C2MARL 在提高利用率方面优于现有的方法(20% sim 86% $)。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Learning+Cooperative+Oversubscription+for+Cloud+by+Chance-Constrained+Multi-Agent+Reinforcement+Learning)|0| -|[CMDiagnostor: An Ambiguity-Aware Root Cause Localization Approach Based on Call Metric Data](https://doi.org/10.1145/3543507.3583302)|Qingyang Yu, Changhua Pei, Bowen Hao, Mingjie Li, Zeyan Li, Shenglin Zhang, Xianglin Lu, Rui Wang, Jiaqi Li, Zhenyu Wu, Dan Pei|Tsinghua University, China; Nankai University, China; Tencent, China; Computer Network Information Center, Chinese Academy of Sciences, China|The availability of online services is vital as its strong relevance to revenue and user experience. To ensure online services’ availability, quickly localizing the root causes of system failures is crucial. Given the high resource consumption of traces, call metric data are widely used by existing approaches to construct call graphs in practice. However, ambiguous correspondences between upstream and downstream calls may exist and result in exploring unexpected edges in the constructed call graph. Conducting root cause localization on this graph may lead to misjudgments of real root causes. To the best of our knowledge, we are the first to investigate such ambiguity, which is overlooked in the existing literature. Inspired by the law of large numbers and the Markov properties of network traffic, we propose a regression-based method (named AmSitor) to address this problem effectively. Based on AmSitor, we propose an ambiguity-aware root cause localization approach based on Call Metric Data named CMDiagnostor, containing metric anomaly detection, ambiguity-free call graph construction, root cause exploration, and candidate root cause ranking modules. The comprehensive experimental evaluations conducted on real-world datasets show that our CMDiagnostor can outperform the state-of-the-art approaches by 14% on the top-5 hit rate. Moreover, AmSitor can also be applied to existing baseline approaches separately to improve their performances one step further. The source code is released at https://github.com/NetManAIOps/CMDiagnostor.|在线服务的提供至关重要，因为它与收入和用户体验密切相关。为了确保在线服务的可用性，快速本地化系统故障的根本原因是至关重要的。由于跟踪的高资源消耗，呼叫度量数据在现有的呼叫图构造方法中得到了广泛的应用。然而，上游或下游呼叫之间可能存在模糊的对应关系，从而导致在构建的呼叫图中探索意外的边缘。在这个图上进行根本原因定位可能会导致对真正根本原因的误判。据我们所知，我们是第一个研究这种模糊性，这是忽视了现有的文献。受大数定律和网络流量的马尔可夫特性的启发，我们提出了一种基于回归的方法(AmSitor)来有效地解决这一问题。基于 AmSitor，我们提出了一种基于调用度量数据的歧义感知根源定位方法 CMDiagnostor，该方法包含度量异常检测、无歧义调用图结构、根源探索和候选根源排序模块。对真实世界数据集进行的综合实验评估表明，我们的 CMDiagnostor 在前5位的命中率方面可以比最先进的方法高出14% 。此外，AmSitor 还可以分别应用于现有的基线方法，以进一步提高其性能。源代码在 https://github.com/netmanaiops/cmdiagnostor 发布。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=CMDiagnostor:+An+Ambiguity-Aware+Root+Cause+Localization+Approach+Based+on+Call+Metric+Data)|0| +|[Learning Cooperative Oversubscription for Cloud by Chance-Constrained Multi-Agent Reinforcement Learning](https://doi.org/10.1145/3543507.3583298)|Junjie Sheng, Lu Wang, Fangkai Yang, Bo Qiao, Hang Dong, Xiangfeng Wang, Bo Jin, Jun Wang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang|East China Normal University, China; Microsoft Research, China; Microsoft 365, USA|Oversubscription is a common practice for improving cloud resource utilization. It allows the cloud service provider to sell more resources than the physical limit, assuming not all users would fully utilize the resources simultaneously. However, how to design an oversubscription policy that improves utilization while satisfying the some safety constraints remains an open problem. Existing methods and industrial practices are over-conservative, ignoring the coordination of diverse resource usage patterns and probabilistic constraints. To address these two limitations, this paper formulates the oversubscription for cloud as a chance-constrained optimization problem and propose an effective Chance Constrained Multi-Agent Reinforcement Learning (C2MARL) method to solve this problem. Specifically, C2MARL reduces the number of constraints by considering their upper bounds and leverages a multi-agent reinforcement learning paradigm to learn a safe and optimal coordination policy. We evaluate our C2MARL on an internal cloud platform and public cloud datasets. Experiments show that our C2MARL outperforms existing methods in improving utilization ($20\%\sim 86\%$) under different levels of safety constraints.|超额订阅是提高云资源利用率的常见做法。它允许云服务提供商出售比物理限制更多的资源，假设并非所有用户都会同时充分利用这些资源。然而，如何设计一个超额订阅策略来提高利用率，同时满足一些安全约束仍然是一个悬而未决的问题。现有的方法和工业实践过于保守，忽视了不同资源使用模式和概率约束的协调。为了解决这两个限制，本文将云计算的超额认购作为一个机会约束的最佳化问题，并提出了一种有效的机会约束多强化学习代理(c2MARL)方法来解决这个问题。具体来说，C2MARL 通过考虑约束的上限来减少约束的数量，并利用多主体强化学习范式来学习安全和最优的协调策略。我们在内部云平台和公共云数据集上评估 C2MARL。实验表明，在不同的安全约束水平下，我们的 C2MARL 在提高利用率方面优于现有的方法(20% sim 86% $)。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Learning+Cooperative+Oversubscription+for+Cloud+by+Chance-Constrained+Multi-Agent+Reinforcement+Learning)|0| +|[CMDiagnostor: An Ambiguity-Aware Root Cause Localization Approach Based on Call Metric Data](https://doi.org/10.1145/3543507.3583302)|Qingyang Yu, Changhua Pei, Bowen Hao, Mingjie Li, Zeyan Li, Shenglin Zhang, Xianglin Lu, Rui Wang, Jiaqi Li, Zhenyu Wu, Dan Pei|Tsinghua University, China; Computer Network Information Center, Chinese Academy of Sciences, China; Tencent, China; Nankai University, China|The availability of online services is vital as its strong relevance to revenue and user experience. To ensure online services’ availability, quickly localizing the root causes of system failures is crucial. Given the high resource consumption of traces, call metric data are widely used by existing approaches to construct call graphs in practice. However, ambiguous correspondences between upstream and downstream calls may exist and result in exploring unexpected edges in the constructed call graph. Conducting root cause localization on this graph may lead to misjudgments of real root causes. To the best of our knowledge, we are the first to investigate such ambiguity, which is overlooked in the existing literature. Inspired by the law of large numbers and the Markov properties of network traffic, we propose a regression-based method (named AmSitor) to address this problem effectively. Based on AmSitor, we propose an ambiguity-aware root cause localization approach based on Call Metric Data named CMDiagnostor, containing metric anomaly detection, ambiguity-free call graph construction, root cause exploration, and candidate root cause ranking modules. The comprehensive experimental evaluations conducted on real-world datasets show that our CMDiagnostor can outperform the state-of-the-art approaches by 14% on the top-5 hit rate. Moreover, AmSitor can also be applied to existing baseline approaches separately to improve their performances one step further. The source code is released at https://github.com/NetManAIOps/CMDiagnostor.|在线服务的提供至关重要，因为它与收入和用户体验密切相关。为了确保在线服务的可用性，快速本地化系统故障的根本原因是至关重要的。由于跟踪的高资源消耗，呼叫度量数据在现有的呼叫图构造方法中得到了广泛的应用。然而，上游或下游呼叫之间可能存在模糊的对应关系，从而导致在构建的呼叫图中探索意外的边缘。在这个图上进行根本原因定位可能会导致对真正根本原因的误判。据我们所知，我们是第一个研究这种模糊性，这是忽视了现有的文献。受大数定律和网络流量的马尔可夫特性的启发，我们提出了一种基于回归的方法(AmSitor)来有效地解决这一问题。基于 AmSitor，我们提出了一种基于调用度量数据的歧义感知根源定位方法 CMDiagnostor，该方法包含度量异常检测、无歧义调用图结构、根源探索和候选根源排序模块。对真实世界数据集进行的综合实验评估表明，我们的 CMDiagnostor 在前5位的命中率方面可以比最先进的方法高出14% 。此外，AmSitor 还可以分别应用于现有的基线方法，以进一步提高其性能。源代码在 https://github.com/netmanaiops/cmdiagnostor 发布。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=CMDiagnostor:+An+Ambiguity-Aware+Root+Cause+Localization+Approach+Based+on+Call+Metric+Data)|0| |[Visual-Aware Testing and Debugging for Web Performance Optimization](https://doi.org/10.1145/3543507.3583323)|Xinlei Yang, Wei Liu, Hao Lin, Zhenhua Li, Feng Qian, Xianlong Wang, Yunhao Liu, Tianyin Xu||Web performance optimization services, or web performance optimizers (WPOs), play a critical role in today’s web ecosystem by improving page load speed and saving network traffic. However, WPOs are known for introducing visual distortions that disrupt the users’ web experience. Unfortunately, visual distortions are hard to analyze, test, and debug, due to their subjective measure, dynamic content, and sophisticated WPO implementations. This paper presents Vetter, a novel and effective system that automatically tests and debugs visual distortions. Its key idea is to reason about the morphology of web pages, which describes the topological forms and scale-free geometrical structures of visual elements. Vetter efficiently calculates morphology and comparatively analyzes the morphologies of web pages before and after a WPO, which acts as a differential test oracle. Such morphology analysis enables Vetter to detect visual distortions accurately and reliably. Vetter further diagnoses the detected visual distortions to pinpoint the root causes in WPOs’ source code. This is achieved by morphological causal inference, which localizes the offending visual elements that trigger the distortion and maps them to the corresponding code. We applied Vetter to four representative WPOs. Vetter discovers 21 unknown defects responsible for 98% visual distortions; 12 of them have been confirmed and 5 have been fixed.|Web 性能优化服务或 Web 性能优化器(WPO)通过提高页面加载速度和节省网络流量在当今的 Web 生态系统中发挥着关键作用。然而，WPO 以引入视觉失真而闻名，这种失真会扰乱用户的网络体验。不幸的是，由于视觉失真的主观测量、动态内容和复杂的 WPO 实现，它们很难进行分析、测试和调试。本文介绍了 Vetter，一个新颖有效的系统，自动测试和调试视觉失真。其核心思想是对网页的形态进行推理，描述视觉元素的拓扑形态和无标度几何结构。Vetter 有效地计算了 WPO 前后网页的形态，并对比分析了 WPO 前后网页的形态，起到了差异化测试的作用。这种形态学分析使维特能够准确可靠地检测视觉失真。Vetter 进一步诊断检测到的视觉失真，以确定 WPO 源代码中的根本原因。这是通过形态学的因果推理来实现的，它定位引发失真的视觉元素，并将它们映射到相应的代码。我们将 Vetter 应用于四个有代表性的 WPO。Vetter 发现了21个未知缺陷，造成了98% 的视觉失真，其中12个已经被证实，5个已经被修复。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Visual-Aware+Testing+and+Debugging+for+Web+Performance+Optimization)|0| -|[Demystifying Mobile Extended Reality in Web Browsers: How Far Can We Go?](https://doi.org/10.1145/3543507.3583329)|Weichen Bi, Yun Ma, Deyu Tian, Qi Yang, Mingtao Zhang, Xiang Jing|School of Software & Microelectronics, Peking University, China; School of Computer Science and Engineering, University of New South Wales, Australia; School of Computer Science, Peking University, China; Institute for Artificial Intelligence, Peking University, China|Mobile extended reality (XR) has developed rapidly in recent years. Compared with the app-based XR, XR in web browsers has the advantages of being lightweight and cross-platform, providing users with a pervasive experience. Therefore, many frameworks are emerging to support the development of XR in web browsers. However, little has been known about how well these frameworks perform and how complex XR apps modern web browsers can support on mobile devices. To fill the knowledge gap, in this paper, we conduct an empirical study of mobile XR in web browsers. We select seven most popular web-based XR frameworks and investigate their runtime performance, including 3D rendering, camera capturing, and real-world understanding. We find that current frameworks have the potential to further enhance their performance by increasing GPU utilization or improving computing parallelism. Besides, for 3D scenes with good rendering performance, developers can feel free to add camera capturing with little influence on performance to support augmented reality (AR) and mixed reality (MR) applications. Based on our findings, we draw several practical implications to provide better XR support in web browsers.|移动扩展现实(XR)近年来发展迅速。与基于应用程序的 XR 相比，Web 浏览器中的 XR 具有轻量级和跨平台的优势，为用户提供了无处不在的体验。因此，许多框架正在出现，以支持在 Web 浏览器中开发 XR。然而，人们对这些框架的性能以及现代网络浏览器在移动设备上支持的复杂 XR 应用知之甚少。为了填补这一知识空白，本文对 Web 浏览器中的移动 XR 进行了实证研究。我们选择了七个最流行的基于 Web 的 XR 框架，并研究了它们的运行时性能，包括3D 渲染、摄像头捕捉和对真实世界的理解。我们发现当前的框架有潜力通过提高 GPU 利用率或改善计算并行性来进一步提高其性能。此外，对于具有良好渲染性能的3D 场景，开发人员可以随意添加对性能影响不大的摄像头捕捉，以支持扩增实境(AR)和混合现实(MR)应用程序。基于我们的发现，我们提出了一些实际的含义，以提供更好的 XR 支持在 Web 浏览器。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Demystifying+Mobile+Extended+Reality+in+Web+Browsers:+How+Far+Can+We+Go?)|0| +|[Demystifying Mobile Extended Reality in Web Browsers: How Far Can We Go?](https://doi.org/10.1145/3543507.3583329)|Weichen Bi, Yun Ma, Deyu Tian, Qi Yang, Mingtao Zhang, Xiang Jing|School of Software & Microelectronics, Peking University, China; Institute for Artificial Intelligence, Peking University, China; School of Computer Science, Peking University, China; School of Computer Science and Engineering, University of New South Wales, Australia|Mobile extended reality (XR) has developed rapidly in recent years. Compared with the app-based XR, XR in web browsers has the advantages of being lightweight and cross-platform, providing users with a pervasive experience. Therefore, many frameworks are emerging to support the development of XR in web browsers. However, little has been known about how well these frameworks perform and how complex XR apps modern web browsers can support on mobile devices. To fill the knowledge gap, in this paper, we conduct an empirical study of mobile XR in web browsers. We select seven most popular web-based XR frameworks and investigate their runtime performance, including 3D rendering, camera capturing, and real-world understanding. We find that current frameworks have the potential to further enhance their performance by increasing GPU utilization or improving computing parallelism. Besides, for 3D scenes with good rendering performance, developers can feel free to add camera capturing with little influence on performance to support augmented reality (AR) and mixed reality (MR) applications. Based on our findings, we draw several practical implications to provide better XR support in web browsers.|移动扩展现实(XR)近年来发展迅速。与基于应用程序的 XR 相比，Web 浏览器中的 XR 具有轻量级和跨平台的优势，为用户提供了无处不在的体验。因此，许多框架正在出现，以支持在 Web 浏览器中开发 XR。然而，人们对这些框架的性能以及现代网络浏览器在移动设备上支持的复杂 XR 应用知之甚少。为了填补这一知识空白，本文对 Web 浏览器中的移动 XR 进行了实证研究。我们选择了七个最流行的基于 Web 的 XR 框架，并研究了它们的运行时性能，包括3D 渲染、摄像头捕捉和对真实世界的理解。我们发现当前的框架有潜力通过提高 GPU 利用率或改善计算并行性来进一步提高其性能。此外，对于具有良好渲染性能的3D 场景，开发人员可以随意添加对性能影响不大的摄像头捕捉，以支持扩增实境(AR)和混合现实(MR)应用程序。基于我们的发现，我们提出了一些实际的含义，以提供更好的 XR 支持在 Web 浏览器。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Demystifying+Mobile+Extended+Reality+in+Web+Browsers:+How+Far+Can+We+Go?)|0| |[Look Deep into the Microservice System Anomaly through Very Sparse Logs](https://doi.org/10.1145/3543507.3583338)|Xinrui Jiang, Yicheng Pan, Meng Ma, Ping Wang|Peking University, China|Intensive monitoring and anomaly diagnosis have become a knotty problem for modern microservice architecture due to the dynamics of service dependency. While most previous studies rely heavily on ample monitoring metrics, we raise a fundamental but always neglected issue - the diagnostic metric integrity problem. This paper solves the problem by proposing MicroCU – a novel approach to diagnose microservice systems using very sparse API logs. We design a structure named dynamic causal curves to portray time-varying service dependencies and a temporal dynamics discovery algorithm based on Granger causal intervals. Our algorithm generates a smoother space of causal curves and designs the concept of causal unimodalization to calibrate the causality infidelities brought by missing metrics. Finally, a path search algorithm on dynamic causality graphs is proposed to pinpoint the root cause. Experiments on commercial system cases show that MicroCU outperforms many state-of-the-art approaches and reflects the superiorities of causal unimodalization to raw metric imputation.|由于服务依赖的动态性，密集监视和异常诊断已经成为现代微服务体系结构的一个棘手问题。虽然以前的大多数研究严重依赖于大量的监测指标，但我们提出了一个基本但总是被忽视的问题——诊断指标完整性问题。本文通过提出 MicroCU 来解决这个问题——一种利用非常稀疏的 API 日志诊断微服务系统的新方法。设计了一种描述时变服务依赖的动态因果曲线结构和一种基于 Granger 因果区间的时间动态发现算法。我们的算法生成一个更光滑的因果曲线空间，并设计了因果单模化的概念来校准因果失真带来的度量缺失。最后，提出了一种基于动态因果图的路径搜索算法来确定根本原因。商业系统案例的实验表明，MicroCU 优于许多最新的方法，反映了因果单模态化对原始度量插补的优越性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Look+Deep+into+the+Microservice+System+Anomaly+through+Very+Sparse+Logs)|0| -|[Analyzing the Communication Clusters in Datacenters✱](https://doi.org/10.1145/3543507.3583410)|KlausTycho Foerster, Thibault Marette, Stefan Neumann, Claudia Plant, Ylli Sadikaj, Stefan Schmid, Yllka Velaj|Faculty of Computer Science, University of Vienna, Austria and UniVie Doctoral School Computer Science, University of Vienna, Austria; Faculty of Computer Science, University of Vienna, Austria and ds:Univie, University of Vienna, Austria; KTH Royal Institute of Technology, Sweden; KTH Royal Institute of Technology, France; TU Berlin & University of Vienna, Germany; TU Dortmund, Germany; Faculty of Computer Science, University of Vienna, Austria|Datacenter networks have become a critical infrastructure of our digital society and over the last years, great efforts have been made to better understand the communication patterns inside datacenters. In particular, existing empirical studies showed that datacenter traffic typically features much temporal and spatial structure, and that at any given time, some communication pairs interact much more frequently than others. This paper generalizes this study to communication groups and analyzes how clustered the datacenter traffic is, and how stable these clusters are over time. To this end, we propose a methodology which revolves around a biclustering approach, allowing us to identify groups of racks and servers which communicate frequently over the network. In particular, we consider communication patterns occurring in three different Facebook datacenters: a Web cluster consisting of web servers serving web traffic, a Database cluster which mainly consists of MySQL servers, and a Hadoop cluster. Interestingly, we find that in all three clusters, small groups of racks and servers can produce a large fraction of the network traffic, and we can determine these groups even when considering short snapshots of network traffic. We also show empirically that these clusters are fairly stable across time. Our insights on the size and stability of communication clusters hence uncover an interesting potential for resource optimizations in datacenter infrastructures.|数据中心网络已经成为我们数字社会的重要基础设施，在过去的几年里，人们付出了巨大的努力来更好地理解数据中心内部的通信模式。特别是，现有的实证研究表明，数据中心流量通常具有很多时间和空间结构，并且在任何给定的时间，一些通信对比其他通信对更频繁地进行交互。本文将这项研究推广到通信组，并分析数据中心流量是如何聚集的，以及随着时间的推移这些聚集是如何稳定的。为此，我们提出了一种围绕双群集方法的方法，该方法允许我们识别经常通过网络进行通信的机架和服务器组。特别是，我们考虑发生在三个不同 Facebook 数据中心的通信模式: 一个由服务于网络流量的 Web 服务器组成的 Web 集群，一个主要由 MySQL 服务器组成的数据库集群，以及一个 Hadoop 集群。有趣的是，我们发现在所有三个集群中，小组的机架和服务器可以产生大部分的网络流量，我们甚至可以在考虑网络流量的短快照时确定这些组。我们还通过经验表明，这些集群在不同的时间段是相当稳定的。因此，我们对通信集群的规模和稳定性的见解揭示了数据中心基础设施中资源优化的有趣潜力。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Analyzing+the+Communication+Clusters+in+Datacenters✱)|0| -|[DDPC: Automated Data-Driven Power-Performance Controller Design on-the-fly for Latency-sensitive Web Services](https://doi.org/10.1145/3543507.3583437)|Mehmet Savasci, Ahmed AliEldin, Johan Eker, Anders Robertsson, Prashant J. Shenoy|Chalmers University of Technology, Sweden; Ericsson Research, Sweden and Lund University, Sweden; University of Massachusetts Amherst, USA; Lund University, Sweden|Traditional power reduction techniques such as DVFS or RAPL are challenging to use with web services because they significantly affect the services’ latency and throughput. Previous work suggested the use of controllers based on control theory or machine learning to reduce performance degradation under constrained power. However, generating these controllers is challenging as every web service applications running in a data center requires a power-performance model and a fine-tuned controller. In this paper, we present DDPC, a system for autonomic data-driven controller generation for power-latency management. DDPC automates the process of designing and deploying controllers for dynamic power allocation to manage the power-performance trade-offs for latency-sensitive web applications such as a social network. For each application, DDPC uses system identification techniques to learn an adaptive power-performance model that captures the application’s power-latency trade-offs which is then used to generate and deploy a Proportional-Integral (PI) power controller with gain-scheduling to dynamically manage the power allocation to the server running application using RAPL. We evaluate DDPC with two realistic latency-sensitive web applications under varying load scenarios. Our results show that DDPC is capable of autonomically generating and deploying controllers within a few minutes reducing the active power allocation of a web-server by more than 50% compared to state-of-the-art techniques while maintaining the latency well below the target of the application.|传统的功耗降低技术，如 DVFS 或 RAPL，对 Web 服务的使用具有挑战性，因为它们严重影响服务的延迟和吞吐量。以往的工作建议使用基于控制理论或机器学习的控制器来减少功率受限情况下的性能下降。然而，生成这些控制器是具有挑战性的，因为在数据中心中运行的每个 Web 服务应用程序都需要电源性能模型和经过微调的控制器。在本文中，我们提出了 DDPC，一个自主数据驱动的控制器生成系统的功率延迟管理。DDPC 自动化设计和部署动态功率分配控制器的过程，以管理对延迟敏感的 Web 应用程序(如社交网络)的功率性能权衡。对于每个应用程序，DDPC 使用系统辨识技术来学习一个自适应功率性能模型，该模型捕捉应用程序的功率延迟权衡，然后用于生成和部署具有增益调度的比例积分(PI)功率控制器，以使用 RAPL 动态管理服务器运行应用程序的功率分配。在不同的负载情况下，我们使用两个真实的对延迟敏感的 Web 应用程序来评估 DDPC。我们的研究结果表明，DDPC 能够在几分钟内自动生成和部署控制器，与最先进的技术相比，网络服务器的有源功率分配减少了50% 以上，同时保持延迟远低于应用程序的目标。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=DDPC:+Automated+Data-Driven+Power-Performance+Controller+Design+on-the-fly+for+Latency-sensitive+Web+Services)|0| +|[Analyzing the Communication Clusters in Datacenters✱](https://doi.org/10.1145/3543507.3583410)|KlausTycho Foerster, Thibault Marette, Stefan Neumann, Claudia Plant, Ylli Sadikaj, Stefan Schmid, Yllka Velaj|TU Dortmund, Germany; Faculty of Computer Science, University of Vienna, Austria; Faculty of Computer Science, University of Vienna, Austria and UniVie Doctoral School Computer Science, University of Vienna, Austria; TU Berlin & University of Vienna, Germany; Faculty of Computer Science, University of Vienna, Austria and ds:Univie, University of Vienna, Austria; KTH Royal Institute of Technology, France; KTH Royal Institute of Technology, Sweden|Datacenter networks have become a critical infrastructure of our digital society and over the last years, great efforts have been made to better understand the communication patterns inside datacenters. In particular, existing empirical studies showed that datacenter traffic typically features much temporal and spatial structure, and that at any given time, some communication pairs interact much more frequently than others. This paper generalizes this study to communication groups and analyzes how clustered the datacenter traffic is, and how stable these clusters are over time. To this end, we propose a methodology which revolves around a biclustering approach, allowing us to identify groups of racks and servers which communicate frequently over the network. In particular, we consider communication patterns occurring in three different Facebook datacenters: a Web cluster consisting of web servers serving web traffic, a Database cluster which mainly consists of MySQL servers, and a Hadoop cluster. Interestingly, we find that in all three clusters, small groups of racks and servers can produce a large fraction of the network traffic, and we can determine these groups even when considering short snapshots of network traffic. We also show empirically that these clusters are fairly stable across time. Our insights on the size and stability of communication clusters hence uncover an interesting potential for resource optimizations in datacenter infrastructures.|数据中心网络已经成为我们数字社会的重要基础设施，在过去的几年里，人们付出了巨大的努力来更好地理解数据中心内部的通信模式。特别是，现有的实证研究表明，数据中心流量通常具有很多时间和空间结构，并且在任何给定的时间，一些通信对比其他通信对更频繁地进行交互。本文将这项研究推广到通信组，并分析数据中心流量是如何聚集的，以及随着时间的推移这些聚集是如何稳定的。为此，我们提出了一种围绕双群集方法的方法，该方法允许我们识别经常通过网络进行通信的机架和服务器组。特别是，我们考虑发生在三个不同 Facebook 数据中心的通信模式: 一个由服务于网络流量的 Web 服务器组成的 Web 集群，一个主要由 MySQL 服务器组成的数据库集群，以及一个 Hadoop 集群。有趣的是，我们发现在所有三个集群中，小组的机架和服务器可以产生大部分的网络流量，我们甚至可以在考虑网络流量的短快照时确定这些组。我们还通过经验表明，这些集群在不同的时间段是相当稳定的。因此，我们对通信集群的规模和稳定性的见解揭示了数据中心基础设施中资源优化的有趣潜力。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Analyzing+the+Communication+Clusters+in+Datacenters✱)|0| +|[DDPC: Automated Data-Driven Power-Performance Controller Design on-the-fly for Latency-sensitive Web Services](https://doi.org/10.1145/3543507.3583437)|Mehmet Savasci, Ahmed AliEldin, Johan Eker, Anders Robertsson, Prashant J. Shenoy|University of Massachusetts Amherst, USA; Ericsson Research, Sweden and Lund University, Sweden; Chalmers University of Technology, Sweden; Lund University, Sweden|Traditional power reduction techniques such as DVFS or RAPL are challenging to use with web services because they significantly affect the services’ latency and throughput. Previous work suggested the use of controllers based on control theory or machine learning to reduce performance degradation under constrained power. However, generating these controllers is challenging as every web service applications running in a data center requires a power-performance model and a fine-tuned controller. In this paper, we present DDPC, a system for autonomic data-driven controller generation for power-latency management. DDPC automates the process of designing and deploying controllers for dynamic power allocation to manage the power-performance trade-offs for latency-sensitive web applications such as a social network. For each application, DDPC uses system identification techniques to learn an adaptive power-performance model that captures the application’s power-latency trade-offs which is then used to generate and deploy a Proportional-Integral (PI) power controller with gain-scheduling to dynamically manage the power allocation to the server running application using RAPL. We evaluate DDPC with two realistic latency-sensitive web applications under varying load scenarios. Our results show that DDPC is capable of autonomically generating and deploying controllers within a few minutes reducing the active power allocation of a web-server by more than 50% compared to state-of-the-art techniques while maintaining the latency well below the target of the application.|传统的功耗降低技术，如 DVFS 或 RAPL，对 Web 服务的使用具有挑战性，因为它们严重影响服务的延迟和吞吐量。以往的工作建议使用基于控制理论或机器学习的控制器来减少功率受限情况下的性能下降。然而，生成这些控制器是具有挑战性的，因为在数据中心中运行的每个 Web 服务应用程序都需要电源性能模型和经过微调的控制器。在本文中，我们提出了 DDPC，一个自主数据驱动的控制器生成系统的功率延迟管理。DDPC 自动化设计和部署动态功率分配控制器的过程，以管理对延迟敏感的 Web 应用程序(如社交网络)的功率性能权衡。对于每个应用程序，DDPC 使用系统辨识技术来学习一个自适应功率性能模型，该模型捕捉应用程序的功率延迟权衡，然后用于生成和部署具有增益调度的比例积分(PI)功率控制器，以使用 RAPL 动态管理服务器运行应用程序的功率分配。在不同的负载情况下，我们使用两个真实的对延迟敏感的 Web 应用程序来评估 DDPC。我们的研究结果表明，DDPC 能够在几分钟内自动生成和部署控制器，与最先进的技术相比，网络服务器的有源功率分配减少了50% 以上，同时保持延迟远低于应用程序的目标。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=DDPC:+Automated+Data-Driven+Power-Performance+Controller+Design+on-the-fly+for+Latency-sensitive+Web+Services)|0| |[Will Admins Cope? Decentralized Moderation in the Fediverse](https://doi.org/10.1145/3543507.3583487)|Ishaku Hassan Anaobi, Aravindh Raman, Ignacio Castro, Haris Bin Zia, Damilola Ibosiola, Gareth Tyson|School of Electronic Engineering and Computer Science, Queen Mary University, United Kingdom; Hong Kong University of Science and Technology, China; Telefonica, Spain|As an alternative to Twitter and other centralized social networks, the Fediverse is growing in popularity. The recent, and polemical, takeover of Twitter by Elon Musk has exacerbated this trend. The Fediverse includes a growing number of decentralized social networks, such as Pleroma or Mastodon, that share the same subscription protocol (ActivityPub). Each of these decentralized social networks is composed of independent instances that are run by different administrators. Users, however, can interact with other users across the Fediverse regardless of the instance they are signed up to. The growing user base of the Fediverse creates key challenges for the administrators, who may experience a growing burden. In this paper, we explore how large that overhead is, and whether there are solutions to alleviate the burden. We study the overhead of moderation on the administrators. We observe a diversity of administrator strategies, with evidence that administrators on larger instances struggle to find sufficient resources. We then propose a tool, WatchGen, to semi-automate the process.|作为 Twitter 和其他集中式社交网络的替代品，Fedifferent 越来越受欢迎。埃隆•马斯克(Elon Musk)最近对 Twitter 的收购加剧了这一趋势。Fedifferent 包括越来越多分散的社交网络，如 Pleroma 或 Mastodon，它们共享相同的订阅协议(ActivityPub)。这些分散的社交网络中的每一个都由不同的管理员运行的独立实例组成。然而，不管用户注册的实例是什么，他们都可以通过 Fedifferent 与其他用户进行交互。不断增长的 Fedifferent 用户基础给管理员带来了关键的挑战，他们可能会面临越来越大的负担。在本文中，我们将探讨这个开销有多大，以及是否存在减轻负担的解决方案。我们研究管理员的节制开销。我们观察到管理员策略的多样性，有证据表明大型实例中的管理员很难找到足够的资源。然后，我们提出一个工具 WatchGen 来半自动化这个过程。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Will+Admins+Cope?+Decentralized+Moderation+in+the+Fediverse)|0| -|[Are Mobile Advertisements in Compliance with App's Age Group?](https://doi.org/10.1145/3543507.3583534)|Yanjie Zhao, Tianming Liu, Haoyu Wang, Yepang Liu, John C. Grundy, Li Li|Huazhong University of Science and Technology, China; School of Software, Beihang University, China and Monash University, Australia; Monash University, Australia; School of Software, Beihang University, China; Southern University of Science and Technology, China|As smartphones and mobile apps permeate every aspect of people’s lives, children are accessing mobile devices at an increasingly younger age. The inescapable exposure of advertisements in mobile apps to children has grown alarmingly. Mobile advertisements are placed by advertisers and subsequently distributed by ad SDKs, under the rare control of app developers and app markets’ content ratings. Indeed, content that is objectionable and harmful to children’s mental health has been reported to appear in advertising, such as pornography. However, few studies have yet concentrated on automatically and comprehensively identifying such kid-unsuitable mobile advertising. In this paper, we first characterize the regulations for mobile ads relating to children. We then propose our novel automated dynamic analysis framework, named AdRambler, that attempts to collect ad content throughout the lifespan of mobile ads and identify their inappropriateness for child app users. Using AdRambler, we conduct a large-scale (25,000 mobile apps) empirical investigation and reveal the non-incidental presence of inappropriate ads in apps with child-included target audiences. We collected 11,270 ad views and identified 1,289 ad violations (from 775 apps) of child user regulations, with roughly half of the app promotions not in compliance with host apps’ content ratings. Our finding indicates that even certified ad SDKs could still propagate inappropriate advertisements. We further delve into the question of accountability for the presence of inappropriate advertising and provide concrete suggestions for all stakeholders to take action for the benefit of children.|随着智能手机和移动应用程序渗透到人们生活的各个方面，儿童使用移动设备的年龄越来越小。移动应用程序中的广告不可避免地暴露在儿童面前，这种现象已经增长到令人担忧的地步。移动广告由广告商投放，然后由广告软件开发工具包(ad SDK)发布，在应用程序开发者和应用程序市场内容评级的罕见控制之下。事实上，有报道称，在色情等广告中出现了对儿童心理健康有害的令人反感的内容。然而，很少有研究集中在自动和全面识别这种儿童不适合的移动广告。在本文中，我们首先描述了与儿童有关的移动广告的规定。然后，我们提出了我们的新型自动化动态分析框架，命名为 AdRambler，该框架试图收集整个移动广告生命周期中的广告内容，并确定其不适合儿童应用程序用户。使用 AdRambler，我们进行了一个大规模(25,000个移动应用程序)的实证调查，并揭示了在包括儿童在内的目标受众的应用程序中非偶然存在不适当的广告。我们收集了11,270个广告浏览量，发现了1,289个违反儿童用户法规的广告(来自775个应用程序) ，其中大约一半的应用程序促销活动不符合主机应用程序的内容评级。我们的发现表明，即使是经过认证的广告 SDK 仍然可以传播不适当的广告。我们进一步探讨了对不适当广告的问责问题，并为所有利益攸关方采取有利于儿童的行动提出了具体建议。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Are+Mobile+Advertisements+in+Compliance+with+App's+Age+Group?)|0| -|[EdgeMove: Pipelining Device-Edge Model Training for Mobile Intelligence](https://doi.org/10.1145/3543507.3583540)|Zeqian Dong, Qiang He, Feifei Chen, Hai Jin, Tao Gu, Yun Yang|Huazhong University of Science and Technology, China; Macquarie University, Australia; Huazhong University of Science and Technology, China and Swinburne University of Technology, Australia; Swinburne University of Technology, Australia; Deakin University, Australia|Training machine learning (ML) models on mobile and Web-of-Things (WoT) has been widely acknowledged and employed as a promising solution to privacy-preserving ML. However, these end-devices often suffer from constrained resources and fail to accommodate increasingly large ML models that crave great computation power. Offloading ML models partially to the cloud for training strikes a trade-off between privacy preservation and resource requirements. However, device-cloud training creates communication overheads that delay model training tremendously. This paper presents EdgeMove, the first device-edge training scheme that enables fast pipelined model training across edge devices and edge servers. It employs probing-based mechanisms to tackle the new challenges raised by device-edge training. Before training begins, it probes nearby edge servers’ training performance and bootstraps model training by constructing a training pipeline with an approximate model partitioning. During the training process, EdgeMove accommodates user mobility and system dynamics by probing nearby edge servers’ training performance adaptively and adapting the training pipeline proactively. Extensive experiments are conducted with two popular DNN models trained on four datasets for three ML tasks. The results demonstrate that EdgeMove achieves a 1.3 × -2.1 × speedup over the state-of-the-art scheme.|基于移动设备和物联网的训练机器学习(ML)模型已经被广泛认可，并被用作保护机器学习隐私的一种有前途的解决方案。然而，这些终端设备往往受到资源的限制，无法适应日益增长的大型机器学习模型，渴望巨大的计算能力。将机器学习模型部分卸载到云中进行培训，在保护隐私和资源需求之间达成了一个平衡。然而，设备云训练产生的通信开销极大地延迟了模型训练。本文介绍了 EdgeMove，这是第一个支持跨边缘设备和边缘服务器的快速流水线模型训练的设备边缘训练方案。它采用基于探测的机制来应对由设备边缘训练带来的新挑战。在训练开始前，通过构造一个具有近似模型划分的训练流水线，探讨了附近边缘服务器的训练性能，并引导模型训练。在训练过程中，EdgeMove 通过自适应地探测附近边缘服务器的训练性能和主动地调整训练流水线来适应用户移动性和系统动态性。广泛的实验进行了两个流行的 DNN 模型训练的四个数据集的三个机器学习任务。实验结果表明，EdgeMove 比最先进的方案提高了1.3 × -2.1 × 的速度。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=EdgeMove:+Pipelining+Device-Edge+Model+Training+for+Mobile+Intelligence)|0| +|[Are Mobile Advertisements in Compliance with App's Age Group?](https://doi.org/10.1145/3543507.3583534)|Yanjie Zhao, Tianming Liu, Haoyu Wang, Yepang Liu, John C. Grundy, Li Li|School of Software, Beihang University, China and Monash University, Australia; Huazhong University of Science and Technology, China; Monash University, Australia; Southern University of Science and Technology, China; School of Software, Beihang University, China|As smartphones and mobile apps permeate every aspect of people’s lives, children are accessing mobile devices at an increasingly younger age. The inescapable exposure of advertisements in mobile apps to children has grown alarmingly. Mobile advertisements are placed by advertisers and subsequently distributed by ad SDKs, under the rare control of app developers and app markets’ content ratings. Indeed, content that is objectionable and harmful to children’s mental health has been reported to appear in advertising, such as pornography. However, few studies have yet concentrated on automatically and comprehensively identifying such kid-unsuitable mobile advertising. In this paper, we first characterize the regulations for mobile ads relating to children. We then propose our novel automated dynamic analysis framework, named AdRambler, that attempts to collect ad content throughout the lifespan of mobile ads and identify their inappropriateness for child app users. Using AdRambler, we conduct a large-scale (25,000 mobile apps) empirical investigation and reveal the non-incidental presence of inappropriate ads in apps with child-included target audiences. We collected 11,270 ad views and identified 1,289 ad violations (from 775 apps) of child user regulations, with roughly half of the app promotions not in compliance with host apps’ content ratings. Our finding indicates that even certified ad SDKs could still propagate inappropriate advertisements. We further delve into the question of accountability for the presence of inappropriate advertising and provide concrete suggestions for all stakeholders to take action for the benefit of children.|随着智能手机和移动应用程序渗透到人们生活的各个方面，儿童使用移动设备的年龄越来越小。移动应用程序中的广告不可避免地暴露在儿童面前，这种现象已经增长到令人担忧的地步。移动广告由广告商投放，然后由广告软件开发工具包(ad SDK)发布，在应用程序开发者和应用程序市场内容评级的罕见控制之下。事实上，有报道称，在色情等广告中出现了对儿童心理健康有害的令人反感的内容。然而，很少有研究集中在自动和全面识别这种儿童不适合的移动广告。在本文中，我们首先描述了与儿童有关的移动广告的规定。然后，我们提出了我们的新型自动化动态分析框架，命名为 AdRambler，该框架试图收集整个移动广告生命周期中的广告内容，并确定其不适合儿童应用程序用户。使用 AdRambler，我们进行了一个大规模(25,000个移动应用程序)的实证调查，并揭示了在包括儿童在内的目标受众的应用程序中非偶然存在不适当的广告。我们收集了11,270个广告浏览量，发现了1,289个违反儿童用户法规的广告(来自775个应用程序) ，其中大约一半的应用程序促销活动不符合主机应用程序的内容评级。我们的发现表明，即使是经过认证的广告 SDK 仍然可以传播不适当的广告。我们进一步探讨了对不适当广告的问责问题，并为所有利益攸关方采取有利于儿童的行动提出了具体建议。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Are+Mobile+Advertisements+in+Compliance+with+App's+Age+Group?)|0| +|[EdgeMove: Pipelining Device-Edge Model Training for Mobile Intelligence](https://doi.org/10.1145/3543507.3583540)|Zeqian Dong, Qiang He, Feifei Chen, Hai Jin, Tao Gu, Yun Yang|Huazhong University of Science and Technology, China; Swinburne University of Technology, Australia; Deakin University, Australia; Macquarie University, Australia; Huazhong University of Science and Technology, China and Swinburne University of Technology, Australia|Training machine learning (ML) models on mobile and Web-of-Things (WoT) has been widely acknowledged and employed as a promising solution to privacy-preserving ML. However, these end-devices often suffer from constrained resources and fail to accommodate increasingly large ML models that crave great computation power. Offloading ML models partially to the cloud for training strikes a trade-off between privacy preservation and resource requirements. However, device-cloud training creates communication overheads that delay model training tremendously. This paper presents EdgeMove, the first device-edge training scheme that enables fast pipelined model training across edge devices and edge servers. It employs probing-based mechanisms to tackle the new challenges raised by device-edge training. Before training begins, it probes nearby edge servers’ training performance and bootstraps model training by constructing a training pipeline with an approximate model partitioning. During the training process, EdgeMove accommodates user mobility and system dynamics by probing nearby edge servers’ training performance adaptively and adapting the training pipeline proactively. Extensive experiments are conducted with two popular DNN models trained on four datasets for three ML tasks. The results demonstrate that EdgeMove achieves a 1.3 × -2.1 × speedup over the state-of-the-art scheme.|基于移动设备和物联网的训练机器学习(ML)模型已经被广泛认可，并被用作保护机器学习隐私的一种有前途的解决方案。然而，这些终端设备往往受到资源的限制，无法适应日益增长的大型机器学习模型，渴望巨大的计算能力。将机器学习模型部分卸载到云中进行培训，在保护隐私和资源需求之间达成了一个平衡。然而，设备云训练产生的通信开销极大地延迟了模型训练。本文介绍了 EdgeMove，这是第一个支持跨边缘设备和边缘服务器的快速流水线模型训练的设备边缘训练方案。它采用基于探测的机制来应对由设备边缘训练带来的新挑战。在训练开始前，通过构造一个具有近似模型划分的训练流水线，探讨了附近边缘服务器的训练性能，并引导模型训练。在训练过程中，EdgeMove 通过自适应地探测附近边缘服务器的训练性能和主动地调整训练流水线来适应用户移动性和系统动态性。广泛的实验进行了两个流行的 DNN 模型训练的四个数据集的三个机器学习任务。实验结果表明，EdgeMove 比最先进的方案提高了1.3 × -2.1 × 的速度。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=EdgeMove:+Pipelining+Device-Edge+Model+Training+for+Mobile+Intelligence)|0| |[HTTP Steady Connections for Robust Web Acceleration](https://doi.org/10.1145/3543507.3583550)|Sunjae Kim, Wonjun Lee|Korea University, Republic of Korea|HTTP’s intrinsic request-and-response traffic pattern makes most web servers often idle, leaving a potential to accelerate page loads. We present the notion of HTTP steady connections, which fully utilizes the server’s available network bandwidth during a page load using the promising HTTP/3 server push, transforming the intermittent workload of loading a page into a more steady one. To construct a proper server push policy to achieve this, we separate the structure of a page, which is a relatively static factor, from the page load environments including client and network characteristics, which are generally dynamic and unknown to servers. We formulate a deadline-based sequencing problem using a page load model with dependency graphs and design a feedback-based reprioritization mechanism within HTTP server push to reactively match client progress robustly. Experiments with a prototype and a wide range of real-world pages show that HTTP steady connections significantly improve web page loads compared with state-of-the-art accelerators, even under packet losses and without any prior knowledge of network environments.|HTTP 内在的请求-响应流量模式使得大多数 Web 服务器经常处于空闲状态，从而有可能加速页面加载。我们提出了 HTTP 稳定连接的概念，它在页面加载期间充分利用服务器的可用网络带宽，使用有前途的 HTTP/3服务器推送，将加载页面的间歇性工作负载转化为更稳定的工作负载。为了构建合适的服务器推策略来实现这一点，我们将页面结构(相对静态的因素)与页面加载环境(包括客户端和网络特性)分离开来，后者通常是动态的，服务器不知道。我们利用一个带有依赖图的页面负载模型，提出了一个基于截止日期的排序问题，并在 HTTP 服务器推送中设计了一个基于反馈的重新排序机制，使得客户端进度能够被动地匹配。对一个原型和大量真实世界页面的实验表明，与最先进的加速器相比，HTTP 稳定连接可以显著提高网页负载，即使在丢包的情况下，也不需要任何关于网络环境的先验知识。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=HTTP+Steady+Connections+for+Robust+Web+Acceleration)|0| |[Dynamic Interventions for Networked Contagions](https://doi.org/10.1145/3543507.3583470)|Marios Papachristou, Siddhartha Banerjee, Jon M. Kleinberg|Cornell University, USA|We study the problem of designing dynamic intervention policies for minimizing networked defaults in financial networks. Formally, we consider a dynamic version of the celebrated Eisenberg-Noe model of financial network liabilities and use this to study the design of external intervention policies. Our controller has a fixed resource budget in each round and can use this to minimize the effect of demand/supply shocks in the network. We formulate the optimal intervention problem as a Markov Decision Process and show how we can leverage the problem structure to efficiently compute optimal intervention policies with continuous interventions and provide approximation algorithms for discrete interventions. Going beyond financial networks, we argue that our model captures dynamic network intervention in a much broader class of dynamic demand/supply settings with networked inter-dependencies. To demonstrate this, we apply our intervention algorithms to various application domains, including ridesharing, online transaction platforms, and financial networks with agent mobility. In each case, we study the relationship between node centrality and intervention strength, as well as the fairness properties of the optimal interventions.|研究了金融网络中最小化网络违约的动态干预策略设计问题。在形式上，我们考虑著名的艾森伯格-诺伊金融网络负债模型的一个动态版本，并用它来研究外部干预政策的设计。我们的控制器在每一轮都有一个固定的资源预算，并且可以使用它来最小化网络中需求/供应冲击的影响。我们将最优干预问题表述为一个马可夫决策过程，并展示了我们如何利用问题结构来有效地计算连续干预的最优干预政策，并为离散干预提供近似算法。超越金融网络，我们认为我们的模型捕捉动态网络干预在一个更广泛的类动态需求/供应设置与网络相互依赖。为了证明这一点，我们将我们的干预算法应用到各种应用领域，包括共乘、在线交易平台和具有代理移动性的金融网络。在每种情况下，我们研究了节点集中度与干预强度之间的关系，以及最优干预的公平性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Dynamic+Interventions+for+Networked+Contagions)|0| -|[Randomized Pricing with Deferred Acceptance for Revenue Maximization with Submodular Objectives](https://doi.org/10.1145/3543507.3583477)|He Huang, Kai Han, Shuang Cui, Jing Tang|The Hong Kong University of Science and Technology (Guangzhou), The Hong Kong University of Science and Technology, China; School of Computer Science and Technology, University of Science and Technology of China, China; School of Computer Science and Technology, Soochow University, China|A lot of applications in web economics need to maximize the revenue under a budget for payments and also guarantee the truthfulness of users, so Budget-Feasible Mechanism (BFM) Design has aroused great interests during last decade. Most of the existing BFMs concentrate on maximizing a monotone submodular function subject to a knapsack constraint, which is insufficient for many applications with complex objectives or constraints. Observing this, the recent studies (e.g., [4, 5, 11]) have considered non-monotone submodular objectives or more complex constraints such as a k-system constraint. In this study, we follow this line of research and propose truthful BFMs with improved performance bounds for non-monotone submodular objectives with or without a k-system constraint. Our BFMs leverage the idea of providing random prices to users while deferring the decision on the final winning set, and are also based on a novel randomized algorithm for the canonical constrained submodular maximization problem achieving better performance bounds compared to the state-of-the-art. Finally, the effectiveness and efficiency of our approach are demonstrated by extensive experiments on several applications about social network marketing, crowdsourcing and personalized recommendation.|网络经济中的许多应用需要在支付预算下实现收益最大化，同时又要保证用户的真实性，因此预算可行机制设计在近十年来引起了人们的极大兴趣。现有的 BFM 大多集中于最大化受背包约束的单调子模函数，这对于许多具有复杂目标或约束的应用来说是不够的。考虑到这一点，最近的研究(例如，[4,5,11])已经考虑了非单调子模目标或更复杂的约束，如 k- 系统约束。在这项研究中，我们遵循这条研究路线，提出了真实的 BFM 与改进的性能界限为非单调子模目标有或没有 k 系统约束。我们的 bfMs 利用向用户提供随机价格的想法，同时推迟对最终获胜集的决定，并且基于一个新的随机化算法，用于规范约束的子模块最大化问题，与最先进的技术相比，获得更好的性能界限。最后，通过在社交网络营销、众包和个性化推荐等多个应用领域的广泛实验，验证了该方法的有效性和高效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Randomized+Pricing+with+Deferred+Acceptance+for+Revenue+Maximization+with+Submodular+Objectives)|0| +|[Randomized Pricing with Deferred Acceptance for Revenue Maximization with Submodular Objectives](https://doi.org/10.1145/3543507.3583477)|He Huang, Kai Han, Shuang Cui, Jing Tang|The Hong Kong University of Science and Technology (Guangzhou), The Hong Kong University of Science and Technology, China; School of Computer Science and Technology, Soochow University, China; School of Computer Science and Technology, University of Science and Technology of China, China|A lot of applications in web economics need to maximize the revenue under a budget for payments and also guarantee the truthfulness of users, so Budget-Feasible Mechanism (BFM) Design has aroused great interests during last decade. Most of the existing BFMs concentrate on maximizing a monotone submodular function subject to a knapsack constraint, which is insufficient for many applications with complex objectives or constraints. Observing this, the recent studies (e.g., [4, 5, 11]) have considered non-monotone submodular objectives or more complex constraints such as a k-system constraint. In this study, we follow this line of research and propose truthful BFMs with improved performance bounds for non-monotone submodular objectives with or without a k-system constraint. Our BFMs leverage the idea of providing random prices to users while deferring the decision on the final winning set, and are also based on a novel randomized algorithm for the canonical constrained submodular maximization problem achieving better performance bounds compared to the state-of-the-art. Finally, the effectiveness and efficiency of our approach are demonstrated by extensive experiments on several applications about social network marketing, crowdsourcing and personalized recommendation.|网络经济中的许多应用需要在支付预算下实现收益最大化，同时又要保证用户的真实性，因此预算可行机制设计在近十年来引起了人们的极大兴趣。现有的 BFM 大多集中于最大化受背包约束的单调子模函数，这对于许多具有复杂目标或约束的应用来说是不够的。考虑到这一点，最近的研究(例如，[4,5,11])已经考虑了非单调子模目标或更复杂的约束，如 k- 系统约束。在这项研究中，我们遵循这条研究路线，提出了真实的 BFM 与改进的性能界限为非单调子模目标有或没有 k 系统约束。我们的 bfMs 利用向用户提供随机价格的想法，同时推迟对最终获胜集的决定，并且基于一个新的随机化算法，用于规范约束的子模块最大化问题，与最先进的技术相比，获得更好的性能界限。最后，通过在社交网络营销、众包和个性化推荐等多个应用领域的广泛实验，验证了该方法的有效性和高效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Randomized+Pricing+with+Deferred+Acceptance+for+Revenue+Maximization+with+Submodular+Objectives)|0| |[Fairness-aware Guaranteed Display Advertising Allocation under Traffic Cost Constraint](https://doi.org/10.1145/3543507.3583501)|Liang Dai, Zhonglin Zu, Hao Wu, Liang Wang, Bo Zheng|Alibaba Group, China|Real-time Bidding (RTB) and Guaranteed Display (GD) advertising are two primary ways to sell impressions for publishers in online display advertising. Although GD contract serves less efficiently compared to RTB ads, it helps advertisers reach numerous target audiences at a lower cost and allows publishers to increase overall advertising revenue. However, with billion-scale requests online per day, it’s a challenging problem for publishers to decide whether and which GD ad to display for each impression. In this paper, we propose an optimal allocation model for GD contracts considering optimizing three objectives: maximizing guaranteed delivery and impressions’ quality and minimizing the extra traffic cost of GD contracts to increase overall revenue. The traffic cost of GD contracts is defined as the potential expected revenue if the impression is allocated to RTB ads. Our model dynamically adjusts the weights for each GD contract between impressions’ quality and traffic cost based on real-time performance, which produces fairness-aware allocation results. A parallel training framework based on Parameter-Server (PS) architecture is utilized to efficiently and periodically update the model. Deriving from the allocation model, we also propose a simple and adaptive online bidding strategy for GD contracts, which can be updated quickly by feedback-based algorithms to achieve optimal impression allocation even in complex and dynamic environments. We demonstrate the effectiveness of our proposed method by using both offline evaluation and online A/B testing.|实时竞价(RTB)和保证显示(GD)广告是出版商在线显示广告销售印象的两种主要方式。尽管 GD 合同的服务效率低于 RTB 广告，但它帮助广告商以较低的成本达到众多目标受众，并使发布商能够增加整体广告收入。然而，由于每天都有数十亿的在线请求，对于出版商来说，决定是否展示每个印象以及展示哪个 GD 广告是一个具有挑战性的问题。在本文中，我们提出了一个 GD 合同的最优分配模型，考虑优化三个目标: 最大化的保证交付和印象的质量和最小化额外的流量成本的 GD 合同，以增加总收入。广东合同的流量成本被定义为潜在的预期收入，如果印象是分配给 RTB 广告。我们的模型基于实时性能动态调整每个 GD 契约在印象质量和流量成本之间的权重，从而产生公平感知的分配结果。利用基于参数服务器(PS)体系结构的并行训练框架对模型进行高效、周期性的更新。在分配模型的基础上，提出了一种简单、自适应的 GD 合同在线投标策略，该策略可以通过基于反馈的算法快速更新，即使在复杂动态环境下也能实现最优印象分配。通过离线评估和在线 A/B 测试，验证了该方法的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Fairness-aware+Guaranteed+Display+Advertising+Allocation+under+Traffic+Cost+Constraint)|0| -|[Is your digital neighbor a reliable investment advisor?](https://doi.org/10.1145/3543507.3583502)|Daisuke Kawai, Alejandro Cuevas, Bryan R. Routledge, Kyle Soska, Ariel ZetlinJones, Nicolas Christin|Carnegie Mellon University, USA; Ramiel Capital, USA|The web and social media platforms have drastically changed how investors produce and consume financial advice. Historically, individual investors were often relying on newsletters and related prospectus backed by the reputation and track record of their issuers. Nowadays, financial advice is frequently offered online, by anonymous or pseudonymous parties with little at stake. As such, a natural question is to investigate whether these modern financial “influencers” operate in good faith, or whether they might be misleading their followers intentionally. To start answering this question, we obtained data from a very large cryptocurrency derivatives exchange, from which we derived individual trading positions. Some of the investors on that platform elect to link to their Twitter profiles. We were thus able to compare the positions publicly espoused on Twitter with those actually taken in the market. We discovered that 1) staunchly “bullish” investors on Twitter often took much more moderate, if not outright opposite, positions in their own trades when the market was down, 2) their followers tended to align their positions with bullish Twitter outlooks, and 3) moderate voices on Twitter (and their own followers) were on the other hand far more consistent with their actual investment strategies. In other words, while social media advice may attempt to foster a sense of camaraderie among people of like-minded beliefs, the reality is that this is merely an illusion, which may result in financial losses for people blindly following advice.|网络和社交媒体平台已经彻底改变了投资者提供和消费金融建议的方式。从历史上看，个人投资者往往依赖时事通讯和相关的招股说明书，并以发行人的声誉和业绩记录作为依据。如今，财务咨询经常是在网上提供的，由匿名或匿名的团体提供，几乎没有什么利害关系。因此，一个自然而然的问题是调查这些现代金融“影响者”是否诚信经营，或者他们是否故意误导他们的追随者。为了开始回答这个问题，我们从一个非常大的加密货币衍生品交易所获得数据，从中我们得到了个人交易头寸。该平台上的一些投资者选择链接到他们的 Twitter 档案。因此，我们可以比较 Twitter 上公开支持的立场和市场上实际采取的立场。我们发现，1) Twitter 上坚定的“看涨”投资者在股市下跌时，往往在自己的交易中持有更为温和(如果不是完全相反的话)的头寸; 2)他们的追随者倾向于将自己的头寸与看涨的 Twitter 前景联系起来; 3) Twitter 上的温和声音(以及他们自己的追随者)则与他们的实际投资策略更为一致。换句话说，虽然社交媒体的建议可能试图培养志同道合者之间的友情，但事实上这只是一种幻觉，可能导致盲目听从建议的人蒙受经济损失。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Is+your+digital+neighbor+a+reliable+investment+advisor?)|0| -|[Impartial Selection with Prior Information](https://doi.org/10.1145/3543507.3583553)|Ioannis Caragiannis, George Christodoulou, Nicos Protopapas|Aristotle University of Thessaloniki, Greece and Archimedes/RC Athena, Greece; Aarhus University, Denmark; University of Patras, Greece|We study the problem of {\em impartial selection}, a topic that lies at the intersection of computational social choice and mechanism design. The goal is to select the most popular individual among a set of community members. The input can be modeled as a directed graph, where each node represents an individual, and a directed edge indicates nomination or approval of a community member to another. An {\em impartial mechanism} is robust to potential selfish behavior of the individuals and provides appropriate incentives to voters to report their true preferences by ensuring that the chance of a node to become a winner does not depend on its outgoing edges. The goal is to design impartial mechanisms that select a node with an in-degree that is as close as possible to the highest in-degree. We measure the efficiency of such a mechanism by the difference of these in-degrees, known as its {\em additive} approximation. In particular, we study the extent to which prior information on voters' preferences could be useful in the design of efficient deterministic impartial selection mechanisms with good additive approximation guarantees. We consider three models of prior information, which we call the {\em opinion poll}, the {\em a prior popularity}, and the {\em uniform} model. We analyze the performance of a natural selection mechanism that we call {\em approval voting with default} (AVD) and show that it achieves a $O(\sqrt{n\ln{n}})$ additive guarantee for opinion poll and a $O(\ln^2n)$ for a priori popularity inputs, where $n$ is the number of individuals. We consider this polylogarithmic bound as our main technical contribution. We complement this last result by showing that our analysis is close to tight, showing an $\Omega(\ln{n})$ lower bound. This holds in the uniform model, which is the simplest among the three models.|我们研究了处于计算社会选择和机制设计交叉点的{ em 公平选择}问题。目标是在一组社区成员中选出最受欢迎的个体。输入可以建模为有向图，其中每个节点代表一个个体，有向边表示社区成员对另一个成员的提名或批准。一个{ em 公正机制}对个体潜在的自私行为具有鲁棒性，并且通过确保节点成为赢家的机会不依赖于其外向边缘，为选民报告他们的真实偏好提供适当的激励。我们的目标是设计公正的机制，选择一个程度尽可能接近程度最高的节点。我们通过这些程度的差异来衡量这种机制的效率，称为它的{ em 加性}近似。特别地，我们研究了在设计具有良好的加性近似保证的有效的确定性公正选择机制时，关于选民偏好的先验信息可以在多大程度上发挥作用。我们考虑先验信息的三种模型，我们称之为{ em 民意调查}、{ em 先验流行}和{ em 统一}模型。我们分析了一个自然选择机制的性能，我们称之为{ em 认可投票与默认}(AVD) ，并表明它实现了一个 $O (sqrt { n ln { n }}}) $附加保证的意见调查和 $O (ln ^ 2n) $的先验受欢迎的输入，其中 $n $是个人的数量。我们认为这个多对数界限是我们的主要技术贡献。我们通过显示我们的分析接近紧凑来补充最后的结果，显示 $Omega (ln { n }) $下界。这在统一模型中是适用的，这是三种模型中最简单的。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Impartial+Selection+with+Prior+Information)|0| +|[Is your digital neighbor a reliable investment advisor?](https://doi.org/10.1145/3543507.3583502)|Daisuke Kawai, Alejandro Cuevas, Bryan R. Routledge, Kyle Soska, Ariel ZetlinJones, Nicolas Christin|Ramiel Capital, USA; Carnegie Mellon University, USA|The web and social media platforms have drastically changed how investors produce and consume financial advice. Historically, individual investors were often relying on newsletters and related prospectus backed by the reputation and track record of their issuers. Nowadays, financial advice is frequently offered online, by anonymous or pseudonymous parties with little at stake. As such, a natural question is to investigate whether these modern financial “influencers” operate in good faith, or whether they might be misleading their followers intentionally. To start answering this question, we obtained data from a very large cryptocurrency derivatives exchange, from which we derived individual trading positions. Some of the investors on that platform elect to link to their Twitter profiles. We were thus able to compare the positions publicly espoused on Twitter with those actually taken in the market. We discovered that 1) staunchly “bullish” investors on Twitter often took much more moderate, if not outright opposite, positions in their own trades when the market was down, 2) their followers tended to align their positions with bullish Twitter outlooks, and 3) moderate voices on Twitter (and their own followers) were on the other hand far more consistent with their actual investment strategies. In other words, while social media advice may attempt to foster a sense of camaraderie among people of like-minded beliefs, the reality is that this is merely an illusion, which may result in financial losses for people blindly following advice.|网络和社交媒体平台已经彻底改变了投资者提供和消费金融建议的方式。从历史上看，个人投资者往往依赖时事通讯和相关的招股说明书，并以发行人的声誉和业绩记录作为依据。如今，财务咨询经常是在网上提供的，由匿名或匿名的团体提供，几乎没有什么利害关系。因此，一个自然而然的问题是调查这些现代金融“影响者”是否诚信经营，或者他们是否故意误导他们的追随者。为了开始回答这个问题，我们从一个非常大的加密货币衍生品交易所获得数据，从中我们得到了个人交易头寸。该平台上的一些投资者选择链接到他们的 Twitter 档案。因此，我们可以比较 Twitter 上公开支持的立场和市场上实际采取的立场。我们发现，1) Twitter 上坚定的“看涨”投资者在股市下跌时，往往在自己的交易中持有更为温和(如果不是完全相反的话)的头寸; 2)他们的追随者倾向于将自己的头寸与看涨的 Twitter 前景联系起来; 3) Twitter 上的温和声音(以及他们自己的追随者)则与他们的实际投资策略更为一致。换句话说，虽然社交媒体的建议可能试图培养志同道合者之间的友情，但事实上这只是一种幻觉，可能导致盲目听从建议的人蒙受经济损失。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Is+your+digital+neighbor+a+reliable+investment+advisor?)|0| +|[Impartial Selection with Prior Information](https://doi.org/10.1145/3543507.3583553)|Ioannis Caragiannis, George Christodoulou, Nicos Protopapas|Aarhus University, Denmark; Aristotle University of Thessaloniki, Greece and Archimedes/RC Athena, Greece; University of Patras, Greece|We study the problem of {\em impartial selection}, a topic that lies at the intersection of computational social choice and mechanism design. The goal is to select the most popular individual among a set of community members. The input can be modeled as a directed graph, where each node represents an individual, and a directed edge indicates nomination or approval of a community member to another. An {\em impartial mechanism} is robust to potential selfish behavior of the individuals and provides appropriate incentives to voters to report their true preferences by ensuring that the chance of a node to become a winner does not depend on its outgoing edges. The goal is to design impartial mechanisms that select a node with an in-degree that is as close as possible to the highest in-degree. We measure the efficiency of such a mechanism by the difference of these in-degrees, known as its {\em additive} approximation. In particular, we study the extent to which prior information on voters' preferences could be useful in the design of efficient deterministic impartial selection mechanisms with good additive approximation guarantees. We consider three models of prior information, which we call the {\em opinion poll}, the {\em a prior popularity}, and the {\em uniform} model. We analyze the performance of a natural selection mechanism that we call {\em approval voting with default} (AVD) and show that it achieves a $O(\sqrt{n\ln{n}})$ additive guarantee for opinion poll and a $O(\ln^2n)$ for a priori popularity inputs, where $n$ is the number of individuals. We consider this polylogarithmic bound as our main technical contribution. We complement this last result by showing that our analysis is close to tight, showing an $\Omega(\ln{n})$ lower bound. This holds in the uniform model, which is the simplest among the three models.|我们研究了处于计算社会选择和机制设计交叉点的{ em 公平选择}问题。目标是在一组社区成员中选出最受欢迎的个体。输入可以建模为有向图，其中每个节点代表一个个体，有向边表示社区成员对另一个成员的提名或批准。一个{ em 公正机制}对个体潜在的自私行为具有鲁棒性，并且通过确保节点成为赢家的机会不依赖于其外向边缘，为选民报告他们的真实偏好提供适当的激励。我们的目标是设计公正的机制，选择一个程度尽可能接近程度最高的节点。我们通过这些程度的差异来衡量这种机制的效率，称为它的{ em 加性}近似。特别地，我们研究了在设计具有良好的加性近似保证的有效的确定性公正选择机制时，关于选民偏好的先验信息可以在多大程度上发挥作用。我们考虑先验信息的三种模型，我们称之为{ em 民意调查}、{ em 先验流行}和{ em 统一}模型。我们分析了一个自然选择机制的性能，我们称之为{ em 认可投票与默认}(AVD) ，并表明它实现了一个 $O (sqrt { n ln { n }}}) $附加保证的意见调查和 $O (ln ^ 2n) $的先验受欢迎的输入，其中 $n $是个人的数量。我们认为这个多对数界限是我们的主要技术贡献。我们通过显示我们的分析接近紧凑来补充最后的结果，显示 $Omega (ln { n }) $下界。这在统一模型中是适用的，这是三种模型中最简单的。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Impartial+Selection+with+Prior+Information)|0| |[Do Language Models Plagiarize?](https://doi.org/10.1145/3543507.3583199)|Jooyoung Lee, Thai Le, Jinghui Chen, Dongwon Lee||Past literature has illustrated that language models (LMs) often memorize parts of training instances and reproduce them in natural language generation (NLG) processes. However, it is unclear to what extent LMs "reuse" a training corpus. For instance, models can generate paraphrased sentences that are contextually similar to training samples. In this work, therefore, we study three types of plagiarism (i.e., verbatim, paraphrase, and idea) among GPT-2 generated texts, in comparison to its training data, and further analyze the plagiarism patterns of fine-tuned LMs with domain-specific corpora which are extensively used in practice. Our results suggest that (1) three types of plagiarism widely exist in LMs beyond memorization, (2) both size and decoding methods of LMs are strongly associated with the degrees of plagiarism they exhibit, and (3) fine-tuned LMs' plagiarism patterns vary based on their corpus similarity and homogeneity. Given that a majority of LMs' training data is scraped from the Web without informing content owners, their reiteration of words, phrases, and even core ideas from training sets into generated texts has ethical implications. Their patterns are likely to exacerbate as both the size of LMs and their training data increase, raising concerns about indiscriminately pursuing larger models with larger training corpora. Plagiarized content can also contain individuals' personal and sensitive information. These findings overall cast doubt on the practicality of current LMs in mission-critical writing tasks and urge more discussions around the observed phenomena. Data and source code are available at https://github.com/Brit7777/LM-plagiarism.|过去的文献表明，语言模型(LM)经常记忆训练实例的一部分，并在自然语言生成(NLG)过程中重现它们。然而，还不清楚 LM 在多大程度上“重用”了一个训练语料库。例如，模型可以生成与训练样本上下文相似的解释句。因此，在这项工作中，我们研究了三种类型的剽窃(即，逐字，释义，和想法)在 GPT-2生成的文本，比较其训练数据，并进一步分析微调的生物多样性与领域特定语料库的剽窃模式在实践中广泛使用。我们的研究结果表明: (1)三种类型的剽窃广泛存在于记忆以外的 LM 中，(2) LM 的大小和解码方法与它们表现出的剽窃程度密切相关，(3)微调 LM 的剽窃模式基于它们的语料相似性和同质性而变化。考虑到 LM 的大部分训练数据都是从网上刮下来的，而没有通知内容所有者，他们重复单词、短语，甚至是将训练集中的核心思想转换成生成的文本，都有伦理上的含义。随着 LM 的规模和培训数据的增加，它们的模式可能会加剧，这引起了人们对不加区分地追求更大的模型和更大的培训语料库的担忧。剽窃的内容也可以包含个人的个人和敏感信息。这些研究结果总体上对当前任务批判性写作任务的语言学习模式的实用性提出了质疑，并敦促围绕所观察到的现象进行更多的讨论。数据和源代码可在 https://github.com/brit7777/lm-plagiarism 下载。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Do+Language+Models+Plagiarize?)|0| -|[Path-specific Causal Fair Prediction via Auxiliary Graph Structure Learning](https://doi.org/10.1145/3543507.3583280)|Liuyi Yao, Yaliang Li, Bolin Ding, Jingren Zhou, Jinduo Liu, Mengdi Huai, Jing Gao|Alibaba, China; Alibaba Group, USA; Purdue University, USA; Alibaba Group, China; Beijing University of Technology, China; Iowa State University, USA|With ubiquitous adoption of machine learning algorithms in web technologies, such as recommendation system and social network, algorithm fairness has become a trending topic, and it has a great impact on social welfare. Among different fairness definitions, path-specific causal fairness is a widely adopted one with great potentials, as it distinguishes the fair and unfair effects that the sensitive attributes exert on algorithm predictions. Existing methods based on path-specific causal fairness either require graph structure as the prior knowledge or have high complexity in the calculation of path-specific effect. To tackle these challenges, we propose a novel casual graph based fair prediction framework which integrates graph structure learning into fair prediction to ensure that unfair pathways are excluded in the causal graph. Furthermore, we generalize the proposed framework to the scenarios where sensitive attributes can be non-root nodes and affected by other variables, which is commonly observed in real-world applications, such as recommendation system, but hardly addressed by existing works. We provide theoretical analysis on the generalization bound for the proposed fair prediction method, and conduct a series of experiments on real-world datasets to demonstrate that the proposed framework can provide better prediction performance and algorithm fairness trade-off.|随着机器学习算法在推荐系统、社交网络等网络技术中的广泛应用，算法公平性已成为一个热门话题，并对社会福利产生重大影响。在不同的公平性定义中，路径特定的因果公平性是一种被广泛采用的有很大潜力的公平性定义，因为它区分了敏感属性对算法预测的公平性和不公平性影响。现有的基于路径特定因果公平性的计算方法，要么以图结构作为先验知识，要么计算路径特定效应的复杂度较高。为了应对这些挑战，我们提出了一种新的基于因果图的公平预测框架，该框架将图结构学习与公平预测相结合，以确保不公平路径被排除在因果图之外。此外，我们还将该框架推广到了敏感属性可能是非根节点且受其他变量影响的情况，这种情况在推荐系统等实际应用中经常出现，但现有的工作很难解决。我们对提出的公平预测方法的泛化界限进行了理论分析，并在实际数据集上进行了一系列实验，结果表明该框架能够提供更好的预测性能和算法的公平性折衷。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Path-specific+Causal+Fair+Prediction+via+Auxiliary+Graph+Structure+Learning)|0| +|[Path-specific Causal Fair Prediction via Auxiliary Graph Structure Learning](https://doi.org/10.1145/3543507.3583280)|Liuyi Yao, Yaliang Li, Bolin Ding, Jingren Zhou, Jinduo Liu, Mengdi Huai, Jing Gao|Alibaba Group, China; Alibaba, China; Alibaba Group, USA; Iowa State University, USA; Purdue University, USA; Beijing University of Technology, China|With ubiquitous adoption of machine learning algorithms in web technologies, such as recommendation system and social network, algorithm fairness has become a trending topic, and it has a great impact on social welfare. Among different fairness definitions, path-specific causal fairness is a widely adopted one with great potentials, as it distinguishes the fair and unfair effects that the sensitive attributes exert on algorithm predictions. Existing methods based on path-specific causal fairness either require graph structure as the prior knowledge or have high complexity in the calculation of path-specific effect. To tackle these challenges, we propose a novel casual graph based fair prediction framework which integrates graph structure learning into fair prediction to ensure that unfair pathways are excluded in the causal graph. Furthermore, we generalize the proposed framework to the scenarios where sensitive attributes can be non-root nodes and affected by other variables, which is commonly observed in real-world applications, such as recommendation system, but hardly addressed by existing works. We provide theoretical analysis on the generalization bound for the proposed fair prediction method, and conduct a series of experiments on real-world datasets to demonstrate that the proposed framework can provide better prediction performance and algorithm fairness trade-off.|随着机器学习算法在推荐系统、社交网络等网络技术中的广泛应用，算法公平性已成为一个热门话题，并对社会福利产生重大影响。在不同的公平性定义中，路径特定的因果公平性是一种被广泛采用的有很大潜力的公平性定义，因为它区分了敏感属性对算法预测的公平性和不公平性影响。现有的基于路径特定因果公平性的计算方法，要么以图结构作为先验知识，要么计算路径特定效应的复杂度较高。为了应对这些挑战，我们提出了一种新的基于因果图的公平预测框架，该框架将图结构学习与公平预测相结合，以确保不公平路径被排除在因果图之外。此外，我们还将该框架推广到了敏感属性可能是非根节点且受其他变量影响的情况，这种情况在推荐系统等实际应用中经常出现，但现有的工作很难解决。我们对提出的公平预测方法的泛化界限进行了理论分析，并在实际数据集上进行了一系列实验，结果表明该框架能够提供更好的预测性能和算法的公平性折衷。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Path-specific+Causal+Fair+Prediction+via+Auxiliary+Graph+Structure+Learning)|0| |[HateProof: Are Hateful Meme Detection Systems really Robust?](https://doi.org/10.1145/3543507.3583356)|Piush Aggarwal, Pranit Chawla, Mithun Das, Punyajoy Saha, Binny Mathew, Torsten Zesch, Animesh Mukherjee||Exploiting social media to spread hate has tremendously increased over the years. Lately, multi-modal hateful content such as memes has drawn relatively more traction than uni-modal content. Moreover, the availability of implicit content payloads makes them fairly challenging to be detected by existing hateful meme detection systems. In this paper, we present a use case study to analyze such systems' vulnerabilities against external adversarial attacks. We find that even very simple perturbations in uni-modal and multi-modal settings performed by humans with little knowledge about the model can make the existing detection models highly vulnerable. Empirically, we find a noticeable performance drop of as high as 10% in the macro-F1 score for certain attacks. As a remedy, we attempt to boost the model's robustness using contrastive learning as well as an adversarial training-based method - VILLA. Using an ensemble of the above two approaches, in two of our high resolution datasets, we are able to (re)gain back the performance to a large extent for certain attacks. We believe that ours is a first step toward addressing this crucial problem in an adversarial setting and would inspire more such investigations in the future.|多年来，利用社交媒体传播仇恨的行为大幅增加。近来，模因等多模态仇恨内容比单模态内容吸引了更多的关注。此外，隐式内容有效载荷的可用性使得它们很难被现有的可恶的文化基因检测系统检测到。在本文中，我们提出了一个用例研究来分析这样的系统的外部对手攻击的脆弱性。我们发现，即使是非常简单的扰动，在单模态和多模态的设置执行人类对模型的知识很少，可以使现有的检测模型高度脆弱。根据经验，我们发现在某些攻击中，宏 F1得分的显著性能下降高达10% 。作为补救措施，我们尝试使用对比学习和基于对抗训练的方法 VILLA 来提高模型的鲁棒性。使用上述两种方法的集合，在我们的两个高分辨率数据集中，我们能够(重新)在很大程度上恢复某些攻击的性能。我们认为，我们的调查是在对抗性背景下解决这一关键问题的第一步，并将激发今后进行更多此类调查。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=HateProof:+Are+Hateful+Meme+Detection+Systems+really+Robust?)|0| |[DualFair: Fair Representation Learning at Both Group and Individual Levels via Contrastive Self-supervision](https://doi.org/10.1145/3543507.3583480)|Sungwon Han, SeungEon Lee, Fangzhao Wu, Sundong Kim, Chuhan Wu, Xiting Wang, Xing Xie, Meeyoung Cha||Algorithmic fairness has become an important machine learning problem, especially for mission-critical Web applications. This work presents a self-supervised model, called DualFair, that can debias sensitive attributes like gender and race from learned representations. Unlike existing models that target a single type of fairness, our model jointly optimizes for two fairness criteria - group fairness and counterfactual fairness - and hence makes fairer predictions at both the group and individual levels. Our model uses contrastive loss to generate embeddings that are indistinguishable for each protected group, while forcing the embeddings of counterfactual pairs to be similar. It then uses a self-knowledge distillation method to maintain the quality of representation for the downstream tasks. Extensive analysis over multiple datasets confirms the model's validity and further shows the synergy of jointly addressing two fairness criteria, suggesting the model's potential value in fair intelligent Web applications.|算法公平性已经成为一个重要的机器学习问题，尤其是对于关键任务的 Web 应用程序。这项工作提出了一个自我监督模型，称为 DualFair，可以从学习表征中去除敏感属性，如性别和种族。与现有的针对单一类型公平的模型不同，我们的模型共同优化了两个公平标准——群体公平和反事实公平——因此在群体和个人层面上做出了更公平的预测。我们的模型使用对比损失来生成对每个受保护群体无法区分的嵌入，同时迫使反事实对的嵌入相似。然后使用自知识精馏方法来维护下游任务的表示质量。通过对多个数据集的广泛分析，证实了该模型的有效性，并进一步显示了联合处理两个公平标准的协同效应，表明了该模型在公平智能 Web 应用中的潜在价值。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=DualFair:+Fair+Representation+Learning+at+Both+Group+and+Individual+Levels+via+Contrastive+Self-supervision)|0| |[PaGE-Link: Path-based Graph Neural Network Explanation for Heterogeneous Link Prediction](https://doi.org/10.1145/3543507.3583511)|Shichang Zhang, Jiani Zhang, Xiang Song, Soji Adeshina, Da Zheng, Christos Faloutsos, Yizhou Sun||Transparency and accountability have become major concerns for black-box machine learning (ML) models. Proper explanations for the model behavior increase model transparency and help researchers develop more accountable models. Graph neural networks (GNN) have recently shown superior performance in many graph ML problems than traditional methods, and explaining them has attracted increased interest. However, GNN explanation for link prediction (LP) is lacking in the literature. LP is an essential GNN task and corresponds to web applications like recommendation and sponsored search on web. Given existing GNN explanation methods only address node/graph-level tasks, we propose Path-based GNN Explanation for heterogeneous Link prediction (PaGE-Link) that generates explanations with connection interpretability, enjoys model scalability, and handles graph heterogeneity. Qualitatively, PaGE-Link can generate explanations as paths connecting a node pair, which naturally captures connections between the two nodes and easily transfer to human-interpretable explanations. Quantitatively, explanations generated by PaGE-Link improve AUC for recommendation on citation and user-item graphs by 9 - 35% and are chosen as better by 78.79% of responses in human evaluation.|透明度和可信度已经成为黑盒机器学习(ML)模型的主要关注点。对模型行为的合理解释增加了模型的透明度，有助于研究人员建立更负责任的模型。近年来，图神经网络(GNN)在许多图 ML 问题中表现出比传统方法更好的性能，并且对它们的解释引起了人们越来越多的兴趣。然而，GNN 对链路预测(LP)的解释在文献中是缺乏的。LP 是一个基本的 GNN 任务，对应于 Web 应用程序，如推荐和网上赞助商搜索。鉴于现有的 GNN 解释方法只能解决节点/图级任务，本文提出了基于路径的 GNN 解释异构链路预测(PaGE-Link)方法，该方法能够产生具有连接可解释性的解释，具有模型可扩展性，并能处理图的异构性。从定性上讲，PaGE-Link 可以将解释作为连接节点对的路径生成，它自然地捕获两个节点之间的连接，并且很容易转换为人类可解释的解释。从数量上看，由 PaGE-Link 产生的解释使 AUC 对引文和用户项图的推荐提高了9-35% ，并且在人类评估中有78.79% 的回答选择了更好的解释。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=PaGE-Link:+Path-based+Graph+Neural+Network+Explanation+for+Heterogeneous+Link+Prediction)|0| |[Fairness in model-sharing games](https://doi.org/10.1145/3543507.3583483)|Kate Donahue, Jon M. Kleinberg|Cornell University, USA|In many real-world situations, data is distributed across multiple self-interested agents. These agents can collaborate to build a machine learning model based on data from multiple agents, potentially reducing the error each experiences. However, sharing models in this way raises questions of fairness: to what extent can the error experienced by one agent be significantly lower than the error experienced by another agent in the same coalition? In this work, we consider two notions of fairness that each may be appropriate in different circumstances: egalitarian fairness (which aims to bound how dissimilar error rates can be) and proportional fairness (which aims to reward players for contributing more data). We similarly consider two common methods of model aggregation, one where a single model is created for all agents (uniform), and one where an individualized model is created for each agent. For egalitarian fairness, we obtain a tight multiplicative bound on how widely error rates can diverge between agents collaborating (which holds for both aggregation methods). For proportional fairness, we show that the individualized aggregation method always gives a small player error that is upper bounded by proportionality. For uniform aggregation, we show that this upper bound is guaranteed for any individually rational coalition (where no player wishes to leave to do local learning).|在许多实际情况中，数据分布在多个自利代理之间。这些代理可以协作建立一个基于多个代理数据的机器学习模型，从而有可能减少每次经验中的错误。然而，以这种方式共享模型提出了公平性的问题: 在多大程度上，一个代理所经历的错误会明显低于同一联盟中另一个代理所经历的错误？在这项工作中，我们考虑了两个公平的概念，每个概念在不同的情况下可能是适当的: 平等主义公平(其目的是约束如何不同的错误率可以)和比例公平(其目的是奖励玩家贡献更多的数据)。我们同样考虑两种常见的模型聚合方法，一种是为所有代理创建单个模型(统一的) ，另一种是为每个代理创建个性化模型。对于平等主义的公平性，我们得到了一个关于协作的代理之间的错误率差异有多大的紧密的乘法界限(这适用于两种聚合方法)。对于比例公平性，我们证明了个性化聚合方法总是给出一个小的参与者误差，这个误差是比例上界的。对于一致聚合，我们证明了这个上界对于任何单独的理性联盟(没有球员希望离开去做局部学习)是有保证的。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Fairness+in+model-sharing+games)|0| -|[Combining Worker Factors for Heterogeneous Crowd Task Assignment](https://doi.org/10.1145/3543507.3583190)|Senuri Wijenayake, Danula Hettiachchi, Jorge Gonçalves|The University of Sydney, Australia; RMIT University, Australia; The University of Melbourne, Australia|Optimising the assignment of tasks to workers is an effective approach to ensure high quality in crowdsourced data - particularly in heterogeneous micro tasks. However, previous attempts at heterogeneous micro task assignment based on worker characteristics are limited to using cognitive skills, despite literature emphasising that worker performance varies based on other parameters. This study is an initial step towards understanding whether and how multiple parameters such as cognitive skills, mood, personality, alertness, comprehension skill, and social and physical context of workers can be leveraged in tandem to improve worker performance estimations in heterogeneous micro tasks. Our predictive models indicate that these parameters have varying effects on worker performance in the five task types considered – sentiment analysis, classification, transcription, named entity recognition and bounding box. Moreover, we note 0.003 - 0.018 reduction in mean absolute error of predicted worker accuracy across all tasks, when task assignment is based on models that consider all parameters vs. models that only consider workers’ cognitive skills. Our findings pave the way for the use of holistic approaches in micro task assignment that effectively quantify worker context.|优化工人的任务分配是一个有效的方法，以确保高质量的众包数据-特别是在异构微型任务。然而，以往基于工人特征的异质微任务分配的尝试仅限于使用认知技能，尽管文献强调工人的表现因其他参数而异。这项研究是了解是否以及如何利用工人的认知技能、情绪、性格、警觉性、理解技能以及社会和身体环境等多个参数来提高异质微任务中工人的绩效评估的第一步。我们的预测模型表明，这些参数在五种任务类型-情绪分析、分类、转录、命名实体识别和边界框中对工人绩效有不同的影响。此外，我们注意到，当任务分配基于考虑所有参数的模型与仅考虑工人认知技能的模型相比时，所有任务中工人预测准确度的平均绝对误差减少了0.003 -0.018。我们的研究结果为在微观任务分配中使用整体方法有效量化工人情境铺平了道路。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Combining+Worker+Factors+for+Heterogeneous+Crowd+Task+Assignment)|0| +|[Combining Worker Factors for Heterogeneous Crowd Task Assignment](https://doi.org/10.1145/3543507.3583190)|Senuri Wijenayake, Danula Hettiachchi, Jorge Gonçalves|The University of Sydney, Australia; The University of Melbourne, Australia; RMIT University, Australia|Optimising the assignment of tasks to workers is an effective approach to ensure high quality in crowdsourced data - particularly in heterogeneous micro tasks. However, previous attempts at heterogeneous micro task assignment based on worker characteristics are limited to using cognitive skills, despite literature emphasising that worker performance varies based on other parameters. This study is an initial step towards understanding whether and how multiple parameters such as cognitive skills, mood, personality, alertness, comprehension skill, and social and physical context of workers can be leveraged in tandem to improve worker performance estimations in heterogeneous micro tasks. Our predictive models indicate that these parameters have varying effects on worker performance in the five task types considered – sentiment analysis, classification, transcription, named entity recognition and bounding box. Moreover, we note 0.003 - 0.018 reduction in mean absolute error of predicted worker accuracy across all tasks, when task assignment is based on models that consider all parameters vs. models that only consider workers’ cognitive skills. Our findings pave the way for the use of holistic approaches in micro task assignment that effectively quantify worker context.|优化工人的任务分配是一个有效的方法，以确保高质量的众包数据-特别是在异构微型任务。然而，以往基于工人特征的异质微任务分配的尝试仅限于使用认知技能，尽管文献强调工人的表现因其他参数而异。这项研究是了解是否以及如何利用工人的认知技能、情绪、性格、警觉性、理解技能以及社会和身体环境等多个参数来提高异质微任务中工人的绩效评估的第一步。我们的预测模型表明，这些参数在五种任务类型-情绪分析、分类、转录、命名实体识别和边界框中对工人绩效有不同的影响。此外，我们注意到，当任务分配基于考虑所有参数的模型与仅考虑工人认知技能的模型相比时，所有任务中工人预测准确度的平均绝对误差减少了0.003 -0.018。我们的研究结果为在微观任务分配中使用整体方法有效量化工人情境铺平了道路。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Combining+Worker+Factors+for+Heterogeneous+Crowd+Task+Assignment)|0| |[Hidden Indicators of Collective Intelligence in Crowdfunding](https://doi.org/10.1145/3543507.3583414)|EmokeÁgnes Horvát, Henry Kudzanai Dambanemuya, Jayaram Uparna, Brian Uzzi|Northwestern University, USA; Indian Institute of Management Udaipur, India|Extensive literature argues that crowds possess essential collective intelligence benefits that allow superior decision-making by untrained individuals working in low-information environments. Classic wisdom of crowds theory is based on evidence gathered from studying large groups of diverse and independent decision-makers. Yet, most human decisions are reached in online settings of interconnected like-minded people that challenge these criteria. This observation raises a key question: Are there surprising expressions of collective intelligence online? Here, we explore whether crowds furnish collective intelligence benefits in crowdfunding systems. Crowdfunding has grown and diversified quickly over the past decade, expanding from funding aspirant creative works and supplying pro-social donations to enabling large citizen-funded urban projects and providing commercial interest-based unsecured loans. Using nearly 10 million loan contributions from a market-dominant lending platform, we find evidence for collective intelligence indicators in crowdfunding. Our results, which are based on a two-stage Heckman selection model, indicate that opinion diversity and the speed at which funds are contributed predict who gets funded and who repays, even after accounting for traditional measures of creditworthiness. Moreover, crowds work consistently well in correctly assessing the outcome of high-risk projects. Finally, diversity and speed serve as early warning signals when inferring fundraising based solely on the initial part of the campaign. Our findings broaden the field of crowd-aware system design and inform discussions about the augmentation of traditional financing systems with tech innovations.|大量文献认为，群体拥有基本的集体智力优势，使未经训练的个人能够在低信息环境中作出优越的决策。群体理论的经典智慧是基于对大量多样化和独立决策者的研究收集到的证据。然而，大多数人类的决定都是在网络环境中做出的，这些网络环境中的人们互相联系，志趣相投，对这些标准提出了挑战。这一观察提出了一个关键问题: 网上是否存在令人惊讶的集体智慧表达？在这里，我们探讨群体是否提供集体智力的好处在众筹系统。过去10年，众筹迅速发展和多样化，从资助有抱负的创意作品和提供亲社会捐助扩大到支持大型公民资助的城市项目和提供基于商业利息的无担保贷款。利用市场主导的贷款平台提供的近1000万笔贷款，我们发现了众筹中集体智慧指标的证据。我们的研究结果基于两阶段 Heckman 选择模型，表明即使在考虑了传统的信用度量标准之后，意见多样性和基金投入的速度也可以预测谁获得了资金，谁偿还了贷款。此外，群体在正确评估高风险项目的结果方面始终表现良好。最后，多样性和速度作为预警信号时，推断筹款完全基于竞选的最初部分。我们的研究结果拓宽了群体感知系统设计的领域，并为关于利用技术创新增强传统融资系统的讨论提供了信息。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Hidden+Indicators+of+Collective+Intelligence+in+Crowdfunding)|0| -|[Multiview Representation Learning from Crowdsourced Triplet Comparisons](https://doi.org/10.1145/3543507.3583431)|Xiaotian Lu, Jiyi Li, Koh Takeuchi, Hisashi Kashima|University of Yamanashi, Japan; Kyoto University, Japan|Crowdsourcing has been used to collect data at scale in numerous fields. Triplet similarity comparison is a type of crowdsourcing task, in which crowd workers are asked the question ``among three given objects, which two are more similar?'', which is relatively easy for humans to answer. However, the comparison can be sometimes based on multiple views, i.e., different independent attributes such as color and shape. Each view may lead to different results for the same three objects. Although an algorithm was proposed in prior work to produce multiview embeddings, it involves at least two problems: (1) the existing algorithm cannot independently predict multiview embeddings for a new sample, and (2) different people may prefer different views. In this study, we propose an end-to-end inductive deep learning framework to solve the multiview representation learning problem. The results show that our proposed method can obtain multiview embeddings of any object, in which each view corresponds to an independent attribute of the object. We collected two datasets from a crowdsourcing platform to experimentally investigate the performance of our proposed approach compared to conventional baseline methods.|众包已经被用来在许多领域大规模收集数据。三重相似性比较是一种众包任务，在这种任务中，众包工作者被问到“在给定的三个对象中，哪两个更相似?”这对人类来说相对容易回答。然而，比较有时可以基于多个视图，即不同的独立属性，如颜色和形状。对于相同的三个对象，每个视图可能导致不同的结果。虽然在以前的工作中提出了一种产生多视图嵌入的算法，但它至少涉及到两个问题: (1)现有的算法不能独立地预测新样本的多视图嵌入，(2)不同的人可能偏好不同的视图。在本研究中，我们提出一个端对端的归纳式深度学习框架来解决多视点表示学习问题。结果表明，该方法可以获得任意对象的多视图嵌入，其中每个视图对应于对象的一个独立属性。我们从一个众包平台收集了两个数据集，以实验研究我们提出的方法与传统的基线方法相比的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Multiview+Representation+Learning+from+Crowdsourced+Triplet+Comparisons)|0| -|[Sedition Hunters: A Quantitative Study of the Crowdsourced Investigation into the 2021 U.S. Capitol Attack](https://doi.org/10.1145/3543507.3583514)|Tianjiao Yu, Sukrit Venkatagiri, Ismini Lourentzou, Kurt Luther|Virginia Tech, USA; University of Washington, USA|Social media platforms have enabled extremists to organize violent events, such as the 2021 U.S. Capitol Attack. Simultaneously, these platforms enable professional investigators and amateur sleuths to collaboratively collect and identify imagery of suspects with the goal of holding them accountable for their actions. Through a case study of Sedition Hunters, a Twitter community whose goal is to identify individuals who participated in the 2021 U.S. Capitol Attack, we explore what are the main topics or targets of the community, who participates in the community, and how. Using topic modeling, we find that information sharing is the main focus of the community. We also note an increase in awareness of privacy concerns. Furthermore, using social network analysis, we show how some participants played important roles in the community. Finally, we discuss implications for the content and structure of online crowdsourced investigations.|社交媒体平台使极端分子能够组织暴力事件，例如2021年的美国国会大厦袭击。同时，这些平台使专业调查人员和业余侦探能够合作收集和识别嫌疑人的图像，目的是让他们对自己的行为负责。通过一个关于煽动者的案例研究，这是一个 Twitter 社区，其目标是识别参与2021年美国国会大厦袭击的个人，我们探讨了社区的主要话题或目标，谁参与了社区，以及如何参与。通过主题建模，我们发现信息共享是社区的主要关注点。我们还注意到，人们对隐私问题的关注有所增加。此外，利用社会网络分析，我们显示了一些参与者如何在社区中发挥重要作用。最后，我们讨论在线众包调查的内容和结构的含义。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Sedition+Hunters:+A+Quantitative+Study+of+the+Crowdsourced+Investigation+into+the+2021+U.S.+Capitol+Attack)|0| +|[Multiview Representation Learning from Crowdsourced Triplet Comparisons](https://doi.org/10.1145/3543507.3583431)|Xiaotian Lu, Jiyi Li, Koh Takeuchi, Hisashi Kashima|Kyoto University, Japan; University of Yamanashi, Japan|Crowdsourcing has been used to collect data at scale in numerous fields. Triplet similarity comparison is a type of crowdsourcing task, in which crowd workers are asked the question ``among three given objects, which two are more similar?'', which is relatively easy for humans to answer. However, the comparison can be sometimes based on multiple views, i.e., different independent attributes such as color and shape. Each view may lead to different results for the same three objects. Although an algorithm was proposed in prior work to produce multiview embeddings, it involves at least two problems: (1) the existing algorithm cannot independently predict multiview embeddings for a new sample, and (2) different people may prefer different views. In this study, we propose an end-to-end inductive deep learning framework to solve the multiview representation learning problem. The results show that our proposed method can obtain multiview embeddings of any object, in which each view corresponds to an independent attribute of the object. We collected two datasets from a crowdsourcing platform to experimentally investigate the performance of our proposed approach compared to conventional baseline methods.|众包已经被用来在许多领域大规模收集数据。三重相似性比较是一种众包任务，在这种任务中，众包工作者被问到“在给定的三个对象中，哪两个更相似?”这对人类来说相对容易回答。然而，比较有时可以基于多个视图，即不同的独立属性，如颜色和形状。对于相同的三个对象，每个视图可能导致不同的结果。虽然在以前的工作中提出了一种产生多视图嵌入的算法，但它至少涉及到两个问题: (1)现有的算法不能独立地预测新样本的多视图嵌入，(2)不同的人可能偏好不同的视图。在本研究中，我们提出一个端对端的归纳式深度学习框架来解决多视点表示学习问题。结果表明，该方法可以获得任意对象的多视图嵌入，其中每个视图对应于对象的一个独立属性。我们从一个众包平台收集了两个数据集，以实验研究我们提出的方法与传统的基线方法相比的性能。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Multiview+Representation+Learning+from+Crowdsourced+Triplet+Comparisons)|0| +|[Sedition Hunters: A Quantitative Study of the Crowdsourced Investigation into the 2021 U.S. Capitol Attack](https://doi.org/10.1145/3543507.3583514)|Tianjiao Yu, Sukrit Venkatagiri, Ismini Lourentzou, Kurt Luther|University of Washington, USA; Virginia Tech, USA|Social media platforms have enabled extremists to organize violent events, such as the 2021 U.S. Capitol Attack. Simultaneously, these platforms enable professional investigators and amateur sleuths to collaboratively collect and identify imagery of suspects with the goal of holding them accountable for their actions. Through a case study of Sedition Hunters, a Twitter community whose goal is to identify individuals who participated in the 2021 U.S. Capitol Attack, we explore what are the main topics or targets of the community, who participates in the community, and how. Using topic modeling, we find that information sharing is the main focus of the community. We also note an increase in awareness of privacy concerns. Furthermore, using social network analysis, we show how some participants played important roles in the community. Finally, we discuss implications for the content and structure of online crowdsourced investigations.|社交媒体平台使极端分子能够组织暴力事件，例如2021年的美国国会大厦袭击。同时，这些平台使专业调查人员和业余侦探能够合作收集和识别嫌疑人的图像，目的是让他们对自己的行为负责。通过一个关于煽动者的案例研究，这是一个 Twitter 社区，其目标是识别参与2021年美国国会大厦袭击的个人，我们探讨了社区的主要话题或目标，谁参与了社区，以及如何参与。通过主题建模，我们发现信息共享是社区的主要关注点。我们还注意到，人们对隐私问题的关注有所增加。此外，利用社会网络分析，我们显示了一些参与者如何在社区中发挥重要作用。最后，我们讨论在线众包调查的内容和结构的含义。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Sedition+Hunters:+A+Quantitative+Study+of+the+Crowdsourced+Investigation+into+the+2021+U.S.+Capitol+Attack)|0| |[Human-in-the-loop Regular Expression Extraction for Single Column Format Inconsistency](https://doi.org/10.1145/3543507.3583515)|Shaochen Yu, Lei Han, Marta Indulska, Shazia W. Sadiq, Gianluca Demartini|The University of Queensland, Australia|Format inconsistency is one of the most frequently appearing data quality issues encountered during data cleaning. Existing automated approaches commonly lack applicability and generalisability, while approaches with human inputs typically require specialized skills such as writing regular expressions. This paper proposes a novel hybrid human-machine system, namely “Data-Scanner-4C”, which leverages crowdsourcing to address syntactic format inconsistencies in a single column effectively. We first ask crowd workers to create examples from single-column data through “data selection” and “result validation” tasks. Then, we propose and use a novel rule-based learning algorithm to infer the regular expressions that propagate formats from created examples to the entire column. Our system integrates crowdsourcing and algorithmic format extraction techniques in a single workflow. Having human experts write regular expressions is no longer required, thereby reducing both the time as well as the opportunity for error. We conducted experiments through both synthetic and real-world datasets, and our results show how the proposed approach is applicable and effective across data types and formats.|格式不一致是数据清理过程中遇到的最常见的数据质量问题之一。现有的自动化方法通常缺乏适用性和普遍性，而具有人工输入的方法通常需要专门技能，如编写正则表达式。本文提出了一种新型的混合式人机系统“ Data-Scanner-4C”，该系统利用众包技术有效地解决单一列中的句法格式不一致问题。我们首先要求人群工作者通过“数据选择”和“结果验证”任务从单列数据创建示例。然后，我们提出并使用一个新的基于规则的学习算法来推断正则表达式，从创建的例子传播格式到整个列。我们的系统集成了众包和算法格式提取技术在一个单一的工作流程。不再需要人工专家编写正则表达式，因此既减少了时间，也减少了出错的机会。我们通过合成数据集和真实数据集进行了实验，结果显示了所提出的方法在跨数据类型和格式方面是如何适用和有效的。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Human-in-the-loop+Regular+Expression+Extraction+for+Single+Column+Format+Inconsistency)|0| |[Identifying Creative Harmful Memes via Prompt based Approach](https://doi.org/10.1145/3543507.3587427)|Junhui Ji, Wei Ren, Usman Naseem|School of Computer Science, University of Sydney, Australia|The creative nature of memes has made it possible for harmful content to spread quickly and widely on the internet. Harmful memes can range from spreading hate speech promoting violence, and causing emotional distress to individuals or communities. These memes are often designed to be misleading, manipulative, and controversial, making it challenging to detect and remove them from online platforms. Previous studies focused on how to fuse visual and language modalities to capture contextual information. However, meme analysis still severely suffers from data deficiency, resulting in insufficient learning of fusion modules. Further, using conventional pretrained encoders for text and images exhibits a greater semantic gap in feature spaces and leads to low performance. To address these gaps, this paper reformulates a harmful meme analysis as an auto-filling and presents a prompt-based approach to identify harmful memes. Specifically, we first transform multimodal data to a single (i.e., textual) modality by generating the captions and attributes of the visual data and then prepend the textual data in the prompt-based pre-trained language model. Experimental results on two benchmark harmful memes datasets demonstrate that our method outperformed state-of-the-art methods. We conclude with the transferability and robustness of our approach to identify creative harmful memes.|文化基因的创造性使得有害内容在互联网上迅速而广泛地传播成为可能。有害的模因包括散布煽动暴力的仇恨言论，以及对个人或社区造成情绪困扰。这些文化基因往往具有误导性、操纵性和争议性，使得从在线平台上检测和移除它们变得具有挑战性。以往的研究集中在如何融合视觉和语言模式，以捕捉上下文信息。然而，模因分析仍然存在严重的数据缺乏问题，导致融合模块学习不足。此外，使用传统的文本和图像预训练编码器表现出更大的特征空间语义差距，并导致低性能。为了解决这些差距，本文将有害模因分析重新表述为自动填充，并提出了一种基于提示的方法来识别有害模因。具体来说，我们首先通过生成可视化数据的标题和属性，将多模态数据转换为单一(即文本)模态，然后在基于提示的预训练语言模型中预置文本数据。在两个基准有害文化基因数据集上的实验结果表明，我们的方法优于最先进的方法。最后，我们总结了我们识别创造性有害模因的方法的可转移性和稳健性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Identifying+Creative+Harmful+Memes+via+Prompt+based+Approach)|0| |[SA-Fusion: Multimodal Fusion Approach for Web-based Human-Computer Interaction in the Wild](https://doi.org/10.1145/3543507.3587429)|Xingyu Liu, Pengfei Ren, Yuchen Chen, Cong Liu, Jing Wang, Haifeng Sun, Qi Qi, Jingyu Wang|State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, China; China Mobile Research Institute, China|Web-based AR technology has broadened human-computer interaction scenes from traditional mechanical devices and flat screens to the real world, resulting in unconstrained environmental challenges such as complex backgrounds, extreme illumination, depth range differences, and hand-object interaction. The previous hand detection and 3D hand pose estimation methods are usually based on single modality such as RGB or depth data, which are not available in some scenarios in unconstrained environments due to the differences between the two modalities. To address this problem, we propose a multimodal fusion approach, named Scene-Adapt Fusion (SA-Fusion), which can fully utilize the complementarity of RGB and depth modalities in web-based HCI tasks. SA-Fusion can be applied in existing hand detection and 3D hand pose estimation frameworks to boost their performance, and can be further integrated into the prototyping AR system to construct a web-based interactive AR application for unconstrained environments. To evaluate the proposed multimodal fusion method, we conduct two user studies on CUG Hand and DexYCB dataset, to demonstrate its effectiveness in terms of accurately detecting hand and estimating 3D hand pose in unconstrained environments and hand-object interaction.|基于网络的增强现实技术已经将人机交互场景从传统的机械设备和平板屏幕拓展到了现实世界，从而带来了无限制的环境挑战，例如复杂的背景、极端的照明、深度范围的差异以及手对象的交互。以往的人手检测和三维人手姿态估计方法通常是基于单一的模式，如 RGB 或深度数据，这是不可用的在一些无约束的环境中的情况下，由于两种模式之间的差异。为了解决这一问题，我们提出了一种多模态融合方法——场景适应融合(Scene-Adapt Fusion，SA-Fusion) ，该方法能够充分利用 RGB 和深度模式在基于 Web 的人机交互任务中的互补性。SA-Fusion 可以应用于现有的手部检测和三维手部姿态估计框架，以提高其性能，并可以进一步集成到原型 AR 系统中，构建基于 Web 的无约束交互式 AR 应用程序。为了评估所提出的多模态融合方法，我们在 CUG Hand 和 DexYCB 数据集上进行了两个用户研究，以验证该方法在无约束环境和手-物交互中准确检测手和估计三维手姿态方面的有效性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=SA-Fusion:+Multimodal+Fusion+Approach+for+Web-based+Human-Computer+Interaction+in+the+Wild)|0| -|[The Harmonic Memory: a Knowledge Graph of harmonic patterns as a trustworthy framework for computational creativity](https://doi.org/10.1145/3543507.3587428)|Jacopo de Berardinis, Albert MeroñoPeñuela, Andrea Poltronieri, Valentina Presutti|Deapartment of Computer Science and Engineering, University of Bologna, Italy; Department of Modern Languages, Literatures, and Cultures, University of Bologna, Italy; Department of Informatics, King's College London, United Kingdom|Computationally creative systems for music have recently achieved impressive results, fuelled by progress in generative machine learning. However, black-box approaches have raised fundamental concerns for ethics, accountability, explainability, and musical plausibility. To enable trustworthy machine creativity, we introduce the Harmonic Memory, a Knowledge Graph (KG) of harmonic patterns extracted from a large and heterogeneous musical corpus. By leveraging a cognitive model of tonal harmony, chord progressions are segmented into meaningful structures, and patterns emerge from their comparison via harmonic similarity. Akin to a music memory, the KG holds temporal connections between consecutive patterns, as well as salient similarity relationships. After demonstrating the validity of our choices, we provide examples of how this design enables novel pathways for combinational creativity. The memory provides a fully accountable and explainable framework to inspire and support creative professionals – allowing for the discovery of progressions consistent with given criteria, the recomposition of harmonic sections, but also the co-creation of new progressions.|计算机创造性的音乐系统最近取得了令人印象深刻的成果，推动了生成机器学习的进步。然而，黑盒方法已经引起了对道德、责任、可解释性和音乐合理性的基本关注。为了实现可信赖的机器创造性，我们引入了谐波记忆，它是一个知识图(KG) ，从一个大型的、异构的音乐语料库中提取出谐波模式。通过利用声调和谐的认知模型，和弦级数被分割成有意义的结构，并且通过和声相似性从它们的比较中产生模式。类似于音乐记忆，KG 持有连续模式之间的时间联系，以及显著的相似性关系。在证明了我们的选择的有效性之后，我们提供了这种设计如何为组合创造力创造新途径的例子。该记忆提供了一个完全负责和可解释的框架，以激励和支持创造性的专业人士-允许发现进展符合给定的标准，重组谐波部分，但也共同创造新的进展。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=The+Harmonic+Memory:+a+Knowledge+Graph+of+harmonic+patterns+as+a+trustworthy+framework+for+computational+creativity)|0| +|[The Harmonic Memory: a Knowledge Graph of harmonic patterns as a trustworthy framework for computational creativity](https://doi.org/10.1145/3543507.3587428)|Jacopo de Berardinis, Albert MeroñoPeñuela, Andrea Poltronieri, Valentina Presutti|Department of Informatics, King's College London, United Kingdom; Department of Modern Languages, Literatures, and Cultures, University of Bologna, Italy; Deapartment of Computer Science and Engineering, University of Bologna, Italy|Computationally creative systems for music have recently achieved impressive results, fuelled by progress in generative machine learning. However, black-box approaches have raised fundamental concerns for ethics, accountability, explainability, and musical plausibility. To enable trustworthy machine creativity, we introduce the Harmonic Memory, a Knowledge Graph (KG) of harmonic patterns extracted from a large and heterogeneous musical corpus. By leveraging a cognitive model of tonal harmony, chord progressions are segmented into meaningful structures, and patterns emerge from their comparison via harmonic similarity. Akin to a music memory, the KG holds temporal connections between consecutive patterns, as well as salient similarity relationships. After demonstrating the validity of our choices, we provide examples of how this design enables novel pathways for combinational creativity. The memory provides a fully accountable and explainable framework to inspire and support creative professionals – allowing for the discovery of progressions consistent with given criteria, the recomposition of harmonic sections, but also the co-creation of new progressions.|计算机创造性的音乐系统最近取得了令人印象深刻的成果，推动了生成机器学习的进步。然而，黑盒方法已经引起了对道德、责任、可解释性和音乐合理性的基本关注。为了实现可信赖的机器创造性，我们引入了谐波记忆，它是一个知识图(KG) ，从一个大型的、异构的音乐语料库中提取出谐波模式。通过利用声调和谐的认知模型，和弦级数被分割成有意义的结构，并且通过和声相似性从它们的比较中产生模式。类似于音乐记忆，KG 持有连续模式之间的时间联系，以及显著的相似性关系。在证明了我们的选择的有效性之后，我们提供了这种设计如何为组合创造力创造新途径的例子。该记忆提供了一个完全负责和可解释的框架，以激励和支持创造性的专业人士-允许发现进展符合给定的标准，重组谐波部分，但也共同创造新的进展。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=The+Harmonic+Memory:+a+Knowledge+Graph+of+harmonic+patterns+as+a+trustworthy+framework+for+computational+creativity)|0| |[A Prompt Log Analysis of Text-to-Image Generation Systems](https://doi.org/10.1145/3543507.3587430)|Yutong Xie, Zhaoying Pan, Jinge Ma, Luo Jie, Qiaozhu Mei|School of Information, University of Michigan, USA; Niantic Inc., USA; Electrical Engineering and Computer Science Department, University of Michigan, USA|Recent developments in large language models (LLM) and generative AI have unleashed the astonishing capabilities of text-to-image generation systems to synthesize high-quality images that are faithful to a given reference text, known as a "prompt". These systems have immediately received lots of attention from researchers, creators, and common users. Despite the plenty of efforts to improve the generative models, there is limited work on understanding the information needs of the users of these systems at scale. We conduct the first comprehensive analysis of large-scale prompt logs collected from multiple text-to-image generation systems. Our work is analogous to analyzing the query logs of Web search engines, a line of work that has made critical contributions to the glory of the Web search industry and research. Compared with Web search queries, text-to-image prompts are significantly longer, often organized into special structures that consist of the subject, form, and intent of the generation tasks and present unique categories of information needs. Users make more edits within creation sessions, which present remarkable exploratory patterns. There is also a considerable gap between the user-input prompts and the captions of the images included in the open training data of the generative models. Our findings provide concrete implications on how to improve text-to-image generation systems for creation purposes.|大型语言模型(LLM)和生成式人工智能的最新发展，已经释放了文本到图像生成系统的惊人能力，可以合成高质量的图像，这些图像忠实于给定的参考文本，即所谓的“提示”。这些系统立即受到了研究人员、创建者和普通用户的广泛关注。尽管在改进生成模型方面做了大量的努力，但在理解这些系统用户的大规模信息需求方面的工作有限。我们对从多个文本到图像生成系统收集的大规模提示日志进行了首次全面分析。我们的工作类似于分析 Web 搜索引擎的查询日志，这一系列工作为 Web 搜索行业和研究的辉煌做出了重要贡献。与 Web 搜索查询相比，文本到图像的提示要长得多，通常组织成特殊的结构，包括生成任务的主题、形式和意图，并呈现独特的信息需求类别。用户在创建会话中进行更多的编辑，这些编辑呈现出非凡的探索模式。生成模型的开放训练数据中包含的用户输入提示和图像标题之间也存在相当大的差距。我们的发现为如何改进文本到图像的生成系统以达到创建目的提供了具体的启示。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Prompt+Log+Analysis+of+Text-to-Image+Generation+Systems)|0| -|[CAM: A Large Language Model-based Creative Analogy Mining Framework](https://doi.org/10.1145/3543507.3587431)|Bhavya, Jinjun Xiong, Chengxiang Zhai|University of Illinois at Urbana-Champaign, USA; University at Buffalo, USA|Analogies inspire creative solutions to problems, and facilitate the creative expression of ideas and the explanation of complex concepts. They have widespread applications in scientific innovation, creative writing, and education. The ability to discover creative analogies that are not explicitly mentioned but can be inferred from the web is highly desirable to power all such applications dynamically and augment human creativity. Recently, Large Pre-trained Language Models (PLMs), trained on massive Web data, have shown great promise in generating mostly known analogies that are explicitly mentioned on the Web. However, it is unclear how they could be leveraged for mining creative analogies not explicitly mentioned on the Web. We address this challenge and propose Creative Analogy Mining (CAM), a novel framework for mining creative analogies, which consists of the following three main steps: 1) Generate analogies using PLMs with effectively designed prompts, 2) Evaluate their quality using scoring functions, and 3) Refine the low-quality analogies by another round of prompt-based generation. We propose both unsupervised and supervised instantiations of the framework so that it can be used even without any annotated data. Based on human evaluation using Amazon Mechanical Turk, we find that our unsupervised framework can mine 13.7% highly-creative and 56.37% somewhat-creative analogies. Moreover, our supervised scores are generally better than the unsupervised ones and correlate moderately with human evaluators, indicating that they would be even more effective at mining creative analogies. These findings also shed light on the creativity of PLMs 1.|类比可以激发对问题的创造性解决方案，促进思想的创造性表达和复杂概念的解释。它们在科学创新、创造性写作和教育中有着广泛的应用。发现创造性类比的能力，没有明确提到，但可以从网络推断是非常理想的动力所有这些应用程序，并增强人类的创造力。最近，大型预训练语言模型(PLM) ，在海量的网络数据的训练，已经显示出巨大的前景，生成大多数已知的类比，明确提到了网络上。然而，目前还不清楚如何利用它们来挖掘网上没有明确提到的创造性类比。我们解决了这个问题，并提出了创造性类比挖掘(CAM) ，一个挖掘创造性类比的新框架，它由以下三个主要步骤组成: 1)使用 PLM 生成具有有效设计提示的类比，2)使用评分函数评估它们的质量，3)通过另一轮基于提示的生成精炼低质量的类比。我们提出了框架的无监督实例化和监督实例化，这样即使没有任何注释数据也可以使用框架。基于亚马逊土耳其机器人的人类评估，我们发现我们的无监督框架可以挖掘13.7% 的高创造性和56.37% 的有创造性的类比。此外，我们的监督分数一般优于无监督分数，并与人类评估者适度相关，表明他们将更有效地挖掘创造性类比。这些发现也揭示了 PLMs 1的创造性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=CAM:+A+Large+Language+Model-based+Creative+Analogy+Mining+Framework)|0| +|[CAM: A Large Language Model-based Creative Analogy Mining Framework](https://doi.org/10.1145/3543507.3587431)|Bhavya, Jinjun Xiong, Chengxiang Zhai|University at Buffalo, USA; University of Illinois at Urbana-Champaign, USA|Analogies inspire creative solutions to problems, and facilitate the creative expression of ideas and the explanation of complex concepts. They have widespread applications in scientific innovation, creative writing, and education. The ability to discover creative analogies that are not explicitly mentioned but can be inferred from the web is highly desirable to power all such applications dynamically and augment human creativity. Recently, Large Pre-trained Language Models (PLMs), trained on massive Web data, have shown great promise in generating mostly known analogies that are explicitly mentioned on the Web. However, it is unclear how they could be leveraged for mining creative analogies not explicitly mentioned on the Web. We address this challenge and propose Creative Analogy Mining (CAM), a novel framework for mining creative analogies, which consists of the following three main steps: 1) Generate analogies using PLMs with effectively designed prompts, 2) Evaluate their quality using scoring functions, and 3) Refine the low-quality analogies by another round of prompt-based generation. We propose both unsupervised and supervised instantiations of the framework so that it can be used even without any annotated data. Based on human evaluation using Amazon Mechanical Turk, we find that our unsupervised framework can mine 13.7% highly-creative and 56.37% somewhat-creative analogies. Moreover, our supervised scores are generally better than the unsupervised ones and correlate moderately with human evaluators, indicating that they would be even more effective at mining creative analogies. These findings also shed light on the creativity of PLMs 1.|类比可以激发对问题的创造性解决方案，促进思想的创造性表达和复杂概念的解释。它们在科学创新、创造性写作和教育中有着广泛的应用。发现创造性类比的能力，没有明确提到，但可以从网络推断是非常理想的动力所有这些应用程序，并增强人类的创造力。最近，大型预训练语言模型(PLM) ，在海量的网络数据的训练，已经显示出巨大的前景，生成大多数已知的类比，明确提到了网络上。然而，目前还不清楚如何利用它们来挖掘网上没有明确提到的创造性类比。我们解决了这个问题，并提出了创造性类比挖掘(CAM) ，一个挖掘创造性类比的新框架，它由以下三个主要步骤组成: 1)使用 PLM 生成具有有效设计提示的类比，2)使用评分函数评估它们的质量，3)通过另一轮基于提示的生成精炼低质量的类比。我们提出了框架的无监督实例化和监督实例化，这样即使没有任何注释数据也可以使用框架。基于亚马逊土耳其机器人的人类评估，我们发现我们的无监督框架可以挖掘13.7% 的高创造性和56.37% 的有创造性的类比。此外，我们的监督分数一般优于无监督分数，并与人类评估者适度相关，表明他们将更有效地挖掘创造性类比。这些发现也揭示了 PLMs 1的创造性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=CAM:+A+Large+Language+Model-based+Creative+Analogy+Mining+Framework)|0| |[Tangible Web: An Interactive Immersion Virtual Reality Creativity System that Travels Across Reality](https://doi.org/10.1145/3543507.3587432)|Simin Yang, Ze Gao, Reza Hadi Mogavi, Pan Hui, Tristan Braud||||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Tangible+Web:+An+Interactive+Immersion+Virtual+Reality+Creativity+System+that+Travels+Across+Reality)|0| -|[Coherent Topic Modeling for Creative Multimodal Data on Social Media](https://doi.org/10.1145/3543507.3587433)|Junaid Rashid, Jungeun Kim, Usman Naseem|School of Computer Science, The University of Sydney, Sydney, Australia, Australia; Department of Data Science, Sejong University, Seoul, Republic of Korea, Republic of Korea; Department of Software, Kongju National University, Cheonan, Republic of Korea, Republic of Korea|The creative web is all about combining different types of media to create a unique and engaging online experience. Multimodal data, such as text and images, is a key component in the creative web. Social media posts that incorporate both text descriptions and images offer a wealth of information and context. Text in social media posts typically relates to one topic, while images often convey information about multiple topics due to the richness of visual content. Despite this potential, many existing multimodal topic models do not take these criteria into account, resulting in poor quality topics being generated. Therefore, we proposed a Coherent Topic modeling for Multimodal Data (CTM-MM), which takes into account that text in social media posts typically relates to one topic, while images can contain information about multiple topics. Our experimental results show that CTM-MM outperforms traditional multimodal topic models in terms of classification and topic coherence.|创意网站就是将不同类型的媒体结合起来，创造一种独特而迷人的在线体验。多模态数据，如文本和图像，是创造性网络的关键组成部分。结合文字描述和图像的社交媒体帖子提供了丰富的信息和背景。社交媒体帖子中的文本通常与一个主题相关，而图像由于视觉内容的丰富性，通常传达关于多个主题的信息。尽管有这种潜力，许多现有的多模式主题模型没有考虑到这些标准，导致生成的主题质量很差。因此，我们提出了一个多模态数据的连贯主题模型(CTM-MM) ，它考虑到了社会媒体帖子中的文本通常与一个主题相关，而图像可以包含关于多个主题的信息。实验结果表明，CTM-MM 模型在主题分类和主题连贯性方面优于传统的多模态主题模型。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Coherent+Topic+Modeling+for+Creative+Multimodal+Data+on+Social+Media)|0| -|[Improving Health Mention Classification Through Emphasising Literal Meanings: A Study Towards Diversity and Generalisation for Public Health Surveillance](https://doi.org/10.1145/3543507.3583877)|Olanrewaju Tahir Aduragba, Jialin Yu, Alexandra I. Cristea, Yang Long|Department of Computer Science, Durham University, United Kingdom and Kwara State University, Nigeria; Department of Computer Science, Durham University, United Kingdom; Department of Computer Science, Durham University, United Kingdom and University College London, United Kingdom|People often use disease or symptom terms on social media and online forums in ways other than to describe their health. Thus the NLP health mention classification (HMC) task aims to identify posts where users are discussing health conditions literally, not figuratively. Existing computational research typically only studies health mentions within well-represented groups in developed nations. Developing countries with limited health surveillance abilities fail to benefit from such data to manage public health crises. To advance the HMC research and benefit more diverse populations, we present the Nairaland health mention dataset (NHMD), a new dataset collected from a dedicated web forum for Nigerians. NHMD consists of 7,763 manually labelled posts extracted based on four prevalent diseases (HIV/AIDS, Malaria, Stroke and Tuberculosis) in Nigeria. With NHMD, we conduct extensive experiments using current state-of-the-art models for HMC and identify that, compared to existing public datasets, NHMD contains out-of-distribution examples. Hence, it is well suited for domain adaptation studies. The introduction of the NHMD dataset imposes better diversity coverage of vulnerable populations and generalisation for HMC tasks in a global public health surveillance setting. Additionally, we present a novel multi-task learning approach for HMC tasks by combining literal word meaning prediction as an auxiliary task. Experimental results demonstrate that the proposed approach outperforms state-of-the-art methods statistically significantly (p < 0.01, Wilcoxon test) in terms of F1 score over the state-of-the-art and shows that our new dataset poses a strong challenge to the existing HMC methods.|人们经常在社交媒体和在线论坛上使用疾病或症状的术语，而不是用来描述自己的健康状况。因此，NLP 健康提及分类(HMC)任务旨在确定用户讨论健康状况的帖子。现有的计算机研究通常只研究发达国家中有代表性的群体中的健康提及情况。卫生监测能力有限的发展中国家无法从这些数据中受益，无法管理公共卫生危机。为了推进健康数据中心的研究，让更多不同的人群受益，我们提出了 Nairaland 健康提及数据集(nhMD) ，这是一个新的数据集，收集自一个专门为尼日利亚人设立的网络论坛。NHMD 包括7,763个根据尼日利亚四种流行疾病(艾滋病毒/艾滋病、疟疾、中风和结核病)手工标记的帖子。使用 NHMD，我们使用当前最先进的 HMC 模型进行了广泛的实验，并发现，与现有的公共数据集相比，NHMD 包含超出分布范围的示例。因此，它非常适合于领域适应性研究。NHMD 数据集的引入提高了脆弱人群的多样性覆盖率，并在全球公共卫生监测背景下推广了 HMC 任务。此外，我们提出了一种新的 HMC 任务的多任务学习方法，结合字面意义预测作为辅助任务。实验结果表明，所提出的方法在 F1评分方面在统计学上显着优于最先进的方法(p < 0.01，Wilcoxon 检验) ，并且表明我们的新数据集对现有的 HMC 方法提出了强烈的挑战。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Improving+Health+Mention+Classification+Through+Emphasising+Literal+Meanings:+A+Study+Towards+Diversity+and+Generalisation+for+Public+Health+Surveillance)|0| +|[Coherent Topic Modeling for Creative Multimodal Data on Social Media](https://doi.org/10.1145/3543507.3587433)|Junaid Rashid, Jungeun Kim, Usman Naseem|Department of Software, Kongju National University, Cheonan, Republic of Korea, Republic of Korea; School of Computer Science, The University of Sydney, Sydney, Australia, Australia; Department of Data Science, Sejong University, Seoul, Republic of Korea, Republic of Korea|The creative web is all about combining different types of media to create a unique and engaging online experience. Multimodal data, such as text and images, is a key component in the creative web. Social media posts that incorporate both text descriptions and images offer a wealth of information and context. Text in social media posts typically relates to one topic, while images often convey information about multiple topics due to the richness of visual content. Despite this potential, many existing multimodal topic models do not take these criteria into account, resulting in poor quality topics being generated. Therefore, we proposed a Coherent Topic modeling for Multimodal Data (CTM-MM), which takes into account that text in social media posts typically relates to one topic, while images can contain information about multiple topics. Our experimental results show that CTM-MM outperforms traditional multimodal topic models in terms of classification and topic coherence.|创意网站就是将不同类型的媒体结合起来，创造一种独特而迷人的在线体验。多模态数据，如文本和图像，是创造性网络的关键组成部分。结合文字描述和图像的社交媒体帖子提供了丰富的信息和背景。社交媒体帖子中的文本通常与一个主题相关，而图像由于视觉内容的丰富性，通常传达关于多个主题的信息。尽管有这种潜力，许多现有的多模式主题模型没有考虑到这些标准，导致生成的主题质量很差。因此，我们提出了一个多模态数据的连贯主题模型(CTM-MM) ，它考虑到了社会媒体帖子中的文本通常与一个主题相关，而图像可以包含关于多个主题的信息。实验结果表明，CTM-MM 模型在主题分类和主题连贯性方面优于传统的多模态主题模型。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Coherent+Topic+Modeling+for+Creative+Multimodal+Data+on+Social+Media)|0| +|[Improving Health Mention Classification Through Emphasising Literal Meanings: A Study Towards Diversity and Generalisation for Public Health Surveillance](https://doi.org/10.1145/3543507.3583877)|Olanrewaju Tahir Aduragba, Jialin Yu, Alexandra I. Cristea, Yang Long|Department of Computer Science, Durham University, United Kingdom and University College London, United Kingdom; Department of Computer Science, Durham University, United Kingdom; Department of Computer Science, Durham University, United Kingdom and Kwara State University, Nigeria|People often use disease or symptom terms on social media and online forums in ways other than to describe their health. Thus the NLP health mention classification (HMC) task aims to identify posts where users are discussing health conditions literally, not figuratively. Existing computational research typically only studies health mentions within well-represented groups in developed nations. Developing countries with limited health surveillance abilities fail to benefit from such data to manage public health crises. To advance the HMC research and benefit more diverse populations, we present the Nairaland health mention dataset (NHMD), a new dataset collected from a dedicated web forum for Nigerians. NHMD consists of 7,763 manually labelled posts extracted based on four prevalent diseases (HIV/AIDS, Malaria, Stroke and Tuberculosis) in Nigeria. With NHMD, we conduct extensive experiments using current state-of-the-art models for HMC and identify that, compared to existing public datasets, NHMD contains out-of-distribution examples. Hence, it is well suited for domain adaptation studies. The introduction of the NHMD dataset imposes better diversity coverage of vulnerable populations and generalisation for HMC tasks in a global public health surveillance setting. Additionally, we present a novel multi-task learning approach for HMC tasks by combining literal word meaning prediction as an auxiliary task. Experimental results demonstrate that the proposed approach outperforms state-of-the-art methods statistically significantly (p < 0.01, Wilcoxon test) in terms of F1 score over the state-of-the-art and shows that our new dataset poses a strong challenge to the existing HMC methods.|人们经常在社交媒体和在线论坛上使用疾病或症状的术语，而不是用来描述自己的健康状况。因此，NLP 健康提及分类(HMC)任务旨在确定用户讨论健康状况的帖子。现有的计算机研究通常只研究发达国家中有代表性的群体中的健康提及情况。卫生监测能力有限的发展中国家无法从这些数据中受益，无法管理公共卫生危机。为了推进健康数据中心的研究，让更多不同的人群受益，我们提出了 Nairaland 健康提及数据集(nhMD) ，这是一个新的数据集，收集自一个专门为尼日利亚人设立的网络论坛。NHMD 包括7,763个根据尼日利亚四种流行疾病(艾滋病毒/艾滋病、疟疾、中风和结核病)手工标记的帖子。使用 NHMD，我们使用当前最先进的 HMC 模型进行了广泛的实验，并发现，与现有的公共数据集相比，NHMD 包含超出分布范围的示例。因此，它非常适合于领域适应性研究。NHMD 数据集的引入提高了脆弱人群的多样性覆盖率，并在全球公共卫生监测背景下推广了 HMC 任务。此外，我们提出了一种新的 HMC 任务的多任务学习方法，结合字面意义预测作为辅助任务。实验结果表明，所提出的方法在 F1评分方面在统计学上显着优于最先进的方法(p < 0.01，Wilcoxon 检验) ，并且表明我们的新数据集对现有的 HMC 方法提出了强烈的挑战。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Improving+Health+Mention+Classification+Through+Emphasising+Literal+Meanings:+A+Study+Towards+Diversity+and+Generalisation+for+Public+Health+Surveillance)|0| |[Learning Faithful Attention for Interpretable Classification of Crisis-Related Microblogs under Constrained Human Budget](https://doi.org/10.1145/3543507.3583861)|Thi Huyen Nguyen, Koustav Rudra|L3S Research Center, Germany; Indian Institute of Technology (Indian School of Mines) Dhanbad, India|The recent widespread use of social media platforms has created convenient ways to obtain and spread up-to-date information during crisis events such as disasters. Time-critical analysis of crisis data can help human organizations gain actionable information and plan for aid responses. Many existing studies have proposed methods to identify informative messages and categorize them into different humanitarian classes. Advanced neural network architectures tend to achieve state-of-the-art performance, but the model decisions are opaque. While attention heatmaps show insights into the model’s prediction, some studies found that standard attention does not provide meaningful explanations. Alternatively, recent works proposed interpretable approaches for the classification of crisis events that rely on human rationales to train and extract short snippets as explanations. However, the rationale annotations are not always available, especially in real-time situations for new tasks and events. In this paper, we propose a two-stage approach to learn the rationales under minimal human supervision and derive faithful machine attention. Extensive experiments over four crisis events show that our model is able to obtain better or comparable classification performance (∼ 86% Macro-F1) to baselines and faithful attention heatmaps using only 40-50% human-level supervision. Further, we employ a zero-shot learning setup to detect actionable tweets along with actionable word snippets as rationales.|最近社交媒体平台的广泛使用为灾害等危机事件期间获取和传播最新信息创造了便利的途径。对危机数据进行时间要求严格的分析，可以帮助人类组织获得可操作的信息，并制定援助应对计划。许多现有研究提出了确定信息性信息并将其分为不同人道主义类别的方法。先进的神经网络架构往往能够实现最先进的性能，但是模型决策是不透明的。虽然注意力热图显示了对模型预测的洞察力，但一些研究发现，标准注意力并不能提供有意义的解释。或者，最近的研究提出了可解释的危机事件分类方法，这些方法依赖于人类的基本原理来训练和提取简短的片段作为解释。但是，基本原理注释并不总是可用的，特别是在新任务和事件的实时情况下。在本文中，我们提出了一个两阶段的方法来学习最小人类监督下的基本原理和获得忠实的机器注意。对四个危机事件的广泛实验表明，我们的模型能够使用40-50% 的人类水平监督获得更好或可比较的分类性能(something 86% 宏观 F1)到基线和忠实的注意热图。此外，我们使用了一个零拍摄学习设置来检测可操作的 tweet 以及可操作的单词片段作为基本原理。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Learning+Faithful+Attention+for+Interpretable+Classification+of+Crisis-Related+Microblogs+under+Constrained+Human+Budget)|0| -|[Attacking Fake News Detectors via Manipulating News Social Engagement](https://doi.org/10.1145/3543507.3583868)|Haoran Wang, Yingtong Dou, Canyu Chen, Lichao Sun, Philip S. Yu, Kai Shu|Department of Computer Science, University of Illinois at Chicago, USA; Department of Computer Science, University of Illinois Chicago, USA and Visa Research, USA; Department of Computer Science, Illinois Institute of Technology, USA; Department of Computer Science and Engineering, Lehigh University, USA|Social media is one of the main sources for news consumption, especially among the younger generation. With the increasing popularity of news consumption on various social media platforms, there has been a surge of misinformation which includes false information or unfounded claims. As various text- and social context-based fake news detectors are proposed to detect misinformation on social media, recent works start to focus on the vulnerabilities of fake news detectors. In this paper, we present the first adversarial attack framework against Graph Neural Network (GNN)-based fake news detectors to probe their robustness. Specifically, we leverage a multi-agent reinforcement learning (MARL) framework to simulate the adversarial behavior of fraudsters on social media. Research has shown that in real-world settings, fraudsters coordinate with each other to share different news in order to evade the detection of fake news detectors. Therefore, we modeled our MARL framework as a Markov Game with bot, cyborg, and crowd worker agents, which have their own distinctive cost, budget, and influence. We then use deep Q-learning to search for the optimal policy that maximizes the rewards. Extensive experimental results on two real-world fake news propagation datasets demonstrate that our proposed framework can effectively sabotage the GNN-based fake news detector performance. We hope this paper can provide insights for future research on fake news detection.|社交媒体是新闻消费的主要来源之一，尤其是在年轻一代中。随着新闻消费在各种社交媒体平台上的日益普及，虚假信息和毫无根据的言论层出不穷。随着各种基于文本和社会背景的假新闻检测器被提出来检测社交媒体上的错误信息，最近的工作开始关注假新闻检测器的脆弱性。针对基于图神经网络(GNN)的虚假新闻检测器提出了第一个对抗性攻击框架，以检验其鲁棒性。具体来说，我们利用一个多代理强化学习框架来模拟欺诈者在社交媒体上的对抗行为。研究表明，在现实世界中，欺诈者相互配合，分享不同的新闻，以逃避假新闻检测器的检测。因此，我们将 MARL 框架建模为一个包含机器人、半机器人和群体工作者代理的马尔可夫博弈，这些代理有自己独特的成本、预算和影响力。然后，我们使用深度 Q 学习来寻找最优的策略，使回报最大化。在两个真实假新闻传播数据集上的大量实验结果表明，我们提出的框架能够有效地破坏基于 GNN 的假新闻检测器的性能。希望本文的研究能为今后假新闻检测的研究提供一些启示。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Attacking+Fake+News+Detectors+via+Manipulating+News+Social+Engagement)|0| +|[Attacking Fake News Detectors via Manipulating News Social Engagement](https://doi.org/10.1145/3543507.3583868)|Haoran Wang, Yingtong Dou, Canyu Chen, Lichao Sun, Philip S. Yu, Kai Shu|Department of Computer Science, University of Illinois Chicago, USA and Visa Research, USA; Department of Computer Science and Engineering, Lehigh University, USA; Department of Computer Science, University of Illinois at Chicago, USA; Department of Computer Science, Illinois Institute of Technology, USA|Social media is one of the main sources for news consumption, especially among the younger generation. With the increasing popularity of news consumption on various social media platforms, there has been a surge of misinformation which includes false information or unfounded claims. As various text- and social context-based fake news detectors are proposed to detect misinformation on social media, recent works start to focus on the vulnerabilities of fake news detectors. In this paper, we present the first adversarial attack framework against Graph Neural Network (GNN)-based fake news detectors to probe their robustness. Specifically, we leverage a multi-agent reinforcement learning (MARL) framework to simulate the adversarial behavior of fraudsters on social media. Research has shown that in real-world settings, fraudsters coordinate with each other to share different news in order to evade the detection of fake news detectors. Therefore, we modeled our MARL framework as a Markov Game with bot, cyborg, and crowd worker agents, which have their own distinctive cost, budget, and influence. We then use deep Q-learning to search for the optimal policy that maximizes the rewards. Extensive experimental results on two real-world fake news propagation datasets demonstrate that our proposed framework can effectively sabotage the GNN-based fake news detector performance. We hope this paper can provide insights for future research on fake news detection.|社交媒体是新闻消费的主要来源之一，尤其是在年轻一代中。随着新闻消费在各种社交媒体平台上的日益普及，虚假信息和毫无根据的言论层出不穷。随着各种基于文本和社会背景的假新闻检测器被提出来检测社交媒体上的错误信息，最近的工作开始关注假新闻检测器的脆弱性。针对基于图神经网络(GNN)的虚假新闻检测器提出了第一个对抗性攻击框架，以检验其鲁棒性。具体来说，我们利用一个多代理强化学习框架来模拟欺诈者在社交媒体上的对抗行为。研究表明，在现实世界中，欺诈者相互配合，分享不同的新闻，以逃避假新闻检测器的检测。因此，我们将 MARL 框架建模为一个包含机器人、半机器人和群体工作者代理的马尔可夫博弈，这些代理有自己独特的成本、预算和影响力。然后，我们使用深度 Q 学习来寻找最优的策略，使回报最大化。在两个真实假新闻传播数据集上的大量实验结果表明，我们提出的框架能够有效地破坏基于 GNN 的假新闻检测器的性能。希望本文的研究能为今后假新闻检测的研究提供一些启示。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Attacking+Fake+News+Detectors+via+Manipulating+News+Social+Engagement)|0| |[ContrastFaux: Sparse Semi-supervised Fauxtography Detection on the Web using Multi-view Contrastive Learning](https://doi.org/10.1145/3543507.3583869)|Ruohan Zong, Yang Zhang, Lanyu Shang, Dong Wang|School of Information Sciences, University of Illinois Urbana-Champaign, USA|The widespread misinformation on the Web has raised many concerns with serious societal consequences. In this paper, we study a critical type of online misinformation, namely fauxtography, where the image and associated text of a social media post jointly convey a questionable or false sense. In particular, we focus on a sparse semi-supervised fauxtography detection problem, which aims to accurately identify fauxtography by only using the sparsely annotated ground truth labels of social media posts. Our problem is motivated by the key limitation of current fauxtography detection approaches that often require a large amount of expensive and inefficient manual annotations to train an effective fauxtography detection model. We identify two key technical challenges in solving the problem: 1) it is non-trivial to train an accurate detection model given the sparse fauxtography annotations, and 2) it is difficult to extract the heterogeneous and complicated fauxtography features from the multi-modal social media posts for accurate fauxtography detection. To address the above challenges, we propose ContrastFaux, a multi-view contrastive learning framework that jointly explores the sparse fauxtography annotations and the cross-modal fauxtography feature similarity between the image and text in multi-modal posts to accurately detect fauxtography on social media. Evaluation results on two social media datasets demonstrate that ContrastFaux consistently outperforms state-of-the-art deep learning and semi-supervised learning fauxtography detection baselines by achieving the highest fauxtography detection accuracy.|网络上广泛存在的错误信息引起了许多严重的社会后果。在本文中，我们研究了一种关键类型的网络虚假信息，即人造图像，其中的图像和相关的文本的社会媒体帖子共同传达一个可疑的或错误的意义。特别地，我们集中在一个稀疏的半监督的人造图像检测问题，其目的是准确地识别人造图像只使用稀疏注释的地面真相标签的社会媒体帖子。我们的问题是由于当前人造地图检测方法的关键局限性而产生的，这些方法通常需要大量昂贵和低效的手工注释来训练一个有效的人造地图检测模型。我们确定了解决这个问题的两个关键技术挑战: 1)在稀疏的人造图注释的情况下，训练一个准确的检测模型是不容易的; 2)难以从多模态社交媒体帖子中提取异构和复杂的人造图特征来进行准确的人造图检测。为了应对上述挑战，我们提出了一个多视图对比学习框架，该框架共同探索稀疏的人造图像注释和多模态文章中图像和文本之间的跨模态人造图像特征的相似性，以准确检测社交媒体上的人造图像。对两个社交媒体数据集的评估结果表明，compastfaux 通过实现最高的人造图像检测准确度，始终优于最先进的深度学习和半监督学习人造图像检测基线。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=ContrastFaux:+Sparse+Semi-supervised+Fauxtography+Detection+on+the+Web+using+Multi-view+Contrastive+Learning)|0| |[Interpreting wealth distribution via poverty map inference using multimodal data](https://doi.org/10.1145/3543507.3583862)|Lisette EspínNoboa, János Kertész, Márton Karsai|Central European University, Austria and Rènyi Institute of Mathematics, Hungary; Central European University, Austria and Complexity Science Hub Vienna, Austria|Poverty maps are essential tools for governments and NGOs to track socioeconomic changes and adequately allocate infrastructure and services in places in need. Sensor and online crowd-sourced data combined with machine learning methods have provided a recent breakthrough in poverty map inference. However, these methods do not capture local wealth fluctuations, and are not optimized to produce accountable results that guarantee accurate predictions to all sub-populations. Here, we propose a pipeline of machine learning models to infer the mean and standard deviation of wealth across multiple geographically clustered populated places, and illustrate their performance in Sierra Leone and Uganda. These models leverage seven independent and freely available feature sources based on satellite images, and metadata collected via online crowd-sourcing and social media. Our models show that combined metadata features are the best predictors of wealth in rural areas, outperforming image-based models, which are the best for predicting the highest wealth quintiles. Our results recover the local mean and variation of wealth, and correctly capture the positive yet non-monotonous correlation between them. We further demonstrate the capabilities and limitations of model transfer across countries and the effects of data recency and other biases. Our methodology provides open tools to build towards more transparent and interpretable models to help governments and NGOs to make informed decisions based on data availability, urbanization level, and poverty thresholds.|贫困地图是政府和非政府组织跟踪社会经济变化和在需要的地方适当分配基础设施和服务的重要工具。传感器和在线众包数据结合机器学习方法为贫困地图推断提供了新的突破。然而，这些方法不能捕捉当地的财富波动，也不能优化以产生可问责的结果，从而保证对所有亚群的准确预测。在这里，我们提出了一系列机器学习模型，用于推断多个地理集群人口密集地区的财富平均值和标准差，并说明它们在塞拉利昂和乌干达的表现。这些模型利用了七个独立和免费提供的特征源，这些特征源基于卫星图像，以及通过在线众包和社交媒体收集的元数据。我们的模型显示，综合元数据特征是农村地区财富的最佳预测因子，表现优于基于图像的模型，后者是预测最高财富五分位数的最佳模型。我们的研究结果恢复了财富的局部均值和变化，并且正确地捕捉了它们之间的正相关和非单调相关。我们进一步展示了跨国模型转移的能力和局限性以及数据新近性和其他偏差的影响。我们的方法提供了开放的工具，以建立更加透明和可解释的模式，帮助政府和非政府组织根据数据可用性、城市化水平和贫困阈值作出知情决定。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Interpreting+wealth+distribution+via+poverty+map+inference+using+multimodal+data)|0| -|[MSQ-BioBERT: Ambiguity Resolution to Enhance BioBERT Medical Question-Answering](https://doi.org/10.1145/3543507.3583878)|Muzhe Guo, Muhao Guo, Edward T. Dougherty, Fang Jin|Roger Williams University, USA; George Washington University, USA; Arizona State University, USA|Question answering (QA) is a task in the field of natural language processing (NLP) and information retrieval, which has pivotal applications in areas such as online reading comprehension and web search engines. Currently, Bidirectional Encoder Representations from Transformers (BERT) and its biomedical variation (BioBERT) achieve impressive results on the reading comprehension QA datasets and medical-related QA datasets, and so they are widely used for a variety of passage-based QA tasks. However, their performances rapidly deteriorate when encountering passage and context ambiguities. This issue is prevalent and unavoidable in many fields, notably the web-based medical field. In this paper, we introduced a novel approach called the Multiple Synonymous Questions BioBERT (MSQ-BioBERT), which integrates question augmentation, rather than the typical single question used by traditional BioBERT, to elevate BioBERT’s performance on medical QA tasks. In addition, we constructed an ambiguous medical dataset based on the information from Wikipedia web. Experiments with both this web-based constructed medical dataset and open biomedical datasets demonstrate the significant performance gains of the MSQ-BioBERT approach, showcasing a new method for addressing ambiguity in medical QA tasks.|问答是自然语言处理和信息检索领域的一项任务，在在线阅读理解和网络搜索引擎等领域有着关键的应用。目前，变压器双向编码器表示(BERT)及其生物医学变异(BioBERT)在阅读理解质量保证数据集和医学相关质量保证数据集上取得了令人印象深刻的结果，因此它们被广泛用于各种基于通道的质量保证任务。然而，当遇到短文和上下文歧义时，它们的表现会迅速恶化。这个问题在许多领域都是普遍的和不可避免的，特别是在基于网络的医学领域。为了提高 BioBERT 在医学 QA 任务中的表现，本文引入了一种新的方法——多同义问题 BioBERT (MSQ-BioBERT) ，该方法集成了问题增强，而不是传统 BioBERT 使用的典型的单个问题。此外，我们基于维基百科网站的信息构建了一个模糊的医学数据集。基于网络构建的医学数据集和开放式生物医学数据集的实验表明，MSQ-BioBERT 方法具有显著的性能增益，为解决医学 QA 任务中的模糊性提供了一种新的方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=MSQ-BioBERT:+Ambiguity+Resolution+to+Enhance+BioBERT+Medical+Question-Answering)|0| -|[Graph-based Village Level Poverty Identification](https://doi.org/10.1145/3543507.3583864)|Jing Ma, Liangwei Yang, Qiong Feng, Weizhi Zhang, Philip S. Yu|University of Illinois Chicago, USA; Southwest Minzu University, China; University of Electronic Science and Technology of China, China|Poverty status identification is the first obstacle to eradicating poverty. Village-level poverty identification is very challenging due to the arduous field investigation and insufficient information. The development of the Web infrastructure and its modeling tools provides fresh approaches to identifying poor villages. Upon those techniques, we build a village graph for village poverty status identification. By modeling the village connections as a graph through the geographic distance, we show the correlation between village poverty status and its graph topological position and identify two key factors (Centrality, Homophily Decaying effect) for identifying villages. We further propose the first graph-based method to identify poor villages. It includes a global Centrality2Vec module to embed village centrality into the dense vector and a local graph distance convolution module that captures the decaying effect. In this paper, we make the first attempt to interpret and identify village-level poverty from a graph perspective.|确定贫困状况是消除贫困的第一个障碍。由于实地调查工作的艰巨性和信息的不足，村级贫困的识别具有很大的挑战性。Web 基础设施及其建模工具的开发为识别贫困村庄提供了新的方法。基于这些技术，我们构建了一个村庄图，用于村庄贫困状况的识别。通过对村庄连接的地理距离建模，揭示了村庄贫困状况与其图形拓扑位置之间的相关关系，并确定了识别村庄的两个关键因素(中心性、同质性衰减效应)。进一步提出了第一种基于图论的贫困村识别方法。它包括一个全局 Centrality2Vec 模块，将村庄中心性嵌入到密集向量中，以及一个捕捉衰减效应的局部图距离卷积模块。本文首次尝试从图形的角度对村级贫困进行解释和识别。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Graph-based+Village+Level+Poverty+Identification)|0| -|[Mapping Flood Exposure, Damage, and Population Needs Using Remote and Social Sensing: A Case Study of 2022 Pakistan Floods](https://doi.org/10.1145/3543507.3583881)|Zainab Akhtar, Umair Qazi, Rizwan Sadiq, Aya ElSakka, Muhammad Sajjad, Ferda Ofli, Muhammad Imran|Qatar Computing Research Institute, Qatar; Department of Geography and Centre for Geocomputation Studies, Hong Kong Baptist University, Hong Kong|The devastating 2022 floods in Pakistan resulted in a catastrophe impacting millions of people and destroying thousands of homes. While disaster management efforts were taken, crisis responders struggled to understand the country-wide flood extent, population exposure, urgent needs of affected people, and various types of damage. To tackle this challenge, we leverage remote and social sensing with geospatial data using state-of-the-art machine learning techniques for text and image processing. Our satellite-based analysis over a one-month period (25 Aug–25 Sep) revealed that 11.48% of Pakistan was inundated. When combined with geospatial data, this meant 18.9 million people were at risk across 160 districts in Pakistan, with adults constituting 50% of the exposed population. Our social sensing data analysis surfaced 106.7k reports pertaining to deaths, injuries, and concerns of the affected people. To understand the urgent needs of the affected population, we analyzed tweet texts and found that South Karachi, Chitral and North Waziristan required the most basic necessities like food and shelter. Further analysis of tweet images revealed that Lasbela, Rajanpur, and Jhal Magsi had the highest damage reports normalized by their population. These damage reports were found to correlate strongly with affected people reports and need reports, achieving an R-Square of 0.96 and 0.94, respectively. Our extensive study shows that combining remote sensing, social sensing, and geospatial data can provide accurate and timely information during a disaster event, which is crucial in prioritizing areas for immediate and gradual response.|巴基斯坦2022年毁灭性的洪水造成了一场灾难，影响到数百万人，摧毁了数千所房屋。在灾害管理工作的同时，应对危机的人员努力了解全国范围的洪水范围、人口暴露、受灾人民的紧急需求以及各种类型的损失。为了应对这一挑战，我们利用地理空间数据进行遥感和社会感应，使用最先进的机器学习技术进行文本和图像处理。我们在一个月期间(8月25日至9月25日)的卫星分析显示，巴基斯坦11.48% 的土地被淹没。结合地理空间数据，这意味着巴基斯坦160个地区的1890万人处于危险之中，成年人占暴露人口的50% 。我们的社会传感数据分析显示，有106.7万份报告与受影响人群的死亡、受伤和担忧有关。为了了解受灾人口的迫切需求，我们分析了推特短信，发现南卡拉奇、 Chitral 和北瓦济里斯坦特区需要食物和住所等最基本的必需品。对推特图片的进一步分析显示，Lasbela、 Rajanpur 和贾尔•马格西(Jhal Magsi)的人口正常化损失报告最高。这些损害报告被发现与受影响的人的报告和需要报告密切相关，分别达到0.96和0.94的 R 平方。我们广泛的研究表明，结合遥感、社会感应和地理空间数据可以在灾害事件期间提供准确和及时的信息，这对于确定立即和逐步响应的优先领域至关重要。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Mapping+Flood+Exposure,+Damage,+and+Population+Needs+Using+Remote+and+Social+Sensing:+A+Case+Study+of+2022+Pakistan+Floods)|0| -|[Web Information Extraction for Social Good: Food Pantry Answering As an Example](https://doi.org/10.1145/3543507.3583880)|HuanYuan Chen, Hong Yu|University of Massachusetts Lowell, USA; University of Massachusetts Amherst, USA|Social determinants of health (SDH) are the conditions in which we are born, live, work, and age. Food insecurity (FI) is an important domain of SDH. FI is associated with poor health outcomes. Food bank/pantry (food pantry) directly addresses FI. Improving the availability and quality of food from food pantries could reduce FI, leading to improved health outcomes. However, it is difficult for a client to access food pantry information. In this study, we built a food pantry answering framework by combining location-aware information retrieval, web information extraction and domain-specific answering. Our proposed framework first retrieves pantry candidates based on geolocation of the client, and utilizes structural information from markup language to extract semantic chunks related to six common client requests. We use BERT and RoBERTa as information extraction models and compare three different web page segmentation methods in the experiments.|健康的社会决定因素(SDH)是我们出生、生活、工作和年龄的条件。粮食不安全是 SDH 研究的一个重要领域。FI 与不良的健康结果相关。食品银行/食品储藏室(食品储藏室)直接地址为 FI。改善食品储藏室食品的供应和质量可以减少流动性感染，从而改善健康结果。然而，客户很难获得食品储藏室的信息。在这项研究中，我们通过结合位置感知信息检索、网络信息抽取和特定领域的应答，建立了一个食品储藏室应答框架。我们提出的框架首先基于客户端的地理位置检索食品储藏室候选者，然后利用标记语言中的结构信息提取与六个常见客户端请求相关的语义块。我们使用 BERT 和 roBERTa 作为信息抽取模型，并在实验中比较了三种不同的网页分割方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Web+Information+Extraction+for+Social+Good:+Food+Pantry+Answering+As+an+Example)|0| -|[Gender Pay Gap in Sports on a Fan-Request Celebrity Video Site](https://doi.org/10.1145/3543507.3583884)|Nazanin Sabri, Stephen Reysen, Ingmar Weber|Texas A&M University-Commerce, USA; Saarland University, Germany; University of California, San Diego, USA|The internet is often thought of as a democratizer, enabling equality in aspects such as pay, as well as a tool introducing novel communication and monetization opportunities. In this study we examine athletes on Cameo, a website that enables bi-directional fan-celebrity interactions, questioning whether the well-documented gender pay gaps in sports persist in this digital setting. Traditional studies into gender pay gaps in sports are mostly in a centralized setting where an organization decides the pay for the players, while Cameo facilitates grass-roots fan engagement where fans pay for video messages from their preferred athletes. The results showed that even on such a platform gender pay gaps persist, both in terms of cost-per-message, and in the number of requests, proxied by number of ratings. For instance, we find that female athletes have a median pay of 30$ per-video, while the same statistic is 40$ for men. The results also contribute to the study of parasocial relationships and personalized fan engagements over a distance. Something that has become more relevant during the ongoing COVID-19 pandemic, where in-person fan engagement has often been limited.|互联网往往被认为是一个民主化者，使平等的方面，如薪酬，以及工具引入新的沟通和货币化的机会。在这项研究中，我们对 Cameo 网站上的运动员进行了调查，这是一个允许粉丝和名人进行双向互动的网站，我们质疑在这种数字环境下，体育运动中的性别收入差距是否依然存在。关于体育运动中性别薪酬差距的传统研究大多集中在一个集中的环境中，由一个组织决定运动员的薪酬，而 Cameo 促进了基层球迷的参与，球迷为他们喜欢的运动员的视频信息付费。结果表明，即使在这样一个平台上，性别薪酬差距依然存在，无论是从每条信息的成本来看，还是从请求数量来看，都是以评级数量为代表的。例如，我们发现女运动员的平均收入是每段视频30美元，而男运动员的平均收入是40美元。这些结果也有助于研究准社会关系和个性化的球迷参与的距离。在当前的2019冠状病毒疾病流行期间，这一点变得更为重要，因为粉丝的亲自参与往往受到限制。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Gender+Pay+Gap+in+Sports+on+a+Fan-Request+Celebrity+Video+Site)|0| -|[Leveraging Existing Literature on the Web and Deep Neural Models to Build a Knowledge Graph Focused on Water Quality and Health Risks](https://doi.org/10.1145/3543507.3584185)|Nikita Gautam, David Shumway, Megan Kowalcyk, Sarthak Khanal, Doina Caragea, Cornelia Caragea, Hande Mcginty, Samuel Dorevitch|Computer Science, Kansas State University, USA; Public Health, University of Illinois Chicago, USA; Computer Science, University of Illinois Chicago, USA|A knowledge graph focusing on water quality in relation to health risks posed by water activities (such as diving or swimming) is not currently available. To address this limitation, we first use existing resources to construct a knowledge graph relevant to water quality and health risks using KNowledge Acquisition and Representation Methodology (KNARM). Subsequently, we explore knowledge graph completion approaches for maintaining and updating the graph. Specifically, we manually identify a set of domain-specific UMLS concepts and use them to extract a graph of approximately 75,000 semantic triples from the Semantic MEDLINE database (which contains head-relation-tail triples extracted from PubMed). Using the resulting knowledge graph, we experiment with the KG-BERT approach for graph completion by employing pre-trained BERT/RoBERTa models and also models fine-tuned on a collection of water quality and health risks abstracts retrieved from the Web of Science. Experimental results show that KG-BERT with BERT/RoBERTa models fine-tuned on a domain-specific corpus improves the performance of KG-BERT with pre-trained models. Furthermore, KG-BERT gives better results than several translational distance or semantic matching baseline models.|目前还没有一个关于水质与水活动(如潜水或游泳)造成的健康风险相关的知识图表。为了解决这一局限性，我们首先利用现有的资源，使用知识获取和表示方法(KNARM)构建了一个与水质和健康风险相关的知识图。随后，我们探讨了维护和更新知识图的知识图完成方法。具体而言，我们手动识别一组特定于领域的 UMLS 概念，并使用它们从 Semantic MEDLINE 数据库(其中包含从 PubMed 提取的头-关系-尾三元组)提取约75,000个语义三元组的图。使用得到的知识图表，我们通过使用预先训练的 BERT/RoBERTa 模型来实验用 KG-BERT 方法完成图表，并且还对从 Web of Science 检索到的一组水质和健康风险摘要进行微调。实验结果表明，带有 BERT/RoBERTa 模型的 KG-BERT 算法在特定领域语料库上进行了微调，提高了带有预训练模型的 KG-BERT 算法的性能。此外，KG-BERT 模型比多个平移距离或语义匹配基线模型得到了更好的结果。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Leveraging+Existing+Literature+on+the+Web+and+Deep+Neural+Models+to+Build+a+Knowledge+Graph+Focused+on+Water+Quality+and+Health+Risks)|0| +|[MSQ-BioBERT: Ambiguity Resolution to Enhance BioBERT Medical Question-Answering](https://doi.org/10.1145/3543507.3583878)|Muzhe Guo, Muhao Guo, Edward T. Dougherty, Fang Jin|Arizona State University, USA; Roger Williams University, USA; George Washington University, USA|Question answering (QA) is a task in the field of natural language processing (NLP) and information retrieval, which has pivotal applications in areas such as online reading comprehension and web search engines. Currently, Bidirectional Encoder Representations from Transformers (BERT) and its biomedical variation (BioBERT) achieve impressive results on the reading comprehension QA datasets and medical-related QA datasets, and so they are widely used for a variety of passage-based QA tasks. However, their performances rapidly deteriorate when encountering passage and context ambiguities. This issue is prevalent and unavoidable in many fields, notably the web-based medical field. In this paper, we introduced a novel approach called the Multiple Synonymous Questions BioBERT (MSQ-BioBERT), which integrates question augmentation, rather than the typical single question used by traditional BioBERT, to elevate BioBERT’s performance on medical QA tasks. In addition, we constructed an ambiguous medical dataset based on the information from Wikipedia web. Experiments with both this web-based constructed medical dataset and open biomedical datasets demonstrate the significant performance gains of the MSQ-BioBERT approach, showcasing a new method for addressing ambiguity in medical QA tasks.|问答是自然语言处理和信息检索领域的一项任务，在在线阅读理解和网络搜索引擎等领域有着关键的应用。目前，变压器双向编码器表示(BERT)及其生物医学变异(BioBERT)在阅读理解质量保证数据集和医学相关质量保证数据集上取得了令人印象深刻的结果，因此它们被广泛用于各种基于通道的质量保证任务。然而，当遇到短文和上下文歧义时，它们的表现会迅速恶化。这个问题在许多领域都是普遍的和不可避免的，特别是在基于网络的医学领域。为了提高 BioBERT 在医学 QA 任务中的表现，本文引入了一种新的方法——多同义问题 BioBERT (MSQ-BioBERT) ，该方法集成了问题增强，而不是传统 BioBERT 使用的典型的单个问题。此外，我们基于维基百科网站的信息构建了一个模糊的医学数据集。基于网络构建的医学数据集和开放式生物医学数据集的实验表明，MSQ-BioBERT 方法具有显著的性能增益，为解决医学 QA 任务中的模糊性提供了一种新的方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=MSQ-BioBERT:+Ambiguity+Resolution+to+Enhance+BioBERT+Medical+Question-Answering)|0| +|[Graph-based Village Level Poverty Identification](https://doi.org/10.1145/3543507.3583864)|Jing Ma, Liangwei Yang, Qiong Feng, Weizhi Zhang, Philip S. Yu|University of Electronic Science and Technology of China, China; Southwest Minzu University, China; University of Illinois Chicago, USA|Poverty status identification is the first obstacle to eradicating poverty. Village-level poverty identification is very challenging due to the arduous field investigation and insufficient information. The development of the Web infrastructure and its modeling tools provides fresh approaches to identifying poor villages. Upon those techniques, we build a village graph for village poverty status identification. By modeling the village connections as a graph through the geographic distance, we show the correlation between village poverty status and its graph topological position and identify two key factors (Centrality, Homophily Decaying effect) for identifying villages. We further propose the first graph-based method to identify poor villages. It includes a global Centrality2Vec module to embed village centrality into the dense vector and a local graph distance convolution module that captures the decaying effect. In this paper, we make the first attempt to interpret and identify village-level poverty from a graph perspective.|确定贫困状况是消除贫困的第一个障碍。由于实地调查工作的艰巨性和信息的不足，村级贫困的识别具有很大的挑战性。Web 基础设施及其建模工具的开发为识别贫困村庄提供了新的方法。基于这些技术，我们构建了一个村庄图，用于村庄贫困状况的识别。通过对村庄连接的地理距离建模，揭示了村庄贫困状况与其图形拓扑位置之间的相关关系，并确定了识别村庄的两个关键因素(中心性、同质性衰减效应)。进一步提出了第一种基于图论的贫困村识别方法。它包括一个全局 Centrality2Vec 模块，将村庄中心性嵌入到密集向量中，以及一个捕捉衰减效应的局部图距离卷积模块。本文首次尝试从图形的角度对村级贫困进行解释和识别。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Graph-based+Village+Level+Poverty+Identification)|0| +|[Mapping Flood Exposure, Damage, and Population Needs Using Remote and Social Sensing: A Case Study of 2022 Pakistan Floods](https://doi.org/10.1145/3543507.3583881)|Zainab Akhtar, Umair Qazi, Rizwan Sadiq, Aya ElSakka, Muhammad Sajjad, Ferda Ofli, Muhammad Imran|Department of Geography and Centre for Geocomputation Studies, Hong Kong Baptist University, Hong Kong; Qatar Computing Research Institute, Qatar|The devastating 2022 floods in Pakistan resulted in a catastrophe impacting millions of people and destroying thousands of homes. While disaster management efforts were taken, crisis responders struggled to understand the country-wide flood extent, population exposure, urgent needs of affected people, and various types of damage. To tackle this challenge, we leverage remote and social sensing with geospatial data using state-of-the-art machine learning techniques for text and image processing. Our satellite-based analysis over a one-month period (25 Aug–25 Sep) revealed that 11.48% of Pakistan was inundated. When combined with geospatial data, this meant 18.9 million people were at risk across 160 districts in Pakistan, with adults constituting 50% of the exposed population. Our social sensing data analysis surfaced 106.7k reports pertaining to deaths, injuries, and concerns of the affected people. To understand the urgent needs of the affected population, we analyzed tweet texts and found that South Karachi, Chitral and North Waziristan required the most basic necessities like food and shelter. Further analysis of tweet images revealed that Lasbela, Rajanpur, and Jhal Magsi had the highest damage reports normalized by their population. These damage reports were found to correlate strongly with affected people reports and need reports, achieving an R-Square of 0.96 and 0.94, respectively. Our extensive study shows that combining remote sensing, social sensing, and geospatial data can provide accurate and timely information during a disaster event, which is crucial in prioritizing areas for immediate and gradual response.|巴基斯坦2022年毁灭性的洪水造成了一场灾难，影响到数百万人，摧毁了数千所房屋。在灾害管理工作的同时，应对危机的人员努力了解全国范围的洪水范围、人口暴露、受灾人民的紧急需求以及各种类型的损失。为了应对这一挑战，我们利用地理空间数据进行遥感和社会感应，使用最先进的机器学习技术进行文本和图像处理。我们在一个月期间(8月25日至9月25日)的卫星分析显示，巴基斯坦11.48% 的土地被淹没。结合地理空间数据，这意味着巴基斯坦160个地区的1890万人处于危险之中，成年人占暴露人口的50% 。我们的社会传感数据分析显示，有106.7万份报告与受影响人群的死亡、受伤和担忧有关。为了了解受灾人口的迫切需求，我们分析了推特短信，发现南卡拉奇、 Chitral 和北瓦济里斯坦特区需要食物和住所等最基本的必需品。对推特图片的进一步分析显示，Lasbela、 Rajanpur 和贾尔•马格西(Jhal Magsi)的人口正常化损失报告最高。这些损害报告被发现与受影响的人的报告和需要报告密切相关，分别达到0.96和0.94的 R 平方。我们广泛的研究表明，结合遥感、社会感应和地理空间数据可以在灾害事件期间提供准确和及时的信息，这对于确定立即和逐步响应的优先领域至关重要。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Mapping+Flood+Exposure,+Damage,+and+Population+Needs+Using+Remote+and+Social+Sensing:+A+Case+Study+of+2022+Pakistan+Floods)|0| +|[Web Information Extraction for Social Good: Food Pantry Answering As an Example](https://doi.org/10.1145/3543507.3583880)|HuanYuan Chen, Hong Yu|University of Massachusetts Amherst, USA; University of Massachusetts Lowell, USA|Social determinants of health (SDH) are the conditions in which we are born, live, work, and age. Food insecurity (FI) is an important domain of SDH. FI is associated with poor health outcomes. Food bank/pantry (food pantry) directly addresses FI. Improving the availability and quality of food from food pantries could reduce FI, leading to improved health outcomes. However, it is difficult for a client to access food pantry information. In this study, we built a food pantry answering framework by combining location-aware information retrieval, web information extraction and domain-specific answering. Our proposed framework first retrieves pantry candidates based on geolocation of the client, and utilizes structural information from markup language to extract semantic chunks related to six common client requests. We use BERT and RoBERTa as information extraction models and compare three different web page segmentation methods in the experiments.|健康的社会决定因素(SDH)是我们出生、生活、工作和年龄的条件。粮食不安全是 SDH 研究的一个重要领域。FI 与不良的健康结果相关。食品银行/食品储藏室(食品储藏室)直接地址为 FI。改善食品储藏室食品的供应和质量可以减少流动性感染，从而改善健康结果。然而，客户很难获得食品储藏室的信息。在这项研究中，我们通过结合位置感知信息检索、网络信息抽取和特定领域的应答，建立了一个食品储藏室应答框架。我们提出的框架首先基于客户端的地理位置检索食品储藏室候选者，然后利用标记语言中的结构信息提取与六个常见客户端请求相关的语义块。我们使用 BERT 和 roBERTa 作为信息抽取模型，并在实验中比较了三种不同的网页分割方法。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Web+Information+Extraction+for+Social+Good:+Food+Pantry+Answering+As+an+Example)|0| +|[Gender Pay Gap in Sports on a Fan-Request Celebrity Video Site](https://doi.org/10.1145/3543507.3583884)|Nazanin Sabri, Stephen Reysen, Ingmar Weber|University of California, San Diego, USA; Texas A&M University-Commerce, USA; Saarland University, Germany|The internet is often thought of as a democratizer, enabling equality in aspects such as pay, as well as a tool introducing novel communication and monetization opportunities. In this study we examine athletes on Cameo, a website that enables bi-directional fan-celebrity interactions, questioning whether the well-documented gender pay gaps in sports persist in this digital setting. Traditional studies into gender pay gaps in sports are mostly in a centralized setting where an organization decides the pay for the players, while Cameo facilitates grass-roots fan engagement where fans pay for video messages from their preferred athletes. The results showed that even on such a platform gender pay gaps persist, both in terms of cost-per-message, and in the number of requests, proxied by number of ratings. For instance, we find that female athletes have a median pay of 30$ per-video, while the same statistic is 40$ for men. The results also contribute to the study of parasocial relationships and personalized fan engagements over a distance. Something that has become more relevant during the ongoing COVID-19 pandemic, where in-person fan engagement has often been limited.|互联网往往被认为是一个民主化者，使平等的方面，如薪酬，以及工具引入新的沟通和货币化的机会。在这项研究中，我们对 Cameo 网站上的运动员进行了调查，这是一个允许粉丝和名人进行双向互动的网站，我们质疑在这种数字环境下，体育运动中的性别收入差距是否依然存在。关于体育运动中性别薪酬差距的传统研究大多集中在一个集中的环境中，由一个组织决定运动员的薪酬，而 Cameo 促进了基层球迷的参与，球迷为他们喜欢的运动员的视频信息付费。结果表明，即使在这样一个平台上，性别薪酬差距依然存在，无论是从每条信息的成本来看，还是从请求数量来看，都是以评级数量为代表的。例如，我们发现女运动员的平均收入是每段视频30美元，而男运动员的平均收入是40美元。这些结果也有助于研究准社会关系和个性化的球迷参与的距离。在当前的2019冠状病毒疾病流行期间，这一点变得更为重要，因为粉丝的亲自参与往往受到限制。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Gender+Pay+Gap+in+Sports+on+a+Fan-Request+Celebrity+Video+Site)|0| +|[Leveraging Existing Literature on the Web and Deep Neural Models to Build a Knowledge Graph Focused on Water Quality and Health Risks](https://doi.org/10.1145/3543507.3584185)|Nikita Gautam, David Shumway, Megan Kowalcyk, Sarthak Khanal, Doina Caragea, Cornelia Caragea, Hande Mcginty, Samuel Dorevitch|Public Health, University of Illinois Chicago, USA; Computer Science, University of Illinois Chicago, USA; Computer Science, Kansas State University, USA|A knowledge graph focusing on water quality in relation to health risks posed by water activities (such as diving or swimming) is not currently available. To address this limitation, we first use existing resources to construct a knowledge graph relevant to water quality and health risks using KNowledge Acquisition and Representation Methodology (KNARM). Subsequently, we explore knowledge graph completion approaches for maintaining and updating the graph. Specifically, we manually identify a set of domain-specific UMLS concepts and use them to extract a graph of approximately 75,000 semantic triples from the Semantic MEDLINE database (which contains head-relation-tail triples extracted from PubMed). Using the resulting knowledge graph, we experiment with the KG-BERT approach for graph completion by employing pre-trained BERT/RoBERTa models and also models fine-tuned on a collection of water quality and health risks abstracts retrieved from the Web of Science. Experimental results show that KG-BERT with BERT/RoBERTa models fine-tuned on a domain-specific corpus improves the performance of KG-BERT with pre-trained models. Furthermore, KG-BERT gives better results than several translational distance or semantic matching baseline models.|目前还没有一个关于水质与水活动(如潜水或游泳)造成的健康风险相关的知识图表。为了解决这一局限性，我们首先利用现有的资源，使用知识获取和表示方法(KNARM)构建了一个与水质和健康风险相关的知识图。随后，我们探讨了维护和更新知识图的知识图完成方法。具体而言，我们手动识别一组特定于领域的 UMLS 概念，并使用它们从 Semantic MEDLINE 数据库(其中包含从 PubMed 提取的头-关系-尾三元组)提取约75,000个语义三元组的图。使用得到的知识图表，我们通过使用预先训练的 BERT/RoBERTa 模型来实验用 KG-BERT 方法完成图表，并且还对从 Web of Science 检索到的一组水质和健康风险摘要进行微调。实验结果表明，带有 BERT/RoBERTa 模型的 KG-BERT 算法在特定领域语料库上进行了微调，提高了带有预训练模型的 KG-BERT 算法的性能。此外，KG-BERT 模型比多个平移距离或语义匹配基线模型得到了更好的结果。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Leveraging+Existing+Literature+on+the+Web+and+Deep+Neural+Models+to+Build+a+Knowledge+Graph+Focused+on+Water+Quality+and+Health+Risks)|0| |[Believability and Harmfulness Shape the Virality of Misleading Social Media Posts](https://doi.org/10.1145/3543507.3583857)|Chiara Patricia Drolsbach, Nicolas Pröllochs||Misinformation on social media presents a major threat to modern societies. While previous research has analyzed the virality across true and false social media posts, not every misleading post is necessarily equally viral. Rather, misinformation has different characteristics and varies in terms of its believability and harmfulness - which might influence its spread. In this work, we study how the perceived believability and harmfulness of misleading posts are associated with their virality on social media. Specifically, we analyze (and validate) a large sample of crowd-annotated social media posts from Twitter's Birdwatch platform, on which users can rate the believability and harmfulness of misleading tweets. To address our research questions, we implement an explanatory regression model and link the crowd ratings for believability and harmfulness to the virality of misleading posts on Twitter. Our findings imply that misinformation that is (i) easily believable and (ii) not particularly harmful is associated with more viral resharing cascades. These results offer insights into how different kinds of crowd fact-checked misinformation spreads and suggest that the most viral misleading posts are often not the ones that are particularly concerning from the perspective of public safety. From a practical view, our findings may help platforms to develop more effective strategies to curb the proliferation of misleading posts on social media.|社交媒体上的错误信息对现代社会构成重大威胁。虽然之前的研究已经分析了真假社交媒体帖子的病毒性，但并不是每一个误导性帖子都一定是病毒性的。相反，错误信息具有不同的特征，在可信度和危害性方面各不相同，这可能会影响其传播。在这项工作中，我们研究了误导性帖子的可信度和危害性是如何与它们在社交媒体上的病毒性相关联的。具体来说，我们分析(并验证)来自 Twitter 的 Birdwatch 平台的大量带有群体注释的社交媒体帖子样本，用户可以在这些样本上评估误导性推文的可信度和危害性。为了解决我们的研究问题，我们实施了一个解释性回归模型，并将可信度和危害性的群体评分与 Twitter 上误导性帖子的病毒性联系起来。我们的研究结果暗示，(i)容易相信和(ii)不是特别有害的错误信息与更多的病毒重新共享级联有关。这些结果提供了不同类型的群众事实核查错误信息如何传播的见解，并表明，病毒性最大的误导性帖子往往不是那些从公共安全的角度特别关注。从实际角度来看，我们的研究结果可能有助于平台制定更有效的策略，以遏制社交媒体上误导性帖子的泛滥。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Believability+and+Harmfulness+Shape+the+Virality+of+Misleading+Social+Media+Posts)|0| -|[Enhancing Deep Knowledge Tracing with Auxiliary Tasks](https://doi.org/10.1145/3543507.3583866)|Zitao Liu, Qiongqiong Liu, Jiahao Chen, Shuyan Huang, Boyu Gao, Weiqi Luo, Jian Weng|College of Information Science and Technology, Jinan University, China; TAL Education Group, China; Guangdong Institute of Smart Education, Jinan University, China|Knowledge tracing (KT) is the problem of predicting students' future performance based on their historical interactions with intelligent tutoring systems. Recent studies have applied multiple types of deep neural networks to solve the KT problem. However, there are two important factors in real-world educational data that are not well represented. First, most existing works augment input representations with the co-occurrence matrix of questions and knowledge components\footnote{\label{ft:kc}A KC is a generalization of everyday terms like concept, principle, fact, or skill.} (KCs) but fail to explicitly integrate such intrinsic relations into the final response prediction task. Second, the individualized historical performance of students has not been well captured. In this paper, we proposed \emph{AT-DKT} to improve the prediction performance of the original deep knowledge tracing model with two auxiliary learning tasks, i.e., \emph{question tagging (QT) prediction task} and \emph{individualized prior knowledge (IK) prediction task}. Specifically, the QT task helps learn better question representations by predicting whether questions contain specific KCs. The IK task captures students' global historical performance by progressively predicting student-level prior knowledge that is hidden in students' historical learning interactions. We conduct comprehensive experiments on three real-world educational datasets and compare the proposed approach to both deep sequential KT models and non-sequential models. Experimental results show that \emph{AT-DKT} outperforms all sequential models with more than 0.9\% improvements of AUC for all datasets, and is almost the second best compared to non-sequential models. Furthermore, we conduct both ablation studies and quantitative analysis to show the effectiveness of auxiliary tasks and the superior prediction outcomes of \emph{AT-DKT}.|知识追踪(KT)是根据学生与智能辅导系统的历史交互关系来预测学生未来表现的问题。最近的研究已经应用了多种类型的深层神经网络来解决 KT 问题。然而，在现实世界的教育数据中有两个重要因素没有得到很好的表示。首先，现有的大多数作品都使用问题和知识组件的共现矩阵脚注{ label { ft: KC }来扩充输入表示。 KC 是概念、原则、事实或技能等日常术语的概括。}但未能将这种内在关系明确整合到最终反应预测任务中。第二，学生的个性化历史表现没有得到很好的捕捉。为了提高原有深度知识追踪模型的预测性能，本文提出了两个辅助学习任务: 重点{问题标注(QT)预测任务}和重点{个性化先验知识(IK)预测任务}。具体来说，QT 任务通过预测问题是否包含特定的关键字来帮助学习更好的问题表征。本课题通过逐步预测隐藏在学生历史学习互动中的学生层次的先验知识，捕捉学生的全球历史表现。我们在三个真实世界的教育数据集上进行了全面的实验，并将所提出的方法与深度序列 KT 模型和非序列模型进行了比较。实验结果表明，方差{ AT-DKT }优于所有序列模型，所有数据集的 AUC 提高了0.9% 以上，与非序列模型相比几乎是第二好的。此外，我们进行了消融研究和定量分析，以显示辅助任务的有效性和优越的预测结果的方法{ AT-DKT }。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Enhancing+Deep+Knowledge+Tracing+with+Auxiliary+Tasks)|0| +|[Enhancing Deep Knowledge Tracing with Auxiliary Tasks](https://doi.org/10.1145/3543507.3583866)|Zitao Liu, Qiongqiong Liu, Jiahao Chen, Shuyan Huang, Boyu Gao, Weiqi Luo, Jian Weng|TAL Education Group, China; Guangdong Institute of Smart Education, Jinan University, China; College of Information Science and Technology, Jinan University, China|Knowledge tracing (KT) is the problem of predicting students' future performance based on their historical interactions with intelligent tutoring systems. Recent studies have applied multiple types of deep neural networks to solve the KT problem. However, there are two important factors in real-world educational data that are not well represented. First, most existing works augment input representations with the co-occurrence matrix of questions and knowledge components\footnote{\label{ft:kc}A KC is a generalization of everyday terms like concept, principle, fact, or skill.} (KCs) but fail to explicitly integrate such intrinsic relations into the final response prediction task. Second, the individualized historical performance of students has not been well captured. In this paper, we proposed \emph{AT-DKT} to improve the prediction performance of the original deep knowledge tracing model with two auxiliary learning tasks, i.e., \emph{question tagging (QT) prediction task} and \emph{individualized prior knowledge (IK) prediction task}. Specifically, the QT task helps learn better question representations by predicting whether questions contain specific KCs. The IK task captures students' global historical performance by progressively predicting student-level prior knowledge that is hidden in students' historical learning interactions. We conduct comprehensive experiments on three real-world educational datasets and compare the proposed approach to both deep sequential KT models and non-sequential models. Experimental results show that \emph{AT-DKT} outperforms all sequential models with more than 0.9\% improvements of AUC for all datasets, and is almost the second best compared to non-sequential models. Furthermore, we conduct both ablation studies and quantitative analysis to show the effectiveness of auxiliary tasks and the superior prediction outcomes of \emph{AT-DKT}.|知识追踪(KT)是根据学生与智能辅导系统的历史交互关系来预测学生未来表现的问题。最近的研究已经应用了多种类型的深层神经网络来解决 KT 问题。然而，在现实世界的教育数据中有两个重要因素没有得到很好的表示。首先，现有的大多数作品都使用问题和知识组件的共现矩阵脚注{ label { ft: KC }来扩充输入表示。 KC 是概念、原则、事实或技能等日常术语的概括。}但未能将这种内在关系明确整合到最终反应预测任务中。第二，学生的个性化历史表现没有得到很好的捕捉。为了提高原有深度知识追踪模型的预测性能，本文提出了两个辅助学习任务: 重点{问题标注(QT)预测任务}和重点{个性化先验知识(IK)预测任务}。具体来说，QT 任务通过预测问题是否包含特定的关键字来帮助学习更好的问题表征。本课题通过逐步预测隐藏在学生历史学习互动中的学生层次的先验知识，捕捉学生的全球历史表现。我们在三个真实世界的教育数据集上进行了全面的实验，并将所提出的方法与深度序列 KT 模型和非序列模型进行了比较。实验结果表明，方差{ AT-DKT }优于所有序列模型，所有数据集的 AUC 提高了0.9% 以上，与非序列模型相比几乎是第二好的。此外，我们进行了消融研究和定量分析，以显示辅助任务的有效性和优越的预测结果的方法{ AT-DKT }。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Enhancing+Deep+Knowledge+Tracing+with+Auxiliary+Tasks)|0| |[Learning to Simulate Crowd Trajectories with Graph Networks](https://doi.org/10.1145/3543507.3583858)|Hongzhi Shi, Quanming Yao, Yong Li|Tsinghua University, China|Crowd stampede disasters often occur, such as recent ones in Indonesia and South Korea, and crowd simulation is particularly important to prevent and avoid such disasters. Most traditional models for crowd simulation, such as the social force model, are hand-designed formulas, which use Newtonian forces to model the interactions between pedestrians. However, such formula-based methods may not be flexible enough to capture the complex interaction patterns in diverse crowd scenarios. Recently, due to the development of the Internet, a large amount of pedestrian movement data has been collected, allowing us to study crowd simulation in a data-driven way. Inspired by the recent success of graph network-based simulation (GNS), we propose a novel method under the framework of GNS, which simulates the crowd in a data-driven way. Specifically, we propose to model the interactions among people and the environment using a heterogeneous graph. Then, we design a heterogeneous gated message-passing network to learn the interaction pattern that depends on the visual field. Finally, the randomness is introduced by modeling the context’s different influences on pedestrians with a probabilistic emission function. Extensive experiments on synthetic data, controlled-environment data and real-world data are performed. Extensive results show that our model can generally capture the three main factors which contribute to crowd trajectories while adapting to the data characteristics beyond the strong assumption of formulas-based methods. As a result, the proposed method outperforms existing methods by a large margin.|人群踩踏灾害经常发生，如最近在印度尼西亚和韩国发生的踩踏事故，人群模拟对于预防和避免这类灾害尤为重要。传统的人群模拟模型，如社会力量模型，大多是手工设计的公式，使用牛顿力来模拟行人之间的相互作用。然而，这种基于公式的方法可能不够灵活，无法在不同的人群场景中捕获复杂的交互模式。近年来，由于互联网的发展，大量的行人运动数据被收集，使我们能够以数据驱动的方式研究人群模拟。受近年来基于图形网络仿真(GNS)的成功启发，本文提出了一种在 GNS 框架下以数据驱动的方式模拟人群的新方法。具体来说，我们建议使用一个异构图来模拟人与环境之间的相互作用。然后，我们设计了一个异构的门限消息传递网络来学习依赖于视场的交互模式。最后，利用概率发射函数模拟环境对行人的不同影响，引入随机性。对合成数据、受控环境数据和真实世界数据进行了广泛的实验。大量的实验结果表明，除了基于公式的方法的强假设外，我们的模型能够在适应数据特征的同时，普遍地捕捉到影响人群轨迹的三个主要因素。因此，提出的方法比现有的方法有很大的优势。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Learning+to+Simulate+Crowd+Trajectories+with+Graph+Networks)|0| diff --git a/papers/www/www2024.md b/papers/www/www2024.md index 52f8a780..dd8d3510 100644 --- a/papers/www/www2024.md +++ b/papers/www/www2024.md @@ -192,7 +192,7 @@ |[Enabling Pre-Shock State Detection using Electrogram Signals from Implantable Cardioverter-Defibrillators](https://doi.org/10.1145/3589335.3651450)|Runze Yan, Neal K. Bhatia, Faisal M. Merchant, Alex Fedorov, Ran Xiao, Cheng Ding, Xiao Hu||Identifying electrical signatures preceding a ventricular arrhythmia from the implantable cardioverter-defibrillators (ICDs) can help predict an upcoming ICD shock. To achieve this, we first deployed a large-scale study (N=326) to continuously monitor the electrogram (EGM) data from the ICDs and select the EGM segments prior to a shock event and under the normal condition. Next, we design a novel cohesive framework that integrates metric learning, prototype learning, and few-shot learning, enabling learning from an imbalanced dataset. We implement metric learning by leveraging a Siamese neural network architecture, which incorporates LSTM units. We innovatively utilize triplet and pair losses in a sequential manner throughout the training process on EGM samples. This approach generates embeddings that significantly enhance the distinction of EGM signals under different conditions. In the inference stage, k-means clustering identifies prototypes representing pre-shock and normal states from these embeddings. In summary, this framework leverages the predictive potential of signals before ICD shocks, addressing the gap in early cardiac arrhythmia detection. Our experimental results show a notable F1 score of 0.87, sensitivity of 0.97, and precision of 0.79. Our framework offers a significant advancement in cardiac care predictive analytics, promising enhanced ICD decision-making for improved patient outcomes.|从植入式心脏复律除颤器(ICD)中识别室性心律失常前的电特征可以帮助预测即将到来的 ICD 休克。为了实现这一点，我们首先部署了一项大规模的研究(N = 326) ，以连续监测来自 ICD 的电图(EGM)数据，并在休克事件之前和正常情况下选择 EGM 片段。接下来，我们设计了一个新的内聚框架，集成度量学习，原型学习和少镜头学习，使学习从一个不平衡的数据集。我们利用结合了 LSTM 单元的暹罗神经网络结构来实现度量学习。在 EGM 样本的训练过程中，我们创新性地以连续的方式利用了三元组和配对损失。这种方法产生的嵌入显着增强了区分 EGM 信号在不同的条件下。在推理阶段，K平均算法从这些嵌入中识别代表预休克和正常状态的原型。总之，这个框架利用了 ICD 冲击前信号的预测潜力，弥补了早期心律不整检测的差距。实验结果表明，F1得分为0.87，灵敏度为0.97，精密度为0.79。我们的框架在心脏护理预测分析方面取得了重大进展，有望增强 ICD 决策，改善患者预后。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Enabling+Pre-Shock+State+Detection+using+Electrogram+Signals+from+Implantable+Cardioverter-Defibrillators)|0| |[In The Beginning, Let There Be The Word: Challenges and Insights in Applying Sentiment Analysis to Social Research](https://doi.org/10.1145/3589335.3651264)|Andrzej Meler||Sentiment analysis based on lexical corpora is widely employed despite its inherent limitations in capturing nuances such as sarcasm and irony. This research delves into the application of sentiment analysis to political communication. To address the limitations of the Bag of Words methodology, a comparative study of sentiment analysis tools and emotion detection from speech is conducted, using automated speech recognition as a benchmark. Emotion recognition from speech has shown promising results, indicating its potential superiority over other methods [1], [2]. This study uses media material from Polish radio and television broadcasts, focusing on political interviews during a significant period marked by a high-profile assassination attempt. Results indicate challenges at the micro-level, but aggregated data reveals a significant correlation between valence measured from voice and text. While sentiment analysis may lack sensitivity in capturing mourning-related discourse, it proves effective in political communication devoid of such nuances. This suggests that valence in sentiment analysis reflects emotional content derived from intonation fairly accurately.|尽管基于词汇语料库的情感分析在捕捉讽刺和反讽等细微差别方面存在固有的局限性，但它仍然得到了广泛的应用。本研究探讨情绪分析在政治传播中的应用。针对“词袋”方法的局限性，以自动语音识别为基准，对情感分析工具和语音情感检测工具进行了比较研究。来自语音的情感识别已经显示出有希望的结果，表明其潜在的优势比其他方法[1] ，[2]。这项研究使用了来自波兰广播和电视广播的媒体材料，重点是在一个引人注目的企图谋杀的重要时期的政治采访。结果表明在微观层面上存在挑战，但汇总数据显示从语音和文本测量的价值之间存在显著的相关性。虽然情绪分析在捕捉与哀悼有关的话语时可能缺乏敏感性，但它在没有这种细微差别的政治交流中被证明是有效的。这表明情感分析中的配价能够相当准确地反映由语调产生的情感内容。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=In+The+Beginning,+Let+There+Be+The+Word:+Challenges+and+Insights+in+Applying+Sentiment+Analysis+to+Social+Research)|0| |[Modeling Multidimensional Cognitive Search in Creativity with Generalized Additive Model](https://doi.org/10.1145/3589335.3651267)|Jia Lin Cheoh||Creativity is the ability to develop innovative functional ideas through unconventional associations. The consensus view on creativity in the literature involves divergence from stereotypical and habitual thought patterns [29, 39]. Creativity relies on search to explore diverse solutions. Search requires charting the mental terrain, leveraging past experiences and knowledge to manipulate and reconfigure components for new solutions [28]. The generally-accepted and overly-narrow view on creativity, however, neglects the fact that creativity is multidimensional [14]. This one-dimensional view of creativity triggers questions such as "Does one consider an unethical but novel creation to be creative?" and "Does one consider a new iPhone with mainstream functionalities but advanced camera features to be creative?" This research challenges the one-dimensional view of creativity, offering a more all-encompassing conceptualization of creativity [14]. The research examines the multidimensional nature of creativity by building a computational model of a designer's mutual search process across multiple mutually dependent search spaces. The research examines the trajectory of mutual search across multiple cognitive search spaces using a Generalized Additive Model (GAM). The field experiment employs 108 designers who develop their web designs through five iterations, utilizing computer graphics methods to extract the images. Through measuring the distance of search by considering changes in visual and source code in each iteration, the study argues that the search patterns differ in the degree of exploration in these search spaces over time. The research concludes that designers' search processes are non-linear and argues that there are more than one or two search spaces. The research also provides perceptual explanations of the multiple search processes in designs and argues for a more encompassing view of creativity.|创造力是通过非传统的联想来发展创新的功能性想法的能力。文献中关于创造力的一致观点涉及到从陈规定型和习惯性思维模式的分歧[29,39]。创造力依赖于探索不同的解决方案。搜索需要绘制心理地形图，利用过去的经验和知识来操纵和重新配置新的解决方案的组件[28]。然而，普遍接受和过于狭隘的创造力观点忽视了这样一个事实，即创造力是多维的[14]。这种关于创造力的一维观点引发了诸如“一个人是否认为一个不道德但新颖的创造是创造性的?”以及“是否有人认为一款拥有主流功能但高级摄像功能的新 iPhone 具有创造性?”这项研究挑战了创造力的一维观点，提供了一个更全面的创造力概念化[14]。该研究通过建立一个设计师在多个相互依赖的搜索空间中相互搜索过程的计算模型，来检验创造力的多维性。本研究使用广义加法模型(GAM)检验了跨多个认知搜索空间的相互搜索轨迹。现场实验雇佣了108名设计师，他们通过五次迭代开发他们的网页设计，利用计算机图形学方法提取图像。通过考虑每次迭代中视觉和源代码的变化来测量搜索距离，研究认为，随着时间的推移，这些搜索空间中的搜索模式在程度上是不同的。研究得出的结论是，设计师的搜索过程是非线性的，并认为有一个或两个以上的搜索空间。该研究还提供了对设计中的多重搜索过程的感性解释，并提出了一个更全面的创造力观点。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Modeling+Multidimensional+Cognitive+Search+in+Creativity+with+Generalized+Additive+Model)|0| -|[Data Augmentation for Conversational AI](https://doi.org/10.1145/3589335.3641238)|Heydar Soudani, Roxana Petcu, Evangelos Kanoulas, Faegheh Hasibi|Radboud Univ Nijmegen, Nijmegen, Netherlands; Univ Amsterdam, Amsterdam, Netherlands|Advancements in conversational systems have revolutionized information access, surpassing the limitations of single queries. However, developing dialogue systems requires a large amount of training data, which is a challenge in low-resource domains and languages. Traditional data collection methods like crowd-sourcing are labor-intensive and time-consuming, making them ineffective in this context. Data augmentation (DA) is an affective approach to alleviate the data scarcity problem in conversational systems. This tutorial provides a comprehensive and up-to-date overview of DA approaches in the context of conversational systems. It highlights recent advances in conversation augmentation, open domain and task-oriented conversation generation, and different paradigms of evaluating these models. We also discuss current challenges and future directions in order to help researchers and practitioners to further advance the field in this area.|会话系统的进步彻底改变了信息访问，超越了单个查询的局限性。然而，开发对话系统需要大量的训练数据，这在资源不足的领域和语言中是一个挑战。传统的数据收集方法，如众包，是劳动密集型和耗时的，使他们在这种情况下无效。数据增强是解决会话系统中数据稀缺问题的有效途径。本教程提供了在会话系统上下文中 DA 方法的全面和最新概述。它强调了会话增强，开放领域和面向任务的会话生成的最新进展，以及评估这些模型的不同范式。我们还讨论了当前的挑战和未来的方向，以帮助研究人员和从业人员进一步推进该领域。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Data+Augmentation+for+Conversational+AI)|0| +|[Data Augmentation for Conversational AI](https://doi.org/10.1145/3589335.3641238)|Heydar Soudani, Roxana Petcu, Evangelos Kanoulas, Faegheh Hasibi|Univ Amsterdam, Amsterdam, Netherlands; Radboud Univ Nijmegen, Nijmegen, Netherlands|Advancements in conversational systems have revolutionized information access, surpassing the limitations of single queries. However, developing dialogue systems requires a large amount of training data, which is a challenge in low-resource domains and languages. Traditional data collection methods like crowd-sourcing are labor-intensive and time-consuming, making them ineffective in this context. Data augmentation (DA) is an affective approach to alleviate the data scarcity problem in conversational systems. This tutorial provides a comprehensive and up-to-date overview of DA approaches in the context of conversational systems. It highlights recent advances in conversation augmentation, open domain and task-oriented conversation generation, and different paradigms of evaluating these models. We also discuss current challenges and future directions in order to help researchers and practitioners to further advance the field in this area.|会话系统的进步彻底改变了信息访问，超越了单个查询的局限性。然而，开发对话系统需要大量的训练数据，这在资源不足的领域和语言中是一个挑战。传统的数据收集方法，如众包，是劳动密集型和耗时的，使他们在这种情况下无效。数据增强是解决会话系统中数据稀缺问题的有效途径。本教程提供了在会话系统上下文中 DA 方法的全面和最新概述。它强调了会话增强，开放领域和面向任务的会话生成的最新进展，以及评估这些模型的不同范式。我们还讨论了当前的挑战和未来的方向，以帮助研究人员和从业人员进一步推进该领域。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Data+Augmentation+for+Conversational+AI)|0| |[Recent Advances in Generative Information Retrieval](https://doi.org/10.1145/3589335.3641239)|Yubao Tang, Ruqing Zhang, Weiwei Sun, Jiafeng Guo, Maarten de Rijke||Generative retrieval (GR) has witnessed significant growth recently in the area of information retrieval. Compared to the traditional "index-retrieve-then-rank'' pipeline, the GR paradigm aims to consolidate all information within a corpus into a single model. Typically, a sequence-to-sequence model is trained to directly map a query to its relevant document identifiers (i.e., docids). This tutorial offers an introduction to the core concepts of the GR paradigm and a comprehensive overview of recent advances in its foundations and applications. We start by providing preliminary information covering foundational aspects and problem formulations of GR. Then, our focus shifts towards recent progress in docid design, training approaches, inference strategies, and applications of GR. We end by outlining challenges and issuing a call for future GR research.This tutorial is intended to be beneficial to both researchers and industry practitioners interested in developing novel GR solutions or applying them in real-world scenarios.|生成性检索(GR)近来在信息检索领域取得了显著的发展。与传统的“索引-检索-然后排名”流水线相比，GR 范式旨在将语料库中的所有信息合并到一个单一模型中。通常，序列到序列模型被训练为直接将查询映射到其相关文档标识符(即 docids)。本教程介绍了 GR 范式的核心概念，并全面概述了其基础和应用方面的最新进展。我们首先提供涵盖 GR 的基础方面和问题表述的初步信息。然后，我们的重点转移到最近的进展，医学设计，训练方法，推理策略，和应用的 GR。最后，我们概述了挑战，并呼吁未来的 GR 研究。本教程的目的是为有兴趣开发新颖 GR 解决方案或将其应用于实际场景的研究人员和行业从业人员提供有益的帮助。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Recent+Advances+in+Generative+Information+Retrieval)|0| |[Large Language Models for Recommendation: Progresses and Future Directions](https://doi.org/10.1145/3589335.3641247)|Jizhi Zhang, Keqin Bao, Yang Zhang, Wenjie Wang, Fuli Feng, Xiangnan He|Natl Univ Singapore, Singapore, Singapore; Univ Sci & Technol China, Hefei, Peoples R China|The powerful large language models (LLMs) have played a pivotal role in advancing recommender systems. Recently, in both academia and industry, there has been a surge of interest in developing LLMs for recommendation, referred to as LLM4Rec. This includes endeavors like leveraging LLMs for generative item retrieval and ranking, as well as the exciting possibility of building universal LLMs for diverse open-ended recommendation tasks. These developments hold the potential to reshape the traditional recommender paradigm, paving the way for the next-generation recommender systems. In this tutorial, we aim to retrospect the evolution of LLM4Rec and conduct a comprehensive review of existing research. In particular, we will clarify how recommender systems benefit from LLMs through a variety of perspectives, including the model architecture, learning paradigm, and the strong abilities of LLMs such as chatting, generalization, planning, and generation. Furthermore, we will discuss the critical challenges and open problems in this emerging field, for instance, the trustworthiness, efficiency, and model retraining issues. Lastly, we will summarize the implications of previous work and outline future research directions. We believe that this tutorial will assist the audience in better understanding the progress and prospects of LLM4Rec, inspiring them for future exploration. This, in turn, will drive the prosperity of LLM4Rec, possibly fostering a paradigm shift in recommendation systems.|强大的大型语言模型(LLM)在推进推荐系统方面发挥了关键作用。最近，在学术界和工业界，对开发推荐 LLM 的兴趣激增，称为 LLM4Rec。这包括利用 LLM 进行生成性项目检索和排名，以及为各种开放式推荐任务构建通用 LLM 的令人兴奋的可能性。这些发展有可能重塑传统的推荐模式，为下一代推荐系统铺平道路。在本教程中，我们的目标是回顾 LLM4Rec 的演变，并对现有的研究进行一个全面的回顾。特别是，我们将通过多种视角阐明推荐系统如何从 LLM 中受益，包括模型体系结构、学习范式以及 LLM 的强大能力，如聊天、泛化、规划和生成。此外，我们将讨论在这个新兴领域的关键挑战和公开问题，例如，可信赖性，效率和模型再培训问题。最后，我们将总结以往工作的启示，并概述未来的研究方向。我们相信，本教程将帮助观众更好地了解 LLM4Rec 的进展和前景，鼓舞他们进行未来的探索。这反过来将推动 LLM4Rec 的繁荣，可能促进推荐系统的范式转变。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Large+Language+Models+for+Recommendation:+Progresses+and+Future+Directions)|0| |[Multimodal Pretraining and Generation for Recommendation: A Tutorial](https://doi.org/10.1145/3589335.3641248)|Jieming Zhu, Xin Zhou, Chuhan Wu, Rui Zhang, Zhenhua Dong||Personalized recommendation stands as a ubiquitous channel for users to explore information or items aligned with their interests. Nevertheless, prevailing recommendation models predominantly rely on unique IDs and categorical features for user-item matching. While this ID-centric approach has witnessed considerable success, it falls short in comprehensively grasping the essence of raw item contents across diverse modalities, such as text, image, audio, and video. This underutilization of multimodal data poses a limitation to recommender systems, particularly in the realm of multimedia services like news, music, and short-video platforms. The recent surge in pretraining and generation techniques presents both opportunities and challenges in the development of multimodal recommender systems. This tutorial seeks to provide a thorough exploration of the latest advancements and future trajectories in multimodal pretraining and generation techniques within the realm of recommender systems. The tutorial comprises three parts: multimodal pretraining, multimodal generation, and industrial applications and open challenges in the field of recommendation. Our target audience encompasses scholars, practitioners, and other parties interested in this domain. By providing a succinct overview of the field, we aspire to facilitate a swift understanding of multimodal recommendation and foster meaningful discussions on the future development of this evolving landscape.|个性化推荐是用户探索符合自己兴趣的信息或项目的一个无处不在的渠道。然而，流行的推荐模型主要依赖于用户项匹配的唯一 ID 和分类特征。虽然这种以 ID 为中心的方法已经取得了相当大的成功，但是它不能全面地掌握原始项目内容在不同模式(如文本、图像、音频和视频)中的本质。这种对多模式数据的利用不足对推荐系统造成了限制，特别是在新闻、音乐和短视频平台等多媒体服务领域。最近在培训前和生成技术方面的激增在开发多式联运推荐系统方面既带来了机遇，也带来了挑战。本教程旨在全面探讨推荐系统领域内多模式预培训和生成技术的最新进展和未来发展轨迹。本教程包括三个部分: 多模式预训练、多模式生成、工业应用和推荐领域的公开挑战。我们的目标受众包括学者、实践者和对该领域感兴趣的其他方面。我们希望通过简要介绍这一领域的情况，促进对多式联运建议的迅速理解，并推动就这一不断变化的格局的未来发展进行有意义的讨论。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Multimodal+Pretraining+and+Generation+for+Recommendation:+A+Tutorial)|0| @@ -202,7 +202,7 @@ |[Decoding YouTube's Recommendation System: A Comparative Study of Metadata and GPT-4 Extracted Narratives](https://doi.org/10.1145/3589335.3651913)|Mayor Inna Gurung, Md Monoarul Islam Bhuiyan, Ahmed AlTaweel, Nitin Agarwal||YouTube's recommendation system is integral to shaping user experiences by suggesting content based on past interactions using collaborative filtering techniques. Nonetheless, concerns about potential biases and homogeneity in these recommendations are prevalent, with the danger of leading users into filter bubbles and echo chambers that reinforce their pre-existing beliefs. Researchers have sought to understand and address these biases in recommendation systems. However, traditionally, such research has relied primarily on metadata, such as video titles, which does not always encapsulate the full content or context of the videos. This reliance on metadata can overlook the nuances and substantive content of videos, potentially perpetuating the very biases and echo chambers that the research aims to unravel. This study advances the examination of sentiment, toxicity, and emotion within YouTube content by conducting a comparative analysis across various depths of titles and narratives extracted by leveraging GPT-4. Our analysis reveals a clear trend in sentiment, emotion, and toxicity levels as the depth of content analysis increases. Notably, there is a general shift from neutral to positive sentiments in both YouTube video titles and narratives. Emotion analysis indicates an increase in positive emotions, particularly joy, with a corresponding decrease in negative emotions such as anger and disgust in narratives, while video titles show a steady decrease in anger. Additionally, toxicity analysis presents a contrasting pattern, with video titles displaying an upward trend in toxicity, peaking at the greatest depth analyzed, whereas narratives exhibit a high initial toxicity level that sharply decreases and stabilizes at lower depths. These findings suggest that the depth of engagement with video content significantly influences emotional and sentiment expressions.|YouTube 的推荐系统是塑造用户体验不可或缺的一部分，它通过使用协同过滤技术，根据过去的互动提供内容建议。尽管如此，对这些建议中潜在的偏见和同质性的担忧普遍存在，有可能导致用户进入过滤器泡沫和回声室，从而加强他们先前存在的信念。研究人员试图理解和解决推荐系统中的这些偏见。然而，传统上，这类研究主要依赖于元数据，如视频标题，它并不总是封装视频的完整内容或上下文。这种对元数据的依赖可能会忽视视频的细微差别和实质性内容，有可能使研究旨在揭示的偏见和回声室永久化。这项研究通过利用 GPT-4对不同深度的标题和叙述进行比较分析，提高了对 YouTube 内容中的情绪、毒性和情感的检查。我们的分析显示，随着内容分析深度的增加，情绪、情绪和毒性水平有明显的趋势。值得注意的是，无论是 YouTube 视频标题还是叙事，都普遍从中性情绪转向积极情绪。情绪分析表明积极情绪的增加，特别是快乐，与相应的消极情绪，如愤怒和厌恶的减少叙述，而视频标题显示了一个稳定的减少愤怒。此外，毒性分析呈现出一种对比模式，视频标题显示毒性呈上升趋势，在分析的最深处达到峰值，而叙述性展示出高的初始毒性水平，在较低深度急剧下降并稳定下来。这些发现表明，视频内容的参与程度显著影响情绪和情绪的表达。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Decoding+YouTube's+Recommendation+System:+A+Comparative+Study+of+Metadata+and+GPT-4+Extracted+Narratives)|0| |[Graph Coarsening via Convolution Matching for Scalable Graph Neural Network Training](https://doi.org/10.1145/3589335.3651920)|Charles Dickens, Edward W. Huang, Aishwarya Reganti, Jiong Zhu, Karthik Subbian, Danai Koutra||Graph summarization as a preprocessing step is an effective and complementary technique for scalable graph neural network (GNN) training. In this work, we propose the Coarsening Via Convolution Matching (CONVMATCH) algorithm and a highly scalable variant, A-CONVMATCH, for creating summarized graphs that preserve the output of graph convolution. We evaluate CONVMATCH on six real-world link prediction and node classification graph datasets, and show it is efficient and preserves prediction performance while significantly reducing the graph size. Notably, CONVMATCH achieves up to 95 performance of GNNs on node classification while trained on graphs summarized down to 1 tasks, CONVMATCH consistently outperforms all baselines, achieving up to a 2x improvement.|图形摘要作为一种预处理步骤，是可扩展图神经网络(GNN)训练的有效补充技术。在这项工作中，我们提出了粗化通过卷积匹配(CONVMATCH)算法和一个高度可伸缩的变体，A-CONVMATCH，用于创建总结图保持图卷积的输出。对实际的六个链路预测和节点分类图数据集进行了 CONVMATCH 评估，结果表明该算法在保持预测性能的同时，显著减小了图的大小。值得注意的是，CONVMATCH 在节点分类上实现了高达95个 GNN 的性能，同时对归纳为1个任务的图进行训练，CONVMATCH 始终优于所有基线，实现了高达2倍的改进。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Graph+Coarsening+via+Convolution+Matching+for+Scalable+Graph+Neural+Network+Training)|0| |[FedHLT: Efficient Federated Low-Rank Adaption with Hierarchical Language Tree for Multilingual Modeling](https://doi.org/10.1145/3589335.3651933)|Zhihan Guo, Yifei Zhang, Zhuo Zhang, Zenglin Xu, Irwin King||Federated Multilingual Modeling (FMM) has become an essential approach in natural language processing (NLP) due to increasing linguistic diversity and the heightened emphasis on data privacy. However, FMM faces two primary challenges: 1) the high communication costs inherent in network operations, and 2) the complexities arising from parameter interference, as languages exhibit both unique characteristics and shared features. To tackle these issues, we introduce a communication-efficient framework for Multilingual Modeling (MM) that combines low-rank adaptation with a hierarchical language tree structure. Our method maintains the base model's weights while focusing on updating only the Low-rank adaptation (LoRA) parameters, significantly reducing communication costs. Additionally, we mitigate parameter conflicts by organizing languages based on their familial ties rather than merging all LoRA parameters together. Our experimental findings reveal that this novel model surpasses established baseline models in performance and markedly decreases communication overhead.|由于语言多样性的增加和对数据隐私的重视，联邦多语言建模(FMM)已经成为自然语言处理(NLP)中的一种重要方法。然而，FMM 面临着两个主要的挑战: 1)网络操作固有的高通信成本，2)参数干扰产生的复杂性，因为语言既表现出独特的特征又表现出共享的特征。为了解决这些问题，我们引入了一个多语言建模(MM)的通信高效框架，它结合了低等级适应性和层次化的语言树结构。该方法在保持基本模型权重的同时，只更新低秩自适应(LoRA)参数，大大降低了通信成本。此外，我们根据语言的亲缘关系来组织语言，而不是将所有 LoRA 参数合并在一起，从而减少了参数冲突。我们的实验结果表明，这种新模型在性能上超过了已建立的基线模型，并显著降低了通信开销。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=FedHLT:+Efficient+Federated+Low-Rank+Adaption+with+Hierarchical+Language+Tree+for+Multilingual+Modeling)|0| -|[Information Retrieval Meets Large Language Models](https://doi.org/10.1145/3589335.3641299)|Zheng Liu, Yujia Zhou, Yutao Zhu, Jianxun Lian, Chaozhuo Li, Zhicheng Dou, Defu Lian, JianYun Nie|Shandong Artificial Intelligence Institute, China; South China University of Technology, China; Wuhan University, China; Beijing University of Posts and Telecommunications, China; Institute of Computing Technology, Chinese Academy of Sciences, China; Zhejiang University, China; University of Science and Technology of China, China; Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, China; Jilin University, China; Tsinghua University, China; Renmin University of China, China; Huawei Technologies Ltd. Co, China; Shandong University, China|The research field of Information Retrieval (IR) has evolved significantly, expanding beyond traditional search to meet diverse user information needs. Recently, Large Language Models (LLMs) have demonstrated exceptional capabilities in text understanding, generation, and knowledge inference, opening up exciting avenues for IR research. LLMs not only facilitate generative retrieval but also offer improved solutions for user understanding, model evaluation, and user-system interactions. More importantly, the synergistic relationship among IR models, LLMs, and humans forms a new technical paradigm that is more powerful for information seeking. IR models provide real-time and relevant information, LLMs contribute internal knowledge, and humans play a central role of demanders and evaluators to the reliability of information services. Nevertheless, significant challenges exist, including computational costs, credibility concerns, domain-specific limitations, and ethical considerations. To thoroughly discuss the transformative impact of LLMs on IR research, the Chinese IR community conducted a strategic workshop in April 2023, yielding valuable insights. This paper provides a summary of the workshop’s outcomes, including the rethinking of IR’s core values, the mutual enhancement of LLMs and IR, the proposal of a novel IR technical paradigm, and open challenges.|信息检索搜索(IR)的研究领域已经发生了显著的变化，超越了传统的搜索，以满足不同的用户信息需求。最近，大语言模型(LLM)在文本理解、生成和知识推理方面展示了非凡的能力，为 IR 研究开辟了令人兴奋的途径。LLM 不仅促进了生成检索，而且为用户理解、模型评估和用户系统交互提供了改进的解决方案。更重要的是，IR 模型、 LLM 和人类之间的协同关系形成了一种新的技术范式，这种范式在信息搜索方面更加强大。IR 模型提供实时和相关的信息，LLM 提供内部知识，而人在信息服务的可靠性中扮演着需求者和评估者的中心角色。尽管如此，仍然存在重大的挑战，包括计算成本、可信度问题、特定领域的局限性和伦理考虑。为了深入讨论 LLM 对国际关系研究的变革性影响，中国国际关系界于2023年4月举办了一次战略研讨会，提出了宝贵的见解。本文提供了研讨会成果的总结，包括重新思考 IR 的核心价值，LLM 和 IR 的相互增强，一个新的 IR 技术范式的提议，以及开放的挑战。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Information+Retrieval+Meets+Large+Language+Models)|0| +|[Information Retrieval Meets Large Language Models](https://doi.org/10.1145/3589335.3641299)|Zheng Liu, Yujia Zhou, Yutao Zhu, Jianxun Lian, Chaozhuo Li, Zhicheng Dou, Defu Lian, JianYun Nie|Beijing University of Posts and Telecommunications, China; Jilin University, China; Renmin University of China, China; Wuhan University, China; South China University of Technology, China; University of Science and Technology of China, China; Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, China; Shandong University, China; Zhejiang University, China; Shandong Artificial Intelligence Institute, China; Tsinghua University, China; Huawei Technologies Ltd. Co, China; Institute of Computing Technology, Chinese Academy of Sciences, China|The research field of Information Retrieval (IR) has evolved significantly, expanding beyond traditional search to meet diverse user information needs. Recently, Large Language Models (LLMs) have demonstrated exceptional capabilities in text understanding, generation, and knowledge inference, opening up exciting avenues for IR research. LLMs not only facilitate generative retrieval but also offer improved solutions for user understanding, model evaluation, and user-system interactions. More importantly, the synergistic relationship among IR models, LLMs, and humans forms a new technical paradigm that is more powerful for information seeking. IR models provide real-time and relevant information, LLMs contribute internal knowledge, and humans play a central role of demanders and evaluators to the reliability of information services. Nevertheless, significant challenges exist, including computational costs, credibility concerns, domain-specific limitations, and ethical considerations. To thoroughly discuss the transformative impact of LLMs on IR research, the Chinese IR community conducted a strategic workshop in April 2023, yielding valuable insights. This paper provides a summary of the workshop’s outcomes, including the rethinking of IR’s core values, the mutual enhancement of LLMs and IR, the proposal of a novel IR technical paradigm, and open challenges.|信息检索搜索(IR)的研究领域已经发生了显著的变化，超越了传统的搜索，以满足不同的用户信息需求。最近，大语言模型(LLM)在文本理解、生成和知识推理方面展示了非凡的能力，为 IR 研究开辟了令人兴奋的途径。LLM 不仅促进了生成检索，而且为用户理解、模型评估和用户系统交互提供了改进的解决方案。更重要的是，IR 模型、 LLM 和人类之间的协同关系形成了一种新的技术范式，这种范式在信息搜索方面更加强大。IR 模型提供实时和相关的信息，LLM 提供内部知识，而人在信息服务的可靠性中扮演着需求者和评估者的中心角色。尽管如此，仍然存在重大的挑战，包括计算成本、可信度问题、特定领域的局限性和伦理考虑。为了深入讨论 LLM 对国际关系研究的变革性影响，中国国际关系界于2023年4月举办了一次战略研讨会，提出了宝贵的见解。本文提供了研讨会成果的总结，包括重新思考 IR 的核心价值，LLM 和 IR 的相互增强，一个新的 IR 技术范式的提议，以及开放的挑战。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Information+Retrieval+Meets+Large+Language+Models)|0| |[Heterogeneous Knowledge Grounding for Medical Question Answering with Retrieval Augmented Large Language Model](https://doi.org/10.1145/3589335.3651941)|Wenting Zhao, Zhongfen Deng, Shweta Yadav, Philip S. Yu||The Large Language Model (LLM) is renowned for its ability to encode a vast amount of general domain knowledge, enabling it to excel in question-answering, dialogue systems, and summarization tasks. However, the medical domain presents a unique challenge to LLM due to the distribution of medical knowledge, which follows a long-tail pattern. Existing approaches address this challenge by injecting medical knowledge into LLM through single sources such as medical textbooks or medical knowledge bases. However, medical knowledge is distributed across multiple heterogeneous information sources. A medical question-answering system can enhance answer coverage and confidence by considering these diverse knowledge sources together. To bridge this gap, we propose a novel approach called Heterogeneous Knowledge Retrieval-Augmented LLM for medical domain question answering. Our experiments, conducted on the MedQA-USMLE dataset, demonstrate promising performance improvements. These results underscore the importance of harnessing heterogeneous knowledge sources in the medical domain.|大型语言模型(LLM)以其编码大量通用领域知识的能力而闻名，使其能够在问答、对话系统和摘要任务方面表现出色。然而，由于医学知识的分布遵循长尾模式，医学领域对 LLM 提出了独特的挑战。现有方法通过单一来源，如医学教科书或医学知识库，将医学知识注入法医学。然而，医学知识是分布在多个异构信息源之间的。一个医学问答系统可以通过综合考虑这些不同的知识来源来提高答案的覆盖范围和信心。为了弥补这一差距，我们提出了一种新的医学领域问题回答方法——异构知识检索增强 LLM。我们在 MedQA-USMLE 数据集上进行的实验证明了有希望的性能改进。这些结果强调了在医学领域利用异质知识来源的重要性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Heterogeneous+Knowledge+Grounding+for+Medical+Question+Answering+with+Retrieval+Augmented+Large+Language+Model)|0| |[Weakly Supervised Video Moment Retrieval via Location-irrelevant Proposal Learning](https://doi.org/10.1145/3589335.3651942)|Wei Ji, Ruiqi Shi, Yinwei Wei, Shanshan Zhao, Roger Zimmermann||This paper deals with Video Moment Retrieval (VMR) in a weakly-supervised fashion, which aims to retrieve local video clips with only global video-level descriptions. Scrutinizing the recent advances in VMR, we find that the fully-supervised models achieve strong performance, but they are heavily relied on the precise temporal annotations. Weakly-supervised methods do not rely on temporal annotations, however, their performance is much weaker than the fully-supervised ones. To fill such gap, we propose to take advantage of a pretrained video-text model as hitchhiker to generate pseudo temporal labels. The pseudo temporal labels, together with the descriptive labels, are then utilized to guide the training of the proposed VMR model. The proposed Location-irrelevant Proposal Learning (LPL) model is based on a pretrained video-text model with cross-modal prompt learning, together with different strategies to generate reasonable proposals with various lengths. Despite the simplicity, we find that our method performs much better than the previous state-of-the-art methods on standard benchmarks, eg., +4.4% and +1.4% in mIoU on the Charades and ActivityNet-Caption datasets respectively, which benefits from training with fine-grained video-text pairs. Further experiments on two synthetic datasets with shuffled temporal location and longer video length demonstrate our model's robustness towards temporal localization bias as well as its strength in handling long video sequences.|本文采用弱监督方式处理视频矩检索(VMR) ，目的是检索只有全局视频级描述的局部视频片段。仔细研究 VMR 的最新进展，我们发现全监督模型取得了很好的性能，但是它们严重依赖于精确的时间注释。弱监督方法不依赖于时间注释，但是它们的性能要比完全监督方法弱得多。为了填补这一空白，我们提出利用预先训练的视频文本模型作为搭便车者来生成伪时态标签。然后利用伪时态标签和描述性标签对 VMR 模型进行训练。提出的位置不相关建议学习(LPL)模型是基于预先训练的视频文本模型和跨模式的提示学习，以及不同的策略来产生不同长度的合理建议。尽管简单，我们发现我们的方法在标准基准上比以前的最先进的方法表现得更好，例如，在 Charades 和 ActivityNet-Caption 数据集上分别增加了4.4% 和1.4% 的 mIoU，这得益于细粒度视频文本对的训练。通过对两个具有混合时间定位和较长视频长度的合成数据集的进一步实验，证明了该模型对时间定位偏差的鲁棒性以及对长视频序列的处理能力。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Weakly+Supervised+Video+Moment+Retrieval+via+Location-irrelevant+Proposal+Learning)|0| |[One-step Reach: LLM-based Keyword Generation for Sponsored Search Advertising](https://doi.org/10.1145/3589335.3651943)|Yang Wang, Zheyi Sha, Kunhai Lin, Chaobing Feng, Kunhong Zhu, Lipeng Wang, Xuewu Jiao, Fei Huang, Chao Ye, Dengwu He, Zhi Guo, Shuanglong Li, Lin Liu||Query keyword matching plays a crucial role in sponsored search advertising by retrieving semantically related keywords of the user query to target relevant advertisements. Conventional technical solutions adopt the retrieve-judge-then-rank retrieval framework structured in cascade funnels. However, it has limitations in accurately depicting the semantic relevance between the query and keyword, and the cumulative funnel losses result in unsatisfactory precision and recall. To address the above issues, this paper proposes a Large Language Model (LLM)-based keyword generation method (LKG) to reach related keywords from the search query in one step. LKG models the query keyword matching as an end-to-end keyword generation task based on the LLM through multi-match prompt tuning. Moreover, it employs the feedback tuning and the prefix tree-based constrained beam search to improve the generation quality and efficiency. Extensive offline experiments and online A/B testing demonstrate the effectiveness and superiority of LKG which is fully deployed in the Baidu sponsored search system bringing significant improvements.|查询关键词匹配通过检索用户查询的语义相关关键词来定位相关广告，在赞助商搜索广告中起着至关重要的作用。传统的技术解决方案采用级联漏斗结构的检索-判决-秩检索框架。然而，它在准确描述查询和关键字之间的语义相关性方面存在局限性，并且累积漏斗损失导致不满意的准确率召回率。针对上述问题，本文提出了一种基于大语言模型(LLM)的关键词生成方法(LKG) ，从搜索查询一步到位地生成相关关键词。LKG 通过多匹配提示调优，将查询关键字匹配建模为基于 LLM 的端到端关键字生成任务。同时，采用反馈调整和基于前缀树的约束波束搜索来提高生成质量和效率。大量的离线实验和在线 A/B 测试证明了 lkG 的有效性和优越性。 LKG 已完全部署在百度赞助的搜索系统中，带来了显著的改进。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=One-step+Reach:+LLM-based+Keyword+Generation+for+Sponsored+Search+Advertising)|0| @@ -321,7 +321,7 @@ |[Efficient Location Sampling Algorithms for Road Networks](https://doi.org/10.1145/3589335.3651497)|Sara Ahmadian, Sreenivas Gollapudi, Kostas Kollias, Vivek Kumar, Ameya Velingker, Santhoshini Velusamy||Many geographic information systems applications rely on data provided by user devices in the road network, including traffic monitoring, driving navigation, and road closure detection. The underlying signal is generally collected by sampling locations from user trajectories. The sampling process, though critical for various applications, has not been studied sufficiently in the literature. While the most natural way to sample a trajectory may be to use a frequency based algorithm, e.g., sampling locations every x seconds, such a sampling strategy can be quite wasteful in resources (e.g., server-side processing, user battery) as well as stored user data. In this work, we conduct a horizontal study of various location sampling algorithms (based on frequency, road geography, reservoir sampling, etc.) and assess their trade-offs in terms of the size of the stored data and the induced quality of training for prediction tasks (specifically predicting speeds on road segments).|许多地理信息系统应用程序依赖于道路网络中用户设备提供的数据，包括交通监控、驾驶导航和道路封闭检测。底层信号通常通过从用户轨迹中采样位置来收集。抽样过程，虽然对各种应用至关重要，但在文献中还没有得到充分的研究。虽然最自然的取样轨迹的方法可能是使用一个基于频率的算法，例如，每 x 秒取样位置，这样的取样策略可能相当浪费资源(例如，服务器端处理，用户电池)以及存储的用户数据。在这项工作中，我们对各种位置采样算法(基于频率、道路地理、水塘抽样等)进行了横向研究，并根据存储数据的大小和预测任务(特别是预测路段速度)的训练诱导质量来评估它们之间的权衡。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Efficient+Location+Sampling+Algorithms+for+Road+Networks)|0| |[A Category-agnostic Graph Attention-based Approach for Determining Notability of Articles for Wikipedia](https://doi.org/10.1145/3589335.3651461)|Gokul Thota, Vasudeva Varma||Wikipedia is a highly essential platform because of its informative, dynamic, and easily accessible nature. To identify topics/titles warranting their own Wikipedia article, editors of Wikipedia defined "Notability" guidelines. So far notability is enforced by humans, which makes scalability an issue. There has been no significant work on Notability determination for titles with complex category dependencies. We design a mechanism to identify such titles. We construct a dataset with 9k such titles and propose a category-agnostic approach utilizing Graph neural networks, for their notability determination. Our system outperforms machine learning-based, transformer-based classifiers and entity salience methods. It provides a scalable alternative for notability detection.|维基百科是一个非常重要的平台，因为它信息量大、动态、易于访问。维基百科的编辑们定义了“知名度”指导原则，以确定哪些主题/标题需要他们自己的维基百科文章。到目前为止，知名度是由人类强制执行的，这使得可伸缩性成为一个问题。在确定具有复杂类别依赖关系的头衔的知名度方面没有重要的工作。我们设计了一个机制来识别这样的标题。我们建立了一个9k 这样的标题的数据集，并提出了一个类别不可知的方法，利用图神经网络，为他们的显着性决定。我们的系统优于基于机器学习、基于变压器的分类器和实体显著性方法。它为知名度检测提供了一个可扩展的替代方案。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Category-agnostic+Graph+Attention-based+Approach+for+Determining+Notability+of+Articles+for+Wikipedia)|0| |[Concentration of Power and Participation in Online Governance: the Ecosystem of Decentralized Autonomous Organizations](https://doi.org/10.1145/3589335.3651481)|Andrea PeñaCalvin, Javier Arroyo, Andrew Schwartz, Samer Hassan||Blockchain technology enables a new form of online community: Decentralized Autonomous Organizations (DAOs), where members typically vote on proposals using tokens. Enthusiasts claim DAOs provide new opportunities for openness, horizontality, and democratization. However, this phenomenon is still under research, especially given the lack of quantitative studies. This paper presents the first census-like quantitative analysis of the whole ecosystem of DAOs, including 30K DAO communities on the main DAO platforms. This enables us to provide insights into the allegedly "democratic'' nature of DAOs, building metrics concerning their lifespan, participation, and power concentration. Most DAOs have a short lifespan and low participation. There is also a positive correlation between community size and voting power concentration. Like other online communities, DAOs seem to follow the iron law: becoming increasingly oligarchic as they grow. Still, a significant amount of DAOs of varying sizes defy this idea by being egalitarian by design.|Blockchain 的技术使一种新的在线社区形式成为可能: 去中心化的自治组织(dalos) ，其成员通常使用令牌对提案进行投票。爱好者声称 DAO 为开放性、横向性和民主化提供了新的机会。然而，这一现象仍在研究中，特别是缺乏定量研究。本文首次对 DAO 的整个生态系统，包括主要 DAO 平台上的30K DAO 群落进行了类似普查的定量分析。这使我们能够深入了解所谓的 DAO 的“民主”本质，构建关于它们的寿命、参与和权力集中的度量。大多数 DAO 的寿命较短，参与度较低。社区规模与选举权集中度呈正相关。与其他在线社区一样，DAO 似乎遵循着铁律: 随着成长，它们变得越来越寡头化。尽管如此，仍然有大量不同大小的 DAO 在设计上是平等的，从而违背了这一理念。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Concentration+of+Power+and+Participation+in+Online+Governance:+the+Ecosystem+of+Decentralized+Autonomous+Organizations)|0| -|["All of Me": Mining Users' Attributes from their Public Spotify Playlists](https://doi.org/10.1145/3589335.3651459)|Pier Paolo Tricomi, Luca Pajola, Luca Pasa, Mauro Conti|Department of Mathematics, University of Padova & Spritz Matter Srl, Padova, Italy; Department of Mathematics, University of Padova, Padova, Italy; Spritz Matter Srl & University of Padova, Padova, Italy|In the age of digital music streaming, playlists on platforms like Spotify have become an integral part of individuals' musical experiences. People create and publicly share their own playlists to express their musical tastes, promote the discovery of their favorite artists, and foster social connections. In this work, we aim to address the question: can we infer users' private attributes from their public Spotify playlists? To this end, we conducted an online survey involving 739 Spotify users, resulting in a dataset of 10,286 publicly shared playlists comprising over 200,000 unique songs and 55,000 artists. Then, we utilize statistical analyses and machine learning algorithms to build accurate predictive models for users' attributes.|在数字音乐流媒体时代，像 Spotify 这样的平台上的播放列表已经成为个人音乐体验不可或缺的一部分。人们创建并公开分享自己的播放列表，以表达自己的音乐品味，促进发现自己喜欢的艺术家，并培养社会关系。在这项工作中，我们的目标是解决这个问题: 我们能从用户的公共 Spotify 播放列表中推断出用户的私人属性吗？为此，我们对739名 Spotify 用户进行了在线调查，得到了10286个公开分享的播放列表数据集，其中包括超过20万首独特歌曲和55000名艺术家。然后，利用统计分析和机器学习算法建立精确的用户属性预测模型。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q="All+of+Me":+Mining+Users'+Attributes+from+their+Public+Spotify+Playlists)|0| +|["All of Me": Mining Users' Attributes from their Public Spotify Playlists](https://doi.org/10.1145/3589335.3651459)|Pier Paolo Tricomi, Luca Pajola, Luca Pasa, Mauro Conti|Department of Mathematics, University of Padova & Spritz Matter Srl, Padova, Italy; Spritz Matter Srl & University of Padova, Padova, Italy; Department of Mathematics, University of Padova, Padova, Italy|In the age of digital music streaming, playlists on platforms like Spotify have become an integral part of individuals' musical experiences. People create and publicly share their own playlists to express their musical tastes, promote the discovery of their favorite artists, and foster social connections. In this work, we aim to address the question: can we infer users' private attributes from their public Spotify playlists? To this end, we conducted an online survey involving 739 Spotify users, resulting in a dataset of 10,286 publicly shared playlists comprising over 200,000 unique songs and 55,000 artists. Then, we utilize statistical analyses and machine learning algorithms to build accurate predictive models for users' attributes.|在数字音乐流媒体时代，像 Spotify 这样的平台上的播放列表已经成为个人音乐体验不可或缺的一部分。人们创建并公开分享自己的播放列表，以表达自己的音乐品味，促进发现自己喜欢的艺术家，并培养社会关系。在这项工作中，我们的目标是解决这个问题: 我们能从用户的公共 Spotify 播放列表中推断出用户的私人属性吗？为此，我们对739名 Spotify 用户进行了在线调查，得到了10286个公开分享的播放列表数据集，其中包括超过20万首独特歌曲和55000名艺术家。然后，利用统计分析和机器学习算法建立精确的用户属性预测模型。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q="All+of+Me":+Mining+Users'+Attributes+from+their+Public+Spotify+Playlists)|0| |[PyGDebias: A Python Library for Debiasing in Graph Learning](https://doi.org/10.1145/3589335.3651239)|Yushun Dong, Zhenyu Lei, Zaiyi Zheng, Song Wang, Jing Ma, Alex Jing Huang, Chen Chen, Jundong Li||Graph-structured data is ubiquitous among a plethora of real-world applications. However, as graph learning algorithms have been increasingly deployed to help decision-making, there has been rising societal concern in the bias these algorithms may exhibit. In certain high-stake decision-making scenarios, the decisions made may be life-changing for the involved individuals. Accordingly, abundant explorations have been made to mitigate the bias for graph learning algorithms in recent years. However, there still lacks a library to collectively consolidate existing debiasing techniques and help practitioners to easily perform bias mitigation for graph learning algorithms. In this paper, we present PyGDebias, an open-source Python library for bias mitigation in graph learning algorithms. As the first comprehensive library of its kind, PyGDebias covers 13 popular debiasing methods under common fairness notions together with 26 commonly used graph datasets. In addition, PyGDebias also comes with comprehensive performance benchmarks and well-documented API designs for both researchers and practitioners. To foster convenient accessibility, PyGDebias is released under a permissive BSD-license together with performance benchmarks, API documentation, and use examples at https://github.com/yushundong/PyGDebias.|图形结构化数据在大量真实世界的应用程序中无处不在。然而，随着图形学习算法越来越多地被用于帮助决策，这些算法可能表现出的偏差引起了社会越来越多的关注。在某些高风险的决策情景中，所做的决定可能会改变相关个人的一生。因此，近年来人们在减小图学习算法的偏差方面进行了大量的探索。然而，仍然缺乏一个库来整合现有的去偏技术，并帮助从业人员轻松地执行图形学习算法的偏差缓解。在本文中，我们介绍了 PyGDebian，一个用于图学习算法中减少偏差的开源 Python 库。作为第一个综合性图书馆，PyGDebian 涵盖了13种常用的公平性概念下的去偏方法和26种常用的图形数据集。此外，PyGDebian 还为研究人员和从业者提供了全面的性能基准和详细记录的 API 设计。为了促进方便的可访问性，PyGdeias 是在一个宽松的 BSD 许可下发布的，同时还有性能基准测试、 API 文档和 https://github.com/yushundong/PyGDebias 使用示例。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=PyGDebias:+A+Python+Library+for+Debiasing+in+Graph+Learning)|0| |[Synslator: An Interactive Machine Translation Tool with Online Learning](https://doi.org/10.1145/3589335.3651240)|Jiayi Wang, Ke Wang, Fengming Zhou, Chengyu Wang, Zhiyong Fu, Zeyu Feng, Yu Zhao, Yuqi Zhang||Interactive machine translation (IMT) has emerged as a progression of the computer-aided translation paradigm, where the machine translation system and the human translator collaborate to produce high-quality translations. This paper introduces Synslator, a user-friendly computer-aided translation (CAT) tool that not only supports IMT, but is adept at online learning with real-time translation memories. To accommodate various deployment environments for CAT services, Synslator integrates two different neural translation models to handle translation memories for online learning. Additionally, the system employs a language model to enhance the fluency of translations in an interactive mode. In evaluation, we have confirmed the effectiveness of online learning through the translation models, and have observed a 13% increase in post-editing efficiency with the interactive functionalities of Synslator. A tutorial video is available at:https://youtu.be/K0vRsb2lTt8.|交互式机器翻译(IMT)已经成为计算机辅助翻译范式的一个发展阶段，机器翻译系统和人工翻译协作生成高质量的翻译。本文介绍了 Synslator，一个用户友好的计算机辅助翻译(CAT)工具，它不仅支持 IMT，而且善于利用实时翻译记忆进行在线学习。为了适应 CAT 服务的不同部署环境，Synslator 集成了两种不同的神经翻译模型来处理在线学习的翻译记忆。此外，该系统采用了一种语言模式，以提高交互式翻译的流畅性。在评估中，我们通过翻译模型证实了在线学习的有效性，并观察到 Synslator 的交互功能使后期编辑效率提高了13% 。教程视频可以在以下 https://youtu.be/k0vrsb2ltt8找到。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Synslator:+An+Interactive+Machine+Translation+Tool+with+Online+Learning)|0| |[ACCORD: Constraint-driven Mediation of Multi-user Conflicts in Cloud Services](https://doi.org/10.1145/3589335.3651244)|Abhiroop Tippavajjula, Primal Pappachan, Anna Cinzia Squicciarini, Jose Such||When multiple users adopt collaborative cloud services like Google Drive to work on a shared resource, incorrect or missing permis- sions may cause conflicting or inconsistent access or use privileges. These issues (or conflicts) compromise resources confidentiality, integrity, or availability leading to a lack of trust in cloud services. An example conflict is when a user with editor permissions changes the permissions on a shared resource without consent from the orig- inal resource owner. In this demonstration, we introduce ACCORD, a web application built on top of Google Drive able to detect and resolve multi-user conflicts. ACCORD employs a simulator to help users preemptively identify potential conflicts and assists them in defining action constraints. Using these constraints, ACCORD can automatically detect and resolve any future conflicts.|当多个用户采用像 Google Drive 这样的协作云服务来处理共享资源时，不正确或缺失的权限可能会导致相互冲突或不一致的访问或使用权限。这些问题(或冲突)损害了资源的机密性、完整性或可用性，导致对云服务缺乏信任。示例冲突是当具有编辑器权限的用户未经原始资源所有者同意而更改对共享资源的权限时。在本演示中，我们将介绍 ACCORD，这是一个构建在 Google Drive 之上的 Web 应用程序，能够检测和解决多用户冲突。ACCORD 使用模拟器帮助用户预先识别潜在的冲突，并协助他们定义行动约束。使用这些约束，ACCORD 可以自动检测和解决任何未来的冲突。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=ACCORD:+Constraint-driven+Mediation+of+Multi-user+Conflicts+in+Cloud+Services)|0| @@ -502,7 +502,7 @@ |[Graph Unlearning with Efficient Partial Retraining](https://doi.org/10.1145/3589335.3651265)|Jiahao Zhang||Graph Neural Networks (GNNs) have achieved remarkable success in various real-world applications. However, GNNs may be trained on undesirable graph data, which can degrade their performance and reliability. To enable trained GNNs to efficiently unlearn unwanted data, a desirable solution is retraining-based graph unlearning, which partitions the training graph into subgraphs and trains sub-models on them, allowing fast unlearning through partial retraining. However, the graph partition process causes information loss in the training graph, resulting in the low model utility of sub-GNN models. In this paper, we propose GraphRevoker, a novel graph unlearning framework that better maintains the model utility of unlearnable GNNs. Specifically, we preserve the graph property with graph property-aware sharding and effectively aggregate the sub-GNN models for prediction with graph contrastive sub-model aggregation. We conduct extensive experiments to demonstrate the superiority of our proposed approach.|图形神经网络(GNN)在各种实际应用中取得了显著的成功。然而，GNN 可能被训练在不需要的图形数据上，这会降低它们的性能和可靠性。为了使经过训练的 GNN 能够有效地去除不需要的数据，一个理想的解决方案是基于再训练的图去除，它将训练图划分为子图，并在子图上训练子模型，允许通过部分再训练来快速去除。然而，图划分过程导致训练图中的信息丢失，从而导致子 GNN 模型的效用较低。在本文中，我们提出了一个新的图形去学习框架 GraphRevoker，它可以更好地维护不可学习 GNN 的模型效用。具体来说，我们采用图属性感知的分片方法保留了图的属性，并有效地聚集了用于预测的子 GNN 模型和图的对比子模型聚集。我们进行了广泛的实验，以证明我们提出的方法的优越性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Graph+Unlearning+with+Efficient+Partial+Retraining)|0| |[Incentives in the Ether: Practical Cryptocurrency Economics & Security](https://doi.org/10.1145/3589335.3651268)|Aviv Yaish||||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Incentives+in+the+Ether:+Practical+Cryptocurrency+Economics+&+Security)|0| |[Social Psychology Meets Social Computing: State of the Art and Future Directions](https://doi.org/10.1145/3589335.3641242)|Sourav S. Bhowmick, Hui Li, S. H. Annabel Chen, Yining Zhao||Social computing platforms typically deal with data that are either related to humans or generated by humans. Consequently, effective design of these platforms needs to be cognizant ofsocial psychology theories. In this tutorial, we review and summarize the research thus far into the paradigm ofpsychology theory-informed design of social computing platforms where the design is guided by theories from social psychology in addition to theories from computer science. Specifically, we review techniques and frameworks that embrace this paradigm in the arena of social influence. In addition, we suggest open problems and new research directions.|社会计算平台通常处理与人类相关或由人类生成的数据。因此，这些平台的有效设计需要社会心理学理论的认识。在本教程中，我们回顾和总结迄今为止的研究范式的心理学理论知情设计的社会计算平台的设计是由社会心理学的理论指导，以及从计算机科学的理论。具体来说，我们回顾了在社会影响领域采用这种范式的技术和框架。此外，还提出了一些开放性问题和新的研究方向。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Social+Psychology+Meets+Social+Computing:+State+of+the+Art+and+Future+Directions)|0| -|[Discrete Choice and Applications](https://doi.org/10.1145/3589335.3641244)|Flavio Chierichetti, Ravi Kumar, Andrew Tomkins|Massachusetts Institute of Technology, Department of Civil and Environmental Engineering, Room 181, United States of America; Massachusetts Institute of Technology, Department of Civil and Environmental Engineering, 77 Massachusetts Avenue, Room 1-181, Cambridge, MA 02139, United States of America; Delft University of Technology, Department of Maritime and Transport Technology, Netherlands; Massachusetts Institute of Technology, Department of Civil and Environmental Engineering, United States of America|This paper presents a framework for estimating and updating user preferences in the context of app-based recommender systems. We specifically consider recommender systems which provide personalized menus of options to users. A Hierarchical Bayes procedure is applied in order to account for inter- and intra-consumer heterogeneity, representing random taste variations among individuals and among choice situations (menus) for a given individual, respectively. Three levels of preference parameters are estimated: population-level, individual-level and menu-specific. In the context of a recommender system, the estimation of these parameters is repeated periodically in an offline process in order to account for trends, such as changing market conditions. Furthermore, the individual-level parameters are updated in real-time as users make choices in order to incorporate the latest information from the users. This online update is computationally efficient which makes it feasible to embed it in a real-time recommender system. The estimated individual-level preferences are stored for each user and retrieved as inputs to a menu optimization model in order to provide recommendations. The proposed methodology is applied to both Monte-Carlo and real data. It is observed that the online update of the parameters is successful in improving the parameter estimates in real-time. This framework is relevant to various recommender systems that generate personalized recommendations ranging from transportation to e-commerce and online marketing, but is particularly useful when the attributes of the alternatives vary over time.|本文提出了一个在基于应用程序的推荐系统中估计和更新用户偏好的框架。我们特别考虑推荐系统，它为用户提供个性化的选项菜单。为了解释消费者之间和消费者内部的异质性，应用层次贝叶斯过程，分别代表个体之间和给定个体的选择情况(菜单)之间的随机口味变化。估计了三个水平的偏好参数: 人口水平、个人水平和菜单特定。在推荐系统的情况下，这些参数的估计会在离线过程中周期性地重复，以便解释趋势，例如不断变化的市场状况。此外，当用户做出选择时，个人级别的参数会实时更新，以便纳入用户提供的最新信息。这种在线更新计算效率高，可以嵌入到实时推荐系统中。为每个用户存储估计的个人级首选项，并将其作为菜单优化模型的输入进行检索，以便提供建议。该方法同时适用于蒙特卡罗和实际数据。实验结果表明，在线更新参数可以有效地提高参数估计的实时性。这一框架与各种推荐系统相关，这些系统产生从运输到电子商务和在线营销的个性化推荐，但是当替代品的属性随着时间的推移而变化时，这一框架特别有用。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Discrete+Choice+and+Applications)|0| +|[Discrete Choice and Applications](https://doi.org/10.1145/3589335.3641244)|Flavio Chierichetti, Ravi Kumar, Andrew Tomkins|Delft University of Technology, Department of Maritime and Transport Technology, Netherlands; Massachusetts Institute of Technology, Department of Civil and Environmental Engineering, 77 Massachusetts Avenue, Room 1-181, Cambridge, MA 02139, United States of America; Massachusetts Institute of Technology, Department of Civil and Environmental Engineering, United States of America; Massachusetts Institute of Technology, Department of Civil and Environmental Engineering, Room 181, United States of America|This paper presents a framework for estimating and updating user preferences in the context of app-based recommender systems. We specifically consider recommender systems which provide personalized menus of options to users. A Hierarchical Bayes procedure is applied in order to account for inter- and intra-consumer heterogeneity, representing random taste variations among individuals and among choice situations (menus) for a given individual, respectively. Three levels of preference parameters are estimated: population-level, individual-level and menu-specific. In the context of a recommender system, the estimation of these parameters is repeated periodically in an offline process in order to account for trends, such as changing market conditions. Furthermore, the individual-level parameters are updated in real-time as users make choices in order to incorporate the latest information from the users. This online update is computationally efficient which makes it feasible to embed it in a real-time recommender system. The estimated individual-level preferences are stored for each user and retrieved as inputs to a menu optimization model in order to provide recommendations. The proposed methodology is applied to both Monte-Carlo and real data. It is observed that the online update of the parameters is successful in improving the parameter estimates in real-time. This framework is relevant to various recommender systems that generate personalized recommendations ranging from transportation to e-commerce and online marketing, but is particularly useful when the attributes of the alternatives vary over time.|本文提出了一个在基于应用程序的推荐系统中估计和更新用户偏好的框架。我们特别考虑推荐系统，它为用户提供个性化的选项菜单。为了解释消费者之间和消费者内部的异质性，应用层次贝叶斯过程，分别代表个体之间和给定个体的选择情况(菜单)之间的随机口味变化。估计了三个水平的偏好参数: 人口水平、个人水平和菜单特定。在推荐系统的情况下，这些参数的估计会在离线过程中周期性地重复，以便解释趋势，例如不断变化的市场状况。此外，当用户做出选择时，个人级别的参数会实时更新，以便纳入用户提供的最新信息。这种在线更新计算效率高，可以嵌入到实时推荐系统中。为每个用户存储估计的个人级首选项，并将其作为菜单优化模型的输入进行检索，以便提供建议。该方法同时适用于蒙特卡罗和实际数据。实验结果表明，在线更新参数可以有效地提高参数估计的实时性。这一框架与各种推荐系统相关，这些系统产生从运输到电子商务和在线营销的个性化推荐，但是当替代品的属性随着时间的推移而变化时，这一框架特别有用。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Discrete+Choice+and+Applications)|0| |[Mining Temporal Networks](https://doi.org/10.1145/3589335.3641245)|Aristides Gionis, Lutz Oettershagen, Ilie Sarpe|Aalto University, Helsinki, Finland; Nordea Data Science Lab, Helsinki, Finland|Networks (or graphs) are used to represent and analyze large datasets of objects and their relations. Naturally, real-world networks have a temporal component: for instance, interactions between objects have a timestamp and a duration. In this tutorial we present models and algorithms for mining temporal networks, i.e., network data with temporal information. We overview different models used to represent temporal networks. We highlight the main differences between static and temporal networks, and discuss the challenges arising from introducing the temporal dimension in the network representation. We present recent papers addressing the most well-studied problems in the setting of temporal networks, including computation of centrality measures, motif detection and counting, community detection and monitoring, event and anomaly detection, analysis of epidemic processes and influence spreading, network summarization, and structure prediction.|网络(或图形)是用来表示和分析大型数据集的对象及其关系。自然地，现实世界的网络有一个时间组件: 例如，对象之间的交互有一个时间戳和一个持续时间。在本教程中，我们介绍了挖掘时态网络的模型和算法，即，带有时态信息的网络数据。我们概述了用于表示时间网络的不同模型。我们强调了静态网络和时态网络的主要区别，并讨论了在网络表示中引入时态维所带来的挑战。我们提交了最近的论文，这些论文涉及时间网络设置中研究最多的问题，包括中心度量的计算、基序检测和计数、社区检测和监测、事件和异常检测、流行病过程和影响传播的分析、网络总结和结构预测。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Mining+Temporal+Networks)|0| |[Lecture-style Tutorial: Towards Graph Foundation Models](https://doi.org/10.1145/3589335.3641246)|Chuan Shi, Cheng Yang, Yuan Fang, Lichao Sun, Philip S. Yu||Emerging as fundamental building blocks for diverse artificial intelligence applications, foundation models have achieved notable success across natural language processing and many other domains. Concurrently, graph machine learning has gradually evolved from shallow methods to deep models to leverage the abundant graph-structured data that constitute an important pillar in the data ecosystem for artificial intelligence. Naturally, the emergence and homogenization capabilities of foundation models have piqued the interest of graph machine learning researchers. This has sparked discussions about developing a next-generation graph learning paradigm, one that is pre-trained on broad graph data and can be adapted to a wide range of downstream graph-based tasks. However, there is currently no clear definition or systematic analysis for this type of work. In this tutorial, we will introduce the concept of graph foundation models (GFMs), and provide a comprehensive exposition on their key characteristics and underpinning technologies. Subsequently, we will thoroughly review existing works that lay the groundwork towards GFMs, which are summarized into three primary categories based on their roots in graph neural networks, large language models, or a hybrid of both. Beyond providing a comprehensive overview and in-depth analysis of the current landscape and progress towards graph foundation models, this tutorial will also explore potential avenues for future research in this important and dynamic field. Finally, to help the audience gain a systematic understanding of the topics covered in this tutorial, we present further details in our recent preprint paper, "Towards Graph Foundation Models: A Survey and Beyond"[4], available at https://arxiv.org/pdf/2310.11829.pdf.|基础模型作为各种人工智能应用的基本构件，已经在自然语言处理和许多其他领域取得了显著的成功。同时，图形机器学习也逐渐从浅层方法演变为深层模型，以利用构成人工智能数据生态系统重要支柱的大量图形结构化数据。自然，基础模型的出现和同质化能力引起了图机学习研究者的兴趣。这引发了关于开发下一代图形学习范式的讨论，这种范式预先对广泛的图形数据进行了培训，可以适应广泛的下游基于图形的任务。然而，目前还没有明确的定义或系统的分析这种类型的工作。在本教程中，我们将介绍图形基础模型(GFMs)的概念，并对其关键特征和支撑技术进行全面的阐述。随后，我们将彻底回顾现有的工作，奠定了基础的通用语言模型，这是总结为三个主要类别的基础上，根据其在图神经网络，大型语言模型，或两者的混合。除了提供当前景观的全面概述和深入分析以及图形基础模型的进展之外，本教程还将探索这一重要和动态领域未来研究的潜在途径。最后，为了帮助读者对本教程所涵盖的主题有一个系统的理解，我们在最近的预印本论文《走向图形基础模型: 调查与超越》[4]中提供了进一步的详细 https://arxiv.org/pdf/2310.11829.pdf。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Lecture-style+Tutorial:+Towards+Graph+Foundation+Models)|0| |[Understanding (Dark) Humour with Internet Meme Analysis](https://doi.org/10.1145/3589335.3641249)|Ming Shan Hee, Rui Cao, Tanmoy Chakraborty, Roy KaWei Lee||Internet memes, in their ubiquitous spread across the digital landscape, have transformed into a potent communicative force. Their significance beckons keen interest from researchers and practitioners alike, necessitating a deep comprehension of their nuanced forms and functions. Recent studies have honed in on diverse facets of memes, particularly in detecting offensive material and discerning sarcasm, yet comprehensive instructional resources remain sparse. Addressing this void, our tutorial delivers an integrated framework for dissecting the complex humor of memes. It weaves together disciplines such as natural language processing, computer vision, and multimodal modeling, empowering participants to decode meanings, analyze sentiments, and identify offensive content within memes. Attendees will engage in hands-on exercises and observe demonstrations, tapping into established datasets and cutting-edge algorithms. This equips them with the expertise to navigate the intricacies of meme analysis and to contribute substantively to this dynamic domain. For more information, please check out our tutorial teaser video.|互联网文化基因在数字世界中无处不在，已经转变成一种强有力的交流力量。它们的重要性引起了研究者和实践者的浓厚兴趣，需要对其微妙的形式和功能有深刻的理解。最近的研究已经打磨在模因的不同方面，特别是在检测攻击性材料和识别讽刺，但是综合的教学资源仍然稀缺。为了填补这个空白，我们的教程提供了一个解剖模因复杂幽默的集成框架。它将自然语言处理、计算机视觉和多模态建模等学科编织在一起，赋予参与者解码意义、分析情绪和识别模因中令人不快的内容的能力。与会者将参与实践练习和观察演示，利用已建立的数据集和最先进的算法。这使他们具备专业知识，能够驾驭模因分析的复杂性，并为这一动态领域做出实质性贡献。欲了解更多信息，请查看我们的教程预告视频。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Understanding+(Dark)+Humour+with+Internet+Meme+Analysis)|0| diff --git a/results.json b/results.json index 71ee1360..fe21e92b 100644 --- a/results.json +++ b/results.json @@ -459241,9 +459241,32 @@ "Shiwei Tong", "Wei Huang" ], - "paper_abstract": "", + "paper_abstract": "Cognitive diagnosis, a fundamental task in education assessments, aims to quantify the students' proficiency level based on the historical test logs. However, the interactions between students and exercises are incomplete and even sparse, which means that only a few exercise scores of a specific student are observed. A key finding is that the pattern of this missingness is non-random, which could induce bias in the estimated proficiency value. To this end, we formulate cognitive diagnosis with a sample selection problem where observations are sampled through non-random probabilities that correlate with both the student's response correctness and the features of the student and exercise. We proposed a simple but effective method called HeckmanCD, adapting the Heckman two-stage approach to mitigate this endogeneity issue. We first employ an interaction model to predict the occurrence probability of a specific student-exercise pair. After that, a selection variable, derived from this interaction model, is incorporated as a controlled independent variable in the cognitive diagnosis framework. Our analysis reveals that the vanilla estimations of the item response theory model are inherently biased in the existence of confounders, and our method can correct this bias by capturing the covariance. The proposed HeckmanCD can be applied to most existing cognitive diagnosis models, including deep models, and the empirical evaluation demonstrates the effectiveness of our method while no other auxiliary information is required such as textual descriptions of exercises.", "paper_code": "#", - "paper_cite": 0 + "paper_cite": 0, + "authors_detail": [ + { + "name": "Dongxuan Han", + "org": "" + }, + { + "name": "Qi Liu", + "org": "" + }, + { + "name": "Siqi Lei", + "org": "" + }, + { + "name": "Shiwei Tong", + "org": "Tencent Company, Shenzhen, China" + }, + { + "name": "Wei Huang", + "org": "Item Bank Department, National Education Examinations Authority, Beijing, China" + } + ], + "translated": "认知诊断，作为教育评估中的基础任务，旨在基于学生的历史测试记录量化其熟练度水平。然而，学生与练习之间的互动往往是不完整的，甚至是稀疏的，这意味着只能观察到特定学生的少数练习成绩。一个关键发现是，这种缺失的模式并非随机，可能会导致估计的熟练度值出现偏差。为此，我们将认知诊断问题形式化为一个样本选择问题，其中观察值是通过与学生回答正确性和学生及练习特征相关的非随机概率进行采样的。我们提出了一种简单但有效的方法，称为HeckmanCD，该方法采用Heckman两阶段方法来缓解这种内生性问题。首先，我们使用一个交互模型来预测特定学生-练习对的发生概率。随后，从该交互模型中得出的选择变量被纳入认知诊断框架中，作为受控的自变量。我们的分析表明，在存在混杂因素的情况下，项目反应理论模型的朴素估计本质上是有偏的，而我们的方法通过捕捉协方差可以纠正这种偏差。所提出的HeckmanCD方法可以应用于大多数现有的认知诊断模型，包括深度模型，并且实证评估显示了该方法的有效性，而无需其他辅助信息，如练习的文本描述。" }, { "paper_name": "Spatio-Temporal Transformer Network with Physical Knowledge Distillation for Weather Forecasting", @@ -459253,9 +459276,27 @@ "Junzhong Ji", "Minglong Lei" ], - "paper_abstract": "", + "paper_abstract": "Weather forecasting has become a popular research topic recently, which mainly benefits from the development of spatio-temporal neural networks to effectively extract useful patterns from weather data. Generally, the weather changes in the meteorological system are governed by physical principles. However, it is challenging for spatio-temporal methods to capture the physical knowledge of meteorological dynamics. To address this problem, we propose in this paper a spatio-temporal Transformer network with physical knowledge distillation (PKD-STTN) for weather forecasting. First, the teacher network is implemented by a differential equation network that models weather changes by the potential energy in the atmosphere to reveal the physical mechanism of atmospheric movements. Second, the student network uses a spatio-temporal Transformer that concurrently utilizes three attention modules to comprehensively capture the semantic spatial correlation, geographical spatial correlation, and temporal correlation from weather data. Finally, the physical knowledge of the teacher network is transferred to the student network by inserting a distillation position encoding into the Transformer. Notice that the output of the teacher network is distilled to the position encoding rather than the output of the student network, which can largely utilize physical knowledge without influencing the feature extraction process of Transformers. Experiments on benchmark datasets show that the proposed method can effectively utilize physical principles of weather changes and has obvious performance advantages compared with several strong baselines.", "paper_code": "#", - "paper_cite": 0 + "paper_cite": 0, + "authors_detail": [ + { + "name": "Jing He", + "org": "College of Computer Science, Beijing University of Technology, Beijing, China", + "orgId": "5f71b3271c455f439fe40dd5" + }, + { + "name": "Junzhong Ji", + "org": "College of Computer Science, Beijing University of Technology, Beijing, China", + "orgId": "5f71b3271c455f439fe40dd5" + }, + { + "name": "Minglong Lei", + "org": "College of Computer Science, Beijing University of Technology, Beijing, China", + "orgId": "5f71b3271c455f439fe40dd5" + } + ], + "translated": "天气预报近年来成为热门研究课题，主要得益于时空神经网络的发展，能够有效从天气数据中提取有用模式。通常，气象系统中的天气变化受物理原理支配。然而，时空方法难以捕捉气象动力学的物理知识。为解决此问题，本文提出一种融入物理知识蒸馏的时空Transformer网络（PKD-STTN）用于天气预报。首先，教师网络采用微分方程网络，通过大气中的势能建模天气变化，揭示大气运动的物理机制。其次，学生网络使用时空Transformer，同时利用三个注意力模块全面捕捉天气数据中的语义空间相关性、地理空间相关性和时间相关性。最后，通过插入蒸馏位置编码，将教师网络的物理知识传递给学生网络。注意，教师网络的输出被蒸馏至位置编码而非学生网络的输出，这样可以在不影响Transformer特征提取过程的情况下充分利用物理知识。在基准数据集上的实验表明，所提方法能有效利用天气变化的物理原理，相比多个强基线方法具有明显的性能优势。" }, { "paper_name": "New Localization Frameworks: User-centric Approaches to Source Localization in Real-world Propagation Scenarios", @@ -459267,9 +459308,37 @@ "Xianghua Li", "Zhen Wang" ], - "paper_abstract": "", + "paper_abstract": "Source localization in social platforms is critical for managing and controlling the misinformation spreading. Despite all the recent advancements, existing methods do not consider the dynamic and heterogeneous propagation behaviors of users and are developed based on simulated data with strong model assumptions, limiting the application in real-world scenarios. This research addresses this limitation by presenting a novel framework for source localization, grounded in real-world propagation cascades from platforms like Weibo and Twitter. What's more, recognizing the user-driven nature of users in information spread, we systematically crawl and integrate user-specific profiles, offering a realistic understanding of user-driven propagation dynamics. In summary, by developing datasets derived from real-world propagation cascades, we set a precedent in enhancing the authenticity and practice of source identification for social media. Our comprehensive experiments not only validate the feasibility and rationale of our novel user-centric localization approaches but also emphasize the significance of considering user profiles in real-world propagation scenarios. The code is available at https://github.com/cgao-comp/NFSL.", "paper_code": "#", - "paper_cite": 0 + "paper_cite": 0, + "authors_detail": [ + { + "name": "Dongpeng Hou", + "org": "Northwestern Polytechnical University, Xi'an, Shaanxi, China", + "orgId": "5f71b36a1c455f439fe42b67" + }, + { + "name": "Yuchen Wang", + "org": "Northwestern Polytechnical University, Xi'an, Shaanxi, China", + "orgId": "5f71b36a1c455f439fe42b67" + }, + { + "name": "Chao Gao", + "org": "Northwestern Polytechnical University, Xi'an, Shaanxi, China", + "orgId": "5f71b36a1c455f439fe42b67" + }, + { + "name": "Xianghua Li", + "org": "Northwestern Polytechnical University, Xi'an, Shaanxi, China", + "orgId": "5f71b36a1c455f439fe42b67" + }, + { + "name": "Zhen Wang", + "org": "Northwestern Polytechnical University, Xi'an, Shaanxi, China", + "orgId": "5f71b36a1c455f439fe42b67" + } + ], + "translated": "社交平台中的信息源定位对于管理和控制虚假信息的传播至关重要。尽管近年来取得了诸多进展，但现有方法并未考虑用户传播行为的动态性和异质性，并且这些方法基于具有强假设的模拟数据开发，限制了其在现实场景中的应用。本研究通过提出一种基于微博和推特等平台真实传播链的全新源定位框架，解决了这一局限性。此外，鉴于信息传播中用户驱动的特性，我们系统地爬取并整合了用户特定的个人资料，从而提供了对用户驱动传播动态的真实理解。总之，通过开发源自真实传播链的数据集，我们为增强社交媒体源识别的真实性和实用性树立了先例。我们的全面实验不仅验证了我们以用户为中心的新型定位方法的可行性和合理性，还强调了在现实传播场景中考虑用户个人资料的重要性。代码已公开，详见 https://github.com/cgao-comp/NFSL。" }, { "paper_name": "Physics-guided Active Sample Reweighting for Urban Flow Prediction", @@ -459283,9 +459352,40 @@ "Zi Huang", "Hongzhi Yin" ], - "paper_abstract": "", + "paper_abstract": "Urban flow prediction is a spatio-temporal modeling task that estimates the throughput of transportation services like buses, taxis, and ride-sharing, where data-driven models have become the most popular solution in the past decade. Meanwhile, the implicitly learned mapping between historical observations to the prediction targets tend to over-simplify the dynamics of real-world urban flows, leading to suboptimal predictions. Some recent spatio-temporal prediction solutions bring remedies with the notion of physics-guided machine learning (PGML), which describes spatio-temporal data with nuanced and principled physics laws, thus enhancing both the prediction accuracy and interpretability. However, these spatio-temporal PGML methods are built upon a strong assumption that the observed data fully conforms to the differential equations that define the physical system, which can quickly become ill-posed in urban flow prediction tasks. The observed urban flow data, especially when sliced into time-dependent snapshots to facilitate predictions, is typically incomplete and sparse, and prone to inherent noise incurred in the collection process. As a result, such physical inconsistency between the data and PGML model significantly limits the predictive power and robustness of the solution. Moreover, due to the interval-based predictions and intermittent nature of data filing in many transportation services, the instantaneous dynamics of urban flows can hardly be captured, rendering differential equation-based continuous modeling a loose fit for this setting. To overcome the challenges, we develop a discretized physics-guided network (PN), and propose a data-aware framework Physics-guided Active Sample Reweighting (P-GASR) to enhance PN. Experimental results in four real-world datasets demonstrate that our method achieves state-of-the-art performance with a demonstrable improvement in robustness.", "paper_code": "#", - "paper_cite": 0 + "paper_cite": 0, + "authors_detail": [ + { + "name": "Wei Jiang", + "org": "" + }, + { + "name": "Tong Chen", + "org": "" + }, + { + "name": "Guanhua Ye", + "org": "" + }, + { + "name": "Wentao Zhang", + "org": "" + }, + { + "name": "Lizhen Cui", + "org": "" + }, + { + "name": "Zi Huang", + "org": "" + }, + { + "name": "Hongzhi Yin", + "org": "" + } + ], + "translated": "城市流量预测是一项时空建模任务，旨在估算公交车、出租车和共享出行等交通服务的吞吐量，其中数据驱动模型在过去十年中已成为最受欢迎的解决方案。然而，隐式学习的历史观测与预测目标之间的映射关系往往过于简化现实世界中城市流量的动态特性，导致预测效果不佳。近期，一些时空预测解决方案引入了物理引导机器学习（PGML）的概念，通过利用细致且基于物理定律的描述来增强预测的准确性和可解释性。然而，这些时空PGML方法基于一个强假设，即观测数据完全符合定义物理系统的微分方程，这在城市流量预测任务中往往难以成立。观测到的城市流量数据，尤其是为了便于预测而分割成时间依赖的快照时，通常是不完整且稀疏的，并且容易受到采集过程中固有噪声的影响。因此，数据与PGML模型之间的物理不一致性显著限制了该解决方案的预测能力和鲁棒性。此外，由于许多交通服务中的数据记录是基于时间间隔的，并且具有间歇性，难以捕捉城市流量的瞬时动态，使得基于微分方程的连续建模方法在此场景下并不适用。为应对这些挑战，我们开发了一种离散化的物理引导网络（PN），并提出了一种数据感知框架——物理引导主动样本重加权（P-GASR），以增强PN的性能。在四个真实世界数据集上的实验结果表明，我们的方法在实现最先进性能的同时，显著提升了模型的鲁棒性。" }, { "paper_name": "Federated Heterogeneous Contrastive Distillation for Molecular Representation Learning", @@ -459298,9 +459398,41 @@ "Bolin Ding", "Hongteng Xu" ], - "paper_abstract": "", + "paper_abstract": "With the increasing application of deep learning to solve scientific problems in biochemistry, molecular federated learning has become popular due to its ability to offer distributed privacy-preserving solutions. However, most existing molecular federated learning methods rely on joint training with public datasets, which are difficult to obtain in practice. These methods also fail to leverage multi-modal molecular representations effectively. To address the above issues, we propose a novel framework, Federated Heterogeneous Contrastive Distillation (FedHCD), which enables to jointly train global models from clients with heterogeneous data modalities, learning tasks, and molecular models. To aggregate data representations of different modalities in a data-free manner, we design a global multi-modal contrastive strategy to align the representation of clients without public dataset. Utilizing intrinsic characteristics of molecular data in different modalities, we tackle the exacerbation of local model drift and data Non-IIDness caused by multi-modal clients. We introduce a multi-view contrastive knowledge transfer to extract features from atoms, substructures, and molecules, solving the issue of information distillation failure due to dimensional biases in different data modalities. Our evaluations on eight real-world molecular datasets and ablation experiments show that FedHCD outperforms other state-of-the-art FL methods, irrespective of whether or not they use public datasets.", "paper_code": "#", - "paper_cite": 0 + "paper_cite": 0, + "authors_detail": [ + { + "name": "Jinjia Feng", + "org": "Peng Cheng Laboratory & Renmin University of China, Shenzhen, China", + "orgId": "5f71b2b11c455f439fe3d913" + }, + { + "name": "Zhen Wang", + "org": "Sun Yat-sen University, Guangzhou, China", + "orgId": "5f71b2af1c455f439fe3d880" + }, + { + "name": "Zhewei Wei", + "org": "Renmin University of China, Beijing, China", + "orgId": "5f71b2b11c455f439fe3d913" + }, + { + "name": "Yaliang Li", + "org": "Alibaba Group, Bellevue, CA, USA" + }, + { + "name": "Bolin Ding", + "org": "Alibaba Group, Bellevue, WA, USA", + "orgId": "5f71b3181c455f439fe406bb" + }, + { + "name": "Hongteng Xu", + "org": "Renmin University of China, Beijing, China", + "orgId": "5f71b2b11c455f439fe3d913" + } + ], + "translated": "随着深度学习在解决生物化学领域科学问题中的应用日益增多，分子联邦学习因其能够提供分布式隐私保护解决方案而受到广泛关注。然而，现有的大多数分子联邦学习方法依赖于与公共数据集的联合训练，这在实际中难以获取。此外，这些方法未能有效利用多模态分子表示。为解决上述问题，我们提出了一种新颖的框架——联邦异构对比蒸馏（Federated Heterogeneous Contrastive Distillation, FedHCD），该框架能够从具有异构数据模态、学习任务和分子模型的客户端中联合训练全局模型。为了在无需数据的情况下聚合不同模态的数据表示，我们设计了一种全局多模态对比策略，以对齐客户端的表示，而不依赖于公共数据集。利用不同模态分子数据的内禀特性，我们解决了多模态客户端导致的局部模型漂移和数据非独立同分布（Non-IID）问题。我们引入了一种多视角对比知识迁移方法，从原子、子结构和分子中提取特征，解决了由于不同数据模态间的维度偏差导致的信息蒸馏失败问题。我们在八个真实世界的分子数据集上进行了评估和消融实验，结果表明，无论是否使用公共数据集，FedHCD均优于其他最先进的联邦学习方法。" }, { "paper_name": "Discrepancy-guided Channel Dropout for Domain Generalization", @@ -459311,9 +459443,32 @@ "Harim Lee", "DongKyu Chae" ], - "paper_abstract": "", + "paper_abstract": "Deep Neural Networks (DNNs) tend to perform poorly on unseen domains due to domain shifts. Domain Generalization (DG) aims to improve the performance on such scenarios by minimizing the distribution discrepancy between source domains. Among many studies, dropout-based DG approaches which remove domain-specific features have gained attention. However, they are limited in minimizing the upper bound of generalization risk because they do not explicitly consider the distribution discrepancy when discarding features. In this paper, we propose a novel Discrepancy-guided Channel Dropout (DgCD) for DG that explicitly derives the discrepancy between domains and drops the channels with significant distribution discrepancy. Given a training batch, we perform two ways of standardization: (1) based on the variance/mean of the batch (i.e., sampled from all source domains) and (2) based on the variance/mean of domain-wise samples in the batch. With the two normal distributions, we explicitly derive the discrepancy using KL-divergence and backpropagate it towards each channel. A channel with a higher contribution to the discrepancy is more likely to be dropped. Experimental results show the superiority of DgCD over the state-of-the-art DG baselines, demonstrating the effectiveness of our dropout strategy which is directly coupled to reducing the domain discrepancy. Our code is available at: https://github.com/gyeomo/DgCD", "paper_code": "#", - "paper_cite": 0 + "paper_cite": 0, + "authors_detail": [ + { + "name": "Seonggyeom Kim", + "org": "Hanyang University, Seoul, Republic of Korea", + "orgId": "5f71b2f51c455f439fe3f7b9" + }, + { + "name": "Byeongtae Park", + "org": "Hanyang University, Seoul, Republic of Korea", + "orgId": "5f71b2f51c455f439fe3f7b9" + }, + { + "name": "Harim Lee", + "org": "Hanyang University, Seoul, Republic of Korea", + "orgId": "5f71b2f51c455f439fe3f7b9" + }, + { + "name": "Dong-Kyu Chae", + "org": "Hanyang University, Seoul, Republic of Korea", + "orgId": "5f71b2f51c455f439fe3f7b9" + } + ], + "translated": "深度神经网络（DNNs）在面对未见过的领域时往往表现不佳，这是由于领域转移造成的。领域泛化（Domain Generalization, DG）旨在通过最小化源领域之间的分布差异来提升在这些场景下的性能。在众多研究中，基于dropout的DG方法因其能够去除领域特定特征而受到关注。然而，这些方法在最小化泛化风险的上界方面存在局限，因为它们在丢弃特征时并未明确考虑分布差异。本文提出了一种新颖的差异引导通道dropout（Discrepancy-guided Channel Dropout, DgCD）方法，用于领域泛化，该方法明确地推导出领域间的差异，并丢弃具有显著分布差异的通道。在给定的训练批次中，我们执行两种标准化方式：（1）基于批次的方差/均值（即从所有源领域中采样）；（2）基于批次中各领域样本的方差/均值。通过这两个正态分布，我们使用KL散度明确地推导出差异，并将其反向传播到每个通道。对差异贡献较大的通道更有可能被丢弃。实验结果表明，DgCD在性能上优于当前最先进的领域泛化基线方法，证明了我们的dropout策略能直接减少领域差异的有效性。我们的代码已公开，地址为：https://github.com/gyeomo/DgCD。" }, { "paper_name": "Efficient and Secure Contribution Estimation in Vertical Federated Learning", @@ -459325,9 +459480,37 @@ "Mingqi Kong", "Kun Zhu" ], - "paper_abstract": "", + "paper_abstract": "As necessary information about whether cooperation can be reached, rewards should be determined in advance in Vertical Federated Learning (VFL). To determine reasonable rewards, participant contributions should be estimated precisely. We propose a Vertically Federated Contribution Estimation (VF-CE) method. VF-CE calculates Mutual Information (MI) between distributed features and the label using a neural network trained via VFL itself. Note that compensation for CE is low as it only covers computation costs, and reward for real VFL training is high as it needs to cover training costs as well as participants' contributions to model performance and the resulting business benefits. Because MI presents a strong positive correlation with the final model performance, contributions to model performance can be estimated based on contributions to MI. We integrate a scalar-level attention mechanism in MI neural network. The attention weights of participants are treated as their contributions. We find that attention weights can effectively measure contribution redundancy, as its Spearman correlation coefficient with Shapley value is as high as 0.963. We demonstrate that VF-CE also satisfies properties of balance, zero element, and symmetry concerning fairness, which are hallmark properties of Shapley value. Compared with existing work, we consider contribution redundancy precisely, efficiently output approximated Shapley values through one MI calculation instead of 2 n where n is the number of participants, and introduce no extra privacy risk except the inherent risk in VFL, i.e., gradient transmission.", "paper_code": "#", - "paper_cite": 0 + "paper_cite": 0, + "authors_detail": [ + { + "name": "Juan Li", + "org": "Nanjing University of Aeronautics and Astronautics, Nanjing, China", + "orgId": "5f71b2c81c455f439fe3e349" + }, + { + "name": "Rui Deng", + "org": "Nanjing University of Aeronautics and Astronautics, Nanjing, China", + "orgId": "5f71b2c81c455f439fe3e349" + }, + { + "name": "Tianzi Zang", + "org": "Nanjing University of Aeronautics and Astronautics, Nanjing, China", + "orgId": "5f71b2c81c455f439fe3e349" + }, + { + "name": "Mingqi Kong", + "org": "Nanjing University of Aeronautics and Astronautics, Nanjing, China", + "orgId": "5f71b2c81c455f439fe3e349" + }, + { + "name": "Kun Zhu", + "org": "Nanjing University of Aeronautics and Astronautics, Nanjing, China", + "orgId": "5f71b2c81c455f439fe3e349" + } + ], + "translated": "在纵向联邦学习（Vertical Federated Learning, VFL）中，为了确定合作是否能够达成，奖励应提前设定。为了设定合理的奖励，参与者的贡献应被精确估计。我们提出了一种纵向联邦贡献估计（Vertically Federated Contribution Estimation, VF-CE）方法。VF-CE通过使用VFL自身训练的神经网络计算分布式特征与标签之间的互信息（Mutual Information, MI）。需要注意的是，CE的补偿较低，因为它仅涵盖计算成本，而实际VFL训练的奖励较高，因为它需要涵盖训练成本以及参与者对模型性能和由此产生的业务效益的贡献。由于MI与最终模型性能呈现强正相关，因此可以通过MI的贡献来估计对模型性能的贡献。我们在MI神经网络中集成了一个标量级别的注意力机制，将参与者的注意力权重视为他们的贡献。我们发现，注意力权重能够有效衡量贡献冗余，其与Shapley值的Spearman相关系数高达0.963。我们证明，VF-CE在公平性方面也满足平衡性、零元素和对称性等特性，这些是Shapley值的标志性特性。与现有工作相比，我们精确考虑了贡献冗余，通过一次MI计算高效输出近似的Shapley值，而不是2^n次计算（其中n是参与者的数量），并且除了VFL固有的梯度传输隐私风险外，不引入额外的隐私风险。" }, { "paper_name": "MoTTo: Scalable Motif Counting with Time-aware Topology Constraint for Large-scale Temporal Graphs", @@ -459340,9 +459523,42 @@ "Yanwei Yu", "Junyu Dong" ], - "paper_abstract": "", + "paper_abstract": "Temporal motifs are recurring subgraph patterns in temporal graphs, and are present in various domains such as social networks, fraud detection, and biological networks. Despite their significance, counting temporal motifs efficiently remains a challenge, particularly on moderately sized datasets with millions of motif instances. To address this challenge, we propose a novel algorithm called Scalable Motif Counting with Time-aware Topology Constraint (MoTTo). MoTTo focuses on accurately counting temporal motifs with up to three nodes and three edges. It first utilizes a topology constraint-based pruning strategy to eliminate nodes that cannot participate in forming temporal motifs before the counting process. Then, it adopts a time-aware topology constraint-based pruning strategy to split large-scale datasets into independent partitions and filter out the unrelated ones, ensuring that the counting results remain unaffected. By investigating the second pruning strategy, we also find that MoTTo can be implemented in a multi-thread manner, further accelerating the counting process significantly. Experimental results on several real-world datasets of varying sizes demonstrate that MoTTo outperforms state-of-the-art methods in terms of efficiency, achieving up to a nine-fold improvement in total temporal motif counting. Specifically, the efficiency of counting triangular temporal motifs is enhanced by up to 31 times compared to state-of-the-art baselines.", "paper_code": "#", - "paper_cite": 0 + "paper_cite": 0, + "authors_detail": [ + { + "name": "Jiantao Li", + "org": "Ocean University of China, Qingdao, Shandong, China", + "orgId": "5f71b2ac1c455f439fe3d6ea" + }, + { + "name": "Jianpeng Qi", + "org": "Ocean University of China, Qingdao, Shandong, China", + "orgId": "5f71b2ac1c455f439fe3d6ea" + }, + { + "name": "Yueling Huang", + "org": "Ocean University of China, Qingdao, China", + "orgId": "5f71b2ac1c455f439fe3d6ea" + }, + { + "name": "Lei Cao", + "org": "University of Arizona, Tucson, USA", + "orgId": "5f71b2841c455f439fe3c6be" + }, + { + "name": "Yanwei Yu", + "org": "Ocean University of China, Qingdao, China", + "orgId": "5f71b2ac1c455f439fe3d6ea" + }, + { + "name": "Junyu Dong", + "org": "Ocean University of China, Qingdao, China", + "orgId": "5f71b2ac1c455f439fe3d6ea" + } + ], + "translated": "时态模体是在时态图中重复出现的子图模式，广泛存在于社交网络、欺诈检测和生物网络等多个领域。尽管它们具有重要意义，但高效地统计时态模体仍然是一个挑战，尤其是在包含数百万模体实例的中等规模数据集上。为了应对这一挑战，我们提出了一种名为“带有时态拓扑约束的可扩展模体计数”（MoTTo）的新算法。MoTTo专注于精确统计包含最多三个节点和三条边的时态模体。首先，它利用基于拓扑约束的剪枝策略，在计数过程之前排除无法参与形成时态模体的节点。接着，它采用基于时态拓扑约束的剪枝策略，将大规模数据集分割成独立的分区并过滤掉无关部分，从而确保计数结果不受影响。通过研究第二种剪枝策略，我们还发现MoTTo可以实现多线程处理，进一步显著加速计数过程。在多个不同规模的实际数据集上的实验结果表明，MoTTo在效率方面优于当前最先进的方法，总时态模体计数效率提升高达九倍。具体而言，与最先进的基线方法相比，三角形时态模体的计数效率提高了多达31倍。" }, { "paper_name": "LagCNN: A Fast yet Effective Model for Multivariate Long-term Time Series Forecasting", @@ -459358,9 +459574,57 @@ "Weihao Jiang", "Jiang Zhu" ], - "paper_abstract": "", + "paper_abstract": "Long-term time series forecasting has gained significant attention in recent years due to its widely-application in various fields. Transformer-based models have gained popularity for the ability to capture long-sequence interactions. However, these models are limited in real-world use because of the memory consumption and computation explosion. The CNN-based models are also one of the main models used for time series prediction, but their performance has always been inferior to the transformer-based models in previous works. We have reconsidered the role of CNN components and redefined the way CNN basic components are used for time series prediction. In addition, the time lags information between periods in the time series is important. Unfortunately, existing works lack consideration of this classic but important information. Motivated by these factors, we propose a fast yet effective CNN model with time lags for multivariate long-term time series forecasting, named LagCNN. Specifically, the time series is transformed into lag-patches to capture the correlation between periods. Then, a fast CNN model is performed in the feature dimension rather than the time dimension like most previous works do. Meanwhile, information aggregation is performed in the time dimension to extract complex temporal patterns. LagCNN significantly outperforms state-of-the-art on multiple publicly available datasets. One step further, LagCNN exhibits significant efficiency advantages over the most efficient Transformer model (PatchTST), resulting in a significant reduction in memory usage (4.4×) and runtime (10.7×).", "paper_code": "#", - "paper_cite": 0 + "paper_cite": 0, + "authors_detail": [ + { + "name": "Linsen Li", + "org": "Zhejiang University & Hikvision Research Institute, Hangzhou, China", + "orgId": "5f71b29c1c455f439fe3d0e1" + }, + { + "name": "Chunfei Jian", + "org": "Hikvision Research Institute, Hangzhou, China", + "orgId": "5f71b5931c455f439fe51d29" + }, + { + "name": "Feng Wan", + "org": "Hikvision Research Institute, Hangzhou, China", + "orgId": "5f71b5931c455f439fe51d29" + }, + { + "name": "Dongdong Geng", + "org": "Hikvision Research Institute, Hangzhou, China", + "orgId": "5f71b5931c455f439fe51d29" + }, + { + "name": "Ziquan Fang", + "org": "Zhejiang University, Hangzhou, China", + "orgId": "5f71b29c1c455f439fe3d0e1" + }, + { + "name": "Lu Chen", + "org": "Zhejiang University, Hangzhou, China", + "orgId": "5f71b29c1c455f439fe3d0e1" + }, + { + "name": "Yunjun Gao", + "org": "Zhejiang University, Hangzhou, China", + "orgId": "5f71b29c1c455f439fe3d0e1" + }, + { + "name": "Weihao Jiang", + "org": "Hikvision Research Institute, Hangzhou, China", + "orgId": "5f71b5931c455f439fe51d29" + }, + { + "name": "Jiang Zhu", + "org": "Hikvision Research Institute, Hangzhou, China", + "orgId": "5f71b5931c455f439fe51d29" + } + ], + "translated": "长期时间序列预测近年来因其广泛的应用而备受关注。基于Transformer的模型因其捕捉长序列交互的能力而受到欢迎。然而，这些模型在实际应用中受到限制，因为它们存在内存消耗和计算爆炸的问题。基于CNN的模型也是用于时间序列预测的主要模型之一，但它们在以往的研究中的表现一直不如基于Transformer的模型。我们重新考虑了CNN组件的作用，并重新定义了用于时间序列预测的CNN基本组件的使用方式。此外，时间序列中各周期之间的时间滞后信息非常重要。遗憾的是，现有研究缺乏对这一经典但重要信息的考虑。受这些因素的启发，我们提出了一种快速且有效的CNN模型，用于多元长期时间序列预测，命名为LagCNN。具体来说，时间序列被转换为滞后补丁，以捕捉周期之间的相关性。然后，在特征维度而不是像大多数先前的工作那样在时间维度上执行快速CNN模型。同时，在时间维度上进行信息聚合，以提取复杂的时间模式。LagCNN在多个公开可用的数据集上显著优于最先进的方法。进一步地，LagCNN在与最有效的Transformer模型（PatchTST）相比时表现出显著的效率优势，导致内存使用量（4.4倍）和运行时间（10.7倍）大幅减少。" }, { "paper_name": "Noise-Resilient Unsupervised Graph Representation Learning via Multi-Hop Feature Quality Estimation", @@ -459372,9 +459636,32 @@ "Geoffrey I. Webb", "Shirui Pan" ], - "paper_abstract": "", + "paper_abstract": "Unsupervised graph representation learning (UGRL) based on graph neural networks (GNNs), has received increasing attention owing to its efficacy in handling graph-structured data. However, existing UGRL methods ideally assume that the node features are noise-free, which makes them fail to distinguish between useful information and noise when applied to real data with noisy features, thus affecting the quality of learned representations. This urges us to take node noisy features into account in real-world UGRL. With empirical analysis, we reveal that feature propagation, the essential operation in GNNs, acts as a \"double-edged sword\" in handling noisy features - it can both denoise and diffuse noise, leading to varying feature quality across nodes, even within the same node at different hops. Building on this insight, we propose a novel UGRL method based on Multi-hop feature Quality Estimation (MQE for short). Unlike most UGRL models that directly utilize propagation-based GNNs to generate representations, our approach aims to learn representations through estimating the quality of propagated features at different hops. Specifically, we introduce a Gaussian model that utilizes a learnable \"meta-representation\" as a condition to estimate the expectation and variance of multi-hop propagated features via neural networks. In this way, the \"meta representation\" captures the semantic and structural information underlying multiple propagated features but is naturally less susceptible to interference by noise, thereby serving as high-quality node representations beneficial for downstream tasks. Extensive experiments on multiple real-world datasets demonstrate that MQE in learning reliable node representations in scenarios with diverse types of feature noise.", "paper_code": "#", - "paper_cite": 0 + "paper_cite": 0, + "authors_detail": [ + { + "name": "Shiyuan Li", + "org": "" + }, + { + "name": "Yixin Liu", + "org": "" + }, + { + "name": "Qingfeng Chen", + "org": "" + }, + { + "name": "Geoffrey I. Webb", + "org": "" + }, + { + "name": "Shirui Pan", + "org": "" + } + ], + "translated": "基于图神经网络（GNN）的无监督图表示学习（UGRL）因其有效处理图结构数据的能力而受到越来越多的关注。然而，现有的UGRL方法理想化地假设节点特征是无噪声的，这使得它们在应用于具有噪声特征的真实数据时，无法区分有用信息和噪声，从而影响学习到的表示质量。这促使我们在现实世界的UGRL中考虑节点噪声特征。通过实证分析，我们揭示了特征传播——GNN中的基本操作——在处理噪声特征时起到了“双刃剑”的作用——它既能去噪又能扩散噪声，导致不同节点之间的特征质量不同，甚至在同一节点的不同跳数之间也存在差异。基于这一见解，我们提出了一种基于多跳特征质量估计（MQE）的新型UGRL方法。与大多数直接利用基于传播的GNN生成表示的UGRL模型不同，我们的方法旨在通过估计不同跳数传播特征的质量来学习表示。具体来说，我们引入了一个高斯模型，该模型利用可学习的“元表示”作为条件，通过神经网络估计多跳传播特征的期望和方差。通过这种方式，“元表示”捕捉了多个传播特征背后的语义和结构信息，但自然较少受到噪声的干扰，从而作为高质量的节点表示，有利于下游任务。在多个真实世界数据集上的广泛实验表明，MQE在处理具有多种特征噪声的场景中能够学习到可靠的节点表示。" }, { "paper_name": "Aligning Large Language Models to a Domain-specific Graph Database for NL2GQL",